. We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process. [Xiong, Heng; Liu, Dongbing; Li, Qiye; Lei, Mengyue; Xu, Liqin; Wu, Liang; Wang, Zongji; Li, Wangsheng; Xia, Min; Lu, Lihua; Lu, Haorong; Hou, Yong; Zhu, Shida; Liu, Xin; Wang, Jian; Yang, Huanming; Wu, Kui; Xu, Xun; Lee, Leo J.] BGI Shenzhen, Shenzhen 518083, Peoples R China; [Xiong, Heng; Liu, Dongbing; Li, Qiye; Lei, Mengyue; Xu, Liqin; Wu, Liang; Wang, Zongji; Li, Wangsheng; Xia, Min; Lu, Lihua; Lu, Haorong; Hou, Yong; Zhu, Shida; Liu, Xin; Wu, Kui; Xu, Xun] BGI Shenzhen, China Natl GeneBank Shenzhen, Shenzhen 518083, Peoples R China; [Ren, Shancheng; Sun, Yinghao] Second Mil Med Univ, Shanghai Changhai Hosp, Dept Urol, Shanghai 200433, Peoples R China; [Hou, Yong; Zhu, Shida; Wu, Kui] Univ Copenhagen, Dept Biol, DK-2200 Copenhagen N, Denmark; [Wang, Jian; Yang, Huanming] James D Watson Inst Genome Sci, Hangzhou 310058, Zhejiang, Peoples R China; [Lee, Leo J.] Univ Toronto, Donnelly Ctr Cellular & Biomol Res, Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada Beijing Genomics Institute (BGI); Beijing Genomics Institute (BGI); Naval Medical University; University of Copenhagen; University of Toronto Lee, LJ (corresponding author), Univ Toronto, Dept Elect & Comp Engn, 10 Kings Coll Rd, Toronto, ON M5S 3G4, Canada.;Xu, X (corresponding author), China Natl GeneBank CNGB, Jinsha Rd, Shenzhen 518120, Peoples R China. xuxun@genomics.cn; ljlee@psi.toronto.edu Li, Qiye/C-1348-2015; 冯, 雅仪/AAS-7511-2021; Xu, Xun/AAI-6763-2020 Li, Qiye/0000-0002-5993-0312; Xu, Xun/0000-0002-5338-5173; wu, liang/0000-0001-6259-261X; Liu, Xin/0000-0003-3256-2940; Liu, Dongbing/0000-0003-2870-5187; hou, yong/0000-0002-0420-0726; Xu, Liqin/0000-0003-3377-3707 Shenzhen Peacock Plan [KQTD20150330171505310] Shenzhen Peacock Plan This project was supported by the Shenzhen Peacock Plan (no. KQTD20150330171505310). We are indebted to colleagues of Beijing Genomics Institute who contributed to this work but were not included in the author list. We also thank Dr. Laurie Goodman and Dr. Scott Edmunds for their valuable suggestions. 34 24 25 0 2 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2047-217X GIGASCIENCE GigaScience MAR 2 2017.0 6 5 10.1093/gigascience/gix012 0.0 8 Biology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics EY1JC 28328004.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000403721200001 0
J Adnan, RM; Petroselli, A; Heddam, S; Santos, CAG; Kisi, O Adnan, Rana Muhammad; Petroselli, Andrea; Heddam, Salim; Santos, Celso Augusto Guimaraes; Kisi, Ozgur Comparison of different methodologies for rainfall-runoff modeling: machine learning vs conceptual approach NATURAL HAZARDS English Article Machine learning; Physically event-based conceptual method; EBA4SUB; Hourly rainfall-runoff modeling ADAPTIVE REGRESSION SPLINES; HIMALAYAN MICRO-WATERSHEDS; ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINE; UNGAUGED BASINS; FLOW; PREDICTION; DESIGN; INTELLIGENCE; STREAM Accurate short-term rainfall-runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall-runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall-runoff modeling. [GRAPHICS] . [Adnan, Rana Muhammad] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China; [Petroselli, Andrea] Univ Tuscia, Dept Econ Engn Soc & Business DEIM, Viterbo, Italy; [Heddam, Salim] Univ 20 Aout 1955, Hydraul Div, Fac Sci, Agron Dept, Route El Hadaik,BP 26, Skikda, Algeria; [Santos, Celso Augusto Guimaraes] Univ Fed Paraiba, Dept Civil & Environm Engn, Joao Pessoa, Paraiba, Brazil; [Kisi, Ozgur] Ilia State Univ, Civil Engn Dept, Tbilisi, Georgia; [Kisi, Ozgur] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Hohai University; Tuscia University; Universite de Skikda; Universidade Federal da Paraiba; Ilia State University; Duy Tan University Kisi, O (corresponding author), Ilia State Univ, Civil Engn Dept, Tbilisi, Georgia.;Kisi, O (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. rana@hhu.edu.cn; petro@unitus.it; heddamsalim@yahoo.fr; celso@ct.ufpb.br; ozgur.kisi@iliauni.edu.ge Santos, Celso Augusto Guimarães/G-1816-2010; Adnan, Rana Muhammad/ABB-1652-2020; HEDDAM, SALIM/B-8647-2015 Santos, Celso Augusto Guimarães/0000-0001-7927-9718; Adnan, Rana Muhammad/0000-0002-2650-8123; HEDDAM, SALIM/0000-0002-8055-8463; Kisi, Ozgur/0000-0001-7847-5872; Petroselli, Andrea/0000-0003-4943-0928 57 15 16 9 32 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0921-030X 1573-0840 NAT HAZARDS Nat. Hazards FEB 2021.0 105 3 2987 3011 10.1007/s11069-020-04438-2 0.0 JAN 2021 25 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences; Water Resources QB5XF 2023-03-23 WOS:000604222800019 0
J Chui, KT; Fung, DCL; Lytras, MD; Lam, TM Chui, Kwok Tai; Fung, Dennis Chun Lok; Lytras, Miltiadis D.; Lam, Tin Miu Predicting at-risk university students in a virtual learning environment via a machine learning algorithm COMPUTERS IN HUMAN BEHAVIOR English Article Academic performance; At-risk students; Event prediction; Higher education; Machine learning; Virtual learning environments SUPPORT VECTOR MACHINE A university education is widely considered essential to social advancement. Ensuring students pass their courses and graduate on time have thus become issues of concern. This paper proposes a reduced training vector-based support vector machine (RTV-SVM) capable of predicting at-risk and marginal students. It also removes redundant training vectors to reduce the training time and support vectors. To examine the effectiveness of the proposed RTV-SVM, 32,593 university students on seven courses were chosen for performance evaluation. Analysis reveals that the RTV-SVM achieved a training vector reduction of at least 59.7% without altering the margin or accuracy of the classifier. Moreover, the results showed the proposed method to be capable of achieving overall accuracy of 92.2-93.8% and 91.3-93.5% in predicting at-risk and marginal students, respectively. [Chui, Kwok Tai] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Fung, Dennis Chun Lok; Lam, Tin Miu] Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China; [Lytras, Miltiadis D.] Amer Coll Greece, Management Informat Syst Dept, Athens, Greece; [Lytras, Miltiadis D.] King Abdulziz Univ, Jeddah, Saudi Arabia City University of Hong Kong; University of Hong Kong; King Abdulaziz University Chui, KT (corresponding author), City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China. ktchui3-c@my.cityu.edu.hk; clfung@hku.hk; mlytras@acg.edu; tinmiu@connect.hku.hk Lytras, Miltiadis/GSM-7668-2022; Lytras, Miltiadis/P-8195-2016; Lytras, Miltiades Demetrios/ABD-5355-2021; Chui, Kwok Tai/T-7346-2019 Lytras, Miltiadis/0000-0002-7281-5458; Lytras, Miltiadis/0000-0002-7281-5458; Chui, Kwok Tai/0000-0001-7992-9901 41 76 76 9 55 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0747-5632 1873-7692 COMPUT HUM BEHAV Comput. Hum. Behav. JUN 2020.0 107 105584 10.1016/j.chb.2018.06.032 0.0 7 Psychology, Multidisciplinary; Psychology, Experimental Social Science Citation Index (SSCI) Psychology KZ9SC 2023-03-23 WOS:000523598100037 0
J Teng, S; Chen, GF; Yan, ZC; Cheng, L; Bassir, D Teng, Shuai; Chen, Gongfa; Yan, Zhaocheng; Cheng, Li; Bassir, David Vibration-based structural damage detection using 1-D convolutional neural network and transfer learning STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL English Article; Early Access Structural damage detection; convolutional neural network; transfer learning; vibration signals; bridge model FAULT-DETECTION; IDENTIFICATION; CURVATURE This paper presents a novel vibration-based structural damage detection approach by using a one-dimensional convolutional neural network (1-D CNN) and transfer learning (TL). The CNN can effectively extract structural damage information from the vibration signals. However, the CNN training needs enough samples, while some damage samples (scenarios) obtained from real structures are limited, which will compromise the CNN ability to detect structural damage. As a solution, the numerical models have potential to provide sufficient CNN training samples; meanwhile, the state-of-the-art TL technique can significantly shorten the network training time and improve the accuracy. Therefore, this paper proposes a new method to detect the damage of a bridge model. The 1-D CNN is firstly trained with the samples of the single damage scenarios of the numerical bridge model. And then it is transferred to the complex scenarios of multi-damage (double or triple simultaneously), random size structures, and experimental model. The results demonstrate that: with the TL, the accuracy of damage detection is increased by about 47% at most, and the convergence speed is increased by at least 50%; in particular, the TL can inhibit over-fitting, and for the real bridge case, the accuracy also increased by 44.4%. It is demonstrated that: the TL can effectively improve the damage detection accuracy and convergence effect, and the application of this method to the random size structures also proves its generalization. [Teng, Shuai; Chen, Gongfa; Yan, Zhaocheng] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou, Peoples R China; [Cheng, Li] Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong, Peoples R China; [Bassir, David] Univ Paris Saclay, Ctr Borelli, CNRS, CMLA,ENS Cachan, Gif Sur Yette, France; [Chen, Gongfa] Guangdong Univ Technol, Sch Civil & Transportat Engn, 100 Waihuan Xi Rd, Guangzhou 510006, Guangdong, Peoples R China Guangdong University of Technology; Hong Kong Polytechnic University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Guangdong University of Technology Chen, GF (corresponding author), Guangdong Univ Technol, Sch Civil & Transportat Engn, 100 Waihuan Xi Rd, Guangzhou 510006, Guangdong, Peoples R China. gongfa.chen@gdut.edu.cn Teng, Shuai/0000-0003-1703-3362 46 1 1 10 10 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 1475-9217 1741-3168 STRUCT HEALTH MONIT Struct. Health Monit. 10.1177/14759217221137931 0.0 DEC 2022 22 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 6U0CT 2023-03-23 WOS:000894039400001 0
C Fischer, F; Xiao, H; Kao, CY; Stachelscheid, Y; Johnson, B; Razar, D; Fawkesley, P; Buckley, N; Bottinger, K; Muntean, P; Grossklags, J USENIX Assoc Fischer, Felix; Xiao, Huang; Kao, Ching-Yu; Stachelscheid, Yannick; Johnson, Benjamin; Razar, Danial; Fawkesley, Paul; Buckley, Nat; Boettinger, Konstantin; Muntean, Paul; Grossklags, Jens Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography PROCEEDINGS OF THE 28TH USENIX SECURITY SYMPOSIUM English Proceedings Paper 28th USENIX Security Symposium AUG 14-16, 2019 Santa Clara, CA USENIX Assoc,Facebook,Microsoft,Intel,Avast,Baidu,Cisco,Google,King Abdullah Univ Sci & Technol,Paloalto Networks,Dropbox,IBM Res,Kaspersky,USC Viterbi, Sch Engn, Informat Sci Inst,Bloomberg,NetApp Stack Overflow is the most popular discussion platform for software developers. However, recent research identified a large amount of insecure encryption code in production systems that has been inspired by examples given on Stack Overflow. By copying and pasting functional code, developers introduced exploitable software vulnerabilities into security-sensitive high-profile applications installed by millions of users every day. Proposed mitigations of this problem suffer from usability flaws and push developers to continue shopping for code examples on Stack Overflow once again. This motivates us to fight the proliferation of insecure code directly at the root before it even reaches the clipboard. By viewing Stack Overflow as a market, implementation of cryptography becomes a decision-making problem. In this context, our goal is to simplify the selection of helpful and secure examples. More specifically, we focus on supporting software developers in making better decisions on Stack Overflow by applying nudges, a concept borrowed from behavioral economics and psychology. This approach is motivated by one of our key findings: For 99.37% of insecure code examples on Stack Overflow, similar alternatives are available that serve the same use case and provide strong cryptography. Our system design that modifies Stack Overflow is based on several nudges that are controlled by a deep neural network. It learns a representation for cryptographic API usage patterns and classification of their security, achieving average AUC-ROC of 0.992. With a user study, we demonstrate that nudge-based security advice significantly helps tackling the most popular and error-prone cryptographic use cases in Android. [Fischer, Felix; Stachelscheid, Yannick; Johnson, Benjamin; Razar, Danial; Muntean, Paul; Grossklags, Jens] Tech Univ Munich, Munich, Germany; [Xiao, Huang] Bosch Ctr Artificial Intelligence, Wuxi, Jiangsu, Peoples R China; [Kao, Ching-Yu; Boettinger, Konstantin] Fraunhofer AISEC, Garching, Germany; [Fawkesley, Paul; Buckley, Nat] Projects IF, London, England Technical University of Munich Fischer, F (corresponding author), Tech Univ Munich, Munich, Germany. flx.fischer@turn.de; huang.xiao@de.bosch.com; ching-yu.kao@aisec.fraunhofer.de; yannick.stachelscheid@turn.de; benjamin.johnson@turn.de; danial.razar@turn.de; paul@projectsbyif.com; nat@projectsbyif.com; konstantin.boettinger@aisec.fraunhofer.de; paul.muntean@turn.de; jens.grossklags@turn.de XIAO, HUANG/AAK-4252-2020 33 6 6 0 0 USENIX ASSOC BERKELEY SUITE 215, 2560 NINTH ST, BERKELEY, CA 94710 USA 978-1-939133-06-9 2019.0 339 356 18 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO3AB 2023-03-23 WOS:000509775000020 0
J Wang, YW; Zhou, J; Zheng, LY; Gogu, C Wang, Yiwei; Zhou, Jian; Zheng, Lianyu; Gogu, Christian An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies JOURNAL OF INTELLIGENT MANUFACTURING English Article Fault diagnostics; Rotating machinery; Vibration signals; Convolutional neural network WAVELET PACKET TRANSFORM; INSTANTANEOUS FREQUENCY; FEATURE-EXTRACTION; SYSTEM; SIGNAL; REPRESENTATION; DECOMPOSITION; RECOGNITION; FEATURES; VECTOR The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of many mechanical systems. In traditional diagnostics approaches, extracting features from raw input is an important prerequisite and normally requires manual extraction based on signal processing techniques. This suffers of some drawbacks such as the strong dependence on domain expertise, the high sensitivity to different mechanical systems, the poor flexibility and generalization ability, and the limitations of mining new features, etc. In this paper, we proposed an end-to-end fault diagnostics model based on a convolutional neural network for rotating machinery using vibration signals. The model learns features directly from the one-dimensional raw vibration signals without any manual feature extraction. To fully validate its effectiveness and robustness, the proposed model is tested on four datasets, including two public ones and two datasets of our own, covering the applications of ball screw, bearing and gearbox. The method of manual, signal processing based feature extraction combined with a classifier is also explored for comparison. The results show that the manually extracted features are sensitive to the various applications, thus needing fine-tuning, while the proposed framework has a good robustness for rotating machinery fault diagnostics with high accuracies for all the four applications, without any application-specific manual fine-tuning. [Wang, Yiwei; Zhou, Jian; Zheng, Lianyu] Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China; [Gogu, Christian] Univ Toulouse, INSA UPS ISAE Mines Albi, Inst Clement Ader UMR CNRS 5312, F-31400 Toulouse, France Beihang University; Universite de Toulouse Zheng, LY (corresponding author), Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China. lyzheng@buaa.edu.cn Zheng, Lianyu/0000-0003-1489-4829 National Natural Science Foundation of China [51805262]; Graduate Student Innovation Fund of Beihang University [YCSJ-03-2019-06] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Graduate Student Innovation Fund of Beihang University The present work was funded by the National Natural Science Foundation of China (No.51805262) and the Graduate Student Innovation Fund of Beihang University (YCSJ-03-2019-06). The authors gratefully acknowledge the Key Laboratory of Performance Test for CNC Machine Tool Components affiliated of Ministry of Industry and Information Technology of China for providing the ball screw test bench and experiment materials. 47 12 12 20 42 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0956-5515 1572-8145 J INTELL MANUF J. Intell. Manuf. MAR 2022.0 33 3 809 830 10.1007/s10845-020-01671-1 0.0 OCT 2020 22 Computer Science, Artificial Intelligence; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YX6YT 2023-03-23 WOS:000578479600001 0
J Wang, D; Brunner, J; Ma, ZY; Lu, H; Hollaus, M; Pang, Y; Pfeifer, N Wang, Di; Brunner, Jasmin; Ma, Zhenyu; Lu, Hao; Hollaus, Markus; Pang, Yong; Pfeifer, Norbert Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging FORESTS English Article Laser scanning; dynamic segmentation; point classification; pattern recognition; wood-leaf classification LEAF-AREA INDEX; TERRESTRIAL LIDAR DATA; RADIOMETRIC CALIBRATION; ABOVEGROUND BIOMASS; INDIVIDUAL TREES; LASER; CLASSIFICATION; WOOD; FORESTS; LEAVES Many biophysical forest properties such as wood volume and leaf area index (LAI) require prior knowledge on either photosynthetic or non-photosynthetic components. Laser scanning appears to be a helpful technique in nondestructively quantifying forest structures, as it can acquire an accurate three-dimensional point cloud of objects. In this study, we propose an unsupervised geometry-based method named Dynamic Segment Merging (DSM) to identify non-photosynthetic components of trees by semantically segmenting tree point clouds, and examining the linear shape prior of each resulting segment. We tested our method using one single tree dataset and four plot-level datasets, and compared our results to a supervised machine learning method. We further demonstrated that by using an optimal neighborhood selection method that involves multi-scale analysis, the results were improved. Our results showed that the overall accuracy ranged from 81.8% to 92.0% with an average value of 87.7%. The supervised machine learning method had an average overall accuracy of 86.4% for all datasets, on account of a collection of manually delineated representative training data. Our study indicates that separating tree photosynthetic and non-photosynthetic components from laser scanning data can be achieved in a fully unsupervised manner without the need of training data and user intervention. [Wang, Di; Brunner, Jasmin; Hollaus, Markus; Pfeifer, Norbert] TU Wien, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria; [Ma, Zhenyu; Pang, Yong] Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China; [Lu, Hao] Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China Technische Universitat Wien; Chinese Academy of Forestry; Research Institute of Forest Resources Information Technique, CAF; Beijing Forestry University Wang, D (corresponding author), TU Wien, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria. di.wang@geo.tuwien.ac.at; brunner.jasmin@a1.net; mazhenyu22@163.com; luhao@bjfu.edu.cn; markus.hollaus@geo.tuwien.ac.at; pangy@ifrit.ac.cn; norbert.pfeifer@geo.tuwien.ac.at Wang, Di/T-2571-2018; Lu, Hao/AAH-1982-2020; Wang, Di/V-3305-2019 Wang, Di/0000-0003-0232-8862; Lu, Hao/0000-0002-3083-4909; Wang, Di/0000-0003-0232-8862; Pang, Yong/0000-0002-9760-6580; Hollaus, Markus/0000-0001-6063-7239; Pfeifer, Norbert/0000-0002-2348-7929 project The influence of Biomass and change on landSLIDE activity (BioSLIDE) within research program Earth System Sciences (ESS) of the Austrian Academy of Science (Osterreichische Akademie der Wissenschaften, OAW); OAW, Ministry of Education, Youth and Sports of the Czech Republic; Slovak Research and Development Agency [DS-2016-0040]; Austrian Research Promotion Agency (FFG) [860021] project The influence of Biomass and change on landSLIDE activity (BioSLIDE) within research program Earth System Sciences (ESS) of the Austrian Academy of Science (Osterreichische Akademie der Wissenschaften, OAW); OAW, Ministry of Education, Youth and Sports of the Czech Republic; Slovak Research and Development Agency(Slovak Research and Development Agency); Austrian Research Promotion Agency (FFG) Special thanks are due to referees for their constructive reviews that improved the manuscript significantly. The study was supported by the project The influence of Biomass and its change on landSLIDE activity (BioSLIDE) within the research program Earth System Sciences (ESS) of the Austrian Academy of Science (Osterreichische Akademie der Wissenschaften, OAW). Partial fund was jointly provided by the OAW, the Ministry of Education, Youth and Sports of the Czech Republic and the Slovak Research and Development Agency under the contract No. DS-2016-0040. The authors also thank the Austrian Research Promotion Agency (FFG) for providing financial support via the project No. 860021. 63 25 27 4 26 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4907 FORESTS Forests MAY 2018.0 9 5 252 10.3390/f9050252 0.0 23 Forestry Science Citation Index Expanded (SCI-EXPANDED) Forestry GJ3KO Green Submitted, gold 2023-03-23 WOS:000435193700027 0
J Tao, Y; Xiong, ST; Muller, JP; Michael, G; Conway, SJ; Paar, G; Cremonese, G; Thomas, N Tao, Yu; Xiong, Siting; Muller, Jan-Peter; Michael, Greg; Conway, Susan J.; Paar, Gerhard; Cremonese, Gabriele; Thomas, Nicolas Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration REMOTE SENSING English Article Mars; 3D mapping; large-area mapping; digital terrain model; deep learning; super-resolution restoration; single image DTM estimation; MADNet; TGO; CaSSIS; HiRISE; HRSC; CTX; Oxia Planum; ExoMars; Rosalind Franklin rover; landing site; planetary mapping; 3D reconstruction ORBITER LASER ALTIMETER; SCIENTIFIC OBJECTIVES; SURFACE; EXPRESS; MISSION; COLOR We propose using coupled deep learning based super-resolution restoration (SRR) and single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel-scale topography from single-view ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) and NASA Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (HiRISE) images. We present qualitative and quantitative assessments of the resultant 2 m/pixel CaSSIS SRR DTM mosaic over the ESA and Roscosmos Rosalind Franklin ExoMars rover's (RFEXM22) planned landing site at Oxia Planum. Quantitative evaluation shows SRR improves the effective resolution of the resultant CaSSIS DTM by a factor of 4 or more, while achieving a fairly good height accuracy measured by root mean squared error (1.876 m) and structural similarity (0.607), compared to the ultra-high-resolution HiRISE SRR DTMs at 12.5 cm/pixel. We make available, along with this paper, the resultant CaSSIS SRR image and SRR DTM mosaics, as well as HiRISE full-strip SRR images and SRR DTMs, to support landing site characterisation and future rover engineering for the RFEXM22. [Tao, Yu; Muller, Jan-Peter] Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Surrey RH5 6NT, England; [Xiong, Siting] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Minist Nat Resources, Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [Michael, Greg] Free Univ Berlin, Inst Geol Sci Planetary Sci & Remote Sensing, Dept Geosci, D-12249 Berlin, Germany; [Conway, Susan J.] Univ Nantes, CNRS, UMR 6112, Lab Planetol & Geodynam, F-44300 Nantes, France; [Paar, Gerhard] Joanneum Res, Steyrergasse 17, A-8010 Graz, Austria; [Cremonese, Gabriele] INAF, Osservatorio Astron Padova, I-35122 Padua, Italy; [Thomas, Nicolas] Univ Bern, Phys Inst, Siedlerstr 5, CH-3012 Bern, Switzerland University of London; University College London; Shenzhen University; Ministry of Natural Resources of the People's Republic of China; Shenzhen University; Shenzhen University; Shenzhen University; Free University of Berlin; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); Nantes Universite; Istituto Nazionale Astrofisica (INAF); University of Padua; University of Bern Tao, Y (corresponding author), Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Surrey RH5 6NT, England. yu.tao@ucl.ac.uik; xiongsiting@szu.edu.cn; j.muller@ucl.ac.uk; gregory.michael@fu-berlin.de; susan.conway@univ-nantes.fr; gerhard.parr@joanneum.at; gabriele.cremonese@inaf.it; nicolas.thomas@space.unibe.ch ; Thomas, Nicolas/G-4693-2015 Cremonese, Gabriele/0000-0001-9021-1140; Muller, Jan-Peter/0000-0002-5077-3736; Thomas, Nicolas/0000-0002-0146-0071; Xiong, Siting/0000-0002-1054-121X; Tao, Yu/0000-0001-9170-6655 UKSA Aurora programme (2018-2021) [ST/S001891/1]; STFC MSSL Consolidated Grant [ST/K000977/1]; Austrian Space Applications Programme Project [882828] UKSA Aurora programme (2018-2021); STFC MSSL Consolidated Grant(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Austrian Space Applications Programme Project The research leading to these results is receiving funding from the UKSA Aurora programme (2018-2021) under grant no. ST/S001891/1 as well as partial funding from the STFC MSSL Consolidated Grant ST/K000977/1. S.C. is grateful to the French Space Agency CNES for supporting her HiRISE related work. PRo3D-based analysis and visualization is funded by the Austrian Space Applications Programme Project 882828 PanCam-3D 2021. 78 0 0 11 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2022.0 14 2 257 10.3390/rs14020257 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology YM4QW Green Published, gold 2023-03-23 WOS:000746561800001 0
J Yin, ZX; Braams, BJ; Guan, YF; Fu, BN; Zhang, DH Yin, Zhengxi; Braams, Bastiaan J.; Guan, Yafu; Fu, Bina; Zhang, Dong H. A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H-3 PHYSICAL CHEMISTRY CHEMICAL PHYSICS English Article POTENTIAL-ENERGY SURFACES; NONADIABATIC COUPLING TERMS; MR-CI LEVEL; H PLUS HD; QUANTUM DYNAMICS; GEOMETRIC PHASE; FEEDFORWARD NETWORKS; ANALYTIC EVALUATION; TRANSFORMATION; ALGORITHM The fundamental invariant neural network (FI-NN) approach is developed to represent coupled potential energy surfaces in quasidiabatic representations with two-dimensional irreducible representations of the complete nuclear permutation and inversion (CNPI) group. The particular symmetry properties of the diabatic potential energy matrix of H-3 for the 1A ' and (2)A ' electronic states were resolved arising from the E symmetry in the D-3h point group. This FI-NN framework with symmetry adaption is used to construct a new quasidiabatic representation of H-3, which reproduces accurately the ab initio energies and derivative information with perfect symmetry behaviors and extremely small fitting errors. The quantum dynamics results on the new FI-NN diabatic PESs give rise to accurate oscillation patterns in the product state-resolved differential cross sections. These results strongly support the accuracy and efficiency of the FI-NN approach to construct reliable diabatic representations with complicated symmetry problems. [Yin, Zhengxi; Guan, Yafu; Fu, Bina; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian, Peoples R China; [Yin, Zhengxi; Guan, Yafu; Fu, Bina; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Dalian, Peoples R China; [Yin, Zhengxi] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Braams, Bastiaan J.] Dutch Natl Ctr Math & Comp Sci, Ctr Wiskunde & Informat CWI, Amsterdam, Netherlands Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Fu, BN; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian, Peoples R China.;Fu, BN; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Dalian, Peoples R China. bina@dicp.ac.cn; zhangdh@dicp.ac.cn National Natural Science Foundation of China [21722307, 21673233, 21590804, 21688102]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB17000000]; LiaoNing Revitalization Talents Program [XLYC1907190] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); LiaoNing Revitalization Talents Program This work was supported by the National Natural Science Foundation of China (Grant No. 21722307, 21673233, 21590804, and 21688102), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB17000000), and the LiaoNing Revitalization Talents Program (XLYC1907190). 64 8 8 9 33 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1463-9076 1463-9084 PHYS CHEM CHEM PHYS Phys. Chem. Chem. Phys. JAN 14 2021.0 23 2 1082 1091 10.1039/d0cp05047d 0.0 10 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics PV7FR 33346765.0 2023-03-23 WOS:000610150200030 0
C Wassermann, S; Casas, P; Ben Houidi, Z; Huet, A; Seufert, M; Wehner, N; Schuler, J; Cai, SM; Shi, H; Xu, JC; Hossfeld, T; Rossi, D ZincirHeywood, N; Ulema, M; Sayit, M; Clayman, S; Kim, MS; Cetinkaya, C Wassermann, Sarah; Casas, Pedro; Ben Houidi, Zied; Huet, Alexis; Seufert, Michael; Wehner, Nikolas; Schueler, Joshua; Cai, Shengming; Shi, Hao; Xu, Jinchun; Hossfeld, Tobias; Rossi, Dario Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traf 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) International Conference on Network and Service Management English Proceedings Paper 16th International Conference on Network and Service Management (CNSM) / 2nd International Workshop on Analytics for Service and Application Management (AnServApp) / 1st International Workshop on the Future Evolution of Internet Protocols (IPFuture) NOV 02-06, 2020 ELECTR NETWORK IFIP,IEEE,FutureWei Technologies,IEEE Commun Soc,Dalhousie Univ Web QoE; Smartphone vs. Desktop; Network Monitoring; Machine Learning; SpeedIndex; Encrypted Traffic Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality-of-Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of application-level-data visibility introduced by the wide adoption of end-toend encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web-page loading sessions, using as input only packet- and flow-level data. In this paper, we study the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web-QoE measurements for the most popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias to the feasibility of these WebQoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of the art, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web-QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios. [Wassermann, Sarah; Casas, Pedro] AIT Austrian Inst Technol, Seibersdorf, Austria; [Ben Houidi, Zied; Huet, Alexis; Cai, Shengming; Shi, Hao; Xu, Jinchun; Rossi, Dario] Huawei Technol Co Ltd, Shenzhen, Peoples R China; [Seufert, Michael; Wehner, Nikolas; Schueler, Joshua; Hossfeld, Tobias] Univ Wurzburg, Wurzburg, Germany Austrian Institute of Technology (AIT); Huawei Technologies; University of Wurzburg Wassermann, S (corresponding author), AIT Austrian Inst Technol, Seibersdorf, Austria. Seufert, Michael/AAR-7846-2021 Seufert, Michael/0000-0002-5036-5206 27 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2165-9605 978-3-903176-31-7 INT CONF NETW SER 2020.0 9 Computer Science, Hardware & Architecture; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ6JK 2023-03-23 WOS:000612229200053 0
J Wang, H; He, HW; Li, JW; Bai, YF; Chang, YH; Yan, BZ Wang, Hao; He, Hongwen; Li, Jianwei; Bai, Yunfei; Chang, Yuhua; Yan, Beizhan Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles APPLIED SCIENCES-BASEL English Article adaptive model predictive control (AMPC); nonlinear autoregressive neural network; electric sanitation vehicle; energy management strategy; dynamic programming Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle's performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time. [Wang, Hao; He, Hongwen; Li, Jianwei; Bai, Yunfei] Beijing Inst Technol BIT, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China; [Wang, Hao; He, Hongwen; Li, Jianwei; Bai, Yunfei] Beijing Inst Technol BIT, Sch Mech Engn, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China; [Chang, Yuhua] Warsaw Univ Technol, Fac Automot & Construct Machinery Engn, PL-00661 Warsaw, Poland; [Yan, Beizhan] Yutong Bus Co Ltd, Zhengzhou 450016, Peoples R China Warsaw University of Technology; Yutong He, HW (corresponding author), Beijing Inst Technol BIT, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China.;He, HW (corresponding author), Beijing Inst Technol BIT, Sch Mech Engn, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China. bitwanghao20@163.com; hwhebit@bit.edu.cn; Jianwei.li@bath.edu; byfbit@163.com; yuhua.chang@pw.edu.pl; yanbz@yutong.com He, Hongwen/Y-2860-2019 Chang, Yuhua/0000-0002-0665-1838; , Hongwen/0000-0003-2874-1858; Wang, Hao/0000-0002-4280-554X National key R&D program of China [2018YFB0106200] National key R&D program of China This work was supported by the National key R&D program of China (2018YFB0106200). 31 0 1 0 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JAN 2021.0 11 2 498 10.3390/app11020498 0.0 24 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics PW8UF gold 2023-03-23 WOS:000610942400001 0
J Zhang, J; Mercado, R; Engkvist, O; Chen, HM Zhang, Jie; Mercado, Rocio; Engkvist, Ola; Chen, Hongming Comparative Study of Deep Generative Models on Chemical Space Coverage JOURNAL OF CHEMICAL INFORMATION AND MODELING English Article NOVO DESIGN; MOLECULAR GENERATION; NETWORKS In recent years, deep molecular generative models have emerged as promising methods for de novo molecular design. Thanks to the rapid advance of deep learning techniques, deep learning architectures such as recurrent neural networks, variational autoencoders, and adversarial networks have been successfully employed for constructing generative models. Recently, quite a few metrics have been proposed to evaluate these deep generative models. However, many of these metrics cannot evaluate the chemical space coverage of sampled molecules. This work presents a novel and complementary metric for evaluating deep molecular generative models. The metric is based on the chemical space coverage of a reference dataset. GDB-13. The performance of seven different molecular generative models was compared by calculating what fraction of the structures, ring systems, and functional groups could be reproduced from the largely unseen reference set when using only a small fraction of GDB-13 for training. The results show that the performance of the generative models studied varies significantly using the benchmark metrics introduced herein, such that the generalization capabilities of the generative models can be clearly differentiated. In addition, the coverages of GDB-13 ring systems and functional groups were compared between the models. Our study provides a useful new metric that can be used for evaluating and comparing generative models. [Zhang, Jie] Guangdong Lab Anim Monitoring Inst, Guangdong Prov Key Lab Lab Anim, Guangzhou 510663, Peoples R China; [Zhang, Jie] Chinese Acad Sci, Guangzhou Inst Biomed & Hlth, State Key Lab Resp Dis, Guangzhou 510530, Peoples R China; [Zhang, Jie; Chen, Hongming] Guangzhou Regenerat Med & Hlth Guangdong Lab, Bioland Lab, Guangzhou 510530, Peoples R China; [Mercado, Rocio; Engkvist, Ola] AstraZeneca, R&D, Discovery Sci, S-43183 Gothenburg, Sweden Chinese Academy of Sciences; Guangzhou Institute of Biomedicine & Health, CAS; State Key Laboratory of Respiratory Disease; Guangzhou Regenerative Medicine & Health Guangdong Laboratory (Bioisland Laboratory); AstraZeneca Chen, HM (corresponding author), Guangzhou Regenerat Med & Hlth Guangdong Lab, Bioland Lab, Guangzhou 510530, Peoples R China. chen_hongming@grmh-gdl.cn Zhang, Jie/0000-0002-5575-303X; Engkvist, Ola/0000-0003-4970-6461 XtalPi Inc; Postdoc Program at AstraZeneca XtalPi Inc; Postdoc Program at AstraZeneca Z. would like to acknowledge funding provided by XtalPi Inc and R. M. thanks the Postdoc Program at AstraZeneca. 51 12 12 2 21 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1549-9596 1549-960X J CHEM INF MODEL J. Chem Inf. Model. JUN 28 2021.0 61 6 2572 2581 10.1021/acs.jcim.0c01328 0.0 MAY 2021 10 Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy; Chemistry; Computer Science TD8AA 34015916.0 Green Submitted 2023-03-23 WOS:000669541400009 0
J Zhou, ZY; Gao, CX; Xu, C; Zhang, Y; Mumtaz, S; Rodriguez, J Zhou, Zhenyu; Gao, Caixia; Xu, Chen; Zhang, Yan; Mumtaz, Shahid; Rodriguez, Jonathan Social Big-Data-Based Content Dissemination in Internet of Vehicles IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Content dissemination; device-to-device vehicle-to-vehicle (D2D-V2V); Internet of things (IoT); Internet of vehicles (IoV); matching theory; Sina Weibo; social big data; Youku D2D; SYSTEMS By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains. [Zhou, Zhenyu; Gao, Caixia; Xu, Chen] North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China; [Zhang, Yan] Univ Oslo, Dept Informat, Oslo, Norway; [Zhang, Yan] Simula Res Lab, Oslo, Norway; [Mumtaz, Shahid; Rodriguez, Jonathan] Inst Telecomunicacoes, P-1049001 Aveiro, Portugal; [Rodriguez, Jonathan] Univ South Wales, Pontypridd CF37 1DL, M Glam, Wales North China Electric Power University; University of Oslo; Universidade de Aveiro; University of South Wales Xu, C (corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China. zhenyu_zhou@ncepu.edu.cn; gcx1026@163.com; chen.xu@ncepu.edu.cn; yanzhang@ieee.org; smumtaz@av.it.pt; jonathan@av.it.pt Zhang, Yan/AFK-8566-2022; Rodriguez, Jonathan/F-6055-2010; Mumtaz, Dr shahid/V-3603-2019 Rodriguez, Jonathan/0000-0001-9829-0955; Mumtaz, Dr shahid/0000-0001-6364-6149; Zhou, Zhenyu/0000-0002-3344-4463 National Science Foundation of China [61601181]; Fundamental Research Funds for the Central Universities [2017MS13]; Beijing Natural Science Foundation [4174104]; Beijing Outstanding Young Talent [2016000020124G081]; Research Council of Norway [240079/F20] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Beijing Outstanding Young Talent; Research Council of Norway(Research Council of Norway) This work was partially supported by the National Science Foundation of China under Grant 61601181, the Fundamental Research Funds for the Central Universities under Grant 2017MS13, by the Beijing Natural Science Foundation under Grant 4174104, and Beijing Outstanding Young Talent under Grant 2016000020124G081. This work was also partially supported by the projects 240079/F20 funded by the Research Council of Norway. 39 147 148 13 146 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2018.0 14 2 768 777 10.1109/TII.2017.2733001 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering FV3QM Green Published 2023-03-23 WOS:000424483600037 0
J Chen, HP; Shi, Y; Bo, B; Zhao, DH; Miao, P; Tong, SB; Wang, CL Chen, Heping; Shi, Yan; Bo, Bin; Zhao, Denghui; Miao, Peng; Tong, Shanbao; Wang, Chunliang Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation FRONTIERS IN NEUROSCIENCE English Article laser speckle contrast imaging; vessel segmentation; CycleGAN; domain adaptation; blood flow imaging Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal-noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%. [Chen, Heping; Shi, Yan; Bo, Bin; Zhao, Denghui; Miao, Peng; Tong, Shanbao] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China; [Chen, Heping; Wang, Chunliang] KTH Royal Inst Technol, Sch Technol & Hlth, Stockholm, Sweden Shanghai Jiao Tong University; Royal Institute of Technology Tong, SB (corresponding author), Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China. stong@sjtu.edu.cn Institute of Medical Robotics in Shanghai Jiao Tong University [IMR2018KY08] Institute of Medical Robotics in Shanghai Jiao Tong University Funding This work is partly supported by the Institute of Medical Robotics in Shanghai Jiao Tong University (No. IMR2018KY08). 31 0 0 13 25 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-453X FRONT NEUROSCI-SWITZ Front. Neurosci. NOV 30 2021.0 15 755198 10.3389/fnins.2021.755198 0.0 10 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology XT5PK 34916898.0 Green Accepted, gold 2023-03-23 WOS:000733639200001 0
J Cheng, JM; Liang, RY; Liang, ZL; Zhao, L; Huang, CW; Schuller, B Cheng, Jiaming; Liang, Ruiyu; Liang, Zhenlin; Zhao, Li; Huang, Chengwei; Schuller, Bjorn A Deep Adaptation Network for Speech Enhancement: Combining a Relativistic Discriminator With Multi-Kernel Maximum Mean Discrepancy IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Adaptation models; Training; Feature extraction; Acoustics; Noise measurement; Task analysis; Speech enhancement; Deep neural network; domain adaptation; maximum mean discrepancy; relativistic discriminator; speech enhancement NOISE In deep-learning-based speech enhancement (SE) systems, trained models are often used to handle unseen noise types and language environments in real-life scenarios. However, since production environments differ from training conditions, mismatch problems arise that may cause a serious decrease in the performance of an SE system. In this study, a domain adaptive method combining two adaptation strategies is proposed to improve the generalization of unlabeled noisy speech. In the proposed encoder-decoder-based SE framework, a domain discriminator and a domain confusion adaptation layer are introduced to conduct adversarial training. The model has two main innovations. First, the algorithm optimizes adversarial training by introducing a relativistic discriminator that relies on relative values by applying the difference, thus avoiding possible bias and better reflecting domain differences. Second, the multi-kernel maximum mean discrepancy (MK-MMD) between domains is taken as the regularization term of the domain adversarial loss, thereby further decreasing the edge distribution distance between domains. The proposed model improves the adaptability to unseen noises by encouraging the feature encoder to generate domain-invariant features. The model was evaluated using cross-noise and cross-language-and-noise experiments, and the results show that the proposed method provides considerable improvements over the baseline without an adaptation in the perceptual evaluation of speech quality (PESQ), the short time objective intelligibility (STOI) and the frequency-weighted signal-to-noise ratio (FWSNR). [Cheng, Jiaming; Liang, Ruiyu; Liang, Zhenlin; Zhao, Li] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China; [Liang, Ruiyu] Nanjing Inst Technol, Sch Informat & Commun Engn, Nanjing 211167, Peoples R China; [Huang, Chengwei] Chinese Acad Sci Co Ltd, Sugon Nanjing Inst, Nanjing 211106, Peoples R China; [Schuller, Bjorn] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, D-86159 Augsburg, Germany; [Schuller, Bjorn] Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England Southeast University - China; Nanjing Institute of Technology; University of Augsburg; Imperial College London Liang, RY (corresponding author), Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China. 230198469@seu.edu.cn; liangry@njit.edu.cn; zhenlinliang1@163.com; zhaoli@seu.edu.cn; huangcwx@126.com; bjoern.schuller@imperial.ac.uk Huang, Chengwei/AER-6849-2022 Huang, Chengwei/0000-0001-9060-6361; cheng, jiaming/0000-0002-7136-7876; Schuller, Bjorn/0000-0002-6478-8699 National Key Research and Development Program of China [2020YFC2004002, 2020YFC2004003]; National Natural Science Foundation of China [62001215] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grants 2020YFC2004002 and 2020YFC2004003, and in part by the National Natural Science Foundation of China under Grant 62001215. 45 3 4 3 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 2329-9304 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. 2021.0 29 41 53 10.1109/TASLP.2020.3036611 0.0 13 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering PC7ES Green Submitted 2023-03-23 WOS:000597160600004 0
J Lalande, A; Chen, ZH; Pommier, T; Decourselle, T; Qayyum, A; Salomon, M; Ginhac, D; Skandarani, Y; Boucher, A; Brahim, K; de Bruijne, M; Camarasa, R; Correia, TM; Feng, X; Girum, KB; Hennemuth, A; Huellebrand, M; Hussain, R; Ivantsits, M; Ma, J; Meyer, C; Sharma, R; Shi, JX; Tsekos, NV; Varela, M; Wang, XY; Yang, S; Zhang, HN; Zhang, YC; Zhou, YC; Zhuang, XH; Couturier, R; Meriaudeau, F Lalande, Alain; Chen, Zhihao; Pommier, Thibaut; Decourselle, Thomas; Qayyum, Abdul; Salomon, Michel; Ginhac, Dominique; Skandarani, Youssef; Boucher, Arnaud; Brahim, Khawla; de Bruijne, Marleen; Camarasa, Robin; Correia, Teresa M.; Feng, Xue; Girum, Kibrom B.; Hennemuth, Anja; Huellebrand, Markus; Hussain, Raabid; Ivantsits, Matthias; Ma, Jun; Meyer, Craig; Sharma, Rishabh; Shi, Jixi; Tsekos, Nikolaos V.; Varela, Marta; Wang, Xiyue; Yang, Sen; Zhang, Hannu; Zhang, Yichi; Zhou, Yuncheng; Zhuang, Xiahai; Couturier, Raphael; Meriaudeau, Fabrice Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge MEDICAL IMAGE ANALYSIS English Article DE-MRI; Myocardium; Infarction; CNN MYOCARDIAL VIABILITY; SEGMENTATION; INFARCT; HEART A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. (c) 2022 Elsevier B.V. All rights reserved. [Lalande, Alain; Qayyum, Abdul; Ginhac, Dominique; Skandarani, Youssef; Boucher, Arnaud; Brahim, Khawla; Girum, Kibrom B.; Hussain, Raabid; Meriaudeau, Fabrice] Univ Burgundy, ImViA Lab, Dijon, France; [Lalande, Alain] Univ Hosp Dijon, MRI Dept, Dijon, France; [Chen, Zhihao; Salomon, Michel; Shi, Jixi; Couturier, Raphael] Univ Franche Comte, Femto ST Lab, Belfort, France; [Pommier, Thibaut] Univ Hosp Dijon, Cardiol Dept, Dijon, France; [Decourselle, Thomas] CASIS CArdiac Simulat & Imaging Software SAS, Quetigny, France; [Brahim, Khawla] Univ Sousse, Natl Engn Sch Sousse, Sousse, Tunisia; [Brahim, Khawla] Univ Monastir, Natl Engn Sch Monastir, LASEE Lab, Monastir, Tunisia; [de Bruijne, Marleen; Camarasa, Robin] Erasmus MC, Biomed Imaging Grp Rotterdam, Rotterdam, Netherlands; [de Bruijne, Marleen; Camarasa, Robin] Erasmus MC, Dept Radiol & Nucl Med, Rotterdam, Netherlands; [de Bruijne, Marleen] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark; [Correia, Teresa M.] Univ Algarve, Ctr Marine Sci, Faro, Portugal; [Correia, Teresa M.] Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England; [Feng, Xue; Meyer, Craig] Univ Virginia, Dept Biomed Engn, Charlottesville, VA USA; [Hennemuth, Anja; Huellebrand, Markus; Ivantsits, Matthias; Zhang, Hannu] Charite Univ Med Berlin, Berlin, Germany; [Hennemuth, Anja; Huellebrand, Markus] Fraunhofer MEVIS, Bremen, Germany; [Hennemuth, Anja] German Ctr Cardiovasc Res, Berlin, Germany; [Ma, Jun] Nanjing Univ Sci & Technol, Dept Math, Nanjing, Peoples R China; [Sharma, Rishabh] Univ Houston, Dept Comp Sci, Data Anal & Intelligent Syst Lab, Houston, TX USA; [Sharma, Rishabh; Tsekos, Nikolaos V.] Univ Houston, Dept Comp Sci, Med Robot & Imaging Lab, Houston, TX USA; [Varela, Marta] Imperial Coll London, Natl Heart & Lung Inst, London, England; [Wang, Xiyue] Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China; [Yang, Sen] Sichuan Univ, Coll Biomed Engn, Chengdu, Peoples R China; [Zhang, Yichi] Beihang Univ, Sch Biol Sci & Med Engn, Beijing, Peoples R China; [Zhou, Yuncheng; Zhuang, Xiahai] Fudan Univ, Sch Data Sci, Shanghai, Peoples R China; [Lalande, Alain] Univ Burgundy, Fac Med, ImViA Lab, 7 Bld Jeanne Arc, F- 21000 Dijon, France Universite de Bourgogne; CHU Dijon Bourgogne; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); CHU Dijon Bourgogne; Universite de Sousse; Universite de Monastir; Erasmus University Rotterdam; Erasmus MC; Erasmus University Rotterdam; Erasmus MC; University of Copenhagen; Universidade do Algarve; University of London; King's College London; University of Virginia; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; German Centre for Cardiovascular Research; Nanjing University of Science & Technology; University of Houston System; University of Houston; University of Houston System; University of Houston; Imperial College London; Sichuan University; Sichuan University; Beihang University; Fudan University; Universite de Bourgogne Lalande, A (corresponding author), Univ Burgundy, Fac Med, ImViA Lab, 7 Bld Jeanne Arc, F- 21000 Dijon, France. alain.lalande@u-bourgogne.fr Correia, Teresa Matias/ABB-4135-2020; Salomon, Michel/T-7328-2019; chen, zhihao/GXM-3816-2022; Varela, Marta/AAU-3335-2021; Zhang, Yichi/GZM-1393-2022; Zhuang, Xiahai/AAH-6334-2019; Lalande, Alain/T-1050-2017; Wang, Xiyue/GXM-5641-2022; de Bruijne, Marleen/G-2662-2010; Ginhac, Dominique/C-5088-2008 Correia, Teresa Matias/0000-0002-1606-9550; Salomon, Michel/0000-0002-1119-2760; Varela, Marta/0000-0003-4057-7851; Zhang, Yichi/0000-0002-4292-6835; Zhuang, Xiahai/0000-0003-4351-4979; Wang, Xiyue/0000-0002-3597-9090; Yang, Sen/0000-0002-0639-4122; de Bruijne, Marleen/0000-0002-6328-902X; Hullebrand, Markus/0000-0003-4948-0917; Hennemuth, Anja/0000-0002-0737-7375; Ivantsits, Matthias/0000-0003-0317-7154; ma, jun/0000-0002-9739-0855; CHEN, Zhihao/0000-0002-6906-813X; POMMIER, THIBAUT/0000-0002-1637-8855; Ginhac, Dominique/0000-0002-5911-2010; decourselle, thomas/0000-0001-6794-5306; Lalande, Alain/0000-0002-7970-366X ISITE-BFC [ANR-17-EURE-0002]; french ADVANCES [ANR-15-IDEX-0003] ISITE-BFC; french ADVANCES This work was supported by the ADVANCES project founded by ISITE-BFC project (number ANR-15-IDEX-0003) and by the french ADVANCES project founded (contract ANR-17-EURE-0002) . 53 2 2 8 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JUL 2022.0 79 102428 10.1016/j.media.2022.102428 0.0 APR 2022 12 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging 1V6YI 35500498.0 Green Published, Green Submitted 2023-03-23 WOS:000806232300004 0
C Liu, TC; Yang, Y; Huang, GB; Lin, ZP; Klanner, F; Denk, C; Rasshofer, RH IEEE Liu, Tianchi; Yang, Yan; Huang, Guang-Bin; Lin, Zhiping; Klanner, Felix; Denk, Cornelia; Rasshofer, Ralph H. Cluster regularized extreme learning machine for detecting mixed-type distraction in driving 2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper 18th IEEE International Conference on Intelligent Transportation Systems SEP 15-18, 2015 SPAIN IEEE,Int Transportat Syst Soc,CVCEI,Univ Las Palmas Gran Canaria,Ayuntamentiao Las Palmas Gran Canaria,LPA,IEEE Comp Soc,Univ Las Palmas Gran Canaria, Inst Univ Ciencias Tecnologias Ciberneticas Distraction was previously studied within each dimension separately, i.e., physical, cognitive and visual. However real-world activities usually involve multiple distraction dimensions in terms of brain resources that might conflict with the driving task. This brings difficulties for classifying dimension/type of distraction even for human experts. On the other hand, many subsequent functional blocks do not utilize distraction type information. For example, a pre-collision system usually makes decision based on distraction level rather than distraction type. Therefore this study aims to detect distraction in general regardless of its type, and proposes an effective machine learning algorithm, i.e., Cluster Regularized Extreme Learning Machine (CR-ELM), to detect mixed-type distraction in driving. Compared to traditional machine learning techniques, CR-ELM is designed to handle problems with multiple clusters per class, and provides more accurate detection performance, which could be used for advanced driver assistance systems. [Liu, Tianchi; Huang, Guang-Bin; Lin, Zhiping] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Yang, Yan; Klanner, Felix] Nanyang Technol Univ, Energy Res Inst NTU ERI N, Singapore 639798, Singapore; [Yang, Yan] Southeast Univ, Sch Informat Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Denk, Cornelia; Rasshofer, Ralph H.] BMW Grp, Projekte ConnectedDrive, Munich, Germany Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Southeast University - China; BMW AG Liu, TC (corresponding author), Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore. tcliu@ntu.edu.sg; y.yang@ntu.edu.sg; egbhuang@ntu.edu.sg; ezplin@ntu.edu.sg; klanner@ntu.edu.sg; cornelia.denk@bmw.de; ralph.rasshofer@bmw.de Lin, Zhiping/AAF-2719-2020; Huang, Guang-Bin/A-5035-2011 Lin, Zhiping/0000-0002-1587-1226; Huang, Guang-Bin/0000-0002-2480-4965 15 12 14 2 12 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-4673-6596-3 IEEE INT C INTELL TR 2015.0 1323 1326 10.1109/ITSC.2015.217 0.0 4 Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Transportation BE8OT 2023-03-23 WOS:000376668801063 0
J Li, HJ; Leung, KS; Wong, MH; Ballester, PJ Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J. Correcting the impact of docking pose generation error on binding affinity prediction BMC BIOINFORMATICS English Article; Proceedings Paper 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) JUN 26-28, 2014 Cambridge, ENGLAND Bioinformat Italian Soc,Source Code Biol & Med,Univ Salerno, Dipartimento Informatica,Vita Salute San Raffaele Univ, Centro Univ Statistica Sci Biomediche,Univ Cambridge, Comp Lab,Int Neural Network Soc, Bioinformat & Intelligence SIG,IEEE CIS BBCT Task Forces Neural Networks & Evolutionary Computat Molecular docking; Binding affinity; Drug discovery; Machine learning RANDOM FOREST; SCORING FUNCTIONS; ACCURACY Background: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes. Results: Against commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand. Conclusions: Binding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/ rf-score-4.tgz and http://ballester.marseille.inserm.fr/rf-score-4.tgz. [Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China; [Ballester, Pedro J.] INSERM, U1068, Canc Res Ctr Marseille, F-13009 Marseille, France; [Ballester, Pedro J.] Inst Paoli Calmettes, F-13009 Marseille, France; [Ballester, Pedro J.] Aix Marseille Univ, F-13284 Marseille, France; [Ballester, Pedro J.] CNRS, UMR7258, F-13009 Marseille, France Chinese University of Hong Kong; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; UNICANCER; Institut Paoli-Calmette (IPC); UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Aix-Marseille Universite Ballester, PJ (corresponding author), INSERM, U1068, Canc Res Ctr Marseille, F-13009 Marseille, France.;Ballester, PJ (corresponding author), Inst Paoli Calmettes, F-13009 Marseille, France. pedro.ballester@inserm.fr Wong, Man Hon/GOH-0560-2022; Ballester, Pedro/A-1148-2008 Ballester, Pedro/0000-0002-4078-743X A*MIDEX grant - French Government Investissements d'Avenir program [ANR-11-IDEX-0001-02]; Chinese University of Hong Kong; Research Grants Council of Hong Kong SAR [414413] A*MIDEX grant - French Government Investissements d'Avenir program(French National Research Agency (ANR)); Chinese University of Hong Kong(Chinese University of Hong Kong); Research Grants Council of Hong Kong SAR(Hong Kong Research Grants Council) This work has been carried out thanks to the support of the A*MIDEX grant (no ANR-11-IDEX-0001-02) funded by the French Government Investissements d'Avenir program, the Direct Grant from the Chinese University of Hong Kong and the GRF Grant (Project Reference 414413) from the Research Grants Council of Hong Kong SAR. Publication of this article was funded by the Direct Grant from the Chinese University of Hong Kong and the GRF Grant (Project Reference 414413) from the Research Grants Council of Hong Kong SAR. This article has been published as part of BMC Bioinformatics Volume 17 Supplement 11, 2016. Selected articles from the 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2014). The full contents of the supplement are available online https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-17supplement- 11. 17 34 34 0 9 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics SEP 22 2016.0 17 11 308 10.1186/s12859-016-1169-4 0.0 13 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology EI3VX 28185549.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000392421500001 0
J Van Lissa, CJ; Stroebe, W; VanDellen, MR; Leander, NP; Agostini, M; Draws, T; Grygoryshyn, A; Gutzgow, B; Kreienkamp, J; Vetter, CS; Abakoumkin, G; Khaiyom, JHA; Ahmedi, V; Akkas, H; Almenara, CA; Atta, M; Bagci, SC; Basel, S; Kida, EB; Bernardo, ABI; Buttrick, NR; Chobthamkit, P; Choi, HS; Cristea, M; Csaba, S; Damnjanovic, K; Danyliuk, I; Dash, A; Di Santo, D; Douglas, KM; Enea, V; Faller, DG; Fitzsimons, GJ; Gheorghiu, A; Gomez, A; Hamaidia, A; Han, Q; Helmy, M; Hudiyana, J; Jeronimus, BF; Jiang, DY; Jovanovic, V; Kamenov, Z; Kende, A; Keng, SL; Kieu, TTT; Koc, Y; Kovyazina, K; Kozytska, I; Krause, J; Kruglanksi, AW; Kurapov, A; Kutlaca, M; Lantos, NA; Lemay, EP; Lesmana, CBJ; Louis, WR; Lueders, A; Malik, NI; Martinez, AP; McCabe, KO; Mehulic, J; Milla, MN; Mohammed, I; Molinario, E; Moyano, M; Muhammad, H; Mula, S; Muluk, H; Myroniuk, S; Najafi, R; Nisa, CF; Nyul, B; O'Keefe, PA; Osuna, JJO; Osin, EN; Park, J; Pica, G; Pierro, A; Rees, JH; Reitsema, AM; Resta, E; Rullo, M; Ryan, MK; Samekin, A; Santtila, P; Sasin, EM; Schumpe, BM; Selim, HA; Stanton, MV; Sultana, S; Sutton, RM; Tseliou, E; Utsugi, A; van Breen, JA; Van Veen, K; Vazquez, A; Wollast, R; Yeung, VWL; Zand, S; Zezelj, IL; Zheng, B; Zick, A; Zuniga, C; Belanger, JJ Van Lissa, Caspar J.; Stroebe, Wolfgang; VanDellen, Michelle R.; Leander, N. Pontus; Agostini, Maximilian; Draws, Tim; Grygoryshyn, Andrii; Gutzgow, Ben; Kreienkamp, Jannis; Vetter, Clara S.; Abakoumkin, Georgios; Khaiyom, Jamilah Hanum Abdul; Ahmedi, Vjolica; Akkas, Handan; Almenara, Carlos A.; Atta, Mohsin; Bagci, Sabahat Cigdem; Basel, Sima; Kida, Edona Berisha; Bernardo, Allan B., I; Buttrick, Nicholas R.; Chobthamkit, Phatthanakit; Choi, Hoon-Seok; Cristea, Mioara; Csaba, Sara; Damnjanovic, Kaja; Danyliuk, Ivan; Dash, Arobindu; Di Santo, Daniela; Douglas, Karen M.; Enea, Violeta; Faller, Daiane Gracieli; Fitzsimons, Gavan J.; Gheorghiu, Alexandra; Gomez, Angel; Hamaidia, Ali; Han, Qing; Helmy, Mai; Hudiyana, Joevarian; Jeronimus, Bertus F.; Jiang, Ding-Yu; Jovanovic, Veljko; Kamenov, Zeljka; Kende, Anna; Keng, Shian-Ling; Tra Thi Thanh Kieu; Koc, Yasin; Kovyazina, Kamila; Kozytska, Inna; Krause, Joshua; Kruglanksi, Arie W.; Kurapov, Anton; Kutlaca, Maja; Lantos, Nora Anna; Lemay, Edward P.; Lesmana, Cokorda Bagus Jaya; Louis, Winnifred R.; Lueders, Adrian; Malik, Najma Iqbal; Martinez, Anton P.; McCabe, Kira O.; Mehulic, Jasmina; Milla, Mirra Noor; Mohammed, Idris; Molinario, Erica; Moyano, Manuel; Muhammad, Hayat; Mula, Silvana; Muluk, Hamdi; Myroniuk, Solomiia; Najafi, Reza; Nisa, Claudia F.; Nyul, Boglarka; O'Keefe, Paul A.; Osuna, Jose Javier Olivas; Osin, Evgeny N.; Park, Joonha; Pica, Gennaro; Pierro, Antonio; Rees, Jonas H.; Reitsema, Anne Margit; Resta, Elena; Rullo, Marika; Ryan, Michelle K.; Samekin, Adil; Santtila, Pekka; Sasin, Edyta M.; Schumpe, Birga M.; Selim, Heyla A.; Stanton, Michael Vicente; Sultana, Samiah; Sutton, Robbie M.; Tseliou, Eleftheria; Utsugi, Akira; van Breen, Jolien Anne; Van Veen, Kees; Vazquez, Alexandra; Wollast, Robin; Yeung, Victoria Wai-Lan; Zand, Somayeh; Zezelj, Iris Lav; Zheng, Bang; Zick, Andreas; Zuniga, Claudia; Belanger, Jocelyn J. Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic PATTERNS English Article COLLECTIVE ACTION; MODEL; HEALTH; VALIDATION; RATIONALE; VALIDITY; SCALE Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample-exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior-and some theoretically derived predictors were relatively unimportant. [Van Lissa, Caspar J.] Univ Utrecht, Utrecht, Netherlands; [Stroebe, Wolfgang; Leander, N. Pontus; Agostini, Maximilian; Gutzgow, Ben; Kreienkamp, Jannis; Jeronimus, Bertus F.; Koc, Yasin; Krause, Joshua; Myroniuk, Solomiia; Reitsema, Anne Margit; Ryan, Michelle K.; Sultana, Samiah; Van Veen, Kees] Univ Groningen, Groningen, Netherlands; [VanDellen, Michelle R.] Univ Georgia, Athens, GA 30602 USA; [Draws, Tim] Delft Univ Technol, Delft, Netherlands; [Grygoryshyn, Andrii; Vetter, Clara S.; Schumpe, Birga M.] Univ Amsterdam, Amsterdam, Netherlands; [Abakoumkin, Georgios; Tseliou, Eleftheria] Univ Thessaly, Volos, Greece; [Khaiyom, Jamilah Hanum Abdul] Int Islamic Univ Malaysia, Gombak, Selangor, Malaysia; [Ahmedi, Vjolica; Kida, Edona Berisha] Univ Pristina, Pristina, Kosovo; [Akkas, Handan] Ankara Sci Univ, Ankara, Turkey; [Almenara, Carlos A.] Univ Peruana Ciencias Aplicadas, Lima, Peru; [Atta, Mohsin; Malik, Najma Iqbal] Univ Sargodha, Punjab, Pakistan; [Bagci, Sabahat Cigdem] Sabanci Univ, Tuzla, Turkey; [Basel, Sima; Faller, Daiane Gracieli; Nisa, Claudia F.; Sasin, Edyta M.; Belanger, Jocelyn J.] New York Univ Abu Dhabi, Abu Dhabi, U Arab Emirates; [Bernardo, Allan B., I] De La Salle Univ, Manila, Philippines; [Buttrick, Nicholas R.] Univ Virginia, Charlottesville, VA USA; [Chobthamkit, Phatthanakit] Thammasat Univ, Bangkok, Thailand; [Choi, Hoon-Seok] Sungkyunkwan Univ, Seoul, South Korea; [Cristea, Mioara] Heriot Watt Univ, Edinburgh, Midlothian, Scotland; [Csaba, Sara; Kende, Anna; Lantos, Nora Anna; Nyul, Boglarka] Eotvos Lorand Univ, Budapest, Hungary; [Damnjanovic, Kaja; Zezelj, Iris Lav] Univ Belgrade, Belgrade, Serbia; [Danyliuk, Ivan; Kozytska, Inna] Taras Shevchenko Natl Univ Kyiv, Kiev, Ukraine; [Dash, Arobindu] Leuphana Univ Luneburg, Luneburg, Germany; [Di Santo, Daniela; Mula, Silvana; Pierro, Antonio; Resta, Elena] Univ Roma La Sapienza, Rome, Italy; [Douglas, Karen M.; Sutton, Robbie M.] Univ Kent, Canterbury, Kent, England; [Enea, Violeta; Gheorghiu, Alexandra] Alexandru Ioan Cuza Univ, Iasi, Romania; [Fitzsimons, Gavan J.] Duke Univ, Durham, NC USA; [Gomez, Angel; Osuna, Jose Javier Olivas; Vazquez, Alexandra] Univ Nacl Educ Distancia, Madrid, Spain; [Hamaidia, Ali] Setif 2 Univ, Setif, Algeria; [Han, Qing] Univ Bristol, Bristol, Avon, England; [Helmy, Mai] Sultan Qaboos Univ, Muscat, Oman; [Helmy, Mai; Muluk, Hamdi] Menoufia Univ, Shibin Al Kawm, Al Minufiyah, Egypt; [Hudiyana, Joevarian; Milla, Mirra Noor] Univ Indonesia, Jawa Barat, Indonesia; [Jiang, Ding-Yu] Natl Chung Cheng Univ, Chiayi, Taiwan; [Jovanovic, Veljko] Univ Novi Sad, Novi Sad, Serbia; [Kamenov, Zeljka; Mehulic, Jasmina] Univ Zagreb, Zagreb, Croatia; [Keng, Shian-Ling] Monash Univ, Melbourne, Vic, Australia; [Tra Thi Thanh Kieu] HCMC Univ Educ, Ho Chi Minh City, Vietnam; [Kruglanksi, Arie W.; Lemay, Edward P.] Univ Maryland, College Pk, MD 20742 USA; [Kurapov, Anton] Univ Durham, Durham, England; [Kutlaca, Maja; Lesmana, Cokorda Bagus Jaya; Wollast, Robin] Udayana Univ, Kuta Selatan, Indonesia; [Louis, Winnifred R.] Univ Queensland, St Lucia, Qld, Australia; [Lueders, Adrian] Univ Limerick, Limerick, Ireland; [Martinez, Anton P.] Univ Sheffield, Sheffield, S Yorkshire, England; [McCabe, Kira O.] Carleton Univ, Ottawa, ON, Canada; [Mohammed, Idris] Usmanu Danfodiyo Univ Sokoto, Sokoto, Nigeria; [Molinario, Erica; Zand, Somayeh] Florida Gulf Coast Univ, Ft Myers, FL USA; [Moyano, Manuel] Univ Cordoba, Cordoba, Spain; [Muhammad, Hayat] Univ Peshawar, Peshawar, Pakistan; [Najafi, Reza] Univ Padua, Padua, Italy; [O'Keefe, Paul A.] Yale NUS Coll, Singapore, Singapore; [Osin, Evgeny N.] HSE Univ, Moscow, Russia; [Park, Joonha] NUCB Business Sch, Nagoya, Aichi, Japan; [Pica, Gennaro] Univ Camerino, Camerino, Italy; [Rees, Jonas H.; Zick, Andreas] Bielefeld Univ, Bielefeld, Germany; [Rullo, Marika] Univ Siena, Arezzo Campus, Siena, Italy; [Ryan, Michelle K.] Univ Exeter, Exeter, Devon, England; [Samekin, Adil] M Narikbayev KAZGUU Univ, Nur Sultan, Kazakhstan; [Santtila, Pekka] New York Univ Shanghai, Shanghai, Peoples R China; [Selim, Heyla A.] King Saud Univ, Riyadh, Saudi Arabia; [Stanton, Michael Vicente] Calif State Univ East Bay, Hayward, CA USA; [Utsugi, Akira] Nagoya Univ, Nagoya, Aichi, Japan; [van Breen, Jolien Anne] Leiden Univ, Leiden, Netherlands; [Yeung, Victoria Wai-Lan] Lingnan Univ, Tuen Mun, Hong Kong, Peoples R China; [Zheng, Bang] Imperial Coll London, London, England; [Zuniga, Claudia] Univ Chile, Santiago, Chile; [Leander, N. Pontus] Wayne State Univ, Detroit, MI USA; [Van Lissa, Caspar J.] Open Sci Community Utrecht, Utrecht, Netherlands Utrecht University; University of Groningen; University System of Georgia; University of Georgia; Delft University of Technology; University of Amsterdam; University of Thessaly; International Islamic University Malaysia; Ankara Science University; Universidad Peruana de Ciencias Aplicadas (UPC); University of Sargodha; Sabanci University; De La Salle University; University of Virginia; Thammasat University; Sungkyunkwan University (SKKU); Heriot Watt University; Eotvos Lorand University; University of Belgrade; Ministry of Education & Science of Ukraine; Taras Shevchenko National University Kiev; Leuphana University Luneburg; Sapienza University Rome; University of Kent; Alexandru Ioan Cuza University; Duke University; Universidad Nacional de Educacion a Distancia (UNED); University of Bristol; Sultan Qaboos University; Egyptian Knowledge Bank (EKB); Menofia University; University of Indonesia; National Chung Cheng University; University of Novi Sad; University of Zagreb; Monash University; University System of Maryland; University of Maryland College Park; Durham University; Universitas Udayana; University of Queensland; University of Limerick; University of Sheffield; Carleton University; State University System of Florida; Florida Gulf Coast University; Universidad de Cordoba; University of Peshawar; University of Padua; Yale NUS College; HSE University (National Research University Higher School of Economics); University of Camerino; University of Bielefeld; University of Siena; University of Exeter; M. Narikbayev KAZGUU University; NYU Shanghai; King Saud University; California State University System; California State University East Bay; Nagoya University; Leiden University; Leiden University - Excl LUMC; Lingnan University; Imperial College London; Universidad de Chile; Wayne State University Van Lissa, CJ (corresponding author), Univ Utrecht, Utrecht, Netherlands.;Van Lissa, CJ (corresponding author), Open Sci Community Utrecht, Utrecht, Netherlands. c.j.vanlissa@uu.nl Koc, Yasin/Y-4811-2019; Gómez, Ángel/C-6108-2011; Damnjanovic, Kaja/AAT-1684-2020; AKKAS, HANDAN/J-1828-2013; Enea, Violeta/GQH-7512-2022; Berisha Kida, Edona/HKN-9805-2023; Mula, Silvana/GXH-3896-2022; Žeželj, Iris/H-9339-2019; Jovanović, Veljko/X-7355-2019; Danyliuk, Ivan/B-9146-2019; Selim, Heyla/GQQ-6663-2022; Osin, Evgeny N./G-6558-2012; Atta, Mohsin/GXZ-9991-2022; Di Santo, Daniela/HGB-6967-2022; Enea, Violeta/AAR-8526-2021; malik, najma iqbal/K-7738-2019; Louis, Winnifred/A-1968-2008; Cristea, Mioara/V-3771-2018; Yeung, Victoria/P-9054-2018 Koc, Yasin/0000-0002-6727-3842; Gómez, Ángel/0000-0002-4287-4916; Damnjanovic, Kaja/0000-0002-9254-1263; AKKAS, HANDAN/0000-0002-2082-0685; Enea, Violeta/0000-0003-3789-2017; Berisha Kida, Edona/0000-0001-6119-1507; Mula, Silvana/0000-0002-8391-630X; Žeželj, Iris/0000-0002-9527-1406; Jovanović, Veljko/0000-0001-9248-2518; Danyliuk, Ivan/0000-0002-6522-5994; Osin, Evgeny N./0000-0003-3330-5647; Atta, Mohsin/0000-0002-5198-5271; Enea, Violeta/0000-0003-3789-2017; malik, najma iqbal/0000-0002-3521-1014; HAMAIDIA, Ali/0000-0002-9224-7208; Schumpe, Birga/0000-0001-5129-9928; Park, Joonha/0000-0002-0764-5173; Krause, Joshua/0000-0002-3165-2584; Kurapov, Anton/0000-0002-1286-9788; Belanger, Jocelyn/0000-0003-3881-0335; Di Santo, Daniela/0000-0002-1438-5832; Kieu, Tra Thi Thanh/0000-0001-6269-7796; Louis, Winnifred/0000-0003-2996-982X; Zheng, Bang/0000-0003-1814-6692; Cristea, Mioara/0000-0002-2944-3791; Draws, Tim/0000-0001-5053-4674; Yeung, Victoria/0000-0002-3479-3198; vanDellen, Michelle/0000-0002-3457-194X; Rees, Jonas/0000-0003-4966-2649; rullo, marika/0000-0003-0642-5985; Myroniuk, Solomiia/0000-0002-6374-7449; Han, Qing/0000-0002-2904-2007 NWO Veni Grant (NWO) [VI.Veni.191G.090]; New York University Abu Dhabi [VCDSF/75-71015]; University of Groningen (Sustainable Society); Instituto de Salud Carlos III - European Regional Development Fund (ERDF) A way to make Europe'' [COV20/00086]; University of Groningen (Ubbo Emmius Fund) NWO Veni Grant (NWO); New York University Abu Dhabi; University of Groningen (Sustainable Society); Instituto de Salud Carlos III - European Regional Development Fund (ERDF) A way to make Europe''; University of Groningen (Ubbo Emmius Fund) The lead author was funded by a NWO Veni Grant (NWO Grant Number VI.Veni.191G.090). This research received support from the New York University Abu Dhabi (VCDSF/75-71015), the University of Groningen (Sustainable Society & Ubbo Emmius Fund), and the Instituto de Salud Carlos III (COV20/00086) co-funded by the European Regional Development Fund (ERDF) ``A way to make Europe.'' 61 3 3 4 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns APR 8 2022.0 3 4 100482 10.1016/j.patter.2022.100482 0.0 APR 2022 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science 1J7XH 35282654.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000798127700003 0
J Tang, C; Liu, XW; Zheng, X; Li, WQ; Xiong, J; Wang, LZ; Zomaya, A; Longo, A Tang, Chang; Liu, Xinwang; Zheng, Xiao; Li, Wanqing; Xiong, Jian; Wang, Lizhe; Zomaya, Albert; Longo, Antonella DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Discriminative Multi-Scale Deep Features IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Feature extraction; Neural networks; Semantics; Image edge detection; Fuses; Task analysis; Machine learning; Defocus blur detection; multi-scale features; feature fusing; channel attention MAP ESTIMATION; SINGLE-IMAGE; NETWORK Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet. [Tang, Chang; Wang, Lizhe] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China; [Liu, Xinwang; Zheng, Xiao] Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Peoples R China; [Zheng, Xiao] Peng Cheng Lab, Shenzhen 518066, Peoples R China; [Li, Wanqing] Univ Wollongong, Sch Comp & Informat Technol, Keiraville, NSW 2500, Australia; [Xiong, Jian] Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Sichuan, Peoples R China; [Zomaya, Albert] Univ Sydney, Sch Informat Technol, Camperdown, NSW 2006, Australia; [Longo, Antonella] Univ Salento, Dept Innovat Engn, I-73100 Lecce, Italy China University of Geosciences; National University of Defense Technology - China; Peng Cheng Laboratory; University of Wollongong; Southwestern University of Finance & Economics - China; University of Sydney; University of Salento Liu, XW; Zheng, X (corresponding author), Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Peoples R China. tangchang@cug.edu.cn; xinwangliu@nudt.edu.cn; endozheng@gmail.com; wanqing@uow.edu.au; xiongjian2017@swufe.edu.cn; Lizhe.Wang@gmail.com; albert.zomaya@sydney.edu.au; antonella.longo@unisalento.it Longo, Antonella/A-5335-2009; Tang, Chang/AAU-8995-2020; Zomaya, Albert Y./G-9697-2017; Wang, Lizhe/L-7453-2014 Longo, Antonella/0000-0002-6902-0160; Tang, Chang/0000-0002-6515-7696; Zomaya, Albert Y./0000-0002-3090-1059; Li, Wanqing/0000-0002-4427-2687; Wang, Lizhe/0000-0003-2766-0845; LIU, Xinwang/0000-0001-9066-1475 National Natural Science Foundation of China [61701451, 61773392, 61901205, U1711266, 41925007]; Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education [GLAB2020ZR18]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors would like to thank the anonymous reviewers for their constructive comments for improving this paper and also like to thank NVIDIA Corporation for the donation of a Titan Xp GPU card and a Titan V GPU card used for this research.. This work was supported by the National Natural Science Foundation of China (No. 61701451, 61773392, 61901205, U1711266, and 41925007) and the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (No. GLAB2020ZR18) and the Fundamental Research Funds for the Central Universities. 61 40 40 31 68 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. FEB 1 2022.0 44 2 955 968 10.1109/TPAMI.2020.3014629 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YC9LS 32759080.0 2023-03-23 WOS:000740006100030 0
J Zhang, ZG; Csanyi, G; Alfe, D Zhang, Zhigang; Csanyi, Gabor; Alfe, Dario Partitioning of sulfur between solid and liquid iron under Earth's core conditions: Constraints from atomistic simulations with machine learning potentials GEOCHIMICA ET COSMOCHIMICA ACTA English Article Partition coefficient; Sulfur; Earth's core; First principles; Machine learning; Light elements; Density functional theory INNER-CORE; OUTER CORE; MELTING EXPERIMENTS; FE-FE3S SYSTEM; FE-S; TEMPERATURES; CONVECTION; DENSITY; ALLOYS; STRATIFICATION Partition coefficients of light elements between the solid and liquid iron phases are crucial for uncovering the state and dynamics of the Earth's core. As one of the major light element candidates, sulfur has attracted extensive interests for measuring its partitioning and phase behaviors over the last several decades, but the relevant experimental data under Earth's core conditions are still scarce. In this study, using a toolkit consisting of electronic structure theory, high-accuracy machine learning potentials and rigorous free energy calculations, we establish an efficient and extendible framework for predicting complex phase behaviors of iron alloys under extreme conditions. As a first application of this framework, we predict the partition coefficients of sulfur over wide range of temperatures and pressures (from 4000 K, 150 GPa to 6000 K, 330 GPa), which are demonstrated to be in good agreement with previous experiments and ab initio simulations. After a continuous increase below similar to 250 GPa, the partition coefficient is found to be around 0.75 +/- 0.07 at higher pressures and are essentially temperature-independent. Given these predictions, the partitioning of sulfur is confirmed to be insufficient to account for the observed density jump across the Earth's inner core boundary and its roles on the geodynamics of the Earth's core should be minor. (C) 2020 Elsevier Ltd. All rights reserved. [Zhang, Zhigang] Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing 100029, Peoples R China; [Zhang, Zhigang] Macau Univ Sci & Technol, State Key Lab Lunar & Planetary Sci, Macau, Peoples R China; [Zhang, Zhigang] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China; [Csanyi, Gabor] Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England; [Alfe, Dario] UCL, Dept Earth Sci, Gower St, London WC1E 6BT, England; [Alfe, Dario] UCL, London Ctr Nanotechnol, Gower St, London WC1E 6BT, England; [Alfe, Dario] Univ Napoli Federico II, Dipartimento Fis Ettore Pancini, Via Cinthia, I-80126 Naples, Italy Chinese Academy of Sciences; Institute of Geology & Geophysics, CAS; Macau University of Science & Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Cambridge; University of London; University College London; University of London; University College London; University of Naples Federico II Zhang, ZG (corresponding author), Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing 100029, Peoples R China. zgzhang@mail.iggcas.ac.cn Alfe`, Dario/C-1521-2008; zhang, zhi gang/E-5331-2014 Alfe`, Dario/0000-0002-9741-8678; zhang, zhi gang/0000-0001-8666-1026 Strategic Priority Research Program (B) of Chinese Academy of Sciences [XDB18000000]; State Key Development Program of Basic Research of China [2014CB845905]; State Key Laboratory of Lunar and Planetary Sciences; NERC [NE/R000425/1] Funding Source: UKRI; Natural Environment Research Council [NE/R000425/1] Funding Source: researchfish Strategic Priority Research Program (B) of Chinese Academy of Sciences; State Key Development Program of Basic Research of China(State Key Development Program for Basic Research of China); State Key Laboratory of Lunar and Planetary Sciences; NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)); Natural Environment Research Council(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) We thank helpful discussions with Drs. Tao Sun, Zuan Chen, Noam Bernstein and Albert Bartok. We thank the assistance of surveying experimental data by Mr. Zhiming Zhang. We are grateful to three anonymous reviewers for their constructive comments and to Marc Blanchard and Jeffrey Catalano for their handling our manuscript. This work was supported by the Strategic Priority Research Program (B) of Chinese Academy of Sciences (#XDB18000000), State Key Development Program of Basic Research of China (2014CB845905) and the opening fund of State Key Laboratory of Lunar and Planetary Sciences. Simulations were carried out on the computational facilities in the Computer Simulation Lab of IGGCAS and Tianhe-2 at the National Supercomputer Center of China (NSCC) in Guangzhou. 81 14 14 4 32 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-7037 1872-9533 GEOCHIM COSMOCHIM AC Geochim. Cosmochim. Acta DEC 15 2020.0 291 5 18 10.1016/j.gca.2020.03.028 0.0 14 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics OP5UM Green Submitted 2023-03-23 WOS:000588151600002 0
J Zhang, SW; Liang, YF; Li, XY; Li, SB; Xiong, XF; Zhang, LH Zhang, Shuwei; Liang, Yefeng; Li, Xingyu; Li, Shibo; Xiong, Xiaofeng; Zhang, Lihai AESR3D: 3D overcomplete autoencoder for trabecular computed tomography super resolution CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY English Article; Early Access deep learning; image resolution; medical image processing; segmentation Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone. The modern way of osteoporosis assessment is through the measurement of bone mineral density, which is not able to unveil the pathological condition from the mesoscale aspect. To obtain mesoscale information from computed tomography (CT), the super-resolution (SR) approach for volumetric imaging data is required. A deep learning model AESR3D is proposed to recover high-resolution (HR) Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement. A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed. The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks. The HR and SR images show a high correlation (r = 0.996, intraclass correlation coefficients = 0.917) on trabecular bone morphological indicators. The results also prove the effectiveness of our regularisation framework when training a large capacity model. [Zhang, Shuwei; Xiong, Xiaofeng] Chinese Peoples Liberat Army Gen Hosp, Dept Orthopaed, Beijing, Peoples R China; [Liang, Yefeng; Li, Shibo] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Minimally Invas Surg Robot & Syst, Shenzhen, Peoples R China; [Liang, Yefeng] Univ Chinese Acad Sci, CAS, Beijing, Peoples R China; [Zhang, Lihai] Natl Univ Singapore, Coll Design & Engn, Singapore, Singapore; [Li, Shibo; Zhang, Lihai] Univ Southern Denmark SDU, Maersk Mc Kinney Moller Inst, SDU Biorobot, Odense, Denmark Chinese People's Liberation Army General Hospital; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National University of Singapore; University of Southern Denmark Li, SB; Zhang, LH (corresponding author), Univ Southern Denmark SDU, Maersk Mc Kinney Moller Inst, SDU Biorobot, Odense, Denmark. sb.li@siat.ac.cn; zhanglihai74@qq.com Beijing Natural Science Foundation-Haidian original Innovation Joint Foundation [L192016]; Joint Funds of the National Natural Science Foundation of China [U21A20489]; National Natural Science Foundation of China [62003330]; Shenzhen Fundamental Research Funds [JCYJ20220818101608019, JCYJ20190807170407391, JCYJ20180507182415428]; Natural Science Foundation of Guangdong Province [2019A1515011699]; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology Beijing Natural Science Foundation-Haidian original Innovation Joint Foundation; Joint Funds of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Fundamental Research Funds; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology Beijing Natural Science Foundation-Haidian original Innovation Joint Foundation, Grant/Award Number: L192016; Joint Funds of the National Natural Science Foundation of China, Grant/Award Number: U21A20489; National Natural Science Foundation of China, Grant/Award Number: 62003330; Shenzhen Fundamental Research Funds, Grant/Award Numbers: JCYJ20220818101608019, JCYJ20190807170407391, JCYJ20180507182415428; Natural Science Foundation of Guangdong Province, Grant/Award Number: 2019A1515011699; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology 32 0 0 0 0 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2468-6557 2468-2322 CAAI T INTELL TECHNO CAAI T. Intell. Technol. 10.1049/cit2.12167 0.0 FEB 2023 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8W5PV gold 2023-03-23 WOS:000931385400001 0
J Zhang, HQ; Xiao, X; Mercaldo, F; Ni, SG; Martinelli, F; Sangaiah, AK Zhang, Hanqi; Xiao, Xi; Mercaldo, Francesco; Ni, Shiguang; Martinelli, Fabio; Sangaiah, Arun Kumar Classification of ransomware families with machine learning based on N-gram of opcodes FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Ransomware classification; Static analysis; Opcode; Machine learning; N-gram Ransomware is a special type of malware that can lock victims' screen and/or encrypt their files to obtain ransoms, resulting in great damage to users. Mapping ransomware into families is useful for identifying the variants of a known ransomware sample and for reducing analysts' workload. However, ransomware that can fingerprint the environment can evade the precious work of dynamic analysis. To the best of our knowledge, to overcome this shortcoming, we are the first to propose an approach based on static analysis to classifying ransomware. First, opcode sequences from ransomware samples are transformed into N-gram sequences. Then, Term frequency-Inverse document frequency (TF-IDF) is calculated for each N-gram to select feature N-grams so that these N-grams exhibit better discrimination between families. Finally, we treat the vectors composed of the TF values of the feature N-grams as the feature vectors and subsequently feed them to five machine-learning methods to perform ransomware classification. Six evaluation criteria are employed to validate the model. Thorough experiments performed using real datasets demonstrate that our approach can achieve the best Accuracy of 91.43%. Furthermore, the average F1-measure of the wannacry ransomware family is up to 99%, and the Accuracy of binary classification is up to 99.3%. The proposed method can detect and classify ransomware that can fingerprint the environment. In addition, we discover that different feature dimensions are required for achieving similar classifier performance with feature N-grams of diverse lengths. (C) 2018 Elsevier B.V. All rights reserved. [Zhang, Hanqi] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Hubei, Peoples R China; [Zhang, Hanqi; Xiao, Xi] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen, Peoples R China; [Ni, Shiguang] Tsinghua Univ, Grad Sch Shenzhen, Div Social Sci & Management, Shenzhen, Peoples R China; [Mercaldo, Francesco] Natl Res Council Italy, Inst Informat & Telemat, Pisa, Italy; [Martinelli, Fabio] Natl Res Council Italy, Inst Informat & Telemat, Secur Grp, Pisa, Italy; [Sangaiah, Arun Kumar] VIT Univ, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India Central China Normal University; Tsinghua University; University Town of Shenzhen; Tsinghua Shenzhen International Graduate School; Tsinghua University; University Town of Shenzhen; Tsinghua Shenzhen International Graduate School; Consiglio Nazionale delle Ricerche (CNR); Istituto di Informatica e Telematica (IIT-CNR); Consiglio Nazionale delle Ricerche (CNR); Istituto di Informatica e Telematica (IIT-CNR); Vellore Institute of Technology (VIT); VIT Vellore Ni, SG (corresponding author), Tsinghua Univ, Grad Sch Shenzhen, Div Social Sci & Management, Shenzhen, Peoples R China. ni.shiguang@sz.tsinghua.edu.cn NI, Shiguang/AAI-7376-2020; Sangaiah, Arun Kumar/U-6785-2019 Sangaiah, Arun Kumar/0000-0002-0229-2460 NSFC [61773229, 61771273, 61202358]; National High-tech RAMP;D Program of China [2015AA016102]; Guangdong Natural Science Foundation [2018A030313422]; RD Program of Shenzhen [JCYJ20160 531174259309, JCYJ20170307153032483, JCYJ20160331184 440545, JCYJ20170307153157440]; Interdisciplinary Research Project of Graduate School at Shenzhen of Tsinghua University [JC2017005] NSFC(National Natural Science Foundation of China (NSFC)); National High-tech RAMP;D Program of China; Guangdong Natural Science Foundation(National Natural Science Foundation of Guangdong Province); RD Program of Shenzhen; Interdisciplinary Research Project of Graduate School at Shenzhen of Tsinghua University This work is supported by the NSFC projects (61773229, 61771273, 61202358), the National High-tech R&D Program of China (2015AA016102), Guangdong Natural Science Foundation (2018A030313422), the RD Program of Shenzhen (JCYJ20160 531174259309, JCYJ20170307153032483, JCYJ20160331184 440545, JCYJ20170307153157440) and the Interdisciplinary Research Project of Graduate School at Shenzhen of Tsinghua University (JC2017005). 41 93 96 2 64 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JAN 2019.0 90 211 221 10.1016/j.future.2018.07.052 0.0 11 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GV7EA 2023-03-23 WOS:000446283600017 0
J Liu, HC; Wu, TT; Rocha, A Liu Huancheng; Wu Tingting; Rocha, Alvaro An Analysis of Research Trends on Data Mining in Chinese Academic Libraries JOURNAL OF GRID COMPUTING English Article Data mining; Bibliometrics; Chinese academic library; Keyword network With the advent of the era of big data, massive data make the informationization work of academic libraries severely challenging in all aspects. The application of data mining technology to the various services of academic libraries to cope with this challenge has gradually become the mainstream trend. In order to better detect the overall trends and research hot spots of data mining in Chinese academic libraries, 329 related studies were extracted from CNKI (China National Knowledge Infrastructure) and analyzed by using bibliometrics and keyword network analyses. Bibliometrics was used to analyze the distribution of the overall trends, authors, institutions, research levels, and keywords in the related field. Cluster analysis was applied to the keyword co-occurrence network to show hot issues in the application research of data mining in Chinese academic libraries. Results indicate that findings in this area generally show an upward trend, but few high-yielding authors are found, the volume of journals is low, research institutions are scattered, and the rate of funding support is not high. At the method level, association rules and cluster analysis are hot data mining technologies in Chinese academic library research. At the application level, the fields related to personalized services and big data continue to be hot research areas. [Liu Huancheng; Wu Tingting] Zhengzhou Univ Aeronaut, 15 Wenyuan West Rd, Zhengzhou, Henan, Peoples R China; [Liu Huancheng] Henan Collaborat Innovat Ctr Aviat Econ Dev, 15 Wenyuan West Rd, Zhengzhou, Henan, Peoples R China; [Rocha, Alvaro] Univ Coimbra, Dept Informat Engn, Polo 2 Pinhal Marrocos, P-3030290 Coimbra, Portugal Zhengzhou University of Aeronautics; Universidade de Coimbra Liu, HC; Wu, TT (corresponding author), Zhengzhou Univ Aeronaut, 15 Wenyuan West Rd, Zhengzhou, Henan, Peoples R China. lhuanc@sina.com; 18203630709@163.com Rocha, Álvaro/O-3273-2013 Rocha, Álvaro/0000-0002-0750-8187 30 2 2 3 32 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1570-7873 1572-9184 J GRID COMPUT J. Comput. SEP 2019.0 17 3 591 601 10.1007/s10723-018-9461-3 0.0 11 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science IZ5LT 2023-03-23 WOS:000487125900009 0
J Chen, J; Wang, B; Huang, J; Deng, SB; Wang, YJ; Blaney, L; Brennan, GL; Cagnetta, G; Jia, QM; Yu, G Chen, Jian; Wang, Bin; Huang, Jun; Deng, Shubo; Wang, Yujue; Blaney, Lee; Brennan, Georgina L.; Cagnetta, Giovanni; Jia, Qimeng; Yu, Gang A machine-learning approach clarifies interactions between contaminants of emerging concern ONE EARTH English Article GREEN-ALGAE; TOXICITY; CHEMICALS; GROWTH; TOXICOLOGY Humans and biotas are exposed to a cocktail of contaminants of emerging concern (CECs), but mixture regulation is lagging behind. This is largely attributed to inadequate experimental data of mixture risk; revealing intricate interactions among CECs in mixtures with random combinations remains a formidable challenge. Here, we propose a new framework comprised of 5,720 lab tests of mixture risk for 100 CECs with random combinations, extended prediction of mixture risk in any CEC combination via a new machine learning model, and validation in field sites. We identify a general concave-down relationship between CEC number and ecological risk of algae, invertebrates, and fish under different lab conditions and in more than 900 field sites worldwide. We propose a new redundancy mechanism'' to clarify interactions among CECs, suggesting implications in grouping CECs by action mode for developing mixture regulatory frameworks. Our framework provides a blueprint for addressing cocktail effects of multi-factors with random combinations in different disciplines. [Chen, Jian; Wang, Bin; Huang, Jun; Deng, Shubo; Wang, Yujue; Cagnetta, Giovanni; Jia, Qimeng; Yu, Gang] Tsinghua Univ, Beijing Key Lab Emerging Organ Contaminants Contr, State Key Joint Lab Environm Simulat & Pollut Con, Beijing Lab Environm Frontier Technol,Sch Environ, Beijing 100084, Peoples R China; [Blaney, Lee] Univ Maryland Baltimore Cty, Dept Chem Biochem & Environm Engn, 1000 Hilltop Circle, Baltimore, MD 21250 USA; [Brennan, Georgina L.] CSIC, Inst Marine Sci, ICM, Barcelona 08003, Spain; [Yu, Gang] Beijing Normal Univ, Adv Interdisciplinary Inst Environm & Ecol, Zhuhai 519087, Peoples R China Tsinghua University; University System of Maryland; University of Maryland Baltimore County; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro Mediterraneo de Investigaciones Marinas y Ambientales (CMIMA); CSIC - Instituto de Ciencias del Mar (ICM); Beijing Normal University Yu, G (corresponding author), Tsinghua Univ, Beijing Key Lab Emerging Organ Contaminants Contr, State Key Joint Lab Environm Simulat & Pollut Con, Beijing Lab Environm Frontier Technol,Sch Environ, Beijing 100084, Peoples R China.;Yu, G (corresponding author), Beijing Normal Univ, Adv Interdisciplinary Inst Environm & Ecol, Zhuhai 519087, Peoples R China. gangyu@bnu.edu.cn Major Project of the National Natural Science Foundation of China [52091544] Major Project of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Major Project of the National Natural Science Foundation of China (52091544). We thank Yonglong Lu (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences) for helpful comments. 59 0 0 13 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2590-3330 2590-3322 ONE EARTH One Earth NOV 18 2022.0 5 11 1239 1249 10.1016/j.oneear.2022.10.006 0.0 NOV 2022 11 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 7M1AL 2023-03-23 WOS:000906387900011 0
C Peng, JY; Gu, RZ; Mosner, L; Plchot, O; Burget, L; Cernocky, J Int Speech Commun Assoc Peng, Junyi; Gu, Rongzhi; Mosner, Ladislav; Plchot, Oldrich; Burget, Lukas; Cernocky, Jan Learnable Sparse Filterbank for Speaker Verification INTERSPEECH 2022 Interspeech English Proceedings Paper Interspeech Conference SEP 18-22, 2022 Incheon, SOUTH KOREA learnable filter; sparse filtering; sparsity; speaker verification Recently, feature extraction with learnable filters was extensively investigated with speaker verification systems, with filters learned both in time- and frequency-domains. Most of the learned schemes however end up with filters close to their initialization (e.g. Mel filterbank) or filters strongly limited by their constraints. In this paper, we propose a novel learnable sparse filterbank, named LearnSF, by exclusively optimizing the sparsity of the filterbank, that does not explicitly constrain the filters to follow pre-defined distribution. After standard pre-processing (STFT and square of the magnitude spectrum), the learnable sparse filterbank is employed, with its normalized outputs fed into a neural network predicting the speaker identity. We evaluated the performance of the proposed approach on both VoxCeleb and CNCeleb datasets. The experimental results demonstrate the effectiveness of the proposed LearnSF compared to both widely-used acoustic features and existing parameterized learnable front-ends. [Peng, Junyi; Mosner, Ladislav; Plchot, Oldrich; Burget, Lukas; Cernocky, Jan] Brno Univ Technol, Fac Informat Technol, Speech FIT, Brno, Czech Republic; [Gu, Rongzhi] Peking Univ, ECE, Beijing, Peoples R China Brno University of Technology; Peking University Peng, JY (corresponding author), Brno Univ Technol, Fac Informat Technol, Speech FIT, Brno, Czech Republic. pengjy@fit.vut.cz Czech National Science Foundation (GACR) project NEUREM3 [19-26934X]; Czech Ministry of Education, Youth and Sports [LTAIN19087]; Horizon 2020 Marie Sklodowska-Curie grant ESPERANTO [101007666]; Czech Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project e-Infrastructure'' [CZ - LM2018140] Czech National Science Foundation (GACR) project NEUREM3; Czech Ministry of Education, Youth and Sports(Ministry of Education, Youth & Sports - Czech Republic); Horizon 2020 Marie Sklodowska-Curie grant ESPERANTO; Czech Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project e-Infrastructure'' The work was partly supported by Czech National Science Foundation (GACR) project NEUREM3 No. 19-26934X, and Czech Ministry of Education, Youth and Sports from project no. LTAIN19087 Multi-linguality in speech technologies and Horizon 2020 Marie Sklodowska-Curie grant ESPERANTO, No. 101007666. Computing on IT4I supercomputer was supported by the Czech Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project e-Infrastructure CZ - LM2018140. 29 0 0 0 0 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2022.0 5110 5114 10.21437/Interspeech.2022-11309 0.0 5 Acoustics; Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Audiology & Speech-Language Pathology; Computer Science; Engineering BU4IZ 2023-03-23 WOS:000900724505058 0
J Qi, W; Liu, XR; Zhang, LB; Wu, LN; Zang, WC; Su, H Qi, Wen; Liu, Xiaorui; Zhang, Longbin; Wu, Lunan; Zang, Wenchuan; Su, Hang Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation ASSEMBLY AUTOMATION English Article Deep learning; EMG; Adaptive sensor fusion; Depth vision; Hand pose recognition Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system's performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement. [Qi, Wen] Politecn Milan, Dept Elect Informat & Bioengn, Milan, Italy; [Liu, Xiaorui] Qingdao Univ, Inst Future, Qingdao, Peoples R China; [Zhang, Longbin] KTH Royal Inst Technol, Stockholm, Sweden; [Wu, Lunan] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin, Peoples R China; [Zang, Wenchuan] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China; [Su, Hang] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy Polytechnic University of Milan; Qingdao University; Royal Institute of Technology; Harbin Institute of Technology; Ocean University of China; Polytechnic University of Milan Su, H (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy. hang.su@polimi.it qi, wen/AAE-5175-2022 32 2 2 7 33 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0144-5154 1758-4078 ASSEMBLY AUTOM Assem. Autom. JUL 22 2021.0 41 3 SI 393 400 10.1108/AA-11-2020-0178 0.0 FEB 2021 8 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering TS0XZ 2023-03-23 WOS:000619109700001 0
J Yang, B; Wu, B; You, YW; Guo, CM; Qiao, L; Lv, ZH Yang, Bin; Wu, Bin; You, Yuwen; Guo, Chunmei; Qiao, Liang; Lv, Zhihan Edge intelligence based digital twins for internet of autonomous unmanned vehicles SOFTWARE-PRACTICE & EXPERIENCE English Article; Early Access deep learning (DL); digital twins (DTs); edge intelligence; intelligent network architecture; Internet of Vehicles (IoV) COMMUNICATION; BLOCKCHAIN; IMPACT It aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet. [Yang, Bin; You, Yuwen; Guo, Chunmei] Tianjin Chengjian Univ, Sch Energy & Safety Engn, Tianjin, Peoples R China; [Wu, Bin] Zhejiang A&F Univ, Informat & Educ Technol Ctr, Hangzhou, Zhejiang, Peoples R China; [Qiao, Liang] Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden Tianjin Chengjian University; Zhejiang A&F University; Qingdao University; Uppsala University Lv, ZH (corresponding author), Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden. lvzhihan@gmail.com Lv, Zhihan/GLR-6000-2022; Lv, Zhihan/GLU-4458-2022; Yang, Bin/GQA-9324-2022; Lv, Zhihan/AHD-0875-2022; Lv, Zhihan/I-3187-2014 Lv, Zhihan/0000-0003-2525-3074; Yang, Bin/0000-0002-1579-7978; Lv, Zhihan/0000-0003-2525-3074 33 3 3 9 19 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0038-0644 1097-024X SOFTWARE PRACT EXPER Softw.-Pract. Exp. 10.1002/spe.3080 0.0 MAR 2022 19 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZO3FG 2023-03-23 WOS:000765613500001 0
J Lin, JW; Chen, XY; Pan, RY; Cao, TB; Cai, JT; Chen, Y; Peng, XS; Cernava, T; Zhang, X Lin, Jianwu; Chen, Xiaoyulong; Pan, Renyong; Cao, Tengbao; Cai, Jitong; Chen, Yang; Peng, Xishun; Cernava, Tomislav; Zhang, Xin GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases AGRICULTURE-BASEL English Article convolutional neural network; residual block; attention mechanism; grape leaf disease RECOGNITION Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3 large, ShuffleNetV2 x 1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases. [Lin, Jianwu; Pan, Renyong; Cao, Tengbao; Cai, Jitong; Chen, Yang; Peng, Xishun; Zhang, Xin] Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China; [Chen, Xiaoyulong] Guizhou Univ, Coll Tobacco Sci, Guiyang 550025, Peoples R China; [Cernava, Tomislav] Graz Univ Technol, Inst Environm Biotechnol, A-8010 Graz, Austria Guizhou University; Guizhou University; Graz University of Technology Zhang, X (corresponding author), Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China. ljw971121@163.com; chenxiaoyulong@sina.cn; panry198@163.com; 15283673634@163.com; gs.jtcai21@gzu.edu.cn; cy52cv2022@163.com; pxs19970921@163.com; tomislav.cernava@tugraz.at; xzhang1@gzu.edu.cn zhang, xin/P-9482-2016; Cernava, Tomislav/S-6277-2019 zhang, xin/0000-0002-8376-617X; Cernava, Tomislav/0000-0001-7772-4080; Peng, Xishun/0000-0002-8612-4746 National Key Research and Development Plan Key Special Projects [2021YFE0107700]; National Nature Science Foundation of China [61865002, 31960555]; Guizhou Science and Technology Program [2019-1410]; Outstanding Young Scientist Program of Guizhou Province [KY2021-026]; Program for Introducing Talents to Chinese Universities, 111 Program [D20023] National Key Research and Development Plan Key Special Projects; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guizhou Science and Technology Program; Outstanding Young Scientist Program of Guizhou Province; Program for Introducing Talents to Chinese Universities, 111 Program This research was funded by National Key Research and Development Plan Key Special Projects, grant number 2021YFE0107700, National Nature Science Foundation of China, grant numbers 61865002 and 31960555, Guizhou Science and Technology Program, grant number 2019-1410, and Outstanding Young Scientist Program of Guizhou Province, grant number KY2021-026. In addition, the study received support by the Program for Introducing Talents to Chinese Universities, 111 Program, grant number D20023. 36 8 8 18 38 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-0472 AGRICULTURE-BASEL Agriculture-Basel JUN 2022.0 12 6 887 10.3390/agriculture12060887 0.0 17 Agronomy Science Citation Index Expanded (SCI-EXPANDED) Agriculture 2M0MV Green Published, gold 2023-03-23 WOS:000817406000001 0
J Zhou, C; Yin, KL; Cao, Y; Intrieri, E; Ahmed, B; Catani, F Zhou, Chao; Yin, Kunlong; Cao, Ying; Intrieri, Emanuele; Ahmed, Bayes; Catani, Filippo Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method LANDSLIDES English Article Step-like landslides; Displacement prediction; Kernel extreme learning machine; Three Gorges Reservoir 3 GORGES RESERVOIR; WAVELET TRANSFORM; MODEL; REGRESSION; RAINFALL; FAILURE; THRESHOLDS; INTENSITY; STABILITY; AREA Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations (i.e., periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the wavelet transform (WT) and particle swarm optimization-kernel extreme learning machine (PSO-KELM) methods, and by considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with that of the multi-factor extreme learning machine (ELM), support vector regression (SVR), backward propagation neural network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms the other models, and the prediction accuracy can be improved by considering causal factors. [Zhou, Chao; Yin, Kunlong; Cao, Ying] China Univ Geosci, Fac Engn, Wuhan 430074, Hubei, Peoples R China; [Zhou, Chao; Intrieri, Emanuele; Catani, Filippo] Univ Florence, Dept Earth Sci, I-50121 Florence, Italy; [Ahmed, Bayes] UCL, Inst Risk & Disaster Reduct, London WC1E 6BT, England China University of Geosciences; University of Florence; University of London; University College London Yin, KL (corresponding author), China Univ Geosci, Fac Engn, Wuhan 430074, Hubei, Peoples R China. yinkl@126.com Catani, Filippo/B-8518-2016; Intrieri, Emanuele/H-5803-2016; Ahmed, Bayes/G-6913-2013; Catani, Filippo/AHC-3484-2022 Catani, Filippo/0000-0001-5185-4725; Intrieri, Emanuele/0000-0002-9227-4409; Ahmed, Bayes/0000-0001-5092-5528; Zhou, Chao/0000-0002-4702-4021 National Natural Science Foundation of China [41572292, 41702330] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This paper was prepared as part of the projects The study of mechanism and forecast criterion of the gentle-dip landslides in The Three Gorges Reservoir Region, China (no. 41572292) and Study on the hydraulic properties and the rainfall infiltration law of the ground surface deformation fissure of colluvial landslides (no. 41702330) funded by the National Natural Science Foundation of China. 55 94 107 17 148 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1612-510X 1612-5118 LANDSLIDES Landslides NOV 2018.0 15 11 2211 2225 10.1007/s10346-018-1022-0 0.0 15 Engineering, Geological; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology GZ4ZN Green Submitted 2023-03-23 WOS:000449421600008 0
J Blessin, NC; Yang, C; Mandelkow, T; Raedler, JB; Li, WC; Bady, E; Simon, R; Vettorazzi, E; Lennartz, M; Bernreuther, C; Fraune, C; Jacobsen, F; Krech, T; Marx, A; Lebok, P; Minner, S; Burandt, E; Clauditz, TS; Wilczak, W; Sauter, G; Heinzer, H; Haese, A; Schlomm, T; Graefen, M; Steurer, S Blessin, Niclas C.; Yang, Cheng; Mandelkow, Tim; Raedler, Jonas B.; Li, Wenchao; Bady, Elena; Simon, Ronald; Vettorazzi, Eik; Lennartz, Maximilian; Bernreuther, Christian; Fraune, Christoph; Jacobsen, Frank; Krech, Till; Marx, Andreas; Lebok, Patrick; Minner, Sarah; Burandt, Eike; Clauditz, Till S.; Wilczak, Waldemar; Sauter, Guido; Heinzer, Hans; Haese, Alexander; Schlomm, Thorsten; Graefen, Markus; Steurer, Stefan Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry JOURNAL OF PATHOLOGY English Article; Early Access artificial intelligence; heterogeneity in prostate cancer; Ki-67 labeling index; multiplex fluorescence immunohistochemistry; prostate cancer DNA FLOW-CYTOMETRY; RADICAL PROSTATECTOMY; PROGNOSTIC VALUE; INTEROBSERVER VARIABILITY; INTERNATIONAL-SOCIETY; ANDROGEN DEPRIVATION; TISSUE MICROARRAYS; DISTANT METASTASIS; SAMPLING STRATEGY; ISUP CONSENSUS The Ki-67 labeling index (Ki-67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki-67 immunostaining in 200-500 tumor cells. To enable automated Ki-67 LI assessment in routine clinical practice, a framework for automated Ki-67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki-67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A 'heterogeneity TMA' containing three to six samples from different tumor areas in each patient was used to model Ki-67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a 'classical' bright field-based Ki-67 analysis with the mfIHC-based framework. The Ki-67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki-67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient: 0.94 [95% confidence interval: 0.87-0.97]). The analysis of the heterogeneity TMA revealed that the Ki-67 LI of the sample with the highest Gleason score (area under the curve [AUC]: 0.68) was as prognostic as the mean Ki-67 LI of all six foci (AUC: 0.71 [p = 0.24]). The combined analysis of the Ki-67 LI and Gleason score obtained on identical tissue spots showed that the Ki-67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC: 0.82 [p = 0.002]) and quantitative Gleason score (AUC: 0.83 [p = 0.018]). The Ki-67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer-positive biopsies, the sole automated analysis of the worst biopsy was sufficient. (c) 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [Blessin, Niclas C.; Yang, Cheng; Mandelkow, Tim; Raedler, Jonas B.; Li, Wenchao; Bady, Elena; Simon, Ronald; Lennartz, Maximilian; Bernreuther, Christian; Fraune, Christoph; Jacobsen, Frank; Krech, Till; Lebok, Patrick; Minner, Sarah; Burandt, Eike; Clauditz, Till S.; Wilczak, Waldemar; Sauter, Guido; Steurer, Stefan] Univ Med Ctr Hamburg Eppendorf, Inst Pathol, Martinistr 52, D-20251 Hamburg, Germany; [Raedler, Jonas B.] Boston Univ, Coll Arts & Sci, Boston, MA USA; [Li, Wenchao] Southeast Univ, Affiliated Zhongda Hosp, Dept Urol, Nanjing, Peoples R China; [Vettorazzi, Eik] Univ Med Ctr Hamburg Eppendorf, Dept Med Biometry & Epidemiol, Hamburg, Germany; [Marx, Andreas] Klinikum Furth, Inst Pathol, Furth, Germany; [Heinzer, Hans; Haese, Alexander; Graefen, Markus] Univ Med Ctr Hamburg Eppendorf, Martini Clin, Prostate Canc Ctr, Hamburg, Germany; [Schlomm, Thorsten] Charite Univ Med Berlin, Dept Urol, Berlin, Germany University of Hamburg; University Medical Center Hamburg-Eppendorf; Boston University; Southeast University - China; University of Hamburg; University Medical Center Hamburg-Eppendorf; University of Hamburg; University Medical Center Hamburg-Eppendorf; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin Blessin, NC (corresponding author), Univ Med Ctr Hamburg Eppendorf, Inst Pathol, Martinistr 52, D-20251 Hamburg, Germany. n.blessin@uke.de Blessin, Niclas Christian/0000-0001-8043-3081; Yang, Cheng/0000-0001-5640-9997; Simon, Ronald/0000-0003-0158-4258 62 0 0 1 1 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0022-3417 1096-9896 J PATHOL J. Pathol. 10.1002/path.6057 0.0 MAR 2023 12 Oncology; Pathology Science Citation Index Expanded (SCI-EXPANDED) Oncology; Pathology 9O1VE 36656126.0 2023-03-23 WOS:000943391600001 0
J Bilicki, M; Dvornik, A; Hoekstra, H; Wright, AH; Chisari, NE; Vakili, M; Asgari, M; Giblin, B; Heymans, C; Hildebrandt, H; Holwerda, BW; Hopkins, A; Johnston, H; Kannawadi, A; Kuijken, K; Nakoneczny, SJ; Shan, HY; Sonnenfeld, A; Valentijn, E Bilicki, M.; Dvornik, A.; Hoekstra, H.; Wright, A. H.; Chisari, N. E.; Vakili, M.; Asgari, M.; Giblin, B.; Heymans, C.; Hildebrandt, H.; Holwerda, B. W.; Hopkins, A.; Johnston, H.; Kannawadi, A.; Kuijken, K.; Nakoneczny, S. J.; Shan, H. Y.; Sonnenfeld, A.; Valentijn, E. Bright galaxy sample in the Kilo-Degree Survey Data Release 4 Selection, photometric redshifts, and physical properties ASTRONOMY & ASTROPHYSICS English Article galaxies: distances and redshifts; catalogs; large-scale structure of Universe; gravitational lensing: weak; methods: data analysis MASS ASSEMBLY GAMA; CHALLENGE LIGHTCONE SIMULATION; LUMINOUS RED GALAXIES; 100 SQUARE DEGREES; POWER-SPECTRUM; STELLAR MASS; COSMOLOGY CONSTRAINTS; TARGET SELECTION; LENSING ANALYSIS; III. APPLICATION We present a bright galaxy sample with accurate and precise photometric redshifts (photo-zs), selected using ugriZYJHK(s) photometry from the Kilo-Degree Survey (KiDS) Data Release 4. The highly pure and complete dataset is flux-limited at r < 20 mag, covers similar to 1000 deg(2), and contains about 1 million galaxies after artifact masking. We exploit the overlap with Galaxy And Mass Assembly spectroscopy as calibration to determine photo-zs with the supervised machine learning neural network algorithm implemented in the ANNz2 software. The photo-zs have a mean error of vertical bar delta z vertical bar similar to 5 x 10(-4) and low scatter (scaled mean absolute deviation of similar to 0:018(1 + z)); they are both practically independent of the r-band magnitude and photo-z at 0:05 < z(phot) < 0:5. Combined with the 9-band photometry, these allow us to estimate robust absolute magnitudes and stellar masses for the full sample. As a demonstration of the usefulness of these data, we split the dataset into red and blue galaxies, used them as lenses, and measured the weak gravitational lensing signal around them for five stellar mass bins. We fit a halo model to these high-precision measurements to constrain the stellar-mass-halo-mass relations for blue and red galaxies. We find that for high stellar mass (M-* > 5 x 10(11) M-circle dot), the red galaxies occupy dark matter halos that are much more massive than those occupied by blue galaxies with the same stellar mass. [Bilicki, M.] Polish Acad Sci, Ctr Theoret Phys, Al Lotnikow 32-46, PL-02668 Warsaw, Poland; [Dvornik, A.; Wright, A. H.; Heymans, C.; Hildebrandt, H.] Ruhr Univ Bochum, Astron Inst AIRUB, Fac Phys & Astron, German Ctr Cosmol Lensing, D-44780 Bochum, Germany; [Dvornik, A.; Hoekstra, H.; Vakili, M.; Kuijken, K.; Sonnenfeld, A.] Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands; [Chisari, N. E.; Hopkins, A.] Univ Utrecht, Inst Theoret Phys, Princetonpl 5, NL-3584 CC Utrecht, Netherlands; [Asgari, M.; Giblin, B.; Heymans, C.] Univ Edinburgh, Inst Astron, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland; [Holwerda, B. W.] Univ Louisville, Dept Phys & Astron, 102 Nat Sci Bldg, Louisville, KY 40292 USA; [Hopkins, A.] Macquarie Univ, Australian Astron Opt, 105 Delhi Rd, N Ryde, NSW 2113, Australia; [Kannawadi, A.] Princeton Univ, Dept Astrophys Sci, 4 Ivy Lane, Princeton, NJ 08544 USA; [Nakoneczny, S. J.] Natl Ctr Nucl Res, Astrophys Div, Ul Pasteura 7, PL-02093 Warsaw, Poland; [Shan, H. Y.] Shanghai Astron Observ SHAO, Nandan Rd 80, Shanghai 200030, Peoples R China; [Shan, H. Y.] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Valentijn, E.] Univ Groningen, Kapteyn Inst, POB 800, NL-9700 AV Groningen, Netherlands Polish Academy of Sciences; Center for Theoretical Physics - Polish Academy of Sciences; Ruhr University Bochum; Leiden University; Leiden University - Excl LUMC; Utrecht University; University of Edinburgh; University of Louisville; Macquarie University; Princeton University; National Centre for Nuclear Research; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Groningen Bilicki, M (corresponding author), Polish Acad Sci, Ctr Theoret Phys, Al Lotnikow 32-46, PL-02668 Warsaw, Poland. bilicki@cft.edu.pl; dvornik@astro.ruhr-uni-bochum.de; hoekstra@strw.leidenuniv.nl Dvornik, Andrej/0000-0001-7415-1977; Wright, Angus/0000-0001-7363-7932; Valentijn, Edwin/0000-0003-1032-6680; Johnston, Harry/0000-0002-4074-0308; Bilicki, Maciej/0000-0002-3910-5809 ESO Telescopes at the La Silla Paranal Observatory under program [177.A-3016, 177.A-3017, 177.A-3018]; NOVA; NWO-M grant; Department of Physics & Astronomy of the University of Padova; Department of Physics of Univ. Federico II (Naples); STFC (UK); ARC (Australia); AAO; Polish National Science Center [2020/38/E/ST9/00395, 2018/30/E/ST9/00698, 2018/31/G/ST9/03388, UMO-2018/31/N/ST9/03975]; Polish Ministry of Science and Higher Education [DIR/WK/2018/12]; European Research Council Consolidator Grant [770935]; Netherlands Organisation for Scientific Research (NWO) [639.043.512]; Dutch Ministry of Education, Culture and Science (OCW); European Research Council [647112]; Royal Society through an Enhancement Award [RGF/EA/181006]; Max Planck Society; Heisenberg grant of the Deutsche Forschungsgemeinschaft [Hi 1495/5-1]; NSFC of China [11973070]; Shanghai Committee of Science and Technology grant [19ZR1466600]; Key Research Program of Frontier Sciences, CAS [ZDBS-LY-7013]; Alexander von Humboldt Foundation of the Max PlanckHumboldt Research Award; Imperial College ESO Telescopes at the La Silla Paranal Observatory under program; NOVA; NWO-M grant; Department of Physics & Astronomy of the University of Padova; Department of Physics of Univ. Federico II (Naples); STFC (UK)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ARC (Australia)(Australian Research Council); AAO; Polish National Science Center; Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); European Research Council Consolidator Grant(European Research Council (ERC)); Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); Dutch Ministry of Education, Culture and Science (OCW); European Research Council(European Research Council (ERC)European Commission); Royal Society through an Enhancement Award; Max Planck Society(Max Planck SocietyFoundation CELLEX); Heisenberg grant of the Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); NSFC of China(National Natural Science Foundation of China (NSFC)); Shanghai Committee of Science and Technology grant; Key Research Program of Frontier Sciences, CAS; Alexander von Humboldt Foundation of the Max PlanckHumboldt Research Award(Alexander von Humboldt Foundation); Imperial College We thank the referee Gabriel Brammer for valuable suggestions on the paper and John Peacock for useful comments on the earlier version of the manuscript. Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under program IDs 177.A-3016, 177.A-3017 and 177.A-3018, and on data products produced by Target/OmegaCEN, INAF-OACN, INAF-OAPD and the KiDS production team, on behalf of the KiDS consortium. OmegaCEN and the KiDS production team acknowledge support by NOVA and NWO-M grants. Members of INAF-OAPD and INAF-OACN also acknowledge the support from the Department of Physics & Astronomy of the University of Padova, and of the Department of Physics of Univ. Federico II (Naples). GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalog is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/.MBi is supported by the Polish National Science Center through grants no. 2020/38/E/ST9/00395, 2018/30/E/ST9/00698 and 2018/31/G/ST9/03388. MBi and SJN acknowledge the support from the Polish Ministry of Science and Higher Education through grant DIR/WK/2018/12. AD, AHW and HHi are supported by an European Research Council Consolidator Grant (No. 770935). HHo acknowledges support from the Netherlands Organisation for Scientific Research (NWO) through grant 639.043.512. This work is part of the Delta ITP consortium, a program of the Netherlands Organisation for Scientific Research (NWO) that is funded by the Dutch Ministry of Education, Culture and Science (OCW). MA, BG and CH acknowledge support from the European Research Council under grant number 647112. BG is supported by the Royal Society through an Enhancement Award (RGF/EA/181006). CH acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max PlanckHumboldt Research Award endowed by the Federal Ministry of Education and Research. HHi is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1). KK acknowledges support from the Royal Society and Imperial College, SJN is supported by the Polish National Science Center through grant UMO-2018/31/N/ST9/03975. HYS acknowledges the support from NSFC of China under grant 11973070, the Shanghai Committee of Science and Technology grant No. 19ZR1466600 and Key Research Program of Frontier Sciences, CAS, Grant No. ZDBS-LY-7013. This work has made use of topcat (Taylor 2005) and stilts (Taylor 2006) software, as well as of python (www.python.org), including the packages NumPy (van der Walt et al. 2011), SciPy (Jones et al. 2001), and Matplotlib (Hunter 2007). Author contributions: All authors contributed to the development and writing of this paper. The authorship list is given in two groups: the lead authors (MB, AD, HHo, AHW, NEC, MV), followed by an alphabetical group which includes those who have either made a significant contribution to the data products, or to the scientific analysis. 130 12 12 0 0 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. SEP 13 2021.0 653 A82 10.1051/0004-6361/202140352 0.0 19 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics UP2XA Green Submitted, Bronze, Green Accepted 2023-03-23 WOS:000695246400004 0
J Li, PH; Zhang, ZJ; Grosu, R; Deng, ZW; Hou, J; Rong, YJ; Wu, R Li, Penghua; Zhang, Zijian; Grosu, Radu; Deng, Zhongwei; Hou, Jie; Rong, Yujun; Wu, Rui An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Article Lithium battery; State-of-health; Remaining useful life; End-to-end framework; Bayesian optimization ION BATTERIES; CAPACITY This study proposes an end-to-end prognostic framework for state-of-health (SOH) estimation and remaining useful life (RUL) prediction. In such a framework, a hybrid neural network (NN), i.e., the concatenation of onedimensional convolutional NN and active-state-tracking long-short-term memory NN, is designed to capture the hierarchical features between several variables affecting battery degeneration, as well as the temporal dependencies embedded in those features. The prior distribution over hyperparameters, specified to the popular NNs applied in SOH or RUL tasks, is built through the Kolmogorov-Smirnov test. Such prior distribution is regarded as a surrogate to investigate the degeneration data's impact on modeling such NNs. Based on such a surrogate, a Bayesian optimization algorithm is proposed to build SOH and RUL models, selecting the most promising configuration automatically in the sequential evolution progress of hyperparameters. Compared with the existing NNs, the experiments indicate that our method hits a lower average RMSE 0.0072 and global average RMSE 0.0269 for SOH and RUL tasks. Code and models are available at https://github.com/LipenghuaCQ/CNN-ASTLSTM. [Li, Penghua; Zhang, Zijian; Hou, Jie] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China; [Grosu, Radu] Vienna Univ Technol, Inst Comp Engn, Treitlstr 3-3, A-1040 Vienna, Austria; [Deng, Zhongwei] Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Rong, Yujun] China Mobile Hangzhou Informat Technol Co Ltd, Hangzhou 310000, Zhejiang, Peoples R China; [Wu, Rui] Chongqing Changan Automobile Co Ltd, Chongqing 400023, Peoples R China Chongqing University of Posts & Telecommunications; Technische Universitat Wien; Chongqing University Li, PH (corresponding author), Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China.;Deng, ZW (corresponding author), Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China. lipenghua88@163.com; dengzhongw@cqu.edu.cn Deng, Zhongwei/AAS-2636-2020 Deng, Zhongwei/0000-0002-5834-1583; Penghua, Li/0000-0003-0615-7781; Grosu, Radu/0000-0001-5715-2142 Chongqing Outstanding Youth Fund Project [cstc2021jcyj-jqX0001]; Science and Technology Research Project of Chongqing Education Commission [KJZD-K202100603]; Na-tional Natural Science Foundation of China [61903057]; Chongqing Natural Science Foundation [cstc2019jcyj-msxmX0129]; Project of Innovation Research Group of Universities in Chongqing [CXQT20016] Chongqing Outstanding Youth Fund Project; Science and Technology Research Project of Chongqing Education Commission; Na-tional Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chongqing Natural Science Foundation(Natural Science Foundation of Chongqing); Project of Innovation Research Group of Universities in Chongqing Acknowledgments This work is supported by Chongqing Outstanding Youth Fund Project (cstc2021jcyj-jqX0001) ; Science and Technology Research Project of Chongqing Education Commission (KJZD-K202100603) ; Na-tional Natural Science Foundation of China (61903057) ; Chongqing Natural Science Foundation (cstc2019jcyj-msxmX0129) ; the Project of Innovation Research Group of Universities in Chongqing (CXQT20016) . 39 14 14 27 61 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. MAR 2022.0 156 111843 10.1016/j.rser.2021.111843 0.0 12 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels 0P7ZZ 2023-03-23 WOS:000784449500007 0
J Heng, WZ; Pang, GY; Xu, FH; Huang, XY; Pang, ZB; Yang, G Heng, Wenzheng; Pang, Gaoyang; Xu, Feihong; Huang, Xiaoyan; Pang, Zhibo; Yang, Geng Flexible Insole Sensors with Stably Connected Electrodes for Gait Phase Detection SENSORS English Article gait phase; insole sensors; surface processing; inkjet printing; visual-based reference; machine learning ROBOT; SYSTEM Gait analysis is an important assessment tool for analyzing vital signals collected from individuals and for providing physical information of the human body, and it is emerging in a diverse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human-robot interaction. Herein, a kind of surface processed conductive rubber was designed and investigated to develop a pressure-sensitive insole to monitor planar pressure in a real-time manner. Due to a novel surface processing method, the pressure sensor was characterized by stable contact resistance, simple manufacturing, and high mechanical durability. In the experiments, it was demonstrated that the developed pressure sensors were easily assembled with the inkjet-printed electrodes and a flexible substrate as a pressure-sensitive insole while maintaining good sensing performance. Moreover, resistive signals were wirelessly transmitted to computers in real time. By analyzing sampled resistive data combined with the gait information monitored by a visual-based reference system based on machine learning method (k-Nearest Neighbor algorithm), the corresponding relationship between plantar pressure distribution and lower limb joint angles was obtained. Finally, the experimental validation of the ability to accurately divide gait into several phases was conducted, illustrating the potential application of the developed device in healthcare and robotics. [Heng, Wenzheng; Pang, Gaoyang; Yang, Geng] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China; [Xu, Feihong] China Acad Engn Phys, Inst Mech Mfg Technol, Mianyang 621900, Sichuan, Peoples R China; [Huang, Xiaoyan] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China; [Pang, Zhibo] ABB Corp Res Sweden, S-72178 Vasteras, Sweden Zhejiang University; Chinese Academy of Engineering Physics; Zhejiang University; ABB Yang, G (corresponding author), Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China. wenzhengheng@zju.edu.cn; gaoyangpang@zju.edu.cn; xufeihong18@gscaep.ac.cn; xiaoyanhuang@zju.edu.cn; pang.zhibo@se.abb.com; yanggeng@zju.edu.cn Pang, Gaoyang/AAI-2716-2020 Pang, Gaoyang/0000-0002-0948-4641; Yang, Geng/0000-0002-8685-5426; Pang, Zhibo/0000-0002-7474-4294 National Natural Science Foundation of China [51,975,513]; Major Research Plan of National Natural Science Foundation of China [51,890,884]; Science Fund for Creative Research Groups of the National Natural Science Foundation of China [51,821,093]; Natural Science Foundation of Zhejiang Province, China [LR20E050003]; China's Thousand Talents Plan Young Professionals Program, Robotics Institute of Zhejiang University [K18-508116-008-03]; Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems [GZKF-201703] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Research Plan of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Fund for Creative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province, China(Natural Science Foundation of Zhejiang Province); China's Thousand Talents Plan Young Professionals Program, Robotics Institute of Zhejiang University; Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems This research was funded by The National Natural Science Foundation of China, grant number 51,975,513, the Major Research Plan of National Natural Science Foundation of China, grant number 51,890,884, the Science Fund for Creative Research Groups of the National Natural Science Foundation of China, grant number 51,821,093, the Natural Science Foundation of Zhejiang Province, China, grant number LR20E050003, the China's Thousand Talents Plan Young Professionals Program, Robotics Institute of Zhejiang University, grant number K18-508116-008-03, and the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems, grant number GZKF-201703. 52 14 15 2 28 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2019.0 19 23 5197 10.3390/s19235197 0.0 20 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation KD1BI 31783618.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000507606200140 0
J Bu, XN; Vahed, AT; Ghassa, S; Chelgani, SC Bu, Xiangning; Vahed, Amir Taghizadeh; Ghassa, Sina; Chelgani, Saeed Chehreh Modelling of coal flotation responses based on operational conditions by random forest INTERNATIONAL JOURNAL OF OIL GAS AND COAL TECHNOLOGY English Article coal; flotation; advanced machine learning; random forest; yield EXPLAINING RELATIONSHIPS; PARAMETERS; PREDICTION; INDEX Coal consumption is one of the critical factors in the economy of China. Flotation separation of coal from its inorganic part (ash) can reduce environmental problems of coal consumption and improve its combustion. This investigation used random forest (RF) as an advanced machine learning method to rank flotation operations by variable importance measurement and predict flotation responses based on operational parameters. Fifty flotation experiments were designed, and performed based on various flotation conditions and by different variables (collector dosage, frother dosage, air flowrate, pulp density, and impeller speed). Statistical assessments indicated that there is a significant negative correlation between yield and ash content. Experiments indicated that in the optimum conditions, yield and ash content would be 80 and 9%, respectively. Variable importance measurement by RF showed that frother has the highest effectiveness on yield. Outcomes of modelling released that RF can accurately be used for ranking flotation parameters, and generating models within complex systems in mineral processing. [Received: May 20, 2020; Accepted: July 19, 2020] [Bu, Xiangning] China Univ Min & Technol, Sch Chem Engn & Technol, Xuzhou 221116, Jiangsu, Peoples R China; [Vahed, Amir Taghizadeh] ePosture AB Lulea, Kvartsstigen 6, SE-97753 Lulea, Sweden; [Ghassa, Sina] Univ Tehran, Coll Engn, Sch Min, Tehran 1684613114, Iran; [Chelgani, Saeed Chehreh] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, Minerals & Met Engn, SE-97187 Lulea, Sweden China University of Mining & Technology; University of Tehran; Lulea University of Technology Chelgani, SC (corresponding author), Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, Minerals & Met Engn, SE-97187 Lulea, Sweden. xiangning.bu@foxmail.com; amir.taghizadeh@eposture.se; sina.ghassa@ut.ac.ir; saeed.chelgani@ltu.se Bu, Xiangning/AAF-5935-2019; Chehreh Chelgani, Saeed/V-4298-2017; Ghassa, Sina/I-5280-2019 Bu, Xiangning/0000-0001-6291-7656; Chehreh Chelgani, Saeed/0000-0002-2265-6321; Ghassa, Sina/0000-0002-2107-8734 Fundamental Research Funds for the Central Universities [2019XKQYMS18] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work is supported by the Fundamental Research Funds for the Central Universities (No. 2019XKQYMS18). 26 3 3 3 15 INDERSCIENCE ENTERPRISES LTD GENEVA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND 1753-3309 1753-3317 INT J OIL GAS COAL T Int. J. Oil Gas Coal Technol. 2021.0 27 4 457 468 10.1504/IJOGCT.2021.116677 0.0 12 Energy & Fuels; Engineering, Chemical; Engineering, Petroleum Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering TX6FX 2023-03-23 WOS:000683186400005 0
J Wang, K; Bi, HB; Zhang, Y; Zhang, C; Liu, ZQ; Zheng, S Wang, Kang; Bi, Hongbo; Zhang, Yi; Zhang, Cong; Liu, Ziqi; Zheng, Shuang (DC)-C-2-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Camouflaged object detection (COD); deep learning; receptive field PATTERN; DEEP In this article, we propose a novel framework for camouflaged object detection (COD), named (DC)-C-2-Net, which contains two new modules: Dual-branch features extraction (DFE) and gradually refined cross fusion (GRCF). Specifically, the DFE simulates the two-stage detection process of human visual mechanisms in observing camouflage scenes. For the first stage, a dense concatenation is employed to aggregate multilevel features and expand the receptive field. The first stage feature maps are then utilized to extract two-direction guidance information, which benefits the second stage. The GRCF consists of a selfrefine attention unit and a cross-refinement unit, with the aim of combining the peer layer features and DFE features for an improved COD performance. The proposed framework outperforms 13 state-of-the-art deep learning-based methods upon three public datasets in terms of five widely used metrics. Finally, we show evidence for the successful applications of the proposed method in the fields of surface defect detection and medical image segmentation. [Wang, Kang; Bi, Hongbo; Zhang, Cong; Liu, Ziqi] Northeast Petr Univ, Sch Elect Engn, Daqing 163318, Peoples R China; [Wang, Kang; Bi, Hongbo; Zhang, Cong; Liu, Ziqi] Northeast Petr Univ, Informat Dept, Daqing 163318, Peoples R China; [Zhang, Yi] INSA Rennes, IETR Lab, F-35510 Rennes, France; [Zheng, Shuang] Tianjin Chain Number Technol Co, Tianjin 300000, Peoples R China Northeast Petroleum University; Northeast Petroleum University; Institut National des Sciences Appliquees de Rennes; Universite de Rennes Bi, HB (corresponding author), Northeast Petr Univ, Sch Elect Engn, Daqing 163318, Peoples R China.;Bi, HB (corresponding author), Northeast Petr Univ, Informat Dept, Daqing 163318, Peoples R China. kangwang@stu.nepu.edu.cn; bihongbo@nepu.edu.cn; yi.zhang1@insa-rennes.fr; Congzhang98@stu.nepu.edu.cn; liuziqi@stu.nepu.edu.cn; s_zheng@linkdata.email Zhang, Yi/0000-0002-7110-5948; Bi, Hongbo/0000-0003-2442-330X; Zhang, Cong/0000-0002-8952-0429 51 5 5 6 40 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. MAY 2022.0 69 5 5364 5374 10.1109/TIE.2021.3078379 0.0 11 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation YG0PS Green Submitted 2023-03-23 WOS:000742200700103 0
J Yang, Y; Shao, AB; Vihinen, M Yang, Yang; Shao, Aibin; Vihinen, Mauno PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms FRONTIERS IN MOLECULAR BIOSCIENCES English Article variation interpretation; mutation; animal variants; plant variants; amino acid substitution; prediction; pathogenicity; machine learning MUTATIONS; BTK; PATHOGENICITY; DATABASE; CLASSIFICATION; PERFORMANCE; GUIDELINES; SERVER; AMIGO Genetic variations are investigated in human and many other organisms for many purposes (e.g., to aid in clinical diagnosis). Interpretation of the identified variations can be challenging. Although some dedicated prediction methods have been developed and some tools for human variants can also be used for other organisms, the performance and species range have been limited. We developed a novel variant pathogenicity/tolerance predictor for amino acid substitutions in any organism. The method, PON-All, is a machine learning tool trained on human, animal, and plant variants. Two versions are provided, one with Gene Ontology (GO) annotations and another without these details. GO annotations are not available or are partial for many organisms of interest. The methods provide predictions for three classes: pathogenic, benign, and variants of unknown significance. On the blind test, when using GO annotations, accuracy was 0.913 and MCC 0.827. When GO features were not used, accuracy was 0.856 and MCC 0.712. The performance is the best for human and plant variants and somewhat lower for animal variants because the number of known disease-causing variants in animals is rather small. The method was compared to several other tools and was found to have superior performance. PON-All is freely available at and . [Yang, Yang; Shao, Aibin] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China; [Yang, Yang] Collaborat Innovat Ctr Novel Software Technol & In, Nanjing, Peoples R China; [Vihinen, Mauno] Lund Univ, Dept Expt Med Sci, Lund, Sweden Soochow University - China; Lund University Vihinen, M (corresponding author), Lund Univ, Dept Expt Med Sci, Lund, Sweden. mauno.vihinen@med.lu.se Natural Science Foundation of the Jiangsu Higher Education Institutions of China [20KJA520010]; Collaborative Innovation Center of Novel Software Technology and Industrialization; Swedish Cancer Society [CAN 2017/699, CAN 20 1350]; Vetenskapsradet [2019-01403] Natural Science Foundation of the Jiangsu Higher Education Institutions of China(National Natural Science Foundation of China (NSFC)); Collaborative Innovation Center of Novel Software Technology and Industrialization; Swedish Cancer Society(Swedish Cancer Society); Vetenskapsradet(Swedish Research Council) This work was supported by the key project of the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJA520010), Collaborative Innovation Center of Novel Software Technology and Industrialization, Vetenskapsradet (2019-01403), and the Swedish Cancer Society (Grant no. CAN 2017/699 and CAN 20 1350). 58 3 3 0 1 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-889X FRONT MOL BIOSCI Front. Mol. Biosci. JUN 16 2022.0 9 867572 10.3389/fmolb.2022.867572 0.0 12 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 2N9KS 35782867.0 gold, Green Accepted 2023-03-23 WOS:000818690500001 0
C Kouliaridis, V; Kambourakis, G; Peng, T Wang, GJ; Ko, R; Bhuiyan, MZA; Pan, Y Kouliaridis, Vasileios; Kambourakis, Georgios; Peng, Tao Feature importance in Android malware detection 2020 IEEE 19TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2020) IEEE International Conference on Trust Security and Privacy in Computing and Communications English Proceedings Paper 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) DEC 29-JAN 01, 2020-2021 Guangzhou, PEOPLES R CHINA IEEE,IEEE Comp Soc Android security; Mobile Malware Detection; Feature selection; Feature importance The topic of mobile malware detection on the Android platform has attracted significant attention over the last several years. However, while much research has been conducted toward mobile malware detection techniques, little attention has been devoted to feature selection and feature importance. That is, which app feature matters more when it comes to machine learning classification. After succinctly surveying all major, dated from 2012 to 2020, datasets used by state-of-the-art malware detection works in the literature, we analyse a critical mass of apps from the most contemporary and prevailing datasets, namely Drebin, VirusShare, and AndroZoo. Next, we rank the importance of app classification features pertaining to permissions and intents using the Information Gain algorithm for all the three above-mentioned datasets. [Kouliaridis, Vasileios] Univ Aegean, Dept Informat & Comm Syst Engn, Samos 83200, Greece; [Kambourakis, Georgios] European Commiss, Joint Res Ctr, I-21027 Ispra, Italy; [Peng, Tao] Guangzhou Univ, Sch Comp Sci, Guangzhou 510006, Peoples R China University of Aegean; European Commission Joint Research Centre; EC JRC ISPRA Site; Guangzhou University Kouliaridis, V (corresponding author), Univ Aegean, Dept Informat & Comm Syst Engn, Samos 83200, Greece. bkouliaridis@aegean.gr; georgios.kampourakis@ec.europa.eu; pengtao_work@163.com 22 3 3 0 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2324-898X 978-1-6654-0392-4 IEEE INT CONF TRUST 2020.0 1450 1455 10.1109/TrustCom50675.2020.00195 0.0 6 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BR8DU Green Submitted 2023-03-23 WOS:000671077600181 0
J Jamal, A; Zahid, M; Rahman, MT; Al-Ahmadi, HM; Almoshaogeh, M; Farooq, D; Ahmad, M Jamal, Arshad; Zahid, Muhammad; Tauhidur Rahman, Muhammad; Al-Ahmadi, Hassan M.; Almoshaogeh, Meshal; Farooq, Danish; Ahmad, Mahmood Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION English Article Traffic crashes; crash injury severity prediction; machine learning; XGBoost; feature sensitivity analysis SUPPORT VECTOR MACHINE; MIXED LOGIT MODEL; ACCIDENT SEVERITY; RANDOM PARAMETER; SINGLE-VEHICLE; STATISTICAL-ANALYSIS; MOTORCYCLE RIDERS; CLUSTER-ANALYSIS; HYBRID APPROACH; DECISION RULES A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that our model outperformed most previous studies. [Jamal, Arshad; Al-Ahmadi, Hassan M.] King Fahd Univ Petr & Minerals, Dept Civil & Environm Engn, Dhahran, Saudi Arabia; [Zahid, Muhammad] Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China; [Tauhidur Rahman, Muhammad] King Fahd Univ Petr & Minerals, Dept City & Reg Planning, Dhahran, Saudi Arabia; [Almoshaogeh, Meshal] Qassim Univ, Coll Engn, Dept Civil Engn, Buraydah, Qassim, Saudi Arabia; [Farooq, Danish] Budapest Univ Technol & Econ, Dept Transport Technol & Econ, Budapest, Hungary; [Farooq, Danish] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Peshawar, Pakistan; [Ahmad, Mahmood] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Peshawar, Pakistan King Fahd University of Petroleum & Minerals; Beijing University of Technology; King Fahd University of Petroleum & Minerals; Qassim University; Budapest University of Technology & Economics; University of Peshawar; University of Peshawar Jamal, A (corresponding author), King Fahd Univ Petr & Minerals, Dept Civil & Environm Engn, Dhahran, Saudi Arabia. arshad.jamal@kfupm.edu.sa Jamal, Arshad/AAP-1419-2021; Ahmad, Mahmood/HHD-1034-2022 Jamal, Arshad/0000-0001-7284-7348; Ahmad, Mahmood/0000-0002-1139-1831; Al-ahmadi, Hasan/0000-0001-5664-1112 KFUPM KFUPM The authors appreciate and gratefully acknowledge the support of KFUPM for conducting this study. The authors also acknowledge the support of the traffic safety center and traffic safety department at the ministry of transport (MOT), Riyadh, for providing the data used in this research. 132 28 28 9 27 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1745-7300 1745-7319 INT J INJ CONTROL SA Int. J. Inj. Control Saf. Promot. OCT 2 2021.0 28 4 408 427 10.1080/17457300.2021.1928233 0.0 MAY 2021 20 Public, Environmental & Occupational Health Social Science Citation Index (SSCI) Public, Environmental & Occupational Health YE4WB 34060410.0 2023-03-23 WOS:000656738400001 0
J Thudi, M; Palakurthi, R; Schnable, JC; Chitikineni, A; Dreisigacker, S; Mace, E; Srivastava, RK; Satyavathi, CT; Odeny, D; Tiwari, VK; Lam, HM; Hong, YB; Singh, VK; Li, GW; Xu, YB; Chen, XP; Kaila, S; Nguyen, H; Sivasankar, S; Jackson, SA; Close, TJ; Shubo, W; Varshney, RK Thudi, Mahendar; Palakurthi, Ramesh; Schnable, James C.; Chitikineni, Annapurna; Dreisigacker, Susanne; Mace, Emma; Srivastava, Rakesh K.; Satyavathi, C. Tara; Odeny, Damaris; Tiwari, Vijay K.; Lam, Hon-Ming; Hong, Yan Bin; Singh, Vikas K.; Li, Guowei; Xu, Yunbi; Chen, Xiaoping; Kaila, Sanjay; Nguyen, Henry; Sivasankar, Sobhana; Jackson, Scott A.; Close, Timothy J.; Shubo, Wan; Varshney, Rajeev K. Genomic resources in plant breeding for sustainable agriculture JOURNAL OF PLANT PHYSIOLOGY English Review Genomics; Sequencing; Genotyping platforms; Sequence-based trait mapping; Genomics-assisted breeding; Genomic breeding; Genomic selection MARKER-ASSISTED SELECTION; BACTERIAL-BLIGHT RESISTANCE; LEAF RUST RESISTANCE; QUANTITATIVE TRAIT LOCI; TARGET SEQUENCING GBTS; SNP GENOTYPING ARRAY; DRAFT GENOME; ORYZA-SATIVA; QTL-SEQ; GENETIC DIVERSITY Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture. [Thudi, Mahendar; Palakurthi, Ramesh; Chitikineni, Annapurna; Srivastava, Rakesh K.; Varshney, Rajeev K.] Int Crops Res Inst Semi Arid Trop, Ctr Excellence Genom & Syst Biol, Hyderabad, India; [Thudi, Mahendar] Univ Southern Queensland, Toowoomba, Qld, Australia; [Schnable, James C.] Univ Nebraska, Lincoln, NE USA; [Dreisigacker, Susanne; Xu, Yunbi] Int Maize & Wheat Improvement Ctr CYMMIT, Mexico City, DF, Mexico; [Mace, Emma] Agri Sci Queensland, Dept Agr & Fisheries DAF, Warwick, Australia; [Satyavathi, C. Tara] Indian Council Agr Res ICAR, Indian Agr Res Inst IARI, New Delhi, India; [Odeny, Damaris] Int Crops Res Inst Semi Arid Trop, Nairobi, Kenya; [Tiwari, Vijay K.] Univ Maryland, College Pk, MD 20742 USA; [Lam, Hon-Ming] Chinese Univ Hong Kong, State Key Lab Agrobiotechnol, Ctr Soybean Res, Shatin, Hong Kong, Peoples R China; [Hong, Yan Bin; Chen, Xiaoping] Chinese Univ Hong Kong, Sch Life Sci, Shatin, Hong Kong, Peoples R China; [Hong, Yan Bin] Guangdong Acad Agr Sci, Crops Res Inst, Guangzhou, Peoples R China; [Singh, Vikas K.] Int Rice Res Inst IRRI, South Asia Hub, Hyderabad, India; [Li, Guowei; Shubo, Wan] Shandong Acad Agr Sci, Jinan, Peoples R China; [Xu, Yunbi] Chinese Acad Agr Sci, Beijing, Peoples R China; [Kaila, Sanjay] Govt India, Minist Sci & Technol, Dept Biotechnol, New Delhi, India; [Nguyen, Henry] Univ Missouri, Natl Ctr Soybean Res, Columbia, MO USA; [Sivasankar, Sobhana] Joint FAO IAEA Div Nucl Techn Food & Agr, Vienna, Austria; [Jackson, Scott A.] Bayer Crop Sci, Leverkusen, Germany; [Close, Timothy J.] Univ Calif Riverside, Riverside, CA 92521 USA CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); University of Southern Queensland; University of Nebraska System; University of Nebraska Lincoln; Queensland Department of Agriculture & Fisheries; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); University System of Maryland; University of Maryland College Park; Chinese University of Hong Kong; Chinese University of Hong Kong; Guangdong Academy of Agricultural Sciences; Shandong Academy of Agricultural Sciences; Chinese Academy of Agricultural Sciences; Department of Biotechnology (DBT) India; University of Missouri System; University of Missouri Columbia; Bayer AG; Bayer CropScience; University of California System; University of California Riverside Varshney, RK (corresponding author), Int Crops Res Inst Semi Arid Trop, Ctr Excellence Genom & Syst Biol, Hyderabad, India. r.k.varshney@cgiar.org Varshney, Rajeev K/C-5295-2014; THUDI, MAHENDAR/GXA-1578-2022; Li, Guowei/HCH-8923-2022; Lam, Hon-Ming/A-9866-2008 Varshney, Rajeev K/0000-0002-4562-9131; THUDI, MAHENDAR/0000-0003-2851-6837; Dreisigacker, Susanne/0000-0002-3546-5989; Lam, Hon-Ming/0000-0002-6673-8740; Xu, Yunbi/0000-0003-3361-4650; Srivastava, Rakesh K/0000-0001-6715-0386; Palakurthi, Ramesh/0000-0002-4399-5867 Science & Engineering Research Board (SERB), Department of Science & Technology, Government of India; Department of Agriculture and Cooparation & Farmers Welfare, Ministry of Agriculture Government of India; Bill and Melinda Gates Foundation, USA; Hong Kong Research Grants Council Area of Excellence Scheme [AoE/M-403/16]; Lo Kwee-Seong Biomedical Research Fund, Hongkong Science & Engineering Research Board (SERB), Department of Science & Technology, Government of India(Department of Science & Technology (India)Science Engineering Research Board (SERB), India); Department of Agriculture and Cooparation & Farmers Welfare, Ministry of Agriculture Government of India; Bill and Melinda Gates Foundation, USA(Bill & Melinda Gates Foundation); Hong Kong Research Grants Council Area of Excellence Scheme(Hong Kong Research Grants Council); Lo Kwee-Seong Biomedical Research Fund, Hongkong RKV and MT are grateful to Science & Engineering Research Board (SERB), Department of Science & Technology, Government of India for awarding JC Bose National Fellowship and an extramural project, under Empowerment and Equity Oppurtunities for Excellence in Science, respectivley. RKV also acknowledges financial support from Department of Agriculture and Cooparation & Farmers Welfare, Ministry of Agriculture Government of India and Bill and Melinda Gates Foundation, USA. H-ML was funded by the Hong Kong Research Grants Council Area of Excellence Scheme [AoE/M-403/16], and Lo Kwee-Seong Biomedical Research Fund, Hongkong. Authors are grateful to Dr Nils Stein, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany for useful discussions especially on barley releated research while preparing the article. 296 36 39 31 123 ELSEVIER GMBH MUNICH HACKERBRUCKE 6, 80335 MUNICH, GERMANY 0176-1617 1618-1328 J PLANT PHYSIOL J. Plant Physiol. FEB 2021.0 257 153351 10.1016/j.jplph.2020.153351 0.0 JAN 2021 18 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences QJ4RU 33412425.0 Green Accepted, hybrid, Green Published 2023-03-23 WOS:000619680000003 0
J Fan, L; Su, H; Zio, E; Chi, LX; Zhang, L; Zhou, J; Liu, Z; Zhan, JJ Fan, Lin; Su, Huai; Zio, Enrico; Chi, Lixun; Zhang, Li; Zhou, Jing; Liu, Zhe; Zhan, Jinjun A deep reinforcement learning-based method for predictive management of demand response in natural gas pipeline networks JOURNAL OF CLEANER PRODUCTION English Article Demand response; Natural gas pipeline network; Reinforcement learning; Deep Q learning ENERGY MANAGEMENT; RELIABILITY ASSESSMENT; OPTIMAL OPERATION; OPTIMIZATION; ALGORITHM; EVOLUTION; FLOW With the increase of natural gas in the world's energy consumption, the efficient and reliable management of natural gas pipeline networks is becoming even more important than before. In recent years, Demand Response (DR) is considered an effective approach for cleaner production and economic strategy, by introducing the participation of customers (CUs). This paper proposes a novel DR method for predictive management in multi-level natural gas markets with different stakeholders. This method is able to make a better trade-off among supplier's profits, gas demand volatility and CU satisfaction. This method includes three parts: dynamic pricing model, intelligent decision making and data-driven demand forecasting. A Markov decision process-based model is developed to illustrate the process of dynamical optimizing energy prices. Then, deep learning and reinforcement learning are integrated to efficiently solve the sequential decision-making problem, based on the physics constraints of natural gas pipeline networks. Besides, to realize the function of predictive optimization, an energy demand forecasting model is developed based on the deep recurrent neural network model. The proposed dynamic pricing method is able to optimize the pricing strategies in accordance to the demand patterns, and dynamically improve the system stability and energy efficiency. Finally, we apply the developed method to a natural gas network with relatively complex topology and different CUs. The results indicate that the proposed method can achieve the targets of peak shaving and valley filling under different pricing periods. Besides, the sensitivity analysis of the critical parameters in the dynamic pricing model is analyzed in detail, which can give a solid criterion for ensuring the effectiveness of this framework. [Fan, Lin; Su, Huai; Chi, Lixun; Zhang, Li; Zhou, Jing; Zhan, Jinjun] China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Beijing 102249, Peoples R China; [Zio, Enrico] Politecn Milan, Dipartimento Energia, Via La Masa 34, I-20156 Milan, Italy; [Zio, Enrico] PSL Res Univ, CRC, MINES ParisTech, Sophia Antipolis, France; [Zio, Enrico] Kyung Hee Univ, Dept Nucl Engn, Coll Engn, Seoul, South Korea; [Liu, Zhe] Petrochina West East Gas Pipeline, Dongfushan Rd 458, Shanghai 200122, Peoples R China China University of Petroleum; Polytechnic University of Milan; UDICE-French Research Universities; Universite PSL; MINES ParisTech; Kyung Hee University Su, H; Zhan, JJ (corresponding author), China Univ Petr, Coll Mech & Transportat Engn, Fuxue Rd 18, Beijing 102249, Peoples R China. suhuai@cup.edu.cn; zhangjj@cup.edu.cn Su, Huai/0000-0002-3752-6773; Zhang, Jinjun/0000-0001-8687-2659 National Natural Science Foundation of China [51904316]; China University of Petroleum, Beijing [2462021YJRC013, 2462018YJRC038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China University of Petroleum, Beijing This work is supported by National Natural Science Foundation of China [grant number 51904316], and the research fund provided by China University of Petroleum, Beijing [grant number 2462021YJRC013, 2462018YJRC038]. 53 2 2 8 12 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. FEB 10 2022.0 335 130274 10.1016/j.jclepro.2021.130274 0.0 16 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 2P6KC 2023-03-23 WOS:000819846400005 0
J Hou, CC; Simos, TE; Famelis, IT Hou, Chih-Chun; Simos, Theodore E.; Famelis, Ioannis Th. Neural network solution of pantograph type differential equations MATHEMATICAL METHODS IN THE APPLIED SCIENCES English Article approximate solutions; neural networks; pantograph equations NUMERICAL-SOLUTION; SOLVING ORDINARY We investigate the approximate solution of pantograph type functional differential equations using neural networks. The methodology is based on the ideas of Lagaris et al, and itis applied to various problems with a proportional delay term subject to initial or boundary conditions. The proposed methodology proves to be very efficient. [Hou, Chih-Chun] Yulin Normal Univ, Yulin 537000, Peoples R China; [Simos, Theodore E.] South Ural State Univ, 76 Lenin Ave, Chelyabinsk 454080, Russia; [Simos, Theodore E.] Ural Fed Univ, Grp Modern Computat Methods, 19 Mira St, Ekaterinburg 620002, Russia; [Simos, Theodore E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi, Greece; [Famelis, Ioannis Th.] MicroSENSES Lab, Dept Elect & Elect Engn, Athens, Greece Yulin Normal University; South Ural State University; Ural Federal University; Democritus University of Thrace Simos, TE (corresponding author), South Ural State Univ, 76 Lenin Ave, Chelyabinsk 454080, Russia. tsimos.conf@gmail.com Famelis, Ioannis/AAP-5476-2021; FAMELIS, Ioannis/GWV-1255-2022 Famelis, Ioannis/0000-0003-0352-9404; Simos, Theodore/0000-0002-9220-6924 27 37 37 6 22 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0170-4214 1099-1476 MATH METHOD APPL SCI Math. Meth. Appl. Sci. APR 2020.0 43 6 3369 3374 10.1002/mma.6126 0.0 JAN 2020 6 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics LQ8FO 2023-03-23 WOS:000505195800001 0
J Huang, JL; Huth, C; Covic, M; Troll, M; Adam, J; Zukunft, S; Prehn, C; Wang, L; Nano, J; Scheerer, MF; Neschen, S; Kastenmuller, G; Suhre, K; Laxy, M; Schliess, F; Gieger, C; Adamski, J; de Angelis, MH; Peters, A; Wang-Sattler, R Huang, Jialing; Huth, Cornelia; Covic, Marcela; Troll, Martina; Adam, Jonathan; Zukunft, Sven; Prehn, Cornelia; Wang, Li; Nano, Jana; Scheerer, Markus F.; Neschen, Susanne; Kastenmuller, Gabi; Suhre, Karsten; Laxy, Michael; Schliess, Freimut; Gieger, Christian; Adamski, Jerzy; Hrabe de Angelis, Martin; Peters, Annette; Wang-Sattler, Rui Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes DIABETES English Article RISK; SPHINGOMYELIN; PREDICTION; ALBUMINURIA; PROGRESSION; POPULATION; CHILDREN Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation. [Huang, Jialing; Covic, Marcela; Troll, Martina; Adam, Jonathan; Wang, Li; Gieger, Christian; Wang-Sattler, Rui] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Res Unit Mol Epidemiol, Neuherberg, Germany; [Huang, Jialing; Huth, Cornelia; Covic, Marcela; Troll, Martina; Adam, Jonathan; Wang, Li; Nano, Jana; Gieger, Christian; Peters, Annette; Wang-Sattler, Rui] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Epidemiol, Neuherberg, Germany; [Huang, Jialing; Huth, Cornelia; Covic, Marcela; Nano, Jana; Gieger, Christian; Hrabe de Angelis, Martin; Peters, Annette; Wang-Sattler, Rui] German Ctr Diabet Res DZD, Munich, Germany; [Zukunft, Sven; Prehn, Cornelia; Adamski, Jerzy] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Res Unit Mol Endocrinol & Metab, Neuherberg, Germany; [Wang, Li] Dept Sci Res, Guangzhou, Guangdong, Peoples R China; [Wang, Li] Shandong Univ, Postdoctoral Work Stn, Liaocheng Peoples Hosp, Jinan, Shandong, Peoples R China; [Scheerer, Markus F.; Neschen, Susanne; Hrabe de Angelis, Martin] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Expt Genet, Neuherberg, Germany; [Kastenmuller, Gabi] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Bioinformat & Syst Biol, Neuherberg, Germany; [Suhre, Karsten] Weill Cornell Med Qatar, Dept Physiol & Biophys, Doha, Qatar; [Laxy, Michael] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Hlth Econ & Hlth Care Management, Neuherberg, Germany; [Schliess, Freimut] Profil Inst Stoffwechselforsch GmbH, Neuss, Germany; [Adamski, Jerzy] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Biochem, Singapore, Singapore; [Adamski, Jerzy; Hrabe de Angelis, Martin] Tech Univ Munich, Ctr Life & Food Sci Weihenstephan, Chair Expt Genet, Freising Weihenstephan, Germany; [Zukunft, Sven] Goethe Univ, Ctr Mol Med, Inst Vasc Signalling, Frankfurt, Germany; [Scheerer, Markus F.] Bayer AG, Med Affairs & Pharmacovigilance, Berlin, Germany; [Neschen, Susanne] Sanofi Aventis Deutschland GmbH, Frankfurt, Germany Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Shandong University; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Qatar Foundation (QF); Weill Cornell Medical College Qatar; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Profil; National University of Singapore; Technical University of Munich; Goethe University Frankfurt; Bayer AG; Sanofi-Aventis Wang-Sattler, R (corresponding author), Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Res Unit Mol Epidemiol, Neuherberg, Germany.;Wang-Sattler, R (corresponding author), Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Epidemiol, Neuherberg, Germany.;Wang-Sattler, R (corresponding author), German Ctr Diabet Res DZD, Munich, Germany. rui.wang-sattler@helmholtz-muenchen.de Peters, Annette/A-6117-2011; Huth, Cornelia/B-5350-2014; Suhre, Karsten/AAF-1778-2020; Wang-Sattler, Rui/B-5354-2014; de Angelis, Martin Hrabe/F-5531-2012 Peters, Annette/0000-0001-6645-0985; Huth, Cornelia/0000-0003-2421-433X; Suhre, Karsten/0000-0001-9638-3912; Wang-Sattler, Rui/0000-0002-8794-8229; de Angelis, Martin Hrabe/0000-0002-7898-2353; Huang, Jialing/0000-0001-7158-3782; Prehn, Cornelia/0000-0002-1274-4715 Helmholtz Zentrum Munchen -German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF); State of Bavaria; Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ; European Union Seventh Framework Programme (EU FP7) [HEALTH-2013-2.4.2-1/602936]; European Institute of Innovation and Technology (EIT) Health [19076, 20679 iPDM-GO]; EIT, a body of the European Union; Biomedical Research Program funds at Weill Cornell Medicine -Qatar, a program - Qatar Foundation Helmholtz Zentrum Munchen -German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); State of Bavaria; Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ; European Union Seventh Framework Programme (EU FP7)(European Commission); European Institute of Innovation and Technology (EIT) Health; EIT, a body of the European Union; Biomedical Research Program funds at Weill Cornell Medicine -Qatar, a program - Qatar Foundation The KORA study was initiated and financed by Helmholtz Zentrum Munchen -German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ. Part of this project was supported by European Union Seventh Framework Programme (EU FP7) grant HEALTH-2013-2.4.2-1/602936 (Project CarTarDis) and the European Institute of Innovation and Technology (EIT) Healthsupported 19076 and 20679 iPDM-GO Integrated Personalized Diabetes Management Goes Europe innovation project. EIT Health is supported by the EIT, a body of the European Union. K.S. is supported by Biomedical Research Program funds at Weill Cornell Medicine -Qatar, a program funded by the Qatar Foundation. 52 13 15 3 14 AMER DIABETES ASSOC ALEXANDRIA 1701 N BEAUREGARD ST, ALEXANDRIA, VA 22311-1717 USA 0012-1797 1939-327X DIABETES Diabetes DEC 2020.0 69 12 2756 2765 10.2337/db20-0586 0.0 10 Endocrinology & Metabolism Science Citation Index Expanded (SCI-EXPANDED) Endocrinology & Metabolism OZ3GW 33024004.0 Bronze, Green Submitted 2023-03-23 WOS:000594819600021 0
J Li, S; Ni, ZY; Zhao, YB; Hu, W; Long, ZR; Ma, HY; Zhou, GL; Luo, YH; Geng, CT Li, Shuai; Ni, Zhongyun; Zhao, Yinbing; Hu, Wei; Long, Zhenrui; Ma, Haiyu; Zhou, Guoli; Luo, Yuhao; Geng, Chuntao Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article geohazard susceptibility; superposition effect; random forest model; Longmenshan fault zone MAPPING LANDSLIDE SUSCEPTIBILITY; MACHINE-LEARNING TECHNIQUES; ARTIFICIAL NEURAL-NETWORK; DATA MINING TECHNIQUES; DEBRIS FLOWS; LOGISTIC-REGRESSION; RANDOM FOREST; FREQUENCY RATIO; RISK-ASSESSMENT; TRIGGERED LANDSLIDES Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009-2012, 2013, and 2014-2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods. [Li, Shuai; Zhao, Yinbing; Hu, Wei; Zhou, Guoli; Luo, Yuhao; Geng, Chuntao] Chengdu Univ Technol, Coll Tourism & Urban Rural Planning, Chengdu 610059, Peoples R China; [Ni, Zhongyun] Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China; [Ni, Zhongyun; Zhao, Yinbing] Natl Univ Ireland, Sch Geog Archaeol & Irish Studies, Galway H91 CF50, Ireland; [Zhao, Yinbing] Chengdu Univ Technol, Human Geog Res Ctr Qinghai Tibet Plateau & Its Ea, Chengdu 610059, Peoples R China; [Long, Zhenrui] Sichuan Res Inst Ecol Restorat Land Space & Geoha, Sichuan Prov Dept Nat Resources, Chengdu 610063, Peoples R China; [Ma, Haiyu] Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China Chengdu University of Technology; Chengdu University of Technology; Chengdu University of Technology; Shanghai Ocean University Ni, ZY (corresponding author), Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China.;Ni, ZY (corresponding author), Natl Univ Ireland, Sch Geog Archaeol & Irish Studies, Galway H91 CF50, Ireland. shuailee.cn@gmail.com; nizhongyun2012@mail.cdut.edu.cn; zhaoyinbing06@cdut.cn; 2019020722@stu.cdut.edu.cn; longzrei@gmail.com; mahaiyu.cn@outlook.com; zhouguoli@stu.cdut.edu.cn; luoyuhao0723@gmail.com; gct199610@gmail.com Li, Shuai/HDN-6910-2022 Ma, Haiyu/0000-0003-2716-4876; Li, Shuai/0000-0002-8098-5262 Key Research Project of National Park Research Center [GJGY 2019-ZD003]; Key Project Open Project of the Qinghai-Tibet Plateau and its Eastern Edge Human Geography Research Center [RWDL2021-ZD002]; Open Fund of the Key Laboratory of Wetland Ecology and Protection of Hebei Province [hklk201902]; Young and Middle-aged Teacher Program of Chengdu University of Technology [2019JXGG01213, 10912-JXGG2021-01679]; China Scholarship Council Fund [20200851509] Key Research Project of National Park Research Center; Key Project Open Project of the Qinghai-Tibet Plateau and its Eastern Edge Human Geography Research Center; Open Fund of the Key Laboratory of Wetland Ecology and Protection of Hebei Province; Young and Middle-aged Teacher Program of Chengdu University of Technology; China Scholarship Council Fund(China Scholarship Council) The Key Research Project of National Park Research Center (GJGY 2019-ZD003), the 2021 Key Project Open Project of the Qinghai-Tibet Plateau and its Eastern Edge Human Geography Research Center (RWDL2021-ZD002), the Open Fund of the Key Laboratory of Wetland Ecology and Protection of Hebei Province (under preparation) (hklk201902), the Young and Middle-aged Teacher Program of Chengdu University of Technology (2019JXGG01213, 10912-JXGG2021-01679) and the China Scholarship Council Fund (NO.20200851509) supported this work. 136 9 9 13 48 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health MAR 2022.0 19 6 3229 10.3390/ijerph19063229 0.0 30 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health 0C5EL 35328915.0 gold, Green Accepted 2023-03-23 WOS:000775336200001 0
J Fang, B; Long, XM; Sun, FC; Liu, HP; Zhang, SX; Fang, C Fang, Bin; Long, Xingming; Sun, Fuchun; Liu, Huaping; Zhang, Shixin; Fang, Cheng Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Fabrics; Feature extraction; Frequency-domain analysis; Tactile sensors; Cameras; Visualization; Sensors; Attention mechanism; defect detection; vision-based tactile sensor TEXTURE This article proposes a fabric structure defect detection method based on the vision-based tactile sensor. The result will be robust by using the tactile sensor regardless of dyeing patterns which can influence the result if some other sensors are used, e.g., vision perception. It also reduces the influence of ambient light on defect detection. Therefore, the proposed method can be more robust and universal than conventional visual methods. A robotic arm equipped with the tactile sensors was used to automate and standardize the data collection process and construct fabric datasets. In addition, a convolutional neural network (CNN) integrated with attention mechanism in the channel domain was developed to detect fabric types. The proposed network employed frequency domain filtering to remove or weaken the influence of normal fabric texture information to improve defect detection efficiency and accuracy. Finally, several experiments were conducted to demonstrate the proposed method's superiority to a visual defect detection method for detecting structural defects. In addition, the efficiency of the proposed method is evaluated. Experimental results show that the proposed method is feasible and efficient to meet the real-world detection requirements. [Fang, Bin; Sun, Fuchun; Liu, Huaping] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Inst Artificial Intelligence, State Key Lab Intelligent Technol & Syst,Dept Com, Beijing 100084, Peoples R China; [Long, Xingming] Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China; [Zhang, Shixin] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China; [Fang, Cheng] Univ Southern Denmark, Maersk McKinney Moller Inst, DK-5230 Odense, Denmark Tsinghua University; Chinese Academy of Sciences; Institute of Computing Technology, CAS; China University of Geosciences; University of Southern Denmark Sun, FC (corresponding author), Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Inst Artificial Intelligence, State Key Lab Intelligent Technol & Syst,Dept Com, Beijing 100084, Peoples R China. fcsun@tsinghua.edu.cn; chfa@mmmi.sdu.dk Fang, Cheng/0000-0002-8535-3774; Long, Xingming/0000-0001-7229-9142 National Natural Science Foundation of China [62173197]; Beijing Science Technology Project [Z191100008019008] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Science Technology Project This work was supported in part by the National Natural Science Foundation of China under Grant 62173197 and in part by the Beijing Science Technology Project under Grant Z191100008019008. 35 5 5 22 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 5011309 10.1109/TIM.2022.3165254 0.0 9 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 1F3XW Green Published 2023-03-23 WOS:000795104800001 0
J Wu, YT; Zhang, YJ; Li, G; Shen, JW; Chen, Z; Liu, YG Wu, Yitao; Zhang, Yuanjian; Li, Guang; Shen, Jiangwei; Chen, Zheng; Liu, Yonggang A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks ENERGY English Article Plug-in hybrid electric vehicles; Model predictive control; Dynamic programming; Neural network; Pontryagin's minimum principle DESIGN Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin's minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin's minimum principle-based model predictive control scheme, thereby proving its online application potential. (C) 2020 Elsevier Ltd. All rights reserved. [Wu, Yitao; Shen, Jiangwei; Chen, Zheng] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China; [Zhang, Yuanjian] Queens Univ Belfast, Sir William Wright Technol Ctr, Belfast BT9 5BS, Antrim, North Ireland; [Li, Guang; Chen, Zheng] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England; [Liu, Yonggang] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Liu, Yonggang] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China Kunming University of Science & Technology; Queens University Belfast; University of London; Queen Mary University London; Chongqing University; Chongqing University Chen, Z (corresponding author), Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China. wuyitaomail@qq.com; y.zhang@qub.ac.uk; g.li@qmul.ac.uk; shenjiangwei6@163.com; chen@kust.edu.cn; andylyg@umich.edu Zhang, Yuanjian/HKN-4832-2023; Chen, Zheng/AAO-6454-2020; Li, Guang/AAC-6883-2021 Zhang, Yuanjian/0000-0001-5563-8480; Chen, Zheng/0000-0002-1634-7231; Li, Guang/0000-0003-1068-0479 National Natural Science Foundation of China [61763021, 51775063]; National Key R&D Program of China [2018YFB0104900]; EU [845102-HOEMEV-H2020-MSCA-IF-2018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; EU(European Commission) This workwas supported in part by the National Natural Science Foundation of China (No. 61763021 and No. 51775063), in part by the National Key R&D Program of China (No. 2018YFB0104900), and in part by the EU-funded Marie Sklodowska-Curie Individual Fellowships Project under Grant 845102-HOEMEV-H2020-MSCA-IF-2018. 43 33 33 9 75 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy OCT 1 2020.0 208 118366 10.1016/j.energy.2020.118366 0.0 17 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels NH4SI Green Submitted 2023-03-23 WOS:000564661300007 0
J Yang, ZB; Xing, LQ; Gu, ZH; Xiao, YM; Zhou, Y; Huang, ZQ; Xue, L Yang, Zhibin; Xing, Linquan; Gu, Zonghua; Xiao, Yingmin; Zhou, Yong; Huang, Zhiqiu; Xue, Lei Model-Based Reinforcement Learning and Neural-Network-Based Policy Compression for Spacecraft Rendezvous on Resource-Constrained Embedded Systems IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Space vehicles; Artificial neural networks; Mathematical models; Vehicle dynamics; Reinforcement learning; Predictive models; Computational modeling; Formal verification; Markov decision process (MDP); model-based reinforcement learning; spacecraft rendezvous guidance DYNAMICS Autonomous spacecraft rendezvous is very challenging in increasingly complex space missions. In this article, we present our approach model-based reinforcement learning for spacecraft rendezvous guidance (MBRL4SRG). We build a Markov decision process model based on the Clohessy-Wiltshire equation of spacecraft dynamics and use dynamic programming to solve it and generate the decision table as the optimal agent policy. Since the onboard computing system of spacecraft is resource constrained in terms of both memory size and processing speed, we train a neural network (NN) as a compact and efficient function approximation to the tabular representation of the decision table. The NN outputs are formally verified using the verification tool ReluVal, and the verification results show that the robustness of the NN is maintained. Experimental results indicate that MBRL4SRG achieves lower computational overhead than the conventional proportional-integral-derivative algorithm and has higher trustworthiness and better computational efficiency during training than the model-free reinforcement learning algorithms. [Yang, Zhibin; Xing, Linquan; Xiao, Yingmin; Zhou, Yong; Huang, Zhiqiu] Nanjing Univ Aeronaut & Astronaut, Sch Comp Sci & Technol, Nanjing 210016, Peoples R China; [Gu, Zonghua] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Xue, Lei] Shanghai Aerosp Elect Technol Inst, Shanghai 201100, Peoples R China Nanjing University of Aeronautics & Astronautics; Umea University Yang, ZB (corresponding author), Nanjing Univ Aeronaut & Astronaut, Sch Comp Sci & Technol, Nanjing 210016, Peoples R China. yangzhibin168@163.com; xinglq@nuaa.edu.cn; zonghua.gu@umu.se; xiaoyingmin@nuaa.edu.cn; zhouyong@nuaa.edu.cn; zqhuang@nuaa.edu.cn; jzgunking@163.com Gu, Zonghua/0000-0003-4228-2774; Yang, Zhibin/0000-0002-9888-6975 National Natural Science Foundation of China [62072233]; Fundamental Research Funds for the Central Universities [NS2019057]; Aeronautical Science Foundation of China [201919052002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Aeronautical Science Foundation of China This work was supported in part by National Natural Science Foundation of China under Grant 62072233, in part by Fundamental Research Funds for the Central Universities under Grant NS2019057, and in part by Aeronautical Science Foundation of China under Grant 201919052002. 30 0 0 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. JAN 2023.0 19 1 1107 1116 10.1109/TII.2022.3192085 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 6A4VJ 2023-03-23 WOS:000880654600114 0
J Lei, TMT; Siu, SWI; Monjardino, J; Mendes, L; Ferreira, F Lei, Thomas M. T.; Siu, Shirley W., I; Monjardino, Joana; Mendes, Luisa; Ferreira, Francisco Using Machine Learning Methods to Forecast Air Quality: A Case Study in Macao ATMOSPHERE English Article random forest; gradient boosting; support vector regression; multiple linear regression; air quality forecast; COVID-19; air quality; air pollution AREA Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R-2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease. [Lei, Thomas M. T.; Siu, Shirley W., I] Univ St Joseph, Inst Sci & Environm, Macau 999078, Peoples R China; [Monjardino, Joana; Ferreira, Francisco] NOVA Univ Lisbon, CENSE Ctr Environm & Sustainabil Res, NOVA Sch Sci & Technol, P-2829516 Caparica, Portugal; [Mendes, Luisa] NOVA Univ Lisbon, NOVA Sch Sci & Technol, Dept Sci & Environm Engn, P-2829516 Caparica, Portugal University of Saint Joseph (Macao); Universidade Nova de Lisboa; Universidade Nova de Lisboa Lei, TMT (corresponding author), Univ St Joseph, Inst Sci & Environm, Macau 999078, Peoples R China. thomas.lei@usj.edu.mo Siu, Shirley/A-2973-2013 Siu, Shirley/0000-0002-3695-7758; Ferreira, Francisco/0000-0001-5815-2136; Lei, Thomas/0000-0001-7726-8820; Monjardino, Joana/0000-0002-5399-995X Fundacao para a Ciencia e Tecnologia, I.P., Portugal [UID/AMB/04085/2020]; CENSE Fundacao para a Ciencia e Tecnologia, I.P., Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); CENSE This research was funded by Fundacao para a Ciencia e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. 30 3 3 4 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4433 ATMOSPHERE-BASEL Atmosphere SEP 2022.0 13 9 1412 10.3390/atmos13091412 0.0 14 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences 4V3CQ gold 2023-03-23 WOS:000859358900001 0
J Wang, K; Hu, Q; Gao, B; Lin, Q; Zhuge, FW; Zhang, DY; Wang, L; He, YH; Scheicher, RH; Tong, H; Miao, XS Wang, Kuan; Hu, Qing; Gao, Bin; Lin, Qi; Zhuge, Fu-Wei; Zhang, Da-You; Wang, Lun; He, Yu-Hui; Scheicher, Ralph H.; Tong, Hao; Miao, Xiang-Shui Threshold switching memristor-based stochastic neurons for probabilistic computing MATERIALS HORIZONS English Article Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%. [Wang, Kuan; Hu, Qing; Lin, Qi; Zhang, Da-You; Wang, Lun; He, Yu-Hui; Tong, Hao; Miao, Xiang-Shui] Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China; [Gao, Bin] Tsinghua Univ, Inst Microelect, Beijing 100084, Peoples R China; [Zhuge, Fu-Wei] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, Sch Mat Sci & Engn, Wuhan 430074, Peoples R China; [Scheicher, Ralph H.] Uppsala Univ, Div Mat Theory, Dept Phys & Astron, SE-75120 Uppsala, Sweden Huazhong University of Science & Technology; Tsinghua University; Huazhong University of Science & Technology; Uppsala University He, YH; Tong, H; Miao, XS (corresponding author), Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China. heyuhui@hust.edu.cn; tonghao@hust.edu.cn; miaoxs@hust.edu.cn Scheicher, Ralph/AEF-6995-2022; he, yh/HJA-5454-2022; Zhuge, Fuwei/I-7171-2013; Scheicher, Ralph/G-1740-2012 Zhuge, Fuwei/0000-0003-3673-2257; He, Yuhui/0000-0002-0546-7692; Wang, Kuan/0000-0002-7406-2867; wang, lun/0000-0002-1181-7345; Scheicher, Ralph/0000-0001-5397-7753 National Science and Technology Major Project [2017ZX02301007-002]; National Natural Science Foundation of China (NSFC) [61974051, 61774068]; Swedish Research Council (VR) [2017-04627] National Science and Technology Major Project; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Swedish Research Council (VR)(Swedish Research Council) This research was supported by the National Science and Technology Major Project, under no. 2017ZX02301007-002, the National Natural Science Foundation of China (NSFC), under no. 61974051, 61774068. R. H. S. thanks the Swedish Research Council (VR grant 2017-04627). 40 31 31 13 93 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2051-6347 2051-6355 MATER HORIZ Mater. Horizons FEB 1 2021.0 8 2 619 629 10.1039/d0mh01759k 0.0 11 Chemistry, Multidisciplinary; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science QH4KA 34821279.0 2023-03-23 WOS:000618243600020 0
J Attoue, N; Shahrour, I; Younes, R Attoue, Nivine; Shahrour, Isam; Younes, Rafic Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting ENERGIES English Article smart building; artificial neural network (ANN); indoor; temperature; facade; outdoor; forecasting; relevance; sensors; recorded data ENERGY-STORAGE TECHNOLOGIES; DISTRIBUTED GENERATION; FLYWHEEL STORAGE; SYSTEM OPERATION; POWER-PLANTS; WIND; ELECTRICITY; COSTS; PENETRATION; INTEGRATION The smart building concept aims to use smart technology to reduce energy consumption, as well as to improve comfort conditions and users' satisfaction. It is based on the use of smart sensors and software to follow both outdoor and indoor conditions for the control of comfort, and security devices for the optimization of energy consumption. This paper presents a data-based model for indoor temperature forecasting, which could be used for the optimization of energy device use. The model is based on an artificial neural network (ANN), which is validated on data recorded in an old building. The novelty of this work consists of the methodology proposed for the development of a simplified model for indoor temperature forecasting. This methodology is based on the selection of pertinent input parameters after a relevance analysis of a large set of input parameters, including solar radiation outdoor temperature history, outdoor humidity, indoor facade temperature, and humidity. It shows that an ANN-based model using outdoor and facade temperature sensors provides good forecasting of indoor temperatures. This model can be easily used in the optimal regulation of buildings' energy devices. [Attoue, Nivine; Shahrour, Isam] Lille Univ, Lab Civil Engn & Geoenvironm, F-59650 Villeneuve Dascq, France; [Shahrour, Isam] Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China; [Younes, Rafic] Lebanese Univ, Modeling Ctr, Hadath 99000, Lebanon Universite de Lille - ISITE; Universite de Lille; Tongji University Shahrour, I (corresponding author), Lille Univ, Lab Civil Engn & Geoenvironm, F-59650 Villeneuve Dascq, France.;Shahrour, I (corresponding author), Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China. nivine.attoue@gmail.com; isam.shahrour@univ-lille1.fr; ryounes@ul.edu.lb Shahrour, isam/A-6865-2008; Shahrour, Isam/I-7151-2019; Younes, Rafic/O-6769-2017 Shahrour, Isam/0000-0001-7279-8005; Younes, Rafic/0000-0002-7957-2057 Italian MISE [CCSEB_00201] Italian MISE(Ministry of Economic Development, Italy) The research activity has been carried out within the project Sviluppo di una innovativa Tecnologia integrata Volano-Batteria per l'accumulo efficiente di energia da rinnovabile per applicazioni di piccola taglia, funded by the Italian MISE (CCSEB_00201). Project partners are acknowledged. 37 57 57 0 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies FEB 2018.0 11 2 395 10.3390/en11020395 0.0 35 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels FY3PY Green Submitted, gold 2023-03-23 WOS:000426734600138 0
J Ibrahim, Y; Kamel, S; Rashad, A; Nasrat, L; Jurado, F Ibrahim, Yousry; Kamel, Salah; Rashad, Ahmed; Nasrat, Loai; Jurado, Francisco Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE English Article Squirrel Cage Induction Generator (SCIG); Doubly Fed Induction Generator (DFIG); Combined Wind Farm (CWF); Static Synchronous Series Compensator (SSSC); Artificial Neural Network (ANN) POWER-SYSTEM; DAMPING CONTROLLER; IMPROVE STABILITY; STATCOM; PSS; GA Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multi-objective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN. [Ibrahim, Yousry; Kamel, Salah; Rashad, Ahmed; Nasrat, Loai] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan 81542, Egypt; [Kamel, Salah] Univ Jaen, Dept Elect Engn, Jaen 23700, Spain; [Jurado, Francisco] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400030, Peoples R China Egyptian Knowledge Bank (EKB); Aswan University; Universidad de Jaen; Chongqing University Jurado, F (corresponding author), Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400030, Peoples R China. fjurado@ujaen.es Rashad, Ahmed/AAF-8050-2019; Nasrat, Loai/AAB-7888-2020; Jurado, Francisco/F-7546-2011 Nasrat, Loai/0000-0002-5751-5860; Jurado, Francisco/0000-0001-8122-7415 NSFC (China)-ASRT (Egypt) Joint Research Fund [51861145406] NSFC (China)-ASRT (Egypt) Joint Research Fund The authors gratefully acknowledge the contribution of the NSFC (China)-ASRT (Egypt) Joint Research Fund, Project No. 51861145406 for providing partial research funding to the work reported in this research. 22 20 20 0 14 UNIV INT RIOJA-UNIR LOGRONO RECTORADO, AVENIDA DE LA PAZ, 137, LOGRONO, 26006, SPAIN 1989-1660 INT J INTERACT MULTI Int. J. Interact. Multimed. Artif. Intell. DEC 2019.0 5 7 118 124 10.9781/ijimai.2019.05.001 0.0 7 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science JJ1HH gold, Green Submitted 2023-03-23 WOS:000493911100013 0
J Wan, LT; Sun, YC; Sun, L; Ning, ZL; Rodrigues, JJPC Wan, Liangtian; Sun, Yuchen; Sun, Lu; Ning, Zhaolong; Rodrigues, Joel J. P. C. Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Safety driving; deep learning; massive MIMO; autonomous vehicles; super resolution DOA estimation OF-ARRIVAL ESTIMATION; CONFORMAL ARRAY ANTENNA; DIRECTION; LOCALIZATION; POLARIZATION; PERSPECTIVE; ALGORITHM; NETWORK; SIGNALS; SDN In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems. The location parameters of autonomous vehicles can be estimated based on the deep unfolding technique, which is a recent advance of deep learning. Traditional vehicle location methods such as the global position system (GPS) can only locate the target vehicles with relatively low accuracy. The super resolution cannot be achieved when two vehicles are too close, which means that the safety incidents exist when autonomous vehicles are deployed in future intelligent transportation systems (ITS). Different from the existing massive MIMO or RIS equipped with a regular array such as uniform rectangular array (URA) and uniform circular array (UCA), we exploit a massive MIMO or a RIS equipped with a conformal array extended from traditional regular array. First, the rotation from the global coordinate system to the local coordinate system is achieved based on geometric algebra. Second, 2D-DOA estimation of autonomous vehicles is modeled as a novel block sparse recovery problem. Third, the deep network architecture SBLNet is implemented to learn the nonlinear characteristic from the DOAs of autonomous vehicles and the data received by massive MIMOs or RISs. The 2D-DOA and polarization parameters can be estimated based on SBLNet with relatively low computational complexity. Simulation results demonstrate that SBLNet performs better than the state-of-the-art methods in terms of estimation accuracy and successful probability. The SBLNet is also suitable for the practical scenario considering fast moving autonomous vehicles, while, the traditional block sparse recovery methods fail in this complex scenario. [Wan, Liangtian; Sun, Yuchen] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China; [Sun, Lu] Dalian Maritime Univ, Inst Informat Sci Technol, Dept Commun Engn, Dalian 116026, Peoples R China; [Ning, Zhaolong] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Mobile Commun Technol, Chongqing 400065, Peoples R China; [Ning, Zhaolong] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China; [Ning, Zhaolong] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Postgrad Program Elect Engn PPGEE, BR-64049550 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-6201001 Covilha, Portugal Dalian University of Technology; Dalian Maritime University; Chongqing University of Posts & Telecommunications; Southeast University - China; Dalian University of Technology; Universidade Federal do Piaui Ning, ZL (corresponding author), Chongqing Univ Posts & Telecommun, Chongqing Key Lab Mobile Commun Technol, Chongqing 400065, Peoples R China. wanliangtian@dlut.edu.cn; sunyuc888@mail.dlut.edu.cn; sunlu5945@gmail.com; zhaolongning@dlut.edu.cn; joeljr@ieee.org Ning, Zhaolong/ABI-3626-2022; Rodrigues, Joel J. P. C./A-8103-2013 Ning, Zhaolong/0000-0002-7870-5524; Rodrigues, Joel J. P. C./0000-0001-8657-3800; Wan, Liangtian/0000-0003-0574-8360 National Natural Science Foundation of China [61801076, 61971084]; Fundamental Research Funds for the Central Universities [DUT20JC29, DUT19JC18]; National Natural Science Foundation of Chongqing [cstc2019jcyj-msxmX0208]; Open Research Fund of National Mobile Communications Research Laboratory, Southeast University [2020D05]; FCT/MCTES; EU funds [UIDB/EEA/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of Chongqing; Open Research Fund of National Mobile Communications Research Laboratory, Southeast University; FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work was supported in part by the National Natural Science Foundation of China under Grant 61801076 and Grant 61971084, in part by the Fundamental Research Funds for the Central Universities DUT20JC29 and DUT19JC18, in part by the National Natural Science Foundation of Chongqing under Grant cstc2019jcyj-msxmX0208, in part by the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University, under Grant 2020D05, in part by the FCT/MCTES through national funds and when applicable co-funded EU funds under Project UIDB/EEA/50008/2020, and in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 309335/2017-5. 52 40 40 31 92 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. JUL 2021.0 22 7 4301 4315 10.1109/TITS.2020.3009223 0.0 15 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation TJ5JU 2023-03-23 WOS:000673518500037 0
J Ding, ZG; Lv, L; Fang, F; Dobre, OA; Karagiannidis, GK; Al-Dhahir, N; Schober, R; Poor, HV Ding, Zhiguo; Lv, Lu; Fang, Fang; Dobre, Octavia A.; Karagiannidis, George K.; Al-Dhahir, Naofal; Schober, Robert; Poor, H. Vincent A State-of-the-Art Survey on Reconfigurable Intelligent Surface-Assisted Non-Orthogonal Multiple Access Networks PROCEEDINGS OF THE IEEE English Article NOMA; 6G mobile communication; Resource management; Security; Gain; Performance gain; 5G mobile communication; Artificial intelligence (AI); multi-input multi-output (MIMO); nonorthogonal multiple access (NOMA); reconfigurable intelligent surface (RIS); resource allocation; wireless security EFFICIENT RESOURCE-ALLOCATION; SIMULTANEOUS WIRELESS INFORMATION; PASSIVE BEAMFORMING DESIGN; EDGE COMPUTING NETWORKS; MASSIVE MIMO SYSTEMS; REFLECTING SURFACE; NOMA SYSTEMS; CHANNEL ESTIMATION; POWER ALLOCATION; UAV COMMUNICATIONS Reconfigurable intelligent surfaces (RISs) and nonorthogonal multiple access (NOMA) have been recognized as key enabling techniques for the envisioned sixth generation (6G) of mobile communication networks. The key feature of RISs is to intelligently reconfigure the wireless propagation environment, which was once considered to be fixed and untunable. The key idea of NOMA is to utilize users' dynamic channel conditions to improve spectral efficiency and user fairness. Naturally, the two communication techniques are complementary to each other and can be integrated to cope with the challenging requirements envisioned for 6G mobile networks. This survey provides a comprehensive overview of the recent progress on the synergistic integration of RISs and NOMA. In particular, the basics of both techniques are introduced first, and then, the fundamentals of RIS-NOMA are discussed for two communication scenarios with different transceiver capabilities. Resource allocation is of paramount importance for the success of RIS-assisted NOMA networks, and various approaches, including artificial intelligence (AI)-empowered designs, are introduced. Security provisioning in RIS-NOMA networks is also discussed as wireless networks are prone to security attacks due to the nature of the shared wireless medium. Finally, the survey is concluded with detailed discussions of the challenges arising in the practical implementation of RIS-NOMA, future research directions, and emerging applications. [Ding, Zhiguo; Poor, H. Vincent] Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA; [Ding, Zhiguo] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England; [Lv, Lu] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China; [Fang, Fang] Western Univ, Dept Elect & Comp Engn, London, ON N6A 3K7, Canada; [Fang, Fang] Western Univ, Dept Comp Sci, London, ON N6A 3K7, Canada; [Dobre, Octavia A.] Mem Univ, Fac Engn & Appl Sci, St John, NF, Canada; [Karagiannidis, George K.] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Wireless Commun & Informat Proc WCIP Grp, Thessaloniki 54124, Greece; [Al-Dhahir, Naofal] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA; [Schober, Robert] Friedrich Alexander Univ Erlangen Nurnberg FAU, Inst Digital Commun, D-91058 Erlangen, Germany Princeton University; University of Manchester; Xidian University; Western University (University of Western Ontario); Western University (University of Western Ontario); Memorial University Newfoundland; Aristotle University of Thessaloniki; University of Texas System; University of Texas Dallas; University of Erlangen Nuremberg Ding, ZG (corresponding author), Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA. zhiguo.ding@manchester.ac.uk; lulv@xidian.edu.cn; fang.fang@uwo.ca; odobre@mun.ca; geokarag@auth.gr; aldhahir@utdallas.edu; robert.schober@fau.de; poor@princeton.edu Karagiannidis, George/A-5190-2014; Dobre, Octavia/B-1435-2008; Aldhahir, Naofal/HCH-5192-2022 Karagiannidis, George/0000-0001-8810-0345; Dobre, Octavia/0000-0001-8528-0512; Poor, H. Vincent/0000-0002-2062-131X; Al-Dhahir, Naofal/0000-0001-9894-8382; Lv, Lu/0000-0002-7609-079X 160 7 7 6 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9219 1558-2256 P IEEE Proc. IEEE SEP 2022.0 110 9 1358 1379 10.1109/JPROC.2022.3174140 0.0 MAY 2022 22 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 4Q5PF Green Published 2023-03-23 WOS:000800797900001 0
J Ren, L; Jia, ZD; Wang, T; Ma, YH; Wang, LH Ren, Lei; Jia, Zidi; Wang, Tao; Ma, Yehan; Wang, Lihui LM-CNN: A Cloud-Edge Collaborative Method for Adaptive Fault Diagnosis With Label Sampling Space Enlarging IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Fault diagnosis; Cloud computing; Collaboration; Data models; Adaptation models; Computer architecture; Training; Cloud-edge collaboration; cloud manufacturing system; fault diagnosis; label-split multiple-inputs convolutional neural network (LM-CNN) INTERNET In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults as a priori knowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method. [Ren, Lei; Jia, Zidi; Wang, Tao] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Ma, Yehan] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden Beihang University; Shanghai Jiao Tong University; Royal Institute of Technology Ren, L; Jia, ZD (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. renlei@buaa.edu.cn; jiazidi@buaa.edu.cn; iamownt@buaa.edu.cn; yehanma@sjtu.edu.cn; lihui.wang@iip.kth.se Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049; Jia, Zidi/0000-0002-3746-7742; Wang, Tao/0000-0003-0201-8100 National Key Research and Development Program of China [2020AAA0109202]; NSFC (National Natural Science Foundation of China) [92167108, 62173023] National Key Research and Development Program of China; NSFC (National Natural Science Foundation of China)(National Natural Science Foundation of China (NSFC)) This work was supported by The National Key Research and Development Program of China under Grant 2020AAA0109202 and the NSFC (National Natural Science Foundation of China)under Project 92167108 and Project 62173023. Paper no. TII-22-2020. 40 2 2 35 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. DEC 2022.0 18 12 9057 9067 10.1109/TII.2022.3180389 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 4Z8CP 2023-03-23 WOS:000862429800070 0
J Cai, HJ; Liu, SN; Shi, HY; Zhou, ZQ; Jiang, SJ; Babovic, V Cai, Hejiang; Liu, Suning; Shi, Haiyun; Zhou, Zhaoqiang; Jiang, Shijie; Babovic, Vladan Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method JOURNAL OF HYDROLOGY English Article Groundwater level predictions; Water balance mechanism; Deep learning; Catchment scale NEURAL-NETWORKS; GLOBAL PATTERNS; DATA SET; RAINFALL; RECHARGE; REGIONS Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Delta NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven ap-proaches on groundwater modellings. [Cai, Hejiang; Babovic, Vladan] Natl Univ Singapore, Dept Civil & Environm Engn, Singapore, Singapore; [Cai, Hejiang; Shi, Haiyun; Zhou, Zhaoqiang] Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Guangdong, Peoples R China; [Cai, Hejiang; Shi, Haiyun; Zhou, Zhaoqiang] Southern Univ Sci & Technol, Sch Environm Sci & Engn, Guangdong Prov Key Lab Soil & Groundwater Pollut C, Shenzhen, Guangdong, Peoples R China; [Liu, Suning] Inst Basic Sci, Ctr Climate Phys, Busan, South Korea; [Jiang, Shijie] Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany National University of Singapore; Southern University of Science & Technology; Southern University of Science & Technology; Institute for Basic Science - Korea (IBS); Helmholtz Association; Helmholtz Center for Environmental Research (UFZ) Shi, HY (corresponding author), Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Guangdong, Peoples R China. shihy@sustech.edu.cn National Natural Science Founda- tion of China; Natural Science Foundation of Shenzhen; [51909117]; [JCYJ20210324105014039] National Natural Science Founda- tion of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shenzhen; ; This study was supported by the National Natural Science Founda- tion of China (51909117) and Natural Science Foundation of Shenzhen (JCYJ20210324105014039) . The groundwater level data are openly available at https://water- data.usgs.gov/nwis/gw, and the CAMELS dataset is freely available at https://ncar.ucar.edu. We gratefully acknowledge the use of the dataset publicly provided by Addor et al. (2017) and Newman et al. (2015) and the codes of P-RNN model publicly provided by Jiang et al. (2020) . 67 0 0 31 31 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. OCT 2022.0 613 B 128495 10.1016/j.jhydrol.2022.128495 0.0 OCT 2022 16 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources 5I4PY 2023-03-23 WOS:000868341800002 0
C Liu, D; Yu, WW; Baldi, S Lu, H; Tang, H; Wang, Z Liu, Di; Yu, Wenwu; Baldi, Simone Broad Learning for Optimal Short-Term Traffic Flow Prediction ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I Lecture Notes in Computer Science English Proceedings Paper 16th International Symposium on Neural Networks (ISNN) JUL 10-12, 2019 Moscow, RUSSIA Skolkovo Inst Sci & Technol,City Univ Hong Kong,Int Neural Network Soc,Asian Pacific Neural Network Soc,Polish Neural Network Soc,Russian Neural Network Soc Broad Learning System; Traffic flow prediction; Flat network; Fast least-square methods DEEP; ARCHITECTURE; NETWORKS; SYSTEM; MODEL In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy. [Liu, Di; Yu, Wenwu] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China; [Yu, Wenwu; Baldi, Simone] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China; [Baldi, Simone] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands Southeast University - China; Southeast University - China; Delft University of Technology Yu, WW (corresponding author), Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China.;Yu, WW (corresponding author), Southeast Univ, Sch Math, Nanjing 210096, Peoples R China. liud923@126.com; wwyu@seu.edu.cn; s.baldi@tudelft.nl Yu, Wenwu/G-5496-2012; Baldi, Simone/C-6796-2015; liu, di/M-5934-2014 Yu, Wenwu/0000-0003-0301-9180; Baldi, Simone/0000-0001-9752-8925; liu, di/0000-0003-0539-9279 National Natural Science Foundation of China [61673107]; National Ten Thousand Talent Program for Young Top-notch Talents [W2070082]; General joint fund of the equipment advance research program of Ministry of Education [6141A020223]; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [BM2017002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Ten Thousand Talent Program for Young Top-notch Talents; General joint fund of the equipment advance research program of Ministry of Education; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence This work was supported by the National Natural Science Foundation of China under Grant No. 61673107, the National Ten Thousand Talent Program for Young Top-notch Talents under Grant No. W2070082, the General joint fund of the equipment advance research program of Ministry of Education under Grant No. 6141A020223, and the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No. BM2017002. 22 4 4 7 18 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-22796-8; 978-3-030-22795-1 LECT NOTES COMPUT SC 2019.0 11554 232 239 10.1007/978-3-030-22796-8_25 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ6HI 2023-03-23 WOS:000611781800025 0
J Di Renzo, M; Tretyakov, S Di Renzo, Marco; Tretyakov, Sergei Reconfigurable Intelligent Surfaces [Scanning the Issue] PROCEEDINGS OF THE IEEE English Article Special issues and sections; Reconfigurable Intelligent Surfaces; Wireless communication; Metamaterials; Network architecture; Channel estimation; Scattering; Software defined networking; Machine learning Wireless connectivity is regarded as a fundamental need for our society. Between 2020 and 2030, it is forecast that the data traffic of the global internet protocol will increase by 55% each year, eventually reaching 5016 EB, with data rates scaling up to 1 Tb/s. Besides supporting very high data rates, future wireless networks are expected to offer several other heterogeneous services, which include sensing, localization, low-latency, and ultrareliable communications. Fifth-generation (5G) networks are, however, not designed to meet these requirements. As the demands and needs become more stringent, in fact, fundamental limitations arise, which are ultimately imposed by the inherent nature of the wireless operation. [Di Renzo, Marco] Paris Saclay Univ, Lab Signals & Syst L2S, CNRS, Paris, France; [Di Renzo, Marco] Cent Supelec, Paris, France; [Di Renzo, Marco] Paris Saclay Univ, Lab Excellence DigiCosme, Commun & Networks Res Area, Paris, France; [Di Renzo, Marco] Paris Saclay Univ, PhD Sch Informat & Commun Technol, Admiss & Evaluat Comm, Paris, France; [Di Renzo, Marco] Cent Supelec, Lab Signals & Syst, Intelligent Phys Commun Grp, Paris, France; [Di Renzo, Marco] IET, London, England; [Di Renzo, Marco] AAIA, Hong Kong, Peoples R China; [Di Renzo, Marco] European Acad Sci & Arts, Salzburg, Austria; [Di Renzo, Marco] Acad Europaea, London, England; [Di Renzo, Marco] European Telecommun Stand Inst ETSI, Ind Specificat Grp ISG RIS, Rapporteur Work Item Commun Models Channel Models, Sophia Antipolis, France; [Tretyakov, Sergei] St Petersburg State Tech Univ, Dept Radiophys, St Petersburg, Russia; [Tretyakov, Sergei] Aalto Univ, Dept Elect & Nanoengn, Radio Sci, Espoo, Finland; [Tretyakov, Sergei] St Petersburg IEEE Electron Devices Microwave The, St Petersburg, Russia; [Tretyakov, Sergei] Virtual Inst Artificial Electromagnet Mat & Metam, Rome, Italy Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; Peter the Great St. Petersburg Polytechnic University; Aalto University Di Renzo, M (corresponding author), Paris Saclay Univ, Lab Signals & Syst L2S, CNRS, Paris, France.;Di Renzo, M (corresponding author), Cent Supelec, Paris, France.;Di Renzo, M (corresponding author), Paris Saclay Univ, Lab Excellence DigiCosme, Commun & Networks Res Area, Paris, France.;Di Renzo, M (corresponding author), Paris Saclay Univ, PhD Sch Informat & Commun Technol, Admiss & Evaluat Comm, Paris, France.;Di Renzo, M (corresponding author), Cent Supelec, Lab Signals & Syst, Intelligent Phys Commun Grp, Paris, France.;Di Renzo, M (corresponding author), IET, London, England.;Di Renzo, M (corresponding author), AAIA, Hong Kong, Peoples R China.;Di Renzo, M (corresponding author), European Acad Sci & Arts, Salzburg, Austria.;Di Renzo, M (corresponding author), Acad Europaea, London, England.;Di Renzo, M (corresponding author), European Telecommun Stand Inst ETSI, Ind Specificat Grp ISG RIS, Rapporteur Work Item Commun Models Channel Models, Sophia Antipolis, France. Tretyakov, Sergei/G-2454-2013; Tretyakov, Sergei/Y-1691-2019 Tretyakov, Sergei/0000-0002-4738-9987 0 0 0 4 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9219 1558-2256 P IEEE Proc. IEEE SEP 2022.0 110 9 1159 1163 10.1109/JPROC.2022.3194589 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 4Q5PF Bronze 2023-03-23 WOS:000856134000007 0
C Bifet, A; Morales, GD; Read, J; Holmes, G; Pfahringer, B Assoc Comp Machinery Bifet, Albert; Morales, Gianmarco De Francisci; Read, Jesse; Holmes, Geoff; Pfahringer, Bernhard Efficient Online Evaluation of Big Data Stream Classifiers KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING English Proceedings Paper 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) AUG 10-13, 2015 Univ Technol Sydney, Adv Analyt Inst, Sydney, AUSTRALIA Assoc Comp Machinery SIGKDD,Assoc Comp Machinery SIGMOD,Taylor & Francis Grp,Univ Technol Sydney,Morgan & Claypool Publishers Univ Technol Sydney, Adv Analyt Inst Data Streams; Evaluation; Online Learning; Classification CLASSIFICATION; AGREEMENT The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models. [Bifet, Albert] HUAWEI, Noahs Ark Lab, Hong Kong, Peoples R China; [Morales, Gianmarco De Francisci] Aalto Univ, Helsinki, Finland; [Read, Jesse] Aalto Univ, HIIT, Helsinki, Finland; [Holmes, Geoff; Pfahringer, Bernhard] Univ Waikato, Hamilton, New Zealand Huawei Technologies; Aalto University; Aalto University; University of Waikato Bifet, A (corresponding author), HUAWEI, Noahs Ark Lab, Hong Kong, Peoples R China. bifet.albert@huawei.com; gdfm@acm.org; jesse.read@aalto.fi; geoff@waikato.ac.nz; bernhard@waikato.ac.nz Bifet, Albert/E-4984-2017; De Francisci Morales, Gianmarco/AAK-2941-2021 Bifet, Albert/0000-0002-8339-7773; De Francisci Morales, Gianmarco/0000-0002-2415-494X 26 92 92 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-3664-2 2015.0 59 68 10.1145/2783258.2783372 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN6IU Green Submitted 2023-03-23 WOS:000485312900010 0
J Noe, F; Olsson, S; Kohler, J; Wu, H Noe, Frank; Olsson, Simon; Koehler, Jonas; Wu, Hao Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning SCIENCE English Article PANCREATIC TRYPSIN-INHIBITOR; MONTE-CARLO METHOD; FREE-ENERGY; TRANSITION; DYNAMICS Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in one shot: vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed Boltzmann generators, which are shown to generate unbiased one-shot equilibrium samples of representative condensed-matter systems and proteins. Boltzmann generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates. [Noe, Frank; Olsson, Simon; Koehler, Jonas; Wu, Hao] FU Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany; [Noe, Frank] FU Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany; [Noe, Frank] Rice Univ, Dept Chem, POB 1892, Houston, TX 77005 USA; [Wu, Hao] Tongji Univ, Sch Math Sci, Shanghai 200092, Peoples R China Free University of Berlin; Free University of Berlin; Rice University; Tongji University Noe, F (corresponding author), FU Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany.;Noe, F (corresponding author), FU Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany.;Noe, F (corresponding author), Rice Univ, Dept Chem, POB 1892, Houston, TX 77005 USA. frank.noe@fu-berlin.de Olsson, Simon/O-5804-2019 Olsson, Simon/0000-0002-3927-7897; Kohler, Jonas/0000-0002-7256-2892; Wu, Hao/0000-0002-2170-0618; Noe, Frank/0000-0003-4169-9324 European Commission [ERC CoG 772230]; Deutsche Forschungsgemeinschaft [CRC1114/A04, GRK2433 DAEDALUS]; MATH+ Berlin Mathematics Research Center [AA1x8, EF1x2]; Alexander von Humboldt Foundation European Commission(European CommissionEuropean Commission Joint Research Centre); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); MATH+ Berlin Mathematics Research Center; Alexander von Humboldt Foundation(Alexander von Humboldt Foundation) We acknowledge funding from the European Commission (ERC CoG 772230 ScaleCell), the Deutsche Forschungsgemeinschaft (CRC1114/A04, GRK2433 DAEDALUS), the MATH+ Berlin Mathematics Research Center (AA1x8, EF1x2), and the Alexander von Humboldt Foundation (postdoctoral fellowship to S.O.) 50 214 215 12 117 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 0036-8075 1095-9203 SCIENCE Science SEP 6 2019.0 365 6457 1001 + eaaw1147 10.1126/science.aaw1147 0.0 30 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics IW1MK 31488660.0 Green Submitted 2023-03-23 WOS:000484732700040 0
J Wang, J; Gui, G; Ohtsuki, T; Adebisi, B; Gacanin, H; Sari, H Wang, Jie; Gui, Guan; Ohtsuki, Tomoaki; Adebisi, Bamidele; Gacanin, Haris; Sari, Hikmet Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems IEEE TRANSACTIONS ON COMMUNICATIONS English Article Downlink; Massive MIMO; Quantization (signal); Correlation; Antenna feeds; Time-frequency analysis; Receiving antennas; Channel state information; feedback; deep learning; frequency division duplexing; massive MIMO LIMITED FEEDBACK Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality. [Wang, Jie; Gui, Guan; Sari, Hikmet] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Ohtsuki, Tomoaki] Keio Univ, Dept Informat & Comp Sci, Yokohama, Kanagawa 2238521, Japan; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester M15 6BH, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany Nanjing University of Posts & Telecommunications; Keio University; Manchester Metropolitan University; RWTH Aachen University Gui, G; Sari, H (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China. 2018010223@njupt.edu.cn; guiguan@njupt.edu.cn; ohtsuki@ics.keio.ac.jp; b.adebisi@mmu.ac.uk; harisg@ice.rwth-aachen.de; hikmet@njupt.edu.cn Gui, Guan/AAG-3593-2019 Gui, Guan/0000-0001-7428-4980; Adebisi, Bamidele/0000-0001-9071-9120; Ohtsuki, Tomoaki/0000-0003-3961-1426 JSPS KAKENHI [JP19H02142]; Major Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]; National Natural Science Foundation of China [61901228]; Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]; Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106] JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Major Project of the Ministry of Industry and Information Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Summit of the Six Top Talents Program of Jiangsu; Program for High-Level Entrepreneurial and Innovative Team; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China(Ministry of Education, China) This work was supported in part by the JSPS KAKENHI under Grant JP19H02142, Major Project of the Ministry of Industry and Information Technology of China under Grant TC190A3WZ-2, National Natural Science Foundation of China under Grant 61901228, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. The associate editor coordinating the review of this article and approving it for publication was F. Gao. 38 18 19 1 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0090-6778 1558-0857 IEEE T COMMUN IEEE Trans. Commun. SEP 2021.0 69 9 5873 5885 10.1109/TCOMM.2021.3086525 0.0 13 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications UQ5BS 2023-03-23 WOS:000696079000020 0
J Hu, WS; Li, HC; Deng, YJ; Sun, X; Du, Q; Plaza, A Hu, Wen-Shuai; Li, Heng-Chao; Deng, Yang-Jun; Sun, Xian; Du, Qian; Plaza, Antonio Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Article Tensors; Feature extraction; Convolution; Computational modeling; Adaptation models; Recurrent neural networks; Logic gates; Attention mechanism; convolutional long short-term memory; hyperspectral image classification; lightweight cell; performance recovery; tensor representation FEATURE-EXTRACTION Recurrent neural networks, especially the convolutional long short-term memory (ConvLSTM), have attracted plenty of attention and shown promising results due to their ability in modeling long-term dependencies in many research fields. In this paper, a lightweight tensor attention-driven ConvLSTM neural network (TACLNN) is proposed for hyperspectral image (HSI) classification. Firstly, to reduce the trainable parameters and memory requirements of ConvLSTM (specifically, the 2-D version of LSTM, i.e., ConvLSTM2D), a lightweight ConvLSTM2D cell is developed by utilizing tensor-train decomposition, resulting in a TT-ConvLSTM2D cell, with which a spatial-spectral TT-ConvLSTM 2-D neural network (SSTTCL2DNN) is built. However, it is inevitable for SSTTCL2DNN to obtain lower accuracies for HSI classification. To recover the accuracy loss caused by the TT-ConvLSTM2D cell in SSTTCL2DNN, a learnable tensor attention residual block (TARB) module is built to further enhance its geometrical structure. When applied to three widely used HSI benchmarks, the proposed TACLNN model outperforms several state-of-the-art methods for HSI classification. In addition, the proposed TACLNN can effectively reduce the number of parameters and storage requirements achieving higher classification accuracies as compared to other competitive baselines. [Hu, Wen-Shuai; Li, Heng-Chao] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Deng, Yang-Jun] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China; [Sun, Xian] Chinese Acad Sci, Key Lab Network Informat Syst Technol NIST, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Caceres 10060, Spain Southwest Jiaotong University; Hunan Agricultural University; Chinese Academy of Sciences; Mississippi State University; Universidad de Extremadura Li, HC (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China. wshu@my.swjtu.edu.cn; lihengchao_78@163.com; dyj2012@yeah.net; sunxian@mail.ie.ac.cn; du@ece.msstate.edu; aplaza@unex.es Plaza, Antonio/C-4455-2008; du, qian/GYJ-7090-2022; Du, Qian/AAB-8840-2022 Plaza, Antonio/0000-0002-9613-1659; Hu, Wenshuai/0000-0002-4757-2765; Sun, Xian/0000-0002-0038-9816 National Natural Science Foundation of China [61871335]; Fundamental Research Funds for the Central Universities [2682020XG02, 2682020ZT35] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61871335 and in part by the Fundamental Research Funds for the Central Universities under Grants 2682020XG02, and 2682020ZT35. 52 9 9 7 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. APR 2021.0 15 3 734 745 10.1109/JSTSP.2021.3063805 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering RJ3XZ 2023-03-23 WOS:000637533400023 0
C Sun, YB; Li, J; Wang, W; Plaza, A; Chen, ZQ IEEE Sun, Yibao; Li, Jun; Wang, Wei; Plaza, Antonio; Chen, Zeqiang ACTIVE LEARNING BASED AUTOENCODER FOR HYPERSPECTRAL IMAGERY CLASSIFICATION 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 10-15, 2016 Beijing, PEOPLES R CHINA Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc,NSSC Autoencoder; hyperspectral imagery classification; active learning; deep learning In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved. [Sun, Yibao; Wang, Wei; Chen, Zeqiang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, CIGIA, Guangzhou 510275, Guangdong, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Hyperspectral Comp Lab Dept Technol Comp & Commun, Caceres, Spain Wuhan University; Sun Yat Sen University; Universidad de Extremadura Sun, YB (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China. Li, Jun/E-6840-2019; Plaza, Antonio/C-4455-2008; Li, Jun/AAD-7233-2020 Li, Jun/0000-0003-1613-9448; Plaza, Antonio/0000-0002-9613-1659; Li, Jun/0000-0003-1613-9448 National Nature Science Foundation of China program [41301441]; China Postdoctoral Science Foundation [2014M562050, 2015T80829] National Nature Science Foundation of China program(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by grants from the National Nature Science Foundation of China program (no. 41301441) and China Postdoctoral Science Foundation funded project (no. 2014M562050, 2015T80829). 15 11 11 2 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5090-3332-4 INT GEOSCI REMOTE SE 2016.0 469 472 10.1109/IGARSS.2016.7729116 0.0 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BG3QG 2023-03-23 WOS:000388114600123 0
J Sforazzini, F; Salome, P; Moustafa, M; Zhou, C; Schwager, C; Rein, K; Bougatf, N; Kudak, A; Woodruff, H; Dubois, L; Lambin, P; Debus, J; Abdollahi, A; Knoll, M Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Kudak, Andreas; Woodruff, Henry; Dubois, Ludwig; Lambin, Philippe; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice RADIOLOGY-ARTIFICIAL INTELLIGENCE English Article Deep Learning; Lung Fibrosis; Radiation Therapy; Segmentation; Animal Studies; CT; Thorax; Lung Purpose: To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions. Materials and Methods: In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n = 1200), validated (n = 300), and tested (n = 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n = 20; group C [n = 16 for training and n = 4 for testing]). Results: The trained model yielded a high median DSC in both test datasets: 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. The median HD in both test datasets was 0.47 mm (IQR, 0-0.51 mm [group A]) and 0.31 mm (IQR, 0.30-0.32 mm [group B]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation. Finally, for the higher-resolution mouse CT images, the median DSC was 0.905 (IQR, 0.902-0.929) and the median 95th percentile of the HD was 0.33 mm (IQR, 2.61-2.78 mm). Conclusion: The developed deep learning-based method for mouse lung segmentation performed well independently of disease state (healthy, fibrotic, emphysematous lungs) and CT resolution. [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Kudak, Andreas; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] German Canc Res Ctr DKEZ, Clin Cooperat Unit Radiat Oncol, Neuenheimer Feld 280, D-69120 Heidelberg, Germany; [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Kudak, Andreas; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] Heidelberg Univ Hosp UKHD, Dept Radiat Oncol, Heidelberg, Germany; [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] Heidelberg Univ Hosp UKHD, Natl Ctr Tumor Dis NCT, Heidelberg, Germany; [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] German Canc Consortium DKTK, Core Ctr Heidelberg, Heidelberg, Germany; [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Kudak, Andreas; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] Heidelberg Inst Radiat Oncol HIRO, Natl Ctr Radiat Oncol NCRO, Heidelberg, Germany; [Sforazzini, Francesco; Salome, Patrick; Moustafa, Mahmoud; Zhou, Cheng; Schwager, Christian; Rein, Katrin; Bougatf, Nina; Debus, Jurgen; Abdollahi, Amir; Knoll, Maximilian] Heidelberg Ion Beam Therapy Ctr HIT, Heidelberg, Germany; [Moustafa, Mahmoud] Suez Canal Univ, Dept Clin Pathol, Ismailia, Egypt; [Zhou, Cheng] Southern Med Univ, Dept Radiat Oncol, Nanfang Hosp, Guangzhou, Peoples R China; [Woodruff, Henry; Dubois, Ludwig; Lambin, Philippe] Maastricht Univ, GROW Sch Oncol, D Lab, Dept Precis Med, Maastricht, Netherlands; [Woodruff, Henry; Dubois, Ludwig; Lambin, Philippe] Maastricht Univ, GROW Sch Oncol, M Lab, Dept Precis Med, Maastricht, Netherlands Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Egyptian Knowledge Bank (EKB); Suez Canal University; Southern Medical University - China; Maastricht University; Maastricht University Knoll, M (corresponding author), German Canc Res Ctr DKEZ, Clin Cooperat Unit Radiat Oncol, Neuenheimer Feld 280, D-69120 Heidelberg, Germany.;Knoll, M (corresponding author), Heidelberg Univ Hosp UKHD, Dept Radiat Oncol, Heidelberg, Germany.;Knoll, M (corresponding author), Heidelberg Univ Hosp UKHD, Natl Ctr Tumor Dis NCT, Heidelberg, Germany.;Knoll, M (corresponding author), Heidelberg Inst Radiat Oncol HIRO, Natl Ctr Radiat Oncol NCRO, Heidelberg, Germany.;Knoll, M (corresponding author), Heidelberg Ion Beam Therapy Ctr HIT, Heidelberg, Germany. m.knoll@dkfz.de Lambin, Philippe/0000-0001-7961-0191; Dubois, Ludwig/0000-0002-8887-4137; Moustafa, Mahmoud/0000-0002-9146-0808; Woodruff, Henry/0000-0001-7911-5123; Salome, Patrick/0000-0003-4978-9156; Kudak, Andreas/0000-0002-1474-1403 European Research Council Advanced Grant (ERC-ADG-2015) [694812]; European Union [766276, 952172, 952103] European Research Council Advanced Grant (ERC-ADG-2015); European Union(European Commission) Financial support from European Research Council Advanced Grant (ERC-ADG-2015 n degrees 694812 -Hypoximmuno), the European Union Horizon 2020 Research and Innovation program under grant agreement: MSCA-ITN-PREDICT n degrees 766276, CHAIMELEON n degrees 952172, and EuCanImage n degrees 952103. 15 2 2 1 4 RADIOLOGICAL SOC NORTH AMERICA (RSNA) OAK BROOK 820 JORIE BLVD, SUITE 200, OAK BROOK, ILLINOIS, UNITED STATES 2638-6100 RADIOL-ARTIF INTELL Radiology-Artificial Intelligence MAR 2022.0 4 2 e210095 10.1148/ryai.210095 0.0 9 Computer Science, Artificial Intelligence; Radiology, Nuclear Medicine & Medical Imaging Emerging Sources Citation Index (ESCI) Computer Science; Radiology, Nuclear Medicine & Medical Imaging 2Z9YB 35391764.0 Green Accepted 2023-03-23 WOS:000826922700005 0
J Liu, WP; Li, CG; Liu, YT; Wu, QW Liu, Weipeng; Li, Changgang; Liu, Yutian; Wu, Qiuwei Predictive control of wind turbine for load reduction during ramping events INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Predictive control; Wind turbine load; Wind power ramping events; Multi -objective optimization; Neural network prediction POWER-SYSTEM; DFIG; FREQUENCY; GENERATOR; STRATEGY; DESIGN With increasing penetration of wind power, the impact of its intermittence and volatility on power systems becomes more severe. A predictive control strategy for wind turbines (WTs) is proposed to deal with wind power ramping events and reduce WT load on the blades. The blade load model is based on the Blade Element Momentum (BEM) theory. The generator speed and pitch angle are simultaneously regulated to realize the control objectives. A two-stage optimization is designed in order to reduce the computational complexity. The objectives of the first stage are minimizing the ramping rate and maximizing the power generation. A trade-off is made between the two contradictory objectives by setting weight coefficients. The second stage reduces the \NT load and meanwhile guarantees the power reference from the first stage is tracked. Feedback is designed based on neural network prediction to compensate the error of the prediction model. Case studies with a 1.5 MW WT were conducted to demonstrate the efficacy of the proposed predictive control strategy. Simulation results show that the proposed control can reduce the WT load during ramping events and the risk of ramping events. (C) 2017 Elsevier Ltd. All rights reserved. [Liu, Weipeng; Li, Changgang; Liu, Yutian; Wu, Qiuwei] Shandong Univ, Key Lab Power Syst Intelligent Dispatch & Control, Minist Educ, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China; [Wu, Qiuwei] Tech Univ Denmark, Dept Elect Engn, Ctr Elect Power & Energy, DK-2800 Lyngby, Denmark Shandong University; Technical University of Denmark Liu, YT (corresponding author), Shandong Univ, Key Lab Power Syst Intelligent Dispatch & Control, Minist Educ, 17923 Jingshi Rd, Jinan 250061, Shandong, Peoples R China. wpliu@mail.sdu.edu.cn; lichgang@sdu.edu.cn; liuyt@sdu.edu.cn; qw@elektro.dtu.dk Liu, Weipeng/HOC-7223-2023; WU, QIUWEI/AAA-4012-2021; Wu, Qiuwei/AAJ-1741-2020 Liu, Weipeng/0000-0001-8368-0643; Wu, Qiuwei/0000-0001-7935-2567 National Basic Research Program of China (973 Program) [2012CB215101] National Basic Research Program of China (973 Program)(National Basic Research Program of China) The authors would like to thank the financial support by National Basic Research Program of China (973 Program) under grant #2012CB215101. 34 7 10 0 38 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. DEC 2017.0 93 135 145 10.1016/j.ijepes.2017.05.025 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering FD4UA 2023-03-23 WOS:000407526300012 0
C Wang, DY; Xu, JW; Li, F; Zhang, LH; Wang, YN; Lansner, A; Hemani, A; Zheng, LR; Zou, Z IEEE Wang, Deyu; Xu, Jiawei; Li, Feng; Zhang, Lianhao; Wang, Yuning; Lansner, Anders; Hemani, Ahmed; Zheng, Li-Rong; Zou, Zhuo Memristor-Based In-Circuit Computation for Trace-Based STDP 2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA English Proceedings Paper IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era JUN 13-15, 2022 Incheon, SOUTH KOREA IEEE,CAS,Minist Sci & ICT,AI Semicond Forum,Incheon Metropolitan City,Incheon Tourism Org,Korea Tourism Org Memristor; trace; spike-timing-dependent plasticity (STDP); online learning; spiking neural network (SNN); neuromorphic computation Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the online learning process. It is challenging for memristor-based implementations to support the trace-based STDP learning rules that have been widely used in neuromorphic applications. This paper proposed a versatile memristor-based architecture to implement the synaptic-level trace-based STDP learning rules. Especially, the similarity between synaptic trace dynamics and the memristor nonlinearity is explored and exploited to emulate the trace variables of trace-based STDP. As two typical trace-based STDP learning rules, the pairwise STDP and the triplet STDP, are simulated on two typical nonlinear bipolar memristor devices. The simulation results show that the behavior of physical memristor devices can be well estimated (below 6% in terms of the relative root-mean-square error), and the memristor-based in-circuit computation for trace-based STDP learning rules can achieve a high correlation coefficient over 98%. [Wang, Deyu; Xu, Jiawei; Li, Feng; Zheng, Li-Rong; Zou, Zhuo] Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Zhang, Lianhao] Tech Univ Denmark, Dept Elect Engn, Lyngby, Denmark; [Wang, Yuning] Univ Turku, Dept Future Technol, Turku, Finland; [Lansner, Anders; Hemani, Ahmed] KTH Royal Inst Technol, Stockholm, Sweden Fudan University; Technical University of Denmark; University of Turku; Royal Institute of Technology Wang, DY (corresponding author), Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China. deyuwang19@fudan.edu.cn; lrzheng@fudan.edu.cn; zhuo@fudan.edu.cn National Natural Science Foundation of China [61876039, 62011530132, 92164301]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0103, 2018SHZDZX01]; Shanghai Platform for Neuromorphic and AI Chip [17DZ2260900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Science and Technology Major Project; Shanghai Platform for Neuromorphic and AI Chip This work was supported in part by the National Natural Science Foundation of China under Grant 61876039, 62011530132 (NSFC-STINT project), and 92164301, and Shanghai Municipal Science and Technology Major Project No. 2021SHZDZX0103 and No.2018SHZDZX01,and in part by the Shanghai Platform for Neuromorphic and AI Chip under Grant 17DZ2260900. 13 1 1 6 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-0996-4 2022.0 1 4 10.1109/AICAS54282.2022.9870015 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT9CO 2023-03-23 WOS:000859273200002 0
J Cheung, CY; Xu, DJ; Cheng, CY; Sabanayagam, C; Tham, YC; Yu, M; Rim, TH; Chai, CY; Gopinath, B; Mitchell, P; Poulton, R; Moffitt, TE; Caspi, A; Yam, JC; Tham, CC; Jonas, JB; Wang, YX; Song, SJ; Burrell, LM; Farouque, O; Li, LJ; Tan, G; Ting, DSW; Hsu, W; Lee, ML; Wong, TY Cheung, Carol Y.; Xu, Dejiang; Cheng, Ching-Yu; Sabanayagam, Charumathi; Tham, Yih-Chung; Yu, Marco; Rim, Tyler Hyungtaek; Chai, Chew Yian; Gopinath, Bamini; Mitchell, Paul; Poulton, Richie; Moffitt, Terrie E.; Caspi, Avshalom; Yam, Jason C.; Tham, Clement C.; Jonas, Jost B.; Wang, Ya Xing; Song, Su Jeong; Burrell, Louise M.; Farouque, Omar; Li, Ling Jun; Tan, Gavin; Ting, Daniel S. W.; Hsu, Wynne; Lee, Mong Li; Wong, Tien Y. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre NATURE BIOMEDICAL ENGINEERING English Article CORONARY-HEART-DISEASE; C-REACTIVE PROTEIN; VASCULAR CALIBER; ATHEROSCLEROSIS RISK; MICROVASCULAR ABNORMALITIES; BLOOD-PRESSURE; DIABETIC-RETINOPATHY; EYE DISEASES; ANTIHYPERTENSIVE TREATMENT; VENULAR CALIBER Deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs perform comparably to or better than expert graders in associations of measurements of retinal-vessel calibre with cardiovascular risk factors. Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs. [Cheung, Carol Y.; Cheng, Ching-Yu; Sabanayagam, Charumathi; Tham, Yih-Chung; Yu, Marco; Rim, Tyler Hyungtaek; Tan, Gavin; Ting, Daniel S. W.; Wong, Tien Y.] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Cheung, Carol Y.; Yam, Jason C.; Tham, Clement C.] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China; [Xu, Dejiang; Hsu, Wynne; Lee, Mong Li] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Cheng, Ching-Yu; Sabanayagam, Charumathi; Tham, Yih-Chung; Rim, Tyler Hyungtaek; Tan, Gavin; Ting, Daniel S. W.; Wong, Tien Y.] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Programme, Singapore, Singapore; [Chai, Chew Yian] Natl Univ Singapore Hosp, Emergency Med Dept, Singapore, Singapore; [Chai, Chew Yian] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Surg, Singapore, Singapore; [Gopinath, Bamini; Mitchell, Paul] Westmead Inst Med Res, Ctr Vis Res, Westmead, NSW, Australia; [Poulton, Richie] Univ Otago, Dept Psychol, Dunedin Multidisciplinary Hlth & Dev Res Unit, Dunedin, New Zealand; [Moffitt, Terrie E.; Caspi, Avshalom] Duke Univ, Dept Psychol & Neurosci, Durham, NC USA; [Jonas, Jost B.] Ruprecht Karls Univ Heidelberg, Med Fac Mannheim, Dept Ophthalmol, Heidelberg, Germany; [Wang, Ya Xing] Capital Med Univ, Beijing Tongren Hosp, Beijing Tongren Eye Ctr, Beijing Ophthalmol & Visual Sci Key Lab,Beijing I, Beijing, Peoples R China; [Song, Su Jeong] Sungkyunkwan Univ, Sch Med, Kangbuk Samsung Hosp, Dept Ophthalmol, Seoul, South Korea; [Burrell, Louise M.; Farouque, Omar] Univ Melbourne, Austin Hosp, Austin Hlth, Dept Cardiol, Heidelberg, Vic, Australia; [Burrell, Louise M.; Farouque, Omar] Univ Melbourne, Dept Med, Heidelberg, Vic, Australia; [Li, Ling Jun] KK Womens & Childrens Hosp, Div Obstet & Gynaecol, Singapore, Singapore National University of Singapore; Singapore National Eye Center; Chinese University of Hong Kong; National University of Singapore; National University of Singapore; National University of Singapore; National University of Singapore; University of Sydney; Westmead Institute for Medical Research; University of Otago; Duke University; Ruprecht Karls University Heidelberg; Capital Medical University; Sungkyunkwan University (SKKU); Samsung Medical Center; Austin Research Institute; Florey Institute of Neuroscience & Mental Health; University of Melbourne; University of Melbourne; KK Women's & Children's Hospital Wong, TY (corresponding author), Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.;Wong, TY (corresponding author), Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Programme, Singapore, Singapore. wong.tien.yin@snec.com.sg Tan, Gavin/HNS-5461-2023; Cheng, Ching-Yu/Y-2229-2019; Wang, Ying/HJI-2509-2023; wang, YA XING/K-9671-2016; Yam, Jason/I-5682-2014; Wong, Tien Yin/AAC-9724-2020; Yam, Jason C./AAI-2522-2020; Sabanayagam, Charumathi/C-1294-2011; Chung, Yih/AIF-2414-2022; Tham, Chee Yung Clement/AAD-6528-2020 Cheng, Ching-Yu/0000-0003-0655-885X; wang, YA XING/0000-0003-2749-7793; Yam, Jason/0000-0002-2156-1486; Wong, Tien Yin/0000-0002-8448-1264; Yam, Jason C./0000-0002-2156-1486; Sabanayagam, Charumathi/0000-0002-4042-4719; Chung, Yih/0000-0002-6752-797X; Tham, Chee Yung Clement/0000-0003-4407-6907; Rim, Tyler Hyungtaek/0000-0001-6465-2620; Bidwai, Pooja Vishal/0000-0002-3077-4395; Burrell, Louise/0000-0003-1863-7539 Singapore Ministry of Health's National Medical Research Council (NMRC) [OFLCG/001/2017, NMRC/STaR/003/2008, NMRC/STaR/0016/2013, NMRC/CIRG/1371/2013]; National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award) [AISG-GC-2019-001] Singapore Ministry of Health's National Medical Research Council (NMRC)(Ministry of Health-SingaporeNational Medical Research Council, SingaporeUK Research & Innovation (UKRI)Medical Research Council UK (MRC)); National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award) We thank all of the staff at the SNEC Ocular Reading Centre (SORC) for their contribution to this study. We acknowledge funding support from Singapore Ministry of Health's National Medical Research Council (NMRC) grants OFLCG/001/2017, NMRC/STaR/003/2008, NMRC/STaR/0016/2013 and NMRC/CIRG/1371/2013. This research is also supported by the National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award no. AISG-GC-2019-001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the National Research Foundation, Singapore. 101 64 69 5 31 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2157-846X NAT BIOMED ENG Nat. Biomed. Eng JUN 2021.0 5 6 498 + 10.1038/s41551-020-00626-4 0.0 OCT 2020 14 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering SU3WH 33046867.0 2023-03-23 WOS:000577092600003 0
J Li, W; Buitenwerf, R; Munk, M; Bocher, PK; Svenning, JC Li, Wang; Buitenwerf, Robert; Munk, Michael; Bocher, Peder Klith; Svenning, Jens-Christian Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem REMOTE SENSING OF ENVIRONMENT English Article Maasai Mara; Savanna ecosystem; Woody densification; Vegetation fractional cover; Savanna vegetation classification; Sentinel-2; WorldView-3; Deep-learning ESTIMATING FRACTIONAL COVER; BUSH ENCROACHMENT; AFRICAN SAVANNA; LAND-COVER; TREE COVER; FOREST; CLASSIFICATION; CONSERVATION; INDEX; SERENGETI The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small-holder fencing. Here, we present the first comprehensive vegetation map of GMME at high (10-m) spatial resolution. The map consists of nine key vegetation cover types (VCTs), which were derived in a two-step process integrating data from high-resolution WorldView-3 images (1.2-m) and Sentinel-2 images using a deep-learning workflow. We evaluate the role of anthropogenic, topographic, and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS-derived browning/greening rates in the preceding 17 years at 250-m resolution. Results show that most VCTs showed a preceding greening trend in the protected land. In contrast, the semi- and unprotected land showed a general preceding greening trend in the woody-dominated cover types, while they exhibited browning trends in grass-dominated cover types. These results suggest that woody vegetation densification may be happening across much of the GMME, alongside vegetation declines within the non-woody covers in the semi- and unprotected lands. Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure. Increasing woody densification across the entire GMME in the future would replace high-quality grass cover and pose a risk to the maintenance of the region's rich savanna megafauna, thus pointing to a need for further investigation using alternative data sources. The increasing availability of high-resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change. [Li, Wang; Buitenwerf, Robert; Munk, Michael; Bocher, Peder Klith; Svenning, Jens-Christian] Aarhus Univ, Ctr Biodivers Dynam Changing World BIOCHANGE, Ny Munkegade 114, DK-8000 Aarhus C, Denmark; [Li, Wang; Buitenwerf, Robert; Munk, Michael; Bocher, Peder Klith; Svenning, Jens-Christian] Aarhus Univ, Dept Biol, Sect Ecoinformat & Biodivers, Ny Munkegade 114, DK-8000 Aarhus C, Denmark; [Li, Wang] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China Aarhus University; Aarhus University; Chinese Academy of Sciences Li, W (corresponding author), Aarhus Univ, Ctr Biodivers Dynam Changing World BIOCHANGE, Ny Munkegade 114, DK-8000 Aarhus C, Denmark. lwwhdz@sina.com Svenning, Jens-Christian/C-8977-2012; Li, Wang/ABC-2320-2021 Svenning, Jens-Christian/0000-0002-3415-0862; Munk, Michael/0000-0003-3281-9160; Li, Wang/0000-0003-1330-2019 European Research Council [ERC2012-StG-310886-HISTFUNC]; Carlsberg Foundation (Semper Ardens project MegaPast2Future) [CF16-000]; VILLUM FONDEN [16549]; China Natural Science Foundation [41701392]; Youth Innovation Promotion Association Chinese Academy of Sciences [2018084] European Research Council(European Research Council (ERC)European Commission); Carlsberg Foundation (Semper Ardens project MegaPast2Future); VILLUM FONDEN(Villum Fonden); China Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Youth Innovation Promotion Association Chinese Academy of Sciences This work was supported by the European Research Council (ERC2012-StG-310886-HISTFUNC, to J.-C.S), Carlsberg Foundation (Semper Ardens project MegaPast2Future, grant CF16-000, to J.-C.S), VILLUM FONDEN (VILLUM Investigator project, grant 16549, to J.-C.S). China Natural Science Foundation (grant 41701392, to W.L), the Youth Innovation Promotion Association Chinese Academy of Sciences (grant 2018084, to W.L). 90 17 17 16 73 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. SEP 15 2020.0 247 111953 10.1016/j.rse.2020.111953 0.0 16 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology ML0TJ 2023-03-23 WOS:000549189200050 0
J Liu, H; Smedskjaer, MM; Bauchy, M Liu, Han; Smedskjaer, Morten M.; Bauchy, Mathieu Deciphering a structural signature of glass dynamics by machine learning PHYSICAL REVIEW B English Article ION MIGRATION MECHANISMS; MOLECULAR-DYNAMICS; SILICATE-GLASSES; EMPIRICAL FORCEFIELDS; CHALLENGES; ALGORITHMS; VISCOSITY; MODEL The dynamics of atoms plays a key role in governing various dynamical and transport properties of glasses. However, it remains elusive which structural features (if any) control atom dynamics in glasses. Here, based on million-atom molecular dynamics simulations and classification-based machine learning, we extract a needle in a haystack by identifying a local, nonintuitive structural signature (a revised version of the recently developed softness metric) that governs glass dynamics. We do so by investigating the ion mobility in sodium silicate glasses-a realistic, archetypal glass-finding that the sodium ion mobility is largely encoded in its initial softness, wherein softer Na atoms exhibit higher mobility. Importantly, our approach allows us to interpret the machine-learned softness metric and thus elucidate the atomistic origin of the ion mobility. Namely, we find that Na mobility is anticorrelated with the local density of defect oxygen neighbors that are located between the nearest two coordination shells. This local packing order offers a potential path to develop glass formulations with tailored dynamical properties. Finally, we demonstrate that the softness is strongly anticorrelated with the activation energy for Na atom reorganization. [Liu, Han] Sichuan Univ, Coll Polymer Sci & Engn, SOlids inFormaT AI Lab SOFT AI Lab, Chengdu 610065, Peoples R China; [Smedskjaer, Morten M.] Aalborg Univ, Dept Chem & Biosci, DK-9220 Aalborg, Denmark; [Bauchy, Mathieu] Univ Calif Los Angeles, Dept Civil & Environm Engn, Phys AmoRphous & Inorgan Solids Lab PARISlab, Los Angeles, CA 90095 USA Sichuan University; Aalborg University; University of California System; University of California Los Angeles Liu, H (corresponding author), Sichuan Univ, Coll Polymer Sci & Engn, SOlids inFormaT AI Lab SOFT AI Lab, Chengdu 610065, Peoples R China. happylife@ucla.edu LIU, HAN/0000-0002-4899-9998 Fundamental Research Funds for the Central Universities [YJ202271]; National Science Foundation [DMR-1928538, DMR-1944510, DMREF-1922167]; Independent Research Fund Denmark [7017-00019] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Science Foundation(National Science Foundation (NSF)); Independent Research Fund Denmark H.L. acknowledges funding from the Fundamental Research Funds for the Central Universities under Grant No. YJ202271. M.B. acknowledges the National Science Foundation under Grants No. DMR-1928538, No. DMR-1944510, and No. DMREF-1922167. M.M.S. acknowledges funding from the Independent Research Fund Denmark (Grant No. 7017-00019) . We acknowledge Dr. Ekin D. Cubuk and Dr. Samuel S. Schoenholz at Google Brain for valuable discussions. Parts of the computational work were performed on TianHe-1 (A) at National Supercomputer Center in Tianjin. 70 0 0 8 8 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B DEC 20 2022.0 106 21 214206 10.1103/PhysRevB.106.214206 0.0 12 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics 7K1YH 2023-03-23 WOS:000905081800002 0
C Wang, CL; Tang, BH; Wu, H; Tang, RL; Li, ZL IEEE Wang, Chunlei; Tang, Bo-Hui; Wu, Hua; Tang, Ronglin; Li, Zhao-Liang ESTIMATION OF DOWNWELLING SURFACE LONGWAVE RADIATION UNDER THIN CIRRUS CLOUD SKY WITH ARTIFICIAL NEURAL NETWORK METHOD 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience & Remote Sensing Symposium JUL 23-28, 2017 Fort Worth, TX Institute of Elect & Electron Engineers Geoscience & Remote Sensing Soc,IEEE,IEEE GRSS downwelling surface long-wave radiation (DSLR); back propagation network (BP); atmosphere water vapor content (WVC); thin cirrus clouds ATMOSPHERIC MODEL; PARAMETERIZATION; SKIES; CLEAR Thin cirrus clouds can reduce land surface long-wave transmission and re-emit energy at a colder temperature and thus making it difficult to estimate downwelling surface longwave radiation (DSLR) from satellite data. In this study, a simulation database is established in terms of radiances observed at the top of the atmosphere (TOA), cloud optical thickness (COT), atmosphere water vapor content (WVC) and height of the cirrus bottom (HCB) and DSLR. And the back propagation (BP) artificial neural network (ANN) was used to estimate DSLR from remotely sensed data for cirrus cloudy skies. Results show that the BP model with TOA thermal radiance, COT, WVC and HCB as inputs provides a practical and efficient tool for remote sensing applications to estimate DSLR under thin cirrus clouds with root mean square error (RMSE) of 11.66 W/m(2). [Wang, Chunlei; Tang, Bo-Hui; Wu, Hua; Tang, Ronglin; Li, Zhao-Liang] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Wang, Chunlei; Tang, Bo-Hui; Wu, Hua; Tang, Ronglin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Li, Zhao-Liang] Chinese Acad Agr Sci, Minist Agr, Inst Agr Resources & Reg Planning, Key Lab Agriinformat, Beijing 100081, Peoples R China; [Li, Zhao-Liang] UdS, CNRS, ICube, 300 Bld Sebastien Brant,CS10413, F-67412 Illkirch Graffenstaden, France Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg Tang, BH (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.;Tang, BH (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. tangbh@igsnrr.ac.cn National Key Research and Development Program of China [2016YFA0600103]; National Natural Science Foundation of China [41571353, 41231170]; Innovation Project of LREIS [O88RA801YA] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Project of LREIS This work was supported in part by the National Key Research and Development Program of China (No. 2016YFA0600103), in part by the National Natural Science Foundation of China under Grant 41571353 and Grant 41231170, and in part by the Innovation Project of LREIS (O88RA801YA). 11 1 1 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5090-4951-6 INT GEOSCI REMOTE SE 2017.0 4855 4858 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BJ6TY 2023-03-23 WOS:000426954604228 0
J Miao, FS; Zhao, FC; Wu, YP; Li, LW; Torok, A Miao, Fasheng; Zhao, Fancheng; Wu, Yiping; Li, Linwei; Torok, Akos Landslide susceptibility mapping in Three Gorges Reservoir area based on GIS and boosting decision tree model STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT English Article; Early Access Landslide; Susceptibility mapping; Three Gorges Reservoir; Machine learning; Uncertainty analysis PREDICTION; DISPLACEMENT; ALGORITHMS; FOREST As one of the most destructive geological disasters, a myriad of landslides has revived and developed in the Three Gorges Reservoir area under the combined action of various detrimental factors. Therefore, the pertinently regional landslide susceptibility mapping (LSM) is of great significance for disaster prevention and mitigation. In this study, LSM is prepared by using a boosting-C5.0 decision tree model. Under the landslide verification of on-site investigations, the study area is divided into accumulation and rock areas, and a total of 12 impact factors are selected. TOL and VIF are employed to determine the multicollinearity among the impact factors. The independent training (80%) and validation (20%) datasets are constructed by random sampling for LSM. ANN, C5.0, and SVM are selected for comparative analysis. The results show that there is no rigorous multicollinearity among the impact factors proposed in this paper. The landslide susceptibility in the study area is divided into low, moderate, high, and very high. The highest susceptibility area distributes along the riverside where the landslide ratio is 37.05% in boosting-C5.0 model. Then the ROCs are expropriated to infer the accuracy of each model. The boosting-C5.0 performs the best with the largest area under the curve in both accumulation and rock areas, reaching at 0.991 and 0.990 in the validation sets, respectively. Finally, the composite modification of the 5 validation sets shows that the uncertainty of boosting-C5.0 is concentrated in the intermediate probability areas of susceptibility. This study reveals the feasibility of machine learning in landslide susceptibility assessment, which could provide a basis for the risk management and control of geological disasters. [Miao, Fasheng; Zhao, Fancheng; Wu, Yiping] China Univ Geosci, Fac Engn, Wuhan, Peoples R China; [Miao, Fasheng] China Univ Geosci, Badong Natl Observat & Res Stn Geohazards, Wuhan, Peoples R China; [Li, Linwei] Guizhou Univ, Coll Resources & Environm Engn, Guiyang, Peoples R China; [Torok, Akos] Budapest Univ Technol & Econ, Fac Civil Engn, Dept Engn Geol & Geotech, Budapest, Hungary China University of Geosciences; China University of Geosciences; Guizhou University; Budapest University of Technology & Economics Wu, YP (corresponding author), China Univ Geosci, Fac Engn, Wuhan, Peoples R China. fsmiao@cug.edu.cn; zhaofancheng@cug.edu.cn; ypwu@cug.edu.cn; lwli@gzu.edu.cn; torok.akos@emk.bme.hu National Natural Science Foundation of China [41977244, 42007267]; Fundamental Research Funds for the Central University; China University of Geosciences (Wuhan); Science and Technology Project of Hubei Provincial Department of Natural Resources [ZRZY2022KJ07] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central University; China University of Geosciences (Wuhan); Science and Technology Project of Hubei Provincial Department of Natural Resources This research was supported by the National Natural Science Foundation of China (41977244, 42007267), the Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan), and Science and Technology Project of Hubei Provincial Department of Natural Resources (ZRZY2022KJ07). The authors thank the colleagues in our laboratory for their constructive comments and assistance. 54 0 0 4 4 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1436-3240 1436-3259 STOCH ENV RES RISK A Stoch. Environ. Res. Risk Assess. 10.1007/s00477-023-02394-4 0.0 FEB 2023 21 Engineering, Environmental; Engineering, Civil; Environmental Sciences; Statistics & Probability; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Mathematics; Water Resources 8T6TU Green Submitted 2023-03-23 WOS:000929392400001 0
J Gerontitis, D; Behera, R; Shi, Y; Stanimirovic, PS Gerontitis, Dimitrios; Behera, Ratikanta; Shi, Yang; Stanimirovic, Predrag S. A robust noise tolerant zeroing neural network for solving time-varying linear matrix equations NEUROCOMPUTING English Article Zeroing neural network; Activation function; noise; Time -varying linear matrix equations SYLVESTER EQUATION; ONLINE SOLUTION; STEIN EQUATION; COMPLEX ZFS; ZNN MODELS; DESIGN; VERIFICATION; SIMULATION; CONVERGENCE A robust noise-tolerant zeroing neural network (ZNN) is introduced for solving time-varying linear matrix equations (TVLME). The convergence speed of designed neural dynamics is analyzed theoretically and compared with the convergence of neural networks which include traditional activation functions, such as the tunable activation function, versatile activation function, and the modified sign-bi-power activation function. The proposed activation is utilized in the development of nonlinear ZNN dynamics for solving time-varying linear matrix equations and the Stein equation. We investigate theoretically and experimentally the behavior of the proposed robust noise-tolerant ZNN with the novel effective acti-vation function. In particular, the convergence analysis of proposed ZNN flows is studied both in the pres-ence of noise and without noise. Simulation tests demonstrate the effectiveness and domination of the suggested activation over already existing activation functions. Further, the introduced noise-tolerant ZNN model is applied in solving the Wheatstone bridge and output tracking control problem.(c) 2022 Elsevier B.V. All rights reserved. [Gerontitis, Dimitrios] Int Hellen Univ, Dept Informat & Elect Engn, Thessaloniki, Greece; [Behera, Ratikanta] Univ Cent Florida, Dept Math, Orlando, FL 32816 USA; [Behera, Ratikanta] Indian Inst Sci, Dept Computat & Data Sci, Bangalore 560012, India; [Shi, Yang] Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Nish 18000, Serbia International Hellenic University; State University System of Florida; University of Central Florida; Indian Institute of Science (IISC) - Bangalore; Yangzhou University; University of Nis Gerontitis, D (corresponding author), Int Hellen Univ, Dept Informat & Elect Engn, Thessaloniki, Greece. dimitrios_gerontitis@yahoo.gr; ratikanta@iisc.ac.in; shiy@yzu.edu.cn; pecko@pmf.ni.ac.rs Stanimirovic, Predrag S./0000-0003-0655-3741 Mohapatra Family Foundation; College of Graduate Studies, University of Central Florida, Orlando, Florida, USA; Ministry of Education, Science and Technological Development, Republic of Serbia [174013/451-03-68/2020-14]; Science Fund of the Republic of Serbia [7750185] Mohapatra Family Foundation; College of Graduate Studies, University of Central Florida, Orlando, Florida, USA; Ministry of Education, Science and Technological Development, Republic of Serbia; Science Fund of the Republic of Serbia Ratikanta Behera is grateful to the Mohapatra Family Foundation and the College of Graduate Studies, University of Central Florida, Orlando, Florida, USA for their financial support for this research. Predrag Stanimirovi acknowledges support from the Ministry of Education, Science and Technological Development, Republic of Serbia, Grant No. 174013/451-03-68/2020-14. Predrag Stanimirovi acknowledges support from the Science Fund of the Republic of Serbia, (No. 7750185, Quantitative Automata Models: Fundamental Problems and Applications-QUAM) . 75 0 0 4 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 7 2022.0 508 254 274 10.1016/j.neucom.2022.08.036 0.0 AUG 2022 21 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4E7SG 2023-03-23 WOS:000848021200003 0
J Song, T; Han, RS; Meng, F; Wang, JR; Wei, W; Peng, SQ Song, Tao; Han, Runsheng; Meng, Fan; Wang, Jiarong; Wei, Wei; Peng, Shiqiu A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves FRONTIERS IN MARINE SCIENCE English Article wave height forecast; deep learning; high spatial and temporal resolution; new mechanism; long time prediction NEURAL-NETWORKS; MODEL; GENERATION; MECHANISM; OCEAN; DECOMPOSITION; LSTM Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R-2 reached 0.69. [Song, Tao; Han, Runsheng; Meng, Fan; Wang, Jiarong; Wei, Wei] China Univ Petr, Coll Comp Sci & Technol, Qingdao, Peoples R China; [Song, Tao] Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Madrid, Spain; [Meng, Fan] DAMO Acad, Alibaba Grp, Hangzhou, Peoples R China; [Meng, Fan] Beijing Normal Univ, Key Lab Environm Change & Nat Disaster, Minist Educ, Beijing, Peoples R China; [Peng, Shiqiu] Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Peoples R China China University of Petroleum; Universidad Politecnica de Madrid; Alibaba Group; Beijing Normal University; Chinese Academy of Sciences; South China Sea Institute of Oceanology, CAS Meng, F (corresponding author), China Univ Petr, Coll Comp Sci & Technol, Qingdao, Peoples R China.;Meng, F (corresponding author), DAMO Acad, Alibaba Grp, Hangzhou, Peoples R China.;Meng, F (corresponding author), Beijing Normal Univ, Key Lab Environm Change & Nat Disaster, Minist Educ, Beijing, Peoples R China.;Peng, SQ (corresponding author), Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Peoples R China. vanmeng@163.com; speng@scsio.ac.cn Song, Tao/T-7360-2018 Song, Tao/0000-0002-0130-3340 Innovation fund project for graduate students of China University of Petroleum (East China) [22CX04008A]; Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University [2022-KF-08] Innovation fund project for graduate students of China University of Petroleum (East China); Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University Innovation fund project for graduate students of China University of Petroleum (East China) (No. 22CX04008A). Project Supported by Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University (Project No. 2022-KF-08). 38 0 0 9 9 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-7745 FRONT MAR SCI Front. Mar. Sci. OCT 3 2022.0 9 983007 10.3389/fmars.2022.983007 0.0 19 Environmental Sciences; Marine & Freshwater Biology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Marine & Freshwater Biology 5L3GI gold 2023-03-23 WOS:000870302600001 0
J Mohammed, S; Elbeltagi, A; Bashir, B; Alsafadi, K; Alsilibe, F; Alsalman, A; Zeraatpisheh, M; Szeles, A; Harsanyi, E Mohammed, Safwan; Elbeltagi, Ahmed; Bashir, Bashar; Alsafadi, Karam; Alsilibe, Firas; Alsalman, Abdullah; Zeraatpisheh, Mojtaba; Szeles, Adrienn; Harsanyi, Endre A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Machine learning; Data mining; Drought prediction; Syria; Mediterranean basin MACHINE LEARNING ALGORITHMS; CLIMATE-CHANGE PROJECTIONS; STANDARDIZED PRECIPITATION; STREAMFLOW DROUGHTS; NORTH-ATLANTIC; HYBRID MODELS; RANDOM TREES; VARIABILITY; INDEX; REGRESSION Drought is a natural hazard which affects ecosystems in the eastern Mediterranean. However, limited historical data for drought monitoring and forecasting are available in the eastern Mediterranean. Thus, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this context, the main goals of this research were to capture agricultural and hydrological drought trends by using the Standardized Precipitation Index (SPI) and to assess the applicability of four ML algorithms (bagging (BG), random subspace (RSS), random tree (RT), and random forest (RF)) in predicting drought events in the eastern Mediterranean based on SPI-3 and SPI-12. The results reveal that hydrological drought (SPI-12,-24) was more severe over the study area, where most stations showed a significant (p < 0.05) negative trend. The accuracy of ML algorithms in drought prediction varied in relation to the implementation stage. In the training stage, RT outperformed the other algorithms (Root mean square error (RMSE) = 0.3, Correlation Coefficient (r) = 0.97); the performance of the algorithms can be ranked as follows: RT > RF > BG > RSS for both SPI-3 and SPI-12. In the testing stage, both the BG and RF algorithms had the highest correlation r (observed vs. predicted) (0.58-0.64) and lowest RMSE (0.68-0.88). In contrast, the lowest correlation r (observed vs. predicted) (0.3-0.41) and highest RMSE (0.94-1.10) was calculated for the RT algorithm. However, BG was more dynamic in drought capturing, with the lowest RMSE and highest correlation. In the validation stage, the BG performance was satisfactory (RMSE = 0.62-0.83, r = 0.58-0.79). The output of this research will help decision-makers with drought mitigation plans by using the new four machine learning algorithms. [Mohammed, Safwan; Szeles, Adrienn; Harsanyi, Endre] Univ Debrecen, Inst Land Use Engn & Precis Farming Technol, Fac Agr & Food Sci & Environm Management, Boszormenyi 138, H-4032 Debrecen, Hungary; [Mohammed, Safwan; Harsanyi, Endre] Univ Debrecen, Inst Agr Res & Educ Farm, Boszormenyi 138, H-4032 Debrecen, Hungary; [Elbeltagi, Ahmed] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt; [Bashir, Bashar; Alsalman, Abdullah] King Saud Univ, Coll Engn, Dept Civil Engn, POBox 800, Riyadh 11421, Saudi Arabia; [Alsafadi, Karam] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China; [Alsilibe, Firas] Szechenyi Istvan Univ, Dept Transport Infrastruct & Water Resources Engn, H-9026 Gyor, Hungary; [Zeraatpisheh, Mojtaba] Univ Vermont, Rubenstein Sch Environm & Nat Resources, 81 Carrigan Dr, Burlington, VT 05405 USA; [Zeraatpisheh, Mojtaba] Univ Vermont, Gund Inst Environm, 210 Colchester Ave, Burlington, VT 05401 USA University of Debrecen; University of Debrecen; Egyptian Knowledge Bank (EKB); Mansoura University; King Saud University; Nanjing University of Information Science & Technology; University of Istvan Szechenyi; University of Vermont; University of Vermont Mohammed, S (corresponding author), Univ Debrecen, Inst Land Use Engn & Precis Farming Technol, Fac Agr & Food Sci & Environm Management, Boszormenyi 138, H-4032 Debrecen, Hungary. safwan@agr.unideb.hu AlSalman, Abdullah/AAG-2339-2021; kumar, Pankaj/HPF-8395-2023; Bashir, Bashar/AAE-1238-2021; Alsafadi, Karam/AAI-5270-2020; Zeraatpisheh, Mojtaba/V-1244-2018; Mohammed (PhD), Safwan/AAA-7126-2019 AlSalman, Abdullah/0000-0002-6655-5717; Bashir, Bashar/0000-0003-0384-9061; Alsafadi, Karam/0000-0001-8925-7918; Zeraatpisheh, Mojtaba/0000-0001-7209-0744; Mohammed (PhD), Safwan/0000-0003-2311-6789; Alsilibe, Firas/0000-0003-3327-262X RESEARCHERS SUPPORTING PROJECT, King Saud University, Riyadh, Saudi Arabia [RSP-2021/296]; National Research, Development and Innovation Fund of Hungary, financed under the TKP2021-NKTA funding scheme [TKP2021-NKTA-32] RESEARCHERS SUPPORTING PROJECT, King Saud University, Riyadh, Saudi Arabia; National Research, Development and Innovation Fund of Hungary, financed under the TKP2021-NKTA funding scheme This research was supported by RESEARCHERS SUPPORTING PROJECT, grant number RSP-2021/296, King Saud University, Riyadh, Saudi Arabia. Also, Project no. TKP2021-NKTA-32 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the TKP2021-NKTA funding scheme 129 6 6 10 14 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. JUN 2022.0 197 106925 10.1016/j.compag.2022.106925 0.0 APR 2022 19 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science 1X9OP hybrid 2023-03-23 WOS:000807778300003 0
J Deringer, VL; Merlet, C; Hu, YC; Lee, TH; Kattirtzi, JA; Pecher, O; Csanyi, G; Elliott, SR; Grey, CP Deringer, Volker L.; Merlet, Celine; Hu, Yuchen; Lee, Tae Hoon; Kattirtzi, John A.; Pecher, Oliver; Csanyi, Gabor; Elliott, Stephen R.; Grey, Clare P. Towards an atomistic understanding of disordered carbon electrode materials CHEMICAL COMMUNICATIONS English Article CARBIDE-DERIVED CARBONS; SODIUM-ION BATTERIES; IN-SITU NMR; SOFT CARBON; INSIGHTS; STORAGE; SPECTROSCOPY; CRYSTAL; LITHIUM; ANODES Disordered nanoporous and hard carbons are widely used in batteries and supercapacitors, but their atomic structures are poorly determined. Here, we combine machine learning and DFT to obtain new atomistic insight into carbonaceous energymaterials. We study structural models of porous and graphitic carbons, and Na intercalation as relevant for sodium- ion batteries. [Deringer, Volker L.; Csanyi, Gabor] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England; [Deringer, Volker L.; Merlet, Celine; Hu, Yuchen; Lee, Tae Hoon; Kattirtzi, John A.; Pecher, Oliver; Elliott, Stephen R.; Grey, Clare P.] Univ Cambridge, Dept Chem, Cambridge CB2 1EW, England; [Merlet, Celine] Univ Toulouse 3 Paul Sabatier, CIRIMAT, CNRS, INPT, Bat CIRIMAT,118 Route Narbonne, F-31062 Toulouse 9, France; [Merlet, Celine] Fed Rech CNRS 3459, Reseau Stockage Electrochim Energie RS2E, F-80039 Amiens, France; [Kattirtzi, John A.] Xiamen Univ, Coll Chem & Chem Engn, Xiamen 361005, Peoples R China; [Pecher, Oliver] NMR Serv GmbH, Blumenstr 70 Haus 3, D-99092 Erfurt, Germany University of Cambridge; University of Cambridge; Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Institut National Polytechnique de Toulouse; Centre National de la Recherche Scientifique (CNRS); Xiamen University Deringer, VL (corresponding author), Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England.;Deringer, VL; Grey, CP (corresponding author), Univ Cambridge, Dept Chem, Cambridge CB2 1EW, England. vld24@cam.ac.uk; cpg27@cam.ac.uk Lee, Tae Hoon/AAB-7490-2021; Merlet, Céline/G-1948-2010; Deringer, Volker/O-6003-2018; Csanyi, Gabor/AAU-6457-2020; Merlet, Celine/AAA-9194-2020 Merlet, Céline/0000-0003-3758-273X; Deringer, Volker/0000-0001-6873-0278; Merlet, Celine/0000-0003-3758-273X Alexander von Humboldt Foundation; Leverhulme Early Career Fellowship; Isaac Newton Trust; School of Physical Sciences, University of Cambridge; European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [714581]; ARCHER UK National Supercomputing Service via EPSRC [EP/K014560/1]; EPSRC [EP/K014560/1, EP/P022596/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/P022596/1] Funding Source: researchfish Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Leverhulme Early Career Fellowship(Leverhulme Trust); Isaac Newton Trust; School of Physical Sciences, University of Cambridge(University of Cambridge); European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme(European Research Council (ERC)); ARCHER UK National Supercomputing Service via EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) We thank Dr Phoebe Allan, Dr Matt Cliffe, and Dr Rachel Kerber for useful discussions. V. L. D. acknowledges a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, a Leverhulme Early Career Fellowship, and support from the Isaac Newton Trust. C. M. acknowledges an Oppenheimer Research Fellowship from the School of Physical Sciences, University of Cambridge. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 714581). This work used the ARCHER UK National Supercomputing Service via EPSRC Grant EP/K014560/1. Data access statement: original data supporting this publication are available as Electronic Supplementary Information (ESI dagger). 44 57 58 18 125 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1359-7345 1364-548X CHEM COMMUN Chem. Commun. JUN 14 2018.0 54 47 5988 5991 10.1039/c8cc01388h 0.0 4 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry GI7JT 29790508.0 Green Published, Green Accepted, hybrid, Green Submitted 2023-03-23 WOS:000434679500011 0
J Elbouchikhi, E; Zia, MF; Benbouzid, M; El Hani, S Elbouchikhi, Elhoussin; Zia, Muhammad Fahad; Benbouzid, Mohamed; El Hani, Soumia Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring ELECTRONICS English Review smart grid; resilience; signal processing; disturbance classification; power quality; PSD estimation; demodulation methods; time-frequency analysis; pattern recognition techniques; machine learning; information theoretical criteria POWER-QUALITY DISTURBANCES; INSTANTANEOUS OSCILLATING PHASOR; EMPIRICAL MODE DECOMPOSITION; FUZZY-EXPERT-SYSTEM; SYNCHROPHASOR ESTIMATION; S-TRANSFORM; RENEWABLE ENERGY; INTERPOLATED-DFT; DYNAMIC SYNCHROPHASOR; PARAMETER-ESTIMATION Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes. [Elbouchikhi, Elhoussin] ISEN Yncrea Ouest, LABISEN, Rue Cuirasse Bretagne, F-29200 Brest, France; [Zia, Muhammad Fahad] Natl Univ Comp & Emerging Sci, Dept Elect Engn, Lahore 54000, Pakistan; [Zia, Muhammad Fahad; Benbouzid, Mohamed] Univ Brest, CNRS, Inst Rech Dupuy Lome, IRDL,UMR 6027, F-29238 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China; [El Hani, Soumia] Mohamed V Univ, Energy Optimizat Diag & Control Res Grp, STIS Res Ctr, ENSAM, Rabat 10100, Morocco Centre National de la Recherche Scientifique (CNRS); Universite de Bretagne Occidentale; Shanghai Maritime University; Mohammed V University in Rabat Elbouchikhi, E (corresponding author), ISEN Yncrea Ouest, LABISEN, Rue Cuirasse Bretagne, F-29200 Brest, France. elhoussin.elbouchikhi@isen-ouest.yncrea.fr; fahad.zia@nu.edu.pk; mohamed.benbouzid@univ-brest.fr; s.elhani@um5r.ac.ma Zia, Muhammad Fahad/0000-0002-8654-6121; ELBOUCHIKHI, Elhoussin/0000-0002-7173-9318 193 7 7 8 25 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics NOV 2021.0 10 21 2725 10.3390/electronics10212725 0.0 40 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics XE1WH gold 2023-03-23 WOS:000723185500001 0
J Wang, X; Li, HL; Wang, LS; Yu, YZ; Zhou, H; Wang, L; Song, T Wang, Xun; Li, Hanlin; Wang, Lisheng; Yu, Yongzhi; Zhou, Hao; Wang, Lei; Song, Tao An improved YOLOv3 model for detecting location information of ovarian cancer from CT images INTELLIGENT DATA ANALYSIS English Article Ovarian cancer; deep learning; CT image; object detection; YOLOv3 Ovarian cancer is a malignant tumor that poses a serious threat to women's lives. Computer-aided diagnosis (CAD) systems can classify the type of ovarian tumors, but few of them can provide exactly the location information of ovarian cancer cells. Recently, deep learning technology becomes hot for automatic detection of cancer cells, particularly for detecting their locations. In this work, we propose a novel end-to-end network YOLO-OC (Ovarian cancer) model, which can extract the characteristics of ovarian cancer more efficiently. In our method, deformable convolution is used to enhance the model's ability to learn geometric deformation in space. Squeeze-and-Excitation (SE) module is proposed to automatically learn the importance of different channel features. Data experiments are conducted on datasets collected from The Affiliated Hospital of Qingdao University Medical College, China. Experimental results show that our YOLO-OC model achieves 91.83%, 85.66% and 73.82% on mean average precision mAP@.5, mAP@.75 and mAP@[.5,.95], respectively, which performs better than Faster R-CNN, SSD and RetinaNet on both accuracy and efficiency. [Wang, Xun; Li, Hanlin; Wang, Lisheng; Yu, Yongzhi; Song, Tao] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China; [Zhou, Hao; Wang, Lei] Qingdao Univ, Dept Gynaecol, Affiliated Hosp, Qingdao, Shandong, Peoples R China; [Song, Tao] Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid, Spain China University of Petroleum; Qingdao University; Universidad Politecnica de Madrid Song, T (corresponding author), China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China. t.song@upm.es Wang, Xun/HOH-8824-2023; Song, Tao/T-7360-2018 Song, Tao/0000-0002-0130-3340 National Natural Science Foundation of China [61873280, 61672033, 61672248, 61972416, 61772376]; Taishan Scholarship [tsqn201812029]; Major projects of the National Natural Science Foundation of China [41890851]; Natural Science Foundation of Shandong Province [ZR2019MF012]; Fundamental Research Funds for the Central Universities [18CX02152A, 19CX05003A-6] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Taishan Scholarship; Major projects of the National Natural Science Foundation of China; Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by National Natural Science Foundation of China (Grant Nos. 61873280, 61672033, 61672248, 61972416, 61772376), Taishan Scholarship (tsqn201812029), Major projects of the National Natural Science Foundation of China (Grant No. 41890851), Natural Science Foundation of Shandong Province (No. ZR2019MF012), Fundamental Research Funds for the Central Universities (18CX02152A, 19CX05003A-6). 33 1 1 11 36 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1088-467X 1571-4128 INTELL DATA ANAL Intell. Data Anal. 2021.0 25 6 1565 1578 10.3233/IDA-205542 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WS6SS 2023-03-23 WOS:000715309500012 0
J Yuan, L; Yang, Y; Hernandez, A; Shi, L Yuan, Lei; Yang, Yuan; Hernandez, Alvaro; Shi, Lin Feature Extraction for Track Section Status Classification Based on UGW Signals SENSORS English Article track status classification; ultrasonic guided wave (UGW); feature extraction; temporal and spatial dependencies; deep learning algorithm Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. [Yuan, Lei; Yang, Yuan; Shi, Lin] Xian Univ Technol, Elect Dept, Xian 710048, Shaanxi, Peoples R China; [Yuan, Lei; Hernandez, Alvaro] Univ Alcala, Elect Dept, Madrid 28805, Spain Xi'an University of Technology; Universidad de Alcala Yang, Y (corresponding author), Xian Univ Technol, Elect Dept, Xian 710048, Shaanxi, Peoples R China. yuanleixut@126.com; yangyuan@xaut.edu.cn; alvaro.hernandez@uah.es; lincoding@163.com Hernandez, Alvaro/AAB-1365-2019 Hernandez, Alvaro/0000-0001-9308-8133; Yuan, Lei/0000-0003-3488-2278 Key Research and Development Program of Shaanxi Province [2017ZDXM-GY-130]; Xi'an City Science and Technology Project [2017080CG/RC043 (XALG009)]; China Scholarship Council [201508610096]; Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) [TEC2015-64835-C3-2-R] Key Research and Development Program of Shaanxi Province; Xi'an City Science and Technology Project; China Scholarship Council(China Scholarship Council); Spanish Ministry of Economy and Competitiveness (MINECO/FEDER)(Spanish Government) This work has been supported by the Key Research and Development Program of Shaanxi Province (no. 2017ZDXM-GY-130), Xi'an City Science and Technology Project (no. 2017080CG/RC043 (XALG009)), the China Scholarship Council (no. 201508610096), and the Spanish Ministry of Economy and Competitiveness (SOC-PLC project, ref. TEC2015-64835-C3-2-R MINECO/FEDER). 33 5 6 3 26 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2018.0 18 4 1225 10.3390/s18041225 0.0 22 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GJ7NS 29673156.0 gold, Green Submitted, Green Accepted, Green Published 2023-03-23 WOS:000435574800297 0
J Ren, HS; Qian, ZD; Lin, B; Huang, QB; Crispino, M; Ketabdari, M Ren, Haisheng; Qian, Zhendong; Lin, Bin; Huang, Qibo; Crispino, Maurizio; Ketabdari, Misagh Effect of recycled concrete aggregate features on adhesion properties of asphalt mortar-aggregate interface CONSTRUCTION AND BUILDING MATERIALS English Article RCA feature; Asphalt -RCA interface; Cohesive zone model; GA -BP artificial neural network model; Interface adhesions behavior SIMULATION; MIXTURE; PREDICTION; STRENGTH; FAILURE Asphalt-aggregate interface's adhesion properties commonly affect the damage initiation and evolution within asphalt concrete materials, related to pavement durability and quality. The scope of this research was to investigate the influence of Recycled Concrete Aggregate (RCA) features on asphalt mortar-aggregate interface adhesion. Firstly, a three-dimensional reconstruction model of RCA was carried out using X-ray CT tomography and digital image processing. In this regard, five feature indicators, namely cement mortar content, sphericity, flat and elongated ratio, angularity, and surface texture, were proposed. Based on a bilinear cohesive zone model, the interface damage behavior of asphalt mortar-RCA was investigated by using a uniaxial compression simu-lation. Finally, a GA-BP artificial neural network was conducted to predict and quantify the effect of each feature indicator of RCA on interface adhesion. The results showed that when RCA had lower cement mortar content, higher sphericity value, and smoother surface, the asphalt mortar-RCA system was less prone to interface adhesion failure. The 5-14-1 GA-BP artificial neural network proposed in this study showed very good perfor-mance in predicting the interfacial dissipation damage energy with a mean-squared error value of 3.52 x 10-4 for testing dataset. The cement mortar content parameter exhibited a remarkable influence on the interface adhesion property, and its global contribution to the interfacial dissipation damage energy (0.3486) was more than twice that of the surface texture parameter (0.1316). In future studies, the performance characteristics of cement mortar can be further investigated, thereby proposing RCA's performance optimization technology. [Ren, Haisheng; Qian, Zhendong; Huang, Qibo] Southeast Univ, Intelligent Transportat Syst Res Ctr, Nanjing 211189, Peoples R China; [Huang, Qibo] Foochow Transportat Dept, Fuzhou 350004, Peoples R China; [Lin, Bin] CCCC Second Highway Consultants Co Ltd, Wuhan 430118, Peoples R China; [Ren, Haisheng; Crispino, Maurizio; Ketabdari, Misagh] Politecn Milan, Dept Civil & Environm Engn, I-20133 Milan, Italy Southeast University - China; Polytechnic University of Milan Qian, ZD (corresponding author), Southeast Univ, Intelligent Transportat Syst Res Ctr, Nanjing 211189, Peoples R China. ren_hs510@seu.edu.cn; qianzd@seu.edu.cn; huangqibo@seu.edu.cn; maurizio.crispino@polimi.it; misagh.ketabdari@polimi.it Ketabdari, Misagh/AGK-5005-2022 Ketabdari, Misagh/0000-0002-0885-4540 Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX21_0125]; China Scholarship Council (CSC) Postgraduate Research & Practice Innovation Program of Jiangsu Province; China Scholarship Council (CSC)(China Scholarship Council) The authors sincerely acknowledge the funding support from the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_0125) and the National Natural Science) . The authors would like to thank the financial support to this research from the China Scholarship Council (CSC) . 45 0 0 17 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. OCT 24 2022.0 353 129097 10.1016/j.conbuildmat.2022.129097 0.0 SEP 2022 11 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science 5V3GH 2023-03-23 WOS:000877120900004 0
J Balezentis, T; Blancard, S; Shen, ZY; Streimikiene, D Balezentis, Tomas; Blancard, Stephane; Shen, Zhiyang; Streimikiene, Dalia Analysis of Environmental Total Factor Productivity Evolution in European Agricultural Sector DECISION SCIENCES English Article By-Production Approach; Environmental Performance; European Agriculture; Luenberger-Hicks-Moorsteen Productivity Indicator; and Undesirable Outputs MODELING UNDESIRABLE FACTORS; DATA ENVELOPMENT ANALYSIS; BY-PRODUCTION; EFFICIENCY EVALUATION; DEA APPROACH; BIG DATA; PERFORMANCE; RESOURCE; INDICATOR; GROWTH Productivity analysis has been an important avenue for economic research. Therefore, medleys of quantitative techniques have been proposed to operationalize productivity analysis. In this article, an extended by-production model is discussed and applied to ensure a link between the production and the pollution-generating subtechnologies. The corresponding dual formulations are provided to interpret the economic role of pollution-generating inputs in the subtechnologies. Finally, we integrate the proposed model with the environmental Luenberger-Hicks-Moorsteen productivity indicator based upon input and output directional distance functions. The proposed model is applied to measure the green economic growth of agricultural sectors of the selected European countries. [Balezentis, Tomas; Streimikiene, Dalia] Lithuanian Inst Agr Econ, 4A A Vivulskio Str, LT-03220 Vilnius, Lithuania; [Blancard, Stephane] Univ Bourgogne Franche Comte, INRA, AgroSup Dijon, CESAER UMR1041, F-21000 Dijon, France; [Shen, Zhiyang] Anhui Univ Finance & Econ, 962 CaoShan RD, Bengbu 233030, Peoples R China; [Shen, Zhiyang] Export Import Bank China, 30 FuXingMenNei Str, Beijing 100030, Peoples R China Lithuanian Institute of Agrarian Economics; INRAE; Institut Agro; AgroSup Dijon; Anhui University of Finance & Economics Shen, ZY (corresponding author), Anhui Univ Finance & Econ, 962 CaoShan RD, Bengbu 233030, Peoples R China.;Shen, ZY (corresponding author), Export Import Bank China, 30 FuXingMenNei Str, Beijing 100030, Peoples R China. tomas@laei.lt; stephane.blancard@agrosupdijon.fr; zhiyang86@163.com; dalia@mail.lei.lt Balezentis, Tomas/D-8609-2015; SHEN, Zhiyang/AAY-7556-2021 Balezentis, Tomas/0000-0002-3906-1711; SHEN, Zhiyang/0000-0002-5126-6461; BLANCARD, Stephane/0000-0001-5996-2368 European Social Fund [09.3.3-LMT-K-712-01-0007]; Research Council of Lithuania (LMTLT) European Social Fund(European Social Fund (ESF)); Research Council of Lithuania (LMTLT)(Research Council of Lithuania (LMTLT)) We appreciate valuable suggestions from editors and anonymous referees. This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-01-0007) under grant agreement with the Research Council of Lithuania (LMTLT). 66 37 37 17 66 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0011-7315 1540-5915 DECISION SCI Decis. Sci. APR 2021.0 52 2 483 511 10.1111/deci.12421 0.0 JAN 2020 29 Management Social Science Citation Index (SSCI) Business & Economics RJ8RH 2023-03-23 WOS:000507139000001 0
J Lu, N; Li, D; Shi, WB; Vijayakumar, P; Piccialli, F; Chang, V Lu, Ning; Li, Dan; Shi, Wenbo; Vijayakumar, Pandi; Piccialli, Francesco; Chang, Victor An efficient combined deep neural network based malware detection framework in 5G environment COMPUTER NETWORKS English Article 5G network; Internet of Things (IoT) networks; Android-based applications; Malware detection; Combined deep neural network While Android smartphones are widely used in 5G networks, third-party application platforms are facing a rapid increase in the screening of applications for market launch. However, on the one hand, due to the receipt of excessive applications for listing, the review requires a lot of time and computing resources. On the other hand, due to the multi-selectivity of Android application features, it is difficult to determine the best feature combination as a criterion for distinguishing benign and malicious software. To address these challenges, this paper proposes an efficient malware detection framework based on deep neural network called DLAMD that can face large-scale samples. An efficient detection framework is designed, which combines the pre-detection phase of rapid detection and the deep detection phase of deep detection. The Android application package (APK) is analyzed in detail, and the permissions and opcodes feature that can distinguish benign from malicious are quickly extracted from the APK. Besides, to obtain the feature subset that can distinguish the attributes most, the random forest with good effect is selected for importance selection and the convolutional neural network (CNN) which automatically extracted the hidden pattern inside features is selected for feature selection. In the experiment, real data from shared malware collection and third-party application download platforms are used to verify the high efficiency of the proposed method. The results show that the comprehensive classification index F1-score of DLAMD can reach 95.69%. [Lu, Ning] Xidian Univ, Sch Comp Sci & Technol, Xian, Peoples R China; [Lu, Ning; Li, Dan; Shi, Wenbo] Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China; [Vijayakumar, Pandi] Univ Coll Engn Tindivanam, Dept Comp Sci & Engn, Tindivanam, India; [Piccialli, Francesco] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Campania, Italy; [Chang, Victor] Teesside Univ, Sch Comp Engn & Digital Technol, Middlesbrough, Cleveland, England Xidian University; Northeastern University - China; University of Naples Federico II; University of Teesside Shi, WB (corresponding author), Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China.;Chang, V (corresponding author), Teesside Univ, Sch Comp Engn & Digital Technol, Middlesbrough, Cleveland, England. shiwb@neuq.edu.cn; ic.victor.chang@gmail.com Chang, Victor/AAC-7582-2019; Vijayakumar, Pandi/Y-4636-2019 Chang, Victor/0000-0002-8012-5852; Vijayakumar, Pandi/0000-0001-5451-8946; Li, Dan/0000-0001-8178-1512 National Natural Science Foundation of China [62072093, 62072092, U1708262]; China Postdoctoral Science Foundation [2019M653568]; Fundamental Research Funds for the Central Universities [N2023020]; Natural Science Foundation of Hebei Province of China [F2020501013, 20310702D, VCR 0000116] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province) This work is supported by the National Natural Science Foundation of China (Nos. 62072093, 62072092, and U1708262); the China Postdoctoral Science Foundation (No. 2019M653568); the Fundamental Research Funds for the Central Universities (No. N2023020); the Natural Science Foundation of Hebei Province of China (No. F2020501013, 20310702D) for co-authors in China and VC Research (VCR 0000116) for Prof. Chang. Dan Li is the co-first author of this paper. 40 10 10 2 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. APR 22 2021.0 189 107932 10.1016/j.comnet.2021.107932 0.0 FEB 2021 11 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RF6OK Green Accepted, Green Submitted 2023-03-23 WOS:000634961400011 0
J Wang, ZF; Miao, YL; Xu, M; Zhu, ZW; Qureshi, S; Chang, Q Wang, Zhifang; Miao, Yinglu; Xu, Min; Zhu, Zhongwei; Qureshi, Salman; Chang, Qing Revealing the differences of urban parks' services to human wellbeing based upon social media data URBAN FORESTRY & URBAN GREENING English Article Big data; Big data science; Ecosystem benefits; Structural assessment; Urban green spaces; Urban ecology; word2vec model CULTURAL ECOSYSTEM SERVICES; TRADE-OFFS; ENVIRONMENTAL-EDUCATION; GREEN SPACE; BIG DATA; LANDSCAPES; SYNERGIES; VALUES; CHINA; PHOTOGRAPHS Urban green spaces provide a range of services to urban residents; however, how these distinct spaces provide different services, if any, to human wellbeing has been seldom addressed. Using large volumes of social media data considering broad user groups and publicly available content, we developed a method to transform unstructured online comments into a structured assessment of nine categories of ecosystem services. An 'ecosystem services lexicon' was created based on 6853 words under twenty-eight subcategories of parks' services to human wellbeing using the word2vec model. The application of the ecosystem service lexicon to urban parks in Beijing revealed that all ecosystem services were perceivable to urban park users; however, the perception frequency of the ecosystem services varied across different parks. Additionally, the perceived ecosystem services were bundled together; four types of bundles were identified with varied dominant services. Technically, this study offered a novel technical procedure that can transfer unstructured free comments into a structured assessment of urban parks' perceived services to human wellbeing. Theoretically, the study revealed small-scale ecosystem service bundles from users' opinions and called for a further cross-scale understanding of ecosystem service bundles. Practically, the study findings can help inform evidence-based park policies, planning, and management. [Wang, Zhifang; Miao, Yinglu; Xu, Min; Zhu, Zhongwei] Peking Univ, Coll Architecture & Landscape Architecture, 5 Yiheyuan Rd, Beijing 100871, Peoples R China; [Qureshi, Salman] Humboldt Univ, Inst Geog Landscape Ecol, Rudower Chaussee 16, D-12489 Berlin, Germany; [Chang, Qing] China Agr Univ, Dept Ornamental Hort & Landscape Architecture, 2 Yuanmingyuan West Rd, Beijing 100193, Peoples R China Peking University; Humboldt University of Berlin; China Agricultural University Xu, M (corresponding author), 5 Yiheyuan Rd, Beijing 100871, Peoples R China. zhifangw@pku.edu.cn; 1146920134@qq.com; xumin7105@pku.edu.cn; zhuzhongwei6@pku.edu.cn; salman.qureshi@geo.hu-berlin.de; changqing@cau.edu.cn Qureshi, Salman/0000-0001-7955-7490; Miao, Yinglu/0000-0002-1060-5359 National Natural Science Foundation of China [41871153, 41625003]; Postdoctoral Research Foundation of China [2020M670050] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Postdoctoral Research Foundation of China(China Postdoctoral Science Foundation) This work was supported by the National Natural Science Foundation of China [grant numbers 41871153, 41625003] ; Postdoctoral Research Foundation of China [grant number 2020M670050] . 89 10 10 41 115 ELSEVIER GMBH MUNICH HACKERBRUCKE 6, 80335 MUNICH, GERMANY 1618-8667 1610-8167 URBAN FOR URBAN GREE Urban For. Urban Green. AUG 2021.0 63 127233 10.1016/j.ufug.2021.127233 0.0 JUL 2021 10 Plant Sciences; Environmental Studies; Forestry; Urban Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Plant Sciences; Environmental Sciences & Ecology; Forestry; Urban Studies TZ0ON 2023-03-23 WOS:000684175900004 0
J Wen, ZY; Hu, HZ; Yang, RY; Qian, B; Sham, RWH; Sun, R; Xu, J; Patel, P; Rana, O; Dustdar, S; Ranjan, R Wen, Zhenyu; Hu, Haozhen; Yang, Renyu; Qian, Bin; Sham, Ringo W. H.; Sun, Rui; Xu, Jie; Patel, Pankesh; Rana, Omer; Dustdar, Schahram; Ranjan, Rajiv Orchestrating Networked Machine Learning Applications Using Autosteer IEEE INTERNET COMPUTING English Article Performance evaluation; Degradation; Training data; Adaptation models; Cloud computing; Runtime; Computational modeling A platform for orchestrating networked machine learning (ML) applications over distributed environments is described. ML applications are transformed into automated pipelines that manage the whole application lifecycle and production-grade implementations are automatically constructed. We present AUTOSTEER, a software platform that can deploy ML applications on various hardware resources & mdash; interconnected using heterogeneous network resources & mdash;across cloud and edge devices. Device placement optimization and model adaptation are used as control actions to support application requirements and maximize the performance of ML model execution over heterogeneous computing resources. The performance of deployed applications is continually monitored at runtime to overcome performance degradation due to incorrect application parameter settings or model decay. Three real-world applications are used to demonstrate how AUTOSTEER can support application deployment and runtime performance guarantees. [Wen, Zhenyu] Zhejiang Univ Technol, Cyberspace Secur, Hangzhou 310023, Peoples R China; [Hu, Haozhen] Zhejiang Univ Technol, Coll Informat, Hangzhou 310023, Peoples R China; [Yang, Renyu] Univ Leeds, Leeds LS2 9JT, England; [Qian, Bin; Sham, Ringo W. H.; Sun, Rui] Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE1 7RU, England; [Patel, Pankesh] Univ South Carolina, AI Inst, Columbia, SC 29208 USA; [Rana, Omer] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales; [Dustdar, Schahram] TU Wien, Comp Sci, A-1040 Vienna, Austria Zhejiang University of Technology; Zhejiang University of Technology; University of Leeds; Newcastle University - UK; University of South Carolina System; University of South Carolina Columbia; Cardiff University; Technische Universitat Wien Wen, ZY (corresponding author), Zhejiang Univ Technol, Cyberspace Secur, Hangzhou 310023, Peoples R China. zhenyuwen@zjut.ed.cn; 2112003108@zjut.edu.cn; r.yang1@leeds.ac.uk; b.qian3@ncl.ac.uk; ringo.sham@newcastle.ac.uk; r.sun5@newcastle.ac.uk; j.xu@leeds.ac.uk; dr.pankesh.patel@gmail.com; ranaof@cardiff.ac.uk; dustdar@dsg.tuwien.ac.at; raj.ranjan@ncl.ac.uk Dustdar, Schahram/G-9877-2015; Rana, Omer/E-4314-2015 Dustdar, Schahram/0000-0001-6872-8821; Rana, Omer/0000-0003-3597-2646 U.K. EPSRC [EP/T01461X/1]; U.K. Alan Turing Institute Post-Doctoral Enrichment Award Programme; U.K. Alan Turing Pilot Project U.K. EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); U.K. Alan Turing Institute Post-Doctoral Enrichment Award Programme; U.K. Alan Turing Pilot Project This work was supported in part by U.K. EPSRC under Grant EP/T01461X/1, in part by U.K. Alan Turing Institute Post-Doctoral Enrichment Award Programme, and in part by U.K. Alan Turing Pilot Project. 12 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1089-7801 1941-0131 IEEE INTERNET COMPUT IEEE Internet Comput. NOV 1 2022.0 26 6 51 58 10.1109/MIC.2022.3180907 0.0 8 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8C6QM Green Accepted 2023-03-23 WOS:000917730600007 0
J Shen, MG; Yang, JH; Jiang, WB; Sanjuan, MAF; Zheng, YQ Shen, Mengen; Yang, Jianhua; Jiang, Wenbo; Sanjuan, Miguel A. F.; Zheng, Yuqiao Stochastic resonance in image denoising as an alternative to traditional methods and deep learning NONLINEAR DYNAMICS English Article Blind image denoising; Monostable system; Stochastic resonance; Deep learning; BM3D ENHANCEMENT; NETWORK; ARRAY Image denoising is a long-standing hard problem, especially in the field of engineering detection. In addition, emerging promising image-based intelligent diagnosis methods are sensitive to image noise. However, existing methods cannot meet the requirements well. Actively utilizing the construction role of noise, we propose an adaptive image denoising method based on stochastic resonance of a monostable model (AMSR). Compared with BM3D which is the representative of traditional methods, the AMSR is able to enhance and preserve weak defect features while achieving a considerable degree of denoising. Further, we introduce an image noise intensity estimation method based on principal component analysis, combined with the optimal region theory of stochastic resonance, and finally improve the AMSR method to achieve blind image denoising (AMSRB). Compared with CBDNet which is the representative method for general image blind denoising, the AMSRB has a leading edge in specific application scenarios where it is difficult to obtain a large amount of training data, with higher accuracy and stability. The results demonstrate the superiority of nonlinear stochastic dynamics in image denoising and related engineering application value. [Shen, Mengen; Yang, Jianhua] China Univ Min & Technol, Sch Mechatron Engn, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou 221116, Jiangsu, Peoples R China; [Jiang, Wenbo] Henan Pinggao Elect Co Ltd, China Elect Equipment Grp Co Ltd, Pingdingshan 467001, Henan, Peoples R China; [Sanjuan, Miguel A. F.] Univ Rey Juan Carlos, Nonlinear Dynam Chaos & Complex Syst Grp, Dept Fis, Tulipan S-N Mostoles, Madrid 28933, Spain; [Zheng, Yuqiao] Lanzhou Univ Technol, Sch Mech & Elect Engn, Lanzhou 730050, Peoples R China China University of Mining & Technology; Universidad Rey Juan Carlos; Lanzhou University of Technology Yang, JH (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou 221116, Jiangsu, Peoples R China. jianhuayang@cumt.edu.cn Yang, Jianhua/S-3753-2018; SANJUAN, MIGUEL A. F./B-7709-2011 Yang, Jianhua/0000-0001-5389-9142; SANJUAN, MIGUEL A. F./0000-0003-3515-0837 National Natural Science Foundation of China [12072362]; Lanzhou Talent Innovation and Entrepreneurship Project [2020-RC-24]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX21_2193]; Spanish State Research Agency (AEI); European Regional Development Fund [PID2019-105554GB-I00]; Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Lanzhou Talent Innovation and Entrepreneurship Project; Postgraduate Research & Practice Innovation Program of Jiangsu Province; Spanish State Research Agency (AEI)(Spanish Government); European Regional Development Fund(European Commission); Priority Academic Program Development of Jiangsu Higher Education Institutions This work is supported by the National Natural Science Foundation of China (Grant No. 12072362), the Lanzhou Talent Innovation and Entrepreneurship Project (Grant No. 2020-RC-24), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX21_2193), the Spanish State Research Agency (AEI) and the European Regional Development Fund (Grant No. PID2019-105554GB-I00), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. 76 0 0 12 30 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. AUG 2022.0 109 3 2163 2183 10.1007/s11071-022-07571-8 0.0 JUN 2022 21 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics 3W3RA 2023-03-23 WOS:000809528900001 0
J Lotfata, A; Grekousis, G; Wang, RY Lotfata, Aynaz; Grekousis, George; Wang, Ruoyu Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE English Article; Early Access Hypertension prevalence; neighborhood determinants; spatial machine learning; random forest; Chicago USA SOCIOECONOMIC-STATUS; HEALTH-CARE; ENVIRONMENT; LANGUAGE; ADULTS; INCOME; CRIME; CITY In the United States, the rise in hypertension prevalence has been connected to neighborhood characteristics. While various studies have found a link between neighborhood and health, they do not evaluate the relative dependence of each component in the growth of hypertension and, more significantly, how this value differs geographically (i.e., across different neighborhoods). This study ranks the contribution of ten socioeconomic neighborhood factors to hypertension prevalence in Chicago, Illinois, using multiple global and local machine learning models at the census tract level. First, we use Geographical Random Forest, a recently proposed non-linear machine learning regression method, to assess each predictive factor's spatial variation and contribution to hypertension prevalence. Then we compare GRF performance to Geographically Weighted Regression (local model), Random Forest (global model), and OLS (global model). The results indicate that GRF outperforms all models and that the importance of variables varies by census tract. Household composition is the most important factor in the Chicago tracts, while on the other hand, Housing type and Transportation is the least important factor. While the household composition is the most important determinant around north Lake Michigan, the socioeconomic condition of the neighborhood in Chicago's mid-north has the most importance on hypertension prevalence. Understanding how the importance of socioeconomic factors associated with hypertension prevalence varies spatially aids in the design and implementation of health policies based on the most critical factors identified at the local level (i.e., tract), rather than relying on broad city-level guidelines (i.e., for entire Chicago and other large cities). [Lotfata, Aynaz] Univ Calif Davis, Sch Vet Med, Dept Vet Pathol, Davis, CA USA; [Grekousis, George] Sun Yat Sen Univ, Sch Geog & Planning, Dept Urban & Reg Planning, Guangzhou, Peoples R China; [Grekousis, George] Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou, Peoples R China; [Grekousis, George] Guangdong Prov Engn Res Ctr Publ Secur & Disaster, Guangzhou, Peoples R China; [Wang, Ruoyu] Queens Univ Belfast, UKCRC Ctr Excellence Publ Hlth, Ctr Publ Hlth, Belfast, North Ireland; [Grekousis, George] Sun Yat Sen Univ, Sch Geog & Planning, Dept Urban & Reg Planning, Waihuandong Rd, Guangzhou 510006, Peoples R China University of California System; University of California Davis; Sun Yat Sen University; Queens University Belfast; Sun Yat Sen University Grekousis, G (corresponding author), Sun Yat Sen Univ, Sch Geog & Planning, Dept Urban & Reg Planning, Waihuandong Rd, Guangzhou 510006, Peoples R China. geogreko@outlook.com 67 0 0 5 5 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 2399-8083 2399-8091 ENVIRON PLAN B-URBAN Env. Plan. B-Urban Anal. City Sci. 10.1177/23998083231153401 0.0 JAN 2023 18 Environmental Studies; Geography; Regional & Urban Planning; Urban Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geography; Public Administration; Urban Studies 8J5FJ 2023-03-23 WOS:000922441100001 0
J Xia, ZQ; Hong, XP; Gao, XY; Feng, XY; Zhao, GY Xia, Zhaoqiang; Hong, Xiaopeng; Gao, Xingyu; Feng, Xiaoyi; Zhao, Guoying Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions IEEE TRANSACTIONS ON MULTIMEDIA English Article Spatiotemporal phenomena; Feature extraction; Task analysis; Videos; Training; Strain; Deep learning; Micro-Expression Recognition; Spatiotemporal Modeling; Temporal Connectivity; Recurrent Convolutional Networks; Data Augmentation; Balanced Loss RECOGNITION; DYNAMICS Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two ways to extend the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, two temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods. [Xia, Zhaoqiang; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Peoples R China; [Hong, Xiaopeng] Xi An Jiao Tong Univ, Xian 710049, Peoples R China; [Gao, Xingyu] Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China; [Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland Northwestern Polytechnical University; Xi'an Jiaotong University; Chinese Academy of Sciences; Institute of Microelectronics, CAS; University of Oulu Gao, XY (corresponding author), Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China. xiazhaoqiang@gmail.com; hongxiaopeng@mail.xjtu.edu.cn; gxy9910@gmail.com; fengxiao@nwpu.edu.cn; guoying.zhao@oulu.fi Xia, Zhaoqiang/AAC-4021-2019; Zhao, Guoying/ABE-7716-2020; Gao, Xingyu/AAL-3288-2021; HONG, Xiaopeng/K-3594-2017 Xia, Zhaoqiang/0000-0003-0630-3339; Zhao, Guoying/0000-0003-3694-206X; Gao, Xingyu/0000-0002-4660-8092; HONG, Xiaopeng/0000-0002-0611-0636 National Natural Science Foundation of China [61702419, 61702491]; Natural Science Basic Research Plan in Shaanxi Province of China [2018JQ6090] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China This work was supported in part by the National Natural Science Foundation of China under Grant 61702419 and Grant 61702491 and in part by the Natural Science Basic Research Plan in Shaanxi Province of China (2018JQ6090). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Zhu Liu. 67 79 83 19 76 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia MAR 2020.0 22 3 626 640 10.1109/TMM.2019.2931351 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications KU2ZL Green Accepted, Green Submitted 2023-03-23 WOS:000519576700005 0
C Li, PF; Lu, H; Zheng, Q; Yang, L; Pan, G Assoc Comp Machinery Li, Pengfei; Lu, Hua; Zheng, Qian; Yang, Long; Pan, Gang LISA: A Learned Index Structure for Spatial Data SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA English Proceedings Paper ACM SIGMOD International Conference on Management of Data (SIGMOD) JUN 14-19, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGMOD Database; Spatial index; Learned Index In spatial query processing, the popular index R-tree may incur large storage consumption and high IO cost. Inspired by the recent learned index [17] that replaces B-tree with machine learning models, we study an analogy problem for spatial data. We propose a novel Learned Index structure for Spatial dAta (LISA for short). Its core idea is to use machine learning models, through several steps, to generate searchable data layout in disk pages for an arbitrary spatial dataset. In particular, LISA consists of a mapping function that maps spatial keys (points) into 1-dimensional mapped values, a learned shard prediction function that partitions the mapped space into shards, and a series of local models that organize shards into pages. Based on LISA, a range query algorithm is designed, followed by a lattice regression model that enables us to convert a KNN query to range queries. Algorithms are also designed for LISA to handle data updates. Extensive experiments demonstrate that LISA clearly outperforms R-tree and other alternatives in terms of storage consumption and IO cost for queries. Moreover, LISA can handle data insertions and deletions efficiently. [Li, Pengfei; Yang, Long; Pan, Gang] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China; [Lu, Hua] Roskilde Univ, Roskilde, Denmark; [Zheng, Qian] Nanyang Technol Univ, Singapore, Singapore Zhejiang University; Roskilde University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Li, PF (corresponding author), Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China. pfl@zju.edu.cn; luhua@ruc.dk; zhengqian@ntu.edu.sg; yanglong@zju.edu.cn; gpan@zju.edu.cn Lu, Hua/0000-0003-1199-6678 Natural Science Foundation of China [61925603, 61772460] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank all anonymous reviewers. This research is supported by Natural Science Foundation of China (No. 61925603, 61772460). Hua Lu's work was conducted when he was employed at Aalborg University, Denmark. Hua Lu and Gang Pan are corresponding authors. 28 27 27 1 4 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6735-6 2020.0 2119 2133 10.1145/3318464.3389703 0.0 15 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR3BZ 2023-03-23 WOS:000644433700140 0
J Jin, C; Jin, SW; Qin, LN Jin, Cong; Jin, Shu-Wei; Qin, Li-Na Attribute selection method based on a hybrid BPNN and PSO algorithms APPLIED SOFT COMPUTING English Article Reduction dimensionality; Attribute selection; Back Propagation Neural Network; Particle Swarm Optimization; Input output correlation High dimensional data contain many redundant or irrelevant attributes, which will be difficult for data mining and a variety of pattern recognition. When implementing data mining or a variety of pattern recognition on high dimensional space, it is necessary to reduce the dimension of high dimensional space. In this paper, a new attribute importance measure and selection methods based on attribute ranking was proposed. In proposed attribute selection method, input output correlation (IOC) is applied for calculating attribute' importance, and then sorts them according to descending order. The hybrid of Back Propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO) algorithms is also proposed. PSO is used to optimize weights and thresholds of BPNN for overcoming the inherent shortcoming of BPNN. The experiment results show the proposed attribute selection method is an effective preproceesing technology. (C) 2012 Elsevier B.V. All rights reserved. [Jin, Cong; Qin, Li-Na] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China; [Jin, Shu-Wei] Univ Lyon 1, Fac Sci & Technol, F-69622 Villeurbanne, France Central China Normal University; UDICE-French Research Universities; Universite Claude Bernard Lyon 1 Jin, C (corresponding author), Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China. jincong@mail.ccnu.edu.cn social science foundation from Chinese Ministry of Education [11YJAZH040] social science foundation from Chinese Ministry of Education This work was supported by social science foundation from Chinese Ministry of Education (Grant No. 11YJAZH040). 20 43 46 3 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. AUG 2012.0 12 8 2147 2155 10.1016/j.asoc.2012.03.015 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 958YB 2023-03-23 WOS:000305275800020 0
J Guo, YD; Wang, HP; Tian, Y; Caldwell, DG Guo, Yida; Wang, Haoping; Tian, Yang; Caldwell, Darwin G. Task performance-based adaptive velocity assist-as-needed control for an upper limb exoskeleton BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Article Upper limb exoskeleton; Assist-as-needed control; Active rehabilitation; Task performance; Neural network SLIDING MODE CONTROL; IMPEDANCE CONTROL; REHABILITATION; ROBOT; WALKING In this paper, a task performance-based adaptive velocity assist-as-needed (TPAVAAN) controller is developed for an upper limb exoskeleton to stimulate subject's participation in the rehabilitation process. This controller includes an inner position and velocity based double impedance control (PVDIC) loop to calculate the assistive force, and an outer barrier Lyapunov function-based time-delay estimation controller with neural network compensation (NN-BLFTDEC) to drive the exoskeleton to provide the required assistive force. In the PVDIC loop, the assistive force is determined by a position based impedance controller to encourage subject in following the desired trajectory, and a velocity based impedance controller to assist the subject to perform the task at the desired velocity. A task performance function that considers position tracking error and assistive force is established to assess the subject's motor capability and adjust the desired velocity. For the inner loop, the NNBLFTDEC is designed based on a barrier Lyapunov function (BLF) to constrain the tracking error. A time-delay estimation (TDE) method and radial basis function neural network (RBFNN) are applied to estimate uncertain exoskeleton dynamics. Co-simulation studies are performed in SolidWorks and Matlab/Simulink. The mean tracking errors of the subject are 0.013 m and 0.039 m with and without the developed controller, respectively. Besides, with the enhancement of subject's motor capability, the value of the performance function decreases from 7.94 to 1.77, while the desired velocity increases from 0.025 m/s to 0.118 m/s. The proposed TPAVAAN controller has potential to improve subjects' task performance and modulate the assistance level adaptively. [Guo, Yida; Wang, Haoping; Tian, Yang] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China; [Caldwell, Darwin G.] Ist Italiano Tecnol, Dept Adv Robot, I-16163 Genoa, Italy Nanjing University of Science & Technology; Istituto Italiano di Tecnologia - IIT Wang, HP (corresponding author), Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China. hp.wang@njust.edu.cn Caldwell, Darwin Gordon/E-9954-2018 Caldwell, Darwin Gordon/0000-0002-6233-9961 National Natural Science Foundation of China [62173182, 61773212] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (No. 62173182, 61773212) 42 1 1 13 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control MAR 2022.0 73 103474 10.1016/j.bspc.2021.103474 0.0 12 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0N1YW 2023-03-23 WOS:000782642900008 0
J Hilbert, M; Barnett, G; Blumenstock, J; Contractor, N; Diesner, J; Frey, S; Gonzalez-Bailon, S; Lamberso, PJ; Pan, J; Peng, TQ; Shen, C; Smaldino, PE; Van Atteveldt, W; Waldherr, A; Zhang, JW; Zhu, JJH Hilbert, Martin; Barnett, George; Blumenstock, Joshua; Contractor, Noshir; Diesner, Jana; Frey, Seth; Gonzalez-Bailon, Sandra; Lamberso, P. J.; Pan, Jennifer; Tai-Quan Peng; Shen, Cuihua (Cindy); Smaldino, Paul E.; Van Atteveldt, Wouter; Waldherr, Annie; Zhang, Jingwen; Zhu, Jonathan J. H. Computational Communication Science: A Methodological Catalyzer for a Maturing Discipline INTERNATIONAL JOURNAL OF COMMUNICATION English Article computational science; research methods; big data; simulations; online experiments; methodology BIG DATA; SOCIAL-SCIENCE; MEDIA; NEWS This article reviews the opportunities and challenges for computational research methods in the field of communication. Among the social sciences, communication stands out as a discipline with a relatively low-profile institutionalized focus on the in-house development of methods. Computational tools are changing this, and they are catalyzing a new set of methods directly suited to tackling foundational research questions in communication. We systematically review how computational methods affect the three fundamental pillars of the scientific method: observational approaches (i.e., digital trace data), theoretical approaches (i.e., computer simulations), and experimental research (i.e., virtual labs and field experiments). We stress that data are a catalyzer but not a requirement for computational science. We explore how observational, theoretical, and experimental approaches can be combined and cross-fertilize one another. We conclude that taking advantage of computational methods will require a systematic effort in our discipline to develop and adjust these methods. [Hilbert, Martin; Barnett, George; Frey, Seth; Shen, Cuihua (Cindy); Zhang, Jingwen] Univ Calif Davis, Davis, CA 95616 USA; [Blumenstock, Joshua] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Contractor, Noshir] Northwestern Univ, Evanston, IL 60208 USA; [Diesner, Jana] Univ Illinois, Urbana, IL USA; [Gonzalez-Bailon, Sandra] Univ Penn, Philadelphia, PA 19104 USA; [Lamberso, P. J.] Univ Calif Los Angeles, Los Angeles, CA USA; [Pan, Jennifer] Stanford Univ, Stanford, CA 94305 USA; [Tai-Quan Peng] Michigan State Univ, E Lansing, MI 48824 USA; [Smaldino, Paul E.] Univ Calif Merced, Merced, TX USA; [Van Atteveldt, Wouter] Vrije Univ Amsterdam, Amsterdam, Netherlands; [Waldherr, Annie] Univ Munster, Munster, Germany; [Zhu, Jonathan J. H.] City Univ Hong Kong, Hong Kong, Peoples R China University of California System; University of California Davis; University of California System; University of California Berkeley; Northwestern University; University of Illinois System; University of Illinois Urbana-Champaign; University of Pennsylvania; University of California System; University of California Los Angeles; Stanford University; Michigan State University; Vrije Universiteit Amsterdam; University of Munster; City University of Hong Kong Hilbert, M (corresponding author), Univ Calif Davis, Davis, CA 95616 USA. hilbert@ucdavis.edu; gabarnett@ucdavis.edu; jblumenstock@berkeley.edu; nosh@northwestern.edu; jdiesner@illinois.edu; sfrey@ucdavis.edu; sgonzalezbailon@asc.upenn.edu; lamberson@ucla.edu; jp1@stanford.edu; winsonpeng@gmail.com; cuishen@ucdavis.edu; paul.smaldino@gmail.com; wouter@vanatteveldt.com; waldherr@uni-muenster.de; jwzzhang@ucdavis.edu; j.zhu@cityu.edu.hk Zhang, Jingwen/AAQ-3561-2020; Contractor, Noshir/AAJ-8371-2020; Contractor, Noshir/B-7456-2009; Blumenstock, Joshua/ADN-3685-2022; Peng, Tai-Quan/B-3176-2011; Zhu, Jonathan J. H./F-9859-2012; Waldherr, Annie/AAU-5418-2021 Zhang, Jingwen/0000-0003-1733-6857; Contractor, Noshir/0000-0002-9989-3018; Blumenstock, Joshua/0000-0002-1813-7414; Zhu, Jonathan J. H./0000-0001-6173-6941; 34 10 10 3 22 USC ANNENBERG PRESS LOS ANGELES UNIV SOUTHERN CALIFORNIA, KERCKHOFF HALL, 734 W ADAMS BLVD, MC7725, LOS ANGELES, CA 90089 USA 1932-8036 INT J COMMUN-US Int. J. Commun. 2019.0 13 3912 3934 23 Communication Social Science Citation Index (SSCI) Communication JB7SS 2023-03-23 WOS:000488766000004 0
J Tang, K; Li, Z; Tian, LL; Wang, LH; Zhu, YM Tang, Kun; Li, Zhi; Tian, Lili; Wang, Lihui; Zhu, Yuemin ADMIR-Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images IEEE ACCESS English Article Deformable image registration; affine registration; convolutional neural network; unsupervised learning; end-to-end registration LEARNING FRAMEWORK; MAXIMIZATION We proposed an unsupervised end-to-end Affine and Deformable Medical Image Registration (ADMIR) method based on convolutional neural network (ConvNet). ADMIR includes three key components: an affine registration module for learning the affine transformation parameters, a deformable registration module for learning the displacement vector field, and a spatial transformer for getting the final warped image from both affine and deformable transformation parameters. To evaluate its performance, the magnetic resonance images of drug-addicted brains were used to train and test the model, and we compared it with two state-of-art methods in terms of Dice score, Hausdorff distance, and average symmetric surface distance. The experimental results demonstrated that our proposed ADMIR model outperforms existing methods even with the images without pre-alignment, which suggests that the ADMIR model can be used to achieve quick medical image registration with high accuracy. [Tang, Kun; Li, Zhi; Tian, Lili; Wang, Lihui] Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang 550025, Peoples R China; [Zhu, Yuemin] Univ Lyon, INSA Lyon, Inserm U1206, CNRS UMR5220, F-69621 Lyon, France Guizhou University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Li, Z (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang 550025, Peoples R China. zhili@gzu.edu.cn Tang, Kun/GSD-3218-2022; Zhu, Yuemin/K-7292-2014 , Li/0000-0001-9813-4979; Zhu, Yuemin/0000-0001-6814-1449 National Nature Science Foundations of China [61661010]; Program PHC-Cai Yuanpei 2018 [41400TC]; Guizhou Science and Technology Plan Project [Qiankehe [2018]5301] National Nature Science Foundations of China(National Natural Science Foundation of China (NSFC)); Program PHC-Cai Yuanpei 2018; Guizhou Science and Technology Plan Project This work was supported in part by the National Nature Science Foundations of China under Grant 61661010, the Program PHC-Cai Yuanpei 2018 (Nffi 41400TC), and the Guizhou Science and Technology Plan Project under Grant Qiankehe [2018]5301. 34 6 7 1 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 70960 70968 10.1109/ACCESS.2020.2986829 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LK4CT gold 2023-03-23 WOS:000530809000021 0
J Yang, BX; Chen, P; Li, XY; Yang, F; Huang, ZS; Fu, GH; Luo, D; Wang, XQ; Li, WT; Wen, L; Zhu, JY; Liu, Q Yang, Bing Xiang; Chen, Pan; Li, Xin Yi; Yang, Fang; Huang, Zhisheng; Fu, Guanghui; Luo, Dan; Wang, Xiao Qin; Li, Wentian; Wen, Li; Zhu, Junyong; Liu, Qian Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo Tree Hole FRONTIERS IN PSYCHIATRY English Article suicide; tree hole of Weibo; artificial intelligence; social media; content analysis BackgroundPeople with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and Zoufan is one of the largest depression Tree Holes. To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the Zoufan tree hole via artificial intelligence robots. ObjectiveTo explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the Zoufan tree hole. MethodsKnowledge graph technology was used to screen high suicide risk comments in the Zoufan tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis. ResultsAmong 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason. ConclusionUsers sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment. [Yang, Bing Xiang; Chen, Pan; Li, Xin Yi; Yang, Fang; Luo, Dan; Wang, Xiao Qin; Liu, Qian] Wuhan Univ, Sch Nursing, Wuhan, Peoples R China; [Yang, Bing Xiang] Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Peoples R China; [Yang, Bing Xiang; Luo, Dan; Liu, Qian] Wuhan Univ, Populat & Hlth Res Ctr, Wuhan, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Fac Sci, Div Math & Comp Sci, Amsterdam, Netherlands; [Fu, Guanghui] Beijing Univ Technol, Dept Informat Sci, Beijing, Peoples R China; [Li, Wentian] Huazhong Univ Sci & Technol, Tongji Med Coll, Affiliated Wuhan Mental Hlth Ctr, Wuhan, Peoples R China; [Wen, Li] Wuhan Univ, Renmin Hosp, Dept Nursing, Wuhan, Peoples R China; [Zhu, Junyong] Wuhan Univ, Sch Publ Hlth, Wuhan, Peoples R China Wuhan University; Wuhan University; Wuhan University; Vrije Universiteit Amsterdam; Beijing University of Technology; Huazhong University of Science & Technology; Wuhan University; Wuhan University Liu, Q (corresponding author), Wuhan Univ, Sch Nursing, Wuhan, Peoples R China.;Liu, Q (corresponding author), Wuhan Univ, Populat & Hlth Res Ctr, Wuhan, Peoples R China.;Wen, L (corresponding author), Wuhan Univ, Renmin Hosp, Dept Nursing, Wuhan, Peoples R China.;Zhu, JY (corresponding author), Wuhan Univ, Sch Publ Hlth, Wuhan, Peoples R China. 360668166@qq.com; zhujunyong1974@whu.edu.cn; hopeliuqian@whu.edu.cn Li, xinyi/HJG-4670-2022; li, xinyi/GWZ-8941-2022; li, xin/HHS-9461-2022 Project of Humanities and Social Sciences of the Ministry of Education in China [20YJCZH204] Project of Humanities and Social Sciences of the Ministry of Education in China Funding This study was supported by the grant from the Project of Humanities and Social Sciences of the Ministry of Education in China (The Proactive Levelled Intervention for Social Network Users' Emotional Crisis-an Automatic Crisis Balance Analysis Model, 20YJCZH204). 24 1 1 14 34 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-0640 FRONT PSYCHIATRY Front. Psychiatry FEB 18 2022.0 13 789504 10.3389/fpsyt.2022.789504 0.0 8 Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Psychiatry ZU3QN 35264986.0 gold 2023-03-23 WOS:000769757400001 0
J Ye, J; Cong, XN; Zhang, PY; Zeng, GM; Hoffmann, E; Wu, Y; Zhang, HB; Fang, W Ye, Jie; Cong, Xiangna; Zhang, Panyue; Zeng, Guangming; Hoffmann, Erhard; Wu, Yan; Zhang, Haibo; Fang, Wei Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud JOURNAL OF MOLECULAR LIQUIDS English Article Phosphate adsorption; Red mud; Artificial neural network; Training algorithm; Importance analysis AQUEOUS-SOLUTION; WASTE-WATER; REMOVAL; OPTIMIZATION; TEMPERATURE; PERFORMANCE; ADSORBENT; KINETICS; CARBON; BATCH In this research the combination of neutralization activation and acid activation processes was employed to improve the physicochemical characters of red mud. In order to better understand the phosphate adsorption behaviors and further improve the phosphate adsorption performance of acid-activated neutralized red mud (AaN-RM), for the first time the impact of operational parameters on phosphate adsorption onto AaN-RM was systematically investigated, and back propagation artificial neural network (ANN) modeling was conducted. The results demonstrated that phosphate adsorption capacity of AaN-RM decreased with the enhancement of adsorbent dosage and the concentration of the competing ion (carbonate), while it increased with the increase of initial phosphate concentration and contact time. The optimal adsorption temperature and initial solution pH for phosphate adsorption onto AaN-RM were 50 degrees C and 4.0, respectively. Moreover, a 6-10-1 feed forward ANN structure with trainlm algorithm was successfully constructed for predicting the phosphate removal by AaN-RM. The RMSE and R-2 values for two subsets (training and validation subset, and testing subset) were 3.06 and 2.61, and 0.9932 and 0.9969, respectively. Furthermore, the importance analysis showed that contact time and initial phosphate concentration were the most influential parameters on phosphate removal by AaN-RM, the importance of which reached 24.64% and 22.16%, respectively. (C) 2016 Elsevier B.V. All rights reserved. [Ye, Jie; Zhang, Panyue; Zeng, Guangming; Wu, Yan; Zhang, Haibo; Fang, Wei] Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Hunan, Peoples R China; [Ye, Jie; Zhang, Panyue; Zeng, Guangming; Wu, Yan; Zhang, Haibo; Fang, Wei] Hunan Univ, Minist Educ, Key Lab Environm Biol & Pollut Control, Changsha 410082, Hunan, Peoples R China; [Ye, Jie; Hoffmann, Erhard] Karlsruhe Inst Technol, Dept Aquat Environm Engn, D-76131 Karlsruhe, Germany; [Cong, Xiangna] Karlsruhe Inst Technol, Inst Appl Mat IAM WK, D-76131 Karlsruhe, Germany Hunan University; Hunan University; Helmholtz Association; Karlsruhe Institute of Technology; Helmholtz Association; Karlsruhe Institute of Technology Zhang, PY; Zeng, GM (corresponding author), Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Hunan, Peoples R China. zhangpanyue@hnu.edu.cn; zgming@hnu.cn Zhang, Haibo/HLP-9266-2023 CSC (China Scholarship Council); National Natural Science Foundation of China [51578068, 51378190, 51039001]; Furong Scholar of Hunan Province CSC (China Scholarship Council)(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Furong Scholar of Hunan Province The authors are thankful to the CSC (China Scholarship Council), the National Natural Science Foundation of China (51578068, 51378190, 51039001), and Furong Scholar of Hunan Province for support. 41 38 40 1 33 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-7322 1873-3166 J MOL LIQ J. Mol. Liq. APR 2016.0 216 35 41 10.1016/j.molliq.2016.01.020 0.0 7 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics DJ2ZJ 2023-03-23 WOS:000374073900006 0
J Nauwelaers, N; Matthews, H; Fan, Y; Croquet, B; Hoskens, H; Mahdi, S; El Sergani, A; Gong, SW; Xu, TM; Bronstein, M; Marazita, M; Weinberg, S; Claes, P Nauwelaers, Nele; Matthews, Harold; Fan, Yi; Croquet, Balder; Hoskens, Hanne; Mahdi, Soha; El Sergani, Ahmed; Gong, Shunwang; Xu, Tianmin; Bronstein, Michael; Marazita, Mary; Weinberg, Seth; Claes, Peter Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning ORTHODONTICS & CRANIOFACIAL RESEARCH English Article biological shape analysis; geometric deep learning; palate CHILDREN; SURFACE; MODELS; NUMBER Objectives Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. Setting and Sample Population This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. Materials and methods Principal component analysis (PCA) and autoencoders (AE) are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity. Results We found that the SAE obtains equivalent results to PCA and AEs for all evaluation metrics. SAE scores were found to be uncorrelated and provided an optimally compact representation of the shapes. Conclusion We conclude that the SAE is a promising tool for 3D palatal shape analysis, which effectively combines the power of PCA with the flexibility of deep learning. This opens future AI driven applications of shape analysis in orthodontics and other related clinical disciplines. [Nauwelaers, Nele; Matthews, Harold; Croquet, Balder; Hoskens, Hanne; Mahdi, Soha; Claes, Peter] UZ Leuven, Med Imaging Res Ctr, Leuven, Belgium; [Nauwelaers, Nele; Croquet, Balder; Hoskens, Hanne; Mahdi, Soha; Claes, Peter] Katholieke Univ Leuven, ESAT PSI, Dept Elect Engn, Leuven, Belgium; [Matthews, Harold; Claes, Peter] Katholieke Univ Leuven, Dept Human Genet, Leuven, Belgium; [Matthews, Harold; Fan, Yi; Claes, Peter] Murdoch Childrens Res Inst, Facial Sci Res Grp, Parkville, MO, Australia; [Fan, Yi; Xu, Tianmin] Peking Univ, Sch & Hosp Stomatol, Dept Orthodont, Beijing, Peoples R China; [Fan, Yi; Xu, Tianmin] Peking Univ, Sch & Hosp Stomatol, Beijing Key Lab Digital Stomatol, Natl Engn Lab Digital & Mat Technol Stomatol, Beijing, Peoples R China; [El Sergani, Ahmed; Marazita, Mary; Weinberg, Seth] Univ Pittsburgh, Dept Oral & Craniofacial Sci, Ctr Craniofacial & Dent Genet, Pittsburgh, PA USA; [Gong, Shunwang; Bronstein, Michael] Imperial Coll London, Dept Comp, London, England; [Bronstein, Michael] USI Lugano, Inst Computat Sci, Lugano, Switzerland; [Bronstein, Michael] Twitter, San Francisco, CA USA; [Marazita, Mary] Univ Pittsburgh, Grad Sch Publ Hlth, Dept Human Genet, Pittsburgh, PA 15261 USA KU Leuven; University Hospital Leuven; KU Leuven; KU Leuven; Murdoch Children's Research Institute; Peking University; Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Imperial College London; Universita della Svizzera Italiana; Twitter, Inc.; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh Nauwelaers, N; Claes, P (corresponding author), UZ Leuven, Med Imaging Res Ctr, Leuven, Belgium. nele.nauwelaers@kuleuven.be; peter.claes@kuleuven.be ; Claes, Peter/X-3923-2018 Mahdi, Soha/0000-0002-0549-7103; Weinberg, Seth/0000-0001-9467-4556; El Sergani, Ahmed/0000-0002-7680-932X; Claes, Peter/0000-0001-9489-9819; Matthews, Harold/0000-0002-0524-0025; Croquet, Balder/0000-0002-9539-5193; Hoskens, Hanne/0000-0002-1467-6461; Nauwelaers, Nele/0000-0002-9763-6106 National Institute of Dental & Craniofacial Research [R01-DE016148, R01-DE027023]; Research Fund, KU Leuven [BOF-C1, C14/15/081, C14/20/081]; Research Program of the Research Foundation Flanders [G078518N]; FWO PhD fellowship [ISB0121N] National Institute of Dental & Craniofacial Research(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Dental & Craniofacial Research (NIDCR)); Research Fund, KU Leuven(KU Leuven); Research Program of the Research Foundation Flanders(FWO); FWO PhD fellowship(FWO) National Institute of Dental & Craniofacial Research, Grant/Award Number: R01-DE016148 and R01-DE027023; Research Fund, KU Leuven, Grant/Award Number: BOF-C1, C14/15/081 and C14/20/081; Research Program of the Research Foundation Flanders, Grant/Award Number: G078518N; FWO PhD fellowship, Grant/Award Number: ISB0121N 33 7 7 1 3 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1601-6335 1601-6343 ORTHOD CRANIOFAC RES Orthod. Craniofac. Res. DEC 2021.0 24 2 SI 134 143 10.1111/ocr.12521 0.0 AUG 2021 10 Dentistry, Oral Surgery & Medicine Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Dentistry, Oral Surgery & Medicine XU5JF 34310057.0 Green Accepted, Bronze 2023-03-23 WOS:000687815600001 0
J Sun, GL; Mareri, B; Liu, GS; Fang, XF; Jiang, W Sun, Guolin; Mareri, Bruce; Liu, Guisong; Fang, Xiufen; Jiang, Wei Big Data Based Dynamic Flow Aggregation over 5G Network Slicing KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS English Article Dynamic aggregation; big data; IoT; flow aggregation Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloudbased big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach. [Sun, Guolin; Mareri, Bruce; Liu, Guisong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China; [Fang, Xiufen] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China; [Jiang, Wei] German Res Ctr Artificial Intelligence DFKI GmbH, Kaiserslautern, Germany; [Jiang, Wei] TU, Dept Elect & Informat Technol EIT, Kaiserslautern, Germany University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; German Research Center for Artificial Intelligence (DFKI) Sun, GL (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China. guolin.sun@uestc.edu.cn Liu, Guisong/S-1263-2019; Jiang, Wei/ADI-7167-2022 Liu, Guisong/0000-0003-2360-0466; Mareri, Bruce/0000-0002-2077-0040 Fundamental Research Funds for the Central Universities [ZYGX2014J060]; Science and Technology Planning project of Sichuan Province, China [2016GZ0075]; ZTE Innovation Research Fund for Universities Program Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Science and Technology Planning project of Sichuan Province, China; ZTE Innovation Research Fund for Universities Program This work is supported by the Fundamental Research Funds for the Central Universities, under grant no. ZYGX2014J060, the Science and Technology Planning project of Sichuan Province, China, under grant no. 2016GZ0075 and the ZTE Innovation Research Fund for Universities Program 2016. 25 2 2 0 22 KSII-KOR SOC INTERNET INFORMATION GANGNAM-GU KOR SCI & TECHNOL CTR, 409 ON 4TH FLR, MAIN BLDG, 635-4 YEOKSAM 1-DONG, GANGNAM-GU, SEOUL 00000, SOUTH KOREA 1976-7277 KSII T INTERNET INF KSII Trans. Internet Inf. Syst. OCT 31 2017.0 11 10 4717 4737 10.3837/tiis.2017.10.003 0.0 21 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications FL1LM Bronze 2023-03-23 WOS:000413976300003 0
J Zhang, JG; Su, RM; Fu, Q; Ren, WQ; Heide, F; Nie, YF Zhang, Jingang; Su, Runmu; Fu, Qiang; Ren, Wenqi; Heide, Felix; Nie, Yunfeng A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging SCIENTIFIC REPORTS English Article LINEAR-MODELS; REFLECTANCE; SURFACE; SPECTROMETER; FREQUENCY; RECOVERY; RANGE; SPACE; FIELD Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging. [Zhang, Jingang; Su, Runmu] Univ Chinese Acad Sci, Sch Future Technol, Beijing 100039, Peoples R China; [Su, Runmu] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China; [Fu, Qiang] King Abdullah Univ Sci & Technol, Thuwal 239556900, Saudi Arabia; [Ren, Wenqi] Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China; [Heide, Felix] Princeton Univ, Computat Imaging Lab, Princeton, NJ 08544 USA; [Nie, Yunfeng] Vrije Univ Brussel, Dept Appl Phys & Photon, B-1050 Brussels, Belgium Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Xidian University; King Abdullah University of Science & Technology; Chinese Academy of Sciences; Institute of Information Engineering, CAS; Princeton University; Vrije Universiteit Brussel Nie, YF (corresponding author), Vrije Univ Brussel, Dept Appl Phys & Photon, B-1050 Brussels, Belgium. Yunfeng.Nie@vub.be Fu, Qiang/0000-0001-6395-8521 Chinese Academy of Sciences [CAS-WX2021-PY-0110, YJKYYQ20180039, Y70X25A1HY]; National Natural Science Foundation of China [61775219, 61771369, 61640422]; Flemish Fund for Scientific Research (FWO) [FWOTM1039] Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Flemish Fund for Scientific Research (FWO)(FWO) This work was supported by the Chinese Academy of Sciences (No. CAS-WX2021-PY-0110, NO. YJKYYQ20180039 and NO. Y70X25A1HY), and the National Natural Science Foundation of China (NO. 61775219, NO. 61771369 and NO. 61640422). Yunfeng Nie acknowledges the Flemish Fund for Scientific Research (FWO) for supporting her research (No. FWOTM1039). The authors would like to thank those who provide open-sources code for the whole community, such as Sparse Coding, SRUNet, SRMSCNN, HSCNN+ and etc. 106 5 5 23 28 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep JUL 13 2022.0 12 1 11905 10.1038/s41598-022-16223-1 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 2X0CZ 35831474.0 Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000824883400075 0
J Sun, SY; Zhou, JH; Wen, JX; Wei, YF; Wang, XJ Sun, Siyuan; Zhou, Junhua; Wen, Jiuxing; Wei, Yifei; Wang, Xiaojun A DQN-Based Cache Strategy for Mobile Edge Networks CMC-COMPUTERS MATERIALS & CONTINUA English Article Mobile edge network; edge caching; energy efficiency RESOURCE-ALLOCATION; RESEARCH ISSUES; PREDICTION The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse, thus the network transmission delay and system throughput can be improved significantly. Since the duplicate content transmissions between edge network and remote cloud can be reduced, the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent. This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm. The deep neural network (DNN) and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network (DQN), in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution. The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method, so the DQN algorithm could converge to the optimal solution quickly, and the network performance of the content caching policy can be optimized. The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly. [Sun, Siyuan; Wei, Yifei] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China; [Zhou, Junhua] Beijing Inst Elect Syst Engn, State Key Lab Intelligent Mfg Syst Technol, Beijing 100854, Peoples R China; [Wen, Jiuxing] Ningbo Sunny Intelligent Technol Co LTD, Yuyao 315400, Peoples R China; [Wang, Xiaojun] Dublin City Univ, Dublin 9, Ireland Beijing University of Posts & Telecommunications; Dublin City University Sun, SY (corresponding author), Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China. sunsy@bupt.edu.cn wei, yifei/AAB-4772-2022 National Natural Science Foundation of China [61871058] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (61871058, WYF, http://www.nsfc.gov.cn/). 36 8 8 11 24 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2022.0 71 2 3277 3291 20471 10.32604/cmc.2022.020471 0.0 15 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science XN7CZ gold 2023-03-23 WOS:000729659700001 0
J Zeng, QF; Gao, Y; Guan, K; Liu, JT; Feng, ZQ Zeng, Qingfeng; Gao, Yong; Guan, Kang; Liu, Jiantao; Feng, Zhiqiang Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide JOURNAL OF ADVANCED CERAMICS English Article machine learning (ML); computational fluid dynamic (CFD); chemical vapor deposition; boron carbide; B; C ratio; kinetic mechanisms CERAMIC-MATRIX COMPOSITES; GAS-PHASE; METHYLTRICHLOROSILANE; PYROLYSIS; CVD; FABRICATION; SIMULATION; REACTOR; BCL3 Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems. [Zeng, Qingfeng; Gao, Yong] Northwestern Polytech Univ, Sch Mat Sci & Engn, Sci & Technol Thermostruct Composite Mat Lab, Xian 710072, Peoples R China; [Zeng, Qingfeng] MSEA Int Inst Mat Genome, Guan 065500, Peoples R China; [Guan, Kang] South China Univ Technol, Sch Mat Sci & Engn, Guangzhou 510640, Peoples R China; [Liu, Jiantao] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China; [Feng, Zhiqiang] Southwest Jiaotong Univ, Sch Mech & Engn, Chengdu 610031, Peoples R China; [Feng, Zhiqiang] Univ Paris Saclay, LMEE UEVE, F-91020 Evry, France Northwestern Polytechnical University; South China University of Technology; Southwest Jiaotong University; Southwest Jiaotong University; UDICE-French Research Universities; Universite Paris Saclay Zeng, QF (corresponding author), Northwestern Polytech Univ, Sch Mat Sci & Engn, Sci & Technol Thermostruct Composite Mat Lab, Xian 710072, Peoples R China.;Zeng, QF (corresponding author), MSEA Int Inst Mat Genome, Guan 065500, Peoples R China.;Guan, K (corresponding author), South China Univ Technol, Sch Mat Sci & Engn, Guangzhou 510640, Peoples R China. qfzeng@nwpu.edu.cn; mskguan@scut.edu.cn 40 4 4 11 28 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2226-4108 2227-8508 J ADV CERAM J. Adv. Ceram. JUN 2021.0 10 3 537 550 10.1007/s40145-021-0456-3 0.0 14 Materials Science, Ceramics Science Citation Index Expanded (SCI-EXPANDED) Materials Science RT7RH Green Submitted, gold 2023-03-23 WOS:000644654000010 0
J Gamatie, A; An, X; Zhang, Y; Kang, A; Sassatelli, G Gamatie, Abdoulaye; An, Xin; Zhang, Ying; Kang, An; Sassatelli, Gilles Empirical model-based performance prediction for application mapping on multicore architectures JOURNAL OF SYSTEMS ARCHITECTURE English Article Resource allocation; Application mapping; Model-based performance prediction; Machine learning MACHINE; OPTIMIZATION; SYSTEMS Application mapping in multicore embedded systems plays a central role in their energy-efficiency. The present paper deals with this issue by focusing on the prediction of performance and energy consumption, induced by task and data allocation on computing resources. It proposes a solution by answering three fundamental questions as follows: (i) how to encode mappings for training performance prediction models? (ii) how to define an adequate criterion for assessing the quality of mapping performance predictors? and (iii) which technique among regression and classification enables the best predictions? Here, the prediction models are obtained by applying carefully selected supervised machine learning techniques on raw data, generated off-line from system executions. These techniques are Support Vector Machines, Adaptive Boosting (AdaBoost) and Artificial Neural Networks (ANNs). Our study is validated on an automotive application case study. The experimental results show that with a limited set of training information, AdaBoost and ANNs can provide very good outcomes (up to 84.8% and 89.05% correct prediction score in some cases, respectively), making them attractive enough for the addressed problem. [Gamatie, Abdoulaye; Sassatelli, Gilles] Univ Montpellier, CNRS, LIRMM, 161 Rue Ada, F-34095 Montpellier, France; [An, Xin; Zhang, Ying; Kang, An] Hefei Univ Technol, Anhui Prov Key Lab Affect Comp & Adv Intelligent, 193 Tunxi Rd, Hefei 230009, Anhui, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite Paul-Valery; Universite Perpignan Via Domitia; Universite de Montpellier; Hefei University of Technology Gamatie, A (corresponding author), Univ Montpellier, CNRS, LIRMM, 161 Rue Ada, F-34095 Montpellier, France. abdoulaye.gamatie@lirmm.fr; xin.an@hfut.edu.cn; yingzhang9403@163.com; kangan03@mail.hfut.edu.cn; gilles.sassatelli@lirmm.fr French ANR agency [ANR-15-CE25-0007-01]; National Natural Science Foundation of China [61502140, 61432004]; European Community [611411] French ANR agency(French National Research Agency (ANR)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Community(European Commission) This work has been supported by the French ANR agency under the grant ANR-15-CE25-0007-01, within the framework of the CONTINUUM project, and by the National Natural Science Foundation of China under grant 61502140 and 61432004. It has been initiated within the European Community's Seventh Framework Programme (FP7/2007-2013) under the DreamCloud project, grant agreement no. 611411. The authors would like to thank Roman Ursu and Manuel Selva who participated in earlier discussions about the current work. 44 6 6 1 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1383-7621 1873-6165 J SYST ARCHITECT J. Syst. Architect. SEP 2019.0 98 1 16 10.1016/j.sysarc.2019.06.001 0.0 16 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science IZ6BH Green Submitted, Bronze 2023-03-23 WOS:000487166300001 0
C Du, YQ; Yang, S; Huang, KB IEEE Du, Yuqing; Yang, Sheng; Huang, Kaibin High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning 2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP) IEEE Global Conference on Signal and Information Processing English Proceedings Paper 7th IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP) NOV 11-14, 2019 Ottawa, CANADA IEEE,IEEE Signal Proc Soc,MathWorks,Carleton Univ, Fac Engn & Design,Univ Ottawa FEEDBACK Edge machine learning involves the deployment of machine learning algorithms at the network edge so as to leverage massive mobile data and distributed computation resources. Many edge learning frameworks (e.g., federated learning) have been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for aggregation for updating a global AI model. Since each gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead may become a bottleneck for edge learning In this work, we propose a novel gradient compression scheme to reduce the aforementioned overhead. Specifically, in the proposed scheme, the norm of the stochastic gradient is quantized using a uniform quantizer while the normalized stochastic gradient is decomposed into block gradients. A Grassmannian codebook is applied to quantizing each normalized block gradients. Their quantized versions are assembled using a so-called hinge vector, which is quantized using another Grassmannian codebook. Furthermore, a practical bit-allocation strategy is developed. By simulations, we show that similar learning performance can be achieved with substantially lower communication overhead as compared to the one-bit scalar quantization schemes used in the state-of-the-art design, namely signed SGD. [Du, Yuqing; Huang, Kaibin] Univ Hong Kong, Hong Kong, Peoples R China; [Yang, Sheng] Univ Paris Saclay, Lab Signals & Syst, Cent Supelec, F-91190 Gif Sur Yvette, France University of Hong Kong; UDICE-French Research Universities; Universite Paris Saclay Du, YQ (corresponding author), Univ Hong Kong, Hong Kong, Peoples R China. yqdu@eee.hku.hk; sheng.yang@centralesupelec.fr; huangkb@eee.hku.hk 16 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-4066 978-1-7281-2723-1 IEEE GLOB CONF SIG 2019.0 5 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP5GC 2023-03-23 WOS:000555454800004 0
J Nassif, R; Vlaski, S; Richard, C; Chen, J; Sayed, AH Nassif, Roula; Vlaski, Stefan; Richard, Cedric; Chen, Jie; Sayed, Ali H. Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning IEEE SIGNAL PROCESSING MAGAZINE English Article WIRELESS SENSOR NETWORKS; REGULARIZATION; LMS The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an approach to inductive transfer learning (using what is learned for one problem to assist with another problem), and it helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias. Several strategies have been derived within this community under the assumption that all data are available beforehand at a fusion center. [Nassif, Roula] Amer Univ Beirut AUB, Beirut, Lebanon; [Nassif, Roula; Vlaski, Stefan] Ecole Polytech Fed Lausanne, Adapt Syst Lab, Lausanne, Switzerland; [Richard, Cedric] Univ Cote Azur, Nice, France; [Richard, Cedric] Inst Univ France, Paris, France; [Richard, Cedric] IEEE Signal Proc Soc SPS, Reg 8 Europe Middle East Africa, Piscataway, NJ USA; [Richard, Cedric] French Fed CNRS Res Assoc Informat, Signal, Image, Vis, Paris, France; [Chen, Jie] Univ Nice Sophia Antipolis, Lagrange Lab, Nice, France; [Chen, Jie] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA; [Chen, Jie] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China; [Sayed, Ali H.] Ecole Polytech Fed Lausanne, Engn, Lausanne, Switzerland; [Sayed, Ali H.] Univ Calif Los Angeles, Elect Engn, Los Angeles, CA 90024 USA American University of Beirut; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; UDICE-French Research Universities; Universite Cote d'Azur; Institut Universitaire de France; UDICE-French Research Universities; Universite Cote d'Azur; Observatoire de la Cote d'Azur; University of Michigan System; University of Michigan; Northwestern Polytechnical University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of California System; University of California Los Angeles Nassif, R (corresponding author), Amer Univ Beirut AUB, Beirut, Lebanon. roula.nassif@aub.edu.lb; stefan.vlaski@epfl.ch; cedric.richard@unice.fr; dr.jie.chen@ieee.org; ali.sayed@epfl.ch Vlaski, Stefan/AAA-2067-2019; Sayed, Ali/D-6251-2012 Vlaski, Stefan/0000-0002-0616-3076; Nassif, Roula/0000-0001-9663-8559; Sayed, Ali/0000-0002-5125-5519 40 38 38 3 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-5888 1558-0792 IEEE SIGNAL PROC MAG IEEE Signal Process. Mag. MAY 2020.0 37 3 14 25 10.1109/MSP.2020.2966273 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering LM4KK Green Submitted 2023-03-23 WOS:000532218500005 0
J Wen, XH; Wang, YH; Ji, XD; Traore, MK Wen, Xiaohuan; Wang, Yanhong; Ji, Xiaodong; Traore, Mamadou Kaba Three-stage churn management framework based on DCN with asymmetric loss EXPERT SYSTEMS WITH APPLICATIONS English Article Churnprediction; Customersegmentation; Fastfeaturededuction; DeepandCrossNetwork; Asymmetricloss CUSTOMER RELATIONSHIP MANAGEMENT; MACHINE LEARNING TECHNIQUES; CLASS IMBALANCE; PREDICTION; INDUSTRY; MODEL Customer retention is always a hot topic in customer relationship management, and machine learning is widely used in this area. However, existing researches focus on increasing the accuracy of predicting customer churn rather than provide a method of making practical strategies to retain customer in business. Such being the case, this paper proposes a three-stage churn management framework, integrating churn prediction, customer segmentation and strategy making effectively. Two open datasets are collected and applied our framework respectively. In customer prediction phase, a Deep and Cross model taking cross features into consideration is built to learn more implicit information. During assessment, 10-fold cross validation is used, and our model outperforms existing mainstream models. In customer segmentation phase, customers are segmented into different groups using the output of our model and the importance scores of cross features are calculated as well. In strategy making phase, possible strategies for business are made based on the results of the previous phases. Then we propose a fast feature deduction approach to verify the efficiency of strategies, which offers instructive information for decision-makers. [Wen, Xiaohuan; Wang, Yanhong; Ji, Xiaodong] Shanghai Univ Engn Sci, 333 Longteng Rd, Shanghai 201620, Peoples R China; [Traore, Mamadou Kaba] Univ Bordeaux, IMS, CNRS, UMR 5218,Bordeaux INP,ENSCBP, F-33405 Talence, France Shanghai University of Engineering Science; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Universite de Bordeaux Wang, YH (corresponding author), Shanghai Univ Engn Sci, 333 Longteng Rd, Shanghai 201620, Peoples R China. wenxhwork@gmail.com; yanhong.wang@hotmail.com; cruiser3a@163.com; mamadou-kaba.traore@u-bordeaux.fr WEN, Xiaohuan/0000-0001-7604-9896; Ji, Xiaodong/0000-0001-9767-9758; WANG, Yanhong/0000-0003-3957-7168 Humanity and Social Science foundation of Ministry of Education of China [20YJC630150]; Soft Science Foundation of Shanghai Science and Tech-nology Committee [21692105300]; Shanghai Sailing Pro-gram [20YF1415900] Humanity and Social Science foundation of Ministry of Education of China; Soft Science Foundation of Shanghai Science and Tech-nology Committee; Shanghai Sailing Pro-gram Acknowledgments This research was supported by the Humanity and Social Science foundation of Ministry of Education of China (Grant No. 20YJC630150) ; Soft Science Foundation of Shanghai Science and Tech-nology Committee (Grant No. 21692105300) ; Shanghai Sailing Pro-gram (Grant No. 20YF1415900) 39 0 0 17 23 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 30 2022.0 207 117998 10.1016/j.eswa.2022.117998 0.0 JUL 2022 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 3B5YV 2023-03-23 WOS:000828016900006 0
J Chen, LP; Yin, H; Yuan, LG; Lopes, AM; Machado, JAT; Wu, RC Chen, Li-ping; Yin, Hao; Yuan, Li-guo; Lopes, Antonio M.; Machado, J. A. Tenreiro; Wu, Ran-chao A novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA sequence operations FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING English Article Fractional-order discrete systems; Neural networks; Deoxyribonucleic acid (DNA) encryption; Color image encryption; TP37 SELF-ADAPTIVE PERMUTATION; SYSTEM; SECURE; DIFFUSION; DYNAMICS A novel color image encryption algorithm based on dynamic deoxyribonucleic acid (DNA) encoding and chaos is presented. A three-neuron fractional-order discrete Hopfield neural network (FODHNN) is employed as a pseudo-random chaotic sequence generator. Its initial value is obtained with the secret key generated by a five-parameter external key and a hash code of the plain image. The external key includes both the FODHNN discrete step size and order. The hash is computed with the SHA-2 function. This ensures a large secret key space and improves the algorithm sensitivity to the plain image. Furthermore, a new three-dimensional projection confusion method is proposed to scramble the pixels among red, green, and blue color components. DNA encoding and diffusion are used to diffuse the image information. Pseudo-random sequences generated by FODHNN are employed to determine the encoding rules for each pixel and to ensure the diversity of the encoding methods. Finally, confusion II and XOR are used to ensure the security of the encryption. Experimental results and the security analysis show that the proposed algorithm has better performance than those reported in the literature and can resist typical attacks. [Chen, Li-ping; Yin, Hao] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China; [Yuan, Li-guo] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China; [Lopes, Antonio M.] Univ Porto, UISPA LAETA INEGI, Fac Engn, Rua Dr Roberto Frias, P-4200465 Porto, Portugal; [Machado, J. A. Tenreiro] Polytech Inst Porto, Dept Elect Engn, R Dr Antonio Bernardino de Almeida 431, P-4249015 Porto, Portugal; [Wu, Ran-chao] Anhui Univ, Sch Math Sci, Hefei 230601, Peoples R China Hefei University of Technology; South China Agricultural University; Universidade do Porto; Polytechnic Institute of Porto; Anhui University Chen, LP (corresponding author), Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China. lip_chenhut@126.com Lopes, António M./A-2740-2016; Machado, J. A. Tenreiro/M-2173-2013 Lopes, António M./0000-0001-7359-4370; Machado, J. A. Tenreiro/0000-0003-4274-4879; Chen, Liping/0000-0002-8110-5378 65 33 34 5 45 ZHEJIANG UNIV HANGZHOU EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA 2095-9184 2095-9230 FRONT INFORM TECH EL Front. Inform. Technol. Elect. Eng. JUN 2020.0 21 6 SI 866 879 10.1631/FITEE.1900709 0.0 14 Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering MF1FP 2023-03-23 WOS:000545096300005 0
J Yao, Y; Chang, C; Ndayisaba, F; Wang, S Yao, Yuan; Chang, Cun; Ndayisaba, Felix; Wang, Shuang A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model IEEE ACCESS English Article Remote sensing; Earth; Artificial satellites; Spatial resolution; Land surface temperature; Urban areas; Spatiotemporal phenomena; Machine learning algorithm; land surface temperature; spatiotemporal fusion; surface urban heat island (SUHI) SUPPORT VECTOR MACHINES; REFLECTANCE FUSION; TEMPORAL RESOLUTION; TIME-SERIES; TEMPERATURE; LANDSAT; CLASSIFICATION; IMPACT; CITIES Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 degrees C in 2009 to 4.04 degrees C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively. [Yao, Yuan] Chengdu Univ, Sch Architecture & Civil Engn, Chengdu 610106, Peoples R China; [Yao, Yuan] Chengdu Univ, Inst Higher Educ Sichuan Prov, Key Lab Pattern Recognit & Intelligent Informat P, Chengdu 610106, Peoples R China; [Chang, Cun] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Ndayisaba, Felix] Univ Ghent, Fac Biosci Engn, B-9000 Ghent, Belgium; [Ndayisaba, Felix] Protestant Inst Arts & Social Sci, Dept Geog, Huye, Rwanda; [Wang, Shuang] Xinjiang Agr Univ, Sch Management, Urumqi 830000, Peoples R China Chengdu University; Chengdu University; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Ghent University; Xinjiang Agricultural University Chang, C (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China. changcun@ms.xjb.ac.cn chang, cun/0000-0002-8962-6127 National Key Research and Development Program of China [2016YFC0500201-02] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2016YFC0500201-02. 46 3 3 5 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 164268 164281 10.1109/ACCESS.2020.3022047 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications NT6AF gold 2023-03-23 WOS:000573020600001 0
J Wang, DS; Lou, LQ; Zhang, M; Boucouvalas, AC; Zhang, CY; Huang, XT Wang, Danshi; Lou, Liqi; Zhang, Min; Boucouvalas, Anthony C.; Zhang, Chunyu; Huang, Xuetian Dealing With Alarms in Optical Networks Using an Intelligent System IEEE ACCESS English Article Alarm pre-processing; K-means; back propagation neural network; alarm compression; alarm correlation analyzing; optical network Millions of alarms in the optical layer may appear in optical transport networks every month, which brings great challenges to network operation, administration and maintenance. In this paper, we deal with this problem and propose a method of alarm pre-processing and correlation analysis for this network. During the alarm pre-processing, we use the method of combined time series segmentation and time sliding window to extract the alarm transactions, and then we use the algorithm of combined K-means and back propagation neural network to evaluate the alarm importance quantitatively. During the alarm correlation analysis, we modify a classic rule mining algorithm, i.e., Apriori algorithm, into a Weighted Apriori to find the high-frequency chain alarm sets among the alarm transactions. Through the actual alarm data from the record in the optical layer of a provincial backbone of China Telecom, we conducted experiments and the results show that our method is able to perform effectively the alarm compressing, alarm correlating, and chain alarm mining. By parameter adjustment, the alarm compression rate is able to vary from 60% to 90% and the average fidelity of chain alarm mining keeps around 84%. The results show our approach and method is promising for trivial alarm identifying, chain alarm mining, and root fault locating in existing optical networks. [Wang, Danshi; Lou, Liqi; Zhang, Min; Zhang, Chunyu] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China; [Boucouvalas, Anthony C.] Univ Peloponnese, Dept Informat & Telecommun, Tripoli 22100, Greece; [Huang, Xuetian] China Telecom Corp, Beijing 100015, Peoples R China Beijing University of Posts & Telecommunications; University of Peloponnese; China Telecom Corp. Ltd. Zhang, M (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China. mzhang@bupt.edu.cn Zhang, Chun-yu/HDP-5776-2022 NSFC Project [61705016]; National Key Research and Development Program of China [2016YFB0901200]; Fundamental Research Funds for the Central Universities [2019RC12] NSFC Project(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the NSFC Project under Grant 61705016, in part by the National Key Research and Development Program of China under Grant 2016YFB0901200, and in part by the support from the Fundamental Research Funds for the Central Universities under Grant 2019RC12. 22 7 7 1 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 97760 97770 10.1109/ACCESS.2019.2929872 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IN8YT gold 2023-03-23 WOS:000478966400016 0
C Jing, HF; Zhang, YF; Zhou, JH; Zhang, WS; Liu, X; Min, GZ; Zhang, ZM IEEE Jing, Haifeng; Zhang, Yafei; Zhou, Jiehan; Zhang, Weishan; Liu, Xin; Min, Guizhi; Zhang, Zhanmin LSTM-based Service Migration for Pervasive Cloud Computing IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY English Proceedings Paper IEEE International Congress on Cybermatics / IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology JUL 30-AUG 03, 2018 Halifax, CANADA IEEE,IEEE Comp Soc Pervasive Cloud Computing; Service Migration; Machine Learning; LSTM Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for pervasive cloud computing, i.e., LSTM4PCC, which supports an accurate prediction of cloud resources. LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions. We evaluate LSTM4PCC and compare it with the ARIMA (AutoRegressive Integrated Moving Average) approach in terms of prediction accuracy. The results show that LSTM4PCC performs better than ARIMA. [Jing, Haifeng; Liu, Xin] China Univ Petr, Sch Comp & Commun Engn, Qingdao, Shandong, Peoples R China; [Zhang, Yafei; Zhang, Weishan] China Univ Petr, Dept Software Engn, Qingdao, Shandong, Peoples R China; [Zhou, Jiehan] Univ Oulu, Oulu, Finland; [Min, Guizhi; Zhang, Zhanmin] Huabei Oilfield Co, PetroChina, Engn Technol Res Inst, Renqiu, Peoples R China China University of Petroleum; China University of Petroleum; University of Oulu; China National Petroleum Corporation Jing, HF (corresponding author), China Univ Petr, Sch Comp & Commun Engn, Qingdao, Shandong, Peoples R China. jinghaifeng@upc.edu.cn; z17070640@s.upc.edu.cn; jiehan.zhou@oulu.fi; zhangws@upc.edu.cn; lx@upc.edu.cn; cyy_mgz@petrochina.com.cn; cyy_zhangzm@petrochina.com.cn National Natural Science Foundation of China [61309024]; Program on Innovation Method Fund of China [2015010300]; Key Research Program of Shandong Province [2017GGX10140]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program on Innovation Method Fund of China; Key Research Program of Shandong Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research is supported by National Natural Science Foundation of China (No. 61309024), the Program on Innovation Method Fund of China (Grant No. 2015010300), the Key Research Program of Shandong Province (No. 2017GGX10140) and also supported by Fundamental Research Funds for the Central Universities. 27 5 5 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-7975-3 2018.0 1835 1840 10.1109/Cybermatics_2018.2018.00305 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BN4AE 2023-03-23 WOS:000481634500269 0
J Yang, CX; Chen, Y; Gong, QY; He, XL; Xiao, Y; Huang, YH; Fu, XM Yang, Chenxi; Chen, Yang; Gong, Qingyuan; He, Xinlei; Xiao, Yu; Huang, Yuhuan; Fu, Xiaoming Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study BIG DATA MINING AND ANALYTICS English Article behavioral difference; online social networks; Yelp; machine learning Given that the USA and Germany are the most populous countries in North America and Western Europe, understanding the behavioral differences between American and German users of online social networks is essential. In this work, we conduct a data-driven study based on the Yelp Open Dataset. We demonstrate the behavioral characteristics of both American and German users from different aspects, i.e., social connectivity, review styles, and spatiotemporal patterns. In addition, we construct a classification model to accurately recognize American and German users according to the behavioral data. Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949. [Yang, Chenxi; Chen, Yang; Gong, Qingyuan; He, Xinlei] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Yang, Chenxi; Chen, Yang; Gong, Qingyuan; He, Xinlei] Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai 200433, Peoples R China; [Xiao, Yu] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Huang, Yuhuan] Guangdong Univ Foreign Studies, Fac European Languages & Cultures, Guangzhou 510420, Peoples R China; [Fu, Xiaoming] Univ Gottingen, Inst Comp Sci, D-37077 Gottingen, Germany Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China.;Chen, Y (corresponding author), Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai 200433, Peoples R China. chenyang@fudan.edu.cn; yu.xiao@aalto.fi; huangyuhuan@gdufs.edu.cn; fu@cs.uni-goettingen.de ; Fu, Xiaoming/B-7208-2016 Xiao, Yu/0000-0002-4517-3779; Fu, Xiaoming/0000-0002-8012-4753 National Natural Science Foundation of China [61602122, 71731004]; Natural Science Foundation of Shanghai [16ZR1402200]; Shanghai Pujiang Program [16PJ1400700]; EU FP7 IRSES MobileCloud project [612212]; Lindemann Foundation [12-2016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Pujiang Program(Shanghai Pujiang Program); EU FP7 IRSES MobileCloud project; Lindemann Foundation This work was supported by the National Natural Science Foundation of China (Nos. 61602122 and 71731004), the Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), EU FP7 IRSES MobileCloud project (No. 612212), and Lindemann Foundation (No. 12-2016). 44 1 1 0 0 TSINGHUA UNIV PRESS BEIJING B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA 2096-0654 BIG DATA MIN ANAL Big Data Min. Anal. DEC 2018.0 1 4 284 296 10.26599/BDMA.2018.9020024 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Information Systems Emerging Sources Citation Index (ESCI) Computer Science VL6HQ Green Submitted, Green Published, gold 2023-03-23 WOS:000895925200003 0
J Ning, M; Guan, JH; Liu, PZ; Zhang, ZP; O'Hare, GMP Ning, Ma; Guan, Jianhe; Liu, Pingzeng; Zhang, Ziqing; O'Hare, Gregory M. P. GA-BP Air Quality Evaluation Method Based on Fuzzy Theory CMC-COMPUTERS MATERIALS & CONTINUA English Article Air quality evaluation; fuzzy theory; genetic algorithm; BP neural network With the rapid development of China's economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application, the four-grade standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy. [Ning, Ma; Guan, Jianhe] China Univ Geosci, Beijing 100083, Peoples R China; [Liu, Pingzeng] Shandong Agr Univ, Tai An 271018, Shandong, Peoples R China; [Zhang, Ziqing] Shandong Vocat Inst Fash Technol, Tai An 271000, Shandong, Peoples R China; [O'Hare, Gregory M. P.] UCD, Dublin 999014, Ireland China University of Geosciences; Shandong Agricultural University Ning, M (corresponding author), China Univ Geosci, Beijing 100083, Peoples R China. yuetianhewu@126.com O'Hare, Gregory M.P./AAA-7756-2022 O'Hare, Gregory M.P./0000-0002-5124-1686 7 12 13 3 18 TECH SCIENCE PRESS NORCROSS 6825 JIMMY CARTER BLVD, STE 1850, NORCROSS, GA 30071 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2019.0 58 1 215 227 10.32604/cmc.2019.03763 0.0 13 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science HI5AU gold 2023-03-23 WOS:000456465400014 0
C Pandya, A; Odunsi, O; Liu, C; Cuzzocrea, A; Wang, JW Wu, XT; Jermaine, C; Xiong, L; Hu, XH; Kotevska, O; Lu, SY; Xu, WJ; Aluru, S; Zhai, CX; Al-Masri, E; Chen, ZY; Saltz, J Pandya, Arjun; Odunsi, Oluwatobiloba; Liu, Chen; Cuzzocrea, Alfredo; Wang, Jianwu Adaptive and Efficient Streaming Time Series Forecasting with Lambda Architecture and Spark 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) IEEE International Conference on Big Data English Proceedings Paper 8th IEEE International Conference on Big Data (Big Data) DEC 10-13, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,IBM,Ankura Time Series Forecasting; Vector Auto Regression (VAR); Concept Drift; Lambda Architecture; Spark MODEL The rise of the Internet of Things (IoT) devices and the streaming platform has tremendously increased the data in motion or streaming data. It incorporates a wide variety of data, for example, social media posts, online gamers in-game activities, mobile or web application logs, online e-commerce transactions, financial trading, or geospatial services. Accurate and efficient forecasting based on real-time data is a critical part of the operation in areas like energy & utility consumption, healthcare, industrial production, supply chain, weather forecasting, financial trading, agriculture, etc. Statistical time series forecasting methods like Autoregression (AR), Autoregressive integrated moving average (ARIMA), and Vector Autoregression (VAR), face the challenge of concept drift in the streaming data, i.e., the properties of the stream may change over time. Another challenge is the efficiency of the system to update the Machine Learning (ML) models which are based on these algorithms to tackle the concept drift. In this paper, we propose a novel framework to tackle both of these challenges. The challenge of adaptability is addressed by applying the Lambda architecture to forecast future state based on three approaches simultaneously: batch (historic) data-based prediction, streaming (real-time) data-based prediction, and hybrid prediction by combining the first two. To address the challenge of efficiency, we implement a distributed VAR algorithm on top of the Apache Spark big data platform. To evaluate our framework, we conducted experiments on streaming time series forecasting with four types of data sets of experiments: data without drift (no drift), data with gradual drift, data with abrupt drift and data with mixed drift. The experiments show the differences of our three forecasting approaches in terms of accuracy and adaptability. [Pandya, Arjun; Odunsi, Oluwatobiloba; Wang, Jianwu] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA; [Liu, Chen] North China Univ Technol, Beijing, Peoples R China; [Cuzzocrea, Alfredo] Univ Calabria, iDEA Lab, Calabria, Italy University System of Maryland; University of Maryland Baltimore County; North China University of Technology; University of Calabria Pandya, A (corresponding author), Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA. apandya1@umbc.edu; oodunsi1@umbc.edu; liuchen@ncut.edu.cn; alfredo.cuzzocrea@unical.it; jianwu@umbc.edu 17 8 8 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2639-1589 978-1-7281-6251-5 IEEE INT CONF BIG DA 2020.0 5182 5190 10.1109/BigData50022.2020.9377947 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR6NZ 2023-03-23 WOS:000662554705030 0
J Chen, G; Bing, ZS; Rohrbein, F; Conradt, J; Huang, K; Cheng, L; Jiang, ZY; Knoll, A Chen, Guang; Bing, Zhenshan; Roehrbein, Florian; Conradt, Joerg; Huang, Kai; Cheng, Long; Jiang, Zhuangyi; Knoll, Alois Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS English Article Dynamic vision sensor (DVS); neuromorphics; neurorobotics; snake-like robot; spiking neural network infrastructure (SpiNNaker) Neurorobotic mimics the structural and functional principles of living creature systems. Modeling a single system by robotic hardware and software has existed for decades. However, an integrated toolset studying the interaction of all systems has not been demonstrated yet. We present a hybrid neuromorphic computing paradigm to bridge this gap by combining the neurorobotics platform (NRP) with the neuromorphic snake-like robot (NeuroSnake). This paradigm encompasses the virtual models, neuromorphic sensing and computing capabilities, and physical bio-inspired bodies, with which an experimenter can design and execute both in-silico and in-vino robotic experimentation easily. The NRP is a public Web-based platform for easily testing brain models with virtual bodies and environments. The NeuroSnake is a bio-inspired robot equipped with a silica-retina sensor and neuromorphic computer for power-efficiency applications. We illustrate the efficiencies of our paradigm with an easy designing of a visual pursuit experiment in the NRP. We study two automatic behavior learning tasks which are further integrated into a complex task of semi-autonomous pole climbing. The result shows that robots could build new learning rules in a less explicit manner inspired by living creatures. Our method gives an alternative way to efficiently develop complex behavior control of the ro As spiking neural network is a bio-inspired neural network and the NeuroSnake robot is equipped with a spike-based silicon retina camera, the control system can be easily implemented via spiking neurons simulated on neuromorphic hardware, such as SpiNNaker.bot. [Chen, Guang; Bing, Zhenshan; Roehrbein, Florian; Cheng, Long; Jiang, Zhuangyi; Knoll, Alois] Tech Univ Munich, Fac Comp Sci, Dept Informat, D-85748 Munich, Germany; [Conradt, Joerg] Tech Univ Munich, Neurosci Syst Theory, D-85748 Munich, Germany; [Huang, Kai] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510257, Guangdong, Peoples R China Technical University of Munich; Technical University of Munich; Sun Yat Sen University Bing, ZS (corresponding author), Tech Univ Munich, Fac Comp Sci, Dept Informat, D-85748 Munich, Germany. guang@in.tum.de; bing@in.tum.de; florian.roehrbein@in.tum.de; conradt@tum.de; huangk36@mail.sysu.edu.cn; cheng1@in.tum.de; jiangz@in.tum.de; knoll@in.tum.de Knoll, Alois/AAN-8417-2021; Bing, Zhenshan/AAF-7965-2020 Knoll, Alois/0000-0003-4840-076X; Conradt, Jorg/0000-0001-5998-9640 European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) [720270] European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) This work was supported by the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under Grant 720270 (Human Brain Project). 33 18 19 4 57 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2379-8920 2379-8939 IEEE T COGN DEV SYST IEEE Trans. Cogn. Dev. Syst. MAR 2019.0 11 1 1 12 10.1109/TCDS.2017.2712712 0.0 12 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology HO9BE Green Published 2023-03-23 WOS:000461254800001 0
J Cheng, YY; Zhou, KF; Wang, JL; Cui, SC; Yan, JN; De Maeyer, P; Van de Voorde, T Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; Yan, Jining; De Maeyer, Philippe; Van de Voorde, Tim A Framework for Big Earth Observation Data Using Horizontal Scaling Strategy IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Distributed databases; Big Data; Earth; Metadata; Indexing; Load management; Routing; Data retrieval; Earth observation (EO) data; horizontal scaling strategy; parallel computing The rapid growth of Earth observation (EO) data poses a challenge to the way of data management. An efficient framework based on big data technology can bring new solutions. Some excellent frameworks have been proposed, which provide efficient organization and management of EO data. However, they are not optimized for data distribution in the storage environment. In this letter, an optimized EO data management strategy is proposed. Different horizontal scaling strategies are designed to explore the optimal scheme of EO data distribution. The MapReduce parallel computing model was used to test the performance of data retrieval in the experiment. The results show that the proposed strategy contributes to the efficient organization and arrangement of data. Remote sensing (RS) data blocks can be evenly distributed to different shards according to the time characteristics and hash characteristics of the strategy, and the logical index of the data reduces the time consumed by the routing process. This distributed management mode that achieves load balancing provides a framework foundation for parallel computing. Therefore, the framework with an efficient strategy can improve the performance of data management. [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao] Chinese Acad Sci, Xinjiang Res Ctr Mineral Resources, State Key Lab Desert & Oasis Ecol, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China; [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao] Xinjiang Key Lab Mineral Resources & Digital Geol, Urumqi 830011, Peoples R China; [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; De Maeyer, Philippe; Van de Voorde, Tim] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Univ Ghent, Dept Geog, B-9000 Ghent, Belgium; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium; [Yan, Jining] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ghent University; Ghent University; China University of Geosciences Wang, JL (corresponding author), Chinese Acad Sci, Xinjiang Res Ctr Mineral Resources, State Key Lab Desert & Oasis Ecol, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China. wangjinlin@ms.xjb.ac.cn Yan, Jining/0000-0003-0680-5427; Cheng, yinyi/0000-0002-7454-1374; Van de Voorde, Tim/0000-0002-9324-5087 Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China [2021A03001-3]; Key Project of Natural Science Foundation of China -Xinjiang Joint Fund [U1803241]; Chinese Academy of Sciences President's International Fellowship Initiative under PIFI Grant [2017VCA0002] Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China; Key Project of Natural Science Foundation of China -Xinjiang Joint Fund; Chinese Academy of Sciences President's International Fellowship Initiative under PIFI Grant This work was supported in part by the Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China under Grant 2021A03001-3; in part by the Key Project of Natural Science Foundation of China -Xinjiang Joint Fund under Grant U1803241; and in part by the Chinese Academy of Sciences President's International Fellowship Initiative under PIFI Grant 2017VCA0002. 24 0 0 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 3513005 10.1109/LGRS.2022.3192640 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3K1YS 2023-03-23 WOS:000833879800008 0
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Dawson, J; De, K; De, S; Debbins, P; De Bonis, I; Decowski, M; De Gouvea, A; De Holanda, PC; Astiz, ILD; Deisting, A; De Jong, P; Delbart, A; Delepine, D; Delgado, M; Dell'Acqua, A; Delmonte, N; De Lurgio, P; Neto, JRD; DeMuth, DM; Dennis, S; Densham, C; Deptuch, GW; De Roeck, A; De Romeri, V; De Souza, G; Devi, R; Dharmapalan, R; Dias, M; Diaz, F; Diaz, J; Di Domizio, S; Di Giulio, L; Ding, P; Di Noto, L; Dirkx, G; Distefano, C; Diurba, R; Diwan, M; Djurcic, Z; Doering, D; Dolan, S; Dolek, F; Dolinski, M; Domine, L; Donon, Y; Douglas, D; Douillet, D; Dragone, A; Drake, G; Drielsma, F; Duarte, L; Duchesneau, D; Duffy, K; Dunne, P; Dutta, B; Duyang, H; Dvornikov, O; Dwyer, D; Dyshkant, A; Eads, M; Earle, A; Edmunds, D; Eisch, J; Emberger, L; Emery, S; Englezos, P; Ereditato, A; Erjavec, T; Escobar, C; Eurin, G; Evans, JJ; Ewart, E; Ezeribe, AC; Fahey, K; Falcone, A; Fani, M; Farnese, C; Farzan, Y; Fedoseev, D; Felix, J; Feng, Y; Fernandez-Martinez, E; Menendez, PF; Morales, MF; Ferraro, F; Fields, L; Filip, P; Filthaut, F; Fiorini, M; Fischer, V; Fitzpatrick, RS; Flanagan, W; Fleming, B; Flight, R; Fogarty, S; Foreman, W; Fowler, J; Fox, W; Franc, J; Francis, K; Franco, D; Freeman, J; Freestone, J; Fried, J; Friedland, A; Robayo, FF; Fuess, S; Furic, IK; Furman, K; Furmanski, AP; Gabrielli, A; Gago, A; Gallagher, H; Gallas, A; Gallego-Ros, A; Gallice, N; Galymov, V; Gamberini, E; Gamble, T; Ganacim, F; Gandhi, R; Gandrajula, R; Gao, F; Gao, S; Garcia-Gamez, D; Garcia-Peris, MA; Gardiner, S; Gastler, D; Gauvreau, J; Ge, G; Geffroy, N; Gelli, B; Gendotti, A; Gent, S; Ghorbani-Moghaddam, Z; Giammaria, P; Giammaria, T; Giangiacomi, N; Gibin, D; Gil-Botella, I; Gilligan, S; Girerd, C; Giri, A; Gnani, D; Gogota, O; Gold, M; Gollapinni, S; Gollwitzer, K; Gomes, RA; Bermeo, LG; Fajardo, LSG; Gonnella, F; Gonzalez-Diaz, D; Gonzalez-Lopez, M; Goodman, MC; Goodwin, O; Goswami, S; Gotti, C; Goudzovski, E; Grace, C; Gran, R; Granados, E; Granger, P; Grant, A; Grant, C; Gratieri, D; Green, P; 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K.; Shaw, T.; Shchablo, K.; Shepherd-Themistocleous, C.; Sheshukov, A.; Shin, S.; Shoemaker, I; Shooltz, D.; Shrock, R.; Siegel, H.; Simard, L.; Sinclair, J.; Sinev, G.; Singh, J.; Singh, J.; Singh, L.; Singh, P.; Singh, V; Sipos, R.; Sippach, F.; Sirri, G.; Sitraka, A.; Siyeon, K.; Skarpaas, K.; Smith, A.; Smith, E.; Smith, P.; Smolik, J.; Smy, M.; Snider, E.; Snopok, P.; Snowden-Ifft, D.; Nunes, M. Soares; Sobel, H.; Soderberg, M.; Sokolov, S.; Solano Salinas, C. J.; Soldner-Rembold, S.; Soleti, S.; Solomey, N.; Solovov, V; Sondheim, W. E.; Sorel, M.; Sotnikov, A.; Soto-Oton, J.; Ugaldi, F. Soto; Sousa, A.; Soustruznik, K.; Spagliardi, F.; Spanu, M.; Spitz, J.; Spooner, N. J. C.; Spurgeon, K.; Stancari, M.; Stanco, L.; Stanford, C.; Stefan, D.; Stein, R.; Steiner, H.; Steklain Lisboa, A. F.; Stewart, J.; Stillwell, B.; Stock, J.; Stocker, F.; Stokes, T.; Strait, M.; Strauss, T.; Strigari, L.; Stuart, A.; Suarez, J. G.; Suarez Suncion, J.; Sulej, R.; Sullivan, H.; Summers, D.; Surdo, A.; Susic, V; Suter, L.; Sutera, C.; Svoboda, R.; Szczerbinska, B.; Szelc, A. M.; Tanaka, H.; Tang, S.; Tapia, A.; Oregui, B. Tapia; Tapper, A.; Tariq, S.; Tarpara, E.; Tata, N.; Tatar, E.; Tayloe, R.; Teklu, A.; Tennessen, P.; Tenti, M.; Terao, K.; Ternes, C. A.; Terranova, F.; Testera, G.; Thakore, T.; Thea, A.; Thompson, J. L.; Thorn, C.; Timm, S.; Tishchenko, V; Tomassetti, L.; Tonazzo, A.; Torbunov, D.; Torti, M.; Tortola, M.; Tortorici, F.; Tosi, N.; Totani, D.; Toups, M.; Touramanis, C.; Travaglini, R.; Trevor, J.; Trilov, S.; Trzaska, W. H.; Tsai, Y.; Tsai, Y.; Tsamalaidze, Z.; Tsang, K.; Tsverava, N.; Tufanli, S.; Tull, C.; Tyley, E.; Tzanov, M.; Uboldi, L.; Uchida, M. A.; Urheim, J.; Usher, T.; Uzunyan, S.; Vagins, M. R.; Vahle, P.; Valder, S.; Valdiviesso, G. A.; Valencia, E.; Valentim, R.; Vallari, Z.; Vallazza, E.; Valle, J. W.; Vallecorsa, S.; VanBerg, R.; Van de Water, R. G.; Vanegas Forero, D.; Vannerom, D.; Varanini, F.; Vargas, D.; Varner, G.; Vasel, J.; Vasina, S.; Vasseur, G.; Vaughan, N.; Vaziri, K.; Ventura, S.; Verdugo, A.; Vergani, S.; Vermeulen, M. A.; Verzocchi, M.; Vicenzi, M.; de Souza, H. Vieira; Vignoli, C.; Vilela, C.; Viren, B.; Vrba, T.; Wachala, T.; Waldron, A., V; Wallbank, M.; Wallis, C.; Wang, H.; Wang, J.; Wang, L.; Wang, M. H.; Wang, X.; Wang, Y.; Wang, Y.; Warburton, K.; Warner, D.; Wascko, M.; Waters, D.; Watson, A.; Wawrowska, K.; Weatherly, P.; Weber, A.; Weber, M.; Wei, H.; Weinstein, A.; Wenman, D.; Wetstein, M.; White, A.; Whitehead, L. H.; Whittington, D.; Wilking, M. J.; Wilkinson, A.; Wilkinson, C.; Williams, Z.; Wilson, F.; Wilson, R. J.; Wisniewski, W.; Wolcott, J.; Wongjirad, T.; Wood, A.; Wood, K.; Worcester, E.; Worcester, M.; Wresilo, K.; Wret, C.; Wu, W.; Wu, W.; Xiao, Y.; Xie, F.; Yaeggy, B.; Yandel, E.; Yang, G.; Yang, K.; Yang, T.; Yankelevich, A.; Yershov, N.; Yonehara, K.; Yoon, Y.; Young, T.; Yu, B.; Yu, H.; Yu, H.; Yu, J.; Yu, Y.; Yuan, W.; Zaki, R.; Zalesak, J.; Zambelli, L.; Zamorano, B.; Zani, A.; Zazueta, L.; Zeller, G.; Zennamo, J.; Zeug, K.; Zhang, C.; Zhang, S.; Zhang, Y.; Zhao, M.; Zhivun, E.; Zhu, G.; Zimmerman, E. D.; Zucchelli, S.; Zuklin, J.; Zutshi, V; Zwaska, R. DUNE Collaboration Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network EUROPEAN PHYSICAL JOURNAL C English Article Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation. 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L.] Ctr Tecnol Informacao Renato Archer, BR-13069901 Campinas, SP, Brazil; [Singh, L.; Singh, V] Cent Univ South Bihar, Gaya 824236, India; [Filip, P.; Kvasnicka, J.; Lokajicek, M.; Pec, V; Zalesak, J.; Zuklin, J.] Czech Acad Sci, Inst Phys, Prague 18200 8, Czech Republic; [Bour, P.; Franc, J.; Jediny, F.; Kus, V; Smolik, J.; Vrba, T.] Czech Tech Univ, Prague 11519 1, Czech Republic; [Maloney, J. A.] Dakota State Univ, Madison, SD 57042 USA; [Flanagan, W.] Univ Dallas, Irving, TX 75062 USA; [Aimard, B.; De Bonis, I; Duchesneau, D.; Geffroy, N.; Karyotakis, Y.; Kunze, P.; Zambelli, L.] Univ Grenoble Alpes, Univ Savoie Mt Blanc, Lab Annecy Phys Particules, CNRS,LAPP IN2P3, F-74000 Annecy, France; [Grant, A.; Muir, A.; Newton, H.] Daresbury Lab, Warrington WA4 4AD, Cheshire, England; [Dolinski, M.; Lane, C. E.; Weatherly, P.] Drexel Univ, Philadelphia, PA 19104 USA; [Conley, E.; Fowler, J.; Major, A.; Pershey, D.; Scholberg, K.] Duke Univ, Durham, NC 27708 USA; [Moffat, K.; Pascoli, S.] Univ Durham, Durham DH1 3LE, England; [Batchelor, C.; Clarke, P.; Muheim, F.; Nebot-Guinot, M.; Perry, J.; Szelc, A. M.; Yuan, W.] Univ Edinburgh, Edinburgh EH8 9YL, Midlothian, Scotland; [Arroyave, M. A.; Betancur Rodriguez, A.; Palacio, G.; Rincon, E., V; Suarez, J. G.] Univ EIA, Envigado, Antioquia, Colombia; [Monsalve, S. Alonso; Alt, C.; Gendotti, A.; Murphy, S.; Radics, B.; Rigaut, Y.; Rubbia, A.; Sgalaberna, D.] Swiss Fed Inst Technol, Zurich, Switzerland; [Barros, N.; Maio, A.; Maneira, J. C.] Univ Lisboa FCUL, Fac Ciencias, P-1749016 Lisbon, Portugal; [Valdiviesso, G. A.] Univ Fed Alfenas, BR-37715400 Pocos De Caldas, MG, Brazil; [Gomes, R. A.] Univ Fed Goias, BR-74690900 Goiania, Go, Brazil; [Marinho, F.] Univ Fed Sao Carlos, BR-13604900 Araras, SP, Brazil; [Kamiya, F.; de Oliveira, M. Leigui; Luzio, V. P.; Moura, C. A.; Paulucci, L.] Univ Fed ABC, BR-09210580 Santo Andre, SP, Brazil; [Peres, L. Da Silva; De Mello Neto, J. R.] Univ Fed Rio de Janeiro, BR-21941901 Rio De Janeiro, RJ, Brazil; [Acciarri, R.; Adamowski, M.; Andrews, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Bellantoni, L.; Berkman, S.; Betancourt, M.; Biery, K.; Blaszczyk, F.; Braga, D.; Brice, S. J.; Bross, A.; Caratelli, D.; Cavanna, F.; Cerati, G.; Childress, S.; Christian, D.; Crisler, M.; David, C.; Ding, P.; Drake, G.; Duffy, K.; Eisch, J.; Escobar, C.; Fahey, K.; Freeman, J.; Fuess, S.; Gardiner, S.; Gollwitzer, K.; Haaf, K.; Hahn, A.; Hamernik, T.; Harris, D. A.; Hatcher, R.; Heavey, A.; Herner, K.; Himmel, A.; Hoff, J.; Howard, B.; Hronek, M. S.; Illingworth, R.; James, E.; Jena, D.; Junk, T.; Ketchum, W.; King, B.; Kirby, M.; Kobilarcik, T.; Lee, C.; Lockwitz, S.; Machado, P.; Macier, J.; Marchan, M.; Marchionni, A.; Mazzacane, A.; McCluskey, E.; Miao, T.; Mishra, C.; Mohayai, T. A.; Mokhov, N.; Montanari, C.; Montanari, D.; Mossey, C.; Mu, W.; Niner, E.; Norman, A.; Norrick, A.; Palamara, O.; Paley, J. M.; Papadimitriou, V; Parke, S.; Paudel, A.; Pavlovic, Z.; Plunkett, R.; Pordes, S.; Psihas, F.; Raaf, J.; Rakhno, I; Rameika, R.; Ramson, B.; Rebel, B.; Rechenmacher, R.; Reitzner, S. D.; Savage, G.; Schellman, H.; Schlabach, P.; Schukraft, A.; Shaw, T.; Snider, E.; Stancari, M.; Strauss, T.; Sulej, R.; Suter, L.; Tariq, S.; Timm, S.; Toups, M.; Vaziri, K.; Verzocchi, M.; Wang, M. H.; Wang, X.; Weber, A.; Wu, W.; Yang, T.; Yonehara, K.; Zeller, G.; Zennamo, J.; Zwaska, R.] Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA; [Andreotti, M.; Calabrese, R.; Fiorini, M.; Giammaria, T.; Guarise, M.; Luppi, E.; Schifano, S.; Tomassetti, L.] Univ Ferrara, Ferrara, Italy; [Furic, I. K.] Univ Florida, Gainesville, FL 32611 USA; [Gratieri, D.] Fluminense Fed Univ, 9 Icarai, BR-24220900 Niteroi, RJ, Brazil; [Di Domizio, S.; Di Noto, L.; Ferraro, F.; Furman, K.; Pallavicini, M.; Vicenzi, M.] Univ Genoa, Genoa, Italy; [Adamov, G.; Bagaturia, I; Chokheli, D.; Kemularia, O.; Khvedelidze, A.; Lomidze, I; Tsamalaidze, Z.; Tsverava, N.] Georgian Tech Univ, Tbilisi, Georgia; [Garcia-Gamez, D.; Nicolas-Arnaldos, F.; Zamorano, B.] Univ Granada, Granada 18002, Spain; [Garcia-Gamez, D.; Nicolas-Arnaldos, F.; Zamorano, B.] CAFPE, Granada 18002, Spain; [Rubbia, C.] Gran Sasso Sci Inst, Laquila, Italy; [Maddalena, A.; Vignoli, C.] Lab Nazl Gran Sasso, Laquila, AQ, Italy; [Kosc, T.; Real, J.; Ricol, J.; Robert, A.] Univ Grenoble Alpes, Grenoble INP, CNRS, LPSC IN2P3, F-38000 Grenoble, France; [Barranco Monarca, J.; Delepine, D.; Felix, J.; Granados, E.; Valencia, E.] Univ Guanajuato, Guanajuato 37000, Mexico; [Gandhi, R.] Harish Chandra Res Inst, Allahabad 211019, Uttar Pradesh, India; [Guenette, R.; Ho, J.; Kubota, S.; Prince, S.; Stanford, C.; Tata, N.] Harvard Univ, Cambridge, MA 02138 USA; [Dharmapalan, R.; Dvornikov, O.; Kumar, J.; Learned, J.; Marfatia, D.; Maricic, J.; Matsuno, S.; Milincic, R.; Nishimura, K.; Varner, G.] Univ Hawaii, Honolulu, HI 96822 USA; [Aduszkiewicz, A.; Cherdack, D.; He, M.; Koerner, L. W.; Morris, C.; Munford, D.; Renshaw, A.; Wood, A.] Univ Houston, Houston, TX 77204 USA; [Bambah, B.; Mohanta, R.] Univ Hyderabad, Hyderabad 500046, India; [Hill, T.; Tatar, E.] Idaho State Univ, Pocatello, ID 83209 USA; [Jesus-Valls, C.; Lux, T.; Vargas, D.] Barcelona Inst Sci & Technol BIST, Inst Fis Altes Energies IFAE, Barcelona, Spain; [Antonova, M.; Barenboim, G.; Cervera Villanueva, A.; De Romeri, V; Fernandez Menendez, P.; Garcia-Peris, M. A.; Martin-Albo, J.; Martinez Mirave, P.; Mena, O.; Bueno, L. Molina; Novella, P.; Pompa, F.; Sorel, M.; Ternes, C. A.; Tortola, M.; Valle, J. W.] CSIC, Inst Fis Corpuscular, Valencia 46980, Spain; [Antonova, M.; Barenboim, G.; Cervera Villanueva, A.; De Romeri, V; Fernandez Menendez, P.; Garcia-Peris, M. A.; Martin-Albo, J.; Martinez Mirave, P.; Mena, O.; Bueno, L. Molina; Novella, P.; Pompa, F.; Sorel, M.; Ternes, C. A.; Tortola, M.; Valle, J. W.] Univ Valencia, Valencia 46980, Spain; [Amedo, P.; Fernandez Morales, M.; Gonzalez-Diaz, D.; Leardini, S.; Saa-Hernandez, A.] Inst Galego Fis Altas Enerxias, La Coruna, Spain; [Chirco, R.; Foreman, W.; Morquecho, M. Hernandez; Kaplan, D. M.; Littlejohn, B. R.; Snopok, P.; Yu, Y.] IIT, Chicago, IL 60616 USA; [Dirkx, G.; Dunne, P.; Hamilton, P.; Iles, G.; Klustova, A.; Long, K.; Pasternak, J.; Pozimski, J.; Tapper, A.; Waldron, A., V; Wascko, M.] Imperial Coll Sci Technol & Med, London SW7 2BZ, England; [Bhattacharjee, M.; Bhuyan, B.; Nath, A.] Indian Inst Technol Guwahati, Gauhati 781039, India; [Giri, A.; Sahu, N.] Indian Inst Technol Hyderabad, Hyderabad 502285, India; [Back, A.; Diaz, J.; Ewart, E.; Fox, W.; Hayes, C.; Johnston, N.; Kostelecky, V; Lehnert, R.; Macias, C.; Plata, M. Manrique; Messier, M.; Mewes, M.; Mufson, S.; Musser, J.; Smith, E.; Smith, P.; Tayloe, R.; Urheim, J.; Vasel, J.; Zhang, S.] Indiana Univ, Bloomington, IN 47405 USA; [Bertolucci, S.; Cagnoli, I; Cervelli, A.; Cicero, V; Gabrielli, A.; Guerzoni, M.; Iliescu, A. M.; Ingratta, G.; Laurenti, G.; Mandrioli, G.; Mauri, N.; Moggi, N.; Montagna, E.; Montanari, A.; Pasqualini, L.; Patrizii, L.; Pia, V; Poppi, F.; Pozzato, M.; Sirri, G.; Tenti, M.; Tosi, N.; Travaglini, R.; Zucchelli, S.] Ist Nazl Fis Nucl, Sez Bologna, I-40127 Bologna, Italy; [Ali-Mohammadzadeh, B.; Bellini, V; Petta, C.; Potenza, R.; Sutera, C.; Tortorici, F.] Ist Nazl Fis Nucl, Sez Catania, I-95123 Catania, Italy; [Andreotti, M.; Calabrese, R.; Fiorini, M.; Giammaria, T.; Guarise, M.; Luppi, E.; Schifano, S.; Tomassetti, L.] Ist Nazl Fis Nucl, Sez Ferrara, I-44122 Ferrara, Italy; [Bersani, A.; Caminata, A.; Copello, S.; Davini, S.; Di Domizio, S.; Di Noto, L.; Ferraro, F.; Ghorbani-Moghaddam, Z.; Pallavicini, M.; Testera, G.; Vicenzi, M.] Ist Nazl Fis Nucl, Sez Genova, I-16146 Genoa, Italy; [Bernardini, P.; Surdo, A.] Ist Nazl Fis Nucl, Sez Lecce, I-73100 Lecce, Italy; [Biassoni, M.; Bomben, L.; Bonesini, M.; Branca, A.; Brizzolari, C.; Brunetti, G.; Carniti, P.; Cattadori, C.; Cavallaro, G.; Delgado, M.; Falcone, A.; Gotti, C.; Mascagna, V; Mazza, R.; Moretti, D.; Parozzi, E.; Pessina, G.; Spanu, M.; Terranova, F.; Torti, M.; Vallazza, E.] Ist Nazl Fis Nucl, Sez Milano Bicocca 3, I-20126 Milan, Italy; [Cova, P.; Delmonte, N.; Gallice, N.; Guffanti, D.; Lazzaroni, M.; Minotti, A.; Riboldi, S.; Sala, P.; Zani, A.] Ist Nazl Fis Nucl, Sez Milano, I-20133 Milan, Italy; [Cocco, A. G.] Ist Nazl Fis Nucl, Sez Napoli, I-80126 Naples, Italy; [Centro, S.; Farnese, C.; Gibin, D.; Guglielmi, A.; Meng, G.; Pietropaolo, F.; Stanco, L.; Varanini, F.; Ventura, S.] Ist Nazl Fis Nucl, Sez Padova, I-35131 Padua, Italy; [Menegolli, A.; Montanari, C.; Rappoldi, A.; Raselli, G.; Rossella, M.; Scaramelli, A.] Ist Nazl Fis Nucl, Sez Pavia, I-27100 Pavia, Italy; [Biagi, S.; Distefano, C.; Papaleo, R.; Riccobene, G.; Sapienza, P.] Ist Nazl Fis Nucl, Lab Nazl Sud, I-95123 Catania, Italy; [Lara, V. Ayala; Castromonte, C.; Neyra, M. Chavarry; Solano Salinas, C. J.; Ugaldi, F. Soto; Suarez Suncion, J.] Univ Nacl Ingn, Lima 25, Peru; [Khabibullin, M.; Khotjantsev, A.; Kudenko, Y.; Kulagin, S.; Mefodiev, A.; Mineev, O.; Yershov, N.] Russian Acad Sci, Inst Nucl Res, Moscow 117312, Russia; [Bomben, L.; Mascagna, V] Univ Insubria, Via Ravasi 2, I-21100 Varese, Italy; [Bilki, B.; Debbins, P.; Jeong, Y.; Karaman, G.; Nachtman, J.; Onel, Y.; Penzo, A.] Univ Iowa, Iowa City, IA 52242 USA; [Feng, Y.; Fischer, V; Krennrich, F.; Poonthottathil, N.; Sanchez, M.; Warburton, K.; Weinstein, A.; Wetstein, M.] Iowa State Univ, Ames, IA 50011 USA; [Autiero, D.; Bechetoille, E.; Caiulo, D.; Carlus, B.; David, Q.; Galymov, V; Girerd, C.; LeBrun, P.; Marteau, J.; Pennacchio, E.; Pugnere, D.; Shchablo, K.] Inst Phys 2 Infinis Lyon, F-69622 Villeurbanne, France; [Farzan, Y.] Inst Res Fundamental Sci, Tehran, Iran; [Barao, F.] Univ Lisbon, Inst Super Tecn IST, P-1049001 Lisbon, Portugal; [Narita, S.; Negishi, K.] Iwate Univ, Morioka, Iwate 0208551, Japan; [Devi, R.; Potukuchi, B., V; Sharma, H. R.] Univ Jammu, Jammu 180006, Jammu & Kashmir, India; [Mehta, P.; Rout, J.; Shafaq, S.] Jawaharlal Nehru Univ, New Delhi 110067, India; [Lee, S.; Shin, S.] Jeonbuk Natl Univ, Jeonrabuk Do 54896, South Korea; [Anfimov, N.; Antoshkin, A.; Balashov, N.; Butorov, I; Chukanov, A.; Fedoseev, D.; Kakorin, I; Kalitkina, A.; Kolupaeva, L.; Korablev, D.; Olshevskiy, A.; Rybnikov, A.; Samoylov, O.; Selyunin, A.; Sheshukov, A.; Sokolov, S.; Sotnikov, A.; Vasina, S.] Dzhelepov Lab Nucl Problems, Joint Inst Nucl Res, 6 Joliot Curie, Dubna 141980, Moscow Region, Russia; [Trzaska, W. H.] Univ Jyvaskyla, Jyvaskyla 40014, Finland; [AlRashed, M.; Bolton, T.; Horton-Smith, G. A.; Liao, H.; Martinez, N.] Kansas State Univ, Manhattan, KS 66506 USA; [Vagins, M. R.] Kavli Inst Phys & Math Universe, Kashiwa, Chiba 2778583, Japan; [Hasegawa, T.] High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki 3050801, Japan; [Cho, K.] Korea Inst Sci & Technol Informat, Daejeon 34141, South Korea; [Guthikonda, K.] KL Univ, Vaddeswaram 522502, Andhra Pradesh, India; [Kasai, S.] Kure Coll, Natl Inst Technol, Hiroshima 7378506, Japan; [Aushev, V; Cross, R.; Gogota, O.; Kadenko, I; Onishchuk, Y.] Taras Shevchenko Natl Univ Kyiv, UA-01601 Kiev, Ukraine; [Blake, A.; Brailsford, D.; Mouster, G.; Nowak, J. A.; Ratoff, P.] Univ Lancaster, Lancaster LA1 4YB, England; [Aguilar, J.; Calafiura, P.; Cline, G.; Dwyer, D.; Gnani, D.; Grace, C.; Karcher, A.; Kohn, S.; Lambert, A.; Lawrence, A.; Leitner, M.; Lin, C.; Loew, T.; Luk, K.; Madigan, P.; Matichard, F.; Patton, S.; Prakash, T.; Russell, B.; Soleti, S.; Steiner, H.; Tennessen, P.; Tull, C.; Wang, L.; Wilkinson, C.; Wood, K.] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; [Andringa, S.; Barao, F.; Barros, N.; Neves, F. Bento; Maio, A.; Maneira, J. C.; Solovov, V] Lab Instrumentacao & Fis Expt Particulas, P-1649003 Lisbon, Portugal; [Andringa, S.; Barao, F.; Barros, N.; Neves, F. Bento; Maio, A.; Maneira, J. C.; Solovov, V] Lab Instrumentacao & Fis Expt Particulas, P-3004516 Coimbra, Portugal; [Abud, A. Abed; Andreopoulos, C.; Mavrokoridis, K.; Payne, D.; Roda, M.; Touramanis, C.] Univ Liverpool, Liverpool L69 7ZE, Merseyside, England; [Boissevain, J.; Gollapinni, S.; Guardincerri, E.; Louis, W. C.; Rivera, D.; Sandberg, V; Sondheim, W. E.; Van de Water, R. G.] Los Alamos Natl Lab, Los Alamos, NM 87545 USA; [Bhardwaj, A.; Kasetti, S. P.; Kutter, T.; Lin, S.; Matthews, J.; Metcalf, W.; Mitchell, M.; Mote, M.; Schefke, T.; Stokes, T.; Tzanov, M.] Louisiana State Univ, Baton Rouge, LA 70803 USA; [Nagu, S.; Singh, J.; Singh, J.] Univ Lucknow, Lucknow 226007, Uttar Pradesh, India; [Fernandez-Martinez, E.; Gonzalez-Lopez, M.; Rosauro-Alcaraz, S.] Madrid Autonoma Univ, Madrid 28049, Spain; [Fernandez-Martinez, E.; Gonzalez-Lopez, M.; Rosauro-Alcaraz, S.] IFT UAM CSIC, Madrid 28049, Spain; [Weber, A.] Johannes Gutenberg Univ Mainz, D-55122 Mainz, Germany; [Arellano, L.; Basque, V; Bitadze, A.; Emberger, L.; Evans, J. J.; Freestone, J.; Goodwin, O.; Green, P.; Guzowski, P.; Langstaff, M.; Lepin, L. M.; Marsden, D.; McConkey, N.; McNab, A.; Miller, G.; Navrer-Agasson, A.; Pater, J.; Reggiani-Guzzo, M.; Soldner-Rembold, S.] Univ Manchester, Manchester M13 9PL, Lancs, England; [Barrow, J. L.; Conrad, J.; Hourlier, A.; Schneider, A. W.; Vannerom, D.] MIT, Cambridge, MA 02139 USA; [Emberger, L.] Max Planck Inst, D-80805 Munich, Germany; [Hoyos, J.; Tapia, A.; Vanegas Forero, D.] Univ Medellin, Medellin 050026, Colombia; [Barnes, C.; Fitzpatrick, R. S.; Mousseau, J.; Spitz, J.] Univ Michigan, Ann Arbor, MI 48109 USA; [Bromberg, C.; Douglas, D.; Edmunds, D.; Gandrajula, R.; Mahn, K.; Pickering, L.; Safford, T.; Shooltz, D.] Michigan State Univ, E Lansing, MI 48824 USA; [Bonesini, M.; Brizzolari, C.; Brunetti, G.; Delgado, M.; Falcone, A.; Minotti, A.; Parozzi, E.; Spanu, M.; Terranova, F.; Torti, M.] Univ Milano Bicocca, I-20126 Milan, Italy; [Gallice, N.; Lazzaroni, M.] Univ Milan, I-20133 Milan, Italy; [Gran, R.; Habig, A.] Univ Minnesota, Duluth, MN 55812 USA; [Diurba, R.; Furmanski, A. P.; Hilgenberg, C.; Hostert, M.; Irwin, B.; Marshak, M.; Miller, W.; Mislivec, A.; Muramatsu, H.; Pawloski, G.; Poling, R.; Rea, E.; Strait, M.; Torbunov, D.] Univ Minnesota Twin Cities, Minneapolis, MN 55455 USA; [Bhattarai, D.; Cremaldi, L.; Crnkovic, J.; Davies, G. S.; Sanders, D. A.; Summers, D.] Univ Mississippi Univ, University, MS 38677 USA; [Gold, M.] Univ New Mexico, Albuquerque, NM 87131 USA; [Wachala, T.] Polish Acad Sci, H Niewodniczanski Inst Nucl Phys, Krakow, Poland; [Decowski, M.; De Jong, P.; Filthaut, F.; Gupta, V; Miedema, T.; Vermeulen, M. A.] Nikhef Natl Inst Subatom Phys, NL-1098 XG Amsterdam, Netherlands; [Barkhouse, W.; Young, T.] Univ North Dakota, Grand Forks, ND 58202 USA; [Blazey, G.; Boyden, D.; Choate, S.; Dyshkant, A.; Eads, M.; Francis, K.; Uzunyan, S.; Zutshi, V] Northern Illinois Univ, De Kalb, IL 60115 USA; [De Gouvea, A.] Northwestern Univ, Evanston, IL 60208 USA; [Bostan, N.; Fields, L.; LoSecco, J.] Univ Notre Dame, Notre Dame, IN 46556 USA; [Gauvreau, J.; Snowden-Ifft, D.] Occidental Coll, Los Angeles, CA 90041 USA; [Beacom, J. F.; Zhu, G.] Ohio State Univ, Columbus, OH 43210 USA; [Calcutt, J.; Gilligan, S.; Schellman, H.; Vaughan, N.] Oregon State Univ, Corvallis, OR 97331 USA; [Sulej, R.] Natl Ctr Nucl Res, A Soltana 7, PL-05400 Otwock, Poland; [Abi, B.; Azfar, F.; Barr, G.; Battisti, F.; Hamacher-Baumann, P.; Hasnip, C.; Kabirnezhad, M.; Plows, K.; Reynolds, A.; Rodrigues, P.; Spagliardi, F.; Yang, K.] Univ Oxford, Oxford OX1 3RH, England; [Back, H.; Church, E.; Hoppe, E.; Jackson, C. M.] Pacific Northwest Natl Lab, Richland, WA 99352 USA; [Centro, S.; Gibin, D.; Stanco, L.] Univ Padua, I-35131 Padua, Italy; [Bhatnagar, V] Panjab Univ, Chandigarh 160014, UT, India; [Beigbeder, C.; Bongrand, M.; Bourgeois, C.; Breton, D.; Cavalier, F.; Douillet, D.; Gallas, A.; Guerard, E.; Maalmi, J.; Manzanillas, L.; Marie, R.; Rosier, P.; Simard, L.] Univ Paris Saclay, IJCLab, CNRS IN2P3, F-91405 Orsay, France; [Chardonnet, E.; Coelho, J.; Dawson, J.; Patzak, T.; Sacerdoti, S.; Tonazzo, A.; de Souza, H. Vieira] Univ Paris, CNRS, Astroparticule & Cosmol, F-75006 Paris, France; [Cova, P.; Delmonte, N.] Univ Parma, I-43121 Parma, Italy; [Menegolli, A.; Montanari, C.; Rappoldi, A.; Raselli, G.; Rossella, M.] Univ Pavia, I-27100 Pavia, Italy; [Chong, P. S.; Keener, P.; Klein, J.; Kroupova, T.; Land, B.; Last, D.; Lunday, B.; Mauger, C.; Newcomer, M.; Nikolica, A.; Delgado, M. Ramirez; VanBerg, R.] Univ Penn, Philadelphia, PA 19104 USA; [Mocioiu, I] Penn State Univ, University Pk, PA 16802 USA; [Goswami, S.; Sensenig, J.] Phys Res Lab, Ahmadabad 380009, Gujarat, India; [Bellettini, G.] Univ Pisa, I-56127 Pisa, Italy; [Chatterjee, A.; Gao, F.; Han, J.; Judah, M.; Naples, D.; Paolone, V; Rice, L. C.; Rosenberg, M.; Savinov, V; Zhang, Y.] Univ Pittsburgh, Pittsburgh, PA 15260 USA; [Bazo Alba, J.; Diaz, F.; Gago, A.] Pontificia Univ Catolica Peru, Lima, Peru; [Mendez, H.; Santana, S.; Santos-Maldonado, M.] Univ Puerto Rico, Mayaguez, PR 00681 USA; [Sharma, R. K.] Punjab Agr Univ, Ludhiana 141004, Punjab, India; [Bevan, A.; Booth, A.; Cremonesi, L.; Hayrapetyan, K.; Hays, J.; Singh, P.] Queen Mary Univ London, London E1 4NS, England; [Filthaut, F.; Miedema, T.] Radboud Univ Nijmegen, NL-6525 AJ Nijmegen, Netherlands; [Budd, H.; Flight, R.; Henry, S.; Howell, R.; Manly, S.; Marshall, C.; McFarland, K. S.; Olivier, A.; Ruterbories, D.; Wret, C.] Univ Rochester, Rochester, NY 14627 USA; [Chen-Wishart, Z.; Deisting, A.; Kaboth, A.; Vazquez, W. Panduro; Ritchie-Yates, H.] Royal Holloway Coll, London TW20 0EX, England; [Englezos, P.; Mastbaum, A.] Rutgers State Univ, Piscataway, NJ 08854 USA; [Andreopoulos, C.; Brew, C.; Densham, C.; Hartnett, T.; Holin, A.; Hristova, I; Kaboth, A.; Manolopoulos, K.; Nandakumar, R.; Newbold, D.; Papanestis, A.; Sankey, D.; Shepherd-Themistocleous, C.; Thea, A.; Wawrowska, K.; Wilson, F.] STFC Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England; [Bernardini, P.] Univ Salento, I-73100 Lecce, Italy; [Hatzikoutelis, A.] San Jose State Univ, San Jose, CA 95192 USA; [Castillo, A.; Gomez Bermeo, L.; Gomez Fajardo, L. S.] Univ Sergio Arboleda, Bogota 11022, Colombia; [Ezeribe, A. C.; Gamble, T.; Kudryavtsev, V. A.; Kumar, P.; Scarff, A.; Spooner, N. J. C.; Thompson, J. L.; Tyley, E.] Univ Sheffield, Sheffield S3 7RH, S Yorkshire, England; [Ankowski, A.; Conley, R.; Convery, M.; Doering, D.; Domine, L.; Dragone, A.; Drielsma, F.; Friedland, A.; Gupta, A.; Hulcher, Z.; Itay, R.; Koh, D. H.; Kurita, N.; LeCompte, T.; Li, S.; Lin, Q.; MacFarlane, D.; Pena Perez, A.; Petrillo, G.; Petti, R.; Rochester, L.; Sinclair, J.; Skarpaas, K.; Tanaka, H.; Terao, K.; Tsai, Y.; Tsang, K.; Usher, T.; Wisniewski, W.] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA; [Duyang, H.; Guo, B.; Kuruppu, C.; Mishra, S.] Univ South Carolina, Columbia, SC 29208 USA; [Haiston, J.; Johnson, W.; Martinez Caicedo, D. A.; Reichenbacher, J.; Rodriguez Rondon, J.; Sinev, G.; Sitraka, A.; Stock, J.; Wang, J.] South Dakota Sch Mines & Technol, Rapid City, SD 57701 USA; [Gent, S.; Michna, G.] South Dakota State Univ, Brookings, SD 57007 USA; [Coan, T. E.] Southern Methodist Univ, Dallas, TX 75275 USA; [Chen, Z.; Jung, C.; Martynenko, S.; McGrew, C.; Raut, S.; Riccio, C.; Shrock, R.; Teklu, A.; Wang, Y.; Wilking, M. J.; Yang, G.] SUNY Stony Brook, Stony Brook, NY 11794 USA; [Ling, J.; Yu, H.] Sun Yat Sen Univ, Guangzhou 510275, Peoples R China; [Heise, J.] Sanford Underground Res Facil, Lead, SD 57754 USA; [Alion, T.; Asquith, L.; Bezerra, T. S.; Borkum, A.; Chisnall, G.; De Icaza Astiz, I. L.; Earle, A.; Griffith, C.; Hartnell, J.; Kralik, R.; Lasorak, P.; Peeters, S. J.; Porter, J.; Valder, S.; Wawrowska, K.; Xie, F.] Univ Sussex, Brighton BN1 9RH, E Sussex, England; [Nunes, M. Soares; Soderberg, M.; Spurgeon, K.; Whittington, D.] Syracuse Univ, Syracuse, NY 13244 USA; [Adames, M. R.; Antoniassi, M.; Barros, A.; Ganacim, F.; Hirsch, L. R.; Ribas, M.; Steklain Lisboa, A. F.] Univ Tecnol Fed Parana, Curitiba, Parana, Brazil; [Dutta, B.; Kim, D.; Strigari, L.] Texas A&M Univ, College Stn, TX 77840 USA; [Szczerbinska, B.] Texas A&M Univ Corpus Christi, Corpus Christi, TX 78412 USA; [Asaadi, J.; Brandt, A.; Carranza, H.; De, K.; Hadavand, H.; Jang, W.; Jones, B.; Sullivan, H.; White, A.; Williams, Z.; Yu, J.] Univ Texas Arlington, Arlington, TX 76019 USA; [Lang, K.; Oregui, B. Tapia] Univ Texas Austin, Austin, TX 78712 USA; [Giangiacomi, N.; Ilic, N.] Univ Toronto, Toronto, ON M5S 1A1, Canada; [Gallagher, H.; Le, T.; Mann, A.; Mason, K.; Mills, J.; Sharankova, R.; Wolcott, J.; Wongjirad, T.] Tufts Univ, Medford, MA 02155 USA; [Dias, M.; Duarte, L.; Senise, C. 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J.; Barker, G.; Brunetti, M.; Chappell, A.; Lord, T.; Lu, X.; Marshall, J.; Mawby, I; Rai, M.] Univ Warwick, Coventry CV4 7AL, W Midlands, England; [Battat, J.; McAskill, T.] Wellesley Coll, Wellesley, MA 02481 USA; [Meyer, H.; Muether, M.; Roy, P.; Solomey, N.] Wichita State Univ, Wichita, KS 67260 USA; [Catano-Mur, E.; Kordosky, M.; Nelson, J. K.; Vahle, P.; Zazueta, L.] Coll William & Mary, Williamsburg, VA 23187 USA; [Carroll, T.; Greenler, L.; Koehler, K.; Laundrie, A.; Lister, A.; Nesbit, J.; Paulos, B.; Rebel, B.; Wenman, D.; Zeug, K.] Univ Wisconsin, Madison, WI 53706 USA; [Ereditato, A.; Fleming, B.; Franco, D.; Heeger, K. M.; Langford, T.] Yale Univ, New Haven, CT 06520 USA; [Ioannisian, A.; Kazaryan, N.; Nalbandyan, M.] Yerevan Inst Theoret Phys & Modeling, Yerevan 0036, Armenia; [Cai, T.; David, C.; Harris, D. A.; Shaker, F.; Zaki, R.] York Univ, Toronto, ON M3J 1P3, Canada State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Amsterdam; Antalya Bilim University; University Antananarivo; Universidad Antonio Narino; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; Universidad Nacional de Asuncion; National & Kapodistrian University of Athens; Banaras Hindu University (BHU); University of Basel; University of Bern; Beykent University; University of Birmingham; University of Bologna; Boston University; University of Bristol; United States Department of Energy (DOE); Brookhaven National Laboratory; University of Bucharest; University of California System; University of California Berkeley; University of California System; University of California Davis; University of California System; University of California Irvine; University of California System; University of California Los Angeles; University of California System; 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Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University Nacional de Ingenieria Lima; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; University of Insubria; University of Iowa; Iowa State University; Universidade de Lisboa; Instituto Superior Tecnico; Iwate University; University of Jammu; Jawaharlal Nehru University, New Delhi; Joint Institute for Nuclear Research - Russia; University of Jyvaskyla; Kansas State University; University of Tokyo; High Energy Accelerator Research Organization (KEK); Korea Institute of Science & Technology Information (KISTI); Koneru Lakshmaiah Education Foundation (K L Deemed to be University); Ministry of Education & Science of Ukraine; Taras Shevchenko National University Kiev; Lancaster University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Coimbra; University of Liverpool; United States Department of Energy (DOE); Los Alamos National Laboratory; Louisiana State University System; Louisiana State University; Lucknow University; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Johannes Gutenberg University of Mainz; University of Manchester; Massachusetts Institute of Technology (MIT); Max Planck Society; Universidad de Medellin; University of Michigan System; University of Michigan; Michigan State University; University of Milano-Bicocca; University of Milan; University of Minnesota System; University of Minnesota Duluth; University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of New Mexico; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; FOM National Institute for Subatomic Physics; University of North Dakota Grand Forks; Northern Illinois University; Northwestern University; University of Notre Dame; Occidental College; University System of Ohio; Ohio State University; Oregon State University; National Centre for Nuclear Research; University of Oxford; United States Department of Energy (DOE); Pacific Northwest National Laboratory; University of Padua; Panjab University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite PSL; Universite Paris Cite; Observatoire de Paris; CEA; Centre National de la Recherche Scientifique (CNRS); University of Parma; University of Pavia; University of Pennsylvania; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Department of Space (DoS), Government of India; Physical Research Laboratory - India; University of Pisa; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pontificia Universidad Catolica del Peru; University of Puerto Rico; University of Puerto Rico Mayaguez; Punjab Agricultural University; University of London; Queen Mary University London; Radboud University Nijmegen; University of Rochester; University of London; Royal Holloway University London; Rutgers State University New Brunswick; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Salento; California State University System; San Jose State University; University Sergio Arboleda; University of Sheffield; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; University of South Carolina System; University of South Carolina Columbia; South Dakota School Mines & Technology; South Dakota State University; Southern Methodist University; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Sun Yat Sen University; University of Sussex; Syracuse University; Pontificia Universidade Catolica do Parana; Universidade Federal do Parana; Universidade Tecnologica Federal do Parana; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University Corpus Christi; University of Texas System; University of Texas Arlington; University of Texas System; University of Texas Austin; University of Toronto; Tufts University; Universidade Federal de Sao Paulo (UNIFESP); Ulsan National Institute of Science & Technology (UNIST); University of London; University College London; Variable Energy Cyclotron Centre; Virginia Polytechnic Institute & State University; University of Warsaw; University of Warwick; Wellesley College; Wichita State University; William & Mary; University of Wisconsin System; University of Wisconsin Madison; Yale University; York University - Canada Yang, T (corresponding author), Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA. tjyang@fnal.gov do Lago Pimentel, Vinicius/M-5017-2015; Vallazza, Erik Silvio/V-1732-2017; Manyam, Venkata Narasimha/AAH-1271-2019; De Romeri, Valentina/B-2588-2019; Strigari, Lidia/B-9352-2017; Neves, Francisco/H-4744-2013; Carini, Gabriella/HNI-4968-2023; Carniti, Paolo/ABC-4407-2020; Prest, Michela/V-1736-2017; Calvo Alamillo, Enrique/L-1203-2014; Anadón, José Ignacio Crespo/T-6399-2019; Carniti, Paolo/HKV-9919-2023; Mascagna, Valerio/HLQ-1103-2023; do Amaral Valdiviesso, Gustavo/G-3404-2011; Cuesta, Clara/AAB-3448-2019; Parvu, Mihaela/ABG-5753-2020; Garcia Gamez, Diego/ABA-4141-2021; 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Manyam, Venkata Narasimha/0000-0003-0367-5057; De Romeri, Valentina/0000-0003-3585-7437; Strigari, Lidia/0000-0003-4293-2298; Neves, Francisco/0000-0003-3635-1083; Prest, Michela/0000-0003-3161-4454; Calvo Alamillo, Enrique/0000-0002-1100-2963; Anadón, José Ignacio Crespo/0000-0002-3260-7072; Carniti, Paolo/0000-0002-7820-2732; Mascagna, Valerio/0000-0003-3612-1608; do Amaral Valdiviesso, Gustavo/0000-0002-0381-3619; Cuesta, Clara/0000-0003-1190-7233; Garcia Gamez, Diego/0000-0003-3452-3478; Caminata, Alessio/0000-0002-5800-5504; Kalra, D/0000-0001-5254-4067; CHATTERJEE, ANIMESH/0000-0002-2935-0958; Franco, Domenico/0000-0003-1278-9478; BELLINI, Vincenzo/0000-0001-6906-7463; Shin, Seodong/0000-0001-6956-841X; Mazzacane, Anna/0000-0001-6091-3870; sala, paola/0000-0001-9859-5564; Kubota, Shion/0000-0001-8247-0265; Behera, Biswaranjan/0000-0002-7381-5898; Sousa, Alexandre/0000-0002-1862-1409; Kabirnezhad, Monireh/0000-0002-3217-2259; Sinclair, James/0000-0002-1012-3800; Kralik, Robert/0000-0001-7557-5085; Tang, Shaochun/0000-0001-6693-332X; Guardincerri, Elena/0000-0002-7703-9988; Wret, Clarence/0000-0002-2288-7605; Peeters, Simon/0000-0002-3260-1439; Acero O, Mario A/0000-0002-0835-0641; Kearns, Edward/0000-0002-1781-150X; Aranda, Alfredo/0000-0002-8639-5167; Hadavand, Haleh/0000-0001-5447-3346; Solovov, Vladimir/0000-0002-0659-7034; Tapper, Alexander/0000-0003-4543-864X; Raaf, Jennifer/0000-0002-4533-929X; abi, babak/0000-0001-7036-9645; Travaglini, Riccardo/0000-0002-5288-1407; Davies, Gavin/0000-0002-5728-5356; Koller, Patrick Pascal/0000-0002-7968-9095; Ding, Pengfei/0000-0002-4050-1753; Goudzovski, Evgueni/0000-0001-9398-4237; Rodriguez Alonso, Manuel Jesus/0000-0002-3845-1136; Lazzaroni, Massimo/0000-0002-4094-1273; Cussans, David/0000-0001-8192-0826; Cui, Yanou/0000-0003-4322-9246; Ho, Johnny/0000-0003-4119-4262; Filthaut, Frank/0000-0003-3338-2247; Dunne, Patrick/0000-0001-7543-1882; Patterson, Ryan/0000-0002-5787-9517; Trzaska, Wladyslaw Henryk/0000-0003-0672-9137; MARINO, ALYSIA/0000-0002-1709-538X; Ereditato, Antonio/0000-0002-5423-8079; Antusch, Stefan/0000-0001-6120-9279; Bonilla, Cesar/0000-0002-4450-5946; Mohayai, Tanaz/0000-0003-0578-752X; Tenti, Matteo/0000-0002-4254-5901; Jesus-Valls, Cesar/0000-0002-0154-2456; Diwan, Milind/0000-0001-7194-5827; Queiroga Bazetto, Maria Cecilia/0000-0002-0835-9144; Cai, Tejin/0000-0003-0851-1625; Schmitz, David/0000-0003-2165-7389; Kolupaeva, Liudmila/0000-0002-3290-6494; Anfimov, Nikolay/0000-0002-9099-7574; Bostan, Nilay/0000-0002-1129-4345; Sergi, Antonino/0000-0001-9495-6115; Sotnikov, Albert/0000-0001-8371-5949; Brooke, James/0000-0003-2529-0684; Castillo, Andres/0000-0002-9887-3293; Wilson, Robert/0000-0002-8184-4103; Franc, Jiri/0000-0002-5761-8675; Hostert, Matheus/0000-0002-9584-8877; Sanchez, Mayly/0000-0001-5128-1330; Lane, Charles/0000-0003-4329-5796; Mooney, Michael/0000-0001-8348-4167; Beacom, John/0000-0002-0005-2631; Qian, Xin/0000-0002-7903-7935; Mariani, Camillo/0000-0003-3284-4681; dolek, furkan/0000-0001-7092-5517; Zhang, Chao/0000-0003-2298-6272; Fernandez, Pablo/0000-0001-9034-1930; Kuruppu, Kuruppumullage Don Chatura/0000-0003-2772-1978; Marchionni, Alberto/0000-0003-3039-9537; Guffanti, Daniele/0000-0002-6182-5618; Schneider, Austin/0000-0002-0895-3477; Cremonesi, Linda/0000-0003-0711-1056; Panduro Vazquez, Jose Guillermo/0000-0003-2605-8940; Norman, Andrew/0000-0001-8572-956X; Sankey, David/0000-0003-0955-4213; Green, Patrick/0000-0001-9872-3685; Zhu, Guanying/0000-0003-0031-634X; Ochoa-Ricoux, Juan Pedro/0000-0001-7376-5555; Tortola, Mariam/0000-0002-5855-2671; Rebel, Brian/0000-0001-6791-8703; Wei, Hanyu/0000-0003-1973-4912; Plunkett, Robert/0000-0002-6770-1771; Thakore, Tarak/0000-0003-0628-3155; Kamiya, Felipe Garcia Ken/0000-0001-8057-5055; Horton-Smith, Glenn/0000-0001-9677-9167; Alonso-Monsalve, Saul/0000-0002-9678-7121; Marshall, Christopher/0000-0003-4668-4877; Wu, Wenjie/0000-0003-4379-9548; Ghorbani moghaddam, Zahra/0000-0002-4410-9505; Cavalier, Fabien/0000-0002-3658-7240; Strigari, Louis/0000-0001-5672-6079; Aurisano, Adam/0000-0001-9568-9338; Kulagin, Sergey/0000-0003-0279-4337; Fields, Laura/0000-0001-8281-3686; Kothekar, Kunal/0000-0001-5102-4326; Machado, Pedro/0000-0002-9118-7354; Spitz, Joshua/0000-0002-6288-7028; Stein, Raul/0000-0002-2583-5755; Bay, Fatih/0000-0002-1017-1896; Luppi, Eleonora/0000-0002-1072-5633; Sorel, Michel/0000-0003-2141-9508; Jones, Benjamin/0000-0003-3400-8986; Nebot Guinot, Miquel/0000-0002-4784-9867; Cavanna, Flavio/0000-0002-5586-9964; Diaz Desposorio, Felix Napoleon/0000-0002-8232-058X; Castromonte Flores, Cesar Manuel/0000-0002-9559-3704; Dharmapalan, Ranjan/0000-0002-5450-6942; Kadenko, Ihor/0000-0001-8766-4229; Matthews, James/0000-0002-1832-4420; Parke, Stephen/0000-0003-2028-6782; Poling, Ronald/0000-0001-7305-4702; Louis, William/0000-0002-7579-3709; Bitadze, Alexander/0000-0001-7979-1092; David, Claire/0000-0002-1794-1443; Habig, Alec/0000-0002-1018-9383; Hasegawa, Takuya/0000-0002-2967-1954; Singh, Prabhjot/0000-0002-3583-1918; Li, Shirley/0000-0002-2157-8982; Tosi, Nicolo/0000-0002-0474-0247; Cherdack, Daniel/0000-0002-3829-728X; Soleti, Stefano Roberto/0000-0002-5526-1414; Gandrajula, Reddy Pratap/0000-0001-9053-3182; Sarcevic, Ina/0000-0003-1364-6858; Palestini, Sandro/0000-0002-4110-096X; Blazey, Gerald Charles/0000-0002-7435-5758; Berner, Roman Matthias/0000-0001-8088-0965; Mualem, Leon/0000-0003-3716-5100; Singh, Jaydip/0000-0003-3413-9548; Kreczko, Luke/0000-0003-2341-8330; Benekos, Nektarios/0000-0001-7831-8762; Dell'Acqua, Andrea/0000-0003-2453-7745; Hoyos, Jaime/0000-0003-2142-1448; McNab, Andrew/0000-0001-5023-2086; Eurin, Guillaume/0000-0002-4511-3960; Muramatsu, Hajime/0000-0003-0305-4461; Ha, Chang Hyon/0000-0002-9598-8589; Nishimura, Kurtis/0000-0001-8818-8922; Leigui de Oliveira, Marcelo Augusto/0000-0001-9067-1577; de Mello Neto, Joao/0000-0002-3234-6634; Solano Salinas, Carlos Javier/0000-0001-7821-498X; Soldner-Rembold, Stefan/0000-0002-9079-6860; Fernandez-Martinez, Enrique/0000-0002-6274-4473; Wang, Michael/0000-0002-4713-9646; Conrad, Janet/0000-0002-6393-0438; Biagi, Simone/0000-0001-8598-0017; Hillier, Stephen/0000-0002-7599-6469; Barrow, Joshua/0000-0002-7319-3339; Lineros, Roberto/0000-0002-9419-5480; Christodoulou, Georgios/0000-0003-0648-0319; Weber, Michele/0000-0002-2770-9031; Gallice, Niccolo/0000-0003-1226-388X; Ventura, Sandro/0000-0002-8938-2193; Chen, Hucheng/0000-0002-9936-0115; Schukraft, Anne/0000-0002-9112-5479; Hilgenberg, Christopher/0000-0001-7847-487X Fermi Research Alliance, LLC (FRA) [DE-AC02-07CH11359]; CNPq, Brazil; FAPERJ, Brazil; FAPEG, Brazil; FAPESP, Brazil; CFI, Canada; IPP, Canada; NSERC, Canada; CERN; MSMT, Czech Republic; ERDF, European Union; H2020-EU, European Union; MSCA, European Union; CNRS/IN2P3, France; CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; CAM, Spain; Fundacion La Caixa, Spain; Junta de Andalucia-FEDER, Spain; MICINN, Spain; Xunta de Galicia, Spain; SERI, Switzerland; SNSF, Switzerland; TuBTAK, Turkey; Royal Society, United Kingdom; UKRI/STFC, United Kingdom; DOE, United States of America; NSF, United States of America; National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231] Fermi Research Alliance, LLC (FRA); CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPERJ, Brazil(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPEG, Brazil(Fundacao de Amparo a Pesquisa do Estado do Goias (FAPEG)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); CFI, Canada(Canada Foundation for Innovation); IPP, Canada; NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); CERN; MSMT, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); ERDF, European Union; H2020-EU, European Union; MSCA, European Union; CNRS/IN2P3, France(Centre National de la Recherche Scientifique (CNRS)); CEA, France(French Atomic Energy Commission); INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); NRF, South Korea(National Research Foundation of Korea); CAM, Spain; Fundacion La Caixa, Spain(La Caixa Foundation); Junta de Andalucia-FEDER, Spain; MICINN, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Xunta de Galicia, Spain(Xunta de Galicia); SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); TuBTAK, Turkey(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); Royal Society, United Kingdom(Royal Society of London); UKRI/STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility The ProtoDUNE-SP detector was constructed and operated on the CERN Neutrino Platform. We gratefully acknowledge the support of the CERN management, and the CERN EP, BE, TE, EN and IT Departments for NP04/ProtoDUNE-SP. This document was prepared by the DUNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, IPP and NSERC, Canada; CERN; MSMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; CAM, Fundacion La Caixa, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain; SERI and SNSF, Switzerland; TuBTAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. 29 0 0 25 25 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C OCT 12 2022.0 82 10 903 10.1140/epjc/s10052-022-10791-2 0.0 19 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 5F7PH Green Submitted, Green Published, gold, Green Accepted 2023-03-23 WOS:000866503200001 0
C Chen, HJ; Zhang, X; Song, T; Wang, X; Zeng, X; Rodriguez-Paton, A Zheng, H; Callejas, Z; Griol, D; Wang, H; Hu, X; Schmidt, H; Baumbach, J; Dickerson, J; Zhang, L Chen, Hongjie; Zhang, Xuan; Song, Tao; Wang, Xun; Zeng, Xiangxiang; Rodriguez-Paton, Alfonso LncRNA-disease association prediction based on neighborhood information aggregation in neural network PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) IEEE International Conference on Bioinformatics and Biomedicine-BIBM English Proceedings Paper IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics DEC 03-06, 2018 Madrid, SPAIN Ulster Univ,Univ Granada,Univ Carlos III Madrid,TCCLS,IEEE,IEEE Comp Soc,Natl Sci Fdn,Syst Med,Mary Ann Liebert Inc publishers LONG NONCODING RNAS; CANCER; METASTASIS; EXPRESSION Recent studies have demonstrated that lncRNAs play pivotal roles in various biological processes. Some computational methods have been developed to infer lncRNA-disease associations. However, the experimental identification is time-consuming. In this paper, we introduce a method named NNHLDA, which is based on neighborhood information aggregation in neural network. NNHLDA outperforms previous methods at lncRNA-disease association prediction in heterogeneous network. To evaluate our method, we conduct several experiments. In leave-one-out cross-validation (LOOCV) experiments, our NNHLDA method performed better than current state-of-the-art approach. Furthermore, we extracted top 100 lncRNA-disease associations identified by our method and conducted case studies on gastric cancer. The predictions have been confirmed by verified experimental results. Therefore, it is anticipated that NNHLDA could be a useful tool for biomedical researches. [Chen, Hongjie] Xiamen Univ, Dept Comp Sci Sch Informat & Technol, Xiamen 361000, Peoples R China; [Zhang, Xuan] Univ Technol Sydney, Fac Engn & Informat Technol, Adv Analyt Inst, Sydney, NSW 120558, Australia; [Song, Tao; Wang, Xun] China Univ Petr, Coll Comp & Commun Engn, Qingdao 266580, Peoples R China; [Zeng, Xiangxiang; Rodriguez-Paton, Alfonso] Univ Politecn Madrid, Dept Inteligencia Artificial, Campus Montegancedo, E-28660 Madrid, Spain Xiamen University; University of Technology Sydney; China University of Petroleum; Universidad Politecnica de Madrid Zeng, X (corresponding author), Univ Politecn Madrid, Dept Inteligencia Artificial, Campus Montegancedo, E-28660 Madrid, Spain. jeffchan@stu.xmu.edu.cn; xzeng@xmu.edu.cn zeng, xiangxiang/H-3771-2014; Wang, Xun/HOH-8824-2023; Rodríguez-Patón, Alfonso/H-1003-2011 zeng, xiangxiang/0000-0003-1081-7658; Rodríguez-Patón, Alfonso/0000-0001-7289-2114; Zhang, Xuan/0000-0002-3089-9809 National Natural Science Foundation of China [61472333, 61772441, 61472335, 61272152, 41476118]; Project of marine economic innovation and development in Xiamen [16PFW034SF02]; Natural Science Foundation of the Higher Education Institutions of Fujian Province [JZ160400]; Natural Science Foundation of Fujian Province [2017J01099]; President Fund of Xiamen University [20720170054, IJCI-2015-26991] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of marine economic innovation and development in Xiamen; Natural Science Foundation of the Higher Education Institutions of Fujian Province; Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); President Fund of Xiamen University The work was supported by the National Natural Science Foundation of China (Grant Nos. 61472333, 61772441, 61472335, 61272152, and 41476118), Project of marine economic innovation and development in Xiamen (No. 16PFW034SF02), Natural Science Foundation of the Higher Education Institutions of Fujian Province (No JZ160400), Natural Science Foundation of Fujian Province (No. 2017J01099), President Fund of Xiamen University (No. 20720170054). X. Zeng is supported by Juan de la Cierva position (code: IJCI-2015-26991). 18 4 4 1 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-1125 2156-1133 978-1-5386-5488-0 IEEE INT C BIOINFORM 2018.0 175 178 4 Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematical & Computational Biology BM0DB 2023-03-23 WOS:000458654000032 0
J Xie, XR; Qu, H; Yi, Z; Kurths, J Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Pattern recognition; selective attention mechanism; spiking neural network (SNN); supervised learning LEARNING ALGORITHM; CLASSIFICATION; RECOGNITION; SIMULATOR; MODEL; TIME The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN. [Xie, Xiurui; Qu, Hong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China; [Qu, Hong; Kurths, Jurgen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Yi, Zhang] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China; [Kurths, Jurgen] Humboldt Univ, Dept Phys, D-12489 Berlin, Germany University of Electronic Science & Technology of China; Potsdam Institut fur Klimafolgenforschung; Sichuan University; Humboldt University of Berlin Xie, XR (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China. xiexiurui@126.com; hongqu@uestc.edu.cn; zhangyi@scu.edu.cn; kurths@pik-potsdam.de National Science Foundation of China [61273308, 61432012, 61573081] National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Science Foundation of China under Grant 61273308, Grant 61432012, and Grant 61573081. 39 31 33 1 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JUN 2017.0 28 6 1411 1424 10.1109/TNNLS.2016.2541339 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering EV7SQ 28113824.0 2023-03-23 WOS:000401982100013 0
J Palesi, F; Nigri, A; Gianeri, R; Aquino, D; Redolfi, A; Biagi, L; Carne, I; De Francesco, S; Ferraro, S; Martucci, P; Medina, JP; Napolitano, A; Pirastru, A; Baglio, F; Tagliavini, F; Bruzzone, MG; Tosetti, M; Wheeler-Kingshott, CAMG Palesi, Fulvia; Nigri, Anna; Gianeri, Ruben; Aquino, Domenico; Redolfi, Alberto; Biagi, Laura; Carne, Irene; De Francesco, Silvia; Ferraro, Stefania; Martucci, Paola; Medina, Jean Paul; Napolitano, Antonio; Pirastru, Alice; Baglio, Francesca; Tagliavini, Fabrizio; Bruzzone, Maria Grazia; Tosetti, Michela; Wheeler-Kingshott, Claudia A. M. Gandini RIN Neuroimaging NetworkG MRI data quality assessment for the RIN-Neuroimaging Network using the ACR phantoms PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS English Article ACR; Quality control; Multisite ASSURANCE Purpose: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra-and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. Methods: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The stan-dard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detect -ability tests were performed using an automatic QC script. Results: Measures were mostly within the ACR tolerance ranges for both T1-and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good repro-ducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). Conclusions: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers. [Palesi, Fulvia; Martucci, Paola; Wheeler-Kingshott, Claudia A. M. Gandini] Univ Pavia, Dept Brain & Behav Sci, Pavia, Italy; [Palesi, Fulvia; Wheeler-Kingshott, Claudia A. M. Gandini] IRCCS Mondino Fdn, Brain Connect Ctr, Pavia, Italy; [Nigri, Anna; Gianeri, Ruben; Aquino, Domenico; Ferraro, Stefania; Medina, Jean Paul; Bruzzone, Maria Grazia] Fdn IRCCS Ist Neurol Carlo Besta, UO Neuroradiol, Via Celoria 11, I-20133 Milan, Italy; [Redolfi, Alberto; De Francesco, Silvia] IRCCS Ist Ctr San Giovanni di Dio Fatebenefratelli, Lab Neuroinformat, Brescia, Italy; [Biagi, Laura; Tosetti, Michela] Fdn IRCCS Stella Maris, Med Phys & MR Lab, Pisa, Italy; [Carne, Irene] IRCCS Ist Clin Sci Maugeri, Pavia, Italy; [Ferraro, Stefania] Univ Elect Sci & Technol China, Sch Life Sci & Technol, MOE Key Lab Neuroinformat, Chengdu, Peoples R China; [Napolitano, Antonio] IRCCS Ist Osped Pediat Bambino Gesu, Rome, Italy; [Pirastru, Alice; Baglio, Francesca] IRCCS Fdn Don Carlo Gnocchi Onlus, Milan, Italy; [Tagliavini, Fabrizio] Fdn IRCCS Ist Neurol Carlo Besta, Direz Sci, Milan, Italy; [Wheeler-Kingshott, Claudia A. M. Gandini] UCL, UCL Queen Sq Inst Neurol, Fac Brain Sci, NMR Res Unit,Dept Neuroinflammat,Queen Sq MS Ctr, Gower St, London WC1E 6BT, England University of Pavia; IRCCS Fondazione Casimiro Mondino; IRCCS Istituto Neurologico Besta; IRCCS Fondazione Stella Maris; Istituti Clinici Scientifici Maugeri IRCCS; University of Electronic Science & Technology of China; IRCCS Istituto Neurologico Besta; University of London; University College London Nigri, A (corresponding author), Fdn IRCCS Ist Neurol Carlo Besta, UO Neuroradiol, Via Celoria 11, I-20133 Milan, Italy. anna.nigri@istituto-besta.it Biagi, Laura/K-5692-2016; Nigri, Anna/AAM-9827-2021; De Francesco, Silvia/ABD-5005-2020; Redolfi, Alberto/J-7675-2016; Palesi, Fulvia/J-5902-2016 Biagi, Laura/0000-0003-2159-439X; Nigri, Anna/0000-0002-1197-5458; De Francesco, Silvia/0000-0002-9277-9087; Redolfi, Alberto/0000-0002-4145-9059; Palesi, Fulvia/0000-0001-5027-8770; Medina Carrion, Jean Paul/0000-0002-6450-0202; Gandini Wheeler-Kingshott, Claudia A.M./0000-0002-4832-1300 Italian Minister of Health [RRC-2016-2361095, RRC-2017-2364915, RRC-2018-2365796, RCR-2019-23669119_001, RCR 2020-23670067, RCR 2022-23682285]; Ministry of Economy and Finance [CCR-2017-23669078] Italian Minister of Health; Ministry of Economy and Finance This project was funded by the Italian Minister of Health (RRC; RRC2016-2361095; RRC-2017-2364915; RRC-2018-2365796; RCR-201923669119_001; RCR 2020-23670067; RCR 2022-23682285) and the Ministry of Economy and Finance (CCR-2017-23669078). The funding sources had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, and approval of the manuscript. 30 0 0 3 3 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1120-1797 1724-191X PHYS MEDICA Phys. Medica DEC 2022.0 104 93 100 10.1016/j.ejmp.2022.10.008 0.0 NOV 2022 8 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 6Q9BF 36379160.0 hybrid, Green Published 2023-03-23 WOS:000891902800004 0
J Li, X; Ding, ML; Pizurica, A Li, Xian; Ding, Mingli; Pizurica, Aleksandra Spectral Feature Fusion Networks With Dual Attention for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Correlation; Hyperspectral imaging; Training; Testing; Convolution; Deep learning; Attention mechanism; deep learning; hyperspectral image classification; spectral feature fusion MARKOV-RANDOM-FIELDS; CNN Recent progress in spectral classification is largely attributed to the use of convolutional neural networks (CNNs). While a variety of successful architectures have been proposed, they all extract spectral features from various portions of adjacent spectral bands. In this article, we take a different approach and develop a deep spectral feature fusion method, which extracts both local and interlocal spectral features, capturing thus also the correlations among nonadjacent bands. To our knowledge, this is the first reported deep spectral feature fusion method. Our model is a two-stream architecture, where an intergroup and a groupwise spectral classifier operate in parallel. The interlocal spectral correlation feature extraction is achieved elegantly, by reshaping the input spectral vectors to form the so-called nonadjacent spectral matrices. We introduce the concept of groupwise band convolution to enable the efficient extraction of discriminative local features with multiple kernels adopting the local spectral content. Another important contribution of this work is a novel dual-channel attention mechanism to identify the most informative spectral features. The model is trained in an end-to-end fashion with a joint loss. Experimental results on real datasets demonstrate excellent performance compared with the current state of the art. [Li, Xian; Ding, Mingli] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China; [Li, Xian; Pizurica, Aleksandra] Univ Ghent, UGent GAIM, Dept Telecommun & Informat Proc, B-9000 Ghent, Belgium Harbin Institute of Technology; Ghent University Ding, ML (corresponding author), Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China. xianli0511@gmail.com; dingml@hit.edu.cn; aleksandra.pizurica@ugent.be Pizurica, Aleksandra/AAG-4687-2021; Pizurica, Aleksandra/K-6866-2015 Pizurica, Aleksandra/0000-0002-9322-4999; Li, Xian/0000-0001-5714-3940 China Scholarship Council; AI Program of the Flemish Government China Scholarship Council(China Scholarship Council); AI Program of the Flemish Government This work was supported in part by the China Scholarship Council and in part by the AI Program of the Flemish Government. 49 5 5 5 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 10.1109/TGRS.2021.3084922 0.0 JUN 2021 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG3HV Green Published 2023-03-23 WOS:000732767500001 0
J Kouchaki, S; Yang, Y; Walker, TM; Walker, AS; Wilson, DJ; Peto, TEA; Crook, DW; Clifton, DA; Hoosdally, SJ; Cruz, ALG; Carter, J; Grazian, C; Fowler, PW; Iqbal, Z; Hunt, M; Smith, EG; Rathod, P; Jarrett, L; Matias, D; Cirillo, DM; Borroni, E; Battaglia, S; Ghodousi, A; Spitaleri, A; Cabibbe, A; Tahseen, S; Nilgiriwala, K; Shah, S; Rodrigues, C; Kambli, P; Surve, U; Khot, R; Niemann, S; Kohl, T; Merker, M; Hoffmann, H; Molodtsov, N; Plesnik, S; Ismail, N; Omar, SV; Joseph, L; Marubini, E; Thwaites, G; Thuong, TNT; Ngoc, NH; Srinivasan, V; Moore, D; Coronel, J; Solano, W; Gao, GF; He, GX; Zhao, YL; Ma, AJ; Liu, CF; Zhu, BL; Laurenson, I; Claxton, P; Wilkinson, RJ; Lalvani, A; Posey, J; Gardy, J; Werngren, J; Paton, N; Jou, RW; Wu, MH; Lin, WH; Ferrazoli, L; de Oliveira, RS Kouchaki, Samaneh; Yang, Yang; Walker, Timothy M.; Walker, A. Sarah; Wilson, Daniel J.; Peto, Timothy E. A.; Crook, Derrick W.; Clifton, David A.; Hoosdally, Sarah J.; Cruz, Ana L. Gibertoni; Carter, Joshua; Grazian, Clara; Fowler, Philip W.; Iqbal, Zamin; Hunt, Martin; Smith, E. Grace; Rathod, Priti; Jarrett, Lisa; Matias, Daniela; Cirillo, Daniela M.; Borroni, Emanuele; Battaglia, Simone; Ghodousi, Arash; Spitaleri, Andrea; Cabibbe, Andrea; Tahseen, Sabira; Nilgiriwala, Kayzad; Shah, Sanchi; Rodrigues, Camilla; Kambli, Priti; Surve, Utkarsha; Khot, Rukhsar; Niemann, Stefan; Kohl, Thomas; Merker, Matthias; Hoffmann, Harald; Molodtsov, Nikolay; Plesnik, Sara; Ismail, Nazir; Omar, Shaheed Vally; Joseph, Lavania; Marubini, Elliott; Thwaites, Guy; Thuong Nguyen Thuy Thuong; Nhung Hoang Ngoc; Srinivasan, Vijay; Moore, David; Coronel, Jorge; Solano, Walter; Gao, George F.; He, Guangxue; Zhao, Yanlin; Ma, Aijing; Liu, Chunfa; Zhu, Baoli; Laurenson, Ian; Claxton, Pauline; Wilkinson, Robert J.; Lalvani, Ajit; Posey, James; Gardy, Jennifer; Werngren, Jim; Paton, Nicholas; Jou, Ruwen; Wu, Mei-Hua; Lin, Wan-Hsuan; Ferrazoli, Lucilaine; de Oliveira, Rosaline Siqueira CRyPTIC Consortium Application of machine learning techniques to tuberculosis drug resistance analysis BIOINFORMATICS English Article MYCOBACTERIUM-TUBERCULOSIS; MATRIX Motivation Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P<0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers. Availability and implementation The source code can be found at http://www.robots.ox.ac.uk/ davidc/code.php [Kouchaki, Samaneh; Yang, Yang; Clifton, David A.] Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England; [Walker, Timothy M.; Walker, A. Sarah; Peto, Timothy E. A.; Crook, Derrick W.] Univ Oxford, Nuffield Dept Med, Oxford, England; [Walker, Timothy M.; Walker, A. Sarah; Peto, Timothy E. A.; Crook, Derrick W.] John Radcliffe Hosp, Natl Inst Hlth Res, Oxford Biomed Res Ctr, Oxford, England; [Walker, A. Sarah] UCL, Clin Trials Unit, MRC, London, England; [Wilson, Daniel J.] Univ Oxford, Li Ka Shing Ctr Hlth Informat & Discovery, Nuffield Dept Populat Hlth, Oxford, England; [Crook, Derrick W.] Publ Hlth England, Natl Infect Serv, London, England; [Kouchaki, Samaneh; Yang, Yang; Walker, Timothy M.; Walker, A. Sarah; Wilson, Daniel J.; Peto, Timothy E. A.; Crook, Derrick W.; Clifton, David A.; Hoosdally, Sarah J.; Cruz, Ana L. Gibertoni; Carter, Joshua; Grazian, Clara; Fowler, Philip W.] Univ Oxford, Oxford, England; [Iqbal, Zamin; Hunt, Martin] European Bioinformat Inst, Cambridge, England; [Smith, E. Grace; Rathod, Priti; Jarrett, Lisa; Matias, Daniela] Publ Hlth England, Birmingham, W Midlands, England; [Cirillo, Daniela M.; Borroni, Emanuele; Battaglia, Simone; Ghodousi, Arash; Spitaleri, Andrea; Cabibbe, Andrea] IRCCS, San Raffaele Sci Inst, Emerging Bacterial Pathogens Unit, Milan, Italy; [Tahseen, Sabira] Natl TB Control Program Pakistan, Islamabad, Pakistan; [Nilgiriwala, Kayzad; Shah, Sanchi] Fdn Med Res, Mumbai, Maharashtra, India; [Rodrigues, Camilla; Kambli, Priti; Surve, Utkarsha; Khot, Rukhsar] Hinduja Natl Hosp & Med Res Ctr, Mumbai, Maharashtra, India; [Niemann, Stefan; Kohl, Thomas; Merker, Matthias] Res Ctr Borstel, Borstel, Germany; [Hoffmann, Harald; Molodtsov, Nikolay; Plesnik, Sara] IML Red, Inst Microbiol & Lab Med, Gauting, Germany; [Ismail, Nazir; Omar, Shaheed Vally; Joseph, Lavania; Marubini, Elliott] Natl Inst Communicable Dis, Johannesburg, South Africa; [Thwaites, Guy; Thuong Nguyen Thuy Thuong; Nhung Hoang Ngoc; Srinivasan, Vijay] Univ Oxford, Clin Res Unit, Ho Chi Minh City, Vietnam; [Moore, David; Coronel, Jorge; Solano, Walter] London Sch Hyg & Trop Med, London, England; [Moore, David; Coronel, Jorge; Solano, Walter] Univ Peruana Cayetano Hereda, Lima, Peru; [Gao, George F.; He, Guangxue; Zhao, Yanlin; Ma, Aijing; Liu, Chunfa] China CDC, Beijing, Peoples R China; [Zhu, Baoli] Chinese Acad Sci, Inst Microbiol, Beijing, Peoples R China; [Laurenson, Ian; Claxton, Pauline] Scottish Mycobacteria Reference Lab, Edinburgh, Midlothian, Scotland; [Wilkinson, Robert J.] Univ Cape Town, Rondebosch, South Africa; [Lalvani, Ajit] Imperial Coll London, London, England; [Posey, James] CDC Atlanta, Atlanta, GA USA; [Gardy, Jennifer] Univ British Columbia, Vancouver, BC, Canada; [Werngren, Jim] Publ Hlth Agcy Sweden, Stockholm, Sweden; [Paton, Nicholas] Natl Univ Singapore, Singapore, Singapore; [Jou, Ruwen; Wu, Mei-Hua; Lin, Wan-Hsuan] CDC Taiwan, Taipei, Taiwan; [Ferrazoli, Lucilaine; de Oliveira, Rosaline Siqueira] Adolfo Lutz Inst, Sao Paulo, Brazil University of Oxford; University of Oxford; University of Oxford; Medical Research Council Clinical Trials Unit; University of London; University College London; University of Oxford; Public Health England; University of Oxford; European Molecular Biology Laboratory (EMBL); Public Health England; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele; Forschungszentrum Borstel; National Institute for Communicable Diseases (NICD); University of Oxford; University of London; London School of Hygiene & Tropical Medicine; Universidad Peruana Cayetano Heredia; Chinese Center for Disease Control & Prevention; Chinese Academy of Sciences; Institute of Microbiology, CAS; University of Cape Town; Imperial College London; Centers for Disease Control & Prevention - USA; University of British Columbia; Public Health Agency of Sweden; National University of Singapore; Centers For Disease Control - Taiwan; Instituto Adolfo Lutz Yang, Y (corresponding author), Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England. yang.yang@eng.ox.ac.uk Ghodousi, Arash/AAB-8698-2022; Kohl, Thomas Andreas/AAB-9782-2020; Omar, Shaheed Vally/AAB-5071-2020; Wilson, Daniel J/AAE-8975-2019; Gao, George Fu/ABD-5229-2021; Liu, Chunfa/AAQ-5348-2021; Walker, Sarah/HDM-8717-2022; Cabibbe, Andrea Maurizio/U-1634-2019; Walker, Timothy/AAD-5991-2019; Cirillo, Daniela Maria/AAB-3646-2019; Niemann, Stefan/C-9327-2011; Merker, Matthias/P-7399-2015; Tahseen, Sabira/AAV-8653-2020; Comas, Iñaki/AAA-1155-2019; Fowler, Philip/C-5116-2009; Borroni, Emanuele/K-5179-2016 Ghodousi, Arash/0000-0001-9334-1573; Kohl, Thomas Andreas/0000-0002-1126-6803; Omar, Shaheed Vally/0000-0001-6983-1636; Wilson, Daniel J/0000-0002-0940-3311; Liu, Chunfa/0000-0002-4510-8643; Cabibbe, Andrea Maurizio/0000-0001-9727-6465; Cirillo, Daniela Maria/0000-0001-6415-1535; Merker, Matthias/0000-0003-1386-2331; Tahseen, Sabira/0000-0002-6887-8149; Comas, Iñaki/0000-0001-5504-9408; Niemann, Stefan/0000-0002-6604-0684; Crook, Derrick/0000-0002-0590-2850; Wilkinson, Robert/0000-0002-2753-1800; Arandjelovic, Irena/0000-0002-3753-8248; Srinivasan, Vijay/0000-0003-3434-5608; Kouchaki, Samaneh/0000-0002-5870-4030; Thwaites, Guy/0000-0002-2858-2087; Walker, Timothy/0000-0003-0421-9264; GRAZIAN, CLARA/0000-0002-4967-7720; Battaglia, Simone/0000-0003-3854-4110; Fowler, Philip/0000-0003-0912-4483; Borroni, Emanuele/0000-0001-6780-5119 National Institute for Health Research (NIHR) Oxford Biomedical Research Centre; CRyPTIC consortium - Wellcome Trust/Newton Fund-MRC Collaborative Award [200205/Z/15/Z]; Bill and Melinda Gates Foundation Trust [OPP1133541]; Wellcome Trust [101237/Z/13/Z]; Royal Society [101237/Z/13/Z]; EPSRC [EP/N020774/1]; EPSRC [EP/N020774/1] Funding Source: UKRI; MRC [MC_PC_16027] Funding Source: UKRI National Institute for Health Research (NIHR) Oxford Biomedical Research Centre(National Institute for Health Research (NIHR)); CRyPTIC consortium - Wellcome Trust/Newton Fund-MRC Collaborative Award; Bill and Melinda Gates Foundation Trust; Wellcome Trust(Wellcome Trust); Royal Society(Royal Society of London); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) The work was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre; the CRyPTIC consortium which is funded by a Wellcome Trust/Newton Fund-MRC Collaborative Award [200205/Z/15/Z] and the Bill and Melinda Gates Foundation Trust [OPP1133541]. D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society [101237/Z/13/Z]. SK was also supported by EPSRC (Grant EP/N020774/1). 19 28 29 1 14 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics JUL 1 2019.0 35 13 2276 2282 10.1093/bioinformatics/bty949 0.0 7 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics IR4PJ 30462147.0 Green Published, hybrid 2023-03-23 WOS:000481416400014 0
C Cohen, R; Zhang, Y; Solomon, O; Toberman, D; Taieb, L; van Sloun, RJG; Eldar, YC IEEE Cohen, Regev; Zhang, Yi; Solomon, Oren; Toberman, Daniel; Taieb, Liran; van Sloun, Ruud J. G.; Eldar, Yonina C. DEEP CONVOLUTIONAL ROBUST PCA WITH APPLICATION TO ULTRASOUND IMAGING 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 12-17, 2019 Brighton, ENGLAND Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Ultrasound; Deep learning; Contrast agents; Unfolding; Low-rank decomposition. THRESHOLDING ALGORITHM; FILTER DESIGN; SEPARATION; SPARSE Sparse and low-rank decomposition, also known as robust principle component analysis, has been applied successfully in numerous applications. Typically, this approach leads to a minimization problem which is solved using iterative algorithms. Drawing inspiration from recurrent networks, in recent years deep-learning strategies have been extended to mimic the behavior of iterative algorithms, with reduced complexity. In this work, we propose an extension of these deep architectures to robust principle component analysis in which fully-connected layers are replaced with convolutional ones. This strategy offers spatial invariance and significant reduction in the number of learned parameters. We then apply the proposed method to contrast-enhanced ultrasound, in which low-rank tissue signal needs to be removed in order to visualize blood vessels. We demonstrate the effectiveness of our approach on simulations and in-vivo rat brain scans. The resulting images exhibit improved visual quality and contrast compared with images obtained by commonly practiced methods. [Cohen, Regev; Solomon, Oren; Toberman, Daniel; Taieb, Liran; Eldar, Yonina C.] Technion, Dept Elect Engn, IL-32000 Haifa, Israel; [Zhang, Yi] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China; [van Sloun, Ruud J. G.] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands Technion Israel Institute of Technology; Tsinghua University; Eindhoven University of Technology Cohen, R (corresponding author), Technion, Dept Elect Engn, IL-32000 Haifa, Israel. van Sloun, Ruud/AGX-0436-2022; Solomon, Oren/AAC-2120-2019 van Sloun, Ruud/0000-0003-2845-0495; European Unions Horizon 2020 research and innovation program [646804-ERC-COG-BNYQ]; Israel Science Foundation [0100101] European Unions Horizon 2020 research and innovation program; Israel Science Foundation(Israel Science Foundation) This project has received funding from the European Unions Horizon 2020 research and innovation program under grant No. 646804-ERC-COG-BNYQ, and from the Israel Science Foundation under grant No. 0100101. 31 8 8 2 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-4799-8131-1 INT CONF ACOUST SPEE 2019.0 3212 3216 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BN4NP 2023-03-23 WOS:000482554003087 0
J Rodrigues, M; Jimenez-Ruano, A; Gelabert, PJ; de Dios, VR; Torres, L; Ribalaygua, J; Vega-Garcia, C Rodrigues, Marcos; Jimenez-Ruano, Adrian; Gelabert, Pere Joan; de Dios, Victor Resco; Torres, Luis; Ribalaygua, Jaime; Vega-Garcia, Cristina Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula INTERNATIONAL JOURNAL OF WILDLAND FIRE English Article; Early Access fire danger; forecast; fuel moisture; Iberian Peninsula; ignition probability; lightning strike; machine learning; wildfires FOREST-FIRE OCCURRENCE; CLIMATE-CHANGE; WILDFIRES; DISTRIBUTIONS; MOISTURE; DRIVERS; TRENDS Background. Lightning is the most common origin of natural fires, being strongly linked to specific synoptic conditions associated with atmospheric instability, such as dry thunderstorms; dry fuels are required for ignition to take place and for subsequent propagation.Aims. The aim was to predict the daily probability of ignition by exploiting a large dataset of lightning and fire data to anticipate ignition over the entire Iberian Peninsula.Methods. We trained and tested a machine learning model using lightning strikes (> 17 million) in the period 2009-2015. For each lightning strike, we extracted information relating to fuel condition, structural features of vegetation, topography, and the specific characteristics of the strikes (polarity, intensity and flash density).Key results. Naturally triggered ignitions are typically initiated at higher elevations (above 1000 m above sea level) under conditions of low dead fuel moisture (< 10-13%) and moderate live moisture content (Drought Code > 300). Negative-polarity lightning strikes (-10 kA) appear to trigger fires more frequently.Conclusions and implications. Our approach was able to provide ignition forecasts at multiple temporal and spatial scales, thus enhancing forest fire risk assessment systems. [Rodrigues, Marcos; Jimenez-Ruano, Adrian] Univ Zaragoza, Dept Geog & Land Management, Pedro Cerbuna 12, Zaragoza 5009, Spain; [Rodrigues, Marcos; Jimenez-Ruano, Adrian] Univ Zaragoza, Univ Inst Res Environm Sci Aragon IUCA, GEOFOREST Grp, Pedro Cerbuna 12, Zaragoza 5009, Spain; [Gelabert, Pere Joan; Vega-Garcia, Cristina] Univ Lleida, Dept Agr & Forest Engn, Alcalde Rovira Roure 191, Lleida 25198, Spain; [de Dios, Victor Resco] Univ Lleida, Dept Crop & Forest Sci, Alcalde Rovira Roure 191, Lleida 25198, Spain; [de Dios, Victor Resco] CERCA Ctr, Joint Res Unit CTFC, AGROTECNIO, Alcalde Rovira Roure 191, Lleida 25198, Spain; [de Dios, Victor Resco] Southwest Univ Sci & Technol, Sch Life Sci & Engn, 59 Qinlong Rd, Mianyang 621010, Peoples R China; [Torres, Luis; Ribalaygua, Jaime] MeteoGRID SL, Calle Almansa 88, Madrid 28040, Spain University of Zaragoza; University of Zaragoza; Universitat de Lleida; Universitat de Lleida; Southwest University of Science & Technology - China Rodrigues, M (corresponding author), Univ Zaragoza, Dept Geog & Land Management, Pedro Cerbuna 12, Zaragoza 5009, Spain. rmarcos@unizar.es Rodrigues, Marcos/L-7931-2014; de Dios, Víctor Resco/AAH-3655-2019 Rodrigues, Marcos/0000-0002-0477-0796; de Dios, Víctor Resco/0000-0002-5721-1656; Gelabert Vadillo, Pere Joan/0000-0001-8020-4932 66 0 0 5 5 CSIRO PUBLISHING CLAYTON UNIPARK, BLDG 1, LEVEL 1, 195 WELLINGTON RD, LOCKED BAG 10, CLAYTON, VIC 3168, AUSTRALIA 1049-8001 1448-5516 INT J WILDLAND FIRE Int. J. Wildland Fire 10.1071/WF22123 0.0 JAN 2023 12 Forestry Science Citation Index Expanded (SCI-EXPANDED) Forestry 8I6IX hybrid 2023-03-23 WOS:000921836500001 0
J Zhang, XL; Zhang, H; Fan, CW; Hildesjo, C; Shen, BR; Sun, XF Zhang, Xueli; Zhang, Hong; Fan, Chuanwen; Hildesjo, Camilla; Shen, Bairong; Sun, Xiao-Feng Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays CANCERS English Article CHGA; protein biomarker; colon cancer; diagnosis; machine learning; tissue microarrays Simple Summary The identification of effective novel biomarkers is emergently needed in colon cancer patients. In the present study, firstly we predicted that CHGA could be a biomarker for colon cancer based on the protein-protein interaction network of all the reported biomarkers that were collected from our colorectal cancer biomarker database (CBD). Then we verified our results using a diagnostic test in gene expression data and an immunohistochemistry test. The results of this study suggest that a loss of CHGA expression from the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon cancer patients. Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein-protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients. [Zhang, Xueli; Zhang, Hong] Orebro Univ, Sch Med, Inst Med Sci, S-70281 Orebro, Sweden; [Zhang, Xueli] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Guangdong Eye Inst, Dept Ophthalmol, Guangzhou 510080, Peoples R China; [Fan, Chuanwen; Hildesjo, Camilla; Sun, Xiao-Feng] Linkoping Univ, Dept Oncol, S-58183 Linkoping, Sweden; [Fan, Chuanwen; Hildesjo, Camilla; Sun, Xiao-Feng] Linkoping Univ, Dept Biomed & Clin Sci, S-58183 Linkoping, Sweden; [Shen, Bairong] Sichuan Univ, Western China Hosp, Inst Syst Genet, Chengdu 610017, Peoples R China Orebro University; Guangdong Academy of Medical Sciences & Guangdong General Hospital; Linkoping University; Linkoping University; Sichuan University Sun, XF (corresponding author), Linkoping Univ, Dept Oncol, S-58183 Linkoping, Sweden.;Sun, XF (corresponding author), Linkoping Univ, Dept Biomed & Clin Sci, S-58183 Linkoping, Sweden. zhangxueli@gdph.org.cn; hong.zhang@oru.se; xuntian2005@163.com; Camilla.Hildesjo@liu.se; bairong.shen@scu.edu.cn; xiao-feng.sun@liu.se Fan, Chuanwen/GLU-5994-2022 Zhang, Hong/0000-0003-1834-1578; Zhang, Xueli/0000-0001-5963-9261 Swedish Cancer Foundation [19 0322 Pj] Swedish Cancer Foundation This work was supported by the grants from Swedish Cancer Foundation (19 0322 Pj). 31 1 1 4 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6694 CANCERS Cancers JUN 2022.0 14 11 2664 10.3390/cancers14112664 0.0 14 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 1Z1MO 35681650.0 Green Submitted, gold, Green Accepted 2023-03-23 WOS:000808597300001 0
J Ma, RX; Wu, F; Zhang, M; Lu, ZH; Wan, JG; Xie, CS Ma, Ruixiang; Wu, Fei; Zhang, Meng; Lu, Zhonghai; Wan, Jiguang; Xie, Changsheng RBER-Aware Lifetime Prediction Scheme for 3D-TLC NAND Flash Memory IEEE ACCESS English Article NAND flash; P/E cycle; retention time; RBER; machine learning NAND flash memory is widely used in various computing systems. However, flash blocks can sustain only a limited number of program/erase (P/E) cycles, which are referred to as the endurance. On one hand, in order to ensure data integrity, flash manufacturers often define the maximum P/E cycles of the worst block as the endurance of flash blocks. On the other hand, blocks exhibit large endurance variations, which introduce two serious problems. The first problem is that the error correcting code (ECC) is often over-provisioned, as it has to be designed to tolerate the worst case to ensure data integrity, which causes longer decoding latency. The second problem is the underutilized block's lifespan due to conservatively defined block endurance. Raw bit error rate (RBER) of most blocks have not arrived the allowable RBER based on the nominal endurance point, which implies that the conventional P/E cycle-based block retirement policies may waste large flash storage space. In this paper, to exploit the storage capacity of each flash block, we propose an RBER-aware lifetime prediction scheme based on machine learning technologies. We consider the problem that the model can lose prediction effectiveness over time and use incremental learning to update the model for adapting the changes at different lifetime stages. At run time, trained data will be gradually discarded, which can reduce memory overhead. For evaluating our purpose, four well-known machine learning techniques have been compared in terms of predictive accuracy and time overhead under our proposed lifetime prediction scheme. We also compared the predicted values with the tested values obtained in the real NAND flash-based test platform, and the experimental results show that the support vector machine (SVM) models based on our proposed lifetime prediction scheme can achieve as high as 95% accuracy for flash blocks. We also apply our proposed lifetime prediction scheme to predict the actual endurance of flash blocks at four different retention times, and the experimental results show that it can significantly improve the maximum P/E cycle of flash blocks from 37.5% to 86.3% on average. Therefore, the proposed lifetime prediction scheme can provide a guide for block endurance prediction. [Ma, Ruixiang; Wu, Fei; Zhang, Meng; Wan, Jiguang; Xie, Changsheng] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China; [Wu, Fei; Xie, Changsheng] Huazhong Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518000, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-11428 Stockholm, Sweden Huazhong University of Science & Technology; Huazhong University of Science & Technology; Royal Institute of Technology Wu, F (corresponding author), Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China.;Wu, F (corresponding author), Huazhong Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518000, Peoples R China. wufei@hust.edu.cn Lu, Zhonghai/AAP-2154-2020 Ma, Ruixiang/0000-0001-8829-408X Creative Research Group Project of NSFC [61821003]; NSFC [61872413, U1709220]; Wuhan Science and Technology Project [2017010201010108]; Shenzhen Basic Research Project [JCYJ20170307160135308, JCYJ20170818162129916]; National Key Research and Development Program of China [2018YFB10033005]; Fundamental Research Funds for the Central Universities [2016YXMS019]; 111 Project [B07038]; Key Laboratory of Data Storage System, Ministry of Education Creative Research Group Project of NSFC(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)); Wuhan Science and Technology Project; Shenzhen Basic Research Project; National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); 111 Project(Ministry of Education, China - 111 Project); Key Laboratory of Data Storage System, Ministry of Education This work was supported in part by the Creative Research Group Project of NSFC under Grant 61821003, in part by the NSFC under Grant 61872413 and Grant U1709220, in part by the Wuhan Science and Technology Project under Grant 2017010201010108, in part by the Shenzhen Basic Research Project under Grant JCYJ20170307160135308 and Grant JCYJ20170818162129916, in part by the National Key Research and Development Program of China under Grant 2018YFB10033005, in part by the Fundamental Research Funds for the Central Universities under Grant 2016YXMS019, in part by the 111 Project under Grant B07038, and in part by the Key Laboratory of Data Storage System, Ministry of Education. 46 16 16 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 44696 44708 10.1109/ACCESS.2019.2909567 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HU6IY gold 2023-03-23 WOS:000465384700001 0
C Zhang, XY; Li, YL; Sanchez-Jurado, R; Pardo, AC; Polemi, A; Gonzalez, A; Alamo, J; Ferrer, J; Majewski, S; Kundu, B IEEE Zhang, Xinyue; Li, Yinlin; Sanchez-Jurado, Raul; Carmen Pardo, Ana; Polemi, Andrew; Gonzalez, Antonio; Alamo, Jorge; Ferrer, Jose; Majewski, Stan; Kundu, Bijoy Segmentation method for breast tumor diagnosis based on Artificial Neural Network algorithm applied to dynamic 18F-FDG PET images 2015 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC) IEEE Nuclear Science Symposium and Medical Imaging Conference English Proceedings Paper IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) OCT 31-NOV 07, 2015 San Diego, CA IEEE DESIGN To establish an accurate and automatic image segmentation method for extracting the tumor from the healthy tissue using the dedicated 18F-FDG Mammography with Molecular Imaging (MAMMI) Positron Emission Tomography (PET) dynamic images of breast in vivo, the paper presents a novel method using Artificial Neural Network (ANN) combined with time activity curves (TAC) of each voxel as input vector. TACs voxel by voxel were obtained from PET images with average filtering and least-squares fitting algorithm to improve the signal noise ratio (SNR). The data was then normalized and constructed as input feature vectors of the ANN network to train or segment the tumor regions. The initial pilot validation with 2 patient's data of the proposed method using FDG has shown promising results. [Zhang, Xinyue; Li, Yinlin] Beijing Inst Technol, Mechatron Engn Sch, Beijing, Peoples R China; [Li, Yinlin; Polemi, Andrew; Majewski, Stan; Kundu, Bijoy] Univ Virginia, Radiol & Med Imaging Dept, Charlottesville, VA USA; [Sanchez-Jurado, Raul; Ferrer, Jose] ERESA Univ Gen Hosp Valencia, Nucl Med Dept, Valencia, Spain; [Carmen Pardo, Ana; Alamo, Jorge] GEM Imaging SA, Oncovis, Valencia, Spain; [Gonzalez, Antonio] Inst Instrumentat Mol Imaging, Valencia, Spain Beijing Institute of Technology; University of Virginia; Consejo Superior de Investigaciones Cientificas (CSIC); Universitat Politecnica de Valencia; CSIC-UPV-CIEMAT - Instituto de Instrumentacion para Imagen Molecular (I3M) Zhang, XY (corresponding author), Beijing Inst Technol, Mechatron Engn Sch, Beijing, Peoples R China. xinyue.zhanng@gmail.com; yinlin.li@gmail.com; rsanchez@eresa.com; apardo@oncovision.com; amp3as@virginia.edu; agonzalez@i3m.upv.es; jalamo@oncovision.com; jferrer@eresa.com; stan.majewski@gmail.com; bkk5a@virginia.edu Martinez, Antonio Javier Gonzalez/G-8126-2016 Martinez, Antonio Javier Gonzalez/0000-0001-6742-5626 12 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1095-7863 978-1-4673-9862-6 IEEE NUCL SCI CONF R 2015.0 4 Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Radiology, Nuclear Medicine & Medical Imaging BI6TR 2023-03-23 WOS:000413680600338 0
J Xu, C; Du, XP; Fan, XT; Giuliani, G; Hu, ZY; Wang, W; Liu, J; Wang, T; Yan, ZZ; Zhu, JJ; Jiang, TY; Guo, HD Xu, Chen; Du, Xiaoping; Fan, Xiangtao; Giuliani, Gregory; Hu, Zhongyang; Wang, Wei; Liu, Jie; Wang, Teng; Yan, Zhenzhen; Zhu, Junjie; Jiang, Tianyang; Guo, Huadong Cloud-based storage and computing for remote sensing big data: a technical review INTERNATIONAL JOURNAL OF DIGITAL EARTH English Review Remote sensing; big data; cloud computing; data cube; analysis ready data; parallel computing; data model EARTH DATA; MODEL; ANALYTICS; FRAMEWORK; OPPORTUNITIES; EFFICIENT; LANDSAT; SCIENCE; SYSTEMS The rapid growth of remote sensing big data (RSBD) has attracted considerable attention from both academia and industry. Despite the progress of computer technologies, conventional computing implementations have become technically inefficient for processing RSBD. Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years. This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science. First, we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications, i.e. raster storage, metadata management, data homogeneity, and computing paradigms. Second, we introduce state-of-the-art cloud-based data management technologies for RSBD storage. The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies, which we name the RSBD data model. Four data models are suggested, i.e. scenes, ARD, data cubes, and composite layers. Third, we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations. Finally, we categorize the architectures of mainstream RSBD platforms. This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers. [Xu, Chen; Du, Xiaoping; Fan, Xiangtao; Yan, Zhenzhen; Zhu, Junjie; Guo, Huadong] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Xu, Chen; Du, Xiaoping; Fan, Xiangtao; Yan, Zhenzhen; Zhu, Junjie; Guo, Huadong] Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China; [Xu, Chen] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; [Giuliani, Gregory] Univ Geneva, Inst Environm Sci GRID Geneva, Geneva, Switzerland; [Hu, Zhongyang] Univ Utrecht, Inst Marine & Atmospher Res Utrecht IMAU, Utrecht, Netherlands; [Wang, Wei; Liu, Jie] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China; [Wang, Teng] Peoples Republ China Telecom Co Ltd, Tianyi Cloud, Guangzhou, Peoples R China; [Jiang, Tianyang] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Geneva; Utrecht University; Chinese Academy of Sciences; Institute of Software, CAS; Tsinghua University Du, XP (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China.;Du, XP (corresponding author), Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China. duxp@aircas.ac.cn Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080103, XDA19080101]; Innovation Drive Development Special Project of Guangxi [GuikeAA20302022]; National Natural Science Foundation of China [41974108] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Innovation Drive Development Special Project of Guangxi; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by Strategic Priority Research Program of the Chinese Academy of Sciences, Project title: CASEarth: [Grant Number XDA19080103,XDA19080101]; Innovation Drive Development Special Project of Guangxi: [Grant Number GuikeAA20302022]; National Natural Science Foundation of China: [Grant Number 41974108]. 173 0 0 51 56 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1753-8947 1753-8955 INT J DIGIT EARTH Int. J. Digit. Earth DEC 31 2022.0 15 1 1417 1445 10.1080/17538947.2022.2115567 0.0 29 Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Remote Sensing 3Y8DG gold, Green Published 2023-03-23 WOS:000843951300001 0
J Li, K; Kou, JQ; Zhang, WW Li, Kai; Kou, Jiaqing; Zhang, Weiwei Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils AEROSPACE SCIENCE AND TECHNOLOGY English Article Reduced-order model; Long short-term memory network; Unsteady aerodynamics; NACA 6-series airfoils LIMIT-CYCLE OSCILLATION; DYNAMICS; FLUTTER; IDENTIFICATION; NETWORKS; SPARSE; ROBUST; FLOW Computational-fluid-dynamics-based prediction of unsteady aerodynamics is an essential research topic in the design of aircraft, which usually requires very high computational cost. Therefore, developing efficient and accurate reduced-order models (ROMs) for unsteady aerodynamics is important. With recent progress in modeling unsteady aerodynamics for a fixed configuration, there is a need of developing reduced-order models across multiple geometrical shapes. In this work, the long short-term memory (LSTM) network, a traditional model in machine learning for modeling time-dependent dynamics, is utilized to construct the unsteady aerodynamic reduced-order model, which takes into account the variation of airfoil geometry. The proposed approach is illustrated by some NACA 6-series airfoils under pitching and plunging motions in the transonic flow, where the geometrical parametrization is based on the maximum camber and thickness. Results show that the model can accurately capture the unsteady aerodynamics for varying shape parameters. Finally, as a proof of concept, the constructed ROM is applied to uncertainty and sensitivity analysis with respect to the variation of geometry parameters. (c) 2021 Elsevier Masson SAS. All rights reserved. [Li, Kai; Kou, Jiaqing; Zhang, Weiwei] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China; [Kou, Jiaqing] Univ Politecn Madrid, ETSIAE UPM Sch Aeronaut, Plaza Cardenal Cisneros 3, E-28040 Madrid, Spain Northwestern Polytechnical University; Universidad Politecnica de Madrid Kou, JQ (corresponding author), Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China. koujiaqing@mail.nwpu.edu.cn 寇, 家庆/N-4534-2019; Zhang, weiwei/P-8005-2015 寇, 家庆/0000-0002-0965-5404; Zhang, weiwei/0000-0001-7799-833X; Li, Kai/0000-0002-8930-9076 66 14 15 10 31 ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER ISSY-LES-MOULINEAUX 65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE 1270-9638 1626-3219 AEROSP SCI TECHNOL Aerosp. Sci. Technol. DEC 2021.0 119 107173 10.1016/j.ast.2021.107173 0.0 OCT 2021 13 Engineering, Aerospace Science Citation Index Expanded (SCI-EXPANDED) Engineering WN2TH 2023-03-23 WOS:000711625800003 0
J Qian, C; Wu, XF; Jianxun, T; Zihao, C; Shi, WJ; Tang, JF; Kahlert, UD; Du, RF Qian, Chen; Wu, Xiufu; Jianxun, Tang; Zihao, Chen; Shi, Wenjie; Tang, Jingfeng; Kahlert, Ulf D.; Du, Renfei A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma FRONTIERS IN GENETICS English Article Glioblastoma; machine learning; ECM; survival prediction; web server GLIOMA; APOPTOSIS; EFFICACY; CANCER Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis. Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high- and low-ECM index groups with significantly different overall survival rates in four datasets. High-ECM index patients exhibited a worse prognosis than low-ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/). Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients. [Qian, Chen; Wu, Xiufu; Jianxun, Tang; Tang, Jingfeng] Guilin Med Univ, Dept Cerebrovascular Dis, Affiliated Hosp 2, Guilin, Peoples R China; [Zihao, Chen; Shi, Wenjie] Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany; [Shi, Wenjie; Kahlert, Ulf D.] Otto Von Guericke Univ, Univ Clin Gen Visceral Vasc & Transplantat Surg, Univ Hosp Magdeburg, Mol & Expt Surg,Med Fac, Magdeburg, Germany; [Du, Renfei] Heinrich Heine Univ Dusseldorf, Med Fac, Clin Neurosurg, Dusseldorf, Germany; [Du, Renfei] Chifeng Municipal Hosp, Chifeng, Inner Mongolia, Peoples R China Guilin Medical University; Pius-Hospital Oldenburg; Otto von Guericke University; University Hospital Magdeburg; Heinrich Heine University Dusseldorf Kahlert, UD (corresponding author), Otto Von Guericke Univ, Univ Clin Gen Visceral Vasc & Transplantat Surg, Univ Hosp Magdeburg, Mol & Expt Surg,Med Fac, Magdeburg, Germany.;Du, RF (corresponding author), Heinrich Heine Univ Dusseldorf, Med Fac, Clin Neurosurg, Dusseldorf, Germany.;Du, RF (corresponding author), Chifeng Municipal Hosp, Chifeng, Inner Mongolia, Peoples R China. ulf.kahlert@med.ovgu.de; redu100@uni-duesseldorf.de Kahlert, Ulf Dietrich/0000-0002-6021-1841 35 0 0 0 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. JUN 17 2022.0 13 851427 10.3389/fgene.2022.851427 0.0 11 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity 2P0UR 35783254.0 Green Accepted, gold 2023-03-23 WOS:000819467500001 0
J Lustrek, M; Lorenz, P; Kreutzer, M; Qian, ZL; Steinbeck, F; Wu, D; Born, N; Ziems, B; Hecker, M; Blank, M; Shoenfeld, Y; Cao, ZW; Glocker, MO; Li, YX; Fuellen, G; Thiesen, HJ Lustrek, Mitja; Lorenz, Peter; Kreutzer, Michael; Qian, Zilliang; Steinbeck, Felix; Wu, Di; Born, Nadine; Ziems, Bjoern; Hecker, Michael; Blank, Miri; Shoenfeld, Yehuda; Cao, Zhiwei; Glocker, Michael O.; Li, Yixue; Fuellen, Georg; Thiesen, Hans-Juergen Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins PLOS ONE English Article B-CELL EPITOPES; RECEPTOR; ANTIGENICITY; INDUCTION; PEPTIDES; DISEASE; SERUM; MODEL Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognitionmodes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR. [Lustrek, Mitja; Fuellen, Georg] Univ Rostock, Univ Med Rostock, Inst Biostat & Informat Med & Ageing Res, D-18055 Rostock, Germany; [Lustrek, Mitja] Jozef Stefan Inst, Dept Intelligent Syst, Ljubljana, Slovenia; [Lorenz, Peter; Kreutzer, Michael; Qian, Zilliang; Steinbeck, Felix; Wu, Di; Born, Nadine; Thiesen, Hans-Juergen] Univ Rostock, Univ Med Rostock, Inst Immunol, D-18055 Rostock, Germany; [Qian, Zilliang; Wu, Di; Li, Yixue] Shanghai Inst Biol Sci, Shanghai, Peoples R China; [Ziems, Bjoern] Gesell Individualisierte Med GmbH, Rostock, Germany; [Hecker, Michael] Steinbeis Transfer Ctr Proteome Anal, Rostock, Germany; [Blank, Miri; Shoenfeld, Yehuda] Sheba Med Ctr, Zabludovicz Ctr Autoimmune Dis, Ramat Gan, Israel; [Cao, Zhiwei] Tongji Univ, Sch Life Sci & Technol, Shanghai 200092, Peoples R China; [Glocker, Michael O.] Univ Rostock, Proteome Ctr Rostock, D-18055 Rostock, Germany University of Rostock; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Rostock; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Chaim Sheba Medical Center; Tongji University; University of Rostock Thiesen, HJ (corresponding author), Univ Rostock, Univ Med Rostock, Inst Immunol, D-18055 Rostock, Germany. HJ.Thiesen@gmx.de Wu, Di/J-4483-2012 Wu, Di/0000-0001-9565-1217; Hecker, Michael/0000-0001-7015-3094; Lorenz, Peter A./0000-0001-7925-1002 Autorome [LSHM-CT-2004-005264]; E-RARE [01GM0803]; BMBF [0315450D]; State of Mecklenburg-Vorpommern MV-Excellence [UR08051, UR09012] Autorome; E-RARE; BMBF(Federal Ministry of Education & Research (BMBF)); State of Mecklenburg-Vorpommern MV-Excellence EU funded Autorome (LSHM-CT-2004-005264) and E-RARE (01GM0803); BMBF support through Med-Sys (0315450D), and funding by the State of Mecklenburg-Vorpommern MV-Excellence: (UR08051 und UR09012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 48 13 13 0 6 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One NOV 11 2013.0 8 11 e78605 10.1371/journal.pone.0078605 0.0 15 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 255EG 24244326.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000327221600036 0
J Li, YD; Mavromatis, S; Zhang, F; Du, ZH; Sequeira, J; Wang, ZY; Zhao, XW; Liu, RY Li, Yadong; Mavromatis, Sebastien; Zhang, Feng; Du, Zhenhong; Sequeira, Jean; Wang, Zhongyi; Zhao, Xianwei; Liu, Renyi Single-Image Super-Resolution for Remote Sensing Images Using a Deep Generative Adversarial Network With Local and Global Attention Mechanisms IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Remote sensing; Feature extraction; Image reconstruction; Spatial resolution; Signal processing algorithms; Biological system modeling; Generative adversarial networks; Convolutional neural networks (CNNs); generative adversarial network (GAN); local and global attention module; remote sensing; single-image super super-resolution (SISR) RECONSTRUCTION Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth's surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines L1 pixel loss, L1 perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method. [Li, Yadong; Zhang, Feng; Du, Zhenhong; Liu, Renyi] Zhejiang Univ, Sch Earth Sci, Zhejiang Prov Lab Geog Informat Syst GIS, Hangzhou 310058, Peoples R China; [Mavromatis, Sebastien; Sequeira, Jean] Aix Marseille Univ, French Natl Ctr Sci Res CNRS, Lab Comp Sci & Syst LIS, F-13001 Marseille, France; [Wang, Zhongyi; Zhao, Xianwei] Huawei Technol Co Ltd, City Intelligence Cloud & AI, Shenzhen 518129, Peoples R China Zhejiang University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Aix-Marseille Universite; Huawei Technologies Du, ZH (corresponding author), Zhejiang Univ, Sch Earth Sci, Zhejiang Prov Lab Geog Informat Syst GIS, Hangzhou 310058, Peoples R China. 11738022@zju.edu.cn; sebastien.mavromatis@unv-amu.fr; duzhenhong@zju.edu.cn; zfcarnation@zju.edu.cn; jean.sequeira@univ-amu.fr; bigbaby.wang@huawei.com; zhaoxianwei@huawei.com; liurenyi@zju.edu.cn Mavromatis, Sébastien/AAW-4368-2020 Mavromatis, Sébastien/0000-0002-2838-5112; Du, Zhenhong/0000-0001-9449-0415; Li, Yadong/0000-0002-6448-9736 National Key Research and Development Project [2018YFB0505000]; Fundamental Research Funds for the Central Universities [2019QNA3013] National Key Research and Development Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development Project under Grant 2018YFB0505000 and in part by the Fundamental Research Funds for the Central Universities under Grant 2019QNA3013. 77 4 4 6 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 10.1109/TGRS.2021.3093043 0.0 JUL 2021 24 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG8XJ 2023-03-23 WOS:000733242200001 0
J Lv, ZH; Chen, DL; Lv, HB Lv, Zhihan; Chen, Dongliang; Lv, Haibin Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS English Article Smart City; digital twins; BIM; multimedia big data; Bayesian Network Structural Learning Algorithm OPTIMIZATION; INTEGRATION; PERFORMANCE; ALGORITHM; FRAMEWORK; MODEL; GIS With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently. [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden; [Chen, Dongliang] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China; [Lv, Haibin] Minist Nat Resources North Sea Bur, North China Sea Offshore Engn Survey Inst, Qingdao 266061, Peoples R China Uppsala University; Qingdao University Lv, ZH (corresponding author), Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden. lvzhihan@gmail.com; cdlord@qq.com; lvhaibinsoa@gmail.com Lv, Zhihan/I-3187-2014 Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China (NSFC) [61902203] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61902203. 40 32 32 2 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1551-6857 1551-6865 ACM T MULTIM COMPUT ACM Trans. Multimed. Comput. Commun. Appl. JUN 2022.0 18 2 S 117 10.1145/3529395 0.0 21 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7R6FY Bronze 2023-03-23 WOS:000910168000004 0
J Han, J; Shen, D; Chien, CJ Han, Jian; Shen, Dong; Chien, Chiang-Ju Terminal iterative learning control for discrete-time nonlinear systems based on neural networks JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS English Article TO-POINT TRACKING; OPTIMIZATION PROBLEMS; TILC The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. [Han, Jian; Shen, Dong] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Han, Jian] Univ Amsterdam, Informat Inst, Fac Sci, NL-1098 XH Amsterdam, Netherlands; [Chien, Chiang-Ju] Huafan Univ, Dept Elect Engn, New Taipei 22301, Taiwan Beijing University of Chemical Technology; University of Amsterdam; Huafan University Shen, D (corresponding author), Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China. J.Han@uva.nl; shendong@mail.buct.edu.cn; cjc@cc.hfu.edu.tw SHEN, Dong/0000-0003-1063-1351 National Natural Science Foundation of China [61673045, 61304085]; Beijing Natural Science Foundation [4152040] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work is supported by National Natural Science Foundation of China (61673045, 61304085), Beijing Natural Science Foundation (4152040). 26 10 10 0 17 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-0032 1879-2693 J FRANKLIN I J. Frankl. Inst.-Eng. Appl. Math. MAY 2018.0 355 8 3641 3658 10.1016/j.jfranklin.2018.03.008 0.0 18 Automation & Control Systems; Engineering, Multidisciplinary; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics GG1IK 2023-03-23 WOS:000432440400012 0
J Yang, QP; Lv, J; Tong, QC; Du, X; Wang, YC; Zhang, ST; Yang, GC; Bergara, A; Ma, YM Yang, Qiuping; Lv, Jian; Tong, Qunchao; Du, Xin; Wang, Yanchao; Zhang, Shoutao; Yang, Guochun; Bergara, Aitor; Ma, Yanming Hard and superconducting cubic boron phase via swarm-intelligence structural prediction driven by a machine-learning potential PHYSICAL REVIEW B English Article TRANSITION-TEMPERATURE; CRYSTAL-STRUCTURE; CARBON; GRAPHENE; FORM; ALLOTROPE; CHEMISTRY; STATE Boron is an intriguing element due to its electron deficiency and the ability to form multicenter bonds in allotropes and borides, exhibiting diversified structures, unique chemical bonds, and interesting properties. Using swarm-intelligence structural prediction driven by a machine learning potential, we identified a boron phase with a 24-atom cubic unit cell, called c-B-24 , consisting of a B-6 octahedron in addition to well-known B-2 pairs and B-12 icosahedra at ambient pressure. There appear unusual four-center-two-electron (4c-2e) bonds in the B-12 icosahedron, originating from the peculiar bonding pattern between the B2 pair and B12 icosahedron, which is in sharp contrast with the 3c-2e and 2c-2e bonds in alpha-B-12 . More interestingly, c-B-24 is a metal with a superconducting critical temperature of 13.8 K at ambient pressure. The predicted Vickers hardness (23.1 GPa) indicates that c-B-24 is a potential hard material. Notably, it also has a good shear/tensile resistance (48.9/29.3 GPa). Our work not only enriches the understanding of the chemical properties of boron, but also sparks efforts on trying to synthesize this particular compound, c-B-24. [Yang, Qiuping; Du, Xin; Zhang, Shoutao; Yang, Guochun] Northeast Normal Univ, Ctr Adv Optoelect Funct Mat Res, Changchun 130024, Peoples R China; [Yang, Qiuping; Du, Xin; Zhang, Shoutao; Yang, Guochun] Northeast Normal Univ, Key Lab UV Light Emitting Mat & Technol, Minist Educ, Changchun 130024, Peoples R China; [Yang, Qiuping; Du, Xin; Yang, Guochun] Yanshan Univ, Sch Sci, State Key Lab Metastable Mat Sci & Technol, Qinhuangdao 066004, Hebei, Peoples R China; [Yang, Qiuping; Du, Xin; Yang, Guochun] Yanshan Univ, Sch Sci, Key Lab Microstruct Mat Phys Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China; [Lv, Jian; Tong, Qunchao; Wang, Yanchao; Ma, Yanming] Jilin Univ, Coll Phys, Int Ctr Computat Method & Software, State Key Lab Superhard Mat, Changchun 130012, Peoples R China; [Bergara, Aitor] Univ Basque Country, Dept Fis Mat Condensada, UPV EHU, Euskal Herriko Unibertsitatea, Bilbao 48080, Spain; [Bergara, Aitor] Donostia Int Phys Ctr DIPC, Donostia San Sebastian 20018, Spain; [Bergara, Aitor] Ctr Mixto CSIC UPV EHU, Ctr Fis Mat CFM, Donostia San Sebastian 20018, Spain Northeast Normal University - China; Northeast Normal University - China; Yanshan University; Yanshan University; Jilin University; University of Basque Country; University of Basque Country Yang, GC (corresponding author), Northeast Normal Univ, Ctr Adv Optoelect Funct Mat Res, Changchun 130024, Peoples R China.;Yang, GC (corresponding author), Northeast Normal Univ, Key Lab UV Light Emitting Mat & Technol, Minist Educ, Changchun 130024, Peoples R China.;Yang, GC (corresponding author), Yanshan Univ, Sch Sci, State Key Lab Metastable Mat Sci & Technol, Qinhuangdao 066004, Hebei, Peoples R China.;Yang, GC (corresponding author), Yanshan Univ, Sch Sci, Key Lab Microstruct Mat Phys Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China.;Ma, YM (corresponding author), Jilin Univ, Coll Phys, Int Ctr Computat Method & Software, State Key Lab Superhard Mat, Changchun 130012, Peoples R China.;Bergara, A (corresponding author), Univ Basque Country, Dept Fis Mat Condensada, UPV EHU, Euskal Herriko Unibertsitatea, Bilbao 48080, Spain.;Bergara, A (corresponding author), Donostia Int Phys Ctr DIPC, Donostia San Sebastian 20018, Spain.;Bergara, A (corresponding author), Ctr Mixto CSIC UPV EHU, Ctr Fis Mat CFM, Donostia San Sebastian 20018, Spain. yanggc468@nenu.edu.cn; a.bergara@ehu.eus; mym@jlu.edu.cn mi, wenhui/GWD-0763-2022; Bergara, Aitor/AAM-7118-2021; Bergara, Aitor/AAD-2027-2021; Wang, Yanchao/A-6634-2015; Tong, Qunchao/GRR-4935-2022; Zhang, Shoutao/I-2668-2015; Ma, Yanming/A-7297-2008 Bergara, Aitor/0000-0003-2707-1856; Wang, Yanchao/0000-0003-4518-925X; Zhang, Shoutao/0000-0002-0971-8831; Ma, Yanming/0000-0003-3711-0011; Tong, Qunchao/0000-0002-7218-8271; Yang, Qiuping/0000-0002-6304-6220 Natural Science Foundation of China [21873017, 21573037, 11704062, 51732003]; Postdoctoral Science Foundation of China [2013M541283]; Natural Science Foundation of Jilin Province [20190201231JC]; 111 Project [B13013]; Spanish Ministry of Science and Innovation [PID2019-105488GB-I00]; Jilin Province Out-standing Young Talents project [20190103040JH] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Postdoctoral Science Foundation of China(China Postdoctoral Science Foundation); Natural Science Foundation of Jilin Province; 111 Project(Ministry of Education, China - 111 Project); Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Jilin Province Out-standing Young Talents project The authors acknowledge the funding support from the Natural Science Foundation of China under Grants No. 21873017, No. 21573037, No. 11704062, and No. 51732003, the Postdoctoral Science Foundation of China under Grant No. 2013M541283, the Natural Science Foundation of Jilin Province (20190201231JC), the 111 Project (No. B13013). The work was carried out at National Supercomputer Center in Tianjin, and the calculations were performed on TianHe-1 (A). A.B. acknowledges financial support from the Spanish Ministry of Science and Innovation (PID2019-105488GB-I00) and from Jilin Province Out-standing Young Talents project (Grant No. 20190103040JH). 89 12 12 4 49 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B JAN 8 2021.0 103 2 24505 10.1103/PhysRevB.103.024505 0.0 6 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics PQ1QT Green Published 2023-03-23 WOS:000606324100005 0
J Zhang, HY; Hung, CL; Liu, MY; Hu, XY; Lin, YY Zhang, Hanyu; Hung, Che-Lun; Liu, Meiyuan; Hu, Xiaoye; Lin, Yi-Yang NCNet: Deep Learning Network Models for Predicting Function o Non-coding DNA FRONTIERS IN GENETICS English Article Non-coding DNA; residual learning; LSTM; sequence to sequence learning; deep learning The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep residual learning and sequence-to-sequence learning networks, to predict the transcription factor (TF) binding sites, which can then be used to predict non-coding functions. In NCNet, deep residual learning networks are used to enhance the identification rate of regulatory patterns of motifs, so that the sequence-to-sequence learning network may make the most out of the sequential dependency between the patterns. With the identity shortcut technique and deep architectures of the networks, NCNet achieves significant improvement compared to the original hybrid model in identifying regulatory markers. [Zhang, Hanyu; Hung, Che-Lun; Liu, Meiyuan; Hu, Xiaoye] Affiliated Canc Hosp, Guangzhou, Guangdong, Peoples R China; [Zhang, Hanyu; Hung, Che-Lun; Liu, Meiyuan; Hu, Xiaoye] Inst Guangzhou Med Univ, Guangzhou, Guangdong, Peoples R China; [Zhang, Hanyu] Providence Univ, Coll Comp & Informat, Taichung, Taiwan; [Zhang, Hanyu] Univ Paris Saclay, Ecole Cent Sup Elec, Labo MICS, Gif Sur Yvette, France; [Hung, Che-Lun] Chang Gung Univ, Dept & Grad Inst Comp Sci & Informat Engn, Taoyuan, Taiwan; [Hung, Che-Lun] Chang Gung Mem Hosp, Div Rheumatol Allergy & Immunol, Taoyuan, Taiwan; [Hung, Che-Lun] Chang Gung Univ, AI Innovat Res Ctr, Taoyuan, Taiwan; [Hung, Che-Lun; Lin, Yi-Yang] Providence Univ, Dept Comp Sci & Commun Engn, Taichung, Taiwan Providence University - Taiwan; UDICE-French Research Universities; Universite Paris Saclay; Chang Gung University; Chang Gung Memorial Hospital; Chang Gung University; Providence University - Taiwan Hung, CL (corresponding author), Affiliated Canc Hosp, Guangzhou, Guangdong, Peoples R China.;Hung, CL (corresponding author), Inst Guangzhou Med Univ, Guangzhou, Guangdong, Peoples R China.;Hung, CL (corresponding author), Chang Gung Univ, Dept & Grad Inst Comp Sci & Informat Engn, Taoyuan, Taiwan.;Hung, CL (corresponding author), Chang Gung Mem Hosp, Div Rheumatol Allergy & Immunol, Taoyuan, Taiwan.;Hung, CL (corresponding author), Chang Gung Univ, AI Innovat Res Ctr, Taoyuan, Taiwan.;Hung, CL (corresponding author), Providence Univ, Dept Comp Sci & Commun Engn, Taichung, Taiwan. clhung@mail.cgu.edu.tw Ministry of Science and Technology [MOST 107-2218-E-126-001, MOST 107-2218-E-029-001] Ministry of Science and Technology(Ministry of Science, ICT & Future Planning, Republic of Korea) The research is supported by the Ministry of Science and Technology under the grants MOST 107-2218-E-126-001 and MOST 107-2218-E-029-001. 32 3 3 0 11 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. MAY 29 2019.0 10 432 10.3389/fgene.2019.00432 0.0 9 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity IA3HZ 31191597.0 gold, Green Published 2023-03-23 WOS:000469455600001 0
J Wang, XH; Gao, LL; Song, JK; Shen, HT Wang, Xuanhan; Gao, Lianli; Song, Jingkuan; Shen, Hengtao Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition IEEE SIGNAL PROCESSING LETTERS English Article Action recognition; deep learning; three-dimensional (3-D) convolution; LSTM; saliency-aware OPTIMIZATION; MOTION Human activity recognition in videos with convolutional neural network (CNN) features has received increasing attention in multimedia understanding. Taking videos as a sequence of frames, a new record was recently set on several benchmark datasets by feeding frame-level CNN sequence features to long short-term memory (LSTM) model for video activity recognition. This recurrentmodel-based visual recognition pipeline is a natural choice for perceptual problems with time-varying visual input or sequential outputs. However, the above-mentioned pipeline takes frame-level CNN sequence features as input for LSTM, which may fail to capture the rich motion information from adjacent frames or maybe multiple clips. Furthermore, an activity is conducted by a subject or multiple subjects. It is important to consider attention that allows for salient features, instead of mapping an entire frame into a static representation. To tackle these issues, we propose a novel pipeline, saliency-aware three-dimensional (3-D) CNN with LSTM, for video action recognition by integrating LSTM with salient-aware deep 3-D CNN features on videos shots. Specifically, we first apply saliency-aware methods to generate saliency-aware videos. Then, we design an end-to-end pipeline by integrating 3-D CNN with LSTM, followed by a time series pooling layer and a soft max layer to predict the activities. Noticeably, we set a new record on two benchmark datasets, i.e., UCF101 with 13 320 videos and HMDB-51 with 6766 videos. Our method outperforms the state-of-the-art end-to-end methods of action recognition by 3.8% and 3.2%, respectively on above two datasets. [Wang, Xuanhan] Univ Elect Sci & Technol China, Chengdu 610051, Peoples R China; [Song, Jingkuan] Univ Trento, I-38122 Trento, Italy; [Shen, Hengtao] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia; [Gao, Lianli] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China University of Electronic Science & Technology of China; University of Trento; University of Queensland; University of Electronic Science & Technology of China Gao, LL (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China. wangxuanhan@std.uestc.edu.cn; lianli.gao@uestc.edu.cn; jingkuan.song@unitn.it; shenht@itee.uq.edu.au Shen, Heng Tao/ABD-5331-2021 song, jingkuan/0000-0002-2549-8322 Fundamental Research Funds for the Central Universities [ZYGX2014J063]; ational Natural Science Foundation of China [61502080] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); ational Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2014J063 and in part by the National Natural Science Foundation of China under Grant 61502080. 33 126 132 4 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. APR 2017.0 24 4 510 514 10.1109/LSP.2016.2611485 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering EX7NN 2023-03-23 WOS:000403435000007 0
C Gruhn, V; Han, YB; Hesenius, M; Reichert, M; Wang, GL; Yu, J; Zhang, L Hacid, H; Kao, O; Mecella, M; Moha, N; Paik, HY Gruhn, Volker; Han, Yanbo; Hesenius, Marc; Reichert, Manfred; Wang, Guiling; Yu, Jian; Zhang, Liang BRIBOT: Towards a Service-Based Methodology for Bridging Business Processes and IoT Big Data SERVICE-ORIENTED COMPUTING (ICSOC 2021) Lecture Notes in Computer Science English Proceedings Paper 19th International Conference on Service-Oriented Computing (ICSOC) NOV 22-25, 2021 ELECTR NETWORK Internet of Things (IoT); IoT Big Data; IoT Services; Business Process Management (BPM); Process-Aware Information Systems (PAIS) INTERNET We envisage that BPM and IoT Big Data will be the two pillars of next-generation Process-Aware Information Systems (PAIS). While IoT enables BPM to perceive and react to realtime events in the physical world, BPM can equip IoT with a well-developed modelling and implementation platform. However, the integration of BPM and IoT is facing paradigm misalignment challenges including mismatch of programming mechanisms, mismatch of resource management mechanisms, and mismatch of adaptation mechanisms. In this paper, we present the vision and architectural solution of the recently funded NSFC-DFG cooperation research project BRIBOT, which aims to develop novel service-based approaches and techniques for these challenges. The paper presents the BRIBOT methodology that comprises four parts: abstraction and servitization of IoT data, resource space that handles service and data assets, modelling and transformation of IoT and business events, and IoT-event-driven process awareness and adaptation. [Gruhn, Volker; Hesenius, Marc] Univ Duisburg Essen, Inst Software Engn, Schutzenbahn 70, D-45127 Essen, Germany; [Han, Yanbo; Wang, Guiling] North China Univ Technol, Beijing Key Lab Integrat & Anal Largescale Stream, 5 Jinyuanzhuang Rd, Beijing 100144, Peoples R China; [Reichert, Manfred] Ulm Univ, Inst Databases & Informat Syst, Ulm, Germany; [Yu, Jian] Auckland Univ Technol, Dept Comp Sci, Auckland 1142, New Zealand; [Zhang, Liang] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai Inst Intelligent Elect & Syst, Shanghai, Peoples R China University of Duisburg Essen; North China University of Technology; Ulm University; Auckland University of Technology; Fudan University Han, YB (corresponding author), North China Univ Technol, Beijing Key Lab Integrat & Anal Largescale Stream, 5 Jinyuanzhuang Rd, Beijing 100144, Peoples R China. volker.gruhn@uni-due.de; yhan@ict.ac.cn; marc.hesenius@uni-due.de; manfred.reichert@uni-ulm.de; wangguiling@ict.ac.cn; jian.yu@aut.ac.nz; lzhang@fudan.edu.cn Hesenius, Marc/0000-0002-6826-9212 Projects of International Cooperation and Exchanges NSFC [62061136006]; Deutsche Forschungsgemeinschaft DFG [449721677] Projects of International Cooperation and Exchanges NSFC; Deutsche Forschungsgemeinschaft DFG(German Research Foundation (DFG)) This work is supported by Projects of International Cooperation and Exchanges NSFC (Grant No. 62061136006), Deutsche Forschungsgemeinschaft DFG (Grant No. 449721677). 29 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-91430-1; 978-3-030-91431-8 LECT NOTES COMPUT SC 2021.0 13121 597 611 10.1007/978-3-030-91431-8_37 0.0 15 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU4II 2023-03-23 WOS:000899291500037 0
J Sun, CL; Bian, Y; Zhou, T; Pan, JL Sun, Chuanliang; Bian, Yan; Zhou, Tao; Pan, Jianjun Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region SENSORS English Article remote sensing; Sentinel-1; Sentinel-2; Landsat-8; crop mapping; urban agriculture region; phenology LAND-COVER CLASSIFICATION; BAND SAR DATA; BIOPHYSICAL VARIABLES; TERRASAR-X; SENTINEL-2; AREA; PERFORMANCE; ACCURACY; IMAGERY; INDEX Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management. [Sun, Chuanliang; Pan, Jianjun] Nanjing Agr Univ, Coll Resources & Environm Sci, Nanjing 210095, Jiangsu, Peoples R China; [Bian, Yan] Nanjing Agr Univ, Coll Agr & Econ Management, Nanjing 210095, Jiangsu, Peoples R China; [Zhou, Tao] Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany; [Zhou, Tao] UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, Permoserstr 15, D-04318 Leipzig, Germany Nanjing Agricultural University; Nanjing Agricultural University; Humboldt University of Berlin; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ) Pan, JL (corresponding author), Nanjing Agr Univ, Coll Resources & Environm Sci, Nanjing 210095, Jiangsu, Peoples R China. chuanliang_sun87@163.com; bianyanlinda@163.com; tao.zhou@ufz.de; jpan@njau.edu.cn Zhou, Tao/0000-0003-1216-0709; Sun, Chuanliang/0000-0001-8219-9153 National Natural Science Foundation of China [41771247, 61701123] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Natural Science Foundation of China (No. 41771247, No. 61701123). 68 61 64 15 66 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2 2019.0 19 10 2401 10.3390/s19102401 0.0 23 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation IC5NI 31130689.0 Green Submitted, Green Accepted, gold, Green Published 2023-03-23 WOS:000471014500188 0
C Kobrle, P; Kost'al, T; Zednik, J; Pavelka, J; Yang, XF IEEE Kobrle, Pavel; Kost'al, Tomas; Zednik, Jakub; Pavelka, Jiri; Yang, Xiaofeng Task of Energy Router in Smart Grids PROCEEDINGS OF THE 2018 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI) International Conference on Electronics Computers and Artificial Intelligence English Proceedings Paper 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) JUN 28-30, 2018 Iasi, ROMANIA IEEE Romania Sect,IEEE Ind Applicat Soc,Univ Pitesti,Guvernul Romaniei,Univ Pitesti, Fac Electronica Comunicatii Calculatoare Distribution Network; Smart Grid; Energy Router; Energy Storage Many buzzwords concerning the problematic of energy production and its effective (smart) utilization in industry as well as in households like Smart Grid, Big Data, Industry 4.0, Energy Router, Green Energy etc. are often used in many different meanings or even only for making one's idea more attractive. Nevertheless, their definitions aren't united or they don't express the essence in their entirely. The aim of this paper isn't to define some of the buzzwords; however, it tries to explain what is really a so called energy router and why is it spoken about in the context of existing and to the near future planned system of electrification in the central Europe. Some kinds of energy storage are integral to the modern grid. The short overview of energy storage devices is presented at the end. This text should make the understanding of the problematic in wider extent easier. [Kobrle, Pavel; Kost'al, Tomas; Zednik, Jakub; Pavelka, Jiri] Czech Tech Univ, Dept Elect Drives & Tract, Prague, Czech Republic; [Yang, Xiaofeng] Beijing Jiaotong Univ, Sch Elect Engn, Beijing, Peoples R China Czech Technical University Prague; Beijing Jiaotong University Kobrle, P (corresponding author), Czech Tech Univ, Dept Elect Drives & Tract, Prague, Czech Republic. kobrlpav@fel.cvut.cz; xfyang@bjtu.edu.cn Yang, Xiaofeng/AAG-5745-2020 Yang, Xiaofeng/0000-0002-3525-3177 Ministry of Education, Youth and Sports of the Czech Republic [LTACH-17001]; National Key Research and Development Program of China [2016YFE0131700] Ministry of Education, Youth and Sports of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); National Key Research and Development Program of China This material is based on the work within the Czech-Chinese common project supported by the Ministry of Education, Youth and Sports of the Czech Republic under the grant for INTER-EXCELLENCE, project No. LTACH-17001 and the National Key Research and Development Program of China No. 2016YFE0131700. 13 1 1 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2378-7147 978-1-5386-4901-5 INT C ELECT COMPUT 2018.0 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM7CM 2023-03-23 WOS:000467734100127 0
J Chen, G; Chen, K; Zhang, LJ; Zhang, LM; Knoll, A Chen, Guang; Chen, Kai; Zhang, Lijun; Zhang, Liming; Knoll, Alois VCANet: Vanishing-Point-Guided Context-Aware Network for Small Road Object Detection AUTOMOTIVE INNOVATION English Article Autonomous driving; Road hazard; Object detection; Deep learning; Vanishing point VISION Advanced deep learning technology has made great progress in generic object detection of autonomous driving, yet it is still challenging to detect small road hazards in a long distance owing to lack of large-scale small-object datasets and dedicated methods. This work addresses the challenge from two aspects. Firstly, a self-collected long-distance road object dataset (TJ-LDRO) is introduced, which consists of 109,337 images and is the largest dataset so far for the small road object detection research. Secondly, a vanishing-point-guided context-aware network (VCANet) is proposed, which utilizes the vanishing point prediction block and the context-aware center detection block to obtain semantic information. The multi-scale feature fusion pipeline and the upsampling block in VCANet are introduced to enhance the region of interest (ROI) feature. The experimental results with TJ-LDRO dataset show that the proposed method achieves better performance than the representative generic object detection methods. This work fills a critical capability gap in small road hazards detection for high-speed autonomous vehicles. [Chen, Guang; Chen, Kai; Zhang, Lijun] Tongji Univ, Shanghai, Peoples R China; [Chen, Guang; Knoll, Alois] Tech Univ Munich, Munich, Germany; [Zhang, Liming] Geely Res Inst, Hangzhou, Peoples R China Tongji University; Technical University of Munich Chen, G; Zhang, LJ (corresponding author), Tongji Univ, Shanghai, Peoples R China.;Chen, G (corresponding author), Tech Univ Munich, Munich, Germany. guangchen@tongji.edu.cn; tjedu_zhanglijun@tongji.edu.cn National Natural Science Foundation of China [61906138]; National Key Research and Development Program of China [2016YFB0100901]; Shanghai AI Innovative Development Project 2018; Shanghai Rising Star Program [21QC1400900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Shanghai AI Innovative Development Project 2018; Shanghai Rising Star Program This research has received funding from the National Natural Science Foundation of China (No. 61906138), National Key Research and Development Program of China (No.2016YFB0100901), Shanghai AI Innovative Development Project 2018, and Shanghai Rising Star Program (No. 21QC1400900). We would like to thank Mingyuan Chen for the support of small objects collection in developing the TJ-LDRO dataset. 63 15 15 1 10 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2096-4250 2522-8765 AUTOMOTIVE INNOV Automot. Innov. NOV 2021.0 4 4 400 412 10.1007/s42154-021-00157-x 0.0 SEP 2021 13 Engineering, Electrical & Electronic; Engineering, Mechanical; Transportation Science & Technology Emerging Sources Citation Index (ESCI) Engineering; Transportation WZ4BR 2023-03-23 WOS:000695377300001 0
J Zhang, MJ; Feng, HH; Tomka, J; Polovka, M; Ma, RQ; Zhang, XS Zhang, Mengjie; Feng, Huanhuan; Tomka, Jan; Polovka, Martin; Ma, Ruiqin; Zhang, Xiaoshuan Predicting of mutton sheep stress coupled with multi-environment sensing and supervised learning network in the transportation process COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Mutton sheep stress; Multi-environment sensing; Supervised learning network; Transportation process; Health traceability RESPONSES; WELFARE; STRATEGY; GOATS; FARM Stress response caused by transportation is an important reason that affects the health and welfare of livestock, and the intelligent and precise monitoring of the stress state during transportation is a blank in research. On-site tracking experiments, literature review, and expert consultation indicate that environmental temperature, relative humidity and vibration intensity of vehicle are important stressors in the transportation process, and cortisol (COR) and adrenocorticotropic hormone (ACTH) are the key biomarkers to characterize the stress level. In this paper, stressors causing stress response were collected through environmental sensor network, and key biomarkers representing stress response were identified through biochemical analysis, and their change rules were analyzed and compared. Furthermore, the predictive models coupled with environmental parameters and stress markers were constructed through supervised learning networks. The results showed that: (1) After testing, the data collection performance of the multi-sensor network is reliable (P < 0.05); transportation will cause significant changes in the concentration of COR and ACTH (P 0.05); the simulation results of stress markers data have no significant difference compared with the sample data (PSpline PPchip > 0.90). (2) The validation results of models showed that: for COR, the absolute error and relative error based on general regression neural network (GRNN) are 2.59 +/- 0.16 ng/mL and 1.30 +/- 0.07%, respectively, which is better than the accuracy based on back propagation neural network (BPNN) and Elman neural network (Elman); for ACTH, the absolute error and relative error based on GRNN models were 1.75 +/- 0.13 pg/mL and 2.14 +/- 0.17%, respectively, which were better than the accuracy based on BPNN and Elman models. (3) For the prediction performance, the prediction accuracy of GRNN model for COR concentration is basically equal to that of Elman model, which is 0.17% higher than that of BPNN model for COR concentration, but the maximum relative error of BPNN model for COR concentration is the smallest. The prediction accuracy of Elman model for ACTH concentration is 0.09% higher than that of GRNN model, and 0.29% higher than that of BPNN model. But the maximum relative error of Elman model for ACTH concentration is the largest, and the running time is significantly longer. Although the performance of the three modeling methods is different, their overall accuracy has reached more than 97%. Therefore, this study can effectively identify the stress state of mutton sheep in the actual transportation process, and provide technical support for the health traceability and control of mutton sheep. [Zhang, Mengjie; Feng, Huanhuan; Ma, Ruiqin; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China; [Zhang, Mengjie; Feng, Huanhuan; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China; [Tomka, Jan; Polovka, Martin] Natl Agr & Food Ctr, SK-82475 Bratislava, Slovakia China Agricultural University Ma, RQ; Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China.;Zhang, XS (corresponding author), Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. maruiqin@cau.edu.cn; zhxshuan@cau.edu.cn Tomka, Jan/0000-0001-5959-1069 National Modern Sheep Industry Technology System Project of China [CARS-38]; European Union [727243-2] National Modern Sheep Industry Technology System Project of China; European Union(European Commission) This research was funded by the National Modern Sheep Industry Technology System Project of China (CARS-38) and the European Union Horizon 2020: Understanding food value chain and network dynamics (727243-2). 41 3 3 5 13 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. NOV 2021.0 190 106422 10.1016/j.compag.2021.106422 0.0 SEP 2021 12 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science WC5HW 2023-03-23 WOS:000704288800002 0
J Hu, CQ; Duan, YX; Liu, SC; Yan, YQ; Tao, N; Osman, A; Ibarra-Castanedo, C; Sfarra, S; Chen, DP; Zhang, CL Hu, Caiqi; Duan, Yuxia; Liu, Shicai; Yan, Yiqian; Tao, Ning; Osman, Ahmad; Ibarra-Castanedo, Clemente; Sfarra, Stefano; Chen, Dapeng; Zhang, Cunlin LSTM-RNN-based defect classification in honeycomb structures using infrared thermography INFRARED PHYSICS & TECHNOLOGY English Article Honeycomb; Thermography; Non-destructive testing; Defect classification ARTIFICIAL NEURAL-NETWORK; RELIABILITY; FLAWS Honeycomb-structured materials are widely used in commercial and military aircraft. Manufacturing defects and damage during operation have become primary safety threats. This has increased the demand for non-destructive testing (NDT) for damage and flaws during aircraft operation and maintenance. Characterizing, or classifying defects, in addition to detecting them, is important. Classifying the liquids trapped in aircraft honeycomb cells is an example. A small amount of ingressed water is often tolerable, whereas a small amount of hydraulic oil may be an early warning of hydraulic system malfunction. This paper proposes an infrared thermography-based NDT technique and a long short term memory recurrent neural network (LSTM-RNN) model which automatically classifies common defects occurring in honeycomb materials. These including debonding, adhesive pooling, and liquid ingress. This LSTM-based algorithm has a greater than 90% sensitivity in classifying water, and hydraulic oil ingress. It has a greater than 70% sensitivity in classifying debonding and adhesive pooling. [Hu, Caiqi; Duan, Yuxia; Liu, Shicai] Cent South Univ, Sch Phys & Elect, 932 Lushan South Rd, Changsha 410083, Hunan, Peoples R China; [Yan, Yiqian; Tao, Ning; Zhang, Cunlin] Capital Normal Univ, Phys Dept, 105 West Sanhuan North Rd, Beijing 100048, Peoples R China; [Osman, Ahmad] Fraunhofer Inst Nondestruct Testing IZFP, Dept Inspect Components & Assemblies, D-66123 Saarbrucken, Germany; [Ibarra-Castanedo, Clemente] Univ Laval, Dept Elect & Comp Engn, Comp Vis & Syst Lab, 1065 Av Med, Quebec City, PQ G1V 0A6, Canada; [Sfarra, Stefano] Univ Aquila, Dept Ind & Informat Engn & Econ, I-67100 Laquila, Italy; [Chen, Dapeng] Sci & Technol Opt Radiat Lab, 50 Yongding Rd, Beijing 100854, Peoples R China Central South University; Capital Normal University; Fraunhofer Gesellschaft; Laval University; University of L'Aquila Duan, YX (corresponding author), Cent South Univ, Sch Phys & Elect, 932 Lushan South Rd, Changsha 410083, Hunan, Peoples R China. yuxia.duan@csu.edu.cn Barros, Jótelly/AAH-4913-2019; Duan, Yuxia/D-7334-2013 Duan, Yuxia/0000-0002-4581-0686 National Natural Science Foundation of China [61505264] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China [grant No. 61505264, 2016]. 42 35 36 5 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1350-4495 1879-0275 INFRARED PHYS TECHN Infrared Phys. Technol. NOV 2019.0 102 103032 10.1016/j.infrared.2019.103032 0.0 8 Instruments & Instrumentation; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation; Optics; Physics JQ1YJ 2023-03-23 WOS:000498748400085 0
J Shao, WH; Luo, HY; Zhao, F; Ma, Y; Zhao, ZL; Crivello, A Shao, Wenhua; Luo, Haiyong; Zhao, Fang; Ma, Yan; Zhao, Zhongliang; Crivello, Antonino Indoor Positioning Based on Fingerprint-Image and Deep Learning IEEE ACCESS English Article Indoor positioning; indoor localization; neural networks; fingerprint; feature extraction LOCALIZATION Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns. [Shao, Wenhua; Zhao, Fang] Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China; [Shao, Wenhua; Ma, Yan] Beijing Univ Posts & Telecommun, Inst Network Technol, Beijing 100876, Peoples R China; [Luo, Haiyong] Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China; [Luo, Haiyong] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China; [Zhao, Zhongliang] Univ Bern, Inst Comp Sci, CH-3012 Bern, Switzerland; [Crivello, Antonino] CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; Institute of Computing Technology, CAS; University of Bern; Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienza e Tecnologie dell'Informazione Alessandro Faedo (ISTI-CNR) Luo, HY (corresponding author), Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China.;Luo, HY (corresponding author), Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China.;Zhao, ZL (corresponding author), Univ Bern, Inst Comp Sci, CH-3012 Bern, Switzerland. yhluo@ict.ac.cn; zhao@inf.unibe.ch Crivello, Antonino/P-8335-2018; MA, YAN/HHN-2912-2022 Crivello, Antonino/0000-0001-7238-2181; National Key Research and Development Program [2018YFB0505200]; National Natural Science Foundation of China [61872046]; BUPT Excellent Ph.D. Students Foundation [CX2017404]; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); BUPT Excellent Ph.D. Students Foundation; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device This work was supported in part by the National Key Research and Development Program under Grant 2018YFB0505200, in part by the National Natural Science Foundation of China under Grant 61872046, in part by the BUPT Excellent Ph.D. Students Foundation under Grant CX2017404, and in part by the Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device. 46 57 57 4 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 74699 74712 10.1109/ACCESS.2018.2884193 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HF7AP gold 2023-03-23 WOS:000454390300001 0
J Wang, RY; Browning, MHEM; Qin, XF; He, JL; Wu, WJ; Yao, Y; Liu, Y Wang, Ruoyu; Browning, Matthew H. E. M.; Qin, Xiaofei; He, Jialv; Wu, Wenjie; Yao, Yao; Liu, Ye Visible green space predicts emotion: Evidence from social media and street view data APPLIED GEOGRAPHY English Article Greenness; Mood; Big data; Machine learning; Health disparities MENTAL-HEALTH; URBAN GREENERY; BLUE SPACES; DETERMINANTS; BLUESPACE; MOOD Social media data are increasingly used to examine associations between environmental exposures and mental wellbeing. In particular, studies highlight that exposure to natural outdoor environments (NOEs) is associated with fewer negative emotions. Also, people in socioeconomically disadvantaged neighbourhoods tend to benefit more from NOEs than their counterparts in more privileged neighbourhoods (equigenic effects). However, exposure to NOEs is principally studied with remotely sensed data that fail to measure peoples' lived experience and the visible environment at eye-level. The current study explored relationships between two forms of NOEs (green and blue spaces) using Tencent Map street view imagery and negative emotions from social media Weibo microblogs in 1540 neighborhoods of Guangzhou, China. Negative emotions and exposure to visible green and blue space were assessed at the neighborhood-level (averaged by neighborhood). Higher levels of visible green space were associated with lower levels of negative emotions (3rd quartile: coefficient [coef.] =-0.006, 95% confidence interval [CI] =-0.012 to-0.000; 4th quartile: coef. =-0.007, CI =-0.013 to-0.001), and these associations were stronger during non-work times. No associations were observed for visible blue space except in interactions with socioeconomic status (SES); blue space provided an equigenic effect whereby people in lower-SES neighborhoods expressed fewer negative emotions than other lower-SES neighbourhoods without blue space. Because negative emotions are strongly linked to depression and anxiety, the importance of green and blue space visible at eye-level should be considered when promoting equitable public health. [Wang, Ruoyu] Queens Univ Belfast, Ctr Publ Hlth, Belfast, North Ireland; [Browning, Matthew H. E. M.] Clemson Univ, Dept Pk Recreat & Tourism Management PRTM, Clemson, SC 29634 USA; [Qin, Xiaofei] Boston Univ, Dept Earth & Environm, Boston, MA USA; [He, Jialv; Liu, Ye] Sun Yat Sen Univ, Sch Geog & Planning, Xingang Xi Rd, Guangzhou 510275, Peoples R China; [He, Jialv; Liu, Ye] Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Xingang Xi Rd, Guangzhou 510275, Peoples R China; [Wu, Wenjie] Wuhan Univ, Sch Urban Design, Wuhan 430072, Hubei, Peoples R China; [Wu, Wenjie] Hubei Habitat Environm Res Ctr Engn & Technol, Wuhan 430072, Hubei, Peoples R China; [Yao, Yao] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China; [Yao, Yao] Univ Tokyo, Ctr Spatial Informat Sci, Chiba, Japan Queens University Belfast; Clemson University; Boston University; Sun Yat Sen University; Sun Yat Sen University; Wuhan University; China University of Geosciences; University of Tokyo Wu, WJ (corresponding author), Wuhan Univ, Sch Urban Design, Wuhan 430072, Hubei, Peoples R China. r.wang@qub.ac.uk; mhb2@clemson.edu; qinxf@bu.edu; hesysugis@foxmail.com; caswwj@foxmail.com; yaoy@cug.edu.cn; liuye25@mail.sysu.edu.cn Browning, Matthew/D-2111-2014 Browning, Matthew/0000-0003-2296-7602; Liu, Ye/0000-0003-2511-5413; Wang, Ruoyu/0000-0002-7240-558X National Natural Science Foundation of China [41871140, 41971194, 52278085, 41801306]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X355]; National Office for Philosophy and Social Sciences [20ZDA037] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; National Office for Philosophy and Social Sciences Funding This work was supported by the National Natural Science Foundation of China (grant numbers 41871140, 41971194, 52278085 and 41801306) , the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (grant number 2017ZT07X355) , National Office for Philosophy and Social Sciences (20ZDA037) . 77 0 0 8 8 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0143-6228 1873-7730 APPL GEOGR Appl. Geogr. NOV 2022.0 148 102803 10.1016/j.apgeog.2022.102803 0.0 9 Geography Social Science Citation Index (SSCI) Geography 9A6BH 2023-03-23 WOS:000934141000002 0
C Zhang, WS; Sun, HY; Zhou, JH; Liu, X; Zhang, ZM; Min, GZ IEEE Zhang, Weishan; Sun, Haoyun; Zhou, Jiehan; Liu, Xin; Zhang, Zhanmin; Min, Guizhi Fully Convolutional Network based Ship Plate Recognition 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 07-10, 2018 IEEE Syst Man & Cybernet Soc, Miyazaki, JAPAN IEEE,Sci Council Japan IEEE Syst Man & Cybernet Soc Ship license plate recognition; Text detection; Fully Convolutional Network; AIS Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages. [Zhang, Weishan; Sun, Haoyun] China Univ Petr, Dept Software Engn, Qingdao, Peoples R China; [Zhou, Jiehan] Univ Oulu, Oulu, Finland; [Liu, Xin] China Univ Petr, Sch Comp & Commun Engn, Qingdao, Peoples R China; [Zhang, Zhanmin; Min, Guizhi] PetroChina, Huabei Oilfield Co, Engn Technol Res Inst, Renqiu, Peoples R China China University of Petroleum; University of Oulu; China University of Petroleum; China National Petroleum Corporation Zhang, WS (corresponding author), China Univ Petr, Dept Software Engn, Qingdao, Peoples R China. zhangws@upc.edu.cn; hys_upc@163.com; jiehan.zhou@oulu.fi; lx@upc.edu.cn; cyy_zhangzm@petrochina.com.cn; cyy_mgz@petrochina.com.cn xu, liang/AAC-4448-2022; Sun, Haoyun/AAC-4369-2022 Key Research Program of Shandong Province [2017GGX10140]; Fundamental Research Funds for the Central Universities [15CX08015A]; National Natural Science Foundation of China [61309024] Key Research Program of Shandong Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140 and in part by the Fundamental Research Funds for the Central Universities(15CX08015A), National Natural Science Foundation of China (No. 61309024). 14 2 2 0 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-5386-6650-0 IEEE SYS MAN CYBERN 2018.0 1803 1808 10.1109/SMC.2018.00312 0.0 6 Computer Science, Cybernetics; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM1JN Green Accepted 2023-03-23 WOS:000459884801149 0
J Sharma, P; Sahoo, BB; Said, Z; Hadiyanto, H; Nguyen, XP; Nizetic, S; Huang, ZH; Hoang, AT; Li, CH Sharma, Prabhakar; Sahoo, Bibhuti B.; Said, Zafar; Hadiyanto, H.; Nguyen, Xuan Phuong; Nizetic, Sandro; Huang, Zuohua; Hoang, Anh Tuan; Li, Changhe Application of machine learning and Box-Behnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas INTERNATIONAL JOURNAL OF HYDROGEN ENERGY English Article Biodiesel; Biogas; Prediction model; Optimization; Neural network; Engine behavior INTERNAL-COMBUSTION ENGINES; COMPRESSION IGNITION ENGINE; INJECTION DIESEL-ENGINE; EMISSION CHARACTERISTICS; PERFORMANCE; RATIO; OPTIMIZATION; OIL; ENRICHMENT; BIOFUELS Waste-derived biogas and third-generation algal biodiesel are attractive alternative fuels to substitute fossil diesel in a diesel engine. However, using biodiesel as a pilot liquid fuel and biogas as the main fuel in a diesel engine is a complicated and highly non-linear process. The current study seeks to predict and optimize the combustion and exhaust emission characteristics of a variable compression dual-fuel combustion engine. Data from experi-ments were obtained at a variety of engine loads, compression ratios, pilot fuel injection pressures, and timings. A multi-layer perceptron network was employed to develop an Artificial Neural Network (ANN) based prognostic model using the experimental data. The developed prognostic model was used to estimate brake thermal efficiency, biogas flow rates, peak in-cylinder pressure, carbon dioxide, unburned hydrocarbons, oxides of ni-trogen, and carbon monoxide. The predictive model's robustness is demonstrated by sta-tistical metrics such as R (0.9723-0.988) and R2 (0.9453-0.9761), Nash-Sutcliffe model efficiency (94-97%), and mean absolute percentage error (0.013-0.128%), Kling-Gupta ef-ficiency (0.9548-0.9836), and Theil's U2 model uncertainty (0.162-0.368). To optimize the parameters of dual-fuel combustion, the Multi-Output Response Surface Methodology (RSM) was employed. The trade-off assessment between emission and efficiency using the desirability approach revealed that 84% engine load, 244 bar of fuel injection pressure, 28 degrees BTDC of injection timing, and 17.5 compression ratio are the best-operating conditions for the test engine. An experimental investigation was used to corroborate the RSM research findings, and errors were less than 9%. It was revealed that ANN-linked RSM is a good hybrid technique for modeling, prediction, and optimization of the performance of a dual -fuel engine. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. [Sharma, Prabhakar] Delhi Skill & Entrepreneurship Univ, Sch Engn Sci, Delhi 110089, India; [Sahoo, Bibhuti B.] Vellore Inst Technol, Sch Mech Engn, Vellore 632007, Tamil Nadu, India; [Said, Zafar] Univ Sharjah, Dept Sustainable & Renewable Energy Engn, POB 27272, Sharjah, U Arab Emirates; [Said, Zafar] Natl Univ Sci & Technol NUST, US Pakistan Ctr Adv Studies Energy USPCAS E, Islamabad, Pakistan; [Hadiyanto, H.] Diponegoro Univ, Ctr Biomass & Renewable Energy CBIORE, Dept Chem Engn, Jl Prof Soedarto SH, Tembalang 50271, Semarang, Indonesia; [Hadiyanto, H.] Diponegoro Univ, Sch Postgrad Studies, Jl Imam Bardjo SH, Semarang 50241, Indonesia; [Nguyen, Xuan Phuong] Ho Chi Minh City Univ Transport, PATET Res Grp, Ho Chi Minh City, Vietnam; [Nizetic, Sandro] Univ Split, Rudjera Boskov 32, Split 21000, Croatia; [Huang, Zuohua] Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian 710049, Peoples R China; [Hoang, Anh Tuan] HUTECH Univ, Inst Engn, Ho Chi Minh City, Vietnam; [Li, Changhe] Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China Vellore Institute of Technology (VIT); VIT Vellore; University of Sharjah; National University of Sciences & Technology - Pakistan; Diponegoro University; Diponegoro University; Ho Chi Minh City University of Transport; University of Split; Xi'an Jiaotong University; Ho Chi Minh City University of Technology (HUTECH); Vietnam National University Hochiminh City; Qingdao University of Technology Sahoo, BB (corresponding author), Vellore Inst Technol, Sch Mech Engn, Vellore 632007, Tamil Nadu, India.;Said, Z (corresponding author), Univ Sharjah, Dept Sustainable & Renewable Energy Engn, POB 27272, Sharjah, U Arab Emirates.;Nguyen, XP (corresponding author), Ho Chi Minh City Univ Transport, PATET Res Grp, Ho Chi Minh City, Vietnam.;Hoang, AT (corresponding author), HUTECH Univ, Inst Engn, Ho Chi Minh City, Vietnam. bbs.iitg@gmail.com; zsaid@sharjah.ac.ae; phuong@ut.edu.vn; hatuan@hutech.edu.vn ; said, Zafar/C-4086-2016; Nguyen Xuan, Phuong/AAT-9669-2021; Hoang, Anh Tuan/C-4780-2019; Hadiyanto, Hady/P-1227-2016; SHARMA, PRABHAKAR/AFS-6314-2022 Li, Changhe/0000-0003-4409-956X; said, Zafar/0000-0003-2376-9309; Nguyen Xuan, Phuong/0000-0003-0354-8648; Hoang, Anh Tuan/0000-0002-1767-8040; Hadiyanto, Hady/0000-0003-0074-7078; SHARMA, PRABHAKAR/0000-0002-7585-6693 100 20 20 1 1 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-3199 1879-3487 INT J HYDROGEN ENERG Int. J. Hydrog. Energy FEB 28 2023.0 48 18 6738 6760 10.1016/j.ijhydene.2022.04.152 0.0 FEB 2023 23 Chemistry, Physical; Electrochemistry; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels 8R3XO 2023-03-23 WOS:000927827800001 0
J Jha, SK; Chishti, Z; Ahmad, Z; Arshad, KUR Jha, Sunil Kumar; Chishti, Zia; Ahmad, Zulfiqar; Arshad, Khaliq-ur-Rehman Enterobacter sp. SWLC2 for biodegradation of chlorpyrifos in the aqueous medium: Modeling of the process using artificial neural network approaches COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Biodegradation; Chlorpyrifos; Aqueous medium, Enterobacter sp.; Optimization; Artificial neural network MICROBIAL-DEGRADATION; DEGRADING BACTERIUM; FOOD SECURITY; PESTICIDES; SOIL; BIOREMEDIATION; 3,5,6-TRICHLORO-2-PYRIDINOL; PREDICTION Chlorpyrifos is a widely used organochlorine pesticide. The unsystematic use of pesticides for the restrain of soil-borne pests and bacterium causes pollution of ecosystems and harming the health of animals and humans. Moreover, due to high persistence, mobility, and higher toxicity in soil environments, it causes severe destruction of species. Therefore the investigation of the degradation process of chlorpyrifos polluted locations to diminish its lethal effect is important. With this objective, the present study reports the efficiency of new isolated bacterial strain SWLC2 for chlorpyrifos degradation in an aqueous medium and the biotic/abiotic factors influencing the process and their optimization using statistical Taguchi design and artificial neural network (ANN) approaches. Results revealed a higher efficiency of bacterium SWLC2 for chlorpyrifos degradation at optimal environmental and general conditions (800 mu L inoculum, glucose (97%), arginine (89%), and succinic acid (83%)). External supplements (carbon and nitrogen sources) had stimulating effects on chlorpyrifos biodegradation depending on the concentration of supplemented substrate. Chlorpyrifos biodegradation by SWLC2 also varied as the initial concentration of the tested compound changes, following first-order kinetics. The finding provides a basic understanding of the application of the bacteria strain SWLC2 in the bioremediation of soils contaminated with chlorpyrifos pesticide. ANN approaches have achieved an average R-2 equal to 0.98-0.99 in the prediction of chlorpyrifos degradation considering the affecting biotic/abiotic factors. [Jha, Sunil Kumar] Univ Informat Technol & Management Rzeszow, Fac Informat Technol, PL-35225 Rzeszow, Poland; [Chishti, Zia; Ahmad, Zulfiqar; Arshad, Khaliq-ur-Rehman] Univ Calif Riverside, Dept Environm Sci, Riverside, CA 92521 USA; [Ahmad, Zulfiqar] Chinese Acad Sci, Inst Hydrobiol, Key Lab Algal Biol, Wuhan 430072, Peoples R China University of Information Technology & Management Rzeszow; University of California System; University of California Riverside; Chinese Academy of Sciences; Institute of Hydrobiology, CAS Jha, SK (corresponding author), Univ Informat Technol & Management Rzeszow, Fac Informat Technol, PL-35225 Rzeszow, Poland. sdrsunil76@gmail.com; zulfiqar@ihb.ac.cn Ahmad, Zulfiqar/0000-0002-8760-0230 69 2 2 2 3 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. FEB 2022.0 193 106680 10.1016/j.compag.2021.106680 0.0 JAN 2022 13 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science YX7GW 2023-03-23 WOS:000754267900001 0
J Sun, CH; Leto, S Sun, Chenghao; Leto, Sebastian A novel joint bidding technique for fuel cell wind turbine photovoltaic storage unit and demand response considering prediction models analysis Effect's INTERNATIONAL JOURNAL OF HYDROGEN ENERGY English Article Integrated offering; Microgrid management; Multiple artificial neural network; Wind and solar system; Stochastic programming; Demand response WATER CYCLE ALGORITHM; DISTRIBUTED RESOURCES; POWER; STRATEGY; HYDROGEN; MARKET; PARTICIPATION The stake of distributed generation resources like fuel cell in daily market is proved to be a major uncertain problem. The volatile character of market price together with the unbalanced nature of power can take hold of economic advancement of distributed generation resources which in turn can culminate in diversion retribution while the market is being struck. This study introduces a market participation model in share conditions to improve the profit for Fuel Cell/wind turbine/storage/photovoltaic and demand response. To solve the mentioned problem, an accurate prediction model is presented in this paper. This model is based on complete ensemble empirical mode decomposition, and multiple artificial neural network which is coupled with Broyden water cycle algorithm. By this algorithm, the prediction accuracy of proposed forecast engine is enhanced and could get the better results. A sure-footed stochastic optimization approach was deployed in order to take prices of markets and distributed generation resources into account. In the generation of distributed generation resources, forecasting error database in everyday, modified, and depressed market was drawn on to induce probabilistic scenario. Improbable variables were discarded by a neuro-fuzzy model. Eventually, to illustrate the joint model strategy suggested in the study, a testing system contains fuel cell/wind turbine/storage unit/photovoltaic and demand response was utilized and the attained results were calculated in two different periods. (C) 2020 Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. [Sun, Chenghao] Shandong Management Univ, Emerging Econ Formats Res Inst, Jinan 250357, Shandong, Peoples R China; [Leto, Sebastian] Tech Univ Kosice, Dept Engn, Kosice, Slovakia Shandong Management University; Technical University Kosice Sun, CH (corresponding author), Shandong Management Univ, Emerging Econ Formats Res Inst, Jinan 250357, Shandong, Peoples R China. sunch2015@163.com Sun, Chenghao/HKN-2713-2023 35 7 7 0 11 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-3199 1879-3487 INT J HYDROGEN ENERG Int. J. Hydrog. Energy FEB 28 2020.0 45 11 6823 6837 10.1016/j.ijhydene.2019.12.210 0.0 15 Chemistry, Physical; Electrochemistry; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels KU4AR 2023-03-23 WOS:000519652800072 0
J Roman, M; Shahid, A; Khan, S; Koubaa, A; Yu, LS Roman, Muhammad; Shahid, Abdul; Khan, Shafiullah; Koubaa, Anis; Yu, Lisu Citation Intent Classification Using Word Embedding IEEE ACCESS English Article Metadata; Citation analysis; Computational modeling; Licenses; Context modeling; Task analysis; Semantics; Citation intent; citation analysis; citation context; citation motivation; citation function classification; word embedding; scholarly dataset RETRIEVAL Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations. [Roman, Muhammad; Shahid, Abdul; Khan, Shafiullah] Kohat Univ Sci & Technol, Inst Comp, Kohat 26000, Pakistan; [Koubaa, Anis] Prince Sultan Univ, Robot & Internet Things Lab, Riyadh 12435, Saudi Arabia; [Koubaa, Anis] Polytech Inst Porto, CISTER INESC TEC, P-4200 Porto, Portugal; [Yu, Lisu] Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Yu, Lisu] Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China Kohat University of Science & Technology; Prince Sultan University; INESC TEC; Polytechnic Institute of Porto; Nanchang University; Chinese Academy of Sciences; Institute of Computing Technology, CAS Yu, LS (corresponding author), Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China.;Yu, LS (corresponding author), Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China. lisuyu@ncu.edu.cn Koubaa, Anis/T-7414-2018; Roman, Muhammad/HKV-8350-2023 Koubaa, Anis/0000-0003-3787-7423; Yu, Lisu/0000-0001-8637-852X; Khan, S/0000-0001-8363-2051; Luangaphirom, Thananan/0000-0002-9082-1237 State Key Laboratory of Computer Architecture (ICT, CAS) Open Project [CARCHB202019] State Key Laboratory of Computer Architecture (ICT, CAS) Open Project This work was supported by the State Key Laboratory of Computer Architecture (ICT, CAS) Open Project under Grant CARCHB202019. 52 17 19 13 50 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 9982 9995 10.1109/ACCESS.2021.3050547 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications PV2CU gold 2023-03-23 WOS:000609801100001 0
J Zhou, H; Cai, RY; Quan, TW; Liu, SJ; Li, SW; Huang, Q; Erturk, A; Zeng, SQ Zhou, Hang; Cai, Ruiyao; Quan, Tingwei; Liu, Shijie; Li, Shiwei; Huang, Qing; Ertuerk, Ali; Zeng, Shaoqun 3D high resolution generative deep-learning network for fluorescence microscopy imaging OPTICS LETTERS English Article MICE Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover 20 x /1.0-NAvolume images from coarsely registered 5 x /0.16-NA volume images collected by light-sheet microscopy. This method. would provide great potential in applications which require high resolution volume imaging. (C) 2020 Optical Society of America [Zhou, Hang; Quan, Tingwei; Liu, Shijie; Li, Shiwei; Huang, Qing; Zeng, Shaoqun] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Britton Chance Ctr Biomed Photon, Wuhan 430074, Hubei, Peoples R China; [Zhou, Hang; Quan, Tingwei; Liu, Shijie; Li, Shiwei; Huang, Qing; Zeng, Shaoqun] Huazhong Univ Sci & Technol, Collaborat Innovat Ctr Biomed Engn, Sch Engn Sci, MoE Key Lab Biomed Photon, Wuhan 430074, Hubei, Peoples R China; [Cai, Ruiyao; Ertuerk, Ali] Helmholtz Ctr Munich, Inst Tissue Engn & Regenerat Med, D-85764 Neuherberg, Germany; [Cai, Ruiyao; Ertuerk, Ali] Ludwig Maximilians Univ Munchen, Klinikum Univ Munchen, Inst Stroke & Dementia Res, D-81377 Munich, Germany; [Ertuerk, Ali] Munich Cluster Syst Neurol SyNergy, D-81377 Munich, Germany Huazhong University of Science & Technology; Huazhong University of Science & Technology; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Munich; University of Munich Quan, TW (corresponding author), Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Britton Chance Ctr Biomed Photon, Wuhan 430074, Hubei, Peoples R China.;Quan, TW (corresponding author), Huazhong Univ Sci & Technol, Collaborat Innovat Ctr Biomed Engn, Sch Engn Sci, MoE Key Lab Biomed Photon, Wuhan 430074, Hubei, Peoples R China. quantingwei@hust.edu.cn; sqzeng@mail.hust.edu.cn Zeng, Shaoqun/0000-0002-1802-337X; Zhou, Hang/0000-0002-0072-926X National Program on Key Basic Research Project of China [2015CB7556003]; Science Fund for Creative Research Groups [61721092]; Vascular Dementia Research Foundation; Synergy Excellence Cluster Munich; ERA-Net Neuron [01EW1501A]; Fritz Thyssen Stiftung [10.17.1.019MN]; Deutsche Forschungsgemeinschaft [ER 810/2-1, TRR127]; National Institutes of Health; Helmholtz ICEMED Alliance; WNLO National Program on Key Basic Research Project of China(National Basic Research Program of China); Science Fund for Creative Research Groups(Science Fund for Creative Research Groups); Vascular Dementia Research Foundation; Synergy Excellence Cluster Munich; ERA-Net Neuron; Fritz Thyssen Stiftung; Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Helmholtz ICEMED Alliance; WNLO National Program on Key Basic Research Project of China (2015CB7556003); Science Fund for Creative Research Groups (61721092); Director Fund of WNLO; Vascular Dementia Research Foundation, Synergy Excellence Cluster Munich; ERA-Net Neuron (01EW1501A); Fritz Thyssen Stiftung (Ref. 10.17.1.019MN); Deutsche Forschungsgemeinschaft (Ref. ER 810/2-1, TRR127); National Institutes ofHealth; HelmholtzICEMEDAlliance. 23 9 10 2 36 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0146-9592 1539-4794 OPT LETT Opt. Lett. APR 1 2020.0 45 7 1695 1698 10.1364/OL.387486 0.0 4 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics KY8AN 32235976.0 Green Submitted 2023-03-23 WOS:000522794100026 0
J Wang, LB; Zhang, C; Li, R; Duan, CX; Meng, XL; Atkinson, PM Wang, Libo; Zhang, Ce; Li, Rui; Duan, Chenxi; Meng, Xiaoliang; Atkinson, Peter M. Scale-Aware Neural Network for Semantic Segmentation of Multi-Resolution Remote Sensing Images REMOTE SENSING English Article deep convolutional neural network; multiple spatial resolutions; remote sensing; scale-aware feature representation; semantic segmentation LAND-COVER; RESOLUTION; CLASSIFIER; MULTISCALE; FRAMEWORK Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: (1) increased scale variation of geo-objects and (2) loss of detailed information at coarse spatial resolutions. To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed imagery. SaNet deploys a densely connected feature network (DCFFM) module to capture high-quality multi-scale context, such that the scale variation is handled properly and the quality of segmentation is increased for both large and small objects. A spatial feature recalibration (SFRM) module was further incorporated into the network to learn intact semantic content with enhanced spatial relationships, where the negative effects of information loss are removed. The combination of DCFFM and SFRM allows SaNet to learn scale-aware feature representation, which outperforms the existing multi-scale feature representation. Extensive experiments on three semantic segmentation datasets demonstrated the effectiveness of the proposed SaNet in cross-resolution segmentation. [Wang, Libo; Li, Rui; Meng, Xiaoliang] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Zhang, Ce; Atkinson, Peter M.] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Zhang, Ce] UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England; [Duan, Chenxi] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7522 Enschede, Netherlands Wuhan University; Lancaster University; UK Centre for Ecology & Hydrology (UKCEH) Meng, XL (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China. wanglibo@whu.edu.cn; c.zhang9@lancaster.ac.uk; lironui@whu.edu.cn; c.duan@utwente.nl; xmeng@whu.edu.cn; pma@lancaster.ac.uk Wang, Libo/GLR-5763-2022; Wang, Libo/GLR-6297-2022; Li, Rui/ADH-9550-2022 Wang, Libo/0000-0001-8096-6531; Li, Rui/0000-0001-7858-3160; Atkinson, Peter/0000-0002-5489-6880; Zhang, Ce/0000-0001-5100-3584 National Natural Science Foundation of China (NSFC) [16000009]; National Key Research and Development Program of China [2018YFB0505003]; [16000011]; [16000012]; [41971352] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; ; ; This research was funded by the National Natural Science Foundation of China (NSFC) under grant number 41971352, National Key Research and Development Program of China under grant number 2018YFB0505003. 50 4 5 1 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. DEC 2021.0 13 24 5015 10.3390/rs13245015 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology XZ1YZ Green Accepted, Green Submitted, gold 2023-03-23 WOS:000737457000001 0
J Kun, Z; Ning, C; Peng, Z; Jian, L; Michael, B Zhang, Kun; Chen, Ning; Zeng, Peng; Liu, Jian; Beer, Michael An efficient reliability analysis method for structures with hybrid time-dependent uncertainty RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Time -dependent reliability analysis; Deep neural network; Active learning; Weighted sampling; Hybrid uncertain model INTERVAL; MODELS; SYSTEMS Performing time-dependent reliability analysis is an effective way to estimate the failure probability of structural system throughout its lifetime. In the engineering practices, uncertain parameters with sufficient sample and limited sample may exist simultaneously. The uncertain parameters with limited sample data are difficult to construct its precise probabilistic characteristics during estimating the accurate time-dependent reliability. To address this issue, this paper first develops a new hybrid time-dependent reliability model involving interval processes. Then, to reduce the high dimensionality, an extension method based on equivalent stochastic process transformation approach is proposed to transform the stochastic processes and the interval processes into cor-responding equivalent random variables respectively. In particular, an instantaneous reliability model is con-structed to envelope all potential system failures that may occur during the time interval. In order to identify the instantaneous failure surface accurately, an active learning method is proposed based on the deep neural network model and the weighted sampling method. With the constructed deep neural network model, the new hybrid time-dependent reliability can be evaluated by performing the Monte Carlo Sampling. Three numerical examples are used to verify the accuracy and efficiency of the proposed method. [Zhang, Kun; Chen, Ning; Zeng, Peng; Liu, Jian] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China; [Beer, Michael] Leibniz Univ Hannover, Inst Risk & Reliabil, Callin Str 34, Hannover, Germany; [Beer, Michael] Univ Liverpool, Inst Risk & Uncertainty, Peach St, Liverpool L69 7ZF, Lancs, England; [Beer, Michael] Tongji Univ, Int Joint Res Ctr Resilient Infrastructure, Shanghai, Peoples R China; [Beer, Michael] Tongji Univ, Int Joint Res Ctr Engn Reliabil & Stochast Mech, Shanghai, Peoples R China Hunan University; Leibniz University Hannover; University of Liverpool; Tongji University; Tongji University Ning, C (corresponding author), Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China. chenning@hnu.edu.cn National Natural Science Foundation of China; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; [51905162]; [51621004]; [2022JJ30132] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); ; ; The paper is supported by the National Natural Science Foundation of China (Grant No. 51905162) , the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51621004) and the Natural Science Foundation of Hunan Province (Grant No. 2022JJ30132) . The author would also like to thank re- viewers for their valuable suggestions. 40 3 3 17 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. DEC 2022.0 228 108794 10.1016/j.ress.2022.108794 0.0 SEP 2022 11 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 5A0IR 2023-03-23 WOS:000862580900010 0
J Muhammad, R; Zhang, WY; Abbas, Z; Guo, F; Gwiazdzinski, L Muhammad, Rizwan; Zhang, Wenyin; Abbas, Zaheer; Guo, Feng; Gwiazdzinski, Luc Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China LAND English Article LULC change; remote sensing; big data; QGIS; impervious surface; prediction; Linyi URBAN EXPANSION; HABITAT FRAGMENTATION; CELLULAR-AUTOMATA; CLIMATE-CHANGE; URBANIZATION; DYNAMICS; IMPACTS; MODEL; AGRICULTURE; TEMPERATURE Land use and land cover (LULC) change analysis is a systematic technique that aids in the comprehension of physical and non-physical interaction with the natural habitat and the pursuit of environmental sustainability. Research regarding LULC's spatiotemporal changing patterns and the simulation of future scenarios offers a complete view of present and future development possibilities. To simulate the spatiotemporal change transition potential and future LULC simulation, we utilized multi-temporal remotely sensed big data from 1990 to 2020 with a 10-year interval. Independent variables (DEM, slope, and distance from roads) and an integrated CA-ANN methodology within the MOLUSCE plugin of QGIS were utilized. The findings reveal that physical and socioeconomic driving variables have a substantial effect on the patterns of the terrain. In the last three decades, the study area had a significant rise in impervious surface from 10.48% to 26.91%, as well as a minor increase in water from 1.30% to 1.67%. As a result, forest cover decreased from 12.60% to 8.74%, green space decreased from 26.34% to 16.57%, and barren land decreased from 49.28% to 46.11%. Additionally, the predictions (2030-2050) support the increasing trend towards impervious surface at the expense of significant quantities of forest and green space. [Muhammad, Rizwan; Zhang, Wenyin; Guo, Feng] Linyi Univ, Sch Informat Sci & Engn, Linyi 276000, Shandong, Peoples R China; [Abbas, Zaheer] South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China; [Gwiazdzinski, Luc] Univ Grenoble Alpes, Inst Geog Alpine IGA, F-38100 Grenoble, France Linyi University; South China Normal University; Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA) Muhammad, R (corresponding author), Linyi Univ, Sch Informat Sci & Engn, Linyi 276000, Shandong, Peoples R China. rizwan@lyu.edu.cn; zhangwenyin@lyu.edu.cn; abbaszaheer@m.scnu.edu.cn; guofeng_g@lyu.edu.cn; lucmarg@gmail.com Muhammad, Dr. Rizwan/R-5074-2016; Abbas, Zaheer/AAU-6131-2021 Muhammad, Dr. Rizwan/0000-0001-8903-5426; Abbas, Zaheer/0000-0002-1696-2795; Zhang, Wenyin/0000-0002-0370-448X Key Research and Development Program of Shandong Province [2019GNC106027, 2019JZZY010134]; Natural Science Foundation of Shandong Province [ZR2020MF058, ZR2020MF029] Key Research and Development Program of Shandong Province; Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work was partially funded by the Key Research and Development Program of Shandong Province (2019GNC106027, 2019JZZY010134) and the Natural Science Foundation of Shandong Province (ZR2020MF058, ZR2020MF029). 66 12 12 10 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-445X LAND-BASEL Land MAR 2022.0 11 3 419 10.3390/land11030419 0.0 24 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology 0C3LP gold 2023-03-23 WOS:000775219100001 0
J Zhou, N; Kawano, Y; Cao, M Zhou, Ning; Kawano, Yu; Cao, Ming Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Attitude tracking; fault-tolerant control (FTC); finite-time control; input saturations; neural network (NN) control FAULT-TOLERANT CONTROL; ATTITUDE TRACKING; RIGID SPACECRAFT In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples. [Zhou, Ning] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China; [Kawano, Yu] Hiroshima Univ, Grad Sch Engn, Higashihiroshima 7398527, Japan; [Cao, Ming] Univ Groningen, Fac Sci & Engn, NL-9747 AG Groningen, Netherlands Fujian Agriculture & Forestry University; Hiroshima University; University of Groningen Zhou, N (corresponding author), Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China. zhouning2010@gmail.com; ykawano@hiroshima-u.ac.jp; m.cao@rug.nl Kawano, Yu/AAZ-8978-2021; Cao, Ming/B-6808-2013 Kawano, Yu/0000-0001-5066-4700; Cao, Ming/0000-0001-5472-562X; , Ning/0000-0002-7457-5048 National Natural Science Foundation of China [61603095, 61972093]; Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University [XJQ201612] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University The work of N. Zhou was supported in part by the National Natural Science Foundation of China under Grant 61603095 and Grant 61972093, in part by the Research Foundation for Outstanding Young Scholars in the University of Fujian Province, and in part by the Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University under Grant XJQ201612. 38 24 27 9 51 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. SEPT 2020.0 31 9 3696 3710 10.1109/TNNLS.2019.2945920 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering NJ9CU 31722494.0 Green Published 2023-03-23 WOS:000566342500043 0
J Zheng, X; Li, MC; Shah, SBH; Do, DT; Chen, YF; Mavromoustakis, CX; Mastorakis, G; Pallis, E Zheng, Xiao; Li, Mingchu; Shah, Syed Bilal Hussain; Dinh-Thuan Do; Chen, Yuanfang; Mavromoustakis, Constandinos X.; Mastorakis, George; Pallis, Evangelos Enhancing Security-Problem-Based Deep Learning in Mobile Edge Computing ACM TRANSACTIONS ON INTERNET TECHNOLOGY English Article Mobile edge computing; synthetic theories; mobile device; suboptimal; security strategies; quality of service; deep learning WIRELESS; ALGORITHMS; INTERNET; ENERGY The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD's task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time. [Zheng, Xiao; Li, Mingchu; Shah, Syed Bilal Hussain] Shandong Univ Technol, Sch Comp Sci & Technol, 266 Xincun West Rd, Zibo, Peoples R China; [Li, Mingchu] Dalian Univ Technol, Sch Software, Tuqiang St, Dalian 116620, Kaifaqu, Peoples R China; [Shah, Syed Bilal Hussain] Manchester Metropolitan Univ, Sch Comp & Math, Manchester, Lancs, England; [Dinh-Thuan Do] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan; [Chen, Yuanfang] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China; [Mavromoustakis, Constandinos X.] Univ Nicosia, Mobile Syst Lab MoSys Lab, Dept Comp Sci, 46 Makedonitissas Ave, Nicosia, Cyprus; [Mavromoustakis, Constandinos X.] Univ Nicosia, Res Fdn, 46 Makedonitissas Ave, Nicosia, Cyprus; [Mastorakis, George] Hellen Mediterranean Univ, Dept Management Sci & Technol, Agios Nikolaos 72100, Crete, Greece; [Pallis, Evangelos] Hellen Mediterranean Univ, Dept Elect & Comp Engn, 1 Thorvald Circle, Iraklion, Greece Shandong University of Technology; Dalian University of Technology; Manchester Metropolitan University; Asia University Taiwan; Zhejiang University; University of Nicosia; University of Nicosia; Hellenic Mediterranean University; Hellenic Mediterranean University Zheng, X (corresponding author), Shandong Univ Technol, Sch Comp Sci & Technol, 266 Xincun West Rd, Zibo, Peoples R China. xiao_zheng0910@163.com; mingchul@dlut.edu.cn; SShah@mmu.ac.uk; inhthuan@asia.edu.tw; yuanfangchen@gmail.com; mavromoustakis.c@unic.ac.cy; gmastorakis@hmu.gr; pallis@pasiphae.eu Do, Dinh-Thuan/C-5477-2017 Do, Dinh-Thuan/0000-0003-2072-069X National Nature Science Foundation of China [61572095, 61877007] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) The article is supported by the National Nature Science Foundation of China under grant Nos 61572095 and 61877007. Authors' addresses: X. Zheng, School of Computer Science and Technology, Shandong University of Technology, 266 XincunWest Road, Zibo, China; email: xiao_zheng0910@163.com;M.Li, School of Software, Dalian University of Technology, Tuqiang Street, Dalian, Kaifaqu, China, 116620; email: mingchul@dlut.edu.cn;S.B.Hussain Shah, School of Computing and Mathematics, Manchester Metropolitan University, Metropolitan, Manchester, UK; email:S.Shah@mmu.ac.uk;D.-T.Do, Department of Computer Science and Information Engineering, Asia University, Taichung, Taichung, Taiwan; email: dodinhthuan@asia.edu.tw;Y.Chen, Zhejiang University, Zhejiang, Hangzhou, China; email: yuanfangchen@gmail.com;C.X.Mavromoustakis, Department of Computer Science, Mobile Systems Laboratory (MoSys Lab), University of Nicosia and University of Nicosia Research Foundation, 46 Makedonitissas Avenue, Nicosia, Cyprus; email:mavromoustakis.c@unic.ac.cy;G.Mastorakis, Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, Greece, 72100; email: gmastorakis@hmu.gr;E.Pallis, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 1 Thorvald Circle, Heraklion, Greece; email: pallis@pasiphae.eu. 38 0 0 7 8 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1533-5399 1557-6051 ACM T INTERNET TECHN ACM Trans. Internet. Technol. MAY 2022.0 22 2 49 10.1145/3458931 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1J8ZI 2023-03-23 WOS:000798201400022 0
J Price, M; Albaugh, M; Hahn, S; Juliano, AC; Fani, N; Brier, ZMF; Legrand, AC; Van Stolk-Cooke, K; Chaarani, B; Potter, A; Peck, K; Allgaier, N; Banaschewski, T; Bokde, ALW; Quinlan, EB; Desrivieres, S; Flor, H; Grigis, A; Gowland, P; Heinz, A; Ittermann, B; Martinot, JL; Paillere, ML; Artiges, E; Nees, F; Orfanos, DP; Poustka, L; Hohmann, S; Frohner, JH; Smolka, MN; Walter, H; Whelan, R; Schumann, G; Garavan, H Price, Matthew; Albaugh, Matthew; Hahn, Sage; Juliano, Anthony C.; Fani, Negar; Brier, Zoe M. F.; Legrand, Alison C.; Van Stolk-Cooke, Katherine; Chaarani, Bader; Potter, Alexandra; Peck, Kelly; Allgaier, Nicholas; Banaschewski, Tobias; Bokde, Arun L. W.; Quinlan, Erin Burke; Desrivieres, Sylvane; Flor, Herta; Grigis, Antoine; Gowland, Penny; Heinz, Andreas; Ittermann, Bernd; Martinot, Jean-Luc; Paillere, Marie-Laure; Artiges, Eric; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Poustka, Luise; Hohmann, Sarah; Froehner, Juliane H.; Smolka, Michael N.; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Garavan, Hugh Examination of the association between exposure to childhood maltreatment and brain structure in young adults: a machine learning analysis NEUROPSYCHOPHARMACOLOGY English Article Exposure to maltreatment during childhood is associated with structural changes throughout the brain. However, the structural differences that are most strongly associated with maltreatment remain unclear given the limited number of whole-brain studies. The present study used machine learning to identify if and how brain structure distinguished young adults with and without a history of maltreatment. Young adults (ages 18-21, n = 384) completed an assessment of childhood trauma exposure and a structural MRI as part of the IMAGEN study. Elastic net regularized regression was used to identify the structural features that identified those with a history of maltreatment. A generalizable model that included 7 cortical thicknesses, 15 surface areas, and 5 subcortical volumes was identified (area under the receiver operating characteristic curve = 0.71, p < 0.001). Those with a maltreatment history had reduced surface areas and cortical thicknesses primarily in fronto-temporal regions. This group also had larger cortical thicknesses in occipital regions and surface areas in frontal regions. The results suggest childhood maltreatment is associated with multiple measures of structure throughout the brain. The use of a large sample without exposure to adulthood trauma provides further evidence for the unique contribution of childhood trauma to brain structure. The identified regions overlapped with regions associated with psychopathology in adults with maltreatment histories, which offers insights as to how these disorders manifest. [Price, Matthew; Brier, Zoe M. F.; Legrand, Alison C.; Van Stolk-Cooke, Katherine] Univ Vermont, Dept Psychol Sci, Ctr Res Emot Stress & Technol, Burlington, VT 05405 USA; [Albaugh, Matthew; Hahn, Sage; Juliano, Anthony C.; Chaarani, Bader; Potter, Alexandra; Peck, Kelly; Allgaier, Nicholas; Garavan, Hugh] Univ Vermont, Dept Psychiat, Burlington, VT USA; [Fani, Negar] Emory Univ, Sch Med, Dept Psychiat & Behav Sci, Atlanta, GA USA; [Peck, Kelly] Vermont Ctr Behav & Hlth, Burlington, VT USA; [Banaschewski, Tobias; Nees, Frauke; Hohmann, Sarah] Heidelberg Univ, Med Fac Mannheim, Cent Inst Mental Hlth, Dept Child & Adolescent Psychiat & Psychotherapy, Mannheim, Germany; [Bokde, Arun L. W.; Nees, Frauke] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England; [Quinlan, Erin Burke] Trinity Coll Dublin, Sch Med, Discipline Psychiat, Dublin, Ireland; [Quinlan, Erin Burke] Trinity Coll Dublin, Trinity Coll, Inst Neurosci, Dublin, Ireland; [Desrivieres, Sylvane; Schumann, Gunter] Kings Coll London, SGDP Ctr, Inst Psychiat Psychol & Neurosci, Ctr Populat Neurosci & Precis Med PONS, London, England; [Flor, Herta] Heidelberg Univ, Med Fac Mannheim, Inst Cognit & Clin Neurosci, Cent Inst Mental Hlth, Mannheim, Germany; [Flor, Herta] Univ Mannheim, Sch Social Sci, Dept Psychol, Mannheim, Germany; [Grigis, Antoine; Orfanos, Dimitri Papadopoulos] Univ Paris Saclay, CEA, NeuroSpin, Gif Sur Yvette, France; [Gowland, Penny] Univ Nottingham, Sir Peter Mansfield Imaging Ctr, Sch Phys & Astron, Univ Pk, Nottingham, England; [Heinz, Andreas; Walter, Henrik] Charite Univ Med Berlin, Dept Psychiat & Psychotherapy CCM, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Free Univ Berlin, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Humboldt Univ, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Berlin Inst Hlth, Berlin, Germany; [Ittermann, Bernd] Phys Tech Bundesanstalt PTB, Braunschweig, Germany; [Martinot, Jean-Luc; Paillere, Marie-Laure; Artiges, Eric] INSERM, INSERM U A10 Trajectoires Dev Psychiat, Paris, France; [Martinot, Jean-Luc; Paillere, Marie-Laure; Artiges, Eric] Univ Paris Saclay, Ecole Normale Super Paris Saclay, CNRS, Ctr Borelli, Gif Sur Yvette, France; [Paillere, Marie-Laure] Sorbonne Univ, Pitie Salpetriere Hosp, AP HP, Dept Child & Adolescent Psychiat, Paris, France; [Artiges, Eric] Orsay Hosp, Psychiat Dept 91G16, Orsay, France; [Nees, Frauke] Univ Kiel, Univ Med Ctr Schleswig Holstein, Inst Med Psychol & Med Sociol, Kiel, Germany; [Poustka, Luise] Univ Med Ctr Gottingen, Dept Child & Adolescent Psychiat & Psychotherapy, Gottingen, Germany; [Froehner, Juliane H.; Smolka, Michael N.] Tech Univ Dresden, Dept Psychiat, Dresden, Germany; [Froehner, Juliane H.; Smolka, Michael N.] Tech Univ Dresden, Neuroimaging Ctr, Dresden, Germany; [Whelan, Robert] Trinity Coll Dublin, Sch Psychol, Dublin, Ireland; [Whelan, Robert] Trinity Coll Dublin, Global Brain Hlth Inst, Dublin, Ireland; [Schumann, Gunter] Humboldt Univ, Campus Charite Mitte, Dept Psychiat & Psychotherapy, PONS Res Grp, Berlin, Germany; [Schumann, Gunter] Leibniz Inst Neurobiol, Magdeburg, Germany; [Schumann, Gunter] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence IS, Shanghai, Peoples R China University of Vermont; University of Vermont; Emory University; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; University of London; King's College London; Trinity College Dublin; Trinity College Dublin; University of London; King's College London; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; University of Mannheim; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of Nottingham; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health; Physikalisch-Technische Bundesanstalt (PTB); Institut National de la Sante et de la Recherche Medicale (Inserm); Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Pitie-Salpetriere - APHP; UDICE-French Research Universities; Sorbonne Universite; University of Kiel; Schleswig Holstein University Hospital; University of Gottingen; Technische Universitat Dresden; Technische Universitat Dresden; Trinity College Dublin; Trinity College Dublin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Leibniz Institut fur Neurobiologie (LIN); Fudan University Price, M (corresponding author), Univ Vermont, Dept Psychol Sci, Ctr Res Emot Stress & Technol, Burlington, VT 05405 USA. Matthew.Price@uvm.edu Fani, Negar/AAW-1445-2021; Desrivières, Sylvane/ABC-4336-2021; Artiges, Eric/AAT-7219-2021; Gowland, Penny A./I-9016-2012; Heinz, Andreas/ABB-7736-2020 Fani, Negar/0000-0002-7720-252X; Desrivières, Sylvane/0000-0002-9120-7060; Artiges, Eric/0000-0003-4461-7646; Gowland, Penny A./0000-0002-4900-4817; Heinz, Andreas/0000-0001-5405-9065; Potter, Alexandra/0000-0001-5813-6259; Quinlan, Erin Burke/0000-0003-2927-1632; Price, Matthew/0000-0001-5637-9230; Walter, Henrik/0000-0002-9403-6121; Flor, Herta/0000-0003-4809-5398; Schumann, Gunter/0000-0003-4905-5523; Whelan, Robert/0000-0002-2790-7281; Nees, Frauke/0000-0002-7796-8234 NIMH [K08MH107661-A1, K08MH121654-A1]; European Union [LSHM-CT-2007-037286]; Horizon 2020 [695313]; ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) [PR-ST0416-10004]; BRIDGET (JPND: Brain Imaging, cognition Dementia and next generation Genomics) [MR/N027558/1]; Human Brain Project (HBP SGA 2) [785907]; FP7 project MATRICS [603016]; Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions) [MR/N000390/1]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; Bundesministeriumfur Bildung und Forschung [01GS08152, 01EV0711, 01EE1406A, 01EE1406B]; Deutsche Forschungsgemeinschaft [SM 80/7-2, SFB 940/2]; Medical Research Foundation; Medical Research Council [MR/R00465X/1, MR/S020306/1]; National Institutes of Health (NIH) [5U54EB02040305, 1R56AG058854-01]; ANR [AF12-NEUR0008-01-WM2NA, ANR12-SAMA-0004]; Fondation de France; Fondation pour la Recherche Medicale; Mission Interministerielle de Luttecontre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hopitaux-de-Paris; INSERM (interface grant); Paris Sud University IDEX 2012; National Institutes of Health; Science Foundation Ireland, USA (Axon, Testosterone and Mental Health during Adolescence) [16/ERCD/3797, RO1 MH085772-01A1]; NIH Consortium grant [U54 EB020403]; cross-NIH alliance NIMH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); European Union(European Commission); Horizon 2020; ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways); BRIDGET (JPND: Brain Imaging, cognition Dementia and next generation Genomics); Human Brain Project (HBP SGA 2); FP7 project MATRICS; Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions)(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London(General Electric); Bundesministeriumfur Bildung und Forschung(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); Medical Research Foundation(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); National Institutes of Health (NIH)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); ANR(French National Research Agency (ANR)); Fondation de France(Fondation de France); Fondation pour la Recherche Medicale(Fondation pour la Recherche Medicale); Mission Interministerielle de Luttecontre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hopitaux-de-Paris; INSERM (interface grant); Paris Sud University IDEX 2012; National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Science Foundation Ireland, USA (Axon, Testosterone and Mental Health during Adolescence)(Science Foundation Ireland); NIH Consortium grant(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); cross-NIH alliance The authors declare no competing interests. This work received support from the following sources: NIMH K08MH107661-A1, NIMH K08MH121654-A1, the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant STRATIFY (Brain network-based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST0416-10004), BRIDGET (JPND: Brain Imaging, cognition Dementia and next generation Genomics) (MR/N027558/1), Human Brain Project (HBP SGA 2, 785907), the FP7 project MATRICS (603016), the Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumfur Bildung und Forschung (BMBF grants 01GS08152, 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940/2), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB02040305 and 1R56AG058854-01). Further support was provided by grants from: ANR (project AF12-NEUR0008-01-WM2NA, and ANR12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Medicale, the Mission Interministerielle de Luttecontre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hopitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. 41 6 6 0 13 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 0893-133X 1740-634X NEUROPSYCHOPHARMACOL Neuropsychopharmacology OCT 2021.0 46 11 1888 1894 10.1038/s41386-021-00987-7 0.0 FEB 2021 7 Neurosciences; Pharmacology & Pharmacy; Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Pharmacology & Pharmacy; Psychiatry UO7BB 33637836.0 Bronze 2023-03-23 WOS:000622319400003 0
J Sun, X; Chen, CR; Dong, JY; Liu, D; Hu, GS Sun, Xin; Chen, Changrui; Dong, Junyu; Liu, Dan; Hu, Guosheng Exploring ubiquitous relations for boosting classification and localization KNOWLEDGE-BASED SYSTEMS English Article Deep learning; Relation knowledge exploring; Computer vision; Few-shot classification; Saliency detection OBJECT; SMOTE Although the weakly supervised learning can effectively avoid the tedious data annotating process of deep learning approaches, the performance is still in urgent need of enhancement. In this paper, we endeavor to mine a ubiquitous and fundamental knowledge-Relation, to boost several existing classification and localization models without changing the original structure. We first propose a universal relation exploring scheme to mine the relations among entities. This scheme can be specialized into different instantiations including object, superpixel and pixel relations to stimulating different learning models. We adopt the object relations on a few-shot classification model to concentrate on the dominant object, and to boost its discriminative capacity. The superpixel relation is utilized to improve the performance of the saliency object detection models. The sensitivity of the pixel relations to the uncertain regions makes it suitable for distinguishing the disputed area in saliency detection results. Our experiments demonstrate that all the three relation instantiations can significantly boost the performance of the state-of-the-art learning models and optimize the visual result. (C) 2020 Elsevier B.V. All rights reserved. [Sun, Xin; Chen, Changrui; Dong, Junyu; Liu, Dan] Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Peoples R China; [Hu, Guosheng] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Ocean University of China; Queens University Belfast Sun, X (corresponding author), Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Peoples R China. sunxin@ouc.edu.cn; ccr@stu.ouc.edu.cn; dongjunyu@ouc.edu.cn; liudandan@stu.ouc.edu.cn; huguosheng100@gmail.com Chen, Changrui/AAN-5773-2020 National Natural Science Foundation of China [61971388, U1706218, 41576011, L1824025]; Key Research and Development Program of Shandong Province, China [2017GGX10105]; Major Program of Natural Science Foundation of Shandong Province, China [ZR2018ZB0852] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Shandong Province, China; Major Program of Natural Science Foundation of Shandong Province, China This work was supported in part by the National Natural Science Foundation of China (No. 61971388, U1706218, 41576011, L1824025), Key Research and Development Program of Shandong Province, China (No. 2017GGX10105), and Major Program of Natural Science Foundation of Shandong Province, China (No. ZR2018ZB0852). The authors gratefully thank anonymous referees for their useful comments and editors for their work. 65 8 8 0 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAY 21 2020.0 196 105824 10.1016/j.knosys.2020.105824 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LF3DO 2023-03-23 WOS:000527301700038 0
J Xu, CY; Liu, H; Li, TH; Zhang, Y; Li, T; Li, G Xu, Chuanyun; Liu, Huan; Li, Tenghui; Zhang, Yang; Li, Tian; Li, Gang Cascaded Feature-Mask Fusion for Foreground Segmentation IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS English Article Feature extraction; Image segmentation; Semantics; Convolution; Decoding; Image edge detection; Task analysis; Deep learning; feature-mask fusion; foreground segmentation; high-level features; video surveillance NETWORK Foreground segmentation aims at extracting moving objects from the background in a robust manner under various challenging scenarios. The deep learning-based methods have achieved remarkable improvement in this field. These methods produce semantically correct predictions based on extracted rich semantic features yet perform poorly on segmentation of edge details. The main reason is that the high-level features extracted by the deep network lose the high-frequency information for the successful edge segmentation. On this basis, we propose a novel segmentation network with a cascade architecture to refine segmentation results step by step by introducing detailed information into high-level features. The network recorrects and optimizes the segmentation maps in each step so that more accurate segmentation results are obtained. Furthermore, we evaluate our approach on the challenging CDnet2014 dataset and achieve an F-measure of 0.9868. Our approach thus outperforms previous methods, such as FgSegNet_v2, FgSegNet, BSPVGan, Cascade CNN, IUTIS-5, WeSamBE, DeepBS, and GMM-Stauffer. [Xu, Chuanyun; Liu, Huan; Li, Tenghui; Li, Gang] Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing 401135, Peoples R China; [Zhang, Yang] Chongqing Normal Univ, Coll Comp & Informat Sci, Chongqing 401331, Peoples R China; [Li, Tian] Rhein Westfal TH Aachen, Comp Sci Dept, D-52074 Aachen, Germany Chongqing University of Technology; Chongqing Normal University; RWTH Aachen University Liu, H (corresponding author), Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing 401135, Peoples R China. with.him777@gmail.com China Chongqing Science and Technology Commission [cstc2020jscx-msxmX0086]; China Chongqing Banan District Science and Technology Commission Project [2020QC413]; China Chongqing Municipal Education Commission [KJQN202001137] China Chongqing Science and Technology Commission; China Chongqing Banan District Science and Technology Commission Project; China Chongqing Municipal Education Commission This work was supported in part by the China Chongqing Science and Technology Commission under Grant cstc2020jscx-msxmX0086; in part by the China Chongqing Banan District Science and Technology Commission Project under Grant 2020QC413; and in part by the China Chongqing Municipal Education Commission under Grant KJQN202001137. 47 0 0 4 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2687-7813 IEEE OPEN J INTEL TR IEEE Open J. Intell. Transp. Syst. 2022.0 3 340 350 10.1109/OJITS.2022.3170075 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Transportation Science & Technology Emerging Sources Citation Index (ESCI) Computer Science; Engineering; Transportation 1A4VB gold 2023-03-23 WOS:000791755300002 0
J Zhu, B; Gao, ZH; Zhao, JK; vanden Broucke, SKLM Zhu, Bing; Gao, Zihan; Zhao, Junkai; vanden Broucke, Seppe K. L. M. IRIC: An R library for binary imbalanced classification SOFTWAREX English Article Imbalanced classification; R language; Integrated library; Parallel implementation PREDICTION Imbalanced classification is a challenging issue in data mining and machine learning, for which a large number of solutions have been proposed. In this paper, we introduce an R library called IRIC, which integrates a wide set of solutions for imbalanced binary classification. IRIC not only provides a new implementation of some state-of-art techniques for imbalanced classification, but also improves the efficiency of model building using parallel techniques. The library and its source code are made freely available. (C) 2019 The Authors. Published by Elsevier B.V. [Zhu, Bing; Gao, Zihan; Zhao, Junkai] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China; [vanden Broucke, Seppe K. L. M.] Katholieke Univ Leuven, Dept Decis Sci & Informat Management, B-3000 Leuven, Belgium Sichuan University; KU Leuven Zhu, B (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China. zhubing@scu.edu.cn Zhu, Bing/HPC-1807-2023; Gao, Zihan/GWQ-5679-2022 National Natural Science Foundation of China [71401115]; Sichuan University, PR China [skqy 201742] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan University, PR China This work is supported by the National Natural Science Foundation of China (Grant No. 71401115) and fund from Sichuan University, PR China (Grant No. skqy 201742). 26 4 4 1 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-7110 SOFTWAREX SoftwareX JUL-DEC 2019.0 10 100341 10.1016/j.softx.2019.100341 0.0 5 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science JX9RT gold, Green Published 2023-03-23 WOS:000504065000023 0
J Shang, QY; Liu, XJ; Zhang, MF; Zhang, PF; Ling, YJ; Cui, GW; Liu, WE; Shi, XF; Yue, J; Tang, B Shang, Qiaoyan; Liu, Xiaojuan; Zhang, Mingfei; Zhang, Pengfei; Ling, Yujie; Cui, Guanwei; Liu, Wenge; Shi, Xifeng; Yue, Jun; Tang, Bo Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm
CHEMICAL ENGINEERING JOURNAL English Article Ofloxacin; TS-1/C3N4
; Artificial neural network; Genetic algorithm; Wastewater components Z-SCHEME PHOTOCATALYST; GRAPHENE OXIDE; DOPED G-C3N4; OXIDATION; NANOPARTICLES; TETRACYCLINE; KINETICS; TIO2 Antibiotics are used worldwide and their residues have been detected in multiple aquatic environments, which threaten both the human health and ecological environment. An efficient process is necessary for the thorough degradation of antibiotic contaminants in wastewater. Photocatalytic degradation of organic pollutants has the advantages of environmental friendliness, safety and thoroughness. In this study, titanium silicon molecular sieve-loaded carbon nitride (TS-1/C3N4) composite photocatalysts were synthesised and used for the photo-catalytic degradation of ofloxacin (OFX) wastewater. An artificial neural network (ANN) was proposed based on the experimental data, and combined with genetic algorithm (GA) to optimise the experimental parameters for the favourable reaction performance. The maximum removal efficiency (RE) at 82.92% was measured under the optimal experimental parameters (1.55 g/L catalyst, 58.60 % TS-1 loading and 49.38 mW/cm(2) luminous power density). Moreover, the influence of wastewater constituents on the RE was investigated both experimentally and through modelling via the ANN model. The experimental results revealed that the adsorption of OFX on the photocatalyst and the RE were reduced in the presence of wastewater constituents, probably due to their competitive adsorption and the subsequent reaction with the reactive species and light-shielding effect. The model gave an absolute relative deviation (ARD %) for the synergistic effect of cations, metal ions and anions at 6.88 %, 1.04 % and 1.77 %, respectively, thus showing a good ability to predict the synergistic interplay of wastewater components. This work may provide important insights and solutions for the photocatalytic treatment of antibiotic wastewater. [Shang, Qiaoyan; Liu, Xiaojuan; Zhang, Mingfei; Zhang, Pengfei; Ling, Yujie; Cui, Guanwei; Liu, Wenge; Shi, Xifeng; Tang, Bo] Shandong Normal Univ, Collaborat Innovat Ctr Functionalized Probes Chem, Inst Mat & Clean Energy, Coll Chem,Chem Engn & Mat Sci, Jinan 250014, Peoples R China; [Shang, Qiaoyan; Yue, Jun] Univ Groningen, Engn & Technol Inst Groningen, Dept Chem Engn, NL-9747 AG Groningen, Netherlands Shandong Normal University; University of Groningen Shi, XF; Tang, B (corresponding author), Shandong Normal Univ, Collaborat Innovat Ctr Functionalized Probes Chem, Inst Mat & Clean Energy, Coll Chem,Chem Engn & Mat Sci, Jinan 250014, Peoples R China.;Yue, J (corresponding author), Univ Groningen, Engn & Technol Inst Groningen, Dept Chem Engn, NL-9747 AG Groningen, Netherlands. shixf@sdnu.edu.cn; yue.jun@rug.nl; tangb@sdnu.edu.cn Yue, Jun/0000-0003-4043-0737 National Natural Science Foundation of China [22106094, 21976110, 21927811, 91753111]; Major Sci-ence and Technology Innovation Project of Shandong Province [2019JZZY020328]; China Scholarship Council [202008370046] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Sci-ence and Technology Innovation Project of Shandong Province; China Scholarship Council(China Scholarship Council) Acknowledgements This work was supported by the National Natural Science Foundation of China (22106094, 21976110, 21927811, 91753111) , the Major Sci-ence and Technology Innovation Project of Shandong Province (2019JZZY020328) and the financial support from China Scholarship Council (202008370046) . 53 6 7 33 65 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1385-8947 1873-3212 CHEM ENG J Chem. Eng. J. SEP 1 2022.0 443 136354 10.1016/j.cej.2022.136354 0.0 APR 2022 13 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 1L9QL Green Published 2023-03-23 WOS:000799614900005 0
J Mohseni, N; Fosel, T; Guo, LZ; Navarrete-Benlloch, C; Marquardt, F Mohseni, Naeimeh; Foesel, Thomas; Guo, Lingzhen; Navarrete-Benlloch, Carlos; Marquardt, Florian Deep Learning of Quantum Many-Body Dynamics via Random Driving QUANTUM English Article FIELD Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is based purely on monitoring expectation values of observables under random driving. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state. This allows our approach to be applied eventually to actual experimental data generated from a quantum many-body system that might be open, noisy, or disordered, without any need for a detailed understanding of the system. This scheme provides considerable speedup for rapid explorations and pulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit. [Mohseni, Naeimeh; Foesel, Thomas; Guo, Lingzhen; Navarrete-Benlloch, Carlos; Marquardt, Florian] Max Planck Inst Phys Lichts, Staudtstr 2, D-91058 Erlangen, Germany; [Mohseni, Naeimeh; Foesel, Thomas; Marquardt, Florian] Univ Erlangen Nurnberg, Dept Phys, Staudtstr 5, D-91058 Erlangen, Germany; [Navarrete-Benlloch, Carlos] Shanghai Jiao Tong Univ, Sch Phys & Astron, Wilczek Quantum Ctr, Shanghai 200240, Peoples R China; [Navarrete-Benlloch, Carlos] Shanghai Res Ctr Quantum Sci, Shanghai 201315, Peoples R China Max Planck Society; University of Erlangen Nuremberg; Shanghai Jiao Tong University Navarrete-Benlloch, C (corresponding author), Max Planck Inst Phys Lichts, Staudtstr 2, D-91058 Erlangen, Germany.;Navarrete-Benlloch, C (corresponding author), Shanghai Jiao Tong Univ, Sch Phys & Astron, Wilczek Quantum Ctr, Shanghai 200240, Peoples R China.;Navarrete-Benlloch, C (corresponding author), Shanghai Res Ctr Quantum Sci, Shanghai 201315, Peoples R China. derekkorg@gmail.com Yangyang Development Fund; Shanghai talent program; Shanghai Municipal Science and Technology Major Project [2019SHZDZX01] Yangyang Development Fund; Shanghai talent program; Shanghai Municipal Science and Technology Major Project CNB thanks sponsorship from the Yangyang Development Fund, as well as support from a Shanghai talent program and from the Shanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX01). 52 1 1 2 6 VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF WIEN BOLTZMANNGASSE 3, WIEN, 1090, AUSTRIA 2521-327X QUANTUM-AUSTRIA Quantum MAY 17 2022.0 6 714 10.22331/chl.2022.0003 0.0 20 Quantum Science & Technology; Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics 1Y8BH 2023-03-23 WOS:000808363000001 0
J Li, SS; Chen, BX; Chen, H; Hua, Z; Shao, Y; Yin, H; Wang, JW Li, Shaoshuo; Chen, Baixing; Chen, Hao; Hua, Zhen; Shao, Yang; Yin, Heng; Wang, Jianwei Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning PLOS ONE English Article HNRNP-C; EXPRESSION; RISK; CYTOSCAPE; PATTERNS; PACKAGE; COMPLEX; WOMEN; RNA Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO. [Li, Shaoshuo] Nanjing Univ Chinese Med, Nanjing, Peoples R China; [Chen, Baixing] Univ Leuven, KU Leuven, Dept Dev & Regenerat, Leuven, Belgium; [Chen, Hao; Hua, Zhen; Shao, Yang; Yin, Heng; Wang, Jianwei] Univ Chinese Med, Wuxi Affiliated Hosp, Dept Traumatol & Orthoped, Wuxi, Jiangsu, Peoples R China Nanjing University of Chinese Medicine; KU Leuven Wang, JW (corresponding author), Univ Chinese Med, Wuxi Affiliated Hosp, Dept Traumatol & Orthoped, Wuxi, Jiangsu, Peoples R China. wxwangjianwei1963@126.com Wang, Jianwei/0000-0002-2484-7922 National Natural Science Foundation of China [81873320, 81973878] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This study was supported by the National Natural Science Foundation of China (No. 81873320, 81973878). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 78 2 2 0 12 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One SEP 23 2021.0 16 9 e0257343 10.1371/journal.pone.0257343 0.0 18 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics XH8RH 34555052.0 Green Published, gold 2023-03-23 WOS:000725694500026 0
J Li, RJ; Wu, Y; Wu, Q; Dey, N; Crespo, RG; Shi, FQ Li, Rongjie; Wu, Yao; Wu, Qun; Dey, Nilanjan; Crespo, Ruben Gonzalez; Shi, Fuqian Emotion stimuli-based surface electromyography signal classification employing Markov transition field and deep neural networks MEASUREMENT English Article Surface electromyography; Markov transition field; Signal classification; Deep neural network; Signal image transition RECOGNITION Surface electromyography (sEMG) has been widely used in clinical medicine, rehabilitation medicine, and intelligent robots. Currently, sEMG signal classification methods promoted the development and industrialization of sEMG control bionic prostheses. Emotion recognition using sEMG signal is crucial in human-computer interaction (HCI) and becoming a research hotspot. While the high rate of emotion recognition is still the key issue for the emotion applications. Employing sEMG to study emotion classification can improve the recognition rate and eliminate subjective interference. In this research, the Markov transition field (MTF) method was applied to convert sEMG signals to images; and this crucial converting process makes convolutional neural networks adopting the input resource. A 69-INPUT-6-OUTPUT primary deep neural network was constructed for classifying the human emotion states under emotion stimuli experiment. The MTF-based deep neural network (MTF-DNN) for classifying sEMG signals was developed and validated subsequently. The result showed that the high effectiveness of the proposed classification model. The proposed MTFDNN performs high efficacy in the indices of classification of Ac (0.9102), Pr (0.1867), and Fm (0.9089) by comparing with different classification models. [Li, Rongjie; Wu, Yao] Wenzhou Business Coll, Wenzhou 325067, Peoples R China; [Wu, Qun] Zhejiang Sci Tech Univ, Inst Universal Design, Hangzhou 310027, Peoples R China; [Dey, Nilanjan] JIS Univ, Dept Comp Sci & Engn, Kolkata 700109, India; [Crespo, Ruben Gonzalez] Univ Int La Rioja, Dept Comp Sci & Technol, Logrono 26001, Spain; [Shi, Fuqian] Rutgers Canc Inst New Jersey, New Brunswick, NJ 08903 USA Zhejiang Sci-Tech University; Universidad Internacional de La Rioja (UNIR); Rutgers State University New Brunswick; Rutgers State University Medical Center; Rutgers Cancer Institute of New Jersey Wu, Y (corresponding author), 1 Univ Rd Chashan High Tech Educ Zone, Wenzhou, Zhejiang, Peoples R China. yaowu890@gmail.com Gonzalez Crespo, Ruben/P-8601-2018 Gonzalez Crespo, Ruben/0000-0001-5541-6319 45 0 0 12 26 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement FEB 2022.0 189 110470 10.1016/j.measurement.2021.110470 0.0 JAN 2022 11 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation YR2DN 2023-03-23 WOS:000749807200003 0
J Yang, K; Lu, KZ; Wu, Y; Yu, J; Liu, BY; Zhao, Y; Chen, JX; Zhou, XZ Yang, Kuo; Lu, Kezhi; Wu, Yang; Yu, Jian; Liu, Baoyan; Zhao, Yi; Chen, Jianxin; Zhou, Xuezhong A network-based machine-learning framework to identify both functional modules and disease genes HUMAN GENETICS English Article HUMAN INTERACTOME; PREDICTION; WALKING Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease. [Yang, Kuo; Lu, Kezhi; Zhou, Xuezhong] Beijing Jiaotong Univ, Inst Med Intelligence, Sch Comp & Informat Technol, Beijing 100044, Peoples R China; [Yang, Kuo] Tsinghua Univ, Dept Automat, MOE Key Lab Bioinformat, Inst TCM X,Bioinformat Div,BNRIST, Beijing 10084, Peoples R China; [Wu, Yang; Zhao, Yi] Chinese Acad Sci, Inst Comp Technol, Adv Comp Res Ctr, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China; [Yu, Jian] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China; [Liu, Baoyan; Zhou, Xuezhong] China Acad Chinese Med Sci, Data Ctr Tradit Chinese Med, Beijing 100700, Peoples R China; [Chen, Jianxin] Beijing Univ Chinese Med, Beijing 100029, Peoples R China; [Lu, Kezhi] Katholieke Univ Leuven, Imec DistriNet, B-3001 Leuven, Belgium Beijing Jiaotong University; Tsinghua University; Chinese Academy of Sciences; Institute of Computing Technology, CAS; Beijing Jiaotong University; China Academy of Chinese Medical Sciences; Beijing University of Chinese Medicine; KU Leuven Zhou, XZ (corresponding author), Beijing Jiaotong Univ, Inst Med Intelligence, Sch Comp & Informat Technol, Beijing 100044, Peoples R China.;Zhou, XZ (corresponding author), China Acad Chinese Med Sci, Data Ctr Tradit Chinese Med, Beijing 100700, Peoples R China. yangkuo@bjtu.edu.cn; lukezhi@bjtu.edu.cn; wuyang@ict.ac.cn; jianyu@bjtu.edu.cn; liuby@mail.cintcm.ac.cn; biozy@ict.ac.cn; cjx@bucm.edu.cn; xzzhou@bjtu.edu.cn Yu, Jian/HJY-2670-2023; Zhou, Xuezhong/AAZ-5025-2021; Zhao, Yi/A-2962-2011 Zhao, Yi/0000-0001-6046-8420; KeZhi, Lu/0000-0003-4979-5097 National Key Research and Development Program [2017YFC1703506, 2017YFC1703502]; National Science and Technology Major Project [2019ZX09201005-002-006]; National Key R&D Program of China [2018AAA0100302]; Fundamental Research Funds for the Central Universities [2018JBZ006, ZZ10-005]; Special Programs of Traditional Chinese Medicine [JDZX2015170, JDZX2015171] National Key Research and Development Program; National Science and Technology Major Project; National Key R&D Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Special Programs of Traditional Chinese Medicine This work was partially supported by the National Key Research and Development Program [2017YFC1703506 and 2017YFC1703502], the National Science and Technology Major Project [2019ZX09201005-002-006], the National Key R&D Program of China [2018AAA0100302], the Fundamental Research Funds for the Central Universities [2018JBZ006 and ZZ10-005], and the Special Programs of Traditional Chinese Medicine [JDZX2015170 and JDZX2015171]. 70 4 5 3 28 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0340-6717 1432-1203 HUM GENET Hum. Genet. JUN 2021.0 140 6 897 913 10.1007/s00439-020-02253-0 0.0 JAN 2021 17 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity RX7MM 33409574.0 2023-03-23 WOS:000605523000001 0
J Tao, QH; Li, Z; Xu, J; Lin, S; De Schutter, B; Suykens, JAK Tao, Qinghua; Li, Zhen; Xu, Jun; Lin, Shu; De Schutter, Bart; Suykens, Johan A. K. Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Predictive models; Artificial neural networks; Neurons; Feature extraction; Analysis of variance; Data models; Numerical models; Traffic flow prediction; piecewise linear neural networks; interpretation; variables analysis MODELS; REGRESSION Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted, e.g., the contributions of specific traffic elements are not explicit, hardly providing informative guidance. In this paper, we aim at addressing more interpretable short-term TF prediction with joint consideration to high accuracy, and thus introduces a pragmatic method by applying the efficient hinging hyperplanes neural network (EHHNN) simply built upon sparse neuron connections. In the proposed method, different traffic factors are incorporated into the inputs, including their spatial-temporal information. Besides the pursuit of accuracy, we further extend the ANOVA decomposition of EHHNNs to the interpretation analysis with specifications to traffic data, in which the contributions concerning specific traffic variables are detected quantitatively. As such, the proposed method firstly applies the EHHNN to filter out more important traffic variables for dimensionality reduction while maintaining accurate prediction. Then, variable interpretation analysis is performed from different perspectives, e.g. to quantitatively investigate the influence of traffic factors and also their spatial-temporal impacts. Therefore, a predictor and an analyzing tool can both be attained for the TF by exerting the flexibility and extending the interpretability of EHHNNs, which is promising to provide informative guidance to future traffic control. Numerical experiments verify the effectiveness and potential of the proposed method in TF prediction and analysis. [Tao, Qinghua; Suykens, Johan A. K.] Katholieke Univ Leuven, Dept Elect Engn ESAT STADIUS, B-3000 Leuven, Belgium; [Li, Zhen; Xu, Jun] Harbin Inst Technol Shenzhen, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China; [Xu, Jun] Minist Educ, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China; [Lin, Shu] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China; [De Schutter, Bart] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands KU Leuven; Harbin Institute of Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Delft University of Technology Xu, J (corresponding author), Harbin Inst Technol Shenzhen, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China.;Xu, J (corresponding author), Minist Educ, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China. qinghua.tao@esat.kuleuven.be; zueslee.hitsz@foxmail.com; xujunqgy@hit.edu.cn; slin@ucas.ac.cn; b.deschutter@tudelft.nl; johan.suykens@esat.kuleuven.be ; Suykens, Johan/C-9781-2014; De Schutter, Bart/B-7978-2011 Tao, Qinghua/0000-0001-9705-7748; Suykens, Johan/0000-0002-8846-6352; De Schutter, Bart/0000-0001-9867-6196; Xu, Jun/0000-0002-2934-4814 European Research Council (ERC) [787960, KUL C14/18/068]; Flemish Government The Research Foundation Flanders (FWO) [GOA4917]; Ford-KU Leuven Research Alliance Project [KUL0076]; EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI-Integrating Reasoning, Learning and Optimization); National Natural Science Foundation of China [U1813224, 61673366, 62173113, 61903304]; Science and Technology Innovation Committee of Shenzhen Municipality [JCYJ20200109113412326, GXWD20201230155427003-20200821173613001] European Research Council (ERC)(European Research Council (ERC)European Commission); Flemish Government The Research Foundation Flanders (FWO); Ford-KU Leuven Research Alliance Project; EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI-Integrating Reasoning, Learning and Optimization); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Innovation Committee of Shenzhen Municipality This work was supported in part by the European Research Council (ERC) Advanced Grant E-DUALITY (787960); in part by the Research Council under Grant KUL C14/18/068; in part by the Flemish Government The Research Foundation Flanders (FWO) Project GOA4917; in part by the Ford-KU Leuven Research Alliance Project KUL0076; in part by the EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI-Integrating Reasoning, Learning and Optimization); in part by the National Natural Science Foundation of China under Grant U1813224, Grant 61673366, Grant 62173113, and Grant 61903304; and in part by the Science and Technology Innovation Committee of Shenzhen Municipality under Grant JCYJ20200109113412326 and Grant GXWD20201230155427003-20200821173613001. 51 4 4 4 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. SEP 2022.0 23 9 15616 15628 10.1109/TITS.2022.3142728 0.0 JAN 2022 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 4U7SI Green Published 2023-03-23 WOS:000751492800001 0
J Netzer, N; Weisser, C; Schelb, P; Wang, XF; Qin, XY; Gortz, M; Schutz, V; Radtke, JP; Hielscher, T; Schwab, C; Stenzinger, A; Kuder, TA; Gnirs, R; Hohenfellner, M; Schlemmer, HP; Maier-Hein, KH; Bonekamp, D Netzer, Nils; Weisser, Cedric; Schelb, Patrick; Wang, Xianfeng; Qin, Xiaoyan; Goertz, Magdalena; Schuetz, Viktoria; Radtke, Jan Philipp; Hielscher, Thomas; Schwab, Constantin; Stenzinger, Albrecht; Kuder, Tristan Anselm; Gnirs, Regula; Hohenfellner, Markus; Schlemmer, Heinz-Peter; Maier-Hein, Klaus H.; Bonekamp, David Fully Automatic Deep Learning in Bi-institutional Prostate Magnetic Resonance Imaging Effects of Cohort Size and Heterogeneity INVESTIGATIVE RADIOLOGY English Article MRI; prostate cancer; deep learning; U-Net; PI-RADS DIAGNOSTIC-ACCURACY; CANCER; DIFFUSION; BIOPSY; MRI Background: The potential of deep learning to support radiologist prostate magnetic resonance imaging (MRI) interpretation has been demonstrated. Purpose: The aim of this study was to evaluate the effects of increased and diversified training data (TD) on deep learning performance for detection and segmentation of clinically significant prostate cancer-suspicious lesions. Materials and Methods: In this retrospective study, biparametric (T2-weighted and diffusion-weighted) prostate MRI acquired with multiple I.5-T and 3.0-T MRI scanners in consecutive men was used for training and testing of prostate segmentation and lesion detection networks. Ground truth was the combination of targeted and extended systematic MRI-transrectal ultrasound fusion biopsies, with significant prostate cancer defined as International Society of Urological Pathology grade group greater than or equal to 2. U-Nets were internally validated on full, reduced, and PROSTATEx-enhanced training sets and subsequently externally validated on the institutional test set and the PROSTATEx test set. U-Net segmentation was calibrated to clinically desired levels in cross-validation, and test performance was subsequently compared using sensitivities, specificities, predictive values, and Dice coefficient. Results: One thousand four hundred eighty-eight institutional examinations (median age, 64 years; interquartile range, 58-70 years) were temporally split into training (2014-2017, 806 examinations, supplemented by 204 PROSTATEx examinations) and test (2018-2020, 682 examinations) sets. In the test set, Prostate Imaging-Reporting and Data System (PI-RADS) cutoffs greater than or equal to 3 and greater than or equal to 4 on a per-patient basis had sensitivity of 97% (241/249) and 90% (223/249) at specificity of 19% (82/433) and 56% (242/433), respectively. The full U-Net had corresponding sensitivity of 97% (241/249) and 88% (219/249) with specificity of 20% (86/433) and 59% (254/433), not statistically different from PI-RADS (P > 0.3 for all comparisons). U-Net trained using a reduced set of 171 consecutive examinations achieved inferior performance (P < 0.001). PROSTATEx training enhancement did not improve performance. Dice coefficients were 0.90 for prostate and 0.42/0.53 for MRI lesion segmentation at PI-RADS category 3/4 equivalents. Conclusions: In a large institutional test set, U-Net confirms similar performance to clinical PI-RADS assessment and benefits from more TD, with neither institutional nor PROSTATEx performance improved by adding multiscanner or bi-institutional TD. [Netzer, Nils; Weisser, Cedric; Schelb, Patrick; Wang, Xianfeng; Qin, Xiaoyan; Radtke, Jan Philipp; Gnirs, Regula; Schlemmer, Heinz-Peter; Bonekamp, David] German Canc Res Ctr, Div Radiol, Neuenheimer Feld 280, D-69120 Heidelberg, Germany; [Netzer, Nils; Weisser, Cedric; Schelb, Patrick; Wang, Xianfeng] Heidelberg Univ, Med Sch, Heidelberg, Germany; [Wang, Xianfeng] Guilin Med Univ, Affiliated Hosp, Dept Radiol, Guilin, Peoples R China; [Qin, Xiaoyan] Nanxishan Hosp Guangei Zhuang Autonomous Reg, Dept Radiol, Guilin, Guangxi, Peoples R China; [Goertz, Magdalena; Schuetz, Viktoria; Radtke, Jan Philipp; Hohenfellner, Markus] Heidelberg Univ, Dept Urol, Med Ctr, Heidelberg, Germany; [Hielscher, Thomas] German Canc Res Ctr, Div Biostat, Heidelberg, Germany; [Schwab, Constantin; Stenzinger, Albrecht] Heidelberg Univ, Med Ctr, Inst Pathol, Heidelberg, Germany; [Kuder, Tristan Anselm] German Canc Res Ctr, Div Med Phys Radiol, Heidelberg, Germany; [Maier-Hein, Klaus H.] German Canc Res Ctr, Med Image Comp, Heidelberg, Germany; [Schlemmer, Heinz-Peter; Maier-Hein, Klaus H.; Bonekamp, David] German Canc Consortium, Heidelberg, Germany Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Guilin Medical University; Ruprecht Karls University Heidelberg; Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Helmholtz Association; German Cancer Research Center (DKFZ); Helmholtz Association; German Cancer Research Center (DKFZ); Helmholtz Association; German Cancer Research Center (DKFZ) Bonekamp, D (corresponding author), German Canc Res Ctr, Div Radiol, Neuenheimer Feld 280, D-69120 Heidelberg, Germany. d.bonekamp@dkfz-heidelbag.de Kuder, Tristan Anselm/AAF-2136-2019 Kuder, Tristan Anselm/0000-0001-6439-8124 Bayer; Bristol Myers Squibb; Chugai; BMBF; Deutsche Krebshilfe; Dietmar-Hopp-Stiftung; Roland-Ernst-Stiftung Bayer(Bayer AG); Bristol Myers Squibb(Bristol-Myers Squibb); Chugai; BMBF(Federal Ministry of Education & Research (BMBF)); Deutsche Krebshilfe(Deutsche Krebshilfe); Dietmar-Hopp-Stiftung; Roland-Ernst-Stiftung N.N., C.W., P.S., X.W., X.Q., M.G., V.S., T.H., C.S., T.A.K., R.G., M.H., and K.H.M.-H. have nothing to declare. J.P.R. declares payment for consultant work from Saegeling Medizintechnik, Siemens Healthineers, and for development of educational presentations from Saegeling Medizintechnik. A.S. declares being part of advisory board/speaker's bureau of AstraZeneca, Bayer, Bristol Myers Squibb, Eli Lilly, Illumina, Janssen, MSD, Novartis, Pfizer, Roche, Seattle Genetics, Thermo Fisher Scientific; and declares receiving grants from Bayer, Bristol Myers Squibb, and Chugai. H.-P.S. declares receiving consulting fee or honorarium from Siemens, Curagita, Profound, and Bayer; declares receiving travel support from Siemens, Curagita, Profound, and Bayer; is a board member of Curagita; provides consultancy for Curagita and Bayer; declares receiving grants/grants pending from BMBF, Deutsche Krebshilfe, Dietmar-Hopp-Stiftung, and Roland-Ernst-Stiftung; and declares receiving payment for lectures from Siemens, Curagita, Profound, and Bayer. D.B. declares receiving payment for lectures from Bayer Vital. 44 11 11 4 8 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA 0020-9996 1536-0210 INVEST RADIOL Invest. Radiol. DEC 2021.0 56 12 799 808 10.1097/RLI.0000000000000791 0.0 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging ZB1RA 34049336.0 2023-03-23 WOS:000756626700003 0
J Guo, JF; Albeshri, AA; Sanchez, YG Guo, Jianfang; Albeshri, Aiiad A.; Sanchez, Yolanda Guerrero PSYCHOLOGICAL MEMORY FORGETTING MODEL USING LINEAR DIFFERENTIAL EQUATION ANALYSIS FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY English Article Long-Term Memory Neural Network Model; Linear Differential Equation; Memory Forgetting Model; Neural Mechanism of Cognitive Memory PREDICTION; EFFICIENCY; TREND To solve the problem of insufficient combination of neural mechanism and psychological mechanism in the study of human brain cognitive memory activity, in this exploration, the memory forgetting model in psychology is analyzed and constructed by fractional order model of linear differential equation. The study of memory in cognitive psychology is combined with the neurophysiological mechanism of memory. At the same time, for the stability of long-term memory, a long-term memory neural network model (LTMNNS) is proposed. The validity and accuracy of the model are tested by the recognition of memory eigenvectors. Numerical test and simulation results show that when the difference between any initial values of the system is not more than delta, the corresponding system solution difference is not more than epsilon in the interval [0, T]. Reasoning correctness of fractional order computation model of memory neural network is verified. The LTMNNS algorithm in this exploration guarantees a high number of completed tasks, and the average number of failed tasks is less than MemNNs, DNC and LSTM algorithm. Compared with other models, the proposed LTMNNS has better generalization ability and higher stability. [Guo, Jianfang] Hohai Univ, Sch Marxism, Nanjing, Peoples R China; [Guo, Jianfang] Nanjing Univ Chinese Med, Sch Marxism, Nanjing, Peoples R China; [Albeshri, Aiiad A.] King Abdulaziz Univ, Fac Comp & IT, Dept Comp Sci, Jeddah, Saudi Arabia; [Sanchez, Yolanda Guerrero] Univ Murcia, Fac Med, Dept Anat & Psicobiol, Murcia, Spain Hohai University; Nanjing University of Chinese Medicine; King Abdulaziz University; University of Murcia Guo, JF (corresponding author), Hohai Univ, Sch Marxism, Nanjing, Peoples R China.;Guo, JF (corresponding author), Nanjing Univ Chinese Med, Sch Marxism, Nanjing, Peoples R China. guojianfang100731@163.com; aaalbeshri@kau.edu.sa; yolanda.guerreros@um.es Albeshri, Aiiad A/E-7159-2015 Albeshri, Aiiad A/0000-0003-3796-0294 Education Department of Jiangsu province Research on the influence and countermeasures of contemporary main social trends of thought on college students values [2017SJB0325] Education Department of Jiangsu province Research on the influence and countermeasures of contemporary main social trends of thought on college students values The study was supported by Education Department of Jiangsu province Research on the influence and countermeasures of contemporary main social trends of thought on college students values (Grant No. 2017SJB0325). 30 0 0 11 13 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-348X 1793-6543 FRACTALS Fractals-Complex Geom. Patterns Scaling Nat. Soc. MAR 2022.0 30 2 2240080 10.1142/S0218348X22400801 0.0 12 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Mathematics; Science & Technology - Other Topics 0E7QU hybrid 2023-03-23 WOS:000776872300009 0
J Zhou, XQ; Yue, XD; Xu, ZK; Denoeux, T; Chen, YF Zhou, Xiaoqian; Yue, Xiaodong; Xu, Zhikang; Denoeux, Thierry; Chen, Yufei PENet: Prior evidence deep neural network for bladder cancer staging METHODS English Article Bladder cancer staging; Deep neural network; Prior evidence fusion CLASSIFICATION Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional Neural Networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it de-mands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, an innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated the bladder wall is measured to get prior distribution parameters of class probability called prior evidence. Second, we formulate the posterior distribution of class probability according to Bayesian Theorem. Last, we modify the loss function based on posterior distribution of class probability which parameters include both prior evidence and prediction evidence in the learning procedure. Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN algorithms for bladder cancer staging. [Zhou, Xiaoqian; Yue, Xiaodong; Xu, Zhikang] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China; [Yue, Xiaodong] Artificial Intelligence Inst Shanghai Univ, Shanghai, Peoples R China; [Denoeux, Thierry] Shanghai Univ, Sino European Sch Technol, Shanghai, Peoples R China; [Denoeux, Thierry] Univ technol Compiegne, Compiegne, France; [Chen, Yufei] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China Shanghai University; Shanghai University; Picardie Universites; Universite de Technologie de Compiegne; Tongji University Yue, XD (corresponding author), Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China. zhouxiaoqian@shu.edu.cn; yswantfly@shu.edu.cn; xuzhikangnba@shu.edu.cn; thierry.denoeux@utc.fr; yufeichen@tongji.edu.cn Denoeux, Thierry/0000-0002-0660-5436 National Natural Science Foundation of China; Natural Science Foundation of Shanghai; Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province, China; [62173252]; [61976134]; [21ZR1423900]; [CICIP2021001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province, China; ; ; ; This work was supported by National Natural Science Foundation of China (Serial Nos. 62173252, 61976134) , Natural Science Foundation of Shanghai (NO. 21ZR1423900) and Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province, China (No. CICIP2021001) . 40 1 1 6 6 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1046-2023 1095-9130 METHODS Methods NOV 2022.0 207 20 28 10.1016/j.ymeth.2022.08.010 0.0 SEP 2022 9 Biochemical Research Methods; Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 5T1OA 36031139.0 Green Submitted 2023-03-23 WOS:000875643900004 0
J Li, BH; Li, G Li, Bohua; Li, Ge Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions JOURNAL OF SENSORS English Article Financial deregulation, financial globalization, and the increasing variety and technological sophistication of the commodities offered by financial services have made the operations of financial institutions more complex. Compared with credit risk and market risk, financial institutions' transaction risk management plays an increasingly important role in financial practice. As an emerging technology, big data mining technology has a unique advantage in optimizing the processing and management of large amounts of data. Big data mining technology not only has the common functions of finding, comprehensively managing all kinds of information, collecting and analyzing data, and conducting statistics but also should have the ability to process information that is hidden and useful in the database through data mining technology. Based on CFPS and data mining technology, this paper analyzes the operational risk of financial institutions, analyzes the causes of the operational risk of financial institutions, discusses the measures to avoid the operational risk of financial institutions, and draws corresponding conclusions. [Li, Bohua] Zhongnan Univ Econ & Law, Sch Finance, Wuhan 430073, Peoples R China; [Li, Ge] Univ Bologna, Sch Engn & Architecture, I-47521 Cesena, Italy Zhongnan University of Economics & Law; University of Bologna Li, BH (corresponding author), Zhongnan Univ Econ & Law, Sch Finance, Wuhan 430073, Peoples R China. lbh@stu.zuel.edu.cn; ge.li2@studio.unibo.it 20 0 0 6 10 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-725X 1687-7268 J SENSORS J. Sens. AUG 11 2022.0 2022 9645142 10.1155/2022/9645142 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3X8HV gold 2023-03-23 WOS:000843276600004 0
J Attaullah, H; Anjum, A; Kanwal, T; Malik, SUR; Asheralieva, A; Malik, H; Zoha, A; Arshad, K; Imran, MA Attaullah, Hasina; Anjum, Adeel; Kanwal, Tehsin; Malik, Saif Ur Rehman; Asheralieva, Alia; Malik, Hassan; Zoha, Ahmed; Arshad, Kamran; Imran, Muhammad Ali F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes SENSORS English Article DCP; F-Classify; membership function; MSA; MST; (p; k) angelization; QT K-ANONYMITY; MODEL With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches. [Attaullah, Hasina; Anjum, Adeel; Kanwal, Tehsin; Malik, Saif Ur Rehman] COMSATS Univ, Dept Comp Sci, Islamabad 45550, Pakistan; [Asheralieva, Alia] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China; [Malik, Saif Ur Rehman] Cybernet AS, EE-12618 Tallinn, Estonia; [Malik, Hassan] Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, Lancs, England; [Zoha, Ahmed; Imran, Muhammad Ali] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Arshad, Kamran] Ajman Univ, Coll Engn & IT, Ajman 20550, U Arab Emirates; [Imran, Muhammad Ali] Ajman Univ, Artificial Intelligence Res Ctr AIRC, Ajman 20550, U Arab Emirates COMSATS University Islamabad (CUI); Southern University of Science & Technology; Cybernetica; Edge Hill University; University of Glasgow; Ajman University; Ajman University Malik, SUR (corresponding author), COMSATS Univ, Dept Comp Sci, Islamabad 45550, Pakistan.;Malik, SUR (corresponding author), Cybernet AS, EE-12618 Tallinn, Estonia. hasina.attaullah12@gmail.com; adeel.anjum@comsats.edu.pk; tehseenkanwal@yahoo.com; saif.rehmanmalik@gmail.com; aasheralieva@gmail.com; malikh@edgehill.ac.uk; Ahmed.Zoha@glasgow.ac.uk; k.arshad@ajman.ac.ae; Muhammad.Imran@glasgow.ac.uk Imran, Muhammad Ali/I-4832-2012; Asheralieva, Alia/L-5500-2017; Zoha, Ahmed/ABB-8933-2020; Anjum, Adeel AA/L-4391-2013 Imran, Muhammad Ali/0000-0003-4743-9136; Asheralieva, Alia/0000-0002-4430-5928; Zoha, Ahmed/0000-0001-7497-9336; Anjum, Adeel AA/0000-0001-5083-0019; Arshad, Kamran/0000-0002-4447-8335 46 3 3 2 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUL 2021.0 21 14 4933 10.3390/s21144933 0.0 23 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation TO6AR 34300673.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000676992400001 0
J Li, C; Rusak, Z; Horvath, I; Ji, LH Li, Chong; Rusak, Zoltan; Horvath, Imre; Ji, Linhong Validation of the reasoning of an entry-level cyber-physical stroke rehabilitation system equipped with engagement enhancing capabilities ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE English Article Cyber-physical stroke rehabilitation system; Engagement enhancing; Reasoning in context; Stimulation strategy; Learning mechanism; Accuracy of suggestions ROBOT-AIDED THERAPY; VIRTUAL-REALITY; NEURAL-NETWORK; CLASSIFICATION; RECOVERY; MOTION; GAMES; MODEL; ARM Maintaining and enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. In the preceding phase of our research, an entry-level cyber-physical stroke rehabilitation system (CP-SRS) has been developed, with the aim of enhancing patients' overall engagement during rehabilitation exercises. As a follow up on the evaluation of the proposed engagement enhancing method and the smart learning mechanism based on the simulated data, this paper presents the validation results of the proposed CP-SRS system based on real-life data. Validation included two aspects: (i) validation of the effectiveness of the applied stimulation strategies (SSs), and (ii) validation of the accuracy of the suggestions of the smart learning mechanism. Methodologically, a within-subject experiment was designed and completed. Eighteen subjects were recruited to participate in the experiments, based on convenience sampling. During the completed game exercises SSs were applied individually as well as in combination. The engagement levels of the participants were evaluated and recorded after applying the SSs individually and combined. The results were processed by within-subject ANOVA in order to test if there was a significant difference between the influences of the different SSs and combinations. In addition, training and testing of the smart learning mechanism (SLM) was also executed in MATLAB. The results indicated that several SSs significantly increased the engagement of the subjects, and that both neural network-based SLM and the Naive Bayes-based SLM were able to learn and discriminate the effects of the various SSs. Our conclusion is that they both can be used to assist making decision on effective application of SSs. However, applying neural network-based SLM is more appropriate in the context of increasing engagement. (C) 2016 Elsevier Ltd. All rights reserved. [Li, Chong; Rusak, Zoltan; Horvath, Imre] Delft Univ Technol, Fac Ind Design Engn, Landbergstr 15, NL-2628 CE Delft, Netherlands; [Li, Chong; Ji, Linhong] Tsinghua Univ, State Key Lab Tribol, Div Intelligent & Biomimet Machinery, Beijing 100084, Peoples R China Delft University of Technology; Tsinghua University Li, C (corresponding author), Delft Univ Technol, Fac Ind Design Engn, Landbergstr 15, NL-2628 CE Delft, Netherlands. C.Li-1@tudelft.nl Horvath, Imre/E-5911-2013; Rusak, Zoltan/E-6890-2013 Horvath, Imre/0000-0002-6008-0570; Rusak, Zoltan/0000-0002-6999-5881 China Scholarship Council [[2013]3009] China Scholarship Council(China Scholarship Council) Chong Li is supported by the China Scholarship Council [2013]3009. 41 6 6 0 33 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0952-1976 1873-6769 ENG APPL ARTIF INTEL Eng. Appl. Artif. Intell. NOV 2016.0 56 185 199 10.1016/j.engappai.2016.09.006 0.0 15 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Automation & Control Systems; Computer Science; Engineering EC3VH 2023-03-23 WOS:000388054400013 0
J Tripathi, V; Chattopadhyaya, S; Mukhopadhyay, AK; Sharma, S; Li, CH; Di Bona, G Tripathi, Varun; Chattopadhyaya, Somnath; Mukhopadhyay, Alok Kumar; Sharma, Shubham; Li, Changhe; Di Bona, Gianpaolo A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0 MATHEMATICS English Article lean manufacturing; smart manufacturing; cyber-physical system; flexible manufacturing system; artificial intelligence; industry 4; 0 technologies 6 SIGMA; INTEGRATION; FRAMEWORK; SYSTEMS; TIME The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber-physical systems, artificial intelligence, and the industrial Internet of Things in the present scenario. These approaches are used for operations management in industries, and specifically productivity maximization with cleaner shop floor environmental management, and issues such as worker safety and product quality. The present research aimed to develop a methodology for cleaner production management using lean and smart manufacturing in industry 4.0. The developed methodology would able to enhance productivity within restricted resources in the production system. The developed methodology was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit. The results reveal that the developed methodology could provide a sustainable production system and problem-solving that are key to controlling production shop floor management in the context of industry 4.0. It is also capable of enhancing the productivity level within limited constraints. The novelty of the present research lies in the fact that this type of methodology, which has been developed for the first time, helps the industry individual to enhance production in Industry 4.0 within confined assets by the elimination of several problems encountered in shop floor management. Therefore, the authors of the present study strongly believe that the developed methodology would be beneficial for industry individuals to enhance shop floor management within constraints in industry 4.0. [Tripathi, Varun] Accurate Inst Management & Technol, Dept Mech Engn, Greater Noida 201306, India; [Chattopadhyaya, Somnath] Indian Inst Technol ISM, Dept Mech Engn, Dhanbad 826004, Bihar, India; [Mukhopadhyay, Alok Kumar] Indian Inst Technol ISM, Dept Min Machinery Engn, Dhanbad 826004, Bihar, India; [Sharma, Shubham] IK Gujral Punjab Tech Univ, Dept Mech Engn, Main Campus Kapurthala, Kapurthala 144603, India; [Sharma, Shubham] Chandigarh Univ, Univ Ctr Res & Dev, Dept Mech Engn, Mohali 140413, India; [Li, Changhe] Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China; [Di Bona, Gianpaolo] Univ Cassino & Southern Lazio, Dept Civil & Ind Engn, I-03043 Cassino, Italy Accurate Institute of Management & Technology; Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad; Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad; I. K. Gujral Punjab Technical University; Chandigarh University; Qingdao University of Technology; University of Cassino Sharma, S (corresponding author), IK Gujral Punjab Tech Univ, Dept Mech Engn, Main Campus Kapurthala, Kapurthala 144603, India.;Sharma, S (corresponding author), Chandigarh Univ, Univ Ctr Res & Dev, Dept Mech Engn, Mohali 140413, India.;Di Bona, G (corresponding author), Univ Cassino & Southern Lazio, Dept Civil & Ind Engn, I-03043 Cassino, Italy. varuncim@gmail.com; somnathchattopadhyaya@iitism.ac.in; akm_emm@yahoo.co.in; shubham543sharma@gmail.com; sy_lichanghe@163.com; dibona@unicas.it Li, Changhe/X-3450-2019 Li, Changhe/0000-0003-4409-956X; Di Bona, Gianpaolo/0000-0001-9567-5534 54 14 14 36 62 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics FEB 2022.0 10 3 347 10.3390/math10030347 0.0 23 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics YZ7HH gold 2023-03-23 WOS:000755642800001 0
J Bai, H; Shi, LJ; Aoues, Y; Huang, CW; Lemosse, D Bai, Hao; Shi, Lujie; Aoues, Younes; Huang, Changwu; Lemosse, Didier Estimation of probability distribution of long-term fatigue damage on wind turbine tower using residual neural network MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Fatigue damage; Residual neural network; Wind turbine tower; Numerical method UNCERTAINTY; RELIABILITY; DESIGN Fatigue is one of the most significant failure modes in structural and mechanical design. As for wind engineering, the fatigue issue on wind turbine towers is more critical since it concerns not only the structural safety but also the power production of the wind turbine. On the one hand, due to the complex fluid-solid interaction and the sophisticated control system of the wind turbine, it is impossible to predict the probability of fatigue-induced failure on wind turbine towers by analytical method. On the other hand, the structural reliability methods based on a numerical approach are usually time-consuming. To overcome these drawbacks, this work firstly proposes a probabilistic fatigue analysis framework to estimate the fatigue damage of wind turbine tower based on numerical simulations. Then, to reduce the computational cost of numerical approach, a residual neural network (ResNet)-assisted fatigue estimation approach is designed for the assessment of long-term fatigue loads under the proposed probabilistic fatigue analysis framework. The proposed probabilistic fatigue analysis framework estimates the cumulative fatigue damage on the cross-section of wind towers in a probabilistic pattern. The designed surrogate-assisted approach learns a model to approximate the relationship between wind speed and the fatigue damage. Then, this surrogate model can be used to predict fatigue damage under different wind speed so that a large number of simulation can be replaced by model prediction. Consequently, the efficiency of the proposed probabilistic fatigue analysis method can be significantly improved. Our proposed method is validated by numerical studies with a state-of-the-art wind turbine and has been applied in a wind turbine design with real-world wind loads. [Bai, Hao] Shenzhen PowerOak Newener Co Ltd, Shenzhen 518055, Peoples R China; [Shi, Lujie; Aoues, Younes; Lemosse, Didier] INSA Rouen Normandie, Lab Mech Normandy LMN, F-76000 Rouen, France; [Huang, Changwu] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China Southern University of Science & Technology Huang, CW (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China. baihao@poweroak.net; huangcw3@sustech.edu.cn French Ministry of Higher Education, Research and Innovation through INSA Rouen Normandie French Ministry of Higher Education, Research and Innovation through INSA Rouen Normandie This research was funded by the French Ministry of Higher Education, Research and Innovation through a grant from INSA Rouen Normandie for a Ph.D. thesis. 49 0 0 3 3 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. MAY 1 2023.0 190 110101 10.1016/j.ymssp.2023.110101 0.0 JAN 2023 20 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 8M5NO 2023-03-23 WOS:000924511600001 0
J Zheng, SY; Guo, JP; Langendijk, JA; Both, S; Veldhuis, RNJ; Oudkerk, M; van Ooijen, PMA; Wijsman, R; Sijtsema, NM Zheng, Sunyi; Guo, Jiapan; Langendijk, Johannes A.; Both, Stefan; Veldhuis, Raymond N. J.; Oudkerk, Matthijs; van Ooijen, Peter M. A.; Wijsman, Robin; Sijtsema, Nanna M. Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning RADIOTHERAPY AND ONCOLOGY English Article Lung cancer; Radiotherapy; Deep learning; Logistic regression; Overall survival RADIATION-THERAPY; RADIOTHERAPY; HYPOFRACTIONATION Background and purpose: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical variables and image features from pre-treatment CT-scans.Materials and methods: NSCLC patients who received stereotactic radiotherapy were prospectively col-lected at the UMCG and split into a training and a hold out test set including 189 and 81 patients, respec-tively. External validation was performed on 228 NSCLC patients who were treated with radiation or concurrent chemoradiation at the Maastro clinic (Lung1 dataset). A hybrid model that integrated both image and clinical features was implemented using deep learning. Image features were learned from cubic patches containing lung tumours extracted from pre-treatment CT scans. Relevant clinical variables were selected by univariable and multivariable analyses.Results: Multivariable analysis showed that age and clinical stage were significant prognostic clinical fac-tors for 2-year OS. Using these two clinical variables in combination with image features from pre-treatment CT scans, the hybrid model achieved a median AUC of 0.76 [95 % CI: 0.65-0.86] and 0.64 [95 % CI: 0.58-0.70] on the complete UMCG and Maastro test sets, respectively. The Kaplan-Meier sur-vival curves showed significant separation between low and high mortality risk groups on these two test sets (log-rank test: p-value < 0.001, p-value = 0.012, respectively)Conclusion: We demonstrated that a hybrid model could achieve reasonable performance by utilizing both clinical and image features for 2-year OS prediction. Such a model has the potential to identify patients with high mortality risk and guide clinical decision making. (c) 2023 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 180 (2023) 1-6 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [Zheng, Sunyi; Guo, Jiapan; Langendijk, Johannes A.; Both, Stefan; van Ooijen, Peter M. A.; Wijsman, Robin; Sijtsema, Nanna M.] Univ Groningen, Univ Med Ctr Groningen, Dept Radiat Oncol, Groningen, Netherlands; [Zheng, Sunyi] Westlake Univ, Sch Engn, Artificial Intelligence & Biomed Image Anal Lab, Hangzhou, Peoples R China; [Zheng, Sunyi] Westlake Inst Adv Study, Inst Adv Technol, Hangzhou, Peoples R China; [Guo, Jiapan] Univ Groningen, Bernoulli Inst Math Comp Sci & Artificial Intelli, Groningen, Netherlands; [Veldhuis, Raymond N. J.] Univ Twente, Fac Elect Engn, Enschede, Netherlands; [Oudkerk, Matthijs] Univ Twente, Fac Med Sci, Groningen, Netherlands; [Zheng, Sunyi] Shilongshan Rd 18, Hangzhou, Zhejiang, Peoples R China University of Groningen; Westlake University; Westlake University; University of Groningen; University of Twente; University of Groningen Zheng, SY (corresponding author), Shilongshan Rd 18, Hangzhou, Zhejiang, Peoples R China. zhengsunyi@westlake.edu.cn Zheng, Sunyi/0000-0002-9005-4875; Sijtsema, Nanna Maria/0000-0001-6644-274X 27 0 0 1 1 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0167-8140 1879-0887 RADIOTHER ONCOL Radiother. Oncol. MAR 2023.0 180 109483 10.1016/j.radonc.2023.109483 0.0 JAN 2023 6 Oncology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Oncology; Radiology, Nuclear Medicine & Medical Imaging 8P7FH 36690302.0 Green Submitted, hybrid 2023-03-23 WOS:000926685700001 0
J Hu, F; Zhang, L; Liu, WZ; Guo, XX; Shi, L; Liu, XY Hu, Fan; Zhang, Lin; Liu, Wenzhe; Guo, Xiaoxiao; Shi, Lei; Liu, Xiang Yang Gel-Based Artificial Photonic Skin to Sense a Gentle Touch by Reflection ACS APPLIED MATERIALS & INTERFACES English Article artificial photonic skin (p-skin); gel; tactile sensing; reflection; photonic crystal PRESSURE SENSOR; CRYSTALS; FABRICATION; INTERFERENCE; RUBBER This work demonstrates that engineering a three-dimensional photonic crystal (3DPC) structure in a highly flexible gel is a potential method to achieve flexible tactile artificial photonic skin (p-skin) for future visible-light communication (VLC). We investigated the photonic output modes of 3DPC-coated gel-based pressure sensors and explored their ability to sense low pressures (<10 kPa) through reflection. Such sensors with high sensitivity, fast response, and adjustable detection range can be fabricated in arrays of dots covering large, complex/uneven surfaces and are promising in the development of stimuli-responsive soft materials for future artificial intelligence, health monitoring, and photonic communication systems. [Hu, Fan; Zhang, Lin; Guo, Xiaoxiao; Liu, Xiang Yang] Xiamen Univ, Coll Mat, Coll Phys Sci & Technol, Res Inst Biomimet & Soft Matter, Xiamen 361005, Peoples R China; [Hu, Fan] Delft Univ Technol, Dept Chem Engn, Adv Soft Matter Grp, Van der Maasweg 9, NL-2629 HZ Delft, Netherlands; [Liu, Wenzhe; Shi, Lei] Fudan Univ, Key Lab Micro & Nano Photon Struct, Minist Educ, Dept Phys, Shanghai 200433, Peoples R China; [Liu, Wenzhe; Shi, Lei] Fudan Univ, State Key Lab Surface Phys, Shanghai 200433, Peoples R China; [Liu, Xiang Yang] Natl Univ Singapore, Dept Phys, 2 Sci Dr 3, Singapore 117542, Singapore Xiamen University; Delft University of Technology; Fudan University; Fudan University; National University of Singapore Liu, XY (corresponding author), Xiamen Univ, Coll Mat, Coll Phys Sci & Technol, Res Inst Biomimet & Soft Matter, Xiamen 361005, Peoples R China.;Shi, L (corresponding author), Fudan Univ, Key Lab Micro & Nano Photon Struct, Minist Educ, Dept Phys, Shanghai 200433, Peoples R China.;Shi, L (corresponding author), Fudan Univ, State Key Lab Surface Phys, Shanghai 200433, Peoples R China.;Liu, XY (corresponding author), Natl Univ Singapore, Dept Phys, 2 Sci Dr 3, Singapore 117542, Singapore. lshi@fudan.edu.cn; phyliuxy@nus.edu.sg Liu, Xiang Yang/W-2052-2019; Liu, XY/E-5090-2010; Liu, Wenzhe/HKW-4545-2023 Liu, Xiang Yang/0000-0002-5280-5578; Liu, XY/0000-0002-4747-1251; Liu, Wenzhe/0000-0002-6582-4161; Hu, Fan/0000-0001-5251-6795 NUS AcRF Tier 1 [R-144-000-367-112]; 111 project [B16029]; National Nature Science Foundation [U1405226]; Doctoral Fund of the Ministry of Education [20130121110018]; 1000 Talents Program funding from Xiamen University; China Scholarship Council [201706310027] NUS AcRF Tier 1; 111 project(Ministry of Education, China - 111 Project); National Nature Science Foundation(National Natural Science Foundation of China (NSFC)); Doctoral Fund of the Ministry of Education(Ministry of Education, China); 1000 Talents Program funding from Xiamen University; China Scholarship Council(China Scholarship Council) This work was funded by the NUS AcRF Tier 1 (R-144-000-367-112), the 111 project (B16029), National Nature Science Foundation (U1405226), Doctoral Fund of the Ministry of Education (20130121110018), the 1000 Talents Program funding from Xiamen University, China Scholarship Council (201706310027). 29 8 9 11 85 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1944-8244 1944-8252 ACS APPL MATER INTER ACS Appl. Mater. Interfaces MAY 1 2019.0 11 17 15195 15200 10.1021/acsami.9b01649 0.0 6 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science HW9CS 30985101.0 2023-03-23 WOS:000466988800003 0
J Lyu, T; Zhang, L; Xu, X; Arias, CA; Brix, H; Carvalho, PN Lyu, Tao; Zhang, Liang; Xu, Xiao; Arias, Carlos A.; Brix, Hans; Carvalho, Pedro N. Removal of the pesticide tebuconazole in constructed wetlands: Design comparison, influencing factors and modelling ENVIRONMENTAL POLLUTION English Article Artificial neural network; Biofilm reactors; Contaminants of emerging concern; Fungicides; Phytoremediation ARTIFICIAL NEURAL-NETWORK; WASTE-WATER TREATMENT; LINEAR-REGRESSION; TREATMENT-PLANT; PREDICTION; IMAZALIL; COMMUNITIES; PERFORMANCE; MACROPHYTES; SIMULATION Constructed wetlands (CWs) are a promising technology to treat pesticide contaminated water, but its implementation is impeded by lack of data to optimize designs and operating factors. Unsaturated and saturated CW designs were used to compare the removal of triazole pesticide, tebuconazole, in unplanted mesocosms and mesocosms planted with five different plant species: Typha lahfolia, Phragmites australis, Iris pseudacorus, Juncus effusus and Berula erecta. Tebuconazole removal efficiencies were significantly higher in unsaturated CWs than saturated CWs, showing for the first time the potential of unsaturated CWs to treat tebuconazole contaminated water. An artificial neural network model was demonstrated to provide more accurate predictions of tebuconazole removal than the traditional linear regression model. Also, tebuconazole removal could be fitted an area-based first order kinetics model in both CW designs. The removal rate constants were consistently higher in unsaturated CWs (range of 2.6-10.9 cm d(-1)) than in saturated CWs (range of 1.7-7.9 cm d(-1)) and higher in planted CWs (range of 3.1-10.9 cm d(-1)) than in unplanted CWs (range of 1.7-2.6 cm d(-1)) for both designs. The low levels of sorption of tebuconazole to the substrate (0.7-2.1%) and plant phytoaccumulation (2.5-12.1%) indicate that the major removal pathways were biodegradation and metabolization inside the plants after plant uptake. The main factors influencing tebuconazole removal in the studied systems were system design, hydraulic loading rate and plant presence. Moreover, tebuconazole removal was positively correlated to dissolved oxygen and all nutrients removal. (C) 2017 Elsevier Ltd. All rights reserved. [Lyu, Tao; Zhang, Liang; Arias, Carlos A.; Brix, Hans; Carvalho, Pedro N.] Aarhus Univ, Dept Biosci, DK-8000 Aarhus C, Denmark; [Xu, Xiao] Tsinghua Univ, Sch Software, Beijing 10084, Peoples R China; [Lyu, Tao] Nottingham Trent Univ, Sch Anim Rural & Environm Sci, Nottingham NG25 0QF, England; [Carvalho, Pedro N.] Aarhus Univ, Dept Environm Sci, DK-4000 Roskilde, Denmark Aarhus University; Tsinghua University; Nottingham Trent University; Aarhus University Lyu, T; Carvalho, PN (corresponding author), Aarhus Univ, Dept Biosci, DK-8000 Aarhus C, Denmark.;Lyu, T (corresponding author), Nottingham Trent Univ, Sch Anim Rural & Environm Sci, Nottingham NG25 0QF, England.;Carvalho, PN (corresponding author), Aarhus Univ, Dept Environm Sci, DK-4000 Roskilde, Denmark. tao.lyu@ntu.ac.uk; pedro.carvalho@bios.au.dk Arias, Carlos A/I-8007-2013; Carvalho, Pedro Neves/K-1894-2012; Brix, Hans/C-5208-2008; Brix, Hans/AAN-5367-2020; Carvalho, Pedro/HHN-1424-2022; Zhang, Liang/AGG-7852-2022; bueno, saimon/AAX-5584-2021; Lyu, Tao/B-8022-2017 Arias, Carlos A/0000-0002-6628-7564; Carvalho, Pedro Neves/0000-0002-7131-9102; Brix, Hans/0000-0003-2771-2983; Brix, Hans/0000-0003-2771-2983; Zhang, Liang/0000-0002-0249-2067; Lyu, Tao/0000-0001-5162-8103 Aarhus University Research Foundation (AUFF) Center for Advanced Water Purification; China Scholarship Council (CSC); Guangzhou Elites Project of Guangzhou Municipal Government Aarhus University Research Foundation (AUFF) Center for Advanced Water Purification; China Scholarship Council (CSC)(China Scholarship Council); Guangzhou Elites Project of Guangzhou Municipal Government This work was funded by the Aarhus University Research Foundation (AUFF) Center for Advanced Water Purification. The PhD fellowship of Tao Lv was supported by the China Scholarship Council (CSC). The PhD fellowship of Liang Zhang was supported by the Guangzhou Elites Project of Guangzhou Municipal Government. 44 40 42 2 70 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0269-7491 1873-6424 ENVIRON POLLUT Environ. Pollut. FEB 2018.0 233 71 80 10.1016/j.envpol.2017.10.040 0.0 10 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology FU9LI 29055837.0 Green Accepted 2023-03-23 WOS:000424177000009 0
J Qin, XY; Zhang, L; Liu, M; Xu, ZW; Liu, GZ Qin, Xinyi; Zhang, Lu; Liu, Min; Xu, Ziwei; Liu, Guangzhong ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS English Article Protein fold recognition; evolutionary features; ACC_HMM and SXG_HMM; selection of variable parameter; feature selection; Full Connected Neural Network SCORING MATRIX; RANDOM FOREST; PREDICTION; CLASSIFICATION; DATABASE; PROBABILITIES; SELECTION; NMR Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievements, but challenges still exist on low sequence similarity datasets. In this study, we propose the ASFold-DNN framework for protein fold recognition research. Above all, four groups of evolutionary features are extracted from the primary structures of proteins, and a preliminary selection of variable parameter is made for two groups of features including ACC_HMM and SXG_HMM, respectively. Then several feature selection algorithms are selected for comparison and the best feature selection scheme is obtained by changing their internal threshold values. Finally, multiple hyper-parameters of Full Connected Neural Network are fully optimized to construct the best model. DD, EDD and TG datasets with low sequence similarities are chosen to evaluate the performance of the models constructed by the framework, and the final prediction accuracy are 85.28, 95.00 and 88.84 percent, respectively Furthermore, the ASTRAL186 and LE datasets are introduced to further verify the generalization ability of our proposed framework. Comprehensive experimental results prove that the ASFold-DNN framework is more prominent than the state-of-the-art studies on protein fold recognition. The source code and data of ASFold-DNN can be downloaded from https://github.com/Bioinformatics-Laboratory/project/tree/master/ASFoId. [Qin, Xinyi; Zhang, Lu; Liu, Min; Liu, Guangzhong] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Xu, Ziwei] Polytech Nantes, Raiment Ireste,Rue Christian Pauc, F-44300 Nantes, France Shanghai Maritime University; Nantes Universite Qin, XY (corresponding author), Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China. 201930310113@stu.shmtu.edu.cn; 201940310007@stu.shmtu.edu.cn; liumin@shmtu.edu.cn; ziwei.xu@etu.univ-nantes.fr; gzhliu@shmtu.edu.cn Qin, Xinyi/0000-0003-2384-2238 53 1 1 2 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5963 1557-9964 IEEE ACM T COMPUT BI IEEE-ACM Trans. Comput. Biol. Bioinform. SEPT 1 2022.0 19 5 2712 2722 10.1109/TCBB.2021.3089168 0.0 11 Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematics, Interdisciplinary Applications; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Computer Science; Mathematics 5F7WN 34133282.0 2023-03-23 WOS:000866522800016 0
C Tang, LH; Zhao, ML; Yang, XL; Dong, YT; Wu, XB IEEE Tang, Lihan; Zhao, Menglian; Yang, Xiaolin; Dong, Yangtao; Wu, Xiaobo Robust Seizure Prediction Based on Multivariate Empirical Mode Decomposition and Maximum Synchronization Modularity IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY IEEE Industrial Electronics Society English Proceedings Paper 46th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON) OCT 19-21, 2020 ELECTR NETWORK IEEE Ind Elect Soc,Nangyang Technol Univ,Inst Elect & Elect Engineers,Smart Grid Power Elect Consortium Singapore seizure prediction; multivariate empirical mode decomposition; synchronization modularity; radial basis function neural network EPILEPTIC SEIZURES Reliable and timely seizure prediction has been increasingly helpful and indispensable for epileptic patients, ensuring safety and improving life quality. Based on electroencephalogram (EEG), a new patient-specific seizure prediction method is proposed in this paper to detect impending seizures automatically and accurately, using a novel indicator called maximum synchronization modularity. As the first step towards this goal, raw EEG signals are decomposed by multivariate empirical mode decomposition (MEMD). Then graph community detection algorithm is applied to characterize the phase synchronization modularity of sub-band EEG signals. Thus, the deep interaction of scalp electrical activity can be effectively revealed. Finally, radial basis function neural network (RBFNN) is used for the classification. The proposed method achieves an average prediction accuracy of 99.06% and an average sensitivity of 100% on CHB-MIT scalp EEG database, outperforming related works based on the same database. [Tang, Lihan; Zhao, Menglian; Wu, Xiaobo] Zhejiang Univ, Inst VLSI Design, Hangzhou, Peoples R China; [Yang, Xiaolin] Interuniv Microelect Ctr, Dept Connected Hlth Solut, Leuven, Belgium; [Dong, Yangtao] Nanyang Technol Univ, Ctr Integrated Circuits & Syst, Singapore, Singapore Zhejiang University; Interuniversity Microelectronics Centre; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Tang, LH (corresponding author), Zhejiang Univ, Inst VLSI Design, Hangzhou, Peoples R China. tanglh_zju@163.com; zhaoml@zju.edu.cn; Xiaolin.yang@imec.be; YTDONG@ntu.edu.sg; wuxbO1@zju.edu.cn Zhejiang Province Natural Science Foundation of China Zhejiang Province Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work was sponsored by the Zhejiang Province Natural Science Foundation of China (No.LY18F040001). 28 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1553-572X 978-1-7281-5414-5 IEEE IND ELEC 2020.0 507 512 6 Automation & Control Systems; Computer Science, Information Systems; Computer Science, Theory & Methods; Green & Sustainable Science & Technology; Energy & Fuels; Engineering, Manufacturing; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Science & Technology - Other Topics; Energy & Fuels; Engineering; Robotics BR2DJ 2023-03-23 WOS:000637323700080 0
J Chen, C; Yao, GR; Wang, CY; Goudos, S; Wan, SH Chen, Chen; Yao, Guorun; Wang, Chenyu; Goudos, Sotirios; Wan, Shaohua Enhancing the robustness of object detection via 6G vehicular edge computing DIGITAL COMMUNICATIONS AND NETWORKS English Article 6G; Vehicular edge computing; Object detection; Feature fusion; Model compression; Model deployment Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available. [Chen, Chen; Yao, Guorun; Wang, Chenyu] Xidian Univ, Sch Telecommun Engn, Xian 710071, Peoples R China; [Goudos, Sotirios] Aristotle Univ Thessaloniki, Dept Phys, ELEDIA AUTH, Thessaloniki 54124, Greece; [Wan, Shaohua] Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China Xidian University; Aristotle University of Thessaloniki; University of Electronic Science & Technology of China Wan, SH (corresponding author), Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China. cc2000@mail.xidian.edu.cn; gryao@stu.xidian.edu.cn; w573592782@163.com; sgoudo@physics.auth.gr; shaohua.wan@uestc.edu.cn Goudos, Sotirios K./HJA-6146-2022; Wan, Shaohua/B-9243-2014 Goudos, Sotirios K./0000-0001-5981-5683; Wan, Shaohua/0000-0001-7013-9081; Yao, Guorun/0000-0003-1762-0794 National Key Research and Development Program of China [2020YFB1807500]; National Natural Science Foundation of China [62072360, 62001357, 62172438, 61901367]; key research and development plan of Shaanxi province [2021ZDLGY02-09, 2020JQ-844]; Natural Science Foundation of Guangdong Province of China [2022A1515010988]; Key Project on Artificial Intelligence of Xi'an Science and Technology Plan [2022JH-RGZN-0003]; Xi'an Science and Technology Plan [20RGZN0005]; Xi'an Key Laboratory of Mobile Edge Computing and Security [201805052-ZD3CG36] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); key research and development plan of Shaanxi province; Natural Science Foundation of Guangdong Province of China(National Natural Science Foundation of Guangdong Province); Key Project on Artificial Intelligence of Xi'an Science and Technology Plan; Xi'an Science and Technology Plan; Xi'an Key Laboratory of Mobile Edge Computing and Security This work was supported by the National Key Research and Development Program of China(2020YFB1807500), the National Natural Science Foundation of China (62072360, 62001357, 62172438, 61901367), the key research and development plan of Shaanxi province (2021ZDLGY02-09, 2020JQ-844), the Natural Science Foundation of Guangdong Province of China (2022A1515010988), Key Project on Artificial Intelligence of Xi'an Science and Technology Plan (2022JH-RGZN-0003), Xi'an Science and Technology Plan(20RGZN0005) and the Xi'an Key Laboratory of Mobile Edge Computing and Security (201805052-ZD3CG36). 62 0 0 1 1 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGCHENG DISTRICT 100717, PEOPLES R CHINA 2468-5925 2352-8648 DIGIT COMMUN NETW Digit. Commun. Netw. DEC 2022.0 8 6 923 931 10.1016/j.dcan.2022.10.013 0.0 9 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications 8P6JS gold 2023-03-23 WOS:000926628700001 0
J Bao, XX; Xiao, WJ; Li, SB; Iglesias, G Bao, Xingxian; Xiao, Weijie; Li, Shubo; Iglesias, Gregorio Parametric study and optimization of a two-body wave energy converter IET RENEWABLE POWER GENERATION English Article HEAVING POINT ABSORBER; RECTANGULAR STRUCTURE; SHAPE OPTIMIZATION; TRUNCATED CYLINDER; RADIATION; DYNAMICS; DESIGN; DIFFRACTION; FRONT The parametric study and optimization of a two-body wave energy converter (WEC) for the wave and current conditions in the region of Zhaitang Island (China) is presented. Nine parameters are considered, and their influence on the power captured by the two-body WEC is investigated following both single-parameter and multi-parameter approaches. A backpropagation neural network model is developed and applied to predict the captured power for any given values of the nine parameters and the wave frequency. Then the robust design method, also known as the Taguchi method, is implemented to study the comprehensive effects of the parameters on the power output of the device. Moreover, scale model experiments are conducted to verify and confirm the influence of the principal parameters on the power output. Combining numerical simulations, a neural network model and experimental work, this study provides an optimization programme for the main parameters of the device in the target sea region and, at a more general level, references for two-body WEC designs based on specific sea states. [Bao, Xingxian; Xiao, Weijie; Li, Shubo] China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China; [Bao, Xingxian] China Univ Petr East China, Natl Engn Lab Offshore Geophys & Explorat Equipme, Qingdao 266580, Peoples R China; [Iglesias, Gregorio] Univ Coll Cork, Environm Res Inst, MaREI, Coll Rd, Cork, Ireland; [Iglesias, Gregorio] Univ Coll Cork, Sch Engn, Coll Rd, Cork, Ireland; [Iglesias, Gregorio] Univ Plymouth, Sch Engn, Marine Bldg, Plymouth PLA 8AA, Devon, England China University of Petroleum; China University of Petroleum; University College Cork; University College Cork; University of Plymouth Bao, XX (corresponding author), China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China. baoxingxian@upc.edu.cn Iglesias, Gregorio/AAD-6108-2021 Iglesias, Gregorio/0000-0003-2719-1663; Bao, Xingxian/0000-0002-8555-8366 National Natural Science Foundation of China [51979283]; Shandong Provincial Natural Science Foundation, China [ZR2018MEE053]; Fundamental Research Funds for the Central Universities [20CX02313A] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Natural Science Foundation, China(Natural Science Foundation of Shandong Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) National Natural Science Foundation of China, Grant/Award Number: 51979283; Shandong Provincial Natural Science Foundation, China, Grant/AwardNumber: ZR2018MEE053; Fundamental Research Funds for the Central Universities, Grant/AwardNumber: 20CX02313A 32 2 2 4 22 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1752-1416 1752-1424 IET RENEW POWER GEN IET Renew. Power Gener. OCT 2021.0 15 14 SI 3319 3330 10.1049/rpg2.12254 0.0 JUL 2021 12 Green & Sustainable Science & Technology; Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels; Engineering WI5GL 2023-03-23 WOS:000676531000001 0
J Sun, JY; Liu, X; Back, T; Xu, ZB Sun, Jianyong; Liu, Xin; Back, Thomas; Xu, Zongben Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION English Article Statistics; Sociology; Optimization; Process control; Deep learning; Reinforcement learning; Convergence; Adaptive differential evolution; deep learning; global optimization; policy gradient (PG); reinforcement learning (RL) REAL-PARAMETER OPTIMIZATION; GLOBAL OPTIMIZATION; ADAPTATION; STRATEGY Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination process for the most suitable parameter setting is troublesome and time consuming. Adaptive control parameter methods that can adapt to problem landscape and optimization environment are more preferable than fixed parameter settings. This article proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. In the approach, the parameter control is modeled as a finite-horizon Markov decision process. A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i.e., parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure. The differential evolution algorithm based on the learned agent is compared against nine well-known evolutionary algorithms on the CEC'13 and CEC'17 test suites. Experimental results show that the proposed algorithm performs competitively against these compared algorithms on the test suites. [Sun, Jianyong; Liu, Xin; Xu, Zongben] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China; [Back, Thomas] Leiden Univ, Leiden Inst Adv Comp Sci, NL-2300 RA Leiden, Netherlands Xi'an Jiaotong University; Leiden University; Leiden University - Excl LUMC Liu, X (corresponding author), Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China. jy.sun@xjtu.edu.cn; liuxin17@stu.xjtu.edu.cn; t.h.w.baeck@liacs.leidenuniv.nl; zbxu@mail.xjtu.edu.cn Back, Thomas/0000-0001-6768-1478; Liu, Xin/0000-0003-4710-2103 National Natural Science Foundation of China [11991023, 62076197]; Major Project of National Science Foundation of China [U1811461]; National Science Foundation of China [61721002, 11690011] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Project of National Science Foundation of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 11991023 and Grant 62076197; in part by the Major Project of National Science Foundation of China under Grant U1811461; in part by the Key Project of National Science Foundation of China under Grant 11690011; and in part by the Project of National Science Foundation of China under Grant 61721002. 64 16 16 19 63 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1089-778X 1941-0026 IEEE T EVOLUT COMPUT IEEE Trans. Evol. Comput. AUG 2021.0 25 4 666 680 10.1109/TEVC.2021.3060811 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science TS3CE Green Submitted 2023-03-23 WOS:000679530900008 0
C Ma, K; Abraham, A IEEE Ma, Kun; Abraham, Ajith Toward Lightweight Transparent Data Middleware in Support of Document Stores 2013 THIRD WORLD CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGIES (WICT) English Proceedings Paper Third World Congress on Information and Communication Technologies (WICT) DEC 15-18, 2013 Hanoi, VIETNAM MapReduce; big data; middleware; NoSQL; Cloud Computing SQL With the advent of rapid increase in the size of data in legacy applications, it is urgent to build efficient and flexible data middleware that supports SQL to MapReduce transformation. In this paper, we propose a data middleware to translate the SQLs to the operations that the NoSQL can understand using the MapReduce framework we design. This middleware transforms the read and write SQL statements into MapReduce jobs. In addition, a set of transformation rules is discussed in detail. This middleware can significantly reduce redundant computations and improve the performance of I/O operations. [Ma, Kun] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China; [Abraham, Ajith] Sci Network Innovat & Res Excellence, Machine Intelligence Res Labs, Seattle, WA USA; [Abraham, Ajith] VSB Tech Univ Ostrava, IT4Innovat, Ostrava, Czech Republic University of Jinan; Technical University of Ostrava Ma, K (corresponding author), Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China. ise_mak@ujn.edu.cn; ajith.abraham@ieee.org Abraham, Ajith/A-1416-2008; Ma, Kun/B-7755-2013 Abraham, Ajith/0000-0002-0169-6738; Ma, Kun/0000-0002-0135-5423 13 4 4 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-3230-6 2013.0 253 257 5 Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BF3GR 2023-03-23 WOS:000380541300043 0
J Wen, ZH; Fang, PY; Yin, YB; Krolczyk, G; Gardoni, P; Li, ZX Wen, Zhenhua; Fang, Pengya; Yin, Yibing; Krolczyk, Grzegorz; Gardoni, Paolo; Li, Zhixiong A novel machine learning model for safety risk analysis in flywheel-battery hybrid energy storage system JOURNAL OF ENERGY STORAGE English Article Flywheel energy storage system; Electric vehicles; Machine learning; Remaining life prediction BEARING; LIFE Flywheel energy storage system (FESS) has been regarded as the most promising hybrid storage technique to manage the battery charging process of electric vehicles. Thanks to properly regulating with the FESS, the battery life can be significantly prolonged. In order to ensure the safety of the hybrid storage system, it is imperative to monitor the mechanical operation condition of the FESS. Because the rolling bearing is a critical mechanical component in the FESS, the performance degradation monitoring and remaining useful life (RUL) prediction of the rolling bearing must be performed. This paper proposes a new machine learning method for the construction of a health indicator to quantitatively evaluate the bearing health status. In this new method, the original feature set is firstly selected through three feature evaluation indicators and the principle component analysis (PCA) is employed to fuse the original feature set as a new health indicator. Then the primary trend of the health indicator is extracted by the empirical mode decomposition (EMD); and the Kriging model-based prediction method is proposed to predict the bearing RUL. The feasibility and superiority of the proposed method is verified through experimental test and analysis result shows that the root mean square error (RMSE) of the prediction is as small as 0.0425. [Wen, Zhenhua; Fang, Pengya] Zhengzhou Univ Aeronaut, Sch Aeronaut Engn, Zhengzhou 450015, Peoples R China; [Yin, Yibing] Qingdao Univ Technol, Sch Mech & Automat Engn, Qingdao 266520, Peoples R China; [Krolczyk, Grzegorz; Li, Zhixiong] Opole Univ Technol, Dept Mfg Engn & Automat Prod, PL-45758 Opole, Poland; [Gardoni, Paolo] Univ Illinois, Dept Civil & Environm Engn, Champaign, IL 61820 USA; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea Zhengzhou University of Aeronautics; Qingdao University of Technology; Opole University of Technology; University of Illinois System; University of Illinois Urbana-Champaign; Yonsei University Fang, PY (corresponding author), Zhengzhou Univ Aeronaut, Sch Aeronaut Engn, Zhengzhou 450015, Peoples R China.;Li, ZX (corresponding author), Opole Univ Technol, Dept Mfg Engn & Automat Prod, PL-45758 Opole, Poland.;Li, ZX (corresponding author), Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea. g.krolczyk@po.opole.pl; gardoni@illinois.edu; zhixiong.li@yonsei.ac.kr Krolczyk, Grzegorz/D-6709-2013 Krolczyk, Grzegorz/0000-0002-2967-1719 National Natural Science Foundation of China [51975539, 51979261]; Aeronautical Science Foun-dation of China [2018ZD55008]; key project ofthe Education Department of Henan Province, China [19A460030]; Key Science and technique RAMP;D Program of Henan Province, China [212102210275]; Narodowego Centrum Nauki, Poland [2020/37/K/ST8/02748, 2017/25/B/ST8/00962] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Aeronautical Science Foun-dation of China; key project ofthe Education Department of Henan Province, China; Key Science and technique RAMP;D Program of Henan Province, China; Narodowego Centrum Nauki, Poland Acknowledgment The work was supported by the National Natural Science Foundation of China (51975539 & 51979261) and the Aeronautical Science Foun-dation of China (2018ZD55008) , and partly support by the key project ofthe Education Department of Henan Province, China (19A460030) , the Key Science and technique R&D Program of Henan Province, China (212102210275) and Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748 & No. 2017/25/B/ST8/00962) . 24 0 0 7 25 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-152X 2352-1538 J ENERGY STORAGE J. Energy Storage MAY 2022.0 49 104072 10.1016/j.est.2022.104072 0.0 FEB 2022 11 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 0J7KR 2023-03-23 WOS:000780281200002 0
J Wang, DY; Luo, HY; Grunder, O; Lin, YB; Guo, HX Wang, Deyun; Luo, Hongyuan; Grunder, Olivier; Lin, Yanbing; Guo, Haixiang Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm APPLIED ENERGY English Article Electricity price forecasting; Multi-step ahead; Fast ensemble empirical mode decomposition; Variational mode decomposition; Firefly algorithm; Back propagation neural network SUPPORT VECTOR MACHINES; DEMAND; SYSTEM; EEMD In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crticial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by firefly algorithm (FA). The proposed model is unique in the sense that VMD is specifically applied to further decompose the high frequency intrinsic mode functions (IMFs) generated by FEEMD into a number of modes in order to improve the forecast accuracy. To validate the effectiveness and accuracy of the proposed model, three electricity price series respectively collected from the real-world electricity markets of Australia and France are adopted to conduct the empirical study. The results indicate that the proposed model outperforms the other considered models over horizons of one-step, two-step, four-step and six-step ahead forecasting, which shows that the proposed model has superior performances for both one-step and multi-step ahead forecasting of electricity price. (C) 2016 Elsevier Ltd. All rights reserved. [Wang, Deyun; Luo, Hongyuan; Lin, Yanbing; Guo, Haixiang] China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China; [Wang, Deyun; Luo, Hongyuan; Lin, Yanbing; Guo, Haixiang] China Univ Geosci, Mineral Resource Strategy & Policy Res Ctr, Wuhan 430074, Peoples R China; [Wang, Deyun; Grunder, Olivier] Univ Bourgogne Franche Comte, UTBM, IRTES, Rue Thierry Mieg, F-90010 Belfort, France China University of Geosciences; China University of Geosciences; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Wang, DY (corresponding author), China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China. wang.deyun@hotmail.com National Natural Science Foundation of China [71301153, 71103163, 71573237]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG140612]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(Fundamental Research Funds for the Central Universities); Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China(Scientific Research Foundation for the Returned Overseas Chinese ScholarsMinistry of Education, China) The authors would like to thank the editor and the two anonymous reviewers for their constructive comments on improving an early version of this paper. This research was supported by the National Natural Science Foundation of China (Grant Nos. 71301153, 71103163, 71573237); the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG140612); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China. 37 194 201 26 282 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy MAR 15 2017.0 190 390 407 10.1016/j.apenergy.2016.12.134 0.0 18 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Energy & Fuels; Engineering EN4DV 2023-03-23 WOS:000395959100033 0
J Chen, L; Pan, XY; Zhang, YH; Liu, M; Huang, T; Cai, YD Chen, Lei; Pan, XiaoYong; Zhang, Yu-Hang; Liu, Min; Huang, Tao; Cai, Yu-Dong Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL English Article Widely expressed gene; Rarely expressed gene; Enrichment theory; Minimum redundancy maximum relevance; Incremental feature selection; Recurrent neural network ACTIVATING POLYPEPTIDE PACAP; ROBUST PREDICTION; UBIQUITIN LIGASES; CANCER-CELLS; IDENTIFICATION; PROFILES; DYSREGULATION; INVOLVEMENT; SIGNATURES; RELEVANCE A tissue-specific gene expression shapes the formation of tissues, while gene expression changes reflect the immune response of the human body to environmental stimulations or pressure, particularly in disease conditions, such as cancers. A few genes are commonly expressed across tissues or various cancers, while others are not. To investigate the functional differences between widely and rarely expressed genes, we defined the genes that were expressed in 32 normal tissues/cancers (i.e., called widely expressed genes; FPKM >1 in all samples) and those that were not detected (i.e., called rarely expressed genes; FPKM <1 in all samples) based on the large gene expression data set provided by Uhlen et al. Each gene was encoded using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores. Minimum redundancy maximum relevance (mRMR) was used to measure and rank these features on the mRMR feature list. Thereafter, we applied the incremental feature selection method with a supervised classifier recurrent neural network (RNN) to select the discriminate features for classifying widely expressed genes from rarely expressed genes and construct an optimum RNN classifier. The Youden's indexes generated by the optimum RNN classifier and evaluated using a 10-fold cross validation were 0.739 for normal tissues and 0.639 for cancers. Furthermore, the underlying mechanisms of the key discriminate GO and KEGG features were analyzed. Results can facilitate the identification of the expression landscape of genes and elucidation of how gene expression shapes tissues and the microenvironment of cancers. (C) 2018 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. [Chen, Lei; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; [Chen, Lei; Liu, Min] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China; [Pan, XiaoYong] Erasmus MC, Dept Med Informat, Rotterdam, Netherlands; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China Shanghai University; Shanghai Maritime University; East China Normal University; Erasmus University Rotterdam; Erasmus MC; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China. liumin@shmtu.edu.cn; huangtao@sibs.ac.cn; caiyudong@staff.shu.edu.cn Zhang, Yu-Hang/O-9774-2015; Liu, Min/AAX-1153-2020; Zhang, Yu-Hang/AAQ-9012-2021; Chen, Lei/A-2275-2011 Zhang, Yu-Hang/0000-0003-3825-0796; Liu, Min/0000-0002-4952-2577; Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3068-1583 National Natural Science Foundation of China [31701151]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; key Laboratory of Stem Cell Biology of Chinese Academyof Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); key Laboratory of Stem Cell Biology of Chinese Academyof Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This study was supported by the National Natural Science Foundation of China [31701151], Natural Science Foundation of Shanghai [17ZR1412500], Shanghai Sailing Program [16YF1413800], the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245], the fund of the key Laboratory of Stem Cell Biology of Chinese Academyof Sciences [201703], Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000]. 94 28 28 1 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2001-0370 COMPUT STRUCT BIOTEC Comp. Struct. Biotechnol. J.. 2019.0 17 49 60 10.1016/j.csbj.2018.12.002 0.0 12 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology JY1TU 30595815.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000504205700006 0
J Guo, XQ; Liu, XH; Zhou, H; Stanislawski, R; Krolczyk, G; Li, ZX Guo, Xiaoqiang; Liu, Xinhua; Zhou, Hao; Stanislawski, Rafal; Krolczyk, Grzegorz; Li, Zhixiong Belt Tear Detection for Coal Mining Conveyors MICROMACHINES English Article machine vision; deep learning; smart mining; smart cities DETECTION SYSTEM; DAMAGE DETECTION; NETWORKS; VISION The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning. [Guo, Xiaoqiang; Liu, Xinhua; Zhou, Hao] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 211006, Jiangsu, Peoples R China; [Zhou, Hao] Suzhou Chien Shiung Inst Technol, Sch Intelligent Mfg, Taicang 215400, Peoples R China; [Stanislawski, Rafal] Opole Univ Technol, Dept Elect Control & Comp Engn, PL-45758 Opole, Poland; [Krolczyk, Grzegorz; Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland China University of Mining & Technology; Opole University of Technology; Opole University of Technology Liu, XH (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 211006, Jiangsu, Peoples R China. godric_guo@cumt.edu.cn; liuxinhua@cumt.edu.cn; 0219@csit.edu.cn; r.stanislawski@po.edu.pl; g.krolczyk@po.edu.pl; zhixiong.li@yonsei.ac.kr Krolczyk, Grzegorz/D-6709-2013 Krolczyk, Grzegorz/0000-0002-2967-1719; Guo, Xiaoqiang/0000-0003-2355-0569; Stanislawski, Rafal/0000-0002-6014-3682 Postgraduate Research and Innovation Program of Jiangsu Province [KYCX21_2188]; Narodowego Centrum Nauki, Poland [2020/37/K/ST8/02748, 2017/25/B/ST8/00962]; Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD) Postgraduate Research and Innovation Program of Jiangsu Province; Narodowego Centrum Nauki, Poland; Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD) This research was funded by the Postgraduate Research and Innovation Program of Jiangsu Province (KYCX21_2188), Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748 & No. 2017/25/B/ST8/00962) and Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD). 90 3 3 34 65 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-666X MICROMACHINES-BASEL Micromachines MAR 2022.0 13 3 449 10.3390/mi13030449 0.0 23 Chemistry, Analytical; Nanoscience & Nanotechnology; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Instruments & Instrumentation; Physics 0A7GZ 35334743.0 gold, Green Accepted 2023-03-23 WOS:000774119500001 0
J Wu, JH; Li, J; Wang, J; Zhang, L; Wang, HD; Wang, GL; Li, XL; Yuan, JX Wu, Jian-Hui; Li, Jing; Wang, Jie; Zhang, Lu; Wang, Hai-Dong; Wang, Guo-Li; Li, Xiao-lin; Yuan, Ju-Xiang Risk prediction of type 2 diabetes in steel workers based on convolutional neural network NEURAL COMPUTING & APPLICATIONS English Article Convolutional neural network; Steel worker; Type 2 diabetes mellitus (T2DM); Prediction of morbidity risk MODEL; GLUCOSE With the change in environment and lifestyle, the number of diabetic patients is increasing rapidly. Diabetes has become one of the most important chronic diseases affecting the health of the Chinese people, and complications, disability, death and treatment costs caused by diabetes have placed a heavy burden on families and society. If the high-risk population of diabetes can be identified and the adverse lifestyle can be changed as soon as possible, the incidence of diabetes can be reduced or the onset of diabetes can be slowed down. Risk prediction model can accurately predict the risk of disease and has been widely used in the field of health management and medical care. This study was based on the special occupational group of steel workers, the risk prediction model of type 2 diabetes was established by using convolutional neural network, and the feasibility of the model was discussed. The results showed that the prediction accuracy of the established model in the training set, verification set, and test set is relatively high, which is 94.5%, 91.0%, and 89.0%, respectively. The area under the ROC curve was 0.950 (95 CI 0.938-0.962), 0.916 (95 CI 0.888-0.945), and 0.899 (95 CI 0.899-0.939), respectively, indicating that the model can accurately predict the risk of type 2 diabetes among steel workers, provide a basis for self-health management of steel workers, facilitate the rational allocation of medical and health resources and the development of health services, and provide a basis for government departments to make decisions. [Wu, Jian-Hui; Li, Jing; Wang, Jie; Zhang, Lu; Wang, Hai-Dong; Wang, Guo-Li; Yuan, Ju-Xiang] North China Univ Sci & Technol, Sch Publ Hlth, Tangshan 063210, Hebei, Peoples R China; [Wu, Jian-Hui; Wang, Guo-Li; Yuan, Ju-Xiang] North China Univ Sci & Technol, Hebei Prov Key Lab Occupat Hlth & Safety Coal Ind, Tangshan 063210, Hebei, Peoples R China; [Li, Xiao-lin] INSA Ctr Val Loire, Campus Blois,3 Rue Chocolaterie,CS 23410, F-41034 Blois, Loir & Cher, France North China University of Science & Technology; North China University of Science & Technology Yuan, JX (corresponding author), North China Univ Sci & Technol, Sch Publ Hlth, Tangshan 063210, Hebei, Peoples R China.;Yuan, JX (corresponding author), North China Univ Sci & Technol, Hebei Prov Key Lab Occupat Hlth & Safety Coal Ind, Tangshan 063210, Hebei, Peoples R China. gwxyjxb@ncst.edu.cn Yuan, Juxiang/0000-0002-5825-4850; wu, jianhui/0000-0002-8440-1404 30 9 9 1 12 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. JUL 2020.0 32 13 9683 9698 10.1007/s00521-019-04489-y 0.0 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science ME6SX 2023-03-23 WOS:000544784200053 0
J Yu, LC; De Mazancourt, M; Hess, A; Ashadi, FR; Klein, I; Mal, H; Courbage, M; Mangin, L Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R.; Klein, Isabelle; Mal, Herve; Courbage, Maurice; Mangin, Laurence Functional Connectivity and Information Flow of the Respiratory Neural Network in Chronic Obstructive Pulmonary Disease HUMAN BRAIN MAPPING English Article breathing; brain; functional connectivity; Granger causality; neural network BRAIN; FLUCTUATIONS; DYSPNEA; ACTIVATION; DIAPHRAGM; RESPONSES; MODELS; DRIVE; MRI Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. (C) 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. [Yu, Lianchun; De Mazancourt, Marine; Ashadi, Fakhrul R.; Courbage, Maurice; Mangin, Laurence] Paris 7 Univ, CNRS, UMR 7057, Dept Phys,Matter & Complex Syst Res Lab, Paris, France; [Yu, Lianchun] Lanzhou Univ, Inst Theoret Phys, Lanzhou, Peoples R China; [De Mazancourt, Marine] Ecole Normale Super, 24 Rue Lhomond, F-75231 Paris, France; [Hess, Agathe; Klein, Isabelle] Hop Bichat Claude Bernard, APHP, Dept Neuroradiol, Paris, France; [Mal, Herve] Hop Bichat Claude Bernard, APHP, Dept Resp Dis, Paris, France; [Mangin, Laurence] Hop Bichat Claude Bernard, APHP, Dept Physiol, Paris, France Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Universite Paris Cite; Lanzhou University; UDICE-French Research Universities; Universite PSL; Ecole Normale Superieure (ENS); Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Ambroise-Pare - APHP; UDICE-French Research Universities; Universite Paris Cite; Hopital Universitaire Bichat-Claude Bernard - APHP; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Ambroise-Pare - APHP; UDICE-French Research Universities; Universite Paris Cite; Hopital Universitaire Bichat-Claude Bernard - APHP; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Ambroise-Pare - APHP; UDICE-French Research Universities; Universite Paris Cite; Hopital Universitaire Bichat-Claude Bernard - APHP Mangin, L (corresponding author), Paris 7 Univ, Dept Phys, Paris, France.;Mangin, L (corresponding author), Paris 7 Univ, Dept Physiol, Paris, France. laurence.mangin@bch.aphp.fr Courbage, Maurice/0000-0001-6615-9039 PHRC APHP; Universite Paris 7; Fondation du souffle; Chancellerie des Universites de Paris-Legs Poix; Region ile de France-DIM-Institut des Systemes Complexes PHRC APHP; Universite Paris 7; Fondation du souffle; Chancellerie des Universites de Paris-Legs Poix; Region ile de France-DIM-Institut des Systemes Complexes(Region Ile-de-France) PHRC APHP, Universite Paris 7, Fondation du souffle, Chancellerie des Universites de Paris-Legs Poix, Region ile de France-DIM-Institut des Systemes Complexes. 56 34 35 0 8 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. AUG 2016.0 37 8 2736 2754 10.1002/hbm.23205 0.0 19 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging DU8UF 27059277.0 Green Published, hybrid 2023-03-23 WOS:000382490000002 0
C Yang, JX; Zhao, YQ; Chan, JCW IEEE Yang, Jingxiang; Zhao, Yong-Qiang; Chan, Jonathan Cheung-Wai HYPERSPECTRAL IMAGE SUPER-RESOLUTION BASED ON MULTI-SCALE WAVELET 3D CONVOLUTIONAL NEURAL NETWORK 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Super-resolution; hyperspectral; wavelet; CNN Super-resolution (SR) of hyperspectral image (HSI) is of significance for its applications. Wavelet decomposition can be used to capture textures and structures in the HSI. In this study, we propose a multi-scale wavelet 3D convolutional neural network (MW-3D-CNN) for HSI SR. Instead of reconstructing the high resolution (HR) HSI directly, we predict the wavelet coefficients of HR HSI with the proposed network, which is composed of an embedding subnet and a predicting subnet. Both of them are built with 3D convolutional layers. The embedding subnet extracts deep spatial-spectral features from the low resolution (LR) HSI and represents the LR HSI as a set of feature cubes. The feature cubes are then fed to the predicting subnet. There are multiple output branches in the predicting subnet, each of which corresponds to a wavelet sub-band and predicts the wavelet coefficients of HR HSI. By applying inverse wavelet transform to the predicted wavelet coefficients, the HR HSI can be obtained. In the training stage, we propose to train MW-3D-CNN with L1 norm loss, which is more suitable than the conventional L2 norm loss for penalizing the errors in different wavelet sub-bands. In the experiment, the performance is tested on several HSI datasets. [Yang, Jingxiang; Zhao, Yong-Qiang] Northwestern Polytech Univ Shenzhen, Res & Dev Inst, Shenzhen 518057, Peoples R China; [Yang, Jingxiang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China; [Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Nanjing University of Science & Technology; Vrije Universiteit Brussel Yang, JX (corresponding author), Northwestern Polytech Univ Shenzhen, Res & Dev Inst, Shenzhen 518057, Peoples R China.;Yang, JX (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. National Natural Science Foundation of China [61771391, 61371152]; Shenzhen Municipal Science and Technology Innovation Committee [JCYJ20170815162956949]; Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University [CX201621] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Municipal Science and Technology Innovation Committee; Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University This work is supported by the National Natural Science Foundation of China (61771391, 61371152), Shenzhen Municipal Science and Technology Innovation Committee under Grant (JCYJ20170815162956949), and Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University (CX201621). 14 2 2 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 2770 2773 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH 2023-03-23 WOS:000519270602213 0
J Mazidi, P; Du, MA; Tjernberg, LB; Bobi, MAS Mazidi, Peyman; Du, Mian; Tjernberg, Lina Bertling; Bobi, Miguel A. Sanz A health condition model for wind turbine monitoring through neural networks and proportional hazard models PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY English Article Wind turbine; condition monitoring; prognostics; maintenance management; neural networks OF-THE-ART; SCADA DATA; MAINTENANCE; RELIABILITY; OPTIMIZATION; CHALLENGES; PREDICTION; SYSTEM In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine's health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition-related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data. [Mazidi, Peyman; Tjernberg, Lina Bertling] KTH Royal Inst Technol, Dept Elect Power & Energy Syst, Osquldasvag 6, SE-10044 Stockholm, Sweden; [Mazidi, Peyman; Bobi, Miguel A. Sanz] Comillas Pontifical Univ, Madrid, Spain; [Du, Mian] China Elect Power Res Inst, Beijing, Peoples R China Royal Institute of Technology; Comillas Pontifical University Mazidi, P (corresponding author), KTH Royal Inst Technol, Dept Elect Power & Energy Syst, Osquldasvag 6, SE-10044 Stockholm, Sweden. mazidi@kth.se Sanz-Bobi, Miguel A./AAG-8470-2020; Mazidi, Peyman/G-6763-2014 Sanz-Bobi, Miguel A./0000-0001-5192-8587; Mazidi, Peyman/0000-0002-0396-3326 European Commission's Directorate-General for Education and Culture European Commission's Directorate-General for Education and Culture The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the Erasmus Mundus Joint Doctorate (EMJD) Fellowship in Sustainable Energy Technologies and Strategies (SETS) program funded by the European Commission's Directorate-General for Education and Culture which provided the opportunity for carrying out this work. 48 12 12 1 47 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 1748-006X 1748-0078 P I MECH ENG O-J RIS Proc. Inst. Mech. Eng. Part O-J. Risk Reliab. OCT 2017.0 231 5 481 494 10.1177/1748006X17707902 0.0 14 Engineering, Multidisciplinary; Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science FH5PB 2023-03-23 WOS:000411218300003 0
J Katsikis, VN; Stanimirovic, PS; Mourtas, SD; Xiao, L; Karabasevic, D; Stanujkic, D Katsikis, Vasilios N.; Stanimirovic, Predrag S.; Mourtas, Spyridon D.; Xiao, Lin; Karabasevic, Darjan; Stanujkic, Dragisa Zeroing Neural Network With Fuzzy Parameter for Computing Pseudoinverse of Arbitrary Matrix IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Fuzzy logic; Mathematical models; TV; Computational modeling; Dynamical systems; Convergence; Vehicle dynamics; Finite-time convergence; fuzzy logic system (FLS); Moore-Penrose (MP) inverse; zeroing neural network (ZNN) MOORE-PENROSE INVERSE; FINITE-TIME CONVERGENCE; SYLVESTER EQUATION; DESIGN FORMULA; COMPLEX ZFS; DYNAMICS; MODELS A correlation between fuzzy logic systems (FLS) and zeroing neural networks (ZNN) design is investigated. It is shown that the gain parameter included in ZNN design can be dynamically adjusted over time by means of an appropriate value derived as the output of a properly defined FLS, which includes appropriately defined membership functions and fuzzy logic rules. Dynamical systems which are applicable to time-varying rank-deficient matrices are proposed. Convergence properties are investigated and illustrative simulation experiments are performed. Presented simulation experiments confirm the superiority of the FLS proposed in this article with respect to previously proposed FLS for dynamic adjustment of gain parameters. Furthermore, the superiority of the FLS-based ZNN model over the corresponding ZNN models based on the classical approach in defining the varying-gain parameter is demonstrated. [Katsikis, Vasilios N.; Mourtas, Spyridon D.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Athens 10559, Greece; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Nish 18000, Serbia; [Xiao, Lin] Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Peoples R China; [Karabasevic, Darjan] Univ Business Acad Novi Sad, Fac Appl Management Econ & Finance, Belgrade 11000, Serbia; [Stanujkic, Dragisa] Univ Belgrade, Tech Fac Bor, Bor 19210, Serbia National & Kapodistrian University of Athens; University of Nis; University of Belgrade Katsikis, VN (corresponding author), Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Athens 10559, Greece. vaskatsikis@econ.tioa.gr; pecko@pmf.ni.ac.rs; spirosmourtas@gmail.com; xiaolin860728@163.com; darjan.karabasevic@mef.edu.rs; dstanujkic@dbor.bg.ac.rs Mourtas, Spyridon D./AAB-3651-2022; Katsikis, Vasilios/AAD-7216-2019 Mourtas, Spyridon D./0000-0002-8299-9916; Stanimirovic, Predrag S./0000-0003-0655-3741; Katsikis, Vasilios/0000-0002-8208-9656; Stanujkic, Dragisa/0000-0002-6846-3074; Karabasevic, Darjan/0000-0001-5308-2503 Ministry of Education, Science, and Technological Development, Republic of Serbia [174013 (451-03-9/2021-14/200124)] Ministry of Education, Science, and Technological Development, Republic of Serbia The work of Predrag Stanimirovic was supported by the Ministry of Education, Science, and Technological Development, Republic of Serbia, under Grant 174013 (451-03-9/2021-14/200124). (Corresponding author: Vasilios N. Katsikis.) 68 11 11 3 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. SEP 2022.0 30 9 3426 3435 10.1109/TFUZZ.2021.3115969 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4F1GT 2023-03-23 WOS:000848264000006 0
C Yu, SC; Gu, CY; Liu, WQ; O'Neill, M IEEE Yu, Shichao; Gu, Chongyan; Liu, Weiqiang; O'Neill, Make A Novel Feature Extraction Strategy for Hardware Trojan Detection 2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) IEEE International Symposium on Circuits and Systems English Proceedings Paper IEEE International Symposium on Circuits and Systems (ISCAS) OCT 10-21, 2020 ELECTR NETWORK IEEE Hardware Trojans; feature extraction; netlist; circuit topology; directed graph; structural features; machine learning CLASSIFICATION; MODELS Hardware Trojans (HTs) are acknowledged as a significant emerging security concern in the IC industry resulting from the globalization of the semiconductor supply chain. Recently, taking advantage of the exponential growth in computing power, machine learning (ML) approaches such as neural networks (NNs) are being considered for HT detection. However, the circuit structure and components of an IC design are different from the data types in the ML models. To efficiently extract HT features from complex IC designs and utilize common ML-based detection approaches is challenging. In this paper, a novel HT feature extraction strategy based on gate-level circuit netlists is proposed to tackle the challenges. The HT features are extracted from the circuit topology rather than statistical analysis in previous research. A commonly utilized support vector machine (SVM)-based HT detection model is employed for data training and testing using the extracted features on HT benchmarks from both open-sourced library and HT generation platform to prove the feasibility and efficiency of the proposed HT feature extraction strategy. The detection results show high recall in nearly all tested benchmarks, achieving at most 97.7% recall on sequential Trojans and 84.8% on combinational ones. [Yu, Shichao; Gu, Chongyan; O'Neill, Make] Queens Univ Belfast, Ctr Secure Informat Technol, ECIT, Belfast, Antrim, North Ireland; [Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll EIE, Nanjing, Peoples R China Queens University Belfast; Nanjing University of Aeronautics & Astronautics Yu, SC (corresponding author), Queens Univ Belfast, Ctr Secure Informat Technol, ECIT, Belfast, Antrim, North Ireland. syu08@qub.ac.uk; c.gu@qub.ac.uk; liuweiqiang@nuaa.edu.cn; m.oneill@ecit.qub.ac.uk GU, CHONGYAN/ABD-5437-2021 GU, CHONGYAN/0000-0002-3028-8004; Yu, Shichao/0000-0001-8861-1671 RISE [EP/R011494/1]; UK Engineering and Physical Sciences Council (EPSRC); National Cyber Security Centre (NCSC) RISE; UK Engineering and Physical Sciences Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Cyber Security Centre (NCSC) This research is supported by RISE (ukrise.org EP/R011494/1) and funded by the UK Engineering and Physical Sciences Council (EPSRC) and the National Cyber Security Centre (NCSC). 14 12 12 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0271-4302 978-1-7281-3320-1 IEEE INT SYMP CIRC S 2020.0 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS1UN 2023-03-23 WOS:000696570700092 0
J Tu, SX; Ding, DX; Chang, YX; Li, CM; Wijns, W; Xu, B Tu, Shengxian; Ding, Daixin; Chang, Yunxiao; Li, Chunming; Wijns, William; Xu, Bo Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS English Article artificial intelligence; coronary angiography; fractional flow reserve; quantitative flow ratio RESERVE; LESIONS Objectives We aimed to evaluate the diagnostic accuracy of computation of fractional flow reserve (FFR) from a single angiographic view in patients with intermediate coronary stenosis. Background Computation of quantitative flow ratio (QFR) from a single angiographic view might increase the feasibility of routine use of computational FFR. In addition, current QFR solutions assume a linear tapering of the reference vessel size, which might decrease the diagnostic accuracy in the presence of the physiologically significant bifurcation lesions. Methods An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of major epicardial coronary arteries including their side branches. A step-down reference diameter function was reconstructed based on the Murray bifurcation fractal law and used for QFR computation. Validation of this Murray law-based QFR (mu QFR) was performed on the FAVOR II China study population. The mu QFR was computed separately in two angiographic projections, starting with the one with optimal angiographic image quality. Hemodynamically significant coronary stenosis was defined by pressure wire-derived FFR <= 0.80. Results The mu QFR was successfully computed in all 330 vessels of 306 patients. There was excellent correlation (r = 0.90, p < .001) and agreement (mean difference = 0.00 +/- 0.05, p = .378) between mu QFR and FFR. The vessel-level diagnostic accuracy for mu QFR to identify hemodynamically significant stenosis was 93.0% (95% CI: 90.3 to 95.8%), with sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of 87.5% (95% CI: 80.2 to 92.8%), 96.2% (95% CI: 92.6 to 98.3%), 92.9% (95% CI: 86.5 to 96.9%), 93.1% (95% CI: 88.9 to 96.1%), 23.0 (95% CI: 11.6 to 45.5), 0.13 (95% CI: 0.08 to 0.20), respectively. Use of suboptimal angiographic image view slightly decreased the diagnostic accuracy of mu QFR (AUC = 0.97 versus 0.92, difference = 0.05, p < .001). Intra- and inter-observer variability for mu QFR computation was 0.00 +/- 0.03, and 0.00 +/- 0.03, respectively. Average analysis time for mu QFR was 67 +/- 22 s. Conclusions Computation of mu QFR from a single angiographic view has high feasibility and excellent diagnostic accuracy in identifying hemodynamically significant coronary stenosis. The short analysis time and good reproducibility of mu QFR bear potential of wider adoption of physiological assessment in the catheterization laboratory. [Tu, Shengxian; Ding, Daixin; Li, Chunming] Shanghai Jiao Tong Univ, Biomed Instrument Inst, Sch Biomed Engn, Shanghai, Peoples R China; [Tu, Shengxian] Shanghai Jiao Tong Univ, Shanghai Med X Engn Res Ctr, Shanghai, Peoples R China; [Ding, Daixin; Wijns, William] Natl Univ Ireland Galway, Lambe Inst Translat Med & Curam, Galway, Ireland; [Chang, Yunxiao] Pulse Med Imaging Technol Co Ltd, Div Sci Res, Shanghai, Peoples R China; [Xu, Bo] Chinese Acad Med Sci & Peking Union Med Coll, Fu Wai Hosp, Natl Ctr Cardiovasc Dis, Catheterizat Labs, Beijing, Peoples R China Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ollscoil na Gaillimhe-University of Galway; Chinese Academy of Medical Sciences - Peking Union Medical College; Fu Wai Hospital - CAMS; Peking Union Medical College Tu, SX (corresponding author), Shanghai Jiao Tong Univ, Med Res Inst X, Room 123,1954 Hua Shan Rd, Shanghai 200030, Peoples R China. sxtu@sjtu.edu.cn Xu, Bo/GQZ-1711-2022 Tu, Shengxian/0000-0001-9681-1067 National Key Research and Development Program of China; Pulse Medical Imaging Technology (Shanghai) Co., Ltd.; Shanghai Science and Technology Commission [19DZ1930600] National Key Research and Development Program of China; Pulse Medical Imaging Technology (Shanghai) Co., Ltd.; Shanghai Science and Technology Commission(Shanghai Science & Technology CommitteeScience & Technology Commission of Shanghai Municipality (STCSM)) National Key Research and Development Program of China; Pulse Medical Imaging Technology (Shanghai) Co., Ltd.; Shanghai Science and Technology Commission, Grant/Award Number: 19DZ1930600 25 25 29 3 8 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1522-1946 1522-726X CATHETER CARDIO INTE Catheter. Cardiovasc. Interv. MAY 1 2021.0 97 2.0 SI 1040 1047 10.1002/ccd.29592 0.0 MAR 2021 8 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology RV6PT 33660921.0 2023-03-23 WOS:000625166800001 0
J Chen, LZ; Wang, JH; Ye, ZH; Zhao, J; Xue, XF; Vander Heyden, Y; Sun, Q Chen, Lanzhen; Wang, Jiahua; Ye, Zhihua; Zhao, Jing; Xue, Xiaofeng; Vander Heyden, Yvan; Sun, Qian Classification of Chinese honeys according to their floral origin by near infrared spectroscopy FOOD CHEMISTRY English Article Honey; Near infrared spectroscopy; Mahalanobis-distance discriminant analysis; Back propagation artificial neural network; Classification techniques UNIFLORAL HONEYS; FEASIBILITY; QUALITY; SPANISH; WINES; MS The feasibility of near infrared (NIR) spectroscopy and multivariate analysis as tools to classify Chinese honey samples according to their different floral origins was explored. Five kinds of honey, namely, acacia, linden, rape, vitex and jujube, were analysed using a NIR spectrophotometer with a fibre optic probe. Classification models based on the NIR spectra were developed using Mahalanobis-distance discriminant analysis (MD-DA) and a back propagation artificial neural network (BP-ANN). By the MD-DA model, total correct classification rates of 87.4% and 85.3% were observed for the calibration and validation samples, respectively, while the ANN model resulted in total correct classification rates of 90.9% and 89.3% for the calibration and validation sets, respectively. By ANN, the respective correct classification rates of linden, acacia, vitex, rape and jujube were 97.1%, 94.3%, 80.0%, 971%, and 85.7% in calibration, and 100%, 93.3%, 80.0%, 100%, and 73.3% in validation. The results indicated that NIR combined with a classification technique could be a suitable technology for the classification of Chinese honeys from different botanical origins. (C) 2012 Elsevier Ltd. All rights reserved. [Ye, Zhihua] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China; [Chen, Lanzhen; Zhao, Jing; Xue, Xiaofeng] Chinese Acad Sci, Bee Res Inst, Beijing 100093, Peoples R China; [Wang, Jiahua; Sun, Qian] Chinese Acad Sci, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China; [Vander Heyden, Yvan] Vrije Univ Brussel, Pharmaceut Res Ctr, Dept Analyt Chem & Pharmaceut Technol, B-1090 Brussels, Belgium Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Vrije Universiteit Brussel Ye, ZH (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. zhihuaye@mail.caas.net.cn zhao, jing/B-3868-2019; chen, lanzhen/AAM-5410-2020 Vander Heyden, Yvan/0000-0002-7788-9886; xue, xiaofeng/0000-0001-6687-0357 National key technology RD program [2006BAD06B04] National key technology RD program(National Key Technology R&D Program) The study was supported by National key technology R&D program (No. 2006BAD06B04). 32 76 81 0 91 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0308-8146 1873-7072 FOOD CHEM Food Chem. NOV 15 2012.0 135 2 338 342 10.1016/j.foodchem.2012.02.156 0.0 5 Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Food Science & Technology; Nutrition & Dietetics 002YF 22868096.0 2023-03-23 WOS:000308574300004 0
J Cao, BN; Niu, HW; Hao, J; Wang, GX Cao, Beining; Niu, Hongwei; Hao, Jia; Wang, Guoxin Building EEG-based CAD object selection intention discrimination model using convolutional neural network (CNN) ADVANCED ENGINEERING INFORMATICS English Article CAD interaction; Selection intention discrimination; EEG; CSP; CNN; Adaptive weights STATE FUNCTIONAL CONNECTIVITY; BRAIN-COMPUTER INTERFACE; DECISION-MAKING; EMOTION RECOGNITION; PREFRONTAL CORTEX; EYE TRACKING; TOP-DOWN; CLASSIFICATION; METAANALYSIS; MECHANISMS Currently, building natural interaction systems based on physiological signals has become an crucial requirement for the development of Computer Aided Design (CAD). As the first step of model operation in CAD, object selection is essential and the efficiency of selecting has a great impact on the experience of users. In the research community, gaze-based interaction for object selection has been well-established. However, this interactive mode is still imperfect due to Midas touch problem. In this work, a selection intention discrimination (SID) model is implemented to decode electroencephalogram (EEG) signals generated during object selection process. Common Spatial Pattern (CSP) is applied to extract spatial features from EEG in four frequency bands. Then these features are learned by a Convolutional Neural Network (CNN) equipped with an adaptive weights training module to realize the SID. To verify the decoding feasibility of this model, a cognitive experiment related to object selection is conducted. The empirical result shows that the performance of this model is good. It turns out that EEG-based object selection is feasible, which can be a intuitive and natural interaction mode for CAD. [Cao, Beining; Niu, Hongwei; Hao, Jia; Wang, Guoxin] Inst Ind & Intelligent Syst Engn, Beijing Inst Technol, Beijing, Peoples R China; [Niu, Hongwei] Katholieke Univ Leuven, Brain & Cognit Res Unit, Leuven, Belgium Beijing Institute of Technology; KU Leuven Hao, J (corresponding author), Inst Ind & Intelligent Syst Engn, Beijing Inst Technol, Beijing, Peoples R China. haojia632@bit.edu.cn hao, Jia/GZK-3937-2022 National Key R&D Program of China [2021YFB1714500]; National Ministry Project of China [JCKY2018204B053] National Key R&D Program of China; National Ministry Project of China Acknowledgment This work is supported by National Key R&D Program of China (2021YFB1714500) , National Ministry Project of China (JCKY2018204B053) . 56 1 2 9 13 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1474-0346 1873-5320 ADV ENG INFORM Adv. Eng. Inform. APR 2022.0 52 101548 10.1016/j.aei.2022.101548 0.0 FEB 2022 12 Computer Science, Artificial Intelligence; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 1K8DE 2023-03-23 WOS:000798825800001 0
J Zhang, QY; Zheng, FF; Zhao, TF; Qu, XH; Aires-de-Sousa, J Zhang, Qingyou; Zheng, Fangfang; Zhao, Tanfeng; Qu, Xiaohui; Aires-de-Sousa, Joao Machine Learning Estimation of Atom Condensed Fukui Functions MOLECULAR INFORMATICS English Article QSPR; Random Forest; Chemoinformatics; Bradley-Terry Models; Quantum Chemistry To enable the fast estimation of atom condensed Fukui functions, machine learning algorithms were trained with databases of DFT pre-calculated values for ca. 23,000 atoms in organic molecules. The problem was approached as the ranking of atom types with the Bradley-Terry (BT) model, and as the regression of the Fukui function. Random Forests (RF) were trained to predict the condensed Fukui function, to rank atoms in a molecule, and to classify atoms as high/low Fukui function. Atomic descriptors were based on counts of atom types in spheres around the kernel atom. The BT coefficients assigned to atom types enabled the identification (93-94% accuracy) of the atom with the highest Fukui function in pairs of atoms in the same molecule with differences 0.1. In whole molecules, the atom with the top Fukui function could be recognized in ca. 50% of the cases and, on the average, about 3 of the top 4 atoms could be recognized in a shortlist of 4. Regression RF yielded predictions for test sets with R-2=0.68-0.69, improving the ability of BT coefficients to rank atoms in a molecule. Atom classification (as high/low Fukui function) was obtained with RF with sensitivity of 55-61% and specificity of 94-95%. [Zhang, Qingyou; Zheng, Fangfang; Zhao, Tanfeng] Henan Univ, Inst Environm & Analyt Sci, Coll Chem & Chem Engn, Kaifeng 475004, Peoples R China; [Qu, Xiaohui] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Environm Energy Technol Div, Berkeley, CA 94720 USA; [Qu, Xiaohui; Aires-de-Sousa, Joao] Univ Nova Lisboa, LAQV REQUIMTE, Dept Quim, Fac Ciencias & Tecnol, P-2829516 Caparica, Portugal Henan University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; Universidade Nova de Lisboa Aires-de-Sousa, J (corresponding author), Univ Nova Lisboa, LAQV REQUIMTE, Dept Quim, Fac Ciencias & Tecnol, P-2829516 Caparica, Portugal. joao@airesdesousa.com Aires-de-Sousa, Joao/C-7826-2013; Aires-de-Sousa, Joao/AAA-9274-2020; ZHENG, FANG/HJZ-0472-2023 Aires-de-Sousa, Joao/0000-0002-5887-2966; Aires-de-Sousa, Joao/0000-0002-5887-2966; Zhang, Qingyou/0000-0002-8923-0843; Zhao, Tanfeng/0000-0002-6654-3679 National Natural Science Foundation of China [20875022]; Portuguese national funds via FCT - Fundacao para a Ciencia e a Tecnologia (Lisboa, Portugal) [PEst-C/EQB/LA0006/2013] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Portuguese national funds via FCT - Fundacao para a Ciencia e a Tecnologia (Lisboa, Portugal) The authors acknowledge the International Science and Technology Cooperation of Henan Province (P. R.China) (No. 114300510009), and thank the financial support of the National Natural Science Foundation of China (No. 20875022). This work was funded by Portuguese national funds via FCT - Fundacao para a Ciencia e a Tecnologia (Lisboa, Portugal) - under project PEst-C/EQB/LA0006/2013. 25 10 10 0 30 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1868-1743 1868-1751 MOL INFORM Mol. Inf. FEB 2016.0 35 2 62 69 10.1002/minf.201500113 0.0 8 Chemistry, Medicinal; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy; Computer Science; Mathematical & Computational Biology DD8JV 27491791.0 2023-03-23 WOS:000370173700003 0
J Wei, H; Zhang, QH; Shang, MH; Gu, Y Wei, Hao; Zhang, Qinghua; Shang, Minghu; Gu, Yu Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform MEASUREMENT English Article Fault diagnosis; Rotating machinery; Neural network; Continuous wavelet transform; Residual learning CONVOLUTIONAL NEURAL-NETWORK; ALGORITHM Effective fault diagnosis of rotating machinery is essential for the predictive maintenance of modern industries. In this study, a novel framework that combines a residual network (ResNet) as a backbone and an extreme learning machine (ELM) as a classifier (RNELM) is proposed to diagnose faults of rotating machinery. Firstly, continuous wavelet transform (CWT) is used to convert the raw time-domain signal into time-frequency domain images. Subsequently, the ResNet backbone in the framework extracts advanced features from the images for the ELM classifier, substantially improving the fault diagnosis performance. The performance of the framework is compared with four other methods using four evaluation metrics on datasets from Case Western Reserve University (CWRU), laboratory results and industrial applications. The experimental results show that the RNELM achieves outstanding results on the test samples of the three datasets, demonstrating the excellent performance and practicability of the proposed framework for fault diagnosis. [Wei, Hao] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Zhang, Qinghua; Gu, Yu] Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China; [Shang, Minghu] Shenyang Blower Works Grp Corp, Shenyang 110869, Peoples R China; [Gu, Yu] Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China; [Gu, Yu] Goethe Univ, Dept Chem, Inst Inorgan & Analyt Chem, Max von Laue Str 9, D-60438 Frankfurt, Germany Beijing University of Chemical Technology; Beijing University of Chemical Technology; Goethe University Frankfurt Gu, Y (corresponding author), Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China. guyu@mail.buct.edu.cn National Natural Science Foundation of China [61876059, 61933013]; Shenyang Blower Works Group Corporation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenyang Blower Works Group Corporation The authors would like to thank the National Natural Science Foundation of China (Grant No. 61876059 and Grant No. 61933013) and Shenyang Blower Works Group Corporation for their support. 46 21 21 21 63 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement OCT 2021.0 183 109864 10.1016/j.measurement.2021.109864 0.0 JUL 2021 10 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation UM8FH 2023-03-23 WOS:000693561300001 0
C Li, S; Ma, XJ; Liang, HZ; Gorner, M; Ruppel, P; Fang, B; Sun, FC; Zhang, JW Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Li, Shuang; Ma, Xiaojian; Liang, Hongzhuo; Gorner, Michael; Ruppel, Philipp; Fang, Bin; Sun, Fuchun; Zhang, Jianwei Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion. The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands. A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulated depth images of a robotic hand. The final training set includes 400K pairwise depth images and joint angles of a Shadow C6 robotic hand. The network evaluation results verify the superiority of TeachNet, especially regarding the high-precision condition. Imitation experiments and grasp tasks teleoperated by novice users demonstrate that TeachNet is more reliable and faster than the state-of-the-art vision-based teleoperation method. [Li, Shuang; Liang, Hongzhuo; Gorner, Michael; Ruppel, Philipp; Zhang, Jianwei] Univ Hamburg, Dept Informat, TAMS, Hamburg, Germany; [Ma, Xiaojian; Fang, Bin; Sun, Fuchun] Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol TNList, State Key Lab Intelligent Technol & Syst, Beijing, Peoples R China University of Hamburg; Tsinghua University Fang, B (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol TNList, State Key Lab Intelligent Technol & Syst, Beijing, Peoples R China. fangbin@mail.tsinghua.edu.cn zhang, jian/HPD-1712-2023 National Science Foundation of China (NSFC); German Research Foundation (DFG) in project Cross Modal Learning [NSFC 61621136008/DFG TRR-169]; National Science Foundation of China [91848206, U1613212]; project STEP2DYNA [691154] National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG) in project Cross Modal Learning(German Research Foundation (DFG)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); project STEP2DYNA This research was funded jointly by the National Science Foundation of China (NSFC) and the German Research Foundation (DFG) in project Cross Modal Learning, NSFC 61621136008/DFG TRR-169. It was also partially supported by National Science Foundation of China (Grant No.91848206, U1613212) and project STEP2DYNA (691154). We would like to thank Chao Yang, Dr. Norman Hendrich, and Prof. Huaping Liu for their generous help and insightful advice. 29 29 30 2 20 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 416 422 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN Green Submitted 2023-03-23 WOS:000494942300050 0
J Mueller-Breckenridge, AJ; Garcia-Alcalde, F; Wildum, S; Smits, SL; de Man, RA; van Campenhout, MJH; Brouwer, WP; Niu, JJ; Young, JAT; Najera, I; Zhu, LN; Wu, D; Racek, T; Hundie, GB; Lin, Y; Boucher, CA; van de Vijver, D; Haagmans, BL Mueller-Breckenridge, Alan J.; Garcia-Alcalde, Fernando; Wildum, Steffen; Smits, Saskia L.; de Man, Robert A.; van Campenhout, Margo J. H.; Brouwer, Willem P.; Niu, Jianjun; Young, John A. T.; Najera, Isabel; Zhu, Lina; Wu, Daitze; Racek, Tomas; Hundie, Gadissa Bedada; Lin, Yong; Boucher, Charles A.; van de Vijver, David; Haagmans, Bart L. Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts SCIENTIFIC REPORTS English Article VIRAL-HEPATITIS; E-ANTIGEN; MUTATIONS; EVOLUTION; VARIANTS; MANAGEMENT; PROMOTER; SEQUENCE; DISEASE Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating 'quasispecies' populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification. [Mueller-Breckenridge, Alan J.; Garcia-Alcalde, Fernando; Wildum, Steffen; Young, John A. T.; Najera, Isabel; Racek, Tomas] Switzerland F Hoffmann La Roche AG, Roche Innovat Ctr, Grenzacherstr 124, CH-4070 Basel, Switzerland; [Smits, Saskia L.; Hundie, Gadissa Bedada; Boucher, Charles A.; van de Vijver, David; Haagmans, Bart L.] Erasmus MC, Dept Virosci, S Gravendijkwal 230, NL-3015 CE Rotterdam, Netherlands; [Niu, Jianjun; Lin, Yong] Xiamen Univ, Zhongshan Hosp, 205 Hubin S Rd, Xiamen 361000, Fujian, Peoples R China; [de Man, Robert A.; van Campenhout, Margo J. H.; Brouwer, Willem P.] Erasmus MC, Dept Gastroenterol & Hepatol, Rotterdam, Netherlands; [Zhu, Lina; Wu, Daitze] Roche R&D Ctr China Ltd, Roche Innovat Ctr Shanghai, 720 Cai Lun Rd, Shanghai 201203, Peoples R China Roche Holding; Erasmus University Rotterdam; Erasmus MC; Xiamen University; Erasmus University Rotterdam; Erasmus MC; Roche Holding Mueller-Breckenridge, AJ (corresponding author), Switzerland F Hoffmann La Roche AG, Roche Innovat Ctr, Grenzacherstr 124, CH-4070 Basel, Switzerland.;Haagmans, BL (corresponding author), Erasmus MC, Dept Virosci, S Gravendijkwal 230, NL-3015 CE Rotterdam, Netherlands. alan.james.mueller-breckenridge@roche.com; b.haagmans@erasmusmc.nl Hundie, Gadissa/GPF-9108-2022; Brouwer, Willem Pieter/P-5444-2014; Haagmans, Bartholomeus Leonardus/E-9791-2015; van Campenhout, Margo J.H./H-2102-2013 Hundie, Gadissa/0000-0002-5627-9675; Brouwer, Willem Pieter/0000-0001-8713-1481; Haagmans, Bartholomeus Leonardus/0000-0001-6221-2015; Mueller-Breckenridge, Alan/0000-0001-5277-0793; Wildum, Steffen/0000-0001-9779-5740; Young, John/0000-0003-1824-2561; van Campenhout, Margo J.H./0000-0003-0460-4920; van de Vijver, David/0000-0001-5877-8526 54 10 10 3 8 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep DEC 11 2019.0 9 18892 10.1038/s41598-019-55445-8 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics JV9YE 31827222.0 Green Published, gold 2023-03-23 WOS:000502715100001 0
J Duan, WS; Ma, JJ; Vannitsem, S Duan, Wansuo; Ma, Junjie; Vannitsem, Stephane An Ensemble Forecasting Method for Dealing with the Combined Effects of the Initial and Model Errors and a Potential Deep Learning Implementation MONTHLY WEATHER REVIEW English Article Uncertainty; Model errors LOCAL LYAPUNOV VECTORS; RANDOM PERTURBATIONS; DATA ASSIMILATION; SINGULAR VECTORS; BRED VECTORS; PREDICTION; WEATHER; DYNAMICS; ECMWF; NCEP In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors. Significance StatementA new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical-deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors. [Duan, Wansuo; Ma, Junjie] Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing, Peoples R China; [Duan, Wansuo; Ma, Junjie] Univ Chinese Acad Sci, Beijing, Peoples R China; [Vannitsem, Stephane] Royal Meteorol Inst Belgium, Brussels, Belgium Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Royal Meteorological Institute of Belgium Duan, WS (corresponding author), Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing, Peoples R China.;Duan, WS (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. duanws@lasg.iap.ac.cn Vannitsem, Stephane/R-7328-2019 Vannitsem, Stephane/0000-0002-1734-1042 National Natural Science Foundation of China [41930971]; project ROADMAP - Belgian Science Policy [B2/20E/P1/ROADMAP] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); project ROADMAP - Belgian Science Policy The authors thank the three anonymous reviewers and the editor, Michael Scheuerer, for their valuable comments and suggestions. This work is supported by the National Natural Science Foundation of China (Grant 41930971). SV is also partly supported by the project ROADMAP, a coordinated JPI-Climate/JPI-Oceans project, financed by the Belgian Science Policy under Contract B2/20E/P1/ROADMAP. 83 0 0 1 1 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693, UNITED STATES 0027-0644 1520-0493 MON WEATHER REV Mon. Weather Rev. NOV 2022.0 150 11 2959 2976 10.1175/MWR-D-22-0007.1 0.0 18 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences 6S5SO 2023-03-23 WOS:000893046800007 0
C Jiang, ZY; Bing, ZS; Huang, K; Chen, G; Cheng, L; Knoll, A Liu, D; Xie, S; Li, Y; Zhao, D; ElAlfy, ESM Jiang, Zhuangyi; Bing, Zhenshan; Huang, Kai; Chen, Guang; Cheng, Long; Knoll, Alois Event-Based Target Tracking Control for a Snake Robot Using a Dynamic Vision Sensor NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI Lecture Notes in Computer Science English Proceedings Paper 24th International Conference on Neural Information Processing (ICONIP) NOV 14-18, 2017 Guangzhou, PEOPLES R CHINA Chinese Acad Sci, Inst Automat,Guangdong Univ Technol,S China Univ Technol,Springers Lecture Notes Comp Sci,IEEE CAA Journal Automatica Sinica,Asia Pacific Neural Network Soc Target tracking; Spiking Neural Network; Dynamic Vision Sensor; Neuromorphic snake robot; Hough transform Dynamic Vision Sensor (DVS) is a promising neuromorphic vision sensor for autonomous locomotion control of mobile robots, as the DVS acquires visual information by mimicking retina to sense and encode the world as neural signals. In this paper, we present an autonomous target detecting and tracking control approach for a snake-like robot with a monocular DVS. By using Hough transform based on the Spiking Neural Network (SNN), the target pole is detected as two parallel lines from the event-based visual input. Then a depth estimation method based on the pose and motion of the robot is proposed. Furthermore, by combining the periodic motion feature of the snake-like robot, an adaptive tracking method based on the estimated depth information is introduced. Experiments are conducted on a snake-like robot to demonstrate the practicality and accuracy of our proposed method to track a target pole dynamically with a monocular DVS. [Jiang, Zhuangyi; Bing, Zhenshan; Chen, Guang; Cheng, Long; Knoll, Alois] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Munich, Germany; [Huang, Kai] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China Technical University of Munich; Sun Yat Sen University Jiang, ZY (corresponding author), Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Munich, Germany. jiangz@in.tum.de; bing@in.tum.de; huangk36@mail.sysu.edu.cn; guang@in.tum.de; chengl@in.tum.de; knoll@in.tum.de Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X; CHen, Guang/0000-0002-7416-592X 14 1 1 3 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-70136-3; 978-3-319-70135-6 LECT NOTES COMPUT SC 2017.0 10639 VI 111 121 10.1007/978-3-319-70136-3_12 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ1LL Green Submitted 2023-03-23 WOS:000576768500012 0
J Zhou, ZY; Yu, HJ; Xu, C; Zhang, Y; Mumtaz, S; Rodriguez, J Zhou, Zhenyu; Yu, Houjian; Xu, Chen; Zhang, Yan; Mumtaz, Shahid; Rodriguez, Jonathan Dependable Content Distribution in D2D-Based Cooperative Vehicular Networks: A Big Data-Integrated Coalition Game Approach IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article D2D-V2V communication; dependable content distribution; cooperative vehicular networks; vehicle trajectory prediction; coalition formation game; big data CELLULAR NETWORKS; COMMUNICATION; DISSEMINATION; NAVIGATION; DELIVERY; INTERNET; SYSTEMS; D2D Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes. [Zhou, Zhenyu; Yu, Houjian; Xu, Chen] North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China; [Zhang, Yan] Univ Oslo, Dept Informat, N-0315 Oslo, Norway; [Zhang, Yan] Simula Res Lab, N-1364 Fornebu, Norway; [Mumtaz, Shahid] Inst Telecomunicacoes, P-1049001 Aveiro, Portugal; [Rodriguez, Jonathan] Inst Telecomunicacoes, P-3810193 Aveiro, Portugal; [Rodriguez, Jonathan] Univ South Wales, Pontypridd CF37 1DL, M Glam, Wales North China Electric Power University; University of Oslo; Universidade de Aveiro; Universidade de Aveiro; University of South Wales Xu, C (corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China. zhenyu_zhou@ncepu.edu.cn; yuhoujian2017@126.com; chen.xu@ncepu.edu.cn; yanzhang@ieee.org; smumtaz@av.it.pt; jonathan@av.it.pt Mumtaz, Dr shahid/V-3603-2019; Yu, Houjian/GRF-1157-2022; Rodriguez, Jonathan/F-6055-2010; Zhang, Yan/AFK-8566-2022 Mumtaz, Dr shahid/0000-0001-6364-6149; Rodriguez, Jonathan/0000-0001-9829-0955; Zhou, Zhenyu/0000-0002-3344-4463 National Science Foundation of China [61601181]; Fundamental Research Funds for the Central Universities [2017MS13]; Beijing Natural Science Foundation [4174104]; Beijing Outstanding Young Talent [2016000020124G081] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Beijing Outstanding Young Talent This work was supported in part by the National Science Foundation of China under Grant 61601181, in part by the Fundamental Research Funds for the Central Universities under Grant 2017MS13, in part by the Beijing Natural Science Foundation under Grant 4174104, and in part by the Beijing Outstanding Young Talent under Grant 2016000020124G081. The Associate Editor for this paper was M. Alam. (Corresponding author: Chen Xu.) 34 111 114 10 90 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. MAR 2018.0 19 3 953 964 10.1109/TITS.2017.2771519 0.0 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation FY9YI Green Published 2023-03-23 WOS:000427222600025 0
J Stanimirovic, PS; Katsikis, VN; Zhang, ZJ; Li, S; Chen, JL; Zhou, MM Stanimirovic, Predrag S.; Katsikis, Vasilios N.; Zhang, Zhijun; Li, Shuai; Chen, Jianlong; Zhou, Mengmeng Varying-parameter Zhang neural network for approximating some expressions involving outer inverses OPTIMIZATION METHODS & SOFTWARE English Article Zhang neural network; generalized inverses; super-exponential convergence; time-varying matrix; varying-parameter ZNN design GENERALIZED INVERSE; ZNN MODELS; COMPUTING PSEUDOINVERSES; COMPLEX ZFS; REPRESENTATION; A(T,S)((2)); LINK A varying-parameter ZNN (VPZNN) neural design is defined for approximating various generalized inverses and expressions involving generalized inverses of complex matrices. The proposed model is termed as C V P Z N N ( A , F , G ) and defined on the basis of the error function which includes three appropriate matrices A,F,G. The C V P Z N N ( A , F , G ) evolution design includes so far defined VPZNN models for computing generalized inverses and also generates a number of matrix expressions involving these generalized inverses. Global and super-exponential convergence properties of the proposed model as well as behaviour of its equilibrium state are investigated. Main contribution of the defined model is its generality. Most important particular cases of the defined model are presented in order to show this fact explicitly. Presented simulation results illustrate generality and effectiveness of the discovered ZNN evolution design. [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Nish, Serbia; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Div Math & Informat, Dept Econ, Athens, Greece; [Zhang, Zhijun] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou, Peoples R China; [Li, Shuai] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Chen, Jianlong; Zhou, Mengmeng] Southeast Univ, Sch Math, Nanjing, Jiangsu, Peoples R China University of Nis; National & Kapodistrian University of Athens; South China University of Technology; Hong Kong Polytechnic University; Southeast University - China Stanimirovic, PS (corresponding author), Univ Nis, Fac Sci & Math, Nish, Serbia. pecko@pmf.ni.ac.rs Katsikis, Vasilios N/AAD-7216-2019; Stanimirović, Predrag S./AAW-3979-2021 Katsikis, Vasilios N/0000-0002-8208-9656; Stanimirović, Predrag S./0000-0003-0655-3741 59 8 8 0 8 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1055-6788 1029-4937 OPTIM METHOD SOFTW Optim. Method Softw. NOV 1 2020.0 35 6 1304 1330 10.1080/10556788.2019.1594806 0.0 27 Computer Science, Software Engineering; Operations Research & Management Science; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Operations Research & Management Science; Mathematics OT0RC 2023-03-23 WOS:000590561200013 0
J Rice, PS; Liu, ZP; Hu, P Rice, Peter S.; Liu, Zhi-Pan; Hu, P. Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT JOURNAL OF PHYSICAL CHEMISTRY LETTERS English Article EVOLUTION REACTION; MOLECULAR-DYNAMICS; WATER FORMATION; SINGLE-CRYSTAL; ENERGY; OXYGEN; REDUCTION; MECHANISM; KINETICS; PT(111) To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H-ad/H2Oad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt. [Rice, Peter S.; Hu, P.] Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, Antrim, North Ireland; [Liu, Zhi-Pan] Fudan Univ, Dept Chem, Shanghai Key Lab Mol Catalysis & Innovat Mat, Key Lab Computat Phys Sci,Minist Educ, Shanghai 200433, Peoples R China Queens University Belfast; Fudan University Hu, P (corresponding author), Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, Antrim, North Ireland. p.hu@qub.ac.uk Liu, Zhipan/X-3057-2019; Liu, Zhi-Pan/A-1988-2011 Liu, Zhipan/0000-0002-2906-5217; Liu, Zhi-Pan/0000-0002-2906-5217; Rice, Peter/0000-0003-2946-3781 EPSRC [EP/P020194/1]; Northern Ireland Department for the Economy (NI-DfE) at the Queen's University of Belfast EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Northern Ireland Department for the Economy (NI-DfE) at the Queen's University of Belfast P.H. and P.S.R. acknowledge the use of computational resources at the UK national high-performance computing service, ARCHER, for which access was obtained via the UKCP consortium. We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1). P.S.R. acknwoledges his Ph.D. studentship funded by the Northern Ireland Department for the Economy (NI-DfE) at the Queen's University of Belfast. 87 7 7 10 29 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1948-7185 J PHYS CHEM LETT J. Phys. Chem. Lett. NOV 4 2021.0 12 43 10637 10645 10.1021/acs.jpclett.1c02998 0.0 OCT 2021 9 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics WU3NW 34704763.0 2023-03-23 WOS:000716456200022 0
J Li, YJ; Zhang, QZ; Kaminski, P; Deifalla, AF; Sufian, M; Dyczko, A; Ben Kahla, N; Atig, M Li, Yongjian; Zhang, Qizhi; Kaminski, Pawel; Deifalla, Ahmed Farouk; Sufian, Muhammad; Dyczko, Artur; Ben Kahla, Nabil; Atig, Miniar Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques MATERIALS English Article concrete; steel fiber; steel fiber-reinforced concrete; compressive strength; mechanical characteristics; construction materials MECHANICAL-PROPERTIES; FLY-ASH; DURABILITY PROPERTIES; SILICA-FUME; BEHAVIOR; PLASTICIZER; SIMULATIONS Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber-reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R-2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R-2 = 0.96, as opposed to SVR bagging and support vector regression, which had R-2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied. [Li, Yongjian] Univ Glasgow, James Watt Engn Sch, Glasgow, Lanark, Scotland; [Zhang, Qizhi] Huanghuai Univ, Sch Architectural Engn, Zhumadian 463000, Peoples R China; [Kaminski, Pawel] AGH Univ Sci & Technol, Fac Civil Engn & Resource Management, PL-30059 Krakow, Poland; [Deifalla, Ahmed Farouk] Future Univ Egypt, Fac Engn & Technol, Struct Engn & Construct Management Dept, Cairo 11835, Egypt; [Sufian, Muhammad] Southeast Univ, Sch Civil Engn, Nanjing 210096, Peoples R China; [Dyczko, Artur] Polish Acad Sci, Mineral & Energy Econ Res Inst, J Wybickiego 7a, PL-31261 Krakow, Poland; [Ben Kahla, Nabil] King Khalid Univ, Coll Engn, Dept Civil Engn, Abha 61421, Saudi Arabia; [Ben Kahla, Nabil; Atig, Miniar] Univ Carthage, Tunisia Polytech Sch, Lab Syst & Appl Mech, Tunis 2078, Tunisia; [Atig, Miniar] Univ Tunis, Higher Natl Engn Sch Tunis, Dept Civil Engn, Tunis, Tunisia University of Glasgow; Huanghuai University; AGH University of Science & Technology; Egyptian Knowledge Bank (EKB); Future University in Egypt; Southeast University - China; Polish Academy of Sciences; Mineral & Energy Economy Research Institute of the Polish Academy of Sciences; King Khalid University; Universite de Carthage; Universite de Tunis Zhang, QZ (corresponding author), Huanghuai Univ, Sch Architectural Engn, Zhumadian 463000, Peoples R China.;Kaminski, P (corresponding author), AGH Univ Sci & Technol, Fac Civil Engn & Resource Management, PL-30059 Krakow, Poland.;Sufian, M (corresponding author), Southeast Univ, Sch Civil Engn, Nanjing 210096, Peoples R China. yongjian4896@outlook.com; zdjs_zgz@163.com; pkamin@agh.edu.pl; ahmed.deifalla@fue.edu.eg; drsufian@seu.edu.cn; arturdyczko@min-pan.krakow.pl; nbohlal@kku.edu.sa; miniar.atig@gmail.com Kamiński, Paweł/T-7756-2017; Dyczko, Artur/GON-7386-2022 Kamiński, Paweł/0000-0002-1450-5881; Dyczko, Artur/0000-0001-6387-5339; Sufian, Muhammad/0000-0001-6777-0219; deifalla, ahmed/0000-0003-1758-3743 Deanship of Scientific Research at King Khalid University [RGP, 2/104/43] Deanship of Scientific Research at King Khalid University The Authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project under grant number RGP. 2/104/43. 72 7 7 6 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials JUN 2022.0 15 12 4209 10.3390/ma15124209 0.0 18 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics 2K3EL 35744270.0 gold, Green Accepted 2023-03-23 WOS:000816223500001 0
J Jiu, MY; Pustelnik, N Jiu, Mingyuan; Pustelnik, Nelly Alternative Design of DeepPDNet in the Context of Image Restoration IEEE SIGNAL PROCESSING LETTERS English Article Image restoration; deep learning; proximal algorithm; Chambolle-Pock algorithm; sparsity INVERSE PROBLEMS; NEURAL-NETWORKS This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a l(2)-norm data-term and an analysis sparse prior. The parameters of our network are the step-sizes of the Chambolle-Pock scheme and the linear operator involved in sparsity-based penalization, including implicitly the regularization parameter. A backpropagation procedure is fully described. Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database. [Jiu, Mingyuan] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China; [Pustelnik, Nelly] Univ Lyon 1, Univ Lyon, ENS Lyon, CNRS,Lab Phys, F-69342 Lyon, France; [Pustelnik, Nelly] UCLouvain, ISPGrp, ICTEAM, B-1348 Louvain, Belgium Zhengzhou University; Centre National de la Recherche Scientifique (CNRS); Ecole Normale Superieure de Lyon (ENS de LYON); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Universite Catholique Louvain Jiu, MY (corresponding author), Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China. iemyjiu@zzu.edu.cn; nelly.pustelnik@ens-lyon.fr National Natural Science Foundation of China [61806180]; ANR (Agence Nationale de la Recherche) of France [ANR-19-CE480009 Multisc'In] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ANR (Agence Nationale de la Recherche) of France(French National Research Agency (ANR)) This work was supported in part by the National Natural Science Foundation of China under Grant 61806180 and in part by the ANR (Agence Nationale de la Recherche) of France under Grant (ANR-19-CE480009 Multisc'In). The associate editor coordinating the reviewof this manuscript and approving it for publication was Mr. Yue Gao. 36 0 0 3 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2022.0 29 932 936 10.1109/LSP.2022.3153229 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 0M8QW Green Submitted 2023-03-23 WOS:000782414200007 0
J Liu, K; Sadoune, N; Rao, N; Greitemann, J; Pollet, L Liu, Ke; Sadoune, Nicolas; Rao, Nihal; Greitemann, Jonas; Pollet, Lode Revealing the phase diagram of Kitaev materials by machine learning: Cooperation and competition between spin liquids PHYSICAL REVIEW RESEARCH English Article SYSTEMS Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases. We use an unsupervised and interpretable machine-learning method, the tensorial-kernel support vector machine, to study the honeycomb Kitaev-Gamma model in a magnetic field. Our machine learns the global classical phase diagram and the associated analytical order parameters, including several distinct spin liquids, two exotic S3 magnets, and two modulated S-3 x Z(3) magnets. We find that the extension of Kitaev spin liquids and a field-induced suppression of magnetic order already occur in the large-S limit, implying that critical parts of the physics of Kitaev materials can be understood at the classical level. Moreover, the two S-3 x Z(3) orders are induced by competition between Kitaev and Gamma spin liquids and feature a different type of spin-lattice entangled modulation, which requires a matrix description instead of scalar phase factors. Our work provides a direct instance of a machine detecting new phases and paves the way towards the development of automated tools to explore unsolved problems in many-body physics. [Liu, Ke; Sadoune, Nicolas; Rao, Nihal; Greitemann, Jonas; Pollet, Lode] Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany; [Liu, Ke; Sadoune, Nicolas; Rao, Nihal; Greitemann, Jonas; Pollet, Lode] Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany; [Pollet, Lode] Shanghai Jiao Tong Univ, Wilczek Quantum Ctr, Sch Phys & Astron, Shanghai 200240, Peoples R China University of Munich; Shanghai Jiao Tong University Liu, K (corresponding author), Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany.;Liu, K (corresponding author), Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany. ke.liu@lmu.de Pollet, Lode C/F-6845-2010; Liu, Ke/A-3401-2017 Pollet, Lode C/0000-0002-7274-2842; Liu, Ke/0000-0002-5768-1631; Rao, Nihal/0000-0003-4824-4381 FP7/ERC Consolidator Grant QSIMCORR [771891]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC-2111, 390814868] FP7/ERC Consolidator Grant QSIMCORR; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy(German Research Foundation (DFG)) We wish to thank Hong-Hao Tu, Simon Trebst, Nic Shannon, and Stephen Nagler for helpful discussions. K.L., N.S., N.R., J.G., and L.P. acknowledge support from FP7/ERC Consolidator Grant QSIMCORR, No. 771891, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2111 - Grant No. 390814868. Our simulations make use of the nu-SVM formulation [106], the LIBSVM library [107,108], and the ALPSCore library [109]. 111 11 11 0 4 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2643-1564 PHYS REV RES Phys. Rev. Res. APR 5 2021.0 3 2 23016 10.1103/PhysRevResearch.3.023016 0.0 15 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics RI3XU gold, Green Submitted 2023-03-23 WOS:000636844500002 0
J Jiang, H; van Genderen, J; Mazzetti, P; Koo, H; Chen, M Jiang, Hao; van Genderen, John; Mazzetti, Paolo; Koo, Hyeongmo; Chen, Min Current status and future directions of geoportals INTERNATIONAL JOURNAL OF DIGITAL EARTH English Article Geoportal(s); big Earth data; Digital Earth; taxonomy of geoportals; geoportals in China SPATIAL DATA INFRASTRUCTURE; GEOSPATIAL WEB SERVICE; REAL-TIME; INFORMATION; SYSTEM; DISCOVERY; HAZARD; CYBERINFRASTRUCTURE; INTEROPERABILITY; IMPLEMENTATION Geoportals are a consolidated web-based solution to provide open spatial data sharing and online geo-information management. Their roles and possible advancements according to the Digital Earth vision and implementation require investigations. This paper presents a review of the literature concerning geoportals and serves the following primary purposes. First, various geoportal approaches for discovering and accessing Earth observation data and geo-information, mainly with scientific purposes, are summarized according to their characteristics and functionalities. Second, current major challenges in geoportals are identified in terms of functionalities, technologies, and especially big data support, from geoportal cases of China. Finally, based on lessons learned from the international and Chinese geoportals, solutions and recommendations for the challenges in geoportals are proposed in terms of their architectures, services, and technologies. The results show that geoportals usually provide access to distributed data systems, offering maps, data discovery, and data downloads. Some of them are also capable of offering online analysis and processing service, enhanced semantic search engines, and dynamic visualization tools. The strength of geoportals could lead to a full-fledged online Digital Earth system that could provide better data sharing and dissemination solutions to the challenges posed by big data. [Jiang, Hao] Univ Chinese Acad Sci, Beijing, Peoples R China; [Jiang, Hao] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China; [van Genderen, John] Univ Twente, Fac Geoinformat Sci & Earth Observat, Enschede, Netherlands; [Mazzetti, Paolo] CNR, Inst Atmospher Pollut Res, Florence, Italy; [Koo, Hyeongmo; Chen, Min] Nanjing Normal Univ, Dept Geog, Key Lab Virtual Geog Environm, Minist Educ, Nanjing, Jiangsu, Peoples R China Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Twente; Consiglio Nazionale delle Ricerche (CNR); Istituto Sull'inquinamento Atmosferico (IIA-CNR); Nanjing Normal University Jiang, H (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. jianghao@radi.ac.cn Mazzetti, Paolo/B-6098-2015; Chen, Min/A-8806-2016; Koo, Hyeongmo/ABA-8261-2021 Mazzetti, Paolo/0000-0002-8291-1128; Chen, Min/0000-0001-8922-8789; Koo, Hyeongmo/0000-0002-5446-1668 99 16 17 7 22 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1753-8947 1753-8955 INT J DIGIT EARTH Int. J. Digit. Earth OCT 2 2020.0 13 10 1093 1114 10.1080/17538947.2019.1603331 0.0 APR 2019 22 Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Remote Sensing NW3YO 2023-03-23 WOS:000466688500001 0
J Wang, KT; Feng, CZ; Li, M; Pei, Q; Li, YQ; Zhu, H; Song, XP; Pei, HP; Tan, FB Wang, Kangtao; Feng, Chenzhe; Li, Ming; Pei, Qian; Li, Yuqiang; Zhu, Hong; Song, Xiangping; Pei, Haiping; Tan, Fengbo A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed THERAPEUTIC ADVANCES IN GASTROENTEROLOGY English Article bibliometric analysis; LDA analyses; machine learning; rectal cancer COLORECTAL-CANCER; SURVIVAL; MORTALITY; CHEMORADIOTHERAPY; PATTERNS; OUTCOMES; SURGERY Background and Aims: The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis. Methods: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. Results: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. Conclusions: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future. [Wang, Kangtao; Pei, Qian; Pei, Haiping; Tan, Fengbo] Cent South Univ, Xiangya Hosp, Dept Gastrointestinal Surg, Xiangya Rd 110, Changsha 410008, Peoples R China; [Feng, Chenzhe] Chinese Acad Med Sci & Peking Union Med Coll, Beijing, Peoples R China; [Li, Ming] Cent South Univ, Coll Basic Med Sci, Dept Immunol, Changsha, Peoples R China; [Li, Yuqiang] Univ Med Ctr Hamburg Eppendorf, Dept Gen Visceral & Thorac Surg, Hamburg, Germany; [Zhu, Hong] Cent South Univ, Xiangya Hosp, Dept Oncol, Changsha, Peoples R China; [Song, Xiangping] UPMC Hillman Canc Ctr, Pittsburgh, PA USA Central South University; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; Central South University; University of Hamburg; University Medical Center Hamburg-Eppendorf; Central South University Pei, HP; Tan, FB (corresponding author), Cent South Univ, Xiangya Hosp, Dept Gastrointestinal Surg, Xiangya Rd 110, Changsha 410008, Peoples R China. haipingpei1966@hotmail.com; fengbotan@csu.edu.cn Li, Yuqiang/0000-0003-1517-3687; Wang, Kangtao/0000-0003-0948-0277 Nature Scientific Foundation of China [81702956]; Strategy-Oriented Special Project of Central South University in China [ZLXD2017003]; XiangYa-Peking University Wei Ming Clinical and Rehabilitation Research Fund [xywm2015I21]; General programme of Natural Science Foundation of Hunan Province [2019JJ40490] Nature Scientific Foundation of China(National Natural Science Foundation of China (NSFC)); Strategy-Oriented Special Project of Central South University in China; XiangYa-Peking University Wei Ming Clinical and Rehabilitation Research Fund; General programme of Natural Science Foundation of Hunan Province The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this research was funded by The Nature Scientific Foundation of China (81702956); The Strategy-Oriented Special Project of Central South University in China (ZLXD2017003); The XiangYa-Peking University Wei Ming Clinical and Rehabilitation Research Fund (xywm2015I21); General programme of Natural Science Foundation of Hunan Province (2019JJ40490). 40 16 16 9 31 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 1756-283X 1756-2848 THER ADV GASTROENTER Ther. Adv. Gastroenterol. JUL 2020.0 13 1756284820934594 10.1177/1756284820934594 0.0 11 Gastroenterology & Hepatology Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology MW2AP 32782478.0 gold, Green Accepted 2023-03-23 WOS:000556846900001 0
C Moyes, A; Zhang, K; Ji, M; Zhou, HY; Crookes, D Papiez, BW; Namburete, AIL; Yaqub, M; Noble, JA Moyes, Andrew; Zhang, Kun; Ji, Ming; Zhou, Huiyu; Crookes, Danny Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images MEDICAL IMAGE UNDERSTANDING AND ANALYSIS Communications in Computer and Information Science English Proceedings Paper 24th Conference on Medical Image Understanding and Analysis (MIUA) JUL 15-17, 2020 St Annes Coll, ELECTR NETWORK Univ Oxford,MathWorks,Brainomix,Natl Consortium Intelligent Med Imaging St Annes Coll Stain separation; Artifact detection; Unsupervised Stain separation is an important pre-processing technique used to aid automated analysis of histopathology images. In this paper, we propose a novel, unsupervised deep learning method for stain separation (Hematoxylin and Eosin). This approach is inspired by Non-Negative Matrix Factorisation (NMF) and decomposes an input image into a stain colour matrix and a stain concentration matrix. In contrast to existing approaches, our method predicts stain colour matrices at the pixel level rather than the image level, thus enabling implicit modelling of tissue-dependant interactions between stains. We demonstrate an 8.81% reduction in mean-squared error on a stain separation task measuring the similarity between predicted and actual hematoxylin images from a publicly available dataset of digitised tissue images. We also present a novel approach to artifact detection in histological images based on a constrained generative adversarial network which we demonstrate is able to detect a variety of artifact types without the use of labels. [Moyes, Andrew; Ji, Ming; Crookes, Danny] Queens Univ Belfast, Belfast, Antrim, North Ireland; [Zhang, Kun] Nantong Univ, Sch Elect Engn, Nantong, Peoples R China; [Zhou, Huiyu] Univ Leicester, Dept Informat, Leicester, Leics, England Queens University Belfast; Nantong University; University of Leicester Zhang, K (corresponding author), Nantong Univ, Sch Elect Engn, Nantong, Peoples R China. amoyes01@qub.ac.uk; zhangkun_nt@163.com; j.ming@qub.ac.uk; hz143@leicester.ac.uk; d.crookes@qub.ac.uk Invest NI; Natural Science Foundation of Jiangsu Province, China [BK20170443]; Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China [17KJB520030] Invest NI; Natural Science Foundation of Jiangsu Province, China(Natural Science Foundation of Jiangsu Province); Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China This work was financially supported by Invest NI, the Natural Science Foundation of Jiangsu Province, China under Grant No. BK20170443 and the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 17KJB520030. 14 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1865-0929 1865-0937 978-3-030-52791-4; 978-3-030-52790-7 COMM COM INF SC 2020.0 1248 221 234 10.1007/978-3-030-52791-4_18 0.0 14 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging BS8BQ 2023-03-23 WOS:000770412700018 0
J Zhan, JM; Alcantud, JCR Zhan, Jianming; Alcantud, Jose Carlos R. A survey of parameter reduction of soft sets and corresponding algorithms ARTIFICIAL INTELLIGENCE REVIEW English Article Parameter reduction; Normal parameter reduction; Soft set; Fuzzy soft set; Decision making GREY RELATIONAL ANALYSIS; DEMPSTER-SHAFER THEORY; ROUGH FUZZY-SETS; DECISION-MAKING; ATTRIBUTE REDUCTION; SIMILARITY MEASURE; OPTIMIZATION; CRITERIA; MODEL; MABAC As is well known, soft set theory can have a bearing on making decisions in many fields. Particularly important is parameter reduction of soft sets, an essential topic both for information sciences and artificial intelligence. Parameter reduction studies the largest pruning of the amount of parameters that define a given soft set without changing its original choice objects. Therefore it can spare computationally costly tests in the decision making process. In the present article, we review some different algorithms of parameter reduction based on some types of (fuzzy) soft sets. Finally, we compare these algorithms to emphasize their respective advantages and disadvantages, and give some examples to illustrate their differences. [Zhan, Jianming] Hubei Univ Nationalities, Dept Math, Enshi 445000, Peoples R China; [Alcantud, Jose Carlos R.] Univ Salamanca, BORDA Res Unit, Salamanca 37007, Spain; [Alcantud, Jose Carlos R.] Univ Salamanca, Multidisciplinary Inst Enterprise IME, Salamanca 37007, Spain Hubei Minzu University; University of Salamanca; University of Salamanca Zhan, JM (corresponding author), Hubei Univ Nationalities, Dept Math, Enshi 445000, Peoples R China. zhanjianming@hotmail.com; jcr@usal.es Alcantud, José Carlos/E-3258-2010 Alcantud, José Carlos/0000-0002-4533-9281; Zhan, Jianming/0000-0003-2510-9515 NNSFC [11461025, 11561023] NNSFC(National Natural Science Foundation of China (NSFC)) The authors are extremely grateful to the editor and the referees for their valuable comments and helpful suggestions which help to improve the presentation of this paper. This research was supported by NNSFC (11461025 and 11561023). 108 89 92 1 37 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. OCT 2019.0 52 3 1839 1872 10.1007/s10462-017-9592-0 0.0 34 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science IY2XT 2023-03-23 WOS:000486256400010 0
J Zeng, B; Sanz-Prieto, I; Luhach, AK Zeng, Bin; Sanz-Prieto, Ivan; Luhach, Ashish Kr Deep learning approach to Automated data collection and processing of video surveillance in sports activity prediction ANNALS OF OPERATIONS RESEARCH English Article; Early Access Video surveillance; Sports activity; Deep learning; Data collection ACTIVITY RECOGNITION; ALGORITHMS; NETWORKS; SENSORS; DESIGN; SYSTEM Human activity recognition is one of today's key fields of automated video surveillance. The technology of smart surveillance technology plays a crucial role. Despite efforts in recent years, it is still difficult to recognize human behaviors from live video. Human activity can vary from basic behaviors to complicated behaviors. Depth cameras currently released have an efficient 3D estimate of body connecting locations in the temporal depth map collection. This article proposed a method for recognizing human behavior and considered the challenge of achieving a descriptive marking of activities by labeling individual sub-activities. The behaviors take place over a long period and have many sequential sub-activities. A sports activity prediction of video surveillance framework is proposed in this article. The suggested operation descriptor considers the sequence classification challenge to be the behavior recognition problem. Deep Learning is used to detect human behaviors in the proposed method. The method is tested on two regular identification benchmark functions. Effects of the research revealed that the solution developed exceeds cutting-edge methodologies. [Zeng, Bin] China West Normal Univ, Presch & Primary Educ Sch, Nanchong 637002, Sichuan, Peoples R China; [Sanz-Prieto, Ivan] Univ Int La Rioja UNIR, Engn & Technol Sch, Logrono, Spain; [Luhach, Ashish Kr] PNG Univ Technol, Dept Elect & Commun Engn, Lae, Papua N Guinea China West Normal University; Universidad Internacional de La Rioja (UNIR) Zeng, B (corresponding author), China West Normal Univ, Presch & Primary Educ Sch, Nanchong 637002, Sichuan, Peoples R China. zengbin0224@sina.com; ivan.sanz@unir.net; ashishluhach@ieee.org Sanz-Prieto, Ivan/0000-0002-5643-4946 29 1 1 3 14 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0254-5330 1572-9338 ANN OPER RES Ann. Oper. Res. 10.1007/s10479-021-04348-x 0.0 OCT 2021 20 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science WM2FL 2023-03-23 WOS:000710906600004 0
J Tang, R; Fong, S; Deb, S; Vasilakos, AV; Millham, RC Tang, Rui; Fong, Simon; Deb, Suash; Vasilakos, Athanasios V.; Millham, Richard C. Dynamic group optimisation algorithm for training feed-forward neural networks NEUROCOMPUTING English Article Dynamic group optimisation algorithm; Metaheuristic algorithm; Neural network EVOLUTION Feed-forward neural networks are efficient at solving various types of problems. However, finding efficient training algorithms for feed-forward neural networks is challenging. The dynamic group optimisation (DGO) algorithm is a recently proposed half-swarm half-evolutionary algorithm, which exhibits a rapid convergence rate and good performance in searching and avoiding local optima. In this paper, we propose a new hybrid algorithm, FNNDGO that integrates the DGO algorithm into a feed-forward neural network. DGO plays an optimisation role in training the neural network, by tuning parameters to their optimal values and configuring the structure of feed-forward neural networks. The performance of the proposed algorithm was determined by comparing its performance with those of other training methods in solving two types of problems. The experimental results show that our proposed algorithm exhibits promising performance for solving real-world problems. (C) 2018 Elsevier B.V. All rights reserved. [Tang, Rui; Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa, Macau, Peoples R China; [Deb, Suash] IT & Educ Consultant, Ranchi, Jharkhand, India; [Deb, Suash] Victoria Univ, Decis Sci & Modelling Program, Melbourne, Vic, Australia; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Lulea, Sweden; [Millham, Richard C.] Durban Univ Technol, Dept Informat Technol, Ritson Campus, Durban, South Africa University of Macau; Victoria University; Lulea University of Technology; Durban University of Technology Fong, S (corresponding author), Univ Macau, Dept Comp & Informat Sci, Taipa, Macau, Peoples R China. yb57463@umac.mo; ccfong@umac.mo; suashdeb@gmail.com; athanasios.vasilakos@ltu.se; Richardm1@dut.ac.za Fong, Simon/C-9388-2009 Fong, Simon/0000-0002-1848-7246; Vasilakos, Athanasios/0000-0003-1902-9877 University of Macau [MYRG2016-00069-FST] University of Macau The authors are grateful for financial support from the research grant for 'Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance' from the University of Macau (grant no. MYRG2016-00069-FST). 35 16 17 0 18 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 7 2018.0 314 1 19 10.1016/j.neucom.2018.03.043 0.0 19 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GS5PJ 2023-03-23 WOS:000443718400001 0
C Cui, K; Jin, Z; Steinbach, E IEEE Cui, Kai; Jin, Zhi; Steinbach, Eckehard COLOR IMAGE DEMOSAICKING USING A 3-STAGE CONVOLUTIONAL NEURAL NETWORK STRUCTURE 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) IEEE International Conference on Image Processing ICIP English Proceedings Paper 25th IEEE International Conference on Image Processing (ICIP) OCT 07-10, 2018 Athens, GREECE Inst Elect & Electron Engneers,IEEE Signal Processing Soc Bayer color filter array (CFA); demosaicking; convolutional neural networks; residual learning Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras. Conventional CDM approaches are mostly based on interpolation schemes and hand-crafted image priors, which result in unpleasant visual artifacts in some cases. Motivated by the special characteristics of inter-channel correlations (higher correlations for R/G and G/B channels than that for R/B), in this paper, a 3-stage convolutional neural network (CNN) structure for CDM is proposed. In the first stage, the G channel is reconstructed independently. Then, by using the reconstructed G channel as guidance, the R and B channels are recovered in the second stage. Finally, high-quality RGB color images are reconstructed in the third stage. The objective and visual quality evaluation results show that the proposed structure achieves noticeable quality improvements in comparison to the state-of-the-art approaches. [Cui, Kai; Jin, Zhi; Steinbach, Eckehard] Tech Univ Munich, Chair Media Technol, Munich, Germany; [Jin, Zhi] Shenzhen Univ, Coll Informat Engn, Shenzhen, Peoples R China Technical University of Munich; Shenzhen University Cui, K (corresponding author), Tech Univ Munich, Chair Media Technol, Munich, Germany. Cui, Kai/Y-5911-2019 Cui, Kai/0000-0003-1476-6309 China Scholarship Council; TUM University Foundation Fellowship; National Natural Science Foundation of China [61701313] China Scholarship Council(China Scholarship Council); TUM University Foundation Fellowship; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work has been supported by a PhD grant from the China Scholarship Council for Kai Cui, a TUM University Foundation Fellowship and the National Natural Science Foundation of China (61701313) for Zhi Jin. 21 15 17 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1522-4880 978-1-4799-7061-2 IEEE IMAGE PROC 2018.0 2177 2181 5 Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BL7MB 2023-03-23 WOS:000455181502060 0
C Fang, YM; Lin, WS; Fang, ZJ; Lei, JJ; Le Callet, P; Yuan, FN IEEE Fang, Yuming; Lin, Weisi; Fang, Zhijun; Lei, Jianjun; Le Callet, Patrick; Yuan, Feiniu LEARNING VISUAL SALIENCY FOR STEREOSCOPIC IMAGES 2014 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (ICMEW) IEEE International Conference on Multimedia and Expo English Proceedings Paper IEEE International Conference on Multimedia and Expo Workshops (ICMEW) JUL 14-18, 2014 Chengdu, PEOPLES R CHINA IEEE,Baidu,NSFC,NSF,QIY,BOCOM,Yahoo,IBM Res,RICOH,Microsoft Res stereoscopic image; 3D image; stereoscopic saliency detection; visual attention MODEL; ATTENTION; OBSERVERS; VIDEO; BIAS Currently, there are various saliency detection models proposed for saliency prediction in 2D images/video in the previous decades. With the rapid development of stereoscopic display techniques, stereoscopic saliency detection is much desired for the emerging stereoscopic applications. Compared with 2D saliency detection, the depth factor has to be considered in stereoscopic saliency detection. Inspired by the wide applications of machine learning techniques in 2D saliency detection, we propose to use the machine learning technique for stereoscopic saliency detection in this paper. The contrast features from color, luminance and texture in 2D images are adopted in the proposed framework. For the depth factor, we consider both the depth contrast and depth degree in the proposed learned model. Additionally, the center-bias factor is also used as an input feature for learning the model. Experimental results on a recent large-scale eye tracking database show the better performance of the proposed model over other existing ones. [Fang, Yuming; Fang, Zhijun; Yuan, Feiniu] Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang, Peoples R China; [Lin, Weisi] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore; [Lei, Jianjun] Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China; [Le Callet, Patrick] Univ Nantes, LUNAM Univ, Polytech Nantes, IRCCyN UMR CNRS 6597, Nantes, France Jiangxi University of Finance & Economics; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Tianjin University; Centre National de la Recherche Scientifique (CNRS); Nantes Universite Fang, YM (corresponding author), Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang, Peoples R China. Le Callet, Patrick/F-5772-2010; Lin, Weisi/A-3696-2011 Lin, Weisi/0000-0001-9866-1947 35 0 0 0 0 IEEE, ELECTRON DEVICES SOC & RELIABILITY GROUP NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1945-7871 978-1-4799-4717-1 IEEE INT CON MULTI 2014.0 6 Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BC8RU 2023-03-23 WOS:000356046900175 0
C Chen, PR; Calis, M; Wijkstra, H; Huang, PT; Hunyadi, B; Mischi, M IEEE Chen, Peiran; Calis, Metin; Wijkstra, Hessel; Huang, Pintong; Hunyadi, Borbala; Mischi, Massimo Multiparametric ultrasound and machine learning for prostate cancer localization 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022) European Signal Processing Conference English Proceedings Paper 30th European Signal Processing Conference (EUSIPCO) AUG 29-SEP 02, 2022 Belgrade, SERBIA European Assoc Signa Proc ultrasound; prostate cancer; machine learning; multilinear singular value decomposition CONTRAST-ENHANCED ULTRASOUND; AGENT DISPERSION; DCE-US; ANGIOGENESIS A cost-effective, widely available, and practical diagnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an additional PCa biomarker that can be quantified by ultrasound shear-wave elastography (SWE). In this work, a dedicated preprocessing of 3D DCE-US acquisitions was investigated by using multilinear singular value decomposition (MLSVD), aiming at improving the CUDI performance. Moreover, the diagnostic potential of a multiparametric ultrasound imaging approach combining 3D CUDI features with SWE tissue elasticity for clinically significant (cs)PCa localization was evaluated by comparison with the histopathological outcome of systematic biopsies. In this multiparametric approach, the performance of five classifiers was evaluated and compared for biopsy-region csPCa classification. The classification performance was assessed by the area under the Receiver Operating Characteristics curve (AUC) in a k-fold cross validation fashion comprising sequential floating forward selection of the features. The combination of CUDI features with MLSVD preprocessing and SWE elasticity yielded the best AUC=0.87 for csPCa localization. Our results suggest 3D multiparametric ultrasound imaging approach combing a dedicated preprocessing step to be a useful tool for PCa diagnostics. [Chen, Peiran; Wijkstra, Hessel; Mischi, Massimo] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands; [Calis, Metin; Hunyadi, Borbala] Delft Univ Technol, Dept Microelect, Delft, Netherlands; [Wijkstra, Hessel] Univ Amsterdam, Med Ctr, Dept Urol, Amsterdam, Netherlands; [Huang, Pintong] Zhejiang Univ, Affiliated Hosp 2, Dept Ultrasound Med, Hangzhou, Peoples R China Eindhoven University of Technology; Delft University of Technology; University of Amsterdam; Zhejiang University Chen, PR (corresponding author), Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands. Hunyadi, Borbala/HNT-0507-2023 Hunyadi, Borbala/0000-0002-9333-9024 Dutch Research Council (NWO-TTW) [15282]; Holland High Tech Dutch Research Council (NWO-TTW); Holland High Tech This work is supported in part by the research programme LOCATE with project number 15282, which is financed by the Dutch Research Council (NWO-TTW) and Holland High Tech with a PPS supplement for research and development in the Topsector HTSM. We would like to thank Angiogenesis Analytics for providing the infrastructure and the tools for running the experiments. 32 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2076-1465 978-90-827970-9-1 EUR SIGNAL PR CONF 2022.0 907 911 5 Acoustics; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU5UP 2023-03-23 WOS:000918827600179 0
J Hopman, H; Chan, S; Chu, WN; Lu, HN; Tse, CY; Chau, S; Lam, L; Mak, A; Neggers, S Hopman, Helene; Chan, Sandra; Chu, Winnie; Lu, Hanna; Tse, Chun-Yu; Chau, Steven; Lam, Linda; Mak, Arthur; Neggers, Sebastiaan Personalized prediction of repetitive transcranial magnetic stimulation clinical response in medication-refractory depression data DATA IN BRIEF English Article Depression; Resting-state functional magnetic resonance imaging; Functional connectivity; Neuroimaging; Biomarkers; Transcranial magnetic stimulation; Machine learning; Support vector machine FUNCTIONAL CONNECTIVITY; SCALE This article describes a dataset that was generated as part of the article: Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning (DOI: 10.1016/j.jad.2021.04.081). We collected resting-state functional Magnetic Resonance Imaging data from 70 medication-refractory depressed subjects before undergoing four weeks of repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex. The data presented here include information about the seed-based analyses such as regions of interest, individual/group functional connectivity maps and contrast maps. The contrast maps are controlled for age, gender, duration of the current depressive episode, duration since the first depressive episode, and symptom scores. Demographics, clinical characteristics, and categorical treatment response variables are reported as well. Further, the individual connectivity values of the identified neuroimaging biomarkers of long-term clinical response were used as features in the support vector machine models are presented in combination with the trained classifiers of the support vector machine models. Post hoc analyses that were not published in the original analyses are presented as well. Finally, the R or MATLAB code scripts for all figures published in the co-submitted paper are included. (C) 2021 The Author(s). Published by Elsevier Inc. [Hopman, Helene; Chan, Sandra; Lu, Hanna; Chau, Steven; Lam, Linda; Mak, Arthur] Chinese Univ Hong Kong, Dept Psychiat, Multictr, Tai Po Hosp, G30,G-F,9 Chuen Rd, Hong Kong, Peoples R China; [Chu, Winnie] Chinese Univ Hong Kong, Dept Radiol, Hong Kong, Peoples R China; [Tse, Chun-Yu] Chinese Univ Hong Kong, Dept Psychol, Hong Kong, Peoples R China; [Neggers, Sebastiaan] Univ Med Ctr Utrecht, Brain Ctr Rudolf Magnus, Utrecht, Netherlands; [Tse, Chun-Yu] City Univ Hong Kong, Dept Social & Behav Sci, Hong Kong, Peoples R China Chinese University of Hong Kong; Chinese University of Hong Kong; Chinese University of Hong Kong; Utrecht University; Utrecht University Medical Center; City University of Hong Kong Hopman, H (corresponding author), Chinese Univ Hong Kong, Dept Psychiat, Multictr, Tai Po Hosp, G30,G-F,9 Chuen Rd, Hong Kong, Peoples R China. hjhopman@cuhk.edu.hk Mak, Arthur/V-6345-2017; Chu, Winnie CW/F-8449-2014 Mak, Arthur/0000-0003-1627-6434; Chu, Winnie CW/0000-0003-4962-4132; Chan, Sau Man Sandra/0000-0002-5916-139X; /0000-0003-4537-296X; Chau, Steven Wai Ho/0000-0002-2986-8677; Tse, Chun-Yu/0000-0002-6120-2971 Research Grants Council, Hong Kong [GRF 14101714] Research Grants Council, Hong Kong(Hong Kong Research Grants Council) This work was supported by the Research Grants Council, Hong Kong [GRF 14101714] . The funding source was not involved in the study design, data collection, data analysis, data inter-pretation, writing of the report, and in the decision to submit the article for publication. We thank Stephy Ho for assistance with data collection and rTMS sessions and the staff from the Chen Wai Wai Vivien Foundation Therapeutic Physical Mental Exercise Centre for providing TMS workspace. Finally, we would like to thank all the participants and their families. 27 0 0 1 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-3409 DATA BRIEF Data Brief AUG 2021.0 37 107264 10.1016/j.dib.2021.107264 0.0 JUL 2021 18 Multidisciplinary Sciences Emerging Sources Citation Index (ESCI) Science & Technology - Other Topics UG6KZ 34345639.0 gold 2023-03-23 WOS:000689360000007 0
J Zhang, YK; Hu, DL; Zhao, QL; Quan, GT; Liu, J; Liu, QG; Zhang, Y; Coatrieux, G; Chen, Y; Yu, HY Zhang, Yikun; Hu, Dianlin; Zhao, Qianlong; Quan, Guotao; Liu, Jin; Liu, Qiegeng; Zhang, Yi; Coatrieux, Gouenou; Chen, Yang; Yu, Hengyong CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING English Article Image reconstruction; Computed tomography; Generative adversarial networks; Generators; X-ray imaging; Noise reduction; Biomedical imaging; Low-dose CT; the comprehensive domain GAN; multi-level consistency loss; one-step reconstruction CONVOLUTIONAL NEURAL-NETWORK; COMPUTED-TOMOGRAPHY; NOISE-REDUCTION; PERFORMANCE; ALGORITHM; SCANS X-ray computed tomography (CT) is of great clinical significance in medical practice because it can provide anatomical information about the human body without invasion, while its radiation risk has continued to attract public concerns. Reducing the radiation dose may induce noise and artifacts to the reconstructed images, which will interfere with the judgments of radiologists. Previous studies have confirmed that deep learning (DL) is promising for improving low-dose CT imaging. However, almost all the DL-based methods suffer from subtle structure degeneration and blurring effect after aggressive denoising, which has become the general challenging issue. This paper develops the Comprehensive Learning Enabled Adversarial Reconstruction (CLEAR) method to tackle the above problems. CLEAR achieves subtle structure enhanced low-dose CT imaging through a progressive improvement strategy. First, the generator established on the comprehensive domain can extract more features than the one built on degraded CT images and directly map raw projections to high-quality CT images, which is significantly different from the routine GAN practice. Second, a multi-level loss is assigned to the generator to push all the network components to be updated towards high-quality reconstruction, preserving the consistency between generated images and gold-standard images. Finally, following the WGAN-GP modality, CLEAR can migrate the real statistical properties to the generated images to alleviate over-smoothing. Qualitative and quantitative analyses have demonstrated the competitive performance of CLEAR in terms of noise suppression, structural fidelity and visual perception improvement. [Zhang, Yikun; Hu, Dianlin; Zhao, Qianlong] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Zhang, Yikun; Hu, Dianlin; Zhao, Qianlong; Chen, Yang] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China; [Quan, Guotao] United Imaging Healthcare Co Ltd, CT RPA Dept, Shanghai 201807, Peoples R China; [Liu, Jin] Anhui Polytech Univ, Coll Comp & Informat, Wuhu 241000, Peoples R China; [Liu, Qiegeng] Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Zhang, Yi] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China; [Coatrieux, Gouenou] IMT Atlantique, Technopole Brest Iroise, F-29238 Brest, France; [Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China; [Yu, Hengyong] Univ Massachusetts Lowell, Dept Elect & Comp Engn, Lowell, MA 01854 USA Southeast University - China; Southeast University - China; Anhui Polytechnic University; Nanchang University; Sichuan University; IMT - Institut Mines-Telecom; IMT Atlantique; Southeast University - China; Southeast University - China; University of Massachusetts System; University of Massachusetts Lowell Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China. yikun@seu.edu.cn; 220171616@seu.edu.cn; 634804624@qq.com; guotao.quan@unitedimaging.com; liujin@ahpu.edu.cn; liuqiegen@ncu.edu.cn; yzhang@scu.edu.cn; gouenou.coatrieux@telecombretagne.eu; chenyang.list@seu.edu.cn; hengyong-yu@ieee.org Yikun, Zhang/HNI-6209-2023 Liu, Jin/0000-0003-1884-671X; Zhang, Yi/0000-0001-7201-2092; Yu, Hengyong/0000-0002-5852-0813; quan, guotao/0000-0002-1079-0718; Zhang, Yikun/0000-0002-4048-4869 State's Key Project of Research and Development Plan [2017YFC0109202, 2017YFA0104302, 2017YFC0107900]; National Natural Science Foundation [81530060, 61871117]; Science and Technology Program of Guangdong [2018B030333001] State's Key Project of Research and Development Plan; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Guangdong This work was supported in part by the State's Key Project of Research and Development Plan under Grant 2017YFC0109202, Grant 2017YFA0104302, and Grant 2017YFC0107900; in part by the National Natural Science Foundation under Grant 81530060 and Grant 61871117; and in part by the Science and Technology Program of Guangdong under Grant 2018B030333001. 72 20 19 16 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging NOV 2021.0 40 11 3089 3101 10.1109/TMI.2021.3097808 0.0 13 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging WN6AL 2023-03-23 WOS:000711848900013 0
J Lyu, Y; Vosselman, G; Xia, GS; Yilmaz, A; Yang, MY Lyu, Ye; Vosselman, George; Xia, Gui-Song; Yilmaz, Alper; Yang, Michael Ying UAVid: A semantic segmentation dataset for UAV imagery ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article UAV; Semantic segmentation; Deep learning; Dataset LIDAR Semantic segmentation has been one of the leading research interests in computer vision recently. It serves as a perception foundation for many fields, such as robotics and autonomous driving. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the driving car. There also exist semantic labeling datasets for the airborne images and the satellite images, where the nadir views of the objects are captured. However, only a few datasets capture urban scenes from an oblique Unmanned Aerial Vehicle (UAV) perspective, where both of the top view and the side view of the objects can be observed, providing more information for object recognition. In this paper, we introduce our UAVid dataset, a new high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. Our UAV dataset consists of 30 video sequences capturing high-resolution images in oblique views. In total, 300 images have been densely labeled with 8 classes for the semantic labeling task. We have provided several deep learning baseline methods with pre-training, among which the proposed Multi-Scale-Dilation net performs the best via multi-scale feature extraction, reaching a mean intersection-over-union (IoU) score around 50%. We have also explored the influence of spatial-temporal regularization for sequence data by leveraging on feature space optimization (FSO) and 3D conditional random field (CRF). Our UAVid website and the labeling tool have been published online (https://uavid.nl/). [Lyu, Ye; Vosselman, George; Yang, Michael Ying] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands; [Xia, Gui-Song] Wuhan Univ, Sch Comp Sci, State Key Lab LIESMARS, Wuhan, Peoples R China; [Yilmaz, Alper] Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43210 USA University of Twente; Wuhan University; University System of Ohio; Ohio State University Yang, MY (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands. michael.yang@utwente.nl Yang, Michael/AAC-6698-2019; Vosselman, George/D-3985-2009; Xia, Gui-Song/HII-9232-2022 Yang, Michael/0000-0002-0649-9987; Vosselman, George/0000-0001-8813-8028; Xia, Gui-Song/0000-0001-7660-6090; Lyu, Ye/0000-0002-6665-7748; Yilmaz, Alper/0000-0003-0755-2628 ISPRS Scientific Initiative project SVSB; National Natural Science Foundation of China [61922065, 61771350] ISPRS Scientific Initiative project SVSB; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work is partially funded by ISPRS Scientific Initiative project SVSB (PI: Michael Ying Yang, co-PI: Alper Yilmaz) and National Natural Science Foundation of China (No. 61922065 and No. 61771350). The authors gratefully acknowledge the support. We also thank several graduate students from University of Twente and Wuhan University for their annotation effort. 47 68 69 12 49 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. JUL 2020.0 165 108 119 10.1016/j.isprsjprs.2020.05.009 0.0 12 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology LY2SN Green Submitted 2023-03-23 WOS:000540373500009 0
J Zhang, JQ; Chen, H; Yao, XM; Fu, XM Zhang, Jiaquan; Chen, Hui; Yao, Xiaoming; Fu, Xiaoming CPFinder: Finding an unknown caller's profession from anonymized mobile phone data DIGITAL COMMUNICATIONS AND NETWORKS English Article Mobile big data; Profession prediction; Machine learning; Classification; Privacy protection Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder -- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively. [Zhang, Jiaquan; Fu, Xiaoming] Univ Gottingen, Inst Comp Sci, D-37077 Gottingen, Germany; [Chen, Hui] Beijing Foreign Studies Univ, Beijing 100089, Peoples R China; [Yao, Xiaoming] China Telecom, Cloud Branch, Beijing 100033, Peoples R China University of Gottingen; Beijing Foreign Studies University; China Telecom Corp. Ltd. Chen, H (corresponding author), Beijing Foreign Studies Univ, Beijing 100089, Peoples R China. jiaquan.zhang@cs.uni-goettingen.de; chenhui@bfsu.edu.cn; yaoxm@chinatelecom.cn; fu@cs.uni-goettingen.de Fu, Xiaoming/AAD-2828-2022 Fu, Xiaoming/0000-0002-8012-4753; Zhang, Jiaquan/0000-0001-9179-9425; Chen, Hui/0000-0003-3015-0248 European Union [824019]; China Scholarship Council (CSC); Fundamental Research Funds for Central Universities [2020JJ014, YY19SSK05] European Union(European Commission); China Scholarship Council (CSC)(China Scholarship Council); Fundamental Research Funds for Central Universities(Fundamental Research Funds for the Central Universities) This work has been partially by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 824019 and China Scholarship Council (CSC), as well as the Fundamental Research Funds for Central Universities (No. 2020JJ014, YY19SSK05). 30 0 0 2 4 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGCHENG DISTRICT 100717, PEOPLES R CHINA 2468-5925 2352-8648 DIGIT COMMUN NETW Digit. Commun. Netw. JUN 2022.0 8 3 324 332 10.1016/j.dcan.2021.08.003 0.0 9 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications 2M9YW gold 2023-03-23 WOS:000818048300010 0
J Wu, YR; Li, H; Feng, X; Casanova, A; Abate, AF; Wan, SH Wu, Yirui; Li, Hao; Feng, Xi; Casanova, Andrea; Abate, Andrea F.; Wan, Shaohua GDRL: An interpretable framework for thoracic pathologic prediction PATTERN RECOGNITION LETTERS English Article Disentangled representation learning; Group-disentangled feature representation; Thoracic pathologic prediction Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like black box without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose a novel Group-Disentangled Representation Learning framework (GDRL). The key contribution is that GDRL completely disentangles latent space into disease concepts with abundant and non-overlapping feature related explanations, thus enhancing interpretability in feature extraction and decision processes. Furthermore, we introduce an implicit group-swap structure by emphasizing the linking relationship between semantical concepts of disease and low-level visual features, other than explicit explanations on general objects and their attributes. We demonstrate our framework on predicting four categories of diseases from chest X-ray images. The AUROC of GDRL on ChestX-ray14 for thoracic pathologic prediction are 0.8630, 0.8980, 0.9269 and 0.8653 respectively, and we showcase the potential of our framework in enhancing interpretability of the factors contributing to different diseases.(c) 2022 Elsevier B.V. All rights reserved. [Wu, Yirui; Li, Hao] Hohai Univ, Key Lab Water Big Data Technol, Minist Water Resources, Nanjing 210093, Peoples R China; [Wu, Yirui; Li, Hao] Hohai Univ, Coll Comp & Informat, Nanjing 210093, Peoples R China; [Feng, Xi] Hohai Univ, Coll Harbor Coastal & Offshore Engn, Nanjing 210093, Peoples R China; [Wu, Yirui] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130015, Peoples R China; [Casanova, Andrea] Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy; [Abate, Andrea F.] Univ Salerno, Via Giovanni Paolo II,132, I-84084 Fisciano, Salerno, Italy; [Wan, Shaohua] Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China Hohai University; Hohai University; Hohai University; Jilin University; University of Cagliari; University of Salerno; University of Electronic Science & Technology of China Wan, SH (corresponding author), Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China. wuyirui@hhu.edu.cn; 211307030003@hhu.edu.cn; xifeng@hhu.edu.cn; casanova@unica.it; abate@unisa.it; shaohua.wan@uestc.edu.cn Wan, Shaohua/B-9243-2014 Wan, Shaohua/0000-0001-7013-9081 National Key R&D Program of China [2021YFB3900601]; Fundamental Research Funds for the Central Universities [B220202074]; Joint Foundation of the Ministry of Edu-cation [8091B022123] National Key R&D Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Joint Foundation of the Ministry of Edu-cation This work was supported in part by a grant from National Key R&D Program of China under Grant No. 2021YFB3900601, the Fundamental Research Funds for the Central Universities under Grant B220202074 , and Joint Foundation of the Ministry of Edu-cation (No. 8091B022123) . 21 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. JAN 2023.0 165 154 160 10.1016/j.patrec.2022.12.020 0.0 DEC 2022 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7W9ZB 2023-03-23 WOS:000913863800001 0
J Liu, MY; Senin, N; Su, R; Leach, R Liu, Mingyu; Senin, Nicola; Su, Rong; Leach, Richard Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning MEASUREMENT SCIENCE AND TECHNOLOGY English Article laser powder bed fusion; surface measurement; light scattering; unsupervised machine learning DEFECT DETECTION; METAL-POWDER; HIGH-SPEED Quality monitoring for laser powder bed fusion (L-PBF), particularly in-process and real-time monitoring, is of importance for part quality assurance and manufacturing cost reduction. Measurement of layer surface topography is critical for quality monitoring, as any anomaly on layer surfaces can result in defects in the final part. In this paper, we propose a surface measurement method, based on the use of scattered light patterns and a convolutional autoencoder-based unsupervised machine learning method, designed and trained using a large set of scattering patterns simulated from reference surfaces using a scattering model. The advantage of using an autoencoder is that the monitoring model can be trained using solely data from acceptable surfaces, without the need to ensure the presence of representative observations for all the types of possible surface defects. The advantage of using simulated data for training is that we can obtain an effective monitoring solution without the need for a large collection of experimental observations. Here we report the results of a preliminary investigation on the performance of the proposed solution, where the trained autoencoder is tested on experimental data obtained off-process, using a dedicated experimental apparatus for generating and collecting light scattering patterns from manufactured L-PBF surfaces. Our results indicate that the proposed monitoring solution is capable of detecting both acceptable and anomalous surfaces. Although further validation is required to fully assess performance within an on-machine and in-process setup, our preliminary results are encouraging and provide a glimpse of the potential benefits of using our surface measurement solution for L-PBF in-process monitoring. [Liu, Mingyu; Senin, Nicola; Leach, Richard] Univ Nottingham, Fac Engn, Mfg Metrol Team, Nottingham, England; [Senin, Nicola] Univ Perugia, Dept Engn, Perugia, Italy; [Su, Rong] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China University of Nottingham; University of Perugia; Chinese Academy of Sciences; Shanghai Institute of Optics & Fine Mechanics Liu, MY (corresponding author), Univ Nottingham, Fac Engn, Mfg Metrol Team, Nottingham, England. mingyu.liu1@nottingham.ac.uk Senin, Nicola/B-5292-2016 Senin, Nicola/0000-0002-9556-0363 Engineering and Physical Sciences Research Council [SPARKLE) [EP/R028826/1]; European Commission [734174]; University of Nottingham Engineering and Physical Sciences Research Council [SPARKLE)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); European Commission(European CommissionEuropean Commission Joint Research Centre); University of Nottingham We acknowledge the support from the Engineering and Physical Sciences Research Council [SPARKLE, EP/R028826/1], European Commission's Horizon 2020 Research and Innovation Staff Exchange Programme [MNR4SCell, 734174], and the University of Nottingham for granting access to the Augusta HPC service. 39 3 3 6 15 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0957-0233 1361-6501 MEAS SCI TECHNOL Meas. Sci. Technol. JUL 1 2022.0 33 7 74006 10.1088/1361-6501/ac6569 0.0 11 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 0T1PJ hybrid, Green Published 2023-03-23 WOS:000786745600001 0
C Chan, NS; Chan, KI; Tse, R; Tang, SK; Pau, G Jiang, X; Fujita, H Chan, Ngai Seng; Chan, Ka Ian; Tse, Rita; Tang, Su-Kit; Pau, Giovanni ReSPEcT: Privacy Respecting Thermal Based Specific Person Recognition THIRTEENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2021) Proceedings of SPIE English Proceedings Paper 13th International Conference on Digital Image Processing (ICDIP) MAY 20-23, 2021 ELECTR NETWORK Int Asoc Comp Sci & Informat Technol Thermal Video Analytic; Machine Learning; Privacy Preserving Computing; Ethical AI SIGNATURE Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT's automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the real-identity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacy-protected scenarios, such as confidential meetings, sensitive spaces, or even public toilets. [Chan, Ngai Seng; Chan, Ka Ian; Tse, Rita; Tang, Su-Kit; Pau, Giovanni] Macao Polytech Inst, Sch Appl Sci, Taipa, Macao, Peoples R China; [Pau, Giovanni] Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy; [Pau, Giovanni] UCLA Comp Sci Dept, Los Angeles, CA USA Macao Polytechnic University; University of Bologna Pau, G (corresponding author), Macao Polytech Inst, Sch Appl Sci, Taipa, Macao, Peoples R China.;Pau, G (corresponding author), Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy. giovanni.pau@unibo.it Chan, Ka Ian/0000-0002-6802-5373; Pau, Giovanni/0000-0003-2216-7170; Tang, Su-Kit/0000-0001-8104-7887 Macao Polytechnic Institute -Edge Sensing and Computing: Enabling Humancentric (Sustainable) Smart Cities [RP/ESCA-01/2020] Macao Polytechnic Institute -Edge Sensing and Computing: Enabling Humancentric (Sustainable) Smart Cities This work was supported in part by the Macao Polytechnic Institute -Edge Sensing and Computing: Enabling Humancentric (Sustainable) Smart Cities (RP/ESCA-01/2020). 12 1 1 0 1 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-4601-8 PROC SPIE 2021.0 11878 1187802 10.1117/12.2599271 0.0 10 Optics; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Optics; Imaging Science & Photographic Technology BS1RI 2023-03-23 WOS:000694937300001 0
J Baronchelli, I; Scarlata, CM; Rodriguez-Munoz, L; Bonato, M; Morselli, L; Vaccari, M; Carraro, R; Barrufet, L; Henry, A; Mehta, V; Rodighiero, G; Baruffolo, A; Bagley, M; Battisti, A; Colbert, J; Dai, YS; De Pascale, M; Dickinson, H; Malkan, M; Mancini, C; Rafelski, M; Teplitz, HI Baronchelli, I.; Scarlata, C. M.; Rodriguez-Munoz, L.; Bonato, M.; Morselli, L.; Vaccari, M.; Carraro, R.; Barrufet, L.; Henry, A.; Mehta, V.; Rodighiero, G.; Baruffolo, A.; Bagley, M.; Battisti, A.; Colbert, J.; Dai, Y. S.; De Pascale, M.; Dickinson, H.; Malkan, M.; Mancini, C.; Rafelski, M.; Teplitz, H. I. Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology (vol 257, 67, 2021) ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES English Correction [Baronchelli, I.; Bonato, M.; Morselli, L.; Baruffolo, A.; De Pascale, M.] INAF Osservatorio Astron Padova, Vicolo Osservatorio 5, I-35122 Padua, Italy; [Baronchelli, I.; Rodriguez-Munoz, L.; Morselli, L.; Rodighiero, G.; Mancini, C.] Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy; [Scarlata, C. M.; Mehta, V.] Univ Minnesota, MN Inst Astrophys, 116 Church St SE, Minneapolis, MN 55455 USA; [Bonato, M.] INAF Ist Radioastron, Via Gobetti 101, I-40129 Bologna, Italy; [Bonato, M.] Italian ALMA Reg Ctr, Via Gobetti 101, I-40129 Bologna, Italy; [Vaccari, M.] Interuniv Inst Data Intens Astron IDIA, Dept Phys & Astron, Cape Town, South Africa; [Vaccari, M.] Univ Western Cape, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa; [Carraro, R.] Univ Valparaiso, Inst Fis & Astron, Gran Bretana 1111, Valparaiso, Chile; [Barrufet, L.] Univ Geneva, Dept Astron, Chemin Pegasi 51, CH-1290 Versoix, Switzerland; [Henry, A.; Rafelski, M.] Space Telescope Sci Inst, Dept Phys & Astron, 3700 San Martin Dr, Baltimore, MD 21218 USA; [Bagley, M.] Univ Texas Austin, Coll Nat Sci, 2515 Speedway, Austin, TX 78712 USA; [Battisti, A.] Australian Natl Univ, Res Sch Astron & Astrophys, Cotter Rd, Weston, ACT 2611, Australia; [Colbert, J.; Teplitz, H. I.] Caltech, IPAC, Mail Code 314-6,1200 E Calif Blvd, Pasadena, CA 91125 USA; [Dai, Y. S.] Chinese Acad Sci South Amer Ctr Astron CASSACA, NAOC, 20A Datun Rd, Beijing 100101, Peoples R China; [Dickinson, H.] Open Univ, Sch Phys Sci, Walton Hall, Milton Keynes MK7 6AA, Bucks, England; [Malkan, M.] UCLA, Dept Phys & Astron, Phys & Astron Bldg 3-714, Los Angeles, CA 90095 USA; [Mancini, C.] INAF IASF, Via Alfonso Corti 12, I-20133 Milan, Italy; [Rafelski, M.] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA Istituto Nazionale Astrofisica (INAF); University of Padua; University of Minnesota System; University of Minnesota Twin Cities; Istituto Nazionale Astrofisica (INAF); University of the Western Cape; Universidad de Valparaiso; University of Geneva; Space Telescope Science Institute; University of Texas System; University of Texas Austin; Australian National University; California Institute of Technology; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Open University - UK; University of California System; University of California Los Angeles; Johns Hopkins University Baronchelli, I (corresponding author), INAF Osservatorio Astron Padova, Vicolo Osservatorio 5, I-35122 Padua, Italy.;Baronchelli, I (corresponding author), Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy. ivano.baronchelli@inaf.it Vaccari, Mattia/R-3431-2016 Vaccari, Mattia/0000-0002-6748-0577; Baronchelli, Ivano/0000-0003-0556-2929; Henry, Alaina/0000-0002-6586-4446; Baruffolo, Andrea/0000-0002-1114-4355; Scarlata, Claudia/0000-0002-9136-8876; Rodriguez Munoz, Lucia/0000-0002-0192-5131 1 0 0 0 0 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0067-0049 1538-4365 ASTROPHYS J SUPPL S Astrophys. J. Suppl. Ser. NOV 1 2022.0 263 1 19 10.3847/1538-4365/ac9963 0.0 1 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 6G3RM gold 2023-03-23 WOS:000884672900001 0
J Zhang, XK; Zhang, BN; Yu, WK; Kang, XD Zhang, Xiaokang; Zhang, Boning; Yu, Weikang; Kang, Xudong Federated Deep Learning With Prototype Matching for Object Extraction From Very-High-Resolution Remote Sensing Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep learning; federated learning; object extrac-tion; remote sensing images; semantic segmentation SEMANTIC SEGMENTATION; NETWORK Deep convolutional neural networks (DCNNs) have become the leading tools for object extraction from very-high-resolution (VHR) remote sensing images. However, the label scarcity problem of local datasets hinders the prediction performances of DCNNs, and privacy concerns regarding remote sensing data often arise in the traditional deep learning schemes. To cope with these problems, we propose a novel federated learning scheme with prototype matching (FedPM) to collaboratively learn a richer DCNN model by leveraging remote sensing data distributed among multiple clients. This scheme conducts the federated optimization of DCNNs by aggregating clients' knowledge in the gradient space without compromising data privacy. Specifically, the prototype matching method is developed to regularize the local training using prototypical representations while reducing the distribution divergence across heterogeneous image data. Furthermore, the derived deviations across local and global prototypes are applied to quantify the effects of local models on the decision boundary and optimize the global model updating via the attention-weighted aggregation scheme. Finally, the sparse ternary compression (STC) method is used to alleviate communication costs. Extensive experimental results derived from VHR aerial and satellite image datasets verify that the FedPM can dramatically improve the prediction performance of DCNNs on object extraction with lower communication costs. To the best of our knowledge, this is the first time that federated learning has been applied for remote sensing visual tasks. [Zhang, Xiaokang] Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Peoples R China; [Zhang, Boning] Univ Glasgow, Sch Comp Sci, Glasgow City G12 8RZ, Scotland; [Yu, Weikang] Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol, D-09599 Freiberg, Germany; [Kang, Xudong] Hunan Univ, Sch Robot, Changsha 410082, Peoples R China Wuhan University of Science & Technology; University of Glasgow; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Hunan University Zhang, BN (corresponding author), Univ Glasgow, Sch Comp Sci, Glasgow City G12 8RZ, Scotland.;Kang, XD (corresponding author), Hunan Univ, Sch Robot, Changsha 410082, Peoples R China. natezhangxk@gmail.com; b.zhang.6@research.gla.ac.uk; w.yu@hzdr.de; xudong_kang@163.com National Key Research and Development Program of China [2021YFA0715203]; Major Program of the National Natural Science Foundation of China [61890962]; National Natural Science Foundation of China [62221002, 62201207, 62101183] National Key Research and Development Program of China; Major Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFA0715203, in part by the Major Program of the National Natural Science Foundation of China under Grant 61890962, and in part by the National Natural Science Foundation of China under Grant 62221002, Grant 61890962, Grant 62201207, and Grant 62101183. 55 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2023.0 61 5603316 10.1109/TGRS.2023.3244136 0.0 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 9J5OV 2023-03-23 WOS:000940236600013 0
J Ke, MJ; Xu, NH; Zhang, ZY; Qiu, DY; Kang, J; Lu, T; Wang, TZ; Peijnenburg, WJGM; Sun, LW; Hu, BL; Qian, HF Ke, Mingjing; Xu, Nuohan; Zhang, Zhenyan; Qiu, Danyan; Kang, Jian; Lu, Tao; Wang, Tingzhang; Peijnenburg, W. J. G. M.; Sun, Liwei; Hu, Baolan; Qian, Haifeng Development of a machine-learning model to identify the impacts of pesticides characteristics on soil microbial communities from high-throughput sequencing data ENVIRONMENTAL MICROBIOLOGY English Article DIVERSITY; REVEALS; BACTERIAL High-throughput sequencing (HTS) of soil environmental DNA provides an advanced insight into the effects of pesticides on soil microbial systems. However, the association between the properties of the pesticide and its ecological impact remains methodically challenging. Risks associated with pesticide use can be minimized if pesticides with optimal structural traits were applied. For this purpose, we merged the 20 independent HTS studies, to reveal that pesticides significantly reduced beneficial bacteria associated with soil and plant immunity, enhanced the human pathogen and weaken the soil's ecological stability. Through the machine-learning approach, correlating these impacts with the physicochemical properties of the pesticides yielded a random forest model with good predictive capabilities. The models revealed that physical pesticide properties such as the dissociation constant (pKa), the molecular weight and water solubility, determined the ecological impact of pesticides to a large extent. Moreover, this study identified that eco-friendly pesticides should possess a value of pKa > 5 and a molecular weight in the range of 200-300 g/mol, which were found to be conducive to bacteria related to plant immunity promotion and exerted the lowest fluctuation of human opportunistic pathogen and keystone species. This guides the design of pesticides for which the impacts on soil biota are minimized. [Ke, Mingjing; Xu, Nuohan; Zhang, Zhenyan; Qiu, Danyan; Kang, Jian; Lu, Tao; Sun, Liwei; Qian, Haifeng] Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China; [Wang, Tingzhang] Key Lab Microbial Technol & Bioinformat Zhejiang, Hangzhou, Peoples R China; [Peijnenburg, W. J. G. M.] Leiden Univ, Inst Environm Sci CML, Leiden, Netherlands; [Peijnenburg, W. J. G. M.] Ctr Safety Subst & Prod, Natl Inst Publ Hlth & Environm RIVM, Bilthoven, Netherlands; [Hu, Baolan] Zhejiang Univ, Dept Environm Sci, Hangzhou, Peoples R China Zhejiang University of Technology; Leiden University; Leiden University - Excl LUMC; Netherlands National Institute for Public Health & the Environment; Zhejiang University Qian, HF (corresponding author), Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China. hfqian@zjut.edu.cn Qian, Haifeng/A-7791-2012; Lu, Tao/Y-3535-2018 Qian, Haifeng/0000-0003-0807-9991; Lu, Tao/0000-0002-2030-3813; Zhang, Zhenyan/0000-0002-9122-2391 Key R&D Program of Zhejiang [2022C02029]; National Natural Science Foundation of China [21577128, 21777144, 21777145, 21976161] Key R&D Program of Zhejiang; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Key R&D Program of Zhejiang, Grant/Award Number: 2022C02029; National Natural Science Foundation of China, Grant/Award Numbers: 21577128, 21777144, 21777145, 21976161 71 1 1 28 33 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1462-2912 1462-2920 ENVIRON MICROBIOL Environ. Microbiol. NOV 2022.0 24 11 SI 5561 5573 10.1111/1462-2920.16175 0.0 AUG 2022 13 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology 6R3ZP 36054535.0 2023-03-23 WOS:000846661200001 0
J Ge, CJ; Gu, IYH; Jakola, AS; Yang, J Ge, Chenjie; Gu, Irene Yu-Hua; Jakola, Asgeir Store; Yang, Jie Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification IEEE ACCESS English Article Molecular-based brain tumor subtype classification; glioma; multi-modal; MRI; data augmentation; generative adversarial networks; deep learning IDH1; MUTATIONS; SURVIVAL; GLIOMAS This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included. [Ge, Chenjie; Gu, Irene Yu-Hua] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden; [Jakola, Asgeir Store] Sahlgrens Acad, Inst Neurosci & Physiol, S-41345 Gothenburg, Sweden; [Jakola, Asgeir Store] Sahlgrens Univ Hosp, S-41345 Gothenburg, Sweden; [Ge, Chenjie; Yang, Jie] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China Chalmers University of Technology; University of Gothenburg; Sahlgrenska University Hospital; Shanghai Jiao Tong University Ge, CJ (corresponding author), Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.;Ge, CJ (corresponding author), Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China. chenjie@chalmers.se Gu, Irene Y.H./D-4044-2018; Jakola, Asgeir/J-1738-2018 Gu, Irene Y.H./0000-0003-4759-7038; Jakola, Asgeir/0000-0002-2860-9331 STINT Joint Swedish-China Mobility Programme in Sweden [CH2015-6193]; Chinese Scholarship Council (CSC); Swedish Research Council VR [2017-00944] STINT Joint Swedish-China Mobility Programme in Sweden; Chinese Scholarship Council (CSC)(China Scholarship Council); Swedish Research Council VR(Swedish Research Council) This work was supported in part by the STINT Joint Swedish-China Mobility Programme in Sweden under Grant CH2015-6193. The work of Chenjie Ge was supported by the Chinese Scholarship Council (CSC). The work of Asgeir Store Jakola was supported by The Swedish Research Council VR under Grant 2017-00944. 28 34 34 4 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 22560 22570 10.1109/ACCESS.2020.2969805 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LC5TQ Green Published, gold 2023-03-23 WOS:000525393900066 0
J Zhang, WS; Guo, WW; Liu, X; Liu, Y; Zhou, JH; Li, B; Lu, QH; Yang, S Zhang, Weishan; Guo, Wuwu; Liu, Xin; Liu, Yan; Zhou, Jiehan; Li, Bo; Lu, Qinghua; Yang, Su LSTM-Based Analysis of Industrial IoT Equipment IEEE ACCESS English Article Time series prediction; LSTM model; power equipment; industry Internet of Things ARTIFICIAL NEURAL-NETWORK; ARMA Industrial Internet of Things (IoT) is producing massive data which are valuable for knowing running status of the underlying equipment. However, these data involve various operation events that span some time, which raise questions on how to model long memory of states, and how to predict the running status based on historical data accurately. This paper aims to develop a method of: (1) analyzing equipment working condition based on the sensed data; (2) building a prediction model for working status forecasting and designing a deep neural network model to predict equipment running data; and (3) improving the prediction accuracy by systematic feature engineering and optimal hyperparameter searching. We evaluate our method with real-world monitoring data collected from 33 sensors of a main pump in a power station for three months. The model achieves less root mean square error than that of autoregressive integrated moving average model. Our method is applicable to general IoT equipment for analyzing time series data and forecasting operation status. [Zhang, Weishan; Guo, Wuwu; Liu, Xin; Li, Bo; Lu, Qinghua] China Univ Petr, Dept Software Engn, Qingdao 102200, Peoples R China; [Liu, Yan] Concordia Univ, Fac Engn & Comp Sci, Montreal, PQ H3G 1M8, Canada; [Zhou, Jiehan] Univ Oulu, Fac Informat Technol & Elect Engn, Oulu 90014, Finland; [Yang, Su] Fudan Univ, Coll Comp Sci & Technol, Shanghai 200433, Peoples R China China University of Petroleum; Concordia University - Canada; University of Oulu; Fudan University Liu, X (corresponding author), China Univ Petr, Dept Software Engn, Qingdao 102200, Peoples R China. lx@upc.edu.cn Lu, Qinghua/AAG-3378-2021; xu, liang/AAC-4448-2022 Zhang, Weishan/0000-0001-9800-1068 Key Research Program of Shandong Province [2017GGX10140]; Fundamental Research Funds for the Central Universities [15CX08015A]; National Natural Science Foundation of China [61309024] Key Research Program of Shandong Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140, in part by the Fundamental Research Funds for the Central Universities under Grant 15CX08015A, and in part by the National Natural Science Foundation of China under Grant 61309024. 22 62 65 3 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 23551 23560 10.1109/ACCESS.2018.2825538 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GG7MV gold 2023-03-23 WOS:000432883300001 0
J Niu, JX; Hu, QS; Niu, Y; Zhang, T; Jha, SK Niu, Jinxing; Hu, Qingsheng; Niu, Yi; Zhang, Tao; Jha, Sunil Kumar Real-Time Recognition and Location of Indoor Objects CMC-COMPUTERS MATERIALS & CONTINUA English Article Object recognition; improved YOLOv3 network; RGB-D camera; object location Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera is used to obtain the RGB map and depth map, the actual distance value is calculated after each pixel in the scene image is mapped to the real scene. Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene. More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement. The running time of objects detection and recognition is reduced to less than half of the original. This improved network has a certain reference value for practical engineering application. [Niu, Jinxing; Hu, Qingsheng; Niu, Yi; Zhang, Tao] North China Univ Water Resources & Elect Power, Inst Mech, Zhengzhou 450011, Peoples R China; [Jha, Sunil Kumar] Univ Informat Technol & Management Rzeszow, IT Fundamentals & Educ Technol Applicat, PL-100031 Rzeszow, Poland North China University of Water Resources & Electric Power; University of Information Technology & Management Rzeszow Niu, JX (corresponding author), North China Univ Water Resources & Elect Power, Inst Mech, Zhengzhou 450011, Peoples R China. njx.mail@163.com Henan Province Science and Technology Project [182102210065] Henan Province Science and Technology Project This work was supported by Henan Province Science and Technology Project under Grant No. 182102210065. 16 0 0 2 17 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2021.0 68 2 2221 2229 10.32604/cmc.2021.017073 0.0 9 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science RN3EP gold 2023-03-23 WOS:000640234500005 0
J Niu, C; Wang, Y; Cohen, AD; Liu, X; Li, HW; Lin, P; Chen, ZY; Min, ZG; Li, WF; Ling, X; Wen, X; Wang, MD; Thompson, HP; Zhang, M Niu, Chen; Wang, Yang; Cohen, Alexander D.; Liu, Xin; Li, Hongwei; Lin, Pan; Chen, Ziyi; Min, Zhigang; Li, Wenfei; Ling, Xiao; Wen, Xin; Wang, Maode; Thompson, Hannah P.; Zhang, Ming Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex EUROPEAN RADIOLOGY English Article Humans; Motor cortex; Magnetic resonance imaging; Brain neoplasms; Neural networks Objective The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). Methods A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. Results The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. Conclusions This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. [Niu, Chen; Min, Zhigang; Li, Wenfei; Ling, Xiao; Wen, Xin; Zhang, Ming] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Med Imaging, Xian 710061, Shaanxi, Peoples R China; [Niu, Chen; Liu, Xin; Li, Hongwei] Tech Univ Munich, Dept Comp Sci, Munich, Germany; [Wang, Yang; Cohen, Alexander D.; Chen, Ziyi; Thompson, Hannah P.] Med Coll Wisconsin, Dept Radiol, 8700 W Wisconsin Ave, Milwaukee, WI 53226 USA; [Lin, Pan] Hunan Normal Univ, Dept Psychol, Cognit & Human Behav Key Lab Hunan Prov, Changsha 410081, Hunan, Peoples R China; [Wang, Maode] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Neurosurg, Xian 710061, Shaanxi, Peoples R China Xi'an Jiaotong University; Technical University of Munich; Medical College of Wisconsin; Hunan Normal University; Xi'an Jiaotong University Zhang, M (corresponding author), Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Med Imaging, Xian 710061, Shaanxi, Peoples R China.;Wang, Y (corresponding author), Med Coll Wisconsin, Dept Radiol, 8700 W Wisconsin Ave, Milwaukee, WI 53226 USA. yangwang@mcw.edu; zhangming01@mail.xjtu.edu.cn Li, Hongwei Bran/HJH-5317-2023 Li, Hongwei Bran/0000-0002-5328-6407 National Natural Science Foundation of China [81871331, 81571640]; Key Research and Development Program of Shaanxi Province [2018SF-113] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Shaanxi Province This study has received funding by the National Natural Science Foundation of China (Grant Nos. 81871331 and 81571640) and the Key Research and Development Program of Shaanxi Province (2018SF-113). 32 5 5 3 14 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. JUL 2021.0 31 7 5253 5262 10.1007/s00330-021-07825-w 0.0 MAR 2021 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging SU8MP 33758954.0 2023-03-23 WOS:000631786700003 0
J Seydi, ST; Hasanlou, M; Chanussot, J Seydi, Seyd Teymoor; Hasanlou, Mahdi; Chanussot, Jocelyn A Quadratic Morphological Deep Neural Network Fusing Radar and Optical Data for the Mapping of Burned Areas IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Fires; Remote sensing; Earth; Artificial satellites; Indexes; Feature extraction; Vegetation mapping; Burned area; convolutional neural networks; deep learning; morphological operator; sentinel-1; sentinel-2 TIME-SERIES; SCALE SPACES; CLASSIFICATION; MULTISENSOR; ALGORITHM; SAVANNAS Wildfires are considered as one of the most disturbing factors in forest areas and high-density vegetation regions. The mapping of wildfires is particularly important for fire prediction and burned biomass estimation. Therefore, accurate and timely mapping of burned areas is of great importance and has a key role in disaster management. Estimation of burned areas from multispectral datasets is challenging, because of the complexity of the background and the different reflections of wildfires on the targets. To this end, this study presents a novel burned-area mapping framework based on the fusion of multitemporal Sentinel-1 coherence imagery and post-event Sentinel-2 imagery. The proposed framework uses hybrid quadratic morphological (QM) operations and convolution layers for deep feature extraction. The proposed architecture is known as the QMDNN-Net, where the overall framework of QMDNN-Net is based on a deep Siamese network. QMDNN-Net has double streams for extracting deep features from multitemporal coherence data and Sentinel-2 imagery. The streams are similar to each other and have the same number of group-dilated convolution blocks and QM layers. QMDNN-Net is defined based on quadratic dilation and erosion, and then it takes the average of these as output. The results of wildfire mapping with QMDNN-Net are evaluated here with two real-world wildfire datasets based on Sentinel-1 and Sentinel-2 imagery. The results show that QMDNN-Net achieves an overall accuracy and Kappa coefficient of 95.5% and 0.9, respectively, and outperforms other state-of-the-art methods. [Seydi, Seyd Teymoor; Hasanlou, Mahdi] Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran 1439957131, Iran; [Chanussot, Jocelyn] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China University of Tehran; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences Seydi, ST (corresponding author), Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran 1439957131, Iran. seydi.teymoor@ut.ac.ir; hasanlou@ut.ac.ir seydi, seyd teymoor/K-3366-2017 Seydi, Seyd Teymoor/0000-0002-3678-4877; Chanussot, Jocelyn/0000-0003-4817-2875 70 1 1 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15 4194 4216 10.1109/JSTARS.2022.3175452 0.0 23 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 1V0PC gold 2023-03-23 WOS:000805801800007 0
J Liu, Y; Guo, B; Li, N; Zhang, J; Chen, JM; Zhang, DQ; Liu, YX; Yu, ZW; Zhang, SZ; Yao, LN Liu, Yan; Guo, Bin; Li, Nuo; Zhang, Jing; Chen, Jingmin; Zhang, Daqing; Liu, Yinxiao; Yu, Zhiwen; Zhang, Sizhe; Yao, Lina DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial Embeddings IEEE INTERNET OF THINGS JOURNAL English Article Attention mechanism; data analytics; deep learning; spatial embedding; store site recommendation SMART CITY; LOCATION; RETAIL Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data ( e. g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attentionbased spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models. [Liu, Yan; Guo, Bin; Li, Nuo; Zhang, Jing; Yu, Zhiwen] Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China; [Chen, Jingmin; Liu, Yinxiao; Zhang, Sizhe] Alibaba Grp, Hangzhou 311121, Zhejiang, Peoples R China; [Zhang, Daqing] Telecom SudParis, Inst Mines Telecom, Dept Reseaux & Serv Multimedia Mobiles, F-91011 Evry, France; [Yao, Lina] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia Northwestern Polytechnical University; Alibaba Group; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; University of New South Wales Sydney Guo, B (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China. liuyan.emily@mail.nwpu.edu.cn; guob@nwpu.edu.cn; 2326375288@qq.com; 1107141724@qq.com; jingmin.cjm@alibaba-inc.com; daqing.zhang@telecom-sudparis.eu; yinxiao.lyx@alibaba-inc.com; zhiwenyu@nwpu.edu.cn; jincheng.zsz@alibaba-inc.com; lina.yao@unsw.edu.au Yu, Zhiwen/0000-0002-9905-3238; Guo, Bin/0000-0001-6097-2467 National Key Research and Development Program of China [2017YFB1001800]; National Natural Science Foundation of China [61772428, 61725205]; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201958] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1001800, in part by the National Natural Science Foundation of China under Grant 61772428 and Grant 61725205, and in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX201958. (Corresponding author: Bin Guo.) 48 10 13 5 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. AUG 2019.0 6 4 7319 7333 10.1109/JIOT.2019.2916143 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IN8VL Green Published 2023-03-23 WOS:000478957600131 0
J Elnaggar, A; Heinzinger, M; Dallago, C; Rehawi, G; Wang, Y; Jones, L; Gibbs, T; Feher, T; Angerer, C; Steinegger, M; Bhowmik, D; Rost, B Elnaggar, Ahmed; Heinzinger, Michael; Dallago, Christian; Rehawi, Ghalia; Wang, Yu; Jones, Llion; Gibbs, Tom; Feher, Tamas; Angerer, Christoph; Steinegger, Martin; Bhowmik, Debsindhu; Rost, Burkhard ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Proteins; Training; Amino acids; Task analysis; Databases; Computational modeling; Three-dimensional displays; Computational biology; high performance computing; machine learning; language modeling; deep learning PROTEIN SECONDARY STRUCTURE; NEURAL-NETWORKS; PREDICTION; LOCALIZATION Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans. [Elnaggar, Ahmed; Heinzinger, Michael; Dallago, Christian; Rehawi, Ghalia; Rost, Burkhard] Tech Univ Munich TUM, Dept Informat Bioinformat & Computat Biol i12, D-85748 Garching, Germany; [Wang, Yu] Med AI Technol Wu Xi Ltd, Wuxi 214000, Jiangsu, Peoples R China; [Jones, Llion] Google, Google AI, Mountain View, CA 94043 USA; [Gibbs, Tom; Feher, Tamas; Angerer, Christoph] NVIDIA, Santa Clara, CA 95051 USA; [Steinegger, Martin] Seoul Natl Univ, Sch Biol Sci, Seoul 08826, South Korea; [Bhowmik, Debsindhu] Oak Ridge Natl Lab, POB 2009, Oak Ridge, TN 37830 USA Technical University of Munich; Google Incorporated; Nvidia Corporation; Seoul National University (SNU); United States Department of Energy (DOE); Oak Ridge National Laboratory Elnaggar, A (corresponding author), Tech Univ Munich TUM, Dept Informat Bioinformat & Computat Biol i12, D-85748 Garching, Germany. ahmed.elnaggar@tum.de; mheinzinger@rostlab.org; christian@dallago.us; ghalia.rihawi93@gmail.com; wang_yu@hotmail.com; llion@google.com; tgibbs@nvidia.com; tfeher@nvidia.com; cangerer@nvidia.com; martin.steinegger@snu.ac.kr; bhowmikd@ornl.gov; assistant@rostlab.org Gibbs, Tom/HJA-8036-2022; Steinegger, Martin/ABD-6525-2021; Rost, Burkhard/A-1908-2016 Steinegger, Martin/0000-0001-8781-9753; Rost, Burkhard/0000-0003-0179-8424; Rehawi, Ghalia/0000-0001-5115-8658 Software Campus 2.0 (TUM) through the German Ministry for Research and Education (BMBF); Alexander von Humboldt foundation through the German Ministry for Research and Education (BMBF); Deutsche Forschungsgemeinschaft [DFG-GZ: RO1320/4-1]; NVIDIA; National Research Foundation of Korea [2019R1A6A1A10073437, NRF-2020M3A9G7103933]; SeoulNational University; Google Cloud; Google Cloud Research Credits Program under Covid19 HPC Consortium grant; DOE Office of Science User Facility [DEAC05-00OR22725]; TPU pods under TensorFlow Research Cloud grant; National Research Foundation of Korea [2019R1A6A1A10073437] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS) Software Campus 2.0 (TUM) through the German Ministry for Research and Education (BMBF)(Federal Ministry of Education & Research (BMBF)); Alexander von Humboldt foundation through the German Ministry for Research and Education (BMBF); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); NVIDIA; National Research Foundation of Korea(National Research Foundation of Korea); SeoulNational University; Google Cloud(Google Incorporated); Google Cloud Research Credits Program under Covid19 HPC Consortium grant; DOE Office of Science User Facility(United States Department of Energy (DOE)); TPU pods under TensorFlow Research Cloud grant; National Research Foundation of Korea(National Research Foundation of Korea) The authors would like to thank Tim Karl, TUM, and Jian Kong, TUM, for invaluable help with hard-and software, Inga Weise, TUM, and Aline Schmidt, TUM, for support with many other aspects of this work, Florian Matthes, TUM, for his generous support and encouragement, crucial support and feedback from NVIDIA, in particular to Ulrich Michaelis, Ada Sedova, Geetika Gupta, Axel Koehler, Frederic Pariente, Jonathan Lefman, and Thomas Bradley, and many at ORNL without whom no aspect of this work could have been realized, particular thanks to John Gounley, Hong-Jun Yoon, Georgia Tourassi, Bill, Brian, Junqi, Graham, and Ver~onica (ORNL Summit). The authors would also like to thank Jack Wells (ORNL) for opening the door to kicking off this project. From IBM, the authors would like to thank Nicolas Castet and Bryant Nelson for their help to fix issues and enhance the performance of IBM PowerAI. From Google, the authors would like to thank Jamie Kinney, Alex Schroeder, Nicole DeSantis, Andrew Stein, Vishal Mishra, Eleazar Ortiz, Nora Limbourg, Cristian Mezzanotte, and all TFRC Team for helping to setup a project on Google Cloud and solving Google cloud issues. No ProtTrans model was easily publicly available without support from the Hugging Face team, including Patrick von Platen, Julien Chaumond, and Clement Delangue. The authors would also like to thank Konstantin Wei ss enow for helping with grant writing and providing early results for the structure prediction task. The authors would also like to thank both Adam Roberts and Colin Raffel for help with the T5 model, and the editor and the anonymous reviewers for essential criticism, especially, for suggesting to compare t-SNEs to randomly initialized models. The authors would also like to thank Leibniz Rechenzentrum (LRZ) for providing access to DGX-1(V100) for the testing phase. This work was supported in part by Software Campus 2.0 (TUM) through the German Ministry for Research and Education (BMBF), in part by the Alexander von Humboldt foundation through the German Ministry for Research and Education (BMBF), in part by the Deutsche Forschungsgemeinschaft under Grant DFG-GZ: RO1320/4-1, and in part by NVIDIA with the donation of 2 Titan GPUs used for the development phase. The work of Martin Steinegger was supported in part by the National Research Foundation of Korea under Grants 2019R1A6A1A10073437 and NRF-2020M3A9G7103933, in part by the New Faculty Startup Fund and the Creative-Pioneering Researchers Program through SeoulNational University. The work of Rostlab was supported in part by Google Cloud and in part by Google Cloud Research Credits Program to fund this project under Covid19 HPC Consortium grant. This work used resources of the Oak Ridge National Laboratory (ORNL) Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Grant DEAC05-00OR22725, and resources of TPU pods under TensorFlow Research Cloud grant. A. Elnaggar and M. Heinzinger contributed equally to thiswork. 99 127 129 57 78 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. OCT 1 2022.0 44 10 7112 7127 10.1109/TPAMI.2021.3095381 0.0 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4N2UL 34232869.0 Green Submitted, hybrid 2023-03-23 WOS:000853875300088 0
J Li, JB; Chen, ZQ; Cheng, L; Liu, XF Li, Jianbin; Chen, Zhiqiang; Cheng, Long; Liu, Xiufeng Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks ENERGY English Article Privacy Protection; Sociodemographic Information; Generative Adversarial Network; Data Sharing RESIDENTIAL ELECTRICITY CONSUMPTION; LOAD PROFILE CHARACTERIZATION; SMART METER DATA; BEHAVIOR Residential energy consumption data and related sociodemographic information are critical for energy demand management, including providing personalized services, ensuring energy supply, and designing demand response programs. However, it is often difficult to collect sufficient data to build machine learning models, primarily due to cost, technical barriers, and privacy. Synthetic data generation becomes a feasible solution to address data availability issues, while most existing work generates data without considering the balance between usability and privacy. In this paper, we first propose a data generation model based on the Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), which is capable of synthesizing fine-grained energy consumption time series and corresponding sociodemographic information. The WDCGAN model can generate realistic data by balancing data usability and privacy level by setting a hyperparameter during training. Next, we take the classification of sociodemographic information as an application example and train four classical classification models with the generated datasets, including CNN, LSTM, SVM, and LightGBM. We evaluate the pro-posed data generator using Irish data, and the results show that the proposed WDCGAN model can generate realistic load profiles with satisfactory similarity in terms of data distribution, patterns, and performance. The classification results validate the usability of the generated data for real-world machine learning applications with privacy guarantee, e.g., most of the differences in classification accuracy and F-1 scores are less than 8% between using real and synthesized data. (c) 2022 The Authors. Published by Elsevier Ltd. [Li, Jianbin; Chen, Zhiqiang; Cheng, Long] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China; [Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, Lyngby, DK-2800 Lyngby, Denmark North China Electric Power University; Technical University of Denmark Cheng, L (corresponding author), North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China. lcheng@ncepu.edu.cn Cheng, Long/ADJ-1122-2022; Tomm, He/E-5457-2013 Cheng, Long/0000-0003-1638-059X; Liu, Xiufeng/0000-0001-5133-6688 National Key Research and Development Plan of China [2021YFE0102400]; Fundamental Research Funds for the Central Universities [2021MS017] National Key Research and Development Plan of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was supported by National Key Research and Development Plan of China (No. 2021YFE0102400) and Fundamental Research Funds for the Central Universities (2021MS017) . 68 14 14 4 4 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy OCT 15 2022.0 257 124694 10.1016/j.energy.2022.124694 0.0 JUL 2022 17 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 5A1ZE hybrid, Green Published 2023-03-23 WOS:000862691500006 0
J Su, QS; Huang, YQ; Wu, XM; Zhang, BY; Lu, PL; Lyu, T Su, Qiushi; Huang, Yanqi; Wu, Xiaomei; Zhang, Biyong; Lu, Peilin; Lyu, Tan Atrial Fibrillation Detection Based on a Residual CNN Using BCG Signals ELECTRONICS English Article ballistocardiogram; atrial fibrillation; residual neural network; classification TASK-FORCE; CLASSIFICATION; MANAGEMENT; UPDATE Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition. [Su, Qiushi; Huang, Yanqi; Wu, Xiaomei] Fudan Univ, Ctr Biomed Engn, Sch Informat Sci & Technol, Shanghai 200438, Peoples R China; [Wu, Xiaomei] Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China; [Wu, Xiaomei] Key Lab Med Imaging Comp & Comp Assisted Interven, Shanghai 200032, Peoples R China; [Wu, Xiaomei] Fudan Univ, Yiwu Res Inst, Yiwu 322000, Peoples R China; [Wu, Xiaomei] Res Ctr Assist Devices, Shanghai 200093, Peoples R China; [Zhang, Biyong] Eindhoven Univ Technol, NL-5612 AP Eindhoven, Netherlands; [Zhang, Biyong] BOBO Technol, Hangzhou 311000, Peoples R China; [Lu, Peilin] Zhejiang Univ, Sir Run Run Shaw Hosp, Neurosci Ctr, Sch Med,Dept Neurol, Hangzhou 310016, Peoples R China; [Lyu, Tan] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Electrocardiog, Hangzhou 310016, Peoples R China Fudan University; Fudan University; Fudan University; Eindhoven University of Technology; Zhejiang University; Zhejiang University Huang, YQ; Wu, XM (corresponding author), Fudan Univ, Ctr Biomed Engn, Sch Informat Sci & Technol, Shanghai 200438, Peoples R China.;Wu, XM (corresponding author), Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China.;Wu, XM (corresponding author), Key Lab Med Imaging Comp & Comp Assisted Interven, Shanghai 200032, Peoples R China. yqhuang@fudan.edu.cn; xiaomeiwu@fudan.edu.cn Huang, Yanqi/0000-0002-8517-0142 National Natural Science Foundation of China [1171009, 61801123]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 16441907900]; Shanghai Municipal Science and Economic and Informatization Commission Project [GYQJ-2018-2-05]; Medical Engineering Fund of Fudan University [yg2021-38]; Zhejiang Provincial Natural Science Foundation of China [LY20H090001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Science and Technology Major Project; Shanghai Municipal Science and Economic and Informatization Commission Project; Medical Engineering Fund of Fudan University; Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work received financial support from National Natural Science Foundation of China, grant no. 1171009 and 61801123; the Shanghai Municipal Science and Technology Major Project, grant no. 2017SHZDZX01 and 16441907900; the Shanghai Municipal Science and Economic and Informatization Commission Project, grant no. GYQJ-2018-2-05; Medical Engineering Fund of Fudan University yg2021-38; Zhejiang Provincial Natural Science Foundation of China, grant no. LY20H090001. 40 0 0 9 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics SEP 2022.0 11 18 2974 10.3390/electronics11182974 0.0 16 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics 4V4ZO gold 2023-03-23 WOS:000859486900001 0
C Wang, K; Liu, R; Qu, Y IEEE Wang, Ke; Liu, Rong; Qu, Yuan Development of a Double-Cavity Climbing Robot for On-Site Inspection of Glass-Curtain-Walls 2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) English Proceedings Paper IEEE International Conference on Information and Automation (ICIA) AUG 26-28, 2013 Yinchuan, PEOPLES R CHINA IEEE,RA double-cavity; climbing robots; on-site inspection; glass-curtain-wall; negative pressure adsorption How to develop practical robots with high artificial intelligence to replace the traditional manual method of on-site inspection of glass-curtain-walls needs to be solved urgently. Therefore, a double-cavity climbing robot that can automatically conduct safety inspection of glass-curtain-wall is researched and presented in this paper. The robot includes adsorption module based on the principle of negative pressure adsorption, servo-drive system to control traveling distance, vibration testing device and safety device. The mechanical structure and its optimization are described in detail, which is followed by a presentation of the robot control system. Finally the wall-climbing and vibration testing experiments are conducted on glass-curtain-wall, and effectiveness of the design is approved. [Wang, Ke] Arts & Metiers ParisTech, Design Mfg & Control Lab LCFC, F-57070 Metz, France; [Liu, Rong; Qu, Yuan] Beihang Univ, Inst Robot, Sch Mech Engn & Automat, Beijing 100191, Peoples R China Arts et Metiers Institute of Technology; Universite de Lorraine; Beihang University Wang, K (corresponding author), Arts & Metiers ParisTech, Design Mfg & Control Lab LCFC, F-57070 Metz, France. swordwangke@gmail.com; rliu@buaa.edu.cn; quyuan85@126.com China Building Material Test and Certification Center China Building Material Test and Certification Center The authors would like to thank Mr. Yiwang Bao and Mr. Yan Qiu in China Building Material Test and Certification Center for their advice on design of the robot and abundant funding support. 10 1 1 2 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-1334-3 2013.0 1183 1188 6 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BB8ES 2023-03-23 WOS:000346483800214 0
C Yao, JJ; Zhou, ZB; Zhao, D; Sun, MY Bie, R; Sun, Y; Yu, J Yao, Jiajia; Zhou, Zhangbing; Zhao, Deng; Sun, Mengyu Energy-Efficient Composition of Configurable Operators in Big Data Environment 2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS Procedia Computer Science English Proceedings Paper 7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) OCT 19-21, 2018 Beijing Normal Univ, Beijing, PEOPLES R CHINA Beijing Normal Univ Operators; Operators Composition; Edge Cluster; Energy Efficiency With the rapid development of edge computing, heterogeneous smart devices, serving as operators, require to be deployed closely to data sources, and cooperate to accomplish big data applications considering factors including energy consumption and fast response. Specifically, an operators composition can be encapsulated and represented as feasible solution, and an operator in a certain solution prefers to be encoded with abstract matrix defined by evaluation indicators including spatial and temporal constraint, priority between consecutive operators, the load balancing factor, and energy consumption. Discovering an optimal composition strategy in the constructed edge cluster can boil down to a multi-objective and multi-constrained optimization problem, which can be solved through adopting heuristic algorithms. Experimental evaluation demonstrates that the Grey Wolf Optimization outperforms Particle Swarm Optimization in discovering an approximately optimal operators composition in the corresponding edge cluster, and can minimize the energy consumption of the network. (C) 2019 The Authors. Published by Elsevier B.V. [Yao, Jiajia; Zhou, Zhangbing; Sun, Mengyu] China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China; [Zhou, Zhangbing; Zhao, Deng] TELECOM SudParis, Comp Sci Dept, F-91011 Evry, France China University of Geosciences; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Yao, JJ (corresponding author), China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China. jiajiayao@cugb.edu.cn Zhao, Deng/GOK-1013-2022 Zhao, Deng/0000-0001-6378-5595 10 2 2 0 1 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-0509 PROCEDIA COMPUT SCI 2019.0 147 354 360 10.1016/j.procs.2019.01.237 0.0 7 Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BM9HR gold 2023-03-23 WOS:000470960800055 0
J Zu, SH; Cao, JX; Qu, S; Chen, YK Zu, Shaohuan; Cao, Junxing; Qu, Shan; Chen, Yangkang Iterative deblending for simultaneous source data using the deep neural network GEOPHYSICS English Article SIMULTANEOUS SOURCES SEPARATION; REVERSE TIME MIGRATION; BLENDED DATA; ACQUISITION; INVERSION; RECONSTRUCTION Simultaneous source technology can accelerate data acquisition and improve subsurface illumination. But those advantages are compromised due to dense interference. To address the intense interference in simultaneous source data, we have investigated a method based on a deep neural network. The designed architecture consists of convolutional and deconvolutional networks. The convolutional network can learn the local features of the training data set, and the deconvolutional network constructs the output using the extracted features to match the ground truth. Because the main computational cost results from the optimization of the network parameters, the trained network can separate simultaneous source data efficiently. Besides, with the given dithering code, we embed the trained network into an iterative framework that can further improve the deblending. A numerical test on synthetic data demonstrates that the iterative framework with the trained network can obtain comparable performance with high efficiency compared to the conventional method. Next, we test our method with two different trained networks (one is from a synthetic data set, and the other is from a field data set) on field data. The test results confirm the performance of our method. [Zu, Shaohuan; Cao, Junxing] Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu, Peoples R China; [Zu, Shaohuan; Cao, Junxing] Chengdu Univ Technol, Coll Geophys, Chengdu 610059, Peoples R China; [Qu, Shan] Delft Univ Technol, Delphi Consortium, Delft, Netherlands; [Chen, Yangkang] Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Peoples R China Chengdu University of Technology; Chengdu University of Technology; Delft University of Technology; Delphi; Zhejiang University Zu, SH (corresponding author), Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu, Peoples R China.;Zu, SH (corresponding author), Chengdu Univ Technol, Coll Geophys, Chengdu 610059, Peoples R China. zushaohuan19@cdut.edu.cn; caojx@cdut.edu.cn; s.qu@tudelft.nl; yangkang.chen@zju.edu.cn Chen, Yangkang/0000-0001-6429-4261; Zu, Shaohuan/0000-0003-0153-1746 National Natural Science Foundation of China [41430323]; National Science and Technology Major Project [2016ZX05024-005-007, 2017ZX05072-002]; Thousand Youth Talents Plan; Zhejiang University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science and Technology Major Project; Thousand Youth Talents Plan; Zhejiang University The authors appreciate K. Hornman and three anonymous reviewers for their constructive suggestions that improved the quality of the paper significantly. The research was partly supported by the National Natural Science Foundation of China (grant no. 41430323) and National Science and Technology Major Project under grant no. 2016ZX05024-005-007 and 2017ZX05072-002. The work of Y. Chen was supported by the Thousand Youth Talents Plan and the Starting Funds from Zhejiang University. 58 35 36 3 23 SOC EXPLORATION GEOPHYSICISTS TULSA 8801 S YALE ST, TULSA, OK 74137 USA 0016-8033 1942-2156 GEOPHYSICS Geophysics MAR-APR 2020.0 85 2 V131 V141 10.1190/GEO2019-0319.1 0.0 11 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics KU2KU 2023-03-23 WOS:000519538200036 0
J Yang, TG; Li, B; Xun, Q Yang, Tongguang; Li, Bin; Xun, Qian LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization IEEE ACCESS English Article LSTM-attention-embedding model; features extraction; deep learning; Bayesian optimization; residual connection NEURAL-NETWORKS; FORECAST; ENERGY Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model. [Yang, Tongguang; Li, Bin] Hunan City Univ, Coll Mechan & Elect Engn, Yiyang 413000, Peoples R China; [Xun, Qian] Chalmers Univ Technol, Dept Elect Engn, S-41279 Gothenburg, Sweden Hunan City University; Chalmers University of Technology Xun, Q (corresponding author), Chalmers Univ Technol, Dept Elect Engn, S-41279 Gothenburg, Sweden. qian.xun@chalmers.se Xun, Qian/P-9373-2018 Xun, Qian/0000-0001-5344-5298; Li, Bin/0000-0003-4678-8369 National Nature Science Foundation for China [61321003, 61290325]; Zhejiang Natural Science Foundation [LY19F030002]; Huzhou Public Welfare Application Research Project [2019GZ02] National Nature Science Foundation for China(National Natural Science Foundation of China (NSFC)); Zhejiang Natural Science Foundation(Natural Science Foundation of Zhejiang Province); Huzhou Public Welfare Application Research Project This work was supported in part by the National Nature Science Foundation for China under Grant 61321003 and Grant 61290325, in part by the Zhejiang Natural Science Foundation under Grant LY19F030002, and in part by the Huzhou Public Welfare Application Research Project under Grant 2019GZ02. 41 19 21 17 53 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 171471 171484 10.1109/ACCESS.2019.2954290 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications KF6TW Green Published, gold 2023-03-23 WOS:000509374200009 0
J Katsikis, VN; Mourtas, SD; Stanimirovic, PS; Li, S; Cao, XW Katsikis, Vasilios N.; Mourtas, Spyridon D.; Stanimirovic, Predrag S.; Li, Shuai; Cao, Xinwei Time-varying mean-variance portfolio selection problem solving via LVI-PDNN COMPUTERS & OPERATIONS RESEARCH English Article Portfolio selection; Time-varying systems; Quadratic programming; Continuous neural networks NEURAL-NETWORK It is widely acclaimed that the Markowitz mean-variance portfolio selection is a very important investment strategy. One approach to solving the static mean-variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study the time-varying mean-variance portfolio selection (TV-MVPS) problem both in the cases of a fixed target portfolio's expected return and for all possible portfolio's expected returns as a time-varying quadratic programming (TVQP) problem. The TV-MVPS also comprises the properties of a moving average. These properties make the TVMVPS an even greater analysis tool suitable to evaluate investments and identify trading opportunities across a continuous-time period. Using an originally developed linear-variational-inequality primal-dual neural network (LVI-PDNN), we also provide an online solution to the static QP problem. To the best of our knowledge, this is an innovative approach that incorporates robust neural network techniques to provide an online, thus more realistic, solution to the TV-MVPS problem. In this way, we present an online solution to a time varying financial problem while eliminating static method limitations. It has been shown that when applied simultaneously to TVQP problems subject to equality, inequality and boundary constraints, the LVI-PDNN approaches the theoretical solution. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods. [Katsikis, Vasilios N.; Mourtas, Spyridon D.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Sofokleous 1 St, Athens 10559, Greece; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Univ Athens Master Business Adm, Athens, Greece; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia; [Li, Shuai] Swansea Univ, Swansea, W Glam, Wales; [Cao, Xinwei] Shanghai Univ, Sch Management, Shanghai, Peoples R China National & Kapodistrian University of Athens; National & Kapodistrian University of Athens; University of Nis; Swansea University; Shanghai University Katsikis, VN (corresponding author), Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Sofokleous 1 St, Athens 10559, Greece.;Katsikis, VN (corresponding author), Natl & Kapodistrian Univ Athens, Univ Athens Master Business Adm, Athens, Greece. vaskatsikis@econ.uoa.gr; spirmour@econ.uoa.gr; pecko@pmf.ni.ac.rs; shuaili@ieee.org; xinweicao@shu.edu.cn Li, Shuai/ABD-5132-2021; Mourtas, Spyridon D./AAB-3651-2022; Katsikis, Vasilios N/AAD-7216-2019 Li, Shuai/0000-0002-9574-9609; Mourtas, Spyridon D./0000-0002-8299-9916; Katsikis, Vasilios N/0000-0002-8208-9656; Stanimirovic, Predrag S./0000-0003-0655-3741 29 8 8 3 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0305-0548 1873-765X COMPUT OPER RES Comput. Oper. Res. FEB 2022.0 138 105582 10.1016/j.cor.2021.105582 0.0 OCT 2021 10 Computer Science, Interdisciplinary Applications; Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science WK8RT 2023-03-23 WOS:000709989000012 0
J Guan, XY; Das, A; Stein, CJ; Heidar-Zadeh, F; Bertels, L; Liu, ML; Haghighatlari, M; Li, J; Zhang, OF; Hao, HX; Leven, I; Head-Gordon, M; Head-Gordon, T Guan, Xingyi; Das, Akshaya; Stein, Christopher J.; Heidar-Zadeh, Farnaz; Bertels, Luke; Liu, Meili; Haghighatlari, Mojtaba; Li, Jie; Zhang, Oufan; Hao, Hongxia; Leven, Itai; Head-Gordon, Martin; Head-Gordon, Teresa A benchmark dataset for Hydrogen Combustion SCIENTIFIC DATA English Article; Data Paper DENSITY-FUNCTIONAL THEORY The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of similar to 290,000 potential energies and similar to 1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, omega B97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction. [Guan, Xingyi; Das, Akshaya; Stein, Christopher J.; Heidar-Zadeh, Farnaz; Bertels, Luke; Liu, Meili; Haghighatlari, Mojtaba; Li, Jie; Zhang, Oufan; Hao, Hongxia; Leven, Itai; Head-Gordon, Martin; Head-Gordon, Teresa] Univ Calif Berkeley, Kenneth S Pitzer Theory Ctr, Berkeley, CA 94720 USA; [Guan, Xingyi; Das, Akshaya; Stein, Christopher J.; Heidar-Zadeh, Farnaz; Bertels, Luke; Liu, Meili; Haghighatlari, Mojtaba; Li, Jie; Zhang, Oufan; Hao, Hongxia; Leven, Itai; Head-Gordon, Martin; Head-Gordon, Teresa] Univ Calif Berkeley, Dept Chem, Berkeley, CA 94720 USA; [Guan, Xingyi; Stein, Christopher J.; Hao, Hongxia; Leven, Itai; Head-Gordon, Martin; Head-Gordon, Teresa] Lawrence Berkeley Natl Lab, Div Chem Sci, Berkeley, CA 94720 USA; [Stein, Christopher J.] Univ Duisburg Essen, Theoret Phys & Ctr Nanointegrat Duisburg Essen CE, D-47048 Duisburg, Germany; [Heidar-Zadeh, Farnaz] Queens Univ, Dept Chem, Kingston, ON K7L 3N6, Canada; [Liu, Meili] Beijing Normal Univ, Dept Chem, Beijing 100875, Peoples R China; [Head-Gordon, Teresa] Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA; [Head-Gordon, Teresa] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA University of California System; University of California Berkeley; University of California System; University of California Berkeley; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of Duisburg Essen; Queens University - Canada; Beijing Normal University; University of California System; University of California Berkeley; University of California System; University of California Berkeley Head-Gordon, T (corresponding author), Univ Calif Berkeley, Kenneth S Pitzer Theory Ctr, Berkeley, CA 94720 USA.;Head-Gordon, T (corresponding author), Lawrence Berkeley Natl Lab, Div Chem Sci, Berkeley, CA 94720 USA.;Head-Gordon, T (corresponding author), Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA.;Head-Gordon, T (corresponding author), Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA. thg@berkeley.edu National Science Foundation [CHE-1955643]; Natural Sciences and Engineering Research Council (NSERC) of Canada; China Scholarship Council; Ministry of Innovation, Science and Research of North Rhine-Westphalia (NRW Ruckkehrerprogramm); Early Postdoc Mobility fellowship from the Swiss National Science Foundation; Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231] National Science Foundation(National Science Foundation (NSF)); Natural Sciences and Engineering Research Council (NSERC) of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); China Scholarship Council(China Scholarship Council); Ministry of Innovation, Science and Research of North Rhine-Westphalia (NRW Ruckkehrerprogramm); Early Postdoc Mobility fellowship from the Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); Office of Science of the U.S. Department of Energy(United States Department of Energy (DOE)) We thank the National Science Foundation under grant CHE-1955643. F.H-Z. acknowledges financial support from Natural Sciences and Engineering Research Council (NSERC) of Canada. M. Liu thanks the China Scholarship Council for a visiting scholar fellowship. C.J.S. acknowledges funding by the Ministry of Innovation, Science and Research of North Rhine-Westphalia (NRW Ruckkehrerprogramm) and an Early Postdoc Mobility fellowship from the Swiss National Science Foundation. This research used computational resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. 29 1 1 6 9 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2052-4463 SCI DATA Sci. Data MAY 17 2022.0 9 1 215 10.1038/s41597-022-01330-5 0.0 7 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 1I1RB 35581204.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000797012100002 0
J Yu, L; Du, ZR; Dong, RM; Zheng, JP; Tu, Y; Chen, X; Hao, PY; Zhong, B; Peng, DL; Zhao, JY; Li, XY; Yang, JY; Fu, HH; Yang, GW; Gong, P Yu, Le; Du, Zhenrong; Dong, Runmin; Zheng, Juepeng; Tu, Ying; Chen, Xin; Hao, Pengyu; Zhong, Bo; Peng, Dailiang; Zhao, Jiyao; Li, Xiyu; Yang, Jianyu; Fu, Haohuan; Yang, Guangwen; Gong, Peng FROM-GLC Plus: toward near real-time and multi-resolution land cover mapping GISCIENCE & REMOTE SENSING English Article Remote sensing; land cover mapping; data fusion; sample migration; machine learning; super-resolution SERIES DATA; SAMPLE SET; CLASSIFICATION; VALIDATION; CHALLENGES; CROPLAND; ACCURACY; DYNAMICS; DATASET; SYSTEM Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there are limitations in the classification detail, spatial resolution, and rapid change monitoring capability of global land cover change data. Building on the earlier Global Land Cover Mapping (Finer Resolution Observation and Monitoring - Global Land Cover, FROM-GLC), we developed the improved Global Land Cover Change Monitoring Platform (FROM-GLC Plus) using methods such as multi-season sample space-time migration, multi-source data time series reconstruction, and machine learning. The FROM-GLC Plus system provides a capacity for producing global land cover change data set from the 1980s with flexibility in spatio-temporal details. The preliminary results show that FROM-GLC Plus provides a framework for near real-time land cover mapping at multi-temporal (annual to daily) and multi-resolution (30 m to sub-meter) levels. [Yu, Le; Du, Zhenrong; Dong, Runmin; Zheng, Juepeng; Tu, Ying; Chen, Xin; Zhao, Jiyao; Li, Xiyu; Fu, Haohuan] Tsinghua Univ, Inst Global Change Studies, Key Lab Earth Syst Modeling, Dept Earth Syst Sci,Minist Educ, Beijing, Peoples R China; [Yu, Le; Gong, Peng] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ Ecol Field Stn East Asian Migrator, Beijing, Peoples R China; [Hao, Pengyu] Digital & Informat Technol Serv Div, Food & Agr Org United Nat, Rome, Italy; [Zhong, Bo; Peng, Dailiang] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China; [Yang, Jianyu] China Agr Univ, Dept Land Sci & Technol, Beijing, Peoples R China; [Yang, Guangwen] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Gong, Peng] Univ Hong Kong, Dept Geog, Dept Earth Sci, Hong Kong, Peoples R China; [Gong, Peng] Univ Hong Kong, Inst Climate & Carbon Neutral, Hong Kong, Peoples R China Tsinghua University; Tsinghua University; Food & Agriculture Organization of the United Nations (FAO); Chinese Academy of Sciences; China Agricultural University; Tsinghua University; University of Hong Kong; University of Hong Kong Yu, L (corresponding author), Tsinghua Univ, Inst Global Change Studies, Key Lab Earth Syst Modeling, Dept Earth Syst Sci,Minist Educ, Beijing, Peoples R China.;Yu, L (corresponding author), Tsinghua Univ, Dept Earth Syst Sci, Minist Educ Ecol Field Stn East Asian Migrator, Beijing, Peoples R China. leyu@tsinghua.edu.cn Yu, Le/C-3701-2008; Dong, Runmin/HGU-1761-2022 Yu, Le/0000-0003-3115-2042; National Key R&D Program of China [2017YFA0604401]; Tsinghua University Initiative Scientific Research Program [2021Z11GHX002, 20223080017]; National Key Scientific and Technological Infrastructure project Earth System Science Numerical Simulator Facility (EarthLab) National Key R&D Program of China; Tsinghua University Initiative Scientific Research Program; National Key Scientific and Technological Infrastructure project Earth System Science Numerical Simulator Facility (EarthLab) This research was funded by the National Key R&D Program of China (grant number: 2017YFA0604401), Tsinghua University Initiative Scientific Research Program (grant number: 2021Z11GHX002, 20223080017), and the National Key Scientific and Technological Infrastructure project Earth System Science Numerical Simulator Facility (EarthLab). 98 7 7 21 49 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1548-1603 1943-7226 GISCI REMOTE SENS GISci. Remote Sens. DEC 31 2022.0 59 1 1026 1047 10.1080/15481603.2022.2096184 0.0 22 Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Remote Sensing 2S3GA gold 2023-03-23 WOS:000821682700001 0
C Cao, JD; Rutkowski, L Rutkowski, L; Scherer, R; Korytkowski, M; Pedrycz, W; Tadeusiewicz, R; Zurada, JM Cao, Jinde; Rutkowski, Leszek On the Global Convergence of the Parzen-Based Generalized Regression Neural Networks Applied to Streaming Data ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I Lecture Notes in Artificial Intelligence English Proceedings Paper 17th International Conference on Artificial Intelligence and Soft Computing (ICAISC) JUN 03-07, 2018 Zakopane, POLAND Polish Neural Network Soc,Univ Social Sci Lodz,Czestochowa Univ Technol, Inst Computat Intelligence,IEEE Computat Intelligence Soc, Poland Chapter Stream data mining; Parzen-type estimator; Global convergence; Generalized regression neural network DECISION TREES; NONSTATIONARY; STABILITY; VARIANCE In the paper we study global (integral) properties of the Parzen-type recursive algorithm dealing with streaming data in the presence of the time-varying noise. The mean integrated squared error of the regression estimate is shown to converge under several conditions. Simulations results illustrate asymptotic properties of the algorithm and its convergence for a wide spectrum of a time-varying noise. [Cao, Jinde] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China; [Rutkowski, Leszek] Czestochowa Tech Univ, Inst Computat Intelligence, Al Armii Krajowej 36, PL-42200 Czestochowa, Poland; [Rutkowski, Leszek] Acad Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland Southeast University - China; Technical University Czestochowa Rutkowski, L (corresponding author), Czestochowa Tech Univ, Inst Computat Intelligence, Al Armii Krajowej 36, PL-42200 Czestochowa, Poland.;Rutkowski, L (corresponding author), Acad Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland. leszek.rutkowski@iisi.pcz.pl Rutkowski, Leszek/AAF-4693-2021 Rutkowski, Leszek/0000-0001-6960-9525 Polish National Science Center [2014/15/B/ST7/05264] Polish National Science Center This work was supported by the Polish National Science Center under Grant No. 2014/15/B/ST7/05264. 35 1 1 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-91253-0; 978-3-319-91252-3 LECT NOTES ARTIF INT 2018.0 10841 25 34 10.1007/978-3-319-91253-0_3 0.0 10 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP4JK 2023-03-23 WOS:000552718500003 0
J Shi, J; He, J; Yan, XD Shi, Jin; He, Jing; Yan, Xinda Sub-column pixel neural network scheme for modulation format shifting based optical camera communications OPTICS LETTERS English Article VISIBLE-LIGHT COMMUNICATION In this Letter, a sub-column pixel neural network (SCPNN) scheme is proposed to label the pixel patterns in modulation format shifting based optical camera communication (MFS-OCC) systems. The MFS scheme, by identifying on-off keying (OOK) and Manchester signal formats, can addi-tionally harvest one-bit information without sacrificing the pixel width per bit, resulting in a higher transmission rate for OCC without multi-level modulation. Compared with the conventional multi-threshold decision (MTD) scheme, the proposed SCPNN can effectively mitigate the impact of inter-symbol interference (ISI) and noise on signal deci-sion by making full use of all pixels in one-bit period. The experimental results show that, in the case of the illumi-nance as low as 300 lux, the SCPNN scheme can attain a data rate of 8.16 Kbps with an average bit-error-rate (BER) of 2.2 x 10-3 below the HD-FEC limitation of 3.8 x 10-3. Meanwhile, the SCPNN scheme achieves remarkable BER performance improvement compared to both MFS-OCC with MTD scheme and conventional OOK-OCC system. (c) 2022 Optica Publishing Group [Shi, Jin] Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China; [He, Jing] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China; [Yan, Xinda] Eindhoven Univ Technol, Electroopt Commun Syst, NL-5612 AZ Eindhoven, Netherlands Wuhan University of Technology; Hunan University; Eindhoven University of Technology Shi, J (corresponding author), Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China. jinshi@whut.edu.cn Natural Science Foundation of Hunan Province; [2020JJ4210] Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Funding. Natural Science Foundation of Hunan Province (2020JJ4210) . 16 0 0 11 11 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0146-9592 1539-4794 OPT LETT Opt. Lett. JAN 1 2023.0 48 1 85 88 10.1364/OL.479716 0.0 4 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 7M4WH 36563375.0 2023-03-23 WOS:000906659100010 0
J Gao, F; Yue, ZY; Wang, J; Sun, JP; Yang, EF; Zhou, HY Gao, Fei; Yue, Zhenyu; Wang, Jun; Sun, Jinping; Yang, Erfu; Zhou, Huiyu A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article CLASSIFICATION; PROPAGATION; MACHINES Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervisedmethod is developed by adding a newregularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples. [Gao, Fei; Yue, Zhenyu; Wang, Jun; Sun, Jinping] Beihang Univ, Elect Informat Engn, Beijing 100191, Peoples R China; [Yang, Erfu] Univ Strathclyde, Dept Design Manufacture & Engn Management, Space Mechatron Syst Technol Lab, Glasgow G1 1XJ, Lanark, Scotland; [Zhou, Huiyu] Queens Univ, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland Beihang University; University of Strathclyde; Queens University Belfast Wang, J (corresponding author), Beihang Univ, Elect Informat Engn, Beijing 100191, Peoples R China. wangj203@buaa.edu.cn SUN, JIN/GPX-9641-2022; Zhou, Huiyu/O-2692-2014; Yang, Erfu/N-2673-2016 Zhou, Huiyu/0000-0003-1634-9840; Gao, Fei/0000-0002-1489-0812; Yang, Erfu/0000-0003-1813-5950 National Natural Science Foundation of China [61771027, 61071139, 61471019, 61171122, 61501011, 61671035]; Aeronautical Science Foundation of China [20142051022]; RSE-NNSFC Joint Project [6161101383]; China University of Petroleum (Huadong); UK EPSRC [EP/N508664/1, EP/R007187/1, EP/N011074/1]; Royal Society-Newton Advanced Fellowship [NA160342]; [9140A07040515HK01009]; EPSRC [EP/N011074/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Aeronautical Science Foundation of China; RSE-NNSFC Joint Project; China University of Petroleum (Huadong); UK EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society-Newton Advanced Fellowship; ; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported by the National Natural Science Foundation of China (61771027; 61071139; 61471019; 61171122; 61501011; 61671035), the Aeronautical Science Foundation of China (20142051022), and the Preresearch Project (9140A07040515HK01009). Dr. E. Yang is supported in part by the RSE-NNSFC Joint Project (2017-2019) (6161101383) with China University of Petroleum (Huadong). Dr. H. Zhou is supported by UK EPSRC under Grants EP/N508664/1, EP/R007187/1, and EP/N011074/1 and Royal Society-Newton Advanced Fellowship under Grant NA160342. 25 28 29 1 8 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. 2017.0 2017 3105053 10.1155/2017/3105053 0.0 8 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology FJ8XN 29118807.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000413052000001 0
J Li, QY; Chen, YS; Ghamisi, P Li, Qingyun; Chen, Yushi; Ghamisi, Pedram Complementary Learning-Based Scene Classification of Remote Sensing Images With Noisy Labels IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Noise measurement; Training; Data models; Remote sensing; Predictive models; Mathematical models; Labeling; Complementary learning (CL); convolutional neural network (CNN); noisy label; remote sensing (RS) image; scene classification (SC) Recently, many deep convolutional neural network (DCNN)-based methods have been proposed for remote sensing (RS) image scene classification (SC). In general, DCNNs obtain good generalization capabilities under the condition of correct labels. Unfortunately, the given samples are sometimes mislabeled. In this letter, the classification of RS images with noisy labels is investigated. First, complementary learning (CL), which learns from complementary labels rather than the original labels, is introduced for RS image classification with noisy labels. CL can decrease the probability of learning from incorrect information, and therefore, it is robust to noisy labels. Then, soft CL, which randomly disturbs the complementary labels of the training samples, is proposed to prevent the overfitting issue in training a DCNN. Moreover, an RS image scene classification framework combining ordinary learning (OL) and CL (RS-COCL) is proposed, which uses CL to obtain a good model and OL to fine-tune the deep model. Additionally, noisy labels filtering is used in RS-COCL (RS-COCL-NLF) to detected and corrected noisy samples. At last, soft CL is used in RS-COCL-NLF to obtain better classification performance. The proposed methods are tested on two widely used datasets (i.e., Northwestern Polytechnical University (NWPU)-RESISC45 and PatternNet) and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods. [Li, Qingyun; Chen, Yushi] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf, D-01328 Dresden, Germany; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Machine Learning Grp, D-09599 Freiberg, Germany; [Ghamisi, Pedram] IARAI, A-1030 Vienna, Austria Harbin Institute of Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Chen, YS (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China. 21b905003@stu.hit.edu.cn; chenyushi@hit.edu.cn; p.ghamisi@gmail.com Li, Qingyun/GZL-4438-2022 Li, Qingyun/0000-0001-5101-4937; Chen, Yushi/0000-0003-2421-0996 Natural Science Foundation of China [61971164] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Natural Science Foundation of China under Grant 61971164. 18 0 0 3 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 10.1109/LGRS.2021.3112960 0.0 SEP 2021 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YC4AN 2023-03-23 WOS:000732876200001 0
J Yin, ZX; Braams, BJ; Fu, BN; Zhang, DH Yin, Zhengxi; Braams, Bastiaan J.; Fu, Bina; Zhang, Dong H. Neural Network Representation of Three-State Quasidiabatic Hamiltonians Based on the Transformation Properties from a Valence Bond Model: Three Singlet States of H-3(+) JOURNAL OF CHEMICAL THEORY AND COMPUTATION English Article A neural network (NN) approach was recently developed to construct accurate quasidiabatic Hamiltonians for two-state systems with conical intersections. Here, we derive the transformation properties of elements of 3 x 3 quasidiabatic Hamiltonians based on a valence bond (VB) model and extend the NN-based method to accurately diabatize the three lowest electronic singlet states of H-3(+), which exhibit the avoided crossing between the ground and first excited states and the conical intersection between the first and second excited states for equilateral triangle configurations (D-3h). The current NN framework uses fundamental invariants (FIs) as the input vector and appropriate symmetry-adapted functions called covariant basis to account for the special symmetry of complete nuclear permutational inversion (CNPI). The resulting diabatic potential energy matrix (DPEM) can reproduce the ab initio adiabatic energies, energy gradients, and derivative couplings between adjacent states as well as the particular symmetry. The accuracy of DPEM is further validated by full-dimensional quantum dynamics calculations. The flexibility of the FI-NN approach based on the VB model shows great potential to resolve diabatization problems for many extended and multistate systems. [Yin, Zhengxi; Fu, Bina; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian 116023, Peoples R China; [Yin, Zhengxi; Fu, Bina; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Dalian 116023, Peoples R China; [Yin, Zhengxi] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Braams, Bastiaan J.] Ctr Wiskunde & Informat CWI, NL-1098 XG Amsterdam, Netherlands Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Fu, BN; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian 116023, Peoples R China.;Fu, BN; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Dalian 116023, Peoples R China. bina@dicp.ac.cn; zhangdh@dicp.ac.cn National Key R&D Program of China [2018YFE0203003]; National Natural Science Foundation of China [21722307, 21673233, 21590804, 21688102]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB17000000]; LiaoNing Revitalization Talents Program [XLYC1907190] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); LiaoNing Revitalization Talents Program This work was supported by the National Key R&D Program of China (Grant No. 2018YFE0203003), the National Natural Science Foundation of China (Grant Nos. 21722307, 21673233, 21590804, and 21688102), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB17000000), and LiaoNing Revitalization Talents Program (XLYC1907190). 76 9 9 5 24 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1549-9618 1549-9626 J CHEM THEORY COMPUT J. Chem. Theory Comput. MAR 9 2021.0 17 3 1678 1690 10.1021/acs.jctc.0c01336 0.0 MAR 2021 13 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics QX1VC 33645221.0 2023-03-23 WOS:000629135700031 0
J Cao, Y; Nakhjiri, AT; Sarkar, SM; Ghadiri, M Cao, Yan; Nakhjiri, Ali Taghvaie; Sarkar, Shaheen M.; Ghadiri, Mahdi Integration of ANN and NSGA-II for Optimization of Nusselt Number and Pressure Drop in a Coiled Heat Exchanger via Water-Based Nanofluid Containing Alumina and Ag Nanoparticles ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING English Article; Early Access Artificial neural network; Nusselt number; NSGA-II optimization algorithm; Pressure drop THERMAL PERFORMANCE; MULTIOBJECTIVE OPTIMIZATION; TRANSFER COEFFICIENT; NEURAL-NETWORK; NANO FLUID; FLOW; CONDUCTIVITY; PARAMETERS; WORKING In this research study, minimizing the pressure drop and maximizing the Nusselt number of water-based nanofluid containing alumina and Ag in a coiled heat exchanger have been considered. The artificial neural network has been implemented to predict nanofluid's pressure drop as well as Nusselt number as a function of nanoparticles volume fraction and Reynolds number. The obtained correlation was found properly accurate as most of the values were accumulated around the line. The value of R, mean error, and STD for the Nusselt number as well as pressure drop were found (0.9996 and 0.9993), (0.007 and - 6.4*10(-5)), and (0.172 and 0.157), respectively. The obtained correlations using ANN are used as input into the NSGA-II optimization algorithm to obtain the optimum pressure drop as well as Nusselt number as a function of nanoparticles volume fraction and Reynolds number. The optimum values were determined in the range of Nusselt number of 8-24 and pressure drop of 2-16 Pa. The optimal point can be chosen according to the process requirement. For the lowest and highest optimum pressures (8.566 and 23.691 Pa), the optimum Nusselt numbers were 2.502 and 115.35, respectively. [Cao, Yan] Xian Technol Univ, Sch Comp Sci & Engn, Xian 710021, Peoples R China; [Nakhjiri, Ali Taghvaie] Islamic Azad Univ, Dept Petr & Chem Engn, Sci & Res Branch, Tehran, Iran; [Sarkar, Shaheen M.] Technol Univ Shannon, Dept Appl Sci, Limerick V94 EC5T, Ireland; [Ghadiri, Mahdi] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Ghadiri, Mahdi] Duy Tan Univ, Fac Environm & Chem Engn, Da Nang 550000, Vietnam Xi'an Technological University; Islamic Azad University; Duy Tan University; Duy Tan University Nakhjiri, AT (corresponding author), Islamic Azad Univ, Dept Petr & Chem Engn, Sci & Res Branch, Tehran, Iran.;Ghadiri, M (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam.;Ghadiri, M (corresponding author), Duy Tan Univ, Fac Environm & Chem Engn, Da Nang 550000, Vietnam. Taghvaiali@yahoo.com; mahdighadiri@duytan.edu.vn 49 0 0 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2193-567X 2191-4281 ARAB J SCI ENG Arab. J. Sci. Eng. 10.1007/s13369-022-07480-3 0.0 DEC 2022 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 7Y1TX 2023-03-23 WOS:000914671800002 0
C Ye, KP; Lou, CT; Suo, XM; Song, YJ; Feng, XX; Ozolins, O; Pang, XD; Zhang, L; Yu, XB Zhang, X; Shum, P; Dong, J Ye, Kangpeng; Lou, Chaoteng; Suo, Xingmeng; Song, Yujie; Feng, Xingxing; Ozolins, Oskars; Pang, Xiaodan; Zhang, Lu; Yu, Xianbin Human Identification by Mean of Optoelectronic Reservoir Computing 13TH INTERNATIONAL PHOTONICS AND OPTOELECTRONICS MEETINGS (POEM 2021) Proceedings of SPIE English Proceedings Paper 13th International Photonics and OptoElectronics Meetings (POEM) NOV 06-08, 2021 Wuhan, PEOPLES R CHINA Huazhong Univ Sci & Technol,Wuhan Natl Lab Optoelectron,Opt Valley Lab Reservoir computing; human identification; optoelectronic; delay feedback loop As an improvement of the traditional recurrent neural networks (RNN), the reservoir computing (RC) only needs to train one output connection weight matrix linearly, which greatly reduces the number of machine learning network calculations. The optoelectronic RC can be realized with a delay feedback loop composed of optical and electrical devices. It has the advantages of lower power consumption and faster speed than the all-electric RC scheme. At the same time, it is easier to be controlled than the all-optical RC scheme. In this paper, we propose to employ the optoelectronic RC to process radar signals to distinguish different persons in the indoor environment. The radar signal required for the simulation is referred from the IDRad data set, which contains the echo signals of the frequency modulated continuous wave (FMCW) radar, and five persons of different ages are free to move around in the room, which is close to the real scene. First, the echo signal is processed and the micro-Doppler features are extracted, and each frame corresponds to a row vector. Then, this vector is used as the input signal of the optoelectronic RC. We numerically studied the impact of parameters such as the size of the RC and the regularization coefficient in the system. Finally, the classification accuracy of five targets reaches 87%. [Ye, Kangpeng; Lou, Chaoteng; Suo, Xingmeng; Song, Yujie; Feng, Xingxing; Zhang, Lu; Yu, Xianbin] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China; [Zhang, Lu; Yu, Xianbin] Zhejiang Lab, Hangzhou 311121, Peoples R China; [Ozolins, Oskars; Pang, Xiaodan] KTH Royal Inst Technol, Sch Engn Sci, S-16440 Kista, Sweden; [Ozolins, Oskars; Pang, Xiaodan] RISE Res Inst Sweden, S-16440 Kista, Sweden; [Ozolins, Oskars] Riga Tech Univ, Inst Telecommun, LV-1048 Riga, Latvia Zhejiang University; Zhejiang Laboratory; Royal Institute of Technology; RISE Research Institutes of Sweden; Riga Technical University Zhang, L (corresponding author), Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China.;Zhang, L (corresponding author), Zhejiang Lab, Hangzhou 311121, Peoples R China. zhanglu1993@zju.edu.cn Pang, Xiaodan/D-5032-2015 Pang, Xiaodan/0000-0003-4906-1704 National Key Research and Development Program of China [2020YFB1805700]; Natural National Science Foundation of China [61771424, 62101483]; Natural Science Foundation of Zhejiang Province [LZ18F010001, LQ21F010015]; Swedish Research Council [2019-05197_VR]; Latvian Council of Science [lzp-2021/1-0062]; State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University; Fundamental Research Funds for the Central Universities [2020QNA5012]; Zhejiang Lab [2020LC0AD01]; RISE SK [P109599] National Key Research and Development Program of China; Natural National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Swedish Research Council(Swedish Research Council); Latvian Council of Science(Latvian Ministry of Education and Science); State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Zhejiang Lab; RISE SK The National Key Research and Development Program of China (2020YFB1805700), in part by the Natural National Science Foundation of China under Grant 61771424 and 62101483, the Natural Science Foundation of Zhejiang Province under Grant LZ18F010001 and LQ21F010015, the Swedish Research Council 2019-05197_VR, RISE SK funded project Optical Neural Networks (P109599), the Latvian Council of Science funded ONN-AI project No. lzp-2021/1-0062, State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University, the Fundamental Research Funds for the Central Universities 2020QNA5012 and Zhejiang Lab (no. 2020LC0AD01). 8 0 0 2 4 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-5185-2; 978-1-5106-5184-5 PROC SPIE 2022.0 12154 1215413 10.1117/12.2625789 0.0 7 Engineering, Electrical & Electronic; Optics Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Optics BT1ED 2023-03-23 WOS:000796864400035 0
C Liu, ZY; Hu, CJ; Li, Y; Hu, JY IEEE Liu, Zhenyu; Hu, Changjun; Li, Yang; Hu, Jingyuan DSDC: A Domain Scientific Data Cloud Based on Virtual Dataspaces 2012 IEEE 26TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS & PHD FORUM (IPDPSW) IEEE International Symposium on Parallel and Distributed Processing Workshops English Proceedings Paper 26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing MAY 21-25, 2012 Shanghai Jiao Tong Univ, Shanghai, PEOPLES R CHINA IEEE,IEEE Comp Soc Tech Comm Parallel Proc (TCPP),ACM SIGARCH,IEEE Comp Soc Tech Comm Comp Architecture,IEEE Comp Soc Tech Comm Distributed Proc (TCDP),IEEE Comp Soc Shanghai Jiao Tong Univ scientific data cloud; virtual dataspaces; semantic integration; ontology; pay-as-you-go New challenges for large-scale data management and sharing have arisen due to the information evolution from massive data to big data. Domain Scientific Data Cloud (DSDC) based on virtual data spaceis proposed in this paper to solve the problems of big data management, reusability and service in scientific field. The application of DSDC (MSDC) is also introduced, which is used in the field of materials science. Virtual dataspace is then defined. In order to build the dataspace, an ontology and logical resources model is discussed. A new mapping and evolutionary model is described as a pay-as-you-go fashion virtual dataspace, which introduces a partial schema, semi-automatically evolution method. A scientific data cloud application model is also proposed for defining service templates. Finally, the DSDC is verified by material service safety evaluation, the results show that DSDC is efficient in scientific data management and suitable for data-intensive application. [Liu, Zhenyu; Hu, Changjun; Li, Yang] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Hu, Jingyuan] Univ Technol Troyes, Troyes, France University of Science & Technology Beijing; Universite de Technologie de Troyes Liu, ZY (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China. zhenyu1984@gmail.com; huchangjun@ies.ustb.edu.cn; mailbox.liyang@gmail.com; hjyfirst@163.com 26 3 3 0 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-7062 978-0-7695-4676-6 IEEE SYM PARA DISTR 2012.0 2176 2182 10.1109/IPDPSW.2012.269 0.0 7 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BCA26 2023-03-23 WOS:000309409400280 0
J Li, XJ; Mou, J; Cao, YH; Santo Banerjee Li, Xuejun; Mou, Jun; Cao, Yinghong; Santo Banerjee An Optical Image Encryption Algorithm Based on a Fractional-Order Laser Hyperchaotic System INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS English Article Optical image encryption; fractional-order laser hyperchaotic system; hyperchaotic system; fractional Fourier transform; BP neural network; GF(17) domain CHAOTIC SYSTEM; GYRATOR TRANSFORM; COMPRESSION; OPERATION; MAP In recent years, image encryption schemes based on optical methods have been extensively studied. However, possible optical encryption methods in combination with fractional-order laser hyperchaotic systems have not been reported. Therefore, an optical image encryption scheme based on fractional Fourier transform and five-dimensional host-induced nonlinearity fractional-order laser hyperchaotic system is studied in this paper. Firstly, the dynamical characteristics of the proposed fractional-order laser hyperchaotic system are analyzed, and the DSP platform is used to realize the system. Then, a novel image encryption scheme is proposed combining BP neural network, GF(17) domain diffusion and hyperchaotic, random point scrambling algorithm. Finally, the performance of the encryption scheme is analyzed in detail. This work provides an experimental basis and theoretical guidance for image secure communication combining fractional-order laser hyperchaotic systems and optical methods and offers a new research perspective for optical image encryption. [Li, Xuejun; Mou, Jun; Cao, Yinghong] Dalian Polytech Univ, Sch Informat Sci & Engn, Dalian 116034, Peoples R China; [Santo Banerjee] Politecn Torino, Dept Math Sci, Giuseppe Luigi Lagrange, Corso Duca Abruzzi 24, Turin, Italy Dalian Polytechnic University; Polytechnic University of Turin Mou, J; Cao, YH (corresponding author), Dalian Polytech Univ, Sch Informat Sci & Engn, Dalian 116034, Peoples R China.;Santo Banerjee (corresponding author), Politecn Torino, Dept Math Sci, Giuseppe Luigi Lagrange, Corso Duca Abruzzi 24, Turin, Italy. moujun@csu.edu.cn; caoyinghong@dlpu.edu.cn; santoban@gmail.com Mou, Jun/0000-0002-7774-2833 National Natural Science Foundation of China [62061014]; Natural Science Foundation of Liaoning province [2020-MS-274]; Basic Scientific Research Projects of Colleges and Universities of Liaoning Province [LJKZ0545] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Liaoning province(Natural Science Foundation of Liaoning Province); Basic Scientific Research Projects of Colleges and Universities of Liaoning Province This work was supported by the National Natural Science Foundation of China (Grant Nos. 62061014); the Natural Science Foundation of Liaoning province (2020-MS-274); the Basic Scientific Research Projects of Colleges and Universities of Liaoning Province (Grant No. LJKZ0545). 53 33 33 42 84 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-1274 1793-6551 INT J BIFURCAT CHAOS Int. J. Bifurcation Chaos MAR 15 2022.0 32 3 2250035 10.1142/S0218127422500353 0.0 25 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Science & Technology - Other Topics ZQ9YX 2023-03-23 WOS:000767452600007 0
J Qiu, CP; Li, H; Guo, WY; Chen, X; Yu, AZ; Tong, XC; Schmitt, M Qiu, Chunping; Li, He; Guo, Wenyue; Chen, Xin; Yu, Anzhu; Tong, Xiaochong; Schmitt, Michael Transferring Transformer-Based Models for Cross-Area Building Extraction From Remote Sensing Images IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Transformers; Adaptation models; Feature extraction; Buildings; Task analysis; Data models; Semantics; Benchmark dataset; building extraction (BE); convolutional neural networks (CNNs); generalization ability; remote sensing (RS); transformer SEGMENTATION; NETWORK Extracting buildings from remote sensing (RS) images is an important task with a variety of applications. Considerable attention has focused on achieving new state-of-the-art (SOTA) accuracy with more and more advanced deep learning (DL) models. However, the developed models still hardly generalize across geographical areas, hindering the practical use of SOTA approaches. To attack this problem, we established a baseline for model cross-area generalization ability using available datasets for building extraction (BE). In addition to two popular fully convolutional neural network (FCN) based models, we first adapted two novel transformer-based models, shifted windows (Swin) transformer and SegFormer, which are all able to output SOTA accuracy with no big difference when tested within one area. However, experimental results show that all models fail to generalize to a different area. We then propose to fine-tune pretrained models from one area on a small subset of an unseen area, the effectiveness of which depends on the model choice and the data size for tuning. By jointly taking advantage of the transfer learning idea and the multiscale feature learning ability of SegFormer, a distinct improvement has been achieved compared to results from Swin transformer and FCN-based models trained on the same amount of data. Commonly used metric, Intersection over Union, can be increased from 38.97% to 70.86%, and from 48.36% to 74.51%, when using 10% and 30% subset of the targeting area, respectively. The influence of model choice and data size for tuning has also been investigated. Our work contributes to complementing the algorithm development and within-area model evaluation in the hot field of BE from RS images. [Qiu, Chunping; Li, He; Guo, Wenyue; Chen, Xin; Yu, Anzhu; Tong, Xiaochong] PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Peoples R China; [Qiu, Chunping] Acad Mil Sci, Res Ctr Artificial Intelligence, Natl Innovat Inst Def Technol, Beijing 100850, Peoples R China; [Schmitt, Michael] Bundeswehr Univ Munich, D-85579 Neubiberg, Germany PLA Information Engineering University; Bundeswehr University Munich Li, H; Guo, WY (corresponding author), PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Peoples R China. chunping.qiu@outlook.com; lihe_5115@zxiat.org; guowyer@163.com; xinchen_costnos@126.com; anzhu_yu@126.com; txchr@aliyun.com; michael.schmitt@unibw.de Schmitt, Michael/J-7326-2013 Schmitt, Michael/0000-0002-0575-2362; Chen, Xin/0000-0001-8332-9132 National Natural Science Foundation of China [42101458, 41801388, 42130112]; Natural Science Foundation of Henan Province of China [212300410096, 222300420592]; National Key Research and Development Program of China [2018YFB0505304] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Henan Province of China; National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 42101458, Grant 41801388, and Grant 42130112, in part by the Natural Science Foundation of Henan Province of China under Grant 212300410096 and Grant 222300420592, and in part by the National Key Research and Development Program of China under Grant 2018YFB0505304. 61 4 4 21 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15 4104 4116 10.1109/JSTARS.2022.3175200 0.0 13 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 1T3MR gold 2023-03-23 WOS:000804637700007 0
J Saeb, MR; Mohammadi, Y; Kermaniyan, TS; Zinck, P; Stadler, FJ Saeb, Mohammad Reza; Mohammadi, Yousef; Kermaniyan, Tayebeh Sadat; Zinck, Philippe; Stadler, Florian J. Unspoken aspects of chain shuttling reactions: Patterning the molecular landscape of olefin multi-block copolymers POLYMER English Article Olefin multi-block copolymer; Artificial neural network; Chain shuttling reaction; Tailored copolymer; Molecular pattern BATCH POLYMERIZATION REACTOR; NEURAL-NETWORK MODELS; BLOCK-COPOLYMERS; BLACK-BOX; MICROSTRUCTURE; REACTIVITIES; ISOPRENE; POLYMERS; ETHYLENE; STYRENE Molecular landscape of olefin block copolymers (OBCs) was patterned by hybridizing capabilities of Kinetic Monte Carlo (KMC) and Artificial Neural Network (ANN) stochastic modeling approaches to explore complexities with chain shuttling copolymerization of ethylene with alpha-olefins. Theoretical data on chain microstructure were obtained by an in-house KMC simulator. The interdependence between microstructure and operating conditions was uncovered by ANN modeling. The average number of linkage points per OBC chain is monitored as a direct criterion reflecting the multi-block nature of OBCs. We also quantified hard and soft block length and ethylene sequence length of both blocks in terms of catalyst composition, ethylene to 1-octene ratio, and chain shuttling agent level, giving useful insights to be applied to developing tailored OBCs. The proposed hybrid stochastic modeling approach successfully predicts the conditions for producing OBCs with predesigned structure; i.e., block length, block number, and ethylene sequence length in hard and soft segments of OBC. As a unique feature of this work, we suggest operation condition for developing and identifying new families of OBCs with microstructures that were previously unexplored. (C) 2017 Elsevier Ltd. All rights reserved. [Saeb, Mohammad Reza] Inst Color Sci & Technol, Dept Resin & Addit, POB 16765-654, Tehran, Iran; [Mohammadi, Yousef] NPC, Petrochem Res & Technol Co NPC Rt, POB 14358-84711, Tehran, Iran; [Kermaniyan, Tayebeh Sadat] Amirkabir Univ Technol, Dept Polymer Engn & Color Technol, Tehran, Iran; [Zinck, Philippe] Univ Lille, ENSCL, UCCS, UMR CNRS 8181, Bat C7, F-59652 Villeneuve Dascq, France; [Stadler, Florian J.] Shenzhen Univ, Nanshan Dist Key Lab Biopolymers & Safety Evaluat, Shenzhen Key Lab Polymer Sci & Technol, Guangdong Res Ctr Interfacial Engn Funct Mat,Coll, Shenzhen 518060, Peoples R China Amirkabir University of Technology; Universite de Lille - ISITE; Centrale Lille; Universite de Lille; Shenzhen University Mohammadi, Y (corresponding author), NPC, Petrochem Res & Technol Co NPC Rt, POB 14358-84711, Tehran, Iran.;Stadler, FJ (corresponding author), Shenzhen Univ, Nanshan Dist Key Lab Biopolymers & Safety Evaluat, Shenzhen Key Lab Polymer Sci & Technol, Guangdong Res Ctr Interfacial Engn Funct Mat,Coll, Shenzhen 518060, Peoples R China. mohammadi@npc-rt.ir; fjstadler@szu.edu.cn Stadler, Florian J./A-7153-2008; Saeb, Mohammad Reza/AER-2724-2022 Stadler, Florian J./0000-0002-5849-1485; Saeb, Mohammad Reza/0000-0001-9907-9414; Mohammadi, Yousef/0000-0001-9401-3715; Mohammadi, Yousef/0000-0002-2166-9913 National Science Foundation of China [21574086]; Nanshan District Key Lab for Biopolymers and Safety Evaluation [KC2014ZDZJ0001A]; Shenzhen Sci & Tech research grant [ZDSYS201507141105130]; Shenzhen City Science and Technology Plan Project [JCYJ20140509172719311] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Nanshan District Key Lab for Biopolymers and Safety Evaluation; Shenzhen Sci & Tech research grant; Shenzhen City Science and Technology Plan Project FJS would like to thank the National Science Foundation of China (21574086), Nanshan District Key Lab for Biopolymers and Safety Evaluation ( No. KC2014ZDZJ0001A), Shenzhen Sci & Tech research grant (ZDSYS201507141105130), and Shenzhen City Science and Technology Plan Project (JCYJ20140509172719311) for financial support. 31 19 19 2 55 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0032-3861 1873-2291 POLYMER Polymer MAY 5 2017.0 116 SI 55 75 10.1016/j.polymer.2017.03.033 0.0 21 Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Polymer Science EU5YE Green Published, Bronze 2023-03-23 WOS:000401109600007 0
J Li, MY; Jiang, ZH; Liu, YT; Chen, SH; Wozniak, M; Scherer, R; Damasevicius, R; Wei, W; Li, ZY; Li, ZX Li, Miaoyu; Jiang, Zhuohan; Liu, Yutong; Chen, Shuheng; Wozniak, Marcin; Scherer, Rafal; Damasevicius, Robertas; Wei, Wei; Li, Ziyi; Li, Zuxin Sitsen: Passive sitting posture sensing based on wireless devices INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS English Review Sitting posture recognition; radio frequency identification; back propagation net; principal component analysis; device-free LOW-BACK-PAIN; RISK-FACTORS; TIME; FLEXION; PEOPLE Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%. [Li, Miaoyu; Jiang, Zhuohan; Liu, Yutong; Chen, Shuheng; Li, Ziyi] Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China; [Wozniak, Marcin] Silesian Tech Univ, Fac Appl Math, Gliwice, Poland; [Scherer, Rafal] Czestochowa Tech Univ, Czestochowa, Poland; [Damasevicius, Robertas] Kaunas Univ Technol, Fac Informat, Multimedia Engn Dept, Kaunas, Lithuania; [Wei, Wei] Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian 710048, Shannxi, Peoples R China; [Li, Zuxin] Huzhou Univ, Sch Engn, Huzhou, Peoples R China Northwest University Xi'an; Silesian University of Technology; Technical University Czestochowa; Kaunas University of Technology; Xi'an University of Technology; Huzhou University Wei, W (corresponding author), Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian 710048, Shannxi, Peoples R China. weiwei@xaut.edu.cn wei, wei/HHR-8613-2022; Damaševičius, Robertas/E-1387-2017; Wei, Wei/ABB-8665-2021; Scherer, Rafal/F-6745-2012 Damaševičius, Robertas/0000-0001-9990-1084; Wei, Wei/0000-0002-8751-9205; Scherer, Rafal/0000-0001-9592-262X; Li, Miaoyu/0000-0002-7326-8409; Li, Ziyi/0000-0002-0850-7960 National Natural Science Foundation of China [61602382]; Science and Technology Innovation Team Supported Project of Shaanxi Province [2018TD-O26]; Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-036, 2019GY-012]; Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Innovation Team Supported Project of Shaanxi Province; Key Research and Development Program of Shaanxi Province; Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the National Natural Science Foundation of China (61602382), the Science and Technology Innovation Team Supported Project of Shaanxi Province (2018TD-O26), the Key Research and Development Program of Shaanxi Province (nos 2018ZDXM-GY-036 and 2019GY-012) and the Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data (no. IPBED7). 29 9 9 6 24 SAGE PUBLICATIONS INC THOUSAND OAKS 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA 1550-1477 INT J DISTRIB SENS N Int. J. Distrib. Sens. Netw. JUL 2021.0 17 7 15501477211024846 10.1177/15501477211024846 0.0 11 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications UI9MS gold 2023-03-23 WOS:000690922100001 0
J Chen, DP; Chen, ZJ; Zhang, YS; Qu, X; Zhang, MY; Wu, CZ Chen, Depeng; Chen, Zhijun; Zhang, Yishi; Qu, Xu; Zhang, Mingyang; Wu, Chaozhong Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model JOURNAL OF ADVANCED TRANSPORTATION English Article DRIVER ASSISTANCE; CLASSIFICATION In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles. [Chen, Depeng; Chen, Zhijun; Wu, Chaozhong] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Peoples R China; [Zhang, Yishi] Wuhan Univ Technol, Sch Management, Wuhan, Peoples R China; [Qu, Xu] Southeast Univ, Sch Transportat, Nanjing, Peoples R China; [Zhang, Mingyang] Aalto Univ, Sch Engn, Espoo, Finland Wuhan University of Technology; Wuhan University of Technology; Southeast University - China; Aalto University Chen, ZJ (corresponding author), Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Peoples R China.;Zhang, YS (corresponding author), Wuhan Univ Technol, Sch Management, Wuhan, Peoples R China. chenzj556@whut.edu.cn; yszhang@whut.edu.cn Zhang, Mingyang/AEC-5093-2022 Zhang, Mingyang/0000-0001-5820-2789; Zhang, Yishi/0000-0001-6269-1239; wu, chaozhong/0000-0003-3338-0436 National Key R&D Program of China [2018YFB1600600]; National Natural Science Foundation of China [61703319, 71702066, 51775396, U1764262]; Major Project of Technological Innovation of Hubei Province [2017CFA008]; Fundamental Research Funds for the Central Universities [WUT:2021VI042] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Project of Technological Innovation of Hubei Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key R&D Program of China under grant 2018YFB1600600; in part by the National Natural Science Foundation of China under grants 61703319, 71702066, 51775396, and U1764262; in part by the Major Project of Technological Innovation of Hubei Province under grant 2017CFA008; and in part by the Fundamental Research Funds for the Central Universities under grant WUT:2021VI042. 31 5 5 9 33 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 0197-6729 2042-3195 J ADV TRANSPORT J. Adv. Transp. JUN 2 2021.0 2021 6687378 10.1155/2021/6687378 0.0 12 Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation TF1BS gold, Green Published 2023-03-23 WOS:000670448600001 0
J Chen, T; Chang, QR; Clevers, JGPW; Kooistra, L Chen, Tao; Chang, Qingrui; Clevers, J. G. P. W.; Kooistra, L. Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy ENVIRONMENTAL POLLUTION English Article Soil cadmium; Visible and near-infrared spectroscopy; Orthogonal signal correction; Back propagation neural network; Indicator kriging HEAVY-METAL CONTAMINATION; PARTIAL LEAST-SQUARES; REFLECTANCE SPECTROSCOPY; ORGANIC-MATTER; MINING AREA; REGRESSION; SPECTRA; IRRIGATION; SALINITY; PLANTS Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates. (C) 2015 Elsevier Ltd. All rights reserved. [Chen, Tao; Chang, Qingrui] Northwest A&F Univ, Coll Resources & Environm, Yangling 712100, Shaanxi, Peoples R China; [Clevers, J. G. P. W.; Kooistra, L.] Wageningen Univ, Lab Geoinformat Sci & Remote Sensing, NL-6708 PB Wageningen, Netherlands Northwest A&F University - China; Wageningen University & Research Chen, T (corresponding author), Northwest A&F Univ, Coll Resources & Environm, Yangling 712100, Shaanxi, Peoples R China. davidlp77@zju.edu.cn; changqr@nwsuaf.edu.cn Kooistra, Lammert/I-6331-2016; Kooistra, Lammert/AAW-2172-2020; Chen, Tao/A-4643-2015; Clevers, Jan/N-1278-2014 Kooistra, Lammert/0000-0001-5549-5993; Kooistra, Lammert/0000-0001-5549-5993; Chen, Tao/0000-0001-9162-4078; Clevers, Jan/0000-0002-0046-082X National High Technology Research and Development Program of China [2013AA102401]; National Study Abroad Funding National High Technology Research and Development Program of China(National High Technology Research and Development Program of China); National Study Abroad Funding The research was sponsored by the National High Technology Research and Development Program of China (2013AA102401) and the National Study Abroad Funding. 63 102 107 4 102 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0269-7491 1873-6424 ENVIRON POLLUT Environ. Pollut. NOV 2015.0 206 217 226 10.1016/j.envpol.2015.07.009 0.0 10 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology CY2JV 26188912.0 2023-03-23 WOS:000366235700028 0
C Li, L; Xia, ZQ; Li, LH; Jiang, XY; Feng, XY; Roli, F IEEE Li, Lei; Xia, Zhaoqiang; Li, Linghan; Jiang, Xiaoyue; Feng, Xiaoyi; Roli, Fabio FACE ANTI-SPOOFING VIA HYBRID CONVOLUTIONAL NEURAL NETWORK 2017 INTERNATIONAL CONFERENCE ON THE FRONTIERS AND ADVANCES IN DATA SCIENCE (FADS) English Proceedings Paper International Conference on the Frontiers and Advances in Data Science (FADS) OCT 23-25, 2017 Xian, PEOPLES R CHINA Univ Essex,NW Univ,IEEE UK & RI Comp Chapter,IEEE Xian Sect,SAGE Publishing Face anti-spoofing techniques have been developed several years since the face recognition systems were successfully applied. Existing approaches in this topic merely use the entire region of human face. However, different facial parts always have different structures and the full-face model maybe weaken the discrepancy of the specific parts. So training the specific model for each facial part can improve the performance of anti-spoofing. Motivated by this, in this paper, we propose a new method of face anti-spoofing leveraging hybrid convolutional neural network (CNN) for facial parts. The main procedure of our work can be summarized as follows: (i) We divide the face into several parts; (ii) Based on different parts, training the corresponding CNN model of each part, which will constitute the hybrid CNN; (iii) Concatenating the last layer of the hybrid model to train a SVM classifier; (iv) By the SVM to distinguish the real and fake faces. We tested the effectiveness of our method on two public available databases, Replay-Attack and CASIA, and the experiments show our proposed method can obtain satisfactory results with respect to the state-of-the-art methods. [Li, Lei; Xia, Zhaoqiang; Li, Linghan; Jiang, Xiaoyue; Feng, Xiaoyi] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China; [Roli, Fabio] Univ Cagliari, Dept Elect & Elect Engn, Cagliari, Italy Northwestern Polytechnical University; University of Cagliari Xia, ZQ (corresponding author), Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China. zxia@nwpu.edu.cn Xia, Zhaoqiang/AAC-4021-2019 Xia, Zhaoqiang/0000-0003-0630-3339 National Undergraduate Training Programs for Innovation and Entrepreneurship [201710699371]; National Nature Science Foundation of China [61702419] National Undergraduate Training Programs for Innovation and Entrepreneurship; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is partly supported by the National Undergraduate Training Programs for Innovation and Entrepreneurship (NO. 201710699371) and the National Nature Science Foundation of China (NO. 61702419). 18 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-3148-5 2017.0 148 152 5 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL6PD 2023-03-23 WOS:000454608700028 0
J Wang, ZW; Li, JY; Wu, JT; Xu, H Wang, Zewen; Li, Jiayi; Wu, Jieting; Xu, Hui Application of Deep Learning Algorithms to Visual Communication Courses FRONTIERS IN PSYCHOLOGY English Article deep learning; visual communication courses; fast style transfer network; image style transfer; TensorFlow NETWORK There are rare studies on the combination of visual communication courses and image style transfer. Nevertheless, such a combination can make students understand the difference in perception brought by image styles more vividly. Therefore, a collaborative application is reported here combining visual communication courses and image style transfer. First, the visual communication courses are sorted out to obtain the relationship between them and image style transfer. Then, a style transfer method based on deep learning is designed, and a fast transfer network is introduced. Moreover, the image rendering is accelerated by separating training and execution. Besides, a fast style conversion network is constructed based on TensorFlow, and a style model is obtained after training. Finally, six types of images are selected from the Google Gallery for the conversion of image style, including landscape images, architectural images, character images, animal images, cartoon images, and hand-painted images. The style transfer method achieves excellent effects on the whole image besides the part hard to be rendered. Furthermore, the increase in iterations of the image style transfer network alleviates lack of image content and image style. The image style transfer method reported here can quickly transmit image style in less than 1 s and realize real-time image style transmission. Besides, this method effectively improves the stylization effect and image quality during the image style conversion. The proposed style transfer system can increase students' understanding of different artistic styles in visual communication courses, thereby improving the learning efficiency of students. [Wang, Zewen] Ningbo Univ, Pan Tianshou Coll Architecture Art & Design, Ningbo, Peoples R China; [Li, Jiayi] Polytech Univ Turin, Dept Control & Comp Engn, Turin, Italy; [Wu, Jieting] Engn Univ Armed Police Force, Urumqi, Peoples R China; [Xu, Hui] Univ Perpetual Help Syst DALTA, Coll Educ, Manila, Philippines Ningbo University; Polytechnic University of Turin Xu, H (corresponding author), Univ Perpetual Help Syst DALTA, Coll Educ, Manila, Philippines. 841206bluesky@163.com 33 0 0 9 34 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. SEP 29 2021.0 12 713723 10.3389/fpsyg.2021.713723 0.0 10 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology WI4LB 34659027.0 Green Accepted, gold 2023-03-23 WOS:000708332700001 0
J Zhang, JC; Jiao, K; Zhang, TT; Zhang, TJ; Yu, B Zhang, Jian-Chen; Jiao, Kang; Zhang, Tingting; Zhang, Tong-Jie; Yu, Bo A Reliable Calibration of H II Galaxies Hubble Diagram with Cosmic Chronometers and Artificial Neural Network ASTROPHYSICAL JOURNAL English Article INDEPENDENT COSMOLOGICAL CONSTRAINTS; LUMINOUS RED GALAXIES; PRECISION COSMOLOGY; SUPERNOVAE; CONSTANT; LAMBDA The L-sigma relation of H II galaxies (HIIGx) calibrated by a distance indicator is a reliable standard candle for measuring the Hubble constant H-0. The most straightforward calibration technique anchors them with the first tier of distance ladders from the same galaxies. Recently another promising method that uses the cosmological model-independent cosmic chronometers as a calibrator has been proposed. We promote this technique by removing the assumptions about the cosmic flatness and using a nonparametric artificial neural network for the data reconstruction process. We observe a correlation between the cosmic curvature density parameter and the slope of the L-sigma relation, thereby improving the reliability of the calibration. Using the calibrated HIIGx Hubble diagram, we obtain a Type Ia supernovae Hubble diagram free of the conventional assumption about H-0. Finally we get a value of H-0 = 65.9(-2.9)(+3.0) km s(-1) Mpc(-1), which is compatible with the latest Planck 18 measurement. [Zhang, Jian-Chen; Jiao, Kang; Zhang, Tong-Jie] Beijing Normal Univ, Inst Frontiers Astron & Astrophys, Beijing 102206, Peoples R China; [Zhang, Jian-Chen; Jiao, Kang; Zhang, Tong-Jie] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China; [Zhang, Jian-Chen] Dezhou Univ, Coll Comp & Informat Engn, Dezhou 253023, Peoples R China; [Zhang, Jian-Chen; Zhang, Tong-Jie; Yu, Bo] Dezhou Univ, Inst Astron Sci, Dezhou 253023, Peoples R China; [Jiao, Kang] Alma Mater Studiorum Univ Bologna, Dipartimento Fis & Astron Augusto Righi, Via Piero Gobetti 93-2, I-40129 Bologna, Italy; [Zhang, Tingting] PLA Army Engn Univ, Coll Command & Control Engn, Nanjing 210000, Peoples R China; [Yu, Bo] Dezhou Univ, Sch Math & Big Data, Dezhou 253023, Peoples R China; [Yu, Bo] Dezhou Univ, Inst Biophys, Shandong Prov Key Lab Biophys, Dezhou 253023, Peoples R China Beijing Normal University; Beijing Normal University; Dezhou University; Dezhou University; University of Bologna; Army Engineering University of PLA; Dezhou University; Dezhou University Zhang, TT (corresponding author), PLA Army Engn Univ, Coll Command & Control Engn, Nanjing 210000, Peoples R China. 101101964@seu.edu.cn; tjzhang@bnu.edu.cn Zhang, Jianchen/0000-0002-1428-8311; Zhang, Tong-Jie/0000-0002-3363-9965; Jiao, Kang/0000-0003-0167-9345; Yu, Bo/0000-0002-9849-1762 National Science Foundation of China [61802428, 11929301]; China Scholarship Council (CSC) from the Ministry of Education of P.R. China National Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC) from the Ministry of Education of P.R. China We appreciate the referee's several useful suggestions, as well as Guo-Jian Wang's kindly instruction about the ReFANN. We also thank Yu-Chen Wang, Yun-Long Li and Cheng-Zong Ruan for valuable discussions. This work is supported by the National Science Foundation of China (grants Nos. 61802428, 11929301). Kang Jiao is also funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China. 46 0 0 3 3 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. SEP 1 2022.0 936 1 21 10.3847/1538-4357/ac85aa 0.0 8 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 4A1AH gold, Green Submitted 2023-03-23 WOS:000844843000001 0
J Hengl, T; de Jesus, JM; Heuvelink, GBM; Gonzalez, MR; Kilibarda, M; Blagotic, A; Shangguan, W; Wright, MN; Geng, XY; Bauer-Marschallinger, B; Guevara, MA; Vargas, R; MacMillan, RA; Batjes, NH; Leenaars, JGB; Ribeiro, E; Wheeler, I; Mantel, S; Kempen, B Hengl, Tomislav; de Jesus, Jorge Mendes; Heuvelink, Gerard B. M.; Gonzalez, Maria Ruiperez; Kilibarda, Milan; Blagotic, Aleksandar; Shangguan, Wei; Wright, Marvin N.; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A.; Batjes, Niels H.; Leenaars, Johan G. B.; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas SoilGrids250m: Global gridded soil information based on machine learning PLOS ONE English Article SPATIOTEMPORAL INTERPOLATION; ORGANIC-CARBON; DATABASE; PREDICTIONS; DATASETS; FORESTS; MODELS; WATER; MAPS; TOOL This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods D random forest and gradient boosting and/or multinomial logistic regression D as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10 -fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License. [Hengl, Tomislav; de Jesus, Jorge Mendes; Heuvelink, Gerard B. M.; Gonzalez, Maria Ruiperez; Batjes, Niels H.; Leenaars, Johan G. B.; Ribeiro, Eloi; Mantel, Stephan; Kempen, Bas] ISRIC World Soil Informat, Wageningen, Netherlands; [Kilibarda, Milan] Univ Belgrade, Fac Civil Engn, Belgrade, Serbia; [Blagotic, Aleksandar] GILab Ltd, Belgrade, Serbia; [Shangguan, Wei] Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China; [Wright, Marvin N.] Inst Med Biometrie & Stat, Lubeck, Germany; [Geng, Xiaoyuan] Agr & Agri Food Canada, Ottawa, ON, Canada; [Bauer-Marschallinger, Bernhard] Vienna Univ Technol, Dept Geodesy & Geoinformat, Vienna, Austria; [Guevara, Mario Antonio; Vargas, Rodrigo] Univ Delaware, Newark, DE USA; [MacMillan, Robert A.] LandMapper Environm Solut Inc, Edmonton, AB, Canada; [Wheeler, Ichsani] Envirometrix Inc, Wageningen, Netherlands University of Belgrade; Sun Yat Sen University; Agriculture & Agri Food Canada; Technische Universitat Wien; University of Delaware Hengl, T (corresponding author), ISRIC World Soil Informat, Wageningen, Netherlands. tom.hengl@isric.org Snow, Val O/O-8693-2016; Heuvelink, Gerard/AAD-5916-2022; Marschallinger, Berhard/AAP-8088-2020; Shangguan, Wei/C-5157-2008; Batjes, Niels H./F-7195-2010; Vargas, Rodrigo/C-4720-2008; Guevara, Mario/AAH-9539-2019; Hengl, Tomislav/AAE-9320-2019 Snow, Val O/0000-0002-6911-8184; Heuvelink, Gerard/0000-0003-0959-9358; Marschallinger, Berhard/0000-0001-7356-7516; Shangguan, Wei/0000-0001-7622-8210; Batjes, Niels H./0000-0003-2367-3067; Vargas, Rodrigo/0000-0001-6829-5333; Guevara, Mario/0000-0002-9788-9947; Hengl, Tomislav/0000-0002-9921-5129; Mendes de Jesus, Jorge Samuel/0000-0001-9890-067X; Ribeiro, Eloi/0000-0002-2324-3000; Leenaars, Johan G.B./0000-0003-1091-7114; Kilibarda, Milan/0000-0002-2930-3596; Wright, Marvin N./0000-0002-8542-6291 Dutch government; GILAB DOO Dutch government; GILAB DOO ISRIC is a non-profit organization primarily funded by the Dutch government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. GILAB DOO provided support in the form of salaries for author AB, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the author contributions' section. 89 1620 1667 129 711 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One FEB 16 2017.0 12 2 e0169748 10.1371/journal.pone.0169748 0.0 40 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics EL2BG 28207752.0 Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000394424500005 0
J Zhang, CX; Li, TJ; Jin, P; Yuan, Y; Ouyang, XP; Marchesoni, F; Huang, JP Zhang, Chuanxin; Li, Tianjiao; Jin, Peng; Yuan, Yuan; Ouyang, Xiaoping; Marchesoni, Fabio; Huang, Jiping Extracting stellar emissivity via a machine learning analysis of MSX and LAMOST catalog data PHYSICAL REVIEW D English Article ANGULAR DIAMETERS; ABUNDANCES; DISTRIBUTIONS; TEMPERATURES; PARAMETERS; STARS Many astronomical studies model stars like radiating blackbodies with unit emissivity. Their conclusions should be reconsidered if the stellar spectral emissivity, epsilon lambda, were to be proven to be appreciably smaller. However, determining epsilon lambda from raw observational data poses serious technical challenges. Here, using a machine learning technique, we implemented an inverse model for calculating the stellar spectral radiation flux in a given spectral band emissivity. Radiation flux data in some spectral bands serve as input to determine the unknown model parameters. To this purpose, we chose 411 stars (361 from the Midcourse Space Experiment (MSX) catalog and 50 from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) catalog) as training samples of a stochastic particle swarm optimization algorithm. The mean values of the emissivity estimates thus obtained deviate significantly from the ideal blackbody value. Knowledge of the model parameters then enabled us to calculate the radiation fluxes in other spectral bands to compare with the existing observational data and thus validate our approach. Finally, based on the trained algorithm, we discuss our predictions for spectral bands where astronomical data are unavailable. Besides providing direct evidence against modeling stars as emitting blackbodies, our conclusions also call for more direct investigations of the stellar emissivity. [Zhang, Chuanxin; Jin, Peng; Huang, Jiping] Fudan Univ, Dept Phys, State Key Lab Surface Phys, Shanghai 200438, Peoples R China; [Zhang, Chuanxin; Jin, Peng; Huang, Jiping] Fudan Univ, Key Lab Micro & Nano Photon Struct MOE, Shanghai 200438, Peoples R China; [Li, Tianjiao] Nanjing Univ Sci & Technol, Sch Energy & Power Engn, MIIT Key Lab Thermal Control Elect Equipment, Nanjing 210094, Peoples R China; [Yuan, Yuan] Harbin Inst Technol, Sch Energy Sci & Engn, Harbin 150001, Peoples R China; [Ouyang, Xiaoping] Xiangtan Univ, Sch Mat Sci & Engn, Xiangtan 411105, Peoples R China; [Marchesoni, Fabio] Tongji Univ, Sch Phys Sci & Engn, Shanghai 200092, Peoples R China; [Marchesoni, Fabio] Univ Camerino, Dept Phys, I-62032 Camerino, Italy Fudan University; Fudan University; Nanjing University of Science & Technology; Harbin Institute of Technology; Xiangtan University; Tongji University; University of Camerino Zhang, CX; Huang, JP (corresponding author), Fudan Univ, Dept Phys, State Key Lab Surface Phys, Shanghai 200438, Peoples R China. cxzhang@fudan.edu.cn; jphuang@fudan.edu.cn Huang, Jiping/HPB-9148-2023 Huang, Jiping/0000-0002-3617-3275 National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; [11725521]; [12035004]; [20JC1414700] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); ; ; ACKNOWLEDGMENTS We acknowledge financial support from the National Natural Science Foundation of China under GrantsNo. 11725521 and No. 12035004 and from the Science and Technology Commission of Shanghai Municipality under Grant No. 20JC1414700. 62 0 0 4 4 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D DEC 30 2022.0 106 12 123035 10.1103/PhysRevD.106.123035 0.0 13 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 7P0JI 2023-03-23 WOS:000908400100004 0
J Yuan, HL; Ma, RH; Atzberger, C; Li, F; Loiselle, SA; Luo, JH Yuan, Huili; Ma, Ronghua; Atzberger, Clement; Li, Fei; Loiselle, Steven Arthur; Luo, Juhua Estimating Forest fAPAR from Multispectral Landsat-8 Data Using the Invertible Forest Reflectance Model INFORM REMOTE SENSING English Article LEAF-AREA INDEX; NEURAL-NETWORK ESTIMATION; BIDIRECTIONAL REFLECTANCE; BOREAL FORESTS; PART 1; VEGETATION; VARIABLES; RADIATION; INVERSION; LAI The estimation of the Fraction of Absorbed Photosynthetically Active Radiation in forests (forest fAPAR) from multi-spectral Landsat-8 data is investigated in this paper using a physically based radiative transfer model (Invertible Forest Reflectance Model, INFORM) combined with an inversion strategy based on artificial neural nets (ANN). To derive the forest fAPAR for the Dabie mountain test site in China in 30 m spatial resolution (size approximately 3000 km(2)), a database of forest canopy spectral reflectances was simulated with INFORM taking into account structural variables such as leaf area index (LAI), crown coverage and stem density as well as leaf composition. To establish the relationship between forest fAPAR and the reflectance modeled by INFORM, a logarithmic relationship between LAI and fAPAR was used previously established using on-site field measurements. On this basis, predictive models between Landsat-8 reflectance and fAPAR were established using an artificial neural network. After calibrating INFORM for the test site, forty-two forest stands were used to validate the performance of the method. The results show that spectral signatures modeled by INFORM correspond reasonably well with the forest canopy reflectance spectra derived from Landsat data. Deviations increase with increasing angle between surface normal of the hilly terrain and sun incidence. The comparison of estimated and measured fAPAR (R-2 = 0.47, RMSE = 0.11) demonstrates that INFORM can be inverted using neural nets to provide acceptable estimates of forest fAPAR. The accuracy of the predictions increased significantly when excluding pixels located in very steep terrain. This demonstrates that the applied topographic correction was not sufficiently accurate and should be improved for making optimum use of radiative transfer models such as INFORM. [Yuan, Huili; Ma, Ronghua; Li, Fei; Luo, Juhua] Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Jiangsu, Peoples R China; [Atzberger, Clement] Univ Nat Resources & Life Sci BOKU, Inst Surveying Remote Sensing & Land Informat IVF, A-1190 Vienna, Austria; [Loiselle, Steven Arthur] Univ Siena, CSGI, Dipartimento Biotechnol Chim & Farm, I-53100 Siena, Italy Chinese Academy of Sciences; Nanjing Institute of Geography & Limnology, CAS; University of Natural Resources & Life Sciences, Vienna; University of Siena Ma, RH (corresponding author), Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Jiangsu, Peoples R China. yuanhuili.student@sina.com; rhma@niglas.ac.cn; clement.atzberger@boku.ac.at; fli@niglas.ac.cn; loiselle@unisi.it; jhluo@niglas.ac.cn Atzberger, Clement/F-8435-2010; Loiselle, Steven/ABA-3324-2020; Luo, Juhua/AHE-4173-2022 Loiselle, Steven/0000-0001-7414-0389; Atzberger, Clement/0000-0003-2169-8009 Chinese Academy of Sciences [XDA05050106] Chinese Academy of Sciences(Chinese Academy of Sciences) This study was financially supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences (Grant No. XDA05050106). The data collection of this study was supported by the Lake and Watershed Data Center, Scientific Data Sharing Platform for Lake and Watershed (SDSPLW), Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences. 50 19 19 0 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUN 2015.0 7 6 7425 7446 10.3390/rs70607425 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology CM3NS Green Submitted, gold, Green Published 2023-03-23 WOS:000357589800039 0
C Du, ZD; Fasthuber, R; Chen, TS; Ienne, P; Li, L; Luo, T; Feng, XB; Chen, YJ; Temam, O IEEE Du, Zidong; Fasthuber, Robert; Chen, Tianshi; Ienne, Paolo; Li, Ling; Luo, Tao; Feng, Xiaobing; Chen, Yunji; Temam, Olivier ShiDianNao: Shifting Vision Processing Closer to the Sensor 2015 ACM/IEEE 42ND ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA) English Proceedings Paper ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA) JUN 13-17, 2015 Portland, OR IEEE,Asso Comp Machinery,SIGARCH,ACM SIGARCH,Intel,AMD,ALTERA,ARM,CAVIUM,Google,hp,IEEE TCCA,HUAWEI,IBM,Microsoft Res,Oracle Labs,QUALCOMM ARCHITECTURE; COPROCESSOR; HARDWARE; NETWORKS In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are Convolutional Neural Networks (CNN), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is 60X more energy efficient than the previous state-of-the-art neural network accelerator. We present a full design down to the layout at 65 nm, with a modest footprint of 4.86 mm(2) and consuming only 320 mW, but still about 30X faster than high-end GPUs. [Du, Zidong; Chen, Tianshi; Li, Ling; Luo, Tao; Feng, Xiaobing; Chen, Yunji] Chinese Acad Sci, ICT, State Key Lab Comp Architecture, Beijing, Peoples R China; [Du, Zidong; Luo, Tao] Univ CAS, Beijing, Peoples R China; [Fasthuber, Robert; Ienne, Paolo] Ecole Polytech Fed Lausanne, Lausanne, Switzerland; [Temam, Olivier] Inria, Le Chesnay, France Chinese Academy of Sciences; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Inria Du, ZD (corresponding author), Chinese Acad Sci, ICT, State Key Lab Comp Architecture, Beijing, Peoples R China. duzidong@ict.ac.cn; robert.fasthuber@epfl.ch; chentianshi@ict.ac.cn; paolo.ienne@epfl.ch; liling@ict.ac.cn; luotao@ict.ac.cn; fxb@ict.ac.cn; cyj@ict.ac.cn; olivier.temam@inria.fr 60 542 602 9 76 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4503-3402-0 2015.0 92 104 10.1145/2749469.2750389 0.0 13 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BF2CN 2023-03-23 WOS:000380455700008 0
J Li, Y; Cheng, G; Chen, XH; Pang, YS Li, Yong; Cheng, Gang; Chen, Xihui; Pang, Yusong Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM ENTROPY English Article bearing; fault diagnosis; MOMLMEDA; filter feature; LSTM ENTROPY; DECONVOLUTION; NETWORKS As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%. [Li, Yong; Cheng, Gang] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China; [Chen, Xihui] Hohai Univ, Coll Mech & Elect Engn, Changzhou 213022, Peoples R China; [Pang, Yusong] Delft Univ Technol, Fac Mech Maritime & Mat Engn, NL-2628 Delft, Netherlands China University of Mining & Technology; Hohai University; Delft University of Technology Cheng, G (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China. liyong2015@cumt.edu.cn; chg@cumt.edu.cn; chenxh@hhu.edu.cn; Y.Pang@tudelft.nl Outstanding Innovation Scholarship for Doctoral Candidate of Double First Class Construction Disciplines of CUMT Outstanding Innovation Scholarship for Doctoral Candidate of Double First Class Construction Disciplines of CUMT The research was supported by Outstanding Innovation Scholarship for Doctoral Candidate of Double First Class Construction Disciplines of CUMT. 24 5 5 2 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy OCT 2019.0 21 10 1025 10.3390/e21101025 0.0 15 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics JK8MK gold, Green Accepted, Green Published 2023-03-23 WOS:000495094000107 0
J Wang, J; Anisetti, M; Jeon, G Wang, Jin; Anisetti, Marco; Jeon, Gwanggil Reconstruction of missing color-channel data using a three-step back propagation neural network INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article BP neural network; Demosaicking; Color channel; Real time process DEMOSAICKING; FILTER; INTERPOLATION Demosaicking aims to approximate missing color pixels through analysis of the geometric structure between given color pixels and missing color pixels. In this paper, we introduce an efficient adaptive demosaicking method based on back propagation (BP) neural network (BP-NN). We firstly reconstruct the green channel using one BPNN, and then refine the green channel utilizing another BPNN based on the color difference. With the whole green channel interpolated, we reconstruct the red/blue channel using the color difference between the green channel and red/blue channel in a local region. Finally, we refine the red/blue channel using the third BPNN. Regarding the interpolation issue, different image features have completely different properties, such as smooth regions, edges, and textures. Consequently, it is necessary to identify an adaptive model to estimate the relation among neighboring color pixels. We provide the adaptive BP-NN based demosaicking algorithm which can reduce blurring through recovery of missing pixels by a learning process, and also use a pre-trained fixed network to reduce computational complexity. Experimental results demonstrate that the proposed method outperforms extant approaches in PSNR, computational complexity, and visual quality. [Wang, Jin] Xidian Univ, Sch Phys & Optoelect Engn, Xian, Shaanxi, Peoples R China; [Anisetti, Marco] Univ Milan, Dipartimento Informat, Crema, Italy; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian, Shaanxi, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea Xidian University; University of Milan; Xidian University; Incheon National University Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian, Shaanxi, Peoples R China.;Jeon, G (corresponding author), Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea. jwang0608@xidian.edu.cn; marco.anisetti@unimi.it; ggjeon@gmail.com Anisetti, Marco/AAC-9656-2021 Anisetti, Marco/0000-0002-5438-9467 National Natural Science Foundation of China [61501359, 61771378]; Ministero degli Affari Esteri e della Cooperazione Internazionale of Italy [PGR00217]; [NRF-2016K1A3A1A25003543] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministero degli Affari Esteri e della Cooperazione Internazionale of Italy; This paper is sponsored by National Natural Science Foundation of China (No. 61501359, 61771378), by the Framework of International Cooperation Program managed by the NRF of Korea under Grant NRF-2016K1A3A1A25003543 and by the Ministero degli Affari Esteri e della Cooperazione Internazionale of Italy under Grant PGR00217. 27 5 5 2 5 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. OCT 2019.0 10 10 SI 2631 2642 10.1007/s13042-018-0850-5 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science JL6AI 2023-03-23 WOS:000495612400005 0
J Benaben, F; Faugere, L; Montreuil, B; Lauras, M; Moradkhani, N; Cerabona, T; Gou, JQ; Mu, WX Benaben, Frederick; Faugere, Louis; Montreuil, Benoit; Lauras, Matthieu; Moradkhani, Nafe; Cerabona, Thibaut; Gou, Juanqiong; Mu, Wenxin Instability is the norm! A physics-based theory to navigate among risks and opportunities ENTERPRISE INFORMATION SYSTEMS English Article Risk management; risks; opportunity; artificial intelligence; physics; force fields; system modelling; decision support Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity. [Benaben, Frederick; Lauras, Matthieu; Moradkhani, Nafe; Cerabona, Thibaut] Univ Toulouse, IMT Mines Albi, Ctr Genie Ind, Campus Jarlard,Route Teillet, F-81000 Albi, France; [Faugere, Louis; Montreuil, Benoit; Moradkhani, Nafe; Cerabona, Thibaut] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Phys Internet Ctr, Atlanta, GA 30332 USA; [Gou, Juanqiong; Mu, Wenxin] Beijing Jiaotong Univ, Sch Econ & Management, Dept Informat Management, 3 Shangyuancun Xizhimenwai, Beijing 100044, Peoples R China IMT - Institut Mines-Telecom; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; IMT Mines Albi; University System of Georgia; Georgia Institute of Technology; Beijing Jiaotong University Benaben, F (corresponding author), Univ Toulouse, IMT Mines Albi, Ctr Genie Ind, Campus Jarlard,Route Teillet, F-81000 Albi, France.;Lauras, M (corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Dept Informat Management, 3 Shangyuancun Xizhimenwai, Beijing 100044, Peoples R China.;Faugere, L (corresponding author), H Milton Stewart Sch Ind & Syst Engn, Phys Internet Ctr, 755 Ferst Dr NW, Atlanta, GA 30332 USA. frederick.benaben@mines-albi.fr; Lauraslouis.faugere@gatech.edu; wxmu@bjtu.edu.cn Faugere, Louis/0000-0001-6835-6714; Montreuil, Benoit/0000-0001-9909-9212; Moradkhani, Nafe/0000-0001-9002-904X; Benaben, Frederick/0000-0002-1676-6524; LAURAS, Matthieu/0000-0003-2901-4038 34 1 1 3 10 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1751-7575 1751-7583 ENTERP INF SYST-UK Enterp. Inf. Syst. JUN 3 2022.0 16 6 10.1080/17517575.2021.1878391 0.0 JAN 2021 28 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1S4QU Green Published 2023-03-23 WOS:000612160200001 0
J Jin, TH; Cheng, L; Zhang, XC; Cao, J; Qian, XD; Witlox, F Jin, Tanhua; Cheng, Long; Zhang, Xucai; Cao, Jun; Qian, Xiaodong; Witlox, Frank Nonlinear effects of the built environment on metro-integrated ridesourcing usage TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT English Article Ridesourcing; Metro; Built environment; Nonlinearity; Machine learning; GBDT TRANSIT SYSTEMS; ON-DEMAND; TRAVEL; TAXI; SERVICES; CHOICE; MODEL Although ridesourcing has served as an emerging feeder mode to the metro system to solve the first/last-mile issue, research on metro-integrated ridesourcing usage is rather limited. This paper applies a gradient boosting decision tree (GBDT) method to investigate the nonlinear relationship between the built environment and metro-originated and metro-destinated usage, using ridesourcing trip record data. The results show that built environment factors (i.e., density, diversity, and destination accessibility) have significant nonlinear and threshold effects on the integrated usage, which differentiate between weekdays and weekends. Different patterns are also observed between metro-originated and metro-destinated usage. Employment density has a more significantly positive effect on weekday metro-originated usage than metro-destinated usage. The distance to metro stations does exist an effective range. These findings could help not only transportation network companies optimize ridesourcing services but also transportation planners formulate tailor-made land use interventions to facilitate intermodal mobility. [Jin, Tanhua; Cheng, Long; Zhang, Xucai; Witlox, Frank] Univ Ghent, Dept Geog, Ghent, Belgium; [Cheng, Long] Southeast Univ, Jiangsu Collaborat Innovat Ctr Modern Urban Traff, Jiangsu Key Lab Urban Its, Nanjing, Peoples R China; [Cao, Jun] Southeast Univ, Sch Architecture, Nanjing, Peoples R China; [Qian, Xiaodong] Wayne State Univ, Dept Civil & Environm Engn, Detroit, MI 48202 USA; [Witlox, Frank] Univ Tartu, Dept Geog, Tartu, Estonia; [Qian, Xiaodong] Univ Calif Davis, Inst Transportat Studies, Dept Civil & Environm Engn, Davis, CA USA Ghent University; Southeast University - China; Southeast University - China; Wayne State University; University of Tartu; University of California System; University of California Davis Cheng, L (corresponding author), Southeast Univ, Jiangsu Collaborat Innovat Ctr Modern Urban Traff, Jiangsu Key Lab Urban Its, Nanjing, Peoples R China. tanhua.jin@ugent.be; long.cheng@ugent.be; xucai.zhang@ugent.be; juncao_0711@seu.edu.cn; hm8925@wayne.edu; frank.witlox@ugent.be Witlox, Frank/A-5143-2008 Witlox, Frank/0000-0002-8966-6823; Qian, Xiaodong/0000-0002-7245-3362; Jin, Tanhua/0000-0001-6657-526X; zhang, xucai/0000-0001-8266-481X; Cheng, Long/0000-0003-3720-4093 Research Foundation - Flanders (FWO) [1257022N]; China Scholarship Council [202008320289]; Fundamental Research Funds for the Central Universities [2242022R10009] Research Foundation - Flanders (FWO)(FWO); China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the Research Foundation - Flanders (FWO) (Grant number 1257022N), the China Scholarship Council (202008320289), and the Fundamental Research Funds for the Central Universities (No. 2242022R10009). 68 1 1 32 32 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1361-9209 1879-2340 TRANSPORT RES D-TR E Transport. Res. Part D-Transport. Environ. SEP 2022.0 110 103426 10.1016/j.trd.2022.103426 0.0 AUG 2022 16 Environmental Studies; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Transportation 4U5NE 2023-03-23 WOS:000858839500005 0
J Fong, IH; Li, TY; Fong, S; Wong, RK; Tallon-Ballesteros, AJ Fong, Iat Hang; Li, Tengyue; Fong, Simon; Wong, Raymond K.; Tallon-Ballesteros, Antonio J. Predicting concentration levels of air pollutants by transfer learning and recurrent neural network KNOWLEDGE-BASED SYSTEMS English Article Forecasting; Environment monitoring; Transfer learning; Recurrent neural network; Airborne particle matter POLLUTION; MORTALITY Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy, The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks. (C) 2020 Published by Elsevier B.V. [Fong, Iat Hang; Li, Tengyue; Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa, Macao, Peoples R China; [Wong, Raymond K.] Univ New South Wales, Fac Engn, Sydney, NSW, Australia; [Tallon-Ballesteros, Antonio J.] Univ Huelva, Dept Elect Comp Syst & Automat Engn, Huelva 21007, Spain University of Macau; University of New South Wales Sydney; Universidad de Huelva Tallon-Ballesteros, AJ (corresponding author), Reina Mercedes AV, Seville 41012, Spain. antonio.tallon.diesia@zimbra.uhu.es Tallón-Ballesteros, Antonio J/HNB-6529-2023; Fong, Simon/C-9388-2009 Fong, Simon/0000-0002-1848-7246 University of Macau [MYRG2016-00069, MYRG2016-00217]; Macau SAR government [MYRG2016-00069, MYRG2016-00217] University of Macau; Macau SAR government The authors are thankful to the financial support from the research grants, (1) MYRG2016-00069, titled 'Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance, China' and (2) MYRG2016-00217, titled 'Improving the Protein-Ligand Scoring Function for Molecular Docking by Fuzzy Rule-based Machine Learning Approaches, China' offered by University of Macau and Macau SAR government. 31 29 29 4 24 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAR 15 2020.0 192 105622 10.1016/j.knosys.2020.105622 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KT9MF 2023-03-23 WOS:000519335400042 0
J Huang, ZX; Huang, X; Fan, JC; Eichhorn, M; An, F; Chen, BQ; Cao, L; Zhu, ZL; Yun, T Huang, Zhixian; Huang, Xiao; Fan, Jiangchuan; Eichhorn, Markus; An, Feng; Chen, Bangqian; Cao, Lin; Zhu, Zhengli; Yun, Ting Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data REMOTE SENSING English Article aerodynamics; computer simulation; foliage clump; hurricane disturbance; laser scanning WIND DAMAGE; MODEL; TYPHOON; PROBABILITY; EXTRACTION; STORM; AREA; GAP Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree's capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage. [Huang, Zhixian; Huang, Xiao; Zhu, Zhengli; Yun, Ting] Nanjing Forestry Univ, Sch Informat Sci & Technol, Nanjing 210037, Peoples R China; [Huang, Zhixian; Huang, Xiao; Cao, Lin; Yun, Ting] Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China; [Fan, Jiangchuan] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Eichhorn, Markus] Univ Coll Cork, Sch Biol Earth & Environm Sci, Cork T23 N73K, Ireland; [An, Feng; Chen, Bangqian] Chinese Acad Trop Agr Sci, Rubber Res Inst, Minist Agr, Danzhou Invest & Expt Stn Trop Crops, Danzhou 571737, Peoples R China Nanjing Forestry University; Nanjing Forestry University; Beijing Academy of Agriculture & Forestry Sciences (BAAFS); University College Cork; Chinese Academy of Tropical Agricultural Sciences; Ministry of Agriculture & Rural Affairs Yun, T (corresponding author), Nanjing Forestry Univ, Sch Informat Sci & Technol, Nanjing 210037, Peoples R China.;Yun, T (corresponding author), Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China. njfuhzx@njfu.edu.cn; njfuhx@njfu.edu.cn; fanjc@nercita.org.cn; markus.eichhorn@ucc.ie; an-f@catas.cn; bqchen@catas.cn; lincao@njfu.edu.cn; zlzhu@njfu.edu.cn; yunting@njfu.edu.cn Yun, Ting/AAM-8099-2021; Eichhorn, Markus/L-5027-2013 Eichhorn, Markus/0000-0002-3381-0822; Ting, Yun/0000-0003-4294-8337; An, Feng/0000-0003-1545-2475 National Natural Science Foundation of China [31770591, 41701510]; Central Public-interest Scientific Institution Basal Research Fund for the Chinese Academy of Tropical Agricultural Sciences [1630022020002]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Central Public-interest Scientific Institution Basal Research Fund for the Chinese Academy of Tropical Agricultural Sciences; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) This work was supported by theNational Natural Science Foundation of China (grant numbers 31770591, 41701510), the Central Public-interest Scientific Institution Basal Research Fund for the Chinese Academy of Tropical Agricultural Sciences (grant numbers 1630022020002) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). 49 8 9 12 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. APR 2020.0 12 8 1318 10.3390/rs12081318 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology LP9II gold 2023-03-23 WOS:000534628800091 0
J Krefl, D; Carrazza, S; Haghighat, B; Kahlen, J Krefl, Daniel; Carrazza, Stefano; Haghighat, Babak; Kahlen, Jens Riemann-Theta Boltzmann machine NEUROCOMPUTING English Article Boltzmann machines; Neural networks; Riemann-Theta function; Density estimation; Data classification NEURAL-NETWORKS A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Riemann-Theta functions. The conditional expectation of a hidden state for given visible states can also be calculated analytically, yielding a derivative of the logarithmic Riemann-Theta function. The conditional expectation can be used as activation function in a feedforward neural network, thereby increasing the modelling capacity of the network. Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques. (C) 2020 Elsevier B.V. All rights reserved. [Krefl, Daniel; Carrazza, Stefano] CERN, Theoret Phys Dept, CH-1211 Geneva 23, Switzerland; [Haghighat, Babak] Tsinghua Univ, Yau Math Sci Ctr, Beijing 100084, Peoples R China; [Kahlen, Jens] Dachsbau 8, D-53757 St Augustin, Germany European Organization for Nuclear Research (CERN); Tsinghua University Carrazza, S (corresponding author), CERN, Theoret Phys Dept, CH-1211 Geneva 23, Switzerland. daniel.krefl@cern.ch; stefano.carrazza@cern.ch Carrazza, Stefano/D-8412-2017 Carrazza, Stefano/0000-0002-0079-6753 HICCUP ERC Consolidator grant [614577]; European Research Council under the European Union's Horizon 2020 research and innovation Programme [740006] HICCUP ERC Consolidator grant; European Research Council under the European Union's Horizon 2020 research and innovation Programme(European Research Council (ERC)) We would like to thank G. van der Geer for valuable discussions and S. M. Tan for help in improving the manuscript. S. C. is supported by the HICCUP ERC Consolidator grant (614577) and by the European Research Council under the European Union's Horizon 2020 research and innovation Programme (grant agreement n. 740006). 26 1 1 0 16 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing MAY 7 2020.0 388 334 345 10.1016/j.neucom.2020.01.011 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KW0HY Green Published, Green Submitted 2023-03-23 WOS:000520855400030 0
J Zhao, JQ; Fernandes, VB; Jiao, LC; Yevseyeya, I; Maulana, A; Li, R; Back, T; Tang, K; Emmerich, MTM Zhao, Jiaqi; Fernandes, Vitor Basto; Jiao, Licheng; Yevseyeya, Iryna; Maulana, Asep; Li, Rui; Back, Thomas; Tang, Ke; Emmerich, Michael T. M. Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms INFORMATION SCIENCES English Article Convex hull; Classification; Evolutionary multiobjective optimization; Parsimony; ROC analysis; Anti-spam filters CLASSIFICATION; STRATEGY The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization. (C) 2016 Elsevier Inc. All rights reserved. [Zhao, Jiaqi; Jiao, Licheng] Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Int Res Ctr Intelligent Percept & Computat,Minist, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi Provinc, Peoples R China; [Fernandes, Vitor Basto] Polytech Inst Leiria, Comp Sci & Commun Res Ctr, Sch Technol & Management, P-2411901 Leiria, Portugal; [Yevseyeya, Iryna] De Montfort Univ, Fac Technol, Gateway House 5-33, Leicester LE1 9BH, Leics, England; [Maulana, Asep; Back, Thomas; Emmerich, Michael T. M.] Leiden Univ, LIACS, Multicriteria Optimizat Design & Analyt Grp, Niels Bohrweg 1, NL-2333 CA Leiden, Netherlands; [Li, Rui] Microsoft Res Asia, Beijing 100190, Peoples R China; [Tang, Ke] Univ Sci & Technol China, USTC Birmingham Joint Res Inst Intelligent Comput, Sch Comp Sci & Technol, NICAL, Hefei 230027, Anhui, Peoples R China Xidian University; De Montfort University; Leiden University; Leiden University - Excl LUMC; Microsoft; Microsoft Research Asia; Chinese Academy of Sciences; University of Science & Technology of China, CAS Zhao, JQ (corresponding author), Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Int Res Ctr Intelligent Percept & Computat,Minist, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi Provinc, Peoples R China. jiaqizhao88@126.com Emmerich, Michael/AAD-4791-2021; Basto-Fernandes, Vitor/N-1891-2016; Baeck, Thomas/ABF-4706-2020 Basto-Fernandes, Vitor/0000-0003-4269-5114; Emmerich, Michael/0000-0002-7342-2090; Back, Thomas/0000-0001-6768-1478; Zhao, Jiaqi/0000-0002-3564-5090; Maulana, Asep/0000-0001-8708-2923 National Basic Research Program (973 Program) of China [2013CB329402]; National Natural Science Foundation of China [61473215, 61371201, 61373111, 61303032, 61271301, 61272279, 61203303, 61571342]; Excellent Young Science Foundation of China [61522311]; Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]; Major Research Plan of the National Natural Science Foundation of China [91438201, 91438103]; Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]; European Union [247619]; Indonesian Endowment Grant National Basic Research Program (973 Program) of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Excellent Young Science Foundation of China; Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project)(Ministry of Education, China - 111 Project); Major Research Plan of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for Cheung Kong Scholars and Innovative Research Team in University(Program for Changjiang Scholars & Innovative Research Team in University (PCSIRT)); European Union(European Commission); Indonesian Endowment Grant This work was partially supported by the National Basic Research Program (973 Program) of China (no. 2013CB329402), the National Natural Science Foundation of China (nos. 61473215, 61371201, 61373111, 61303032, 61271301, 61272279, 61203303 and 61571342), the Excellent Young Science Foundation of China (no. 61522311), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (no. B07048), the Major Research Plan of the National Natural Science Foundation of China (nos. 91438201 and 91438103), the Program for Cheung Kong Scholars and Innovative Research Team in University (no. IRT_15R53), and the European Union Seventh Framework Programme under Grant agreements (no. 247619) on Nature Inspired Computation and its Applications (NICaiA). Asep Maulana thanks for financial support by the Indonesian Endowment Grant. 62 17 18 0 26 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. NOV 1 2016.0 367 80 104 10.1016/j.ins.2016.05.026 0.0 25 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science DV3AW Green Submitted, Green Accepted 2023-03-23 WOS:000382794400006 0
J Ma, D; Fang, HY; Xue, BH; Wang, FM; Msekh, MA; Chan, CL Ma, Duo; Fang, Hongyuan; Xue, Binghan; Wang, Fuming; Msekh, Mohammed A.; Chan, Chiu Ling Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES English Article Fully convolutional neural network; pavement crack; intelligent detection; crack image database The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too few layers, extracted crack features are not complete, and the method lacks the efficiency to calculate the whole picture. In this study, a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions. This method, using a convolutional layer instead of a fully connected layer, realizes full convolution and accelerates calculation. The region proposals come from the feature map at the end of the base network, which avoids multiple computations of the same picture. Online hard example mining and data-augmentation techniques are adopted to improve the model's recognition accuracy. We trained and tested Concrete Crack Images for Classification (CCIC), which is a public dataset collected using smartphones, and the Crack Image Database (CIDB), which was automatically collected using vehicle-mounted charge-coupled device cameras, with identification accuracy reaching 91.4% and 86.4%, respectively. The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models, and can extract more complete and accurate crack features in CIDB. We also analyzed translation processing, fuzzy, scaling, and distorted images. The proposed model shows a strong robustness and stability, and can automatically identify image cracks of different forms. It has broad application prospects in practical engineering problems. [Ma, Duo; Fang, Hongyuan; Xue, Binghan; Wang, Fuming] Zhengzhou Univ, Sch Water Conservancy Engn, Zhengzhou 450001, Peoples R China; [Ma, Duo; Fang, Hongyuan; Xue, Binghan; Wang, Fuming] Natl Local Joint Engn Lab Major Infrastruct Testi, Zhengzhou 450001, Peoples R China; [Ma, Duo; Fang, Hongyuan; Xue, Binghan; Wang, Fuming] Collaborat Innovat Ctr Water Conservancy & Transp, Zhengzhou 450001, Peoples R China; [Msekh, Mohammed A.] Univ Babylon, Civil Engn Dept, Coll Engn, Babylon, Iraq; [Chan, Chiu Ling] Bauhaus Univ Weimar, Inst Struct Mech, Weimar, Germany Zhengzhou University; University of Babylon; Bauhaus-Universitat Weimar Fang, HY (corresponding author), Zhengzhou Univ, Sch Water Conservancy Engn, Zhengzhou 450001, Peoples R China.;Fang, HY (corresponding author), Natl Local Joint Engn Lab Major Infrastruct Testi, Zhengzhou 450001, Peoples R China.;Fang, HY (corresponding author), Collaborat Innovat Ctr Water Conservancy & Transp, Zhengzhou 450001, Peoples R China. 18337192244@163.com Msekh, Mohammed Abdulrazzak/J-8204-2015; Xue, Binghan/AHD-5526-2022 Msekh, Mohammed Abdulrazzak/0000-0003-1708-1241; Fang, Hongyuan/0000-0001-5827-7158 National Key Research and Development Program of China [2017YFC1501200]; National Natural Science Foundation of China [51678536, 41404096]; Department of education's Production-Study-Research combined innovation FundingBlue fire plan (Huizhou) [CXZJHZ01742]; Program for Science and Technology Innovation Talents in Universities of Henan Province [19HASTIT043]; Outstanding Young Talent Research Fund of Zhengzhou University [1621323001] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Department of education's Production-Study-Research combined innovation FundingBlue fire plan (Huizhou); Program for Science and Technology Innovation Talents in Universities of Henan Province; Outstanding Young Talent Research Fund of Zhengzhou University This research was funded by the National Key Research and Development Program of China (No. 2017YFC1501200), the National Natural Science Foundation of China (Nos. 51678536, 41404096). This project was supported by Department of education's Production-Study-Research combined innovation FundingBlue fire plan (Huizhou) (CXZJHZ01742), the Program for Science and Technology Innovation Talents in Universities of Henan Province (Grant No. 19HASTIT043), the Outstanding Young Talent Research Fund of Zhengzhou University (1621323001). The authors would like to thank for these financial supports. 55 9 9 3 46 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1526-1492 1526-1506 CMES-COMP MODEL ENG CMES-Comp. Model. Eng. Sci. 2020.0 123 3 1267 1291 10.32604/cmes.2020.09122 0.0 25 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics LT9DW gold 2023-03-23 WOS:000537367400019 0
J Zong, GD; Wang, YD; Karimi, HR; Shi, KB Zong, Guangdeng; Wang, Yudi; Karimi, Hamid Reza; Shi, Kaibo Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints NEURAL NETWORKS English Article Dynamic surface control; Input dead-zone; Neural network; Output feedback; Prescribed performance control STATE This paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead zone constraints. First, an NN is utilized to approximate the unknown nonlinear functions, then a state observer is developed to estimate the unmeasurable states. Second, based on the command filter method, an output feedback NN learning backstepping control algorithm is established. Third, a prescribed performance function is employed to ensure the transient performance of the closed loop systems and forces the tracking error to fall within the prescribed performance boundary. It is rigorously proved mathematically that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error can converge to an arbitrarily small neighborhood of the origin. Finally, a numerical example and an application example of the electromechanical system are given to show effectiveness of the acquired control algorithm.(c) 2021 Elsevier Ltd. All rights reserved. [Zong, Guangdeng; Wang, Yudi] Tiangong Univ, Sch Control Sci & Engn, Tianjin 300387, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Shi, Kaibo] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China Tiangong University; Polytechnic University of Milan; Chengdu University Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. lovelyletian@gmail.com; hamidreza.karimi@polimi.it National Natural Science Foundation of China [61773225]; Taishan Scholar Project of Shandong Province [TSQN20161033]; Italian Ministry of Education, University and Research National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Taishan Scholar Project of Shandong Province; Italian Ministry of Education, University and Research(Ministry of Education, Universities and Research (MIUR)) This work was supported in part by the National Natural Science Foundation of China under grant (61773225) and in part by the Taishan Scholar Project of Shandong Province (TSQN20161033) and in part by the Italian Ministry of Education, University and Research for the support provided through the Project 'Department of Excellence LIS4.0 -Lightweight and Smart Structures for Industry 4'. 37 27 27 21 48 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. MAR 2022.0 147 126 135 10.1016/j.neunet.2021.12.019 0.0 JAN 2022 10 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology 0U8HO 35021127.0 2023-03-23 WOS:000787888500011 0
J Che, YH; Zheng, YS; Wu, Y; Sui, X; Bharadwaj, P; Stroe, DI; Yang, YL; Hu, XS; Teodorescu, R Che, Yunhong; Zheng, Yusheng; Wu, Yue; Sui, Xin; Bharadwaj, Pallavi; Stroe, Daniel-Ioan; Yang, Yalian; Hu, Xiaosong; Teodorescu, Remus Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network APPLIED ENERGY English Article Battery health prognostic; Degradation prediction; Probabilistic neural network; Transfer learning; Gaussian mixture model clustering SHORT-TERM-MEMORY; STATE; ALGORITHM; PACKS Accurate and reliable prediction of the battery capacity degradation is vital for predictive health management. This paper proposes a novel framework to improve the accuracy and reliability of battery health prognostic. Firstly, sequential information-ensembled health indicators, which have high correlations with battery capacity and lifetime, are proposed based on partial voltage and capacity sequences. Then, the Gaussian mixture model is adopted for lifetime clustering to verify the effectiveness of the proposed health indicators and an automatic reference batteries selection method is proposed to find out the most relative candidates for degradation base model training. A long short-term memory network with probabilistic regression is leveraged for battery health prognostic, which provides the predicted mean value and confidence interval via Bayesian inference. Finally, the model migration is presented to further improve the accuracy and reliability, with only a few checkpoints used for re-training. The proposed framework for battery health prognostic is validated against four battery datasets, showing high accuracy and reliability. Specifically, the root mean square error and mean absolute error of health prognostic on all the battery cells in four battery dataset can be within 2% and 1.5%, respectively. The mean relative reductions of the above two errors reach 43.7% and 45.3% respectively compared to the conventional method. [Che, Yunhong; Zheng, Yusheng; Wu, Yue; Sui, Xin; Bharadwaj, Pallavi; Stroe, Daniel-Ioan; Teodorescu, Remus] Aalborg Univ, DOE, Aalborg 9220, Denmark; [Wu, Yue] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Yang, Yalian; Hu, Xiaosong] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China Aalborg University; Central South University; Chongqing University Yang, YL; Hu, XS (corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China. yyl@cqu.edu.cn; xiaosonghu@ieee.org Che, Yunhong/GPT-3920-2022; Wu, Yue/GZL-1293-2022; Sui, Xin/AAE-7208-2022; Hu, Xiao/GQI-0007-2022; Che, Yunhong/GNW-5568-2022; Che, Yunhong/GQI-2342-2022; Teodorescu, Remus/O-5224-2015 Che, Yunhong/0000-0002-7350-0001; Wu, Yue/0000-0003-2387-4766; Sui, Xin/0000-0002-6180-469X; Hu, Xiao/0000-0003-1128-4099; Che, Yunhong/0000-0002-7350-0001; Zheng, Yusheng/0000-0003-4901-1846; Stroe, Daniel-Ioan/0000-0002-2938-8921; Teodorescu, Remus/0000-0002-2617-7168 Villum Foundation for Smart Battery project [222860]; National Natural Science Foundation of China [51875054, U1864212] Villum Foundation for Smart Battery project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partially supported by the Villum Foundation for Smart Battery project (No. 222860) and the National Natural Science Foundation of China (Grant No. 51875054 and No. U1864212) 46 3 3 14 18 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy OCT 1 2022.0 323 119663 10.1016/j.apenergy.2022.119663 0.0 JUL 2022 13 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 3K5YJ 2023-03-23 WOS:000834151300001 0
J Sun, B; Cheng, W; Goswami, P; Bai, GH Sun, Bin; Cheng, Wei; Goswami, Prashant; Bai, Guohua Short-term traffic forecasting using self-adjusting k-nearest neighbours IET INTELLIGENT TRANSPORT SYSTEMS English Article road traffic; intelligent transportation systems; learning (artificial intelligence); short-term traffic forecasting; self-adjusting k-nearest neighbours; dynamic procedure kNN; DP-kNN; intelligent transportation systems; road traffic NONPARAMETRIC REGRESSION; FLOW PREDICTION; OUTLIER DETECTION; NEURAL-NETWORKS Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This study proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that the DP-kNN can perform better than the manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement. [Sun, Bin; Cheng, Wei; Goswami, Prashant; Bai, Guohua] Blekinge Inst Technol, S-37179 Karlskrona, Sweden; [Cheng, Wei] Kunming Univ Sci & Technol, Kunming 650093, Yunnan, Peoples R China Blekinge Institute Technology; Kunming University of Science & Technology Cheng, W (corresponding author), Blekinge Inst Technol, S-37179 Karlskrona, Sweden.;Cheng, W (corresponding author), Kunming Univ Sci & Technol, Kunming 650093, Yunnan, Peoples R China. wei.cheng@bth.se Sun, Bin/A-2075-2015 Sun, Bin/0000-0001-5824-425X National Natural Science Foundation of China (NSFC) [61364019] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) We thank all anonymous reviewers for their thorough review and detailed comments which have significantly improved the quality of this paper. We owe great thanks to Niklas Lavesson and Bengt Aspvall for constructive comments and suggestions. We are also thankful to get support from Siva Krishna Dasari and Florian Westphal for proof reading and advice. This work was supported by the National Natural Science Foundation of China (NSFC), under grant agreement number 61364019. Fig. 1 is reprinted from publication [49] with permission from Elsevier, license number 4045330384711. 56 45 48 8 94 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-956X 1751-9578 IET INTELL TRANSP SY IET Intell. Transp. Syst. FEB 2018.0 12 1 41 48 10.1049/iet-its.2016.0263 0.0 8 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation FX4KT Green Submitted 2023-03-23 WOS:000426045200006 0
J Gui, XR; Zhang, X; Xin, YW; Liu, Q; Wang, YF; Zhang, YL; Xu, YF; Liu, ZL; Liu, W; Schioth, HB; Sun, CX; Zhang, ZL; Zhang, Y Gui, Xinru; Zhang, Xin; Xin, Yiwei; Liu, Qi; Wang, Yifeng; Zhang, Yanli; Xu, Yunfei; Liu, Zengli; Liu, Wen; Schioth, Helgi B.; Sun, Chengxi; Zhang, Zongli; Zhang, Yi Identification and validation of volatile organic compounds in bile for differential diagnosis of perihilar cholangiocarcinoma CLINICA CHIMICA ACTA English Article Perihilar cholangiocarcinoma; Volatile organic compounds; Differential diagnosis; Bile CANCER; PANCREATITIS; HEADSPACE Early and differential diagnosis of perihilar cholangiocarcinoma (PHCCA) is highly challenging. This study aimed to evaluate whether volatile organic compounds (VOCs) in bile samples could be emerging diagnostic biomarkers for PHCCA. We collected 200 bile samples from patients with PHCCA and benign biliary diseases (BBD), including a 140-patient training cohort and an 60-patient test cohort. Gas chromatography-ion mobility spec-trometry (GC-IMS) was used for VOCs detection. The predictive models were constructed using machine learning algorithms. Our analysis detected 19 VOC substances using GC-IMS in the bile samples and resulted in the identification of three new VOCs, 2-methoxyfuran, propyl isovalerate, and diethyl malonate that were found in bile. Unsupervised hierarchical clustering analysis supported that VOCs detected in the bile could distinguish PHCCA from BBD. Twelve VOCs defined according to 32 signal peaks had significant statistical significance between BBD and PHCCA, including four up-regulated VOCs in PHCCA, such as 2-ethyl-1-hexanol, propyl iso-valerate, cyclohexanone, and acetophenone, while the rest eight VOCs were down-regulated. ROC curve analysis revealed that machine learning models based on VOCs could help diagnosing PHCCA. Among them, SVM pro-vided the highest AUC of 0.966, with a sensitivity and specificity of 93.1% and 100%, respectively. The diag-nostic model based on different VOC spectra could be a feasible method for the differential diagnosis of PHCCA. [Gui, Xinru; Zhang, Xin; Xin, Yiwei; Liu, Qi; Wang, Yifeng; Sun, Chengxi; Zhang, Yi] Shandong Univ, Dept Clin Lab, Qilu Hosp, 107 Wenhua Xi Rd, Jinan 250012, Shandong, Peoples R China; [Gui, Xinru; Zhang, Xin; Xin, Yiwei; Liu, Qi; Wang, Yifeng; Sun, Chengxi; Zhang, Yi] Shandong Engn Res Ctr Biomarker & Artificial Inte, 107 Wenhua Xi Rd, Jinan 250012, Shandong, Peoples R China; [Zhang, Yanli] Shandong Prov Third Hosp, Dept Clin Lab, 12 Wuyingshan Zhong Rd, Jinan 250012, Shandong, Peoples R China; [Xu, Yunfei; Liu, Zengli; Zhang, Zongli] Shandong Univ, Dept Gen Surg, Qilu Hosp, 107 Wenhua Xi Rd, Jinan 250012, Shandong, Peoples R China; [Liu, Wen; Schioth, Helgi B.] Uppsala Univ, Dept Surg Sci Funct Pharmacol & Neurosci, SE-75124 Uppsala, Sweden Zhang, ZL; Zhang, Y (corresponding author), 107 Wenhua Xi Rd, Jinan 250012, Shandong, Peoples R China. zzlzzl1900@163.com; yizhang@sdu.edu.cn National Natural Science Foundation of China [82272410, 82172339]; Natural Science Foundation of Shandong Province [ZR2020MH238, ZR2021MH110]; Major Scientific and Tech- nological Innovation Project of Shandong Province [2021CXGC010603, 2021CXGC011105] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Major Scientific and Tech- nological Innovation Project of Shandong Province This work was supported by National Natural Science Foundation of China (82272410, 82172339) , Natural Science Foundation of Shandong Province (ZR2020MH238, ZR2021MH110) , Major Scientific and Tech- nological Innovation Project of Shandong Province (2021CXGC010603, 2021CXGC011105) . 37 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0009-8981 1873-3492 CLIN CHIM ACTA Clin. Chim. Acta FEB 15 2023.0 541 117235 10.1016/j.cca.2023.117235 0.0 9 Medical Laboratory Technology Science Citation Index Expanded (SCI-EXPANDED) Medical Laboratory Technology 9P9EG 36716909.0 hybrid 2023-03-23 WOS:000944578900001 0
J Castro, MR; Van der Heiden, B; Henderson, EL Castro, Mayra Ruiz; Van der Heiden, Beatrice; Henderson, Emma L. Catalysts in career transitions: Academic researchers transitioning into sustainable careers in data science JOURNAL OF VOCATIONAL BEHAVIOR English Article Career transitions; Sustainable careers; Academic careers; (Big) data science; Career adaptability ADAPT-ABILITIES SCALE; BOUNDARYLESS CAREER; BIG DATA; CONTEMPORARY CAREERS; ADAPTABILITY; PERFORMANCE; MANAGEMENT; WORK; LIFE; PERSPECTIVE Academic careers are becoming less linear and secure, and are increasingly shaped by environmental constraints. As a result, highly qualified early and mid-career researchers, in particular from STEM (Science, Technology, Engineering, and Mathematics) disciplines, are pursuing careers outside academia. This paper advances theory and empirical research on career transitions and sustainable careers by investigating how junior academics transition into the field of data science by exploring the facilitators of their career transition and the ways in which they experience career sustainability in their new occupational field. This study relies on 28 in-depth interviews with early and mid-career STEM researchers from elite universities who decided to join a data science bootcamp to pursue a new career as data scientists. Our study reveals the career barriers that junior researchers experience in academia and how career catalysts increase their career adaptability, facilitating a career transition into sustainable careers in data science. Our study shows that career sustainability is experienced through the reaffirmation of interviewees' identity as researchers outside of academia as well as in the reconciliation between their previous career expectations and actual career outcomes after transitioning into data science. [Castro, Mayra Ruiz] Univ Roehampton, Roehampton Business Sch, Roehampton Lane, London SW15 5SL, England; [Van der Heiden, Beatrice] Radboud Univ Nijmegen, Inst Management Res, Heyendaalseweg 141, NL-6525 AJ Nijmegen, Netherlands; [Van der Heiden, Beatrice] Open Univ, Heerlen, Netherlands; [Van der Heiden, Beatrice] Univ Ghent, Ghent, Belgium; [Van der Heiden, Beatrice] Kingston Univ, London, England; [Van der Heiden, Beatrice] Hubei Univ, Wuhan, Peoples R China; [Henderson, Emma L.] Kingston Univ, Kingston Business Sch, Kingston Hill, London KT2 7LB, England Roehampton University; Radboud University Nijmegen; Open University Netherlands; Ghent University; Kingston University; Hubei University; Kingston University Castro, MR (corresponding author), Univ Roehampton, Roehampton Business Sch, Roehampton Lane, London SW15 5SL, England. mayra.ruizcastro@roehampton.ac.uk; b.vanderheijden@fm.ru.nl; e.henderson@kingston.ac.uk Henderson, Emma/AAH-7161-2019 Henderson, Emma/0000-0002-5396-2321; Van der Heijden, Beatrice/0000-0001-8672-5368; Ruiz Castro, Mayra/0000-0002-1586-8270 Kingston Business School Kingston Business School The authors would like to thank Associate Editor Cort W. Rudolph and the anonymous reviewers for their constructive feedback and guidance. We are grateful to Maria Simosi, the conveners and participants of the sub-theme Occupational Membership, Careers and Resources in Flux at the 35th EGOS Colloquium in Edinburgh, UK, and members of the People & Organization research cluster of the Roehampton Business School for their insightful comments on earlier versions of this article. The first author acknowledges the financial support granted by the Kingston Business School. 81 7 7 20 55 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0001-8791 1095-9084 J VOCAT BEHAV J. Vocat. Behav. OCT 2020.0 122 103479 10.1016/j.jvb.2020.103479 0.0 17 Psychology, Applied Social Science Citation Index (SSCI) Psychology OH4AJ Green Accepted 2023-03-23 WOS:000582513000003 0
J Zhu, XZ; Xu, ZB; You, SM; Komarek, M; Alessi, DS; Yuan, XZ; Palansooriya, KN; Ok, YS; Tsang, DCW Zhu, Xinzhe; Xu, Zibo; You, Siming; Komarek, Michael; Alessi, Daniel S.; Yuan, Xiangzhou; Palansooriya, Kumuduni Niroshika; Ok, Yong Sik; Tsang, Daniel C. W. Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar CHEMICAL ENGINEERING JOURNAL English Article Iron-biochar composite; Hexavalent chromium; Redox properties; Sustainable waste management; Environmental remediation; Machine learning HEXAVALENT CHROMIUM; AQUEOUS-SOLUTIONS; ELECTRON-DONOR; IRON; MECHANISMS; REDUCTION; SORPTION; SYSTEM Data mining and knowledge discovery by machine learning (ML) have recently come into application in environmental remediation, especially the exploration for the multifactorial process such as hexavalent chromium [Cr(VI)] removal by iron-biochar composite (Fe-BC). The Cr(VI) removal capacity of Fe-BC was concurrently controlled by impregnated iron species (Fe-0/Fe2+/Fe3+), carbon properties, and iron-carbon interactions, while the current lab-scale research could hardly untangle the overall relationships with the Cr(VI) removal experiments of one or several Fe-BCs under different research frameworks. Herein, we investigated the impacts of various microscopic material properties of Fe-BC on aqueous Cr(VI) removal by ML approach and highlighted the variations of biochar properties after iron impregnation. Our results suggested that the direct impacts of impregnated-iron contents on the Cr(VI) removal were limited, possibly related to undistinguished Fe species in the ML models, in which the roles of different iron species on Cr(VI) removal might be counteracted. Instead, the impacts of impregnated iron on the Cr(VI) removal were embodied indirectly by altering the biochar properties. Surface oxygen-containing functional groups (SOFGs) contents on biochar played a pivotal role in Cr(VI) removal according to the ML models. The condensed polyaromatic carbon matrices of BC and Fe-BC with a high content of non-polar carbon were also proved to be conducive to Cr(VI) removal. The ML models developed in this study consider surface functionalities information of BC and Fe-BC and offer a more accurate prediction for Cr(VI) removal, and the information mining behind models can act as a vital reference for the rational design of engineered biochar to remove aqueous Cr(VI). [Zhu, Xinzhe; Xu, Zibo; Tsang, Daniel C. W.] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China; [You, Siming] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Komarek, Michael] Czech Univ Life Sci, Fac Environm Sci, Kamycka 129, Prague 16500, Czech Republic; [Alessi, Daniel S.] Univ Alberta, Dept Earth & Atmospher Sci, Edmonton, AB, Canada; [Yuan, Xiangzhou; Palansooriya, Kumuduni Niroshika; Ok, Yong Sik] Korea Univ, Korea Biochar Res Ctr, APRU Sustainable Waste Management Program, Seoul 02841, South Korea; [Yuan, Xiangzhou; Palansooriya, Kumuduni Niroshika; Ok, Yong Sik] Korea Univ, Div Environm Sci & Ecol Engn, Seoul 02841, South Korea; [Yuan, Xiangzhou] Sun Brand Ind Inc, R&D Ctr, Jeollanam Do 57248, South Korea Hong Kong Polytechnic University; University of Glasgow; Czech University of Life Sciences Prague; University of Alberta; Korea University; Korea University Tsang, DCW (corresponding author), Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China. dan.tsang@polyu.edu.hk Yuan, Xiangzhou/I-3671-2018; Tsang, Dan/E-5442-2012; Komárek, Michael/B-8307-2008; Zhu, Xinzhe/GNP-6013-2022 Yuan, Xiangzhou/0000-0002-6480-3983; Tsang, Dan/0000-0002-6850-733X; Komárek, Michael/0000-0003-3644-9961; Xu, Zibo/0000-0001-8887-9344 Hong Kong Research Grants Council [PolyU 15222020]; Hong Kong Environment and Conservation Fund [87/2017]; Czech Science Foundation [21-23794J] Hong Kong Research Grants Council(Hong Kong Research Grants Council); Hong Kong Environment and Conservation Fund; Czech Science Foundation(Grant Agency of the Czech Republic) The authors appreciate the financial support from the Hong Kong Research Grants Council (PolyU 15222020) , Hong Kong Environment and Conservation Fund (Project 87/2017) , and Czech Science Foundation (Project 21-23794J) for this study. 59 18 18 37 72 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1385-8947 1873-3212 CHEM ENG J Chem. Eng. J. JAN 15 2022.0 428 131967 10.1016/j.cej.2021.131967 0.0 AUG 2021 9 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering XP1FU Green Accepted 2023-03-23 WOS:000730619800005 0
J Latif, A; Maqbool, A; Zhou, RZ; Arsalan, M; Sun, K; Si, YB Latif, Abdul; Maqbool, Ahsan; Zhou, Runzhan; Arsalan, Muhammad; Sun, Kai; Si, Youbin Optimized degradation of bisphenol A by immobilized laccase from Trametes versicolor using Box-Behnken design (BBD) and artificial neural network (ANN) JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING English Article Immobilized laccase; Bisphenol A degradation; Box-Behnken design; Artificial neural network; Optimization AQUEOUS-SOLUTIONS; FUNGAL LACCASES; ACID; DYE; NANOPARTICLES; SUSPENSION; ALGINATE; REMOVAL; SYSTEM; CELLS This study represents one of the first attempts towards bisphenol A (BPA) degradation by Trametes versicolor laccase immobilized on Ba-alginate beads. The effect of input variables including temperature (20-40 degrees C), BPA concentration (2-10 mg/L) and time (10-90 min) were studied using Box-Behnken design (BBD) and artificial neural network (ANN). The maximum BPA degradation of 84.34% was obtained when the temperature was 40 degrees C, BPA concentration 2 mg/L and time 50 min, which was accurately predicted by BBD (83.48%) and ANN (84.33%), proving the accuracy of prediction for both models. The values of R-2 and MSE for BBD were found to be 0.98, 9.88, while for ANN were 0.97, 38.25, respectively. Based on higher R-2 value and lower MSE, BBD was slightly better than ANN. The immobilized laccase showed higher storage stability than free laccase by retaining 68.64% and 44.62% of their activity at the same experimental conditions. Furthermore, BPA transformation was confirmed by Fourier-transform infrared spectroscopy (FTIR) analysis. GC-MS had detected the oxidative degradation products from BPA. Results showed that Ba-alginate immobilized laccase could be a promising biocatalyst in treating organic pollutants. [Latif, Abdul; Zhou, Runzhan; Sun, Kai; Si, Youbin] Anhui Agr Univ, Anhui Prov Key Lab Farmland Ecol Conservat & Poll, Sch Resources & Environm, Hefei 230036, Peoples R China; [Latif, Abdul; Arsalan, Muhammad] Barani Agr Res Inst, Chakwal, Pakistan; [Maqbool, Ahsan] Spanish Natl Res Council, Inst Sustainable Agr, Cordoba 14001, Spain Anhui Agricultural University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Agricultura Sostenible (IAS) Si, YB (corresponding author), Anhui Agr Univ, Anhui Prov Key Lab Farmland Ecol Conservat & Poll, Sch Resources & Environm, Hefei 230036, Peoples R China. latif@ahau.edu.cn; amaqbool@ias.csic.es; z13956604270@163.com; arsalanwasif@gmail.com; sunkai@ahau.edu.cn; youbinsi@ahau.edu.cn Maqbool, Ahsan/AAI-7554-2021 Maqbool, Ahsan/0000-0002-5968-3390; Sun, Kai/0000-0002-1907-9711 National Key Research and Development Program of China [2019YFC1805203]; National Natural Science Foundation of China [41907314, 42077123]; University Synergy Innovation Program of Anhui Province [GXXT-2021-061] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); University Synergy Innovation Program of Anhui Province This work was financially supported by the National Key Research and Development Program of China (2019YFC1805203) , the National Natural Science Foundation of China (41907314, 42077123) and the University Synergy Innovation Program of Anhui Province (GXXT-2021-061) . 55 3 3 12 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2213-2929 2213-3437 J ENVIRON CHEM ENG J. Environ. Chem. Eng. APR 2022.0 10 2 107331 10.1016/j.jece.2022.107331 0.0 FEB 2022 10 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0V1JZ 2023-03-23 WOS:000788102800001 0
J Wang, RY; Helbich, M; Yao, Y; Zhang, JB; Liu, PH; Yuan, Y; Liu, Y Wang, Ruoyu; Helbich, Marco; Yao, Yao; Zhang, Jinbao; Liu, Penghua; Yuan, Yuan; Liu, Ye Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures ENVIRONMENTAL RESEARCH English Article Green space; Street view data; Deep learning; Mediation; Mental wellbeing; China NATURAL OUTDOOR ENVIRONMENTS; PHYSICAL-ACTIVITY; NOISE EXPOSURE; AIR-POLLUTION; HEALTH STRESS; SPACE; ASSOCIATIONS; GREENSPACE; VEGETATION; VIEW Background: Multiple mechanisms have been proposed to explain how greenery in the vicinity of peoples homes enhances their mental health and wellbeing. Mediation studies, however, focus on a limited number of mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground. Objective: To examine: 1) how streetscape and remote sensing-based greenery affect peoples mental wellbeing; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred. Methods: We used a population sample of 1029 adult residents of the metropolis of Guangzhou, China, from 2016. Mental wellbeing was quantified by the World Health Organization Well-Being Index (WHO-5). Two objective greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the normalized difference vegetation index (NDVI) from Landsat 8 remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted. Results: Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association. Conclusions: Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to peoples wellbeing by means of different mechanisms. [Wang, Ruoyu; Zhang, Jinbao; Liu, Penghua; Yuan, Yuan; Liu, Ye] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China; [Wang, Ruoyu; Zhang, Jinbao; Liu, Penghua; Yuan, Yuan; Liu, Ye] Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou, Guangdong, Peoples R China; [Helbich, Marco] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands; [Yao, Yao] China Univ Geosci, Sch Informat Engn, Wuhan, Hubei, Peoples R China Sun Yat Sen University; Sun Yat Sen University; Utrecht University; China University of Geosciences Yuan, Y; Liu, Y (corresponding author), Sun Yat Sen Univ, 135 Xingang Xi Rd, Guangzhou 510275, Guangdong, Peoples R China. m.helbich@uu.nl; yaoy@cug.edu.cn; kampau@foxmail.com; liuph3@mail2.sysu.edu.cn; yyuanah@163.com; liuye25@mail.sysu.edu.cn Yao, Yao/L-7252-2018; Liu, Ye/J-2787-2019; Wang, Ruoyu/S-3360-2019; yao, yao/HHZ-7438-2022 Liu, Ye/0000-0003-2511-5413; Wang, Ruoyu/0000-0002-7240-558X; European Research Council (ERC) under the European Union [714993]; National Natural Science Foundation of China [41871140, 41801306, 51678577]; Innovative Research and Development Team Introduction Program of Guangdong Province [2017ZT07X355] European Research Council (ERC) under the European Union(European Research Council (ERC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovative Research and Development Team Introduction Program of Guangdong Province MH was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 714993). YL was supported by the National Natural Science Foundation of China (grant numbers 41871140, 41801306, and 51678577) and by the Innovative Research and Development Team Introduction Program of Guangdong Province (grant number 2017ZT07X355). 62 98 107 28 180 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0013-9351 1096-0953 ENVIRON RES Environ. Res. SEP 2019.0 176 108535 10.1016/j.envres.2019.108535 0.0 8 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health IU2KV 31260914.0 Green Submitted 2023-03-23 WOS:000483410200041 0
J White, RL; Sutton, AE; Salguero-Gomez, R; Bray, TC; Campbell, H; Cieraad, E; Geekiyanage, N; Gherardi, L; Hughes, AC; Jorgensen, PS; Poisot, T; DeSoto, L; Zimmerman, N White, Rachel L.; Sutton, Alexandra E.; Salguero-Gomez, Roberto; Bray, Timothy C.; Campbell, Heather; Cieraad, Ellen; Geekiyanage, Nalaka; Gherardi, Laureano; Hughes, Alice C.; Jorgensen, Peter Sogaard; Poisot, Timothee; DeSoto, Lucia; Zimmerman, Naupaka The next generation of action ecology: novel approaches towards global ecological research ECOSPHERE English Article action ecology; applied ecology; big data; citizen science; environmental leadership; environmental management; ESA Centennial Paper; planetary stewardship; training; transdisciplinary; translational ecology OPEN-ACCESS; CITIZEN SCIENCE; DATA-MANAGEMENT; BIG DATA; CONSERVATION; PUBLICATION; CHALLENGES; SYSTEMS; TOOL; OPPORTUNITIES Advances in the acquisition and dissemination of knowledge over the last decade have dramatically reshaped the way that ecological research is conducted. The advent of large, technology-based resources such as iNaturalist, Genbank, or the Global Biodiversity Information Facility (GBIF) allow ecologists to work at spatio-temporal scales previously unimaginable. This has generated a new approach in ecological research: one that relies on large datasets and rapid synthesis for theory testing and development, and findings that provide specific recommendations to policymakers and managers. This new approach has been termed action ecology, and here we aim to expand on earlier definitions to delineate its characteristics so as to distinguish it from related subfields in applied ecology and ecological management. Our new, more nuanced definition describes action ecology as ecological research that is (1) explicitly motivated by the need for immediate insights into current, pressing problems, (2) collaborative and transdisciplinary, incorporating sociological in addition to ecological considerations throughout all steps of the research, (3) technology-mediated, innovative, and aggregative (i.e., reliant on 'big data'), and (4) designed and disseminated with the intention to inform policy and management. We provide tangible examples of existing work in the domain of action ecology, and offer suggestions for its implementation and future growth, with explicit recommendations for individuals, research institutions, and ecological societies. [White, Rachel L.; Sutton, Alexandra E.; Salguero-Gomez, Roberto; Bray, Timothy C.; Campbell, Heather; Cieraad, Ellen; Geekiyanage, Nalaka; Gherardi, Laureano; Hughes, Alice C.; Jorgensen, Peter Sogaard; Poisot, Timothee; DeSoto, Lucia; Zimmerman, Naupaka] Univ Brighton, Int Network Next Generat Ecologists, Brighton BN2 4GJ, E Sussex, England; [White, Rachel L.] Univ Brighton, Biol & Biomed Sci Div, Brighton BN2 4GJ, E Sussex, England; [Sutton, Alexandra E.] Duke Univ, Nicholas Sch Environm, Durham, NC 27705 USA; [Salguero-Gomez, Roberto] Univ Queensland, Ctr Excellence Environm Decis, St Lucia, Qld 4072, Australia; [Salguero-Gomez, Roberto] Max Planck Inst Demog Res, Evolutionary Demog Lab, DE-18057 Rostock, Germany; [Salguero-Gomez, Roberto] Trinity Coll Dublin, Sch Nat Sci, Dept Zool, Dublin 2, Ireland; [Bray, Timothy C.] Palacky Univ, Fac Sci, Dept Zool, Olomouc 77146, Czech Republic; [Campbell, Heather] Univ Pretoria, Dept Zool & Entomol, ZA-0028 Hatfield, South Africa; [Cieraad, Ellen] Landcare Res Ecosyst & Global Change, Lincoln 7640, New Zealand; [Geekiyanage, Nalaka] Kyoto Univ, Grad Sch Agr, Div Forest & Biomat Sci, Trop Forest Resources & Environments Lab, Kyoto 6068502, Japan; [Geekiyanage, Nalaka] Rajarata Univ, Fac Agr, Dept Plant Sci, Anuradhapura 50000, Sri Lanka; [Gherardi, Laureano] Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA; [Hughes, Alice C.] Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Ctr Integrat Conservat, Mengla 666303, Yunnan, Peoples R China; [Jorgensen, Peter Sogaard] Univ Copenhagen, Nat Hist Museum Denmark, Ctr Macroecol Evolut & Climate, DK-2100 Copenhagen, Denmark; [Poisot, Timothee] Univ Montreal, Dept Sci Biol, Montreal, PQ H2V 2S9, Canada; [DeSoto, Lucia] Univ Coimbra, Dept Life Sci, Ctr Funct Ecol, P-3000456 Coimbra, Portugal; [Zimmerman, Naupaka] Univ Arizona, Sch Plant Sci, Tucson, AZ 85745 USA University of Brighton; University of Brighton; Duke University; University of Queensland; Max Planck Society; Trinity College Dublin; Palacky University Olomouc; University of Pretoria; Kyoto University; Rajarata University of Sri Lanka; Arizona State University; Arizona State University-Tempe; Chinese Academy of Sciences; Xishuangbanna Tropical Botanical Garden, CAS; University of Copenhagen; Universite de Montreal; Universidade de Coimbra; University of Arizona White, RL (corresponding author), Univ Brighton, Int Network Next Generat Ecologists, Brighton BN2 4GJ, E Sussex, England. r.white2@brighton.ac.uk Sutton, Alexandra E./B-2539-2010; Salguero-Gómez, Rob/N-6016-2016; Cieraad, Ellen/AAJ-6535-2021; Zimmerman, Naupaka/N-9128-2015; Geekiyanage, Nalaka/AAB-6171-2021; hughes, Alice/HCH-3838-2022; Salguero-Gomez, Rob/AAB-8488-2022; Jørgensen, Peter Søgaard/AAF-8570-2020; DeSoto, Lucía/B-6196-2009 Sutton, Alexandra E./0000-0001-5927-2850; Salguero-Gómez, Rob/0000-0002-6085-4433; Cieraad, Ellen/0000-0002-9813-9590; Zimmerman, Naupaka/0000-0003-2168-6390; Geekiyanage, Nalaka/0000-0002-7400-3453; hughes, Alice/0000-0002-4899-3158; Jørgensen, Peter Søgaard/0000-0002-2621-378X; DeSoto, Lucía/0000-0002-5814-5865; White, Rachel/0000-0003-0694-7847; Campbell, Heather/0000-0001-7483-3281; Poisot, Timothee/0000-0002-0735-5184 106 20 21 4 139 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2150-8925 ECOSPHERE Ecosphere AUG 2015.0 6 8 134 10.1890/ES14-00485.1 0.0 16 Ecology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology CP1ON Green Published, Green Submitted 2023-03-23 WOS:000359645200006 0
J Li, YF; Huang, YH; Pedersen, GF; Shen, M Li, Yunfeng; Huang, Yonghui; Pedersen, Gert Frolund; Shen, Ming Recurrent NEAT Assisted 2D-DOA Estimation With Reduced Complexity for Satellite Communication Systems IEEE ACCESS English Article Direction-of-arrival estimation; Covariance matrices; Antenna arrays; Multiple signal classification; Estimation; Topology; Pattern classification; Direction-of-arrival (DOA); neuroevolution of augmenting topologies (NEAT); multiple signal classification (MUSIC); RNEAT-MUSIC; satellite communication DOA ESTIMATION; MUSIC ALGORITHM; ANTENNA-ARRAY; NETWORKS Direction-of-arrival (DOA) estimation plays a vital role in the field of array signal processing. However, the need for heavy computing tasks in most traditional DOA algorithms, e.g., multiple signal classification (MUSIC), makes their engineering practicality significantly compromised in satellite communication systems. The neuroevolution of augmenting topologies (NEAT) can quickly search for appropriate topologies and weights of neural network functions, but its computational complexity is still too high for satellite systems. This paper proposes a modified NEAT architecture featuring a recurrent structure (RNEAT) that only needs a small number of phase components of the received signal covariance matrix as inputs to reduce the complexity and simplify the neural network architecture. The proposed RNEAT incorporated with multiple signal classification (RNEAT-MUSIC) features low complexity to achieve high resolution and low complexity simultaneously. Validation has been done by applying the proposed method in a two-dimensional direction of arrival estimation (2D-DOA) problem. Results show that the proposed RNEAT-MUSIC efficiently restricts the scanning region before forwarding the covariance matrix to the MUSIC stage. Consequently, the computational workload is reduced by 3/4 compared with the traditional 2D-MUSIC algorithm while maintaining satisfactory DOA resolution. [Li, Yunfeng] Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China; [Li, Yunfeng; Huang, Yonghui] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China; [Pedersen, Gert Frolund; Shen, Ming] Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark Chinese Academy of Sciences; National Space Science Center, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Aalborg University Huang, YH (corresponding author), Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China. yonghui@nssc.ac.cn huang, yonghui/GQA-6797-2022 Pedersen, Gert F./0000-0002-6570-7387; Huang, Yonghui/0000-0002-9745-4177; Shen, Ming/0000-0002-9388-3513 42 1 1 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2022.0 10 11551 11563 10.1109/ACCESS.2022.3145583 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications YS1KK Green Submitted, gold 2023-03-23 WOS:000750443100001 0
J Anwar, S; Al-Obeidat, F; Tubaishat, A; Din, S; Ahmad, A; Khan, FA; Jeon, G; Loo, J Anwar, Sajid; Al-Obeidat, Feras; Tubaishat, Abdallah; Din, Sadia; Ahmad, Awais; Khan, Fakhri Alam; Jeon, Gwanggil; Loo, Jonathan Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert IEEE INTERNET OF THINGS JOURNAL English Article Uniform resource locators; Feature extraction; Radio frequency; Classification algorithms; Phishing; Blacklisting; Internet of Things; Feature extraction; malicious URLs; naive Bayes (NB); simulated experts (SEs); URL classification STATIC CODE ATTRIBUTES; PREVENTION This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naive Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate. [Anwar, Sajid; Khan, Fakhri Alam] Inst Management Sci, Peshawar 25000, Pakistan; [Al-Obeidat, Feras; Tubaishat, Abdallah] Zayed Univ, Coll Technol Innovat, Abu Dhabi, U Arab Emirates; [Din, Sadia] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; [Ahmad, Awais] Univ Milan, Dipartimento Informat, I-20133 Milan, Italy; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea; [Loo, Jonathan] Univ West London, Sch Comp & Engn, London W5 5RF, England Zayed University; Kyungpook National University; University of Milan; Xidian University; Incheon National University Ahmad, A (corresponding author), Univ Milan, Dipartimento Informat, I-20133 Milan, Italy.;Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China. sajid.anwar@imsciences.edu.pk; feras.al-obeidat@zu.ac.ae; abdallah.tubaishat@zu.ac.ae; saadia.deen@gmail.com; aahmad.marwat@gmail.com; fakhri.alam@imsciences.edu.pk; ggjeon@gmail.com; jonathan.loo@uwl.ac.uk Khan, Fakhri Alam/S-5340-2017; Loo, Jonathan/E-6075-2019 Loo, Jonathan/0000-0002-2197-8126; Anwar, Sajid/0000-0003-3393-1656; Tubaishat, Abdallah/0000-0002-9621-3574; Ahmad, Ph.D, Awais/0000-0001-5483-2732; Khan, Fakhri Alam/0000-0002-9130-1874 Higher Education Commission Pakistan [21-3/HEC/RD/PPCR/2017]; Research Incentive Fund through Zayed University, Abu Dhabi, UAE [R18037]; National Research Foundation of Korea - Korea Government [2018045330] Higher Education Commission Pakistan(Higher Education Commission of Pakistan); Research Incentive Fund through Zayed University, Abu Dhabi, UAE; National Research Foundation of Korea - Korea Government(National Research Foundation of KoreaKorean Government) This work was supported in part by the Higher Education Commission Pakistan under Grant 21-3/HEC/R&D/PPCR/2017; in part by the Research Incentive Fund through Zayed University, Abu Dhabi, UAE, under Grant R18037; and in part by the National Research Foundation of Korea grant funded by the Korea Government under Grant 2018045330. 28 4 4 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAY 2020.0 7 5 4497 4504 10.1109/JIOT.2019.2954919 0.0 8 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LS0EE 2023-03-23 WOS:000536066300071 0
C Zhang, WS; Liu, X; Yuan, JR; Xu, L; Sun, HY; Zhou, JH; Liu, X Bie, R; Sun, Y; Yu, J Zhang, Weishan; Liu, Xia; Yuan, Jiangru; Xu, Liang; Sun, Haoyun; Zhou, Jiehan; Liu, Xin RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL) 2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS Procedia Computer Science English Proceedings Paper 7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) OCT 19-21, 2018 Beijing Normal Univ, Beijing, PEOPLES R CHINA Beijing Normal Univ Pattern recognition; RCNN; Power transmission line; CNN HOUGH TRANSFORM This paper proposes a new deep learning network - RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable for detecting foreign objects on power transmission lines. The RCNN4SPTL uses RPN (Region Proposal Network) to generate aspect ratio of the region proposals to align with the size of foreign objects. The RCNN4SPTL uses an end to end training to improve its performance. Experimental results show that the RCNN4SPTL significantly improves the detection speed and recognition accuracy, compared with the original Faster RCNN. (C) 2019 The Authors. Published by Elsevier B.V. [Zhang, Weishan; Liu, Xia; Sun, Haoyun; Liu, Xin] China Univ Petr, Coll Comp & Commun Engn, Qingdao, Shandong, Peoples R China; [Yuan, Jiangru] CNPC, Res Inst Petr Explorat & Dev, Beijing, Peoples R China; [Xu, Liang] Beijing Univ Sci & Technol, Coll Comp & Commun Engn, Beijing, Peoples R China; [Xu, Liang] Qingdao Deep Intelligence Informat Technol Co LTD, Qingdao, Shandong, Peoples R China; Univ Oulu, Oulu, Finland China University of Petroleum; China National Petroleum Corporation; University of Science & Technology Beijing; University of Oulu Zhang, WS (corresponding author), China Univ Petr, Coll Comp & Commun Engn, Qingdao, Shandong, Peoples R China. zhangws@upc.edu.cn Sun, Haoyun/AAC-4369-2022; xu, liang/AAC-4448-2022 National Major Science and Technology Project [2017ZX05013-002]; Key Research Program of Shandong Province [2017GGX10140]; Fundamental Research Funds for the Central Universities [2015020031]; National Natural Science Foundation of China [61309024] National Major Science and Technology Project; Key Research Program of Shandong Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported in part by the National Major Science and Technology Project (2017ZX05013-002), the Key Research Program of Shandong Province (2017GGX10140) and in part by the Fundamental Research Funds for the Central Universities (2015020031), National Natural Science Foundation of China (61309024). 17 15 16 1 12 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-0509 PROCEDIA COMPUT SCI 2019.0 147 331 337 10.1016/j.procs.2019.01.232 0.0 7 Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BM9HR gold, Green Published 2023-03-23 WOS:000470960800052 0
J Li, W; Zhao, DS; He, CY; Shen, Y; Hu, AD; Zhang, KF Li, Wang; Zhao, Dongsheng; He, Changyong; Shen, Yi; Hu, Andong; Zhang, Kefei Application of a Multi-Layer Artificial Neural Network in a 3-D Global Electron Density Model Using the Long-Term Observations of COSMIC, Fengyun-3C, and Digisonde SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS English Article COSMIC mission; equatorial ionization anomaly; FY‐ 3C; ionospheric model; neural network GENERAL-CIRCULATION MODEL; IONOSPHERE; THERMOSPHERE The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005-2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 x 10(4) el/cm(3). Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%-60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC-2 mission. Furthermore, the ANN-TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid-latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. [Li, Wang; Zhao, Dongsheng; Zhang, Kefei] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou, Jiangsu, Peoples R China; [Li, Wang; He, Changyong; Hu, Andong; Zhang, Kefei] RMIT Univ, SPACE Res Ctr, Sch Sci, Melbourne, Vic, Australia; [He, Changyong] ENSG, IGN, Champs Sur Marne, Marne La Vallee, France; [Shen, Yi] Xinyang Normal Univ, Key Lab Synergist Prevent Water & Soil Environm P, Xinyang, Peoples R China; [Hu, Andong] Ctr Wiskunde & Informat CWI, Multiscale Grp, Amsterdam, Netherlands China University of Mining & Technology; Royal Melbourne Institute of Technology (RMIT); Universite Gustave-Eiffel; Xinyang Normal University Li, W; Zhang, KF (corresponding author), China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou, Jiangsu, Peoples R China.;Li, W; Zhang, KF (corresponding author), RMIT Univ, SPACE Res Ctr, Sch Sci, Melbourne, Vic, Australia. liwang@cumt.edu.cn; profkzhang@cumt.edu.cn Hu, Andong/0000-0002-6929-2158 National Natural Science Foundations of China [41730109, 41804013]; Natural Science Foundation of Jiangsu Province [BK20200646, BK20200664]; Fundamental Research Funds for the Central Universities [2020QN31, 2020QN30]; China Postdoctoral Science Foundation [2020M671645]; Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution [KLSPWSEP-A06]; Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping); Jiangsu Dual Creative Talents Project; Jiangsu Dual Creative Teams Program Project National Natural Science Foundations of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution; Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping); Jiangsu Dual Creative Talents Project; Jiangsu Dual Creative Teams Program Project The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109, 41804013), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200646, BK20200664), the Fundamental Research Funds for the Central Universities (Grant No. 2020QN31, 2020QN30), the Project funded by China Postdoctoral Science Foundation (Grant No. 2020M671645), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping). The authors would like to acknowledge the support of the Jiangsu Dual Creative Talents and Jiangsu Dual Creative Teams Program Projects awarded in 2017. The authors are very grateful to UCAR community programs for providing COSMIC data, the Lowell GIRO Data Center for providing Ionosonde data, the GSFC for providing solar-magnetic indices. 45 4 4 8 26 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 1542-7390 SPACE WEATHER Space Weather MAR 2021.0 19 3 e2020SW002605 10.1029/2020SW002605 0.0 19 Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology & Atmospheric Sciences RH5TA Green Published, hybrid 2023-03-23 WOS:000636279600001 0
J Shi, Z; Li, ZQ; Hou, WZ; Mei, LL; Sun, L; Jia, C; Zhang, Y; Li, KT; Xu, H; Liu, ZH; Ge, BY; Hong, J; Qiao, YL Shi, Zheng; Li, Zhengqiang; Hou, Weizhen; Mei, Linlu; Sun, Lin; Jia, Chen; Zhang, Ying; Li, Kaitao; Xu, Hua; Liu, Zhenhai; Ge, Bangyu; Hong, Jin; Qiao, Yanli Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Aerosols; Reflectivity; Land surface; Sensors; Atmospheric modeling; Satellite broadcasting; Training; Aerosol optical depth (AOD); aerosol retrieval; neural network; polarized scanning atmospheric corrector (PSAC); remote sensing LAND; MODIS; CHINA; CLOUD; REFLECTANCE; ALGORITHM; AERONET; CLIMATOLOGY; VALIDATION; POLLUTION As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring a high-speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network-based AOD retrieval model (named the AODNet) that takes full advantage of the multispectral measurements of PSAC for AOD retrieval at a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well-trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either the Sun-sky radiometer Observation NETwork (SONET) AOD or the AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85, and more than 60% of the AODNet AOD can fall into the expected error envelope of +/-(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS dark target (DT) and deep blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data. [Shi, Zheng; Li, Zhengqiang; Hou, Weizhen; Zhang, Ying; Li, Kaitao; Xu, Hua; Ge, Bangyu] Chinese Acad Sci, State Environm Protect Key Lab Satellite Remote S, Aerosp Informat Res Inst, Beijing 100101, Peoples R China; [Shi, Zheng; Li, Zhengqiang; Hou, Weizhen; Zhang, Ying] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Mei, Linlu] Univ Bremen, Inst Environm Phys, D-28359 Bremen, Germany; [Sun, Lin; Jia, Chen] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Shandong, Peoples R China; [Liu, Zhenhai; Hong, Jin; Qiao, Yanli] Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China; [Liu, Zhenhai; Hong, Jin; Qiao, Yanli] Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Bremen; Shandong University of Science & Technology; Chinese Academy of Sciences; Anhui Institute of Optics & Fine Mechanics (AIOFM), CAS; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS Li, ZQ (corresponding author), Chinese Acad Sci, State Environm Protect Key Lab Satellite Remote S, Aerosp Informat Res Inst, Beijing 100101, Peoples R China. shizheng@radi.ac.cn; lizq@radi.ac.cn; houwz@radi.ac.cn; mei@iup.physik.uni-bremen.de; sunlin@sdust.edu.cn; jiachen@sdust.edu.cn; zhangying20148@aircas.ac.cn; likt@radi.ac.cn; xuhua@radi.ac.cn; lzhenhai@aiofm.ac.cn; geby@radi.ac.cn; hongjin@aiofm.ac.cn; ylqiao@aiofm.ac.cn LI, Kaitao/O-2945-2015; li, zhengqiang/C-5678-2013 LI, Kaitao/0000-0002-2610-0064; li, zhengqiang/0000-0002-7795-3630; liu, zhen hai/0000-0001-5570-8450; Jia, Chen/0000-0001-5924-8674; Shi, Zheng/0000-0002-3117-9061; hou, wei zhen/0000-0003-0610-7944 National Outstanding Youth Foundation of China [41925019]; National Natural Science Foundation of China [41871269, 42175146]; K. C. Wong Education Foundation [GJTD2018-15]; Deutsche Forschungsgemeinschaft (DFG); German Research Foundation [(TRR) 172, 268020496]; ESA Copernicus Climate Change Service (C3S) [C3S2_120a] National Outstanding Youth Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); K. C. Wong Education Foundation; Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); German Research Foundation(German Research Foundation (DFG)); ESA Copernicus Climate Change Service (C3S) This work was supported in part by the National Outstanding Youth Foundation of China under Grant 41925019, in part by the National Natural Science Foundation of China under Grant 41871269 and Grant 42175146, and in part by the K. C. Wong Education Foundation under Grant GJTD2018-15. The work of Linlu Mei was supported in part by the Deutsche Forschungsgemeinschaft (DFG), German Research Foundation, within the project ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3 as the Transregional Collaborative Research Center (TRR) 172 under Project 268020496 and in part by the ESA Copernicus Climate Change Service (C3S) under Project C3S2_120a. 61 2 2 14 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 4109018 10.1109/TGRS.2022.3192908 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3P1HJ 2023-03-23 WOS:000837291200016 0
J Li, L; Riom, T; Bissyande, TF; Wang, HY; Klein, J; Yves, L Li, Li; Riom, Timothee; Bissyande, Tegawende F.; Wang, Haoyu; Klein, Jacques; Yves, Le Traon Revisiting the impact of common libraries for android-related investigations JOURNAL OF SYSTEMS AND SOFTWARE English Article The packaging model of Android apps requires the entire code to be shipped into a single APK file in order to be installed and executed on a device. This model introduces noises to Android app analyses, e.g., detection of repackaged applications, malware classification, as not only the core developer code but also the other assistant code will be visited. Such assistant code is often contributed by common libraries that are used pervasively by all apps. Despite much effort has been put in our community to investigate Android libraries, the momentum of Android research has not yet produced a complete and reliable set of common libraries for supporting thorough analyses of Android apps. In this work, we hence leverage a dataset of about 1.5 million apps from Google Play to identify potential common libraries, including advertisement libraries, and their abstract representations. With several steps of refinements, we finally collect 1113 libraries supporting common functions and 240 libraries for advertisement. For each library, we also collected its various abstract representations that could be leveraged to find new usages, including obfuscated cases. Based on these datasets, we further empirically revisit three popular Android app analyses, namely (1) repackaged app detection, (2) machine learning-based malware detection, and (3) static code analysis, aiming at measuring the impact of common libraries on their analysing performance. Our experimental results demonstrate that common library can indeed impact the performance of Android app analysis approaches. Indeed, common libraries can introduce both false positive and false negative results to repackaged app detection approaches. The existence of common libraries in Android apps may also impact the performance of machine learning-based classifications as well as that of static code analysers. All in all, the aforementioned results suggest that it is essential to harvest a reliable list of common libraries and also important to pay special attention to them when conducting Android-related investigations. (C) 2019 Elsevier Inc. All rights reserved. [Li, Li] Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia; [Riom, Timothee; Bissyande, Tegawende F.; Klein, Jacques; Yves, Le Traon] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Esch Sur Alzette, Luxembourg; [Wang, Haoyu] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China Monash University; University of Luxembourg; Beijing University of Posts & Telecommunications Li, L (corresponding author), Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia. Li.Li@monash.edu Li, Li/AAR-3316-2020 Klein, Jacques/0000-0003-4052-475X Monash-Warwick Alliance Catalyst Fund [2018/2019]; European Union, under the SPARTA project; Fonds National de la Recherche (FNR), Luxembourg [CHARACTERIZE C17/IS/11693861, Recommend C15/IS/10449467]; University of Luxembourg; National Key Research and Development Program of China [2017YFB0801903]; National Natural Science Foundation of China [61702045] Monash-Warwick Alliance Catalyst Fund; European Union, under the SPARTA project; Fonds National de la Recherche (FNR), Luxembourg(Luxembourg National Research Fund); University of Luxembourg; National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank the anonymous reviewers who have provided insightful and constructive comments that have led to important improvements in several parts of the manuscript. This work was supported by the Monash-Warwick Alliance Catalyst Fund (2018/2019), by the European Union, under the SPARTA project, by the Fonds National de la Recherche (FNR), Luxembourg, under projects CHARACTERIZE C17/IS/11693861 and Recommend C15/IS/10449467, by the University of Luxembourg, under the Vul-Fix project, by the National Key Research and Development Program of China (grant No.2017YFB0801903), and by the National Natural Science Foundation of China (grant No. 61702045). 58 10 10 0 0 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0164-1212 1873-1228 J SYST SOFTWARE J. Syst. Softw. AUG 2019.0 154 157 175 10.1016/j.jss.2019.04.065 0.0 19 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science IC4NT Green Submitted 2023-03-23 WOS:000470943300011 0
J Cheng, L; Chen, XW; De Vos, J; Lai, XJ; Witlox, F Cheng, Long; Chen, Xuewu; De Vos, Jonas; Lai, Xinjun; Witlox, Frank Applying a random forest method approach to model travel mode choice behavior TRAVEL BEHAVIOUR AND SOCIETY English Article Travel mode choice; Prediction performance; Variable importance; Random forest; Nanjing (China) BUILT ENVIRONMENT; RECOGNITION; UNCERTAINTY; ATTITUDES; SELECTION; WORK The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people's travel behavior. [Cheng, Long; Chen, Xuewu] Southeast Univ, Jiangsu Key Lab Urban ITS, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China; [Cheng, Long; De Vos, Jonas; Witlox, Frank] Univ Ghent, Dept Geog, Krijgslaan 281 S8, B-9000 Ghent, Belgium; [Lai, Xinjun] Guangdong Univ Technol, Sch Electromech Engn, 100 Waihuan Xi Rd, Guangzhou 510006, Guangdong, Peoples R China; [Witlox, Frank] Univ Tartu, Dept Geog, Vanemuise 46, EE-51014 Tartu, Estonia; [Witlox, Frank] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, 29 Yudao St, Nanjing 210016, Jiangsu, Peoples R China Southeast University - China; Ghent University; Guangdong University of Technology; University of Tartu; Nanjing University of Aeronautics & Astronautics Chen, XW (corresponding author), Southeast Univ, Jiangsu Key Lab Urban ITS, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China. chenxuewu@seu.edu.cn; jonas.devos@ugent.be; xinjun.lai@gdut.edu.cn; frank.witlox@ugent.be De Vos, Jonas/F-7095-2019; Cheng, Long/ABB-8122-2020; Witlox, Frank/A-5143-2008; De Vos, Jonas/AAJ-1298-2021 De Vos, Jonas/0000-0003-4919-2157; Cheng, Long/0000-0003-3720-4093; Witlox, Frank/0000-0002-8966-6823; De Vos, Jonas/0000-0003-4919-2157; Lai, Xinjun/0000-0003-3740-7026 Research Grants Council of the Hong Kong Special Administrative Region [PolyU 152095/17E]; Research Committee of The Hong Kong Polytechnic University [4-ZZFY]; National Natural Science Foundation of China [71801041, 51338003, 71601052, 71771049] Research Grants Council of the Hong Kong Special Administrative Region(Hong Kong Research Grants Council); Research Committee of The Hong Kong Polytechnic University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is sponsored by the Research Grants Council of the Hong Kong Special Administrative Region (PolyU 152095/17E), the Research Committee of The Hong Kong Polytechnic University (Project No. 4-ZZFY), and the National Natural Science Foundation of China (71801041, 51338003, 71601052 and 71771049). The authors appreciate the Nanjing Institute of City and Transport Planning for providing the data used in this study, and helpful suggestions from Prof. William H.K. Lam and Prof. Sheng-Guo Wang on this research. We also would like to thank the anonymous referees whose comments on the earlier version led to significant improvements. 44 133 140 61 206 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-367X 2214-3688 TRAVEL BEHAV SOC Travel Behav. Soc. JAN 2019.0 14 1 10 10.1016/j.tbs.2018.09.002 0.0 10 Transportation Social Science Citation Index (SSCI) Transportation HA6DZ 2023-03-23 WOS:000450371200001 0
J Zahra, O; Tolu, S; Navarro-Alarcon, D Zahra, Omar; Tolu, Silvia; Navarro-Alarcon, David Differential mapping spiking neural network for sensor-based robot control BIOINSPIRATION & BIOMIMETICS English Article robotics; visual servoing; sensor-based control; spiking neural networks TIMING-DEPENDENT PLASTICITY; MODEL; NEURONS In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot. [Zahra, Omar; Navarro-Alarcon, David] Hong Kong Polytech Univ, Hong Kong, Peoples R China; [Tolu, Silvia] Tech Univ Denmark, Lyngby, Denmark Hong Kong Polytechnic University; Technical University of Denmark Navarro-Alarcon, D (corresponding author), Hong Kong Polytech Univ, Hong Kong, Peoples R China. david.navarro-alarcon@polyu.edu.hk Zahra, Omar/AGY-2830-2022; Nuñez, David/HLW-7333-2023; Tolu, Silvia/G-3818-2016; Navarro-Alarcon, David/V-6917-2017 Zahra, Omar/0000-0003-1644-6480; Tolu, Silvia/0000-0003-1825-8440; Navarro-Alarcon, David/0000-0002-3426-6638 Research Grants Council (RGC) of Hong Kong [14203917]; PROCOREFrance/Hong Kong Joint Research Scheme - RGC; Consulate General of France in Hong Kong [F-PolyU503/18]; Chinese National Engineering Research Centre for Steel Construction (Hong Kong Branch) at PolyU [BBV8]; Key-Area Research and Development Program of Guangdong Province 2020 [76]; Hong Kong Polytechnic University [4-ZZHJ, G-YBYT] Research Grants Council (RGC) of Hong Kong(Hong Kong Research Grants Council); PROCOREFrance/Hong Kong Joint Research Scheme - RGC; Consulate General of France in Hong Kong; Chinese National Engineering Research Centre for Steel Construction (Hong Kong Branch) at PolyU; Key-Area Research and Development Program of Guangdong Province 2020; Hong Kong Polytechnic University(Hong Kong Polytechnic University) This research work is supported in part by the Research Grants Council (RGC) of Hong Kong under Grant No. 14203917, in part by PROCOREFrance/Hong Kong Joint Research Scheme sponsored by the RGC and the Consulate General of France in Hong Kong under Grant F-PolyU503/18, in part by the Chinese National Engineering Research Centre for Steel Construction (Hong Kong Branch) at PolyU under Grant BBV8, in part by the Key-Area Research and Development Program of Guangdong Province 2020 under project 76 and in part by The Hong Kong Polytechnic University under Grant G-YBYT and 4-ZZHJ. 58 5 5 1 23 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-3182 1748-3190 BIOINSPIR BIOMIM Bioinspir. Biomim. MAY 2021.0 16 3 36008 10.1088/1748-3190/abedce 0.0 22 Engineering, Multidisciplinary; Materials Science, Biomaterials; Robotics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science; Robotics RH4VC 33706302.0 Green Published, Green Submitted 2023-03-23 WOS:000636217300001 0
J Dubey, R; Gunasekaran, A; Childe, SJ; Papadopoulos, T; Luo, ZW; Wamba, SF; Roubaud, D Dubey, Rameshwar; Gunasekaran, Angappa; Childe, Stephen J.; Papadopoulos, Thanos; Luo, Zongwei; Wamba, Samuel Fosso; Roubaud, David Can big data and predictive analytics improve social and environmental sustainability? TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE English Article Big data; Predictive analytics; Dynamic capability view; Supply chains; Social sustainability; Environmental sustainability SUPPLY CHAIN MANAGEMENT; RESOURCE-BASED THEORY; INFORMATION-TECHNOLOGY; COMPETITIVE ADVANTAGE; DATA SCIENCE; PERFORMANCE; STRATEGY; IMPACT; CHALLENGES; CULTURE Although literature indicates that big data and predictive analytics (BDPA) convey a distinct organisational capability, little is known about their performance effects in particular contextual conditions (inter alia, national context and culture, and firm size). Grounding our investigation in the dynamic capability views and organisational culture and based on a sample of 205 Indian manufacturing organisations, we empirically investigate the effects of BDPA on social performance (SP) and environmental performance (EP) using variance based structural equation modelling (i.e. PLS). We find that BDPA has significant impact on SP/EP. However, we did not find evidence for moderating role of flexible orientation and control orientation in the links between BDPA and SP/EP. Our findings offer a more nuanced understanding of the performance implications of BDPA, thereby addressing the crucial questions of how and when BDPA can enhance social/environmental sustainability in supply chains. [Dubey, Rameshwar; Roubaud, David] Montpellier Res Management, Montpellier Business Sch, 2300 Ave Moulins, F-34000 Montpellier, France; [Gunasekaran, Angappa] Calif State Univ, Sch Business & Publ Adm, 9001 Stockdale Highway, Bakersfield, CA 93311 USA; [Childe, Stephen J.] Plymouth Univ, Plymouth Business Sch, Plymouth PL4 8AA, Devon, England; [Papadopoulos, Thanos] Univ Kent, Kent Business Sch, Sail & Colour Loft, Hist Dockyard, Chatham ME4 4TE, Kent, England; [Luo, Zongwei] Southern Univ Sci & Technol, Comp Sci & Engn, 1088 Xueyuan Blvd, Sherzzen 518055, Guangdong, Peoples R China; [Wamba, Samuel Fosso] Univ Toulouse, Toulouse Business Sch, 20 Blvd Lascrosses, F-31068 Toulouse, France Montpellier Business School; Universite de Montpellier; California State University System; California State University Bakersfield; University of Plymouth; University of Kent; Southern University of Science & Technology; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; TBS Education Papadopoulos, T (corresponding author), Univ Kent, Kent Business Sch, Sail & Colour Loft, Hist Dockyard, Chatham ME4 4TE, Kent, England.;Luo, ZW (corresponding author), Southern Univ Sci & Technol, Comp Sci & Engn, 1088 Xueyuan Blvd, Sherzzen 518055, Guangdong, Peoples R China. agunasekaran@umassd.edu; stephen.childe@plymouth.ac.uk; A.Papadopoulos@kent.ac.uk; luozw@sustc.edu.cn; d.roubaud@montpellier-bs.com Wamba, Samuel Fosso/AAB-4953-2019; Papadopoulos, Thanos/ABD-5724-2021; Dubey, Rameshwar/AAT-1348-2020; Childe, Stephen J/ABE-8243-2020; Dubey, Rameshwar/U-7022-2018; ROUBAUD, DAVID/ABA-8123-2020; Luo, Zongwei/N-1985-2015 Wamba, Samuel Fosso/0000-0002-1073-058X; Childe, Stephen J/0000-0002-9476-4209; Dubey, Rameshwar/0000-0002-3913-030X; ROUBAUD, DAVID/0000-0003-3827-6187; Luo, Zongwei/0000-0001-9322-959X 81 228 232 44 232 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0040-1625 1873-5509 TECHNOL FORECAST SOC Technol. Forecast. Soc. Chang. JUL 2019.0 144 534 545 10.1016/j.techfore.2017.06.020 0.0 12 Business; Regional & Urban Planning Social Science Citation Index (SSCI) Business & Economics; Public Administration ID5SG Green Accepted, Green Submitted 2023-03-23 WOS:000471735700048 0
J Fan, XK; Wu, J; Comes, HP; Feng, Y; Wang, T; Yang, SZ; Iwasaki, T; Zhu, H; Jiang, Y; Lee, J; Li, P Fan, Xiao-Kai; Wu, Jing; Comes, Hans Peter; Feng, Yu; Wang, Ting; Yang, Shu-Zhen; Iwasaki, Takaya; Zhu, Hong; Jiang, Yun; Lee, Joongku; Li, Pan Phylogenomic, morphological, and niche differentiation analyses unveil species delimitation and evolutionary history of endangered maples in Acer series Campestria (Sapindaceae) JOURNAL OF SYSTEMATICS AND EVOLUTION English Article; Early Access Acer; deep learning; ecological niche; multiomics; phylogeny; series Campestria; systematics CHLOROPLAST CAPTURE; HYBRID SPECIATION; DNA BARCODES; NUCLEAR; HYBRIDIZATION; INTROGRESSION; DISTRIBUTIONS; ACERACEAE; ALIGNMENT; RESOURCE Accurate species delimitation is crucial for biodiversity conservation. The Acer series Campestria comprises four species, A. campestre L., A. miyabei Maxim., A. miaotaiense P. C. Tsoong, and A. yangjuechi Fang & P. L. Chiu. To clarify controversies over the taxonomic status of the latter three endangered species, we undertook phylogenomic, morphological, and niche differentiation analyses in series Campestria. Our coalescent species tree of 544 and 77 single-copy nuclear genes supported series Campestria as monophyletic, with A. yangjuechi having the closest relationship with A. miaotaiense. However, in the plastome-derived tree based on 64 protein coding sequences, the four species did not cluster together, and each of them grouped with some other sympatric Acer species. Given this nuclear-cytoplasmic conflict, we hypothesize that A. yangjuechi have been subject to nuclear gene introgression and plastid (pt) capture involving another sympatric maple, that is, A. amplum Rehder. Principal component analysis and machine learning based on morphological data could not separate A. yangjuechi and A. miaotaiense, but they both could be clearly distinguished from A. miyabei. Moreover, the niche overlap tests of the two more widespread species, A. miyabei and A. miaotaiense, showed they clearly occupy distinct niches. Overall, we conclude that A. miyabei and A. miaotaiense are distinct species, while A. yangjuechi (endemic to Mt. Tianmu/East China) should be treated as a subspecies of A. miaotaiense. Our study points out that multiple lines of phylogenomic, morphological, and ecological evidence prove highly useful in species delimitation. Additionally, our results should help to inform conservation measures for endangered species of the genus Acer/series Campestria in East Asia. [Fan, Xiao-Kai; Wu, Jing; Li, Pan] Zhejiang Univ, Coll Life Sci, MOE Lab Biosyst Homeostasis & Protect, Systemat & Evolutionary Bot & Biodivers Grp, Hangzhou 310058, Peoples R China; [Comes, Hans Peter] Univ Salzburg, Dept Biosci, A-5020 Salzburg, Austria; [Feng, Yu] Chengdu Inst Biol, Chinese Acad Sci, CAS Key Lab Mt Ecol Restorat & Bioresource Utiliza, Chengdu 610041, Peoples R China; [Feng, Yu] Chinese Acad Sci, Chengdu Inst Biol, Ecol Restorat & Biodivers Conservat Key Lab Sichua, Chengdu 610041, Peoples R China; [Wang, Ting] Hangzhou West Lake Acad Landscape Sci, Hangzhou Bot Garden, Hangzhou 310013, Peoples R China; [Yang, Shu-Zhen] Zhejiang Tianmushan Natl Nat Reserve Management Bu, Hangzhou 311311, Peoples R China; [Iwasaki, Takaya] Ochanomizu Univ, Fac Core Res, Nat Sci Div, Tokyo 1128610, Japan; [Zhu, Hong] Zhejiang Forestry Acad, Hangzhou 310023, Peoples R China; [Jiang, Yun] Shanghai Chenshan Bot Garden, Eastern China Conservat Ctr Wild Endangered Plant, Shanghai 201602, Peoples R China; [Lee, Joongku] Chungnam Natl Univ, Dept Environm & Forest Resources, Daejeon 34134, South Korea Zhejiang University; Salzburg University; Chinese Academy of Sciences; Chengdu Institute of Biology, CAS; Chinese Academy of Sciences; Chengdu Institute of Biology, CAS; Ochanomizu University; Chinese Academy of Sciences; Chungnam National University Li, P (corresponding author), Zhejiang Univ, Coll Life Sci, MOE Lab Biosyst Homeostasis & Protect, Systemat & Evolutionary Bot & Biodivers Grp, Hangzhou 310058, Peoples R China. panli@zju.edu.cn Zhu, Hong/0000-0003-4048-2748; Li, Pan/0000-0002-9407-7740 National Natural Science Foundation of China; Open Fund of Shanghai Key Laboratory of Plant Functional Genomics and Resources; National Science and Technology Basic Project of China; [31970225]; [PFGR202104]; [2015FY110200] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund of Shanghai Key Laboratory of Plant Functional Genomics and Resources; National Science and Technology Basic Project of China; ; ; Acknowledgements This research was supported by the National Natural Science Foundation of China (grant number 31970225), the Open Fund of Shanghai Key Laboratory of Plant Functional Genomics and Resources (grant number PFGR202104), and the National Science and Technology Basic Project of China (grant number 2015FY110200). We thank Mr. Chong Zhu for help with the deep learning analysis, and Dr. Hongliang Chen, Dr. Shanshan Zhu, Dr. Hanyang Lin, and Mr. Junjie Wu for helpful discussions. The authors declare no conflict of interest. 114 0 0 10 10 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1674-4918 1759-6831 J SYST EVOL J. Syst. Evol. 10.1111/jse.12919 0.0 NOV 2022 15 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences 6N0IL 2023-03-23 WOS:000889245100001 0
J Luan, F; Wang, T; Tang, LL; Zhang, S; Cordeiro, MNDS Luan, Feng; Wang, Ting; Tang, Lili; Zhang, Shuang; Natalia Dias Soeiro Cordeiro, M. Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach MOLECULES English Article substituted aromatic compounds; toxicity; quantitative structure-activity relationship (QSAR); multiple linear regression (MLR); radial basis function neural network (RBFNN) SELENASTRUM-CAPRICORNUTUM; BENZENE-DERIVATIVES; PREDICTION; NITROAROMATICS; MODELS Nowadays, quantitative structure-activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (-NO2, -X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R-2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R-2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R-2 = 0.803, RMS = 0.222, RBFNN: n = 60, R-2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R-2 = 0.852, RMS = 0.192; RBFNN: n = 31, R-2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity. [Luan, Feng; Wang, Ting; Tang, Lili; Zhang, Shuang] Yantai Univ, Coll Chem & Chem Engn, Yantai 264005, Peoples R China; [Natalia Dias Soeiro Cordeiro, M.] Univ Porto, Dept Chem & Biochem, LAQV REQUIMTE, Fac Sci, P-4169007 Porto, Portugal Yantai University; Universidade do Porto Luan, F (corresponding author), Yantai Univ, Coll Chem & Chem Engn, Yantai 264005, Peoples R China. fluan@sina.com; 18865557652@163.com; tangtang20160216@sina.com; zs20160903@163.com; ncordeir@fc.up.pt Cordeiro, Maria Natália D. S./A-7413-2012 Cordeiro, Maria Natália D. S./0000-0003-3375-8670 National Natural Science Foundation of China [21675138] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was financially supported by the National Natural Science Foundation of China (No. 21675138). 48 6 6 1 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1420-3049 MOLECULES Molecules MAY 2018.0 23 5 1002 10.3390/molecules23051002 0.0 16 Biochemistry & Molecular Biology; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry GJ3NX 29695132.0 Green Submitted, gold, Green Published, Green Accepted 2023-03-23 WOS:000435204000017 0
J Zahra, O; Tolu, S; Zhou, P; Duan, AQ; Navarro-Alarcon, D Zahra, Omar; Tolu, Silvia; Zhou, Peng; Duan, Anqing; Navarro-Alarcon, David A Bio-Inspired Mechanism for Learning Robot Motion From Mirrored Human Demonstrations FRONTIERS IN NEUROROBOTICS English Article robotics; spiking neural networks; sensor-based control; visual servoing; imitation learning NEURONS; IMITATION Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing movements allows to reinforce our motor abilities. This implies the presence of a biological mechanism that allows creating models of others' movements and linking them to the self-model for achieving mirroring. Inspired by such ability, we propose to build a map of movements executed by a teaching agent and mirror the agent's state to the robot's configuration space. Hence, in this study, a neural network is proposed to integrate a motor cortex-like differential map transforming motor plans from task-space to joint-space motor commands and a static map correlating joint-spaces of the robot and a teaching agent. The differential map is developed based on spiking neural networks while the static map is built as a self-organizing map. The developed neural network allows the robot to mirror the actions performed by a human teaching agent to its own joint-space and the reaching skill is refined by the complementary examples provided. Hence, experiments are conducted to quantify the improvement achieved thanks to the proposed learning approach and control scheme. [Zahra, Omar; Zhou, Peng; Duan, Anqing; Navarro-Alarcon, David] Hong Kong Polytech Univ, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China; [Tolu, Silvia] Tech Univ Denmark, Dept Elect Engn, Copenhagen, Denmark Hong Kong Polytechnic University; Technical University of Denmark Zahra, O (corresponding author), Hong Kong Polytech Univ, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China. omar.zahra@connect.polyu.hk Zahra, Omar/AGY-2830-2022; Navarro-Alarcon, David/V-6917-2017; Tolu, Silvia/G-3818-2016 Zahra, Omar/0000-0003-1644-6480; Zhou, Peng/0000-0002-7020-0943; Navarro-Alarcon, David/0000-0002-3426-6638; Tolu, Silvia/0000-0003-1825-8440 Key-Area Research and Development Program of Guangdong Province 2020 [2020B090928001]; Research Grants Council of Hong Kong [14203917, 15212721]; Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme [ZG9V]; Hong Kong Polytechnic University under grant UAKU Key-Area Research and Development Program of Guangdong Province 2020; Research Grants Council of Hong Kong(Hong Kong Research Grants Council); Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme; Hong Kong Polytechnic University under grant UAKU(Hong Kong Polytechnic University) This work is supported in part by the Key-Area Research and Development Program of Guangdong Province 2020 under grant 2020B090928001, in part by the Research Grants Council of Hong Kong under grants 14203917 and 15212721, in part by the Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme under grant ZG9V, and in part by The Hong Kong Polytechnic University under grant UAKU. 29 4 4 4 6 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics MAR 14 2022.0 16 826410 10.3389/fnbot.2022.826410 0.0 16 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology 0H1JL 35360830.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000778495000001 0
J Le, QN; Nguyen, VD; Dobre, OA; Nguyen, NP; Zhao, RQ; Chatzinotas, S Quang Nhat Le; Van-Dinh Nguyen; Dobre, Octavia A.; Nam-Phong Nguyen; Zhao, Ruiqin; Chatzinotas, Symeon Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article NOMA; Clustering algorithms; Downlink; Complexity theory; Uplink; Optimization; Training; Cell-free massive multiple-input multiple-output; full-pilot zero-forcing; k-means; machine learning; non-orthogonal multiple access; power allocation; user clustering K-MEANS; PROPAGATION; ALGORITHM The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Adopting full-pilot zero-forcing at access points (APs) to comprehensively assess the system performance, we formulate the sum SE optimization problem taking into account power constraints at APs, necessary conditions for implementing successive interference cancellation, and required SE constraints at user equipments. The formulated optimization problem is highly non-convex, and thus, it is difficult to obtain the global optimal solution. Therefore, we develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated. [Quang Nhat Le; Dobre, Octavia A.] Mem Univ, Dept Elect & Comp Engn, St John, NF A1B 3X9, Canada; [Van-Dinh Nguyen; Chatzinotas, Symeon] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, L-1855 Luxembourg, Luxembourg; [Nam-Phong Nguyen] Hanoi Univ Sci & Technol, Sch Elect & Telecommun, Hanoi 100000, Vietnam; [Zhao, Ruiqin] Northwestern Polytech Univ, Sch Marine Sci & Technol, Key Lab Ocean Acoust & Sensing, Xian 710072, Peoples R China Memorial University Newfoundland; University of Luxembourg; Hanoi University of Science & Technology; Northwestern Polytechnical University Zhao, RQ (corresponding author), Northwestern Polytech Univ, Sch Marine Sci & Technol, Key Lab Ocean Acoust & Sensing, Xian 710072, Peoples R China. qnle@mun.ca; dinh.nguyen@uni.lu; odobre@mun.ca; nguyennamphong1989@gmail.com; rqzhao@nwpu.edu.cn; symeon.chatzinotas@uni.lu Dobre, Octavia/B-1435-2008; Chatzinotas, Symeon/D-4191-2015; Le, Quang Nhat/AAJ-5020-2021 Dobre, Octavia/0000-0001-8528-0512; Chatzinotas, Symeon/0000-0001-5122-0001; Le, Quang Nhat/0000-0003-0412-5524; Zhao, Ruiqin/0000-0002-8879-5922; NGUYEN, Van-Dinh/0000-0002-9575-1063 Memorial University Research Chair; Natural Sciences and Engineering Research Council of Canada (NSERC); National Natural Science Foundation of China [62071385, 61571367]; Luxembourg National Research Fund (FNR) - ROSETTA Project [C17/IS/11632107]; ERC AGNOSTIC Project [H2020/ERC2020POC/957570] Memorial University Research Chair; Natural Sciences and Engineering Research Council of Canada (NSERC)(Natural Sciences and Engineering Research Council of Canada (NSERC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Luxembourg National Research Fund (FNR) - ROSETTA Project(Luxembourg National Research Fund); ERC AGNOSTIC Project This work was supported in part by the Memorial University Research Chair and the Natural Sciences and Engineering Research Council of Canada (NSERC) through its Discovery Program, in part by the NationalNatural Science Foundation of China under Grants 62071385 and 61571367, in part by the ERC AGNOSTIC Project, ref. H2020/ERC2020POC/957570, and in part by the Luxembourg National Research Fund (FNR) - ROSETTA Project, ref. C17/IS/11632107. 46 8 8 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. DEC 2021.0 70 12 12872 12887 10.1109/TVT.2021.3121217 0.0 16 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation XP9AT Green Submitted 2023-03-23 WOS:000731151400049 0
J Zhu, GB; Ma, Y; Li, ZX; Malekian, R; Sotelo, M Zhu, Guibing; Ma, Yong; Li, Zhixiong; Malekian, Reza; Sotelo, M. Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article; Early Access Uncertainty; Vehicle dynamics; Actuators; Artificial neural networks; Output feedback; Adaptive systems; Technological innovation; Marine surface vehicles; classification reconstruction; adaptive neural network; disturbance observer; output feedback; event-triggered control TRACKING CONTROL; SURFACE VESSELS; NONLINEAR-SYSTEMS; VEHICLES; INPUT This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution. [Zhu, Guibing] Zhejiang Ocean Univ, Sch Maritime, Zhoushan 316022, Peoples R China; [Ma, Yong] Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China; [Ma, Yong] Wuhan Univ Technol, Sanya Sci & Educ Innovat Pk, Sanya 572000, Peoples R China; [Ma, Yong] Wuhan Univ Technol, Chongqing Res Inst, Chongqing 401120, Peoples R China; [Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Malekian, Reza] Malmo Univ, Dept Comp Sci & Media Technol, S-20506 Malmo, Sweden; [Sotelo, M.] Univ Alcal, Dept Comp Engn, Alcala De Henares 28801, Madrid, Spain Zhejiang Ocean University; Hubei Key Laboratory of Inland Shipping Technology; Wuhan University of Technology; Wuhan University of Technology; Wuhan University of Technology; Opole University of Technology; Yonsei University; Malmo University; Universidad de Alcala Ma, Y (corresponding author), Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China.;Li, ZX (corresponding author), Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland. zhuguibing2003@163.com; myongdl@whut.edu.cn; zhixiong.li@yonsei.ac.kr; reza.malekiau@ieee.org; miguel.sotelo@uah.es Zhu, Guibing/HHZ-2170-2022; Sotelo, Miguel Angel/A-8663-2013 Zhu, Guibing/0000-0002-8267-9437; Ma, Yong/0000-0003-0418-9210; Sotelo, Miguel Angel/0000-0001-8809-2103 National Science Foundation of China [52022073, 62073251]; Excellent Youth Foundation of Hubei Scientific Committee [2020CFA055]; Natural Science Foundation of Zhejiang Province [LY21E090005]; Innovation Research Team Project of the Hainan Natural Science Foundation [722CXTD518]; Research Project of Wuhan University of Technology Chongqing Research Institute [YF2021-12]; Science and Technology Project of Zhoushan [2022C41006]; Knowledge Innovation Program of Wuhan-Basic Research [2022010801010181]; Norwegian Financial Mechanism 2014-2021 [2020/37/K/ST8/02748] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Excellent Youth Foundation of Hubei Scientific Committee; Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Innovation Research Team Project of the Hainan Natural Science Foundation; Research Project of Wuhan University of Technology Chongqing Research Institute; Science and Technology Project of Zhoushan; Knowledge Innovation Program of Wuhan-Basic Research; Norwegian Financial Mechanism 2014-2021 This work was supported in part by the National Science Foundation of China under Grant 52022073 and Grant 62073251, in part by the Excellent Youth Foundation of Hubei Scientific Committee under Grant 2020CFA055, in part by the Natural Science Foundation of Zhejiang Province under Grant LY21E090005, in part by the Innovation Research Team Project of the Hainan Natural Science Foundation under Grant 722CXTD518, in part by the Knowledge Innovation Program of Wuhan-Basic Research under Grant 2022010801010181, in part by the Research Project of Wuhan University of Technology Chongqing Research Institute under Grant YF2021-12, in part by the Science and Technology Project of Zhoushan under Grant 2022C41006, and in part by the Norwegian Financial Mechanism 2014-2021 under Project 2020/37/K/ST8/02748. 53 1 1 19 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. 10.1109/TITS.2022.3217152 0.0 NOV 2022 14 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 6C3XZ 2023-03-23 WOS:000881952800001 0
J Ding, WX; Wang, HY; Zhu, XS; Zimmer, B Ding, Wenxiu; Wang, Huiyang; Zhu, Xisong; Zimmer, Bernhard Prediction and Analysis of Respiratory Circulation System in Radiotherapy Patients by Four-Dimensional Computed Tomography Image Technology JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS English Article Prediction; Computed Tomography; Radiotherapy; Neural Network MOTION Usually, radiotherapy not only acts on the parts that need to be treated, but also radiates on the normal parts of the human body, resulting in damage to the normal parts. Recently, some scholars have used the four-dimensional CT image technology to monitor and adjust the radiotherapy process in real time, which can reduce the damage to the normal parts. Therefore, it is a very meaningful subject to apply the real-time monitoring technology to the radiotherapy process of the respiratory circulation system. In order to reduce the damage of related organs in the respiratory circulation system during radiotherapy, this paper optimizes the particle swarm optimization (PSO) algorithm and combines it with artificial neural network (ANN) technology to predict and analyze the respiratory circulation system of human body. The results show that in the analysis of CT image results, there is no significant difference in respiratory flow velocity between the left lung and the right lung from three-dimensional point of view (P > 0.05). Different algorithms are used to analyze the respiratory circulation system, and it is found that the model constructed in this paper can effectively reduce the probability of BP-NN (Back Propagation-Neural Network) falling into the local optimum, and the error is reduced by 25%, while the effect of the original PSO algorithm is inferior to that of the model constructed. Therefore, through this study, it is found that the PSO algorithm can be used in real-time monitoring of the radiotherapy process, and its prediction accuracy has been significantly improved, which achieves the expected effect, and provides experimental basis for the later clinical radiotherapy of respiratory circulation system. [Ding, Wenxiu; Wang, Huiyang] Zhejiang Chinese Med Univ, Clin Med Coll 2, Imaging & Nucl Med, Hangzhou 310000, Zhejiang, Peoples R China; [Zhu, Xisong] Zhejiang Chinese Med Univ, Radiol Dept, Quzhou Cent Hosp, Bell Tower 2, Quzhou 324000, Zhejiang, Peoples R China; [Zimmer, Bernhard] Klinikum Rechts Der Isar, Dept Radiol, Ismaninger Str 22, D-80807 Munich, Germany Zhejiang Chinese Medical University; Zhejiang Chinese Medical University; Technical University of Munich Zhu, XS (corresponding author), Zhejiang Chinese Med Univ, Radiol Dept, Quzhou Cent Hosp, Bell Tower 2, Quzhou 324000, Zhejiang, Peoples R China. 12 0 0 1 3 AMER SCIENTIFIC PUBLISHERS VALENCIA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA 2156-7018 2156-7026 J MED IMAG HEALTH IN J. Med. Imaging Health Inform. AUG 2020.0 10 8 1851 1856 10.1166/jmihi.2020.3192 0.0 6 Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging LY8HG 2023-03-23 WOS:000540768700010 0
J He, X; Chen, YS; Ghamisi, P He, Xin; Chen, Yushi; Ghamisi, Pedram Heterogeneous Transfer Learning for Hyperspectral Image Classification Based on Convolutional Neural Network IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Training; Hyperspectral imaging; Convolutional neural nets; Data models; Kernel; Classification; convolutional neural network (CNN); hyperspectral image (HSI); transfer learning SPECTRAL-SPATIAL CLASSIFICATION; SELECTION; PROFILES; BAND Deep convolutional neural networks (CNNs) have shown their outstanding performance in the hyperspectral image (HSI) classification. The success of CNN-based HSI classification relies on the availability sufficient training samples. However, the collection of training samples is expensive and time consuming. Besides, there are many pretrained models on large-scale data sets, which extract the general and discriminative features. The proper reusage of low-level and midlevel representations will significantly improve the HSI classification accuracy. The large-scale ImageNet data set has three channels, but HSI contains hundreds of channels. Therefore, there are several difficulties to simply adapt the pretrained models for the classification of HSIs. In this article, heterogeneous transfer learning for HSI classification is proposed. First, a mapping layer is used to handle the issue of having different numbers of channels. Then, the model architectures and weights of the CNN trained on the ImageNet data sets are used to initialize the model and weights of the HSI classification network. Finally, a well-designed neural network is used to perform the HSI classification task. Furthermore, attention mechanism is used to adjust the feature maps due to the difference between the heterogeneous data sets. Moreover, controlled random sampling is used as another training sample selection method to test the effectiveness of the proposed methods. Experimental results on four popular hyperspectral data sets with two training sample selection strategies show that the transferred CNN obtains better classification accuracy than that of state-of-the-art methods. In addition, the idea of heterogeneous transfer learning may open a new window for further research. [He, Xin; Chen, Yushi] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol, D-09599 Freiberg, Germany Harbin Institute of Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Chen, YS (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China. 19b905007@stu.hit.edu.cn; chenyushi@hit.edu.cn; p.ghamisi@gmail.com Ghamisi, Pedram/ABD-5419-2021 He, Xin/0000-0003-0455-4230; Chen, Yushi/0000-0003-2421-0996 Natural Science Foundation of China [61971164] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Natural Science Foundation of China under Grant 61971164. 65 79 79 12 72 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing MAY 2020.0 58 5 3246 3263 10.1109/TGRS.2019.2951445 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology LJ0MO 2023-03-23 WOS:000529868700020 0
J Ruan, DW; Song, XZ; Guhmann, C; Yan, JP Ruan, Diwang; Song, Xinzhou; Guehmann, Clemens; Yan, Jianping Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets LUBRICANTS English Article fault data generation; Convolutional Neural Network (CNN); Generative Adversarial Network (GAN); bearing fault diagnosis; unbalanced datasets NEURAL-NETWORK Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN's performance reduces inevitably. To solve the dataset imbalance problem, a Generative Adversarial Network (GAN) has been preferably adopted for the data generation. In published research studies, GAN only focuses on the overall similarity of generated data to the original measurement. The similarity in the fault characteristics is ignored, which carries more information for the fault diagnosis. To bridge this gap, this paper proposes two modifications for the general GAN. Firstly, a CNN, together with a GAN, and two networks are optimized collaboratively. The GAN provides a more balanced dataset for the CNN, and the CNN outputs the fault diagnosis result as a correction term in the GAN generator's loss function to improve the GAN's performance. Secondly, the similarity of the envelope spectrum between the generated data and the original measurement is considered. The envelope spectrum error from the 1st to 5th order of the Fault Characteristic Frequencies (FCF) is taken as another correction in the GAN generator's loss function. Experimental results show that the bearing fault samples generated by the optimized GAN contain more fault information than the samples produced by the general GAN. Furthermore, after the data augmentation for the unbalanced training sets, the CNN's accuracy in the fault classification has been significantly improved. [Ruan, Diwang; Guehmann, Clemens] Tech Univ Berlin, Chair Elect Measurement & Diagnost Technol, D-10587 Berlin, Germany; [Song, Xinzhou] Tech Univ Berlin, Sch Elect Engn & Comp Sci, D-10587 Berlin, Germany; [Yan, Jianping] Tsinghua Univ, Sch Vehide & Mobil, Beijing 100084, Peoples R China Technical University of Berlin; Technical University of Berlin; Tsinghua University Ruan, DW (corresponding author), Tech Univ Berlin, Chair Elect Measurement & Diagnost Technol, D-10587 Berlin, Germany. diwang.ruan@campus.tu-berlin.de; sxztthxq@gmail.com; clemens.guehmann@tu-berlin.de; jianping_yan_de@yahoo.de CSC (China Scholarship Council) scholarship [201806250024]; Zhejiang Labs International Talent Fund for Young Professionals CSC (China Scholarship Council) scholarship; Zhejiang Labs International Talent Fund for Young Professionals This research was funded by CSC (China Scholarship Council) scholarship (201806250024) and Zhejiang Labs International Talent Fund for Young Professionals. 24 6 6 34 94 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-4442 LUBRICANTS Lubricants OCT 2021.0 9 10 105 10.3390/lubricants9100105 0.0 18 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering WT8WT gold 2023-03-23 WOS:000716140100001 0
J Srivastava, D; Chui, KT; Arya, V; Penalvo, FJG; Kumar, P; Singh, AK Srivastava, Deepak; Chui, Kwok Tai; Arya, Varsha; Garcia Penalvo, Francisco Jose; Kumar, Pramod; Singh, Anuj Kumar Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm INTERNATIONAL JOURNAL OF SOFTWARE SCIENCE AND COMPUTATIONAL INTELLIGENCE-IJSSCI English Article Drug Target Interaction; Feature Selection; Machine Learning Classifier; Principle Component Analysis; Protein Sequence PREDICTION; CLASSIFICATION Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier. [Srivastava, Deepak] Swami Rama Himalayan Univ, Himalayan Sch Sci & Technol, Dehra Dun, Uttarakhand, India; [Chui, Kwok Tai] Hong Kong Metropolitan Univ, Hong Kong, Peoples R China; [Arya, Varsha] Insights2Techinfo, Dehra Dun, India; [Garcia Penalvo, Francisco Jose] Univ Salamanca, Salamanca, Spain; [Kumar, Pramod] Meerut Inst Engn & Technol, Meerut, Uttar Pradesh, India; [Singh, Anuj Kumar] Ajay Kumar Garg Engn Coll, Ghaziabad, India Hong Kong Metropolitan University; University of Salamanca; Meerut Institute of Engineering & Technology Srivastava, D (corresponding author), Swami Rama Himalayan Univ, Himalayan Sch Sci & Technol, Dehra Dun, Uttarakhand, India. GARCIA-PENALVO, Francisco Jose/D-5445-2013 GARCIA-PENALVO, Francisco Jose/0000-0001-9987-5584 20 0 0 0 0 IGI GLOBAL HERSHEY 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA 1942-9045 1942-9037 INT J SOFTW SCI COMP Int. J. Softw. Sci. Comput. Intell. 2022.0 14 1 10.4018/IJSSCI.312562 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering Emerging Sources Citation Index (ESCI) Computer Science 8M2FC 2023-03-23 WOS:000924286200003 0
J Liu, XY; Wang, XP; Gao, SY; Chang, V; Tom, R; Yu, MT; Ghiringhelli, LM; Marom, N Liu, Xingyu; Wang, Xiaopeng; Gao, Siyu; Chang, Vincent; Tom, Rithwik; Yu, Maituo; Ghiringhelli, Luca M.; Marom, Noa Finding predictive models for singlet fission by machine learning NPJ COMPUTATIONAL MATERIALS English Article TRIPLET-TRIPLET ANNIHILATION; EXCITON-FISSION; THIN-FILMS; ORGANIC PHOTOVOLTAICS; MATERIALS SCIENCE; UP-CONVERSION; TD-DFT; PENTACENE; DYNAMICS; DESIGN Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set. [Liu, Xingyu; Gao, Siyu; Chang, Vincent; Yu, Maituo; Marom, Noa] Carnegie Mellon Univ, Dept Mat Sci & Engn, Pittsburgh, PA 15213 USA; [Wang, Xiaopeng] Shandong Univ, Qingdao Inst Theoret & Computat Sci, Qingdao 266237, Shandong, Peoples R China; [Tom, Rithwik; Marom, Noa] Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15213 USA; [Ghiringhelli, Luca M.] Max Planck Gesell, Fritz Haber Inst, NOMAD Lab, Berlin, Germany; [Ghiringhelli, Luca M.] Humboldt Univ, Berlin, Germany; [Marom, Noa] Carnegie Mellon Univ, Dept Chem, 4400 5th Ave, Pittsburgh, PA 15213 USA Carnegie Mellon University; Shandong University; Carnegie Mellon University; Max Planck Society; Fritz Haber Institute of the Max Planck Society; Humboldt University of Berlin; Carnegie Mellon University Marom, N (corresponding author), Carnegie Mellon Univ, Dept Mat Sci & Engn, Pittsburgh, PA 15213 USA.;Marom, N (corresponding author), Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15213 USA.;Marom, N (corresponding author), Carnegie Mellon Univ, Dept Chem, 4400 5th Ave, Pittsburgh, PA 15213 USA. nmarom@andrew.cmu.edu Liu, Xingyu/0000-0001-9736-3948; Yu, Maituo/0000-0002-2944-3150; Marom, Noa/0000-0002-1508-1312 National Science Foundation (NSF) Division of Materials Research [DMR-2021803]; Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility [DE-AC02-06CH11357]; Office of Science of the US Department of Energy [DE-AC0205CH11231] National Science Foundation (NSF) Division of Materials Research(National Science Foundation (NSF)); Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility; Office of Science of the US Department of Energy(United States Department of Energy (DOE)) We thank Dr. Runhai Ouyang from Shanghai University for his support in training SISSO and interpreting the results. We thank Dr. Volker Blum and Dr. Yi Yao from Duke University, and Dr. William Paul Huhn from Argonne National Laboratory for their support on DFT calculations with FHI-aims. Work at CMU was supported by the National Science Foundation (NSF) Division of Materials Research through grant DMR-2021803. This research used resources of the Argonne Leadership Computing Facility (ALCF), which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357, and of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy, under Contract DE-AC0205CH11231. 117 3 3 6 21 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2057-3960 NPJ COMPUT MATER npj Comput. Mater. APR 19 2022.0 8 1 70 10.1038/s41524-022-00758-y 0.0 10 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science 0O9CF gold 2023-03-23 WOS:000783820100001 0
J Bi, RN; Schleier, M; Rohn, J; Ehret, D; Xiang, W Bi, Renneng; Schleier, Markus; Rohn, Joachim; Ehret, Dominik; Xiang, Wei Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region, China ENVIRONMENTAL EARTH SCIENCES English Article Three Gorges Reservoir; Landslide susceptibility; Landslide causative factors; Artificial Neural Network; ArcGIS XIANGXI CATCHMENT; RESERVOIR AREA; RATIO A landslide susceptibility map is very important and necessary to efficiently prevent and mitigate the losses brought by natural hazard for a large area. For the purpose of landslide susceptibility analysis for the whole Xiangxi catchment (3,209 km(2)), Artificial Neural Network (ANN) analysis was applied as the main method. The whole catchment was divided into two parts: the training area and the implementation area. The backwater area (559 km(2)) of Xiangxi catchment was used as the training area for the ANN method. In the training area the correlations between the landslide distribution and its causative factors, which includes lithology, slope angle, slope curvature and river network, have been analyzed based on the geological map and digital elevation model (DEM). The back-propagation training algorithm in ANN was selected to train the sample data from the training area, which were composed of input data (causative factors) and target output data (landslide occurrence), in order to find the correlations between them. Based on these correlations and input data in the implementation area (causative factors), the network output data were obtained for the implementation area. In the end, a map of landslide susceptibility, which was established by network output data, was presented for Xiangxi catchment. ArcGIS was applied to extract and quantify input information from a DEM for susceptibility analysis and also to present the result visually. As a result, a landslide susceptibility map, in which 70 % of all landslides are rightly classified in the training area (backwater area), was created for Xiangxi catchment. [Bi, Renneng; Schleier, Markus; Rohn, Joachim] Univ Erlangen Nurnberg, GeoZentrum Nordbayern, D-91054 Erlangen, Germany; [Bi, Renneng; Xiang, Wei] China Univ Geosci, Three Gorges Reservoir Ctr Geohazard, Minist Educ, Wuhan 430074, Peoples R China; [Ehret, Dominik] State Off Geol Resources & Min Baden Wurttemberg, Freiburg, Germany University of Erlangen Nuremberg; China University of Geosciences Bi, RN (corresponding author), Univ Erlangen Nurnberg, GeoZentrum Nordbayern, D-91054 Erlangen, Germany. rennengbi@yahoo.com Hermanns, Reginald/J-1418-2019; Schleier, Markus/I-2357-2013 Schleier, Markus/0000-0003-1204-3510 German Federal Ministry of Education and Research (BMBF) German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) The studies were carried out as a part of the Yangtze-Project which is supported by the German Federal Ministry of Education and Research (BMBF). The authors would like to thank the working group of Prof. Xiang Wei from China University of Geosciences (Wuhan) and the students from Prof. Joachim Rohn in GeoZentrum Nordbayern (FAU) in Germany for their intensive field work in China. 29 21 23 4 55 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1866-6280 1866-6299 ENVIRON EARTH SCI Environ. Earth Sci. SEP 2014.0 72 6 1925 1938 10.1007/s12665-014-3100-5 0.0 14 Environmental Sciences; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Water Resources AO1OV 2023-03-23 WOS:000341083800015 0
J Ji, K; Ren, YF; Wen, RZ; Zhu, CB; Liu, Y; Zhou, BF Ji, Kun; Ren, Yefei; Wen, Ruizhi; Zhu, ChuanBin; Liu, Ye; Zhou, Baofeng HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations JOURNAL OF EARTHQUAKE ENGINEERING English Article Site classification; general regression neural network (GRNN); horizontal-to-vertical spectral ratio (HVSR); pattern classification; Chinese seismic code SPECTRAL RATIO; H/V Seismic site classification, which is fundamental for site-specific seismic hazard assessment, also plays an important role in accurate interpretation of ground motion data. However, detailed borehole information is not always available in many countries, e.g., China. Therefore, this study investigated application of the generalized regression neural network (GRNN) method to seismic site classification using China strong motion stations as example case. First, stations from KiK-net in Japan were classified based on their borehole information and individually assigned to I, II, III, and IV site classes as defined in Chinese seismic code. Then, mean horizontal-to-vertical spectral ratio (HVSR) curves for each site class were calculated as reference patterns. The overall recall rates for I, II, and III sites could reach 66.60%, 67.57%, and 68.42%, respectively, regarding use of KiK-net stations. The GRNN site classification scheme was validated using borehole information of K-NET stations, with recall rates for I and II site classes reaching 68% and 60%, respectively. Finally, based on HVSR curves calculated using strong ground motion data acquired during 2007-2015 in China, the site conditions of 167 National Strong Motion Observation Network System stations were estimated using the GRNN classification scheme. The results were partially validated using borehole information of 73 stations. The similarity between the mean HVSR curves and reference pattern curves indicated that the GRNN seismic site classification scheme is robust and could produce plausible results succinctly. [Ji, Kun; Ren, Yefei; Wen, Ruizhi; Liu, Ye; Zhou, Baofeng] China Earthquake Adm, Key Lab Earthquake Engn & Engn Vibrat, Inst Engn Mech, Harbin, Peoples R China; [Zhu, ChuanBin] Helmholtz Ctr Potsdam, GFZ German Res Ctr Geosci, Potsdam, Germany China Earthquake Administration; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences Ren, YF (corresponding author), China Earthquake Adm, Key Lab Earthquake Engn & Engn Vibrat, Inst Engn Mech, Harbin, Peoples R China. renyefei@iem.net.cn REN, Yefei/C-2865-2013 REN, Yefei/0000-0003-1097-8259; Kun, Ji/0000-0003-0314-5216 National Key Research and Development Program of China [2018YFE0109800]; Chinese National Natural Science Fund [51908518]; Heilongjiang Provincial Natural Science Foundation of China [LH2020E022]; Science Foundation of the Institute of Engineering Mechanics, CEA [CEA 2019B09, 2020C01] National Key Research and Development Program of China; Chinese National Natural Science Fund(National Natural Science Foundation of China (NSFC)); Heilongjiang Provincial Natural Science Foundation of China(Natural Science Foundation of Heilongjiang Province); Science Foundation of the Institute of Engineering Mechanics, CEA This work is supported by National Key Research and Development Program of China under grant number 2018YFE0109800; Chinese National Natural Science Fund under grant number. 51908518; Heilongjiang Provincial Natural Science Foundation of China under grant number LH2020E022; Science Foundation of the Institute of Engineering Mechanics, CEA under grant number CEA 2019B09 and 2020C01. 25 3 3 0 7 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1363-2469 1559-808X J EARTHQ ENG J. Earthqu. Eng. DEC 10 2022.0 26 16 8423 8445 10.1080/13632469.2021.1991520 0.0 DEC 2021 23 Engineering, Civil; Engineering, Geological; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology 6F9EC 2023-03-23 WOS:000730490300001 0
J Zhang, Y; Wang, JK; Lian, CY; Bai, YN; Wang, GD; Zhang, ZZ; Zheng, ZY; Chen, L; Zhang, K; Sirakoulis, G; Zhang, YG Zhang, Yue; Wang, Jinkai; Lian, Chenyu; Bai, Yining; Wang, Guanda; Zhang, Zhizhong; Zheng, Zhenyi; Chen, Lei; Zhang, Kun; Sirakoulis, Georgios; Zhang, Youguang Time-Domain Computing in Memory Using Spintronics for Energy-Efficient Convolutional Neural Network IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS English Article Computing in memory; time-domain; spintronics; digit recognition; convolutional neural networks LOW-POWER; SENSING SCHEME; FULL-ADDER; UNIT-MACRO; HIGH-SPEED; SRAM; COMPUTATION; DESIGN; XOR The data transfer bottleneck in Von Neumann architecture owing to the separation between processor and memory hinders the development of high-performance computing. The computing in memory (CIM) concept is widely considered as a promising solution for overcoming this issue. In this article, we present a time-domain CIM (TD-CIM) scheme using spintronics, which can be applied to construct the energy-efficient convolutional neural network (CNN). Basic Boolean logic operations are implemented through recording the bit-line output at different moments. A multi-addend addition mechanism is then introduced based on the TD-CIM circuit, which can eliminate the cascaded full adders. To further optimize the compatibility of TD-CIM circuit for CNN, we also propose a quantization method that transforms floating-point parameters of pre-trained CNN models into fixed-point parameters. Finally, we build a TD-CIM architecture integrating with a highly reconfigurable array of field-free spin-orbit torque magnetic random access memory (SOT-MRAM) and evaluate its benefits for the quantized CNN. By performing digit recognition with the MNIST dataset, we find that the delay and energy are respectively reduced by 1.22.7 times and 2.4x10(3) -1.1x10(4) times compared with STT-CIM and CRAM based on spintronic memory. Finally, the recognition accuracy can reach 98.65% and 91.11% on MNIST and CIFAR10, respectively. [Zhang, Yue; Wang, Jinkai; Lian, Chenyu; Bai, Yining; Wang, Guanda; Zhang, Zhizhong; Zheng, Zhenyi; Chen, Lei; Zhang, Kun; Zhang, Youguang] Beihang Univ, MITT Key Lab Spintron Sch Integrated Circuit Sci, Fert Beijing Inst, Beijing 100191, Peoples R China; [Zhang, Yue; Wang, Jinkai; Lian, Chenyu; Bai, Yining; Wang, Guanda; Zhang, Zhizhong; Zheng, Zhenyi; Chen, Lei; Zhang, Kun; Zhang, Youguang] Beihang Univ, Hefei Innovat Res Inst, Nanoelect Sci & Technol Ctr, Hefei 230013, Peoples R China; [Sirakoulis, Georgios] Democritus Univ Thrace, Dept Elect & Comp Engn, Xanthi 67100, Greece Beihang University; Beihang University; Democritus University of Thrace Zhang, Y (corresponding author), Beihang Univ, MITT Key Lab Spintron Sch Integrated Circuit Sci, Fert Beijing Inst, Beijing 100191, Peoples R China.;Zhang, Y (corresponding author), Beihang Univ, Hefei Innovat Res Inst, Nanoelect Sci & Technol Ctr, Hefei 230013, Peoples R China. yz@buaa.edu.cn Sirakoulis, Georgios/AAE-9654-2020 Sirakoulis, Georgios/0000-0001-8240-484X; Zhang, Yue/0000-0001-6893-7199; Wang, Guanda/0000-0003-4581-879X National Natural Science Foundation of China [61971024, 51901008]; International Mobility Project [B16001]; National Key Technology Program of China [2017ZX01032101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); International Mobility Project; National Key Technology Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 61971024 and Grant 51901008, in part by the International Mobility Project under Grant B16001, and in part by the National Key Technology Program of China under Grant 2017ZX01032101. 47 16 16 11 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-8328 1558-0806 IEEE T CIRCUITS-I IEEE Trans. Circuits Syst. I-Regul. Pap. MAR 2021.0 68 3 1193 1205 10.1109/TCSI.2021.3055830 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QG2JW 2023-03-23 WOS:000617417200021 0
C Xu, JW; Huang, YX; Zheng, LR; Zou, Z Stan, M; Bhatia, K; Li, H; Alioto, M; Sridhar, R Xu, Jiawei; Huang, Yuxiang; Zheng, Li-Rong; Zou, Zhuo A Low-Power Arithmetic Element for Multi-Base Logarithmic Computation on Deep Neural Networks 2018 31ST IEEE INTERNATIONAL SYSTEM-ON-CHIP CONFERENCE (SOCC) International SoC Design Conference English Proceedings Paper 31st IEEE International System-on-Chip Conference (SOCC) SEP 04-07, 2018 Arlington, VA IEEE,IEEE Circuits & Syst Soc Computational complexity and memory intensity are crucial in deep convolutional neural network algorithms for deployment to embedded systems. Recent advances in logarithmic quantization has manifested great potential in reducing the inference cost of neural network models. However, current base-2 logarithmic quantization suffers from performance upper limit and there is few work that studies hardware implementation of other bases. This paper presents a multi-base logarithmic scheme for Deep Neural Networks (DNNs). The performance of Alexnet is studied with respects to different quantization resolutions. Base-root 2 logarithmic quantization is able to raise the ceiling of top-5 classifying accuracy from 693% to 75.5% at 5-bit resolution. A segmented logarithmic quantization method that combines both base-2 and base-root 2 is then proposed to improve the network top-5 accuracy to 72.3% in 4-bit resolution. The corresponding arithmetic element hardware has been designed, which supports base-root 2 logarithmic quantization and segmented logarithmic quantization respectively. Evaluated in UMC 65nm process, the proposed arithmetic element operating at 500MHz and 1.2V consumes as low as 120 mu W. Compared with 16-bit fixed point multiplier, our design achieves 58.03% smaller in area, with 73.74% energy reduction. [Xu, Jiawei; Zheng, Li-Rong; Zou, Zhuo] Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Huang, Yuxiang] KTH Royal Inst Technol, Stockholm, Sweden Fudan University; Royal Institute of Technology Xu, JW (corresponding author), Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China. jwxu16@fudan.edu.cn; lrzheng@fudan.edu.cn; zhuo@fudan.edu.cn ZHENG, Lirong/ABF-9274-2020; xu, jiawei/GWM-9710-2022 ZHENG, Lirong/0000-0001-9588-0239; Shanghai Pujiang Program [17PJ1400800]; NSFC [61571137]; Shanghai Science and Technology Innovation Program [171C1401400]; Shanghai Institute of Intelligent Electronics Systems Shanghai Pujiang Program(Shanghai Pujiang Program); NSFC(National Natural Science Foundation of China (NSFC)); Shanghai Science and Technology Innovation Program; Shanghai Institute of Intelligent Electronics Systems This work was supported in part by the Shanghai Pujiang Program under Grant 17PJ1400800, in part by NSFC under Grant 61571137, in part by the Shanghai Science and Technology Innovation Program under Grant 171C1401400, and in part by Shanghai Institute of Intelligent Electronics & Systems. 12 3 3 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2163-9612 978-1-5386-1491-4 INT SOC DESIGN CONF 2018.0 43 48 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM3FS 2023-03-23 WOS:000462047000009 0
C Voznak, M; Rozhon, J; Mikulec, M; Rezac, F; Komosny, D; Lin, JCW; Fournier-Viger, P Obaidat, MS; Davoli, F; Giordano, S; Szczerbicka, H; Louta, M; Entrialgo, J; Saldana, J Voznak, M.; Rozhon, J.; Mikulec, M.; Rezac, F.; Komosny, D.; Lin, J. C. -W.; Fournier-Viger, P. Employing the Neural Networks to Parametrically Assess the Quality of a Voice Call PROCEEDINGS OF THE 2016 INTERNATIONAL SYMPOSIUM ON PERFORMANCE EVALUATION OF COMPUTER AND TELECOMMUNICATION SYSTEMS (SPECTS) English Proceedings Paper International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) Part of SummerSim 2016 Multiconference JUL 24-27, 2016 Montreal, CANADA Soc Modeling & Simulat Int,IEEE,IEEE Commun Soc Delay; E-model; Jitter; Neural Networks; Packet Loss; PESQ The paper deals with the modelling of the network effects on the quality of speech in the Voice over IP networks. The main purpose of the ideas presented here is to achieve high-precision estimation of the speech quality in the environment in which the classical approaches of speech quality determination fail. To achieve such a high precision a modular neural network model is used to map the effects of a packet loss on the speech quality based on the PESQ (Perceptual Evaluation of the Speech Quality) reference. To incorporate the temporal effects, the E-model is partially utilized as well. This way a universal tool capable of harnessing the information about the speech quality for stress testing and monitoring of the local infrastructure has been developed enabling the telephony infrastructure administrators to evaluate the performance and stability of the systems in their hands. Moreover, a high-performance simulation environment has been developed. [Voznak, M.; Rozhon, J.; Mikulec, M.; Rezac, F.] VSB Tech Univ Ostrava, Dept Telecommun, 17 Listopadu 15, Ostrava 70800, Czech Republic; [Komosny, D.] Brno Univ Technol, Dept Telecommun, Tech 3082-12, Brno 61600, Czech Republic; [Lin, J. C. -W.; Fournier-Viger, P.] Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China Technical University of Ostrava; Brno University of Technology; Harbin Institute of Technology; University Town of Shenzhen Voznak, M (corresponding author), VSB Tech Univ Ostrava, Dept Telecommun, 17 Listopadu 15, Ostrava 70800, Czech Republic. voznak@ieee.org; jan.rozhon@vsb.cz; martin.mikulec@vsb.cz; filip.rezac@vsb.cz; komosny@feec.vutbr.cz; jerrylin@ieee.org; philfv@hitsz.edu.cn Fournier-viger, Philippe/W-1275-2019; Voznak, Miroslav/E-6448-2016; Lin, Jerry Chun-Wei/C-1514-2011 Voznak, Miroslav/0000-0001-5135-7980; Rozhon, Jan/0000-0003-4768-6073; Rezac, Filip/0000-0001-5742-8970; Fournier-Viger, Philippe/0000-0002-7680-9899 18 1 1 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5108-2423-2 2016.0 10.1109/SPECTS.2016.7570521 0.0 5 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BG0XZ 2023-03-23 WOS:000386571900016 0
J Wang, Y; Ao, W; Ortega-Fernandez, I; Liang, DL; Li, HP; Jiang, B; Huang, XF; Bielsa, D Wang, Yang; Ao, Wen; Ortega-Fernandez, Inigo; Liang, Daolun; Li, Heping; Jiang, Bo; Huang, Xuefeng; Bielsa, Daniel Research of time-varying performance of solar distributed thermal-power plant with neutral network prediction ENERGY CONVERSION AND MANAGEMENT English Article Solar; Thermal-power; Thermocline; Genetic algorithm; Neural network ANAEROBIC-DIGESTION; PACKED-BED; HYDROTHERMAL PRETREATMENT; MICROALGAL BIOMASS; NEURAL-NETWORKS; ELECTRICITY; GENERATION; HEAT; SYSTEM; MODEL Solar energy is potential in the future, but until now its commercial application is limited due to its high price. Distributed solar thermal-power plant provides electricity and heat simultaneously to the nearby users. It is competitive with traditional power plant due to its improved efficiency and low price. In this paper, a unique distributed solar thermal-power plant is designed. It is located in Nanjing Chemical Industry Park in China. Its reliability and advantage are studied through theoretical simulation. The theoretical model consists of 6 modules: solar radiation, solar collectors, thermal energy storage tanks, heat hub, bioreactors and organic rankine cycle generator. The building and bioreactors play the roles of heat and electricity users, respectively. However, both the heat source and users are time-varying, which are especially harmful to the system due to its small scale. Therefore, a thermal energy storage system and a heat hub are applied to solve the problems. Furthermore, the research focuses on the contradiction between the time-varying load requirement and the real-time response of the heat hub. It is solved by applying neural network to predict the load requirement, which enables the system to work with proper operation parameters in real time. The probability model of prediction error is built to test the system reliability with Monte Carlo method. The simulation results show how the distributed solar thermal power plant working with neural network satisfies the time-varying load requirement. While the load requirement varies from 4472 to 21638 kW during one day, the heat hub responds with heat transfer capacity from 5552 to 22272 kW. In most of the time, the temperature deviation rate of heat hub after optimization is lower than 0.03, and the corresponding bias due to prediction error is lower than 0.01. [Wang, Yang; Liang, Daolun] Southeast Univ, Sch Energy & Environm, Sipailou Campus, Nanjing 210001, Peoples R China; [Ao, Wen] Northwestern Polytech Univ, Xian 710072, Peoples R China; [Ortega-Fernandez, Inigo; Bielsa, Daniel] CIC Energigune, Albert Einstein 48, Minano 01510, Alava, Spain; [Li, Heping; Huang, Xuefeng] Hangzhou Dianzi Univ, Inst Energy Studies, Hangzhou 310018, Peoples R China; [Jiang, Bo] Nanjing Univ Sci & Technol, Sch Energy & Power Engn, Nanjing 210094, Peoples R China Southeast University - China; Northwestern Polytechnical University; CIC Energigune; Hangzhou Dianzi University; Nanjing University of Science & Technology Ao, W (corresponding author), Northwestern Polytech Univ, Xian 710072, Peoples R China. 101012382@seu.edu.cn Bielsa, Daniel/AAR-4051-2021; Huang, Xuefeng/AAS-4294-2021; wang, yang/AAP-5750-2021 Bielsa, Daniel/0000-0001-8019-0788; wang, yang/0000-0002-5383-8152; Liang, Daolun/0000-0002-3222-5242; Huang, Xuefeng/0000-0002-6511-3538; Ortega-Fernandez, Inigo/0000-0003-2583-5007 theNational Nature Science Foundation of China [51776181]; Sichuan Science and Technology Program of China [2018GZ0466]; Horizon 2020 program of European Union [657690] theNational Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program of China; Horizon 2020 program of European Union This work is supported by theNational Nature Science Foundation of China (NO. 51776181); Sichuan Science and Technology Program of China (NO. 2018GZ0466); Horizon 2020 program of European Union (NO. 657690);Thanks for Prof. Ao Xia, and Prof. Yun Huang in Chongqing University for providing part of experimental data. 50 1 1 2 22 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. NOV 15 2020.0 224 113333 10.1016/j.enconman.2020.113333 0.0 13 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics PL7UZ 2023-03-23 WOS:000603323200002 0
J Kong, ZH; Jiang, JH; Burn, R; Fahy, KA; Zhu, ZH Kong, Zhihui; Jiang, Jonathan H.; Burn, Remo; Fahy, Kristen A.; Zhu, Zonghong Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning ASTROPHYSICAL JOURNAL English Article STAR SYSTEMS; STABILITY; DYNAMICS Exoplanet detection in the past decade by efforts including NASA's Kepler and TESS missions has revealed many worlds that differ substantially from planets in our own solar system, including more than 150 exoplanets orbiting binary or multi-star systems. This not only broadens our understanding of the diversity of exoplanets, but also promotes our study of exoplanets in the complex binary systems and provides motivation to explore their habitability. In this study, we investigate the habitable zones of circumbinary planets (P-type) based on planetary trajectory and dynamically informed habitable zones. Our results indicate that the mass ratio and orbital eccentricity of binary stars are important factors affecting the orbital stability and habitability of planetary systems. Moreover, planetary trajectory and dynamically informed habitable zones divide planetary habitability into three categories: habitable, periodic habitable, and non-habitable. Therefore, we successfully train a machine-learning model to quickly and efficiently classify these planetary systems, which provides more useful constraints. [Kong, Zhihui; Zhu, Zonghong] Beijing Normal Univ, Dept Astron, Beijing, Peoples R China; [Jiang, Jonathan H.; Fahy, Kristen A.] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA; [Burn, Remo] Max Planck Inst Astron, Konigstuhl 17, D-69117 Heidelberg, Germany Beijing Normal University; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); Max Planck Society Jiang, JH (corresponding author), CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA. Jonathan.H.Jiang@jpl.nasa.gov Burn, Remo/ABE-3993-2020 Burn, Remo/0000-0002-9020-7309; Kong, Zhi-Hui/0000-0002-2236-0627; Jiang, Jonathan/0000-0002-5929-8951 Jet Propulsion Laboratory, California Institute of Technology; NASA; NASA ROSES Exoplanet Research Program [NNH18ZDA-001N-2XRP]; National Natural Science Foundation of China [11633001, 11920101003, 12021003]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB23000000]; SNSF [P2BEP2_195285] Jet Propulsion Laboratory, California Institute of Technology; NASA(National Aeronautics & Space Administration (NASA)); NASA ROSES Exoplanet Research Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); SNSF(Swiss National Science Foundation (SNSF)) Authors J.H.J. and K.A.F. were supported by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. We acknowledge the funding support from the NASA ROSES Exoplanet Research Program NNH18ZDA-001N-2XRP. Authors Z.K. and Z.Z. thanks the support by the National Natural Science Foundation of China under Research grant Nos. 11633001, 11920101003, and 12021003, and the Strategic Priority Research Program of the Chinese Academy of Sciences, grant No. XDB23000000. R.B. acknowledges the financial support from the SNSF under grant No. P2BEP2_195285. We thank Xiaoming Jiang for constructive comments and discussions during development of this study and thank Bingkun Li for helping optimize the ML algorithms. 33 0 0 2 7 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. APR 1 2022.0 929 2 187 10.3847/1538-4357/ac5c5a 0.0 8 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 0W0DK Green Submitted, gold 2023-03-23 WOS:000788708700001 0
C Chochlov, M; Ahmed, GA; Patten, JV; Lu, GX; Hou, W; Gregg, D; Buckley, J IEEE Comp Soc Chochlov, Muslim; Ahmed, Gul Aftab; Patten, James Vincent; Lu, Guoxian; Hou, Wei; Gregg, David; Buckley, Jim Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection 2022 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2022) Proceedings-IEEE International Conference on Software Maintenance English Proceedings Paper 39th IEEE International Conference on Software Maintenance and Evolution (ICSME) OCT 02-07, 2022 Limassol, CYPRUS IEEE,Univ Cyprus,IEEE Comp Soc,Cyprus Tourist Org,Lufthansa Grp Clone detection; semantic clones; scalable; deep neural networks CODE Code clones can detrimentally impact software maintenance and manually detecting them in very large codebases is impractical. Additionally, automated approaches find detection of Type 3 and Type 4 (inexact) clones very challenging. While the most recent artificial deep neural networks (for example BERT-based artificial neural networks) seem to be highly effective in detecting such clones, their pairwise comparison of every code pair in the target system(s) is inefficient and scales poorly on large codebases. We therefore introduce SSCD, a BERT-based clone detection approach that targets high recall of Type 3 and Type 4 clones at scale (in line with our industrial partner's requirements). It does so by computing a representative embedding for each code fragment and finding similar fragments using a nearest neighbour search. SSCD thus avoids the pairwise-comparison bottleneck of other Neural Network approaches while also using parallel, GPU-accelerated search to tackle scalability. This paper details the approach and an empirical assessment towards configuring and evaluating that approach in industrial setting. The configuration analysis suggests that shorter input lengths and text-only based neural network models demonstrate better efficiency in SSCD, while only slightly decreasing effectiveness. The evaluation results suggest that SSCD is more effective than state-of-the-art approaches like SAGA and SourcererCC. It is also highly efficient: in its optimal setting, SSCD effectively locates clones in the entire 320 million LOC BigCloneBench (a standard clone detection benchmark) in just under three hours. [Chochlov, Muslim; Patten, James Vincent; Buckley, Jim] Univ Limerick, Dept Comp Sci & Informat Syst, Limerick, Ireland; [Ahmed, Gul Aftab; Gregg, David] Trinity Coll Dublin, Dept Comp Sci, Dublin, Ireland; [Lu, Guoxian] Huawei Technol Co Ltd, WN Digital IPD & Trustworthiness Enabling, Shanghai, Peoples R China; [Hou, Wei] Huawei Technol Co Ltd, Huawei Vulnerabil Management Ctr, Shenzhen, Guangdong, Peoples R China University of Limerick; Trinity College Dublin; Huawei Technologies; Huawei Technologies Chochlov, M (corresponding author), Univ Limerick, Dept Comp Sci & Informat Syst, Limerick, Ireland. muslim.chochlov@ul.ie; ahmedga@tcd.ie; james.patten@ul.ie; luguoxian@huawei.com; wei.hou@huawei.com; david.gregg@tcd.ie; jim.buckley@ul.ie Patten, James/0000-0002-1937-2774 Science Foundation Ireland [16/RC/3918] Science Foundation Ireland(Science Foundation Ireland) This work was supported, in part, by Science Foundation Ireland grant 16/RC/3918. 32 0 0 1 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6773 978-1-6654-7956-1 PROC IEEE INT CONF S 2022.0 582 591 10.1109/ICSME55016.2022.00080 0.0 10 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU4WM 2023-03-23 WOS:000908031000072 0
J Zhang, ZB; Liu, JB; Wang, L; Guo, GY; Zheng, XY; Gong, XD; Yang, S; Huang, GG Zhang, Zhenbing; Liu, Jingbin; Wang, Lei; Guo, Guangyi; Zheng, Xingyu; Gong, Xiaodong; Yang, Sheng; Huang, Gege An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning REMOTE SENSING English Article smartphone; indoor positioning; outlier detection and removal; PDR; WiFi; Kalman Filter KALMAN FILTER; ALGORITHM; LOCALIZATION; AUTOENCODER; NAVIGATION; FUSION In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness. [Zhang, Zhenbing; Liu, Jingbin; Wang, Lei; Guo, Guangyi; Zheng, Xingyu; Gong, Xiaodong; Yang, Sheng; Huang, Gege] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China; [Zhang, Zhenbing; Wang, Lei] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China; [Liu, Jingbin] Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, Masala 02430, Finland; [Liu, Jingbin] Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, Masala 02430, Finland Wuhan University; The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI) Liu, JB (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.;Liu, JB (corresponding author), Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, Masala 02430, Finland.;Liu, JB (corresponding author), Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, Masala 02430, Finland. zhangzhenbing@whu.edu.cn; jingbin.liu@whu.edu.cn; lei.wang@whu.edu.cn; guangyi.guo@whu.edu.cn; xingyu.zheng@whu.edu.cn; gongxiaodong@whu.edu.cn; shengy@whu.edu.cn; geehuang@whu.edu.cn Wang, Lei/D-8164-2019; cao, xiaoxiang/AAR-9291-2021; Liu, Jingbin/AAE-8803-2020 Wang, Lei/0000-0003-1887-6222; Gong, Xiaodong/0000-0003-0691-9686; Liu, Jingbin/0000-0001-6216-0956; guo, guangyi/0000-0002-7494-0914 National Key Research Development Program of China [2016YFB0502204]; Natural Science Fund of China [41874031, 61872431]; Technology Innovation Program of Hubei Province [2018AAA070]; Natural Science Fund of Hubei Province [2018CFA007]; Postdoctoral Research Foundation of China [2020M682480]; Fundamental Research Funds for the Central Universities [2016YFB0502204] National Key Research Development Program of China; Natural Science Fund of China(National Natural Science Foundation of China (NSFC)); Technology Innovation Program of Hubei Province; Natural Science Fund of Hubei Province; Postdoctoral Research Foundation of China(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was funded in part by the National Key Research Development Program of China, grant number 2016YFB0502204, in part by the Natural Science Fund of China, grant number 41874031 and 61872431, in part by the Technology Innovation Program of Hubei Province, grant number 2018AAA070, in part by the Natural Science Fund of Hubei Province, grant number 2018CFA007, in part by the Postdoctoral Research Foundation of China, grant number 2020M682480, and in part by the Fundamental Research Funds for the Central Universities. The APC was funded by 2016YFB0502204. 69 2 2 9 37 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2021.0 13 6 1106 10.3390/rs13061106 0.0 23 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology SE2TT Green Published, gold 2023-03-23 WOS:000651926200001 0
J Guo, YR; Chen, L; Luo, YB; Liu, Y Guo, Yanrong; Chen, Liang; Luo, Yubo; Liu, Yang Forest biomass turnover time estimation in China based on spatially explicit root : shoot ratios GLOBAL ECOLOGY AND BIOGEOGRAPHY English Article aboveground biomass; belowground biomass; biomass turnover time; machine learning; root; shoot ratios; stand age CARBON-CYCLE; CLIMATE; ALLOCATION; PRODUCTIVITY; UNCERTAINTY; PATTERNS; MODELS; FEEDBACKS; STORAGE; BOREAL Aim Spatial patterns of root : shoot ratios (R:S) are critical to better estimate biomass turnover time (tau). Given the difficulty of measuring belowground biomass directly, we still lack an understanding of high-resolution spatial patterns of R:S in China's forests. Here, we explored patterns of R:S and their association with stand age, tree height, elevation, land cover, and climatic and edaphic variables, and estimated tau for China's forests. Location China. Time period 2004 and 2010. Major taxa studied Forests. Methods We used a machine learning algorithm trained with an R:S dataset of 1,907 observations covering five forest types to upscale the spatial distribution of R:S in China as a function of stand age, tree height, elevation, land cover, and climatic and edaphic variables. We calculated tau using the estimated R:S together with satellite-based aboveground biomass and net primary productivity data at 1 km x 1 km resolution. Results The estimated mean tau for total biomass in China's forests was 10.8-2.0+3.2years. The longest tau (tau > 20 years) was observed in central China, whereas southern China exhibited the shortest tau (tau < 10 years). Large differences in partial correlations between tau and climatic variables (mean annual precipitation, climatological water deficit, and mean annual temperature) were observed among forest types. Main conclusions Our data-driven gridded maps of estimated R:S and tau provide valuable baseline information for assessing model performance and improving future carbon-cycle assessments. [Guo, Yanrong] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ Key Lab Earth Syst Modeling, Beijing, Peoples R China; [Chen, Liang] Univ Eastern Finland, Dept Environm & Biol Sci, Joensuu, Finland; [Luo, Yubo] Beijing Technol & Business Univ, Sch Math & Stat, Beijing, Peoples R China; [Liu, Yang] Inner Mongolia Agr Univ, Coll Forestry, Hohhot 010019, Peoples R China Tsinghua University; University of Eastern Finland; Beijing Technology & Business University; Inner Mongolia Agricultural University Liu, Y (corresponding author), Inner Mongolia Agr Univ, Coll Forestry, Hohhot 010019, Peoples R China. liuyangvip@hotmail.com Liu, Yang/0000-0003-3143-8008; Chen, Liang/0000-0002-1811-5717 National Natural Science Foundation of China [32160363]; Science and Technology Planning Program of Inner Mongolia Autonomous Region [2020GG0067] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Planning Program of Inner Mongolia Autonomous Region National Natural Science Foundation of China, Grant/Award Number: 32160363; Science and Technology Planning Program of Inner Mongolia Autonomous Region, Grant/Award Number: 2020GG0067 61 0 0 16 24 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1466-822X 1466-8238 GLOBAL ECOL BIOGEOGR Glob. Ecol. Biogeogr. JUL 2022.0 31 7 1332 1344 10.1111/geb.13508 0.0 APR 2022 13 Ecology; Geography, Physical Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Physical Geography 2E2EW 2023-03-23 WOS:000784281600001 0
J Xie, TF; Zhou, XL Xie Ting-fan; Zhou Xin-long Neural networks for optimal approximation of continuous functions in ae (d) APPLIED MATHEMATICS-A JOURNAL OF CHINESE UNIVERSITIES SERIES B English Article approximation rate; neural network; ridge function; sigmoidal function Using some regular matrices we present a method to express any multivariate algebraic polynomial of total order n in a normal form. Consequently, we prove constructively that, to approximate continuous target functions defined on some compact set of ae (d) , neural networks are at least as good as algebraic polynomials. [Xie Ting-fan] China Jiliang Univ, Dept Math, Hangzhou 310018, Zhejiang, Peoples R China; [Zhou Xin-long] Univ Duisburg Essen, Dept Math, D-47048 Duisburg, Germany China Jiliang University; University of Duisburg Essen Xie, TF (corresponding author), China Jiliang Univ, Dept Math, Hangzhou 310018, Zhejiang, Peoples R China. xietf@cjlu.edu.cn; xinlong.zhou@uni-due.de 13 0 1 0 0 ZHEJIANG UNIV, EDITORIAL COMMITTEE APPLIED MATHEMATICS ZHEJIANG C/O EDITORIAL COMMITTEE, DEPT MATHEMATICS, HANGZHOU, ZHEJIANG, 310027, PEOPLES R CHINA 1005-1031 1993-0445 APPL MATH SER B Appl. Math.-J. Chin. Univ. Ser. B SEP 2012.0 27 3 335 344 10.1007/s11766-012-2789-8 0.0 10 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics 002SR 2023-03-23 WOS:000308555800008 0
J Stjelja, D; Jokisalo, J; Kosonen, R Stjelja, Davor; Jokisalo, Juha; Kosonen, Risto From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach APPLIED SCIENCES-BASEL English Article occupancy prediction; smart meter; machine learning; cluster analysis; data-driven; smart building DATA ANALYTICS; SIMULATION; PATTERNS; BEHAVIOR; ALGORITHM; DEMAND Climate change and technological development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters, and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence that would offer new insights to the building's operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption. [Stjelja, Davor; Jokisalo, Juha; Kosonen, Risto] Aalto Univ, Dept Mech Engn, Espoo 02150, Finland; [Stjelja, Davor] Granlund Oy, Innovat & Dev, Helsinki 00701, Finland; [Kosonen, Risto] Nanjing Tech Univ, Coll Urban Construct, Nanjing 211800, Peoples R China Aalto University; Nanjing Tech University Stjelja, D (corresponding author), Aalto Univ, Dept Mech Engn, Espoo 02150, Finland.;Stjelja, D (corresponding author), Granlund Oy, Innovat & Dev, Helsinki 00701, Finland. davor.stjelja@granlund.fi; juha.jokisalo@aalto.fi; risto.kosonen@aalto.fi Stjelja, Davor/0000-0003-2091-8292; Jokisalo, Juha/0000-0002-7703-8838; Kosonen, Risto/0000-0002-9717-7552 Business Finland through the project Smart Otaniemi Business Finland through the project Smart Otaniemi This research was funded by Business Finland through the project Smart Otaniemi. 37 2 2 1 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel DEC 2020.0 10 24 9089 10.3390/app10249089 0.0 23 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics PL0BN gold, Green Published 2023-03-23 WOS:000602798700001 0
J Liu, D; Wang, L; Sun, DW; Zeng, XA; Qu, JH; Ma, J Liu, Dan; Wang, Lu; Sun, Da-Wen; Zeng, Xin-An; Qu, Jiahuan; Ma, Ji Lychee Variety Discrimination by Hyperspectral Imaging Coupled with Multivariate Classification FOOD ANALYTICAL METHODS English Article Nondestructive; Hyperspectral imaging; Multivariate classification; Lychee; Discrimination; Support vector machine; Back propagation neural network MOISTURE TRANSFER CHARACTERISTICS; NIR SPECTROSCOPY; PATTERN-RECOGNITION; ISOTHERM EQUATIONS; FRUIT-QUALITY; LITCHI; COLOR; IDENTIFICATION; CALIBRATION; PREDICTION This current study was carried out to investigate the ability of hyperspectral imaging (HSI) technique and multivariate classification for the differentiation of lychee varieties. A total of 122 lychee samples from three varieties (Baila, Jizhui, and Guiwei) were used. The relationship between reflectance spectra and lychee varieties were established. Principal component analysis (PCA) was implemented on the region of interest (ROI) image to reduce data dimensionality and visualize the cluster trend. The first two principal components (PCs) explained over 97 % of variances of all spectral bands. Linear (soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA)) and nonlinear (back propagation neural network (BPNN) and support vector machine (SVM)) multivariate classification methods were used to develop discrimination models. The results revealed that SVM model achieved the best result, with the identification rate of 100 % in the calibration set and 87.81 % in the prediction set. BPNN had a discrimination rate of 100 % for the training set and 85.37 % for prediction set, while PSL-DA and SIMCA model had a discrimination rate of 78.05 % and 60.98 % for prediction sets, respectively. The nonlinear classification methods used were superior to the linear ones. The overall results showed that HSI system with SVM classification tool could be used in identification of lychee varieties. [Liu, Dan; Wang, Lu; Sun, Da-Wen; Zeng, Xin-An; Qu, Jiahuan; Ma, Ji] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Agr & Food Sci Ctr, Univ Coll Dublin, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Ma, Ji/U-7550-2019; Sun, Da-Wen/B-1899-2012; WANG, LU/AAO-9121-2020 Sun, Da-Wen/0000-0002-3634-9963; Ma, Ji/0000-0002-1437-464X Guangdong Province Government (China); China Postdoctoral Science Foundation [2013M530366] Guangdong Province Government (China); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors are grateful to the Guangdong Province Government (China) for the support through the program of Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by China Postdoctoral Science Foundation (2013M530366). 57 15 17 6 62 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1936-9751 1936-976X FOOD ANAL METHOD Food Anal. Meth. OCT 2014.0 7 9 1848 1857 10.1007/s12161-014-9826-6 0.0 10 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology AP9RO 2023-03-23 WOS:000342417700016 0
J Lian, YC; Feng, T; Zhou, JL; Jia, MX; Li, AJ; Wu, ZY; Jiao, LC; Brown, M; Hager, G; Yokoya, N; Hansch, R; Le Saux, B Lian, Yanchao; Feng, Tuo; Zhou, Jinliu; Jia, Meixia; Li, Aijin; Wu, Zhaoyang; Jiao, Licheng; Brown, Myron; Hager, Gregory; Yokoya, Naoto; Hansch, Ronny; Le Saux, Bertrand Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest-Part B IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Three-dimensional displays; Semantics; Laser radar; Training; Earth; Data integration; Satellites; Classification; convolutional neural networks; data fusion contest (DFC); deep learning; image analysis and data fusion; light detection and ranging (LiDAR); point cloud; semantic labeling; semantic mapping HIGH-RESOLUTION LIDAR; LAND-USE; CLASSIFICATION; ALGORITHMS We present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest included challenges with large-scale datasets for semantic 3-D reconstruction from satellite images and also semantic 3-D point cloud classification from airborne LiDAR. 3-D reconstruction results are discussed separately in Part-A. In this Part-B, we report the results of the two best-performing approaches for 3-D point cloud classification. Both are deep learning methods that improve upon the PointSIFT model with mechanisms to combine multiscale features and task-specific postprocessing to refine model outputs. [Lian, Yanchao; Feng, Tuo; Zhou, Jinliu; Jia, Meixia; Li, Aijin; Wu, Zhaoyang; Jiao, Licheng] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ,Sch Artificial Intelligence, Int Res Ctr Intelligent Percept & Computat,Joint, Xian 710071, Peoples R China; [Brown, Myron] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 11100 USA; [Hager, Gregory] Johns Hopkins Univ, Baltimore, MD 21218 USA; [Yokoya, Naoto] Univ Tokyo, Grad Sch Frontier Sci, Chiba 2778561, Japan; [Yokoya, Naoto] RIKEN Ctr Adv Intelligence Project, Tokyo 1030027, Japan; [Hansch, Ronny] German Aerosp Ctr DLR, D-82234 Wessling, Germany; [Le Saux, Bertrand] ESA ESRIN, Lab, I-00044 Frascati, RM, Italy Xidian University; Johns Hopkins University; Johns Hopkins University Applied Physics Laboratory; Johns Hopkins University; University of Tokyo; RIKEN; Helmholtz Association; German Aerospace Centre (DLR); European Space Agency Hansch, R (corresponding author), German Aerosp Ctr DLR, D-82234 Wessling, Germany. yclian@stu.xidian.edu.cn; fengt@stu.xidian.edu.cn; zhoujinliu@stu.xidian.edu.cn; mxjia@stu.xidian.edu.cn; aijinli@stu.xidian.edu.cn; wuzhaoyang@stu.xidian.edu.cn; lchjiao@mail.xidian.edu.cn; myron.brown@jhuapl.edu; hager@cs.jhu.edu; naoto.yokoya@riken.jp; rww.haensch@gmail.com; bls@ieee.org Jia, Mei/GXZ-9967-2022; Yokoya, Naoto/AAC-1530-2022 Le Saux, Bertrand/0000-0001-7162-6746; Jiao, Licheng/0000-0003-3354-9617; Feng, Tuo/0000-0001-5882-3315; Brown, Myron/0000-0001-8302-2106; Li, Aijin/0000-0002-3675-5793 StateKey Program of National Natural Science of China [61836009]; National Natural Science Foundation of China [U1701267]; Major Research Plan of the National Natural Science Foundation of China [91438201]; IARPA [2017-17032700004] StateKey Program of National Natural Science of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Research Plan of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); IARPA The work of Yanchao Lian, Tuo Feng, Jinliu Zhou, Meixia Jia, Aijin Li, ZhaoyangWu, and Licheng Jiao were supported in part by the StateKey Program of National Natural Science of China under Grant 61836009, in part by the National Natural Science Foundation of China under Grant U1701267, and in part by the Major Research Plan of the National Natural Science Foundation of China under Grant 91438201. The work of Myron Brown was supported by IARPA under Contract 2017-17032700004. 40 9 9 1 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14 1158 1170 10.1109/JSTARS.2020.3035274 0.0 13 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology PR7LT gold 2023-03-23 WOS:000607413900037 0
C Li, LB; Sun, LL; Jin, J IEEE Li, Lebao; Sun, Lingling; Jin, Jie Survey of Advances in Control Algorithms of Quadrotor Unmanned Aerial Vehicle 2015 IEEE 16TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT) English Proceedings Paper IEEE 16th International Conference on Communication Technology (ICCT) OCT 18-20, 2015 Beijing, PEOPLES R CHINA IEEE Quadrotor unmanned aerial vehicle (UAV); Control algorithms; Linear and nonlinear; Learning ATTITUDE-CONTROL; TRACKING; ROTORCRAFT First, the basic structure and principle of quadrotor UAV and its practical applications are introduced. Then, some control algorithms are also presented, such as, PID, LQR/LQG, H-infinity, sliding mode, feedback linearization, back stepping, model predictive, robust, adaptive, nested saturation, fuzzy logic, neural network, reinforcement learning, iterative learning, memory and brain emotional learning-based intelligent controllers, and the merits and drawbacks of them are analyzed. At last, we predict the main research direction on the future researches of quadrotor UAV. [Li, Lebao] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China; [Li, Lebao; Sun, Lingling] Hangzhou Dianzi Univ, Key Lab RF Circuits & Syst, Hangzhou 310018, Peoples R China; [Jin, Jie] Dublin City Univ, Sch Elect Engn, Dublin 9, Ireland Zhejiang University; Hangzhou Dianzi University; Dublin City University Li, LB (corresponding author), Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China. lilebao0305@gmail.com; sunll@hdu.edu.cn; jiejin4@mail.dcu.ie 38 19 20 0 18 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-7005-9 2015.0 107 111 5 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BF3LC 2023-03-23 WOS:000380553200022 0
J Li, X; Wang, LY; Abawajy, JH; Qin, XL; Pau, G; You, I Li, Xin; Wang, Liangyuan; Abawajy, Jemal H.; Qin, Xiaolin; Pau, Giovanni; You, Ilsun Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System ENERGIES English Article big data analysis; heterogeneous data-intensive task; IoT system; service response delay; task scheduling MAPREDUCE; DELAY Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks. [Li, Xin; Wang, Liangyuan; Qin, Xiaolin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210023, Peoples R China; [Abawajy, Jemal H.] Deakin Univ, Sch Informat Technol, Locked Bag 20000, Geelong, Vic 3220, Australia; [Pau, Giovanni] Kore Univ Enna, Fac Engn & Architecture, I-94100 Enna, Italy; [You, Ilsun] Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea Nanjing University of Aeronautics & Astronautics; Deakin University; Universita Kore di ENNA; Soonchunhyang University You, I (corresponding author), Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea. lics@nuaa.edu.cn; myteng@nuaa.edu.cn; jemal.abawajy@deakin.edu.au; qinxcs@nuaa.edu.cn; giovanni.pau@unikore.it; isyou@sch.ac.kr You, Ilsun/AAR-7793-2021; Pau, Giovanni/K-2944-2015 Pau, Giovanni/0000-0002-5798-398X; You, Ilsun/0000-0002-0604-3445; Abawajy, Jemal/0000-0001-8962-1222 National Natural Science Foundation of China [61802182]; Soonchunhyang University Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Soonchunhyang University Research Fund This research was funded by the National Natural Science Foundation of China under Grant 61802182 as well as in part by the Soonchunhyang University Research Fund. 26 1 1 1 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies SEP 2020.0 13 17 4508 10.3390/en13174508 0.0 14 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels NQ8ME Green Published, gold 2023-03-23 WOS:000571121200001 0
J Dian, RW; Li, ST; Kang, XD Dian, Renwei; Li, Shutao; Kang, Xudong Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Spatial resolution; Tensile stress; Estimation; Hyperspectral imaging; Dictionaries; Correlation; Convolutional neural network (CNN); fusion; hyperspectral imaging; superresolution MULTIBAND IMAGES; SUPERRESOLUTION; CLASSIFICATION; SPARSE Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a common scheme to obtain high-resolution HSI (HR-HSI). This article presents a novel HSI and MSI fusion method (called as CNN-Fus), which is based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising. Our method only needs to train the CNN on the more accessible gray images and can be directly used for any HSI and MSI data sets without retraining. First, to exploit the high correlations among the spectral bands, we approximate the desired HR-HSI with the low-dimensional subspace multiplied by the coefficients, which can not only speed up the algorithm but also lead to more accurate recovery. Since the spectral information mainly exists in the LR-HSI, we learn the subspace from it via singular value decomposition. Due to the powerful learning performance and high speed of CNN, we use the well-trained CNN for gray image denoising to regularize the estimation of coefficients. Specifically, we plug the CNN denoiser into the alternating direction method of multipliers (ADMM) algorithm to estimate the coefficients. Experiments demonstrate that our method has superior performance over the state-of-the-art fusion methods. [Dian, Renwei; Li, Shutao; Kang, Xudong] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Dian, Renwei; Li, Shutao; Kang, Xudong] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Dian, Renwei] Univ Lisbon, Inst Telecomunicacoes, Inst Super Tecn, P-1049001 Lisbon, Portugal Hunan University; Hunan University; Instituto de Telecomunicacoes; Universidade de Lisboa; Instituto Superior Tecnico Kang, XD (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. drw@hnu.edu.cn; shutao_li@hnu.edu.cn; xudong_kang@163.com Li, Shutao/0000-0002-0585-9848 Major Program of the National Natural Science Foundation of China [61890962]; National Natural Science Foundation of China [61601179, 6187119]; National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]; Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]; Fund of Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province [2018TP1013]; Natural Science Foundation of Hunan Province [2019JJ50036]; Portuguese Science and Technology Foundation [UID/EEA/50008/2019]; Hunan Provincial Innovation Foundation for Postgraduate; China Scholarship Council Major Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Fund of China for International Cooperation and Exchanges; Fund of Hunan Province for Science and Technology Plan Project; Fund of Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province; Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Portuguese Science and Technology Foundation(Fundacao para a Ciencia e a Tecnologia (FCT)); Hunan Provincial Innovation Foundation for Postgraduate; China Scholarship Council(China Scholarship Council) This work was supported by in part by the Major Program of the National Natural Science Foundation of China under Grant 61890962, in part by the National Natural Science Foundation of China under Grant 61601179 and Grant 6187119, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, in part by the Fund of Hunan Province for Science and Technology Plan Project under Grant 2017RS3024, in part by the Fund of Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province under Grant 2018TP1013, in part by the Natural Science Foundation of Hunan Province under Grant 2019JJ50036, in part by the Portuguese Science and Technology Foundation under Project UID/EEA/50008/2019, in part by the Hunan Provincial Innovation Foundation for Postgraduate, and in part by the China Scholarship Council. 56 110 110 31 131 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. MAR 2021.0 32 3 1124 1135 10.1109/TNNLS.2020.2980398 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering QT1DL 32310788.0 2023-03-23 WOS:000626332700015 0
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M.; Pomeroy, D.; Pommes, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Bueso, X. Portell; Posch, C.; Pospelov, G. E.; Pospisil, S.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Prabhu, R.; Pralavorio, P.; Pranko, A.; Prasad, S.; Pravahan, R.; Prell, S.; Pretzl, K.; Pribyl, L.; Price, D.; Price, J.; Price, L. E.; Price, M. J.; Prieur, D.; Primavera, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Prudent, X.; Przybycien, M.; Przysiezniak, H.; Psoroulas, S.; Ptacek, E.; Pueschel, E.; Purdham, J.; Purohit, M.; Puzo, P.; Pylypchenko, Y.; Qian, J.; Qian, Z.; Qin, Z.; Quadt, A.; Quarrie, D. R.; Quayle, W. B.; Quinonez, F.; Raas, M.; Radescu, V.; Radics, B.; Radloff, P.; Rador, T.; Ragusa, F.; Rahal, G.; Rahimi, A. M.; Rahm, D.; Rajagopalan, S.; Rammensee, M.; Rammes, M.; Randle-Conde, A. S.; Randrianarivony, K.; Ratoff, P. N.; Rauscher, F.; Rave, T. C.; Raymond, M.; Read, A. L.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Reichold, A.; Reinherz-Aronis, E.; Reinsch, A.; Reisinger, I.; Rembser, C.; Ren, Z. L.; Renaud, A.; Renkel, P.; Rescigno, M.; Resconi, S.; Resende, B.; Reznicek, P.; Rezvani, R.; Richards, A.; Richter, R.; Richter-Was, E.; Ridel, M.; Rijpstra, M.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Rios, R. R.; Riu, I.; Rivoltella, G.; Rizatdinova, F.; Rizvi, E.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robinson, M.; Robson, A.; de Lima, J. G. Rocha; Roda, C.; Dos Santos, D. Roda; Rodriguez, D.; Roe, A.; Roe, S.; Rohne, O.; Rojo, V.; Rolli, S.; Romaniouk, A.; Romano, M.; Romanov, V. M.; Romeo, G.; Romero Adam, E.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, A.; Rose, M.; Rosenbaum, G. A.; Rosenberg, E. I.; Rosendahl, P. L.; Rosenthal, O.; Rosselet, L.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubinskiy, I.; Ruckert, B.; Ruckstuhl, N.; Rud, V. I.; Rudolph, C.; Rudolph, G.; Ruehr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rumiantsev, V.; Rumyantsev, L.; Runge, K.; Rurikova, Z.; Rusakovich, N. A.; Rutherfoord, J. P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y. F.; Ryadovikov, V.; Ryan, P.; Rybar, M.; Rybkin, G.; Ryder, N. C.; Rzaeva, S.; Saavedra, A. F.; Sadeh, I.; Sadrozinski, H. F-W.; Sadykov, R.; Tehrani, F. Safai; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.; Salihagic, D.; Salnikov, A.; Salt, J.; Ferrando, B. M. Salvachua; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sampsonidis, D.; Samset, B. H.; Sanchez, A.; Sanchez Martinez, V.; Sandaker, H.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, T.; Sandoval, C.; Sandstroem, R.; Sandvoss, S.; Sankey, D. P. C.; Sansoni, A.; Rios, C. Santamarina; Santoni, C.; Santonico, R.; Santos, H.; Saraiva, J. G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sartisohn, G.; Sasaki, O.; Sasao, N.; Satsounkevitch, I.; ; ATLAS Collaboration Search for FCNC single top-quark production at root s=7 TeV with the ATLAS detector PHYSICS LETTERS B English Article Top physics; Heavy-quark production; FCNC; Single top quark CHANGING NEUTRAL CURRENTS; HADRON COLLIDERS; FLAVOR; DECAYS A search for the production of single top-quarks via flavour-changing neutral-currents is presented. Data collected with the ATLAS detector at a centre-of-mass energy of root s = 7 TeV, corresponding to an integrated luminosity of 2.05 fb(-1), are used. Candidate events with a semileptonic top-quark decay signature are classified as signal- or background-like events by using several kinematic variables as input to a neural network. No signal is observed in the neural network output distribution and a Bayesian upper limit is placed on the production cross-section. The observed upper limit at 95% confidence level on the cross-section multiplied by the t -> Wb branching fraction is measured to be sigma(qg -> t) x B(t -> Wb) < 3.9 pb. This upper limit is converted using a model-independent approach into upper limits on the coupling strengths kappa(ugt)/Lambda < 6.9.10(-3) TeV-1 and kappa(cgt)/Lambda < 1.6.10(-2) TeV-1, where A is the new physics scale, and on the branching fractions B(t -> ug) < 5.7.10(-5) and B(t -> cg) < 2.7.10(-4). (C) 2012 CERN. Published by Elsevier B.V. All rights reserved. [Akdogan, T.; Arik, E.; Arik, M.; Istin, S.; Ozcan, V. E.; Rador, T.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Cetin, S. A.] Dogus Univ, Div Phys, Istanbul, Turkey; [Beddall, A. J.; Beddall, A.; Bingul, A.; Diblen, F.] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkey; Istanbul Tech Univ, Dept Phys, TR-80626 Istanbul, Turkey; [Alam, M. S.; Ernst, J.; Rojo, V.] SUNY Albany, Albany, NY 12222 USA; [Bahinipati, S.; Buchanan, N. J.; Chan, K.; Gingrich, D. M.; Kim, M. S.; Moore, R. W.; Pinfold, J. L.; Soni, N.; Subramania, H. S.; Vaque, F. Vives] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Ciftci, A. K.; Ciftci, R.; Yildiz, H. Duran; Kuday, S.; Persembe, S.] Ankara Univ, Dept Phys, TR-06100 Ankara, Turkey; Dumlupinar Univ, Dept Phys, Kutahya, Turkey; [Yilmaz, M.] Gazi Univ, Dept Phys, Ankara, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Cakir, I. Turk] Turkish Atom Energy Commiss, Ankara, Turkey; [Bella, L. Aperio; Aubert, B.; Berger, N.; Colas, J.; Delmastro, M.; Di Ciaccio, L.; Doan, T. K. O.; Elles, S.; Goy, C.; Helary, L.; Hryn'ova, T.; Jeremie, A.; Jezequel, S.; Kataoka, M.; Labbe, J.; Lafaye, R.; Leveque, J.; Lombardo, V. P.; Massol, N.; Perrodo, P.; Petit, E.; Przysiezniak, H.; Richter-Was, E.; Sauvage, G.; Sauvan, E.; Schwoerer, M.; Todorov, T.; Tsionou, D.; Wingerter-Seez, I.; Zitoun, R.; Zolnierowski, Y.] CNRS IN2P3, LAPP, Annecy Le Vieux, France; [Bella, L. Aperio; Aubert, B.; Berger, N.; Colas, J.; Delmastro, M.; Di Ciaccio, L.; Doan, T. K. O.; Elles, S.; Goy, C.; Helary, L.; Hryn'ova, T.; Jeremie, A.; Jezequel, S.; Kataoka, M.; Labbe, J.; Lafaye, R.; Leveque, J.; Lombardo, V. P.; Massol, N.; Perrodo, P.; Petit, E.; Przysiezniak, H.; Richter-Was, E.; Sauvage, G.; Sauvan, E.; Schwoerer, M.; Todorov, T.; Tsionou, D.; Wingerter-Seez, I.; Zitoun, R.; Zolnierowski, Y.] Univ Savoie, Annecy Le Vieux, France; [Asquith, L.; Blair, R. E.; Chekanov, S.; Dawson, J. W.; Fellmann, D.; Guarino, V. J.; Hill, D.; Hill, N.; Karr, K.; LeCompte, T.; Malon, D.; May, E. N.; Nodulman, L.; Paramonov, A.; Price, L. E.; Proudfoot, J.; Ferrando, B. M. Salvachua; Schlereth, J. L.; Stanek, R. W.; Underwood, D. G.; van Gemmeren, P.; Vaniachine, A.; Yoshida, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Cheu, E.; Johns, K. A.; Kaushik, V.; Lampen, C. L.; Lampl, W.; Lei, X.; Loch, P.; Paleari, C. P.; Ruehr, F.; Rutherfoord, J. P.; Shaver, L.; Shupe, M. A.; Varnes, E. W.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Brown, H.; De, K.; Farbin, A.; Heelan, L.; Hernandez, C. M.; Nilsson, P.; Ozturk, N.; Pravahan, R.; Sarkisyan-Grinbaum, E.; Sosebee, M.; Spurlock, B.; Stradling, A. R.; Usai, G.; Vartapetian, A.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, Arlington, TX 76019 USA; [Antonaki, A.; Fassouliotis, D.; Giakoumopoulou, V.; Giokaris, N.; Ioannou, P.; Kourkoumelis, C.; Manousakis-Katsikakis, A.; Tzanakos, G.; Vellidis, C.] Univ Athens, Dept Phys, Athens, Greece; [Alexopoulos, T.; Avramidou, R.; Dris, M.; Filippas, A.; Fokitis, M.; Gazis, E. N.; Iakovidis, G.; Katsoufis, E.; Le Menedeu, E.; Leontsinis, S.; Maltezos, S.; Mountricha, E.; Panagiotopoulou, E.; Papadopoulou, Th. D.; Tsipolitis, G.; Vlachos, S.] Natl Tech Univ Athens, Dept Phys, Zografos, Greece; [Abbott, B.; Abdinov, O.; Aliyev, M.; Huseynov, N.; Khalil-zada, F.; Rzaeva, S.] Azerbaijan Acad Sci, Inst Phys, Baku 370143, Azerbaijan; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Juste Rozas, A.; Korolkov, I.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Perez Codina, E.; Riu, I.; Rossetti, V.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] Univ Autonoma Barcelona, Inst Fis Altes Energies, E-08193 Barcelona, Spain; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Juste Rozas, A.; Korolkov, I.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Perez Codina, E.; Riu, I.; Rossetti, V.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] Univ Autonoma Barcelona, Dept Fis, E-08193 Barcelona, Spain; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Juste Rozas, A.; Korolkov, I.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Perez Codina, E.; Riu, I.; Rossetti, V.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] ICREA, Barcelona, Spain; [Borjanovic, I.; Krstic, J.; Popovic, D. S.; Sijacki, Dj.; Simic, Lj.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Bozovic-Jelisavcic, I.; Jovin, T.; Mamuzic, J.; Mudrinic, M.] Univ Belgrade, Vinca Inst Nucl Sci, Belgrade, Serbia; [Buanes, T.; Burgess, T.; Eigen, G.; Johansen, L. G.; Kastanas, A.; Liebig, W.; Lipniacka, A.; Rosendahl, P. L.; Sandaker, H.; Sjursen, T. B.; Stugu, B.; Tonoyan, A.; Ugland, M.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Arguin, J-F.; Bach, A. M.; Galtieri, A. Barbaro; Barnett, R. M.; Beringer, J.; Biesiada, J.; Calafiura, P.; Caminada, L. M.; Ciocio, A.; Cooke, M.; Copic, K.; Dube, S.; Einsweiler, K.; Gaponenko, A.; Garcia-Sciveres, M.; Haber, C.; Hance, M.; Heinemann, B.; Hinchliffe, I.; Hsu, S. -C.; Hurwitz, M.; Joseph, J.; Lavrijsen, W.; Leggett, C.; Loscutoff, P.; Lys, J.; Madaras, R. J.; Ovcharova, A.; Griso, S. Pagan; Pranko, A.; Quarrie, D. R.; Ruwiedel, C.; Shapiro, M.; Skinnari, L. A.; Tatarkhanov, M.; Tibbetts, M. J.; Tsulaia, V.; Vahsen, S.; Varouchas, D.; Virzi, J.; Yao, Y.; Zenz, S.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA 94720 USA; [Aliev, M.; Giorgi, F. M.; Grancagnolo, S.; Haug, S.; Herrberg, R.; Hristova, I.; Kind, O.; Kolanoski, H.; Kwee, R.; Lacker, H.; Leyton, M.; Lohse, T.; Mandryschls, R.; Nikiforov, A.; Schulz, H.; Wendland, D.; zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Ancu, L. S.; Battaglia, A.; Beck, H. P.; Borer, C.; Ereditato, A.; Martin, T. Fonseca; Gallo, V.; Kabana, S.; Kruker, T.; Pretzl, K.; Topfel, C.; Weber, M. S.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Ancu, L. S.; Battaglia, A.; Beck, H. P.; Borer, C.; Ereditato, A.; Martin, T. Fonseca; Gallo, V.; Kabana, S.; Kruker, T.; Pretzl, K.; Topfel, C.; Weber, M. S.] Univ Bern, High Energy Phys Lab, Bern, Switzerland; [Allbrooke, B. M. M.; Bansil, H. S.; Bracinik, J.; Charlton, D. G.; Chisholm, A. S.; Collins, N. J.; Curtis, C. J.; Dowell, J. D.; Garvey, J.; Hadley, D. R.; Harrison, K.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Mahout, G.; Martin, T. A.; Mclaughlan, T.; Newman, P. R.; O'Neale, S. W.; Palmer, J. D.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Bellagamba, L.; Bertin, A.; Bindi, M.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Caforio, D.; Ciocca, C.; Corradi, M.; De Castro, S.; Di Sipio, R.; Fabbri, L.; Giacobbe, B.; Giusti, P.; Jha, M. K.; Massa, I.; Mengarelli, A.; Monzani, S.; Piccinini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Spighi, R.; Valentinetti, S.; Villa, M.; Zoccoli, A.] INFN Sez Bologna, Bologna, Italy; [Bertin, A.; Bindi, M.; Caforio, D.; De Castro, S.; Di Sipio, R.; Fabbri, L.; Massa, I.; Mengarelli, A.; Monzani, S.; Piccinini, M.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Univ Bologna, Dipartimento Fis, Bologna, Italy; [Alhroob, M.; Anders, C. F.; Arutinov, D.; Backhaus, M.; Barbero, M.; Bechtle, P.; Brock, I.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Fischer, P.; Gaycken, G.; Geich-Gimbel, Ch.; Gonella, L.; Havranek, M.; Hellmich, D.; Hillert, S.; Huegging, F.; Ince, T.; Janus, M.; Karagounis, M.; Khoriauli, G.; Koevesarki, P.; Kokott, T.; Kostyukhin, V. V.; Kraus, J. K.; Kroseberg, J.; Krueger, H.; Kruth, A.; Lapoire, C.; Lehmacher, M.; Leyko, A. M.; Limbach, C.; Loddenkoetter, T.; Mazur, M.; Moeser, N.; Mueller, K.; Nanava, G.; Nattermann, T.; Nuncio-Quiroz, A. -E.; Poghosyan, T.; Psoroulas, S.; Radics, B.; Schaepe, S.; Schmieden, K.; Schmitz, M.; Schultens, M. J.; Schumacher, J. W.; Schwindt, T.; Stillings, J. A.; Therhaag, J.; Tsung, J. -W.; Uchida, K.; Uhlenbrock, M.; Vlasov, N.; Vogel, A.; von Toerne, E.; Wermes, N.; Wienemann, P.; Zendler, C.; Zimmermann, R.; Zimmermann, S.] Univ Bonn, Inst Phys, Bonn, Germany; [Ahlen, S. P.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Hazen, E.; Love, J.; Nation, N. R.; Posch, C.; Shank, J. T.; Whitaker, S. P.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, Boston, MA 02215 USA; [Aefsky, S.; Amelung, C.; Bensinger, J. R.; Blocker, C.; Daya-Ishmukhametova, R. K.; Gozpinar, S.; Kirsch, L. E.; Pomeroy, D.; Skvorodnev, N.; Wellenstein, H.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Caloba, L. P.; Coura Torres, R.; Da Silva, P. V. M.; Maidantchik, C.; de Andrade Filho, L. Manhaes; Marroquim, F.; Nepomuceno, A. A.; Perantoni, M.; Seixas, J. M.] Univ Fed Rio De Janeiro COPPE EE IF, Rio De Janeiro, Brazil; [Cerqueira, A. S.] Fed Univ Juiz de Fora UFJF, Juiz De Fora, MG, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao del Rei UFSJ, Sao Joao Del Rei, MG, Brazil; [Donadelli, M.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, BR-01498 Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Baker, M. D.; Begel, M.; Bernius, C.; Caramarcu, C.; Chen, H.; Chernyatin, V.; Salgado, P. E. De Castro Faria; Debbe, R.; Dhullipudi, R.; Ernst, M.; Gadfort, T.; Gibbard, B.; Gordon, H. A.; Greenwood, Z. D.; Klimentov, A.; Lanni, F.; Lissauer, D.; Lynn, D.; Ma, H.; Maeno, T.; Majewski, S.; Nevski, P.; Nikolopoulos, K.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Park, W.; Pleier, M. -A.; Poblaguev, A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Rahm, D.; Rajagopalan, S.; Redlinger, G.; Sawyer, L.; Sircar, A.; Snyder, S.; Steinberg, P.; Stumer, I.; Takai, H.; Tamsett, M. C.; Trivedi, A.; Undrus, A.; Wenaus, T.; Ye, S.; Yu, D.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Alexa, C.; Badescu, E.; Boldea, V.; Buda, S. I.; Caprini, I.; Caprini, M.; Ciubancan, M.; Constantinescu, S.; Cuciuc, C. -M.; Dita, P.; Dita, S.; Micu, L.; Olariu, A.; Pantea, D.; Popeneciu, G. A.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Darlea, G. L.] Univ Politehn Bucuresti, Bucharest, Romania; W Univ Timisoara, Timisoara, Romania; [Gonzalez Silva, M. L.; Otero y Garzon, G.; Piegaia, R.; Romeo, G.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Ask, S.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Buttinger, W.; Carter, J. R.; Chapman, J. D.; Cowden, C.; French, S. T.; Frost, J. A.; Hill, J. C.; Kaneti, S.; Khoo, T. J.; Lester, C. G.; Moeller, V.; Parker, M. A.; Robinson, D.; Sandoval, T.; Thomson, M.; Ward, C. P.] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England; [Gillberg, D.; Koffas, T.; Liu, C.; Marchand, J. F.; McCarthy, T. G.; Oakham, F. G.; Randrianarivony, K.; Tarrade, F.; Ueno, R.; Vincter, M. G.; Whalen, K.] Carleton Univ, Dept Phys, Ottawa, ON K1S 5B6, Canada; [Aleksa, M.; Anastopoulos, C.; Anghinolfi, F.; Bachas, K.; Banfi, D.; Battistin, M.; Bellina, F.; Bellomo, M.; Berge, D.; Bertinelli, F.; Blanchot, G.; Bogaerts, J. A.; Boyd, J.; Braem, A.; Bremer, J.; Burckhart, H.; Butin, F.; Cataneo, F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Cerri, A.; Barajas, C. A. Chavez; Childers, J. T.; Chromek-Burckhart, D.; Cook, J.; Cote, D.; Danielsson, H. O.; Dell'Acqua, A.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Dittus, F.; Dobos, D.; Dobson, E.; Dopke, J.; Drevermann, H.; Dudarev, A.; Duehrssen, M.; Dunford, M.; Dydak, F.; Elsing, M.; Fabre, C.; Foussat, A.; Francis, D.; Froidevaux, D.; Torregrosa, E. Fullana; Gabaldon, C.; Gayde, J-C.; Gianotti, F.; Godlewski, J.; Gonidec, A.; Goossens, L.; Gorini, B.; Gray, H. M.; Hahn, F.; Hatch, M.; Hauschild, M.; Heller, M.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Inigo-Golfin, J.; Jansen, H.; Joram, C.; Kaneda, M.; Kaplon, J.; Knobloch, J.; Koeneke, K.; Lamanna, M.; Lasseur, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Magnoni, L.; Malaescu, B.; Mapelli, A.; Mapelli, L.; Martin, B.; Maugain, J. M.; Menot, C.; Messina, A.; Molina-Perez, J.; Morley, A. K.; Mornacchi, G.; Muenstermann, D.; Nairz, A. M.; Negri, G.; Nessi, M.; Nicquevert, B.; Pernegger, H.; Peters, K.; Petersen, B. A.; Petersen, J.; Piacquadio, G.; Pommes, K.; Poppleton, A.; Poulard, G.; Pribyl, L.; Rembser, C.; Dos Santos, D. Roda; Salzburger, A.; Savu, D. O.; Schuler, G.; Sfyrla, A.; Sloper, J.; Spigo, G.; Stewart, G. A.; Szeless, B.; Tappern, G. P.; Ten Kate, H.; Viegas, F. J. Tique Aires; Torchiani, I.; Tremblet, L.; Tricoli, A.; Tsarouchas, C.; Unal, G.; van der Ster, D.; Vandoni, G.; Rodriguez, F. Varela; Vinek, E.; Wilkens, H. G.; Winklmeier, F.; Wotschack, J.; Zsenei, A.; Zwalinski, L.] CERN, Geneva, Switzerland; [Anderson, K. J.; Boveia, A.; Canelli, F.; Choudalakis, G.; Costin, T.; Feng, E. J.; Fiascaris, M.; Gardner, R. W.; Gupta, A.; Jen-La Plante, I.; Kapliy, A.; Melachrinos, C.; Merritt, F. S.; Meyer, C.; Miller, D. W.; Onyisi, P. U. E.; Oreglia, M. J.; Penning, B.; Pilcher, J. E.; Shochet, M. J.; Tompkins, L.; Tuggle, J. M.] Univ Chicago, Enrico Fermi Inst, Chicago, IL 60637 USA; [Diaz, M. A.; Olivares Pino, S. A.; Panes, B.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Kuleshov, S.; Pezoa, R.; Prokoshin, F.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Bai, Y.; Cheng, S.; Han, H.; Jin, S.; Lu, F.; Ouyang, Q.; Ruan, X.; Shan, L. Y.; Tong, G.; Xie, Y.; Xu, G.; Yang, Y.; Yuan, L.; Zheng, S.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Han, L.; Jiang, Y.; Jin, G.; Li, S.; Liu, M.; Liu, Y.; Peng, H.; Wang, H.; Wu, Y.; Xu, C.; Zhang, D.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Chen, S.; Chen, T.; Ping, J.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Feng, C.; Ge, P.; He, M.; Li, H.; Meng, Z.; Miao, J.; Zhan, Z.; Zhang, X.; Zhu, C. G.] Shandong Univ, Sch Phys, Jinan, Shandong, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] Clermont Univ, Phys Corpusculaire Lab, Aubiere, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] Univ Blaise Pascal, Aubiere, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] CNRS IN2P3, Aubiere, France; [Andeen, T.; Angerami, A.; Brooijmans, G.; Dodd, J.; Grau, N.; Guo, J.; Hughes, E. W.; Leltchouk, M.; Nikiforou, N.; Parsons, J. A.; Penson, A.; Perez, K.; Reale, V. Perez; Scherzer, M. I.; Thompson, E. N.; Tian, F.; Tuts, P. M.; Urbaniec, D.; Williams, E.; Willis, W.; Wulf, E.; Zivkovic, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Boelaert, N.; Dam, M.; Driouichi, C.; Gregersen, K.; Hansen, J. R.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Heisterkamp, S.; Jakobsen, S.; Jez, P.; Joergensen, M. D.; Kadlecik, P.; Klinkby, E. B.; Lundquist, J.; Mackeprang, R.; Mehlhase, S.; Petersen, T. C.; Simonyan, M.; Thomsen, L. A.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Crosetti, G.; Fazio, S.; La Rotonda, L.; Lavorini, V.; Mastroberardino, A.; Morello, G.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Susinno, G.; Tassi, E.] INFN Grp Collegato Cosenza, Cosenza, Italy; [Capua, M.; Crosetti, G.; Fazio, S.; La Rotonda, L.; Lavorini, V.; Mastroberardino, A.; Morello, G.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Ciba, K.; Dabrowski, W.; Dwuznik, M.; Grabowska-Bold, I.; Jelen, K.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Mindur, B.; Przybycien, M.; Toczek, B.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Banas, E.; Blocki, J.; de Renstrom, P. A. Bruckman; Derendarz, D.; Gornicki, E.; Hajduk, Z.; Iwanski, W.; Kaczmarska, A.; Korcyl, K.; Malecki, Pa.; Malecki, P.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Trzebinski, M.; Trzupek, A.; Turala, M.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.; Zemla, A.] Polish Acad Sci, Hemyk Niewodniczanski Inst Nucl Phys, Krakow, Poland; [Yagci, K. Dindar; Firan, A.; Hadavand, H. K.; Hoffman, J.; Ilchenko, Y.; Ishmukhametov, R.; Joffe, D.; Kama, S.; Kehoe, R.; Randle-Conde, A. S.; Renkel, P.; Rios, R. R.; Stroynowski, R.; Ye, J.] So Methodist Univ, Dept Phys, Dallas, TX 75275 USA; [Ahsan, M.; Izen, J. M.; Lou, X.; Reeves, K.; Wong, W. C.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Kuutmann, E. Bergeaas; Boehler, M.; Dietrich, J.; Ehrenfeld, W.; Ferrara, V.; Fischer, G.; Glazov, A.; Goebel, M.; Fajardo, L. S. Gomez; Da Costa, J. Goncalves Pinto Firmino; Gosdzik, B.; Grahn, K-J.; Gregor, I. M.; Hiller, K. H.; Huettmann, A.; Husemann, U.; Belenguer, M. Jimenez; Johnert, S.; Karnevskiy, M.; Katzy, J.; Kono, T.; Kuhl, T.; Lange, C.; Lobodzinska, E.; Ludwig, D.; Maettig, S.; Medinnis, M.; Mijovic, L.; Moenig, K.; Naumann, T.; Cavalcanti, T. Perez; Petschull, D.; Piec, S. M.; Qin, Z.; Rubinskiy, I.; Sedov, G.; Styles, N. A.; Tackmann, K.; Vankov, P.; Viti, M.; Wildt, M. A.; Zhu, H.] DESY, D-2000 Hamburg, Germany; [Kuutmann, E. Bergeaas; Boehler, M.; Dietrich, J.; Ehrenfeld, W.; Ferrara, V.; Fischer, G.; Glazov, A.; Goebel, M.; Fajardo, L. S. Gomez; Da Costa, J. Goncalves Pinto Firmino; Gosdzik, B.; Grahn, K-J.; Gregor, I. M.; Hiller, K. H.; Huettmann, A.; Husemann, U.; Belenguer, M. Jimenez; Johnert, S.; Karnevskiy, M.; Katzy, J.; Kono, T.; Kuhl, T.; Lange, C.; Lobodzinska, E.; Ludwig, D.; Maettig, S.; Medinnis, M.; Mijovic, L.; Moenig, K.; Naumann, T.; Cavalcanti, T. Perez; Petschull, D.; Piec, S. M.; Qin, Z.; Rubinskiy, I.; Sedov, G.; Styles, N. A.; Tackmann, K.; Vankov, P.; Viti, M.; Wildt, M. A.; Zhu, H.] DESY, Zeuthen, Germany; [Bunse, M.; Goessling, C.; Hirsch, F.; Jung, C. A.; Klaiber-Lodewigs, J.; Klingenberg, R.; Reisinger, I.; Walbersloh, J.; Wunstorf, R.] Tech Univ Dortmund, Inst Expt Phys 4, Dortmund, Germany; [Czodrowski, P.; Friedrich, F.; Goepfert, T.; Kar, D.; Kobel, M.; Leonhardt, K.; Ludwig, A.; Mader, W. F.; Morgenstern, M.; Prudent, X.; Rudolph, C.; Schwierz, R.; Seifert, F.; Steinbach, P.; Straessner, A.; Vest, A.] Tech Univ Dresden, Inst Kern & Teilchenphys, D-01062 Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bocci, A.; Ebenstein, W. L.; Fowler, A. J.; Ko, B. R.; Kotwal, A.; Oh, S. H.; Wang, C.; Yamaoka, J.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bhimji, W.; Buckley, A. G.; Clark, P. J.; Debenedetti, C.; Harrington, R. D.; Martin, V. J.; O'Brien, B. J.; Selbach, K. E.; Smart, B. H.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; Fachhsch Wiener Neustadt, A-2700 Wiener Neustadt, Austria; [Annovi, A.; Antonelli, M.; Bilokon, H.; Cerutti, F.; Curatolo, M.; Di Nardo, R.; Esposito, B.; Ferrer, M. L.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Sansoni, A.; Testa, M.; Vilucchi, E.; Volpi, G.] INFN Lab Nazl Frascati, Frascati, Italy; [Aad, G.; Ahles, F.; Barber, T.; Bernhard, R.; Bitenc, U.; Bruneliere, R.; Christov, A.; Consorti, V.; Fehling-Kaschek, M.; Flechl, M.; Glatzer, J.; Hartert, J.; Herten, G.; Horner, S.; Jakobs, K.; Kollefrath, M.; Kononov, A. I.; Kuehn, S.; Lai, S.; Landgraf, U.; Lohwasser, K.; Ludwig, I.; Ludwig, J.; Lumb, D.; Mahboubi, K.; Mohr, W.; Nilsen, H.; Parzefall, U.; Rammensee, M.; Rave, T. C.; Runge, K.; Rurikova, Z.; Schmidt, E.; Schumacher, M.; Siegert, F.; Stoerig, K.; Sundermann, J. E.; Temming, K. K.; Thoma, S.; Tsiskaridze, V.; Venturi, M.; Vivarelli, I.; von Radziewski, H.; Anh, T. Vu; Warsinsky, M.; Weiser, C.; Werner, M.; Wiik-Fuchs, L. A. M.; Winkelmann, S.; Xie, S.; Zimmermann, S.] Univ Freiburg, Fak Math & Phys, Freiburg, Germany; [Abdelalim, A. A.; Alexandre, G.; Backes, M.; Barone, G.; Bell, P. J.; Bell, W. H.; Noccioli, E. Benhar; Blondel, A.; Bucci, F.; Clark, A.; Dao, V.; Doglioni, C.; Ferrere, D.; Gadomski, S.; Gonzalez-Sevilla, S.; Goulette, M. P.; Iacobucci, G.; La Rosa, A.; Leger, A.; Lister, A.; Latour, B. Martin Dit; Mermod, P.; Herrera, C. Mora; Nektarijevic, S.; Nessi, M.; Nikolics, K.; Pasztor, G.; Pohl, M.; Rosbach, K.; Rosselet, L.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Caso, C.; Dameri, M.; Parodi, A. Ferretto; Gagliardi, G.; Osculati, B.; Parodi, F.; Schiavi, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Beccherle, R.; Caso, C.; Dameri, M.; Darbo, G.; Parodi, A. Ferretto; Gagliardi, G.; Gemme, C.; Morettini, P.; Olcese, M.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Schiavi, C.] INFN Sez Genova, Genoa, Italy; [Chikovani, L.; Tskhadadze, E. G.] Tbilisi State Univ, E Andronikashvili Inst Phys, GE-380086 Tbilisi, Georgia; [Djobava, T.; Khubua, J.; Mchedlidze, G.; Mosidze, M.] Tbilisi State Univ, High Energy Phys Inst, GE-380086 Tbilisi, Georgia; [Astvatsatourov, A.; Dueren, M.; Stenzel, H.] Univ Giessen, Inst Phys 2, D-6300 Giessen, Germany; [Allwood-Spiers, S. E.; Bates, R. L.; Britton, D.; Bussey, P.; Buttar, C. M.; Collins-Tooth, C.; D'Auria, S.; Doherty, T.; Doyle, A. T.; Edwards, N. C.; Ferrag, S.; Ferrando, J.; de Lima, D. E. Ferreira; Gemmell, A.; Kenyon, M.; McGlone, H.; Moraes, A.; O'Shea, V.; Barrera, C. Oropeza; Robson, A.; Saxon, D. H.; Smith, K. M.; St Denis, R. D.; Steele, G.; Thompson, A. S.; Wraight, K.; Wright, C.; Wright, M.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Ay, C.; Bierwagen, K.; Blumenschein, U.; Brandt, O.; Erdmann, J.; Evangelakou, D.; George, M.; Grosse-Knetter, J.; Guindon, S.; Haller, J.; Hamer, M.; Henrichs, A.; Hensel, C.; Keil, M.; Knue, A.; Kohn, F.; Krieger, N.; Kroeninger, K.; Lemmer, B.; Magradze, E.; Mann, A.; Meyer, J.; Morel, J.; Quadt, A.; Roe, A.; Serkin, L.; Shabalina, E.; Uhrmacher, M.; Schroeder, T. Vazquez; Weber, P.; Weingarten, J.] Univ Gottingen, Inst Phys 2, Gottingen, Germany; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] Univ Grenoble 1, Lab Phys Subatom & Cosmol, Grenoble, France; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] CNRS IN2P3, Grenoble, France; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] Inst Natl Polytech Grenoble, F-38031 Grenoble, France; [Addy, T. N.; Harvey, A.; McFarlane, K. W.; Shin, T.; Vassilakopoulos, V. I.] Hampton Univ, Dept Phys, Hampton, VA 23668 USA; [da Costa, J. Barreiro Guimaraes; Belloni, A.; Brandenburg, G. W.; Franklin, M.; Hurst, P.; Huth, J.; Jeanty, L.; Kagan, M.; Mateos, D. Lopez; Outschoorn, V. Martinez; Mercurio, K. M.; Mills, C.; Moed, S.; Morii, M.; Prasad, S.; Skottowe, H. P.; Smith, B. C.; della Porta, G. Zevi] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Anders, G.; Andrei, V.; Davygora, Y.; Dietzsch, T. A.; Hanke, P.; Henke, M.; Kluge, E. -E.; Lendermann, V.; Meier, K.; Mueller, F.; Poddar, S.; Scharf, V.; Schultz-Coulon, H. -C.; Stamen, R.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Kasieczka, G.; Narayan, R.; Radescu, V.; Schaetzel, S.; Schmitt, S.; Schoening, A.] Heidelberg Univ, Inst Phys, Heidelberg, Germany; [Kugel, A.; Maenner, R.; Schroer, N.] Heidelberg Univ, ZITI Inst Tech Infomat, D-6800 Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Brunet, S.; Cwetanski, P.; Evans, H.; Gagnon, P.; Jain, V.; Luehring, F.; Ogren, H.; Penwell, J.; Price, D.; Whittington, D.; Yang, Y.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Epp, B.; Jussel, P.; Kneringer, E.; Kuhn, D.; Rudolph, G.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Behera, P. K.; Limper, M.; Mallik, U.; Pylypchenko, Y.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; Dudziak, F.; Krumnack, N.; Mete, A. S.; Meyer, W. T.; Prell, S.; Rosenberg, E. I.; Ruiz-Martinez, A.; Shrestha, S.; Yamamoto, K.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Abdesselam, A.; Aleksandrov, I. N.; Anisenkov, A.; Annovi, A.; Barashkou, A.; Bardin, D. Y.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chepurnov, V. F.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Glonti, G. L.; Gostkin, M. I.; Grigalashvili, N.; Gusakov, Y.; Huseynov, N.; Kalinovskaya, L. V.; Kazarinov, M. Y.; Kekelidze, G. D.; Kharchenko, D.; Khovanskiy, N.; Khramov, E.; Kolesnikov, V.; Kotov, V. M.; Kruchonak, U.; Krumshteyn, Z. V.; Kukhtin, V.; Ladygin, E.; Lazarev, A. B.; Manjavidze, I. D.; Minashvili, I. A.; Mineev, M.; Nikolaev, K.; Olchevski, A. G.; Peshekhonov, V. D.; Pozdnyakov, V.; Romanov, V. M.; Rumyantsev, L.; Rusakovich, N. A.; Sadykov, R.; Shiyakova, M.; Sisakyan, A. N.; Topilin, N. D.; Vinogradov, V. B.; Zhemchugov, A.] Joint Inst Nucl Res Dubna, Joint Inst Nucl Res, Dubna, Russia; [Amako, K.; Arai, Y.; Doi, Y.; Haruyama, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kohriki, T.; Kondo, T.; Makida, Y.; Manabe, A.; Mitsui, S.; Nagano, K.; Nozaki, M.; Odaka, S.; Sasaki, O.; Suzuki, Y.; Takubo, Y.; Tanaka, S.; Terada, S.; Tojo, J.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Yamada, M.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Akiyama, A.; Hayakawa, T.; Homma, Y.; Ichimiya, R.; Ishikawa, A.; Kawagoe, K.; King, M.; Kishimoto, T.; Kurashige, H.; Matsushita, T.; Miyazaki, K.; Nishiyama, T.; Ochi, A.; Okada, S.; Omachi, C.; Suita, K.; Suzuki, Y.; Takeda, H.; Tani, K.; Tokunaga, K.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo 657, Japan; [Ishino, M.; Sasao, N.; Sumida, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto 612, Japan; [Anduaga, X. S.; Dova, M. T.; Monticelli, F.; Tripiana, M. F.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Anduaga, X. S.; Dova, M. T.; Monticelli, F.; Tripiana, M. F.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Borissov, G.; Bouhova-Thacker, E. V.; Brodbeck, T. J.; Chilingarov, A.; Davidson, R.; de Mora, L.; Dearnaley, W. J.; Fox, H.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Ratoff, P. N.; Smizanska, M.; Walder, J.] Univ Lancaster, Dept Phys, Lancaster, England; [Bianco, M.; Cataldi, G.; Chiodini, G.; Crupi, R.; Gorini, E.; Grancagnolo, F.; Guida, A.; Perrino, R.; Primavera, M.; Spagnolo, S.; Ventura, A.] INFN Sez Lecce, Lecce, Italy; [Bianco, M.; Crupi, R.; Gorini, E.; Guida, A.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Fis, Lecce, Italy; [Allport, P. P.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Greenshaw, T.; Gwilliam, C. B.; Hayward, H. S.; Houlden, M. A.; Jackson, J. N.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kluge, T.; Kretzschmar, J.; Laycock, P.; Maxfield, S. J.; Mehta, A.; Migas, S.; Price, J.; Sellers, G.; Vossebeld, J. H.; Waller, P.; Wrona, B.] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 3BX, Merseyside, England; [Cindro, V.; Deliyergiyev, M.; Dolenc, I.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Tykhonov, A.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Cindro, V.; Deliyergiyev, M.; Dolenc, I.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Tykhonov, A.] Univ Ljubljana, Ljubljana, Slovenia; [Adragna, P.; Bona, M.; Carter, A. A.; Cerrito, L.; Eisenhandler, E.; Ellis, K.; Goddard, J. R.; Landon, M. P. J.; Lloyd, S. L.; Morin, J.; Morris, J. D.; Piccaro, E.; Poll, J.; Rizvi, E.; Salamanna, G.; Stevenson, K.; Castanheira, M. Teixeira Dias; Wiglesworth, C.] Queen Mary Univ London, Sch Phys & Astron, London, England; [Alam, M. A.; Berry, T.; Boisvert, V.; Cowan, G.; Edwards, C. A.; George, S.; Goncalo, R.; Hayden, D.; Misiejuk, A.; Pastore, Fr.; Rose, M.; Spano, F.; Strong, J. A.; Teixeira-Dias, P.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Baker, S.; Bernat, P.; Bieniek, S. P.; Butterworth, J. M.; Campanelli, M.; Christidi, I. A.; Cooper, B. D.; Davison, A. R.; Dean, S.; Dobson, E.; Hesketh, G. G.; Jansen, E.; Jones, T. W.; Konstantinidis, N.; Monk, J.; Nash, M.; Nurse, E.; Prabhu, R.; Richards, A.; Robinson, J. E. M.; Sherwood, P.; Simmons, B.; Taylor, C.; Waugh, B. M.; Wijeratne, P. A.] UCL, Dept Phys & Astron, London, England; [Beau, T.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] Univ Paris Diderot, Paris, France; [Beau, T.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] CNRS IN2P3, Paris, France; [Akesson, T. P. A.; Alonso, A.; Bocchetta, S. S.; Floderus, A.; Hawkins, A. D.; Hedberg, V.; Jarlskog, G.; Lundberg, B.; Lytken, E.; Meirose, B.; Mjornmark, J. U.; Smirnova, O.] Lund Univ, Inst Fys, Lund, Sweden; [Barreiro, F.; Cantero, J.; De la Torre, H.; Del Peso, J.; Glasman, C.; Labarga, L.; Lagouri, T.; Llorente Merino, J.; March, L.; Nebot, E.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor, Madrid, Spain; [Aharrouche, M.; Arnaez, O.; Bendel, M.; Blum, W.; Buescher, V.; Caputo, R.; Eckweiler, S.; Edmonds, K.; Ellinghaus, F.; Ertel, E.; Fiedler, F.; Fleckner, J.; Goeringer, C.; Handel, C.; Hohlfeld, M.; Hsu, P. J.; Ji, W.; Kawamura, G.; Kleinknecht, K.; Koenig, S.; Koepke, L.; Lungwitz, M.; Masetti, L.; Meyer, C.; Moreno, D.; Mueller, T.; Neusiedl, A.; Sander, H. G.; Schaefer, U.; Schmitt, C.; Schroeder, C.; Tapprogge, S.] Johannes Gutenberg Univ Mainz, Inst Phys, D-6500 Mainz, Germany; [Almond, J.; Borri, M.; Brown, G.; Chavda, V.; Cox, B. E.; Da Via, C.; Duerdoth, I. P.; Forti, A.; Foster, J. M.; Howarth, J.; Hughes-Jones, R. E.; Ibbotson, M.; Klinger, J. A.; Kolya, S. D.; Lane, J. L.; Loebinger, F. K.; Marshall, R.; Marx, M.; Masik, J.; Neep, T. J.; Oh, A.; Owen, M.; Pater, J. R.; Pilkington, A. D.; Schwanenberger, C.; Snow, S. W.; Watts, S.; Yang, U. K.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Aoun, S.; Bee, C. P.; Benchouk, C.; Bertella, C.; Bousson, N.; Clemens, J. C.; Coadou, Y.; Djama, F.; Etienne, F.; Feligioni, L.; Henry-Couannier, F.; Hoffmann, D.; Hubaut, F.; Knoops, E. B. F. 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Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Osaka University; University of Oslo; University of Oxford; University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; University of Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; University of Granada; University of Granada; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Charles University Prague; 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Uppsala University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Valencia; University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Barcelona (IMB-CNM); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); University of British Columbia; University of Victoria; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Hiroshima University; Universidade de Lisboa; Universidade de Lisboa; California State University System; California State University Fresno; Novosibirsk State University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; Universidade de Coimbra; Parthenope University Naples; Middle East Technical University; University of Louisiana System; Louisiana Technical University; University of Cape Town; University of Hamburg; Manhattan College; Sun Yat Sen University; Universidade do Minho; University of South Carolina System; University of South Carolina Columbia; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; California Institute of Technology; Jagiellonian University Arik, E (corresponding author), Bogazici Univ, Dept Phys, Istanbul, Turkey. 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Berger, N/ABE-4064-2020; Saraiva, Joao Gentil Mendes/D-1596-2017; SULIN, VLADIMIR V/N-2793-2015; Sýkora, Tomáš/Q-3174-2017; valente, paolo/A-6640-2010; Coccaro, Andrea/AAB-6990-2021; Nessi, Marzio/L-5194-2017; Salamon, Andrea/AGN-6159-2022; Messina, Andrea/C-2753-2013; Glasman, Claudia/Y-8858-2019; García, Elena Oliver/AAZ-1125-2020; Samset, Bjørn H./H-6828-2016; Ohm, Christian/AAU-6572-2020; Kumar, Dr Ashok/HNR-6570-2023; Gutierrez, Phillip/C-1161-2011; Morozov, Sergey/C-1396-2014; Huseynov, Nazim/AAE-4663-2019; de la Hoz, Santiago Gonzalez/E-2494-2016; de Renstrom, Pawel Bruckman/AAG-7725-2021; Ventura, Andrea/A-9544-2015; Olshevskiy, Alexander/I-1580-2016; Grinstein, Sebastian/ABE-1880-2020; Gonzalez, Barbara Alvarez/ABG-7021-2020; della Volpe, Domenico/B-4482-2012; Behera, Prafulla Kumar/AAK-8686-2020; Ruggieri, Federico/T-5300-2019; Šolc, Jaroslav/AAC-5868-2021; Lagouri, Theodota/AAC-7358-2021; Ozcan, Veysi Erkcan/AAS-4508-2020; Weigell, Philipp/I-9356-2012; Kabana, Sonia/ABF-5974-2020; Caprini, Irinel/AAP-3760-2020; Urbán, Susana Cabrera/H-1376-2015; Ros, Eduardo/P-2625-2018; Padilla, Cristobal/C-3218-2017; Vandelli, Wainer/ABI-8185-2020; Casadei, Diego/I-1785-2013; Derendarz, Dominik/AAO-3512-2021; Coccaro, Andrea/P-5261-2016; Christov, Asen/S-4508-2018; Kuutmann, Elin Bergeaas/A-5204-2013; Chelkov, Georgy A./G-9934-2019; Torregrosa, Esteban Fullana/A-7305-2016; Pages, Andres Pacheco/C-5353-2011; Amorim, Antonio/C-8460-2013; Gladilin, Leonid/B-5226-2011; Malek, Fairouz/E-4410-2018; Gonçalo, Ricardo/M-3153-2016; Ciftci, Abbas Kenan/AAC-5487-2022; Nevski, Pavel/AAH-1839-2019; Doyle, Anthony T/C-5889-2009; Parodi, Fabrizio/AHE-5089-2022; Busato, Emmanuel/AAZ-4423-2020; Bachas, Konstantinos/C-8101-2019; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Fassi, Farida/F-3571-2016; Laycock, Paul/AAL-8261-2021; Benslama, Kamal/AAO-2056-2021; Llácer, María Moreno/AAQ-7522-2020; Radics, Balint/ABE-7286-2020; García, Josu Cantero/AAS-9810-2020; Darbo, Giovanni/C-8175-2012; Nordberg, Markus/L-4255-2017; 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Petersen, Troels/0000-0003-0221-3037; Vivarelli, Iacopo/0000-0003-0097-123X; Morettini, Paolo/0000-0001-7139-7912; Calafiura, Paolo/0000-0002-1692-1678; Mudrinic, Mihajlo/0000-0002-5219-2177; De Lotto, Barbara/0000-0003-3624-4480; Fiolhais, Miguel/0000-0001-9035-0335; Lokajicek, Milos/0000-0001-8929-1243; Vercesi, Valerio/0000-0001-7670-4563; Federic, Pavol/0000-0001-5137-473X; Butterworth, Jonathan/0000-0002-5905-5394; TAYLOR, GEOFFREY/0000-0002-1831-4871; Korol, Aleksandr/0000-0001-8448-218X; Musto, Elisa/0000-0003-3213-7848; Gomes, Agostinho/0000-0002-5940-9893; Soldatov, Evgeny/0000-0003-0694-3272; Serkin, Leonid/0000-0003-4749-5250 ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; Lundbeck Foundation, Denmark; EPLANET, European Union; ERC, European Union; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, Germany; DFG, Germany; HGF, Germany; MPG, Germany; AvH Foundation, Germany; GSRT, Greece; ISF, Israel; MINERVA, Germany; GIF, Israel; DIP, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; RCN, Norway; MNiSW, Poland; GRICES, Portugal; FCT, Portugal; MERYS (MECTS), Romania; MES of Russia, Russian Federation; ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, United Kingdom; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; DOE, United States; NSF, United States; ICREA Funding Source: Custom; Science and Technology Facilities Council [ST/I005811/1, PP/E000347/1, ST/K001361/1 LHCb Upgrades, ST/H001042/1, ST/H001042/2, ST/H001093/1, ST/K001361/1, ST/K501840/1, ST/K001337/1, ST/K001264/1 ATLAS, ST/I005803/1 GRIDPP, ST/K001248/1, ST/K001361/1 ATLAS, ST/L001195/1, ST/I003525/1, PP/E002757/1, ST/K001329/1 ATLAS, ST/I003142/1, ST/M001474/1, ST/I005803/1, ST/K000705/1, ST/J005576/1, ST/K001310/1 LHCb Upgrades, ST/I000186/1, ST/K001337/1 ATLAS, ST/I006080/1, ST/J004928/1, PP/E003087/1, ST/I003517/1, ST/H00100X/2, ST/L001144/1, ST/K001310/1, PP/E003699/2, GRIDPP, ST/J005460/1, ST/K00073X/1, ST/K001310/1 ATLAS Upgrades, ST/F007337/1, ST/K001361/1 ATLAS Upgrades, ST/K001310/1 ATLAS, ST/K001361/1 MINOS/MINOS+, ST/H001069/2, ST/H001026/1, ST/M001512/1, ST/M001431/1, PP/E003699/1, ST/G502320/1, ST/K501840/1 GRIDPP, ST/H001093/2, ST/J004944/1, ST/K001418/1, ATLAS, ST/K001310/1 LHCb, ST/J004928/1 ATLAS Upgrade, ST/K001361/1 LHCb, ST/H00100X/1, ST/J002798/1, ST/K001248/1 ATLAS, PP/E000355/1, ST/H001026/2] Funding Source: researchfish; STFC [ST/M001431/1, ST/H00100X/1, ST/L001144/1, ST/I003517/1, ST/H00100X/2, PP/E000347/1, ST/K001337/1, ST/H001026/2, PP/E003699/1, ST/M001512/1, ST/K00073X/1, ST/K001248/1, ST/K001329/1, ST/J002798/1, ST/I000186/1, PP/E003087/1, ST/J005460/1, ST/I005803/1, ST/I003525/1, ST/K001361/1, ST/H001069/2, ST/J005576/1, PP/E003699/2, ST/J004944/1, ST/H001042/2, ST/F007337/1, ST/K000705/1, PP/E002757/1, ST/M000753/1, ST/H001093/2, ST/I003142/1, ST/H001026/1, ST/K501840/1, ST/H001093/1, ST/L001195/1, ST/I006080/1, ST/G502320/1, ST/J004928/1, PP/E000355/1, ST/H001042/1, ST/I005811/1] Funding Source: UKRI; Direct For Mathematical & Physical Scien; Division Of Physics [1119200] Funding Source: National Science Foundation ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWF, Austria; ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); Lundbeck Foundation, Denmark(Lundbeckfonden); EPLANET, European Union; ERC, European Union(European Research Council (ERC)); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); DFG, Germany(German Research Foundation (DFG)); HGF, Germany; MPG, Germany(Max Planck Society); AvH Foundation, Germany(Alexander von Humboldt Foundation); GSRT, Greece(Greek Ministry of Development-GSRT); ISF, Israel(Israel Science Foundation); MINERVA, Germany; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); DIP, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); GRICES, Portugal; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MERYS (MECTS), Romania; MES of Russia, Russian Federation(Russian Federation); ROSATOM, Russian Federation(Russian Federation); JINR; MSTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); DOE, United States(United States Department of Energy (DOE)); NSF, United States(National Science Foundation (NSF)); ICREA(ICREA); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET and ERC, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT, Greece; ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW, Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States. 54 52 53 1 93 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B JUN 12 2012.0 712 4-5 351 369 10.1016/j.physletb.2012.05.022 0.0 19 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 960HI hybrid, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000305378200006 0
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J.; Da Via, C.; Dabrowski, W.; Dafinca, A.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Daniells, A. C.; Danninger, M.; Hoffmann, M. Dano; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, E.; Davies, M.; Davison, P.; Davygora, Y.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Nooij, L.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Regie, J. B. De Vivie; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Domenico, A.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Mattia, A.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Dubreuil, E.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Duflot, L.; Duguid, L.; Duehrssen, M.; Dunford, M.; Yildiz, H. Duran; Dueren, M.; Durglishvili, A.; Duschinger, D.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edson, W.; Edwards, N. C.; Ehrenfeld, W.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Endo, M.; Engelmann, R.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fayard, L.; Federic, P.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Fernandez Martinez, P.; Perez, S. Fernandez; Ferrag, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Parodi, A. Ferretto; Fiascaris, M.; Fiedler, F.; Filipcic, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Fitzgerald, E. A.; Flechl, M.; Fleck, I.; Fleischmann, P.; Fleischmann, S.; Fletcher, G. T.; Fletcher, G.; Flick, T.; Floderus, A.; Castillo, L. R. Flores; Flowerdew, M. J.; Formica, A.; Forti, A.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Fraternali, M.; Freeborn, D.; French, S. T.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Torregrosa, E. Fullana; Fulsom, B. G.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Walls, F. M. Garay; Garberson, F.; Garcia, C.; Garcia Navarro, J. E.; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gauzzi, P.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Ge, P.; Gecse, Z.; Gee, C. N. P.; Geerts, D. A. A.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Gentile, S.; George, M.; George, S.; Gerbaudo, D.; Gershon, A.; Ghazlane, H.; Giacobbe, B.; Giagu, S.; Giangiobbe, V.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Goddard, J. R.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Goncalo, R.; Da Costa, J. Goncalves Pinto Firmino; Gonella, L.; Gonzalez de la Hoz, S.; Gonzalez Parra, G.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorisek, A.; Gornicki, E.; Goshaw, A. T.; Goessling, C.; Gostkin, M. I.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Grabas, H. M. X.; Graber, L.; Grabowska-Bold, I.; Grafstroem, P.; Grahn, K-J.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Grassi, V.; Gratchev, V.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J. -F.; Grohs, J. P.; Grohsjean, A.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Guan, L.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Gupta, S.; Gutierrez, P.; Ortiz, N. G. Gutierrez; Gutschow, C.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Haefner, P.; Hageboeck, S.; Hajduk, Z.; Hakobyan, H.; Haleem, M.; Haley, J.; Hall, D.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamer, M.; Hamilton, A.; Hamilton, S.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Hanagaki, K.; Hanawa, K.; Hance, M.; Hanke, P.; Hann, R.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartjes, F.; Hasegawa, M.; Hasegawa, S.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, A. D.; Hayashi, T.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, L.; Hejbal, J.; Helary, L.; Hellman, S.; Hellmich, D.; Helsens, C.; Henderson, J.; Henderson, R. C. W.; Heng, Y.; Hengler, C.; Henkelmann, S.; Henrichs, A.; Correia, A. M. Henriques; Henrot-Versille, S.; Herbert, G. H.; Hernandez Jimenez, Y.; Herrberg-Schubert, R.; Herten, G.; Hertenberger, R.; Hervas, L.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Hickling, R.; Higon-Rodriguez, E.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohlfeld, M.; Hohn, D.; Holmes, T. R.; Hong, T. M.; van Huysduynen, L. Hooft; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J-Y.; Hou, S.; Hoummada, A.; Howard, J.; Howarth, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S. -C.; Hu, D.; Hu, Q.; Hu, X.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Hulsing, T. A.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Ideal, E.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikematsu, K.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Inamaru, Y.; Ince, T.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Irles Quiles, A.; Isaksson, C.; Ishino, M.; Ishitsuka, M.; Ishmukhametov, R.; Issever, C.; Istin, S.; Ponce, J. M. Iturbe; Iuppa, R.; Ivarsson, J.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, B.; Jackson, M.; Jackson, P.; Jaekel, M. R.; Jain, V.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jakubek, J.; Jamin, D. O.; Jana, D. K.; Jansen, E.; Jansky, R. W.; Janssen, J.; Janus, M.; Jarlskog, G.; Javadov, N.; Javurek, T.; Jeanty, L.; Jejelava, J.; Jeng, G. -Y.; Jennens, D.; Jenni, P.; Jentzsch, J.; Jeske, C.; Jezequel, S.; Ji, H.; Jia, J.; Jiang, Y.; Jiggins, S.; Jimenez Pena, J.; Jin, S.; Jinaru, A.; Jinnouchi, O.; Joergensen, M. D.; Johansson, P.; Johns, K. A.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Joshi, K. D.; Jovicevic, J.; Ju, X.; Jung, C. A.; Jussel, P.; Juste Rozas, A.; Kaci, M.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kajomovitz, E.; Kalderon, C. W.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kaneda, M.; Kaneti, S.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kapliy, A.; Kar, D.; Karakostas, K.; Karamaoun, A.; Karastathis, N.; Kareem, M. J.; Karnevskiy, M.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Katre, A.; Katzy, J.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kazama, S.; Kazanin, V. F.; Kazarinov, M. Y.; Keeler, R.; Kehoe, R.; Keil, M.; Keller, J. S.; Kempster, J. J.; Keoshkerian, H.; Kepka, O.; Kersevan, B. P.; Kersten, S.; Keyes, R. A.; Khalil-zada, F.; Khandanyan, H.; Khanov, A.; Kharlamov, A. G.; Khoo, T. 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V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotov, V. M.; Kotwal, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kouskoura, V.; Koutsman, A.; Kowalewski, R.; Kowalski, T. Z.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Kraus, J. K.; Kravchenko, A.; Kreiss, S.; Kretz, M.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krueger, H.; Krumnack, N.; Krumshteyn, Z. V.; Kruse, A.; Kruse, M. C.; Kruskal, M.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, A.; Kuhl, T.; Kukhtin, V.; Kulchitsky, Y.; Kuleshov, S.; Kuna, M.; Kunigo, T.; Kupco, A.; Kurashige, H.; Kurochkin, Y. A.; Kurumida, R.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; La Rosa, A.; Navarro, J. L. 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N.; Smirnova, O.; Smith, M. N. K.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snidero, G.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Solans, C. A.; Solar, M.; Solc, J.; Soldatov, E. Yu; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Song, H. Y.; Soni, N.; Sood, A.; Sopczak, A.; Sopko, B.; Sopko, V.; Sorin, V.; Sosa, D.; Sosebee, M.; Sotiropoulou, C. L.; ; ATLAS Collaboration Search for the Standard Model Higgs boson produced in association with top quarks and decaying into in collisions at with the ATLAS detector EUROPEAN PHYSICAL JOURNAL C English Article PRODUCTION CROSS-SECTION; LARGE HADRON COLLIDER; NLO QCD CORRECTIONS; PAIR PRODUCTION; T(T)OVER-BAR-H PRODUCTION; PARTON DISTRIBUTIONS; BROKEN SYMMETRIES; PP COLLISIONS; MONTE-CARLO; PLUS PLUS A search for the Standard Model Higgs boson produced in association with a top-quark pair, , is presented. The analysis uses 20.3 fb(-1) of pp collision data at , collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by +jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95 % confidence level. 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R.; Ishitsuka, M.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Loscutoff, P.; Marshall, Z.; Ohm, C. C.; Ovcharova, A.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Quarrie, D. R.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Dietrich, J.; Giorgi, F. M.; Grancagnolo, S.; Herbert, G. H.; Herrberg-Schubert, R.; Hristova, I.; Kind, O. M.; Kolanoski, H.; Lacker, H.; Lohse, T.; Nikiforov, A.; Rehnisch, L.; Rieck, P.; Schulz, H.; Stamm, S.; zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Agustoni, M.; Aloisio, A.; Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Marti, L. F.; Meloni, F.; Sciacca, F. G.; Stramaglia, M. E.; Stucci, S. A.; Weber, M. S.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Agustoni, M.; Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Marti, L. F.; Meloni, F.; Sciacca, F. G.; Stramaglia, M. E.; Stucci, S. A.; Weber, M. S.] Univ Bern, High Energy Phys Lab, Bern, Switzerland; [Allbrooke, B. M. M.; Bella, L. Aperio; Bansil, H. S.; Bracinik, J.; Charlton, D. G.; Chisholm, A. S.; Daniells, A. C.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Levy, M.; Mudd, R. D.; Quijada, J. A. Murillo; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Arik, M.; Istin, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Cetin, S. A.] Dogus Univ, Dept Phys, Istanbul, Turkey; [Beddall, A. J.; Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkey; [Alberghi, G. L.; Bellagamba, L.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Corradi, M.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giorgi, F. M.; Grafstroem, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Negrini, M.; Piccinini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Spighi, R.; Tupputi, S. A.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Ist Nazl Fis Nucl, Sez Bologna, I-40126 Bologna, Italy; [Alberghi, G. L.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Grafstroem, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Piccinini, M.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Tupputi, S. A.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Arslan, O.; Bechtle, P.; Bernlochner, F. U.; Brock, I.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Ehrenfeld, W.; Gaycken, G.; Geich-Gimbel, Ch.; Gonella, L.; Haefner, P.; Hageboeck, S.; Hansen, M. C.; Hellmich, D.; Hohn, D.; Huegging, F.; Janssen, J.; Khoriauli, G.; Kostyukhin, V. V.; Kraus, J. K.; Kroseberg, J.; Krueger, H.; Lenz, T.; Leyko, A. M.; Liebal, J.; Limbach, C.; Mergelmeyer, S.; Mijovic, L.; Mueller, K.; Nanava, G.; Obermann, T.; Pohl, D.; Ricken, O.; Sarrazin, B.; Schaepe, S.; Schopf, E.; Schultens, M. J.; Schwindt, T.; Scutti, F.; Seema, P.; Stillings, J. A.; Tannoury, N.; Therhaag, J.; Uhlenbrock, M.; Velz, T.; von Toerne, E.; Wagner, P.; Wang, T.; Wermes, N.; Wienemann, P.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wong, K. H. Yau; Zimmermann, R.] Univ Bonn, Inst Phys, Bonn, Germany; [Ahlen, S. P.; Bernard, C.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Helary, L.; Kruskal, M.; Long, B. A.; Shank, J. T.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, Boston, MA 02215 USA; [Amelung, C.; Amundsen, G.; Artoni, G.; Bensinger, J. R.; Bianchini, L.; Blocker, C.; Coffey, L.; Fitzgerald, E. A.; Sciolla, G.; Venturini, A.; Zambito, S.; Zengel, K.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Amaral Coutinho, Y.; Caloba, L. P.; Maidantchik, C.; Marroquim, F.; Nepomuceno, A. A.; Seixas, J. M.] Univ Fed Rio de Janeiro, COPPE, EE, IF, Rio De Janeiro, Brazil; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.] Fed Univ Juiz de Fora UFJF, Elect Circuits Dept, Juiz De Fora, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; Navarro, J. L. La Rosa; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, BR-01498 Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Begel, M.; Buttinger, W.; Chen, H.; Chernyatin, V.; Debbe, R.; Ernst, M.; Gibbard, B.; Gordon, H. A.; Iakovidis, G.; Klimentov, A.; Kouskoura, V.; Kravchenko, A.; Lanni, F.; Lissauer, D.; Lynn, D.; Ma, H.; Maeno, T.; Metcalfe, J.; Mountricha, E.; Nevski, P.; Nilsson, P.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Perepelitsa, D. V.; Pleier, M. -A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Radeka, V.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Takai, H.; Undrus, A.; Wenaus, T.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Alexa, C.; Badescu, E.; Boldea, V.; Buda, S. I.; Caprini, I.; Caprini, M.; Chitan, A.; Ciubancan, M.; Constantinescu, S.; Dobre, M.; Ducu, O. A.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Dept Phys, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; West Univ Timisoara, Timisoara, Romania; [Otero y Garzon, G.; Piegaia, R.; Reisin, H.; Sacerdoti, S.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Carter, J. R.; Chapman, J. D.; Cottin, G.; French, S. T.; Gillam, T. P. S.; Hill, J. C.; Kaneti, S.; Khoo, T. J.; Lester, C. G.; Mueller, T.; Parker, M. A.; Robinson, D.; Thomson, M.; Ward, C. P.; Yusuff, I.] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England; [Bellerive, A.; Cree, G.; Di Valentino, D.; Koffas, T.; Lacey, J.; Leight, W. A.; McCarthy, T. G.; Nomidis, I.; Oakham, F. G.; Pasztor, G.; Tarrade, F.; Ueno, R.; Vincter, M. G.; Whalen, K.] Carleton Univ, Dept Phys, Ottawa, ON K1S 5B6, Canada; [Abreu, R.; Aleksa, M.; Andari, N.; Anders, G.; Anghinolfi, F.; Armbruster, A. J.; Arnaez, O.; Avolio, G.; Baak, M. A.; Backes, M.; Backhaus, M.; Barak, L.; Beltramello, O.; Bianco, M.; Bogaerts, J. A.; Boveia, A.; Boyd, J.; Burckhart, H.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Cattai, A.; Cerv, M.; Chromek-Burckhart, D.; Conti, G.; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Girolamo, B.; Dittus, F.; Dobos, D.; Dudarev, A.; Duehrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Farthouat, P.; Fassnacht, P.; Feigl, S.; Perez, S. Fernandez; Francis, D.; Froidevaux, D.; Gillberg, D.; Glatzer, J.; Goossens, L.; Gorini, B.; Gray, H. M.; Hawkings, R. J.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Hubacek, Z.; Huhtinen, M.; Iengo, P.; Jaekel, M. R.; Jakobsen, S.; Jenni, P.; Kaneda, M.; Klioutchnikova, T.; Krasznahorkay, A.; Lantzsch, K.; Lapoire, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Lichard, P.; Macina, D.; Malyukov, S.; Mandelli, B.; Mapelli, L.; Marzin, A.; Milic, A.; Mornacchi, G.; Nairz, A. M.; Nakahama, Y.; Nessi, M.; Nicquevert, B.; Nordberg, M.; Oide, H.; Palestini, S.; Pauly, T.; Pernegger, H.; Peters, K.; Petersen, B. A.; Pommes, K.; Poppleton, A.; Poulard, G.; Poveda, J.; Prasad, S.; Rammensee, M.; Raymond, M.; Rembser, C.; Roe, S.; Ruiz-Martinez, A.; Salzburger, A.; Schaefer, D.; Schlenker, S.; Schmieden, K.; Serfon, C.; Sfyrla, A.; Solans, C. A.; Spigo, G.; Stelzer, H. J.; Teischinger, F. A.; Ten Kate, H.; Tremblet, L.; Tricoli, A.; Tsarouchas, C.; Unal, G.; van Woerden, M. C.; Vandelli, W.; Vigne, R.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Wotschack, J.; Young, C. J. S.; Zwalinski, L.] CERN, CH-1211 Geneva 23, Switzerland; [Alison, J.; Anderson, K. J.; Cheng, Y.; Dandoy, J. R.; Facini, G.; Fiascaris, M.; Gardner, R. W.; Ilchenko, Y.; Kapliy, A.; Kim, Y.; Krizka, K.; Li, H. L.; Merritt, F. S.; Miller, D. W.; Okumura, Y.; Onyisi, P. U. E.; Oreglia, M. J.; Penning, B.; Pilcher, J. E.; Saxon, J.; Shochet, M. J.; Vukotic, I.; Webster, J. S.; Wu, M.; ATLAS Collaboration] Univ Chicago, Enrico Fermi Inst, Chicago, IL 60637 USA; [Carquin, E.; Diaz, M. A.; Vogel, M.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Kuleshov, S.; Pezoa, R.; Prokoshin, F.; White, R.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Bai, Y.; Fang, Y.; Jin, S.; Lou, X.; Ouyang, Q.; Ren, H.; Shan, L. Y.; Sun, X.; Wang, J.; Xu, D.; Yao, L.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Gao, J.; Guan, L.; Han, L.; Hu, Q.; Jiang, Y.; Li, B.; Liu, J. B.; Liu, M.; Liu, Y.; Peng, H.; Song, H. Y.; Xu, L.; Zhang, R.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Chen, S.; Li, Y.; Wang, C.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Chen, L.; Feng, C.; Ge, P.; Ma, L. L.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Sch Phys, Jinan 250100, Shandong, Peoples R China; [Guo, J.; Li, L.; Yang, H.] Shanghai Jiao Tong Univ, Dept Phys & Astron, Shanghai Key Lab Particle Phys & Cosmol, Shanghai 200030, Peoples R China; [Chen, X.] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] Univ Clermont Ferrand 2, Lab Phys Corpusculaire, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] Univ Blaise Pascal, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] CNRS IN2P3, Clermont Ferrand, France; [Alkire, S. P.; Altheimer, A.; Andeen, T.; Angerami, A.; Bain, T.; Brooijmans, G.; Cole, B.; Hu, D.; Hughes, E. W.; Iordanidou, K.; Klein, M. H.; Mohapatra, S.; Nikiforou, N.; Parsons, J. A.; Smith, M. N. K.; Thompson, E. N.; Tuts, P. M.; Zhou, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Dam, M.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Joergensen, M. D.; Loevschall-Jensen, A. E.; Monk, J.; Mortensen, S. S.; Pedersen, L. E.; Petersen, T. C.; Pingel, A.; Thomsen, L. A.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, DK-2100 Copenhagen, Denmark; [Capua, M.; Crosetti, G.; La Rotonda, L.; Mastroberardino, A.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Collegato Cosenza, I-00044 Frascati, Italy; [Capua, M.; Crosetti, G.; La Rotonda, L.; Mastroberardino, A.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, Dipartmento Fis, I-87036 Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Dyndal, M.; Grabowska-Bold, I.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Mindur, B.; Przybycien, M.; Zemla, A.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Banas, E.; de Renstrom, P. A. Bruckman; Chwastowski, J. J.; Derendarz, D.; Godlewski, J.; Gornicki, E.; Hajduk, Z.; Iwanski, W.; Kaczmarska, A.; Korcyl, K.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Cao, T.; Firan, A.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Sekula, S. J.; Stroynowski, R.; Turvey, A. J.; Varol, T.; Wang, H.; Ye, J.; Zhao, X.; Zhou, L.] So Methodist Univ, Dept Phys, Dallas, TX 75275 USA; [Izen, J. M.; Leyton, M.; Meirose, B.; Namasivayam, H.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Bessner, M.; Bloch, I.; Britzger, D.; Deterre, C.; Eckardt, C.; Glazov, A.; Grahn, K-J.; Gregor, I. M.; Grohsjean, A.; Hengler, C.; Hiller, K. H.; Howarth, J.; Katzy, J.; Keller, J. S.; Lobodzinska, E.; Lohwasser, K.; Mamuzic, J.; Moenig, K.; Petit, E.; Rubinskiy, I.; Schaefer, R.; Sedov, G.; South, D.; Tackmann, K.; Wang, J.; Yatsenko, E.; Yildirim, E.] DESY, Hamburg, Germany; [Argyropoulos, S.; Asbah, N.; Bessner, M.; Bloch, I.; Borroni, S.; Britzger, D.; Camarda, S.; Deterre, C.; Eckardt, C.; Filipuzzi, M.; Glazov, A.; Grahn, K-J.; Gregor, I. M.; Grohsjean, A.; Haleem, M.; Hamnett, P. G.; Hengler, C.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lisovyi, M.; Lobodzinska, E.; Lohwasser, K.; Mamuzic, J.; Medinnis, M.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; Peschke, R.; Petit, E.; Radescu, V.; Rubinskiy, I.; Schaefer, R.; Schmitt, S.; Sedov, G.; Shushkevich, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Starovoitov, P.; Styles, N. A.; Tackmann, K.; Wang, J.; Yatsenko, E.; Yildirim, E.] DESY, Zeuthen, Germany; [Burmeister, I.; Erdmann, J.; Esch, H.; Goessling, C.; Jentzsch, J.; Jung, C. A.; Klingenberg, R.; Kroeninger, K.; Wasicki, C.] Tech Univ Dortmund, Inst Expt Phys 4, Dortmund, Germany; [Anger, P.; Duschinger, D.; Friedrich, F.; Grohs, J. P.; Gumpert, C.; Gutschow, C.; Hauswald, L.; Kobel, M.; Mader, W. F.; Morgenstern, M.; Novgorodova, O.; Rudolph, C.; Schnoor, U.; Siegert, F.; Socher, F.; Staerz, S.; Straessner, A.; Vest, A.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, D-01062 Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bocci, A.; Cerio, B. C.; Goshaw, A. T.; Kajomovitz, E.; Kotwal, A.; Kruse, M. C.; Li, L.; Li, S.; Liu, M.; Oh, S. H.; Zhou, C.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bhimji, W.; Bristow, T. M.; Clark, P. J.; Dias, F. A.; Edwards, N. C.; Gao, Y.; Walls, F. M. Garay; Glaysher, P. C. F.; Harrington, R. D.; Leonidopoulos, C.; Martin, V. J.; Mills, C.; O'Brien, B. J.; Pino, S. A. Olivares; Proissl, M.; Selbach, K. E.; Smart, B. H.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Di Nardo, R.; Esposito, B.; Gatti, C.; Giromini, P.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Italy; [Amoroso, S.; Arnold, H.; Betancourt, C.; Boehler, M.; Bruneliere, R.; Buehrer, F.; Buescher, D.; Coniavitis, E.; Consorti, V.; Dao, V.; Di Simone, A.; Flechl, M.; Giuliani, C.; Herten, G.; Jakobs, K.; Javurek, T.; Jenni, P.; Kiss, F.; Koeneke, K.; Kopp, A. K.; Kuehn, S.; Lai, S.; Landgraf, U.; Mahboubi, K.; Mohr, W.; Pagacova, M.; Parzefall, U.; Ronzani, M.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Ruthmann, N.; Schillo, C.; Schmidt, E.; Schumacher, M.; Sommer, P.; Sundermann, J. E.; Temming, K. K.; Tsiskaridze, V.; Ungaro, F. C.; von Radziewski, H.; Warsinsky, M.; Weiser, C.; Werner, M.; Zhang, L.; Zimmermann, S.] Univ Freiburg, Fak Math & Phys, D-79106 Freiburg, Germany; [Ancu, L. S.; Barone, G.; Bell, P. J.; Bell, W. H.; Noccioli, E. Benhar; De Mendizabal, J. Bilbao; Toro, R. Camacho; Clark, A.; Delitzsch, C. M.; della Volpe, D.; Doglioni, C.; Ferrere, D.; Gadomski, S.; Golling, T.; Gonzalez-Sevilla, S.; Gramling, J.; Guescini, F.; Iacobucci, G.; Katre, A.; La Rosa, A.; Mermod, P.; Miucci, A.; Muenstermann, D.; Picazio, A.; Ristic, B.; Tykhonov, A.; Vallecorsa, S.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Darbo, G.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Guido, E.; Morettini, P.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.] Ist Nazl Fis Nucl, Sez Genova, I-16146 Genoa, Italy; [Barberis, D.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Guido, E.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Jejelava, J.; Tskhadadze, E. G.] Iv Javakhishvili Tbilisi State Univ, E Andronikashvili Inst Phys, Tbilisi, Georgia; [Djobava, T.; Durglishvili, A.; Khubua, J.; Mosidze, M.] Tbilisi State Univ, Inst High Energy Phys, Tbilisi, Georgia; [Dueren, M.; Kreutzfeldt, K.; Stenzel, H.] Univ Giessen, Inst Phys 2, Giessen, Germany; [Bates, R. L.; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Buzatu, A.; Cinca, D.; D'Auria, S.; Doyle, A. T.; Ferrag, S.; Ferrando, J.; de Lima, D. E. Ferreira; Gul, U.; Ortiz, N. G. Gutierrez; Kar, D.; Knue, A.; Morton, A.; Mullen, P.; O'Shea, V.; Barrera, C. Oropeza; Owen, M.; Pollard, C. S.; Qin, G.; Quilty, D.; Ravenscroft, T.; Robson, A.; St Denis, R. D.; Stewart, G. A.; Thompson, A. S.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Bindi, M.; Blumenschein, U.; Drechsler, E.; George, M.; Graber, L.; Grosse-Knetter, J.; Hamer, M.; Kareem, M. J.; Kawamura, G.; Keil, M.; Lemmer, B.; Magradze, E.; Mantoani, M.; Mchedlidze, G.; Llacer, M. Moreno; Musheghyan, H.; Nackenhorst, O.; Nadal, J.; Quadt, A.; Rieger, J.; Schorlemmer, A. L. S.; Shabalina, E.; Stolte, P.; Weingarten, J.; Zinonos, Z.] Univ Gottingen, Inst Phys 2, Gottingen, Germany; [Albrand, S.; Brown, J.; Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Gabaldon, C.; Genest, M. H.; Hostachy, J-Y.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Monini, C.; Stark, J.; Trocme, B.; Wu, M.] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, Grenoble, France; [McFarlane, K. W.] Hampton Univ, Dept Phys, Hampton, VA 23668 USA; [da Costa, J. Barreiro Guimares; Catastini, P.; Clark, B. L.; Franklin, M.; Huth, J.; Ippolito, V.; Keyes, R. A.; Mateos, D. Lopez; Mercurio, K. M.; Morii, M.; Skottowe, H. P.; Spearman, W. R.; Sun, S.; Tolley, E.; Yen, A. L.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Andrei, V.; Baas, A. E.; Brandt, O.; Davygora, Y.; Djuvsland, J. I.; Dunford, M.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kluge, E. -E.; Lang, V. S.; Meier, K.; Scharf, V.; Schultz-Coulon, H. -C.; Stamen, R.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Giulini, M.; Narayan, R.; Schaetzel, S.; Schmitt, S.; Schoening, A.; Sosa, D.] Heidelberg Univ, Inst Phys, Heidelberg, Germany; [Colombo, T.; Kretz, M.; Kugel, A.] Heidelberg Univ, ZITI Inst Tech Informat, Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Bortolotto, V.; Castillo, L. R. Flores] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Bortolotto, V.] Univ Hong Kong, Dept Phys, Hong Kong, Hong Kong, Peoples R China; [Bortolotto, V.; Prokofiev, K.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Hong Kong, Peoples R China; [Choi, K.; Dattagupta, A.; Evans, H.; Gagnon, P.; Lammers, S.; Martinez, N. Lorenzo; Luehring, F.; Ogren, H.; Penwell, J.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Jansky, R. W.; Jussel, P.; Kneringer, E.; Lukas, W.; Ritsch, E.; Usanova, A.] Univ Innsbruck, Inst Astro & Teilchenphys, A-6020 Innsbruck, Austria; [Mallik, U.; Mandrysch, R.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; De Lorenzi, F.; Krumnack, N.; Pluth, D.; Prell, S.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Kazarinov, M. Y.; Khramov, E.; Kotov, V. M.; Kruchonak, U.; Krumshteyn, Z. V.; Ladygin, E.; Minashvili, I. A.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Pozdnyakov, V.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Sisakyan, A. N.; Soloshenko, A.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] JINR Dubna, Joint Inst Nucl Res, Dubna, Russia; [Amako, K.; Aoki, M.; Arai, Y.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kohriki, T.; Kondo, T.; Kono, T.; Makida, Y.; Nagano, K.; Nakamura, K.; Nozaki, M.; Odaka, S.; Sasaki, O.; Suzuki, S.; Suzuki, Y.; Takubo, Y.; Tanaka, S.; Terada, S.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Yamada, M.; Yamamoto, A.; Yasu, Y.] KEK, High Energy Accelerator Res Org, Tsukuba, Ibaraki, Japan; [Chen, Y.; Hasegawa, M.; Inamaru, Y.; Kishimoto, T.; Kurashige, H.; Kurumida, R.; Ochi, A.; Shimizu, S.; Takeda, H.; Yakabe, R.; Yamazaki, Y.; Yuan, L.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo 657, Japan; [Ishino, M.; Kukhtin, V.; Kunigo, T.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto 612, Japan; [Kawagoe, K.; Oda, S.; Otono, H.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka 812, Japan; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Monticelli, F.; Wahlberg, H.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Borissov, G.; Bouhova-Thacker, E. V.; Dearnaley, W. J.; Fox, H.; Grimm, K.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Maddocks, H. J.; Skinner, M. B.; Smizanska, M.; Walder, J.; Wharton, A. M.] Univ Lancaster, Dept Phys, Lancaster, England; [Chiodini, G.; Gorini, E.; Primavera, M.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, I-73100 Lecce, Italy; [Gorini, E.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Affolder, A. A.; Allport, P. P.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Gwilliam, C. B.; Hayward, H. S.; Jackson, M.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Lehan, A.; Maxfield, S. J.; Mehta, A.; Readioff, N. P.; Schnellbach, Y. J.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 3BX, Merseyside, England; [Cindro, V.; Deliyergiyev, M.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Mandic, I.; Mikuz, M.; Sfiligoj, T.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Cindro, V.; Deliyergiyev, M.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Mandic, I.; Mikuz, M.; Sfiligoj, T.] Univ Ljubljana, Ljubljana, Slovenia; [Alpigiani, C.; Bevan, A. J.; Bona, M.; Bret, M. Cano; Cerrito, L.; Fletcher, G.; Goddard, J. R.; Hays, J. M.; Hickling, R.; Landon, M. P. J.; Lloyd, S. L.; Morris, J. D.; Nooney, T.; Piccaro, E.; Rizvi, E.; Sandbach, R. L.; Snidero, G.; Castanheira, M. Teixeira Dias] Queen Mary Univ London, Sch Phys & Astron, London, England; [Berry, T.; Blanco, J. E.; Boisvert, V.; Brooks, T.; Connelly, I. A.; Cowan, G.; Duguid, L.; Giannelli, M. Faucci; George, S.; Gibson, S. M.; Kempster, J. J.; Vazquez, J. G. Panduro; Pastore, Fr; Savage, G.; Spano, F.; Teixeira-Dias, P.; Thomas-Wilsker, J.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Bieniek, S. P.; Butterworth, J. M.; Campanelli, M.; Casadei, D.; Chislett, R. T.; Christodoulou, V.; Cooper, B. D.; Davison, P.; Falla, R. J.; Freeborn, D.; Gregersen, K.; Hesketh, G. G.; Jansen, E.; Jiggins, S.; Konstantinidis, N.; Korn, A.; Kucuk, H.; Lambourne, L.; Leney, K. J. C.; Martyniuk, A. C.; Mcfayden, J. A.; Nurse, E.; Ochoa, I.; Pilkington, A. D.; Richter, S.; Scanlon, T.; Sherwood, P.; Simmons, B.; Wardrope, D. R.; Waugh, B. M.] UCL, Dept Phys & Astron, London, England; [Greenwood, Z. D.; Grossi, G. C.; Jana, D. K.; Sawyer, L.; Subramaniam, R.] Louisiana Tech Univ, Ruston, LA 71270 USA; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] Univ Paris Diderot, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] CNRS IN2P3, Paris, France; [Akesson, T. P. A.; Bocchetta, S. S.; Bryngemark, L.; Floderus, A.; Hawkins, A. D.; Hedberg, V.; Ivarsson, J.; Jarlskog, G.; Lytken, E.; Mjornmark, J. U.; Smirnova, O.; Viazlo, O.] Lund Univ, Fys Inst, Lund, Sweden; [Arnal, V.; Barreiro, F.; Cantero, J.; De la Torre, H.; Del Peso, J.; Glasman, C.; Llorente Merino, J.; Mochizuki, K.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor C 15, E-28049 Madrid, Spain; [Becker, M.; Bertella, C.; Blum, W.; Buescher, V.; Caputo, R.; Caudron, J.; Ellinghaus, F.; Endner, O. C.; Ertel, E.; Fiedler, F.; Torregrosa, E. Fullana; Heck, T.; Hohlfeld, M.; Hulsing, T. A.; Karnevskiy, M.; Kleinknecht, K.; Koenig, S.; Koepke, L.; Lin, T. H.; Lungwitz, M.; Masetti, L.; Mattmann, J.; Meyer, C.; Moritz, S.; Poettgen, R.; Rave, S.; Sander, H. G.; Schaeffer, J.; Schaefer, U.; Schmitt, C.; Schott, M.; Schroeder, C.; Schuh, N.; Simioni, E.; Tapprogge, S.; Urrejola, P.; Valderanis, C.; Wollstadt, S. J.; Zimmermann, C.; Zinser, M.] Johannes Gutenberg Univ Mainz, Inst Phys, Mainz, Germany; [Balli, F.; Barnes, S. L.; Cox, B. E.; Da Via, C.; Forti, A.; Ponce, J. M. Iturbe; Joshi, K. D.; Keoshkerian, H.; Klinger, J. A.; Loebinger, F. K.; Marsden, S. P.; Masik, J.; Neep, T. J.; Oh, A.; Ospanov, R.; Pater, J. R.; Peters, R. F. Y.; Price, D.; Qin, Y.; Queitsch-Maitland, M.; Robinson, J. E. M.; Schwanenberger, C.; Thompson, R. J.; Tomlinson, L.; Watts, S.; Webb, S.; Woudstra, M. J.; Wyatt, T. R.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Aad, G.; Alio, L.; Barbero, M.; Chen, L.; Coadou, Y.; Diaconu, C.; Diglio, S.; Djama, F.; Ducu, O. A.; Feligioni, L.; Gao, J.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagai, Y.; Nagy, E.; Pralavorio, P.; Rozanov, A.; Serre, T.; Talby, M.; Torres, R. E. Ticse; Tiouchichine, E.; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.] Aix Marseille Univ, CPPM, Marseille, France; [Aad, G.; Alio, L.; Barbero, M.; Chen, L.; Coadou, Y.; Diaconu, C.; Diglio, S.; Djama, F.; Ducu, O. A.; Feligioni, L.; Gao, J.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagai, Y.; Nagy, E.; Pralavorio, P.; Rozanov, A.; Serre, T.; Talby, M.; Torres, R. E. Ticse; Tiouchichine, E.; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.] CNRS IN2P3, Marseille, France; [Bellomo, M.; Bernard, N. R.; Brau, B.; Dallapiccola, C.; Daya-Ishmukhametova, R. K.; Moyse, E. J. W.; Pais, P.; Pueschel, E.; Ventura, D.; Willocq, S.] Univ Massachusetts, Dept Phys, Amherst, MA USA; [Belanger-Champagne, C.; Chapleau, B.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Autonomous University of Barcelona; 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Harvard University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; Indiana University System; Indiana University Bloomington; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; High Energy Accelerator Research Organization (KEK); Kobe University; Kyoto University; Kyoto University of Education; Kyushu University; National University of La Plata; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; Istituto Nazionale di Fisica Nucleare (INFN); University of Salento; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; UDICE-French Research Universities; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Lund University; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; Istituto Nazionale di Fisica Nucleare (INFN); University of Milan; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; National Center of Particles & High Energy Physics - Belarus; Massachusetts Institute of Technology (MIT); Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Osaka University; University of Oslo; University of Oxford; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Ritsumeikan University; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; CEA; University of California System; University of California Santa Cruz; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Comenius University Bratislava; Slovak Academy of Sciences; University of Cape Town; University of Johannesburg; University of Witwatersrand; Stockholm University; Oskar Klein Centre; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; Tufts University; Universidad Antonio Narino; University of California System; University of California Irvine; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; University of Illinois System; University of Illinois Urbana-Champaign; Uppsala University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Valencia; University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Barcelona (IMB-CNM); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); University of British Columbia; University of Victoria; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; University of London; King's College London; Novosibirsk State University; California State University System; California State University Fresno; University of Fribourg; Universidade do Porto; Tomsk State University; Parthenope University Naples; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Peter the Great St. Petersburg Polytechnic University; University of Louisiana System; Louisiana Technical University; ICREA; National Tsing Hua University; University of Texas System; University of Texas Austin; Ilia State University; Georgian Technical University; Ochanomizu University; Manhattan College; Academia Sinica - Taiwan; Academia Sinica - Taiwan; Moscow Institute of Physics & Technology; University of Geneva; International School for Advanced Studies (SISSA); University of South Carolina System; University of South Carolina Columbia; Sun Yat Sen University; Lomonosov Moscow State University; Stanford University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; University of Kwazulu Natal; Universiti Malaya Aad, G (corresponding author), Aix Marseille Univ, CPPM, Marseille, France. García, Josu Cantero/AAS-9810-2020; da via, cinzia/AAS-3978-2021; Sapronov, Andrey/HGB-0392-2022; Saavedra, Juan Antonio Aguilar/F-1256-2016; Valero, Alberto/G-9866-2015; Stoicea, Gabriel/L-5602-2019; li, li/GPX-3938-2022; Staszewski, Rafał/AAO-6844-2021; Castillo, Luis Roberto Flores/W-3928-2018; Boyko, Igor R/J-3659-2013; de la Hoz, Santiago Gonzalez/E-2494-2016; Luci, Claudio/HKW-0143-2023; Lagouri, Theodota/AAC-7358-2021; Darbo, Giovanni/C-8175-2012; SULIN, VLADIMIR V/N-2793-2015; Brooks, William K/C-8636-2013; Petrucci, Fabrizio/G-8348-2012; Li, Liang/O-1107-2015; Oh, Alexander/HHZ-4386-2022; Grabowska-Bołd, Iwona/ABI-7829-2020; Svatos, Michal/ABA-2041-2020; Derendarz, Dominik/AAO-3512-2021; Šolc, Jaroslav/AAC-5868-2021; Ventura, Andrea/CAH-0226-2022; Kepka, Oldrich/ABF-5327-2021; Negrini, Matteo/C-8906-2014; Watson, Ian James/AFL-9360-2022; Maleev, Victor/R-4140-2016; Laycock, Paul/AAL-8261-2021; Buttar, Craig M/D-3706-2011; Mitsou, Vasiliki A./D-1967-2009; Balaji, S/J-1864-2019; Capua, Marcella/A-8549-2015; Bassalat, Ahmed Azzam/HHY-9901-2022; de Almeida, Fernando Marroquim Leao/D-6947-2019; Torregrosa, Esteban Fullana/A-7305-2016; Llácer, María Moreno/AAU-6582-2020; Zenin, Oleg/E-6116-2018; Orlando, Nicola/AAL-1723-2021; Nevski, Pavel/AAH-1839-2019; Ahmadov, Faig N/B-3723-2018; Zanzi, Daniele/AAY-4102-2020; Hejbal, Jiri/H-1358-2014; O'Shea, Val/G-1279-2010; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Cavaliere, Viviana/CAE-8597-2022; Fiorini, Luca/W-6250-2018; White, Ryan M/E-2979-2015; Gonella, Laura/GLR-3838-2022; Ohm, Christian/AAU-6572-2020; Lambourne, Luke/AAG-7298-2020; Bin Yusuff, Imran/ABA-5986-2020; Cheng, Hok-Chuen/GNP-8341-2022; Koperny, Stefan/ABB-4747-2020; Grinstein, Sebastian/ABE-1880-2020; Shmeleva, Alevtina/M-6199-2015; Górnicki, Edward/C-4075-2019; Lysak, Roman/H-2995-2014; Tassi, Enrico/K-3958-2015; Berge, David/P-2179-2017; Chelkov, Georgy A./G-9934-2019; Fabbri, Laura/H-3442-2012; Livan, Michele M/D-7531-2012; Lee, Lawrence/AAU-6790-2021; Llorente Merino, Javier/AGP-3432-2022; Ferrer, Antonio/H-2942-2015; Sadykov, Renat/AAN-9602-2020; beddall, ayda/AAG-6531-2020; Cheremushkina, Evgeniya/E-9881-2018; Jiménez, Yesenia Hernández/ABH-1107-2020; Klimentov, Alexei A/I-4270-2013; Evangelos, Gazis/L-3966-2017; Caprini, Irinel/AAP-3760-2020; Coccaro, Andrea/AAB-6990-2021; Berta, Peter/AAL-7109-2020; Cottin, Giovanna/AFR-3846-2022; Bachas, Konstantinos/C-8101-2019; Alimonti, Gianluca/AAG-4603-2020; Price, Darren/AAB-5991-2021; Gutierrez, Phillip/C-1161-2011; Herten, Gregor/HNQ-9546-2023; Amorim, Antonio/C-8460-2013; Lee, Shih-Chang/AAV-7016-2021; Glasman, Claudia/Y-8858-2019; Gao, Y./GWZ-3665-2022; Kuze, Masahiro/V-4251-2018; Staroba, Pavel/G-8850-2014; Vaniachine, Alexandre/H-7796-2013; Castro, Nuno/AAB-3648-2019; Tikhomirov, Vladimir/M-6194-2015; Villaplana Perez, Miguel/B-5772-2014; CAKIR, Orhan/L-1893-2015; Lacasta, Carlos/C-7254-2008; Busato, Emmanuel/AAS-5459-2020; Bates, Richard L/D-6596-2013; Alexopoulos, Theodoros/AAC-9633-2022; Hrabovsky, Milan/H-4413-2014; Coccaro, Andrea/P-5261-2016; Gabrielli, Alessandro/H-4931-2012; Quijada, Javier Alberto Murillo/AAS-4234-2021; Martinez, Mario/I-3549-2015; Testa, Marianna/L-9768-2019; Sultansoy, Saleh/AAA-8267-2019; Nevski, Pavel/M-6292-2015; Artoni, Giacomo/ABA-2164-2020; García, Elena Oliver/AAZ-1125-2020; Kazanin, Vasily F./HHM-2056-2022; Albert, Justin/J-4152-2017; Benekos, Nektarios/J-8629-2017; Burdin, Sergey/AAZ-9062-2021; Donadelli, Marisilvia/ABD-5330-2021; Saraiva, Joao Gentil Mendes/D-1596-2017; Rosati, Stefano/AAF-6968-2019; Wu, Xin/ABH-1729-2020; Romaniouk, Anatoli/Q-6674-2017; Costa, Maria Jose/H-5962-2015; Beddall, Andrew John/AAE-5820-2022; Gonçalo, Ricardo/M-3153-2016; Tsybychev, Dmitri/J-3733-2017; De, Kaushik/N-1953-2013; Maevskiy, Artem/P-4101-2016; Cavaliere, Viviana/CAG-2384-2022; de la Torre Perez, Hector/ABG-6942-2020; Fuster, Juan/W-6189-2018; Woźniak, Krzysztof/AAQ-6119-2021; Monzani, Simone/D-6328-2017; Milosavljevic, Marija Vranjes/F-9847-2016; Klok, Peter/AAF-7393-2019; Talyshev, Alexey/HGC-6910-2022; Dolejší, Jiří/P-1509-2017; Iengo, Paolo/AAR-7518-2020; Schernau, Michael/GRY-3808-2022; Andreazza, Attilio/E-5642-2011; MEONI, Evelin/ABD-9498-2021; Alexa, Calin/F-6345-2010; Kosek, Tomáš/Q-1906-2017; de Renstrom, Pawel Bruckman/AAG-7725-2021; Nordberg, Markus/L-4255-2017; do Vale, Maria Aline Barros/D-2357-2019; Pásztor, Gabriella/D-9716-2016; Trzupek, Adam/N-2448-2018; Banaś, Elżbieta A/W-4583-2017; Price, Darren D/E-6162-2012; Fullana Torregrosa, Esteban/AAC-9079-2022; Di Nardo, Roberto/J-4993-2012; Garcia, Carmen/W-2465-2018; YILDIZ, Hatice DURAN/AAN-3727-2021; Monticeli, Francisco Maciel/AAO-9697-2020; Marcisovsky, Michal/AAM-2404-2020; Gorelov, Igor V/J-9010-2015; Ventura, Andrea/A-9544-2015; Malek, Fairouz/E-4410-2018; Snesarev, Andrei/H-5090-2013; Ozcan, Veysi Erkcan/AAS-4508-2020; Chwastowski, Janusz J./I-4480-2012; Amoroso, Simone/AAW-4334-2021; Olszewski, Andrzej J/W-1825-2018; Olszowska, Jolanta/P-2889-2017; Coadou, Yann/G-2263-2010; Donadelli, Marisilvia/H-3710-2016; Giordani, Mario P/Q-6211-2018; Ibáñez, Carlos Escobar/B-3761-2017; Berger, N/ABE-4064-2020; Franchini, Matteo/AAC-9259-2021; Dyndal, Mateusz/AAB-1528-2020; Leyton, Michael/G-2214-2016; Ciftci, Abbas Kenan/AAC-5487-2022; Guo, Jun/O-5202-2015; Bobrovnikov, Victor/AAB-8328-2022; Reznicek, Pavel/C-1989-2017; Carquin López, Edson/GZG-4712-2022; Nagai, Yoshikazu/AAG-6157-2021; della Volpe, Domenico/B-4482-2012; Tassi, Enrico/AAJ-9661-2020; Kartvelishvili, Vakhtang G/K-2312-2013; Francavilla, Paolo/HKE-1206-2023; Bosman, Martine/J-9917-2014; Muino, Patricia Conde/F-7696-2011; Kostyukhin, Vadim/ABA-9033-2020; Vandelli, Wainer/ABI-8185-2020; Padilla, Cristobal/C-3218-2017; Huseynov, Nazim/AAE-4663-2019; Casado, Pilar/H-1484-2015; Kamenshchikov, Andrey/R-1112-2017; kucuk, hilal/AAM-5530-2020; Fassi, Farida/F-3571-2016; Peleganchuk, Sergey V/J-6722-2014; Grancagnolo, Sergio/J-3957-2015; Nessi, Marzio/L-5194-2017; Buttar, Craig/AAG-5392-2021; Li, Hao/GPS-9834-2022; Sbarra, Carla/M-9669-2018; Bogdanchikov, Alexander/AAB-9414-2022; Legger, Federica/HNC-2568-2023; Giagu, Stefano/H-6455-2013; Ciubancan, Liviu Mihai/L-2412-2015; Kantserov, Vadim/M-9761-2015; Boldyrev, Alexey/M-9684-2015; Mindur, Bartosz/A-2253-2017; Benekos, Nektarios/I-9928-2017; Kostyukhin, Vadim/F-3171-2019; Garcia, Jose E/H-6339-2015; Crispin-Ortuzar, Mireia/AAN-7108-2020; Ros, Eduardo/P-2625-2018; Gonzalez, Barbara Alvarez/ABG-7021-2020; perez, miguel/GXF-5077-2022; Llácer, María Moreno/AAQ-7522-2020; Salamon, Andrea/AGN-6159-2022; Prokoshin, Fedor/E-2795-2012; Gavrilenko, Igor/M-8260-2015; Warburton, Andreas/N-8028-2013; Franchini, Matteo/HNI-8314-2023; Fassouliotis, Dimitrios/AAL-7081-2021; Irles, Adrian/AAW-1879-2021; Trzebinski, Maciej/W-1748-2018; Solodkov, Alexander A/B-8623-2017; spagnolo, stefania/A-6359-2012; Della Pietra, Massimo/J-5008-2012; Sopczak, Andre/I-4951-2015; zhang, jin/GXV-9154-2022; Rozen, Yoram/H-1880-2017; Torro Pastor, Emma/AAB-5979-2021; SULIN, Vladimir V/J-6966-2014; Sultansoy, Saleh/AAV-6853-2021; Gladilin, Leonid/B-5226-2011; Barreiro, Fernando/D-9808-2012; Perez, Miguel Villaplana/B-2717-2015; Mashinistov, Ruslan/M-8356-2015; Urbán, Susana Cabrera/H-1376-2015; Masik, Jiri/AAE-1203-2021; Annovi, Alberto/AAA-8638-2020; Veneziano, Stefano/J-1610-2012; Černý, Karel/AAK-7746-2021; Zaitsev, Alexandre M/B-8989-2017; Cetin, Serkant Ali/AGF-0147-2022; Smirnov, Sergei Y/F-1014-2011; Trzebinski, Maciej/AAO-6821-2021; Nechaeva, Polina Y/N-1148-2015; Palka, Marek/GXW-2506-2022; Busato, Emmanuel/AAZ-4423-2020; Chitan, Adrian/AAC-8946-2022; Jones, Roger WL/H-5578-2011; Maneira, Jose/D-8486-2011; Vanadia, Marco/K-5870-2016; Kodys, Peter/P-2636-2017; Capua, Marcella/AAJ-8419-2020; Gauzzi, Paolo/D-2615-2009; Dabrowski, Wladyslaw/AAS-6369-2020; Zhukov, Konstantin I/M-6027-2015; Dam, Mogens/C-2081-2015; Wozniak, Krzysztof W/P-4475-2017; Pages, Andres Pacheco/C-5353-2011; Jiménez Peña, Javier/AFY-1817-2022; Chizhov, Mihail/CAG-2076-2022; gaudio, gabriella/AAN-6039-2021; Leisos, Antonios/AAJ-2351-2021; Vykydal, Zdenek/H-6426-2016; Potamianos, Karolos/V-2525-2019; Di Domenico, Antonio/G-6301-2011; Carquin, Edson/GLU-9641-2022; Carvalho, Joao/M-4060-2013; Mamuzic, Judita/U-3509-2017; Mikestikova, Marcela/H-1996-2014; Doyle, Anthony T/C-5889-2009; Ippolito, Valerio/L-1435-2016; Smirnova, Oxana/A-4401-2013; Chekulaev, Sergey V/O-1145-2015; Zhemchugov, Alexey/N-1717-2017; Paganis, Stathes/J-8413-2017; de Groot, Nicolo/A-2675-2009; Sýkora, Tomáš/Q-3174-2017; Ungaro, Francesca C/G-4554-2018; Stoicea, Gabriel/B-6717-2011; Kaczmarska, Anna/B-2753-2019; Watson, Ian James/AAH-1423-2022; Davidek, Tomas/P-2697-2017; Schultz-Coulon, Hans-Christian/X-5006-2018; Carli, Ina/C-2189-2017; Faltova, Jana/P-6842-2017; Tykhonov, Andrii/Q-8688-2018; Monk, James/A-9021-2017; Rebuzzi, Daniela Marcella/D-9727-2018; xella, stefania/E-6752-2015; McKee, Shawn/B-6435-2012; Skaerso, Morten/E-6847-2015; Korcyl, Krzysztof/W-2111-2018; Konig, Adriaan/B-6439-2013; la rotonda, laura/B-4028-2016; Sykora, Ivan/T-5252-2018; Britton, David/F-2602-2010; Carquin, Edson/G-5221-2015; Korol, Aleksandr/A-6244-2014; Wolter, Marcin/A-7412-2012; Juste, Aurelio/I-2531-2015; Hansen, Peter Henrik/C-2098-2015; Zenis, Tibor/T-5270-2018; Connell, Simon/F-2962-2015; cerri, alessandro/Q-6884-2016; Vos, Marcel/G-8123-2015; La Rosa, Alessandro/I-1856-2013; Petersen, Troels/P-5538-2015 García, Josu Cantero/0000-0001-8449-1019; da via, cinzia/0000-0001-6154-7323; Saavedra, Juan Antonio Aguilar/0000-0002-5475-8920; Valero, Alberto/0000-0002-9776-5880; Staszewski, Rafał/0000-0001-7708-9259; Castillo, Luis Roberto Flores/0000-0003-1551-5974; Boyko, Igor R/0000-0002-3355-4662; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Darbo, Giovanni/0000-0003-2165-0638; SULIN, VLADIMIR V/0000-0003-3943-2495; Brooks, William K/0000-0001-6161-3570; Petrucci, Fabrizio/0000-0002-5278-2206; Li, Liang/0000-0001-6411-6107; Grabowska-Bołd, Iwona/0000-0001-9159-1210; Svatos, Michal/0000-0002-7199-3383; Derendarz, Dominik/0000-0001-5660-3095; 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BOUMEDIENE, Djamel/0000-0002-7809-3118; Gregersen, Kristian/0000-0003-0295-1670; Terashi, Koji/0000-0001-6520-8070; Oreglia, Mark/0000-0001-6203-2209; Duckeck, Guenter/0000-0002-7756-7801; Nagel, Martin/0000-0002-2588-6691; Bona, Marcella/0000-0002-9660-580X; Grivaz, Jean-Francois/0000-0003-4793-7995; Schoeffel, Laurent/0000-0002-8081-2353; Bednyakov, Vadim/0000-0003-4864-8909; Lokajicek, Milos/0000-0001-8929-1243; Erdmann, Johannes/0000-0002-8073-2740; Giannetti, Paola/0000-0002-3721-9490; Salvatore, Fabrizio/0000-0002-3709-1554; Jiggins, Stephen/0000-0003-2906-1977; Redlinger, George/0000-0002-6437-9991; Taylor, Wendy/0000-0002-6596-9125; Barak, Liron/0000-0002-3436-2726; La Rosa, Alessandro/0000-0001-6291-2142; Capeans, Mar/0000-0001-7727-9175; Stugu, Bjarne/0000-0002-1728-9272; Willocq, Stephane/0000-0002-4120-1453; Galhardo, Bruno/0000-0003-0641-301X; Palestini, Sandro/0000-0002-4110-096X; Panduro Vazquez, Jose Guillermo/0000-0003-2605-8940; Besson, Nathalie/0000-0001-9248-6252; Elliot, Alison/0000-0003-0921-0314; Pleier, Marc-Andre/0000-0002-9461-3494; Beck, Hans Peter/0000-0001-7212-1096; Iliadis, Dimitrios/0000-0001-6303-2761; Lester, Christopher Gorham/0000-0001-5770-4883; Alconada Verzini, Maria Josefina/0000-0003-2212-7830; Berghaus, Frank/0000-0003-1887-3910; Lefebvre, Michel/0000-0002-5560-0586; Burke, Stephen/0000-0002-1962-8493; Petersen, Troels/0000-0003-0221-3037; Makovec, Nikola/0000-0001-5124-904X; Dietrich, Janet/0000-0001-7061-1585; Weiser, Christian/0000-0002-6456-6834; gabrielli, andrea/0000-0003-0768-9325; Nairz, Armin/0000-0003-3561-0880; Curatolo, Maria/0000-0003-0978-4879; Mezquita, Costa/0000-0002-2064-2954; Varvell, Kevin/0000-0003-1017-1295; Hill, John/0000-0002-8650-2807; Queitsch-Maitland, Michaela/0000-0003-4643-515X; March, Luis/0000-0002-0849-2244; jia, Jiangyong/0000-0002-5725-3397; Reeves, Kendall/0000-0003-3504-4882; Luehring, Frederick/0000-0001-8721-6901 ANPCyT, Argentina; Yer-PhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; Lundbeck Foundation, Denmark; EPLANET, European Union; ERC, European Union; NSRF, European Union; DFG, Germany; HGF, Germany; MPG, Germany; AvH Foundation, Germany; GSRT, Greece; NSRF, Greece; RGC, Hong Kong SAR, China; ISF, Israel; MINERVA, Israel; GIF, Israel; I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; BRF, Norway; RCN, Norway; MNiSW, Poland; NCN, Poland; GRICES, Portugal; FCT, Portugal; MNE/IFA, Romania; MES of Russia, Russian Federation; ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, United Kingdom; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; DOE, United States of America; NSF, United States of America; STFC [ST/L00352X/1, ST/M503575/1, ST/M002306/1, ST/L003511/1, ST/K001302/1, ST/L003325/1, ST/J004944/1, ST/K00140X/1, ST/M001504/1, ST/J00474X/1, ST/N000463/1, ST/J501074/1, ST/L000326/1, ST/N000420/1, ST/M003213/1, ST/I005803/1, ST/I000178/1, PP/E000355/1, ST/L003112/1, ST/H00100X/1, ST/L001144/1, ST/H00100X/2, PP/E000347/1, ST/L006480/1, ST/I006056/1, ST/K00137X/1, ST/K00073X/1, ST/K001248/1, ST/G50228X/1, ST/H001093/2, ST/M001431/1, ST/K001329/1, ST/M001733/1, ST/K001388/1, ST/I000186/1, ST/N000277/1, ST/K001361/1, ST/L000970/1, ST/M000664/1, ST/J005576/1, ST/M000761/1, ST/K50208X/1, ST/M004821/1, PP/D002915/1, PP/E003087/1, PP/E002846/1, ST/J004928/1, ST/I00372X/1, ST/K003658/1, ST/L001179/1, ST/L003414/1, ST/G502320/1, ST/I006080/1, ST/L001195/1, ST/H001093/1, ST/N000307/1, ST/J005487/1, ST/J005525/1, ST/K000705/1, ST/M001407/1, ST/M006980/1] Funding Source: UKRI; ICREA Funding Source: Custom; Fundação para a Ciência e a Tecnologia [CERN/FIS-NUC/0005/2015] Funding Source: FCT; Direct For Mathematical & Physical Scien; Division Of Physics [1119200] Funding Source: National Science Foundation; Direct For Mathematical & Physical Scien; Division Of Physics [1506173] Funding Source: National Science Foundation; Science and Technology Facilities Council [ST/L000970/1 ATLAS Upgrades, ST/M004821/1 GRIDPP, ST/L003325/1, ST/I00372X/1, ST/J00474X/1, PP/E000347/1, ST/K001361/1 LHCb Upgrades, ST/L003112/1, ST/L00352X/1, ST/H001093/1, 1366825, ST/K001361/1, ST/K001302/1, ST/K001264/1 ATLAS, ST/I005803/1 GRIDPP, ST/M000761/1, ST/K001248/1, ST/K001361/1 ATLAS, ST/M003213/1, ST/G50228X/1, ST/L001195/1, ST/M001407/1, ST/M002306/1 ATLAS Upgrades, ST/L000970/1 ATLAS Upgrade, ST/K001396/1 ATLAS, ST/I000178/1, ST/K003658/1 GRIDPP, ST/M006980/1, ST/M002071/1 ATLAS Upgrades, 1521422, PP/D002915/1, ST/J005525/1, ST/M001474/1, ST/M000664/1, ST/J501074/1, ST/K003658/1, ST/L001179/1 ATLAS Upgrades, ST/M503575/1, ST/I005803/1, ST/L006480/1, ST/M007103/1 ATLAS Upgrades, ST/K001310/1 ATLAS, ST/K001361/1 MINOS/MINOS+, ST/I00372X/1 GRIDPP, ST/M001431/1, ST/G502320/1, ST/L001209/1 ATLAS Upgrades, ST/K00137X/1, ST/L001179/1, ST/N000463/1, ST/H001093/2, ST/L003414/1, ST/N000277/1, 1366364, ST/J004944/1, ST/M001504/1, ST/I006056/1, ST/K001418/1, ATLAS, ST/K001329/1, 1575161, ST/K000705/1, ST/N000420/1, ST/J005576/1, ST/K001310/1 LHCb Upgrades, ST/M006980/1 ATLAS Upgrades, ST/K001388/1, ST/I000186/1, ST/I006080/1, 1090338, ST/N000307/1, ST/J004928/1, PP/E003087/1, ST/H00100X/2, ST/L001144/1, ST/M004821/1, PP/E002846/1, ST/L000326/1, ST/M001733/1, ST/J00474X/1 ATLAS Upgrades, 1334588, ST/K001310/1, PP/E000444/1, 1201187, 1082012, GRIDPP, ST/K00073X/1, ST/M002306/1, ST/L001179/1 ATLAS Upgrade, ST/K001310/1 ATLAS Upgrades, ST/K001361/1 ATLAS Upgrades, ST/I006056/1 ATLAS Upgrade, ST/I006056/1 ATLAS Upgrades, PP/E000355/1, ST/K50208X/1, ST/J005487/1, ST/J004928/1 ATLAS Upgrades, ST/H00100X/1, ST/L000970/1, ST/K001361/1 LHCb, ST/J004928/1 ATLAS Upgrade, ST/L003511/1, ST/K001310/1 LHCb, ST/K001426/1 ATLAS, ST/K00140X/1] Funding Source: researchfish ANPCyT, Argentina(ANPCyT); Yer-PhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); Lundbeck Foundation, Denmark(Lundbeckfonden); EPLANET, European Union; ERC, European Union(European Research Council (ERC)); NSRF, European Union; DFG, Germany(German Research Foundation (DFG)); HGF, Germany; MPG, Germany(Max Planck Society); AvH Foundation, Germany(Alexander von Humboldt Foundation); GSRT, Greece(Greek Ministry of Development-GSRT); NSRF, Greece; RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); MINERVA, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); BRF, Norway; RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; GRICES, Portugal; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia, Russian Federation(Russian Federation); ROSATOM, Russian Federation(Russian Federation); JINR; MSTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ICREA(ICREA); Fundação para a Ciência e a Tecnologia; Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We honour the memory of our colleague Richard St. Denis, who was a driving force of the work described here for a long time and died shortly before its completion. We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; Yer-PhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; RGC, Hong Kong SAR, China; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide. 115 116 116 0 9 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C JUL 29 2015.0 75 7 349 10.1140/epjc/s10052-015-3543-1 0.0 50 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics CN7IB 26269691.0 Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000358606700001 0
J Pu, HB; Sun, DW; Ma, J; Cheng, JH Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis MEAT SCIENCE English Article Hyperspectral imaging; Texture; Fresh pork; Frozen-thawed pork; Probabilistic neural network COMPUTER VISION SYSTEM; PROBABILISTIC NEURAL NETWORKS; FOOD QUALITY EVALUATION; WATER-HOLDING CAPACITY; MACHINE VISION; REFLECTANCE SPECTROSCOPY; ISOTHERM EQUATIONS; MEAT QUALITY; COLOR; BEEF The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. (C) 2014 Elsevier Ltd. All rights reserved. [Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Sch Biosyst Engn, Agr & Food Sci Ctr, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), Natl Univ Ireland, Univ Coll Dublin, Sch Biosyst Engn, Agr & Food Sci Ctr, Dublin 4, Ireland. dawen.sun@ucd.ie Hongbin, Pu/ABE-8433-2020; Ma, Ji/U-7550-2019; Sun, Da-Wen/B-1899-2012; Cheng, Jun-Hu/AAW-9490-2020; Cheng, Jun-Hu/I-9574-2016 Hongbin, Pu/0000-0002-7977-1902; Sun, Da-Wen/0000-0002-3634-9963; Cheng, Jun-Hu/0000-0003-3928-1770; Cheng, Jun-Hu/0000-0003-3928-1770; Ma, Ji/0000-0002-1437-464X Guangdong Province Government (China); Fundamental Research Funds for the Central Universities [2014ZM0027] Guangdong Province Government (China); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors are grateful to the Guangdong Province Government (China) for support through the program of Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by the Fundamental Research Funds for the Central Universities (2014ZM0027). 63 139 143 9 188 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0309-1740 1873-4138 MEAT SCI Meat Sci. JAN 2015.0 99 81 88 10.1016/j.meatsci.2014.09.001 0.0 8 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology AU3BO 25282703.0 2023-03-23 WOS:000345488100012 0
J Duan, YJ; Liu, X; Jatowt, A; Zhuang, CY; Yu, HT; Lynden, S; Kim, KS; Matono, A Duan, Yijun; Liu, Xin; Jatowt, Adam; Zhuang, Chenyi; Yu, Hai-Tao; Lynden, Steven; Kim, Kyoung-Sook; Matono, Akiyoshi Multiview Actionable Knowledge Graph Generation From Wikipedia Biographies IEEE ACCESS English Article Online services; Internet; Encyclopedias; Biographies; Task analysis; Electronic mail; Licenses; Actionable knowledge graph; neural network matrix factorization; text mining; web mining Actionable knowledge graph (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization-based frameworks for AKG generation from three different perspectives: subject, context and functionality of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs. [Duan, Yijun; Liu, Xin; Lynden, Steven; Kim, Kyoung-Sook; Matono, Akiyoshi] Natl Inst Adv Ind Sci & Technol Tokyo Waterfront, Koto City, Tokyo 1350064, Japan; [Jatowt, Adam] Univ Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria; [Zhuang, Chenyi] Ant Grp, Hangzhou 311121, Peoples R China; [Yu, Hai-Tao] Univ Tsukuba, Fac Lib Informat & Media Sci, Tsukuba, Ibaraki 3058577, Japan National Institute of Advanced Industrial Science & Technology (AIST); University of Innsbruck; University of Tsukuba Duan, YJ (corresponding author), Natl Inst Adv Ind Sci & Technol Tokyo Waterfront, Koto City, Tokyo 1350064, Japan. yijun.duan@aist.go.jp DUAN, YIJUN/GSN-5984-2022 DUAN, YIJUN/0000-0002-5098-8593 New Energy and Industrial Technology Development Organization (NEDO) [JPNP20006] New Energy and Industrial Technology Development Organization (NEDO)(New Energy and Industrial Technology Development Organization (NEDO)) This work was supported by the New Energy and Industrial Technology Development Organization (NEDO) under Project JPNP20006. 38 0 0 2 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2022.0 10 73879 73892 10.1109/ACCESS.2022.3189769 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 3Q9DU gold 2023-03-23 WOS:000838524200001 0
J Li, B; Cao, H; Qu, ZN; Hu, YB; Wang, ZK; Liang, ZC Li, Bin; Cao, Hu; Qu, Zhongnan; Hu, Yingbai; Wang, Zhenke; Liang, Zichen Event-Based Robotic Grasping Detection With Neuromorphic Vision Sensor and Event-Grasping Dataset FRONTIERS IN NEUROROBOTICS English Article neuromorphic vision sensor; SMP filter; event-grasping dataset; grasping detection; deep learning Robotic grasping plays an important role in the field of robotics. The current state-of-the-art robotic grasping detection systems are usually built on the conventional vision, such as the RGB-D camera. Compared to traditional frame-based computer vision, neuromorphic vision is a small and young community of research. Currently, there are limited event-based datasets due to the troublesome annotation of the asynchronous event stream. Annotating large scale vision datasets often takes lots of computation resources, especially when it comes to troublesome data for video-level annotation. In this work, we consider the problem of detecting robotic grasps in a moving camera view of a scene containing objects. To obtain more agile robotic perception, a neuromorphic vision sensor (Dynamic and Active-pixel Vision Sensor, DAVIS) attaching to the robot gripper is introduced to explore the potential usage in grasping detection. We construct a robotic grasping dataset namedEvent-Graspingdataset with 91 objects. A spatial-temporal mixed particle filter (SMP Filter) is proposed to track the LED-based grasp rectangles, which enables video-level annotation of a single grasp rectangle per object. As LEDs blink at high frequency, theEvent-Graspingdataset is annotated at a high frequency of 1 kHz. Based on theEvent-Graspingdataset, we develop a deep neural network for grasping detection that considers the angle learning problem as classification instead of regression. The method performs high detection accuracy on ourEvent-Graspingdataset with 93% precision at an object-wise level split. This work provides a large-scale and well-annotated dataset and promotes the neuromorphic vision applications in agile robot. [Li, Bin] JingDong Grp, Beijing, Peoples R China; [Cao, Hu; Hu, Yingbai] Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany; [Qu, Zhongnan] Eidgenoss Tech Hsch ETH Zurich, Comp Engn & Networks Lab, Zurich, Switzerland; [Wang, Zhenke; Liang, Zichen] Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China Technical University of Munich; Tongji University Cao, H (corresponding author), Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany. hu.cao@tum.de Cao, Hu/HJP-8442-2023 Cao, Hu/0000-0001-8225-858X German Research Foundation (DFG); Technical University of Munich (TUM); China Scholarship Council (CSC) [201906130181]; Zhejiang Lab's International Talent Fund for Young Professionals German Research Foundation (DFG)(German Research Foundation (DFG)); Technical University of Munich (TUM); China Scholarship Council (CSC)(China Scholarship Council); Zhejiang Lab's International Talent Fund for Young Professionals This work was financially supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. This work was also supported in part by the scholarship from China Scholarship Council (CSC) under the Grant No. 201906130181 and the Zhejiang Lab's International Talent Fund for Young Professionals. 32 9 9 2 12 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics OCT 8 2020.0 14 51 10.3389/fnbot.2020.00051 0.0 14 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology OF2OV 33162883.0 Green Published, gold 2023-03-23 WOS:000581054800001 0
J Li, QP; Mou, LC; Xu, QZ; Zhang, Y; Zhu, XX Li, Qingpeng; Mou, Lichao; Xu, Qizhi; Zhang, Yun; Zhu, Xiao Xiang R-3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Aerial images and videos; deep learning; multioriented detection; remote sensing; vehicle detection OBJECT DETECTION; CARS Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R-3-Net), to detect multioriented vehicles in aerial images and videos. More specially, R-3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable positionsensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudNO. [Li, Qingpeng; Xu, Qizhi] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China; [Li, Qingpeng; Xu, Qizhi] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Mou, Lichao; Zhu, Xiao Xiang] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany; [Mou, Lichao; Zhu, Xiao Xiang] Tech Univ Munich, Signal Proc Earth Observat, D-80333 Munich, Germany; [Zhang, Yun] Univ New Brunswick, Dept Geodesy & Geomat Engn, Canada Res Chair Lab Adv Geomat Image Proc, Fredericton, NB E3B 5A3, Canada Beihang University; Beijing University of Chemical Technology; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich; University of New Brunswick Xu, QZ (corresponding author), Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China. liqingpeng@buaa.edu.cn; lichao.mou@dlr.de; qizhi@buaa.odu.cn; yunzhang@unb.ca; xiao.zhu@dlr.de Zhu, Xiao Xiang/ABE-7138-2020 Zhu, Xiao Xiang/0000-0001-5530-3613 National Defense Science and Technology Innovation Zone; National Natural Science Foundation of China [61672076, 61331017]; China Scholarship Council; European Research Council through the European Unions Horizon 2020 Research and Innovation Programme (So2Sat) [ERC-2016-StG-714087]; Helmholtz Association in the framework of the Young Investigators Group (SiPEO) [VH-NG-1018]; Bavarian Academy of Sciences and Humanities National Defense Science and Technology Innovation Zone; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); European Research Council through the European Unions Horizon 2020 Research and Innovation Programme (So2Sat); Helmholtz Association in the framework of the Young Investigators Group (SiPEO); Bavarian Academy of Sciences and Humanities This work was supported in part by the National Defense Science and Technology Innovation Zone, in part by the National Natural Science Foundation of China under Grant 61672076 and Grant 61331017, in part by the China Scholarship Council, in part by the European Research Council through the European Unions Horizon 2020 Research and Innovation Programme (So2Sat) under Grant ERC-2016-StG-714087, in part by the Helmholtz Association in the framework of the Young Investigators Group (SiPEO) under Grant VH-NG-1018, and in part by the Bavarian Academy of Sciences and Humanities in the framework of Junges Kolleg. 58 32 32 4 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUL 2019.0 57 7 5028 5042 10.1109/TGRS.2019.2895362 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology IF9TS Green Accepted, Green Submitted 2023-03-23 WOS:000473436000067 0
J Chen, TS; Du, ZD; Sun, NH; Wang, J; Wu, CY; Chen, YJ; Temam, O Chen, Tianshi; Du, Zidong; Sun, Ninghui; Wang, Jia; Wu, Chengyong; Chen, Yunji; Temam, Olivier A HIGH-THROUGHPUT NEURAL NETWORK ACCELERATOR IEEE MICRO English Article THE AUTHORS DESIGNED AN ACCELERATOR ARCHITECTURE FOR LARGE-SCALE NEURAL NETWORKS, WITH AN EMPHASIS ON THE IMPACT OF MEMORY ON ACCELERATOR DESIGN, PERFORMANCE, AND ENERGY. IN THIS ARTICLE, THEY PRESENT A CONCRETE DESIGN AT 65 NM THAT CAN PERFORM 496 16-BIT FIXED-POINT OPERATIONS IN PARALLEL EVERY 1.02 NS, THAT IS, 452 GOP/S, IN A 3.02MM(2), 485-MW FOOTPRINT (EXCLUDING MAIN MEMORY ACCESSES). [Chen, Tianshi; Du, Zidong; Sun, Ninghui; Wang, Jia; Wu, Chengyong] Chinese Acad Sci, State Key Lab Comp Architecture, Beijing 100864, Peoples R China; [Temam, Olivier] INRIA, Bordeaux, France Chinese Academy of Sciences; Inria Chen, TS (corresponding author), Chinese Acad Sci, State Key Lab Comp Architecture, Beijing 100864, Peoples R China. chentianshi@ict.ac.cn; duzidong@ict.ac.cn; snh@ict.ac.cn; brownwj@163.com; cwu@ict.ac.cn; cyj@ict.ac.cn; olivier.temam@inria.fr Sun, Ning/HLX-6289-2023 Chen, Yunji/0000-0003-3925-5185 NSF of China [61100163, 61133004, 61222204, 61221062, 61303158, 61432016, 61472396, 61473275]; 973 Program of China [2015CB358800]; Strategic Priority Research Program of the CAS [XDA06010403, XDB02040009]; International Collaboration Key Program of the CAS [171111KYSB20130002]; 10000 talent program NSF of China(National Natural Science Foundation of China (NSFC)); 973 Program of China(National Basic Research Program of China); Strategic Priority Research Program of the CAS; International Collaboration Key Program of the CAS; 10000 talent program Previous versions of this article appear in Proceedings of 19th International Conference on Architectural Support for Programming Languages and Operating Systems (2014) and ACM Transactions on Computer Systems (accepted in 2014). This work is partially supported by the NSF of China (under grants 61100163, 61133004, 61222204, 61221062, 61303158, 61432016, 61472396, and 61473275), the 973 Program of China (under grant 2015CB358800), the Strategic Priority Research Program of the CAS (under grants XDA06010403 and XDB02040009), the International Collaboration Key Program of the CAS (under grant 171111KYSB20130002), and the 10000 talent program. Olivier Temam is the corresponding author of this paper. 23 21 24 1 28 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0272-1732 1937-4143 IEEE MICRO IEEE Micro MAY-JUN 2015.0 35 3 24 32 10.1109/MM.2015.41 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science CL1IJ 2023-03-23 WOS:000356695700005 0
J Belhi, A; Al-Ali, AK; Bouras, A; Foufou, S; Yu, X; Zhang, HQ Belhi, Abdelhak; Al-Ali, Abdulaziz Khalid; Bouras, Abdelaziz; Foufou, Sebti; Yu, Xi; Zhang, Haiqing Investigating low-delay deep learning-based cultural image reconstruction JOURNAL OF REAL-TIME IMAGE PROCESSING English Article Digital heritage; Image reconstruction; Low-delay reconstruction; Image inpainting; Deep learning; Image clustering RESOLUTION Numerous cultural assets host a great historical and moral value, but due to their degradation, this value is heavily affected as their attractiveness is lost. One of the solutions that most heritage organizations and museums currently choose is to leverage the knowledge of art and history experts in addition to curators to recover and restore the damaged assets. This process is labor-intensive, expensive and more often results in just an assumption over the damaged or missing region. In this work, we tackle the issue of completing missing regions in artwork through advanced deep learning and image reconstruction (inpainting) techniques. Following our analysis of different image completion and reconstruction approaches, we noticed that these methods suffer from various limitations such as lengthy processing times and hard generalization when trained with multiple visual contexts. Most of the existing learning-based image completion and reconstruction techniques are trained on large datasets with the objective of retrieving the original data distribution of the training samples. However, this distribution becomes more complex when the training data is diverse making the training process difficult and the reconstruction inefficient. Through this paper, we present a clustering-based low-delay image completion and reconstruction approach which combines supervised and unsupervised learning to address the highlighted issues. We compare our technique to the current state of the art using a real-world dataset of artwork collected from various cultural institutions. Our approach is evaluated using statistical methods and a surveyed audience to better interpret our results objectively and subjectively. [Belhi, Abdelhak; Al-Ali, Abdulaziz Khalid; Bouras, Abdelaziz] Qatar Univ, Coll Engn, CSE, Doha, Qatar; [Belhi, Abdelhak] Univ Lumiere Lyon 2, DISP Lab, Lyon, France; [Foufou, Sebti] Univ Bourgogne, Le2i Lab, Dijon, France; [Yu, Xi] Chengdu Univ, Sch Informat Sci & Engn, Chengdu, Peoples R China; [Zhang, Haiqing] Chengdu Univ Informat Technol, Chengdu, Peoples R China Qatar University; Institut National des Sciences Appliquees de Lyon - INSA Lyon; Universite Lyon 2; Universite de Bourgogne; Chengdu University; Chengdu University of Information Technology Belhi, A (corresponding author), Qatar Univ, Coll Engn, CSE, Doha, Qatar.;Belhi, A (corresponding author), Univ Lumiere Lyon 2, DISP Lab, Lyon, France. abdelhak.belhi@qu.edu.qa; a.alali@qu.edu.qa; abdelaziz.bouras@qu.edu.qa; sfoufou@u-bourgogne.fr; yuxi@cdu.edu.cn; haiqing_zhang_zhq@163.com NPRP grant from the Qatar National Research Fund (a member of Qatar Foundation) [9-181-1-036] NPRP grant from the Qatar National Research Fund (a member of Qatar Foundation) This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa).The authors would also like to thank the Museum of Islamic Art (MIA), the MIA Multimedia team, Mr. Marc Pelletreau, the Art Curators and the management staff of the Museum of Islamic Art, Doha Qatar for their help and contribution in the data acquisition. 36 5 5 2 12 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1861-8200 1861-8219 J REAL-TIME IMAGE PR J. Real-Time Image Process. DEC 2020.0 17 6 SI 1911 1926 10.1007/s11554-020-00975-y 0.0 JUN 2020 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology OP5TA 2023-03-23 WOS:000539200300001 0
J Li, XX; Chang, DL; Ma, ZY; Tan, ZH; Xue, JH; Cao, J; Yu, JY; Guo, J Li, Xiaoxu; Chang, Dongliang; Ma, Zhanyu; Tan, Zheng-Hua; Xue, Jing-Hao; Cao, Jie; Yu, Jingyi; Guo, Jun OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Deep neural network; Orthogonal softmax layer; overfitting; small-sample classification NEURAL-NETWORKS A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data. To mitigate overfitting in small-sample classification, learning more discriminative features from small-sample data is becoming a new trend. To this end, this paper aims to find a subspace of neural networks that can facilitate a large decision margin. Specifically, we propose the Orthogonal Softmax Layer (OSL), which makes the weight vectors in the classification layer remain orthogonal during both the training and test processes. The Rademacher complexity of a network using the OSL is only k, where K is the number of classes, of that of a network using the fully connected classification layer, leading to a tighter generalization error bound. Experimental results demonstrate that the proposed OSL has better performance than the methods used for comparison on four small-sample benchmark datasets, as well as its applicability to large-sample datasets. Codes are available at: https://github.com/dongliangchang/OSLNet. [Li, Xiaoxu; Cao, Jie] Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; [Li, Xiaoxu; Chang, Dongliang; Ma, Zhanyu; Guo, Jun] Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Sch Artificial Intelligence, Beijing 100876, Peoples R China; [Tan, Zheng-Hua] Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark; [Xue, Jing-Hao] UCL, Dept Stat Sci, London W1T 7PJ, England; [Yu, Jingyi] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China Lanzhou University of Technology; Beijing University of Posts & Telecommunications; Aalborg University; University of London; University College London; ShanghaiTech University Ma, ZY (corresponding author), Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Sch Artificial Intelligence, Beijing 100876, Peoples R China. mazhanyu@bupt.edu.cn Chang, Dongliang/ABE-9202-2022; Tan, Zheng-Hua/B-6889-2015 Xue, Jing-Hao/0000-0003-1174-610X; Chang, Dongliang/0000-0002-4081-3001; Tan, Zheng-Hua/0000-0001-6856-8928 National Key Research and Development Program of China [2019YFF0303300, 2019YFF0303302, 2018YFB2100500]; National Natural Science Foundation of China (NSFC) [61906080, 61763028, 61773071, 61922015, U19B2036, 61976138, 61977047]; National Science and Technology Major Program of the Ministry of Science and Technology [2018ZX03001031]; Beijing Nova Programme Interdisciplinary Cooperation Project [Z191100001119140]; Key Program of Beijing Municipal Natural Science Foundation [L172030]; Hong-liu Outstanding Youth Talents Foundation of Lanzhou University of Technology; Shanghai Science and Technology Committee (STCSM) [2015F0203-000-06]; Shanghai Municipal Education Commission (SHMEC) [2019-01-07-00-01-E00003]; BUPT Excellent Ph.D. Students Foundation; Beijing Academy of Artificial Intelligence (BAAI) [BAAI2020ZJ0204] National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Science and Technology Major Program of the Ministry of Science and Technology; Beijing Nova Programme Interdisciplinary Cooperation Project; Key Program of Beijing Municipal Natural Science Foundation; Hong-liu Outstanding Youth Talents Foundation of Lanzhou University of Technology; Shanghai Science and Technology Committee (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)Shanghai Science & Technology Committee); Shanghai Municipal Education Commission (SHMEC)(Shanghai Municipal Education Commission (SHMEC)); BUPT Excellent Ph.D. Students Foundation; Beijing Academy of Artificial Intelligence (BAAI) This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFF0303300, Grant 2019YFF0303302, and Grant 2018YFB2100500, in part by the National Natural Science Foundation of China (NSFC) under Grant 61906080, Grant 61763028, Grant 61773071, Grant 61922015, Grant U19B2036, Grant 61976138, and Grant 61977047, in part by the National Science and Technology Major Program of the Ministry of Science and Technology under Grant 2018ZX03001031, in part by the Beijing Academy of Artificial Intelligence (BAAI) under Grant BAAI2020ZJ0204, in part by the Beijing Nova Programme Interdisciplinary Cooperation Project under Grant Z191100001119140, in part by the Key Program of Beijing Municipal Natural Science Foundation under Grant L172030, in part by the Hong-liu Outstanding Youth Talents Foundation of Lanzhou University of Technology, in part by the Shanghai Science and Technology Committee (STCSM) under Grant 2015F0203-000-06, in part by the Shanghai Municipal Education Commission (SHMEC) (2019-01-07-00-01-E00003), and in part by the BUPT Excellent Ph.D. Students Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ming-Ming Cheng. 85 15 15 2 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2020.0 29 6482 6495 10.1109/TIP.2020.2990277 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering MF0ZC 32386152.0 Green Published, Green Submitted 2023-03-23 WOS:000545079400008 0
J Wang, WC; LangIois, R; Langlois, M; Genchev, GZ; Wang, XL; Lu, H Wang, Wenchuan; LangIois, Robert; Langlois, Marina; Genchev, Georgi Z.; Wang, Xiaolei; Lu, Hui Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins FRONTIERS IN GENETICS English Article machine learning; protein sequence and structural analysis; multiple-instance learning; decision trees; semi supervised learning; protein function annotation; DNA binding proteins; RNA binding proteins DNA-BINDING; RNA-BINDING; MICROARRAY ANALYSIS; PREDICTION; CLASSIFICATION; CANCER; SPECIFICITY; RECOGNITION; ALGORITHMS; REGRESSION Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein's function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification. [Wang, Wenchuan; Genchev, Georgi Z.; Wang, Xiaolei; Lu, Hui] Shanghai Jiao Tong Univ, Dept Bioinformat & Biostat, SJTU Yale Joint Ctr Biostat & Data Sci, Coll Life Sci & Biotechnol, Shanghai, Peoples R China; [LangIois, Robert; Langlois, Marina; Genchev, Georgi Z.; Lu, Hui] Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA; [LangIois, Robert; Langlois, Marina; Genchev, Georgi Z.; Lu, Hui] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA; [Genchev, Georgi Z.] Bulgarian Inst Genom & Precis Med, Sofia, Bulgaria; [Wang, Xiaolei] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China; [Lu, Hui] Shanghai Childrens Hosp, Ctr Biomed Informat, Shanghai, Peoples R China Shanghai Jiao Tong University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Fudan University; Shanghai Jiao Tong University Lu, H (corresponding author), Shanghai Jiao Tong Univ, Dept Bioinformat & Biostat, SJTU Yale Joint Ctr Biostat & Data Sci, Coll Life Sci & Biotechnol, Shanghai, Peoples R China.;Lu, H (corresponding author), Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA.;Lu, H (corresponding author), Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA.;Lu, H (corresponding author), Shanghai Childrens Hosp, Ctr Biomed Informat, Shanghai, Peoples R China. huilu@sjtu.edu.cn National Key R&D Program of China [2018YFC0910500]; Neil Shen's SJTU Medical Research Fund; SJTU-Yale Collaborative Research Seed Fund; Science and Technology Commission of Shanghai Municipality (STCSM) [17DZ 22512000]; Shanghai Municipal Science and Technology Major Project [2018SHZDZX01]; NIH [T32 HL 07692]; NSFC [31728012]; LCNBI; ZJLab National Key R&D Program of China; Neil Shen's SJTU Medical Research Fund; SJTU-Yale Collaborative Research Seed Fund; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)); Shanghai Municipal Science and Technology Major Project; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NSFC(National Natural Science Foundation of China (NSFC)); LCNBI; ZJLab This work is partially supported by National Key R&D Program of China 2018YFC0910500, the Neil Shen's SJTU Medical Research Fund, SJTU-Yale Collaborative Research Seed Fund; NSFC 31728012, Science and Technology Commission of Shanghai Municipality (STCSM) grant 17DZ 22512000, Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), LCNBI and ZJLab. RL acknowledges the support from NIH training grant T32 HL 07692. 78 1 1 2 10 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. AUG 30 2019.0 10 729 10.3389/fgene.2019.00729 0.0 10 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity IU1KZ 31543893.0 Green Published, gold 2023-03-23 WOS:000483336500001 0
J Cao, ZJ; Markowitch, O Cao, Zhengjun; Markowitch, Olivier Comment on Efficient Secure Outsourcing of Large-Scale Sparse Linear Systems of Equations IEEE TRANSACTIONS ON BIG DATA English Editorial Material Cloud computing; sparse linear equations; linear least squares problem; conjugate gradient method The scheme [IEEE Trans. Big Data, 4 (1), 2018, 26-39] is flawed because the sparsity of coefficient matrix is neglected. In the discussed scenario, we find it is unnecessary to outsource such a problem because the client can solve it locally. Even if the matrix is not sparse, the proposed paradigm which requires a great number of interactions, is rarely adopted because of the possibly incurred massive communication cost. [Cao, Zhengjun] Shanghai Univ, Dept Math, Shangda Rd 99, Shanghai 200444, Peoples R China; [Markowitch, Olivier] Univ Libre Bruxelles, Dept Comp Sci, Blvd Triomphe, B-2121050 Brussels, Belgium Shanghai University; Universite Libre de Bruxelles Cao, ZJ (corresponding author), Shanghai Univ, Dept Math, Shangda Rd 99, Shanghai 200444, Peoples R China. caozhj@shu.edu.cn; olivier.markowitch@ulb.ac.be National Natural Science Foundation of China [61411146001]; Brussels Region Innoviris; SeCloud Project National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Brussels Region Innoviris(Innoviris); SeCloud Project The authors would like to thank the National Natural Science Foundation of China (61411146001), the Brussels Region Innoviris and the SeCloud Project. 5 0 0 5 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7790 IEEE T BIG DATA IEEE Trans. Big Data DEC 1 2021.0 7 6 973 974 10.1109/TBDATA.2020.2995200 0.0 2 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science XG0UI 2023-03-23 WOS:000724476300007 0
J Hu, XB; Guo, R; Chen, JN; Li, HW; Waldmannstetter, D; Zhao, Y; Li, B; Shi, KY; Menze, B Hu, Xiaobin; Guo, Rui; Chen, Jieneng; Li, Hongwei; Waldmannstetter, Diana; Zhao, Yu; Li, Biao; Shi, Kuangyu; Menze, Bjoern Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Coarse-to-fine adversarial network; multi-zone uncertainty estimate; medical image segmentation; Monte Carlo dropout DROPOUT Extranodal natural killer/T cell lymphoma (ENKL), nasal type is a kind of rare disease with a low survival rate that primarily affects Asian and South American populations. Segmentation of ENKL lesions is crucial for clinical decision support and treatment planning. This paper is the first study on computer-aided diagnosis systems for the ENKL segmentation problem. We propose an automatic, coarse-to-fine approach for ENKL segmentation using adversarial networks. In the coarse stage, we extract the region of interest bounding the lesions utilizing a segmentation neural network. In the fine stage, we use an adversarial segmentation network and further introduce a multi-scale L-1 loss function to drive the network to learn both global and local features. The generator and discriminator are alternately trained by backpropagation in an adversarial fashion in a min-max game. Furthermore, we present the first exploration of zone-based uncertainty estimates based on Monte Carlo dropout technique in the context of deep networks for medical image segmentation. Specifically, we propose the uncertainty criteria based on the lesion and the background, and then linearly normalize them to a specific interval. This is not only the crucial criterion for evaluating the superiority of the algorithm, but also permits subsequent optimization by engineers and revision by clinicians after quantitatively understanding the main source of uncertainty from the background or the lesion zone. Experimental results demonstrate that the proposed method is more effective and lesion-zone stable than state-of-the-art deep-learning based segmentation model. [Hu, Xiaobin; Li, Hongwei; Waldmannstetter, Diana; Zhao, Yu; Shi, Kuangyu; Menze, Bjoern] Tech Univ Munich, Dept Comp Sci, D-80333 Munich, Germany; [Guo, Rui; Li, Biao] Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med, Dept Nucl Med, Shanghai 200240, Peoples R China; [Chen, Jieneng] Tongji Univ, Coll Elect & Informat Engn, Shanghai 200082, Peoples R China; [Shi, Kuangyu] Univ Bern, Dept Nucl Med, Lab Artificial Intelligence & Translat Theranost, CH-3012 Bern, Switzerland Technical University of Munich; Shanghai Jiao Tong University; Tongji University; University of Bern Zhao, Y (corresponding author), Tech Univ Munich, Dept Comp Sci, D-80333 Munich, Germany. xiaobin.hu@tum.de; gr11734@rjh.com.cn; chenjn@tongji.edu.cn; hongwei.li@tum.de; waldmannstetter@tum.de; yu.zhao@tum.de; lb10363@rjh.com.cn; k.shi@tum.de; bjoern.menze@tum.de Li, Hongwei Bran/HJH-5317-2023 Li, Hongwei Bran/0000-0002-5328-6407; Zhao, Yu/0000-0001-8179-4903; Guo, Rui/0000-0003-1542-6111; Shi, Kuangyu/0000-0002-8714-3084; Menze, Bjoern/0000-0003-4136-5690 NVIDIA Corporation; China Scholarship Council (CSC) NVIDIA Corporation; China Scholarship Council (CSC)(China Scholarship Council) The author would like to acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research. We give our gratitude to the financial support of China Scholarship Council (CSC). 39 20 20 3 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. SEPT 2020.0 24 9 2599 2608 10.1109/JBHI.2020.2972694 0.0 10 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics NL5DD 32054593.0 2023-03-23 WOS:000567435400018 0
J Xiong, SQ; Li, X; Wu, XW; Yu, J; Gorbatov, OI; Di Marco, I; Kent, PRC; Sun, WW Xiong, Sangqi; Li, Xin; Wu, Xiangwei; Yu, Jin; Gorbatov, Oleg, I; Di Marco, Igor; Kent, Paul R. C.; Sun, Weiwei A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young's modulus COMPUTATIONAL MATERIALS SCIENCE English Article Combinatorial materials science; Density functional theory; High-throughput and data mining; Energetic stability and Young 's modulus TOTAL-ENERGY CALCULATIONS; PHASE-STABILITY; MARTENSITIC-TRANSFORMATION; ELECTRONIC-STRUCTURE; ELASTIC PROPERTIES; X ALLOYS; 1ST-PRINCIPLES; APPROXIMATION; PARAMETERS; OXYGEN The multicomponent Ti alloys, specifically the fl-phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young's modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nband Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young's moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young's modulus. We implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables. [Xiong, Sangqi; Wu, Xiangwei; Yu, Jin] Southeast Univ, Sch Mat Sci & Engn, Nanjing 211189, Peoples R China; [Li, Xin] Florida State Univ, Dept Ind & Mfg Engn, Tallahassee, FL 32306 USA; [Li, Xin; Kent, Paul R. C.; Sun, Weiwei] Oak Ridge Natl Lab, Ctr Nanophase Mat Sci, POB 2009, Oak Ridge, TN 37831 USA; [Gorbatov, Oleg, I] Lulea Univ Technol, Dept Engn Sci & Math, Div Mat Sci, Appl Phys, S-97187 Lulea, Sweden; [Gorbatov, Oleg, I] Nosov Magnitogorsk State Tech Univ, Lab Mech Gradient Nanomat, Magnitogorsk 455000, Russia; [Di Marco, Igor; Sun, Weiwei] Asia Pacific Ctr Theoret Phys, Pohang 790784, Gyeonbuk, South Korea; [Di Marco, Igor] POSTECH, Dept Phys, Pohang 790784, Gyeonbuk, South Korea; [Di Marco, Igor] Uppsala Univ, Dept Phys & Astron, Box 516, SE-75120 Uppsala, Sweden; [Kent, Paul R. C.] Oak Ridge Natl Lab, Computat Sci & Engn Div, POB 2009, Oak Ridge, TN 37831 USA; [Sun, Weiwei] Southeast Univ, SEU FEI Nanop Ctr, Key Lab MEMS, Minist Educ, Nanjing 210096, Peoples R China Southeast University - China; State University System of Florida; Florida State University; United States Department of Energy (DOE); Oak Ridge National Laboratory; Center for Nanophase Materials Sciences; Lulea University of Technology; Nosov Magnitogorsk State Technical University; Asia Pacific Center for Theoretical Physics; Pohang University of Science & Technology (POSTECH); Uppsala University; United States Department of Energy (DOE); Oak Ridge National Laboratory; Southeast University - China Li, X; Sun, WW (corresponding author), Southeast Univ, SEU FEI Nanop Ctr, Key Lab MEMS, Minist Educ, Nanjing 210096, Peoples R China. xl12d@my.fsu.edu; provels8467@gmail.com Gorbatov, Oleg/G-9172-2012; Di Marco, Igor/O-5190-2014; Kent, Paul/A-6756-2008 Gorbatov, Oleg/0000-0001-8629-5193; Di Marco, Igor/0000-0003-1714-0942; Kent, Paul/0000-0001-5539-4017 National Natural Science Foundation of China [51902052]; Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences; Ministry of Education and Science of the Russian Federation [074-02-2018-329]; Swedish National Infrastructure for Computing (SNIC) [SNIC2019/3-580]; National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences(United States Department of Energy (DOE)); Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Swedish National Infrastructure for Computing (SNIC); National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility W.S. acknowledges the National Natural Science Foundation of China (Grant No. 51902052). P.K. and (partially) W.S. were supported as part of the Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. I.D.M. was supported by appointments to the JRG program at the APCTP through the Science and Technology Promotion Fund and Lottery Fund of the Korean Government, the Korean Local Governments-Gyeongsangbuk-do Proving and Pohang City. O.I.G. acknowledges support provided by the Ministry of Education and Science of the Russian Federation (No. 074-02-2018-329 from May 16, 2018). This research used computational resources of Swedish National Infrastructure for Computing (SNIC) under the project of SNIC2019/3-580 as well as the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. We also thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations in this paper. 67 4 4 9 63 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0927-0256 1879-0801 COMP MATER SCI Comput. Mater. Sci. NOV 2020.0 184 109830 10.1016/j.commatsci.2020.109830 0.0 8 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science NN4WR Green Submitted, Bronze 2023-03-23 WOS:000568791000003 0
J Shen, WQ; Cao, YJ; Shao, JF; Liu, ZB Shen, W. Q.; Cao, Y. J.; Shao, J. F.; Liu, Z. B. Prediction of plastic yield surface for porous materials by a machine learning approach MATERIALS TODAY COMMUNICATIONS English Article Yield surface; Artificial neuron network; Machine learning; Porosity; Porous material; Micro-mechanics 2 POPULATIONS; MACROSCOPIC CRITERION; NONSPHERICAL VOIDS; APPROXIMATE MODELS; CONSTITUTIVE LAW; DUCTILE METALS; STRENGTH; COALESCENCE; SOLIDS; MEDIA The present paper focuses on the prediction of effective plastic yield surface of porous materials having a von Mises type solid matrix. Some typical explicit yield criteria obtained by different analytical homogenization methods are briefly reviewed and evaluated by using numerical results obtained from direct finite element simulations with different values of porosity. Each criterion has its own advantage and weakness. In order to get a better prediction, the Artificial Neuron Network (ANN) algorithm is adopted specially for the prediction of macroscopic yield stress of porous materials, seen as a regression problem with two input parameters and one output value. For the training purpose which is a key step in the ANN approach, new numerical results are presented in the present work with a wide range of porosity and of macroscopic stress triaxiality. Based on these data, the ANN approach is trained and it converges quickly. Then the ANN predictions are compared with numerical test data, a good agreement is found for all loading cases. Comparing with the existing yield criteria, the prediction given by the ANN approach is much more accuracy and easy to apply. [Shen, W. Q.; Shao, J. F.; Liu, Z. B.] Northeastern Univ, Coll Resources & Civil Engn, Key Lab, Minist Educ Safe Min Deep Met Mines, Shenyang 110819, Peoples R China; [Shen, W. Q.; Shao, J. F.] Univ Lille, CNRS, LaMcube Lab Mecan Multiphys, Cent Lille,Multiphys,Multiechelle,UMR 9013, F-59000 Lille, France; [Cao, Y. J.] Hohai Univ, Key Lab, Minist Educ Geomech & Embankment Engn, Nanjing 210098, Peoples R China Northeastern University - China; Centre National de la Recherche Scientifique (CNRS); Universite de Lille - ISITE; Centrale Lille; Universite de Lille; Hohai University Shen, WQ; Shao, JF (corresponding author), Northeastern Univ, Coll Resources & Civil Engn, Key Lab, Minist Educ Safe Min Deep Met Mines, Shenyang 110819, Peoples R China.;Shen, WQ; Shao, JF (corresponding author), Univ Lille, CNRS, LaMcube Lab Mecan Multiphys, Cent Lille,Multiphys,Multiechelle,UMR 9013, F-59000 Lille, France. wanqing.shen@polytech-lille.fr; jian-fu.shao@polytech-lille.fr Shen, Wanqing/H-7538-2016; shao, jianfu/N-2447-2018 Shen, Wanqing/0000-0001-9164-9451; shao, jianfu/0000-0002-6632-8207; CAO, Yajun/0000-0002-6004-1416 National Natural Science Foundation of China [11902069]; Young Talent Program of Liaoning Province [XLYC1807094] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Young Talent Program of Liaoning Province This study was supported by the National Natural Science Foundation of China (Grant No. 11902069), in part by the Young Talent Program of Liaoning Province under Grant XLYC1807094 and the framework of Sino-Franco Joint Research Laboratory on Multiphysics and Multiscale Rock Mechanics. 41 7 7 5 21 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4928 MATER TODAY COMMUN Mater. Today Commun. DEC 2020.0 25 101477 10.1016/j.mtcomm.2020.101477 0.0 9 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science PI2ZV Green Published, Bronze 2023-03-23 WOS:000600965600006 0
J Xie, XW; Carre, P; Perrine, C; Pousset, Y; Zhou, NR; Wu, JH Xie, Xinwen; Carre, Philippe; Perrine, Clency; Pousset, Yannis; Zhou, Nanrun; Wu, Jianhua Reduced-reference image quality metric based on statistic model in complex wavelet transform domain SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Reduced-reference image quality metric; Dual-tree complex wavelet transform; Information criterion; Probability density function; Generalized regression neural network COEFFICIENTS; RECOGNITION; INFORMATION A new Reduced-Reference (RR) image quality metric based on statistical models in the complex wavelet transform domain is proposed. The magnitude and the relative phase information of the complex wavelet coefficients is modeled by using probability density function, and a strategy based on the information criterion is proposed to optimally approximate the distribution. To further improve the accuracy of the metric, a comparison of the candidate models is studied, and the inverse Gaussian distribution and the wrapped Cauchy distribution are selected to model the magnitude and the relative phase distributions, respectively. The Kullback-Leibler divergence between the distributions of the reference image and the distorted one serves as the RR feature to measure the distortion. Finally, a generalized regression neural network is employed to map the RR feature into an objective score. Experimental studies confirmed that the proposed RR image quality metric is quality-aware and highly correlated with the human visual system. [Xie, Xinwen; Carre, Philippe; Perrine, Clency; Pousset, Yannis] Univ Poitiers, XLIM Inst, F-86962 Futuroscope, France; [Xie, Xinwen; Zhou, Nanrun; Wu, Jianhua] Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Xie, Xinwen] Jiujiang Univ, Dept Elect Engn, Jiujiang 332005, Peoples R China Universite de Poitiers; Nanchang University; Jiujiang University Xie, XW (corresponding author), Univ Poitiers, XLIM Inst, F-86962 Futuroscope, France. xinwen.xie@univ-poitiers.fr Zhou, Nanrun/HGC-4650-2022; Wu, Jianhua/AFL-8480-2022 Wu, Jianhua/0000-0002-4505-0568 National Natural Science Foundation of China [61662047, 61462061] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China [grant numbers 61662047, 61462061]. 53 4 4 0 6 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. MAY 2019.0 74 218 230 10.1016/j.image.2019.02.006 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering HU6CL Bronze, Green Published 2023-03-23 WOS:000465366200020 0
C Wu, YF; Gao, LR; Liao, WZ; Gamba, P; Zhang, B IEEE Wu, Yuanfeng; Gao, Lianru; Liao, Wenzhi; Gamba, Paolo; Zhang, Bing POTENTIAL ANALYSIS OF FEATURE EXTRACTION BASED QUICK RESPONSE FOR ENVIRONMENTAL CHANGE WITH SOCIAL MEDIA PHOTOS IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 22-27, 2018 Valencia, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc,European Space Agcy Big data; social media; remote sensing; feature exaction; air pollution monitoring A framework based on color feature extraction of social media photos and correlation analysis with air quality parameters is proposed to monitor environmental change. More specifically, photos of the Beijing Olympic Park from Panoramio website have been analyzed as a case study. The aerosol optical depth data at 500 nm wavelength (AOD 500) obtained from sun-photometer observation network station has been used as reference. Results show a proof of concept that social media photos have an interesting potential for air pollution estimate and remote sensing parameter validation with a low cost. [Wu, Yuanfeng; Gao, Lianru; Zhang, Bing] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Liao, Wenzhi] Univ Ghent, TELIN, IPI, IMEC, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium; [Gamba, Paolo] Univ Pavia, Dipartimento Ingn Ind & Informaz, I-27100 Pavia, Italy Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Ghent University; IMEC; University of Pavia Zhang, B (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. Gamba, Paolo E/G-1959-2010; wu, yuanfeng/GVU-6762-2022 Gamba, Paolo E/0000-0002-9576-6337; wu, yuan feng/0000-0001-8427-9851 Natural Science Foundation of China [91638201, 41301384] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors thank Prof. Mingmin Chi from school of computer science, Shanghai key laboratory of data science, Fudan University and Sunphotometer Observation NETwork for providing the datasets used in the experiments. This work was supported by Natural Science Foundation of China under Grant 91638201, 41301384. 7 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-7150-4 INT GEOSCI REMOTE SE 2018.0 1132 1135 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BL4XL 2023-03-23 WOS:000451039801082 0
J Chen, RJ; Romani, L; Scott, M Chen, Renjie; Romani, Lucia; Scott, Michael GMP2021-15th International Conference on Geometric Modeling and Processing COMPUTER AIDED GEOMETRIC DESIGN English Editorial Material Computer-aided geometric design; Computational geometry; Geometric and solid modeling; Curve surface and volume representation; Deep learning This foreword provides an overview of the contributions presented at the fifteenth International Conference on Geometric Modeling and Processing (GMP2021 -originally scheduled for May 10-13, 2021 in Pilsen, Czech Republic). (C) 2021 Elsevier B.V. All rights reserved. [Chen, Renjie] Univ Sci & Technol China, Hefei 230026, Peoples R China; [Romani, Lucia] Alma Mater Studiorum Univ Bologna, Dipartimento Matemat, Pza Porta S Donato 5, I-40126 Bologna, Italy; [Scott, Michael] Brigham Young Univ, Provo, UT 84602 USA Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Bologna; Brigham Young University Romani, L (corresponding author), Alma Mater Studiorum Univ Bologna, Dipartimento Matemat, Pza Porta S Donato 5, I-40126 Bologna, Italy. renjiec@ustc.edu.cn; lucia.romani@unibo.it; michael.scott@byu.edu Romani, Lucia/E-6687-2010 Romani, Lucia/0000-0002-3294-3228 17 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8396 1879-2332 COMPUT AIDED GEOM D Comput. Aided Geom. Des. MAR 2021.0 86 101980 10.1016/j.cagd.2021.101980 0.0 MAR 2021 3 Computer Science, Software Engineering; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics RP2XG 2023-03-23 WOS:000641596300002 0
J Liu, KL; Hu, XS; Meng, JH; Guerrero, JM; Teodorescu, R Liu, Kailong; Hu, Xiaosong; Meng, Jinhao; Guerrero, Josep M.; Teodorescu, Remus RUBoost-Based Ensemble Machine Learning for Electrode Quality Classification in Li-ion Battery Manufacturing IEEE-ASME TRANSACTIONS ON MECHATRONICS English Article Li-ion battery; machine learning; quality prediction; smarter manufacturing; unbalanced classification MANAGEMENT As a typical mechatronics system, the battery manufacturing chain becomes a hot research topic because it directly determines electrode quality, further affecting manufactured battery performance. Due to the complexity of battery manufacturing, an effective sensitivity analysis solution that could quantify variable importance or correlations and explore impact variables toward resulting the electrode quality is urgently needed. This article scrutinizes the effects of component parameters from the mixing stage on the manufactured results of Li-ion battery electrode via classification modeling. Specifically, an effective RUBoostbased ensemble learning framework is proposed to compensate for class imbalance issue and well classify three key quality indicators including the electronic conductivity, thickness, and half-cell capacity for both LiFePO4- and Li-4 Ti5O12-based electrode. Experimental results reveal that the proposed models could well handle the class imbalance issues and accurately classify/predict the qualities of the manufactured electrode. Moreover, the importance weights of variables and the correlations of variable pairs could be effectively quantified. Due to the superiority in terms of accuracy, interpretability, and data-driven nature, the proposed ensemble learning approach could not only help to conduct reliable multiclassification of manufactured electrode but also benefit smarter battery manufacturing. [Liu, Kailong; Hu, Xiaosong] Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England; [Hu, Xiaosong] Chongqing Univ, Dept Automot Engn, Chongqing 400044, Peoples R China; [Meng, Jinhao] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China; [Meng, Jinhao; Guerrero, Josep M.] Aalborg Univ, Ctr Res Microgrids CROM, AAU Energy, DK-9220 Aalborg, Denmark; [Teodorescu, Remus] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark University of Warwick; Chongqing University; Sichuan University; Aalborg University; Aalborg University Hu, XS (corresponding author), Chongqing Univ, Dept Automot Engn, Chongqing 400044, Peoples R China.;Meng, JH (corresponding author), Aalborg Univ, Ctr Res Microgrids CROM, AAU Energy, DK-9220 Aalborg, Denmark. kliu02@qub.ac.uk; xiaosonghu@ieee.org; scmjh2008@163.com; joz@et.aau.dk; ret@et.aau.dk Liu, Kailong/Y-1797-2019; Guerrero, Josep M./D-5519-2014; Meng, Jinhao/U-5118-2019; Hu, Xiao/GQI-0007-2022 Liu, Kailong/0000-0002-3564-6966; Guerrero, Josep M./0000-0001-5236-4592; Meng, Jinhao/0000-0003-3490-5089; Hu, Xiao/0000-0003-1128-4099 National Natural Science Foundation of China [51875054]; VILLUM FONDEN [25920]; China Postdoctoral Science Foundation [2020M673218] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); VILLUM FONDEN(Villum Fonden); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 51875054, in part by the VILLUM FONDEN under the VILLUM Investigator Grant 25920, and in part by the China Postdoctoral Science Foundation under Grant 2020M673218. 34 16 16 11 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1083-4435 1941-014X IEEE-ASME T MECH IEEE-ASME Trans. Mechatron. OCT 2022.0 27 5 2474 2483 10.1109/TMECH.2021.3115997 0.0 OCT 2021 10 Automation & Control Systems; Engineering, Manufacturing; Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering 5L4XP 2023-03-23 WOS:000732667100001 0
J Xiao, X; Zhang, SF; Mercaldo, F; Hu, GW; Sangaiah, AK Xiao, Xi; Zhang, Shaofeng; Mercaldo, Francesco; Hu, Guangwu; Sangaiah, Arun Kumar Android malware detection based on system call sequences and LSTM MULTIMEDIA TOOLS AND APPLICATIONS English Article Android malware detection; System call sequences; Deep learning; LSTM language model As Android-based mobile devices become increasingly popular, malware detection on Android is very crucial nowadays. In this paper, a novel detection method based on deep learning is proposed to distinguish malware from trusted applications. Considering there is some semantic information in system call sequences as the natural language, we treat one system call sequence as a sentence in the language and construct a classifier based on the Long Short-Term Memory (LSTM) language model. In the classifier, at first two LSTM models are trained respectively by the system call sequences from malware and those from benign applications. Then according to these models, two similarity scores are computed. Finally, the classifier determines whether the application under analysis is malicious or trusted by the greater score. Thorough experiments show that our approach can achieve high efficiency and reach high recall of 96.6% with low false positive rate of 9.3%, which is better than the other methods. [Xiao, Xi; Zhang, Shaofeng] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China; [Mercaldo, Francesco] Natl Res Council Italy, Inst Informat & Telemat, I-56124 Pisa, Italy; [Hu, Guangwu] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China; [Sangaiah, Arun Kumar] VIT Univ, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India Tsinghua University; University Town of Shenzhen; Tsinghua Shenzhen International Graduate School; Consiglio Nazionale delle Ricerche (CNR); Istituto di Informatica e Telematica (IIT-CNR); Shenzhen Institute of Information Technology; Vellore Institute of Technology (VIT); VIT Vellore Hu, GW (corresponding author), Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China. hugw@sziit.edu.cn Sangaiah, Arun Kumar/U-6785-2019 Sangaiah, Arun Kumar/0000-0002-0229-2460; HU, Guangwu/0000-0003-3947-9998 NSFC [61375054, 61402255, 61202358]; National High-tech R&D Program of China [2015AA016102]; Guangdong Natural Science Foundation [2015A030310492, 2014A030313745]; RD Program of Shenzhen [JCYJ20150630170146831, JCYJ20160301152145171, JCYJ20160531174259309, JSGG20150512162853495, Shenfagai [2015] 986]; Cross fund of Graduate School at Shenzhen, Tsinghua University [JC20140001] NSFC(National Natural Science Foundation of China (NSFC)); National High-tech R&D Program of China(National High Technology Research and Development Program of China); Guangdong Natural Science Foundation(National Natural Science Foundation of Guangdong Province); RD Program of Shenzhen; Cross fund of Graduate School at Shenzhen, Tsinghua University This work is supported by the NSFC projects (61375054, 61402255, 61202358), the National High-tech R&D Program of China (2015AA016102), Guangdong Natural Science Foundation (2015A030310492, 2014A030313745) and the RD Program of Shenzhen (JCYJ20150630170146831, JCYJ20160301152145171, JCYJ20160531174259309, JSGG20150512162853495, Shenfagai [2015] 986), and Cross fund of Graduate School at Shenzhen, Tsinghua University (JC20140001). 32 97 102 4 39 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. FEB 2019.0 78 4 3979 3999 10.1007/s11042-017-5104-0 0.0 21 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering HS5NE 2023-03-23 WOS:000463917200007 0
J Bian, FY; You, S; Zhao, HR; Chen, CN; Yu, ZT; Lai, YH Bian, Feiyu; You, Shi; Zhao, Haoran; Chen, Changnian; Yu, Zeting; Lai, Yanhua Energy, exergy, economy analysis, and multi-objective optimization of a novel integrated energy system by combining artificial neural network and whale optimization algorithm INTERNATIONAL JOURNAL OF ENERGY RESEARCH English Article artificial neural network; optimization; parameter analysis; solar and biogas energy; solid oxide fuel cell; whale optimization algorithm MUNICIPAL SOLID-WASTE; PREDICTION This study proposed a novel solid oxide fuel cell (SOFC)-integrated system fueled by biogas production from anaerobic fermentation of organic municipal solid wastes. The proposed system composed of the organic Rankine cycle (ORC), the anaerobic digester (AD), the SOFC, Kalina cycle (KC), and the parabolic trough solar collector (PTSC) which provided heat for the continuous and stable operation of anaerobic fermentation. Firstly, the mathematical model was established and then the energy, exergy, and economy analysis were evaluated. The results showed that the total exergy efficiency and the cost rate achieved 30.96% and 19.68$/h under design conditions. It was found that the total exergy efficiency increased as the increasing in the evaporating pressure of ORC and the basic ammonia solution concentration, but it decreased with the increase in the solar radiation intensity. When the input temperature and the current density of SOFC were increased, the total exergy efficiency was increased firstly then decreased and reached the maximum value of 439.2 and 481 kW at the SOFC input temperature of 628.6 degrees C and the current density of 8286 A/m(2). Besides, the cost rate was increased with the increase of the power consumption of the main components. The Pareto frontier was obtained by using the non-dominated sorting whale optimization algorithm (NSWOA) which was employed to perform the multi-objective optimization, and the comprehensive decision-making method (TOPSIS) was used to get the optimal solution. The optimal solution showed that the total exergy efficiency and the cost rate could reach 39.56% and 14.23 $/h, respectively. Furthermore, in order to reduce optimization time and improve the accuracy of surrogate model, this study examined an improved artificial neural network (ANN) algorithm combining data-driven surrogate model and whale optimization algorithm (WOA) to replace the physical model. It was also found that the time using the developed surrogate model to obtain the optimal solution set spent only 5 minutes which is far less than the physical method which spent more than 40 hours under the same computer configuration. [Bian, Feiyu; Chen, Changnian; Yu, Zeting; Lai, Yanhua] Shandong Univ, Sch Energy & Power Engn, Jinan 250061, Peoples R China; [You, Shi] Tech Univ Denmark, Dept Wind & Energy Syst, Lyngby, Denmark; [Zhao, Haoran] Shandong Univ, Sch Elect Engn, Jinan, Peoples R China Shandong University; Technical University of Denmark; Shandong University Yu, ZT (corresponding author), Shandong Univ, Sch Energy & Power Engn, Jinan 250061, Peoples R China. yuzt@sdu.edu.cn Major Science and Technology Project of Inner Mongolia Autonomous Region [2021ZD0032]; National Key R&D Program of China [2018YFA0702200]; National Natural Science Foundation of China [62192753]; Shandong Provincial Natural Science Foundation of China [ZR2019MEE045] Major Science and Technology Project of Inner Mongolia Autonomous Region; National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Natural Science Foundation of China(Natural Science Foundation of Shandong Province) Major Science and Technology Project of Inner Mongolia Autonomous Region, Grant/Award Number: 2021ZD0032; National Key R&D Program of China, Grant/Award Number: 2018YFA0702200; National Natural Science Foundation of China, Grant/Award Number: 62192753; Shandong Provincial Natural Science Foundation of China, Grant/Award Number: ZR2019MEE045 35 0 0 22 22 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 0363-907X 1099-114X INT J ENERG RES Int. J. Energy Res. DEC 2022.0 46 15 24179 24196 10.1002/er.8724 0.0 SEP 2022 18 Energy & Fuels; Nuclear Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Nuclear Science & Technology 7T4UW gold 2023-03-23 WOS:000854376600001 0
J Yu, ZW; Lu, Y; Zhang, J; You, JE; Wong, HS; Wang, YD; Han, GQ Yu, Zhiwen; Lu, Ye; Zhang, Jun; You, Jane; Wong, Hau-San; Wang, Yide; Han, Guoqiang Progressive Semisupervised Learning of Multiple Classifiers IEEE TRANSACTIONS ON CYBERNETICS English Article Ensemble learning; machine learning; optimization; random subspace; semisupervised learning SUPPORT VECTOR MACHINES; RANDOM SUBSPACE; FRAMEWORK; ENSEMBLE; LABEL; CLASSIFICATION; INFORMATION; REGRESSION; ALGORITHM; MODELS Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets. [Yu, Zhiwen; Lu, Ye; Zhang, Jun; Han, Guoqiang] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China; [You, Jane] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China; [Wong, Hau-San] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Wang, Yide] Univ Nantes, Ecole Polytech, F-44306 Nantes, France South China University of Technology; Hong Kong Polytechnic University; City University of Hong Kong; Nantes Universite Yu, ZW (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China. zhwyu@scut.edu.cn Wang, Yide/AAP-4487-2020; Zhang, jun/E-9359-2011 Zhang, jun/0000-0001-7835-9871; WANG, Yide/0000-0002-1461-2003; You, Jane/0000-0002-8181-4836; Yu, Zhiwen/0000-0002-0935-5890; WONG, Hau-San/0000-0002-1530-7529 NSFC [61572199, U1611461, 61472145, 61502173]; Guangdong Natural Science Funds for Distinguished Young Scholars [S2013050014677]; Science and Technology Planning Project of Guangdong Province, China [2015A050502011, 2016B090918042, 2016A050503015, 2016B010127003]; Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 11300715]; City University of Hong Kong [7004674]; Hong Kong General Research [152202/14E]; Hong Kong Polytechnic University [G-YM05, G-YN39] NSFC(National Natural Science Foundation of China (NSFC)); Guangdong Natural Science Funds for Distinguished Young Scholars; Science and Technology Planning Project of Guangdong Province, China; Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council); City University of Hong Kong(City University of Hong Kong); Hong Kong General Research; Hong Kong Polytechnic University(Hong Kong Polytechnic University) This work was supported in part by the NSFC under Grant 61572199, Grant U1611461, Grant 61472145, and Grant 61502173, in part by the Guangdong Natural Science Funds for Distinguished Young Scholars under Grant S2013050014677, in part by the Science and Technology Planning Project of Guangdong Province, China, under Grant 2015A050502011, Grant 2016B090918042, Grant 2016A050503015, and Grant 2016B010127003, in part by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant CityU 11300715, in part by the City University of Hong Kong under Grant 7004674, in part by the Hong Kong General Research under Grant 152202/14E, and in part by the Hong Kong Polytechnic University under Grant G-YM05 and Grant G-YN39. 97 27 29 3 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. FEB 2018.0 48 2 689 702 10.1109/TCYB.2017.2651114 0.0 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science FT1VS 28113355.0 2023-03-23 WOS:000422925700020 0
C Chan, THH; Guerquin, A; Sozio, M Assoc Comp Machinery Chan, T-H. Hubert; Guerquin, Arnaud; Sozio, Mauro Fully Dynamic k-Center Clustering WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018) English Proceedings Paper 27th World Wide Web (WWW) Conference APR 23-27, 2018 Lyon, FRANCE Assoc Comp Machinery,Int World Wide Web Conf Steering Comm,Univ Lyon,Digital League,Inria,Amazon,Baidu,Google,IDEXLYON,NAVER LINE,Yahoo! Res,Webcastor,Caisse Depots,Facebook,Inst Carnot, Telecom Soc Numerique,Mozilla,Spotify,MEDEF Lyon Rhone,MEDEF Auvergne Rhone Alpes,EFFEKTIV,Charli Charger Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model (where at any given point in time only the most recent data items are retained) or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have beenfl agged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a (2 + epsilon)-approximation algorithm for the k-center clustering problem with small amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while k and are constant. Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach. [Chan, T-H. Hubert] Univ Hong Kong, Hong Kong, Peoples R China; [Guerquin, Arnaud; Sozio, Mauro] Telecom ParisTech Univ, LTCI, Paris, France University of Hong Kong Chan, THH (corresponding author), Univ Hong Kong, Hong Kong, Peoples R China. hubert@cs.hku.hk; arnaud.guerquin@ens-paris-saclay.fr; sozio@telecom-paristech.fr PROCORE France-Hong Kong Joint Research Scheme - Research Grants Council of Hong Kong; Consulate General of France in Hong Kong [F-HKU702/16]; Hong Kong RGC [17217716]; French National Agency (ANR) [ANR-15-CE23- 0006] PROCORE France-Hong Kong Joint Research Scheme - Research Grants Council of Hong Kong; Consulate General of France in Hong Kong; Hong Kong RGC(Hong Kong Research Grants Council); French National Agency (ANR)(French National Research Agency (ANR)) This research was partially supported by a grant from the PROCORE France-Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the Consulate General of France in Hong Kong under the project F-HKU702/16.; This research was partially supported by the Hong Kong RGC under the grants 17217716.; This research was partially supported by French National Agency (ANR) under project FIELDS (ANR-15-CE23- 0006). 16 15 15 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5639-8 2018.0 579 587 10.1145/3178876.3186124 0.0 9 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM1PP Green Published, Bronze 2023-03-23 WOS:000460379000057 0
C Lokoc, J; Soucek, T; Vesely, P; Mejzlik, F; Ji, JQ; Xu, CX; Li, XR ASSOC COMP MACHINERY Lokoc, Jakub; Soucek, Tomas; Vesely, Patrik; Mejzlik, Frantisek; Ji, Jiaqi; Xu, Chaoxi; Li, Xirong A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA English Proceedings Paper 28th ACM International Conference on Multimedia (MM) OCT 12-16, 2020 ELECTR NETWORK ACM SIGMM,Assoc Comp Machinery datasets; neural networks; ad-hoc search; known-item search; representation learning IMAGE RETRIEVAL; SEARCH As reported by respected evaluation campaigns focusing both on automated and interactive video search approaches, deep learning started to dominate the video retrieval area. However, the results are still not satisfactory for many types of search tasks focusing on high recall. To report on this challenging problem, we present two orthogonal task-based performance studies centered around the state-of-the-art W2VV++ query representation learning model for video retrieval. First, an ablation study is presented to investigate which components of the model are effective in two types of benchmark tasks focusing on high recall. Second, interactive search scenarios from the Video Browser Showdown are analyzed for two winning prototype systems implementing a selected variant of the model and providing additional querying and visualization components. The analysis of collected logs demonstrates that even with the state-of-the-art text search video retrieval model, it is still auspicious to integrate users into the search process for task types, where high recall is essential. [Lokoc, Jakub; Soucek, Tomas; Vesely, Patrik; Mejzlik, Frantisek] Charles Univ Prague, Fac Math & Phys, Dept Software Engn, SIRET Res Grp, Prague, Czech Republic; [Ji, Jiaqi; Xu, Chaoxi; Li, Xirong] Renmin Univ China, Sch Informat, Key Lab Data Engn & Knowledge Engn, AI & Media Comp Grp, Beijing, Peoples R China Charles University Prague; Renmin University of China Lokoc, J (corresponding author), Charles Univ Prague, Fac Math & Phys, Dept Software Engn, SIRET Res Grp, Prague, Czech Republic. lokoc@ksi.mff.cuni.cz; xirong@ruc.edu.cn Li, Xirong/0000-0002-0220-8310 Czech Science Foundation (GACR) [19-22071Y]; Charles University [SVV-260451]; National Natural Science Foundation of China [61672523]; Beijing Natural Science Foundation [4202033]; Fundamental Research Funds for the Central Universities of Renmin University of China [18XNLG19]; Research Funds of Renmin University of China [18XNLG19] Czech Science Foundation (GACR)(Grant Agency of the Czech Republic); Charles University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central Universities of Renmin University of China; Research Funds of Renmin University of China This research has been supported by Czech Science Foundation (GACR) project 19-22071Y, Charles University grant SVV-260451, National Natural Science Foundation of China (No. 61672523), Beijing Natural Science Foundation (No. 4202033), and the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG19). We would also like to thank Gregor Kovalcik for his help with VIRET and Miroslav Kratochvil for his help with SOMHunter. 39 6 6 2 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7988-5 2020.0 2553 2561 10.1145/3394171.3414002 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BT2LS 2023-03-23 WOS:000810735002069 0
C Cheriguene, S; Azizi, N; Dey, N; Ashour, AS; Mnerie, CA; Olariu, T; Shi, FQ Balas, VE; Jain, LC; Balas, MM Cheriguene, Soraya; Azizi, Nabiha; Dey, Nilanjan; Ashour, Amira S.; Mnerie, Corina A.; Olariu, Teodora; Shi, Fuqian Classifier Ensemble Selection Based on mRMR Algorithm and Diversity Measures: An Application of Medical Data Classification SOFT COMPUTING APPLICATIONS, SOFA 2016, VOL 1 Advances in Intelligent Systems and Computing English Proceedings Paper 7th International Workshop on Soft Computing Applications (SOFA) AUG 24-26, 2016 Arad, ROMANIA Aurel Vlaicu Univ Arad,Univ Belgrade,Romanian Acad, Iasi Branch, Inst Comp Sci,IEEE Romanian Sect,Romanian Soc Control Engn & Tech Informat, Arad Sect,Gen Assoc Engineers Romania, Arad Sect,BTM Resources Arad Medical data classification; Classifiers selection; Diversity measures; Relevance; Minimum Redundancy Maximum Relevance Method AUTOMATED DIAGNOSIS; NEURAL-NETWORKS; DISEASE; RECOGNITION; PERFORMANCE Classifier selection is a significant problem in machine learning to reduce the computational time and the number of ensemble members. Over the past decade, multiple classifier systems (MCS) have been actively exploited to enhance the classification accuracy. Finding a pertinent objective function for measuring the competence of base classifier is a critical issue to select the appropriate subset from a pool of classifiers. Along with the accuracy, diversity measures are designed as objective functions for ensemble selection. This current work proposed a new selection method based on accuracy and diversity in order to achieve better medical data classification performance. The classifiers correlation was calculated using Minimum Redundancy Maximum Relevance (mRMR) method based on relevance and diversity measures. Experiments were carried out on five data sets from UCI Machine Learning Repository and LudmilaKuncheva Collection. The experimental results proved the superiority of the proposed classifiers selection method. [Cheriguene, Soraya; Azizi, Nabiha] Labged Lab, Annaba, Algeria; [Cheriguene, Soraya; Azizi, Nabiha] BadjiMokhtar Univ, Comp Sci Dept, POB 12, Annaba 23000, Algeria; [Dey, Nilanjan] Techno India Coll Technol, Dept Informat Technol, Kolkata, India; [Ashour, Amira S.] Tanta Univ, Dept Elect & Elect Commun Engn, Fac Engn, Tanta, Egypt; [Mnerie, Corina A.] Aurel Vlaicu Univ Arad, Fac Engn, Arad, Romania; [Olariu, Teodora] Vasile Goldis Western Univ Arad, Arad, Romania; [Shi, Fuqian] Wenzhou Med Univ, Coll Informat & Engn, Wenzhou, Peoples R China Egyptian Knowledge Bank (EKB); Tanta University; Aurel Vlaicu University of Arad; Vasile Goldis Western University of Arad; Wenzhou Medical University Dey, N (corresponding author), Techno India Coll Technol, Dept Informat Technol, Kolkata, India. cheriguene@labged.net; azizi@labged.net; neelanjandey@gmail.com; amirasashour@yahoo.com; corina.mnerie@gmail.com; olariu_teodora@yahoo.com; sfq@wmu.edu.cn Mnerie, Corina Anca/J-2933-2012; Azizi, Nabiha/GYU-8970-2022; Ashour, Amira S./T-5454-2019; azizi, nabiha/G-4139-2013 Mnerie, Corina Anca/0000-0002-9710-7781; Ashour, Amira S./0000-0003-3217-6185; azizi, nabiha/0000-0002-2713-4792; cheriguene, soraya/0000-0003-0470-3865 46 1 1 0 6 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-5357 2194-5365 978-3-319-62521-8 ADV INTELL SYST 2018.0 633 375 384 10.1007/978-3-319-62521-8_32 0.0 10 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK2MA 2023-03-23 WOS:000433138300032 0
J Tornatore, M; Wong, E; Zhu, ZQ; Casellas, R; Bathula, BG; Wosinska, L Tornatore, Massimo; Wong, Elaine; Zhu, Zuqing; Casellas, Ramon; Bathula, Balagangadhar G.; Wosinska, Lena Guest Editorial Latest Advances in Optical Networks for 5G Communications and Beyond IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Editorial Material Optical networks; machine learning; fronthaul; telemetry; free space optics; physical layer security; software-defined networking (SDN); network functions virtualization (NFV) This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field. [Tornatore, Massimo] Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy; [Wong, Elaine] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia; [Zhu, Zuqing] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Anhui, Peoples R China; [Casellas, Ramon] Ctr Tecnol Telecomunicac Catalunya CTTC CERCA, Castelldefels 08860, Spain; [Bathula, Balagangadhar G.] AT&T Labs, Network Core & Infrastruct Serv, Middletown, NJ 07748 USA; [Wosinska, Lena] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden Polytechnic University of Milan; University of Melbourne; Chinese Academy of Sciences; University of Science & Technology of China, CAS; AT&T; Chalmers University of Technology Tornatore, M (corresponding author), Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy. massimo.tornatore@polimi.it; ewon@unimelb.edu.au; zqzhu@ieee.org; ramon.casellas@cttc.es; bb4341@att.com; wosinska@chalmers.se Zhu (朱祖勍), Zuqing/J-8431-2017; Tornatore, Massimo/AAJ-5988-2020 Zhu (朱祖勍), Zuqing/0000-0002-4251-788X; Tornatore, Massimo/0000-0003-0740-1061; Casellas, Ramon/0000-0002-2663-6571; Wosinska, Lena/0000-0001-6704-6554 17 1 1 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. SEP 2021.0 39 9 2667 2671 10.1109/JSAC.2021.3094600 0.0 5 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications UB5ND Green Submitted, Bronze 2023-03-23 WOS:000685891700004 0
C Yu, WW; Madani, K; Sabourin, C Madani, K; Dourado, A; Rosa, A; Filipe, J Yu, Weiwei; Madani, Kurosh; Sabourin, Christophe CMAC Structure Optimization Based on Modified Q-Learning Approach and Its Applications COMPUTATIONAL INTELLIGENCE Studies in Computational Intelligence English Proceedings Paper 3rd International Joint Conference on Computational IntelligenceInternational Conference on Complex Analysis and Potential Theory OCT 24-26, 2011JUN 20-23, 2011 Paris, FRANCECtr Rech Math (CRM), Montreal, CANADA Ctr Rech Math (CRM) CMAC neural network; Structural parameters; Q-learning; Structure optimization ADAPTIVE QUANTIZATION Comparing with other neural network based models, CMAC has been applied successfully in many nonlinear control systems because of its computational speed and learning ability. However, for high-dimensional input CMAC in real world applications such as robot, the useable memory is finite or pre-allocated, thus we often have to make our choice between learning accuracy and memory size. This paper discusses how both the number of layer and step quantization influence the approximation quality of CMAC. By experimental enquiry, it is shown that it is possible to decrease the memory size without losing the approximation quality by selecting the adaptive structural parameters. Based on modified Q-learning approach, the CMAC structural parameters can be optimized automatically without increasing the complexity of its structure. The choice of this optimized CMAC structure can achieve a tradeoff between the learning accuracy and finite memory size. At last, this Q-learning based CMAC structure optimization approach is applied on the walk pattern generating for biped robot and workpiece orientation estimation for robot arm assembly respectively. [Yu, Weiwei] Northwestern Polytech Univ, Sch Mechatron Engn, Youyi Xilu 127Hao, Xian 710072, Peoples R China; [Madani, Kurosh; Sabourin, Christophe] Paris Est Univ, Signals Images & Intelligent Syst Lab, LISSI EA 3956, F-77127 Paris, France Northwestern Polytechnical University; Universite Paris-Est-Creteil-Val-de-Marne (UPEC) Yu, WW (corresponding author), Northwestern Polytech Univ, Sch Mechatron Engn, Youyi Xilu 127Hao, Xian 710072, Peoples R China. yuweiwei@nwpu.edu.cn; madani@u-pe.fr; sabourin@u-pe.fr Yu, Wei/GZL-3831-2022; sabourin, christophe/A-3263-2013 sabourin, christophe/0000-0002-7316-0801 Northwestern Polytechnical University [JC20100211] Northwestern Polytechnical University This research is supported by the fundamental research fund of Northwestern Polytechnical University (JC20100211), P.R. China. 11 0 0 0 9 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 1860-949X 978-3-642-35637-7 STUD COMPUT INTELL 2013.0 465 347 + 10.1007/978-3-642-35638-4_23 0.0 3 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BDW81 2023-03-23 WOS:000315429600023 0
J Wang, MY; Shen, LQ; Pasquale, LR; Wang, H; Li, D; Choi, EY; Yousefi, S; Bex, PJ; Elze, T Wang, Mengyu; Shen, Lucy Q.; Pasquale, Louis R.; Wang, Hui; Li, Dian; Choi, Eun Young; Yousefi, Siamak; Bex, Peter J.; Elze, Tobias An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma TRANSLATIONAL VISION SCIENCE & TECHNOLOGY English Article structure-function relationships; unsupervised artificial intelligence; macular vulnerability zone OPTICAL COHERENCE TOMOGRAPHY; VISUAL-FIELD; PROGRESSION; REPRODUCIBILITY; DEFECTS; DAMAGE Purpose: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma. Methods: We used paired reliable 24-2 VFs and optical coherence tomography scans of 691 eyes from 691 patients. The RNFLT maps were used to determine the RNFLT patterns (RPs) by non-negative matrix factorization (NMF). The RPs were correlated with mean deviation (MD), spherical equivalent (SE), and major blood vessel locations. The RPs were further used to predict the 52 total deviation (TD) values by linear regression compared with models using 24 15-degree sectors. Last, we associated the RPs with average TDs of the central upper two locations (C2-TD). Stepwise regression was applied to remove redundant features. Results: NMF highlighted 16 distinct RPs. Twelve RPs had arcuate-like informative zones (iZones): six with superior iZones, five with inferior iZones, and one RP with a bihemifield iZone, and four with non-arcuate-like temporal or nasal iZones. Twelve, nine, nine, and nine RPs were significantly (P < 0.05) correlated to MD, SE, and superior and inferior artery locations, respectively. Using RPs significantly (P < 0.05) improved the prediction of 52 TDs compared with using 24 15-degree sectors. Using RPs significantly (P < 0.001) improved the C2-TD prediction related to thinning in the inferior vulnerability zone compared with using the 24 sectoral RNFLTs. Conclusions: Using RPs improved the VF prediction compared with using sectoral RNFLTs. Translational Relevance: The RPs characterizing both pathological and anatomical variations can potentially assist clinicians better assess RNFLT loss. [Wang, Mengyu; Wang, Hui; Li, Dian; Choi, Eun Young; Elze, Tobias] Harvard Med Sch, Schepens Eye Res Inst, Massachusetts Eye & Ear, Boston, MA 02114 USA; [Shen, Lucy Q.] Harvard Med Sch, Massachusetts Eye & Ear, Boston, MA 02114 USA; [Pasquale, Louis R.] Icahn Sch Med Mt Sinai, Eye & Vis Res Inst, New York, NY 10029 USA; [Pasquale, Louis R.] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02114 USA; [Wang, Hui] Jilin Univ Finance & Econ, Inst Psychol & Behav, Changchun, Peoples R China; [Yousefi, Siamak] Univ Tennessee, Hamilton Eye Inst, Hlth Sci Ctr, Memphis, TN USA; [Bex, Peter J.] Northeastern Univ, Dept Psychol, Boston, MA 02115 USA; [Elze, Tobias] Max Planck Inst Math Sci, Leipzig, Germany Harvard University; Harvard Medical School; Massachusetts Eye & Ear Infirmary; Schepens Eye Research Institute; Harvard University; Harvard Medical School; Massachusetts Eye & Ear Infirmary; Icahn School of Medicine at Mount Sinai; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Jilin University of Finance & Economics; University of Tennessee System; University of Tennessee Health Science Center; Northeastern University; Max Planck Society Elze, T (corresponding author), Harvard Med Sch, Schepens Eye Res Inst, 20 Staniford St, Boston, MA 02114 USA. tobias-elze@tobias-elze.de Li, Dian/AAQ-3482-2020 Li, Dian/0000-0002-5374-7314 National Institutes of Health (NIH) [K99 EY028631]; NIH [R21 EY030142, R21 EY030631, R01 EY030575, R01 EY015473]; BrightFocus Foundation; Lions Foundation; Grimshaw-Gudewicz Foundation; Research to Prevent Blindness; Alice Adler Fellowship; NIH NEI Core Grant [P30 EY003790] National Institutes of Health (NIH)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); BrightFocus Foundation(BrightFocus Foundation); Lions Foundation; Grimshaw-Gudewicz Foundation; Research to Prevent Blindness(Research to Prevent Blindness (RPB)); Alice Adler Fellowship; NIH NEI Core Grant(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) Supported by the National Institutes of Health (NIH) K99 EY028631 (M.W.), NIH R21 EY030142 (S.Y. and T.E.), NIH R21 EY030631 (T.E.), NIH R01 EY030575 (T.E.), NIH R01 EY015473 (L.R.P.), BrightFocus Foundation (M.W. and T.E.), Lions Foundation (M.W. and T.E.), Grimshaw-Gudewicz Foundation (M.W. and T.E.), Research to Prevent Blindness (M.W. and T.E.), the Alice Adler Fellowship (T.E.), and NIH NEI Core Grant P30 EY003790 (M.W. and T.E.). 41 10 10 0 1 ASSOC RESEARCH VISION OPHTHALMOLOGY INC ROCKVILLE 12300 TWINBROOK PARKWAY, ROCKVILLE, MD 20852-1606 USA 2164-2591 TRANSL VIS SCI TECHN Transl. Vis. Sci. Technol. AUG 2020.0 9 9 41 10.1167/tvst.9.9.41 0.0 12 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology OH9SL 32908804.0 Green Accepted, gold 2023-03-23 WOS:000582929100041 0
J Arabameri, A; Danesh, AS; Santosh, M; Cerda, A; Pal, SC; Ghorbanzadeh, O; Roy, P; Chowdhuri, I Arabameri, Alireza; Danesh, Amir Seyed; Santosh, M.; Cerda, Artemi; Pal, Subodh Chandra; Ghorbanzadeh, Omid; Roy, Paramita; Chowdhuri, Indrajit Flood susceptibility mapping using meta-heuristic algorithms GEOMATICS NATURAL HAZARDS & RISK English Article Support vector machine; particle swarm optimization; genetic algorithm; remote sensing; Iran FUZZY INFERENCE SYSTEM; GENETIC ALGORITHM; CLIMATE-CHANGE; FREQUENCY-ANALYSIS; RISK-MANAGEMENT; HYBRID APPROACH; SOIL-EROSION; MACHINE; OPTIMIZATION; PREDICTION Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds. [Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorthol, Tehran, Iran; [Danesh, Amir Seyed] Univ Guilan, Fac Technol & Engn East Guilan, Rudsar Vajargah, Iran; [Santosh, M.] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing, Peoples R China; [Santosh, M.] Univ Adelaide, Dept Earth Sci, Adelaide, SA, Australia; [Cerda, Artemi] Univ Valencia, Dept Geog, Soil Eros & Degradat Res Grp, Valencia, Spain; [Pal, Subodh Chandra; Roy, Paramita; Chowdhuri, Indrajit] Univ Burdwan, Dept Geog, Bardhaman, W Bengal, India; [Ghorbanzadeh, Omid] Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria Tarbiat Modares University; University of Guilan; China University of Geosciences; University of Adelaide; University of Valencia; University of Burdwan; Salzburg University Ghorbanzadeh, O (corresponding author), Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria. omid.ghorbanzadeh@stud.sbg.ac.at Ghorbanzadeh, Omid/ACX-0397-2022 Ghorbanzadeh, Omid/0000-0002-9664-8770; Pal, Subodh Chandra/0000-0003-0805-8007 Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg [DK W 1237-N23] Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg(Austrian Science Fund (FWF)) This research was partly funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W 1237-N23) at the University of Salzburg. 94 7 7 18 64 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1947-5705 1947-5713 GEOMAT NAT HAZ RISK Geomat. Nat. Hazards Risk DEC 31 2022.0 13 1 949 974 10.1080/19475705.2022.2060138 0.0 26 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences; Water Resources 0L6LP gold 2023-03-23 WOS:000781584500001 0
J Monte-Moreno, E; Yang, H; Hernandez-Pajares, M Monte-Moreno, Enric; Yang, Heng; Hernandez-Pajares, Manuel Forecast of the Global TEC by Nearest Neighbour Technique REMOTE SENSING English Article Global Ionospheric Maps; Total Electron Content forecasting; machine learning; Nearest Neighbour method We propose a method for Global Ionospheric Maps of Total Electron Content forecasting using the Nearest Neighbour method. The assumption is that in a database of global ionosphere maps spanning more than two solar cycles, one can select a set of past observations that have similar geomagnetic conditions to those of the current map. The assumption is that the current ionospheric condition can be expressed by a linear combination of conditions seen in the past. The average of these maps leads to common geomagnetic components being preserved and those not shared by several maps being reduced. The method is based on searching the historical database for the dates of the maps closest to the current map and using as a prediction the maps in the database that correspond to time shifts on the prediction horizons. In contrast to other methods of machine learning, the implementation only requires a distance computation and does not need a previous step of model training and adjustment for each prediction horizon. It also provides confidence intervals for the forecast. The method has been analyzed for two full years (2015 and 2018), for selected days of 2015 and 2018, i.e., two storm days and two non-storm days and the performance of the system has been compared with CODE (24- and 48-h forecast horizons). [Monte-Moreno, Enric] Univ Politecn Cataluna, Dept TSC, TALP, Barcelona 08034, Spain; [Yang, Heng] Yangtze Normal Univ, Sch Elect Informat & Engn, Chongqing 408100, Peoples R China; [Yang, Heng; Hernandez-Pajares, Manuel] Univ Politecn Cataluna, Dept Math, UPC IonSAT, Barcelona 08034, Spain; [Hernandez-Pajares, Manuel] Inst Estudis Espacials Catalunya IEEC, Barcelona 08034, Spain Universitat Politecnica de Catalunya; Yangtze Normal University; Universitat Politecnica de Catalunya; Institut d'Estudis Espacials de Catalunya (IEEC) Monte-Moreno, E (corresponding author), Univ Politecn Cataluna, Dept TSC, TALP, Barcelona 08034, Spain. enric.monte@upc.edu; h.yang@upc.edu; manuel.hernandez@upc.edu Yang, Heng/GSN-0455-2022; Monte, Enrique/F-8218-2013; Hernandez-Pajares, Manuel/O-5338-2017 Monte, Enrique/0000-0002-4907-0494; Hernandez-Pajares, Manuel/0000-0002-9687-5850; Yang, Heng/0000-0002-1583-2307 MICINN [PID2019-107579RB-I00]; PITHIA NRF EU project; Natural Science Foundation of Chongqing, China [cstc2021jcyj-msxmX0191]; Science and Technology Research Program of Chongqing Municipal Education Commission of China [KJQN202101414]; Cooperative Projects between Undergraduate Universities in Chongqing and Institutes affiliated with Chinese Academy of Sciences [HZ2021014] MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); PITHIA NRF EU project; Natural Science Foundation of Chongqing, China(Natural Science Foundation of Chongqing); Science and Technology Research Program of Chongqing Municipal Education Commission of China; Cooperative Projects between Undergraduate Universities in Chongqing and Institutes affiliated with Chinese Academy of Sciences This work was been partially supported by the project PID2019-107579RB-I00 (MICINN) and done in the context of PITHIA NRF EU project. The work of Heng Yang was also partially supported by Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-msxmX0191), by the Science and Technology Research Program of Chongqing Municipal Education Commission of China (Grant No. KJQN202101414), and by the Cooperative Projects between Undergraduate Universities in Chongqing and Institutes affiliated with Chinese Academy of Sciences (No.HZ2021014). 22 2 2 1 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2022.0 14 6 1361 10.3390/rs14061361 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 0B6MF Green Published, gold 2023-03-23 WOS:000774745900001 0
J Leone, M Leone, Massimo From Fingers to Faces: Visual Semiotics and Digital Forensics INTERNATIONAL JOURNAL FOR THE SEMIOTICS OF LAW-REVUE INTERNATIONALE DE SEMIOTIQUE JURIDIQUE English Article Face; Semiotics; Forensics; Fingerprinting; Artificial intelligence PRINTS Identification is a primary need of societies. It is even more central in law enforcement. In the history of crime, a dialectics takes place between felonious attempts at concealing, disguising, or forging identities and societal efforts at unmasking the impostures. Semiotics offers specialistic skills at studying the signs of societal detection and identification, including those of forensics and criminology. In human history, no sign more than the face is attached a value of personal identity. Yet, modern forensics realizes that the face can mislead and, inspired by eastern models (China, Japan, India), adopts fingerprinting. In the digital era, however, fingerprinting first goes digital, then it is increasingly replaced by facial recognition. The face is back in digital AI forensics, together with a tangle of sociocultural biases. Semiotics can play a key role in studying their surreptitious influence. [Leone, Massimo] Univ Turin, Dept Philosophy & Educ Studies, Turin, Italy; [Leone, Massimo] Univ Shanghai, Dept Chinese Language & Literature, Shanghai, Peoples R China University of Turin; Shanghai University Leone, M (corresponding author), Univ Turin, Dept Philosophy & Educ Studies, Turin, Italy.;Leone, M (corresponding author), Univ Shanghai, Dept Chinese Language & Literature, Shanghai, Peoples R China. massimo.leone@unito.it Leone, Massimo/0000-0002-8144-4337 European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [819649] European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme(European Research Council (ERC)) This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 819649 - FACETS) 99 7 8 1 4 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0952-8059 1572-8722 INT J SEMIOTIC LAW Int. J. Semiotics Law APR 2021.0 34 2 579 599 10.1007/s11196-020-09766-x 0.0 SEP 2020 21 Law; Language & Linguistics; Social Sciences, Interdisciplinary Emerging Sources Citation Index (ESCI) Government & Law; Linguistics; Social Sciences - Other Topics QL3CV 33679005.0 Green Accepted, hybrid 2023-03-23 WOS:000568749400001 0
C Lamperti, G; Zhao, XF Czarnowski, I; Howlett, RJ; Jain, LC Lamperti, Gianfranco; Zhao, Xiangfu Decremental Subset Construction INTELLIGENT DECISION TECHNOLOGIES 2017, KES-IDT 2017, PT I Smart Innovation Systems and Technologies English Proceedings Paper 9th KES International Conference on Intelligent Decision Technologies (KES-IDT) JUN 21-23, 2017 Algarve, PORTUGAL KES Int FINITE ACYCLIC AUTOMATA; ACTIVE SYSTEMS; INCREMENTAL DETERMINIZATION; TEMPORAL OBSERVATIONS; STATE AUTOMATA; DIAGNOSIS Finite automata are exploited in several domains, including language processing, artificial intelligence, automatic control, and software engineering. Let N be a nondeterministic finite automaton (NFA) and D the deterministic finite automaton (DFA) equivalent to N. Assume to decrement N by Delta N, thus obtaining the decremented NFA N' = N \ Delta N, where some states and/or transitions are missing. Consider determinizing N'. Instead of determinizing N' from scratch using the classical Subset Construction algorithm, we propose Decremental Subset Construction, an algorithm which generates the DFA D' equivalent to N' by updating D based on N and Delta N. This way, only the actions necessary to transform D into D' are applied. Although evidence from worst-case complexity analysis indicates that Decremental Subset Construction is not better than Subset Construction, in practice, when N is large and Delta N relatively small, Decremental Subset Construction may outperform Subset Construction significantly. [Lamperti, Gianfranco] Univ Brescia, Dept Informat Engn, Brescia, Italy; [Zhao, Xiangfu] Zhejiang Normal Univ, Coll Maths Phys & Informat Engn, Jinhua, Peoples R China University of Brescia; Zhejiang Normal University Lamperti, G (corresponding author), Univ Brescia, Dept Informat Engn, Brescia, Italy. gianfranco.lamperti@unibs.it Zhao, Xiangfu/AAP-7918-2020 Zhao, Xiangfu/0000-0001-5870-5730; LAMPERTI, Gian Franco/0000-0002-1915-6932 Zhejiang Provincial Natural Science Foundation of China [LY16F020004]; National Natural Science Foundation of China [61472369] Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by Zhejiang Provincial Natural Science Foundation of China (No. LY16F020004); National Natural Science Foundation of China (No. 61472369). 24 3 3 0 1 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 2190-3018 978-3-319-59421-7; 978-3-319-59420-0 SMART INNOV SYST TEC 2018.0 72 22 36 10.1007/978-3-319-59421-7_3 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK2GJ Green Submitted 2023-03-23 WOS:000432721700003 0
J Xia, HS; Weng, J; Boubaker, S; Zhang, ZP; Jasimuddin, SM Xia, Huosong; Weng, Juan; Boubaker, Sabri; Zhang, Zuopeng; Jasimuddin, Sajjad M. Cross-influence of information and risk effects on the IPO market: exploring risk disclosure with a machine learning approach ANNALS OF OPERATIONS RESEARCH English Article; Early Access Risk disclosure; Information effect; Risk effect; Enterprise information transparency; Unsupervised machine learning algorithm INITIAL RETURNS; TRANSPARENCY; ANALYSTS; MEDIA; FIRMS; SELL; TONE The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives, this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low, the impact of semantic novelty and content richness on the IPO market is respectively enhanced. [Xia, Huosong; Weng, Juan] Wuhan Text Univ, Sch Management, Wuhan 430073, Peoples R China; [Xia, Huosong] Res Ctr Enterprise Decis Support, Key Res Inst Humanities & Social Sci, Wuhan 430073, Peoples R China; [Boubaker, Sabri] EM Normandie Business Sch, Metis Lab, Caen, France; [Zhang, Zuopeng] Vietnam Natl Univ, Int Sch, Hanoi, Vietnam; [Jasimuddin, Sajjad M.] Swansea Univ, Swansea, W Glam, Wales; [Zhang, Zuopeng] Univ North Florida, Coggin Coll Business, Jacksonville, FL 32224 USA; [Jasimuddin, Sajjad M.] Kedge Business Sch, Marseille, France Wuhan Textile University; Vietnam National University Hanoi; Swansea University; State University System of Florida; University of North Florida; Kedge Business School Jasimuddin, SM (corresponding author), Swansea Univ, Swansea, W Glam, Wales.;Jasimuddin, SM (corresponding author), Kedge Business Sch, Marseille, France. sajjad.jasimuddin@kedgebs.com Boubaker, Sabri/0000-0002-6416-2952 National Natural Science Foundation of China [71871172, 71571139]; Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei province [DSS20180204] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei province This research has been supported by the National Natural Science Foundation of China:71871172, Model of Risk knowledge acquisition and Platform governance in FinTech based on deep learning; 71571139, Outlier Analytics and Model of Outlier Knowledge Management in the context of Big Data; We deeply appreciate the suggestions from fellow members of Xia's project team and Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei province (DSS20180204). 117 1 1 17 17 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0254-5330 1572-9338 ANN OPER RES Ann. Oper. Res. 10.1007/s10479-022-05012-8 0.0 OCT 2022 37 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science 5M5XV 36312208.0 Bronze, Green Accepted 2023-03-23 WOS:000871168500002 0
J Franzoni, V; Milani, A; Mengoni, P; Piccinato, F Franzoni, Valentina; Milani, Alfredo; Mengoni, Paolo; Piccinato, Fabrizio Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics APPLIED SCIENCES-BASEL English Article teachers self-evaluation; information visualization; Arttificial Intelligence– based visual interface; learner continuous monitoring; course evaluation; usability This work proposes an innovative visual tool for real-time continuous learners analytics. The purpose of the work is to improve the design, functionality, and usability of learning management systems to monitor user activity to allow educators to make informed decisions on e-learning design, usually limited to dashboards graphs, tables, and low-usability user logs. The standard visualisation is currently scarce, and often inadequate to inform educators about the design quality and students engagement on their learning objects. The same low usability can be found in learning analytics tools, which mostly focus on post-course analysis, demanding specific skills to be effectively used, e.g., for statistical analysis and database queries. We propose a tool for student analytics embedded in a Learning Management System, based on the innovative visual metaphor of interface morphing. Artificial intelligence provides in remote learning immediate feedback, crucial in a face-to-face setting, highlighting the students' engagement in each single learning object. A visual metaphor is the representation of a person, group, learning object, or concept through a visual image that suggests a particular association or point of similarity. The basic idea is that elements of the application interface, e.g., learning objects' icons and student avatars, can be modified in colour and dimension to reflect key performance indicators of learner's activities. The goal is to provide high-affordance information on the student engagement and usage of learning objects, where aggregation functions on subsets of users allow a dynamic evaluation of cohorts with different granularity. The proposed visual metaphors (i.e., thermometer bar, dimensional morphing, and tag cloud morphing) have been implemented and experimented within academic-level courses. Experimental results have been evaluated with a comparative analysis of user logs and a subjective usability survey, which show that the tool obtains quantitative, measurable effectiveness and the qualitative appreciation of educators. Among metaphors, the highest success is obtained by Dimensional morphing and Tag cloud transformation. [Franzoni, Valentina; Milani, Alfredo; Piccinato, Fabrizio] Univ Perugia, Dept Math & Comp Sci, I-6100 Perugia, Italy; [Mengoni, Paolo] Hong Kong Baptist Univ, Dept Journalism, Hong Kong, Peoples R China University of Perugia; Hong Kong Baptist University Franzoni, V; Milani, A (corresponding author), Univ Perugia, Dept Math & Comp Sci, I-6100 Perugia, Italy. valentina.franzoni@dmi.unipg.it; alfredo.milani@unipg.it; pmengoni@hkbu.edu.hk; fabrizio.piccinato@studenti.unipg.it Franzoni, Valentina/AAC-7783-2021 Franzoni, Valentina/0000-0002-2972-7188; MILANI, Alfredo/0000-0003-4534-1805; MENGONI, Paolo/0000-0003-0713-6261 Italian P.R.I.N. e-learning project PHRAME-Phraseological Complexity Measures Italian P.R.I.N. e-learning project PHRAME-Phraseological Complexity Measures This research has been partially funded by the Italian P.R.I.N. e-learning project PHRAME-Phraseological Complexity Measures 31 9 9 15 40 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel OCT 2020.0 10 20 7195 10.3390/app10207195 0.0 25 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Chemistry; Engineering; Materials Science; Physics ON6KL Green Submitted, gold 2023-03-23 WOS:000586807200001 0
J Ran, T; Liu, Y; Li, HZ; Tang, SX; He, ZX; Munteanu, CR; Gonzalez-Diaz, H; Tan, ZL; Zhou, CS Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R.; Gonzalez-Diaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory SCIENTIFIC REPORTS English Article TISSUE DISTRIBUTION; DEVELOPMENTAL-CHANGES; FOOD-INTAKE; GHR GENE; NANOPARTICLES; SELECTION; PEPTIDE; GOAT; POLYMORPHISM; REGRESSION The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R-2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system. [Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Tan, Zhiliang; Zhou, Chuanshe] Chinese Acad Sci, South Cent Expt Stn Anim Nutr & Feed Sci, Key Lab Agroecol Proc Subtrop Reg,Inst Subtrop Ag, Hunan Res Ctr Livestock & Poultry Sci,Minist Agr, Changsha 410125, Hunan, Peoples R China; [Ran, Tao; Li, Hengzhi] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China; [Liu, Yong; Munteanu, Cristian R.] Univ A Coruna, Fac Comp Sci, RNASA, Campus Elvina S-N, La Coruna 15071, Spain; [Gonzalez-Diaz, Humberto] Univ Basque Country UPV EHU, Dept Organ Chem 2, Leioa 48940, Spain; [Gonzalez-Diaz, Humberto] Basque Fdn Sci, IKERBASQUE, Bilbao 48011, Spain Chinese Academy of Sciences; Ministry of Agriculture & Rural Affairs; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade da Coruna; University of Basque Country; Basque Foundation for Science Zhou, CS (corresponding author), Chinese Acad Sci, South Cent Expt Stn Anim Nutr & Feed Sci, Key Lab Agroecol Proc Subtrop Reg,Inst Subtrop Ag, Hunan Res Ctr Livestock & Poultry Sci,Minist Agr, Changsha 410125, Hunan, Peoples R China. zcs@isa.ac.cn Munteanu, Cristian-Robert/G-1714-2011; Gonzalez-Diaz, Humbert G. Díaz/A-6785-2012 Munteanu, Cristian-Robert/0000-0002-5628-2268; Gonzalez-Diaz, Humbert G. Díaz/0000-0002-9392-2797; He, Zhixiong/0000-0002-4802-3506 National Natural Science Foundation of China [31320103917]; Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues [XDA05020700]; CAS Visiting Professorship for Senior International Scientists [2010T2S13, 2012T1S0009]; Hunan Provincial Creation Development Project [2013TF3006]; University Management of Xunta de Galicia, Spain [GRC2014/049]; General Directorate of Culture, Education National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues(Chinese Academy of Sciences); CAS Visiting Professorship for Senior International Scientists; Hunan Provincial Creation Development Project; University Management of Xunta de Galicia, Spain; General Directorate of Culture, Education The authors acknowledge the Key Laboratory of Subtropical Agro-ecological Processes, Public Service Technology Center, Institute of Subtropical Agriculture, the Chinese Academy of Sciences (CAS) for providing all the experimental materials and apparatus. We would like to thank Dr. Carlos Fernandez-Lozano (University of A Coruna, Spain) for the machine learning advices. This project was supported by grants from the National Natural Science Foundation of China (Grant No. 31320103917), Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05020700), CAS Visiting Professorship for Senior International Scientists (Grant No. 2010T2S13, 2012T1S0009), and Hunan Provincial Creation Development Project (Grant No. 2013TF3006). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049), Spain. 56 10 10 0 28 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep JUL 27 2016.0 6 30174 10.1038/srep30174 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics DS1AG 27460882.0 gold, Green Published 2023-03-23 WOS:000380327500001 0
J Wu, Y; Li, Z; Wang, QL; Legros, G; Li, CM; Yan, ZW Wu, Yi; Li, Zhen; Wang, Qianlong; Legros, Guillaume; Li, Chaomin; Yan, Zhiwen Predicting simultaneously fields of soot temperature and volume fraction in laminar sooting flames from soot radiation measurements - a convolutional neural networks approach OPTICS EXPRESS English Article NITROGEN An original convolutional neural network, i.e. U-net approach, has been designed to retrieve simultaneously local soot temperature and volume fraction fields from line-of-sight measurements of soot radiation fields. A five-stage U-net architecture is established and detailed. Based on a set of N2 diluted ethylene non-premixed flames, the minimum batch size requirement for U-net model training is discussed and the U-net model prediction ability is validated fbr the first time by fields provided by the modulated absorption emission (MAE) technique documenting the N-2 diluted flame. Additionally, the U-net model's flexibility and robustness to noise are also quantitatively studied by introducing 5% & 10% Gaussian random noises into training together with the testing data. Eventually, the U-net predictive results are directly contrasted with those of Bayesian optimized back propagation neural network (BPNN) in terms of testing score, prediction absolute error (AE), soot parameter field smoothness, and time cost. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement [Wu, Yi] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China; [Li, Zhen; Wang, Qianlong; Li, Chaomin; Yan, Zhiwen] Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China; [Legros, Guillaume] Sorbonne Univ, CNRS, UMR7190, Inst Jean Rond dAlembert, F-75005 Paris, France Beijing Institute of Technology; Tianjin University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Sorbonne Universite Wang, QL (corresponding author), Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China. wangqianlong@tju.edu.cn wu, yi/0000-0001-5403-0473 National Natural Science Foundation of China [51906016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China (51906016). 23 1 1 2 5 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUN 6 2022.0 30 12 21230 21240 10.1364/OE.458096 0.0 11 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 2B9XG 36224846.0 gold 2023-03-23 WOS:000810533400081 0
C Zhu, KK; Du, ZY; Li, WX; Huang, D; Wang, YH; Chen, LM IEEE Zhu, Kangkang; Du, Zhengyin; Li, Weixin; Huang, Di; Wang, Yunhong; Chen, Liming Discriminative Attention-based Convolutional Neural Network for 3D Facial Expression Recognition 2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2019) IEEE International Conference on Automatic Face and Gesture Recognition and Workshops English Proceedings Paper 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG) MAY 14-18, 2019 Lille, FRANCE IEEE,Univ Lille,Inst Mines Telecom,Univ Lille, Inst Mines Telecom, Ecole Mines Telecom, IMT Lille Douai,INRIA,3DMD,Google,I Site Univ Lille Nord Europe,Centre Rech Informatique Signal Automatique Lille,IEEE Comp Soc,IEEE Biometr Council 3D Facial Expression Recognition ( FER) is an active research area in computer vision. Although previous methods report promising results, two key issues still remain to be solved. On the one hand, different facial areas contribute unequally to performing various expressions, but most existing methods extract features from the entire 3D surface. On the other hand, the difference between expressions varies, while previous methods generally treat different emotions equally, making some of them extremely hard to be distinguished. To solve these problems, we propose a novel approach for 3D FER, namely Discriminative Attention-based Convolution Neural Network ( DA-CNN), to generate more comprehensive expression related representations. DA-CNN introduces an attention module to the CNN models, which helps the deep model selectively focus on emotional salient regions in a learnable way. Furthermore, a novel loss named Dimensional Distribution ( DD) loss is proposed to model the inter-expression relationship. Supervised by DD loss, DA-CNN can generate more discriminative expression representation. Extensive experiments are conducted on BU-3DFE dataset, and the results show that DA-CNN achieves significant improvement over the state-of-the-art. [Zhu, Kangkang; Du, Zhengyin; Li, Weixin; Huang, Di; Wang, Yunhong; Chen, Liming] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China; [Zhu, Kangkang; Du, Zhengyin; Huang, Di; Wang, Yunhong] Beihang Univ, Sch Comp Sci & Engn, IRIP Lab, Beijing 100191, Peoples R China; [Chen, Liming] Univ Lyon, LIRIS, Ecole Cent Lyon, CNRS,UMR 5205, F-69134 Ecully, France Beihang University; Beihang University; Centre National de la Recherche Scientifique (CNRS); Ecole Centrale de Lyon; Institut National des Sciences Appliquees de Lyon - INSA Lyon Huang, D (corresponding author), Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China.;Huang, D (corresponding author), Beihang Univ, Sch Comp Sci & Engn, IRIP Lab, Beijing 100191, Peoples R China. zhukangkang@buaa.edu.cn; duzhy@buaa.edu.cn; weixinli@buaa.edu.cn; dhuang@buaa.edu.cn; yhwang@buaa.edu.cn; Liming.Chen@ec-lyon.fr National Natural Science Foundation of China [61673033] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China under Grant No. 61673033. 45 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2326-5396 978-1-7281-0089-0 IEEE INT CONF AUTOMA 2019.0 590 597 8 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BN6AU 2023-03-23 WOS:000484960400084 0
J He, J; Chen, SH; Shahrour, I He Ji; Chen Sheng-hong; Shahrour, Isam A REVISED SOLUTION OF EQUIVALENT PERMEABILITY TENSOR FOR DISCONTINUOUS FRACTURES JOURNAL OF HYDRODYNAMICS English Article discontinuous fractures; permeability tensor; back analysis; packer test; Artificial Neural Network (ANN) HYDRAULIC CONDUCTIVITY TENSOR; ROCK MASSES; FIELD DETERMINATION; ANISOTROPIC MEDIA; TESTS The equivalent permeability tensor is essential to the application of the equivalent porous media model in the numerical seepage simulation for fractured rock masses. In this paper, a revised solution of the equivalent permeability tensor is proposed to represent the influence of the fracture connectivity in discontinuous fractures. A correction coefficient is involved to reflect the complex seepage flow type through the rock bridge. This correction coefficient is back analyzed from single-hole packer tests, based on the Artificial Neural Network (ANN) back analysis and the Finite Element Method (FEM) seepage simulation. The limitation of this back analysis algorithm is that the number of single-hole packer tests should be equal or greater than the number of fracture sets, and three is the maximum number of the fracture sets. The proposed solution and the back analysis algorithm are applied in the permeability measurement and the seepage simulation for the Xiaowan arch dam foundation. [He Ji; Chen Sheng-hong] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Shahrour, Isam] Univ Lille 1, Lab Genie Civil & Geoenvironm, F-59650 Villeneuve Dascq, France Wuhan University; Universite de Lille - ISITE; Universite de Lille He, J (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. jihechn@gmail.com Shahrour, isam/A-6865-2008; Shahrour, Isam/I-7151-2019 Shahrour, Isam/0000-0001-7279-8005 National Natural Science Foundation of China [51079109] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Project supported by the National Natural Science Foundation of China (Grant No. 51079109). 19 7 7 7 59 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 1001-6058 J HYDRODYN J. Hydrodyn. OCT 2012.0 24 5 711 717 10.1016/S1001-6058(11)60295-3 0.0 7 Mechanics Science Citation Index Expanded (SCI-EXPANDED) Mechanics 033GI 2023-03-23 WOS:000310781800009 0
C Li, L; Feng, XY; Wu, XT; Xia, ZQ; Hadid, A Campilho, A; Karray, F Li, Lei; Feng, Xiaoyi; Wu, Xiaoting; Xia, Zhaoqiang; Hadid, Abdenour Kinship Verification from Faces via Similarity Metric Based Convolutional Neural Network IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016) Lecture Notes in Computer Science English Proceedings Paper 13th International Conference on Image Analysis and Recognition in Memory of Mohamed Kamel (ICIAR) JUL 13-15, 2016 Povoa de Varzim, PORTUGAL Assoc Image & Machine Intelligence Kinship verification; Similarity metric learning; Convolutional neural networks; Siamese architecture The ability to automatically determine whether two persons are from the same family or not is referred to as Kinship (or family) verification. This is a recent and challenging research topic in computer vision. We propose in this paper a novel approach to kinship verification from facial images. Our solution uses similarity metric based convolutional neural networks. The system is trained using Siamese architecture specific constraints. Extensive experiments on the benchmark KinFaceW-I & II kinship face datasets showed promising results compared to many state-of-the-art methods. [Li, Lei; Feng, Xiaoyi; Wu, Xiaoting; Xia, Zhaoqiang; Hadid, Abdenour] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Shaanxi, Peoples R China; [Hadid, Abdenour] Univ Oulu, Ctr Machine Vis Res, Oulu, Finland Northwestern Polytechnical University; University of Oulu Hadid, A (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Shaanxi, Peoples R China.;Hadid, A (corresponding author), Univ Oulu, Ctr Machine Vis Res, Oulu, Finland. hadid@ee.oulu.fi Xia, Zhaoqiang/AAC-4021-2019 Xia, Zhaoqiang/0000-0003-0630-3339 16 27 27 0 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-41501-7; 978-3-319-41500-0 LECT NOTES COMPUT SC 2016.0 9730 539 548 10.1007/978-3-319-41501-7_60 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BG0ZP 2023-03-23 WOS:000386604000060 0
C Wang, CY; Yang, T; Cui, JH; Li, Y; Zhou, TQ; Cai, ZP Lai, Y; Wang, T; Jiang, M; Xu, G; Liang, W; Castiglione, A Wang, Chengyu; Yang, Tao; Cui, Jinhua; Li, Yu; Zhou, Tongqing; Cai, Zhiping TSAEns: Ensemble Learning for KPI Anomaly Detection ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT I Lecture Notes in Computer Science English Proceedings Paper 21st International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP) DEC 03-05, 2021 ELECTR NETWORK Swinburne Univ Technol,Springer, Lecture Notes Comp Sci,FAAI Time series anomaly detection; Ensemble learning; AIOps; Time series; Anomaly detection Time series anomaly detection is a critical task in the domain of Artificial Intelligence for IT Operations (AIOps). Large companies that provide Internet-based services need to closely monitor massive time-series data from applications and hardware in real-time and provide timely troubleshooting to keep reliable services and smooth business. However, selecting and ensembling diverse detectors for better detection results is challenging due to the complexity of time series data. In this paper, an selection framework for time series anomaly detection, which can select proper detectors for time series data of diverse characteristics according to the detector's performance on historical data. Also, we combine active learning methods to propose unseen samples for labeling, which can significantly alleviate the labeling overhead of operators. Experimental results show the effectiveness of the proposed framework. [Wang, Chengyu; Yang, Tao; Cui, Jinhua; Zhou, Tongqing; Cai, Zhiping] Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China; [Li, Yu] Tech Univ Berlin, Dept Comp Engn & Microelect, Berlin, Germany National University of Defense Technology - China; Technical University of Berlin Cui, JH (corresponding author), Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China. jhcui.gid@gmail.com National Key Research and Development Program of China [2020YFC2003400]; National Natural Science Foundation of China [62172155, 62102425, 62072465]; Science and Technology Innovation Program of Hunan Province [2021RC2071] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Innovation Program of Hunan Province This work is supported by the National Key Research and Development Program of China (2020YFC2003400), the National Natural Science Foundation of China (62172155, 62102425, 62072465), and the Science and Technology Innovation Program of Hunan Province (2021RC2071). 22 0 0 6 11 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-95384-3; 978-3-030-95383-6 LECT NOTES COMPUT SC 2022.0 13155 162 177 10.1007/978-3-030-95384-3_11 0.0 16 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS8EO 2023-03-23 WOS:000771729400011 0
J Tu, B; Wang, Z; Yang, XC; Li, J; Plaza, A Tu, Bing; Wang, Zhi; Yang, Xianchang; Li, Jun; Plaza, Antonio Hyperspectral Anomaly Detection Using Quantum Potential Clustering IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Anomaly detection; Wave functions; Hyperspectral imaging; Kernel; Detectors; Principal component analysis; Tensors; density-based spatial clustering of applications with noise (DBSCAN); hyperspectral images (HSIs); quantum potential clustering; unsupervised salient strategy DETECTION ALGORITHMS; REPRESENTATION; PATTERN Quantum machine learning has attracted significant attention in recent years due to its capacity to reflect a particle's aggregation in the quantum domain. In this work, quantum machine learning is used to perform anomaly detection in hyperspectral images (HSIs) by considering pixels as particles in the quantum domain, and using the quantum potential to cluster the anomalies and background. Specifically, a new anomaly detection method is proposed based on quantum potential by first reformulating and applying quantum potential concepts to traditional HSI anomaly detection. Then, by using the weight matrix (also called distance matrix), the original wave function is modified to improve the anomaly detection accuracy. Finally, by considering the similarities between anomaly detection and human vision in the task of highlighting targets, an unsupervised salient strategy is also defined to achieve the final detection results. Through this method, we build the internal relationship between quantum potential energy clustering and hyperspectral anomaly detection (HAD). Our experimental results on four real HSIs reveal that the proposed method compares favorably with regards to other anomaly detection methods. Specially, the proposed method called quantum potential anomaly detection (QPAD) obtains a 0.2% increase on the average of the background suppression ability indicator. [Tu, Bing; Wang, Zhi; Yang, Xianchang] Hunan Inst Sci & Technol, Sch Informat Sci & Technol, Yueyang 414000, Peoples R China; [Li, Jun] China Univ Geosci, Sch Comp Sci, Wuhan 430078, Peoples R China; [Li, Jun] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain Hunan Institute of Science & Technology; China University of Geosciences; China University of Geosciences; Universidad de Extremadura Tu, B (corresponding author), Hunan Inst Sci & Technol, Sch Informat Sci & Technol, Yueyang 414000, Peoples R China. tubing@hnist.edu.cn; wangzhi98190506@outlook.com; xianchang_yang@foxmail.com; lijuncug@cug.edu.cn; aplaza@unex.es ; Plaza, Antonio/C-4455-2008 Yang, Xianchang/0000-0002-1486-0496; Plaza, Antonio/0000-0002-9613-1659 National Natural Science Foundation of China [61977022, 62271200]; Science Foundation for Distinguished Young Scholars of Hunan Province [2020JJ2017]; Key Research and Development Program of Hunan Province [2019SK2012]; Foundation of Department of Water Resources of Hunan Province [XSKJ2021000-12, XSKJ2021000-13, XSKJ2022068-48]; Natural Science Foundation of Hunan Province [2021JJ40226]; Foundation of Education Bureau of Hunan Province [21B0590, 21B0595, 20B062]; Junta de Extremadura [GR18060]; Spanish Ministerio de Ciencia e Innovacion (APRISA) [PID2019-110315RB-I00]; European Union's Horizon 2020 Research and Innovation Program (EXPOSURE) [734541] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation for Distinguished Young Scholars of Hunan Province(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Key Research and Development Program of Hunan Province; Foundation of Department of Water Resources of Hunan Province; Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Foundation of Education Bureau of Hunan Province; Junta de Extremadura; Spanish Ministerio de Ciencia e Innovacion (APRISA)(Ministry of Science and Innovation, Spain (MICINN)); European Union's Horizon 2020 Research and Innovation Program (EXPOSURE) This work was supported in part by the National Natural Science Foundation of China under Grant 61977022 and Grant 62271200; in part by the Science Foundation for Distinguished Young Scholars of Hunan Province under Grant 2020JJ2017; in part by the Key Research and Development Program of Hunan Province under Grant 2019SK2012; in part by the Foundation of Department of Water Resources of Hunan Province under Grant XSKJ2021000-12, Grant XSKJ2021000-13, and Grant XSKJ2022068-48; in part by the Natural Science Foundation of Hunan Province under Grant 2021JJ40226; in part by the Foundation of Education Bureau of Hunan Province under Grant 21B0590, Grant 21B0595, and Grant 20B062; in part by the Junta de Extremadura under Grant GR18060; in part by the Spanish Ministerio de Ciencia e Innovacion (APRISA) under Project PID2019-110315RB-I00; and in part by the European Union's Horizon 2020 Research and Innovation Program (EXPOSURE) under Grant 734541. 49 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 10.1109/TIM.2022.3218561 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 6J6KW 2023-03-23 WOS:000886932000026 0
J Leng, KJ; Jing, LB; Lin, IC; Chang, SH; Lam, A Leng, Kaijun; Jing, Linbo; Lin, I-Ching; Chang, Sheng-Hung; Lam, Anthony Research on mining collaborative behaviour patterns of dynamic supply chain network from the perspective of big data NEURAL COMPUTING & APPLICATIONS English Article Supply chain; Cooperative behaviour pattern; Network efficiency; Identification of important nodes ORDER ALLOCATION; DATA ANALYTICS; SELECTION; MODEL; QFD In view of the limitations of the linear model used in the traditional supply chain solution method, it cannot adapt to the dynamic supply chain network solution method in the multi-source big data environment of the Internet, and maps the dynamic supply chain into a network diagram model, and proposes e-commerce. Supply chain network model, based on this model, presents a semi-instance pattern detection method based on collaborative matrix decomposition, which is used to detect a semi-instantiated collaborative behaviour pattern in the supply chain network. According to the given collaborative behaviour model, the collaborative supply matrix decomposition method is first used to calculate the candidate supply chain of the personalized supply chain, and the degree of intimacy between node entities in the supply chain network is calculated. Using the A* graph search algorithm, a supply chain result candidate chain set is generated based on a personalized supply chain candidate set. According to personalized time, cost and other constraints, the final supply chain solution is tailored. The correctness, efficiency and accuracy of the method were verified by the e-commerce supply chain data set for apparel. [Leng, Kaijun; Jing, Linbo] China Acad Social Sci, Natl Acad Econ Strategy, Beijing, Peoples R China; [Leng, Kaijun; Lin, I-Ching; Chang, Sheng-Hung] Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China; [Lam, Anthony] Katholieke Univ Leuven, Fac Econ & Business, Louvain, Belgium Chinese Academy of Social Sciences; Hubei University of Economics; KU Leuven Lin, IC (corresponding author), Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China. liniching@hbue.edu.cn Leng, Kaijun/AAI-4417-2020; 穆, 儒/HGC-6534-2022 Leng, Kaijun/0000-0002-6962-2524; Lin, Yichin/0000-0003-1154-6554 National Natural Science Foundation of China [71402048]; Hubei society of social sciences [2016101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hubei society of social sciences The authors acknowledge the National Natural Science Foundation of China (Grant: 71402048) and Hubei society of social sciences (Grant: 2016101). 21 5 5 5 35 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. JAN 2019.0 31 1.0 SI 113 121 10.1007/s00521-018-3666-z 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HU7HY 2023-03-23 WOS:000465453100011 0
J Chen, Q; Ren, XM; Oliver, JA Chen, Qiang; Ren, Xuemei; Angel Oliver, Jesus Identifier-based adaptive neural dynamic surface control for uncertain DC-DC buck converter system with input constraint COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION English Article Adaptive dynamic surface control; Neural compensator; Buck converter; Finite-time identifier NONLINEAR-SYSTEMS; DIGITAL-CONTROL; DESIGN In this paper, an identifier-based adaptive neural dynamic surface control (IANDSC) is proposed for the uncertain DC DC buck converter system with input constraint. Based on the analysis of the effect of input constraint in the buck converter, the neural network compensator is employed to ensure the controller output within the permissible range. Subsequently, the constrained adaptive control scheme combined with the neural network compensator is developed for the buck converter with uncertain load current. In this scheme, a newly presented finite-time identifier is utilized to accelerate the parameter tuning process and to heighten the accuracy of parameter estimation. By utilizing the adaptive dynamic surface control (ADSC) technique, the problem of explosion of complexity inherently in the traditional adaptive backstepping design can be overcome. The proposed control law can guarantee the uniformly ultimate boundedness of all signals in the closed-loop system via Lyapunov synthesis. Numerical simulations are provided to illustrate the effectiveness of the proposed control method. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved. [Chen, Qiang; Ren, Xuemei] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China; [Angel Oliver, Jesus] UPM, Ctr Elect Ind, Madrid 28006, Spain Beijing Institute of Technology; Universidad Politecnica de Madrid Chen, Q (corresponding author), Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China. chenqiang8404@yahoo.com.cn chen, qiang/HGU-5418-2022; chen, qiang/GWZ-7308-2022 National Natural Science Foundation of China [60974046, 61011130163] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Supported by the National Natural Science Foundation of China under Grant Nos. 60974046 and 61011130163. 28 16 19 0 22 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1007-5704 1878-7274 COMMUN NONLINEAR SCI Commun. Nonlinear Sci. Numer. Simul. APR 2012.0 17 4 1871 1883 10.1016/j.cnsns.2011.08.028 0.0 13 Mathematics, Applied; Mathematics, Interdisciplinary Applications; Mechanics; Physics, Fluids & Plasmas; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Mechanics; Physics 859RP 2023-03-23 WOS:000297893800037 0
J Zhu, JD; San-Segundo, R; Pardo, JM Zhu, Jiadong; San-Segundo, Ruben; Pardo, Jose M. Feature extraction for robust physical activity recognition HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES English Article Physical activity recognition; Human Activity Recognition; Feature extraction robustness; Type of sensor; Signal normalization; REALDISP Activity Recognition dataset; OPPORTUNITY dataset; Random Forest; Pattern recognition; Machine learning This paper presents the development of a Human Activity Recognition (HAR) system that uses a network of nine inertial measurement units situated in different body parts. Every unit provides 3D (3-dimension) acceleration, 3D angular velocity, 3D magnetic field orientation, and 4D quaternions. This system identifies 33 different physical activities (walking, running, cycling, lateral elevation of arms, etc.). The system is composed of two main modules: a feature extractor for obtaining the most relevant characteristics from the inertial signals every second, and a machine learning algorithm for classifying between the different activities. This paper focuses on the feature extractor module, evaluating several types of features and proposing different normalization approaches. This paper also analyses the performance of every sensor included in the inertial measurement units. The main experiments have been done using a public available dataset named REALDISP Activity Recognition dataset. This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios. Final results demonstrate that the proposed HAR system significantly improves the classification accuracy compared to previous works on this dataset. For the best configuration, the system accuracy is 99.1%. This system has been also evaluated with the OPPORTUNITY dataset obtaining competitive results. [Zhu, Jiadong] Beijing Univ Posts & Telecommun, Sch Comp Sci, 10 Xitucheng Rd, Beijing 100876, Peoples R China; [San-Segundo, Ruben; Pardo, Jose M.] Univ Politecn Madrid, ETSI Telecomunicac, Speech Technol Grp, Ciudad Univ, E-28040 Madrid, Spain Beijing University of Posts & Telecommunications; Universidad Politecnica de Madrid San-Segundo, R (corresponding author), Univ Politecn Madrid, ETSI Telecomunicac, Speech Technol Grp, Ciudad Univ, E-28040 Madrid, Spain. ruben.sansegundo@upm.es Pardo, Jose/AAT-2834-2020 Volpe Martinez, Alfonso/0000-0002-0132-9698 China Scholarship Council; ASLP-MULAN [TIN2014-54288-C4-1-R]; NAVEGABLE; MICINN [DPI2014-53525-C3-2-R] China Scholarship Council(China Scholarship Council); ASLP-MULAN; NAVEGABLE; MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government) This work has been supported by China Scholarship Council scholarship and ASLP-MULAN (TIN2014-54288-C4-1- R) and NAVEGABLE (MICINN, DPI2014-53525-C3-2-R) projects. 31 37 37 0 11 KOREA INFORMATION PROCESSING SOC SEOUL 1002HO YONGSUNGBIZTEL 314-1 2GA HANKANGRO YONGSAN-GU, SEOUL, 140-750, SOUTH KOREA 2192-1962 HUM-CENT COMPUT INFO Human-centric Comput. Inf. Sci. JUN 2 2017.0 7 16 10.1186/s13673-017-0097-2 0.0 16 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science EW7IB hybrid 2023-03-23 WOS:000402681900001 0
J Peng, HP; Tian, Y; Kurths, J Peng, Haipeng; Tian, Ye; Kurths, Juergen Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks MATHEMATICAL PROBLEMS IN ENGINEERING English Article DETERMINISTIC CONSTRUCTION; MEASUREMENT MATRIX; RECOVERY; SYSTEM Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption. [Peng, Haipeng; Tian, Ye] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China; [Peng, Haipeng; Tian, Ye] Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Potsdam Institut fur Klimafolgenforschung Peng, HP (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China.;Peng, HP (corresponding author), Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China. penghaipeng@bupt.edu.cn National Key Research and Development Program of China [2016YFB0800602, 2016YFB0800604]; National Natural Science Foundation of China [61573067, 61472045]; Beijing City Board of Education Science and Technology Project [KM201510015009]; Beijing City Board of Education Science and Technology Key Project [KZ201510015015] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing City Board of Education Science and Technology Project; Beijing City Board of Education Science and Technology Key Project This paper is supported by the National Key Research and Development Program of China (Grants nos. 2016YFB0800602 and 2016YFB0800604), the National Natural Science Foundation of China (Grants nos. 61573067 and 61472045), the Beijing City Board of Education Science and Technology Project (Grant no. KM201510015009), and the Beijing City Board of Education Science and Technology Key Project (Grant no. KZ201510015015). 28 1 1 0 15 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. 2017.0 2017 8158465 10.1155/2017/8158465 0.0 8 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics ER0MT gold, Green Submitted 2023-03-23 WOS:000398482700001 0
J Song, T; Wang, JR; Xu, DY; Wei, W; Han, RS; Meng, F; Li, Y; Xie, PF Song, Tao; Wang, Jiarong; Xu, Danya; Wei, Wei; Han, Runsheng; Meng, Fan; Li, Ying; Xie, Pengfei Unsupervised Machine Learning for Improved Delaunay Triangulation JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article unstructured grid generation and optimization; K-means clustering; global ocean model; Delaunay triangulation; grid quality TROPICAL CYCLONES; GRID GENERATION; FRAMEWORK Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively. [Song, Tao; Wang, Jiarong; Wei, Wei; Han, Runsheng; Li, Ying; Xie, Pengfei] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China; [Song, Tao] Univ Politecn Madrid, Dept Artificial Intelligence, Fac Comp Sci, Campus Montegancedo, Madrid 28660, Spain; [Xu, Danya] Guangdong Lab Marine Sci & Engn, Zhuhai 519080, Peoples R China; [Meng, Fan] China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China China University of Petroleum; Universidad Politecnica de Madrid; China University of Petroleum Xu, DY (corresponding author), Guangdong Lab Marine Sci & Engn, Zhuhai 519080, Peoples R China. tsong@upc.edu.cn; wangjiarong1024@163.com; xudanya@sml-zhuhai.cn; weiwei@upc.edu.cn; z20070023@s.upc.edu.cn; vanmeng@163.com; leeying422@163.com; pengfeixie1996@163.com Song, Tao/T-7360-2018 Song, Tao/0000-0002-0130-3340; Wang, Jiarong/0000-0003-0751-0217; Meng, Fan/0000-0002-1462-4696; Xie, Pengfei/0000-0002-7521-157X Natural Science Foundation of China [U1811464]; National Key Research and Development Program [2018YFC1406201, 2018YFC1406204]; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [311021009]; Natural Science Foundation of Shandong Province [405 ZR2019MF012]; Taishan Scholars Fund [ZX20190157] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Taishan Scholars Fund FundingThis work was supported by Natural Science Foundation of China (Grant: U1811464); National Key Research and Development Program (No. 2018YFC1406201 and No. 2018YFC1406204); Project supported by Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021009); Natural Science Foundation of Shandong Province (Grant No. 405 ZR2019MF012); Taishan Scholars Fund (Grant No. ZX20190157). 32 1 1 1 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. DEC 2021.0 9 12 1398 10.3390/jmse9121398 0.0 18 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography YA2DV gold 2023-03-23 WOS:000738151300001 0
J Liu, C; Wang, W; Wang, ZX; Chen, SF; Su, PF; Gao, HY; Xu, C; Ge, BY; Ding, HF; Liu, L Liu, Chang; Wang, Wei; Wang, Zhixun; Chen, Shangfa; Su, Peifang; Gao, Hongyuan; Xu, Chao; Ge, Biyuan; Ding, Hongfa; Liu, Liang Optimal operation and locating method of new energy building with shared charging service FRONTIERS IN ENERGY RESEARCH English Article location problem; data analysis; new energy building; analytic hierarchy process; electric vehicle; optimal operating strategy; mixed-integer linear programming ACCESSIBILITY; EMISSIONS In order to cope with global climate change, an electric vehicle (EV) and new energy building are constantly being innovated and improved. With the popularity and application of big data and Internet of Things, the new energy building with available charging piles may also become a charging station, which can solve the problem of difficult charging of EVs and promote the local energy consumption of the building. Therefore, this study proposes a shared charging concept for buildings, that is, shared photovoltaic, charging, and energy storage building (sPCEB). First, based on the analysis results of big data in cities or settlements of people, a locating method of the sPCEB system is introduced, and further proposes an optimal operating strategy that maximizes the combined benefit of the building. The efficiency and effectiveness of the proposed methods are verified by simulation. [Liu, Chang; Wang, Wei; Chen, Shangfa; Su, Peifang; Gao, Hongyuan; Xu, Chao] Changjiang Inst Survey Planning Design & Res, Wuhan, Peoples R China; [Wang, Zhixun] State Grid Hubei Econ Res Inst, Wuhan, Peoples R China; [Ge, Biyuan] Tech Univ Darmstadt, Dept Architecture, Darmstadt, Germany; [Ding, Hongfa] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan, Peoples R China; [Liu, Liang] Changsha Univ Sci & Technol, Coll Energy & Power Engn, Changsha, Peoples R China Technical University of Darmstadt; Huazhong University of Science & Technology; Changsha University of Science & Technology Liu, L (corresponding author), Changsha Univ Sci & Technol, Coll Energy & Power Engn, Changsha, Peoples R China. liu_0826@126.com 27 1 1 3 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-598X FRONT ENERGY RES Front. Energy Res. AUG 8 2022.0 10 865060 10.3389/fenrg.2022.865060 0.0 15 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 3Y1IJ Green Published, gold 2023-03-23 WOS:000843482800001 0
J Zhang, B; Hu, WH; Cao, D; Huang, Q; Chen, Z; Blaabjerg, F Zhang, Bin; Hu, Weihao; Cao, Di; Huang, Qi; Chen, Zhe; Blaabjerg, Frede Deep reinforcement learning-based approach for optimizing energy conversion in integrated electrical and heating system with renewable energy ENERGY CONVERSION AND MANAGEMENT English Article Integrated energy system; Dynamic energy conversion; Artificial intelligence; Deep reinforcement learning; Proximal policy optimization WIND POWER; OPTIMIZATION; MANAGEMENT; GENERATION; OPERATION; NETWORK; STORAGE; DEMAND; DESIGN; MODEL With advanced information technologies applied in integrated energy systems (IESs), controlling the energy conversion has become an effective method for improving grid flexibility and reducing the operating cost of IESs. This study proposes a dynamic energy conversion strategy for the energy management of an IES with renewable energy, which considers the system operator's (SO) operating cost. Deep reinforcement learning (DRL) is used to illustrate the hierarchical decision-making process, in which the dynamic energy conversion problem is formulated as a discrete finite Markov decision process, and proximal policy optimization (PPO) is adopted to solve the decision-making problem. Using DRL, the SO can adaptively decide the wind power conversion ratio during the online learning process, where the uncertainties of customers' load demand profiles, flexibility of spot electricity prices, and wind power generation are addressed. Simulations show that the proposed PPO-based renewable energy conversion algorithm can effectively reduce the SO's operating cost. [Zhang, Bin; Hu, Weihao; Cao, Di; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Sichuan, Peoples R China; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark University of Electronic Science & Technology of China; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Sichuan, Peoples R China. sven@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; hwong@uestc.edu.cn; zch@et.aau.dk Blaabjerg, Frede/A-5008-2008; Hu, Weihao/AAE-7945-2019 Blaabjerg, Frede/0000-0001-8311-7412; Hu, Weihao/0000-0002-7019-7289; Zhang, Bin/0000-0002-9237-8144; Huang, Qi/0000-0002-8637-0269 Open Fund of State Key Laboratory of Operation and Control of Renewable Energy Storage System [NYB51201901204] Open Fund of State Key Laboratory of Operation and Control of Renewable Energy Storage System This work was supported by Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage System (NYB51201901204). 54 39 43 18 120 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. DEC 15 2019.0 202 112199 10.1016/j.enconman.2019.112199 0.0 13 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics JU4PK 2023-03-23 WOS:000501659300003 0
J Gao, X; Han, WC; Hu, QY; Qin, YT; Wang, SJ; Lun, F; Sun, J; Wu, JC; Xiao, X; Lan, Y; Li, H Gao, Xiang; Han, Wenchao; Hu, Qiyuan; Qin, Yuting; Wang, Sijia; Lun, Fei; Sun, Jing; Wu, Jiechen; Xiao, Xiao; Lan, Yang; Li, Hong Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model REMOTE SENSING English Article apple yield; logistic growth model; planting age; spectral endmember; BP neural network VEGETATION; SATELLITE; COVER In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future. [Gao, Xiang; Han, Wenchao; Hu, Qiyuan; Qin, Yuting; Wang, Sijia; Lun, Fei] China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China; [Sun, Jing] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China; [Wu, Jiechen] KTH Royal Inst Technol, Dept Sustainable Dev Environm Sci & Engn SEED, S-10044 Stockholm, Sweden; [Xiao, Xiao] China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China; [Lan, Yang] UCL, Bartlett Sch Environm Energy & Resources, London WC1E 6BT, England; [Li, Hong] Beijing Acad Agr & Forestry Sci, Inst Plant Nutr & Resources, Beijing 100097, Peoples R China China Agricultural University; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Royal Institute of Technology; China Agricultural University; University of London; University College London; Beijing Academy of Agriculture & Forestry Sciences (BAAFS) Li, H (corresponding author), Beijing Acad Agr & Forestry Sci, Inst Plant Nutr & Resources, Beijing 100097, Peoples R China. lihong@baafs.net.cn 43 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2023.0 15 3 642 10.3390/rs15030642 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 8W5HA gold 2023-03-23 WOS:000931362500001 0
J Wang, SH; Ye, FF; Yang, LH; Liu, J; Wang, H; Martinez, L Wang, Suhui; Ye, Fei-Fei; Yang, Long-Hao; Liu, Jun; Wang, Hui; Martinez, Luis Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Extended belief rule base; Data increment; Parameter optimization; Activation factor; Industrial cost prediction WEIGHT CALCULATION; RISK-ASSESSMENT; EXPERT-SYSTEM Industrial cost is a comprehensive indicator to reflect industrial behaviors, whereas industrial data with annotation are rare because the annotation process is very expensive. Data increment transformation is a feasible solution to enrich annotated industrial data, but it bring a new challenge in system modeling because the size of transformational data is the quadratical relationship with that of collected data, and even turn into big data problem. Hence, a novel rule-based system proposed for handling big data problems, called micro-extended belief rule-based system (Micro-EBRBS), is introduced for industrial cost prediction. Firstly, the Micro-EBRBS is improved by (1) the use of activation factor to revise the calculation of individual matching degrees; (2) the use of parameter optimization to determine the optimal value of basic parameters. Afterwards, on the basis of data increment transformation, a novel industrial cost prediction model, called data increment-based Micro-EBRBS (DIME) model, is developed to accurately predict industrial costs. In case study, 13 state-own holding industries with historical data from 1999 to 2019 in China are used to illustrate the effectiveness of the DIME model. Comparative results show that the DIME model is more accurate than some existing models in industrial cost prediction. [Wang, Suhui; Yang, Long-Hao] Fuzhou Univ, Decis Sci Inst, Fuzhou, Fujian, Peoples R China; [Ye, Fei-Fei] Fujian Normal Univ, Sch Publ Management, Fuzhou, Peoples R China; [Ye, Fei-Fei; Yang, Long-Hao; Liu, Jun; Wang, Hui; Martinez, Luis] Ulster Univ, Sch Comp, Coleraine, North Ireland; [Martinez, Luis] Univ Jaen, Dept Comp Sci, Jaen, Spain Fuzhou University; Fujian Normal University; Ulster University; Universidad de Jaen Yang, LH (corresponding author), Fuzhou Univ, Decis Sci Inst, Fuzhou, Fujian, Peoples R China.;Yang, LH (corresponding author), Ulster Univ, Sch Comp, Coleraine, North Ireland. more026@hotmail.com Martinez, Luis/A-1746-2009; Yang, Long-Hao/H-4430-2017 Martinez, Luis/0000-0003-4245-8813; Yang, Long-Hao/0000-0003-2239-1447 National Natural Science Foundation of China [72001043, 61773123]; National Science Foundation of Fujian Province, China [2020J05122]; Humanities and Social Science Foundation of the Ministry of Education of China [20YJC630188] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Foundation of Fujian Province, China; Humanities and Social Science Foundation of the Ministry of Education of China(Ministry of Education, China) This research was supported by the National Natural Science Foundation of China (Nos. 72001043 and 61773123), the National Science Foundation of Fujian Province, China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188). 27 0 0 5 8 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. JAN 2023.0 14 1 SI 63 78 10.1007/s13042-021-01485-4 0.0 JAN 2022 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8C1OT Green Submitted 2023-03-23 WOS:000746362100004 0
J Jerbi, H; Ben Aoun, S; Omri, M; Simos, TE; Tsitouras, C Jerbi, Houssem; Ben Aoun, Sondess; Omri, Mohamed; Simos, Theodore E.; Tsitouras, Charalampos A Neural Network Type Approach for Constructing Runge-Kutta Pairs of Orders Six and Five That Perform Best on Problems with Oscillatory Solutions MATHEMATICS English Article initial value problem; Runge-Kutta pairs; differential evolution; periodic orbits 5TH-ORDER; FORMULAS; FAMILY We analyze a set of explicit Runge-Kutta pairs of orders six and five that share no extra properties, e.g., long intervals of periodicity or vanishing phase-lag etc. This family of pairs provides five parameters from which one can freely pick. Here, we use a Neural Network-like approach where these coefficients are trained on a couple of model periodic problems. The aim of this training is to produce a pair that furnishes best results after using certain intervals and tolerance. Then we see that this pair performs very well on a wide range of problems with periodic solutions. [Jerbi, Houssem] Univ Hail, Coll Engn, Dept Ind Engn, Hail 1234, Saudi Arabia; [Ben Aoun, Sondess] Univ Hail, Coll Comp Sci & Engn, Dept Comp Engn, Hail 1234, Saudi Arabia; [Omri, Mohamed] King Abdulaziz Univ, Deanship Sci Res DSR, Jeddah 21589, Saudi Arabia; [Simos, Theodore E.] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan; [Simos, Theodore E.] Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China; [Simos, Theodore E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, GR-67100 Xanthi, Greece; [Tsitouras, Charalampos] Natl & Kapodistrian Univ Athens, Gen Dept, GR-34400 Psachna, Greece University Ha'il; University Ha'il; King Abdulaziz University; China Medical University Taiwan; China Medical University Hospital - Taiwan; Democritus University of Thrace; National & Kapodistrian University of Athens Simos, TE (corresponding author), China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan.;Simos, TE (corresponding author), Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China.;Simos, TE (corresponding author), Democritus Univ Thrace, Dept Civil Engn, Sect Math, GR-67100 Xanthi, Greece. h.jerbi@uoh.edu.sa; s.benaoun@uoh.edu.sa; Mnomri@kau.edu.sa; tsimos.conf@gmail.com; tsitourasc@uoa.gr Omri, Mohamed/N-4035-2015; JERBI, Houssem/AEL-1686-2022; Jerbi, Houssem/AAP-4572-2020; Ben Aoun, Sondess/GRY-4694-2022 JERBI, Houssem/0000-0003-1816-3767; Jerbi, Houssem/0000-0003-1816-3767; Tsitouras, Charalampos/0000-0001-6801-8117; Simos, Theodore/0000-0002-9220-6924 Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [FP-057-43] Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia FundingThe Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia has funded this project, under grant no. (FP-057-43). 25 2 2 0 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics MAR 2022.0 10 5 827 10.3390/math10050827 0.0 10 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics ZS0JU gold 2023-03-23 WOS:000768160300001 0
J Gou, SQ; Nik, VM; Scartezzini, JL; Zhao, Q; Li, ZR Gou, Shaoqing; Nik, Vahid M.; Scartezzini, Jean-Louis; Zhao, Qun; Li, Zhengrong Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand ENERGY AND BUILDINGS English Review Passive design; Residential buildings; Multi-objective optimization; Artificial neural network; Genetic algorithm; Thermal comfort; Energy demand SIMULATION-BASED OPTIMIZATION; ARTIFICIAL NEURAL-NETWORK; VECTOR MACHINE APPROACH; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHM; CLIMATE ANALYSIS; HOT SUMMER; PERFORMANCE; EFFICIENCY; SYSTEM The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm It (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay's theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. (C) 2017 Published by Elsevier B.V. [Gou, Shaoqing; Li, Zhengrong] Tongji Univ, Sch Mech Engn, HVAC & Gas Inst, Shanghai 200092, Peoples R China; [Nik, Vahid M.] Lund Univ, Div Bldg Phys, Dept Bldg & Environm Technol, Lund, Sweden; [Nik, Vahid M.] Chalmers Univ Technol, Dept Architecture & Civil Engn, Div Bldg Technol, Gothenburg, Sweden; [Scartezzini, Jean-Louis] Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland; [Zhao, Qun] Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China Tongji University; Lund University; Chalmers University of Technology; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Tongji University Li, ZR (corresponding author), Tongji Univ, Sch Mech Engn, HVAC & Gas Inst, Shanghai 200092, Peoples R China. shaoqing.gou@hotmail.com; vahid.nik@byggtek.lth.se; jean-louis.scartezzini@epfl.ch; zhaoqun@tongji.edu.cn; lizhengrongtongji@126.com ZHAO, QUN/HMP-0262-2023; Nik, Vahid M./K-2632-2016 Nik, Vahid M./0000-0002-1497-4813; Scartezzini, Jean-Louis/0000-0001-8892-2592 50 131 135 34 192 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7788 1872-6178 ENERG BUILDINGS Energy Build. JUN 15 2018.0 169 484 506 10.1016/j.enbuild.2017.09.095 0.0 23 Construction & Building Technology; Energy & Fuels; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Energy & Fuels; Engineering GH9RD 2023-03-23 WOS:000434005700042 0
J Gao, Y; Guo, X; Yao, R; Zhou, WN; Cattani, C Gao, Yan; Guo, Xin; Yao, Rao; Zhou, Wuneng; Cattani, Carlo Stability analysis of neural network controller based on event triggering JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS English Article LINEAR-SYSTEMS; STABILIZATION; DELAY In this paper, we investigated the stability of continuous-time nonlinear systems and designed a Neural-Network-Controller based event-triggered. First, we designed a system delay model for the transmission time-delay contained in the system. Based on the designed time-delay model, the input delay method was used to convert the synchronous controller solution problem into a stability problem for the corresponding time-delay system. Second, by using a piecewise Lyapunov-Krasovskii functional combined with the Jensen's inequality, both the stability condition of the continuous-time nonlinear systems and the solution method of sampling controller were given. The designed sampling controller is able to update the control parameters only when the triggering rules are fulfilled, thus reducing the transmission rate of the network. Finally, the effectiveness of the proposed method was verified by numerical simulation. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. [Gao, Yan; Guo, Xin; Yao, Rao] Shanghai Univ Engn Sciencering, Sch Elect & Elect Engn, Shanghai 201600, Peoples R China; [Zhou, Wuneng] Donghua Univ, Sch Informat Sci & Engn, Shanghai 201600, Peoples R China; [Cattani, Carlo] Univ Tuscia, Engn Sch, DEIM, I-01100 Viterbo, Italy; [Cattani, Carlo] Azerbaijan Univ, Dept Math & Informat, Baku 1007, Azerbaijan Donghua University; Tuscia University Gao, Y (corresponding author), Shanghai Univ Engn Sciencering, Sch Elect & Elect Engn, Shanghai 201600, Peoples R China. gy@sues.edu.cn; yaorao@sues.edu.com; cattani@unitus.it Cattani, Carlo/I-5051-2013 Cattani, Carlo/0000-0002-7504-0424 26 4 4 1 15 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-0032 1879-2693 J FRANKLIN I J. Frankl. Inst.-Eng. Appl. Math. SEP 2020.0 357 14 9960 9975 10.1016/j.jfranklin.2020.07.040 0.0 16 Automation & Control Systems; Engineering, Multidisciplinary; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics OA0IT 2023-03-23 WOS:000577481300040 0
J Yucesoy, B; Wang, XD; Huang, JM; Barabasi, AL Yucesoy, Burcu; Wang, Xindi; Huang, Junming; Barabasi, Albert-Laszlo Success in books: a big data approach to bestsellers EPJ DATA SCIENCE English Article Success; Books; Bestsellers; Big data WORD-OF-MOUTH; DISTRIBUTIONS; GENDER; SALES Reading remains the preferred leisure activity for most individuals, continuing to offer a unique path to knowledge and learning. As such, books remain an important cultural product, consumed widely. Yet, while over 3 million books are published each year, very few are read widely and less than 500 make it to the New York Times bestseller lists. And once there, only a handful of authors can command the lists for more than a few weeks. Here we bring a big data approach to book success by investigating the properties and sales trajectories of bestsellers. We find that there are seasonal patterns to book sales with more books being sold during holidays, and even among bestsellers, fiction books sell more copies than nonfiction books. General fiction and biographies make the list more often than any other genre books, and the higher a book's initial place in the rankings, the longer the book stays on the list as well. Looking at patterns characterizing authors, we find that fiction writers are more productive than nonfiction writers, commonly achieving bestseller status with multiple books. Additionally, there is no gender disparity among bestselling fiction authors but nonfiction, most bestsellers are written by male authors. Finally we find that there is a universal pattern to book sales. Using this universality we introduce a statistical model to explain the time evolution of sales. This model not only reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers. The analysis of the bestseller characteristics and the discovery of the universal nature of sales patterns with its driving forces are crucial for our understanding of the book industry, and more generally, of how we as a society interact with cultural products. [Yucesoy, Burcu; Wang, Xindi; Huang, Junming; Barabasi, Albert-Laszlo] Northeastern Univ, Ctr Complex Network Res, Boston, MA 02115 USA; [Yucesoy, Burcu; Wang, Xindi; Huang, Junming; Barabasi, Albert-Laszlo] Northeastern Univ, Dept Phys, Boston, MA 02115 USA; [Huang, Junming] Univ Elect Sci & Technol China, Web Sci Ctr, CompleX Lab, Chengdu, Sichuan, Peoples R China; [Barabasi, Albert-Laszlo] Dana Farber Canc Inst, Ctr Canc Syst Biol, Boston, MA 02115 USA; [Barabasi, Albert-Laszlo] Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA; [Barabasi, Albert-Laszlo] Cent European Univ, Ctr Network Sci, Budapest, Hungary Northeastern University; Northeastern University; University of Electronic Science & Technology of China; Harvard University; Dana-Farber Cancer Institute; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Central European University Barabasi, AL (corresponding author), Northeastern Univ, Ctr Complex Network Res, Boston, MA 02115 USA.;Barabasi, AL (corresponding author), Northeastern Univ, Dept Phys, Boston, MA 02115 USA.;Barabasi, AL (corresponding author), Dana Farber Canc Inst, Ctr Canc Syst Biol, Boston, MA 02115 USA.;Barabasi, AL (corresponding author), Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA.;Barabasi, AL (corresponding author), Cent European Univ, Ctr Network Sci, Budapest, Hungary. barabasi@gmail.com Barabasi, Albert-Laszlo/S-6474-2017; Huang, Junming/H-9135-2019 Huang, Junming/0000-0002-2532-4090; Barabasi, Albert/0000-0002-4028-3522 Air Force Office of Scientific Research (AFOSR) [FA9550-15-1-0077]; John Templeton Foundation [51977]; DARPA [N66001-16-1-4067] Air Force Office of Scientific Research (AFOSR)(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)); John Templeton Foundation; DARPA(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)) This research was supported by Air Force Office of Scientific Research (AFOSR) under agreement FA9550-15-1-0077, John Templeton Foundation under agreement 51977 and DARPA under agreement N66001-16-1-4067. 29 22 22 9 44 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2193-1127 EPJ DATA SCI EPJ Data Sci. APR 6 2018.0 7 7 10.1140/epjds/s13688-018-0135-y 0.0 25 Mathematics, Interdisciplinary Applications; Social Sciences, Mathematical Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Mathematics; Mathematical Methods In Social Sciences GC2LZ gold 2023-03-23 WOS:000429615700001 0
J Chen, HY; He, YH; Lal, S; Majumder, S Chen, Heng-Yu; He, Yang-Hui; Lal, Shailesh; Majumder, Suvajit Machine learning Lie structures & applications to physics PHYSICS LETTERS B English Article Classical and exceptional Lie algebras and their representations are among the most important tools in the analysis of symmetry in physical systems. In this letter we show how the computation of tensor products and branching rules of irreducible representations is machine-learnable, and can achieve relative speed-ups of orders of magnitude in comparison to the non-ML algorithms. (C) 2021 The Author(s). Published by Elsevier B.V. [Chen, Heng-Yu] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan; [He, Yang-Hui; Majumder, Suvajit] City Univ London, Dept Math, London EC1V 0HB, England; [He, Yang-Hui] Univ Oxford, Merton Coll, Oxford OX1 4JD, England; [He, Yang-Hui] Nankai Univ, Sch Phys, Tianjin 300071, Peoples R China; [Lal, Shailesh] Univ Porto, Fac Ciencias, 687 Rua Campo Alegre, P-4169007 Porto, Portugal National Taiwan University; City University London; University of Oxford; Nankai University; Universidade do Porto Majumder, S (corresponding author), City Univ London, Dept Math, London EC1V 0HB, England. heng.yu.chen@phys.ntu.edu.tw; hey@maths.ox.ac.uk; shailesh.hri@gmail.com; Suvajit.Majumder@city.ac.uk Lal, Shailesh/ABC-1516-2021 Majumder, Suvajit/0000-0001-9861-2934 Ministry of Science and Technology (MOST) [108-2112-M-002-004-]; STFC UK [ST/J00037X/1]; Simons Foundation [488637]; Foundation for Science and Technology of Portugal (FCT) [UID-04650-FCUP]; SMCSE Doctoral Studentship at City, University of London; [CERN/FISPAR/0019/2017] Ministry of Science and Technology (MOST)(Ministry of Science and Technology, China); STFC UK(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Simons Foundation; Foundation for Science and Technology of Portugal (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); SMCSE Doctoral Studentship at City, University of London; HYC is supported in part by Ministry of Science and Technology (MOST) through the grant 1082112M002004. YHH is indebted to STFC UK, for grant ST/J00037X/1. SL's work was supported by the Simons Foundation grant 488637 (Simons Collaboration on the Nonperturbative bootstrap) and the project CERN/FISPAR/0019/2017. Centro de Fisica do Porto is partially funded by the Foundation for Science and Technology of Portugal (FCT) under the grant UID04650FCUP. SM's work is supported by dSMCSE Doctoral Studentship at City, University of London. 34 2 2 0 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B JUN 10 2021.0 817 136297 10.1016/j.physletb.2021.136297 0.0 APR 2021 5 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics SM5NO gold, Green Accepted, Green Submitted 2023-03-23 WOS:000657652200009 0
J Tang, DYY; Chew, KW; Ting, HY; Sia, YH; Gentili, FG; Park, YK; Banat, F; Culaba, AB; Ma, ZL; Show, PL Tang, Doris Ying Ying; Chew, Kit Wayne; Ting, Huong-Yong; Sia, Yuk-Heng; Gentili, Francesco G.; Park, Young-Kwon; Banat, Fawzi; Culaba, Alvin B.; Ma, Zengling; Show, Pau Loke Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae BIORESOURCE TECHNOLOGY English Article Chlorophyll; Microalgae; Prediction; Multilayer perceptron; Regression MODEL This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive. [Tang, Doris Ying Ying; Ma, Zengling; Show, Pau Loke] Wenzhou Univ, Zhejiang Prov Key Lab Subtrop Water Environm & Mar, Wenzhou 325035, Peoples R China; [Tang, Doris Ying Ying; Show, Pau Loke] Univ Nottingham Malaysia, Fac Sci & Engn, Dept Chem & Environm Engn, Jalan Broga, Semenyih 43500, Selangor Darul, Malaysia; [Chew, Kit Wayne] Nanyang Technol Univ, Sch Chem Chem Engn & Biotechnol, 62 Nanyang Dr, Singapore 637459, Singapore; [Ting, Huong-Yong; Sia, Yuk-Heng] Univ Technol Sarawak, Drone Res & Applicat Ctr, Sarawak, Malaysia; [Gentili, Francesco G.] Swedish Univ Agr Sci SLU, Dept Forest Biomat & Technol SBT, S-90183 Umea, Sweden; [Park, Young-Kwon] Univ Seoul, Sch Environm Engn, Seoul 02504, South Korea; [Banat, Fawzi] Khalifa Univ, Dept Chem Engn, POB 127788, Abu Dhabi, U Arab Emirates; [Culaba, Alvin B.] De La Salle Univ, Dept Mech Engn, 2401 Taft Ave, Manila 0922, Philippines; [Culaba, Alvin B.] De La Salle Univ, Ctr Engn & Sustainable Dev Res, 2401 Taft Ave, Manila 0922, Philippines; [Show, Pau Loke] SIMATS, Saveetha Sch Engn, Dept Sustainable Engn, Chennai 602105, India Wenzhou University; University of Nottingham Malaysia; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Swedish University of Agricultural Sciences; University of Seoul; Khalifa University of Science & Technology; De La Salle University; De La Salle University; Saveetha Institute of Medical & Technical Science; Saveetha School of Engineering Show, PL (corresponding author), Wenzhou Univ, Zhejiang Prov Key Lab Subtrop Water Environm & Mar, Wenzhou 325035, Peoples R China.;Show, PL (corresponding author), Univ Nottingham Malaysia, Fac Sci & Engn, Dept Chem & Environm Engn, Jalan Broga, Semenyih 43500, Selangor Darul, Malaysia. PauLoke.Show@nottingham.edu.my Pau Loke, Show/A-7953-2015; Tang, Doris Ying Ying/HPD-9162-2023; Chew, Kit Wayne/R-2427-2019; Ma, Zengling/F-9485-2017 Pau Loke, Show/0000-0002-0913-5409; Chew, Kit Wayne/0000-0003-2622-6916; Ting, Huong Yong/0000-0001-7950-2676; Ma, Zengling/0000-0002-4165-0339; Tang, Doris Ying Ying/0000-0001-7723-8445 Fundamental Research Grant Scheme, Malaysia [FRGS/1/2019/STG05/UNIM/02/2]; Vinnova [2017-03301]; National Research Foundation of Korea [2021R1A2C3011274]; MyPAIR-PHC-Hibiscus Grant [MyPAIR/1/2020/STG05/UNIM/1] Fundamental Research Grant Scheme, Malaysia; Vinnova(Vinnova); National Research Foundation of Korea(National Research Foundation of Korea); MyPAIR-PHC-Hibiscus Grant This work was supported by the Fundamental Research Grant Scheme, Malaysia [FRGS/1/2019/STG05/UNIM/02/2] and MyPAIR-PHC-Hibiscus Grant [MyPAIR/1/2020/STG05/UNIM/1] . The financial support of Vinnova (2017-03301) was greatly appreciated. Young-KwonPark also acknowledges for the support from National Research Foun-dation of Korea (2021R1A2C3011274) . 37 0 0 4 4 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0960-8524 1873-2976 BIORESOURCE TECHNOL Bioresour. Technol. FEB 2023.0 370 128503 10.1016/j.biortech.2022.128503 0.0 DEC 2022 7 Agricultural Engineering; Biotechnology & Applied Microbiology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels 7U1UR 36535615.0 2023-03-23 WOS:000911921800001 0
J Sarfraz, Y; Basharat, M; Riaz, MT; Akram, MS; Xu, C; Ahmed, KS; Shahzad, A; Al-Ansari, N; Linh, NTT Sarfraz, Yasir; Basharat, Muhammad; Riaz, Muhammad Tayyib; Akram, Mian Sohail; Xu, Chong; Ahmed, Khawaja Shoaib; Shahzad, Amir; Al-Ansari, Nadhir; Linh, Nguyen Thi Thuy Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors OPEN GEOSCIENCES English Article landslide susceptibility; machine learning; the weight of evidence; analytical hierarchy process; random forest; road corridors 2005 KASHMIR EARTHQUAKE; ANALYTICAL HIERARCHY PROCESS; SUPPORT VECTOR MACHINE; LOGISTIC-REGRESSION; RANDOM FOREST; GIS; MODELS; BIVARIATE; WEIGHTS; HAZARD Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, and machine learning (ML), along the main road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performance matrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelum road is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslide-prone zones along road corridors. [Sarfraz, Yasir; Basharat, Muhammad; Riaz, Muhammad Tayyib; Ahmed, Khawaja Shoaib; Shahzad, Amir] Univ Azad Jammu & Kashmir, Inst Geol, Muzaffarabad 13100, Pakistan; [Linh, Nguyen Thi Thuy] Thu Dau Mot Univ, Inst Appl Technol, Thu Dau Mot, Binh Duong Prov, Vietnam; [Akram, Mian Sohail] Univ Punjab, Inst Geol, Lahore, Pakistan; [Xu, Chong] Minist Emergency Management China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China; [Al-Ansari, Nadhir] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden University of Azad Jammu & Kashmir; Thu Dau Mot University; University of Punjab; Lulea University of Technology Sarfraz, Y (corresponding author), Univ Azad Jammu & Kashmir, Inst Geol, Muzaffarabad 13100, Pakistan.;Linh, NTT (corresponding author), Thu Dau Mot Univ, Inst Appl Technol, Thu Dau Mot, Binh Duong Prov, Vietnam. yasir.sarfraz@ajku.edu.pk; basharatgeo@ajku.edu.pk; tayyibriaz@ajku.edu.pk; sohail.geo@pu.edu.pk; chongxu@ninhm.ac.cn; shoaib.ahmed@ajku.edu.pk; amir.shahzad@ajku.edu.pk; nadhir.alansari@ltu.se; nguyenthuylinh@tdmu.edu.vn 77 0 0 4 4 DE GRUYTER POLAND SP Z O O WARSAW BOGUMILA ZUGA 32A STR, 01-811 WARSAW, MAZOVIA, POLAND 2391-5447 OPEN GEOSCI Open Geosci. DEC 31 2022.0 14 1 1606 1635 10.1515/geo-2022-0424 0.0 30 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology 7W9PV Green Published, gold 2023-03-23 WOS:000913839200001 0
J Zhang, TL; Sodhro, AH; Luo, ZW; Zahid, N; Nawaz, MW; Pirbhulal, S; Muzammal, M Zhang, Tianle; Sodhro, Ali Hassan; Luo, Zongwei; Zahid, Noman; Nawaz, Muhammad Wasim; Pirbhulal, Sandeep; Muzammal, Muhammad y A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients IEEE ACCESS English Article Medical services; Biomedical imaging; Wireless communication; Senior citizens; Batteries; Reliability; Real-time systems; Deep learning; elderly healthcare; cost-effective; intelligent systems; IoMT; reliability TRANSMISSION; SYSTEM Deep learning (DL) driven cardiac image processing methods manage and monitor the massive medical data collected by the internet of things (IoT) based on wearable devices. A Joint DL and IoT platform are known as Deep-IoMT that extracts the accurate cardiac image data from noisy conventional devices and tools. Besides, smart and dynamic technological trends have caught the attention of every corner such as, healthcare, which is possible through portable and lightweight sensor-enabled devices. Tiny size and resource-constrained nature restrict them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the keys challenges to be tackled carefully for ubiquitous medical care. Sustainability (i.e., longer battery lifetime), energy efficiency, and reliability are the vital ingredients for wearable devices to empower a cost-effective and pervasive healthcare environment. Thus, the key contribution of this paper is the sixth fold. First, a novel self-adaptive power control-based enhanced efficient-aware approach (EEA) is proposed to reduce energy consumption and enhance the battery lifetime and reliability. The proposed EEA and conventional constant TPC are evaluated by adopting real-time data traces of static (i.e., sitting) and dynamic (i.e., cycling) activities and cardiac images. Second, a novel joint DL-IoMT framework is proposed for the cardiac image processing of remote elderly patients. Third, DL driven layered architecture for IoMT is proposed. Forth, the battery model for IoMT is proposed by adopting the features of a wireless channel and body postures. Fifth, network performance is optimized by introducing sustainability, energy drain, and PLR and average threshold RSSI indicators. Sixth, a Use-case for cardiac image-enabled elderly patient's monitoring is proposed. Finally, it is revealed through experimental results in MATLAB that the proposed EEA scheme performs better than the constant TPC by enhancing energy efficiency, sustainability, and reliability during data transmission for elderly healthcare. [Zhang, Tianle] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China; [Sodhro, Ali Hassan; Zahid, Noman] Sukkur IBA Univ, Elect Engn Dept, Sindh 65200, Pakistan; [Luo, Zongwei] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China; [Nawaz, Muhammad Wasim] Univ Lahore, Dept Comp Engn, Lahore 54590, Pakistan; [Pirbhulal, Sandeep] Univ Beira Interior, Inst Telecomunicacoes, P-6201001 Covilha, Portugal; [Muzammal, Muhammad] Bahria Univ, Dept Comp Sci, Islamabad 44000, Pakistan Guangzhou University; Sukkur IBA University; Southern University of Science & Technology; University of Lahore; Universidade da Beira Interior Luo, ZW (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China.;Pirbhulal, S (corresponding author), Univ Beira Interior, Inst Telecomunicacoes, P-6201001 Covilha, Portugal. luozw@sustech.edu.cn; sandeep.hemnani28@gmail.com Muzammal, Muhammad/AAE-8185-2022; Luo, Zongwei/N-1985-2015; Sodhro, Ali Hassan/AAE-9467-2021; , 叶叶叶/ABE-1619-2020; Pirbhulal, Sandeep/X-7191-2019; Zahid, Noman/ADI-9651-2022; Sodhro, Ali Hassan/ABE-1975-2021 Muzammal, Muhammad/0000-0001-8817-1629; Luo, Zongwei/0000-0001-9322-959X; Sodhro, Ali Hassan/0000-0001-5502-530X; Pirbhulal, Sandeep/0000-0003-0843-8974; Zahid, Noman/0000-0002-7304-398X; Zhang, Tianle/0000-0001-8459-9273 Research grant of PIFI 2020 [2020VBC0002]; FCT [UID/EEA/50008/2019]; Beijing Municipal Natural Science Foundation [4172006]; Guangdong Province Key Research and Development Plan [2019B010137004]; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019); Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio a Investigacao Cientifica e Tecnologica Programas Integrados de ICDT; Technologies and Equipment Guangdong Education Bureau Fund [2017KTSCX166]; Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ201708171 12037041, ZDSYS201703031748284002E]; [010145-FEDER-000019-C4] Research grant of PIFI 2020; FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Guangdong Province Key Research and Development Plan; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019); Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio a Investigacao Cientifica e Tecnologica Programas Integrados de ICDT; Technologies and Equipment Guangdong Education Bureau Fund; Science and Technology Innovation Committee Foundation of Shenzhen; This work was supported by the Research grant of PIFI 2020 under Grant 2020VBC0002, in part by FCT Project (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2019) under Grant UID/EEA/50008/2019, in part by the Beijing Municipal Natural Science Foundation under Grant 4172006, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010137004, in part by the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019), in part by the Operacao Centro-010145-FEDER-000019-C4-Centro de Competencias em Cloud Computing, in part by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio a Investigacao Cientifica e Tecnologica Programas Integrados de IC&DT, in part by the Technologies and Equipment Guangdong Education Bureau Fund under Grant 2017KTSCX166, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant JCYJ201708171 12037041, and in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284002E. 25 38 38 0 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 75822 75832 10.1109/ACCESS.2020.2989143 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LK3ZL Green Submitted, gold 2023-03-23 WOS:000530800200018 0
J Wang, W; Yan, YX; Guo, Z; Hou, HF; Garcia, M; Tan, XR; Anto, EO; Mahara, G; Zheng, YL; Li, B; Kang, T; Zhong, ZH; Wang, YX; Guo, XH; Golubnitschaja, O Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Wang, Youxin; Guo, Xiuhua; Golubnitschaja, Olga Suboptimal Hlth Study Consortium; European Assoc Predictive Persona All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine EPMA JOURNAL English Article Predictive preventive personalised medicine (PPPM; 3PM); Suboptimal health status (SHS); Traditional medicine; Body mass index (BMI); Multi-level diagnostics; Omics; Glycan; Liquid biopsy; Modifiable preventable risks; Risk assessment; Communicable; Non-communicable diseases; Adolescence; Stress overload; Cardiovascular disease; Cancers; Neurologic diseases; Mood disorders; Microbiome; Periodontal health; Natural substances; Lifestyle; Dietary habits; Behavioural patterns; Sleep medicine; Individualised patient profile; Artificial intelligence (AI); Multi-parametric analysis; Big data management; Medical ethics; Health economy; Health policy; Epidemics; Pandemics; COVID-19 PROMOTING LIFE-STYLE; BREAST-CANCER; DIABETES-MELLITUS; COLLEGE-STUDENTS; RISK-FACTORS; N-GLYCAN; BEHAVIORS; IMPACT; GLYCOSYLATION; ADOLESCENTS First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions: Stress overload associated pathologies Male and female health Planned pregnancies Periodontal health Eye disorders Inflammatory disorders, wound healing and pain management with associated complications Metabolic disorders and suboptimal body weight Cardiovascular pathologies Cancers Stroke, particularly of unknown aetiology and in young individuals Sleep medicine Sports medicine Improved individual outcomes under pandemic conditions such as COVID-19. [Wang, Wei; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Anto, Enoch Odame; Zheng, Yulu; Wang, Youxin] Edith Cowan Univ, Ctr Precis Hlth, Perth, WA, Australia; [Wang, Wei; Yan, Yuxiang; Wang, Youxin; Guo, Xiuhua] Capital Med Univ, Beijing Key Lab Clin Epidemiol, Beijing, Peoples R China; [Wang, Wei; Hou, Haifeng] Shandong First Med Univ & Shandong Acad Med Sci, Sch Publ Hlth, Tai An, Shandong, Peoples R China; [Wang, Wei; Tan, Xuerui; Mahara, Gehendra] Shantou Univ, Med Coll, Affiliated Hosp 1, Shantou, Peoples R China; [Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Wang, Youxin; Guo, Xiuhua; Golubnitschaja, Olga] Suboptimal Hlth Study Consortium, Kumasi, Ghana; [Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Wang, Youxin; Guo, Xiuhua; Golubnitschaja, Olga] Suboptimal Hlth Study Consortium, Perth, WA, Australia; [Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Wang, Youxin; Guo, Xiuhua; Golubnitschaja, Olga] Suboptimal Hlth Study Consortium, Beijing, Peoples R China; [Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Wang, Youxin; Guo, Xiuhua; Golubnitschaja, Olga] Suboptimal Hlth Study Consortium, Bonn, Germany; [Wang, Wei; Yan, Yuxiang; Guo, Zheng; Hou, Haifeng; Garcia, Monique; Tan, Xuerui; Anto, Enoch Odame; Mahara, Gehendra; Zheng, Yulu; Li, Bo; Kang, Timothy; Zhong, Zhaohua; Guo, Xiuhua; Golubnitschaja, Olga] European Assoc Predict Prevent & Personalised, Med, Brussels, Belgium; [Anto, Enoch Odame; Wang, Youxin] Kwame Nkrumah Univ Sci & Technol, Coll Hlth Sci, Dept Med Diagnost, Kumasi, Ghana; [Li, Bo] Henan Univ, Sch Nursing & Hlth, Kaifeng, Peoples R China; [Kang, Timothy] Inst Chinese Acuol, Perth, WA, Australia; [Zhong, Zhaohua] Harbin Med Univ, Sch Basic Med, Harbin, Peoples R China; [Golubnitschaja, Olga] Rhein Friedrich Wilhelm Univ Bonn, Univ Hosp Bonn, Dept Radiat Oncol, Predict Prevent & Personalised 3P Med, Bonn, Germany Edith Cowan University; Capital Medical University; Shandong First Medical University & Shandong Academy of Medical Sciences; Shantou University; Kwame Nkrumah University Science & Technology; Henan University; Harbin Medical University; University of Bonn Wang, W (corresponding author), Edith Cowan Univ, Ctr Precis Hlth, Perth, WA, Australia.;Wang, W (corresponding author), Capital Med Univ, Beijing Key Lab Clin Epidemiol, Beijing, Peoples R China.;Wang, W (corresponding author), Shandong First Med Univ & Shandong Acad Med Sci, Sch Publ Hlth, Tai An, Shandong, Peoples R China.;Wang, W (corresponding author), Shantou Univ, Med Coll, Affiliated Hosp 1, Shantou, Peoples R China.;Wang, W; Golubnitschaja, O (corresponding author), Suboptimal Hlth Study Consortium, Kumasi, Ghana.;Wang, W; Golubnitschaja, O (corresponding author), Suboptimal Hlth Study Consortium, Perth, WA, Australia.;Wang, W; Golubnitschaja, O (corresponding author), Suboptimal Hlth Study Consortium, Beijing, Peoples R China.;Wang, W; Golubnitschaja, O (corresponding author), Suboptimal Hlth Study Consortium, Bonn, Germany.;Wang, W; Golubnitschaja, O (corresponding author), European Assoc Predict Prevent & Personalised, Med, Brussels, Belgium.;Golubnitschaja, O (corresponding author), Rhein Friedrich Wilhelm Univ Bonn, Univ Hosp Bonn, Dept Radiat Oncol, Predict Prevent & Personalised 3P Med, Bonn, Germany. wei.wang@ecu.edu.au; Olga.Golubnitschaja@ukbonn.de liu, li/HGC-0900-2022 wang, wei/0000-0002-1430-1360; Golubnitschaja, Prof. Dr., Olga/0000-0001-5427-2018 Project DEAL Project DEAL Open Access funding enabled and organised by Project DEAL. 188 31 32 8 20 SPRINGER INT PUBL AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1878-5077 1878-5085 EPMA J EPMA J. DEC 2021.0 12 4 SI 403 433 10.1007/s13167-021-00253-2 0.0 SEP 2021 31 Medicine, General & Internal; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) General & Internal Medicine; Research & Experimental Medicine XK3JG 34539937.0 Green Published, Green Accepted, hybrid 2023-03-23 WOS:000695384500001 0
J Mao, RB; Wen, B; Kazemi, A; Zhao, YH; Laguna, AF; Lin, R; Wong, N; Niemier, M; Hu, XS; Sheng, X; Graves, CE; Strachan, JP; Li, C Mao, Ruibin; Wen, Bo; Kazemi, Arman; Zhao, Yahui; Laguna, Ann Franchesca; Lin, Rui; Wong, Ngai; Niemier, Michael; Hu, X. Sharon; Sheng, Xia; Graves, Catherine E.; Strachan, John Paul; Li, Can Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search NATURE COMMUNICATIONS English Article IN-MEMORY; TCAM Memory augmented neural network for lifelong on-device learning is bottlenecked by limited bandwidth in conventional hardware. Here, the authors demonstrate its efficient in-memristor realization with a close-software accuracy, supported by hashing and similarity search in crossbars. Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an implementation can be efficiently scaled up for one-shot learning on more complex tasks. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms that were not possible in conventional hardware. [Mao, Ruibin; Wen, Bo; Zhao, Yahui; Lin, Rui; Wong, Ngai; Li, Can] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China; [Kazemi, Arman; Sheng, Xia; Graves, Catherine E.] Hewlett Packard Enterprise, Hewlett Packard Labs, Milpitas, CA 95035 USA; [Kazemi, Arman; Laguna, Ann Franchesca; Niemier, Michael; Hu, X. Sharon] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 USA; [Strachan, John Paul] Forschungszentrum Julich, Peter Grunberg Inst PGI 14, Julich, Germany; [Strachan, John Paul] Rhein Westfal TH Aachen, Aachen, Germany; [Laguna, Ann Franchesca] De La Salle Univ, Dept Comp Technol, Manila, Philippines University of Hong Kong; Hewlett-Packard; University of Notre Dame; Helmholtz Association; Research Center Julich; RWTH Aachen University; De La Salle University Li, C (corresponding author), Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China.;Graves, CE (corresponding author), Hewlett Packard Enterprise, Hewlett Packard Labs, Milpitas, CA 95035 USA.;Strachan, JP (corresponding author), Forschungszentrum Julich, Peter Grunberg Inst PGI 14, Julich, Germany.;Strachan, JP (corresponding author), Rhein Westfal TH Aachen, Aachen, Germany. catherine.graves@hpe.com; j.strachan@fz-juelich.de; canl@hku.hk Laguna, Ann Franchesca/GLV-3028-2022; Wong, Ngai/C-1866-2009; Kazemi, Arman/G-1968-2019 Laguna, Ann Franchesca/0000-0001-8267-1040; MAO, RUIBIN/0000-0001-6085-0486; Wong, Ngai/0000-0002-3026-0108; Kazemi, Arman/0000-0002-2009-5516; Wen, Bo/0000-0002-6106-9509 Early Career Scheme from the Research Grant Council of Hong Kong SAR [27210321]; NSFC Excellent Young Scientist Fund (HKMacau) [62122005]; Mainland-Hong Kong Joint Funding Scheme (MHKJFS) Project [MHP/066/20]; ACCESS - AI Chip Center for Emerging Smart Systems - InnoHK funding; Hong Kong SAR; Semiconductor Research Corporation (SRC) via the ASCENT center-one of six research centers in the Joint University Microelectronics Program (JUMP) Early Career Scheme from the Research Grant Council of Hong Kong SAR; NSFC Excellent Young Scientist Fund (HKMacau); Mainland-Hong Kong Joint Funding Scheme (MHKJFS) Project; ACCESS - AI Chip Center for Emerging Smart Systems - InnoHK funding; Hong Kong SAR; Semiconductor Research Corporation (SRC) via the ASCENT center-one of six research centers in the Joint University Microelectronics Program (JUMP) This work was supported in part by the Early Career Scheme (Grant No. 27210321) from the Research Grant Council of Hong Kong SAR, NSFC Excellent Young Scientist Fund (HK&Macau) (Grant No. 62122005), Mainland-Hong Kong Joint Funding Scheme (MHKJFS) Project MHP/066/20, ACCESS - AI Chip Center for Emerging Smart Systems, sponsored by InnoHK funding, Hong Kong SAR, and Semiconductor Research Corporation (SRC) via the ASCENT center-one of six research centers in the Joint University Microelectronics Program (JUMP). 50 0 0 10 10 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. OCT 21 2022.0 13 1 6284 10.1038/s41467-022-33629-7 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 5M5GS 36271072.0 gold, Green Submitted, Green Accepted 2023-03-23 WOS:000871124000008 0
J Pei, EC; Jiang, DM; Alioscha-Perez, M; Sahli, H Pei, Ercheng; Jiang, Dongmei; Alioscha-Perez, Mitchel; Sahli, Hichem Continuous affect recognition with weakly supervised learning MULTIMEDIA TOOLS AND APPLICATIONS English Article Continuous affect recognition; DNN-BLSTM; Weak supervision Recognizing a person's affective state from audio-visual signals is an essential capability for intelligent interaction. Insufficient training data and the unreliable labels of affective dimensions (e.g., valence and arousal) are two major challenges in continuous affect recognition. In this paper, we propose a weakly supervised learning approach based on hybrid deep neural network and bidirectional long short-term memory recurrent neural network (DNN-BLSTM). It firstly maps the audio/visual features into a more discriminative space via the powerful modelling capacities of DNN, then models the temporal dynamics of affect via BLSTM. To reduce the negative impact of the unreliable labels, we utilize a temporal label (TL) along with a robust loss function (RL) for incorporating weak supervision into the learning process of the DNN-BLSTM model. Therefore, the proposed method not only has a simpler structure than the deep BLSTM model in He et al. (24) which requires more training data, but also is robust to noisy and unreliable labels. Single modal and multimodal affect recognition experiments have been carried out on the RECOLA dataset. Single modal recognition results show that the proposed method with TL and RL obtains remarkable improvements on both arousal and valence in terms of concordance correlation coefficient (CCC), while multimodal recognition results show that with less feature streams, our proposed approach obtains better or comparable results with the state-of-the-art methods. [Pei, Ercheng; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, VUB NPU Joint AVSP Lab, Xian 710072, Peoples R China; [Alioscha-Perez, Mitchel; Sahli, Hichem] Vrije Univ Brussel, Dept ETRO, Pl Laan 2, B-1050 Brussels, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel Pei, EC (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, VUB NPU Joint AVSP Lab, Xian 710072, Peoples R China. peiercheng@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn; maperezg@etrovub.be; hsahli@etrovub.be ARSLAN, Okan/AAA-3232-2020 Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14]; Chinese Scholarship Council (CSC) [201706290115]; VUB Interdisciplinary Research Program through the EMO-App project Shaanxi Provincial International Science and Technology Collaboration Project; Chinese Scholarship Council (CSC)(China Scholarship Council); VUB Interdisciplinary Research Program through the EMO-App project This work is supported by the Shaanxi Provincial International Science and Technology Collaboration Project (grant 2017KW-ZD-14), the Chinese Scholarship Council (CSC) (grant 201706290115), the VUB Interdisciplinary Research Program through the EMO-App project. 72 4 4 0 13 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUL 2019.0 78 14 19387 19412 10.1007/s11042-019-7313-1 0.0 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IJ2BS 2023-03-23 WOS:000475703800021 0
J Zhou, JG; Wong, AHH; Wang, HT; Tan, FY; Chen, XF; Jin, SH; He, SS; Shen, G; Wang, YJ; Frey, B; Fietkau, R; Hecht, M; Ma, H; Gaipl, US Zhou, Jian-Guo; Wong, Ada Hang-Heng; Wang, Haitao; Tan, Fangya; Chen, Xiaofei; Jin, Su-Han; He, Si-Si; Shen, Gang; Wang, Yun-Jia; Frey, Benjamin; Fietkau, Rainer; Hecht, Markus; Ma, Hu; Gaipl, Udo S. Elucidation of the Application of Blood Test Biomarkers to Predict Immune-Related Adverse Events in Atezolizumab-Treated NSCLC Patients Using Machine Learning Methods FRONTIERS IN IMMUNOLOGY English Article blood test; irAE prediction; NSCLC; atezolizumab; machine learning C-REACTIVE PROTEIN; CHECKPOINT INHIBITORS; OPEN-LABEL; MULTICENTER; DOCETAXEL; LIVER BackgroundDevelopment of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now. MethodThis study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients. ResultsXGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio (training cohort AUC = 0.692, test cohort AUC = 0.681). This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method [for the 8:2 training:test cohort ratio, training cohort AUC difference = -0.035 (p < 0.0001), and test cohort AUC difference = 0.001 (p=0.965)]. ConclusionBlood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation. [Zhou, Jian-Guo; He, Si-Si; Shen, Gang; Wang, Yun-Jia; Ma, Hu] Zunyi Med Univ, Dept Oncol, Affiliated Hosp 2, Zunyi, Peoples R China; [Zhou, Jian-Guo; Frey, Benjamin; Gaipl, Udo S.] Univ Klinikum Erlangen, Dept Radiat Oncol, Translat Radiobiol, Erlangen, Germany; [Zhou, Jian-Guo; Frey, Benjamin; Fietkau, Rainer; Hecht, Markus; Gaipl, Udo S.] Univ Klinikum Erlangen, Dept Radiat Oncol, Erlangen, Germany; [Zhou, Jian-Guo; Frey, Benjamin; Fietkau, Rainer; Hecht, Markus; Gaipl, Udo S.] Comprehens Canc Ctr Erlangen EMN, Erlangen, Germany; [Wong, Ada Hang-Heng] AW Med Co Ltd, Macau, Macau, Peoples R China; [Wang, Haitao] NCI, NIH, Thorac Surg Branch, Bethesda, MD USA; [Tan, Fangya] Harrisburg Univ Sci & Technol, Dept Analyt, Harrisburg, PA USA; [Chen, Xiaofei] Sanofi, Dept Biostat & Programming, Bridgewater, NJ USA; [Jin, Su-Han] Zunyi Med Univ, Sch Stomatol, Dept Orthodont, Zunyi, Peoples R China Zunyi Medical University; University of Erlangen Nuremberg; University of Erlangen Nuremberg; University of Erlangen Nuremberg; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Sanofi-Aventis; Zunyi Medical University Zhou, JG; Ma, H (corresponding author), Zunyi Med Univ, Dept Oncol, Affiliated Hosp 2, Zunyi, Peoples R China.;Zhou, JG; Gaipl, US (corresponding author), Univ Klinikum Erlangen, Dept Radiat Oncol, Translat Radiobiol, Erlangen, Germany.;Zhou, JG; Gaipl, US (corresponding author), Univ Klinikum Erlangen, Dept Radiat Oncol, Erlangen, Germany.;Zhou, JG; Gaipl, US (corresponding author), Comprehens Canc Ctr Erlangen EMN, Erlangen, Germany. jianguo.zhou@yahoo.com; mahuab@163.com; udo.gaipl@uk-erlangen.de Zhou, Jian-Guo/AFK-7643-2022; Zhou, Jianguo/GRX-6659-2022; Hecht, Markus/AAX-1792-2020 Zhou, Jian-Guo/0000-0002-5021-3739; Zhou, Jianguo/0000-0002-5021-3739; Hecht, Markus/0000-0003-2082-216X National Natural Science Foundation of China [82060475]; National Natural Science Foundation of Guizhou Province [[2020]1Z062, ZK2021-YB435, ZK2022-YB632]; Lian Yun Gang Shi Hui Lan Public Foundation [HL-HS2020-92]; University Development Fund of University of Macau National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of Guizhou Province; Lian Yun Gang Shi Hui Lan Public Foundation; University Development Fund of University of Macau This research was funded by the National Natural Science Foundation of China (Grant No. 82060475), the National Natural Science Foundation of Guizhou Province (Grant Nos. [2020]1Z062, ZK2021-YB435, and ZK2022-YB632), and Lian Yun Gang Shi Hui Lan Public Foundation (Grant No. HL-HS2020-92). AW Medical Company Limited received startup funding from the University Development Fund of University of Macau. 38 0 0 6 6 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. JUN 30 2022.0 13 862752 10.3389/fimmu.2022.862752 0.0 9 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology 3A5TK 35844547.0 Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000827322000001 0
J Jiang, QM; Chan, CS; Eichelberger, S; Ma, H; Pikkemaat, B Jiang, Qiumin; Chan, Chung-Shing; Eichelberger, Sarah; Ma, Hang; Pikkemaat, Birgit Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists CURRENT ISSUES IN TOURISM English Article Big data in tourism; Chinese outbound market; information and communication technology; online reviews; online user sentiment; smart urban destination Online user-generated data are useful to researchers and practitioners to investigate an online destination image, which contributes significantly to the development of a tourism destination brand. However, finding an effective and efficient way to analyse big data remains a key challenge in the tourism industry. This study aims to figure out Hong Kong's online destination image from reviews of tourism websites by mainland Chinese tourists. Precisely 72,284 online reviews from three tourism websites in mainland China were selected and analysed with the help of data mining and sentiment analysis. The key findings demonstrate that Chinese tourists have an optimistic image of Hong Kong; mainland tourists are of the view that Hong Kong's weather is both hot and stuffy, Hong Kong houses pleasant and convenient yet small hotels, together with authentic yet expensive food, convenient shopping with a good night view, friendly locals, clean and lively atmosphere and also poor service. Overall, the sentiments, manifested online by mainland tourists about Hong Kong's tourism industry, are positive although this group also observed some negative factors. This paper deepens the understanding of the destination image of Hong Kong, while noting substantial implications for both tourism management and the authorities. [Jiang, Qiumin; Chan, Chung-Shing] Chinese Univ Hong Kong, Dept Geog & Resource Management, Sha Tin, Hong Kong, Peoples R China; [Eichelberger, Sarah; Pikkemaat, Birgit] Univ Innsbruck, Sch Management, Dept Strateg Management Mkt & Tourism, Innsbruck, Austria; [Ma, Hang] Harbin Inst Technol, Shenzhen, Peoples R China Chinese University of Hong Kong; University of Innsbruck; Harbin Institute of Technology Chan, CS (corresponding author), Chinese Univ Hong Kong, Dept Geog & Resource Management, Sha Tin, Hong Kong, Peoples R China. ccs_johnson@cuhk.edu.hk Eichelberger, Sarah/AAU-5940-2021; Chan, Chung Shing/W-4838-2018 Eichelberger, Sarah/0000-0001-6835-9034; Chan, Chung Shing/0000-0002-3847-3781 98 13 13 11 99 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1368-3500 1747-7603 CURR ISSUES TOUR Curr. Issues Tour. SEP 2 2021.0 24 17 2501 2522 10.1080/13683500.2021.1874312 0.0 JAN 2021 22 Hospitality, Leisure, Sport & Tourism Social Science Citation Index (SSCI) Social Sciences - Other Topics UC8TG 2023-03-23 WOS:000608877900001 0
J Rogal, J; Schneider, E; Tuckerman, ME Rogal, Jutta; Schneider, Elia; Tuckerman, Mark E. Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations PHYSICAL REVIEW LETTERS English Article ACCELERATED MOLECULAR-DYNAMICS; CLOSE-PACKED PHASES; FREE-ENERGY; TIME-SCALE; SIMULATION; EFFICIENT; SURFACES The investigation of the microscopic processes underlying structural phase transformations in solids is extremely challenging for both simulation and experiment. Atomistic simulations of solid-solid phase transitions require extensive sampling of the corresponding high-dimensional and often rugged energy landscape. Here, we propose a rigorous construction of a 1D path collective variable that is used in combination with enhanced sampling techniques for efficient exploration of the transformation mechanisms. The path collective variable is defined in a space spanned by global classifiers that arc derived from local structural units. A reliable identification of the local structural environments is achieved by employing a neural-network-based classification scheme. The proposed path collective variable is generally applicable and enables the investigation of both transformation mechanisms and kinetics. [Rogal, Jutta] Ruhr Univ Bochum, Interdisciplinary Ctr Adv Mat Simulat, D-44780 Bochum, Germany; [Rogal, Jutta; Schneider, Elia; Tuckerman, Mark E.] NYU, Dept Chem, New York, NY 10003 USA; [Tuckerman, Mark E.] NYU, Courant Inst Math Sci, New York, NY 10012 USA; [Tuckerman, Mark E.] NYU Shanghai, NYU ECNU Ctr Computat Chem, 3663 Zhongshan Rd North, Shanghai 200062, Peoples R China Ruhr University Bochum; New York University; New York University; NYU Shanghai Rogal, J (corresponding author), Ruhr Univ Bochum, Interdisciplinary Ctr Adv Mat Simulat, D-44780 Bochum, Germany.;Rogal, J (corresponding author), NYU, Dept Chem, New York, NY 10003 USA. jutta.rogal@rub.de Rogal, Jutta/A-2976-2019 Rogal, Jutta/0000-0002-6268-380X Alexander von Humboldt Foundation; National Science Foundation through the Materials Research Science and Engineering Center (MRSEC) program [DMR-1420073]; National Science Foundation [CHE-1565980] Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); National Science Foundation through the Materials Research Science and Engineering Center (MRSEC) program; National Science Foundation(National Science Foundation (NSF)) J. R. acknowledges financial support by the Alexander von Humboldt Foundation. M. E. T. acknowledges support from the National Science Foundation partially through the Materials Research Science and Engineering Center (MRSEC) program DMR-1420073 and partially through CHE-1565980. The authors acknowledge computing time by the Center for Interface-Dominated High Performance Materials (ZGH, Ruhr-Universitat Bochum). 47 30 30 4 27 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. DEC 13 2019.0 123 24 245701 10.1103/PhysRevLett.123.245701 0.0 6 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics JW1DE 31922858.0 Green Submitted 2023-03-23 WOS:000502798200013 0
J Wang, J; Shi, L; Zhao, Y; Zhang, HX; Szczerbicki, E Wang, Juan; Shi, Lei; Zhao, Yang; Zhang, Haoxi; Szczerbicki, Edward Adversarial attack algorithm for traffic sign recognition MULTIMEDIA TOOLS AND APPLICATIONS English Article; Early Access Adversarial attack; Black box; Traffic sign recognition; Algorithm security FACE RECOGNITION; BLACK Deep learning suffers from the threat of adversarial attacks, and its defense methods have become a research hotspot. In all applications of deep learning, intelligent driving is an important and promising one, facing serious threat of adversarial attack in the meanwhile. To address the adversarial attack, this paper takes the traffic sign recognition as a typical object, for it is the core function of intelligent driving. Considering that the black box attack does not need to know the internal characteristics of the model, it can have more practical value. However, the existing black box attack algorithm has high visit time and low efficiency in attacking sample generation. In this regard, the SimBA algorithm with high efficiency is selected and improved according to the characteristics of traffic signs, named the L-SimBA algorithm. According to the graphic characteristics of traffic signs that are already known, L-SimBA algorithm limits the search subspace consciously and specifies the set of search directions, and that is the core idea of it. By this way, L-SimBA algorithm can generate adversarial samples faster. Experimental comparison shows that in the field of traffic sign recognition, L-SimBA algorithm is better than SimBA algorithm. On the premise of obtaining similar quality adversarial attack samples, the success rate of adversarial measures gets higher, and the number of model visits reduces considerably, thus the attack efficiency of the algorithm improves greatly. [Wang, Juan; Shi, Lei; Zhao, Yang; Zhang, Haoxi] Chengdu Univ Informat Technol, Sch Cyberspace Secur, Chengdu 610225, Peoples R China; [Wang, Juan; Zhang, Haoxi] Adv Cryptog & Syst Secur Key Lab Sichuan Prov, Chengdu 610225, Peoples R China; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Dept Management, Gabriela Narutowicza 11-12, PL-80233 Gdansk, Poland Chengdu University of Information Technology; Fahrenheit Universities; Gdansk University of Technology Shi, L (corresponding author), Chengdu Univ Informat Technol, Sch Cyberspace Secur, Chengdu 610225, Peoples R China. wangjuan@cuit.edu.cn; 1402687676@qq.com; haoxi@cuit.edu.cn; Edward.Szczerbicki@zie.pg.gda.pl Wang, Juan/0000-0002-5893-2367 Sichuan Science and Technology Program [2021YFH0076] Sichuan Science and Technology Program This research was funded by Sichuan Science and Technology Program No.2021YFH0076. 16 0 0 5 5 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. 10.1007/s11042-022-14067-5 0.0 OCT 2022 13 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5P9JI 2023-03-23 WOS:000873458400002 0
J Yuan, RH; Liu, Z; Xu, YY; Yin, RW; He, JJ; Bai, Y; Zhou, YM; Li, JS; Xue, DZ; Lookman, T Yuan, Ruihao; Liu, Zhen; Xu, Yangyang; Yin, Ruowei; He, Jingjin; Bai, Yang; Zhou, Yumei; Li, Jinshan; Xue, Dezhen; Lookman, Turab Optimizing Electrocaloric Effect in Barium Titanate-based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis ACTA MATERIALIA English Article Machine learning; Landau theory; Experimental design; Electrocaloric effect RELAXOR FERROELECTRICS; MATERIALS DISCOVERY; CERAMICS; DESIGN; RANGE The search for ferroelectrics with optimized electrocaloric effects is often hindered due to the large compositional space and lack of reliable property data. Phenomenological theory, such as the Landau approach, then becomes useful, provided such models can be reliably parametrized with experimental data. We propose an approach in which we combine a surrogate model formed from widely available polarization data to predict entropy changes using polarization from the machine learned model as input to the Landau model. Our objective is to search for room temperature BaTiO3 based calorics, and we validate our approach by synthesizing compounds guided by optimal experimental design using the surrogate model for polarization. We iterate our design loop four times, predicting and synthesizing four compounds, the electrocaloric effects of which are estimated using both indirect and direct characterization. The compositions show competitive electrocaloric performance at room temperature with a large adiabatic temperature change delta T~0.6 K at 20 kV/cm in a wide temperature window (~30 K with 90%.delta T). Moreover, we find that the homogeneous Landau models are largely predictive in the linear regime where the change in entropy varies quadratically with polarization. We discuss the role of nonlinear and diffusive effects for larger entropy behavior. The proposed approach shows promise in the use of theory or physics based models to bridge with output from machine learning methods when limited data is available for a property of interest. (C) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. [Yuan, Ruihao; Li, Jinshan] Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Peoples R China; [Yuan, Ruihao] Los Alamos Alamos Natl Lab, Theoret Div, Los Alamos, NM 87545 USA; [Liu, Zhen] Tech Univ Darmstadt, Dept Mat Sci, Mech Funct Mat, D-64287 Darmstadt, Germany; [Xu, Yangyang; Zhou, Yumei; Xue, Dezhen] Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China; [Yin, Ruowei; He, Jingjin; Bai, Yang] Univ Sci & Technol Beijing, Inst Adv Mat & Technol, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China; [Lookman, Turab] AiMaterials Res, Santa Fe, NM 87501 USA Northwestern Polytechnical University; Technical University of Darmstadt; Xi'an Jiaotong University; University of Science & Technology Beijing Xue, DZ (corresponding author), Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China. LiuZ_hit@hotmail.com; xuedezhen@xjtu.edu.cn; turablookman@gmail.com Zhou, Yu/HHN-3071-2022; XUE, Dezhen/A-6062-2010; xu, yang/HOC-0456-2023 XUE, Dezhen/0000-0001-6132-1236; Yuan, Ruihao/0000-0002-8214-5400; Liu, Zhen/0000-0002-9682-6178 National Key Research and Development Program of China [2021YFB3802102]; National Natural Science Foundation of China [52002326]; Fundamental Research Funds for the Cen-tral Universities [G2020KY05122]; Los Alamos National Laboratory; LOEWE program of the State of Hesse, Germany, within the project FLAME (Fermi Level Engineering of Antiferroelectric Materials for Energy Storage and Insulation Systems) National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Cen-tral Universities(Fundamental Research Funds for the Central Universities); Los Alamos National Laboratory(United States Department of Energy (DOE)Los Alamos National Laboratory); LOEWE program of the State of Hesse, Germany, within the project FLAME (Fermi Level Engineering of Antiferroelectric Materials for Energy Storage and Insulation Systems) This work was financially supported by the National Key Re-search and Development Program of China (2021YFB3802102) , National Natural Science Foundation of China (grant no. 52002326) , and the Fundamental Research Funds for the Cen-tral Universities (G2020KY05122) . R.Y. and T.L. acknowledge support from Los Alamos National Laboratory where this work was carried out. Z. L. acknowledges support from the LOEWE program of the State of Hesse, Germany, within the project FLAME (Fermi Level Engineering of Antiferroelectric Materials for Energy Storage and Insulation Systems) . We thank Mr. Junning Li at Queen Mary University of London for helpful discussion. 69 1 1 38 57 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1359-6454 1873-2453 ACTA MATER Acta Mater. AUG 15 2022.0 235 118054 10.1016/j.actamat.2022.118054 0.0 JUN 2022 11 Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Metallurgy & Metallurgical Engineering 1Y9OX 2023-03-23 WOS:000808467000007 0
J Niu, C; Cohen, AD; Wen, X; Chen, ZY; Lin, P; Liu, X; Menze, BH; Wiestler, B; Wang, Y; Zhang, M Niu, Chen; Cohen, Alexander D.; Wen, Xin; Chen, Ziyi; Lin, Pan; Liu, Xin; Menze, Bjoern H.; Wiestler, Benedikt; Wang, Yang; Zhang, Ming Modeling motor task activation from resting-state fMRI using machine learning in individual subjects BRAIN IMAGING AND BEHAVIOR English Article Machine learning; Functional MRI; Resting state; Independent component analysis; Motor function; General linear model FUNCTIONAL CONNECTIVITY; BRAIN REPRESENTATION; LANGUAGE AREAS; NETWORKS; CORTEX; TUMOR; STIMULATION; SURGERY; STROKE; REGION Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications. [Niu, Chen; Wen, Xin; Zhang, Ming] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Med Imaging, 277 West Yanta Rd, Xian 710061, Shaanxi, Peoples R China; [Niu, Chen; Liu, Xin; Menze, Bjoern H.] Tech Univ Munich, Inst Biomed Engn, Munich, Germany; [Cohen, Alexander D.; Chen, Ziyi; Wang, Yang] Med Coll Wisconsin, Dept Radiol, 8701 Watertown Plank Rd, Milwaukee, WI 53226 USA; [Lin, Pan] Hunan Normal Univ, Dept Psychol, Changsha, Hunan, Peoples R China; [Lin, Pan] Hunan Normal Univ, Cognit & Human Behav Key Lab Hunan Prov, Changsha, Hunan, Peoples R China; [Menze, Bjoern H.] Tech Univ Munich, Dept Comp Sci, Munich, Germany; [Wiestler, Benedikt] Tech Univ Munich, Klinikum Rechts Isar, Dept Neuroradiol, Munich, Germany Xi'an Jiaotong University; Technical University of Munich; Medical College of Wisconsin; Hunan Normal University; Hunan Normal University; Technical University of Munich; Technical University of Munich Zhang, M (corresponding author), Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Med Imaging, 277 West Yanta Rd, Xian 710061, Shaanxi, Peoples R China.;Wang, Y (corresponding author), Med Coll Wisconsin, Dept Radiol, 8701 Watertown Plank Rd, Milwaukee, WI 53226 USA. yangwang@mcw.edu; zhangming01@mail.xjtu.edu.cn Menze, Bjoern/0000-0003-4136-5690; Wiestler, Benedikt/0000-0002-2963-7772; Lin, Pan/0000-0001-7473-905X National Natural Science Foundation of China [81871331]; Key Research and Development Program of Shaanxi Province [2018SF-113] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Shaanxi Province This study was partially supported by the National Natural Science Foundation of China (Project No.81871331, to M.Z.) and the Key Research and Development Program of Shaanxi Province (2018SF-113, to C.N.). 44 3 3 3 17 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1931-7557 1931-7565 BRAIN IMAGING BEHAV Brain Imaging Behav. FEB 2021.0 15 1 122 132 10.1007/s11682-019-00239-9 0.0 JAN 2020 11 Neuroimaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology PX7WX 31903530.0 2023-03-23 WOS:000505416100004 0
J Xie, ZP; McLoughlina, I; Zhang, HM; Song, Y; Xiao, W Xie, Zhipeng; McLoughlina, Ian; Zhang, Haomin; Song, Yan; Xiao, Wei A new variance-based approach for discriminative feature extraction in machine hearing classification using spectrogram features DIGITAL SIGNAL PROCESSING English Article Machine hearing; Auditory event detection; Robust auditory classification; Sound classification; Discriminative sound features SOUND EVENT CLASSIFICATION; AUDIO CLASSIFICATION; IMAGE FEATURE; RECOGNITION Machine hearing is an emerging research field that is analogous to machine vision in that it aims to equip computers with the ability to hear and recognise a variety of sounds. It is a key enabler of natural human-computer speech interfacing, as well as in areas such as automated security surveillance, environmental monitoring, smart homes/buildings/cities. Recent advances in machine learning allow current systems to accurately recognise a diverse range of sounds under controlled conditions. However doing so in real-world noisy conditions remains a challenging task. Several front-end feature extraction methods have been used for machine hearing, employing speech recognition features like MFCC and PLP, as well as image-like features such as AIM and SIF. The best choice of feature is found to be dependent upon the noise environment and machine learning techniques used. Machine learning methods such as deep neural networks have been shown capable of inferring discriminative classification rules from less structured front-end features in related domains. In the machine hearing field, spectrogram image features have recently shown good performance for noise-corrupted classification using deep neural networks. However there are many methods of extracting features from spectrograms. This paper explores a novel data-driven feature extraction method that uses variance-based criteria to define spectral pooling of features from spectrograms. The proposed method, based on maximising the pooled spectral variance of foreground and background sound models, is shown to achieve very good performance for robust classification. (C) 2016 Elsevier Inc. All rights reserved. [Xie, Zhipeng; McLoughlina, Ian; Zhang, Haomin; Song, Yan] Univ Sci & Technol China, Natl Engn Lab Speech & Language Informat Proc, Hefei 230026, Peoples R China; [McLoughlina, Ian] Univ Kent, Sch Comp, Medway, England; [Xiao, Wei] Huawei Technol Duesseldorf GmbH, European Res Ctr, Munich, Germany Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Kent; Huawei Technologies McLoughlina, I (corresponding author), Univ Sci & Technol China, Natl Engn Lab Speech & Language Informat Proc, Hefei 230026, Peoples R China.;McLoughlina, I (corresponding author), Univ Kent, Sch Comp, Medway, England. ivm@kent.ac.uk McLoughlin, Ian/AAS-9081-2020 McLoughlin, Ian/0000-0001-7111-2008 Huawei Innovation Research Program under Machine Hearing and Perception Project [YB2012120147]; Natural Science Foundation of China (NSFC) [61172158] Huawei Innovation Research Program under Machine Hearing and Perception Project(Huawei Technologies); Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was partly supported by the Huawei Innovation Research Program under Machine Hearing and Perception Project Contract No. YB2012120147. Yan Song was supported by the Natural Science Foundation of China (NSFC), grant no. 61172158. 32 11 11 0 42 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1051-2004 1095-4333 DIGIT SIGNAL PROCESS Digit. Signal Prog. JUL 2016.0 54 119 128 10.1016/j.dsp.2016.04.005 0.0 10 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering DM9SY Green Accepted 2023-03-23 WOS:000376707200011 0
C Liu, X; Zhang, XM; Wang, YW; Zhou, JH; Helal, S; Xu, ZD; Zhang, WS; Cao, S Jin, H; Wang, Q; Zhang, LJ Liu, Xin; Zhang, Xiaomiao; Wang, Yiwen; Zhou, Jiehan; Helal, Sumi; Xu, Zhidong; Zhang, Weishan; Cao, Shuai PARMTRD: Parallel Association Rules Based Multiple-Topic Relationships Detection WEB SERVICES - ICWS 2018 Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Web Services (ICWS) held as Part of the Services Conference Federation (SCF) JUN 25-30, 2018 Seattle, WA Topic relationship detection; Parallel association rules; Association keyword set; Public opinion MODEL Lots of events happened everyday make social big data have plenty of topics. A topic usually comprises a series of stories. Clues of associations among stories are usually clear, but hidden associations among topics are not always intuitive. It is challenging to find topic associations due to intrinsic complexities of social big data, while analyzing relationships among topics is valuable to explore and reach to origination sources of specific events. Existing research rarely pay attention to analyze multiple-topic relationships. This paper proposes a mining approach for topic relationships detection based on parallel association rules, namely PARMTRD (Parallel Association Rules based Multiple-Topic Relationships Detection). PARMTRD obtains association keyword sets for each topic using parallel association rules based on large-scale frequent keyword sets, which mines association rules for multiple topics in parallel. PARMTRD detects the relevance among multiple topics by selecting and assembling association keywords from association keyword sets, which help to find sources of events. Experiments show that PARMTRD can detect the hidden relationships among multiple topics accurately and efficiently. [Liu, Xin; Zhang, Xiaomiao; Wang, Yiwen; Zhang, Weishan; Cao, Shuai] China Univ Petr, Qingdao 266580, Peoples R China; [Zhou, Jiehan] Univ Oulu, Oulu, Finland; [Helal, Sumi] Univ Lancaster, Lancaster, England; [Xu, Zhidong] Beijing Normal Univ, Beijing, Peoples R China China University of Petroleum; University of Oulu; Lancaster University; Beijing Normal University Liu, X (corresponding author), China Univ Petr, Qingdao 266580, Peoples R China. lx@upc.edu.cn; jiehan.zhou@oulu.fi Wang, Yixuan/GZK-6559-2022; Wang, Yijun/GXW-1763-2022; wang, yixuan/GXW-2866-2022; wang, yi/GVT-8516-2022; Wang, Yin/HCI-9352-2022 Key Program of Shandong Province [2017GGX10140]; National Natural Science Foundation of China [61309024]; National Social Science Foundation of China [15CKS031]; Fundamental Research Funds for the Central Universities Key Program of Shandong Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Social Science Foundation of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The work presented in this paper is supported by the Key Program of Shandong Province (No. 2017GGX10140), National Natural Science Foundation of China (No. 61309024), National Social Science Foundation of China (No. 15CKS031), and also supported by Fundamental Research Funds for the Central Universities. 28 2 2 1 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-94289-6; 978-3-319-94288-9 LECT NOTES COMPUT SC 2018.0 10966 422 436 10.1007/978-3-319-94289-6_27 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP3BH 2023-03-23 WOS:000546169700026 0
J Jiang, JH; Shi, KY; Peng, FY; Hsu, CY; Kung, WM Jiang, Jiehui; Shi, Kuangyu; Peng, Fangyu; Hsu, Chih-Yu; Kung, Woon-Man Editorial: Translational Advances in Alzheimer's, Parkinson's, and Other Neurodegenerative Dementias FRONTIERS IN AGING NEUROSCIENCE English Editorial Material Alzheimer's disease; Parkinson's disease; dementia; neurodegenerative disorders; big data mining; imaging methods; bioinformatic applications [Jiang, Jiehui] Shanghai Univ, Inst Biomed Engn, Sch Life Sci, Shanghai, Peoples R China; [Shi, Kuangyu] Univ Bern, Bern Univ Hosp, Dept Nucl Med, Inselspital, Bern, Switzerland; [Shi, Kuangyu] Tech Univ Munich, Dept Informat, Munich, Germany; [Peng, Fangyu] Univ Texas Southwestern Med Ctr Dallas, Dept Radiol, Dallas, TX 75390 USA; [Hsu, Chih-Yu] Fujian Univ Technol, Sch Comp Sci & Math, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou, Peoples R China; [Kung, Woon-Man] Taipei Tzu Chi Hosp, Buddhist Tzu Chi Med Fdn, Dept Surg, Div Neurosurg, New Taipei, Taiwan Shanghai University; University of Bern; University Hospital of Bern; Technical University of Munich; University of Texas System; University of Texas Southwestern Medical Center Dallas; Fujian University of Technology; Buddhist Tzu Chi General Hospital; Taipei Tzu Chi Hospital Kung, WM (corresponding author), Taipei Tzu Chi Hosp, Buddhist Tzu Chi Med Fdn, Dept Surg, Div Neurosurg, New Taipei, Taiwan. nskungwm@yahoo.com.tw Kung, Woon-Man/Q-3012-2019 Kung, Woon-Man/0000-0001-8311-2902; Shi, Kuangyu/0000-0002-8714-3084 5 0 0 1 4 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1663-4365 FRONT AGING NEUROSCI Front. Aging Neurosci. MAR 17 2022.0 14 858467 10.3389/fnagi.2022.858467 0.0 5 Geriatrics & Gerontology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Geriatrics & Gerontology; Neurosciences & Neurology 0U6IQ 35370606.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000787753400001 0
C Li, SM; Gao, C; Yu, HT; Zhang, JP IEEE Li, Shaomei; Gao, Chao; Yu, Hongtao; Zhang, Jianpeng Person Re-identification via Person DPM based Partition 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 23rd International Conference on Pattern Recognition (ICPR) DEC 04-08, 2016 Mexican Assoc Comp Vis Robot & Neural Comp, Cancun, MEXICO Int Assoc Pattern Recognit,Int Conf Pattern Recognit, Org Comm,Elsevier,IBM Res,INTEL,CONACYT Mexican Assoc Comp Vis Robot & Neural Comp Person re-identification; Deformable Part Model; Deep learning feature; Part matching In surveillance videos, the pictures of a same person often present significant variation which makes person re-identification difficult. Though the globe appearances may present great difference, some local patches still have great similarities, and human eyes can be used to distinguish the identity of each person via these local patches. Inspired from it, patch matching is introduced in person re-identification and has been shown to be an efficient method to solve these problems caused by different viewpoints, poses, camera settings, illumination and occlusion. But until now there is no guide for how to decide the size of patch, and most researches got these patches either by dense sampling or coarse partition. In both case, the person structure information is missing. To improve re-identification accuracy, we propose a method via person DPM (Deformable Parts Model) partition. First, both compared appearances are partitioned into several body parts by pre-trained person DPM and these parts are grouped according to their positions in the body; Second, part matching is conducted between two appearances' parts in each group based on deep learning features; Finally, fusing the similarities of each group are to decide whether these two appearances are from the same person or not. Experiments on VIPeR dataset illustrate that without supervised training, the proposed method can obtain good re-identification performances compared with state-of-the-art methods. [Li, Shaomei; Gao, Chao; Yu, Hongtao; Zhang, Jianpeng] Natl Digital Switching Syst Engn & Technol Res &, Zhengzhou, Henan, Peoples R China; [Zhang, Jianpeng] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands PLA Information Engineering University; Eindhoven University of Technology Li, SM (corresponding author), Natl Digital Switching Syst Engn & Technol Res &, Zhengzhou, Henan, Peoples R China. lishaomeimay@126.com National Science and Technology Support Plan of China [2014BAH30B01]; National Natural Science Foundation of China [61521003] National Science and Technology Support Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Science and Technology Support Plan of China (No. 2014BAH30B01), and National Natural Science Foundation of China(No. 61521003). 27 3 3 0 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1051-4651 978-1-5090-4847-2 INT C PATT RECOG 2016.0 3856 3861 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI1RM 2023-03-23 WOS:000406771303140 0
J Lou, XY; Suykens, JAK Lou, Xuyang; Suykens, Johan A. K. Stability of Coupled Local Minimizers Within the Lagrange Programming Network Framework IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS English Article Coupled local minimizers; Lagrange programming network; augmented Lagrangian stability; synchronization RECURRENT NEURAL-NETWORK; GLOBAL OPTIMIZATION; CONVERGENCE; SYNCHRONIZATION; SEARCH Coupled local minimizers (CLMs) turn out to be a potential global optimization strategy to explore a search space, avoid overfitting and produce good generalization. In this paper, convergence properties of CLMs based on an augmented Lagrangian function in the context of equality constrained minimization, are studied. We first consider the augmented Lagrangian by taking the objective of minimizing the average cost of an ensemble of local minimizers subject to pairwise synchronization constraints. Then we study an array of CLMs within the Lagrange programming network framework and analyze the local stability of CLMs using a linearization strategy. We further show that, under some mild conditions, global asymptotical stability of the unique equilibrium point of the network can be guaranteed. Afterwards, some sufficient conditions are presented to ensure the stability of synchronization between any two minimizers via a directed graph method. The results show that the CLMs usually can be synchronized if the penalty factors in the array of CLMs are chosen large enough. It is worth pointing out that CLMs possess the capability of global exploration in the search space and the advantage of faster running time on convex problems in comparison with most of the neural network approaches, which is also illustrated through two test functions and their numerical simulations. [Lou, Xuyang] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China; [Lou, Xuyang] Jiangnan Univ, Sch IoT Engn, Minist Educ, Wuxi 214122, Peoples R China; [Suykens, Johan A. K.] Katholieke Univ Leuven, Dept Elect Engn ESAT SCD SISTA, B-3001 Louvain, Belgium Jiangnan University; Jiangnan University; KU Leuven Lou, XY (corresponding author), Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China. Xuyang.Lou@gmail.com; Johan.Suykens@esat.kuleuven.be Suykens, Johan A.K./C-9781-2014 Suykens, Johan A.K./0000-0002-8846-6352 Research Council KUL [GOA/11/05, GOA/10/09, CoE EF/05/006, IOF-SCORES4CHEM]; Flemish Government: FWO [G0226.06, G0321.06, G.0302.07, G.0320.08, G.0558.08, G.0557.08, G.0588.09, G.0377.09]; Belgian Federal Science Policy Office [IUAP P6/04]; EU: ERNSI; FP7-HD-MPC [INFSO-ICT-223854, ICT-248940]; National Natural Science Foundation of China [61174021, 61104155]; 111 Project [B12018] Research Council KUL(KU Leuven); Flemish Government: FWO(FWO); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); EU: ERNSI; FP7-HD-MPC; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project) This work was supported by Research Council KUL: GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM, several Ph.D./postdoc and fellow grants; Flemish Government: FWO: Ph.D./postdoc grants, projects: G0226.06 (cooperative systems and optimization), G0321.06 (Tensors), G.0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08 (Glycemia2), G.0588.09 (Brain-machine) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC), IWT: Ph.D. Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O & O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelliCIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; Other: Helmholtz: viCERP, ACCM. This work was also supported in part by the National Natural Science Foundation of China (61174021, 61104155) and the 111 Project (B12018). This paper was approved by Associate Editor Xiang Li. 39 13 13 1 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-8328 1558-0806 IEEE T CIRCUITS-I IEEE Trans. Circuits Syst. I-Regul. Pap. FEB 2013.0 60 2 377 388 10.1109/TCSI.2012.2215782 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 080TU 2023-03-23 WOS:000314267000011 0
J Cheng, TCE; Kamble, SS; Belhadi, A; Ndubisi, NO; Lai, KH; Kharat, MG Edwin Cheng, T. C.; Kamble, Sachin S.; Belhadi, Amine; Ndubisi, Nelson Oly; Lai, Kee-hung; Kharat, Manoj Govind Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH English Article Big data analytics; circular economy; supply chain flexibility; supply chain performance; sustainable STRATEGIES The digital supply chains enabled by Big Data Analytics (BDA) capabilities have become a business significance to develop a competitive and sustainable supply chain. The implementation of BDA for sustainable supply chains introduces various organisational challenges for the manufacturing firms demanding complementary capabilities. This study presents an investigation of the linkages between BDA capabilities, circular economy (CE) practices, and sustainable supply chain (SSC) flexibility on the SSC performance. Based on a survey of 320 manufacturing organisations, the study presents very interesting results. It is revealed that the BDA does not have a direct effect on sustainable performance. The CE practices and SSC flexibility are significant mediating variables between the BDA capabilities and SSC performance. The BDA is found to drive the implementation of CE practices. Further, CE practices are found to develop SSC flexibility with the BDA complementing these flexibilities. [Edwin Cheng, T. C.] Hong Kong Polytech Univ, PolyU Business Sch, Hong Kong, Peoples R China; [Kamble, Sachin S.] EDHEC Business Sch, Strategy Operat & SCM, Roubaix, France; [Belhadi, Amine] Cadi Ayyad Univ, LaPSSII, Marrakech, Morocco; [Ndubisi, Nelson Oly] Qatar Univ, Coll Business & Econ, Doha, Qatar; [Lai, Kee-hung] Hong Kong Polytech Univ, Fac Business, Hong Kong, Peoples R China; [Kharat, Manoj Govind] Natl Inst Ind Engn, Mumbai, Maharashtra, India Hong Kong Polytechnic University; Universite Catholique de Lille; EDHEC Business School; Cadi Ayyad University of Marrakech; Qatar University; Hong Kong Polytechnic University; National Institute of Industrial Engineering (NITIE) Kamble, SS (corresponding author), EDHEC Business Sch, Strategy Operat & SCM, Roubaix, France. sachin.kamble@edhec.edu KAMBLE, SACHIN/L-4304-2018; Belhadi, Amine/AAN-7540-2020; Lai, Kee-hung/B-4054-2009 KAMBLE, SACHIN/0000-0003-4922-8172; Belhadi, Amine/0000-0003-4831-4941; Lai, Kee-hung/0000-0001-9296-0882 50 61 61 35 141 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0020-7543 1366-588X INT J PROD RES Int. J. Prod. Res. NOV 17 2022.0 60 22 SI 6908 6922 10.1080/00207543.2021.1906971 0.0 APR 2021 15 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 6C2MZ 2023-03-23 WOS:000636897300001 0
J Gao, F; Wang, W; Tan, MM; Zhu, LN; Zhang, YC; Fessler, E; Vermeulen, L; Wang, X Gao, Feng; Wang, Wei; Tan, Miaomiao; Zhu, Lina; Zhang, Yuchen; Fessler, Evelyn; Vermeulen, Louis; Wang, Xin DeepCC: a novel deep learning-based framework for cancer molecular subtype classification ONCOGENESIS English Article II COLON-CANCER; GENE-EXPRESSION; BREAST-CANCER; VALIDATION; SIGNATURE; HETEROGENEITY; RECURRENCE; PROGNOSIS; DISCOVERY; THERAPY Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping. [Gao, Feng; Wang, Wei; Tan, Miaomiao; Zhu, Lina; Zhang, Yuchen; Wang, Xin] City Univ Hong Kong, Dept Biomed Sci, Hong Kong, Peoples R China; [Gao, Feng] Sun Yat Sen Univ, Affiliated Hosp 6, Dept Colorectal Surg, Guangzhou, Guangdong, Peoples R China; [Gao, Feng] Guangdong Inst Gastroenterol, Guangdong Prov Key Lab Colorectal & Pelv Floor Di, Supported Natl Key Clin Discipline, Guangzhou, Guangdong, Peoples R China; [Fessler, Evelyn; Vermeulen, Louis] Univ Amsterdam, AMC, CEMM, Lab Expt Oncol & Radiobiol LEXOR, Amsterdam, Netherlands; [Wang, Xin] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China City University of Hong Kong; Sun Yat Sen University; University of Amsterdam; Academic Medical Center Amsterdam; City University of Hong Kong Wang, X (corresponding author), City Univ Hong Kong, Dept Biomed Sci, Hong Kong, Peoples R China.;Vermeulen, L (corresponding author), Univ Amsterdam, AMC, CEMM, Lab Expt Oncol & Radiobiol LEXOR, Amsterdam, Netherlands.;Wang, X (corresponding author), City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China. l.vermeulen@amc.uva.nl; Xin.Wang@cityu.edu.hk Wang, Xin/A-1130-2012; Zhang, Yuchen/GYI-8858-2022; Gao, Feng/AAE-4201-2020 Wang, Xin/0000-0002-5122-2418; Gao, Feng/0000-0002-0500-5527; Tan, Miaomiao/0000-0001-6450-7116; ZHU, Lina/0000-0002-8558-2120 Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 21101115, 11102317, 11103718]; Science Technology and Innovation Committee of Shenzhen Municipality [JCYJ20170307091256048]; Shenzhen Research Institute, City University of Hong Kong; KWF [UVA2014-7245]; European Research Council [ERG-StG 638193]; ZonMw [Vidi 016.156.308] Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council); Science Technology and Innovation Committee of Shenzhen Municipality; Shenzhen Research Institute, City University of Hong Kong(City University of Hong Kong); KWF; European Research Council(European Research Council (ERC)European Commission); ZonMw(Netherlands Organization for Health Research and Development) This work was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. CityU 21101115, 11102317, 11103718), as well as a grant from The Science Technology and Innovation Committee of Shenzhen Municipality (JCYJ20170307091256048) to X. W. and partially supported by the Shenzhen Research Institute, City University of Hong Kong, and grants from KWF (UVA2014-7245), the European Research Council (ERG-StG 638193) and ZonMw (Vidi 016.156.308) to L. V. 52 69 70 4 31 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2157-9024 ONCOGENESIS Oncogenesis AUG 16 2019.0 8 44 10.1038/s41389-019-0157-8 0.0 12 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology IR6IC 31420533.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000481541900001 0
J Li, XY; He, Y; Fioranelli, F; Jing, XJ; Yarovoy, A; Yang, Y Li, Xinyu; He, Yuan; Fioranelli, Francesco; Jing, Xiaojun; Yarovoy, Alexander; Yang, Yang Human Motion Recognition With Limited Radar Micro-Doppler Signatures IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep learning (DL); human motion recognition (HMR); radar micro-Doppler (MD); transfer learning CLASSIFICATION The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons' motions in a small-scale target data set, ITL can also classify the persons' motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method. [Li, Xinyu; He, Yuan; Jing, Xiaojun] Beijing Univ Posts & Telecommun BUPT, Key Lab Trustworthy Distributed Comp & Serv, Beijing 100876, Peoples R China; [Fioranelli, Francesco; Yarovoy, Alexander] Delft Univ Technol, Dept Microelect, NL-2628 CD Delft, Netherlands; [Yang, Yang] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China Delft University of Technology; Tianjin University He, Y (corresponding author), Beijing Univ Posts & Telecommun BUPT, Key Lab Trustworthy Distributed Comp & Serv, Beijing 100876, Peoples R China. lixinyu@bupt.edu.cn; yuanhe@bupt.edu.cn; f.fioranelli@tudelft.nl; jxiaojun@bupt.edu.cn; a.yarovoy@tudelft.nl; yang_yang@tju.edu.cn Fioranelli, Francesco/Y-8612-2019 yang, yang/0000-0002-1364-8653; Li, Xinyu/0000-0002-1919-2327; Fioranelli, Francesco/0000-0001-8254-8093; he, yuan/0000-0001-7578-8515 National Nature Science Foundation of China [61901049]; BUPT Basic Research Fund [500419757]; BUPT Excellent Ph.D.; Students Foundation [CX2020208] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); BUPT Basic Research Fund; BUPT Excellent Ph.D.; Students Foundation This work was supported in part by the National Nature Science Foundation of China under Grant 61901049, in part by the BUPT Basic Research Fund under Grant 500419757, and in part by the BUPT Excellent Ph.D. Students Foundation under Grant CX2020208. 39 11 11 19 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2021.0 59 8 6586 6599 10.1109/TGRS.2020.3028223 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology TM2SS Green Published 2023-03-23 WOS:000675402300030 0
J Liu, JH; Zhao, BK; Xin, Q; Su, JS; Ou, W Liu, Jiahao; Zhao, Baokang; Xin, Qin; Su, Jinshu; Ou, Wei DRL-ER: An Intelligent Energy-Aware Routing Protocol With Guaranteed Delay Bounds in Satellite Mega-Constellations IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING English Article Satellites; Deep learning; Batteries; Delays; Routing protocols; Network topology; Reinforcement learning; Energy efficiency; Deep learning; Deep reinforcement learning; end-to-end delay; energy-efficient routing; satellite mega-constellations Major space companies are developing satellite mega-constellations to provide global Internet coverage and services. Limited battery capacity is one of the biggest obstacles on mega-constellations due to the restricted weight and volume of satellites. Massive Internet packet routing tasks pose a big challenge to the energy system in such mega constellations. Incorrect use of satellite batteries during routing phases may significantly increase the energy consumption and cause node failure quickly. Existing state-of-the-art works on energy-saving routing for satellite networks paid much attention on traffic distribution and end-to-end delay issues. However, these methodologies were using many real-time network information for optimization which is not practical in mega-constellations. Note also that these works did not consider energy efficiency and guaranteed end-to-end delay simultaneously. In this paper, we propose a novel deep reinforcement learning based energy-efficient routing protocol called DRL-ER, which avoids the battery energy imbalance of constellations and can also guarantee a required bounded end-to-end delay. In DRL-ER, satellites can learn a routing policy that will balance energy usage among satellites. Extensive simulation results show that our proposed DRL-ER protocol reduces the energy consumption of satellites in average by more than 55% compared to the current state-of-the-art work, and prolongs the lifetime of constellations significantly. [Liu, Jiahao; Zhao, Baokang; Su, Jinshu] Natl Univ Def Technol, Sch Comp, Changsha 410073, Peoples R China; [Xin, Qin] Univ Faroe Isl, Fac Sci & Technol, FO-100 Torshavn, Faroe Islands, Denmark; [Ou, Wei] Hunan Univ Sci, Dept Elect & Informat Engn, Yongzhou 425100, Peoples R China National University of Defense Technology - China Liu, JH (corresponding author), Natl Univ Def Technol, Sch Comp, Changsha 410073, Peoples R China. liujiahao14@nudt.edu.cn; bkzhao@nudt.edu.cn; qinx@setur.fo; sjs@nudt.edu.cn; ouwei@huse.edu.cn Xin, Qin/0000-0002-6178-8538 National Natural Science Foundation [61972412] National Natural Science Foundation(National Natural Science Foundation of China (NSFC)) This work was partially supported by the National Natural Science Foundation under research grant 61972412. 40 9 10 11 21 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 2327-4697 IEEE T NETW SCI ENG IEEE Trans. Netw. Sci. Eng. OCT 1 2021.0 8 4 2872 2884 10.1109/TNSE.2020.3039499 0.0 13 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics XM6IW 2023-03-23 WOS:000728929300015 0
J Zhang, GH; Li, KR; Chen, ZX; Sun, L; Zhang, JW; Pan, XP Zhang, Guanghua; Li, Keran; Chen, Zhixian; Sun, Li; Zhang, Jianwei; Pan, Xueping Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations JOURNAL OF HEALTHCARE ENGINEERING English Article Diabetic retinopathy (DR) is currently one of the severe complications leading to blindness, and computer-aided, diagnosis technology-assisted DR grading has become a popular research trend especially for the development of deep learning methods. However, most deep learning-based DR grading models require a large number of annotations to provide data guidance, and it is laborious for experts to find subtle lesion areas from fundus images, making accurate annotation more expensive than other vision tasks. In contrast, large-scale unlabeled data are easily accessible, becoming a potential solution to reduce the annotating workload in DR grading. Thus, this paper explores the internal correlations from unknown fundus images assisted by limited labeled fundus images to solve the semisupervised DR grading problem and proposes an augmentation-consistent clustering network (ACCN) to address the above-mentioned challenges. Specifically, the augmentation provides an efficient cue for the similarity information of unlabeled fundus images, assisting the supervision from the labeled data. By mining the consistent correlations from augmentation and raw images, the ACCN can discover subtle lesion features by clustering with fewer annotations. Experiments on Messidor and APTOS 2019 datasets show that the ACCN surpasses many state-of-the-art methods in a semisupervised manner. [Zhang, Guanghua; Chen, Zhixian] Taiyuan Univ, Dept Intelligence & Automat, Taiyuan 030000, Peoples R China; [Li, Keran] Nanjing Med Univ, Affiliated Eye Hosp, Nanjing, Jiangsu, Peoples R China; [Sun, Li] Nanjing Univ Chinese Med, Affiliated Hosp Integrated Tradit Chinese & Weste, Nanjing 210028, Peoples R China; [Sun, Li] Jiangsu Prov Acad Tradit Chinese Med, Nanjing 210028, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Tech Aspects Multimodal Syst TAMS, D-22527 Hamburg, Germany; [Pan, Xueping] First Peoples Hosp Huzhou, Huzhou 313000, Peoples R China Taiyuan University; Nanjing Medical University; Nanjing University of Chinese Medicine; Nanjing University of Chinese Medicine; University of Hamburg; Huzhou University Pan, XP (corresponding author), First Peoples Hosp Huzhou, Huzhou 313000, Peoples R China. panxueping1006@139.com Pan, Xueping/0000-0003-0916-0118 Research Funds of the Shanxi Transformation and Comprehensive Reform Demonstration Zone [2018KJCX04]; Fund for Shanxi 1331 Project; Key Research and Development Program of Shanxi Province [201903D311009]; Research Foundation of Education Bureau of Shanxi Province [HLW-20132]; Scientific Innovation Plan of Universities in Shanxi Province [2021L575]; Shanxi Scholarship Council of China [2020-149]; Zhejiang Medical and Health Research Project [2020PY027]; Huzhou Science and Technology Planning Program [2019GY13] Research Funds of the Shanxi Transformation and Comprehensive Reform Demonstration Zone; Fund for Shanxi 1331 Project; Key Research and Development Program of Shanxi Province; Research Foundation of Education Bureau of Shanxi Province; Scientific Innovation Plan of Universities in Shanxi Province; Shanxi Scholarship Council of China; Zhejiang Medical and Health Research Project; Huzhou Science and Technology Planning Program This work was supported by the Research Funds of the Shanxi Transformation and Comprehensive Reform Demonstration Zone (Grant no. 2018KJCX04), the Fund for Shanxi 1331 Project, and the Key Research and Development Program of Shanxi Province (No. 201903D311009). The work was also partially sponsored by the Research Foundation of Education Bureau of Shanxi Province (Grant No. HLW-20132), the Scientific Innovation Plan of Universities in Shanxi Province (Grant no. 2021L575), and, the Shanxi Scholarship Council of China (Grant No. 2020-149). The work was also sponsored by the Zhejiang Medical and Health Research Project (2020PY027) and the Huzhou Science and Technology Planning Program (2019GY13). 41 0 0 2 3 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 2040-2295 2040-2309 J HEALTHC ENG J. Healthc. Eng. MAR 28 2022.0 2022 4246239 10.1155/2022/4246239 0.0 10 Health Care Sciences & Services Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services 1D1AA 35388319.0 Green Accepted, gold 2023-03-23 WOS:000793539600009 0
J Bogojeski, M; Vogt-Maranto, L; Tuckerman, ME; Muller, KR; Burke, K Bogojeski, Mihail; Vogt-Maranto, Leslie; Tuckerman, Mark E.; Mueller, Klaus-Robert; Burke, Kieron Quantum chemical accuracy from density functional approximations via machine learning NATURE COMMUNICATIONS English Article MOLECULAR-DYNAMICS; PSEUDOPOTENTIALS; PROTON; POTENTIALS; PROGRAM; MODELS; ATOMS Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal . mol(-1) with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal . mol(-1)) on test data. Moreover, density-based Delta -learning (learning only the correction to a standard DFT calculation, termed Delta -DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Delta -DFT is highlighted by correcting on the fly DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)(2)) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Delta -DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities. [Bogojeski, Mihail; Mueller, Klaus-Robert] Tech Univ Berlin, Machine Learning Grp, Marchstr 23, D-10587 Berlin, Germany; [Vogt-Maranto, Leslie; Tuckerman, Mark E.] NYU, Dept Chem, 4 Washington Pl, New York, NY 10003 USA; [Tuckerman, Mark E.] NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10012 USA; [Tuckerman, Mark E.] NYU Shanghai, NYU ECNU Ctr Computat Chem, 3663 Zhongshan Rd North, Shanghai 200062, Peoples R China; [Mueller, Klaus-Robert] Korea Univ, Dept Artificial Intelligence, Seoul 02841, South Korea; [Mueller, Klaus-Robert] Max Planck Inst Informat, Stuhlsatzenhausweg, D-66123 Saarbrucken, Germany; [Burke, Kieron] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA; [Burke, Kieron] Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA Technical University of Berlin; New York University; New York University; NYU Shanghai; Korea University; Max Planck Society; University of California System; University of California Irvine; University of California System; University of California Irvine Muller, KR (corresponding author), Tech Univ Berlin, Machine Learning Grp, Marchstr 23, D-10587 Berlin, Germany.;Tuckerman, ME (corresponding author), NYU, Dept Chem, 4 Washington Pl, New York, NY 10003 USA.;Tuckerman, ME (corresponding author), NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10012 USA.;Tuckerman, ME (corresponding author), NYU Shanghai, NYU ECNU Ctr Computat Chem, 3663 Zhongshan Rd North, Shanghai 200062, Peoples R China.;Muller, KR (corresponding author), Korea Univ, Dept Artificial Intelligence, Seoul 02841, South Korea.;Muller, KR (corresponding author), Max Planck Inst Informat, Stuhlsatzenhausweg, D-66123 Saarbrucken, Germany.;Burke, K (corresponding author), Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.;Burke, K (corresponding author), Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA. mark.tuckerman@nyu.edu; klaus-robert.mueller@tu-berlin.de; kieron@uci.edu Mueller, Klaus-Robert/Y-3547-2019; Bogojeski, Mihail/ABD-3740-2020; Mueller, Klaus-Robert/C-3196-2013 Mueller, Klaus-Robert/0000-0002-3861-7685; Bogojeski, Mihail/0000-0002-1839-7320; Mueller, Klaus-Robert/0000-0002-3861-7685; Vogt-Maranto, Leslie/0000-0002-7006-4582; Tuckerman, Mark/0000-0003-2194-9955 U.S. Army Research Office [W911NF-13-1-0387]; Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea Government [2019-0-00079]; Artificial Intelligence Graduate School Program, Korea University; German Ministry for Education and Research (BMBF) [01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D, 01IS18037A]; German Research Foundation (DFG) [EXC 2046/1, 390685689]; NSF [CHE 1856165] U.S. Army Research Office; Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea Government(Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea); Artificial Intelligence Graduate School Program, Korea University; German Ministry for Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); German Research Foundation (DFG)(German Research Foundation (DFG)); NSF(National Science Foundation (NSF)) The authors thank Dr. Felix Brockherde and Joseph Cendagorta for helpful discussions, Dr. Li Li for the initial water dataset, and Dr. Huziel Sauceda and Dr. Stefan Chmiela for the optimized geometries of ethanol and for helpful discussions. Calculations were run on NYU IT High Performance Computing resources and at TUB. Work at NYU was supported by the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (L.V.-M. and M.E.T.). K.-R.M. was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. K.B. was supported by NSF grant CHE 1856165. This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. 115 105 105 47 135 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. OCT 16 2020.0 11 1 5223 10.1038/s41467-020-19093-1 0.0 11 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics OG7JC 33067479.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000582054700002 0
C Zhang, X; Xue, R; Soergel, U Yilmaz, A; Wegner, JD; Qin, R; Remondino, F; Fuse, T; Toschi, I Zhang, X.; Xue, R.; Soergel, U. A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS XXIV ISPRS CONGRESS IMAGING TODAY, FORESEEING TOMORROW, COMMISSION II International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences English Proceedings Paper 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow JUN 06-11, 2022 Nice, FRANCE Int Soc Photogrammetry & Remote Sensing Deep Learning; Transformer; Two-Stage Approach; Rare Classes; Imbalanced Classes; Semantic Segmentation; Urban Point Clouds Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods. This paper proposes a two-stage segmentation framework with automated workflows for imbalanced rare classes based on general semantic segmentation. The proposed approach includes two stages: general semantic segmentation and object-based refined semantic segmentation. Firstly, general segmentation networks are utilized to segment general large objects. Secondly, refined semantic segmentation is conducted by an automated workflow: 3D clustering and bounding box (BBox) generation are applied to the point cloud of rare fine-grained objects during the training, followed by object detection to extract fine-grained objects. Afterwards, as the constraints, the extracted BBoxes further refine the segmentation results. Our approach is evaluated on the Hessigheim High-Resolution 3D Point Cloud (H3D) Benchmark and obtains state-of-the-art 89.35% overall accuracy and outstanding 75.70% mean F1-Score. Furthermore, rare classes Vehicle and Chimney achieve breakthroughs from zero to 63.63% and 52.00% in F1-score, respectively. [Zhang, X.; Xue, R.; Soergel, U.] Univ Stuttgart, Inst Photogrammetry, D-70174 Stuttgart, Germany; [Xue, R.] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China University of Stuttgart; Xidian University Xue, R (corresponding author), Univ Stuttgart, Inst Photogrammetry, D-70174 Stuttgart, Germany.;Xue, R (corresponding author), Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China. xinlong.zhang@ifp.uni-stuttgart.de; ruihang.xue@ifp.uni-stuttgart.de; uwe.soergel@ifp.uni-stuttgart.de China Scholarship Council [202008080145] China Scholarship Council(China Scholarship Council) The research work in this paper has been funded by China Scholarship Council (No. 202008080145). 19 0 0 1 1 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLE 1E, GOTTINGEN, 37081, GERMANY 1682-1750 2194-9034 INT ARCH PHOTOGRAMM 2022.0 43-B2 329 334 10.5194/isprs-archives-XLIII-B2-2022-329-2022 0.0 6 Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Physical Geography; Remote Sensing; Imaging Science & Photographic Technology BT8VT gold 2023-03-23 WOS:000855635300046 0
J Barbano, R; Kereta, Z; Hauptmann, A; Arridge, SR; Jin, BT Barbano, Riccardo; Kereta, Zeljko; Hauptmann, Andreas; Arridge, Simon R.; Jin, Bangti Unsupervised knowledge-transfer for learned image reconstruction* INVERSE PROBLEMS English Article unsupervised learning; pretraining; image reconstruction; Bayesian deep learning; computed tomography INVERSE PROBLEMS; DEEP; NETWORK; UNCERTAINTY; EFFICIENT; SIGNAL Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not available in medical imaging. To circumvent this issue we develop a novel unsupervised knowledge-transfer paradigm for learned reconstruction within a Bayesian framework. The proposed approach learns a reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images of ellipses and the corresponding simulated measurement data. The second phase fine-tunes the pretrained network to more realistic measurement data without supervision. By construction, the framework is capable of delivering predictive uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography showing that the approach is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques. Moreover, for test data distributed differently from the training data, the proposed framework can significantly improve reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, when compared with learned methods trained on the synthetic dataset only. [Barbano, Riccardo; Kereta, Zeljko; Hauptmann, Andreas; Arridge, Simon R.] UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England; [Hauptmann, Andreas] Univ Oulu, Res Unit Math Sci, Oulu, Finland; [Jin, Bangti] Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong, Peoples R China University of London; University College London; University of Oulu; Chinese University of Hong Kong Barbano, R (corresponding author), UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England. riccardo.barbano.19@ucl.ac.uk; z.kereta@ucl.ac.uk; Andreas.Hauptmann@oulu.fi; s.arridge@ucl.ac.uk; bangti.jin@gmail.com Jin, Bangti/B-4226-2009 Jin, Bangti/0000-0002-3775-9155; barbano, riccardo/0000-0003-1863-2092; Arridge, Simon/0000-0003-1292-0210 i4health PhD studentship (UK EPSRC) [EP/S021930/1]; Alan Turing Institute (UK EPSRC) [EP/N510129/1]; UK EPSRC [EP/V026259/1]; Academy of Finland [336796, 334817, 338408]; Academy of Finland (AKA) [334817] Funding Source: Academy of Finland (AKA) i4health PhD studentship (UK EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Alan Turing Institute (UK EPSRC); UK EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) The work of RB is substantially supported by the i4health PhD studentship (UK EPSRC EP/S021930/1) and from The Alan Turing Institute (UK EPSRC EP/N510129/1), and that of ZK, SA and BJ by UK EPSRC EP/T000864/1, and that of SA and BJ also by UK EPSRC EP/V026259/1. AH acknowledges funding by Academy of Finland Projects 336796, 334817, 338408. 60 1 1 3 5 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0266-5611 1361-6420 INVERSE PROBL Inverse Probl. OCT 1 2022.0 38 10 104004 10.1088/1361-6420/ac8a91 0.0 28 Mathematics, Applied; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics 4J5IR hybrid, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000851297000001 0
J Xu, ZH; Zhang, Z; Wang, SL; Jolfaei, A; Bashir, AK; Yan, Y; Mumtaz, S Xu, Zuohong; Zhang, Zhou; Wang, Shilian; Jolfaei, Alireza; Bashir, Ali Kashif; Yan, Ye; Mumtaz, Shahid Decentralized Opportunistic Channel Access in CRNs Using Big-Data Driven Learning Algorithm IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE English Article Wireless sensor networks; Channel estimation; Markov processes; Sensors; Channel models; Cognitive radio; Multiuser channels; Multi-user channel sensing and access; multi-armed bandit problem; logarithmic regret EFFICIENT ALLOCATION RULES; MULTIARMED BANDIT PROBLEM; COGNITIVE RADIO NETWORKS; MULTIPLE PLAYS Opportunistic channel access in cognitive radio networks (CRNs) under an unknown environment is gradually receiving a great deal of attention. This article studies the basic problem of decentralized secondary users (SUs) performing multiple channel sensing and access in CRN, when sensing is not perfect. The channel availability information is unknown and must be estimated and learned through big-data samples from the wireless channels by SUs. Both the independent identical distribution (i.i.d.) channel model and the Markov channel model are considered. In the i.i.d. model, the availability of each channel is modeled as an i.i.d. process, while in the Markov model, the availability of each channel is set as a Markov chain with an unknown probability transition matrix. If multiple SUs access to the identical channel, collision will occur and none of SUs gets a reward. Learning loss, which is also referred to as regret, is thus inevitable. To handle with the sampling data on large scale, we formulate the channel sensing and access process as a multi-armed bandit problem (MABP), based on which big-data driven online algorithms are proposed. The theoretical analysis and simulations prove that the regret of our algorithms is both logarithmic in finite time and asymptotically. [Xu, Zuohong; Wang, Shilian] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Hunan, Peoples R China; [Zhang, Zhou; Yan, Ye] Tianjin Artificial Intelligence Innovat Ctr TAIIC, Tianjin 300451, Peoples R China; [Jolfaei, Alireza] Macquarie Univ, Dept Comp, Sydney, NSW 2113, Australia; [Bashir, Ali Kashif] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England; [Mumtaz, Shahid] Inst Telecomunicac, P-3810193 Aveiro, Portugal National University of Defense Technology - China; Macquarie University; Manchester Metropolitan University; Instituto de Telecomunicacoes Zhang, Z (corresponding author), Tianjin Artificial Intelligence Innovat Ctr TAIIC, Tianjin 300451, Peoples R China. zuohong.xu@outlook.com; zt.sy1986@163.com; wangsl@nudt.edu.cn; alireza.jolfaei@mq.edu.au; dr.alikashif.b@ieee.org; yanye1971@sohu.com; smumtaz@av.it.pt Mumtaz, Dr shahid/V-3603-2019; Bashir, Ali Kashif/R-4015-2019; Jolfaei, Alireza/GQH-6907-2022 Mumtaz, Dr shahid/0000-0001-6364-6149; Bashir, Ali Kashif/0000-0003-2601-9327; Jolfaei, Alireza/0000-0001-7818-459X; shilian, wang/0000-0003-4132-8750 25 4 4 2 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2471-285X IEEE TETCI IEEE Trans. Emerg. Top. Comput. Intell. FEB 2021.0 5 1 57 69 10.1109/TETCI.2020.3018779 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science TP8VS 2023-03-23 WOS:000677872800006 0
J Guo, GD; Yu, K; Wang, H; Lin, S; Xu, YZ; Chen, XF Guo, Gongde; Yu, Kai; Wang, Hui; Lin, Song; Xu, Yongzhen; Chen, Xiaofeng Quantum Hierarchical Agglomerative Clustering Based on One Dimension Discrete Quantum Walk with Single-Point Phase Defects CMC-COMPUTERS MATERIALS & CONTINUA English Article Quantum machine learning; discrete quantum walk; hierarchical agglomerative clustering As an important branch of machine learning, clustering analysis is widely used in some fields, e.g., image pattern recognition, social network analysis, information security, and so on. In this paper, we consider the designing of clustering algorithm in quantum scenario, and propose a quantum hierarchical agglomerative clustering algorithm, which is based on one dimension discrete quantum walk with single-point phase defects. In the proposed algorithm, two nonclassical characters of this kind of quantum walk, localization and ballistic effects, are exploited. At first, each data point is viewed as a particle and performed this kind of quantum walk with a parameter, which is determined by its neighbors. After that, the particles are measured in a calculation basis. In terms of the measurement result, every attribute value of the corresponding data point is modified appropriately. In this way, each data point interacts with its neighbors and moves toward a certain center point. At last, this process is repeated several times until similar data points cluster together and form distinct classes. Simulation experiments on the synthetic and real world data demonstrate the effectiveness of the presented algorithm. Compared with some classical algorithms, the proposed algorithm achieves better clustering results. Moreover, combining quantum cluster assignment method, the presented algorithm can speed up the calculating velocity. [Guo, Gongde; Yu, Kai; Lin, Song; Xu, Yongzhen] Fujian Normal Univ, Coll Math & Informat, Fuzhou 350007, Peoples R China; [Wang, Hui] Univ Ulster, Sch Comp & Math, Coleraine BT37 0QB, Londonderry, North Ireland; [Chen, Xiaofeng] Fujian Jiangxia Univ, Sch Elect Informat Sci, Fuzhou 350108, Peoples R China Fujian Normal University; Ulster University; Fujian Jiangxia University Lin, S (corresponding author), Fujian Normal Univ, Coll Math & Informat, Fuzhou 350007, Peoples R China. lins95@gmail.com Yu, Kai/0000-0002-7296-2589 National Natural Science Foundation of China [61976053, 61772134]; Fujian Province Natural Science Foundation [2018J01776]; Program for New Century Excellent Talents in Fujian Province University, Probability and Statistics: Theory and Application [IRTL1704]; Program for Innovative Research Team in Science and Technology in Fujian Province University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fujian Province Natural Science Foundation(Natural Science Foundation of Fujian Province); Program for New Century Excellent Talents in Fujian Province University, Probability and Statistics: Theory and Application(Program for New Century Excellent Talents in University (NCET)); Program for Innovative Research Team in Science and Technology in Fujian Province University(Program for Changjiang Scholars & Innovative Research Team in University (PCSIRT)) This work was supported by National Natural Science Foundation of China (Grants Nos. 61976053 and 61772134), Fujian Province Natural Science Foundation (Grant No. 2018J01776), Program for New Century Excellent Talents in Fujian Province University, Probability and Statistics: Theory and Application (Grant No. IRTL1704), and the Program for Innovative Research Team in Science and Technology in Fujian Province University. 25 1 2 1 5 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2020.0 65 2 1397 1409 10.32604/cmc.2020.011399 0.0 13 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science NG7HQ gold 2023-03-23 WOS:000564151800024 0
C Zhou, MC; Wang, XJ; Weiss, J; Eslami, A; Huang, K; Maier, M; Lohmann, CP; Navab, N; Knoll, A; Nasseri, MA Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Zhou, Mingchuan; Wang, Xijia; Weiss, Jakob; Eslami, Abouzar; Huang, Kai; Maier, Mathias; Lohmann, Chris P.; Navab, Nassir; Knoll, Alois; Nasseri, M. Ali Needle Localization for Robot-assisted Subretinal Injection based on Deep Learning 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster Subretinal injection is known to be a complicated task for ophthalmologists to perform, the main sources of difficulties are the fine anatomy of the retina, insufficient visual feedback, and high surgical precision. Image guided robot-assisted surgery is one of the promising solutions that bring significant surgical enhancement in treatment outcome and reduces the physical limitations of human surgeons. In this paper, we demonstrate a robust framework for needle detection and localization in subretinal injection using microscope-integrated Optical Coherence Tomography (MI-OCT) based on deep learning. The proposed method consists of two main steps: a) the preprocessing of OCT volumetric images; b) needle localization in the processed images. The first step is to coarsely localize the needle position based on the needle information above the retinal surface and crop the original image into a small region of interest (ROI). Afterward, the cropped small image is fed into a well trained network for detection and localization of the needle segment. The entire framework is extensively validated in ex-vivo pig eye experiments with robotic subretinal injection. The results show that the proposed method can localize the needle accurately with a confidence of 99.2%. [Zhou, Mingchuan; Wang, Xijia; Knoll, Alois] Tech Univ Munich, Chair Robot & Embedded Syst, Munich, Germany; [Weiss, Jakob; Navab, Nassir] Tech Univ Munich, Chair Comp Aided Med Procedures & Augmented Real, Munich, Germany; [Eslami, Abouzar] Carl Zeiss Meditec, Jena, Germany; [Huang, Kai] Sun Yat Sen Univ, China Sch Data & Comp Sci, Minist Educ, Key Lab Machine Intelligence & Adv Comp, Guangzhou, Guangdong, Peoples R China; [Maier, Mathias; Lohmann, Chris P.; Nasseri, M. Ali] Tech Univ Munich, Klinikum Rechts Isar, Augenklin & Poliklin, Munich, Germany Technical University of Munich; Technical University of Munich; Carl Zeiss AG; Sun Yat Sen University; Technical University of Munich Zhou, MC (corresponding author), Tech Univ Munich, Chair Robot & Embedded Syst, Munich, Germany. zhoum@tum.de; xijia.wang@tum.de; jakob.weiss@tum.de; abouzar.eslami@zeiss.com; huangk36@mail.sysu.edu.cn; mathias.maier@mri.tum.de; chris.lohmann@mri.tum.de; navab@tum.de; knoll@tum.de; ali.nasseri@mri.tum.de Maier, Mathias/ABH-6630-2020; Nasseri, M. Ali/AAX-3939-2020; Knoll, Alois/AAN-8417-2021 Nasseri, M. Ali/0000-0002-9764-5731; Knoll, Alois/0000-0003-4840-076X NVIDIA Corporation NVIDIA Corporation We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. We also thank Carl Zeiss Meditec AG for providing imaging device. 18 7 7 2 13 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 8727 8732 6 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN Green Published 2023-03-23 WOS:000494942306062 0
C Gao, Y; Chen, AQ; Chi, SS; Zhang, GT; Hao, AM IEEE Comp Soc Gao, Yang; Chen, Anqi; Chi, Susan; Zhang, Guangtao; Hao, Aimin Analysis of Emotional Tendency and Syntactic Properties of VR Game Reviews 2022 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS (VRW 2022) English Proceedings Paper IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR) MAR 12-16, 2022 ELECTR NETWORK IEEE,IEEE Comp Soc,ChristchurchNZ,Virbela,Univ Canterbury,Immers Learning Res Network,Qualcomm,HIT Lab NZ, Appl Immers Gaming Initiat Applied computing; Computers in other domains; Personal computers and PC applications; Computer games; Human-centered computing; Human-computer interaction (HCI); HCI design and evaluation methods; User studies The studies of player reviews can help game developers design and optimize VR games. To this end, we investigated 288,685 reviews from 506 VR games on the Steam platform to analyze their sentiment tendencies using the machine learning-based model SKEP, which finds that although some of the reviews are recommend, they actually have opposite emotional tendencies. We also study the syntactic properties based on the natural language processing (NLP) kits Stanza and NLTK library, and we find that cybersickness is a significant concern for players. [Gao, Yang; Chen, Anqi; Hao, Aimin] Beihang Univ, Beijing, Peoples R China; [Chi, Susan] NetEase Games, Hangzhou, Peoples R China; [Zhang, Guangtao] Tech Univ Denmark, Lyngby, Denmark Beihang University; Technical University of Denmark Hao, AM (corresponding author), Beihang Univ, Beijing, Peoples R China. ham@buaa.edu.cn Gao, Yang/0000-0002-9149-3554; Zhang, Guangtao/0000-0003-0794-3338 11 0 0 3 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 978-1-6654-8402-2 2022.0 639 640 10.1109/VRW55335.2022.00175 0.0 2 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT2FN 2023-03-23 WOS:000808111800166 0
J Arabameri, A; Chen, W; Lombardo, L; Blaschke, T; Bui, DT Arabameri, Alireza; Chen, Wei; Lombardo, Luigi; Blaschke, Thomas; Dieu Tien Bui Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment REMOTE SENSING English Article land degradation; soil conservation; ensemble; machine learning; environmental management LANDSLIDE SUSCEPTIBILITY ASSESSMENT; EVIDENTIAL BELIEF FUNCTION; LOGISTIC-REGRESSION; SPATIAL PREDICTION; CONDITIONING FACTORS; STATISTICAL-MODELS; EVENTS APPLICATION; HAZARD ASSESSMENT; NEURAL-NETWORKS; RANDOM FOREST Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher's linear discriminant analysis (FLDA), logistic model tree (LMT) and naive Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data. [Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Jalal Ale Ahmad Highway, Tehran 9821, Iran; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Peoples R China; [Lombardo, Luigi] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands; [Blaschke, Thomas] Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; [Dieu Tien Bui] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Tarbiat Modares University; Ministry of Natural Resources of the People's Republic of China; Xi'an University of Science & Technology; University of Twente; Salzburg University; Duy Tan University Arabameri, A (corresponding author), Tarbiat Modares Univ, Dept Geomorphol, Jalal Ale Ahmad Highway, Tehran 9821, Iran.;Bui, DT (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. alireza.arab@ut.ac.ir; chenwei0930@xust.edu.cn; luigi.lombardo@kaust.edu.sa; thomas.blaschke@sbg.ac.at; buitiendieu@duytan.edu.vn Blaschke, Thomas/AAR-9037-2021; Blaschke, Thomas/F-3342-2011; Lombardo, Luigi/AAY-6370-2021; Chen, Wei/ABB-8669-2020 Blaschke, Thomas/0000-0002-1860-8458; Blaschke, Thomas/0000-0002-1860-8458; Lombardo, Luigi/0000-0003-4348-7288; Chen, Wei/0000-0002-5825-1422; ARABAMERI, ALIREZA/0000-0002-1142-1666 Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg [DK W 1237-N23] Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg(Austrian Science Fund (FWF)) This research was partly funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W 1237-N23) at the University of Salzburg. 82 26 26 2 27 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2020.0 12 1 140 10.3390/rs12010140 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology KO2PM gold 2023-03-23 WOS:000515391700140 0
J Wang, YL; Wang, CH; Gu, CY; Cui, YJ; O'Neill, M; Liu, WQ Wang, Yale; Wang, Chenghua; Gu, Chongyan; Cui, Yijun; O'Neill, Maire; Liu, Weiqiang A Generic Dynamic Responding Mechanism and Secure Authentication Protocol for Strong PUFs IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS English Article Authentication protocol; dynamically configurable; machine learning (ML); physical unclonable function (PUF) ARBITER PUF; ATTACK; DESIGN As a lightweight hardware security primitive, physical unclonable functions (PUFs) can provide reliable identity authentication for devices of Internet of Things (IoTs) with limited resources. However, the delay-based PUF structures in authentication protocols have static responding behaviors, which make them vulnerable to modeling attacks. To address this issue, many complex PUF designs have been designed to increase the nonlinearity of their models. However, most of them can still be broken by modeling-based machine learning (ML) attacks. In this article, a dynamic responding mechanism for PUF designs to generate dynamic responses is proposed. Different from the concept of logically reconfigurable PUFs, the proposed mechanism does not rely on external inputs to provide reconfiguration signals. And different from the conventional PUF authentication protocols that use large-size linear feedback shift register (LFSR) to extend the master challenge, the proposed scheme uses internally generated dynamic signals to obfuscate the master challenge to generate multiple subchallenges. These subchallenges are then input to the underlying strong PUF to generate multibit dynamic responses. It can prevent an attacker from obtaining valid challenge-response pairs (CRPs) for the underlying PUF. A security authentication protocol is also proposed, the special authentication bit-string design can resist both conventional ML attacks and the latest covariance matrix adaptation evolution strategies (CMA-ES) variant. [Wang, Yale; Wang, Chenghua; Cui, Yijun; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut NUAA, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China; [Gu, Chongyan; O'Neill, Maire] Queens Univ Belfast QUB, Inst Elect Commun & Informat Technol ECIT, Ctr Secure Informat Technol CSIT, Belfast BT3 9DT, Antrim, North Ireland Nanjing University of Aeronautics & Astronautics Liu, WQ (corresponding author), Nanjing Univ Aeronaut & Astronaut NUAA, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China. yalewang@nuaa.edu.cn; chwang@nuaa.edu.cn; c.gu@qub.ac.uk; yijun.cui@nuaa.edu.cn; m.oneill@ecit.qub.ac.uk; liuweiqiang@nuaa.edu.cn Wang, Yale/0000-0002-2183-4886; Liu, Weiqiang/0000-0001-8398-8648 National Natural Science Foundation of China (NSFC) [62022041]; Fundamental Research Funds for the Central Universities [NP2022103]; Royal Society Research [IEC/NSFC/211024]; Engineering and Physical Sciences Research Council (EPSRC) [EP/N508664]; EPSRC Quantum Communications Hub [EP/T001011/1] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Royal Society Research(Royal Society of London); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC Quantum Communications Hub(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62022041, in part by the Fundamental Research Funds for the Central Universities under Grant NP2022103, in part by the Royal Society Research under Grant IEC/NSFC/211024, in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N508664, and in part by the EPSRC Quantum Communications Hub under Grant EP/T001011/1. 41 0 0 4 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-8210 1557-9999 IEEE T VLSI SYST IEEE Trans. Very Large Scale Integr. (VLSI) Syst. SEP 2022.0 30 9 1256 1268 10.1109/TVLSI.2022.3189953 0.0 JUL 2022 13 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4G5VK Green Accepted 2023-03-23 WOS:000830322300001 0
J Zhong, CN; Ng, WWY; Zhang, S; Nugent, CD; Shewell, C; Medina-Quero, J Zhong, Cankun; Ng, Wing W. Y.; Zhang, Shuai; Nugent, Chris D.; Shewell, Colin; Medina-Quero, Javier Multi-Occupancy Fall Detection Using Non-Invasive Thermal Vision Sensor IEEE SENSORS JOURNAL English Article Thermal sensors; Feature extraction; Sensors; Training; Perturbation methods; Image sensors; Temperature sensors; Multi-occupancy fall detection; thermal vision sensor; MoT-LoGNN; smart environments; neural networks SYSTEM; ERROR Falling is a common issue within the aging population. The immediate detection of a fall is key to guarantee early and immediate attention to avoid other potential immobility risks and reduction in recovery time. Video-based approaches for monitoring fall detection, although being highly accurate, are largely perceived as being intrusive if deployed within living environments. As an alternative, thermal vision-based methods can be deployed to offer a more acceptable level of privacy. To date, thermal vision-based fall detection methods have largely focused on single-occupancy scenarios, which are not fully representative of real living environments with multi-occupancy. This work proposes a non-invasive thermal vision-based approach of multi-occupancy fall detection (MoT-LoGNN) which discriminates between a fall or no-fall. The approach consists of four major components: i) a multi-occupancy decomposer, ii) a sensitivity-based sample selector, iii) the T-LoGNN for single-occupancy fall detection, and iv) a fine-tuning mechanism. The T-LoGNN consists of a robust neural network minimizing a Localized Generalization Error (L-GEM) and thermal image features extracted by a Convolutional Neural Network (CNN). Comparing to other methods, the MoT-LoGNN achieved the highest average accuracy of 98.39% within the context of a multi-occupancy fall detection experiment. [Zhong, Cankun; Ng, Wing W. Y.] South China Univ Technol, Guangdong Prov Key Lab Computat Intelligence & Cy, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China; [Zhang, Shuai; Nugent, Chris D.; Shewell, Colin] Ulster Univ, Sch Comp, Jordanstown BT37 0QB, North Ireland; [Medina-Quero, Javier] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain South China University of Technology; Ulster University; Universidad de Jaen Ng, WWY (corresponding author), South China Univ Technol, Guangdong Prov Key Lab Computat Intelligence & Cy, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China. curran.z@qq.com; wingng@ieee.org; s.zhang@ulster.ac.uk; cd.nugent@ulster.ac.uk; cp.shewell@ulster.ac.uk; jmquero@ujaen.es Nugent, Chris/0000-0003-0882-7902; Ng, Wing W. Y./0000-0003-0783-3585; Zhong, Cankun/0000-0002-4271-6483; Shewell, Colin/0000-0002-6618-6587; Medina Quero, Javier/0000-0002-8577-8772 2020 Research and Development Program in Key Areas of Guangdong Province [2020B010166002]; National Natural Science Foundation of China [61876066]; Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) [2019A050510006]; REMIND project - Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 [734355] 2020 Research and Development Program in Key Areas of Guangdong Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction); REMIND project - Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 This work was supported in part by the 2020 Research and Development Program in Key Areas of Guangdong Province under Grant 2020B010166002, in part by the National Natural Science Foundation of China under Grant 61876066, in part by the Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) under Grant 2019A050510006, and the REMIND project funded by Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 under Grant Agreement No. 734355. 37 9 9 6 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. FEB 15 2021.0 21 4 5377 5388 10.1109/JSEN.2020.3032728 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics PX1OT Green Published 2023-03-23 WOS:000611133100144 0
J Wang, W; Hao, SY; Wei, YC; Xia, ST; Feng, JS; Sebe, N Wang, Wei; Hao, Siyuan; Wei, Yunchao; Xia, Shengtao; Feng, Jiashi; Sebe, Nicu Temporal Spiking Recurrent Neural Network for Action Recognition IEEE ACCESS English Article Action recognition; temporal spiking; recurrent neural network HISTOGRAMS In this paper, we propose a novel temporal spiking recurrent neural network (TSRNN) to perform robust action recognition in videos. The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals. The conventional CNN-RNNs typically used for this problem treat all the frames equally important such that they are error-prone to noisy frames. The TSRNN solves this problem by employing a temporal pooling architecture which can help RNN select sparse and reliable frames and enhances its capability in modelling long-range temporal information. Besides, a message passing bridge is added between the spiking signals and the recurrent unit. In this way, the spiking signals can guide RNN to correct its long-term memory across multiple frames from contamination caused by noisy frames with distracting factors (e.g., occlusion, rapid scene transition). With these two novel components, TSRNN achieves competitive performance compared with the state-of-the-art CNN-RNN architectures on two large scale public benchmarks, UCF101 and HMDB51. [Wang, Wei] Ecole Polytech Fed Lausanne, Comp Vis Lab, CH-1015 Lausanne, Switzerland; [Hao, Siyuan] Qingdao Univ Technol, Informat & Control Engn Coll, Qingdao 266520, Shandong, Peoples R China; [Wei, Yunchao] Univ Illinois, Beckman Inst, Urbana, IL 61820 USA; [Xia, Shengtao; Feng, Jiashi] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 259776, Singapore; [Sebe, Nicu] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Qingdao University of Technology; University of Illinois System; University of Illinois Urbana-Champaign; National University of Singapore; University of Trento Hao, SY (corresponding author), Qingdao Univ Technol, Informat & Control Engn Coll, Qingdao 266520, Shandong, Peoples R China. lemonbananan@163.com Feng, Jiashi/AGX-6209-2022; Wang, Wei/AAK-5521-2021 Wang, Wei/0000-0002-5477-1017; Sebe, Niculae/0000-0002-6597-7248 National Natural Science Foundation of China [61701272] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61701272. 42 9 9 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 117165 117175 10.1109/ACCESS.2019.2936604 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IV6AC Green Submitted, gold 2023-03-23 WOS:000484350300001 0
J Shayakhmetov, R; Kuznetsov, M; Zhebrak, A; Kadurin, A; Nikolenko, S; Aliper, A; Polykovskiy, D Shayakhmetov, Rim; Kuznetsov, Maksim; Zhebrak, Alexander; Kadurin, Artur; Nikolenko, Sergey; Aliper, Alexander; Polykovskiy, Daniil Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders FRONTIERS IN PHARMACOLOGY English Article deep learning; generative models; adversarial autoencoders; conditional generation; representation learning; drug discovery; gene expression DRUG DISCOVERY; DEEP; SMILES Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those related and unrelated to the drug incubation. The model uses related features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE. [Shayakhmetov, Rim; Kuznetsov, Maksim; Zhebrak, Alexander; Kadurin, Artur; Nikolenko, Sergey; Aliper, Alexander; Polykovskiy, Daniil] Insilico Med, Hong Kong, Hong Kong, Peoples R China; [Nikolenko, Sergey] Neuromat OU, Tallinn, Estonia Polykovskiy, D (corresponding author), Insilico Med, Hong Kong, Hong Kong, Peoples R China. daniil@insilico.com Nikolenko, Sergey/AAU-3615-2020; Kadurin, Artur/ABF-9143-2021 Nikolenko, Sergey/0000-0001-7787-2251; Kadurin, Artur/0000-0003-1482-9365 65 10 10 2 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1663-9812 FRONT PHARMACOL Front. Pharmacol. APR 17 2020.0 11 269 10.3389/fphar.2020.00269 0.0 11 Pharmacology & Pharmacy Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy LJ9JK 32362822.0 Green Published, gold 2023-03-23 WOS:000530476500001 0
J Batyrshin, I; Pamucar, DS; Crippa, P; Liu, F Batyrshin, Ildar; Pamucar, Dragan S.; Crippa, Paolo; Liu, Feng The fuzzy system and its application in East Asia JOURNAL OF INTELLIGENT & FUZZY SYSTEMS English Editorial Material Fuzzy logic; artificial intelligence system; data mining The Fuzzy Logic and system are widely used in many fields during the last decades. Many tools and methods are developed to mine the data in the interdisciplinary science, which witnesses the novel discovery in Science and new technologies in Engineering. To exchange the ideas and research in East Asia, the 2015 International Conference on Fuzzy System and Data Mining (FSDM 2015) is held during 12th similar to 15th December 2015. This special issue is selected some papers related to the fuzzy system and its application in East Asia, which will have a snapshot of the regional research in the field. [Batyrshin, Ildar] Natl Polytech Inst Mexico, Ctr Comp Res, Mexico City, DF, Mexico; [Pamucar, Dragan S.] Univ Def Belgrade, Dept Logist, Belgrade, Serbia; [Crippa, Paolo] Univ Politecn Marche, Dept Informat Engn, Marche, Italy; [Liu, Feng] Wuhan Univ, Int Sch Software, Wuhan 430072, Peoples R China Instituto Politecnico Nacional - Mexico; Marche Polytechnic University; Wuhan University Liu, F (corresponding author), Wuhan Univ, Int Sch Software, Wuhan 430072, Peoples R China. fliuwhu@whu.edu.cn Pamucar, Dragan/AAG-8288-2019; Batyrshin, Ildar/AAT-3126-2020; Pamucar, Dragan/C-4303-2016; Crippa, Paolo/C-1615-2010 Pamucar, Dragan/0000-0001-8522-1942; Pamucar, Dragan/0000-0001-8522-1942; Crippa, Paolo/0000-0003-4504-7550 0 1 1 0 8 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1064-1246 1875-8967 J INTELL FUZZY SYST J. Intell. Fuzzy Syst. 2015.0 29 6 2317 + 10.3233/IFS-151929 0.0 2 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CY8QU 2023-03-23 WOS:000366674200001 0
J Tan, BQ; Wang, FF; Liu, J; Kang, K; Costa, F Tan, Bing Qing; Wang, Fangfang; Liu, Jia; Kang, Kai; Costa, Federica A Blockchain-Based Framework for Green Logistics in Supply Chains SUSTAINABILITY English Article green logistics; blockchain; Internet of Things; supply chains BIG DATA; MANAGEMENT; TECHNOLOGY; INDUSTRY; SYSTEM; PERFORMANCE; DESIGN; MODEL The logistics industry around the world has proliferated over recent years as a large number of business organizations have come to recognize the importance of logistics. Cost control used to be emphasized to remain competitive, but recently green logistics has gained attention with the awareness of the integration of economy and society as a whole. Nowadays, green logistics is a useful concept to improve the sustainability of logistics operations, and its related policies and theoretical research have been investigated and explored. However, the practical applications of green logistics are impeded by real-time data sharing, which is common in the logistics industry. Blockchain technology is adopted to address this challenge and enable data sharing among related stakeholders. This paper presents a reference framework for green logistics based on blockchain to reach the sustainable operations of logistics, with the integration of the Internet of Things and big data. Finally, potential benefits and limitations are analyzed when implementing this framework. [Tan, Bing Qing] Jinan Univ, Management Sch, 601 West Huangpu Ave, Guangzhou 510632, Peoples R China; [Wang, Fangfang; Liu, Jia] Guangdong Univ Finance & Econ, Natl Econ Res Ctr, 21 Luntou Rd, Guangzhou 510320, Peoples R China; [Kang, Kai] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Hong Kong, Peoples R China; [Costa, Federica] Politecn Milan, Dept Management Econ & Ind Engn, I-20156 Milan, Italy Jinan University; Guangdong University of Finance & Economics; University of Hong Kong; Polytechnic University of Milan Liu, J (corresponding author), Guangdong Univ Finance & Econ, Natl Econ Res Ctr, 21 Luntou Rd, Guangzhou 510320, Peoples R China. tanbingqing0910@stu2018.jnu.edu.cn; wff@gdufe.edu.cn; jennysccm@gdufe.edu.cn; kangkai@connect.hku.hk; Federica.costa@polimi.it wang, fang/GYD-4295-2022; Wang, Fang/HPC-5174-2023; Molina, Nicholle/AAA-7370-2022 Kang, Kai/0000-0002-3142-5883; Bing Qing, Tan/0000-0002-3534-0529 National Statistical Science Research Project [2018LY26]; Guangzhou Philosophy and Social Science Project [2019GZQN35]; Guangdong Soft Science Research Project [2018A070712039] National Statistical Science Research Project; Guangzhou Philosophy and Social Science Project; Guangdong Soft Science Research Project This research was funded by National Statistical Science Research Project, grant number 2018LY26; Guangzhou Philosophy and Social Science Project, grant number 2019GZQN35; Guangdong Soft Science Research Project, grant number 2018A070712039. 59 45 45 32 120 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JUN 2020.0 12 11 4656 10.3390/su12114656 0.0 13 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology MC6JX Green Published, gold 2023-03-23 WOS:000543391800319 0
J Shamshirband, S; Rabczuk, T; Chau, KW Shamshirband, Shahab; Rabczuk, Timon; Chau, Kwok-Wing A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources IEEE ACCESS English Article Big dataset; deep learning; modeling; optimizing; solar energy; wind energy ARTIFICIAL NEURAL-NETWORKS; RENEWABLE ENERGY; HYDROGEN-PRODUCTION; COMPUTATIONAL INTELLIGENCE; PREDICTION; RADIATION; HYDROPOWER; EMISSIONS; SYSTEMS; CONSUMPTION Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques. [Shamshirband, Shahab] Norwegian Univ Sci & Technol, Dept Comp Sci, N-7491 Trondheim, Norway; [Rabczuk, Timon] Bauhaus Univ Weimar, Inst Struct Mech, D-99423 Weimar, Germany; [Chau, Kwok-Wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China Norwegian University of Science & Technology (NTNU); Bauhaus-Universitat Weimar; Hong Kong Polytechnic University Shamshirband, S (corresponding author), Norwegian Univ Sci & Technol, Dept Comp Sci, N-7491 Trondheim, Norway. shahab.shamshirband@ntnu.no S.Band, Shahab/ABI-7388-2020; S. Band, Shahab/ABB-2469-2020; S.Band, Shahab/AAD-3311-2021; Chau, Kwok-wing/E-5235-2011; Rabczuk, Timon/A-3067-2009 S.Band, Shahab/0000-0002-8963-731X; S. Band, Shahab/0000-0001-6109-1311; Chau, Kwok-wing/0000-0001-6457-161X; Rabczuk, Timon/0000-0002-7150-296X Norwegian Open Artificial Intelligence Lab, Department of Computer Science, NTNU, Trondheim, Norway Norwegian Open Artificial Intelligence Lab, Department of Computer Science, NTNU, Trondheim, Norway This work was supported by Norwegian Open Artificial Intelligence Lab, Department of Computer Science, NTNU, Trondheim, Norway. 83 156 156 9 41 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 164650 164666 10.1109/ACCESS.2019.2951750 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications KF8HZ gold 2023-03-23 WOS:000509479200008 0
J Wang, WQ; Chen, YS; Ghamisi, P Wang, Weiquan; Chen, Yushi; Ghamisi, Pedram Transferring CNN With Adaptive Learning for Remote Sensing Scene Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Adaptation models; Task analysis; Predictive models; Convolutional neural networks; Transfer learning; Smoothing methods; Convolutional neural network (CNN); remote sensing (RS) scene classification; transfer learning CONVOLUTIONAL NEURAL-NETWORK; ATTENTION Accurate classification of remote sensing (RS) images is a perennial topic of interest in the RS community. Recently, transfer learning, especially for fine-tuning pretrained convolutional neural networks (CNNs), has been proposed as a feasible strategy for RS scene classification. However, because the target domain (i.e., the RS images) and the source domain (e.g., ImageNet) are quite different, simply using the model pretrained on an ImageNet dataset presents some difficulties. The RS images and the pretrained models need to be properly adjusted to build a better classification system. In this study, an adaptive learning strategy for transferring a CNN-based model is proposed. First, an adaptive transform is used to adjust the original size of the RS image to a certain size, which is tailored to the input of the subsequent pretrained model. Then, an adaptive transferring model is proposed to automatically learn what knowledge from the pretrained model should be transferred to the RS scene classification model. Finally, in combination with a label smoothing approach, an adaptive label is presented to generate soft labels based on the statistics of the classification model predictions for each category, which is beneficial for learning the relationships between the target and nontarget categories of scenes. In general, the proposed methods adaptively manage the input, model, and label simultaneously, which leads to better classification performance for RS scene classification. The proposed methods are tested on three widely used datasets, and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods. [Wang, Weiquan; Chen, Yushi] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany; [Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, A-1030 Vienna, Austria Harbin Institute of Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Chen, YS (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China. 215005085@stu.hit.edu.cn; chenyushi@hit.edu.cn; p.ghamisi@gmail.com Chen, Yushi/0000-0003-2421-0996 Natural Science Foundation of China [61971164, U20B2041] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Natural Science Foundation of China under Grant 61971164 and Grant U20B2041. 70 7 7 23 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5533918 10.1109/TGRS.2022.3190934 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3D1HW 2023-03-23 WOS:000829062600007 0
J Deng, SQ; Katoh, M; Yu, XW; Hyyppa, J; Gao, T Deng, Songqiu; Katoh, Masato; Yu, Xiaowei; Hyyppa, Juha; Gao, Tian Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms REMOTE SENSING English Article forest resource measurement; airborne laser scanning; RGB imagery; individual tree detection; tree crown-based classification; machine learning approaches SMALL-FOOTPRINT LIDAR; HIGH-DENSITY LIDAR; AIRBORNE LIDAR; STEM VOLUME; FOREST DATA; SEGMENTATION; BIOMASS; EXTRACTION; PARAMETERS; FEATURES Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classesPinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved treesusing a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blueRGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications. [Deng, Songqiu; Katoh, Masato] Shinshu Univ, Inst Mt Sci, Nagano 3994598, Japan; [Yu, Xiaowei; Hyyppa, Juha] Finnish Geospatial Res Inst, Geodeetinrinne 2, Masala 02430, Finland; [Gao, Tian] Chinese Acad Sci, Inst Appl Ecol, Key Lab Forest Ecol & Management, Shenyang 110016, Peoples R China Shinshu University; The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); Chinese Academy of Sciences; Shenyang Institute of Applied Ecology, CAS Deng, SQ (corresponding author), Shinshu Univ, Inst Mt Sci, Nagano 3994598, Japan. deng0316@yahoo.com; mkatoh@shinshu-u.ac.jp; xiaowei.yu@nls.fi; juha.hyyppa@nls.fi; tiangao@iae.ac.cn Hyyppä, Juha M./ABH-1852-2021 Deng, Songqiu/0000-0002-0502-6424; Hyyppa, Juha/0000-0001-5360-4017 Japan Society for the Promotion of Science (JSPS KAKENHI) [JP16K18716]; Grants-in-Aid for Scientific Research [16K18716] Funding Source: KAKEN Japan Society for the Promotion of Science (JSPS KAKENHI)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This study was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS KAKENHI Grant Number JP16K18716). We gratefully acknowledge a number of students of Shinshu University for their support in the field and plot surveys. We would like to thank the members of the Forest Measurement and Planning Laboratory, Shinshu University, for their advice and assistance with this study. Moreover, we wish to express our heartfelt thanks to Jiaojun Zhu for his constructive suggestions for the manuscript. Finally, we wish to acknowledge the helpful comments from the anonymous reviewers and editors. 72 25 26 5 52 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. DEC 2016.0 8 12 1034 10.3390/rs8121034 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology EI4UQ Green Published, gold 2023-03-23 WOS:000392489400063 0
J Pan, CY; Hao, M; Barraza, N; Solano, E; Albarran-Arriagada, F Pan, C-Y; Hao, M.; Barraza, N.; Solano, E.; Albarran-Arriagada, F. Experimental semi-autonomous eigensolver using reinforcement learning SCIENTIFIC REPORTS English Article QUANTUM; LEVEL; ALGORITHM; GO The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence. [Pan, C-Y; Hao, M.; Barraza, N.; Solano, E.; Albarran-Arriagada, F.] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Pan, C-Y; Hao, M.; Barraza, N.; Solano, E.; Albarran-Arriagada, F.] Shanghai Univ, Phys Dept, Shanghai 200444, Peoples R China; [Solano, E.] Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Solano, E.] Basque Fdn Sci, Ikerbasque, Plaza Euskadi 5, Bilbao 48009, Spain; [Solano, E.] Kipu Quantum, Kurwenalstr 1, D-80804 Munich, Germany Shanghai University; Shanghai University; University of Basque Country; Basque Foundation for Science Solano, E; Albarran-Arriagada, F (corresponding author), Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China.;Solano, E; Albarran-Arriagada, F (corresponding author), Shanghai Univ, Phys Dept, Shanghai 200444, Peoples R China.;Solano, E (corresponding author), Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain.;Solano, E (corresponding author), Basque Fdn Sci, Ikerbasque, Plaza Euskadi 5, Bilbao 48009, Spain.;Solano, E (corresponding author), Kipu Quantum, Kurwenalstr 1, D-80804 Munich, Germany. enr.solano@gmail.com; pancho.albarran@gmail.com Albarran-Arriagada, Francisco/0000-0001-8899-3673 Spanish MCIU/AEI/FEDER [PGC2018-095113-B-I00]; Basque Government [IT986-16]; project QMiCS of EU Flagship on Quantum Technologies [820505]; project OpenSuperQ of EU Flagship on Quantum Technologies [820363]; EU FET Open Grant Quromorphic; EPIQUS; Shanghai STCSM [2019SHZDZX01-ZX04] Spanish MCIU/AEI/FEDER; Basque Government(Basque Government); project QMiCS of EU Flagship on Quantum Technologies; project OpenSuperQ of EU Flagship on Quantum Technologies; EU FET Open Grant Quromorphic; EPIQUS; Shanghai STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)) We acknowledge financial support from Spanish MCIU/AEI/FEDER (PGC2018-095113-B-I00), Basque Government IT986-16, projects QMiCS (820505) and OpenSuperQ (820363) of EU Flagship on Quantum Technologies, EU FET Open Grant Quromorphic, EPIQUS, and Shanghai STCSM (Grant No. 2019SHZDZX01-ZX04). 45 2 2 0 4 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep JUN 10 2021.0 11 1 12241 10.1038/s41598-021-90534-7 0.0 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics SV4IR 34112819.0 gold, Green Published, Green Submitted 2023-03-23 WOS:000663784500011 0
J Stanimirovic, PS; Katsikis, VN; Li, S Stanimirovic, Predrag S.; Katsikis, Vasilios N.; Li, Shuai Hybrid GNN-ZNN models for solving linear matrix equations NEUROCOMPUTING English Article Zhang neural network; Gradient neural network; Moore-Penrose inverse; Outer inverse; Dynamic equation; Activation function RECURRENT NEURAL-NETWORKS; COMPLEX ZFS; DYNAMICS New dynamical models for solving the matrix equations BX = D and XC = D are developed in time-invariant case. These models are derived as a combination of GNN and ZNN models. They do not posses GNN dynamic due to their implicit dynamics. Formally observed, they can be derived by multiplying the right hand side in the ZNN dynamics by an appropriate symmetric positive definite matrix which improves the convergence rate. For this purpose, these models are termed as HZNN. The convergence of HZNN models is global and exponential. Also, the convergence rate of HZNN models is superior with respect to the convergence rate of the classical GNN model as well as with respect to ZNN models in time-invariant case. Capability of the HZNN models to overcome unavoidable implementation noises is considered theoretically and numerically. The Matlab implementation of HZNN models is proposed and used in numerical experiments for solving matrix equations and computing various appearances of outer inverses with prescribed range and null space. (C) 2018 Elsevier B.V. All rights reserved. [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Nish 18000, Serbia; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Div Math & Informat, Dept Econ, Sofokleous 1 St, Athens 10559, Greece; [Li, Shuai] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China University of Nis; National & Kapodistrian University of Athens; Hong Kong Polytechnic University Stanimirovic, PS (corresponding author), Univ Nis, Fac Sci & Math, Nish 18000, Serbia. pecko@pmf.ni.ac.rs; vaskatsikis@econ.uoa.gr; shuaili@polyu.edu.hk Stanimirović, Predrag S./AAW-3979-2021; Katsikis, Vasilios N/AAD-7216-2019; Li, Shuai/ABD-5132-2021; Li, Shuai/ABD-6426-2021 Stanimirović, Predrag S./0000-0003-0655-3741; Katsikis, Vasilios N/0000-0002-8208-9656; Li, Shuai/0000-0002-9574-9609; Li, Shuai/0000-0001-8316-5289 Serbian Ministry of Science [174013]; bilateral project between China and Serbia; Special Account Research Grant [13294] Serbian Ministry of Science(Ministry of Education, Science & Technological Development, Serbia); bilateral project between China and Serbia; Special Account Research Grant The first author gratefully acknowledge support from the Research Project 174013 of the Serbian Ministry of Science and the bilateral project between China and Serbia, The theory of tensors, operator matrices and applications (no. 4-5).r The second author gratefully acknowledges that this work was carried out at the Department of Economics, University of Athens, Greece, and supported by Special Account Research Grant 13294. 37 24 24 1 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 17 2018.0 316 124 134 10.1016/j.neucom.2018.07.058 0.0 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GS8OQ 2023-03-23 WOS:000443971900013 0
J Wang, C; Liu, GH; Yang, ZR; Li, J; Zhang, T; Jiang, HL; He, M; Luo, M; Li, WT Wang, Chao; Liu, Gonghui; Yang, Zhirong; Li, Jun; Zhang, Tao; Jiang, Hailong; He, Miao; Luo, Ming; Li, Wentuo Downhole gas-kick transient simulation and detection with downhole dual-measurement points in water-based drilling fluid JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING English Article Gas-kick detection; Advection upstream splitting; Machine learning; Downhole pressure measurement; Unscented kalman filter; Attenuation of pressure wave 2-PHASE FLOW; NUMERICAL-SIMULATION; MODEL; DIAGNOSIS Gas kick is generally difficult to discover in time using traditional surface detection methods, which results in a significant wastage of time and money. Owing to the restriction of the low data-transmission speed of measurement-while-drilling system, downhole measured data is usually ignored in gas-kick detection. Furthermore, surface detection methods comprising the use of pressure and flow-rate sensors require professional knowledge and many input parameters, some of which are required to be assumed. In this study, we used downhole dual measurement points for detecting gas kick without the use of other surface input parameters. Firstly, we developed an end-to-end supervised neural network to determine the still and circulation working conditions, which were used for calculating the drilling fluid density and viscosity. Secondly, an unscented Kalman filter was applied to perform a backward gas fraction calculation dynamically. However, this downhole calculation method cannot be used in highly deviated and horizontal wells. Because there is a downhole fluctuating pressure generated during the rock breaking, we proposed an auxiliary gas-kick detection method based on the theory of pressure wave attenuation. This method can be applied to all well types. To evaluate the proposed gas-kick detection method, we used a gas-liquid flow simulation model combined with a pump rate model, screw-drilling-tool pressure-consumption model, rock-breaking model, and formation permeability model to generate transient data with the highest possible accuracy. The advection upstream splitting model was used as the numerical scheme. The accuracy of the simulation model was successfully validated using two field experimental data sets. Finally, we generated a set of vertical and horizontal well data each with the simulation model to test the gas-kick detection method. The experiment results showed that the proposed gas-kick detection model was successful in detecting gas kick and obtaining the accurate gas fraction. [Wang, Chao; Liu, Gonghui] China Univ Petr, Beijing, Peoples R China; [Yang, Zhirong] Norwegian Univ Sci & Technol, Trondheim, Norway; [Yang, Zhirong] Aalto Univ, Espoo, Finland; [Li, Jun] China Univ Petr Beijing Karamay, Karamay, Peoples R China; [Zhang, Tao; Jiang, Hailong] Beijing Informat Sci & Technol Univ, Beijing, Peoples R China; [He, Miao] Yangtze Univ, Wuhan, Peoples R China; [Luo, Ming; Li, Wentuo] CNOOC China Ltd, Zhanjiang Branch, Beijing, Peoples R China China University of Petroleum; Norwegian University of Science & Technology (NTNU); Aalto University; China University of Petroleum; Beijing Information Science & Technology University; Yangtze University; China National Offshore Oil Corporation (CNOOC) Li, J (corresponding author), China Univ Petr Beijing Karamay, Karamay, Peoples R China. lijun446@vip163.com zhang, tao/0000-0001-7216-0423 National Natural Science Foundation of China [51734010, U1762211]; National Science and Technology Major Project [2016ZX05020003]; National Natural Science Foundation Youth Project of China [51904034]; Academy of Finland [307929, 314177]; Research Council of Norway [287284] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science and Technology Major Project; National Natural Science Foundation Youth Project of China; Academy of Finland(Academy of Finland); Research Council of Norway(Research Council of Norway) The authors express their appreciation to the National Natural Science Foundation of China (Grant No.51734010, No.U1762211), National Science and Technology Major Project (Grant No.2016ZX05020003) and National Natural Science Foundation Youth Project of China (Grant No.51904034) for financial support to this paper. Zhirong Yang is supported by Academy of Finland (project no.307929 and 314177) and the Research Council of Norway (project no. 287284). 50 4 5 4 16 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1875-5100 2212-3865 J NAT GAS SCI ENG J. Nat. Gas Sci. Eng. DEC 2020.0 84 103678 10.1016/j.jngse.2020.103678 0.0 17 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering PB8WE Green Published 2023-03-23 WOS:000596593900002 0
J Agarwal, A; Dowsley, R; McKinney, ND; Wu, DR; Lin, CT; De Cock, M; Nascimento, ACA Agarwal, Anisha; Dowsley, Rafael; McKinney, Nicholas D.; Wu, Dongrui; Lin, Chin-Teng; De Cock, Martine; Nascimento, Anderson C. A. Protecting Privacy of Users in Brain-Computer Interface Applications IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING English Article Secure multiparty computation; cryptography; machine learning; linear regression; driver drowsiness estimation LINEAR-REGRESSION; UNCONDITIONALLY SECURE Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations. [Agarwal, Anisha; McKinney, Nicholas D.; De Cock, Martine; Nascimento, Anderson C. A.] Univ Washington Tacoma, Sch Engn & Technol, Tacoma, WA 98402 USA; [Agarwal, Anisha] Amazon, Seattle, WA 98121 USA; [Dowsley, Rafael] Aarhus Univ, Dept Comp Sci, DK-8200 Aarhus, Denmark; [Dowsley, Rafael] Bar Ilan Univ, Dept Comp Sci, IL-5290002 Ramat Gan, Israel; [McKinney, Nicholas D.] Breakpoint Labs, Washington, DC 20016 USA; [Wu, Dongrui] Huazhong Univ Sci & Technol, Sch Automat, Key Lab, Minist Educ Image Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China; [Lin, Chin-Teng] Univ Technol Sydney, Sch Software, Ultimo, NSW 2007, Australia; [De Cock, Martine] Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium University of Washington; University of Washington Tacoma; Amazon.com; Aarhus University; Bar Ilan University; Huazhong University of Science & Technology; University of Technology Sydney; Ghent University De Cock, M (corresponding author), Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium. anisha3@uw.edu; rafael@dowsley.net; mckinnnd@uw.edu; drwu09@gmail.com; chin-teng.lin@uts.edu.au; mdecock@uw.edu; clay@uw.edu Lin, Chin-Teng (CT)/G-8129-2017; Wu, Dongrui/E-1956-2011 Lin, Chin-Teng (CT)/0000-0001-8371-8197; Nascimento, Anderson/0000-0002-8298-6250; Dowsley, Rafael/0000-0002-7588-2410; Wu, Dongrui/0000-0002-7153-9703; De Cock, Martine/0000-0001-7917-0771; , Nicholas/0000-0001-9191-1528 BIU Center for Research in Applied Cryptography and Cyber Security; European Research Council (ERC) through the European Union [669255]; Israel National Cyber Bureau in the Prime Minister's Office BIU Center for Research in Applied Cryptography and Cyber Security; European Research Council (ERC) through the European Union(European Research Council (ERC)); Israel National Cyber Bureau in the Prime Minister's Office The work of R. Dowsley was supported in part by the BIU Center for Research in Applied Cryptography and Cyber Security in conjunction with the Israel National Cyber Bureau in the Prime Minister's Office and in part by the European Research Council (ERC) through the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 669255 (MPCPRO). 54 17 18 2 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1534-4320 1558-0210 IEEE T NEUR SYS REH IEEE Trans. Neural Syst. Rehabil. Eng. AUG 2019.0 27 8 1546 1555 10.1109/TNSRE.2019.2926965 0.0 10 Engineering, Biomedical; Rehabilitation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Rehabilitation IP9FX 31283483.0 Green Published, Green Submitted 2023-03-23 WOS:000480356700006 0
J Nanehkaran, YA; Pusatli, T; Jin, CY; Chen, JD; Cemiloglu, A; Azarafza, M; Derakhshani, R Nanehkaran, Yaser Ahangari; Pusatli, Tolga; Jin Chengyong; Chen, Junde; Cemiloglu, Ahmed; Azarafza, Mohammad; Derakhshani, Reza Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis WATER English Article slope stability; factor of safety; machine learning; prediction; soil slope LIMIT-EQUILIBRIUM; PREDICTION Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (beta), dry density (gamma(d)), cohesion (c), and internal friction angle (phi), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470. [Nanehkaran, Yaser Ahangari; Cemiloglu, Ahmed] Yancheng Teachers Univ, Sch Informat Engn, Yancheng 224002, Peoples R China; [Pusatli, Tolga] Cankaya Univ, Dept Management Informat Syst, TR-06790 Ankara, Turkey; [Jin Chengyong] Yang En Univ, Acad Engn & Technol, Quanzhou 362014, Peoples R China; [Chen, Junde] Xiangtan Univ, Dept Elect Commerce, Xiangtan 411100, Peoples R China; [Azarafza, Mohammad] Univ Tabriz, Dept Civil Engn, Tabriz 5166616471, Iran; [Derakhshani, Reza] Univ Utrecht, Dept Earth Sci, NL-3584 CB Utrecht, Netherlands Yancheng Teachers University; Cankaya University; Xiangtan University; University of Tabriz; Utrecht University Derakhshani, R (corresponding author), Univ Utrecht, Dept Earth Sci, NL-3584 CB Utrecht, Netherlands. r.derakhshani@uu.nl Derakhshani, Reza/P-1194-2019; Cemiloğlu, Ahmed/HJZ-4981-2023; PUSATLI, Tolga/C-6912-2019 Derakhshani, Reza/0000-0001-7499-4384; Cemiloğlu, Ahmed/0000-0003-2633-0924; PUSATLI, Tolga/0000-0002-2303-8023; Ahangari Nanehkaran, Yaser/0000-0002-8055-3195; Azarafza, Mohammad/0000-0001-7777-3800 37 2 2 9 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water NOV 2022.0 14 22 3743 10.3390/w14223743 0.0 14 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources 6K9FR Green Published, gold 2023-03-23 WOS:000887798800001 0
J Guo, WD; Sun, ZC; Vilsen, SB; Meng, JH; Stroe, DI Guo, Wendi; Sun, Zhongchao; Vilsen, Soren Byg; Meng, Jinhao; Stroe, Daniel Ioan Review of grey box lifetime modeling for lithium-ion battery: Combining physics and data-driven methods JOURNAL OF ENERGY STORAGE English Review Lithium-ion batteries; Lifetime modeling; Physics-based models; Data-driven methods; Data-driven assisted physical models; Physics-guided data-driven REMAINING USEFUL LIFE; CYCLE LIFE; ELECTROCHEMICAL MODEL; CHARGE ESTIMATION; CHEMICAL DEGRADATION; PARAMETER-ESTIMATION; GENETIC ALGORITHM; HEALTH ESTIMATION; STATE ESTIMATION; THERMAL-MODEL Lithium-ion batteries are a popular choice for a wide range of energy storage system applications. The current motivation to improve the robustness of lithium-ion battery applications has stimulated the need for in-depth research into aging effects and the establishment of lifetime prediction models. This paper reviews different combination approaches of physics-based models and data-driven models. The three basic physics-based battery lifetime models are introduced, and requirements and features are compared from an application perspective. Then, state-of-the-art approaches for integrating physics and data-driven methods are systematically reviewed. Flowcharts present each approach to offer the readers a clear understanding. Next, the publication trends are represented by line graphs, and pie charts, including data-driven assisted physical models and physics-guided data-driven, different physical model applications, and data-driven approaches. It is concluded that electrochemical models have great potential to describe complex aging behavior under various conditions. Moreover, machine learning is a promising tool to overcome mechanistic absence and highly nonlinear performance, occupying 78 % of all data-driven methods. Physics-guided data-driven approach started to emerge as an innovative lifetime prediction method after 2020. The application advantages and limitations are compared according to the description of different methods. Furthermore, future perspectives are discussed, with opportunities and challenges. The Prospect of applying physics-guided machine learning looks forward to more inspiration. [Guo, Wendi; Sun, Zhongchao; Vilsen, Soren Byg; Stroe, Daniel Ioan] Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark; [Vilsen, Soren Byg] Aalborg Univ, Dept Math Sci, DK-9220 Aalborg, Denmark; [Meng, Jinhao] Sichuan Univ, Coll Elect Engn, Chengdu, Peoples R China Aalborg University; Aalborg University; Sichuan University Sun, ZC (corresponding author), Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark. zs@energy.aau.dk ; Vilsen, Soren/C-4852-2016 Stroe, Daniel-Ioan/0000-0002-2938-8921; Sun, Zhongchao/0000-0002-6378-6376; Guo, Wendi/0000-0001-5832-8971; Vilsen, Soren/0000-0002-9718-8147 China Scholarship Council [202106020069] China Scholarship Council(China Scholarship Council) This work has been funded by China Scholarship Council. The fund number is 202106020069. 191 2 2 33 33 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-152X 2352-1538 J ENERGY STORAGE J. Energy Storage DEC 1 2022.0 56 A 105992 10.1016/j.est.2022.105992 0.0 NOV 2022 22 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 6P5OH hybrid, Green Published 2023-03-23 WOS:000890979000007 0
S Kurakin, A; Goodfellow, I; Bengio, S; Dong, YP; Liao, FZ; Liang, M; Pang, TY; Zhu, J; Hu, XL; Xie, CH; Wang, JY; Zhang, ZS; Ren, Z; Yuille, A; Huang, SX; Zhao, Y; Zhao, YZ; Han, ZL; Long, JJJ; Berdibekov, Y; Akiba, T; Tokui, S; Abe, M Escalera, S; Weimer, M Kurakin, Alexey; Goodfellow, Ian; Bengio, Samy; Dong, Yinpeng; Liao, Fangzhou; Liang, Ming; Pang, Tianyu; Zhu, Jun; Hu, Xiaolin; Xie, Cihang; Wang, Jianyu; Zhang, Zhishuai; Ren, Zhou; Yuille, Alan; Huang, Sangxia; Zhao, Yao; Zhao, Yuzhe; Han, Zhonglin; Long, Junjiajia; Berdibekov, Yerkebulan; Akiba, Takuya; Tokui, Seiya; Abe, Motoki Adversarial Attacks and Defences Competition NIPS'17 COMPETITION: BUILDING INTELLIGENT SYSTEMS Springer Series on Challenges in Machine Learning English Article; Book Chapter To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams. [Kurakin, Alexey; Goodfellow, Ian; Bengio, Samy] Google Brain, Mountain View, CA 94043 USA; [Dong, Yinpeng; Liao, Fangzhou; Liang, Ming; Pang, Tianyu; Zhu, Jun; Hu, Xiaolin] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Xie, Cihang; Zhang, Zhishuai; Yuille, Alan] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA; [Wang, Jianyu] Baidu Res, Sunnyvale, CA USA; [Ren, Zhou] Snap Inc, Los Angeles, CA USA; [Huang, Sangxia] Sony Mobile Commun, Lund, Sweden; [Zhao, Yao] Microsoft Corp, Redmond, WA 98052 USA; [Zhao, Yao] Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA; [Han, Zhonglin] Smule Inc, San Francisco, CA USA; [Long, Junjiajia] Yale Univ, Dept Phys, New Haven, CT USA; [Akiba, Takuya; Tokui, Seiya; Abe, Motoki] Preferred Networks Inc, Tokyo, Japan Google Incorporated; Tsinghua University; Johns Hopkins University; Baidu; Sony Corporation; Microsoft; Yale University; Yale University Kurakin, A (corresponding author), Google Brain, Mountain View, CA 94043 USA. Yuille, Alan L./0000-0001-5207-9249 42 91 93 0 6 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2520-131X 978-3-319-94042-7; 978-3-319-94041-0 SPRING SER CHALLENGE 2018.0 195 231 10.1007/978-3-319-94042-7_11 0.0 10.1007/978-3-319-94042-7 37 Computer Science, Artificial Intelligence Book Citation Index – Science (BKCI-S) Computer Science BP1ZA Green Submitted 2023-03-23 WOS:000541378900013 0
J Tang, D; Tang, L; Dai, R; Chen, JW; Li, X; Rodrigues, JJPC Tang, Dan; Tang, Liu; Dai, Rui; Chen, Jingwen; Li, Xiong; Rodrigues, Joel J. P. C. MF-Adaboost: LDoS attack detection based on multi-features and improved Adaboost FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article LDoS; Internet of Things; Feature set; Feature selection; Adaboost algorithm; Machine learning FEATURE-SELECTION; ALGORITHM A low-rate denial of service (LDoS) attack is a precise network attack that aims at reducing the quality of the network service. Many networks do not have an effective mechanism for defending against LDoS attacks, including the emerging Internet of Things. In this paper, we propose an LDoS attack detection method that is based on multiple features of network traffic and an improved Adaboost algorithm (MF-Adaboost). Based on an analysis of the network traffic, we construct a network feature set that is used for feature calculation and feature selection of network traffic data. Feature calculation can extract the most useful information from the network traffic data and reduce the scale of the network data. Feature selection is used to select the optimal classification features to ensure that the detection algorithm can be effectively trained. This method utilizes the Adaboost algorithm, which is a classification algorithm in the field of machine learning. The well-trained Adaboost algorithm can effectively identify LDoS attack traffic. We improve the Adaboost algorithm to alleviate the imbalance of the sample weights. Experiments are conducted on the NS2 simulation platform and a test-bed platform to evaluate the performance of our method. The experimental results demonstrate that our method can detect LDoS attacks effectively. (C) 2020 Elsevier B.V. All rights reserved. [Tang, Dan; Tang, Liu; Dai, Rui; Chen, Jingwen] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China; [Li, Xiong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Inst Cyber Secur, Chengdu 611731, Peoples R China; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Teresina, PI, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, Lisbon, Portugal Hunan University; University of Electronic Science & Technology of China; Universidade Federal do Piaui; Instituto de Telecomunicacoes Rodrigues, JJPC (corresponding author), Univ Fed Piaui, Teresina, PI, Brazil. tangliu@hnu.edu.cn; lixiong@uestc.edu.cn; joeljr@ieee.org Li, Xiong/K-7233-2012; Rodrigues, Joel J. P. C./A-8103-2013; Tang, Dan/V-6478-2019 Li, Xiong/0000-0001-6619-554X; Rodrigues, Joel J. P. C./0000-0001-8657-3800; National Natural Science Foundation of China [61772189, 61702173]; Hunan Provincial Natural Science Foundation of China [2019JJ40037, 2018JJ3191]; Scientific Research Fund of Hunan Provincial Education Department [18A178]; FCT/MCTES through national funds; EU funds [UIDB/EEA/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hunan Provincial Natural Science Foundation of China(Natural Science Foundation of Hunan Province); Scientific Research Fund of Hunan Provincial Education Department(Hunan Provincial Education Department); FCT/MCTES through national funds; EU funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work was supported by National Natural Science Foundation of China (61772189 and 61702173), Hunan Provincial Natural Science Foundation of China (2019JJ40037 and 2018JJ3191), and the Scientific Research Fund of Hunan Provincial Education Department (18A178), and by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/EEA/50008/2020; and by Brazilian National Council for Scientific and Technological Development (CNPq) via Grant No. 309335/2017-5. 50 45 47 1 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. MAY 2020.0 106 347 359 10.1016/j.future.2019.12.034 0.0 13 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science LF3KP 2023-03-23 WOS:000527320000028 0
J Ge, CJ; Gu, IYH; Jakola, AS; Yang, J Ge, Chenjie; Gu, Irene Yu-Hua; Jakola, Asgeir Store; Yang, Jie Deep semi-supervised learning for brain tumor classification BMC MEDICAL IMAGING English Article Glioma; Molecular-based brain tumor classification; Grading; MRI; Semi-supervised learning; Deep learning IDH1; MUTATIONS; GLIOMAS Background This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art. [Ge, Chenjie; Gu, Irene Yu-Hua] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden; [Jakola, Asgeir Store] Sahlgrens Univ Hosp, S-41345 Gothenburg, Sweden; [Jakola, Asgeir Store] Sahlgrens Acad, Inst Neurosci & Physiol, S-41345 Gothenburg, Sweden; [Yang, Jie] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China Chalmers University of Technology; Sahlgrenska University Hospital; University of Gothenburg; Shanghai Jiao Tong University Ge, CJ (corresponding author), Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden. chenjie@chalmers.se Swedish Research Council VR [2017-00944]; Chalmers University of Technology Swedish Research Council VR(Swedish Research Council); Chalmers University of Technology The work of Asgeir Jakola was supported by The Swedish Research Council VR under the grant 2017-00944, whose role is purely on supporting promising scientific research work. Open access funding provided by Chalmers University of Technology. 26 25 27 4 33 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2342 BMC MED IMAGING BMC Med. Imag. JUL 29 2020.0 20 1 87 10.1186/s12880-020-00485-0 0.0 11 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging MX4LZ 32727476.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000557696800002 0
J Xia, ZQ; Feng, XY; Lin, J; Hadid, A Xia, Zhaoqiang; Feng, Xiaoyi; Lin, Jie; Hadid, Abdenour Deep convolutional hashing using pairwise multi-label supervision for large-scale visual search SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Learning based hashing; Deep learning; Convolutional neural networks; Pairwise multi-label supervision; Label relevance BINARY; QUANTIZATION; SCENE Image hashing has attracted much attention in the field of large-scale visual search, and learning based approaches have benefited from recent advances of deep learning, which outperforms the shallow models. Most existing deep hashing approaches tend to learn hierarchical models with single-label images limiting the semantic representations. However, few methods have utilized multi-label images to explore rich semantic supervision. In this paper, we propose a new deep convolutional hashing approach by leveraging multi-label images and exploring the label relevance. The proposed method utilizes pairwise supervision to hierarchically transform images into hash codes. Within the deep hashing framework, the Convolutional Neural Networks (CNNs) are considered to automatically learn visual features with smaller semantic gaps. Then a hashing layer using nonlinear mapping is employed to obtain hash codes. A regularized loss function based on pairwise multi label supervision is proposed to simultaneously learn the features and hash codes. Besides, pairwise multi-label supervision utilizes label relevance to compute semantic similarity of images. The experiments of visual search on two multi-label datasets demonstrate the competitiveness of our proposed approach compared to several state-of-the-art multi-label approaches. (C) 2017 Elsevier B.V. All rights reserved. [Xia, Zhaoqiang; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Shaanxi, Peoples R China; [Lin, Jie] Inst Infocomm Res, Visual Comp Dept, Singapore 138632, Singapore; [Hadid, Abdenour] Univ Oulu, Dept Comp Sci & Engn, Oulu 90014, Finland Northwestern Polytechnical University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); University of Oulu Xia, ZQ (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Shaanxi, Peoples R China. xiazhaoqiang@gmail.com Xia, Zhaoqiang/AAC-4021-2019 Xia, Zhaoqiang/0000-0003-0630-3339 Northwestern Polytechnical University [G2015KY0302]; National Nature Science Foundation of China [61502388] Northwestern Polytechnical University; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is partly supported by the Fundamental Research Funds of Northwestern Polytechnical University (No. G2015KY0302) and the National Nature Science Foundation of China (No. 61502388). 43 16 16 1 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. NOV 2017.0 59 SI 109 116 10.1016/j.image.2017.06.008 0.0 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering FO1FC 2023-03-23 WOS:000416501100013 0
J Katsikis, VN; Mourtas, SD; Stanimirovic, PS; Zhang, YN Katsikis, Vasilios N.; Mourtas, Spyridon D.; Stanimirovic, Predrag S.; Zhang, Yunong Solving Complex-Valued Time-Varying Linear Matrix Equations via QR Decomposition With Applications to Robotic Motion Tracking and on Angle-of-Arrival Localization IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Mathematical model; Matrix decomposition; Numerical models; Neural networks; Convergence; Computational modeling; Tracking; Continuous-time model; dynamical system; linear system; QR decomposition; zeroing neural dynamics (ZND) RECURRENT NEURAL-NETWORK; SYLVESTER EQUATION; ZNN MODELS; OPTIMIZATION; CONVERGENCE; INVERSE; ZFS The problem of solving linear equations is considered as one of the fundamental problems commonly encountered in science and engineering. In this article, the complex-valued time-varying linear matrix equation (CVTV-LME) problem is investigated. Then, by employing a complex-valued, time-varying QR (CVTVQR) decomposition, the zeroing neural network (ZNN) method, equivalent transformations, Kronecker product, and vectorization techniques, we propose and study a CVTVQR decomposition-based linear matrix equation (CVTVQR-LME) model. In addition to the usage of the QR decomposition, the further advantage of the CVTVQR-LME model is reflected in the fact that it can handle a linear system with square or rectangular coefficient matrix in both the matrix and vector cases. Its efficacy in solving the CVTV-LME problems have been tested in a variety of numerical simulations as well as in two applications, one in robotic motion tracking and the other in angle-of-arrival localization. [Katsikis, Vasilios N.; Mourtas, Spyridon D.] Natl & Kapodistrian Univ Athens, Div Math & Informat, Dept Econ, Athens 10559, Greece; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Nish 18000, Serbia; [Zhang, Yunong] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China National & Kapodistrian University of Athens; University of Nis; Sun Yat Sen University Katsikis, VN (corresponding author), Natl & Kapodistrian Univ Athens, Div Math & Informat, Dept Econ, Athens 10559, Greece. vaskatsikis@econ.uoa.gr; spirosmourtas@gmail.com; pecko@pmf.ni.ac.rs; zhynong@mail.sysu.edu.cn Katsikis, Vasilios N/AAD-7216-2019; ZHANG, YUTING/HOH-4131-2023; zhang, yu/HNS-5948-2023; Mourtas, Spyridon D./AAB-3651-2022 Katsikis, Vasilios N/0000-0002-8208-9656; Mourtas, Spyridon D./0000-0002-8299-9916; Stanimirovic, Predrag S./0000-0003-0655-3741 Ministry of Education, Science and Technological Development, Republic of Serbia [174013/451-03-68/2020-14/200124] Ministry of Education, Science and Technological Development, Republic of Serbia The work of Predrag S. Stanimirovi ' c was supported by the Ministry of Education, Science and Technological Development, Republic of Serbia under Grant 174013/451-03-68/2020-14/200124. 48 24 24 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. AUG 2022.0 33 8 3415 3424 10.1109/TNNLS.2021.3052896 0.0 JAN 2021 10 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3Z9UL 33513117.0 2023-03-23 WOS:000732913600001 0
C Liu, L; Huo, H; Liu, XF; Palade, V; Peng, DL; Chen, QK Pei, J; Manolopoulos, Y; Sadiq, S; Li, J Liu, Liang; Huo, Huan; Liu, Xiufeng; Palade, Vasile; Peng, Dunlu; Chen, Qingkui Recognizing Textual Entailment with Attentive Reading and Writing Operations DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2018, PT I Lecture Notes in Computer Science English Proceedings Paper 23rd International Conference on Database Systems for Advanced Applications (DASFAA). MAY 21-24, 2018 Gold Coast, AUSTRALIA Inferencing the entailment relations between natural language sentence pairs is fundamental to artificial intelligence. Recently, there is a rising interest in modeling the task with neural attentive models. However, those existing models have a major limitation to keep track of the attention history because usually only one single vector is utilized to memorize the past attention information. We argue its importance based on our observation that the potential alignment clues are not always centralized. Instead, they may diverge substantially, which could cause the problem of long-range dependency. In this paper, we propose to facilitate the conventional attentive reading operations with two sophisticated writing operations - forget and update. Instead of utilizing a single vector that accommodates the attention history, we write the past attention information directly into the sentence representations. Therefore, higher memory capacity of attention history could be achieved. Experiments on Stanford Natural Language Inference corpus (SNLI) demonstrate the superior efficacy of our proposed architecture. [Liu, Liang; Huo, Huan; Peng, Dunlu; Chen, Qingkui] Univ Shanghai Sci & Technol, Dept Comp Sci, Shanghai, Peoples R China; [Liu, Xiufeng] Tech Univ Denmark, Lyngby, Denmark; [Palade, Vasile] Coventry Univ, Fac Engn Environm & Comp, Coventry, W Midlands, England University of Shanghai for Science & Technology; Technical University of Denmark; Coventry University Huo, H (corresponding author), Univ Shanghai Sci & Technol, Dept Comp Sci, Shanghai, Peoples R China. huo_huan@yahoo.com Liu, Xiufeng/K-6142-2019; Peng, Dunlu/AAL-5071-2021; Tomm, He/E-5457-2013 Liu, Xiufeng/0000-0001-5133-6688; 28 1 1 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-91452-7; 978-3-319-91451-0 LECT NOTES COMPUT SC 2018.0 10827 I 847 860 10.1007/978-3-319-91452-7_54 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP3HF 2023-03-23 WOS:000546494600054 0
J Bao, XX; Wang, ZC; Fu, DF; Shi, C; Iglesias, G; Cui, HL; Sun, ZY Bao, Xingxian; Wang, Zhichao; Fu, Dianfu; Shi, Chen; Iglesias, Gregorio; Cui, Hongliang; Sun, Zhengyi Machine learning methods for damage detection of thermoplastic composite pipes under noise conditions OCEAN ENGINEERING English Article Long and short -term memory networks; Random forest; Random decrement technique; Damage detection; Thermoplastic composite pipes LEAK DETECTION METHOD; PRESSURE; OIL Machine learning methods for damage detection of thermoplastic composite pipes (TCPs) under noise conditions are presented, which combine the random decrement technique (RDT) with random forest (RF) and long and short-term memory (LSTM) networks. RDT is applied first to process the measured noisy strain response data of the TCP under random excitation. Then the RF or LSTM method is used to conduct the damage localization and severity estimation of the pipe. The applicability of the proposed methods is verified by means of numerical and experimental studies. The numerical example consists of a TCP subjected to internal pressure and random wave excitation considering several noise levels. The damages are simulated as circular holes on different layers of the pipe and varying severity, characterized by their radii and depths. The damage detection is carried out using RDT-RF and RDT-LSTM methods. The experimental studies consist of laboratory tests of a TCP model using Fiber Bragg Grating sensors. The damage cases, simulated as cracks with different lengths and depths on the rein-forcement layer, are discussed. Both the numerical simulation and experimental tests show that the proposed RDT-RF and RDT-LSTM methods have an excellent performance in damage detection of TCPs. [Bao, Xingxian; Wang, Zhichao] China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China; [Bao, Xingxian] China Univ Petr East China, Natl Engn Lab Offshore Geophys & Explorat Equipme, Qingdao 266580, Shandong, Peoples R China; [Fu, Dianfu] CNOOC Res Inst Ltd, Beijing 100028, Peoples R China; [Shi, Chen] Harbin Inst Technol Weihai, Sch Ocean Engn, Weihai 264209, Peoples R China; [Iglesias, Gregorio] Univ Coll Cork, Environm Res Inst, MaREI, Coll Rd, Cork, Ireland; [Iglesias, Gregorio] Univ Coll Cork, Sch Engn, Coll Rd, Cork, Ireland; [Iglesias, Gregorio] Univ Plymouth, Sch Engn, Marine Bldg, Plymouth PL4 8AA, England; [Cui, Hongliang; Sun, Zhengyi] Qingdao Pegasus Photoelect Technol Co Ltd, Qingdao 266114, Shandong, Peoples R China China University of Petroleum; China University of Petroleum; Harbin Institute of Technology; University College Cork; University College Cork; University of Plymouth Bao, XX (corresponding author), China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China. baoxingxian@upc.edu.cn Iglesias, Gregorio/E-5114-2012 Iglesias, Gregorio/0000-0003-2719-1663; Shi, Chen/0000-0002-5192-5172; Bao, Xingxian/0000-0002-8555-8366 National Natural Science Foundation of China [51979283]; Fundamental Research Funds for the Central Universities [20CX02313A]; Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment [20CX02313A]; Science and Technology Project of West Coast of Qingdao, China [2020-87] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment; Science and Technology Project of West Coast of Qingdao, China This research is supported by the National Natural Science Foundation of China (Grant No. 51979283), the Fundamental Research Funds for the Central Universities (Grant No. 20CX02313A), the Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment (Grant No. 20CX02313A) and the Science and Technology Project of West Coast of Qingdao, China (Grant No. 2020-87). 31 3 3 9 13 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. MAR 15 2022.0 248 110817 10.1016/j.oceaneng.2022.110817 0.0 FEB 2022 14 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 0P6KN 2023-03-23 WOS:000784336800004 0
J Cui, BB; Creput, JC; Zhang, L Cui, Beibei; Creput, Jean-Charles; Zhang, Lei Self-organizing maps and full GPU parallel approach to graph matching COMPUTER COMMUNICATIONS English Article Graph matching; Distributed local search; Self-organizing map; GPU OPTIMIZATION ALGORITHMS Graph matching is an essential problem in computer science and communications. It can be applied to a variety of issues such as artificial intelligence, computer vision, and communication systems. In this paper, we propose a new Graphics Processing Unit framework written in CUDA C++ specifically dedicated to geometric graph matching but providing new parallel algorithms, with low computational complexity, as the self-organizing map in the plane, and a distributed local search method. Unlike state-of-the-art graph matching algorithms, available from Matlab platform, that most often need at least O(N2) memory size, with N the problem size, our proposals only require O(N) space and allows massively parallel execution. These parallel algorithms are evaluated and compared to the state-of-the-art methods available for graph matching and following the same experimental protocol. [Cui, Beibei; Zhang, Lei] Henan Univ Technol, Coll Elect Engn, Zhengzhou 450001, Henan, Peoples R China; [Cui, Beibei; Creput, Jean-Charles] Univ Bourgogne Franche Comte, UTBM, CIAD, F-90010 Belfort, France Henan University of Technology; Universite de Bourgogne; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Cui, BB (corresponding author), Henan Univ Technol, Coll Elect Engn, Zhengzhou 450001, Henan, Peoples R China. beibei.cui@haut.edu.cn Ministry of Higher Education of China; Key Scientific Research Project Plans of Higher Education Institutions in Henan Province, China; Science Foundation of Henan University of Technology, China; Innovation and Entrepreneurship Training Program for College Stu-dents, China; [202110463002S]; [23B410002]; [22A410002]; [2021BS060]; [2020BS059]; [2018BS068] Ministry of Higher Education of China; Key Scientific Research Project Plans of Higher Education Institutions in Henan Province, China; Science Foundation of Henan University of Technology, China; Innovation and Entrepreneurship Training Program for College Stu-dents, China; ; ; ; ; ; This work was supported by the Ministry of Higher Education of China (No. 202110463002S) ; the Key Scientific Research Project Plans of Higher Education Institutions in Henan Province, China (No. 23B410002, 22A410002) ; the Science Foundation of Henan University of Technology, China (No. 2021BS060, 2020BS059, 2018BS068) ; the Innovation and Entrepreneurship Training Program for College Stu-dents, China (No. 202110463002S) . 30 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0140-3664 1873-703X COMPUT COMMUN Comput. Commun. JAN 15 2023.0 198 217 227 10.1016/j.comcom.2022.12.005 0.0 DEC 2022 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 7Q8MT 2023-03-23 WOS:000909639400001 0
J van der Gaast, JP; Weidinger, F van der Gaast, Jelmer Pier; Weidinger, Felix A deep learning approach for the selection of an order picking system EUROPEAN JOURNAL OF OPERATIONAL RESEARCH English Article Logistics; Order picking; Warehouse management; Deep learning; System selection AUTOMATED STORAGE; RETRIEVAL-SYSTEMS; BUCKET BRIGADES; PERFORMANCE; FRAMEWORK; DESIGN; WORK This paper develops a novel strategic decision support framework for the design of an order picking system, which can be used whenever different systems and/or control mechanisms need to be compared for a given customer order structure. Warehousing companies frequently struggle in selecting the most suitable design for their order picking system. Traditionally, a comparison of different order picking systems is based on time-consuming simulation runs. In addition, the only source of consultancy is most often carried out by the order picking system manufacturers themselves. Our framework, using recent advancements in deep neural networks, provides an efficient methodology for selecting not only the best order picking system for a given order structure but also the most suited design parameters. This enables warehouse companies to compare objectively an extensive number of systems and allows them to identify the most promising order picking systems. We demonstrate our framework for a comprehensive comparison of three different fixed-path order picking systems to find one best suited for a provided order structure. (C) 2022 Elsevier B.V. All rights reserved. [van der Gaast, Jelmer Pier] Fudan Univ, Dept Management Sci, Shanghai, Peoples R China; [Weidinger, Felix] Tech Univ Darmstadt, Chair Management Sci Operat Res, Darmstadt, Germany Fudan University; Technical University of Darmstadt van der Gaast, JP (corresponding author), Fudan Univ, Dept Management Sci, Shanghai, Peoples R China. jpgaast@fudan.edu.cn; felix.weidinger@tu-darmstadt.de Weidinger, Felix/0000-0002-1814-397X 51 0 0 13 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0377-2217 1872-6860 EUR J OPER RES Eur. J. Oper. Res. OCT 16 2022.0 302 2 530 543 10.1016/j.ejor.2022.01.006 0.0 MAY 2022 14 Management; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Business & Economics; Operations Research & Management Science 3E1QJ 2023-03-23 WOS:000829764400009 0
J Khan, AH; Cao, XW; Li, S; Katsikis, VN; Liao, LF Khan, Ameer Hamza; Cao, Xinwei; Li, Shuai; Katsikis, Vasilios N.; Liao, Liefa BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Article Adaptive moment estimation (ADAM); Beetle antennae search (BAM); gradient estimation; metaheuristic optimization; nature-inspired algorithms; neural network SINE COSINE ALGORITHM; SYSTEMS; CONTROLLER; MODEL In this paper, we propose enhancements to Beetle Antennae search (BAS) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation (ADAM) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer (PSO) and the original BAS algorithm. [Khan, Ameer Hamza] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Cao, Xinwei] Shanghai Univ, Sch Management, Shanghai 201900, Peoples R China; [Li, Shuai] Swansea Univ, Dept Elect & Elect Engn, Swansea SA1 8EN, W Glam, Wales; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Athens 10679, Greece; [Liao, Liefa] Jiangxi Univ Sci & Technol, Sch Informat Engn, Ganzhou 341000, Peoples R China Hong Kong Polytechnic University; Shanghai University; Swansea University; National & Kapodistrian University of Athens; Jiangxi University of Science & Technology Cao, XW (corresponding author), Shanghai Univ, Sch Management, Shanghai 201900, Peoples R China. ameer.h.khan@connect.polyu.hk; xinweicao@shu.edu.cn; shuaili@ieee.org; vaskatsikis@econ.uoa.gr; liaolf@126.com Katsikis, Vasilios N/AAD-7216-2019; Li, Shuai/ABD-5132-2021 Katsikis, Vasilios N/0000-0002-8208-9656; Li, Shuai/0000-0002-9574-9609 80 88 92 10 63 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. MAR 2020.0 7 2 461 471 10.1109/JAS.2020.1003048 0.0 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems KU3GB Green Accepted 2023-03-23 WOS:000519596200013 0
J Chen, C; Batselier, K; Yu, WJ; Wong, N Chen, Cong; Batselier, Kim; Yu, Wenjian; Wong, Ngai Kernelized support tensor train machines PATTERN RECOGNITION English Article Image classification; Tensor; Support tensor machine VECTOR MACHINES Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning commu-nity. Traditional machine learning approaches are vector-or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for high-dimensional image classifica-tion with very small number of training samples. Specifically, we propose a kernelized support tensor train machine that accepts tensorial input and preserves the intrinsic kernel property. The main con-tributions are threefold. First, we propose a TT-based feature mapping procedure that maintains the TT structure in the feature space. Second, we demonstrate two ways to construct the TT-based kernel func-tion while considering consistency with the TT inner product and preservation of information. Third, we show that it is possible to apply different kernel functions on different data modes. In principle, our method tensorizes the standard SVM on its input structure and kernel mapping scheme. This reduces the storage and computation complexity of kernel matrix construction from exponential to polynomial. The validity proof and computation complexity of the proposed TT-based kernel functions are provided elabo-rately. Extensive experiments are performed on high-dimensional fMRI and color images datasets, which demonstrates the superiority of the proposed scheme compared with the state-of-the-art techniques. (c) 2021 Elsevier Ltd. All rights reserved. [Chen, Cong; Wong, Ngai] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China; [Batselier, Kim] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands; [Yu, Wenjian] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China University of Hong Kong; Delft University of Technology; Tsinghua University Wong, N (corresponding author), Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China.;Yu, WJ (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. chencong@eee.hku.hk; k.batselier@tudelft.nl; yu-wj@tsinghua.edu.cn; nwong@eee.hku.hk Hong Kong Research Grants Council [17246416]; University Research Committee of The University of Hong Kong, Tsinghua University Initiative Scientific Research Program; NSFC [61872206] Hong Kong Research Grants Council(Hong Kong Research Grants Council); University Research Committee of The University of Hong Kong, Tsinghua University Initiative Scientific Research Program; NSFC(National Natural Science Foundation of China (NSFC)) This work is partially supported by the Hong Kong Research Grants Council under Project 17246416, the University Research Committee of The University of Hong Kong, Tsinghua University Initiative Scientific Research Program, and NSFC under grant no. 61872206 . 31 5 5 4 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. FEB 2022.0 122 108337 10.1016/j.patcog.2021.108337 0.0 OCT 2021 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering WD4DN Green Published, Green Submitted 2023-03-23 WOS:000704893500002 0
J Wang, SY; Yuan, JB; Li, X; Qian, ZZ; Arena, F; You, I Wang, Songyun; Yuan, Jiabin; Li, Xin; Qian, Zhuzhong; Arena, Fabio; You, Ilsun Active Data Replica Recovery for Quality-Assurance Big Data Analysis in IC-IoT IEEE ACCESS English Article Big data analysis; data recovery; IC-IoT; NVM; QoS improvement SECURE; FAILURE; CLOUD QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time. [Wang, Songyun; Yuan, Jiabin; Li, Xin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Jiangsu, Peoples R China; [Li, Xin; Qian, Zhuzhong] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China; [Arena, Fabio] Kore Univ Enna, Fac Engn & Architecture, I-94100 Enna, Italy; [You, Ilsun] Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea Nanjing University of Aeronautics & Astronautics; Nanjing University; Universita Kore di ENNA; Soonchunhyang University You, I (corresponding author), Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea. ilsunu@gmail.com Qian, Zhuzhong/HPD-4328-2023; , 叶叶叶/ABE-1619-2020; Arena, Fabio/AAJ-9977-2020; You, Ilsun/AAR-7793-2021 Qian, Zhuzhong/0000-0003-1625-7575; Arena, Fabio/0000-0002-6656-1797; You, Ilsun/0000-0002-0604-3445 Soonchunhyang University Research Fund; National Natural Science Foundation of China [61802182] Soonchunhyang University Research Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Soonchunhyang University Research Fund as well as the National Natural Science Foundation of China under Grant 61802182. 38 12 12 2 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 106997 107005 10.1109/ACCESS.2019.2932259 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IS2GX gold, Green Submitted 2023-03-23 WOS:000481972100139 0
J Liu, WW; Yang, ZL; Bi, KX Liu, Weiwei; Yang, Zhile; Bi, Kexin Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach COMPLEXITY English Article OFF COMPANIES; PREDICTION; CREATION; MODEL; PRODUCTIVITY; CAPABILITIES; PERFORMANCE; MANAGEMENT; REGRESSION; ALGORITHM University spin-outs (USOs), creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to this distribution, a radial basis function (RBF) neural network (NN) model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university's equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs. [Liu, Weiwei; Bi, Kexin] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Heilongjiang, Peoples R China; [Liu, Weiwei] Queens Univ Belfast, Management Sch, Belfast BT9 5EE, Antrim, North Ireland; [Yang, Zhile] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Bi, Kexin] Harbin Univ Sci & Technol, Sch Management, Harbin 150080, Heilongjiang, Peoples R China Harbin Engineering University; Queens University Belfast; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Harbin University of Science & Technology Yang, ZL (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China. zyang07@qub.ac.uk National Natural Science Foundation of China [71302028, 71472057, 71602041]; Heilongjiang Province Natural Science Fund [LC2017031]; Fundamental Scientific Research Funds for the Central Universities [HEUCF170903, HEUCFW170908, HEUCFW170904] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Heilongjiang Province Natural Science Fund; Fundamental Scientific Research Funds for the Central Universities This research is financially supported by the National Natural Science Foundation of China under Grants 71302028, 71472057, and 71602041, Heilongjiang Province Natural Science Fund (LC2017031), and Fundamental Scientific Research Funds for the Central Universities (HEUCF170903, HEUCFW170908, and HEUCFW170904). 34 2 2 1 11 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1076-2787 1099-0526 COMPLEXITY Complexity 2017.0 6920904 10.1155/2017/6920904 0.0 8 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Mathematics; Science & Technology - Other Topics FL2ZT gold, Green Submitted 2023-03-23 WOS:000414090800001 0
J Angelini, F; Widera, P; Mobasheri, A; Blair, J; Struglics, A; Uebelhoer, M; Henrotin, Y; Marijnissen, AC; Kloppenburg, M; Blanco, FJ; Haugen, IK; Berenbaum, F; Ladel, C; Larkin, J; Bay-Jensen, AC; Bacardit, J Angelini, Federico; Widera, Pawel; Mobasheri, Ali; Blair, Joseph; Struglics, Andre; Uebelhoer, Melanie; Henrotin, Yves; Marijnissen, Anne Ca; Kloppenburg, Margreet; Blanco, Francisco J.; Haugen, Ida K.; Berenbaum, Francis; Ladel, Christoph; Larkin, Jonathan; Bay-Jensen, Anne C.; Bacardit, Jaume Osteoarthritis endotype discovery via clustering of biochemical marker data ANNALS OF THE RHEUMATIC DISEASES English Article KNEE OSTEOARTHRITIS; GLOBAL BURDEN; DISEASE; INFLAMMATION; BIOMARKERS; PHENOTYPES; TURNOVER Objectives Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning. Method Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. Results Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. Conclusions This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future. [Angelini, Federico; Widera, Pawel; Bacardit, Jaume] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England; [Mobasheri, Ali] Univ Oulu, Fac Med, Res Unit Med Imaging Phys & Technol, Oulu, Finland; [Mobasheri, Ali] State Res Inst Ctr Innovat Med, Dept Regenerat Med, Vilnius, Lithuania; [Mobasheri, Ali; Marijnissen, Anne Ca] UMC Utrecht, Rheumatol & Clin Immunol, Utrecht, Netherlands; [Mobasheri, Ali] Sun Yat Sen Univ, Dept Joint Surg, Affiliated Hosp 1, Guangzhou, Peoples R China; [Mobasheri, Ali] World Hlth Org Collaborating Ctr Publ Hlth Aspect, Liege, Belgium; [Blair, Joseph; Bay-Jensen, Anne C.] Nordic Biosci, ImmunoSci, Herlev, Denmark; [Struglics, Andre] Lund Univ, Fac Med, Dept Clin Sci Lund, Orthopaed, Lund, Sweden; [Uebelhoer, Melanie; Henrotin, Yves] Artialis SA, Liege, Belgium; [Henrotin, Yves] Univ Liege, Ctr Interdisciplinary Res Med CIRM, Liege, Belgium; [Kloppenburg, Margreet] Leiden Univ, Rheumatol, Med Ctr, Leiden, Netherlands; [Kloppenburg, Margreet] Leiden Univ, Dept Clin Epidemiol, Med Ctr, Leiden, Netherlands; [Blanco, Francisco J.] Univ A Coruna, Serv Reumatol, INIBIC Hosp, La Coruna, Spain; [Haugen, Ida K.] Diakonhjemmet Hosp, Div Rheumatol & Res, Oslo, Norway; [Berenbaum, Francis] Sorbonne Univ, INSERM, Paris, France; [Ladel, Christoph] BioBone BV, Darmstadt, Germany; [Larkin, Jonathan] GlaxoSmithKline USA, Philadelphia, PA USA Newcastle University - UK; University of Oulu; State Research Institute Centre for Innovative Medicine; Utrecht University; Utrecht University Medical Center; Sun Yat Sen University; World Health Organization; Lund University; University of Liege; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Universidade da Coruna; Instituto de Investigacion Biomedica de A Coruna (INIBIC); Diakonhjemmet Hospital; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; GlaxoSmithKline Bacardit, J (corresponding author), Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England. jaume.bacardit@newcastle.ac.uk Bay-Jensen, Anne-Christine/AEQ-7281-2022; Ladel, Christoph/ABG-3165-2021; Blanco, Francisco J./C-3192-2014; Mobasheri, Ali/D-5770-2011 Bay-Jensen, Anne-Christine/0000-0001-7952-9297; Blanco, Francisco J./0000-0001-9821-7635; Mobasheri, Ali/0000-0001-6261-1286; Struglics, Andre/0000-0003-4289-1393; Larkin, Jonathan/0000-0002-1202-9287; Kloppenburg, Margreet/0000-0002-9294-2307; Blair, Joseph/0000-0003-3248-7039 Innovative Medicines Initiative Joint Undertaking [115770]; European Union; EFPIA Innovative Medicines Initiative Joint Undertaking; European Union(European Commission); EFPIA The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement no 115770, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in-kind contribution. See http://www.imi.europa.eu/and http://wwwapproachprojecteu/. 55 7 7 5 13 BMJ PUBLISHING GROUP LONDON BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND 0003-4967 1468-2060 ANN RHEUM DIS Ann. Rheum. Dis. MAY 2022.0 81 5 666 675 10.1136/annrheumdis-2021-221763 0.0 MAR 2022 10 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology 0K8NU 35246457.0 Green Published, Green Submitted, Bronze 2023-03-23 WOS:000765866400001 0
C Zecchetto, S; Zanchetta, A IEEE Zecchetto, Stefano; Zanchetta, Andrea Validation of high resolution SAR winds fields obtained by Deep Learning 2022 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA LEARNING TO MEASURE SEA HEALTH PARAMETERS (METROSEA) English Proceedings Paper IEEE International Workshop on Metrology for the Sea Learning to Measure Sea Health Parameters (MetroSea) OCT 03-05, 2022 Milazzo, ITALY IEEE,Univ Messina Synthetic Aperture Radar; Sea Surface Wind field; ResNet; Validation A Deep Learning methodology based on ResNet, recently developed to retrieve the wind direction exclusively from the SAR images at 500 m of resolution, produces wind fields with unprecedented spatial details. As a consequence, the classical validation method comparing the SAR-derived with model and in-situ winds is not sufficient because of the natural lack of small scale structures provided by the models and the limited spatial coverage of the in-situ data. This paper proposes a complementary approach to the classical validation, estimating the spatial gradients of SAR-derived wind direction theta and speed U and verifying their compatibility with the typical values obtained from experimental wind time series. Hence getting a consistency test of the spatial information of the SAR-derived wind fields obtained with the ResNet methodology. This analysis on five Sentinel-1 images over the northern Adriatic Sea shows a good compatibility of the local spatial variations of the ResNet SAR-derived wind fields with those derived from experimental time series. These results, together with the statistical agreement with model and in-situ data sets, enforce the reliability of the wind maps obtained with the ResNet methodology, which describe real features of the wind fields. [Zecchetto, Stefano] CNR, Ist Sci Polari, I-35127 Padua, Italy; [Zanchetta, Andrea] Hong Kong Baptist Univ, Dept Music, Hong Kong, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Polari (ISP-CNR); Hong Kong Baptist University Zecchetto, S (corresponding author), CNR, Ist Sci Polari, I-35127 Padua, Italy. stefano.zecchetto@cnr.it; andyzanchetta@gmail.com Italian Space Agency (ASI) [2021-4-U.0 (CUP F65F21000070005)] Italian Space Agency (ASI)(Agenzia Spaziale Italiana (ASI)) The Sentinel-1 images have been downloaded from the ESA Sentinels Scientific Data Hub (https://scihub.copernicus.eu/).The Consortium for Smallscale Modeling (COSMO) wind fields have been obtained from https://meteohub.mistralportal.it.The work has been partially funded by the Italian Space Agency (ASI), Contract Call ASI DC-UOT-2019-017, Project APPLICAVEMARS n. 2021-4-U.0 (CUP F65F21000070005). 18 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-9942-2 2022.0 501 505 10.1109/METROSEA55331.2022.9950836 0.0 5 Engineering, Ocean; Oceanography Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Oceanography BU3ZQ 2023-03-23 WOS:000894276700099 0
J Chen, M; Hwang, K; Wang, HY; Leung, VCM; Humar, I Chen, Min; Hwang, Kai; Wang, Haiyang; Leung, Victor C. M.; Humar, Iztok Guest Editorial Special Issue on Internet of Things for Smart Health and Emotion Care IEEE INTERNET OF THINGS JOURNAL English Editorial Material As an information carrier, the Internet of Things (IoT) based on the Internet and sensing equipment makes all physical objects form an interconnected network. The 5th generation mobile networks (5G) technology has many advantages, such as high data rates, reduced latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity, realize the real-time data collection, transmission, analysis, management, and application in the era of global Internet of Everything. In order to quickly respond to people's daily requirements and provide the smart application based on artificial intelligence technology in various scenarios, the number of IoT devices will further increase. The integration of mobile-edge computing (MEC) and IoT is imperative, especially in industries needing real-time data computing, such as smart home, public security, automobile transportation, smart health, emotion care, etc. As a new form of IoT terminal combining 5G and MEC, wearable device based on intelligent fabrics plays an important role in smart health and emotion care, which is one of the potential development directions of the next generation of intelligent medical and rehabilitation systems. [Chen, Min] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China; [Hwang, Kai] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China; [Wang, Haiyang] Univ Minnesota Duluth, Dept Comp Sci, Duluth, MN 55812 USA; [Leung, Victor C. M.] Shenzhen Univ, Sch Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [Humar, Iztok] Univ Ljubljana, Sch Financial Engn, Ljubljana 1000, Slovenia Huazhong University of Science & Technology; Chinese University of Hong Kong, Shenzhen; University of Minnesota System; University of Minnesota Duluth; University of Minnesota Twin Cities; University of Minnesota Hospital; Shenzhen University; University of Ljubljana Chen, M (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China. minchen@ieee.org; minchen@ieee.org; haiyang@d.umn.edu; vleung@ieee.org; iztok.humar@fe.uni-lj.si Leung, Victor C. M./AGU-2462-2022 Leung, Victor C. M./0000-0003-3529-2640 0 1 1 1 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. DEC 1 2021.0 8 23 16718 16722 10.1109/JIOT.2021.3116407 0.0 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications XA2YL Bronze 2023-03-23 WOS:000720519000006 0
C Wang, C; Zhang, F; Liang, P; Daneva, M; van Sinderen, M ACM Wang, Chong; Zhang, Fan; Liang, Peng; Daneva, Maya; van Sinderen, Marten Can App Changelogs Improve Requirements Classification from App Reviews? An Exploratory Study PROCEEDINGS OF THE 12TH ACM/IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2018) English Proceedings Paper 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) as part of the Empirical Software Engineering International Week (ESEIW) OCT 11-12, 2018 Oulu, FINLAND ACM,IEEE App reviews; app changelogs; requirements analysis; machine learning; data-driven requirements engineering [Background] Recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources. However, only a few studies reported results of leveraging app changelogs together with app reviews. [Aims] Motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews. We aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non-functional requirements from app reviews. We also want to know which classification technique works better in this context. [Method] We did a case study on the effect of app changelogs on automatic classification of app reviews. Specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data. [Results] We compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs. Among the four algorithms, Naive Bayes was found to be more accurate for categorizing app reviews. [Conclusions] The results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews. In addition, Naive Bayes seems to be more suitable for our further research on this topic. [Wang, Chong; Zhang, Fan; Liang, Peng] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China; [Daneva, Maya; van Sinderen, Marten] Univ Twente, Sch Comp Sci, Enschede, Netherlands Wuhan University; University of Twente Wang, C (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China. cwang@whu.edu.cn; liangp@whu.edu.cn; m.daneva@utwente.nl; m.j.vansinderen@utwente.nl peng, liang/HKE-0294-2023 Liang, Peng/0000-0002-2056-5346; Daneva, Maya/0000-0001-7359-8013 National Basic Research Program of China [2014CB340404]; National Natural Science Foundation of China [61702378, 61672387] National Basic Research Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Basic Research Program of China under grant No. 2014CB340404, and the National Natural Science Foundation of China under grant Nos. 61702378 and 61672387. 12 3 3 1 3 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5823-1 2018.0 10.1145/3239235.3267428 0.0 4 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM8PJ 2023-03-23 WOS:000469776800043 0
J Gregoire, E; Lagniez, JM; Zhang, D Gregoire, Eric; Lagniez, Jean-Marie; Zhang, Du Consensus-finding that preserves mutually conflicting hypothetical information from a same agent AI COMMUNICATIONS English Article Artificial Intelligence; Boolean logic; consensus-finding; negotiation; multi-agents ALGORITHM; MAXSAT; MODEL A consensus among logically conflicting agents is defined as a subset of all the information conveyed by the agents such that this subset does not conflict with the information of any of the agents. Hopefully, each agent might be inclined to endorse such a consensus since this latter one does not conflict with the agent's own information. This form of consensus is investigated from a practical computational point of view within an extended Boolean logic-based knowledge representation framework. A method that is based on a single step discrete optimisation process to compute one maximal consensus is shown viable even for very large information sets. Interestingly, we allow each agent to require some additional hypothetical - possibly mutually conflicting - information to be point-wise compatible with any consensus. [Gregoire, Eric; Lagniez, Jean-Marie] Univ Artois, CRIL, F-62300 Lens, France; [Gregoire, Eric; Lagniez, Jean-Marie] CNRS, F-62300 Lens, France; [Gregoire, Eric; Zhang, Du] MUST, Fac Informat Technol, Ave Wai Long, Taipa, Macau, Peoples R China Universite d'Artois; Centre National de la Recherche Scientifique (CNRS); Macau University of Science & Technology Gregoire, E (corresponding author), Univ Artois, CRIL, F-62300 Lens, France.;Gregoire, E (corresponding author), CNRS, F-62300 Lens, France.;Gregoire, E (corresponding author), MUST, Fac Informat Technol, Ave Wai Long, Taipa, Macau, Peoples R China. gregoire@cril.fr; lagniez@cril.fr; duzhang@must.edu.mo Gregoire, Eric/0000-0001-6707-3106 28 0 0 0 2 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 0921-7126 1875-8452 AI COMMUN AI Commun. 2018.0 31 3 303 317 10.3233/AIC-170749 0.0 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GG2TE 2023-03-23 WOS:000432544800006 0
C Yu, NG; Weber, C; Hu, XL Tetko, IV; Kurkova, V; Karpov, P; Theis, F Yu, Niange; Weber, Cornelius; Hu, Xiaolin Learning Sparse Hidden States in Long Short-Term Memory ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: DEEP LEARNING, PT II Lecture Notes in Computer Science English Proceedings Paper 28th International Conference on Artificial Neural Networks (ICANN) SEP 17-19, 2019 Tech Univ Munchen, Klinikum Rechts Isar, Munich, GERMANY Tech Univ Munchen, Klinikum Rechts Isar Recurrent neural network (RNN); Long Short-Term Memory (LSTM); Language modeling; Image captioning; Network acceleration Long Short-Term Memory (LSTM) is a powerful recurrent neural network architecture that is successfully used in many sequence modeling applications. Inside an LSTM unit, a vector called memory cell is used to memorize the history. Another important vector, which works along with the memory cell, represents hidden states and is used to make a prediction at a specific step. Memory cells record the entire history, while the hidden states at a specific time step in general need to attend only to very limited information thereof. Therefore, there exists an imbalance between the huge information carried by a memory cell and the small amount of information requested by the hidden states at a specific step. We propose to explicitly impose sparsity on the hidden states to adapt them to the required information. Extensive experiments show that sparsity reduces the computational complexity and improves the performance of LSTM networks (The source code is available at https://github.com/feiyuhug/SHS_LSTM/tree/master). [Yu, Niange; Hu, Xiaolin] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China; [Weber, Cornelius] Univ Hamburg, Dept Informat, Hamburg, Germany Tsinghua University; University of Hamburg Hu, XL (corresponding author), Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. niangeyu@gmail.com; weber@informatik.uni-hamburg.de; xlhu@tsinghua.edu.cn Hu, Xiaolin/0000-0002-4907-7354 NSFC [61621136008]; German Research Foundation (DFG) [CML (TRR 169)] NSFC(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG)(German Research Foundation (DFG)) We acknowledge support by NSFC (61621136008) and German Research Foundation (DFG) under project CML (TRR 169). 23 2 2 2 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-30484-3; 978-3-030-30483-6 LECT NOTES COMPUT SC 2019.0 11728 288 298 10.1007/978-3-030-30484-3_24 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP3AP 2023-03-23 WOS:000545998100024 0
C Duntsch, I; Gediga, G IOP Duntsch, Ivo; Gediga, Guenther Confusion Matrices and Rough Set Data Analysis 2019 3RD INTERNATIONAL CONFERENCE ON MACHINE VISION AND INFORMATION TECHNOLOGY (CMVIT 2019) Journal of Physics Conference Series English Proceedings Paper 3rd International Conference on Machine Vision and Information Technology (CMVIT) FEB 22-24, 2019 Guangzhou, PEOPLES R CHINA Asia Pacific Inst Sci & Engn A widespread approach in machine learning to evaluate the quality of a classifier is to cross classify predicted and actual decision classes in a confusion matrix, also called error matrix. A classification tool which does not assume distributional parameters but only information contained in the data is based on rough set data model which assumes that knowledge is given only up to a certain granularity. Using this assumption and the technique of confusion matrices, we define various indices and classifiers based on rough confusion matrices. [Duntsch, Ivo] Fujian Normal Univ, Coll Math & Informat, Fuzhou, Fujian, Peoples R China; [Gediga, Guenther] Inst Evaluat & Marktanalysen, Brinkstr 19, D-49143 Jeggen, Germany Fujian Normal University Duntsch, I (corresponding author), Fujian Normal Univ, Coll Math & Informat, Fuzhou, Fujian, Peoples R China. D.Ivo@fjnu.edu.cn; gediga@eval-institut.de Duntsch, Ivo/A-4852-2008 Duntsch, Ivo/0000-0001-8907-2382 8 16 16 3 13 IOP PUBLISHING LTD BRISTOL DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND 1742-6588 1742-6596 J PHYS CONF SER 2019.0 1229 12055 10.1088/1742-6596/1229/1/012055 0.0 6 Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BO0QJ Green Submitted, gold 2023-03-23 WOS:000492959200055 0
J Yao, JH; Lin, MX; Liao, WJ; Fan, WJ; Xu, XX; Shi, HR; Wu, SX Yao, Jia-Hui; Lin, Meng-Xian; Liao, Wen-Jun; Fan, Wei-Jie; Xu, Xiao-Xin; Shi, Haoran; Wu, Shu-Xiang M1ARegpred: Epitranscriptome Target Prediction of N1-methyladenosine (m1A) Regulators Based on Sequencing Features and Genomic Features FRONTIERS IN BIOSCIENCE-LANDMARK English Article m1A; substrate; machine learning RNA MODIFICATIONS; M(6)A RNA; DATABASE; TRANSLATION; METHYLOME; REVEALS; NUCLEAR; BINDING Background: N1-methyladenosine (m1A) is a reversible post-transcriptional modification in mRNA, which has been proved to play critical roles in various biological processes through interaction with different m1A regulators. There are several m1A regulators existing in the human genome, including YTHDF1-3 and YTHDC1. Methods: Several techniques have been developed to identify the substrates of m1A regulators, but their binding specificity and biological functions are not yet fully understood due to the limitations of wetlab approaches. Here, we submitted the framework m1ARegpred (m1A regulators substrate prediction), which is based on machine learning and the combination of sequence-derived and genome-derived features. Results: Our framework achieved area under the receiver operating characteristic (AUROC) scores of 0.92 in the full transcript model and 0.857 in the mature mRNA model, showing an improvement compared to the existing sequence-derived methods. In addition, motif search and gene ontology enrichment analysis were performed to explore the biological functions of each m1A regulator. Conclusions: Our work may facilitate the discovery of m1A regulators substrates of interest, and thereby provide new opportunities to understand their roles in human bodies. [Yao, Jia-Hui; Lin, Meng-Xian; Liao, Wen-Jun; Fan, Wei-Jie; Xu, Xiao-Xin; Wu, Shu-Xiang] Fujian Med Univ, Key Lab Gastrointestinal Canc, Sch Basic Med Sci, Minist Educ, Fuzhou 350005, Fujian, Peoples R China; [Yao, Jia-Hui] Xian Jiaotong Liverpool Univ, Dept Biol Sci, Suzhou 215123, Jiangsu, Peoples R China; [Shi, Haoran] Justus Liebig Univ Giessen, Rescarch Ctr BioSyst Land Use & Nutr 1FZ, Inst Appl Microbiol, D-35392 Giessen, Germany; [Wu, Shu-Xiang] Fujian Med Univ, Dept Med Microbiol, Fujian Key Lab Tumor Microbiol, Fuzhou 350005, Fujian, Peoples R China Fujian Medical University; Xi'an Jiaotong-Liverpool University; Justus Liebig University Giessen; Fujian Medical University Wu, SX (corresponding author), Fujian Med Univ, Key Lab Gastrointestinal Canc, Sch Basic Med Sci, Minist Educ, Fuzhou 350005, Fujian, Peoples R China.;Wu, SX (corresponding author), Fujian Med Univ, Dept Med Microbiol, Fujian Key Lab Tumor Microbiol, Fuzhou 350005, Fujian, Peoples R China. wushuxiang@fjmu.edu.cn Liu, Han/HMD-9231-2023; li, xiao/HKV-8405-2023; zhang, yu/HNS-5948-2023 Liu, Han/0000-0002-5269-8477; Shi, Haoran/0000-0001-5997-6946 Scientific Research Foundation for Advanced Talents of Fujian Medical University [XRCZX2020012] Scientific Research Foundation for Advanced Talents of Fujian Medical University Scientific Research Foundation for Advanced Talents of Fujian Medical University (XRCZX2020012). 66 1 1 4 4 IMR PRESS ROBINSON 112 ROBINSON RD, ROBINSON, SINGAPORE 2768-6701 2768-6698 FRONT BIOSCI-LANDMRK Front. Biosci. AUG 31 2022.0 27 9 10.31083/j.fbl2709269 0.0 9 Biochemistry & Molecular Biology; Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Cell Biology 5O6BQ 36224013.0 gold 2023-03-23 WOS:000872556700023 0
J Zhang, JH; Xu, ZY; Xu, WS; Zhu, FY; Lyu, XY; FU, M Zhang, Jiahui; Xu, Zhiyu; Xu, Weisheng; Zhu, Feiyu; Lyu, Xiaoyu; FU, Min Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization APPLIED SCIENCES-BASEL English Article multi-energy virtual power plant; economic cost; power quality; bi-objective dispatch; long short-term memory; multi-objective particle swarm optimization MANAGEMENT This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, improvisational and foresighted, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that foresighted strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h. [Zhang, Jiahui; Xu, Zhiyu; Xu, Weisheng; Zhu, Feiyu; Lyu, Xiaoyu; FU, Min] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China; [Zhang, Jiahui; Zhu, Feiyu] Univ Bologna, Dept Elect Elect & Informat Engn, I-40136 Bologna, Italy; [Xu, Weisheng] Tongji Univ, Ctr Bigdata & Informatizat, Shanghai 20092, Peoples R China Tongji University; University of Bologna; Tongji University Xu, ZY (corresponding author), Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China. karlzhangjh@163.com; xuzhiyu@tongji.edu.cn; xuweisheng@tongji.edu.cn; zhufy2015@163.com; 1730751@tongji.edu.cn; fumin96704@163.com Zhang, Jiahui/0000-0002-7723-5460 National Natural Science Foundation of China [61773292, 71401125]; Research & Development Program of Guizhou Power Grid Co., Ltd. [GZKJXM20160635] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research & Development Program of Guizhou Power Grid Co., Ltd. This research was funded by National Natural Science Foundation of China, grant number 61773292, 71401125; Research & Development Program of Guizhou Power Grid Co., Ltd., grant number GZKJXM20160635. 30 11 11 2 24 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JAN 2 2019.0 9 2 292 10.3390/app9020292 0.0 20 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics HM4IG Green Submitted, gold 2023-03-23 WOS:000459436300082 0
J Lu, HM; Wang, D; Li, YJ; Li, JR; Li, X; Kim, H; Serikawa, S; Humar, I Lu, Huimin; Wang, Dong; Li, Yujie; Li, Jianru; Li, Xin; Kim, Hyoungseop; Serikawa, Seiichi; Humar, Iztok CONET: A COGNITIVE OCEAN NETWORK IEEE WIRELESS COMMUNICATIONS English Article The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the sea floor. Approximately 70.8 percent of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the ocean network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The cognitive ocean network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the article are as follows: a CONet architecture is proposed and described in detail; important and useful demonstration applications of the CONet are proposed; and future trends in CONet research are presented. [Lu, Huimin; Serikawa, Seiichi] Kyushu Inst Technol, Kitakyushu, Fukuoka, Japan; [Lu, Huimin; Li, Yujie] Yangzhou Univ, Yangzhou, Jiangsu, Peoples R China; [Wang, Dong] Dalian Univ Technol, Sch Informat & Commun Engn, Dalian, Peoples R China; [Li, Jianru] Tongji Univ, Shanghai, Peoples R China; [Li, Xin] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Kim, Hyoungseop] Kyushu Inst Technol, Dept Control Engn, Kitakyushu, Fukuoka, Japan; [Humar, Iztok] Univ Ljubljana, Ljubljana, Slovenia Kyushu Institute of Technology; Yangzhou University; Dalian University of Technology; Tongji University; Shanghai Jiao Tong University; Kyushu Institute of Technology; University of Ljubljana Lu, HM (corresponding author), Kyushu Inst Technol, Kitakyushu, Fukuoka, Japan.;Lu, HM (corresponding author), Yangzhou Univ, Yangzhou, Jiangsu, Peoples R China. luhuimin@ieee.org; wdice@dlut.edu.cn; yzyjli@gmail.com; kimhs@cntl.kyutech.ac.jp; serikawa@elcs.kyutech.ac.jp; iztok.humar@fe.uni-lj.si Li, YuJie/HGT-8657-2022; Li, Yujie/AAH-3298-2019; , 王栋/AAB-5194-2020; Li, yu/HHZ-5236-2022 Li, Yujie/0000-0002-0275-2797; Leading Initiative for Excellent Young Researcher of Ministry of Education, Culture, Sports, Science and Technology - Japan [16809746]; JSPS [17K14694]; Research Fund of the State Key Laboratory of Marine Geology in Tongji University [MGK1803]; Research Fund of the State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University [1510]; Research Fund of the Telecommunications Advancement Foundation; Fundamental Research Developing Association for Shipbuilding and Offshore, Japan-China Scientific Cooperation Program [6171101454]; International Exchange Program of the National Institute of Information and Communications; Collaboration Program of the National Institute of Informatics; Slovenian Research Agency [P2-0246] Leading Initiative for Excellent Young Researcher of Ministry of Education, Culture, Sports, Science and Technology - Japan; JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Research Fund of the State Key Laboratory of Marine Geology in Tongji University; Research Fund of the State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University; Research Fund of the Telecommunications Advancement Foundation; Fundamental Research Developing Association for Shipbuilding and Offshore, Japan-China Scientific Cooperation Program; International Exchange Program of the National Institute of Information and Communications; Collaboration Program of the National Institute of Informatics; Slovenian Research Agency(Slovenian Research Agency - Slovenia) This work was supported by the Leading Initiative for Excellent Young Researcher of Ministry of Education, Culture, Sports, Science and Technology - Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17K14694), the Research Fund of the State Key Laboratory of Marine Geology in Tongji University (MGK1803), the Research Fund of the State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510), the Research Fund of the Telecommunications Advancement Foundation, Fundamental Research Developing Association for Shipbuilding and Offshore, Japan-China Scientific Cooperation Program (6171101454), the International Exchange Program of the National Institute of Information and Communications, and the Collaboration Program of the National Institute of Informatics. Dr. Humar would like to acknowledge financial support from the Slovenian Research Agency (research core funding No. P2-0246). 15 90 94 2 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. JUN 2019.0 26 3 90 96 10.1109/MWC.2019.1800325 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IH6HZ Green Submitted 2023-03-23 WOS:000474597100014 0
J Fu, GH; Song, CW; Li, JQ; Ma, Y; Chen, P; Wang, RQ; Yang, BX; Huang, ZS Fu, Guanghui; Song, Changwei; Li, Jianqiang; Ma, Yue; Chen, Pan; Wang, Ruiqian; Yang, Bing Xiang; Huang, Zhisheng Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study JOURNAL OF MEDICAL INTERNET RESEARCH English Article deep learning; distant supervision; mental health; crisis prevention Background: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide. [Fu, Guanghui; Song, Changwei; Li, Jianqiang; Wang, Ruiqian] Beijing Univ Technol, Sch Software Engn, Beijing, Peoples R China; [Ma, Yue] Univ Paris Saclay, Ctr Natl Rech Sci, Interdisciplinary Lab Digital Sci, Orsay, France; [Chen, Pan; Yang, Bing Xiang] Wuhan Univ, Sch Hlth Sci, Wuhan 430072, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Artificial Intelligence, Amsterdam, Netherlands Beijing University of Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Wuhan University; Vrije Universiteit Amsterdam Yang, BX (corresponding author), Wuhan Univ, Sch Hlth Sci, Wuhan 430072, Peoples R China. 00009312@whu.wdu.cn Fu, Guanghui/ABA-7593-2021; LI, JIAN/GRY-2197-2022; Li, Jing/GYU-5036-2022; li, jian/GSE-0245-2022; LI, Jing/HNB-5575-2023; Huang, Zhisheng/P-3284-2014 Ma, Yue/0000-0002-2805-2473; Huang, Zhisheng/0000-0003-3794-9829 National Natural Science Foundation of China [72174152]; Project of Humanities and Social Sciences of the Ministry of Education in China [20YJCZH204] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of Humanities and Social Sciences of the Ministry of Education in China This study is supported by the National Natural Science Foundation of China (No. 72174152) and the Project of Humanities and Social Sciences of the Ministry of Education in China (The Proactive Levelled Intervention for Social Network Users' Emotional Crisis-an Automatic Crisis Balance Analysis Model, 20YJCZH204). 33 1 1 9 26 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA 1438-8871 J MED INTERNET RES J. Med. Internet Res. AUG 26 2021.0 23 8 e26119 10.2196/26119 0.0 13 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Health Care Sciences & Services; Medical Informatics UH1WU 34435964.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000689731100004 0
J Guo, YH; Chen, SZ; Li, XX; Cunha, M; Jayavelu, S; Cammarano, D; Fu, YS Guo, Yahui; Chen, Shouzhi; Li, Xinxi; Cunha, Mario; Jayavelu, Senthilnath; Cammarano, Davide; Fu, Yongshuo Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images REMOTE SENSING English Article empirical line calibration method (ELCM); machine learning; soil and plant analyzer development (SPAD); spectral and textural indices; stepwise regression model (SRM); unmanned aerial vehicle (UAV) SUPPORT-VECTOR-MACHINE; RANDOM FOREST CLASSIFIER; EMPIRICAL LINE METHOD; RADIOMETRIC CALIBRATION; CHLOROPHYLL CONTENT; REFLECTANCE INDEXES; CANOPY REFLECTANCE; NITROGEN STATUS; SENSED IMAGES; WATER-STRESS Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R-2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R-2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods. [Guo, Yahui; Chen, Shouzhi; Li, Xinxi; Fu, Yongshuo] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China; [Cunha, Mario] Univ Porto, Sci Fac, Rua Campo Alegre, P-4169007 Porto, Portugal; [Cunha, Mario] Univ Porto, Inst Syst & Comp Engn Technol & Sci INESC TEC, Campus Fac Engn, Rua Dr Roberto Frias, P-4200465 Porto, Portugal; [Jayavelu, Senthilnath] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore; [Cammarano, Davide] Purdue Univ, Dept Agron, W Lafayette, IN 47907 USA Beijing Normal University; Universidade do Porto; INESC TEC; Universidade do Porto; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); Purdue University System; Purdue University; Purdue University West Lafayette Campus Fu, YS (corresponding author), Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China. guoyh@mail.bnu.edu.cn; 201921470001@mail.bnu.edu.cn; 201921470012@mail.bnu.edu.cn; mccunha@fc.up.pt; jsenthilnath@alum.iisc.ac.in; dcammar@purdue.edu; yfu@bnu.edu.cn Cammarano, Davide/CAF-3057-2022; Guo, Yahui/GQP-9675-2022; Fu, Yongshuo/AAF-9446-2021; Guo, Yahui/AAA-1131-2020; Senthilnath, J./L-2949-2013 Guo, Yahui/0000-0002-0099-0759; Senthilnath, J./0000-0002-1737-7985; Fu, Yongshuo/0000-0002-9761-5292; Cunha, Mario/0000-0002-8299-324X; Chen, Shouzhi/0000-0002-3156-7953 National Funds for Distinguished Young Youths [42025101]; National Key Research and Development Program of China [2017YFA06036001]; National Natural Science Foundation of China [31770516]; 111 Project [B18006] National Funds for Distinguished Young Youths; National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project) This study was funded by the National Funds for Distinguished Young Youths (Grant No. 42025101), the National Key Research and Development Program of China (Grant No.2017YFA06036001), the National Natural Science Foundation of China (Grant No. 31770516), the 111 Project (Grant No. B18006). 109 10 10 21 31 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2022.0 14 6 1337 10.3390/rs14061337 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 0B3YM gold 2023-03-23 WOS:000774574400001 0
C Zhan, Q; Hu, JJ; Yu, ZT; Li, XB; Wang, WJ IEEE Zhan, Qi; Hu, Jingjing; Yu, Zitong; Li, Xiaobai; Wang, Wenjin Revisiting motion-based respiration measurement from videos 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) JUL 20-24, 2020 Montreal, CANADA IEEE Engn Med & Biol Soc Video-based motion analysis gave rise to contactless respiration rate monitoring that measures subtle respiratory movement from a human chest or belly. In this paper, we revisit this technology via a large video benchmark that includes six categories of practical challenges. We analyze two video properties (i.e. pixel intensity variation and pixel movement) that are essential for respiratory motion analysis and various signal extraction approaches (i.e. from conventional to recent Convolutional Neural Network (CNN)-based methods). We find that pixel movement can better quantify respiratory motion than pixel intensity variation in various conditions. We also conclude that the simple conventional approach (e.g. Zero-phase Component Analysis) can achieve better performance than CNN that uses data training to define the extraction of respiration signal, which thus raises a more general question whether CNN can improve video-based physiological signal measurement. [Zhan, Qi; Hu, Jingjing] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China; [Yu, Zitong; Li, Xiaobai] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland; [Wang, Wenjin] Eindhoven Univ Technol, Eindhoven, Netherlands Hunan University; University of Oulu; Eindhoven University of Technology Wang, WJ (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. zhanqi@hnu.edu.cn; jingjingh@hnu.edu.cn; Zitong.Yu@oulu.fi; Xiaobai.Li@oulu.fi; wwang@tue.nl National Natural Science Foundation of China [61671204] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the National Natural Science Foundation of China (Grant No. 61671204). 13 2 2 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1990-8 IEEE ENG MED BIO 2020.0 5909 5912 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BQ8TK 33019319.0 Green Accepted 2023-03-23 WOS:000621592206061 0
J Du, ZD; Liu, SL; Fasthuber, R; Chen, TS; Ienne, P; Li, L; Luo, T; Guo, Q; Feng, XB; Chen, YJ; Temam, O Du, Zidong; Liu, Shaoli; Fasthuber, Robert; Chen, Tianshi; Ienne, Paolo; Li, Ling; Luo, Tao; Guo, Qi; Feng, Xiaobing; Chen, Yunji; Temam, Olivier An Accelerator for High Efficient Vision Processing IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS English Article Accelerator architectures; convolutional neural network; vision sensor ARCHITECTURE; COPROCESSOR; HARDWARE In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are convolutional neural networks (CNNs), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is highly energy-efficient. We present a single-core implementation down to the layout at 65 nm, with a modest footprint of 5.94mm(2) and consuming only 336mm(2), but still about 30x faster than high-end GPUs. For visual processing with higher resolution and frame-rate requirements, we further present a multicore implementation with elevated performance. [Du, Zidong; Liu, Shaoli; Chen, Tianshi; Li, Ling; Luo, Tao; Guo, Qi; Feng, Xiaobing; Chen, Yunji] Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China; [Du, Zidong; Luo, Tao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Fasthuber, Robert; Ienne, Paolo] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland; [Chen, Yunji] Chinese Acad Sci, CAS Ctr Excellence Brain Sci, Beijing 100190, Peoples R China; [Temam, Olivier] INRIA, F-91120 Palaiseau, France Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Chinese Academy of Sciences; Inria Du, ZD (corresponding author), Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China.;Du, ZD (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. duzidong@ict.ac.cn; liushaoli@ict.ac.cn; robert.fasthuber@epfl.ch; chentianshi@ict.ac.cn; paolo.ienne@epfl.ch; liling@ict.ac.cn; luotao@ict.ac.cn; guoqi@ict.ac.cn; fxb@ict.ac.cn; cyj@ict.ac.cn; olivier.temam@inria.fr Chen, Yunji/0000-0003-3925-5185 NSF of China [61133004, 61303158, 61432016, 61472396, 61473275, 61522211, 61532016, 61521092]; 973 Program of China [XDA06010403, XDB02040009]; International Collaboration Key Program of the CAS [171111KYSB20130002]; 10000 talent program NSF of China(National Natural Science Foundation of China (NSFC)); 973 Program of China(National Basic Research Program of China); International Collaboration Key Program of the CAS; 10000 talent program This work is partially supported by the NSF of China (under Grants 61133004, 61303158, 61432016, 61472396, 61473275, 61522211, 61532016, 61521092), the 973 Program of China (under Grant 2015CB358800), the Strategic Priority Research Program of the CAS (under Grants XDA06010403, XDB02040009), the International Collaboration Key Program of the CAS (under Grant 171111KYSB20130002), and the 10000 talent program. 63 4 4 1 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0070 1937-4151 IEEE T COMPUT AID D IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. FEB 2017.0 36 2 227 240 10.1109/TCAD.2016.2584062 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering EL5SV 2023-03-23 WOS:000394682200003 0
S Deng, JY; Yuan, QJ; Mamitsuka, H; Zhu, SF Mamitsuka, H Deng, Jieyao; Yuan, Qingjun; Mamitsuka, Hiroshi; Zhu, Shanfeng DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank DATA MINING FOR SYSTEMS BIOLOGY, 2 EDITION: Methods and Protocols Methods in Molecular Biology English Article; Book Chapter DrugE-Rank; Learning to rank; Drug discovery Identifying drug-target interactions is crucial for the success of drug discovery. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. By utilizing the Learning to rank framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at http://datamining-iip.fudan.edu.cn/service/DrugE-Rank/. [Deng, Jieyao; Yuan, Qingjun; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Deng, Jieyao; Yuan, Qingjun; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Mamitsuka, Hiroshi] Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Japan; [Mamitsuka, Hiroshi] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Zhu, Shanfeng] Fudan Univ, Ctr Computat Syst Biol, Shanghai, Peoples R China Fudan University; Fudan University; Kyoto University; Aalto University; Fudan University Deng, JY (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Deng, JY (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China. Mamitsuka, Hiroshi/R-1110-2016 Mamitsuka, Hiroshi/0000-0002-6607-5617 National Natural Science Foundation of China [61572139]; MEXT KAKENHI [16H02868]; FiDiPro by Tekes National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MEXT KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); FiDiPro by Tekes This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868, and FiDiPro by Tekes. 15 2 2 1 11 HUMANA PRESS INC TOTOWA 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA 1064-3745 1940-6029 978-1-4939-8561-6; 978-1-4939-8560-9 METHODS MOL BIOL Methods Mol. Biol. 2018.0 1807 195 202 10.1007/978-1-4939-8561-6_14 0.0 10.1007/978-1-4939-8561-6 8 Biochemical Research Methods; Biology; Mathematical & Computational Biology Book Citation Index – Science (BKCI-S) Biochemistry & Molecular Biology; Life Sciences & Biomedicine - Other Topics; Mathematical & Computational Biology BS0SX 30030812.0 Green Accepted 2023-03-23 WOS:000684257200015 0
J Li, WK; Lu, SR; Deng, DL Li, Weikang; Lu, Sirui; Deng, Dong-Ling Quantum federated learning through blind quantum computing SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY English Article quantum federated learning; blind quantum computing; differential privacy; quantum classifier GAME; GO Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooperation between quantum physics and machine learning may lead to unparalleled prospect for solving private distributed learning tasks. In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe. For concreteness, we first introduce a protocol for private single-party delegated training of variational quantum classifiers based on blind quantum computing and then extend this protocol to multiparty private distributed learning incorporated with differential privacy. We carry out extensive numerical simulations with different real-life datasets and encoding strategies to benchmark the effectiveness of our protocol. We find that our protocol is robust to experimental imperfections and is secure under the gradient attack after the incorporation of differential privacy. Our results show the potential for handling computationally expensive distributed learning tasks with privacy guarantees, thus providing a valuable guide for exploring quantum advantages from the security perspective in the field of machine learning with real-life applications. [Li, Weikang; Lu, Sirui; Deng, Dong-Ling] Tsinghua Univ, Inst Inteldisciplinary Informat Sci, Ctr Quantum Informat, Beijing 100084, Peoples R China; [Lu, Sirui] Mar Planck Inst Quantenopt, D-85748 Garching, Germany; [Deng, Dong-Ling] Shanghai Qi Zhi Inst, Shanghai 200232, Peoples R China Tsinghua University Deng, DL (corresponding author), Tsinghua Univ, Inst Inteldisciplinary Informat Sci, Ctr Quantum Informat, Beijing 100084, Peoples R China.;Deng, DL (corresponding author), Shanghai Qi Zhi Inst, Shanghai 200232, Peoples R China. dldeng@tsinghua.edu.cn Lu, Sirui/AAD-6461-2022 Lu, Sirui/0000-0003-2807-6288; Li, Weikang/0000-0002-7137-5390 Tsinghua University [53330300320]; National Natural Science Foundation of China [12075128]; Shanghai Qi Zhi Institute Tsinghua University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Qi Zhi Institute This work was supported by the start-up fund from Tsinghua University (Grant No. 53330300320), the National Natural Science Foundation of China (Grant No. 12075128), and the Shanghai Qi Zhi Institute. We thank Haoyu Guo, Wenjie Jiang, Zhide Lu, and Peixin Shen for helpful discussions. 57 12 13 7 29 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-7348 1869-1927 SCI CHINA PHYS MECH Sci. China-Phys. Mech. Astron. OCT 2021.0 64 10 100312 10.1007/s11433-021-1753-3 0.0 8 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics UM8PD Green Submitted 2023-03-23 WOS:000693586900003 0
J Zhong, Y; Zheng, W; Chen, ZY; Xia, F; Yu, LM; Wu, QQ; Ai, XK; Shen, CS; Yang, ZY; Yan, W; Ding, YH; Liang, YF; Chen, ZP; Tong, RH; Bai, W; Fang, JG; Li, F Zhong, Y.; Zheng, W.; Chen, Z. Y.; Xia, F.; Yu, L. M.; Wu, Q. Q.; Ai, X. K.; Shen, C. S.; Yang, Z. Y.; Yan, W.; Ding, Y. H.; Liang, Y. F.; Chen, Z. P.; Tong, R. H.; Bai, W.; Fang, J. G.; Li, F. J-TEXT Team Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A PLASMA PHYSICS AND CONTROLLED FUSION English Article disruption prediction; machine learning; LightGBM; feature importance TOOL Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model's performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors. [Zhong, Y.; Zheng, W.; Chen, Z. Y.; Wu, Q. Q.; Ai, X. K.; Shen, C. S.; Yan, W.; Ding, Y. H.; Liang, Y. F.; Chen, Z. P.; Bai, W.; Fang, J. G.; Li, F.] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Int Joint Res Lab Magnet Confinement Fus & Plasma, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China; [Chen, Z. Y.] Chengdu Univ, Chengdu 610106, Peoples R China; [Xia, F.; Yu, L. M.; Yang, Z. Y.; Tong, R. H.] Southwestern Inst Phys, Chengdu 610041, Peoples R China; [Liang, Y. F.] Forschungszentrum Julich, Inst Energie & Klimaforsch Plasmaphys, D-52425 Julich, Germany; [Liang, Y. F.] Chinese Acad Sci, Inst Plasma Phys, POB 1126, Hefei 230031, Peoples R China Huazhong University of Science & Technology; Chengdu University; Southwestern Institute of Physics - China; Helmholtz Association; Research Center Julich; Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS Zheng, W; Chen, ZY (corresponding author), Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Int Joint Res Lab Magnet Confinement Fus & Plasma, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China.;Chen, ZY (corresponding author), Chengdu Univ, Chengdu 610106, Peoples R China. zhengwei@hust.edu.cn; zychen@hust.edu.cn Fang, Jiangang/GQZ-0561-2022; Chen, Zhipeng/HHC-0796-2022; shen, chengshuo/GXA-1891-2022; YU, Liming/AFT-9836-2022 Yu, Liming/0000-0002-1177-3063 National Key Research and Development Program of China [2017YFE0301202, 2017YFE0300500, 2017YFE0300501]; National Magnetic Confinement Fusion Science Program [2015GB111002, 2015GB104000]; National Natural Science Foundation of China [11775089, 51821005, 71762031, 11875022, 11905077, 11575068]; National MCF Energy R&D Program of China [2019YFE03010004]; China Postdoctoral Science Foundation [2019M652615] National Key Research and Development Program of China; National Magnetic Confinement Fusion Science Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National MCF Energy R&D Program of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors are very grateful for help from the J-TEXT team. This work is supported by the National Key Research and Development Program of China (Grant Nos. 2017YFE0301202, 2017YFE0300500, 2017YFE0300501), by the National Magnetic Confinement Fusion Science Program (Grant Nos. 2015GB111002, 2015GB104000), by the National Natural Science Foundation of China (Grant Nos. 11775089, 51821005, 71762031, 11875022, 11905077 and 11575068), by the National MCF Energy R&D Program of China (Grant No. 2019YFE03010004), and by the China Postdoctoral Science Foundation (Grant No. 2019M652615). 34 1 1 1 17 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0741-3335 1361-6587 PLASMA PHYS CONTR F Plasma Phys. Control. Fusion JUL 2021.0 63 7 75008 10.1088/1361-6587/abfa74 0.0 11 Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Physics SJ2UP 2023-03-23 WOS:000655386600001 0
J Heydari, M; Fan, YA; Li, XY; Zhou, XH; Lai, KK Heydari, Mohammad; Fan Yanan; Li, Xiaoyang; Zhou Xiaohu; Lai, Kin Keung RELATIONSHIP BETWEEN THE RATE OF FLUCTUATION SCALE AND INDEX CHANGES IN TEHRAN STOCK EXCHANGE LEX HUMANA English Article Fluctuation scale; Index changes; Tehran Stock Exchange; Wavelet model; Educational guide The objective of this study was to the relationship between the rate of fluctuation scale and index changes in Tehran Stock Exchange using the wavelet model. The method of this research is using data technique. One of the most widely used techniques in financial time series is neural network. Due to the comprehensiveness of this technique and the lack of some assumptions about the data, it has become more widespread compared to statistical data. However, noise in time series, especially in financial and economic time series, reduces the accuracy of neural network (NN) predictions. One method of descaling in time series is wavelet transform. The results showed that with constant inflation rate as a controlling variable of exchange rate fluctuation scales and stock price index, from 2005 to 2016, there is a negative and very coherent coherence in long-term scales. According to the results, it can be said that one of the most fundamental issues in terms of training guidelines is the economic situation and the adoption of investment strategies. Accordingly, this shows that in recent years, the long-term decline in the stock price index has reduced exchange rate fluctuations. The results of this research can be an educational guide for those who want to invest in the stock market. [Heydari, Mohammad; Li, Xiaoyang] Southwest Univ, Business Coll, Chongqing, Peoples R China; [Fan Yanan] Univ Wroclaw, Wroclaw, Poland; [Zhou Xiaohu] Nanjing Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Lai, Kin Keung] Shaanxi Normal Univ, Int Business Sch, Xian 710062, Peoples R China Southwest University - China; University of Wroclaw; Nanjing University of Science & Technology; Shaanxi Normal University Heydari, M (corresponding author), Southwest Univ, Business Coll, Chongqing, Peoples R China. MohammadHeydari1992@yahoo.com; 15735925764@163.com; zjclxy@swu.edu.cn; njustzxh@njust.edu.cn; mskklai@outlook.com Heydari, Mohammad/D-7221-2017; Lai, Kin Keung/AEB-4278-2022 Heydari, Mohammad/0000-0003-3942-2107; 34 0 0 2 2 UNIV CATOLICA PETROPOLIS PETROPOLIS UNIV CATOLICA PETROPOLIS, PETROPOLIS, 00000, BRAZIL 2175-0947 LEX HUMANA Lex Humana AUG-DEC 2021.0 13 2 153 176 24 Law Emerging Sources Citation Index (ESCI) Government & Law YD5TQ 2023-03-23 WOS:000740504900009 0
J Yu, JY; Hou, BX; Lelyakin, A; Xu, ZJ; Jordan, T Yu, Jiyang; Hou, Bingxu; Lelyakin, Alexander; Xu, Zhanjie; Jordan, Thomas Gas detonation cell width prediction model based on support vector regression NUCLEAR ENGINEERING AND TECHNOLOGY English Article Detonation Cell Width; Hydrogen Safety; Machine Learning; Support Vector Regression Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, lambda, is highly correlated with the characteristic reaction zone width, lambda. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of lambda/delta. The obtained correlations formulate the dependency of the ratio lambda/delta on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered. (C) 2017 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license [Yu, Jiyang] Tsinghua Univ, Dept Engn Phys, Qinghuayuan 1, Beijing 100084, Peoples R China; [Hou, Bingxu] CNNC China Zhongyuan Engn Corp, Xizhimenwai St 140, Beijing 100044, Peoples R China; [Lelyakin, Alexander; Xu, Zhanjie; Jordan, Thomas] Karlsruhe Inst Technol, Inst Nucl & Energy Technol, POB 3640, D-76021 Karlsruhe, Germany Tsinghua University; Helmholtz Association; Karlsruhe Institute of Technology Hou, BX (corresponding author), CNNC China Zhongyuan Engn Corp, Xizhimenwai St 140, Beijing 100044, Peoples R China. h.bx@163.com National Key Laboratory of Science and Technology on Reactor System Design Technology [HT-A100K-02-201406] National Key Laboratory of Science and Technology on Reactor System Design Technology The work is financially supported by National Key Laboratory of Science and Technology on Reactor System Design Technology (HT-A100K-02-201406). Meanwhile, the authors appreciate Dr M. Kuznetsov of IKET, Karlsruhe Institute of Technology for his kind support on a part of the experimental data used in the study. 20 8 8 4 11 KOREAN NUCLEAR SOC DAEJEON NUTOPIA BLDG, 342-1 JANGDAE-DONG, DAEJEON, 305-308, SOUTH KOREA 1738-5733 NUCL ENG TECHNOL Nucl. Eng. Technol. OCT 2017.0 49 7 1423 1430 10.1016/j.net.2017.06.014 0.0 8 Nuclear Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Nuclear Science & Technology FL4UF gold 2023-03-23 WOS:000414226300007 0
J Liu, XL; Li, YC; Wu, Y; Wang, ZY; He, W; Li, ZX Liu, Xinglong; Li, Yicheng; Wu, Yong; Wang, Zhiyuan; He, Wei; Li, Zhixiong A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article marine vessel recognition; multi-scale matching; convolutional neural network (CNN); radar-camera calibration; vessel net (VesNet) PATTERN Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness. [Liu, Xinglong; Wang, Zhiyuan; He, Wei] Minjiang Univ, Coll Phys & Elect Informat Engn, Fuzhou 350108, Peoples R China; [Liu, Xinglong; Wang, Zhiyuan; He, Wei] Minjiang Univ, Fujian Engn Res Ctr Safety Control Ship Intellige, Fuzhou 350108, Peoples R China; [Liu, Xinglong; Wang, Zhiyuan; He, Wei] Minjiang Univ, Fujian Univ Marine Intelligent Ship Equipment, Engn Res Ctr, Fuzhou 350108, Peoples R China; [Li, Yicheng] Wuhan Univ Technol, Hubei Key Lab Transportat Internet Things, Wuhan 430063, Peoples R China; [Wu, Yong] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China; [Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland Minjiang University; Minjiang University; Minjiang University; Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; Opole University of Technology Li, YC (corresponding author), Wuhan Univ Technol, Hubei Key Lab Transportat Internet Things, Wuhan 430063, Peoples R China. liuxl@mju.edu.cn; liyicheng070@ujs.edu.cn; wy118116@sina.com; wangzhiyuan@mju.edu.cn; hewei11@mju.edu.cn; zhixiong.li@ieee.org ; Li, Zhixiong/G-8418-2018 Wei, He/0000-0001-9192-9728; Wang, Zhiyuan/0000-0002-7521-2150; Li, Zhixiong/0000-0002-7265-0008 Fujian Province Natural Science Foundation [2021J011028, 2020J01860]; University-industry cooperation program of Department of Science and Technology of Fujian Province [2019H6018]; Key Project of Science and Technology of Wuhan [201701021010132]; Fund of National Engineering Research Center for Water Transport Safety [A2021001]; Fund of Hubei Key Laboratory of Transportation Internet of Things [WHUTIOT-2019002]; Narodowego Centrum Nauki, Poland [2020/37/K/ST8/02748] Fujian Province Natural Science Foundation(Natural Science Foundation of Fujian Province); University-industry cooperation program of Department of Science and Technology of Fujian Province; Key Project of Science and Technology of Wuhan; Fund of National Engineering Research Center for Water Transport Safety; Fund of Hubei Key Laboratory of Transportation Internet of Things; Narodowego Centrum Nauki, Poland This research was funded by the Fujian Province Natural Science Foundation (No: 2021J011028, 2020J01860) and University-industry cooperation program of Department of Science and Technology of Fujian Province (No. 2019H6018) and The Key Project of Science and Technology of Wuhan (No. 201701021010132) and Fund of National Engineering Research Center for Water Transport Safety (No. A2021001) and Fund of Hubei Key Laboratory of Transportation Internet of Things (No. WHUTIOT-2019002) and Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748). 24 1 1 3 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. NOV 2021.0 9 11 1199 10.3390/jmse9111199 0.0 16 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography XF9OL gold 2023-03-23 WOS:000724393400001 0
J Wong, JK; Deuschl, G; Wolke, R; Bergman, H; Muthuraman, M; Groppa, S; Sheth, SA; Bronte-Stewart, HM; Wilkins, KB; Petrucci, MN; Lambert, E; Kehnemouyi, Y; Starr, PA; Little, S; Anso, J; Gilron, R; Poree, L; Kalamangalam, GP; Worrell, GA; Miller, KJ; Schiff, ND; Butson, CR; Henderson, JM; Judy, JW; Ramirez-Zamora, A; Foote, KD; Silburn, PA; Li, LM; Oyama, G; Kamo, H; Sekimoto, S; Hattori, N; Giordano, JJ; DiEuliis, D; Shook, JR; Doughtery, DD; Widge, AS; Mayberg, HS; Cha, JH; Choi, K; Heisig, S; Obatusin, M; Opri, E; Kaufman, SB; Shirvalkar, P; Rozell, CJ; Alagapan, S; Raike, RS; Bokil, H; Green, D; Okun, MS Wong, Joshua K.; Deuschl, Guenther; Wolke, Robin; Bergman, Hagai; Muthuraman, Muthuraman; Groppa, Sergiu; Sheth, Sameer A.; Bronte-Stewart, Helen M.; Wilkins, Kevin B.; Petrucci, Matthew N.; Lambert, Emilia; Kehnemouyi, Yasmine; Starr, Philip A.; Little, Simon; Anso, Juan; Gilron, Ro'ee; Poree, Lawrence; Kalamangalam, Giridhar P.; Worrell, Gregory A.; Miller, Kai J.; Schiff, Nicholas D.; Butson, Christopher R.; Henderson, Jaimie M.; Judy, Jack W.; Ramirez-Zamora, Adolfo; Foote, Kelly D.; Silburn, Peter A.; Li, Luming; Oyama, Genko; Kamo, Hikaru; Sekimoto, Satoko; Hattori, Nobutaka; Giordano, James J.; DiEuliis, Diane; Shook, John R.; Doughtery, Darin D.; Widge, Alik S.; Mayberg, Helen S.; Cha, Jungho; Choi, Kisueng; Heisig, Stephen; Obatusin, Mosadolu; Opri, Enrico; Kaufman, Scott B.; Shirvalkar, Prasad; Rozell, Christopher J.; Alagapan, Sankaraleengam; Raike, Robert S.; Bokil, Hemant; Green, David; Okun, Michael S. Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury FRONTIERS IN HUMAN NEUROSCIENCE English Article deep brain stimulation; artificial intelligence; neuroethics; pain; interventional psychiatry; adaptive DBS; epilepsy; traumatic brain injury RESPONSIVE NEUROSTIMULATION; THALAMIC-STIMULATION; PARKINSONS-DISEASE; SEIZURE OUTCOMES; ESSENTIAL TREMOR; NUCLEUS; SURGERY DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury. [Wong, Joshua K.; Butson, Christopher R.; Ramirez-Zamora, Adolfo; Okun, Michael S.] Univ Florida, Fixel Inst Neurol Dis, Dept Neurol, Gainesville, FL 32611 USA; [Deuschl, Guenther; Wolke, Robin] Univ Kiel, Dept Neurol, Kiel, Germany; [Bergman, Hagai] Hebrew Univ Jerusalem, Inst Med Res Israel Canada, Dept Med Neurobiol Physiol, Jerusalem, Israel; [Muthuraman, Muthuraman; Groppa, Sergiu] Johannes Gutenberg Univ Mainz, Dept Neurol,Univ Med Ctr, Biomed Stat & Multimodal Signal Proc Unit, Sect Movement Disorders & Neurostimulat,Focus Pro, Mainz, Germany; [Sheth, Sameer A.] Baylor Coll Med, Dept Neurol Surg, Houston, TX 77030 USA; [Bronte-Stewart, Helen M.; Wilkins, Kevin B.; Petrucci, Matthew N.; Lambert, Emilia; Kehnemouyi, Yasmine; Shirvalkar, Prasad] Stanford Univ, Sch Med, Dept Neurol & Neurol Sci, Human Motor Control & Neuromodulat Lab, Stanford, CA 94305 USA; [Starr, Philip A.; Little, Simon; Anso, Juan; Gilron, Ro'ee] Univ Calif San Francisco, Dept Neurol Surg, Kavli Inst Fundamental Neurosci, San Francisco, CA USA; [Poree, Lawrence] Univ Calif San Francisco, Dept Anesthesia, San Francisco, CA USA; [Kalamangalam, Giridhar P.] Univ Florida, Dept Neurol, Wilder Ctr Epilepsy Res, Gainesville, FL USA; [Worrell, Gregory A.] Mayo Clin, Dept Neurol, Rochester, NY USA; [Miller, Kai J.] Mayo Clin, Dept Neurosurg, Rochester, NY USA; [Schiff, Nicholas D.] Weill Cornell Med, Dept Neurol, Weill Cornell Brain & Spine Inst, New York, NY USA; [Henderson, Jaimie M.] Stanford Univ, Dept Neurosurg, Stanford, CA 94305 USA; [Judy, Jack W.] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL USA; [Foote, Kelly D.] Univ Florida, Fixel Inst Neurol Dis, Dept Neurosurg, Gainesville, FL USA; [Silburn, Peter A.] Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia; [Silburn, Peter A.] St Andrews War Mem Hosp, Brisbane, Qld, Australia; [Li, Luming] Tsinghua Univ, Sch Aerosp Engn, Natl Engn Lab Neuromodulat, Beijing, Peoples R China; [Oyama, Genko; Kamo, Hikaru; Sekimoto, Satoko; Hattori, Nobutaka] Juntendo Univ, Fac Med, Dept Neurol, Tokyo, Japan; [Giordano, James J.] Georgetown Univ Med Ctr, Dept Neurol, Neuroeth Studies Program, Washington, DC USA; [DiEuliis, Diane] Natl Def Univ, US Dept Def Ft Lesley J McNair, Washington, DC USA; [Shook, John R.] Univ Buffalo, Dept Philosophy & Sci Educ, Buffalo, NY USA; [Doughtery, Darin D.] Massachusetts Gen Hosp, Dept Psychiat, Boston, MA 02114 USA; [Doughtery, Darin D.] Harvard Med Sch, Boston, MA 02115 USA; [Widge, Alik S.] Univ Minnesota, Dept Psychiat, Minneapolis, MN 55455 USA; [Mayberg, Helen S.; Cha, Jungho; Choi, Kisueng; Heisig, Stephen; Obatusin, Mosadolu] Icahn Sch Med Mt Sinai, Dept Neurol & Neurosurg, New York, NY 10029 USA; [Opri, Enrico] Emory Univ, Dept Neurol, Atlanta, GA 30322 USA; [Kaufman, Scott B.] Columbia Univ, Dept Psychol, New York, NY 10027 USA; [Shirvalkar, Prasad] Univ Calif San Francisco, Dept Anesthesiol Pain Management & Neurol, San Francisco, CA 94143 USA; [Rozell, Christopher J.; Alagapan, Sankaraleengam] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Raike, Robert S.] Medtronic Inc, Restorat Therapies Grp Implantables Res & Core Te, Minneapolis, MN USA; [Bokil, Hemant] Boston Sci Neuromodulat Corp, Valencia, CA USA; [Green, David] NeuroPace Inc, Mountain View, CA USA State University System of Florida; University of Florida; University of Kiel; Hebrew University of Jerusalem; Johannes Gutenberg University of Mainz; Baylor College of Medicine; Stanford University; University of California System; University of California San Francisco; University of California System; University of California San Francisco; State University System of Florida; University of Florida; Mayo Clinic; Mayo Clinic; Cornell University; Weill Cornell Medicine; Stanford University; State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Queensland; Tsinghua University; Juntendo University; Georgetown University; National Defense University - USA; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; University of Minnesota System; University of Minnesota Twin Cities; Icahn School of Medicine at Mount Sinai; Emory University; Columbia University; University of California System; University of California San Francisco; University System of Georgia; Georgia Institute of Technology; Medtronic Wong, JK (corresponding author), Univ Florida, Fixel Inst Neurol Dis, Dept Neurol, Gainesville, FL 32611 USA. joshua.wong@neurology.ufl.edu Widge, Alik/M-9064-2017; Deuschl, Gunther/A-7986-2010 Widge, Alik/0000-0001-8510-341X; Deuschl, Gunther/0000-0002-4176-9196; Tan, Huiling/0000-0001-8038-3029; Rozell, Christopher/0000-0001-5173-1661; Wilkins, Kevin/0000-0001-5907-4029; Henderson, Jaimie/0000-0002-3276-2267; Lambert, Emilia/0000-0001-5667-0132 NIH [R25NS108939, UH3 NS103549, UH3NS103550, 2R01MH102238, R01 NR014852, R01NS096008]; McNair Foundation; Dana Foundation; Medtronic; BRAIN Initiative; National Institute of Neurological Disorders and Stroke; James S. McDonnell Foundation; Boston Scientific; Michael J. Fox Foundation; Hope for Depression Research Foundation; Parkinson's Foundation; Parkinson Alliance; Smallwood Foundation; BachmannStrauss Foundation; Tourette Syndrome Association; UF Foundation NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); McNair Foundation; Dana Foundation; Medtronic(Medtronic); BRAIN Initiative; National Institute of Neurological Disorders and Stroke(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); James S. McDonnell Foundation; Boston Scientific(Boston Scientific); Michael J. Fox Foundation; Hope for Depression Research Foundation; Parkinson's Foundation; Parkinson Alliance; Smallwood Foundation; BachmannStrauss Foundation; Tourette Syndrome Association; UF Foundation JW's research was supported by NIH R25NS108939. SAS was a consultant for Boston Scientific, Abbott, Zimmer Biomet, and Neuropace. SAS was also funded by NIH UH3 NS103549, the McNair Foundation, and the Dana Foundation. HB-S served on the Clinical Advisory Board for Medtronic. PAS received research support and fellowship training support from Medtronic. NS was a member of the scientific advisory board for EnspireDBS, Inc. and QuantalX, Inc. NS also received current grant funding from the BRAIN Initiative, National Institute of Neurological Disorders and Stroke, and the James S. McDonnell Foundation. CB holds intellectual property related to some of the results presented. CB has served as a consultant for NeuroPace, Advanced Bionics, Boston Scientific, IntelectMedical, Abbott (St. Jude Medical), Functional Neuromodulation, NeuraModix. JH served on the advisory board for Enspire DBS, a consultant for Neuralink and received research support from Boston Scientific. JH also received hardware support from Medtronic for the study described in this article. JJ has received research grants from the Michael J. Fox Foundation. DDD received research support and honoraria from Medtronic. HMreceived grant support from NIH UH3NS103550, 2R01MH102238, and the Hope for Depression Research Foundation, and consulting and licensing fees from Abbott Labs. PSh received research support from Medtronic but not financial support. MSO served as a consultant for the Parkinson's Foundation, and has received research grants from NIH, Parkinson's Foundation, the Michael J. Fox Foundation, the Parkinson Alliance, Smallwood Foundation, the BachmannStrauss Foundation, the Tourette Syndrome Association, and the UF Foundation. MSO's DBS research was supported by: NIH R01 NR014852 and R01NS096008. MSO was PI of the NIH R25NS108939 Training Grant. 105 6 6 7 18 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5161 FRONT HUM NEUROSCI Front. Hum. Neurosci. MAR 4 2022.0 16 813387 10.3389/fnhum.2022.813387 0.0 21 Neurosciences; Psychology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Psychology 0P5RJ 35308605.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000784285100001 0
C Tang, S; Chen, LJ; Mi, JP; Ye, M; Li, QD; Zhang, JW IEEE Tang, Song; Chen, Lijuan; Mi, Jinpeng; Ye, Mao; Li, Qingdu; Zhang, Jianwei Adaptive Pedestrian Detection by Modulating Features in Dynamical Environment 2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017) English Proceedings Paper IEEE International Conference on Robotics and Biomimetics (ROBIO) DEC 05-08, 2017 Macau, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,City Univ Hong Kong,Beijing Inst Technol,Univ Hong Kong,Univ Macau,Shenzhen Acad Robot The accuracy of a trained pedestrian detector is always decreased in a new scenario, if the distributions of the samples in the testing and training scenarios are different. Traditional methods solve this problem based on domain adaption techniques. Unfortunately, most of existing methods need to keep source samples or label target samples in the detection phase. Therefore, they are hard to be applied in the real applications with dynamical environment. For this problem, we propose a feature modulation model, which consists of a Simple Dynamical Neural Network (SDNN) and a Modulating Neural Network (MNN). In SDNN, a dynamical layer is adopt to adaptively weight the feature maps, whose parameters are predicted by MNN. For each candidate proposal, the SDNN generates a proprietary deep feature respectively. Our contributions include 1) the first feature-based unsupervised domain adaptation method which is very suitable for real applications and 2) a new scheme of dynamically weighting feature maps, in which the corresponding training method is also given. Experimental results confirm that our method can achieve the competitive results on two pedestrian datasets. [Tang, Song; Mi, Jinpeng; Li, Qingdu; Zhang, Jianwei] Univ Hamburg, TAMS, Dept Informat, D-22527 Hamburg, Germany; [Chen, Lijuan] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China; [Ye, Mao] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Key Lab Neurolnformat, Ctr Robot,Minist Educ, Chengdu 611731, Peoples R China University of Hamburg; University of Electronic Science & Technology of China; University of Electronic Science & Technology of China Ye, M (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Key Lab Neurolnformat, Ctr Robot,Minist Educ, Chengdu 611731, Peoples R China. cvlab.uestc@gmail.com Li, Qingdu/E-9396-2011 Li, Qingdu/0000-0001-9928-7272 German Science Foundation (DFG); National Natural Science Foundation of China (NSFC) [Sonderforschungsbereich Transregio 169, 61375038, 61773083] German Science Foundation (DFG)(German Research Foundation (DFG)); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is partly funded by the German Science Foundation (DFG) and National Natural Science Foundation of China (NSFC) in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169, NSFC (Grant 61375038, 61773083). 24 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-3742-5 2017.0 62 67 6 Robotics; Multidisciplinary Sciences Conference Proceedings Citation Index - Science (CPCI-S) Robotics; Science & Technology - Other Topics BQ1KU 2023-03-23 WOS:000576754200011 0
C Cao, MX; Li, AJ; Fang, Q; Kroger, BJ IEEE Cao, Mengxue; Li, Aijun; Fang, Qiang; Kroeger, Bernd J. Growing Self-Organizing Map Approach for Semantic Acquisition Modeling 2013 IEEE 4TH INTERNATIONAL CONFERENCE ON COGNITIVE INFOCOMMUNICATIONS (COGINFOCOM) International Conference on Cognitive Infocommunications English Proceedings Paper 4th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) DEC 02-05, 2013 Budapest, HUNGARY IEEE Hungary Sect,IEEE,IEEE Joint Chapter IES & RAS,IEEE Chapter SMC,IEEE Chapter CI,Obuda Univ,BME Dept Telecommunicat & Media Informat,3D Internet Based Control & Commun Lab,Hungarian Acad Sci, Comp & Automat Res Inst Neural network; growing self-organizing map; semantic feature map; language acquisition Based on the incremental nature of knowledge learning, in this study a growing self-organizing neural network approach for modeling the acquisition process of semantic features is proposed. The Growing Self-Organizing Map (GSOM) algorithm is extended and applied to the problem of language acquisition. Based on that algorithm, experiments are conducted using Standard German children's books corpus. A cyclic reinforcing and reviewing training procedure is introduced to model the teaching and learning process between children and their communication partners. Experimental results indicate that (1) GSOM has good ability to learn the semantic categories presented within the training data, that (2) clear semantic boundaries can be found in the network representation, and that (3) cyclic reinforcing and reviewing training leads to a detailed categorization of lexical items as well as to a detailed clustering, while keeping the already-learned clusters and already-developed network structure stable. Experiments show that our GSOM approach is a good method for modeling semantic learning during language acquisition. [Cao, Mengxue; Li, Aijun; Fang, Qiang] Chinese Acad Social Sci, Inst Linguist, Lab Phonet & Speech Sci, Beijing, Peoples R China; [Kroeger, Bernd J.] Rhein Westfal TH Aachen Univ, Sch Med, Dept Phoniat Pedaudiol & Commun Disorders, Neurophonet Grp, Aachen, Germany; [Kroeger, Bernd J.] Tianjin Univ, Sch Comp Sci & Technol, Cognit Comutat & Applicat Lab, Tianjin, Peoples R China Chinese Academy of Social Sciences; RWTH Aachen University; Tianjin University Cao, MX (corresponding author), Chinese Acad Social Sci, Inst Linguist, Lab Phonet & Speech Sci, Beijing, Peoples R China. mengxuecao@outlook.com; liaj@cass.org.cn; fangqiang@cass.org.cn; bkroeger@ukaachen.de Kröger, Bernd/AAW-5031-2021; Kröger, Bernd/A-4435-2009 Kröger, Bernd/0000-0002-4727-2957; Kröger, Bernd/0000-0002-4727-2957 NSFC [61233009]; CASS Innovation Program NSFC(National Natural Science Foundation of China (NSFC)); CASS Innovation Program This research was funded by the NSFC Key Project with No. 61233009, and the CASS Innovation Program. 14 3 3 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2375-1312 978-1-4799-1543-9; 978-1-4799-1546-0 INT CONF COGN INFO 2013.0 33 38 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BC0WY 2023-03-23 WOS:000349770000005 0
J Tian, WC; Liao, ZL; Zhang, J Tian, Wenchong; Liao, Zhenliang; Zhang, Jin An optimization of artificial neural network model for predicting chlorophyll dynamics ECOLOGICAL MODELLING English Article Artificial neural networks; Chlorophyll dynamics; Algal bloom; Non-stationary time series ALGAL BLOOMS; ABUNDANCE; VARIABLES; RIVER As one of the factors to represent some species of algae, chlorophyll dynamics model has been regarded as one of the early-warning proactive approaches to prevent or mitigate the occurrence of some algal blooms. To decrease the cost of aquatic environmental in-situ monitoring and increase the accuracy of bloom forecasting, a traditional artificial neural network (ANN) based chlorophyll dynamics prediction model had been optimized. This optimization approach was conducted by presenting the change of chlorophyll value rather than the base value of chlorophyll as the output variable of the network. Both of the optimized and traditional networks had been applied to a case study. The results of model performance indices show that the optimized network predicts better than the traditional network. Furthermore, the non-stationary time series was employed to explain this phenomenon from a theoretical aspect. The proposed approach for chlorophyll dynamics ANN model optimization could assist the essential proactive strategy for algal bloom control. (C) 2017 Elsevier B.V. All rights reserved. [Tian, Wenchong; Liao, Zhenliang] Tongji Univ, Minist Educ, Coll Environm Sci & Engn, Key Lab Yangtze River Water Environm, Shanghai 200092, Peoples R China; [Zhang, Jin] Tech Univ Dresden, Inst Urban Water Management, D-01062 Dresden, Germany Tongji University; Technische Universitat Dresden Liao, ZL (corresponding author), Tongji Univ, Minist Educ, Coll Environm Sci & Engn, Key Lab Yangtze River Water Environm, Shanghai 200092, Peoples R China.;Zhang, J (corresponding author), Tech Univ Dresden, Inst Urban Water Management, D-01062 Dresden, Germany. zl_liao@tongji.edu.cn; jin.zhang@hotmail.com Zhang, Jin/L-6993-2017; Zhang, Jin/AAJ-8982-2021 Zhang, Jin/0000-0002-0946-5520; Zhang, Jin/0000-0002-0946-5520 National Natural Science Foundation of China [51578396]; German BMBF (Bundesministerium fur Bildung und Forschung, Federal Ministry of Education and Research) [2016YFE0123300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); German BMBF (Bundesministerium fur Bildung und Forschung, Federal Ministry of Education and Research)(Federal Ministry of Education & Research (BMBF)) This study was financially supported by National Natural Science Foundation of China (Grant No. 51578396), and German BMBF (Bundesministerium fur Bildung und Forschung, Federal Ministry of Education and Research) CLIENT project Managing Water Resources for Urban Catchments in the framework of the Sino-German Key Technologies and Management Modes for the Water Environmental Rehabilitation of a Lake City from the Catchment Viewpoint (Grant No. 2016YFE0123300). The authors would like to gratefully acknowledge Mr. Haibing Shao and Prof. Olaf Kolditz for their valuable suggestions for this study. 23 48 50 7 53 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0304-3800 1872-7026 ECOL MODEL Ecol. Model. NOV 24 2017.0 364 42 52 10.1016/j.ecolmodel.2017.09.013 0.0 11 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology FM2MM 2023-03-23 WOS:000414818900004 0
J Luo, JF; Dong, CY; Lin, KR; Chen, XH; Zhao, LQ; Menzel, L Luo, Jianfeng; Dong, Chunyu; Lin, Kairong; Chen, Xiaohong; Zhao, Liqiang; Menzel, Lucas Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography REMOTE SENSING OF ENVIRONMENT English Article Forest snow mapping; Random forest; Time-lapse photography; MODIS; NDSI RIVER-BASIN; GRAIN-SIZE; MODIS DATA; RETRIEVAL; ACCURACY; PRODUCTS; CLIMATE; AREA; VARIABILITY; RESOLUTION The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1-7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8-14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales. [Luo, Jianfeng; Dong, Chunyu; Lin, Kairong; Chen, Xiaohong] Sun Yat sen Univ, Ctr Water Resources & Environm, Sch Civil Engn, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China; [Zhao, Liqiang] Lanzhou Univ, Coll Earth & Environm Sci, Ctr Glacier & Desert Res, Lanzhou 730000, Peoples R China; [Menzel, Lucas] Heidelberg Univ, Inst Geog, Professorship Hydrol & Climatol, D-69120 Heidelberg, Germany Southern Marine Science & Engineering Guangdong Laboratory; Sun Yat Sen University; Lanzhou University; Ruprecht Karls University Heidelberg Dong, CY; Lin, KR (corresponding author), Sun Yat sen Univ, Ctr Water Resources & Environm, Sch Civil Engn, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China. dongchy7@mail.sysu.edu.cn; linkr@mail.sysu.edu.cn Dong, Chunyu/P-1741-2019; Menzel, Lucas/A-6881-2018 Dong, Chunyu/0000-0001-5721-9247; Menzel, Lucas/0000-0002-2201-3406; Luo, Jianfeng/0000-0001-8344-7016 National Natural Science Foundation of China [41801254, 51822908]; National Key R&D Program of China [2021YFC3001000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China Acknowledgements The authors acknowledge the financial support from the National Natural Science Foundation of China (Grants No. 41801254 and 51822908) , and the National Key R&D Program of China (2021YFC3001000) . MODIS data were downloaded from the Earth Observing System Data and Information System (EOSDIS, https://earth data.nasa.gov) , which is operated by NASA?s Earth Science Data Systems Program. The revision of this paper benefited from insightful comments and suggestions from the editor and three anonymous reviewers. 81 4 4 17 27 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. JUN 15 2022.0 275 113017 10.1016/j.rse.2022.113017 0.0 APR 2022 14 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology 1L0KH 2023-03-23 WOS:000798981000003 0
C Hillebrand, M; Greinert, M; Dumitrescu, R; Herzog, O IEEE Hillebrand, Michael; Greinert, Matthias; Dumitrescu, Roman; Herzog, Otthein Advanced Monkey Testing for connected autonomous systems 2020 IEEE 15TH INTERNATIONAL CONFERENCE OF SYSTEM OF SYSTEMS ENGINEERING (SOSE 2020) English Proceedings Paper IEEE 15th International Conference of System of Systems Engineering (SoSE) JUN 02-04, 2020 Budapest, HUNGARY IEEE,IEE Hungary Sect,IEEE SMC Chapter,IEEE SMC Soc,IEEE Syst Council,Rochester Inst Technol, MABL Lab,Univ Texas San Antonio, ACE Lab Agent-Based Testing; Model-Based Perturbation Injection Techniques; Autonomous Systems; Advanced Systems Engineering; System of Systems FAULT INJECTION Sensors, actuators, machine learning, communication and robotics are paving the way for the introduction of autonomous systems. Autonomous Systems in safety-critical applications require resilient operation of the intended functionality throughout the mission. Especially they must be safe and highly available. However, it is not possible to fully anticipate evolving threats, vulnerabilities and faults during the lifetime of those systems. This requires a resilient systems architecture of the autonomous system. Therefore, a thorough testing and evaluation of such systems is mandatory. In this paper, we present a monkey testing framework for evaluating resilience capabilities of autonomous systems. The framework contains a set of agents with specific role concepts and strategy sets. The framework can be applied to virtual, physical and hybrid testbeds. Due to its modularity the framework is extensible, scalable and also adaptable to different autonomous systems (e.g. mobile robot, manipulator). The monkey testing framework is able to work pseudo-randomized and thus reproducible on a connected system. A logging mechanism annotates the data so that the data can be used for machine learning (e.g. anomaly detection algorithm, self-healing). We applied the framework on a mobile robotic system in virtual scenarios. [Hillebrand, Michael; Greinert, Matthias; Dumitrescu, Roman] Fraunhofer IEM, Prod Engn, Paderborn, Germany; [Herzog, Otthein] Univ Bremen, Bremen, Germany; [Herzog, Otthein] Tongji Univ, Shanghai, Peoples R China University of Bremen; Tongji University Hillebrand, M (corresponding author), Fraunhofer IEM, Prod Engn, Paderborn, Germany. michael.hillebrand@iem.fraunhofer.de; matthias.greinert@iem.fraunhofer.de; roman.dumitrescu@iem.fraunhofer.de; otthein.herzog@uni-bremen.de German Federal Ministry of Education and Research (BMBF) [03VP02551] German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) This research was funded by the German Federal Ministry of Education and Research (BMBF) in the project Validation of Artificial Immune Systems for Autonomous Systems (KI4AS), grant number 03VP02551. The contents of this publication are the sole responsibility of the authors. 29 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-8050-2 2020.0 163 168 6 Engineering, Industrial; Engineering, Electrical & Electronic; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Operations Research & Management Science BQ8UT 2023-03-23 WOS:000621805400025 0
J Zhang, T; Ding, BY; Zhao, X; Liu, GJ; Pang, ZB Zhang, Tao; Ding, Biyun; Zhao, Xin; Liu, Ganjun; Pang, Zhibo LearningADD: Machine learning based acoustic defect detection in factory automation JOURNAL OF MANUFACTURING SYSTEMS English Article Acoustic defect detection; Edge computing; Factory automation; Feature extraction algorithm OF-THE-ART; FAULT-DETECTION; INTELLIGENCE; PROGNOSIS; ALGORITHM; FRAMEWORK Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability. [Zhang, Tao; Ding, Biyun; Zhao, Xin; Liu, Ganjun] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China; [Pang, Zhibo] ABB Corp Res Sweden, Dept Automat Technol, S-72178 Vasteras, Sweden; [Pang, Zhibo] Royal Inst Technol KTH, Dept Intelligent Syst, S-11428 Stockholm, Sweden Tianjin University; ABB; Royal Institute of Technology Zhang, T (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China.;Pang, ZB (corresponding author), ABB Corp Res Sweden, Dept Automat Technol, S-72178 Vasteras, Sweden. zhangtao@tju.edu.cn; pang.zhibo@se.abb.com Zhang, Tao/ABD-8875-2021 Ding, Biyun/0000-0001-6387-5857 37 8 8 6 23 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-6125 1878-6642 J MANUF SYST J. Manuf. Syst. JUL 2021.0 60 48 58 10.1016/j.jmsy.2021.04.005 0.0 MAY 2021 11 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science UI8LI 2023-03-23 WOS:000690850900005 0
J Peng, JY; Kimmig, A; Niu, ZB; Wang, JH; Liu, XF; Wang, DK; Ovtcharova, J Peng, Jieyang; Kimmig, Andreas; Niu, Zhibin; Wang, Jiahai; Liu, Xiufeng; Wang, Dongkun; Ovtcharova, Jivka Wind turbine failure prediction and health assessment based on adaptive maximum mean discrepancy INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article SCADA data; Failure prediction; Performance assessment; Adaptive maximum mean discrepancy; Convolutional neural network DIAGNOSIS; KERNEL Wind turbines are being designed with increasingly sophisticated electrical and mechanical components, leading to more complicated maintenance procedures and higher failure costs. Much of the research in this area has provided reliable early warning of wind turbine failures in an effort to reduce downtime and maintenance costs. Recently, data-driven approaches have been introduced into the health status assessment of wind turbines. However, obtaining high-precision failure predictions and interpretable health status assessments is still challenging. In this research, we propose a unified framework for predicting failures in and assessing the health of wind turbines. First, we empirically grouped wind turbine failures into four categories. Low frequency failures involving extensive downtime are critical, due to their tendency to gradually deteriorate and the severe consequences they introduce; thus, these were adopted here for use in the predictive maintenance strategy. The adaptive maximum mean discrepancy algorithm, a novel similarity metric for distributions, was used as a means of capturing the dynamic weights of sensor attributes and predicting potential failures. A convolutional neural network was then employed to identify false alarms and improve the precision of the model. This approach will significantly improve the prediction rate and reflect the relationship between failures and features/sensors, which is essential to further improve the performance of wind turbines. Experiments showed that our health assessment method had a higher failure prediction rate and a lower false alarm rate than the traditional method. The process also provides the gradual degradation trend for wind turbines, as influenced by specific failures. An empirical study of one-year of SCADA data from six wind turbines was further used to demonstrate the interpretability of the approach. [Peng, Jieyang; Wang, Jiahai; Wang, Dongkun] Tongji Univ, Sch Mech Engn, Shanghai 200092, Peoples R China; [Peng, Jieyang; Kimmig, Andreas; Ovtcharova, Jivka] Karlsruhe Inst Technol, D-76133 Karlsruhe, Germany; [Niu, Zhibin] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300072, Peoples R China; [Niu, Zhibin; Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark Tongji University; Helmholtz Association; Karlsruhe Institute of Technology; Tianjin University; Technical University of Denmark Niu, ZB (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin 300072, Peoples R China. mind3str@gmail.com Niu, Zhibin/AAE-7765-2020; Tomm, He/E-5457-2013 Niu, Zhibin/0000-0002-5171-7648; Liu, Xiufeng/0000-0001-5133-6688; PENG, jieyang/0000-0001-6544-1825 National Key R&D Program of China [2017YFE0101400]; National Natural Science Foundation of China [61802278]; China Scholarship Council; EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant [754462]; Innovation Fund Denmark [8090-00046A]; European Union'sHorizon 2020 research and innovation programme [775970, 91352] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant; Innovation Fund Denmark; European Union'sHorizon 2020 research and innovation programme The research is supported by National Key R&D Program of China (No.2017YFE0101400) , National Natural Science Foundation of China (No.61802278) , China Scholarship Council, and EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement (754462) , the Heat4.0 project (No.8090-00046A) funded by Innovation Fund Denmark, and the Flexibility for Smart Urban Energy Systems project (FlexSUS) (91352) funded by the European Union'sHorizon 2020 research and innovation programme under grant agree-ment (No.775970) . 29 4 5 11 40 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. JAN 2022.0 134 107391 10.1016/j.ijepes.2021.107391 0.0 JUL 2021 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering UR8YZ 2023-03-23 WOS:000697028800008 0
J Wang, CH; Li, FF; Liu, Q; Wang, HN; Benmoussa, P; Jeschke, S; Oeser, M Wang, Chonghui; Li, Feifei; Liu, Quan; Wang, Hainian; Benmoussa, Pia; Jeschke, Sabina; Oeser, Markus Establishment and extension of digital aggregate database using auxiliary classifier Wasserstein GAN with gradient penalty CONSTRUCTION AND BUILDING MATERIALS English Article ACWGAN-gp; Generative adversarial networks; Discrete element; Aggregate morphology; Angularity; Machine learning; Pavement NONSPHERICAL PARTICLES; MODEL; SHAPE For road construction, the morphological characteristics of coarse aggregates such as angularity and sphericity have a considerable influence on asphalt pavement performance. In traditional aggregate simulation processes, images of real coarse grains are captured, and their parameters are extracted manually for reproducing them in a numerical simulation such as Discrete Element Modeling (DEM). Generative Adversarial Networks can generate aggregate images, which can be stored in the Aggregate DEM Database directly. In this paper, it has been demonstrated that applying Auxiliary Classifier Wasserstein GANs with gradient penalty (ACWGAN-gp) is reliable and efficient for the establishment of an aggregate image database. In addition, the distribution of original images was compared with that of images generated based on ACGAN and ACWGAN-gp models. Generated images were validated through obtaining identifiable edge coordinates and represented as DEM input in the simulation process. The results prove that the ACWGAN-gp approach can be used for generating aggregate images for the DEM database. It successfully generates high-quality images of aggregates with a representative distribution of morphologies used for DEM simulation. This work shows convenience and efficiency for machine learning applications in the road construction field. [Wang, Chonghui; Liu, Quan; Oeser, Markus] Rhein Westfal TH Aachen, Inst Highway Engn, D-52074 Aachen, Germany; [Li, Feifei; Benmoussa, Pia; Jeschke, Sabina] Rhein Westfal TH Aachen, Cybernet Lab IMA & IfU, D-52074 Aachen, Germany; [Wang, Hainian] Changan Univ, Highway Sch, Xizn 710064, Peoples R China; [Jeschke, Sabina] Deutsch Bahn AG, Digitizat & Technol, Berlin, Germany RWTH Aachen University; RWTH Aachen University; Chang'an University; Deutsche Bahn Wang, CH (corresponding author), Rhein Westfal TH Aachen, Inst Highway Engn, D-52074 Aachen, Germany.;Li, FF (corresponding author), Rhein Westfal TH Aachen, Cybernet Lab IMA & IfU, D-52074 Aachen, Germany. c.wang@isac.rwth-aachen.de; Feifei.Li@ima.rwth-aachen.de; q.liu@isac.rwth-aachen.de; Pia.Benmoussa@ifu.rwth-aachen.de; Sabina.Jeschke@ima-zlw-ifu.rwth-aachen.de; oeser@isac.rwth-aachen.de Liu, Quan/0000-0002-6730-539X German Research Foundation (DFG) [OE 514/1-2, FOR2089]; China Scholarship Council [201706560034, 201708220090] German Research Foundation (DFG)(German Research Foundation (DFG)); China Scholarship Council(China Scholarship Council) This research is supported by the German Research Foundation (DFG) under research project No. OE 514/1-2 (FOR2089) . The authors also appreciate the support from the China Scholarship Council (Grant No. 201706560034, 201708220090) . The authors are solely responsible for the content. 49 3 3 5 34 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. SEP 20 2021.0 300 124217 10.1016/j.conbuildmat.2021.124217 0.0 JUL 2021 11 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science UD7KC 2023-03-23 WOS:000687383000004 0
J He, Q; Qin, K; Cohen, JB; Loyola, D; Li, D; Shi, JC; Xue, Y He, Qin; Qin, Kai; Cohen, Jason Blake; Loyola, Diego; Li, Ding; Shi, Jincheng; Xue, Yong Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements ENVIRONMENTAL RESEARCH LETTERS English Article reconstruction; NO2; gaps; OMI; GOME-2B; machine learning SURFACE REFLECTANCE ANISOTROPY; MASS FACTOR CALCULATION; MAX-DOAS OBSERVATIONS; NITROGEN-DIOXIDE; SATELLITE-OBSERVATIONS; EMISSION TRENDS; COLUMN DENSITY; AIR-QUALITY; AEROSOLS; RETRIEVAL Tropospheric NO2 columns retrieved from ozone monitoring instrument (OMI) are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO2 data over China by using machine learning and an adaptive weighted temporal fitting method with NO2 measurements from Global Ozone Monitoring Experiment-2B, and surface measurements. The reconstructed NO2 has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with Multi-AXis Differential Optical Absorption Spectroscopy measurements (R: 0.75-0.85). Finally, the mean of reconstructed NO2 vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over Mainland China. This novel finding is expected to contribute to both air quality and climate studies. [He, Qin; Qin, Kai; Cohen, Jason Blake; Li, Ding; Shi, Jincheng; Xue, Yong] China Univ Min & Technol, Sch Environm & Spatial Informat, Xuzhou, Jiangsu, Peoples R China; [Cohen, Jason Blake] Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai, Peoples R China; [Cohen, Jason Blake] Southern Marine Sci & Engn Guangdong Lab, Zhuhai, Peoples R China; [Loyola, Diego] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Oberpfaffenhofen, Germany China University of Mining & Technology; Sun Yat Sen University; Southern Marine Science & Engineering Guangdong Laboratory; Helmholtz Association; German Aerospace Centre (DLR) Qin, K (corresponding author), China Univ Min & Technol, Sch Environm & Spatial Informat, Xuzhou, Jiangsu, Peoples R China. qinkai@cumt.edu.cn Cohen, Jason/AAQ-2977-2021; Loyola, Diego/GYO-3213-2022 Loyola R., Diego G./0000-0002-8547-9350; Cohen, Jason Blake/0000-0002-9889-8175; he, qin/0000-0002-6087-5293 National Natural Science Foundation of China [41975041, 41871260]; Xuzhou Key RD Program [KC18225]; Ministry of Science and Technology of China [2016YFC0200506] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Xuzhou Key RD Program; Ministry of Science and Technology of China(Ministry of Science and Technology, China) This work was funded by National Natural Science Foundation of China (Grant No. 41975041 and No. 41871260), Xuzhou Key R&D Program (Grant No. KC18225), and the Ministry of Science and Technology of China (Grant No. 2016YFC0200506). 75 16 17 7 39 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-9326 ENVIRON RES LETT Environ. Res. Lett. DEC 2020.0 15 12 125011 10.1088/1748-9326/abc7df 0.0 11 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences PF5PJ Green Accepted, gold 2023-03-23 WOS:000599105800001 0
J Shahrour, I; Bian, HB; Xie, XY; Zhang, ZX Shahrour, Isam; Bian, Hanbing; Xie, Xiongyao; Zhang, Zixin Smart technology applications for the optimal management of underground facilities UNDERGROUND SPACE English Article Artificial intelligence; Lifecycle; Safety; Smart city; Maintenance; Smart technology; Underground facilities OF-THE-ART; RISK-MANAGEMENT; SYSTEM; INFORMATION; TRACKING; FUTURE; SAFETY; INFRASTRUCTURE; CHALLENGES; DESIGN This study centers on the use of smart technology to improve the lifecycle management of underground facilities. It presents a comprehensive digital solution that addresses the challenges of underground facilities, particularly those related to the extensive use of underground space, as well as the requirements for safety, sustainability, and quality of services. The proposed solution emerged from discussions with experts, companies, and cities involved in the design, construction, and management of underground facilities. In this paper, we first discuss the major challenges of underground facilities, then, we present the development of a smart solution to address these challenges. This study demonstrates a promising perspective for the use of smart technology in the optimal management of underground facilities, and paves the way for its implementation. [Shahrour, Isam; Xie, Xiongyao; Zhang, Zixin] Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China; [Shahrour, Isam; Bian, Hanbing] Univ Lille, Lab Genie Civil & Geoenvironm, F-59000 Lille, France Tongji University; Universite de Lille - ISITE; Universite de Lille Shahrour, I (corresponding author), Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China. isam.shahrour@univ-lille.fr xie, xiongyao/AAH-3550-2022; xie, xiongyao/AFM-0926-2022 60 4 4 2 24 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGHENG DISTRICT 100717, PEOPLES R CHINA 2096-2754 2467-9674 UNDERGR SPACE Undergr. Space OCT 2021.0 6 5 551 559 10.1016/j.undsp.2020.12.002 0.0 9 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering UQ1SN gold 2023-03-23 WOS:000695851000007 0
J Li, XY; Zhang, C; Behrens, H; Holtz, F Li, Xiaoyan; Zhang, Chao; Behrens, Harald; Holtz, Francois Calculating amphibole formula from electron microprobe analysis data using a machine learning method based on principal components regression LITHOS English Article Amphibole formula; Amphibole nomenclature; Machine learning; Principal components regression; Electron microprobe analysis HIGH-TEMPERATURE BEHAVIOR; FERRIC IRON; CRYSTAL-CHEMISTRY; CALCIC AMPHIBOLES; SODIC AMPHIBOLES; FE OXIDATION; MODEL; BIOTITE; TI; DEPROTONATION Wepresent a new method for calculating amphibole formula from routine electron microprobe analysis (EMPA) data by applying a principal components regression (PCR)-based machine learning algorithm on reference amphibole data. The reference amphibole data collected from literature are grouped in two datasets, for Li-free and Li-bearing amphiboles respectively, which include Fe2+, Fe3+, OH contents and the ion site assignments determined by single crystal structure refinement. We established two PCR models, for Li-free and Li-bearing amphiboles respectively, by the 10-fold cross validation of training datasets and evaluated by independent test datasets. The results show that our models can successfully reproduce the reference data for most ions with an error less than +/- 0.01 atom per formula unit (apfu), for Fe3+ within an error less than +/- 0.2 apfu and for (OH)-O-W and O-W(2-) with errors less than +/- 0.3 apfu. The error in estimated Fe3+/Sigma Fe ratio shows a rough negative dependence on FeOT content (total iron expressed as FeO), ranging within +/- 0.3 for amphiboles with FeOT >= 5 wt% and within +/- 0.2 for amphiboles with FeOT >= 10 wt%. Our models are applicable to both (W)(OH, F, Cl)-dominant and O-W-dominant amphiboles. It is notable that this method is not suitable for calculating mineral formula of amphiboles that have been affected by deprotonation as a result of secondary oxidation, but it could offer an estimation of initial (OH)-O-W prior to the post-formation oxidation. A user-friendly Excel worksheet is provided with two independent PCR models for calculating the formula of Li-free amphibole and Li-bearing amphibole, respectively. An automatic nomenclature function is also provided according to the nomenclature criteria of the 2012 International Mineralogical Association (IMA) report. (C) 2020 Elsevier B.V. All rights reserved. [Li, Xiaoyan; Zhang, Chao] Northwest Univ, Dept Geol, State Key Lab Continental Dynam, Xian 710069, Peoples R China; [Li, Xiaoyan; Zhang, Chao; Behrens, Harald; Holtz, Francois] Leibniz Univ Hannover, Inst Mineral, D-30167 Hannover, Germany Northwest University Xi'an; Leibniz University Hannover Zhang, C (corresponding author), Northwest Univ, Dept Geol, State Key Lab Continental Dynam, Xian 710069, Peoples R China. zhangchao@nwu.edu.cn Zhang, Chao/P-4689-2019 Holtz, Francois/0000-0002-2043-8603; Zhang, Chao/0000-0001-7019-5075; Li, Xiaoyan/0000-0001-7802-6479 German Research Foundation (DFG) [BE 1720/40]; National Natural Science Foundation of China (NSFC) [41902052] German Research Foundation (DFG)(German Research Foundation (DFG)); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This study was supported by German Research Foundation (DFG) (BE 1720/40) and National Natural Science Foundation of China (NSFC) (41902052). We thank He Huang and an anonymous reviewer for their insightful comments and Michael Roden for efficient editorial handling. The Microsoft Excel spreadsheet for calculating amphibole formula from EMPA data is provided as supplementary file online. 63 26 26 7 40 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0024-4937 1872-6143 LITHOS Lithos JUN 2020.0 362 105469 10.1016/j.lithos.2020.105469 0.0 13 Geochemistry & Geophysics; Mineralogy Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Mineralogy LG6JZ 2023-03-23 WOS:000528206200002 0
J Miao, YZ; Ma, XP; Bu, SP Miao, Y. Z.; Ma, X. P.; Bu, S. P. Research on the Learning Method Based on PCA-ELM INTELLIGENT AUTOMATION AND SOFT COMPUTING English Article Extreme learning machine (ELM); Principal component analysis (PCA); Feed-forward neural network MACHINE The Single-hidden Layer Feed-forward Neural Network has been widely applied in the fields such as pattern recognition, automatic control and data mining. However, the speed of the traditional learning method, since it is far from enough to satisfy the actual demand has become the main bottleneck, which restricts its development. As one of the new learning methods, the extreme learning machine (ELM) has its own remarkable characteristics, but the fact that ELM is based on the Empirical Risk Minimization may lead to over fitting. In addition, ELM does not consider the weight of error, so its performance will be severely affected when there are outliers in data integration. To solve the above problems, this paper referred to the two algorithms including PCA (Principal Component Analysis) and ELM, and put forward a learning method and prediction model, which combined PCA and ELM. From the results of simulation analysis, as combining advantages of PCA and ELM algorithms, the network structure can be simplified to improve the learning ability and its prediction precision. [Miao, Y. Z.; Ma, X. P.; Bu, S. P.] China Univ Ming & Technol, Sch Informat & Elect Engn, Xuzhou, Peoples R China; [Miao, Y. Z.] Uni Hamburg, Dept Informat, Inst Tech Aspects Multimodal Syst, Hamburg, Germany China University of Mining & Technology; University of Hamburg Miao, YZ (corresponding author), China Univ Ming & Technol, Sch Informat & Elect Engn, Xuzhou, Peoples R China.;Miao, YZ (corresponding author), Uni Hamburg, Dept Informat, Inst Tech Aspects Multimodal Syst, Hamburg, Germany. miaoyanzi81@163.com 23 4 4 2 13 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1079-8587 2326-005X INTELL AUTOM SOFT CO Intell. Autom. Soft Comput. 2017.0 23 4 637 642 10.1080/10798587.2017.1316071 0.0 6 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science FL4SB 2023-03-23 WOS:000414220300014 0
J Ali, L; Chakraborty, C; He, ZQ; Cao, WM; Imrana, Y; Rodrigues, JJPC Ali, Liaqat; Chakraborty, Chinmay; He, Zhiquan; Cao, Wenming; Imrana, Yakubu; Rodrigues, Joel J. P. C. A novel sample and feature dependent ensemble approach for Parkinson's disease detection NEURAL COMPUTING & APPLICATIONS English Article; Early Access Ensemble learning; F-score based feature selection; hyperparameters optimization; deep neural networks; Parkinson's disease VOICE RECORDING REPLICATIONS; HYBRID INTELLIGENT SYSTEM; MULTIPLE TYPES; FEATURE-SELECTION; EARLY-DIAGNOSIS; CLASSIFICATION; PREDICTION; CANCER Parkinson's disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%. [Ali, Liaqat] Univ Sci & Technol, Dept Elect Engn, Bannu, Pakistan; [Chakraborty, Chinmay] Birla Inst Technol, Dept Elect & Commun Engn, Jharkhand, India; [He, Zhiquan; Cao, Wenming] Shenzhen Univ, Guangdong Multimedia Informat Serv Engn Technol R, Shenzhen 518000, Peoples R China; [Imrana, Yakubu] Univ Elect Sci & Technol China UESTC, Sch Comp Sci & Engn, Chengdu, Peoples R China; [Rodrigues, Joel J. P. C.] China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266555, Peoples R China; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, Covilha Delegat, Covilha, Portugal University of Science & Technology Bannu; Birla Institute of Technology Mesra; Shenzhen University; University of Electronic Science & Technology of China; China University of Petroleum He, ZQ (corresponding author), Shenzhen Univ, Guangdong Multimedia Informat Serv Engn Technol R, Shenzhen 518000, Peoples R China. cchakrabarty@bitmesra.ac.in; zhiquan@szu.edu.cn; wmcao@szu.edu.cn; yakubu.imrana@uds.edu.gh; joeljr@ieee.org Chakraborty, Chinmay/N-3608-2017; Rodrigues, Joel J. P. C./A-8103-2013 Chakraborty, Chinmay/0000-0002-4385-0975; Rodrigues, Joel J. P. C./0000-0001-8657-3800; Imrana, Yakubu/0000-0002-9901-4909 National Natural Science Foundation of China [61971290, 61771322, 61871186]; Fundamental Research Foundation of Shenzhen [JCYJ20190808160815125] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Foundation of Shenzhen This work is supported by the National Natural Science Foundation of China under grants 61971290, 61771322, and 61871186 and the Fundamental Research Foundation of Shenzhen under Grant JCYJ20190808160815125. 59 6 6 7 10 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. 10.1007/s00521-022-07046-2 0.0 MAR 2022 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZR4FQ hybrid 2023-03-23 WOS:000767741400003 0
J Liu, L; Shibasaki, R; Zhang, Y; Kosuge, N; Zhang, MY; Hu, Y Liu, Lei; Shibasaki, Ryuichi; Zhang, Yong; Kosuge, Naoki; Zhang, Mingyang; Hu, Yue Data-driven framework for extracting global maritime shipping networks by machine learning OCEAN ENGINEERING English Article Maritime shipping network; Automatic identification system (AIS); Waypoint; Shipping route; DBSCAN; A* algorithm TRAJECTORIES; PREDICTION Maritime shipping network is essential for ship routing, scheduling, and flexibility analysis of the shipping system. This paper proposes a framework for extracting global maritime shipping traffic networks using auto-matic identification system (AIS) data based on machine learning methods. The framework consists of berthing area identification, trajectory selection and separation, waypoint area identification, edge generation, and network construction. Simultaneously, a route planning method using the A* algorithm based on a probability-directed graph model is proposed to verify the effectiveness of the maritime shipping network. The real-world global AIS data of bulk carriers in 2018 was used to extract maritime shipping networks to prove the frame-work. The framework successfully extracts maritime shipping networks containing 2769 berthing areas and 2688 waypoint areas over the world's oceans, and the results demonstrate that the estimated networks can be used to analyze the speed of navigation on edges and the size of flows between nodes. Additionally, along with the estimated shipping networks, distance-based route planning is still more stable even if generated routes considering node connection probabilities usually match the observed trajectories. It is concluded that the proposed framework and methods may help (1) provide a thorough framework to obtain and analyze maritime shipping traffic networks and (2) enrich route planning methods by considering historical navigation patterns. [Liu, Lei; Zhang, Yong] Southeast Univ, Sch Transportat, Nanjing 211189, Peoples R China; [Liu, Lei; Shibasaki, Ryuichi; Kosuge, Naoki] Univ Tokyo, Sch Engn, Tokyo 1138656, Japan; [Zhang, Mingyang] Aalto Univ, Sch Mech Engn, Espoo 02210, Finland; [Hu, Yue] Tongji Univ, Coll Transportat Engn, Shanghai 201804, Peoples R China Southeast University - China; University of Tokyo; Aalto University; Tongji University Zhang, Y (corresponding author), Southeast Univ, Sch Transportat, Nanjing 211189, Peoples R China. lei1992@seu.edu.cn; zhangyong@seu.edu.cn Zhang, Mingyang/AEC-5093-2022 Zhang, Mingyang/0000-0001-5820-2789 China Scholarship Council scholarship (CSC) [202106090198]; JSPS KAKENHI [JP20H00286]; Transportation Science and Technology Demonstration Project of Jiangsu Province [2018Y02]; China Freight System Efficient Green Development System Construction Project [P159883]; Southeast University -Nanjing Kirin Science, and Technology Innovation Department 2021 Special Funds [8521008862] China Scholarship Council scholarship (CSC)(China Scholarship Council); JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Transportation Science and Technology Demonstration Project of Jiangsu Province; China Freight System Efficient Green Development System Construction Project; Southeast University -Nanjing Kirin Science, and Technology Innovation Department 2021 Special Funds This work was supported by the China Scholarship Council scholarship (CSC, grant number: 202106090198) , JSPS KAKENHI (grant number: JP20H00286) , the Transportation Science and Technology Demonstration Project of Jiangsu Province (grant number: 2018Y02) , the China Freight System Efficient Green Development System Con- struction Project (grant number: P159883 (CFT/CS-6) ) , and the Southeast University -Nanjing Kirin Science, and Technology Innovation Department 2021 Special Funds (grant number: 8521008862) . 53 0 0 18 18 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. FEB 1 2023.0 269 113494 10.1016/j.oceaneng.2022.113494 0.0 19 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 8A0QG 2023-03-23 WOS:000915952500001 0
J Zhou, X; Cheng, J; Cao, JD; Ragulskis, M Zhou, Xia; Cheng, Jun; Cao, Jinde; Ragulskis, Minvydas Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts NEURAL NETWORKS English Article Nonhomogeneous Markov switching; Neural network; Asynchronous filter; Variable packet dropout JUMP SYSTEMS; TIME; STABILITY This work focuses on the problem of asynchronous filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts (VPDs). The discrete-time nonhomogeneous Markov process is adopted to depict the modes switching of target plant, where time-varying transition probabilities are revealed by utilizing a polytope technology. By means of the Bernoulli distributed sequence, the randomly occurring packet dropouts are presented, where VPD rates are mode-dependent and remain variable. Unlike the existing results, the hidden Markov model scheme is formulated to describe the asynchronization between nonhomogeneous neural networks and filter, and resilient filters are presented, which makes the designed filters more general. Eventually, a simulation example is established to verify the effectiveness of the developed filter scheme. (C) 2020 Elsevier Ltd. All rights reserved. [Zhou, Xia] Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Peoples R China; [Cheng, Jun] Guangxi Normal Univ, Coll Math & Stat, Guilin 541006, Peoples R China; [Cao, Jinde] Southeast Univ, Sch Math, Nanjing 21189, Peoples R China; [Ragulskis, Minvydas] Kaunas Univ Technol, Ctr Nonlinear Syst, Studentu 50-146, LT-51368 Kaunas, Lithuania Guilin University of Electronic Technology; Guangxi Normal University; Southeast University - China; Kaunas University of Technology Cheng, J (corresponding author), Guangxi Normal Univ, Coll Math & Stat, Guilin 541006, Peoples R China. xiazhou201612@guet.edu.cn; jcheng@gxnu.edu.cn; jdcao@seu.edu.cn; minvydas.ragulskis@ktu.lt zhang, hanyun/ABH-7128-2020; Cheng, Jun/AAE-6928-2019; Ragulskis, Minvydas/A-1546-2008 Cheng, Jun/0000-0002-3483-747X; Ragulskis, Minvydas/0000-0002-3348-9717 Guangxi Science and Technology Base and Specialized Talents [Guike AD18281026]; Guangxi Natural Science Foundation [2017GXNSFBA198179, 2019JJA110041, 2019AC20190]; Training Program for 1,000 Young and Middle-aged Cadre Teachers in Universities of Guangxi Province; Science and Technology Joint Foundation of Guizhou Province [LKM[2013]21]; National Natural Science Foundation of China [61703150] Guangxi Science and Technology Base and Specialized Talents; Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Training Program for 1,000 Young and Middle-aged Cadre Teachers in Universities of Guangxi Province; Science and Technology Joint Foundation of Guizhou Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by Guangxi Science and Technology Base and Specialized Talents (No. Guike AD18281026), Guangxi Natural Science Foundation Project (No. 2017GXNSFBA198179, No. 2019JJA110041, No. 2019AC20190), the Training Program for 1,000 Young and Middle-aged Cadre Teachers in Universities of Guangxi Province, the Science and Technology Joint Foundation of Guizhou Province (No. LKM[2013]21), and the National Natural Science Foundation of China (No. 61703150). 32 13 13 4 22 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. OCT 2020.0 130 229 237 10.1016/j.neunet.2020.07.012 0.0 9 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology NM0RU 32693351.0 2023-03-23 WOS:000567813200020 0
C Pan, ZQ; Cai, F; Chen, WY; Chen, HH; de Rijke, M ASSOC COMP MACHINERY Pan, Zhiqiang; Cai, Fei; Chen, Wanyu; Chen, Honghui; de Rijke, Maarten Star Graph Neural Networks for Session-based Recommendation CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT English Proceedings Paper 29th ACM International Conference on Information and Knowledge Management (CIKM) OCT 19-23, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGIR,ACM SIGWEB Session-based recommendation; graph neural networks; highway networks Session-based recommendation is a challenging task. Without access to a user's historical user-item interactions, the information available in an ongoing session may be very limited. Previous work on session-based recommendation has considered sequences of items that users have interacted with sequentially. Such item sequences may not fully capture complex transition relationship between items that go beyond inspection order. Thus graph neural network (GNN) based models have been proposed to capture the transition relationship between items. However, GNNs typically propagate information from adjacent items only, thus neglecting information from items without direct connections. Importantly, GNN-based approaches often face serious overfitting problems. We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation. The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session. To avoid overfitting, we employ highway networks (HN) to adaptively select embeddings from item representations. Finally, we aggregate the item embeddings generated by the SGNN in an ongoing session to represent a user's final preference for item prediction. Experiments on two public benchmark datasets show that SGNN-HN can outperform state-of-the-art models in terms of P@20 and MRR@20 for session-based recommendation. [Pan, Zhiqiang; Cai, Fei; Chen, Wanyu; Chen, Honghui] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Peoples R China; [de Rijke, Maarten] Univ Amsterdam, Amsterdam, Netherlands; [de Rijke, Maarten] Ahold Delhaize, Zaandam, Netherlands National University of Defense Technology - China; University of Amsterdam Cai, F (corresponding author), Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Peoples R China. panzhiqiang@nudt.edu.cn; caifei@nudt.edu.cn; wanyuchen@nudt.edu.cn; chenhonghui@nudt.edu.cn; derijke@uva.nl de Rijke, Maarten/0000-0002-1086-0202 National Natural Science Foundation of China [61702526]; Defense Industrial Technology Development Program [JCKY2017204B064]; Postgraduate Scientific Research Innovation Project of Hunan Province [CX20200055] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Defense Industrial Technology Development Program; Postgraduate Scientific Research Innovation Project of Hunan Province This work was partially supported by the National Natural Science Foundation of China under No. 61702526, the Defense Industrial Technology Development Program under No. JCKY2017204B064, and the Postgraduate Scientific Research Innovation Project of Hunan Province under No. CX20200055. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. 39 51 51 7 11 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6859-9 2020.0 1195 1204 10.1145/3340531.3412014 0.0 10 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LW Green Published 2023-03-23 WOS:000749561301020 0
J Onojeghuo, AO; Blackburn, GA; Wang, QM; Atkinson, PM; Kindred, D; Miao, YX Onojeghuo, Alex O.; Blackburn, George A.; Wang, Qunming; Atkinson, Peter M.; Kindred, Daniel; Miao, Yuxin Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data INTERNATIONAL JOURNAL OF REMOTE SENSING English Article CHINA; CROP; CLASSIFICATION; SAR; COVER; AREA; AGRICULTURE; DYNAMICS; SENSORS; SURFACE Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May-October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world. [Onojeghuo, Alex O.; Blackburn, George A.] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Blackburn, George A.] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China; [Wang, Qunming; Atkinson, Peter M.] Univ Lancaster, Fac Sci & Technol, Engn Bldg, Lancaster, England; [Atkinson, Peter M.] Univ Southampton, Geog & Environm, Southampton, Hants, England; [Atkinson, Peter M.] Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast, Antrim, North Ireland; [Kindred, Daniel] ADAS UK Ltd, Cambridge, England; [Miao, Yuxin] China Agr Univ, Dept Plant Nutr, Coll Resource & Environm Sci, Beijing, Peoples R China Lancaster University; Hong Kong Polytechnic University; Lancaster University; University of Southampton; Queens University Belfast; China Agricultural University Onojeghuo, AO (corresponding author), Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England. lexisgis@yahoo.com ONOJEGHUO, ALEX/AAY-2170-2020 ONOJEGHUO, ALEX/0000-0003-2944-3967; Atkinson, Peter/0000-0002-5489-6880; Kindred, Daniel/0000-0001-7910-7676 UK Science and Technology Facilities Council (STFC) Newton Agri-Tech programme UK Science and Technology Facilities Council (STFC) Newton Agri-Tech programme(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) The UK Science and Technology Facilities Council (STFC) Newton Agri-Tech programme financially supported this research, under project 'Remote Sensing for Sustainable Intensification in China.' 68 69 70 7 99 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. 2018.0 39 4 1042 1067 10.1080/01431161.2017.1395969 0.0 26 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Remote Sensing; Imaging Science & Photographic Technology FR8UK Green Accepted 2023-03-23 WOS:000419349800006 0
J Abera, TA; Vuorinne, I; Munyao, M; Pellikka, PKE; Heiskanen, J Abera, Temesgen Alemayehu; Vuorinne, Ilja; Munyao, Martha; Pellikka, Petri K. E.; Heiskanen, Janne Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data DATA English Article; Data Paper Taita Taveta; land cover; reference database; machine learning; Sentinel-1; Sentinel-2 Taita Taveta County (TTC) is one of the world's biodiversity hotspots in the highlands with some of the world's megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 +/- 2.3% and it is freely accessible for land use planners, conservation managers, and researchers. [Abera, Temesgen Alemayehu; Vuorinne, Ilja; Munyao, Martha; Pellikka, Petri K. E.; Heiskanen, Janne] Univ Helsinki, Dept Geosci & Geog, POB 68, FI-00014 Helsinki, Finland; [Abera, Temesgen Alemayehu; Vuorinne, Ilja; Pellikka, Petri K. E.] Univ Helsinki, Inst Atmospher & Earth Syst Res, Fac Sci, POB 4, FI-00014 Helsinki, Finland; [Munyao, Martha] Kenya Wildlife Serv, POB 40241, Nairobi 00100, Kenya; [Pellikka, Petri K. E.] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China University of Helsinki; University of Helsinki; Wuhan University Abera, TA (corresponding author), Univ Helsinki, Dept Geosci & Geog, POB 68, FI-00014 Helsinki, Finland.;Abera, TA (corresponding author), Univ Helsinki, Inst Atmospher & Earth Syst Res, Fac Sci, POB 4, FI-00014 Helsinki, Finland. temesgen.abera@helsinki.fi; ilja.vuorinne@helsinki.fi; martha.munyao@helsinki.fi; petri.pellikka@helsinki.fi; janne.heiskanen@helsinki.fi Pellikka, Petri/0000-0002-5996-9268; Heiskanen, Janne/0000-0002-3899-8860; Vuorinne, Ilja/0000-0002-6425-8604 European Union DG International Partnerships under the DeSIRA (Development of Smart Innovation through Research in Agriculture) program through the ESSA (Earth observation and environmental sensing for climate-smart sustainable agropastoral ecosystem trans [FOOD/2020/418132] European Union DG International Partnerships under the DeSIRA (Development of Smart Innovation through Research in Agriculture) program through the ESSA (Earth observation and environmental sensing for climate-smart sustainable agropastoral ecosystem trans This work is funded by the European Union DG International Partnerships under the DeSIRA (Development of Smart Innovation through Research in Agriculture) program (FOOD/2020/418132) through the ESSA (Earth observation and environmental sensing for climate-smart sustainable agropastoral ecosystem transformation in East Africa) project. 21 0 0 4 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2306-5729 DATA Data MAR 2022.0 7 3 36 10.3390/data7030036 0.0 12 Computer Science, Information Systems; Multidisciplinary Sciences Emerging Sources Citation Index (ESCI) Computer Science; Science & Technology - Other Topics 0E6NO Green Published, gold 2023-03-23 WOS:000776796300001 0
J Zhang, YD; Yang, XJ; Cattani, C; Rao, RV; Wang, SH; Phillips, P Zhang, Yudong; Yang, Xiaojun; Cattani, Carlo; Rao, Ravipudi Venkata; Wang, Shuihua; Phillips, Preetha Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm ENTROPY English Article tea-category identification; fractional Fourier entropy; color histogram; kernel principal component analysis; feed-forward neural network; Jaya algorithm; stratified cross validation LEARNING-BASED OPTIMIZATION; PATHOLOGICAL BRAIN DETECTION; ARTIFICIAL NEURAL-NETWORK; GREEN TEA; COMPUTER VISION; ALZHEIMERS-DISEASE; WAVELET-ENTROPY; OOLONG TEAS; BLACK TEAS; SPA-LDA This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 x 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification. [Zhang, Yudong; Wang, Shuihua] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China; [Zhang, Yudong; Wang, Shuihua] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China; [Zhang, Yudong] Guangxi Univ, Coll Mech Engn, Guangxi Key Lab Mfg Syst & Adv Mfg Technol, Nanning 530021, Peoples R China; [Yang, Xiaojun] China Univ Min & Technol, Dept Math & Mech, Xuzhou 221008, Peoples R China; [Cattani, Carlo] Univ Tuscia, Engn Sch DEIM, I-01100 Viterbo, Italy; [Rao, Ravipudi Venkata] Sardar Vallabhbhai Natl Inst Technol, Dept Mech Engn, Surat 395007, India; [Phillips, Preetha] Shepherd Univ, Sch Nat Sci & Math, Shepherdstown, WV 25443 USA Nanjing Normal University; Guangxi University; China University of Mining & Technology; Tuscia University; National Institute of Technology (NIT System); Sardar Vallabhbhai National Institute of Technology Zhang, YD; Wang, SH (corresponding author), Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.;Zhang, YD; Wang, SH (corresponding author), Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.;Zhang, YD (corresponding author), Guangxi Univ, Coll Mech Engn, Guangxi Key Lab Mfg Syst & Adv Mfg Technol, Nanning 530021, Peoples R China. zhangyudong@njnu.edu.cn; dyangxiaojun@163.com; cattani@unitus.it; ravipudirao@gmail.com; wangshuihua@njnu.edu.cn; pphill01@rams.shepherd.edu Zhang, Yudong/I-7633-2013; rao, ravipudi v/A-1337-2016; Wang, Shuihua/G-7326-2016; Cattani, Carlo/I-5051-2013; Rai, Dhiraj/A-6686-2016; Yang, Xiao-Jun/E-8311-2011 Zhang, Yudong/0000-0002-4870-1493; Wang, Shuihua/0000-0003-4713-2791; Cattani, Carlo/0000-0002-7504-0424; Yang, Xiao-Jun/0000-0003-0009-4599 National Natural Science Foundation of China (NSFC) [51407095, 61503188]; Natural Science Foundation of Jiangsu Province [BK20150983, BK20150982, BK20150973, BK20151548]; Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]; Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2013012-2, BE2014009-3]; Program of Natural Science Research of Jiangsu Higher Education Institutions [15KJB470010, 15KJB510018, 15KJB510016, 13KJB460011, 14KJB480004, 14KJB520021]; Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]; Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]; Open Fund of Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology [15-140-30-008K] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing; Key Supporting Science and Technology Program (Industry) of Jiangsu Province; Program of Natural Science Research of Jiangsu Higher Education Institutions; Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province; Nanjing Normal University Research Foundation for Talented Scholars; Open Fund of Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology This paper was supported by the National Natural Science Foundation of China (NSFC) (51407095, 61503188), the Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982, BK20150973, BK20151548), the Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), the Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2013012-2, BE2014009-3), the Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010, 15KJB510018, 15KJB510016, 13KJB460011, 14KJB480004, 14KJB520021), the Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), the Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), and the Open Fund of Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology (15-140-30-008K). 60 76 77 0 67 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy MAR 2016.0 18 3 77 10.3390/e18030077 0.0 17 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics DI5IH Green Submitted, gold, Green Published 2023-03-23 WOS:000373531600021 0
J Wang, W; Yan, Y; Cui, Z; Feng, JS; Yan, SC; Sebe, N Wang, Wei; Yan, Yan; Cui, Zhen; Feng, Jiashi; Yan, Shuicheng; Sebe, Nicu Recurrent Face Aging with Hierarchical AutoRegressive Memory IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Face aging; face normalization; recurrent neural network; gated recurrent unit; autoencoder NEURAL-NETWORK Modeling the aging process of human faces is important for cross-age face verification and recognition. In this paper, we propose a Recurrent Face Aging (RFA) framework which takes as input a single image and automatically outputs a series of aged faces. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. Due to the lack of labeled face data of the same person captured in a long range of ages, traditional face aging models split the ages into discrete groups and learn a one-step face transformation for each pair of adjacent age groups. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network whose recurrent module is a hierarchical triple-layer gated recurrent unit which functions as an autoencoder. The bottom layer of the module encodes the input to a latent representation, and the top layer decodes the representation to a corresponding aged face. The experimental results demonstrate the effectiveness of our framework. [Wang, Wei; Sebe, Nicu] Univ Trento, Dept Informat Engn & Comp Sci DISI, I-38123 Trento, Italy; [Yan, Yan] Texas State Univ, Dept Comp Sci, San Marcos, TX 78666 USA; [Cui, Zhen] Nanjing Univ Sci & Technol, Dept Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China; [Feng, Jiashi; Yan, Shuicheng] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119077, Singapore; [Yan, Shuicheng] Qihoo 360 Co, Beijing 100015, Peoples R China University of Trento; Texas State University System; Texas State University San Marcos; Nanjing University of Science & Technology; National University of Singapore Wang, W (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci DISI, I-38123 Trento, Italy. wei.wang@unitn.it; tom_yan@txstate.edu; zhen.cui@njust.edu.cn; elefjia@nus.edu.sg; eleyans@nus.edu.sg; sebe@disi.unitn.it Wang, Wei/AAK-5521-2021; Feng, Jiashi/AGX-6209-2022; Yan, Shuicheng/HCI-1431-2022 Wang, Wei/0000-0002-5477-1017; Sebe, Niculae/0000-0002-6597-7248 50 11 11 1 8 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. MAR 2019.0 41 3 654 668 10.1109/TPAMI.2018.2803166 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering HK7LA 29994505.0 2023-03-23 WOS:000458168800010 0
J Hou, MM; Hu, XF; Cai, JT; Han, XG; Yuan, SQ Hou, Miaomiao; Hu, Xiaofeng; Cai, Jitao; Han, Xinge; Yuan, Shuaiqi An Integrated Graph Model for Spatial-Temporal Urban Crime Prediction Based on Attention Mechanism ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article urban crime; graph convolutional network; attention mechanism; spatial-temporal prediction; LSTM network NEURAL-NETWORK; PROPERTY CRIME; IMPACTS; RATES; LSTM Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial-temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial-temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an online integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial-temporal features, topological graphs, and external features. Then, the online integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial-temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation. [Hou, Miaomiao; Hu, Xiaofeng] Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China; [Cai, Jitao; Han, Xinge] China Univ Min & Technol, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China; [Yuan, Shuaiqi] Delft Univ Technol, Safety & Secur Sci Sect, Fac Technol Policy & Management, NL-2628 BX Delft, Netherlands People's Public Security University of China; China University of Mining & Technology; Delft University of Technology Hu, XF (corresponding author), Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China. 201621220042@stu.ppsuc.edu.cn; huxiaofeng@ppsuc.edu.cn; sqt1910101006@student.cumtb.edu.cn; bqt2110104039@student.cumtb.edu.cn; s.yuan-2@tudelft.nl Yuan, Shuaiqi/HDM-9886-2022 Yuan, Shuaiqi/0000-0003-2758-546X National Natural Science Foundation of China [72174203] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Natural Science Foundation of China (Grant No. 72174203). 53 2 2 6 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. MAY 2022.0 11 5 294 10.3390/ijgi11050294 0.0 19 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physical Geography; Remote Sensing 1R2CA gold 2023-03-23 WOS:000803182200001 0
J Han, PC; He, XQ; Ren, HJ; Wang, Y; Peng, X; Shu, ZL; Gao, SB; Wang, YB; Chen, Z Han, Pengcheng; He, Xiaoqiong; Ren, Haijun; Wang, Yi; Peng, Xu; Shu, Zeliang; Gao, Shibin; Wang, Yanbo; Chen, Zhe Fault Diagnosis and System Reconfiguration Strategy of a Single-Phase Three-Level Neutral-Point-Clamped Cascaded Inverter IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS English Article; Proceedings Paper IEEE Transportation Electrification Conference and Expo, Asia-Pacific NOV 19-22, 2017 Harbin, PEOPLES R CHINA IEEE Fault diagnosis; neural network (NN); modulation reconfiguration; single-phase three-level neutral-point-clamped cascaded inverter (3L-NPC-CI); spectrum analysis OPEN-CIRCUIT FAULT; TOLERANT OPERATION; 3-PHASE; CONVERTER This paper presents a fault diagnosis method and system reconfiguration strategy of a three-level neutral-point-clamped cascaded inverter (3L-NPC-CI) to enhance the reliability of power supply in an electrified railway. First, the neural network (NN) is employed to perform an open-circuit fault diagnosis and identify fault switches. Then, system reconfiguration strategy is proposed to perform a single-phase 3L-NPC-CI reconfiguration. Through the analysis of the operation status of 3L-NPC, there are eight fault modes in a single-bridge arm, including the single-switch fault and double-switch fault. By the feature analysis for the output arm voltage harmonic of normal and eight fault modes, seven harmonic parameters are selected as fault feature vectors. Meanwhile, a three-layer NN is constructed, and the seven feature vectors are the input layer of the NN. By training algorithm, the fault switches location can be identified accurately about one modulation period. Then, the fault module is bypassed and the 3L-NPC-CI system is stable by reconfiguring the modulation strategy of other normal modular. Simulation and experimental results are given to validate the proposed fault diagnosis and system reconfiguration strategy. The proposed method is able to improve robustness and reliability of 3L-NPC-CI in the traction power supply system. [Han, Pengcheng; He, Xiaoqiong; Ren, Haijun; Wang, Yi; Shu, Zeliang; Gao, Shibin] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Sichuan, Peoples R China; [Han, Pengcheng; He, Xiaoqiong; Ren, Haijun; Wang, Yi; Shu, Zeliang; Gao, Shibin] Southwest Jiaotong Univ, Natl Rail Transit Elect & Automat Engn Tech Res C, Chengdu 611756, Sichuan, Peoples R China; [Peng, Xu] Civil Aviat Flight Univ China, Guanghan 618307, Peoples R China; [Wang, Yi; Wang, Yanbo; Chen, Zhe] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Southwest Jiaotong University; Southwest Jiaotong University; Civil Aviation Flight University of China; Aalborg University He, XQ (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Sichuan, Peoples R China.;He, XQ (corresponding author), Southwest Jiaotong Univ, Natl Rail Transit Elect & Automat Engn Tech Res C, Chengdu 611756, Sichuan, Peoples R China. birdhpc@my.swjtu.edu.cn; hexq@home.swjtu.edu.cn; haidaoren@outlook.com; de_yiwang@163.com; pengxuswjtu@foxmail.com; shuzeliang@swjtu.edu.cn; gao_shi_bin@126.com; ywa@et.aau.dk; zch@et.aau.dk Wang, Yanbo/HFZ-8018-2022; wangwangwang, yuanyaun/HHN-6432-2022; wang, yan/GSE-6489-2022; Wang, Yanbo/GWC-2305-2022; Wang, Yuan/HHC-1520-2022; Wang, Yin/HCI-9352-2022; Wang, Ying/HJI-2509-2023; wang, yi/HOF-6668-2023; Wang, Yu/GZL-9655-2022 National Natural Science Foundation of China [51477144]; National Rail Transit Electrification and Automation Engineering Technique Research Center [NEEC-2017-A01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Rail Transit Electrification and Automation Engineering Technique Research Center This work was supported in part by the National Natural Science Foundation of China under Grant 51477144 and in part by the National Rail Transit Electrification and Automation Engineering Technique Research Center under Grant NEEC-2017-A01. 30 29 33 8 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0093-9994 1939-9367 IEEE T IND APPL IEEE Trans. Ind. Appl. JUL-AUG 2019.0 55 4 3863 3876 10.1109/TIA.2019.2901359 0.0 14 Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering IH5VZ Green Published 2023-03-23 WOS:000474562900054 0
J Mondal, PK; Sanchez, LPA; Benedetto, E; Shen, Y; Guo, MY Mondal, Pritom Kumar; Aguirre Sanchez, Lizeth P.; Benedetto, Emmanuele; Shen, Yao; Guo, Minyi A dynamic network traffic classifier using supervised ML for a Docker-based SDN network CONNECTION SCIENCE English Article Quality of service; software-defined network; traffic classification; machine learning With the rapid technological growth in the last decades, the number of devices and users has drastically increased. Software-defined networking (SDN) with machine learning (ML) has become an emerging solution for network scheduling, quality of service (QoS), resource allocations, and security. This paper focuses on the implementation of a network traffic classifier using a novel Docker-based SDN network. ML offers good performance to real-time traffic solutions without depending on well-known TCP or UDP port numbers, IP addresses, or encrypted payloads. In this paper, using three ML techniques, we first classify network flows with 3, 5, and 7 parameters giving up to 97.14% accuracy. Additionally, we present a new performance accelerator algorithm (PAA), which incorporates these three ML classifiers and accelerates the overall performance significantly. We then propose a dynamic network classifier (DNC) generated from PAA over a novel Docker-based SDN network. Finally, we propose a new controller algorithm for Ryu platforms, which integrates the DNC and classifies both TCP and UDP flows in real-time. Based on the evaluations, an improvement in latency performance has been demonstrated, where analysing a packet, controller processing time takes on an average of 10 mu s. This study will certainly serve to further research on optimising SDN and QoS. [Mondal, Pritom Kumar; Aguirre Sanchez, Lizeth P.; Benedetto, Emmanuele; Shen, Yao; Guo, Minyi] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China; [Benedetto, Emmanuele] Politecn Milan, Milan, Italy Shanghai Jiao Tong University; Polytechnic University of Milan Shen, Y (corresponding author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China. yshen@cs.sjtu.edu.cn Benedetto, Emmanuele/0000-0001-9834-3877 Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China [201802005]; Key Program of the National Natural Science Foundation of China [61932014]; Pudong New Area Science & Technology Development Fund [PKX2019-R02] Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China; Key Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Pudong New Area Science & Technology Development Fund This work was partially supported by the Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China under grant number 201802005, the Key Program of the National Natural Science Foundation of China under grant number 61932014, and Pudong New Area Science & Technology Development Fund under Grant No. PKX2019-R02. 37 3 3 1 14 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0954-0091 1360-0494 CONNECT SCI Connect. Sci. JUL 3 2021.0 33 3 693 718 10.1080/09540091.2020.1870437 0.0 JAN 2021 26 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science UA2LG Bronze 2023-03-23 WOS:000608321300001 0
J Sun, Y; Hu, JJ; Wang, WJ; He, M; de With, PHN Sun, Yue; Hu, Jingjing; Wang, Wenjin; He, Min; de With, Peter H. N. Camera-based discomfort detection using multi-channel attention 3D-CNN for hospitalized infants QUANTITATIVE IMAGING IN MEDICINE AND SURGERY English Article Infant discomfort; discomfort detection; video health monitoring; 3D convolutional neural network (3D-CNN) PAIN; FEATURES Background: Detecting discomfort in infants is an important topic for their well-being and development. In this paper, we present an automatic and continuous video-based system for monitoring and detecting discomfort in infants. Methods: The proposed system employs a novel and efficient 3D convolutional neural network (CNN), which achieves an end- to-end solution without the conventional face detection and tracking steps. In the scheme of this study, we thoroughly investigate the video characteristics (e.g., intensity images and motion images) and CNN architectures (e.g., 2D and 3D) for infant discomfort detection. The realized improvements of the 3D-CNN are based on capturing both the motion and the facial expression information of the infants. Results: The performance of the system is assessed using videos recorded from 24 hospitalized infants by visualizing receiver operating characteristic (ROC) curves and measuring the values of area under the ROC curve (AUC). Additional performance metrics (labeling accuracy) are also calculated. Experimental results show that the proposed system achieves an AUC of 0.99, while the overall labeling accuracy is 0.98. Conclusions: These results confirms the robustness by using the 3D-CNN for infant discomfort monitoring and capturing both motion and facial expressions simultaneously. [Sun, Yue; Wang, Wenjin; de With, Peter H. N.] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands; [Hu, Jingjing; He, Min] Hunan Univ, Dept Elect Engn, Changsha, Peoples R China Eindhoven University of Technology; Hunan University Wang, WJ (corresponding author), Bldg Flux POB 513, NL-5600 MB Eindhoven, Netherlands. wwang@tue.nl 30 2 2 2 9 AME PUBL CO SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2223-4292 2223-4306 QUANT IMAG MED SURG Quant. Imaging Med. Surg. JUL 2021.0 11 7 3059 3069 10.21037/qims-20-1302 0.0 11 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging SD8SF 34249635.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000651645600015 0
J Chen, JW; Liu, ZG; Wang, HR; Nunez, A; Han, ZW Chen, Junwen; Liu, Zhigang; Wang, Hongrui; Nunez, Alfredo; Han, Zhiwei Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Automatic defect detection; catenary support device; deep convolutional neural network (DCNN); fastener; high-speed railway VISUAL INSPECTION SYSTEM; CASCADE; VISION The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners' defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan-Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments. [Chen, Junwen; Liu, Zhigang; Wang, Hongrui; Han, Zhiwei] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Sichuan, Peoples R China; [Wang, Hongrui; Nunez, Alfredo] Delft Univ Technol, Sect Railway Engn, NL-2628 CN Delft, Netherlands Southwest Jiaotong University; Delft University of Technology Liu, ZG (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Sichuan, Peoples R China. junwenchen@yeah.net; liuzg_cd@126.com; soul_wang0@163.com; a.a.nunezvicencio@tudelft.nl; zw.han@my.swjtu.edu.cn Wang, Hongrui/D-2091-2016; Vicencio, Alfredo Núñez/S-7874-2019; Núñez, Alfredo/AAH-9173-2021; Wang, Hongrui/AAQ-5219-2021; Liu, Zhigang/G-9639-2018 Wang, Hongrui/0000-0001-7194-4728; Vicencio, Alfredo Núñez/0000-0001-5610-6689; Núñez, Alfredo/0000-0001-5610-6689; Wang, Hongrui/0000-0001-7194-4728; Liu, Zhigang/0000-0003-4154-5587 National Natural Science Foundation of China [U1434203, 51377136, 51407147]; Sichuan Province Youth Science and Technology Innovation Team [2016TD0012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Province Youth Science and Technology Innovation Team This work was supported in part by the National Natural Science Foundation of China under Grant U1434203, Grant 51377136, and Grant 51407147, and in part by the Sichuan Province Youth Science and Technology Innovation Team under Grant 2016TD0012. The Associate Editor coordinating the review process was Dr. Jochen Lang. (Corresponding author: Zhigang Liu.) 37 234 253 43 250 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. FEB 2018.0 67 2 257 269 10.1109/TIM.2017.2775345 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation FS1LE Green Submitted 2023-03-23 WOS:000419536500002 0
J Nie, D; Trullo, R; Lian, J; Wang, L; Petitjean, C; Ruan, S; Wang, Q; Shen, D Nie, Dong; Trullo, Roger; Lian, Jun; Wang, Li; Petitjean, Caroline; Ruan, Su; Wang, Qian; Shen, Dinggang Medical Image Synthesis with Deep Convolutional Adversarial Networks IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING English Article Adversarial learning; auto-context model; deep learning; image synthesis; residual learning ESTIMATING CT IMAGE; ATTENUATION-CORRECTION; 7T-LIKE IMAGES; NEURAL-NETWORK; AUTO-CONTEXT; SUPERRESOLUTION; RECONSTRUCTION; PET/MRI; ATLAS Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks. [Nie, Dong] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27510 USA; [Nie, Dong; Trullo, Roger; Wang, Li; Shen, Dinggang] Univ N Carolina, Dept Radiol, Chapel Hill, NC 27510 USA; [Nie, Dong; Trullo, Roger; Wang, Li; Shen, Dinggang] Univ N Carolina, BRIC, Chapel Hill, NC 27510 USA; [Trullo, Roger; Petitjean, Caroline; Ruan, Su] Univ Normandy, Dept Comp Sci, Caen, France; [Lian, Jun] Univ N Carolina, Dept Radiat Oncol, Chapel Hill, NC USA; [Wang, Qian] Shanghai Jiao Tong Univ, Sch Biomed Engn, MedX Res Inst, Shanghai 200240, Peoples R China; [Shen, Dinggang] Korea Univ, Dept Brain & Cognit Engn, Seoul 02841, South Korea University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Shanghai Jiao Tong University; Korea University Shen, D (corresponding author), Univ N Carolina, Dept Radiol, Chapel Hill, NC 27510 USA.;Shen, D (corresponding author), Univ N Carolina, BRIC, Chapel Hill, NC 27510 USA.;Wang, Q (corresponding author), Shanghai Jiao Tong Univ, Sch Biomed Engn, MedX Res Inst, Shanghai 200240, Peoples R China. dongnie@cs.unc.edu; wang.qian@sjtu.edu.cn; dgshen@med.unc.edu Shen, Dinggang/ABF-6812-2020; Petitjean, Caroline/S-7296-2019; Wang, Qian/AAY-9494-2020; Wang, Li/O-1425-2015 Shen, Dinggang/0000-0002-7934-5698; Wang, Qian/0000-0002-3490-3836; Ruan, Su/0000-0001-8785-6917; Wang, Li/0000-0003-2165-0080 National Institutes of Health [CA206100]; National Key Research and Development Program of China [2017YFC0107600]; National Natural Science Foundation of China [61473190, 81471733]; Science and Technology Commission of Shanghai Municipality [16511101100, 16410722400] National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) This work was supported in part by the National Institutes of Health under Grant CA206100 for D. Shen; in part by the National Key Research and Development Program of China under Grant 2017YFC0107600; in part by the National Natural Science Foundation of China under Grant 61473190, 81471733; and in part by the Science and Technology Commission of Shanghai Municipality under Grants 16511101100, 16410722400 for Q. Wang. 52 263 275 17 162 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9294 1558-2531 IEEE T BIO-MED ENG IEEE Trans. Biomed. Eng. DEC 2018.0 65 12 2720 2730 10.1109/TBME.2018.2814538 0.0 11 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering HB7JK 29993445.0 Green Accepted 2023-03-23 WOS:000451253600007 0
J Ahmad, OF; Mori, Y; Misawa, M; Kudo, SE; Anderson, JT; Bernal, J; Berzin, TM; Bisschops, R; Byrne, MF; Chen, PJ; East, JE; Eelbode, T; Elson, DS; Gurudu, SR; Histace, A; Karnes, WE; Repici, A; Singh, R; Valdastri, P; Wallace, MB; Wang, P; Stoyanov, D; Lovat, LB Ahmad, Omer F.; Mori, Yuichi; Misawa, Masashi; Kudo, Shin-ei; Anderson, John T.; Bernal, Jorge; Berzin, Tyler M.; Bisschops, Raf; Byrne, Michael F.; Chen, Peng-Jen; East, James E.; Eelbode, Tom; Elson, Daniel S.; Gurudu, Suryakanth R.; Histace, Aymeric; Karnes, William E.; Repici, Alessandro; Singh, Rajvinder; Valdastri, Pietro; Wallace, Michael B.; Wang, Pu; Stoyanov, Danail; Lovat, Laurence B. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method ENDOSCOPY English Article COMPUTER-AIDED DETECTION; GASTROINTESTINAL ENDOSCOPY; COLORECTAL NEOPLASIA; EUROPEAN-SOCIETY; RESEARCH AGENDA; POLYP DETECTION; OPTICAL BIOPSY; DIAGNOSIS; TRIAL Background Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy. [Ahmad, Omer F.; Stoyanov, Danail; Lovat, Laurence B.] UCL, Wellcome EPSRC Ctr Intervent & Surg Sci, London, England; [Mori, Yuichi; Misawa, Masashi; Kudo, Shin-ei] Showa Univ, Digest Dis Ctr, Northern Yokohama Hosp, Yokohama, Kanagawa, Japan; [Mori, Yuichi] Univ Oslo, Inst Hlth & Soc, Clin Effectiveness Res Grp, Oslo, Norway; [Anderson, John T.] Gloucestershire Hosp NHS Fdn Trust, Dept Gastroenterol, Gloucester, England; [Bernal, Jorge] Univ Autonoma Barcelona, Comp Sci Dept, Barcelona, Spain; [Bernal, Jorge] Comp Vis Ctr, Barcelona, Spain; [Berzin, Tyler M.] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Ctr Adv Endoscopy, Boston, MA USA; [Bisschops, Raf] Univ Hosp Leuven, Dept Gastroenterol & Hepatol, TARGID KU Leuven, Leuven, Belgium; [Byrne, Michael F.] Univ British Columbia, Vancouver Gen Hosp, Div Gastroenterol, Vancouver, BC, Canada; [Chen, Peng-Jen] Triserv Gen Hosp, Natl Def Med Ctr, Div Gastroenterol, Taipei, Taiwan; [East, James E.] John Radcliffe Hosp, Translat Gastroenterol Unit, Oxford, England; [East, James E.] Univ Oxford, Oxford NIHR Biomed Res Ctr, Oxford, England; [Eelbode, Tom] Katholieke Univ Leuven, Med Imaging Res Ctr, ESAT PSI, Leuven, Belgium; [Elson, Daniel S.] Imperial Coll London, Hamlyn Ctr Robot Surg, Inst Global Hlth Innovat, London, England; [Elson, Daniel S.] Imperial Coll London, Dept Surg & Canc, London, England; [Gurudu, Suryakanth R.] Mayo Clin, Div Gastroenterol & Hepatol, Scottsdale, AZ USA; [Histace, Aymeric] Univ Cergy Pointoise, Cergy Pointoise Cedex, CNRS, ENSEA,ETIS, Cergy Pontoise, France; [Karnes, William E.] Univ Calif Irvine, HH Chao Comprehens Digest Dis Ctr, Dept Med, Div Gastroenterol & Hepatol, Irvine, CA USA; [Repici, Alessandro] IRCCS, Dept Gastroenterol, Humanitas Clin & Res Ctr, Milan, Italy; [Repici, Alessandro] Humanitas Univ, Dept Biomed Sci, Milan, Italy; [Singh, Rajvinder] Lyell McEwan Hosp, Dept Gastroenterol & Hepatol, Adelaide, SA, Australia; [Valdastri, Pietro] Univ Leeds, Sch Elect & Elect Engn, Leeds, W Yorkshire, England; [Wallace, Michael B.] Mayo Clin, Div Gastroenterol & Hepatol, Jacksonville, FL USA; [Wang, Pu] Sichuan Acad Med Sci & Sichuan Prov Peoples Hosp, Dept Gastroenterol, Chengdu, Peoples R China; [Lovat, Laurence B.] Univ Coll London Hosp, Gastrointestinal Serv, London, England UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC); University of London; University College London; Showa University; University of Oslo; Gloucestershire Hospitals NHS Foundation Trust; Autonomous University of Barcelona; Centre de Visio per Computador (CVC); Harvard University; Beth Israel Deaconess Medical Center; Harvard Medical School; KU Leuven; University Hospital Leuven; University of British Columbia; National Defense Medical Center; Tri-Service General Hospital; University of Oxford; University of Oxford; KU Leuven; Imperial College London; Imperial College London; Mayo Clinic; Mayo Clinic Phoenix; Centre National de la Recherche Scientifique (CNRS); CY Cergy Paris Universite; University of California System; University of California Irvine; Humanitas University; Lyell McEwin Hospital; University of Leeds; Mayo Clinic; Sichuan Provincial People's Hospital; University College London Hospitals NHS Foundation Trust Ahmad, OF (corresponding author), Wellcome EPSRC Ctr Intervent & Surg Sci, Charles Bell House,43-45 Foley St, London W1W 7TS, England. ofahmad123@gmail.com Wallace, Michael/GZL-9731-2022; Misawa, Masashi/H-9004-2019; Repici, Alessandro/HFH-8162-2022; Mori, Yuichi/AAU-5406-2020; Elson, Daniel/B-4921-2008; Lovat, Laurence/C-1986-2009 Wallace, Michael/0000-0002-6446-5785; Misawa, Masashi/0000-0002-8520-2036; Repici, Alessandro/0000-0002-1621-6450; Chen, Peng Jen/0000-0001-5400-905X; Bisschops, Raf/0000-0002-9994-8226; Karnes, William/0000-0002-6225-9080; Wang, Pu/0000-0002-1234-309X; Singh, Rajvinder/0000-0001-9116-6054; Anderson, John/0000-0002-6625-114X; Mori, Yuichi/0000-0003-2262-0334; Elson, Daniel/0000-0002-5578-3941; Lovat, Laurence/0000-0003-4542-3915; Kudo, Toyoki/0000-0002-2953-9603 Wellcome Trust 203145Z/16/Z [203145Z/16/Z EP/P027938/1]; Engineering and Physical Sciences Research Council; EPSRC [EP/P012841/1, EP/P027938/1, EP/R004080/1] Funding Source: UKRI Wellcome Trust 203145Z/16/Z(Wellcome Trust); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) Wellcome Trust 203145Z/16/Z; Engineering and Physical Sciences Research Council 203145Z/16/Z EP/P027938/1 33 24 24 0 2 GEORG THIEME VERLAG KG STUTTGART RUDIGERSTR 14, D-70469 STUTTGART, GERMANY 0013-726X 1438-8812 ENDOSCOPY Endoscopy SEP 2021.0 53 9 893 901 10.1055/a-1306-7590 0.0 JAN 2021 9 Gastroenterology & Hepatology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology; Surgery YN3PU 33167043.0 Green Published, hybrid 2023-03-23 WOS:000607390000001 0
J Awais, M; Long, X; Yin, B; Chen, C; Akbarzadeh, S; Abbasi, SF; Irfan, M; Lu, CM; Wang, XH; Wang, LS; Chen, W Awais, Muhammad; Long, Xi; Yin, Bin; Chen, Chen; Akbarzadeh, Saeed; Abbasi, Saadullah Farooq; Irfan, Muhammad; Lu, Chunmei; Wang, Xinhua; Wang, Laishuan; Chen, Wei Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification? BMC RESEARCH NOTES English Article Convolutional neural networks (CNNs); Video electroencephalogram (VEEG); Neonatal sleep; Sleep and wake classification; Feature extraction INFANTS; RESPIRATION Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke(R) facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL's), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke(R) video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke(R) (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required. [Awais, Muhammad; Chen, Chen; Akbarzadeh, Saeed; Abbasi, Saadullah Farooq; Irfan, Muhammad; Chen, Wei] Fudan Univ, Ctr Intelligent Med Elect, Dept Elect Engn, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Long, Xi] Eindhoven Univ Technol, Dept Elect Engn, Den Dolech 2, NL-5612 AZ Eindhoven, Netherlands; [Yin, Bin] Philips Res, Connected Care & Personal Hlth Dept, Shanghai, Peoples R China; [Lu, Chunmei; Wang, Laishuan] Fudan Univ, Dept Neonatol, Childrens Hosp, Shanghai 200032, Peoples R China; [Wang, Xinhua] Fudan Univ, Dept Neurol, Childrens Hosp, Shanghai 200032, Peoples R China; [Chen, Wei] Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China; [Long, Xi] Philips Res, Dept Family Care Solut, NL-5656 AE Eindhoven, Netherlands Fudan University; Eindhoven University of Technology; Philips; Philips Research; Fudan University; Fudan University; Fudan University; Philips; Philips Research Chen, W (corresponding author), Fudan Univ, Ctr Intelligent Med Elect, Dept Elect Engn, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.;Lu, CM (corresponding author), Fudan Univ, Dept Neonatol, Childrens Hosp, Shanghai 200032, Peoples R China. luchunmei1975@163.com; w_chen@fudan.edu.cn Abbasi, Saadullah Farooq/AAJ-1154-2020; Wang, Xin/AAN-8435-2021; Abbasi, Saadullah/HNB-9444-2023; Long, Xi/AAU-4595-2020 Wang, Xin/0000-0002-4457-7376; Long, Xi/0000-0001-9505-1270; Abbasi, Saadullah Farooq/0000-0001-9814-3023; Irfan, Dr. Muhammad/0000-0003-4161-6875 National Key RAMP;D Program of China [2017YFE0112000]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; China Postdoctoral Science Foundation [2018T110346, 2018M632019] National Key RAMP;D Program of China; Shanghai Municipal Science and Technology Major Project; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work is supported by National Key R&D Program of China (Grant No. 2017YFE0112000), Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01), and China Postdoctoral Science Foundation Grant (Grant No. 2018T110346 and No. 2018M632019). 41 6 6 1 6 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1756-0500 BMC RES NOTES BMC Res. Notes NOV 4 2020.0 13 1 507 10.1186/s13104-020-05343-4 0.0 6 Biology; Multidisciplinary Sciences Emerging Sources Citation Index (ESCI) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics OS4BB 33148327.0 Green Submitted, Green Accepted, gold 2023-03-23 WOS:000590107300001 0
J Fan, C; Fu, GL; Di Noia, A; Smit, M; Rietjens, JHH; Ferrare, RA; Burton, S; Li, ZQ; Hasekamp, OP Fan, Cheng; Fu, Guangliang; Di Noia, Antonio; Smit, Martijn; Rietjens, Jeroen H. H.; Ferrare, Richard A.; Burton, Sharon; Li, Zhengqiang; Hasekamp, Otto P. Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements REMOTE SENSING English Article neural network; aerosols; multi-angle; polarimetry SPECTRAL-RESOLUTION LIDAR; WATER-LEAVING RADIANCE; PHOTOPOLARIMETRIC MEASUREMENTS; INFORMATION-CONTENT; OPTICAL-PROPERTIES; LAND; ALGORITHM; POLARIZATION; MISSION; INVERSION For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties [Fan, Cheng; Li, Zhengqiang] Chinese Acad Sci, Aerosp Informat Res Inst, State Environm Protect Key Lab Satellite Remote S, Beijing 100101, Peoples R China; [Fan, Cheng; Fu, Guangliang; Smit, Martijn; Rietjens, Jeroen H. H.; Hasekamp, Otto P.] SRON Netherlands Inst Space Res, Sorbonnelaan 2, NL-3584 CA Utrecht, Netherlands; [Fan, Cheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Di Noia, Antonio] Univ Leicester, Sch Phys & Astron, Univ Rd, Leicester LE1 7RH, Leics, England; [Ferrare, Richard A.; Burton, Sharon] NASA, Langley Res Ctr LaRC, Hampton, VA 23666 USA Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Leicester; National Aeronautics & Space Administration (NASA); NASA Langley Research Center Hasekamp, OP (corresponding author), SRON Netherlands Inst Space Res, Sorbonnelaan 2, NL-3584 CA Utrecht, Netherlands. fancheng@radi.ac.cn; g.fu@sron.nl; adn9@le.ac.uk; J.M.Smit@sron.nl; J.H.H.Rietjens@sron.nl; richard.a.ferrare@nasa.gov; Sharon.p.burton@nasa.gov; lizq@radi.ac.cn; o.hasekamp@sron.nl li, zhengqiang/C-5678-2013; Hasekamp, Otto/AAO-7661-2020; 樊, 程/HJY-1661-2023; Di Noia, Antonio/K-4206-2019 li, zhengqiang/0000-0002-7795-3630; Di Noia, Antonio/0000-0002-5052-0763; Fan, Cheng/0000-0003-1547-9758 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080303]; Ministry of Science and Technology (MOST) of China [2016YFC0200500]; National Natural Science Foundation of China [41871260, 41590855]; International team of Advanced Polarization Remote Sensing Technology and Application of the Chinese Academy of Sciences [GJTD-2018-15]; NWO/NSO project ACEPOL: Aerosol Characterization from Polarimeter and Lidar [ALW-GO/16-09]; Aerosol, Cloud, Ecosystem (ACE) program at NASA's Earth Science Division Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Ministry of Science and Technology (MOST) of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); International team of Advanced Polarization Remote Sensing Technology and Application of the Chinese Academy of Sciences; NWO/NSO project ACEPOL: Aerosol Characterization from Polarimeter and Lidar; Aerosol, Cloud, Ecosystem (ACE) program at NASA's Earth Science Division This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, grant no. XDA19080303, the Ministry of Science and Technology (MOST) of China under grant 2016YFC0200500, the National Natural Science Foundation of China, grant nos. 41871260 and 41590855 and the International team of Advanced Polarization Remote Sensing Technology and Application of the Chinese Academy of Sciences, grant nos. GJTD-2018-15, and also the NWO/NSO project ACEPOL: Aerosol Characterization from Polarimeter and Lidar under project number ALW-GO/16-09. ACEPOL flights were sponsored by the former Aerosol, Cloud, Ecosystem (ACE) program at NASA's Earth Science Division. 89 15 18 2 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. DEC 1 2019.0 11 23 2877 10.3390/rs11232877 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology KE2IO gold 2023-03-23 WOS:000508382100158 0
J Dong, J; Varbanov, M; Philippot, S; Vreken, F; Zeng, WB; Blay, V Dong, Jie; Varbanov, Mihayl; Philippot, Stephanie; Vreken, Fanny; Zeng, Wen-Bin; Blay, Vincent Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY English Article Coronavirus; ligand-based drug design; machine learning; cheminformatics; drug discovery SARS-COV-2; COVID-19; PLPRO Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M-pro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M-pro, with half-maximal inhibitory concentration values (IC50) in the range 0.18-18.82 mu M. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M-pro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells. [Dong, Jie; Zeng, Wen-Bin] Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha, Peoples R China; [Varbanov, Mihayl; Philippot, Stephanie; Vreken, Fanny] Univ Lorraine, CNRS, Nancy, France; [Varbanov, Mihayl] CHRU Nancy Brabois, Lab Virol, Vandoeuvre Les Nancy, France; [Blay, Vincent] Univ Calif Santa Cruz, Dept Microbiol & Environm Toxicol, Santa Cruz, CA 95064 USA Central South University; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; CHU de Nancy; University of California System; University of California Santa Cruz Blay, V (corresponding author), Univ Calif Santa Cruz, Dept Microbiol & Environm Toxicol, Santa Cruz, CA 95064 USA. vroger@ucsc.edu Blay, Vincent/AEZ-2180-2022 Blay, Vincent/0000-0001-9602-2375; Dong, Jie/0000-0002-3324-9000; Varbanov, Mihayl/0000-0002-2515-3009 Project of Innovation-driven Plan in Central South University Project of Innovation-driven Plan in Central South University This work was financially supported by grants from the Project of Innovation-driven Plan in Central South University. The authors gratefully acknowledge the University of Lorraine, CNRS and the FEDER-FSE Fire Light. 69 1 1 11 11 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1475-6366 1475-6374 J ENZYM INHIB MED CH J. Enzym. Inhib. Med. Chem. JAN 1 2023.0 38 1 24 35 10.1080/14756366.2022.2132486 0.0 12 Biochemistry & Molecular Biology; Chemistry, Medicinal Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Pharmacology & Pharmacy 5S7JH 36305272.0 gold, Green Accepted 2023-03-23 WOS:000875360800001 0
J Feng, XX; Ye, KP; Lou, CT; Suo, XM; Song, YJ; Pang, XD; Ozolins, O; Zhang, L; Yu, XB Feng, Xingxing; Ye, Kangpeng; Lou, Chaoteng; Suo, Xingmeng; Song, Yujie; Pang, Xiaodan; Ozolins, Oskars; Zhang, Lu; Yu, Xianbin Human recognition with the optoelectronic reservoir-computing-based micro-Doppler radar signal processing APPLIED OPTICS English Article IDENTIFICATION; SYSTEM Current perception and monitoring systems, such as human recognition, are affected by several environmental factors, such as limited light intensity, weather changes, occlusion of targets, and public privacy. Human recognition using radar signals is a promising direction to overcome these defects; however, the low signal-to-noise ratio of radar signals still makes this task challenging. Therefore, it is necessary to use suitable tools that can efficiently deal with radar signals to identify targets. Reservoir computing (RC) is an efficient machine learning scheme that is easy to train and demonstrates excellent performance in processing complex time-series signals. The RC hardware implementation structure based on nonlinear nodes and delay feedback loops endows it with the potential for real-time fast signal processing. In this paper, we numerically study the performance of the optoelectronic RC composed of optical and electrical components in the task of human recognition with noisy micro-Doppler radar signals. A single-loop optoelectronic RC is employed to verify the application of RC in this field, and a parallel dual-loop optoelectronic RC scheme with a dual-polarization Mach-Zehnder modulator (DPol-MZM) is also used for performance comparison. The result is verified to be comparable with other machine learning tools, which demonstrates the ability of the optoelectronic RC in capturing gait information and dealing with noisy radar signals; it also indicates that optoelectronic RC is a powerful tool in the field of human target recognition based on micro-Doppler radar signals. Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. [Feng, Xingxing; Ye, Kangpeng; Lou, Chaoteng; Suo, Xingmeng; Song, Yujie; Zhang, Lu; Yu, Xianbin] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China; [Pang, Xiaodan; Ozolins, Oskars] KTH Royal Inst Technol, Appl Phys Dept, S-10691 Stockholm, Sweden; [Ozolins, Oskars] RISE Res Inst Sweden, Networks Unit, S-16440 Kista, Sweden; [Zhang, Lu; Yu, Xianbin] Zhejiang Lab, Hangzhou 311121, Peoples R China Zhejiang University; Royal Institute of Technology; RISE Research Institutes of Sweden; Zhejiang Laboratory Zhang, L (corresponding author), Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China.;Zhang, L (corresponding author), Zhejiang Lab, Hangzhou 311121, Peoples R China. zhanglu1993@zju.edu.cn Pang, Xiaodan/D-5032-2015 Pang, Xiaodan/0000-0003-4906-1704; Zhang, Lu/0000-0001-9567-155X; Ozolins, Oskars/0000-0001-9839-7488 National Key Research and Development Program of China [2020YFB1805700]; National Natural Science Foundation of China [62101483]; Natural Science Foundation of Zhejiang Province [LQ21F010015]; Vetenskapsradet [2022-04798]; Zhejiang Lab [2020LC0AD01] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Vetenskapsradet(Swedish Research Council); Zhejiang Lab National Key Research and Development Program of China (2020YFB1805700); National Natural Science Foundation of China (62101483); Natural Science Foundation of Zhejiang Province (LQ21F010015); Zhejiang Lab (2020LC0AD01); Vetenskapsradet (2022-04798). 29 0 0 9 14 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics JUL 1 2022.0 61 19 5782 5789 10.1364/AO.462299 0.0 8 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 2S8DB 36255813.0 2023-03-23 WOS:000822017300036 0
J You, RH; Yao, SW; Mamitsuka, H; Zhu, SF You, Ronghui; Yao, Shuwei; Mamitsuka, Hiroshi; Zhu, Shanfeng DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction BIOINFORMATICS English Article; Proceedings Paper Biennial Joint Meeting of 29th Annual Conference on Intelligent Systems for Molecular Biology (ISMB) / 20th European Conference on Computational biology (ECCB) JUL 25-30, 2021 ELECTR NETWORK ONTOLOGY; DATABASE Motivation: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations. Results: We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement. [You, Ronghui; Yao, Shuwei] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Mamitsuka, Hiroshi] Kyoto Univ, Bioinformat Ctr, Inst Chem Res, Uji, Kyoto 6110011, Japan; [Mamitsuka, Hiroshi] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Zhu, Shanfeng] Fudan Univ, Inst Sci & Technol Brain Inspired Intellige, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, Shanghai Inst Artificial Intelligence Algorithms, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, Key Lab Computat Neurosci & Brain Inspired Intell, Minist Educ, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Zhangjiang Fudan Int Innovat Ctr, Shanghai 200433, Peoples R China Fudan University; Kyoto University; Aalto University; Fudan University; Fudan University; Fudan University; Fudan University; Fudan University Zhu, SF (corresponding author), Fudan Univ, Inst Sci & Technol Brain Inspired Intellige, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Shanghai Inst Artificial Intelligence Algorithms, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Key Lab Computat Neurosci & Brain Inspired Intell, Minist Educ, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Zhangjiang Fudan Int Innovat Ctr, Shanghai 200433, Peoples R China. zhusf@fudan.edu.cn ; Mamitsuka, Hiroshi/R-1110-2016 Zhu, Shanfeng/0000-0002-6067-5312; Mamitsuka, Hiroshi/0000-0002-6607-5617 National Natural Science Foundation of China [61872094]; Shanghai Municipal Science and Technology Major Project [2018SHZDZX01, 2017SHZDZX01]; ZJ Lab; Shanghai Center for BrainScience and Brain-Inspired Technology; 111 Project [B18015]; Information Technology Facility; CAS-MPG Partner Institute for Computational Biology; Shanghai Institute for Biological Sciences, Chinese Academy of Sciences; Academy of Finland [315896]; JST ACCEL [JPMJAC1503]; NEXT KAKENHI [19H04169]; Grants-in-Aid for Scientific Research [19H04169] Funding Source: KAKEN National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Science and Technology Major Project; ZJ Lab; Shanghai Center for BrainScience and Brain-Inspired Technology; 111 Project(Ministry of Education, China - 111 Project); Information Technology Facility; CAS-MPG Partner Institute for Computational Biology; Shanghai Institute for Biological Sciences, Chinese Academy of Sciences; Academy of Finland(Academy of Finland); JST ACCEL(Japan Science & Technology Agency (JST)); NEXT KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) Financial Support: S.Z. was supported by National Natural Science Foundation of China (No. 61872094), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), ZJ Lab, and Shanghai Center for BrainScience and Brain-Inspired Technology. S.Y. and R.Y. have been supported by the 111 Project (No. B18015), Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01) and Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. H. M. has been supported partially by Academy of Finland (315896), JST ACCEL (JPMJAC1503), NEXT KAKENHI (19H04169). 38 10 10 8 15 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics JUL 2021.0 37 1.0 I262 I271 10.1093/bioinformatics/btab270 0.0 10 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics US8VF 34252926.0 Green Published 2023-03-23 WOS:000697703700032 0
J Liu, H; Cui, SW; Zhao, XH; Cong, FY Liu, Hang; Cui, Shaowei; Zhao, Xiaohui; Cong, Fengyu Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Article Obstructive sleep apnea; ECG; Transformer; Deep learning AUTOMATIC DETECTION; ALGORITHM Obstructive sleep apnea (OSA) is a sleep breathing disorder that can seriously affect the health of patients. The manual diagnostic of OSA through the Polysomnography (PSG) recordings is time-consuming and tedious. Electrocardiogram (ECG) signals have been an alternative for OSA detection. This paper proposes a CNN -Transformer architecture for automatic OSA detection based on single-channel ECG signals. The proposed architecture has two fundamental parts. The first part has the aim of learning a feature representation from ECG signals by using the CNN. The second part consists mainly of the Transformer, a model structure built solely with self-attention mechanism, which is used to model the global temporal context and to perform classification tasks. The effectiveness of the proposed method was validated on Apnea-ECG dataset. The dataset consists of 70 ECG recordings with an annotation for each minute of each recording. The current and adjacent 1-min epochs were combined to form the 3-min input epoch. Besides, experiments were set up with different baseline deep learning models for sequence modeling to verify their effects on classification performance. The per -segment classification accuracy reached 88.2% and the area under the receiver operating characteristic curve (AUC) was 0.95. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 4.33. Experimental results demonstrate that the Transformer structure and a 3-min input time window both effectively improve the classification performance. The proposed method can accurately detect OSA from single-channel ECG signals and provides a promising and reliable solution for home portable detection of OSA. [Liu, Hang; Cui, Shaowei; Cong, Fengyu] Dalian Univ Technol, Fac Med, Sch Biomed Engn, Dalian, Peoples R China; [Liu, Hang; Cui, Shaowei; Cong, Fengyu] Dalian Univ Technol, Liaoning Key Lab Integrated Circuit & Biomed Elect, Dalian, Peoples R China; [Zhao, Xiaohui] Dalian Municipal Cent Hosp, Dept Resp & Crit Care Med, Dalian, Peoples R China; [Liu, Hang; Cui, Shaowei; Cong, Fengyu] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla, Finland Dalian University of Technology; Dalian University of Technology; University of Jyvaskyla Liu, H (corresponding author), Dalian Univ Technol, Fac Med, Sch Biomed Engn, Dalian, Peoples R China. liuhang@dlut.edu.cn 36 0 0 11 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control APR 2023.0 82 104581 10.1016/j.bspc.2023.104581 0.0 JAN 2023 8 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering 8O4VD 2023-03-23 WOS:000925833200001 0
C Das, A; Ferrer, MA; Morales, A; Diaz, M; Pal, U; Impedovo, D; Li, HL; Yang, WT; Ota, K; Yao, T; Hung, LQ; Cuong, NQ; Kim, S; Gattal, A Llados, J; Lopresti, D; Uchida, S Das, Abhijit; Ferrer, Miguel A.; Morales, Aythami; Diaz, Moises; Pal, Umapada; Impedovo, Donato; Li, Hongliang; Yang, Wentao; Ota, Kensho; Yao, Tadahito; Le Quang Hung; Nguyen Quoc Cuong; Kim, Seungjae; Gattal, Abdeljalil ICDAR 2021 Competition on Script Identification in the Wild DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT IV Lecture Notes in Computer Science English Proceedings Paper 16th IAPR International Conference on Document Analysis and Recognition (ICDAR) SEP 05-10, 2021 Lausanne, SWITZERLAND IAPR Handwritten and printed script identification; Wild; Deep learning; Multi-script The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th International Conference on Document Analysis and Recognition (ICDAR 2021). The goal of SIW is to evaluate the limits of script identification approaches through a large scale in the wild database including 13 scripts (MDIW-13 dataset) and two different scenarios (handwritten and printed). The competition includes the evaluation over three different tasks depending of the nature of the data used for training and testing. Nineteen research groups registered for SIW 2021, out of which 6 teams from both academia and industry took part in the final round and submitted a total of 166 algorithms for scoring. Submissions included a wide variety of deep-learning solutions as well as approaches based on standard image processing techniques. The performance achieved by the participants prove the elevate accuracy of deep learning methods in comparison with traditional statistical approaches. The best approach obtained classification accuracies of 99% in all three tasks with experiments over more than 50K test samples. The results suggest that there is still room for improvements, specially over handwritten samples and specific scripts. [Das, Abhijit; Pal, Umapada] Indian Stat Inst, Kolkata, India; [Das, Abhijit] Thapar Univ, Patiala, Punjab, India; [Ferrer, Miguel A.] Univ Las Palmas Gran Canaria, Las Palmas Gran Canaria, Spain; [Morales, Aythami] Univ Autonoma Madrid, Madrid, Spain; [Diaz, Moises] Univ Atlantico Medio, Las Palmas Gran Canaria, Spain; [Impedovo, Donato] Univ Bari Aldo Moro, Bari, Italy; [Li, Hongliang; Yang, Wentao] South China Univ Technol, Guangzhou, Peoples R China; [Ota, Kensho; Yao, Tadahito] Canon IT Solut Inc, Tokyo, Japan; [Le Quang Hung; Nguyen Quoc Cuong] Univ Informat Technol, Ho Chi Minh City, Vietnam; [Kim, Seungjae] NAVER Papago, Seoul, South Korea; [Gattal, Abdeljalil] Larbi Tebessi Univ, Tebessa, Algeria Indian Statistical Institute; Indian Statistical Institute Kolkata; Thapar Institute of Engineering & Technology; Universidad de Las Palmas de Gran Canaria; Autonomous University of Madrid; Universita degli Studi di Bari Aldo Moro; South China University of Technology; Canon Incorporated; Naver; Universite de Tebessa Das, A (corresponding author), Indian Stat Inst, Kolkata, India.;Das, A (corresponding author), Thapar Univ, Patiala, Punjab, India. abhijit.das@thapar.edu; aythami.morales@uam.es; moises.diaz@atlanticomedio.es; donato.impedovo@uniba.it Ferrer, Miguel A A/L-3863-2013; Ferrer, Miguel/AFU-8286-2022; Moreno, Aythami/ABF-8166-2021; Pal, Umapada/AAC-4930-2022; Diaz, Moises/L-3637-2013 Ferrer, Miguel A A/0000-0002-2924-1225; Diaz, Moises/0000-0003-3878-3867 39 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-86337-1; 978-3-030-86336-4 LECT NOTES COMPUT SC 2021.0 12824 738 753 10.1007/978-3-030-86337-1_49 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS3IB 2023-03-23 WOS:000711880100049 0
J Jing, Z; Liu, J; Ibrahim, MS; Fan, JJ; Fan, XJ; Zhang, GQ Jing, Zhou; Liu, Jie; Ibrahim, Mesfin Seid; Fan, Jiajie; Fan, Xuejun; Zhang, Guoqi Lifetime Prediction of Ultraviolet Light-Emitting Diodes Using a Long Short-Term Memory Recurrent Neural Network IEEE ELECTRON DEVICE LETTERS English Article Light emitting diodes; Neural networks; Fans; Maintenance engineering; Degradation; Recurrent neural networks; Standards; Ultraviolet LED; long short-term memory; recurrent neural network; IESNA TM-21 recommendation; lifetime prediction Ultraviolet light-emitting diodes (UV LEDs) play an important role in inactivating novel coronavirus pneumonia, but the lack of rapid lifetime prediction can easily cause untimely failure detection, long product development cycles, and high costs. This study predicts the lifetime of UV LEDs based on the long short-term memory (LSTM) recurrent neural network (RNN). First, the equipment setup was designed to conduct an aging test to obtain a predicted length of life for the UV LED samples using a Weibull distribution. Next, LSTM RNN was employed to predict the lifetime of the UV LEDs based on the radiation power degradation. The results were then compared with those from nonlinear least squares (NLS) regression recommended by the IESNA TM-21 industry standard. Finally, the robustness of the two methods was analyzed by changing the starting times of the predictions. The results showed that the LSTM RNN proposed in this letter reveals not only a 29.7% lower lifetime prediction error compared with the NLS regression, but also a more stable robustness. Thus, the LSTM RNN method is found to be more accurate and more robust in predicting the lifetime of UV LEDs. [Jing, Zhou] Hohai Univ, Coll Mech & Elect Engn, Changzhou 213022, Jiangsu, Peoples R China; [Liu, Jie] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Ibrahim, Mesfin Seid] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China; [Ibrahim, Mesfin Seid] Wollo Univ, Kombolcha Inst Technol, Coll Engn, Kombolcha 208, Ethiopia; [Fan, Jiajie] Acad Engn & Technol, Inst Future Lighting, Shanghai 200433, Peoples R China; [Fan, Jiajie] Fudan Univ, Shanghai Res Ctr Silicon Carbide Power Devices En, Shanghai 200433, Peoples R China; [Fan, Jiajie] Changzhou Inst Technol Res Solid State Lighting, Changzhou 213161, Jiangsu, Peoples R China; [Fan, Xuejun] Lamar Univ, Dept Mech Engn, Beaumont, TX 77710 USA; [Zhang, Guoqi] Delft Univ Technol, Dept Microelect Engn, NL-2628 Delft, Netherlands Hohai University; Fudan University; Hong Kong Polytechnic University; Fudan University; Texas State University System; Lamar University; Delft University of Technology Fan, JJ (corresponding author), Acad Engn & Technol, Inst Future Lighting, Shanghai 200433, Peoples R China. jay.fan@connect.polyu.hk Ibrahim, Mesfin Seid/GXV-6143-2022 Ibrahim, Mesfin Seid/0000-0001-5202-4539; Zhang, Guoqi/0000-0002-8023-5170; Fan, Xuejun/0000-0003-0525-4424; Jing, Zhou/0000-0003-2928-5348 National Natural Science Foundation of China [51805147]; Six Talent Peaks Project in Jiangsu Province [GDZB-017]; Fundamental Research Funds for the Central Universities [B200203031]; Shanghai Science and Technology Development Funds [19DZ2253400] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Six Talent Peaks Project in Jiangsu Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Shanghai Science and Technology Development Funds This work was supported in part by the National Natural Science Foundation of China under Grant 51805147, in part by the Six Talent Peaks Project in Jiangsu Province under Grant GDZB-017, in part by the Fundamental Research Funds for the Central Universities under Grant B200203031, and in part by the Shanghai Science and Technology Development Funds under Grant 19DZ2253400. 18 10 10 7 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0741-3106 1558-0563 IEEE ELECTR DEVICE L IEEE Electron Device Lett. DEC 2020.0 41 12 1817 1820 10.1109/LED.2020.3034567 0.0 4 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering OZ4ZF 2023-03-23 WOS:000594935300020 0
C Tang, ZY; Li, P; Guo, S; Liao, XF; Jin, H; Zhang, DQ Lee, K; Liu, L Tang, Zaiyang; Li, Peng; Guo, Song; Liao, Xiaofei; Jin, Hai; Zhang, Daqing Selective Traffic Offloading On the Fly: a Machine Learning Approach 2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017) IEEE International Conference on Distributed Computing Systems English Proceedings Paper 37th IEEE International Conference on Distributed Computing Systems (ICDCS) JUN 05-08, 2017 Atlanta, GA IEEE,Natl Sci Fdn,IEEE Tech Comm Distributed Proc,IEEE Comp Soc,Japan Sci & Technol Agcy It has been well recognized that network transmission constitutes a large portion of smartphone energy consumption, mainly because of the tail energy caused by cellular network interface. Traffic offloading has been proposed to reduce energy by letting a smartphone offload network traffic to its neighbors in vicinity via low-power direct connections (e.g., WiFi Direct or Bluetooth). Our experiments conducted in a realistic environment reveal that energy efficiency cannot be improved or even deteriorates without a carefully designed offloading strategy. In this paper, we propose a selective traffic offloading scheme implemented as a smartphone middleware in a software-defined fashion, which consists of a packet classifier and a traffic scheduler. Using a light-weight machine learning approach exploiting unique smartphone context information, the packet classifier identifies packets generated on the fly as offloadable or not with substantially improved efficiency and feasibility on resource limited smartphones compared to traditional approaches. Both testbed and simulation based experiments are conducted and the results show that our proposal always attains the superior performance on a number of comparison metrics. [Tang, Zaiyang; Liao, Xiaofei; Jin, Hai] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Serv Comp Technol & Syst Lab, Wuhan 430074, Hubei, Peoples R China; [Li, Peng] Univ Aizu, Sch Comp Sci & Engn, Aizu Wakamatsu, Fukushima, Japan; [Guo, Song] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China; [Zhang, Daqing] Telecom SudParis, Inst Mines Telecom, Paris, France Huazhong University of Science & Technology; University of Aizu; Hong Kong Polytechnic University; IMT - Institut Mines-Telecom; IMT Atlantique; Institut Polytechnique de Paris Tang, ZY (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Serv Comp Technol & Syst Lab, Wuhan 430074, Hubei, Peoples R China. tangzaiyang@hust.edu.cn; pengli@u-aizu.ac.jp; song.guo@polyu.edu.hk; xfliao@hust.edu.cn; hjin@hust.edu.cn; daqing.zhang@telecom-sudparis.eu Guo, Song/AAZ-4542-2020 Guo, Song/0000-0001-9831-2202 Program of International ST Cooperation [2015DFE12860]; National High-tech Research and Development Program of China (863 Program) [2015AA01A203]; Science and Technology Planning Project of Guangdong Province in China [2016B030305002] Program of International ST Cooperation; National High-tech Research and Development Program of China (863 Program)(National High Technology Research and Development Program of China); Science and Technology Planning Project of Guangdong Province in China This research is supported by the Program of International S&T Cooperation under grant No. 2015DFE12860, the National High-tech Research and Development Program of China (863 Program) under grant No.2015AA01A203 and the Science and Technology Planning Project of Guangdong Province in China under grant No.2016B030305002. 22 0 0 0 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6927 978-1-5386-1791-5 INT CON DISTR COMP S 2017.0 2386 2392 10.1109/ICDCS.2017.113 0.0 7 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI5RN 2023-03-23 WOS:000412759500250 0
J Zhang, YJ; Thangavelu, L; Taban, TZ; Abdelbasset, WK; Suksatan, W; Sarjadi, MS; Rahman, ML; Sarkar, SM; Alashwal, M; Zwawi, M; Algarni, M Zhang, Yanjie; Thangavelu, Lakshmi; Taban, Talib Zeedan; Abdelbasset, Walid Kamal; Suksatan, Wanich; Sarjadi, Mohd Sani; Rahman, Md Lutfor; Sarkar, Shaheen M.; Alashwal, May; Zwawi, Mohammed; Algarni, Mohammed Development of hybrid machine learning model for simulation of chemical reactors in water treatment applications: Absorption in amino acid ENVIRONMENTAL TECHNOLOGY & INNOVATION English Article Separation; Amino acid solutions; Environmental; CO2 capture
; Machine learning GAUSSIAN-PROCESSES; PREDICTION; REGRESSION; INTELLIGENCE; CO2 Separation and capture of CO(2 )from gas mixtures is of great importance from environmental point of view which can be effectively achieved using amino acids as new class of chemical absorbents. However, screening the proper absorbent with desired separation properties using experimental measurements is tedious and costly. The predictive computational techniques can be employed to overcome this problem. In this study, for estimating and analyzing CO2 solubility in chemical solvents based on amino acid salt solutions, we created two regression models from different classes of machine learning methods. The main aim is to analyze the effect of physicochemical parameters on the CO2 dissolution in solvent which can be carried out in chemical reactors for separation/conversion of CO2 for environmental applications. A number of CO2 solubility data are collected from resources and used for training and validation of machine learning computations. Several inputs were considered for the developed machine learning models. Inputs in this regression task are T (temperature), weight% (overall mass percentage of solvent), P-CO2 (partial pressure of CO2 in the gas), MW-am (molecular weight of amino acid salt), MPC (melting point of amino acid salt), M-WC (molecular mass of cation). In this task, we must predict alpha (CO2 loading in the amino acid solution) as the only output of the developed models. The models studied in this research are the Gaussian process and the decision tree boosted with Gradient boosting. With the R-2 criterion, the scores of the two Gradient boosting and Gaussian process models were obtained 0.985 and 0.993, respectively. As the third efficiency metric of the models, the Gradient boosting and regression of the Gaussian process with the RMSE criterion is the error rates of 1.10E-01 and 1.44E-01. The models developed in this work indicated to be reliable and robust enough for screening the solvents for a particular application and to save time and cost of experimental measurements. (C)& nbsp;2022 The Author(s). Published by Elsevier B.V.& nbsp; [Zhang, Yanjie] Tianjin Youmei Environm Protect Technol Co LTD, Tianjin 300383, Peoples R China; [Thangavelu, Lakshmi] Saveetha Univ, Saveetha Inst Med & Tech Sci, Saveetha Dent Coll, Dept Pharmacol, Chennai, India; [Taban, Talib Zeedan] Kut Univ Coll, Laser & Optoelect Engn Dept, Al Kut 52001, Wasit, Iraq; [Abdelbasset, Walid Kamal] Prince Sattam bin Abdulaziz Univ, Coll Appl Med Sci, Dept Hlth & Rehabil Sci, Al Kharj, Saudi Arabia; [Abdelbasset, Walid Kamal] Cairo Univ, Kasr Al Aini Hosp, Dept Phys Therapy, Giza, Egypt; [Suksatan, Wanich] Chulabhorn Royal Acad, HRH Princess Chulabhorn Coll Med Sci, Fac Nursing, Bangkok, Thailand; [Sarjadi, Mohd Sani; Rahman, Md Lutfor] Univ Malaysia Sabah, Fac Sci & Nat Resources, Kota Kinabalu 88400, Sabah, Malaysia; [Sarkar, Shaheen M.] Technol Univ Shannon Midlands Midwest, Dept Appl Sci, Limerick V94 EC5T, Ireland; [Alashwal, May] Jeddah Int Coll, Dept Comp Sci, Jeddah, Saudi Arabia; [Zwawi, Mohammed; Algarni, Mohammed] King Abdulaziz Univ, Fac Engn, Mech Engn Dept, POB 344, Rabigh 21911, Saudi Arabia Saveetha Institute of Medical & Technical Science; Saveetha Dental College & Hospital; Prince Sattam Bin Abdulaziz University; Egyptian Knowledge Bank (EKB); Cairo University; Chulabhorn Royal Academy; HRH Princess Chulabhorn College of Medical Science; Universiti Kebangsaan Malaysia; Universiti Malaysia Sabah; King Abdulaziz University Zhang, YJ (corresponding author), Tianjin Youmei Environm Protect Technol Co LTD, Tianjin 300383, Peoples R China.;Rahman, ML (corresponding author), Univ Malaysia Sabah, Fac Sci & Nat Resources, Kota Kinabalu 88400, Sabah, Malaysia. yiying5120@126.com; lotfor@ums.edu.my Algarni, Mohammed/AHA-8346-2022; t, l/GZN-1073-2022; Suksatan, Wanich/AAJ-1920-2021; Abdelbasset, Walid Kamal/T-8648-2017; Taban, Talib Zeedan/HJI-3172-2023; T, Lakshmi/AAL-4695-2020 Suksatan, Wanich/0000-0003-1797-1260; Abdelbasset, Walid Kamal/0000-0003-4703-661X; Taban, Talib Zeedan/0000-0002-6187-9819; Sarkar, Md. Shaheen/0000-0002-7741-678X 51 4 4 2 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-1864 ENVIRON TECHNOL INNO Environ. Technol. Innov. AUG 2022.0 27 102417 10.1016/j.eti.2022.102417 0.0 MAR 2022 12 Biotechnology & Applied Microbiology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Engineering; Environmental Sciences & Ecology 1I9AH gold 2023-03-23 WOS:000797517900007 0
J Mortazavi, B; Silani, M; Podryabinkin, EV; Rabczuk, T; Zhuang, XY; Shapeev, AV Mortazavi, Bohayra; Silani, Mohammad; Podryabinkin, Evgeny, V; Rabczuk, Timon; Zhuang, Xiaoying; Shapeev, Alexander, V First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials ADVANCED MATERIALS English Article first-principles calculations; machine learning; mechanical; failure response; multiscale modeling TOTAL-ENERGY CALCULATIONS; THERMAL-CONDUCTIVITY; GRAPHENE SHEETS; BOROPHENE; STABILITY; CHEMISTRY; STRENGTH; BEHAVIOR Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become exceedingly expensive for large systems at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. Herein the authors propose the concept of first-principles multiscale modeling of mechanical properties, where ab initio level of accuracy is hierarchically bridged to explore the mechanical/failure response of macroscopic systems. It is demonstrated that machine-learning interatomic potentials (MLIPs) fitted to ab initio datasets play a pivotal role in achieving this goal. To practically illustrate this novel possibility, the mechanical/failure response of graphene/borophene coplanar heterostructures is examined. It is shown that MLIPs conveniently outperform popular CMD models for graphene and borophene and they can evaluate the mechanical properties of pristine and heterostructure phases at room temperature. Based on the information provided by the MLIP-based CMD, continuum models of heterostructures using the finite element method can be constructed. The study highlights that MLIPs were the missing block for conducting first-principles multiscale modeling, and their employment empowers a straightforward route to bridge ab initio level accuracy and flexibility to explore the mechanical/failure response of nanostructures at continuum scale. [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Dept Math & Phys, Computat Sci & Simulat Technol, Inst Photon, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Opt & Engn Inn, D-30169 Hannover, Germany; [Silani, Mohammad] Isfahan Univ Technol, Dept Mech Engn, Esfahan 8415683111, Iran; [Podryabinkin, Evgeny, V; Shapeev, Alexander, V] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Nobel St 3, Moscow 143026, Russia; [Rabczuk, Timon] Tongji Univ, Dept Geotech Engn, Coll Civil Engn, Shanghai 1239, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Isfahan University of Technology; Skolkovo Institute of Science & Technology; Tongji University Mortazavi, B; Zhuang, XY (corresponding author), Leibniz Univ Hannover, Dept Math & Phys, Computat Sci & Simulat Technol, Inst Photon, Appelstr 11, D-30167 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Opt & Engn Inn, D-30169 Hannover, Germany. bohayra.mortazavi@gmail.com; zhuang@iop.uni-hannover.de Rabczuk, Timon/A-3067-2009; Zhuang, Xiaoying/G-4754-2011; Podryabinkin, Evgeny/GZK-2538-2022; Silani, Mohammad/ABA-5230-2021 Rabczuk, Timon/0000-0002-7150-296X; Zhuang, Xiaoying/0000-0001-6562-2618; Podryabinkin, Evgeny/0000-0001-9057-7249; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Russian Science Foundation [18-13-00479]; Iran National Science Foundation (INSF) [940004]; European Research Council Starting Grant COTOFLEXI [802205]; Projekt DEAL Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Russian Science Foundation(Russian Science Foundation (RSF)); Iran National Science Foundation (INSF)(Iran National Science Foundation (INSF)); European Research Council Starting Grant COTOFLEXI; Projekt DEAL M.S. and E.V.P. contributed equally to this work. B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479, ). M.S. thanks the financial support of the Iran National Science Foundation (INSF) under project no. 940004. X.Z. acknowledges the sponsorship from the European Research Council Starting Grant COTOFLEXI (No. 802205). B.M. and T.R. are greatly thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources. Open Access funding enabled and organized by Projekt DEAL. 55 63 63 30 91 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0935-9648 1521-4095 ADV MATER Adv. Mater. SEP 2021.0 33 35 2102807 10.1002/adma.202102807 0.0 JUL 2021 9 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics UK8DR 34296779.0 hybrid 2023-03-23 WOS:000676114900001 0
J Mortazavi, B; Shojaei, F; Shahrokhi, M; Rabczuk, T; Shapeev, AV; Zhuang, XY Mortazavi, Bohayra; Shojaei, Fazel; Shahrokhi, Masoud; Rabczuk, Timon; Shapeev, Alexander, V; Zhuang, Xiaoying Electronic, Optical, Mechanical and Li-Ion Storage Properties of Novel Benzotrithiophene-Based Graphdiyne Monolayers Explored by First Principles and Machine Learning BATTERIES-BASEL English Article graphdiyne; semiconductors; mechanical; optical; machine learning ANODE MATERIAL; GRAPHENE; CARBON; GRAPHYNE; BATTERY Recently, benzotrithiophene graphdiyne (BTT-GDY), a novel two-dimensional (2D) carbon-based material, was grown via a bottom-up synthesis strategy. Using the BTT-GDY lattice and by replacing the S atoms with N, NH and O, we designed three novel GDY lattices, which we named BTHP-, BTP- and BTF-GDY, respectively. Next, we explored structural, electronic, mechanical, optical, photocatalytic and Li-ion storage properties, as well as carrier mobilities, of novel GDY monolayers. Phonon dispersion relations, mechanical and failure behavior were explored using the machine learning interatomic potentials (MLIPs). The obtained HSE06 results reveal that BTX-GDYs (X = P, F, T) are direct gap semiconductors with band gaps in the range of 2.49-2.65 eV, whereas the BTHP-GDY shows a narrow indirect band gap of 0.06 eV. With appropriate band offsets, good carrier mobilities and a strong capability for the absorption of visible and ultraviolet range of light, BTF- and BTT-GDYs were predicted to be promising candidates for overall photocatalytic water splitting. The BTHP-GDY nanosheet, noticeably, was found to yield an ultrahigh Li-ion storage capacity of over 2400 mAh/g. The obtained findings provide a comprehensive vision of the critical physical properties of the novel BTT-based GDY nanosheets and highlight their potential for applications in nanoelectronics and energy storage and conversion systems. [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Cluster Excellence PhoenixD, Photon Opt & Engn Innovat Disciplines, D-30167 Hannover, Germany; [Shojaei, Fazel] Persian Gulf Univ, Fac Nano & Biosci & Technol, Dept Chem, Bushehr 75169, Iran; [Rabczuk, Timon; Zhuang, Xiaoying] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai 200070, Peoples R China; [Shapeev, Alexander, V] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Bolshoy Bulvar 30, Moscow 143026, Russia Leibniz University Hannover; Leibniz University Hannover; Persian Gulf University; Tongji University; Skolkovo Institute of Science & Technology Mortazavi, B; Zhuang, XY (corresponding author), Leibniz Univ Hannover, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Cluster Excellence PhoenixD, Photon Opt & Engn Innovat Disciplines, D-30167 Hannover, Germany.;Zhuang, XY (corresponding author), Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai 200070, Peoples R China. bohayra.mortazavi@gmail.com; zhuang@iop.uni-hannover.de Mortazavi, Bohayra/0000-0003-3031-5057 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Russian Science Foundation [18-13-00479] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Russian Science Foundation(Russian Science Foundation (RSF)) This research was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). A.V.S. is supported by the Russian Science Foundation (Grant No. 18-13-00479, https://rscf.ru/project/18-13-00479/, accessed on 24 August 2022). 40 0 0 7 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2313-0105 BATTERIES-BASEL Batteries-Basel OCT 2022.0 8 10 194 10.3390/batteries8100194 0.0 14 Electrochemistry; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Electrochemistry; Energy & Fuels; Materials Science 5O2TZ gold 2023-03-23 WOS:000872333500001 0
J Aldaya, I; Giacoumidis, E; Tsokanos, A; Jarajreh, M; Wen, YN; Wei, JL; Campuzano, G; Abbade, MLF; Barry, LP Aldaya, Ivan; Giacoumidis, Elias; Tsokanos, Athanasios; Jarajreh, Mutsam; Wen, Yannuo; Wei, Jinlong; Campuzano, Gabriel; Abbade, Marcelo L. F.; Barry, Liam P. Compensation of nonlinear distortion in coherent optical OFDM systems using a MIMO deep neural network-based equalizer OPTICS LETTERS English Article A novel nonlinear equalizer based on a multiple-input multiple-output (MIMO) deep neural network (DNN) is proposed and experimentally demonstrated for compensation of inter-subcarrier nonlinearities in a 40 Gb/s coherent optical orthogonal frequency division multiplexing system. Experimental results reveal that MIMO-DNN can extend the power margin by 4 dB at 2000 km of standard single-mode fiber transmission when compared to linear compensation or conventional single-input single-output DNN. It is also found that MIMO-DNN outperforms digital back propagation by increasing up to 1 dB the effective Q-factor and reducing by a factor of three the computational cost. (C) 2020 Optical Society of America [Aldaya, Ivan; Abbade, Marcelo L. F.] Sao Paulo State Univ, Campus Sao Joao da Boa Vista, Sao Joao Da Boa Vista, SP, Brazil; [Giacoumidis, Elias; Barry, Liam P.] Dublin City Univ, Sch Elect Engn, Dublin 9, Ireland; [Tsokanos, Athanasios] Univ Hertfordshire, Hatfield, Herts, England; [Jarajreh, Mutsam] Fahad Bin Sultan Univ, Tabuk, Saudi Arabia; [Wen, Yannuo] Beijing Univ Technol, 100 Pingleyuan, Beijing, Peoples R China; [Wei, Jinlong] Huawei Technol Duesseldorf GmbH, European Res Ctr, Riesstr 25, Munich, Germany; [Campuzano, Gabriel] Tecnol Monterrey, Sch Engn, Monterrey, Mexico Universidade Estadual Paulista; Dublin City University; University of Hertfordshire; Fahad Bin Sultan University; Beijing University of Technology; Huawei Technologies; Tecnologico de Monterrey Aldaya, I (corresponding author), Sao Paulo State Univ, Campus Sao Joao da Boa Vista, Sao Joao Da Boa Vista, SP, Brazil. ivan.a.aldaya@ieee.org Aldaya, Ivan/O-6793-2017; Wei, Jinlong/K-5803-2012 Aldaya, Ivan/0000-0002-7969-3051; Barry, Liam/0000-0001-8366-4790; Wei, Jinlong/0000-0001-7714-5003; Abbade, Marcelo/0000-0003-2823-4725 Fundacao de Amparo a Pesquisa do Estado de Sao Paulo [2018/25339-4]; Science Foundation Ireland [12/RC/2276, 13/RC/2077, 15/US-C2C/I3132]; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [311035/2018-3, 432303/2018-9] Fundacao de Amparo a Pesquisa do Estado de Sao Paulo(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Science Foundation Ireland(Science Foundation Ireland); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (2018/25339-4); Science Foundation Ireland (12/RC/2276, 13/RC/2077, 15/US-C2C/I3132); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (311035/2018-3, 432303/2018-9). 14 4 4 3 10 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0146-9592 1539-4794 OPT LETT Opt. Lett. OCT 15 2020.0 45 20 5820 5823 10.1364/OL.403778 0.0 4 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics OF4RY 33057293.0 2023-03-23 WOS:000581198200047 0
J Lin, XX; Wang, Y; Liu, ZS; Lin, SN; Tan, JY; He, JS; Hu, F; Wu, XM; Ghosh, S; Chen, MH; Liu, F; Mao, R Lin, Xiaoxuan; Wang, Yu; Liu, Zishan; Lin, Sinan; Tan, Jinyu; He, Jinshen; Hu, Fan; Wu, Xiaomin; Ghosh, Subrata; Chen, Minhu; Liu, Fen; Mao, Ren Intestinal strictures in Crohn's disease: a 2021 update THERAPEUTIC ADVANCES IN GASTROENTEROLOGY English Review Crohn's disease; diagnosis; fibrosis; intestinal strictures; pathogenesis; treatment COMPUTED-TOMOGRAPHY; BOWEL STRICTURES; FIBROSIS; MANAGEMENT; MYOFIBROBLASTS; COMPLICATIONS; ENTEROGRAPHY; PATHOGENESIS; FIBROCYTES; ENDOSCOPY Intestinal strictures remain one of the most intractable and common complications of Crohn's disease (CD). Approximately 70% of CD patients will develop fibrotic strictures after 10 years of CD diagnosis. Since specific antifibrotic therapies are unavailable, endoscopic balloon dilation and surgery remain the mainstay treatments despite a high recurrence rate. Besides, there are no reliable methods for accurately evaluating intestinal fibrosis. This is largely due to the fact that the mechanisms of initiation and propagation of intestinal fibrosis are poorly understood. There is growing evidence implying that the pathogenesis of stricturing CD involves the intricate interplay of factors including aberrant immune and nonimmune responses, host-microbiome dysbiosis, and genetic susceptibility. Currently, the progress on intestinal strictures has been fueled by the advent of novel techniques, such as single-cell sequencing, multi-omics, and artificial intelligence. Here, we perform a timely and comprehensive review of the substantial advances in intestinal strictures in 2021, aiming to provide prompt information regarding fibrosis and set the stage for the improvement of diagnosis, treatment, and prognosis of intestinal strictures. [Lin, Xiaoxuan; Wang, Yu; Liu, Zishan; Lin, Sinan; Tan, Jinyu; He, Jinshen; Hu, Fan; Wu, Xiaomin; Chen, Minhu; Liu, Fen; Mao, Ren] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Gastroenterol, 58 Zhongshan Rd 2nd, Guangzhou 510080, Peoples R China; [Mao, Ren] Huidong Peoples Hosp, Dept Gastroenterol, Huizhou 516399, Peoples R China; [Ghosh, Subrata] Univ Coll Cork, APC Microbiome Ireland, Cork, Ireland Sun Yat Sen University; University College Cork Liu, F; Mao, R (corresponding author), Sun Yat Sen Univ, Affiliated Hosp 1, Dept Gastroenterol, 58 Zhongshan Rd 2nd, Guangzhou 510080, Peoples R China.;Mao, R (corresponding author), Huidong Peoples Hosp, Dept Gastroenterol, Huizhou 516399, Peoples R China. liuf237@mail.sysu.edu.cn; maor5@mail.sysu.edu.cn Wang, Yu/0000-0002-5224-3333; Ghosh, Subrata/0000-0002-1713-7797; Lin, Xiaoxuan/0000-0002-4668-9101 National Natural Science Foundation of China [81970483, 82170537] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (NSFC grant No 81970483, and 82170537 to Ren Mao). 96 0 0 2 2 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 1756-283X 1756-2848 THER ADV GASTROENTER Ther. Adv. Gastroenterol. JUN 2022.0 15 17562848221104952 10.1177/17562848221104951 0.0 15 Gastroenterology & Hepatology Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology 2H7FE 35757383.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000814455400001 0
C Kosuda, M; Lipovsky, P; Yang, WJ; Bai, ML; Novotnak, J; Szoke, Z Andoga, R; Fozo, L Kosuda, Marek; Lipovsky, Pavol; Yang, Wenjiang; Bai, Mingliang; Novotnak, Jozef; Szoke, Zoltan Discussion on blockchain applications in unmanned aerial systems domain 2020 NEW TRENDS IN AVIATION DEVELOPMENT (NTAD): XV INTERNATIONAL SCIENTIFIC CONFERENCE NTINAD English Proceedings Paper 15th International Scientific Conference on New Trends in Aviation Development (NTinAD) SEP 17-18, 2020 Stary Smokovec, SLOVAKIA IEEE, Czechoslovakia Sect DRONES A rise in usage of unmanned aerial systems (UAS) in commercial and civilian applications as a result of the recent advancement in manufacturing processes, communications, and networking technology led to the demand for access to non-segregated airspace. The steady development of UAS has created a dynamic environment moving at faster pace than manned aviation. In particular, there is an intense pressure on operations in very low altitudes where market is driven by new business models. Commercial UAS operators are expected to monitor weather, changes in airspace structure, avoid buildings and temporary obstacles, consider potential risks arising from the ground, and maintain separation from manned aviation. On the other hand, authorities are expected to monitor, and control unmanned aerial traffic as well as enforce the airspace rules. To address this arising requirements unmanned traffic management is being developed and deployed in numerous countries, which aims to enable safe and efficient operations of highly automated UAS in automated unmanned traffic management (UTM) system developed on foundations of modern technologies such as artificial intelligence, the Internet-of-Things, 5G networks while considering the need to address cybersecurity requirements. Among cybersecurity requirements a potential candidate with promising applications is blockchain technology, which is a distributed ledger of immutable records stored in a decentralized database. This paper discusses potential applications of blockchain technology that could support UAS operations and increase security in storing crucial flight data. [Kosuda, Marek; Lipovsky, Pavol; Novotnak, Jozef; Szoke, Zoltan] Tech Univ Kosice, Fac Aeronaut, Dept Aviat Tech Studies, Kosice, Slovakia; [Yang, Wenjiang; Bai, Mingliang] Beihang Univ, Sch Astronaut, Beijing, Peoples R China Technical University Kosice; Beihang University Kosuda, M (corresponding author), Tech Univ Kosice, Fac Aeronaut, Dept Aviat Tech Studies, Kosice, Slovakia. marek.kosuda@tuke.sk; pavol.lipovsky@tuke.sk; yangwjbuaa@buaa.edu.cn; baiml@buaa.edu.cn; jozef.novotnak@tuke.sk; zoltan.szoke@tuke.sk Lipovský, Pavol/AAA-6077-2020; Novotňák, Jozef/ABC-7153-2020 Lipovský, Pavol/0000-0002-6827-242X; Novotňák, Jozef/0000-0003-2226-4077 KEGA [052TUKE-4-2018, 058TUKE-4-2018]; ITMS [313011T557]; [APVV-17-0184]; [APVV-18-0248]; [019/2019/1.1.3/OPVaI/DP] KEGA; ITMS; ; ; This work was supported by the national grant agencies under APVV-17-0184, APVV-18-0248, KEGA 052TUKE-4-2018 and KEGA 058TUKE-4-2018 grants.; This work was supported by the project No. 019/2019/1.1.3/OPVaI/DP with the ITMS code 313011T557 entitled Support of the research and development potential in the area of transportation vehicles. 27 0 0 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-7325-2 2020.0 142 145 10.1109/NTAD51447.2020.9379109 0.0 4 Engineering, Aerospace Conference Proceedings Citation Index - Science (CPCI-S) Engineering BR8YL 2023-03-23 WOS:000674701800028 0
J Mankin, R; Hagstrum, D; Guo, M; Eliopoulos, P; Njoroge, A Mankin, Richard; Hagstrum, David; Guo, Min; Eliopoulos, Panagiotis; Njoroge, Anastasia Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management INSECTS English Article Sitophilus oryzae; Tribolium castaneum; abundance; population density; neural networks; machine learning FERRUGINEUS COLEOPTERA DRYOPHTHORIDAE; TRIBOLIUM-CASTANEUM COLEOPTERA; IMPROVE SPEECH-INTELLIGIBILITY; COPTOTERMES-FORMOSANUS SHIRAKI; MACULATUS F COLEOPTERA; FRUIT-FLY DIPTERA; CALLOSOBRUCHUS-MACULATUS; INCISITERMES-MINOR; SITOPHILUS-ORYZAE; NONDESTRUCTIVE DETECTION Simple Summary A variety of different acoustic devices has been commercialized for detection of hidden insect infestations in stored products, trees, and soil, including a recently introduced device demonstrated in this report to successfully detect rice weevil immatures and adults in grain. Several of the systems have incorporated digital signal processing and statistical analyses such as neural networks and machine learning to distinguish targeted pests from each other and from background noise, enabling automated monitoring of the abundance and distribution of pest insects in stored products, and potentially reducing the need for chemical control. Current and previously available devices are reviewed in the context of the extensive research in stored product insect acoustic detection since 2011. It is expected that further development of acoustic technology for detection and management of stored product insect pests will continue, facilitating automation and decreasing detection and management costs. Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future. [Mankin, Richard] ARS, USDA, Ctr Med Agr & Vet Entomol CMAVE, Gainesville, FL 32608 USA; [Hagstrum, David] Kansas State Univ, Dept Entomol, Manhattan, KS 66502 USA; [Guo, Min] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China; [Eliopoulos, Panagiotis] Univ Thessaly, Dept Agrotechnol, Larisa 41500, Greece; [Njoroge, Anastasia] Univ Florida, Inst Food & Agr Sci, Trop Res & Educ Ctr, Homestead, FL 33031 USA United States Department of Agriculture (USDA); Kansas State University; Shaanxi Normal University; University of Thessaly; State University System of Florida; University of Florida Mankin, R (corresponding author), ARS, USDA, Ctr Med Agr & Vet Entomol CMAVE, Gainesville, FL 32608 USA. Richard.Mankin@usda.gov; hgstr@ksu.edu; guomin@snnu.edu.cn; eliopoulos@uth.gr; anjoroge@ufl.edu Eliopoulos, Panagiotis A./AAB-4736-2020; Njoroge, Anastasia/L-5783-2019 Eliopoulos, Panagiotis A./0000-0003-2055-0950; Njoroge, Anastasia/0000-0001-9155-6423; Mankin, Richard/0000-0003-3369-8110 King Abdullah University of Science and Technology (Saudi Arabia) [58-6036-8-024F]; Fundamental Research Funds for the Central Universities (China) [GK202105006] King Abdullah University of Science and Technology (Saudi Arabia)(King Abdullah University of Science & Technology); Fundamental Research Funds for the Central Universities (China)(Fundamental Research Funds for the Central Universities) This research was funded in part by a grant from King Abdullah University of Science and Technology (Agreement #58-6036-8-024F, Saudi Arabia), and was supported by the Fundamental Research Funds for the Central Universities (Grant No.GK202105006, China). 287 10 10 9 65 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-4450 INSECTS Insects MAR 2021.0 12 3 259 10.3390/insects12030259 0.0 23 Entomology Science Citation Index Expanded (SCI-EXPANDED) Entomology RD9GJ 33808747.0 Green Accepted, gold 2023-03-23 WOS:000633776400001 0
J Ibrar, M; Wang, L; Muntean, GM; Akbar, A; Shah, N; Malik, KR Ibrar, Muhammad; Wang, Lei; Muntean, Gabriel-Miro; Akbar, Aamir; Shah, Nadir; Malik, Kaleem Razzaq PrePass-Flow: A Machine Learning based technique to minimize ACL policy violation due to links failure in hybrid SDN COMPUTER NETWORKS English Article Hybrid SDN; Machine Learning; ACL; Link Failure Prediction; Network reachability SOFTWARE-DEFINED NETWORKING; PREDICTION; REGRESSION; DEPLOYMENT The centralized architecture of Software-Defined Networking (SDN) reduces networking complexity and improves network manageability by omitting the need for box-by-box troubleshooting and management. However, due to both budget constraints and maturity level of the SDN-capable devices, organizations often are reluctant to adopt SDN in practice. Therefore, instead of migrating to a pure SDN architecture, an incremental SDN deployment strategy is preferred in practice. In this paper, we consider an incremental SDN deployment strategy known as hybrid SDN -involving simultaneous use of both SDN switches and legacy switches. The links connected to an SDN switch are called SDN links, and the rest are called legacy links. An SDN controller can directly poll the status of the SDN links via the connected SDN switches. At the same time, the status of the legacy links passes through SDN switches and reaches the controller, causing delay. As a result, the controller does not have the current status of legacy links in real-time. This delay may lead to undesired outcomes. For example, it causes network reachability problems due to Access Control List (ACL) policies. Therefore, to minimize the impact of network-layer failure in hybrid SDN, we propose a Machine Learning (ML) based technique called PrePass-Flow. PrePass-Flow predicts link failures before their occurrence, recomputes the locations of ACL policies, and installs the ACL policies in the recomputed locations in a proactive manner. The main objective of PrePass-Flow is to minimize the ACL policy violations and network reachability problems due to ACL policies in case of link failures. For the link status prediction, PrePass-Flow uses two supervised ML-based models: 1) a Logistic Regression (LR) model, and 2) a Support Vector Machine (SVM) model. Testing results show that the LR model performs better than both the SVM model and an existing approach in terms of Packet Delivery Ratio (PDR) and ACL policy violations. For instance, the LR model's accuracy is 4% better, precision is 5% higher, sensitivity is 10% better, and Area Under the Curve (AUC) is 6% greater than the SVM model's corresponding results. [Ibrar, Muhammad; Wang, Lei] Dalian Univ Technol, Sch Software, Dalian, Peoples R China; [Muntean, Gabriel-Miro] Dublin City Univ, Sch Elect Engn, Dublin, Ireland; [Akbar, Aamir] Abdul Wali Khan Univ Mardan AWKUM, Dept Comp Sci, Mardan, Pakistan; [Shah, Nadir] COMSATS Univ Islamabad, Dept Comp Sci, Wah Campus, Rawalpindi, Pakistan; [Malik, Kaleem Razzaq] Air Univ, Dept Comp Sci, Multan Campus, Multan, Pakistan; [Wang, Lei] Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China; [Wang, Lei] Peng Cheng Lab, Shenzhen, Peoples R China Dalian University of Technology; Dublin City University; COMSATS University Islamabad (CUI); Air University Islamabad; Peng Cheng Laboratory Wang, L (corresponding author), Dalian Univ Technol, Sch Software, Dalian, Peoples R China.;Wang, L (corresponding author), Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China.;Wang, L (corresponding author), Peng Cheng Lab, Shenzhen, Peoples R China. mibrar@mail.dlut.edu.cn; lei.wang@dlut.edu.cn Akbar, Aamir/AAC-8033-2020; Ibrar, Muhammad/ABI-7976-2020 Akbar, Aamir/0000-0002-9421-7379; Ibrar, Muhammad/0000-0002-6813-5003; Malik, Kaleem/0000-0003-4538-3095; Shah, Nadir/0000-0003-1173-4272 National Key Research and Development Plan [2017YFC0821003-2]; Dalian Science and Technology Innovation Fund [2019J11CY004]; Science Foundation Ireland Research Centres Programme [12/RC/2289_P2, 16/SP/3804]; Higher Education Commission (HEC), Pakistan [5372/Federal/NRPU/RD/HEC/2016] National Key Research and Development Plan; Dalian Science and Technology Innovation Fund; Science Foundation Ireland Research Centres Programme(Science Foundation Ireland); Higher Education Commission (HEC), Pakistan(Higher Education Commission of Pakistan) This work was supported in part by the ''National Key Research and Development Planwith No. 2017YFC0821003-2, ''Dalian Science and Technology Innovation Fund with No. 2019J11CY004. G.-M. Muntean acknowledges the support of the Science Foundation Ireland Research Centres Programme grants 12/RC/2289_P2 (Insight) and 16/SP/3804 (ENABLE). Nadir Shah wishes to acknowledge the support of Higher Education Commission (HEC), Pakistan under the project No. 5372/Federal/NRPU/RD/HEC/2016, titled ``FAULT LOCALIZATION IN TERM OF SECURITY AND MANAGEMENT POLICY IN SOFTWARE DEFINED NETWORKS. 79 11 11 1 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. JAN 15 2021.0 184 107706 10.1016/j.comnet.2020.107706 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications YI9AG 2023-03-23 WOS:000744133100006 0
C Shu, C; Dosyn, D; Lytvyn, V; Vysotska, V; Sachenko, A; Jun, S IEEE Shu, Chang; Dosyn, Dmytro; Lytvyn, Vasyl; Vysotska, Victoria; Sachenko, Anatoly; Jun, Su Building of the Predicate Recognition System for the NLP Ontology Learning Module PROCEEDINGS OF THE 2019 10TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS - TECHNOLOGY AND APPLICATIONS (IDAACS), VOL. 2 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems-Technology and Applications-IDAACS English Proceedings Paper 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS) SEP 18-21, 2019 Metz, FRANCE IEEE,IEEE Ukraine Sect I & M CI Joint Soc Chapter,Ternopil Natl Econ Univ, Res Inst Intelligent Comp Syst,Natl Acad Sci Ukraine, V M Glushkov Inst Cybernet,Ecole Natl Ing Metz,Univ Lorraine, Lab Concept Optimisat & Modelling Syst,IEEE Ukraine Sect,IEEE France Sect,MDPI Sensors,River Publishers,Fondat ENIM natural language processing; logical predicate; ontology learning; knowledge base The practical approach to recognizing a description logic predicate in a natural language sentence is presented. The approach is based on using the open source tool - Link Grammar Parser in combination with Protege-OWL API. Machine learning method of recognition based on naive Bayes allows distinguishing kind of previously learned binary predicate and roles of its attributes. The predicate recognition system had been included into ontology learning module of the CROCUS application. [Shu, Chang] NorthWest Univ Polit Sci & Law, Xian 710122, Peoples R China; [Dosyn, Dmytro; Lytvyn, Vasyl; Vysotska, Victoria] Lviv Polytech Natl Univ, Lvov, Ukraine; [Sachenko, Anatoly] Univ Informat Technol & Management Rzeszow, PL-35225 Rzeszow, Poland; [Jun, Su] Hubei Univ Tehcnol, Wuhan 430068, Peoples R China Northwest University of Political Science & Law; Ministry of Education & Science of Ukraine; Lviv Polytech National University; University of Information Technology & Management Rzeszow Shu, C (corresponding author), NorthWest Univ Polit Sci & Law, Xian 710122, Peoples R China. changshucn@126.com; Vasyl.V.Lytvyn@lpnu.ua; sjhosix@gmail.com Lytvyn, Vasyl/AAY-9371-2020; Vysotska, Victoria/P-7714-2016; Dosyn, Dmytro/P-8184-2016 Lytvyn, Vasyl/0000-0002-9676-0180; Vysotska, Victoria/0000-0001-6417-3689; Dosyn, Dmytro/0000-0003-4040-4467 27 12 12 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-4068-1 INT WORKSH INT DATA 2019.0 802 808 7 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP0KO 2023-03-23 WOS:000535131000043 0
J Douglass, EF; Allaway, RJ; Szalai, B; Wang, WY; Tian, TZ; Fernandez-Torras, A; Realubit, R; Karan, C; Zheng, SY; Pessia, A; Tanoli, Z; Jafari, M; Wan, FP; Li, SY; Xiong, YP; Duran-Frigola, M; Bertoni, M; Badia-I-Mompel, P; Mateo, L; Guitart-Pla, O; Chung, V; Tang, J; Zeng, JY; Aloy, P; Saez-Rodriguez, J; Guinney, J; Gerhard, DS; Califano, A Douglass, Eugene F.; Allaway, Robert J.; Szalai, Bence; Wang, Wenyu; Tian, Tingzhong; Fernandez-Torras, Adria; Realubit, Ron; Karan, Charles; Zheng, Shuyu; Pessia, Alberto; Tanoli, Ziaurrehman; Jafari, Mohieddin; Wan, Fangping; Li, Shuya; Xiong, Yuanpeng; Duran-Frigola, Miquel; Bertoni, Martino; Badia-I-Mompel, Pau; Mateo, Lidia; Guitart-Pla, Oriol; Chung, Verena; Tang, Jing; Zeng, Jianyang; Aloy, Patrick; Saez-Rodriguez, Julio; Guinney, Justin; Gerhard, Daniela S.; Califano, Andrea A community challenge for a pancancer drug mechanism of action inference from perturbational profile data CELL REPORTS MEDICINE English Article CONNECTIVITY MAP; SMALL MOLECULES; CANCER; TARGET; IDENTIFICATION; SIMILARITY; SIGNATURES; PREDICT The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with similar to 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among similar to 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action. [Douglass, Eugene F.; Realubit, Ron; Karan, Charles; Califano, Andrea] Columbia Univ Irving Med Ctr, Dept Syst Biol, 1130 St Nicholas Ave, New York, NY 10032 USA; [Douglass, Eugene F.] Univ Georgia, Pharmaceut & Biomed Sci, 250 W Green St, Athens, GA 30602 USA; [Allaway, Robert J.; Chung, Verena; Guinney, Justin] Sage Bionetworks, Computat Oncol Grp, 2901 Third Ave,Ste 330, Seattle, WA 98121 USA; [Szalai, Bence] Semmelweis Univ, Fac Med, Dept Physiol, Budapest, Hungary; [Wang, Wenyu; Zheng, Shuyu; Pessia, Alberto; Tanoli, Ziaurrehman; Jafari, Mohieddin; Tang, Jing] Univ Helsinki, Fac Med, Res Program Syst Oncol, Helsinki, Finland; [Tian, Tingzhong; Wan, Fangping; Li, Shuya; Zeng, Jianyang] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing 100084, Peoples R China; [Fernandez-Torras, Adria; Duran-Frigola, Miquel; Bertoni, Martino; Badia-I-Mompel, Pau; Mateo, Lidia; Guitart-Pla, Oriol; Aloy, Patrick] Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Joint IRB BSC CRG Program Computat Biol, Barcelona, Spain; [Xiong, Yuanpeng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Zeng, Jianyang] Tsinghua Univ, MOE Key Lab Bioinformat, Beijing 100084, Peoples R China; [Aloy, Patrick] Inst Catalana Recerca & Estudis Avancats ICREA, Barcelona, Spain; [Saez-Rodriguez, Julio] Heidelberg Univ, Fac Med, Heidelberg, Germany; [Saez-Rodriguez, Julio] Heidelberg Univ Hosp, Inst Computat Biomed, Bioquant, Heidelberg, Germany; [Gerhard, Daniela S.] NCI, Off Canc Genom, NIH, Bethesda, MD 20892 USA; [Califano, Andrea] Columbia Univ Irving Med Ctr, Herbert Irving Comprehens Canc Ctr, 1130 St Nicholas Ave, New York, NY 10032 USA; [Califano, Andrea] Columbia Univ Irving Med Ctr, Dept Med, 630 W 168th St, New York, NY 10032 USA; [Califano, Andrea] Columbia Univ Irving Med Ctr, Dept Biochem & Mol Biophys, 701 W 168th St, New York, NY 10032 USA; [Califano, Andrea] Columbia Univ Irving Med Ctr, Dept Biomed Informat, 622 W 168th St, New York, NY 10032 USA NewYork-Presbyterian Hospital; University System of Georgia; University of Georgia; Semmelweis University; University of Helsinki; Tsinghua University; Barcelona Institute of Science & Technology; Institute for Research in Biomedicine - IRB Barcelona; Tsinghua University; Tsinghua University; ICREA; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Center for Cancer Genomics (CCG); NewYork-Presbyterian Hospital; NewYork-Presbyterian Hospital; NewYork-Presbyterian Hospital; NewYork-Presbyterian Hospital Califano, A (corresponding author), Columbia Univ Irving Med Ctr, Dept Syst Biol, 1130 St Nicholas Ave, New York, NY 10032 USA.;Califano, A (corresponding author), Columbia Univ Irving Med Ctr, Herbert Irving Comprehens Canc Ctr, 1130 St Nicholas Ave, New York, NY 10032 USA.;Califano, A (corresponding author), Columbia Univ Irving Med Ctr, Dept Med, 630 W 168th St, New York, NY 10032 USA.;Califano, A (corresponding author), Columbia Univ Irving Med Ctr, Dept Biochem & Mol Biophys, 701 W 168th St, New York, NY 10032 USA.;Califano, A (corresponding author), Columbia Univ Irving Med Ctr, Dept Biomed Informat, 622 W 168th St, New York, NY 10032 USA. ac2248@cumc.columbia.edu tang, jing/HHR-9815-2022; Tang, Jing/W-1764-2019; Tang, Jing/H-4084-2012; Jafari, Mohieddin/M-2755-2019; Douglass, Eugene/O-5003-2018; Allaway, Robert/ABE-1264-2021 Tang, Jing/0000-0001-7480-7710; Tang, Jing/0000-0001-7480-7710; Jafari, Mohieddin/0000-0002-6991-8587; Saez-Rodriguez, Julio/0000-0002-8552-8976; Wan, Fangping/0000-0003-1647-3278; Duran-Frigola, Miquel/0000-0002-9906-6936; Zheng, Shuyu/0000-0003-0624-8077; Szalai, Bence/0000-0002-9320-5704; Tanoli, Ziaurrehman/0000-0003-2435-9862; Tian, Tingzhong/0000-0003-4899-0632; Douglass, Eugene/0000-0003-3418-9203; Chung, Verena/0000-0002-5622-7998; Allaway, Robert/0000-0003-3573-3565 National Cancer Institute's Office of Cancer Genomics Cancer Target Discovery and Development (CTD2) initiative; National Cancer Institute's Office of Cancer Genomics; Generalitat de Catalunya [001-P-001682, 001-P-001647]; Spanish Ministerio de Economi'f Cancer Genomics [BIO2016-77038-R]; European Research Council [614944]; European Commission [824087]; Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences [460044]; Cancer Target Discovery and Development Center grant [U01-CA168426]; NIH [S10-OD012351, S10OD021764]; CTD2 program [U01-CA217862]; European Research Council (ERC) [716063]; Academy of Finland [332454]; Doctoral Program of Biomedicine, University of Helsinki; K. Albin Johanssons stiftelse; CSC IT Center for Science, Finland; Academy of Finland (AKA) [332454] Funding Source: Academy of Finland (AKA) National Cancer Institute's Office of Cancer Genomics Cancer Target Discovery and Development (CTD2) initiative; National Cancer Institute's Office of Cancer Genomics; Generalitat de Catalunya(Generalitat de Catalunya); Spanish Ministerio de Economi'f Cancer Genomics; European Research Council(European Research Council (ERC)European Commission); European Commission(European CommissionEuropean Commission Joint Research Centre); Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Cancer Target Discovery and Development Center grant; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); CTD2 program; European Research Council (ERC)(European Research Council (ERC)European Commission); Academy of Finland(Academy of Finland); Doctoral Program of Biomedicine, University of Helsinki; K. Albin Johanssons stiftelse; CSC IT Center for Science, Finland; Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported by the National Cancer Institute's Office of Cancer Genomics Cancer Target Discovery and Development (CTD2) initiative. The results published here are based in whole or in part upon data generated by CTD2 Network (https://ocg.cancer.gov/programs/ctd2/data-portal), established by the National Cancer Institute's Office of Cancer Genomics. P.A. acknowledges the support of the Generalitat de Catalunya (RIS3CAT Emergents CECH: 001-P-001682 and VEIS: 001-P-001647), the Spanish Ministerio de Economi ' a y Competitividad (BIO2016-77038-R), the European Research Council (SysPharmAD: 614944), and the European Commission (RiPCoN: 101003633). B.S. was supported by the Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences (460044). A.C. was supported by a Cancer Target Discovery and Development Center grant (U01-CA168426) and by NIH shared instrumentation grants S10-OD012351 and S10OD021764. A.C., J.G., R.J.A., and E.F.D. were supported through supplemental funding from the CTD2 program (U01-CA217862). J.T. acknowledges the support of European Research Council (ERC) starting grant DrugComb (no. 716063) and the Academy of Finland (no. 317680). J.T. and Z.T. were supported by European Commission H2020 EOSC-life (no. 824087). W.W. was funded by the Doctoral Program of Biomedicine, University of Helsinki, and K. Albin Johanssons stiftelse. M.J. was supported by the Academy of Finland (no. 332454). The Challenge team NetPhar acknowledges their colleagues Bulat Zagidullin and Jehad Aldahdooh for technical support, as well as the CSC IT Center for Science, Finland, for computational resources. 67 5 5 5 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3791 CELL REP MED Cell Rep. Med. JAN 18 2022.0 3 1 100492 10.1016/j.xcrm.2021.100492 0.0 JAN 2022 20 Cell Biology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Cell Biology; Research & Experimental Medicine YK2GC 35106508.0 gold, Green Submitted, Green Published 2023-03-23 WOS:000745037100009 0
J Wei, Y; Varanasi, RS; Schwarz, T; Gomell, L; Zhao, H; Larson, DJ; Sun, BH; Liu, G; Chen, H; Raabe, D; Gault, B Wei, Ye; Varanasi, Rama Srinivas; Schwarz, Torsten; Gomell, Leonie; Zhao, Huan; Larson, David J.; Sun, Binhan; Liu, Geng; Chen, Hao; Raabe, Dierk; Gault, Baptiste Machine-learning-enhanced time-of-flight mass spectrometry analysis PATTERNS English Article ATOM-PROBE TOMOGRAPHY; OPTIMIZATION; PERSPECTIVES; IONIZATION; COMPONENT; FRACTION; SPECTRA; FUTURE; IMAGES; IONS Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. [Wei, Ye; Varanasi, Rama Srinivas; Schwarz, Torsten; Gomell, Leonie; Zhao, Huan; Sun, Binhan; Raabe, Dierk; Gault, Baptiste] Max Planck Inst Eisenforsch GmbH, Max Planck Str 1, D-40237 Dusseldorf, Germany; [Gault, Baptiste] Imperial Coll, Royal Sch Mines, Dept Mat, London SW7 2AZ, England; [Liu, Geng; Chen, Hao] Tsinghua Univ, Sch Mat Sci & Engn, Key Lab Adv Mat, Minist Educ, Beijing 100084, Peoples R China; [Larson, David J.] CAMECA Instruments, 5470 Nobel Dr, Madison, WI 53711 USA Max Planck Society; Imperial College London; Tsinghua University Wei, Y; Gault, B (corresponding author), Max Planck Inst Eisenforsch GmbH, Max Planck Str 1, D-40237 Dusseldorf, Germany.;Gault, B (corresponding author), Imperial Coll, Royal Sch Mines, Dept Mat, London SW7 2AZ, England. y.wei@mpie.de; b.gault@mpie.de Sun, Binhan/AAE-7860-2021 WEI, YE/0000-0003-1965-2298; Sun, Binhan/0000-0001-9561-7019; Zhao, Huan/0000-0001-8840-084X; Varanasi, Rama Srinivas/0000-0003-3837-6129 BiGmax, Max Planck Society's Research Network on Big-Data-Driven Materials Science; Stiftung des Deutschen Volkes; IMPRS-SURMAT; CSC; German Research Foundation (DFG) [GA 2450/1-1]; Luxembourgish Fonds National de la Recherche (CORRKEST); [SFB TR270]; [ERC-CoG-SHINE-771602] BiGmax, Max Planck Society's Research Network on Big-Data-Driven Materials Science; Stiftung des Deutschen Volkes; IMPRS-SURMAT; CSC; German Research Foundation (DFG)(German Research Foundation (DFG)); Luxembourgish Fonds National de la Recherche (CORRKEST); ; B.G. acknowledges support fromthe SFB TR270 -Hommage, project Z01. Y.W. appreciates funding by BiGmax, theMax Planck Society's Research Network on Big-Data-Driven Materials Science. The authors are grateful to Professors Leopoldo Molina-Luna and Oliver Gutfleisch for providing the Sm-Co hard magnet sample. B.G. and Y.W. acknowledge financial support from the ERC-CoG-SHINE-771602. L.G. is grateful for funding from the Stiftung des Deutschen Volkes. R.S.V. is grateful for funding from IMPRS-SURMAT. H.Z. is grateful for funding from the CSC. T.S. is grateful for funding from the German Research Foundation (DFG) (contract GA 2450/1-1) and the Luxembourgish Fonds National de la Recherche (CORRKEST). The authors are grateful toUwe Tezins, Andreas Sturm, and Christian Bross for their support of the APT and FIB facilities at the Max-Planck-Institut f_ur Eisenforschung GmbH. 67 8 9 5 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns FEB 12 2021.0 2 2 100192 10.1016/j.patter.2020.100192 0.0 FEB 2021 13 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science SH0OR 33659909.0 Green Submitted, Green Accepted, Green Published, gold 2023-03-23 WOS:000653837800004 0
C Huo, ZQ; Mukherjee, M; Shu, L; Chen, YF; Zhou, ZB IEEE Huo, Zhiqiang; Mukherjee, Mithun; Shu, Lei; Chen, Yuanfang; Zhou, Zhangbing Cloud-based Data-intensive Framework towards Fault Diagnosis in Large-scale Petrochemical Plants 2016 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) International Wireless Communications and Mobile Computing Conference English Proceedings Paper 12th International Wireless Communications & Mobile Computing Conference (IWCMC) SEP 05-09, 2016 Paphos, CYPRUS IEEE,IEEE Cyprus Sect,Frederick Univ IWSNs; Fault Diagnosis; Big Data; Cloud Computing; Hadoop; Industrial Sensing Ecosystem Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE. [Huo, Zhiqiang; Mukherjee, Mithun; Shu, Lei; Chen, Yuanfang] Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming, Peoples R China; [Huo, Zhiqiang; Zhou, Zhangbing] China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China; [Chen, Yuanfang; Zhou, Zhangbing] Telecom SudParis, Sch Informat Engn, Evry, France Guangdong University of Petrochemical Technology; China University of Geosciences; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Shu, L (corresponding author), Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming, Peoples R China. zqhuo@cugb.edu.cn; m.mukherjee@ieee.org; lei.shu@ieee.org; yuanfang.chen.2009@ieee.org; zbzhou@cugb.edu.cn Shu, Lei/HHT-0694-2022; Chen, Yuanfang/AAJ-5465-2020 Shu, Lei/0000-0002-6700-9347; Chen, Yuanfang/0000-0002-1557-087X 18 11 13 0 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-6492 978-1-5090-0304-4 INT WIREL COMMUN 2016.0 1080 1085 6 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BG1ZO Green Submitted 2023-03-23 WOS:000387181100186 0
J Deng, JF; Sun, J; Peng, W; Zhang, DH; Vyatkin, V Deng, Jifei; Sun, Jie; Peng, Wen; Zhang, Dianhua; Vyatkin, Valeriy Imbalanced multiclass classification with active learning in strip rolling process KNOWLEDGE-BASED SYSTEMS English Article Imbalanced learning; Multiclass classification; Active learning; Deep learning; Strip rolling PREDICTION In the strip rolling process, conventional supervised methods cannot effectively address data with an imbalanced number of normal and faulty instances. In this paper, based on a deep belief network, a resampling method is combined with active learning (AL) to address imbalanced multiclass problems. The support vector machine-based synthetic minority oversampling technique was adapted to enrich the training data, whereas the true data distribution and model generalization were changed. A new selection strategy of AL is proposed that forms a function using uncertainty and diversity. AL uses an optimizing set that has a similar distribution with the whole dataset to calculate the informativeness of instances to optimize the model. Based on this step, the model study instances approach decision boundaries to promote performance. The proposed method is validated by five UCI benchmark datasets and strip rolling data, and experiments show that it outperforms conventional methods in tackling imbalanced multiclass problems.(c) 2022 Elsevier B.V. All rights reserved. [Deng, Jifei; Sun, Jie; Peng, Wen; Zhang, Dianhua] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Peoples R China; [Deng, Jifei; Vyatkin, Valeriy] Aalto Univ, Sch Elect Engn, Dept Elect Engn & Automat, Espoo, Finland; [Sun, Jie; Peng, Wen; Zhang, Dianhua] Northeastern Univ, Inst Frontier Technol Low Carbon Steelmaking, Shenyang, Peoples R China; [Vyatkin, Valeriy] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Lulea, Sweden Northeastern University - China; Aalto University; Northeastern University - China; Lulea University of Technology Sun, J (corresponding author), Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Peoples R China. sunjie@ral.neu.edu.cn Vyatkin, Valeriy/J-9178-2013 Vyatkin, Valeriy/0000-0002-9315-9920; Deng, Jifei/0000-0001-6448-0494 National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; LiaoNing Revitalization Talents Program; [52074085]; [U21A20117]; [N2004010]; [XLYC1907065] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); LiaoNing Revitalization Talents Program; ; ; ; Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant Nos. 52074085 and U21A20117) , the Fundamental Research Funds for the Central Universities (Grant No. N2004010) , and the LiaoNing Revitalization Talents Program (XLYC1907065) . 35 0 0 17 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. NOV 14 2022.0 255 109754 10.1016/j.knosys.2022.109754 0.0 SEP 2022 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4Z9WF 2023-03-23 WOS:000862548500018 0
J Zhang, LQ; Liu, Y; Ren, LL; Teuling, AJ; Zhang, XX; Jiang, SH; Yang, XL; Wei, LY; Zhong, F; Zheng, LH Zhang, Linqi; Liu, Yi; Ren, Liliang; Teuling, Adriaan J.; Zhang, Xiaoxiang; Jiang, Shanhu; Yang, Xiaoli; Wei, Linyong; Zhong, Feng; Zheng, Lihong Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology SCIENCE OF THE TOTAL ENVIRONMENT English Article Soil moisture; ESA CCI; Reconstruction; Artificial neural network; Original kriging AMSR-E; PRODUCTS; WATER; VALIDATION; DATASETS; AREA; INTERPOLATION; DYNAMICS; PLATEAU; FOREST Soil moisture (SM) is a vital variable for controlling water and heat exchange between the atmosphere and land surface. Spatiotemporally continuous satellite-derived SM information is urgently needed for large-scale meteorological and hydrological applications. This study proposes the application of a spatial gap-Idling method using an artificial neural network (ANN) to reconstruct missing records of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI) in the growing seasons of 1982-2015 across China. Ten environmental variables were taken into consideration, including meteorological forcing, geographic and topographic features, vegetation conditions, and soil texture. The ANN-reconstructed SM and RF-reconstructed SM were validated using the ground-based observations and three global reanalysis SM datasets. The gap-filling results by the ANN model were compared to that by the original kriging (OK) model under three simulation scenarios: SM images with removed swaths, varying vegetation densities, and different percentages of data gaps. The results showed that the ANN-reconstructed SM was in good agreement with in-situ SM observations, with the CC more than 0.607 and the RMSE less than 0.074 m(3)/m(3) over the northwestern and eastern parts of China. Compared with the original ESA CCI SM, the ANN model performed better than the OK model in reconstructing SM with absent swaths and in densely and sparsely vegetated regions. Specifically, the ANN-reconstructed SM provided a higher estimation accuracy in regions with low-density vegetation than in those with high-density vegetation. The weaker performance may due to the complex interactions between surface SM and various environmental variables underneath the dense vegetation. In addition, the ANN model significantly outperformed the OK model by accurately estimating SM when the percentage of data gaps succeeded 40%. Our study is valuable for providing an alternative reference to reconstruct satellite-derived SM and highlights the potential of using the ANN model. (C) 2021 Elsevier B.V. All rights reserved. [Liu, Yi; Ren, Liliang; Jiang, Shanhu; Yang, Xiaoli] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China; [Zhang, Linqi; Liu, Yi; Ren, Liliang; Zhang, Xiaoxiang; Jiang, Shanhu; Yang, Xiaoli; Wei, Linyong; Zhong, Feng; Zheng, Lihong] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Teuling, Adriaan J.] Wageningen Univ, Hydrol & Quantitat Water Management Grp, NL-6708 PB Wageningen, Netherlands Hohai University; Hohai University; Wageningen University & Research Liu, Y; Ren, LL (corresponding author), Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China. liuyihhdx@126.com; RLL@hhu.edu.cn Teuling, Ryan/D-2318-2014; wei, linyong/GRS-2027-2022 Teuling, Ryan/0000-0003-4302-2835; National Key Research and Development Program; National Key Research and Development Program by the Ministry of Science and Technology, the People's Republic of China [2019YFC1510600, 2016YFA0601504]; Fundamental Research Funds for the Central Universities [2019B05214, B200204029]; National Natural Science Foundation of China [41807165]; Natural Science Foundation of Jiangsu Province, China [BK20180512] National Key Research and Development Program; National Key Research and Development Program by the Ministry of Science and Technology, the People's Republic of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province, China(Natural Science Foundation of Jiangsu Province) This study was supported by the National Key Research and Development Program approved by the Ministry of Science and Technology, the People's Republic of China, under Grant No. 2019YFC1510600 and 2016YFA0601504; the Fundamental Research Funds for the Central Universities under Grant No. B200204029; the National Natural Science Foundation of China under Grant No. 41807165; the Natural Science Foundation of Jiangsu Province, China under Grant No. BK20180512; and the Fundamental Research Funds for the Central Universities under Grant No. 2019B05214. 69 13 13 20 61 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. AUG 15 2021.0 782 146602 10.1016/j.scitotenv.2021.146602 0.0 APR 2021 16 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology SJ4TT 2023-03-23 WOS:000655529300008 0
J Jonas, R; Earls, J; Marques, H; Chang, HJ; Choi, JH; Doh, JH; Her, AY; Koo, BK; Nam, CW; Park, HB; Shin, S; Cole, J; Gimelli, A; Khan, MA; Lu, B; Gao, Y; Nabi, F; Nakazato, R; Schoepf, UJ; Driessen, RS; Bom, MJ; Thompson, RC; Jang, JJ; Ridner, M; Rowan, C; Avelar, E; Genereux, P; Knaapen, P; de Waard, GA; Pontone, G; Andreini, D; Al-Mallah, MH; Jennings, R; Crabtree, TR; Villines, TC; Min, JK; Choi, AD Jonas, Rebecca; Earls, James; Marques, Hugo; Chang, Hyuk-Jae; Choi, Jung Hyun; Doh, Joon-Hyung; Her, Ae-Young; Koo, Bon Kwon; Nam, Chang-Wook; Park, Hyung-Bok; Shin, Sanghoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lu, Bin; Gao, Yang; Nabi, Faisal; Nakazato, Ryo; Schoepf, U. Joseph; Driessen, Roel S.; Bom, Michiel J.; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Genereux, Philippe; Knaapen, Paul; de Waard, Guus A.; Pontone, Gianluca; Andreini, Daniele; Al-Mallah, Mouaz H.; Jennings, Robert; Crabtree, Tami R.; Villines, Todd C.; Min, James K.; Choi, Andrew D. Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence OPEN HEART English Article carotid artery diseases; computed tomography angiography; atherosclerosis; diagnostic imaging; coronary angiography COMPUTED-TOMOGRAPHY ANGIOGRAPHY; CORONARY CT ANGIOGRAPHY; NORTH-AMERICAN SOCIETY; ARTERY-DISEASE; SCCT GUIDELINES; PLAQUE FEATURES; YOUNG-ADULTS; PERFORMANCE; ISCHEMIA; RISK Objective The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (>= 50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and >= 65 years. Results The cohort was 64.4 +/- 10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm(3) vs 48.2 mm(3); p<0.04) and non-obstructive lesions (22.1 mm(3) vs 49.4 mm(3); p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment. [Jonas, Rebecca] Thomas Jefferson Univ Hosp, Philadelphia, PA 19107 USA; [Earls, James; Jennings, Robert; Crabtree, Tami R.; Min, James K.] Cleerly Hlth, New York, NY USA; [Marques, Hugo] CHRC Campus Nova Med Sch, Unit Cardiovasc Imaging, UNICA, Lisbon, Portugal; [Chang, Hyuk-Jae] Yonsei Univ Hlth Syst, Cardiol, Seoul, South Korea; [Choi, Jung Hyun] Ontact Hlth Inc, Seoul, South Korea; [Doh, Joon-Hyung] Inje Univ, Ilsan Paik Hosp, Dept Med, Goyang, South Korea; [Her, Ae-Young] Kangwon Natl Univ Hosp, Cardiol, Chunchon, Kangwon, South Korea; [Koo, Bon Kwon] Seoul Natl Univ Hosp, Dept Internal Med, Seoul, South Korea; [Nam, Chang-Wook] Keimyung Univ, Dongsan Hosp, Cardiovasc Ctr, Daegu, South Korea; [Park, Hyung-Bok] Catholic Kwandong Univ, Int St Marys Hosp, Dept Internal Med, Div Cardiol, Incheon, South Korea; [Shin, Sanghoon] Ewha Womans Univ, Cardiol, Mokdong Hosp, Seoul, South Korea; [Cole, Jason] Mobile Cardiol Associates, Mobile, AL USA; [Gimelli, Alessia] Fdn Toscana Gabriele Monasterio, Dept Imaging, Pisa, Italy; [Khan, Muhammad Akram] Cardiac Ctr Texas, Mckinney, TX USA; [Lu, Bin] Fuwai Hosp, Dept Radiol, State Key Lab Cardiovasc Dis, Beijing, Peoples R China; [Gao, Yang] Fuwai Hosp, State Key Lab Cardiovasc Dis, Beijing, Peoples R China; [Nabi, Faisal] Houston Methodist Hosp, Houston, TX 77030 USA; [Nakazato, Ryo] St Lukes Int Hosp, Cardiovasc Ctr, Chuo Ku, Tokyo, Japan; [Schoepf, U. Joseph] Med Univ South Carolina, Dept Radiol & Radiol Sci, Charleston, SC 29425 USA; [Driessen, Roel S.] VU Univ Med Ctr Amsterdam, Amsterdam, Noord Holland, Netherlands; [Bom, Michiel J.] Vrije Univ Amsterdam, Dept Cardiol, Amsterdam, Netherlands; [Thompson, Randall C.] St Lukes Mid Amer Heart Inst, Kansas City, MO USA; [Jang, James J.] Kaiser Permanente, Cardiol, San Jose, CA USA; [Ridner, Michael] Heart Ctr Res, Huntsville, AL USA; [Rowan, Chris] Renown Hlth, Reno, NV USA; [Avelar, Erick] St Marys Med Grp, Oconee Heart & Vasc Ctr, Athens, GA USA; [Genereux, Philippe] Hop Sacre Coeur Montreal, Div Cardiol, Montreal, PQ, Canada; [Knaapen, Paul; de Waard, Guus A.] VU Univ Med Ctr Amsterdam, Cardiol, Amsterdam, Noord Holland, Netherlands; [Pontone, Gianluca; Andreini, Daniele] Ctr Cardiol Monzino Ist Ricovero & Cura Carattere, Milan, Lombardia, Italy; [Al-Mallah, Mouaz H.] Houston Methodist Hosp, Cardiol, Houston, TX 77030 USA; [Villines, Todd C.] Univ Virginia Hlth Syst, Med Cardiol, Charlottesville, VA USA; [Choi, Andrew D.] George Washington Univ, Sch Med & Hlth Sci, Div Cardiol, Washington, DC 20052 USA; [Choi, Andrew D.] George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA Jefferson University; Yonsei University; Yonsei University Health System; Inje University; Kangwon National University; Kangwon National University Hospital; Seoul National University (SNU); Seoul National University Hospital; Keimyung University; Catholic Kwandong University; Ewha Womans University; Chinese Academy of Medical Sciences - Peking Union Medical College; Fu Wai Hospital - CAMS; Chinese Academy of Medical Sciences - Peking Union Medical College; Fu Wai Hospital - CAMS; The Methodist Hospital System; The Methodist Hospital - Houston; St. Luke's International Hospital; Medical University of South Carolina; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; Vrije Universiteit Amsterdam; Saint Luke's Mid America Heart Institute; Kaiser Permanente; Universite de Montreal; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; The Methodist Hospital System; The Methodist Hospital - Houston; University of Virginia; George Washington University; George Washington University Choi, AD (corresponding author), George Washington Univ, Sch Med & Hlth Sci, Div Cardiol, Washington, DC 20052 USA.;Choi, AD (corresponding author), George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA. adchoi@mfa.gwu.edu lu, bin/HPE-4790-2023 Chang, Hyuk-Jae/0000-0002-6139-7545; Pinto Marques, Hugo/0000-0003-3540-0488; Marques, Hugo/0000-0001-8210-2075; Al-Mallah, Mouaz/0000-0003-2348-0484 GW Heart and Vascular Institute GW Heart and Vascular Institute ADC is supported by a grant from the GW Heart and Vascular Institute. 29 1 1 1 3 BMJ PUBLISHING GROUP LONDON BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND 2053-3624 OPEN HEART Open Heart NOV 2021.0 8 2 e001832 10.1136/openhrt-2021-001832 0.0 9 Cardiac & Cardiovascular Systems Emerging Sources Citation Index (ESCI) Cardiovascular System & Cardiology WZ4JD 34785589.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000719934500001 0
J Jin, TH; Cheng, L; Liu, ZC; Cao, J; Huang, HS; Witlox, F Jin, Tanhua; Cheng, Long; Liu, Zhicheng; Cao, Jun; Huang, Haosheng; Witlox, Frank Nonlinear public transit accessibility effects on housing prices: Heterogeneity across price segments TRANSPORT POLICY English Article Nonlinearity; Public transit accessibility; Residential property value; Inequity; Machine learning; Shanghai (China) REAL-ESTATE VALUES; LAND-VALUE CAPTURE; BUS RAPID-TRANSIT; PROPERTY-VALUES; INDUCED GENTRIFICATION; RAIL TRANSIT; TRANSPORT; IMPACT; STATIONS; MARKET Although numerous studies have been conducted to analyze the relationship between public transit accessibility and housing prices, they often assumed a linear relationship. Using a dataset of 196,232 s-hand residential properties in Shanghai (China), this study applies the gradient boosting regression trees (GBRT) method to investigate the complicated relationships between public transit accessibility and housing prices across price segments. The results show that for low- and median-priced houses, the travel time by public transit to the central business district (CBD) contributes the most to housing prices while for high-priced houses, the systemwide metro accessibility contributes the most. Public transit accessibility has significant nonlinear and threshold effects on housing prices. Public transit accessibility has an overall positive effect on housing prices, though some negative effects occur within certain intervals. Different patterns are observed across price segments. High-priced houses receive a much larger premium from the closeness to CBD and systemwide metro accessibility but are more negatively affected by both local and systemwide bus accessibility, compared to low-and median-priced houses. The differences in transit's value-added effects across price segments may bring about inequity in accessibility across different income groups. Relevant policies on how to reduce such an inequity issue are finally discussed. [Jin, Tanhua; Cheng, Long; Huang, Haosheng; Witlox, Frank] Univ Ghent, Dept Geog, Ghent, Belgium; [Cheng, Long] Southeast Univ, Sch Transportat, Nanjing, Peoples R China; [Liu, Zhicheng] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China; [Cao, Jun] Southeast Univ, Sch Architecture, Nanjing, Peoples R China; [Witlox, Frank] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, Nanjing, Peoples R China; [Witlox, Frank] Univ Tartu, Dept Geog, Tartu, Estonia Ghent University; Southeast University - China; Tsinghua University; Southeast University - China; Nanjing University of Aeronautics & Astronautics; University of Tartu Cheng, L (corresponding author), Sipailou 2, Nanjing 210096, Peoples R China. tanhua.jin@ugent.be; long.cheng@ugent.be; zhichengliu@tsinghua.edu.cn; juncao_0711@seu.edu.cn; haosheng.huang@ugent.be; frank.witlox@ugent.be Witlox, Frank/A-5143-2008; Huang, Haosheng/AFE-5924-2022 Witlox, Frank/0000-0002-8966-6823; Huang, Haosheng/0000-0001-8399-3607; Jin, Tanhua/0000-0001-6657-526X; Cheng, Long/0000-0003-3720-4093 Research Foundation - Flanders (FWO) [1257022N]; China Scholarship Council [202008320289] Research Foundation - Flanders (FWO)(FWO); China Scholarship Council(China Scholarship Council) This work was supported by the Research Foundation - Flanders (FWO) (Grant number 1257022N) and the China Scholarship Council (202008320289). 84 5 5 12 42 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0967-070X 1879-310X TRANSPORT POLICY Transp. Policy MAR 2022.0 117 48 59 10.1016/j.tranpol.2022.01.004 0.0 JAN 2022 12 Economics; Transportation Social Science Citation Index (SSCI) Business & Economics; Transportation YT9KK 2023-03-23 WOS:000751672000005 0
J Wang, SF; Esperanca, JP; Wu, Q Wang, Shaofeng; Esperanca, Jose Paulo; Wu, Qiao Effects of Live Streaming Proneness, Engagement and Intelligent Recommendation on Users' Purchase Intention in Short Video Community: Take TikTok (DouYin) Online Courses as an Example INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION English Article; Early Access ARTIFICIAL-INTELLIGENCE; CUSTOMER ENGAGEMENT; PERCEIVED EASE; CO-CREATION; INFORMATION; DECISION; BEHAVIOR; BRAND; ERA Shopping through short video communities has become a new trend, but few currently understand users' purchase intention toward TikTok online courses. This research developed a purchasing intention model of TikTok online courses by extending the technology acceptance model. This study used a questionnaire survey method and collected 413 valid questionnaires. Partial least squares structural equation modeling (PLS-SEM) was adopted to test the hypotheses and found: (1) perceived usefulness and ease of use positively affect perceived value. Free trial negatively affects perceived cost. Perceived usefulness, perceived ease of use, and free trial indirectly affect purchase intention through two mediation variables; (2) live streaming proneness, live streaming engagement, and perceived value positively affect purchase intention, while the perceived cost will negatively affect purchase intention; (3) an exciting conclusion is that intelligent recommendation positively moderates the relationship between perceived value and live streaming engagement with purchase intention, but intelligent recommendation negatively moderates the relationship between perceived cost and purchase intention. These findings make theoretical contributions and provide managerial insights based on empirical results for short video community purchase intentions. [Wang, Shaofeng; Wu, Qiao] Zhejiang Wanli Univ, Sch Logist & E Commerce, Ningbo, Peoples R China; [Wang, Shaofeng] Beijing Normal Univ, Smart Learning Inst, Beijing, Peoples R China; [Esperanca, Jose Paulo] Lisbon Univ Inst, ISCTE Business Sch, Lisbon, Portugal; [Esperanca, Jose Paulo] Lisbon Univ Inst, Business Res Unit, Lisbon, Portugal Zhejiang Wanli University; Beijing Normal University; Instituto Universitario de Lisboa; Instituto Universitario de Lisboa Wu, Q (corresponding author), Zhejiang Wanli Univ, Sch Logist & E Commerce, Ningbo, Peoples R China. qiaowu101@163.com Wang, Shaofeng/AAZ-8797-2021; Wang, Shaofeng/GRF-5527-2022 Wang, Shaofeng/0000-0002-0300-2453; Wang, Shaofeng/0000-0002-0300-2453 Zhejiang Province Association for Higher Education Project [KT2022412]; Ningbo Education Science Planning Project [2022YZD012]; Ningbo Soft Science Research Program [2022R040, 2021R030] Zhejiang Province Association for Higher Education Project; Ningbo Education Science Planning Project; Ningbo Soft Science Research Program This work was supported by the Zhejiang Province Association for Higher Education Project under grant [KT2022412], Ningbo Education Science Planning Project under Grant [2022YZD012], Ningbo Soft Science Research Program under Grant [2022R040], and Ningbo Soft Science Research Program under Grant [2021R030]. 90 1 1 91 101 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1044-7318 1532-7590 INT J HUM-COMPUT INT Int. J. Hum.-Comput. Interact. 10.1080/10447318.2022.2091653 0.0 AUG 2022 13 Computer Science, Cybernetics; Ergonomics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering 3Z0EZ 2023-03-23 WOS:000844095400001 0
J Chen, J; Patil, KR; Yeo, BTT; Eickhoff, SB Chen, Ji; Patil, Kaustubh R.; Yeo, B. T. Thomas; Eickhoff, Simon B. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research BIOLOGICAL PSYCHIATRY English Review SCHIZOPHRENIA SUBTYPES; CO-MORBIDITY; CONNECTIVITY; HETEROGENEITY; DISORDERS; PATTERNS; MOTION; PSYCHOPATHOLOGY; IDENTIFICATION; DIMENSIONS Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging???derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs. [Chen, Ji] Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China; [Chen, Ji] Zhejiang Univ, Sch Med, Dept Psychiat, Affiliated Hosp 4, Yiwu, Zhejiang, Peoples R China; [Chen, Ji; Patil, Kaustubh R.; Eickhoff, Simon B.] Res Ctr Julich, Inst Neurosci & Med Brain & Behav INM 7, Julich, Germany; [Patil, Kaustubh R.; Eickhoff, Simon B.] Heinrich Heine Univ Dusseldorf, Med Fac, Inst Syst Neurosci, Dusseldorf, Germany; [Yeo, B. T. Thomas] Natl Univ Singapore, Ctr Sleep & Cognit, Yong Loo Lin Sch Med, Singapore, Singapore; [Yeo, B. T. Thomas] Natl Univ Singapore, Ctr Translat MR Res, Yong Loo Lin Sch Med, Singapore, Singapore; [Yeo, B. T. Thomas] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore; [Yeo, B. T. Thomas] Natl Univ Singapore, Integrat Sci & Engn Programme, Singapore, Singapore; [Yeo, B. T. Thomas] Massachusetts Gen Hosp, Martinos Ctr Biomed Imaging, Charlestown, MA USA Zhejiang University; Zhejiang University; Helmholtz Association; Research Center Julich; Heinrich Heine University Dusseldorf; National University of Singapore; National University of Singapore; National University of Singapore; National University of Singapore; Harvard University; Massachusetts General Hospital Chen, J (corresponding author), Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China.;Chen, J (corresponding author), Zhejiang Univ, Sch Med, Dept Psychiat, Affiliated Hosp 4, Yiwu, Zhejiang, Peoples R China.;Chen, J (corresponding author), Res Ctr Julich, Inst Neurosci & Med Brain & Behav INM 7, Julich, Germany. jichen.allen@hotmail.com National Human Brain Project of China [2022ZD0214000]; National Key R&D Program of China [2021YFC2502200]; Deutsche Forschungsgemeinschaft [EI 816/4-1]; National Institute of Mental Health [R01-MH074457]; Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union [785907, 945539]; Singapore National Research Foundation Fellowship; NUS Yong Loo Lin School of Medicine [NUHSRO/2020/124/TMR/LOA]; Singapore National Medical Research Council LCG [OFLCG19May-0035]; NMRC STaR [STaR20nov-0003]; National Institutes of Health [R01-MH120080] National Human Brain Project of China; National Key R&D Program of China; Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); National Institute of Mental Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union(European Commission); Singapore National Research Foundation Fellowship(National Research Foundation, Singapore); NUS Yong Loo Lin School of Medicine; Singapore National Medical Research Council LCG; NMRC STaR; National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) This study was supported by the National Human Brain Project of China (Grant No. 2022ZD0214000 [to JC]), the National Key R&D Program of China (Grant No. 2021YFC2502200 [to JC]), Deutsche Forschungsgemeinschaft (Grant No. EI 816/4-1 [to SBE]), the National Institute of Mental Health (Grant No. R01-MH074457 [to SBE]), the Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain, and the European Union's Horizon 2020 Research and Innovation Programme (Grant Nos. 785907; HBP SGA2 [to SBE] and 945539; HBP SGA3 [to SBE]), the Singapore National Research Foundation Fellowship (Class of 2017), the NUS Yong Loo Lin School of Medicine (Grant No. NUHSRO/2020/124/TMR/LOA [to BTTY]), the Singapore National Medical Research Council LCG (Grant No. OFLCG19May-0035 [to BTTY]), NMRC STaR (Grant No. STaR20nov-0003 [to BTTY]), and the National Institutes of Health (Grant No. R01-MH120080 [to BTTY]). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore NRF or the Singapore NMRC. 140 0 0 5 5 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0006-3223 1873-2402 BIOL PSYCHIAT Biol. Psychiatry JAN 1 2023.0 93 1 18 28 10.1016/j.biopsych.2022.07.025 0.0 11 Neurosciences; Psychiatry Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychiatry 7U0RJ 2023-03-23 WOS:000911845300006 0
J Nguyen, XC; Ly, QV; Peng, WX; Nguyen, VH; Nguyen, DD; Tran, QB; Nguyen, TTH; Sonne, C; Lam, SS; Ngo, HH; Goethals, P; Le, QV Xuan Cuong Nguyen; Quang Viet Ly; Peng, Wanxi; Van-Huy Nguyen; Dinh Duc Nguyen; Quoc Ba Tran; Thi Thanh Huyen Nguyen; Sonne, Christian; Lam, Su Shiung; Ngo, Huu Hao; Goethals, Peter; Quyet Van Le Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning JOURNAL OF HAZARDOUS MATERIALS English Article Biochar; Constructed wetland; Expanded clay; Machine learning; Vertical flow HYDRAULIC RETENTION TIME; NITROGEN REMOVAL; NUTRIENT REMOVAL; OXYGEN-TRANSFER; TREATMENT-PLANT; PERFORMANCE; MODEL; RECIRCULATION; GENERATION; DESIGN This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF1) and biochar (VF2), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5. m (-2).d(-1) and 3.5 - 3.6 g.NH4-N.m(-2).d(-1), respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 8(2).4 +/- 5.7 and 84.6 +/- 6.4%, respectively (P < 0.05). The results obtained from the designed systems were further subject to machine learning to clarify the effecting factors and predict the effluents. The optimal algorithms were random forest, generalized linear model, and support vector machine. The values of the coefficient of determination (R-2) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg.L-1, 80.0% and 0.3 mg.L-1, 90.1% and 2.9 mg.L-1, and 48.5% and 0.5 mg.L-1 for BOD5_VF1, NH4-N_VF1, BOD5_VF2, and NH4-N_VF2, respectively [Xuan Cuong Nguyen; Quoc Ba Tran; Thi Thanh Huyen Nguyen; Quyet Van Le] Duy Tan Univ, Inst Res & Dev, Lab Energy & Environm Sci, Da Nang 550000, Vietnam; [Xuan Cuong Nguyen; Quoc Ba Tran; Thi Thanh Huyen Nguyen] Duy Tan Univ, Fac Environm & Chem Engn, Da Nang 550000, Vietnam; [Quang Viet Ly] Sun Yat Sen Univ, Sch Environm Sci & Engn, Guangdong Prov Key Lab Environm Pollut Control &, Guangzhou 510275, Peoples R China; [Peng, Wanxi] Henan Agr Univ, Henan Prov Engn Res Ctr Biomass Value Added Prod, Sch Forestry, Zhengzhou 450002, Peoples R China; [Van-Huy Nguyen] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; [Van-Huy Nguyen] Ton Duc Thang Univ, Fac Appl Sci, Ho Chi Minh City, Vietnam; [Dinh Duc Nguyen] Nguyen Tat Thanh Univ, Fac Environm & Food Engn, 300A Nguyen Tat Thanh,Dist 4, Ho Chi Minh City 755414, Vietnam; [Dinh Duc Nguyen] Kyonggi Univ, Dept Environm Energy Engn, Suwon 16227, South Korea; [Sonne, Christian] Aarhus Univ, Arctic Res Ctr ARC, Dept Biosci, Frederiksborgvej 399,POB 358, DK-4000 Roskilde, Denmark; [Lam, Su Shiung] Univ Malaysia Terengganu, Higher Inst Ctr Excellence HICoE, Inst Trop Aquaculture & Fisheries AKUATROP, Terengganu 21030, Malaysia; [Ngo, Huu Hao] Univ Technol Sydney, Ctr Technol Water & Wastewater, Sch Civil & Environm Engn, Sydney, NWS 2007, Australia; [Goethals, Peter] Univ Ghent, Lab Environm Toxicol & Aquat Ecol, Jozef Plateaustr 22, B-9000 Ghent, Belgium Duy Tan University; Duy Tan University; Sun Yat Sen University; Henan Agricultural University; Ton Duc Thang University; Ton Duc Thang University; Nguyen Tat Thanh University (NTTU); Kyonggi University; Aarhus University; Universiti Malaysia Terengganu; University of Technology Sydney; Ghent University Le, QV (corresponding author), Duy Tan Univ, Inst Res & Dev, Lab Energy & Environm Sci, Da Nang 550000, Vietnam. nguyenvanhuy@tdtu.edu.vn; levanquyet@dtu.edu.vn Nguyen, Van-Huy/AAD-7854-2022; Lam, Su Shiung/K-7436-2012; Le, Quyet Van/GRS-9136-2022; Van Le, Quyet/AAN-3619-2020; Nguyen, Cuong Xuan/AAU-4979-2021; Van Le, Quyet/AAH-6385-2020; Nguyen, D. Duc/Q-6703-2017; Nguyen, Cuong Xuan/GQA-3379-2022; Sonne, Christian/I-7532-2013 Lam, Su Shiung/0000-0002-5318-1760; Le, Quyet Van/0000-0002-4313-301X; Van Le, Quyet/0000-0002-4313-301X; Nguyen, Cuong Xuan/0000-0002-1953-1456; Nguyen, D. Duc/0000-0002-6004-3657; Sonne, Christian/0000-0001-5723-5263; Quoc Ba, Tran/0000-0002-0522-501X; Nguyen, Van-Huy/0000-0001-8556-1955 Vietnam National Foundation for Science and Technology Development (NAFOSTED) [105.99-2019.25] Vietnam National Foundation for Science and Technology Development (NAFOSTED)(National Foundation for Science & Technology Development (NAFOSTED)) This research is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.99-2019.25. 68 10 10 19 110 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3894 1873-3336 J HAZARD MATER J. Hazard. Mater. JUL 5 2021.0 413 125426 10.1016/j.jhazmat.2021.125426 0.0 FEB 2021 12 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology RY0EZ 33621772.0 2023-03-23 WOS:000647589100004 0
J Ding, YC; Martin-Guerrero, JD; Sanz, M; Magdalena-Benedicto, R; Chen, X; Solano, E Ding, Yongcheng; Martin-Guerrero, Jose D.; Sanz, Mikel; Magdalena-Benedicto, Rafael; Chen, Xi; Solano, Enrique Retrieving Quantum Information with Active Learning PHYSICAL REVIEW LETTERS English Article ALGORITHM Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies. [Ding, Yongcheng; Chen, Xi; Solano, Enrique] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Chen, Xi; Solano, Enrique] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Sanz, Mikel; Chen, Xi; Solano, Enrique] Univ Basque Country, Dept Phys Chem, UPV EHU, Apartado 644, Bilbao 48080, Spain; [Martin-Guerrero, Jose D.; Magdalena-Benedicto, Rafael] Univ Valencia, Elect Engn Dept, IDAL, Avinguda Univ S-N, E-46100 Valencia, Spain; [Solano, Enrique] Ikerbasque, Basque Fdn Sci, Maria Diaz de Haro 3, Bilbao 48013, Spain; [Solano, Enrique] IQM, Munich, Germany Shanghai University; Shanghai University; University of Basque Country; University of Valencia; Basque Foundation for Science Ding, YC; Chen, X; Solano, E (corresponding author), Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China.;Ding, YC; Chen, X; Solano, E (corresponding author), Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China.;Ding, YC; Chen, X; Solano, E (corresponding author), Univ Basque Country, Dept Phys Chem, UPV EHU, Apartado 644, Bilbao 48080, Spain.;Martin-Guerrero, JD (corresponding author), Univ Valencia, Elect Engn Dept, IDAL, Avinguda Univ S-N, E-46100 Valencia, Spain.;Solano, E (corresponding author), Ikerbasque, Basque Fdn Sci, Maria Diaz de Haro 3, Bilbao 48013, Spain.;Solano, E (corresponding author), IQM, Munich, Germany. jonzen.ding@gmail.com; jose.d.martin@uv.es; xchen@shu.edu.cn; enr.solano@gmail.com Sanz, Mikel/W-8637-2019; Ding, Yongcheng/AAQ-5097-2021; Martín-Guerrero, José D./L-8146-2014; chen, xi/GXH-3653-2022; Chen, Xi/ABE-6575-2020 Sanz, Mikel/0000-0003-1615-9035; Ding, Yongcheng/0000-0002-6008-0001; Martín-Guerrero, José D./0000-0001-9378-0285; Chen, Xi/0000-0003-4221-4288; Magdalena, Rafael/0000-0003-3752-8231 National Natural Science Foundation of China (NSFC) [11474193]; STCSM [2019SHZDZX01-ZX04, 18010500400, 18ZR1415500]; Program for Eastern Scholar; Ramon y Cajal program of the Spanish MCIU [RYC-2017-22482]; QMiCS [820505]; OpenSuperQ of the EU Flagship on Quantum Technologies [820363]; Spanish Government [PGC2018-095113-B-I00]; Basque Government [IT986-16]; EU FET Open Grant Quromorphic; U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program [ERKJ333] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); Program for Eastern Scholar; Ramon y Cajal program of the Spanish MCIU(German Research Foundation (DFG)); QMiCS; OpenSuperQ of the EU Flagship on Quantum Technologies; Spanish Government(Spanish Government); Basque Government(Basque Government); EU FET Open Grant Quromorphic; U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program We acknowledge support from National Natural Science Foundation of China (NSFC) (Grant No. 11474193), STCSM (Grants No. 2019SHZDZX01-ZX04, No. 18010500400, and No. 18ZR1415500), Program for Eastern Scholar, Ramon y Cajal program of the Spanish MCIU (Grant No. RYC-2017-22482), QMiCS (Grant No. 820505) and OpenSuperQ (Grant No. 820363) of the EU Flagship on Quantum Technologies, Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE), Basque Government IT986-16, as well as the and EU FET Open Grant Quromorphic. This work is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program, under field work Proposal No. ERKJ333. 39 8 8 0 20 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. APR 10 2020.0 124 14 140504 10.1103/PhysRevLett.124.140504 0.0 6 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics LC1XO 32338974.0 Green Submitted, hybrid 2023-03-23 WOS:000525122200002 0
J Xue, L; Yi, LL; Lin, R; Huang, LY; Chen, JJ Xue, Lei; Yi, Lilin; Lin, Rui; Huang, Luyao; Chen, Jiajia SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON OPTICS EXPRESS English Article TRANSMISSION Semiconductor optical amplifier (SOA) is widely used for power amplification in O-band, particularly for passive optical networks (PONs) which can greatly benefit its advantages of simple structure, low power consumption and integrability with photonics circuits. However, the annoying nonlinear pattern effect degrades system performance when the SOA is needed as a pre-amplifier in PONs. Conventional solutions for pattern effect mitigation are either based on optical filtering or gain clamping. They are not simple or sufficiently flexible for practical deployment. Neural network (NN) has been demonstrated for impairment compensation in optical communications thanks to its powerful nonlinear fitting ability. In this paper, for the first time, NN-based equalizer is proposed to mitigate the SOA pattern effect for 50G PON with intensity modulation and direct detection. The experimental results confirm that the NN-based equalizer can effectively mitigate the SOA nonlinear pattern effect and significantly improve the dynamic range of receiver, achieving 29-dB power budget with the FEC limit at 1e(-2). Moreover, the well-trained NN model in the receiver side can be directly placed at the transmitter in the optical line terminal to pre-equalize the signal for transmission so as to simplify digital signal processing in the optical network unit. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement [Xue, Lei; Yi, Lilin; Huang, Luyao] Shanghai Jiao Tong Univ, State Key Lab Adv Opt Commun Syst & Networks, Shanghai 200240, Peoples R China; [Xue, Lei; Lin, Rui; Chen, Jiajia] Chalmers Univ Technol, S-41296 Gothenburg, Sweden Shanghai Jiao Tong University; Chalmers University of Technology Yi, LL (corresponding author), Shanghai Jiao Tong Univ, State Key Lab Adv Opt Commun Syst & Networks, Shanghai 200240, Peoples R China. lilinyi@sjtu.edu.cn EU [722429]; Key TechnologiesResearch and Development Program [2019YFB1803803] EU(European Commission); Key TechnologiesResearch and Development Program EU H2020 5G STEP-FWD (722429); Key TechnologiesResearch and Development Program (2019YFB1803803). 17 7 7 3 6 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express AUG 2 2021.0 29 16 24714 24722 10.1364/OE.426781 0.0 9 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics TR1TR 34614821.0 Green Published, gold 2023-03-23 WOS:000678755000010 0
J You, ZZ; Jiang, M; Shi, ZH; Zhao, MH; Shi, C; Du, SL; Herard, AS; Souedet, N; Delzescaux, T You, Zhenzhen; Jiang, Ming; Shi, Zhenghao; Zhao, Minghua; Shi, Cheng; Du, Shuangli; Herard, Anne-Sophie; Souedet, Nicolas; Delzescaux, Thierry Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images MICROSCOPY RESEARCH AND TECHNIQUE English Article macaque brain; microscopic images; MSEG-iCNN; Neuron semantic segmentation UNET PLUS PLUS; TEXTURE CLASSIFICATION; U-NET This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. [You, Zhenzhen; Shi, Zhenghao; Zhao, Minghua; Shi, Cheng; Du, Shuangli] Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian, Peoples R China; [You, Zhenzhen; Herard, Anne-Sophie; Souedet, Nicolas; Delzescaux, Thierry] Univ Paris Saclay, MIRCen, Lab Malad Neurodegenerat, CEA CNRS UMR 9199, Paris, France; [Jiang, Ming] Xidian Univ, Natl Lab Radar Signal Proc, Xian, Peoples R China Xi'an University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Xidian University You, ZZ (corresponding author), Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian, Peoples R China. zhenzhen_you89@163.com YOU, Zhenzhen/0000-0003-3882-4737 Fund of Doctoral Start-up of Xi'an University of Technology [112/256081811]; General Program of National Natural Science Foundation of China [62076198]; Guangdong Basic and Applied Basic Research Foundation [2021A1515110057]; Natural Science Foundation of Shaanxi Province of China [2020GXLH-Y005]; Natural Science Basic Research Program of Shaanxi [2020JM-463, 2020JQ-652, 2022JQ-049]; Natural Science Foundation of Shaanxi Provincial Department of Education [20JK0795] Fund of Doctoral Start-up of Xi'an University of Technology; General Program of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Natural Science Foundation of Shaanxi Province of China(Natural Science Foundation of Shaanxi Province); Natural Science Basic Research Program of Shaanxi; Natural Science Foundation of Shaanxi Provincial Department of Education Fund of Doctoral Start-up of Xi'an University of Technology, Grant/Award Number: 112/256081811; General Program of National Natural Science Foundation of China, Grant/Award Number: 62076198; Guangdong Basic and Applied Basic Research Foundation, Grant/Award Number: 2021A1515110057; Key Program of Natural Science Foundation of Shaanxi Province of China, Grant/Award Number: 2020GXLH-Y005; Natural Science Basic Research Program of Shaanxi, Grant/Award Numbers: 2020JM-463, 2020JQ-652, 2022JQ-049; Natural Science Foundation of Shaanxi Provincial Department of Education, Grant/Award Number: 20JK0795 34 0 0 5 7 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1059-910X 1097-0029 MICROSC RES TECHNIQ Microsc. Res. Tech. NOV 2022.0 85 11 3541 3552 10.1002/jemt.24206 0.0 JUL 2022 12 Anatomy & Morphology; Biology; Microscopy Science Citation Index Expanded (SCI-EXPANDED) Anatomy & Morphology; Life Sciences & Biomedicine - Other Topics; Microscopy 5I9OZ 35855638.0 2023-03-23 WOS:000827700500001 0
J Spiekermann, S; Acquisti, A; Bohme, R; Hui, KL Spiekermann, Sarah; Acquisti, Alessandro; Boehme, Rainer; Hui, Kai-Lung The challenges of personal data markets and privacy ELECTRONIC MARKETS English Article Privacy; Data protection; Personal data markets; Internet economics; Big data E-COMMERCE; ONLINE; WILLINGNESS; ECONOMICS; PARADOX; SYSTEMS; MODEL Personal data is increasingly conceived as a tradable asset. Markets for personal information are emerging and new ways of valuating individuals' data are being proposed. At the same time, legal obligations over protection of personal data and individuals' concerns over its privacy persist. This article outlines some of the economic, technical, social, and ethical issues associated with personal data markets, focusing on the privacy challenges they raise. [Spiekermann, Sarah] Vienna Univ Econ & Business WU Vienna, A-1020 Vienna, Austria; [Acquisti, Alessandro] Carnegie Mellon Univ, Heinz Coll, Pittsburgh, PA 15213 USA; [Boehme, Rainer] Univ Munster, Inst Wirtschaftsinformat, D-48149 Munster, Germany; [Hui, Kai-Lung] Hong Kong Univ Sci & Technol, Sch Business & Management, Hong Kong, Hong Kong, Peoples R China Vienna University of Economics & Business; Carnegie Mellon University; University of Munster; Hong Kong University of Science & Technology Spiekermann, S (corresponding author), Vienna Univ Econ & Business WU Vienna, Welthandelspl 1, A-1020 Vienna, Austria. sspieker@wu.ac.at Jia, Chuanchang/AAA-5513-2020; Acquisti, Alessandro/AAW-3231-2020 Acquisti, Alessandro/0000-0001-6582-6178; Hui, Kai Lung/0000-0002-7074-1176; Bohme, Rainer/0000-0003-4518-6227 Div Of Information & Intelligent Systems; Direct For Computer & Info Scie & Enginr [1413864] Funding Source: National Science Foundation Div Of Information & Intelligent Systems; Direct For Computer & Info Scie & Enginr(National Science Foundation (NSF)NSF - Directorate for Computer & Information Science & Engineering (CISE)) 65 115 118 18 173 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1019-6781 1422-8890 ELECTRON MARK Electron. Mark. JUN 2015.0 25 2 161 167 10.1007/s12525-015-0191-0 0.0 7 Business; Management Social Science Citation Index (SSCI) Business & Economics CJ2UZ Green Submitted 2023-03-23 WOS:000355341500008 0
J Pahlevan, N; Smith, B; Schalles, J; Binding, C; Cao, ZG; Ma, RH; Alikas, K; Kangro, K; Gurlin, D; Ha, N; Matsushita, B; Moses, W; Greb, S; Lehmann, MK; Ondrusek, M; Oppelt, N; Stumpf, R Pahlevan, Nima; Smith, Brandon; Schalles, John; Binding, Caren; Cao, Zhigang; Ma, Ronghua; Alikas, Krista; Kangro, Kersti; Gurlin, Daniela; Nguyen Ha; Matsushita, Bunkei; Moses, Wesley; Greb, Steven; Lehmann, Moritz K.; Ondrusek, Michael; Oppelt, Natascha; Stumpf, Richard Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach REMOTE SENSING OF ENVIRONMENT English Article Chlorophyll-a; Sentinel missions; Water quality; Inland and coastal waters; Earth observation; Machine learning; Algorithm development INHERENT OPTICAL-PROPERTIES; REMOTE-SENSING REFLECTANCE; ATMOSPHERIC CORRECTION; ALGAL BLOOMS; CHESAPEAKE BAY; LEAVING RADIANCE; PUBLIC-HEALTH; COLOR; ALGORITHMS; MERIS Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m(3) and a mean Chla of 21.7 mg/m(3), suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40-60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column. [Pahlevan, Nima; Smith, Brandon] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Pahlevan, Nima; Smith, Brandon] SSAI, Lanham, MD USA; [Schalles, John] Creighton Univ, Dept Biol, Omaha, NE 68178 USA; [Binding, Caren] Environm & Climate Change Canada, Burlington, ON, Canada; [Cao, Zhigang; Ma, Ronghua] Nanjing Inst Geog & Limnol, Nanjing, Peoples R China; [Alikas, Krista; Kangro, Kersti] Univ Tartu, Tartu, Estonia; [Gurlin, Daniela] Wisconsin Dept Nat Resources, Madison, WI USA; [Nguyen Ha] Vietnam Natl Univ, VNU Univ Sci, Hanoi, Vietnam; [Matsushita, Bunkei] Univ Tsukuba, Ibaraki, Japan; [Moses, Wesley] Naval Res Lab, Washington, DC 20375 USA; [Greb, Steven] Univ Wisconsin, Space Sci & Engn, Madison, WI USA; [Lehmann, Moritz K.] Xerra Earth Observat Inst, Hamilton, New Zealand; [Lehmann, Moritz K.] Univ Waikato, Hamilton, New Zealand; [Ondrusek, Michael] NOAA Ctr Satellite Applicat & Res, College Pk, MD USA; [Oppelt, Natascha] Univ Kiel, Dept Geog, Kiel, Germany; [Stumpf, Richard] NOAA Natl Ctr Coastal Sci Studies, Silver Spring, MD USA National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; Creighton University; Environment & Climate Change Canada; Chinese Academy of Sciences; Nanjing Institute of Geography & Limnology, CAS; University of Tartu; Vietnam National University Hanoi; University of Tsukuba; United States Department of Defense; United States Navy; Naval Research Laboratory; University of Wisconsin System; University of Wisconsin Madison; University of Waikato; University of Kiel Pahlevan, N (corresponding author), NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA. nima.pahlevan@nasa.gov Alikas, Krista/ABE-1431-2020; Cao, Zhigang/O-2165-2019; Alikas, Krista/F-7279-2017; Kangro, Kersti/ABH-7713-2020; Pahlevan, Nima/L-7746-2014; Moses, Wesley/FVK-3856-2022; Cao, Zhigang/L-5337-2017; Kangro, Kersti/D-8972-2015; ma, rong/GVU-3096-2022 Alikas, Krista/0000-0003-3855-6525; Cao, Zhigang/0000-0001-5329-2906; Alikas, Krista/0000-0003-3855-6525; Pahlevan, Nima/0000-0002-5454-5212; Cao, Zhigang/0000-0001-5329-2906; Kangro, Kersti/0000-0001-6143-9330; Matsushita, Bunkei/0000-0001-6397-1144; Nguyen, Ha/0000-0002-5010-5615; Moses, Wesley/0000-0003-3551-6093; Lehmann, Moritz/0000-0001-7346-3901 NASA ROSES [80NSSC18K0077, 80HQTR19C0015]; USGS Landsat Science Team Award [140G0118C0011] NASA ROSES(National Aeronautics & Space Administration (NASA)); USGS Landsat Science Team Award The algorithm development, the implementation, and the analyses were funded under NASA ROSES Awards #80NSSC18K0077, #80HQTR19C0015, and the USGS Landsat Science Team Award #140G0118C0011. We further appreciate dedicated review notes provided by two anonymous reviewers assisting in improving the overall quality of this manuscript. 110 160 166 37 145 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. APR 2020.0 240 111604 10.1016/j.rse.2019.111604 0.0 21 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology LA4YZ hybrid, Green Published 2023-03-23 WOS:000523955300036 0
J Jain, N; Jhunthra, S; Garg, H; Gupta, V; Mohan, S; Ahmadian, A; Salahshour, S; Ferrara, M Jain, Nikita; Jhunthra, Srishti; Garg, Harshit; Gupta, Vedika; Mohan, Senthilkumar; Ahmadian, Ali; Salahshour, Soheil; Ferrara, Massimiliano Prediction modelling of COVID using machine learning methods from B-cell dataset RESULTS IN PHYSICS English Article SARS-CoV; SARS-CoV-2; Coronavirus; Support vector machine (SVM); Naive Bayes; K - nearest neighbors (KNN); Logistic regression; Ensembles; Gradient boosting; Random forest; XGBoost; AdaBoost; Multilayer perceptron (MLP); B-cells; COVD-19 Coronavirus is a pandemic that has become a concern for the whole world. This disease has stepped out to its greatest extent and is expanding day by day. Coronavirus, termed as a worldwide disease, has caused more than 8 lakh deaths worldwide. The foremost cause of the spread of coronavirus is SARS-CoV and SARS-CoV-2, which are part of the coronavirus family. Thus, predicting the patients suffering from such pandemic diseases would help to formulate the difference in inaccurate and infeasible time duration. This paper mainly focuses on the prediction of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble learning strategies that came out to be beneficial while making predictions. The predictions are made using various machine learning models. The numerous machine learning models, such as SVM, Naive Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks are used in predicting and analyzing the dataset. The most accurate result was obtained using the proposed algorithm with 0.919 AUC score and 87.248% validation accuracy for predicting SARS-CoV and 0.923 AUC and 87.7934% validation accuracy for predicting SARS-CoV-2 virus. [Jain, Nikita; Jhunthra, Srishti; Garg, Harshit; Gupta, Vedika] Bharati Vidyapeeths Coll Engn, Dept Comp Sci & Engn, New Delhi 110063, India; [Mohan, Senthilkumar] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India; [Ahmadian, Ali] Natl Univ Malaysia, Inst IR 4 0, Ukm 43600, Selangor, Malaysia; [Ahmadian, Ali] Wenzhou Kean Univ, Coll Sci & Technol, Sch Math Sci, Wenzhou, Peoples R China; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Ferrara, Massimiliano] Bocconi Univ, ICRIOS Invernizzi Ctr Res Innovat Org Strategy &, Dept Management & Technol, Via Sarfatti 25, I-20136 Milan, MI, Italy Vellore Institute of Technology (VIT); VIT Vellore; Wenzhou-Kean University; Bahcesehir University; Bocconi University Ahmadian, A (corresponding author), Natl Univ Malaysia, Inst IR 4 0, Ukm 43600, Selangor, Malaysia.;Ahmadian, A (corresponding author), Wenzhou Kean Univ, Coll Sci & Technol, Sch Math Sci, Wenzhou, Peoples R China. ahmadian.hosseini@gmail.com Ahmadian, Ali/N-3697-2015; Salahshour, Soheil/K-4817-2019; , m.senthilkumar/L-5551-2015; Ferrara, Massimiliano/P-8797-2014 Ahmadian, Ali/0000-0002-0106-7050; Salahshour, Soheil/0000-0003-1390-3551; , m.senthilkumar/0000-0002-8114-3147; Ferrara, Massimiliano/0000-0002-3663-836X 31 34 34 5 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2211-3797 RESULTS PHYS Results Phys. FEB 2021.0 21 103813 10.1016/j.rinp.2021.103813 0.0 18 Materials Science, Multidisciplinary; Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics ZH2WX 33495725.0 Green Accepted, gold 2023-03-23 WOS:000760805400002 0
J Chen, YF; Crespi, N; Ortiz, AM; Shu, L Chen, Yuanfang; Crespi, Noel; Ortiz, Antonio M.; Shu, Lei Reality mining: A prediction algorithm for disease dynamics based on mobile big data INFORMATION SCIENCES English Article Reality mining; Disease dynamics; Prediction algorithm; Mobile big data EPIDEMIC DYNAMICS; TRANSMISSION; OUTBREAKS; SYSTEMS; MODELS Predicting disease dynamics during an epidemic is an important aspect of e-Health applications. In such prediction, Realistic Contact Networks (RCNs) have been widely used to characterize disease dynamics. The structure of such networks is dynamically changed during an epidemic. Capturing such kind of dynamic structure is the basis of prediction. With the popularity of mobile devices, it is possible to capture the dynamic change of the network structure. On this basis, in this study, we evaluate the impact of the network structure on disease dynamics, by analyzing massive spatiotemporal data collected by mobile devices. These devices are carried by the volunteers of Ebola outbreak areas. Based on the results of this evaluation, a model is designed to recognize the dynamic structure of RCNs. On the basis of this model, we propose a prediction algorithm for disease dynamics. By extensive experiments, we show that our algorithm improves the accuracy of the disease prediction. (C) 2016 Elsevier Inc. All rights reserved. [Chen, Yuanfang; Crespi, Noel] Telecom SudParis, Inst Mines Telecom, Paris, France; [Ortiz, Antonio M.] R&D Dept, Montimage, France; [Chen, Yuanfang; Shu, Lei] Guangdong Univ Petrochem Technol, Maoming, Guangdong, Peoples R China IMT - Institut Mines-Telecom; IMT Atlantique; Institut Polytechnique de Paris; Guangdong University of Petrochemical Technology Chen, YF (corresponding author), Telecom SudParis, Inst Mines Telecom, Paris, France.;Chen, YF (corresponding author), Guangdong Univ Petrochem Technol, Maoming, Guangdong, Peoples R China. yuanfang.chen.2009@ieee.org; noel.crespi@mines-telecom.fr; antonio.ortiz@montimage.com; lei.shu@ieee.org Crespi, Noel/ABE-7052-2020; Shu, Lei/HHT-0694-2022; Chen, Yuanfang/AAJ-5465-2020 Crespi, Noel/0000-0003-2962-192X; Shu, Lei/0000-0002-6700-9347; Chen, Yuanfang/0000-0002-1557-087X; Ortiz, Antonio M./0000-0002-7145-8241 Special Fund of Guangdong Higher School Talent Recruitment, Educational Commission of Guangdong Province, China [2013KJCX0131]; Guangdong High-Tech Development Fund [2013B010401035]; National Natural Science Foundation of China [61401107] Special Fund of Guangdong Higher School Talent Recruitment, Educational Commission of Guangdong Province, China; Guangdong High-Tech Development Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by 2013 Special Fund of Guangdong Higher School Talent Recruitment, Educational Commission of Guangdong Province, China Project No. 2013KJCX0131, Guangdong High-Tech Development Fund No. 2013B010401035, National Natural Science Foundation of China Grant No. 61401107. 35 21 21 0 120 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. FEB 10 2017.0 379 82 93 10.1016/j.ins.2016.07.075 0.0 12 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science ED8ET 2023-03-23 WOS:000389105500006 0
J Petrauskiene, V; Pal, M; Cao, MS; Wang, J; Ragulskis, M Petrauskiene, Vilma; Pal, Mayur; Cao, Maosen; Wang, Jie; Ragulskis, Minvydas Color Recurrence Plots for Bearing Fault Diagnosis SENSORS English Article recurrence plot; nonuniform embedding; transfer learning; feature extraction; bearing fault diagnosis SUPPORT VECTOR MACHINE; EMBEDDING DIMENSION; TIME-SERIES; PARAMETERS; ENTROPY This paper presents bearing fault diagnosis using the image classification of different fault patterns. Feature extraction for image classification is carried out using a novel approach of Color recurrence plots, which is presented for the first time. Color recurrence plots are created using non-linear embedding of the vibration signals into delay coordinate space with variable time lags. Deep learning-based image classification is then performed by building the database of the extracted features of the bearing vibration signals in the form of Color recurrence plots. A Series of computational experiments are performed to compare the accuracy of bearing fault classification using Color recurrence plots. The standard bearing vibration dataset of Case Western Reserve University is used for those purposes. The paper demonstrates the efficacy and the accuracy of a new and unique approach of scalar time series extraction into two-dimensional Color recurrence plots for bearing fault diagnosis. [Petrauskiene, Vilma; Pal, Mayur; Ragulskis, Minvydas] Kaunas Univ Technol, Dept Math Modelling, Studentu 50-146, LT-51368 Kaunas, Lithuania; [Cao, Maosen] Hohai Univ, Dept Engn Mech, Hohai 210098, Peoples R China; [Cao, Maosen] Chuzhou Univ, Coll Civil & Architecture Engn, Chuzhou 239000, Peoples R China; [Wang, Jie] Jiangsu Dongjiao Intelligent Control Technol Grp, Intelligent Transportat & Intelligent Construct E, Nanjing 211161, Peoples R China Kaunas University of Technology; Hohai University; Chuzhou University Ragulskis, M (corresponding author), Kaunas Univ Technol, Dept Math Modelling, Studentu 50-146, LT-51368 Kaunas, Lithuania. minvydas.ragulskis@ktu.lt Cao, Maosen/HNI-6007-2023; Ragulskis, Minvydas/A-1546-2008 Cao, Maosen/0000-0001-6640-1905; Pal, Mayur/0000-0002-1387-4124; Ragulskis, Minvydas/0000-0002-3348-9717 Key R&D Project of Anhui Science and Technology Department [202004b11020026]; Jiangsu International Joint Research and Development Program [BZ2022010]; Nanjing International Joint Research and Development Program [20211200] Key R&D Project of Anhui Science and Technology Department; Jiangsu International Joint Research and Development Program; Nanjing International Joint Research and Development Program The authors are grateful for the support from the Key R&D Project of Anhui Science and Technology Department (202004b11020026), the Jiangsu International Joint Research and Development Program (No. BZ2022010), the Nanjing International Joint Research and Development Program (No. 20211200). 41 0 0 8 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors NOV 2022.0 22 22 8870 10.3390/s22228870 0.0 18 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 6L0KX 36433467.0 gold, Green Accepted 2023-03-23 WOS:000887880000001 0
J Fu, ZT; Zhao, S; Zhang, XS; Polovka, M; Wang, X Fu, Zetian; Zhao, Shuang; Zhang, Xiaoshuan; Polovka, Martin; Wang, Xiang Quality Characteristics Analysis and Remaining Shelf Life Prediction of Fresh Tibetan Tricholoma matsutake under Modified Atmosphere Packaging in Cold Chain FOODS English Article Tibetan T; matsutake; quality characteristics; shelf life; modified atmosphere packaging; cold chain BP-NEURAL-NETWORK Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 degrees C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S-1, and they changed gradually in period S-2. In the period S-3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain. [Fu, Zetian; Zhao, Shuang; Zhang, Xiaoshuan; Wang, Xiang] China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China; [Polovka, Martin] Natl Agr & Food Ctr Priemyselna, Radlinskeho 9, SK-81237 Bratislava, Slovakia China Agricultural University Wang, X (corresponding author), China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. fzt@cau.edu.cn; zhaosh@cau.edu.cn; zhxshuan@cau.edu.cn; polovka@vup.sk; wxzrjj@cau.edu.cn Polovka, Martin/AAD-7303-2019 ZHANG, Xiaoshuan/0000-0002-0395-928X; Wang, Xiang/0000-0003-3286-9539 National Nature Science Foundation of China [31460421]; China-Slovak bilateral project (8-2) National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); China-Slovak bilateral project (8-2) This research was funded by the National Nature Science Foundation of China for the project entitled Multiple information fusion based quality monitoring of Tibet edible fungi in cold chain (31460421), and the China-Slovak bilateral project (8-2). 42 12 12 13 64 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2304-8158 FOODS Foods APR 2019.0 8 4 136 10.3390/foods8040136 0.0 14 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology HX3LS 31013609.0 Green Submitted, Green Accepted, gold, Green Published 2023-03-23 WOS:000467294000025 0
J Qu, JH; Sun, DW; Pu, HB Qu, Jia-Huan; Sun, Da-Wen; Pu, Hongbin Vis/NIR Chemical Imaging Technique for Predicting Sodium Humate Contents in Aquaculture Environment WATER AIR AND SOIL POLLUTION English Article Imaging spectroscopy; Vis/NIR; Humic; Gray reference image; Successive projections algorithm; Back-propagation artificial neural network LEAST-SQUARES REGRESSION; NONDESTRUCTIVE DETERMINATION; QUALITY EVALUATION; GENETIC ALGORITHM; HUMIC SUBSTANCES; FISH; SELECTION; SPOILAGE; WATER; TOXICITY This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. Successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed (R-C(2) = 0.986, R-CV(2) = 0.985, R-P(2) = 0. 9 9 3, RMSEC = 0. 329 m g /L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths (R-C(2) = 0.978, R-CV(2) = 0.996, R-P(2) = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water. [Qu, Jia-Huan; Sun, Da-Wen; Pu, Hongbin] South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China; [Qu, Jia-Huan; Sun, Da-Wen; Pu, Hongbin] South China Univ Technol, Guangzhou Higher Educ Mega Ctr, Acad Contemporary Food Engn, Guangzhou 510006, Guangdong, Peoples R China; [Qu, Jia-Huan; Sun, Da-Wen; Pu, Hongbin] Guangzhou Higher Educ Mega Ctr, Engn & Technol Res Ctr Guangdong Prov Intelligent, Guangzhou 510006, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, FRCFT, Dublin 4, Ireland South China University of Technology; South China University of Technology; University College Dublin Sun, DW (corresponding author), Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, FRCFT, Dublin 4, Ireland. dawen.sun@ucd.ie Sun, Da-Wen/B-1899-2012; Hongbin, Pu/ABE-8433-2020 Sun, Da-Wen/0000-0002-3634-9963; Hongbin, Pu/0000-0002-7977-1902; Qu, Jia-Huan/0000-0001-6041-7500 International S&T Cooperation Program of China [2015DFA71150]; Collaborative Innovation Major Special Projects of Guangzhou City [201508020097, 201604020007, 201604020057]; Guangdong Provincial Science and Technology Plan Projects [2015A020209016, 2016A040403040]; Key Projects of Administration of Ocean and Fisheries of Guangdong Province [A201401C04]; National Key Technologies RD Program [2015BAD19B03]; International and Hong Kong - Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology Equipment [2015KGJHZ001]; Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products [2016LM2154] International S&T Cooperation Program of China; Collaborative Innovation Major Special Projects of Guangzhou City; Guangdong Provincial Science and Technology Plan Projects; Key Projects of Administration of Ocean and Fisheries of Guangdong Province; National Key Technologies RD Program(National Key Technology R&D Program); International and Hong Kong - Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology Equipment; Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products The authors are grateful to the International S&T Cooperation Program of China (2015DFA71150) for its support. This research was also supported by the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097, 201604020007, 201604020057), the Guangdong Provincial Science and Technology Plan Projects (2015A020209016, 2016A040403040), the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the National Key Technologies R&D Program (2015BAD19B03), the International and Hong Kong - Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology & Equipment (2015KGJHZ001), the Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products and the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154). 47 3 4 0 22 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0049-6979 1573-2932 WATER AIR SOIL POLL Water Air Soil Pollut. MAY 2017.0 228 5 177 10.1007/s11270-017-3355-z 0.0 10 Environmental Sciences; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences; Water Resources EU5KM 2023-03-23 WOS:000401070100010 0
J Liu, C; Jin, J; Daly, I; Li, SR; Sun, H; Huang, YT; Wang, XY; Cichocki, A Liu, Chang; Jin, Jing; Daly, Ian; Li, Shurui; Sun, Hao; Huang, Yitao; Wang, Xingyu; Cichocki, Andrzej SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING English Article Electroencephalography; Feature extraction; Convolution; Kernel; Convolutional neural networks; Filter banks; Task analysis; Brain-computer interface; motor imagery; SincNet; neural network; gated recurrent unit BRAIN-COMPUTER-INTERFACE It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets. [Liu, Chang; Li, Shurui; Sun, Hao; Huang, Yitao; Wang, Xingyu] East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China; [Jin, Jing] East China Univ Sci & Technol, Shenzhen Res Inst, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China; [Daly, Ian] Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia; [Cichocki, Andrzej] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland; [Cichocki, Andrzej] Nicolaus Copernicus Univ, Dept Informat, PL-87100 Torun, Poland East China University of Science & Technology; East China University of Science & Technology; University of Essex; Skolkovo Institute of Science & Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Nicolaus Copernicus University Jin, J (corresponding author), East China Univ Sci & Technol, Shenzhen Res Inst, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China. liuchang@mail.ecust.edu.cn; jinjingat@gmail.com; i.daly@essex.ac.uk; shuruili1008@163.com; 13916245741@163.com; hyt2242@gmail.com; xywang@ecust.edu.cn; a.cichocki@skoltech.ru ; Daly, Ian/Q-7322-2017 Li, Shurui/0000-0002-6244-6396; Jin, Jing/0000-0002-6133-5491; Liu, Chang/0000-0002-2930-306X; Daly, Ian/0000-0001-5489-0393 National Key Research and Development Program [2017YFB13003002]; National Natural Science Foundation of China [61573142, 61773164]; Program of Introducing Talents of Discipline to Universities (the 111 Project) [B17017]; Shuguang Project - Shanghai Municipal Education Commission; Shanghai Education Development Foundation [19SG25]; Ministry of Education and Science of the Russian Federation [14.756.31.0001]; Polish National Science Center [UMO-2016/20/W/NZ4/00354] National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program of Introducing Talents of Discipline to Universities (the 111 Project)(Ministry of Education, China - 111 Project); Shuguang Project - Shanghai Municipal Education Commission(Chinese Academy of Sciences); Shanghai Education Development Foundation; Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Polish National Science Center This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002, in part by the Grant National Natural Science Foundation of China under Grant 61573142 and Grant 61773164, in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Shuguang Project funded by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 19SG25, in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001, and in part by the Polish National Science Center under Grant UMO-2016/20/W/NZ4/00354. 39 3 3 15 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1534-4320 1558-0210 IEEE T NEUR SYS REH IEEE Trans. Neural Syst. Rehabil. Eng. 2022.0 30 540 549 10.1109/TNSRE.2022.3156076 0.0 10 Engineering, Biomedical; Rehabilitation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Rehabilitation ZU6RR 35235515.0 hybrid, Green Published 2023-03-23 WOS:000769969000001 0
J Mishra, D; Luo, ZW; Hazen, B; Hassini, E; Foropon, C Mishra, Deepa; Luo, Zongwei; Hazen, Benjamin; Hassini, Elkafi; Foropon, Cyril Organizational capabilities that enable big data and predictive analytics diffusion and organizational performance A resource-based perspective MANAGEMENT DECISION English Article Big data; Resource-based theory; Organization performance; Predictive analytics; HR capabilities; IT deployment capabilities SUPPLY CHAIN INTEGRATION; DYNAMIC CAPABILITIES; INFORMATION-TECHNOLOGY; FIRM PERFORMANCE; COMPETITIVE ADVANTAGE; OPERATIONAL CAPABILITIES; BUSINESS STRATEGY; VALUE CREATION; MANAGEMENT; SYSTEMS Purpose Big data and predictive analytics (BDPA) has received great attention in terms of its role in making business decisions. However, current knowledge on BDPA regarding how it might link organizational capabilities and organizational performance (OP) remains unclear. Drawing from the resource-based view, the purpose of this paper is to propose a model to examine how information technology (IT) deployment (i.e. strategic IT flexibility, business-BDPA partnership and business-BDPA alignment) and HR capabilities affect OP through BDPA. Design/methodology/approach To test the proposed hypotheses, structural equation modeling is applied on survey data collected from 159 Indian firms. Findings The results show that BDPA diffusion mediates the influence of IT deployment and HR capabilities on OP. In addition, there is a direct effect of IT deployment and HR capabilities on BDPA diffusion, which also has a direct relationship with OP. Originality/value Through this study, authors demonstrate that IT deployment and HR capabilities have an indirect impact on OP through BDPA diffusion. [Mishra, Deepa; Hassini, Elkafi] McMaster Univ, DeGroote Sch Business, Hamilton, ON, Canada; [Luo, Zongwei] SUSTech, Dept Comp Sci & Engn, Shenzhen, Peoples R China; [Hazen, Benjamin] Univ Tennessee, Dept Mkt & SCM, Knoxville, TN USA; [Foropon, Cyril] Montpellier Business Sch, Dept Management, Montpellier, France McMaster University; Southern University of Science & Technology; University of Tennessee System; University of Tennessee Knoxville; Montpellier Business School; Universite de Montpellier Luo, ZW (corresponding author), SUSTech, Dept Comp Sci & Engn, Shenzhen, Peoples R China. deepamishra904@gmail.com; luozw@sustc.edu.cn; benjamin.hazen@live.com; hassini@mcmaster.ca; c.foropon@montpellier-bs.com BHATT, DEEPA/AAR-5580-2021; Foropon, Cyril/AAF-5640-2019; Luo, Zongwei/N-1985-2015 Foropon, Cyril/0000-0003-3083-0016; Luo, Zongwei/0000-0001-9322-959X National Natural Science Foundation of China [6171101169]; Natural Science Foundation of Guangdong Province [2015A030313782]; Guangdong Education Bureau Fund [2017KTSCX166]; SUSTech startup fund [Y01236215]; Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ20170817112037041] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Guangdong Education Bureau Fund; SUSTech startup fund; Science and Technology Innovation Committee Foundation of Shenzhen This work is partially supported with the following funds: National Natural Science Foundation of China (6171101169), Natural Science Foundation of Guangdong Province (2015A030313782), Guangdong Education Bureau Fund 2017KTSCX166, SUSTech startup fund (Y01236215) and the Science and Technology Innovation Committee Foundation of Shenzhen (JCYJ20170817112037041). 130 27 27 15 109 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0025-1747 1758-6070 MANAGE DECIS Manag. Decis. SEP 12 2019.0 57 8 SI 1734 1755 10.1108/MD-03-2018-0324 0.0 22 Business; Management Social Science Citation Index (SSCI) Business & Economics IZ4EP 2023-03-23 WOS:000487037400002 0
J Yang, B; Cao, XL; Huang, CW; Guan, YL; Yuen, C; Di Renzo, M; Niyato, D; Debbah, M; Hanzo, L Yang, Bo; Cao, Xuelin; Huang, Chongwen; Guan, Yong Liang; Yuen, Chau; Di Renzo, Marco; Niyato, Dusit; Debbah, Merouane; Hanzo, Lajos Spectrum-Learning-Aided Reconfigurable Intelligent Surfaces for Green 6G Networks IEEE NETWORK English Article 6G mobile communication; Wireless communication; Wireless sensor networks; Green products; Learning (artificial intelligence); Interference; Reconfigurable intelligent surfaces DESIGN In the sixth generation (6G) era, emerging large-scale computing-based applications (e.g., processing enormous amounts of images in real time in autonomous driving) tend to lead to excessive energy consumption for end users, whose devices are usually energy-constrained. In this context, energy efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize green 6G networks. As a remedy, reconfigurable intelligent surfaces (RISs) have been proposed for improving energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-in-terference-plus-noise ratio (SINR) sometimes may become degraded. This is because the signals impinging on an RIS are typically contaminated by interfering signals that are usually dynamic and unknown. To address this issue, learning the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, referred to here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently thinking and deciding whether or not to reflect the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions. [Yang, Bo; Cao, Xuelin; Yuen, Chau] Singapore Univ Technol & Design, Singapore, Singapore; [Huang, Chongwen] Zhejiang Univ, Hangzhou, Peoples R China; [Huang, Chongwen] Zhejiang Prov Key Lab Informat Proc Commun & Netw, Hangzhou, Peoples R China; [Guan, Yong Liang; Niyato, Dusit] Nanyang Technol Univ, Singapore, Singapore; [Di Renzo, Marco] Univ Paris Saclay, CNRS, CentraleSupelec, Lab Signaux & Syst, Paris, France; [Debbah, Merouane] Univ Paris Saclay, Technol Innovat Inst, Paris, France; [Debbah, Merouane] Univ Paris Saclay, CentraleSupelec, Paris, France; [Hanzo, Lajos] Univ Southampton, Southampton, Hants, England Singapore University of Technology & Design; Zhejiang University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; University of Southampton Yang, B (corresponding author), Singapore Univ Technol & Design, Singapore, Singapore. bo_yang@sutd.edu.sg; xuelin_cao@sutd.edu.sg; chongwenhuang@zju.edu.cn; eylguan@ntu.edu.sg; yuenchau@sutd.edu.sg; marco.di-renzo@universite-paris-saclay.fr; dniyato@ntu.edu.sg; merouane.debbah@tii.ae; lh@ecs.soton.ac.uk Yuen, Chau/C-5493-2013 Yuen, Chau/0000-0002-9307-2120 MOE Tier 2 [MOE000168-01]; A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund -Pre Positioning (IAFPP) [A19D6a0053]; National Natural Science Foundation of China [62101492]; Zhejiang University Education Foundation Qizhen Scholar Foundation; Fundamental Research Funds for the Central Universities [2021FZZX001-21]; European Commission [871464, 101017011]; National Research Foundation, Singapore under the AI Singapore Programme (AISG) [AISG2-RP-2020-019]; WASP/NTU grant [M4082187 (4080)]; Singapore Ministry of Education (MOE) Tier 1 [RG16/20]; Engineering and Physical Sciences Research Council projects [EP/P034284/1, EP/P003990/1]; European Research Council's Advanced Fellow Grant QuantCom [789028] MOE Tier 2(Ministry of Education, Singapore); A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund -Pre Positioning (IAFPP)(Agency for Science Technology & Research (A*STAR)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang University Education Foundation Qizhen Scholar Foundation; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); European Commission(European CommissionEuropean Commission Joint Research Centre); National Research Foundation, Singapore under the AI Singapore Programme (AISG); WASP/NTU grant; Singapore Ministry of Education (MOE) Tier 1(Ministry of Education, Singapore); Engineering and Physical Sciences Research Council projects(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); European Research Council's Advanced Fellow Grant QuantCom This research is supported by MOE Tier 2 MOE000168-01 and A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund -Pre Positioning (IAFPP) (Grant No. A19D6a0053). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of A*STAR. The work of C. Huang was supported in part by the National Natural Science Foundation of China under Grant 62101492, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Fundamental Research Funds for the Central Universities under Grant 2021FZZX001-21. The work of M. Di Renzo was supported in part by the European Commission through the H2020 ARIADNE project under grant agreement number 871464 and through the H2020 RISE-6G project under grant agreement number 101017011. The work of D. Niyato was supported in part by the National Research Foundation, Singapore under the AI Singapore Programme (AISG) (AISG2-RP-2020-019), WASP/NTU grant M4082187 (4080) and Singapore Ministry of Education (MOE) Tier 1 (RG16/20). The work of L. Hanzo was supported in part by the Engineering and Physical Sciences Research Council projects EP/P034284/1 and EP/P003990/1 (COALESCE) as well as the European Research Council's Advanced Fellow Grant QuantCom (Grant No. 789028). 15 4 4 2 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. NOV-DEC 2021.0 35 6 20 26 10.1109/MNET.110.2100301 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications YK8ZD Green Submitted, Green Accepted 2023-03-23 WOS:000745493100010 0
C Fan, GH; He, ZR; Sun, JL; Gui, G; Gacanin, H; Adebisi, B IEEE Fan, Guanghui; He, Zhengran; Sun, Jinlong; Gui, Guan; Gacanin, Haris; Adebisi, Bamidele Downlink Channel State Information Limited Feedback Using Fully Convolutional Network 2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) IEEE Wireless Communications and Networking Conference English Proceedings Paper IEEE Wireless Communications and Networking Conference (WCNC) MAR 29-APR 01, 2021 Nanjing, PEOPLES R CHINA IEEE Fully convolutional network; downlink CSI; limited feedback; deep learning In massive multiple input multiple output (MIMO) systems, the base station (BS) requires channel state information (CSI) to better utilize the available spatial diversity and multiplexing gains. However, in frequency division duplex (FDD) systems, user equipment (UE) needs to keep on feeding downlink CSI back to the BS, thereby consuming precious bandwidth resources. In this paper, we propose a deep learning (DL) based downlink CSI limited feedback scheme, called FullyConv, which is composed of all convolutional layers to compress and decompress the downlink CSI. FullyConv will improve reconstruction accuracy and robustness as well as reduce the time and space complexity, thus enhancing the system feasibility. Experimental results demonstrate that the FullyConv has a gain of nearly 5 dB compared to baseline. The performance of the FullyConv degrades slightly in the noisy uplink channel, which shows the robustness of FullyConv. Meanwhile, the complexity of the model composed of time complexity and space complexity is significantly reduced. [Fan, Guanghui; He, Zhengran; Sun, Jinlong; Gui, Guan] NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, Aachen, Germany; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester, Lancs, England Nanjing University of Posts & Telecommunications; RWTH Aachen University; Manchester Metropolitan University Fan, GH (corresponding author), NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China. SUN, JIN/GPX-9641-2022; Gui, Guan/AAG-3593-2019 Gui, Guan/0000-0001-7428-4980; Adebisi, Bamidele/0000-0001-9071-9120 Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX20 0813]; Major Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]; National Natural Science Foundation of China [61901228, 61671253]; Six Top Talents Program of Jiangsu [XYDXX-010]; Chongqing Municipal Key Laboratory of Institutions of Higher Education [cqupt-mct-201802]; 1311 Talent Plan of Nanjing University of Posts and Telecommunications Postgraduate Research and Practice Innovation Program of Jiangsu Province; Major Project of the Ministry of Industry and Information Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Six Top Talents Program of Jiangsu; Chongqing Municipal Key Laboratory of Institutions of Higher Education; 1311 Talent Plan of Nanjing University of Posts and Telecommunications This work was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX20 0813), the Major Project of the Ministry of Industry and Information Technology of China under Grant TC190A3WZ-2, National Natural Science Foundation of China under Grant 61901228 and 61671253, the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Project Supported by Chongqing Municipal Key Laboratory of Institutions of Higher Education under Grant cqupt-mct-201802, the 1311 Talent Plan of Nanjing University of Posts and Telecommunications. 22 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1525-3511 978-1-7281-9505-6 IEEE WCNC 2021.0 10.1109/WCNC49053.2021.9417350 0.0 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS2OB 2023-03-23 WOS:000704226500123 0
J Zhang, L; Qin, XY; Liu, M; Xu, ZW; Liu, GZ Zhang, Lu; Qin, Xinyi; Liu, Min; Xu, Ziwei; Liu, Guangzhong DNN-m6A: A Cross-Species Method for Identifying RNA N6-methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion GENES English Article N6-methyladenosine sites; multi-information fusion; elastic net; deep neural network; Bayesian hyper-parameter optimization MESSENGER-RNA; N-6-METHYLADENOSINE SITES; 5-METHYLCYTOSINE SITES; NUCLEAR-RNA; SEQUENCES; M(6)A; METHYLATION; PREDICTION; SELECTION As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58-83.38% and an area under the curve (AUC) of 81.39-91.04%. Furthermore, the independent datasets achieved an ACC of 72.95-83.04% and an AUC of 80.79-91.09%, which shows an excellent generalization ability of our proposed method. [Zhang, Lu; Qin, Xinyi; Liu, Min; Liu, Guangzhong] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Xu, Ziwei] Polytech Nantes, Batiment Ireste, F-44300 Nantes, France Shanghai Maritime University; Nantes Universite Liu, GZ (corresponding author), Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China. 201940310007@stu.shmtu.edu.cn; 201930310113@stu.shmtu.edu.cn; liumin@shmtu.edu.cn; ziwei.xu@etu.univ-nantes.fr; gzhliu@shmtu.edu.cn Zhang, Lu/0000-0002-0765-9284 Natural Science Foundation of Shanghai [17ZR1412500]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This study was supported by the Natural Science Foundation of Shanghai (17ZR1412500) and the Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000). 60 10 10 9 25 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4425 GENES-BASEL Genes MAR 2021.0 12 3 354 10.3390/genes12030354 0.0 25 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity RD7XH 33670877.0 Green Accepted, gold 2023-03-23 WOS:000633685200001 0
J Niya, AK; Huang, JL; Kazemzadeh-Zow, A; Naimi, B Niya, Ali Kourosh; Huang, Jinliang; Kazemzadeh-Zow, Ali; Naimi, Babak An adding/deleting approach to improve land change modeling: a case study in Qeshm Island, Iran ARABIAN JOURNAL OF GEOSCIENCES English Article Land use; cover change modeling; Cellular automata-Markov chain; Adding; deleting; Qeshm Island CELLULAR-AUTOMATA MODELS; URBAN-GROWTH SIMULATION; COVER CHANGE; MARKOV-CHAIN; SENSITIVITY-ANALYSIS; PREDICTION; ACCURACY; INTEGRATION; ALLOCATION; REGRESSION Land use/cover change (LUCC) simulation models are helpful tools for decision makers because of their capacity of predicting the landscape dynamics under various scenarios and thereby developing countermeasures. Developing LUCC models with high reliability still remains challenging due to complicated influencing of natural and anthropogenic factors. An adding/deleting approach is proposed in this study to explore whether and to what extent it can improve the accuracy of a hybrid LUCC model involved with cellular automata, Markov chain, and artificial neural network in the Qeshm Island, the biggest island in the Persian Gulf. The accuracy assessment was conducted by comparing the simulation results obtained from the models with the maps derived from Landsat image in 2014. The results revealed that the adding/deleting approach could improve the prediction accuracy of the model for the majority of land use classes as the area of the correctly predicted classes increased to 7.2km(2), which is greater than 6.09km(2) without using the approach. We further compared the results derived from the proposed approach with those from cellular automata-Markov chain-artificial neural network, Markov chain-artificial neural network, and cellular automata-Markov chain-logistic regression, resulting in the Figure of Merit index of 7.8 with this approach, compared to 6.7, 5.1, and 4.5 with the other three models mentioned above. This study demonstrates that the proposed approach is effective for improving the performance of LUCC modeling. [Niya, Ali Kourosh; Huang, Jinliang] Xiamen Univ, Coastal & Ocean Management Inst, Xiamen, Peoples R China; [Kazemzadeh-Zow, Ali] Univ Tehran, Geog Fac, Dept RS & GIS, Tehran, Iran; [Naimi, Babak] Univ Amsterdam, Inst Biodivers & Ecosyst Dynam, Amsterdam, Netherlands Xiamen University; University of Tehran; University of Amsterdam Huang, JL (corresponding author), Xiamen Univ, Coastal & Ocean Management Inst, Xiamen, Peoples R China. ali_kooroshnia@yahoo.com; jlhuang@xmu.edu.cn; ali_kazemzadeh@ut.ac.ir; naimi.b@gmail.com Naimi, Babak/D-8804-2016; Kazemzadeh-zow, Ali/AAA-2933-2019 Naimi, Babak/0000-0001-5431-2729; Kazemzadeh Haghighi, Ali/0000-0002-6492-032X Chinese Government Marine Scholarship [2016SOA016] Chinese Government Marine Scholarship This research was partially supported by Chinese Government Marine Scholarship (Grant No: 2016SOA016). 76 4 4 2 19 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1866-7511 1866-7538 ARAB J GEOSCI Arab. J. Geosci. JUN 2019.0 12 11 333 10.1007/s12517-019-4504-z 0.0 17 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Geology HY8MT 2023-03-23 WOS:000468393600001 0
J Jiang, JD; Zhao, JW; Pang, SM; Meraghni, F; Siadat, A; Chen, Q Jiang, Jindong; Zhao, Jiawei; Pang, Shanmin; Meraghni, Fodil; Siadat, Ali; Chen, Qiang Physics-informed deep neural network enabled discovery of size-dependent deformation mechanisms in nanostructures INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES English Article Deep neural network; Nanostructures; Surface effects; Displacement and stress fields; Deep energy approach; Gurtin-Murdoch model FINITE-VOLUME HOMOGENIZATION; NANOPOROUS MATERIALS; TOPOLOGY OPTIMIZATION; SURFACE STRESS; ELASTIC STATE; LOCALIZATION; FIELDS; ENERGY; BEHAVIOR The surface effects based on the Gurtin-Murdoch interface model are incorporated into a physics-informed deep neural network (DNN) to enable the discovery of the size-dependent mechanical response in nanoscale structures for the first time. The DNN directly deals with the minimization of a cost function with contributions from total potential energies and penalties representing the displacement boundary conditions. Following this spirit, the Gurtin-Murdoch interface model is directly implemented into the DNN cost function through additional surface energies associated with the surfaces of the nanostructures without resorting to the implementation of Young Laplace equations explicitly employed in the analytical approaches. The DNN technique is first validated on both displacement and stress fields vis-`a-vis the analytical solution for the modified Kirsch problem with the Gurtin-Murdoch interface under far-field shear loading. Then, the DNN technique is critically and vigorously assessed against the Q4, Q8, and Q9 finite-element results of nanocylinders under diametrical loading. Notably, the accuracy of the DNN is demonstrated to be comparable to the analytical, Q8 and Q9 finite element-based results without the apparent stress discontinuities noticeable in the Q4 element. The numerical example indicates that the selection of collocation points in the DNN approach can be made completely random using the Monte-Carlo simulation without sacrificing the accuracy of the predicted stress field. An inverse load identification problem is also considered to demonstrate the special strength of the DNN approach, namely, both the forward and inverse problems can be handled using the same framework. The present contribution provides a compelling alternative and independent means of identifying surface elasticity effect and hence is a good tool in assessing the finite-element method as well as other approaches' predictive capability for this class of materials. [Meraghni, Fodil; Chen, Qiang] Univ Lorraine, CNRS, Arts & Metiers Inst Technol, LEM3,UMR7239, F-57000 Metz, France; [Jiang, Jindong; Siadat, Ali] HESAM Univ, LCFC, Arts & Metiers Inst Technol, F-57070 Metz, France; [Zhao, Jiawei; Pang, Shanmin] Xi An Jiao Tong Univ, Sch Soft Engn, Xian 710049, Shaanxi, Peoples R China Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Lorraine; heSam Universite; Xi'an Jiaotong University Chen, Q (corresponding author), Univ Lorraine, CNRS, Arts & Metiers Inst Technol, LEM3,UMR7239, F-57000 Metz, France. qiang.chen@ensam.eu Chen, Qiang/HMP-1748-2023 Chen, Qiang/0000-0002-1623-8756; MERAGHNI, Fodil/0000-0002-5043-8700 62 2 2 7 28 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0020-7683 1879-2146 INT J SOLIDS STRUCT Int. J. Solids Struct. FEB 1 2022.0 236 111320 10.1016/j.ijsolstr.2021.111320 0.0 NOV 2021 21 Mechanics Science Citation Index Expanded (SCI-EXPANDED) Mechanics WW8GX 2023-03-23 WOS:000718149000003 0
C Li, BJ; Chen, S; Lu, YX; He, WS; Zeng, XM Xu, B Li Baojie; Chen Shuo; Lu Yuxin; He Weisheng; Zeng Xiaoming Three-Stage Lossless Compression Technique For Processing PMU Big Data In Modern Smart Grid PROCEEDINGS OF THE 2017 2ND JOINT INTERNATIONAL INFORMATION TECHNOLOGY, MECHANICAL AND ELECTRONIC ENGINEERING CONFERENCE (JIMEC 2017) ACSR-Advances in Comptuer Science Research English Proceedings Paper 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC) OCT 04-05, 2017 Chongqing, PEOPLES R CHINA Data compression; Lossless compression; Big data; PMU; Adaptive Huffman; Smart grid WAMS With the widespread proliferation and application of high data sampling rate device-Phasor Measurement Units (PMUs) in modern smart grid, large volumes of data are produced in the wide-area monitoring system, which results in increasing challenge in real-time analysis, data storage and data transmission. This paper proposes an improved lossless compression technique which is fully based on the analysis of PMU data structure and consists by three stages algorithm for handling massive data. In the first stage, adjacent coordinate data of one period are operated subtraction and the waveform difference method is adopted to simplify data of other periods. Then, adaptive dynamic Huffman algorithm is selected to compress the processed data. Finally, Run Length Encoding method is employed to decrease furthermore the redundancy. The performance of the improved approach was evaluated under simulation using field PMU data. Experimental results under both dynamic and steady states presented at the end of the paper prove the efficiency of the proposed technique. [Li Baojie] Ecole Cent Nantes, IRCCyN, F-45300 Nantes, France; [Chen Shuo; Lu Yuxin; He Weisheng; Zeng Xiaoming] Xi An Jiao Tong Univ, State Key Lab Elect Insulat Power Equipment, Xian 710049, Shaanxi, Peoples R China Nantes Universite; Ecole Centrale de Nantes; Xi'an Jiaotong University Li, BJ (corresponding author), Ecole Cent Nantes, IRCCyN, F-45300 Nantes, France. libaojie@stu.xjtu.edu.cn 8 0 0 0 0 ATLANTIS PRESS PARIS 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE 2352-538X 978-94-6252-366-1 ACSR ADV COMPUT 2017.0 62 59 63 5 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ8XC 2023-03-23 WOS:000428892800012 0
J Al-Waisy, AS; Al-Fahdawi, S; Mohammed, MA; Abdulkareem, KH; Mostafa, SA; Maashi, MS; Arif, M; Garcia-Zapirain, B Al-Waisy, Alaa S.; Al-Fahdawi, Shumoos; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A.; Maashi, Mashael S.; Arif, Muhammad; Garcia-Zapirain, Begonya COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images SOFT COMPUTING English Article Coronavirus COVID-19 epidemic; Deep learning; Transfer learning; ResNet34 model; Chest radiography imaging; Chest X-ray images The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result. [Al-Waisy, Alaa S.] Imam Jaafar Al Sadiq Univ, Commun Engn Tech Dept, Informat Technol Coll, Baghdad, Iraq; [Al-Fahdawi, Shumoos] Al Maaref Univ Coll, Comp Sci Dept, Anbar, Iraq; [Mohammed, Mazin Abed] Univ Anbar, Coll Comp Sci & Informat Technol, Ramadi 11, Anbar, Iraq; [Abdulkareem, Karrar Hameed] Al Muthanna Univ, Coll Agr, Samawah 66001, Iraq; [Mostafa, Salama A.] Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Batu Pahat 86400, Johor, Malaysia; [Maashi, Mashael S.] King Saud Univ, Coll Comp & Informat Sci, Software Engn Dept, Riyadh 11451, Saudi Arabia; [Arif, Muhammad] Guangzhou Univ, Sch Comp Sci, Guangzhou, Peoples R China; [Garcia-Zapirain, Begonya] Univ Deusto, eVIDA Lab, Avda Univ 24, Bilbao 48007, Spain Imam Jaa'far al-Sadiq University; University of Anbar; Al-Muthanna University; University of Tun Hussein Onn Malaysia; King Saud University; Guangzhou University; University of Deusto Al-Waisy, AS (corresponding author), Imam Jaafar Al Sadiq Univ, Commun Engn Tech Dept, Informat Technol Coll, Baghdad, Iraq. king_alaa87@yahoo.com; sun_algold@yahoo.com; mazinalshujeary@uoanbar.edu.iq; khak9784@mu.edu.iq; salama@uthm.edu.my; mmaashi@ksu.edu.sa; arifmuhammad36@hotmail.com; mbgarciazapi@deusto.es Abdulkareem, Karrar Hameed/V-1741-2017; Mohammed, Mazin Abed/E-3910-2018; Maashi, Mashael/AAJ-3501-2020; Mostafa, Salama/N-2437-2017; Arif, Muhammad/J-8700-2019; Al-Waisy, Alaa S./AAZ-9675-2021 Abdulkareem, Karrar Hameed/0000-0001-7302-2049; Mohammed, Mazin Abed/0000-0001-9030-8102; Maashi, Mashael/0000-0003-0446-5430; Mostafa, Salama/0000-0001-5348-502X; Al-Waisy, Alaa S./0000-0001-7544-2709 36 68 68 2 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1432-7643 1433-7479 SOFT COMPUT Soft Comput. MAR 2023.0 27 5 2657 2672 10.1007/s00500-020-05424-3 0.0 NOV 2020 16 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 9D8KQ 33250662.0 hybrid, Green Accepted 2023-03-23 WOS:000591392000004 0
J Zhang, WT; Yang, D; Wang, HC; Huang, XF; Gidlund, M Zhang, Weiting; Yang, Dong; Wang, Hongchao; Huang, Xuefeng; Gidlund, Mikael CarNet: A Dual Correlation Method for Health Perception of Rotating Machinery IEEE SENSORS JOURNAL English Article Health perception; convolutional neural network; gated recurrent unit; attention mechanism; temporal and spatial correlation FAULT-DIAGNOSIS; NEURAL-NETWORK As a key component of rotating machinery, the health perception of hearings is essential to ensure the safe and reliable operation of industrial equipment. In recent years, research on equipment health perception based on data-driven methods has received extensive attention. Overall, most studies focus on several public datasets to verify the effectiveness of their algorithms. However, the scale of these datasets cannot completely satisfy the representation learning of deep models. Therefore, this paper proposes a novel method, called CarNet, to obtain a more robust model and ensure that the model is sufficiently trained on a limited dataset. Specifically, it is composed of a data augmentation method named equitable sliding stride segmentation (ESSS) and a hybrid-stacked deep model (HSDM). The ESSS not only amplifies the scale of the original dataset but also enables newly generated data with both spatial and temporal correlations. The HSDM can, therefore, extract shallow spatial features and deep temporal information from the strongly correlated 2-dimensional (2-D) sensor array using a CNN and a bi-GRU, respectively. Moreover, the integrated attention mechanism contributes to focusing limited resources on informative areas. The effectiveness of CarNet is evaluated on the CWRU dataset, and an optimal diagnostic accuracy of 99.92% is achieved. [Zhang, Weiting; Yang, Dong; Wang, Hongchao] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China; [Huang, Xuefeng] Beijing Sheenline Technol, Beijing 100044, Peoples R China; [Gidlund, Mikael] Mid Sweden Univ, Informat & Commun Syst, S-85170 Sundsvall, Sweden Beijing Jiaotong University; Mid-Sweden University Yang, D (corresponding author), Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China. 17111018@bjtu.edu.cn; dyang@bjtu.edu.cn; hcwang@bjtu.edu.cn; huangxuefeng@shenzhou-gaotie.com; mikael.gidlund@miun.se Gidlund, Mikael/L-7643-2018 Gidlund, Mikael/0000-0003-0873-7827; Zhang, Weiting/0000-0002-7473-2234 Fundamental Research Funds for the Central Universities [2018YJS013]; National Key Research and Development Program of China [2018YFB1702001]; National Natural Science Foundation of China [61771040] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2018YJS013, in part by the National Key Research and Development Program of China under Grant 2018YFB1702001, and in part by the National Natural Science Foundation of China under 61771040. The associate editor coordinating the review of this paper and approving it for publication was Prof. Jun Zhao. (Corresponding author: Dong Yang.) 39 8 8 2 45 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. AUG 15 2019.0 19 16 7095 7106 10.1109/JSEN.2019.2912934 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics IK7TR 2023-03-23 WOS:000476795500059 0
J Zhang, YY; Gao, X; Smith, K; Inial, G; Liu, SM; Conil, LB; Pan, BC Zhang, Yanyang; Gao, Xiang; Smith, Kate; Inial, Goulven; Liu, Shuming; Conil, Lenny B.; Pan, Bingcai Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network WATER RESEARCH English Article Artificial neural network; Genetic algorithm; Drinking water treatment; Water production AQUEOUS-SOLUTION; MODEL; REMOVAL; INTELLIGENCE; VARIABLES; COAGULATION; PARAMETERS; DOSAGE; ANN Stringent regulations and deteriorating source water quality could greatly influence the water production capacity of drinking water treatment plants (DWTPs). Using models to predict the performance of DWTPs under stress provides valuable information for decision making and future planning. A hybrid statistic model named HANN was established by combining artificial neural network (ANN) with genetic algorithm (GA) aiming at forecasting the overall performance of DWTPs nationwide in China. Monthly data from 45 DWTPs across China was employed. Water quality parameters like temperature and chemical oxygen demand (COD) and operational parameters like electricity consumption and chemical consumption were selected as input variables, while drinking water production was employed as the output. Both preliminary data analysis and principal component analysis (PCA) suggested a clear nonlinear relationship between the input and output variables. The structure of the HANN model was optimized by employing the lowest mean squared error (MSE) as the indicator. The resultant HANN model performed well when simulating the training datasets. Its predictive accuracy for the independent test datasets was enhanced when feeding more training datasets and the performance was constantly higher than the independent multi-layered ANN models using the coefficient of determination (R-2) as the indicator, indicating the HANN model was capable of capturing complex non-linear relationship and extrapolation. Results from Accuracy test, Garson sensitivity analysis and Analysis of Variance (ANOVA) suggested the quantity of water produced by DWTPs was closely linked to water quality and operational parameters. The scenario analysis showed that the HANN model was capable of predicting water production variation based on the parameter variations, indicating that the HANN model could be a general management tool for decision makers and DWTP managers to make plans in advance of regulatory changes, source water quality variations and market demand. (C) 2019 Elsevier Ltd. All rights reserved. [Zhang, Yanyang; Gao, Xiang; Pan, Bingcai] Nanjing Univ, State Key Lab Pollut Control & Resource Reuse, Sch Environm, Nanjing 210023, Jiangsu, Peoples R China; [Zhang, Yanyang; Pan, Bingcai] Nanjing Univ, Res Ctr Environm Nanotechnol ReCENT, Nanjing 210023, Jiangsu, Peoples R China; [Smith, Kate; Liu, Shuming] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China; [Inial, Goulven] Veolia Water Technol, PMT, St Maurice, France; [Conil, Lenny B.] Veolia Res & Innovat VeRI, Hong Kong, Peoples R China Nanjing University; Nanjing University; Tsinghua University Zhang, YY (corresponding author), Nanjing Univ, State Key Lab Pollut Control & Resource Reuse, Sch Environm, Nanjing 210023, Jiangsu, Peoples R China. zhangyanyang@nju.edu.cn Pan, Bingcai/AGE-9460-2022; liu, shuming/F-3081-2018 liu, shuming/0000-0002-4949-4318; Conil, Lenny B/0000-0003-4092-7399; Gao, Xiang/0000-0002-6096-1829; Pan, Bingcai/0000-0003-3626-1539 National Key Research and Development Program of China [2016YFA0203104/2017YFE0107200]; Natural Science Foundation of China [51708280]; Veolia and Projects on Social Development of Jiangsu Province of China [BE2016734] National Key Research and Development Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Veolia and Projects on Social Development of Jiangsu Province of China We appreciate the financial support from National Key Research and Development Program of China (Grant No. 2016YFA0203104/2017YFE0107200) and Natural Science Foundation of China (Grant No. 51708280). We also greatly appreciate the support and financial aid from Veolia and Projects on Social Development of Jiangsu Province of China (Grant No. BE2016734). 53 55 59 18 217 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0043-1354 WATER RES Water Res. NOV 1 2019.0 164 114888 10.1016/j.watres.2019.114888 0.0 12 Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Water Resources IW0IO 31377525.0 2023-03-23 WOS:000484645800007 0
C Melinte, O; Viadareanu, L; Munteanu, L; Hou, ZG; Bian, GB; Yu, HN; Cang, S; Wang, HB IEEE Melinte, Octavian; Viadareanu, Luigc; Munteanu, Ligia; Hou, Zeng-Guang; Bian, Gui-Bin; Yu, Hongnian; Cang, Shuang; Wang, Hongbo Haptic Intelligent Interfaces for NAO Robot Hand Control 2015 INTERNATIONAL CONFERENCE ON ADVANCED MECHATRONIC SYSTEMS (ICAMECHS) International Conference on Advanced Mechatronic Systems English Proceedings Paper International conference on Advanced Mechatronic systems AUG 22-24, 2015 Beijing, PEOPLES R CHINA Adv Mechatronic Syst,IEEE,Beijing Univ of Post and Telecommun,Modelling Identification and control humanoid robots; haptic interfaces; intelligent control; Nao robot The paper presents a new approach on haptic interface control for NAO robotic hand. The haptic teleoperation of NAO robot hand raises some issues, mapping the haptic device dynamic being the most important. The mapping was achieved through Neural Network by determining a dynamic gain of robot haptic feedback. The results lead to the achievement of the haptic intelligent interfaces for the NAO robot hand control which can be integrated into an innovative haptic robot control system. [Melinte, Octavian; Viadareanu, Luigc; Munteanu, Ligia] Inst Solid Mech Romanian Acad, Dept Robot & Mechatron, Bucharest, Romania; [Hou, Zeng-Guang; Bian, Gui-Bin] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100900, Peoples R China; [Yu, Hongnian; Cang, Shuang] Bournemouth Univ, Sch Design Engn & Comp, Poole BH12 5BB, Dorset, England; [Wang, Hongbo] Yanshan Univ, Parallel Robot & Mechatron Sys Lab, Qinhuangdao, Peoples R China Romanian Academy of Sciences; Chinese Academy of Sciences; Institute of Automation, CAS; Bournemouth University; Yanshan University Viadareanu, L (corresponding author), Inst Solid Mech Romanian Acad, Dept Robot & Mechatron, Bucharest, Romania. octavian.melinte@yahoo.com; luigiv2007@yahoo.com.sg; guibin.bian@ia.ac.cn; yuh@bournemouth.ac.uk; hongbo_w@ysu.edu.cn Yu, Hongnian/AFV-5287-2022; Melinte, Daniel Octavian/AAR-3343-2021; Chiroiu, Veturia V/A-7174-2008 Melinte, Daniel Octavian/0000-0001-7999-0438; Chiroiu, Veturia V/0000-0002-1086-8634; Yu, Hongnian/0000-0003-2894-2086 19 2 2 2 20 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2325-0682 978-1-4673-7996-0 INT C ADV MECH SYST 2015.0 50 55 6 Engineering, Electrical & Electronic; Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BF2HC 2023-03-23 WOS:000380467100010 0
J Al-qaness, MAA; Abbasi, AA; Fan, H; Ibrahim, RA; Alsamhi, SH; Hawbani, A Al-qaness, Mohammed A. A.; Abbasi, Aaqif Afzaal; Fan, Hong; Ibrahim, Rehab Ali; Alsamhi, Saeed H.; Hawbani, Ammar An improved YOLO-based road traffic monitoring system COMPUTING English Article Intelligent traffic; Neural network; Computer vision; YOLOv3; Traffic analysis VEHICLE DETECTION; TRACKING; RECOGNITION; MODEL The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios. [Al-qaness, Mohammed A. A.; Fan, Hong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China; [Abbasi, Aaqif Afzaal] Fdn Univ, Dept Software Engn, Islamabad 44000, Pakistan; [Ibrahim, Rehab Ali] Zagazig Univ, Fac Sci, Dept Math, Zagazig 44519, Egypt; [Alsamhi, Saeed H.] Athlone Inst Technol, Software Res Inst, Athlone, Ireland; [Hawbani, Ammar] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Anhui, Peoples R China Wuhan University; Egyptian Knowledge Bank (EKB); Zagazig University; Technological University of the Shannon: Midlands Midwest; Chinese Academy of Sciences; University of Science & Technology of China, CAS Al-qaness, MAA (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China. alqaness@whu.edu.cn; aaqif.afzaal@fui.edu.pk; hfan3@whu.edu.cn; rehab100r@yahoo.com; salsamhi@ait.ie; anmande@ustc.edu.cn Hawbani, Ammar/S-3356-2019; Abbasi, Aaqif Afzaal/AFG-9482-2022; Al-qaness, Mohammed/ABE-7552-2020 Hawbani, Ammar/0000-0002-1069-3993; Abbasi, Aaqif Afzaal/0000-0002-9982-1321; Al-qaness, Mohammed/0000-0002-6956-7641; Alsamhi, Saeed/0000-0003-2857-6979 National Key Research and Development Program of China [2019YFB1405600] National Key Research and Development Program of China This research was supported by the National Key Research and Development Program of China (Grant No. 2019YFB1405600). 38 23 24 6 59 SPRINGER WIEN WIEN SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA 0010-485X 1436-5057 COMPUTING Computing FEB 2021.0 103 2 SI 211 230 10.1007/s00607-020-00869-8 0.0 JAN 2021 20 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science QJ8AZ 2023-03-23 WOS:000605535400001 0
C Fan, BY; Ouyang, ZC; Niu, JW; Yu, S; Rodrigues, J IEEE Fan, Boyu; Ouyang, Zhenchao; Niu, Jianwei; Yu, Shui; Rodrigues, Joel Smart Water Flosser: A Novel Smart Oral Cleaner with IMU Sensor 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV DEC 09-13, 2018 Abu Dhabi, U ARAB EMIRATES IEEE water flosser; IMU; machine learning; signal process; Android App Among various tools invented to help improve people's oral health, water flossers can achieve better performance than traditional and electronic toothbrushes, and are less harmful than dental floss, especially for those with orthodontic teeth or tooth implant surgeries. However, the water flossers available in the market serve no monitoring or recording functions that can help consumers clean their teeth in a more efficient way. To capture users' motions, this study develops a novel smart water flosser, installing an Inertial Measurement Unit (IMU) sensor on the handle of the flosser. We determine the motion cycle using signal processing techniques and extract a set of statistical characteristics from the data set. We then train and compare different machine learning models as classifiers to recognize the motions of the handle. We find that the Random Forest model achieves the best detection accuracy at 97% and 85% of the whole feature set and optimized set, respectively. Finally we implement an Android App that connects the smart water flosser with a Bluetooth module to show the washing area in real-time and record relevant information for further guidance. [Fan, Boyu; Ouyang, Zhenchao; Niu, Jianwei] State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China; [Niu, Jianwei] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China; [Yu, Shui] Deakin Univ, Geelong, Vic, Australia; [Rodrigues, Joel] Natl Inst Telecommun Inatel, Santa Rita Do Sapucai, MG, Brazil; [Rodrigues, Joel] Inst Telecomunicacoes, Lisbon, Portugal; [Rodrigues, Joel] Univ Fortaleza UNIFOR, Fortaleza, Ceara, Brazil Beihang University; Deakin University; Instituto Nacional de Telecomunicacoes (INATEL); Instituto de Telecomunicacoes; Universidade Fortaleza Niu, JW (corresponding author), State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China.;Niu, JW (corresponding author), Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China. niujianwei@buaa.edu.cn Rodrigues, Joel J. P. C./A-8103-2013 Rodrigues, Joel J. P. C./0000-0001-8657-3800 National Key R&D Program of China [2017YFB1301100]; National Natural Science Foundation of China [61572060, 61772060, 61728201]; CERNET Innovation Project [NGII20160316, NGII20160701, NGII20170315] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CERNET Innovation Project This work was supported by National Key R&D Program of China (2017YFB1301100), National Natural Science Foundation of China (61572060, 61772060 and 61728201), and CERNET Innovation Project (NGII20160316, NGII20160701 and NGII20170315). 13 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-5386-4727-1 IEEE GLOB COMM CONF 2018.0 7 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BM5YU 2023-03-23 WOS:000465774303075 0
C Li, ZX; Wang, T; Yang, Y; Wang, Y; Shi, P; Snoussi, H IEEE Li, ZeXian; Wang, Tian; Yang, Yi; Wang, Yan; Shi, Peng; Snoussi, Hichem Event prediction via spatio-temporal sequence analysis 2019 CHINESE AUTOMATION CONGRESS (CAC2019) Chinese Automation Congress English Proceedings Paper Chinese Automation Congress (CAC) NOV 22-24, 2019 Hangzhou, PEOPLES R CHINA IEEE,CAA,IEEE Syst, Man, & Cybernet Soc event prediction; computer version; pose detection; pose prediction; image generation Event prediction often refers to the process of inferring dynamic information such as human pose change, object motion trajectory and event development direction from static images or partial video, which has broad application prospects in security monitoring, automatic driving, human-computer interaction and other fields. This article proposes the means of deep learning to conduct research on sequence images' event prediction in time and space dimensions. To solve the problem of missing semantic information and lack of external information in the current event prediction research, we propose to adopt human skeleton constraint as guiding information, then complete the task from two aspects: skeleton detection and prediction in two-dimensional sequence images, image generation under skeleton guidance. The sequence images event prediction and generation model can generate realistic images with good continuity and perfect details on the basis of reasonable prediction in a short time. To verify the feasibility of our algorithm, we carried out a series of experiments on multiple deep learning network structures. The experimental results of the traditional motion detection dataset and the automatic driving dataset show the theoretical and practical value of the proposed algorithm in the field of sequence image event prediction, which has great potential for development. [Li, ZeXian; Wang, Tian; Wang, Yan] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; [Yang, Yi] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo, Henan, Peoples R China; [Shi, Peng] Fujian Normal Univ, Sch Math & Comp Sci, Fuzhou, Fujian, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, Inst Charles Delaunay LM2S, FRE CNRS 2019, Troyes, France Beihang University; Henan Polytechnic University; Fujian Normal University; Universite de Technologie de Troyes Wang, T (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China. lizexian0427@gmail.com; wangtian@buaa.edu.cn; yangyi@hpu.edu.cn; w-yan@buaa.edu.cn; pshi@fjnu.edu.cn; hichem.snoussi@utt.fr National Key Research and Development Program of China [2018AAA0101400]; National Natural Science Foundation of China [61972016]; Fundamental Research Funds for the Central Universities [YWF-19-BJ-J-237]; Open Research Fund of Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou, China National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Open Research Fund of Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou, China This work is partially supported by the National Key Research and Development Program of China (2018AAA0101400), the National Natural Science Foundation of China (61972016), the Fundamental Research Funds for the Central Universities (YWF-19-BJ-J-237), the Open Research Fund of Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou, China. 32 0 0 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2688-092X 2688-0938 978-1-7281-4094-0 CHIN AUTOM CONGR 2019.0 1558 1563 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BR9NT 2023-03-23 WOS:000679040101107 0
J Yao, YT; Ciais, P; Viovy, N; Li, W; Yang, H; Joetzjer, E; Ben Bond-Lamberty Yao, Yitong; Ciais, Philippe; Viovy, Nicolas; Li, Wei; Yang, Hui; Joetzjer, Emilie; Ben Bond-Lamberty A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability GLOBAL BIOGEOCHEMICAL CYCLES English Article soil heterotrophic respiration; random forest; inter-annual variability; precipitation; soil moisture CARBON-DIOXIDE EMISSIONS; LAND-SURFACE MODEL; NITROGEN ADDITION; AUTOTROPHIC RESPIRATION; MICROBIAL RESPIRATION; ECOSYSTEM RESPIRATION; CLIMATE-CHANGE; TEMPERATURE SENSITIVITY; WHEAT YIELD; FOREST Soil heterotrophic respiration (SHR) is important for carbon-climate feedbacks because of its sensitivity to soil carbon, climatic conditions and nutrient availability. However, available global SHR estimates have either a coarse spatial resolution or rely on simple upscaling formulations. To better quantify the global distribution of SHR and its response to climate variability, we produced a new global SHR data set using Random Forest, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity. We estimated a global total SHR of 46.838.656.3 Pg C yr(-1) over 1985-2013 with a significant increasing trend of 0.03 Pg C yr(-2). Among the inputs to generate SHR products, the choice of soil moisture datasets contributes more to the difference among SHR ensemble. Water availability dominates SHR inter-annual variability (IAV) at the global scale; more precisely, temperature strongly controls the SHR IAV in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning SHR ensemble of data-driven gridded estimates and outputs from process-based models (TRENDYv6) shows agreement for a strong association between water variability and SHR IAV at the global scale, but ensemble members exhibit different ecosystem-level SHR IAV controllers. The important role of water availability in driving SHR suggests both a direct effect limiting decomposition and an indirect effect on litter available from productivity. Considering potential uncertainties remaining in our data-driven SHR datasets, we call for more scientifically designed SHR observation network and deep-learning methods making maximum use of observation data. [Yao, Yitong; Ciais, Philippe; Viovy, Nicolas; Yang, Hui] Univ Paris Saclay, Lab Sci Climat & Environm, CEA CNRS UVSQ, LSCE IPSL, Gif Sur Yvette, France; [Li, Wei] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China; [Joetzjer, Emilie] Univ Toulouse, Meteo France, CNRM, CNRS, Toulouse, France; [Ben Bond-Lamberty] Univ Maryland, Joint Global Change Res Inst, Pacific Northwest Natl Lab, College Pk, MD 20742 USA UDICE-French Research Universities; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Tsinghua University; Centre National de la Recherche Scientifique (CNRS); Meteo France; Universite de Toulouse; United States Department of Energy (DOE); Pacific Northwest National Laboratory; University System of Maryland; University of Maryland College Park Yao, YT (corresponding author), Univ Paris Saclay, Lab Sci Climat & Environm, CEA CNRS UVSQ, LSCE IPSL, Gif Sur Yvette, France. yitong.yao@lsce.ipsl.fr Bond-Lamberty, Benjamin/C-6058-2008; Joetzjer, Emilie/AAE-9799-2022; Joetzjer, Emilie/AFF-1116-2022; viovy, nicolas/S-2631-2018 Bond-Lamberty, Benjamin/0000-0001-9525-4633; Li, Wei/0000-0003-2543-2558; YAO, YITONG/0000-0002-1713-6719; viovy, nicolas/0000-0002-9197-6417 CLAND Convergence Institute - ANR [16-CONV-0003]; Make Our Planet Great Again (MOPGA) Scholarship CLAND Convergence Institute - ANR(French National Research Agency (ANR)); Make Our Planet Great Again (MOPGA) Scholarship This work was financially supported by the CLAND Convergence Institute funded by ANR (16-CONV-0003). Y. Yao also acknowledges support from Make Our Planet Great Again (MOPGA) Scholarship. 151 8 8 14 69 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0886-6236 1944-9224 GLOBAL BIOGEOCHEM CY Glob. Biogeochem. Cycle AUG 2021.0 35 8 e2020GB006918 10.1029/2020GB006918 0.0 23 Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences UI7HQ Green Published 2023-03-23 WOS:000690773700005 0
J Vrabel, J; Kepes, E; Duponchel, L; Motto-Ros, V; Fabre, C; Connemann, S; Schreckenberg, F; Prasse, P; Riebe, D; Junjuri, R; Gundawar, MK; Tan, XF; Porizka, P; Kaiser, J Vrabel, Jakub; Kepes, Erik; Duponchel, Ludovic; Motto-Ros, Vincent; Fabre, Cecile; Connemann, Sven; Schreckenberg, Frederik; Prasse, Paul; Riebe, Daniel; Junjuri, Rajendhar; Gundawar, Manoj Kumar; Tan, Xiaofeng; Porizka, Pavel; Kaiser, Jozef Classification of challenging Laser-Induced Breakdown Spectroscopy soil sample data - EMSLIBS contest SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY English Article; Proceedings Paper 10th Euro-Mediterranean Symposium on Laser-Induced Breakdown Spectroscopy (EMSLIBS) SEP 08-13, 2019 Brno, CZECH REPUBLIC EMSLIBS contest; Chemometrics; Laser-induced breakdown spectroscopy (LIBS); Machine learning; Classification benchmark We present results of the classification contest organized for the EMSLIBS 2019 conference. For this publication, we chose only the five best approaches and discussed their algorithm in detail. The main focus of the contest reflected both recent and long-term challenges of Laser-Induced Breakdown Spectroscopy (LIBS) data processing. The contest was designed with a purpose to raise a challenge in handling and processing a very large dataset, containing high-dimensional elemental spectra. For the contest, 138 samples were measured using a lab-based LIBS system. In total, the data set consisted of 70,000 spectra, separated into 12 classes according to their elemental composition. Due to its extensivity and complexity, the data set is unique within the LIBS community. The central idea was to simulate the so-called out-of-sample classification (i.e. according to similar elemental composition), implying various real-world applications. Even more, it reflects the current level of expertise in the LIBS community and the capability of the LIBS method itself. [Vrabel, Jakub; Kepes, Erik; Porizka, Pavel; Kaiser, Jozef] Brno Univ Technol, Cent European Inst Technol, Purkynova 123, Brno 61200, Czech Republic; [Duponchel, Ludovic] Univ Lille, CNRS, UMR 8516, LASIRe Lab Spect Interact React & Environm, F-59000 Lille, France; [Motto-Ros, Vincent] Univ Lyon, Univ Lyon 1, CNRS, Inst Lumiere Mat,UMR 5306, F-69622 Villeurbanne, France; [Fabre, Cecile] Univ Lorraine, Lab GeoRessources UMR CNRS 7359, F-54506 Vandoeuvre Les Nancy, France; [Connemann, Sven; Schreckenberg, Frederik] Fraunhofer Inst Laser Technol ILT, Steinbachstr 15, D-52074 Aachen, Germany; [Prasse, Paul] Univ Potsdam, Dept Comp Sci, August Bebel Str 89, D-14482 Potsdam, Germany; [Riebe, Daniel] Univ Potsdam, Phys Chem, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany; [Junjuri, Rajendhar; Gundawar, Manoj Kumar] Univ Hyderabad, Adv Ctr Res High Energy Mat, Prof CR Rao Rd,Cent Univ Campus PO, Hyderabad 500046, Telangana, India; [Tan, Xiaofeng] X Sci, Shanghai, Peoples R China Brno University of Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Chemistry (INC); Universite de Lille - ISITE; Universite de Lille; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Universite de Lorraine; Fraunhofer Gesellschaft; University of Potsdam; University of Potsdam; Defence Research & Development Organisation (DRDO); Advanced Centre Research in High Energy Materials (ACRHEM); University of Hyderabad Vrabel, J (corresponding author), Brno Univ Technol, Cent European Inst Technol, Purkynova 123, Brno 61200, Czech Republic. jakub.vrabel@ceitec.vutbr.cz; ludovic.duponchel@univ-lille.fr; frederik.schreckenberg@ilt.fraunhofer.de; prasse@uni-potsdam.de; manoj@uohyd.ac.in; x.tan@jhu.edu Képeš, Erik/AAQ-9055-2020; FABRE, Cécile/B-5827-2012; Kaiser, Jozef/D-6800-2012; Junjuri, Rajendhar/AAX-3213-2021; Pořízka, Pavel/G-9463-2014; Porizka, Pavel/AGF-2639-2022 Képeš, Erik/0000-0002-7086-2613; FABRE, Cécile/0000-0001-8627-4050; Kaiser, Jozef/0000-0002-7397-125X; Junjuri, Rajendhar/0000-0003-4857-5615; Pořízka, Pavel/0000-0002-8604-7365; Porizka, Pavel/0000-0002-8604-7365; Riebe, Daniel/0000-0002-1234-6733; Tan, Xiaofeng/0000-0002-1114-3571 Ministry of Education, Youth and Sports of the Czech Republic under the project CEITEC 2020 [LQ1601]; CEITEC Nano Research Infrastructure (MEYS CR); CEITEC Nano+ project [CZ.02.1.01/0.0/0.0/16_013/0001728]; German ministry of education and research (BMBF) in the project PLUS [033R181]; German federal state of Brandenburg; European Regional Development Fund (ERDF) (ERDF 2014-2020); economic development agency Brandenburg (WFBB) in the LIBSqORE project [80172489]; DRDO, India Ministry of Education, Youth and Sports of the Czech Republic under the project CEITEC 2020; CEITEC Nano Research Infrastructure (MEYS CR); CEITEC Nano+ project; German ministry of education and research (BMBF) in the project PLUS(Federal Ministry of Education & Research (BMBF)); German federal state of Brandenburg; European Regional Development Fund (ERDF) (ERDF 2014-2020); economic development agency Brandenburg (WFBB) in the LIBSqORE project; DRDO, India(Defence Research & Development Organisation (DRDO)) The creation of the contest has been supported by the Ministry of Education, Youth and Sports of the Czech Republic under the project CEITEC 2020 (LQ1601). This work was also carried out with the support of CEITEC Nano Research Infrastructure (MEYS CR, 2016-2019), CEITEC Nano+ project, ID CZ.02.1.01/0.0/0.0/16_013/0001728. FS and SC were supported by funding from the German ministry of education and research (BMBF) in the project PLUS (033R181). PauPr and DR are grateful for financial support from the German federal state of Brandenburg and the European Regional Development Fund (ERDF 2014-2020) as well as for project management by the Investitionsbank des Landes Brandenburg (ILB) and the economic development agency Brandenburg (WFBB) in the LIBSqORE project (grant number 80172489). RJ and MKG acknowledge the support of DRDO, India. 29 26 26 6 13 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0584-8547 1873-3565 SPECTROCHIM ACTA B Spectroc. Acta Pt. B-Atom. Spectr. JUL 2020.0 169 105872 10.1016/j.sab.2020.105872 0.0 12 Spectroscopy Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Spectroscopy MK2GY Green Published, hybrid 2023-03-23 WOS:000548605200007 0
C Luo, C; Zhao, P; Qiao, B; Wu, YJ; Zhang, HY; Wu, W; Lu, WH; Dang, YN; Rajmohan, S; Lin, QW; Zhang, DM ACM Luo, Chuan; Zhao, Pu; Qiao, Bo; Wu, Youjiang; Zhang, Hongyu; Wu, Wei; Lu, Weihai; Dang, Yingnong; Rajmohan, Saravanakumar; Lin, Qingwei; Zhang, Dongmei NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021) English Proceedings Paper 30th World Wide Web Conference (WWW) APR 12-23, 2021 ELECTR NETWORK Assoc Comp Machinery Disk Failure Prediction; Cloud Platforms; High Service Reliability; Neighborhood-Temporal Attention Model; Data Imbalance With the rapid deployment of cloud platforms, high service reliability is of critical importance. An industrial cloud platform contains a huge number of disks, and disk failure is a common cause of service unreliability. In recent years, many machine learning based disk failure prediction approaches have been proposed, and they can predict disk failures based on disk status data before the failures actually happen. In this way, proactive actions can be taken in advance to improve service reliability. However, existing approaches treat each disk individually and do not explore the influence of the neighboring disks. In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. When predicting whether or not a disk will fail in near future, NTAM is a novel approach that not only utilizes a disk's own status data, but also considers its neighbors' status data. Moreover, NTAM includes a novel attention-based temporal component to capture the temporal nature of the disk status data. Besides, we propose a data enhancement method, called Temporal Progressive Sampling (TPS), to handle the extreme data imbalance issue. We evaluate NTAM on a public dataset as well as two industrial datasets collected from millions of disks in Microsoft Azure. Our experimental results show that NTAM significantly outperforms state-of-the-art competitors. Also, our empirical evaluations indicate the effectiveness of the neighborhood-ware component and the temporal component underlying NTAM as well as the effectiveness of TPS. More encouragingly, we have successfully applied NTAM and TPS to Microsoft cloud platforms (including Microsoft Azure and Microsoft 365) and obtained benefits in industrial practice. [Luo, Chuan; Zhao, Pu; Qiao, Bo; Lu, Weihai; Lin, Qingwei; Zhang, Dongmei] Microsoft Res, Beijing, Peoples R China; [Wu, Youjiang; Dang, Yingnong] Microsoft Azure, Redmond, WA USA; [Zhang, Hongyu] Univ Newcastle, Newcastle, NSW, Australia; [Wu, Wei] Leibniz Univ Hannover, Hannover, Germany; [Rajmohan, Saravanakumar] Microsoft 365, Redmond, WA USA Microsoft; University of Newcastle; Leibniz University Hannover Luo, C (corresponding author), Microsoft Res, Beijing, Peoples R China. chuan.luo@microsoft.com zhang, hongyu/ABC-3702-2021 zhang, hongyu/0000-0002-3063-9425; Qiao, Bo/0000-0002-8997-8317 49 3 3 0 4 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8312-7 2021.0 1181 1191 10.1145/3442381.3449867 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS5MC 2023-03-23 WOS:000733621801017 0
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Andrean, SY; Andreazza, A; Andrei, V; Anelli, CR; Angelidakis, S; Angerami, A; Anisenkov, AV; Annovi, A; Antel, C; Anthony, MT; Antipov, E; Antonelli, M; Antrim, DJA; Anulli, F; Aoki, M; Pozo, JAA; Aparo, MA; Bella, LA; Ferraz, VA; Pereira, RA; Arcangeletti, C; Arce, ATH; Arduh, FA; Arguin, JF; Argyropoulos, S; Arling, JH; Armbruster, AJ; Armstrong, A; Arnaez, O; Arnold, H; Tame, ZPA; Artoni, G; Artz, S; Asai, S; Asawatavonvanich, T; Asbah, N; Asimakopoulou, EM; Asquith, L; Assahsah, J; Assamagan, K; Astalos, R; Atkin, RJ; Atkinson, M; Atlay, NB; Atmani, H; Augsten, K; Avolio, G; Ayoub, MK; Azuelos, G; Bachacou, H; Bachas, K; Backes, M; Backman, F; Bagnaia, P; Bahmani, M; Bahrasemani, H; Bailey, AJ; Bailey, VR; Baines, JT; Bakalis, C; Baker, OK; Bakker, PJ; Gupta, DB; Balaji, S; Baldin, EM; Balek, P; Balli, F; Balunas, WK; Balz, J; Banas, E; Bandieramonte, M; Bandyopadhyay, A; Banerjee, S; Barak, L; Barbe, WM; Barberio, EL; Barberis, D; Barbero, M; Barbour, G; Barillari, T; Barisits, MS; 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A.; Schune, Ph; Schwartzman, A.; ; ATLAS Collaboration Dijet Resonance Search with Weak Supervision Using root S=13 TeV pp Collisions in the ATLAS Detector PHYSICAL REVIEW LETTERS English Article MASS; DISTRIBUTIONS; ASSOCIATION; PHOTON; JET This Letter describes a search for narrowly resonant new physics using a machine -learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A -> BC, for m(A) similar to O(TeV), m(B), m(C) similar to O(100 GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 root s = 13 TeV pp collision dataset of 139 fb(-1) recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV, Cross-section limits for narrow -width A, B, and C particles vary with m(A), m(B), and m(C). For example, when m(A) = 3 TeV and m(B) greater than or similar to 200 GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on m(C). For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons. [Deliot, F.; Duvnjak, D.; Jackson, P.; Kong, A. X. Y.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Jain, V; Swift, S. 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M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Bartos, P.; Blazek, T.; Dubovsky, M.; Hyrych, S.; Majersky, O.; Melo, M.; Racko, M.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Murin, P.; Smiesko, J.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Inst Expt Phys, Dept Subnucl Phys, Kosice, Slovakia; [Assamagan, K.; Barone, G.; Begel, M.; Brost, E.; Cavaliere, V; Chen, H.; D'amen, G.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Hu, Q.; Iakovidis, G.; Kalderon, C. W.; Klimentov, A.; Kouskoura, V; Lancon, E.; Lanni, F.; Lee, C. A.; Lim, S.; Liu, H. B.; Lynn, D.; Ma, H.; Maeno, T.; Matakias, D.; Nagle, J. L.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Ouellette, J.; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Roloff, J.; Seidlitz, B. D.; Serfon, C.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Tricoli, A.; Undrus, A.; Weber, C.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Bawa, H. S.; Gao, Y. S.; Grimm, K.; Moss, J.; Parker, A. J.] Calif State Univ Los Angeles, Los Angeles, CA 90032 USA; [Batley, J. R.; Brandt, O.; Chapman, J. D.; Cowley, J. W.; Fawcett, W. J.; Henkelmann, L.; Hill, J. C.; Hommels, L. B. A. H.; Lester, C. G.; Lin, C. Y.; Malone, C.; Noel, D. L.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson, D.; Tombs, R.; Ward, C. P.; Williams, S.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; iThemba Labs, Western Cape, South Africa; [Boye, D.; Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; Univ South Africa, Dept Phys, Pretoria, South Africa; [Christopher, L. D.; Dahbi, S.; Jimenez, Y. Hernnadez; Hlaluku, D. R.; Jivan, H.; Kar, D.; Mdhluli, J. E.; Garcia, B. R. Mellado; Roy, D.; Ruan, X.; Shrif, E. M.; Haddad, E. Sideras; Tomiwa, K. G.; von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Keller, J. S.; Klein, C.; Koffas, T.; Laurier, A.; Oakham, F. G.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chadi, Z.; Hoummada, A.; Imam, H.] Univ Hassan 2, Fac Sci Ain Chock, Reseau Univ Phys Hautes Energies, Casablanca, Morocco; [Gouighri, M.] Univ Ibn Tofail, Fac Sci, Kenitra, Morocco; Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA Marrakech, Marrakech, Morocco; [Assahsah, J.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Assahsah, J.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; El Jarrari, H.; Fassi, F.; Hamdaoui, H.; Ngair, B.; Tayalati, Y.; Zaazoua, M.] Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Abud, A. Abed; Ahmad, A.; Alderweireldt, S.; Aleksa, M.; Allaire, C.; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Bielski, R.; Bortfeldt, J.; Boyd, J.; Buat, Q.; Butti, P.; Buttinger, W.; Calace, N.; Camarda, S.; Camincher, C.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Cattai, A.; Coimbra, A. E. C.; Czodrowski, P.; Dachs, F.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Dittus, F.; Dudarev, A.; Duehrssen, M.; Dyndal, M.; Ellis, N.; Elsing, M.; Errenst, M.; Falke, S.; Farthouat, P.; Fassnacht, P.; Francis, D.; Froidevaux, D.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Hawkings, R. J.; Heinrich, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Javurek, T.; Jenni, P.; Jovicevic, J.; Klioutchnikova, T.; Koehler, N. M.; Krasznahorkay, A.; Kuehn, S.; Kuwertz, E. S.; Lassnig, M.; Miotto, G. Lehmann; Longo, L.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Meehan, S.; Meng, L.; Berlingen, J. Montejo; Morley, A. K.; Mornacchi, G.; Moschovakos, P.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pandini, C. E.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Pontecorvo, L.; Astigarraga, M. E. Pozo; Queitsch-Maitland, M.; Raymond, M.; Rembser, C.; Rizzi, C.; Roe, S.; Rummler, A.; Saimpert, M.; Salzburger, A.; Samarati, J.; Pineda, A. Sanchez; Schaepe, S.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sidiropoulou, O.; Sanchez, C. A. Solans; Spigo, G.; Stewart, G. A.; Stockton, M. C.; Ten Kate, H.; Unal, G.; Vallier, A.; Vandelli, W.; Schroeder, T. Vazquez; Venturi, N.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Wilkens, H. G.; Young, C. J. S.; Zanzi, D.; Zwalinski, L.] CERN, Geneva, Switzerland; [Gardner, R. W.; Hank, M. D.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Liu, J. K. K.; Miller, D. W.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Vukotic, I; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Agaras, M. N.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Morales, F. A. Jimenez; Madar, R.; Megy, T.; Nibigira, E.; Pallin, D.; Rustige, L.; Santoni, C.; Vazeille, F.] Univ Clermont Auvergne, IN2P3, CNRS, LPC, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Chen, J.; Clark, M. R.; Cole, B.; Emerman, A.; Gonski, J. L.; Havener, L. B.; Kahn, A.; Kennedy, K. E.; Mahon, D. J.; Mohapatra, S.; Ochoa, I; Parsons, J. A.; Rybar, M.; Tuts, P. M.; Yin, P.] Columbia Univ, Nevis Lab, Irvington, NY USA; [AbouZeid, O. S.; Camplani, A.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Nielsen, D. S.; Petersen, T. C.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Carducci, G.; Crosetti, G.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Capua, M.; Carducci, G.; Crosetti, G.; Gnesi, I; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Collegato Cosenza, Frascati, Italy; [Betti, A.; Deiana, A. M.; Kehoe, R.; Leney, K. J. C.; McKay, M. A.; Milke, C. D.; Narayan, R.; Parajuli, S.; Sekula, S. J.; Stroynowski, R.; Thomas, J. O.; Wang, P.; Ye, J.] Southern Methodist Univ, Phys Dept, Dallas, TX USA; [Ferguson, S. W.; Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Phys Dept, Richardson, TX 75083 USA; [Fanourakis, G.; Geralis, T.; Stavropoulos, G.] Natl Ctr Sci Res Demokritos, Aghia Paraskevi, Greece; [Andrean, S. Y.; Backman, F.; Barranco Navarro, L.; Bohm, C.; Clement, C.; Hellman, S.; Kastanas, A.; Milstead, D. A.; Moa, T.; Nelson, M. E.; Pasuwan, P.; Sanchez, L. Pereira; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Andrean, S. Y.; Backman, F.; Barranco Navarro, L.; Clement, C.; Hellman, S.; Kastanas, A.; Milstead, D. A.; Moa, T.; Nelson, M. E.; Pasuwan, P.; Sanchez, L. Pereira; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Amoroso, S.; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Bloch, I; Boerner, D.; Braren, F.; Brendlinger, K.; Brenner, L.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Daubney, T.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Issever, C.; Jacobs, R. M.; Jeong, N.; Katzy, J.; Kitali, V; Kuechler, J. T.; Kuhl, T.; Leight, W. A.; Li, X.; Li, Y.; Liberatore, M.; Linss, A.; Liu, Y.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Potamianos, K.; Pownall, G.; Rauch, D. M.; Renardi, A.; Richter, S.; Rifki, O.; Rimoldi, M.; Rivadeneira, P.; Robin, M.; Rossi, E.; Iglesias, J. A. Sabater; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; Scodeggio, M.; Seitz, C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Thompson, E. A.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Worm, S. D.; Yap, Y. C.] DESY, Hamburg, Germany; [Amoroso, S.; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Bloch, I; Boerner, D.; Braren, F.; Brendlinger, K.; Brenner, L.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Daubney, T.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Issever, C.; Jacobs, R. M.; Jeong, N.; Katzy, J.; Kitali, V; Kuechler, J. T.; Kuhl, T.; Leight, W. A.; Li, X.; Li, Y.; Liberatore, M.; Linss, A.; Liu, Y.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Potamianos, K.; Pownall, G.; Rauch, D. M.; Renardi, A.; Richter, S.; Rifki, O.; Rimoldi, M.; Rivadeneira, P.; Robin, M.; Rossi, E.; Iglesias, J. A. Sabater; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; Scodeggio, M.; Seitz, C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Thompson, E. A.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Worm, S. D.; Yap, Y. C.] DESY, Zeuthen, Germany; [Cinca, D.; Dado, T.; Erdmann, J.; Freundlich, E. M.; Gessner, G.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Rustige, L.; Weingarten, J.; Wendland, B.; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Berthold, A.; Bittrich, C.; Duschinger, D.; Herrmann, T.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Maerker, M.; Ramos, J. Manjarres; Morgenstern, S.; Nag, A. Nag; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wiel, C.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Beacham, J. B.; Bocci, A.; Davis, D. R.; Eggleston, M. G.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Pachal, K.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Carter, T. M.; Clark, P. J.; Farrington, S. M.; Giannelli, M. Faucci; Gao, Y.; Hamity, G. N.; Hasib, A.; Heath, M. P.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Palazzo, S.; Sogaard, A.; Strubig, A.; Takeva, E. P.; Taylor, A. J.; Themistokleous, N.; Villhauer, E. M.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Frascati, Italy; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Argyropoulos, S.; Becherer, F.; Buehrer, F.; Garcia-Argos, C.; Gargiulo, S.; Gugel, R.; Guth, M.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hirose, S.; Hohn, D.; Honig, J. C.; Jakobs, K.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Kuprash, O.; Landgraf, U.; Lang, V. S.; Loesle, A.; Luedtke, C.; Magerl, V; Parzefall, U.; Roland, B.; Rottler, B.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Sauerburger, F.; Schleicher, K. E.; Scholer, P. G.; Schumacher, M.; Sperlich, D.; Weiser, C.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wollrath, J.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Abeling, K.; Achkar, B.; Beck, H. C.; Bindi, M.; Bisanz, T.; Bokan, P.; Buschmann, E.; Dreyer, T.; Gerlach, L. O.; Gama, R. Goncalves; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lange, J. C.; Mellenthin, J. D.; Mobius, S.; Oerdek, S.; Peiffer, T.; Pokharel, I; Quadt, A.; Sabatini, P.; Shabalina, E.; Skaf, A.; Smith, J. W.; Sohns, F.; Torres, R. E. Ticse; Veatch, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Adorni, S.; Akilli, E.; Amrouche, C. S.; Antel, C.; Benoit, M.; Clark, A.; Della Volpe, D.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; Mermod, P.; Nessi, M.; Nindhito, H. R.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Parodi, F.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Caforio, D.; Deuren, M.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P. J.; Buttar, C. M.; Callea, G.; Connelly, I. A.; Crawley, S. J.; Cunningham, W. R.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Gray, C.; Gul, U.; Howarth, J.; Jamieson, J.; O'Shea, V; Owen, M.; Robson, A.; Spiteri, D. P.; Warrack, N.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Quintero, D. M. Portillo; Readioff, N. P.; Stark, J.; Trocme, B.; Yang, X.] Univ Grenoble Alpes, Grenoble INP, IN2P3, CNRS,LPSC, Grenoble, France; [Asbah, N.; Bullard, B. A.; Di Petrillo, K. F.; Franklin, M.; Huth, J.; Lee, L.; Morii, M.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Chen, Y.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xie, X.; Xu, H.; Yang, S.; Yang, Z.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Peoples R China; [Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Chen, Y.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xie, X.; Xu, H.; Yang, S.; Yang, Z.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, D.; Duan, Y.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Tariq, K.; Xu, Z.; Yang, X.; Yuan, R.; Zhang, X.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, D.; Duan, Y.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Tariq, K.; Xu, Z.; Yang, X.; Yuan, R.; Zhang, X.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Peoples R China; [Dong, B.; Guo, J.; Hu, S.; Kato, C.; Li, F.; Li, J.; Li, L.; Li, S.; Mittal, M.; Nishu, N.; Su, W.; Wang, C.; Wang, Z.; Yan, J.; Yang, H. J.; Yatsenko, E.; Zhang, X.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, KLPPAC MoE, Shanghai, Peoples R China; [Kato, C.; Li, S.; Liu, K.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Bartels, F.; Dunford, M.; Franchino, S.; Jongmanns, J.; Klassen, M.; Kugel, A.; Zu Theenhausen, H. Meyer; Mkrtchyan, T.; Napolitano, F.; Ott, P. S.; Sahinsoy, M.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; Czurylo, M. M.; de Lima, D. E. Ferreira; Helary, L.; Krishnan, A.; Schoening, A.; Vigani, L.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Cheng, H. C.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Salvucci, A.; Wang, J.; Zhang, Z.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Lo, C. Y.; Hernandez, D. Paredes; Salvucci, A.; Tam, K. C.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Salvucci, A.; Xiang, J.; Yang, T.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Salvucci, A.; Xiang, J.; Yang, T.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Agapopoulou, C.; Al Khoury, K.; Atmani, H.; Bassalat, A.; De Regie, J. B. De Vivie; Delgove, D.; Delporte, C.; Duflot, L.; Escalier, M.; Fayard, L.; Fournier, D.; Ghosh, A.; Grivaz, J-F; Guerguichon, A.; Han, K.; Hohov, D.; Hrivnac, J.; Iconomidou-Fayard, L.; Kotsokechagia, A.; Laudrain, A.; Lounis, A.; Makovec, N.; Morange, N.; Perego, M. M.; Petroff, P.; Puzo, P.; Rashid, T.; Rousseau, D.; Rybkin, G.; Sacerdoti, S.; Schaffer, A. C.; Serin, L.; Simion, S.; Tanaka, R.; Trofymov, A.; Varouchas, D.; Zerwas, D.; Zhang, Z.] Univ Paris Saclay, IJCLab, IN2P3, CNRS, F-91405 Orsay, France; [Calfayan, P.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Meyer, C.; Palacino, G.; Roland, C. P. A.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Faraj, M.; Giordani, M. P.; Giugliarelli, G.; Magro, J.; Panizzo, G.; Pinamonti, M.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] Ist Nazl Fis Nucl, Sez Trieste, Grp Collegato Udine, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Faraj, M.; Giordani, M. P.; Giugliarelli, G.; Magro, J.; Panizzo, G.; Pinamonti, M.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Politecn Ingn & Architettura, Udine, Italy; [Chiodini, G.; Gorini, E.; Gravili, F. G.; Mirto, A.; Primavera, M.; Reale, M.; Schioppa, E. J.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, Lecce, Italy; [Gorini, E.; Gravili, F. G.; Mirto, A.; Reale, M.; Schioppa, E. J.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Citterio, M.; D'Auria, S.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Meroni, C.; Monzani, S.; Mungo, D. P.; Murrone, A.; Perini, L.; Ragusa, F.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.] Ist Nazl Fis Nucl, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; D'Auria, S.; Fanti, M.; Lazzaroni, M.; Mungo, D. P.; Murrone, A.; Perini, L.; Ragusa, F.; Rossini, L.; Stabile, A.] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V; Lavorgna, M.; Massarotti, P.; Merola, L.; Rossi, E.; Sekhniaidze, G.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Pezzotti, L.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.; Vercesi, V.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Farina, E. M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Pezzotti, L.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; Di Gregorio, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Mastrandrea, P.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.; Verducci, M.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; Di Gregorio, G.; Francavilla, P.; Mastrandrea, P.; Roda, C.; Sotiropoulou, C. L.; Verducci, M.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Bruscino, N.; Chomont, A. R.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Di Bello, F. A.; Falciano, S.; Francescato, S.; Frattari, G.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Vannicola, D.; Vari, R.; Veneziano, S.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Bruscino, N.; Chomont, A. R.; Corradi, M.; De Cecco, S.; Di Bello, F. A.; Francescato, S.; Frattari, G.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.; Vannicola, D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Camelia, E. Alunno; Bruno, S.; Caltabiano, A.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Liberti, B.; Loffredo, S.; Marcoccia, L.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Camelia, E. Alunno; Bruno, S.; Caltabiano, A.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Loffredo, S.; Marcoccia, L.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Biglietti, M.; Camerlingo, M. T.; D'Amico, V; Di Micco, B.; Di Nardo, R.; Farilla, A.; Iodice, M.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.] Ist Nazl Fis Nucl, Sez Roma Tre, Rome, Italy; [Camerlingo, M. T.; D'Amico, V; Di Micco, B.; Di Nardo, R.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] INFN TIFPA, Trento, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Hrdinka, J.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Bret, M. Cano; Ghosh, A.; Mallik, U.] Univ Iowa, Iowa City, IA USA; [An, F.; Chen, B.; Chen, C. H.; Cochran, J.; Heidorn, W. D.; Jiang, H.; Kang, S.; Krumnack, N.; Lebedev, A.; Lee, S.; Liu, B.; Magini, N.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.; Yoshihara, K.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; 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Istituto Nazionale di Fisica Nucleare (INFN); Southern Methodist University; University of Texas System; University of Texas Dallas; National Centre of Scientific Research Demokritos; Stockholm University; Oskar Klein Centre; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; 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University of Milan; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; Universidade Federal de Juiz de Fora; Universidade Federal do Rio de Janeiro; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; High Energy Accelerator Research Organization (KEK); Kobe University; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; 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New York University; Ochanomizu University; University System of Ohio; Ohio State University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Universidade de Lisboa; 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University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; City University of New York (CUNY) System; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; Autonomous University of Barcelona; University of Sharjah; University of Aegean; University of Louisville; Ben Gurion University; California State University System; California State University East Bay; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; University of Fribourg; University of Udine; Lomonosov Moscow State University; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Bulgarian Academy of Sciences; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Moscow Institute of Physics & Technology; An Najah National University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aad, G (corresponding author), Aix Marseille Univ, IN2P3, CNRS, CPPM, Marseille, France. 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Munoz/AAH-3909-2019; Song, Weimin/AAJ-5415-2020; AGHEORGHIESEI, Catalin/B-8596-2014; Leisos, Antonios/AAJ-2351-2021; Fassouliotis, Dimitrios/AAL-7081-2021; Simsek, Sinem/AGG-2640-2022; Lefebvre, Benoit/ABB-2272-2021; Bergmann, Benedikt/L-8368-2019; Brahimi, Nihal/HNJ-5325-2023; Ozcan, Veysi Erkcan/AAS-4508-2020; Introzzi, Gianluca/K-2497-2015; Lampoudis, Christos/AAU-5016-2021; Gravila, Paul/HLP-6245-2023; Schioppa, Enrico Junior/F-4731-2019; Betti, Alessandra/AAD-9964-2019; Masik, Jiri/AAE-1203-2021; Banaś, Elżbieta A/W-4583-2017; Garcia, Carmen/W-2465-2018; Kharlamova, Tatyana/AAQ-5430-2020; MEONI, Evelin/ABD-9498-2021; Beddall, Andrew John/AAE-5820-2022; Wolters, Helmut/M-4154-2013; Mindur, Bartosz/A-2253-2017; Černý, Karel/AAK-7746-2021; Herten, Gregor/HNQ-9546-2023; Trzupek, Adam/N-2448-2018; Gao, Y./GWZ-3665-2022; Cetin, Serkant Ali/AGF-0147-2022; Dubinin, Filipp/M-9546-2015; Lagouri, Theodota/AAC-7358-2021; gaudio, gabriella/AAN-6039-2021; Adiguzel, Aytul/AAC-9049-2020; Didenko, Mariia/ABB-7470-2021; 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zhang, jin/GXV-9154-2022; Hamal, Petr/G-5540-2014; Wu, Xin/ABH-1729-2020; Chizhov, Mihail/CAG-2076-2022; Darbo, Giovanni/C-8175-2012; Sapronov, Andrey/HGB-0392-2022; Kazanin, Vasily F./HHM-2056-2022; Talyshev, Alexey/HGC-6910-2022; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Romano, Marino/ACW-0715-2022; Tikhomirov, Vladimir/M-6194-2015; Castro, Nuno/AAB-3648-2019; Olszewski, Andrzej/AGV-6131-2022; Cavaliere, Viviana/CAE-8597-2022; Petrucci, Fabrizio/G-8348-2012; Kuze, Masahiro/V-4251-2018; Pereira, Rodrigo Araujo/AFV-9983-2022; Manthos, Ioannis/GWD-1076-2022; Trzebinski, Maciej/AAO-6821-2021; Olszowska, Jolanta/P-2889-2017; Meshkov, Oleg/AAM-8539-2021; Franchini, Matteo/AAC-9259-2021; gao, yi/HCI-8298-2022; Tapia, Sebastian/ABB-6644-2021; Cieśla, Krzysztof/AAM-4181-2021; Gustavino, Giuliano/AAK-6591-2020; Malek, Fairouz/E-4410-2018; Smirnova, Oxana/A-4401-2013; Ippolito, Valerio/L-1435-2016; Lee, Lawrence/AAU-6790-2021; CAKIR, Orhan/L-1893-2015; Amoroso, Simone/AAW-4334-2021; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Suster, Carl J E/L-3714-2016; Coccaro, Andrea/AAB-6990-2021; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Gil, Damian/AAK-4749-2020; Bogdanchikov, Alexander/AAB-9414-2022; Casado, Pilar/H-1484-2015; Franchini, Matteo/HNI-8314-2023; , Alex/N-8715-2016; Dziedzic, Bartosz/Q-4189-2017; Blue, Andrew/C-9882-2016; Kodys, Peter/P-2636-2017; Maj, Klaudia/Q-4624-2017; Britton, David/F-2602-2010; Konig, Adriaan/B-6439-2013; Schultz-Coulon, Hans-Christian/X-5006-2018; Buckley, Andy/B-8362-2014; Grancagnolo, Sergio/J-3957-2015; Wolter, Marcin/A-7412-2012; uysal, zekeriya/AAD-1226-2019; xella, stefania/E-6752-2015; Baldin, Evgenii/A-6186-2014; Worm, Steven/I-3575-2012; Berta, Peter/AAL-7109-2020; Tartarelli, Giuseppe Francesco/A-5629-2016; LIVAN, Michele/D-7531-2012; Bachas, Konstantinos/C-8101-2019; Faltova, Jana/P-6842-2017; Carli, Ina/C-2189-2017; D'Eramo, Louis/Q-5816-2017; Vos, Marcel/G-8123-2015; Korcyl, Krzysztof/W-2111-2018; Mlynarikova, Michaela/P-2701-2017; Zhang, Kaili/H-2805-2016; Penc, Ondrej/H-3032-2014; Vranjes Milosavljevic, Marija/F-9847-2016; Monzani, Simone/D-6328-2017; cerri, alessandro/Q-6884-2016; Zenis, Tibor/T-5270-2018; Ungaro, Francesca/G-4554-2018; Della Pietra, Massimo/J-5008-2012; Dabrowski, Wladyslaw/AAS-6369-2020; Bosman, Martine/J-9917-2014; MARTINEZ, MARIO/I-3549-2015; Sioli, Maximiliano/Q-1597-2016; Juste, Aurelio/I-2531-2015; Flores Castillo, Luis Roberto/W-3928-2018; Giordani, Mario/Q-6211-2018; Sykora, Ivan/T-5252-2018; Chu, Ming-chung/M-2655-2018; Camarri, Paolo/M-7979-2015; Li, Liang/O-1107-2015; Lazzaroni, Massimo/N-3675-2015; Rodriguez Bosca, Sergi/HPC-6167-2023; Vanadia, Marco/K-5870-2016; Goncalo, Jose/M-3153-2016; Benekos, Nektarios/I-9928-2017; Stanecka, Ewa/V-6242-2018; Gonzalez Suarez, Rebeca/L-6128-2014; Lysak, Roman/H-2995-2014; Aguilar Saavedra, Juan Antonio/F-1256-2016; Albert, Justin/J-4152-2017; Rebuzzi, Daniela Marcella/D-9727-2018; Carquin, Edson/G-5221-2015; Hansen, Peter Henrik/C-2098-2015; McKee, Shawn/B-6435-2012; Kaczmarska, Anna/B-2753-2019; 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Barranco Navarro, Laura/0000-0002-3380-8167; David, Claire/0000-0002-1794-1443; Longo, Luigi/0000-0002-2357-7043; Alonso, Francisco/0000-0001-9431-8156; Valente, Marco/0000-0002-0486-9569; Liu, Xiaotian/0000-0003-1366-5530; Hansen, Peter Henrik/0000-0002-6764-4789; McKee, Shawn/0000-0002-4551-4502; Raine, John/0000-0002-5987-4648; Portales, Louis/0000-0002-9860-9185; Poulsen, Trine/0000-0001-7207-6029; Ricci, Ester/0000-0002-4222-9976; Mastroberardino, Anna/0000-0002-8933-9494; Romain, Madar/0000-0002-6875-6408; Beretta, Matteo Mario/0000-0002-7026-8171; DA FONSECA PINTO, JOAO VICTOR/0000-0003-1746-1914; Horn, Philipp/0000-0002-5640-0447; Ellinghaus, Frank/0000-0003-3596-5331; Thompson, Paul/0000-0002-6239-7715; Schopf, Elisabeth/0000-0002-9340-2214; Zaazoua, Mohamed/0000-0002-4105-2988; Starovoitov, Pavel/0000-0003-1990-0992; Mildner, Hannes/0000-0002-0384-6955; TERRON, JUAN/0000-0003-0132-5723; Marcoccia, Lorenzo/0000-0001-6422-7018; Pachal, Katherine/0000-0002-8332-243X; Lari, Tommaso/0000-0002-1388-869X; Ragusa, Francesco/0000-0002-4064-0489; Saito, Masahiko/0000-0001-5564-0935; Kartvelishvili, Vakhtang/0000-0002-1957-3787; Junggeburth, Johannes/0000-0001-7205-1171; Vercesi, Valerio/0000-0001-7670-4563; Hays, Chris/0000-0003-2371-9723; Mezquita, Costa/0000-0002-2064-2954; Besson, Nathalie/0000-0001-9248-6252; Guenther, Jaroslav/0000-0003-3189-3959; Walkowiak, Wolfgang/0000-0002-0385-3784; Mkrtchyan, Tigran/0000-0002-5786-3136; Bellagamba, Lorenzo/0000-0001-7098-9393; /0000-0001-5765-1750; Kaczmarska, Anna/0000-0002-8880-4120; Ravina, Baptiste/0000-0002-1622-6640; Karpov, Sergey/0000-0002-2230-5353; FANTI, MARCELLO/0000-0002-8773-145X; Beau, Tristan/0000-0002-2022-2140; Nisati, Aleandro/0000-0002-5080-2293; Grummer, Aidan/0000-0003-2752-1183; Levchenko, Mikhail/0000-0002-5495-0656; Morange, Nicolas/0000-0003-0047-7215; Unal, Guillaume/0000-0001-8130-7423; Carlson, Benjamin/0000-0002-7550-7821; Rohne, Ole Myren/0000-0001-7744-9584; Zhang, Matt/0000-0001-8659-5727; Kiryunin, Andrey/0000-0001-7490-6890; Steinhebel, Amanda/0000-0002-4080-2919; Bhattacharya, Deb Sankar/0000-0003-3837-4166; Shapiro, Marjorie/0000-0001-8540-9654; Kuchinskaia, Olesia/0000-0002-3664-2465; Hrynevich, Aliaksei/0000-0002-5411-114X; Ali, Shahzad/0000-0001-5216-3133; Pascuzzi, Vincent R./0000-0003-3167-8773; Petersen, Troels/0000-0003-0221-3037; Adam, Lennart/0000-0001-6005-2812; Maeda, Junpei/0000-0002-9084-3305; Cardillo, Fabio/0000-0002-4478-3524; Wu, Yusheng/0000-0002-1528-4865; Alhroob, Muhammad/0000-0001-7569-7111; snyder, scott/0000-0001-8610-8423; Sabatini, Paolo/0000-0003-0159-697X; Potti, Harish/0000-0002-0800-9902; Takeva, Emily/0000-0003-3142-030X; Cueto Gomez, Ana Rosario/0000-0003-1494-7898; Sfyrla, Anna/0000-0002-3003-9905; Vickey Boeriu, Oana/0000-0002-6497-6809; Hank, Michael/0000-0002-4731-6120; Fawcett, William/0000-0003-2596-8264; Lucotte, Arnaud/0000-0002-5992-0640; Lleres, Annick/0000-0003-1769-8524; Thomson, Evelyn/0000-0001-6031-2768; Weirich, Marcel/0000-0002-5129-872X; ; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, China; Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NBC KI, Russia Federation; JINR; MESTD, Serbia; MSS R, Slovakia; ARRS; Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; CRC, Canada; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions,European Union; COST, European Union; Investissements d' Avenir Labex, France; Investissements d' Avenir Idex, France; ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF; Thales programme - EU-ESF; Aristeia programme - EU-ESF; Greek NS RF, Greece; BSF-NSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC [ST/S000879/1, ST/N000331/1, ST/T000414/1, ST/N000277/1, ST/S000747/1, ST/S00095X/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, China; Hong Kong SAR, China; ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NBC KI, Russia Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSS R, Slovakia; ARRS(Slovenian Research Agency - Slovenia); Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; CRC, Canada; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions,European Union; COST, European Union; Investissements d' Avenir Labex, France(French National Research Agency (ANR)); Investissements d' Avenir Idex, France(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme - EU-ESF; Thales programme - EU-ESF(Thales Group); Aristeia programme - EU-ESF; Greek NS RF, Greece; BSF-NSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST, and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR, and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NBC KI, Russia Federation; JINR; MESTD, Serbia; MSS R, Slovakia; ARRS and Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF, and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Sklodowska-Curie Actions and COST, European Union; Investissements d' Avenir Labex, Investissements d' Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales, and Aristeia programmes co-financed by EU-ESF and the Greek NS RF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all W'LCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CCIN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-Tl (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK), and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. 1105]. 114 15 15 2 36 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. SEP 21 2020.0 125 13 131801 10.1103/PhysRevLett.125.131801 0.0 23 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics NR2NQ 33034503.0 Green Published, Green Accepted, hybrid, Green Submitted 2023-03-23 WOS:000571399800004 0
J Edmunds, SC; Hunter, CI; Smith, V; Stoev, P; Penev, L Edmunds, Scott C.; Hunter, Chris I.; Smith, Vincent; Stoev, Pavel; Penev, Lyubomir Biodiversity research in the big data era: GigaScience and Pensoft work together to publish the most data-rich species description GIGASCIENCE English Editorial Material With the publication of the first eukaryotic species description, combining transcriptomic, DNA barcoding, and micro-CT imaging data, GigaScience and Pensoft demonstrate how classical taxonomic description of a new species can be enhanced by applying new generation molecular methods, and novel computing and imaging technologies. This 'holistic' approach in taxonomic description of a new species of cave-dwelling centipede is published in the Biodiversity Data Journal (BDJ), with coordinated data release in the GigaScience GigaDB database. [Edmunds, Scott C.; Hunter, Chris I.] BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peoples R China; [Smith, Vincent] Nat Hist Museum, London SW7 5BD, England; [Stoev, Pavel; Penev, Lyubomir] Pensoft Publishers, Sofia, Bulgaria; [Stoev, Pavel] Nat Hist Museum, Natl Museum, Sofia, Bulgaria; [Penev, Lyubomir] Bulgarian Acad Sci, Inst Biodivers & Ecosyst Res, Sofia, Bulgaria Natural History Museum London; Bulgarian Academy of Sciences; Bulgarian Academy of Sciences; Bulgarian Academy of Sciences Edmunds, SC (corresponding author), BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peoples R China. scott@gigasciencejournal.com Penev, Lyubomir/O-9982-2019; Stoev, Pavel/H-5112-2019; Smith, Vincent/AAK-7335-2020 Stoev, Pavel/0000-0002-5702-5677; Smith, Vincent/0000-0001-5297-7452; Hunter, Christopher/0000-0002-1335-0881; Edmunds, Scott/0000-0001-6444-1436; Penev, Lyubomir/0000-0002-2186-5033 NERC [NE/H02185X/1] Funding Source: UKRI; Natural Environment Research Council [NE/H02185X/1] Funding Source: researchfish NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)); Natural Environment Research Council(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) 10 3 3 0 13 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2047-217X GIGASCIENCE GigaScience 2013.0 2 14 10.1186/2047-217X-2-14 0.0 3 Biology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics V41HA 24229463.0 Green Published, gold 2023-03-23 WOS:000209536100003 0
J Zheng, YZ; Shadloo, MS; Nasiri, H; Maleki, A; Karimipour, A; Tlili, I Zheng, Yuanzhou; Shadloo, Mostafa Safdari; Nasiri, Hossein; Maleki, Akbar; Karimipour, Arash; Tlili, Iskander Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations RENEWABLE ENERGY English Article Biodiesel blends; Viscosity; Intelligence approaches; Empirical correlations KINEMATIC VISCOSITY; DIESEL BLENDS; PERFORMANCE; FUELS; OIL; ENGINE; ESTERS; ANN; OPTIMIZATION; COMPONENTS From the perspective of renewability and environmental pollution, biodiesels are appropriate alternatives to conventional diesel fuels due to their proper combustion behavior and atomization characteristics, which can be influenced by the viscosity as an essential factor. Therefore, the viscosity prediction would be of importance for blend of biodiesel/diesel fuels. For biodiesel/diesel mixtures, the blended viscosity has been predicted with numerous empirical correlations available in the literature. In this work, the viscosity of fuel mixtures was evaluated through Generalized regression neural network (GRNN), Radial Basis Neural Networks (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Cascade Feed-forward Neural Network (CFNN), based on various experimental data. These developed models were then compared in terms of predictive accuracy and available empirical correlations to select the best model. Finally, the proposed model was compared with the most prominent biodiesel viscosity models confirming that the developed model has been superior in predicting the value of viscosity for biodiesel blends with reported values of 0.9997 and 0.87% for parameters of the coefficient of determination (R-2) and absolute average relative deviation (AARD%), respectively. (C) 2020 Elsevier Ltd. All rights reserved. [Zheng, Yuanzhou] Wuhan Univ Technol, Hubei Key Lab Inland Shipping Technol, 588 Youyi Ave, Wuhan, Peoples R China; [Zheng, Yuanzhou] Wuhan Univ Technol, Sch Nav, 588 Youyi Ave, Wuhan, Peoples R China; [Shadloo, Mostafa Safdari] Normandie Univ, INSA Rouen, CORIA CNRS UMR6614, F-76000 Rouen, France; [Nasiri, Hossein] Daneshpajoohan Higher Educ Inst, Dept Mech Engn, Esfahan, Iran; [Maleki, Akbar] Shahrood Univ Technol, Fac Mech Engn, Shahrood, Iran; [Karimipour, Arash] Islamic Azad Univ, Dept Mech Engn, Najafabad Branch, Najafabad, Iran; [Tlili, Iskander] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; [Tlili, Iskander] Ton Duc Thang Univ, Fac Appl Sci, Ho Chi Minh City, Vietnam Hubei Key Laboratory of Inland Shipping Technology; Wuhan University of Technology; Wuhan University of Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Rouen Normandie; Shahrood University of Technology; Islamic Azad University; Ton Duc Thang University; Ton Duc Thang University Tlili, I (corresponding author), Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam. a_maleki@shahroodut.ac.ir; iskander.tlili@tdtu.edu.vn Maleki, Akbar/AFG-0723-2022; Karimipour, Arash/A-3478-2019; Safdari Shadloo, Mostafa/H-5825-2013; NASIRI, Hossein/AAD-8260-2021 Maleki, Akbar/0000-0002-5830-4934; Karimipour, Arash/0000-0001-7596-7134; Safdari Shadloo, Mostafa/0000-0002-0631-3046; National Natural Science Foundation of China [51979215] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Grant Number 51979215). 50 76 76 2 28 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-1481 RENEW ENERG Renew. Energy JUN 2020.0 153 1296 1306 10.1016/j.renene.2020.02.087 0.0 11 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels LT3DX 2023-03-23 WOS:000536952200107 0
J Jiang, YY; Zheng, YJ; Sui, XD; Jiao, WZ; He, YL; Jia, WK Jiang, Yanyun; Zheng, Yuanjie; Sui, Xiaodan; Jiao, Wanzhen; He, Yunlong; Jia, Weikuan ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images COMPUTER SYSTEMS SCIENCE AND ENGINEERING English Article Deep learning; deformable image registration; image segmentation; multispectral imaging (MSI) LEARNING FRAMEWORK Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called Adversarial Segmentation and Registration Nets (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets. [Jiang, Yanyun; Zheng, Yuanjie; Sui, Xiaodan; Jia, Weikuan] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China; [Zheng, Yuanjie] Shandong Normal Univ, Key Lab Intelligent Comp & Informat Secur Univ Sh, Inst Biomed Sci, Shandong Prov Key Lab Novel Distributed Comp Soft, Jinan 250358, Peoples R China; [Jiao, Wanzhen] Shandong Univ, Shandong Prov Hosp, Jinan 250021, Peoples R China; [He, Yunlong] Univ Lyon, INSERM, CNRS, INSA Lyon, F-69621 Villeurbanne, France Shandong Normal University; Shandong Normal University; Shandong First Medical University & Shandong Academy of Medical Sciences; Shandong University; Centre National de la Recherche Scientifique (CNRS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Zheng, YJ (corresponding author), Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.;Zheng, YJ (corresponding author), Shandong Normal Univ, Key Lab Intelligent Comp & Informat Secur Univ Sh, Inst Biomed Sci, Shandong Prov Key Lab Novel Distributed Comp Soft, Jinan 250358, Peoples R China. yjzheng@sdnu.edu.cn 于, 于增臣/AAH-4657-2021 National Natural Science Foundation of China [81871508, 61773246]; Major Program of Shandong Province Natural Science Foundation [ZR2019ZD04, ZR2018ZB0419]; Taishan Scholar Program of Shandong Province of China [TSHW201502038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Program of Shandong Province Natural Science Foundation; Taishan Scholar Program of Shandong Province of China This work was supported by the National Natural Science Foundation of China (Grant Nos. 81871508 and 61773246); the Major Program of Shandong Province Natural Science Foundation (Grant No. ZR2019ZD04 and ZR2018ZB0419); and the Taishan Scholar Program of Shandong Province of China (Grant No. TSHW201502038). 43 1 1 6 21 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 0267-6192 COMPUT SYST SCI ENG Comput. Syst. Sci. Eng. MAY 2021.0 36 3 537 549 10.32604/csse.2021.014578 0.0 13 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science PU3PY hybrid 2023-03-23 WOS:000609218500009 0
J Liao, H; Dong, WH; Huang, HS; Gartner, G; Liu, HP Liao, Hua; Dong, Weihua; Huang, Haosheng; Gartner, Georg; Liu, Huiping Inferring user tasks in pedestrian navigation from eye movement data in real-world environments INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE English Article Wayfinding; random forests; task inference; eye tracking; machine learning OBSERVERS TASK; TRACKING; ATTENTION; GUIDANCE; YARBUS; MAPS Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing. [Liao, Hua; Dong, Weihua; Liu, Huiping] Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing, Peoples R China; [Liao, Hua; Gartner, Georg] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Liao, Hua; Gartner, Georg] Vienna Univ Technol, Dept Geodesy & Geoinformat, Vienna, Austria; [Huang, Haosheng] Univ Zurich, Dept Geog, GISci Ctr, Zurich, Switzerland Beijing Normal University; Beijing Normal University; Technische Universitat Wien; University of Zurich Dong, WH (corresponding author), Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing, Peoples R China. dongweihua@bnu.edu.cn Huang, Haosheng/F-2941-2011; Huang, Haosheng/AFE-5924-2022 Huang, Haosheng/0000-0001-8399-3607; Gartner, Georg/0000-0003-2002-5339; Liao, Hua/0000-0002-6304-329X National Key Research and Development Program of China [2017YFB0503602]; National Natural Science Foundation of China [NSFC] [41471382]; Beijing Normal University Teaching Construction and Reform Project [14-07-01]; China Scholarship Council [201606040097] National Key Research and Development Program of China; National Natural Science Foundation of China [NSFC](National Natural Science Foundation of China (NSFC)); Beijing Normal University Teaching Construction and Reform Project; China Scholarship Council(China Scholarship Council) This research is supported by National Key Research and Development Program of China [Grant No. 2017YFB0503602], National Natural Science Foundation of China [NSFC, Grant No. 41471382], Beijing Normal University Teaching Construction and Reform Project [Grant No. 14-07-01], and China Scholarship Council [Grant No. 201606040097]. 57 30 35 8 55 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1365-8816 1362-3087 INT J GEOGR INF SCI Int. J. Geogr. Inf. Sci. 2019.0 33 4 739 763 10.1080/13658816.2018.1482554 0.0 25 Computer Science, Information Systems; Geography; Geography, Physical; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Geography; Physical Geography; Information Science & Library Science HM6CM 2023-03-23 WOS:000459561600006 0
J Qiu, YC; Zhou, GZ; Zhang, Y; Cichocki, A Qiu, Yichun; Zhou, Guoxu; Zhang, Yu; Cichocki, Andrzej Canonical polyadic decomposition (CPD) of big tensors with low multilinear rank MULTIMEDIA TOOLS AND APPLICATIONS English Article Tucker decompositions; Canonical polyadic decomposition (CPD); Tensor decomposition NONNEGATIVE MATRIX; ALGORITHMS; SPEED Tensor decomposition methods have been widely applied to big data analysis as they bring multiple modes and aspects of data to a unified framework, which allows us to discover complex internal structures and correlations of data. Unfortunately most existing approaches are not designed to meet the challenges posed by big data dilemma. This paper attempts to improve the scalability of tensor decompositions and makes two contributions: A flexible and fast algorithm for the CP decomposition (FFCP) of tensors based on their Tucker compression; A distributed randomized Tucker decomposition approach for arbitrarily big tensors but with relatively low multilinear rank. These two algorithms can deal with huge tensors, even if they are dense. Extensive simulations provide empirical evidence of the validity and efficiency of the proposed algorithms. [Qiu, Yichun; Zhou, Guoxu] Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China; [Zhou, Guoxu] Guangdong Univ Technol, Sch Automat, Guangdong HongKong Macao Joint Lab Smart Discrete, Guangzhou 510006, Peoples R China; [Zhang, Yu] Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia; [Cichocki, Andrzej] Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland Guangdong University of Technology; Guangdong University of Technology; Stanford University; Skolkovo Institute of Science & Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Zhou, GZ (corresponding author), Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China.;Zhou, GZ (corresponding author), Guangdong Univ Technol, Sch Automat, Guangdong HongKong Macao Joint Lab Smart Discrete, Guangzhou 510006, Peoples R China. ycqiu@gdut.edu.cn; gx.zhou@gdut.edu.cn; yzhangsu@stanford.edu Zhang, Yu/J-1796-2015; Zhou, Guoxu/D-2040-2014 Zhang, Yu/0000-0003-4087-6544; Zhou, Guoxu/0000-0003-1187-577X National Natural Science Foundation of China (NSFC) [61673124, 61973090, 61727810] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was partially supported by the National Natural Science Foundation of China (NSFC) Grant 61673124, Grant 61973090, and Grant 61727810. 45 3 3 3 11 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2021.0 80 15 22987 23007 10.1007/s11042-020-08711-1 0.0 APR 2020 21 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering TD4QX 2023-03-23 WOS:000528160700001 0
J Isozaki, A; Mikami, H; Tezuka, H; Matsumura, H; Huang, KR; Akamine, M; Hiramatsu, K; Iino, T; Ito, T; Karakawa, H; Kasai, Y; Li, Y; Nakagawa, Y; Ohnuki, S; Ota, T; Qian, Y; Sakuma, S; Sekiya, T; Shirasaki, Y; Suzuki, N; Tayyabi, E; Wakamiya, T; Xu, MZ; Yamagishi, M; Yan, HC; Yu, Q; Yan, S; Yuan, D; Zhang, W; Zhao, YQ; Arai, F; Campbell, RE; Danelon, C; Di Carlo, D; Hiraki, K; Hoshino, Y; Hosokawa, Y; Inaba, M; Nakagawa, A; Ohya, Y; Oikawa, M; Uemura, S; Ozeki, Y; Sugimura, T; Nitta, N; Goda, K Isozaki, Akihiro; Mikami, Hideharu; Tezuka, Hiroshi; Matsumura, Hiroki; Huang, Kangrui; Akamine, Marino; Hiramatsu, Kotaro; Iino, Takanori; Ito, Takuro; Karakawa, Hiroshi; Kasai, Yusuke; Li, Yan; Nakagawa, Yuta; Ohnuki, Shinsuke; Ota, Tadataka; Qian, Yong; Sakuma, Shinya; Sekiya, Takeichiro; Shirasaki, Yoshitaka; Suzuki, Nobutake; Tayyabi, Ehsen; Wakamiya, Tsubasa; Xu, Muzhen; Yamagishi, Mai; Yan, Haochen; Yu, Qiang; Yan, Sheng; Yuan, Dan; Zhang, Wei; Zhao, Yaqi; Arai, Fumihito; Campbell, Robert E.; Danelon, Christophe; Di Carlo, Dino; Hiraki, Kei; Hoshino, Yu; Hosokawa, Yoichiroh; Inaba, Mary; Nakagawa, Atsuhiro; Ohya, Yoshikazu; Oikawa, Minoru; Uemura, Sotaro; Ozeki, Yasuyuki; Sugimura, Takeaki; Nitta, Nao; Goda, Keisuke Intelligent image-activated cell sorting 2.0 LAB ON A CHIP English Article FLOW-CYTOMETRY; IDENTIFICATION; FLUORESCENT; MICROSCOPY The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of similar to 2000 events per second and a high sensitivity of similar to 50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology. [Isozaki, Akihiro; Mikami, Hideharu; Matsumura, Hiroki; Huang, Kangrui; Akamine, Marino; Hiramatsu, Kotaro; Ito, Takuro; Karakawa, Hiroshi; Nakagawa, Yuta; Ota, Tadataka; Wakamiya, Tsubasa; Xu, Muzhen; Yan, Haochen; Yan, Sheng; Yuan, Dan; Zhao, Yaqi; Campbell, Robert E.; Di Carlo, Dino; Hiraki, Kei; Sugimura, Takeaki; Nitta, Nao; Goda, Keisuke] Univ Tokyo, Dept Chem, Tokyo 1130033, Japan; [Isozaki, Akihiro] Kanagawa Inst Ind Sci & Technol, 705-1 Shimoimaizumi, Ebina, Kanagawa 2430435, Japan; [Tezuka, Hiroshi; Inaba, Mary] Univ Tokyo, Dept Creat Informat, Tokyo 1130033, Japan; [Iino, Takanori; Sekiya, Takeichiro; Yu, Qiang; Ozeki, Yasuyuki] Univ Tokyo, Dept Elect Engn & Informat Syst, Tokyo 1138656, Japan; [Ito, Takuro; Sugimura, Takeaki; Nitta, Nao; Goda, Keisuke] Japan Sci & Technol Agcy, Kawaguchi, Saitama 3320012, Japan; [Kasai, Yusuke; Sakuma, Shinya; Arai, Fumihito] Nagoya Univ, Dept Micronano Mech Sci & Engn, Nagoya, Aichi 4648601, Japan; [Li, Yan; Qian, Yong; Zhang, Wei; Campbell, Robert E.] Univ Alberta, Dept Chem, Edmonton, AB T6G 2G2, Canada; [Ohnuki, Shinsuke; Ohya, Yoshikazu] Univ Tokyo, Dept Integrated Biosci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778562, Japan; [Shirasaki, Yoshitaka; Suzuki, Nobutake; Yamagishi, Mai; Uemura, Sotaro] Univ Tokyo, Dept Biol Sci, Tokyo 1130033, Japan; [Tayyabi, Ehsen] Univ Toronto, Dept Chem, Toronto, ON M5S 3H6, Canada; [Danelon, Christophe] Delft Univ Technol, Dept Bionanosci, van der Maasweg 9, NL-2629 HZ Delft, Netherlands; [Di Carlo, Dino; Goda, Keisuke] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90095 USA; [Di Carlo, Dino] Univ Calif Los Angeles, Dept Mech Engn, Los Angeles, CA 90095 USA; [Di Carlo, Dino] Univ Calif Los Angeles, Calif NanoSyst Inst, Los Angeles, CA 90095 USA; [Hoshino, Yu] Kyushu Univ, Dept Chem Engn, Fukuoka 8190395, Japan; [Hosokawa, Yoichiroh] Nara Inst Sci & Technol, Div Mat Sci, Ikoma 6300192, Japan; [Nakagawa, Atsuhiro] Tohoku Univ, Dept Neurosurg, Sendai, Miyagi 9808577, Japan; [Ohya, Yoshikazu] Natl Inst Adv Ind Sci & Technol, AIST UTokyo Adv Operandomeasurement Technol Open, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778589, Japan; [Oikawa, Minoru] Kochi Univ, Sci Unit, Nat Sci Cluster, Kochi 7808520, Japan; [Sugimura, Takeaki; Nitta, Nao] CYBO, Tokyo 1010022, Japan; [Goda, Keisuke] Wuhan Univ, Inst Technol Sci, Wuhan 430072, Hubei, Peoples R China University of Tokyo; University of Tokyo; University of Tokyo; Japan Science & Technology Agency (JST); Nagoya University; University of Alberta; University of Tokyo; University of Tokyo; University of Toronto; Delft University of Technology; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; Kyushu University; Nara Institute of Science & Technology; Tohoku University; National Institute of Advanced Industrial Science & Technology (AIST); Kochi University; Wuhan University Goda, K (corresponding author), Univ Tokyo, Dept Chem, Tokyo 1130033, Japan.;Goda, K (corresponding author), Japan Sci & Technol Agcy, Kawaguchi, Saitama 3320012, Japan.;Goda, K (corresponding author), Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90095 USA.;Goda, K (corresponding author), Wuhan Univ, Inst Technol Sci, Wuhan 430072, Hubei, Peoples R China. goda@chem.s.u-tokyo.ac.jp Isozaki, Akihiro/AAV-4271-2021; Ohya, Yoshikazu/AAW-2200-2021; Shirsaki, Yoshitaka/HLH-4624-2023; Hiramatsu, Kotaro/P-4567-2018; Danelon, Christophe/AAW-5303-2021; GODA, KEISUKE/G-4997-2014 Ohya, Yoshikazu/0000-0003-0837-1239; Hiramatsu, Kotaro/0000-0003-0767-019X; Danelon, Christophe/0000-0002-0961-6640; Yuan, Dan/0000-0002-5472-232X; Kasai, Yusuke/0000-0003-0637-4575; Huang, Kangrui/0000-0001-7873-7246; Goda, Keisuke/0000-0001-6302-6038; Isozaki, Akihiro/0000-0003-0391-9832; Matsumura, Hiroki/0000-0003-3638-6956 ImPACT program (CSTI, Cabinet Office, Government of Japan); JSPS Core-to-Core Program; JSPS KAKENHI [19H05633]; White Rock Founda-tion; Nakatani Foundation; Precise Measurement Technology Promotion Foundation; KISTEC; Grants-in-Aid for Scientific Research [19H05633] Funding Source: KAKEN ImPACT program (CSTI, Cabinet Office, Government of Japan); JSPS Core-to-Core Program(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); White Rock Founda-tion; Nakatani Foundation; Precise Measurement Technology Promotion Foundation; KISTEC; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported by the ImPACT program (CSTI, Cabinet Office, Government of Japan), JSPS Core-to-Core Program, JSPS KAKENHI grant number 19H05633, White Rock Founda-tion, KISTEC, Nakatani Foundation, and Precise Measurement Technology Promotion Foundation. N. N. is an ISAC Marylou Ingram Scholar. We thank members of the Marileen Dogterom lab for discussions about the Btub proteins, Anne Doerr for purifying and labeling the Btub proteins, Johannes Kattan for performing preliminary experiments, and members of the Danelon lab for preparing the lipid-coated beads. 44 67 68 13 82 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1473-0197 1473-0189 LAB CHIP Lab Chip JUL 7 2020.0 20 13 2263 2273 10.1039/d0lc00080a 0.0 11 Biochemical Research Methods; Chemistry, Multidisciplinary; Chemistry, Analytical; Nanoscience & Nanotechnology; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry; Science & Technology - Other Topics; Instruments & Instrumentation MG2LE 32459276.0 Green Published 2023-03-23 WOS:000545864300013 0
J Farnoud, A; Tofighian, H; Baumann, I; Ahookhosh, K; Pourmehran, O; Cui, XG; Heuveline, V; Song, C; Vreugde, S; Wormald, PJ; Menden, MP; Schmid, O Farnoud, Ali; Tofighian, Hesam; Baumann, Ingo; Ahookhosh, Kaveh; Pourmehran, Oveis; Cui, Xinguang; Heuveline, Vincent; Song, Chen; Vreugde, Sarah; Wormald, Peter-John; Menden, Michael P.; Schmid, Otmar Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs PHARMACEUTICALS English Article nasal drug delivery; machine learning; numerical modelling; nanodrug delivery; targeted drug delivery PARTICLE DEPOSITION; NANOPARTICLE DEPOSITION; OLFACTORY DEPOSITION; AIR-FLOW; CAVITIES; RAT The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 mu m) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 mu m and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1-20 mu m) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R-2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively. [Farnoud, Ali; Menden, Michael P.] Helmholtz Munich, Computat Hlth Ctr, D-85764 Neuherberg, Germany; [Farnoud, Ali; Schmid, Otmar] Comprehens Pneumol Ctr CPC M, D-81377 Munich, Germany; [Farnoud, Ali; Schmid, Otmar] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Lung Hlth & Immun, D-85764 Neuherberg, Germany; [Tofighian, Hesam] Amirkabir Univ Technol, Dept Mech Engn, Tehran 1591634311, Iran; [Baumann, Ingo] Heidelberg Univ, Dept Otorhinolaryngol Head & Neck Surg, Med Ctr, D-69120 Heidelberg, Germany; [Ahookhosh, Kaveh] Katholieke Univ Leuven, Dept Imaging & Pathol, Biomed MRI & MoSAIC, B-3000 Leuven, Belgium; [Pourmehran, Oveis; Vreugde, Sarah; Wormald, Peter-John] Univ Adelaide, Adelaide Med Sch, Dept Otolaryngol Head & Neck Surg, Adelaide 5011, Australia; [Pourmehran, Oveis] Univ Adelaide, Sch Mech Engn, Adelaide 5005, Australia; [Cui, Xinguang] Huazhong Univ Sci & Technol, Sch Aerosp Engn, Wuhan 430074, Peoples R China; [Heuveline, Vincent; Song, Chen] Heidelberg Univ, Engn Math & Comp Lab EMCL, D-69120 Heidelberg, Germany; [Menden, Michael P.] Ludwig Maximilian Univ Munich, Dept Biol, D-82152 Planegg, Germany; [Menden, Michael P.] German Ctr Diabet Res DZD eV, D-85764 Neuherberg, Germany Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Amirkabir University of Technology; Ruprecht Karls University Heidelberg; KU Leuven; University of Adelaide; University of Adelaide; Huazhong University of Science & Technology; Ruprecht Karls University Heidelberg; University of Munich Farnoud, A (corresponding author), Helmholtz Munich, Computat Hlth Ctr, D-85764 Neuherberg, Germany.;Farnoud, A (corresponding author), Comprehens Pneumol Ctr CPC M, D-81377 Munich, Germany.;Farnoud, A (corresponding author), Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Lung Hlth & Immun, D-85764 Neuherberg, Germany. alifarnod@gmail.com Schmid, Otmar/F-1078-2013 Schmid, Otmar/0000-0002-8012-7786; Song, Chen/0000-0003-3775-8184; Ahookhosh, Kaveh/0000-0002-1789-2303; Farnoud, Ali/0000-0002-9298-5497 46 0 0 1 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8247 PHARMACEUTICALS-BASE Pharmaceuticals JAN 2023.0 16 1 81 10.3390/ph16010081 0.0 14 Chemistry, Medicinal; Pharmacology & Pharmacy Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy 8A2KA 36678578.0 gold 2023-03-23 WOS:000916071600001 0
J Liao, H; Zhao, WD; Zhang, CB; Dong, WH; Huang, HS Liao, Hua; Zhao, Wendi; Zhang, Changbo; Dong, Weihua; Huang, Haosheng Detecting individuals' spatial familiarity with urban environments using eye movement data COMPUTERS ENVIRONMENT AND URBAN SYSTEMS English Article Pedestrian navigation; Eye tracking; Machine learning; Random forest; Wayfinding; Spatial familiarity LOCATION-BASED SERVICES; PEDESTRIAN NAVIGATION; ATTENTION; RESPONSES; TASK; LOAD The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users' familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users' familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data - eye movement data to infer users' spatial familiarity of environments using a machine learning approach. We collected 38 participants' eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users' spatial familiarity. Our results indicate that detecting users' spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users' familiarity with the environments. [Liao, Hua; Zhao, Wendi; Zhang, Changbo] Hunan Normal Univ, Sch Geog Sci, Changsha, Peoples R China; [Liao, Hua; Zhao, Wendi; Zhang, Changbo] Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha, Peoples R China; [Liao, Hua; Dong, Weihua] Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing, Peoples R China; [Liao, Hua; Dong, Weihua] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Huang, Haosheng] Univ Ghent, Dept Geog, Res Grp CartoGIS, Ghent, Belgium Hunan Normal University; Hunan Normal University; Beijing Normal University; Beijing Normal University; Ghent University Dong, WH (corresponding author), Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;Dong, WH (corresponding author), Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China. dongweihua@bnu.edu.cn Huang, Haosheng/AFE-5924-2022 Huang, Haosheng/0000-0001-8399-3607; Liao, Hua/0000-0002-6304-329X National Natural Science Foundation of China [NSFC] [42001410, 41871366]; Natural Science Foundation of Hunan Province [2021JJ40350]; Scientific Research Foundation of Hunan Provincial Education Department [19B367] National Natural Science Foundation of China [NSFC](National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Scientific Research Foundation of Hunan Provincial Education Department(Hunan Provincial Education Department) This research is supported by the National Natural Science Foundation of China [NSFC, Grant Nos. 42001410 and 41871366], Natural Science Foundation of Hunan Province [Grant No. 2021JJ40350] and Scientific Research Foundation of Hunan Provincial Education Department [Grant No. 19B367]. 48 4 4 10 29 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0198-9715 1873-7587 COMPUT ENVIRON URBAN Comput. Environ. Urban Syst. APR 2022.0 93 101758 10.1016/j.compenvurbsys.2022.101758 0.0 JAN 2022 12 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science; Regional & Urban Planning Social Science Citation Index (SSCI) Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science; Public Administration YX5VH 2023-03-23 WOS:000754170000006 0
J Jahanshahi, H; Shahriari-Kahkeshi, M; Alcaraz, R; Wang, X; Singh, VP; Pham, VT Jahanshahi, Hadi; Shahriari-Kahkeshi, Maryam; Alcaraz, Raul; Wang, Xiong; Singh, Vijay P.; Viet-Thanh Pham Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors ENTROPY English Article Non-equilibrium four-dimensional chaotic system; entropy measure; adaptive approximator-based control; neural network; uncertain dynamics APPROXIMATE ENTROPY; FEEDBACK-CONTROL; SYNCHRONIZATION; MULTISTABILITY; REALIZATION; COEXISTENCE; OSCILLATOR; INFINITY Today, four-dimensional chaotic systems are attracting considerable attention because of their special characteristics. This paper presents a non-equilibrium four-dimensional chaotic system with hidden attractors and investigates its dynamical behavior using a bifurcation diagram, as well as three well-known entropy measures, such as approximate entropy, sample entropy, and Fuzzy entropy. In order to stabilize the proposed chaotic system, an adaptive radial-basis function neural network (RBF-NN)-based control method is proposed to represent the model of the uncertain nonlinear dynamics of the system. The Lyapunov direct method-based stability analysis of the proposed approach guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, adaptive learning laws are proposed to tune the weight coefficients of the RBF-NN. The proposed adaptive control approach requires neither the prior information about the uncertain dynamics nor the parameters value of the considered system. Results of simulation validate the performance of the proposed control method. [Jahanshahi, Hadi] Univ Tehran, Fac New Sci & Technol, Dept Aerosp Engn, Tehran 143951561, Iran; [Shahriari-Kahkeshi, Maryam] Shahrekord Univ, Fac Engn, Shahrekord 64165478, Iran; [Alcaraz, Raul] UCLM, Res Grp Elect Biomed & Telecommun Engn, Cuenca 16071, Spain; [Wang, Xiong] Shenzhen Univ, Inst Adv Study, Shenzhen 518060, Peoples R China; [Singh, Vijay P.] Texas A&M Univ, Dept Biol & Agr Engn, 2117 TAMU, College Stn, TX 77843 USA; [Singh, Vijay P.] Texas A&M Univ, Zachry Dept Civil Engn, 2117 TAMU, College Stn, TX 77843 USA; [Viet-Thanh Pham] Ton Duc Thang Univ, Fac Elect & Elect Engn, Nonlinear Syst & Applicat, Ho Chi Minh City 700000, Vietnam University of Tehran; Shahrekord University; Universidad de Castilla-La Mancha; Shenzhen University; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station; Ton Duc Thang University Pham, VT (corresponding author), Ton Duc Thang Univ, Fac Elect & Elect Engn, Nonlinear Syst & Applicat, Ho Chi Minh City 700000, Vietnam. hadi_jahanshahi@ut.ac.ir; m.shahriyarikahkeshi@ec.iut.ac.ir; raul.alcaraz@uclm.es; wangxiong8686@szu.edu.cn; vsingh@tamu.edu; phamvietthanh@tdtu.edu.vn Jahanshahi, Hadi/L-5815-2019; Pham, Viet Thanh/F-3715-2019; Shahriari-kahkeshi, Maryam/ABA-3006-2021; Alcaraz, Raul/L-7592-2014 Pham, Viet Thanh/0000-0001-9045-3633; Alcaraz, Raul/0000-0002-0942-3638 National Natural Science Foundation of China [61601306]; Shenzhen Overseas High Level Talent Peacock Project Fund [20150215145C] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Overseas High Level Talent Peacock Project Fund The author Xiong Wang was supported by the National Natural Science Foundation of China (No. 61601306) and the Shenzhen Overseas High Level Talent Peacock Project Fund (No. 20150215145C). 63 68 68 2 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy FEB 2019.0 21 2 156 10.3390/e21020156 0.0 15 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics HO2JZ 33266872.0 Green Submitted, gold, Green Accepted, Green Published 2023-03-23 WOS:000460742200057 0
J Shi, L; Li, J; Palansooriya, KN; Chen, YH; Hou, DY; Meers, E; Tsang, DCW; Wang, XN; Ok, YS Shi, Liang; Li, Jie; Palansooriya, Kumuduni Niroshika; Chen, Yahua; Hou, Deyi; Meers, Erik; Tsang, Daniel C. W.; Wang, Xiaonan; Ok, Yong Sik Modeling phytoremediation of heavy metal contaminated soils through machine learning JOURNAL OF HAZARDOUS MATERIALS English Article Heavy metal; Hyperaccumulator; Machine learning; Phytoextraction; Soil remediation PHYTOEXTRACTION; CADMIUM; ACCUMULATION; PREDICTION As an important subtopic within phytoremediation, hyperaccumulators have garnered significant attention due to their ability of super-enriching heavy metals. Identifying the factors that affecting phytoextraction efficiency has important application value in guiding the efficient remediation of heavy metal contaminated soil. However, it is challenging to identify the critical factors that affect the phytoextraction of heavy metals in soil-hyperaccumulator ecosystems because the current projections on phytoremediation extrapolations are rudimentary at best using simple linear models. Here, machine learning (ML) approaches were used to predict the important factors that affecting phytoextraction efficiency of hyperaccumulators. ML analysis was based on 173 data points with consideration of soil properties, experimental conditions, plant families, low-molecular -weight organic acids from plants, plant genes, and heavy metal properties. Heavy metal properties, especially the metal ion radius, were the most important factors that affect heavy metal accumulation in shoots, and the plant family was the most important factor that affect the bioconcentration factor, metal extraction ratio, and remediation time. Furthermore, the Crassulaceae family had the highest potential as hyperaccumulators for phytoremediation, which was related to the expression of genes encoding heavy metal transporting ATPase (HMA), Metallothioneins (MTL), and natural resistance associated macrophage protein (NRAMP), and also the secretion of malate and threonine. New insights into the effects of plant characteristics, experimental conditions, soil characteristics, and heavy metal properties on phytoextraction efficiency from ML model interpretation could guide the efficient phytoremediation by identifying the best hyperaccumulators and resolving its efficient remediation mechanisms. [Shi, Liang; Palansooriya, Kumuduni Niroshika; Ok, Yong Sik] Korea Univ, Korea Biochar Res Ctr, APRU Sustainable Waste Management Program, Div Environm Sci & Ecol Engn, Seoul 02841, South Korea; [Shi, Liang; Chen, Yahua] Nanjing Agr Univ, Coll Life Sci, Nanjing 210095, Peoples R China; [Li, Jie] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore; [Li, Jie] Chinese Acad Sci, Inst Urban Environm, CAS Key Lab Urban Pollutant Convers, Xiamen 361021, Peoples R China; [Palansooriya, Kumuduni Niroshika] Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Hangzhou 311300, Peoples R China; [Hou, Deyi] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China; [Meers, Erik] Univ Ghent, Fac Biosci Engn, Dept Green Chem & Technol, Coupure Links 653, B-9000 Ghent, Belgium; [Tsang, Daniel C. W.] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Wang, Xiaonan] Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China Korea University; Nanjing Agricultural University; National University of Singapore; Chinese Academy of Sciences; Institute of Urban Environment, CAS; Zhejiang A&F University; Tsinghua University; Ghent University; Hong Kong Polytechnic University; Tsinghua University Ok, YS (corresponding author), Korea Univ, Korea Biochar Res Ctr, APRU Sustainable Waste Management Program, Div Environm Sci & Ecol Engn, Seoul 02841, South Korea.;Wang, XN (corresponding author), Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China. wangxiaonan@tsinghua.edu.cn; yongsikok@korea.ac.kr Tsang, Dan/E-5442-2012; Wang, Xiaonan/T-1102-2017 Tsang, Dan/0000-0002-6850-733X; Wang, Xiaonan/0000-0001-9775-2417 Cooperative Research Program for Agriculture Science and Technology Development from Rural Development Administration, the Republic of Korea [PJ01475801]; International Postdoctoral Exchange Program Fellowship [PC2020041]; National Research Foundation of Korea (NRF) - Korea government (MSIT) [2021R1A2C2011734]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF- 2021R1A6A1A10045235] Cooperative Research Program for Agriculture Science and Technology Development from Rural Development Administration, the Republic of Korea; International Postdoctoral Exchange Program Fellowship; National Research Foundation of Korea (NRF) - Korea government (MSIT)(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of KoreaMinistry of Science & ICT (MSIT), Republic of Korea); Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education(National Research Foundation of KoreaMinistry of Education (MOE), Republic of KoreaNational Research Council for Economics, Humanities & Social Sciences, Republic of Korea) This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475801) from Rural Development Administration, the Republic of Korea. This work was also supported by the International Postdoctoral Exchange Program Fellowship (PC2020041). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2011734). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2021R1A6A1A10045235). 48 3 3 119 121 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3894 1873-3336 J HAZARD MATER J. Hazard. Mater. JAN 5 2023.0 441 129904 10.1016/j.jhazmat.2022.129904 0.0 10 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology 4O5TF 36096061.0 2023-03-23 WOS:000854759400004 0
C Chen, QY; Sun, CY; Lu, ZH; Gao, C IEEE Chen, Qinyu; Sun, Congyi; Lu, Zhonghai; Gao, Chang Enabling Energy-Efficient Inference for Self-Attention Mechanisms in Neural Networks 2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA English Proceedings Paper IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era JUN 13-15, 2022 Incheon, SOUTH KOREA IEEE,CAS,Minist Sci & ICT,AI Semicond Forum,Incheon Metropolitan City,Incheon Tourism Org,Korea Tourism Org Self-attention; approximate computing; VLSI The study of specialized accelerators tailored for neural networks is becoming a promising topic in recent years. Such existing neural network accelerators are usually designed for convolutional neural networks (CNNs) or recurrent neural networks have been (RNNs), however, less attention has been paid to the attention mechanisms, which is an emerging neural network primitive with the ability to identify the relations within input entities. The self-attention-oriented models such as Transformer have achieved great performance on natural language processing, computer vision and machine translation. However, the self-attention mechanism has intrinsically expensive computational workloads, which increase quadratically with the number of input entities. Therefore, in this work, we propose an software-hardware co-design solution for energy-efficient self-attention inference. A prediction-based approximate self-attention mechanism is introduced to substantially reduce the runtime as well as power consumption, and then a specialized hardware architecture is designed to further increase the speedup. The design is implemented on a Xilinx XC7Z035 FPGA, and the results show that the energy efficiency is improved by 5.7x with less than 1% accuracy loss. [Chen, Qinyu] Univ Shanghai Sci & Technol, Inst Photon Chips, Shanghai, Peoples R China; [Sun, Congyi] Nanjing Univ, Sch Elect Sci & Engn, Nanjing, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Stockholm, Sweden; [Gao, Chang] Univ Zurich, Inst Neuroinformat, Zurich, Switzerland; [Gao, Chang] Swiss Fed Inst Technol, Zurich, Switzerland University of Shanghai for Science & Technology; Nanjing University; Royal Institute of Technology; University of Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich Gao, C (corresponding author), Univ Zurich, Inst Neuroinformat, Zurich, Switzerland.;Gao, C (corresponding author), Swiss Fed Inst Technol, Zurich, Switzerland. Gao, Chang/0000-0002-3284-4078 13 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-0996-4 2022.0 25 28 10.1109/AICAS54282.2022.9869924 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT9CO Green Accepted 2023-03-23 WOS:000859273200008 0
C Gu, CY; Chang, CH; Liu, WQ; Yu, SC; Ma, QQ; O'Neill, M IEEE Gu, Chongyan; Chang, Chip Hong; Liu, Weiqiang; Yu, Shichao; Ma, Qingqing; O'Neill, Maire A Modeling Attack Resistant Deception Technique for Securing PUF based Authentication PROCEEDINGS OF THE 2019 ASIAN HARDWARE ORIENTED SECURITY AND TRUST SYMPOSIUM (ASIANHOST) English Proceedings Paper Asian Hardware Oriented Security and Trust Symposium (AsianHOST) DEC 16-17, 2019 Xian, PEOPLES R CHINA IEEE,IEEE Syst Council,IEEE Council Elect Design Automat,Open Secur Res Physical Unclonable Function (PUF); machine learning attacks; authentication protocol Due to practical constraints in preventing phishing through public network or insecure communication channels, simple physical unclonable function (PUF)-based authentication protocol with unrestricted queries and transparent responses is vulnerable to modeling and replay attacks. In this paper, we present a PUF-based authentication method to mitigate the practical limitations in applications where a resource-rich server authenticates a device with no strong restriction imposed on the type of PUF designs or any additional protection on the binary channel used for the authentication. Our scheme uses an active deception protocol to prevent machine learning (ML) attacks on a device. The monolithic system makes collection of challenge response pairs (CRPs) easy for model building during enrollment but prohibitively time consuming upon device deployment. A genuine server can perform a mutual authentication with the device at any time with a combined fresh challenge contributed by both the server and the device. The message exchanged in clear does not expose the authentic CRPs. The false PUF multiplexing is fortified against prediction of waiting time by doubling the time penalty for every unsuccessful authentication. [Gu, Chongyan; Yu, Shichao; O'Neill, Maire] Queens Univ Belfast, ECIT, CSIT, Belfast, Antrim, North Ireland; [Chang, Chip Hong] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore; [Liu, Weiqiang; Ma, Qingqing] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing, Peoples R China Queens University Belfast; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanjing University of Aeronautics & Astronautics Gu, CY (corresponding author), Queens Univ Belfast, ECIT, CSIT, Belfast, Antrim, North Ireland. cgu01@qub.ac.uk; echchang@ntu.edu.sg; liuweiqiang@nuaa.edu.cn; syu08@qub.ac.uk; m.oneill@ecit.qub.ac.uk Singapore MOE Tier 2 [MOE2015-T2-2-013]; Institute for Information & communications Technology Promotion(IITP) - Korean government(MSIT) [2016-0-00399]; Engineering and Physical Sciences Research Council (EPSRC) [EP/N508664/-CSIT2]; National Natural Science Foundation China [61771239]; Nature Science Foundation of Jiangsu Province [BK20151477] Singapore MOE Tier 2(Ministry of Education, Singapore); Institute for Information & communications Technology Promotion(IITP) - Korean government(MSIT)(Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of KoreaMinistry of Science & ICT (MSIT), Republic of Korea); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation China(National Natural Science Foundation of China (NSFC)); Nature Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) This work was partly supported by the Singapore MOE Tier 2 Grant No. MOE2015-T2-2-013, by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korean government(MSIT) (No. 2016-0-00399, Study on secure key hiding technology for loT devices [KeyHAS Project]), by the Engineering and Physical Sciences Research Council (EPSRC) (EP/N508664/-CSIT2), by National Natural Science Foundation China (61771239) and by Nature Science Foundation of Jiangsu Province (BK20151477). 22 13 13 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-3544-1 2019.0 6 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BU6LE 2023-03-23 WOS:000926002700019 0
J Liu, KX; Nie, T; Liu, W; Liu, YQ; Lai, DY Liu, Kuixing; Nie, Ting; Liu, Wei; Liu, Yiqing; Lai, Dayi A machine learning approach to predict outdoor thermal comfort using local skin temperatures SUSTAINABLE CITIES AND SOCIETY English Article Outdoor thermal comfort; Urban; Environment; Machine learning; Support vector machine (SVM); Skin temperature; Physiological parameters URBAN PARK; SENSATION; MICROCLIMATE; PERFORMANCE; PATTERNS; SPACES; MODEL; INDEX Under the warming climate, providing thermal comfort to large urban population in city open spaces has become an important research topic. However, because of its dynamic and complex nature, the outdoor thermal comfort is difficult to predict. Skin temperature of human body may contain useful information of outdoor thermal comfort. In this paper, a Support Vector Machine (SVM) model was developed to predict the cool discomfort, comfort, and warm discomfort in outdoor environments using local skin temperatures and thermal load as inputs. In this study, the performances of models using different inputs were compared with each other. The results revealed that when using single local skin temperature as input, the skin temperature of exposed body parts exhibited the highest prediction accuracy (66 %-70 %), while that of abdomen or thorax was the lowest (42 %-58 %). The prediction accuracy increased by 1 %-5 % when the thermal load was added as an extra input feature, while that could be improved by 4%-7% when using skin temperature of two body parts as inputs. This study demonstrated that human outdoor thermal state can be captured with reasonable accuracy by monitoring skin temperatures from two local body parts. [Liu, Kuixing] Tianjin Univ, Sch Architecture, Tianjin 300072, Peoples R China; [Nie, Ting] Tianjin Univ, Tianjin Int Engn Inst, Tianjin 300072, Peoples R China; [Liu, Wei] KTH Royal Inst Technol, Div Sustainable Bldg, Dept Civil & Architectural Engn, Brinellvagen 23, S-10044 Stockholm, Sweden; [Liu, Yiqing] Univ Reading, Sch Built Environm, Reading, Berks, England; [Lai, Dayi] Shanghai Jiao Tong Univ, Dept Architecture, Sch Design, Shanghai 200240, Peoples R China Tianjin University; Tianjin University; Royal Institute of Technology; University of Reading; Shanghai Jiao Tong University Lai, DY (corresponding author), Shanghai Jiao Tong Univ, Dept Architecture, Sch Design, Shanghai 200240, Peoples R China. dayi_lai@sjtu.edu.cn Liu, Wei/M-4376-2019 Liu, Wei/0000-0003-1285-2334 Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality [19YF1424000]; National Natural Science Foundation of China [51708400, 51708343] Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported by Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality through Grant No. 19YF1424000. This research also receives support from the National Natural Science Foundation of China (No. 51708400 and No. 51708343). 53 21 21 15 58 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. AUG 2020.0 59 102216 10.1016/j.scs.2020.102216 0.0 13 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels LU0OC 2023-03-23 WOS:000537462200006 0
J Lei, XM; Xia, Y; Komarizadehasl, S; Sun, LM Lei, Xiaoming; Xia, Ye; Komarizadehasl, Seyedmilad; Sun, Limin Condition level deteriorations modeling of RC beam bridges with U-Net convolutional neural networks STRUCTURES English Article Condition assessment; Regional bridges; Inspection reports; Deterioration modeling; Deep learning; Nondestructive evaluation STRUCTURAL ASSESSMENT; SUSPENSION BRIDGES; PERFORMANCE Reinforced concrete (RC) beam bridges have suffered structural deterioration due to loads, environmental conditions, etc. Regular visual inspections of bridges effectively monitor the structural condition level and provide a vast amount of condition-related data for years. This study proposes a deep learning-based condition level deterioration modeling method with a U-Net model to improve the prediction accuracy of future structural conditions. The proposed method is supported by the data gathered from the years of regional bridge inspection reports. Before training the model, the regional condition-related features regarding the influence of bridge ages and the superstructure types are investigated, and the correlations between selected features and structural conditions are also revealed. The acquired inspection database validated the high prediction accuracy and classification performance of each bridge's main part and system with the proposed deterioration modeling method. Its robustness is tested under a variety of data missing rate scenarios. The optimum model architecture and its effectiveness are also validated through comparative studies. This study provides a novel method to predict the future structural condition with inspection data and could serve as a reference for more reasonable utilization of the bridge condition deterioration model in structural condition assessment and management. [Lei, Xiaoming; Xia, Ye; Sun, Limin] Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China; [Xia, Ye; Sun, Limin] Shanghai Qizhi Inst, Shanghai, Peoples R China; [Komarizadehasl, Seyedmilad] Univ Politecn Cataluna, Dept Civil & Environm Engn, BarcelonaTech, Barcelona, Spain; [Sun, Limin] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai, Peoples R China Tongji University; Universitat Politecnica de Catalunya; Tongji University Xia, Y (corresponding author), Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China. yxia@tongji.edu.cn; seyedmilad.komarizadehasl@upc.edu; lmsun@tongji.edu.cn Komarizadehasl, Seyedmilad/ABC-1816-2021 Komarizadehasl, Seyedmilad/0000-0002-9010-2611 National Natural Science Foundation of China [51978508]; Technology Cooperation Project of Shanghai Qizhi Institute [SYXF0120020109]; Transportation Science and Technology Program of Shandong Province [2021B51] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Technology Cooperation Project of Shanghai Qizhi Institute; Transportation Science and Technology Program of Shandong Province This paper is supported by the National Natural Science Foundation of China (51978508) , Technology Cooperation Project of Shanghai Qizhi Institute (SYXF0120020109) , and Transportation Science and Technology Program of Shandong Province (2021B51) . 49 4 4 13 18 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 2352-0124 STRUCTURES Structures AUG 2022.0 42 333 342 10.1016/j.istruc.2022.06.013 0.0 JUN 2022 10 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering 2H6CD 2023-03-23 WOS:000814379300002 0
J Cao, JD; Stamov, G; Stamova, I; Simeonov, S Cao, Jinde; Stamov, Gani; Stamova, Ivanka; Simeonov, Stanislav Almost Periodicity in Impulsive Fractional-Order Reaction-Diffusion Neural Networks With Time-Varying Delays IEEE TRANSACTIONS ON CYBERNETICS English Article Delays; Neural networks; Mathematical model; Perturbation methods; Stability criteria; Almost periodic processes; fractional derivatives; impulsive control; neural networks; reaction– diffusion terms; stability; time-varying delays; uncertain terms GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; SYNCHRONIZATION; EXISTENCE; SYSTEMS; EQUATIONS; LYAPUNOV A neural-network model of fractional order with impulsive perturbations, time-varying delays, and reaction-diffusion terms is investigated in this article. The focus is on investigating qualitative properties of the states and developing new almost periodicity and stability criteria. The uncertain case is also considered. Examples are established and the effectiveness of the obtained criteria is demonstrated. [Cao, Jinde] Southeast Univ, Sch Math, Jiangsu Prov Key Lab Networked Collect Intelligen, Nanjing 211189, Peoples R China; [Stamov, Gani] Tech Univ Sofia, Dept Math, Sliven 8800, Bulgaria; [Stamova, Ivanka] Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA; [Simeonov, Stanislav] Burgas Prof Dr Assen Zlatarov Univ, Dept Comp Syst & Technol, Burgas 8010, Bulgaria Southeast University - China; Technical University Sofia; University of Texas System; University of Texas at San Antonio (UTSA) Stamova, I (corresponding author), Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA. jdcao@seu.edu.cn; gstamov@abv.bg; ivanka.stamova@utsa.edu; stsim@yahoo.com Cao, Jinde/D-1482-2012; Simeonov, Stanislav/M-1073-2016 Cao, Jinde/0000-0003-3133-7119; Simeonov, Stanislav/0000-0002-8145-405X; Stamova, Ivanka/0000-0001-6723-2699 National Natural Science Foundation of China [61573096, 61833005]; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [BM2017002]; European Regional Development Fund through the Operational Programme Science and Education for Smart Growth [UNITe BG05M2OP001-1.001-0004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Provincial Key Laboratory of Networked Collective Intelligence; European Regional Development Fund through the Operational Programme Science and Education for Smart Growth This work was supported in part by the National Natural Science Foundation of China under Grant 61573096 and Grant 61833005, in part by the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant BM2017002, and in part by the European Regional Development Fund through the Operational Programme Science and Education for Smart Growth under Contract UNITe BG05M2OP001-1.001-0004 (2018-2023). This article was recommended by Associate Editor Y. Pan. 61 54 54 4 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JAN 2021.0 51 1 151 161 10.1109/TCYB.2020.2967625 0.0 11 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science PK8TB 32071019.0 2023-03-23 WOS:000602709000013 0
J Hu, XB; Song, J; Liao, ZH; Liu, YH; Gao, J; Menze, B; Liu, WQ Hu, Xiaobin; Song, Jian; Liao, Zhenhua; Liu, Yuhong; Gao, Jian; Menze, Bjoern; Liu, Weiqiang Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints FRICTION English Article wear particles classifier; morphological priors; data augmentation; deep residual network IN-VIVO; HIP; CLASSIFICATION; OSTEOLYSIS; DISEASE Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints. [Hu, Xiaobin; Menze, Bjoern] Tech Univ Munich, Dept Comp Sci, D-85748 Garching, Germany; [Song, Jian] Sun Yat Sen Univ, Sch Biomed Engn, Guangzhou 510006, Guangdong, Peoples R China; [Liao, Zhenhua; Liu, Weiqiang] Tsinghua Univ Shenzhen, Res Inst, Key Lab Biomed Mat & Implant Devices, Shenzhen 518057, Peoples R China; [Liu, Yuhong; Liu, Weiqiang] Tsinghua Univ, State Key Lab Tribol, Beijing 100084, Peoples R China; [Gao, Jian] Washington Univ, Mckelvey Sch Engn, St Louis, MO 63130 USA Technical University of Munich; Sun Yat Sen University; Tsinghua University; Tsinghua University; Washington University (WUSTL) Song, J (corresponding author), Sun Yat Sen Univ, Sch Biomed Engn, Guangzhou 510006, Guangdong, Peoples R China.;Liu, YH (corresponding author), Tsinghua Univ, State Key Lab Tribol, Beijing 100084, Peoples R China. songj67@mail.sysu.edu.cn; liuyuhong@tsinghua.edu.cn Menze, Bjoern/0000-0003-4136-5690 National Natural Science Foundation of China [51875303]; Sun Yat-sen University; China Scholarship Council (CSC) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sun Yat-sen University; China Scholarship Council (CSC)(China Scholarship Council) This work is financially supported by the National Natural Science Foundation of China (No. 51875303). Support through the start-up foundation from Sun Yat-sen University is also gratefully acknowledged. Xiaobin Hu acknowledges the funding from the China Scholarship Council (CSC). 34 1 1 14 37 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2223-7690 2223-7704 FRICTION Friction APR 2022.0 10 4 560 572 10.1007/s40544-021-0516-2 0.0 AUG 2021 13 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering ZU6BI gold 2023-03-23 WOS:000686029500001 0
J Wang, YH; Luan, SH; Gigerenzer, G Wang, Yuhui; Luan, Shenghua; Gigerenzer, Gerd Modeling fast-and-frugal heuristics PSYCH JOURNAL English Review adaptive toolbox; artificial intelligence; computer simulation; ecological rationality; inference TAKE-THE-BEST; DECISION STRATEGIES; PERFORMANCE; JUDGMENT; BIAS Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics. [Wang, Yuhui; Luan, Shenghua] Chinese Acad Sci, Inst Psychol, 16 Lincui Rd, Beijing 100101, Peoples R China; [Wang, Yuhui; Luan, Shenghua] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China; [Gigerenzer, Gerd] Max Planck Inst Human Dev, Berlin, Germany Chinese Academy of Sciences; Institute of Psychology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Max Planck Society Luan, SH (corresponding author), Chinese Acad Sci, Inst Psychol, 16 Lincui Rd, Beijing 100101, Peoples R China. luansh@psych.ac.cn 王, 宇慧/GXZ-7548-2022 National Natural Science Foundation of China [32171074] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China, Grant/Award Number: 32171074 71 0 0 3 7 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2046-0252 2046-0260 PSYCH J PsyCh J. AUG 2022.0 11 4 600 611 10.1002/pchj.576 0.0 JUL 2022 12 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology 3L0WC 35778774.0 2023-03-23 WOS:000819661900001 0
J Xiong, J; Zuo, XT; Zhang, S; Liao, W; Chen, ZB Xiong, Juan; Zuo, Xingtao; Zhang, Shi; Liao, Wei; Chen, Zhongbing Model-based evaluation of fouling mechanisms in powdered activated carbon/membrane bioreactor system WATER SCIENCE AND TECHNOLOGY English Article fouling mechanism; MBR; model; transmembrane pressure ARTIFICIAL NEURAL-NETWORK; MEMBRANE; MICROFILTRATION; IDENTIFICATION; ULTRAFILTRATION; LAWS; MBRS Identifying the fouling degree of a membrane bioreactor (MBR) provides guidance on the determination of suitable membrane cleaning methods. There is still a lack of knowledge on the effects of powdered activated carbon (PAC) refresh ratio on the MBR fouling mechanism. Major fouling mechanisms of an MBR with constant flow rate at different PAC replenishment ratios were investigated by individual and combined mechanistic fouling models. The root mean square errors were employed to assess the prediction accuracy of the used fouling models. The combined models showed better prediction. The cake-complete model provided far better fits of the transmembrane pressure data, and provided good fits of other individual model predictions regardless of the PAC refreshment ratio. Fourier transform infrared spectroscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy confirmed that the cake layer clogging was the main fouling mechanism followed by complete blockage and standard plugging. The cake-complete model may be used to predict the fouling mechanisms in PAC/MBR systems. [Xiong, Juan] Huazhong Agr Univ, Coll Sci, Wuhan 430070, Hubei, Peoples R China; [Zuo, Xingtao; Zhang, Shi; Liao, Wei] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Hubei, Peoples R China; [Chen, Zhongbing] Czech Univ Life Sci Prague, Fac Environm Sci, Prague 16500, Czech Republic Huazhong Agricultural University; Huazhong Agricultural University; Czech University of Life Sciences Prague Zuo, XT (corresponding author), Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Hubei, Peoples R China. xingtaozuo@aliyun.com Chen, Zhongbing/H-5001-2018 Chen, Zhongbing/0000-0001-8801-4842 Fundamental Research Funds for the Central Universities of China [2662018JC013, 2662017JC019] Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) This work was supported by the Fundamental Research Funds for the Central Universities of China (No. 2662018JC013, 2662017JC019). 19 6 6 2 26 IWA PUBLISHING LONDON ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND 0273-1223 1996-9732 WATER SCI TECHNOL Water Sci. Technol. MAY 15 2019.0 79 10 1844 1852 10.2166/wst.2019.167 0.0 9 Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Water Resources IK8JJ 31294700.0 Bronze 2023-03-23 WOS:000476841100002 0
J Wang, YY; Ramachandra, N; Salazar-Canizales, EM; Feldman, HA; Watkins, R; Dolag, K Wang, Yuyu; Ramachandra, Nesar; Salazar-Canizales, Edgar M.; Feldman, Hume A.; Watkins, Richard; Dolag, Klaus Peculiar velocity estimation from kinetic SZ effect using deep neural networks MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article methods: statistical; techniques: radial velocities; cosmic background radiation STRONG GRAVITATIONAL LENSES; CLUSTER VELOCITIES; GALAXY CLUSTERS; CONSTRAINTS; SIGNALS; CODE; MASS The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the independent estimation of the optical depth. Images of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to determine its ability of estimating peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17 per cent compared to the analytical approach. [Wang, Yuyu] Shanghai Jiao Tong Univ, Sch Phys & Astron, Dept Astron, Shanghai 200240, Peoples R China; [Wang, Yuyu; Feldman, Hume A.] Univ Kansas, Dept Phys & Astron, Lawrence, KS 66045 USA; [Ramachandra, Nesar] Argonne Natl Lab, Div High Energy Phys, Lemont, IL 60439 USA; [Ramachandra, Nesar] Univ Chicago, Kavli Inst Cosmol Phys, 5640 South Ellis Ave, Chicago, IL 60637 USA; [Salazar-Canizales, Edgar M.] Univ Sonora, Dept Fis, Hermosillo 83000, Sonora, Mexico; [Salazar-Canizales, Edgar M.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Watkins, Richard] Willamette Univ, Dept Phys, Salem, OR 97301 USA; [Dolag, Klaus] Univ Observ Munich, Scheinerstr 1, D-81679 Munich, Germany; [Dolag, Klaus] Max Planck Inst Astrophys MPA, Karl Schwarzschild Str 1, D-85748 Garching, Germany Shanghai Jiao Tong University; University of Kansas; United States Department of Energy (DOE); Argonne National Laboratory; University of Chicago; Universidad de Sonora; University of Arizona; Willamette University; University of Munich; Max Planck Society Wang, YY (corresponding author), Shanghai Jiao Tong Univ, Sch Phys & Astron, Dept Astron, Shanghai 200240, Peoples R China.;Wang, YY (corresponding author), Univ Kansas, Dept Phys & Astron, Lawrence, KS 66045 USA.;Ramachandra, N (corresponding author), Argonne Natl Lab, Div High Energy Phys, Lemont, IL 60439 USA.;Ramachandra, N (corresponding author), Univ Chicago, Kavli Inst Cosmol Phys, 5640 South Ellis Ave, Chicago, IL 60637 USA. yuyuwang@sjtu.edu.cn; nramachandra@anl.gov Wang, Yuyu/0000-0002-0245-8547; Ramachandra, Nesar/0000-0001-7772-0346 National Science Foundation [ACI-1548562]; U.S. Department of Energy [DE-AC02-06CH11357]; NSF [AST-1907404]; INCITE programme [DE-AC02-06CH11357]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [390783311] National Science Foundation(National Science Foundation (NSF)); U.S. Department of Energy(United States Department of Energy (DOE)); NSF(National Science Foundation (NSF)); INCITE programme; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.; NR's work at Argonne National Laboratory was supported under the U.S. Department of Energy contract DE-AC02-06CH11357.; HAF and RW were partially supported by NSF grant AST-1907404. An award of computer time was provided by the INCITE programme. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.; KD acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2094 - 390783311.; The calculations were carried out at the Leibniz Supercomputer Center (LRZ) under the project `pr86re'. We are especially grateful for the support by M. Petkova through the Computational Center for Particle and Astrophysics (C2PAP) and the support by N. Hammer at LRZ when carrying out the Box0 simulation within the Extreme Scale-Out Phase on the new SuperMUC Haswell extension system. NR thanks Jon ' as Chaves-Montero for discussions and help with the manuscript. 66 1 1 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. SEP 2021.0 506 1 1427 1437 10.1093/mnras/stab1715 0.0 JUL 2021 11 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics WB9XQ Green Submitted 2023-03-23 WOS:000703918800094 0
J Chen, G; Cao, H; Ye, CB; Zhang, ZY; Liu, XB; Mo, XH; Qu, ZN; Conradt, J; Rohrbein, F; Knoll, A Chen, Guang; Cao, Hu; Ye, Canbo; Zhang, Zhenyan; Liu, Xingbo; Mo, Xuhui; Qu, Zhongnan; Conradt, Jorg; Roehrbein, Florian; Knoll, Alois Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors FRONTIERS IN NEUROROBOTICS English Article neuromorphic vision sensor; event-stream encoding; object detection; convolutional neural network; multi-Cue event information fusion Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors. [Chen, Guang; Ye, Canbo; Zhang, Zhenyan; Liu, Xingbo] Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China; [Chen, Guang; Roehrbein, Florian; Knoll, Alois] Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany; [Cao, Hu; Mo, Xuhui] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China; [Qu, Zhongnan] Swiss Fed Inst Technol, Comp Engn & Networks Lab, Zurich, Switzerland; [Conradt, Jorg] KTH Royal Inst Technol, Dept Computat Sci & Technol, Stockholm, Sweden Tongji University; Technical University of Munich; Hunan University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Royal Institute of Technology Chen, G (corresponding author), Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Chen, G (corresponding author), Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany.;Qu, ZN (corresponding author), Swiss Fed Inst Technol, Comp Engn & Networks Lab, Zurich, Switzerland. guang@in.tum.de; zhongnanqu@ethz.ch Knoll, Alois/AAN-8417-2021; Cao, Hu/HJP-8442-2023 Knoll, Alois/0000-0003-4840-076X; Cao, Hu/0000-0001-8225-858X; Conradt, Jorg/0000-0001-5998-9640 European Union's Horizon 2020 Research and Innovation Program [785907]; Shanghai Automotive Industry SciTech Development Program [1838]; Fundamental Research Funds for the Central Universities of Tongji University European Union's Horizon 2020 Research and Innovation Program; Shanghai Automotive Industry SciTech Development Program; Fundamental Research Funds for the Central Universities of Tongji University The research leading to these results has partially received funding from the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No. 785907 (HBP SGA2), from the Shanghai Automotive Industry SciTech Development Program under Grant Agreement No. 1838, and from the Fundamental Research Funds for the Central Universities of Tongji University. 25 19 19 0 10 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics APR 2 2019.0 13 10 10.3389/fnbot.2019.00010 0.0 11 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology HT4IW 31001104.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000464528200001 0
J Ferrari, L; Dell'Acqua, F; Zhang, P; Du, PJ Ferrari, Luca; Dell'Acqua, Fabio; Zhang, Peng; Du, Peijun Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data REMOTE SENSING English Article attention mechanism; building mapping; data fusion; EfficientNet; HAFNet; high-resolution imagery (HRI); light detection and ranging (LiDAR); mapping; urban areas CONVOLUTIONAL NEURAL-NETWORK; REMOTE-SENSING IMAGES; SEMANTIC SEGMENTATION; CLASSIFICATION; FUSION; MULTISCALE; MODEL; EDGE Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of joint features from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future urban mapping satellite constellation. [Ferrari, Luca; Dell'Acqua, Fabio] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia Unit, CNIT, I-27100 Pavia, Italy; [Zhang, Peng; Du, Peijun] Univ Nanjing, Dept Geog Informat Sci, Nanjing 210093, Peoples R China University of Pavia; Nanjing University Dell'Acqua, F (corresponding author), Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia Unit, CNIT, I-27100 Pavia, Italy. luca.ferrari11@universitadipavia.it; fabio.dellacqua@unipv.it; pzhangrs@smail.nju.edu.cn; peijun@nju.edu.cn Dell'Acqua, Fabio/L-5498-2013 Dell'Acqua, Fabio/0000-0002-0044-2998; Du, Peijun/0000-0002-2488-2656 39 4 4 4 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2021.0 13 21 4361 10.3390/rs13214361 0.0 15 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology WX8TQ gold 2023-03-23 WOS:000718862400001 0
J Xiao, B; Zhang, YX; Chen, Y; Yin, XF Xiao, Bo; Zhang, Yuxuan; Chen, Yuan; Yin, Xianfei A semi-supervised learning detection method for vision-based monitoring of construction sites by integrating teacher-student networks and data augmentation ADVANCED ENGINEERING INFORMATICS English Article Deep learning; Object detection; Teacher-student networks; Data augmentation; Vision-based monitoring; Construction sites ACTION RECOGNITION; TRACKING; WORKERS Recently, deep-learning detection methods have achieved huge success in the vision-based monitoring of construction sites in terms of safety control and productivity analysis. However, deep-learning detection methods require large-scale datasets for training purposes, and such datasets are difficult to develop due to the limited accessibility of construction images and the need for labor-intensive annotations. To address this problem, this research proposes a semi-supervised learning detection method for construction site monitoring based on teacher-student networks and data augmentation. The proposed method requires a limited number of labeled data to achieve high detection performance in construction scenarios. Initially, the proposed method trains the teacher object detector with labeled data following weak data augmentation. Next, the trained teacher object detector generates pseudo-detection results from unlabeled images that have been weakly augmented. Finally, the student object detector is trained with the pseudo-detection results and unlabeled images that have been both weakly and strongly augmented. In our experiments, 10,000 annotated construction images from the Alberta Construction Image Dataset (ACID) have been divided into a training set (70%) and a validation set (30%). The proposed method achieved a 91% mean average precision (mAP) on the validation set while only requiring 30% of the training set. In comparison, the existing supervised learning method ResNet50 Faster R-CNN achieved a mAP of 90.8% when training on the full training set. These experimental results show the potential of the proposed method in terms of reducing the time, effort, and costs spent on developing construction datasets. As such, this research has explored the potential of semi-supervised learning methods and increased the practicality of vision-based monitoring systems in the construction industry. [Xiao, Bo; Zhang, Yuxuan] Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB T6G 2R3, Canada; [Chen, Yuan] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China; [Yin, Xianfei] Univ Twente, Dept Construct Management & Engn, Enschede, Netherlands University of Alberta; Tianjin University; University of Twente Yin, XF (corresponding author), Univ Twente, Dept Construct Management & Engn, Enschede, Netherlands. bxiao2@ualberta.ca; yuxuan.zhang@ualberta.ca; yuanchen_2020@tju.edu.cn; x.yin@utwente.nl Zhang, Yuxuan/HOF-1490-2023; Yin, Xianfei/AAK-4041-2020 Zhang, Yuxuan/0000-0002-3181-239X; Yin, Xianfei/0000-0001-8270-8632; Chen, Yuan/0000-0002-1945-2512; Xiao, Bo/0000-0003-0798-8018 National Natural Science Foundation of China [72002152] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank Professor Shih-Chung Kang for publishing the ACID dataset to the community. More information about the ACID dataset can be found on the link: www.acidb.ca.We gratefully acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 72002152). 52 3 3 8 25 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1474-0346 1873-5320 ADV ENG INFORM Adv. Eng. Inform. OCT 2021.0 50 101372 10.1016/j.aei.2021.101372 0.0 AUG 2021 14 Computer Science, Artificial Intelligence; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering UX1AW Green Published, hybrid 2023-03-23 WOS:000700580900004 0
C Toro, FG; Fuentes, DED; Becker, U; Manz, H; Lu, DB; Cai, BG IEEE Toro, Federico Grasso; Fuentes, Damian Eduardo Diaz; Becker, Uwe; Manz, Hansjoerg; Lu, Debiao; Cai, Baigen Particle Filter technique for position estimation in GNSS-based localisation systems 2015 INTERNATIONAL ASSOCIATION OF INSTITUTES OF NAVIGATION WORLD CONGRESS (IAIN) English Proceedings Paper 15th World Congress of the International-Association-of-Institutes-of-Navigation (lAIN) OCT 20-23, 2015 Prague, CZECH REPUBLIC Int Assoc Inst Navigat,IEEE,Cesky Inst Navigace GNSS; Particle Filter; Map Matching; Artificial Intelligence Ethics The usage of filter techniques for position estimation for safety-relevant purposes is an extensive field of research. Depending on the needed application for the Global Navigation Satellite System (GNSS) the state estimation can be achieved by several techniques. State estimation by means of Kalman Filter (KF), as well as Extended Kalman Filter (EKF) and Particle Filter (PF) have been developed and tested. Map-matching algorithms integrate the localisation data provided by GNSS with spatial road network data (also called digital map) to identify the correct line (or track) on which a vehicle is traveling and to determine the location of a vehicle within the line (or track). The goal of map-matching techniques is to exploit prior information contained in road or railway networks. However, incorporating digital map information within the conventional KF framework is not easy, because this constraint leads to highly non-Gaussian posterior densities that are difficult to represent accurately using conventional techniques. Since the PF approach presents no restrictions regarding non-linearity of models and noise distribution the velocity and heading measurement errors can be accurately modelled. The most significant advantages of the PF approach for map-matching application are: 1) PF approach provides a natural way for road map information to be incorporated into vehicle position estimation. 2) PF approach is capable of capturing multi-modal distributions. The selected PF-based location estimators presented here is oriented to work within an intelligent GNSS-based localisation system. The PF-based map matching techniques are presented in a mathematical ground and tests are performed, as part of a developed satellite localisation system based on artificial intelligence (AI) tools. In the railway domain the same integration can be performed by means of a digital trap map to identify the location within the tracks. A map-matching algorithm can be the key component of the data fusion to improve the performance of localisation systems that support the navigation function of intelligent transport systems (ITS). [Toro, Federico Grasso; Fuentes, Damian Eduardo Diaz] Inst Qual Safety & Transportat, Braunschweig, Germany; [Becker, Uwe; Manz, Hansjoerg] Tech Univ Carolo Wilhelmina Braunschweig, Inst Traff Safety & Automat Engn, Braunschweig, Germany; [Lu, Debiao] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China; [Cai, Baigen] Beijing Jiaotong Univ, Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China Braunschweig University of Technology; Beijing Jiaotong University; Beijing Jiaotong University Toro, FG (corresponding author), Inst Qual Safety & Transportat, Braunschweig, Germany. federico.grassotoro@iqst.de; u.becker@tu-bs.de 17 0 0 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-7634-1 2015.0 8 Engineering, Electrical & Electronic; Remote Sensing; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Remote Sensing; Telecommunications BG0DJ 2023-03-23 WOS:000386184800017 0
J Zhan, H; Huang, DY; Yang, CG Zhan, Hong; Huang, Dianye; Yang, Chenguang Adaptive dynamic programming enhanced admittance control for robots with environment interaction and actuator saturation INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS English Article Adaptive dynamic programming; Admittance control; Robot-environment interaction; Actuator saturation; Optimal control; Neural network NONLINEAR-SYSTEMS; TRACKING CONTROL; IMPEDANCE; STABILIZATION; MANIPULATORS; SENSORLESS; DESIGN This paper focuses on the optimal tracking control problem for robot systems with environment interaction and actuator saturation. A control scheme combined with admittance adaptation and adaptive dynamic programming (ADP) is developed. The unknown environment is modelled as a linear system and admittance controller is derived to achieve compliant behaviour of the robot. In the ADP framework, the cost function is defined with non-quadratic form and the critic network is designed with radial basis function neural network which introduces to obtain an approximate optimal control of the Hamilton-Jacobi-Bellman equation, which guarantees the optimal trajectory tracking. The system stability is analysed by Lyapunov theorem and simulations demonstrate the effectiveness of the proposed strategy. [Zhan, Hong] South China Univ Technol, Key Lab Autonomous Syst & Networked Control, Minist Educ, Guangzhou 510640, Peoples R China; [Huang, Dianye] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Yang, Chenguang] Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England South China University of Technology; Technical University of Munich; University of Bristol; University of West England Yang, CG (corresponding author), Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England. cyang@ieee.org 34 3 3 4 16 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2366-5971 2366-598X INT J INTELL ROBOT Int. J. Intell. Robot. MAR 2021.0 5 1 89 100 10.1007/s41315-020-00159-8 0.0 FEB 2021 12 Robotics Emerging Sources Citation Index (ESCI) Robotics QS5XX hybrid 2023-03-23 WOS:000614022800001 0
J Zhang, ZJ; Zheng, LN; Chen, ZM; Kong, LD; Karimi, HR Zhang, Zhijun; Zheng, Lunan; Chen, Zhuoming; Kong, Lingdong; Karimi, Hamid Reza Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Manipulators; Collision avoidance; Task analysis; Recurrent neural networks; Kinematics; Collision avoidance; cooperative task; dual robot manipulators; motion planning; quadratic programming (QP); recurrent neural network (RNN) Collision between dual robot manipulators during working process will lead to task failure and even robot damage. To avoid mutual collision of dual robot manipulators while doing collaboration tasks, a novel recurrent neural network (RNN)-based mutual-collision-avoidance (MCA) scheme for solving the motion planning problem of dual manipulators is proposed and exploited. Because of the high accuracy and low computation complexity, the linear variational inequality-based primal-dual neural network is used to solve the proposed scheme. The proposed scheme is applied to the collaboration trajectory tracking and cup-stacking tasks, and shows its effectiveness for avoiding collision between the dual robot manipulators. Through network iteration and online learning, the dual robot manipulators will learn the ability of MCA. Moreover, a line-segment-based distance measure algorithm is proposed to calculate the minimum distance between the dual manipulators. If the computed minimum distance is less than the first safe-related distance threshold, a speed brake operation is executed and guarantees that the robot cannot exceed the second safe-related distance threshold. Furthermore, the proposed MCA strategy is formulated as a standard quadratic programming problem, which is further solved by an RNN. Computer simulations and a real dual robot experiment further verify the effectiveness, accuracy, and physical realizability of the RNN-based MCA scheme when manipulators cooperatively execute the end-effector tasks. [Zhang, Zhijun; Zheng, Lunan; Chen, Zhuoming] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China; [Kong, Lingdong] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy South China University of Technology; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Polytechnic University of Milan Zhang, ZJ (corresponding author), South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China. drzhangzhijun@gmail.com; ldkong@ieee.org; hamidreza.karimi@polimi.it Karimi, Hamid Reza/Y-2403-2019; Zheng, Lunan/AAF-2055-2019 Zheng, Lunan/0000-0002-7671-6051 National Key Research and Development Program of China [2017YFB1002505]; National Natural Science Foundation of China [61976096, 61603142, 61633010]; Guangdong Foundation for Distinguished Young Scholars [2017A030306009]; Guangdong Special Support Program [2017TQ04X475]; Science and Technology Program of Guangzhou [201707010225]; Fundamental Research Funds for Central Universities [2017MS049]; Scientific Research Starting Foundation of South China University of Technology; National Key Basic Research Program of China (973 Program) [2015CB351703]; Italian Ministry of Education, University and Research under the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0; Guangdong Key Research and Development Program [2018B030339001]; Guangdong Natural Science Foundation Research Team Program [1414060000024] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Foundation for Distinguished Young Scholars; Guangdong Special Support Program; Science and Technology Program of Guangzhou; Fundamental Research Funds for Central Universities(Fundamental Research Funds for the Central Universities); Scientific Research Starting Foundation of South China University of Technology; National Key Basic Research Program of China (973 Program)(National Basic Research Program of China); Italian Ministry of Education, University and Research under the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0; Guangdong Key Research and Development Program; Guangdong Natural Science Foundation Research Team Program This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1002505, in part by the National Natural Science Foundation of China under Grant 61976096, Grant 61603142, and Grant 61633010, in part by the Guangdong Foundation for Distinguished Young Scholars under Grant 2017A030306009, in part by the Guangdong Special Support Program under Grant 2017TQ04X475, in part by the Science and Technology Program of Guangzhou under Grant 201707010225, in part by the Fundamental Research Funds for Central Universities under Grant 2017MS049, in part by the Scientific Research Starting Foundation of South China University of Technology, in part by the National Key Basic Research Program of China (973 Program) under Grant 2015CB351703, in part by the Italian Ministry of Education, University and Research under the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0, in part by the Guangdong Key Research and Development Program under Grant 2018B030339001, and in part by Guangdong Natural Science Foundation Research Team Program 1414060000024. 38 25 25 15 50 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. MAR 2021.0 32 3 1052 1066 10.1109/TNNLS.2020.2980038 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering QT1DL 32310785.0 2023-03-23 WOS:000626332700010 0
C Le Kernec, J; Fioranelli, F; Ding, C; Zhao, H; Sun, L; Hong, H; Romain, O; Lorandel, J IEEE Le Kernec, J.; Fioranelli, F.; Ding, C.; Zhao, H.; Sun, L.; Hong, H.; Romain, O.; Lorandel, J. Radar Sensing in Assisted Living: an Overview 2019 IEEE MTT-S INTERNATIONAL MICROWAVE BIOMEDICAL CONFERENCE (IMBIOC 2019) English Proceedings Paper IEEE MTT-S International Microwave Biomedical Conference (IMBioC) MAY 06-08, 2019 Nanjing, PEOPLES R CHINA IEEE,MTT S,Nanjing Univ Sci & Technol,APS,Tiebeihongshan Newtown Radar; signal processing; human activity recognition; vital signs FALL DETECTION This paper gives an overview of trends in radar sensing for assisted living. It focuses on signal processing and classification, looking at conventional approaches, deep learning and fusion techniques. The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition. [Le Kernec, J.; Fioranelli, F.] Univ Glasgow, Univ Ave, Glasgow G12 8QQ, Lanark, Scotland; [Ding, C.; Zhao, H.; Sun, L.; Hong, H.] Nanjing Univ Sci & Technol, 200 Xiaolingwei St, Nanjing 210094, Jiangsu, Peoples R China; [Romain, O.; Lorandel, J.] Univ Cergy Pontoise, ETIS, 6 Av Ponceau, F-95000 Cergy Pontoise, France University of Glasgow; Nanjing University of Science & Technology; CY Cergy Paris Universite Le Kernec, J (corresponding author), Univ Glasgow, Univ Ave, Glasgow G12 8QQ, Lanark, Scotland. Fioranelli, Francesco/Y-8612-2019 le kernec, julien/0000-0003-2124-6803 UK EPSRC grant [EP/R041679/1 INSHEP]; Campus France PHC Cai Yuanpei [41457UK]; Horse Betting Levy Board [SP006]; National Science Foundation China [61871224] UK EPSRC grant(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Campus France PHC Cai Yuanpei; Horse Betting Levy Board; National Science Foundation China(National Natural Science Foundation of China (NSFC)) This work was partly supported support by UK EPSRC grant EP/R041679/1 INSHEP, Campus France PHC Cai Yuanpei 41457UK, Horse Betting Levy Board SP006, and the National Science Foundation China under grant 61871224. 20 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-7395-9 2019.0 4 Engineering, Biomedical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BR9JB 2023-03-23 WOS:000676326800009 0
J Hu, L; Fu, CX; Song, XY; Grimm, R; Von Busch, H; Benkert, T; Kamen, A; Lou, B; Huisman, H; Tong, AEL; Penzkofer, T; Choi, MH; Shabunin, I; Winkel, D; Xing, PY; Szolar, D; Coakley, F; Shea, S; Szurowska, E; Guo, JY; Li, L; Li, YH; Zhao, JG Hu, Lei; Fu, Caixia; Song, Xinyang; Grimm, Robert; Von Busch, Heinrich; Benkert, Thomas; Kamen, Ali; Lou, Bin; Huisman, Henkjan; Tong, Angela; Penzkofer, Tobias; Choi, Moon Hyung; Shabunin, Ivan; Winkel, David; Xing, Pengyi; Szolar, Dieter; Coakley, Fergus; Shea, Steven; Szurowska, Edyta; Guo, Jing-yi; Li, Liang; Li, Yue-hua; Zhao, Jun-gong Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique CANCER IMAGING English Article Diffusion magnetic resonance imaging; Deep learning; Prostatic neoplasms; Artifact; Risk factor 3 T; IMPACT; BIOPSIES; IMPROVES; QUALITY Background Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency.Methods This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant.Results DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC(patient): 0.89 vs. 0.86; AUC(lesion): 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P <= .03) of DL-CAD.Conclusions Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. [Hu, Lei; Li, Yue-hua; Zhao, Jun-gong] Shanghai Jiao Tong Univ, Dept Diagnost & Intervent Radiol, Shanghai Sith Peoples Hosp 6, Sch Med, 600 Yi Shan Rd, Shanghai 200233, Peoples R China; [Fu, Caixia] Siemens Shenzhen Magnet Resonance Ltd, MR Applicat Dev, Shenzhen, Peoples R China; [Song, Xinyang] Hubei Univ Med, Xiangyang Peoples Hosp No 1, Dept Radiol, Xiangyang 441000, Peoples R China; [Grimm, Robert; Benkert, Thomas] Siemens Healthcare GmbH, MR Applicat Predev, Erlangen, Germany; [Von Busch, Heinrich] Siemens Healthcare GmbH, Innovat Owner Artificial Intelligence Oncol, Erlangen, Germany; [Kamen, Ali; Lou, Bin] Siemens Healthineers, Digital Technol & Innovat, Princeton, NJ USA; [Huisman, Henkjan] Radboud Univ Nijmegen Med Ctr, Nijmegen, Netherlands; [Tong, Angela] NYU, New York, NY USA; [Penzkofer, Tobias] Charite, Univ Med Berlin, Berlin, Germany; [Choi, Moon Hyung] Catholic Univ Korea, Eunpyeong St Marys Hosp, Eunpyeong St, Seoul, South Korea; [Shabunin, Ivan] Patero Clin, Moscow, Russia; [Winkel, David] Univ Spital Basel, Basel, Switzerland; [Xing, Pengyi] Changhai Hosp, Shanghai, Peoples R China; [Szolar, Dieter] Diagnostikum Graz Sud West, Graz, Austria; [Coakley, Fergus] Oregon Hlth & Sci Univ, Portland, OR USA; [Shea, Steven] Loyola Univ Med Ctr, Maywood, IL USA; [Szurowska, Edyta] Med Univ Gdansk, Gdansk, Poland; [Guo, Jing-yi] Shanghai Jiao Tong Univ, Clin Res Ctr, Shanghai Peoples Hosp 6, Sch Med, Shanghai 200233, Peoples R China; [Li, Liang] Renmin Hosp Wuhan Univ, Dept Radiol, Wuhan 430060, Peoples R China Shanghai Jiao Tong University; Siemens AG; Hubei University of Medicine; Siemens AG; Siemens AG; Siemens AG; Radboud University Nijmegen; New York University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Catholic University of Korea; University of Basel; Naval Medical University; Oregon Health & Science University; Loyola University Chicago; Fahrenheit Universities; Medical University Gdansk; Shanghai Jiao Tong University; Wuhan University Zhao, JG (corresponding author), Shanghai Jiao Tong Univ, Dept Diagnost & Intervent Radiol, Shanghai Sith Peoples Hosp 6, Sch Med, 600 Yi Shan Rd, Shanghai 200233, Peoples R China. zhaojungongradio@hotmail.com hu, lei/0000-0002-9124-5411; Szurowska, Edyta/0000-0002-7042-4381 National Natural Science Foundation of China [81901845, 81671791]; Science Foundation of Shanghai Jiaotong University Affiliated Sixth People's Hospital [201818]; Shanghai key discipline of medical imaging [2017ZZ02005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation of Shanghai Jiaotong University Affiliated Sixth People's Hospital; Shanghai key discipline of medical imaging This study received funding by the National Natural Science Foundation of China (Nos. 81901845, 81671791), Science Foundation of Shanghai Jiaotong University Affiliated Sixth People's Hospital (No. 201818), and Shanghai key discipline of medical imaging (No: 2017ZZ02005). 34 0 0 4 4 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1740-5025 1470-7330 CANCER IMAGING Cancer Imaging JAN 17 2023.0 23 1 6 10.1186/s40644-023-00527-0 0.0 12 Oncology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Oncology; Radiology, Nuclear Medicine & Medical Imaging 8B5HO 36647150.0 Green Accepted, gold 2023-03-23 WOS:000916954000001 0
J Chang, T; Lu, JQ; Shen, ZX; Huang, Y; Lu, D; Wang, X; Cao, JJ; Morent, R Chang, Tian; Lu, Jiaqi; Shen, Zhenxing; Huang, Yu; Lu, Di; Wang, Xin; Cao, Junji; Morent, Rino Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method APPLIED CATALYSIS B-ENVIRONMENTAL English Article Post-plasma-catalytic system; Dielectric barrier discharge; Toluene removal; Artificial neural network; Non-dominating sorting genetic algorithm II LOW-TEMPERATURE OXIDATION; NONTHERMAL PLASMA; OXIDE CATALYSTS; BY-PRODUCTS; REMOVAL; DISCHARGE; PERFORMANCE; CO; BENZENE; VOCS In this study, a post-non-thermal plasma (NTP)-catalytic system was developed for the removal of toluene over a series of MnCoOx/gamma-Al2O3 catalysts. The addition of the MnCoOx/gamma-Al2O3 catalysts markedly promoted the toluene removal efficiency, CO. yield, CO2 yield and energy yield (EY) compared with the plasma alone system. The 5 wt% MnCoOx/gamma-Al2O3 catalyst exhibited the best reaction performance, which could be attributed to the reducibility and surface active oxygen species of the catalyst. With artificial neural network (ANN), the effects of experimental parameters on the reaction performance of toluene degradation were modeled and analyzed; for this analysis, four parameters were considered, namely, discharge power, initial concentration of toluene, flow rate, and relative humidity. The results indicated that the predicted results fitted well with the experimental results. The discharge power was the most significant factor for the toluene removal efficiency and CO. yield, whereas the EY was the most influenced by the gas flow rate. A multi-objective optimization model was proposed to determine optimal experimental parameters, which was then solved using the non-dominating sorting genetic algorithm II (NSGA-II). The results revealed that the Pareto front obtained from the hybrid ANN and NSGA-II method provided a series of feasible and optimal process parameters for the post-NTP-catalytic system. This hybrid method also served as an effective tool to select process parameters according to application conditions and preferences. [Chang, Tian; Lu, Jiaqi; Shen, Zhenxing; Lu, Di] Xi An Jiao Tong Univ, Dept Environm Sci & Engn, Xian 710049, Shaanxi, Peoples R China; [Shen, Zhenxing; Huang, Yu; Cao, Junji] Chinese Acad Sci, Inst Earth Environm, Key Lab Aerosol Chem Phys, Xian 710049, Shaanxi, Peoples R China; [Chang, Tian; Morent, Rino] Univ Ghent, Dept Appl Phys, Res Unit Plasma Technol, Fac Engn & Architecture, Sint Pietersnieuwstr 41-B4, B-9000 Ghent, Belgium; [Wang, Xin] Max Planck Inst Chem, Multiphase Chem Dept, D-55128 Mainz, Germany Xi'an Jiaotong University; Chinese Academy of Sciences; Institute of Earth Environment, CAS; Ghent University; Max Planck Society Shen, ZX (corresponding author), Xi An Jiao Tong Univ, Dept Environm Sci & Engn, Xian 710049, Shaanxi, Peoples R China. zxshen@mail.xjtu.edu.cn Junji, Cao/D-3259-2014; Huang, Yu/Y-5411-2018; 黄, 婷婷/AAG-3454-2021; Shen, Zhenxing/F-3998-2011 Junji, Cao/0000-0003-1000-7241; Huang, Yu/0000-0003-3334-4849; National Science Foundation of China [41573138, 41877383]; Fundamental Research Funding for Central Universities in China [xkjc2015002]; China Scholarship Council [201806280087]; Hundred Talent Program of the Chinese Academy of Sciences National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funding for Central Universities in China; China Scholarship Council(China Scholarship Council); Hundred Talent Program of the Chinese Academy of Sciences(Chinese Academy of Sciences) This research was financially supported by the National Science Foundation of China (41573138, 41877383), the Fundamental Research Funding for Central Universities in China (xkjc2015002) and China Scholarship Council (No. 201806280087). Yu Huang is also supported by the Hundred Talent Program of the Chinese Academy of Sciences. 62 42 42 6 191 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0926-3373 1873-3883 APPL CATAL B-ENVIRON Appl. Catal. B-Environ. MAY 5 2019.0 244 107 119 10.1016/j.apcatb.2018.11.025 0.0 13 Chemistry, Physical; Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering HK4SE 2023-03-23 WOS:000457952600011 0
J Caruso, F; Dong, LJ; Lin, ML; Liu, MM; Gong, ZN; Xu, WX; Aionge, G; Li, SH Caruso, Francesco; Dong, Lijun; Lin, Mingli; Liu, Mingming; Gong, Zining; Xu, Wanxue; Aionge, Giuseppe; Li, Songhai Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques FRONTIERS IN MARINE SCIENCE English Article passive acoustic monitoring; Indo-Pacific humpback dolphin; spatiotemporal patterns; distribution; acoustic behavior; coastal waters PACIFIC HUMPBACK DOLPHINS; BOTTLE-NOSED DOLPHINS; PEARL RIVER ESTUARY; SOUSA-CHINENSIS; ECHOLOCATION SIGNALS; OCEAN MEASUREMENTS; FORAGING ACTIVITY; SNAPPING SHRIMP; MARINE MAMMALS; SANNIANG BAY Passive acoustic monitoring (PAM) is increasingly being adopted as a non-invasive method for the assessment of ocean ecological dynamics. PAM is an important sampling approach for acquiring critical information about marine mammals, especially in areas where data are lacking and where evaluations of threats for vulnerable populations are required. The Indo-Pacific humpback dolphin (IPHD, Sousa chinensis) is a coastal species which inhabits tropical and warm-temperate waters from the eastern Indian Ocean throughout Southeast Asia to central China. A new population of this species was recently discovered in waters southwest of Hainan Island, China. An array of passive acoustic platforms was deployed at depths of 10-20 m (the preferred habitat of humpback dolphins), across sites covering more than 100 km of coastline. In this study, we explored whether the acoustic data recorded by the array could be used to classify IPHD echolocation clicks, with the aim of investigating the spatiotemporal patterns of distribution and acoustic behavior of this species. A number of supervised machine learning algorithms were trained to automatically classify echolocation clicks from the different types of short-broadband pulses recorded. The best performance was reported by a cubic support vector machine (Cubic SVM), which was applied to 19,215 5-min recordings (similar to 4.2 TB), collected over a period of 75 days at six locations. Subsequently, using spectrogram visualization and audio listening, human operators confirmed the presence of clicks within the selected files. Additionally, other dolphin vocalizations (including whistles, buzzes, and burst pulses) and different sound sources (soniferous fishes, snapping shrimps, human activities) were also reported. The detection range of IPHD clicks was estimated using a transmission loss (IL) model and the performance of the trained classifier was compared with data synchronously collected by an acoustic data logger (A-tag). This study demonstrates that the distribution and habitat use of a coastal and resident dolphin species can be monitored over a large spatiotemporal scale, using an array of passive acoustic platforms and a data analysis protocol that includes both machine learning techniques and spectrogram inspection. [Caruso, Francesco; Dong, Lijun; Lin, Mingli; Liu, Mingming; Gong, Zining; Xu, Wanxue; Li, Songhai] Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Sanya, Peoples R China; [Liu, Mingming] Univ Chinese Acad Sci, Dept Earth Sci, Beijing, Peoples R China; [Gong, Zining; Xu, Wanxue] Univ Chinese Acad Sci, Dept Elect Elect & Commun Engn, Beijing, Peoples R China; [Aionge, Giuseppe] Italian Natl Agcy New Technol, Observat & Measures Environm & Climate, Energy & Sustainable Econ Dev ENEA, Palermo, Italy Chinese Academy of Sciences; Institute of Deep-Sea Science & Engineering, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Italian National Agency New Technical Energy & Sustainable Economics Development Li, SH (corresponding author), Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Sanya, Peoples R China. lish@idsse.ac.cn Caruso, Francesco/AAB-4845-2019 Caruso, Francesco/0000-0001-6881-3786; Alonge, Giuseppe/0000-0003-2564-186X major science and technology project in Hainan Province [ZDKJ2016009-1-1]; National Natural Science Foundation of China [41422604, 41306169]; Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection; China Ocean Mineral Resources RD Association [DY135-E2-4] major science and technology project in Hainan Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection; China Ocean Mineral Resources RD Association This study was financially supported by a major science and technology project in Hainan Province (ZDKJ2016009-1-1), National Natural Science Foundation of China (Nos. 41422604 and 41306169), the Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China, and Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection, and the China Ocean Mineral Resources R&D Association (Grant No. DY135-E2-4). 103 27 27 4 30 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-7745 FRONT MAR SCI Front. Mar. Sci. APR 28 2020.0 7 267 10.3389/fmars.2020.00267 0.0 19 Environmental Sciences; Marine & Freshwater Biology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Marine & Freshwater Biology LI1AR gold, Green Published 2023-03-23 WOS:000529218200001 0
J Akhazhanov, A; More, A; Amini, A; Hazlett, C; Treu, T; Birrer, S; Shajib, A; Liao, K; Lemon, C; Agnello, A; Nord, B; Aguena, M; Allam, S; Andrade-Oliveira, F; Annis, J; Brooks, D; Buckley-Geer, E; Burke, DL; Rosell, AC; Kind, MC; Carretero, J; Choi, A; Conselice, C; Costanzi, M; da Costa, LN; Pereira, MES; De Vicente, J; Desai, S; Dietrich, JP; Doel, P; Everett, S; Ferrero, I; Finley, DA; Flaugher, B; Frieman, J; Garcia-Bellido, J; Gerdes, DW; Gruen, D; Gruendl, RA; Gschwend, J; Gutierrez, G; Hinton, SR; Hollowood, DL; Honscheid, K; James, DJ; Kim, AG; Kuehn, K; Kuropatkin, N; Lahav, O; Lima, M; Lin, H; Maia, MAG; March, M; Menanteau, F; Miquel, R; Morgan, R; Palmese, A; Paz-Chinchon, F; Pieres, A; Malagon, AAP; Sanchez, E; Scarpine, V; Serrano, S; Sevilla-Noarbe, I; Smith, M; Soares-Santos, M; Suchyta, E; Swanson, MEC; Tarle, G; To, C; Varga, TN; Weller, J Akhazhanov, A.; More, A.; Amini, A.; Hazlett, C.; Treu, T.; Birrer, S.; Shajib, A.; Liao, K.; Lemon, C.; Agnello, A.; Nord, B.; Aguena, M.; Allam, S.; Andrade-Oliveira, F.; Annis, J.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Choi, A.; Conselice, C.; Costanzi, M.; da Costa, L. N.; Pereira, M. E. S.; De Vicente, J.; Desai, S.; Dietrich, J. P.; Doel, P.; Everett, S.; Ferrero, I; Finley, D. A.; Flaugher, B.; Frieman, J.; Garcia-Bellido, J.; Gerdes, D. W.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; James, D. J.; Kim, A. G.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; Lin, H.; Maia, M. A. G.; March, M.; Menanteau, F.; Miquel, R.; Morgan, R.; Palmese, A.; Paz-Chinchon, F.; Pieres, A.; Malagon, A. A. Plazas; Sanchez, E.; Scarpine, V; Serrano, S.; Sevilla-Noarbe, I; Smith, M.; Soares-Santos, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; To, C.; Varga, T. N.; Weller, J. DES Collaboration Finding quadruply imaged quasars with machine learning - I. Methods MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article gravitational lensing: strong; methods: statistical; astronomical data bases: surveys STRONG GRAVITATIONAL LENSES; CANDIDATES; MODEL Strongly lensed quadruply imaged quasars (quads) arc extraordinary objects. They arc very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic 'needle in a haystack' problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86-0.89. Recall is close to 100 per cent down to total magnitude i similar to 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i similar to 17-21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 10(8) multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. [Akhazhanov, A.] Univ Calif Los Angeles, Dept Elect & Comp Engn, Los Angeles, CA 90095 USA; [Akhazhanov, A.] Nazarbayev Univ, Dept Elect & Comp Engn, Nur Sultan 010000, Kazakhstan; [More, A.] Interuniv Ctr Astron & Astrophys IUCAA, Post Bag 4, Pune 411007, Maharashtra, India; [More, A.] Univ Tokyo, UTIAS, Kavli IPMU WPI, Kashiwa, Chiba 2778583, Japan; [Amini, A.; Hazlett, C.] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA; [Hazlett, C.] Univ Calif Los Angeles, Dept Polit Sci, Los Angeles, CA 90095 USA; [Treu, T.] PAB, Dept Phys & Astron, 430 Portola Plaza,Box 951547, Los Angeles, CA 90095 USA; [Birrer, S.; Burke, D. L.; To, C.] Stanford Univ, Kavli Inst Particle Astrophys & Cosmol, Stanford, CA 94305 USA; [Shajib, A.; Nord, B.; Buckley-Geer, E.] Univ Chicago, Dept Astron & Astrophys, Chicago, IL 60637 USA; [Liao, K.] Wuhan Univ, Sch Phys & Technol, Wuhan 430072, Peoples R China; [Lemon, C.] Ecole Polytech Fed Lausanne EPFL, Lab Astrophys, Observ Sauverny, CH-1290 Versoix, Switzerland; [Agnello, A.] Niels Bohr Inst, DARK, Jagtvej 128, DK-2200 Copenhagen, Denmark; [Nord, B.; Allam, S.; Annis, J.; Buckley-Geer, E.; Finley, D. A.; Flaugher, B.; Frieman, J.; Gutierrez, G.; Kuropatkin, N.; Lin, H.; Palmese, A.; Scarpine, V] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA; [Nord, B.; Frieman, J.; Palmese, A.] Univ Chicago, Kavli Inst Cosmol Phys, Chicago, IL 60637 USA; [Aguena, M.; Andrade-Oliveira, F.; Rosell, A. Carnero; da Costa, L. N.; Gschwend, J.; Lima, M.; Maia, M. A. G.; Pieres, A.] Lab Interinst E Astron LIneA, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, RJ, Brazil; [Andrade-Oliveira, F.] Univ Estadual Paulista, Inst Fis Teor, BR-01140070 Sao Paulo, Brazil; [Brooks, D.; Doel, P.; Lahav, O.] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England; [Burke, D. L.; To, C.] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA; [Kind, M. Carrasco; Gruendl, R. A.; Menanteau, F.; Paz-Chinchon, F.; Swanson, M. E. C.] Natl Ctr Supercomp Applicat, Ctr Astrophys Surveys, 1205 West Clark St, Urbana, IL 61801 USA; [Kind, M. Carrasco; Gruendl, R. A.; Menanteau, F.] Univ Illinois, Dept Astron, 1002 W Green St, Urbana, IL 61801 USA; [Carretero, J.; Miquel, R.] Barcelona Inst Sci & Technol, Inst Fis Altes Energies IFAE, Campus UAB, E-08193 Bellaterra, Barcelona, Spain; [Choi, A.; Honscheid, K.] Ohio State Univ, Ctr Cosmol & Astroparticle Phys, Columbus, OH 43210 USA; [Conselice, C.] Univ Manchester, Jodrell Bank Ctr Astrophys, Sch Phys & Astron, Oxford Rd, Manchester M13 9PL, Lancs, England; [Conselice, C.] Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England; [Costanzi, M.] Univ Trieste, Dept Phys, Astron Unit, Via Tiepolo 11, I-34131 Trieste, Italy; [Costanzi, M.] INAF Osservatorio Astron Trieste, Via GB Tiepolo 11, I-34143 Trieste, Italy; [Costanzi, M.] Inst Fundamental Phys Universe, Via Beirut 2, I-34014 Trieste, Italy; [da Costa, L. N.; Gschwend, J.; Maia, M. A. G.; Pieres, A.] Observ Nacl, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, RJ, Brazil; [Pereira, M. E. S.; Gerdes, D. W.; Soares-Santos, M.; Tarle, G.] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA; [Pereira, M. E. S.] Univ Hamburg, Hamburger Sternwarte, Gojenbergsweg 112, D-21029 Hamburg, Germany; [De Vicente, J.; Sanchez, E.; Sevilla-Noarbe, I] Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid 28040, Spain; [Desai, S.] IIT Hyderabad, Dept Phys, Kandi 502285, Telangana, India; [Dietrich, J. P.] Ludwig Maximilians Univ Munchen, Fac Phys, Scheinerstr 1, D-81679 Munich, Germany; [Everett, S.; Hollowood, D. L.] Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA; [Ferrero, I] Univ Oslo, Inst Theoret Astrophys, POB 1029 Blindern, NO-0315 Oslo, Norway; [Garcia-Bellido, J.] Univ Autonoma Madrid, Inst Fis Teor UAM CSIC, Madrid 28049, Spain; [Gerdes, D. W.] Univ Michigan, Dept Astron, Ann Arbor, MI 48109 USA; [Gruen, D.; Varga, T. N.; Weller, J.] Ludwig Maximilians Univ Munchen, Univ Sternwarte, Fak Phys, Scheinerstr 1, D-81679 Munich, Germany; [Hinton, S. R.] Univ Queensland, Sch Math & Phys, Brisbane, Qld 4072, Australia; [Honscheid, K.] Ohio State Univ, Dept Phys, Columbus, OH 43210 USA; [James, D. J.] Ctr Astrophys Harvard & Smithsonian, 60 Garden St, Cambridge, MA 02138 USA; [Kim, A. G.] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA 94720 USA; [Kuehn, K.] Macquarie Univ, Australian Astron Opt, N Ryde, NSW 2113, Australia; [Kuehn, K.] Lowell Observ, 1400 Mars Hill Rd, Flagstaff, AZ 86001 USA; [Lima, M.] Univ Sao Paulo, Inst Fis, Dept Fis Matemat, CP 66318, BR-05314970 Sao Paulo, SP, Brazil; [March, M.] Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA; [Miquel, R.] Inst Catalana Recerca & Estudis Avancats, E-08010 Barcelona, Spain; [Morgan, R.] Univ Wisconsin, Phys Dept, 2320 Chamberlin Hall,1150 Univ Ave, Madison, WI 53706 USA; [Paz-Chinchon, F.] Univ Cambridge, Inst Astron, Madingley Rd, Cambridge CB3 0HA, England; [Malagon, A. A. Plazas] Princeton Univ, Dept Astrophys Sci, Peyton Hall, Princeton, NJ 08544 USA; [Serrano, S.] Inst Estudis Espacials Catalunya IEEC, E-08034 Barcelona, Spain; [Serrano, S.] CSIC, Inst Space Sci ICE, Campus UAB,Carrer Can Magrans S-N, E-08193 Barcelona, Spain; [Smith, M.] Univ Southampton, Sch Phys & Astron, Southampton SO17 1BJ, Hants, England; [Suchyta, E.] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN 37831 USA; [To, C.] Stanford Univ, Dept Phys, 382 Via Pueblo Mall, Stanford, CA 94305 USA; [Varga, T. N.; Weller, J.] Max Planck Inst Extraterr Phys, Giessenbachstr, D-85748 Garching, Germany University of California System; University of California Los Angeles; Nazarbayev University; Inter-University Centre for Astronomy & Astrophysics; University of Tokyo; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; Stanford University; University of Chicago; Wuhan University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Copenhagen; Niels Bohr Institute; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; University of Chicago; Universidade Estadual Paulista; University of London; University College London; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; University of Illinois System; University of Illinois Urbana-Champaign; Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); University System of Ohio; Ohio State University; University of Manchester; Jodrell Bank Centre for Astrophysics; University of Nottingham; University of Trieste; Istituto Nazionale Astrofisica (INAF); University of Michigan System; University of Michigan; University of Hamburg; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Hyderabad; University of Munich; University of Oslo; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); University of Michigan System; University of Michigan; University of Munich; University of Queensland; University System of Ohio; Ohio State University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Macquarie University; Universidade de Sao Paulo; University of Pennsylvania; ICREA; University of Wisconsin System; University of Wisconsin Madison; University of Cambridge; Princeton University; Institut d'Estudis Espacials de Catalunya (IEEC); Autonomous University of Barcelona; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Ciencias del Espacio (ICE); University of Southampton; United States Department of Energy (DOE); Oak Ridge National Laboratory; Stanford University; Max Planck Society Treu, T (corresponding author), PAB, Dept Phys & Astron, 430 Portola Plaza,Box 951547, Los Angeles, CA 90095 USA. tt@astro.ucla.edu Pereira, Maria/GZA-9681-2022; Carretero, Jorge/L-8237-2014 Carretero, Jorge/0000-0002-3130-0204; Serrano Elorduy, Santiago/0000-0002-0211-2861; Weller, Jochen/0000-0002-8282-2010; Tarle, Gregory/0000-0003-1704-0781; Treu, Tommaso/0000-0002-8460-0390; Agnello, Adriano/0000-0001-9775-0331; Ferrero, Ismael/0000-0002-1295-1132; Costanzi, Matteo/0000-0001-8158-1449; Menanteau, Felipe/0000-0002-1372-2534 National Science Foundation [NSF-AST-1906976, AST-1138766, AST-1536171]; NASA [HST-GO-15320, HST-GO-15652]; Packard Foundation through a Packard Research Fellowship; U.S. Department of Energy; U.S. National Science Foundation; Ministry of Science and Education of Spain; Science and Technology Facilities Council of the United Kingdom; Higher Education Funding Council for England; National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign; Kavli Institute of Cosmological Physics at the University of Chicago; Center for Cosmology and Astro-Particle Physics at the Ohio State University; Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University; Financiadora de Estudos e Projetos; FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; Ministerio da Ciencia, Tecnologia e InovacAo; Deutsche Forschungsgemeinschaft; Collaborating Institutions in the Dark Energy Survey; MICINN [ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509]; ERDF funds from the European Union; CERCA programme of the Generalitat de Catalunya; European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013); ERC grant [240672, 291329, 306478]; Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) do e-Universo (CNPq) [465376/2014-2]; U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-AC02-07CH11359] National Science Foundation(National Science Foundation (NSF)); NASA(National Aeronautics & Space Administration (NASA)); Packard Foundation through a Packard Research Fellowship; U.S. Department of Energy(United States Department of Energy (DOE)); U.S. National Science Foundation(National Science Foundation (NSF)); Ministry of Science and Education of Spain(Spanish Government); Science and Technology Facilities Council of the United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Higher Education Funding Council for England(UK Research & Innovation (UKRI)Higher Education Funding Council for England (HEFCE)); National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign; Kavli Institute of Cosmological Physics at the University of Chicago; Center for Cosmology and Astro-Particle Physics at the Ohio State University(Ohio State University); Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University; Financiadora de Estudos e Projetos(Financiadora de Inovacao e Pesquisa (Finep)); FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Ministerio da Ciencia, Tecnologia e InovacAo(Spanish Government); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); Collaborating Institutions in the Dark Energy Survey; MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); ERDF funds from the European Union; CERCA programme of the Generalitat de Catalunya; European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013); ERC grant; Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) do e-Universo (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)); U.S. Department of Energy, Office of Science, Office of High Energy Physics(United States Department of Energy (DOE)) We are grateful to Paul Schechter for numerous insightful conversations on lens finding and training sets. TT acknowledges support from the National Science Foundation through grant NSF-AST-1906976, from NASA through grants HST-GO-15320and HST-GO-15652, and from the Packard Foundation through a Packard Research Fellowship. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e InovacAo, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitat Munchen and the associated Excellence Cluster Universe, the University of Michigan, NSF's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. 44 0 0 1 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. MAY 5 2022.0 513 2 2407 2421 10.1093/mnras/stac925 0.0 15 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 1A1WR Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000791556200018 0
C Velev, D; Zlateva, P; Zong, XJ ACM Velev, Dimiter; Zlateva, Plamena; Zong, Xuejun Challenges of 5G Usability in Disaster Management PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON COMPUTING AND ARTIFICIAL INTELLIGENCE (ICCAI 2018) English Proceedings Paper International Conference on Computing and Artificial Intelligence (ICCAI) MAR 12-14, 2018 Chengdu, PEOPLES R CHINA Sichuan Univ 5G; IoT; Big Data; Cloud Computing; Natural Disasters In recent years, there is a registered increase in the number and intensity of disasters caused by natural phenomena or human activities around the world. Such calamities adversely affect social relations, economic growth and sustainable development of the countries. Although many information systems for disaster management try to reduce the possible aftereffects of disasters and assess the damages, not always they are capable of handling the consequences in the right way regardless of the advanced information technologies used. Recently all technical achievements are being planned and focused on the forthcoming 5G technology with its numerous possible applications in different aspects of real life. The aim of the paper is to investigate the 5G usability in disasters and how this technology could improve the management of the negative consequences. [Velev, Dimiter] Univ Natl & World Econ, 8 Mi Dekemvri Bldv, Sofia 1700, Bulgaria; [Zlateva, Plamena] Bulgarian Acad Sci, Inst Robot, Acad G Bonchev Str,Bl 2, BU-1113 Sofia, Bulgaria; [Zong, Xuejun] Shenyang Univ Chem Technol, Shenyang Econ & Technol Dvlp, Zone 11th St, Shenyang 110142, Liaoning, Peoples R China University of National & World Economics - Bulgaria; Bulgarian Academy of Sciences; Shenyang University of Chemical Technology Velev, D (corresponding author), Univ Natl & World Econ, 8 Mi Dekemvri Bldv, Sofia 1700, Bulgaria. dgvelev@unwe.bg; plamzlateva@abv.bg; syictauto@163.com Zlateva, Plamena V./K-2881-2017; Velev, Dimiter G/G-3791-2019 Zlateva, Plamena V./0000-0002-0153-5811; Velev, Dimiter G/0000-0003-3030-1819 Bulgarian National Science Fund [DFNI-I02/15] Bulgarian National Science Fund(National Science Fund of Bulgaria) The authors wish to thank the Bulgarian National Science Fund for the partial financial support under the Grant. DFNI-I02/15 from 12.12.2014, titled Information System for Integrated Risk Assessment from Natural Disasters. 26 8 8 0 5 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6419-5 2018.0 71 75 10.1145/3194452.3194475 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL9RH 2023-03-23 WOS:000458155100014 0
C Guo, SW; Hohn, S; Xu, FY; Schommer, C VanDenHerik, J; Rocha, AP Guo, Siwen; Hohn, Sviatlana; Xu, Feiyu; Schommer, Christoph Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2018 Lecture Notes in Artificial Intelligence English Proceedings Paper 10th International Conference on Agents and Artificial Intelligence (ICAART) JAN 16-18, 2018 Funchal, PORTUGAL Sentiment analysis; Hawkes process; Personalized model; Attention network; Recurrent neural networks People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people's opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person's past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research. [Guo, Siwen; Hohn, Sviatlana; Schommer, Christoph] Univ Luxembourg, ILIAS Res Lab, CSC, Esch Sur Alzette, Luxembourg; [Xu, Feiyu] Lenovo, AI Lab, Beijing, Peoples R China University of Luxembourg; Legend Holdings; Lenovo Guo, SW (corresponding author), Univ Luxembourg, ILIAS Res Lab, CSC, Esch Sur Alzette, Luxembourg. siwen.guo@uni.lu Hohn, Sviatlana/0000-0003-0646-3738 40 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-05453-3; 978-3-030-05452-6 LECT NOTES ARTIF INT 2019.0 11352 202 222 10.1007/978-3-030-05453-3_10 0.0 21 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BS4TB Green Submitted 2023-03-23 WOS:000722460300010 0
C Shi, HY; Jaulent, MC; Pfaender, F Shakshuki, E; Yasar, A; Malik, H Shi, Hongyi; Jaulent, Marie-Christine; Pfaender, Fabien Semantic Interpretation of the map with Diabetes-Related Websites 10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS Procedia Computer Science English Proceedings Paper 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH) NOV 04-07, 2019 Coimbra, PORTUGAL diabetes mellitus; information networks; data visualization; clustering; semantic interpretation; machine learning Diabetes as a chronic disease requires continuous medical care and constant patient self-management which involve several stakeholders to improve health outcome and patient quality of life. In our prior work, we used the networks of World Wide Web to highlight how stakeholders of diabetes link to each other online. The aim of this study is to use a semantic approach focusing on the diabetes-related websites to better understand the common interest shared by the same clusters which were detected in our previous study of stakeholders on diabetes. To achieve this, we employed the data annotation and machine learning to study which combinations of tags can predict or explain the clusters. In the end, a total of 430 websites which are detected into 5 clusters have been tagged with 38 different tags from 6 different dimensions in this study. Although the result shows a very low prediction performance using tags to determine the clusters of diabetes-related websites, except for cluster 1 and cluster 2, this reflects the community reality: a mix of websites of different types that create a mixed but localized space. It proves the community can have a tagging scheme occasionally but it is still hard to use semantical approach to predict accurately the clusters. (C) 2019 The Authors. Published by Elsevier B.V. [Shi, Hongyi; Jaulent, Marie-Christine] Univ Paris 13, Sorbonne Univ, INSERM, LIMICS, F-75011 Paris, France; [Pfaender, Fabien] Shanghai Univ, UTSEUS, Shanghai, Peoples R China; [Pfaender, Fabien] Univ Technol Compiegne, Costech EA2223, Compiegne, France Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; Universite Paris 13; Shanghai University; Picardie Universites; Universite de Technologie de Compiegne Shi, HY (corresponding author), Univ Paris 13, Sorbonne Univ, INSERM, LIMICS, F-75011 Paris, France. maryshi@live.cn ; Jaulent, Marie-Christine/G-1065-2013 Pfaender, Fabien/0000-0001-6338-9058; Jaulent, Marie-Christine/0000-0003-4445-7494 Chinese Scholarship Council; LIMICS, Laboratoire d'Informatique Medicale et d'Ingenierie des Connaissances en e-Sante, UMRS_1142; Complex City Laboratory, Shanghai Chinese Scholarship Council(China Scholarship Council); LIMICS, Laboratoire d'Informatique Medicale et d'Ingenierie des Connaissances en e-Sante, UMRS_1142; Complex City Laboratory, Shanghai This paper was made possible by the cooperation program by Sorbonne Universite and Chinese Scholarship Council. Chinese Scholarship Council offers fellowship during I'm studying PhD in Sorbonne Universite. I would like to thank LIMICS, Laboratoire d'Informatique Medicale et d'Ingenierie des Connaissances en e-Sante, UMRS_1142 for the support and this work is also supported partly by Complex City Laboratory, Shanghai. 13 1 1 1 3 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-0509 PROCEDIA COMPUT SCI 2019.0 160 330 337 10.1016/j.procs.2019.11.083 0.0 8 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Medical Informatics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Medical Informatics BO4UP Green Published, gold 2023-03-23 WOS:000515510100043 0
J Yang, Z; Baraldi, P; Zio, E Yang, Zhe; Baraldi, Piero; Zio, Enrico A novel method for maintenance record clustering and its application to a case study of maintenance optimization RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Maintenance record; Text clustering; Convolutional neural network; Stochastic multi-state model; Maintenance optimization; Mining; Excavator bucket CORRECTIVE MAINTENANCE; NEURAL-NETWORKS; SUBJECT; SYSTEMS; MODEL Maintenance records contain information about equipment degradation, which is typically not used as data for maintenance modeling and optimization given the difficulty of analyzing text to extract relevant and usable information. In this work, we present a method for using such textual information by: 1) the identification of equipment degradation states through clustering the maintenance records using a Convolutional Neural Network (CNN) and 2) the development of a stochastic multi-state degradation model based on the information therein. The method is applied to a database of maintenance records collected from excavator buckets used for mining. The results show that the proposed CNN-based clustering method identifies homogeneous clusters of maintenance records representing different equipment degradation states and that the proposed stochastic multi-state degradation model based on this information can be used for improving maintenance. [Yang, Zhe] Dongguan Univ Technol, Sch Mech Engn, Dongguan 523808, Peoples R China; [Yang, Zhe; Baraldi, Piero; Zio, Enrico] Politecn Milan, Energy Dept, Via La Masa 34, I-20156 Milan, Italy; [Zio, Enrico] PSL Res Univ, CRC, MINES ParisTech, Sophia Antipolis, France; [Zio, Enrico] Aramis Srl, Via Pergolesi 5, Milan, Italy; [Zio, Enrico] Kyung Hee Univ, Coll Engn, Dept Nucl Engn, Seoul, South Korea Dongguan University of Technology; Polytechnic University of Milan; UDICE-French Research Universities; Universite PSL; MINES ParisTech; Kyung Hee University Baraldi, P (corresponding author), Politecn Milan, Energy Dept, Via La Masa 34, I-20156 Milan, Italy. piero.baraldi@polimi.it China Scholarship Council [201506280015]; INAIL [11] China Scholarship Council(China Scholarship Council); INAIL Zhe Yang gratefully acknowledges the financial support from the China Scholarship Council (No. 201506280015) for performing this work while at Department of Energy, Politecnico di Milano. The participation of Piero Baraldi and Enrico Zio has been funded by INAIL within the BRIC/2018, ID = 11 framework, project MAC4PRO. We greatly appreciate the help from Melinda Hodkiewicz who is with University of Western Australia, Crawley WA 6009, AUS, for providing the database of maintenance records. 58 9 9 6 31 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. NOV 2020.0 203 107103 10.1016/j.ress.2020.107103 0.0 10 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science NM2DB Bronze 2023-03-23 WOS:000567911600045 0
C Mei, ZY; Ivanov, K; Lubich, L; Wang, L Yu, H; Liu, J; Liu, L; Ju, Z; Liu, Y; Zhou, D Mei, Zhanyong; Ivanov, Kamen; Lubich, Ludwig; Wang, Lei Recognition of Pes Cavus Foot Using Smart Insole: A Pilot Study INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III Lecture Notes in Artificial Intelligence English Proceedings Paper 12th International Conference on Intelligent Robotics and Applications (ICIRA) AUG 08-11, 2019 Shenyang, PEOPLES R CHINA Natl Nat Sci Fdn China,Springer,Chinese Acad Sci, Shenyang Inst Automat,Huazhong Univ Sci & Technol,Shanghai Jiao Tong Univ,Univ Portsmouth,Shenyang Assoc Sci & Technol Pes cavus; Smart insole; Plantar pressure; 1D CNN PLANTAR PRESSURE; HEEL STRIKE; CLASSIFICATION; PLANUS; GAIT The presence of pes cavus, a high-arched foot, is a potential reason for some neuromuscular problems. Active research efforts are being made to devise portable systems for monitoring and early detection of foot deviations. In line with that, we have developed instrumented insoles that incorporate force and inertial sensors and used them to capture data from sixty-four subjects; among them, there were forty-four subjects with normal feet arches and twenty subjects exhibiting pes cavus. We applied a 1D convolutional neural network to extract features and classify data. The trained model allowed for a recognition rate of more than 96%. The presented use case could inspire further research on using smart footwear for pes cavus screening and progression monitoring. [Mei, Zhanyong] Chengdu Univ Technol, Coll Cyber Secur, Chengdu 610059, Peoples R China; [Ivanov, Kamen; Wang, Lei] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Ivanov, Kamen] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen 518055, Peoples R China; [Lubich, Ludwig] Tech Univ Sofia, Fac Telecommun, Sofia 1000, Bulgaria Chengdu University of Technology; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Technical University Sofia Wang, L (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China. zhanyongm99@163.com; kamen@siat.ac.cn; lvl@tu-sofia.bg; wang.lei@siat.ac.cn Ivanov, Kamen Valentinov/A-6498-2019; Lubich, Ludwig/R-7403-2016 Ivanov, Kamen Valentinov/0000-0002-1038-4277; Lubich, Ludwig/0000-0003-2010-4766 Science and Technology Department of Sichuan province [2017GZ0304]; [U1505251] Science and Technology Department of Sichuan province; This project was supported in parts by the Key Project 2017GZ0304 of the Science and Technology Department of Sichuan province and the Key Program of Joint Funds of the National Natural Science Foundation of China, grant U1505251. 23 3 3 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-27535-8; 978-3-030-27534-1 LECT NOTES ARTIF INT 2019.0 11742 654 662 10.1007/978-3-030-27535-8_58 0.0 9 Computer Science, Artificial Intelligence; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics BP9GN 2023-03-23 WOS:000569253000058 0
J Zhao, MM; Beurier, G; Wang, HY; Wang, XG Zhao, Mingming; Beurier, Georges; Wang, Hongyan; Wang, Xuguang Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution SENSORS English Article driver posture monitoring; pressure measurement; sensor layout; machine learning SMART CUSHION; DISTRACTION Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver's intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs. [Zhao, Mingming; Wang, Hongyan] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China; [Zhao, Mingming; Beurier, Georges; Wang, Xuguang] Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T9406, F-69622 Lyon, France Tongji University; UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Universite Gustave-Eiffel Wang, XG (corresponding author), Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T9406, F-69622 Lyon, France. 1710085@tongji.edu.cn; georges.beurier@univ-eiffel.fr; why-sos@vip.sina.com; xuguang.wang@univ-eiffel.fr Zhao, Mingming/GWN-2646-2022; shi, zhensheng/GXN-3967-2022 Zhao, Mingming/0000-0003-4355-7688; Wang, Xuguang/0000-0001-9181-6045 China Scholarship Council [201806260131]; French National Project ANR AutoConduct [ANR-16-CE22-0007]; Universite Gustave Eiffel China Scholarship Council(China Scholarship Council); French National Project ANR AutoConduct(French National Research Agency (ANR)); Universite Gustave Eiffel This research was funded by the China Scholarship Council, grant number 201806260131 and the French National Project ANR AutoConduct, grant number ANR-16-CE22-0007. The APC was funded by Universite Gustave Eiffel. 32 4 4 2 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2021.0 21 10 3346 10.3390/s21103346 0.0 13 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation ST7SO 34065797.0 Green Published, gold 2023-03-23 WOS:000662639800001 0
J Zhou, Y; Yang, XM; Zhang, RZ; Liu, K; Anisetti, M; Jeon, G Zhou, Yang; Yang, Xiaomin; Zhang, Rongzhu; Liu, Kai; Anisetti, Marco; Jeon, Gwanggil Gradient-based multi-focus image fusion method using convolution neural network COMPUTERS & ELECTRICAL ENGINEERING English Article Image fusion; Multi-focus fusion; Convolution neural network; Gradient-based PERFORMANCE; TRANSFORM Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods. Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods. [Zhou, Yang; Yang, Xiaomin; Zhang, Rongzhu] Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China; [Zhou, Yang; Yang, Xiaomin] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610064, Sichuan, Peoples R China; [Liu, Kai] Sichuan Univ, Coll Elect Engn, Chengdu 610064, Sichuan, Peoples R China; [Anisetti, Marco] Univ Milan, Dipartimento Informat DI, Via Celoria 18, I-20133 Milan, MI, Italy; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China Sichuan University; Sichuan University; Sichuan University; University of Milan; Incheon National University; Xidian University Yang, XM (corresponding author), Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China.;Yang, XM (corresponding author), Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610064, Sichuan, Peoples R China. arielyang@scu.edu.cn yang, xiao/HJI-7815-2023; Anisetti, Marco/AAC-9656-2021 Anisetti, Marco/0000-0002-5438-9467 National Natural Science Foundation of China [61701327]; Key Research and Development Project of Science and Technology Commission Foundation of Sichuan Province, China [2018FZ0036]; Sichuan University, China [2020SCUNG205] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Project of Science and Technology Commission Foundation of Sichuan Province, China; Sichuan University, China(Sichuan University) This work is sponsored by the National Natural Science Foundation of China (Grant No.61701327), the Key Research and Development Project of Science and Technology Commission Foundation of Sichuan Province, China (Grant No. 2018FZ0036) and the Funding from Sichuan University, China (Grant No. 2020SCUNG205). 30 1 1 1 17 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. JUN 2021.0 92 107174 10.1016/j.compeleceng.2021.107174 0.0 MAY 2021 15 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SV3EX 2023-03-23 WOS:000663706800006 0
J Zhang, H; Wan, LX; Haug, T; Mok, WK; Paesani, S; Shi, YZ; Cai, H; Chin, LK; Karim, MF; Xiao, LM; Luo, XS; Gao, F; Dong, B; Assad, S; Kim, MS; Laing, A; Kwek, LC; Liu, AQ Zhang, Hui; Wan, Lingxiao; Haug, Tobias; Mok, Wai-Keong; Paesani, Stefano; Shi, Yuzhi; Cai, Hong; Chin, Lip Ket; Karim, Muhammad Faeyz; Xiao, Limin; Luo, Xianshu; Gao, Feng; Dong, Bin; Assad, Syed; Kim, M. S.; Laing, Anthony; Kwek, Leong Chuan; Liu, Ai Qun Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder SCIENCE ADVANCES English Article NEURAL-NETWORKS; ENTANGLEMENT; GENERATION; STATES Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsu-pervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (-0.971), obtain-ing a total teleportation fidelity of-0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking. [Zhang, Hui; Wan, Lingxiao; Chin, Lip Ket; Karim, Muhammad Faeyz; Kwek, Leong Chuan; Liu, Ai Qun] Nanyang Technol Univ, Quantum Sci & Engn Ctr QSec, 50 Nanyang Ave, Singapore 639798, Singapore; [Haug, Tobias; Kim, M. S.] Imperial Coll London, Quantum Opt & Laser Sci, Exhibit Rd, London SW7 2AZ, England; [Mok, Wai-Keong; Kwek, Leong Chuan] Natl Univ Singapore, Ctr Quantum Technol, BlockS15,3 Sci Dr 2, Singapore 117543, Singapore; [Paesani, Stefano] Univ Copenhagen, Niels Bohr Inst, Ctr Hybrid Quantum Networks Hy Q, Blegdamsvej 17, DK-2100 Copenhagen, Denmark; [Paesani, Stefano; Laing, Anthony] Univ Bristol, HH Wills Phys Lab, Quantum Engn Technol Labs, Bristol BS8 1TH, England; [Paesani, Stefano; Laing, Anthony] Univ Bristol, Dept Elect & Elect Engn, Bristol BS8 1TH, England; [Shi, Yuzhi] Tongji Univ, Inst Precis Opt Engn, Sch Phys Sci & Engn, Shanghai 200092, Peoples R China; [Cai, Hong] ASTAR, Inst Microelect, Singapore 138634, Singapore; [Xiao, Limin] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Luo, Xianshu; Gao, Feng; Dong, Bin] Adv Micro Foundry, 11 Sci Pk Rd, Singapore 117685, Singapore; [Assad, Syed] Australian Natl Univ, Ctr Quantum Computat & Commun Technol, Dept Quantum Sci, Canberra, ACT 2600, Australia Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Imperial College London; National University of Singapore; University of Copenhagen; Niels Bohr Institute; University of Bristol; University of Bristol; Tongji University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Microelectronics (IME); Fudan University; Australian National University Kwek, LC; Liu, AQ (corresponding author), Nanyang Technol Univ, Quantum Sci & Engn Ctr QSec, 50 Nanyang Ave, Singapore 639798, Singapore.;Kwek, LC (corresponding author), Natl Univ Singapore, Ctr Quantum Technol, BlockS15,3 Sci Dr 2, Singapore 117543, Singapore.;Shi, YZ (corresponding author), Tongji Univ, Inst Precis Opt Engn, Sch Phys Sci & Engn, Shanghai 200092, Peoples R China. yuzhi_shi@163.com; cqtklc@nus.edu.sg; eaqliu@ntu.edu.sg Shi, Yuzhi/AGM-3241-2022; Kwek, Leong-Chuan L.C./P-2303-2014 Shi, Yuzhi/0000-0002-9041-0462; Kwek, Leong-Chuan L.C./0000-0002-0879-0591; Wan, Lingxiao/0000-0002-1551-0949; Xiao, Limin/0000-0002-8791-3456; Mok, Wai Keong/0000-0002-1920-5407; Haug, Tobias/0000-0003-2707-9962; Zhang, Hui/0000-0003-2989-7547; CHIN, LIP KET/0000-0001-9020-7782 Singapore Ministry of Education (MOE) Tier 3 grant [MOE2017-T3-1-001]; Singapore National Research Foundation (NRF) National Natural Science Foundation of China (NSFC) joint grant [NRF2017NRF-NSFC002-014]; Samsung GRC project; U.K. National Quantum Technologies Programme; UKRI EPSRC [EP/T001062/1]; UK Hub in Quantum Computing and Simulation Singapore Ministry of Education (MOE) Tier 3 grant(Ministry of Education, Singapore); Singapore National Research Foundation (NRF) National Natural Science Foundation of China (NSFC) joint grant; Samsung GRC project(Samsung); U.K. National Quantum Technologies Programme; UKRI EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); UK Hub in Quantum Computing and Simulation This work was supported by the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001), Singapore National Research Foundation (NRF) National Natural Science Foundation of China (NSFC) joint grant (NRF2017NRF-NSFC002-014), Samsung GRC project and the UK Hub in Quantum Computing and Simulation, and part of the U.K. National Quantum Technologies Programme with funding from UKRI EPSRC grant EP/T001062/1. 70 0 0 20 20 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 2375-2548 SCI ADV Sci. Adv. OCT 7 2022.0 8 40 eabn9783 10.1126/sciadv.abn9783 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 6I3VZ 36206336.0 Green Published, gold 2023-03-23 WOS:000886057300010 0
J Loureiro, SMC; Al-Ansi, A; Ryu, HB; Ariza-Montes, A; Han, HS Loureiro, Sandra Maria Correia; Al-Ansi, Amr; Ryu, Hyungseo Bobby; Ariza-Montes, Antonio; Han, Heesup Culture, heritage looting, and tourism: A text mining review approach FRONTIERS IN PSYCHOLOGY English Review cultural heritage looting; heritage destruction; public access; human rights; cultural heritage preservation; protection of cultural property PROPERTY PROTECTION; DESTRUCTION; CONTEXT Tourism scholars have been devoted to exploring the significance of cultural heritage in generating economic, environmental, and social values. However, limited efforts were found to verify potential threats that demolish these values such as looting issue in the global heritage tourism industry. Therefore, this study has reviewed extant publications to demonstrate the potential emerged textual clusters discussed by previous studies. It also summarized the network distribution of articles journals and authors' affiliations to capture the mobility and diversity with a focus on the business and tourism management field. Hence, the core clusters discovered were related to heritage destruction, public access, world heritage, human rights, cultural heritage preservation, and protection of cultural heritage in the event. The results have established theoretical insights and research agendas for future tourism studies, while it determined critical drawbacks in employing technology tools including virtual reality, augmented reality, and artificial intelligence for cultural heritage preservation/protection. [Loureiro, Sandra Maria Correia] Univ Lisbon, ISCTE Inst, Lisbon, Portugal; [Loureiro, Sandra Maria Correia] Business Res Unit, Lisbon, Portugal; [Al-Ansi, Amr] Macau Univ Sci & Technol, Fac Hospitality & Tourism Management, Taipa, Macao, Peoples R China; [Ryu, Hyungseo Bobby] Kyungnam Univ, Coll Hlth Sci, Food Franchise Dept, Changwon Si, South Korea; [Ariza-Montes, Antonio] Univ Loyola Andalucia, Social Matters Res Grp, Cordoba, Spain; [Han, Heesup] Sejong Univ, Coll Hospitality & Tourism Management, Seoul, South Korea Instituto Universitario de Lisboa; Universidade de Lisboa; Macau University of Science & Technology; Kyungnam University; Universidad Loyola Andalucia; Sejong University Ryu, HB (corresponding author), Kyungnam Univ, Coll Hlth Sci, Food Franchise Dept, Changwon Si, South Korea.;Han, HS (corresponding author), Sejong Univ, Coll Hospitality & Tourism Management, Seoul, South Korea. bobbyryu414@hanmail.net; heesup.han@gmail.com Al-Ansi, Amr/E-4238-2018 Al-Ansi, Amr/0000-0001-8021-6749; Loureiro, Sandra Maria Correia/0000-0001-8362-4430 63 0 0 14 14 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. AUG 8 2022.0 13 944250 10.3389/fpsyg.2022.944250 0.0 10 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology 6Q6HH 36003102.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000891712900001 0
J Guo, CB; Chen, YS; Liu, H; Lu, Y; Qu, X; Yuan, H; Lek, S; Xie, SG Guo, Chuanbo; Chen, Yushun; Liu, Han; Lu, Yin; Qu, Xiao; Yuan, Hui; Lek, Sovan; Xie, Songguang Modelling fish communities in relation to water quality in the impounded lakes of China's South-to-North Water Diversion Project ECOLOGICAL MODELLING English Article Ensemble modelling; Fish communities; Ecological impacts; Machine learning; South-to-North Water Diversion Project; Water quality changes SPECIES DISTRIBUTIONS; CLIMATE-CHANGE; SHALLOW LAKES; ASSEMBLAGES; RIVER; PATTERNS; UNCERTAINTY; RICHNESS; IMPACTS; MACROPHYTES Large scale inter-basin water diversion projects would have a set of ecological and environmental impacts on aquatic ecosystems. However, knowledge regarding water transfer induced water quality changes in determining fish communities in the impounding ecosystems remain largely unknown. In the current study, we filled this research gap by using a set of machine learning algorithms to ensemble modelling fish community indices with water quality indicators in the impounded lakes along the eastern route of China's South-to-North Water Diversion Project (SNWDP). Overall, our results realed that water quality changes can be good predictors for the variation of fish community structures in these lakes. Although different model techniques highlighted different variable importance of water quality in determining fish communities, there is generally a consensus that the hydrological related water quality indicators like: water clarity (e.g., total suspended solids) and nutrient loading (e.g., phosphate) contributed the most to the changes of fish communities. Our results also indicated that water diversions could bring knock-on effects on fish communities. Thus, more attention should be paid to long-term ecological effects from future water diversions. [Guo, Chuanbo; Chen, Yushun; Liu, Han; Lu, Yin; Qu, Xiao; Lek, Sovan; Xie, Songguang] Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Hubei, Peoples R China; [Chen, Yushun; Liu, Han; Lu, Yin; Qu, Xiao; Xie, Songguang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Yuan, Hui] Fisheries Management Commiss Gao Bao Shaobo Lakes, Yangzhou 225009, Jiangsu, Peoples R China; [Lek, Sovan] Univ Paul Sabatier, CNRS, UMR EDB 5174, F-31062 Toulouse, France Chinese Academy of Sciences; Institute of Hydrobiology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Ecole Nationale Formation Agronomique (ENSFEA) Chen, YS (corresponding author), Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Hubei, Peoples R China. yushunchen@ihb.ac.cn National Natural Science Foundation of China [31602158]; Chinese Academy of Sciences (Key Research Program of Frontier Sciences) [QYZDB-SSW-SMC041]; Chinese Academy of Sciences (Feature Institute Program) [Y55Z06]; Chinese Academy of Sciences (Hundred Talent Program) [Y62302]; Chinese Academy of Sciences (Institute Talent Program) [Y45Z04] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (Key Research Program of Frontier Sciences); Chinese Academy of Sciences (Feature Institute Program); Chinese Academy of Sciences (Hundred Talent Program)(Chinese Academy of Sciences); Chinese Academy of Sciences (Institute Talent Program) We are grateful to the Fisheries Management Commissions of Hongze Lake, Luoma Lake, Gao-Bao-Shaobo lake, Dongping Lake and Nansi Lake for providing the convenience and kindly assistance in field sampling. We also highly appreciated our colleagues from the group of Aquatic Ecosystems Health for the assistance in field sampling and water quality measurements. Thanks so much for the great efforts and fruitful comments made by the editors and two reviewers on the early version of the manuscript. This work was co-funded by National Natural Science Foundation of China (No.31602158) and Chinese Academy of Sciences (Key Research Program of Frontier Sciences QYZDB-SSW-SMC041, Feature Institute Program Y55Z06, Hundred Talent Program Y62302, and Institute Talent Program Y45Z04). 71 19 21 21 118 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3800 1872-7026 ECOL MODEL Ecol. Model. APR 1 2019.0 397 25 35 10.1016/j.ecolmodel.2019.01.014 0.0 11 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology HP1CD 2023-03-23 WOS:000461402100003 0
J Feng, G; Zheng, KI; Li, YY; Rios, RS; Zhu, PW; Pan, XY; Li, G; Ma, HL; Tang, LJ; Byrne, CD; Targher, G; He, N; Mi, M; Chen, YP; Zheng, MH Feng, Gong; Zheng, Kenneth I.; Li, Yang-Yang; Rios, Rafael S.; Zhu, Pei-Wu; Pan, Xiao-Yan; Li, Gang; Ma, Hong-Lei; Tang, Liang-Jie; Byrne, Christopher D.; Targher, Giovanni; He, Na; Mi, Man; Chen, Yong-Ping; Zheng, Ming-Hua Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES English Article diagnosis; fibrosis; liver biopsy; machine learning algorithm; NAFLD LIVER-DISEASE; PROGRESSION; FEATURES; SYSTEM Background The presence of significant liver fibrosis is a key determinant of long-term prognosis in non-alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non-invasive fibrosis biomarkers. Methods We used a cohort of 553 adults with biopsy-proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F >= 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Results In the training cohort, the variables selected by LASSO algorithm were body mass index, pro-collagen type III, collagen type IV, aspartate aminotransferase and albumin-to-globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869-0.904) for identifying fibrosis F >= 2. The LRM AUROC was 0.764, 95% CI 0.710-0.816 and significantly better than the AST-to-Platelet ratio (AUROC 0.684, 95% CI 0.605-0.762), FIB-4 score (AUROC 0.594, 95% CI 0.503-0.685) and NAFLD Fibrosis Score (AUROC 0.557, 95% CI 0.470-0.644). In the validation cohort, MLA also showed the highest AUROC (0.893, 95% CI 0.864-0.901). The diagnostic accuracy of MLA outperformed that of LRM in all subgroups considered. Conclusions Our newly developed MLA algorithm has excellent diagnostic performance for predicting fibrosis F >= 2 in patients with biopsy-confirmed NAFLD. [Feng, Gong; Mi, Man] Xian Med Univ, Xian, Peoples R China; [Zheng, Kenneth I.; Rios, Rafael S.; Li, Gang; Ma, Hong-Lei; Tang, Liang-Jie; Chen, Yong-Ping; Zheng, Ming-Hua] Wenzhou Med Univ, Dept Hepatol, NAFLD Res Ctr, Affiliated Hosp 1, 2 Fuxue Lane, Wenzhou 325000, Peoples R China; [Li, Yang-Yang] Wenzhou Med Univ, Dept Pathol, Affiliated Hosp 1, Wenzhou, Peoples R China; [Zhu, Pei-Wu] Wenzhou Med Univ, Dept Lab Med, Affiliated Hosp 1, Wenzhou, Peoples R China; [Pan, Xiao-Yan] Wenzhou Med Univ, Dept Endocrinol, Affiliated Hosp 1, Wenzhou, Peoples R China; [Byrne, Christopher D.] Univ Hosp Southampton, Southampton Natl Inst Hlth Res Biomed Res Ctr, Southampton Gen Hosp, Southampton, Hants, England; [Targher, Giovanni] Univ Verona, Sect Endocrinol Diabet & Metab, Dept Med, Verona, Italy; [Targher, Giovanni] Azienda Osped Univ Integrata Verona, Verona, Italy; [He, Na] Xian Med Univ, Dept Gastroenterol, Affiliated Hosp 1, Xian, Peoples R China; [Chen, Yong-Ping; Zheng, Ming-Hua] Wenzhou Med Univ, Inst Hepatol, Wenzhou, Peoples R China; [Chen, Yong-Ping; Zheng, Ming-Hua] Key Lab Diag & Treatment Dev Chron Liver Dis Zhej, Wenzhou, Peoples R China Xi'an Medical University; Wenzhou Medical University; Wenzhou Medical University; Wenzhou Medical University; Wenzhou Medical University; University of Southampton; University of Verona; University of Verona; Azienda Ospedaliera Universitaria Integrata Verona; Xi'an Medical University; Wenzhou Medical University Zheng, MH (corresponding author), Wenzhou Med Univ, Dept Hepatol, NAFLD Res Ctr, Affiliated Hosp 1, 2 Fuxue Lane, Wenzhou 325000, Peoples R China. zhengmh@wmu.edu.cn Zheng, Ming-Hua/H-5584-2019 Zheng, Ming-Hua/0000-0003-4984-2631; Targher, Giovanni/0000-0002-4325-3900; Feng, Gong/0000-0001-5394-0029; Byrne, Christopher D/0000-0001-6322-7753 National Natural Science Foundation of China [82070588]; High Level Creative Talents from Department of Public Health in Zhejiang Province [S2032102600032]; Project of New Century 551 Talent Nurturing in Wenzhou; Special Research Fund of Youan Medical Alliance for the Liver and Infectious Diseases [LM202003]; School of Medicine, University of Verona, Verona, Italy; Southampton NIHR Biomedical Research Centre, UK [IS-BRC-20004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High Level Creative Talents from Department of Public Health in Zhejiang Province; Project of New Century 551 Talent Nurturing in Wenzhou; Special Research Fund of Youan Medical Alliance for the Liver and Infectious Diseases; School of Medicine, University of Verona, Verona, Italy; Southampton NIHR Biomedical Research Centre, UK This work was supported by grants from the National Natural Science Foundation of China (82070588), High Level Creative Talents from Department of Public Health in Zhejiang Province (S2032102600032) and Project of New Century 551 Talent Nurturing in Wenzhou. Na He is supported in part by Special Research Fund of Youan Medical Alliance for the Liver and Infectious Diseases (LM202003). GT is supported in part by grants from the School of Medicine, University of Verona, Verona, Italy. CDB is supported in part by the Southampton NIHR Biomedical Research Centre (IS-BRC-20004), UK. 29 8 9 3 9 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1868-6974 1868-6982 J HEPATO-BIL-PAN SCI J. Hepato-Biliary-Pancreat. Sci. JUL 2021.0 28 7 593 603 10.1002/jhbp.972 0.0 MAY 2021 11 Gastroenterology & Hepatology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology; Surgery TL9WV 33908180.0 Green Accepted 2023-03-23 WOS:000649415200001 0
J Korkmaz, ME; Waqar, S; Garcia-Collado, A; Gupta, MK; Krolczyk, GM Korkmaz, Mehmet Erdi; Waqar, Saad; Garcia-Collado, A.; Gupta, Munish Kumar; Krolczyk, Grzegorz M. A technical overview of metallic parts in hybrid additive manufacturing industry JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T English Article Subtractive manufacturing; Additive manufacturing; Hybrid manufacturing; Artificial intelligence; Industry 4; 0 PERFORMANCE; FABRICATION; DEPOSITION; OPTIMIZATION; INTEGRATION; SUPERALLOY; DESIGN; SHAPE; HARD; CELL Additive manufacturing technologies have emerged as the promising alternatives of conventional manufacturing techniques. Conventional manufacturing techniques involves cutting and removal of material by mechanical procedures to achieve final product. Whereas, discrete chunks of material in any form are combined point by point and layer by layer for the fabrication of final product in additive manufacturing processes. Numerous advantages and inefficiencies of these manufacturing techniques are reflected in factors such as the design, fabrication, material properties and working condition etc. Therefore, development of a production technology by combining the benefits of both conventional and additive techniques is significantly important. Hybrid Manufacturing jointly apply additive and conventional production methods to attain final products. Hence, this short overview covers the operation aspects of both additive and subtractive manufacturing of metallic materials. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [Korkmaz, Mehmet Erdi] Karabuk Univ, Dept Mech Engn, Karabuk, Turkey; [Waqar, Saad] Shandong Univ, Sch Mech Engn, Key Lab High Efficiency & Clean Mech Manufacture, Minist Educ, Jinan 250061, Peoples R China; [Garcia-Collado, A.] Univ Jaen, Dept Mech & Min Engn, EPS Jaen, Campus Las, Jaen 23071, Spain; [Gupta, Munish Kumar; Krolczyk, Grzegorz M.] Opole Univ Technol, Fac Mech Engn, 76 Proszkowska St, PL-45758 Opole, Poland Karabuk University; Shandong University; Universidad de Jaen; Opole University of Technology Garcia-Collado, A (corresponding author), Univ Jaen, Dept Mech & Min Engn, EPS Jaen, Campus Las, Jaen 23071, Spain.;Gupta, MK (corresponding author), Opole Univ Technol, Fac Mech Engn, 76 Proszkowska St, PL-45758 Opole, Poland. acollado@ujaen.es; munishguptanit@gmail.com Krolczyk, Grzegorz/D-6709-2013; Waqar, Saad/AAF-7837-2021 Krolczyk, Grzegorz/0000-0002-2967-1719; Garcia Collado, Alberto/0000-0002-3079-066X; KORKMAZ, Mehmet Erdi/0000-0002-0481-6002; Waqar, Saad/0000-0002-3645-4720 Regional Operational Programme for the Opole Voivodeship - European Union; State budget of Poland; [RPOP.01.01.00-16-0015/17] Regional Operational Programme for the Opole Voivodeship - European Union; State budget of Poland; The research is supported by the Regional Operational Programme for the Opole Voivodeship financed by the Structural Funds of the European Union and the State budget of Poland, RPOP.01.01.00-16-0015/17. 84 11 11 28 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2238-7854 2214-0697 J MATER RES TECHNOL J. Mater. Res. Technol-JMRT MAY-JUN 2022.0 18 384 395 10.1016/j.jmrt.2022.02.085 0.0 MAR 2022 12 Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Metallurgy & Metallurgical Engineering 0I0JW gold 2023-03-23 WOS:000779115500002 0
J Stamov, G; Stamova, I; Li, XD; Gospodinova, E Stamov, Gani; Stamova, Ivanka; Li, Xiaodi; Gospodinova, Ekaterina Practical Stability with Respect to h-Manifolds for Impulsive Control Functional Differential Equations with Variable Impulsive Perturbations MATHEMATICS English Article practical stability; h-manifolds; impulsive functional differential equations; variable impulsive perturbations; Lyapunov-Razumikhin method CELLULAR NEURAL-NETWORKS; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; SYSTEMS; STABILIZATION; CONTROLLABILITY; EXISTENCE; DESIGN; TERMS; DELAY The present paper is devoted to the problems of practical stability with respect to h-manifolds for impulsive control differential equations with variable impulsive perturbations. We will consider these problems in light of the Lyapunov-Razumikhin method of piecewise continuous functions. The new results are applied to an impulsive control cellular neural network model with variable impulsive perturbations. [Stamov, Gani; Gospodinova, Ekaterina] Prof Dr Assen Zlatarov Univ, Dept Math & Phys, Burgas 8010, Bulgaria; [Stamov, Gani; Stamova, Ivanka] Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA; [Li, Xiaodi] Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Shandong, Peoples R China University of Burgas; University of Texas System; University of Texas at San Antonio (UTSA); Shandong Normal University Stamova, I (corresponding author), Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA. ivanka.stamova@utsa.edu Stamov, Gani/R-2024-2016; Stamova, Ivanka/AAQ-1586-2020 Stamova, Ivanka/0000-0001-6723-2699; Gospodinova, Ekaterina/0000-0001-9083-7135 European Regional Development Fund through the Operational Programme Science and Education for Smart Growth [BG05M2OP001-1.001-0004] European Regional Development Fund through the Operational Programme Science and Education for Smart Growth This research was funded in part by the European Regional Development Fund through the Operational Programme Science and Education for Smart Growth under Contract UNITe No. BG05M2OP001-1.001-0004 (2018-2023). 52 5 5 2 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics JUL 2019.0 7 7 656 10.3390/math7070656 0.0 16 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics IN6DB Green Submitted, gold 2023-03-23 WOS:000478765700056 0
J Huang, WY; Wang, HW; Qin, HT; Wei, YG; Chevallier, J Huang, Wenyang; Wang, Huiwen; Qin, Haotong; Wei, Yigang; Chevallier, Julien Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method ENERGY ECONOMICS English Article OHLC data; Trading strategies; EUA; CNN; Unconstrained transformation TECHNICAL TRADING STRATEGIES; EU-ETS; CARBON PRICE; ELECTRICITY LOAD; MARKET; EMISSIONS; PHASE; VOLATILITY; CLIMATE; SYSTEM This paper develops an open-high-low-close (OHLC) data forecasting framework to forecast EUA futures price based on EU ETS data and extended exogenous variables from 2013 to 2020. The challenge of forecasting such an OHLC structure lies in handling its three intrinsic constraints, i.e., the positive constraint, interval constraint, and boundary constraint. This paper proposes a novel unconstrained transformation method for OHLC data and combines it with various forecasting models. Out-of-sample modelings identify the extraordinary performance of the convolutional neural network (CNN) in terms of MAPE (1.371%), MAE (0.274), RMSE (0.370), and AR (0.621), better than that of multiple linear regression (MLR), vector auto-regression (VAR) and vector error correction model (VECM), support vector regression (SVR), and multi-layer perceptron (MLP). The proposed transformation-based forecasting framework demonstrates the considerable potential for OHLC data forecasting in the energy finance field, e.g., crude and natural gas. Practicable and concrete suggestions are provided to ensure the profitability of trading EUA futures. [Huang, Wenyang; Wang, Huiwen; Wei, Yigang] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China; [Huang, Wenyang; Qin, Haotong] Beihang Univ, Shen Yuan Honors Coll, Beijing, Peoples R China; [Huang, Wenyang; Wei, Yigang] Beijing Key Lab Emergency Support Simulat Technol, Beijing, Peoples R China; [Wang, Huiwen] Beihang Univ, Key Lab Complex Syst Anal, Management & Decis, Minist Educ, Beijing, Peoples R China; [Qin, Haotong] Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China; [Wei, Yigang] Beihang Univ, Lab Low carbon Intelligent Governance, Beijing, Peoples R China; [Chevallier, Julien] IPAG Business Sch, Ipag Lab, 184 bd St Germain, F-75006 Paris, France; [Chevallier, Julien] Univ Paris 8 LED, 2 Ave Liberte, F-93526 Saint Denis, France Beihang University; Beihang University; Beihang University; Beihang University; Beihang University; IPAG Business School Wei, YG (corresponding author), Beihang Univ, Sch Econ & Management, Beijing, Peoples R China. wenyanghuang@buaa.edu.cn; wanghw@vip.sina.com; qinhaotong@buaa.edu.cn; weiyg@buaa.edu.cn; julien.chevallier@ipag.fr Huang, Wenyang/0000-0001-8485-9346 National Science Foundation of China [71904009, 72021001, 72174020]; MOE (Ministry of Education in China) Project of Humanities and Social Sciences [18YJC840041]; Beijing Social Science Fund [20GLC054] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); MOE (Ministry of Education in China) Project of Humanities and Social Sciences; Beijing Social Science Fund Acknowledgments The authors are grateful for the financial support from the National Science Foundation of China (NO.71904009, 72021001, 72174020) , MOE (Ministry of Education in China) Project of Humanities and Social Sciences (NO.18YJC840041) , and Beijing Social Science Fund (NO.20GLC054) . Besides, the authors also thank the anonymous re-viewers for insightful comments that helped us improve the quality of the paper. 106 8 8 15 29 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0140-9883 1873-6181 ENERG ECON Energy Econ. JUN 2022.0 110 106049 10.1016/j.eneco.2022.106049 0.0 MAY 2022 16 Economics Social Science Citation Index (SSCI) Business & Economics 1S1CG 2023-03-23 WOS:000803795000002 0
J Yang, JN; Wen, YF; Wang, Y; Zhang, SJ; Pinto, JP; Pennington, EA; Wang, Z; Wu, Y; Sander, SP; Jiang, JH; Hao, JM; Yung, YL; Seinfeld, JH Yang, Jiani; Wen, Yifan; Wang, Yuan; Zhang, Shaojun; Pinto, Joseph P.; Pennington, Elyse A.; Wang, Zhou; Wu, Ye; Sander, Stanley P.; Jiang, Jonathan H.; Hao, Jiming; Yung, Yuk L.; Seinfeld, John H. From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA English Article COVID-19; machine learning; air pollution; traffic emissions; vehicular electrification LOS-ANGELES; CLARS-FTS; IN-USE; EMISSIONS; DIESEL; TRENDS; GASOLINE; CO2 The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 mu m by -30.1% and -17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future trafficemission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels. [Yang, Jiani; Wang, Yuan; Yung, Yuk L.] CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA; [Wen, Yifan; Zhang, Shaojun; Wu, Ye; Hao, Jiming] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China; [Wang, Yuan; Sander, Stanley P.; Jiang, Jonathan H.; Yung, Yuk L.] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA; [Pinto, Joseph P.] Univ N Carolina, Gillings Sch Global Publ Hlth, Chapel Hill, NC 27599 USA; [Pennington, Elyse A.; Seinfeld, John H.] CALTECH, Div Chem & Chem Engn, Pasadena, CA 91125 USA; [Wang, Zhou] Johannes Gutenberg Univ Mainz, Dept Geog, D-55099 Mainz, Germany California Institute of Technology; Tsinghua University; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); University of North Carolina; University of North Carolina Chapel Hill; California Institute of Technology; Johannes Gutenberg University of Mainz Wang, Y (corresponding author), CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA.;Zhang, SJ (corresponding author), Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China.;Wang, Y (corresponding author), CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA.;Seinfeld, JH (corresponding author), CALTECH, Div Chem & Chem Engn, Pasadena, CA 91125 USA. yuan.wang@caltech.edu; zhsjun@tsinghua.edu.cn; seinfeld@caltech.edu Yung, Yuk/AAM-4850-2021; Wu, Ye/O-9779-2015; Zhang, Shaojun/AAU-3131-2020; Wang, Yuan/A-5816-2013 Yung, Yuk/0000-0002-4263-2562; Zhang, Shaojun/0000-0002-2176-6174; Wang, Yuan/0000-0001-6657-8401; Pinto, Joe/0000-0001-5639-8458; Yang, Jiani/0000-0003-0037-2413; Wen, Yifan/0000-0003-1876-7990; Jiang, Jonathan/0000-0002-5929-8951; Pennington, Elyse/0000-0003-1736-2342 Jet Propulsion Laboratory, California Institute of Technology; Samsung Corporation [SAMS.2019GRO]; National Key Research and Development Program of China [2017YFC0212100]; National Natural Science Foundation of China [41977180]; Ford Motor Company; NASA Jet Propulsion Laboratory, California Institute of Technology; Samsung Corporation(Samsung); National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ford Motor Company; NASA(National Aeronautics & Space Administration (NASA)) Y. Wang, S.P.S., J.H.J., and Y.L.Y. acknowledge support by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. E.A.P. and J.H.S. acknowledge support by the Samsung Corporation (award SAMS.2019GRO) . S.Z. acknowledges support by the National Key Research and Development Program of China (grant 2017YFC0212100) , the National Natural Science Foundation of China (grant 41977180) , and Ford Motor Company. J.Y. acknowledges Leo Gallagher and Daniel Kitowski at the California Department of Transportation, Thomas E. Morrell at the Caltech Library, Jin Tao for helpful information on data inputs, and Yu Zhou at Tsinghua University for useful discussions. 44 29 30 34 170 NATL ACAD SCIENCES WASHINGTON 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA 0027-8424 P NATL ACAD SCI USA Proc. Natl. Acad. Sci. U. S. A. JUN 29 2021.0 118 26 e2102705118 10.1073/pnas.2102705118 0.0 7 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics TH0EO 34155113.0 hybrid, Green Accepted 2023-03-23 WOS:000671767800005 0
J Ai, MZ; Ding, YC; Ban, Y; Martin-Guerrero, JD; Casanova, J; Cui, JM; Huang, YF; Chen, X; Li, CF; Guo, GC Ai, Ming-Zhong; Ding, Yongcheng; Ban, Yue; Martin-Guerrero, Jose D.; Casanova, Jorge; Cui, Jin-Ming; Huang, Yun-Feng; Chen, Xi; Li, Chuan-Feng; Guo, Guang-Can Experimentally realizing efficient quantum control with reinforcement learning SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY English Article quantum control; reinforcement learning; trapped ion; quantum computing; noise robustness We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence approach to control a quantum system. We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity. In particular, the protocol's robustness against both over-rotations and off-resonance errors can still be achieved simultaneously without any priori input. For the thorough comparison, we choose the task as single-qubit flipping, in which various analytical methods are well-developed as the benchmark, ensuring their feasibility in the quantum system as well. Consequently, a gate operation is demonstrated on a trapped Yb-171(+) ion, significantly outperforming analytical pulses in the gate time and energy cost with hybrid robustness, as well as the fidelity after repetitive operations under time-varying stochastic errors. Our experiments reveal a framework of computer-inspired quantum control, which can be extended to other complicated tasks without loss of generality. [Ai, Ming-Zhong; Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China; [Ai, Ming-Zhong; Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phy, Hefei 230026, Peoples R China; [Ding, Yongcheng; Chen, Xi] Shanghai Univ, Int Ctr Quantum Artifcial Intelligence Sci & Tech, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Chen, Xi] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Ban, Yue; Casanova, Jorge; Chen, Xi] Univ Basque Country UPV EHU, Dept Phys Chem, Bilbao 48080, Spain; [Ban, Yue] Shanghai Univ, Sch Mat Sci & Engn, Shanghai 200444, Peoples R China; [Martin-Guerrero, Jose D.] Univ Valencia, Elect Engn Dept, IDAL, Valencia 46100, Spain; [Casanova, Jorge] Basque Fdn Sci, IKERBASQUE, Bilbao 48009, Spain Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shanghai University; Shanghai University; University of Basque Country; Shanghai University; University of Valencia; Basque Foundation for Science Cui, JM; Huang, YF (corresponding author), Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China.;Cui, JM; Huang, YF (corresponding author), Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phy, Hefei 230026, Peoples R China.;Chen, X (corresponding author), Shanghai Univ, Int Ctr Quantum Artifcial Intelligence Sci & Tech, Shanghai 200444, Peoples R China.;Chen, X (corresponding author), Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China.;Chen, X (corresponding author), Univ Basque Country UPV EHU, Dept Phys Chem, Bilbao 48080, Spain. jmcui@ustc.edu.cn; hyf@ustc.edu.cn; xchen@shu.edu.cn Martín-Guerrero, José D./L-8146-2014; Chen, Xi/ABE-6575-2020; chen, xi/GXH-3653-2022; Ban, Yue/AAR-9240-2021 Martín-Guerrero, José D./0000-0001-9378-0285; Chen, Xi/0000-0003-4221-4288; Ban, Yue/0000-0003-1764-4470; Ding, Yongcheng/0000-0002-6008-0001; Ai, Ming-Zhong/0000-0003-3269-2534 National Natural Science Foundation of China [11874343, 61327901, 11774335, 11734015, 12075145]; An-hui Initiative in Quantum Information Technologies [AHY020100, AHY070000]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDYSSW-SLH003]; STCSM [2019SHZDZX01-ZX04]; EU [820505, 820363]; Spanish Government (MCIU/AEI/FEDER, UE) [PGC2018-095113-B-I00]; Basque Government [IT986-16]; EU FET Open Grant Quromorphic [828826]; EPIQUS [899368]; Ramon y Cajal Program [RYC-2017-22482, RYC2018-025197-I]; Spanish MICINN [EUR2020-112117]; UPV/EHU through the grant EHUrOPE; Program for Eastern Scholia National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); An-hui Initiative in Quantum Information Technologies; Key Research Program of Frontier Sciences, Chinese Academy of Sciences; STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); EU(European Commission); Spanish Government (MCIU/AEI/FEDER, UE)(Spanish Government); Basque Government(Basque Government); EU FET Open Grant Quromorphic; EPIQUS; Ramon y Cajal Program(Spanish Government); Spanish MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); UPV/EHU through the grant EHUrOPE; Program for Eastern Scholia This work was supported by the National Natural Science Foundation of China (Grant Nos. 11874343, 61327901, 11774335, and 11734015), An-hui Initiative in Quantum Information Technologies (Grant Nos. AHY020100, and AHY070000), and Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No. QYZDYSSW-SLH003). The theoretical part of the work was also partially supported by the National Natural Science Foundation of China (Grant No. 12075145), STCSM (Grant No. 2019SHZDZX01-ZX04), Program for Eastern Scholia; QMiCS (Grant No. 820505) and OpenSuperQ (Grant No. 820363) of the EU Flagship on Quantum Technologies, Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE), Basque Government IT986-16, EU FET Open Grant Quromorphic (Grant No. 828826) as well as EPIQUS (Grant No. 899368). Xi Chen acknowledges the Ramon y Cajal Program (Grant No. RYC-2017-22482). Jorge Casanova acknowledges the Ramon y Cajal Program (Grant No. RYC2018-025197-I) and the EUR2020-112117 Project of the Spanish MICINN, as well as the support from the UPV/EHU through the grant EHUrOPE. 68 3 3 15 24 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-7348 1869-1927 SCI CHINA PHYS MECH Sci. China-Phys. Mech. Astron. MAY 2022.0 65 5 250312 10.1007/s11433-021-1841-2 0.0 8 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics ZU6RK Green Submitted 2023-03-23 WOS:000769968200001 0
C Li, HJ; Leung, KS; Wong, MH; Ballester, PJ DiSerio, C; Lio, P; Nonis, A; Tagliaferri, R Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J. The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2014 Lecture Notes in Bioinformatics English Proceedings Paper 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) JUN 26-28, 2014 Cambridge, ENGLAND Bioinformat Italian Soc,Source Code Biol & Med,Univ Salerno, Dipartimento Informatica,Vita Salute San Raffaele Univ, Centro Univ Statistica Sci Biomediche,Univ Cambridge, Comp Lab,Int Neural Network Soc, Bioinformat & Intelligence SIG,IEEE CIS BBCT Task Forces Neural Networks & Evolutionary Computat molecular docking; scoring functions; random forest; chemical informatics; structural bioinformatics SCORING FUNCTIONS; PROTEIN Docking is a computational technique that predicts the preferred conformation and binding affinity of a ligand molecule as bound to a protein pocket. It is often employed to identify a molecule that binds tightly to the target, so that a small concentration of the molecule is sufficient to modulate its biochemical function. The use of non-parametric machine learning, a data-driven approach that circumvents the need of modeling assumptions, has recently been shown to introduce a large improvement in the accuracy of docking scoring. However, the impact of pose generation error on binding affinity prediction is still to be investigated. Here we show that the impact of pose generation is generally limited to a small decline in the accuracy of scoring. These machine-learning scoring functions retained the highest performance on PDBbind v2007 core set in this common scenario where one has to predict the binding affinity of docked poses instead of that of co-crystallized poses (e.g. drug lead optimization). Nevertheless, we observed that these functions do not perform so well at predicting the near-native pose of a ligand. This suggests that having different scoring functions for different problems is a better approach than using the same scoring function for all problems. [Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China; [Ballester, Pedro J.] INSERM, U1068, Canc Res Ctr Marseille, F-13009 Marseille, France; [Ballester, Pedro J.] Inst J Paoli I Calmettes, F-13009 Marseille, France; [Ballester, Pedro J.] Aix Marseille Univ, F-13284 Marseille, France; [Ballester, Pedro J.] CNRS, UMR7258, F-13009 Marseille, France Chinese University of Hong Kong; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; UNICANCER; Institut Paoli-Calmette (IPC); UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Aix-Marseille Universite Ballester, PJ (corresponding author), INSERM, U1068, Canc Res Ctr Marseille, F-13009 Marseille, France. pedro.ballester@inserm.fr Ballester, Pedro/A-1148-2008; Wong, Man Hon/GOH-0560-2022 Ballester, Pedro/0000-0002-4078-743X; 12 5 5 0 4 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 1611-3349 978-3-319-24462-4; 978-3-319-24461-7 LECT N BIOINFORMAT Lect. Notes Bioinforma. 2015.0 8623 231 241 10.1007/978-3-319-24462-4_20 0.0 11 Biochemical Research Methods; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Computer Science; Mathematical & Computational Biology BE0MG 2023-03-23 WOS:000366296700020 0
J Li, W; He, CY; Hu, AD; Zhao, DS; Shen, Y; Zhang, KF Li, Wang; He, Changyong; Hu, Andong; Zhao, Dongsheng; Shen, Yi; Zhang, Kefei A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network ADVANCES IN SPACE RESEARCH English Article Artificial neural network; Ionospheric model; Data correction; COSMIC; Ionosonde GNSS-RADIO OCCULTATION; ELECTRON-DENSITY; PLASMA BUBBLES; IONOSONDE; TEC; VALUES There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde's observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde's data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14-25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved. [Li, Wang; Zhao, Dongsheng; Zhang, Kefei] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China; [Li, Wang; He, Changyong; Hu, Andong; Zhang, Kefei] RMIT Univ, SPACE Res Ctr, Sch Sci, Melbourne, Vic 3001, Australia; [He, Changyong] ENSG, IGN, Cite Descartes, F-77455 Champs Sur Marne, Marne La Vallee, France; [Hu, Andong] Ctr Wiskunde & Informat CWI, Multiscale Grp, Sci Pk 123, NL-1098 XG Amsterdam, Netherlands; [Shen, Yi] Xinyang Normal Univ, Key Lab Synergist Prevent Water & Soil Environm P, Xinyang 464000, Peoples R China China University of Mining & Technology; Royal Melbourne Institute of Technology (RMIT); Universite Gustave-Eiffel; Xinyang Normal University Zhang, KF (corresponding author), China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China. profkzhang@cumt.edu.cn Zhao, Dongsheng/0000-0001-7297-8639 National Natural Science Foundations of China [41730109, 41870404]; Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution [KLSPWSEP-A06]; China Postdoctoral Science Foundation [2020M671645]; Fundamental Research Funds for the Central Universities [2020QN31]; Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping); Jiangsu Dual Creative Talents Project; Jiangsu Dual Creative Teams Programme Project; Xuzhou Key Project [KC19111] National Natural Science Foundations of China(National Natural Science Foundation of China (NSFC)); Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping); Jiangsu Dual Creative Talents Project; Jiangsu Dual Creative Teams Programme Project; Xuzhou Key Project The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109. 41870404), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), the Project funded by China Postdoctoral Science Foundation (Grant No. 2020M671645), the Fundamental Research Funds for the Central Universities (Grant No. 2020QN31), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping). We would like to acknowledge the support of the Jiangsu Dual Creative Talents and Jiangsu Dual Creative Teams Programme Projects awarded in 2017 and the Xuzhou Key Project (KC19111). We are very grateful to UCAR community programs for providing COSMIC data, the Lowell GIRO Data Center for providing Ionosonde data, the GSFC for providing solar-magnetic indices, the IZMIRAN for providing global foF2 and hmF2 maps. 53 5 5 0 6 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. JAN 1 2021.0 67 1 20 34 10.1016/j.asr.2020.07.032 0.0 15 Engineering, Aerospace; Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences PI1OC Green Published 2023-03-23 WOS:000600867300004 0
C Fan, WF; Geerts, F; Libkin, L ACM Fan, Wenfei; Geerts, Floris; Libkin, Leonid On Scale Independence for Querying Big Data PODS'14: PROCEEDINGS OF THE 33RD ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS English Proceedings Paper 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems JUN 22-27, 2014 Snowbird, UT ACM SIGACT,ACM Special Interest Group on Artificial Intelligence,ACM Special Interest Group on Management of Data,ACM Special Interest Group on Theoretical Computer Science Scale independence; big data; query answering To make query answering feasible in big datasets, practitioners have been looking into the notion of scale independence of queries. Intuitively, such queries require only a relatively small subset of the data, whose size is determined by the query and access methods rather than the size of the dataset itself. This paper aims to formalize this notion and study its properties. We start by defining what it means to be scale-independent, and provide matching upper and lower bounds for checking scale independence, for queries in various languages, and for combined and data complexity. Since the complexity turns out to be rather high, and since scale-independent queries cannot be captured syntactically, we develop sufficient conditions for scale independence. We formulate them based on access schemas, which combine indexing and constraints together with bounds on the sizes of retrieved data sets. We then study two variations of scale-independent query answering, inspired by existing practical systems. One concerns incremental query answering: we check when query answers can be maintained in response to updates scale-independently. The other explores scale-independent query rewriting using views. [Fan, Wenfei] Univ Edinburgh, Edinburgh, Midlothian, Scotland; [Fan, Wenfei] Beihang Univ, RCDB & SKLSDE Lab, Beijing, Peoples R China; [Geerts, Floris] Univ Antwerp, Dept Math & Comp Sci, Antwerp, Belgium; [Libkin, Leonid] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland University of Edinburgh; Beihang University; University of Antwerp; University of Edinburgh Fan, WF (corresponding author), Univ Edinburgh, Edinburgh, Midlothian, Scotland. wenfei@inf.ed.ac.uk; floris.geerts@uantwerpen.be; libkin@inf.ed.ac.uk EPSRC, UK [EP/J015377/1]; 973 Programs [2012CB316200, 2014CB340302]; Shenzhen Peacock Program [1105100030834361]; Guangdong Innovative Research Team Program, China [2011D005]; NSFC [61133002] EPSRC, UK(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); 973 Programs(National Basic Research Program of China); Shenzhen Peacock Program; Guangdong Innovative Research Team Program, China; NSFC(National Natural Science Foundation of China (NSFC)) Fan and Libkin are supported in part by EPSRC EP/J015377/1, UK. Fan is also supported by 973 Programs 2012CB316200 and 2014CB340302, NSFC 61133002, Shenzhen Peacock Program 1105100030834361 and Guangdong Innovative Research Team Program 2011D005, China. 28 18 18 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-2375-8 2014.0 51 62 12 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL4TR 2023-03-23 WOS:000450809200005 0
C Zheng, YM; Fei, LK; Jia, W; Wen, J; Teng, SH; Rida, I Magnenat-Thalmann, N; Interrante, V; Thalmann, D; Papagiannakis, G; Sheng, B; Kim, J; Gavrilova, M Zheng, Yongmin; Fei, Lunke; Jia, Wei; Wen, Jie; Teng, Shaohua; Rida, Imad Compact Double Attention Module Embedded CNN for Palmprint Recognition ADVANCES IN COMPUTER GRAPHICS, CGI 2021 Lecture Notes in Computer Science English Proceedings Paper 38th Computer Graphics International Conference (CGI) SEP 06-10, 2021 ELECTR NETWORK Univ Geneva, MIRALab Pattern recognition; Biometric recognition; Palmprint recognition; Attention mechanism; Convolutional neural network Palmprint-based biometric recognition has received tremendous attention due to its several advantages such as high security, noninvasive and good hygiene propensities. Recent deep convolutional neural network (CNN) has been successfully applied for palmprint recognition and achieved promising performance due to its breakthroughs in image classification, which however usually requires a massive amount of labeled samples to finetune the network. In this paper, we propose a compact CNN with limited layers for palmprint recognition by embedding double attention mechanisms into the convolutional layers. Specifically, we first design a channel attention module to learn and select the discriminative channel maps by adaptively optimizing the attention weights among all channels. Then, we engineer a location attention module to learn the position-specific features of the palmprints. Both the channel and location attention modules are subsequently embedded into each convolutional layer, such that more discriminative features can be efficiently exploited during feature learning. Lastly, we train a fully convolutional network as the classifier for feature identification. Extensive experimental results on three widely used databases demonstrate the effectiveness of the proposed method in comparison with the state-of-the-arts. [Zheng, Yongmin; Fei, Lunke; Teng, Shaohua] Guangdong Univ Technol, Sch Comp, Guangzhou, Peoples R China; [Jia, Wei] Hefei Univ Technol, Sch Comp & Informat, Hefei, Peoples R China; [Wen, Jie] Harbin Inst Technol, Lab Shenzhen Med Biometr Percept & Anal Engn, Shenzhen, Peoples R China; [Rida, Imad] Univ Technol Compiegne, Lab Biomech & Bioengn, Compiegne, France Guangdong University of Technology; Hefei University of Technology; Harbin Institute of Technology; Picardie Universites; Universite de Technologie de Compiegne Fei, LK (corresponding author), Guangdong Univ Technol, Sch Comp, Guangzhou, Peoples R China. RIDA, Imad/0000-0003-2789-5070; Fei, Lunke/0000-0001-6072-7875 Guangzhou Science and technology plan project [202002030110]; Natural Science Foundation of Guangdong Province [2019A1515011811]; National Natural Science Foundation of China [62076086, 61972102] Guangzhou Science and technology plan project; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Guangzhou Science and technology plan project under Grant 202002030110, in part by the Natural Science Foundation of Guangdong Province under Grant 2019A1515011811, and in part by the National Natural Science Foundation of China under Grants 62076086 and 61972102. 27 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-89028-5; 978-3-030-89029-2 LECT NOTES COMPUT SC 2021.0 13002 264 275 10.1007/978-3-030-89029-2_21 0.0 12 Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU5SI 2023-03-23 WOS:000917831900021 0
J Wang, YF; Tabari, H; Xu, YP; Xu, Y; Wang, Q Wang, Yuefeng; Tabari, Hossein; Xu, Youpeng; Xu, Yu; Wang, Qiang Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network WATER English Article water level variation; contribution analysis; multilayer perceptron network; human activities; climate variability YANGTZE-RIVER DELTA; EASTERN CHINA; BASIN; PRECIPITATION; URBANIZATION; IMPACTS; LAKE; MODEL; STREAMFLOW; RUNOFF Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954-2014) were divided into baseline (1954-1987) and evaluation (1988-2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)). [Wang, Yuefeng] Chongqing Normal Univ, Sch Geog & Tourism, Chongqing 401331, Peoples R China; [Wang, Yuefeng; Xu, Youpeng; Xu, Yu; Wang, Qiang] Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210023, Jiangsu, Peoples R China; [Tabari, Hossein] Katholieke Univ Leuven, Dept Civil Engn, Hydraul Sect, B-3000 Leuven, Belgium Chongqing Normal University; Nanjing University; KU Leuven Xu, YP (corresponding author), Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210023, Jiangsu, Peoples R China. wyf2046@163.com; hossein.tabari@kuleuven.be; xuyu906@163.com; xypnju@163.com; wqianghy@163.com Xu, Yu/GXV-4894-2022; WANG, YUE/GWQ-9256-2022; Tabari, Hossein/E-2911-2015 Tabari, Hossein/0000-0003-2052-4541; Wang, Qiang/0000-0002-7941-880X; Xu, Youpeng/0000-0001-5411-3842; XU, Yu/0000-0003-0014-2298 National Key Technology Support Program [2018YFC1508201]; National Natural Science Foundation of China [41771032]; Water Conservancy Science and Technology Foundation of Jiangsu Province [2015003]; China Scholarship Council; Nanjing University [201702B066] National Key Technology Support Program(National Key Technology R&D Program); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Water Conservancy Science and Technology Foundation of Jiangsu Province; China Scholarship Council(China Scholarship Council); Nanjing University(Nanjing University) This work was funded by the National Key Technology Support Program (No. 2018YFC1508201), the National Natural Science Foundation of China (No. 41771032), the Water Conservancy Science and Technology Foundation of Jiangsu Province (No. 2015003), and the Program of China Scholarship Council (2016); the Program B for Outstanding PhD candidate of Nanjing University (201702B066). 66 6 6 1 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water APR 2019.0 11 4 720 10.3390/w11040720 0.0 16 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources IF5FC gold, Green Published 2023-03-23 WOS:000473105700090 0
J Sousa, LBDE; Jonkers, I; Syx, L; Dunkel, I; Chaumeil, J; Picard, C; Foret, B; Chen, CJ; Lis, JT; Heard, E; Schulz, EG; Marsico, A de Andrade e Sousa, Lisa Barros; Jonkers, Iris; Syx, Laurene; Dunkel, Ilona; Chaumeil, Julie; Picard, Christel; Foret, Benjamin; Chen, Chong-Jian; Lis, John T.; Heard, Edith; Schulz, Edda G.; Marsico, Annalisa Kinetics of Xist-induced gene silencing can be predicted from combinations of epigenetic and genomic features GENOME RESEARCH English Article X-CHROMOSOME INACTIVATION; REGULATORY ELEMENTS; RNA; METHYLATION; DOMAINS; MAPS; HETEROCHROMATIN; IDENTIFICATION; PROTEINS; ESCAPE To initiate X-Chromosome inactivation (XCI), the long noncoding RNA Xist mediates chromosome-wide gene silencing of one X Chromosome in female mammals to equalize gene dosage between the sexes. The efficiency of gene silencing is highly variable across genes, with some genes even escaping XCI in somatic cells. A gene's susceptibility to Xist-mediated silencing appears to be determined by a complex interplay of epigenetic and genomic features; however, the underlying rules remain poorly understood. We have quantified chromosome-wide gene silencing kinetics at the level of the nascent transcriptome using allele-specific Precision nuclear Run-On sequencing (PRO-seq). We have developed a Random Forest machine-learning model that can predict the measured silencing dynamics based on a large set of epigenetic and genomic features and tested its predictive power experimentally. The genomic distance to the Xist locus, followed by gene density and distance to LINE elements, are the prime determinants of the speed of gene silencing. Moreover, we find two distinct gene classes associated with different silencing pathways: a class that requires Xist-repeat A for silencing, which is known to activate the SPEN pathway, and a second class in which genes are premarked by Polycomb complexes and tend to rely on the B repeat in Xist for silencing, known to recruit Polycomb complexes during XCI. Moreover, a series of features associated with active transcriptional elongation and chromatin 3D structure are enriched at rapidly silenced genes. Our machine-learning approach can thus uncover the complex combinatorial rules underlying gene silencing during X inactivation. [de Andrade e Sousa, Lisa Barros; Dunkel, Ilona; Schulz, Edda G.; Marsico, Annalisa] Max Planck Inst Mol Genet, Otto Warburg Labs, D-14195 Berlin, Germany; [Jonkers, Iris; Lis, John T.] Cornell Univ, Dept Mol Biol & Genet, Ithaca, NY 14853 USA; [Syx, Laurene; Chaumeil, Julie; Picard, Christel; Foret, Benjamin; Chen, Chong-Jian; Heard, Edith] PSL Res Univ, UPMC Paris Sorbonne, Inst Curie, CNRS,UMR3215,INSERM,U934, F-75005 Paris, France; [Syx, Laurene] PSL Res Univ, Mines Paris Tech, Inst Curie, INSERM,U900, F-75005 Paris, France; [Marsico, Annalisa] Free Univ Berlin, Dept Math & Informat, D-14195 Berlin, Germany; [Chen, Chong-Jian] Annoroad Gene Technol Co Ltd, Beijing 100176, Peoples R China; [Jonkers, Iris] Univ Groningen, Univ Med Ctr Groningen, Dept Genet, NL-9700 RB Groningen, Netherlands; [Chaumeil, Julie] Univ Paris 05, Sorbonne Paris Cite, Inst Cochin, INSERM,U1016,CNRS,UMR8104, F-74014 Paris, France; [Marsico, Annalisa] Helmholtz Zentrum Munchen, ICB, D-85764 Oberschleissheim, Germany; [Heard, Edith] EMBL, Directors Res Unit, D-69117 Heidelberg, Germany Max Planck Society; Cornell University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Sorbonne Universite; Universite PSL; UNICANCER; Institut Curie; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite PSL; MINES ParisTech; UNICANCER; Institut Curie; Institut National de la Sante et de la Recherche Medicale (Inserm); Free University of Berlin; University of Groningen; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Cochin - APHP; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; European Molecular Biology Laboratory (EMBL) Schulz, EG; Marsico, A (corresponding author), Max Planck Inst Mol Genet, Otto Warburg Labs, D-14195 Berlin, Germany.;Lis, JT (corresponding author), Cornell Univ, Dept Mol Biol & Genet, Ithaca, NY 14853 USA.;Heard, E (corresponding author), PSL Res Univ, UPMC Paris Sorbonne, Inst Curie, CNRS,UMR3215,INSERM,U934, F-75005 Paris, France.;Marsico, A (corresponding author), Free Univ Berlin, Dept Math & Informat, D-14195 Berlin, Germany.;Marsico, A (corresponding author), Helmholtz Zentrum Munchen, ICB, D-85764 Oberschleissheim, Germany.;Heard, E (corresponding author), EMBL, Directors Res Unit, D-69117 Heidelberg, Germany. johnlis@cornell.edu; Edith.Heard@embl.org; edda.schulz@molgen.mpg.de; annalisa.marsico@helmholtz-muenchen.de Marsico, Annalisa/AFU-3262-2022 CHAUMEIL, Julie/0000-0002-8976-2890 NIH [GM025332]; Center for Vertebrate Genomics (Cornell University); University of Groningen; ERC [671027]; Labelisation La Ligue, FRM [DEI20151234398]; ANR DoseX 2017; Labex DEEP, part of the IDEX Idex PSL [ANR-11-LBX-0044, ANR-10IDEX-0001-02 PSL]; ABS4NGS [ANR-11-BINF-0001]; ARC Foundation for Cancer Research (France) [R17016KK]; ATIP-AVENIR program (INSERM/CNRS, France) [C16002KS]; IMPRS; HFSP long-term fellowship [LT000597/2010-L]; Max-Planck Research Group Leader program; E: bio Module III-Xnet grant [BMBF 031L0072] NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Center for Vertebrate Genomics (Cornell University); University of Groningen; ERC(European Research Council (ERC)European Commission); Labelisation La Ligue, FRM; ANR DoseX 2017(French National Research Agency (ANR)); Labex DEEP, part of the IDEX Idex PSL; ABS4NGS; ARC Foundation for Cancer Research (France); ATIP-AVENIR program (INSERM/CNRS, France); IMPRS; HFSP long-term fellowship; Max-Planck Research Group Leader program; E: bio Module III-Xnet grant We thank the NGS platform of the Curie Institute for sequencing, J. Helmuth and Q. Deletang for help with data analysis, and S. Rocha and A. Bousard for providing RNA-seq data of Xist mutants associated with a recent preprint. This work was supported by NIH grant GM025332 to J.T.L.; Center for Vertebrate Genomics (Cornell University) stipend to J.T.L. and I. J.; Helen Hay Whitney Postdoctoral Fellowship and Rosalind Franklin Fellowship (University of Groningen) to I.J.; ERC Advanced Investigator award (ERC-2014-AdG no. 671027), Labelisation La Ligue, FRM (grant DEI20151234398), ANR DoseX 2017, Labex DEEP (ANR-11-LBX-0044), part of the IDEX Idex PSL (ANR-10IDEX-0001-02 PSL), and ABS4NGS (ANR-11-BINF-0001) funding to E.H.; ARC Foundation for Cancer Research (France; post-doctoral fellowship and grant R17016KK) and the ATIP-AVENIR program (INSERM/CNRS, France; grant C16002KS) to J.C.; IMPRS for Computational Biology and Scientific Computing funding to L.B.A.S.; and HFSP long-term fellowship (LT000597/2010-L), Max-Planck Research Group Leader program and E: bio Module III-Xnet grant (BMBF 031L0072) to E.G.S. 47 22 22 0 6 COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT COLD SPRING HARBOR 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA 1088-9051 1549-5469 GENOME RES Genome Res. JUL 2019.0 29 7 1087 1099 10.1101/gr.245027.118 0.0 13 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Genetics & Heredity IG3UJ 31175153.0 Green Published, Green Accepted, Green Submitted, hybrid 2023-03-23 WOS:000473730600005 0
C Li, C; De Causmaecker, P; Chen, YW Lang, J Li, Chao; De Causmaecker, Patrick; Chen, Yingwu Data-driven Onboard Scheduling for an Autonomous Observation Satellite PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE English Proceedings Paper 27th International Joint Conference on Artificial Intelligence (IJCAI) JUL 13-19, 2018 Stockholm, SWEDEN Int Joint Conf Artifical Intelligence,Didi ChuXing,Xiaoi,Tencent,JD.com,Alibaba Grp,Baidu,Tieto,Adobe,Pwc,Artificial Intelligence Journal,Ericsson,Recorded Future,Songshu AI Learning,Microsoft,Node Pole,Nnaisense,Vinnova,IBM,Hitachi,Sondrel,Inovia ai,Figure Eight,Prowler.io,Nuance Observation requests for autonomous observation satellites are dynamically generated. Considering the limited computing resources, a data-driven onboard scheduling method combining AI techniques and polynomial-time heuristics is proposed in this work. To construct observation schedules, a framework with offline learning and onboard scheduling is adopted. A neural network is trained offline in ground stations to assign the scheduling priority to observation requests in the onboard scheduling, based on the optimized historical schedules obtained by genetic algorithms which are computationally demanding to run onboard. The computational simulations show that the performance of the scheduling heuristic is enhanced using the data-driven framework. [Li, Chao; De Causmaecker, Patrick] Katholieke Univ Leuven, Dept Comp Sci, CODeS, Leuven, Belgium; [Chen, Yingwu] Natl Univ Def Technol, Coll Syst Engn, Changsha, Peoples R China KU Leuven; National University of Defense Technology - China Li, C (corresponding author), Katholieke Univ Leuven, Dept Comp Sci, CODeS, Leuven, Belgium. chao.li@kuleuven.be; patrick.decausmaecker@kuleuven.be; ywchen@nudt.edu.cn ; De Causmaecker, Patrick/E-5635-2017 Li, Chao/0000-0002-9102-3923; De Causmaecker, Patrick/0000-0002-6763-1945 4 2 2 0 0 IJCAI-INT JOINT CONF ARTIF INTELL FREIBURG ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY 978-0-9992411-2-7 2018.0 5773 5774 2 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS7MQ 2023-03-23 WOS:000764175405157 0
C Zheng, Z; Zhou, B; Zhou, D; Zheng, X; Cheng, G; Soylu, A; Kharlamov, E Sattler, U; Hogan, A; Keet, M; Presutti, V; Almeida, JPA; Takeda, H; Monnin, P; Pirro, G; DAmato, C Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Zheng, Xianda; Cheng, Gong; Soylu, Ahmet; Kharlamov, Evgeny Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring SEMANTIC WEB - ISWC 2022 Lecture Notes in Computer Science English Proceedings Paper 21st International Semantic Web Conference (ISWC) OCT 23-27, 2022 ELECTR NETWORK ChinaScope,Elsevier,IBM Res,Metaphacts,MEMECT,Oracle,Springer Knowledge graph; Machine learning; Data analytics; Industrial application; Welding monitoring Data analysis including ML are essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG). It relies on semantic technologies to formally encode ML knowledge and solutions in KGs, which can be translated to executable scripts in a reusable and modularised fashion. In addition, the executable KGs also serve as common language between ML experts and non-ML experts, and facilitate their communication. We evaluated our system extensively with an impactful industrial use case at Bosch, including a user study, workshops and scalability evaluation. The evaluation demonstrates the system offers a user-friendly way for even non-ML experts to discuss, customise, and reuse ML methods. [Zheng, Zhuoxun; Kharlamov, Evgeny] Bosch Ctr Artificial Intelligence, Renningen, Germany; [Zheng, Zhuoxun; Soylu, Ahmet] Oslo Metropolitan Univ, Dept Comp Sci, Oslo, Norway; [Zhou, Baifan; Zhou, Dongzhuoran; Kharlamov, Evgeny] Univ Oslo, SIRIUS Ctr, Oslo, Norway; [Zheng, Xianda] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China; [Cheng, Gong] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China Oslo Metropolitan University (OsloMet); University of Oslo; Nanjing University; Southeast University - China Zhou, B (corresponding author), Univ Oslo, SIRIUS Ctr, Oslo, Norway. zhuoxun.zheng@de.bosch.com; baifanz@ifi.uio.no H2020 project Dome 4.0 [953163]; H2020 project OntoCommons [958371]; H2020 project DataCloud [101016835]; SIRIUS Centre, Norwegian Research Council [237898] H2020 project Dome 4.0; H2020 project OntoCommons; H2020 project DataCloud; SIRIUS Centre, Norwegian Research Council The work was partially supported by the H2020 projects Dome 4.0 (Grant Agreement No. 953163), OntoCommons (Grant Agreement No. 958371), and DataCloud (Grant Agreement No. 101016835) and the SIRIUS Centre, Norwegian Research Council project number 237898. 35 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-19432-0; 978-3-031-19433-7 LECT NOTES COMPUT SC 2022.0 13489 791 809 10.1007/978-3-031-19433-7_45 0.0 19 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU2UR 2023-03-23 WOS:000886782800045 0
J Musa, A; Tripathi, S; Dehmer, M; Emmert-Streib, F Musa, Aliyu; Tripathi, Shailesh; Dehmer, Matthias; Emmert-Streib, Frank L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository FRONTIERS IN GENETICS English Article gene expression; big data; pharmacogenomics; web application; visualization; data science GENE-EXPRESSION; CONNECTIVITY MAP; R PACKAGE; SIGNATURES; BIOLOGY The LINCS L1000 data repository contains almost two million gene expression profiles for thousands of small molecules and drugs. However, due to the complexity and the size of the data repository and a lack of an interoperable interface, the creation of pharmacologically meaningful workflows utilizing these data is severely hampered. In order to overcome this limitation, we developed the L1000 Viewer, a search engine and graphical web interface for the LINCS data repository. The web interface serves as an interactive platform allowing the user to select different forms of perturbation profiles, e.g., for specific cell lines, drugs, dosages, time points and combinations thereof. At its core, our method has a database we created from inferring and utilizing the intricate dependency graph structure among the data files. The L1000 Viewer is accessible via http://L1000viewer.bio-complexity.com/. [Musa, Aliyu; Tripathi, Shailesh; Emmert-Streib, Frank] Tampere Univ, Fac Informat Technol & Commun Sci, Predict Soc & Data Analyt Lab, Tampere, Finland; [Musa, Aliyu; Emmert-Streib, Frank] Inst Biosci & Med Technol, Tampere, Finland; [Tripathi, Shailesh; Dehmer, Matthias] Univ Appl Sci Upper Austria, Fac Management, Inst Intelligent Prod, Linz, Austria; [Dehmer, Matthias] UMIT, Dept Mechatron & Biomed Comp Sci, Hall In Tirol, Austria; [Dehmer, Matthias] Nankai Univ, Coll Comp & Control Engn, Tianjin, Peoples R China Tampere University; Nankai University Emmert-Streib, F (corresponding author), Tampere Univ, Fac Informat Technol & Commun Sci, Predict Soc & Data Analyt Lab, Tampere, Finland.;Emmert-Streib, F (corresponding author), Inst Biosci & Med Technol, Tampere, Finland. v@bio-complexity.com Emmert-Streib, Frank/AAF-2878-2020; Trpathi, Shailesh/T-2123-2017 Emmert-Streib, Frank/0000-0003-0745-5641; Musa, Aliyu/0000-0003-1217-2638 Austrian Science Funds [P 30031] Austrian Science Funds(Austrian Science Fund (FWF)) MD thanks the Austrian Science Funds for supporting this work (project P 30031). 31 7 7 1 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. JUN 14 2019.0 10 557 10.3389/fgene.2019.00557 0.0 11 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity IE0DL 31258549.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000472056800001 0
J Chen, J; Wensing, T; Hoffstaedter, F; Cieslik, EC; Muller, VI; Patil, KR; Aleman, A; Derntl, B; Gruber, O; Jardri, R; Kogler, L; Sommer, IE; Eickhoff, SB; Nickl-Jockschat, T Chen, Ji; Wensing, Tobias; Hoffstaedter, Felix; Cieslik, Edna C.; Muller, Veronika, I; Patil, Kaustubh R.; Aleman, Andre; Derntl, Birgit; Gruber, Oliver; Jardri, Renaud; Kogler, Lydia; Sommer, Iris E.; Eickhoff, Simon B.; Nickl-Jockschat, Thomas Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling NEUROIMAGE-CLINICAL English Article Formal thought disorder; Neuroimaging; Machine learning FUNCTIONAL CONNECTIVITY; MOTION CORRECTION; BRAIN SIGNAL; ACTIVATION; LANGUAGE; FMRI; SCALE; NETWORKS; THALAMUS; CORTEX Formal thought disorder (FTD) is a core symptom cluster of schizophrenia, but its neurobiological substrates remain poorly understood. Here we collected resting-state fMRI data from 276 subjects at seven sites and employed machine-learning to investigate the neurobiological correlates of FTD along positive and negative symptom dimensions in schizophrenia. Three a priori, meta-analytically defined FTD-related brain regions were used as seeds to generate whole-brain resting-state functional connectivity (rsFC) maps, which were then compared between schizophrenia patients and controls. A repeated cross-validation procedure was realized within the patient group to identify clusters whose rsFC patterns to the seeds were repeatedly observed as significantly associated with specific FTD dimensions. These repeatedly identified clusters (i.e., robust clusters) were functionally characterized and the rsFC patterns were used for predictive modeling to investigate predictive capacities for individual FTD dimensional-scores. Compared with controls, differential rsFC was found in patients in fronto-temporo-thalamic regions. Our cross-validation procedure revealed significant clusters only when assessing the seed-to-whole-brain rsFC patterns associated with positive-FTD. RsFC patterns of three frontotemporal clusters, associated with higher-order cognitive processes (e.g., executive functions), specifically predicted individual positive-FTD scores (p = 0.005), but not other positive symptoms, and the PANSS general psychopathology subscale (p > 0.05). The prediction of positive-FTD was moreover generalized to an independent dataset (p = 0.013). Our study has identified neurobiological correlates of positive FTD in schizophrenia in a network associated with higher-order cognitive functions, suggesting a dysexecutive contribution to FTD in schizophrenia. We regard our findings as robust, as they allow a prediction of individual-level symptom severity. [Chen, Ji] Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China; [Chen, Ji; Hoffstaedter, Felix; Cieslik, Edna C.; Muller, Veronika, I; Patil, Kaustubh R.; Eickhoff, Simon B.] Res Ctr Julich, Inst Neurosci & Med Brain & Behav INM 7, Julich, Germany; [Chen, Ji; Hoffstaedter, Felix; Cieslik, Edna C.; Muller, Veronika, I; Patil, Kaustubh R.; Eickhoff, Simon B.] Heinrich Heine Univ Dusseldorf, Med Fac, Inst Syst Neurosci, Dusseldorf, Germany; [Wensing, Tobias] Rhein Westfal TH Aachen, Med Fac, Dept Psychiat Psychotherapy & Psychosomat, Aachen, Germany; [Wensing, Tobias] JARA Translat Brain Med, Aachen, Germany; [Aleman, Andre] Univ Groningen, Univ Med Ctr Groningen, Dept Neurosci, Groningen, Netherlands; [Derntl, Birgit; Kogler, Lydia] Univ Tubingen, Med Sch, Dept Psychiat & Psychotherapy, Tubingen, Germany; [Gruber, Oliver] Heidelberg Univ, Dept Gen Psychiat, Sect Expt Psychopathol & Neuroimaging, Heidelberg, Germany; [Jardri, Renaud] Univ Lille, INSERM U1172, Lille Neurosci & Cognit Ctr, Plastic & Subject Team, Lille, France; [Jardri, Renaud] CHU Lille, Fontan Hosp, CURE Platform, Lille, France; [Sommer, Iris E.] Univ Groningen, Univ Med Ctr Groningen, Dept Biomed Sci Cells & Syst, Groningen, Netherlands; [Nickl-Jockschat, Thomas] Univ Iowa, Iowa Neurosci Inst, Carver Coll Med, Iowa City, IA 52242 USA; [Nickl-Jockschat, Thomas] Univ Iowa, Carver Coll Med, Dept Psychiat, Iowa City, IA 52242 USA Zhejiang University; Helmholtz Association; Research Center Julich; Heinrich Heine University Dusseldorf; RWTH Aachen University; University of Groningen; Eberhard Karls University of Tubingen; Ruprecht Karls University Heidelberg; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Lille - ISITE; Universite de Lille; Universite de Lille - ISITE; CHU Lille; University of Groningen; University of Iowa; University of Iowa Nickl-Jockschat, T (corresponding author), Univ Iowa, Iowa Neurosci Inst, Dept Psychiat, 2312 Pappajohn Biomed Discovery Bldg, Iowa City, IA 52242 USA. thomas-nickl-jockschat@uiowa.edu Jardri, Renaud/J-9202-2012; Gruber, Oliver/AAY-5563-2020 Jardri, Renaud/0000-0003-4596-1502; Nickl-Jockschat, Thomas/0000-0003-2616-6503; Patil, Kaustubh/0000-0002-0289-5480; Chen, Ji/0000-0002-9476-1737; Hoffstaedter, Felix/0000-0001-7163-3110; Cieslik, Edna/0000-0001-5491-5837; Kogler, Lydia/0000-0002-9493-2321; Sommer, Iris/0000-0001-5597-3262 Deutsche Forschungsgemeinschaft (DFG) [EI 816/4-1]; National Institute of Mental Health [R01-MH074457]; Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union [720270, 785907]; Chinese Scholarship Council Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); National Institute of Mental Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union(European Commission); Chinese Scholarship Council(China Scholarship Council) We thank the researchers at each of the medical centers for their efforts in recruiting the subjects and sharing the data that made the current work possible. Gratitude is expressed to each of the subjects who spent their time and energy in participating this study and their cooperation. This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1 to S.B.E.), the National Institute of Mental Health (R01-MH074457 to S.B.E.), the Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain, the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and 785907 (HBP SGA2), and the Chinese Scholarship Council to J.C. Author contributions 88 7 7 4 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2213-1582 NEUROIMAGE-CLIN NeuroImage-Clin. 2021.0 30 102666 10.1016/j.nicl.2021.102666 0.0 APR 2021 11 Neuroimaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology TE9KA 34215141.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000670324000006 0
J Jin, C; Jin, SW Jin, Cong; Jin, Shu-Wei Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization APPLIED SOFT COMPUTING English Article ANN; Software metrics; Fault-prone prediction; QPSO BEE COLONY ALGORITHM; FEATURE-SELECTION; GENETIC ALGORITHM; DEFECT PREDICTION; CLASSIFICATION; PARAMETERS; IDENTIFICATION; REGRESSION; QUALITY The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules' fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance. (C) 2015 Published by Elsevier B.V. [Jin, Cong] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China; [Jin, Shu-Wei] Ecole Normale Super, Dept Phys, F-75231 Paris 5, France Central China Normal University; UDICE-French Research Universities; Universite PSL; Ecole Normale Superieure (ENS) Jin, C (corresponding author), Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China. jinc26@aliyun.com , Cong/0000-0002-3703-359X science and technology research program of Wuhan of China [201210121023] science and technology research program of Wuhan of China This work was supported by science and technology research program of Wuhan of China (Grant No. 201210121023). 62 49 50 0 33 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. OCT 2015.0 35 717 725 10.1016/j.asoc.2015.07.006 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science CP7ZT 2023-03-23 WOS:000360109900054 0
J Kessler, RC; Rose, S; Koenen, KC; Karam, EG; Stang, PE; Stein, DJ; Heeringa, SG; Hill, ED; Liberzon, I; McLaughlin, KA; McLean, SA; Pennell, BE; Petukhova, M; Rosellini, AJ; Ruscio, AM; Shahly, V; Shalev, AY; Silove, D; Zaslavsky, AM; Angermeyer, MC; Bromet, EJ; de Almeida, JMC; de Girolamo, G; de Jonge, P; Demyttenaere, K; Florescu, SE; Gureje, O; Haro, JM; Hinkov, H; Kawakami, N; Kovess-Masfety, V; Lee, S; Medina-Mora, ME; Murphy, SD; Navarro-Mateu, F; Piazza, M; Posada-Villa, J; Scott, K; Torres, Y; Viana, MC Kessler, Ronald C.; Rose, Sherri; Koenen, Karestan C.; Karam, Elie G.; Stang, Paul E.; Stein, Dan J.; Heeringa, Steven G.; Hill, Eric D.; Liberzon, Israel; McLaughlin, Katie A.; McLean, Samuel A.; Pennell, Beth E.; Petukhova, Maria; Rosellini, Anthony J.; Ruscio, Ayelet M.; Shahly, Victoria; Shalev, Arieh Y.; Silove, Derrick; Zaslavsky, Alan M.; Angermeyer, Matthias C.; Bromet, Evelyn J.; Caldas de Almeida, Jose Miguel; de Girolamo, Giovanni; de Jonge, Peter; Demyttenaere, Koen; Florescu, Silvia E.; Gureje, Oye; Haro, Josep Maria; Hinkov, Hristo; Kawakami, Norito; Kovess-Masfety, Viviane; Lee, Sing; Medina-Mora, Maria Elena; Murphy, Samuel D.; Navarro-Mateu, Fernando; Piazza, Marina; Posada-Villa, Jose; Scott, Kate; Torres, Yolanda; Viana, Maria Carmen How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys WORLD PSYCHIATRY English Article Post-traumatic stress disorder; predictive modeling; machine learning; penalized regression; random forests; ridge regression PTSD; PREVENTION; METAANALYSIS; SYMPTOMS; EXPOSURE; VERSION Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice. [Kessler, Ronald C.; Rose, Sherri; Hill, Eric D.; Petukhova, Maria; Rosellini, Anthony J.; Shahly, Victoria; Zaslavsky, Alan M.] Harvard Univ, Sch Med, Dept Hlth Care Policy, Boston, MA 02115 USA; [Koenen, Karestan C.] Columbia Univ, Mailman Sch Publ Hlth, New York, NY USA; [Karam, Elie G.] Balamand Univ, Sch Med, Beirut, Lebanon; [Karam, Elie G.] IDRAAC, Beirut, Lebanon; [Stang, Paul E.] Janssen Res & Dev, Titusville, NJ USA; [Stein, Dan J.] Univ Cape Town, ZA-7925 Cape Town, South Africa; [Heeringa, Steven G.; Pennell, Beth E.] Univ Michigan, Inst Social Res, Ann Arbor, MI USA; [Liberzon, Israel] Univ Michigan, Dept Psychol, Ann Arbor, MI USA; [McLaughlin, Katie A.] Univ Washington, Seattle, WA 98195 USA; [McLean, Samuel A.] Univ N Carolina, Chapel Hill, NC USA; [Ruscio, Ayelet M.] Univ Penn, Philadelphia, PA 19104 USA; [Shalev, Arieh Y.] Hadassah Univ Hosp, IL-91120 Jerusalem, Israel; [Silove, Derrick] Univ New S Wales, Sydney, NSW, Australia; [Silove, Derrick] Liverpool Hosp, Sydney, NSW, Australia; [Angermeyer, Matthias C.] Ctr Publ Mental Hlth, Gosingam Wagram, Austria; [Bromet, Evelyn J.] SUNY Stony Brook, Stony Brook, NY 11794 USA; [Caldas de Almeida, Jose Miguel] Univ Nova Lisboa, P-1200 Lisbon, Portugal; [de Girolamo, Giovanni] IRCCS Ctr San Giovanni di Dio Fatebenefratelli, Brescia, Italy; [de Jonge, Peter] Univ Groningen, Univ Med Ctr Groningen, Groningen, Netherlands; [Demyttenaere, Koen] Univ Hosp Gasthuisberg, Leuven, Belgium; [Florescu, Silvia E.] Natl Sch Publ Hlth Management & Profess Dev, Bucharest, Romania; [Gureje, Oye] Univ Ibadan, Ibadan, Nigeria; [Haro, Josep Maria] Parc Sanitari Sant Joan de Deu, Sant Boi De Llobregat, Spain; [Hinkov, Hristo] Natl Ctr Publ Hlth Protect, Sofia, Bulgaria; [Kawakami, Norito] Univ Tokyo, Tokyo, Japan; [Kovess-Masfety, Viviane] Univ Paris 05, Paris, France; [Kovess-Masfety, Viviane] EHESP Sch Publ Hlth, Paris, France; [Lee, Sing] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China; [Medina-Mora, Maria Elena] Inst Nacl Psiquiatria Ramon de La Fuente Muniz, Mexico City, DF, Mexico; [Murphy, Samuel D.] Univ Ulster, Coleraine BT52 1SA, Londonderry, North Ireland; [Navarro-Mateu, Fernando] Serv Murciano Salud, El Palmar, Spain; [Navarro-Mateu, Fernando] CIBERESP, El Palmar, Spain; [Piazza, Marina] Univ Peruana Cayetano Heredia, Lima, Peru; [Posada-Villa, Jose] Univ Colegio Mayor de Cundinamarca, Bogota, Colombia; [Scott, Kate] Univ Otago, Dunedin, New Zealand; [Torres, Yolanda] Univ Ctr Excellence Mental Hlth Res, Medellin, Colombia; [Viana, Maria Carmen] Univ Fed Espirito Santo, Vitoria, Brazil Harvard University; Harvard Medical School; Columbia University; University Balamand; Johnson & Johnson; Janssen Pharmaceuticals; University of Cape Town; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Washington; University of Washington Seattle; University of North Carolina; University of North Carolina Chapel Hill; University of Pennsylvania; Hebrew University of Jerusalem; University of New South Wales Sydney; Liverpool Hospital; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Universidade Nova de Lisboa; IRCCS Fatebenefratelli; University of Groningen; KU Leuven; University Hospital Leuven; University of Ibadan; Medical University Sofia; University of Tokyo; UDICE-French Research Universities; Universite Paris Cite; Chinese University of Hong Kong; Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz; Ulster University; CIBER - Centro de Investigacion Biomedica en Red; CIBERESP; Universidad Peruana Cayetano Heredia; Universidad Colegio Mayor de Cundinamarca; University of Otago; Universidade Federal do Espirito Santo Kessler, RC (corresponding author), Harvard Univ, Sch Med, Dept Hlth Care Policy, 180 Longwood Ave, Boston, MA 02115 USA. Lee, Sing/O-2136-2015; de Girolamo, Giovanni/K-3062-2016; Gureje, Oye/J-1183-2014; Navarro-Mateu, Fernando/H-9055-2017; Stein, Dan/A-1752-2008; McLean, Samuel/GPT-1213-2022; Posada-Villa, Jose/AAA-2355-2019; Adewuya, Abiodun/N-4997-2017; demyttenaere, koen/AAG-4716-2021; Medina-Mora, María Elena I/T-5937-2018; Haro, Josep Maria/D-1423-2011; Florescu, Silvia/AAX-7951-2020; de Jonge, Peter/L-6395-2013 de Girolamo, Giovanni/0000-0002-1611-8324; Gureje, Oye/0000-0003-0094-5947; Navarro-Mateu, Fernando/0000-0002-3228-623X; Stein, Dan/0000-0001-7218-7810; McLean, Samuel/0000-0001-9482-3582; Posada-Villa, Jose/0000-0001-5840-3118; Adewuya, Abiodun/0000-0002-7611-6953; Medina-Mora, María Elena I/0000-0001-9300-0752; Haro, Josep Maria/0000-0002-3984-277X; Rosellini, Anthony/0000-0002-8385-8621; PIAZZA, MARINA/0000-0002-8446-6419; Shalev, Arieh/0000-0001-9425-050X; Liberzon, Israel/0000-0002-4990-556X; Torres de Galvis, Yolanda/0000-0002-6712-365X; kovess-masfety, viviane/0000-0001-7045-6175; de Jonge, Peter/0000-0002-0866-6929 U.S. Public Health Service [1 R01MH101227-01A1, R13-MH066849, R01-MH069864, R01 DA016558]; National Institute of Mental Health [R01 MH070884, R01 MH093612-01]; John D. and Catherine T. MacArthur Foundation; Pfizer Foundation; Fogarty International Center (FIRCA) [R03-TW006481]; Pan American Health Organization; Eli Lilly and Company; Ortho-McNeil Pharmaceutical; GlaxoSmithKline; Bristol-Myers Squibb U.S. Public Health Service(United States Department of Health & Human ServicesUnited States Public Health Service); National Institute of Mental Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); John D. and Catherine T. MacArthur Foundation; Pfizer Foundation; Fogarty International Center (FIRCA)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH Fogarty International Center (FIC)); Pan American Health Organization; Eli Lilly and Company(Eli Lilly); Ortho-McNeil Pharmaceutical; GlaxoSmithKline(GlaxoSmithKline); Bristol-Myers Squibb(Bristol-Myers Squibb) The research reported here is supported by U.S. Public Health Service grant 1 R01MH101227-01A1 and is carried out in conjunction with the WHO's WMH Survey initiative, which is supported by the National Institute of Mental Health (R01 MH070884 and R01 MH093612-01), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the U.S. Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, GlaxoSmithKline, and Bristol-Myers Squibb. The authors thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. None of the funders had any role in the design, analysis, interpretation of results, or preparation of this paper. A complete list of all within-country and cross-national WMH Survey publications can be found at http://www.hcp.med.harvard.edu/WMH/. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of the sponsoring organizations, agencies, or governments. 32 158 159 0 63 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1723-8617 2051-5545 WORLD PSYCHIATRY World Psychiatry OCT 2014.0 13 3 265 274 10.1002/wps.20150 0.0 10 Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Psychiatry AQ5LR 25273300.0 Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000342851200017 0
J Kumar, SA; Subathra, MSP; Kumar, NM; Malvoni, M; Sairamya, NJ; George, ST; Suviseshamuthu, ES; Chopra, SS Kumar, S. Ananda; Subathra, M. S. P.; Kumar, Nallapaneni Manoj; Malvoni, Maria; Sairamya, N. J.; George, S. Thomas; Suviseshamuthu, Easter S.; Chopra, Shauhrat S. A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network ENERGIES English Article photovoltaics; distributed generation; grid faults; islanding detection; islanding issues in power system; resilient photovoltaic system; robust power system; signal processing; tunable-Q wavelet transform; artificial neural network SLANTLET TRANSFORM; PROTECTION Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue. [Kumar, S. Ananda; Subathra, M. S. P.] Karunya Inst Technol & Sci, Sch Engn & Technol, Dept Elect & Elect Engn, Coimbatore 641114, Tamil Nadu, India; [Kumar, Nallapaneni Manoj; Chopra, Shauhrat S.] City Univ Hong Kong, Sch Energy & Environm, Hong Kong, Peoples R China; [Malvoni, Maria] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15780, Greece; [Sairamya, N. J.] Karunya Inst Technol & Sci, Sch Engn & Technol, Dept Elect & Commun Engn, Coimbatore 641114, Tamil Nadu, India; [George, S. Thomas] Karunya Inst Technol & Sci, Sch Engn & Technol, Dept Biomed Engn, Coimbatore 641114, Tamil Nadu, India; [Suviseshamuthu, Easter S.] Kessler Fdn, Ctr Mobil & Rehabil Engn Res, W Orange, NJ 07052 USA Karunya Institute of Technology & Sciences; City University of Hong Kong; National Technical University of Athens; Karunya Institute of Technology & Sciences; Karunya Institute of Technology & Sciences; Kessler Institute for Rehabilitation Subathra, MSP (corresponding author), Karunya Inst Technol & Sci, Sch Engn & Technol, Dept Elect & Elect Engn, Coimbatore 641114, Tamil Nadu, India.;Kumar, NM; Chopra, SS (corresponding author), City Univ Hong Kong, Sch Energy & Environm, Hong Kong, Peoples R China.;Malvoni, M (corresponding author), Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15780, Greece. johnanand777@gmail.com; subathra@karunya.edu; mnallapan2-c@my.cityu.edu.hk; maria.malvoni@gmail.com; sairamyanj@karunya.edu.in; thomasgeorge@karunya.edu; eselvan@kesslerfoundation.org; sschopra@cityu.edu.hk Subathra, M.S.P./AAM-1001-2021; Manoj Kumar, Nallapaneni/R-8605-2018; George, S.Thomas/F-1931-2018; Chopra, Shauhrat S./HIK-2137-2022; Chopra, Shauhrat S./AAS-8691-2020 Manoj Kumar, Nallapaneni/0000-0002-7382-7784; George, S.Thomas/0000-0003-0304-495X; Chopra, Shauhrat S./0000-0001-9067-4321; MALVONI, MARIA/0000-0002-6626-8849 49 12 12 2 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies AUG 2020.0 13 16 4238 10.3390/en13164238 0.0 22 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels NH6TB Green Published, gold 2023-03-23 WOS:000564799600001 0
C Quan, WZ; Wang, K; Yan, DM; Pellerin, D; Zhang, XP Barnaghi, P; Gottlob, G; Katsaros, D; Manolopoulos, Y; Pandey, R; Tzouramanis, T; Vakali, A Quan, Weize; Wang, Kai; Yan, Dong-Ming; Pellerin, Denis; Zhang, Xiaopeng Impact of Data Preparation and CNN's First Layer on Performance of Image Forensics: A Case Study of Detecting Colorized Images 2019 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE WORKSHOPS (WI 2019 COMPANION) English Proceedings Paper 19th IEEE/WIC/ACM International Conference on Web Intelligence (WI) OCT 13-17, 2019 Thessaloniki, GREECE IEEE,WIC,Assoc Comp Machinery,Aristotle Univ Thessaloniki,Open Univ Cyprus image forensics; colorized image; JPEG compression; convolutional neural network; generalization STEGANALYSIS In the field of image forensics, many convolutional neural network (CNN)-based forensic methods have been proposed and generally achieved the state-of-the-art performance. However, some questions are worth studying and answering regarding the trustworthiness of such methods, including for example the appropriateness of the discriminative information automatically extracted by CNN and the generalization performance on unseen data during the testing phase. In this paper, we study these questions in the case of a specific forensic problem of distinguishing between natural images (NIs) and colorized images (CIs). Through a series of experiments, we analyze the impact of data preparation and setting of the first layer of a recent state-of-the-art CNN-based method on the detector's forensic performance, in particular the generalization capability. We obtain some interesting observations which can serve as useful hints for carrying out image forensics experiments. Moreover, we propose a very simple method to improve the generalization performance of colorized image detection by combining decision results from CNN models with different settings at the network's first layer. [Quan, Weize; Yan, Dong-Ming; Zhang, Xiaopeng] Univ CAS, Chinese Acad Sci, NLPR, Beijing, Peoples R China; [Quan, Weize; Wang, Kai; Pellerin, Denis] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, Grenoble, France Chinese Academy of Sciences; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Yan, DM (corresponding author), Univ CAS, Chinese Acad Sci, NLPR, Beijing, Peoples R China. qweizework@gmail.com; kai.wang@gipsa-lab.grenoble-inp.fr; yandongming@gmail.com pellerin, denis/0000-0002-3792-1706 Beijing Natural Science Foundation [L182059]; National Natural Science Foundation of China [61620106003, 61772523]; French National Research Agency (DEFALS) [ANR-16-DEFA-0003, ANR-15-IDEX-02]; UCAS Joint PhD Training Program Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); French National Research Agency (DEFALS)(French National Research Agency (ANR)); UCAS Joint PhD Training Program This work is funded in part by Beijing Natural Science Foundation (L182059), National Natural Science Foundation of China (61620106003 and 61772523), and the French National Research Agency (DEFALS ANR-16-DEFA-0003, ANR-15-IDEX-02). W. Quan acknowledges the support from the UCAS Joint PhD Training Program. We thank L. Zhuo for helpful discussions about the details of [15] and Dr. W. Fan for sharing implementation of the JPEG blocking measure [4]. 15 3 3 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6988-6 2019.0 127 131 10.1145/3358695.3360890 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO5PR Green Published 2023-03-23 WOS:000518627400019 0
J Ganchev, I; Ji, ZL; O'Droma, M; Zhao, L Ganchev, Ivan; Ji, Zhanlin; O'Droma, Mairtin; Zhao, Li Smart Recommendation of Mobile Services to Consumers IEEE TRANSACTIONS ON CONSUMER ELECTRONICS English Article ubiquitous consumer wireless world; always best connected and best served; service recommendation system; data management platform SYSTEM This paper describes the design and implementation of a smart recommendation system used to discover and suggest to consumers the 'best' mobile services in the emerging ubiquitous consumer wireless world (UCWW). The cloud-based system builds up and dynamically manages personal profiles of consumers with big data processing techniques. A consumer identity module (CIM), based on the Java Card technology, supports a third-party authentication, authorization and accounting procedure (3P-AAA), and provides a trusted execution environment for corresponding mobile applications running on the consumer's mobile device. A distributed data management platform (DMP), built with the Hadoop, Storm, and Kafka technologies, provides an efficient computing environment for real-time data scheduling and message processing in the cloud. With this distributed architecture, the system is able to turn mobile services' activities of consumers into actionable analytic datasets and recommend the 'best' mobile services applicable to each particular consumer under the 'always best connected and best served' (ABC&S) paradigm. A number of novel software solutions, both on the consumer and cloud side, are presented. An approach towards running this service recommendation system in a distributed way and high throughput capacity is elaborated. [Ganchev, Ivan] Plovdiv Univ Paisii Hilendarski, Dept Comp Syst, Plovdiv, Bulgaria; [Ganchev, Ivan; Ji, Zhanlin; O'Droma, Mairtin] Univ Limerick, TRC, Limerick, Ireland; [Ji, Zhanlin] North China Univ Sci & Technol, Informat Coll, Tangshan, Hebei, Peoples R China; [Zhao, Li] Tsinghua Univ, Res Inst Informat Technol, Beijing, Peoples R China Plovdiv University; University of Limerick; North China University of Science & Technology; Tsinghua University Ji, ZL (corresponding author), Univ Limerick, TRC, Limerick, Ireland.;Ji, ZL (corresponding author), North China Univ Sci & Technol, Informat Coll, Tangshan, Hebei, Peoples R China. Ivan.Ganchev@ul.ie; Zhanlin.Ji@ul.ie; Mairtin.ODroma@ul.ie; Zhaoli@tsinghua.edu.cn Ganchev, Ivan/D-4973-2012; Ji, Zhanlin/AAW-5661-2021; O'Droma, Mairtin/D-2743-2015 Ganchev, Ivan/0000-0003-0535-7087; O'Droma, Mairtin/0000-0002-1672-1668; ji, zhanlin/0000-0003-3527-3773 Irish Research Council New Foundations scheme, Ireland; Telecommunications Research Centre (TRC), University of Limerick, Ireland Irish Research Council New Foundations scheme, Ireland; Telecommunications Research Centre (TRC), University of Limerick, Ireland This publication has been supported and funded by the Irish Research Council New Foundations scheme, Ireland, and by the Telecommunications Research Centre (TRC), University of Limerick, Ireland. 14 8 8 1 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0098-3063 1558-4127 IEEE T CONSUM ELECTR IEEE Trans. Consum. Electron. NOV 2017.0 63 4 499 508 10.1109/TCE.2017.015115 0.0 10 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications FT6PT 2023-03-23 WOS:000423276800020 0
J Wang, ZH; Jiang, DD; Lv, ZH Wang, Zhihao; Jiang, Dingde; Lv, Zhihan AI-Assisted Trustworthy Architecture for Industrial IoT Based on Dynamic Heterogeneous Redundancy IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Artificial intelligence (AI); built-in security; deep reinforcement learning; double deep Q network (DDQN); dynamic heterogeneous redundancy (DHR) MIMIC DEFENSE; REINFORCEMENT; FRAMEWORK; GAME Current cyberspace is confronted with unprecedented security risks, whereas traditional passive protection techniques are ill-equipped for attacks or defects with unknown features. Dynamic heterogeneous redundancy (DHR), a built-in active defense approach, deploys uncertain, random, dynamic systems to change the asymmetry of attack and defense, where arbitration is one of the key mechanisms. In this article, an AI-assisted trustworthy architecture based on DHR and deep reinforcement learning-based intelligent arbitration (DRLIA) algorithm is presented to enhance security for industrial Internet of things (IIoT). A double deep Q network (DDQN) is introduced, which is capable to distinguish the reliable and credible IIoT message from executors through interaction with the DHR environment. Finally, the DRLIA is implemented to conduct arbitration tasks in an IIoT critical message transmission scenario, where several comparison experiments between DRLIA and other traditional algorithms are designed. The result on the testbed empirically demonstrates the effectiveness of the proposed architecture and the security enhancement. [Wang, Zhihao; Jiang, Dingde] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China; [Jiang, Dingde] Lab Electromagnet Space Cognit & Intelligent Contr, Chengdu, Peoples R China; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, S-75236 Uppsala, Sweden University of Electronic Science & Technology of China; Uppsala University Jiang, DD (corresponding author), Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China.;Jiang, DD (corresponding author), Lab Electromagnet Space Cognit & Intelligent Contr, Chengdu, Peoples R China. wangzhihao1998@foxmail.com; jiangdd@uestc.edu.cn; lvzhihan@gmail.com National Natural Science Foundation of China [61571104, U20B2070]; Sichuan Science and Technology Program [2018JY0539]; Key Projects of the Sichuan Provincial Education Department [18ZA0219]; Fundamental Research Funds for the Central Universities [ZYGX2017KYQD170]; CERNET Innovation Project [NGII20190111]; Open Fund of Digital Media Art, Key Laboratory of Sichuan Province [2020-JCJQ-ZD-016-11, 61403110405, 315075802, JZX6Y202001010161]; [20DMAKL01]; [2018510007000134] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program; Key Projects of the Sichuan Provincial Education Department; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); CERNET Innovation Project; Open Fund of Digital Media Art, Key Laboratory of Sichuan Province; ; This work was supported in part by the National Natural Science Foundation of China under Grant 61571104 and Grant U20B2070, in part by the Sichuan Science and Technology Program under Grant 2018JY0539, in part by the Key Projects of the Sichuan Provincial Education Department under Grant 18ZA0219, in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2017KYQD170, in part by the CERNET Innovation Project under Grant NGII20190111, in part by the Fund Projects under Grant 2020-JCJQ-ZD-016-11, Grant 61403110405, Grant 315075802, and Grant JZX6Y202001010161, in part by the Open Fund of Digital Media Art, Key Laboratory of Sichuan Province under Grant 20DMAKL01, and in part by the Innovation Funding under Grant 2018510007000134. Paper no. TII-22-2269. (Corresponding author: Dingde Jiang.) 26 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2023.0 19 2 2019 2027 10.1109/TII.2022.3210139 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 8Q1HM 2023-03-23 WOS:000926964700086 0
J Li, ZR; Dey, N; Ashour, AS; Cao, LY; Wang, Y; Wang, D; McCauley, P; Balas, VE; Shi, K; Shi, FQ Li, Zairan; Dey, Nilanjan; Ashour, Amira S.; Cao, Luying; Wang, Yu; Wang, Dan; McCauley, Pamela; Balas, Valentina E.; Shi, Kai; Shi, Fuqian Convolutional Neural Network Based Clustering and Manifold Learning Method for Diabetic Plantar Pressure Imaging Dataset JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS English Article Diabetic Mellitus; Plantar Pressure Imaging; Convolutional Neural Network; Clustering; Parameterized Manifold Learning DIMENSIONALITY REDUCTION METHODS; CLASSIFICATION; SEGMENTATION; FOOT; PREDICTION; SYSTEM; ALGORITHM; FRAMEWORK; MELLITUS; IMPACT Foot plantar pressure characteristics can be used to investigate and characterize diabetic patients. The current work proposed an effective method for analyzing plantar pressure images in order to obtain the key areas of foot plantar pressure characteristics. A collected data of plantar pressure of diabetic patients is involved to evaluate the proposed method based on image analysis. Initially, the plantar pressure imaging dataset was preprocessed by using watershed transformation to determine the region of interest (ROI) as well as to decrease the computation complexity. Afterward, the convolutional neural network (CNN) based K-mean clustering and parameterized manifold learning using an improved isometric mapping algorithm (ISOMAP) were applied to attain segments of the imaging dataset. The proposed method was discussed and was compared on ten areas of plantar including toes, mid-foots and heels. For the clustering result, the experiments established superior performance with root mean square error (RMSE) of 70%, average accuracy of 80% and 80% time consuming. Furthermore, the proposed manifold learning method achieved an average accuracy of 87.2%, which was superior to other seven algorithms including multi-dimensional scaling (MDS), principal components analysis (PCA), locally linear embedding (LLE), Hessian LLE, Laplacian eigenmap method (LE), diffusion map, and local tangent space alignment (LTSA). The proposed approach established potential application on shoe-last customization of diabetic foot. [Li, Zairan; Wang, Dan] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China; [Li, Zairan; Shi, Kai] Wenzhou Vocat & Tech Coll, Wenzhou 325035, Peoples R China; [Dey, Nilanjan] Techno India Coll Technol, Dept Informat Technol, Kolkata 740000, W Bengal, India; [Ashour, Amira S.] Tanta Univ, Fac Engn, Dept Elect & Elect Commun Engn, Tanta 31111, Egypt; [Cao, Luying; Wang, Yu; Shi, Fuqian] Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 320065, Peoples R China; [McCauley, Pamela] Univ Cent Florida, Dept Ind Engn & Management Syst, Orlando, FL 32825 USA; [Balas, Valentina E.] Aurel Vlaicu Univ Arad, Dept Automat & Appl Informat, Arad 310130, Romania Tianjin University; Egyptian Knowledge Bank (EKB); Tanta University; Wenzhou Medical University; State University System of Florida; University of Central Florida; Aurel Vlaicu University of Arad Shi, FQ (corresponding author), Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 320065, Peoples R China. Ashour, Amira S./T-5454-2019; Balas, Valentina Emilia/F-4525-2010 Ashour, Amira S./0000-0003-3217-6185; Balas, Valentina Emilia/0000-0003-0885-1283 Zhejiang Provincial Natural Science Fund [LY17F030014]; Science and Research Project of Education of Zhejiang Province [FX2016102] Zhejiang Provincial Natural Science Fund; Science and Research Project of Education of Zhejiang Province This work was sponsored by Zhejiang Provincial Natural Science Fund under Grant No. (LY17F030014) and Science and Research Project of Education of Zhejiang Province under Grant No. (FX2016102). 71 31 31 3 28 AMER SCIENTIFIC PUBLISHERS VALENCIA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA 2156-7018 2156-7026 J MED IMAG HEALTH IN J. Med. Imaging Health Inform. JUN 2017.0 7 3 639 652 10.1166/jmihi.2017.2082 0.0 14 Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging FB3SW 2023-03-23 WOS:000406064100018 0
J Wang, M; Kasmarik, K; Bezerianos, A; Tan, KC; Abbass, H Wang, Min; Kasmarik, Kathryn; Bezerianos, Anastasios; Tan, Kay Chen; Abbass, Hussein On the channel density of EEG signals for reliable biometric recognition PATTERN RECOGNITION LETTERS English Article EEG biometrics; Data augmentation; Deep learning; Current source density FUNCTIONAL CONNECTIVITY; IDENTIFICATION; ELECTRODE Electroencephalography (EEG) provides appealing biometrics by encompassing unique attributes including robustness against forgery, privacy compliance, and aliveness detection. Among the main challenges in deploying EEG biometric systems in real-world applications, stability and usability are two important ones. They respectively reflect the capacity of the system to provide stable performance within and across different states, and the ease of use of the system. Previous studies indicate that the usability of an EEG biometric system is largely affected by the number of electrodes and reducing channel density is an effective way to enhance usability. However, it is still unclear what is the impact of channel density on recognition performance and stability. This study examines this issue for systems using different feature extraction and classification methods. Our results reveal a trade-off between channel density and stability. With low-density EEG, the recognition accuracy and stability are compromised to varying degrees. Based on the analysis, we propose a framework that integrates channel density augmentation, functional connectivity estimation and deep learning models for practical and stable EEG biometric systems. The framework helps to improve the stability of EEG biometric systems that use consumer-grade low channel density devices, while retaining the advantages of high usability. (C)& nbsp;2021 Elsevier B.V. All rights reserved. [Wang, Min; Kasmarik, Kathryn; Abbass, Hussein] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia; [Bezerianos, Anastasios] Hellen Inst Transport, Ctr Res & Technol Hellas, Thessaloniki, Greece; [Tan, Kay Chen] Hong Kong Polytech Univ, Hong Kong, Peoples R China University of New South Wales Sydney; Centre for Research & Technology Hellas; Hong Kong Polytechnic University Wang, M (corresponding author), Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia. maggie.wang1@adfa.edu.au Bezerianos, Tasos/AAC-2499-2022; Abbass, Hussein/C-9563-2009 Bezerianos, Tasos/0000-0002-8199-6000; Wang, Min/0000-0002-1580-6387; Abbass, Hussein/0000-0002-8837-0748 Australian Research Council [DP160102037] Australian Research Council(Australian Research Council) This project is funded by an Australian Research Council Discovery Grant DP160102037. 30 6 7 2 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. JUL 2021.0 147 134 141 10.1016/j.patrec.2021.04.003 0.0 MAY 2021 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science SM5CR 2023-03-23 WOS:000657623900018 0
C Ran, XC; Zhang, YW; Shen, CY; Yvert, B; Chen, WD; Zhang, SM IEEE Ran, Xingchen; Zhang, Yiwei; Shen, Chenye; Yvert, Blaise; Chen, Weidong; Zhang, Shaomin Dimensionality Reduction of Local Field Potential Features with Convolution Neural Network in Neural Decoding: A Pilot Study 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol Local field potentials (LFPs) have better long-term stability compared with spikes in brain-machine interfaces (BMIs). Many studies have shown promising results of LFP decoding, but the high-dimensional feature of LFP still hurdle the development of the BMIs to low-cost. In this paper, we proposed a framework of a 1D convolution neural network (CNN) to reduce the dimensionality of the LFP features. For evaluating the performance of this architecture, the reduced LFP features were decoded to cursor position (Center-out task) by a Kalman filter. The Principal components analysis (PCA) was also performed as a comparison. The results showed that the CNN model could reduce the dimensionality of LFP features to a smaller size without significant performance loss. The decoding result based on the CNN features outperformed that based on the PCA features. Moreover, the reduced features by CNN also showed robustness across different sessions. These results demonstrated that the LFP features reduced by the CNN model achieved low cost without sacrificing high-performance and robustness, suggesting that this method could be used for portable BMI systems in the future. [Ran, Xingchen; Zhang, Yiwei] Zhejiang Univ, Qiushi Acad Adv Studies, Dept Biomed Engn, Hangzhou, Peoples R China; [Shen, Chenye] Zhejiang Univ, Dept Biomed Engn & Instrument Sci, Hangzhou, Peoples R China; [Yvert, Blaise] Inserm, Grenoble, France; [Yvert, Blaise] Univ Grenoble Alpes, BrainTech Lab U1205, Grenoble, France; [Chen, Weidong; Zhang, Shaomin] Zhejiang Univ, Key Lab Biomed Engn, Zhejiang Prov Key Lab Cardio Cerebral Vasc Detect, Qiushi Acad Adv Studies,Minist Educ, Hangzhou, Zhejiang, Peoples R China Zhejiang University; Zhejiang University; Institut National de la Sante et de la Recherche Medicale (Inserm); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Zhejiang University Zhang, SM (corresponding author), Zhejiang Univ, Key Lab Biomed Engn, Zhejiang Prov Key Lab Cardio Cerebral Vasc Detect, Qiushi Acad Adv Studies,Minist Educ, Hangzhou, Zhejiang, Peoples R China. shaomin@zju.edu.cn National Key RD Program [2017YFE0195500]; NSFC [31627802, 31371001]; Key R&D Program of Zhejiang [2021C03003, 2021C03050] National Key RD Program; NSFC(National Natural Science Foundation of China (NSFC)); Key R&D Program of Zhejiang This research supported by the National Key R&D Program (2017YFE0195500) and the NSFC (31627802, 31371001), the Key R&D Program of Zhejiang (2021C03003, 2021C03050). 25 0 0 2 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 1047 1050 10.1109/EMBC46164.2021.9630630 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34891468.0 2023-03-23 WOS:000760910501019 0
J Ye, WL; Jiang, W; Tong, Z; Yuan, DD; Xiao, JJ Ye, Wanli; Jiang, Wei; Tong, Zheng; Yuan, Dongdong; Xiao, Jingjing Convolutional neural network for pothole detection in asphalt pavement ROAD MATERIALS AND PAVEMENT DESIGN English Article pothole; convolutional neural network (CNN); feature extraction; structure optimisation; object recognition STRUCTURAL DAMAGE DETECTION; RECOGNITION; DROPOUT; CRACKS Many image processing techniques (IPTs) were proposed to inspect pavement defects for improving the precision and efficiency of the on-site inspections by humans. However, the various pavement conditions led to the unacceptable stability of IPTs. Therefore, an application of convolutional neural networks (CNNs) is presented for pothole detection using digital images in this study. Two CNNs, called conventional CNN and pre-pooling CNN, were designed, trained, and tested using 96,000 pavement images. Additionally, a stability study and comparative study were conducted based on the testing results. The main difference between the two CNNs was that a pre-pooling layer was used in the pre-pooling CNN to pre-process pavement images before the first convolutional layer. The results demonstrated that the optimised pre-pooling CNN had the 98.95% recognition precision in the testing. The stability study indicated that the optimised CNN model had the robustness in various real-world situations (e.g. light conditions and pavement materials). Compared with the traditional IPT methods, the CNN had a higher precision for extracting pothole features autonomously. [Ye, Wanli; Jiang, Wei; Yuan, Dongdong] Changan Univ, Minist Educ, Key Lab Special Area Highway Engn, Xian, Shaanxi, Peoples R China; [Ye, Wanli; Jiang, Wei; Tong, Zheng; Yuan, Dongdong] Changan Univ, Sch Highway, Xian, Shaanxi, Peoples R China; [Tong, Zheng] Univ Technol Compiegne, Sorbonne Univ, CNRS, Compiegne, France; [Xiao, Jingjing] Changan Univ, Sch Civil Engn, Xian, Shaanxi, Peoples R China Chang'an University; Chang'an University; Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; UDICE-French Research Universities; Sorbonne Universite; Chang'an University Tong, Z (corresponding author), Changan Univ, Sch Highway, Xian, Shaanxi, Peoples R China.;Tong, Z (corresponding author), Univ Technol Compiegne, Sorbonne Univ, CNRS, Compiegne, France. zheng.tong@hds.utc.fr Tong, Zheng/0000-0001-6894-3521; Ye, Wanli/0000-0001-8164-0517 National Natural Science Foundation of China [51608043]; Youth Top-notch Talent Support Program of Shaanxi Province; Fok Ying-Tong Education Foundation [161072]; Fundamental Research Funds for the Central Universities [300102219317] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Top-notch Talent Support Program of Shaanxi Province; Fok Ying-Tong Education Foundation(Fok Ying Tung Education Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This project was jointly supported by the National Natural Science Foundation of China (grant number 51608043), the Youth Top-notch Talent Support Program of Shaanxi Province, the Fok Ying-Tong Education Foundation (grant number 161072), and the Fundamental Research Funds for the Central Universities (grant number 300102219317). 32 29 29 11 90 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1468-0629 2164-7402 ROAD MATER PAVEMENT Road Mater. Pavement Des. JAN 2 2021.0 22 1 42 58 10.1080/14680629.2019.1615533 0.0 MAY 2019 17 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science PN3KR 2023-03-23 WOS:000470633300001 0
J Pan, HW; Zheng, GW; Hutter, A; Huang, ZK Pan, Haowei; Zheng, Gewei; Hutter, Akos; Huang, Zhenkai Building Interior Layout Design Based on Building Information Model and Deep Learning Technology: Taking the Interior Renewal Design of the Fifth Floor of the Procuratorate of Dong Xi Hu District as an Example COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article With the development of the Internet era and the information age, electronic information technology has profoundly affected human life and work, and the architectural design industry is no exception. Architectural design has a complex design period, and the arbitrary division of labour reflects the particularity of its working process. At present, the integration of electronic information technology and scientific and technological software into architectural design has dramatically improved the work efficiency of designers. However, due to the enormous workload of drawing and modification of building drawings, and many professional departments involved in cooperation, the error rate of drawing design is still high, which eventually leads to a series of serious problems such as stagnation of the design process and rework of construction. However, due to the enormous workload of drawing and modification of building drawings, and many professional departments involved in cooperation, the error rate of drawing design is still high, which eventually leads to a series of serious problems such as stagnation of the design process and rework of construction. The BIM building information model system is an electronic information platform for comprehensive architectural design majors. It can integrate the design drawings of various majors in the design process, correct the design problems caused by inaccurate pictures at the first time, and reduce the errors in the design process. Through the BIM system and Python programming software, cross-platform cooperation is carried out to carry out computer deep learning and a series of extra design work. This paper puts forward the interior design method of the BIM system and shows the research work of interior design by the BIM building information platform through practical cases. By comparing with traditional design methods, the advantages of this technology in interior design are demonstrated, and a reference for future interior design informatization is provided. [Pan, Haowei; Hutter, Akos; Huang, Zhenkai] Univ Pecs, Fac Engn & Informat Technol, Pecs, Hungary; [Zheng, Gewei] Hubei Univ Technol, Sch Art & Design, Wuhan, Peoples R China University of Pecs; Hubei University of Technology Pan, HW (corresponding author), Univ Pecs, Fac Engn & Informat Technol, Pecs, Hungary. lrudd59228@student.napavalley.edu; 413389131@qq.com; wake5566@foxmail.com; 420740070@qq.com 32 0 0 8 8 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. OCT 10 2022.0 2022 3746393 10.1155/2022/3746393 0.0 8 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology 5U2YX 36262612.0 gold, Green Published 2023-03-23 WOS:000876420000018 0
C Wu, SL; Huang, CL; Li, JY IEEE Wu, Shengli; Huang, Chunlan; Li, Jieyu Combining Retrieval Results for Balanced Effectiveness and Efficiency in the Big Data Search Environment 2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) International Conference on Computer and Information Technology English Proceedings Paper IEEE International Conference on Computer and Information Technology (CIT 2014) SEP 11-13, 2014 Xian, PEOPLES R CHINA IEEE,IEEE Computer Soc,TCSC IEEE,Xidian Univ,ISN,Natl Project for Wirless Networks,ITDCN,CISC,EIN,KC Wong Educ Fdn Hong Kong results combination; data fusion; information retrieval; web search; linear combination In the big data age, we have to deal with tremendous amount of information, which is collected from various types of sources. For information retrieval systems, the collection of documents becomes larger and larger. For some query, an information retrieval system needs to retrieve a large number of documents as the result to the query. In reality, very often people mainly care about some top-ranked documents rather than the complete long list of documents. In such a situation, how to develop a retrieval system with desirable efficiency and effectiveness is a research problem. In this paper, we focus on the data fusion approach to information retrieval, in which each component retrieval system contributes a result and all the results are combined by a combination method. The goal of this research is to find a feasible combination method that is able to balance effectiveness and efficiency. Using 3 groups of historical runs from TREC for the experiment, we find that with the weights trained by weighted linear regression, the linear combination method can achieve good results in effectiveness and efficiency. [Wu, Shengli; Huang, Chunlan] Jiangsu Univ, Sch Comp Sci & Telecommun Engn, Zhenjiang, Peoples R China; [Wu, Shengli] Ulster Univ, Sch Comp & Math, Newtownabbey, North Ireland; [Li, Jieyu] Jiangsu Univ Lib, Dept Resource Management, Zhenjiang, Peoples R China Jiangsu University; Ulster University Wu, SL (corresponding author), Jiangsu Univ, Sch Comp Sci & Telecommun Engn, Zhenjiang, Peoples R China. swu@ujs.edu.cn; palaceo77@163.com; jli@ujs.edu 14 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2474-9648 978-1-4799-6239-6 INT CONF COMPUT INFO 2014.0 555 560 10.1109/CIT.2014.137 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BI3TD 2023-03-23 WOS:000411451900092 0
C Wang, Y; Qin, ZX; Pang, J; Zhang, Y; Xin, J Assoc Comp Machinery Wang, Yan; Qin, Zongxu; Pang, Jun; Zhang, Yang; Xin, Jin Semantic Annotation for Places in LBSN through Graph Embedding CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT English Proceedings Paper ACM Conference on Information and Knowledge Management (CIKM) NOV 06-10, 2017 Singapore, SINGAPORE Assoc Comp Machinery,Assoc Comp Machinery, Special Interest Grp Informat Retrieval,ACM SIGWEB,Grab,Living Analyt Res Ctr,Shopee Semantic Tag; Deep Learning; Graph Representation With the prevalence of location-based social networks (LBSNs), automated semantic annotation for places plays a critical role in many LBSN-related applications. Although a line of research continues to enhance labeling accuracy, there is still a lot of room for improvement. The crucial problem is to find a high-quality representation for each place. In previous works, the representation is usually derived directly from observed patterns of places or indirectly from calculated proximity amongst places or their combination. In this paper, we also exploit the combination to represent places but present a novel semi-supervised learning framework based on graph embedding, called Predictive Place Embedding (PPE). For place proximity, PPE first learns user embeddings from a user-tag bipartite graph by minimizing supervised loss in order to preserve the similarity of users visiting analogous places. User similarity is then transformed into place proximity by optimizing each place embedding as the centroid of the vectors of its check-in users. Our underlying idea is that a place can be considered as a representative of all its visitors. For observed patterns, a place-temporal bipartite graph is used to further adjust place embeddings by reducing unsupervised loss. Extensive experiments on real large LBSNs show that PPE outperforms state-of-the-art methods significantly. [Wang, Yan; Qin, Zongxu; Xin, Jin] Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China; [Pang, Jun] Univ Luxembourg, FSTC & SnT, Luxembourg, Luxembourg; [Zhang, Yang] Univ Saarland, CISPA, Saarland Informat Campus, Saarbrucken, Germany Central University of Finance & Economics; University of Luxembourg; Saarland University Wang, Y (corresponding author), Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China. dayanking@gmail.com; qinzongxu@163.com; jun.pang@uni.lu; yang.zhang@cispa.saarland; jinxin@cufe.edu.cn Pang, Jun/0000-0002-4521-4112 National Key Research Program of China [2016YFB1001101]; National Nature Science Foundation of China [U1509214] National Key Research Program of China; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work has been partially supported by National Key Research Program of China (2016YFB1001101) and the Joint Funds of the National Nature Science Foundation of China (U1509214). 12 11 12 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-4918-5 2017.0 2343 2346 10.1145/3132847.3133075 0.0 4 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK6SW Green Published 2023-03-23 WOS:000440845300289 0
J Mensah, RA; Xiao, J; Das, O; Jiang, L; Xu, Q; Alhassan, MO Mensah, Rhoda Afriyie; Xiao, Jie; Das, Oisik; Jiang, Lin; Xu, Qiang; Alhassan, Mohammed Okoe Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction POLYMERS English Article flammability; heat release rate; microscale combustion calorimetry; multiple linear regression; adaptive neuro-fuzzy inference system HEAT RELEASE CAPACITY; CONE CALORIMETRY; COMBUSTION; POLYSTYRENE; NETWORKS; ANFIS; MODEL The fire behavior of materials is usually modeled on the basis of fire physics and material composition. However, significant strides have been made recently in applying soft computing methods such as artificial intelligence in flammability studies. In this paper, multiple linear regression (MLR) was employed to test the degree of non-linearities in flammability parameter modeling by assessing the linear relationship between sample mass, heating rate, heat release capacity (HRC) and total heat release (THR). Adaptive neuro-fuzzy inference system (ANFIS) was then adopted to predict the HRC and THR of the extruded polystyrene measured from microscale combustion calorimetry experiments. The ANFIS models presented excellent predictions, showing very low mean training and testing errors as well as reasonable agreements between experimental and predicted datasets. Hence, it can be inferred that ANFIS can handle the non-linearities in flammability modeling, making it apt as a modeling technique for accurate and effective flammability assessments. [Mensah, Rhoda Afriyie; Xiao, Jie; Jiang, Lin; Xu, Qiang; Alhassan, Mohammed Okoe] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China; [Das, Oisik] Lulea Univ Technol, Dept Engn Sci & Math, Div Mat Sci, S-97187 Lulea, Sweden Nanjing University of Science & Technology; Lulea University of Technology Jiang, L; Xu, Q (corresponding author), Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China. ramensah@ymail.com; xiaojieblue@163.com; oisik.das@ltu.se; ljiang@njust.edu.cn; xuqiang@njust.edu.cn; mohaokoe2020@yahoo.com Alhassan, Mohammed Okoe/HLG-2290-2023; Xiao, Jie/GRR-6203-2022 Mensah, Rhoda Afriyie/0000-0003-4720-5380; Jiang, Lin/0000-0003-0928-4583 National Natural Science Foundation of China (NSFC) [51776098]; NSFC [51911530151, CH2018-7733, 51706219]; STINT [51911530151, CH2018-7733] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)); STINT The authors would like to thank the National Natural Science Foundation of China (NSFC, Grant 51776098), the joint project of NSFC and STINT (51911530151, China side, and CH2018-7733, Sweden side), and NSFC (Grant 51706219). Oisik Das expresses his gratitude to Bio4Energy. 32 33 33 2 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4360 POLYMERS-BASEL Polymers JAN 2020.0 12 1 122 10.3390/polym12010122 0.0 16 Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Polymer Science KU6TX 31948059.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000519848300122 0
C Yue, Q; Liu, FS; Song, CQ; Liu, YM; Cao, N; Higgs, R; Li, JQ; O'Hare, GMP; Yang, CN; Zhou, Q IEEE Yue, Qiang; Liu, Fusheng; Song, Changqing; Liu, Yanmin; Cao, Ning; Higgs, Russell; Li, Junqing; O'Hare, Gregory M. P.; Yang, Cuinan; Zhou, Qiang A Study on the Reservoir Intelligent Inspection System Based on the Internet Things Technology 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 2 IEEE International Conference on Computational Science and Engineering English Proceedings Paper 20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC) JUL 21-24, 2017 Guangzhou, PEOPLES R CHINA IEEE,IEEE Comp Soc,IFIP,IEEE Tech Comm Scalable Comp,Univ Porto,Xilinx,Univ Guangzhou big data; early warning; water resources data; reservoir; decision tree The application of the Internet of things technology brings opportunity for modern dam safety intelligent inspection. Aiming at the present situation of reservoir dam safety inspection data, information island serious deficiencies and poor timeliness problem, the research applied big data thoughts into water management to solve the numerous data, demand diversifications of related interest groups, overall water difficulties and other problems confronted by hydraulic engineering. By using the historical database which contains large amount of data and feedback information, a health early warning model of reservoir was designed and established in large data environment based on decision tree C5.0 algorithm. The Dingdong Reservoirs health status was forecasted with the model as a case study. The results show that the accuracy of the early warning model is 91.92%. The reservoir is in health state, corresponding to no warning level. The early warning model is feasible and effective for utilizing abundant case resources, it can be used widely in reservoir health management. The results obtained in this paper are beneficial to the sustainable development and scientific management of reservoir. [Yue, Qiang; Liu, Fusheng] Shandong Agr Univ, Coll Water Conservancy & Civil Engn, Tai An, Shandong, Peoples R China; [Song, Changqing; Li, Junqing] Shandong Agr Univ, Agr Big Data Res Ctr, Tai An, Shandong, Peoples R China; [Liu, Yanmin] Dalhousie Univ, Fac Comp Sci, Halifax, NS, Canada; [Cao, Ning; Yang, Cuinan] Qingdao Binhai Univ, Coll Informat Engn, Qingdao, Peoples R China; [Higgs, Russell] Univ Coll Dublin, Sch Math & Stat, Dublin, Ireland; [O'Hare, Gregory M. P.] Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland; [Zhou, Qiang] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Shandong Agricultural University; Shandong Agricultural University; Dalhousie University; University College Dublin; University College Dublin; Tsinghua University Liu, FS (corresponding author), Shandong Agr Univ, Coll Water Conservancy & Civil Engn, Tai An, Shandong, Peoples R China.;Song, CQ; Li, JQ (corresponding author), Shandong Agr Univ, Agr Big Data Res Ctr, Tai An, Shandong, Peoples R China.;Cao, N (corresponding author), Qingdao Binhai Univ, Coll Informat Engn, Qingdao, Peoples R China. yueqiang406@163.com; liufsh@sdau.edu.cn; cqsong@sdau.edu.cn; ning.cao2008@hotmail.com; a397858801@126.com O'Hare, Gregory M.P./AAA-7756-2022; Cao, Ning/P-6652-2017 O'Hare, Gregory M.P./0000-0002-5124-1686; Higgs, Russell/0000-0002-0896-6788 National Key Technology Research and Development Program of the Ministry of Science and Technology of China [2015BAB07B05]; Water Conservancy Scientific and Technical Program of Shandong Province [SDSLKY201305]; significant application of agriculture technology innovation Program of Shandong province [SDNYCX1531963] National Key Technology Research and Development Program of the Ministry of Science and Technology of China(National Key Technology R&D Program); Water Conservancy Scientific and Technical Program of Shandong Province; significant application of agriculture technology innovation Program of Shandong province This work is supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAB07B05), the Water Conservancy Scientific and Technical Program of Shandong Province (SDSLKY201305) and the significant application of agriculture technology innovation Program of Shandong province (SDNYCX1531963). 13 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1949-0828 978-1-5386-3221-5 IEEE INT C COMPUT 2017.0 181 183 10.1109/CSE-EUC.2017.216 0.0 3 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL5AZ 2023-03-23 WOS:000451193100029 0
J Tuli, S; Gill, SS; Xu, MX; Garraghan, P; Bahsoon, R; Dustdar, S; Sakellariou, R; Rana, O; Buyya, R; Casale, G; Jennings, NR Tuli, Shreshth; Gill, Sukhpal Singh; Xu, Minxian; Garraghan, Peter; Bahsoon, Rami; Dustdar, Schahram; Sakellariou, Rizos; Rana, Omer; Buyya, Rajkumar; Casale, Giuliano; Jennings, Nicholas R. HUNTER: AI based holistic resource management for sustainable cloud computing JOURNAL OF SYSTEMS AND SOFTWARE English Article Holistic resource management; Energy-efficiency; Cloud computing; Artificial intelligence; Thermal management DATA CENTERS; WORKLOAD MANAGEMENT; ENERGY; NETWORK; CONSOLIDATION; SIMULATION; ALGORITHM The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. (c) 2021 Elsevier Inc. All rights reserved. [Tuli, Shreshth; Casale, Giuliano; Jennings, Nicholas R.] Imperial Coll London, Dept Comp, London, England; [Gill, Sukhpal Singh] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Xu, Minxian] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China; [Garraghan, Peter] Univ Lancaster, Sch Comp & Commun, Lancaster, England; [Bahsoon, Rami] Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England; [Dustdar, Schahram] Vienna Univ Technol, Distributed Syst Grp, Vienna, Austria; [Sakellariou, Rizos] Univ Manchester, Dept Comp Sci, Manchester, Lancs, England; [Rana, Omer] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales; [Buyya, Rajkumar] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst Clouds Lab, Melbourne, FL USA; [Jennings, Nicholas R.] Loughborough Univ, Loughborough, Leics, England Imperial College London; University of London; Queen Mary University London; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Lancaster University; University of Birmingham; Technische Universitat Wien; University of Manchester; Cardiff University; University of Melbourne; Loughborough University Gill, SS (corresponding author), Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England. s.tuli20@imperial.ac.uk; s.s.gill@qmul.ac.uk; mx.xu@siat.ac.cn; p.garraghan@lancaster.ac.uk; r.bahsoon@cs.bham.ac.uk; dustdar@dsg.tuwien.ac.at; rizos@manchester.ac.uk; ranaof@cardiff.ac.uk; rbuyya@unimelb.edu.au; g.casale@imperial.ac.uk; n.r.jennings@lboro.ac.uk Rana, Omer/E-4314-2015 Rana, Omer/0000-0003-3597-2646 EPSRC Research Grant [EP/V007092/1]; National Natural Science Foundation of China [62102408]; SIAT Innovation Program for Excellent Young Researchers; President's Ph.D. Scholarship at the Imperial College London EPSRC Research Grant(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); SIAT Innovation Program for Excellent Young Researchers; President's Ph.D. Scholarship at the Imperial College London Shreshth Tuli is supported by the President's Ph.D. Scholarship at the Imperial College London. This research work is partially supported by the EPSRC Research Grant (EP/V007092/1), National Natural Science Foundation of China (62102408) and SIAT Innovation Program for Excellent Young Researchers. The authors thank Muhammad Hassaan Anwar for his suggestions at the initial stages of this work. 37 7 7 3 13 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0164-1212 1873-1228 J SYST SOFTWARE J. Syst. Softw. FEB 2022.0 184 111124 15 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 9D3KY 2023-03-23 WOS:000936000800009 0
J Ye, XK; Yin, XF; Cai, XS; Yuste, AP; Xu, HL Ye, Xiaokang; Yin, Xuefeng; Cai, Xuesong; Perez Yuste, Antonio; Xu, Hongliang Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks IEEE ACCESS English Article Long-term-evolution; fingerprint; radio propagation; channel impulse response; multipath component; feature extraction; neural networks; user equipment localization INDOOR LOCALIZATION; FEATURE-SELECTION; GPS In this paper, a novel fingerprint-based localization technique is proposed, which is applicable for positioning user equipments (UEs) in cellular communication networks such as the long-term evolution (LIE) system. This technique utilizes a unique mapping between the characteristics of a radio channel formulated as a fingerprint vector and a geographical location. A feature-extraction algorithm is applied to selecting channel parameters with non-redundant information that are calculated from the LIE down-link signals. A feedforward neural network with the input of fingerprint vectors and the output of UEs' known locations is trained and used by UEs to estimate their positions. The results of experiments conducted in an in-service LTE system demonstrate that by using only one LTE eNodeB, the proposed technique yields a median error distance of 6 and 75 meters in indoor and outdoor environments, respectively. This localization technique is applicable in the cases where the Global Navigation Satellite System (GNSS) is unavailable, e.g., in indoor environments or in dense-urban scenarios with closely spaced skyscrapers heavily blocking the line-of-sight paths between a UE and GNSS satellites. [Ye, Xiaokang; Yin, Xuefeng; Cai, Xuesong] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China; [Perez Yuste, Antonio] Tech Univ Madrid, Sch Telecommun Engn, Madrid 28040, Spain; [Xu, Hongliang] Shanghai Radio Monitoring Stn, Shanghai 200031, Peoples R China Tongji University; Universidad Politecnica de Madrid Yin, XF (corresponding author), Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China. yinxuefeng@tongji.edu.cn Yuste, Antonio Perez/B-4601-2017; Cai, Xuesong/L-9278-2019 Yuste, Antonio Perez/0000-0002-3503-5065; Cai, Xuesong/0000-0001-7759-7448; yin, xuefeng/0000-0001-7216-8994 76 48 48 1 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2017.0 5 12071 12087 10.1109/ACCESS.2017.2712131 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications FI2GT gold 2023-03-23 WOS:000411757500013 0
C Lu, JH; Yang, LY; Mac Namee, B; Zhang, Y Assoc Computat Linguist Lu, Jinghui; Yang, Linyi; Mac Namee, Brian; Zhang, Yue A Rationale-Centric Framework for Human-in-the-loop Machine Learning PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) English Proceedings Paper 60th Annual Meeting of the Association-for-Computational-Linguistics (ACL) MAY 22-27, 2022 Dublin, IRELAND Assoc Computat Linguist,Amazon Sci,Bloomberg Engn,Google Res,Liveperson,Meta,Baidu,ByteDance,DeepMind,Grammarly,GTCOM,IBM,Megagon Labs,Microsoft,Alibaba Grp,Bosch,Cohere,G Res,ServiceNow,Relativity,Naver,ASAPP,Duolingo,BabelSpace,Spotiry,Adobe,D & I Special Initiat,AppTek,YaiGlobal,Aixplain,Apple We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. [Lu, Jinghui; Mac Namee, Brian] Univ Coll Dublin, Insight Ctr Data Analyt, Dublin, Ireland; [Lu, Jinghui; Mac Namee, Brian] Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland; [Yang, Linyi; Zhang, Yue] Westlake Univ, Sch Engn, Hangzhou, Peoples R China; [Yang, Linyi; Zhang, Yue] Westlake Inst Adv Study, Inst Adv Technol, Hangzhou, Peoples R China; [Lu, Jinghui] SenseTime Res, Hangzhou, Peoples R China University College Dublin; University College Dublin; Westlake University; Westlake University Lu, JH (corresponding author), Univ Coll Dublin, Insight Ctr Data Analyt, Dublin, Ireland.;Lu, JH (corresponding author), Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland.;Lu, JH (corresponding author), SenseTime Res, Hangzhou, Peoples R China. jinghui.lu@ucd.ie; yanglinyi@westlake.edu.cn; brian.macnamee@ucd.ie; zhangyue@westlake.edu.cn Pioneer; Leading Goose R&D Program of Zhejiang [2022SDXHDX0003]; Science Foundation Ireland (SFI) [12/RC/2289_P2] Pioneer; Leading Goose R&D Program of Zhejiang; Science Foundation Ireland (SFI)(Science Foundation Ireland) We acknowledge with thanks the discussion with Chenyang Lyu from Dublin City University, as well as the many others who have helped. We would also like to thank anonymous reviewers for their insightful comments and suggestions to help improve the paper. This publication has emanated from research conducted with the financial support of the Pioneer and Leading Goose R&D Program of Zhejiang under Grant Number 2022SDXHDX0003 and Science Foundation Ireland (SFI) under Grant Number [12/RC/2289_P2]. Yue Zhang is the corresponding author. 47 0 0 1 1 ASSOC COMPUTATIONAL LINGUISTICS-ACL STROUDSBURG 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA 978-1-955917-21-6 2022.0 6986 6996 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BT4EM 2023-03-23 WOS:000828702307008 0
C Alameda-Pineda, X; Hospedales, TM; Ricci, E; Sebe, N; Wang, XG ACM Alameda-Pineda, Xavier; Hospedales, Timothy M.; Ricci, Elisa; Sebe, Nicu; Wang, Xiaogang Emerging Topics in Learning from Noisy and Missing Data MM'16: PROCEEDINGS OF THE 2016 ACM MULTIMEDIA CONFERENCE English Proceedings Paper 24th ACM Multimedia Conference (MM) OCT 15-19, 2016 Amsterdam, NETHERLANDS ACM,ACM SIGMM Noisy and missing data; Low rank models; Zero-shot learning; Deep learning While vital for handling most multimedia and computer vision problems, collecting large scale fully annotated datasets is a resource-consuming, often unaffordable task. Indeed, on the one hand datasets need to be large and variate enough so that learning strategies can successfully exploit the variability inherently present in real data, but on the other hand they should be small enough so that they can be fully annotated at a reasonable cost. With the overwhelming success of (deep) learning methods, the traditional problem of balancing between dataset dimensions and resources needed for annotations became a full-fledged dilemma In this context, methodological approaches able to deal with partially described data sets represent a one-of-a-kind opportunity to find the right balance between data variability and resource consumption in annotation. These include methods able to deal with noisy, weak or partial annotations. In this tutorial we will present several recent methodologies addressing different visual tasks under the assumption of noisy, weakly annotated data sets. [Alameda-Pineda, Xavier; Sebe, Nicu] Univ Trento, Trento, Italy; [Hospedales, Timothy M.] Queen Mary Univ, London, England; [Ricci, Elisa] FBK, Trento, Italy; [Ricci, Elisa] Univ Perugia, Perugia, Italy; [Wang, Xiaogang] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China University of Trento; University of London; Queen Mary University London; Fondazione Bruno Kessler; University of Perugia; Chinese University of Hong Kong Alameda-Pineda, X (corresponding author), Univ Trento, Trento, Italy. xavier.alamedapineda@unitn.it; t.hospedales@qmul.ac.uk; eliricci@fbk.eu; sebe@disi.unitn.it; xgwang@ee.cuhk.edu.hk Ricci, Elisa/0000-0002-0228-1147; Sebe, Niculae/0000-0002-6597-7248 6 1 1 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-3603-1 2016.0 1469 1470 10.1145/2964284.2986910 0.0 2 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BG2WX Green Accepted 2023-03-23 WOS:000387733800121 0
J Papadimitriou, G; Chatzidimitriou, A; Gizopoulos, D; Reddi, VJ; Leng, JW; Salami, B; Unsal, OS; Cristal, A Papadimitriou, George; Chatzidimitriou, Athanasios; Gizopoulos, Dimitris; Reddi, Vijay Janapa; Leng, Jingwen; Salami, Behzad; Unsal, Osman S.; Cristal, Adrian Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins IEEE TRANSACTIONS ON DEVICE AND MATERIALS RELIABILITY English Article Materials reliability; Three-dimensional displays; 6G mobile communication; Iron; Benchmark testing; FAA; Artificial intelligence; Voltage margins; power consumption; energy efficiency; multicore CPU; many-core GPU; FPGA; accelerators VOLTAGE NOISE Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and programmable FPGAs. The effective utilization of these architectures poses several challenges, among which a primary one is power consumption. Voltage reduction is one of the most efficient methods to reduce power consumption of a chip. With the galloping adoption of hardware accelerators (i.e., GPUs and FPGAs) in large datacenters and other large-scale computing infrastructures, a comprehensive evaluation of the safe voltage reduction levels for each different chip can be employed for efficient reduction of the total power. We present a survey of recent studies in voltage margins reduction at the system level for modern CPUs, GPUs and FPGAs. The pessimistic voltage guardbands inserted by the silicon vendors can be exploited in all devices for significant power savings. On average, voltage reduction can reach 12% in multicore CPUs, 20% in manycore GPUs and 39% in FPGAs. [Papadimitriou, George; Chatzidimitriou, Athanasios; Gizopoulos, Dimitris] Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 10679, Greece; [Reddi, Vijay Janapa] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA; [Leng, Jingwen] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China; [Salami, Behzad; Unsal, Osman S.; Cristal, Adrian] Barcelona Supercomp Ctr, Dept Comp Sci, Barcelona 08034, Spain National & Kapodistrian University of Athens; Harvard University; Shanghai Jiao Tong University; Universitat Politecnica de Catalunya; Barcelona Supercomputer Center (BSC-CNS) Papadimitriou, G (corresponding author), Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 10679, Greece. georgepap@di.uoa.gr; achatz@di.uoa.gr; dgizop@di.uoa.gr; vj@eecs.harvard.edu; leng-jw@sjtu.edu.cn; behzad.salami@bsc.es; osman.unsal@bsc.es; adrian.cristal@bsc.es Papadimitriou, George/AAB-7546-2019; Gizopoulos, Dimitris/ABD-7951-2021 Papadimitriou, George/0000-0002-3772-5019; Gizopoulos, Dimitris/0000-0002-1613-9061; Leng, Jingwen/0000-0002-5660-5493; Chatzidimitriou, Athanasios/0000-0001-8161-7165 EU H2020 Programme under the UniServer project [688540]; EU H2020 Programme under the LEGaTO project [780681] EU H2020 Programme under the UniServer project; EU H2020 Programme under the LEGaTO project The research leading to results in the CPU part was funded by the EU H2020 Programme under the UniServer project (http://www.uniserver2020.eu), grant agreement no 688540. Also, the research leading to results in FPGA part was funded by the EU H2020 Programme under the LEGaTO project (https://legato-project.eu), grant agreement no 780681. 56 12 12 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-4388 1558-2574 IEEE T DEVICE MAT RE IEEE Trans. Device Mater. Reliab. JUN 2020.0 20 2 341 350 10.1109/TDMR.2020.2989813 0.0 10 Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physics MC0EB Green Submitted 2023-03-23 WOS:000542969400015 0
C Luo, YL; Lu, Q; Liu, JX; Fu, Q; Harkin, J; McDaid, L; Martinez-Corral, J; Biot-Mari, G Assoc Comp Machinery Luo, Yuling; Lu, Qian; Liu, Junxiu; Fu, Qiang; Harkin, Jim; McDaid, Liam; Martinez-Corral, Jordi; Biot-Mari, Guillermo Forest Fire Detection using Spiking Neural Networks 2018 ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS English Proceedings Paper 15th ACM International Conference on Computing Frontiers MAY 08-10, 2018 Ischia, ITALY Assoc Comp Machinery,ACM SIGMICRO,IBM,Intel,ManyCoreSoft,arm,BITalino forest fire detection; spiking neural networks; detection model MODEL Forest fires is one of the main causes of environmental degradation and its detection and forecasting is challenging. A novel method of forest fire detection based on spiking neural networks is proposed in this paper. Data obtained from controlled experiments are used as input training samples and a detection model is established by considering the factors of temperature, humidity, carbon monoxide concentration, wind speed and wind direction. Experimental results show that the spiking neural network can achieve a detection accuracy of similar to 91%, and therefore provides a better power/accuracy trade-off against existing approaches. [Luo, Yuling; Lu, Qian; Liu, Junxiu; Fu, Qiang] Guangxi Normal Univ, Fac Elect Engn, Guilin 541004, Peoples R China; [Harkin, Jim; McDaid, Liam] Ulster Univ, Sch Comp Engn & Intelligent Syst, Derry BT48 7JL, Londonderry, North Ireland; [Martinez-Corral, Jordi; Biot-Mari, Guillermo] Senticnel, C Valencia 3, Valencia 46135, Spain Guangxi Normal University; Ulster University Luo, YL (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guilin 541004, Peoples R China. yuling0616@mailbox.gxnu.edu.cn; jg.harkin@ulster.ac.uk; jmc@ntforest.com Fu, Qiang/0000-0002-3498-5915 National Natural Science Foundation of China [61603104]; Guangxi Natural Science Foundation [2017GXNSFAA198180, 2015GXNSFBA139256, 2016GXNSFCA380017]; Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China [KY2016YB059]; Guangxi Key Lab of Multi-source Information Mining Security [MIMS15-07]; Doctoral Research Foundation of Guangxi Normal University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China; Guangxi Key Lab of Multi-source Information Mining Security; Doctoral Research Foundation of Guangxi Normal University This research was supported by the National Natural Science Foundation of China under Grant 61603104, the Guangxi Natural Science Foundation under Grant 2017GXNSFAA198180, 2015GXNSFBA139256 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining & Security under Grant MIMS15-07, and the Doctoral Research Foundation of Guangxi Normal University. 31 3 3 2 7 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-5761-6 2018.0 371 375 10.1145/3203217.3203231 0.0 5 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL7LO 2023-03-23 WOS:000455156500058 0
J Chen, S; Jiang, ZY; Zhou, S; Niu, ZS; He, ZY; Marinescu, A; DaSilva, LA Chen, Sheng; Jiang, Zhiyuan; Zhou, Sheng; Niu, Zhisheng; He, Ziyan; Marinescu, Andrei; DaSilva, Luiz A. Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS English Article Channel state information; MIMO; deep neural networks; channel models; beamforming MASSIVE MIMO SYSTEMS; NEURAL-NETWORKS; INFORMATION; WIRELESS; MODEL Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cramer-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams. [Chen, Sheng; Zhou, Sheng; Niu, Zhisheng] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China; [Jiang, Zhiyuan] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China; [He, Ziyan] Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30332 USA; [Marinescu, Andrei; DaSilva, Luiz A.] Trinity Coll Dublin, CONNECT, Dublin D02 PN40 2, Ireland Tsinghua University; Shanghai University; University System of Georgia; Georgia Institute of Technology; Trinity College Dublin Jiang, ZY (corresponding author), Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China. chen-s16@tsinghua.edu.cn; zhiyjiang@foxmail.com; sheng.zhou@tsinghua.edu.cn; niuzhs@tsinghua.edu.cn; heziyan@gatech.edu; marinesa@tcd.ie; dasilval@tcd.ie Dasilva, Luiz/AAA-4678-2021; zhou, sheng/GYJ-6012-2022; Jiang, Zhiyuan/AAH-6710-2019 Jiang, Zhiyuan/0000-0002-8522-5721; Chen, Sheng/0000-0002-0907-735X; He, Ziyan/0000-0003-1025-8171; Zhou, Sheng/0000-0003-0651-0071 Nature Science Foundation of China [61861136003, 61871254, 91638204, 61571265, 61621091]; National Key R&D Program of China [2018YFB0105005]; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles; Science Foundation Ireland [17/NSFC/5224] Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles; Science Foundation Ireland(Science Foundation Ireland) This work was supported in part by the Nature Science Foundation of China under Grant 61861136003, Grant 61871254, Grant 91638204, Grant 61571265, and Grant 61621091, in part by the National Key R&D Program of China under Grant 2018YFB0105005, in part by the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles, and in part by the Science Foundation Ireland under Grant 17/NSFC/5224. This paper was presented in part at the IEEE GLOBECOM 2017 [1] and the IEEE GLOBECOM 2018 [2]. The associate editor coordinating the review of this paper and approving it for publication was S. Wang. 41 9 9 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1276 1558-2248 IEEE T WIREL COMMUN IEEE Trans. Wirel. Commun. JUL 2019.0 18 7 3554 3568 10.1109/TWC.2019.2915683 0.0 15 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications II6YP 2023-03-23 WOS:000475339700016 0
J Parsons, MJG; Lin, TH; Mooney, TA; Erbe, C; Juanes, F; Lammers, M; Li, SH; Linke, S; Looby, A; Nedelec, SL; Van Opzeeland, I; Radford, C; Rice, AN; Sayigh, L; Stanley, J; Urban, E; Di Iorio, L Parsons, Miles J. G.; Lin, Tzu-Hao; Mooney, T. Aran; Erbe, Christine; Juanes, Francis; Lammers, Marc; Li, Songhai; Linke, Simon; Looby, Audrey; Nedelec, Sophie L.; Van Opzeeland, Ilse; Radford, Craig; Rice, Aaron N.; Sayigh, Laela; Stanley, Jenni; Urban, Edward; Di Iorio, Lucia Sounding the Call for a Global Library of Underwater Biological Sounds FRONTIERS IN ECOLOGY AND EVOLUTION English Review soundscape; bioacoustics database; artificial intelligence; biodiversity; passive acoustic monitoring; ecological informatics MULLOWAY ARGYROSOMUS-JAPONICUS; BOTTLE-NOSED DOLPHINS; OPHIDION-MARGINATUM OPHIDIIDAE; FISH SOUNDS; SPAWNING AGGREGATION; AUTOMATIC DETECTION; PASSIVE ACOUSTICS; WHALE SONG; BALAENOPTERA-PHYSALUS; MARINE MAMMALS Aquatic environments encompass the world's most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources-potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future. [Parsons, Miles J. G.] Australian Inst Marine Sci, Perth, WA, Australia; [Lin, Tzu-Hao] Acad Sinica, Biodivers Res Ctr, Taipei, Taiwan; [Mooney, T. Aran; Sayigh, Laela] Woods Hole Oceanog Inst, Dept Biol, Woods Hole, MA 02543 USA; [Erbe, Christine] Curtin Univ, Ctr Marine Sci & Technol, Perth, WA, Australia; [Juanes, Francis] Univ Victoria, Dept Biol, Victoria, BC, Canada; [Lammers, Marc] Hawaiian Isl Humpback Whale Natl Marine Sanctuary, Kihei, HI USA; [Li, Songhai] Chinese Acad Sci, Marine Mammal & Marine Bioacoust Lab, Inst Deep Sea Sci & Engn, Sanya, Peoples R China; [Linke, Simon] CSIRO, Dutton Pk, Qld, Australia; [Looby, Audrey] Univ Florida, Fisheries & Aquat Sci, Gainesville, FL USA; [Looby, Audrey] Univ Florida, Inst Food & Agr Sci UF IFAS Nat Coast Biol Stn, Cedar Key, FL USA; [Nedelec, Sophie L.] Univ Exeter, Coll Life & Environm Sci, Biosci, Hatherly Labs, Exeter, Devon, England; [Van Opzeeland, Ilse] Helmholtz Zentrum Polar & Marine Res, Ocean Acoust Lab, Alfred Wegener Inst, Bremerhaven, Germany; [Van Opzeeland, Ilse] Carl von Ossietzky Univ Oldenburg, Helmholtz Inst Funct Marine Biodivers HIFMB, Oldenburg, Germany; [Radford, Craig] Univ Auckland, Inst Marine Sci, Leigh Marine Lab, Warkworth, New Zealand; [Rice, Aaron N.] Cornell Univ, Cornell Lab Ornithol, K Lisa Yang Ctr Conservat Bioacoust, Ithaca, NY USA; [Sayigh, Laela] Hampshire Coll, Amherst, MA 01002 USA; [Stanley, Jenni] Univ Waikato, Sch Sci, Coastal Marine Field Stn, Tauranga, New Zealand; [Urban, Edward] Univ Delaware, Sci Comm Ocean Res, Newark, DE USA; [Di Iorio, Lucia] Univ Perpignan Via Domitia, CNRS, Ctr Format & Rech Sur Environm Mediterraneens, Perpignan, France; [Di Iorio, Lucia] CHORUS Inst, Grenoble, France Australian Institute of Marine Science; Academia Sinica - Taiwan; Woods Hole Oceanographic Institution; Curtin University; University of Victoria; National Oceanic Atmospheric Admin (NOAA) - USA; Chinese Academy of Sciences; Institute of Deep-Sea Science & Engineering, CAS; Commonwealth Scientific & Industrial Research Organisation (CSIRO); State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Exeter; Helmholtz Association; Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research; Carl von Ossietzky Universitat Oldenburg; University of Auckland; Cornell University; University of Waikato; University of Delaware; Centre National de la Recherche Scientifique (CNRS) Parsons, MJG (corresponding author), Australian Inst Marine Sci, Perth, WA, Australia. m.parsons@aims.gov.au Barrela, Cristina/HJY-8958-2023; Lin, Tzu-Hao/GLT-9168-2022 Lin, Tzu-Hao/0000-0002-6973-3953; Di Iorio, Lucia/0000-0002-3354-830X; Looby, Audrey/0000-0003-1833-8643 U.S. National Science Foundation [OCE-1840868] U.S. National Science Foundation(National Science Foundation (NSF)) Support for the initial author group to meet, discuss, and build consensus on the issues within this manuscript was provided by the Scientific Committee on Oceanic Research, Monmouth University Urban Coast Institute, and Rockefeller Program for the Human Environment. The U.S. National Science Foundation supported the publication of this article through Grant OCE-1840868 to the Scientific Committee on Oceanic Research. 270 9 9 13 30 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-701X FRONT ECOL EVOL Front. Ecol. Evol. FEB 8 2022.0 10 810156 10.3389/fevo.2022.810156 0.0 20 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology ZG9RI Green Published, gold 2023-03-23 WOS:000760587900001 0
J He, NJ; Fang, LY; Li, ST; Plaza, J; Plaza, A He, Nanjun; Fang, Leyuan; Li, Shutao; Plaza, Javier; Plaza, Antonio Skip-Connected Covariance Network for Remote Sensing Scene Classification IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Feature extraction; Learning systems; Neural networks; Training; Aggregates; Computational modeling; Remote sensing; Covariance pooling; deep neural network; multi-layer feature; scene recognition CONVOLUTIONAL NEURAL-NETWORKS; FEATURES; RECOGNITION This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC). The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, i.e., skip connections and covariance pooling. The advantages of newly developed SCCov are twofold. First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets. Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps. This allows the CNN to achieve more representative feature learning when dealing with RSSC problems. Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters. Specifically, our SCCov only needs 10% of the parameters used by its counterparts. [He, Nanjun; Fang, Leyuan; Li, Shutao] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China; [He, Nanjun; Fang, Leyuan; Li, Shutao] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Peoples R China; [Plaza, Javier; Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, E-10003 Caceres, Spain Hunan University; Hunan University; Universidad de Extremadura Fang, LY (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China. henanjun@hnu.edu.cn; fangleyuan@gmail.com; shutao_li@hnu.edu.cn; jplaza@unex.es; aplaza@unex.es Plaza, Javier/C-4452-2008; Li, Shutao/Y-3102-2019; Plaza, Antonio/C-4455-2008 Plaza, Javier/0000-0002-2384-9141; Li, Shutao/0000-0002-0585-9848; Plaza, Antonio/0000-0002-9613-1659; He, Nanjun/0000-0003-3105-6499; Fang, Leyuan/0000-0003-2351-4461 National Natural Science Fund of China [61890962, 61520106001, 61771192]; Science and Technology Plan Project Fund of Hunan Province [CX2018B171, 2017RS3024, 2018TP1013]; Science and Technology Talents Program of Hunan Association for Science and Technology [2017TJ-Q09]; National Key R&D Program of China [2018YFB1305200] National Natural Science Fund of China(National Natural Science Foundation of China (NSFC)); Science and Technology Plan Project Fund of Hunan Province; Science and Technology Talents Program of Hunan Association for Science and Technology; National Key R&D Program of China This work was supported in part by the National Natural Science Fund of China under Grant 61890962, Grant 61520106001, and Grant 61771192, in part by the Science and Technology Plan Project Fund of Hunan Province under Grant CX2018B171, Grant 2017RS3024, and Grant 2018TP1013, in part by the Science and Technology Talents Program of Hunan Association for Science and Technology under Grant 2017TJ-Q09, and in part by the National Key R&D Program of China under Grant 2018YFB1305200. 65 90 96 6 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. MAY 2020.0 31 5 1461 1474 10.1109/TNNLS.2019.2920374 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering LM6OA 31295122.0 2023-03-23 WOS:000532368000004 0
J Li, X; Ding, ML; Pizurica, A Li, Xian; Ding, Mingli; Pizurica, Aleksandra Fully Group Convolutional Neural Networks for Robust Spectral-Spatial Feature Learning IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Hyperspectral imaging; Correlation; Dimensionality reduction; Data models; Data mining; Convolutional neural networks; Group convolution; hyperspectral image (HSI) classification; lightweight feature fusion; multiscale spectral feature extraction (MSSFE); spectral-spatial feature learning HYPERSPECTRAL IMAGE CLASSIFICATION; MARKOV-RANDOM-FIELDS Convolutional neural network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. Weak generalization of CNN models to different datasets is a common issue in this domain largely because of limited amount of labeled training samples. In this article, we propose a shapefully group convolutional neural network (FGCNN) method that integrates cascades of shuffled group convolutions tailored to different network stages. To our knowledge, this is the first reported full-group CNN model in general, and we design it in particular for robust spectral-spatial classification of HSI. In the primary feature extraction stage, we develop an original multiscale spectral feature extraction approach based on a novel concept of multikernel depthwise convolution that we define in terms of shuffled and importance-weighted group convolution. In the subsequent stage, we introduce a discriminative spectral-spatial feature extraction method with a novel group competition block to capture informative features with relatively few parameters. The final feature fusion stage is defined as a novel lightweight group feature fusion method that sharply reduces fusion weights compared to traditional methods with fully connected layers. Experimental results on three datasets show that the proposed FGCNN yields robust classification accuracy under the same hyperparameter settings compared to the current state-of-the-art. [Li, Xian; Ding, Mingli] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China; [Li, Xian; Pizurica, Aleksandra] Univ Ghent, UGent GAIM, Dept Telecommun & Informat Proc, B-9000 Ghent, Belgium Harbin Institute of Technology; Ghent University Ding, ML (corresponding author), Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China. xianli0511@gmail.com; dingm@hit.edu.cn; aleksandra.pizurica@ugent.be Pizurica, Aleksandra/AAG-4687-2021; Pizurica, Aleksandra/K-6866-2015 Pizurica, Aleksandra/0000-0002-9322-4999; Li, Xian/0000-0001-5714-3940 China Scholarship Council; AI Program of the Flemish Government China Scholarship Council(China Scholarship Council); AI Program of the Flemish Government This work was supported in part by the China Scholarship Council and in part by the AI Program of the Flemish Government. (Corresponding author: Mingli Ding.) 55 0 0 1 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 10.1109/TGRS.2021.3091618 0.0 JUN 2021 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG3HV Green Published 2023-03-23 WOS:000732768400001 0
J Egger, J; Wild, D; Weber, M; Bedoya, CAR; Karner, F; Prutsch, A; Schmied, M; Dionysio, C; Krobath, D; Jin, Y; Gsaxner, C; Li, JN; Pepe, A Egger, Jan; Wild, Daniel; Weber, Maximilian; Bedoya, Christopher A. Ramirez; Karner, Florian; Prutsch, Alexander; Schmied, Michael; Dionysio, Christina; Krobath, Dominik; Jin, Yuan; Gsaxner, Christina; Li, Jianning; Pepe, Antonio Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform JOURNAL OF DIGITAL IMAGING English Article Medical image analysis; Cloud; Client/server; Python; ITK; VTK; Deep learning; CNN; GAN; Virtual reality; Augmented reality; Whitepaper SEGMENTATION; SOFTWARE; THERAPY; SYSTEM; VTK Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster (www.studierfenster.at): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448-456, 68], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology. [Egger, Jan; Wild, Daniel; Weber, Maximilian; Bedoya, Christopher A. Ramirez; Karner, Florian; Prutsch, Alexander; Schmied, Michael; Dionysio, Christina; Krobath, Dominik; Jin, Yuan; Gsaxner, Christina; Li, Jianning; Pepe, Antonio] Graz Univ Technol, Inst Comp Graph & Vis, Fac Comp Sci & Biomed Engn, Inffeldgasse 16, A-8010 Graz, Austria; [Egger, Jan; Wild, Daniel; Weber, Maximilian; Bedoya, Christopher A. Ramirez; Karner, Florian; Prutsch, Alexander; Schmied, Michael; Dionysio, Christina; Krobath, Dominik; Jin, Yuan; Gsaxner, Christina; Li, Jianning; Pepe, Antonio] Comp Algorithms Med Lab, Graz, Austria; [Egger, Jan; Li, Jianning] Univ Hosp Essen, Inst Artificial Intelligence Med, AI Guided Therapies, Girardetstr 2, D-45131 Essen, Germany; [Jin, Yuan] Res Ctr Connected Healthcare Big Data, ZhejiangLab, Hangzhou 311121, Zhejiang, Peoples R China Graz University of Technology; University of Duisburg Essen Egger, J (corresponding author), Graz Univ Technol, Inst Comp Graph & Vis, Fac Comp Sci & Biomed Engn, Inffeldgasse 16, A-8010 Graz, Austria.;Egger, J (corresponding author), Comp Algorithms Med Lab, Graz, Austria.;Egger, J (corresponding author), Univ Hosp Essen, Inst Artificial Intelligence Med, AI Guided Therapies, Girardetstr 2, D-45131 Essen, Germany. egger@tugraz.at Austrian Science Fund (FWF) [KLI 678-B31]; TU Graz Lead Project (Mechanics, Modeling, and Simulation of Aortic Dissection); Austrian Federal Ministry of Transport, Innovation, and Technology (BMVIT) [871132]; Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Styrian Business Promotion Agency (SFG); REACT-EU project KITE (Plattform fur KI-Translation Essen) Austrian Science Fund (FWF)(Austrian Science Fund (FWF)); TU Graz Lead Project (Mechanics, Modeling, and Simulation of Aortic Dissection); Austrian Federal Ministry of Transport, Innovation, and Technology (BMVIT); Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Styrian Business Promotion Agency (SFG); REACT-EU project KITE (Plattform fur KI-Translation Essen) This work received funding from the Austrian Science Fund (FWF) KLI 678-B31: enFaced: Virtual and Augmented Reality Training and Navigation Module for 3D-Printed Facial Defect Reconstructions and the TU Graz Lead Project (Mechanics, Modeling, and Simulation of Aortic Dissection). Moreover, this work was supported by CAMed (COMET K-Project 871132), which is funded by the Austrian Federal Ministry of Transport, Innovation, and Technology (BMVIT); the Austrian Federal Ministry for Digital and Economic Affairs (BMDW); and the Styrian Business Promotion Agency (SFG). Finally, the REACT-EU project KITE (Plattform fur KI-Translation Essen) also provided support. 75 4 4 8 20 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0897-1889 1618-727X J DIGIT IMAGING J. Digit. Imaging APR 2022.0 35 2 340 355 10.1007/s10278-021-00574-8 0.0 JAN 2022 16 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging ZT0ZZ 35064372.0 hybrid, Green Accepted 2023-03-23 WOS:000745403100003 0
J Zhang, X; Kloosterman, RC; Li, YJ Zhang, Xu; Kloosterman, Robert C.; Li, Yajuan 'Stars' and places: exploring the spatial organization of media and entertainment industries in China JOURNAL OF CULTURAL ECONOMY English Article Media and entertainment industries; cultural and creative industries; celebrity; big data; China CULTURAL INDUSTRIES; EMPLOYMENT; ECONOMY; EMERGENCE; VANCOUVER; MOBILITY; WORLD; CITY Drawing on an innovative 'big data' method, this paper presents a comprehensive analysis of the geography of media and entertainment industries (MEIs) in China, by examining the specific activities that are performed by celebrities, the key actors in these industries. Compared to previous research on cultural and creative industries, which primarily rely on more traditional statistical data, our study demonstrates a new approach to systemically investigate the spatial organization of the dynamic production process in MEIs. The outcomes reveal that activities focusing on the different value segments of MEIs display rather diverse geographical patterns. Depending on the availability and combination of specific local assets, different locations perform varying functions in the production networks of MEIs. Therefore, the agglomeration of MEIs only partially overlaps with the existing urban hierarchy. In addition, celebrities catering to different media and entertainment markets also exhibit distinct activity patterns, indicating a close and reciprocal relationship between the popularity of celebrities, the activities they perform, the platforms that are chosen, and the commercial value that can be generated as a result. This study contributes to our understanding of the complex development patterns and consequences of MEIs in emerging economies. [Zhang, Xu] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan, Peoples R China; [Kloosterman, Robert C.] Univ Amsterdam, Fac Social & Behav Sci, Amsterdam, Netherlands; [Li, Yajuan] Cent China Normal Univ, Coll Urban & Environm Sci, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan Branch,China Tourism Acad, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China Wuhan University of Technology; University of Amsterdam; Central China Normal University Li, YJ (corresponding author), Cent China Normal Univ, Coll Urban & Environm Sci, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan Branch,China Tourism Acad, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. yajuan.li@mail.ccnu.edu.cn li, yajuan/0000-0002-5787-0459 National Natural Science Foundation of China [41601163] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China [grant number 41601163]. 44 0 0 13 35 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1753-0350 1753-0369 J CULT ECON-UK J. Cult. Econ. SEP 2 2020.0 13 5 626 641 10.1080/17530350.2020.1751677 0.0 APR 2020 16 Cultural Studies; Economics; Sociology Social Science Citation Index (SSCI); Arts & Humanities Citation Index (A&HCI) Cultural Studies; Business & Economics; Sociology NS2QK 2023-03-23 WOS:000527651700001 0
C Cheng, L; Wang, Y; Pei, YL; Epema, D IEEE Cheng, Long; Wang, Ying; Pei, Yulong; Epema, Dick A Coflow-based Co-optimization Framework for High-performance Data Analytics 2017 46TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING (ICPP) Proceedings of the International Conference on Parallel Processing English Proceedings Paper 46th International Conference on Parallel Processing Workshops (ICPPW) AUG 14-17, 2017 Bristol, ENGLAND CRAY Supercomp Company,Intel,ARM,OCF,SGI,Univ Bristol,VirginiaTech,Int Assoc Comp & Communications big data; coflow scheduling; distributed joins; network communications; data-intensive applications Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the network communication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved by using state-of-the-art methods, such as reducing network traffic designed in the data management domain, and data flow scheduling in the data communications domain. However, the proposed techniques in both fields just view each other as a black box, and performance gains from a co-optimization perspective have not yet been explored. In this paper, based on current research in coflow scheduling, we propose a novel Coflow-based Co-optimization Framework (CCF), which can co-optimize application-level data movement and network-level data communications for distributed operators, and consequently contribute to their performance in large distributed environments. We present the detailed design and implementation of CCF, and conduct an experimental evaluation of CCF using large-scale simulations on large data joins. Our results demonstrate that CCF can always perform faster than current approaches on network communications in large-scale distributed scenarios. [Cheng, Long; Pei, Yulong] Eindhoven Univ Technol, Eindhoven, Netherlands; [Wang, Ying] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China; [Epema, Dick] Delft Univ Technol, Delft, Netherlands Eindhoven University of Technology; Chinese Academy of Sciences; Institute of Computing Technology, CAS; Delft University of Technology Cheng, L (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. l.cheng@tue.nl; wangying2009@ict.ac.cn; y.pei.1@tue.nl; d.h.j.epema@tudelft.nl Cheng, Long/ADJ-1122-2022; Wang, Ying/ABD-4070-2020 Cheng, Long/0000-0003-1638-059X; 29 8 8 0 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 0190-3918 978-1-5386-1042-8 PROC INT CONF PARAL 2017.0 392 401 10.1109/ICPP.2017.48 0.0 10 Computer Science, Hardware & Architecture Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ6TR Green Submitted 2023-03-23 WOS:000426952300040 0
J Porwal, P; Pachade, S; Kokare, M; Deshmukh, G; Son, J; Bae, W; Liu, LH; Wang, J; Liu, XH; Gao, LX; Wu, TB; Xiao, J; Wang, FY; Yin, BC; Wang, YZ; Danala, G; He, LS; Choi, YH; Lee, YC; Jung, SH; Li, ZY; Sui, XD; Wu, JY; Li, XL; Zhou, T; Toth, J; Bara, A; Kori, A; Chennamsetty, SS; Safwan, M; Alex, V; Lyu, XZ; Cheng, L; Chu, QH; Li, PC; Ji, X; Zhang, SY; Shen, YX; Dai, L; Saha, O; Sathish, R; Melo, T; Araujo, T; Harangi, B; Sheng, B; Fang, RG; Sheet, D; Hajdu, A; Zheng, YJ; Mendonca, AM; Zhang, ST; Campilho, A; Zheng, B; Shen, D; Giancardo, L; Quellec, G; Meriaudeau, F Porwal, Prasanna; Pachade, Samiksha; Kokare, Manesh; Deshmukh, Girish; Son, Jaemin; Bae, Woong; Liu, Lihong; Wang, Jianzong; Liu, Xinhui; Gao, Liangxin; Wu, TianBo; Xiao, Jing; Wang, Fengyan; Yin, Baocai; Wang, Yunzhi; Danala, Gopichandh; He, Linsheng; Choi, Yoon Ho; Lee, Yeong Chan; Jung, Sang-Hyuk; Li, Zhongyu; Sui, Xiaodan; Wu, Junyan; Li, Xiaolong; Zhou, Ting; Toth, Janos; Bara, Agnes; Kori, Avinash; Chennamsetty, Sai Saketh; Safwan, Mohammed; Alex, Varghese; Lyu, Xingzheng; Cheng, Li; Chu, Qinhao; Li, Pengcheng; Ji, Xin; Zhang, Sanyuan; Shen, Yaxin; Dai, Ling; Saha, Oindrila; Sathish, Rachana; Melo, Tania; Araujo, Teresa; Harangi, Balazs; Sheng, Bin; Fang, Ruogu; Sheet, Debdoot; Hajdu, Andras; Zheng, Yuanjie; Mendonca, Ana Maria; Zhang, Shaoting; Campilho, Aurelio; Zheng, Bin; Shen, Dinggang; Giancardo, Luca; Quellec, Gwenole; Meriaudeau, Fabrice IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge MEDICAL IMAGE ANALYSIS English Article Diabetic Retinopathy; Retinal image analysis; Deep learning; Challenge CONVOLUTIONAL NEURAL-NETWORKS; RETINAL IMAGE-ANALYSIS; RED LESION DETECTION; HIGHER-ORDER SPECTRA; COLOR FUNDUS IMAGES; OPTIC DISC; AUTOMATIC DETECTION; MACULAR EDEMA; MICROANEURYSM DETECTION; FEATURE-EXTRACTION Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on Diabetic Retinopathy - Segmentation and Grading was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI-2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal subchallenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular. (C) 2019 Elsevier B.V. All rights reserved. [Porwal, Prasanna; Pachade, Samiksha; Kokare, Manesh] Shri Guru Gobind Sirighji Inst Engn & Technol, Nanded, India; [Porwal, Prasanna; Pachade, Samiksha; Giancardo, Luca] Univ Texas Hlth Sci Ctr Houston, Sch Biomed Informat, Houston, TX 77030 USA; [Deshmukh, Girish] Sushrusha Hosp, Eye Clin, Nanded, Maharashtra, India; [Son, Jaemin; Bae, Woong] VUNO Inc, Seoul, South Korea; [Liu, Lihong; Wang, Jianzong; Liu, Xinhui; Gao, Liangxin; Wu, TianBo; Xiao, Jing] Ping An Technol Shenzhen Co Ltd, Shenzhen, Guangdong, Peoples R China; [Wang, Fengyan; Yin, Baocai] iFLYTEK Res, Hefei, Anhui, Peoples R China; [Wang, Yunzhi; Danala, Gopichandh; He, Linsheng; Zheng, Bin] Univ Oklahoma, Sch Elect & Comp Engn, Norman, OK 73019 USA; [Choi, Yoon Ho; Lee, Yeong Chan; Jung, Sang-Hyuk] Sungkyunkwan Univ, SAIHST, Seoul, South Korea; [Li, Zhongyu; Zhang, Shaoting] Univ N Carolina, Dept Comp Sci, Charlotte, NC USA; [Sui, Xiaodan; Zheng, Yuanjie] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China; [Shen, Dinggang] Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA; [Shen, Dinggang] Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA; [Wu, Junyan] Cleerly Inc, New York, NY USA; [Li, Xiaolong] Virginia Tech, Blacksburg, VA USA; [Zhou, Ting] SUNY Buffalo, Buffalo, NY USA; [Toth, Janos; Bara, Agnes; Harangi, Balazs; Hajdu, Andras] Univ Debrecen, Fac Informat, POB 400, H-4002 Debrecen, Hungary; [Lyu, Xingzheng; Zhang, Sanyuan] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China; [Lyu, Xingzheng; Cheng, Li] ASTAR, Bioinformat Inst, Machine Learning Bioimage Anal Grp, Singapore, Singapore; [Chu, Qinhao; Li, Pengcheng] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Ji, Xin] Beijing Shanggong Med Technol Co Ltd, Beijing, Peoples R China; [Shen, Yaxin; Dai, Ling; Sheng, Bin] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China; [Shen, Yaxin; Dai, Ling; Sheng, Bin] Shanghai Jiao Tong Univ, AI Inst, MoE Key Lab Artificial Intelligence, Shanghai, Peoples R China; [Fang, Ruogu] Univ Florida, J Crayton Pruitt Family Dept Biomed Engn, Gainesville, FL 32611 USA; [Saha, Oindrila; Sathish, Rachana; Sheet, Debdoot] Indian Inst Technol Kharagpur, Kharagpur, W Bengal, India; [Melo, Tania; Araujo, Teresa; Mendonca, Ana Maria; Campilho, Aurelio] INESC TEC Inst Syst & Comp Engn Technol & Sci, Porto, Portugal; [Araujo, Teresa; Mendonca, Ana Maria; Campilho, Aurelio] Univ Porto, FEUP Fac Engn, Porto, Portugal; [Quellec, Gwenole] INSERM, UMR 1101, Brest, France; [Meriaudeau, Fabrice] Univ Teknol PETRONAS, Dept Elect & Elect Engn, Perak, Malaysia; [Meriaudeau, Fabrice] Univ Bourgogne, ImViA IFTIM, Dijon, France; [Cheng, Li] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada Shri Guru Gobind Singhji Institute of Engineering & Technology; University of Texas System; University of Texas Health Science Center Houston; University of Oklahoma System; University of Oklahoma - Norman; Sungkyunkwan University (SKKU); University of North Carolina; University of North Carolina Charlotte; Shandong Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Virginia Polytechnic Institute & State University; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; University of Debrecen; Zhejiang University; Agency for Science Technology & Research (A*STAR); A*STAR - Bioinformatics Institute (BII); National University of Singapore; Shanghai Jiao Tong University; Shanghai Jiao Tong University; State University System of Florida; University of Florida; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; INESC TEC; Universidade do Porto; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; Universiti Teknologi Petronas; Universite de Bourgogne; University of Alberta Porwal, P (corresponding author), Shri Guru Gobind Singhji Inst Engn & Technol, Ctr Excellence Signal & Image Proc, Nanded Ms, India. porwal.prasanna@sggs.ac.in Hajdu, Andras/F-4885-2013; DANALA, GOPICHANDH/AFK-3123-2022; Giancardo, Luca/AGY-0035-2022; Son, Jaemin/GQQ-8857-2022; Tóth, János/GRX-2383-2022; Shen, Dinggang/ABF-6812-2020; Fang, Ruogu/AAY-6687-2020; Mendonça, Ana Maria/AIC-8438-2022; Wu, Jun/HJP-1242-2023; Mendonça, Ana Maria/M-7629-2013; Quellec, Gwenole/L-9946-2015; li, xiao/GSN-6181-2022; liu, li/HGC-0900-2022; Cheng, Li/AAU-6734-2020; DANALA, GOPICHANDH/AAG-1674-2022; li, xiao/HJP-5134-2023; Kokare, Manesh/AAJ-5588-2020; Porwal, Prasanna/O-5447-2018; Li, xiaolong/GRS-9148-2022; Fang, Ruogu/AAY-8923-2020; Campilho, Aurelio/M-4869-2013; Melo, Tania/E-9251-2019 Hajdu, Andras/0000-0003-1718-9770; DANALA, GOPICHANDH/0000-0001-6857-9408; Giancardo, Luca/0000-0002-4862-2277; Shen, Dinggang/0000-0002-7934-5698; Fang, Ruogu/0000-0003-3980-3532; Mendonça, Ana Maria/0000-0002-4319-738X; Mendonça, Ana Maria/0000-0002-4319-738X; Quellec, Gwenole/0000-0003-1669-7140; Cheng, Li/0000-0003-3261-3533; Porwal, Prasanna/0000-0003-2268-7504; Fang, Ruogu/0000-0003-3980-3532; Lee, Yeong Chan/0000-0002-2093-3161; Campilho, Aurelio/0000-0002-5317-6275; Pachade, Samiksha/0000-0001-5226-6706; Melo, Tania/0000-0003-1781-8696 Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded (M.S.), INDIA [ISBI-2018]; Research Grant for Intelligence Information Service Expansion Project [NIPA-0O202-17-1045]; National Natural Science Foundation of China [61572300]; National Key Research and Development Program of China [2016YFC1300302, 2017YFE0104000]; Science and Technology Commission of Shanghai Municipality [16=501100, 17411952600]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences [EFOP-3.6.2-16-2017-00015]; European Union; State of Hungary; European Social Fund; Beijing Shanggong Medical Technology Co., Ltd.; A*STAR [A1818g0022]; ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme; National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [CMUP-ERI/TIC/0028/2014]; FCT [SFRH/BD/122365/2016] Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded (M.S.), INDIA; Research Grant for Intelligence Information Service Expansion Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences(Hungarian Academy of Sciences); European Union(European Commission); State of Hungary; European Social Fund(European Social Fund (ESF)); Beijing Shanggong Medical Technology Co., Ltd.; A*STAR(Agency for Science Technology & Research (A*STAR)); ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme; National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology)(Fundacao para a Ciencia e a Tecnologia (FCT)); FCT(Fundacao para a Ciencia e a Tecnologia (FCT)) This work is sponsored by the Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded (M.S.), INDIA. The authors would like to thank the following people for their help in various aspects of organizing the ISBI-2018 Diabetic Retinopathy Segmentation and Grading Challenge: Prof. Emanuele Trucco (University of Dundee, Scotland), Tom MacGillivray (University of Edinburgh, Scotland), Ravi Kamble (SGGS Institute of Engineering and Technology, Nanded), Prof. Vivek Sahasrabuddhe (Government Medical College, Nanded) and Desire Sidibe (Universite de Bourgogne, France). We would also like to thank Prof. Jorge Cuadros, University of California, Berkeley (Organizer of Kaggle Diabetic Retinopathy challenge) for his kind permission for reporting the results of the models trained on their dataset. VRT: This study was supported by the Research Grant for Intelligence Information Service Expansion Project, which is funded by.National IT Industry Promotion Agency (NIPA-0O202-17-1045) in South Korea. DeepDR: This work was supported in part by the National Natural Science Foundation of China under Grant Grant 61872241, Grant 61572316, in part by the National Key Research and Development Program of China under Grant 2016YFC1300302 and Grant 2017YFE0104000, in part by the Science and Technology Commission of Shanghai Municipality under Grant 16=501100 and Grant 17411952600. HarangiM1 -M2: Research was supported in part by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences and the project EFOP-3.6.2-16-2017-00015 supported by the European Union and the State of Hungary, co -financed by the European Social Fund. ZJU-BII-SGEX: This work is supported by Beijing Shanggong Medical Technology Co., Ltd., which provided ocular healthcare solutions in China. This research is partially supported by the A*STAR A1818g0022 grant of Singapore. Many thanks to the labeled images from Image Annotation Group of Beijing Shanggong Medical Technology. Team CBER (A.M. Mendonca, T. Melo, T. Aratijo and A. Campilho) is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme, and by National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project CMUP-ERI/TIC/0028/2014. Teresa Araujo is funded by the FCT grant SFRH/BD/122365/2016. SDNU: This study was supported by the National Natural Science Foundation of China (Grant No. 61572300). 166 84 87 12 58 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JAN 2020.0 59 101561 10.1016/j.media.2019.101561 0.0 26 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging JU0YT 31671320.0 2023-03-23 WOS:000501405800010 0
J Wen, YQ; Tao, W; Sui, ZY; Piera, MA; Song, RX Wen, Yuanqiao; Tao, Wei; Sui, Zhongyi; Piera, Miquel Angel; Song, Rongxin Dynamic model-based method for the analysis of ship behavior in marine traffic situation OCEAN ENGINEERING English Article Maritime traffic; Dynamic model; Ship behavior; Social force model; Complex network; Structure entropy SOCIAL FORCE MODEL; NETWORK; SIMULATION; FLOW; PERFORMANCE With the continual development of modern transportation technology and artificial intelligence technology, how to recognize the complex phenomenon of ship behavior existing in maritime traffic has become a hot topic. Maritime traffic is a complex system, the emergence of ship behavior is a leading cause of traffic complexity, and make up the core ideas of this research. This research studies ship behavior from three aspects: ship individual behavior, ship-ship interaction and multi-ship behavior. According to the movement state attribute, the improved Social Force Model has been developed by considering of the interactive effects between ships. On that foundation, the complex network model has been built to analyze the emergence of multi-ship behavior in a macroscopic view. Through experimental analysis of ship behavior in different scenarios, the results show that the repulsive force between ships changes in the ship behavior dynamic model can express the dynamic characteristics of the ship. And structural entropy in marine traffic situation complex network has been proved to describe the maritime traffic system. As such, the framework proposed in this paper can provide a new perspective for further understanding and research of ship behavior. [Wen, Yuanqiao; Tao, Wei] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Peoples R China; [Wen, Yuanqiao] Natl Engn Res Ctr Water Transport Safety, Wuhan, Peoples R China; [Sui, Zhongyi] Wuhan Univ Technol, Sch Nav, Wuhan, Peoples R China; [Sui, Zhongyi; Piera, Miquel Angel] Autonomous Univ Barcelona, Dept Telecommun & Syst Engn, Sabadell, Spain; [Tao, Wei] Delft Univ Technol, Fac Mech Maritime & Mat Engn, Dept Maritime & Transport Technol, Delft, Netherlands; [Song, Rongxin] Delft Univ Technol, Fac Technol Policy & Management, Safety & Secur Sci Grp, Delft, Netherlands Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; Autonomous University of Barcelona; Delft University of Technology; Delft University of Technology Sui, ZY (corresponding author), Wuhan Univ Technol, Sch Nav, Wuhan, Peoples R China.;Sui, ZY (corresponding author), Autonomous Univ Barcelona, Dept Telecommun & Syst Engn, Sabadell, Spain. suizy@whut.edu.cn Sui, Zhongyi/0000-0001-9240-1714 National Natural Science Foundation of China (NSFC) [52072287, U2141234]; China Scholarship Council [202106950051, 202006950077] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) The authors would like to thank the anonymous reviewers and edi-tors for their constructive comments, which is very helpful to improve the paper. This work is supported by the National Natural Science Foundation of China (NSFC) through Grant No.52072287, No. U2141234 and fellowship from the China Scholarship Council through Gant No.202106950051, No.202006950077. We wish to thank Yihao Liu and Shunqiang Xu for their assistance in this manuscript. 78 0 0 29 52 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. AUG 1 2022.0 257 111578 10.1016/j.oceaneng.2022.111578 0.0 JUN 2022 11 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 2F1QV 2023-03-23 WOS:000812691200001 0
J Ruan, DW; Chen, YX; Guhmann, C; Yan, JP; Li, ZR Ruan, Diwang; Chen, Yuxiang; Guehmann, Clemens; Yan, Jianping; Li, Zhirou Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis ELECTRONICS English Article bearing; transfer learning; dynamics model; fault diagnosis; modelica; Convolutional Neural Network (CNN) CONVOLUTIONAL NEURAL-NETWORK; ROLLING ELEMENT BEARINGS; VIBRATION ANALYSIS; SYSTEM; CNN In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica model for the whole bearing test rig, including the test bearing, driving motor and hydraulic loading system. First, a five degree-of-freedom (5-DoF) model was proposed for the test bearing to identify the normal bearing dynamics. Next, a fault model was applied to characterize the defect position, defect size, defect shape and multiple defects. The virtual bearing test bench was first developed with OpenModelica and then called in Python with OMPython. For validation of the positive effect of the dynamics model in the direct transfer learning for bearing fault diagnosis, the simulation data from the Modelica model and experimental data from the Case Western Reserve University were fed separately or jointly to train a Convolution Neural Network (CNN). Then the well-trained CNN was transferred directly to achieve the fault diagnosis under the test set consisting of experiment data. Additionally, 157 features were extracted from both time-domain and frequency-domain and fed into CNN as input, and then four different validation cases were designed. The results confirmed the positive effect of simulation data in the CNN transfer learning, especially when the simulation data were added as auxiliary to experimental data, and improved CNN classification accuracy. Furthermore, it indicated that the simulation data from the bearing dynamics model could play a part in the actual experimental measurement when the collected data were insufficient. [Ruan, Diwang; Guehmann, Clemens] Tech Univ Berlin, Chair Elect Measurement & Diagnost Technol, D-10587 Berlin, Germany; [Chen, Yuxiang; Li, Zhirou] Tech Univ Berlin, Sch Elect Engn & Comp Sci, D-10587 Berlin, Germany; [Yan, Jianping] Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China Technical University of Berlin; Technical University of Berlin; Tsinghua University Guhmann, C (corresponding author), Tech Univ Berlin, Chair Elect Measurement & Diagnost Technol, D-10587 Berlin, Germany.;Yan, JP (corresponding author), Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China. diwang.ruan@campus.tu-berlin.de; yuxiangchen525@foxmail.com; clemens.guehmann@tu-berlin.de; jianping_yan_de@yahoo.de; lizhirou0423@hotmail.com 27 2 2 3 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics FEB 2022.0 11 4 622 10.3390/electronics11040622 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics ZO8LX gold 2023-03-23 WOS:000765979500001 0
J Tsai, CW; Chiang, MC; Ksentini, A; Chen, M Tsai, Chun-Wei; Chiang, Ming-Chao; Ksentini, Adlen; Chen, Min Metaheuristic Algorithms for Healthcare: Open Issues and Challenges COMPUTERS & ELECTRICAL ENGINEERING English Article Metaheuristic algorithm data healthcare and smart home PARTICLE SWARM OPTIMIZATION; WIRELESS SENSOR NETWORKS; ANT COLONY SYSTEM; GENETIC ALGORITHM; DESIGN; CLOUD; TECHNOLOGIES; DATABASE Inspired by the observation that a healthcare system usually involves various intelligent technologies from different disciplines, especially metaheuristics and data mining, this paper provides a brief survey of metaheuristics for healthcare system and a roadmap for researchers working on metaheuristics and healthcare to develop a more efficient and effective healthcare system. This paper begins with a discussion of changes for healthcare, followed by a brief review of the features of up-to-date technologies for healthcare.. Then, a learnable big data analytics framework for healthcare system is presented which provides a high performance solution to the forthcoming challenges of big data. Finally, changes, potentials, open issues, and future trends of metaheuristics for healthcare are addressed. (C) 2016 Elsevier Ltd. All rights reserved. [Tsai, Chun-Wei] Natl Ilan Univ, Dept Comp Sci & Informat Engn, Yitan 26047, Taiwan; [Chiang, Ming-Chao] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan; [Ksentini, Adlen] Univ Rennes 1, IRISA Lab, Rennes, France; [Chen, Min] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China National Ilan University; National Sun Yat Sen University; Universite de Rennes; Huazhong University of Science & Technology Chen, M (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China. minchen2012@hust.edu.cn Chen, Min/N-9350-2015; Tsai, Chun-Wei/R-6389-2019 Chen, Min/0000-0002-0960-4447; Ministry of Science and Technology of Taiwan, R.O.C. [MOST104-2221-E-197-005, MOST104-2221-E-110-014] Ministry of Science and Technology of Taiwan, R.O.C. The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions on the papers. This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST104-2221-E-197-005 and MOST104-2221-E-110-014. 81 25 25 1 21 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. JUL 2016.0 53 421 434 10.1016/j.compeleceng.2016.03.005 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering DZ1MI 2023-03-23 WOS:000385602100033 0
J Gupta, S; Qian, XY; Bhushan, B; Luo, ZW Gupta, Shivam; Qian, Xiaoyan; Bhushan, Bharat; Luo, Zongwei Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective MANAGEMENT DECISION English Article Big data; Organizational culture; Structural equation modelling; Firm performance; Cloud ERP; Dynamic capability view RESOURCE-BASED VIEW; SUPPLY CHAIN MANAGEMENT; DATA ANALYTICS; ORGANIZATIONAL PERFORMANCE; INFORMATION-TECHNOLOGY; PREDICTIVE ANALYTICS; MEASUREMENT SYSTEMS; OPERATIONS; CULTURE; BUSINESS Purpose Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm. Design/methodology/approach A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling. Findings SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data. Originality/value This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory. [Gupta, Shivam] Montpellier Business Sch, Montpellier Res Management, Montpellier, France; [Qian, Xiaoyan; Luo, Zongwei] Southern Univ Sci & Technol China, Dept Comp Sci & Engn, Shenzhen, Peoples R China; [Bhushan, Bharat] XLRI Xavier Sch Management, Sch Management, Jamshedpur, Bihar, India Montpellier Business School; Universite de Montpellier; Southern University of Science & Technology; XLRI -Xavier School of Management Luo, ZW (corresponding author), Southern Univ Sci & Technol China, Dept Comp Sci & Engn, Shenzhen, Peoples R China. sh.gupta@montpellier-bs.com; xiaoyanqian1010@gmail.com; bharat.bhushan.08@gmail.com; luozw@sustc.edu.cn Gupta, Shivam/R-2996-2016; Luo, Zongwei/N-1985-2015 Gupta, Shivam/0000-0002-2714-4958; Luo, Zongwei/0000-0001-9322-959X Natural Science Foundation of China [6171101169]; National Key R&D Plan - Key Special Plan on Public Security Risk Mitigation/Response [2017YFC0804003]; Technologies and Equipment Guangdong Education Bureau Fund [2017KTSCX166]; Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ20170817112037041] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Plan - Key Special Plan on Public Security Risk Mitigation/Response; Technologies and Equipment Guangdong Education Bureau Fund; Science and Technology Innovation Committee Foundation of Shenzhen This work is partially supported by Natural Science Foundation of China 6171101169, National Key R&D Plan - Key Special Plan on Public Security Risk Mitigation/Response 2017YFC0804003, Technologies and Equipment Guangdong Education Bureau Fund 2017KTSCX166, the Science and Technology Innovation Committee Foundation of Shenzhen JCYJ20170817112037041. 105 44 45 19 128 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0025-1747 1758-6070 MANAGE DECIS Manag. Decis. SEP 12 2019.0 57 8 SI 1857 1882 10.1108/MD-06-2018-0633 0.0 26 Business; Management Social Science Citation Index (SSCI) Business & Economics IZ4EP 2023-03-23 WOS:000487037400007 0
J Nikulin, P; Hofheinz, F; Maus, J; Li, YM; Butof, R; Lange, C; Furth, C; Zschaeck, S; Kreissl, MC; Kotzerke, J; van den Hoff, J Nikulin, Pavel; Hofheinz, Frank; Maus, Jens; Li, Yimin; Buetof, Rebecca; Lange, Catharina; Furth, Christian; Zschaeck, Sebastian; Kreissl, Michael C.; Kotzerke, Joerg; van den Hoff, Joerg A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING English Article FDG-PET; Standardized uptake value; SUV; Standardized uptake ratio; SUR; Convolutional neural network UPTAKE RATIO SUR; STANDARD UPTAKE; SEGMENTATION; VARIABILITY; AORTA Purpose The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor's glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload, which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 946 FDG PET/CT scans from several sites were used for network training (N= 366) and testing (N= 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spillover from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean +/- SD) = (- 0.5 +/- 2.2)% with a 95% confidence interval of [- 5.1,3.8]%and a total range of [- 10.0, 12.0]%. For four test cases, the derived ROIs were unusable (< 1 ml). Conclusion CNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting. [Nikulin, Pavel; Hofheinz, Frank; Maus, Jens; van den Hoff, Joerg] Helmholtz Zentrum Dresden Rossendorf, Inst Radiopharmaceut Canc Res, PET Ctr, Bautzner Landstr 400, D-01328 Dresden, Germany; [Li, Yimin] Xiamen Univ, Affiliated Hosp 1, Xiamen Canc Ctr, Dept Radiat Oncol, Xiamen, Peoples R China; [Buetof, Rebecca] Tech Univ Dresden, Fac Med, OncoRay Natl Ctr Radiat Res Oncol, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany; [Buetof, Rebecca] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany; [Buetof, Rebecca] Tech Univ Dresden, Fac Med, Dept Radiotherapy & Radiat Oncol, Dresden, Germany; [Buetof, Rebecca] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dresden, Germany; [Buetof, Rebecca] Natl Ctr Tumor Dis NCT, Partner Site Dresden, Dresden, Germany; [Buetof, Rebecca] German Canc Res Ctr, Heidelberg, Germany; [Buetof, Rebecca] Tech Univ Dresden, Fac Med, Dresden, Germany; [Buetof, Rebecca] Helmholtz Zentrum Dresden Rossendorf HZDR, Helmholtz Assoc, Dresden, Germany; [Lange, Catharina; Furth, Christian] Charite Univ Med Berlin, Dept Nucl Med, Berlin, Germany; [Lange, Catharina; Furth, Christian; Zschaeck, Sebastian] Free Univ Berlin, Berlin, Germany; [Lange, Catharina; Furth, Christian; Zschaeck, Sebastian] Humboldt Univ, Berlin, Germany; [Lange, Catharina; Furth, Christian; Zschaeck, Sebastian] Berlin Inst Hlth, Berlin, Germany; [Zschaeck, Sebastian] Charite Univ Med Berlin, Dept Radiat Oncol, Berlin, Germany; [Kreissl, Michael C.] Otto von Guericke Univ, Univ Hosp Magdeburg, Dept Radiol & Nucl Med, Div Nucl Med, Magdeburg, Germany; [Kotzerke, Joerg; van den Hoff, Joerg] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dept Nucl Med, Dresden, Germany Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Xiamen University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Technische Universitat Dresden; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Technische Universitat Dresden; Carl Gustav Carus University Hospital; Technische Universitat Dresden; Technische Universitat Dresden; Carl Gustav Carus University Hospital; Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Helmholtz Association; German Cancer Research Center (DKFZ); Technische Universitat Dresden; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Otto von Guericke University; University Hospital Magdeburg; Technische Universitat Dresden; Carl Gustav Carus University Hospital Nikulin, P (corresponding author), Helmholtz Zentrum Dresden Rossendorf, Inst Radiopharmaceut Canc Res, PET Ctr, Bautzner Landstr 400, D-01328 Dresden, Germany. p.nikulin@hzdr.de Kreissl, Michael/GRR-9938-2022; Kreissl, Michael/AAB-7270-2021; Lange, Catharina/ABE-1563-2020 Kreissl, Michael/0000-0001-5905-6015; Lange, Catharina/0000-0002-6745-8441; Furth, Christian/0000-0001-7302-7886; Zschaeck, Sebastian/0000-0003-3109-0662 Projekt DEAL; Major Projects of Fujian Natural Science Foundation [2008-59-11]; Nature Science Foundation of China [81101066]; Xiamen city science and technology project guidance [3502Z20164009]; German Federal Ministry of Education and Research (BMBF) [03ZIK041]; Berliner Krebsgesellschaft [ZSF201720]; Foundation for the National Institutes of Health [U01-CA-80098, U01-CA-79778] Projekt DEAL; Major Projects of Fujian Natural Science Foundation; Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Xiamen city science and technology project guidance; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); Berliner Krebsgesellschaft; Foundation for the National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) Open Access funding provided by Projekt DEAL. This work was partly supported by the Major Projects of Fujian Natural Science Foundation (No. 2008-59-11), the Nature Science Foundation of China (No. 81101066), the Xiamen city science and technology project guidance (3502Z20164009), the German Federal Ministry of Education and Research (BMBF contract 03ZIK041), the Berliner Krebsgesellschaft (ZSF201720), and the Foundation for the National Institutes of Health (U01-CA-80098 and U01-CA-79778). 29 5 5 0 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1619-7070 1619-7089 EUR J NUCL MED MOL I Eur. J. Nucl. Med. Mol. Imaging APR 2021.0 48 4 995 1004 10.1007/s00259-020-04991-9 0.0 OCT 2020 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging RM2WC 33006022.0 hybrid 2023-03-23 WOS:000574319400002 0
J Zhao, DY; Zhang, Z; Lu, H; Cheng, S; Si, BL; Feng, XS Zhao, Dongye; Zhang, Zheng; Lu, Hong; Cheng, Sen; Si, Bailu; Feng, Xisheng Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration IEEE TRANSACTIONS ON CYBERNETICS English Article Visualization; Navigation; Robot sensing systems; Brain modeling; Hippocampus; Biological system modeling; Cognitive map; hippocampus; navigation; path integration; place cells; self-motion cues; sensorimotor integration; sensory-motor integration network model (SeMINet); visual cues HIPPOCAMPAL PLACE CELLS; PATH-INTEGRATION; GRID CELLS; CORTEX; PERCEPTION; NETWORKS; DYNAMICS; NEURONS; SYSTEM; FIELDS How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central questions in the navigation research. Studies in neuroscience have shown that place cells in the hippocampus of the rodent brains form dynamic cognitive representations of locations in the environment. We propose a neural-network model called sensory-motor integration network model (SeMINet) to learn cognitive map representations by integrating sensory and motor information while an agent is exploring a virtual environment. This biologically inspired model consists of a deep neural network representing visual features of the environment, a recurrent network of place units encoding spatial information by sensorimotor integration, and a secondary network to decode the locations of the agent from spatial representations. The recurrent connections between the place units sustain an activity bump in the network without the need of sensory inputs, and the asymmetry in the connections propagates the activity bump in the network, forming a dynamic memory state which matches the motion of the agent. A competitive learning process establishes the association between the sensory representations and the memory state of the place units, and is able to correct the cumulative path-integration errors. The simulation results demonstrate that the network forms neural codes that convey location information of the agent independent of its head direction. The decoding network reliably predicts the location even when the movement is subject to noise. The proposed SeMINet thus provides a brain-inspired neural-network model for cognitive map updated by both self-motion cues and visual cues. [Zhao, Dongye; Feng, Xisheng] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China; [Zhao, Dongye; Feng, Xisheng] Chinese Acad Sci, Inst Robot, Shenyang 110169, Peoples R China; [Zhao, Dongye; Feng, Xisheng] Chinese Acad Sci, Inst Intelligent Mfg, Shenyang 110169, Peoples R China; [Zhao, Dongye; Feng, Xisheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Zhang, Zheng] New York Univ Shanghai, Dept Comp Sci, Shanghai 316021, Peoples R China; [Lu, Hong] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Cheng, Sen] Ruhr Univ Bochum, Inst Neuroinformat, D-44801 Bochum, Germany; [Si, Bailu] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China Chinese Academy of Sciences; Shenyang Institute of Automation, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; NYU Shanghai; Fudan University; Ruhr University Bochum; Beijing Normal University Si, BL (corresponding author), Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China. zhaodongye@sia.cn; zz@nyu.edu; honglu@fudan.edu.cn; sen.cheng@rub.de; bailusi@bnu.edu.cn; fxs@sia.cn Cheng, Sen/A-5722-2009; Si, Bailu/O-9575-2014 Cheng, Sen/0000-0002-6719-8029; Lu, Hong/0000-0002-4572-2854; Si, Bailu/0000-0002-0260-3433 National Key Research and Development Program of China [2016YFC0801808]; German Research Foundation [SFB 1280] National Key Research and Development Program of China; German Research Foundation(German Research Foundation (DFG)) The work of Bailu Si was supported by the National Key Research and Development Program of China under Grant 2016YFC0801808. The work of Sen Cheng was supported by the German Research Foundation through SFB 1280, Project A14. 59 1 1 9 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JAN 2022.0 52 1 508 521 10.1109/TCYB.2020.2977999 0.0 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science YG0IU 32275629.0 2023-03-23 WOS:000742182700046 0
J Zou, Y; Zheng, C; Alzahrani, AM; Ahmad, W; Ahmad, A; Mohamed, AM; Khallaf, R; Elattar, S Zou, Yong; Zheng, Chao; Alzahrani, Abdullah Mossa; Ahmad, Waqas; Ahmad, Ayaz; Mohamed, Abdeliazim Mustafa; Khallaf, Rana; Elattar, Samia Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers GELS English Article geopolymers; concrete; modeling; predictions; compressive strength FLY-ASH; MECHANICAL-PROPERTIES; SILICA-FUME; CONCRETE; FORMULATIONS; BEHAVIOR; SODIUM The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R-2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R-2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models' lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials. [Zou, Yong] Wuhan Univ, Sch Civil Engn, Wuhan 430072, Peoples R China; [Zheng, Chao] Univ Texas San Antonio, Dept Civil & Environm Engn, San Antonio, TX 78249 USA; [Alzahrani, Abdullah Mossa] Taif Univ, Coll Engn, Dept Civil Engn, POB 11099, Taif 21944, Saudi Arabia; [Ahmad, Waqas; Ahmad, Ayaz] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan; [Ahmad, Ayaz] Natl Univ Ireland Galway, Coll Sci & Engn, MaREI Ctr, Ryan Inst, Galway H91 HX31, Ireland; [Ahmad, Ayaz] Natl Univ Ireland Galway, Coll Sci & Engn, Sch Engn, Galway H91 HX31, Ireland; [Mohamed, Abdeliazim Mustafa] Prince Sattam Bin Abdulaziz Univ, Coll Engn Al Kharj, Dept Civil Engn, Al Kharj 11942, Saudi Arabia; [Mohamed, Abdeliazim Mustafa] Bayan Coll Sci & Technol, Bldg & Construct Technol Dept, Khartoum 210, Sudan; [Khallaf, Rana] Future Univ Egypt, Fac Engn & Technol, Struct Engn & Construct Management Dept, New Cairo 11845, Egypt; [Elattar, Samia] Princess Nourah Bint Abdulrahman Univ, Coll Engn, Dept Ind & Syst Engn, POB 84428, Riyadh 11671, Saudi Arabia Wuhan University; University of Texas System; University of Texas at San Antonio (UTSA); Taif University; COMSATS University Islamabad (CUI); Prince Sattam Bin Abdulaziz University; Egyptian Knowledge Bank (EKB); Future University in Egypt; Princess Nourah bint Abdulrahman University Zou, Y (corresponding author), Wuhan Univ, Sch Civil Engn, Wuhan 430072, Peoples R China.;Ahmad, W (corresponding author), COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan. yzou1008@sina.com; 13808698522@163.com; amyalzahrani@tu.edu.sa; waqasahmad@cuiatd.edu.pk; a.ahmad8@nuigalway.ie; a.bilal@psau.edu.sa; rana.khallaf@fue.edu.eg; saelattar@pnu.edu.sa alzahrani, Abdullah M Y/HPD-2877-2023; Elattar, Samia/GQP-6407-2022; Ahmed, Abdeliazim/GPC-7287-2022 alzahrani, Abdullah M Y/0000-0003-4812-4283; Elattar, Samia/0000-0001-6724-6783; Ahmed, Abdeliazim/0000-0002-7141-5057; Ahmad, Waqas/0000-0002-1668-7607; Ahmad, Ayaz/0000-0002-0312-2965 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2022R163] Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia(Princess Nourah bint Abdulrahman University) Funding was courtesy of Princess Nourah bint Abdulrahman University Researchers under Supporting Project number (PNURSP2022R163), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. 60 19 19 20 24 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2310-2861 GELS-BASEL Gels MAY 2022.0 8 5 271 10.3390/gels8050271 0.0 23 Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Polymer Science 1S8PW 35621569.0 Green Accepted, gold 2023-03-23 WOS:000804307500001 0
J Zhang, B; Zhang, M; Kang, J; Hong, DF; Xu, J; Zhu, XX Zhang, Bo; Zhang, Meng; Kang, Jian; Hong, Danfeng; Xu, Jian; Zhu, Xiaoxiang Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network REMOTE SENSING English Article multilayer perceptron; neural network; Landsat 8 OLI; remote sensing image; estimation; PMx concentrations AEROSOL OPTICAL-THICKNESS; GROUND-LEVEL PM2.5; PARTICULATE MATTER; AIR-POLLUTION; SATELLITE; RETRIEVAL; CHINA; PREDICTION; MORTALITY; EXPOSURE The estimation of PMx (incl. PM10 and PM2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (incl. PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R-2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 mu g/m(3) and 31.27 mu g/m(3). Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China. [Zhang, Bo; Zhang, Meng] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian 710049, Shaanxi, Peoples R China; [Zhang, Bo; Hong, Danfeng; Xu, Jian; Zhu, Xiaoxiang] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany; [Kang, Jian; Hong, Danfeng; Zhu, Xiaoxiang] TUM, Signal Proc Earth Observat SiPEO, D-80333 Munich, Germany Xi'an Jiaotong University; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich Zhang, M (corresponding author), Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian 710049, Shaanxi, Peoples R China. zhangbo5005@stu.xjtu.edu.cn; sql_zhang@hotmail.com; jian.kang@tum.de; danfeng.hong@dlr.de; jian.xu@dlr.de; xiaoxiang.zhu@dlr.de kang, jian/V-3055-2019; Hong, Danfeng/U-6082-2019; Xu, Jian/AAP-5179-2021; zhang, bx/HNR-3314-2023 kang, jian/0000-0001-6284-3044; Hong, Danfeng/0000-0002-3212-9584; Xu, Jian/0000-0003-2348-125X; Zhang, Meng/0000-0002-8744-2922 National Natural Science Foundation of China [41871315]; European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [ERC-2016-StG-714087] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program(European Research Council (ERC)) This research was funded by (a) the National Natural Science Foundation of China, Grant No. 41871315, and (b) the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program, Grant No. ERC-2016-StG-714087. 50 17 17 4 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 16 2019.0 11 6 646 10.3390/rs11060646 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology HT4TA Green Accepted, Green Published, gold, Green Submitted 2023-03-23 WOS:000464554900008 0
J Tangpanitanon, J; Thanasilp, S; Dangniam, N; Lemonde, MA; Angelakis, DG Tangpanitanon, Jirawat; Thanasilp, Supanut; Dangniam, Ninnat; Lemonde, Marc-Antoine; Angelakis, Dimitris G. Expressibility and trainability of parametrized analog quantum systems for machine learning applications PHYSICAL REVIEW RESEARCH English Article MANY-BODY LOCALIZATION; THERMALIZATION; SUPREMACY Parametrized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices. In digital quantum computing, it has been shown that random parametrized quantum circuits are able to express complex distributions intractable by a classical computer, leading to the demonstration of quantum supremacy. However, their chaotic nature makes parameter optimization challenging in variational approaches. Evidence of similar classically intractable expressibility has been recently demonstrated in analog quantum computing with driven many-body systems. A thorough investigation of trainability of such analog systems is yet to be performed. In this work we investigate how the interplay between external driving and disorder in the system dictates the trainability and expressibility of interacting quantum systems. We show that if the system thermalizes, the training fails at the expense of the large expressibility, while the opposite happens when the system enters the many-body localized (MBL) phase. From this observation we devise a protocol using quenched MBL dynamics which allows accurate trainability while keeping the overall dynamics in the quantum supremacy regime. Our work shows the fundamental connection between quantum many-body physics and its application in machine learning. We conclude our work with an example application in generative modeling employing a well studied analog many-body model of a driven Ising spin chain. Our approach can be implemented with a variety of available quantum platforms including cold ions, atoms, and superconducting circuits. [Tangpanitanon, Jirawat; Thanasilp, Supanut; Lemonde, Marc-Antoine; Angelakis, Dimitris G.] Natl Univ Singapore, Ctr Quantum Technol, 3 Sci Dr 2, Singapore 117543, Singapore; [Dangniam, Ninnat] Fudan Univ, Dept Phys, Shanghai 200433, Peoples R China; [Dangniam, Ninnat] Fudan Univ, Ctr Field Theory & Particle Phys, Shanghai 200433, Peoples R China; [Dangniam, Ninnat] Fudan Univ, State Key Lab Surface Phys, Shanghai 200433, Peoples R China; [Angelakis, Dimitris G.] Tech Univ Crete, Sch Elect & Comp Engn, Khania 73100, Greece National University of Singapore; Fudan University; Fudan University; Fudan University; Technical University of Crete Tangpanitanon, J (corresponding author), Natl Univ Singapore, Ctr Quantum Technol, 3 Sci Dr 2, Singapore 117543, Singapore. cqtjt@nus.edu.sg; dimitris.angelakis@nus.edu.sg National Research Foundation; Ministry of Education Singapore under the Research Centres of Excellence programme; Polisimulator project; EU Regional Development Fund; European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013); National Natural Science Foundation of China [11875110] National Research Foundation; Ministry of Education Singapore under the Research Centres of Excellence programme(Ministry of Education, Singapore); Polisimulator project; EU Regional Development Fund; European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors gratefully acknowledge financial support through the National Research Foundation and Ministry of Education Singapore under the Research Centres of Excellence programme. It was also partially funded by Polisimulator project co-financed by Greece and the EU Regional Development Fund, the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013). Ninnat Dangniam is supported by the National Natural Science Foundation of China (Grant No. 11875110). 68 7 7 0 2 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2643-1564 PHYS REV RES Phys. Rev. Res. DEC 14 2020.0 2 4 43364 10.1103/PhysRevResearch.2.043364 0.0 9 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics PO9OZ Green Submitted, gold 2023-03-23 WOS:000605502200003 0
J Lee, DK; Chan, YC; Chen, W; Wang, LW; van Beek, A; Chen, W Lee, Doksoo; Chan, Yu-Chin; Chen, Wei (Wayne); Wang, Liwei; van Beek, Anton; Chen, Wei t-METASET: Task-Aware Acquisition of Metamaterial Datasets Through Diversity-Based Active Learning JOURNAL OF MECHANICAL DESIGN English Article data-driven design; data acquisition; active learning; metamaterial; machine learning OPTIMIZATION; DESIGN Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Often built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active learning-based data acquisition framework aiming to guide both diverse and task-aware data gen-eration. Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (similar to O(104)) shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets are used to demonstrate the efficacy. Applicable to general image-based design representations, t-METASET could boost future advancements in data-driven design. [Lee, Doksoo; Chen, Wei (Wayne); Chen, Wei] Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA; [Chan, Yu-Chin] Siemens Corp Technol, Princeton, NJ 08540 USA; [Wang, Liwei] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China; [van Beek, Anton] Univ Coll Dublin, Sch Mech & Mat Engn, Dublin D04V1W8, Ireland Northwestern University; Siemens AG; Shanghai Jiao Tong University; University College Dublin Chen, W (corresponding author), Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA. dslee@northwestern.edu; yu-chin.chan@siemens.com; wei.wayne.chen@northwestern.edu; iridescence@sjtu.edu.cn; anton.vanbeek@ucd.ie; weichen@northwestern.edu National Science Foundation (NSF) through the CSSI program [OAC 1835782] National Science Foundation (NSF) through the CSSI program(National Science Foundation (NSF)) National Science Foundation (NSF) through the CSSI program (Award # OAC 1835782). 69 0 0 0 0 ASME NEW YORK TWO PARK AVE, NEW YORK, NY 10016-5990 USA 1050-0472 1528-9001 J MECH DESIGN J. Mech. Des. MAR 1 2023.0 145 3 31704 10.1115/1.4055925 0.0 15 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 9G7BD 2023-03-23 WOS:000938302900009 0
C Chen, C; Batselier, K; Ko, CY; Wong, N IEEE Chen, Cong; Batselier, Kim; Ko, Ching-Yun; Wong, Ngai A Support Tensor Train Machine 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 14-19, 2019 Budapest, HUNGARY support vector machine; tensor train; classification RANK There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. Support tensor machine (STM) and support Tucker machine (STuM) are two typical tensor generalization of the conventional support vector machine (SVM). However, the expressive power of STM is restrictive due to its rank-one tensor constraint, and STuM is not scalable because of the exponentially sized Tucker core tensor. To overcome these limitations, we introduce a novel and effective support tensor train machine (STTM) by employing a general and scalable tensor train as the parameter model. Experiments validate and confirm the superiority of the STTM over SVM, STM and STuM. [Chen, Cong; Ko, Ching-Yun; Wong, Ngai] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China; [Batselier, Kim] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands University of Hong Kong; Delft University of Technology Chen, C (corresponding author), Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China. chencong@eee.hku.hk; k.batselier@tudelft.nl; cyko@eee.hku.hk; nwong@eee.hku.hk Hong Kong Research Grants Council under General Research Fund (GRF) Project [17246416]; University Research Committee of The University of Hong Kong Hong Kong Research Grants Council under General Research Fund (GRF) Project; University Research Committee of The University of Hong Kong(University of Hong Kong) This work is supported by the Hong Kong Research Grants Council under General Research Fund (GRF) Project 17246416, and the University Research Committee of The University of Hong Kong. 22 4 4 3 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-7281-1985-4 IEEE IJCNN 2019.0 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO9HA 2023-03-23 WOS:000530893802046 0
J Qian, Z; Chen, M; Yang, Y; Zhong, T; Zhang, F; Zhu, R; Zhang, K; Zhang, ZX; Sun, Z; Ma, PL; Lu, GN; Ye, Y; Yan, JY Qian, Zhen; Chen, Min; Yang, Yue; Zhong, Teng; Zhang, Fan; Zhu, Rui; Zhang, Kai; Zhang, Zhixin; Sun, Zhuo; Ma, Peilong; Lu, Guonian; Ye, Yu; Yan, Jinyue Vectorized dataset of roadside noise barriers in China using street view imagery EARTH SYSTEM SCIENCE DATA English Article; Data Paper TRAFFIC NOISE; CITIES; DISPERSION; POLLUTION; GROWTH Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021). [Qian, Zhen; Chen, Min; Yang, Yue; Zhong, Teng; Zhang, Kai; Zhang, Zhixin; Sun, Zhuo; Ma, Peilong; Lu, Guonian] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China; [Qian, Zhen; Chen, Min; Yang, Yue; Zhong, Teng; Zhang, Kai; Sun, Zhuo; Ma, Peilong; Lu, Guonian] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China; [Qian, Zhen; Chen, Min; Yang, Yue; Zhong, Teng; Zhang, Kai; Sun, Zhuo; Ma, Peilong; Lu, Guonian] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China; [Chen, Min] Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China; [Zhang, Fan] MIT, Senseable City Lab, Cambridge, MA 02139 USA; [Zhu, Rui] Hong Kong Polytech Univ, Dept Land Surveying & Geo Informat, Kowloon, Hong Kong, Peoples R China; [Zhang, Zhixin] Nanjing Univ, Coll Geog & Marine, POB 2100913, Nanjing, Peoples R China; [Ye, Yu] Tongji Univ, Dept Architecture, Coll Architecture & Urban Planning, Shanghai, Peoples R China; [Yan, Jinyue] Malardalen Univ, Future Energy Ctr, S-72123 Vasteras, Sweden; [Yan, Jinyue] KTH Royal Inst Technol, Dept Chem Engn, S-10044 Stockholm, Sweden Nanjing Normal University; Nanjing Normal University; Massachusetts Institute of Technology (MIT); Hong Kong Polytechnic University; Nanjing University; Tongji University; Malardalen University; Royal Institute of Technology Chen, M (corresponding author), Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China. chenmin0902@163.com Qian, Zhen/0000-0002-0423-7430 National Natural Science Foundation of China; National Natural Science Foundation of China-Guangdong Joint Fund [U1811464]; Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX22_1567] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China-Guangdong Joint Fund; Postgraduate Research and Practice Innovation Program of Jiangsu Province This research has been supported by the National Natural Science Foundation of China and National Natural Science Foundation of China-Guangdong Joint Fund (grant no. U1811464) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (grant no. KYCX22_1567). 67 2 2 16 18 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1866-3508 1866-3516 EARTH SYST SCI DATA Earth Syst. Sci. Data SEP 6 2022.0 14 9 4057 4076 10.5194/essd-14-4057-2022 0.0 20 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences 4I0IM gold, Green Submitted 2023-03-23 WOS:000850260100001 0
J Li, GF; Zhu, PJ; Cao, N; Wu, M; Chen, ZY; Cao, GS; Li, HJ; Gong, CJ Li, Guofu; Zhu, Pengjia; Cao, Ning; Wu, Mei; Chen, Zhiyi; Cao, Guangsheng; Li, Hongjun; Gong, Chenjing Improving the system log analysis with language model and semi-supervised classifier MULTIMEDIA TOOLS AND APPLICATIONS English Article Log analysis; Language model; Machine learning Mining the vast amount of server-side logging data is an essential step to boost the business intelligence, as well as to facilitate the system maintenance for multimedia or IoT oriented services. Considering the vast volume of the data repository, designers of these logging-data analysis systems need to carefully balance the speed of the processing and the accuracy of the message classification. Conventional keyword-based log data monitoring and classification is sufficiently fast, but does not scale well in complex systems, especially when the target system is contributed by a large group of developers, each may differ in the way to encode the logging messages, and often carrying misleading labels. Conversely, many of the sophisticated approaches may suffer from their considerable time consumption, such that delayed processing jobs may begin to accumulate, and can hardly support the timely decision requirements. Meanwhile, we also suggest that the design of a large scale online log analysis should follow a principle that requires the least prior knowledge, in which unsupervised or semi-supervised solution is preferred. In this paper, we propose a two-stage machine learning based method, in which the system logs are regarded as the output of a quasi-natural language, pre-filtered by a perplexity score threshold, and then undergo a fine-grained classification procedure. Empirical studies on our web-services show that our method has obvious advantage in terms of processing speed and classification accuracy. [Li, Guofu; Chen, Zhiyi] Univ Shanghai Sci & Technol, Coll Commun & Art Design, Shanghai, Peoples R China; [Li, Guofu] Univ Coll Dublin, Comp Sci & Informat, Dublin, Ireland; [Zhu, Pengjia] State St Corp, Boston, MA USA; [Cao, Ning] Sanming Univ, Coll Informat Engn, Sanming, Peoples R China; [Wu, Mei] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan, Hubei, Peoples R China; [Cao, Guangsheng; Li, Hongjun; Gong, Chenjing] Qingdao Binhai Univ, Coll Informat Engn, Qingdao, Shandong, Peoples R China University of Shanghai for Science & Technology; University College Dublin; Sanming University; Wuhan Institute of Technology Cao, N (corresponding author), Sanming Univ, Coll Informat Engn, Sanming, Peoples R China. li.guofu.l@gmail.com; zhupengjia@gmail.com; ning.cao2008@hotmail.com; wumei0604@hotmail.com; iamedithchen@gmail.com; 52605560@qq.com; 911845094@qq.com; 821670587@qq.com Cao, Ning/P-6652-2017 Shanghai University Youth Teacher Training Funding Scheme [ZZslg16054]; National Social Science Foundation [16BXW031]; Grant of Shandong Province Vocational Education Educational Reform Research Project Study on Vocational Colleges Professional Building Service Regional Upgrade Industries [2017209] Shanghai University Youth Teacher Training Funding Scheme; National Social Science Foundation; Grant of Shandong Province Vocational Education Educational Reform Research Project Study on Vocational Colleges Professional Building Service Regional Upgrade Industries This research is supported by Shanghai University Youth Teacher Training Funding Scheme (ZZslg16054), National Social Science Foundation (16BXW031), Grant of Shandong Province Vocational Education Educational Reform Research Project Study on Vocational Colleges Professional Building Service Regional Upgrade Industries (2017209). 29 1 1 1 11 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. AUG 2019.0 78 15 21521 21535 10.1007/s11042-018-7020-3 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IQ0SO 2023-03-23 WOS:000480461400044 0
J Kakkos, I; Dimitrakopoulos, GN; Sun, Y; Yuan, JJ; Matsopoulos, GK; Bezerianos, A; Sun, Y Kakkos, Ioannis; Dimitrakopoulos, Georgios N.; Sun, Yi; Yuan, Jingjia; Matsopoulos, George K.; Bezerianos, Anastasios; Sun, Yu EEG Fingerprints of Task-Independent Mental Workload Discrimination IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Task analysis; Electroencephalography; Feature extraction; Bioinformatics; Sun; Machine learning; Electrodes; Cross-task classification; functional connectivity; mental workload; feature fusion; EEG WORKING-MEMORY; COGNITIVE WORKLOAD; FUNCTIONAL CONNECTIVITY; FEATURE-SELECTION; TERM-MEMORY; CLASSIFICATION; NETWORK; POWER; SYNCHRONIZATION; INHIBITION In the nascent field of neuroergonomics, mental workload assessment is one of the most important issues and has an apparent significance in real-world applications. Although prior research has achieved efficient single-task classification, scatted studies on cross-task mental workload assessment usually result in unsatisfactory performance. Here, we introduce a data-driven analysis framework to overcome the challenges regarding task-independent workload assessment using a fusion of EEG spectral characteristics and unveil the common neural mechanisms underlying mental workload. Specifically, multi-frequency power spectrum and functional connectivity (FC) were estimated for two workload levels in two working-memory tasks performed by 40 healthy participants, subsequently being fed into a machine learning approach to obtain the importance of each feature vector and evaluate classification performance in a cross-task fashion. Our framework achieved a classification accuracy of 0.94 for task-independent mental workload discrimination. Further investigation of the designated features in terms of their spectral and localization properties revealed task-independent common patterns in the neural mechanisms governing workload. In particular, increased workload was associated with elevated frontal delta and theta power but reduced parietal alpha power, whereas FC exhibited complex frequency- and region-dependent alterations. By implication, the employment of the EEG feature fusion emphasized their utility in serving as promising indicators for different workload conditions applications. [Kakkos, Ioannis; Matsopoulos, George K.] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15780, Greece; [Dimitrakopoulos, Georgios N.] Univ Patras, Dept Med, Patras 26500, Greece; [Sun, Yi] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Neurol, Hangzhou 310027, Zhejiang, Peoples R China; [Yuan, Jingjia] Zhejiang Univ, Key Lab Biomed Engn, Minist Educ China, Hangzhou 310027, Zhejiang, Peoples R China; [Bezerianos, Anastasios] Natl Univ Singapore, N1 Inst, Ctr Life Sci, Singapore 117456, Singapore; [Sun, Yu] Zhejiang Univ, Minist Educ China, Key Lab Biomed Engn, Hangzhou 310027, Zhejiang, Peoples R China; [Sun, Yu] Zhejiang Lab, Hangzhou 310027, Zhejiang, Peoples R China National Technical University of Athens; University of Patras; Zhejiang University; Ministry of Education, China; Zhejiang University; National University of Singapore; Ministry of Education, China; Zhejiang University; Zhejiang Laboratory Sun, Y (corresponding author), Zhejiang Univ, Minist Educ China, Key Lab Biomed Engn, Hangzhou 310027, Zhejiang, Peoples R China. ioakakkos@gmail.com; geo.dimitrakopoulos@gmail.com; sunyieeg@zju.edu.cn; qiaqiayuan@gmail.com; gmatso@esd.ece.ntua.gr; bezer@upatras.gr; yusun@zju.edu.cn Dimitrakopoulos, Georgios N./AAB-7851-2022; Bezerianos, Tasos/AAC-2499-2022 Dimitrakopoulos, Georgios N./0000-0002-7817-9552; Bezerianos, Tasos/0000-0002-8199-6000; SUN, Yu/0000-0002-6666-8586 National Natural Science Foundation of China [81801785]; Fundamental Research Funds for the Central Universities [2020FZZX01005]; Zhejiang Lab [2019KE0AD01]; Zhejiang University Global Partnership Fund [100000-11320]; Ministry of Education of Singapore [MOE2014-T2-1-115] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Zhejiang Lab; Zhejiang University Global Partnership Fund; Ministry of Education of Singapore(Ministry of Education, Singapore) This work was funded by the National Natural Science Foundation of China under Grant 81801785, the Fundamental Research Funds for the Central Universities under Grant 2020FZZX01005, in part by the grants from Zhejiang Lab under Grant 2019KE0AD01, in part by Zhejiang University Global Partnership Fund under Grant 100000-11320, and in part by the grants from the Ministry of Education of Singapore under Grant MOE2014-T2-1-115. 67 16 16 11 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. OCT 2021.0 25 10 3824 3833 10.1109/JBHI.2021.3085131 0.0 10 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics WC2SH 34061753.0 2023-03-23 WOS:000704111100019 0
J Schwarz, S; Tsiftsis, T Schwarz, Stefan; Tsiftsis, Theodoros Codebook Training for Trellis-Based Hierarchical Grassmannian Classification IEEE WIRELESS COMMUNICATIONS LETTERS English Article Grassmannian classification; CSI quantization; non-coherent transmission; trellis network training LIMITED FEEDBACK; MIMO; COMMUNICATION We consider classification of points on a complex-valued Grassmann manifold of m-dimensional subspaces within the n-dimensional complex Euclidean space. We introduce a trellis-based hierarchical classification network, which is based on an orthogonal product decomposition of the orthogonal basis representing the m-dimensional subspace. Exploiting the similarity of the proposed trellis classifier with a neural network, we propose stochastic gradient-based training techniques. We apply the proposed methods to two important applications in wireless communication, namely Grassmannian channel state information quantization in multiple-input multiple-output communications and non-coherent Grassmannian multi-resolution transmission. [Schwarz, Stefan] Vienna Univ Technol, Inst Telecommun, CD Lab Dependable Wireless Connect, A-1040 Vienna, Austria; [Tsiftsis, Theodoros] Jinan Univ, Inst Phys Internet & Sch Intelligent Syst Sci, Zhuhai 519070, Peoples R China Technische Universitat Wien; Jinan University Schwarz, S (corresponding author), Vienna Univ Technol, Inst Telecommun, CD Lab Dependable Wireless Connect, A-1040 Vienna, Austria. stefan.schwarz@tuwien.ac.at Schwarz, Stefan/AAA-3908-2022 Schwarz, Stefan/0000-0002-4065-2906 27 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-2337 2162-2345 IEEE WIREL COMMUN LE IEEE Wirel. Commun. Lett. MAR 2022.0 11 3 636 640 10.1109/LWC.2021.3139166 0.0 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications ZP7PO hybrid 2023-03-23 WOS:000766612300044 0
J Qi, XZ; Meng, J; Li, M; Yang, YY; Hu, Y; Li, B; Zhang, JW; Tian, W Qi, Xiaozhi; Meng, Jin; Li, Meng; Yang, Yuanyuan; Hu, Ying; Li, Bing; Zhang, Jianwei; Tian, Wei An Automatic Path Planning Method of Pedicle Screw Placement Based on Preoperative CT Images IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS English Article; Proceedings Paper 14th Hamlyn Symposium on Medical Robotics (HSMR) 2021 ELECTR NETWORK 3D image processing; spine surgery robot; pedicle screw placement; automatic path planning; deep learning SPINE SURGERY; FUSION; ACCURACY; SIZE Due to good fixation and obvious clinical advantages in the treatment of spinal diseases, pedicle screw technology is widely employed in spinal surgery. However, most screw placement paths are manually selected, which is a complicated and time-consuming process. Therefore, various kinds of spine surgery robots have been invented to assist during surgeries. An automatic path planning method of pedicle screw placement is proposed in this article. First, based on preoperative CT scans, the spine of the patient can be segmented and each individual vertebra is classified by using a trained deep learning network model. Additionally, a local coordinate system in every single vertebra is established according to its anatomical characteristics. Second, after analyzing the images to recognize the feature points, the pedicle region and route of screw placement are identified, which renders completion of the surgical path planning automatic. The experimental results show that 93% of the planning results can be directly applied in surgeries after surgeon evaluation. In addition to the improvement in accuracy, the whole algorithm does not rely on any human assistance. This fully automatic process greatly reduces the time required for surgical planning. [Qi, Xiaozhi] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sy, Shenzhen 518055, Peoples R China; [Meng, Jin; Li, Meng; Li, Bing] Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China; [Meng, Jin; Li, Meng] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Yang, Yuanyuan; Hu, Ying] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Hong Kong Macao Joint Lab Human Machine, Shenzhen 518055, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Dept Informat, D-22527 Hamburg, Germany; [Tian, Wei] Beijing Jishuitan Hosp, Dept Spine Surg, Beijing 100035, Peoples R China Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Harbin Institute of Technology; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; University of Hamburg Yang, YY (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Hong Kong Macao Joint Lab Human Machine, Shenzhen 518055, Peoples R China. xz.qi@siat.ac.cn; jin.meng@siat.ac.cn; meng.li3@siat.ac.cn; yy.yang1@siat.ac.cn; ying.hu@siat.ac.cn; libing.sgs@hit.edu.cn; zhang@informatik.uni-hamburg.de; tianweijst@vip.163.com zhang, jian/HPD-1712-2023; yang, yuanyuan/HPG-2020-2023 Guangdong Natural Science Foundation [2021A1515012264]; National Natural Science Foundation of China [U1713221, 12026604]; Shenzhen Institute of Artificial Intelligence and Robotics for Society; Shenzhen Fundamental Research Funds [JCYJ20200109115627045, JCYJ20190807170407391] Guangdong Natural Science Foundation(National Natural Science Foundation of Guangdong Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Institute of Artificial Intelligence and Robotics for Society; Shenzhen Fundamental Research Funds This work was supported in part by the Guangdong Natural Science Foundation under Grant 2021A1515012264; in part by the National Natural Science Foundation of China under Grant U1713221 and Grant 12026604; in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society; and in part by the Shenzhen Fundamental Research Funds under Grant JCYJ20200109115627045 and Grant JCYJ20190807170407391. 38 2 2 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2576-3202 IEEE T MED ROBOT BIO IEEE Trans. Med. Robotics Bionics MAY 2022.0 4 2 403 413 10.1109/TMRB.2022.3155288 0.0 11 Engineering, Biomedical; Robotics Emerging Sources Citation Index (ESCI) Engineering; Robotics 6X8XV 2023-03-23 WOS:000896692200016 0
J Ji, ZL; Ganchev, I; O'Droma, M; Zhang, X; Zhang, XJ Ji, Zhanlin; Ganchev, Ivan; O'Droma, Mairtin; Zhang, Xin; Zhang, Xueji A Cloud-Based X73 Ubiquitous Mobile Healthcare System: Design and Implementation SCIENTIFIC WORLD JOURNAL English Article Based on the user-centric paradigm for next generation networks, this paper describes a ubiquitous mobile healthcare (uHealth) system based on the ISO/IEEE 11073 personal health data (PHD) standards (X73) and cloud computing techniques. A number of design issues associated with the system implementation are outlined. The system includes a middleware on the user side, providing a plug-and-play environment for heterogeneous wireless sensors and mobile terminals utilizing different communication protocols and a distributed big data processing subsystem in the cloud. The design and implementation of this system are envisaged as an efficient solution for the next generation of uHealth systems. [Ji, Zhanlin; Ganchev, Ivan; O'Droma, Mairtin] Univ Limerick, TRC, Limerick, Ireland; [Ji, Zhanlin; Zhang, Xin] Hebei United Univ, Coll Informat, Tangshan 063009, Peoples R China; [Zhang, Xueji] Univ Sci & Technol, Res Ctr Bioengn & Sensing Technol, Beijing 100080, Peoples R China University of Limerick; North China University of Science & Technology; University of Science & Technology Beijing Zhang, XJ (corresponding author), Univ Sci & Technol, Res Ctr Bioengn & Sensing Technol, Beijing 100080, Peoples R China. zhangxueji@ustb.edu.cn zhang, xueji/B-1904-2010; Ji, Zhanlin/AAW-5661-2021; PAN, ZEQIANG/X-6341-2018; Ganchev, Ivan/D-4973-2012; zhang, xueji/ABE-6497-2020; O'Droma, Máirtín/D-2743-2015 Ganchev, Ivan/0000-0003-0535-7087; zhang, xueji/0000-0002-0035-3821; O'Droma, Máirtín/0000-0002-1672-1668; ji, zhanlin/0000-0003-3527-3773 Government of Ireland Post-Doctoral Fellowship [GOIPD/2013/243]; Telecommunications Research Centre (TRC) of the University of Limerick; Natural Science Foundation of Hebei Province of China [F2012401050] Government of Ireland Post-Doctoral Fellowship; Telecommunications Research Centre (TRC) of the University of Limerick; Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province) This work has been supported by the Government of Ireland Post-Doctoral Fellowship (GOIPD/2013/243), the Telecommunications Research Centre (TRC) of the University of Limerick, and the Natural Science Foundation of Hebei Province of China (F2012401050). 34 17 17 0 23 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1537-744X SCI WORLD J Sci. World J. 2014.0 145803 10.1155/2014/145803 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics AD5RN 24737958.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000333310900001 0
J Hu, WS; Li, HC; Wang, R; Gao, F; Du, Q; Plaza, A Hu, Wen-Shuai; Li, Heng-Chao; Wang, Rui; Gao, Feng; Du, Qian; Plaza, Antonio Pseudo Complex-Valued Deformable ConvLSTM Neural Network With Mutual Attention Learning for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Neural networks; Convolution; Training; Representation learning; Logic gates; Data models; Complex-valued (CV) representation; convolutional long short-term memory (ConvLSTM); feature fusion; hyperspectral image (HSI) classification; mutual attention learning TRANSFORM Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, traditional hand-crafted methods extract the features with basic attributes of HSIs, which can provide the lack of details and interpretability of deep semantic features. However, existing methods fail to incorporate their complementarity for HSI classification. As such, a Pseudo complex-valued (CV) Deformable ConvLSTM Neural Network with mutual Attention learning (APDCLNN) is proposed, providing a new way to realize the collaborative learning of hand-crafted and deep features for HSI classification. First, a 2-D pseudo CV deformable ConvLSTM (PDConvLSTM2D) cell is designed using deformable convolution and complex operations, with which a spatial-spectral PDConvLSTM2D neural network (SSPDCL2DNN) is built to extract scale- and spectral-enhanced deep spatial-spectral features. Then, 3-D Gabor filter is used to extract hand-crafted features, and a mutual attention-based multimodality feature learning and fusion (MAMLF) module is designed to integrate them into deep features for training and optimization of SSPDCL2DNN. Finally, an attention loss subnetwork is designed to refine the classification results. As we know, this is the first attempt to apply the idea of mutual attention learning to fuse hand-crafted and deep features for HSI classification. Extensive experiments on three widely used HSI datasets show the advantages of our model over other deep methods in terms of both quantitative and visual quality. [Hu, Wen-Shuai; Li, Heng-Chao; Wang, Rui] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Li, Heng-Chao] Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Peoples R China; [Gao, Feng] Ocean Univ China, Sch Comp Sci & Technol, Qingdao 266100, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10060, Spain Southwest Jiaotong University; Southwest Jiaotong University; Ocean University of China; Mississippi State University; Universidad de Extremadura Li, HC (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China.;Li, HC (corresponding author), Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Peoples R China. lihengchao_78@163.com Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Gao, Feng/0000-0002-1825-328X; Hu, Wenshuai/0000-0002-4757-2765 National Natural Science Foundation of China [61871335, 61801404]; Fundamental Research Funds for the Central Universities [2682020ZT35]; National Key Research and Development Program of China [2019YFB1706104] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 61871335 and Grant 61801404, in part by the Fundamental Research Funds for the Central Universities under Grant 2682020ZT35, and in part by the National Key Research and Development Program of China under Grant 2019YFB1706104. 67 0 0 21 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5533017 10.1109/TGRS.2022.3188791 0.0 17 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3Q8XU 2023-03-23 WOS:000838508100011 0
J Ismail, A; Saidi, L; Sayadi, M; Benbouzid, M Ismail, Adla; Saidi, Lotfi; Sayadi, Mounir; Benbouzid, Mohamed A New Data-Driven Approach for Power IGBT Remaining Useful Life Estimation Based On Feature Reduction Technique and Neural Network ELECTRONICS English Article data-driven approach; IGBT; feedforward neural network; prognostic; power converter; remaining useful life; time-domain feature; wind energy system; feature reduction RELIABILITY The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%. [Ismail, Adla; Saidi, Lotfi; Sayadi, Mounir] Univ Tunis, Elect Engn Dept, Lab Signal Image & Energy Mastery SIME, ENSIT, LR 13ES03, Tunis 1008, Tunisia; [Saidi, Lotfi; Benbouzid, Mohamed] Univ Brest, Inst Rech Dupuy Lome, UMR CNRS IRDL 6027, F-29238 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Engn Logist Coll, Shanghai 201306, Peoples R China Universite de Tunis; Universite de Bretagne Occidentale; Shanghai Maritime University Benbouzid, M (corresponding author), Univ Brest, Inst Rech Dupuy Lome, UMR CNRS IRDL 6027, F-29238 Brest, France.;Benbouzid, M (corresponding author), Shanghai Maritime Univ, Engn Logist Coll, Shanghai 201306, Peoples R China. adla.ismail@ensit.u-tunis.tn; lotfi.saidi@esstt.rnu.tn; mounir.sayadi@esstt.rnu.tn; mohamed.benbouzid@univ-brest.fr ISMAIL, Adla/0000-0002-7462-8111; Saidi, Lotfi/0000-0002-3419-8583 29 5 6 6 26 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics OCT 2020.0 9 10 1571 10.3390/electronics9101571 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics OL1VX gold 2023-03-23 WOS:000585130900001 0
J Wen, X; Huang, YQ; Wu, XM; Zhang, BY Wen, Xin; Huang, Yanqi; Wu, Xiaomei; Zhang, Biyong A Feasible Feature Extraction Method for Atrial Fibrillation Detection From BCG IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Feature extraction; Hypertension; Monitoring; Machine learning algorithms; Rhythm; Support vector machines; Heart rate variability; Ballistocardiogram; atrial fibrillation; feature extraction; classification SEISMOCARDIOGRAPHY; CLASSIFICATION; UPDATE Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here, we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database collected by a non-contact sensor. The BCG signals, including both with AF and sinus rhythm (SR), were collected from 37 subjects during overnight sleep (approximately 8 h). The signals were split into 2915 1-min segments (AF: 1494, SR: 1421) without overlap and labeled as AF and SR. BCG signals were transformed into BCG energy signals in order to highlight the features of AF and SR BCG signals; and four new data sequences representing different characteristics of the BCG energy signals were generated. The mean value, variance, skewness, and kurtosis of the four data sequences were calculated and 16 features were extracted for each segment. Five machine learning algorithms were used for classification. The results of this study show that the support vector machine performed the best among the five tested classifiers and achieved sensitivity, precision, and accuracy of 0.968, 0.928, and 0.945, respectively. These results indicate that the proposed feature extraction method can be well applied to AF and SR classification and may lay foundations for the development of systems for long-term home cardiac monitoring and AF screening. [Wen, Xin; Huang, Yanqi; Wu, Xiaomei] Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China; [Wu, Xiaomei] Fudan Univ, Key Lab Med Imaging Comp & Comp Assisted Interven, Shanghai 200032, Peoples R China; [Wu, Xiaomei] Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China; [Zhang, Biyong] Eindhoven Univ Technol, NL-5612 AP Eindhoven, Netherlands; [Zhang, Biyong] BOBO Technol, Hangzhou 310000, Peoples R China Fudan University; Fudan University; Eindhoven University of Technology Wu, XM (corresponding author), Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China.;Wu, XM (corresponding author), Fudan Univ, Key Lab Med Imaging Comp & Comp Assisted Interven, Shanghai 200032, Peoples R China. wenx17@fudan.edu.cn; yanqihuang17@fudan.edu.cn; xiaomeiwu@fudan.edu.cn; biyong.zhang@ish-lab.org Huang, Yanqi/0000-0002-8517-0142 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01] Shanghai Municipal Science and Technology Major Project This work was supported by the Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01. 43 22 23 7 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. APR 2020.0 24 4 1093 1103 10.1109/JBHI.2019.2927165 0.0 11 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics LC5GX 31295128.0 2023-03-23 WOS:000525354900016 0
J Gao, LH; Wei, FY; Yan, ZW; Ma, J; Xia, JJ Gao, Lihao; Wei, Fengying; Yan, Zhongwei; Ma, Jin; Xia, Jiangjiang A Study of Objective Prediction for Summer Precipitation Patterns Over Eastern China Based on a Multinomial Logistic Regression Model ATMOSPHERE English Article summer precipitation pattern; objective prediction; machine learning; multinomial logistic regression; selection of predictors; generalized ability RAINFALL PATTERNS; MONSOON; VARIABILITY; CIRCULATION; ENSO; SNOW The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships between summer precipitations and corresponding predictors, and might induce the instability of prediction models, especially in cases with few predictors. This study aims to investigate how to learn as much information as possible from various and numerous predictors reflecting different climate conditions. An objective prediction method based on the multinomial logistic regression (MLR) model is proposed to facilitate the study. The predictors are objectively selected from a machine learning perspective. The effectiveness of the objective prediction model is assessed by considering the influence of collinearity and number of predictors. The prediction accuracy is found to be comparable to traditionally estimated predictability, ranging between 0.6 and 0.7. The objective prediction model is capable of learning the intrinsic structure of the predictors, and is significantly superior to the prediction model with randomly-selected predictors and the single best predictor. A robust prediction can be generally obtained by learning information from plenty of predictors, although the most effective model may be constructed with fewer predictors through proper methods of predictor selection. In addition, the effectiveness of objective prediction is found to generally improve as observation increases, highlighting its potential for improvement during application as time passes. [Gao, Lihao; Wei, Fengying] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China; [Yan, Zhongwei; Xia, Jiangjiang] Univ Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm East Asia, Beijing 100029, Peoples R China; [Ma, Jin] Univ Utrecht, Inst Marine & Atmospher Res, NL-3584 CC Utrecht, Netherlands Chinese Academy of Meteorological Sciences (CAMS); Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Utrecht University Yan, ZW (corresponding author), Univ Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm East Asia, Beijing 100029, Peoples R China. gaolh@cma.gov.cn; weifengying_bj@sina.cn; yzw@tea.ac.cn; mj1985.2015@gmail.com; xiajj@tea.ac.cn Yan, Zhongwei/AAF-7451-2020 Yan, Zhongwei/0000-0003-0638-137X; Ma, Jin/0000-0002-4076-5740; Gao, Lihao/0000-0003-2646-7584 Chinese Academy Sciences [XDA20020201, 134111KYSB20160028]; Basic Research Special Project of Chinese Academy of Meteorological Sciences [2015Y007] Chinese Academy Sciences(Chinese Academy of Sciences); Basic Research Special Project of Chinese Academy of Meteorological Sciences This research was funded by grants from the Chinese Academy Sciences projects (XDA20020201 and 134111KYSB20160028) and the Basic Research Special Project of Chinese Academy of Meteorological Sciences (grant 2015Y007). 42 6 7 1 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4433 ATMOSPHERE-BASEL Atmosphere APR 2019.0 10 4 213 10.3390/atmos10040213 0.0 18 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences HX3SR Green Submitted, Green Published, gold 2023-03-23 WOS:000467313400052 0
J Guo, L; Weng, M; Galea, M; Wu, XW; Zhang, P; Lu, WQ Guo, Liang; Weng, Mian; Galea, Michael; Wu, Xiaowen; Zhang, Peng; Lu, Wenqi Few-sample multi-objective optimisation of a double-sided tubular machine with hybrid segmented permanent magnet IET ELECTRIC POWER APPLICATIONS English Article DESIGN; MOTOR; THRUST; FORCE Double-sided tubular machine (DSTM) is very suitable for wave energy conversion but easily suffers from high thrust ripple. In order to get the minimum cogging force with the maximum thrust force, a new DSTM with hybrid segmented permanent magnet array is proposed and optimised by a novel iterative few-sample multi-objective optimisation method. The novel optimisation method is based on an iterative Taguchi method framework to obtain optimal design with only few samples. To solve the low precision problem of the iterative Taguchi method, a surrogate-model based multi-objective optimisation algorithm that uses a general regression neural network, a speed-constrained multi-objective particle swarm optimisation and an exponentially weighted moving average are embedded into this framework. The optimisation result is compared with other alternative topologies and methods, and a prototype is manufactured for testing experiment. [Guo, Liang; Weng, Mian; Lu, Wenqi] Zhejiang Sci Tech Univ, Fac Mech Engn & Automat, Hangzhou, Peoples R China; [Galea, Michael] Univ Malta, Dept Ind Elect Power Convers, Msida, Malta; [Galea, Michael] Univ Nottingham, Machine & Control Grp, Power Elect, Nottingham, England; [Wu, Xiaowen; Zhang, Peng] Zhejiang Fangyuan Test Grp CO LTD, Hangzhou, Peoples R China Zhejiang Sci-Tech University; University of Malta; University of Nottingham Guo, L (corresponding author), Zhejiang Sci Tech Univ, Fac Mech Engn & Automat, 928 Second Ave, Hangzhou 310018, Peoples R China. lguo@zstu.edu.cn Guo, Liang/0000-0001-8277-260X; Galea, Michael/0000-0002-9094-611X Key Research and Development Program of Zhejiang Science and Technology Department [2021C01071]; National Natural Science Foundation of China [51677172]; Zhejiang Provincial Natural Science Foundation of China [LY19E070006]; Zhejiang Key Laboratory of More Electric Aircraft Technology, University of Nottingham Ningbo China [PKLMEA20OF01] Key Research and Development Program of Zhejiang Science and Technology Department; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); Zhejiang Key Laboratory of More Electric Aircraft Technology, University of Nottingham Ningbo China Key Research and Development Program of Zhejiang Science and Technology Department, Grant/Award Number: 2021C01071; National Natural Science Foundation of China, Grant/Award Number: 51677172; Zhejiang Provincial Natural Science Foundation of China, Grant/Award Number: LY19E070006; Zhejiang Key Laboratory of More Electric Aircraft Technology, University of Nottingham Ningbo China, Grant/Award Number: PKLMEA20OF01 32 1 1 5 8 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-8660 1751-8679 IET ELECTR POWER APP IET Electr. Power Appl. SEP 2022.0 16 9 953 965 10.1049/elp2.12202 0.0 APR 2022 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 3T2KK 2023-03-23 WOS:000788368700001 0
J Samalan, A; Tytgat, M; Zaganidis, N; Alves, GA; Marujo, F; De Araujo, FTD; Da Costa, EM; Damiao, DD; Nogima, H; Santoro, A; De Souza, SF; Aleksandrov, A; Hadjiiska, R; Iaydjiev, P; Rodozov, M; Shopova, M; Sultanov, G; Bonchev, M; Dimitrov, A; Litov, L; Pavlov, B; Petkov, P; Petrov, A; Qian, SJ; Bernal, C; Cabrera, A; Fraga, J; Sarkar, A; Elsayed, S; Assran, Y; El Sawy, M; Mahmoud, MA; Mohammed, Y; Combaret, C; Gouzevitch, M; Grenier, G; Laktineh, I; Mirabito, L; Shchablo, K; Bagaturia, I; Lomidze, D; Lomidze, I; Bhatnagar, V; Gupta, R; Kumari, P; Singh, J; Amoozegar, V; Boghrati, B; Ebraimi, M; Ghasemi, R; Najafabadi, MM; Zareian, E; Abbrescia, M; Aly, R; Elmetenawee, W; Filippis, N; Gelmi, A; Iaselli, G; Leszki, S; Loddo, F; Margjeka, I; Pugliese, G; Ramos, D; Benussi, L; Bianco, S; Piccolo, D; Buontempo, S; Di Crescenzo, A; Fienga, F; De Lellis, G; Lista, L; Meola, S; Paolucci, P; Braghieri, A; Salvini, P; Montagna, P; Riccardi, C; Vitulo, P; Francois, B; Kim, TJ; Park, J; Choi, SY; Hong, B; Lee, KS; Goh, J; Lee, H; Eysermans, J; Estrada, CU; Pedraza, I; Castilla-Valdez, H; Sanchez-Hernandez, A; Herrera, CAM; Navarro, DAP; Sanchez, GAA; Carrillo, S; Vazquez, E; Radi, A; Ahmad, A; Asghar, I; Hoorani, H; Muhammad, S; Shah, MA; Crottya, I Samalan, A.; Tytgat, M.; Zaganidis, N.; Alves, G. A.; Marujo, F.; Da Silva De Araujo, F. Torres; Da Costa, E. M.; De Jesus Damiao, D.; Nogima, H.; Santoro, A.; Fonseca De Souza, S.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Shopova, M.; Sultanov, G.; Bonchev, M.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.; Qian, S. J.; Bernal, C.; Cabrera, A.; Fraga, J.; Sarkar, A.; Elsayed, S.; Assran, Y.; El Sawy, M.; Mahmoud, M. A.; Mohammed, Y.; Combaret, C.; Gouzevitch, M.; Grenier, G.; Laktineh, I; Mirabito, L.; Shchablo, K.; Bagaturia, I; Lomidze, D.; Lomidze, I; Bhatnagar, V; Gupta, R.; Kumari, P.; Singh, J.; Amoozegar, V; Boghrati, B.; Ebraimi, M.; Ghasemi, R.; Najafabadi, M. Mohammadi; Zareian, E.; Abbrescia, M.; Aly, R.; Elmetenawee, W.; Filippis, N.; Gelmi, A.; Iaselli, G.; Leszki, S.; Loddo, F.; Margjeka, I; Pugliese, G.; Ramos, D.; Benussi, L.; Bianco, S.; Piccolo, D.; Buontempo, S.; Di Crescenzo, A.; Fienga, F.; De Lellis, G.; Lista, L.; Meola, S.; Paolucci, P.; Braghieri, A.; Salvini, P.; Montagna, P.; Riccardi, C.; Vitulo, P.; Francois, B.; Kim, T. J.; Park, J.; Choi, S. Y.; Hong, B.; Lee, K. S.; Goh, J.; Lee, H.; Eysermans, J.; Uribe Estrada, C.; Pedraza, I; Castilla-Valdez, H.; Sanchez-Hernandez, A.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Ayala Sanchez, G. A.; Carrillo, S.; Vazquez, E.; Radi, A.; Ahmad, A.; Asghar, I; Hoorani, H.; Muhammad, S.; Shah, M. A.; Crottya, I CMS Collaboration A new approach for CMS RPC current monitoring using Machine Learning techniques JOURNAL OF INSTRUMENTATION English Article; Proceedings Paper 15th Workshop on Resistive Plate Chambers and Related Detectors FEB 10-14, 2020 Univ Rome Tor Vergata, Rome, ITALY Univ Rome Tor Vergata Large detector-systems performance; Resistive-plate chambers The CMS experiment has 1054 RPCs in its muon system. Monitoring their currents is the first essential step towards maintaining the stability of the CMS RPC detector performance. The current depends on several parameters such as applied voltage, luminosity, environmental conditions, etc. Knowing the influence of these parameters on the RPC current is essential for the correct interpretation of its instabilities as they can be caused either by changes in external conditions or by malfunctioning of the detector in the ideal case. We propose a Machine Learning(ML) based approach to be used for monitoring the CMS RPC currents. The approach is crucial for the development of an automated monitoring system capable of warning for possible hardware problems at a very early stage, which will contribute further to the stable operation of the CMS RPC detector. [Samalan, A.; Tytgat, M.; Zaganidis, N.] Univ Ghent, Dept Phys & Astron, Proeftuinstr 86, B-9000 Ghent, Belgium; [Alves, G. A.; Marujo, F.] Urca, Ctr Brasileiro Pesquisas Fis, R Dr Xavier Sigaud 150, BR-22290180 Rio De Janeiro, RJ, Brazil; [Da Silva De Araujo, F. Torres; Da Costa, E. M.; De Jesus Damiao, D.; Nogima, H.; Santoro, A.; Fonseca De Souza, S.] Univ Estado Rio de Janeiro, Dept Fis Nucl & Altas Energias, Inst Fis, Rua Sao Francisco Xavier 524, BR-20559900 Rio De Janeiro, RJ, Brazil; [Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Shopova, M.; Sultanov, G.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Tzarigradsko Shaussee Blvd 72, BG-1784 Sofia, Bulgaria; [Bonchev, M.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.] Univ Sofia, Fac Phys, 5 James Bourchier Blvd, BG-1164 Sofia, Bulgaria; [Qian, S. J.] Peking Univ, Sch Phys, Beijing 100871, Peoples R China; [Bernal, C.; Cabrera, A.; Fraga, J.; Sarkar, A.] Univ Los Andes, Apartado Aereo 4976,Carrera 1E,18A 10, Bogota, Colombia; [Elsayed, S.] Acad Sci Res & Technol, Egyptian Network High Energy Phys, 101 Kasr El Einy St, Cairo, Egypt; [Mahmoud, M. A.; Mohammed, Y.] Fayoum Univ, Fac Sci, Ctr High Energy Phys, Al Fayyum 63514, Egypt; [Combaret, C.; Gouzevitch, M.; Grenier, G.; Laktineh, I; Mirabito, L.; Shchablo, K.] Univ Claude Bernard Lyon 1, Univ Lyon, CNRS, IN2P3,Inst Phys Nucl Lyon, Villeurbanne, France; [Bagaturia, I; Lomidze, D.; Lomidze, I] Georgian Tech Univ, 77 Kostava Str, GE-0175 Tbilisi, Georgia; [Bhatnagar, V; Gupta, R.; Kumari, P.; Singh, J.] Panjab Univ, Dept Phys, Chandigarh 160014, India; [Amoozegar, V; Boghrati, B.; Ebraimi, M.; Ghasemi, R.; Najafabadi, M. Mohammadi; Zareian, E.] Inst Res Fundamental Sci IPM, Sch Particles & Accelerators, POB 19395-5531, Tehran, Iran; [Abbrescia, M.; Aly, R.; Elmetenawee, W.; Filippis, N.; Gelmi, A.; Iaselli, G.; Leszki, S.; Loddo, F.; Margjeka, I; Pugliese, G.; Ramos, D.] Ist Nazl Fis Nucl, Sez Bari, Via Orabona 4, IT-70126 Bari, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] Ist Nazl Fis Nucl, Lab Nazl Frascati LNF, Via Enrico Fermi 40, IT-00044 Frascati, Italy; [Buontempo, S.; Di Crescenzo, A.; Fienga, F.; De Lellis, G.; Lista, L.; Meola, S.; Paolucci, P.] Complesso Univ Monte S Angelo, Sez Napoli, Ist Nazl Fis Nucl, Via Cintia, IT-80126 Naples, Italy; [Braghieri, A.; Salvini, P.; Montagna, P.; Riccardi, C.; Vitulo, P.] Ist Nazl Fis Nucl, Sez Pavia, Via Bassi 6, Pavia, Italy; [Francois, B.; Kim, T. J.; Park, J.] Hanyang Univ, 222 Wangsimni Ro, Seoul, South Korea; [Choi, S. Y.; Hong, B.; Lee, K. S.] Korea Univ, Dept Phys, 145 Anam Ro, Seoul 02841, South Korea; [Goh, J.] Kyung Hee Univ, 26 Kyungheedae Ro, Seoul, South Korea; [Lee, H.] Sungkyunkwan Univ, 2066 Seobu Ro, Seoul 16419, South Korea; [Eysermans, J.; Uribe Estrada, C.; Pedraza, I] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Castilla-Valdez, H.; Sanchez-Hernandez, A.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Ayala Sanchez, G. A.] CINVESTAV, Av Inst Politecn Nacl 2508, Ciudad De Mexico 07360, DF, Mexico; [Carrillo, S.; Vazquez, E.] Univ Iberoamer, Mexico City, DF, Mexico; [Radi, A.] Sultan Qaboos Univ, Muscat 123, Oman; [Ahmad, A.; Asghar, I; Hoorani, H.; Muhammad, S.; Shah, M. A.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Crottya, I] Wisconsin Univ, Dept Phys, Madison, WI 53706 USA; [Assran, Y.; El Sawy, M.] British Univ Egypt BUE, Suez Desert Rd,POB 43, Cairo 11837, Egypt; [El Sawy, M.] Beni Suef Univ, Fac Sci, Dept Phys, Bani Suwayf, Egypt; [Boghrati, B.] Damghan Univ, Sch Engn, Damghan 3671641167, Iran; [Montagna, P.; Riccardi, C.; Vitulo, P.] Univ Pavia, Via Bassi 6, Pavia, Italy; [Assran, Y.] Suez Univ, Suez Cairo Rd, Suez 43522, Egypt Ghent University; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Bulgarian Academy of Sciences; University of Sofia; Peking University; Universidad de los Andes (Colombia); Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Arab Academy for Science, Technology & Maritime Transport; Egyptian Knowledge Bank (EKB); Fayoum University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Georgian Technical University; Panjab University; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Hanyang University; Korea University; Kyung Hee University; Sungkyunkwan University (SKKU); Benemerita Universidad Autonoma de Puebla; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Universidad Iberoamericana Ciudad de Mexico; Sultan Qaboos University; National Centre for Physics - Pakistan; Quaid I Azam University; Egyptian Knowledge Bank (EKB); British University in Egypt; Egyptian Knowledge Bank (EKB); Beni Suef University; University of Pavia; Egyptian Knowledge Bank (EKB); Suez University Petkov, P (corresponding author), Univ Sofia, Fac Phys, 5 James Bourchier Blvd, BG-1164 Sofia, Bulgaria. Peicho.Petkov@cern.ch Shopova, Mariana Filipova/HKW-0149-2023; Sanchez-Hernandez, Alberto/L-4955-2017; de Souza Sandro, Fonseca/ABB-8505-2020; Petkov, Peicho/M-2080-2016; mohammed, yasser/P-1462-2016; Pedraza Morales, Maria Isabel/ABC-7289-2021; de Jesus Damiao, Dilson/G-6218-2012; Singh, J B/ABF-5574-2021; Benussi, Luigi/O-9684-2014; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Gupta, Rajat/GVS-7964-2022; da Silva, Fabio Marujo/AAA-4495-2020; Fienga, Francesco/HKE-1204-2023; Bhatnagar, Vipin/Z-1079-2018 Shopova, Mariana Filipova/0000-0001-6664-2493; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; de Souza Sandro, Fonseca/0000-0001-7830-0837; Petkov, Peicho/0000-0002-0420-9480; mohammed, yasser/0000-0001-8399-3017; Pedraza Morales, Maria Isabel/0000-0002-2669-4659; de Jesus Damiao, Dilson/0000-0002-3769-1680; Singh, J B/0000-0001-9029-2462; Benussi, Luigi/0000-0002-2363-8889; da Silva, Fabio Marujo/0000-0002-3555-0489; Fienga, Francesco/0000-0001-5978-4952; Vitulo, Paolo/0000-0001-9247-7778; Riccardi, Cristina/0000-0003-0165-3962; Di Crescenzo, Antonia/0000-0003-4276-8512; Assran, Yasser/0000-0001-6974-9595; Buontempo, Salvatore/0000-0001-9526-556X; Montagna, Paolo Maria/0000-0001-9647-9420; Bhatnagar, Vipin/0000-0002-8392-9610; Pugliese, Gabriella/0000-0001-5460-2638; Alves, Gilvan/0000-0002-8369-1446; Aly, Reham/0000-0001-6808-1335 Bulgarian Ministry of Education and Science under the National Science Program Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES) [D01-205/23.11.2018] Bulgarian Ministry of Education and Science under the National Science Program Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES) This paper is partially supported by the Bulgarian Ministry of Education and Science (contract D01-205/23.11.2018) under the National Science Program Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES), approved by DCM#577/17.08.2018. We would like to thank especially to Anton Dimitrov and Osvaldo Miguel Colin who mainly developed the RPC Condition Data Automation concept and implementation respectively, and all our colleagues from the CMS RPC group and L1 muon trigger group for their dedicated work to keep the stable performance of the RPC system. We wish to congratulate our colleagues in the CERN accelerator Department for the excellent performance of the LHC machine. We thank the technical and administrative staff at CERN and all CMS institutes. 10 0 0 0 2 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-0221 J INSTRUM J. Instrum. OCT 2020.0 15 10 C10009 10.1088/1748-0221/15/10/C10009 0.0 9 Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Instruments & Instrumentation NZ7IP Green Published, Green Submitted 2023-03-23 WOS:000577277600009 0
J Mortazavi, B; Shojaei, F; Yagmurcukardes, M; Shapeev, AV; Zhuang, XY Mortazavi, Bohayra; Shojaei, Fazel; Yagmurcukardes, Mehmet; V. Shapeev, Alexander; Zhuang, Xiaoying Anisotropic and outstanding mechanical, thermal conduction, optical, and piezoelectric responses in a novel semiconducting BCN monolayer confirmed by first-principles and machine learning CARBON English Article h-BCN; Piezoelectric; Semiconductor; Machine -learning; Thermal conductivity CARRIER MOBILITY; STRAIN BEHAVIOR; CARBON; BC6N; C3N; STRENGTH Graphene-like nanomembranes made of the neighboring elements of boron, carbon and nitrogen elements, are well-known of showing outstanding physical properties. Herein, with the aid of density functional theory (DFT) calculations, various atomic configurations of the graphene-like BCN nanosheets are investigated. DFT results reveal that depending on the atomic arrangement, the BCN monolayers may display semimetallic Dirac cone or semiconducting electronic nature. BCN nanosheets are also found to exhibit high piezoelectricity and carrier mobilities with considerable in-plane anisotropy, depending on the atomic arrangement. For the predicted most stable BCN monolayer, thermal and mechanical properties are explored using machine learning interatomic potentials. The room temperature tensile strength and lattice thermal conductivity of the most stable BCN monolayer are estimated to be orientation-dependent and remarkably high, over 78 GPa and 290 W/m.K, respectively. In addition, the thermal expansion coefficient of the monolayer BCN at room temperature is esti-mated to be-3.2 x 10-6 K-1, which is close to that of the graphene. The piezoelectric response of the herein proposed BCN lattice is also predicted to be close to that of the h-BN monolayer. Presented results highlight outstanding physics of the BCN nanosheets. [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Photon Opt & Engn Innovat Disciplines, Cluster Excellence PhoenixD, Hannover, Germany; [Shojaei, Fazel] Persian Gulf Univ, Fac Nano & Biosci & Technol, Dept Chem, Bushehr 75169, Iran; [Yagmurcukardes, Mehmet] Izmir Inst Technol, Dept Photon, Izmir, Turkey; [V. Shapeev, Alexander] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Bolshoy Bulvar 30, Moscow 143026, Russia; [Zhuang, Xiaoying] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Persian Gulf University; Izmir Institute of Technology; Skolkovo Institute of Science & Technology; Tongji University Mortazavi, B; Zhuang, XY (corresponding author), Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany. bohayra.mortazavi@gmail.com; zhuang@iop.uni-hannover.de Shapeev, Alexander/0000-0002-7497-5594 Deutsche For-schungsgemeinschaft (DFG, German Reuter Foundation) [EXC 2122, 390833453]; Persian Gulf Uni-versity Research Council, Iran; Russian Science Foundation [18-13-00479]; BAGEP Award of the Science Academy; Sevinc-Erdal Inonu Foundation Deutsche For-schungsgemeinschaft (DFG, German Reuter Foundation); Persian Gulf Uni-versity Research Council, Iran; Russian Science Foundation(Russian Science Foundation (RSF)); BAGEP Award of the Science Academy; Sevinc-Erdal Inonu Foundation B. M. appreciates the funding by the Deutsche For-schungsgemeinschaft (DFG, German Reuter Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453) . F.S. thanks the Persian Gulf University Research Council, Iran, for the support of this study. B. M is greatly thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources. A.V.S. is supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/p roject/18-13-00479/) . Computational resources were partially provided by TUBITAKULAKBIM, High Performance and Grid Computing Center (TR-Grid e-Infrastructure) . This work was partially supported by the BAGEP Award of the Science Academy with funding supplied by Sevinc-Erdal Inonu Foundation. 62 4 4 23 23 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0008-6223 1873-3891 CARBON Carbon NOV 5 2022.0 200 500 509 10.1016/j.carbon.2022.08.077 0.0 SEP 2022 10 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science 4X2DV 2023-03-23 WOS:000860659200005 0
J Gumaei, A; Al-Rakhami, M; Hassan, MM; Pace, P; Alai, G; Lin, K; Fortino, G Gumaei, Abdu; Al-Rakhami, Mabrook; Hassan, Mohammad Mehedi; Pace, Pasquale; Alai, Gianluca; Lin, Kai; Fortino, Giancarlo Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection IEEE NETWORK English Article 5G mobile communication; Image edge detection; Computational modeling; RF signals; Blockchain; Data models; Drones TECHNOLOGY; NETWORKS; SECURITY; INTERNET Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using radio frequency (RF) signals and through 5G networks. Recently, few studies have been proposed to use deep neural networks (DNNs) on RF signals for identifying drones and detecting their flight modes, such as off, on and connected, flying, hovering, and video recording. However, transmitting RF signals between drones and 5G nodes needs to be secure and decentralized; in addition, the performance of identification and detection needs to be more accurate. In this article, we introduce a framework that combines a blockchain with a deep recurrent neural network (DRNN) and edge computing for 5G-enabled drone identification and flight mode detection. In the proposed framework, raw RF signals of different drones under several flight modes are remotely sensed and collected on a cloud server to train a DRNN model and then distribute the trained model on edge devices for detecting drones and their flight modes. Blockchain is used in the proposed framework for data integrity and securing data transmission. The DRNN model is evaluated on a public dataset, called DroneRF. Experimental evaluation results show that the DRNN model of the proposed framework can detect drones and their flight modes from real RF signals with high accuracy as compared to recent related work. [Gumaei, Abdu; Al-Rakhami, Mabrook; Hassan, Mohammad Mehedi] King Saud Univ, Res Chair Pervas & Mobile Comp, Riyadh, Saudi Arabia; [Pace, Pasquale; Alai, Gianluca; Fortino, Giancarlo] Univ Calabria, Calabria, Italy; [Lin, Kai] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China King Saud University; University of Calabria; Dalian University of Technology Gumaei, A (corresponding author), King Saud Univ, Res Chair Pervas & Mobile Comp, Riyadh, Saudi Arabia. Hassan, Mohammad Mehedi/D-4946-2016; Pace, Pasquale/L-8666-2017; Gumaei, Abdu/J-6288-2019; Fortino, Giancarlo/J-2950-2017; Pace, Pasquale/AAF-2126-2019; Hassan, Mohammad/GZA-7507-2022; Aloi, Gianluca/ABE-4944-2020 Hassan, Mohammad Mehedi/0000-0002-3479-3606; Pace, Pasquale/0000-0002-4159-2765; Gumaei, Abdu/0000-0001-8512-9687; Fortino, Giancarlo/0000-0002-4039-891X; Pace, Pasquale/0000-0002-4159-2765; Hassan, Mohammad/0000-0002-1712-0004; Aloi, Gianluca/0000-0002-4688-1021 Deanship of Scientific Research at King Saud University Deanship of Scientific Research at King Saud University(King Saud University) The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientifi c Research Chairs: Chair of Pervasive and Mobile Computing. Mohammad Mehedi Hassan is the corresponding author of this paper. 15 21 21 0 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. JAN-FEB 2021.0 35 1 94 100 10.1109/MNET.011.2000204 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RO6NJ 2023-03-23 WOS:000641159800015 0
J Liu, Y; Liu, L; Guo, YM; Lew, MS Liu, Yu; Liu, Li; Guo, Yanming; Lew, Michael S. Learning visual and textual representations for multimodal matching and classification PATTERN RECOGNITION English Article Vision and language; Multimodal matching; Multimodal classification; Deep learning Multimodal learning has been an important and challenging problem for decades, which aims to bridge the modality gap between heterogeneous representations, such as vision and language. Unlike many current approaches which only focus on either multimodal matching or classification, we propose a unified network to jointly learn multimodal matching and classification (MMC-Net) between images and texts. The proposed MMC-Net model can seamlessly integrate the matching and classification components. It first learns visual and textual embedding features in the matching component, and then generates discriminative multimodal representations in the classification component. Combining the two components in a unified model can help in improving their performance. Moreover, we present a multi-stage training algorithm by minimizing both of the matching and classification loss functions. Experimental results on four well-known multimodal benchmarks demonstrate the effectiveness and efficiency of the proposed approach, which achieves competitive performance for multimodal matching and classification compared to state-of-the-art approaches. (C) 2018 Published by Elsevier Ltd. [Liu, Yu; Lew, Michael S.] Leiden Univ, Dept Comp Sci, NL-2333 CA Leiden, Netherlands; [Liu, Li; Guo, Yanming] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 8000, Finland Leiden University; Leiden University - Excl LUMC; National University of Defense Technology - China; University of Oulu Lew, MS (corresponding author), Leiden Univ, Dept Comp Sci, NL-2333 CA Leiden, Netherlands. m.s.k.lew@liacs.leidenuniv.nl ARSLAN, Okan/AAA-3232-2020 LIACS Media Lab at Leiden University [2006002026]; China Scholarship Council [201406060010] LIACS Media Lab at Leiden University; China Scholarship Council(China Scholarship Council) This work was supported mainly by the LIACS Media Lab at Leiden University: grant 2006002026 and in part by the China Scholarship Council: grant 201406060010. We are also grateful to the support of NVIDIA with the donation of GPU cards. 74 16 17 3 40 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. DEC 2018.0 84 51 67 10.1016/j.patcog.2018.07.001 0.0 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GT6VO Green Accepted 2023-03-23 WOS:000444659200005 0
J Ma, H; Zhang, YX; Thuerey, N; Null, XYH; Haidn, OJ Ma, Hao; Zhang, Yuxuan; Thuerey, Nils; Null, Xiangyu Hu; Haidn, Oskar J. Physics-Driven Learning of the SteadyNavier-Stokes Equations using Deep Convolutional Nural Networks COMMUNICATIONS IN COMPUTATIONAL PHYSICS English Article Deep learning; physics-drivenmethod; convolutional neural networks; Navier-Stokes equations NEURAL-NETWORKS; FLOW Recently, physics-driven deep learning methods have shown particular promise for the prediction of physical fields, especially to reduce the dependency on large amounts of pre-computed training data. In this work, we target the physicsdriven learning of complex flow fields with high resolutions. We propose the use of Convolutional neural networks (CNN) based U- net architectures to efficiently represent and reconstruct the input and output fields, respectively. By introducingNavier-Stokes equations and boundary conditions into loss functions, the physics-driven CNN is designed to predict corresponding steady flow fields directly. In particular, this prevents many of the difficulties associated with approaches employing fully connected neural networks. Several numerical experiments are conducted to investigate the behavior of the CNN approach, and the results indicate that a first-order accuracy has been achieved. Specifically for the case of a flow around a cylinder, different flow regimes can be learned and the adhered twin-vortices are predicted correctly. The numerical results also show that the training for multiple cases is accelerated significantly, especially for the difficult cases at low Reynolds numbers, and when limited reference solutions are used as supplementary learning targets. [Ma, Hao; Haidn, Oskar J.] Tech Univ Munich, Dept Aerosp & Geodesy, D-85748 Garching, Germany; [Zhang, Yuxuan] Beijing Aerosp Prop Inst, Beijing 100076, Peoples R China; [Thuerey, Nils] Tech Univ Munich, D-85748 Garching, Germany; [Null, Xiangyu Hu] Tech Univ Munich, Dept Mech Engn, D-85748 Garching, Germany Technical University of Munich; Technical University of Munich; Technical University of Munich Null, XYH (corresponding author), Tech Univ Munich, Dept Mech Engn, D-85748 Garching, Germany. hao.ma@tum.de; uruz7@live.com; nils.thuerey@tum.de; xiangyu.hu@tum.de; oskar..haidn@tum.de China Scholarship Council [201703170250, 201804980021] China Scholarship Council(China Scholarship Council) Hao Ma (No. 201703170250) and Yuxuan Zhang (No. 201804980021) are supported by China Scholarship Council when they conduct the work this paper represents. 43 0 0 4 4 GLOBAL SCIENCE PRESS Hong Kong Office B, 9/F, Kings Wing Plaza2, No.1 On Kwan St, Shek Mun, NT, Hong Kong, 00000, PEOPLES R CHINA 1815-2406 1991-7120 COMMUN COMPUT PHYS Commun. Comput. Phys. SEP 2022.0 32 3 715 736 10.4208/cicp.OA-2021-0146 0.0 22 Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics 8K1ZS Green Submitted 2023-03-23 WOS:000922907100004 0
J Zhang, HL; Fu, R; Wang, C; Guo, YS; Yuan, W Zhang, Hailun; Fu, Rui; Wang, Chang; Guo, Yingshi; Yuan, Wei Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach IEEE INTERNET OF THINGS JOURNAL English Article Behavioral sciences; Hidden Markov models; Vehicles; Turning; Predictive models; Machine learning; Dictionaries; Graphical lasso; LogDet divergence; signalized intersection; turning maneuver prediction; Vehicle-to-Infrastructure (V2I) DRIVER BEHAVIOR; RECOGNITION; FRAMEWORK; MODEL; CLASSIFICATION; SELECTION; SYSTEM Vehicle-to-Infrastructure (V2I) communication has provided a solution for the improvement of the traffic efficiency of smart city intersections. For example, turning maneuvers prediction at signalized intersections in a connected environment helps traffic command centers time traffic lights and dynamically predict traffic flow. However, the modeling methods used in existing research on this topic have some limitations, such as poor scalability and interpretability of machine learning. Thus, this study proposes a dictionary learning-based approach to predict turning maneuvers before the intersection. The proposed dictionary model estimates the LogDet divergence-based sparse inverse covariance matrix (LDbSICM) of driving behavior samples. The graphical lasso method is used to estimate the sparse inverse covariance matrix of the driving samples to construct a dictionary library of the maneuver behavior. The LogDet divergence is used to calculate the difference between each inverse covariance matrix. A driving simulator is utilized to collect experimental data consisting of turning left (TL), turning right (TR), and going straight (GS) behaviors to establish and evaluate the proposed model. The experimental results demonstrate that the proposed dictionary learning-based turning maneuver prediction model achieves 100% prediction accuracy for TL and GS and 97.2% for TR. The proposed model has substantial advantages over existing methods. The model can predict TL, TR, and GS in a connected environment 270, 280, and 290 m, respectively, before the intersection. [Zhang, Hailun; Fu, Rui; Wang, Chang; Guo, Yingshi; Yuan, Wei] Changan Univ, Sch Automobile, Xian 710064, Peoples R China; [Zhang, Hailun] Delft Univ Technol, Fac Civil Engn & Geosci, Dept Transport & Planning, NL-2600 Delft, Netherlands Chang'an University; Delft University of Technology Fu, R (corresponding author), Changan Univ, Sch Automobile, Xian 710064, Peoples R China. iszhanghailun@outlook.com; furui@chd.edu.cn; wangchang@chd.edu.cn; guoys@chd.edu.cn; yuanwei@chd.edu.cn Wang, Chang/0000-0003-3531-1215 National Key Research and Development Program of China [2018YFB1600501]; China Scholarship Council [202106560009]; Fundamental Research Funds for the Central Universities [CHD 300102222709] National Key Research and Development Program of China; China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1600501; in part by the China Scholarship Council under Grant 202106560009; and in part by the Fundamental Research Funds for the Central Universities under Grant CHD 300102222709. 76 0 0 11 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. NOV 15 2022.0 9 22 23142 23159 10.1109/JIOT.2022.3188312 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5Y1KT 2023-03-23 WOS:000879049400092 0
J Zou, YL; Weinacker, H; Koch, B Zou, Yanling; Weinacker, Holger; Koch, Barbara Towards Urban Scene Semantic Segmentation with Deep Learning from LiDAR Point Clouds: A Case Study in Baden-Wurttemberg, Germany REMOTE SENSING English Article urban scene; mobile mapping; deep learning; remote sensing; point clouds; semantic segmentation; unbalanced classes AUTOMATIC SEGMENTATION; REGISTRATION An accurate understanding of urban objects is critical for urban modeling, intelligent infrastructure planning and city management. The semantic segmentation of light detection and ranging (LiDAR) point clouds is a fundamental approach for urban scene analysis. Over the last years, several methods have been developed to segment urban furniture with point clouds. However, the traditional processing of large amounts of spatial data has become increasingly costly, both time-wise and financially. Recently, deep learning (DL) techniques have been increasingly used for 3D segmentation tasks. Yet, most of these deep neural networks (DNNs) were conducted on benchmarks. It is, therefore, arguable whether DL approaches can achieve the state-of-the-art performance of 3D point clouds segmentation in real-life scenarios. In this research, we apply an adapted DNN (ARandLA-Net) to directly process large-scale point clouds. In particular, we develop a new paradigm for training and validation, which presents a typical urban scene in central Europe (Munzingen, Freiburg, Baden-Wurttemberg, Germany). Our dataset consists of nearly 390 million dense points acquired by Mobile Laser Scanning (MLS), which has a rather larger quantity of sample points in comparison to existing datasets and includes meaningful object categories that are particular to applications for smart cities and urban planning. We further assess the DNN on our dataset and investigate a number of key challenges from varying aspects, such as data preparation strategies, the advantage of color information and the unbalanced class distribution in the real world. The final segmentation model achieved a mean Intersection-over-Union (mIoU) score of 54.4% and an overall accuracy score of 83.9%. Our experiments indicated that different data preparation strategies influenced the model performance. Additional RGB information yielded an approximately 4% higher mIoU score. Our results also demonstrate that the use of weighted cross-entropy with inverse square root frequency loss led to better segmentation performance than when other losses were considered. [Zou, Yanling; Weinacker, Holger; Koch, Barbara] Univ Freiburg, Chair Remote Sensing & Landscape Informat Syst, Tennenbacherstr 4, D-79106 Freiburg, Germany; [Zou, Yanling] Chengdu Univ Informat Technol, Coll Control Engn, Chengdu 610225, Peoples R China University of Freiburg; Chengdu University of Information Technology Zou, YL (corresponding author), Univ Freiburg, Chair Remote Sensing & Landscape Informat Syst, Tennenbacherstr 4, D-79106 Freiburg, Germany.;Zou, YL (corresponding author), Chengdu Univ Informat Technol, Coll Control Engn, Chengdu 610225, Peoples R China. yanling.zou@felis.uni-freiburg.de; holger.weinacker@felis.uni-freiburg.de; barbara.koch@felis.uni-freiburg.de zou, yanling/0000-0002-1975-3235 China Scholarship Council (CSC); Sichuan Ministry of Education, China China Scholarship Council (CSC)(China Scholarship Council); Sichuan Ministry of Education, China This study was funded by the China Scholarship Council (CSC) and partially supported by the Sichuan Ministry of Education, China. 47 3 3 7 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13 16 3220 10.3390/rs13163220 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology UH2NU gold 2023-03-23 WOS:000689775400001 0
J Das, P; Das, AK; Nayak, J; Pelusi, D; Ding, WP Das, Priyanka; Das, Asit Kumar; Nayak, Janmenjoy; Pelusi, Danilo; Ding, Weiping Group incremental adaptive clustering based on neural network and rough set theory for crime report categorization NEUROCOMPUTING English Article Crime report categorization; Group incremental clustering; Neural network; Adaptive resonance theory; Rough set theory; Unsupervised learning COMMUNITY STRUCTURE Explosively growing online text reports are mostly unstructured in nature. Many state-of-the-art tech-niques involving supervised, unsupervised or semi-supervised approaches have been developed in the recent years for automatic clustering of these reports. Annotation of online crime reports is a challenging task as various types of crime reports are frequently generated over time. To the best of the authors' knowledge, this is the first attempt taken for group incremental adaptive clustering of crime reports inte-grating neural network and rough set theory. The proposed work initially identifies the named entities and selects only the context words within a pair of entities as a phrase. Thus every report is described by a collection of phrases. The phrases are vectorized using GloVe and a graph based clustering algorithm is applied to cluster all the collected phrases. The phrases within a cluster are considered as the similar type of phrases, called paraphrases and each report is represented by a binary vector of dimension equal to the number of clusters obtained. If a phrase of the report lies in a cluster then a '1' is set at the corre-sponding position of the binary vector; otherwise it is set as '0'. Next, an adaptive resonance theory neural network is applied on the binary vector representation of the crime reports to generate a set of clusters of crime reports. When a new group of reports is available, the reports are transformed into binary form in the similar way and the rough set theory is applied on them. It puts many reports into existing clusters and for the remaining reports, adaptive resonance theory is further applied to modify the existing clusters and possibly generate the new clusters. Thus, in the dynamic environment when data are generated grad-ually over time, the proposed group incremental clustering algorithm is adapted to provide the updated set of clusters. The method has been applied on various crime report datasets and validated with the help of several cluster validation indices. The method is also compared with some state-of-the-art clustering algorithms to express its effectiveness and statistical significance in the domain of crime corpora. (c) 2019 Elsevier B.V. All rights reserved. [Das, Priyanka; Das, Asit Kumar] Indian Inst Engn Sci & Technol, Sibpur 711103, Howrah, India; [Nayak, Janmenjoy] Aditya Inst Technol & Management AITAM, Dept CSE, Tekkali, AP, India; [Pelusi, Danilo] Univ Teramo, Dept Commun Sci, Teramo, Italy; [Ding, Weiping] Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China Indian Institute of Engineering Science Technology Shibpur (IIEST); University of Teramo; Nantong University Ding, WP (corresponding author), Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China. priyankadas700@gmail.com; akdas@cs.iiests.ac.in; mailforjnayak@gmail.com; dpelusi@unite.it; ding.wp@ntu.edu.cn NAYAK, JANMENJOY/V-6663-2018 NAYAK, JANMENJOY/0000-0002-9746-6557; Das, Asit Kumar/0000-0002-9886-1735 National Natural Science Foundation of China [61976120]; Natural Science Foundation of Jiangsu Province [BK20191445]; Six Talent Peaks Project of Jiangsu Province [XYDXXJS-048]; Qing Lan Project of Jiangsu Province National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Six Talent Peaks Project of Jiangsu Province; Qing Lan Project of Jiangsu Province This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province. 71 4 4 0 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 12 2021.0 459 465 480 10.1016/j.neucom.2019.10.109 0.0 SEP 2021 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WM4QE 2023-03-23 WOS:000711070700020 0
J Xie, YY; Song, KL; Wu, QW; Zhou, Q Xie, Yunyun; Song, Kunlong; Wu, Qiuwei; Zhou, Qian Orthogonal genetic algorithm based power system restoration path optimization INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS English Article orthogonal experimental design; orthogonal genetic algorithm; path optimization; power system restoration NETWORK; STRATEGY Optimizing the power system restoration path is a key issue for the system restoration after a blackout. Because the optimization is a complex nonlinear programming problem, artificial intelligent algorithms are widely employed to solve this problem due to its modeling flexibility and strong optimization capability. However, because the dimension of restoration path optimization is very high especially for large-scale systems, artificial intelligent algorithms in current works are easy to be trapped in the local optima. To improve the optimal solution from the artificial intelligence algorithms, an orthogonal genetic algorithm is employed in this paper to optimize the restoration path, which can search the solution space in a statistically sound manner. First, the experimental design method was employed to generate orthogonal array as the initial population which was scattered uniformly over the feasible solution space. Then, the orthogonal crossover operator based on the orthogonal experimental design was employed to generate a small but representative feasible solution as the potential offspring. Finally, the proposed method is validated by using the IEEE 118-bus test system and part of the Jiangsu power grid in China. [Xie, Yunyun; Song, Kunlong] Nanjing Univ Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China; [Song, Kunlong] Guodian Sci & Technol Res Inst, Chengdu 610041, Peoples R China; [Wu, Qiuwei] Tech Univ Denmark, Dept Elect Engn, Ctr Elect Power & Energy Syst, DK-2800 Lyngby, Denmark; [Zhou, Qian] State Grid Jiangsu Elect Power Res Inst, Nanjing 211103, Jiangsu, Peoples R China Nanjing University of Science & Technology; Technical University of Denmark; State Grid Corporation of China Xie, YY (corresponding author), Nanjing Univ Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China. xyy_njust@163.com Wu, Qiuwei/AAJ-1741-2020; WU, QIUWEI/AAA-4012-2021 Wu, Qiuwei/0000-0001-7935-2567; National Natural Science Foundation of China [51507080, 61673213]; Fundamental Research Funds for the Central Universities [30918011330]; Science and Technology Project of State Grid Corporation of China [5228001600DT] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Science and Technology Project of State Grid Corporation of China National Natural Science Foundation of China, Grant/Award Number: 51507080, 61673213; The Fundamental Research Funds for the Central Universities, Grant/Award Number: 30918011330; the Science and Technology Project of State Grid Corporation of China, Grant/Award Number: 5228001600DT 31 7 7 2 19 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2050-7038 INT T ELECTR ENERGY Int. Trans. Electr. Energy Syst. DEC 2018.0 28 12 e2630 10.1002/etep.2630 0.0 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering HF1DS Green Submitted, gold 2023-03-23 WOS:000453906400014 0
J Li, H; Ghamisi, P; Rasti, B; Wu, ZY; Shapiro, A; Schultz, M; Zipf, A Li, Hao; Ghamisi, Pedram; Rasti, Behnood; Wu, Zhaoyan; Shapiro, Aurelie; Schultz, Michael; Zipf, Alexander A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks REMOTE SENSING English Article deep learning; data fusion; hyperspectral image classification; residual learning; multi-sensor fusion; convolutional neural networks (CNNs); auxiliary loss function HYPERSPECTRAL IMAGE; EXTINCTION PROFILES; CLASSIFICATION; EARTH; 3-D Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations: 1- a unique adaptation of the coupled residual networks to address multi-sensor data classification; 2- a smart auxiliary training via adjusting the loss function to address classifications with limited samples; and 3- a unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features. The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods. [Li, Hao; Wu, Zhaoyan; Schultz, Michael; Zipf, Alexander] Heidelberg Univ, Inst Geog, GISci Chair, D-69120 Heidelberg, Germany; [Ghamisi, Pedram; Rasti, Behnood] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Explorat, Chemnitzer Str 40, D-09599 Freiberg, Germany; [Wu, Zhaoyan] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Shapiro, Aurelie] Here There Mapping Solut, D-10115 Berlin, Germany Ruprecht Karls University Heidelberg; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Wuhan University Li, H (corresponding author), Heidelberg Univ, Inst Geog, GISci Chair, D-69120 Heidelberg, Germany. hao.li@uni-heidelberg.de; p.ghamisi@gmail.com; b.rasti@hzdr.de; zhaoyan.wu@uni-heidelberg.de; aurelie@here-there-mapping.com; michael.schultz@uni-heidelberg.de; zipf@uni-heidelberg.de Ghamisi, Pedram/ABD-5419-2021 Wu, Zhaoyan/0000-0001-5639-0317; Ghamisi, Pedram/0000-0003-1203-741X; Zipf, Alexander/0000-0003-4916-9838; Li, Hao/0000-0002-6336-8772 Klaus Tschira Stiftung, Heidelberg; Alexander von Humboldt foundation Klaus Tschira Stiftung, Heidelberg; Alexander von Humboldt foundation(Alexander von Humboldt Foundation) The authors would like to thank Lorenzo Bruzzone of the University of Trento for providing the Trento data set, and the same thanks to the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the Houston data set and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for distributing the Houston 2013 and 2018 dataset. The work of Behnood Rasti was funded by Alexander von Humboldt foundation. This research was partly funded by the Klaus Tschira Stiftung, Heidelberg. 39 7 7 4 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUN 2020.0 12 12 2067 10.3390/rs12122067 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology MM7LT gold 2023-03-23 WOS:000550335600001 0
J Li, JH; Weinmann, M; Sun, X; Diao, WH; Feng, YC; Hinz, S; Fu, K Li, Jihao; Weinmann, Martin; Sun, Xian; Diao, Wenhui; Feng, Yingchao; Hinz, Stefan; Fu, Kun VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article ALS Point Cloud; Classification; Deep Learning; Decoupled High-level Feature; Local-global Information Aggregation LIDAR DATA; SEMANTIC SEGMENTATION; AERIAL LIDAR; CONVOLUTIONS; VEGETATION Airborne Laser Scanning (ALS) point cloud classification is a valuable and practical task in the fields of photogrammetry and remote sensing. It takes an extremely important role in many applications of surveying, monitoring, planning, production and living. Recently, driven by the wave of deep learning, the classification of ALS point clouds has also been gradually shifting from traditional feature design to careful deep network architecture construction. Although significant progress has been achieved by leveraging deep learning technology, there are still some matters demanding prompt solution: (1) the coupling phenomenon of high-level semantic features from multiple field-of-views; (2) information propagation without aggregated local-global features in different levels of symmetrical structure; (3) quite serious class-imbalanced distribution problems in large-scale ALS point clouds. In this paper, to tackle these matters, we propose a novel View-Decoupled Network with Local-global Aggregation Bridge (VD-LAB) model. More concretely, a View-Decoupled (VD) grouping method is set at the deepest layer of the network. Then, we establish a Local-global Aggregation Bridge (LAB) between down sampling path and up-sampling path of the same level. After that, a Self-Amelioration (SA) loss is taken as the optimization objective to train the whole model in an end-to-end manner. Extensive experiments on four challenging ALS point cloud datasets (LASDU, US3D, ISPRS 3D and GML) demonstrate that our VD-LAB is able to achieve state-of-the-art performance in terms of Overall Accuracy (OA) and mean F1-score (e.g., reaching 88.01% and 78.42% for LASDU dataset, respectively) with very few model parameters and it possesses a strong generalization capability. In addition, the visualization of achieved results also reveals more satisfactory classification for some categories, such as Water in the US3D dataset and Powerline in the ISPRS 3D dataset. Ultimately, the effect of each module in VD-LAB is assessed in detailed ablation analyses as well. [Li, Jihao; Sun, Xian; Diao, Wenhui; Feng, Yingchao; Fu, Kun] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China; [Li, Jihao; Sun, Xian; Diao, Wenhui; Feng, Yingchao; Fu, Kun] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China; [Li, Jihao; Sun, Xian; Feng, Yingchao; Fu, Kun] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Li, Jihao; Sun, Xian; Feng, Yingchao; Fu, Kun] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China; [Weinmann, Martin; Hinz, Stefan] Karlsruhe Inst Technol, Inst Photogrammetry & Remote Sensing, Karlsruhe, Germany Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Helmholtz Association; Karlsruhe Institute of Technology Li, JH (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.;Weinmann, M (corresponding author), Karlsruhe Inst Technol, Inst Photogrammetry & Remote Sensing, Karlsruhe, Germany. lijihao17@mails.ucas.edu.cn; martin.weinmann@kit.edu; sunxian@aircas.ac.cn; diaowh@aircas.ac.cn; fengyingchao17@mails.ucas.edu.cn; stefan.hinz@kit.edu; kunfuiecas@gmail.com Hinz, Stefan/HDN-5781-2022; Feng, Yingchao/HKE-4766-2023 Hinz, Stefan/0000-0002-7323-9800; National Natural Science Foundation of China [61725105]; [II/4] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); The authors would like to thank the College of Surveying and Geo-informatics, Tongji University and The Department of Photogrammetry and Remote Sensing, Technical University of Munich (TUM) for providing the LASDU point cloud dataset to support our study. The US3D dataset was kindly provided by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE-GRSS) , the Johns Hopkins University (JHU) , and the Intelligence Advanced Research Projects Activity (IARPA) . The authors also appreciate the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group (WG) II/4 to provide the ISPRS 3D point cloud dataset. Furthermore, the authors would like to thank the Graphics and Media Lab (GML) , Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University for providing the GML dataset. The authors are deeply grateful for the effort of all anonymous re-viewers and would like to thank them for their precious comments. The research of this paper is supported by the National Natural Science Foundation of China under Grants 61725105. 71 2 2 4 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. APR 2022.0 186 19 33 10.1016/j.isprsjprs.2022.01.012 0.0 FEB 2022 15 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology 0N1DT 2023-03-23 WOS:000782587800002 0
J Jiang, XF; Xiang, S; Wang, M; Tang, P Jiang, Xiaofan; Xiang, Shao; Wang, Mi; Tang, Peng Dual-Pathway Change Detection Network Based on the Adaptive Fusion Module IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Feature extraction; Decoding; Training; Encoding; Indexes; Fuses; Remote sensing; Adaptive fusion; auxiliary supervision; change detection (CD); deep learning; dual-pathway feature difference networks (FDNs) In recent years, with the development of high-resolution remote sensing (RS) images and deep learning technology, high-quality source data and state-of-the-art methods have become increasingly available, and great progress has been made in change detection (CD) in RS fields. However, existing methods still suffer from weak network feature representation and poor CD performance. To address these problems, we propose a novel CD network, called dual-pathway CD network (DP-CD-Net), which can help enhance feature representation and achieve a more accurate difference map. The proposed method contains a dual-pathway feature difference network (FDN), an adaptive fusion module (AFM), and an auxiliary supervision strategy. Dual-pathway FDNs can effectively enhance feature representation by supplementing the detailed information from the encoding layers. Then, we use the AFM method to fuse the difference maps. To solve the problem of training difficulty, we use the auxiliary supervision strategy to improve the performance of DP-CD-Net. We conduct extensive experiments to validate the performance of the proposed method on the LEVIR-CD dataset. The results demonstrate that the proposed method performs better than existing methods. [Jiang, Xiaofan; Xiang, Shao; Wang, Mi] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China; [Tang, Peng] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany Wuhan University; Technical University of Munich Wang, M (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China. jiangxiaofan2014@163.com; xiangshao@whu.edu.cn; wangmi@whu.edu.cn; tangp@in.tum.de Xiang, Shao/V-3790-2018 Xiang, Shao/0000-0002-2797-1937; Jiang, Xiaofan/0000-0002-2152-8926; TANG, Peng/0000-0003-4099-6677 National Natural Science Foundation of China [61825103, 91838303, 91738302] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Project 61825103, Project 91838303, and Project 91738302. 15 0 0 1 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 10.1109/LGRS.2021.3103991 0.0 SEP 2021 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XY1VP 2023-03-23 WOS:000732105200001 0
J Yang, ZY; Wei, D; Zaccone, A; Wang, YJ Yang, Zeng-Yu; Wei, Dan; Zaccone, Alessio; Wang, Yun-Jiang Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space PHYSICAL REVIEW B English Article MECHANICAL-BEHAVIOR; INHOMOGENEOUS FLOW; RELAXATION; LIQUIDS; DEFORMATION; ORDER Optimizing materials' properties and functions by controlling defects in the crystalline phase has been a cornerstone of materials science and condensed matter physics. However, this paradigm has yet to be established in the broadly defined amorphous materials, which implies the identification of very subtle structural features in an otherwise uniformly disordered medium. Here we propose and define a new integrated glassy defect (IGD), based on machine learning strategy informed by atomistic physics, and also by an extremely wide configurational, thermodynamic, and dynamic variables space of the disordered state. The IGD simultaneously includes positional topology and vibrational features, as well as the local morphology of the potential energy landscape. This unprecedented combination gives rise to a much more comprehensive and more effective definition of the glassy defect, much beyond the conventional, purely structural input. IGD can be used not only as an efficient predictor of athermal plasticity but is also transferable to detect both short-time vibrational anomalies (the boson peak), and long-time relaxation and diffusion dynamics in glasses. The integrated strategy is instrumental to build the long-sought structure-property relationship in complex media. [Yang, Zeng-Yu; Wei, Dan; Wang, Yun-Jiang] Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; [Yang, Zeng-Yu; Wei, Dan; Wang, Yun-Jiang] Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; [Zaccone, Alessio] Univ Milan, Dept Phys A Pontremoli, Via Celoria 16, I-20133 Milan, Italy; [Zaccone, Alessio] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England; [Zaccone, Alessio] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England Chinese Academy of Sciences; Institute of Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Milan; University of Cambridge; University of Cambridge Wang, YJ (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. yjwang@imech.ac.cn Wang, Yunjiang/H-6062-2011; Yang, Zeng-Yu/AAC-8577-2019 Wang, Yunjiang/0000-0002-2969-3889; Yang, Zeng-Yu/0000-0003-0510-8785; Zaccone, Alessio/0000-0002-6673-7043 National Key Research and Development Program of China [2017YFB0701502]; National Natural Science Foundation of China [12072344]; Youth Innovation Promotion Association of Chinese Academy of Sciences [2017025] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Innovation Promotion Association of Chinese Academy of Sciences This work is financially supported by the National Key Research and Development Program of China (Grant No. 2017YFB0701502), the National Natural Science Foundation of China (Grant No. 12072344), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2017025). 67 8 8 2 24 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B AUG 13 2021.0 104 6 64108 10.1103/PhysRevB.104.064108 0.0 14 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics UA4BK Green Submitted, Green Published 2023-03-23 WOS:000685105800002 0
J Wong, WKM; Joglekar, MV; Saini, V; Jiang, GZ; Dong, CX; Chaitarvornkit, A; Maciag, GJ; Gerace, D; Farr, RJ; Satoor, SN; Sahu, S; Sharangdhar, T; Ahmed, AS; Chew, YV; Liuwantara, D; Heng, B; Lim, CK; Hunter, J; Januszewski, AS; Sorensen, AE; Akil, ASA; Gamble, JR; Loudovaris, T; Kay, TW; Thomas, HE; O'Connell, PJ; Guillemin, GJ; Martin, D; Simpson, AM; Hawthorne, WJ; Dalgaard, LT; Ma, RCW; Hardikar, AA Wong, Wilson K. M.; Joglekar, Mugdha, V; Saini, Vijit; Jiang, Guozhi; Dong, Charlotte X.; Chaitarvornkit, Alissa; Maciag, Grzegorz J.; Gerace, Dario; Farr, Ryan J.; Satoor, Sarang N.; Sahu, Subhshri; Sharangdhar, Tejaswini; Ahmed, Asma S.; Chew, Yi Vee; Liuwantara, David; Heng, Benjamin; Lim, Chai K.; Hunter, Julie; Januszewski, Andrzej S.; Sorensen, Anja E.; Akil, Ammira S. A.; Gamble, Jennifer R.; Loudovaris, Thomas; Kay, Thomas W.; Thomas, Helen E.; O'Connell, Philip J.; Guillemin, Gilles J.; Martin, David; Simpson, Ann M.; Hawthorne, Wayne J.; Dalgaard, Louise T.; Ma, Ronald C. W.; Hardikar, Anandwardhan A. Machine learning workflows identify a microRNA signature of insulin transcription in human tissues ISCIENCE English Article THIOREDOXIN-INTERACTING PROTEIN; PANCREATIC BETA-CELLS; STEM-CELLS; DIFFERENTIATION; EXPRESSION; MIR-375; PROLIFERATION; DYSFUNCTION; VALIDATION; RESISTANCE Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic beta-cell function. Studies to identify specific microRNA( s) associated with human (pro-)endocrine gene expression are needed. We profiled microRNAs and key pancreatic genes in 353 human tissue samples. Machine learning workflows identified microRNAs associated with (pro-)insulin transcripts in a discovery set of islets (n = 30) and insulin-negative tissues (n = 62). This microRNA signature was validated in remaining 261 tissues that include nine islet samples from individuals with type 2 diabetes. Top eight microRNAs (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, and -429-3p) were confirmed to be associated with and predictive of (pro-)insulin transcript levels. Use of doxycycline-inducible microRNA-overexpressing human pancreatic duct cell lines confirmed the regulatory roles of thesemicroRNAs in (pro-)endocrine gene expression. Knockdown of these microRNAs in human islet cells reduced (pro-)insulin transcript abundance. Our data provide specific microRNAs to further study microRNA-mRNA interactions in regulating insulin transcription. [Wong, Wilson K. M.; Joglekar, Mugdha, V; Saini, Vijit; Dong, Charlotte X.; Chaitarvornkit, Alissa; Farr, Ryan J.; Satoor, Sarang N.; Sahu, Subhshri; Sharangdhar, Tejaswini; Ahmed, Asma S.; Hardikar, Anandwardhan A.] Western Sydney Univ, Sch Med, Diabet & Islet Biol Grp, Narellan Rd & Gilchrist Dr, Campbelltown, NSW 2560, Australia; [Wong, Wilson K. M.; Joglekar, Mugdha, V; Dong, Charlotte X.; Chaitarvornkit, Alissa; Farr, Ryan J.; Satoor, Sarang N.; Sahu, Subhshri; Sharangdhar, Tejaswini; Ahmed, Asma S.; Hardikar, Anandwardhan A.] Univ Sydney, Fac Med & Hlth, Diabet & Islet Biol Grp, 92-94 Parramatta Rd, Camperdown, NSW 2050, Australia; [Saini, Vijit; Gerace, Dario; Simpson, Ann M.] Univ Technol Sydney, Sch Life Sci, 15 Broadway, Ultimo, NSW 2007, Australia; [Saini, Vijit; Gerace, Dario; Simpson, Ann M.] Univ Technol Sydney, Ctr Hlth Technol, 15 Broadway, Ultimo, NSW 2007, Australia; [Jiang, Guozhi; Ma, Ronald C. W.] Chinese Univ Hong Kong, Prince Wales Hosp, Dept Med & Therapeut, Hong Kong, Peoples R China; [Jiang, Guozhi; Ma, Ronald C. W.] Chinese Univ Hong Kong, Prince Wales Hosp, Hong Kong Inst Diabet & Obes, Hong Kong, Peoples R China; [Jiang, Guozhi; Ma, Ronald C. W.] Chinese Univ Hong Kong, Prince Wales Hosp, Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China; [Maciag, Grzegorz J.; Sorensen, Anja E.; Dalgaard, Louise T.; Hardikar, Anandwardhan A.] Roskilde Univ, Dept Sci & Environm, Univ Vej 1, DK-4000 Roskilde, Denmark; [Chew, Yi Vee; Liuwantara, David; O'Connell, Philip J.; Hawthorne, Wayne J.] Univ Sydney, Ctr Transplant & Renal Res, Westmead Inst Med Res, 176 Hawkesbury Rd, Westmead, NSW 2145, Australia; [Heng, Benjamin; Lim, Chai K.; Guillemin, Gilles J.] Macquarie Univ, Fac Med Hlth & Human Sci, Sydney, NSW 2019, Australia; [Hunter, Julie; Gamble, Jennifer R.] Univ Sydney, Med Sch, Centenary Inst, Ctr Endothelium,Vasc Biol Program, Locked Bag 6, Newtown, NSW 2042, Australia; [Januszewski, Andrzej S.] Univ Sydney, NHMRC Clin Trials Ctr, 92-94 Parramatta Rd, Camperdown, NSW 2050, Australia; [Akil, Ammira S. A.] Sidra Med, Dept Human Genet, Precis Med Program, POB 26999, Doha, Qatar; [Loudovaris, Thomas; Kay, Thomas W.; Thomas, Helen E.] St Vincents Inst, 9 Princes St, Fitzroy, Vic, Australia; [Loudovaris, Thomas; Kay, Thomas W.; Thomas, Helen E.] Univ Melbourne, Dept Med, 9 Princes St, Fitzroy, Vic, Australia; [Martin, David] Strathfield Hosp, Upper GI Surg, 2-3 Everton Rd, Strathfield, NSW 2135, Australia University of Sydney; Western Sydney University; University of Sydney; University of Technology Sydney; University of Technology Sydney; Chinese University of Hong Kong; Prince of Wales Hospital; Chinese University of Hong Kong; Prince of Wales Hospital; Chinese University of Hong Kong; Prince of Wales Hospital; Roskilde University; University of Sydney; Westmead Institute for Medical Research; Macquarie University; University of Sydney; Centenary Institute; University of Sydney; Sidra Medical & Research Center; St. Vincent's Institute of Medical Research; University of Melbourne Hardikar, AA (corresponding author), Western Sydney Univ, Sch Med, Diabet & Islet Biol Grp, Narellan Rd & Gilchrist Dr, Campbelltown, NSW 2560, Australia.;Hardikar, AA (corresponding author), Univ Sydney, Fac Med & Hlth, Diabet & Islet Biol Grp, 92-94 Parramatta Rd, Camperdown, NSW 2050, Australia.;Hardikar, AA (corresponding author), Roskilde Univ, Dept Sci & Environm, Univ Vej 1, DK-4000 Roskilde, Denmark. a.hardikar@wostomsydney.edu.au Joglekar, Mugdha V./AFP-0659-2022; Guillemin, Gilles J/C-3878-2015; Wong, Wilson/AAC-1438-2022; Farr, Ryan J/N-5585-2017; O'Connell, Philip John/AFK-9353-2022; Lim, Edwin/R-5001-2019; Hardikar, Anandwardhan/ACF-7730-2022; Heng, Ruiwen benjamin/U-7383-2019; Ma, Ronald/C-2788-2009; Dalgaard, Louise/O-3788-2015 Guillemin, Gilles J/0000-0001-8105-4470; Farr, Ryan J/0000-0003-1636-1815; O'Connell, Philip John/0000-0002-2036-283X; Lim, Edwin/0000-0003-3526-8405; Heng, Ruiwen benjamin/0000-0002-0247-2920; Ma, Ronald/0000-0002-1227-803X; Dalgaard, Louise/0000-0002-3598-2775; Loudovaris, Thomas/0000-0002-7038-2302; Gamble, Jennifer/0000-0002-0179-9964; Wong, Wilson/0000-0001-7736-6834 Australian Research Council Future Fellowship [FT110100254]; Juvenile Diabetes Research Foundation (JDRF) Australia T1D Clinical Research Network [JDRF/4-CDA2016-228-MB]; University of Sydney CDIP grants; Danish Diabetes Academy - Novo Nordisk Foundation [NNF17SA0031406]; Australian Postgraduate Award; University of Sydney; JDRF Australia PhD top-up award; JDRF Australia/Helmsley Charitable Trust; JDRF USA advanced post-doctoral award [3-APF-2016-178-A-N]; JDRF, USA; NHMRC program grant; JDRF; Operational Infrastructure Support Scheme of the Government of Victoria; RGC Theme-based Research Scheme [T12-402/13N]; RGC Research Impact Fund [R4012-18]; Focused Innovation Scheme; Faculty Postdoctoral Scheme of the Chinese University of Hong Kong; Danish Diabetes Academy post-doctoral grant - Novo Nordisk Foundation; Wenkart Chair of the Endothelium; Australian Research Council [FT110100254] Funding Source: Australian Research Council Australian Research Council Future Fellowship(Australian Research Council); Juvenile Diabetes Research Foundation (JDRF) Australia T1D Clinical Research Network; University of Sydney CDIP grants; Danish Diabetes Academy - Novo Nordisk Foundation; Australian Postgraduate Award(Australian Government); University of Sydney(University of Sydney); JDRF Australia PhD top-up award; JDRF Australia/Helmsley Charitable Trust; JDRF USA advanced post-doctoral award; JDRF, USA; NHMRC program grant(National Health and Medical Research Council (NHMRC) of Australia); JDRF(Juvenile Diabetes Research Foundation); Operational Infrastructure Support Scheme of the Government of Victoria; RGC Theme-based Research Scheme; RGC Research Impact Fund; Focused Innovation Scheme; Faculty Postdoctoral Scheme of the Chinese University of Hong Kong; Danish Diabetes Academy post-doctoral grant - Novo Nordisk Foundation; Wenkart Chair of the Endothelium; Australian Research Council(Australian Research Council) The research presented herein has been funded through grants from the Australian Research Council Future Fellowship (FT110100254), the Juvenile Diabetes Research Foundation (JDRF) Australia T1D Clinical Research Network (JDRF/4-CDA2016-228-MB), and the University of Sydney CDIP grants to A.A.H. A.A.H, L. T.D., and A.E.S. are also supported through visiting professorships from the Danish Diabetes Academy, funded by the Novo Nordisk Foundation, grant number NNF17SA0031406 (2016-18 and 2019-22). W.K.M.W. acknowledges previous support from the Australian Postgraduate Award, University of Sydney, JDRF Australia PhD top-up award, and current funding through JDRF Australia/Helmsley Charitable Trust. M.V.J. was supported through a JDRF USA advanced post-doctoral award (3-APF-2016-178-A-N) and currently a transition award from JDRF, USA. J.R.G. holds the Wenkart Chair of the Endothelium. P. O.C. was an NHMRC senior practitioner fellow and funded by an NHMRC program grant and the JDRF. The St Vincent's Institute receives support from the Operational Infrastructure Support Scheme of the Government of Victoria. R.C.W. M. acknowledges support from the RGC Theme-based Research Scheme (T12-402/13N) and Research Impact Fund (R4012-18), the Focused Innovation Scheme, and Faculty Postdoctoral Scheme of the Chinese University of Hong Kong. A.E.S. was supported through a Danish Diabetes Academy post-doctoral grant, supported by the Novo Nordisk Foundation. We thank Ms. Cody Lee-Maynard, Ms. Dana AlRijjal, and Dr. Najeeb Syed for assistance in laboratory analytical work. A.A.H. acknowledges interaction(s) with Dr. Khalid Fakhro, Ms. Shihana Fathima, Prof. Alicia J. Jenkins, Prof. Anthony C. Keech, Prof. Val Gebski and infrastructure support from the NHMRC CTC, Faculty of Medicine & Health, University of Sydney; Australia, School of Medicine, Western Sydney University, Australia; Western Sydney University, Ingham Institute, Liverpool; Australia and the Rebecca L. Cooper Medical Research Foundation. The support of all surgical team members contributing to the consenting and acquisition of research tissue samples, all organ donors, and supporting family members is gratefully acknowledged. 77 6 6 0 5 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 2589-0042 ISCIENCE iScience APR 23 2021.0 24 4 102379 10.1016/j.isci.2021.102379 0.0 46 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics RQ2PJ 33981968.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000642261700120 0
J Yang, MH; Xu, DY; Chen, SC; Li, HY; Shi, Z Yang, Meihua; Xu, Dongyun; Chen, Songchao; Li, Hongyi; Shi, Zhou Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra SENSORS English Article machine learning approaches; vis-NIR spectra; paddy soil; soil organic matter; pH PARTIAL LEAST-SQUARES; REFLECTANCE SPECTROSCOPY; VARIABLE SELECTION; QUANTITATIVE-ANALYSIS; GENETIC ALGORITHMS; REGRESSION; CARBON; NITROGEN; LIBRARY; REGION Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approachespartial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R-2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R-2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R-2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R-2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R-2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R-2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R-2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R-2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R-2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain. [Yang, Meihua; Xu, Dongyun; Shi, Zhou] Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China; [Yang, Meihua] Yuzhang Normal Univ, Dept Environm Engn, Nanchang 330103, Jiangxi, Peoples R China; [Chen, Songchao] INRA, Unite InfoSol, F-45075 Orleans, France; [Li, Hongyi] Jiangxi Univ Finance & Econ, Dept Land Resource Management, Nanchang 330013, Jiangxi, Peoples R China; [Shi, Zhou] Minist Agr, Key Lab Spect Sensing, Hangzhou 310058, Zhejiang, Peoples R China Zhejiang University; INRAE; Jiangxi University of Finance & Economics; Ministry of Agriculture & Rural Affairs Shi, Z (corresponding author), Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China.;Shi, Z (corresponding author), Minist Agr, Key Lab Spect Sensing, Hangzhou 310058, Zhejiang, Peoples R China. 0015862@zju.edu.cn; xudongyun@zju.edu.cn; songchao.chen@inra.fr; lihongyi1981@zju.edu.cn; shizhou@zju.edu.cn shi, Zhou/A-2283-2015; shi, Zhou/M-7845-2019; Moraes, Edison/C-2443-2015; Chen, Songchao/S-7982-2019; LI, H.Y./B-3427-2014 shi, Zhou/0000-0003-3914-5402; shi, Zhou/0000-0003-3914-5402; Chen, Songchao/0000-0003-1245-0482; National Key Research and Development Program [2017YFD0700501]; National Science Foundation of China [41561049]; Natural Science Fund for Distinguished Young Scholar of Jiangxi Province [20171BCB23049]; Science and Technology Project of Jiangxi Provincial Education Department [GJJ150481] National Key Research and Development Program; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fund for Distinguished Young Scholar of Jiangxi Province; Science and Technology Project of Jiangxi Provincial Education Department This research was supported by the National Key Research and Development Program (2017YFD0700501), the National Science Foundation of China (No. 41561049), the Natural Science Fund for Distinguished Young Scholar of Jiangxi Province (No 20171BCB23049), the Science and Technology Project of Jiangxi Provincial Education Department (No GJJ150481). 47 54 57 13 69 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JAN 2 2019.0 19 2 263 10.3390/s19020263 0.0 14 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HL2WI 30641879.0 Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000458569300046 0
C Khan, MG; Taheri, J; Khoshkholghi, MA; Kassler, A; Cartwright, C; Darula, M; Deng, SG DeTurck, F; Chemouil, P; Wauters, T; Zhani, MF; Cerroni, W; Pasquini, R; Zhu, Z Khan, Michel Gokan; Taheri, Javid; Khoshkholghi, Mohammad Ali; Kassler, Andreas; Cartwright, Carolyn; Darula, Marian; Deng, Shuiguang A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions PROCEEDINGS OF THE 2020 6TH IEEE CONFERENCE ON NETWORK SOFTWARIZATION (NETSOFT 2020): BRIDGING THE GAP BETWEEN AI AND NETWORK SOFTWARIZATION English Proceedings Paper 6th IEEE International Conference on Network Softwarization (NetSoft) - Bridging the Gap Between AI and Network Softwarization JUN 29-JUL 03, 2020 ELECTR NETWORK IEEE,IEEE Commun Soc,Intel,Huawei Network Function Virtualization; Cloud Native Network Functions; Machine Learning; Service Level Agreement; Quality of Service Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2-5 times more computing resources than the PTE. [Khan, Michel Gokan; Taheri, Javid; Khoshkholghi, Mohammad Ali; Kassler, Andreas] Karlstad Univ, Dept Comp Sci, Karlstad, Sweden; [Khan, Michel Gokan; Cartwright, Carolyn; Darula, Marian] Ericsson, Dept R&D & Portfolio, Stockholm, Sweden; [Deng, Shuiguang] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China Karlstad University; Ericsson; Zhejiang University Khan, MG (corresponding author), Karlstad Univ, Dept Comp Sci, Karlstad, Sweden.;Khan, MG (corresponding author), Ericsson, Dept R&D & Portfolio, Stockholm, Sweden. michel.gokan@kau.se; javid.taheri@kau.se; ali.khosh-kholghi@kau.se; andreas.kassler@kau.se; carolyn.cartwright@ericsson.com; marian.darula@ericsson.com; dengsg@zju.edu.cn Khoshkholghi, Mohammad Ali/GQP-9963-2022; Gokan Khan, Michel/GQQ-5487-2022; Khan, Michel Gokan/S-9356-2017 Khoshkholghi, Mohammad Ali/0000-0002-6101-4305; Khan, Michel Gokan/0000-0002-4825-8831; Deng, Shuiguang/0000-0001-5015-6095 Knowledge Foundation of Sweden [20160182] Knowledge Foundation of Sweden This work was partially supported by the Grant No. 20160182 funded by the Knowledge Foundation of Sweden. 24 5 5 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-5684-2 2020.0 292 300 9 Computer Science, Software Engineering; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BQ8ZA 2023-03-23 WOS:000623436400048 0
J Nie, XB; Min, C; Pan, YJ; Li, K; Li, ZX Nie, Xiaobo; Min, Chuan; Pan, Yongjun; Li, Ke; Li, Zhixiong Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving SENSORS English Article deep neural networks; longitudinal-lateral dynamics; autonomous vehicle; real-time simulation MOBILITY Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle's initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety. [Nie, Xiaobo; Min, Chuan; Pan, Yongjun] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China; [Pan, Yongjun] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China; [Li, Ke] Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China; [Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland Chongqing University; Dalian University of Technology; Chongqing University; Opole University of Technology Pan, YJ (corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China.;Pan, YJ (corresponding author), Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China. xiaobo.nie@cqu.edu.cn; chuan.min@cqu.edu.cn; yongjun.pan@cqu.edu.cn; keli-bridge@cqu.edu.cn; z.li@po.edu.pl National Natural Science Foundation of China [12072050]; Research Project of State Key Laboratory of Mechanical System and Vibration [MSV202216]; Narodowego Centrum Nauki, Poland [2020/37/K/ST8/02748] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Project of State Key Laboratory of Mechanical System and Vibration; Narodowego Centrum Nauki, Poland This work was supported by the National Natural Science Foundation of China (Project No. 12072050), the Research Project of State Key Laboratory of Mechanical System and Vibration (Project No. MSV202216), and the Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748). 41 6 6 8 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAR 2022.0 22 5 2013 10.3390/s22052013 0.0 16 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation ZX3JN 35271160.0 gold, Green Accepted 2023-03-23 WOS:000771794400001 0
C Corradi, F; You, HZ; Giulioni, M; Indiveri, G IEEE Corradi, Federico; You, Hongzhi; Giulioni, Massimiliano; Indiveri, Giacomo Decision Making and Perceptual Bistability in Spike-Based Neuromorphic VLSI Systems 2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) IEEE International Symposium on Circuits and Systems English Proceedings Paper IEEE International Symposium on Circuits and Systems (ISCAS) MAY 24-27, 2015 Lisbon, PORTUGAL IEEE MODEL; MECHANISM; NETWORKS; DYNAMICS; MEMORY Understanding how to reproduce robust and reliable decision making behavior in neuromorphic systems can be useful for developing information processing architectures in subthreshold analog circuits as well as future emerging nano-technologies, that comprise inhomogeneous and unreliable components. To this end, we explore the computational properties of a recurrent neural network, implemented in a custom mixed signal analog/digital neuromorphic chip, for realizing perceptual decision-making, bi-stable perception, and working memory. The chip comprises conductance-based integrate-and-fire neurons and configurable synapses with realistic dynamics. These circuits are configured to implement a recurrent neural network, composed of excitatory and inhibitory pools of silicon neurons coupled with local excitation and global inhibition. We show how the interplay between excitation and inhibition produces competitive winner-take-all dynamics, which is a feature of decision-making and persistent activity models, and demonstrate that the system generates reliable dynamics capable of reproducing both neurophysiological data and psycho-physical performances in coding and collective distributed computation. [Corradi, Federico; Indiveri, Giacomo] Univ Zurich, Inst Neuroinformat, CH-8006 Zurich, Switzerland; [Corradi, Federico; Indiveri, Giacomo] Swiss Fed Inst Technol, Zurich, Switzerland; [You, Hongzhi] Univ Elect Sci & Technol, Minist Educ, Key Lab Neuroinformat, Beijing, Peoples R China; [Giulioni, Massimiliano] Ist Super Sanita, Dept Technol & Hlth, I-00161 Rome, Italy University of Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Electronic Science & Technology of China; Istituto Superiore di Sanita (ISS) Corradi, F (corresponding author), Univ Zurich, Inst Neuroinformat, CH-8006 Zurich, Switzerland. federico@ini.phys.ethz.ch Indiveri, Giacomo/AAX-3310-2020; Corradi, Federico/ABF-1431-2020; Corradi, Federico/ABA-9135-2020; Indiveri, Giacomo/A-4282-2010 Indiveri, Giacomo/0000-0002-7109-1689; Corradi, Federico/0000-0002-5868-8077; Indiveri, Giacomo/0000-0002-7109-1689 21 10 10 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0271-4302 978-1-4799-8391-9 IEEE INT SYMP CIRC S 2015.0 2708 2711 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BE4BG 2023-03-23 WOS:000371471002260 0
J Ji, MT; Chen, XY; Genchev, GZ; Wei, MY; Yu, GJ Ji, Mengting; Chen, Xiaoyun; Genchev, Georgi Z.; Wei, Mingyue; Yu, Guangjun Status of AI-Enabled Clinical Decision Support Systems Implementations in China METHODS OF INFORMATION IN MEDICINE English Article artificial intelligence; AI-enabled clinical decision support systems; clinical decision support systems; clinical implementation; survey CONDUCT Background AI-enabled Clinical Decision Support Systems (AI+CDSSs) were heralded to contribute greatly to the advancement of health care services. There is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, understanding the actual system implementation status in clinical practice is imperative. Objectives The aim of the study is to understand (1) the current situation of AI+CDSSs clinical implementations in Chinese hospitals and (2) concerns regarding AI+CDSSs current and future implementations. Methods We investigated 160 tertiary hospitals from six provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney U -test were utilized for analysis. Results Thirty-eight of the surveyed hospitals (23.75%) had implemented AI+CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI+CDSSs, p <0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as just neutral to satisfied. The three most common concerns were system functions improvement and integration into the clinical process, data quality and availability, and methodological bias. Conclusion While AI+CDSSs were not yet widespread in Chinese clinical settings, professionals recognize the potential benefits and challenges regarding in-hospital AI+CDSSs. [Ji, Mengting] Shanghai Jiao Tong Univ, Ren Ji Hosp, Sch Med, Dept Oncol, Shanghai, Peoples R China; [Chen, Xiaoyun] China Digital Med Press, Beijing, Peoples R China; [Genchev, Georgi Z.; Wei, Mingyue; Yu, Guangjun] Shanghai Childrens Hosp, Dept Informat Technol, Shanghai 200062, Peoples R China; [Genchev, Georgi Z.] Bulgarian Inst Genom & Precis Med, Sofia, Bulgaria Shanghai Jiao Tong University; Shanghai Jiao Tong University Yu, GJ (corresponding author), Shanghai Childrens Hosp, Dept Informat Technol, Shanghai 200062, Peoples R China. yugj1688@shchildren.com.cn 2019 China Medical AI Development Research Project of the National Institute of Hospital Administration 2019 China Medical AI Development Research Project of the National Institute of Hospital Administration This work was supported by the 2019 China Medical AI Development Research Project of the National Institute of Hospital Administration. 43 2 2 7 16 GEORG THIEME VERLAG KG STUTTGART RUDIGERSTR 14, D-70469 STUTTGART, GERMANY 0026-1270 2511-705X METHOD INFORM MED Methods Inf. Med. DEC 2021.0 60 05/06 123 132 10.1055/s-0041-1736461 0.0 OCT 2021 10 Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Health Care Sciences & Services; Medical Informatics XU0KU 34695871.0 2023-03-23 WOS:000710648700001 0
J Anderer, M; Li, F Anderer, Matthias; Li, Feng Hierarchical forecasting with a top-down alignment of independent-level forecasts INTERNATIONAL JOURNAL OF FORECASTING English Article M5 competition; Forecasting reconciliation; Hierarchical forecasting; Hierarchical alignment; Deep learning forecasting STOCK CONTROL; INTERMITTENT Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then, hierarchical reconciliation can be used to improve the overall performance further. In this paper, we present a hierarchical-forecasting-with-alignment approach that treats the bottom-level forecasts as mutable to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach, N-BEATS, for continu-ous time series at the top levels, and a widely used tree-based algorithm, LightGBM, for intermittent time series at the bottom level. The hierarchical-forecasting-with-alignment approach is a simple yet effective variant of the bottom-up method, accounting for biases that are difficult to observe at the bottom level. It allows suboptimal forecasts at the lower level to retain a higher overall performance. The approach in this empirical study was developed by the first author during the M5 Accuracy competition, ranking second place. The method is also business orientated and can be used to facilitate strategic business planning. (c) 2022 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. [Anderer, Matthias] Matthias Anderer GmbH, Holzkirchen, Germany; [Li, Feng] Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China Central University of Finance & Economics Li, F (corresponding author), Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China. matthias@matthiasanderer.de; feng.li@cufe.edu.cn Beijing Universities Advanced Disciplines Initiative, China [GJJ2019163]; Emerging Interdisciplinary Project of CUFE; CUFE, China Beijing Universities Advanced Disciplines Initiative, China; Emerging Interdisciplinary Project of CUFE; CUFE, China The authors are grateful to the editors and two anonymous reviewers for helpful comments that improved the contents of the paper. The authors also appreciate the public discussions on the time series features, models, and performance on the Kaggle open forum during the M5 competition. Feng Li is supported by the Beijing Universities Advanced Disciplines Initiative, China (No. GJJ2019163) , the Emerging Interdisciplinary Project of CUFE, and the disciplinary funding of CUFE, China. 25 3 3 9 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0169-2070 1872-8200 INT J FORECASTING Int. J. Forecast. OCT-DEC 2022.0 38 4 SI 1405 1414 10.1016/j.ijforecast.2021.12.015 0.0 OCT 2022 10 Economics; Management Social Science Citation Index (SSCI) Business & Economics 5U0AG Green Submitted 2023-03-23 WOS:000876216700010 0
J He, X; Wang, AL; Ghamisi, P; Li, GY; Chen, YS He, Xin; Wang, Aili; Ghamisi, Pedram; Li, Guoyu; Chen, Yushi LiDAR Data Classification Using Spatial Transformation and CNN IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Convolutional neural networks (CNNs); deep learning; feature extraction; light detection and ranging (LiDAR); morphological profile (MP); spatial transformation network (STN) DEEP Light detection and ranging (LiDAR) is a useful data acquisition technique, which is widely used in a variety of practical applications. The classification of LiDAR-derived rasterized digital surface model (LiDAR-DSM) is a fundamental technique in LiDAR data processing. In recent years, deep learning methods, especially convolutional neural networks (CNNs), have shown their capability in remote sensing areas, including LiDAR data processing. Traditional deep models empirically use a fixed neighborhood system as input to the network. Therefore, the weight and height of the input rectangle may not be optimal. In order to modify such handcrafted setting, a spatial transformation network is used here to identify optimal inputs. The transformed inputs are fed into a well-designed CNN to obtain the final classification results. Furthermore, morphological profiles are combined with spatial transformation CNN to further improve the classification accuracy. The proposed frameworks are tested on two LiDAR-DSMs (i.e., the Recology and Houston data sets). The experimental results show that the proposed models provide competitive results compared to the state-of-the-art methods. Furthermore, the proposed optimal input identification approach can also be found beneficial for other remote sensing applications. [He, Xin; Wang, Aili] Harbin Univ Sci & Technol, Higher Educ Key Lab Measure & Control Technol & I, Harbin 150080, Heilongjiang, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol Explorat, D-09599 Freiberg, Germany; [Li, Guoyu] Chinese Acad Sci, State Key Lab Frozen Soil Engn, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China; [Chen, Yushi] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China Harbin University of Science & Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Chinese Academy of Sciences; Cold & Arid Regions Environmental & Engineering Research Institute, CAS; Harbin Institute of Technology Chen, YS (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China. 1091636421@qq.com; aili925@hrbust.edu.cn; p.ghamisi@gmail.com; guoyuli@lzb.ac.cn; chenyushi@hit.edu.cn Ghamisi, Pedram/ABD-5419-2021 Chen, Yushi/0000-0003-2421-0996; Li, Guoyu/0000-0002-4651-6251 Natural Science Foundation of China [61771171, 61871157]; Open Fund of State Key Laboratory of Frozen Soil Engineering [SKLFSE201614]; High Potential Program of Helmholtz-Zentrum Dresden-Rossendorf Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund of State Key Laboratory of Frozen Soil Engineering; High Potential Program of Helmholtz-Zentrum Dresden-Rossendorf This work was supported in part by the Natural Science Foundation of China under Grant 61771171 and Grant 61871157, in part by the Open Fund of State Key Laboratory of Frozen Soil Engineering under Grant SKLFSE201614, and in part by the High Potential Program of Helmholtz-Zentrum Dresden-Rossendorf. 13 31 32 2 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. JAN 2019.0 16 1 125 129 10.1109/LGRS.2018.2868378 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology HG7OP 2023-03-23 WOS:000455181800026 0
J Qi, J; Du, J; Siniscalchi, SM; Ma, XL; Lee, CH Qi, Jun; Du, Jun; Siniscalchi, Sabato Marco; Ma, Xiaoli; Lee, Chin-Hui Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Upper bound; Speech enhancement; Neural networks; Optimization; Complexity theory; Signal processing algorithms; Estimation error; Deep neural network; mean absolute error; vector-to-vector regression; non-convex optimization; image de-noising; speech enhancement TIME-VARYING DELAYS; SPACE SCALES; APPROXIMATION In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the over-parametrization technique. [Qi, Jun; Ma, Xiaoli; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Du, Jun] Univ Sci & Technol China, Natl Engn Lab Speech & Language Informat Proc, Hefei 230027, Peoples R China; [Siniscalchi, Sabato Marco] Univ Enna Kore, Fac Architecture & Engn, I-94100 Enna, Italy; [Siniscalchi, Sabato Marco] Georgia Inst Technol, Atlanta, GA 30332 USA University System of Georgia; Georgia Institute of Technology; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universita Kore di ENNA; University System of Georgia; Georgia Institute of Technology Qi, J (corresponding author), Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA. qij41@gatech.edu; jundu@ustc.edu.cn; marco.siniscalchi@unikore.it; xiaoli@gatech.edu; chl@ece.gatech.edu Siniscalchi, Sabato Marco Marco/I-3423-2012 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507; Ma, Xiaoli/0000-0002-3076-2589; Qi, Jun/0000-0001-7533-2630 National Natural Science Foundation of China [61671422] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61671422. 66 22 22 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2020.0 68 3411 3422 10.1109/TSP.2020.2993164 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering MB8GK Green Submitted 2023-03-23 WOS:000542835400005 0
J Dercle, L; Ma, JC; Xie, CM; Chen, AP; Wang, DL; Luk, L; Revel-Mouroz, P; Otal, P; Peron, JM; Rousseau, H; Lu, L; Schwartz, LH; Mokrane, FZ; Zhao, BS Dercle, Laurent; Ma, Jingchen; Xie, Chuanmiao; Chen, Ai-ping; Wang, Deling; Luk, Lyndon; Revel-Mouroz, Paul; Otal, Philippe; Peron, Jean-Marie; Rousseau, Herve; Lu, Lin; Schwartz, Lawrence H.; Mokrane, Fatima-Zohra; Zhao, Binsheng Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine EUROPEAN JOURNAL OF RADIOLOGY English Article Machine learning; Radiomics; Quality control; Contrast media; Liver neoplasms HEPATOCELLULAR-CARCINOMA; NONINVASIVE DIAGNOSIS; PORTAL-HYPERTENSION; IMATINIB MESYLATE; CIRRHOSIS; CRITERIA; CT; VALIDATION; SORAFENIB; ACCURACY Purpose: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC). Method: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [0-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D. Results: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms. Conclusions: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis. [Dercle, Laurent; Ma, Jingchen; Chen, Ai-ping; Luk, Lyndon; Lu, Lin; Schwartz, Lawrence H.; Mokrane, Fatima-Zohra; Zhao, Binsheng] Columbia Univ, Dept Radiol, Vagellos Coll Phys & Surg, New York, NY 10032 USA; [Dercle, Laurent; Ma, Jingchen; Chen, Ai-ping; Luk, Lyndon; Lu, Lin; Schwartz, Lawrence H.; Mokrane, Fatima-Zohra; Zhao, Binsheng] New York Presbyterian Hosp, Dept Radiol, New York, NY USA; [Xie, Chuanmiao; Wang, Deling] Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Radiol, Canc Ctr,State Key Lab Oncol South China, Guangzhou 510060, Peoples R China; [Revel-Mouroz, Paul; Otal, Philippe; Rousseau, Herve; Mokrane, Fatima-Zohra] Rangueil Univ Hosp, Radiol Dept, Toulouse, France; [Peron, Jean-Marie] Purpan Univ Hosp, Hepatol Dept, Toulouse, France Columbia University; NewYork-Presbyterian Hospital; State Key Lab Oncology South China; Sun Yat Sen University; CHU de Toulouse; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; CHU de Toulouse Dercle, L (corresponding author), Columbia Univ, New York Presbyterian Hosp, Dept Radiol, Med Ctr, 168th St, New York, NY 10032 USA. ld2752@cumc.columbia.edu Ma, Jingchen/HGA-8264-2022; Dercle, Laurent/C-9740-2018 Ma, Jingchen/0000-0002-7000-2277; Dercle, Laurent/0000-0002-1322-0710 National Cancer Institute (NCI) [U01 CA225431]; Fondation Philanthropia; Fondation Nuovo-Soldati; Alain Rahmouni French Society of Radiology-CERF 2018 National Cancer Institute (NCI)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Fondation Philanthropia; Fondation Nuovo-Soldati; Alain Rahmouni French Society of Radiology-CERF 2018 National Cancer Institute (NCI) supported this work in part by Grant U01 CA225431. The content is solely the responsibility of the authors and does not necessarily represent the views of the funding source. The work was partially funded by a grant from Fondation Philanthropia (LD), Fondation Nuovo-Soldati (LD), and through research grants from Alain Rahmouni French Society of Radiology-CERF 2018 (FZM). 31 7 8 0 8 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0720-048X 1872-7727 EUR J RADIOL Eur. J. Radiol. APR 2020.0 125 108850 10.1016/j.ejrad.2020.108850 0.0 9 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging KY2HJ 32070870.0 Green Accepted 2023-03-23 WOS:000522392200036 0
J Li, HJ; Leung, KS; Wong, MH; Ballester, PJ Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study BMC BIOINFORMATICS English Article Molecular docking; Binding affinity; Drug discovery; Machine learning PROTEIN-LIGAND-BINDING; OUT CROSS-VALIDATION; PDBBIND DATABASE; COMPLEXES; DISCOVERY; APPROPRIATE; LEAD Background: State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. Results: In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Conclusions: Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development. [Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China; [Ballester, Pedro J.] Wellcome Trust Genome Campus, European Bioinformat Inst, Cambridge, England; [Ballester, Pedro J.] Univ Aix Marseille, Canc Res Ctr Marseille, INSERM U1068, UM105,IPC, F-13009 Marseille, France Chinese University of Hong Kong; European Molecular Biology Laboratory (EMBL); Wellcome Trust Sanger Institute; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; UNICANCER; Institut Paoli-Calmette (IPC) Li, HJ (corresponding author), Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. jackyleehongjian@gmail.com Wong, Man Hon/GOH-0560-2022; Ballester, Pedro/A-1148-2008 Ballester, Pedro/0000-0002-4078-743X Chinese University of Hong Kong; GRF Grant from the Research Grants Council of Hong Kong SAR [414413]; Medical Research Council for a Methodology Research Fellowship [G0902106]; MRC [G0902106] Funding Source: UKRI; Medical Research Council [G0902106] Funding Source: researchfish Chinese University of Hong Kong(Chinese University of Hong Kong); GRF Grant from the Research Grants Council of Hong Kong SAR; Medical Research Council for a Methodology Research Fellowship(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) We gratefully acknowledge the Direct Grant from the Chinese University of Hong Kong, the GRF Grant (Project Reference 414413) from the Research Grants Council of Hong Kong SAR, the Inserm funding (P. J. B.), and the Medical Research Council for a Methodology Research Fellowship (Grant No. G0902106, awarded to P. J. B.). We thank Yang Cao for helping us reproduce the Cyscore results. 31 55 56 1 23 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics AUG 27 2014.0 15 291 10.1186/1471-2105-15-291 0.0 12 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology AN9DR 25159129.0 Green Published, gold 2023-03-23 WOS:000340906700001 0
J Kopciewicz, P; Akiba, KC; Szumlak, T; Sitko, S; Barter, W; Buytaert, J; Eklund, L; Hennessy, K; Koppenburg, P; Latham, T; Majewski, M; Oblakowska-Mucha, A; Parkes, C; Qian, WN; Velthuis, J; Williams, M Kopciewicz, Pawel; Akiba, Kazuyoshi Carvalho; Szumlak, Tomasz; Sitko, Sebastian; Barter, William; Buytaert, Jan; Eklund, Lars; Hennessy, Karol; Koppenburg, Patrick; Latham, Thomas; Majewski, Maciej; Oblakowska-Mucha, Agnieszka; Parkes, Chris; Qian, Wenbin; Velthuis, Jaap; Williams, Mark Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction SENSORS English Article particle tracking detectors; sensor optimization; readout systems The optimization of the Beetle readout ASIC and the performance of the software for the signal processing based on machine learning methods are presented. The Beetle readout chip was developed for the LHCb (Large Hadron Collider beauty) tracking detectors and was used in the VELO (Vertex Locator) during Run 1 and 2 of LHC data taking. The VELO, surrounding the LHC beam crossing region, was a leading part of the LHCb tracking system. The Beetle chip was used to read out the signal from silicon microstrips, integrating and amplifying it. The studies presented in this paper cover the optimization of its electronic configuration to achieve the lower power consumption footprint and the lower operational temperature of the sensors, while maintaining a good condition of the analogue response of the whole chip. The studies have shown that optimizing the operational temperature is possible and can be beneficial when the detector is highly irradiated. Even a single degree drop in silicon temperature can result in a significant reduction in the leakage current. Similar studies are being performed for the future silicon tracker, the Upstream Tracker (UT), which will start operating at LHC in 2021. It is expected that the inner part of the UT detector will suffer radiation damage similar to the most irradiated VELO sensors in Run 2. In the course of analysis we also developed a general approach for the pulse shape reconstruction using an ANN approach. This technique can be reused in case of any type of front-end readout chip. [Kopciewicz, Pawel; Szumlak, Tomasz; Sitko, Sebastian; Majewski, Maciej; Oblakowska-Mucha, Agnieszka] AGH Univ Sci & Technol, Dept Particle Interact & Detect Tech, Fac Phys & Appl Comp Sci, PL-30059 Krakow, Poland; [Akiba, Kazuyoshi Carvalho; Koppenburg, Patrick] Nikhef Natl Inst Subatom Phys, NL-1098 XG Amsterdam, Netherlands; [Barter, William] Imperial Coll, Dept Phys, London SW7 2AZ, England; [Buytaert, Jan] European Org Nucl Res CERN, CH-1211 Geneva, Switzerland; [Eklund, Lars] Univ Glasgow, Sch Phys & Astron, Glasgow G12 8QQ, Lanark, Scotland; [Eklund, Lars] Uppsala Univ, Dept Phys & Astron, S-75105 Uppsala, Sweden; [Hennessy, Karol] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 7ZE, Merseyside, England; [Latham, Thomas] Univ Warwick, Dept Phys, Warwick CV4 7AL, England; [Parkes, Chris] Univ Manchester, Sch Phys & Astron, Manchester M13 9PL, Lancs, England; [Qian, Wenbin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Velthuis, Jaap] Univ Bristol, HH Wills Phys Lab, Bristol BS8 1TH, Avon, England; [Williams, Mark] Univ Edinburgh, Sch Phys & Astron, Edinburgh EH8 9YL, Midlothian, Scotland AGH University of Science & Technology; FOM National Institute for Subatomic Physics; Imperial College London; European Organization for Nuclear Research (CERN); University of Glasgow; Uppsala University; University of Liverpool; University of Warwick; University of Manchester; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Bristol; University of Edinburgh Kopciewicz, P (corresponding author), AGH Univ Sci & Technol, Dept Particle Interact & Detect Tech, Fac Phys & Appl Comp Sci, PL-30059 Krakow, Poland. Pawel.Kopciewicz@fis.agh.edu.pl; Kazu.Akiba@cern.ch; Szumlak@agh.edu.pl; Sebastian.Sitko@fis.agh.edu.pl; William.Barter@cern.ch; Jan.Buytaert@cern.ch; Lars.Eklund@cern.ch; Karol.Hennessy@cern.ch; Patrick.Koppenburg@cern.ch; T.Latham@warwick.ac.uk; Maciej.Witold.Majewski@cern.ch; AMucha@agh.edu.pl; Chris.Parkes@cern.ch; Wenbin.Qian@cern.ch; Jaap.Velthuis@bris.ac.uk; Mark.Williams@cern.ch Szumlak, Tomasz/B-3131-2018 Szumlak, Tomasz/0000-0002-2562-7163; Majewski, Maciej/0000-0002-4658-6962; Williams, Mark/0000-0001-5448-4213; Oblakowska-Mucha, Agnieszka/0000-0003-1328-0534; Kopciewicz, Pawel/0000-0001-9092-3527 Polish National Science Center Preludium grant [UMO2020/37/N/ST2/04008]; CERN-Organisation Europeenne pour la Recherche Nucleaire; NVIDIA Corporation; MEiN [DIR/WK/2017/2020/04-1]; LHCb-Large Hadron Collider beauty; Science and Technology Facilities Council [ST/S000879/1] Funding Source: researchfish Polish National Science Center Preludium grant; CERN-Organisation Europeenne pour la Recherche Nucleaire; NVIDIA Corporation; MEiN; LHCb-Large Hadron Collider beauty; Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge support from Polish National Science Center Preludium grant (UMO2020/37/N/ST2/04008) and CERN-Organisation Europeenne pour la Recherche Nucleaire and LHCb-Large Hadron Collider beauty. We also acknowledge support from NVIDIA Corporation and MEiN DIR/WK/2017/2020/04-1. 38 1 1 0 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2021.0 21 18 6075 10.3390/s21186075 0.0 12 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation UW5CU 34577286.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000700174500001 0
J Mukherjee, M; Matam, R; Mavromoustakis, CX; Jiang, H; Mastorakis, G; Guo, M Mukherjee, Mithun; Matam, Rakesh; Mavromoustakis, Constandinos X.; Jiang, Hao; Mastorakis, George; Guo, Mian Intelligent Edge Computing: Security and Privacy Challenges IEEE COMMUNICATIONS MAGAZINE English Article AGGREGATION Edge computing has already shown its potential benefits to support the delay-sensitive and computation-intensive service provisioning in the Internet-of-Things-based environments. Simultaneously, artificial intelligence is expected to enhance the cognizance and intelligence of edge computing, resulting in a new paradigm, intelligent edge computing. However, as is evident, several new security and privacy-related issues will arise due to the distinct characteristics and working principles of intelligent edge computing. In this article, we focus on malicious attacks targeting the intelligent engines of edge computing. These attacks counteract the gains from intelligent systems and also compromise the edge computing system. We provide the main factors that are responsible for the security and privacy-related challenges in intelligent edge computing. Finally, the potential research directions in privacy and security-related issues are outlined for intelligent edge computing. [Mukherjee, Mithun; Jiang, Hao] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China; [Matam, Rakesh] Indian Inst Informat Technol Guwahati, Gauhati, India; [Mavromoustakis, Constandinos X.] Univ Nicosia, Comp Sci Dept, Nicosia, Cyprus; [Mavromoustakis, Constandinos X.] Univ Nicosia Res Fdn UNRF, Nicosia, Cyprus; [Mastorakis, George] Hellen Mediterranean Univ, Sitia, Greece; [Guo, Mian] Guangdong Univ Petrochem Technol, Maoming, Peoples R China Nanjing University of Information Science & Technology; University of Nicosia; University of Nicosia; Hellenic Mediterranean University; Guangdong University of Petrochemical Technology Guo, M (corresponding author), Guangdong Univ Petrochem Technol, Maoming, Peoples R China. Mavromoustakis, Constandinos/M-8305-2014 Mavromoustakis, Constandinos/0000-0003-0333-8034 National Natural Science Foundation of China [61901128]; Ambient Assisted Living (AAL) project vINCI: Clinically-validated INtegrated Support for Assistive Care and Lifestyle Improvement: the Human Link - Research Promotion Foundation in Cyprus under the AAL framework [vINCI/P2P/AAL/0217/0016]; Digital Ecosystem Utilisation (CySloP); IoF2020, European Union's Horizon 2020 research and innovation programme [731884] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ambient Assisted Living (AAL) project vINCI: Clinically-validated INtegrated Support for Assistive Care and Lifestyle Improvement: the Human Link - Research Promotion Foundation in Cyprus under the AAL framework; Digital Ecosystem Utilisation (CySloP); IoF2020, European Union's Horizon 2020 research and innovation programme This work was supported in part by the National Natural Science Foundation of China under Grant 61901128. Parts of this work were supported by the Ambient Assisted Living (AAL) project vINCI: Clinically-validated INtegrated Support for Assistive Care and Lifestyle Improvement: the Human Link funded by the Research Promotion Foundation in Cyprus under the AAL framework with Grant No. vINCI/P2P/AAL/0217/0016; and partly supported by Digital Ecosystem Utilisation (CySloP), which has received funding from IoF2020 (http://www.iof2020.eu), the European Union's Horizon 2020 research and innovation programme under grant agreement no. 731884. 15 7 8 7 49 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. SEP 2020.0 58 9 26 31 10.1109/MCOM.001.2000297 0.0 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications NY2VH 2023-03-23 WOS:000576253200006 0
J Zhang, MH; Yao, BZ; Gang, LH; Chen, G Zhang Mingheng; Yao Baozhen; Gang Longhui; Chen Gang Accurate Multisteps Traffic Flow Prediction Based on SVM MATHEMATICAL PROBLEMS IN ENGINEERING English Article SUPPORT-VECTOR-MACHINE; KALMAN FILTER Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM) are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction. [Zhang Mingheng] Dalian Univ Technol, Sch Automot Engn, Dalian 116024, Peoples R China; [Yao Baozhen] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China; [Gang Longhui] Dalian Maritime Univ, Sch Nav, Dalian 116026, Peoples R China; [Chen Gang] Aalborg Univ, Dept Mech & Mfg Engn, DK-169220 Aalborg, Denmark Dalian University of Technology; Dalian University of Technology; Dalian Maritime University; Aalborg University Zhang, MH (corresponding author), Dalian Univ Technol, Sch Automot Engn, Dalian 116024, Peoples R China. gloriazhang@163.com yao, Baozhen/I-9650-2014; Chen, Gang/E-1327-2015 Chen, Gang/0000-0002-3462-4060 Humanities and Social Sciences Foundation of the Ministry of Education in China [12YJCZH280]; Ph.D. Programs Foundation of Ministry of Education of China [20112125120004]; National Natural Science Foundation of China [11272075] Humanities and Social Sciences Foundation of the Ministry of Education in China; Ph.D. Programs Foundation of Ministry of Education of China(Ministry of Education, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was jointly supported by grants from the Humanities and Social Sciences Foundation of the Ministry of Education in China (Project no. 12YJCZH280), the Ph.D. Programs Foundation of Ministry of Education of China (Project no. 20112125120004), and National Natural Science Foundation of China (Project no. 11272075). 16 36 36 4 50 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. 2013.0 2013 418303 10.1155/2013/418303 0.0 8 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics 250BX gold, Green Published, Green Submitted 2023-03-23 WOS:000326826300001 0
J Heydari, M; Fan, YA; Li, XY; Zhou, XH; Lai, KK Heydari, Mohammad; Fan Yanan; Li, Xiaoyang; Zhou Xiaohu; Lai, Kin Keung Socio-educational overview between the fluctuation rate scale and the index changes in the stock exchange APUNTES UNIVERSITARIOS English Article Fluctuation scale; index changes; Tehran Stock Exchange; Wavelet model; educational guide ARTIFICIAL NEURAL-NETWORKS; LONG-TERM-MEMORY; CHAOS; MODEL; MARKETS; FAMA The objective of this study was to the relationship between the rate of fluctuation scale and index changes in Tehran Stock Exchange using the wavelet model. The method of this research is using data technique. One of the most widely used techniques in financial time series is neural network. Due to the comprehensiveness of this technique and the lack of some assumptions about the data, it has become more widespread compared to statistical data. However, noise in time series, especially in financial and economic time series, reduces the accuracy of neural network (NN) predictions. One method of descaling in time series is wavelet transform. The results showed that with constant inflation rate as a controlling variable of exchange rate fluctuation scales and stock price index, from 2005 to 2016, there is a negative and very coherent coherence in long-term scales. According to the results, it can be said that one of the most fundamental issues in terms of training guidelines is the economic situation and the adoption of investment strategies. Accordingly, this shows that in recent years, the long-term decline in the stock price index has reduced exchange rate fluctuations. The results of this research can be an educational guide for those who want to invest in the stock market. [Heydari, Mohammad; Li, Xiaoyang] Southwest Univ, Business Coll, Chongqing, Peoples R China; [Fan Yanan] Univ Wroclaw, Wroclaw, Poland; [Zhou Xiaohu] Nanjing Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Lai, Kin Keung] Shaanxi Normal Univ, Int Business Sch, Xian, Peoples R China Southwest University - China; University of Wroclaw; Nanjing University of Science & Technology; Shaanxi Normal University Heydari, M (corresponding author), Southwest Univ, Business Coll, Chongqing, Peoples R China. Mohammad_Heydari@njust.edu.cn Lai, Kin Keung/AEB-4278-2022 35 0 0 1 4 UNIV PERUANA UNION LIMA ALTURA KM 19 5 CARRETERA CENTRAL, NANA, LIMA, 00000, PERU 2225-7136 2304-0335 APUNT UNIV Apunt. Univ. OCT-DEC 2021.0 11 4 123 145 23 Education & Educational Research Emerging Sources Citation Index (ESCI) Education & Educational Research TE6PK 2023-03-23 WOS:000670132400007 0
J Heng, JL; Zheng, KF; Feng, XY; Veljkovic, M; Zhou, ZX Heng, Junlin; Zheng, Kaifeng; Feng, Xiaoyang; Veljkovic, Milan; Zhou, Zhixiang Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks ENGINEERING STRUCTURES English Article Orthotropic steel decks; Rib-to-deck joints; Probabilistic fatigue assessment; Gaussian process regression; dynamic Bayesian network CRACK GROWTH-BEHAVIOR; DETERIORATING SYSTEMS; RELIABILITY-ANALYSIS; WELDED-JOINTS; PREDICTION This study integrates the fatigue test and numerical prediction to derive a comprehensive probability-stress-life (P-S-N) curve for rib-to-deck (RD) welded joints in orthotropic steel decks. Fatigue tests of RD joints are con-ducted to measure fatigue strength and crack growth data. Based on the test, a probabilistic fatigue crack growth (PFCG) model is established to predict the distribution of fatigue life under various stress ranges. Two machine learning tools are adopted to assist the PFCG model-based prediction, i.e., the Gaussian process regression (GPR) and dynamic Bayesian network (DBN). The GPR is used to train a surrogate model solving stress intensity factors for the PFCG prediction, using 2,000 samples generated from finite element (FE) analyses. The trained model is then validated by a new dataset of 100 FE samples. An adapted DBN model is proposed to update the PFCG model with the fatigue crack growth data measured from ten specimens. According to the result, the application of GPR can reduce the solution cost of the PFCG prediction by approximately 1,875 times. Compared with the prior PFCG model, the updated posterior model shows an improved agreement with the test data, i.e., the maximum difference in fatigue strength between model prediction and test data decreases from 12% to 3%. Based on the posterior PFCG model, the P-S-N curve of RD joints is statistically derived using sufficient numerical samples. [Heng, Junlin; Zhou, Zhixiang] Shenzhen Univ, Coll Civil & Transportat Engn, Dept Civil Engn, Shenzhen 518060, Peoples R China; [Heng, Junlin; Veljkovic, Milan] Delft Univ Technol, Fac Civil & Environm Engn, Dept Engn Struct, NL-2628 CN Delft, Netherlands; [Zheng, Kaifeng; Feng, Xiaoyang] Southwest Jiaotong Univ, Sch Civil Engn, Dept Bridge Engn, Chengdu 610031, Peoples R China Shenzhen University; Delft University of Technology; Southwest Jiaotong University Zhou, ZX (corresponding author), Shenzhen Univ, Coll Civil & Transportat Engn, Dept Civil Engn, Shenzhen 518060, Peoples R China. zhixiangzhou@szu.edu.cn Veljkovic, Milan/0000-0003-4791-2341 National Natural Science Foundation of China [51778536]; Shenzhen Key Laboratory of Structure Safety and Health Monitoring of Marine Infrastructures [ZDSYS20201020162400001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Key Laboratory of Structure Safety and Health Monitoring of Marine Infrastructures Acknowledgement The study is supported by the National Natural Science Foundation of China (grant number: 51778536) and Shenzhen Key Laboratory of Structure Safety and Health Monitoring of Marine Infrastructures (Inpreparation, grant number: ZDSYS20201020162400001) . 68 4 4 11 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0141-0296 1873-7323 ENG STRUCT Eng. Struct. AUG 15 2022.0 265 114496 10.1016/j.engstruct.2022.114496 0.0 JUN 2022 14 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering 2M7EH Green Published 2023-03-23 WOS:000817858000002 0
J Deng, TC; Li, YG; Liu, X; Wang, LH Deng, Tianchi; Li, Yingguang; Liu, Xu; Wang, Lihui Federated learning-based collaborative manufacturing for complex parts JOURNAL OF INTELLIGENT MANUFACTURING English Article; Early Access Complex parts machining; Federated learning; Machining parameter planning; Domain adaptation The manufacturing of complex parts, such as aircraft structural parts and aero-engine casing parts, has always been one of the focuses in the manufacturing field. The machining process involves a variety of hard problems (e.g. tool wear prediction, smart process planning), which require assumptions, simplifications and approximations during the mechanism-based modelling. For these problems, supervised machine learning methods have achieved good results by fitting input-output relations from plenty of labelled data. However, the data acquisition is difficult, time consuming, and of high cost, thus the amount of data in a single enterprise is often limited. To address this issue, this research aims to realise the equivalent manufacturing data sharing based on federated learning (FL), which is a new machine learning framework to use the scattered data while protecting the data privacy. An enterprise-oriented framework is first proposed to find FL participants with similar data resources. Then, the machining parameter planning task for aircraft structural parts is taken as an example to propose an FL model, which mines the knowledge and rules in the historical processing files from multiple enterprises. In addition, to solve the data difference among enterprises, domain adaptation method in transfer learning is used to obtain domain-invariant features. In the case study, a prototype platform is developed, and to validate the performance of the proposed model, a data set is built based on the historical processing files from three aircraft manufacturing enterprises. The proposed model achieves the best performance compared with the model trained only with the data from a single enterprise, and the model without domain adaptation. [Deng, Tianchi; Li, Yingguang] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Peoples R China; [Liu, Xu] Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden Nanjing University of Aeronautics & Astronautics; Nanjing Tech University; Royal Institute of Technology Li, YG (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Peoples R China. liyingguang@nuaa.edu.cn Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049; Li, Yingguang/0000-0003-4425-8073 National Natural Science Foundation of China [51925505, U21B2081] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Funding was provided by National Natural Science Foundation of China (Grant Nos. 51925505, U21B2081). 28 1 1 15 17 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0956-5515 1572-8145 J INTELL MANUF J. Intell. Manuf. 10.1007/s10845-022-01968-3 0.0 AUG 2022 14 Computer Science, Artificial Intelligence; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3N5AV 2023-03-23 WOS:000836160900001 0
J Wu, YZ; Wu, DH; Fei, MH; Sorensen, H; Ren, Y; Mou, JG Wu, Yuezhong; Wu, Denghao; Fei, Minghao; Sorensen, Henrik; Ren, Yun; Mou, Jiegang Application of GA-BPNN on estimating the flow rate of a centrifugal pump ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE English Article Centrifugal pump; Flow rate estimation; Genetic algorithm; BPNN; Flow sensorless estimation NEURAL-NETWORK; GENETIC ALGORITHM; MODEL; SELECTION; PREDICTION; PARAMETERS Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps' monitoring and control technology without flow sensors. [Wu, Yuezhong; Wu, Denghao; Fei, Minghao; Mou, Jiegang] China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou 310018, Peoples R China; [Wu, Denghao; Sorensen, Henrik] Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark; [Ren, Yun] Zhejiang Univ Technol, Zhijiang Coll, Shaoxing 312030, Peoples R China China Jiliang University; Aalborg University; Zhejiang University of Technology Wu, DH (corresponding author), China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou 310018, Peoples R China. wdh@cjlu.edu.cn Sorensen, Henrik/0000-0003-1462-6314 Natural Science Foundation of Zhejiang Province; China Post-doctoral Science Foundation; Science and Technology Program of Zhejiang Province; [LGG21E090002]; [LY21E060004]; [2021M691383]; [2021C01052] Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); China Post-doctoral Science Foundation(China Postdoctoral Science Foundation); Science and Technology Program of Zhejiang Province; ; ; ; Funding This research was funded by Natural Science Foundation of Zhejiang Province (Grant No. LGG21E090002, LY21E060004) , the China Post-doctoral Science Foundation (Grant No. 2021M691383) , and Science and Technology Program of Zhejiang Province (Grant No. 2021C01052) . 38 0 0 8 8 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0952-1976 1873-6769 ENG APPL ARTIF INTEL Eng. Appl. Artif. Intell. MAR 2023.0 119 105738 10.1016/j.engappai.2022.105738 0.0 DEC 2022 13 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 7P7DS 2023-03-23 WOS:000908862100001 0
J Cai, X; Liu, FC; Yu, AR; Qin, JJ; Hatamvand, M; Ahmed, I; Luo, JY; Zhang, YM; Zhang, H; Zhan, YQ Cai, Xia; Liu, Fengcai; Yu, Anran; Qin, Jiajun; Hatamvand, Mohammad; Ahmed, Irfan; Luo, Jiayan; Zhang, Yiming; Zhang, Hao; Zhan, Yiqiang Data-driven design of high-performance MASn(x)Pb(1-x)I(3) perovskite materials by machine learning and experimental realization LIGHT-SCIENCE & APPLICATIONS English Article SOLAR-CELLS; HALIDE PEROVSKITES; TIN TRIIODIDE; EFFICIENCY; PHOTOVOLTAICS; 24.8-PERCENT The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASn(x)Pb(1-x)I(3) perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and opencircuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn (4+ )content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development. [Cai, Xia; Liu, Fengcai; Yu, Anran; Hatamvand, Mohammad; Ahmed, Irfan; Luo, Jiayan; Zhang, Yiming; Zhang, Hao; Zhan, Yiqiang] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Cai, Xia] Shanghai Normal Univ, Coll Informat Mech & Elect Engn, Shanghai 200234, Peoples R China; [Cai, Xia; Liu, Fengcai; Yu, Anran; Hatamvand, Mohammad; Ahmed, Irfan; Luo, Jiayan; Zhan, Yiqiang] Fudan Univ, Ctr Micronano Syst, Shanghai 200433, Peoples R China; [Qin, Jiajun] Linkoping Univ, Dept Phys Chem & Biol, SE-58183 Linkoping, Sweden; [Zhang, Yiming; Zhang, Hao] Fudan Univ, Key Lab Micro & Nano Photon Struct, Shanghai 200433, Peoples R China; [Zhang, Yiming; Zhang, Hao] Fudan Univ, Dept Opt Sci & Engn, Shanghai 200433, Peoples R China; [Zhang, Hao] Fudan Univ, Yiwu Res Inst, Chengbei Rd, Yiwu City 322000, Zhejiang, Peoples R China Fudan University; Shanghai Normal University; Fudan University; Linkoping University; Fudan University; Fudan University; Fudan University Zhang, H; Zhan, YQ (corresponding author), Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.;Zhan, YQ (corresponding author), Fudan Univ, Ctr Micronano Syst, Shanghai 200433, Peoples R China.;Zhang, H (corresponding author), Fudan Univ, Key Lab Micro & Nano Photon Struct, Shanghai 200433, Peoples R China.;Zhang, H (corresponding author), Fudan Univ, Dept Opt Sci & Engn, Shanghai 200433, Peoples R China.;Zhang, H (corresponding author), Fudan Univ, Yiwu Res Inst, Chengbei Rd, Yiwu City 322000, Zhejiang, Peoples R China. zhangh@fudan.edu.cn; yqzhan@fudan.edu.cn Ahmed, Irfan/HHD-1948-2022; Zhan, Yiqiang/A-6687-2008; AHMED, IRFAN/AFW-6944-2022; Zhang, Yiming/HGB-7344-2022 Zhan, Yiqiang/0000-0001-8391-2555; Qin, Jiajun/0000-0002-9956-2358 National Natural Science Foundation of China [61774046, 11374063]; Shanghai Municipal Natural Science Foundation [19ZR1402900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Natural Science Foundation This work is supported by the National Natural Science Foundation of China (grant numbers 61774046 and 11374063), and by Shanghai Municipal Natural Science Foundation under Grant Nos. 19ZR1402900. 56 0 0 16 25 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2095-5545 2047-7538 LIGHT-SCI APPL Light-Sci. Appl. JUL 26 2022.0 11 1 234 10.1038/s41377-022-00924-3 0.0 12 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 3F5MD 35882845.0 gold, Green Published 2023-03-23 WOS:000830712300003 0
J Zhang, J; Yue, M; Hu, Y; Bergquist, R; Su, C; Gao, FH; Cao, ZG; Zhang, ZJ Zhang, Jun; Yue, Ming; Hu, Yi; Bergquist, Robert; Su, Chuan; Gao, Fenghua; Cao, Zhi-Guo; Zhang, Zhijie Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches PLOS NEGLECTED TROPICAL DISEASES English Article SCHISTOSOMIASIS CONTROL; ONCOMELANIA-HUPENSIS; TRANSMISSION; MANSONI Elimination of the intermediate snail host of Schistosoma is the most effective way to control schistosomiasis and the most important first step is to accurately identify the snail habitats. Due to the substantial resources required for traditional, manual snail-searching in the field, and potential risk of miss-classification of potential snail habitats by remote sensing, more convenient and precise methods are urgently needed. Snail data (N = 15,000) from two types of snail habitats (lake/marshland and hilly areas) in Anhui Province, a typical endemic area for schistosomiasis, were collected together with 36 environmental variables covering the whole province. Twelve different models were built and evaluated with indices, such as area under the curve (AUC), Kappa, percent correctly classified (PCC), sensitivity and specificity. We found the presence-absence models performing better than those based on presence-only. However, those derived from machine-learning, especially the random forest (RF) approach were preferable with all indices above 0.90. Distance to nearest river was found to be the most important variable for the lake/marshlands, while the climatic variables were more important for the hilly endemic areas. The predicted high-risk areas for potential snail habitats of the lake/marshland type exist mainly along the Yangtze River, while those of the hilly type are dispersed in the areas south of the Yangtze River. We provide here the first comprehensive risk profile of potential snail habitats based on precise examinations revealing the true distribution and habitat type, thereby improving efficiency and accuracy of snail control including better allocation of limited health resources. Author summary Schistosomiasis is a parasitic disease caused by parasitic worms of the genus Schistosoma. In China, the sole intermediate snail host is Oncomelania hupensis whose elimination has proved to be the most effective way to interrupt this disease. However, manual snail-searching is labour-intensive, expensive and time-consuming and can lead to inaccurate results. For a better approach, 12 models were employed and compared to characterise the typical snail habitats that vary between the lake/marshlands and the hilly areas. We found that the two types of snail habitats showed notable differences during the modelling process, mainly due to the impact of environmental variables that can form different types of habitats. We further found that habitat characterization contributed to better prediction of areas at risk, and that the precision was high, especially of models based on machine-learning algorithms such as random forest (RF). The highest level of accuracy was achieved by the support vector machine (SVM) approach and artificial neural networks (ANN). Our study provides new insights into accurate prediction of the spatial distribution of potential snail habitats with machine-learning as the preferred approach. [Zhang, Jun; Hu, Yi; Zhang, Zhijie] Fudan Univ, Sch Publ Hlth, Minist Educ, Dept Epidemiol & Biostat,Key Lab Public Hlth Safe, Shanghai, Peoples R China; [Yue, Ming] Nanjing Med Univ, Dept Infect Dis, Affiliated Hosp 1, Nanjing, Jiangsu, Peoples R China; [Bergquist, Robert] Ingerod, Brastad, Sweden; [Su, Chuan] Nanjing Med Univ, Ctr Global Hlth, Jiangsu Key Lab Pathogen Biol, Dept Pathogen Biol & Immunol, Nanjing, Jiangsu, Peoples R China; [Gao, Fenghua; Cao, Zhi-Guo] Anhui Inst Schistosomiasis Control, Hefei, Anhui, Peoples R China Fudan University; Nanjing Medical University; Nanjing Medical University Zhang, ZJ (corresponding author), Fudan Univ, Sch Publ Hlth, Minist Educ, Dept Epidemiol & Biostat,Key Lab Public Hlth Safe, Shanghai, Peoples R China. epistat@gmail.com 岳, 明/AAX-5776-2021 岳, 明/0000-0002-7677-0832; zhang, zhijie/0000-0002-1276-787X National Natural Science Foundation of China [81673239, 81973102, 81773487] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported by the National Natural Science Foundation of China (81673239, 81973102, 81773487). The funder had no role in study design, data collection and analysis, decision to publish and preparation of the manuscript. 40 6 6 2 13 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1935-2735 PLOS NEGLECT TROP D Plos Neglect. Trop. Dis. APR 2020.0 14 4 e0008178 10.1371/journal.pntd.0008178 0.0 13 Infectious Diseases; Parasitology; Tropical Medicine Science Citation Index Expanded (SCI-EXPANDED) Infectious Diseases; Parasitology; Tropical Medicine MX9YW 32251421.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000558074700040 0
J Kang, J; Fernandez-Beltran, R; Ye, Z; Tong, XH; Ghamisi, P; Plaza, A Kang, Jian; Fernandez-Beltran, Ruben; Ye, Zhen; Tong, Xiaohua; Ghamisi, Pedram; Plaza, Antonio Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Measurement; Semantics; Remote sensing; Feature extraction; Training; Complexity theory; Encoding; Deep learning; dimensionality reduction; metric learning; remote sensing (RS) scene characterization CLASSIFICATION; CHALLENGES With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community. [Kang, Jian] Tech Univ Berlin, Fac Elect Engn & Comp Sci, D-10587 Berlin, Germany; [Fernandez-Beltran, Ruben] Univ Jaume 1, Inst New Imaging Technol, Castellon De La Plana 12071, Spain; [Ye, Zhen; Tong, Xiaohua] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf, Explorat Div, Machine Learning Grp, Helmholtz Inst Freiberg Resource Technol, D-09599 Freiberg, Germany; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10003, Spain Technical University of Berlin; Universitat Jaume I; Tongji University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Universidad de Extremadura Ye, Z (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. jian.kang@tu-berlin.de; rufernan@uji.es; yezhen0402@126.com; xhtong@tongji.edu.cn; p.ghamisi@gmail.com; aplaza@unex.es Plaza, Antonio/C-4455-2008; kang, jian/V-3055-2019; Fernandez-Beltran, Ruben/GLT-5907-2022; Ghamisi, Pedram/ABD-5419-2021 Plaza, Antonio/0000-0002-9613-1659; kang, jian/0000-0001-6284-3044; Ye, Zhen/0000-0002-7178-9817 National Key Research and Development Project of China [2018YFB0505400, 2017YFB0502700]; National Natural Science Foundation of China [41631178]; Spanish Ministry of Economy [RTI2018-098651-B-C54]; FEDER-Junta de Extremadura [GR18060]; European Union [734541] National Key Research and Development Project of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Economy(Spanish Government); FEDER-Junta de Extremadura(European Commission); European Union(European Commission) This work was supported in part by the National Key Research and Development Project of China under Grant 2018YFB0505400 and Grant 2017YFB0502700, in part by the National Natural Science Foundation of China under Grant 41631178, in part by the Spanish Ministry of Economy under Grant RTI2018-098651-B-C54, in part by FEDER-Junta de Extremadura under Grant GR18060, and in part by the European Union under the H2020 EOXPOSURE Project under Grant 734541. 62 43 43 2 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing DEC 2020.0 58 12 8905 8918 10.1109/TGRS.2020.2991657 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology OY6YF Green Published 2023-03-23 WOS:000594389800049 0
J Tang, J; Yang, YH; Blaabjerg, F; Chen, J; Diao, LJ; Liu, ZG Tang, Jing; Yang, Yongheng; Blaabjerg, Frede; Chen, Jie; Diao, Lijun; Liu, Zhigang Parameter Identification of Inverter-Fed Induction Motors: A Review ENERGIES English Review induction motor; parameter identification; offline parameter identification; online parameter identification; recursive least square; model reference adaptive system; signal injection; extend Luenberger observer; sliding mode observer; extend Kalman observer; artificial intelligence ROTOR TIME-CONSTANT; RESISTANCE ESTIMATION SCHEME; ARTIFICIAL NEURAL-NETWORKS; SLIDING MODE OBSERVER; TEMPERATURE ESTIMATION; AC DRIVES; GENETIC ALGORITHMS; SENSORLESS CONTROL; ADAPTIVE SCHEME; TORQUE CONTROL Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods. [Tang, Jing; Chen, Jie; Diao, Lijun; Liu, Zhigang] Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China; [Yang, Yongheng; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Liu, Zhigang] Beijing Engn Res Ctr Elect Rail Transit, Beijing 100044, Peoples R China Beijing Jiaotong University; Aalborg University Tang, J (corresponding author), Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China. 15117396@bjtu.edu.cn; yoy@et.aau.dk; fbl@et.aau.dk; jiechen@bjtu.edu.cn; ljdiao@bjtu.edu.cn; zhgliu@bjtu.edu.cn Yang, Yongheng/N-7735-2015; Blaabjerg, Frede/A-5008-2008 Yang, Yongheng/0000-0002-1488-4762; Blaabjerg, Frede/0000-0001-8311-7412 Fundamental Research Funds for the Central Universities [2017YJS187, 2018JBZ004] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was funded by the Fundamental Research Funds for the Central Universities (2017YJS187, 2018JBZ004). 69 22 22 7 35 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies SEP 2018.0 11 9 2194 10.3390/en11092194 0.0 21 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels GW1BL gold, Green Published 2023-03-23 WOS:000446604500015 0
J Zhou, JP; Murphy, E Zhou, Jiangping; Murphy, Enda Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia JOURNAL OF TRANSPORT GEOGRAPHY English Article Excess commuting; Indicator; Variation; Big data; Temporal; Brisbane JOBS-HOUSING BALANCE; EFFICIENCY; IMPACTS; WORKERS Commuting patterns where most if not all 'trips are optimised relative to a given distribution of jobs and housing can result in personal and socio-economic benefits. Excess commuting indicators provide useful information for academics and policy analysts to evaluate how the actual commuting pattern deviates from an optimal pattern where commuting costs are minimised. While actual commuting patterns vary from day-to-day, academic researchers have yet to quantify the temporal variation in these indicators over short time periods. This may be due to the lack of available longitudinal data as input for excess commuting indicators. This study shows that new, open and/or big data (NOBD) (e.g. smartcard data) can be exploited to serve as the input for such analysis. In this regard, our study uses half a year's worth of smartcard data from Brisbane, Australia to first derive/aggregate origins and destinations by small areas of the probable commuting trips by transit on all 122 weekdays over the study period. The study quantifies the day-to-day variation in excess commuting indicators for these trips and finds that excess commuting indicators vary considerably from one day to the next. Nevertheless, daily variations occur within a relatively consistent range which can be planned for. Our research suggests that more conscious and systematic utilisation of NOBD could change how commuting flows in cities are quantified, monitored and planned. In addition, with NOBD, we can more efficiently detect daily outliers in commuting patterns. [Zhou, Jiangping] Univ Hong Kong, Dept Urban Planning & Design, Fac Architecture, Hong Kong, Peoples R China; [Murphy, Enda] Univ Coll Dublin, Sch Architecture Planning & Environm Policy, Dublin, Ireland University of Hong Kong; University College Dublin Zhou, JP (corresponding author), Univ Hong Kong, Dept Urban Planning & Design, Fac Architecture, Hong Kong, Peoples R China. zhoujp@hku.hk; enda.murphy@ucd.ie Zhou, Jiangping/AAA-9772-2020 Zhou, Jiangping/0000-0002-1623-5002 42 14 18 5 34 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0966-6923 1873-1236 J TRANSP GEOGR J. Transp. Geogr. JAN 2019.0 74 223 232 10.1016/j.jtrangeo.2018.11.014 0.0 10 Economics; Geography; Transportation Social Science Citation Index (SSCI) Business & Economics; Geography; Transportation HM5MM 2023-03-23 WOS:000459519800020 0
C Bermejo, C; Braud, T; Yang, J; Mirjafari, S; Shi, BW; Xiao, Y; Hui, P ASSOC COMP MACHINERY Bermejo, Carlos; Braud, Tristan; Yang, Ji; Mirjafari, Shayan; Shi, Bowen; Xiao, Yu; Hui, Pan VIMES: A Wearable Memory Assistance System for Automatic Information Retrieval MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA English Proceedings Paper 28th ACM International Conference on Multimedia (MM) OCT 12-16, 2020 ELECTR NETWORK ACM SIGMM,Assoc Comp Machinery Augmented reality; wearable; video; face recognition; event detection; memory assistance AUTOBIOGRAPHICAL MEMORY; SENSECAM; RECOGNITION; DEMENTIA; CAMERA; AIDS The advancement of artificial intelligence and wearable computing triggers the radical innovation of cognitive applications. In this work, we propose VIMES, an augmented reality-based memory assistance system that helps recall declarative memory, such as whom the user meets and what they chat. Through a collaborative method with 20 participants, we design VIMES, a system that runs on smartglasses, takes the first-person audio and video as input, and extracts personal profiles and event information to display on the embedded display or a smartphone. We perform an extensive evaluation with 50 participants to show the effectiveness of VIMES for memory recall. VIMES outperforms (90% memory accuracy) other traditional methods such as self-recall (34%) while offering the best memory experience (Vividness, Coherence, and Visual Perspective all score over 4/5). The user study results show that most participants find VIMES useful (3.75/5) and easy to use (3.46/5). [Bermejo, Carlos; Braud, Tristan; Yang, Ji; Shi, Bowen; Hui, Pan] HKUST, Hong Kong, Peoples R China; [Mirjafari, Shayan] Dartmouth Coll, Hanover, NH 03755 USA; [Xiao, Yu] Aalto Univ, Espoo, Finland; [Hui, Pan] Univ Helsinki, Helsinki, Finland Hong Kong University of Science & Technology; Dartmouth College; Aalto University; University of Helsinki Bermejo, C (corresponding author), HKUST, Hong Kong, Peoples R China. cbf@cse.ust.hk; braudt@ust.hk; jyangaa@cse.ust.hk; shayan@cs.dartmouth.edu; bshi@cse.ust.hk; yu.xiao@aalto.fi; panhui@cse.ust.hk Mirjafari, Shayan/0000-0002-7165-2781; Xiao, Yu/0000-0002-4517-3779 Research Grants Council of Hong Kong [16214817]; Academy of Finland Research Grants Council of Hong Kong(Hong Kong Research Grants Council); Academy of Finland(Academy of Finland) This research has been supported in part by project 16214817 from the Research Grants Council of Hong Kong,and the 5GEAR and FIT projects from Academy of Finland. 62 2 2 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7988-5 2020.0 3191 3200 10.1145/3394171.3413663 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BT2LS Green Accepted 2023-03-23 WOS:000810735003029 0
J Zhang, TP; Song, YZ; Kong, ZJ; Guo, TT; Lopez-Benitez, M; Lim, E; Ma, F; Yu, LM Zhang, Tongpo; Song, Yunze; Kong, Zejian; Guo, Tiantian; Lopez-Benitez, Miguel; Lim, Enggee; Ma, Fei; Yu, Limin Mobile Robot Tracking with Deep Learning Models under the Specific Environments APPLIED SCIENCES-BASEL English Article deep learning (DL); computer vision; robot tracking FASTER R-CNN; RECOGNITION Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design. [Zhang, Tongpo; Song, Yunze; Kong, Zejian; Guo, Tiantian; Lim, Enggee; Yu, Limin] Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China; [Lopez-Benitez, Miguel] Univ Liverpool, Elect Engn & Elect, Liverpool L69, Merseyside, England; [Lopez-Benitez, Miguel] Antonio Nebrija Univ, ARIES Res Ctr, Madrid 28015, Spain; [Ma, Fei] Xian Jiaotong Liverpool Univ, Sch Sci, Suzhou 215123, Peoples R China Xi'an Jiaotong-Liverpool University; University of Liverpool; Universidad Antonio de Nebrija; Xi'an Jiaotong-Liverpool University Yu, LM (corresponding author), Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China. limin.yu@xjtlu.edu.cn Yu, Limin/0000-0002-6891-0604 Research Enhancement Fund of XJTLU [REF-19-01-04]; National Natural Science Foundation of China (NSFC) [61501380]; AI University Research Center (AI-URC); XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund [KSF-P-02]; Jiangsu Data Science and Cognitive Computational Engineering Research Centre; Suzhou Key Lab of Broadband wireless Access Technology (BWAT); ARIES Research Centre Research Enhancement Fund of XJTLU; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); AI University Research Center (AI-URC); XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund; Jiangsu Data Science and Cognitive Computational Engineering Research Centre; Suzhou Key Lab of Broadband wireless Access Technology (BWAT); ARIES Research Centre This research was partially funded by the Research Enhancement Fund of XJTLU (REF-19-01-04), the National Natural Science Foundation of China (NSFC) (Grant No. 61501380), and by AI University Research Center (AI-URC) and XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund (KSF-P-02), Jiangsu Data Science and Cognitive Computational Engineering Research Centre, and ARIES Research Centre and Suzhou Key Lab of Broadband wireless Access Technology (BWAT). 32 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JAN 2023.0 13 1 273 10.3390/app13010273 0.0 17 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 7R3GC gold 2023-03-23 WOS:000909963000001 0
J Li, L; Chen, SW; Yang, CF; Meng, FJ; Sigrimis, N Li, Li; Chen, Shiwang; Yang, Chengfei; Meng, Fanjia; Sigrimis, Nick Prediction of plant transpiration from environmental parameters and relative leaf area index using the random forest regression algorithm JOURNAL OF CLEANER PRODUCTION English Article Relative leaf area index; Image processing; Random forest; Greenhouse irrigation; Save energy CROP; WATER; NETWORKS; IMAGES The accurate prediction of plant transpiration is essential for automatic precision irrigation in greenhouses; thus, an accurate transpiration prediction model is needed. The main objective of this study was to establish a plant transpiration prediction model by integrating plant and environmental parameters based on random forest regression (RFR). The relative leaf area index (RLAI) was selected as the plant parameter, which could be obtained in real time by image processing. The environmental parameters included air temperature, relative humidity and illumination intensity, which were obtained in real time by sensors. More than 500 samples were collected in each of the seedling and florescence phases, of which 70% were used for training, and 30% were used for testing the RFR model. The performance of the RFR model was then compared with that of a BP neural network and a GA-BP neural network. The results showed that the RFR model significantly outperformed the other models, achieving the highest prediction accuracy (R2 = 0.9472 and 0.9654) and smallest error (RMSE = 19.75 g and 18.85 g) among the test sets. In addition, the results confirmed the importance of all the parameters used to predict plant transpiration. Among the parameters, illumination intensity had the greatest influence on the mean square of the prediction error rate, and RLAI had the smallest influence. However, RLAI was very important for improving the prediction accuracy of the RFR model and achieving model significance. This research provides a scientific reference for the efficient production and intelligent irrigation of greenhouse tomatoes, providing a way to conserve water and energy. (C) 2020 Elsevier Ltd. All rights reserved. [Li, Li; Meng, Fanjia] China Agr Univ, Key Lab Modern Precis Syst Integrat Res, Minist Educ, Beijing 100083, Peoples R China; [Chen, Shiwang; Yang, Chengfei] China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr, Beijing 100083, Peoples R China; [Sigrimis, Nick] Agr Univ Athens, Dept Agr Engn, Athens 11855, Greece China Agricultural University; China Agricultural University; Ministry of Agriculture & Rural Affairs; Agricultural University of Athens Meng, FJ (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China. lily@cau.edu.cn; csw112@126.com; mengfanjia@126.com; ns@aua.gr National Key Research and Development Program of China [2019YFD1001903]; Yunnan Academician Expert Workstation [LJGZZ-2018001] National Key Research and Development Program of China; Yunnan Academician Expert Workstation This work was supported by the National Key Research and Development Program of China [project number 2019YFD1001903] and Yunnan Academician Expert Workstation (Wang Maohua, in Li jiang) [project number LJGZZ-2018001]. 44 17 19 7 56 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. JUL 10 2020.0 261 121136 10.1016/j.jclepro.2020.121136 0.0 13 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology LO3NY 2023-03-23 WOS:000533536800004 0
C Jing, XM; Lin, SF; Huang, ZS Siuly, S; Wang, H; Chen, L; Guo, Y; Xing, C Jing, Xiaomin; Lin, Shaofu; Huang, Zhisheng Research on Suicide Identification Method Based on Microblog Tree Hole Crowd HEALTH INFORMATION SCIENCE, HIS 2021 Lecture Notes in Computer Science English Proceedings Paper 10th International Conference on Health Information Science (HIS) OCT 25-28, 2021 Melbourne, AUSTRALIA Suicide; Microblog tree hole; Temporal characteristics; Suicide identification; Monitoring and rescue Suicide has always been a key issue of social and health organizations and research of scholars. In recent years, with the increasing popularity of Internet and social media, more and more people record their lives and feelings in social media, even more, they publish suicide speeches, especially in the youth. The microblog tree hole is generated because a depressed patient has left, a large number of potential depression patients or suicide prone people to leave messages under her account until today, including venting negative emotions, suicide messages and even committing suicide together. In view of this situation, the paper analyzes the actual needs and business scenarios of monitoring and identifying online depression suicide messages. Based on the data of tree hole message on microblog, this paper designs and implements a multi-channel convolutional neural network social suicide warning model. By mining the time of message of tree hole people in microblog, the feature matrix is obtained after quantifying some knowledge elements; After word orientation quantization, word vector matrix and word vector matrix are obtained. Feature matrix and text vector matrix are used as three inputs of convolutional neural network respectively to identify and warn social suicide. The model is proved to be better by experiments and improve the accuracy of early warning. [Jing, Xiaomin; Lin, Shaofu] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands; [Huang, Zhisheng] Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China Beijing University of Technology; Vrije Universiteit Amsterdam; Capital Medical University Lin, SF (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China. linshaofu@bjut.edu.cn 10 0 0 1 10 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-90885-0; 978-3-030-90884-3 LECT NOTES COMPUT SC 2021.0 13079 141 149 10.1007/978-3-030-90885-0_13 0.0 9 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Primary Health Care Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; General & Internal Medicine BS7CX 2023-03-23 WOS:000758752400013 0
J Sousa, JJ; Liu, G; Fan, JH; Perski, Z; Steger, S; Bai, SB; Wei, LH; Salvi, S; Wang, Q; Tu, JA; Tong, LQ; Mayrhofer, P; Sonnenschein, R; Liu, SJ; Mao, YC; Tolomei, C; Bignami, C; Atzori, S; Pezzo, G; Wu, LX; Yan, SY; Peres, E Sousa, Joaquim J.; Liu, Guang; Fan, Jinghui; Perski, Zbigniew; Steger, Stefan; Bai, Shibiao; Wei, Lianhuan; Salvi, Stefano; Wang, Qun; Tu, Jienan; Tong, Liqiang; Mayrhofer, Peter; Sonnenschein, Ruth; Liu, Shanjun; Mao, Yachun; Tolomei, Cristiano; Bignami, Christian; Atzori, Simone; Pezzo, Giuseppe; Wu, Lixin; Yan, Shiyong; Peres, Emanuel Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques REMOTE SENSING English Article geohazards; landslide detection; landslide inventory; remote sensing; InSAR; MT-InSAR; offset tracking; change vector analysis; land cover change SYNTHETIC-APERTURE RADAR; DIFFERENTIAL SAR INTERFEROMETRY; SLOW-MOVING LANDSLIDES; GLACIER MOTION; PERMANENT SCATTERERS; LAND SUBSIDENCE; TRACKING METHOD; INSAR; DISPLACEMENT; DEFORMATION Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere-both in remote and/or in highly populated areas-and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology-especially satellite-based-can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course. [Sousa, Joaquim J.; Peres, Emanuel] Univ Tras Os Montes & Alto Douro, Sch Sci & Technol, Engn Dept, P-5000801 Vila Real, Portugal; [Sousa, Joaquim J.; Peres, Emanuel] INESC Technol & Sci, Ctr Robot Ind & Intelligent Syst CRIIS, P-4200465 Porto, Portugal; [Liu, Guang] Chinese Acad Sci, Key Lab Digital Earth Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Fan, Jinghui; Tu, Jienan; Tong, Liqiang] China Aero Geophys Survey, Beijing 100083, Peoples R China; [Fan, Jinghui; Tu, Jienan; Tong, Liqiang] Remote Sensing Ctr Nat Resources, Beijing 100083, Peoples R China; [Perski, Zbigniew] Natl Res Inst, Polish Geol Inst, Carpathian Branch, PL-31560 Krakow, Poland; [Steger, Stefan; Mayrhofer, Peter; Sonnenschein, Ruth] Eurac Res, Inst Earth Observat, I-39100 Bolzano, Italy; [Bai, Shibiao] Nanjing Normal Univ, Coll Marine Sci & Engn, Nanjing 210046, Peoples R China; [Wei, Lianhuan; Liu, Shanjun; Mao, Yachun] Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Peoples R China; [Salvi, Stefano; Tolomei, Cristiano; Bignami, Christian; Atzori, Simone; Pezzo, Giuseppe] Natl Earthquake Observ, Ist Nazl Geofis & Vulcanol, Via Vigna Murata 605, I-00143 Rome, Italy; [Wang, Qun] China Highway Engn Consultants Corp, Beijing 100081, Peoples R China; [Wu, Lixin] Cent South Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China; [Yan, Shiyong] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China University of Tras-os-Montes & Alto Douro; INESC TEC; Chinese Academy of Sciences; Polish Geological Institute - National Research Institute; European Academy of Bozen-Bolzano; Nanjing Normal University; Northeastern University - China; Istituto Nazionale Geofisica e Vulcanologia (INGV); Central South University; China University of Mining & Technology Sousa, JJ (corresponding author), Univ Tras Os Montes & Alto Douro, Sch Sci & Technol, Engn Dept, P-5000801 Vila Real, Portugal.;Sousa, JJ (corresponding author), INESC Technol & Sci, Ctr Robot Ind & Intelligent Syst CRIIS, P-4200465 Porto, Portugal. jjsousa@utad.pt; liuguang@radi.ac.cn; jh15fan@agrs.cn; zper@pgi.gov.pl; Stefan.Steger@eurac.edu; Shibiaobai@njnu.edu.cn; weilianhuan@mail.neu.edu.cn; stefano.salvi@ingv.it; wangqun3@ccccltd.cn; tujienan@mail.cgs.gov.cn; tongliqiang@mail.cgs.gov.cn; petmayr@gmail.com; ruth.sonnenschein@eurac.edu; liushanjun@mail.neu.edu.cn; maoyachun@mail.neu.edu.cn; cristiano.tolomei@ingv.it; christian.bignami@ingv.it; simone.atzori@ingv.it; guseppe.pezzo@ingv.it; wulx66@csu.edu.cn; yanshiyong@cumt.edu.cn; eperes@utad.pt Salvi, Stefano/G-3905-2011; Peres, Emanuel/E-8392-2011 Salvi, Stefano/0000-0002-7776-6544; Peres, Emanuel/0000-0001-5669-7976; Tolomei, Cristiano/0000-0001-7378-0712; bai, shibiao/0000-0002-1699-0825; Steger, Stefan/0000-0003-0886-5191; Sonnenschein, Ruth/0000-0002-9112-0897; Sousa, Joaquim Joao/0000-0003-4533-930X; Bignami, Christian/0000-0002-8632-9979; Pezzo, Giuseppe/0000-0003-4851-4193; Atzori, Simone/0000-0002-5031-9904 Strategic Priority Research Program of the Chinese Academy of Sciences [32365]; China Geological Surve [DD20160342, DD20190515]; Key Research Program of Frontier Sciences, CAS [QYZDY-SSW-DQC026]; National Natural Science Foundation of China (NSFC) [41590852, 41001264]; National Science and Technology Major Project [2016ZX05054013]; [3393]; [XDA19070202] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); China Geological Surve; Key Research Program of Frontier Sciences, CAS; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Science and Technology Major Project; ; This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA19070202], the China Geological Survey [grant number DD20160342, DD20190515], the Key Research Program of Frontier Sciences, CAS [grant number QYZDY-SSW-DQC026], the National Natural Science Foundation of China (NSFC) [grant number 41590852, 41001264] and the National Science and Technology Major Project [grant number 2016ZX05054013], and the APC was funded by [XDA19070202]. 83 3 3 9 43 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2021.0 13 21 4269 10.3390/rs13214269 0.0 29 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology WX0AZ gold 2023-03-23 WOS:000718269000001 0
C Liu, TZ; Hou, JC; Xiong, G; Nyberg, TR; Li, XH IEEE Liu, TaoZhong; Hou, Jiachen; Xiong, Gang; Nyberg, Timo R.; Li, Xiaohui Smart Cloud-based Platform for Construction Sites PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS (SOLI) English Proceedings Paper IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) JUL 10-12, 2016 Beijing, PEOPLES R CHINA IEEE,IEEE Intelligent Transportat Syst Soc,Informs Serv Sci smart construction site; cloud computing; monitor and control This paper discusses the concept of smart construction site, summarizes the smart construction site's content and its core technology, and cloud computing platform's architecture is proposed in order to realize the construction site's smart management and control, and intelligence services. Mainly discusses how to effectively collect and use construction site's big data, meets the high efficiency of construction site's administration, business management, service applications needs, and gives the smart construction site's cloud platform framework, put forward the smart construction site's application system solutions, lists some typical applications, finally lookouts smart city's good prospects for the future. [Liu, TaoZhong; Hou, Jiachen; Li, Xiaohui] Hainan Zhongke TianNan Technol Co Ltd, Sanya, Hainan, Peoples R China; [Hou, Jiachen; Xiong, Gang] Chinese Acad Sci, Cloud Comp Ctr, Dongguan, Guangdong, Peoples R China; [Xiong, Gang; Nyberg, Timo R.] Aalto Univ, Sch Sci, Dept Ind Engn & Management, Aalto, Finland Chinese Academy of Sciences; Aalto University Liu, TZ (corresponding author), Hainan Zhongke TianNan Technol Co Ltd, Sanya, Hainan, Peoples R China. hou, jiachen/AAB-1461-2022 10 2 2 10 42 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-2927-3 2016.0 168 173 6 Computer Science, Interdisciplinary Applications; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Operations Research & Management Science BG5MM 2023-03-23 WOS:000389544400030 0
J Cheng, HT; Yan, L; Ma, ZM; Ribaric, S Cheng, Haitao; Yan, Li; Ma, Zongmin; Ribaric, Slobodan Fuzzy spatio-temporal ontologies and formal construction based on fuzzy Petri nets COMPUTATIONAL INTELLIGENCE English Article construction; fuzzy Petri nets; fuzzy spatio-temporal ontologies; ontologies; Semantic Web TEMPORAL RELATIONS; KNOWLEDGE Fuzzy spatio-temporal knowledge is required in a wide range of application fields, including GIS, spatio-temporal database, and artificial intelligence. Ontology, as a formal method of knowledge representation, plays a key role in the Semantic Web. Therefore, how to extend ontologies to represent fuzzy spatio-temporal knowledge needs to be solved. In this paper, we propose an approach for representing fuzzy spatio-temporal knowledge with ontologies and investigate a formal construction of fuzzy spatio-temporal ontologies based on fuzzy Petri nets. First, we propose a formal definition of fuzzy spatio-temporal ontologies. In addition, based on fuzzy description logic, a description logic named the fuzzy spatio-temporal description logic, which provides a logical basis for the fuzzy spatio-temporal ontologies, is presented. Furthermore, a fuzzy spatio-temporal web ontology language, which is fuzzy and spatio-temporal extension of standard ontology language OWL, is proposed to make the fuzzy spatio-temporal ontologies easy to use and support efficient reasoning. On the basis of fuzzy spatio-temporal ontologies, we investigate a formal approach for constructing fuzzy spatio-temporal ontologies from fuzzy spatio-temporal Petri nets, ie, transforming fuzzy spatio-temporal Petri nets (including Petri net model and Petri net instance) into fuzzy spatio-temporal ontologies at both structure and instance levels. Finally, we prove the correctness of the transformation and provide a detailed transformation example. [Cheng, Haitao] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China; [Yan, Li; Ma, Zongmin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Ribaric, Slobodan] Univ Zagreb, Fac Elect Engn & Comp, Zagreb, Croatia Nanjing University of Posts & Telecommunications; Nanjing University of Aeronautics & Astronautics; University of Zagreb Ma, ZM (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China. zongminma@gmail.com Ma, Zongmin/0000-0001-7780-6473 National Natural Science Foundation of China [61772269, 61370075]; NUPTSF [NY217133]; Chinese-Croatian Scientific and Technological Cooperation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NUPTSF; Chinese-Croatian Scientific and Technological Cooperation National Natural Science Foundation of China, Grant/Award Number: 61772269 and 61370075; NUPTSF, Grant/Award Number: NY217133; Chinese-Croatian Scientific and Technological Cooperation 58 9 9 3 15 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0824-7935 1467-8640 COMPUT INTELL-US Comput. Intell. FEB 2019.0 35 1 204 239 10.1111/coin.12199 0.0 36 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HK1GS 2023-03-23 WOS:000457653600010 0
J Tu, W; Santi, P; Zhao, TH; He, XY; Li, QQ; Dong, L; Wallington, TJ; Ratti, C Tu, Wei; Santi, Paolo; Zhao, Tianhong; He, Xiaoyi; Li, Qingquan; Dong, Lei; Wallington, Timothy J.; Ratti, Carlo Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing APPLIED ENERGY English Article Ride-hailing; Urban mobility; GPS trajectories; Electrification; Machine learning; Big data CHARGING STATIONS; EMISSIONS; PATTERNS; MOBILITY; TRAVEL; CYCLE; ELECTRIFICATION; INFORMATION; ACCEPTANCE; PROFILES The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation. [Tu, Wei; Zhao, Tianhong; Li, Qingquan] Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen Key Lab Spatial Informat Smart Sensing &, Shenzhen 518060, Peoples R China; [Tu, Wei; Zhao, Tianhong; Li, Qingquan] Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China; [Tu, Wei; Zhao, Tianhong; Li, Qingquan] Shenzhen Univ, Guandong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Tu, Wei; Santi, Paolo; Dong, Lei; Ratti, Carlo] MIT, Senseable City Lab, Cambridge, MA 02142 USA; [Tu, Wei; Li, Qingquan] Shenzhen Univ, Natl Adm Surveying Mapping & GeoInformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China; [He, Xiaoyi] Univ Michigan, Sch Environm & Sustainabil, Dana Bldg 440 Church, Ann Arbor, MI 48109 USA; [Wallington, Timothy J.] Ford Motor Co, Res & Adv Engn, 2101 Village Rd MD 3182, Dearborn, MI 48121 USA; [Santi, Paolo] CNR, Ist Informat & Telemat, Pisa, Italy Shenzhen University; Shenzhen University; Shenzhen University; Massachusetts Institute of Technology (MIT); Shenzhen University; University of Michigan System; University of Michigan; Ford Motor Company; Consiglio Nazionale delle Ricerche (CNR); Istituto di Informatica e Telematica (IIT-CNR) Tu, W; Li, QQ (corresponding author), Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen Key Lab Spatial Informat Smart Sensing &, Shenzhen 518060, Peoples R China.;Tu, W; Li, QQ (corresponding author), Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China. tuwei@szu.edu.cn; liqq@szu.edu.cn Dong, Lei/AAZ-8537-2020; He, Xiaoyi/AAL-3526-2020; Zhao, Tianhong/AGX-5760-2022; TU, Wei/H-4073-2014 Dong, Lei/0000-0002-1615-5424; He, Xiaoyi/0000-0002-6263-0329; TU, Wei/0000-0002-0255-4037; Zhao, Tianhong/0000-0002-9290-2049; Li, Qingquan/0000-0002-2438-6046 National Natural Science Foundation of China [41401444]; Basic Research Program of Shenzhen Science and Technology Innovation Committee [JCYJ20180305125113883, JCYJ20170302144402023]; China Scholarship Council [201708440434]; Allianz; Amsterdam Institute for Advanced Metropolitan Solutions; Brose; Cisco; Ericsson; Fraunhofer Institute; Liberty Mutual Institute; Kuwait-MIT Center for Natural Resources and the Environment; Shenzhen; Singapore-MIT Alliance for Research and Technology (SMART); UBER; Vitoria State Government; Volkswagen Group America National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Basic Research Program of Shenzhen Science and Technology Innovation Committee; China Scholarship Council(China Scholarship Council); Allianz; Amsterdam Institute for Advanced Metropolitan Solutions; Brose; Cisco; Ericsson(Ericsson); Fraunhofer Institute; Liberty Mutual Institute; Kuwait-MIT Center for Natural Resources and the Environment; Shenzhen; Singapore-MIT Alliance for Research and Technology (SMART)(Singapore-MIT Alliance for Research & Technology Centre (SMART)); UBER; Vitoria State Government; Volkswagen Group America This work was jointly supported by the National Natural Science Foundation of China [grant number 41401444], the Basic Research Program of Shenzhen Science and Technology Innovation Committee [grant numbers JCYJ20180305125113883, JCYJ20170302144402023], and China Scholarship Council [grant number 201708440434]. Paolo and Carlo would like to thank Allianz, Amsterdam Institute for Advanced Metropolitan Solutions, Brose, Cisco, Ericsson, Fraunhofer Institute, Liberty Mutual Institute, Kuwait-MIT Center for Natural Resources and the Environment, Shenzhen, Singapore-MIT Alliance for Research and Technology (SMART), UBER, Vitoria State Government, Volkswagen Group America, and all the members of the MIT Senseable City Lab Consortium for supporting this research. We thank Ford colleagues Wei Shen and Mike Degner for helpful discussions. 57 44 44 9 81 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy SEP 15 2019.0 250 147 160 10.1016/j.apenergy.2019.04.157 0.0 14 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Energy & Fuels; Engineering IS6EB 2023-03-23 WOS:000482244000013 0
J Kan, ZH; Tang, LL; Kwan, MP; Zhang, X Kan, Zihan; Tang, Luliang; Kwan, Mei-Po; Zhang, Xia Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article fuel consumption; emissions estimation; GPS trace; big data ENERGY/EMISSIONS ESTIMATION; TRAVEL PATTERNS; MODELS; SHANGHAI The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles' mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles' activities in road networks. [Kan, Zihan; Tang, Luliang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China; [Kwan, Mei-Po] Univ Illinois, Dept Geog & Geog Informat Sci, 1301 W Green St, Urbana, IL 61801 USA; [Kwan, Mei-Po] Univ Utrecht, Fac Geosci, Dept Human Geog & Spatial Planning, POB 80125, NL-3508 TC Utrecht, Netherlands; [Zhang, Xia] Wuhan Univ, Sch Urban Design, Wuhan 430070, Hubei, Peoples R China Wuhan University; University of Illinois System; University of Illinois Urbana-Champaign; Utrecht University; Wuhan University Tang, LL (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. kzh@whu.edu.cn; tll@whu.edu.cn; mpk654@gmail.com; xiazhang@whu.edu.cn Kwan, Mei-Po/S-4162-2016 Kwan, Mei-Po/0000-0001-8602-9258; TANG, Luliang/0000-0003-3523-8994; Kan, Zihan/0000-0002-6364-0537 National Key Research and Development Plan of China [2017YFB0503604, 2016YFE0200400]; National Natural Science Foundation of China [41671442, 41571430, 41529101, 41271442]; Joint Foundation of Ministry of Education of China [6141A02022341]; John Simon Guggenheim Memorial Foundation Fellowship National Key Research and Development Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Joint Foundation of Ministry of Education of China; John Simon Guggenheim Memorial Foundation Fellowship This work was supported by the grants from National Key Research and Development Plan of China (2017YFB0503604, 2016YFE0200400), the grants from the National Natural Science Foundation of China (41671442, 41571430, 41529101, 41271442), and the Joint Foundation of Ministry of Education of China (6141A02022341). In addition, Mei-Po Kwan was supported by a John Simon Guggenheim Memorial Foundation Fellowship. 36 43 44 6 44 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health APR 2018.0 15 4 566 10.3390/ijerph15040566 0.0 23 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health GI9TK 29561813.0 Green Accepted, gold, Green Published, Green Submitted 2023-03-23 WOS:000434868800005 0
J Cao, CL; Wen, W; Chen, AB; Wang, SC; Xu, GZ; Niu, CS; Song, J Cao, Chenglong; Wen, Wen; Chen, Aobo; Wang, Shuochen; Xu, Guozheng; Niu, Chaoshi; Song, Jian Neuropsychological Alterations of Prolactinomas' Cognitive Flexibility in Task Switching BRAIN SCIENCES English Article prolactinomas; cognitive flexibility; theta oscillation; machine-learning; connectivity THETA OSCILLATIONS; PITUITARY PATIENTS; CORTEX; HYPERPROLACTINEMIA; DYSFUNCTION; EXPRESSION; DOPAMINE; DYNAMICS; ALPHA Prolactinomas have been reported to impair cognition in broad aspects. However, few studies investigated the influence of prolactinomas on cognitive flexibility never mentioning the underlying neural and electrophysiological mechanism. We recorded scalp electroencephalography (EEG) in a colour-shape switching task. Patients with prolactinomas showed longer reaction time in switch trials and larger switch costs relative to healthy controls (HCs). Compared to HCs who showed stronger frontal theta activity in switch trials, the generally weak frontal theta activity in patients implied that they could not afford the executive control to configure task sets. Meanwhile, machine-learning based classification revealed that patients manifested non-selective brain patterns in response to different task types (colour vs. shape task) and different task states (switch vs. repeat state), which collectively suggested the cognitive dysfunction in preparation for a changing environment. Compared to HCs who showed stronger frontoparietal synchronization in switch trials, this enhanced frontoparietal connectivity was disrupted among patients with severe prolactinomas. This finding implicated greater hyperprolactinemia was linked to a larger decrease in cognitive performance. Taken together, the present study highlighted frontal theta power, and frontoparietal connectivity at theta band as the electrophysiological markers of the impaired cognitive flexibility and task control in patients with prolactinomas. [Cao, Chenglong; Chen, Aobo; Niu, Chaoshi] Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Neurosurg, Div Life Sci & Med, Hefei 230001, Peoples R China; [Cao, Chenglong] Maastricht Univ, Fac Psychol & Neurosci, Dept Cognit Neurosci, POB 616, NL-6200 MD Maastricht, Netherlands; [Cao, Chenglong; Wang, Shuochen; Xu, Guozheng; Song, Jian] Gen Hosp Chinese PLA Cent Theater Command, Dept Neurosurg, Wuhan 430074, Peoples R China; [Wen, Wen] Boston Univ, Dept Psychol & Brain Sci, Boston, MA 02215 USA Chinese Academy of Sciences; University of Science & Technology of China, CAS; Maastricht University; Boston University Niu, CS (corresponding author), Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Neurosurg, Div Life Sci & Med, Hefei 230001, Peoples R China.;Song, J (corresponding author), Gen Hosp Chinese PLA Cent Theater Command, Dept Neurosurg, Wuhan 430074, Peoples R China. niuchaoshi@ustc.edu.cn; drsongjian@163.com Wen, Wen/0000-0002-6868-5830 National Natural Science Foundation of China [81870863] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the funding from the National Natural Science Foundation of China (81870863). The Institutes of National Natural Science had no further role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; or in the decision to submit the article for publication. 52 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3425 BRAIN SCI Brain Sci. JAN 2023.0 13 1 82 10.3390/brainsci13010082 0.0 11 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 7Y0UQ 36672063.0 gold 2023-03-23 WOS:000914605900001 0
J Zhang, HX; Li, F; Wang, J; Wang, ZL; Sanin, C; Szczerbicki, E Zhang, Haoxi; Li, Fei; Wang, Juan; Wang, Zuli; Sanin, Cesar; Szczerbicki, Edward Experience-Oriented Intelligence for Internet of Things CYBERNETICS AND SYSTEMS English Article Decisional DNA; deep learning; internet of things; knowledge representation DECISIONAL DNA; KNOWLEDGE STRUCTURE; NEURAL-NETWORKS; ALGORITHM; REPRESENTATION; TECHNOLOGY; ONTOLOGY; SETS; IOT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allowing people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing among different IoT systems are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems. [Zhang, Haoxi; Li, Fei; Wang, Juan; Wang, Zuli] Chengdu Univ Informat Technol, Coll Informat Secur Engn, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China; [Sanin, Cesar] Univ Newcastle, Sch Mech Engn, Newcastle, NSW, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Coll Informat Secur Engn, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China. haoxi@cuit.edu.cn Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Szczerbicki, Edward/0000-0001-7794-2862; Zhang, Haoxi/0000-0002-1341-1912 Scientific Research Foundation of CUIT [KYTZ201422]; Application Basic Research Project of Sichuan Province [2014JY0071]; Open Project of Network and Data Security Key Laboratory of Sichuan province [NDS2015-01] Scientific Research Foundation of CUIT; Application Basic Research Project of Sichuan Province; Open Project of Network and Data Security Key Laboratory of Sichuan province This work is supported by the Project KYTZ201422 by the Scientific Research Foundation of CUIT, the Application Basic Research Project of Sichuan Province (No. 2014JY0071), and the Open Project of Network and Data Security Key Laboratory of Sichuan province (NDS2015-01). 78 5 5 0 11 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. 2017.0 48 3 SI 162 181 10.1080/01969722.2016.1276771 0.0 20 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science EO7QM 2023-03-23 WOS:000396885200003 0
C Kouliaridis, V; Barmpatsalou, K; Kambourakis, G; Wang, GJ Wang, G; Han, Q; Bhuiyan, MZA; Ma, X; Loulergue, F; Li, P; Roveri, M; Chen, L Kouliaridis, Vasileios; Barmpatsalou, Konstantia; Kambourakis, Georgios; Wang, Guojun Mal-warehouse: A data collection-as-a-service of mobile malware behavioral patterns 2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) English Proceedings Paper IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) NOV 07-11, 2018 Guangzhou, PEOPLES R CHINA IEEE Comp Soc,Guangzhou Univ Smartphones are pervasively used in many everyday life extents, and have been both targets and victims of malware. While there are many anti-malware applications available in mobile markets, so far there are no public services that collect mobile usage data, so as to observe malware effects on mobile devices. The main contribution of this paper is the Mal-warehouse, an open-source tool performing data collection-as-a-service for Android malware behavioral patterns. During its initial development and experimentation phase, the tool extracts mobile device statistics, including CPU, memory and battery usage, process reports, and network statistics for 14 Android malware applications from a target device. It then stores them in a classified manner on a cloud database. Despite the fact that the work at hand is still in an early stage, the detection model is enhanced with a preliminary detection module. Machine learning techniques are used as a proof-of-concept so as to evaluate the detection capabilities of the detection model, when compared to a clean snapshot of the target device. Mal-warehouse is publicly available, meaning that anyone can download and use it locally and then upload their findings to the cloud service for further evaluation and processing by others. [Kouliaridis, Vasileios; Kambourakis, Georgios] Univ Aegean, Dept Informat & Commun Syst Engn, Lesbos, Greece; [Barmpatsalou, Konstantia] Univ Coimbra, Ctr Informat & Syst, Coimbra, Portugal; [Wang, Guojun] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou, Guangdong, Peoples R China University of Aegean; Universidade de Coimbra; Guangzhou University Kouliaridis, V (corresponding author), Univ Aegean, Dept Informat & Commun Syst Engn, Lesbos, Greece. bkouliaridis@aegean.gr; konstantia@dei.uc.pt; gkamb@aegean.gr; csgjwang@gzhu.edu.cn 29 6 6 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-9380-3 2018.0 1503 1508 10.1109/SmartWorld.2018.00260 0.0 6 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM0GQ 2023-03-23 WOS:000458742900224 0
C Hu, SF; Rueangsirarak, W; Ee, MBP; Aslam, N; Shum, HPH IEEE Hu, Shanfeng; Rueangsirarak, Worasak; Ee, Maxime Bouch Prime; Aslam, Nauman; Shum, Hubert P. H. A Motion Classification Approach to Fall Detection 2017 11TH INTERNATIONAL CONFERENCE ON SOFTWARE, KNOWLEDGE, INFORMATION MANAGEMENT AND APPLICATIONS (SKIMA) International Conference on Software Knowledge Information Management and Applications English Proceedings Paper 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) DEC 06-08, 2017 Malabe, SRI LANKA IEEE,Sri Lanka Inst Informat Technol,IEEE Sri Lanka Sect fall detection; motion capture; motion analysis; motion classification; machine learning ACCELEROMETRY The population of older people in the world has grown rapidly in recent years. To alleviate the increasing burden on health systems, automated health monitoring of older people can be very economical for requesting urgent medical support when a harmful accident has been detected. One of the accidents that happens frequently to older people in a household environment is a fall, which can cause serious injuries if not handled immediately. In this paper, we propose a motion classification approach to fall detection, by integrating the techniques of motion capture and machine learning. The motion of a person is recorded with a set of inertial sensors, which provides a comprehensive and structural description of body movements, while being robust to variations in the working environment. We build a database comprising motions of both falls and normal activities. We experiment with several combinations of joint selection, feature extraction, and classification algorithms, showing that accurate fall detection can be achieved by our motion classification approach. [Hu, Shanfeng; Aslam, Nauman; Shum, Hubert P. H.] Northumbria Univ, Newcastle Upon Tyne, Tyne & Wear, England; [Hu, Shanfeng] Beihang Univ, Beijing, Peoples R China; [Rueangsirarak, Worasak] Mae Fah Luang Univ, Chiang Rai, Thailand; [Ee, Maxime Bouch Prime] Seatech Univ Toulon, Toulon, France Northumbria University; Beihang University; Mae Fah Luang University Hu, SF (corresponding author), Northumbria Univ, Newcastle Upon Tyne, Tyne & Wear, England.;Hu, SF (corresponding author), Beihang Univ, Beijing, Peoples R China. shanfeng.hu@northumbria.ac.uk; worasak.rue@mfu.ac.th; maxime.bouchee.18@seatech.fr; nauman.aslam@northumbria.ac.uk; hubert.shum@northumbria.ac.uk Shum, Hubert P. H./E-8060-2015 Shum, Hubert P. H./0000-0001-5651-6039; Hu, Shanfeng/0000-0002-0709-8384 Erasmus Mundus Action 2 Programme [2014-0861/001-001]; Engineering and Physical Sciences Research Council (EPSRC) [EP/M002632/1]; Royal Society [IE160609] Erasmus Mundus Action 2 Programme; Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society(Royal Society of London) This work was supported in part by the Erasmus Mundus Action 2 Programme (Ref: 2014-0861/001-001), the Engineering and Physical Sciences Research Council (EPSRC) (Ref: EP/M002632/1) and the Royal Society (Ref: IE160609). 13 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2373-082X 978-1-5386-4602-1 I C SOFTWARE KNOWL I 2017.0 6 Computer Science, Information Systems; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ8KX 2023-03-23 WOS:000428407800006 0
J Lytras, MD; Al-Halabi, W; Zhang, JX; Haraty, RA; Masud, M Lytras, Miltiadis D.; Al-Halabi, Wadee; Zhang, Jacky Xi; Haraty, Ramzi A.; Masud, Mehedi Enabling Technologies and Business Infrastructures for Next Generation Social Media: Big Data, Cloud Computing, Internet of Things and Virtual Reality JOURNAL OF UNIVERSAL COMPUTER SCIENCE English Article Social Media; Internet of Things; Big Data; Virtual Reality; Immersive Technology; Mixed Reality SYSTEMS The last years we faced a tremendous development of social media powered by innovative technologies related to web services, web 2.0 and social networks. Nowadays we are entering in to the next digitally enriched generation of social media and enabled by thrilling, currently under extensive development technologies, like Big Data, Cloud Computing, Virtual/Augmented Reality and Internet of Things. To our understanding the new generation of social media and the integrated business models that will support them are within a converging area where social features and ubiquitous technologies are met. Social elements, related to behavior, self-esteem, attachment and other psychological dimensions of personality will be transparently integrated to a number of technologies. Thus for the next years we have to wait for a number of new applications and systems, all targeted in a meta-existence level where the characteristics of human identity will be mixed with various digital identity elements. This basic trend and direction in the Social Media research in the next decade will boost the transparency of technologies and will stimulate an extremely different Web than the one we are exploiting together. The key dimension of the Social Media sphere will be the dependent social identification of humans by a number of technologies. [Lytras, Miltiadis D.] Amer Coll Greece, Athens, Greece; [Al-Halabi, Wadee] King Abdulaziz Univ, Jeddah 21413, Saudi Arabia; [Zhang, Jacky Xi] Tianjin Univ, Tianjin 300072, Peoples R China; [Haraty, Ramzi A.] Lebanese Amer Univ, Beirut, Lebanon; [Masud, Mehedi] Taif Univ, At Taif, Saudi Arabia King Abdulaziz University; Tianjin University; Lebanese American University; Taif University Lytras, MD (corresponding author), Amer Coll Greece, Athens, Greece. mlytras@acg.edu; wsalhalabi@kau.edu.sa; jackyzhang@tju.edu.cn; rharaty@lau.edu.lb; mmasud@tu.edu.sa Lytras, Miltiades Demetrios/ABD-5355-2021; Haraty, Ramzi A./AAH-2901-2019; Alhalabi, Wadee/GQG-9111-2022; Lytras, Miltiadis/GSM-7668-2022; Masud, Mehedi/AAZ-7022-2020; Zhang, Jacky/HHS-9302-2022; Lytras, Miltiadis/P-8195-2016; Alhalabi, Wadee/GOG-9206-2022; Lytras, Miltiadis/ABD-5607-2021 Haraty, Ramzi A./0000-0002-6978-3627; Alhalabi, Wadee/0000-0002-4505-7268; Lytras, Miltiadis/0000-0002-7281-5458; Masud, Mehedi/0000-0001-6019-7245; Lytras, Miltiadis/0000-0002-7281-5458; Zhang, Xi/0000-0002-1105-9417 7 22 22 0 24 GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM GRAZ INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA 0948-695X J UNIVERS COMPUT SCI J. Univers. Comput. Sci. 2015.0 21 11 1379 1384 6 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science DB4BI 2023-03-23 WOS:000368457300001 0
J Jia, Y; Jin, SG; Savi, P; Gao, Y; Tang, J; Chen, YX; Li, WM Jia, Yan; Jin, Shuanggen; Savi, Patrizia; Gao, Yun; Tang, Jing; Chen, Yixiang; Li, Wenmei GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation REMOTE SENSING English Article global navigation satellite system (GNSS)-reflectometry; soil moisture retrieval; signal-to-noise ratio (SNR); XGBoost MICROWAVE DIELECTRIC BEHAVIOR; GPS SIGNALS; REFLECTED SIGNALS; BISTATIC RADAR; WATER CONTENT; WET SOIL; REFLECTOMETRY; OCEAN; LAND; SCATTERING Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data. [Jia, Yan; Gao, Yun; Tang, Jing; Chen, Yixiang; Li, Wenmei] Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Jiangsu, Peoples R China; [Jia, Yan; Chen, Yixiang; Li, Wenmei] Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji, Nanjing 210023, Jiangsu, Peoples R China; [Jin, Shuanggen] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Jiangsu, Peoples R China; [Jin, Shuanggen] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Savi, Patrizia] Politecn Torino, Corso Duca Abruzzi 24, I-10129 Turin, Italy Nanjing University of Posts & Telecommunications; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Polytechnic University of Turin Jia, Y (corresponding author), Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Jiangsu, Peoples R China.;Jia, Y (corresponding author), Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji, Nanjing 210023, Jiangsu, Peoples R China. jiayan@njupt.edu.cn Jin, Shuanggen/B-8094-2008 Jin, Shuanggen/0000-0002-5108-4828; JIA, YAN/0000-0002-8282-8105; SAVI, Patrizia/0000-0001-9585-310X; Gao, Yun/0000-0002-9694-0907 Natural Science Foundation of Jiangsu Province [BK20180765, BK20170897]; Nanjing Technology Innovation Foundation for Selected Overseas Scientists [RK032YZZ18003]; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) [217152, 219066]; National Natural Science Foundation of China [41401480] Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Nanjing Technology Innovation Foundation for Selected Overseas Scientists; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180765, BK20170897, in part by the Nanjing Technology Innovation Foundation for Selected Overseas Scientists under Grant RK032YZZ18003. Research on Advanced Land Surface Detection System using GNSS-R', in part by the Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) under Grant 217152, 219066, by the National Natural Science Foundation of China under Grant 41401480. 65 42 45 7 60 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUL 2 2019.0 11 14 1655 10.3390/rs11141655 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology IQ1RK gold 2023-03-23 WOS:000480527800025 0
J Bi, WB; Yu, F; Cao, N; Higgs, R Bi, Wenbin; Yu, Fang; Cao, Ning; Higgs, Russell Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing CMC-COMPUTERS MATERIALS & CONTINUA English Article Internet of Vehicles mobile cloud computing; resource load predicting; multi distributed resource computing scheduling; chaos analysis algorithm; improved artificial bee colony function MODEL Load-time series data in mobile cloud computing of Internet of Vehicles (IoV) usually have linear and nonlinear composite characteristics. In order to accurately describe the dynamic change trend of such loads, this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV. Firstly, a chaotic analysis algorithm is implemented to process the load-time series, while some learning samples of load prediction are constructed. Secondly, a support vector machine (SVM) is used to establish a load prediction model, and an improved artificial bee colony (IABC) function is designed to enhance the learning ability of the SVM. Finally, a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system, which is composed of 50 vehicles as the data set; and a comparison experiment is conducted by using a grey model, a back propagation neural network, a radial basis function (RBF) neural network and a RBF kernel function of SVM. As shown in the experimental results, the prediction accuracy of the method proposed in this study is significantly higher than other models, with a significantly reduced real-time prediction error for resource loading in mobile cloud environments. Compared with single-prediction models, the prediction method proposed can build up multidimensional time series in capturing complex load time series, fit and describe the load change trends, approximate the load time variability more precisely, and deliver strong generalization ability to load prediction models for mobile cloud computing resources. [Bi, Wenbin] Dalian Neusoft Univ Informat, Sch Comp & Software, Dalian 116023, Peoples R China; [Yu, Fang] Qingdao Bin Hai Univ, Sch Informat Engn, Qingdao, Peoples R China; [Cao, Ning] Wuxi Vocat Coll Sci & Technol, Sch Internet Things & Software Technol, Wuxi 214028, Jiangsu, Peoples R China; [Higgs, Russell] Univ Coll Dublin, Sch Math & Stat, Dublin, Ireland University College Dublin Cao, N (corresponding author), Wuxi Vocat Coll Sci & Technol, Sch Internet Things & Software Technol, Wuxi 214028, Jiangsu, Peoples R China. ning.cao2008@hotmail.com Higgs, Russell/0000-0002-0896-6788 Shandong medical and health science and technology development plan project [202012070393] Shandong medical and health science and technology development plan project This work was supported by Shandong medical and health science and technology development plan project (No.202012070393) . 32 0 0 3 3 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2022.0 73 1 165 180 10.32604/cmc.2022.027776 0.0 16 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science 1W4CG gold 2023-03-23 WOS:000806722000006 0
C Wang, Z; Zhang, X; Wang, WX; Shi, LN; Huang, CX; Wang, JT; Zhang, Y IEEE Wang, Zhan; Zhang, Xun; Wang, Wenxiao; Shi, Lina; Huang, Chuanxi; Wang, Jintao; Zhang, Yue Deep Convolutional Auto-Encoder based Indoor Visible Light Positioning Using RSS Temporal Image 2019 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB) IEEE International Symposium on Broadband Multimedia Systems and Broadcasting English Proceedings Paper IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) JUN 05-07, 2019 SOUTH KOREA IEEE,IEEE Broadcast Technol Soc,Korean Inst Broadcast & Media Engineers,Elect & Telecommunicat Res Inst,Sungkyunkwan Univ, Inst Informat & Commun Technol Visible Light Communication; Received Signal Strength (RSS); Convolutional Auto-Encoder; Deep learning Visible light communication is a promising candidate for achieving low-cost, high data rate and massive coverage in future wireless network. To fulfill the multimedia or broadcasting service, the prerequisite is to determine the user's position. However, traditional RSS fingerprint based positioning approach suffers the random signal fluctuation problem which significantly limits the positioning performance. To tackle this problem, this paper presents a deep learning scheme which utilize a regression DNN combined with convolutional auto-encoder (CAE) to provide robust indoor positioning. Instead of using the separate fluctuated RSS reading for positioning, the proposed method takes the RSS temporal image (RTI) composed from a set of consecutive RSS readings as input. By leveraging the spatial and temporal dependency of RTI data with stacked denoising CAE, the proposed network is expected to learn more complex and consistent features from the fluctuating RSS readings and perform better location estimation. Simulation results have shown that the proposed method is capable of providing more stable positioning result from fluctuating RSS data than traditional DNN based method. [Wang, Zhan; Zhang, Xun; Wang, Wenxiao; Shi, Lina; Huang, Chuanxi] Inst Super Elect, Paris, France; [Wang, Jintao] Univ Tsinghua, Dept Elect Engn, Beijing, Peoples R China; [Zhang, Yue] Univ Leicester, Dept Elect Engn, Leicester, Leics, England University of Leicester Zhang, X (corresponding author), Inst Super Elect, Paris, France. xun.zhang@isep.fr National Key R&D Program of China [2017YFE011230]; EU Horizon 2020 program towards the Internet of Radio-Light project [H2020-ICT 761992] National Key R&D Program of China; EU Horizon 2020 program towards the Internet of Radio-Light project The authors gratefully acknowledge the financial support of the National Key R&D Program of China (Grant No.2017YFE011230) and the EU Horizon 2020 program towards the Internet of Radio-Light project H2020-ICT 761992. 24 0 0 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2155-5044 978-1-7281-2150-5 IEEE INT SYM BROADB 2019.0 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BS4XX 2023-03-23 WOS:000723995900025 0
J Wei, XD; Sun, QZ; Liu, XL; Yue, CX; Liang, SY; Wang, LH Wei, Xudong; Sun, Qingzhen; Liu, Xianli; Yue, Caixu; Liang, Steven Y.; Wang, Lihui Research on parallel distributed clustering algorithm applied to cutting parameter optimization INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Big data; Data mining; Distributed clustering; T; K-means algorithm; MapReduce framework; Cutting parameter optimization In the big data era, traditional data mining technology cannot meet the requirements of massive data processing with the background of intelligent manufacturing. Aiming at insufficient computing power and low efficiency in mining process, this paper proposes a improved K-means clustering algorithm based on the concept of distributed clustering in cloud computing environment. The improved algorithm (T.K-means) is combined with MapReduce computing framework of Hadoop platform to realize parallel computing, so as to perform processing tasks of massive data. In order to verify the practical performance of T.K-means algorithm, taking machining data of milling Ti-6Al-4V alloy as the mining object. The mapping relationship among cutting parameters, surface roughness, and material removal rate is mined, and the optimized value for cutting parameters is obtained. The results show that T.K-means algorithm can be used to mine the optimal cutting parameters, so that the best surface roughness can be obtained in milling Ti-6Al-4V titanium alloy. [Wei, Xudong; Sun, Qingzhen; Liu, Xianli; Yue, Caixu] Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China; [Liang, Steven Y.] Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden Harbin University of Science & Technology; University System of Georgia; Georgia Institute of Technology; Royal Institute of Technology Sun, QZ (corresponding author), Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China. qingzhs@163.com Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049 Projects of International Cooperation and Exchanges NSFC [51720105009]; National Key Research and Development Project of China [2019YFB1704800] Projects of International Cooperation and Exchanges NSFC; National Key Research and Development Project of China This research was funded by Projects of International Cooperation and Exchanges NSFC (Grant Number 51720105009) and National Key Research and Development Project of China (Grant Number 2019YFB1704800). 31 0 0 4 7 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. JUN 2022.0 120 11-12 7895 7904 10.1007/s00170-022-09252-7 0.0 MAY 2022 10 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering 1L7GY 2023-03-23 WOS:000791141200002 0
J Zeng, R; Rossiter, DG; Zhang, JP; Cai, K; Gao, WC; Pan, WJ; Zeng, YT; Jiang, CY; Li, DC Zeng, Rong; Rossiter, David G.; Zhang, Jiapeng; Cai, Kai; Gao, Weichang; Pan, Wenjie; Zeng, Yuntao; Jiang, Chaoying; Li, Decheng How Well Can Reflectance Spectroscopy Allocate Samples to Soil Fertility Classes? AGRONOMY-BASEL English Article soil fertility class; reflectance spectroscopy; allocation accuracy; machine learning PARTIAL LEAST-SQUARES; INFRARED-SPECTROSCOPY; ACCURACY ASSESSMENT; CLASSIFICATION; REGRESSION; QUALITY Fertilization decisions depend on the measurement of a large set of soil fertility indicators, usually through laboratory determination, which is costly and time-consuming. Visible and nearinfrared (vis-NIR) spectroscopy combined with machine learning can simultaneously predict various soil fertility indicators. Spectroscopy is inherently less accurate than direct laboratory determination. However, in many fertilization recommendation contexts, farmers mainly fertilize according to classified fertility indicators, rather than by continuous soil property values. These classes have defined limits of property values. We hypothesized that the additional inaccuracy from spectroscopy may not be important for properties grouped into classes. This study compared the indirect and direct prediction of soil fertility classes. Indirectly, by (1) using vis-NIR spectra with machine learning to predict 20 soil fertility indicators (pH, soil organic matter (SOM), cation exchange capacity (CEC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), calcium (Ca), magnesium (Mg), silicon (Si), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), Zinc (Zn), molybdenum (Mo) and chlorine (Cl)) and (2) allocating the indicators to soil fertility classes. Directly, by predicting soil fertility classes directly from vis-NIR spectra using machine learning. The prediction accuracy of these two methods were compared and the accuracies needed for the acceptable class allocation of the fertility indicators were determined. The example dataset is a soil spectral library from the Guizhou Province, southwest China. The model performance was evaluated by the overall allocation accuracy and tau index, which accounts for class imbalance. For direct allocation based on three fertility classes (low, medium and high), the overall allocation accuracy of eight properties (CEC, Cu, Si, Zn, S, Mn, Ca and Mg), nine properties (B, AN, TK, AK, SOM, TN, TP, Fe and Mo) and three properties (Cl, AP and pH) were within the range of 0.80-1.0, 0.60-0.80 and 0.40-0.60, respectively. For indirect allocation based on the same classes, the allocation accuracy of nine properties (TN, CEC, Cu, S, Zn, Si, Mn, Ca and Mg), nine properties (B, TK, pH, TP, AK, AN, Fe, Mo and SOM) and two properties (Cl and AP) were within the range of 0.80-1.0, 0.60-0.80 and 0.40-0.60, respectively. We conclude that vis-NIR spectroscopy was fairly successful for soil fertility class allocation for most of the soil properties, using either direct or indirect models. The advantage of indirect models is that both specific property values and soil fertility classes can be obtained at no increase in cost, while direct models are suggested when only soil fertility class information are available. [Zeng, Rong; Zhang, Jiapeng] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China; [Zeng, Rong; Rossiter, David G.; Li, Decheng] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China; [Rossiter, David G.] Cornell Univ, Sect Soil & Crop Sci, Ithaca, NY 14853 USA; [Rossiter, David G.] ISRIC World Soil Informat, NL-6700 AJ Wageningen, Netherlands; [Cai, Kai; Gao, Weichang] Guizhou Acad Tobacco Sci, Upland Flue Cured Tobacco Qual & Ecol Key Lab Chi, Guiyang 550081, Peoples R China; [Cai, Kai] Southwest Univ, Coll Resources & Environm, Chongqing 400715, Peoples R China; [Pan, Wenjie; Zeng, Yuntao; Jiang, Chaoying] China Natl Tobacco Corp Guizhou Prov Co, Guiyang 550004, Peoples R China Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Institute of Soil Science, CAS; Cornell University; China National Tobacco Corporation; Southwest University - China; China National Tobacco Corporation Li, DC (corresponding author), Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China. dcli@issas.ac.cn Rossiter, D G/D-3842-2009 Rossiter, D G/0000-0003-4558-1286 National Natural Science Foundation of China [42107322]; China Tobacco Corporation Guizhou Provincial Company Science and Technology Project [201910]; Natural Science Foundation of colleges and universities of Jiangsu Province [20KJB210009]; Key deployment projects of Chinese Academy of Sciences [KGFZD-135-19-10] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Tobacco Corporation Guizhou Provincial Company Science and Technology Project; Natural Science Foundation of colleges and universities of Jiangsu Province; Key deployment projects of Chinese Academy of Sciences(Chinese Academy of Sciences) This research was funded by National Natural Science Foundation of China (42107322), China Tobacco Corporation Guizhou Provincial Company Science and Technology Project (201910), Natural Science Foundation of colleges and universities of Jiangsu Province (20KJB210009), and Key deployment projects of Chinese Academy of Sciences (KGFZD-135-19-10). 42 0 0 12 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4395 AGRONOMY-BASEL Agronomy-Basel AUG 2022.0 12 8 1964 10.3390/agronomy12081964 0.0 21 Agronomy; Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Plant Sciences 4C2LA gold 2023-03-23 WOS:000846290700001 0
J Li, Y; Cheng, G; Pang, YS; Kuai, M Li, Yong; Cheng, Gang; Pang, Yusong; Kuai, Moshen Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum SENSORS English Article planetary gear; fault diagnosis; VMD; center frequency; feature image; CNN Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective. [Li, Yong; Cheng, Gang; Kuai, Moshen] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China; [Pang, Yusong] Delft Univ Technol, Fac Mech Maritime & Mat Engn, NL-2628 Delft, Netherlands China University of Mining & Technology; Delft University of Technology Cheng, G (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China. liyong2015@cumt.edu.cn; chg@cumt.edu.cn; Y.Pang@tudelft.nl; kuaimoshen2016@cumt.edu.cn Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities [2015YC02] Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Fundamental Research Funds for the Central Universities (grant number 2015YC02). This support is gratefully acknowledged. 25 22 23 7 36 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUN 2018.0 18 6 1735 10.3390/s18061735 0.0 15 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GL0KD 29843418.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000436774300069 0
J Xing, XF; Xiong, YK; Yang, RP; Wang, R; Wang, WB; Kan, HD; Lu, T; Li, DS; Cao, JJ; Penuelas, J; Ciais, P; Bauer, N; Boucher, O; Balkanski, Y; Hauglustaine, D; Brasseur, G; Morawska, L; Janssens, IA; Wang, XR; Sardans, J; Wang, YJ; Deng, YF; Wang, L; Chen, JM; Tang, X; Zhang, RH Xing, Xiaofan; Xiong, Yuankang; Yang, Ruipu; Wang, Rong; Wang, Weibing; Kan, Haidong; Lu, Tun; Li, Dongsheng; Cao, Junji; Penuelas, Josep; Ciais, Philippe; Bauer, Nico; Boucher, Olivier; Balkanski, Yves; Hauglustaine, Didier; Brasseur, Guy; Morawska, Lidia; Janssens, Ivan A.; Wang, Xiangrong; Sardans, Jordi; Wang, Yijing; Deng, Yifei; Wang, Lin; Chen, Jianmin; Tang, Xu; Zhang, Renhe Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA English Article COVID-19; machine learning; satellite observation; air pollution; pandemic management NO2; TIME; INFECTION; ERROR; CHINA The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available. [Xing, Xiaofan; Xiong, Yuankang; Yang, Ruipu; Wang, Rong; Wang, Xiangrong; Wang, Yijing; Deng, Yifei; Wang, Lin; Chen, Jianmin] Fudan Univ, Shanghai Key Lab Atmospher Particle Pollut & Pre, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China; [Wang, Rong; Wang, Lin; Chen, Jianmin; Tang, Xu; Zhang, Renhe] Fudan Univ, BIntegrated Res Disaster Risk Int Ctr Excellence, Shanghai 200438, Peoples R China; [Wang, Rong; Wang, Lin; Tang, Xu; Zhang, Renhe] Fudan Univ, Governance Weather Climate Extremes Impact & Publ, Shanghai 200438, Peoples R China; [Wang, Rong; Wang, Xiangrong] Fudan Univ, Inst Atmospher Sci, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China; [Wang, Rong] Fudan Univ, Ctr Urban Ecoplanning & Design, Shanghai 200438, Peoples R China; [Wang, Rong] Fudan Univ, Big Data Inst Carbon Emiss & Environm Pollut, Shanghai 200438, Peoples R China; [Wang, Weibing; Kan, Haidong] Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China; [Lu, Tun] Fudan Univ, Key Lab Publ Hlth Safety, Minist Educ, Shanghai 200438, Peoples R China; [Lu, Tun] Fudan Univ, Natl Hlth Commiss, Key Lab Hlth Technol Assessment, Sch Publ Hlth, Shanghai 200438, Peoples R China; [Li, Dongsheng; Sardans, Jordi] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai 200438, Peoples R China; [Cao, Junji] Microsoft Res Asia, Shanghai 200232, Peoples R China; [Penuelas, Josep; Sardans, Jordi] Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China; [Penuelas, Josep] CREAF, Barcelona 08193, Spain; [Ciais, Philippe; Balkanski, Yves; Hauglustaine, Didier] Univ Autonoma Barcelona, Consejo Super Invest Cient CSIC, Global Ecol Unit Ctr Invest Ecol & Aplicaciones F, Bellaterra 08193, Spain; [Ciais, Philippe] Univ Versailles St Quentin, CNRS, Commissariat Energie Atom & Energies Alternat, Lab Sci Climat & Environnem, F-91190 Gif Sur Yvette, France; [Bauer, Nico] Cyprus Inst, Climate & Atmosphere Res Ctr, CY-2121 Nicosia, Cyprus; [Boucher, Olivier] Leibniz Assoc, Potsdam Inst Climate Impact Res, D-14412 Potsdam, Germany; [Brasseur, Guy] Sorbonne Univ, CNRS, Inst Pierre Simon Lapl, F-75252 Paris, France; [Brasseur, Guy] Max Planck Inst Meteorol, Environm Modeling Grp, D-20146 Hamburg, Germany; [Morawska, Lidia] Natl Ctr Atmospher Res, Atmospher Chem Observat & Modeling Lab, Boulder, CO 80307 USA; [Janssens, Ivan A.] Queensland Univ Technol, Int Lab Air Qual & Hlth, Brisbane, Qld 4001, Australia; [Janssens, Ivan A.] Univ Antwerp, Dept Biol, B-2610 Antwerp, Belgium Fudan University; Fudan University; Fudan University; Fudan University; Fudan University; Fudan University; Fudan University; Fudan University; Fudan University; Microsoft; Microsoft Research Asia; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); University of Barcelona; Autonomous University of Barcelona; Consejo Superior de Investigaciones Cientificas (CSIC); UDICE-French Research Universities; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Potsdam Institut fur Klimafolgenforschung; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Max Planck Society; National Center Atmospheric Research (NCAR) - USA; Queensland University of Technology (QUT); University of Antwerp Wang, R (corresponding author), Fudan Univ, Shanghai Key Lab Atmospher Particle Pollut & Pre, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China.;Wang, R (corresponding author), Fudan Univ, BIntegrated Res Disaster Risk Int Ctr Excellence, Shanghai 200438, Peoples R China.;Wang, R (corresponding author), Fudan Univ, Governance Weather Climate Extremes Impact & Publ, Shanghai 200438, Peoples R China.;Wang, R (corresponding author), Fudan Univ, Inst Atmospher Sci, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China.;Wang, R (corresponding author), Fudan Univ, Ctr Urban Ecoplanning & Design, Shanghai 200438, Peoples R China.;Wang, R (corresponding author), Fudan Univ, Big Data Inst Carbon Emiss & Environm Pollut, Shanghai 200438, Peoples R China. rongwang@fudan.edu.cn Wang, Lin/E-8546-2010; Junji, Cao/D-3259-2014; Kan, Haidong/AGU-0328-2022; Sardans, Jordi/AEM-0228-2022; Zhang, Renhe/AAA-6301-2020; Liu, Hai-Ying/P-5557-2014; Penuelas, Josep/D-9704-2011; Balkanski, Yves/A-6616-2011; Morawska, Lidia/B-4140-2011 Wang, Lin/0000-0002-4905-3432; Junji, Cao/0000-0003-1000-7241; Sardans, Jordi/0000-0003-2478-0219; Zhang, Renhe/0000-0001-7750-8679; Liu, Hai-Ying/0000-0001-8667-3465; Penuelas, Josep/0000-0002-7215-0150; Balkanski, Yves/0000-0001-8241-2858; Wang, Rong/0000-0003-1962-0165; Li, Dongsheng/0000-0003-3103-8442; Wang, Weibing/0000-0002-4497-5251; Kan, Haidong/0000-0002-1871-8999; Morawska, Lidia/0000-0002-0594-9683 National Natural Science Foundation of China [41877506]; PolEASIA-ANR project [ANR-15-CE04-0005]; Australia-China Centre for Air Quality Science and Management; Catalan Government [AGAUR-2020PANDE00117]; European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project [ESRIN/4000123002/18/I-NB]; Observation-based system for monitoring and verification of greenhouse gases project (VERIFY) [725546]; Fudan's Wangdao Undergraduate Research Opportunities Program [18107]; Chinese Thousand Youth Talents Program National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); PolEASIA-ANR project; Australia-China Centre for Air Quality Science and Management; Catalan Government; European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project; Observation-based system for monitoring and verification of greenhouse gases project (VERIFY); Fudan's Wangdao Undergraduate Research Opportunities Program; Chinese Thousand Youth Talents Program We thank H. J. Yu for important comments and J. H. Pan for help in compiling the epidemiological data. This work was supported by the National Natural Science Foundation of China (41877506) , the Fudan's Wangdao Undergraduate Research Opportunities Program (18107) , the Chinese Thousand Youth Talents Program, the PolEASIA-ANR project (ANR-15-CE04-0005) , and the Australia-China Centre for Air Quality Science and Man-agement. J.P. and J.S. acknowledge financial support from the Catalan Govern-ment grant AGAUR-2020PANDE00117. P.C. acknowledges support from the European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project (ESRIN/4000123002/18/I-NB) and the Observation-based system for monitoring and verification of greenhouse gases project (VERIFY, grant 725546) . 72 9 9 17 64 NATL ACAD SCIENCES WASHINGTON 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA 0027-8424 1091-6490 P NATL ACAD SCI USA Proc. Natl. Acad. Sci. U. S. A. AUG 17 2021.0 118 33 e2109098118 10.1073/pnas.2109098118 0.0 11 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics UH1VM 34380740.0 Green Submitted, Green Accepted 2023-03-23 WOS:000689727700005 0
J Liu, KL; Hu, XS; Wei, ZB; Li, Y; Jiang, Y Liu, Kailong; Hu, Xiaosong; Wei, Zhongbao; Li, Yi; Jiang, Yan Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION English Article Cyclic capacity prediction; cycling aging; data-driven modeling; lithium-ion (Li-ion) battery; machine learning; state of health (SOH) STATE-OF-HEALTH; REMAINING USEFUL LIFE; PROGNOSTICS This article presents the development of machine-learning-enabled data-driven models for effective capacity predictions for lithium-ion (Li-ion) batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the considerations of battery aging tendency and the corresponding operational temperature and depth-of-discharge. Then based on a systematic understanding of the covariance functions within the Gaussian process regression (GPR), two related data-driven models are developed. Specifically, by modifying the isotropic squared exponential kernel with an automatic relevance determination structure, Model A could extract the highly relevant input features for capacity predictions. Through coupling the Arrhenius law and a polynomial equation into a compositional kernel, Model B is capable of considering the electrochemical and empirical knowledge of battery degradation. The developed models are validated and compared on the nickel-manganese-cobalt (NMC) oxide Li-ion batteries with various cycling patterns. The experimental results demonstrate that the modified GPR model considering the battery electrochemical and empirical aging signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multistep predictions. The proposed technique is promising for battery capacity predictions under various cycling cases. [Liu, Kailong] Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England; [Hu, Xiaosong] Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Wei, Zhongbao] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China; [Li, Yi] Univ Lancaster, Dept Chem Lancaster, Lancaster LA1 4YB, England; [Li, Yi] Vrije Univ Brussel, Dept Mobil, Logist & Automot Technol Ctr, B-1050 Brussels, Belgium; [Jiang, Yan] Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr NANTEC, Beijing 100044, Peoples R China University of Warwick; Chongqing University; Beijing Institute of Technology; Lancaster University; Vrije Universiteit Brussel; Beijing Jiaotong University Liu, KL (corresponding author), Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England.;Hu, XS (corresponding author), Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China. kliu02@qub.ac.uk; xiaosonghu@ieee.org; weizb@bit.edu.cn; y.li82@lancaster.ac.uk; yanjiang@bjtu.edu.cn Wei, Zhongbao/AAC-3263-2021; Liu, Kailong/Y-1797-2019 Wei, Zhongbao/0000-0003-0051-5648; Liu, Kailong/0000-0002-3564-6966 National Science Foundation of China (NSFC) [51875054]; EU [685716] National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); EU(European Commission) This work was supported in part by the National Science Foundation of China (NSFC) under Grant 51875054 and in part by the EU funded Project under Grant 685716. 39 161 163 42 204 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7782 IEEE T TRANSP ELECTR IEEE Trans. Transp. Electrif. DEC 2019.0 5 4 1225 1236 10.1109/TTE.2019.2944802 0.0 12 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation KE4YX Green Submitted 2023-03-23 WOS:000508563900032 0
C Flores, H; Nurmi, P; Hui, P IEEE Flores, Huber; Nurmi, Petteri; Hui, Pan AI-powered Multi-device Systems and Applications (PerCrowd 2019 Panel Discussion) 2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) International Conference on Pervasive Computing and Communications English Proceedings Paper IEEE International Conference on Pervasive Computing and Communications (PerCom) MAR 11-15, 2019 Kyoto, JAPAN IEEE Cloud; Edge; Cloudlet; Artificial Intelligence; Device-to-Device; Data Analytics Multi-device computing is an emerging research area that envisions autonomous, proactive and dynamic self-organizing collaborations between several devices, e.g., to improve the energy footprint of the individual devices, to accelerate their performance, or to capture information that is not possible to acquire without multiple devices working together. However, merging the resources of multiple devices to work in collaboration in a self-organizing manner is a profound challenge. Devices in the wild are not autonomous nor social entities that are aware of their context, e.g., location, local resources, other devices, I/O interfaces, and users preferences, among others. The theme of the panel is to overview how by leveraging the power of AI, it is possible to design and develop seamless integration of devices to provide better multi-modal interactions to users and to reduce the burden of computations in the wild for devices. [Flores, Huber; Nurmi, Petteri; Hui, Pan] Univ Helsinki, Helsinki, Finland; [Nurmi, Petteri] Univ Lancaster, Lancaster, England; [Hui, Pan] HKUST, Hong Kong, Peoples R China University of Helsinki; Lancaster University; Hong Kong University of Science & Technology Flores, H (corresponding author), Univ Helsinki, Helsinki, Finland. huber.flores@helsinki.fi; p.nurmi@lancaster.ac.uk; panhui@cse.ust.hk Hui, Pan/AAK-6660-2020; Flores, Huber/AAP-5274-2021; Nurmi, Petteri/AAV-4865-2020 Hui, Pan/0000-0001-6026-1083; Flores, Huber/0000-0003-4551-629X; 0 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2474-2503 978-1-5386-9151-9 INT CONF PERVAS COMP 2019.0 334 334 1 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BN2MP 2023-03-23 WOS:000476951900060 0
J Wu, Z; Fan, DW; Zhou, Y; Li, RY; Noack, BR Wu, Zhi; Fan, Dewei; Zhou, Yu; Li, Ruiying; Noack, Bernd R. Jet mixing optimization using machine learning control EXPERIMENTS IN FLUIDS English Article CLOSED-LOOP CONTROL; EXTREMUM-SEEKING; SUBHARMONIC RESONANCE; SQUARE CYLINDER; FLOW-CONTROL; FREQUENCY; MANIPULATION; AMPLITUDE We experimentally optimize mixing of a turbulent round jet using machine learning control (MLC) following Li et al. (Exp Fluids 58(article 103):1-20, 2017). The jet is manipulated with one unsteady minijet blowing in wall-normal direction close to the nozzle exit. The flow is monitored with two hotwire sensors. The first sensor is positioned on the centerline five jet diameters downstream of the nozzle exit, i.e. the end of the potential core, while the second is located three jet diameters downstream and displaced towards the shear-layer. The mixing performance is monitored with mean velocity at the first sensor. A reduction of this velocity correlates with increased entrainment near the potential core. MLC is employed to optimize sensor feedback, a general open-loop broadband frequency actuation and combinations of both. MLC has identified the optimal periodic forcing with small duty cycle as the best control policy employing only 400 actuation measurements, each lasting for 5 s. This learning rate is comparable if not faster than typical optimization of periodic forcing with two free parameters (frequency and duty cycle). In addition, MLC results indicate that neither new frequencies nor sensor feedback improves mixing further-contrary to many of other turbulence control experiments. The optimality of pure periodic actuation may be attributed to the simple jet flapping mechanism in the minijet plane. The performance of sensor feedback is shown to face a challenge for small duty cycles. The jet mixing results demonstrate the untapped potential of MLC in quickly learning optimal general control policies, even deciding between open- and closed-loop control. [GRAPHICS] . [Wu, Zhi; Fan, Dewei; Zhou, Yu; Noack, Bernd R.] Harbin Inst Technol, Inst Turbulence Noise Vibrat Interact & Control, Shenzhen Campus, Shenzhen, Peoples R China; [Wu, Zhi; Fan, Dewei; Zhou, Yu] Digital Engn Lab Offshore Equipment, Shenzhen, Peoples R China; [Li, Ruiying] Univ Poitiers, CNRS, Inst PPRIME, ISAE,ENSMA, F-86962 Futuroscope, France; [Noack, Bernd R.] Campus Univ Orsay, CNRS, LIMSI, Rue John Neumann,Bat 508, F-91403 Orsay, France; [Noack, Bernd R.] Tech Univ Berlin, ISTA, Muller Breslau Str 8, D-10623 Berlin, Germany Harbin Institute of Technology; Centre National de la Recherche Scientifique (CNRS); Ecole Nationale Superieure de Mecanique et d'Aerotechnique (ISAE-ENSMA); Universite de Poitiers; Universite de Toulouse; Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO); Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Technical University of Berlin Zhou, Y; Noack, BR (corresponding author), Harbin Inst Technol, Inst Turbulence Noise Vibrat Interact & Control, Shenzhen Campus, Shenzhen, Peoples R China.;Zhou, Y (corresponding author), Digital Engn Lab Offshore Equipment, Shenzhen, Peoples R China.;Noack, BR (corresponding author), Campus Univ Orsay, CNRS, LIMSI, Rue John Neumann,Bat 508, F-91403 Orsay, France.;Noack, BR (corresponding author), Tech Univ Berlin, ISTA, Muller Breslau Str 8, D-10623 Berlin, Germany. yuzhou@hit.edu.cn; Bernd.Noack@limsi.fr Wu, Zhi/H-4193-2017; ZHOU, YU/G-5230-2017; Fan, Dewei/P-3631-2019; Noack, Bernd R./B-1242-2012; Zhou, Yu/HHN-3071-2022 Wu, Zhi/0000-0001-8495-1740; Fan, Dewei/0000-0002-2652-4414; Noack, Bernd R./0000-0001-5935-1962; French National Research Agency (ANR) as part of the Investissement dAvenir program, through the iCODE Institute project - IDEX Paris-Saclay [ANR-11-IDEX-0003-02]; ANR Grant 'ACTIV_ROAD'; ANR Grant 'FlowCon'; PSA Peugeot-Citroen; Institute Pprime; NSFC [91752109] French National Research Agency (ANR) as part of the Investissement dAvenir program, through the iCODE Institute project - IDEX Paris-Saclay(French National Research Agency (ANR)); ANR Grant 'ACTIV_ROAD'(French National Research Agency (ANR)); ANR Grant 'FlowCon'(French National Research Agency (ANR)); PSA Peugeot-Citroen; Institute Pprime; NSFC(National Natural Science Foundation of China (NSFC)) This work is supported by a public Grant overseen by the French National Research Agency (ANR) as part of the Investissement dAvenir program, through the iCODE Institute project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02, by the ANR Grants 'ACTIV_ROAD' and 'FlowCon'. The thesis of RL is supported by the OpenLab Fluidics between PSA Peugeot-Citroen and Institute Pprime (Fluidics@poitiers). The financial support of NSFC via Grant (approval no. 91752109) is acknowledged. We appreciate valuable stimulating discussions with Steven Brunton, Camila Chovet, Eurika Kaiser, Laurent Keirsbulck, Nathan Kutz, Richard Semaan and the French-German-Canadian-American pinball team: Guy Yoslan Cornejo-Maceda, Nan Deng, Francois Lusseyran, Robert Martinuzzi, Cedric Raibaudo and Luc Pastur. 50 18 19 3 49 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0723-4864 1432-1114 EXP FLUIDS Exp. Fluids AUG 2018.0 59 8 131 10.1007/s00348-018-2582-4 0.0 17 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics GP0AC Green Submitted 2023-03-23 WOS:000440468600001 0
J Xu, X; Li, HW; Sun, C; Ramos, TB; Darouich, H; Xiong, YW; Qu, ZY; Huang, GH Xu, Xu; Li, Huawei; Sun, Chen; Ramos, Tiago B.; Darouich, Hanaa; Xiong, Yunwu; Qu, Zhongyi; Huang, Guanhua Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs* IRRIGATION AND DRAINAGE English Article neural network; non-linear fitting; pedotransfer functions; soil hydraulic parameters; soil water model; textural data conversion Many agro-environmental studies focusing on the efficient management of soils and water resources make use of soil water simulation models. Reliable soil hydraulic properties are critical for ensuring the accuracy of model simulations. However, soil hydraulic parameters in northern China are generally derived using external pedotransfer functions (PTFs) that do not take into account the specificities of local edaphoclimatic conditions due to the lack of a better alternative. Therefore, the main objective of this paper was to develop PTFs for estimating the soil water retention curve (SWRC) in northern China agricultural soils (named PTF-ANC). A total of 440 soil horizons were collected from the existing literature. A flexible soil-textural conversion program was first developed to harmonize soil particle-size data into the United States Department of Agriculture (USDA) classification system. The SWRC parameters of the van Genuchten model were also generated by curve fitting. Then, the PTF-ANC were developed using artificial neural networks, with soil texture and bulk density being used as input data and with a basic three-layer back-propagation neural network. The PTF-ANC showed an acceptable accuracy when predicting the SWRC, indicating a strong application potential for northern China soils. Comparison of estimates with two widely used external PTFs also showed that these were not suitable for characterizing the SWRC of northern China agricultural soils. This is due to the fact that the main soil textures (silt and silty loam textures) found in northern China soils were misrepresented in those external soil databases. Overall, this paper presented the absolute necessity of developing specific PTFs for northern China agricultural soils. [Xu, Xu; Xiong, Yunwu; Huang, Guanhua] China Agr Univ, Chinese Israeli Int Ctr Res & Training Agr, Beijing 100083, Peoples R China; [Li, Huawei] Henan Tech Coll Construct, Dept Engn Management, Zhengzhou, Peoples R China; [Sun, Chen] Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing, Peoples R China; [Ramos, Tiago B.] Univ Lisbon, Inst Super Tecn, MARETEC, Lisbon, Portugal; [Darouich, Hanaa] Univ Lisbon, Inst Super Agron, Ctr Invest Agron Alimentos Ambiente & Paisagem LE, Lisbon, Portugal; [Qu, Zhongyi] Inner Mongolia Agr Univ, Coll Water Conservancy & Civil Engn, Hohhot, Peoples R China China Agricultural University; Chinese Academy of Agricultural Sciences; Institute of Environment & Sustainable Development in Agriculture, CAAS; Universidade de Lisboa; Instituto Superior Tecnico; Universidade de Lisboa; Inner Mongolia Agricultural University Huang, GH (corresponding author), China Agr Univ, Chinese Israeli Int Ctr Res & Training Agr, Beijing 100083, Peoples R China. ghuang@cau.edu.cn Ramos, Tiago/D-6758-2013 Ramos, Tiago/0000-0002-7823-4048; Darouich, Hanaa/0000-0001-8811-1175 13th Five-year National Key Research and Development Program of the Chinese Ministry of Science and Technology [2016YFC0400107]; National Natural Science Foundation of China [52022108, 51639009] 13th Five-year National Key Research and Development Program of the Chinese Ministry of Science and Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) 13th Five-year National Key Research and Development Program of the Chinese Ministry of Science and Technology, Grant/Award Number: 2016YFC0400107; National Natural Science Foundation of China, Grant/Award Numbers: 52022108, 51639009 52 2 2 2 18 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1531-0353 1531-0361 IRRIG DRAIN Irrig. Drain. OCT 2021.0 70 4 593 608 10.1002/ird.2584 0.0 FEB 2021 16 Agronomy; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Water Resources WF3WI 2023-03-23 WOS:000620464100001 0
J Long, NN; Lei, YX; Peng, LH; Xu, P; Mao, P Long, Nannan; Lei, Yongxiang; Peng, Lianhua; Xu, Ping; Mao, Ping A scoping review on monitoring mental health using smart wearable devices MATHEMATICAL BIOSCIENCES AND ENGINEERING English Review artificial intelligence; mental health; psychological prediction; signal indicator STRESS DETECTION; SENSORS; TECHNOLOGY; LANGUAGE; CHILDREN With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Longterm and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health. [Long, Nannan; Mao, Ping] Cent South Univ, Xiangya Hosp 3, Dept Nursing, Changsha 410013, Peoples R China; [Long, Nannan; Peng, Lianhua] Cent South Univ, Xiangya Nursing Sch, Changsha 410031, Peoples R China; [Lei, Yongxiang] Politecn Milan, Dept Mech Engn, I-10056 Milan, Italy; [Peng, Lianhua] Jinggangshan Univ, Affiliated Hosp, Jianggangshan 343100, Peoples R China; [Xu, Ping] ZiBo Hosp Tradit Chinese & Western Med, Zibo 255020, Peoples R China; [Mao, Ping] Hunan Key Lab Nursing, Changsha 410013, Peoples R China Central South University; Central South University; Polytechnic University of Milan; Jinggangshan University Mao, P (corresponding author), Cent South Univ, Xiangya Hosp 3, Dept Nursing, Changsha 410013, Peoples R China.;Mao, P (corresponding author), Hunan Key Lab Nursing, Changsha 410013, Peoples R China. ping.mao@csu.edu.cn Changsha Natural Science Foundation [kq2202422]; Chinese Scholarship Council (CSC) [202006370101] Changsha Natural Science Foundation; Chinese Scholarship Council (CSC)(China Scholarship Council) This work is supported by the Changsha Natural Science Foundation No. kq2202422 and the Chinese Scholarship Council (CSC) under grant No. 202006370101. 77 2 2 10 19 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA 1547-1063 1551-0018 MATH BIOSCI ENG Math. Biosci. Eng. 2022.0 19 8 7899 7919 10.3934/mbe.2022369 0.0 21 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology 1W6GT 35801449.0 gold 2023-03-23 WOS:000806871700002 0
J Leone, M Leone, Massimo Digital Cosmetics A semiotic study on the Chinese and global meanings of the face in image-processing apps CHINESE SEMIOTIC STUDIES English Article digital beautification; racial biases; self-representation; semiotics of the face; social networks ART The earliest extant depictions of the human face are not simply realistic but represented through specific technologies (means) and techniques (styles). In these representations, the face was probably idealized in order to empower its agency through simulacra. The history of art sees humans become increasingly aware of the impact of technology and technique on the production of visual representations of the face. With photography, and even more so with its digital version, technology is developed, hidden, and miniaturized so as to democratize and market technique. The result, however, a naturalization of technology, is increasingly problematic in the era of algorithms: artificial intelligence absorbs the social bias of its engineers. This is particularly evident in the domain of digital cosmetics: successful apps are used to process and share billions of facial images, yet few critically reflect on the aesthetic ideology underpinning them. This is an urgent task for visual, social, and cultural semiotics. [Leone, Massimo] Univ Turin, Dept Philosophy & Educ Sci, Turin, Italy; [Leone, Massimo] Shanghai Univ, Dept Chinese Language & Literature, Shanghai, Peoples R China University of Turin; Shanghai University Leone, M (corresponding author), Univ Turin, Dept Philosophy & Educ Sci, Turin, Italy.;Leone, M (corresponding author), Shanghai Univ, Dept Chinese Language & Literature, Shanghai, Peoples R China. massimo.leone@unito.it European Research Council (ERC) under the European Union [819649] European Research Council (ERC) under the European Union(European Research Council (ERC)) This publication is part of a project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 819649 -FACETS). 19 1 1 0 3 DE GRUYTER MOUTON BERLIN GENTHINER STRASSE 13, 10785 BERLIN, GERMANY 2198-9605 2198-9613 CHIN SEMIOT STUD Chin. Semiot. Stud. NOV 2020.0 16 4 551 580 10.1515/css-2020-0030 0.0 30 Humanities, Multidisciplinary Emerging Sources Citation Index (ESCI) Arts & Humanities - Other Topics PA4EG Green Submitted, hybrid 2023-03-23 WOS:000595590500003 0
J Kim, J; Nili, H; Truong, ND; Ahmed, T; Yang, JW; Jeong, DS; Sriram, S; Ranasinghe, DC; Ippolito, S; Chun, H; Kavehei, O Kim, Jeeson; Nili, Hussein; Nhan Duy Truong; Ahmed, Taimur; Yang, Jiawei; Jeong, Doo Seok; Sriram, Sharath; Ranasinghe, Damith C.; Ippolito, Samuel; Chun, Hosung; Kavehei, Omid Nano-Intrinsic True Random Number Generation: A Device to Data Study IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS English Article Low-frequency noise; non-volatile memory; random number generation; random telegraph noise; resistive memory RANDOM TELEGRAPH NOISE; MEMORY-PART I; STATISTICAL FLUCTUATIONS; CMOS; RRAM; SIGNAL; STATE We present a circuit technique to extract true random numbers from carrier capture and emission in oxide traps in the emerging redox-based resistive memory (ReRAM). This phenomenon that appears as small changes in current magnitude passing through the device is known as random telegraph noise (RTN) and is increasingly becoming a source of reliability issues in nanometer-scale devices. We demonstrate a circuit that exploits TRN suitable for a true random number generator (TRNG) in security applications, where the system is secure from different adversarial attacks, including side-channel monitoring and machine learning analysis. We experimentally characterize RTN in ReRAMs and extract its dependency to temperature, voltage, and area. We introduce an RTN harvesting circuit to mitigate sensitivities to temperature fluctuations, injected supply noise, and power signal monitoring. We reduced bias and imbalance in data due to high-speed sampling via von Neumann whitening. The circuit is compared to conventional non-differential readout approach. Our approach shows a 7.26 times improvement in autocorrelation and significant resilience against the injected supply noise. We also demonstrate the TRNG's quality and robustness using statistical tests and machine learning attacks. The output of the generator satisfies statistical tests for randomness and is immune to modeling attacks based on the machine learning methods. [Kim, Jeeson; Nhan Duy Truong; Kavehei, Omid] Univ Sydney, Nanoneuroinspired Res Lab, Sydney, NSW 2006, Australia; [Kim, Jeeson; Ahmed, Taimur; Sriram, Sharath; Ippolito, Samuel] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia; [Nili, Hussein] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA; [Nhan Duy Truong; Kavehei, Omid] Univ Sydney, Nano Inst, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia; [Yang, Jiawei] Wenzhou Med Univ, Wenzhou Hosp, Wenzhou 325035, Peoples R China; [Yang, Jiawei] Nanochap Elect Pty Ltd, Hangzhou 311121, Zhejiang, Peoples R China; [Jeong, Doo Seok] Hanyang Univ, Seoul 04763, South Korea; [Ranasinghe, Damith C.] Univ Adelaide, Sch Comp Sci, Auto Id Labs, Adelaide, SA 5005, Australia; [Chun, Hosung] IMEC, B-3001 Leuven, Belgium University of Sydney; Royal Melbourne Institute of Technology (RMIT); University of California System; University of California Santa Barbara; University of Sydney; Wenzhou Medical University; Hanyang University; University of Adelaide; IMEC Kavehei, O (corresponding author), Univ Sydney, Nanoneuroinspired Res Lab, Sydney, NSW 2006, Australia.;Kavehei, O (corresponding author), Univ Sydney, Nano Inst, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia. jeeson.kim@rmit.edu.au; omid.kavehei@sydney.edu.au Yang, Jiawei/HNJ-5012-2023; Kavehei, Omid/C-1294-2012; Ranasinghe, Damith/AAC-1387-2021; Ahmed, Taimur/AAM-3033-2020; Nili, Hussein/M-1851-2013; Sriram, Sharath/C-6200-2011 Kavehei, Omid/0000-0002-2753-5553; Ranasinghe, Damith/0000-0002-2008-9255; Ahmed, Taimur/0000-0002-4092-3323; Nili, Hussein/0000-0001-6629-7749; Sriram, Sharath/0000-0002-8365-7504; Ippolito, Samuel/0000-0002-6844-7963; Kim, Jeeson/0000-0002-7854-7335; Truong, Nhan/0000-0003-4350-8026 Australian Research Council [DP140103448]; National Natural Science Foundation of China [61501332] Australian Research Council(Australian Research Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Australian Research Council under Grant DP140103448 and in part by the National Natural Science Foundation of China under Grant 61501332. 64 14 16 3 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-8328 1558-0806 IEEE T CIRCUITS-I IEEE Trans. Circuits Syst. I-Regul. Pap. JUL 2019.0 66 7 2615 2626 10.1109/TCSI.2019.2895045 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering IE8IC 2023-03-23 WOS:000472616100019 0
J Mortazavi, B; Shahrokhi, M; Shojaei, F; Rabczuk, T; Zhuang, XY; Shapeev, AV Mortazavi, Bohayra; Shahrokhi, Masoud; Shojaei, Fazel; Rabczuk, Timon; Zhuang, Xiaoying; Shapeev, Alexander, V A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C5N monolayers NANOSCALE English Article THERMAL-CONDUCTIVITIES; CARBON; EFFICIENT; MORPHOLOGY; DYNAMICS; C3N Carbon nitride nanomembranes are currently among the most appealing two-dimensional (2D) materials. As a nonstop endeavor in this field, a novel 2D fused aromatic nanoporous network with a C5N stoichiometry has been most recently synthesized. Inspired by this experimental advance and exciting physics of nanoporous carbon nitrides, herein we conduct extensive density functional theory calculations to explore the electronic, optical and photocatalytic properties of the C5N monolayer. In order to examine the dynamic stability and evaluate the mechanical and heat transport properties under ambient conditions, we employ state of the art methods on the basis of machine-learning interatomic potentials. The C5N monolayer is found to be a direct band gap semiconductor, with a band-gap of 2.63 eV according to the HSE06 method. The obtained results confirm the dynamic stability, remarkable tensile strengths over 10 GPa and a low lattice thermal conductivity of similar to 9.5 W m(-1) K-1 for the C5N monolayer at room temperature. The first absorption peak of the single-layer C5N along the in-plane polarization is predicted to appear in the visible range of light. With a combination of high carrier mobility, appropriate band edge positions and strong absorption of visible light, the C5N monolayer might be an appealing candidate for photocatalytic water splitting reactions. The presented results provide an extensive understanding concerning the critical physical properties of the C5N nanosheets and also highlight the robustness of machine-learning interatomic potentials in the exploration of complex physical behaviors. [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Dept Math & Phys, Chair Computat Sci & Simulat Technol, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Opt & Engn Inn, Hannover, Germany; [Shahrokhi, Masoud] Islamic Azad Univ, Kermanshah Branch, Young Researchers Club, Kermanshah, Iran; [Shojaei, Fazel] Persian Gulf Univ, Fac Nano & Biosci & Technol, Dept Chem, Bushehr 75169, Iran; [Rabczuk, Timon; Zhuang, Xiaoying] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai, Peoples R China; [Shapeev, Alexander, V] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Bolshoy Bulvar 30, Moscow 143026, Russia Leibniz University Hannover; Leibniz University Hannover; Islamic Azad University; Persian Gulf University; Tongji University; Skolkovo Institute of Science & Technology Mortazavi, B (corresponding author), Leibniz Univ Hannover, Dept Math & Phys, Chair Computat Sci & Simulat Technol, Appelstr 11, D-30167 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Opt & Engn Inn, Hannover, Germany.;Shapeev, AV (corresponding author), Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Bolshoy Bulvar 30, Moscow 143026, Russia. Bohayra.mortazavi@gmail.com; a.shapeev@skoltech.ru Zhuang, Xiaoying/G-4754-2011 Zhuang, Xiaoying/0000-0001-6562-2618 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Russian Science Foundation [18-1300479]; Persian Gulf University Research Council Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Russian Science Foundation(Russian Science Foundation (RSF)); Persian Gulf University Research Council B. M. and X. Z. appreciate the funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). A. V. S. is supported by the Russian Science Foundation (grant no. 18-1300479, https://rscf.ru/project/18-13-00479/).F.S.thanks the Persian Gulf University Research Council for support of this study. The authors are thankful to the VEGAS cluster at the Bauhaus University of Weimar for providing the computational resources. 42 9 9 22 55 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2040-3364 2040-3372 NANOSCALE Nanoscale MAR 17 2022.0 14 11 4324 4333 10.1039/d1nr06449e 0.0 MAR 2022 10 Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics ZU8OP 35253027.0 2023-03-23 WOS:000765401000001 0
C Gao, D; Zhang, GL; Yin, XZ; Li, B; Schlichtmann, U; Zhuo, C IEEE Gao, Di; Li Zhang, Grace; Yin, Xunzhao; Li, Bing; Schlichtmann, Ulf; Zhuo, Cheng Reliable Memristor-based Neuromorphic Design Using Variation- and Defect-Aware Training 2021 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN (ICCAD) ICCAD-IEEE ACM International Conference on Computer-Aided Design English Proceedings Paper 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD) NOV 01-04, 2021 ELECTR NETWORK IEEE,ACM,IEEE CAS,IEEE Council Elect Design Automat,ACM SIGDA,IEEE Electron Devices Soc The memristor crossbar provides a unique opportunity to develop a neuromorphic computing system (NCS) with high scalability and energy efficiency. However, the reliability issues that arise from the immature fabrication process and physical device limitations, i.e., variations and stuck-at-faults (SAF), dramatically prevent its wide application in practice. Specifically, variations make the programmed weights deviate from their expected values. On the other hand, defective memristors cannot even represent the weights effectively. In this work, we propose a variation- and defect-aware framework to improve the reliability of memristor-based NCS while minimizing the inference performance loss. We propose to develop analytical weight models to characterize the non-ideal effects of variations and SAFs, which can then be incorporated into a Bayesian neural network as priori and constraint. We then convert the reliability improvement to the neural network training for optimal weights that can accommodate variations and defects across the chips, which does not require computation-intensive retraining or cost-expensive testing. Extensive experimental results with the proposed framework confirm its effective capability of improving the reliability of NCS, while significantly mitigating the inference accuracy degradation under even severe variations and SAFs. [Gao, Di; Yin, Xunzhao; Zhuo, Cheng] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China; [Li Zhang, Grace; Li, Bing; Schlichtmann, Ulf] Tech Univ Munich, Dept Elect Design Automat, Munich, Germany Zhejiang University; Technical University of Munich Yin, XZ; Zhuo, C (corresponding author), Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China. xzyin1@zju.edu.cn; czhuo@zju.edu.cn Zhuo, Cheng/HLV-8134-2023 Zhejiang Provincial Key RD program [2020C01052]; NSFC [62034007]; Zhejiang Provincial NSF [LD21F040003, LQ21F040006]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [457473137] Zhejiang Provincial Key RD program; NSFC(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial NSF(Natural Science Foundation of Zhejiang Province); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) This work was supported by Zhejiang Provincial Key R&D program with Grant No. 2020C01052, NSFC with Grant No. 62034007, Zhejiang Provincial NSF with Grant No. LD2lF040003 and LQ2lF040006, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -457473137. 35 0 0 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1933-7760 978-1-6654-4507-8 ICCAD-IEEE ACM INT 2021.0 10.1109/ICCAD51958.2021.9643468 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Manufacturing; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS6FI 2023-03-23 WOS:000747493600034 0
J Nian, R; He, B; Zheng, B; van Heeswijk, M; Yu, Q; Miche, Y; Lendasse, A Nian, Rui; He, Bo; Zheng, Bing; van Heeswijk, Mark; Yu, Qi; Miche, Yoan; Lendasse, Amaury Extreme learning machine towards dynamic model hypothesis in fish ethology research NEUROCOMPUTING English Article; Proceedings Paper International Workshop of Extreme Learning Machines (ELM) DEC 11-13, 2012 Singapore, SINGAPORE Extreme learning machine; Fish ethology; Video surveillance system; Dynamic state space; Color distribution; Object recognition ELM; ALGORITHM; KERNEL In this paper, we present one dynamic model hypothesis to perform fish trajectory tracking in the fish ethology research and develop the relevant mathematical criterion on the basis of the Extreme Learning Machine (ELM). It is shown that the proposed scheme can conduct the non-linear and non Gaussian tracking process by multiple historical cues and current predictions - the state vector motion, the color distribution and the appearance recognition, all of which can be extracted from the single-hidden layer feedforward neural network (SLFN) at diverse levels with ELM. The strategy of the hierarchical hybrid ELM ensemble then combines the individual SLFN of the tracking cues for the performance improvements. The simulation results have shown the excellent performance in both robustness and accuracy of the developed approach. (C) 2013 Elsevier B.V. All rights reserved. [Nian, Rui; He, Bo] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266003, Peoples R China; [van Heeswijk, Mark; Yu, Qi; Miche, Yoan; Lendasse, Amaury] Aalto Univ, Dept Informat & Comp Sci, FI-00076 Aalto, Finland; [Lendasse, Amaury] Basque Fdn Sci, IKERBASQUE, Bilbao 48011, Spain; [Lendasse, Amaury] Univ Basque Country, Fac Comp Sci, Computat Intelligence Grp, Donostia San Sebastian, Spain; [Lendasse, Amaury] Arcada Univ Appl Sci, Helsinki 00550, Finland Ocean University of China; Aalto University; Basque Foundation for Science; University of Basque Country; Arcada University of Applied Sciences He, B (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266003, Peoples R China. bhe@ouc.edu.cn Miche, Yoan/J-9079-2014 Miche, Yoan/0000-0001-8864-2312 44 44 44 0 16 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing MAR 27 2014.0 128 273 284 10.1016/j.neucom.2013.03.054 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science AB5TN 2023-03-23 WOS:000331851700033 0
C Shukla, A; Hudemann, KN; Hecker, A; Schmid, S Assoc Comp Machinery Shukla, Apoory; Hudemann, Kevin Nico; Hecker, Artur; Schmid, Stefan Runtime Verification of P4 Switches with Reinforcement Learning NETAI'19: PROCEEDINGS OF THE 2019 ACM SIGCOMM WORKSHOP ON NETWORK MEETS AI & ML English Proceedings Paper ACM SIGCOMM Workshop on Network Meets AI and ML (NetAI) AUG 23, 2019 Beijing, PEOPLES R CHINA ACM SIGCOMM,Assoc Comp Machinery Network Verification; P4; Machine Learning; Fuzzing We present the design and early implementation of P4RL, a system that uses reinforcement learning-guided fuzz testing to execute the verification of P4 switches automatically at runtime. P4RL system uses our novel user-friendly query language, p4q to conveniently specify the intended properties in simple conditional statements (if-else) and check the actual runtime behavior of the P4 switch against such properties. In P4RL, user-specified p4q queries with the control plane configuration, Agent, and the Reward System guide the fuzzing process to trigger runtime bugs automatically during Agent training. To illustrate the strength of P4RL, we developed and evaluated an early prototype of P4RL system that executes runtime verification of a P4 network device, e.g., L3 (Layer-3) switch. Our initial results are promising and show that P4RL automatically detects diverse bugs while outperforming the baseline approach. [Shukla, Apoory; Hudemann, Kevin Nico] TU Berlin, Berlin, Germany; [Hecker, Artur] Huawei Technol, Shenzhen, Peoples R China; [Schmid, Stefan] Univ Vienna, Fac Copm Sci, Vienna, Austria Technical University of Berlin; Huawei Technologies; University of Vienna Shukla, A (corresponding author), TU Berlin, Berlin, Germany. Hecker, Artur/0000-0003-3604-2686 German Bundesministerium fur Bildung und Forschung (BMBF) Software Campus [01IS17052]; European Research Council (ERC) grant ResolutioNet [ERC-StG-679158] German Bundesministerium fur Bildung und Forschung (BMBF) Software Campus(Federal Ministry of Education & Research (BMBF)); European Research Council (ERC) grant ResolutioNet(European Research Council (ERC)) We thank Anja Feldmann, Georgios Smargadakis, Bhargava Shastry, and our anonymous reviewers for their helpful feedback. This work and its dissemination efforts were conducted as a part of Verify project supported by the German Bundesministerium fur Bildung und Forschung (BMBF) Software Campus grant 01IS17052 and by the European Research Council (ERC) grant ResolutioNet (ERC-StG-679158). 22 25 26 0 4 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6872-8 2019.0 1 7 10.1145/3341216.3342206 0.0 7 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO2PA 2023-03-23 WOS:000506862100001 0
J Yang, YC; Sun, DW; Pu, HB; Wang, NN; Zhu, ZW Yang, Yi-Chao; Sun, Da-Wen; Pu, Hongbin; Wang, Nan-Nan; Zhu, Zhiwei Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion POSTHARVEST BIOLOGY AND TECHNOLOGY English Article Hyperspectral imaging (HSI); Litchi; Anthocyanin; Radial basis function support vector regression (RBF-SVR); Model fusion; Radial basis function neural network (RBF-NN) NEAR-INFRARED SPECTROSCOPY; RADIAL BASIS FUNCTIONS; QUALITY EVALUATION; COMPUTER VISION; MULTIVARIATE-ANALYSIS; DESORPTION ISOTHERMS; POSTHARVEST STORAGE; LITCHI FRUIT; COLOR; BEEF A quantitative approach was proposed to evaluate anthocyanin content of lychee pericarp using hyperspectral imaging (HSI) technique. A HSI system working in the range of 350-1050 nm was used to acquire a 3-D lychee image. Successive projection algorithm (SPA) and stepwise regression (SWR) algorithm were utilized to reduce data dimensionality and search for optimal wavelengths related with anthocyanin content in pericarp. Radial basis function support vector regression (RBF-SVR) was adopted to establish quantitative relationship between hyperspectral image information in two sets of optimal wavelengths and anthocyanin content of pericarp. Finally, in order to improve prediction accuracy, SPA-RBF-SVR and SWR-RBF-SVR models were fused into a single model by radial basis function neural network (RBF-NN) algorithm. The results revealed that the fused model possessed a better performance than either SPA-RBF-SVR or SWR-RBF-SVR models alone, as the fused model showed higher coefficients of determination (R-2) of 0.891 and 0.872, and lower root mean square errors (RMSEs) of 0.567% and 0.610% for the training and the testing sets, respectively. Visualization maps based on the fused model were generated to display the anthocyanin distribution within lychee pericarp. This study demonstrates that HSI is capable of predicting and visualizing anthocyanin evolution in the pericarp of lychee during storage. (C) 2015 Elsevier B.V. All rights reserved. [Yang, Yi-Chao; Sun, Da-Wen; Pu, Hongbin; Wang, Nan-Nan; Zhu, Zhiwei] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland Univ Coll Dublin, Univ Coll Dublin, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Hongbin, Pu/ABE-8433-2020; Wang, Nan/GRY-3150-2022; Sun, Da-Wen/B-1899-2012; Wang, Nan/HLV-7836-2023 Hongbin, Pu/0000-0002-7977-1902; Sun, Da-Wen/0000-0002-3634-9963; Guangdong Province Government (China); National Key Technologies RD Program [2014BAD08B09, 2015BAD19B03]; International S&T Cooperation Programme of China [2015DFA71150]; International S&T Cooperation Projects of Guangdong Province [2013B051000010] Guangdong Province Government (China); National Key Technologies RD Program(National Key Technology R&D Program); International S&T Cooperation Programme of China; International S&T Cooperation Projects of Guangdong Province The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by the National Key Technologies R&D Program (2014BAD08B09, 2015BAD19B03), the International S&T Cooperation Programme of China (2015DFA71150), and the International S&T Cooperation Projects of Guangdong Province (2013B051000010). 74 27 28 4 61 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-5214 1873-2356 POSTHARVEST BIOL TEC Postharvest Biol. Technol. MAY 2015.0 103 55 65 10.1016/j.postharvbio.2015.02.008 0.0 11 Agronomy; Food Science & Technology; Horticulture Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Food Science & Technology CG2EU 2023-03-23 WOS:000353088500007 0
J Basto-Fernandes, V; Yevseyeva, I; Mendez, JR; Zhao, JQ; Fdez-Riverola, F; Emmerich, MTM Basto-Fernandes, Vitor; Yevseyeva, Iryna; Mendez, Jose R.; Zhao, Jiaqi; Fdez-Riverola, Florentino; Emmerich, Michael T. M. A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification APPLIED SOFT COMPUTING English Article Spam filtering; Multi-objective optimization; Parsimony; Three-way classification; Rule-based classifiers; SpamAssassin HYPERVOLUME Classifier performance optimization in machine learning can be stated as a multi-objective optimization problem. In this context, recent works have shown the utility of simple evolutionary multi-objective algorithms (NSGA-II, SPEA2) to conveniently optimize the global performance of different anti-spam filters. The present work extends existing contributions in the spam filtering domain by using three novel indicator-based (SMS-EMOA, CH-EMOA) and decomposition-based (MOEA/D) evolutionary multi objective algorithms. The proposed approaches are used to optimize the performance of a heterogeneous ensemble of classifiers into two different but complementary scenarios: parsimony maximization and e-mail classification under low confidence level. Experimental results using a publicly available standard corpus allowed us to identify interesting conclusions regarding both the utility of rule-based classification filters and the appropriateness of a three-way classification system in the spam filtering domain. (C) 2016 Elsevier B.V. All rights reserved. [Basto-Fernandes, Vitor] Polytech Inst Leiria, Comp Sci & Commun Res Ctr, Sch Technol & Management, P-2411901 Leiria, Portugal; [Yevseyeva, Iryna] De Montfort Univ, Fac Technol, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England; [Mendez, Jose R.; Fdez-Riverola, Florentino] Univ Vigo, Informat Engn Sch, Campus Univ As Lagoas S-N, Orense 32004, Spain; [Zhao, Jiaqi] Xidian Univ, Int Res Ctr Intelligent Percept & Computat, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi, Peoples R China; [Emmerich, Michael T. M.] Leiden Univ, Fac Sci, Leiden Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands De Montfort University; Universidade de Vigo; Xidian University; Leiden University; Leiden University - Excl LUMC Fdez-Riverola, F (corresponding author), ESEI Escuela Super Ingn Informat, Edificio Politecn,Campus Univ Lagoas S-N, Orense 32004, Spain. vitor.basto.fernandes@ipleiria.pt; iryna.yevseyeva@dmu.ac.uk; moncho.mendez@uvigo.es; jiaqizhao@stu.xidian.edu.cn; riverola@uvigo.es; emmerich@liacs.nl Emmerich, Michael/AAD-4791-2021; Méndez Reboredo, José Ramón/F-3993-2016; Fdez-Riverola, Florentino/G-1411-2011; Basto-Fernandes, Vitor/N-1891-2016 Méndez Reboredo, José Ramón/0000-0002-1935-4760; Fdez-Riverola, Florentino/0000-0002-3943-8013; Basto-Fernandes, Vitor/0000-0003-4269-5114; Emmerich, Michael/0000-0002-7342-2090; Zhao, Jiaqi/0000-0002-3564-5090 University of Vigo [14VI05] University of Vigo This work has been partially funded by the [14VI05] Contract Programme from the University of Vigo. SING group thanks CITI (Centro de Investigation, Transferencia e Innovation) from University of Vigo for hosting its IT infrastructure. The authors would like to thank Pu Wang for sharing the code of CH-MOGP. 51 17 19 3 26 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. NOV 2016.0 48 111 123 10.1016/j.asoc.2016.06.043 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science EE4CL Green Submitted, Green Accepted 2023-03-23 WOS:000389549400009 0
C Massa, A; Oliveri, G; Salucci, M IEEE Massa, Andrea; Oliveri, Giacomo; Salucci, Marco Recent Advances in Complex Reflectarray Synthesis within the System-by-Design Framework 2022 16TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP) Proceedings of the European Conference on Antennas and Propagation English Proceedings Paper 16th European Conference on Antennas and Propagation (EuCAP) MAR 27-APR 01, 2022 Madrid, SPAIN European Assoc Antenna & Propagat reflectarray antennas; system-by-design; metasurfaces; artificial intelligence; wave manipulation; surface electromagnetics System-by-Design (SbD) has recently emerged as a powerful and efficient approach in reflectarray antenna (RA) synthesis and optimization. The features/properties of SbD (e.g., its flexibility and natural capability to handle complex multi-scale task-oriented problems) and the intrinsic challenges associated to advanced RA synthesis have motivated a great interest in developing and applying SbD-based approaches in reflectarray engineering. Within this framework, the objective of this contribution is to present a review of the current and most recent advances of SbD formulations as applied to reflectarray design developed at the ELEDIA Research Center. Towards this end, the customization of the SbD loop to the RA synthesis problem is illustrated, and the integration of advanced micro-scale modeling techniques based on learning-by-example strategies with macro-scale task-oriented optimization strategies is discussed. Current challenges and trends in the application of SbD to the solution of advanced RA design problems are remarked, as well. [Massa, Andrea; Oliveri, Giacomo; Salucci, Marco] UniTN Univ Trento, ELEDIA Res Ctr ELEDIA, DICAM, Trento, Italy; [Massa, Andrea] UESTC UESTC, ELEDIA Res Ctr ELEDIA, Chengdu, Peoples R China; [Massa, Andrea] TSINGHUA Tsinghua Univ, ELEDIA Res Ctr ELEDIA, Beijing, Peoples R China University of Trento Massa, A (corresponding author), UniTN Univ Trento, ELEDIA Res Ctr ELEDIA, DICAM, Trento, Italy.;Massa, A (corresponding author), UESTC UESTC, ELEDIA Res Ctr ELEDIA, Chengdu, Peoples R China.;Massa, A (corresponding author), TSINGHUA Tsinghua Univ, ELEDIA Res Ctr ELEDIA, Beijing, Peoples R China. andrea.massa@unitn.it; giacomo.oliveri@unitn.it; marco.salucci@unitn.it Oliveri, Giacomo/F-1030-2011; Salucci, Marco/S-8654-2016; Massa, Andrea/F-3276-2013 Oliveri, Giacomo/0000-0002-5238-358X; Salucci, Marco/0000-0002-6948-8636; Massa, Andrea/0000-0002-8429-8937 Italian Ministry of Education, University, and Research [SCN_00166, CUP: E44G14000040008, CUP: E64I19000560001]; National Natural Science Foundation of China [6721001] Italian Ministry of Education, University, and Research(Ministry of Education, Universities and Research (MIUR)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work benefited from the networking activities carried out within the Project SMARTOUR -Piattaforma Intelligente per il Turismo (Grant no. SCN_00166) funded by the Italian Ministry of Education, University, and Research within the Program Smart cities and communities and Social Innovation (CUP: E44G14000040008), the Project SPEED (Grant No. 6721001) funded by the National Natural Science Foundation of China, and the Project CLOAKING METASURFACES FOR A NEW GENERATION OF INTELLIGENT ANTENNA SYSTEMS (MANTLES) funded by the Italian Ministry of Education, University, and Research within the PRIN2017 Program under grant 2017BHFZKH (CUP: E64I19000560001). 16 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-3342 978-88-31299-04-6 PROC EUR CONF ANTENN 2022.0 4 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BT2XZ 2023-03-23 WOS:000815113901110 0
C Jiang, ZY; Otto, R; Bing, ZS; Huang, K; Knoll, A IEEE Jiang, Zhuangyi; Otto, Richard; Bing, Zhenshan; Huang, Kai; Knoll, Alois Target Tracking Control of a Wheel-less Snake Robot Based on a Supervised Multi-layered SNN 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) OCT 24-JAN 24, 2020-2021 ELECTR NETWORK IEEE,RSJ The snake-like robot without wheels is a bio-inspired robot whose high degree of freedom results in a challenge in autonomous locomotion control. The use of a Spiking Neural Network (SNN) which is a biologically plausible artificial neural network can help to achieve the autonomous locomotion behavior of snake robots in an energy-efficient manner. Approaches that use an SNN without hidden layers have been applied in the single-target tracking task. However, due to the complexity of the 3D gaits on a wheel-less snake robot and the imprecision of the pose control while in motion, they have some fluctuation that adversely affects their performances. In this work, we design two multi-layered SNNs with different topology for a wheel-less snake robot to track a certain moving object. The visual signals obtained from a Dynamic Vision Sensor (DVS) are fed into the SNN to drive the locomotion controller. Furthermore, the Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) learning rule is utilized to train the SNN end-to-end. Compared to the SNN without hidden layers, the proposed multi-layered SNN with a separated hidden layer shows its advantage in terms of robustness. [Jiang, Zhuangyi; Otto, Richard; Bing, Zhenshan; Knoll, Alois] Tech Univ Munich, Dept Informat, Munich, Germany; [Huang, Kai] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China; [Huang, Kai] Peng Cheng Lab, Shenzhen, Peoples R China Technical University of Munich; Sun Yat Sen University; Peng Cheng Laboratory Jiang, ZY (corresponding author), Tech Univ Munich, Dept Informat, Munich, Germany. jiangz@in.tum.de; ottor@in.tum.de; bing@in.tum.de; huangk36@mail.sysu.edu.cn; knoll@in.tum.de European Unions Horizon 2020 Framework Programme for Research and Innovation [785907]; China Scholarship Council (CSC) [201606270201]; Science and Technology Planning Project of Guangzhou city of China [202007050004] European Unions Horizon 2020 Framework Programme for Research and Innovation; China Scholarship Council (CSC)(China Scholarship Council); Science and Technology Planning Project of Guangzhou city of China This research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Speci~c Grant Agreement No. 785907 (Human Brain Project SGA2). This work is supported in part by the scholarship from China Scholarship Council (CSC) under the Grant No. 201606270201 and the funding from Science and Technology Planning Project of Guangzhou city of China under Grant 202007050004. 22 0 0 8 10 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-7281-6212-6 IEEE INT C INT ROBOT 2020.0 7124 7130 10.1109/IROS45743.2020.9341520 0.0 7 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering; Robotics BS4YL Green Published 2023-03-23 WOS:000724145801096 0
J Dietrich, CF; Douira-Khomsi, W; Gharbi, H; Sharma, M; Cui, XW; Sparchez, Z; Richter, J; Kabaalioglu, A; Atkinson, NSS; Schreiber-Dietrich, D; Yi, D Dietrich, Christoph F.; Douira-Khomsi, Wiem; Gharbi, Hassen; Sharma, Malay; Cui, Xin Wu; Sparchez, Zeno; Richter, Joachim; Kabaalioglu, Adnan; Atkinson, Nathan S. S.; Schreiber-Dietrich, Dagmar; Yi, Dong Cystic and alveolar echinococcosis of the hepatobiliary tract - the role of new imaging techniques for improved diagnosis MEDICAL ULTRASONOGRAPHY English Review Tropical medicine; parasites; guidelines; contrast enhanced ultrasound; elastography CONTRAST-ENHANCED-ULTRASOUND; CLINICAL-PRACTICE RECOMMENDATIONS; 1 BASIC PRINCIPLES; EFSUMB-GUIDELINES; WFUMB GUIDELINES; HYDATID CYST; GENERAL-ASPECTS; PERIHEPATIC LYMPHADENOPATHY; ARTIFICIAL-INTELLIGENCE; INTRABILIARY RUPTURE Cystic echinococcosis (CE) or hydatidosis (hydatid cysts) is an infection with a wide spectrum of manifestations, from asymptomatic infection to fatal disease. Ultrasomd (US) allows screening. diagnosis, differential diagnosis, treatment guidance and follow-up of CE. under many circumstances. Hydatid cysts are predominantly observed in the liver. Herewith we present a review to demonstrate established and innovative imaging features of CE of the hepatobiliary tract. [Dietrich, Christoph F.] DAIM, Kliniken Hirslanden, Beau Site, Bern, Switzerland; [Dietrich, Christoph F.] Zhengzhou Univ, Affiliated Hosp 1, Sino German Res Ctr Ultrasound Med, Zhengzhou, Peoples R China; [Douira-Khomsi, Wiem] Hop Enfants Bechir Hamza, Tunis, Tunisia; [Gharbi, Hassen] Ibn Zohr Clin, Dept Radiol, Tunis, Tunisia; [Sharma, Malay] Jaswant Rai Special Hosp, Dept Gastroenterol, Meerut, Uttar Pradesh, India; [Cui, Xin Wu] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Med Ultrasound, Wuhan, Peoples R China; [Sparchez, Zeno] Univ Med & Pharm Iuliu Hatieganu, Inst Gastroenterol & Hepatol O Fodor Cluj Napoca, Med Dept 3, Cluj Napoca, Romania; [Richter, Joachim] Charite, Inst Trop Med & Int Hlth, Berlin, Germany; [Kabaalioglu, Adnan] Akdeniz Univ Hosp, Dept Radiol, Antalya, Turkey; [Atkinson, Nathan S. S.] Waitemata Dist Hlth Board, Dept Gastroenterol, Auckland, New Zealand; [Schreiber-Dietrich, Dagmar] Univ Wurzburg, Pediat Dept, Caritas Krankenhaus Bad Mergentheim, Acad Teaching Hosp, Wurzburg, Germany; [Yi, Dong] Fudan Univ, Zhongshan Hosp, Dept Ultrasound, Shanghai, Peoples R China Zhengzhou University; Universite de Tunis-El-Manar; Hopital d'Enfants; Huazhong University of Science & Technology; Iuliu Hatieganu University of Medicine & Pharmacy; Regional Institute of Gastroenterology & Hepatology; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Akdeniz University; Caritas Hospital Bad Mergentheim; University of Wurzburg; Fudan University Dietrich, CF (corresponding author), DAIM, Kliniken Hirslanden, Beau Site, Bern, Switzerland. c.f.dietrich@googlemail.com Kabaalioglu, Adnan/C-6912-2016 Kabaalioglu, Adnan/0000-0001-6247-1738 Bad Mergentheimer Leberzentrum e.V. Bad Mergentheimer Leberzentrum e.V. The authors thank the Bad Mergentheimer Leberzentrum e.V. for support. 104 8 8 0 11 SOC ROMANA ULTRASONOGRAFE MEDICINA BIOLOGIE-SRUMB CLUJ-NAPOCA STR CROITORIOR NR 19-21, CLUJ-NAPOCA, 400162, ROMANIA 1844-4172 2066-8643 MED ULTRASON Med. Ultrason. 2020.0 22 1 77 86 10.11152/mu-2421 0.0 10 Acoustics; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Radiology, Nuclear Medicine & Medical Imaging KQ9GF 32096792.0 gold 2023-03-23 WOS:000517228300013 0
C Huang, T; Baiocchi, M; Lei, X IEEE Huang, Tao; Baiocchi, Martina; Lei, Xia An ontological approach for automatic tracking causes of blackouts in power systems 2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON 2022) IEEE Mediterranean Electrotechnical Conference-MELECON English Proceedings Paper 21st IEEE Mediterranean Electrotechnical Conference (IEEE MELECON) JUN 14-16, 2022 Palermo, ITALY IEEE,IEEE Reg 8,IEEE Italy Sect,Univ Studi Palermo Blackout Analysis; Ontology; Natural Language Processing; Named Entity Recognition In recent years, the occurrence and damages of the blackouts in power systems saw an increase. Many of them are due to the impacts of climate change. However, tracing all blackouts that happened in the world and associating them with specific causes is quite challenging due to the huge amount of information over the Internet. In this paper, we proposed a framework for automatically analyzing the information collected from Internet based on the Ontological Approach coupled with Artificial Intelligence (AI) tailored for Natural Language Processing, which permits information extraction from reports to better understand the major entities of a power outage, their role, and the connections among them. However, our test shows that, despite the promising results of Named Entity Recognition in other fields, the lack of a standard ontology for blackout analysis is both the proof and the cause for the missed chance of exploiting this AI branch. [Huang, Tao; Baiocchi, Martina] Politecn Torino, Turin, Italy; [Huang, Tao; Lei, Xia] Xihua Univ, Chengdu, Peoples R China Polytechnic University of Turin; Xihua University Huang, T (corresponding author), Xihua Univ, Chengdu, Peoples R China. tao.huang@polito.it; martina.baiocchi@studenti.polito.it; snow_lei246@mail.xhu.edu.cn National Natural Science Foundation of China [51877181] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 51877181. 21 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2158-8481 978-1-6654-4280-0 IEEE MEDITERR ELECT 2022.0 813 818 10.1109/MELECON53508.2022.9843078 0.0 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT8NR 2023-03-23 WOS:000854091100143 0
J Aladejare, AE; Wang, Y Aladejare, Adeyemi Emman; Wang, Yu Estimation of rock mass deformation modulus using indirect information from multiple sources TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY English Article Deformation modulus; Rock mass; Big data analytics; Rock engineering projects; Probability; Uncertainty UNIAXIAL COMPRESSIVE STRENGTH; YOUNGS MODULUS; VARIABILITY; SELECTION; INDEX; MODEL Deformation modulus (E-m) of rock masses is an important design parameter in many rock engineering projects such as tunnels excavation, slopes, and foundations. As direct (field) measurements of Em are time consuming and sometimes impossible to perform, data of E-m is often limited or not available in many rock engineering projects. When there is limited or non-availability of E-m data from rock engineering projects, various indirect information from multiple sources (e.g., different rock mass classifications) often available during preliminary stages of rock engineering projects can be combined to estimate E-m. How to combine various information from multiple sources to estimate values of E-m remains a difficult task. This study aims to address this challenge by developing a Bayesian sequential updating approach, which uses information from multiple sources to estimate E-m. The proposed approach is formulated to combine information from tunneling quality index (Q) and rock mass rating (RMR) obtained during rock mass classifications to estimate E-m, in terms of its statistics and probability distribution. The proposed approach is illustrated using real Q and RMR data as inputs, and it is shown to satisfactorily estimate E-m values. The approach can also be used in big data analytics, by using multiple sources of information to uncover patterns and trends of rock properties and other useful information, especially during investigation into rock engineering projects such as underground excavations. [Aladejare, Adeyemi Emman] Univ Oulu, Oulu Min Sch, Pentti Kaiteran Katu 1, Oulu 90014, Finland; [Wang, Yu] City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China University of Oulu; City University of Hong Kong Aladejare, AE (corresponding author), Univ Oulu, Oulu Min Sch, Pentti Kaiteran Katu 1, Oulu 90014, Finland. adeyemi.aladejare@oulu.fi; yuwang@cityu.edu.hk Wang, Yu/AAM-1349-2020; Aladejare, Adeyemi/AGO-4158-2022; Aladejare, Adeyemi/AAC-6196-2019 Wang, Yu/0000-0003-4635-7059; Aladejare, Adeyemi/0000-0002-0599-3799 Research Grants Council of the Hong Kong Special Administrative Region, China [9042172 (CityU 11200115), 9042331 (CityU 11225216)] Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council) The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. 9042172 (CityU 11200115) and Project No. 9042331 (CityU 11225216)]. This financial support is gratefully acknowledged. 40 21 21 1 36 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0886-7798 TUNN UNDERGR SP TECH Tunn. Undergr. Space Technol. MAR 2019.0 85 76 83 10.1016/j.tust.2018.11.047 0.0 8 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering HJ9IL 2023-03-23 WOS:000457512000008 0
J Alshehri, AS; Tula, AK; You, FQ; Gani, R Alshehri, Abdulelah S.; Tula, Anjan K.; You, Fengqi; Gani, Rafiqul Next generation pure component property estimation models: With and without machine learning techniques AICHE JOURNAL English Article data analysis; group-contribution; machine learning; pure component property prediction GAUSSIAN-PROCESSES; ORGANIC-COMPOUNDS; DESIGN; PREDICTION; SELECTION; PRODUCT Physiochemical properties of pure components serve as the basis for the design and simulation of chemical products and processes. Models based on the molecular structural information of chemicals for the following 25 pure component properties are presented in this work: (critical-) temperature, pressure, volume, acentric factor; (normal-) boiling point, melting point, auto-ignition temperature; flash point; (standard-) enthalpy of formation, Gibbs energy of formation, enthalpy of fusion, enthalpy of vaporization, liquid molar volume; (environmental-) (lethal dose-) LC50 and LD50, photo-chemical oxidation potential, bioconcentration factor, permissible exposure limit; (physicochemical-) acid dissociation constant, water-solubility, octanol-water partition coefficient, Hildebrandt solubility parameter, Hansen solubility parameters. Utilizing functional groups for molecular representation, two parallel property estimation models where the group contributions for each property are regressed through traditional regression techniques and machine learning techniques are presented. Both techniques use an a priori data analysis before regression of model parameters. A dataset with more than 24,000 chemicals for the 25 pure component properties has been utilized for the development of the two sets of property models. The efficacy of the developed models and their use are highlighted together with a discussion on the overall performance, application range, and predictive capabilities with implications to product and/or process engineering problem solutions. [Alshehri, Abdulelah S.; You, Fengqi] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY USA; [Alshehri, Abdulelah S.] King Saud Univ, Coll Engn, Dept Chem Engn, Riyadh, Saudi Arabia; [Tula, Anjan K.] Zhejiang Univ, Coll Control Sci & Engn, Hangzhou, Peoples R China; [Gani, Rafiqul] Korea Adv Inst Sci & Technol KAIST, Dept Chem & Biomol Engn, Daejeon, South Korea Cornell University; King Saud University; Zhejiang University; Korea Advanced Institute of Science & Technology (KAIST) Gani, R (corresponding author), PSE SPEED Co, Skyttemosen 6, DK-3450 Allerod, Denmark.;You, FQ (corresponding author), Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA. fengqi.you@cornell.edu; rgani2018@gmail.com Gani, Rafiqul/A-4865-2011; Alshehri, Abdulelah S./AAZ-5921-2021; Alshehri, Abdulelah S./AAT-6066-2020; You, Fengqi/B-5040-2011 Gani, Rafiqul/0000-0002-6719-9283; Alshehri, Abdulelah S./0000-0001-5213-3575; Alshehri, Abdulelah S./0000-0001-5213-3575; You, Fengqi/0000-0001-9609-4299 61 13 13 13 27 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0001-1541 1547-5905 AICHE J AICHE J. JUN 2022.0 68 6 e17469 10.1002/aic.17469 0.0 OCT 2021 16 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 1H9RA 2023-03-23 WOS:000704911100001 0
J Qian, LQ; Zhou, YJ; Zeng, WQ; Chen, XK; Ding, ZP; Shen, YJ; Qian, YF; Tose, D; Silva, M; Han, YC; Fu, XL Qian, Liqiang; Zhou, Yinjie; Zeng, Wanqin; Chen, Xiaoke; Ding, Zhengping; Shen, Yujia; Qian, Yifeng; Tose, Davide; Silva, Mario; Han, Yuchen; Fu, Xiaolong A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules TRANSLATIONAL LUNG CANCER RESEARCH English Article Solitary pulmonary nodule (SPN); frozen section (FS); surgical resection; diagnostic accuracy; random forest (RF) GROUND-GLASS OPACITY; CELL LUNG-CANCER; ARTIFICIAL-INTELLIGENCE; TUMOR-MARKER; DIAGNOSIS; RESECTION; CLASSIFICATION; ACCURACY; LESIONS; WILL Background: Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection. Methods: Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017???December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them. Results: The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and Conclusions: Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection. [Qian, Liqiang; Chen, Xiaoke; Ding, Zhengping] Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Shanghai Lung Canc Ctr, Shanghai, Peoples R China; [Zhou, Yinjie] Univ Chinese Acad Sci, Ningbo 2 Hosp, Hwa Mei Hosp, Dept Thorac Surg, Ningbo, Peoples R China; [Zeng, Wanqin; Shen, Yujia; Fu, Xiaolong] Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiat Oncol, 241 West Huaihai Rd, Shanghai 200030, Peoples R China; [Qian, Yifeng] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Natl Clin Res Ctr Oral Dis, Shanghai, Peoples R China; [Tose, Davide] Fdn IRCCS Ca Granda Osped Maggiore Polidin, Thorac Surg & Lung Transplant Unit, Milan, Italy; [Silva, Mario] Univ Parma, Dept Med & Surg DiMeC, Sci Radiol, Parma, Italy; [Han, Yuchen] Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Pathol, 241 West Huaihai Rd, Shanghai 200030, Peoples R China Shanghai Jiao Tong University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Shanghai Jiao Tong University; Shanghai Jiao Tong University; IRCCS Ca Granda Ospedale Maggiore Policlinico; University of Parma; Shanghai Jiao Tong University Fu, XL (corresponding author), Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiat Oncol, 241 West Huaihai Rd, Shanghai 200030, Peoples R China.;Han, YC (corresponding author), Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Pathol, 241 West Huaihai Rd, Shanghai 200030, Peoples R China. ychan@cmu.edu.cn; xlfu1964@hotmail.com Han, Yu/GZA-9220-2022 Major Research Plan of the National Natural Science Foundation of China [92059206] Major Research Plan of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Major Research Plan of the National Natural Science Foundation of China (grant number 92059206) . The Foundation sponsored a part of the data collection, random forest algorithm, and statistical analyses cost. 37 1 1 1 1 AME PUBL CO SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2218-6751 2226-4477 TRANSL LUNG CANCER R Transl. Lung Cancer Res. JUN 2022.0 11 6 1132 + 10.21037/tlcr-22-395 0.0 JUN 2022 14 Oncology; Respiratory System Science Citation Index Expanded (SCI-EXPANDED) Oncology; Respiratory System 2T4DX 35832446.0 Green Accepted, gold 2023-03-23 WOS:000817056900001 0
J Zhang, GQ; Hu, P Zhang, Gengquan; Hu, Pan Early Warning of Financial and Business Management Based on Three Improved BP-NN Algorithms WIRELESS COMMUNICATIONS & MOBILE COMPUTING English Article To address the issue of early warning in financial management and economics, this article presents a study based on our improved BP-NN algorithms. This approach improves the benefits of early warning systems in financial and business management based on BP neural network algorithm technology and improves BP neural network algorithms. Based on the analysis and calculation of the results, the inconsistency of the financial model industry estimates is 66.3% and 72.7% for CT and non-CT companies. The actual discrimination rate of the hedge fund model is 81.3% and 83.9% for ST and ST companies, respectively. Compared with the net structure of the financial index, the general guidance model improved the ability of ST companies and non-ST companies to withstand risks by 14.27% and 8.76%, respectively. It can be concluded that the integration of nonfinancial indicators into the estimation model can improve the accuracy of the estimation of the model. Experiments have shown that research based on our improved BP-NN algorithms can not only eliminate BP network inadequacies but also improve the accuracy of early warning in financial markets. [Zhang, Gengquan] Anhui Inst Informat Technol, Wuhu 241100, Anhui, Peoples R China; [Hu, Pan] Tech Univ Munich, Sch Civil & Architectural Engn, D-80333 Munich, Germany Technical University of Munich Zhang, GQ (corresponding author), Anhui Inst Informat Technol, Wuhu 241100, Anhui, Peoples R China. 2022001333@aiit.edu.cn 25 0 0 4 4 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1530-8669 1530-8677 WIREL COMMUN MOB COM Wirel. Commun. Mob. Comput. SEP 1 2022.0 2022 5135260 10.1155/2022/5135260 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5Y4GM gold 2023-03-23 WOS:000879242600005 0
J Liu, J; Zhang, BC; Zeng, HR; Shen, LL; Liu, JZ; Zhao, J Liu, Juan; Zhang, Baochang; Zeng, Haoran; Shen, Linlin; Liu, Jianzhuang; Zhao, Jason The BeiHang Keystroke Dynamics Systems, Databases and baselines NEUROCOMPUTING English Article Keystroke dynamics; Gabor feature; SVM; Random projection; Embedded system design NEURAL-NETWORK SYSTEM; AUTHENTICATION Keystroke dynamics is an important biometric branch for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits transformation methods to characterize keystroke dynamics, and provides benchmark results of several popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbor classifier. (C) 2014 Elsevier B.V. All rights reserved. [Liu, Juan; Zeng, Haoran] Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China; [Zhang, Baochang] Beihang Univ, Sci & Technol Aircraft Control Lab, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Shen, Linlin] Shenzhen Univ, Sch Comp Sci & Software Engn, Shenzhen, Peoples R China; [Shen, Linlin; Liu, Jianzhuang] Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Hong Kong, Peoples R China; [Zhao, Jason] Microdone Network Technol Co Ltd, Beijing, Peoples R China; [Zhang, Baochang] IIT, Pattern Anal & Comp Vis PAVIS, I-16163 Genoa, Italy Beihang University; Beihang University; Shenzhen University; Chinese University of Hong Kong; Istituto Italiano di Tecnologia - IIT Zhang, BC (corresponding author), Beihang Univ, Sci & Technol Aircraft Control Lab, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. bczhang@buaa.edu.cn Shen, Linlin/AEX-9392-2022 Shen, Linlin/0000-0003-1420-0815 Natural Science Foundation of China [60903065, 61039003, 61272052]; Fundamental Research Funds for the Central Universities; Program for New Century Excellent Talents University of Ministry of Education of China Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Program for New Century Excellent Talents University of Ministry of Education of China(Program for New Century Excellent Talents in University (NCET)) The work was supported in part by the Natural Science Foundation of China, under Contracts 60903065, 61039003 and 61272052, in part by the Fundamental Research Funds for the Central Universities, and by the Program for New Century Excellent Talents University of Ministry of Education of China. 26 5 5 0 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 20 2014.0 144 271 281 10.1016/j.neucom.2014.04.044 0.0 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science AO9LS 2023-03-23 WOS:000341677800025 0
J Zhu, JY; Li, XH; Gao, C; Wang, Z; Kurths, J Zhu, Junyou; Li, Xianghua; Gao, Chao; Wang, Zhen; Kurths, Jurgen Unsupervised community detection in attributed networks based on mutual information maximization NEW JOURNAL OF PHYSICS English Article community detection; attributed networks; graph convolutional networks; mutual information Community detection is of great significance for understanding network functions and behaviors. With the successful application of deep learning in network-based analyses, recent studies have turned to utilizing graph convolutional networks (GCNs) to this problem due to their capability in capturing network attributes. Nevertheless, most existing GCN-based community detection approaches are semi-supervised and local structure-aware, even though community detection is an unsupervised learning problem essentially. In this paper, we develop a novel GCN method for unsupervised community detection under the framework of mutual information (MI) maximization, called UCDMI. Specifically, a novel MI maximization mechanism is developed to capture more fine-grained information of the global network structure in an unsupervised manner. Moreover, a new aggregation function is proposed for GCN to distinguish the importance between different neighboring nodes, which enables our method to identify more high-quality node representations and improve the community detection performance. Our extensive experiments demonstrate the effectiveness of our proposed UCDMI compared with several state-of-the-art community detection methods. [Zhu, Junyou; Li, Xianghua; Gao, Chao; Wang, Zhen] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China; [Zhu, Junyou; Gao, Chao] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China; [Zhu, Junyou; Gao, Chao] Southwest Univ, Coll Software, Chongqing 400715, Peoples R China; [Kurths, Jurgen] Potsdam Inst Climate Impact Res, Potsdam, Germany; [Kurths, Jurgen] Univ Maribor, Fac Nat Sci & Math, Koroska Cesta 160, Maribor 2000, Slovenia Northwestern Polytechnical University; Southwest University - China; Southwest University - China; Potsdam Institut fur Klimafolgenforschung; University of Maribor Li, XH; Wang, Z (corresponding author), Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China. li_xianghua@nwpu.edu.cn; zhenwang0@gmail.com Zhu, Junyou/GPC-8570-2022 National Natural Science Foundation for Distinguished Young Scholars [62025602]; National Natural Science Foundation of China [61976181, U1803263, 11931015, 61762020]; Fok Ying-Tong Education Foundation, China [171105]; Technological Innovation Team of Shaanxi Province [2020TD-013]; Science and Technology Support Program of Guizhou [QKHZC2021YB531]; Russian Ministry of Science and Education Agreement [13.1902.21.0026] National Natural Science Foundation for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fok Ying-Tong Education Foundation, China(Fok Ying Tung Education Foundation); Technological Innovation Team of Shaanxi Province; Science and Technology Support Program of Guizhou; Russian Ministry of Science and Education Agreement Dr. Xianghua Li and Professor Zhen Wang are the corresponding authors of this paper. This work was supported by the National Natural Science Foundation for Distinguished Young Scholars (Grant No. 62025602), National Natural Science Foundation of China (Grant Nos. 61976181, U1803263, 11931015 and 61762020), the Fok Ying-Tong Education Foundation, China (Grant No. 171105), the Technological Innovation Team of Shaanxi Province (No. 2020TD-013), the Science and Technology Support Program of Guizhou (Grant No. QKHZC2021YB531), and the Russian Ministry of Science and Education Agreement (Grant No. 13.1902.21.0026). 48 4 4 4 9 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1367-2630 NEW J PHYS New J. Phys. NOV 2021.0 23 11 113016 10.1088/1367-2630/ac2fbd 0.0 11 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics YC2FB gold 2023-03-23 WOS:000739511000001 0
J Shi, XJ; Wei, YJ; Ji, BY Shi, Xiao-Jing; Wei, Yongjun; Ji, Boyang Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment FRONTIERS IN MOLECULAR BIOSCIENCES English Review gastric cancer; omics; systems biology; data integration; personalized medicine GENE-EXPRESSION PATTERN; MOLECULAR CLASSIFICATION; CLINICAL-SIGNIFICANCE; HELICOBACTER-PYLORI; NETWORK ANALYSIS; SUBTYPES; CHEMOTHERAPY; IDENTIFICATION; MICROBIOME; MUTATIONS Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics. [Shi, Xiao-Jing] Zhengzhou Univ, Lab Anim Ctr, State Key Lab Esophageal Canc Prevent & Treatment, Acad Med Sci, Zhengzhou, Peoples R China; [Wei, Yongjun] Zhengzhou Univ, Key Lab Adv Drug Preparat Technol, Minist Educ, Sch Pharmaceut Sci, Zhengzhou, Peoples R China; [Ji, Boyang] Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden; [Ji, Boyang] Tech Univ Denmark, Novo Nordisk Fdn, Ctr Biosustainabil, Lyngby, Denmark Zhengzhou University; Zhengzhou University; Chalmers University of Technology; Novo Nordisk Foundation; Technical University of Denmark Wei, YJ (corresponding author), Zhengzhou Univ, Key Lab Adv Drug Preparat Technol, Minist Educ, Sch Pharmaceut Sci, Zhengzhou, Peoples R China.;Ji, BY (corresponding author), Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden.;Ji, BY (corresponding author), Tech Univ Denmark, Novo Nordisk Fdn, Ctr Biosustainabil, Lyngby, Denmark. yongjunwei@zzu.edu.cn; boyangji@gmail.com Wei, Yongjun/AAY-5100-2021; Ji, Boyang/HMV-6662-2023 Wei, Yongjun/0000-0002-1554-1999; Ji, Boyang/0000-0002-7269-4342 National Natural Science Foundation of China [31800079, 31801957]; Scientific Program of Henan Province [19A350012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific Program of Henan Province This work was supported by the National Natural Science Foundation of China (Nos. 31800079 and 31801957) and the Scientific Program of Henan Province (No. 19A350012 for X-JS). 78 12 13 11 19 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-889X FRONT MOL BIOSCI Front. Mol. Biosci. AUG 26 2020.0 7 203 10.3389/fmolb.2020.00203 0.0 8 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology NQ0VD 33005629.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000570585500001 0
J Li, X; Shen, X; Zhou, YX; Wang, XH; Li, TQ Li, Xia; Shen, Xi; Zhou, Yongxia; Wang, Xiuhui; Li, Tie-Qiang Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet) PLOS ONE English Article In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature. [Li, Xia; Shen, Xi; Zhou, Yongxia; Wang, Xiuhui; Li, Tie-Qiang] China Jiliang Univ, Coll Informat Engn, Hangzhou, Peoples R China; [Li, Tie-Qiang] Karolinska Inst, Dept Clin Sci, Intervent & Technol, Stockholm, Sweden; [Li, Tie-Qiang] Karolinska Univ Hosp, Dept Med Radiat & Nucl Med, Stockholm, Sweden China Jiliang University; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital Li, TQ (corresponding author), China Jiliang Univ, Coll Informat Engn, Hangzhou, Peoples R China.;Li, TQ (corresponding author), Karolinska Inst, Dept Clin Sci, Intervent & Technol, Stockholm, Sweden.;Li, TQ (corresponding author), Karolinska Univ Hosp, Dept Med Radiat & Nucl Med, Stockholm, Sweden. tie-qiang.li@ki.se Wang, Xiuhui/0000-0003-1773-9760; Li, Tie-Qiang/0000-0002-4866-5904 China Scholarship Council, Zhejiang Natural Science Foundation of China [LY18E070005]; National Natural Science Foundation of China [51377186] China Scholarship Council, Zhejiang Natural Science Foundation of China(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by China Scholarship Council, Zhejiang Natural Science Foundation of China (No. LY18E070005) and the National Natural Science Foundation of China (No. 51377186). 21 39 42 4 35 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One MAY 4 2020.0 15 5 e0232127 10.1371/journal.pone.0232127 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics LT3GS 32365142.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000536959700016 0
J Liu, X; Zhao, JW; Li, J; Cao, B; Lv, ZH Liu, Xin; Zhao, Jianwei; Li, Jie; Cao, Bin; Lv, Zhihan Federated Neural Architecture Search for Medical Data Security IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Fuzzy logic; Medical diagnostic imaging; Data models; Data privacy; Data security; Rough sets; Privacy; Federated learning (FL); interval type-2 fuzzy rough neural network; medical data security; multiobjective evolution; neural architecture search (NAS) ALGORITHM Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people's panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security. [Liu, Xin; Li, Jie] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China; [Liu, Xin; Zhao, Jianwei; Li, Jie; Cao, Bin] Southern Univ Sci & Technol, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China; [Zhao, Jianwei; Cao, Bin] Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China; [Zhao, Jianwei; Cao, Bin] Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China; [Lv, Zhihan] Uppsala Univ, S-75105 Uppsala, Sweden Hebei University of Technology; Southern University of Science & Technology; Hebei University of Technology; Hebei University of Technology; Uppsala University Li, J (corresponding author), Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China.;Cao, B (corresponding author), Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China. 202011701002@stu.hebut.edu.cn; 201422102003@stu.hebut.edu.cn; lijie@hebut.edu.cn; caobin@scse.hebut.edu.cn; lvzhihan@qdu.edu.cn Lv, Zhihan/I-3187-2014; Lv, Zhihan/GLR-6000-2022; Zhao, Jianwei/O-1441-2017 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Li, Jie/0000-0002-2053-4662; Zhao, Jianwei/0000-0003-0424-4056 National Natural Science Foundation of China (NSFC) [61976242]; Natural Science Fund of Hebei Province for Distinguished Young Scholars [F2021202010]; Natural Science Fund of Hebei Province [G2019202350]; Fundamental Scientific Research Funds for Interdisciplinary Team of Hebei University of Technology [JBKYTD2002]; Guangdong Provincial Key Laboratory [2020B121201001, TII-21-2760] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Fund of Hebei Province for Distinguished Young Scholars; Natural Science Fund of Hebei Province; Fundamental Scientific Research Funds for Interdisciplinary Team of Hebei University of Technology; Guangdong Provincial Key Laboratory This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61976242, in part by the Natural Science Fund of Hebei Province for Distinguished Young Scholars under Grant F2021202010, in part by the Natural Science Fund of Hebei Province under Grant G2019202350, in part by the Fundamental Scientific Research Funds for Interdisciplinary Team of Hebei University of Technology under Grant JBKYTD2002, and in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001. Paper no. TII-21-2760. 44 35 35 59 86 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. AUG 2022.0 18 8 5628 5636 10.1109/TII.2022.3144016 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 1D5NR 2023-03-23 WOS:000793847600064 0
J Sun, B; Zhang, H; Wu, ZX; Zhang, YY; Li, T Sun, Biao; Zhang, Han; Wu, Zexu; Zhang, Yunyan; Li, Ting Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification IEEE SIGNAL PROCESSING LETTERS English Article Convolution; Electroencephalography; Spatiotemporal phenomena; Feature extraction; Electrodes; Task analysis; Adaptive systems; Brain computer interfaces (BCI); electroencephalogram (EEG); motor imagery (MI); graph neural network; spatiotemporal structure Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time-variability and individual diversity of EEG signals, a novel framework based on graph neural network is proposed for MI-EEG classification. First, an adaptive graph convolutional layer (AGCL) is constructed, by which the electrode channel information are integrated dynamically. We further propose an adaptive spatiotemporal graph convolutional network (ASTGCN), which fully exploits the characteristics of EEG signals in time domain and the channel correlations in spatial domain simultaneously. We execute the experiments using EEG signals recorded at motor imagery scenarios, where twenty-five healthy subjects performed MI movements of the right hand and feet to generate motor commands. Experimental results reveal that the proposed method outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of ASTGCN include high accuracy, high efficiency, and robustness to cross-trial and cross-subject variations, making it an ideal candidate for long-term MI-EEG applications. [Sun, Biao; Zhang, Han; Wu, Zexu] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China; [Zhang, Yunyan] Paderborn Univ, Dept Phys, D-33098 Paderborn, Germany; [Li, Ting] Chinese Acad Med Sci, Inst Biomed Engn, Tianjin 300020, Peoples R China; [Li, Ting] Pecking Union Med Coll, Tianjin 300020, Peoples R China Tianjin University; University of Paderborn; Chinese Academy of Medical Sciences - Peking Union Medical College; Institute of BioMedical Engineering - CAMS Li, T (corresponding author), Chinese Acad Med Sci, Inst Biomed Engn, Tianjin 300020, Peoples R China.;Li, T (corresponding author), Pecking Union Med Coll, Tianjin 300020, Peoples R China. sunbiao@tju.edu.cn; zhanghanup137@163.com; wuzexuxuexi@163.com; bsuntju@gmail.com; t.li619@foxmail.com Zhang, Han/0000-0003-1109-4738 National Natural Science Foundation of China [61971303, 81971660]; Tianjin Outstanding Youth Fund Project [20JCJQIC00230]; Program of Chinese Institute for Brain Research in Beijing [2020-NKX-XM-14]; Beijing Major Science and Technology Project [Z191100010618004]; Tianjin Key Project [18JCZDJC32700]; Sichuan International Cooperation [2021YFH0004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tianjin Outstanding Youth Fund Project; Program of Chinese Institute for Brain Research in Beijing; Beijing Major Science and Technology Project; Tianjin Key Project; Sichuan International Cooperation This work was supported in part by the National Natural Science Foundation of China under Grant 61971303 and Grant 81971660, in part by the Tianjin Outstanding Youth Fund Project under Grant 20JCJQIC00230, in part by the Program of Chinese Institute for Brain Research in Beijing under Grant 2020-NKX-XM-14, in part by the Beijing Major Science and Technology Project under Grant Z191100010618004, in part by the Tianjin Key Project under Grant 18JCZDJC32700, and in part by Sichuan International Cooperation under Grant 2021YFH0004. 23 7 8 33 100 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2021.0 28 219 223 10.1109/LSP.2021.3049683 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QC7SI 2023-03-23 WOS:000615032700003 0
J Liao, XM; Shi, J; Li, Z; Zhang, L; Xia, BQ Liao, Xiaomin; Shi, Jia; Li, Zan; Zhang, Lei; Xia, Baiqiang A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Ultra-dense cellular networks; resource allocation; deep reinforcement learning; model-driven; optimization ENERGY EFFICIENCY; USER ASSOCIATION; SCHEME; OPTIMIZATION; MANAGEMENT; FAIRNESS Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness. [Liao, Xiaomin; Shi, Jia; Li, Zan] Xidian Univ, State Key Lab Integrated Serv Networks, Sch Telecommun Engn, Xian 710071, Peoples R China; [Liao, Xiaomin] Natl Univ Def Technol, Sch Informat & Commun, Xian 710106, Peoples R China; [Zhang, Lei] Univ Glasgow, Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Xia, Baiqiang] Silo AI Oy, Helsinki 00180, Finland Xidian University; National University of Defense Technology - China; University of Glasgow Shi, J; Li, Z (corresponding author), Xidian Univ, State Key Lab Integrated Serv Networks, Sch Telecommun Engn, Xian 710071, Peoples R China. lxm8410@163.com; jiashi@xidian.edu.cn; zanli@xidian.edu.cn; lei.zhang@glasgow.ac.uk; baiqiang.xia@topdatascience.com liao, xiaomin/0000-0001-8844-210X; Zhang, Lei/0000-0002-4767-3849 National Natural Science Foundation of China [61631015, 61901327]; National Natural Science Foundation for Distinguished Young Scholar of China [61825104]; China Postdoctoral Science Foundation [2018M631122]; Key Laboratory Foundation of Ministry of Industry and Information Technology [KF20181912] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation for Distinguished Young Scholar of China(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Key Laboratory Foundation of Ministry of Industry and Information Technology This work was supported in part by the Projects of the National Natural Science Foundation of China under Grants 61631015 and 61901327, in part by the National Natural Science Foundation for Distinguished Young Scholar of China under Grant 61825104, in part by the China Postdoctoral Science Foundation under Grant 2018M631122, and in part by the Key Laboratory Foundation of Ministry of Industry and Information Technology under Grant KF20181912. 39 24 25 5 36 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. JAN 2020.0 69 1 983 997 10.1109/TVT.2019.2954538 0.0 15 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation KK2AF Green Accepted 2023-03-23 WOS:000512550600080 0
J Cui, C; Li, WT; Ye, XT; Hei, YQ; Rocca, P; Shi, XW Cui, Can; Li, Wen Tao; Ye, Xiu Tiao; Hei, Yong Qiang; Rocca, Paolo; Shi, Xiao Wei Synthesis of Mask-Constrained Pattern-Reconfigurable Nonuniformly Spaced Linear Arrays Using Artificial Neural Networks IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION English Article Antenna radiation patterns; Antenna arrays; Decoding; Artificial neural networks; Linear antenna arrays; Training; Synthesizers; Array synthesis; artificial neural network (ANN); nonuniformly spaced linear array; pattern-reconfigurable array OPTIMIZATION; DESIGN; NUMBER In this article, a novel multibranch encoder-decoder-based artificial neural network (ANN) framework is proposed for pattern-reconfigurable nonuniformly spaced linear array synthesis. In the proposed framework, different encoder-decoder branches are devoted to synthesizing different radiation patterns to satisfy the desired radiation patterns. In each encoder-decoder branch, the encoder, and the decoder serve as the array synthesizer and analyzer, respectively. By minimizing a suitably-defined loss function with respect to multiple radiation patterns, element amplitudes, and positions, not only multiple mask-constrained radiation patterns can be successfully achieved, but also the common element amplitudes and positions with minimum inter-element spacing control can be obtained. Thus, different radiation patterns can be switched by phase-only control. In addition, by using different training samples, the proposed method can consider the ideal synthesis of the array factor, as well as the actual synthesis of antenna arrays to consider the mutual coupling effects in the synthesis process. Simulation results are provided to validate the capability, efficiency, and attractiveness of the proposed method. [Cui, Can; Li, Wen Tao; Shi, Xiao Wei] Xidian Univ, Dept Elect Engn, Sci & Technol Antenna & Microwave Lab, Xian 710071, Peoples R China; [Ye, Xiu Tiao] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Int Res Ctr Intelligent Percept & Computat, Xian 710071, Peoples R China; [Hei, Yong Qiang] Xidian Univ, State Key Lab ISN, Xian 710071, Peoples R China; [Rocca, Paolo] ELEDIA Res Ctr ELEDIA Univ Trento, I-38123 Trento, Italy; [Rocca, Paolo] ELEDIA Res Ctr ELEDIA Xidian Univ, Xian 710071, Shanxi, Peoples R China Xidian University; Xidian University; Xidian University Li, WT (corresponding author), Xidian Univ, Dept Elect Engn, Sci & Technol Antenna & Microwave Lab, Xian 710071, Peoples R China. wtli@mail.xidian.edu.cn; paolo.rocca@unitn.it Rocca, Paolo/F-1145-2011 Rocca, Paolo/0000-0003-4522-6903; Cui, Can/0000-0002-9605-6759 National Nature Science Foundation [61876143]; Aeronautical Science Foundation of China [20200020081001]; Fundamental Research Project [173] National Nature Science Foundation(National Natural Science Foundation of China (NSFC)); Aeronautical Science Foundation of China; Fundamental Research Project This work was supported in part by the National Nature Science Foundation under Grant 61876143, in part by the Aeronautical Science Foundation of China under Grant 20200020081001, and in part by the Fundamental Research Project under Grant 173. 32 1 1 6 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-926X 1558-2221 IEEE T ANTENN PROPAG IEEE Trans. Antennas Propag. JUN 2022.0 70 6 4355 4368 10.1109/TAP.2022.3140214 0.0 14 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 2D6GE 2023-03-23 WOS:000811642200040 0
J Zhu, Y; Liang, JS; Shi, X; Zhang, Z Zhu, Yuan; Liang, Jia-sheng; Shi, Xun; Zhang, Zhen Full-Inorganic Flexible Ag2S Memristor with Interface Resistance-Switching for Energy-Efficient Computing ACS APPLIED MATERIALS & INTERFACES English Article; Early Access flexible memristor; Ag2S; interface resistance-switching; switching energy; energy-efficient computing ARTIFICIAL SYNAPSE; MEMORY; BEHAVIOR Flexible memristor-based neural network hardware is capable of implementing parallel computation within the memory units, thus holding great promise for fast and energy-efficient neuromorphic computing in flexible electronics. However, the current flexible memristor (FM) is mostly operated with a filamentary mechanism, which demands large energy consumption in both setting and computing. Herein, we report an Ag2S-based FM working with distinct interface resistance-switching (RS) mechanism. In direct contrast to conventional filamentary memristors, RS in this Ag2S device is facilitated by the space charge-induced Schottky barrier modification at the Ag/A(2)S interface, which can be achieved with the setting voltage below the threshold voltage required for filament formation. The memristor based on interface RS exhibits 105 endurance cycles and 104 s retention under bending condition, and multiple level conductive states with exceptional tunability and stability. Since interface RS does not require the formation of a continuous Ag filament via Ag+ ion reduction, it can achieve an ultralow switching energy of similar to 0.2 fJ. Furthermore, a hardware-based image processing with a software-comparable computing accuracy is demonstrated using the flexible Ag2S memristor array. And the image processing with interface RS indeed consumes 2 orders of magnitude lower power than that with filamentary RS on the same hardware. This study demonstrates a new resistance-switching mechanism for energy-efficient flexible neural network hardware. [Zhu, Yuan; Zhang, Zhen] Uppsala Univ, Dept Elect Engn, Div Solid State Elect, S-75121 Uppsala, Sweden; [Liang, Jia-sheng; Shi, Xun] Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine M, Shanghai 200050, Peoples R China Uppsala University; Chinese Academy of Sciences; Shanghai Institute of Ceramics, CAS Zhang, Z (corresponding author), Uppsala Univ, Dept Elect Engn, Div Solid State Elect, S-75121 Uppsala, Sweden.;Shi, X (corresponding author), Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine M, Shanghai 200050, Peoples R China. xshi@mail.sic.ac.cn; zhen.zhang@angstrom.uu.se Shi, Xun/A-1764-2019 Shi, Xun/0000-0002-8086-6407 National Natural Science Foundation of China (NSFC) [5181101519, 51625205]; Shanghai government [20JC1415100]; Uppsala University, Sweden; Swedish Strategic Research Foundation [SSF FFL15-0174]; Swedish Research Council [VR 2018-06030]; Wallenberg Academy Fellow Program [KAW 2015-0127, 2020-0190] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Shanghai government; Uppsala University, Sweden; Swedish Strategic Research Foundation(Swedish Foundation for Strategic Research); Swedish Research Council(Swedish Research Council); Wallenberg Academy Fellow Program The synthesis of Ag 2 S film (by J.L. and X.S.) was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 5181101519 and 51625205 and the Shanghai government under Grant No. 20JC1415100. The Ag 2 S FM devices were fabricated in the cleanroom at ?ngstr?m Microstructure Laboratory (MSL) , Uppsala University, Sweden and the technical staff of MSL are acknowledged for their process support. The device fabrication (by Y.Z. and Z.Z.) was supported by the Swedish Strategic Research Foundation (Grant No. SSF FFL15-0174 to Z.Z.) , the Swedish Research Council (Grant No. VR 2018-06030 to Z.Z.) , and the Wallenberg Academy Fellow Program (Grants No. KAW 2015-0127 and 2020-0190 to Z.Z.) . 34 2 2 32 32 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1944-8244 1944-8252 ACS APPL MATER INTER ACS Appl. Mater. Interfaces 10.1021/acsami.2c11183 0.0 SEP 2022 8 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 4Z0QK 36102604.0 Green Published 2023-03-23 WOS:000861923900001 0
J Zhang, L; Jiang, BB; Wisselink, HJ; Vliegenthart, R; Xie, XQ Zhang, Lin; Jiang, Beibei; Wisselink, Hendrik Joost; Vliegenthart, Rozemarijn; Xie, Xueqian COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images BRITISH JOURNAL OF RADIOLOGY English Article COMPUTED-TOMOGRAPHY; AIRWAY SEGMENTATION; CANCER; EMPHYSEMA; VALIDATION Objective Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD. Methods Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (n = 373) were retrospectively included as the training cohort, and subjects from another hospital (n = 226) were used as the external test cohort. According to the PFT results, all subjects were labeled as Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1, 2, 3, 4 or normal. Two DenseNet-201 CNNs were trained using CT images of lung parenchyma and bronchial wall to generate two corresponding confidence levels to indicate the possibility of COPD, then combined with logistic regression analysis. Quantitative CT was used for comparison. Results: In the test cohort, CNN achieved an area under the curve of 0.899 (95%CI: 0.853-0.935) to deter-mine the existence of COPD, and an accuracy of 81.7% (76.2-86.7%), which was significantly higher than the accuracy 68.1% (61.6%-74.2%) using quantitative CT method (p < 0.05). For three- way (normal, GOLD 1-2, and GOLD 3-4) and five- way (normal, GOLD 1, 2, 3, and 4) classifications, CNN reached accuracies of 77.4 and 67.9%, respectively. Conclusion CNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD. It provides an alternative way to detect COPD using the extensively available chest CT. Advances in knowledge CNN can identify the main pathological changes of COPD (emphysema and airway wall remodeling) based on CT images, to infer lung function and determine the existence and severity of COPD. CNN reached an area under the curve of 0.853 to deter-mine the existence of COPD in the external test cohort. The CNN approach provides an alternative and effective way for early detection of COPD using extensively used chest CT, as an important alternative to pulmonary func-tion test. [Zhang, Lin; Jiang, Beibei; Xie, Xueqian] Shanghai Jiao Tong Univ, Sch Med, Shanghai Gen Hosp, Dept Radiol, Shanghai, Peoples R China; [Wisselink, Hendrik Joost; Vliegenthart, Rozemarijn] Univ Groningen, Univ Med Ctr Groningen, Dept Radiol, Groningen, Netherlands Shanghai Jiao Tong University; University of Groningen Xie, XQ (corresponding author), Shanghai Jiao Tong Univ, Sch Med, Shanghai Gen Hosp, Dept Radiol, Shanghai, Peoples R China. xiexueqian@hotmail.com Xie, Xueqian/AAE-2292-2022 Xie, Xueqian/0000-0002-6669-0097; Wisselink, Hendrik Joost/0000-0002-1706-5076 Ministry of Science and Technology of China [2016YFE0103000]; National Natural Science Foundation of China [81971612, 81471662]; Shanghai Municipal Education Commission- Gaofeng Clinical Medicine Grant Support [20181814]; Shanghai Jiao Tong University [ZH2018ZDB10] Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Education Commission- Gaofeng Clinical Medicine Grant Support; Shanghai Jiao Tong University This study was sponsored by Ministry of Science and Technology of China (2016YFE0103000) , National Natural Science Foundation of China (81971612 and 81471662) , Shanghai Municipal Education Commission- Gaofeng Clinical Medicine Grant Support (20181814) , Shanghai Jiao Tong University (ZH2018ZDB10) . The funders played no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. 30 3 3 5 5 BRITISH INST RADIOLOGY LONDON 48-50 ST JOHN ST, LONDON, ENGLAND 0007-1285 1748-880X BRIT J RADIOL Br. J. Radiol. 2022.0 95 1133 20210637 10.1259/bjr.20210637 0.0 9 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 4I6OI 35143286.0 Green Published 2023-03-23 WOS:000850694500012 0
J Song, R; Giunchiglia, F; Shen, Q; Li, N; Xu, H Song, Rui; Giunchiglia, Fausto; Shen, Qiang; Li, Nan; Xu, Hao Improving Abusive Language Detection with online interaction network INFORMATION PROCESSING & MANAGEMENT English Article Abusive Language Detection; BERT; Social network; Graph neural network; Interaction network The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation -Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results. [Song, Rui; Giunchiglia, Fausto; Xu, Hao] Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Peoples R China; [Shen, Qiang; Li, Nan; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China; [Giunchiglia, Fausto] Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy; [Xu, Hao] Jilin Univ, Chongqing Res Inst, Chongqing 401123, Peoples R China Jilin University; Jilin University; University of Trento; Jilin University Xu, H (corresponding author), Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China. songrui20@mails.jlu.edu.cn; fausto@disi.unitn.it; shenqiang19@mails.jlu.edu.cn; linan19@mails.jlu.edu.cn; xuhao@jlu.edu.cn Giunchiglia, Fausto/0000-0002-5903-6150; Li, Nan/0000-0001-9011-9213 National Natural Science Foundation of China (NSFC) [62077027]; European Union [823783]; Education Department of Jilin Province, China [JJKH20200993K]; Department of Science and Technology of Jilin Province, China [20200801002GH] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); European Union(European Commission); Education Department of Jilin Province, China; Department of Science and Technology of Jilin Province, China This work was supported by National Natural Science Foundation of China (NSFC) , ?From Learning Outcome to Proactive Learning: Towards a Humancentered AI Based Approach to Intervention on Learning Motivation? (No. 62077027) , and the European Union?s Horizon 2020 FET Proactive project ?WeNet-The Internet of us? (No. 823783) and the work was also supported by the Education Department of Jilin Province, China (JJKH20200993K) and the Department of Science and Technology of Jilin Province, China (20200801002GH) . 60 1 1 6 6 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. SEP 2022.0 59 5 103009 10.1016/j.ipm.2022.103009 0.0 JUL 2022 14 Computer Science, Information Systems; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science 4E9VB 2023-03-23 WOS:000848165100006 0
J Zhang, FH; Martinez, CM; Clarke, D; Cao, DP; Knoll, A Zhang, Feihu; Martinez, Clara Marina; Clarke, Daniel; Cao, Dongpu; Knoll, Alois Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application FRONTIERS IN NEUROROBOTICS English Article neural network; autonomous driving; uncertainty prediction; localization; odometry FUSION; MANAGEMENT; ALGORITHM; MODEL This paper proposes a framework for uncertainty prediction in complex fusion networks, where signals become available sporadically. Assuming there is no information of the sensor characteristics available, a surrogated model of the sensor uncertainty is yielded directly from data through artificial neural networks. The strategy developed is applied to autonomous vehicle localization through odometry sensors (speed and orientation), so as to determine the location uncertainty in the trajectory. The results obtained allow for fusion of autonomous vehicle location measurements, and effective correction of the accumulated odometry error in most scenarios. The neural networks applicability and generalization capacity are proven, evidencing the suitability of the presented methodology for uncertainty estimation in non-linear and intractable processes. [Zhang, Feihu] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian, Shaanxi, Peoples R China; [Martinez, Clara Marina] Porsche Engn Serv GmbH, Bietigheim Bissingen, Germany; [Clarke, Daniel] Cogsense Technol Ltd, London, England; [Cao, Dongpu] Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON, Canada; [Knoll, Alois] Tech Univ Munich, Dept Informat, Munich, Germany Northwestern Polytechnical University; University of Waterloo; Technical University of Munich Zhang, FH (corresponding author), Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian, Shaanxi, Peoples R China. feihu.zhang@nwpu.edu.cn Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X; Cao, Dongpu/0000-0003-2541-5272 National Natural Science Foundation of China (NSFC) [61703335]; Fundamental Research Funds for the Central Universities [G2017KY0302]; 111 Project [B18041] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); 111 Project(Ministry of Education, China - 111 Project) This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 61703335, the Fundamental Research Funds for the Central Universities under Grants G2017KY0302, and the 111 Project under Grant B18041. 37 7 7 5 17 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics MAY 10 2019.0 13 12 10.3389/fnbot.2019.00012 0.0 17 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology IB0MH 31133839.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000469952200001 0
J Shi, SS; Wu, HP; Song, YT; Handroos, H Shi, Shanshuang; Wu, Huapeng; Song, Yuntao; Handroos, Heikki Mechanical design and error prediction of a flexible manipulator system applied in nuclear fusion environment INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION English Article Robot design; Nuclear fusion; Flexible manipulator; Error prediction; BP neural network Purpose - The purpose of this paper is to introduce a development and error modeling of a serial redundant manipulator system applied in nuclear fusion environment. Detailed mechanical design of vacuum-compatible arms and actuators are presented, and manipulator flexibility is studied through experiments and model identification algorithm to evaluate the deformation. Design/methodology/approach - First, the manipulator is designed to be several modular segments to obtain enough and flexible workspace inside the fusion device with narrow and complex geometries. Joint actuators with rotation-linear-rotation chains are developed to provide both huge reduction ratios and vacuum sealing. The redundant manipulator system has 11 degree of freedoms in total with a storage cask system to dock with the device vacuum vessel. In addition, to improve the position accuracy, an error prediction model is built based on the experimental study and back-propagation neural network (BPNN) algorithm. Findings - Currently, the implementation of the manipulator system has been successfully carried out in both atmosphere and vacuum condition. Excellent performance indicates that the mechanical design is suitable. The validation of BPNN model shows an acceptable prediction accuracy (94 similar to 98 per cent) compared with the real measurement. Originality/value - This is a special robot system which is practically used in a nuclear fusion device in China. It will allow remote inspection and maintenance of plasma facing components in the vacuum vessel of fusion device without breaking the ultra-high vacuum condition during physical experiments. Its design, mechanism and error prediction strategy have great reference values to the similar robots in vacuum and temperature applications. [Shi, Shanshuang; Song, Yuntao] Chinese Acad Sci, Inst Plasma Phys, Hefei, Anhui, Peoples R China; [Shi, Shanshuang; Wu, Huapeng; Handroos, Heikki] Lappeenrannan Teknillinen Yliopisto, Lab Intelligent Machines, Lappeenranta, Finland Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS Shi, SS (corresponding author), Chinese Acad Sci, Inst Plasma Phys, Hefei, Anhui, Peoples R China.;Shi, SS (corresponding author), Lappeenrannan Teknillinen Yliopisto, Lab Intelligent Machines, Lappeenranta, Finland. shiss@ipp.ac.cn 19 8 8 2 19 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0143-991X 1758-5791 IND ROBOT Ind. Robot. 2017.0 44 6 711 719 10.1108/IR-04-2017-0066 0.0 9 Engineering, Industrial; Robotics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Robotics FL0XR 2023-03-23 WOS:000413936600004 0
J Qi, XB; Li, CG; Zhao, GY; Hong, XP; Pietikainen, M Qi, Xianbiao; Li, Chun-Guang; Zhao, Guoying; Hong, Xiaopeng; Pietikainen, Matti Dynamic texture and scene classification by transferring deep image features NEUROCOMPUTING English Article Dynamic texture classification; Dynamic scene classification; Transferred ConvNet feature; Convolutional neural network REPRESENTATION Dynamic texture and scene classification are two fundamental problems in understanding natural video content. Extracting robust and effective features is a crucial step towards solving these problems. However, the existing approaches suffer from the sensitivity to either varying illumination, or viewpoint changes, or even camera motion, and/or the lack of spatial information. Inspired by the success of deep structures in image classification, we attempt to leverage a deep structure to extract features for dynamic texture and scene classification. To tackle with the challenges in training a deep structure, we propose to transfer some prior knowledge from image domain to video domain. To be more specific, we propose to apply a well-trained Convolutional Neural Network (ConvNet) as a feature extractor to extract mid-level features from each frame, and then form the video-level representation by concatenating the first and the second order statistics over the mid-level features. We term this two-level feature extraction scheme as a Transferred ConvNet Feature (TCoF). Moreover, we explore two different implementations of the TCoF scheme, i.e., the spatial TCoF and the temporal TCoF. In the spatial TCoF, the mean-removed frames are used as the inputs of the ConvNet; whereas in the temporal TCoF, the differences between two adjacent frames are used as the inputs of the ConvNet We evaluate systematically the proposed spatial TCoF and the temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN, and Maryland, and demonstrate that the proposed approach yields superior performance., (C) 2015 Elsevier B.V. All rights reserved. [Qi, Xianbiao; Zhao, Guoying; Hong, Xiaopeng; Pietikainen, Matti] Univ Oulu, Ctr Machine Vis Res, FIN-90014 Oulu, Finland; [Qi, Xianbiao; Li, Chun-Guang] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China University of Oulu; Beijing University of Posts & Telecommunications Li, CG (corresponding author), Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China. qixianbiao@gmail.com; lichunguang@bupt.edu.cn; gyzhao@ee.oulu.fi; xhong@ee.oulu.fi; mkp@ee.oulu.fi Zhao, Guoying/ABE-7716-2020; HONG, Xiaopeng/V-6078-2019 Zhao, Guoying/0000-0003-3694-206X; HONG, Xiaopeng/0000-0002-0611-0636 Academy of Finland; Infotech Oulu; NSFC [61273217, 61175011]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China Academy of Finland(Academy of Finland); Infotech Oulu; NSFC(National Natural Science Foundation of China (NSFC)); Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China(Scientific Research Foundation for the Returned Overseas Chinese ScholarsMinistry of Education, China) X. Qi, G. Zhao, X. Hong, and M. Pietikainen are supported in part by the Academy of Finland and Infotech Oulu. C.-G. Li is supported partially by NSFC under Grant nos. 61273217 and 61175011, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China. The authors would like to thank Renaud Peteri, Richard P. Wildes and Pavan Turaga for sharing the DynTex dynamic texture, YUPENN dynamic scene, and Maryland dynamic scene data sets. 46 76 76 0 36 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 1 2016.0 171 1230 1241 10.1016/j.neucom.2015.07.071 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CW3HX Green Submitted 2023-03-23 WOS:000364883900121 0
J Hong, YS; Chen, YY; Chen, SC; Shen, RL; Hu, BF; Peng, J; Wang, N; Guo, L; Zhuo, ZQ; Yang, YY; Liu, YL; Mouazen, AM; Shi, Z Hong, Yongsheng; Chen, Yiyun; Chen, Songchao; Shen, Ruili; Hu, Bifeng; Peng, Jie; Wang, Nan; Guo, Long; Zhuo, Zhiqing; Yang, Yuanyuan; Liu, Yaolin; Mouazen, Abdul Mounem; Shi, Zhou Data mining of urban soil spectral library for estimating organic carbon GEODERMA English Article Urban soil; Soil spectral library; Soil organic carbon; Stratified modeling INFRARED REFLECTANCE SPECTROSCOPY; LEAST-SQUARES REGRESSION; NIR; PREDICTION; GEOSTATISTICS; CALIBRATION; SYSTEM; STRATIFICATION; CLASSIFICATION; PERFORMANCE Accurate quantification of urban soil organic carbon (SOC) is essential for understanding anthropogenic changes and further guiding effective city managements. Visible and near infrared (vis-NIR) spectroscopy can monitor the SOC content in a time-and cost-effective manner. However, processes and mechanisms dominating the re-lationships between SOC and spectral data in urban soils remain unknown. The main objective of this paper was to evaluate whether multiple stratification strategies (i.e., based on land-use/land-cover [LULC], pH, and spectral clustering) resulted in better predicted performance for SOC compared to the non-stratified (global) models. Results showed that regarding the non-stratified models, the convolutional neural network (CNN) model exhibited the best performance (validation R2 = 0.73), followed by Cubist (validation R2 = 0.66) and memory-based learning (validation R2 = 0.65). After LULC stratification, Cubist model achieved the best prediction (validation R2 = 0.76), improving the value of ratio of performance to interquartile distance by 0.11 compared to the global CNN model. Areas with high SOC values were mainly located in the city center. Stratification by LULC class is a promising strategy for addressing the impact of the soil-landscape diversity and complexity on vis-NIR spectral estimation of SOC in urban soil spectral library. [Hong, Yongsheng; Wang, Nan; Shi, Zhou] Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applica, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China; [Chen, Yiyun; Liu, Yaolin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China; [Chen, Songchao] ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China; [Shen, Ruili] Hubei Acad Environm Sci, Wuhan 430072, Peoples R China; [Hu, Bifeng] Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Nanchang 330013, Peoples R China; [Peng, Jie] Tarim Univ, Coll Plant Sci, Alar 843300, Peoples R China; [Guo, Long] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China; [Zhuo, Zhiqing] Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China; [Yang, Yuanyuan] Zhejiang Univ City Coll, Sch Spatial Planning & Design, Hangzhou 310015, Peoples R China; [Mouazen, Abdul Mounem] Univ Ghent, Dept Environm, Coupure Links 653, B-9000 Ghent, Belgium; [Mouazen, Abdul Mounem] VYTAUTAS MAGNUS Univ, Fac Engn, Dept Agr Engn & Safety, Kaunas, Lithuania Zhejiang University; Wuhan University; Jiangxi University of Finance & Economics; Tarim University; Huazhong Agricultural University; Zhejiang Academy of Agricultural Sciences; Zhejiang University City College; Ghent University; Vytautas Magnus University Shi, Z (corresponding author), Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applica, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China. shizhou@zju.edu.cn CHEN, YIYUN/AAD-9501-2019; yang, yuanyuan/HPG-2020-2023; Mouazen, Abdul M./AAO-9709-2020; shi, Zhou/M-7845-2019; Chen, Songchao/S-7982-2019 CHEN, YIYUN/0000-0002-7442-3239; Mouazen, Abdul M./0000-0002-0354-0067; shi, Zhou/0000-0003-3914-5402; Chen, Songchao/0000-0003-1245-0482 National Key Research and Develop- ment Program of China [2018YFE0107000]; National Natural Sci- ence Foundation of China [U1901601, 41771440, 42101061]; Research Foundation-Flanders (FWO) [G0F9216N]; China Postdoctoral Science Foundation [2021M702840] National Key Research and Develop- ment Program of China; National Natural Sci- ence Foundation of China(National Natural Science Foundation of China (NSFC)); Research Foundation-Flanders (FWO)(FWO); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The research is supported by National Key Research and Develop- ment Program of China (2018YFE0107000) and National Natural Sci- ence Foundation of China (grant number: U1901601; 41771440; 42101061) . Authors acknowledged the financial support received from the Research Foundation-Flanders (FWO) for Odysseus I SiTeMan Project (Nr. G0F9216N) . Yongsheng Hong also gratefully acknowledges the Project funded by China Postdoctoral Science Foundation (2021M702840) . Wuhan vis-NIR spectral library was initially devel- oped by the second author of this article (Yiyun Chen) . The authors thank the Dr. Yiyun Chen and staff members at the Soil Survey and Monitoring Lab of the Wuhan University for maintaining and querying the Wuhan spectral database. 104 2 2 37 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0016-7061 1872-6259 GEODERMA Geoderma NOV 15 2022.0 426 116102 10.1016/j.geoderma.2022.116102 0.0 AUG 2022 14 Soil Science Science Citation Index Expanded (SCI-EXPANDED) Agriculture 4I9LV 2023-03-23 WOS:000850894800002 0
J Ding, L; Zheng, K; Lin, D; Chen, YX; Liu, B; Li, JS; Bruzzone, L Ding, Lei; Zheng, Kai; Lin, Dong; Chen, Yuxing; Liu, Bing; Li, Jiansheng; Bruzzone, Lorenzo MP-ResNet: Multipath Residual Network for the Semantic Segmentation of High-Resolution PolSAR Images IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Convolutional neural network (CNN); polarimetric synthetic aperture radar (PolSAR) image analysis; remote sensing; semantic segmentation There are limited studies on the semantic segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images due to the scarcity of training data and the complexity of managing speckle noise. The Gaofen contest has provided open access a high-quality PolSAR semantic segmentation dataset. Taking this opportunity, we propose a multipath residual network (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder-decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multiscale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multilevel feature fusion design in its decoder to effectively exploit the features learned from its different branches. Comparisons with the baseline method of fully connected network (FCN with ResNet34) show that the MP-ResNet has achieved significant accuracy improvements. It also surpasses several state-of-the-art methods in terms of overall accuracy (OA), mF(1) and frequency weighted intersection over union (fwIoU), with only a limited increase of computational costs. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. [Ding, Lei; Chen, Yuxing; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy; [Zheng, Kai; Liu, Bing; Li, Jiansheng] PLA Strateg Support Force Informat Engn Univ, Inst Surveying & Mapping, Zhengzhou 450001, Peoples R China; [Lin, Dong] Space Engn Univ, NCO Sch, Beijing 102200, Peoples R China University of Trento; PLA Information Engineering University; Aerospace Engineering University Bruzzone, L (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy. lei.ding@unitn.it; yuxing.chen@unitn.it; lorenzo.bruzzone@ing.unitn.it Ding, Lei/AAK-2573-2021; Bruzzone, Lorenzo/A-2076-2012 Ding, Lei/0000-0003-0653-8373; Yuxing, Chen/0000-0001-9788-3556; Bruzzone, Lorenzo/0000-0002-6036-459X; Bing, Liu/0000-0003-0848-8453 China Scholarship Council through the Scholarship [201703170123] China Scholarship Council through the Scholarship(China Scholarship Council) This work was supported by the China Scholarship Council through the Scholarship under Grant 201703170123. 22 12 12 2 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 10.1109/LGRS.2021.3079925 0.0 MAY 2021 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XW4AV Green Submitted 2023-03-23 WOS:000732201100001 0
J Zhao, YQ; Yang, JX; Chan, JCW Zhao, Yongqiang; Yang, Jingxiang; Chan, Jonathan Cheung-Wai Hyperspectral Imagery Super-Resolution by Spatial-Spectral Joint Nonlocal Similarity IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Hyperspectral (HS) imaging; nonlocal similarity; spectral unmixing; super-resolution HOPFIELD NEURAL-NETWORK; RESOLUTION ENHANCEMENT; CONTOURING METHODS; FUSION Hyperspectral (HS) super-resolution reconstruction is an ill-posed inversion problem, for which the solution from reconstruction constraint is not unique. To address this, an HS image super-resolution method is proposed to first utilize the joint regulation of spatial and spectral nonlocal similarities. We then fused the HS and panchromatic images with sparse regulation. With these two regulation terms, edge sharpness and spectrum consistency are preserved and noises are suppressed. The proposed method is tested with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion images and evaluated by quantitative measures. The resulting enhanced images from the proposed method are superior to the results obtained by other well-known methods. [Zhao, Yongqiang; Yang, Jingxiang] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China; [Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat ETRO, B-1050 Brussels, Belgium; [Chan, Jonathan Cheung-Wai] Fdn Edmund Mach, Foxlab, I-38010 San Michele All Adige, Trentino, Italy Northwestern Polytechnical University; Vrije Universiteit Brussel; Fondazione Edmund Mach Zhao, YQ (corresponding author), Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China. zhaoyq@nwpu.edu.cn; yang123jx@mail.nwpu.edu.cn; jcheungw@etro.vub.ac.be Zhao, Yongqiang/X-8054-2019 Zhao, Yongqiang/0000-0002-6974-7327 National Natural Science Found of China [61371152, 61071172, 61301196]; New Century Excellent Talents Award Program from Ministry of Education of China [NCET-12-0464]; Ministry of Education Scientific Research Foundation for the Returned Overseas; Sciences Foundation of Northwestern Polytechnical University [C200941, JC201251]; Northwestern Polytechnical University [Z2013121, Z2013122] National Natural Science Found of China(National Natural Science Foundation of China (NSFC)); New Century Excellent Talents Award Program from Ministry of Education of China; Ministry of Education Scientific Research Foundation for the Returned Overseas; Sciences Foundation of Northwestern Polytechnical University; Northwestern Polytechnical University This work is supported in part by the National Natural Science Found of China (61371152, 61071172, and 61301196), in part by New Century Excellent Talents Award Program from Ministry of Education of China (NCET-12-0464), in part by the Ministry of Education Scientific Research Foundation for the Returned Overseas, in part by Sciences Foundation of Northwestern Polytechnical University (C200941 and JC201251), and in part by Graduate Starting Seed Fund of Northwestern Polytechnical University (Z2013121 and Z2013122). 37 60 60 2 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. JUN 2014.0 7 6 SI 2671 2679 10.1109/JSTARS.2013.2292824 0.0 9 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology AN5HK 2023-03-23 WOS:000340621200071 0
J Zhu, L; Su, CW; Zhang, JH; Cui, GC; Cichocki, A; Zhou, CL; Li, JH Zhu, Li; Su, Chongwei; Zhang, Jianhai; Cui, Gaochao; Cichocki, Andrzej; Zhou, Changle; Li, Junhua EEG-based approach for recognizing human social emotion perception ADVANCED ENGINEERING INFORMATICS English Article Hyper-scanning; EEG; Emotion; Phase lag index; Deep learning SINGLE-TRIAL EEG; PHASE-LAG INDEX; BRAIN; RECOGNITION; SCHIZOPHRENIA; DEPRESSION Social emotion perception plays an important role in our daily social interactions and is involved in the treatments for mental disorders. Hyper-scanning technique enables to measure brain activities simultaneously from two or more persons, which was employed in this study to explore social emotion perception. We analyzed the recorded electroencephalogram (EEG) to explore emotion perception in terms of event related potential (ERP) and phase synchronization, and classified emotion categories based on convolutional neural network (CNN). The results showed that (1) ERP was significantly different among four emotion categories (i.e., anger, disgust, neutral, and happy), but there was no significant difference for ERP in the comparison of rating orders (the order of rating actions of the paired participants); (2) the intra-brain phase lag index (PLI) was higher than the inter-brain PLI but its number of connections exhibiting significant difference was less in all typical frequency bands (from delta to gamma); (3) the emotion classification accuracy of inter-PLI-Conv outperformed that of intra-PLI-Conv for all cases of using each frequency band (five frequency bands totally). In particular, the classification accuracies averaged across all participants in the alpha band were 65.55% and 50.77% (much higher than the chance level) for the inter-PLI-Conv and intra-PLI-Conv, respectively. According to our results, the emotion category of happiness can be classified with a higher performance compared to the other categories. [Zhu, Li; Zhou, Changle] Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Xiamen 361005, Peoples R China; [Su, Chongwei; Zhang, Jianhai; Cichocki, Andrzej] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China; [Cui, Gaochao] RIKEN, Rhythm Based Brain Informat Proc Unit, Wako, Saitama 3510198, Japan; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol Skoltech, Moscow 143026, Russia; [Cichocki, Andrzej] Nicolaus Copernicus Univ, Dept Informat, PL-87100 Torun, Poland; [Li, Junhua] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England; [Li, Junhua] Wuyi Univ, Lab Brain Bion Intelligence & Computat Neurosci, Jiangmen 529020, Peoples R China; [Li, Junhua] Natl Univ Singapore, Singapore Inst Neurotechnol, Singapore 117456, Singapore; [Li, Junhua] Northwestern Polytech Univ, Ctr Multidisciplinary Convergence Comp, Sch Comp Sci & Engn, Xian 710072, Peoples R China Xiamen University; Hangzhou Dianzi University; RIKEN; Skolkovo Institute of Science & Technology; Nicolaus Copernicus University; University of Essex; Wuyi University; National University of Singapore; Northwestern Polytechnical University Li, JH (corresponding author), Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England. juhalee.bcmi@gmail.com National Natural Science Foundation of China [61633010, 61806149, 61673322]; National Key Basic Research Program of China [2013CB329502]; Ministry of Education and Science of the Russian Federation [14.756.31.0001]; Polish National Science Center, Poland [2016/20/W/N24/00354] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Basic Research Program of China(National Basic Research Program of China); Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Polish National Science Center, Poland The work presented in this paper was supported by the National Natural Science Foundation of China (No. 61633010, 61806149, 61673322), and the National Key Basic Research Program of China (No. 2013CB329502). This research was also partially supported by the Ministry of Education and Science of the Russian Federation (grant 14.756.31.0001) and the Polish National Science Center, Poland (grant 2016/20/W/N24/00354). 62 5 5 6 34 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1474-0346 1873-5320 ADV ENG INFORM Adv. Eng. Inform. OCT 2020.0 46 101191 10.1016/j.aei.2020.101191 0.0 10 Computer Science, Artificial Intelligence; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering PR9UJ Green Accepted 2023-03-23 WOS:000607575400041 0
J Fu, X; Gui, G; Wang, Y; Ohtsuki, T; Adebisi, B; Gacanin, H; Adachi, F Fu, Xue; Gui, Guan; Wang, Yu; Ohtsuki, Tomoaki; Adebisi, Bamidele; Gacanin, Haris; Adachi, Fumiyuki Lightweight Automatic Modulation Classification Based on Decentralized Learning IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Automatic modulation classification (AMC); convolutional neural network (CNN); centralized learning; decentralized learning DEEP NEURAL-NETWORK; RESOURCE-ALLOCATION; RECOGNITION; COMPRESSION; ALGORITHM; SYSTEMS; CNN Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model consolidation and lightweight design. Specifically, the model consolidation is realized by a central device (CD) for edge device (ED) model averaging (MA) and multiple EDs for ED model training. The lightweight is designed by separable convolutional neural network (S-CNN), in which the separable convolutional layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolutional neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about N times of it, where N is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset D-1 = {2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} and about 5 times that of S-CNN-based CentAMC in dataset D-2 = {2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM}), while the communication overhead is reduced more than 35%. [Fu, Xue; Gui, Guan; Wang, Yu] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Ohtsuki, Tomoaki] Keio Univ, Dept Informat & Comp Sci, Yokohama, Kanagawa 2238521, Japan; [Adebisi, Bamidele] Manchester Metropolitan Univ, Dept Engn, Fac Sci & Engn, Manchester M1 5GD, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, D-52074 Aachen, Germany; [Adachi, Fumiyuki] Tohoku Univ, Res Org Elect Commun, Sendai, Miyagi 9808577, Japan Nanjing University of Posts & Telecommunications; Keio University; Manchester Metropolitan University; RWTH Aachen University; Tohoku University Gui, G (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China. 1020010415@njupt.edu.cn; 1018010407@njupt.edu.cn; guiguan@njupt.edu.cn; ohtsuki@ics.keio.ac.jp; b.adebisi@mmu.ac.uk; harisg@ice.rwth-aachen.de; adachi@ecei.tohoku.ac.jp Gui, Guan/AAG-3593-2019 Gui, Guan/0000-0001-7428-4980; Fu, Xue/0000-0001-9518-4081; Ohtsuki, Tomoaki/0000-0003-3961-1426; Adebisi, Bamidele/0000-0001-9071-9120 Japan Society for the Promotion of Science KAKENHI [JP19H02142]; National Science and Technology Major Project of the Ministry of Science and Technology of China [TC190A3WZ-2]; National Natural Science Foundation of China [61901228]; Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]; Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106] Japan Society for the Promotion of Science KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); National Science and Technology Major Project of the Ministry of Science and Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Summit of the Six Top Talents Program of Jiangsu; Program for High-Level Entrepreneurial and Innovative Team; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China(Ministry of Education, China) This work was supported in part by the Japan Society for the Promotion of Science KAKENHI under Grant JP19H02142, the Project Funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant TC190A3WZ-2, National Natural Science Foundation of China under Grant 61901228, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. 49 15 15 11 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. MAR 2022.0 8 1 57 70 10.1109/TCCN.2021.3089178 0.0 14 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications ZP7VX 2023-03-23 WOS:000766629000008 0
C Zhang, WS; Xue, B; Zhou, JH; Liu, X; Lv, H Wang, G; Han, Q; Bhuiyan, MZA; Ma, X; Loulergue, F; Li, P; Roveri, M; Chen, L Zhang, Weishan; Xue, Bing; Zhou, Jiehan; Liu, Xin; Lv, Hao A Scalable and Efficient Multi-label CNN-based License Plate Recognition On Spark 2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) English Proceedings Paper IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) NOV 07-11, 2018 Guangzhou, PEOPLES R CHINA IEEE Comp Soc,Guangzhou Univ Surveillance cameras are being rapidly deployed for facilitating smart transportation. Recognizing the vehicle license plate from massive videos becomes a challenge in context of system scalability and efficiency. This paper proposes a novel algorithm for scalable and efficient license plate recognition (SELPR). The SELPR algorithm first locates the license plate using a YOLO (You Look Only Once) network and recognizes the license plate using multi-label convolutional neural network (Multi-label CNN). We deploy the SELPR algorithm to the Apache Spark framework to evaluate its scalability and efficiency in parallel processing. The results demonstrates that SELPR can achieve synthesized performance with 95% recognition accuracy, better processing efficiency and scalability on a Spark cluster. [Zhang, Weishan; Xue, Bing] China Univ Petr, Dept Software Engn, Qingdao, Peoples R China; [Zhou, Jiehan] Oulu Univ, Oulu, Finland; [Liu, Xin] China Univ Petr, Sch Comp & Commun Engn, Qingdao, Peoples R China; [Lv, Hao] China Univ Petr, Coll Comp & Commun Engn, Qingdao, Peoples R China China University of Petroleum; University of Oulu; China University of Petroleum; China University of Petroleum Zhang, WS (corresponding author), China Univ Petr, Dept Software Engn, Qingdao, Peoples R China. zhangws@upc.edu.cn; b199402030210@qq.com; jiehan.zhou@oulu.fi; lx@upc.edu.cn; lvhao.upc@gmail.com xu, liang/AAC-4448-2022 Key Research Program of Shandong Province [2017GGX10140]; Fundamental Research Funds for the Central Universities [15CX08015A]; National Natural Science Foundation of China [61309024] Key Research Program of Shandong Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140 and in part by the Fundamental Research Funds for the Central Universities(15CX08015A), National Natural Science Foundation of China (No. 61309024). 18 2 2 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-9380-3 2018.0 1738 1744 10.1109/SmartWorld.2018.00294 0.0 7 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM0GQ Green Accepted 2023-03-23 WOS:000458742900258 0
J Teng, SZ; Zhang, SL; Huang, QM; Sebe, N Teng, Shangzhi; Zhang, Shiliang; Huang, Qingming; Sebe, Nicu Multi-View Spatial Attention Embedding for Vehicle Re-Identification IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Feature extraction; Task analysis; Measurement; Visualization; Computer science; Training; Neural networks; Vehicle re-identification; convolutional neural network; attention; multi-view Vehicle Re-Identification (Re-ID) is a challenging vision task mainly because the appearance of a vehicle varies dramatically under different viewpoints. Moreover, different vehicles with the same model and color commonly show similar appearance, thus are hard to be distinguished. To alleviate negative effects of viewpoint variance, we design a multi-view branch network where each branch learns a viewpoint-specific feature without parameter sharing. Being able to focus on a limited range of viewpoints, this viewpoint-specific feature performs substantially better than the general feature learned by an uniform network. To further differentiate visually similar vehicles, we strengthen the discriminative power on their subtle local differences by introducing a spatial attention model into each feature learning branch. The multi-view feature learning and spatial attention learning compose our neural network architecture, which is trained end to end with the softmax loss and triplet loss, respectively. We evaluate our methods on two large vehicle Re-ID datasets, i.e., VehicleID and VeRi-776, respectively. Extensive experiments show that our methods achieve promising performance. For example, we achieve mAP accuracy of 76.78% and 72.53% on VehicleID and VeRi-776 dataset respectively, substantially better than current state-of-the art. [Teng, Shangzhi; Huang, Qingming] Univ Chinese Acad Sci, Sch Comp Sci & Tecnol, Beijing 100049, Peoples R China; [Zhang, Shiliang] Peking Univ, Sch Elect Engn & Comp Sci, Dept Comp Sci, Beijing 100871, Peoples R China; [Sebe, Nicu] Univ Trento, Dept Informat Engn & Comp Sci, I-38122 Trento, Italy Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Peking University; University of Trento Zhang, SL (corresponding author), Peking Univ, Sch Elect Engn & Comp Sci, Dept Comp Sci, Beijing 100871, Peoples R China. shangzhi.teng@vipl.ict.ac.cn; slzhang.jdl@pku.edu.cn; qmhuang@ucas.ac.cn; sebe@disi.unitn.it teng, shangzhi/HMD-1791-2023; Teng, Shangzhi/AAN-9030-2021 teng, shangzhi/0000-0001-7098-9932; Sebe, Niculae/0000-0002-6597-7248 National Natural Science Foundation of China [61620106009, 61931008, U1636214, 61836002, 61936011]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) [QYZDJ-SSW-SYS013]; Italy-China collaboration project TALENT [2018YFE0118400]; Beijing Natural Science Foundation [JQ18012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS); Italy-China collaboration project TALENT; Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 61620106009, Grant 61931008, Grant U1636214, Grant 61836002, and Grant 61936011, in part by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) under Grant QYZDJ-SSW-SYS013, in part by the Italy-China collaboration project TALENT under Grant 2018YFE0118400, and in part by the Beijing Natural Science Foundation under Grant JQ18012. This article was recommended by Associate Editor J. Yuan. 65 15 15 5 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. FEB 2021.0 31 2 816 827 10.1109/TCSVT.2020.2980283 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QC7WT 2023-03-23 WOS:000615044400030 0
J Huang, YK; Li, B; Barni, M; Huang, JW Huang, Yuankun; Li, Bin; Barni, Mauro; Huang, Jiwu Identification of VoIP Speech With Multiple Domain Deep Features IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Audio forensics; convolutional neural network; voice over Internet protocol QUALITY ASSESSMENT Identifying whether a phone call comes from VoIP (Voice over Internet Protocol) is a challenging but less-investigated audio forensic issue. As shown in a previous study, existing feature based methods do not work well. In this paper, we propose a robust data-driven approach, called CNN-MLS (convolutional neural network based multi-domain learning scheme), to distinguish VoIP calls from mobile phone calls. To better explore the differences between VoIP and mobile phone calls, we first process data with high-pass filtering, and then extract deep features from both temporal domain and spectral domain. Two CNN architectures are designed for accepting data from respective domains, and some tricks such as auxiliary classifiers and individual subnet training are used for accelerating network convergence. The deep features are finally fused in a classification module for identifying the phone call type. The proposed method is evaluated on VPCID (VoIP Phone Call Identification Database) dataset, under various testing conditions. We pay particular attention to tests on data belonging to a source mismatched with the training sources. Experimental results show that, compared with existing methods, our method can achieve satisfactory and better accuracy on two-second-long inputs, implying that an alert may be activated shortly after a VoIP call is made. [Huang, Yuankun; Li, Bin; Huang, Jiwu] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen Key Lab Media Secur, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518060, Peoples R China; [Huang, Yuankun; Li, Bin; Huang, Jiwu] Inst Artificial Intelligence & Robot Soc, Shenzhen 518172, Peoples R China; [Li, Bin] Peng Cheng Lab, Shenzhen 518066, Peoples R China; [Barni, Mauro] Univ Siena, Dept Informat Engn & Math, I-53100 Siena, Italy Shenzhen University; Peng Cheng Laboratory; University of Siena Li, B (corresponding author), Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen Key Lab Media Secur, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518060, Peoples R China.;Li, B (corresponding author), Inst Artificial Intelligence & Robot Soc, Shenzhen 518172, Peoples R China.;Li, B (corresponding author), Peng Cheng Lab, Shenzhen 518066, Peoples R China. huangyuankun2016@email.szu.edu.cn; libin@szu.edu.cn; barni@dii.unisi.it; jwhuang@szu.edu.cn BARNI, Mauro/0000-0002-7368-0866; Huang, Yuankun/0000-0003-3539-839X National Natural Science Foundation of China (NSFC) [U19B2022, 61872244, 61772349]; Guangdong Basic and Applied Basic Research Foundation [2019B151502001]; Guangdong R&D Program in Key Areas [2019B010139003]; Shenzhen RD Program [JCYJ20180305124325555, GJHZ20180928155814437] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Guangdong R&D Program in Key Areas; Shenzhen RD Program This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant U19B2022, Grant 61872244, and Grant 61772349, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019B151502001, in part by the Guangdong R&D Program in Key Areas under Grant 2019B010139003, and in part by the Shenzhen R&D Program under Grant JCYJ20180305124325555 and Grant GJHZ20180928155814437. The associate editor coordinating the review of this manuscript and approving it for publication was Ms. Elham Tabassi. (Corresponding author: Bin Li.) 41 2 2 2 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2020.0 15 2253 2267 10.1109/TIFS.2019.2960635 0.0 15 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering KO7CQ 2023-03-23 WOS:000515707000003 0
C Khan, A; Zhang, HP; Boudjellal, N; Dai, L; Ahmad, A; Shang, JY; Haindl, P Kotsis, G; Tjoa, AM; Khalil, I; Moser, B; Taudes, A; Mashkoor, A; Sametinger, J; Martinez-Gil, J; Sobieczky, F; Fischer, L; Ramler, R; Khan, M; Czech, G Khan, Asif; Zhang, Huaping; Boudjellal, Nada; Dai, Lin; Ahmad, Arshad; Shang, Jianyun; Haindl, Philipp A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2022 WORKSHOPS Communications in Computer and Information Science English Proceedings Paper 33rd International Conference on Database and Expert Systems Applications (DEXA) AUG 22-24, 2022 Vienna Univ Econ & Business, Vienna, AUSTRIA Software Competence Ctr Hagenberg,JVU, Inst Telecooperat,IIWAS Org Vienna Univ Econ & Business Twitter; USA election; Sentiment analysis; Rule-based; Transformers; Deep learning; Vote-share TWITTER Social media platforms (SMPs) attracted people from all over the world for they allow them to discuss and share their opinions about any topic including politics. The comprehensive use of these SMPs has radically transformed newfangled politics. Election campaigns and political discussions are increasingly held on these SMPs. Studying these discussions aids in predicting the outcomes of any political event. In this study, we analyze and predict the 2020 US Presidential Election using Twitter data. Almost 2.5 million tweets are collected and categorized into Location-considered (LC) (USA only), and Location-unconsidered (LUC) (either location not mentioned or out of USA). Two different sentiment analysis (SA) approaches are employed: dictionary-based SA, and transformers-based SA. We investigated if the deployment of deep learning techniques can improve prediction accuracy. Furthermore, we predict a vote-share for each candidate at LC and LUC levels. Afterward, the predicted results are compared with the five polls' predicted results as well as the real results of the election. The results show that dictionary-based SA outperformed all the five polls' predicted results including the transformers with MAE 0.85 at LC and LUC levels, and RMSE 0.867 and 0.858 at LC and LUC levels. [Khan, Asif; Zhang, Huaping; Boudjellal, Nada; Dai, Lin; Shang, Jianyun] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China; [Ahmad, Arshad] Johannes Kepler Univ Linz, Inst Software Syst Engn, A-4040 Linz, Austria; [Ahmad, Arshad] Inst Appl Sci & Technol, Pak Austria Fachhsch, Dept IT & Comp Sci, Mang Khanpur Rd, Haripur 22620, Pakistan; [Haindl, Philipp] Software Competence Ctr Hagenberg SCCH, Hagenberg, Austria Beijing Institute of Technology; Johannes Kepler University Linz; Softwarepark Hagenberg Zhang, HP; Boudjellal, N (corresponding author), Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China. kevinzhang@bit.edu.cn; nada@bit.edu.cn Boudjellal, Nada/0000-0002-0420-6511; Ahmad, Arshad/0000-0002-3576-8365; Khan, Asif/0000-0002-5863-8467; Zhang, Huaping/0000-0002-0137-4069 35 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1865-0929 1865-0937 978-3-031-14343-4; 978-3-031-14342-7 COMM COM INF SC 2022.0 1633 32 43 10.1007/978-3-031-14343-4_4 0.0 12 Computer Science, Information Systems; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU0OO 2023-03-23 WOS:000870761200004 0
J Zhang, T; Song, YT; Wu, HP; Handroos, H; Cheng, Y; Zhang, XC Zhang, Tao; Song, Yuntao; Wu, Huapeng; Handroos, Heikki; Cheng, Yong; Zhang, Xuanchen Deformation modeling of remote handling EAMA robot by recurrent neural networks INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION English Article Deformation estimation; EAMA; Flexible robot; Recurrent neural network TECHNOLOGY; PROGRESS; DESIGN Purpose Remote handling (RH) manipulators have been widely studied for maintenance tasks in fusion reactors. Those tasks always require heavy load, high accuracy and large work space for manipulators. Traditionally, the maintenance of fusion devices always depends on manual RH. With the development of calculating ability, the intelligent automatic maintenance makes it possible for a fusion device instead of the previous manual operation. As the flexibility of arm and the deformation of manipulator will cause problems, which are mainly inaccuracy and lower efficiency. This paper aims to study an effective way to promote the arm behavior to solve these problems. Design/methodology/approach By making use of the experimental advanced superconducting tokamak articulated maintenance arm as a platform, a series of experiments is designed to measure errors of kinematics and to collect the database of the flexible arm. Through studying the data and the arm structure, recurrent neural network (RNN) method was adopted to estimate the deformation of flexible arm and eventually compensate deformation in robot control to achieve higher accuracy. Findings By means of delicate RNN modeling, errors of kinematics have been reduced to a smaller order than the RH mode. This intelligent maintenance method will also reduce complexity of operations in maintenance. Originality/value This paper presents the use of an artificial intelligent algorithm to solve a nonlinear deformation problem of the flexible arm. The results demonstrate that it is efficient in dealing with this problem in fusion application. The RNN's successful application has also shown that intelligent algorithms can be widely applied in fusion maintenance. [Zhang, Tao; Song, Yuntao] Chinese Acad Sci, Inst Plasma Phys, Chinese Acad Sci ASIPP, Hefei, Anhui, Peoples R China; [Zhang, Tao] Univ Sci & Technol China, Hefei, Anhui, Peoples R China; [Zhang, Tao] Lappeenranta Univ Technol, Sch Energy Syst, Lappeenranta, Finland; [Wu, Huapeng; Handroos, Heikki] Lappeenranta Univ Technol, Sch Energy Syst, Dept Mech Engn, Lappeenranta, Finland; [Cheng, Yong] Huainan New Energy Res Ctr, Anhui Prov Key Lab Special Welding, Hefei, Anhui, Peoples R China; [Zhang, Xuanchen] Chinese Acad Sci, Inst Plasma Phys, Hefei, Anhui, Peoples R China Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Lappeenranta University of Technology; Lappeenranta University of Technology; Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS Zhang, T (corresponding author), Chinese Acad Sci, Inst Plasma Phys, Chinese Acad Sci ASIPP, Hefei, Anhui, Peoples R China.;Zhang, T (corresponding author), Univ Sci & Technol China, Hefei, Anhui, Peoples R China.;Zhang, T (corresponding author), Lappeenranta Univ Technol, Sch Energy Syst, Lappeenranta, Finland. zhangtao@ipp.ac.cn Zhang, Tao/AAE-7539-2019 Zhang, Tao/0000-0003-0750-0123 20 3 3 0 6 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0143-991X 1758-5791 IND ROBOT Ind. Robot. 2019.0 46 2 300 310 10.1108/IR-08-2018-0171 0.0 11 Engineering, Industrial; Robotics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Robotics HX9HF 2023-03-23 WOS:000467717100013 0
J Zhang, D; Chen, YY; Guo, FH; Karimi, HR; Dong, H; Xuan, Q Zhang, Dan; Chen, Yongyi; Guo, Fanghong; Karimi, Hamid Reza; Dong, Hui; Xuan, Qi A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Convolutional neural network (CNN); fault diagnosis; fuzzy C-means (FCM); principal component analysis (PCA); rolling bearing DISCRETE WAVELET TRANSFORM; FUZZY C-MEANS In modern manufacturing processes, requirements for automatic fault diagnosis have been growing increasingly as it plays a vitally important role in the reliability and safety of industrial facilities. Rolling bearing systems represent a critical part in most of the industrial applications. In view of the strong environmental noise in the working environment of rolling bearing, its vibration signals have nonstationary and nonlinear characteristics, and those features are difficult to be extracted. In this article, we proposed a new intelligent fault diagnosis method for rolling bearing with unlabeled data by using the convolutional neural network (CNN) and fuzzy C-means (FCM) clustering algorithm. CNN is first utilized to automatically extract features from rolling bearing vibration signals. Then, the principal component analysis (PCA) technique is used to reduce the dimension of the extracted features, and the first two principal components are selected as the fault feature vectors. Finally, the FCM algorithm is introduced to cluster those rolling bearing data in the derived feature space and identify the different fault types of rolling bearing. The results indicate that the newly proposed fault diagnosis method can achieve higher accuracy than other existing results in the literature. [Zhang, Dan; Chen, Yongyi; Guo, Fanghong; Dong, Hui; Xuan, Qi] Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy Zhejiang University of Technology; Polytechnic University of Milan Zhang, D (corresponding author), Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Peoples R China. danzhang@zjut.edu.cn Zhang, Dan/ABD-4909-2021 Major Project of Science and Technology Innovation in Ningbo City [2019B1003]; National Nature Science Foundation of China [61873237, 61903333] Major Project of Science and Technology Innovation in Ningbo City; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Major Project of Science and Technology Innovation in Ningbo City under Grant 2019B1003 and in part by the National Nature Science Foundation of China under Grant 61873237 and Grant 61903333. 30 28 28 17 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70 3507010 10.1109/TIM.2020.3043873 0.0 10 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation PO0RK 2023-03-23 WOS:000604879000023 0
C Guo, P; Wang, J; Gao, XZ; Tanskanen, JMA IEEE Guo, Ping; Wang, Jing; Gao, X. Z.; Tanskanen, Jarno M. A. Epileptic EEG Signal Classification with Marching Pursuit based on Harmony Search Method PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 14-17, 2012 Seoul, SOUTH KOREA IEEE Systems, Man, and Cybernetics Soc (SMC),IEEE,Korea Univ,Korean Soc Cognit Sci (KSCS),Korean Inst Informat Scientists and Engineers Soc Computat Intelligence (KSCI),Hi Seoul,Korea Tourism Org,Asian Off Aerosp Res and Dev (AOARD),Natl Res Fdn Korea (NRF),World Class Univ,Korea Univ, WCU Res Div Brain and Cognit Engn Harmony search method; Electroencephalogram; Seizure detection; Overcomplete dictionary; Sparse representation NEURAL-NETWORK; AUTOMATIC DETECTION; OPTIMIZATION; ALGORITHM In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results. [Guo, Ping; Wang, Jing] Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China; [Gao, X. Z.] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, Espoo, Finland; [Tanskanen, Jarno M. A.] Tampere Univ Technol, Dept Biomed Engn, Tampere, Finland; [Wang, Jing] Peking Univ Hlth Sci Ctr, Sch Foundat Educ, Beijing, Peoples R China Beijing Normal University; Aalto University; Tampere University; Peking University Guo, P (corresponding author), Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China. pguo@ieee.org; wang_jing@bjmu.edu.cn; xiao-zhi.gao@aalto.fi; tanskanen@ieee.org Guo, Peng/GWC-0572-2022; guo, peng/AAG-4052-2019; Gao, Xiao-Zhi/Q-3038-2016; GUO, Ping/AAG-2160-2019; GUO, Ping/A-3482-2015 Gao, Xiao-Zhi/0000-0002-0078-5675; GUO, Ping/0000-0002-7122-1084; GUO, Ping/0000-0002-7122-1084 National Natural Science Foundation of China; Academy of Finland; National Natural Science Foundation of China [60911130513]; Academy of Finland [135225, 135220]; Academy of Finland (AKA) [135220] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) The research work in this paper was fully supported by the grants from a joint call by the National Natural Science Foundation of China and Academy of Finland. J. Wang's and P. Guo's research work was funded by the National Natural Science Foundation of China ( Project No 60911130513). X. Z. Gao's work was funded by the Academy of Finland under Grant 135225, and J. M. A. Tanskanen's by the Academy of Finland under Grant 135220. 23 3 3 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-4673-1714-6 IEEE SYS MAN CYBERN 2012.0 291 296 6 Computer Science, Cybernetics; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BEJ33 2023-03-23 WOS:000316869200050 0
J Jia, HL; Qu, MH; Wang, G; Walsh, MJ; Yao, JR; Guo, H; Liu, HL Jia, Honglei; Qu, Minghao; Wang, Gang; Walsh, Michael J.; Yao, Jurong; Guo, Hui; Liu, Huili Dough-Stage Maize (Zea mays L.) Ear Recognition Based on Multiscale Hierarchical Features and Multifeature Fusion MATHEMATICAL PROBLEMS IN ENGINEERING English Article FRUIT; YIELD; ROBOT; WIRE Crop-related object recognition is of great importance in realizing intelligent agricultural machinery. Maize (Zea mays. L.) ear recognition could be a representative of crop-related object recognition, which is a critical technological premise for realizing automatic maize ear picking and maize yield prediction. In order to recognize maize ears in dough stage, this study combined deep learning and image processing, which have advantages of feature extraction and hardware flexibility, respectively. LabelImage was applied to mark and label maize plants, based on the deep learning framework TensorFlow, and this study developed multiscale hierarchical feature extraction together with quadruple-expanded convolutional kernels. To recognize the whole maize plant, 1250 images were acquired for training the recognition model, and its performance in a test set showed that the recognition accuracy was 99.47%. Subsequently, multifeatures of maize ear were determined, and the optimum binary threshold was obtained by fitting Gaussian distribution in the subblock image. Consequently, the maize ear was recognized by morphological process which was conducted by Python and OpenCV. Experiment was conducted in August 2018, and 10800 images were acquired for testing this algorithm. Experimental results showed that the average recognition accuracy was 97.02% and time consumption was 0.39 s for each image, which could meet a forward speed of 4.61 km/h for combine harvesters. [Jia, Honglei; Guo, Hui] Jilin Univ, Key Lab Bion Engn, Minist Educ, Changchun 130022, Peoples R China; [Qu, Minghao; Wang, Gang; Liu, Huili] Jilin Univ, Coll Biol & Agr Engn, Changchun 130022, Peoples R China; [Wang, Gang] Univ Vienna, Dept Geog & Reg Res, A-1090 Vienna, Austria; [Walsh, Michael J.] Cornell Univ, Dept Biol & Environm Engn, Ithaca, NY 14853 USA; [Yao, Jurong] First Adm Dept Peoples Canal, Chengdu 611000, Dujiangyan Sich, Peoples R China Jilin University; Jilin University; University of Vienna; Cornell University Wang, G (corresponding author), Jilin Univ, Coll Biol & Agr Engn, Changchun 130022, Peoples R China.;Wang, G (corresponding author), Univ Vienna, Dept Geog & Reg Res, A-1090 Vienna, Austria. gw1988@jlu.edu.cn Wang, Gang/0000-0003-2234-1896 National Natural Science Foundation of China [31901408]; Science and Technology Development Plan of Jilin Province [20180414074GH]; National Key Research and Development Program [2018YFD0300207] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Development Plan of Jilin Province; National Key Research and Development Program This work was supported by the National Natural Science Foundation of China (31901408), the Science and Technology Development Plan of Jilin Province (20180414074GH), and the National Key Research and Development Program (2018YFD0300207). Special thanks are due to the Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH). 39 2 2 3 25 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. MAY 18 2020.0 2020 9825472 10.1155/2020/9825472 0.0 14 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics LW5IU gold 2023-03-23 WOS:000539181000008 0
J Sha, Y; Faber, J; Gou, SP; Liu, B; Li, W; Schramm, S; Stoecker, H; Steckenreiter, T; Vnucec, D; Wetzstein, N; Widl, A; Zhou, K Sha, Yu; Faber, Johannes; Gou, Shuiping; Liu, Bo; Li, Wei; Schramm, Stefan; Stoecker, Horst; Steckenreiter, Thomas; Vnucec, Domagoj; Wetzstein, Nadine; Widl, Andreas; Zhou, Kai A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE English Article Valves acoustics signal; Cavitation intensity recognition; Cavitation detection; Multi-task learning; 1-D convolutional neural network; 1-D double hierarchical residual block ARTIFICIAL NEURAL-NETWORKS; FAULT-DIAGNOSIS; CLASSIFICATION; COLLAPSE; PERFORMANCE; ALGORITHM With the rapid development of smart manufacturing, data-driven machinery health management has received a growing attention. As one of the most popular methods in machinery health management, deep learning (DL) has achieved remarkable successes. However, due to the issues of limited samples and poor separability of different cavitation states of acoustic signals, which greatly hinder the eventual performance of DL modes for cavitation intensity recognition and cavitation detection. Also different tasks were performed separately conventionally. In this work, a novel multi-task learning framework for simultaneous cavitation detection and cavitation intensity recognition framework using 1-D double hierarchical residual networks (1-D DHRN) is proposed for analyzing valves acoustic signals. Firstly, a data augmentation method based on sliding window with fast Fourier transform (Swin-FFT) is developed to alleviate the small-sample issue confronted in this study. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is constructed to capture sensitive features from the frequency domain acoustic signals of valve. Then, a new structure of 1-D DHRN is proposed. Finally, the devised 1-D DHRN is evaluated on two datasets of valve acoustic signals without noise (Dataset 1 and Dataset 2) and one dataset of valve acoustic signals with realistic surrounding noise (Dataset 3) provided by SAMSON AG (Frankfurt). Our method has achieved state-of-the-art results. The prediction accuracies of 1-D DHRN for cavitation intensitys recognition are as high as 93.75%, 94.31% and 100%, which indicates that 1-D DHRN outperforms other DL models and conventional methods. At the same time, the testing accuracies of 1-D DHRN for cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, 1-D DHRN has also been tested for different frequencies of samples and shows excellent results for frequency of samples that mobile phones can accommodate. [Sha, Yu; Gou, Shuiping; Liu, Bo] Xidian Univ, Acad Adv Interdisciplinary Res, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China; [Sha, Yu; Faber, Johannes; Li, Wei; Schramm, Stefan; Stoecker, Horst; Zhou, Kai] Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany; [Sha, Yu] Giersch Sci Ctr, Xidian FIAS Int Joint Res Ctr, D-60438 Frankfurt, Germany; [Stoecker, Horst] Goethe Univ Frankfurt, Inst Theoret Phys, D-60438 Frankfurt, Germany; [Stoecker, Horst] GSI Helmholtzzentrum Schwerionenforsch GmbH, D-64291 Darmstadt, Germany; [Steckenreiter, Thomas; Vnucec, Domagoj; Wetzstein, Nadine; Widl, Andreas] SAMSON AG, D-60314 Frankfurt, Germany Xidian University; Goethe University Frankfurt; Helmholtz Association; GSI Helmholtz-Center for Heavy Ion Research Zhou, K (corresponding author), Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany. zhou@fias.uni-frankfurt.de Sha, Yu/0000-0003-4521-2077; Zhou, Kai/0000-0001-9859-1758 Xidian - FIAS International Joint Research Center; AI grant at FIAS through SAMSON AG; BMBF; SAMSON AG; Walter GreinerGesellschaft zur Forderung der physikalischen Grundla -genforschung e.V. through the Judah M. Eisenberg Laureatus Chair at Goethe Universitat Frankfurt am Main; NVIDIA Corporation Xidian - FIAS International Joint Research Center; AI grant at FIAS through SAMSON AG; BMBF(Federal Ministry of Education & Research (BMBF)); SAMSON AG; Walter GreinerGesellschaft zur Forderung der physikalischen Grundla -genforschung e.V. through the Judah M. Eisenberg Laureatus Chair at Goethe Universitat Frankfurt am Main; NVIDIA Corporation This research is supported by Xidian - FIAS International Joint Research Center (Y. S.), by the AI grant at FIAS through SAMSON AG (J. F., K. Z.), by the BMBF funding through the ErUM -Data project (K. Z.), by SAMSON AG (D. V., T. S., A. W.), by the Walter GreinerGesellschaft zur Forderung der physikalischen Grundla -genforschung e.V. through the Judah M. Eisenberg Laureatus Chair at Goethe Universitat Frankfurt am Main (H. S.), by the NVIDIA GPU grant through NVIDIA Corporation (K. Z.). 70 3 3 6 11 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0952-1976 1873-6769 ENG APPL ARTIF INTEL Eng. Appl. Artif. Intell. AUG 2022.0 113 104904 10.1016/j.engappai.2022.104904 0.0 MAY 2022 20 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 3E7NH Green Submitted 2023-03-23 WOS:000830167100002 0
J Zhang, K; Zhang, HB; Zhou, HY; Crookes, D; Li, L; Shao, YQ; Liu, D Zhang, Kun; Zhang, Hongbin; Zhou, Huiyu; Crookes, Danny; Li, Ling; Shao, Yeqin; Liu, Dong Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article 3D SEGMENTATION Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments. [Zhang, Kun; Zhang, Hongbin] Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China; [Zhou, Huiyu] Univ Leicester, Dept Informat, Leicester, Leics, England; [Crookes, Danny] Queens Univ, ECIT, Belfast, Antrim, North Ireland; [Li, Ling] Univ Kent, Sch Comp, Canterbury, Kent, England; [Shao, Yeqin] Nantong Univ, Sch Transportat, Nantong 226019, Peoples R China; [Liu, Dong] Nantong Univ, Jiangsu Key Lab Neuroregenerat, Coinnovat Ctr Neuroregenerat, Nantong 226001, Peoples R China Nantong University; University of Leicester; Queens University Belfast; University of Kent; Nantong University; Nantong University Zhang, K (corresponding author), Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China.;Liu, D (corresponding author), Nantong Univ, Jiangsu Key Lab Neuroregenerat, Coinnovat Ctr Neuroregenerat, Nantong 226001, Peoples R China. zhangkun_nt@163.com; 1951299040@qq.com; hz143@leicester.ac.uk; d.crookes@qub.ac.uk; c.li@kent.ac.uk; hnsyk@ntu.edu.cn; liudongtom@gmail.com Zhou, Huiyu/O-2692-2014; Liu, Dong/O-1271-2013 Zhou, Huiyu/0000-0003-1634-9840; zhang, kun/0000-0001-5675-8046; Li, Ling/0000-0002-4026-0216; Liu, Dong/0000-0002-2764-6544 National Natural Science Foundation of China [81570447, 61671255, 81870359]; Natural Science Foundation of Jiangsu Province, China [BK20170443]; Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China [17KJB520030]; UK EPSRC [EP/N011074/1]; Royal Society-Newton Advanced Fellowship [NA160342]; European Union's Horizon 2020 Research and Innovation Program under the Marie-Sklodowska-Curie Grant [720325]; EPSRC [EP/N011074/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province, China(Natural Science Foundation of Jiangsu Province); Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China; UK EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society-Newton Advanced Fellowship; European Union's Horizon 2020 Research and Innovation Program under the Marie-Sklodowska-Curie Grant; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This is work was supported by the National Natural Science Foundation of China (Nos. 81570447, 61671255, and 81870359), the Natural Science Foundation of Jiangsu Province, China, under Grant No. BK20170443, the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 17KJB520030. H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union's Horizon 2020 Research and Innovation Program under the Marie-Sklodowska-Curie Grant Agreement No. 720325. 45 13 13 2 19 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. 2019.0 2019 8214975 10.1155/2019/8214975 0.0 14 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology HL8NV 30863436.0 Green Published, gold 2023-03-23 WOS:000459000800001 0
J Fang, YM; Lei, JJ; Li, J; Xu, L; Lin, WS; Le Callet, P Fang, Yuming; Lei, Jianjun; Li, Jia; Xu, Long; Lin, Weisi; Le Callet, Patrick Learning visual saliency from human fixations for stereoscopic images NEUROCOMPUTING English Article Stereoscopic image; 3D image; Stereoscopic saliency detection; Visual attention; Machine learning; Support Vector Regression ATTENTION; MODEL; SCENE; MECHANISMS; FEATURES; VIDEOS In the previous years, a lot of saliency detection algorithms have been designed for saliency computation of visual content. Recently, stereoscopic display techniques have developed rapidly, which results in much requirement of stereoscopic saliency detection for emerging stereoscopic applications. Different from 2D saliency prediction, stereoscopic saliency detection methods have to consider depth factor. We design a novel stereoscopic saliency detection algorithm by machine learning technique. First, the features of luminance, color and texture are extracted to calculate the feature contract for predicting feature maps of stereoscopic images. Furthermore, the depth features are extracted for depth feature map computation. Sematic features including the center-bias factor and other top-down cues are also applied as the features in the proposed stereoscopic saliency detection method. Support Vector Regression (SVR) is applied to learn the saliency detection model of stereoscopic images. Experimental results obtained on a public large-scale eye tracking database demonstrate that the proposed method can predict better saliency results for stereoscopic images than other existing ones. (C) 2017 Elsevier B.V. All rights reserved. [Fang, Yuming] Jiangxi Univ Finance & Econ, Sch Informat Technol, Jiangxi Prov Key Lab Digital Media, Nanchang 330032, Jiangxi, Peoples R China; [Lei, Jianjun] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China; [Li, Jia] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China; [Xu, Long] Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China; [Lin, Weisi] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore; [Le Callet, Patrick] Univ Nantes, Polytech Nantes, LUNAM Univ, CNRS,IRCCyN,UMR 6597, Nantes, France Jiangxi University of Finance & Economics; Tianjin University; Beihang University; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Centre National de la Recherche Scientifique (CNRS); Nantes Universite; Ecole Centrale de Nantes Lei, JJ (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China. fa0001ng@e.ntu.edu.sg; jjlei@tju.edu.cn; jiali@buaa.edu.cn; lxu@nao.cas.cn; wslin@ntu.edu.sg; patrick.lecallet@univ-nantes.fr Xu, Long/AAH-9908-2019; Li, Jia/AAB-6431-2019; Lei, Jianjun/P-2539-2018; Lin, Weisi/A-3696-2011; Le Callet, Patrick/F-5772-2010 Xu, Long/0000-0002-9286-2876; Li, Jia/0000-0002-4346-8696; Lei, Jianjun/0000-0003-3171-7680; Lin, Weisi/0000-0001-9866-1947; Natural Science Foundation of China [61571212]; Natural Science Foundation of Jiangxi Province in China [GJJ160420, 20161ACB21014, 20151BDH80003] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangxi Province in China(Natural Science Foundation of Jiangxi Province) This work was partially funded by the Natural Science Foundation of China under grant 61571212, and by Natural Science Foundation of Jiangxi Province in China under grant GJJ160420, grant 20161ACB21014 and grant 20151BDH80003. 66 10 10 3 45 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 29 2017.0 266 284 292 10.1016/j.neucom.2017.05.050 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FE4KX 2023-03-23 WOS:000408183900027 0
S Feigelson, ED; de Souza, RS; Ishida, EEO; Babu, GJ Reid, N Feigelson, Eric D.; de Souza, Rafael S.; Ishida, Emille E. O.; Babu, Gutti Jogesh Twenty-First-Century Statistical and Computational Challenges in Astrophysics ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 8, 2021 Annual Review of Statistics and Its Application English Article; Book Chapter astronomy; astrophysics; astrostatistics; cosmology; galaxies; stars; exoplanets; gravitational waves; Bayesian inference; likelihood-free modeling; signal detection; periodic time series; machine learning; measurement errors GENERALIZED LINEAR-MODELS; SUPERNOVA PHOTOMETRIC CLASSIFICATION; APPROXIMATE BAYESIAN COMPUTATION; LIKELIHOOD-FREE INFERENCE; DENSITY-ESTIMATION; SPECTRAL-ANALYSIS; COSMIC WEB; MASS; REGRESSION; ASTRONOMY Modern astronomy has been rapidly increasing our ability to see deeper into the Universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these data sets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and joint development of new statistical methodologies. This collaboration will yield more astrophysical insights into astronomical populations and the cosmos itself. [Feigelson, Eric D.; Babu, Gutti Jogesh] Penn State Univ, Dept Astron & Astrophys, University Pk, PA 16802 USA; [Feigelson, Eric D.; Babu, Gutti Jogesh] Penn State Univ, Dept Stat, University Pk, PA 16802 USA; [Feigelson, Eric D.; Babu, Gutti Jogesh] Penn State Univ, Ctr Astrostat, University Pk, PA 16802 USA; [de Souza, Rafael S.] Chinese Acad Sci, Shanghai Astron Observ, Key Lab Res Galaxies & Cosmol, Shanghai 200030, Peoples R China; [Ishida, Emille E. O.] Univ Clermont Auvergne, CNRS, IN2P3, LPC, F-63000 Clermont Ferrand, France Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA) Feigelson, ED (corresponding author), Penn State Univ, Dept Astron & Astrophys, University Pk, PA 16802 USA.;Feigelson, ED (corresponding author), Penn State Univ, Dept Stat, University Pk, PA 16802 USA.;Feigelson, ED (corresponding author), Penn State Univ, Ctr Astrostat, University Pk, PA 16802 USA. e5f@psu.edu de Souza, Rafael S./O-2990-2019 de Souza, Rafael S./0000-0001-7207-4584 NSF [AST-1614690]; NASA [80NSSC17K0122]; Eberly College of Science through the Center for Astrostatistics; 2018-20 CNRS MOMENTUM fellowship NSF(National Science Foundation (NSF)); NASA(National Aeronautics & Space Administration (NASA)); Eberly College of Science through the Center for Astrostatistics; 2018-20 CNRS MOMENTUM fellowship Astrostatistics at Penn State is supported by NSF grant AST-1614690, NASA grant 80NSSC17K0122, and the Eberly College of Science through the Center for Astrostatistics. E.E.O.I. is supported by a 2018-20 CNRS MOMENTUM fellowship. 124 4 4 1 5 ANNUAL REVIEWS PALO ALTO 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0897 USA 2326-8298 2326-831X ANNU REV STAT APPL 2021.0 8 493 517 10.1146/annurev-statistics-042720-112045 0.0 25 Mathematics, Interdisciplinary Applications; Statistics & Probability Book Citation Index – Science (BKCI-S); Science Citation Index Expanded (SCI-EXPANDED) Mathematics BR0BI Green Submitted 2023-03-23 WOS:000627718500022 0
J Zhang, RH; Zhou, JC; Hai, T; Zhang, SX; Iwendi, M; Biamba, C; Anumbe, N Zhang, Ruihua; Zhou, Jincheng; Hai, Tao; Zhang, Shixue; Iwendi, Marvellous; Biamba, Cresantus; Anumbe, Noble Quality assurance awareness in higher education in China: big data challenges JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS English Article Quality assurance; Higher education; Quality management; Big data; Analytics; HEEC The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation's universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model. [Zhang, Ruihua; Zhou, Jincheng; Hai, Tao] Qiannan Normal Univ Nationalities, Sch Comp & Informat, Duyun 558000, Guizhou, Peoples R China; [Zhang, Ruihua; Zhang, Shixue] Segi Univ, Fac Educ, Language & Psychol Postgrad Dept, Petaling Jaya, Malaysia; [Zhou, Jincheng; Hai, Tao] Qiannan Normal Univ Nationalities, Key Lab Complex Syst & Intelligent Optimizat Guiz, Duyun 558000, Peoples R China; [Hai, Tao] Univ Teknol Malaysia UTM, Sch Comp, Fac Engn, Johor Baharu 81310, Johor, Malaysia; [Zhang, Shixue] Baoji Univ Arts & Sci, Inst Int Cooperat & Exchange, Baoji 721013, Shaanxi, Peoples R China; [Iwendi, Marvellous] Univ Nigeria, Nsukka, Nigeria; [Biamba, Cresantus] Univ Gavle, Dept Culture Studies Religious Studies & Educ Sci, S-80176 Gavle, Sweden; [Anumbe, Noble] Univ South Carolina, Mech Engn Dept, Columbia, SC 29208 USA SEGi University; Universiti Teknologi Malaysia; Baoji University of Arts & Sciences; University of Nigeria; University of Gavle; University of South Carolina System; University of South Carolina Columbia Zhang, SX (corresponding author), Segi Univ, Fac Educ, Language & Psychol Postgrad Dept, Petaling Jaya, Malaysia.;Zhang, SX (corresponding author), Baoji Univ Arts & Sci, Inst Int Cooperat & Exchange, Baoji 721013, Shaanxi, Peoples R China.;Biamba, C (corresponding author), Univ Gavle, Dept Culture Studies Religious Studies & Educ Sci, S-80176 Gavle, Sweden. zhangshixue@bjwlxy.edu.cn; cresantus.biamba@hig.se Zhang, Ruihua/HMP-3996-2023 Zhou, Jincheng/0000-0002-1995-4002 University of Gavle; Department of Culture Studies, Religious Studies and Educational Sciences, University of Gavle, Gavle, Sweden University of Gavle; Department of Culture Studies, Religious Studies and Educational Sciences, University of Gavle, Gavle, Sweden Open access funding provided by University of Gavle. The project was supported by the Department of Culture Studies, Religious Studies and Educational Sciences, University of Gavle, Gavle, Sweden. 29 0 0 4 4 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2192-113X J CLOUD COMPUT-ADV S J. Cloud Comput.-Adv. Syst. Appl. SEP 29 2022.0 11 1 56 10.1186/s13677-022-00321-6 0.0 9 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4Z0HM Green Submitted, gold, Green Published 2023-03-23 WOS:000861900500004 0
J Zhang, XD; Li, XP; Du, HA; Ruiz, R Zhang, Xiaodong; Li, Xiaoping; Du, Houan; Ruiz, Ruben Task Scheduling for Spark Applications With Data Affinity on Heterogeneous Clusters IEEE INTERNET OF THINGS JOURNAL English Article Task analysis; Sparks; Servers; Scheduling; Schedules; Data communication; Internet of Things; Data affinity; heterogeneous cluster; spark; task scheduling DATA PLACEMENT; TIME The Internet of Things (IoT)-enabled applications use sensors and actuators to collect big data, which are processed by big data models, e.g., Spark. Generally, data processing tasks are precedence constrained and the computation results are transmitted to other IoT devices. In this article, we consider the Spark workflow problem of scheduling tasks with data affinity to heterogeneous servers to minimize the maximum completion time. In a Spark instance, jobs are precedence constrained and stages for each job are also precedence constrained. There are a large number of topological stage orders. A balance between task execution times, determined by heterogeneous servers, and transmission times caused by data affinity is difficult to achieve. A scheduling optimization algorithm framework is proposed, which consists of five components: 1) temporal parameter calculation; 2) ready stage adding; 3) task sequencing; 4) resource allocation; and 5) schedule improvement. Strategies for each component are developed. The algorithmic components are statistically calibrated over a comprehensive set of instances. The proposed algorithm is compared to two modified classic algorithms for similar problems on typical scientific workflow instances. The experimental results demonstrate the effectiveness of the proposal for the considered problem. [Zhang, Xiaodong] Nanjing Audit Univ, Sch Informat & Engn, Nanjing 211815, Peoples R China; [Li, Xiaoping; Du, Houan] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China; [Ruiz, Ruben] Univ Politecn Valencia, Grp Sistemas Optimizac Aplicada, Valencia 46022, Spain Nanjing Audit University; Southeast University - China; Universitat Politecnica de Valencia Li, XP (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China. xpli@seu.edu.cn zhang, xiaodong/HNS-5895-2023 National Key Research and Development Program of China [2019YFB1404602]; National Natural Science Foundation of China [61872077, 61832004]; Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization; FEDER Funds [RTI2018-094940-B-I00] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization; FEDER Funds(European Commission) This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1404602, and in part by the National Natural Science Foundation of China under Grant 61872077 and Grant 61832004. The work of Ruben Ruiz was supported in part by the Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization financed with FEDER Funds under Grant RTI2018-094940-B-I00. 30 0 0 8 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. NOV 1 2022.0 9 21 21792 21801 10.1109/JIOT.2022.3181997 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5M4QC 2023-03-23 WOS:000871080800081 0
J Dong, YC; Ding, ZG; Chiclana, F; Herrera-Viedma, E Dong, Yucheng; Ding, Zhaogang; Chiclana, Francisco; Herrera-Viedma, Enrique Dynamics of Public Opinions in an Online and Offline Social Network IEEE TRANSACTIONS ON BIG DATA English Article Social network services; Mathematical model; Big data; Internet; Context modeling; Security; Context; Opinion dynamics; social network; consensus; security; online and offline context; big data CONSENSUS; INFORMATION; AGENTS With the development of the information and Internet technology, the public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to the security crisis for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, in this paper we propose the public opinion dynamics model in an online-offline social network context. Next, in the theory aspect we investigate the analytical conditions to form a consensus in the public opinion dynamics model. Furthermore, we conduct the extensive simulations to investigate how the online agents impact the dynamics of public opinion formation, and unfold that the online agents shorten the steady-state time, decrease the number of opinion clusters, and smoothen the opinion changes in the opinion dynamics. The increase in the size of the online agents often enhances these effects. The results in this paper can provide a basis for the management of the public opinions in the Internet age. [Dong, Yucheng; Ding, Zhaogang] Sichuan Univ, Sch Business, Chengdu 610065, Sichuan, Peoples R China; [Chiclana, Francisco] De Montfort Univ, Ctr Computat Intelligence, Fac Technol, Leicester LE1 9BH, Leics, England; [Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18010, Spain; [Herrera-Viedma, Enrique] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 23218, Saudi Arabia Sichuan University; De Montfort University; University of Granada; King Abdulaziz University Dong, YC (corresponding author), Sichuan Univ, Sch Business, Chengdu 610065, Sichuan, Peoples R China. ycdong@scu.edu.cn; dzgzhaogang@stu.scu.edu.cn; chiclana@dmu.ac.uk; viedma@decsai.ugr.es Dong, Yucheng/J-1668-2017; Chiclana, Francisco/B-9031-2008; Herrera-Viedma, Enrique/C-2704-2008 Dong, Yucheng/0000-0002-0028-6796; Chiclana, Francisco/0000-0002-3952-4210; Herrera-Viedma, Enrique/0000-0002-7922-4984 NSF of China [71571124]; FEDER funds [TIN2013-40658-P, TIN2016-75850-R]; Andalusian Excellence Project NSF of China(National Natural Science Foundation of China (NSFC)); FEDER funds(European Commission); Andalusian Excellence Project This work was supported by the grants (No. 71571124) from NSF of China, the grant (Nos. TIN2013-40658-P and TIN2016-75850-R) from the FEDER funds, and the grant (No. TIC-5991) from the Andalusian Excellence Project. 43 46 47 123 277 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7790 IEEE T BIG DATA IEEE Trans. Big Data SEPT 1 2021.0 7 4 610 618 10.1109/TBDATA.2017.2676810 0.0 9 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science TY7VN Green Submitted 2023-03-23 WOS:000683988500001 0
J Jiang, JH; Shi, KY; Huang, YH; Hsu, CY; Hettie, KS; Kung, WM Jiang, Jiehui; Shi, Kuangyu; Huang, Yiyun Henry; Hsu, Chih-Yu; Hettie, Kenneth Scott; Kung, Woon-Man Editorial: Translational advances in Alzheimer's, Parkinson's, and other dementia: Molecular mechanisms, biomarkers, diagnosis, and therapies, volume II FRONTIERS IN AGING NEUROSCIENCE English Editorial Material Alzheimer's disease; Parkinson's disease; dementia; neurodegenerative disorders; big data mining; imaging methods; bioinformatic applications [Jiang, Jiehui] Shanghai Univ, Sch Life Sci, Inst Biomed Engn, Shanghai, Peoples R China; [Shi, Kuangyu] Univ Bern, Bern Univ Hosp, Inselspital, Dept Nucl Med, Bern, Switzerland; [Shi, Kuangyu] Tech Univ Munich, Dept Informat, Munich, Germany; [Huang, Yiyun Henry] Yale Univ, PET Ctr, Sch Med, Dept Radiol & Biomed Imaging, New Haven, CT USA; [Hsu, Chih-Yu] Fujian Univ Technol, Sch Transportat, Fuzhou, Peoples R China; [Hettie, Kenneth Scott] Stanford Univ, Sch Med, Dept Radiol Otolaryngol Head & Neck Surg, Mol Imaging Program Stanford MIPS, Palo Alto, CA 94304 USA; [Kung, Woon-Man] Buddhist Tzu Chi Med Fdn, Taipei Tzu Chi Hosp, Dept Surg, Div Neurosurg, New Taipei, Taiwan; [Kung, Woon-Man] Chinese Culture Univ, Coll Kinesiol & Hlth, Dept Exercise & Hlth Promot, Taipei, Taiwan Shanghai University; University of Bern; University Hospital of Bern; Technical University of Munich; Yale University; Fujian University of Technology; Stanford University; Buddhist Tzu Chi General Hospital; Taipei Tzu Chi Hospital; Chinese Culture University Kung, WM (corresponding author), Buddhist Tzu Chi Med Fdn, Taipei Tzu Chi Hosp, Dept Surg, Div Neurosurg, New Taipei, Taiwan.;Kung, WM (corresponding author), Chinese Culture Univ, Coll Kinesiol & Hlth, Dept Exercise & Hlth Promot, Taipei, Taiwan. nskungwm@yahoo.com.tw Shi, Kuangyu/0000-0002-8714-3084 5 0 0 5 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1663-4365 FRONT AGING NEUROSCI Front. Aging Neurosci. OCT 10 2022.0 14 1045828 10.3389/fnagi.2022.1045828 0.0 4 Geriatrics & Gerontology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Geriatrics & Gerontology; Neurosciences & Neurology 5T8BX 36299613.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000876085700001 0
J Mortazavi, B; Podryabinkin, EV; Roche, S; Rabczuk, T; Zhuang, XY; Shapeev, AV Mortazavi, Bohayra; Podryabinkin, Evgeny, V; Roche, Stephan; Rabczuk, Timon; Zhuang, Xiaoying; Shapeev, Alexander, V Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures MATERIALS HORIZONS English Article TOTAL-ENERGY CALCULATIONS; TRANSPORT; GRAPHENE One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over shortab initiomolecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019,5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modelingviahierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures. [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30157 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Cluster Excellence PhoenixD, Photon Opt & Engn Innovat Disciplines, Hannover, Germany; [Podryabinkin, Evgeny, V; Shapeev, Alexander, V] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Nobel St 3, Moscow 143026, Russia; [Roche, Stephan] CSIC, Catalan Inst Nanosci & Nanotechnol ICN2, Campus UAB, Barcelona 08193, Spain; [Roche, Stephan] BIST, Campus UAB, Barcelona 08193, Spain; [Roche, Stephan] ICREA Inst Catalan Recerca & Estudis Avancats, Barcelona 08010, Spain; [Rabczuk, Timon; Zhuang, Xiaoying] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Skolkovo Institute of Science & Technology; Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Catalan Institute of Nanoscience & Nanotechnology (ICN2); Consejo Superior de Investigaciones Cientificas (CSIC); Autonomous University of Barcelona; Barcelona Institute of Science & Technology; ICREA; Tongji University Mortazavi, B (corresponding author), Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30157 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Cluster Excellence PhoenixD, Photon Opt & Engn Innovat Disciplines, Hannover, Germany. bohayra.mortazavi@gmail.com Rabczuk, Timon/A-3067-2009; Podryabinkin, Evgeny/GZK-2538-2022; Roche, Stephan/B-1116-2012 Rabczuk, Timon/0000-0002-7150-296X; Podryabinkin, Evgeny/0000-0001-9057-7249; Roche, Stephan/0000-0003-0323-4665 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Russian Science Foundation [18-13-00479]; Severo Ochoa program from Spanish MINECO [SEV-2017-0706]; CERCA Programme/Generalitat de Catalunya; Russian Science Foundation [18-13-00479] Funding Source: Russian Science Foundation Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Russian Science Foundation(Russian Science Foundation (RSF)); Severo Ochoa program from Spanish MINECO(Spanish Government); CERCA Programme/Generalitat de Catalunya; Russian Science Foundation(Russian Science Foundation (RSF)) B. M. and X. Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E. V. P and A. V. S. were supported by the Russian Science Foundation (Grant No. 18-13-00479). ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and funded by the CERCA Programme/Generalitat de Catalunya. 71 82 82 8 89 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2051-6347 2051-6355 MATER HORIZ Mater. Horizons SEP 1 2020.0 7 9 2359 2367 10.1039/d0mh00787k 0.0 9 Chemistry, Multidisciplinary; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science NI1JZ Green Submitted 2023-03-23 WOS:000565115500009 0
C He, F; Zhu, YM; Wang, WB; Nanding, AY; Kuai, ZX; Li, XM; Liu, ZJ IEEE He, Feng; Zhu, Yuemin; Wang, Weibo; Nanding, Abiyasi; Kuai, Zixiang; Li, Xiaomei; Liu, Zhengjun Multi-Instance Classification of Histopathological Breast Cancer Images with Visual Explanation 2022 16TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP2022), VOL 1 International Conference on Signal Processing English Proceedings Paper 16th IEEE International Conference on Signal Processing (ICSP) OCT 21-24, 2022 Beijing, PEOPLES R CHINA Inst Elect & Elect Engineers,Beijing Jiaotong Univ,IET,Union Radio Scientifique Int,Chinese Inst Elect,IEEE Beijing Sect,IET Beijing Local Network,Natl Nat Sci Fdn China,IEEE Signal Proc Soc Beijing Chapter,IEEE Comp Soc Beijing Chapter,Japan China Sci & Technol Exchange Assoc,CIE Signal Proc Soc,Shenzhen Univ, Intelligent Informat Inst,CIC Commun & Signal Proc Soc,Beijing Inst Elect histopathological images; deep learning; breast cancer classification; multiple instance learning; visual explanation COMPUTER-AIDED DIAGNOSIS Breast cancer is a big concern for women due to its higher mortality compared to other cancers. Objective and accurate early diagnosis is primordial for the treatment and survival improvement of patients. Histopathological image classification is considered the gold standard and is usually the last and most dependent diagnosis approach for doctors to make patient treatment proposals. In particular, recent deep learning-based methods provide remarkable classification results. However, these methods ignore rationale or logical explanation that is important for diagnosis reliability and human-level understanding. This paper proposes a multi-instance classification network (MICNet) with the mechanism of visual explanation to achieve the explainable classification of histopathological breast cancer images. The method uses a simple two-dimensional convolution kernel to generate explanation maps (i.e., visual explanation) through features coming from the end of the feature extractor in the VGG11 model pre-trained by ImageNet. Multiple instance learning (MIL) based on mirror padding and overlap cropping is adopted to improve the network's classification performance. We also design a weighted average pooling method to encourage the network to learn more accurate visual explanation. Experiments on BreakHis and Camelyon16 patch-based datasets demonstrate that our MICNet outperforms other CNN models in classification and is able to provide a logical visual explanation that supports the network's prediction. [He, Feng; Wang, Weibo] Harbin Inst Technol, Inst Ultra Precis Optoelect Instrument Engn, Harbin 150001, Peoples R China; [He, Feng; Wang, Weibo] Harbin Inst Technol, Key Lab Ultra Precis Intelligent Instrumentat, Harbin 150001, Peoples R China; [Liu, Zhengjun] Harbin Inst Technol, Sch Phys, Harbin 150001, Peoples R China; [He, Feng; Zhu, Yuemin] Univ Lyon, CNRS UMR 5220, CREATIS, INSA Lyon,INSERM U1294, F-69621 Villeurbanne, France; [Nanding, Abiyasi; Li, Xiaomei] Harbin Med Univ Canc Hosp, Dept Pathol, Harbin 150081, Peoples R China; [Kuai, Zixiang] Harbin Med Univ Canc Hosp, Imaging Ctr, Harbin 150081, Peoples R China Harbin Institute of Technology; Harbin Institute of Technology; Harbin Institute of Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon He, F (corresponding author), Harbin Inst Technol, Inst Ultra Precis Optoelect Instrument Engn, Harbin 150001, Peoples R China.;He, F (corresponding author), Harbin Inst Technol, Key Lab Ultra Precis Intelligent Instrumentat, Harbin 150001, Peoples R China.;He, F (corresponding author), Univ Lyon, CNRS UMR 5220, CREATIS, INSA Lyon,INSERM U1294, F-69621 Villeurbanne, France. fenghe@hit.edu.cn; yue-min.zhu@creatis.insa-lyon.fr; wwbhit@hit.edu.cn; abiyasi001@163.com; zixiangkuai@126.com; fanliwenqi@163.com; zjliu@hit.edu.cn National Natural Science Foundation of China [51775148]; China Scholarship Council; International Research Project METISLAB National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); International Research Project METISLAB This work was partly supported by National Natural Science Foundation of China (51775148), the International Research Project METISLAB and China Scholarship Council. 34 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-5221 978-1-6654-6056-9 INT CONF SIGN PROCES 2022.0 431 436 10.1109/ICSP56322.2022.9965331 0.0 6 Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU4ET 2023-03-23 WOS:000896487300085 0
J Pan, LQ; Paun, G; Zhang, GX; Neri, F Pan, Linqiang; Paun, Gheorghe; Zhang, Gexiang; Neri, Ferrante Spiking Neural P Systems with Communication on Request INTERNATIONAL JOURNAL OF NEURAL SYSTEMS English Article Bio-inspired computing; membrane computing; P system; artificial neural network; spiking neural network NETWORK; NEURONS; POWER Spiking Neural P Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural P systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these P systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. In the present work, a novel communication strategy among neurons of Spiking Neural P Systems is proposed. In the resulting models, called Spiking Neural P Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural P systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). The Spiking Neural P Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems. [Pan, Linqiang] Huazhong Univ Sci & Technol, Sch Automat, Educ Minist China, Key Lab Image Informat Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China; [Pan, Linqiang] Zhengzhou Univ Light Ind, Zhengzhou 450002, Henan, Peoples R China; [Paun, Gheorghe] Romanian Acad, Inst Math, POB 1-764, RO-014700 Bucharest, Romania; [Zhang, Gexiang] Xihua Univ, Robot Res Ctr, Chengdu 610039, Sichuan, Peoples R China; [Zhang, Gexiang] Xihua Univ, Minist Educ, Key Lab Fluid & Power Machinery, Chengdu 610039, Sichuan, Peoples R China; [Zhang, Gexiang] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Sichuan, Peoples R China; [Neri, Ferrante] De Montfort Univ, Ctr Computat Intelligence, Sch Comp Sci & Informat, Gateway, Leicester LE1 9BH, Leics, England Huazhong University of Science & Technology; Zhengzhou University of Light Industry; Institute of Mathematics of the Romanian Academy; Romanian Academy of Sciences; University of Bucharest; Xihua University; Xihua University; Southwest Jiaotong University; De Montfort University Zhang, GX (corresponding author), Xihua Univ, Robot Res Ctr, Chengdu 610039, Sichuan, Peoples R China.;Zhang, GX (corresponding author), Xihua Univ, Minist Educ, Key Lab Fluid & Power Machinery, Chengdu 610039, Sichuan, Peoples R China.;Zhang, GX (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Sichuan, Peoples R China. lqpan@mail.hust.edu.cn; gpaun@us.es; zhgxdylan@126.com; fneri@dmu.ac.uk Neri, Ferrante/U-7036-2019 Neri, Ferrante/0000-0002-6100-6532; Zhang, Gexiang/0000-0001-8034-0977 National Natural Science Foundation of China [61320106005, 61772214, 61373047, 61672437, 61702428, 51641506]; Innovation Scientists and Technicians Troop Construction Projects of Henan Province [154200510012]; Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, P. R. China [JYBFXYQ-1] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Scientists and Technicians Troop Construction Projects of Henan Province; Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, P. R. China The work of L. Pan was supported by National Natural Science Foundation of China (61320106005 and 61772214) and the Innovation Scientists and Technicians Troop Construction Projects of Henan Province (154200510012). The work of G. Zhang was supported by National Natural Science Foundation of China (61373047, 61672437, 61702428 and 51641506) and the Research Project of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, P. R. China (JYBFXYQ-1). 58 149 149 4 58 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0129-0657 1793-6462 INT J NEURAL SYST Int. J. Neural Syst. DEC 2017.0 27 8 1750042 10.1142/S0129065717500423 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FL5JM 28982286.0 Green Submitted 2023-03-23 WOS:000414271800003 0
J Orujov, F; Maskeliunas, R; Damasevicius, R; Wei, W Orujov, F.; Maskeliunas, R.; Damasevicius, R.; Wei, W. Fuzzy based image edge detection algorithm for blood vessel detection in retinal images APPLIED SOFT COMPUTING English Article Fuzzy logic; Retinal fundus images; Retinal blood vessels; Retinal vessel segmentation; Edge detection; Medical image processing NEURAL-NETWORKS; FUNDUS IMAGES; SEGMENTATION; SYSTEM; TYPE-2; LOGIC; MODEL We developed a contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method uses the green channel data from eye fundus images as input, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filter for background exclusion. The Mamdani (Type-2) fuzzy rules applied on image gradient value are used for edge detection. The results of experiments on the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE) and CHASEdb datasets show the applicability of the proposed method as a flexible approach which can be adapted to numerous edge detection/contour based applications. We achieved an accuracy of 0.865 for STARE dataset, an accuracy of 0.939 for the DRIVE dataset, and the accuracy of 0.950 for the ChaseDB dataset. In relation to works of other authors, our method offered a similar performance, but it offers an improved dynamics and flexibility in formulation of the linguistic threshold criteria, which can be a leading factor in design of image processing systems with dynamic and flexible rules, such as Type 2 fuzzy rules would allow, offering an interesting alternative to currently widespread deep learning applications. (C) 2020 Elsevier B.V. All rights reserved. [Orujov, F.; Maskeliunas, R.] Kaunas Univ Technol, Dept Multimedia Engn, Kaunas, Lithuania; [Damasevicius, R.] Vytautas Magnus Univ, Dept Appl Informat, Kaunas, Lithuania; [Damasevicius, R.] Silesian Tech Univ, Fac Appl Math, Gliwice, Poland; [Wei, W.] Xian Univ Technol, Sch Comp Sci & Engn, Xian, Shaanxi, Peoples R China Kaunas University of Technology; Vytautas Magnus University; Silesian University of Technology; Xi'an University of Technology Damasevicius, R (corresponding author), Vileikos 8, Kaunas, Lithuania. robertas.damasevicius@vdu.lt Maskeliunas, Rytis/J-7173-2017; wei, wei/HHR-8613-2022; Wei, Wei/ABB-8665-2021; Damaševičius, Robertas/E-1387-2017 Maskeliunas, Rytis/0000-0002-2809-2213; Wei, Wei/0000-0002-8751-9205; Damaševičius, Robertas/0000-0001-9990-1084 69 87 87 16 96 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. SEP 2020.0 94 106452 10.1016/j.asoc.2020.106452 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science NJ0BB 2023-03-23 WOS:000565708500008 0
J Burdack, J; Horst, F; Aragones, D; Eekhoff, A; Schollhorn, WI Burdack, Johannes; Horst, Fabian; Aragones, Daniel; Eekhoff, Alexander; Schoellhorn, Wolfgang Immanuel Fatigue-Related and Timescale-Dependent Changes in Individual Movement Patterns Identified Using Support Vector Machine FRONTIERS IN PSYCHOLOGY English Article situatedness; individuality; kinematic data; optimal movement; fatigue; support vector machine; machine learning; movement classification DYNAMICAL-SYSTEMS APPROACH; GAIT PATTERNS; PHASE-TRANSITIONS; VARIABILITY; MUSCLE; LEG; OPTIMIZATION; ACQUISITION; KINEMATICS; RETENTION The scientific and practical fields-especially high-performance sports-increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement-specifically, the frontal foot kick (mae-geri) in karate-in expert athletes. The two leading research questions presented for consideration are (1) Can characteristics of individual movement patterns be observed throughout the entire training session despite continuous changes, i.e., even as fatigue-related processes increase? and (2) How do intra-individual movement patterns change as fatigue-related processes increase throughout a training session? Sixteen expert karatekas performed 606 frontal foot kicks directed toward an imaginary target. The kicks were performed in nine sets at 80% (K-80) of the self-experienced maximal intensity. In addition, six kicks at maximal intensity (K-100) were performed after each of the nine sets. Between the sets, the participants took a 90-s break. Three-dimensional full-body kinematic data of all kicks were recorded with 10 infrared cameras. The normalized waveforms of nine upper- and lower-body joint angles were classified using a supervised machine learning method (support vector machine). The results of the classification revealed a disjunct distinction between the kinematic movement patterns of individual athletes. The identification of unique movement patterns of individual athletes was independent of the intensity and the degree of fatigue-related processes. In other words, even with the accumulation of fatigue-related processes, the unique movement patterns of an individual athlete can be clearly identified. During the training session, changes in intra-individual movement patterns could also be detected, indicating the occurrence of adaptations in individual movement patterns throughout the fatigue-related accumulation process. The results suggest that these adaptations can be modeled in terms of changes in patterns rather than increasing variance. Practical consequences are critically discussed. [Burdack, Johannes; Horst, Fabian; Aragones, Daniel; Eekhoff, Alexander; Schoellhorn, Wolfgang Immanuel] Johannes Gutenberg Univ Mainz, Dept Training & Movement Sci, Inst Sports Sci, Mainz, Germany; [Schoellhorn, Wolfgang Immanuel] Shanghai Univ Sport, Sch Martial Arts, Dept Wushu, Shanghai, Peoples R China Johannes Gutenberg University of Mainz; Shanghai University of Sport Horst, F (corresponding author), Johannes Gutenberg Univ Mainz, Dept Training & Movement Sci, Inst Sports Sci, Mainz, Germany. horst@uni-mainz.de Aragonés, Daniel/AGO-5510-2022; Schoellhorn, Wolfgang/AGY-7067-2022; Horst, Fabian/V-2242-2019 Aragonés, Daniel/0000-0003-1184-9045; Schoellhorn, Wolfgang/0000-0002-4558-1991; Burdack, Johannes/0000-0001-8894-4214 119 8 8 2 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. SEP 30 2020.0 11 551548 10.3389/fpsyg.2020.551548 0.0 18 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology OB9HP 33101124.0 Green Accepted, gold 2023-03-23 WOS:000578774000001 0
J Zheng, P; Wu, ZB; Sun, J; Zhang, Y; Zhu, YQ; Shen, Y; Yang, JD; Wei, ZH; Plaza, A Zheng, Peng; Wu, Zebin; Sun, Jin; Zhang, Yi; Zhu, Yaoqin; Shen, Yuan; Yang, Jiandong; Wei, Zhihui; Plaza, Antonio A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms REMOTE SENSING English Article content-based image retrieval; cloud computing; unmixing; hyperspectral images BIG DATA; ALGORITHM As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy. [Zheng, Peng; Wu, Zebin; Sun, Jin; Zhang, Yi; Zhu, Yaoqin; Shen, Yuan; Wei, Zhihui] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China; [Yang, Jiandong] China Satellite Maritime Tracking & Control Dept, Jiangyin 214431, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10071, Spain Nanjing University of Science & Technology; Universidad de Extremadura Wu, ZB (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. 318106020161@njust.edu.cn; gswei@njust.edu.cn; sunj@njust.edu.cn; yzhang@njust.edu.cn; zhuyaoqin@njust.edu.cn; shenyuan@njust.edu.cn; yjdjs250@126.com; gswei@njust.edu.cn; aplaza@unex.es SUN, JIN/GPX-9641-2022; Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Wu, Zebin/0000-0002-7162-0202; Zheng, Peng/0000-0001-5072-4224 National Natural Science Foundation of China [61772274, 61976117, 61701238, 61501241, 61671243, 61802190]; Jiangsu Provincial Natural Science Foundation of China [BK20180018, BK20170858, BK20191409]; Fundamental Research Funds for the Central Universities [30917015104, 30919011103, 30919011402, 30919011234] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Provincial Natural Science Foundation of China(Natural Science Foundation of Jiangsu Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was funded in part by the National Natural Science Foundation of China under Grant 61772274, Grant 61976117, Grant 61701238, Grant 61501241, Grant 61671243, and Grant 61802190, in part by the Jiangsu Provincial Natural Science Foundation of China under Grant BK20180018, Grant BK20170858, and Grant BK20191409, in part by the Fundamental Research Funds for the Central Universities under Grant 30917015104, Grant 30919011103, and Grant 30919011402, Grant 30919011234. 38 6 6 2 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2021.0 13 2 176 10.3390/rs13020176 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology PX7QI Green Published 2023-03-23 WOS:000611548500001 0
J Bessagnet, B; Couvidat, F; Lemaire, V Bessagnet, Bertrand; Couvidat, Florian; Lemaire, Vincent A statistical physics approach to perform fast highly-resolved air quality simulations - A new step towards the meta-modelling of chemistry transport models ENVIRONMENTAL MODELLING & SOFTWARE English Article Air quality modelling; Linear regression; Statistics; Metamodel; Resolution; Increment STREET-LEVEL; URBAN; EUROPE A methodology rested on model-based machine learning using simple linear regressions and the parameterizations of the main physics and chemistry processes has been developed to perform highly-resolved air quality simulations. The training of the methodology is (i) completed over a 6-month period using the outputs of the chemical transport model CHIMERE, and (ii) then applied over the subsequent 6 months. Despite rough assumptions, this new methodology performs as well as the raw CHIMERE simulation for daily mean concentrations of the main criteria air pollutants (NO2, Ozone, PM10 and PM2.5) with correlations ranging from 0.75 to 0.83 for the particulate matter and up to 0.86 for the maximum ozone concentrations. Some improvements are investigated to expand this methodology to several other uses, but at this stage the method can be used for air quality forecasting, analysis of pollution episodes and mapping. This study also confirms that including a minimum set of selected physical parameterizations brings a high added value on machine learning processes. [Bessagnet, Bertrand; Couvidat, Florian; Lemaire, Vincent] Natl Inst Ind Environm & Risks, Parc Technol ALATA, F-60550 Verneuil En Halatte, France; [Bessagnet, Bertrand] Hangzhou Futuris Environm Technol Co Ltd, Zhejiang Overseas High Level Talent Innovat Pk, Hangzhou 311121, Zhejiang, Peoples R China; [Lemaire, Vincent] Univ Chile, Ctr Climate & Resilience Res CR 2, Dept Geofis, Blanco Encalada 2002, Santiago, Chile Institut National de l'Environnement Industriel et des Risques (INERIS); Universidad de Chile Bessagnet, B (corresponding author), Natl Inst Ind Environm & Risks, Parc Technol ALATA, F-60550 Verneuil En Halatte, France. bertrand.bessagnet@futuris-environment.com Bessagnet, Bertrand/AAB-9241-2019 Bessagnet, Bertrand/0000-0003-2062-4681; Lemaire, Vincent/0000-0002-8048-1048 French Ministry in charge of Ecology (MTES); UN-ECE CLRTAP (EMEP); AMAP; NILU French Ministry in charge of Ecology (MTES); UN-ECE CLRTAP (EMEP); AMAP; NILU This work is partly funded by the French Ministry in charge of Ecology (MTES). The EBAS database has largely been funded by the UN-ECE CLRTAP (EMEP), AMAP and through NILU internal resources. Specific developments have been possible due to projects like EUSAAR (EU-FP5) (EBAS web interface), EBAS-Online (Norwegian Research Council INFRA) (upgrading of database platform) and HTAP (European Commission DG-ENV) (import and export routines to build a secondary repository in support of www.htap.org). 29 6 6 0 35 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1364-8152 1873-6726 ENVIRON MODELL SOFTW Environ. Modell. Softw. JUN 2019.0 116 100 109 10.1016/j.envsoft.2019.02.017 0.0 10 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Environmental Sciences & Ecology; Water Resources HQ1HY Bronze, Green Submitted 2023-03-23 WOS:000462150100008 0
J Zhu, JH; Jin, J; Gao, Z; Reviriego, P Zhu, Jinhua; Jin, Jie; Gao, Zhen; Reviriego, Pedro Single Event Transient tolerant Count Min Sketches MICROELECTRONICS RELIABILITY English Article Count Min Sketch; Fault tolerance; Frequency estimation; Single Event Transients FREQUENT Frequency estimation is a common operation in big data processing. In many big data applications, computing the exact data frequency is not practical as it requires a large computational effort. Instead, a reasonably accurate estimate is commonly used. The Count Min Sketch (CMS) is a popular method for frequency estimation due to its simple implementation and low memory requirements. However, Single Event Transients (SETs) can affect the logic functions of the CMS. Based on our previous research, nearly half of the SETs will lead to CMS estimation errors in the worst case. Moreover, the most frequent elements are more likely to be underestimated when SETs occur, which is not acceptable in practice. Therefore, this paper proposes several fault-tolerant schemes to protect the CMS against SETs, especially to avoid underestimation. In particular, space redundancy-based schemes and partial time redundancy-based schemes are proposed, and the probabilities for underestimation and overestimation are analyzed theoretically. Experiments are performed to compare the reliability of the CMS protected by different schemes and validate the theoretical predictions. Finally, the selection of the best CMS parameters for practical applications is discussed with a comprehensive analysis of the overhead and performance of the different protection schemes. [Zhu, Jinhua; Jin, Jie; Gao, Zhen] Tianjin Univ, Tianjin 300072, Peoples R China; [Reviriego, Pedro] Univ Carlos III Madrid, Av Univ 30, Madrid, Spain Tianjin University; Universidad Carlos III de Madrid Gao, Z (corresponding author), Tianjin Univ, Tianjin 300072, Peoples R China. zhujh@tju.edu.cn; jinjie@tju.edu.cn; zgao@tju.edu.cn; revirieg@it.uc3m.es National Natural Science Foundation of China (NSFC) [62171313, PID2019-104207RB-I00, RED2018-102585-T]; Spanish Ministry of Science and Innovation; Department of Research and Innovation of Madrid Regional Authority [Y2018/TCS-5046] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Department of Research and Innovation of Madrid Regional Authority This work is supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62171313, in part by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and in part by the Department of Research and Innovation of Madrid Regional Authority with the EMPATIA-CM Research Project (Reference Y2018/TCS-5046) . 25 0 0 2 4 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0026-2714 1872-941X MICROELECTRON RELIAB Microelectron. Reliab. FEB 2022.0 129 114486 10.1016/j.microrel.2022.114486 0.0 JAN 2022 8 Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Science & Technology - Other Topics; Physics 0V6CY 2023-03-23 WOS:000788430800003 0
J Zhang, C; Lafond, C; Barateau, A; Leseur, J; Rigaud, B; Line, DBCS; Yang, GY; Shu, HZ; Dillenseger, JL; de Crevoisier, R; Simon, A Zhang, Chen; Lafond, Caroline; Barateau, Anais; Leseur, Julie; Rigaud, Bastien; Chan Sock Line, Diane Barbara; Yang, Guanyu; Shu, Huazhong; Dillenseger, Jean-Louis; de Crevoisier, Renaud; Simon, Antoine Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer PHYSICS IN MEDICINE AND BIOLOGY English Article adaptive radiation therapy; cervical cancer; plan-of-the-day; CBCT segmentation RADIOTHERAPY; STRATEGIES; MOTION; MANAGEMENT; CARCINOMA; MOVEMENT; TOXICITY; BLADDER Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images. Approach. The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations. Main results. In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage). Significance. The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library. [Zhang, Chen; Yang, Guanyu; Shu, Huazhong] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, PeoplesRepubl China, Nanjing, Peoples R China; [Zhang, Chen; Lafond, Caroline; Barateau, Anais; Rigaud, Bastien; Chan Sock Line, Diane Barbara; Dillenseger, Jean-Louis; de Crevoisier, Renaud; Simon, Antoine] Univ Rennes, CLCC Eugene Marquis, Inserm, LTSI UMR 1099, F-35000 Rennes, France; [Zhang, Chen; Yang, Guanyu; Shu, Huazhong] Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing, Peoples R China; [Leseur, Julie] CLCC Eugene Marquis, Radiotherapy Dept, F-35000 Rennes, France; [Yang, Guanyu; Shu, Huazhong; Dillenseger, Jean-Louis; Simon, Antoine] Ctr Rech Informat Biomed Sino francais CRIBs, Sino Francais, France Southeast University - China; Institut National de la Sante et de la Recherche Medicale (Inserm); UNICANCER; Centre Eugene Marquis; Universite de Rennes; Southeast University - China; UNICANCER; Centre Eugene Marquis; Universite de Rennes Shu, HZ (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, PeoplesRepubl China, Nanjing, Peoples R China.;Shu, HZ (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing, Peoples R China.;Shu, HZ (corresponding author), Ctr Rech Informat Biomed Sino francais CRIBs, Sino Francais, France. shu.list@seu.edu.cn Rigaud, Bastien/ABA-8816-2021 Rigaud, Bastien/0000-0002-0038-429X; Yang, Guanyu/0000-0003-3704-1722; shu, hua zhong/0000-0002-3833-7915 French National Research Association (ANR), Investissement d'Avenir programme (Labex CAMI) [ANR- 11-LABX-0004]; National Key Research and Development Program of China [2022YFE0116700]; National Natural Science Foundation [62171125]; ITMO Cancer AVIESAN (National Alliance for Life Sciences Health); Embassy of France in China; French organizations Ligue contre le Cancer' and La Vannetaise' French National Research Association (ANR), Investissement d'Avenir programme (Labex CAMI)(French National Research Agency (ANR)); National Key Research and Development Program of China; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); ITMO Cancer AVIESAN (National Alliance for Life Sciences Health); Embassy of France in China; French organizations Ligue contre le Cancer' and La Vannetaise' This work was partly funded by the French National Research Association (ANR) as part of the Investissement d'Avenir programme (Labex CAMI) under reference ANR- 11-LABX-0004, by the National Key Research and Development Program of China under Grant 2022YFE0116700, by National Natural Science Foundation under grant 62171125, by the DELPEL project with financial support from ITMO Cancer AVIESAN (National Alliance for Life Sciences & Health) within the framework of the Cancer Plan, by the French organizations Ligue contre le Cancer' and La Vannetaise', and by the Embassy of France in China. 27 0 0 3 3 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0031-9155 1361-6560 PHYS MED BIOL Phys. Med. Biol. DEC 21 2022.0 67 24 245020 10.1088/1361-6560/aca5e5 0.0 10 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging 7B1ZO 36541494.0 Green Submitted 2023-03-23 WOS:000898940500001 0
J Ting, DSW; Cheung, CYL; Lim, G; Tan, GSW; Quang, ND; Gan, A; Hamzah, H; Garcia-Franco, R; Yeo, IYS; Lee, SY; Wong, EYM; Sabanayagam, C; Baskaran, M; Ibrahim, F; Tan, NC; Finkelstein, EA; Lamoureux, EL; Wong, IY; Bressler, NM; Sivaprasad, S; Varma, R; Jonas, JB; He, MG; Cheng, CY; Cheung, GCM; Aung, T; Hsu, W; Lee, ML; Wong, TY Ting, Daniel Shu Wei; Cheung, Carol Yim-Lui; Lim, Gilbert; Tan, Gavin Siew Wei; Quang, Nguyen D.; Gan, Alfred; Hamzah, Haslina; Garcia-Franco, Renata; Yeo, Ian Yew San; Lee, Shu Yen; Wong, Edmund Yick Mun; Sabanayagam, Charumathi; Baskaran, Mani; Ibrahim, Farah; Tan, Ngiap Chuan; Finkelstein, Eric A.; Lamoureux, Ecosse L.; Wong, Ian Y.; Bressler, Neil M.; Sivaprasad, Sobha; Varma, Rohit; Jonas, Jost B.; He, Ming Guang; Cheng, Ching-Yu; Cheung, Gemmy Chui Ming; Aung, Tin; Hsu, Wynne; Lee, Mong Li; Wong, Tien Yin Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION English Article MAJOR RISK-FACTORS; MACULAR DEGENERATION; SINGAPORE EPIDEMIOLOGY; AUTOMATED DETECTION; GLOBAL PREVALENCE; TELEMEDICINE; CHALLENGES; MANAGEMENT; BARRIERS; COHORT IMPORTANCE A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. OBJECTIVE To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. DESIGN, SETTING, AND PARTICIPANTS Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD(72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD(35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy orworse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. EXPOSURES Use of a deep learning system. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. RESULTS In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5%(95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4%(95% CI, 81.7%-99.9%), and specificity was 87.2%(95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2%(95% CI, 91.1%-99.8%), and specificity was 88.7%(95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). CONCLUSIONS AND RELEVANCE In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes. [Ting, Daniel Shu Wei; Cheung, Carol Yim-Lui; Tan, Gavin Siew Wei; Quang, Nguyen D.; Gan, Alfred; Hamzah, Haslina; Yeo, Ian Yew San; Lee, Shu Yen; Wong, Edmund Yick Mun; Sabanayagam, Charumathi; Baskaran, Mani; Lamoureux, Ecosse L.; Cheng, Ching-Yu; Cheung, Gemmy Chui Ming; Aung, Tin; Wong, Tien Yin] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Ting, Daniel Shu Wei; Tan, Gavin Siew Wei; Yeo, Ian Yew San; Lee, Shu Yen; Wong, Edmund Yick Mun; Sabanayagam, Charumathi; Baskaran, Mani; Ibrahim, Farah; Tan, Ngiap Chuan; Lamoureux, Ecosse L.; Cheng, Ching-Yu; Cheung, Gemmy Chui Ming; Aung, Tin; Wong, Tien Yin] Natl Univ Singapore, Duke NUS Med Sch, Singapore, Singapore; [Cheung, Carol Yim-Lui] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Hong Kong, Peoples R China; [Lim, Gilbert; Hsu, Wynne; Lee, Mong Li] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Garcia-Franco, Renata] IAP, Inst Mexicano Oftalmol, Queretaro, Mexico; [Tan, Ngiap Chuan] Singapore Hlth Serv, SingHlth Polyclin, Singapore, Singapore; [Finkelstein, Eric A.] Duke NUS Grad Med Sch, Lien Ctr Palliat Care, Hlth Serv & Syst Res Program, Singapore, Singapore; [Wong, Ian Y.] Univ Hong Kong, Dept Ophthalmol, Hong Kong, Hong Kong, Peoples R China; [Bressler, Neil M.] Johns Hopkins Wilmer Eye Inst, Baltimore, MD USA; [Sivaprasad, Sobha] Natl Hlth Serv Fdn Trust, Moorfields Eye Hosp, London, England; [Varma, Rohit] Univ Southern Calif, Gayle & Edward Roski Eye Inst, Los Angeles, CA USA; [Jonas, Jost B.] Heidelberg Univ, Dept Ophthalmol, Heidelberg, Germany; [He, Ming Guang] Sun Yatsen Univ, State Key Lab Ophthalmol, Zhongshan Ophthalm Ctr, Guangzhou, Guangdong, Peoples R China National University of Singapore; Singapore National Eye Center; National University of Singapore; Chinese University of Hong Kong; National University of Singapore; National University of Singapore; University of Hong Kong; Johns Hopkins University; Johns Hopkins Medicine; University of London; University College London; Moorfields Eye Hospital NHS Foundation Trust; University of Southern California; Ruprecht Karls University Heidelberg; Sun Yat Sen University Wong, TY (corresponding author), Singapore Natl Eye Ctr, 11 Third Hosp Ave, Singapore 168751, Singapore. wong.tien.yin@snec.com.sg Wong, Tien Yin/AAC-9724-2020; Lamoureux, Ecosse/Z-5482-2019; Cheung, Carol Y./G-7895-2016; Nguyen, Duc Quang/AHC-6430-2022; Sabanayagam, Charumathi/C-1294-2011; Lim, Gilbert/N-7322-2017; Tan, Gavin/HNS-5461-2023; Cheng, Ching-Yu/Y-2229-2019; He, Mingguang/AAY-5239-2020; Sivaprasad, S./D-6876-2015; Mani, Baskaran/GWZ-8299-2022; Cheung, Carol/AAF-1101-2020 Wong, Tien Yin/0000-0002-8448-1264; Nguyen, Duc Quang/0000-0001-6919-5211; Sabanayagam, Charumathi/0000-0002-4042-4719; Lim, Gilbert/0000-0002-5381-9250; Cheng, Ching-Yu/0000-0003-0655-885X; He, Mingguang/0000-0002-6912-2810; Sivaprasad, S./0000-0001-8952-0659; Cheung, Carol/0000-0002-9672-1819; Cheung, Chui Ming Gemmy/0000-0003-3358-3516; Bidwai, Pooja Vishal/0000-0002-3077-4395; Hsu, Wynne/0000-0002-4142-8893; Lee, Mong Li/0000-0002-9636-388X; Tan, Ngiap-Chuan/0000-0002-5946-1149 Singapore National Health Innovation Center (NHIC) [NHIC-I2D-1409022]; SingHealth Foundation Research Grant [SHF/FG648S/2015]; Tanoto Foundation; NMRC, MOH [0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, STaR/2013, CG/SERI/2010]; Biomedical Research Council [08/1/35/19/550, 09/1/35/19/616]; MOH, Singapore [AIC/RPDD/SIDRP/SERI/FY2013/0018, AIC/HPD/FY2016/0912]; National Medical Research Council (NMRC), Ministry of Health (MOH) Singapore National Health Innovation Center (NHIC); SingHealth Foundation Research Grant(SingHealth); Tanoto Foundation; NMRC, MOH; Biomedical Research Council(Agency for Science Technology & Research (A*STAR)); MOH, Singapore(Ministry of Health-Singapore); National Medical Research Council (NMRC), Ministry of Health (MOH)(Ministry of Health-SingaporeNational Medical Research Council, Singapore) This project received funding from National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore National Health Innovation Center (NHIC), Innovation to Develop Grant (NHIC-I2D-1409022); SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation; unrestricted donations to the Retina Division, Johns Hopkins University School of Medicine. For the Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008 and STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550 and 09/1/35/19/616). The Singapore Diabetic Retinopathy Program received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 and AIC/HPD/FY2016/0912). 40 944 991 35 286 AMER MEDICAL ASSOC CHICAGO 330 N WABASH AVE, STE 39300, CHICAGO, IL 60611-5885 USA 0098-7484 1538-3598 JAMA-J AM MED ASSOC JAMA-J. Am. Med. Assoc. DEC 12 2017.0 318 22 2211 2223 10.1001/jama.2017.18152 0.0 13 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine FP7NR 29234807.0 Bronze, Green Published, Green Accepted 2023-03-23 WOS:000417822700019 0
J Hong, DF; Gao, LR; Hang, RL; Zhang, B; Chanussot, J Hong, Danfeng; Gao, Lianru; Hang, Renlong; Zhang, Bing; Chanussot, Jocelyn Deep EncoderDecoder Networks for Classification of Hyperspectral and LiDAR Data IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Laser radar; Feature extraction; Training; Hyperspectral imaging; Testing; Image reconstruction; Classification; deep learning (DL); encoder--decoder; hyperspectral; light detection and ranging (LiDAR); multimodality; remote sensing (RS) FUSION Deep learning (DL) has been garnering increasing attention in remote sensing (RS) due to its powerful data representation ability. In particular, deep models have been proven to be effective for RS data classification based on a single given modality. However, with one single modality, the ability in identifying the materials remains limited due to the lack of feature diversity. To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoderx2013;decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data. EndNet fuses the multimodal information by enforcing the fused features to reconstruct the multimodal input in turn. Such a reconstruction strategy is capable of better activating the neurons across modalities compared with some conventional and widely used fusion strategies, e.g., early fusion, middle fusion, and late fusion. Extensive experiments conducted on two popular hyperspectral and LiDAR data sets demonstrate the superiority and effectiveness of the proposed EndNet in comparison with several state-of-the-art baselines in the hyperspectral-LiDAR classification task. The codes will be available at https://github.com/danfenghong/IEEE_GRSL_EndNet, contributing to the RS community. [Hong, Danfeng] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France; [Gao, Lianru; Zhang, Bing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Hang, Renlong] Nanjing Univ Informat Sci & Technol, Sch Automat, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Peoples R China; [Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, LJK, Grenoble INP, Inria,CNRS, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Chinese Academy of Sciences Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. hongdanfeng1989@gmail.com; gaolr@aircas.ac.cn; renlong_hang@163.com; zb@radi.ac.cn; jocelyn@hi.is Chanussot, Jocelyn/0000-0003-4817-2875 National Key Research and Development Program of China [2016YFB0500502]; National Natural Science Foundation of China [41722108]; AXA Research Fund National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); AXA Research Fund(AXA Research Fund) This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0500502, in part by the National Natural Science Foundation of China under Grant 41722108, and in part by AXA Research Fund. 23 43 43 51 85 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 5500205 10.1109/LGRS.2020.3017414 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XW4AV 2023-03-23 WOS:000735565000005 0
J Wu, LF; Zeng, WZ; Lei, GQ; Ma, T; Wu, JW; Huang, JS; Gaiser, T; Srivastava, AK Wu, Lifeng; Zeng, Wenzhi; Lei, Guoqing; Ma, Tao; Wu, Jingwei; Huang, Jiesheng; Gaiser, Thomas; Srivastava, Amit Kumar Simulating root length density dynamics of sunflower in saline soils based on machine learning COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Sunflower; Root length density; Salt stress; Machine learning; Elementary function WATER-UPTAKE; GROWTH; MODEL; REGRESSION Root length density (RLD) is an indispensable input for driving almost all agro-hydrological models, but it is difficult to measure and has strong plasticity to soil environments, thus it is a challenge to characterize the dynamics of RLD if crops suffering adverse soil stress at field scale. Soil salinity is a major abiotic stress that restricts crop shoot growth and yield formation, but it may stimulate root growth of salt-tolerant crops. In this study, based on 256 datasets of actual root length density (ARLD) for sunflower grown under saline conditions and influencing factors as days after sowing (DAS), root depth (RD), soil salt content (SSC), soil water content (SWC), and leaf area index (LAI), we (1) clarified the limitations of cubic polynomial, exponential, and power elementary functions of root depth (RD) for ARLD prediction; and (2) established and compared three novel machine learning models (MLMs) including gaussian process regression (GPR), multivariate adaptive regression spline (MARS), and random forest (RF) with eight combinations of inputs (COIs). Results show the distribution of the sunflower's ARLD was significantly different in fields with different salinity levels, the peak value of ARLD in the low-salt field was smaller than 1.2 cm.cm(-3), but it could be over 3.0 cm.cm(-3) in the high-salt field. Except at the early growth stage (DAS = 28-30), all the elementary functions failed to fit the ARLD accurately with the RMSE ranging from 0.39 to 0.97 cm.cm(-3) and R2 lower than 0.38. The higher prediction accuracies for ARLD were obtained in MLMs, especially with the COI of DAS + RD + SSC + LAI. Moreover, the accuracies of RF and GPR models (RMSE ranging from 0.36 to 0.37 cm.cm(-3) and R(2 )greater than 0.73 in the test) were higher than the MARS. The spatiotemporal distribution of simulated ARLD in the GPR model was relatively smooth, while it presented certain discontinuity in the RF model. In general, the RLD plasticity of sunflower in saline soil should not be ignored, the MLMs models, such as the GPR and RF models, are more applicable for RLD prediction than the elementary function models. [Wu, Lifeng] Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China; [Zeng, Wenzhi; Lei, Guoqing; Wu, Jingwei; Huang, Jiesheng] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Ma, Tao] Hohai Univ, Coll Agr Sci & Engn, Nanjing 210098, Peoples R China; [Gaiser, Thomas; Srivastava, Amit Kumar] Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Crop Sci Grp, Katzenburgweg 5, D-53115 Bonn, Germany Nanchang Institute Technology; Wuhan University; Hohai University; University of Bonn Zeng, WZ; Lei, GQ (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. zengwenzhi1989@whu.edu.cn Huang, Jie/GWQ-5005-2022; Zeng, Wen/GPS-8058-2022; Ma, Tao/GZA-7024-2022; Srivastava, Amit Kumar/D-9159-2018 Srivastava, Amit Kumar/0000-0001-8219-4854; Gaiser, Thomas/0000-0002-5820-2364 Program of the National Natural Science Foundation of China (NSFC) [51879196, 52179039, 51790533]; Mobility Programme of Sino-German Center [M-0009]; Fundamental Research Funds for the Central Universities [B200201006, 2042021kf0051]; Postdoctoral Research Foundation of China [2020M682475]; Nat-ural Science Foundation of Jiangsu Province [BK20200513] Program of the National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Mobility Programme of Sino-German Center; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Postdoctoral Research Foundation of China(China Postdoctoral Science Foundation); Nat-ural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) We are grateful for the Program of the National Natural Science Foundation of China (NSFC) (Grant Nos. 51879196, 52179039, and 51790533) , Mobility Programme of Sino-German Center (Grant No. M-0009) , Fundamental Research Funds for the Central Universities (Grant No. B200201006, 2042021kf0051) , Postdoctoral Research Foundation of China (No. 2020M682475) , and the financial support from the Nat-ural Science Foundation of Jiangsu Province (Grant Nos. BK20200513) . 57 1 1 23 35 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. JUN 2022.0 197 106918 10.1016/j.compag.2022.106918 0.0 APR 2022 11 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science 1W2JW 2023-03-23 WOS:000806604800003 0
J Hussain, S; Ayoub, M; Jilani, G; Yu, Y; Khan, A; Wahid, JA; Butt, MFA; Yang, GQ; Moller, DPF; Hou, WY Hussain, Shabir; Ayoub, Muhammad; Jilani, Ghulam; Yu, Yang; Khan, Akmal; Wahid, Junaid Abdul; Butt, Muhammad Farhan Ali; Yang, Guangqin; Moller, Dietmar P. F.; Hou Weiyan Aspect2Labels: A novelistic decision support system for higher educational institutions by using multi-layer topic modelling approach EXPERT SYSTEMS WITH APPLICATIONS English Article Aspect-based sentiment analysis; Machine learning applications in education; Aspect extraction; Topic modelling; Opinion mining; Situational awareness FEATURE-SELECTION SCHEME; SENTIMENT ANALYSIS; SITUATION AWARENESS; FREQUENCY; FEEDBACK Aspect-based sentiment analysis (ABSA) has gained a rising concentration recently. It aims to provide a set of aspect terms and sentiments from a piece of text. Educational Data Mining (EDM) is now an essential tool for analysing pedagogical data. In academic institutions, student feedback is an influential gauge to measure the quality of the teaching-learning process. It helps higher education institutions to reconsider and improve their policies for student recruitment and retention. This paper proposed a situation awareness multi-layer topic modelling and enhanced hybrid machine learning approach for evaluating students' textual feedback data in academic institutions. The proposed Aspect2Labels (A2L) approach is divided our system into three layers. To preserve semantic information, we extracted general aspects terms in the first layer known as high-level aspects. We pulled low-level aspects terms associated with high-level aspect terms in the second layer and the third layer used for sentiment orientation. We used zero-shot learning, LDA, and different variants of LDA for the aspect extraction process. We performed annotation on unlabelled students' comments using our proposed A2L approach, and we obtained 91.3% accuracy in this process. We developed and tested novel algorithms for aspect terms mapping to label each aspect term to corresponding feedback. Different machine learning algorithms have been used to classify sentiments according to extracted aspects. We have also proposed and used Variable Global Feature Selection Scheme (VGFSS) and Variable Stopwords Filtering (VSF) to improve the performance of classifiers. We have managed to get 97% and 93% accuracy on the test dataset using Support Vector Machine (SVM) and Artificial Neural Networks (ANN), respectively. We highly suggest that our novel approach of aspect-oriented sentiment analysis could provide adequate understanding to analyse students' feedback. [Hussain, Shabir; Wahid, Junaid Abdul; Hou Weiyan] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China; [Jilani, Ghulam; Yang, Guangqin] Zhengzhou Univ, Sch Educ, Zhengzhou 450001, Henan, Peoples R China; [Ayoub, Muhammad] Cent South Univ, Sch Comp Sci & Engn, Changsha 336017, Hunan, Peoples R China; [Yu, Yang] Univ Duisburg Essen, Distributed Syst Grp, D-47057 Duisburg, Germany; [Khan, Akmal] Islamia Univ Bahawalpur, Dept Data Sci, Bahawalpur 63100, Punjab, Pakistan; [Butt, Muhammad Farhan Ali] NCBA&E, Dept Comp Sci, Rahim Yar Khan 64200, Punjab, Pakistan; [Moller, Dietmar P. F.] Tech Univ Clausthal, D-38678 Clausthal Zellerfeld, Germany Zhengzhou University; Zhengzhou University; Central South University; University of Duisburg Essen; Islamia University of Bahawalpur; TU Clausthal Hou, WY (corresponding author), Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China. shabir@gs.zzu.edu.cn; ayoubncbae@gmail.com; jilani_101@gs.zzu.edu.cn; yang.yu@uni-due.de; akmal.shahbaz@iub.edu.pk; junaid.a.wahid@gs.zzu.edu.cn; farhanfauz007@gmail.com; guangqinyang@sina.com; dietmar.moeller@tu-clausthal.de; houwy@zzu.edu.cn Yu, Yang/AAG-7823-2021; Hussain, Shabir/GQB-0376-2022 Yu, Yang/0000-0003-1895-9722; Khan, Akmal/0000-0003-3636-8053 National Natural Science Foundation of China [92067106]; Ministry of Education of the People's Republic of China [E-GCCRC20200309] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Education of the People's Republic of China(Ministry of Education, China) This research work supported by the National Natural Science Foundation of China [grant number: 92067106] and by the Ministry of Education of the People's Republic of China [grant number: E-GCCRC20200309]. 100 2 2 2 2 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. DEC 15 2022.0 209 118119 10.1016/j.eswa.2022.118119 0.0 23 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 6M3UI 2023-03-23 WOS:000888796100001 0
J He, ZW; Cao, YP; Dong, YF; Yang, JX; Cao, YL; Tisse, CL He, Zewei; Cao, Yanpeng; Dong, Yafei; Yang, Jiangxin; Cao, Yanlong; Tisse, Christel-Loic Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: a deep-learning approach APPLIED OPTICS English Article NOISE REMOVAL; STRIPE; SEQUENCES; ALGORITHM Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focalplane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation. Specifically, a comprehensive column FPN model is utilized to depict the nonlinear characteristics of column amplifiers in the readout circuit of FPA. A large number of high-fidelity training images are simulated based on this model and the end-to-end residual deep network is capable of learning the intrinsic difference between undesirable FPN and original image details. Therefore, column FPN can be accurately estimated and further subtracted from the raw infrared images to obtain NUC results. Comparative results with state-of-the-art single-image-based NUC methods, using real-captured noisy infrared images, demonstrate that our proposed deep-learning-based approach delivers better performances of FPN removal, detail preservation, and artifact suppression. (C) 2018 Optical Society of America. [He, Zewei; Cao, Yanpeng; Dong, Yafei; Yang, Jiangxin; Cao, Yanlong] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Zhejiang, Peoples R China; [He, Zewei; Cao, Yanpeng; Dong, Yafei; Yang, Jiangxin; Cao, Yanlong] Zhejiang Univ, Sch Mech Engn, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou 310027, Zhejiang, Peoples R China; [Tisse, Christel-Loic] ULIS, ZI Iles Cordees BP27, F-38113 Veurey Voroize, France Zhejiang University; Zhejiang University Cao, YP (corresponding author), Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Zhejiang, Peoples R China.;Cao, YP (corresponding author), Zhejiang Univ, Sch Mech Engn, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou 310027, Zhejiang, Peoples R China. caoyp@zju.edu.cn National Natural Science Foundation of China (NSFC) [51575486, 51605428, U1664264]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) National Natural Science Foundation of China (NSFC) (51575486, 51605428, U1664264); Fundamental Research Funds for the Central Universities. 39 50 55 4 59 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics JUN 20 2018.0 57 18 D155 D164 10.1364/AO.57.00D155 0.0 10 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics GK0ZL 30117949.0 2023-03-23 WOS:000435841800022 0
J Song, XH; Jiang, SQ; Herranz, L; Chen, CP Song, Xinhang; Jiang, Shuqiang; Herranz, Luis; Chen, Chengpeng Learning Effective RGB-D Representations for Scene Recognition IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Scene recognition; deep learning; multimodal; RGB-D; video; CNN; RNN Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can he addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition. [Song, Xinhang; Jiang, Shuqiang; Chen, Chengpeng] Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China; [Song, Xinhang; Jiang, Shuqiang; Chen, Chengpeng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Herranz, Luis] Univ Autonoma Barcelona, Comp Vis Ctr, E-08193 Barcelona, Spain Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Autonomous University of Barcelona; Centre de Visio per Computador (CVC) Jiang, SQ (corresponding author), Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China. xinhang.song@vipl.ict.ac.cn; sqjiang@ict.ac.cn; lherranz@cvc.uab.es; chengpeng.chen@vipl.ict.ac.cn Herranz, Luis/B-4573-2016; ARSLAN, Okan/AAA-3232-2020 Herranz, Luis/0000-0002-7022-3395; song, xinhang/0000-0002-0895-1076 National Natural Science Foundation of China [61532018]; Lenovo Outstanding Young Scientists Program; National Program for Special Support of Eminent Professionals; National Program for Support of Top-notch Young Professionals; National Postdoctoral Program for Innovative Talents [BX201700255]; China Postdoctoral Science Foundation [2018M631583]; European Union Research and Innovation Program under the Marie Sklodowska-Curie Grant [6655919] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Lenovo Outstanding Young Scientists Program; National Program for Special Support of Eminent Professionals; National Program for Support of Top-notch Young Professionals; National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); European Union Research and Innovation Program under the Marie Sklodowska-Curie Grant This work was supported in part by the National Natural Science Foundation of China under Grant 61532018, in part by the Lenovo Outstanding Young Scientists Program, in part by the National Program for Special Support of Eminent Professionals, in part by the National Program for Support of Top-notch Young Professionals, in part by the National Postdoctoral Program for Innovative Talents under Grant BX201700255, in part by the China Postdoctoral Science Foundation under Grant 2018M631583, and in part by the European Union Research and Innovation Program under the Marie Sklodowska-Curie Grant 6655919. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Julian Fierrez. 43 27 29 5 41 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. FEB 2019.0 28 2 980 993 10.1109/TIP.2018.2872629 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GY4BC 30281448.0 Green Submitted 2023-03-23 WOS:000448501800011 0
J Gao, JL; He, JL; Zhang, FF; Xiao, Q; Cai, X; Yi, X; Zheng, SQ; Zhang, Y; Wang, DL; Zhu, GJ; Wang, J; Shen, B; Ralser, M; Guo, TN; Zhu, Y Gao, Jinlong; He, Jiale; Zhang, Fangfei; Xiao, Qi; Cai, Xue; Yi, Xiao; Zheng, Siqi; Zhang, Ying; Wang, Donglian; Zhu, Guangjun; Wang, Jing; Shen, Bo; Ralser, Markus; Guo, Tiannan; Zhu, Yi Integration of protein context improves protein-based COVID-19 patient stratification CLINICAL PROTEOMICS English Article COVID-19; Severe cases; Proteomics; Protein complex; Stoichiometric ratio INFLAMMATION; NETWORK; DISEASE Background Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. Results Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction. [Gao, Jinlong; He, Jiale; Zhang, Fangfei; Xiao, Qi; Cai, Xue; Yi, Xiao; Zheng, Siqi; Guo, Tiannan; Zhu, Yi] Westlake Univ, Sch Life Sci, Westlake Lab Life Sci & Biomed, Key Lab Struct Biol Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China; [Gao, Jinlong; He, Jiale; Zhang, Fangfei; Xiao, Qi; Cai, Xue; Yi, Xiao; Zheng, Siqi; Guo, Tiannan; Zhu, Yi] Westlake Inst Adv Study, Inst Basic Med Sci, Hangzhou, Zhejiang, Peoples R China; [Zhang, Ying; Wang, Donglian; Zhu, Guangjun; Wang, Jing; Shen, Bo] Wenzhou Med Univ, Taizhou Hosp, Linhai, Zhejiang, Peoples R China; [Ralser, Markus] Francis Crick Inst, Mol Biol Metab Lab, London, England; [Ralser, Markus] Charite Univ Med Berlin, Dept Biochem, Berlin, Germany; [Ralser, Markus] Free Univ Berlin, Berlin, Germany; [Ralser, Markus] Humboldt Univ, Berlin, Germany Westlake University; Westlake University; Wenzhou Medical University; Francis Crick Institute; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin Guo, TN; Zhu, Y (corresponding author), Westlake Univ, Sch Life Sci, Westlake Lab Life Sci & Biomed, Key Lab Struct Biol Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China.;Guo, TN; Zhu, Y (corresponding author), Westlake Inst Adv Study, Inst Basic Med Sci, Hangzhou, Zhejiang, Peoples R China. guotiannan@westlake.edu.cn; zhuyi@westlake.edu.cn He, Jiale/0000-0002-0535-6685; Yi, Xiao/0000-0002-6288-5233; Ralser, Markus/0000-0001-9535-7413 National Key R&D Program of China [2020YFE0202200]; National Science Fund for Young Scholars [21904107]; National Natural Science Foundation of China [81972492]; Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars [LR19C050001]; Hangzhou Agriculture and Society Advancement Program [20190101A04]; Westlake Education Foundation; Tencent Foundation; ERC [SyG-2020 951475]; Wellcome Trust [IA 200829/Z/16/Z]; Ministry of Education and Research (BMBF), as part of the NationalResearch Node 'Mass spectrometry in Systems Medicine (MSCoresys) [031L0220, FKZ: 01KX2021] National Key R&D Program of China; National Science Fund for Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars; Hangzhou Agriculture and Society Advancement Program; Westlake Education Foundation; Tencent Foundation; ERC(European Research Council (ERC)European Commission); Wellcome Trust(Wellcome Trust); Ministry of Education and Research (BMBF), as part of the NationalResearch Node 'Mass spectrometry in Systems Medicine (MSCoresys)(Federal Ministry of Education & Research (BMBF)) This work is supported by Grants from the National Key R&D Program of China (No. 2020YFE0202200), the National Science Fund for Young Scholars (21904107), the National Natural Science Foundation of China (81972492), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), and Westlake Education Foundation, Tencent Foundation. Work in the MR lab is supported by the ERC (SyG-2020 951475) and the Wellcome Trust (IA 200829/Z/16/Z), and the specific work received funding from the Ministry of Education and Research (BMBF), as part of the NationalResearch Node `Mass spectrometry in Systems Medicine (MSCoresys), under Grant agreements 031L0220, and Research (NaFoUniMedCOVID-19-NUMNAPKON, FKZ: 01KX2021). 51 0 0 4 8 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1542-6416 1559-0275 CLIN PROTEOM Clin. Proteom. DEC 2022.0 19 1 31 10.1186/s12014-022-09370-0 0.0 13 Biochemical Research Methods Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 3S5PQ 35953823.0 Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000839649100001 0
J Hai, T; Zhou, JC; Srividhya, SR; Jain, SK; Young, P; Agrawal, S Hai, Tao; Zhou, Jincheng; Srividhya, S. R.; Jain, Sanjiv Kumar; Young, Praise; Agrawal, Shweta BVFLEMR: an integrated federated learning and blockchain technology for cloud-based medical records recommendation system JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS English Article Collaborative learning; Federated learning; Recommendation system; Blockchain; Cloud security; EHR FRAMEWORK Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient's cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security. [Hai, Tao; Zhou, Jincheng] Qiannan Normal Univ Nationalities, Sch Comp & Informat, Duyun 558000, Guizhou, Peoples R China; [Hai, Tao; Zhou, Jincheng] Key Lab Complex Syst & Intelligent Optimizat Guiz, Duyun 558000, Guizhou, Peoples R China; [Hai, Tao] Univ Teknol MARA, Inst Big Data Analyt & Artificial Intelligence IB, Shah Alam 40450, Selangor, Malaysia; [Srividhya, S. R.] Sathyabama Inst Sci & Technol, Chennai, Tamil Nadu, India; [Jain, Sanjiv Kumar] Medi Caps Univ, Elect Engn Dept, Indore, Madhya Pradesh, India; [Young, Praise] Univ Eastern Finland, Dept Linguist Data Sci, Joensuu 80101, Finland; [Agrawal, Shweta] SAGE Univ, Inst Adv Comp, Indore, India Universiti Teknologi MARA; Sathyabama Institute of Science & Technology; University of Eastern Finland Zhou, JC (corresponding author), Qiannan Normal Univ Nationalities, Sch Comp & Informat, Duyun 558000, Guizhou, Peoples R China.;Zhou, JC (corresponding author), Key Lab Complex Syst & Intelligent Optimizat Guiz, Duyun 558000, Guizhou, Peoples R China. guideaaa@126.com SR, Srividhya/AAY-3713-2020; Agrawal, Dr. Shweta/ABE-3459-2021; Jain, Sanjiv Kumar/ABF-3137-2021 SR, Srividhya/0000-0002-7601-8288; Agrawal, Dr. Shweta/0000-0002-0372-6128; Jain, Sanjiv Kumar/0000-0001-8942-7681; Zhou, Jincheng/0000-0002-1995-4002 National Natural Science Foundation of China [61862051]; Science and Technology Foundation of Guizhou Province [[2019]1299]; Top-notch Talent Program of of Guizhou province [KY[2018]080]; Qiannan Normal University for Nationalities [QNSY2018JS013, QNSYRC201715] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Foundation of Guizhou Province; Top-notch Talent Program of of Guizhou province; Qiannan Normal University for Nationalities The National Natural Science Foundation of China under Grant No. 61862051; the Science and Technology Foundation of Guizhou Province under Grant No. [2019]1299; the Top-notch Talent Program of of Guizhou province under Grant No. KY[2018]080; the program of Qiannan Normal University for Nationalities under Grant Nos. QNSY2018JS013, QNSYRC201715. 31 1 1 14 19 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2192-113X J CLOUD COMPUT-ADV S J. Cloud Comput.-Adv. Syst. Appl. JUL 28 2022.0 11 1 22 10.1186/s13677-022-00294-6 0.0 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 3I4VP Green Submitted, gold 2023-03-23 WOS:000832716800001 0
C Chen, GX; Ding, LP; Chen, GX; Qin, PK; Zhou, GM; Liu, Q Destech Publicat Inc Chen, Guang-xuan; Ding, Li-ping; Chen, Guang-xiao; Qin, Pan-ke; Zhou, Guo-ming; Liu, Qiang Risk Analysis of Online Lending Platform Based on Bigdata Technology 3RD INTERNATIONAL CONFERENCE ON E-COMMERCE AND CONTEMPORARY ECONOMIC DEVELOPMENT (ECED 2017) DEStech Transactions on Economics Business and Management English Proceedings Paper 3rd International Conference on E-commerce and Contemporary Economic Development (ECED) APR 08-09, 2017 Xian, PEOPLES R CHINA Adv Informat Sci Res Ctr Online Lending; Prediction Model; Risk Warning; Bigdata In recent years, online lending platforms have emerged a lot of insecurity events. How to judge the security state and health degree of the online lending platforms are becoming the most concerned problems for the investors and supervision departments. In this paper, we've adopted the massive data acquisition technology. Spark distributed computing, machine learning and other bigdata technologies to analyze the risks of the online lending platform. Then, a risk early warning model based on factor analysis method was proposed. Practice has proved that the model can help to improve the accuracy of the risk prediction to online lending platforms. [Chen, Guang-xuan; Zhou, Guo-ming; Liu, Qiang] Zhejiang Police Coll, Hangzhou, Zhejiang, Peoples R China; [Chen, Guang-xuan; Ding, Li-ping] Guangzhou & Chinese Acad Sci, Inst Software Applicat Technol, Digital Forens Lab, Guangzhou, Guangdong, Peoples R China; [Chen, Guang-xiao] Univ Carlos III Madrid, Madrid, Spain; [Qin, Pan-ke] Henan Polytech Univ, Jiaozuo, Henan, Peoples R China Zhejiang Police College; Universidad Carlos III de Madrid; Henan Polytechnic University Ding, LP (corresponding author), Guangzhou & Chinese Acad Sci, Inst Software Applicat Technol, Digital Forens Lab, Guangzhou, Guangdong, Peoples R China. ericcgx@163.com; liping@nfs.iscas.ac.cn Scientific Research Project of Zhejiang Police College [20150615]; Educational Technology Planning Program of Zhejiang Province [JB058]; University Laboratory of Zhejiang Province [YB201536]; NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1509219]; National High Technology Research and Development Program of China [2015AA016003] Scientific Research Project of Zhejiang Police College; Educational Technology Planning Program of Zhejiang Province; University Laboratory of Zhejiang Province; NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; National High Technology Research and Development Program of China(National High Technology Research and Development Program of China) This research was financially supported by the Scientific Research Project of Zhejiang Police College (NO. 20150615), Educational Technology Planning Program of Zhejiang Province (NO. JB058), Research Program of University Laboratory of Zhejiang Province (NO. YB201536), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (NO. U1509219), and the National High Technology Research and Development Program of China (No. 2015AA016003). 9 0 0 0 4 DESTECH PUBLICATIONS, INC LANCASTER 439 DUKE STREET, LANCASTER, PA 17602-4967 USA 2475-8868 978-1-60595-446-2 DESTECH TRANS ECON 2017.0 118 122 5 Business; Economics; Management Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Business & Economics BI0AD 2023-03-23 WOS:000404468200023 0
J Huang, JY; Tu, SX; Masuda, S; Ninomiya, K; Dijkstra, J; Chu, M; Ding, DX; Hynes, SO; O'Leary, N; Onuma, Y; Serruys, PW; Wijns, W Huang, Jiayue; Tu, Shengxian; Masuda, Shinichiro; Ninomiya, Kai; Dijkstra, Jouke; Chu, Miao; Ding, Daixin; Hynes, Sean O. O.; O'Leary, Neil; Onuma, Yoshinobu; Serruys, Patrick W. W.; Wijns, William Plaque burden estimated from optical coherence tomography with deep learning: In vivo validation using co-registered intravascular ultrasound CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS English Article; Early Access bioresorbable scaffolds; intravascular ultrasound; optical coherence tomography PERCUTANEOUS CORONARY INTERVENTION; EXPERT CONSENSUS DOCUMENT; TRI-MODALITY SYSTEM; VULNERABLE PLAQUES; IVUS; ANGIOGRAPHY; CALCIFICATION; FEASIBILITY; ASSOCIATION; MORPHOLOGY ObjectivesThe objective of the present study was to compare plaque burden (PB) calculated from optical coherence tomography (OCT) using deep learning (DL) with PB derived from co-registered intravascular ultrasound (IVUS). BackgroundA DL algorithm was developed for automated plaque characterization and PB quantification from OCT images. However, the performance of this algorithm for PB quantification has not been validated. MethodsFive-year follow-up OCT and IVUS images from 15 patients implanted with bioresorbable vascular scaffold (BVS) at baseline were analyzed. Precise co-registration for 72 anatomical slices was achieved utilizing unique BVS radiopaque markers. PB derived from OCT DL and IVUS were compared. OCT cross-sections were divided into four subgroups with different media visibility level. The impact of media visibility on the numerical difference between OCT-derived and IVUS-derived PB was investigated. The stent sizes selected by OCT DL and IVUS were compared. ResultsSixty-four paired OCT and IVUS cross-sections were compared. OCT DL showed good concordance with IVUS for PB assessment (ICC = 0.81, difference = -3.53 +/- 6.17%, p < 0.001). The numerical difference between OCT DL-derived PB and IVUS-derived PB was not substantially impacted by missing segments of media visualization (p = 0.21). OCT DL showed a diagnostic accuracy of 92% in identifying PB > 65%. The stent sizes selected by OCT DL were smaller compared to the ones selected by IVUS (difference = 0.30 +/- 0.34 mm, p < 0.001). ConclusionsThe DL algorithm provides a feasible and reliable method for automated PB estimation from OCT, irrespective of media visibility. OCT DL showed good diagnostic accuracy in identifying PB > 65%, revealing its potential to complement conventional OCT imaging. [Huang, Jiayue; Ding, Daixin; Wijns, William] Univ Galway, Lambe Inst Translat Med, Smart Sensors Lab & CURAM, Galway, Ireland; [Tu, Shengxian; Chu, Miao] Shanghai Jiao Tong Univ, Biomed Instrument Inst, Sch Biomed Engn, Shanghai, Peoples R China; [Masuda, Shinichiro; Ninomiya, Kai; O'Leary, Neil; Onuma, Yoshinobu; Serruys, Patrick W. W.] Univ Galway, Dept Cardiol, Galway, Ireland; [Dijkstra, Jouke] Leiden Univ, Dept Radiol, Div Image Proc, Med Ctr, Leiden, Netherlands; [Hynes, Sean O. O.] Univ Galway, Univ Hosp Galway, Dept Histopathol, Galway, Ireland; [Serruys, Patrick W. W.] Imperial Coll London, Natl Heart & Lung Inst, Cardiovasc Sci Div, London, England; [Tu, Shengxian] Shanghai Jiao Tong Univ, Biomed Instrument Inst, Med X Res Inst, Sch Biomed Engn, Room 123, 1954, Hua Shan Rd, Shanghai 200030, Peoples R China Ollscoil na Gaillimhe-University of Galway; Shanghai Jiao Tong University; Ollscoil na Gaillimhe-University of Galway; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Ollscoil na Gaillimhe-University of Galway; Imperial College London; Shanghai Jiao Tong University Tu, SX (corresponding author), Shanghai Jiao Tong Univ, Biomed Instrument Inst, Med X Res Inst, Sch Biomed Engn, Room 123, 1954, Hua Shan Rd, Shanghai 200030, Peoples R China. sxtu@sjtu.edu.cn Huang, Jiayue/0000-0003-2003-7823; Tu, Shengxian/0000-0001-9681-1067 Natural Science Foundation of China; Science Foundation Ireland Research Professorship; [82020108015]; [81871460]; [15/RP/2765] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation Ireland Research Professorship(Science Foundation Ireland); ; ; ACKNOWLEDGMENT This work was supported in part by grants from the Natural Science Foundation of China [No. 82020108015 and 81871460] to S. T. and by Science Foundation Ireland Research Professorship [15/RP/2765] to W. W., J. H. and D. D. 48 0 0 4 4 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1522-1946 1522-726X CATHETER CARDIO INTE Catheter. Cardiovasc. Interv. 10.1002/ccd.30525 0.0 DEC 2022 10 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology 6Y6QQ 36519717.0 2023-03-23 WOS:000897217800001 0
J Haacke, EM; Chen, YS; Utriainen, D; Wu, B; Wang, Y; Xia, S; He, NY; Zhang, CY; Wang, X; Lagana, MM; Luo, Y; Fatemi, A; Liu, SF; Gharabaghi, S; Wu, DM; Sethi, SK; Huang, F; Sun, TT; Qu, FF; Yadav, BK; Ma, XY; Bai, Y; Wang, MY; Cheng, JL; Yan, FH Haacke, E. Mark; Chen, Yongsheng; Utriainen, David; Wu, Bo; Wang, Yu; Xia, Shuang; He, Naying; Zhang, Chunyan; Wang, Xiao; Lagana, M. Marcella; Luo, Yu; Fatemi, Ali; Liu, Saifeng; Gharabaghi, Sara; Wu, Dongmei; Sethi, Sean K.; Huang, Feng; Sun, Taotao; Qu, Feifei; Yadav, Brijesh K.; Ma, Xiaoyue; Bai, Yan; Wang, Meiyun; Cheng, Jingliang; Yan, Fuhua STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method MAGNETIC RESONANCE IMAGING English Article Quantitative magnetic resonance imaging; Susceptibility weighted imaging; T1 mapping; Quantitative susceptibility mapping; Multi-parametric magnetic resonance imaging; Strategically acquired gradient echo FLAIR-ASTERISK; PROTON-DENSITY; SYNTHETIC-MRI; 7 T; SCLEROSIS; LESIONS; ANGLE; GUIDELINES; ACCURATE; T-1 One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors' product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinson's disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images. [Haacke, E. Mark; Chen, Yongsheng; Sethi, Sean K.; Qu, Feifei; Yadav, Brijesh K.] Wayne State Univ, Sch Med, Dept Radiol, Detroit, MI USA; [Haacke, E. Mark; Utriainen, David; Liu, Saifeng; Sethi, Sean K.] MRI Inst Biomed Res, Bingham Farms, MI USA; [Haacke, E. Mark; Utriainen, David; Wu, Bo; Gharabaghi, Sara; Sethi, Sean K.] Magnet Resonance Innovat Inc, Bingham Farms, MI USA; [Chen, Yongsheng] Wayne State Univ, Sch Med, Dept Neurol, Detroit, MI 48201 USA; [Wang, Yu; Wu, Dongmei] East China Normal Univ, Sch Phys & Elect Sci, Shanghai Key Lab Magnet Resonance, Shanghai, Peoples R China; [Wang, Yu; Huang, Feng] Neusoft Med Syst Co Ltd, Shanghai, Peoples R China; [Xia, Shuang] Tianjin First Cent Hosp, Dept Radiol, Tianjin, Peoples R China; [He, Naying; Yan, Fuhua] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Radiol, Shanghai, Peoples R China; [Zhang, Chunyan; Wang, Xiao; Cheng, Jingliang] Zhengzhou Univ, Affiliated Hosp 1, Dept MRI, Zhengzhou, Henan, Peoples R China; [Lagana, M. Marcella] Fdn Don Carlo Gnocchi ONLUS, IRCCS, Milan, Italy; [Luo, Yu] Tongji Univ, Sch Med, Shanghai Peoples Hosp 4, Dept Radiol,Translat Res Inst Brain & Brain Like, Shanghai, Peoples R China; [Fatemi, Ali] Univ Mississippi, Med Ctr, Dept Radiol, Jackson, MS 39216 USA; [Fatemi, Ali] Univ Mississippi, Med Ctr, Dept Radiat Oncol, Jackson, MS 39216 USA; [Sun, Taotao] Shanghai Jiao Tong Univ, Sch Med, Int Peace Matern & Child Hlth Hosp, Dept Radiol, Shanghai, Peoples R China; [Ma, Xiaoyue; Bai, Yan; Wang, Meiyun] Henan Prov Peoples Hosp, Dept Radiol, Zhengzhou, Henan, Peoples R China; [Ma, Xiaoyue; Bai, Yan; Wang, Meiyun] Zhengzhou Univ, Peoples Hosp, Dept Radiol, Zhengzhou, Henan, Peoples R China Wayne State University; Wayne State University; East China Normal University; Tianjin Medical University; Shanghai Jiao Tong University; Zhengzhou University; IRCCS Fondazione Don Carlo Gnocchi Onlus; Tongji University; University of Mississippi; University of Mississippi Medical Center; University of Mississippi; University of Mississippi Medical Center; Shanghai Jiao Tong University; Zhengzhou University; Zhengzhou University Haacke, EM (corresponding author), Harper Grace Hosp, MR Res Core, 3990 John R, Detroit, MI 48201 USA.;Wang, MY (corresponding author), Henan Prov Peoples Hosp, Zhengzhou, Henan, Peoples R China. nmrimaging@aol.com; mywang@ha.edu.cn Sun, Taotao/ABD-1872-2020; Lagana, Maria Marcella/L-8050-2016; Chen, Yongsheng/U-9800-2019 Lagana, Maria Marcella/0000-0001-7848-1711; Chen, Yongsheng/0000-0001-7114-5731; Sun, Taotao/0000-0002-4103-6902; Gharabaghi, Sara/0000-0002-9890-4053 71 28 29 1 13 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0730-725X 1873-5894 MAGN RESON IMAGING Magn. Reson. Imaging JAN 2020.0 65 15 26 10.1016/j.mri.2019.09.006 0.0 12 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging JZ0NL 31629075.0 2023-03-23 WOS:000504801100003 0
J Chen, KJ; Vantuch, OM; Zhang, Y; Hu, J; He, JL Chen, Kunjin; Vantuch, Tomas; Zhang, Yu; Hu, Jun; He, Jinliang Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features IEEE TRANSACTIONS ON SMART GRID English Article Conductors; Feature extraction; Partial discharges; Fault detection; Noise level; Noise measurement; Shape; Covered conductor; partial discharges; clustering methods; gradient boosting trees PARTIAL-DISCHARGE; WAVELET ANALYSIS; CLASSIFICATION The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capitalizing on those pulses as referential patterns, we construct insightful features and develop a novel machine learning model with a superior detection performance for early-stage covered conductor faults. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field. [Chen, Kunjin; Hu, Jun; He, Jinliang] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China; [Vantuch, Tomas] VSB Tech Univ Ostrava, Ctr ENET, Ostrava 70833, Czech Republic; [Zhang, Yu] Univ Calif Santa Cruz, Dept Elect & Comp Engn, Santa Cruz, CA 95064 USA Tsinghua University; Technical University of Ostrava; University of California System; University of California Santa Cruz He, JL (corresponding author), Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China. hejl@tsinghua.edu.cn Zhang, Yu/AAW-8020-2021 Zhang, Yu/0000-0001-7889-2676 Natural Science Foundation of China [51720105004, 51921005]; Development of the ENET Centre Research Infrastructure [CZ.1.05/2.1.00/19.0389]; Hellman Fellowship; CITRIS; Banatao Institute at the University of California Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Development of the ENET Centre Research Infrastructure; Hellman Fellowship; CITRIS; Banatao Institute at the University of California This work was supported in part by the Natural Science Foundation of China under Grant 51720105004 and Grant 51921005; in part by the Development of the ENET Centre Research Infrastructure under Grant CZ.1.05/2.1.00/19.0389; and in part by a Hellman Fellowship and a Seed Fund Award from CITRIS and the Banatao Institute at the University of California. 35 6 7 0 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3053 1949-3061 IEEE T SMART GRID IEEE Trans. Smart Grid MAR 2021.0 12 2 1602 1614 10.1109/TSG.2020.3032527 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QO8XO Green Submitted 2023-03-23 WOS:000623420700057 0
J Shao, HD; Lin, J; Zhang, LW; Galar, D; Kumar, U Shao, Haidong; Lin, Jing; Zhang, Liangwei; Galar, Diego; Kumar, Uday A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance INFORMATION FUSION English Article Collaborative maintenance; Prognostics and health management; Multi-sensor information fusion; Fault diagnosis; Stacked wavelet auto-encoder; Planetary gearbox NETWORK; AUTOENCODER; MANAGEMENT Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches. [Shao, Haidong] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China; [Shao, Haidong; Lin, Jing; Galar, Diego; Kumar, Uday] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Zhang, Liangwei] Dongguan Univ Technol, Dept Ind Engn, Dongguan 523808, Peoples R China; [Galar, Diego] TECNALIA, Donostia San 20009, Sebastian, Spain Hunan University; Lulea University of Technology; Dongguan University of Technology Zhang, LW (corresponding author), Dongguan Univ Technol, Dept Ind Engn, Dongguan 523808, Peoples R China. liangwei.zhang@dgut.edu.cn Lin, Jing/B-3076-2015; Shao, Haidong/AAE-5814-2022; Kumar, Uday/HDM-7357-2022 Lin, Jing/0000-0002-7458-6820; Shao, Haidong/0000-0002-8018-1774 National Natural Science Foundation of China [51905160, 71801045]; Natural Science Foundation of Hunan Province [2020JJ5072]; National Key Research and Development Program of China [2020YFB1712103]; Fundamental Research Funds for the Central Universities [531118010335]; DGUT [GC300502-46] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); DGUT The authors wish to thank the CRRC-LTU joint research center and CSSC-LTU joint smart Maintenance Lab, Sweden. The research was also supported by the National Natural Science Foundation of China (51905160, 71801045), the Natural Science Foundation of Hunan Province (2020JJ5072), the National Key Research and Development Program of China (2020YFB1712103), the Fundamental Research Funds for the Central Universities (531118010335), and in part by the Research start-up funds of DGUT (GC300502-46). 34 90 92 65 176 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion OCT 2021.0 74 65 76 10.1016/j.inffus.2021.03.008 0.0 APR 2021 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science SO7EE 2023-03-23 WOS:000659136200005 0
J Liu, Y; Tang, SX; Fernandez-Lozano, C; Munteanu, CR; Pazos, A; Yu, YZ; Tan, ZL; Gonzalez-Diaz, H Liu, Yong; Tang, Shaoxun; Fernandez-Lozano, Carlos; Munteanu, Cristian R.; Pazos, Alejandro; Yu, Yi-zun; Tan, Zhiliang; Gonzalez-Diaz, Humberto Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity EXPERT SYSTEMS WITH APPLICATIONS English Article Machine learning; Expected values; Moving averages; Cell properties; Perturbation theory; Time series analysis STAPHYLOCOCCUS-EPIDERMIDIS; ESCHERICHIA-COLI; ADHESION; ADHERENCE; BACTERIA; SELECTION; CHARGE; BIOMATERIALS; HYDROCARBON; REGRESSION The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average - Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations,of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia - nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale. (C) 2016 Elsevier Ltd. All rights reserved. [Liu, Yong; Tang, Shaoxun; Tan, Zhiliang] Chinese Acad Sci, Key Lab Agroecol Proc Subtrop Reg, Changsha 410125, Hunan, Peoples R China; [Liu, Yong; Tang, Shaoxun; Tan, Zhiliang] Chinese Acad Sci, Hunan Coinnovat Ctr Anim Prod Safety, CICAPS, Inst Subtrop Agr, Changsha 410125, Hunan, Peoples R China; [Liu, Yong; Fernandez-Lozano, Carlos; Munteanu, Cristian R.; Pazos, Alejandro] Univ A Coruna, Fac Comp Sci, Campus Elvina S-N, La Coruna 15071, Spain; [Pazos, Alejandro] CHUAC, Inst Invest Biomed A Coruna INIBIC, La Coruna 15006, Spain; [Yu, Yi-zun] Jiangxi Acad Sci, Inst Biol Resources, Nanchang 330096, Jiangxi, Peoples R China; [Gonzalez-Diaz, Humberto] Univ Basque Country UPV EHU, Dept Organ Chem 2, Leioa 48940, Spain; [Gonzalez-Diaz, Humberto] Basque Fdn Sci, Ikerbasque, Bilbao 48011, Spain Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Subtropical Agriculture, CAS; Universidade da Coruna; Complejo Hospitalario Universitario A Coruna; Universidade da Coruna; Instituto de Investigacion Biomedica de A Coruna (INIBIC); University of Basque Country; Basque Foundation for Science Munteanu, CR (corresponding author), Univ A Coruna, Fac Comp Sci, Campus Elvina S-N, La Coruna 15071, Spain. y.liu86@outlook.com; shaoxuntang@163.com; carlos.fernandez@udc.es; c.munteanu@udc.es; apazos@udc.es; yizun808@163.com; zltan@isa.ac.cn; gonzalezdiazh@yahoo.es Munteanu, Cristian-Robert/G-1714-2011; Pazos, Alejandro/AAI-5887-2020; Gonzalez-Diaz, Humbert G. Díaz/A-6785-2012; Fernandez-Lozano, Carlos/D-2469-2014; Pazos Sierra, Alejandro/K-8927-2017 Munteanu, Cristian-Robert/0000-0002-5628-2268; Gonzalez-Diaz, Humbert G. Díaz/0000-0002-9392-2797; Fernandez-Lozano, Carlos/0000-0003-0413-5677; Pazos Sierra, Alejandro/0000-0003-2324-238X National Natural Science Foundation of China [31172234, 31260556]; Planned Science and Technology Project of Hunan Province [2015NK3041]; Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Science [XDA05020700]; Hunan Provincial Creation Development Project [2013TF3006]; Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture; General Directorate of Culture, Education and University Management of Xunta de Galicia, Spain [GRC2014/049]; Spanish Ministry of Economy and Competitiveness; BIOCAI [UNLC08-1E-002, UNLC13-13-3503]; European Regional Development Funds (FEDER) by the European Union; Juan de la Cierva fellowship program - Spanish Ministry of Economy and Competitiveness [FJCI-2015-26071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Planned Science and Technology Project of Hunan Province; Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Science; Hunan Provincial Creation Development Project; Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture; General Directorate of Culture, Education and University Management of Xunta de Galicia, Spain; Spanish Ministry of Economy and Competitiveness(Spanish Government); BIOCAI; European Regional Development Funds (FEDER) by the European Union; Juan de la Cierva fellowship program - Spanish Ministry of Economy and Competitiveness The authors would like to thank for the grants from the National Natural Science Foundation of China (Grant No. 31172234, and No. 31260556), the Planned Science and Technology Project of Hunan Province (2015NK3041), Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Science (Grant No. XDA05020700) and Hunan Provincial Creation Development Project (Grant No. 2013TF3006). The authors gratefully acknowledge the support from the Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture, and the Chinese Academy of Sciences (CAS) for providing all the experimental materials and apparatus. The authors would also like to thank Tao Ran for the assistance in reviewing the manuscript. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049), Spain and finally by the Spanish Ministry of Economy and Competitiveness for its support with the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union and the Juan de la Cierva fellowship program supported by the Spanish Ministry of Economy and Competitiveness (Carlos Fernandez-Lozano, Ref. FJCI-2015-26071). The authors declare that there are no conflicts of interest. 61 21 22 3 91 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. APR 15 2017.0 72 306 316 10.1016/j.eswa.2016.10.058 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science EI8PW Green Submitted 2023-03-23 WOS:000392770900026 0
J Yu, X; Yuan, XM; Dong, EZ; Riha, K Yu, Xiao; Yuan, Ximei; Dong, Enzeng; Riha, Kamil Target extraction of banded blurred infrared images by immune dynamical algorithm with two-dimensional minimum distance immune field INFRARED PHYSICS & TECHNOLOGY English Article Target extraction; Blurred infrared image; Immune dynamical network NEURAL-NETWORK; SEGMENTATION Banded blurred Infrared image segmentation is a challenging topic since banded blurred infrared images are characterized by high noise, low contrast, and weak edges. Based on the interconnected and networked collaborative mechanism between innate immune factors and adaptive immune factors, this paper presents an immune dynamical algorithm with two-dimensional minimum distance immune field to solve this puzzle. Firstly, using the original characteristics as antigen surface molecular patterns, innate immune factors in the first layer of immune dynamical network extract banded blurred regions from the whole banded blurred infrared image region. Secondly, innate immune factors in the second layer of immune dynamical network extract new characteristics to design the complex of major histocompatibility complex (MHC) and antigen peptide. Lastly, adaptive immune factors in the last layer will extract object and background antigens from all the banded blurred image antigens, and design the optimal immune field. of every adaptive immune factors. Experimental results on hand trace infrared images verified that the proposed algorithm could efficiently extract targets from images, and produce better extraction accuracy. (C) 2016 Elsevier B.V. All rights reserved. [Yu, Xiao; Yuan, Ximei; Dong, Enzeng] Tianjin Univ Technol, Sch Elect Engn, Tianjin 300384, Peoples R China; [Yu, Xiao; Yuan, Ximei; Dong, Enzeng] Tianjin Univ Technol, Tianjin Key Lab Control Theory & Applicat Complic, Tianjin 300384, Peoples R China; [Yuan, Ximei; Riha, Kamil] Brno Univ Technol, Dept Telecommun, Brno 61200, Czech Republic Tianjin University of Technology; Tianjin University of Technology; Brno University of Technology Yu, X; Dong, EZ (corresponding author), Tianjin Univ Technol, Sch Elect Engn, Tianjin 300384, Peoples R China.;Yu, X; Dong, EZ (corresponding author), Tianjin Univ Technol, Tianjin Key Lab Control Theory & Applicat Complic, Tianjin 300384, Peoples R China. yx_ustb@163.com yu, xiao/HHT-0722-2022 National Natural Science Foundation of China [61272358] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the National Natural Science Foundation of China (No. 61272358). 19 3 4 1 22 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1350-4495 1879-0275 INFRARED PHYS TECHN Infrared Phys. Technol. JUL 2016.0 77 94 99 10.1016/j.infrared.2016.05.017 0.0 6 Instruments & Instrumentation; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation; Optics; Physics DT5OQ 2023-03-23 WOS:000381532900015 0
C Yan, YJ; Zhao, HM; Kao, FJ; Vargas, VM; Zhao, SP; Ren, JC Ren, J; Hussain, A; Zheng, J; Liu, CL; Luo, B; Zhao, H; Zhao, X Yan, Yijun; Zhao, Huimin; Kao, Fu-Jen; Masero Vargas, Valentin; Zhao, Sophia; Ren, Jinchang Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos ADVANCES IN BRAIN INSPIRED COGNITIVE SYSTEMS, BICS 2018 Lecture Notes in Artificial Intelligence English Proceedings Paper 9th International Conference on Brain-Inspired Cognitive Systems (BICS) JUL 07-08, 2018 Xian, PEOPLES R CHINA Univ Strathclyde,Univ Stirling,IEEE Brain Initiat Deep neural network (DNN); Video salient objects; Pedestrian detection SALIENCY DETECTION; OBJECT DETECTION; FUSION In this paper, we introduce an efficient framework to subtract the background from both visible and thermal imagery for pedestrians' detection in the urban scene. We use a deep neural network (DNN) to train the background subtraction model. For the training of the DNN, we first generate an initial background map and then employ randomly 5% video frames, background map, and manually segmented ground truth. Then we apply a cognition-based postprocessing to further smooth the foreground detection result. We evaluate our method against our previous work and 11 recently widely cited method on three challenge video series selected from a publicly available color-thermal benchmark dataset OCTBVS. Promising results have been shown that the proposed DNN-based approach can successfully detect the pedestrians with good shape in most scenes regardless of illuminate changes and occlusion problem. [Yan, Yijun; Zhao, Sophia; Ren, Jinchang] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Scotland; [Zhao, Huimin] Guangdong Polytechn Univ, Sch Comp Sci, Guangzhou, Peoples R China; [Zhao, Huimin] Guangzhou Key Lab Digital Content Proc & Secur Te, Guangzhou, Peoples R China; [Kao, Fu-Jen] Natl Yang Ming Univ, Inst Biophoton, Taipei, Taiwan; [Masero Vargas, Valentin] Univ Extremadura, Dept Comp Syst & Telemat Engn, Badajoz, Spain University of Strathclyde; National Yang Ming Chiao Tung University; Universidad de Extremadura Ren, JC (corresponding author), Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Scotland. jinchang.ren@strath.ac.uk Kao, Fu-Jen/GYQ-6902-2022 Yan, Yijun/0000-0003-0224-0078; Masero Vargas, Valentin/0000-0003-4839-8292; Kao, Fu-Jen/0000-0001-8361-3942; Ren, Jinchang/0000-0001-6116-3194 28 2 2 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-00563-4; 978-3-030-00562-7 LECT NOTES ARTIF INT 2018.0 10989 75 84 10.1007/978-3-030-00563-4_8 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Neurosciences; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Neurosciences & Neurology; Imaging Science & Photographic Technology BT9YM 2023-03-23 WOS:000865938200008 0
J Adeel, M; Tejedor, J; Macias-Guarasa, J; Lu, C Adeel, Muhammad; Tejedor, Javier; Macias-Guarasa, Javier; Lu, Chao Improved Perturbation Detection in Direct Detected phi-OTDR Systems using Matched Filtering IEEE PHOTONICS TECHNOLOGY LETTERS English Article Distributed acoustic sensing; phase-OTDR; perturbation detection FREQUENCY-RESPONSE; EXTINCTION-RATIO; ENHANCEMENT; PERFORMANCE; TRANSFORM; SENSOR Nuisance Alarm Rate (NAR) is critical in phi-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forest-based machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications. [Adeel, Muhammad; Lu, Chao] Hong Kong Polytech Univ PolyU, Dept Elect Engn, Photon Res Ctr, Hong Kong, Peoples R China; [Tejedor, Javier] Univ CEU San Pablo, Madrid 28003, Spain; [Macias-Guarasa, Javier] Univ Alcala, Madrid 28801, Spain Hong Kong Polytechnic University; San Pablo CEU University; Universidad de Alcala Adeel, M (corresponding author), Hong Kong Polytech Univ PolyU, Dept Elect Engn, Photon Res Ctr, Hong Kong, Peoples R China. m.adeel@connnect.polyu.hk; javier.tejedornoguerales@ceu.es; javier.maciasguarasa@uah.es; enluchao@polyu.edu.hk Macias-Guarasa, Javier/J-4625-2012; Tejedor, Javier/L-2075-2015; Adeel, Muhammad/O-9981-2016 Macias-Guarasa, Javier/0000-0002-3303-3963; Tejedor, Javier/0000-0001-7699-5620; Adeel, Muhammad/0000-0001-7021-8812 Spanish Ministry of Economy and Competivity [TIN2016-75982-C2-1-R]; General Research Fund (GRF) [PolyU 152658/16E, 152168/17E] Spanish Ministry of Economy and Competivity(Spanish Government); General Research Fund (GRF) This work was supported in part by the Spanish Ministry of Economy and Competivity under Project TIN2016-75982-C2-1-R, and in part by the General Research Fund (GRF: PolyU 152658/16E, 152168/17E). 15 4 4 3 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1041-1135 1941-0174 IEEE PHOTONIC TECH L IEEE Photonics Technol. Lett. NOV 1 2019.0 31 21 1689 1692 10.1109/LPT.2019.2940297 0.0 4 Engineering, Electrical & Electronic; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Optics; Physics NG8BE Green Accepted 2023-03-23 WOS:000564204200003 0
J Ju, CS; Zhou, JD; Lee, SR; Tan, MS; Liu, T; Bazoukis, G; Jeevaratnam, K; Chan, EWY; Wong, ICK; Wei, L; Zhang, QP; Tse, G Ju, Chengsheng; Zhou, Jiandong; Lee, Sharen; Tan, Martin Sebastian; Liu, Tong; Bazoukis, George; Jeevaratnam, Kamalan; Chan, Esther W. Y.; Wong, Ian Chi Kei; Wei, Li; Zhang, Qingpeng; Tse, Gary Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach ESC HEART FAILURE English Article Frailty index; Heart failure; Mortality; Inflammation; Nutrition; Machine learning OLDER-ADULTS; INFLAMMATION; ASSOCIATION; SURVIVAL; RISK Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods and results This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (>= 2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques. [Ju, Chengsheng; Wong, Ian Chi Kei; Wei, Li] UCL, Sch Pharm, Res Dept Practice & Policy, London, England; [Zhou, Jiandong; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China; [Lee, Sharen] Chinese Univ Hong Kong, LKS Inst Hlth Sci, Lab Cardiovasc Physiol, Cardiovasc Analyt Grp, Hong Kong, Peoples R China; [Tan, Martin Sebastian] Univ Toronto, Fac Arts & Sci, Toronto, ON, Canada; [Liu, Tong; Tse, Gary] Tianjin Med Univ, Hosp 2, Tianjin Inst Cardiol, Dept Cardiol,Tianjin Key Lab Ion Mol Funct Cardio, Tianjin 300211, Peoples R China; [Bazoukis, George; Tse, Gary] Evangelismos Gen Hosp, Dept Cardiol 2, Athens, Greece; [Jeevaratnam, Kamalan] Univ Surrey, Fac Hlth & Med Sci, Guildford, Surrey, England; [Chan, Esther W. Y.; Wong, Ian Chi Kei] Univ Hong Kong, Dept Pharmacol & Pharm, Ctr Safe Medicat Practice & Res, Hong Kong, Peoples R China University of London; University College London; University of London School of Pharmacy; City University of Hong Kong; Chinese University of Hong Kong; University of Toronto; Tianjin Medical University; Evangelismos Hospital; University of Surrey; University of Hong Kong Zhang, QP (corresponding author), City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.;Tse, G (corresponding author), Tianjin Med Univ, Hosp 2, Tianjin Inst Cardiol, Dept Cardiol,Tianjin Key Lab Ion Mol Funct Cardio, Tianjin 300211, Peoples R China.;Tse, G (corresponding author), Univ Surrey, Fac Hlth & Med Sci, Guildford, Surrey, England. qingpeng.zhang@cityu.edu.hk; g.tse@surrey.ac.uk Ju, Chengsheng/HNS-3776-2023; Bazoukis, George/AAM-4006-2020; Zhang, Qingpeng/D-4682-2011; Tse, Gary/P-1171-2015; Lee, Sharen/AAW-7488-2021; Chan, Esther Wai Yin/B-3522-2012 Zhang, Qingpeng/0000-0002-6819-0686; Tse, Gary/0000-0001-5510-1253; Lee, Sharen/0000-0002-2401-2837; Ju, Chengsheng/0000-0001-7860-6262; Chan, Esther Wai Yin/0000-0002-7602-9470; Zhou, Jiandong/0000-0003-3780-9033; liu, ying/0000-0001-9385-992X 57 14 15 2 12 WILEY PERIODICALS, INC SAN FRANCISCO ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA 2055-5822 ESC HEART FAIL ESC Heart Fail. AUG 2021.0 8 4 2837 2845 10.1002/ehf2.13358 0.0 JUN 2021 9 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology TQ4FN 34080784.0 Green Accepted, Green Published 2023-03-23 WOS:000657315300001 0
J Wang, SW; Jin, X; Mao, S; Vasilakos, AV; Tang, Y Wang, Saiwei; Jin, Xin; Mao, Shuai; Vasilakos, Athanasios V.; Tang, Yang Model-Free Event-Triggered Optimal Consensus Control of Multiple Euler-Lagrange Systems via Reinforcement Learning IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING English Article Mathematical model; Neural networks; Reinforcement learning; System dynamics; Heuristic algorithms; Computational modeling; Protocols; Augmented system; Euler-Lagrange system; event-triggered control; reinforcement learning This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered optimal controller. Thirdly, we present a policy iteration (PI) algorithm derived from RL, which converges to the optimal policy. Then, the value function of each agent is represented through a neural network to realize the PI algorithm. Moreover, the gradient descent method is used to update the neural network only at a series of discrete event-triggered instants. The specific form of the event-triggered condition is then proposed, and it is guaranteed that the closed-loop augmented system under the event-triggered mechanism is uniformly ultimately bounded (UUB). Meanwhile, the Zeno behavior is also eliminated. Finally, the validity of this approach is verified by a simulation example. [Wang, Saiwei; Jin, Xin; Mao, Shuai; Tang, Yang] East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China; [Vasilakos, Athanasios V.] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, NSW 2007, Australia; [Vasilakos, Athanasios V.] Fuzhou Univ, Dept Comp Sci & Technol, Fuzhou 350116, Peoples R China; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden East China University of Science & Technology; University of Technology Sydney; Fuzhou University; Lulea University of Technology Tang, Y (corresponding author), East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China. 842376017@qq.com; jx_9810@163.com; mshecust@163.com; th.vasilakos@gmail.com; tangtany@gmail.com Vasilakos, Athanasios/0000-0003-1902-9877; Mao, Shuai/0000-0002-1960-3456; Tang, Yang/0000-0002-2750-8029 National Key Research and Development Program of China [2018YFC0809302]; National Natural Science Foundation of China [61751305, 61673176]; Program of Shanghai Academic Research Leader [20XD1401300]; Programme of Introducing Talents of Discipline to Universities (the 111 Project) [B17017] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program of Shanghai Academic Research Leader; Programme of Introducing Talents of Discipline to Universities (the 111 Project)(Ministry of Education, China - 111 Project) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0809302, the National Natural Science Foundation of China under Grants 61751305 and 61673176, in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017. 44 14 14 19 69 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 2327-4697 IEEE T NETW SCI ENG IEEE Trans. Netw. Sci. Eng. JAN 1 2021.0 8 1 246 258 10.1109/TNSE.2020.3036604 0.0 13 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics RA1UK 2023-03-23 WOS:000631202700021 0
C Chen, SQ; Hao, JY; Weiss, G; Zhou, S; Zhang, ZL IEEE Chen, Siqi; Hao, Jianye; Weiss, Gerhard; Zhou, Shuang; Zhang, Zili Toward Efficient Agreements in Real-time Multilateral Agent-based Negotiations 2015 IEEE 27TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2015) Proceedings-International Conference on Tools With Artificial Intelligence English Proceedings Paper 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) NOV 09-11, 2015 Vietri sul Mare, ITALY IEEE,IEEE Comp Soc,Biol & Artificial Intelligence Fdn,ICTAI,Seconda Univ Studi Napoli,Italian Assoc Artificial Intelligence,Soc Italiana Reti Neuroniche,Int Inst Adv Sci Studies,Univ Patras Distributed Artificial Intelligence; Multi-agent systems; Automated Negotiation; Empirical game theory Negotiations among autonomous agents have gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences over the space of negotiation outcomes. We propose a novel negotiation approach which enables an agent to reach an efficient agreement with multiple opponents. The proposed approach achieves that goal by, 1) employing sparse pseudo-input Gaussian processes to model the behavior of opponents, 2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and 3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation. The experimental results show, both from the standard mean-score perspective and the perspective of empirical game theory, that the agent applying the proposed approach outperforms the state-of-the-art negotiation agents from the recent Automated Negotiating Agents Competition (ANAC) in a variety of negotiation domains. [Chen, Siqi; Zhang, Zili] Southwest Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China; [Hao, Jianye] Tianjin Univ, Sch Software, Tianjin 300072, Peoples R China; [Weiss, Gerhard; Zhou, Shuang] Maastricht Univ, Dept Knowledge Engn, NL-6200 MD Maastricht, Netherlands Southwest University - China; Tianjin University; Maastricht University Chen, SQ (corresponding author), Southwest Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China. siqi.chen09@gmail.com; jianye.hao@tju.edu.cn; gerhard.weiss@maastrichtuniversity.nl; shuang.zhou@maastrichtuniversity.nl; zili.zhang@swu.eud.cn Zhang, Zili/HKW-2171-2023 16 3 3 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1082-3409 978-1-5090-0163-7 PROC INT C TOOLS ART 2015.0 896 903 10.1109/ICTAI.2015.130 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BE6QD 2023-03-23 WOS:000374592500116 0
J Li, FP; Kusche, J; Chao, NF; Wang, ZT; Locher, A Li, Fupeng; Kusche, Juergen; Chao, Nengfang; Wang, Zhengtao; Loecher, Anno Long-Term (1979-Present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE Data GEOPHYSICAL RESEARCH LETTERS English Article SEA-LEVEL RISE; RIVER-BASIN; MASS CHANGE; MODELS; TRENDS The Gravity Recovery and Climate Experiment (GRACE) mission has monitored global total water storage anomalies (TWSA) with an unprecedented accuracy since 2002. Yet, many applications require a longer record, that is, extending prior to the GRACE period. Here, we present a new global reconstruction of long-term (1979-2020) TWSA fields by combining machine learning with time series decomposition and statistical decomposition techniques. We find that the long-term TWSA reconstructed from GRACE fits well with the GRACE-FO observation over most grids (0.5 degrees resolution) of the global land and successfully reproduces the strong El Nino signal. Comparisons to Satellite Laser Ranging solutions and to observed global mean sea level change suggest our reconstruction (doi:https://doi.org/10.5061/dryad.z612jm6bt ) is more reliable than previously published products. This study provides a viable approach for both reconstructing past TWSA and filling the GRACE data gap at the global scale. Plain Language Summary Since 2002, temporal changes of vertically integrated water storage have been detected globally with unprecedented accuracy via satellite gravimetry, as first implemented with the Gravity Recovery and Climate Experiment (GRACE) satellites. However, no similar observation system has been in place before 2002, that could have used to measure the total water storage anomalies (TWSA) with an accuracy comparable to GRACE. Several approaches, such as machine learning, have been proposed in previous studies to reconstruct past TWSA based on their empirical relationships with the climatic/hydrological drivers (e.g., precipitation and soil moisture). Different from traditional approaches, we here combine machine learning with statistical decomposition techniques to reconstruct GRACE-like TWSA (0.5 degrees resolution) for the period 1979-2020 over the global land. We find that the reconstructed TWSA shows strong abnormal signals during El Nino years. In addition, we compare our reconstruction to recent TWSA data obtained using Satellite Laser Ranging analysis to five satellites, that is, Lageos-1, Lageos-2, Ajisai, Stella, and Starlette, in the period since 1993, and to the observed global mean sea level change as validations. The comparison suggests that our reconstruction is more accurate than previously published products. We suggest our method provides a viable alternative for reconstructing long-term TWSA. [Li, Fupeng; Chao, Nengfang] China Univ Geosci, Coll Marine Sci & Technol, Wuhan, Peoples R China; [Li, Fupeng; Wang, Zhengtao] Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China; [Li, Fupeng; Kusche, Juergen; Loecher, Anno] Univ Bonn, Inst Geodesy & Geoinformat, Bonn, Germany China University of Geosciences; Wuhan University; University of Bonn Chao, NF (corresponding author), China Univ Geosci, Coll Marine Sci & Technol, Wuhan, Peoples R China. chaonf@cug.edu.cn Chao, Nengfang/0000-0002-6211-4308; Li, Fupeng/0000-0002-3329-1989 NSFC (China) [41974019, 41704011, 41274032, 41474018, 414294019, 41974007]; project of Key Laboratory of National Bureau of surveying, mapping and geographic information for earth observation and space-time information science [201812]; Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education [GLAB2020ZR12]; Fundamental Research Funds for the Central Universities; German Research Foundation DFG [KU1207/26-1, RI2657/2-2] NSFC (China)(National Natural Science Foundation of China (NSFC)); project of Key Laboratory of National Bureau of surveying, mapping and geographic information for earth observation and space-time information science; Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); German Research Foundation DFG(German Research Foundation (DFG)) The authors thank the following data providers CSR, NOAA, NASA, CPC, University of Colorado, and University of Bonn for making the data available: GRACE data; GRACE-FO data; climatic data; hydrological data; GMSL; SLR data. This study is supported by the NSFC (China) under Grants 41974019, 41704011, 41274032, 41474018, 414294019, and 41974007, by funding project of Key Laboratory of National Bureau of surveying, mapping and geographic information for earth observation and space-time information science (201812), by the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (Grant No. GLAB2020ZR12) and the Fundamental Research Funds for the Central Universities. The authors also acknowledge funding by the German Research Foundation DFG under grants KU1207/26-1 and RI2657/2-2. 33 37 37 25 88 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. APR 28 2021.0 48 8 e2021GL093492 10.1029/2021GL093492 0.0 10 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology TH8HN 2023-03-23 WOS:000672324900094 0
J Niyato, D; Huang, KB; Bennis, M; Pan, M; Xiong, ZH; Le, LB; Kim, DI; Wang, LC Niyato, Dusit; Huang, Kaibin; Bennis, Mehdi; Pan, Miao; Xiong, Zehui; Le, Long Bao; Kim, Dong In; Wang, Li-Chun IEEE TCCN Special Section Editorial: Convergence of Collaborative Distributed Machine Learning and Edge Computing Toward Intelligent Networking IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Editorial Material We are delighted to introduce the readers to this special Section of the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN), which aims at soliciting the original research and practical contributions on novel architectures, applications, and technologies of the convergence of Collaborative Distributed Machine Learning (CD-ML) and edge computing towards the realization of edge intelligence and/or intelligent edge. The objective of this special Section is to introduce recent advances in convergence of CD-ML and edge computing, and its applications to tackle both fundamental and practical challenges in intelligent communication systems. We have received a total number of 26 submissions, and after a rigorous peer-review process, 9 articles have been selected for publication, which are briefly discussed as follows. [Niyato, Dusit] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Huang, Kaibin] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong 999077, Peoples R China; [Bennis, Mehdi] Univ Oulu, Ctr Wireless Commun, Oulu 90570, Finland; [Pan, Miao] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA; [Xiong, Zehui] Singapore Univ Technol & Design, Pillar Informat Syst Technol & Design, Singapore 487372, Singapore; [Le, Long Bao] Univ Quebec, Inst Natl Rech Sci, Montreal, PQ H5A 1K6, Canada; [Kim, Dong In] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea; [Wang, Li-Chun] Natl Yang Ming Chiao Tung Univ, Dept Elect & Comp Engn, Hsinchu 300, Taiwan Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; University of Hong Kong; University of Oulu; University of Houston System; University of Houston; Singapore University of Technology & Design; University of Quebec; Institut national de la recherche scientifique (INRS); University of Quebec Montreal; Sungkyunkwan University (SKKU); National Yang Ming Chiao Tung University Niyato, D (corresponding author), Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore. Le, Long Bao/A-1003-2016; Wang, Li-Chun/AAD-7164-2022; Bennis, Mehdi/ABE-5838-2020; Xiong, Zehui/B-9792-2019 Le, Long Bao/0000-0003-3577-6530; Wang, Li-Chun/0000-0002-7883-6217; Bennis, Mehdi/0000-0003-0261-0171; Xiong, Zehui/0000-0002-4440-941X 9 0 0 10 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. DEC 2021.0 7 4 1016 1020 10.1109/TCCN.2021.3127563 0.0 5 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications XL4WA Bronze 2023-03-23 WOS:000728144400004 0
J Huang, K; Li, BW; Xiong, DL; Jiang, HT; Jiang, XW; Yan, XL; Claesen, L; Liu, DH; Chen, JJ; Liu, ZL Huang, Kai; Li, Bowen; Xiong, Dongliang; Jiang, Haitian; Jiang, Xiaowen; Yan, Xiaolang; Claesen, Luc; Liu, Dehong; Chen, Junjian; Liu, Zhili Structured Dynamic Precision for Deep Neural Networks Quantization ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS English Article Neural networks; compression and acceleration; systolic array; algorithm-architecture co-design Deep Neural Networks (DNNs) have achieved remarkable success in various Artificial Intelligence applications. Quantization is a critical step in DNNs compression and acceleration for deployment. To further boost DNN execution efficiency, many works explore to leverage the input-dependent redundancy with dynamic quantization for different regions. However, the sensitive regions in the feature map are irregularly distributed, which restricts the real speed up for existing accelerators. To this end, we propose an algorithm-architecture co-design, named Structured Dynamic Precision (SDP). Specifically, we propose a quantization scheme in which the high-order bit part and the low-order bit part of data can be masked independently. And a fixed number of term parts are dynamically selected for computation based on the importance of each term in the group. We also present a hardware design to enable the algorithm efficiently with small overheads, whose inference time mainly scales with the precision proportionally. Evaluation experiments on extensive networks demonstrate that compared to the state-of-the-art dynamic quantization accelerator DRQ, our SDP can achieve 29% performance gain and 51% energy reduction for the same level of model accuracy. [Huang, Kai; Li, Bowen; Xiong, Dongliang; Jiang, Haitian; Jiang, Xiaowen; Yan, Xiaolang] Zhejiang Univ, Inst VLSI Design, 38 Zheda Rd, Hangzhou 310030, Zhejiang, Peoples R China; [Claesen, Luc] Univ Hasselt, Engn Technol Elect ICT Dept, B-3590 Diepenbeek, Belgium; [Liu, Dehong; Chen, Junjian] China Southern Power Grid Co Ltd, Guangzhou 510670, Peoples R China; [Liu, Zhili] Sec Chip Technol Co Ltd, Hangzhou 310030, Peoples R China Zhejiang University; Hasselt University; China Southern Power Grid Xiong, DL (corresponding author), Zhejiang Univ, Inst VLSI Design, 38 Zheda Rd, Hangzhou 310030, Zhejiang, Peoples R China. huangk@zju.edu.cn; libw@zju.edu.cn; xiongdl@zju.edu.cn; jianghaitian@zju.edu.cn; xiaowen_jiang@zju.edu.cn; yan@vlsi.zju.edu.cn; luc.claesen@uhasselt.be; liudh@csg.cn; chenjj4@csg.cn; liuzhili@sec-chip.com Jiang, Haitian/0000-0001-8215-166X National Key R&D Program of China [2020YFB0906000, 2020YFB0906001] National Key R&D Program of China This work is supported by the National Key R&D Program of China (2020YFB0906000, 2020YFB0906001). 48 0 0 1 1 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1084-4309 1557-7309 ACM T DES AUTOMAT EL ACM Transact. Des. Automat. Electron. Syst. JAN 2023.0 28 1 12 10.1145/3549535 0.0 24 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8B6LG Bronze 2023-03-23 WOS:000917034400012 0
J Fernandez-Beltran, R; Ibanez, D; Kang, J; Pla, F Fernandez-Beltran, Ruben; Ibanez, Damian; Kang, Jian; Pla, Filiberto Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression Networks IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Vegetation mapping; Spatial resolution; Satellites; Earth; Instruments; Flexible printed circuits; MODIS; Inter-sensor regression; Sentinel-2 (S2); Sentinel-3 (S3); temporal resolution; vegetation indices (VI) MISSION; FLEX Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation, and many other factors may severely constrain the availability of data involving multiple satellites. In response, this letter proposes a novel deep 3-D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 (S3) VI by taking advantage of inter-sensor Sentinel-2 (S2) observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension to exploit not only the higher spatial resolution of the S2 instrument but also its own temporal evolution to better estimate time-resolved VI in S3. To validate the proposed approach, we built a database made of multiple day-synchronized S2 and S3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-the-art regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available at https://github.com/rufernan/3CRN. [Fernandez-Beltran, Ruben; Ibanez, Damian; Pla, Filiberto] Janne I Univ, Inst New Imaging Technol, Castellon De La Plana 12071, Spain; [Kang, Jian] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China Soochow University - China Kang, J (corresponding author), Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China. rufernan@uji.es; ibanezd@uji.es; jiankang@suda.edu.cn; pla@uji.es Ibáñez Fernández, Damián/ABA-9238-2021; kang, jian/V-3055-2019; Pla, Filiberto/AAD-1208-2022 Ibáñez Fernández, Damián/0000-0002-3252-1252; kang, jian/0000-0001-6284-3044; Pla, Filiberto/0000-0003-0054-3489 Ministerio de Ciencia, Innovacion y Universidades [RTI2018-098651-B-C54]; Generalitat Valenciana [GV/2020/167]; National Natural Science Foundation of China [62101371] Ministerio de Ciencia, Innovacion y Universidades(Spanish Government); Generalitat Valenciana(Center for Forestry Research & Experimentation (CIEF)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Grant RTI2018-098651-B-C54, in part by the Generalitat Valenciana under Grant GV/2020/167, and in part by the National Natural Science Foundation of China under Grant 62101371. 24 1 1 2 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 2501505 10.1109/LGRS.2021.3108856 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XP3SW Green Published 2023-03-23 WOS:000730789400017 0
J Li, DM; Dai, XF; Wang, JX; Xu, Q; Wang, Y; Fu, TY; Hafez, A; Grant, J Li, Deming; Dai, Xuefei; Wang, Jianxing; Xu, Qun; Wang, Yong; Fu, Tianyou; Hafez, Ali; Grant, Joshua Evaluation of College Students' Classroom Learning Effect Based on the Neural Network Algorithm MOBILE INFORMATION SYSTEMS English Article With the advent of the digital society, the amount of information we face will increase exponentially, which will challenge our level of educational knowledge, so we begin to pay attention to the effect of education and teaching in the context of digitalization. The purpose of this paper is to study the evaluation of students' classroom learning effect based on the neural network algorithm and scientific objectivity. Assessment and other principles are to create an assessment system for students' learning outcomes in the classroom. The system includes three first-level indicators, including first-level indicators and their weights. By selecting a university to test the system, the results show that the system can quantitatively evaluate the learning outcomes, and the corresponding scores and grades can be obtained through the formula. After adjusting the parameters of the hidden layer nodes, the BP elastic gradient algorithm is used to complete the evaluation model generation. The training error results show that the target curve and the output curve almost coincide, and the error curve is also between -0.2 and 0.5. Therefore, the learning outcome score obtained by the BP neural network based on the principal component factor data is basically consistent with the given learning outcome score. [Li, Deming] Jilin Int Studies Univ, Sch Educ, Changchun 130117, Jilin, Peoples R China; [Dai, Xuefei] Jilin Int Studies Univ, Sch Int Media, Changchun 130117, Jilin, Peoples R China; [Wang, Jianxing] Chongqing Jiaotong Univ, Informat Sci & Engn, Chongqing 400074, Peoples R China; [Xu, Qun] Shandong Univ Technol, Zibo, Peoples R China; [Wang, Yong] Sejong Univ, Dept Econ, Seoul 05006, South Korea; [Fu, Tianyou] Shanghai Maritime Univ, Shanghai, Peoples R China; [Hafez, Ali] Univ Basque Country, Leioa, Spain; [Grant, Joshua] Univ Melbourne, Melbourne, Australia Jilin International Studies University; Jilin International Studies University; Chongqing Jiaotong University; Shandong University of Technology; Sejong University; Shanghai Maritime University; University of Basque Country; University of Melbourne Dai, XF (corresponding author), Jilin Int Studies Univ, Sch Int Media, Changchun 130117, Jilin, Peoples R China. lideming@jisu.edu.cn; daixuefei@jisu.edu.cn; 631418040232@mails.cqjtu.edu.cn; 15121404019@stumail.sdut.edu.cn; wangyong256@126.com; 201410230073@stu.shmtu.edu.cn; alihafez56788@gmail.com; joshuagrant8146@gmail.com 18 0 0 1 1 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1574-017X 1875-905X MOB INF SYST Mob. Inf. Syst. OCT 3 2022.0 2022 7772620 10.1155/2022/7772620 0.0 8 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 5K8IJ gold 2023-03-23 WOS:000869963900012 0
J Shen, JY; Chen, JB; Zheng, ZQ; Zheng, JB; Liu, ZR; Song, J; Wong, SY; Wang, XL; Huang, MQ; Fang, PH; Jiang, BS; Tsang, WH; He, ZL; Liu, TR; Akinwunmi, B; Wang, CC; Zhang, CJP; Huang, J; Ming, WK Shen, Jiayi; Chen, Jiebin; Zheng, Zequan; Zheng, Jiabin; Liu, Zherui; Song, Jian; Wong, Sum Yi; Wang, Xiaoling; Huang, Mengqi; Fang, Po-Han; Jiang, Bangsheng; Tsang, Winghei; He, Zonglin; Liu, Taoran; Akinwunmi, Babatunde; Wang, Chi Chiu; Zhang, Casper J. P.; Huang, Jian; Ming, Wai-Kit An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study JOURNAL OF MEDICAL INTERNET RESEARCH English Article AI; application; disease diagnosis; maternal health care; artificial intelligence; app; women; rural; innovation; diabetes; gestational diabetes; diagnosis PREDICTION; FUTURE Background: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms. [Shen, Jiayi; Tsang, Winghei; Ming, Wai-Kit] Jinan Univ, Sch Med, Dept Publ Hlth & Prevent Med, Guangzhou, Peoples R China; [Shen, Jiayi] Sun Yat Sen Univ, Ctr Canc, Guangzhou, Peoples R China; [Chen, Jiebin; Liu, Zherui] Jinan Univ, Coll Informat Sci & Technol, Guangzhou, Peoples R China; [Zheng, Zequan; Zheng, Jiabin; Wong, Sum Yi; Fang, Po-Han; Jiang, Bangsheng; He, Zonglin] Jinan Univ, Int Sch, Guangzhou, Peoples R China; [Song, Jian] Sun Yat Sen Univ, Sch Int Studies, Guangzhou, Peoples R China; [Wang, Xiaoling; Huang, Mengqi] Jinan Univ, Sch Journalism & Commun, Guangzhou, Peoples R China; [Liu, Taoran] Univ Groningen, Fac Econ & Business, Groningen, Netherlands; [Akinwunmi, Babatunde] Brigham & Womens Hosp, Dept Obstet & Gynecol, 75 Francis St, Boston, MA 02115 USA; [Akinwunmi, Babatunde] Harvard Univ, Harvard Med Sch, Massachusetts Gen Hosp, Ctr Genom Med, Boston, MA 02115 USA; [Wang, Chi Chiu] Chinese Univ Hong Kong, Dept Obstet & Gynaecol, Hong Kong, Hong Kong, Peoples R China; [Zhang, Casper J. P.] Univ Hong Kong, Sch Publ Hlth, Hong Kong, Hong Kong, Peoples R China; [Huang, Jian] Imperial Coll London, Sch Publ Hlth, Dept Epidemiol & Biostat, Multidisciplinary Collaborat Res Ctr Environm & H, St Marys Campus, London, England Jinan University; Sun Yat Sen University; Jinan University; Jinan University; Sun Yat Sen University; Jinan University; University of Groningen; Harvard University; Brigham & Women's Hospital; Harvard University; Harvard Medical School; Massachusetts General Hospital; Chinese University of Hong Kong; University of Hong Kong; Imperial College London Ming, WK (corresponding author), Jinan Univ, Sch Med, Dept Publ Hlth & Prevent Med, Guangzhou, Peoples R China. wkming@connect.hku.hk Wang, Ronald/C-6541-2014; Akinwunmi, Babatunde/AAV-8259-2020; Huang, huangmengqi/HDM-8111-2022; Zhang, Casper J. P./AAH-6101-2019; Liu, Scott/AAC-7891-2021; Huang, Jian/AAV-1355-2021; Liu, Taoran/HMP-5195-2023; Ming, Wai-kit/AAH-5989-2019 Wang, Ronald/0000-0002-3928-7278; Akinwunmi, Babatunde/0000-0001-8316-1552; Zhang, Casper J. P./0000-0003-1047-0287; Liu, Scott/0000-0002-7806-1263; Huang, Jian/0000-0002-3931-5013; Liu, Taoran/0000-0002-7806-1263; Ming, Wai-kit/0000-0002-8846-7515; Zheng, Zequan/0000-0002-6031-6643; Tsang, Winghei/0000-0003-4350-3559; Zheng, Jiabin/0000-0001-7961-4301; Shen, Jiayi/0000-0002-7022-6940 28 7 7 6 28 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA 1438-8871 J MED INTERNET RES J. Med. Internet Res. SEP 15 2020.0 22 9 e21573 10.2196/21573 0.0 11 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics PF6DZ 32930674.0 Green Accepted, gold 2023-03-23 WOS:000599143800002 0
J Zou, XC; Zhu, SN; Mottus, M Zou, Xiaochen; Zhu, Sunan; Mottus, Matti Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms REMOTE SENSING English Article canopy structure; LAI; leaf angle distribution; Sentinel-2; vegetation indices; machine learning LEAF-AREA INDEX; CHLOROPHYLL CONTENT; SPECTRAL REFLECTANCE; ANGLE DISTRIBUTION; NITROGEN STATUS; GREEN LAI; RETRIEVAL; MODEL; GROWTH; RED Leaf angle distribution (LAD), or the leaf mean tilt angle (MTA) capturing its central value, is used to quantify the direction of the leaf surface in a canopy and is one of the most important canopy structuraltraits. Combined with the other important structure parameter, leaf area index (LAI), LAD determines the light interception of a crop canopy. However, unlike LAI, only few studies have addressed the direct retrieval of LAD or MTA from remote sensing data. Recently, it has been shown that the red edge is a key spectral region where the effect of leaf angle on crop spectral reflectance can be separated from that of other structural variables. The Multispectral imager (MSI) onboard the Sentinel-2 (S2) satellite has two specially designed red-edge channels in this spectral region and thus can potentially be used for large-scale mapping of MTA at high spatial and temporal resolutions. Unfortunately, no field data on leaf angles at the scale of S2 pixel are available. Therefore, we simulated 5000 observations of different crops using the PROSAIL canopy reflectance model. Further, we used the MTA and LAI data of six crop species growing in 162 experimental plots in Finland and simulated their reflectance signal in S2 bands by resampling AISA airborne imaging spectroscopy data. Four common machine learning regression algorithms (random forest, support vector machine, multilayer perceptron network and partial least squares regression) were examined for retrieving canopy structure parameters, including leaf angle, from the simulated reflectances. Further, we analyzed the utility of 12 vegetation indices (VIs) well known to be sensitive to canopy structure for canopy structure estimation. Six of the studied indices used information from the visible part of the spectrum and the near infrared (NIR) while another six were selected to also utilize the red edge bands specific to S2. We found that S2 band 6 in the red edge had a strong correlation with MTA (R-2 = 0.79 in model simulation and R-2 = 0.87 in field measurements) but a low correlation with LAI (R-2 = 0.07 in model simulation and R-2= 0.06 in field measurements). Of the six red edge-based VIs, four (NDVIRE, CIRE, WDRVIRE and MSRRE) depended less on MTA than the visible NIR-based VIs and thus could be useful for estimating LAI for any LAD. The other two red edge-based VIs, IRECI and S2REP, had stronger correlations with MTA (R-2 = 0.67 and 0.52, respectively) than LAI (R-2 = 0.24 and 0.19, respectively). Additionally, MTA was accurately estimated (RMSE = 1.1-2.4 degrees in model simulations and RMSE = 2.2-3.9 degrees in field measurements) using the four 10 m spatial resolution bands with the RF, SVM and MLP algorithms, without information in the red edge. These promising results indicate the capability of S2 in accurately mapping the MTA of field crops on a large scale. [Zou, Xiaochen; Zhu, Sunan] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Mottus, Matti] VTT Tech Res Ctr Finland Ltd, FI-02044 Espoo, Finland Nanjing University of Information Science & Technology; VTT Technical Research Center Finland Zou, XC (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China. xiaochen.zou@nuist.edu.cn; 20191235007@nuist.edu.cn; matti.mottus@vtt.fi National Science Foundation of China [41801243]; Academy of Finland [317387]; Academy of Finland (AKA) [317387] Funding Source: Academy of Finland (AKA) National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This research was supported by the National Science Foundation of China (grant No. 41801243) and the Academy of Finland (grant No. 317387). 72 2 2 14 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUN 2022.0 14 12 2849 10.3390/rs14122849 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 2K7IQ gold 2023-03-23 WOS:000816505300001 0
J Lin, X; Cicchella, D; Hong, J; Meng, GG Lin, Xin; Cicchella, Domenico; Hong, Jun; Meng, Ganggang A Test of the Hypothesis That Syn-Collisional Felsic Magmatism Contributes to Continental Crustal Growth Via Deep Learning Modeling and Principal Component Analysis of Big Geochemical Datasets JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH English Article crustal growth; collision-zone hypothesis; deep learning; principal component analysis; GEOROC Database; Tibetan Magmatism Database MULTIVARIATE-ANALYSIS; GANGDESE BELT; ARC MAGMAS; EVOLUTION; ROCKS; CLASSIFICATION; PETROGENESIS; DERIVATION; TERRANE; ORIGIN The origin, way of growth, and compositional transition (from basaltic to andesitic) of continental crust remain enigmatic. To better understand the evolution of the Earth's crust, geoscientists have hypothesized two competing models, one is the widely accepted island-arc model, the other is the newly proposed collision-zone model that continental collision produces and preserves syn-collisional Mantle-derived Bulk-continental-crust-like Granitoids (MBGs), and hence maintains net continental crust growth. Here, we tested the collision-zone model by investigating the existence, temporal-spatial distribution, geochemical signatures, and possible sources of the syn-collisional MBGs. We applied deep learning (DL) algorithm and principal component analysis (PCA) to the database GEOROC and Tibetan Magmatism Database. DL successfully built a regression model of whole-rock element compositional data and mean zircon epsilon(Hf)(t) data of igneous rocks. This can not only assign values to the missing Hf data, but statistically unveil the potential relations between the compositions (both isotopic and geochemical) and the possible sources of the igneous rocks. The DL and PCA enabled to recognize the MBGs and define their geochemical and isotopic fingerprints differing noticeably from arc magmas (e.g., Kohistan arc type and Tibetan adakite-like type). Besides, our observations suggest that MBGs are common in collisional settings as a response to known collision events. Moreover, the MBGs' distinct geochemical and isotopic signatures indicate that they are likely sourced from subducted ocean crust. Our results therefore generally support evident contribution of syn-collisional felsic magmatism to net continental crust growth. However, further refinement of the petrogenesis and estimation of the (relative) volume are critically needed. [Lin, Xin] Changan Univ, Sch Earth Sci & Resources, Xian, Peoples R China; [Lin, Xin] Laurentian Univ, Harquail Sch Earth Sci, Sudbury, ON, Canada; [Cicchella, Domenico] Univ Sannio, Dept Sci & Technol, Benevento, Italy; [Hong, Jun] Xian Ctr Geol Survey, Xian, Peoples R China; [Meng, Ganggang] Jinchuan Grp Co LTD, Jinchang, Peoples R China Chang'an University; Laurentian University; University of Sannio; China Geological Survey Lin, X (corresponding author), Changan Univ, Sch Earth Sci & Resources, Xian, Peoples R China.;Lin, X (corresponding author), Laurentian Univ, Harquail Sch Earth Sci, Sudbury, ON, Canada.;Cicchella, D (corresponding author), Univ Sannio, Dept Sci & Technol, Benevento, Italy.;Hong, J (corresponding author), Xian Ctr Geol Survey, Xian, Peoples R China. xlin1@laurentian.ca; cidom@unisannio.it; 250439578@qq.com Cicchella, Domenico/A-2977-2012 Cicchella, Domenico/0000-0001-8001-3896; Jun, Hong/0000-0003-4793-8568; Lin, Xin/0000-0001-9393-1370 Special Fund for Basic Research of Central Universities (CHD) [300102271206]; China Scholarship Council [201906565036] Special Fund for Basic Research of Central Universities (CHD); China Scholarship Council(China Scholarship Council) This study was financially supported by the Special Fund for Basic Research of Central Universities (CHD), grant 300102271206. This paper was completed while the first author was a visiting scholar at Laurentian University, which was financially supported by China Scholarship Council, grant 201906565036. The authors are grateful to editor Stephen Parman and an anonymous associate editor for editorial handling and review of the manuscript, to Chris Hawkesworth, and three anonymous reviewers for insightful and constructive comments. 83 1 1 7 15 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-9313 2169-9356 J GEOPHYS RES-SOL EA J. Geophys. Res.-Solid Earth FEB 2022.0 127 2 e2021JB023002 10.1029/2021JB023002 0.0 17 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics ZO3QU 2023-03-23 WOS:000765643700003 0
C Song, Y; Tang, F; Dong, WM; Zhang, XP; Deussen, O; Lee, TY ACM Song, Yu; Tang, Fan; Dong, Weiming; Zhang, Xiaopeng; Deussen, Oliver; Lee, Tong-Yee Photo Squarization by Deep Multi-Operator Retargeting PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18) English Proceedings Paper 26th ACM Multimedia Conference (MM) OCT 22-26, 2018 Seoul, SOUTH KOREA Assoc Comp Machinery,ACM SIGMM Image retargeting; photo squarization; retargeting visual perception issues; distillation Squared forms of photos are widely used in social media as album covers or thumbnails of image streams. In this study, we realize photo squarization by modeling Retargeting Visual Perception Issues, which reflect human perception preference toward image ratargeting. General image retargeting techniques deal with three common issues, namely, salient content, object shape, and scene composition, to preserve the important information of original image. We propose a new way based on multi-operator techniques to investigate human behavior in balancing the three issues. We establish a new dataset and observe human behavior by inviting investigators to retarget images to square manually. We propose a data-driven approach composed of perception and distillation modules by using deep learning techniques to predict human perception preference. The perception part learns the relations among the three issues, and the distillation part transfers the learned relations to a simple but effective network. Our study contributes to deep learning literature by optimizing a network index and lightening its running burden. Experimental results show that photo squarization results generated by the proposed model are consistent with human visual perception results. [Song, Yu; Tang, Fan; Dong, Weiming; Zhang, Xiaopeng] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China; [Song, Yu; Tang, Fan] Univ Chinese Acad Sci, Beijing, Peoples R China; [Deussen, Oliver] VCC SIAT Shenzhen, Shenzhen, Peoples R China; [Deussen, Oliver] Univ Konstanz, Constance, Germany; [Lee, Tong-Yee] Natl Cheng Kung Univ, Tainan, Taiwan Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Konstanz; National Cheng Kung University Dong, WM (corresponding author), Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China. songyu2017@ia.ac.cn; tangfan2013@ia.ac.cn; weiming.dong@ia.ac.cn; xiaopeng.zhang@ia.ac.cn; oliver.deussen@uni-konstanz.de; tonylee@mail.ncku.edu.tw Tang, Fan/O-3923-2018; Deussen, Oliver/HKF-2004-2023; DONG, Weiming/AAG-7678-2020 Tang, Fan/0000-0002-3975-2483; DONG, Weiming/0000-0001-6502-145X National Natural Science Foundation of China [61832016, 61672520, 61702488]; Beijing Natural Science Foundation [4162056]; National Laboratory of Pattern Recognition; Leading Talents of Guangdong Program [00201509]; Ministry of Science and Technology, Taiwan [MOST-104-2221-E-006-044-MY3] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Laboratory of Pattern Recognition; Leading Talents of Guangdong Program; Ministry of Science and Technology, Taiwan(Ministry of Science and Technology, Taiwan) We thank the users on Pexels and Pixabay who share their great works under CC0 Creative Commons. This work was supported by National Natural Science Foundation of China under nos. 61832016, 61672520 and 61702488, by Beijing Natural Science Foundation under No. 4162056, by the independent research project of National Laboratory of Pattern Recognition, by the Leading Talents of Guangdong Program under No. 00201509, and by the Ministry of Science and Technology under No. MOST-104-2221-E-006-044-MY3, Taiwan. 37 7 7 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-5665-7 2018.0 1047 1055 10.1145/3240508.3240623 0.0 9 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO2ZN 2023-03-23 WOS:000509665700120 0
J Yang, FF; Zhu, JW; Wang, JF; Zhang, LW; Wang, WJ; Chen, X; Lin, XX; Wang, QS; Burkhoff, D; Zhou, SK; He, KL Yang, Feifei; Zhu, Jiuwen; Wang, Junfeng; Zhang, Liwei; Wang, Wenjun; Chen, Xu; Lin, Xixiang; Wang, Qiushuang; Burkhoff, Daniel; Zhou, S. Kevin; He, Kunlun Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography ANNALS OF TRANSLATIONAL MEDICINE English Article Mitral regurgitation (MR); self-supervised learning (SSL); color Doppler echocardiography; mitral regurgitation grading (MR grading) ARTIFICIAL-INTELLIGENCE; DISEASE Background: Mitral regurgitation (MR) is the most common valve lesion worldwide. However, the quantitative assessment of MR severity based on current guidelines is challenging and time-consuming; strict adherence to applying these guidelines is therefore relatively infrequent. We aimed to develop an automatic, reliable and reproducible artificial intelligence (AI) diagnostic system to assist physicians in grading MR severity based on color video Doppler echocardiography via a self-supervised learning (SSL) algorithm. Methods: We constructed a retrospective cohort of 2,766 consecutive echocardiographic studies of patients with MR diagnosed based on clinical criteria from two hospitals in China. One hundred and forty- eight studies with reference standards were selected in the main analysis and also served as the test set for the AI segmentation model. Five hundred and ninety-two and 148 studies were selected with stratified random sampling as the training and validation datasets, respectively. The self-supervised algorithm captures features and segments the MR jet and left atrium (LA) area, and the output is used to assist physicians in MR severity grading. The diagnostic performance of physicians without and with the support from AI was estimated and compared. Results: The performance of SSL algorithm yielded 89.2% and 85.3% average segmentation dice similarity coefficient (DICE) on the validation and test datasets, which achieved 6.2% and 8.1% improvement compared to Residual U-shape Network (ResNet-UNet), respectively. When physicians were provided the output of algorithm for grading MR severity, the sensitivity increased from 77.0% (95% CI: 70.9-82.1%) to 86.7% (95% CI: 80.3-91.2%) and the specificity was largely unchanged: 91.5% (95% CI: 87.8-94.1%) vs. 90.5% (95% CI: 86.7-93.2%). Conclusions: This study provides a new, practical, accurate, plug-and-play AI-assisted approach for assisting physicians in MR severity grading that can be easily implemented in clinical practice. [Yang, Feifei; Wang, Wenjun; Chen, Xu; Lin, Xixiang; He, Kunlun] Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing, Peoples R China; [Yang, Feifei; He, Kunlun] Chinese Peoples Liberat Army Gen Hosp, Beijing Key Lab Precis Med Chron Heart Failure, Beijing, Peoples R China; [Yang, Feifei; He, Kunlun] Chinese Peoples Liberat Army Gen Hosp, Key Lab, Minist Ind & Informat Technol Biomed Engn & Trans, Beijing, Peoples R China; [Zhu, Jiuwen; Zhou, S. Kevin] Univ Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, MIRACLE Grp, Beijing, Peoples R China; [Wang, Junfeng] Univ Utrecht, Utrecht Inst Pharmaceut Sci, Div Pharmacoepidemiol & Clin Pharmacol, Utrecht, Netherlands; [Zhang, Liwei; Wang, Qiushuang] Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Med Ctr 4, Beijing, Peoples R China; [Chen, Xu; Lin, Xixiang] Med Sch Chinese PLA, Beijing, Peoples R China; [Burkhoff, Daniel] Cardiovasc Res Fdn, New York, NY USA; [Zhou, S. Kevin] Univ Sci & Technol China, MIRACLE Ctr, Sch Biomed Engn, Suzhou, Peoples R China; [Zhou, S. Kevin] Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China Chinese People's Liberation Army General Hospital; Chinese People's Liberation Army General Hospital; Chinese People's Liberation Army General Hospital; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Utrecht University; Chinese People's Liberation Army General Hospital; Chinese People's Liberation Army General Hospital; Cardiovascular Research Foundation (CRF); Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS He, KL (corresponding author), Chinese Peoples Liberat Army Gen Hosp, 28 Fuxing Rd, Beijing 100853, Peoples R China. kunlunhe@plagh.org yang, fei/HPF-9658-2023 Beijing Natural Science and Technology Foundation [7202198]; Ministry of Industry and Information Technology of China [2020-0103-3-1] Beijing Natural Science and Technology Foundation; Ministry of Industry and Information Technology of China This research received support from the Beijing Natural Science and Technology Foundation (7202198) and Ministry of Industry and Information Technology of China (2020-0103-3-1). 32 0 0 1 10 AME PUBL CO SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2305-5839 2305-5847 ANN TRANSL MED ANN. TRANSL. MED. JAN 2021.0 10 1 3 10.21037/atm-21-3449 0.0 OCT 2021 14 Oncology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Oncology; Research & Experimental Medicine ZT0PW 35242848.0 gold 2023-03-23 WOS:000730237900001 0
C Zhao, T; Lazendic, S; Zhao, YX; Montereale-Gavazzi, G; Pizurica, A Choras, M; Choras, RS Zhao, Ting; Lazendic, Srdan; Zhao, Yuxin; Montereale-Gavazzi, Giacomo; Pizurica, Aleksandra Classification of Multibeam Sonar Image Using the Weyl Transform IMAGE PROCESSING AND COMMUNICATIONS: TECHNIQUES, ALGORITHMS AND APPLICATIONS Advances in Intelligent Systems and Computing English Proceedings Paper 11th International Image Processing and Communications Conference (IP and C) SEP 11-13, 2019 Bydgoszcz, POLAND Multibeam data processing; Multibeam sonar image; Feature extraction; Weyl transform; Acoustic sediment classification In this paper we develop a novel classification method for multibeam sonar images based on the Weyl transform. The texture descriptor based on Weyl coefficients describes effectively the multiscale correlation features appearing in the sonar images. Our classification approach combines the Weyl coefficients with statistical features that are commonly used in the analysis of seabed sonar images and captures the morphological variation and geoacoustic characteristics of the seafloor. We employ a neural network as a classifier. The proposed combined feature extraction method demonstrates better performance than the commonly used statistical methods in this application. [Zhao, Ting; Zhao, Yuxin] Harbin Engn Univ, Coll Automat, Harbin, Peoples R China; [Zhao, Ting; Lazendic, Srdan; Pizurica, Aleksandra] Univ Ghent, TELIN GAIM, Dept Telecommun & Informat Proc, Ghent, Belgium; [Lazendic, Srdan] Univ Ghent, Dept Math Anal, Ghent, Belgium; [Montereale-Gavazzi, Giacomo] Royal Belgian Inst Nat Sci, Operat Directorate Nat Environm, Brussels, Belgium; [Montereale-Gavazzi, Giacomo] Univ Ghent, Renard Ctr Marine Geol, Dept Geol, Ghent, Belgium Harbin Engineering University; Ghent University; Ghent University; Royal Belgian Institute of Natural Sciences; Ghent University Zhao, T (corresponding author), Harbin Engn Univ, Coll Automat, Harbin, Peoples R China.;Zhao, T (corresponding author), Univ Ghent, TELIN GAIM, Dept Telecommun & Informat Proc, Ghent, Belgium. zhaoting@hrbeu.edu.cn; Srdan.Lazendic@UGent.be; zhaoyuxin@hrbeu.edu.cn; gmonterealegavazzi@naturalsciences.be; Aleksandra.Pizurica@UGent.be Pizurica, Aleksandra/AAG-4687-2021; Zhao, Yuxin/HNS-3187-2023; Lazendic, Srdan/N-7586-2018; Pizurica, Aleksandra/K-6866-2015 Lazendic, Srdan/0000-0003-2772-9240; Pizurica, Aleksandra/0000-0002-9322-4999; Montereale Gavazzi, Giacomo/0000-0001-9599-2425 Major Project of Chinese National Programs for Fundamental Research and Development [613317]; China Scholarship Council Major Project of Chinese National Programs for Fundamental Research and Development; China Scholarship Council(China Scholarship Council) This work was supported in part by Major Project of Chinese National Programs for Fundamental Research and Development (No. 613317) and in part by the China Scholarship Council. 16 1 1 1 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-5357 2194-5365 978-3-030-31254-1; 978-3-030-31253-4 ADV INTELL SYST COMP 2020.0 1062 206 213 10.1007/978-3-030-31254-1_25 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BR3UX 2023-03-23 WOS:000649312100025 0
J Meng, WZ; Li, WJ; Su, CH; Zhou, JY; Lu, RX Meng, Weizhi; Li, Wenjuan; Su, Chunhua; Zhou, Jianying; Lu, Rongxing Enhancing Trust Management for Wireless Intrusion Detection via Traffic Sampling in the Era of Big Data IEEE ACCESS English Article Intrusion detection; traffic sampling; wireless sensor network; trust computation; Bayesian model; big data DETECTION SYSTEMS; SENSOR; FRAMEWORK; SECURITY; INTERNET Internet of Things (IoT) has been widely used in our daily life, which enables various objects to be interconnected for data exchange, including physical devices, vehicles, and other items embedded with network connectivity. Wireless sensor network (WSN) is a vital application of IoT, providing many kinds of information among sensors, whereas such network is vulnerable to a wide range of attacks, especially insider attacks, due to its natural environment and inherent unreliable transmission. To safeguard its security, intrusion detection systems (IDSs) are widely adopted in a WSN to defend against insider attacks through implementing proper trust-based mechanisms. However, in the era of big data, sensors may generate excessive information and data, which could degrade the effectiveness of trust computation. In this paper, we focus on this challenge and propose a way of combining Bayesian-based trust management with traffic sampling for wireless intrusion detection under a hierarchical structure. In the evaluation, we investigate the performance of our approach in both a simulated and a real network environment. Experimental results demonstrate that packet-based trust management would become ineffective in a heavy traffic environment, and that our approach can help lighten the burden of IDSs in handling traffic, while maintaining the detection of insider attacks. [Meng, Weizhi; Li, Wenjuan] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark; [Li, Wenjuan] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Su, Chunhua] Univ Aizu, Div Comp Sci, Aizu Wakamatsu, Fukushima 9658580, Japan; [Zhou, Jianying] Singapore Univ Technol & Design, Informat Syst Technol & Design, Singapore 487372, Singapore; [Lu, Rongxing] Univ New Brunswick, Fac Comp Sci, Fredericton, NB E3B 5A3, Canada Technical University of Denmark; City University of Hong Kong; University of Aizu; Singapore University of Technology & Design; University of New Brunswick Su, CH (corresponding author), Univ Aizu, Div Comp Sci, Aizu Wakamatsu, Fukushima 9658580, Japan. chsu@u-aizu.ac.jp Li, Wenjuan/AAE-4027-2019; Meng, Weizhi/N-9638-2019 Li, Wenjuan/0000-0003-3745-5669; Meng, Weizhi/0000-0003-4384-5786; Zhou, Jianying/0000-0003-0594-0432 JSPS [WAKATE B-15K16005]; University of Aizu; SUTD start-up Research Grant [SRG-ISTD-2017-124]; Grants-in-Aid for Scientific Research [15K16005] Funding Source: KAKEN JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); University of Aizu; SUTD start-up Research Grant; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported in part by JSPS Grants-in-Aid for Scientific Research KAKENHI under Grant WAKATE B-15K16005, in part by the Competitive Research Funding from the University of Aizu P-21, and in part by SUTD start-up Research Grant SRG-ISTD-2017-124. 49 55 55 1 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 7234 7243 10.1109/ACCESS.2017.2772294 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications FZ3RK Green Submitted, gold 2023-03-23 WOS:000427507500001 0
J Fang, WL; Love, PED; Luo, HB; Xu, SJ Fang, Weili; Love, Peter E. D.; Luo, Hanbin; Xu, Shuangjie A deep learning fusion approach to retrieve images of People's unsafe behavior from construction sites DEVELOPMENTS IN THE BUILT ENVIRONMENT English Article Digital image; Image retrieval; Deep learning; Feature fusion; Content-based image retrieval MODEL; FALLS Retrieving unsafe behaviours from an existing digital database can provide managers and the like with the necessary information to put in place strategies to improve safety in construction. Prevailing studies have focused on developing content-based image retrieval (CBIR) approaches (e.g., color-based) to retrieve objects and ma-terials obtained from construction sites. While CBIR approaches are effective in extracting low-level features from digital images they are unable to accurately retrieve unsafe behaviours those from existing databases. To address this limitation, we develop an improved CBIR approach to retrieve unsafe behaviour images more accurately and automatically, which combines features extracted from different models. We utilise a digital database developed by Huazhong University of Science and Technology to validate the feasibility of our pro-posed approach. Our research demonstrates that the fusion of ResNet-101 and VGG-19 can obtain higher levels of Top-K recall and outperform the one feature extraction method. [Fang, Weili] Tech Univ Berlin, Dept Civil & Bldg Syst, Gustav Meyer Allee 25, D-13156 Berlin, Germany; [Love, Peter E. D.] Curtin Univ, Sch Civil & Mech Engn, GPO U1987, Perth, WA 6845, Australia; [Luo, Hanbin] Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan 430074, Hubei, Peoples R China; [Xu, Shuangjie] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China Technical University of Berlin; Curtin University; Huazhong University of Science & Technology; Hong Kong University of Science & Technology Luo, HB (corresponding author), Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan 430074, Hubei, Peoples R China. luohbcem@hust.edu.cn Alexander von Humboldt Stiftung/Foundation; National Natural Science Foundation of China [51978302, U21A20151] Alexander von Humboldt Stiftung/Foundation(Alexander von Humboldt Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to acknowledge the financial support of the Alexander von Humboldt Stiftung/Foundation, National Natural Science Foundation of China (Grant No. No. 51978302, U21A20151) . 45 0 0 23 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-1659 DEV BUILT ENVIRON Dev. Built Environ. DEC 2022.0 12 100085 10.1016/j.dibe.2022.100085 0.0 SEP 2022 10 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 4N4UM 2023-03-23 WOS:000854014800001 0
C Liu, JX; Sun, TN; Luo, YL; Fu, Q; Cao, Y; Zhai, J; Ding, XM Cheng, L; Leung, ACS; Ozawa, S Liu, Junxiu; Sun, Tiening; Luo, Yuling; Fu, Qiang; Cao, Yi; Zhai, Jia; Ding, Xuemei Financial Data Forecasting Using Optimized Echo State Network NEURAL INFORMATION PROCESSING (ICONIP 2018), PT V Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Neural Information Processing (ICONIP) DEC 13-16, 2018 Siem Reap, CAMBODIA Asia Pacific Neural Network Soc,Springer Echo state network; Fruit fly algorithm; Time series; Algorithm optimization The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (similar to 50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy. [Liu, Junxiu; Sun, Tiening; Luo, Yuling; Fu, Qiang] Guangxi Normal Univ, Fac Elect Engn, Guilin 541004, Peoples R China; [Cao, Yi] Univ Surrey, Surrey Business Sch, Dept Business Transformat & Sustainable Enterpris, Guildford GU2 7XH, Surrey, England; [Zhai, Jia] Univ Salford, Salford Business Sch, 43 Crescent, Salford M5 4WT, Lancs, England; [Ding, Xuemei] Fujian Normal Univ, Coll Math & Informat, Fuzhou 350108, Peoples R China; [Ding, Xuemei] Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine BT48 7JL, Londonderry, North Ireland Guangxi Normal University; University of Surrey; University of Salford; Fujian Normal University; Ulster University Luo, YL (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guilin 541004, Peoples R China. yuling0616@mailbox.gxnu.edu.cn Fu, Qiang/0000-0002-3498-5915 National Natural Science Foundation of China [61603104]; Guangxi Natural Science Foundation [2016GXNSFCA380017, 2015GXNSFBA139256, 2017GXNSFAA198180]; funding of Overseas 100 Talents Program of Guangxi Higher Education; Doctoral Research Foundation of Guangxi Normal University [2016BQ005]; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry; Funds for Young Key Program of Education Department from Fujian Province, China [JZ160425]; Program of Education Department of Fujian Province, China [I201501005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); funding of Overseas 100 Talents Program of Guangxi Higher Education; Doctoral Research Foundation of Guangxi Normal University; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry(Scientific Research Foundation for the Returned Overseas Chinese Scholars); Funds for Young Key Program of Education Department from Fujian Province, China; Program of Education Department of Fujian Province, China This research is supported by the National Natural Science Foundation of China under Grant 61603104, the Guangxi Natural Science Foundation under Grants 2016GXNSFCA380017, 2015GXNSFBA139256 and 2017GXNSFAA198180, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Doctoral Research Foundation of Guangxi Normal University under Grant 2016BQ005, the Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry, the Funds for Young Key Program of Education Department from Fujian Province, China (Grant No. JZ160425), and Program of Education Department of Fujian Province, China (Grant No. I201501005). 19 4 4 0 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-04221-9; 978-3-030-04220-2 LECT NOTES COMPUT SC 2018.0 11305 138 149 10.1007/978-3-030-04221-9_13 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ6OZ 2023-03-23 WOS:000612948800013 0
J Ouyang, YC; He, W; Li, XJ Ouyang, Yuncheng; He, Wei; Li, Xiajing Reinforcement learning control of a single-link flexible robotic manipulator IET CONTROL THEORY AND APPLICATIONS English Article flexible manipulators; learning (artificial intelligence); manipulator kinematics; vibration control; lightweight structures; radial basis function networks; stability; Lyapunov methods; reinforcement learning control; single-link flexible robotic manipulator; lightweight structure; Lagrange equation; radial basis function neural networks; actor NN; system stability; Lyapunov direct method; Matlab simulation; Quanser flexible link platform ADAPTIVE TRACKING CONTROL; VIBRATION CONTROL; FEEDBACK-CONTROL; STABILITY ANALYSIS; NONLINEAR-SYSTEMS; RESONANT CONTROL; CONSTRAINT; DESIGN; PARAMETERS; NETWORKS In this study, the authors focus on the reinforcement learning control of a single-link flexible manipulator and attempt to suppress the vibration due to its flexibility and lightweight structure. The assumed mode method and the Lagrange's equation are adopted in modelling to enhance the satisfaction of precision. Two radial basis function neural networks (NNs) are employed in the designed control algorithm, actor NN for generating a policy and critic NN for evaluating the cost-to-go. Rigorous stability of the system has been proven via Lyapunov's direct method. Through Matlab simulation and experiment on the Quanser flexible link platform, the superiority and feasibility of the reinforcement learning control are verified. [Ouyang, Yuncheng] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China; [Ouyang, Yuncheng] Univ Elect Sci & Technol China, Ctr Robot, Chengdu 611731, Peoples R China; [He, Wei] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China; [Li, Xiajing] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; University of Science & Technology Beijing; Delft University of Technology He, W (corresponding author), Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China. weihe@ieee.org He, Wei/B-2355-2016; He, Wei/K-4900-2019 He, Wei/0000-0002-8944-9861; He, Wei/0000-0002-8944-9861 National Natural Science Foundation of China [61522302, 6161101273]; National Basic Research Program of China (973 Program) [2014CB744206]; Beijing Natural Science Foundation [4172041]; Fundamental Research Funds for the China Central Universities of USTB [FRF-BD-16-005A, FRF-TP-15-005C1] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Basic Research Program of China (973 Program)(National Basic Research Program of China); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the China Central Universities of USTB The authors thank the Editor-In-Chief, the Associate Editor and the anonymous reviewers for their constructive comments in improving the quality and presentation of this paper. This work was supported by the National Natural Science Foundation of China under Grant 61522302, 6161101273, the National Basic Research Program of China (973 Program) under Grant 2014CB744206, the Beijing Natural Science Foundation under Grant 4172041, and the Fundamental Research Funds for the China Central Universities of USTB under Grant FRF-BD-16-005A and FRF-TP-15-005C1. 50 39 40 11 142 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-8644 1751-8652 IET CONTROL THEORY A IET Contr. Theory Appl. JUN 2 2017.0 11 9 1426 1433 10.1049/iet-cta.2016.1540 0.0 8 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation EV0PZ 2023-03-23 WOS:000401446200013 0
C Hooper, A; Wright, TJ; Weiss, JR; Rollins, C; Gaddes, M; Lazecky, M; Morishita, Y; Walters, RJ; Wang, H; Hussain, E IEEE Hooper, Andrew; Wright, Tim J.; Weiss, Jonathan R.; Rollins, Chris; Gaddes, Matthew; Lazecky, Milan; Morishita, Yu; Walters, Richard J.; Wang, Hua; Hussain, Ekbal EXPLOITING INSAR ON A LARGE SCALE FOR TECTONICS AND VOLCANO MONITORING IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) SEP 26-OCT 02, 2020 ELECTR NETWORK Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc Sentinel-1; InSAR; Tectonic strain; Volcano monitoring; Machine learning Geodetic measurements of crustal deformation rates provide important information on earthquake hazard, indicating that strain is accumulating either faster or slower than the rate suggested by known earthquakes. With the COMET-LiCSAR InSAR processing system, which performs large-scale automated processing and timeseries analysis of Sentinel-1 data, we aim to generate strain rate maps for the entire Alpine-Himalayan Belt, and use these to investigate seismic hazard. In this paper we will present results for Anatolia. Deformation is also a key indicator of volcanic unrest, and is often associated with the flow of magma to shallower depths. The operational nature of Sentinel-1, with frequent revisits and rapid data delivery, makes it suitable for monitoring subaerial volcanoes globally. In order to deal with the large quantity of new data that is continuously being generated, we have developed machine learning approaches to flag when either a new deformation pattern emerges, or an existing deformation pattern changes rate. [Hooper, Andrew; Wright, Tim J.; Rollins, Chris; Gaddes, Matthew; Lazecky, Milan] Univ Leeds, Sch Earth & Environm, COMET, Leeds, W Yorkshire, England; [Weiss, Jonathan R.] Univ Potsdam, Inst Geosci, Potsdam, Germany; [Morishita, Yu] Geospatial Informat Author Japan, Tsukuba, Ibaraki, Japan; [Walters, Richard J.] Univ Durham, Dept Earth Sci, COMET, Durham, England; [Wang, Hua] Guangdong Univ Technol, Dept Surveying Engn, Guangzhou, Peoples R China; [Hussain, Ekbal] British Geol Survey, Keyworth, Notts, England University of Leeds; University of Potsdam; Durham University; Guangdong University of Technology; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC British Geological Survey Hooper, A (corresponding author), Univ Leeds, Sch Earth & Environm, COMET, Leeds, W Yorkshire, England. ; Wright, Tim/A-5892-2011 Hussain, Ekbal/0000-0001-6921-2843; Wright, Tim/0000-0001-8338-5935; Lazecky, Milan/0000-0001-8179-5949 NERC [come30001] Funding Source: UKRI NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) 3 0 0 2 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-7281-6374-1 INT GEOSCI REMOTE SE 2020.0 6857 6858 10.1109/IGARSS39084.2020.9323491 0.0 2 Computer Science, Artificial Intelligence; Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Optics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Environmental Sciences & Ecology; Geology; Remote Sensing; Optics BR6WH 2023-03-23 WOS:000664335306126 0
C Zha, X; Chai, Y; Witlox, F; Ma, L Jia, Y; Du, J; Li, H; Zhang, W Zha, Xi; Chai, Yi; Witlox, Frank; Ma, Le Container Throughput Time Series Forecasting Using a Hybrid Approach PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT SYSTEMS CONFERENCE, VOL 1 Lecture Notes in Electrical Engineering English Proceedings Paper Chinese Intelligent Systems Conference (CISC) 2015 Yangzhou, PEOPLES R CHINA Port throughput forecasting; SARIMA; ANN; Hybrid models This paper proposed a novel two-stage hybrid container throughput forecasting model. Time series in reality exhibits both linear and nonlinear characteristics and individual models are not able to describe the two features simultaneously. Therefore, we combine linear model SARIMA (seasonal autoregressive integrated moving average) and nonlinear model ANN (artificial neural network). In order to break through the limitations of traditional hybrid models, based on the identified parameters of SARIMA in first stage, the structures of several ANN in second stage could be decided. Finally, we validate the proposed hybrid model 5 performs best with case study in Shanghai port. [Zha, Xi; Chai, Yi; Ma, Le] Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China; [Zha, Xi] Inst Transport & Maritime Management Antwerp, B-2000 Antwerp, Belgium; [Witlox, Frank] Univ Ghent, Dept Geog, Krijgslaan 281 S8, B-9000 Ghent, Belgium Chongqing University; Ghent University Chai, Y (corresponding author), Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China. chaiyi@cqu.edu.cn Witlox, Frank/A-5143-2008 Witlox, Frank/0000-0002-8966-6823 12 1 2 0 10 SPRINGER NEW YORK 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES 1876-1100 1876-1119 978-3-662-48386-2; 978-3-662-48384-8 LECT NOTES ELECTR EN 2016.0 359 639 650 10.1007/978-3-662-48386-2_65 0.0 12 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Robotics BE9LO 2023-03-23 WOS:000377755300065 0
J Bi, YJ; Li, C; Benezeth, Y; Yang, F Bi, Yanjing; Li, Chao; Benezeth, Yannick; Yang, Fan Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training PLOS ONE English Article NEURAL-NETWORKS; RECOGNITION Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 - 8.47% comparing to state-of-the-arts with different data training data density. [Bi, Yanjing] Capital Univ Econ & Business, Sch Foreign Studies, Beijing, Peoples R China; [Li, Chao] Chinese Acad Sci, Inst Acoust, Beijing, Peoples R China; [Li, Chao] Univ Chinese Acad Sci, Beijing, Peoples R China; [Benezeth, Yannick; Yang, Fan] Univ Bourgogne Franche Comte, Lab ImViA, Dijon, France Capital University of Economics & Business; Chinese Academy of Sciences; Institute of Acoustics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universite de Bourgogne Li, C (corresponding author), Chinese Acad Sci, Inst Acoust, Beijing, Peoples R China.;Li, C (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. chao.li@mail.ioa.ac.cn LI, Chao/0000-0002-3782-7955 25 1 1 0 6 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One OCT 18 2021.0 16 10 e0257901 10.1371/journal.pone.0257901 0.0 21 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics XU0SG 34662367.0 gold, Green Published 2023-03-23 WOS:000733985600006 0
J Li, J; Zhang, L; Li, CX; Tian, HL; Ning, J; Zhang, JX; Tong, YW; Wang, XN Li, Jie; Zhang, Le; Li, Chunxing; Tian, Hailin; Ning, Jing; Zhang, Jingxin; Tong, Yen Wah; Wang, Xiaonan Data-Driven Based In-Depth Interpretation and Inverse Design of Anaerobic Digestion for CH4-Rich Biogas Production ACS ES&T ENGINEERING English Article ensemble machine learning; anaerobic fermentation; waste to energy; microbial community; inverse experimental design REACTOR; PERFORMANCE; MANURE Anaerobic digestion (AD) is one of the most widely used bioconversion technologies for renewable energy production from wet biowaste. However, such an AD system is so complicated that it is challenging to fully comprehend this process and design the operational conditions for a specific biowaste to achieve CH4-rich biogas. In this context, ensemble machine learning (ML) algorithms were employed to develop multitask models for jointly predicting the CH4 yield and content in biogas and understanding this complicated process. Based on the best ensemble model with the R-2 values of 0.82 and 0.86 for the multitask prediction of CH4 yield and content, the top three critical factors for CH4 yield/contents were identified and their interactions with process acid generation and microbial community in the AD process were comprehensively interpreted to unveil their importance on CH4 generation. Moreover, the well-developed ensemble model was integrated with an optimization algorithm to inversely design the AD process for a real-world food waste, in which the CH4 yield was as high as 468.7 mL/gVS and the calculation results were experimentally validated with relative errors of 9-16%. This work provides a creative approach to gain insights and inverse design for AD reactors, which is helpful to waste-to-energy technologists and practitioners. [Li, Jie; Tong, Yen Wah] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore; [Zhang, Le; Tian, Hailin; Tong, Yen Wah] Natl Univ Singapore, NUS Environm Res Inst, Singapore 138602, Singapore; [Zhang, Le; Tian, Hailin; Zhang, Jingxin; Tong, Yen Wah] Energy & Environm Sustainabil Megac E2S2 Phase II, Campus Res Excellence & Technol Enterprise CREATE, Singapore 138602, Singapore; [Li, Chunxing] Tech Univ Denmark, Dept Chem & Biochem Engn, DK-2800 Lyngby, Denmark; [Ning, Jing] Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Pollutant Convers, Xiamen 361021, Peoples R China; [Zhang, Jingxin] Shanghai Jiao Tong Univ, China UK Low Carbon Coll, Shanghai 201306, Peoples R China; [Wang, Xiaonan] Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China National University of Singapore; National University of Singapore; Technical University of Denmark; Chinese Academy of Sciences; Institute of Urban Environment, CAS; Shanghai Jiao Tong University; Tsinghua University Tong, YW (corresponding author), Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore.;Tong, YW (corresponding author), Natl Univ Singapore, NUS Environm Res Inst, Singapore 138602, Singapore.;Tong, YW (corresponding author), Energy & Environm Sustainabil Megac E2S2 Phase II, Campus Res Excellence & Technol Enterprise CREATE, Singapore 138602, Singapore.;Wang, XN (corresponding author), Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China. wangxiaonan@isinghua.edu.cn; chetyw@nus.edu.sg Wang, Xiaonan/T-1102-2017; Li, Chunxing/HOF-2375-2023; Tian, Hailin/AAQ-2084-2021; Li, Jie/GNP-6986-2022; Tong, Yen Wah/D-4602-2009 Wang, Xiaonan/0000-0001-9775-2417; Li, Chunxing/0000-0003-1941-0387; Li, Jie/0000-0001-7969-4997; Tong, Yen Wah/0000-0002-6004-6284; ZHANG, Le/0000-0002-9103-3750 National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program [R-706-000-103-281, R-706-001-102-281]; Agency for Science, Technology and Research [A1898b0043] National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program(National Research Foundation, Singapore); Agency for Science, Technology and Research(Agency for Science Technology & Research (A*STAR)) This work was supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program (grant number R-706-000-103-281 and R-706-001-102-281) and the Singapore RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic grant Accelerated Materials Development for Manufacturing by the Agency for Science, Technology and Research under grant no. A1898b0043. 39 10 10 14 37 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2690-0645 ACS EST ENG ACS ES&T Eng. APR 8 2022.0 2 4 642 652 10.1021/acsestengg.1c00316 0.0 JAN 2022 11 Engineering, Environmental Emerging Sources Citation Index (ESCI) Engineering 0S5SH Green Submitted 2023-03-23 WOS:000746680700001 0
J Dixit, R; Khambhati, K; Supraja, KV; Singh, V; Lederer, F; Show, PL; Awasthi, MK; Sharma, A; Jain, R Dixit, Rewati; Khambhati, Khushal; Supraja, Kolli Venkata; Singh, Vijai; Lederer, Franziska; Show, Pau-Loke; Awasthi, Mukesh Kumar; Sharma, Abhinav; Jain, Rohan Application of machine learning on understanding biomolecule interactions in cellular machinery BIORESOURCE TECHNOLOGY English Article Protein mutation; Protein -solid interaction; Protein -carbohydrate interaction; Aptamer design; Nanomedicine LIGAND-BINDING; PROTEIN INTERACTIONS; PREDICTION; SITES; DISCOVERY; APTAMERS Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques. [Dixit, Rewati; Supraja, Kolli Venkata] Indian Inst Technol Delhi, Dept Biochem Engn & Biotechnol, Waste Treatment Lab, New Delhi 110016, India; [Khambhati, Khushal; Singh, Vijai] Indrashil Univ, Sch Sci, Dept Biosci, Mehsana 382715, Gujarat, India; [Lederer, Franziska; Jain, Rohan] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Bautzner landstr 400, D-01328 Dresden, Germany; [Show, Pau-Loke] Wenzhou Univ, Zhejiang Prov Key Lab Subtrop Water Environm & Mar, Wenzhou 325035, Peoples R China; [Show, Pau-Loke] SIMATS, Saveetha Sch Engn, Dept Sustainable Engn, Chennai 602105, India; [Show, Pau-Loke] Univ Nottingham, Dept Chem & Environm Engn, Semenyih 43500, Selangor Darul, Malaysia; [Awasthi, Mukesh Kumar] Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Peoples R China; [Sharma, Abhinav] Leibniz Inst Polymer Res Dresden, Inst Theory Polymers, Hohe Str 6, D-01069 Dresden, Germany Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Wenzhou University; Saveetha Institute of Medical & Technical Science; Saveetha School of Engineering; University of Nottingham Malaysia; Northwest A&F University - China; Leibniz Institut fur Polymerforschung Dresden Jain, R (corresponding author), Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Bautzner landstr 400, D-01328 Dresden, Germany. r.jain@hzdr.de Pau Loke, Show/A-7953-2015 Pau Loke, Show/0000-0002-0913-5409; Singh, Vijai/0000-0002-6394-4370 Ministry of Education, Government of India [1400791, 1401210]; Indian Council of Medical Research of the Government of India [AMR/Fellowship/36/2022- ECD-II] Ministry of Education, Government of India; Indian Council of Medical Research of the Government of India Rewati Dixit and Kolli Venkata Supraja thank Ministry of Education, Government of India for the Prime Minister's research fellowship to support their doctoral research (PMRF ID: 1400791, 1401210, respectively). Khushal Khambhati acknowledges the Indian Council of Medical Research of the Government of India for financial assistance in the form of a Senior Research Fellowship (File No. AMR/Fellowship/36/2022- ECD-II) 76 0 0 17 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0960-8524 1873-2976 BIORESOURCE TECHNOL Bioresour. Technol. FEB 2023.0 370 128522 10.1016/j.biortech.2022.128522 0.0 JAN 2023 12 Agricultural Engineering; Biotechnology & Applied Microbiology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels 8E0FU 36565819.0 2023-03-23 WOS:000918659700001 0
J Chan, THH; Guerquin, A; Hu, SG; Sozio, M Chan, T-H Hubert; Guerquin, Arnaud; Hu, Shuguang; Sozio, Mauro Fully Dynamic k-Center Clustering With Improved Memory Efficiency IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Clustering; k-center; approximation algorithm; fully-dynamic Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been flagged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a (2 + epsilon)-approximation algorithm for the k-center clustering problem with small amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while k and t are constant. Furthermore, we significantly improve the memory requirement of our fully dynamic algorithm, although at the cost of a worse approximation ratio of 4 + epsilon. Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach. [Chan, T-H Hubert; Hu, Shuguang] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Guerquin, Arnaud; Sozio, Mauro] Telecom ParisTech Univ, LTCI, F-91120 Paris, France University of Hong Kong Chan, THH (corresponding author), Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. hubert@cs.hku.hk; arnaud.guerquin@ens-paris-saclay.fr; sghu@cs.hku.hk; sozio@telecom-paristech.fr Hong Kong RGC [17200817] Hong Kong RGC(Hong Kong Research Grants Council) This work was partially supported by the Hong Kong RGC under the grant 17200817. The conference version of this paper appeared inWWW2018 [1]. 16 1 1 1 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. JUL 1 2022.0 34 7 3255 3266 10.1109/TKDE.2020.3023020 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 1V0JL 2023-03-23 WOS:000805787100017 0
J Das, A; Mohanty, MN; Mallick, PK; Tiwari, P; Muhammad, K; Zhu, HY Das, Abhishek; Mohanty, Mihir Narayan; Mallick, Pradeep Kumar; Tiwari, Prayag; Muhammad, Khan; Zhu, Hongyin Breast cancer detection using an ensemble deep learning method BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Article Convolutional neural networks; Multilayer perceptron; Empirical wavelet transform; Variational mode decomposition; Stacking ensemble; Breast cancer; Invasive ductal carcinoma MODE DECOMPOSITION; CLASSIFICATION In this work, the effectiveness of the deep learning model is applied for one-dimensional data when converted to images. This work is based on the effective conversion of one-dimensional data to images and designing a stacked ensemble deep learning model that can increase the performance of classification accuracy in comparison to single models. Breast cancer detection from gene expression dataset and breast histopathology images is considered using the proposed ensemble model. The gene expression data is one-dimensional. Using the tDistributed Stochastic Neighbor embedding technique and Convex Hull algorithm the one-dimensional data is converted to an image. Existing methods are using the datasets directly for training and classification, whereas the proposed method uses the dataset as well as the decomposed forms of the same for improving the performance. It involves two-stage classification. The first stage consists of three Convolutional Neural Networks as the base classifiers. Empirical Wavelet Transform and Variational Mode Decomposition are the two methods used to decompose the dataset so that the models can be trained at the molecular level, making our model robust in comparison to state-of-the-art methods. The first stage classification outcomes are used to train the second stage classifier Multilayer Perceptron. The gene expression dataset collected from Mendeley and is used for the generation of two-dimensional synthetic datasets. The synthetic datasets and breast histopathology image datasets are used for the training and validation of the proposedmodel. The improved results obtained in this work show the effectiveness of our method [Das, Abhishek; Mohanty, Mihir Narayan] Siksha O Anusandhan, ITER, Bhubaneswar 751030, Odisha, India; [Mallick, Pradeep Kumar] KIIT, Sch Comp Engn, Bhubaneswar 751024, Odisha, India; [Tiwari, Prayag] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland; [Muhammad, Khan] Sungkyunkwan Univ, Sch Convergence, Coll Comp & Informat, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 03063, South Korea; [Zhu, Hongyin] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Siksha 'O' Anusandhan University; Kalinga Institute of Industrial Technology (KIIT); Aalto University; Sungkyunkwan University (SKKU); Tsinghua University Mohanty, MN (corresponding author), Siksha O Anusandhan, ITER, Bhubaneswar 751030, Odisha, India. mihir.n.mohanty@gmail.com; pradeep.mallickfcs@kiit.ac.in; prayag.tiwari@aalto.fi; khanmuhammad@sju.ac.kr; zhuhongyin2020@mail.tsinghua.edu.cn Mohanty, Dr. Mihir Narayan/GYU-0128-2022; Zhu, Hongyin/HJA-5160-2022; Das, Abhishek/ADP-4815-2022; Pandey, Hari Mohan/M-9658-2015; Tiwari, Prayag/N-6261-2017; Muhammad, Khan/L-9059-2016 Zhu, Hongyin/0000-0001-5786-7594; Pandey, Hari Mohan/0000-0002-9128-068X; Tiwari, Prayag/0000-0002-2851-4260; Muhammad, Khan/0000-0003-4055-7412; zhu, hong yin/0000-0002-4223-5209; Das, Abhishek/0000-0002-6474-2332 52 12 12 3 7 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control SEP 2021.0 70 103009 10.1016/j.bspc.2021.103009 0.0 AUG 2021 11 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering US6OS 2023-03-23 WOS:000697546700007 0
J Ye, GM; Wan, YF; Wu, JM; Zhuang, WB; Zhou, ZQ; Jin, TS; Zi, JY; Zhang, DD; Geng, XM; Yang, P Ye, Guomin; Wan, Yanfen; Wu, Jiaming; Zhuang, Wenbo; Zhou, Ziquan; Jin, Taosha; Zi, Jinyu; Zhang, Dadong; Geng, Xuemin; Yang, Peng Multifunctional device integrating dual-temperature regulator for outdoor personal thermal comfort and triboelectric nanogenerator for self-powered human-machine interaction NANO ENERGY English Article Human thermal comfort; Human-Machine Interaction; Self-powered multifunctional system; Machine learning; Integrated device MANAGEMENT; ABSORPTION Reasonable depletion of energy resource to realize energy affair is critical to ever-lasting advancement of human society, such as passive thermal management strategy and self-powered technologies, promoting emergence of integrated and functionalized energy device. Here, series of multiply-utilization-type composite nanofibers were prepared by electrospinning to apply for personal thermal comfort and tactile triboelectric nanogenerator (T-TENG). Our heating/cooling pattern enables a skin simulator's temperature increase/decrease of 8.7 degrees C/2.5 degrees C respectively under sunlight exposure. Thermoelectricity was utilized to transform the heat into electricity with a maximum voltage of 157.6 mV. Besides, with optimization of structure and materials of T-TENG, optimum output performance with open-circuit voltage (V-OC) of 215.1 V, short-circuit current (I-SC) of 2.54 mu A, transfer charge (Q(T)) of 70.72 nC and superior effective peak power density of 362.58 mW M-2 were acquired. Furthermore, a self-powered game controller with signal acquiring and processing circuits for manipulation of game characters was achieved, and the International Morse Code that relay on motion recognition was first time realized with assistance of machine learning. The flexible and self-powered T-TENG manifests an imperative application value in electronics for HMI and wearable device. [Ye, Guomin; Wan, Yanfen; Zhuang, Wenbo; Zhou, Ziquan; Jin, Taosha; Zi, Jinyu; Zhang, Dadong; Geng, Xuemin; Yang, Peng] Yunnan Univ, Natl Ctr Int Res Photoelect & Energy Mat, Sch Mat & Energy, Kunming 650091, Peoples R China; [Wu, Jiaming] Univ Paris Saclay, CNRS, Lab Phys Solides, F-91405 Orsay, France Yunnan University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Yang, P (corresponding author), Yunnan Univ, Natl Ctr Int Res Photoelect & Energy Mat, Sch Mat & Energy, Kunming 650091, Peoples R China. pyang@ynu.edu.cn Yang, Peng/B-3819-2012 Yang, Peng/0000-0002-5473-5454 National Natural Science Foundation of China [52071282]; Applied Basic Research Foundation of Yunnan Province [202001BB050046] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Applied Basic Research Foundation of Yunnan Province Acknowledgments This work was financially supported by the National Natural Science Foundation of China (No. 52071282) and Applied Basic Research Foundation of Yunnan Province (No. 202001BB050046) . Authors thank Advanced Analysis and Measurement Center of Yunnan University for the sample testing service. 72 7 7 37 71 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2211-2855 2211-3282 NANO ENERGY Nano Energy JUN 15 2022.0 97 107148 10.1016/j.nanoen.2022.107148 0.0 MAR 2022 14 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics 0Z7SU 2023-03-23 WOS:000791274300001 0
J Jiang, JY; Weiss, M; Liu, SY; Baret, F Jiang, Jingyi; Weiss, Marie; Liu, Shouyang; Baret, Frederic Effective GAI is best estimated from reflectance observations as compared to GAI and LAI: Demonstration for wheat and maize crops based on 3D radiative transfer simulations FIELD CROPS RESEARCH English Article Effective GAI; Wheat; Maize; 3D radiative transfer model; Canopy reflectance LEAF-AREA INDEX; VEGETATION INDEXES; HEMISPHERICAL PHOTOGRAPHY; BIOPHYSICAL VARIABLES; CHLOROPHYLL CONTENT; TRANSFER MODELS; WINTER-WHEAT; GREEN LAI; CANOPY; RETRIEVAL The definition of LAI (Leaf Area Index) is important when deriving it from reflectance observation for model application and validation. Canopy reflectance and the corresponding quantities of LAI, PAI (Plant Area Index), GAI (Green Area Index) and effective GAI (GAI(eff )) are first calculated using a 3D radiative transfer model (RTM) applied to 3D wheat and maize architecture models. A range of phenological stages, leaf optical properties, soil reflectance, canopy structure and sun directions is considered. Several retrieval methods are compared, including vegetation indices (VIs) combined with a semi-empirical model, and 1D or 3D RTM combined with a machine learning inversion approach. Results show that GAI(eff ) is best estimated from remote sensing observations. The RTM inversion using a 3D model provides more accurate GAI(eff ) estimates compared with VIs and the 1D PRO-SAIL model with RMSE = 0.33 for wheat and RMSE= 0.43 for maize. GAI(eff )offers the advantage to be easily accessible from ground measurements at the decametric resolution. It was therefore concluded that the most efficient retrieval approach would be to use machine learning algorithms trained over paired GAI(eff ) and the corresponding canopy reflectance derived either from realistic 3D canopy models or from experimental measurements. [Jiang, Jingyi] Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China; [Jiang, Jingyi; Weiss, Marie; Liu, Shouyang; Baret, Frederic] Avignon Univ, UMR EMMAH, INRAE, F-84000 Avignon, France; [Liu, Shouyang] Nanjing Agr Univ, Acad Adv Interdisciplinary Studies, PheniX, Nanjing 210095, Peoples R China Beijing Forestry University; Avignon Universite; INRAE; Nanjing Agricultural University Jiang, JY (corresponding author), Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China. jiangjingyi1989@163.com Fundamental Research Funds for the Central Universities [2021ZY13]; National Natural Science Foundation of China [42101329]; Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS202115] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund of State Key Laboratory of Remote Sensing Science Acknowledgment This work was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. 2021ZY13) , the National Natural Science Foundation of China (Grant No. 42101329) , and Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS202115) . 72 0 0 6 19 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-4290 1872-6852 FIELD CROP RES Field Crop. Res. JUL 1 2022.0 283 108538 10.1016/j.fcr.2022.108538 0.0 APR 2022 12 Agronomy Science Citation Index Expanded (SCI-EXPANDED) Agriculture 1C5SE 2023-03-23 WOS:000793177900005 0
J Liu, QC; Xiao, L; Yang, JX; Chan, JCW Liu, Qichao; Xiao, Liang; Yang, Jingxiang; Chan, Jonathan Cheung-Wai Content-Guided Convolutional Neural Network for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Convolution; Kernel; Shape; Feature extraction; Convolutional neural networks; Principal component analysis; Hyperspectral sensors; Content-guided convolution; convolutional neural network (CNN); guided kernel; hyperspectral image (HSI) classification; latent guide map (LGM) SPECTRAL-SPATIAL CLASSIFICATION; LOGISTIC-REGRESSION; KERNEL Convolutional neural networks (CNNs) are of great interest and have demonstrated remarkable performance in hyperspectral images (HSIs) classification. However, due to the current configuration of the convolution layers with a fixed kernel shape, regular CNNs are inherently limited in modeling the diverse land-cover structures, particularly in the cross-classes edge regions, where irregular class boundaries would lead to high classification errors. To address this issue, we propose a content-guided CNN (CGCNN) for HSI classification. Compared with the shape-fixed kernel in the traditional CNN, the proposed content-guided convolution adaptively adjusts its kernel shape according to the spatial distribution of land covers. The content pattern is reflected by a latent guide map automatically learned from HSI. Such content-adaptive kernel with CGCNN could suppress the irregularity and unexpected features in class boundaries and, thus, improve the feature learning in cross-classes regions. Based on the content-guided convolution, a novel guided feature extraction unit (GFEU) is constructed for spectral-spatial feature learning of HSI. Finally, the CGCNN classification framework is established by stacking multiple GFEUs with dense connection, which is helpful for mitigating the gradient vanishing and increasing the robustness to overfitting. Extensive experiments on several HSIs demonstrate that the proposed approach possesses great details' preserving ability and its performance outperforms other state-of-the-art methods. [Liu, Qichao; Xiao, Liang; Yang, Jingxiang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China; [Xiao, Liang] Nanjing Univ Sci & Technol, Minist Educ, Key Lab Intelligent Percept & Syst High Dimens In, Nanjing 210094, Peoples R China; [Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Nanjing University of Science & Technology; Nanjing University of Science & Technology; Vrije Universiteit Brussel Xiao, L (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. qc.l@qq.com; xiaoliang@mail.njust.edu.cn; yang123jx@njust.edu.cn; jcheungw@etrovub.be Liu, Qichao/GOH-1414-2022; xiao, liang/G-2968-2010 Liu, Qichao/0000-0003-0134-9450; xiao, liang/0000-0003-0178-9384 National Natural Science Foundation of China [61871226, 61571230]; Jiangsu Provincial Social Developing Project [BE2018727]; Fundamental Research Funds for the Central Universities [30918011104]; National Major Research Plan of China [2016YFF0103604] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Provincial Social Developing Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Major Research Plan of China This work was supported in part by the National Natural Science Foundation of China under Grant 61871226 and Grant 61571230, in part by the Jiangsu Provincial Social Developing Project under Grant BE2018727, in part by the Fundamental Research Funds for the Central Universities under Grant 30918011104, and in part by the National Major Research Plan of China under Grant 2016YFF0103604. 49 25 29 6 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEPT 2020.0 58 9 6124 6137 10.1109/TGRS.2020.2974134 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology NH1SA 2023-03-23 WOS:000564455700010 0
J Liu, YW; Liu, X; Mu, XD; Hou, TW; Xu, JQ; Di Renzo, M; Al-Dhahir, N Liu, Yuanwei; Liu, Xiao; Mu, Xidong; Hou, Tianwei; Xu, Jiaqi; Di Renzo, Marco; Al-Dhahir, Naofal Reconfigurable Intelligent Surfaces: Principles and Opportunities IEEE COMMUNICATIONS SURVEYS AND TUTORIALS English Article Wireless networks; Communication system security; Wireless sensor networks; Optimization; Array signal processing; Tutorials; Performance evaluation; 6G; intelligent reflecting surfaces (IRSs); large intelligent surfaces (LISs); machine learning; performance optimization; reconfigurable intelligent surfaces (RISs); wireless networks NONORTHOGONAL MULTIPLE-ACCESS; REFLECTING SURFACE; WIRELESS COMMUNICATIONS; MISO COMMUNICATION; BEAMFORMING OPTIMIZATION; RESOURCE-ALLOCATION; CHANNEL ESTIMATION; ENERGY EFFICIENCY; PHASE-SHIFT; NETWORKS Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs),(1) have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multiantenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks. (1) Without loss of generality, we use the name of RIS in the remainder of this paper. [Liu, Yuanwei; Liu, Xiao; Xu, Jiaqi] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Mu, Xidong] Beijing Univ Posts & Telecommun, Minist Educ, Sch Artificial Intelligence, Beijing 100876, Peoples R China; [Mu, Xidong] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing 100876, Peoples R China; [Hou, Tianwei] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China; [Di Renzo, Marco] Univ Paris Saclay, CNRS, Cent Supelec, Lab Signaux & Syst, F-91192 Gif Sur Yvette, France; [Al-Dhahir, Naofal] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA University of London; Queen Mary University London; Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Beijing Jiaotong University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; University of Texas System; University of Texas Dallas Liu, YW (corresponding author), Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England. yuanwei.liu@qmul.ac.uk; x.liu@qmul.ac.uk; muxidong@bupt.edu.cn; 16111019@bjtu.edu.cn; jiaqi.xu@qmul.ac.uk; marco.di-renzo@universite-paris-saclay.fr; aldhahir@utdallas.edu Liu, Yuanwei/AAA-2444-2020; Aldhahir, Naofal/HCH-5192-2022; XU, JIAQI/ADL-8942-2022; Mu, Xidong/ABD-8992-2021 Liu, Yuanwei/0000-0002-6389-8941; XU, JIAQI/0000-0002-3539-181X; Mu, Xidong/0000-0001-8351-360X European Commission [871464, 101017011] European Commission(European CommissionEuropean Commission Joint Research Centre) The work of Marco Di Renzo was supported in part by the European Commission through the H2020 ARIADNE Project under Grant 871464 and through the H2020 RISE-6G Project under Grant 101017011. 181 173 174 39 99 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1553-877X IEEE COMMUN SURV TUT IEEE Commun. Surv. Tutor. 2021.0 23 3 1546 1577 10.1109/COMST.2021.3077737 0.0 32 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications UF3AJ Green Submitted 2023-03-23 WOS:000688449200005 0
J Zhang, Y; Farrugia, N; Bellec, P Zhang, Yu; Farrugia, Nicolas; Bellec, Pierre Deep learning models of cognitive processes constrained by human brain connectomes MEDICAL IMAGE ANALYSIS English Article fMRI; Cognitive decoding; Human connectome; graph neural network VISUAL WORKING-MEMORY; INDIVIDUAL-DIFFERENCES; STRUCTURAL COVARIANCE; NETWORKS; STATES; FMRI; REPRESENTATIONS; PARCELLATION Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database. Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale. (C) 2022 The Authors. Published by Elsevier B.V. [Zhang, Yu] Res Inst Artificial Intelligence, Zhejiang Lab, Hangzhou, Peoples R China; [Zhang, Yu; Bellec, Pierre] Ctr Rech Inst Univ Geriatrie Montreal, Montreal, PQ H3W 1W6, Canada; [Zhang, Yu; Bellec, Pierre] Univ Montreal, Dept Psychol, Montreal, PQ H3C 3J7, Canada; [Farrugia, Nicolas] IMT Atlantique, Dept Math & Elect Engn, Brest, France Zhejiang Laboratory; Universite de Montreal; Universite de Montreal; IMT - Institut Mines-Telecom; IMT Atlantique Zhang, Y (corresponding author), Artificial Intelligence Res Inst, Zhejiang Lab, Zhongtai St, Hangzhou 311100, Zhejiang, Peoples R China.;Bellec, P (corresponding author), Univ Montreal, Dept Psychol, 4565 Chemin Queen Mary, Montreal, PQ H3W 1W5, Canada. yuzhang2bic@gmail.com; pierre.bellec@gmail.com FARRUGIA, Nicolas/0000-0002-1159-3513 Science and Technology Innovation 2030 -Brain Science and Brain-Inspired Intelligence Project [2021ZD0200201, 2022ZD0211500]; Scientific Project of Zhejiang Lab [2021ND0PI01, 2022ND0AN01]; Courtois foundation; IVADO Postdoctoral Scholarships Program; Fonds de recherche du Quebec -Sante Science and Technology Innovation 2030 -Brain Science and Brain-Inspired Intelligence Project; Scientific Project of Zhejiang Lab; Courtois foundation; IVADO Postdoctoral Scholarships Program; Fonds de recherche du Quebec -Sante(Fonds de la Recherche en Sante du Quebec) This work was partially supported by the Science and Technology Innovation 2030 -Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200201, No. 2022ZD0211500, No. 2022ZD0211500), Scientific Project of Zhejiang Lab (No. 2021ND0PI01, No. 2022ND0AN01), Courtois foundation through the Courtois NeuroMod Project and the IVADO Postdoctoral Scholarships Program. PB is supported by a salary award of Fonds de recherche du Quebec -Sante, chercheur boursier junior 2. 60 2 2 12 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. AUG 2022.0 80 102507 10.1016/j.media.2022.102507 0.0 17 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging 4Y6DB 35738052.0 Green Submitted, hybrid 2023-03-23 WOS:000861615200001 0
J He, SX; Huang, ZB; Wang, XH; Fang, L; Li, SK; Zhang, Y; Zhang, GY He, Shixu; Huang, Zhibo; Wang, Xiaohan; Fang, Lin; Li, Shengkang; Zhang, Yong; Zhang, Gengyun SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters BIOINFORMATICS English Article Rapid increase of the data size in metagenome researches has raised the demand for new tools to process large datasets efficiently. To accelerate the metagenome profiling process in the scenario of big data, we developed SOAPMetaS, a marker gene-based multiple-sample metagenome profiling tool built on Apache Spark. SOAPMetaS demonstrates high performance and scalability to process large datasets. It can process 80 samples of FASTQ data, summing up to 416 GiB, in around half an hour; and the accuracy of species profiling results of SOAPMetaS is similar to that of MetaPhlAn2. SOAPMetaS can deal with a large volume of metagenome data more efficiently than common-used single-machine tools. [He, Shixu] Univ Chinese Acad Sci, BGI Educ Ctr, Shenzhen 518083, Peoples R China; [Huang, Zhibo; Wang, Xiaohan; Fang, Lin; Li, Shengkang; Zhang, Yong; Zhang, Gengyun] BGI Shenzhen, Shenzhen 518083, Peoples R China; [Fang, Lin; Zhang, Yong] Univ Copenhagen, Dept Biol, DK-2100 Copenhagen, Denmark Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Beijing Genomics Institute (BGI); University of Copenhagen Zhang, Y; Zhang, GY (corresponding author), BGI Shenzhen, Shenzhen 518083, Peoples R China.;Zhang, Y (corresponding author), Univ Copenhagen, Dept Biol, DK-2100 Copenhagen, Denmark. zhangyong2@genomics.cn; zhanggengyun@genomics.cn huang, zhibo/ABB-3600-2021 Zhang, Yong/0000-0001-9950-1793 Science Technology and Innovation Commission of Shenzhen Municipality [JCYJ20150831201123287]; Key Laboratory of Genomics,Ministry of Agriculture, BGI-Shenzhen, Shenzhen, China; Guangdong Provincial Key Laboratory of Core Collection of Crop Genetic Resources Research and Application, BGI-Shenzhen, Shenzhen, China Science Technology and Innovation Commission of Shenzhen Municipality; Key Laboratory of Genomics,Ministry of Agriculture, BGI-Shenzhen, Shenzhen, China; Guangdong Provincial Key Laboratory of Core Collection of Crop Genetic Resources Research and Application, BGI-Shenzhen, Shenzhen, China The authors thank Miss Xu, Huixin for her advice on the statistical analysis. The authors thank Miss Cai, Kaiye for sharing her knowledge of metagenomics. The authors also thank Mr. Fang, Chao and Mr. Lin, Yuxiang for their help on understanding IGC-related content. We need to thank the support from Science Technology and Innovation Commission of Shenzhen Municipality under grant No. JCYJ20150831201123287. We also need to thank two laboratories for their supports: Key Laboratory of Genomics,Ministry of Agriculture, BGI-Shenzhen, Shenzhen 518120, China; Guangdong Provincial Key Laboratory of Core Collection of Crop Genetic Resources Research and Application, BGI-Shenzhen, Shenzhen 518120, China. 13 0 1 0 26 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics APR 1 2021.0 37 7 1021 1023 10.1093/bioinformatics/btaa697 0.0 3 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics SI3EM 32766813.0 Bronze 2023-03-23 WOS:000654708400020 0
J Firoozbakhtian, A; Hosseini, M; Sheikholeslami, MN; Salehnia, F; Xu, GB; Rabbani, H; Sobhanie, E Firoozbakhtian, Ali; Hosseini, Morteza; Sheikholeslami, Mahsa Naghavi; Salehnia, Foad; Xu, Guobao; Rabbani, Hodjattallah; Sobhanie, Ebtesam Detection of COVID-19: A Smartphone-Based Machine-Learning- Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction ANALYTICAL CHEMISTRY English Article; Early Access GRAPHENE QUANTUM DOTS; ELECTROCHEMILUMINESCENCE SENSOR; OXIDE The unstoppable spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has severely threatened public health over the past 2 years. The current ubiquitously accepted method for its diagnosis provides sensitive detection of the virus; however, it is relatively time-consuming and costly, not to mention the need for highly skilled personnel. There is a clear need to develop novel computer-based diagnostic tools to provide rapid, cost-efficient, and time-saving detection in places where massive traditional testing is not practical. Here, we develop an electrochemiluminescence (ECL)-based detection system whose results are quantified as reverse transcriptase polymerase chain reaction (RT-PCR) cyclic threshold (CT) values. A concentration -dependent signal is generated upon the introduction of the virus to the electrode and is recorded with a smartphone camera. The ECL images are used to train machine learning algorithms, and a model using artificial neural networks (ANNs) for 45 samples was developed. The model demonstrated more than 90% accuracy in the diagnosis of 50 unknown real samples, detecting up to a CT value of 32 and a limit of detection (LOD) of 10-12 g mL-1 in the testing of artificial samples. [Firoozbakhtian, Ali; Hosseini, Morteza; Sobhanie, Ebtesam] Univ Tehran, Fac New Sci & Technol, Dept Life Sci Engn, Nanobiosensors Lab, Tehran 1439817435, Iran; [Sheikholeslami, Mahsa Naghavi] Univ Tehran, Coll Sci, Ctr Excellence Electrochem, Sch Chem, Tehran 1439817435, Iran; [Salehnia, Foad] Univ Rovira & Virgili, Dept Engn Elect, Escola Tecn Super Engn, Tarragona 43007, Spain; [Xu, Guobao] Chinese Acad Sci, Changchun Inst Appl Chem, State Key Lab Electroanalyt Chem, Changchun 130022, Peoples R China; [Xu, Guobao] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China; [Rabbani, Hodjattallah] ACECR, Avicenna Res Inst, Monoclonal Antibody Res Ctr, Tehran 1439817435, Iran; [Hosseini, Morteza] Natl Inst Genet Engn & Biotechnol NIGEB, Tehran 1439817435, Iran University of Tehran; University of Tehran; Universitat Rovira i Virgili; Chinese Academy of Sciences; Changchun Institute of Applied Chemistry, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Academic Center for Education, Culture & Research (ACECR) Hosseini, M (corresponding author), Univ Tehran, Fac New Sci & Technol, Dept Life Sci Engn, Nanobiosensors Lab, Tehran 1439817435, Iran.;Hosseini, M (corresponding author), Natl Inst Genet Engn & Biotechnol NIGEB, Tehran 1439817435, Iran. hosseini_m@ut.ac.ir Firoozbakhtian, Ali/HHZ-2416-2022; Hosseini, Morteza/HHY-8202-2022; xu, guo bao/J-2452-2017 Firoozbakhtian, Ali/0000-0002-3024-5258; N. sheikholeslami, Mahsa/0000-0001-8254-3153; Salehnia, Foad/0000-0002-0979-4690; xu, guo bao/0000-0001-9747-0575 Agence Universitaire de la Francophonie (AUF); Iran National Science Foundation and Chinese Academy of Sciences [INSF 99008701]; CAS-VPST Silk Road Science [GJHZ202125]; University of Tehran Agence Universitaire de la Francophonie (AUF); Iran National Science Foundation and Chinese Academy of Sciences; CAS-VPST Silk Road Science; University of Tehran(University of Tehran) This work was supported by Agence Universitaire de la Francophonie (AUF), Iran National Science Foundation and Chinese Academy of Sciences (INSF 99008701, CAS-VPST Silk Road Science, GJHZ202125), and the University of Tehran. 38 1 1 21 21 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0003-2700 1520-6882 ANAL CHEM Anal. Chem. 10.1021/acs.analchem.2c03502 0.0 NOV 2022 8 Chemistry, Analytical Science Citation Index Expanded (SCI-EXPANDED) Chemistry 6N9DP 36383743.0 2023-03-23 WOS:000889849400001 0
J Al-Waisy, AS; Mohammed, MA; Al-Fandawi, S; Maashi, MS; Garcia-Zapirain, B; Abdulkareem, KH; Mostafa, SA; Kumar, NM; Le, DN Al-Waisy, A. S.; Mohammed, Mazin Abed; Al-Fandawi, Shumoos; Maashi, M. S.; Garcia-Zapirain, Begonya; Abdulkareem, Karrar Hameed; Mostafa, S. A.; Kumar, Nallapaneni Manoj; Dac-Nhuong Le COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images CMC-COMPUTERS MATERIALS & CONTINUA English Article Coronavirus epidemic; deep learning; deep belief network; con-volutional deep belief network; chest radiography imaging Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision. [Al-Waisy, A. S.; Mohammed, Mazin Abed; Al-Fandawi, Shumoos] Univ Anbar, Coll Comp Sci & Informat Technol, Anbar 31001, Iraq; [Maashi, M. S.] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11451, Saudi Arabia; [Garcia-Zapirain, Begonya] Univ Deusto, eVIDA Lab, Avda Univ, Bilbao 48007 24, Spain; [Abdulkareem, Karrar Hameed] Al Muthanna Univ, Coll Agr, Samawah 66001, Iraq; [Mostafa, S. A.] Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Johor Baharu 86400, Malaysia; [Kumar, Nallapaneni Manoj] City Univ Hong Kong, Sch Energy & Environm, Kowloon, Hong Kong 83, Peoples R China; [Dac-Nhuong Le] Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam; [Dac-Nhuong Le] Duy Tan Univ, Fac Informat Technol, Danang 550000, Vietnam University of Anbar; King Saud University; University of Deusto; Al-Muthanna University; University of Tun Hussein Onn Malaysia; City University of Hong Kong; Duy Tan University; Duy Tan University Le, DN (corresponding author), Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam.;Le, DN (corresponding author), Duy Tan Univ, Fac Informat Technol, Danang 550000, Vietnam. Nhuongld@dhhp.edu.vn Abdulkareem, Karrar Hameed/V-1741-2017; Al-Waisy, Alaa S./AAZ-9675-2021; Mohammed, Mazin Abed/E-3910-2018; Manoj Kumar, Nallapaneni/R-8605-2018; Maashi, Mashael/AAJ-3501-2020; Le, Dac-Nhuong/E-4197-2016 Abdulkareem, Karrar Hameed/0000-0001-7302-2049; Al-Waisy, Alaa S./0000-0001-7544-2709; Mohammed, Mazin Abed/0000-0001-9030-8102; Manoj Kumar, Nallapaneni/0000-0002-7382-7784; Maashi, Mashael/0000-0003-0446-5430; Le, Dac-Nhuong/0000-0003-2601-2803 39 56 56 5 23 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2021.0 67 2 2409 2429 10.32604/cmc.2021.012955 0.0 21 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science QF2EU gold 2023-03-23 WOS:000616713000029 0
J Wang, ZL; Wang, QX; Han, YQ; Ma, Y; Zhao, H; Nowak, A; Li, JJ Wang, Zhilong; Wang, Qingxun; Han, Yanqiang; Ma, Yan; Zhao, Hua; Nowak, Andrzej; Li, Jinjin Deep learning for ultra-fast and high precision screening of energy materials ENERGY STORAGE MATERIALS English Article Multifunctional energy storage materials; Nanostructured materials; band gap; Transfer learning; ab initio calculations BAND-GAPS; DATABASE; REDUCTION; DISCOVERY; DESIGN; NIO Semiconductor materials for energy storage are the core and foundation of modern information society and play important roles in photovoltaic system, integrated circuit, spacecraft technology, lighting applications, and other fields. Unfortunately, due to the long experiment period and high calculation cost, the high-precision band gap (the basic characteristic parameter) of semiconductor is difficult to obtain, which hinders the development of new semiconductor materials. Since the traditional Perdew-Burke-Ernzerhof (PBE) functional not only requires a long calculation time, but also significantly underestimates the band gap, we developed a deep learning model that can predict the more precise Heyd-Scuseria-Ernzerhof (HSE06) band gaps in milliseconds for 1,503 binary metallic oxides, nitrides, and sulfides, with a mean absolute error (MAE) of 0.35 eV, a mean squared error (MSE) of 0.21 eV, and a coefficient of determination (R-2) of 0.98. Based on transfer learning, only < 5% of the data set (64 structures) was required to train the model and predict the band gaps of the remaining 1,439 structures. From the 1,503 candidate materials, we quickly identified 75 carrier transport materials, 33 electrode and electrocatalytic materials, 299 power switching materials, and 114 sensing materials. This work is the first to demonstrate the feasibility of transfer learning in band gap prediction, from the low-level PBE to the high-level HSE06 calculation, with a computation speed at least 10(4) times faster than the ab initio calculation. The proposed method could be further expanded to incorporate entire organic/inorganic crystal materials databases (> 10(6) crystals), which is of great significance for the screening and discovery of new semiconductor energy storage materials. [Wang, Zhilong; Wang, Qingxun; Han, Yanqiang; Li, Jinjin] Shanghai Jiao Tong Univ, Dept Micronanoelect, Minist Educ, Key Lab Thin Film & Microfabricat, Shanghai 200240, Peoples R China; [Ma, Yan; Zhao, Hua] Beijing Inst Tracking & Telecommun Technol, Beijing 100094, Peoples R China; [Nowak, Andrzej] Gdansk Univ Technol, Fac Chem, Narutowicza 11-12, PL-80233 Gdansk, Poland Shanghai Jiao Tong University; Fahrenheit Universities; Gdansk University of Technology Li, JJ (corresponding author), Shanghai Jiao Tong Univ, Dept Micronanoelect, Minist Educ, Key Lab Thin Film & Microfabricat, Shanghai 200240, Peoples R China. lijinjin@sjtu.edu.cn MA, YAN/HHN-2912-2022; Han, Yanqiang/AAY-5102-2020; Wang, Zhilong/AAU-1200-2020 Han, Yanqiang/0000-0001-5454-0617; Wang, Zhilong/0000-0002-1910-3654 National Natural Science Foundation of China [21901157]; SJTU Global Strategic Partnership Fund (2020 SJTU-HUJI) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); SJTU Global Strategic Partnership Fund (2020 SJTU-HUJI) The authors are grateful for the financial support provided by the National Natural Science Foundation of China (No.21901157), and the SJTU Global Strategic Partnership Fund (2020 SJTU-HUJI). 68 11 12 10 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8297 ENERGY STORAGE MATER Energy Storage Mater. AUG 2021.0 39 45 53 10.1016/j.ensm.2021.04.006 0.0 APR 2021 9 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science SN6GC 2023-03-23 WOS:000658385000004 0
J Golesorkhi, M; Gomez-Pilar, J; Tumati, S; Fraser, M; Northoff, G Golesorkhi, Mehrshad; Gomez-Pilar, Javier; Tumati, Shankar; Fraser, Maia; Northoff, Georg Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization COMMUNICATIONS BIOLOGY English Article The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region's time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex's temporal hierarchy converges with its spatial core-periphery hierarchy. Golesorkhi et al. use a combination of magnetoencephalography data, machine learning and simulation to investigate intrinsic neural timescales during resting and task states. They provide insight into how the temporal hierarchy of the human cortex converges with its spatial core-periphery hierarchy. [Golesorkhi, Mehrshad] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada; [Golesorkhi, Mehrshad; Tumati, Shankar; Northoff, Georg] Royal Ottawa Mental Hlth Ctr, Inst Mental Hlth Res, Mind Brain Imaging & Neuroeth Res Unit, Ottawa, ON, Canada; [Gomez-Pilar, Javier] Univ Valladolid, Biomed Engn Grp, Valladolid, Spain; [Gomez-Pilar, Javier] Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI, Madrid, Spain; [Tumati, Shankar] Univ Toronto, Sunnybrook Res Inst, Neuropsychopharmacol Res Grp, Toronto, ON, Canada; [Fraser, Maia] Univ Ottawa, Dept Math & Stat, Ottawa, ON, Canada; [Northoff, Georg] Hangzhou Normal Univ, Ctr Cognit & Brain Disorders, Hangzhou, Peoples R China; [Northoff, Georg] Zhejiang Univ, Mental Hlth Ctr, Sch Med, Hangzhou, Zhejiang, Peoples R China University of Ottawa; University of Ottawa; Universidad de Valladolid; CIBER - Centro de Investigacion Biomedica en Red; CIBERBBN; University of Toronto; Sunnybrook Health Science Center; Sunnybrook Research Institute; University of Ottawa; Hangzhou Normal University; Zhejiang University Northoff, G (corresponding author), Royal Ottawa Mental Hlth Ctr, Inst Mental Hlth Res, Mind Brain Imaging & Neuroeth Res Unit, Ottawa, ON, Canada.;Northoff, G (corresponding author), Hangzhou Normal Univ, Ctr Cognit & Brain Disorders, Hangzhou, Peoples R China.;Northoff, G (corresponding author), Zhejiang Univ, Mental Hlth Ctr, Sch Med, Hangzhou, Zhejiang, Peoples R China. georg.northoff@theroyal.ca Gomez-Pilar, Javier/B-9123-2018; Tumati, Shankar/AAD-2092-2020 Gomez-Pilar, Javier/0000-0001-7882-7890; Tumati, Shankar/0000-0001-5703-2044 European Union [785907]; UMRF; uOBMRI; CIHR; PSI; NSERC [ES/T01279X/1]; SHERRC [ES/T01279X/1]; CIHR [ES/T01279X/1] European Union(European Commission); UMRF; uOBMRI; CIHR(Canadian Institutes of Health Research (CIHR)); PSI; NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); SHERRC; CIHR(Canadian Institutes of Health Research (CIHR)) This research has received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). G.N. is grateful for funding provided by UMRF, uOBMRI, CIHR and PSI. We are also grateful to Chris J. Honey for giving useful suggestions. We are also grateful to CIHR, NSERC, and SHERRC for supporting our tri-council grant from the Canada-UK Artificial Intelligence (AI) Initiative `The self as agent-environment nexus: crossing disciplinary boundaries to help human selves and anticipate artificial selves' (ES/T01279X/1) (together with Karl J. Friston from the UK). 56 22 22 1 8 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2399-3642 COMMUN BIOL Commun. Biol. MAR 4 2021.0 4 1 277 10.1038/s42003-021-01785-z 0.0 14 Biology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics QT4UR 33664456.0 gold, Green Published, Green Submitted 2023-03-23 WOS:000626584900001 0
J Chen, WS; Demirkol, I; Mostafa, M; Perotti, A Chen, Wanshi; Demirkol, Ilker; Mostafa, Miraj; Perotti, Alberto MOBILE COMMUNICATIONS AND NETWORKS IEEE COMMUNICATIONS MAGAZINE English Editorial Material Special sections and issues; 5G mobile communication; Physical layer; Machine learning; Communication networks One of the most captivating aspects of last-generation mobile communication networks is their multitude of built-in features conceived to address use cases and applications in a variety of domains. As each feature brings about advantages to address applications demands, such a multi-faceted technology puts a number of new challenges in front of researchers and designers. [Chen, Wanshi] Qualcomm Inc, Technol, San Diego, CA 92121 USA; [Demirkol, Ilker] Univ Politecn Cataluna, Dept Min Ind & ICT Syst Engn, Barcelona, Spain; [Mostafa, Miraj] Amer Tower Corp, Network Engn, Bhubaneswar, Odisha, India; [Perotti, Alberto] Huawei Technol, Shenzhen, Guangdong, Peoples R China Qualcomm; Universitat Politecnica de Catalunya; Huawei Technologies Chen, WS (corresponding author), Qualcomm Inc, Technol, San Diego, CA 92121 USA. wanshic@qti.qualcomm.com; ilker.demirkol@upc.edu; miraj.mostafa@americantower.com; alberto.perotti@ieee.org Chen, Wanshi/0000-0001-5061-2177 0 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. NOV 2021.0 59 11 36 37 10.1109/MCOM.2021.9665442 0.0 2 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications XY1KM Bronze 2023-03-23 WOS:000736740400018 0
J Chen, WJ; Ogiela, U; Zhang, JD Chen, Weijun; Ogiela, Urszula; Zhang, Jindan Customer-oriented sales modeling strategy in a big data environment CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE English Article customer oriented; small profits and quick returns; turnover; profit margin; discount rate; big data analysis In order to provide personally oriented solutions in sales strategies for different retailers in the fierce market competition,data mining is being increasingly used in retail industry. We use customer-oriented data mining technology to extract valuable information from a great amount of historical commercial data, so that we can provide different sales promotions for merchants to extend the business. Small profits and quick returns is a popular sales strategy to increase sales volume by many shopping malls. By using statistical modeling, we establish a mathematical model to analysis the relationships among the discount rate,the turnover and the profit margin according to the historical sales data of a shopping mall in recent two years. Base on this,the paper conducts the empirical research on small profits and quick returns and make sales recommendations on promotions. Our analysis indicates that the effect of discount rate on turnover is greater than the effect of profit rate on turnover in the retail industry. It is also necessary to consider the different nature of goods. The final computing results are also influenced by different types of goods. Furthermore, we consider the classification of goods into three types: the sell-well goods, the common goods, and the unsalable goods and analyze them in the similar framework. The paper shows that there are different characteristics for the different types. [Chen, Weijun] Jiaxing Vocat & Tech Coll, Dept Basic Educ, Jiaxing, Peoples R China; [Ogiela, Urszula] Pedag Univ Krakow, Krakow, Poland; [Zhang, Jindan] Xianyang Vocat Syst Coll, Sch Elect Informat, Xianyang, Peoples R China Pedagogical University of Cracow Chen, WJ (corresponding author), Jiaxing Vocat & Tech Coll, Dept Basic Educ, Jiaxing, Peoples R China. cwj0617@163.com Chen, Wei/GZK-7348-2022 Zhang, Jindan/0000-0002-1499-8460; chen, weijun/0000-0002-5300-8407 23 1 1 3 20 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1532-0626 1532-0634 CONCURR COMP-PRACT E Concurr. Comput.-Pract. Exp. OCT 10 2021.0 33 19 SI e6366 10.1002/cpe.6366 0.0 MAY 2021 9 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science UP9KH 2023-03-23 WOS:000651010100001 0
J Chatterjee, S; Nguyen, B; Ghosh, SK; Bhattacharjee, KK; Chaudhuri, S Chatterjee, Sheshadri; Nguyen, Bang; Ghosh, Soumya Kanti; Bhattacharjee, Kalyan Kumar; Chaudhuri, Sumana Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations BOTTOM LINE English Article Indian organizations; Technology adoption; Hedonic attitude; Utilitarian attitude; AI integrated CRM system INFORMATION-TECHNOLOGY; PLANNED BEHAVIOR; USER ACCEPTANCE; MODEL; BUSINESS; MANAGEMENT; LEADERSHIP; EXTENSION; FRAMEWORK; EFFICACY Purpose The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations. Design/methodology/approach To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation using a survey by considering 308 usable respondents. Findings The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%. Research limitations/implications By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations. Practical implications The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations. Originality/value Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness. [Chatterjee, Sheshadri; Ghosh, Soumya Kanti] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, W Bengal, India; [Nguyen, Bang] Univ Southern Denmark, Odense, Denmark; [Nguyen, Bang] Shanghai Univ, Ctr Innovat & Entrepreneurship, Shanghai, Peoples R China; [Bhattacharjee, Kalyan Kumar] Indian Inst Technol Delhi, New Delhi, India; [Chaudhuri, Sumana] Mumbai Univ, Dept Econ & Management Studies DSIMS, Mumbai, Maharashtra, India Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; University of Southern Denmark; Shanghai University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi; University of Mumbai Chatterjee, S (corresponding author), Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, W Bengal, India. sheshadri.academic@gmail.com; bang.london@gmail.com; skg@cse.iitkgp.ac.in; kalyan_bhat@hotmail.com; sumana.chaudhuri@dsims.org.in Bhattacharjee, Kalyan/AAM-8341-2021 Bhattacharjee, Kalyan/0000-0001-9844-6091; Chatterjee, Sheshadri/0000-0003-1075-5549 77 35 35 5 28 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0888-045X 2054-1724 BOTTOM LINE Bottom Line NOV 14 2020.0 33 4 359 375 10.1108/BL-08-2020-0057 0.0 OCT 2020 17 Information Science & Library Science Emerging Sources Citation Index (ESCI) Information Science & Library Science OY0TX 2023-03-23 WOS:000582266700001 0
J Jacob, AW; Vicente-Guijalba, F; Lopez-Martinez, C; Lopez-Sanchez, JM; Litzinger, M; Kristen, H; Mestre-Quereda, A; Ziolkowski, D; Lavalle, M; Notarnicola, C; Suresh, G; Antropov, O; Ge, SJ; Praks, J; Ban, YF; Pottier, E; Franquet, JJM; Duro, J; Engdahl, ME Jacob, Alexander W.; Vicente-Guijalba, Fernando; Lopez-Martinez, Carlos; Lopez-Sanchez, Juan M.; Litzinger, Marius; Kristen, Harald; Mestre-Quereda, Alejandro; Ziolkowski, Dariusz; Lavalle, Marco; Notarnicola, Claudia; Suresh, Gopika; Antropov, Oleg; Ge, Shaojia; Praks, Jaan; Ban, Yifang; Pottier, Eric; Franquet, Jordi Joan Mallorqui; Duro, Javier; Engdahl, Marcus E. Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Copernicus; interferometric coherence; land cover mapping; Sentinel-1; synthetic aperture radar (SAR) CLASSIFICATION; MACHINE This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain-interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Donana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered. [Jacob, Alexander W.; Litzinger, Marius; Kristen, Harald; Notarnicola, Claudia] Eurac Res, Inst Earth Observat, I-39100 Bolzano, Italy; [Vicente-Guijalba, Fernando; Duro, Javier] Dares Technol, Barcelona 08860, Spain; [Lopez-Martinez, Carlos; Franquet, Jordi Joan Mallorqui] Univ Politecn Cataluna, Signal Theory & Commun Dept, ES-08034 Barcelona, Spain; [Lopez-Sanchez, Juan M.; Mestre-Quereda, Alejandro] Univ Alicante, Dept Phys Syst Engn & Signal Theory, Alicante 03690, Spain; [Ziolkowski, Dariusz] Inst Geodesy & Cartog, PL-02679 Warsaw, Poland; [Lavalle, Marco] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA; [Suresh, Gopika] Fed Agcy Cartog & Geodesy, D-60598 Frankfurt, Germany; [Antropov, Oleg] VTT Tech Res Ctr Finland, Espoo 02044, Finland; [Ge, Shaojia] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing, Peoples R China; [Praks, Jaan] Aalto Univ, Dept Elect & Nanoengn, Sch Elect Engn, Helsinki 02150, Finland; [Ban, Yifang] KTH Royal Inst Technol, Div Geoinformat, Dept Urban Planning & Environm, S-11428 Stockholm, Sweden; [Pottier, Eric] Univ Rennes 1, Inst Elect & Telecommun Rennes, F-35000 Rennes, France; [Engdahl, Marcus E.] European Space Agcy, European Space Res Inst, I-00044 Frascati, Italy European Academy of Bozen-Bolzano; Universitat Politecnica de Catalunya; Universitat d'Alacant; Institute of Geodesy & Cartography; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); VTT Technical Research Center Finland; Nanjing University of Science & Technology; Aalto University; Royal Institute of Technology; Universite de Rennes; European Space Agency Jacob, AW (corresponding author), Eurac Res, Inst Earth Observat, I-39100 Bolzano, Italy. alexander.jacob@eurac.edu; fvicente@dares.tech; carlos.lopez@tsc.upc.edu; juanma.lopez@ua.es; marius.litzinger@gmail.com; haraldkristen@posteo.at; alejandro.mestre@ua.es; Dariusz.Ziolkowski@igik.edu.pl; marco.lavalle@jpl.nasa.gov; claudia.notarnicola@eurac.edu; gopika7787@gmail.com; oleg.antropov@vtt.fi; geshaojia@njust.edu.cn; jaan.praks@aalto.fi; yifang@kth.se; eric.pottier@univ-rennes1.fr; mallorqui@tsc.upc.es; jduro@dares.tech; marcus.engdahl@esa.int Mallorquí, Jordi J./F-6594-2013; Lopez-Martinez, Carlos/J-7446-2012; Antropov, Oleg/S-7200-2019; Lopez-Sanchez, Juan M M/C-2135-2011; Praks, Jaan/M-4488-2016 Mallorquí, Jordi J./0000-0002-9424-1889; Lopez-Martinez, Carlos/0000-0002-1366-9446; Antropov, Oleg/0000-0001-8576-404X; Lopez-Sanchez, Juan M M/0000-0002-4216-5175; Suresh, Gopika/0000-0001-5185-6050; Lavalle, Marco/0000-0002-9858-6454; Notarnicola, Claudia/0000-0003-1968-0125; Ge, Shaojia/0000-0002-9655-1207; Praks, Jaan/0000-0001-7466-3569; Ziolkowski, Dariusz/0000-0002-1331-0919; POTTIER, Eric/0000-0002-5165-1423; Mestre-Quereda, Alejandro/0000-0003-4437-2095; Jacob, Alexander/0000-0003-4434-7244 European Space Agency via the ESA SEOM Program ITT [AO/1-8306/15/I-NB]; European Commission [778360]; Spanish Ministry of Science, Innovation and Universities; State Agency of Research (AEI); European Funds for Regional Development (EFRD) [TEC2017-85244-C2-1-P] European Space Agency via the ESA SEOM Program ITT; European Commission(European CommissionEuropean Commission Joint Research Centre); Spanish Ministry of Science, Innovation and Universities(Spanish Government); State Agency of Research (AEI); European Funds for Regional Development (EFRD)(European Commission) This work was supported in part by the European Space Agency via the ESA SEOM Program ITT under Grant AO/1-8306/15/I-NB SEOM-S14SCI Land, in part by the European Commission under Grant 778360 for the possibility of funding exchange of researchers between DARES Technologies and EURAC Research with the H2020 Cost Action Project HERCULES, and in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P. 51 51 53 3 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2020.0 13 535 552 10.1109/JSTARS.2019.2958847 0.0 18 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology LE5PY Green Submitted, Green Published, gold 2023-03-23 WOS:000526772200003 0
J He, PX; Jiang, CL; Xu, ZX; Shen, CA He, Peixu; Jiang, Cuiling; Xu, Zhixing; Shen, Chuangang Knowledge Hiding: Current Research Status and Future Research Directions FRONTIERS IN PSYCHOLOGY English Review knowledge hiding; systematic literature review; future research directions; content analysis; bibliometric analysis; descriptive analysis MODERATING ROLE; ARTIFICIAL-INTELLIGENCE; WORKPLACE DEVIANCE; EMPIRICAL-EVIDENCE; MEDIATING ROLE; BEHAVIOR; CONSEQUENCES; TEAMS; ANTECEDENTS; PERFORMANCE This article provides a review of scientific articles addressing the topic of knowledge hiding in organizations. Based on a descriptive analysis, bibliometric analysis, and content analysis of a sample of 81 articles published in the academic journals in the Web of Science from 2012 to 2020, we identify the main areas and current dynamics of knowledge hiding research. Our results show that the central research themes of knowledge hiding include five clusters: concept and dimensions, antecedents, consequences, theories, and influence mechanisms. Based on our findings, we suggest future research should further develop the concept and dimensions of knowledge hiding; probe deeper into the consequences of knowledge hiding; explore multilateral, cross-level, and collective knowledge hiding; employ innovative theoretical perspectives and research methods to study knowledge hiding; and address how cultural and other contextual factors may shape the knowledge hiding behavior. [He, Peixu; Shen, Chuangang] Huaqiao Univ, Business Sch, Quanzhou, Peoples R China; [Jiang, Cuiling] Kedge Business Sch, Dept Management, Talence, France; [Xu, Zhixing] Beijing Normal Univ, Business Sch, Beijing, Peoples R China Huaqiao University; Kedge Business School; Beijing Normal University Shen, CA (corresponding author), Huaqiao Univ, Business Sch, Quanzhou, Peoples R China.;Xu, ZX (corresponding author), Beijing Normal Univ, Business Sch, Beijing, Peoples R China. xuzhixing@bnu.edu.cn; psychshen@hqu.edu.cn JIANG, Cuiling/GLS-9101-2022 JIANG, Cuiling/0000-0002-8117-1113 Huaqiao University's Academic Project - Fundamental Research Funds for the Central Universities [20SKGC-QT02]; National Natural Science Foundation of China [72172048] Huaqiao University's Academic Project - Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Funding was provided by Huaqiao University's Academic Project Supported by the Fundamental Research Funds for the Central Universities (20SKGC-QT02) and the National Natural Science Foundation of China (72172048). 83 38 38 76 172 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. OCT 29 2021.0 12 748237 10.3389/fpsyg.2021.748237 0.0 19 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology WW6JD 34777143.0 gold 2023-03-23 WOS:000718019400001 0
C Borijindakul, P; Jinuntuya, N; Manoonpong, P Yu, H; Liu, J; Liu, L; Ju, Z; Liu, Y; Zhou, D Borijindakul, Pongsiri; Jinuntuya, Noparit; Manoonpong, Poramate Haptic Feedback with a Reservoir Computing-Based Recurrent Neural Network for Multiple Terrain Classification of a Walking Robot INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT IV Lecture Notes in Artificial Intelligence English Proceedings Paper 12th International Conference on Intelligent Robotics and Applications (ICIRA) AUG 08-11, 2019 Shenyang, PEOPLES R CHINA Natl Nat Sci Fdn China,Springer,Chinese Acad Sci, Shenyang Inst Automat,Huazhong Univ Sci & Technol,Shanghai Jiao Tong Univ,Univ Portsmouth,Shenyang Assoc Sci & Technol Terrain classification; Soft terrains; Haptic feedback; Neural networks; Walking machines Terrain classification is an important feature for walking robots because it allows the robots to stably move and operate on the terrain. Different terrain classification techniques have been developed. The techniques include the use of different exteroceptive and proprioceptive sensors with different classification methods. Whereas these techniques have been widely used to classify flat, hard, and rough terrains, their application to soft terrains has not been fully addressed. Achieving soft-terrain classification will expand the operational range of walking robots. Thus, in this study, we propose a new technique to classify various terrains including soft ones. The technique exploits haptic feedback (expressed only through ground contact force measurement of a legged robot) and neurodynamics with the temporal memory of a reservoir computing-based recurrent neural network. We used six different terrains to evaluate the performance of the proposed technique. The terrains include sand (loose ground), foams with different softness levels (soft ground), and floor (hard ground). The experimental results show that we can successfully classify all terrains with an accuracy of above 70%. Furthermore, owing to the temporal memory of the network, if the haptic feedback is transiently missing, the network will be still be able to classify the terrain considerably well. [Borijindakul, Pongsiri; Manoonpong, Poramate] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Inst Bioinspired Struct & Surface Engn, Nanjing, Peoples R China; [Jinuntuya, Noparit] Kasetsart Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Manoonpong, Poramate] Univ Southern Denmark, MaerskMc Kinney Moller Inst, CBR Embodied AI & Neurorobot Lab, Odense M, Denmark Nanjing University of Aeronautics & Astronautics; Kasetsart University; University of Southern Denmark Manoonpong, P (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Inst Bioinspired Struct & Surface Engn, Nanjing, Peoples R China.;Manoonpong, P (corresponding author), Univ Southern Denmark, MaerskMc Kinney Moller Inst, CBR Embodied AI & Neurorobot Lab, Odense M, Denmark. kobekang@nuaa.edu.cn; poma@nuaa.edu.cn Capacity Building on Academic Competency of KU Students from Kasetsart University, Thailand; Thousand Talents program of China; National Natural Science Foundation of China [51861135306] Capacity Building on Academic Competency of KU Students from Kasetsart University, Thailand; Thousand Talents program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Capacity Building on Academic Competency of KU Students from Kasetsart University, Thailand, the Thousand Talents program of China, and the National Natural Science Foundation of China (Grant No. 51861135306). 18 0 0 0 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-27538-9; 978-3-030-27537-2 LECT NOTES ARTIF INT 2019.0 11743 233 244 10.1007/978-3-030-27538-9_20 0.0 12 Computer Science, Artificial Intelligence; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics BR5IE 2023-03-23 WOS:000655487600020 0
J Lin, H; Leon, JI; Luo, WS; Marquez, A; Liu, JX; Vazquez, S; Franquelo, LG Lin, Hao; Leon, Jose I.; Luo, Wensheng; Marquez, Abraham; Liu, Jianxing; Vazquez, Sergio; Franquelo, L. G. Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters ENERGIES English Article three-level neutral-point-clamped converter; integral sliding-mode control; extended state observer; radial basis function neural network MODULATION TECHNIQUES; RECTIFIER; TRACKING; SYSTEMS; DESIGN Three-level neutral-point-clamped (NPC) converter is widely used in energy conversion systems due to its good properties for high-power systems presenting output waveforms with reduced harmonic distortion. To obtain better system performance, an integral sliding-mode control (ISMC)-based direct power control (DPC) strategy is proposed for NPC converters. The controller achieves three objectives. First, an extended state observer (ESO)-based ISMC strategy, to enforce the active and reactive power to their reference values, is applied in the power tracking loop. ESO is used to reduce the influence of parameter uncertainties. Next, in the voltage regulation loop, a radial basis function neural network (RBFNN)-based adaptive ISMC strategy is applied to regulate the DC-link voltage. RBFNN is used to estimate the load variation, which is considered as a disturbance, to improve the system disturbance rejection ability. An adaptive law is used in the controller to reduce the chattering of reference active power which can reduce the current harmonic distortion. Finally, a proportional-integral (PI) control strategy is applied in the voltage balancing loop to achieve voltage balance between two DC-link capacitors. Experimental results show the effectiveness and superiority of the proposed control strategy for the NPC power converter compared with PI-based DPC strategy. [Lin, Hao; Leon, Jose I.; Luo, Wensheng; Liu, Jianxing; Franquelo, L. G.] Harbin Inst Technol, Dept Control Sci & Engn, Harbin 150001, Peoples R China; [Leon, Jose I.; Marquez, Abraham; Vazquez, Sergio; Franquelo, L. G.] Univ Seville, Elect Engn Dept, Seville 41004, Spain Harbin Institute of Technology; University of Sevilla Leon, JI (corresponding author), Harbin Inst Technol, Dept Control Sci & Engn, Harbin 150001, Peoples R China.;Leon, JI (corresponding author), Univ Seville, Elect Engn Dept, Seville 41004, Spain. haolin@hit.edu.cn; jileon@us.es; wensheng.luo@hit.edu.cn; amarquez@ieee.org; jx.liu@hit.edu.cn; sergi@us.es; lgfranquelo@us.es Franquelo, Leopoldo Garcia/D-5450-2009; Vazquez, Sergio/C-1669-2016; Marquez Alcaide, Abraham/H-1781-2018 Franquelo, Leopoldo Garcia/0000-0002-1976-9747; Vazquez, Sergio/0000-0002-7438-8904; Marquez Alcaide, Abraham/0000-0003-1647-7527 National Natural Science Foundation of China [61525303, 41772377, 61673130]; Top-Notch Young Talents Program of China; State grid heilongjiang electric power company limited; Self-Planned Task of State Key Laboratory of Robotics and System (HIT) [SKLRS201806B] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Top-Notch Young Talents Program of China; State grid heilongjiang electric power company limited; Self-Planned Task of State Key Laboratory of Robotics and System (HIT) This work was supported in part by the National Natural Science Foundation of China (61525303, 41772377 and 61673130), Top-Notch Young Talents Program of China (Ligang Wu), State grid heilongjiang electric power company limited funded project and the Self-Planned Task of State Key Laboratory of Robotics and System (HIT) (SKLRS201806B). 33 10 10 1 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies JAN 2020.0 13 1 227 10.3390/en13010227 0.0 20 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels KV4AW Green Published, gold, Green Submitted 2023-03-23 WOS:000520425800227 0
J Yang, L; Wang, YJ; Kloeden, PE Yang, Lin; Wang, Yejuan; Kloeden, Peter E. Pullback Exponential Attractors for Non-autonomous Recurrent Neural Networks with Discrete and Distributed Time-Varying Delays JOURNAL OF DYNAMICS AND DIFFERENTIAL EQUATIONS English Article Infinite lattice; Non-autonomous recurrent neural network; Variable delays; Pullback exponential attractors First some sufficient conditions are presented for the existence and the construction of pullback exponential attractors for infinite dimensional non-autonomous dynamical systems with delays. This abstract result is then used to establish the existence of pullback exponential attractors for an infinite lattice model of non-autonomous recurrent neural networks with discrete and distributed time-varying delays. [Yang, Lin; Wang, Yejuan] Lanzhou Univ, Sch Math & Stat, Gansu Key Lab Appl Math & Complex Syst, Lanzhou 730000, Peoples R China; [Kloeden, Peter E.] Univ Tubingen, Math Inst, D-72076 Tubingen, Germany Lanzhou University; Eberhard Karls University of Tubingen Wang, YJ (corresponding author), Lanzhou Univ, Sch Math & Stat, Gansu Key Lab Appl Math & Complex Syst, Lanzhou 730000, Peoples R China. yangl17@lzu.edu.cn; wangyj@lzu.edu.cn; kloeden@math.uni-frankfurt.de National Natural Science Foundation of China [41875084] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant No. 41875084. 26 0 0 1 9 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1040-7294 1572-9222 J DYN DIFFER EQU J. Dyn. Differ. Equ. MAR 2023.0 35 1 303 327 10.1007/s10884-021-09991-3 0.0 APR 2021 25 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 9E1CL 2023-03-23 WOS:000638831600001 0
C Yu, L; Hannuksela, M; Tillo, T; Lin, CY; Gabbouj, M IEEE Yu, Li; Hannuksela, Miska; Tillo, Tammam; Lin, Chunyu; Gabbouj, Moncef Is the transmission of depth data always necessary for 3D video streaming? 2018 EIGHTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) International Conference on Image Processing Theory Tools and Applications English Proceedings Paper 8th International Conference on Image Processing Theory, Tools and Applications (IPTA) NOV 07-10, 2018 Xian, PEOPLES R CHINA IEEE UKRI Comp Chapter,IEEE Xian Sect,European Assoc Signal Proc,NW Polytechn Univ,NW Univ,Key Lab Aerosp Informat Percept & Photoectr Control Depth map; Depth/Disparity estimation; Multi-view video; Stereo video; Free-viewpoint; Neural network MULTIVIEW-VIDEO; VIEW SYNTHESIS Depth data is of vital importance in the 3D video streaming, which allows the flexible rendering of views at arbitrary viewpoints. Given the importance of the depth data, the Multi-view Video plus Depth (MVD) format has been conventionally used. In the MVD format, the depth data is transmitted along with the texture data. In this work, we argue that the transmission of the depth data is not necessary in cases, when 1) the bandwidth is limited and 2) viewpoint switching is not frequent. We propose that the depth transmission could be replaced by a receiver-side unit that can estimate the depth from the received multi-view videos. This replacement does not only spare the bandwidth dedicated for the transmission of the depth, but also achieves a competitive rate-distortion performance with the MVD method. [Yu, Li; Gabbouj, Moncef] Tampere Univ Technol, Signal Proc Lab, Tampere, Finland; [Hannuksela, Miska] Nokia Technol, Tampere, Finland; [Tillo, Tammam] Libera Univ Bolzano Bozen, Fac Comp Sci, Bolzano, Italy; [Lin, Chunyu] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China Tampere University; Nokia Corporation; Nokia Finland; Free University of Bozen-Bolzano; Beijing Jiaotong University Yu, L (corresponding author), Tampere Univ Technol, Signal Proc Lab, Tampere, Finland. li.yu@tut.fi; miska.hannuksela@nokia.com; Tammam.Tillo@unibz.it; cylin@bjtu.edu.cn; moncef.gabbouj@tut.fi Gabbouj, Moncef/G-4293-2014; Lin, Chunyu/AAI-5185-2021 Gabbouj, Moncef/0000-0002-9788-2323; Lin, Chunyu/0000-0003-2847-0349 Business Finland [IMD-4072] Business Finland This work has been funded by Business Finland, within Project IMD-4072. 16 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2154-512X 978-1-5386-6428-5 INT CONF IMAG PROC 2018.0 247 251 5 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BM2PI 2023-03-23 WOS:000461275700044 0
C Liu, X; Shi, HL; Chen, HY; Yu, ZT; Li, XB; Zhao, GY IEEE COMP SOC Liu, Xin; Shi, Henglin; Chen, Haoyu; Yu, Zitong; Li, Xiaobai; Zhao, Guoying iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF DATABASE We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI. [Liu, Xin] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Shi, Henglin; Chen, Haoyu; Yu, Zitong; Li, Xiaobai; Zhao, Guoying] Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China; [Liu, Xin] Univ Oulu, Oulu, Finland University of Oulu; Tianjin University; University of Oulu Zhao, GY (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China. Shi, Henglin/GQI-2735-2022; Liu, Xin/AAD-5166-2019 Shi, Henglin/0000-0001-5884-8475; Liu, Xin/0000-0002-2242-6139; Chen, Haoyu/0000-0003-3267-2664 Academy of Finland; KAUTE foundation; National Natural Science Foundation of China Academy of Finland(Academy of Finland); KAUTE foundation; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This paper is supported by Academy of Finland, KAUTE foundation, and National Natural Science Foundation of China. 99 10 10 2 10 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 10626 10637 10.1109/CVPR46437.2021.01049 0.0 12 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5UT Green Submitted, Green Accepted 2023-03-23 WOS:000742075000062 0
J Carela-Espanol, V; Barlet-Ros, P; Bifet, A; Fukuda, K Carela-Espanol, Valentin; Barlet-Ros, Pere; Bifet, Albert; Fukuda, Kensuke A streaming flow-based technique for traffic classification applied to 12+1 years of Internet traffic TELECOMMUNICATION SYSTEMS English Article Traffic classification; Machine learning; Stream classification; Hoeffding adaptive tree; Network monitoring IDENTIFICATION The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e., they build a model or a set of patterns from a static, invariable dataset). However, very little work has addressed the practical limitations that arise when facing a more realistic scenario with an infinite, continuously evolving stream of network traffic flows. In this paper, we propose a streaming flow-based classification solution based on Hoeffding Adaptive Tree, a machine learning technique specifically designed for evolving data streams. The main novelty of our proposal is that it is able to automatically adapt to the continuous evolution of the network traffic without storing any traffic data. We apply our solution to a 12 + 1 year-long dataset from a transit link in Japan, and show that it can sustain a very high accuracy over the years, with significantly less cost and complexity than existing alternatives based on static learning algorithms, such as C4.5. [Carela-Espanol, Valentin] UPC BarcelonaTech, Broadband Commun Res Grp CBA, Barcelona, Spain; [Barlet-Ros, Pere] UPC BarcelonaTech, Comp Architecture Dept, Barcelona, Spain; [Bifet, Albert] HUAWEI Noahs Ark Lab, Shatin, Hong Kong, Peoples R China; [Fukuda, Kensuke] NII, Tokyo, Japan Universitat Politecnica de Catalunya; Universitat Politecnica de Catalunya; Huawei Technologies; Research Organization of Information & Systems (ROIS); National Institute of Informatics (NII) - Japan Carela-Espanol, V (corresponding author), UPC BarcelonaTech, Broadband Commun Res Grp CBA, Barcelona, Spain. vcarela@ac.upc.edu; pbarlet@ac.upc.edu; bifet.albert@huawei.com; kensuke@nii.ac.jp Bifet, Albert/E-4984-2017; Barlet-Ros, Pere/L-5022-2014 Bifet, Albert/0000-0002-8339-7773; Barlet-Ros, Pere/0000-0001-7837-0886 NII International Internship Program; Spanish Ministry of Economy and Competitiveness [TEC2011-27474]; AGAUR [2014-SGR-1427] NII International Internship Program; Spanish Ministry of Economy and Competitiveness(Spanish Government); AGAUR(Agencia de Gestio D'Ajuts Universitaris de Recerca Agaur (AGAUR)) This research was funded by the NII International Internship Program, by the Spanish Ministry of Economy and Competitiveness under contract TEC2011-27474 (NOMADS project) and by AGAUR (ref. 2014-SGR-1427). 27 14 15 0 7 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1018-4864 1572-9451 TELECOMMUN SYST Telecommun. Syst. OCT 2016.0 63 2 191 204 10.1007/s11235-015-0114-6 0.0 14 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications DU7AN Green Submitted 2023-03-23 WOS:000382365800006 0
J Shao, Q; Gao, EL; Mara, T; Hu, H; Liu, T; Makradi, A Shao, Qian; Gao, Enlai; Mara, Thierry; Hu, Heng; Liu, Tong; Makradi, Ahmed Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions APPLIED ENERGY English Article Solid oxide fuel cell; Electrochemical model; Global sensitivity analysis; Polynomial chaos expansion; Bayesian model evidence HEAT-TRANSFER; CRACK-GROWTH; SHORT STACK; FLUID-FLOW; SOFC; MODEL; PERFORMANCE; OPTIMIZATION; RELIABILITY; SIMULATION Uncertainties that commonly exist in mathematical models prevent accurate predictions of solid oxide fuel cell performances and consequently impede the development and application of solid oxide fuel cell technologies. Assessing the impact of uncertain input parameters on cell performance variability is of utmost importance to the improvement of fuel cell models. To this end, a global sensitivity analysis is performed on the electrochemical model of a fuel cell using the Bayesian sparse polynomial chaos expansion approach. With this approach, machine-learning models are constructed to approximate the input-output relationship of the electrochemical model. The first-order, second-order, and total Sobol' indices are then computed analytically to quantify the individual impact of each parameter, the pairwise interaction between them and the total coupling effects over the entire input parameter space. These sensitivity indices show that the kinetic parameters of the electrochemical reaction, such as the activation energy, pre-exponential coefficients, and the electronic transfer coefficient, are the most sensitive parameters that significantly contribute to the variation of cell output voltage, which indicates the requirement for in-depth investigations of these parameters to enhance the accuracy in fuel cell model predictions. This work uncovers the possibility to apply data science techniques to the field of fuel cells. The results of this study not only demonstrate the effectiveness of the Bayesian approach for performing sensitivity analysis on the electrochemical model of a fuel cell, but also shed light on the rational design and optimization of solid oxide fuel cells. [Shao, Qian; Gao, Enlai; Hu, Heng] Wuhan Univ, Sch Civil Engn, 8 South Rd East Lake, Wuhan 430072, Peoples R China; [Mara, Thierry] Univ Reunion, EA 4518, FST, PIMENT, 15 Ave Rene Cassin, F-97715 St Denis, Reunion, France; [Mara, Thierry] European Commiss, Joint Res Ctr, Directorate Foresight Modelling Behav Insights &, I-21027 Ispra, VA, Italy; [Liu, Tong] Wuhan Univ, Sch Power & Mech Engn, 8 South Rd East Lake, Wuhan 430072, Peoples R China; [Makradi, Ahmed] Luxembourg Inst Sci & Technol, Esch Sur Alzette, Luxembourg Wuhan University; University of La Reunion; European Commission Joint Research Centre; EC JRC ISPRA Site; Wuhan University; Luxembourg Institute of Science & Technology Shao, Q (corresponding author), Wuhan Univ, Sch Civil Engn, 8 South Rd East Lake, Wuhan 430072, Peoples R China. qian.shao@whu.edu.cn Gao, Enlai/AAH-8453-2020; Liu, Tong/P-4043-2018; Shao, Qian/AAP-9101-2021; HU, Heng/K-4705-2012 Gao, Enlai/0000-0003-1960-0260; Liu, Tong/0000-0001-5311-9687; Shao, Qian/0000-0001-8074-4365; HU, Heng/0000-0002-5561-9578; Makradi, Ahmed/0000-0001-9772-1768 National Natural Science Foundation of China [11702199, 11902225]; Natural Science Foundation of Hubei Province [2017CFB147] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province(Natural Science Foundation of Hubei Province) This work has been supported by the National Natural Science Foundation of China (Grant No.: 11702199 and 11902225), and the Natural Science Foundation of Hubei Province (Grant No.: 2017CFB147). 49 12 13 7 38 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy FEB 15 2020.0 260 114318 10.1016/j.apenergy.2019.114318 0.0 14 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering KN8PZ 2023-03-23 WOS:000515108700070 0
J Mahendran, N; Vincent, DR; Srinivasan, K; Chang, CY; Garg, A; Gao, L; Reina, DG Mahendran, Nivedhitha; Vincent, Durai Raj; Srinivasan, Kathiravan; Chang, Chuan-Yu; Garg, Akhil; Gao, Liang; Gutierrez Reina, Daniel Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder SENSORS English Article correlation-based feature selection; random forest; weighted average ensemble; major depressive disorder; smartwatch sensor LOGISTIC-REGRESSION; SMART WATCH; CLASSIFICATION; FEATURES The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches. [Mahendran, Nivedhitha; Vincent, Durai Raj; Srinivasan, Kathiravan] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India; [Chang, Chuan-Yu] Natl Yunlin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Touliu 64002, Yunlin, Taiwan; [Garg, Akhil; Gao, Liang] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China; [Gutierrez Reina, Daniel] Univ Seville, Elect Engn Dept, Seville 41092, Spain Vellore Institute of Technology (VIT); VIT Vellore; National Yunlin University Science & Technology; Huazhong University of Science & Technology; University of Sevilla Chang, CY (corresponding author), Natl Yunlin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Touliu 64002, Yunlin, Taiwan. nivedhitha.m2019@vitstudent.ac.in; pmvincent@vit.ac.in; kathiravan.srinivasan@vit.ac.in; chuanyu@yuntech.edu.tw; akhilgarg@hust.edu.cn; gaoliang@mail.hust.edu.cn; dgutierrezreina@us.es GARG, AKHIL/GWC-5056-2022; Srinivasan, Kathiravan/P-6125-2014; GAO, Liang/C-7528-2009; P.M., Durai Raj Vincent/S-6371-2019 Srinivasan, Kathiravan/0000-0002-9352-0237; GAO, Liang/0000-0002-1485-0722; P.M., Durai Raj Vincent/0000-0002-7598-1363; Gutierrez, Daniel/0000-0002-2481-5058 Intelligent Recognition Industry Service Research Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project, Ministry of Education (MOE) in Taiwan Intelligent Recognition Industry Service Research Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project, Ministry of Education (MOE) in Taiwan Part of this work was financially supported by the Intelligent Recognition Industry Service Research Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. 45 17 17 0 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors NOV 2019.0 19 22 4822 10.3390/s19224822 0.0 16 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Chemistry; Engineering; Instruments & Instrumentation JW9QY 31698678.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000503381500001 0
J Gao, DJ; Jiang, HY; Shi, WF; Wang, TZ; Wang, YD Gao, Diju; Jiang, Haoyang; Shi, Weifeng; Wang, Tianzhen; Wang, Yide Adaptive equivalent consumption minimization strategy for hybrid electric ship ENERGY SCIENCE & ENGINEERING English Article adaptive equivalent consumption minimization strategy; battery aging; BP neural network; dynamic programming; hybrid electric ships MANAGEMENT; OPTIMIZATION; SYSTEMS In recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce the fuel consumption and emissions, has become a research hotspot. However, frequent use of the battery will accelerate the aging of the battery, and the replacement of scrapped battery will increase the cost of the ship. Therefore, it is necessary to consider delaying battery aging into the energy control strategy of HES. The equivalent consumption minimization strategy (ECMS) is a feasible energy control strategy because it can be implemented in real time. However, under the condition of uncertain initial state of charge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumption unless the equivalent factor (EF) is optimized in real time. In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) is proposed, which extracts the global optimal EF trajectory from the dynamic programming (DP) solution and uses the back propagation (BP) neural network to adjust the EF in real time. A trade-off between the fuel consumption and battery aging is made in the cost function by introducing a weight coefficient. Finally, the effectiveness and the adaptability of the proposed strategy are verified in MATLAB. [Gao, Diju; Jiang, Haoyang; Shi, Weifeng; Wang, Tianzhen] Shanghai Maritime Univ, Key Lab Transport Ind Marine Technol & Control En, Shanghai 201306, Peoples R China; [Shi, Weifeng; Wang, Tianzhen] Shanghai Maritime Univ, Logist Engn Coll, Shanghai, Peoples R China; [Wang, Yide] Univ Nantes, UMR CNRS 6164, Inst Elect & Technol Num Erique, Nantes, France Shanghai Maritime University; Shanghai Maritime University; Nantes Universite Jiang, HY (corresponding author), Shanghai Maritime Univ, Key Lab Transport Ind Marine Technol & Control En, Shanghai 201306, Peoples R China. jhy139@foxmail.com Gao, Diju/0000-0001-7564-8061 Shanghai Science and Technology planning project [20040501200]; Science AMP; Technology Commission of Shanghai Municipality; Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency [20DZ2252300] Shanghai Science and Technology planning project; Science AMP; Technology Commission of Shanghai Municipality; Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency This work was supported by Shanghai Science and Technology planning project under Grant 20040501200. This work was also supported by the Science & Technology Commission of Shanghai Municipality and Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency under Grant 20DZ2252300. 20 3 3 25 49 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2050-0505 ENERGY SCI ENG Energy Sci. Eng. MAR 2022.0 10 3 840 852 10.1002/ese3.1060 0.0 JAN 2022 13 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels ZP5IP Green Published 2023-03-23 WOS:000743727300001 0
J Lou, Y; Liu, YP; Kaakinen, JK; Li, XS Lou, Ya; Liu, Yanping; Kaakinen, Johanna K.; Li, Xingshan Using support vector machines to identify literacy skills: Evidence from eye movements BEHAVIOR RESEARCH METHODS English Article Eye movements; Literacy skills; Support vector machines E-Z READER; INDIVIDUAL-DIFFERENCES; PERCEPTUAL SPAN; FEATURE-SELECTION; COMPREHENSION; FREQUENCY; FIXATION; PATTERNS; MODEL Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities. [Lou, Ya; Liu, Yanping; Li, Xingshan] Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, 16 Lincui Rd, Beijing, Peoples R China; [Lou, Ya] Univ Chinese Acad Sci, Beijing, Peoples R China; [Kaakinen, Johanna K.] Univ Turku, Dept Psychol, Turku, Finland Chinese Academy of Sciences; Institute of Psychology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Turku Li, XS (corresponding author), Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, 16 Lincui Rd, Beijing, Peoples R China. lixs@psych.ac.cn Li, Xingshan/HHM-6328-2022; liu, yan/HGV-1365-2022; liu, yan/HCI-5542-2022 Natural Science Foundation of China [31571125] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported by a grant from the Natural Science Foundation of China (31571125). We thank Erik D. Reichle and an anonymous reviewer for their constructive comments and detailed suggestions when we revised the manuscript. 42 20 21 7 37 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1554-351X 1554-3528 BEHAV RES METHODS Behav. Res. Methods JUN 2017.0 49 3 887 895 10.3758/s13428-016-0748-7 0.0 9 Psychology, Mathematical; Psychology, Experimental Social Science Citation Index (SSCI) Psychology EY8LF 27287447.0 Bronze 2023-03-23 WOS:000404245700006 0
J Fung, PL; Zaidan, MA; Timonen, H; Niemi, JV; Kousa, A; Kuula, J; Luoma, K; Tarkoma, S; Petaja, T; Kulmala, M; Hussein, T Fung, Pak L.; Zaidan, Martha A.; Timonen, Hilkka; Niemi, Jarkko, V; Kousa, Anu; Kuula, Joel; Luoma, Krista; Tarkoma, Sasu; Petaja, Tuukka; Kulmala, Markku; Hussein, Tareq Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration JOURNAL OF AEROSOL SCIENCE English Article Air quality; Statistical modelling; Random forest; Neural network; Support vector; Input-adaptive proxy AIR-POLLUTION; AEROSOL-PARTICLES; REGRESSION; URBAN; HELSINKI; NETWORK; STREET; ABSORPTION; POLLUTANTS; PREDICTION Air quality prediction with black-box (BB) modelling is gaining widespread interest in research and industry. This type of data-driven models work generally better in terms of accuracy but are limited to capture physical, chemical and meteorological processes and therefore accountability for interpretation. In this paper, we evaluated different white-box (WB) and BB methods that estimate atmospheric black carbon (BC) concentration by a suite of observations from the same measurement site. This study involves data in the period of 1st January 2017-31st December 2018 from two measurement sites, from a street canyon site in Makelankatu and from an urban background site in Kumpula, in Helsinki, Finland. At the street canyon site, WB models performed (R-2 = 0.81-0.87) in a similar way as the BB models did (R-2 = 0.86-0.87). The overall performance of the BC concentration estimation methods at the urban background site was much worse probably because of a combination of smaller dynamic variability in the BC values and longer data gaps. However, the difference in WB (R-2 = 0.44-0.60) and BB models (R-2 = 0.41-0.64) was not significant. Furthermore, the WB models are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable and determine if the model output makes sense. This feature outweighs slightly higher performance of some individual BB models, and inherently the WB models are a better choice due to their transparency in the model architecture. Among all the WB models, IAP and LASSO are recommended due to its flexibility and its efficiency, respectively. Our findings also ascertain the importance of temporal properties in statistical modelling. In the future, the developed BC estimation model could serve as a virtual sensor and complement the current air quality monitoring. Main findings: White-box models are preferred over black-box models in estimating black carbon because they are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable. The black carbon model could serve as a virtual sensor integrating into air quality network in support with real measurements, so as to complement the current air quality index. [Fung, Pak L.; Zaidan, Martha A.; Luoma, Krista; Petaja, Tuukka; Kulmala, Markku; Hussein, Tareq] Univ Helsinki, Fac Sci, Inst Atmospher & Earth Syst Res Phys, Helsinki, Finland; [Zaidan, Martha A.] Nanjing Univ, Sch Atmospher Sci, Joint Int Res Lab Atmospher & Earth Syst Sci, Nanjing 210023, Peoples R China; [Timonen, Hilkka; Kuula, Joel] Finnish Meteorol Inst, Atmospher Composit Res, FI-00560 Helsinki, Finland; [Niemi, Jarkko, V; Kousa, Anu] Helsinki Reg Environm Serv Author HSY, POB 100, FI-00066 Helsinki, Finland; [Tarkoma, Sasu] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Hussein, Tareq] Fraunhofer WKI, Dept Mat Anal & Indoor Chem, D-38108 Braunschweig, Germany; [Hussein, Tareq] Univ Jordan, Dept Phys, Amman 11942, Jordan University of Helsinki; Nanjing University; Finnish Meteorological Institute; University of Helsinki; University of Jordan Fung, PL; Hussein, T (corresponding author), Univ Helsinki, Fac Sci, Inst Atmospher & Earth Syst Res Phys, Helsinki, Finland. pak.fung@helsinki.fi; martha.zaidan@helsinki.fi; hilkka.timonen@fmi.fi; jarkko.niemi@hsy.fi; anu.kousa@hsy.fi; joel.kuula@fmi.fi; krista.q.luoma@helsinki.fi; sasu.tarkoma@helsinki.fi; tuukka.petaja@helsinki.fi; markku.kulmala@helsinki.fi; tareq.hussein@helsinki.fi Petäjä, Tuukka/A-8009-2008; Luoma, Krista/AAG-3426-2022; Luoma, Krista/ADT-5270-2022; Fung, Pak Lun/ABG-9705-2021; Kuula, Joel/AAM-4298-2020; Zaidan, Martha Arbayani/G-4575-2011 Petäjä, Tuukka/0000-0002-1881-9044; Luoma, Krista/0000-0001-8841-3050; Luoma, Krista/0000-0001-8841-3050; Fung, Pak Lun/0000-0003-3493-1383; Kuula, Joel/0000-0003-3270-5972; Zaidan, Martha Arbayani/0000-0002-6348-1230; Kousa, Anu/0000-0002-2556-3489; Hussein, Tareq/0000-0002-0241-6435 MegaSense program; City of Helsinki Innovation Fund; Business Finland; European Union through the Urban Innovative Action Healthy Outdoor Premises for Everyone (HOPE) [UIA03-240]; European Research Council (ERC) [742206]; European Union [654109]; ERA-PLANET and its trans-national project SMURBS [689443]; Academy of Finland [272041, 1307537] MegaSense program; City of Helsinki Innovation Fund; Business Finland; European Union through the Urban Innovative Action Healthy Outdoor Premises for Everyone (HOPE); European Research Council (ERC)(European Research Council (ERC)European Commission); European Union(European Commission); ERA-PLANET and its trans-national project SMURBS; Academy of Finland(Academy of Finland) The work is supported by MegaSense program, the City of Helsinki Innovation Fund, Business Finland, the European Union through the Urban Innovative Action Healthy Outdoor Premises for Everyone (HOPE, project No. UIA03-240). Grants are also received from the European Research Council (ERC, grant No. 742206), the European Union's Horizon 2020 Research and Innovation Framework Program (grant agreement No. 654109), and ERA-PLANET (www.era-planet.eu) and its trans-national project SMURBS (www.smurbs.eu) funded under the same program (grant agreement No. 689443). The authors show gratitude to Academy of Finland for the funding via the Centre of Excellence in Atmospheric sciences (project No. 272041) and NanoBioMass (project No. 1307537). The authors acknowledge the City of Helsinki for providing traffic data. 71 11 11 3 18 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0021-8502 1879-1964 J AEROSOL SCI J. Aerosol. Sci. FEB 2021.0 152 105694 10.1016/j.jaerosci.2020.105694 0.0 21 Engineering, Chemical; Engineering, Mechanical; Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences PO4AY Green Published, hybrid 2023-03-23 WOS:000605111600003 0
J Chetouani, A; Li, LD Chetouani, Aladine; Li, Leida On the use of a scanpath predictor and convolutional neural network for blind image quality assessment SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Image quality; CNN model; Saliency; Scanpath prediction SALIENCY DETECTION; MODEL Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the stateof-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity. [Chetouani, Aladine] Univ Orleans, PRISME Lab, Orleans, France; [Li, Leida] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China Universite de Orleans; Xidian University Chetouani, A (corresponding author), Univ Orleans, PRISME Lab, Orleans, France. aladine.chetouani@univ-orleans.fr li, li/HII-4157-2022; Li, Li/AEM-3636-2022 59 14 14 1 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. NOV 2020.0 89 115963 10.1016/j.image.2020.115963 0.0 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering OC5HB Green Published 2023-03-23 WOS:000579186200002 0
J Wan, LL; Chen, T; Plaza, A; Cai, HJ Wan, Lulu; Chen, Tao; Plaza, Antonio; Cai, Haojie Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Convolutional neural networks (CNNs); hyperspectral unmixing; spectral information RECOGNITION Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers. Considering real-world situations, a sparse constraint is normally added to the linear unmixing model. However, the linear model does not take into account that the spectrum of mixed pixels is not simply linearlymixed. To fully study themixing characteristics of ground object spectra before being imaged by the sensor, we propose a supervised unmixing architecture based on a one-dimensional convolutional neural network (CNN) by considering the spectral information and the sparse characteristics in the mixed pixel. Since 1-D CNN only considers feature learning, we combine the traditional root-mean-square error (RMSE) and l(1) regularization in its loss function to minimize training error. The performance of our proposed unmixing model is assessed by comparing the unmixing resultswith three traditional linear sparse unmixing algorithms and the fuzzy ARTMAP neural network in a simulated dataset and three real datasets. The RMSE was used to verify the unmixing accuracy of the different methods. The results showed that the RMSE obtained by our proposed CNN-based method was the lowest among themethods on all three real datasets, proving the effectiveness and stability of the CNN in unmixing tasks. [Wan, Lulu; Chen, Tao; Cai, Haojie] China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China; [Chen, Tao] Geomat Technol & Applicat Key Lab Qinghai Prov, Xining 810001, Peoples R China; [Chen, Tao] Beijing Key Lab Urban Spatial Informat Engn, Beijing 100038, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun Escuela Politecn, Hyperspectral Computing Lab, Caceres 10071, Spain China University of Geosciences; Universidad de Extremadura Chen, T (corresponding author), China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China. lucy@cug.edu.cn; taochen@cug.edu.cn; aplaza@unex.es; cason@cug.edu.cn Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; chen, tao/0000-0001-6965-1256 National Natural Science Foundation of China [62071439, 61871259]; Opening Foundation of Qilian Mountain National Park Research Center (Qinghai) [GKQ2019-01]; Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering [20210209]; Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province [QHDX-2019-01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Foundation of Qilian Mountain National Park Research Center (Qinghai); Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering; Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province This work was supported in part by the National Natural Science Foundation of China under Grant 62071439 and Grant 61871259, in part by the Opening Foundation of Qilian Mountain National Park Research Center (Qinghai) under Grant GKQ2019-01, in part by the Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering under Grant 20210209, and in part by the Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province under Grant QHDX-2019-01. 48 6 6 5 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14 11669 11682 10.1109/JSTARS.2021.3126755 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology XG0VV gold 2023-03-23 WOS:000724480200002 0
J Abdallah, M; Mohammadi, B; Zaroug, MAH; Omer, A; Cheraghalizadeh, M; Eldow, MEE; Duan, Z Abdallah, Mohammed; Mohammadi, Babak; Zaroug, Modathir A. H.; Omer, Abubaker; Cheraghalizadeh, Majid; Eldow, Mohamed E. E.; Duan, Zheng Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models JOURNAL OF HYDROLOGY-REGIONAL STUDIES English Article Reference evapotranspiration; Empirical models; Quantile regression; Machine learning; Sudan; Hyper -arid region LIMITED CLIMATIC DATA; CROP EVAPOTRANSPIRATION; METEOROLOGICAL DATA; RANDOM FORESTS; PREDICTION; EQUATIONS; SVM; COMBINATION; NORTHWEST; ACCURACY Study region: Two hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focus: The study aims to evaluate the potential of the D-vine Copula-based quantile regression (DVQR) model for estimating daily ETo during 2000-2015 based on various input structures. Further, the DVQR model was compared with Multivariate Linear quantile regression (MLQR), Bayesians Model Averaging quantile regression (BMAQR), Empirical Models (EMMs), and Classical Machine Learning (CML). Besides, the CML models including Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Extreme Gradient Boosting (XGBoost), and M5 Model Tree (M5Tree) were employed. New hydrological insights for the region: The original EMMs showed poor performance, which improved after calibration techniques. The DVQR, MLQR, and BMAQR models showed better performance than the calibrated EMMs. However, the DVQR model exhibited the highest accu-racy than the MLQR and BMAQR models over two study sites. The M5Tree, SVM, and XGBoost models perfumed better than ELM and RF models at both study sites. The DVQR and XGBoost models showed equivalent performance (R-2, NSE, and WIA > 0.99, MAE, and RMSE < 0.2) to the M5Tree and SVM models, but they had significantly more accuracy than the calibrated EMMs, MLQR, BMAQR, ELM, and RF models in two hyper-arid regions. Overall, the high dimensional DVQR model is recommended as a promising alternative technique for estimating daily ETo in hyper-arid climate conditions around the world. [Abdallah, Mohammed] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Jiangsu, Peoples R China; [Abdallah, Mohammed; Eldow, Mohamed E. E.] Hydraul Res Stn, POB 318, Wad Madani, Sudan; [Mohammadi, Babak; Duan, Zheng] Lund Univ, Dept Phys Geog & Ecosyst Sci, Solvegatan 12, SE-22362 Lund, Sweden; [Zaroug, Modathir A. H.] Nile Basin Initiat Secretariat, Entebbe, Uganda; [Omer, Abubaker] Nanjing Univ Informat Sci & Technol, Sch Hydrol & Water Resources, Nanjing, Peoples R China; [Cheraghalizadeh, Majid] Univ Tehran, Dept Irrigat & Reclamat Engn, Karaj, Iran Hohai University; Lund University; Nanjing University of Information Science & Technology; University of Tehran Mohammadi, B (corresponding author), Lund Univ, Dept Phys Geog & Ecosyst Sci, Solvegatan 12, SE-22362 Lund, Sweden. babak.mohammadi@nateko.lu.se Mohammadi, Babak/G-4012-2018; ALsiddig, Mohammed/ACC-8628-2022 Mohammadi, Babak/0000-0001-8427-5965; ALsiddig, Mohammed/0000-0002-7151-6923; Omer, Abubaker/0000-0002-1811-9574 95 0 0 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-5818 J HYDROL-REG STUD J. Hydrol.-Reg. Stud. DEC 2022.0 44 101259 10.1016/j.ejrh.2022.101259 0.0 NOV 2022 26 Water Resources Science Citation Index Expanded (SCI-EXPANDED) Water Resources 6Q2FQ gold 2023-03-23 WOS:000891433200002 0
J Yuan, J; Zhang, GD; Chen, Z; Li, Z; Zhang, Y; Yu, SS Yuan, Jun; Zhang, Guidong; Chen, Zhe; Li, Zhong; Zhang, Yun; Yu, Samson S. A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method KNOWLEDGE-BASED SYSTEMS English Article Adaptive dynamic programming; Energy management system; Multi-neural network fusion prediction algorithm; Hardware-in-loop MODEL; LSTM The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C-means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform. (c) 2022 Elsevier B.V. All rights reserved. [Yuan, Jun; Zhang, Guidong; Zhang, Yun] Guangdong Univ Technol, Sch Automation, Guangzhou, Peoples R China; [Yu, Samson S.] Deakin Univ, Sch Engn, Melbourne, Australia; [Chen, Zhe] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark; [Li, Zhong] Minnan Normal Univ, Sch Math & Stat, Zhangzhou, Peoples R China Guangdong University of Technology; Deakin University; Aalborg University; MinNan Normal University Zhang, GD (corresponding author), Guangdong Univ Technol, Sch Automation, Guangzhou, Peoples R China. guidong.zhang@gdut.edu.cn Yu, Shenglong/R-4063-2019; Zhang, Guidong/W-8760-2019 Yu, Shenglong/0000-0002-2983-7344; Zhang, Guidong/0000-0002-2371-2890 National Natural Science Foundation of China [62003091]; Natural Science Foundation of Fujian Province, China [2020J01800]; China Scholarship Council (CSC) [CSC2020084 40641] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province); China Scholarship Council (CSC)(China Scholarship Council) This work was supported in part by the National Natural Science Foundation of China (Grant Number: 62003091) and Natural Science Foundation of Fujian Province, China (Grant Number: 2020J01800) . Jun Yuan, as a visiting Ph.D. student, was supported in part by China Scholarship Council (CSC Number: CSC2020084 40641) . 20 4 4 7 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. APR 6 2022.0 241 108284 10.1016/j.knosys.2022.108284 0.0 FEB 2022 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0W0LY 2023-03-23 WOS:000788730900014 0
C Liu, QX; Chen, Y; Cheng, G; Kharlamov, E; Li, JY; Qu, YZ Harth, A; Kirrane, S; Ngomo, AN; Paulheim, H; Rula, A; Gentile, AL; Haase, P; Cochez, M Liu, Qingxia; Chen, Yue; Cheng, Gong; Kharlamov, Evgeny; Li, Junyou; Qu, Yuzhong Entity Summarization with User Feedback SEMANTIC WEB (ESWC 2020) Lecture Notes in Computer Science English Proceedings Paper 17th Extended Semantic Web Conference (ESWC) MAY 31-JUN 04, 2020 GREECE AWS,Elsevier,Ontotext,Metaphacts,STI Innsbruck,videolectures.net Entity summarization; User feedback; Reinforcement learning; Deep neural network Semantic Web applications have benefited from entity summarization techniques which compute a compact summary for an entity by selecting a set of key triples from underlying data. A wide variety of entity summarizers have been developed. However, the quality of summaries they generate are still not satisfying, and we lack mechanisms for improving computed summaries. To address this challenge, in this paper we present the first study of entity summarization with user feedback. We consider a cooperative environment where a user reads the current entity summary and provides feedback to help an entity summarizer compute an improved summary. Our approach represents this iterative process as a Markov decision process where the entity summarizer is modeled as a reinforcement learning agent. To exploit user feedback, we represent the interdependence of triples in the current summary and the user feedback by a novel deep neural network which is incorporated into the policy of the agent. Our approach outperforms five baseline methods in extensive experiments with both real users and simulated users. [Liu, Qingxia; Chen, Yue; Cheng, Gong; Li, Junyou; Qu, Yuzhong] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Peoples R China; [Kharlamov, Evgeny] Univ Oslo, Dept Informat, Oslo, Norway; [Kharlamov, Evgeny] Robert Bosch GmbH, Bosch Ctr Artificial Intelligence, Renningen, Germany Nanjing University; University of Oslo; Bosch Cheng, G (corresponding author), Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Peoples R China. qxliu2013@smail.nju.edu.cn; mg1933005@smail.nju.edu.cn; gcheng@nju.edu.cn; evgeny.kharlamov@ifi.uio.no; 141220056@smail.nju.edu.cn; yzqu@nju.edu.cn NSFC [61772264]; Qing Lan Program of Jiangsu Province NSFC(National Natural Science Foundation of China (NSFC)); Qing Lan Program of Jiangsu Province This work was supported in part by the NSFC under Grant 61772264 and in part by the Qing Lan Program of Jiangsu Province. 28 13 13 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-49461-2; 978-3-030-49460-5 LECT NOTES COMPUT SC 2020.0 12123 376 392 10.1007/978-3-030-49461-2_22 0.0 17 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU3DK Bronze 2023-03-23 WOS:000890144000022 0
J Quan, WZ; Wang, K; Yan, DM; Zhang, XP; Pellerin, D Quan, Weize; Wang, Kai; Yan, Dong-Ming; Zhang, Xiaopeng; Pellerin, Denis Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION English Article Image forensics; Computer-generated image; Convolutional neural network; Generalization; Negative samples NATURAL IMAGES; DOMAIN Advanced computer graphics rendering software tools can now produce computer-generated (CG) images with increasingly high level of photorealism. This makes it more and more difficult to distinguish natural images (Nis) from CG images by naked human eyes. For this forensic problem, recently some CNN(convolutional neural network)-based methods have been proposed. However, researchers rarely pay attention to the blind detection (or generalization) problem, i.e., no training sample is available from unknown computer graphics rendering tools that we may encounter during the testing phase. We observe that detector performance decreases, sometimes drastically, in this challenging but realistic setting. To study this challenging problem, we first collect four high-quality CG image datasets, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-branch network with different initializations in the first layer to capture diverse features. Moreover, we introduce a gradient-based method to construct harder negative samples and conduct enhanced training to further improve the generalization of CNN-based detectors. Experimental results demonstrate the effectiveness of our method in improving the performance for the challenging task of blind detection of CG images. (C) 2020 Elsevier Ltd. All rights reserved. [Quan, Weize; Yan, Dong-Ming; Zhang, Xiaopeng] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China; [Quan, Weize; Yan, Dong-Ming; Zhang, Xiaopeng] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Quan, Weize; Wang, Kai; Pellerin, Denis] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Yan, DM (corresponding author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China. qweizework@gmail.com; kai.wang@gipsa-lab.grenoble-inp.fr; yandongming@gmail.com; xiaopeng.zhang@ia.ac.cn; denis.pellerin@gipsa-lab.grenoble-inp.fr Yan, Dong-Ming/0000-0003-2209-2404; pellerin, denis/0000-0002-3792-1706 National Key R&D Program of China [2019YFB2204104, 2018YFB2100602]; National Natural Science Foundation of China [61772523, 61620106003, 61972459]; Beijing Natural Science Foundation [L182059]; French National Agency for Research through PERSYVAL-lab [ANR-11-LABX-002501]; Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering; UCAS Joint PhD Training Program; French National Agency for Research through DEFALS [ANR-16-DEFA-0003]; Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); French National Agency for Research through PERSYVAL-lab; Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering; UCAS Joint PhD Training Program; French National Agency for Research through DEFALS; Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance We thank Dr. D.-T. Dang-Nguyen and Dr. D. Shullani for kindly sharing the RAISE (Dang-Nguyen et al., 2015) and VISION (Shullani et al., 2017) dataset respectively. This work was supported by the National Key R&D Program of China (2019YFB2204104 and 2018YFB2100602), the National Natural Science Foundation of China (61772523, 61620106003 and 61972459), the Beijing Natural Science Foundation (L182059), the French National Agency for Research through PERSYVAL-lab under Grant ANR-11-LABX-002501 and through DEFALS under Grant ANR-16-DEFA-0003, and the Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering and Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance. W. Quan acknowledges the support from the UCAS Joint PhD Training Program. 32 6 6 2 9 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2666-2817 FORENS SCI INT-DIGIT Forens. Sci. Int.-Digit. Investig. DEC 2020.0 35 301023 10.1016/j.fsidi.2020.301023 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science PH6XC Green Submitted 2023-03-23 WOS:000600551900008 0
J Leung, H; Long, XX; Ni, YQ; Qian, LL; Nychas, E; Siliceo, SL; Pohl, D; Hanhineva, K; Liu, Y; Xu, AM; Nielsen, HB; Belda, E; Clement, K; Loomba, R; Li, HT; Jia, WP; Panagiotou, G Leung, Howell; Long, Xiaoxue; Ni, Yueqiong; Qian, Lingling; Nychas, Emmanouil; Siliceo, Sara Leal; Pohl, Dennis; Hanhineva, Kati; Liu, Yan; Xu, Aimin; Nielsen, Henrik B.; Belda, Eugeni; Clement, Karine; Loomba, Rohit; Li, Huating; Jia, Weiping; Panagiotou, Gianni Risk assessment with gut microbiome and metabolite markers in NAFLD development SCIENCE TRANSLATIONAL MEDICINE English Article FATTY LIVER-DISEASE; GROWTH-FACTOR 21; CARDIOVASCULAR-DISEASE; PREDICTION; MANAGEMENT; DIAGNOSIS; OBESITY; EPIDEMIOLOGY; PREVENTION; POPULATION A growing body of evidence suggests interplay between the gut microbiota and the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of the gut microbiome in early detection of NAFLD is unclear. Prospective studies are necessary for identifying reliable, microbiome markers for early NAFLD. We evaluated 2487 individuals in a community-based cohort who were followed up 4.6 years after initial clinical examination and biospecimen sampling. Metagenomic and metabolomic characterizations using stool and serum samples taken at baseline were performed for 90 participants who progressed to NAFLD and 90 controls who remained NAFLD free at the follow-up visit. Cases and controls were matched for gender, age, body mass index (BMI) at baseline and follow-up, and 4-year BMI change. Machine learning models integrating baseline microbial signatures (14 features) correctly classified participants (auROCs of 0.72 to 0.80) based on their NAFLD status and liver fat accumulation at the 4-year follow up, outperforming other prognostic clinical models (auROCs of 0.58 to 0.60). We confirmed the biological relevance of the microbiome features by testing their diagnostic ability in four external NAFLD case-control cohorts examined by biopsy or magnetic resonance spectroscopy, from Asia, Europe, and the United States. Our findings raise the possibility of using gut microbiota for early clinical warning of NAFLD development. [Leung, Howell; Ni, Yueqiong; Nychas, Emmanouil; Siliceo, Sara Leal; Panagiotou, Gianni] Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Syst Biol & Bioinformat Unit, Beutenbergstr 11A, D-07745 Jena, Germany; [Long, Xiaoxue; Ni, Yueqiong; Qian, Lingling; Li, Huating; Jia, Weiping] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Shanghai Diabet Inst, Dept Endocrinol & Metab, Shanghai Key Lab Diabet Mellitus,Shanghai Clin Ct, Shanghai 200233, Peoples R China; [Pohl, Dennis; Nielsen, Henrik B.] Clin Microbi, Fruebjergvej 3, DK-2100 Copenhagen, Denmark; [Pohl, Dennis] Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, Bldg 220, DK-2800 Lyngby, Denmark; [Hanhineva, Kati] Univ Turku, Dept Life Technol, Food Chem & Food Dev Unit, Turku 20014, Finland; [Hanhineva, Kati] Chalmers Univ Technol, Dept Biol & Biol Engn, Div Food & Nutr Sci, S-41296 Gothenburg, Sweden; [Hanhineva, Kati] Univ Eastern Finland, Sch Med, Inst Publ Hlth & Clin Nutr, Kuopio 70211, Finland; [Liu, Yan; Xu, Aimin; Panagiotou, Gianni] Univ Hong Kong, State Key Lab Pharmaceut Biotechnol, Hong Kong, Peoples R China; [Liu, Yan; Xu, Aimin; Panagiotou, Gianni] Univ Hong Kong, Dept Med, Hong Kong, Peoples R China; [Xu, Aimin] Univ Hong Kong, Dept Pharmacol & Pharm, Hong Kong, Peoples R China; [Belda, Eugeni; Clement, Karine] Sorbonne Univ, Pitie Salpetriere Hosp, Assistance Publ Hop Paris, INSERM,Nutri Res Unit,Nutr Dept, F-75013 Paris, France; [Loomba, Rohit] Univ Calif San Diego, NAFLD Res Ctr, Dept Med, La Jolla, CA 92093 USA Hans Knoll Institute (HKI); Shanghai Jiao Tong University; Technical University of Denmark; University of Turku; Chalmers University of Technology; University of Eastern Finland; University of Hong Kong; University of Hong Kong; University of Hong Kong; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Pitie-Salpetriere - APHP; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; Assistance Publique-Hopitaux de Marseille; Sorbonne Universite; University of California System; University of California San Diego Ni, YQ; Panagiotou, G (corresponding author), Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Syst Biol & Bioinformat Unit, Beutenbergstr 11A, D-07745 Jena, Germany.;Ni, YQ; Li, HT; Jia, WP (corresponding author), Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Shanghai Diabet Inst, Dept Endocrinol & Metab, Shanghai Key Lab Diabet Mellitus,Shanghai Clin Ct, Shanghai 200233, Peoples R China.;Panagiotou, G (corresponding author), Univ Hong Kong, State Key Lab Pharmaceut Biotechnol, Hong Kong, Peoples R China.;Panagiotou, G (corresponding author), Univ Hong Kong, Dept Med, Hong Kong, Peoples R China. yueqiong.ni@leibniz-hki.de; huarting99@sjtu.edu.cn; wpjia@sjtu.edu.cn; gianni.panagiotou@hki-jena.de Jia, Weiping/B-7483-2012; xu, aimin/D-3291-2013 Jia, Weiping/0000-0002-6244-2168; Liu, Yan/0000-0001-6358-4056; Leung, Hok Yuen Howell/0000-0002-7943-480X; Ni, Yueqiong/0000-0003-1285-7354; Leal Siliceo, Sara/0000-0002-9076-3962; Nychas, Emmanouil/0000-0002-1306-683X; Pohl, Dennis/0000-0003-2644-9263; xu, aimin/0000-0002-0668-033X; Hanhineva, Kati/0000-0001-6834-7375 Marie Sklodowska-Curie Actions (MSCA) [H2020-MSCA-ITN-2018 813781]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [EXC 2051, 390713860]; Hong Kong Research Grants Council/Area of Excellence [AoE/M/707-18]; EU [E/HKU703/20]; National Natural Science Foundation of China (NSFC)-NHMRC joint research grant [81561128016]; Shanghai Municipal Key Clinical Specialty; National Key Research and Development Program of China [2018YFA0800402]; NSFC [81870598, 82022012]; Two Hundred Program from Shanghai Jiao Tong University School of Medicine [20191830]; Innovative Research Team of High-level Local Universities in Shanghai [SHSMU-ZDCX20212700]; Assistance Publique Hopitaux de Paris (APHP) DGOS; NCATS [5UL1TR001442]; NIDDK [U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, P30DK120515]; NHLBI [P01HL147835]; NIAAA [U01AA029019] Marie Sklodowska-Curie Actions (MSCA); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)); Hong Kong Research Grants Council/Area of Excellence; EU(European Commission); National Natural Science Foundation of China (NSFC)-NHMRC joint research grant(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Key Clinical Specialty; National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Two Hundred Program from Shanghai Jiao Tong University School of Medicine; Innovative Research Team of High-level Local Universities in Shanghai; Assistance Publique Hopitaux de Paris (APHP) DGOS; NCATS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Center for Advancing Translational Sciences (NCATS)); NIDDK(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK)); NHLBI(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung & Blood Institute (NHLBI)); NIAAA(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse & Alcoholism (NIAAA)) This work was supported by Marie Sklodowska-Curie Actions (MSCA) and Innovative Training Networks, H2020-MSCA-ITN-2018 813781 BestTreat (G.P., H. Leung, S.L.S., D.P., E.N., and H.B.N.); the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy, EXC 2051, Project ID 390713860 (G.P.); the Hong Kong Research Grants Council/Area of Excellence (AoE/M/707-18) (A.X.); the EU-HK Co-funding Mechanism (E/HKU703/20) (A.X.); the National Natural Science Foundation of China (NSFC)-NHMRC joint research grant (81561128016) (W.J.); the Shanghai Municipal Key Clinical Specialty (W.J.); the National Key Research and Development Program of China (2018YFA0800402) (W.J.); the General Program of NSFC (81870598) (H. Li); the Excellent Young Scholars of NSFC (82022012) (H. Li); the Two Hundred Program from Shanghai Jiao Tong University School of Medicine (20191830) (H. Li); the Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212700) (H. Li); the Assistance Publique Hopitaux de Paris (APHP) DGOS for the Programme hospitalier de Recherche clinique (microbaria) predictive value in identifying individuals with a high risk of developing NAFLD and EU transatlantic project Leducq and JPI-Microdiet (K.C.); the NCATS (5UL1TR001442) (R.L.); the NIDDK (U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, and P30DK120515) (R.L.); the NHLBI (P01HL147835) (R.L.); and the NIAAA (U01AA029019) (R.L.). 109 7 7 16 34 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 1946-6234 1946-6242 SCI TRANSL MED Sci. Transl. Med. JUN 8 2022.0 14 648 eabk0855 10.1126/scitranslmed.abk0855 0.0 14 Cell Biology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Cell Biology; Research & Experimental Medicine 2D4NG 35675435.0 Green Accepted 2023-03-23 WOS:000811525200005 0
C Feng, YB; Wu, DA; Xuan, PD; Izikson, P; Qi, PN; You, HN; Chai, Y; Venselaar, JJ; Bottegal, G; Sanguinetti, G; Verstraeten, B; Nooitgedagt, T; Mozooni, B Adan, O; Robinson, JC Feng, Yaobin; Wu, Dean; Xuan, Pandeng; Izikson, Pavel; Qi, Payne; You, Huanian; Chai, Yvon; Venselaar, Jan Jitse; Bottegal, Giulio; Sanguinetti, Gonzalo; Verstraeten, Bert; Nooitgedagt, Tjitte; Mozooni, Babak A Novel Accurate and Robust Technique in After-Etch Overlay Metrology of 3D-NAND's Memory Holes METROLOGY, INSPECTION, AND PROCESS CONTROL FOR MICROLITHOGRAPHY XXXIV Proceedings of SPIE English Proceedings Paper Conference on Metrology, Inspection, and Process Control for Microlithography XXXIV FEB 24-27, 2020 San Jose, CA SPIE,Nova Measuring Ltd 3D-NAND; Metrology; Overlay; Robustness; After-etch; Intra-die stress; In-Device Metrology; DUV scanner; Lithography In this work a novel machine learning algorithm is used to calculate the after etch overlay of the memory holes in a 3D-NAND device based on OCD metrology by YieldStar S1375. It is shown that the method can distinguish the overlay signals from the process induced signals in the acquired pupil image and therefore, enables for an overlay metrology approach which is highly robust to process variations. This metrology data is used to characterize and correct the process induced intra-die stress and the DUV scanner application fingerprint. [Feng, Yaobin; Wu, Dean; Xuan, Pandeng] Yangtze Memory Technol Co Ltd YMTC, Wuhan, Peoples R China; [Izikson, Pavel; Venselaar, Jan Jitse; Bottegal, Giulio; Sanguinetti, Gonzalo; Verstraeten, Bert; Nooitgedagt, Tjitte; Mozooni, Babak] ASML BV, Veldhoven, Netherlands; [Qi, Payne; You, Huanian; Chai, Yvon] ASML China, Wuhan, Peoples R China ASML Holding Mozooni, B (corresponding author), ASML BV, Veldhoven, Netherlands. babak.mozooni@asml.com 2 1 1 0 9 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-3418-3 PROC SPIE 2020.0 11325 1132522 10.1117/12.2553054 0.0 9 Optics; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Optics; Imaging Science & Photographic Technology BQ1IJ 2023-03-23 WOS:000576666600050 0
J Chabi, D; Wang, ZH; Bennett, C; Klein, JO; Zhao, WS Chabi, Djaafar; Wang, Zhaohao; Bennett, Christopher; Klein, Jacques-Olivier; Zhao, Weisheng Ultrahigh Density Memristor Neural Crossbar for On-Chip Supervised Learning IEEE TRANSACTIONS ON NANOTECHNOLOGY English Article Crossbar; ferroelectric tunnel memristor; memristor; neural network; on-chip supervised learning NETWORK; DECOMPOSITION; CIRCUITS; SYNAPSE Although there are many candidates for future computing systems, memristor-based neural crossbar (NC) is considered especially promising, thanks to their low power consumption, high density, and fault tolerance. However, their implementation is still hindered by the limitations of CMOS neuron and learning cells. In this paper, we present a memristor-based NC that implements on-chip supervised learning. Instead of using a standard CMOS neuron, a simple CMOS inverter realizes the activation function. More importantly, we propose a compact learning cell that consists of a crossbar latch of two antiparallel oriented binary memristors. This design allows for higher density integration and could be naturally extended to a multilayer neural network. Using the CMOS 40-nm design kit and a physics-based compact model of high-performance ferroelectric tunnel memristor, we performed transient simulations to validate the function of the proposed neural crossbar. Then, we construct a multilayer NC by cascading monolayer networks; thereby, enabling the network to learn non-linearly separable functions (e.g., XOR function). Finally, the fault tolerance is evaluated with Monte Carlo simulation. Analysis of simulation results demonstrates promising applications of our proposed neural crossbar for on-chip supervised learning. [Chabi, Djaafar; Wang, Zhaohao; Bennett, Christopher; Klein, Jacques-Olivier; Zhao, Weisheng] Univ Paris 11, CNRS, UMR, Inst Elect Fondamentale, F-91400 Orsay, France; [Zhao, Weisheng] Beihang Univ, Spintron Interdisciplinary Ctr, Beijing 100191, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Beihang University Chabi, D (corresponding author), Univ Paris 11, CNRS, UMR, Inst Elect Fondamentale, F-91400 Orsay, France. djaafar.chabi@gmail.com; Zhaohao.wang@u-psud.fr; christopher.bennett@u-psud.fr; Jacques-olivier.klein@u-psud.fr; weisheng.zhao@u-psud.fr KLEIN, Jacques-Olivier/HKN-6702-2023; KLEIN, Jacques-Olivier/B-3014-2010 KLEIN, Jacques-Olivier/0000-0002-6923-5276; KLEIN, Jacques-Olivier/0000-0002-6923-5276 ANR-MOOREA; ANR-MARS ANR-MOOREA(French National Research Agency (ANR)); ANR-MARS(French National Research Agency (ANR)) This work was supported by the Research Projects ANR-MOOREA and ANR-MARS. The review of this paper was arranged by Associate Editor C. A. Moritz. 38 36 36 1 36 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-125X 1941-0085 IEEE T NANOTECHNOL IEEE Trans. Nanotechnol. NOV 2015.0 14 6 954 962 10.1109/TNANO.2015.2448554 0.0 9 Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Science & Technology - Other Topics; Materials Science; Physics CW4UR 2023-03-23 WOS:000364992600003 0
J Wei, WJ; Liu, Z; Huang, LJ; Nebout, A; Le Meur, O; Zhang, TH; Wang, JJ; Xu, LH Wei, Weijie; Liu, Zhi; Huang, Lijin; Nebout, Alexis; Le Meur, Olivier; Zhang, Tianhong; Wang, Jijun; Xu, Lihua Predicting atypical visual saliency for autism spectrum disorder via scale-adaptive inception module and discriminative region enhancement loss NEUROCOMPUTING English Article Autism spectrum disorder; Atypical visual saliency; Saliency prediction; Visual attention; Deep neural network ENCODER-DECODER NETWORK; CIRCUMSCRIBED ATTENTION; AMYGDALA LESIONS; SOCIAL ATTENTION; OBJECT DETECTION; CHILDREN Autism spectrum disorder (ASD) is a prevalent lifelong neurodevelopmental disorder. Individuals with ASD perform atypical attention toward the visual stimulus. Despite that the number and performance of visual attention models are ongoing, the atypical attention prediction stagnates. To better understand abnormal visual behaviors of individuals with ASD, this paper models the atypical visual saliency via a deep neural network. First, the multi-level side-out feature maps are extracted from images. Second, the representation of features is enhanced by a scale-adaptive coarse-and-fine inception module that is specialized in integrating multi-scale features. Consequently, the enhanced features are transmitted from deep layers to shallow layers, boosting the precision of saliency maps. Furthermore, to facilitate our model transferring from typical saliency prediction to atypical saliency prediction, a loss function, namely discriminative region enhancement loss, is designed to intensify the discrepancy between the atypical saliency and typical saliency, driving the model to fit the atypical pattern of visual attention. Qualitative and quantitative experiments demonstrate that the proposed model achieves state-of-theart performance in terms of various metrics. (c) 2020 Elsevier B.V. All rights reserved. [Wei, Weijie; Liu, Zhi; Huang, Lijin] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China; [Wei, Weijie; Liu, Zhi; Huang, Lijin] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China; [Nebout, Alexis; Le Meur, Olivier] Univ Rennes 1, IRISA, F-35042 Rennes, France; [Zhang, Tianhong; Wang, Jijun; Xu, Lihua] Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Shanghai Key Lab Psychot Disorders, Sch Med, Shanghai 200030, Peoples R China Shanghai University; Shanghai University; Universite de Rennes; Shanghai Jiao Tong University Liu, Z (corresponding author), Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China. liuzhisjtu@163.com LIU, Zhi/D-4518-2012; Xu, Lihua/ABF-4012-2021 LIU, Zhi/0000-0002-8428-1131; National Natural Science Foundation of China [61771301] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant No. 61771301. 84 2 2 0 6 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 17 2021.0 453 610 622 10.1016/j.neucom.2020.06.125 0.0 JUN 2021 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science SU8ZB 2023-03-23 WOS:000663418700017 0
J Bonini, N; Vitolo, M; Imberti, JF; Proietti, M; Romiti, GF; Boriani, G; Johnsen, SP; Guo, YT; Lip, GYH Bonini, Niccolo; Vitolo, Marco; Imberti, Jacopo Francesco; Proietti, Marco; Romiti, Giulio Francesco; Boriani, Giuseppe; Paaske Johnsen, Soren; Guo, Yutao; Lip, Gregory Y. H. Mobile health technology in atrial fibrillation EXPERT REVIEW OF MEDICAL DEVICES English Review Atrial fibrillation; handheld; integrated care; mobile health; photoplethysmography; telemedicine; wearables; artificial intelligence HEART RHYTHM SOCIETY; CRYPTOGENIC STROKE; HAND-HELD; DECISION-SUPPORT; EUROPEAN-SOCIETY; AF; CARE; CARDIOLOGY; DEVICES; ELECTROCARDIOGRAM Introduction Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices, such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines. Areas covered mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health-care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life. Expert opinion The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention. [Bonini, Niccolo; Vitolo, Marco; Imberti, Jacopo Francesco; Proietti, Marco; Romiti, Giulio Francesco; Lip, Gregory Y. H.] Univ Liverpool, Liverpool Ctr Cardiovasc Sci, Liverpool, Merseyside, England; [Bonini, Niccolo; Vitolo, Marco; Imberti, Jacopo Francesco; Romiti, Giulio Francesco; Lip, Gregory Y. H.] Liverpool Heart & Chest Hosp, Liverpool, Merseyside, England; [Bonini, Niccolo; Vitolo, Marco; Imberti, Jacopo Francesco; Boriani, Giuseppe] Univ Modena & Reggio Emilia, Policlin Modena, Dept Biomed Metab & Neural Sci, Cardiol Div, Modena, Italy; [Vitolo, Marco; Imberti, Jacopo Francesco] Univ Modena & Reggio Emilia, Clin & Expt Med PhD Program, Modena, Italy; [Proietti, Marco] Univ Milan, Dept Clin Sci & Community Hlth, Milan, Italy; [Proietti, Marco] IRCCS Ist Clin Sci Maugeri, Geriatr Unit, Milan, Italy; [Romiti, Giulio Francesco] Sapienza Univ Rome, Dept Translat & Precis Med, Rome, Italy; [Paaske Johnsen, Soren; Lip, Gregory Y. H.] Aalborg Univ, Dept Clin Med, Danish Ctr Clin Hlth Serv Res DACS, Aalborg, Denmark; [Guo, Yutao] Chinese Peoples Liberat Army Gen Hosp, Med Ctr 6, Dept Pulm Vessel & Thrombot Dis, Beijing, Hebei, Peoples R China University of Liverpool; Liverpool Heart & Chest Hospital; Universita di Modena e Reggio Emilia; Universita di Modena e Reggio Emilia; University of Milan; Istituti Clinici Scientifici Maugeri IRCCS; Sapienza University Rome; Aalborg University; Chinese People's Liberation Army General Hospital Lip, GYH (corresponding author), Liverpool Ctr Cardiovasc Sci, William Henry Duncan Bldg,6 West Derby St, Liverpool L7 8TX, Merseyside, England. gregory.lip@liverpool.ac.uk Vitolo, Marco/W-1038-2019; Bonini, Niccolò/AGC-3462-2022; Proietti, Marco/K-8060-2016; Romiti, Giulio Francesco/J-6882-2019; Imberti, Jacopo/AAT-1134-2021; BORIANI, GIUSEPPE/A-4852-2015 Vitolo, Marco/0000-0002-5196-6249; Bonini, Niccolò/0000-0002-2766-5286; Proietti, Marco/0000-0003-1452-2478; Romiti, Giulio Francesco/0000-0002-3788-8942; Imberti, Jacopo/0000-0003-3403-3364; BORIANI, GIUSEPPE/0000-0002-9820-4815 114 7 7 7 16 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1743-4440 1745-2422 EXPERT REV MED DEVIC Expert Rev. Med. Devices APR 3 2022.0 19 4 327 340 10.1080/17434440.2022.2070005 0.0 APR 2022 14 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering 2L0WJ 35451347.0 hybrid, Green Published 2023-03-23 WOS:000787684300001 0
J Liu, H; Chen, J; Hissel, D; Su, HY Liu, Hao; Chen, Jian; Hissel, Daniel; Su, Hongye Short-Term Prognostics of PEM Fuel Cells: A Comparative and Improvement Study IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Neural network (NN); particle swarm optimization (PSO); proton exchange membrane fuel cells (PEMFCs); short-term prognostics KALMAN FILTER; DEGRADATION; LIFE As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications. [Liu, Hao; Chen, Jian; Su, Hongye] Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China; [Hissel, Daniel] Univ Bourgogne Franche Comte, CNRS, FEMTO ST, FCLAB, F-90010 Belfort, France Zhejiang University; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Chen, J (corresponding author), Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China. haoliu7850052@zju.edu.cn; jchen@zju.edu.cn; daniel.hissel@univ-fcomte.fr; hysu@iipc.zju.edu.cn Hissel, Daniel/AFY-3459-2022 Hissel, Daniel/0000-0003-0657-3320 National Natural Science Foundation of China [61433013, 61621002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61433013 and Grant 61621002. 34 28 30 4 119 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. AUG 2019.0 66 8 6077 6086 10.1109/TIE.2018.2873105 0.0 10 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation HR9IA 2023-03-23 WOS:000463471400027 0
J Cai, H; Bian, ZJ; Fan, ZH; Liu, B; Naviner, L Cai, Hao; Bian, Zhongjian; Fan, Zhonghua; Liu, Bo; Naviner, Lirida Commodity Bit-Cell Sponsored MRAM Interaction Design for Binary Neural Network IEEE TRANSACTIONS ON ELECTRON DEVICES English Article Convolution; Magnetic tunneling; Sensors; Transistors; Latches; Random access memory; Kernel; Binary neural network (BNN); in-memory computing; magnetic random access memory (MRAM); multi-XAC operation; one-step convolution IN-MEMORY MACRO Binary neural networks (BNNs) can transform multiply-and-accumulate (MAC) operations into XNOR and accumulation (XAC), which has been proven to greatly reduce the hardware resource consumption and improve the computing speed with small accuracy loss. Among various emerging nonvolatile memories (NVMs), spin transfer torque magnetic random access memory (STT-MRAM) shows great prospect for in-memory computing framework. In this work, a device-circuit interaction design approach is investigated with commodity MRAM bit-cell. The bit-cell array (BCA) and related peripheral circuits are minimally modified to realize in-magnetic random access memory (MRAM) one-step and unlimited-width convolution operations. The in-memory computing framework is implemented with 16-nm FinFET process and magnetic tunnel junction (MTJ) compact model. Mixed National Institute of Standards and Technology database (MNIST) handwritten digit recognition is demonstrated using this in-memory computing proposal. The recognition latency of one-step convolution is 21% improved than that of XAC convolution, whereas energy consumption of XAC is 30% lower than the one-step operations. The recognition accuracy of one-step/XAC convolution achieves 93.3%/96.5%, respectively. [Cai, Hao; Bian, Zhongjian; Fan, Zhonghua; Liu, Bo] Southeast Univ, Natl ASIC Syst Engn Ctr, Sch Elect Sci & Engn, Nanjing 210096, Peoples R China; [Naviner, Lirida] Telecom Paris, Lab Traitement & Commun Informat, F-91120 Palaiseau, France Southeast University - China; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Liu, B (corresponding author), Southeast Univ, Natl ASIC Syst Engn Ctr, Sch Elect Sci & Engn, Nanjing 210096, Peoples R China. hao.cai@seu.edu.cn; liubo_cnasic@seu.edu.cn Cai, Hao/L-7083-2019 Naviner, Lirida/0000-0002-6320-4153 National Natural Science Foundation of China [61904028] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61904028. The review of this article was arranged by Editor B. K. Kaushik. (Hao Cai and Zhongjian Bian contributed equally to this work.) 31 1 1 5 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9383 1557-9646 IEEE T ELECTRON DEV IEEE Trans. Electron Devices APR 2022.0 69 4 1721 1726 10.1109/TED.2021.3134588 0.0 DEC 2021 6 Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physics 1C2CN hybrid 2023-03-23 WOS:000736863500001 0
J Jin, C; Jin, SW; Ye, JM Jin, C.; Jin, S. -W.; Ye, J. -M. Artificial neural network-based metric selection for software fault-prone prediction model IET SOFTWARE English Article CLASSIFICATION; ALGORITHMS The identification of a module's fault-proneness is very important for minimising cost and improving the effectiveness of the software development process. How to obtain the relation between software metrics and a module's fault-proneness has been the focus of much research. One technical challenge to obtain this relation is that there is relevance between software metrics. To overcome this problem, the authors propose a reduction dimensionality phase, which can be generally implemented in any software fault-prone prediction model. In this study, the authors present applications of artificial neural network (ANN) and support vector machine in software fault-prone prediction using metrics. A new evaluation function for computing the contribution of each metric is also proposed in order to adapt to the characteristics of software data. The vital characteristic of this approach is the automatic determination of ANN architecture during metrics selection. Four software datasets are used for evaluating the performance of the proposed model. The experimental results show that the proposed model can establish the relation between software metrics and modules' fault-proneness. Moreover, it is also very simple because its implementation requires neither extra cost nor expert's knowledge. The proposed model has good performance, and can provide software project managers with trustworthy indicators of fault prone components. [Jin, C.; Ye, J. -M.] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China; [Jin, S. -W.] Univ Lyon 1, Fac Sci & Technol, F-69622 Villeurbanne, France Central China Normal University; UDICE-French Research Universities; Universite Claude Bernard Lyon 1 Jin, C (corresponding author), Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China. jincong@mail.ccnu.edu.cn social science foundation from Chinese Ministry of Education [11YJAZH040]; Science and Technology Research Programme of Wuhan of China [201210121023] social science foundation from Chinese Ministry of Education; Science and Technology Research Programme of Wuhan of China This work was supported by the social science foundation from Chinese Ministry of Education (Grant no. 11YJAZH040), and the Science and Technology Research Programme of Wuhan of China (Grant no. 201210121023). 37 15 18 1 15 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-8806 1751-8814 IET SOFTW IET Softw. DEC 2012.0 6 6 479 487 10.1049/iet-sen.2011.0138 0.0 9 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 029SY 2023-03-23 WOS:000310517200003 0
J Qi, CC; Zheng, JS; Meng, CQ; Wu, MT Qi, Chongchong; Zheng, Jiashuai; Meng, Chuiqian; Wu, Mengting Efficient Machine Learning Model for Predicting the Stiffness of Circular Footings on Clay Overlying Sand APPLIED SCIENCES-BASEL English Article elastic stiffness; circular footing; machine learning; soil-structure interaction Assessing the stiffness of circular foundations is the key to evaluating their deformation; thus, it is important for foundation design. The current determination methods for the stiffness coefficient are either time-consuming or inaccurate. In this paper, a novel stiffness prediction model has been proposed, using the decision tree (DT) algorithm optimized by particle size optimization (PSO). The condition of the embedded foundation, the embedded depth (Z(D)/2R), the thickness of the clay layer beneath the foundation base (T/2R), and the ratio of shear stiffness between clay and sand (G(sand)/G(clay)) were used as input variables, while the elastic stiffness coefficients (K-c, K-h, K-m, and K-v) were used as output variables. The optimum DT model has undergone comprehensive validation, and independent model verification using extra simulations. The results illustrate that PSO could promote further increases in the capability of DT modeling in predicting stiffness coefficients. The optimum DT model achieved a good level of performance on stiffness coefficient modeling. (The R for the training set was greater than 0.98 for all of the stiffness coefficients.) The variable importance analysis showed that the T/2R was the most significant variable for all stiffness coefficients, followed by G(sand)/G(clay). The optimum DT model achieved good predictive performance upon independent verification, with the R being 0.97, 0.99, 0.99, and 0.95 for K-v, K-h, K-m, and K-c, respectively. The proposed reliable and efficient DT-PSO model for stiffness coefficients in layered soil could further promote the safe and efficient utilization of circular foundations. [Qi, Chongchong; Zheng, Jiashuai; Wu, Mengting] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China; [Meng, Chuiqian] Delft Univ Technol, Fac Civil Engn & Geosci, NL-2628 Delft, Netherlands Central South University; Delft University of Technology Wu, MT (corresponding author), Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China. 215512109@csu.edu.cn State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University [HESS-2120] State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University This work was supported by the State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University (No. HESS-2120). 38 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel FEB 2023.0 13 4 2653 10.3390/app13042653 0.0 17 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 9H3WO gold 2023-03-23 WOS:000938765400001 0
C Han, WJ; Ruan, HB; Chen, XM; Wang, ZX; Li, HF; Schuller, B Int Speech Commun Assoc Han, Wenjing; Ruan, Huabin; Chen, Xiaomin; Wang, Zhixiang; Li, Haifeng; Schuller, Bjoern Towards Temporal Modelling of Categorical Speech Emotion Recognition 19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES Interspeech English Proceedings Paper 19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018) AUG 02-SEP 06, 2018 Hyderabad, INDIA Int Speech Commun Assoc speech emotion recognition; recurrent neural network; connectionist temporal classification; sequence-to-sequence To model the categorical speech emotion recognition task in a temporal manner, the first challenge arising is how to transfer the categorical label for each utterance into a label sequence. To settle this, we make a hypothesis that an utterance is consisting of emotional and non-emotional segments, and these non-emotional segments correspond to silent regions, short pauses, transitions between phonemes; unvoiced phonemes, etc. With this hypothesis, we propose to treat an utterance's label sequence as a chain of two states: the emotional state denoting the emotional frame and Null denoting the non-emotional frame. Then, we exploit a recurrent neural network based connectionist temporal classification model to automatically label and align an utterance's emotional segments with emotional labels, while non-emotional segments with Nulls. Experimental results on the IEMOCAP corpus validate our hypothesis and also demonstrate the effectiveness of our proposed method compared to the state-of-the-art algorithms. [Han, Wenjing; Wang, Zhixiang] Samsung Res Inst China Beijing SRC B, Beijing, Peoples R China; [Ruan, Huabin] Tsinghua Univ, Prot Res Technol Ctr, Beijing, Peoples R China; [Chen, Xiaomin; Li, Haifeng; Schuller, Bjoern] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China; [Schuller, Bjoern] Imperial Coll London, GLAM, London, England; [Schuller, Bjoern] Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, Augsburg, Germany Tsinghua University; Harbin Institute of Technology; Imperial College London; University of Augsburg Han, WJ (corresponding author), Samsung Res Inst China Beijing SRC B, Beijing, Peoples R China. wenjing.han@samsung.com chen, xia/GXM-5435-2022; chen, xi/GXH-3653-2022; chen, xia/GYR-3948-2022 Schuller, Bjorn/0000-0002-6478-8699 35 29 30 1 6 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X 978-1-5108-7221-9 INTERSPEECH 2018.0 932 936 10.21437/Interspeech.2018-1858 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM5PH 2023-03-23 WOS:000465363900194 0
J Peng, W; Shi, JG; Varanka, T; Zhao, GY Peng, Wei; Shi, Jingang; Varanka, Tuomas; Zhao, Guoying Rethinking the ST-GCNs for 3D skeleton-based human action recognition NEUROCOMPUTING English Article Human action recognition; ST-GCNs; Dynamic graph modeling; Deep neural networks The skeletal data has been an alternative for the human action recognition task as it provides more com-pact and distinct information compared to the traditional RGB input. However, unlike the RGB input, the skeleton data lies in a non-Euclidean space that traditional deep learning methods are not able to use their fullest potential. Fortunately, with the emerging trend of Geometric deep learning, the spatial -temporal graph convolutional network (ST-GCN) has been proposed to deal with the action recognition problem from skeleton data. ST-GCN and its variants fit well with skeleton-based action recognition and are becoming the mainstream frameworks for this task. However, the efficiency and the performance of the task are hindered by either fixing the skeleton joint correlations or providing a computational expen-sive strategy to construct a dynamic topology for the skeleton. We argue that many of these operations are either unnecessary or even harmful for the task. By theoretically and experimentally analysing the state-of-the-art ST-GCNs, we provide a simple but efficient strategy to capture the global graph correla-tions and thus efficiently model the representation of the input graph sequences. Moreover, the global graph strategy also reduces the graph sequence into the Euclidean space, thus a multi-scale temporal fil-ter is introduced to efficiently capture the dynamic information. With the method, we are not only able to better extract the graph correlations with much fewer parameters (only 12.6% of the current best), but we also achieve a superior performance. Extensive experiments on current largest 3D datasets, NTU-RGB+D and NTU-RGB+D 120, demonstrate the ability of our network to perform efficient and lightweight priority on this task. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). [Peng, Wei; Varanka, Tuomas; Zhao, Guoying] Univ Oulu, CMVS, Oulu, Finland; [Shi, Jingang] Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China University of Oulu; Xi'an Jiaotong University Zhao, GY (corresponding author), Univ Oulu, CMVS, Oulu, Finland. Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X Academy ICT 2023 project [328115]; Academy of Finland [316765]; National Natural Science Foundation of China [62002283]; Infotech Oulu Academy ICT 2023 project; Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Infotech Oulu This work was supported by Academy ICT 2023 project (grant 328115) , the Academy of Finland for project MiGA (grant 316765) , the National Natural Science Foundation of China under Grant 62002283, and Infotech Oulu. As well, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. 45 16 17 4 25 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 24 2021.0 454 45 53 10.1016/j.neucom.2021.05.004 0.0 MAY 2021 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science TI0KI hybrid, Green Published 2023-03-23 WOS:000672469900005 0
J Chand, GB; Dwyer, DB; Erus, G; Sotiras, A; Varol, E; Srinivasan, D; Doshi, J; Pomponio, R; Pigoni, A; Dazzan, P; Kahn, RS; Schnack, HG; Zanetti, MV; Meisenzahl, E; Busatto, GF; Crespo-Facorro, B; Pantelis, C; Wood, SJ; Zhuo, CJ; Shinohara, RT; Shou, HC; Fan, Y; Gur, RC; Gur, RE; Satterthwaite, TD; Koutsouleris, N; Wolf, DH; Davatzikos, C Chand, Ganesh B.; Dwyer, Dominic B.; Erus, Guray; Sotiras, Aristeidis; Varol, Erdem; Srinivasan, Dhivya; Doshi, Jimit; Pomponio, Raymond; Pigoni, Alessandro; Dazzan, Paola; Kahn, Rene S.; Schnack, Hugo G.; Zanetti, Marcus V.; Meisenzahl, Eva; Busatto, Geraldo F.; Crespo-Facorro, Benedicto; Pantelis, Christos; Wood, Stephen J.; Zhuo, Chuanjun; Shinohara, Russell T.; Shou, Haochang; Fan, Yong; Gur, Ruben C.; Gur, Raquel E.; Satterthwaite, Theodore D.; Koutsouleris, Nikolaos; Wolf, Daniel H.; Davatzikos, Christos Two distinc neuroanatomica subtypes of schizophrenia revealed using machine learning BRAIN English Article schizophrenia; structural MRI; neuroanatomical heterogeneity; semi-supervised machine learning; voxel-wise analysis TREATMENT RESPONSE; BRAIN STRUCTURE; HETEROGENEITY; VALIDATION; PATTERNS; BIOMARKERS; SUBGROUPS; DOPAMINE; SYMPTOMS; VOLUMES Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 ( r = -0.201, P= 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P. 0.109), sex (chi-square = 0.013, df = 1, P. 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P. 0.177), positive symptoms (t = 0.249, df = 289, P. 0.803), negative symptoms (t = 0.151, df = 289, P. 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P= 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P. 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics. [Chand, Ganesh B.; Erus, Guray; Sotiras, Aristeidis; Varol, Erdem; Srinivasan, Dhivya; Doshi, Jimit; Pomponio, Raymond; Fan, Yong; Gur, Ruben C.; Gur, Raquel E.; Davatzikos, Christos] Univ Penn, Perelman Sch Med, Dept Radiol, Philadelphia, PA 19104 USA; [Chand, Ganesh B.; Erus, Guray; Sotiras, Aristeidis; Varol, Erdem; Srinivasan, Dhivya; Doshi, Jimit; Pomponio, Raymond; Shinohara, Russell T.; Shou, Haochang; Fan, Yong; Satterthwaite, Theodore D.; Wolf, Daniel H.; Davatzikos, Christos] Univ Penn, Perelman Sch Med, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; [Dwyer, Dominic B.; Pigoni, Alessandro; Koutsouleris, Nikolaos] Ludwig Maximilians Univ Munchen, Dept Psychiat & Psychotherapy, Munich, Germany; [Sotiras, Aristeidis] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA; [Varol, Erdem] Columbia Univ, Dept Stat, Zuckerman Inst, New York, NY USA; [Pigoni, Alessandro] Univ Milan, Dept Neurosci & Mental Hlth, Milan, Italy; [Dazzan, Paola] Kings Coll London, Inst Psychiat, London, England; [Kahn, Rene S.] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY 10029 USA; [Schnack, Hugo G.] Univ Med Ctr Utrecht, Dept Psychiat, Utrecht, Netherlands; [Zanetti, Marcus V.; Busatto, Geraldo F.] Univ Sao Paulo, Fac Med, Inst Psychiat, Sao Paulo, Brazil; [Zanetti, Marcus V.] Hosp Sirio Libanes, Sao Paulo, Brazil; [Meisenzahl, Eva] Kliniken Heinrich Heine Univ, LVR Klinikum Dusseldorf, Dusseldorf, Germany; [Crespo-Facorro, Benedicto] Univ Cantabria, IDIVAL CIBERSAM, Cantabria, Spain; [Crespo-Facorro, Benedicto] Univ Seville, Univ Hosp Virgen Rocio, Dept Psychiat, Sch Med, Seville, Spain; [Pantelis, Christos] Univ Melbourne, Dept Psychiat, Melbourne Neuropsychiatry Ctr, Carlton, SA, Australia; [Pantelis, Christos] Melbourne Hlth, Carlton, SA, Australia; [Wood, Stephen J.] Natl Ctr Excellence Youth Mental Hlth, Orygen, Melbourne, Vic, Australia; [Wood, Stephen J.] Univ Melbourne, Ctr Youth Mental Hlth, Melbourne, Vic, Australia; [Wood, Stephen J.] Univ Birmingham, Sch Psychol, Edgbaston, England; [Zhuo, Chuanjun] Tianjin Med Univ, Nankai Univ, Neuroimagin Genet & Comorbid Lab PNGC Lab, Dept Psychiat,Affiliated Tianjin Anding Hosp, Tianjin, Peoples R China; [Zhuo, Chuanjun] Tianjin Med Univ, Dept Psychiat, Tianjin, Peoples R China; [Shinohara, Russell T.; Shou, Haochang] Univ Penn, Perelman Sch Med, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA; [Gur, Ruben C.; Gur, Raquel E.; Satterthwaite, Theodore D.; Wolf, Daniel H.] Univ Penn, Dept Psychiat, Perelman Sch Med, Philadelphia, PA 19104 USA University of Pennsylvania; Pennsylvania Medicine; University of Pennsylvania; Pennsylvania Medicine; University of Munich; Washington University (WUSTL); Columbia University; University of Milan; University of London; King's College London; Icahn School of Medicine at Mount Sinai; Utrecht University; Utrecht University Medical Center; Universidade de Sao Paulo; Hospital Sirio Libanes; Heinrich Heine University Dusseldorf; Heinrich Heine University Dusseldorf Hospital; Universidad de Cantabria; University of Sevilla; Virgen del Rocio University Hospital; University of Melbourne; Orygen, The National Centre of Excellence in Youth Mental Health; Orygen, The National Centre of Excellence in Youth Mental Health; University of Melbourne; University of Birmingham; Nankai University; Tianjin Medical University; Tianjin Medical University; University of Pennsylvania; Pennsylvania Medicine; University of Pennsylvania; Pennsylvania Medicine Davatzikos, C (corresponding author), Univ Penn, Perelman Sch Med, Dept Radiol, Philadelphia, PA 19104 USA.;Davatzikos, C (corresponding author), Univ Penn, Perelman Sch Med, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA.;Chand, GB (corresponding author), 3700 Hamilton Walk, Philadelphia, PA 19104 USA. ganesh.chand@pennmedicine.upenn.edu; christos.davatzikos@pennmedicine.upenn.edu Pantelis, Christos/H-7722-2014; Crespo-Facorro, BENEDICTO/GXZ-9830-2022; Davatzikos, Christos/ABE-2057-2021; Wood, Stephen/ABF-4086-2020; Busatto, Geraldo F/D-4431-2009; Crespo-Facorro, BENEDICTO/AAY-2238-2021; Dwyer, Dominic/Z-1225-2018 Pantelis, Christos/0000-0002-9565-0238; Crespo-Facorro, BENEDICTO/0000-0003-0033-7132; Wood, Stephen/0000-0003-4186-5919; Busatto, Geraldo F/0000-0003-4749-4602; Crespo-Facorro, BENEDICTO/0000-0003-0033-7132; Dazzan, Paola/0000-0002-8427-3617; Chand, Ganesh/0000-0002-9857-5800; Dwyer, Dominic/0000-0003-3949-5867 United States National Institutes of Health [R01MH112070, S10OD023495, R01MH101111, R01MH113565, R01MH113550, R01MH112847, R01EB022573]; PRONIA project - European Union [602152]; MRC [MC_PC_11003] Funding Source: UKRI United States National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); PRONIA project - European Union; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) This work was supported by United States National Institutes of Health grant R01MH112070. Additional support was provided by S10OD023495, R01MH101111, R01MH113565, R01MH113550, R01MH112847, and R01EB022573. The work was also supported by the PRONIA project as funded by the European Union 7th Framework Program grant 602152. 66 79 81 12 29 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0006-8950 1460-2156 BRAIN Brain MAR 2020.0 143 3 1027 1038 10.1093/brain/awaa025 0.0 12 Clinical Neurology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology KY5VS 32103250.0 Green Published, Green Accepted, Bronze 2023-03-23 WOS:000522641900033 0
J He, L; Liu, W; Zhuang, KX; Meng, J; Qiu, J He, Li; Liu, Wei; Zhuang, Kaixiang; Meng, Jie; Qiu, Jiang Executive function-related functional connectomes predict intellectual abilities INTELLIGENCE English Article Executive functions; Intellectual abilities; Functional connectivity; Machine learning; Individual differences TOOLBOX COGNITION BATTERY; FINGERPRINTING IDENTIFYING INDIVIDUALS; WORKING-MEMORY; FLUID INTELLIGENCE; GENERAL INTELLIGENCE; PROCESSING SPEED; GENETIC OVERLAP; CHILDREN BORN; BRAIN; FMRI Executive functions (EFs) refer to a range of cognitive control and regulation processes that coordinate thoughts and actions in a goal-directed way and are regarded as a hallmark of intellectual abilities. However, most studies have used a single measurement to explore the relationship between EFs and intelligence, and there is a lack of robust evidence to demonstrate the link between EF-related neural substrates and intelligence under an integrative framework. To address this issue, we employed a large sample (primary dataset, n = 881; hold-out dataset, n = 181) from the Human Connectome Project, which included high-quality MRI data and multiple EF and intelligence measurements. Based on a machine learning framework, we examined the predictive effect of EF-related functional connectivity (FC) on fluid intelligence (Gf) and crystallized intelligence (Gc) using a connectome-based predictive model. The results showed that all types of EF-related FCs (i.e., EF-common, updating-, shifting-, and inhibition-specific FCs) predicted novel subjects? Gf and Gc in the primary dataset and successfully generalized to the hold-out dataset. Additionally, EF-related FCs appeared to demonstrate better performance in predicting Gc. Identified predictive FCs revealed the domain-general and domain-specific connectivity patterns of EFs, and the network hubs were mainly located in the default mode, cognitive control, salience, and visual networks. These findings facilitate our understanding of the relation between multiple EF domains and intelligence from the perspective of network neuroscience, suggesting that different intellectual abilities and EFs share similar neural bases to some extent, which allows the link between EFs and intelligence to be revisited. [He, Li; Zhuang, Kaixiang; Meng, Jie; Qiu, Jiang] Southwest Univ, Key Lab Cognit & Personal, Minist Educ, Chongqing, Peoples R China; [He, Li; Zhuang, Kaixiang; Meng, Jie; Qiu, Jiang] Southwest Univ, Fac Psychol, 2 TianSheng Rd, Chongqing 400715, Peoples R China; [Liu, Wei] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Med Ctr, Nijmegen, Netherlands; [Qiu, Jiang] Beijing Normal Univ, Southwest Univ Branch, Collaborat Innovat Ctr Assessment Basic Educ Qual, Beijing, Peoples R China Southwest University - China; Southwest University - China; Radboud University Nijmegen; Beijing Normal University Qiu, J (corresponding author), Southwest Univ, Fac Psychol, 2 TianSheng Rd, Chongqing 400715, Peoples R China. qiuj318@swu.edu.cn He, Li/AAL-4867-2021 He, Li/0000-0002-2213-2499 National Natural Science Foundation of China [31771231, 32071070]; Natural Science Foundation of Chongqing [cstc2019jcyjmsxmX0520, cstc2020jcyjmsxmX0299]; planned project of Chongqing humanities and Social Sciences [2018PY80, 2019PY51]; Fundamental Research Funds for the Central Universities [SWU119007]; Chang Jiang Scholars Program; National Outstanding Young People Plan; Chongqing Talent Program National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Chongqing(Natural Science Foundation of Chongqing); planned project of Chongqing humanities and Social Sciences; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Chang Jiang Scholars Program(Program for Changjiang Scholars & Innovative Research Team in University (PCSIRT)); National Outstanding Young People Plan; Chongqing Talent Program Funding This research was supported by the National Natural Science Foundation of China (31771231, 32071070) , Natural Science Foundation of Chongqing (cstc2019jcyjmsxmX0520, cstc2020jcyjmsxmX0299) , the planned project of Chongqing humanities and Social Sciences (2018PY80, 2019PY51) , Fundamental Research Funds for the Central Universities (SWU119007) , Chang Jiang Scholars Program, National Outstanding Young People Plan, and Chongqing Talent Program. 90 6 6 6 19 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0160-2896 1873-7935 INTELLIGENCE Intelligence MAR-APR 2021.0 85 101527 10.1016/j.intell.2021.101527 0.0 FEB 2021 11 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology RC0QW 2023-03-23 WOS:000632509500001 0
C Su, YC; Li, J; Plaza, A; Marinoni, A; Gamba, P; Huang, YC IEEE Su, Yuanchao; Li, Jun; Plaza, Antonio; Marinoni, Andrea; Gamba, Paolo; Huang, Yuancheng DEEP AUTO-ENCODER NETWORK FOR HYPERSPECTRAL IMAGE UNMIXING IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 22-27, 2018 Valencia, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc,European Space Agcy Hyperspectral unmixing; Non-negative sparse auto-encoder; Variational auto-encoder; Deep learning ENDMEMBER EXTRACTION; FAST ALGORITHM In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder network composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a variational auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic data set. In our comparison with other state-of-the-art unmixing methods, the proposed approach demonstrates highly competitive performance. [Su, Yuanchao; Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, E-10071 Caceres, Spain; [Marinoni, Andrea; Gamba, Paolo] Univ Pavia, Dipartimento Ingn Ind Informaz, I-27100 Pavia, Italy; [Huang, Yuancheng] Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Shaanxi, Peoples R China Sun Yat Sen University; Universidad de Extremadura; University of Pavia; Xi'an University of Science & Technology Su, YC (corresponding author), Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China. Li, Jun/E-6840-2019; Plaza, Antonio/C-4455-2008; Gamba, Paolo E/G-1959-2010; Li, Jun/AAD-7233-2020; Su, Yuanchao/ABD-6108-2021 Li, Jun/0000-0003-1613-9448; Plaza, Antonio/0000-0002-9613-1659; Gamba, Paolo E/0000-0002-9576-6337; Li, Jun/0000-0003-1613-9448; Su, Yuanchao/0000-0002-4776-0862 National Natural Science Foundation of China [61771496]; National Key Research and Development Program of China [2017YFB0502900]; Guangdong Provincial Natural Science Foundation [2016A030313254] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province) This work was supported by National Natural Science Foundation of China under Grant 61771496, National Key Research and Development Program of China under Grant 2017YFB0502900, Guangdong Provincial Natural Science Foundation under Grant 2016A030313254. 16 5 5 2 14 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-7150-4 INT GEOSCI REMOTE SE 2018.0 6400 6403 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BL4XL 2023-03-23 WOS:000451039806050 0
J Upreti, D; Huang, WJ; Kong, WP; Pascucci, S; Pignatti, S; Zhou, XF; Ye, HC; Casa, R Upreti, Deepak; Huang, Wenjiang; Kong, Weiping; Pascucci, Simone; Pignatti, Stefano; Zhou, Xianfeng; Ye, Huichun; Casa, Raffaele A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2 REMOTE SENSING English Article LAI; LCC; FAPAR; FVC; CCC; PROSAIL; GPR; machine learning; active learning LEAF-AREA INDEX; NITROGEN STATUS ASSESSMENT; CHLOROPHYLL CONTENT; VEGETATION INDEXES; NEURAL-NETWORKS; SUGAR-BEET; LAI; REFLECTANCE; REGRESSION; INVERSION This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R-2 of 0.78, rott mean squared error (RMSE) of 0.68 m(2) m(-2) and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R-2 = 0.26, RMSE = 8.88 g cm(-2), RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R-2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm. [Upreti, Deepak; Casa, Raffaele] Univ Tuscia, DAFNE, Via San Camillo de Lellis, I-01100 Viterbo, Italy; [Huang, Wenjiang; Kong, Weiping; Ye, Huichun] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Kong, Weiping] Chinese Acad Sci, Acad Optoelect, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China; [Pascucci, Simone; Pignatti, Stefano] CNR, Inst Methodol Environm Anal, IMAA, Via Fosso Cavaliere 100, I-00133 Rome, Italy; [Zhou, Xianfeng] Hangzhou Dianzi Univ, Coll Life Informat Sci & Instrument Engn, Hangzhou 310018, Zhejiang, Peoples R China Tuscia University; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Chinese Academy of Sciences; Academy of Opto-Electronics, CAS; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Hangzhou Dianzi University Casa, R (corresponding author), Univ Tuscia, DAFNE, Via San Camillo de Lellis, I-01100 Viterbo, Italy. dupreti@unitus.it; huangwj@radi.ac.cn; kongwp@radi.ac.cn; simone.pascucci@cnr.it; stefano.pignatti@cnr.it; zhouxf@radi.ac.cn; yehc@radi.ac.cn; rcasa@unitus.it Pascucci, Simone/I-8172-2019; huang, wenjiang/AAM-6844-2021 Pascucci, Simone/0000-0002-8311-8615; , huangwenjiang/0000-0003-1710-8301; , Weiping/0000-0003-3371-7908; Upreti, Deepak/0000-0001-8936-1889; CASA, Raffaele/0000-0003-3091-7680; YE, Huichun/0000-0001-7836-497X European Space Agency (ESA); Ministry of Science and Technology of China (MOST), under the Dragon 4 Programme [ID32275-Topic1]; National Natural Science Foundation of China [41871339]; Italian National Research Council within the CNR-CAS Bilateral Agreement European Space Agency (ESA)(European Space AgencyEuropean Commission); Ministry of Science and Technology of China (MOST), under the Dragon 4 Programme; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Italian National Research Council within the CNR-CAS Bilateral Agreement This research was funded by the European Space Agency (ESA) and the Ministry of Science and Technology of China (MOST), under the Dragon 4 Programme, Project ID32275-Topic1; it was also supported by the National Natural Science Foundation of China (41871339) and by the Italian National Research Council within the CNR-CAS Bilateral Agreement 2017-2019. 65 75 75 4 41 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 1 2019.0 11 5 481 10.3390/rs11050481 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology HQ6RA gold, Green Published, Green Submitted 2023-03-23 WOS:000462544500009 0
J He, L; Xie, JH; Li, J; Plaza, A; Chanussot, J; Zhu, JW He, Lin; Xie, Jinhua; Li, Jun; Plaza, Antonio; Chanussot, Jocelyn; Zhu, Jiawei Variable Subpixel Convolution Based Arbitrary-Resolution Hyperspectral Pansharpening IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Pansharpening; Spatial resolution; Training; Standards; Task analysis; Convolution; Optimized production technology; Arbitrary resolution; convolution neural network (CNN); hyperspectral (HS) pansharpening; spatial pattern; variable subpixel convolution (VSPC) MULTISPECTRAL DATA; FUSION; IMAGES; QUALITY; MS Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution HS (ARHS) pansharpening seeks to pansharpen LRHS images to any user-customized resolutions. For such a new HS pansharpening task, it is not feasible to train and store convolution neural network (CNN) models for all possible candidate scales, which implies that the single model acquired from the training phase should be capable of being generalized to yield HS images with any resolutions in the pansharpening phase. To address the challenge, a novel variable subpixel convolution (VSPC)-based CNN (VSPC-CNN) method following our arbitrary upsampling CNN (AU-CNN) framework is developed for ARHS pansharpening. The VSPC-CNN method comprises a two-stage elevating thread. The first stage is to improve the spatial resolution of the input HS image to that of the PAN image through a prepansharpening module, and then, a VSPC-encapsulated arbitrary scale attention upsampling (ASAU) module is cascaded for arbitrary resolution adjustment. After training with given scales, it can be generalized to pansharpen HS image to arbitrary scales under the spatial patterns invariance across the training and pansharpening phases. Experimental results from several specific VSPC-CNNs on both simulated and real HS datasets show the superiority of the proposed method. [He, Lin; Xie, Jinhua; Zhu, Jiawei] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China; [Li, Jun] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China; [Plaza, Antonio] Univ Extremadura, Hyperspectral Comp Lab, Caceres 10003, Spain; [Chanussot, Jocelyn] Univ Grenoble Alpes, Jean Kuntzmann Lab LJK, Natl Inst Res Digital Sci & Technol Inria, Grenoble Inst Technol Grenoble INP,CNRS, F-38000 Grenoble, France South China University of Technology; China University of Geosciences; Universidad de Extremadura; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA) Li, J (corresponding author), China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China. helin@scut.edu.cn; lijuncug@cug.edu.cn; aplaza@unex.es; jocelyn.chanussot@gipsa-lab.grenoble-inp.fr Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [62071184, 61571195, 42030111, 61836003]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011615]; Guangzhou Science and Technology Program [202002030395] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Guangzhou Science and Technology Program This work was supported in part by the National Natural Science Foundation of China under Grant 62071184, Grant 61571195, Grant 42030111, and Grant 61836003; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011615; and in part by the Guangzhou Science and Technology Program under Grant 202002030395. 53 1 1 2 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5534519 10.1109/TGRS.2022.3189624 0.0 19 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3K0RQ 2023-03-23 WOS:000833793800011 0
J Tang, T; Hong, T; Hong, HH; Ji, SY; Mumtaz, S; Cheriet, M Tang, Tao; Hong, Tao; Hong, Haohui; Ji, Senyuan; Mumtaz, Shahid; Cheriet, Mohamed An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios ELECTRONICS English Article 5G; IoT; UAV; multitarget tracking; GM-PHD filter; UAV-PHD filter; machine learning CHALLENGES; INTERNET The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter. [Tang, Tao; Hong, Tao; Hong, Haohui] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Ji, Senyuan] Hainan Univ, Sch Comp Sci & Cyberspace Secur, Haikou 570100, Hainan, Peoples R China; [Mumtaz, Shahid] Inst Telecomunicacoes, P-1049001 Aveiro, Portugal; [Cheriet, Mohamed] Univ Quebec, ETS, Montreal, PQ H1A 0A1, Canada Beihang University; Hainan University; Universidade de Aveiro; University of Quebec; Ecole de Technologie Superieure - Canada; University of Quebec Montreal Tang, T; Hong, T (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China. tangtao@buaa.edu.cn; hongtao@buaa.edu.cn; 2015016427@mail.buct.edu.cn; jisenyuan@hainanu.edu.cn; smumtaz@ay.it.pt; mohamed.cheriet@etsmtl.ca Mumtaz, Dr shahid/V-3603-2019 Mumtaz, Dr shahid/0000-0001-6364-6149; Tang, Tao/0000-0003-2987-8346; Hong, Tao/0000-0002-9607-4260 National Natural Science Foundation of China [61827901, 61671056, U1633122] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is sponsored National Natural Science Foundation of China under Grant No.61827901, No.61671056 and U1633122. 34 13 13 3 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics OCT 2019.0 8 10 1188 10.3390/electronics8101188 0.0 16 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics JP4UW Green Published, gold 2023-03-23 WOS:000498262700128 0
J Jin, Y; Ge, Y; Liu, YJ; Chen, YH; Zhang, HT; Heuvelink, GBM Jin, Yan; Ge, Yong; Liu, Yaojie; Chen, Yuehong; Zhang, Haitao; Heuvelink, Gerard B. M. A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Spatial resolution; Interpolation; Support vector machines; Mathematical model; Market research; Soil moisture; Monitoring; Area-to-area kriging; downscaling; soil moisture; support vector regression REGRESSION; RADIOMETER; ECOSYSTEM; FOREST The surface soil moisture (SSM) products derived from microwave remote sensing have a coarse spatial resolution; therefore, downscaling is required to obtain accurate SSM at high spatial resolution. An effective way to handle the stratified heterogeneity is to model for various stratifications; however, the number of samples is often limited under each stratification, influencing the downscaling accuracy. In this study, a machine learning-based geostatistical model, which combines various kinds of ancillary information at fine spatial scale, is developed for spatial downscaling. The proposed support vector area-to-area regression kriging (SVATARK) model incorporates support vector regression and area-to-area kriging by considering the nonlinear relationships among variables for various stratifications. SVATARK also considers the change of support problem in the downscaling interpolation process as well as for solving the small sample size in trend prediction. The SVATARK method is evaluated in the Naqu region on the Tibetan Plateau, China, to downscale the European Space Agency's (ESA) 25-km-resolution SSM product. The 1-km-resolution SSM predictions have been produced every eight days over a six-year period (2010-2015). Compared with five other downscaling methods, the downscaled predictions from the SVATARK method performs the best with in situ observations, resulting in a 24.4% reduction in root-mean-square error with 0.08 m(3)center dot m(-3) and a 8.2% increase in correlation coefficient with 0.72, on average. Additionally, anomalously low SSM values, an indicator of drought, had a record low anomaly in mid-July for 2015, as noted by previous studies, indicating that SVATARK could be utilized for drought monitoring. [Jin, Yan; Zhang, Haitao] Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China; [Ge, Yong] Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Liu, Yaojie] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China; [Chen, Yuehong] Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Peoples R China; [Heuvelink, Gerard B. M.] Wageningen Univ, Soil Geog & Landscape Grp, NL-6700 AA Wageningen, Netherlands Nanjing University of Posts & Telecommunications; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Nanjing University; Hohai University; Wageningen University & Research Ge, Y (corresponding author), Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. jinyan@njupt.edu.cn; gey@lreis.ac.cn; liuyaojie@nju.edu.cn; yuehong@hhu.edu.cn; zhanghaitao@njupt.edu.cn; gerard.heuvelink@wur.nl Liu, Yaojie/HGB-4640-2022; Heuvelink, Gerard/AAD-5916-2022 Liu, Yaojie/0000-0002-3112-9590; Heuvelink, Gerard/0000-0003-0959-9358; Jin, Yan/0000-0002-6978-163X National Natural Science Foundation for Distinguished Young Scholars of China [41725006]; National Natural Science Foundation of China [42001332, 42001375, 41531174, 41531179]; Strategic Priority Research Program of Chinese Academy of Sciences [XDA20030302]; Scientific Research Fund of Nanjing University of Posts and Telecommunications under NUPTSF [NY219035] National Natural Science Foundation for Distinguished Young Scholars of China(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); Scientific Research Fund of Nanjing University of Posts and Telecommunications under NUPTSF This work was supported in part by the National Natural Science Foundation for Distinguished Young Scholars of China under Grant 41725006, in part by the National Natural Science Foundation of China under Grant 42001332, Grant 42001375, Grant 41531174, and Grant 41531179, in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA20030302, and in part by the Scientific Research Fund of Nanjing University of Posts and Telecommunications under NUPTSF Grant NY219035. 52 6 6 7 36 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14 1025 1037 10.1109/JSTARS.2020.3035386 0.0 13 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology PR7LT Green Submitted, gold 2023-03-23 WOS:000607413900032 0
J Li, R; Xiong, BY; Zhang, SF; Zhang, XA; Li, YF; Iu, H; Fernando, T Li, Ran; Xiong, Binyu; Zhang, Shaofeng; Zhang, Xinan; Li, Yifeng; Iu, Herbert; Fernando, Tyrone A novel one dimensional convolutional neural network based data-driven vanadium redox flow battery modelling algorithm JOURNAL OF ENERGY STORAGE English Article vanadium redox flow battery; Modelling; One dimensional conventional neural network; Data-driven RECOGNITION power ;nore energy ]. For possess high good maintenance cost cost-effectiveness battery, applications with and states are electrochemical variables instead of electrical variables. Moreover, the usage of such models re. uires some electrochemical knowledge, which is an obstacle for electrical engineers. In recent years, VRB modelling for power system studies has been explored. For example, in 2015, a comprehensive electrical equivalent circuit model for system-level analysis was proposed [6]. The topology of proposed model is much simpler than the traditional electrochemical model. However, the prerequisite of accurate modelling is the knowledge of many interna] VRB parameters, such as battery cell dimensions, thermal hydraulic design, and effect of shunt current, which can be easily affected by manufacturing techniques and are subject to change in different operating environments. In 2016, an online model identiiica-tion method was to address the issue [Li, Ran; Zhang, Xinan; Iu, Herbert; Fernando, Tyrone] Univ Western Australia, Dept Elect Elect & Comp Engn, Perth 6009, Australia; [Xiong, Binyu; Zhang, Shaofeng] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China; [Li, Yifeng] Voith Grp, Heidenheim, Germany University of Western Australia; Wuhan University of Technology Zhang, XA (corresponding author), Univ Western Australia, Dept Elect Elect & Comp Engn, Perth 6009, Australia. xinan.zhang@uwa.edu.au ; Zhang, Xinan/B-2872-2017 Fernando, Tyrone/0000-0003-0140-8887; Zhang, Xinan/0000-0002-9472-8785 Australian Future Battery Industries Corporate Research Center ?; project [BU/PG: 00660/55005100] Australian Future Battery Industries Corporate Research Center ?; project Acknowledgement This work is funded by Australian Future Battery Industries Corporate Research Center ?Mine Electrification? project (grant number BU/PG: 00660/55005100) . 29 0 0 3 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-152X 2352-1538 J ENERGY STORAGE J. Energy Storage MAY 2023.0 61 106767 10.1016/j.est.2023.106767 0.0 FEB 2023 11 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 9C9PO 2023-03-23 WOS:000935740500001 0
J Sui, MY; Wang, YJ; Han, XY; Kloeden, PE Sui, Meiyu; Wang, Yejuan; Han, Xiaoying; Kloeden, Peter E. Random recurrent neural networks with delays JOURNAL OF DIFFERENTIAL EQUATIONS English Article Random recurrent neural network; Random attractor; Multi-valued non compact random dynamical system; Extremal random complete trajectory; Variable delay; Monotone GLOBAL EXPONENTIAL STABILITY; PULLBACK ATTRACTORS; STOCHASTIC STABILITY; ASYMPTOTIC STABILITY; DYNAMICAL-SYSTEMS; LATTICE SYSTEMS; DISCRETE; EQUATIONS An infinite lattice model of a recurrent neural network with random connection strengths between neurons is developed and analyzed. To incorporate the presence of various type of delays in the neural networks, both discrete and distributed time varying delays are considered in the model. For the existence of random pullback attractors and periodic attractors, the nonlinear terms of the resulting system are not expected to be Lipschitz continuous, but only satisfy a weaker continuity assumption along with growth conditions, under which the uniqueness of the underlying Cauchy problem may not hold. Then after extending the concept and theory of monotone multi-valued semiflows to the random context, the structure of random pullback attractors with or without periodicity is investigated. In particular, the existence and stability properties of extremal random complete trajectories are studied. (c) 2020 Elsevier Inc. All rights reserved. [Sui, Meiyu; Wang, Yejuan] Lanzhou Univ, Sch Math & Stat, Gansu Key Lab Appl Math & Complex Syst, Lanzhou 730000, Peoples R China; [Han, Xiaoying] Auburn Univ, Dept Math & Stat, Auburn, AL 36849 USA; [Kloeden, Peter E.] Univ Tubingen, Math Inst, D-72076 Tubingen, Germany Lanzhou University; Auburn University System; Auburn University; Eberhard Karls University of Tubingen Wang, YJ (corresponding author), Lanzhou Univ, Sch Math & Stat, Gansu Key Lab Appl Math & Complex Syst, Lanzhou 730000, Peoples R China. suimy17@lzu.edu.cn; wangyj@lzu.edu.cn; xzh0003@auburn.edu; kloeden@na.uni-tuebingen.de NSF of China [41875084, 11571153]; Fundamental Research Funds for the Central Universities [lzujbky-2018-it58, lzujbky-2018-ot03] NSF of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was partially supported by NSF of China (Grant No. 41875084, 11571153) and the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2018-it58, lzujbky-2018-ot03). 42 15 15 2 27 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0022-0396 1090-2732 J DIFFER EQUATIONS J. Differ. Equ. NOV 5 2020.0 269 10 8597 8639 10.1016/j.jde.2020.06.008 0.0 43 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics MH2PA 2023-03-23 WOS:000546572400028 0
J Shi, YC; Song, RZ; Wang, LP; Qi, YJ; Zhang, HX; Zhu, JL; Zhang, XB; Tang, XW; Zhan, QQ; Zhao, Y; Swaab, DF; Bao, AM; Zhang, ZJ Shi, Yachen; Song, Ruize; Wang, Liping; Qi, Yangjian; Zhang, Hongxing; Zhu, Jianli; Zhang, Xiaobin; Tang, Xiaowei; Zhan, Qiongqiong; Zhao, Yang; Swaab, Dick F.; Bao, Ai-Min; Zhang, Zhijun Identifying Plasma Biomarkers with high specificity for major depressive disorder: A multi-level proteomics study JOURNAL OF AFFECTIVE DISORDERS English Article Machine learning; Major depressive disorder; Plasma; Proteomics; Postmortem brain tissue; Vitamin D-binding protein VITAMIN-D-BINDING; C-REACTIVE PROTEIN; GROUP-SPECIFIC COMPONENT; BIPOLAR DISORDER; RATING-SCALE; PSYCHIATRIC-DISORDERS; SERUM; IDENTIFICATION; GENE; SCHIZOPHRENIA Background: There are currently no objective diagnostic biomarkers for major depressive disorder (MDD) due to the biological complexity of the disorder. The existence of blood-based biomarkers with high specificity would be convenient for the clinical diagnosis of MDD. Methods: A comprehensive plasma proteomic analysis was conducted in a highly homogeneous cohort [7 drug-naive MDD patients and 7 healthy controls (HCs)], with bioinformatics analysis combined with machine learning used to screen candidate proteins. Verification of reproducibility and specificity was conducted in independent cohorts [60 HCs and 74 MDD, 42 schizophrenia (SZ) and 39 bipolar I disorder (BD-I) drug-naive patients]. Furthermore, verification of consistency was accomplished by proteomic analysis of postmortem brain tissue from 16 MDD patients and 16 HCs. Results: Levels of C-reactive protein (CRP), antithrombin III (ATIII), interalpha-trypsin inhibitor heavy chain 4 (ITIH4) and vitamin D-binding protein (VDB) were significantly higher in MDD patients, both in the discovery cohort and independent replication cohort. In comparison with SZ or BD-I patients, two proteins (VDB and ITIH4) were significantly elevated only in MDD patients. In addition, increased VDB and ITIH4 were observed consistently in both plasma and postmortem dorsolateral prefrontal cortex tissues of MDD patients. Furthermore, a panel consisting of all four plasma proteins was able to distinguish MDD patients from HCs or SZ or BD-I patients with the highest accuracy. Conclusion: Plasma ITIH4 and VDB may be potential plasma biomarkers of MDD with high specificity. The four-protein panel is more suitable as a potential clinical diagnostic marker for MDD. [Shi, Yachen; Song, Ruize; Zhang, Zhijun] Southeast Univ, Sch Med, ZhongDa Hosp, Dept Neurol, Nanjing 210009, Jiangsu, Peoples R China; [Wang, Liping] Nanjing Univ Aeronaut & Astronaut, Dept Math, Nanjing 210016, Jiangsu, Peoples R China; [Qi, Yangjian; Bao, Ai-Min] Zhejiang Univ, Dept Neurobiol, Key Lab Med Neurobiol,Sch Med, Minist Hlth China,Zhejiang Prov Key Lab Neurobiol, Hangzhou 310058, Zhejiang, Peoples R China; [Zhang, Hongxing; Zhu, Jianli; Zhang, Zhijun] Xinxiang Med Univ, Dept Psychol, Xinxiang 453003, Henan, Peoples R China; [Zhang, Xiaobin; Tang, Xiaowei; Zhan, Qiongqiong] Yangzhou Univ, Dept Psychiat, WuTaiShan Hosp, Coll Med, Yangzhou 225003, Jiangsu, Peoples R China; [Zhang, Xiaobin] Soochow Univ, Suzhou Psychiat Hosp, Guangji Hosp, Suzhou 215008, Jiangsu, Peoples R China; [Zhao, Yang] Nanjing Med Univ, Sch Publ Hlth, Dept Biostat, Nanjing 211100, Jiangsu, Peoples R China; [Swaab, Dick F.] Royal Netherlands Acad Arts & Sci, Netherlands Inst Neurosci, NL-1105 BA Amsterdam, Netherlands; [Zhang, Zhijun] Zhejiang Univ, Mental Hlth Ctr, Sch Med, Hangzhou 310013, Zhejiang, Peoples R China Southeast University - China; Nanjing University of Aeronautics & Astronautics; Zhejiang University; Xinxiang Medical University; Yangzhou University; Soochow University - China; Nanjing Medical University; Royal Netherlands Academy of Arts & Sciences; Netherlands Institute for Neuroscience (NIN-KNAW); Zhejiang University Zhang, ZJ (corresponding author), Southeast Univ, Sch Med, ZhongDa Hosp, Dept Neurol, Nanjing 210009, Jiangsu, Peoples R China.;Bao, AM (corresponding author), Zhejiang Univ, Dept Neurobiol, Key Lab Med Neurobiol,Sch Med, Minist Hlth China,Zhejiang Prov Key Lab Neurobiol, Hangzhou 310058, Zhejiang, Peoples R China.;Zhang, ZJ (corresponding author), Xinxiang Med Univ, Dept Psychol, Xinxiang 453003, Henan, Peoples R China.;Zhang, ZJ (corresponding author), Zhejiang Univ, Mental Hlth Ctr, Sch Med, Hangzhou 310013, Zhejiang, Peoples R China. baoaimin@zju.edu.cn; janemengzhang@vip.163.com zhao, yang/GXF-4424-2022 National Key Research and Development Plan of China [2016YFC1306700]; National Natural Science Key Foundation of China [81830040]; Science and Technology Program of Guangdong [2018B030334001]; Program of Excellent Talents in Medical Science of Jiangsu Province [JCRCA2016006]; National Natural Science Foundation of China [81971268, 11971231, 31271130]; Programme of Introducing Talents of Discipline to Universities of China [B13026]; Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences [2017PT31038, 2018PT31041] National Key Research and Development Plan of China; National Natural Science Key Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Guangdong; Program of Excellent Talents in Medical Science of Jiangsu Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Programme of Introducing Talents of Discipline to Universities of China(Ministry of Education, China - 111 Project); Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences This study was partly supported by the National Key Research and Development Plan of China (No. 2016YFC1306700), the National Natural Science Key Foundation of China (No. 81830040), Science and Technology Program of Guangdong (No. 2018B030334001), Program of Excellent Talents in Medical Science of Jiangsu Province (No. JCRCA2016006), National Natural Science Foundation of China (No. 81971268, No. 11971231, and No. 31271130), the Programme of Introducing Talents of Discipline to Universities of China (No. B13026), and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (No. 2017PT31038 and No. 2018PT31041). 66 11 12 2 28 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0165-0327 1573-2517 J AFFECT DISORDERS J. Affect. Disord. DEC 1 2020.0 277 620 630 10.1016/j.jad.2020.08.078 0.0 11 Clinical Neurology; Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Psychiatry OA0DM 32905914.0 2023-03-23 WOS:000577467600010 0
C Tallon-Ballesteros, AJ; Correia, L; Fong, S Alsmirat, M; Jararweh, Y; Awaysheh, F; Aloqaily, M Tallon-Ballesteros, Antonio J.; Correia, Luis; Fong, Simon Hybrid scatter and ant search feature subset selection: applications in classification problems 2021 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT DATA SCIENCE TECHNOLOGIES AND APPLICATIONS (IDSTA) English Proceedings Paper 2nd International Conference on Intelligent Data Science Technologies and Applications (IDSTA) NOV 15-17, 2021 ELECTR NETWORK IEEE Estonia Sect,Univ Tartu,Intelligent Technologies Org metaheuristic; feature subset selection; machine learning; scatter search; ant search OPTIMIZATION; ALGORITHM; PSO This paper presents an approach to metaheuristic-based feature subset selection. Feature selection is an NP-problem. This means that finding the optimal feature set is an intractable problem. Approximate algorithms are very convenient since the exhaustive search is prohibitive due to the extremely high computational cost. Metaheuristics are a key approach in the field of data mining and especially in data pre-processing. Scatter search has been applied in context of feature selection although ant search is more widespread in the data preparation area. This contribution hybridises both search strategies to get a prediction model which is able to predict faster and more accurately with further unseen data. First the scatter search takes place and the reached solution is improved by means of an ant search. The test-bed comprises a good number of high-dimensional data sets. Results are very substantial since the second metaheuristic improves greatly the performance of the classifiers and reduces the models' complexity in terms of input feature space. [Tallon-Ballesteros, Antonio J.] Univ Huelva, Dept Electr Comp Sys & Autom Engn, Huelva, Spain; [Correia, Luis] Univ Lisbon, Dept Informat LASIGE Ciencias, Lisbon, Portugal; [Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa, Macau Sar, Peoples R China Universidad de Huelva; Universidade de Lisboa; University of Macau Tallon-Ballesteros, AJ (corresponding author), Univ Huelva, Dept Electr Comp Sys & Autom Engn, Huelva, Spain. antonio.tallon.diesia@zimbra.uhu.es; Luis.Correia@ciencias.ulisboa.pt; ccfong@um.edu.mo Tallón-Ballesteros, Antonio J/HNB-6529-2023 (Spanish Inter-Ministerial Commission of Science and Technology), FEDER funds [TIN2017-88209-C2-2-R]; FCT through LASIGE Research Unit [UIDP/00408/2020]; Junta de Andalucia [US-1263341] (Spanish Inter-Ministerial Commission of Science and Technology), FEDER funds; FCT through LASIGE Research Unit(Fundacao para a Ciencia e a Tecnologia (FCT)); Junta de Andalucia(Junta de Andalucia) A. T.-B. was partly subsidised by these projects: TIN2017-88209-C2-2-R (Spanish Inter-Ministerial Commission of Science and Technology), FEDER funds and US-1263341 (Junta de Andalucia), L. C. was partly funded by FCT through LASIGE Research Unit, ref. UIDB, UIDP/00408/2020. 13 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2180-5 2021.0 150 153 10.1109/IDSTA53674.2021.9660823 0.0 4 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT8DN 2023-03-23 WOS:000852877600022 0
C Hu, YB; Chen, G; Ning, XY; Dong, JH; Liu, S; Knoll, A IEEE Hu, Yingbai; Chen, Guang; Ning, Xiangyu; Dong, Jinhu; Liu, Shu; Knoll, Alois Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) NOV 04-08, 2019 Macau, PEOPLES R CHINA IEEE,RSJ Learning by imitation is a powerful way that can reduce the complexly in searching space. It could help the mobile robot to acquire new skills from interaction with a human-being in natural way. In this paper, the dynamic movement primitives (DMPs) is utilized to imitate the trajectory from human walking. DMPs is a modified formulation of virtual spring-dampers (VSD) system that enjoys better fitting performance in learning. Further, while dealing with the trajectory tracking problem of mobile robots, a novel nonlinear model predictive control (MPC) approach is proposed for motion control. The nonlinear MPC scheme applies a new neural network named Varying-parameter Lagrangian Neural Network (VP-LNN) to solve a Quadratic Programming (QP) problem by iterating over a finite receding horizon. The new network of VP-LNN can converge to the global optimal solution. Thus, a new human-robot interaction (HRI) scheme for mobile robot is proposed, which can reduce the complexity in motion planning in various applications. [Hu, Yingbai; Chen, Guang; Knoll, Alois] Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, D-85748 Munich, Germany; [Chen, Guang; Ning, Xiangyu; Dong, Jinhu] Tongji Univ, Coll Automot Engn, Shanghai 201804, Peoples R China; [Liu, Shu] ETHZurich, D MTEC, CH-8092 Zurich, Switzerland Technical University of Munich; Tongji University Chen, G (corresponding author), Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, D-85748 Munich, Germany.;Chen, G (corresponding author), Tongji Univ, Coll Automot Engn, Shanghai 201804, Peoples R China. yingbai.hu@tum.de; guangchen@tongji.edu.cn; sheldonnxy@gmail.com; 317684920@qq.com; liush@ethz.ch; knoll@in.tum.de Knoll, Alois/AAN-8417-2021; Liu, Shu/AFT-1390-2022 Knoll, Alois/0000-0003-4840-076X; Liu, Shu/0000-0002-9576-9335 European Union [785907]; Shanghai AI Innovative Development Project European Union(European Commission); Shanghai AI Innovative Development Project The research leading to these results has partially received funding from the European Union's Horizon 2020 Research and Innovation Program in accordance with Grant Agreement No. 785907 (HBP SGA2), and from the Shanghai AI Innovative Development Project. 19 6 6 2 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-7281-4004-9 IEEE INT C INT ROBOT 2019.0 5057 5062 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics BP2QS Green Published 2023-03-23 WOS:000544658404022 0
J Portelli, B; Scaboro, S; Tonino, R; Chersoni, E; Santus, E; Serra, G Portelli, Beatrice; Scaboro, Simone; Tonino, Roberto; Chersoni, Emmanuele; Santus, Enrico; Serra, Giuseppe Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets JOURNAL OF MEDICAL INTERNET RESEARCH English Article adverse drug events; COVID-19; digital pharmacovigilance; opinion mining; vaccines; social media; machine learning; deep learning; learning models; sentiment analysis; Twitter analysis; Twitter; web portal; public health Background: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot-related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions: We present a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model. [Portelli, Beatrice; Scaboro, Simone; Tonino, Roberto; Serra, Giuseppe] Univ Udine, Dept Math Comp Sci & Phys, 206 Via Sci, I-33100 Udine, Italy; [Portelli, Beatrice] Univ Napoli Federico II, Dept Biol, Naples, Italy; [Chersoni, Emmanuele] Hong Kong Polytech Univ, Dept Chinese & Bilingual Studies, Hung Hom, Hong Kong, Peoples R China; [Santus, Enrico] Bayer Pharmaceut, Decis Sci & Adv Analyt Med Affairs & Pharmacovigi, Whippany, NJ USA University of Udine; University of Naples Federico II; Hong Kong Polytechnic University; Bayer AG; Bayer Healthcare Pharmaceuticals Portelli, B (corresponding author), Univ Udine, Dept Math Comp Sci & Phys, 206 Via Sci, I-33100 Udine, Italy. portelli.beatrice@spes.uniud.it ; Serra, Giuseppe/M-3572-2015 Tonino, Roberto/0000-0001-7630-7330; Serra, Giuseppe/0000-0002-4269-4501; Portelli, Beatrice/0000-0001-8887-616X; Scaboro, Simone/0000-0003-2533-1298; Chersoni, Emmanuele/0000-0001-8742-0451 54 0 0 5 10 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA 1438-8871 J MED INTERNET RES J. Med. Internet Res. MAY 13 2022.0 24 5 e35115 10.2196/35115 0.0 17 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics 1N3XW 35446781.0 gold, Green Accepted 2023-03-23 WOS:000800593100002 0
J Youn, SY; Choi, MH; Kim, DH; Lee, YJ; Huisman, H; Johnson, E; Penzkofer, T; Shabunin, I; Winkel, DJ; Xing, PY; Szolar, D; Grimm, R; von Busch, H; Son, Y; Lou, B; Kamen, A Youn, Seo Yeon; Choi, Moon Hyung; Kim, Dong Hwan; Lee, Young Joon; Huisman, Henkjan; Johnson, Evan; Penzkofer, Tobias; Shabunin, Ivan; Winkel, David Jean; Xing, Pengyi; Szolar, Dieter; Grimm, Robert; von Busch, Heinrich; Son, Yohan; Lou, Bin; Kamen, Ali Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience EUROPEAN JOURNAL OF RADIOLOGY English Article Deep learning; Prostate; Prostate neoplasms; Prostate imaging reporting and data system; Magnetic resonance imaging DATA SYSTEM; CANCER; ACCURACY; BIOPSY Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PIRADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI. Methods: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value > 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test. Results: Clinically significant cancer [CSC, Gleason score > 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value > 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value > 4. Conclusions: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice. [Youn, Seo Yeon; Choi, Moon Hyung; Kim, Dong Hwan; Lee, Young Joon] Catholic Univ Korea, Coll Med, Dept Radiol, Seoul St Marys Hosp, Seoul, South Korea; [Choi, Moon Hyung; Lee, Young Joon] Catholic Univ Korea, Coll Med, Dept Radiol, Eunpyeong St Marys Hosp, 1021 Tongil Ro, Seoul 03312, South Korea; [Huisman, Henkjan] Radboud Univ Nijmegen, Dept Radiol, Med Ctr, Nijmegen, Netherlands; [Johnson, Evan] NYU, Dept Radiol, New York, NY 10003 USA; [Penzkofer, Tobias] Charite Univ Med Berlin, Dept Radiol, Berlin, Germany; [Shabunin, Ivan] Patero Clin, Moscow, Russia; [Winkel, David Jean] Univ Hosp Basel, Dept Radiol, Basel, Switzerland; [Xing, Pengyi] Changhai Hosp, Dept Radiol, Shanghai, Peoples R China; [Szolar, Dieter] Diagnostikum Graz Sud West, Graz, Austria; [Grimm, Robert; von Busch, Heinrich] Siemens Healthcare, Diagnost Imaging, Erlangen, Germany; [Son, Yohan] Siemens Healthineers Ltd, Seoul, South Korea; [Lou, Bin; Kamen, Ali] Siemens Healthineers, Digital Technol & Innovat, Princeton, NJ USA Catholic University of Korea; Seoul St. Mary's Hospital; Catholic University of Korea; Radboud University Nijmegen; New York University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; University of Basel; Naval Medical University; Siemens AG; Siemens Germany; Siemens AG Choi, MH (corresponding author), Catholic Univ Korea, Coll Med, Dept Radiol, Eunpyeong St Marys Hosp, 1021 Tongil Ro, Seoul 03312, South Korea. cmh@catholic.ac.kr; rhdwngkwk333@naver.com; yjleerad@catholic.ac.kr; henkjan.huisman@radboudumc.nl; tobias.penzkofer@charite.de; shabunin@pateroclinic.ru; davidjean.winkel@usb.ch; 746992685@qq.com; dieter.szolar@diagnostikum-graz.at; robertgrimm@siemens-healthineers.com; heinrich.von_busch@siemens-healthineers.com; yohan.son@siemens-healthineers.com; bin.lou@siemens-healthineers.com; ali.kamen@siemens-healthineers.com Penzkofer, Tobias/AFN-1192-2022; xing, peng/HDM-6843-2022 Penzkofer, Tobias/0000-0001-9591-8575; National Research Foundation of Korea (NRF) [2018R1D1A1B07050160] National Research Foundation of Korea (NRF)(National Research Foundation of Korea) This work was supported by the National Research Foundation of Korea (NRF) under Grant (2018R1D1A1B07050160) . 27 5 5 0 2 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0720-048X 1872-7727 EUR J RADIOL Eur. J. Radiol. SEP 2021.0 142 109894 10.1016/j.ejrad.2021.109894 0.0 AUG 2021 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging UE4SJ 34388625.0 Bronze 2023-03-23 WOS:000687879800002 0
J Fang, J; Wang, T; Li, CC; Hu, XP; Ngai, E; Seet, BC; Cheng, J; Guo, Y; Jiang, X Fang, Jing; Wang, Tao; Li, Cancheng; Hu, Xiping; Ngai, Edith; Seet, Boon-Chong; Cheng, Jun; Guo, Yi; Jiang, Xin Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality IEEE ACCESS English Article Depression prevalence; depression analysis; gait abnormality; machine learning SUICIDAL IDEATION; MAJOR DEPRESSION; COLLEGE-STUDENTS; ANXIETY; SYMPTOMS; VALIDITY; INDIVIDUALS; RECOGNITION; PATTERNS; DISORDER In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis. [Fang, Jing; Hu, Xiping; Cheng, Jun] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Wang, Tao; Li, Cancheng; Hu, Xiping] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China; [Ngai, Edith] Uppsala Univ, Dept Informat Technol, S-75105 Uppsala, Sweden; [Seet, Boon-Chong] Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand; [Guo, Yi] Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Neurol,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China; [Jiang, Xin] Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Geriatr,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Lanzhou University; Uppsala University; Auckland University of Technology; Jinan University; Southern University of Science & Technology; Jinan University; Southern University of Science & Technology Hu, XP (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China.;Hu, XP (corresponding author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China.;Jiang, X (corresponding author), Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Geriatr,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China. xp.hu@siat.ac.cn; jiangxinsz@163.com Li, Cancheng/AAX-4702-2020; Wang, Tao/AAN-2496-2021; Hu, Xiping/GSE-5065-2022 Li, Cancheng/0000-0002-1952-4547; Wang, Tao/0000-0002-3038-0097; Hu, Xiping/0000-0002-4952-699X; Guo, Yi/0000-0002-2614-6118 Shenzhen Technology [JSGG20170413171746130]; National Natural Science Foundation of China [61632014, 61802159, 61210010, 61772508, 61402211]; Program of Beijing Municipal Science and Technology Commission [Z171100000117005] Shenzhen Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program of Beijing Municipal Science and Technology Commission This work was supported in part by Shenzhen Technology under Project JSGG20170413171746130, in part by the National Natural Science Foundation of China under Grant 61632014, Grant 61802159, Grant 61210010, Grant 61772508, and Grant 61402211, and in part by the Program of Beijing Municipal Science and Technology Commission under Grant Z171100000117005. 60 20 21 11 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 174425 174437 10.1109/ACCESS.2019.2957179 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications KF6XN gold, Green Submitted 2023-03-23 WOS:000509384100004 0
J Gao, L; Zhang, LX; Meng, HY; Ren, YH; Lai, YK; Kobbelt, L Gao, Lin; Zhang, Ling-Xiao; Meng, Hsien-Yu; Ren, Yi-Hui; Lai, Yu-Kun; Kobbelt, Leif PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS English Article Three-dimensional displays; Shape; Geometry; Two dimensional displays; Feature extraction; Solid modeling; Computational modeling; Unsupervised learning; convolutional neural network; symmetry detection; 3D models; planar reflective symmetry In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this article, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces. [Gao, Lin; Zhang, Ling-Xiao; Ren, Yi-Hui] Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China; [Gao, Lin; Zhang, Ling-Xiao; Ren, Yi-Hui] Univ Chinese Acad Sci, Beijing 100190, Peoples R China; [Meng, Hsien-Yu] Univ Maryland, College Pk, MD 20742 USA; [Lai, Yu-Kun] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales; [Kobbelt, Leif] Rhein Westfal TH Aachen, Inst Comp Graph & Multimedia, D-52062 Aachen, Germany Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University System of Maryland; University of Maryland College Park; Cardiff University; RWTH Aachen University Gao, L (corresponding author), Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China. gaolin@ict.ac.cn; zhanglingxiao@ict.ac.cn; mengxy19@cs.umd.edu; renyihui17@mails.ucas.ac.cn; LaiY4@cardiff.ac.uk; kobbelt@cs.rwth-aachen.de Kobbelt, Leif/0000-0002-7880-9470 National Natural Science Foundation of China [61872440, 61828204]; Beijing Municipal Natural Science Foundation [L182016]; Royal Society Newton Advanced Fellowship [NAF\R2\192151]; Youth Innovation Promotion Association CAS; CCF-Tencent Open Fund; Tencent AI Lab Rhino-Bird Focused Research Program [JR202024]; Open Project Program of the National Laboratory of Pattern Recognition [201900055] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Royal Society Newton Advanced Fellowship; Youth Innovation Promotion Association CAS; CCF-Tencent Open Fund; Tencent AI Lab Rhino-Bird Focused Research Program; Open Project Program of the National Laboratory of Pattern Recognition This work was supported by National Natural Science Foundation of China (No. 61872440 and No. 61828204), Beijing Municipal Natural Science Foundation (No. L182016), the Royal Society Newton Advanced Fellowship (No. NAF\R2\192151), Youth Innovation Promotion Association CAS, CCF-Tencent Open Fund, Tencent AI Lab RhinoBird Focused Research Program (No.JR202024) and Open Project Program of the National Laboratory of Pattern Recognition (No. 201900055). 61 10 10 2 18 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1077-2626 1941-0506 IEEE T VIS COMPUT GR IEEE Trans. Vis. Comput. Graph. JUN 1 2021.0 27 6 3007 3018 10.1109/TVCG.2020.3003823 0.0 12 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science SA9KC 32746265.0 Green Accepted, Green Submitted 2023-03-23 WOS:000649620700018 0
J Kong, QQ; Yu, CS; Xu, Y; Iqbal, T; Wang, WW; Plumbley, MD Kong, Qiuqiang; Yu, Changsong; Xu, Yong; Iqbal, Turab; Wang, Wenwu; Plumbley, Mark D. Weakly Labelled AudioSet Tagging With Attention Neural Networks IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Audio tagging; AudioSet; attention neural network; weakly labelled data; multiple instance learning ACOUSTIC SCENES; CLASSIFICATION; RECOGNITION Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognizing a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, whereas the onset and offset times are unknown. To address the weakly labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths, and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision of 0.369, outperforming the best MIL method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet-embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class. [Kong, Qiuqiang; Iqbal, Turab; Wang, Wenwu; Plumbley, Mark D.] Univ Surrey, Ctr Vis Speech & Signal Proc, Guildford GU2 7XH, Surrey, England; [Yu, Changsong] Univ Stuttgart, D-70174 Stuttgart, Germany; [Xu, Yong] Tencent AI Lab, Bellevue, WA 98004 USA; [Wang, Wenwu] Qingdao Univ Sci & Technol, Qingdao 266071, Shandong, Peoples R China University of Surrey; University of Stuttgart; Qingdao University of Science & Technology Xu, Y (corresponding author), Tencent AI Lab, Bellevue, WA 98004 USA. q.kong@surrey.ac.uk; changsong_yu@yahoo.de; lucayongxu@tencent.com; t.iqbal@surrey.ac.uk; w.wang@surrey.ac.uk; m.plumbley@surrey.ac.uk wang, wenwu/HOF-4371-2023; xu, yong/AAQ-8221-2020; Plumbley, Mark D/A-7298-2008 xu, yong/0000-0003-4944-6890; Plumbley, Mark D/0000-0002-9708-1075; Iqbal, Turab/0000-0003-3393-2544; Wang, Wenwu/0000-0002-8393-5703 EPSRC [EP/N014111/1]; China Scholarship Council [201406150082]; EPSRC Doctoral Training Partnership [1976218, EP/N509772/1]; EPSRC [EP/N014111/1] Funding Source: UKRI EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); China Scholarship Council(China Scholarship Council); EPSRC Doctoral Training Partnership(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the EPSRC Grant EP/N014111/1 Making Sense of Sounds, in part by the Research Scholarship from the China Scholarship Council 201406150082, and in part by a studentship (Reference: 1976218) from the EPSRC Doctoral Training Partnership under Grant EP/N509772/1. 58 46 46 2 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 2329-9304 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. NOV 2019.0 27 11 1791 1802 10.1109/TASLP.2019.2930913 0.0 12 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering IT1EW Green Published, Green Submitted 2023-03-23 WOS:000482589900001 0
J Meyer-Base, A; Amani, AM; Meyer-Base, U; Foo, S; Stadlbauer, A; Yu, W Meyer-Base, A.; Amani, A. Moradi; Meyer-Base, U.; Foo, S.; Stadlbauer, A.; Yu, W. Pinning observability of competitive neural networks with different time-constants NEUROCOMPUTING English Article Multi-time-scale competitive neural network; Synchronization; Pinning observability COMPLEX DYNAMICAL NETWORK; EXPONENTIAL STABILITY; SYNCHRONIZATION The new concept called pinning observability is proposed for competitive neural networks with different time-scales and a distributed observer structure, which is determined to estimate the states of this large scale network. This network observer has local distinct sub-observers that process local information at the node level but exchange their state estimates with the neighboring observers and thus reflect the interconnection structure of the neural network. The goal is to employ only a minimum number of measurements at certain nodes within the whole neural network, however to be able to estimate the entire state of the network. This can be interpreted as the dual problem to the longer studied pinning control problem. In this paper, we formulate the proposed approach for the two most common competitive neural networks with different time-scales and derive some decoupled and simple conditions for pinning observability. The sub-observers are driven by only local neuron level information but communicate the estimated local states with the neighboring observers. This exchange of local information is the basis of cortical neural processing. The monitoring of few signals from the network holds important practical application for brain signal processing. Simulation examples are given to illustrate the theoretical concepts. (C) 2018 Published by Elsevier B.V. [Meyer-Base, A.] Florida State Univ, Dept Sci Comp, Tallahassee, FL 32306 USA; [Amani, A. Moradi] RMIT Univ, Sch Engn, Melbourne, Vic 3001, Australia; [Meyer-Base, U.; Foo, S.] Florida State Univ, Dept Elect & Comp Engn, Tallahassee, FL 32310 USA; [Stadlbauer, A.] Univ Erlangen Nurnberg, Dept Neurosurg, Nurnberg, Germany; [Yu, W.] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China State University System of Florida; Florida State University; Royal Melbourne Institute of Technology (RMIT); State University System of Florida; Florida State University; University of Erlangen Nuremberg; Southeast University - China Amani, AM (corresponding author), RMIT Univ, Sch Engn, Melbourne, Vic 3001, Australia. ali.moradiamani@rmit.edu.au Amani, Ali Moradi/AAL-5202-2020; Amani, Ali Moradi/AAT-1251-2021; Yu, Wenwu/G-5496-2012; Meyer-Baese, Anke/E-9745-2010; Amani, Ali Mohammad/O-3690-2017; Stadlbauer, Andreas/ABF-6235-2020 Yu, Wenwu/0000-0003-0301-9180; Meyer-Baese, Anke/0000-0001-6363-2687; Amani, Ali Mohammad/0000-0002-7034-3071; Moradi Amani, Ali/0000-0001-5158-4307 Fulbright Award Fulbright Award We acknowledge funding through a Fulbright Award. 22 3 3 1 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing FEB 15 2019.0 329 97 102 10.1016/j.neucom.2018.09.053 0.0 6 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HF1KD 2023-03-23 WOS:000453924300010 0
J Siddiqui, SA; Zhang, Y; Feng, ZQ; Kos, A Siddiqui, Sarah Ali; Zhang, Yuan; Feng, Zhiquan; Kos, Anton A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera JOURNAL OF MEDICAL SYSTEMS English Article Pulse rate; PhotoPlethysmoGraph (PPG); Smartphone sensor; Mobile health HEART-RATE; SIGNAL; SYSTEM The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively. [Siddiqui, Sarah Ali; Zhang, Yuan; Feng, Zhiquan] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China; [Kos, Anton] Univ Ljubljana, Fac Elect Engn, Ljubljana, Slovenia University of Jinan; University of Ljubljana Zhang, Y (corresponding author), Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China. yzhang@ujn.edu.cn Kos, Anton/0000-0001-6234-8561; Zhang, Yuan/0000-0003-2726-2855 National Natural Science Foundation of China [61572231, 61173079, 61472163]; Natural Science Foundation of Shandong, China [ZR2013FM004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong, China(Natural Science Foundation of Shandong Province) This work was supported in part by the National Natural Science Foundation of China under Grant 61572231, 61173079 and 61472163, in part by the Natural Science Foundation of Shandong, China under Grant ZR2013FM004. The corresponding author is Yuan Zhang. 28 29 30 5 34 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0148-5598 1573-689X J MED SYST J. Med. Syst. MAY 2016.0 40 5 126 10.1007/s10916-016-0485-6 0.0 6 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics DK5KR 27067432.0 2023-03-23 WOS:000374959300006 0
J Brockherde, F; Vogt, L; Li, L; Tuckerman, ME; Burke, K; Muller, KR Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E.; Burke, Kieron; Mueller, Klaus-Robert Bypassing the Kohn-Sham equations with machine learning NATURE COMMUNICATIONS English Article DENSITY; PSEUDOPOTENTIALS; ENERGY; APPROXIMATION; SPACE; DFT Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems. [Brockherde, Felix; Mueller, Klaus-Robert] Tech Univ Berlin, Machine Learning Grp, Marchstr 23, D-10587 Berlin, Germany; [Brockherde, Felix] Max Planck Inst Mikrostrukturphys, Weinberg 2, D-06120 Halle, Germany; [Vogt, Leslie; Tuckerman, Mark E.] NYU, Dept Chem, New York, NY 10003 USA; [Li, Li; Burke, Kieron] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA; [Tuckerman, Mark E.] NYU, Courant Inst Math Sci, New York, NY 10003 USA; [Tuckerman, Mark E.] NYU Shanghai, NYU ECNU Ctr Computat Chem, 3663 Zhongshan Rd North, Shanghai 200062, Peoples R China; [Burke, Kieron] Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA; [Mueller, Klaus-Robert] Korea Univ, Dept Brain & Cognit Engn, Seoul 136713, South Korea; [Mueller, Klaus-Robert] Max Planck Inst Informat, Stuhlsatzenhausweg, D-66123 Saarbrucken, Germany Technical University of Berlin; Max Planck Society; New York University; University of California System; University of California Irvine; New York University; NYU Shanghai; University of California System; University of California Irvine; Korea University; Max Planck Society Muller, KR (corresponding author), Tech Univ Berlin, Machine Learning Grp, Marchstr 23, D-10587 Berlin, Germany.;Tuckerman, ME (corresponding author), NYU, Dept Chem, New York, NY 10003 USA.;Burke, K (corresponding author), Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.;Tuckerman, ME (corresponding author), NYU, Courant Inst Math Sci, New York, NY 10003 USA.;Tuckerman, ME (corresponding author), NYU Shanghai, NYU ECNU Ctr Computat Chem, 3663 Zhongshan Rd North, Shanghai 200062, Peoples R China.;Burke, K (corresponding author), Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA.;Muller, KR (corresponding author), Korea Univ, Dept Brain & Cognit Engn, Seoul 136713, South Korea.;Muller, KR (corresponding author), Max Planck Inst Informat, Stuhlsatzenhausweg, D-66123 Saarbrucken, Germany. mark.tuckerman@nyu.edu; kieron@uci.edu; klaus-robert.mueller@tu-berlin.de Mueller, Klaus-Robert/C-3196-2013; Mueller, Klaus-Robert/Y-3547-2019 Mueller, Klaus-Robert/0000-0002-3861-7685; Mueller, Klaus-Robert/0000-0002-3861-7685; Vogt-Maranto, Leslie/0000-0002-7006-4582 NSF [CHE-1464795]; Einstein Foundation; Institute for Information & Communications Technology Promotion (IITP) - Korea government [2017-0-00451]; US Army Research Office [W911NF-13-1-0387]; Direct For Mathematical & Physical Scien; Division Of Chemistry [1464795] Funding Source: National Science Foundation NSF(National Science Foundation (NSF)); Einstein Foundation; Institute for Information & Communications Technology Promotion (IITP) - Korea government(Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea); US Army Research Office; Direct For Mathematical & Physical Scien; Division Of Chemistry(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)) We thank J.C. Snyder for early discussions and H. Glawe for helpful guidance regarding the 3D reference computations. We thank IPAM at UCLA for repeated hospitality. Work at UC Irvine supported by NSF CHE-1464795. K.R.M. and F.B. thank the Einstein Foundation for generously funding the ETERNAL project. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (no. 2017-0-00451). Work at NYU supported by the US Army Research Office under contract/grant number W911NF-13-1-0387 (M.E.T. and L.V.). Ab initio trajectories were run using High Performance Computing resources at NYU. Other DFT simulations were run using High Performance Computing resources at MPI Halle. 65 379 386 20 212 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. OCT 11 2017.0 8 872 10.1038/s41467-017-00839-3 0.0 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics FJ5GE 29021555.0 Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000412776200009 0
J Xiao, JJ; Liu, T; Zhang, HX; Szczerbicki, E Xiao, Junjie; Liu, Tao; Zhang, Haoxi; Szczerbicki, Edward Adding Interpretability to Neural Knowledge DNA CYBERNETICS AND SYSTEMS English Article Decision trees; deep reinforcement learning; interpretable AI; neural knowledge DNA This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems. [Xiao, Junjie; Liu, Tao; Zhang, Haoxi] Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China; [Zhang, Haoxi] Adv Cryptog & Syst Secur Key Lab Sichuan Prov, Chengdu, Peoples R China; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Gdansk, Poland Chengdu University of Information Technology; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China. haoxi@cuit.edu.cn Zhang, Haoxi/0000-0002-1341-1912 Sichuan Science and Technology Program [2019YFH0185] Sichuan Science and Technology Program This work was supported by the Sichuan Science and Technology Program under Grant 2019YFH0185. 15 0 0 0 3 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. APR 22 2022.0 53 5 SI 500 509 10.1080/01969722.2021.2018548 0.0 DEC 2021 10 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1E2GJ 2023-03-23 WOS:000732579500001 0
C Yang, GY; Li, GQ; Pan, T; Kong, YY; Wu, JS; Shu, HZ; Luo, LM; Dillenseger, JL; Coatrieux, JL; Tang, LJ; Zhu, XM IEEE Yang, Guanyu; Li, Guoqing; Pan, Tan; Kong, Youyong; Wu, Jiasong; Shu, Huazhong; Luo, Limin; Dillenseger, Jean-Louis; Coatrieux, Jean-Louis; Tang, Lijun; Zhu, Xiaomei Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 24th International Conference on Pattern Recognition (ICPR) AUG 20-24, 2018 Chinese Acad Sci, Inst Automat, Beijing, PEOPLES R CHINA Int Assoc Pattern Recognit,Chinese Assoc Automat Chinese Acad Sci, Inst Automat CT image; segmentation; kidney; renal tumor; 3D fully convolutional neural network; pyramid pooling CLASSIFICATION; CANCER Renal cancer is one of ten most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) becomes the main therapeutic approach in treating renal cancer. Accurate kidney and tumor segmentation in CT images is a prerequisite step in the surgery planning. However, automatic and accurate kidney and renal tumor segmentation in CT images remains a challenge. In this paper, we propose a new method to perform a precise segmentation of kidney and renal tumor in CT angiography images. This method relies on a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM). The proposed network is implemented as an end-to-end learning system directly on 3D volumetric images. It can make use of the 3D spatial contextual information to improve the segmentation of the kidney as well as the tumor lesion. The experiments conducted on 140 patients show that these target structures can be segmented with a high accuracy. The resulting average dice coefficients obtained for kidney and renal tumor are equal to 0.931 and 0.802 respectively. These values are higher than those obtained from the other two neural networks. [Yang, Guanyu; Li, Guoqing; Pan, Tan; Kong, Youyong; Wu, Jiasong; Shu, Huazhong; Luo, Limin] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Lab Image Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Yang, Guanyu; Li, Guoqing; Pan, Tan; Kong, Youyong; Wu, Jiasong; Shu, Huazhong; Luo, Limin; Dillenseger, Jean-Louis; Coatrieux, Jean-Louis] Ctr Rech Informat Biomed SinoFrancais CRIBs, Rennes, France; [Dillenseger, Jean-Louis; Coatrieux, Jean-Louis] Univ Rennes 1, INSERM, U1099, LTSI, F-35000 Rennes, France; [Tang, Lijun; Zhu, Xiaomei] Nanjing Med Univ, Affiliated Hosp 1, Dept Radiol, Nanjing, Jiangsu, Peoples R China Southeast University - China; Universite de Rennes; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes; Nanjing Medical University Yang, GY (corresponding author), Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Lab Image Sci & Technol, Nanjing, Jiangsu, Peoples R China.;Yang, GY (corresponding author), Ctr Rech Informat Biomed SinoFrancais CRIBs, Rennes, France. yang.list@seu.edu.cn Dillenseger, Jean-Louis/Y-8683-2019; Coatrieux, Jean-Louis/ABC-8154-2020 Dillenseger, Jean-Louis/0000-0001-8840-3944; National Natural Science Foundation [31571001]; National Key Research and Development Program of China [2017YFC0107903]; Short-Term Recruitment Program of Foreign Experts [WQ20163200398]; Science Foundation for The Excellent Youth Scholars of Southeast University National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Short-Term Recruitment Program of Foreign Experts; Science Foundation for The Excellent Youth Scholars of Southeast University This research was supported by National Natural Science Foundation under grants (31571001), the National Key Research and Development Program of China (2017YFC0107903), the Short-Term Recruitment Program of Foreign Experts (WQ20163200398), and Science Foundation for The Excellent Youth Scholars of Southeast University. 19 30 32 0 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1051-4651 978-1-5386-3788-3 INT C PATT RECOG 2018.0 3790 3795 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL7LC Green Submitted 2023-03-23 WOS:000455146803132 0
J Duan, GD; Wang, XT; Huang, TX; Kurths, J Duan, Guiduo; Wang, Xiaotong; Huang, Tianxi; Kurths, Jurgen An Improved Group Similarity-Based Association Rule Mining Algorithm in Complex Scenes INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE English Article Association rule mining; speeding up; business big data; apache spark Association rule (AR) mining in complex scene has attracted extensive attention of researchers in recent years. Typically, many researchers focused on an algorithm itself and ignored a generalization method to improve the performance of AR mining. Tuna et al., presented a general data structure Speeding-Up AR Structure with Inverted Index Compression (SAII) which could be utilized in most of the existing algorithms to improve their performance IEEE Trans. Cybern. 46(12) (2016) 3059-3072. However, we found that this algorithm consumes a lot of time in reordering data because a one-to-one comparison method is used in this process, which is the main reason that the speeding-up structure is difficult to establish when coping with much more large amount of data. To overcome these problems, this paper aims to propose an improved speeding-up AR algorithm based on group similarity and Apache Spark framework to further reduce the memory requirements and runtime. Our simulation results on the police business big dataset make clear that our improved approach performs well and is more suitable for a big data environment. [Duan, Guiduo] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China; [Duan, Guiduo; Kurths, Jurgen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Duan, Guiduo; Kurths, Jurgen] Humboldt Univ, Dept Phys, D-12489 Berlin, Germany; [Duan, Guiduo] UESTC Guangdong, Inst Elect & Informat Engn, Guangzhou 511700, Guangdong, Peoples R China; [Wang, Xiaotong] CECT Ocean Informat Co Ltd, Beijing 100043, Peoples R China; [Huang, Tianxi] Chengdu Text Coll, Dept Fundamental Courses, Chengdu, Peoples R China University of Electronic Science & Technology of China; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin Huang, TX (corresponding author), Chengdu Text Coll, Dept Fundamental Courses, Chengdu, Peoples R China. Guiduo.Duan@ueste.edu.cn; huang_tianxi@163.com Ministry of Science and Technology Department Foundation of Sichuan Province [2016FZ0108, 2017JY0027, 2018JY0067, 2017GFW0128]; Natural Science Foundation of Guangdong Province, China [2017A030313380]; China Scholarship Council Ministry of Science and Technology Department Foundation of Sichuan Province; Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); China Scholarship Council(China Scholarship Council) This work is supported by the Ministry of Science and Technology Department Foundation of Sichuan Province (Nos. 2016FZ0108, 2017JY0027, 2018JY0067 and 2017GFW0128) and by the Natural Science Foundation of Guangdong Province, China (No. 2017A030313380) and by the China Scholarship Council. 21 0 0 3 15 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-0014 1793-6381 INT J PATTERN RECOGN Int. J. Pattern Recognit. Artif. Intell. FEB 2020.0 34 2 2059005 10.1142/S0218001420590053 0.0 20 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KX5UA 2023-03-23 WOS:000521944600014 0
J Lo, HC; Koedijk, KG; Gao, X; Hsu, YT Lo, Huai-Chun; Koedijk, Kees G.; Gao, Xiang; Hsu, Yuan-Teng How do job vacancy rates predict firm performance? A web crawling massive data perspective PACIFIC-BASIN FINANCE JOURNAL English Article Job vacancy; Web crawler; Big data; Firm performance; Corporate strategy CORPORATE SOCIAL-RESPONSIBILITY; BOOK-TO-MARKET; STOCK RETURNS; OPERATING PERFORMANCE; ORGANIZATIONAL PERFORMANCE; COLLECTIVE TURNOVER; GOVERNANCE; EARNINGS; RISK Traditionally, the relationship between a firm's performance and its business strategy is studied using structured data taken from proxy statements and financial reports. However, there have been increasing efforts to explore the linkages between corporate outcomes and unstructured information, such as text or image/audio/video files. Until recently, semi-structured data had been largely overlooked. Given that a substantial amount of such data can be extracted using web crawler techniques and then processed using big data solutions, the current study employed this procedure to investigate whether dynamic job vacancy postings by Taiwanese publicly listed companies are associated with subsequent stock returns and operating ratios. We report that new job openings foreshadow a firm's operating performance, both indirectly, by boosting stock prices, and directly, by signaling positive developments. This finding remains robust to tests addressing endogeneity concerns and the adoption of alternative specifications. We thus shed light on the role of metadata in financial analysis. [Lo, Huai-Chun] Yuan Ze Univ, Coll Management, Taoyuan, Taiwan; [Koedijk, Kees G.] Tilburg Univ, Ctr Econ Policy Res, Tilburg, Netherlands; [Gao, Xiang; Hsu, Yuan-Teng] Shanghai Business Sch, Res Ctr Finance, 2271 West Zhong Shan Rd, Shanghai 200235, Peoples R China Tilburg University; Shanghai Business School Hsu, YT (corresponding author), Shanghai Business Sch, Res Ctr Finance, 2271 West Zhong Shan Rd, Shanghai 200235, Peoples R China. yuanteng.hsu@gmail.com Gao, Xiang/ABF-8025-2021; Hsu, Yuan-Teng/Q-3546-2019 Gao, Xiang/0000-0003-4914-557X; Hsu, Yuan-Teng/0000-0001-8805-3964 Ministry of Science and Technology in Taiwan [MOST 108-2410-H-155-005]; MOE Project of Humanities and Social Sciences [20YJCZH199]; SBS Rising-Star Program [18KY-PQMX-04] Ministry of Science and Technology in Taiwan(Ministry of Science and Technology, Taiwan); MOE Project of Humanities and Social Sciences; SBS Rising-Star Program Huai-Chun Lo acknowledges funding from the Ministry of Science and Technology in Taiwan (MOST 108-2410-H-155-005). Yuan-Teng Hsu gratefully acknowledge financial support from MOE Project of Humanities and Social Sciences (No. 20YJCZH199), SBS Rising-Star Program (No. 18KY-PQMX-04). All errors are our own. 40 0 0 0 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0927-538X 1879-0585 PAC-BASIN FINANC J Pac.-Basin Financ. J. SEP 2020.0 62 101371 10.1016/j.pacfin.2020.101371 0.0 18 Business, Finance Social Science Citation Index (SSCI) Business & Economics NX0GL 2023-03-23 WOS:000575397400001 0
J Tsai, CW; Chen, MY; Piccialli, F; Qiu, T; Jung, JJ; Hung, PCK; Zeadally, S Tsai, Chun-Wei; Chen, Mu-Yen; Piccialli, Francesco; Qiu, Tie; Jung, Jason J.; Hung, Patrick C. K.; Zeadally, Sherali IEEE ACCESS SPECIAL SECTION EDITORIAL: DATA MINING FOR INTERNET OF THINGS IEEE ACCESS English Editorial Material It is an irrefutable fact that the Internet of Things (IoT) will eventually change our daily lives because its applications and relevant technologies have been or will be penetrating our daily lives. Also, the IoT is aimed to connect all the things (e.g., devices and systems) together via the Internet, thus making it easy to collect the data of users or environments and to find out useful information from the gathered data by using data mining technologies. As a consequence, how intelligent systems are developed for the IoT has become a critical research topic today. This means that artificial intelligence (AI) technologies (e.g., supervised learning, unsupervised learning, and semi-supervised learning) were used in the development of intelligent systems for analyzing the data captured from IoT devices or making decisions for IoT systems. It can be easily seen that AI can make an IoT system more intelligent and thus more accurate. For example, various sensors can be used for a smart home system to pinpoint the location and analyze the behavior of a human; however, with AI technologies, a more accurate prediction can be provided on the two pieces of information of a human. One of the most important uses for AI technologies is to make IoT systems more intelligent in order to provide a more convenient environment for users; thus, how to use existing AI technologies or develop new AI technologies to construct a better IoT system has attracted the attention of researchers from different disciplines in recent years. That is why, besides using existing supervised, unsupervised, semi-supervised learning algorithms, data mining algorithms, and machine learning algorithms, several recent studies have also attempted to develop new intelligent methods for the devices or systems for the IoT. All these approaches for making an IoT system more intelligent can also be found in the articles of this Special Section. [Tsai, Chun-Wei] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan; [Chen, Mu-Yen] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan; [Piccialli, Francesco] Univ Naples Federico II UNINA, Dept Math & Applicat R Caccioppoli, I-80138 Naples, Italy; [Qiu, Tie] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China; [Jung, Jason J.] Chung Ang Univ, Dept Comp Engn, Seoul 06974, South Korea; [Hung, Patrick C. K.] Univ Ontario Inst Technol, Fac Business & Informat Technol, Oshawa, ON L1G 0C5, Canada; [Zeadally, Sherali] Univ Kentucky, Coll Commun & Informat, Lexington, KY 40506 USA National Sun Yat Sen University; National Cheng Kung University; University of Naples Federico II; Tianjin University; Chung Ang University; Ontario Tech University; University of Kentucky Tsai, CW (corresponding author), Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan. Jung, Jason J./B-9622-2012 Jung, Jason J./0000-0003-0050-7445 0 1 1 1 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 90418 90427 10.1109/ACCESS.2021.3090137 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications TJ8EP gold 2023-03-23 WOS:000673708200001 0
C Liu, L; Fieguth, P; Wang, XG; Pietikainen, M; Hu, DW Leibe, B; Matas, J; Sebe, N; Welling, M Liu, Li; Fieguth, Paul; Wang, Xiaogang; Pietikainen, Matti; Hu, Dewen Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark COMPUTER VISION - ECCV 2016, PT III Lecture Notes in Computer Science English Proceedings Paper 14th European Conference on Computer Vision (ECCV) OCT 08-16, 2016 Amsterdam, NETHERLANDS Local binary pattern; Deep learning; Performance evaluation; Texture classification LOCAL BINARY PATTERN; CLASSIFICATION; FEATURES In recent years, a wide variety of different texture descriptors has been proposed, including many LBP variants. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we propose a new extensive benchmark (RoTeB) for measuring the robustness of texture operators against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. Fourteen datasets from the eight most commonly used texture sources are used in the benchmark. An extensive evaluation of the recent most promising LBP variants and some non-LBP descriptors based on deep convolutional networks is carried out. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered. [Liu, Li] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China; [Fieguth, Paul] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada; [Wang, Xiaogang] Chinese Univ Hong Kong, Dept Elect Engn, Shatin, Hong Kong, Peoples R China; [Pietikainen, Matti] Univ Oulu, Ctr Machine Vis Res, Oulu, Finland; [Hu, Dewen] Natl Univ Def Technol, Coll Mechatron & Automat, Changsha, Hunan, Peoples R China National University of Defense Technology - China; University of Waterloo; Chinese University of Hong Kong; University of Oulu; National University of Defense Technology - China Liu, L (corresponding author), Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China. liuli_nudt@nudt.edu.cn; pfieguth@uwaterloo.ca; xgwang@ee.cuhk.edu.hk; mkp@ee.oulu.fi; dwhu@nudt.edu.cn 50 30 31 0 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-46487-9; 978-3-319-46486-2 LECT NOTES COMPUT SC 2016.0 9907 69 86 10.1007/978-3-319-46487-9_5 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BG5DH 2023-03-23 WOS:000389384800005 0
J Pan, XY; Chen, L; Feng, KY; Hu, XH; Zhang, YH; Kong, XY; Huang, T; Cai, YD Pan, Xiaoyong; Chen, Lei; Feng, Kai-Yan; Hu, Xiao-Hua; Zhang, Yu-Hang; Kong, Xiang-Yin; Huang, Tao; Cai, Yu-Dong Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES English Article snoRNA; cancer type; Monte Carlo feature selection; support vector machine; RIPPER algorithm SMALL NUCLEOLAR RNAS; CARLO FEATURE-SELECTION; NONCODING RNA; RIBOSOMAL-RNA; LUNG-CANCER; BOX C/D; IDENTIFICATION; HOST; BINDING; GENES Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew's correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules. [Pan, Xiaoyong; Cai, Yu-Dong] Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; [Pan, Xiaoyong] Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China; [Feng, Kai-Yan] Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China; [Hu, Xiao-Hua] Fudan Univ, Sch Life Sci, Dept Biostat & Computat Biol, Shanghai 200438, Peoples R China; [Zhang, Yu-Hang; Kong, Xiang-Yin; Huang, Tao] Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China Shanghai University; Erasmus University Rotterdam; Erasmus MC; Shanghai Maritime University; East China Normal University; Fudan University; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; Shanghai Institutes for Biological Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China.;Kong, XY; Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China. xypan172436@gmail.com; chen_lei1@163.com; addland@126.com; xhhu@fudan.edu.cn; zhangyh825@163.com; xykong@sibs.ac.cn; huangtao@sibs.ac.cn; cai_yud@126.com Zhang, Yu-Hang/O-9774-2015; Chen, Lei/A-2275-2011; Zhang, Yu-Hang/AAQ-9012-2021 Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; cai, yudong/0000-0001-5664-7979 National Natural Science Foundation of China [31701151, 31571343]; Natural Science Foundation of Shanghai [17ZR1412500, 16ZR1403100]; National Key R&D Program of China [2018YFC0910403]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); National Key R&D Program of China; Shanghai Municipal Science and Technology Major Project; Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This research was funded by the National Natural Science Foundation of China (31701151, 31571343), Natural Science Foundation of Shanghai (17ZR1412500, 16ZR1403100), National Key R&D Program of China (2018YFC0910403), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), Shanghai Sailing Program (16YF1413800), the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000). 105 27 27 2 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1422-0067 INT J MOL SCI Int. J. Mol. Sci. MAY 1 2019.0 20 9 2185 10.3390/ijms20092185 0.0 16 Biochemistry & Molecular Biology; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry IA7RK 31052553.0 gold, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000469753500132 0
C Ma, QS; Meng, FD; Zheng, DQ; Wang, MX; Graham, Y; Jiang, WB; Liu, Q Lin, CY; Xue, N; Zhao, D; Huang, X; Feng, Y Ma, Qingsong; Meng, Fandong; Zheng, Daqi; Wang, Mingxuan; Graham, Yvette; Jiang, Wenbin; Liu, Qun MaxSD: A Neural Machine Translation Evaluation Metric Optimized by Maximizing Similarity Distance NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016) Lecture Notes in Computer Science English Proceedings Paper 5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL) DEC 02-06, 2016 Kunming Univ Sci & Technol, Kunming, PEOPLES R CHINA China Comp Federat,State Key Lab Digital Publishing,Microsoft Res Asia,DeepShare,Baidu Inc,GridSum,Sogou Inc,RSVP Technologies Inc,NiuTrans,Asian Federat Nat Language Proc,Chinese & Oriental Language Comp Soc Kunming Univ Sci & Technol Machine translation evaluation; Neural networks; Similarity distance; Maximization We propose a novel metric for machine translation evaluation based on neural networks. In the training phrase, we maximize the distance between the similarity scores of high and low-quality hypotheses. Then, the trained neural network is used to evaluate the new hypotheses in the testing phase. The proposed metric can efficiently incorporate lexical and syntactic metrics as features in the network and thus is able to capture different levels of linguistic information. Experiments on WMT-14 show state-of-the-art performance is achieved in two out of five language pairs on the system-level and one on the segment-level. Comparative results are also achieved in the remaining language pairs. [Ma, Qingsong; Meng, Fandong; Zheng, Daqi; Wang, Mingxuan; Jiang, Wenbin; Liu, Qun] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China; [Ma, Qingsong; Meng, Fandong; Zheng, Daqi; Wang, Mingxuan; Jiang, Wenbin] Univ Chinese Acad Sci, Beijing, Peoples R China; [Graham, Yvette; Liu, Qun] Dublin City Univ, Sch Comp, ADAPT Ctr, Dublin, Ireland Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Dublin City University Ma, QS (corresponding author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.;Ma, QS (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. maqingsong@ict.ac.cn; mengfandong@ict.ac.cn; zhengdaqi@ict.ac.cn; wangmingxuan@ict.ac.cn; graham.yvette@gmail.com; jiangwenbin@ict.ac.cn; qun.liu@dcu.ie National Natural Science Foundation of P. R. China [61379086]; European Union Horizon 2020 research and innovation programme [645452 (QT21)]; ADAPT Centre for Digital Content Technology at Dublin City University under the SFI Research Centres Programme - European Regional Development Fund [13/RC/2106] National Natural Science Foundation of P. R. China(National Natural Science Foundation of China (NSFC)); European Union Horizon 2020 research and innovation programme; ADAPT Centre for Digital Content Technology at Dublin City University under the SFI Research Centres Programme - European Regional Development Fund This work is supported by National Natural Science Foundation of P. R. China under Grant No. 61379086, European Union Horizon 2020 research and innovation programme under grant agreement 645452 (QT21), and the ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) at Dublin City University funded under the SFI Research Centres Programme (Grant 13/RC/2106) co-funded under the European Regional Development Fund. 14 1 1 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-50496-4; 978-3-319-50495-7 LECT NOTES COMPUT SC 2016.0 10102 153 161 10.1007/978-3-319-50496-4_13 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI1BH 2023-03-23 WOS:000405476800013 0
J Deng, JF; Sierla, S; Sun, J; Vyatkin, V Deng, Jifei; Sierla, Seppo; Sun, Jie; Vyatkin, Valeriy Reinforcement learning for industrial process control: A case study in flatness control in steel industry COMPUTERS IN INDUSTRY English Article Strip rolling; Process control; Reinforcement learning; Ensemble learning Strip rolling is a typical manufacturing process, in which conventional control approaches are widely applied. Development of the control algorithms requires a mathematical expression of the process by means of the first principles or empirical models. However, it is difficult to upgrade the conventional control approaches in response to the ever-changing requirements and environmental conditions because domain knowledge of control engineering, mechanical engineering, and material science is required. Reinforcement learning is a machine learning method that can make the agent learn from interacting with the environment, thus avoiding the need for the above mentioned mathematical expression. This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. Based on the proximal policy optimization (PPO), a multi-actor PPO is proposed. Each randomly initialized actor interacts with the environment in parallel, but only the experience from the actor that obtains the highest reward is used for updating the actors. Simulation results show that the proposed method outperforms the conventional control methods and the state-of-the-art reinforcement learning methods in terms of process capability and smoothness. [Deng, Jifei; Sierla, Seppo; Vyatkin, Valeriy] Aalto Univ, Sch Elect Engn, Dept Elect Engn & Automat, Espoo, Finland; [Sun, Jie] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Peoples R China; [Vyatkin, Valeriy] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Lulea, Sweden Aalto University; Northeastern University - China; Lulea University of Technology Deng, JF (corresponding author), Aalto Univ, Sch Elect Engn, Dept Elect Engn & Automat, Espoo, Finland.;Sun, J (corresponding author), Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Peoples R China. jifei.deng@aalto.fi; sunjie@ral.neu.edu.cn Deng, Jifei/0000-0001-6448-0494 China Scholarship Council [202006080008]; National Natural Science Foundation of China [52074085, U21A20117]; Fundamental Research Funds for the Central Universities [N2004010]; LiaoNing Revitalization Talents Program [XLYC1907065] China Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); LiaoNing Revitalization Talents Program Acknowledgements This work was supported by China Scholarship Council (No. 202006080008) , the National Natural Science Foundation of China (Grant Nos. 52074085 and U21A20117) , the Fundamental Research Funds for the Central Universities (Grant No. N2004010) , and the LiaoNing Revitalization Talents Program (XLYC1907065) . 39 0 0 25 25 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0166-3615 1872-6194 COMPUT IND Comput. Ind. DEC 2022.0 143 103748 10.1016/j.compind.2022.103748 0.0 AUG 2022 10 Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5A0GL Green Published 2023-03-23 WOS:000862575100002 0
J Wang, J; Jia, P; Cuadros, DF; Xu, M; Wang, XL; Guo, WD; Portnov, BA; Bao, YH; Chang, YS; Song, GX; Chen, N; Stein, A Wang, Jiao; Jia, Peng; Cuadros, Diego F.; Xu, Min; Wang, Xianliang; Guo, Weidong; Portnov, Boris A.; Bao, Yuhai; Chang, Yushan; Song, Genxin; Chen, Nan; Stein, Alfred A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis REMOTE SENSING English Article infectious disease; climate-sensitive; artificial neural network (ANN); remote sensing; brucellosis; disease outbreak prediction; environmental health; Inner Mongolia ALACHUA COUNTY; POPULATION; CHINA Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify important environmental determinants of the disease and predict its outbreaks. A novel artificial neural network (ANN) model was developed, using annual county-level numbers of HB cases and data on 37 environmental variables, potentially associated with HB in Inner Mongolia, China. Data from 2006 to 2008 were used to train, validate and test the model, while data for 2009-2010 were used to assess the model's performance. The Enhanced Vegetation Index was identified as the most important predictor of HB incidence, followed by land surface temperature and other temperature- and precipitation-related variables. The suitable ecological niche of HB was modeled based on these predictors. Model estimates were found to be in good agreement with reported numbers of HB cases in both the model development and assessment phases. The study suggests that HB outbreaks may be predicted, with a reasonable degree of accuracy, using the ANN model and environmental variables obtained from satellite data. The study deepened the understanding of environmental determinants of HB and advanced the methodology for prediction of climate-sensitive infectious disease outbreaks. [Wang, Jiao; Song, Genxin] Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow, Minist Educ, Kaifeng 475004, Peoples R China; [Wang, Jiao; Wang, Xianliang] Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, Beijing 100021, Peoples R China; [Jia, Peng; Stein, Alfred] Univ Twente, Fac Geoinformat Sci & Earth Observat, Dept Earth Observat Sci, NL-7500 Enschede, Netherlands; [Cuadros, Diego F.] Univ Cincinnati, Dept Geog & Geog Informat Sci, Cincinnati, OH 45221 USA; [Cuadros, Diego F.] Univ Cincinnati, Hlth Geog & Dis Modeling Lab, Cincinnati, OH 45221 USA; [Xu, Min] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Guo, Weidong] Inner Mongolia Ctr Dis Control & Prevent, Hohhot 010031, Peoples R China; [Portnov, Boris A.] Univ Haifa, Dept Nat Resources & Environm Management, Fac Management, IL-3498838 Haifa, Israel; [Bao, Yuhai; Chang, Yushan] Inner Mongolia Normal Univ, Inner Mongolian Key Lab Remote Sensing & GIS, Hohhot 010022, Peoples R China; [Chen, Nan] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350003, Fujian, Peoples R China Henan University; Chinese Center for Disease Control & Prevention; National Institute of Environmental Health, Chinese Center for Disease Control & Prevention; University System of Ohio; University of Cincinnati; University System of Ohio; University of Cincinnati; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; University of Haifa; Inner Mongolia Normal University; Fuzhou University Jia, P (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat, Dept Earth Observat Sci, NL-7500 Enschede, Netherlands. jwanghjs@163.com; jiapengff@hotmail.com; cuadrodo@ucmail.uc.edu; xumin@radi.ac.cn; ahyxwx1@163.com; gwd.com@126.com; portnov@research.haifa.ac.il; baoyuhai@imnu.edu.cn; yushangis@163.com; shengliking@126.com; fjcn99@163.com; a.stein@utwente.nl /AHB-5989-2022; Xu, Min/HHY-9228-2022; Jia, Peng/K-1446-2017; Stein, Alfred/D-4731-2009 Jia, Peng/0000-0003-0110-3637; Stein, Alfred/0000-0002-9456-1233 Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education [JOF201704]; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University [2017LSDMIS08]; Key Laboratory for National Geographic State Monitoring (National Administration of Surveying, Mapping and Geoinformation) [2015NGCM] Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University; Key Laboratory for National Geographic State Monitoring (National Administration of Surveying, Mapping and Geoinformation) All authors acknowledge funding support from the Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education (No. JOF201704); the Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University (No. 2017LSDMIS08); and the Key Laboratory for National Geographic State Monitoring (National Administration of Surveying, Mapping and Geoinformation) (No. 2015NGCM). We would also like to thank three anonymous reviewers for very helpful comments. 48 15 15 4 28 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. OCT 2017.0 9 10 1018 10.3390/rs9101018 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology FM0JS gold, Green Published 2023-03-23 WOS:000414650600048 0
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A.; Sonnenschein, L.; Steggemann, J.; Teyssier, D.; Thueer, S.; Weber, M.; Bontenackels, M.; Cherepanov, V.; Erdogan, Y.; Fluegge, G.; Geenen, H.; Geisler, M.; Ahmad, W. Haj; Hoehle, F.; Kargoll, B.; Kress, T.; Kuessel, Y.; Lingemann, J.; Nowack, A.; Perchalla, L.; Pooth, O.; Sauerland, P.; Stahl, A.; Martin, M. Aldaya; Behr, J.; Behrenhoff, W.; Behrens, U.; Bergholz, M.; Bethani, A.; Borras, K.; Burgmeier, A.; Cakir, A.; Calligaris, L.; Campbell, A.; Castro, E.; Costanza, F.; Dammann, D.; Pardos, C. Diez; Dorland, T.; Eckerlin, G.; Eckstein, D.; Flucke, G.; Geiser, A.; Glushkov, I.; Gunnellini, P.; Habib, S.; Hauk, J.; Hellwig, G.; Jung, H.; Kasemann, M.; Katsas, P.; Kleinwort, C.; Kluge, H.; Knutsson, A.; Kraemer, M.; Kruecker, D.; Kuznetsova, E.; Lange, W.; Leonard, J.; Lohmann, W.; Lutz, B.; Mankel, R.; Marfin, I.; Marienfeld, M.; Melzer-Pellmann, I. -A.; Meyer, A. B.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Novgorodova, O.; Nowak, F.; Olzem, J.; Perrey, H.; Petrukhin, A.; Pitzl, D.; Raspereza, A.; Cipriano, P. M. Ribeiro; Riedl, C.; Ron, E.; Rosin, M.; Salfeld-Nebgen, J.; Schmidt, R.; Schoerner-Sadenius, T.; Sen, N.; Spiridonov, A.; Stein, M.; Walsh, R.; Wissing, C.; Blobel, V.; Enderle, H.; Erfle, J.; Gebbert, U.; Goerner, M.; Gosselink, M.; Haller, J.; Hermanns, T.; Hoeing, R. S.; Kaschube, K.; Kaussen, G.; Kirschenmann, H.; Klanner, R.; Lange, J.; Peiffer, T.; Pietsch, N.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Schroeder, M.; Schum, T.; Seidel, M.; Sibille, J.; Sola, V.; Stadie, H.; Steinbrueck, G.; Thomsen, J.; Vanelderen, L.; Barth, C.; Berger, J.; Boeser, C.; Chwalek, T.; De Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Guthoff, M.; Hackstein, C.; Hartmann, F.; Hauth, T.; Heinrich, M.; Held, H.; Hoffmann, K. H.; Husemann, U.; Katkov, I.; Komaragiri, J. R.; Pardo, P. 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G.; Barone, L.; Cavallari, F.; Del Re, D.; Diemoz, M.; Fanelli, C.; Grassi, M.; Longo, E.; Meridiani, P.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Soffi, L.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Demaria, N.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Pastrone, N.; Pelliccioni, M.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; Marone, M.; Montanino, D.; Penzo, A.; Schizzi, A.; Kim, T. Y.; Nam, S. K.; Chang, S.; Kim, D. H.; Kim, G. N.; Kong, D. J.; Park, H.; Son, D. C.; Son, T.; Kim, J. Y.; Kim, Z. J.; Song, S.; Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, T. J.; Lee, K. S.; Moon, D. H.; Park, S. K.; Roh, Y.; Choi, M.; Kim, J. H.; Park, C.; Park, I. C.; Park, S.; Ryu, G.; Choi, Y.; Choi, Y. K.; Goh, J.; Kim, M. 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D.; Ma, T.; Nahn, S.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Rudolph, M.; Stephans, G. S. F.; Stoeckli, F.; Sumorok, K.; Sung, K.; Velicanu, D.; Wenger, E. A.; Wolf, R.; Wyslouch, B.; Yang, M.; Yilmaz, Y.; Yoon, A. S.; Zanetti, M.; Zhukova, V.; Cooper, S. I.; Dahmes, B.; De Benedetti, A.; Franzoni, G.; Gude, A.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Mans, J.; Pastika, N.; Rusack, R.; Sasseville, M.; Singovsky, A.; Tambe, N.; Turkewitz, J.; Cremaldi, L. M.; Kroeger, R.; Perera, L.; Rahmat, R.; Sanders, D. A.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Eads, M.; Keller, J.; Kravchenko, I.; Lazo-Flores, J.; Malik, S.; Snow, G. R.; Godshalk, A.; Iashvili, I.; Jain, S.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Haley, J.; Nash, D.; Orimoto, T.; Trocino, D.; Wood, D.; Zhang, J.; Anastassov, A.; Hahn, K. A.; Kubik, A.; Lusito, L.; Mucia, N.; Odell, N.; Ofierzynski, R. A.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Velasco, M.; Won, S.; Antonelli, L.; Berry, D.; Brinkerhoff, A.; Chan, K. M.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kolb, J.; Lannon, K.; Luo, W.; Lynch, S.; Marinelli, N.; Morse, D. M.; Pearson, T.; Planer, M.; Ruchti, R.; Slaunwhite, J.; Valls, N.; Wayne, M.; Wolf, M.; Bylsma, B.; Durkin, L. S.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Puigh, D.; Rodenburg, M.; Vuosalo, C.; Williams, G.; Winer, B. L.; Berry, E.; Elmer, P.; Halyo, V.; Hebda, P.; Hegeman, J.; Hunt, A.; Jindal, P.; Koay, S. A.; Pegna, D. Lopes; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroue, P.; Quan, X.; Raval, A.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zenz, S. C.; Zuranski, A.; Brownson, E.; Lopez, A.; Mendez, H.; Vargas, J. E. Ramirez; Alagoz, E.; Barnes, V. E.; Benedetti, D.; Bolla, G.; Bortoletto, D.; De Mattia, M.; Everett, A.; Hu, Z.; Jones, M.; Koybasi, O.; Kress, M.; Laasanen, A. T.; Leonardo, N.; Maroussov, V.; Merkel, P.; Miller, D. H.; Neumeister, N.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Marono, M. Vidal; Yoo, H. D.; Zablocki, J.; Zheng, Y.; Guragain, S.; Parashar, N.; Adair, A.; Akgun, B.; Boulahouache, C.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Chung, Y. S.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Miner, D. C.; Vishnevskiy, D.; Zielinski, M.; Bhatti, A.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Lungu, G.; Malik, S.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Rekovic, V.; Robles, J.; Rose, K.; Salur, S.; Schnetzer, S.; Seitz, C.; Somalwar, S.; Stone, R.; Thomas, S.; Walker, M.; Cerizza, G.; Hollingsworth, M.; Spanier, S.; Yang, Z. C.; York, A.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Safonov, A.; Sakuma, T.; Sengupta, S.; Suarez, I.; Tatarinov, A.; Toback, D.; Akchurin, N.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Jeong, C.; Kovitanggoon, K.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Florez, C.; Greene, S.; Gurrola, A.; Johns, W.; Kurt, P.; Maguire, C.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Balazs, M.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Lin, C.; Neu, C.; Wood, J.; Gollapinni, S.; Harr, R.; Karchin, P. E.; Don, C. Kottachchi Kankanamge; Lamichhane, P.; Sakharov, A.; Anderson, M.; Belknap, D. A.; Borrello, L.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Friis, E.; Gray, L.; Grogg, K. S.; Grothe, M.; Hall-Wilton, R.; Herndon, M.; Herve, A.; Klabbers, P.; Klukas, J.; Lanaro, A.; Lazaridis, C.; Loveless, R.; Mohapatra, A.; Mozer, M. U.; Ojalvo, I.; Palmonari, F.; Pierro, G. A.; Ross, I.; Savin, A.; Smith, W. H.; Swanson, J. CMS Collaboration Measurement of the t(t)over-bar production cross section in the tau plus jets channel in pp collisions at root s=7 TeV EUROPEAN PHYSICAL JOURNAL C English Article The top-quark pair production cross section in 7 TeV center-of-mass energy proton-proton collisions is measured using data collected by the CMS detector at the LHC. The measurement uses events with one jet identified as a hadronically decaying tau lepton and at least four additional energetic jets, at least one of which is identified as coming from a b quark. The analyzed data sample corresponds to an integrated luminosity of 3.9 fb(-1) recorded by a dedicated multijet plus hadronically decaying tau trigger. A neural network has been developed to separate the top-quark pairs from the W + jets and multijet backgrounds. The measured value of sigma(t (t) over bar) = 152 +/- 12 (stat.) +/- 32 (syst.) +/- 3 (lum.) pb is consistent with the standard model predictions. [Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.] Yerevan Phys Inst, Yerevan 375036, Armenia; [Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.; Eroe, J.; Fabjan, C.; Friedl, M.; Fruehwirth, R.; Ghete, V. M.; Hoermann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knuenz, V.; Krammer, M.; Kraetschmer, I.; Liko, D.; Mikulec, I.; Pernicka, M.; Rabady, D.; Rahbaran, B.; Rohringer, C.; Rohringer, H.; Schoefbeck, R.; Strauss, J.; Taurok, A.; Waltenberger, W.; Wulz, C. -E.] OeAW, Inst Hochenergiephys, Vienna, Austria; [Mossolov, V.; Shumeiko, N.; Gonzalez, J. Suarez] Natl Ctr Particle & High Energy Phys, Minsk, BELARUS; [Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Luyckx, S.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Selvaggi, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.] Univ Antwerp, B-2020 Antwerp, Belgium; [Blekman, F.; Blyweert, S.; D'Hondt, J.; Suarez, R. Gonzalez; Kalogeropoulos, A.; Maes, M.; Olbrechts, A.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.] Vrije Univ Brussel, Brussels, Belgium; [Clerbaux, B.; De Lentdecker, G.; Dero, V.; Gay, A. P. R.; Hreus, T.; Leonard, A.; Marage, P. E.; Mohammadi, A.; Reis, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.] Univ Libre Bruxelles, Brussels, Belgium; [Adler, V.; Beernaert, K.; Cimmino, A.; Costantini, S.; Garcia, G.; Grunewald, M.; Klein, B.; Lellouch, J.; Marinov, A.; Mccartin, J.; Rios, A. A. Ocampo; Ryckbosch, D.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Walsh, S.; Yazgan, E.; Zaganidis, N.] Univ Ghent, B-9000 Ghent, Belgium; [Basegmez, S.; Bruno, G.; Castello, R.; Ceard, L.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Lemaitre, V.; Liao, J.; Militaru, O.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Garcia, J. M. Vizan] Catholic Univ Louvain, B-1348 Louvain, Belgium; [Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.] Univ Mons, B-7000 Mons, Belgium; [Alves, G. A.; Correa Martins Junior, M.; Martins, T.; Pol, M. E.; Souza, M. H. G.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Alda Junior, W. L.; Carvalho, W.; Custodio, A.; Da Costa, E. 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H.; Liang, D.; Liang, S.; Meng, X.; Tao, J.; Wang, J.; Wang, X.; Wang, Z.; Xiao, H.; Xu, M.; Zang, J.; Zhang, Z.] Inst High Energy Phys, Beijing 100039, Peoples R China; [Asawatangtrakuldee, C.; Ban, Y.; Guo, Y.; Li, Q.; Li, W.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Zhang, L.; Zou, W.] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China; [Avila, C.; Carrillo Montoya, C. A.; Gomez, J. P.; Gomez Moreno, B.; Osorio Oliveros, A. F.; Sanabria, J. C.] Univ Los Andes, Bogota, Colombia; [Godinovic, N.; Lelas, D.; Plestina, R.; Polic, D.; Puljak, I.] Tech Univ Split, Split, Croatia; [Antunovic, Z.; Kovac, M.] Univ Split, Split, Croatia; [Brigljevic, V.; Duric, S.; Kadija, K.; Luetic, J.; Mekterovic, D.; Morovic, S.] Rudjer Boskovic Inst, Zagreb, Croatia; [Attikis, A.; Galanti, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. 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L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Locci, E.; Malcles, J.; Millischer, L.; Nayak, A.; Rander, J.; Rosowsky, A.; Titov, M.] CEA Saclay, DSM IRFU, F-91191 Gif Sur Yvette, France; [Baffioni, S.; Beaudette, F.; Benhabib, L.; Bianchini, L.; Bluj, M.; Busson, P.; Charlot, C.; Daci, N.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Florent, A.; de Cassagnac, R. Granier; Haguenauer, M.; Mine, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Paganini, P.; Sabes, D.; Salerno, R.; Sirois, Y.; Veelken, C.; Zabi, A.] Ecole Polytech, CNRS, IN2P3, Lab Leprince Ringuet, F-91128 Palaiseau, France; [Agram, J. -L.; Andrea, J.; Bloch, D.; Bodin, D.; Brom, J. -M.; Cardaci, M.; Chabert, E. 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T.; Foa, L.; Ligabue, F.] Scuola Normale Super Pisa, Pisa, Italy; [Barone, L.; Cavallari, F.; Del Re, D.; Diemoz, M.; Fanelli, C.; Grassi, M.; Longo, E.; Meridiani, P.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Soffi, L.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Barone, L.; Del Re, D.; Fanelli, C.; Grassi, M.; Longo, E.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Rahatlou, S.; Soffi, L.] Univ Rome, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Demaria, N.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Pastrone, N.; Pelliccioni, M.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; [Amapane, N.; Argiro, S.; Casasso, S.; Costa, M.; Migliore, E.; Monaco, V.; Potenza, A.; Romero, A.; Sacchi, R.; Solano, A.] Univ Turin, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Obertino, M. M.; Ruspa, M.] Univ Piemonte Orientale, Turin, Italy; [Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; Marone, M.; Montanino, D.; Penzo, A.; Schizzi, A.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Candelise, V.; Della Ricca, G.; Marone, M.; Montanino, D.; Schizzi, A.] Univ Trieste, Trieste, Italy; [Kim, T. Y.; Nam, S. K.] Kangwon Natl Univ, Chunchon, South Korea; [Chang, S.; Kim, D. H.; Kim, G. N.; Kong, D. J.; Park, H.; Son, D. C.; Son, T.] Kyungpook Natl Univ, Taegu, South Korea; [Kim, J. Y.; Kim, Z. J.; Song, S.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, T. J.; Lee, K. S.; Moon, D. H.; Park, S. K.; Roh, Y.] Korea Univ, Seoul, South Korea; [Choi, M.; Kim, J. H.; Park, C.; Park, I. C.; Park, S.; Ryu, G.] Univ Seoul, Seoul, South Korea; [Choi, Y.; Choi, Y. K.; Goh, J.; Kim, M. S.; Kwon, E.; Lee, B.; Lee, J.; Lee, S.; Seo, H.; Yu, I.] Sungkyunkwan Univ, Suwon, South Korea; [Bilinskas, M. J.; Grigelionis, I.; Janulis, M.; Juodagalvis, A.] Vilnius Univ, Vilnius, Lithuania; [Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Lopez-Fernandez, R.; Martinez-Ortega, J.; Sanchez-Hernandez, A.; Villasenor-Cendejas, L. M.] IPN, Ctr Invest & Estudios Avanzados, Mexico City 07738, DF, Mexico; [Carrillo Moreno, S.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Salazar Ibarguen, H. A.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Casimiro Linares, E.; Morelos Pineda, A.; Reyes-Santos, M. A.] Univ Autonoma San Luis Potosi, San Luis Potosi, Mexico; [Krofcheck, D.] Univ Auckland, Auckland 1, New Zealand; [Bell, A. J.; Butler, P. H.; Doesburg, R.; Reucroft, S.; Silverwood, H.] Univ Canterbury, Christchurch 1, New Zealand; [Ahmad, M.; Asghar, M. I.; Butt, J.; Hoorani, H. R.; Khalid, S.; Khan, W. A.; Khurshid, T.; Qazi, S.; Shah, M. A.; Shoaib, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Bialkowska, H.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Wrochna, G.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Brona, G.; Bunkowski, K.; Cwiok, M.; Dominik, W.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.] Univ Warsaw, Inst Expt Phys, Fac Phys, Warsaw, Poland; [Almeida, N.; Bargassa, P.; David, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Seixas, J.; Varela, J.; Vischia, P.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Belotelov, I.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Kozlov, G.; Lanev, A.; Malakhov, A.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Smirnov, V.; Volodko, A.; Zarubin, A.] Joint Inst Nucl Res, Dubna, Russia; [Evstyukhin, S.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Vorobyev, An.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Kirsanov, M.; Krasnikov, N.; Matveev, V.; Pashenkov, A.; Tlisov, D.; Toropin, A.] Russian Acad Sci, Inst Nucl Res, Moscow 117312, Russia; [Epshteyn, V.; Erofeeva, M.; Gavrilov, V.; Kossov, M.; Lychkovskaya, N.; Popov, V.; Safronov, G.; Semenov, S.; Shreyber, I.; Stolin, V.; Vlasov, E.; Zhokin, A.] Inst Theoret & Expt Phys, Moscow 117259, Russia; [Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Vinogradov, A.] PN Lebedev Phys Inst, Moscow 117924, Russia; [Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Markina, A.; Obraztsov, S.; Perfilov, M.; Popov, A.; Sarycheva, L.; Savrin, V.; Snigirev, A.] Moscow MV Lomonosov State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Grishin, V.; Kachanov, V.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Inst High Energy Phys, State Res Ctr Russian Federat, Protvino, Russia; [Adzic, P.; Djordjevic, M.; Ekmedzic, M.; Krpic, D.; Milosevic, J.] Univ Belgrade, Fac Phys, Belgrade 11001, Serbia; [Adzic, P.; Djordjevic, M.; Ekmedzic, M.; Krpic, D.; Milosevic, J.] Vinca Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Arce, P.; Battilana, C.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Dominguez Vazquez, D.; Fernandez Bedoya, C.; Fernandez Ramos, J. P.; Ferrando, A.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Merino, G.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Santaolalla, J.; Soares, M. S.; Willmott, C.] CIEMAT, E-28040 Madrid, Spain; [Albajar, C.; Codispoti, G.; de Troconiz, J. F.] Univ Autonoma Madrid, Madrid, Spain; [Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Lloret Iglesias, L.; Piedra Gomez, J.] Univ Oviedo, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Chuang, S. H.; Duarte Campderros, J.; Felcini, M.; Fernandez, M.; Gomez, G.; Gonzalez Sanchez, J.; Graziano, A.; Jorda, C.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Rodrigo, T.; Rodriguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, E-39005 Santander, Spain; [Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benitez, J. F.; Bernet, C.; Bianchi, G.; Bloch, P.; Bocci, A.; Bonato, A.; Botta, C.; Breuker, H.; Camporesi, T.; Cerminara, G.; Christiansen, T.; Perez, J. A. Coarasa; D'Enterria, D.; Dabrowski, A.; De Roeck, A.; Di Guida, S.; Dobson, M.; Dupont-Sagorin, N.; Elliott-Peisert, A.; Frisch, B.; Funk, W.; Georgiou, G.; Giffels, M.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Giunta, M.; Glege, F.; Garrido, R. Gomez-Reino; Govoni, P.; Gowdy, S.; Guida, R.; Gundacker, S.; Hammer, J.; Hansen, M.; Harris, P.; Hartl, C.; Harvey, J.; Hegner, B.; Hinzmann, A.; Innocente, V.; Janot, P.; Kaadze, K.; Karavakis, E.; Kousouris, K.; Lecoq, P.; Lee, Y. -J.; Lenzi, P.; Lourenco, C.; Magini, N.; Maeki, T.; Malberti, M.; Malgeri, L.; Mannelli, M.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moser, R.; Mulders, M.; Musella, P.; Nesvold, E.; Orsini, L.; Cortezon, E. Palencia; Perez, E.; Perrozzi, L.; Petrilli, A.; Pfeiffer, A.; Pierini, M.; Pimia, M.; Piparo, D.; Polese, G.; Quertenmont, L.; Racz, A.; Reece, W.; Antunes, J. Rodrigues; Rolandi, G.; Rovelli, C.; Rovere, M.; Sakulin, H.; Santanastasio, F.; Schaefer, C.; Schwick, C.; Segoni, I.; Sekmen, S.; Sharma, A.; Siegrist, P.; Silva, P.; Simon, M.; Sphicas, P.; Spiga, D.; Tsirou, A.; Veres, G. I.; Vlimant, J. R.; Woehri, H. K.; Worm, S. D.; Zeuner, W. D.] CERN, European Org Nucl Res, CH-1211 Geneva, Switzerland; [Bertl, W.; Deiters, K.; Erdmann, W.; Gabathuler, K.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Koenig, S.; Kotlinski, D.; Langenegger, U.; Meier, F.; Renker, D.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Baeni, L.; Bortignon, P.; Buchmann, M. A.; Casal, B.; Chanon, N.; Deisher, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Duenser, M.; Eller, P.; Eugster, J.; Freudenreich, K.; Grab, C.; Hits, D.; Lecomte, P.; Lustermann, W.; Marini, A. C.; del Arbol, P. Martinez Ruiz; Mohr, N.; Moortgat, F.; Naegeli, C.; Nef, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pape, L.; Pauss, F.; Peruzzi, M.; Ronga, F. J.; Rossini, M.; Sala, L.; Sanchez, A. K.; Starodumov, A.; Stieger, B.; Takahashi, M.; Tauscher, L.; Thea, A.; Theofilatos, K.; Treille, D.; Urscheler, C.; Wallny, R.; Weber, H. A.; Wehrli, L.] ETH, Inst Particle Phys, Zurich, Switzerland; [Amsler, C.; Chiochia, V.; De Visscher, S.; Favaro, C.; Rikova, M. Ivova; Kilminster, B.; Mejias, B. Millan; Otiougova, P.; Robmann, P.; Snoek, H.; Tupputi, S.; Verzetti, M.] Univ Zurich, Zurich, Switzerland; [Chang, Y. H.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Li, S. W.; Lin, W.; Lu, Y. J.; Singh, A. P.; Volpe, R.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Bartalini, P.; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Dietz, C.; Grundler, U.; Hou, W. -S.; Hsiung, Y.; Kao, K. Y.; Lei, Y. J.; Lu, R. -S.; Majumder, D.; Petrakou, E.; Shi, X.; Shiu, J. G.; Tzeng, Y. M.; Wan, X.; Wang, M.] Natl Taiwan Univ, Taipei 10764, Taiwan; [Asavapibhop, B.; Srimanobhas, N.] Chulalongkorn Univ, Bangkok, Thailand; [Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Karaman, T.; Karapinar, G.; Topaksu, A. Kayis; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sogut, K.; Cerci, D. Sunar; Tali, B.; Topakli, H.; Vergili, L. N.; Vergili, M.] Cukurova Univ, Adana, Turkey; [Akin, I. V.; Aliev, T.; Bilin, B.; Bilmis, S.; Deniz, M.; Gamsizkan, H.; Guler, A. M.; Ocalan, K.; Ozpineci, A.; Serin, M.; Sever, R.; Surat, U. E.; Yalvac, M.; Yildirim, E.; Zeyrek, M.] Middle E Tech Univ, Dept Phys, TR-06531 Ankara, Turkey; [Guelmez, E.; Isildak, B.; Kaya, M.; Kaya, O.; Ozkorucuklu, S.; Sonmez, N.] Bogazici Univ, Istanbul, Turkey; [Bahtiyar, H.; Barlas, E.; Cankocak, K.; Vardarli, F. I.; Yucel, M.] Istanbul Tech Univ, TR-80626 Istanbul, Turkey; [Levchuk, L.] Kharkov Inst Phys & Technol, Natl Sci Ctr, Kharkov, Ukraine; [Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Frazier, R.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Metson, S.; Newbold, D. M.; Nirunpong, K.; Poll, A.; Senkin, S.; Smith, V. J.; Williams, T.] Univ Bristol, Bristol, Avon, England; [Basso, L.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Jackson, J.; Kennedy, B. W.; Olaiya, E.; Petyt, D.; Radburn-Smith, B. C.; Shepherd-Themistocleous, C. H.; Tomalin, I. R.; Womersley, W. J.] Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England; [Bainbridge, R.; Ball, G.; Beuselinck, R.; Buchmuller, O.; Colling, D.; Cripps, N.; Cutajar, M.; Dauncey, P.; Davies, G.; Della Negra, M.; Ferguson, W.; Fulcher, J.; Futyan, D.; Gilbert, A.; Bryer, A. Guneratne; Hall, G.; Hatherell, Z.; Hays, J.; Iles, G.; Jarvis, M.; Karapostoli, G.; Lyons, L.; Magnan, A. -M.; Marrouche, J.; Mathias, B.; Nandi, R.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Pioppi, M.; Raymond, D. M.; Rogerson, S.; Rose, A.; Ryan, M. J.; Seez, C.; Sharp, P.; Sparrow, A.; Stoye, M.; Tapper, A.; Acosta, M. Vazquez; Virdee, T.; Wakefield, S.; Wardle, N.; Whyntie, T.] Univ London Imperial Coll Sci Technol & Med, London, England; [Chadwick, M.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Martin, W.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.] Brunel Univ, Uxbridge UB8 3PH, Middx, England; [Hatakeyama, K.; Liu, H.; Scarborough, T.] Baylor Univ, Waco, TX 76798 USA; [Charaf, O.; Henderson, C.; Rumerio, P.] Univ Alabama, Tuscaloosa, AL USA; [Avetisyan, A.; Bose, T.; Fantasia, C.; Heister, A.; Lawson, P.; Lazic, D.; Rohlf, J.; Sperka, D.; St John, J.; Sulak, L.] Boston Univ, Boston, MA 02215 USA; [Alimena, J.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Jabeen, S.; Kukartsev, G.; Laird, E.; Landsberg, G.; Luk, M.; Narain, M.; Nguyen, D.; Segala, M.; Sinthuprasith, T.; Speer, T.] Brown Univ, Providence, RI 02912 USA; [Breedon, R.; Breto, G.; Sanchez, M. Calderon De La Barca; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Dolen, J.; Erbacher, R.; Gardner, M.; Houtz, R.; Ko, W.; Kopecky, A.; Lander, R.; Mall, O.; Miceli, T.; Pellett, D.; Ricci-Tam, F.; Rutherford, B.; Searle, M.; Smith, J.; Squires, M.; Tripathi, M.; Sierra, R. Vasquez; Yohay, R.] Univ Calif Davis, Davis, CA 95616 USA; [Andreev, V.; Cline, D.; Cousins, R.; Duris, J.; Erhan, S.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Jarvis, C.; Rakness, G.; Schlein, P.; Traczyk, P.; Valuev, V.; Weber, M.] Univ Calif Los Angeles, Los Angeles, CA USA; [Babb, J.; Clare, R.; Dinardo, M. E.; Ellison, J.; Gary, J. W.; Giordano, F.; Hanson, G.; Liu, H.; Long, O. R.; Luthra, A.; Nguyen, H.; Paramesvaran, S.; Sturdy, J.; Sumowidagdo, S.; Wilken, R.; Wimpenny, S.] Univ Calif Riverside, Riverside, CA 92521 USA; [Andrews, W.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; Evans, D.; Holzner, A.; Kelley, R.; Lebourgeois, M.; Letts, J.; Macneill, I.; Mangano, B.; Padhi, S.; Palmer, C.; Petrucciani, G.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Sudano, E.; Tadel, M.; Tu, Y.; Vartak, A.; Wasserbaech, S.; Wuerthwein, F.; Yagil, A.; Yoo, J.] Univ Calif San Diego, La Jolla, CA 92093 USA; [Barge, D.; Bellan, R.; Campagnari, C.; D'Alfonso, M.; Danielson, T.; Flowers, K.; Geffert, P.; George, C.; Golf, F.; Incandela, J.; Justus, C.; Kalavase, P.; Kovalskyi, D.; Krutelyov, V.; Lowette, S.; Villalba, R. Magana; Mccoll, N.; Pavlunin, V.; Ribnik, J.; Richman, J.; Rossin, R.; Stuart, D.; To, W.; West, C.] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA; [Apresyan, A.; Bornheim, A.; Chen, Y.; Di Marco, E.; Duarte, J.; Gataullin, M.; Ma, Y.; Mott, A.; Newman, H. B.; Rogan, C.; Spiropulu, M.; Timciuc, V.; Veverka, J.; Wilkinson, R.; Xie, S.; Yang, Y.; Zhu, R. Y.] CALTECH, Pasadena, CA 91125 USA; [Azzolini, V.; Calamba, A.; Carroll, R.; Ferguson, T.; Iiyama, Y.; Jang, D. W.; Liu, Y. F.; Paulini, M.; Vogel, H.; Vorobiev, I.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Cumalat, J. P.; Drell, B. R.; Ford, W. T.; Gaz, A.; Lopez, E. Luiggi; Smith, J. G.; Stenson, K.; Ulmer, K. A.; Wagner, S. R.] Univ Colorado, Boulder, CO 80309 USA; [Alexander, J.; Chatterjee, A.; Eggert, N.; Gibbons, L. K.; Heltsley, B.; Hopkins, W.; Khukhunaishvili, A.; Kreis, B.; Mirman, N.; Kaufman, G. Nicolas; Patterson, J. R.; Ryd, A.; Salvati, E.; Sun, W.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Vaughan, J.; Weng, Y.; Winstrom, L.; Wittich, P.] Cornell Univ, Ithaca, NY USA; [Winn, D.] Fairfield Univ, Fairfield, CT 06430 USA; [Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Burkett, K.; Butler, J. N.; Chetluru, V.; Cheung, H. W. K.; Chlebana, F.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gao, Y.; Green, D.; Gutsche, O.; Hanlon, J.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kunori, S.; Kwan, S.; Leonidopoulos, C.; Linacre, J.; Lincoln, D.; Lipton, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Mishra, K.; Mrenna, S.; Musienko, Y.; Newman-Holmes, C.; O'Dell, V.; Sexton-Kennedy, E.; Sharma, S.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitmore, J.; Wu, W.; Yang, F.; Yun, J. C.] Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA; [Acosta, D.; Avery, P.; Bourilkov, D.; Chen, M.; Cheng, T.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Dobur, D.; Drozdetskiy, A.; Field, R. D.; Fisher, M.; Fu, Y.; Furic, I. K.; Gartner, J.; Hugon, J.; Kim, B.; Konigsberg, J.; Korytov, A.; Kropivnitskaya, A.; Kypreos, T.; Low, J. F.; Matchev, K.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Park, M.; Remington, R.; ; Yerevan Physics Institute; Belarusian State University; National Center of Particles & High Energy Physics - Belarus; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; University of Mons; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Universidad de los Andes (Colombia); University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Universite de Haute-Alsace (UHA); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Ivane Javakhishvili Tbilisi State University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National & Kapodistrian University of Athens; University of Ioannina; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Panjab University; University of Delhi; Saha Institute of Nuclear Physics; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kangwon National University; Kyungpook National University; Chonnam National University; Korea University; University of Seoul; Sungkyunkwan University (SKKU); Vilnius University; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; Universidad Autonoma de San Luis Potosi; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; Fairfield University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida International University; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Purdue University System; Purdue University; Rice University; University of Rochester; Rockefeller University; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; European Organization for Nuclear Research (CERN); National Institute of Chemical Physics & Biophysics (NICPB); California Institute of Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Egyptian Knowledge Bank (EKB); Suez Canal University; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Fayoum University; Egyptian Knowledge Bank (EKB); Helwan University; Egyptian Knowledge Bank (EKB); British University in Egypt; National Centre for Nuclear Research; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Joint Institute for Nuclear Research - Russia; Lomonosov Moscow State University; Brandenburg University of Technology Cottbus; University of Kansas; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; Eotvos Lorand University; Tata Institute of Fundamental Research (TIFR); Visva Bharati University; Sharif University of Technology; Isfahan University of Technology; Shiraz University; Islamic Azad University; Sapienza University Rome; Guglielmo Marconi University; University of Siena; University of Bucharest; University of Belgrade; University of California System; University of California Los Angeles; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); National & Kapodistrian University of Athens; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; University of Bern; Gaziosmanpasa University; Adiyaman University; Izmir Institute of Technology; University of Iowa; Mersin University; Ozyegin University; Kafkas University; Suleyman Demirel University; Ege University; Kahramanmaras Sutcu Imam University; University of Southampton; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Utah System of Higher Education; Utah Valley University; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; University of Belgrade; University of Belgrade; United States Department of Energy (DOE); Argonne National Laboratory; Erzincan Binali Yildirim University; Mimar Sinan Guzel Sanatlar University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Kyungpook National University Chatrchyan, S (corresponding author), Yerevan Phys Inst, Yerevan 375036, Armenia. cms-publication-committee-chair@cern.ch González Caballero, Isidro/E-7350-2010; Iorio, Alberto Orso Maria/GXA-0233-2022; Safronov, Grigory/F-8494-2017; Singh, J B/ABF-5574-2021; Bunichev, Viacheslav/E-2285-2012; Zalewski, Piotr/H-7335-2013; SONMEZ, Nasuf/Y-8399-2018; Grandi, Claudio/B-5654-2015; de la Hoz, Santiago Gonzalez/E-2494-2016; BARRILLON, Stephanie/ABA-6873-2021; Wulz, Claudia-Elisabeth E/H-5657-2011; de Paula Carvalho, Wagner/V-3343-2019; Tiras, Emrah/ABG-2354-2020; Kirakosyan, Martin R./N-2701-2015; VARDARLI, Fuat Ilkehan/B-6360-2013; Tsirou, Andromachi/HHC-6869-2022; Gamsızkan, Halil/AAF-5296-2019; Gomez, Jaime Arturo/P-5242-2018; Morovic, Srecko/T-9019-2019; Lychkovskaya, Natalia/F-8341-2017; De Guio, Federico/ABI-2018-2020; Ferencek, Dinko/G-6411-2015; Volpe, Roberta/AAR-4100-2021; Grigelionis, Ignas/ABG-4286-2021; Dremin, Igor/K-8053-2015; Puerta Pelayo, Jesus/L-1632-2014; Tinti, Gemma/I-5886-2013; Smirnov, Vitaly A/B-5001-2017; Petkov, Peicho/M-2080-2016; Civinini, Carlo/AAL-6403-2020; Goh, Junghwan/Q-3720-2016; Peris, Antonio Delgado/AAA-5165-2019; Zhu, Ren-Yuan/V-8966-2019; Shmatov, Sergei/J-5621-2014; Stahl, Achim/E-8846-2011; Arce, Pedro/L-1268-2014; radi, amr/Y-1592-2019; Verwilligen, Piet/M-2968-2014; de gruttola, michele/AAN-7271-2020; TUVE', Cristina/P-3933-2015; Fasanella, Daniele/ABD-9759-2020; Paktinat Mehdiabadi, Saeid/AAY-8501-2020; Mertzimekis, Theo J./M-1053-2019; Novaes, Sergio F./D-3532-2012; Yagil, Avi/GMX-0958-2022; Calvo Alamillo, Enrique/L-1203-2014; Ligabue, Franco/F-3432-2014; Waltenberger, Wolfgang/H-9330-2018; Tytgat, Michael G./F-3732-2018; Ocalan, Kadir/AAX-5235-2021; KIM, Tae Jeong/P-7848-2015; Heredia De La Cruz, Ivan/L-2610-2018; Acosta, Monica Vazquez/AAB-2841-2019; Ozturk, Sertac/AGO-2476-2022; Bedoya, Cristina F/K-8066-2014; Venturi, Andrea/J-1877-2012; Dahms, Torsten/A-8453-2015; Palencia Cortezon, Jose Enrique/F-1059-2017; Casarsa, Massimo/L-3623-2018; Kim, Min Suk/E-4161-2015; Bortignon, Pierluigi/AAF-6561-2020; Doğangün, Oktay/AAC-1579-2022; Santoro, Alberto F.S./E-7932-2014; Deniz, Muhammed/I-2517-2017; polatoz, ayse/C-3463-2018; Hos, ilknur/T-1165-2019; Marlow, Daniel R/C-9132-2014; Daskalakis, Georgios/M-4480-2013; Andreev, Vladimir F/M-8665-2015; Gozalez-Lopez, Oscar/AAH-3533-2019; Malbouisson, H./N-6733-2015; Hill, Christopher/B-5371-2012; Montanari, Alessandro/J-2420-2012; Park, Michael/HNJ-2201-2023; Calligaris, Luigi/K-2018-2017; Hernández, José M./H-9127-2015; banerjee, swagato/AAN-3165-2021; MANJHVAR, AMIT KUMAR/HNB-6459-2023; Dudko, Lev/D-7127-2012; Akgun, Bora/AAM-8311-2021; Trocsanyi, Zoltan/A-5598-2009; Polatoz, Ayse/AAF-6722-2021; Janssen, Xavier/E-1915-2013; Bourilkov, Dimitri/AAO-4908-2020; Rabady, Dinyar/GSN-9210-2022; Goy Lopez, Silvia/K-9200-2017; SEVINC KAYA, OZLEM/AAE-8069-2020; Cepeda, Maria/AAF-4992-2019; Gribushin, Andrei M/J-4225-2012; M, F/HGF-3166-2022; Doroba, krzysztof/AAA-3419-2021; Ozkorucuklu, Suat/AAE-7535-2020; Ferguson, Thomas A/O-3444-2014; Yang, Fan/GYU-4249-2022; Selvaggi, Michele/CAJ-2129-2022; GULER, E GURPINAR/AAH-4748-2019; Gao, Y./GWZ-3665-2022; Kozhuharov, Venelin/AAL-1658-2021; Cerizza, Giordano/AAO-5885-2020; Zhukov, Valery/K-3615-2013; Duarte-Campderrós, Jordi/F-5025-2018; Fanò, Livio/K-2460-2015; CAKIR, Altan/ABD-4450-2020; Leonidov, Andrey V/P-3197-2014; Vischia, Pietro/AAB-7811-2021; Pereira, Antonio Vilela/L-4142-2016; Hoorani, Hafeez R/D-1791-2013; Verwilligen, Piet Omer J/AAA-8819-2019; Luukka, Panja-Riina/S-8956-2018; Fouz, Maria Cruz/AAF-1105-2019; Amapane, Nicola/HDN-9630-2022; Ramos, Juan Pablo Fernández/AAF-3114-2019; Scodellaro, Luca/K-9091-2014; Demortier, Luc/AAE-6344-2020; Calderon, Alicia/K-3658-2014; De Filippis, Nicola/AAD-6280-2019; Mousa, Jehad/I-8019-2019; polatoz, ayse/AAV-1190-2020; Giammanco, Andrea/O-5017-2019; Travaglini, Riccardo/AAR-1089-2021; Loukas, Dimitris/ABF-1478-2020; Tomei, Thiago R F P/E-7091-2012; Ocalan, Kadir/ABA-7004-2021; fahim, ali/C-2400-2016; Mundim, Luiz M/A-1291-2012; Günaydin, Yusuf/F-7300-2014; yagil, avi/I-3278-2018; Dhingra, Nitish/ABA-9507-2020; Steggemann, Jan/AAL-5700-2020; Yetkin, Taylan/ABH-2774-2021; Bornheim, Adi/L-2750-2019; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Cavallo, Nicola/F-8913-2012; Paganoni, Marco/A-4235-2016; Vogel, Helmut/N-8882-2014; Sanchez-Hernandez, Alberto/L-4955-2017; Faccioli, Pietro/AAF-1499-2019; Romero, Luciano/B-8917-2018; Meridiani, Paolo/ABH-1655-2020; de Jesus Damiao, Dilson/G-6218-2012; D'Hondt, Jorgen/ABD-7930-2020; Zeyrek, Mehmet/ABA-7235-2020; Sunar Cerci, Deniz/AHE-1706-2022; Cankocak, Kerem/A-1507-2018; Cerrada, Marcos/J-6934-2014; Malvezzi, Sandra/B-8524-2009; Bagliesi, Giuseppe/C-2230-2013; Najafabadi, Mojtaba Mohammadi/AAF-7564-2019; Soares, Mara Senghi/GZM-6676-2022; Grigelionis, Ignas/AAI-2131-2021; Cimmino, Anna/AAA-1673-2020; Liu, Sheng/K-2815-2013; Benussi, Luigi/O-9684-2014; Sznajder, Andre/L-1621-2016; Lutz, Benjamin/U-4283-2017; Gao, Yu/HGE-1937-2022; Ivanov, Andrew G/A-7982-2013; kaya, mithat/D-8062-2011; Pompili, Alexis/ABG-3556-2021; Kim, Victor T/L-1648-2013; Adiguzel, Aytul/AAC-9049-2020; Blekman, Freya/ABD-7916-2020; Leonidov, Andrey/ABI-6671-2020; Konecki, Marcin/G-4164-2015; D'Alessandro, Raffaello/F-5897-2015; Padula, Sandra S./AAE-4304-2020; Flix, Josep/G-5414-2012; Klyukhin, Vyacheslav I/D-6850-2012; Torassa, Ezio/I-1788-2012; Lannon, Kevin/HGU-5755-2022; Lokhtin, Igor/D-7004-2012; Bodek, Arie/AAD-2811-2019; Rossin, Roberto/C-8376-2019; Arcidiacono, Roberta/ABF-3918-2020; Kodolova, Olga/D-7158-2012; Dallavalle, Gaetano Marco/AFW-4654-2022; Battilana, Carlo/AAA-7345-2020; Perieanu, Adrian/D-3347-2018; ozok, ferhat/A-6803-2018; Paulini, Manfred/N-7794-2014; Sanchez, Francisca J. Munoz/AAH-3909-2019; Dubinin, Mikhail N/I-3942-2016; khurshid, taimoor/AAF-4900-2019; Rodrigo, Teresa/F-6183-2018; Sguazzoni, Giacomo/J-4620-2015; De La Cruz Burelo, Eduard/Q-6021-2019; Menichelli, Mauro/AAQ-4026-2020; Fabozzi, Francesco/T-6468-2019; Mesyats, Gennady A/L-3138-2015; Majumder, Devdatta/X-2542-2019; Asavapibhop, Burin/X-1477-2019; Raidal, Martti/F-4436-2012; Aliev, Tahmasib/ABA-4487-2020; Paoletti, Simone/R-2295-2016; Oehler, Andreas/GQH-8718-2022; Yazgan, Efe/C-4521-2014; Bartalini, Paolo/E-2512-2014; Güler, Ali Murat/ABB-3382-2020; Traczyk, Piotr/ABA-8929-2020; Doğangün, Oktay/L-9252-2013; Dorigo, Tommaso/AAY-6673-2020; Ragazzi, Stefano/D-2463-2009; Lowette, Steven/HKV-3341-2023; Leonidov, Andrey/M-4440-2013; Tuo, Shengquan/ABA-9032-2021; Eskut, Eda/B-8905-2018; Giacomelli, Paolo/B-8076-2009; De La Cruz Burelo, Eduard/AAR-9103-2021; Banerjee, Swagato/AAN-3326-2021; Bernardes, Cesar Augusto/D-2408-2015; Cepeda, Maria/HNO-9314-2023; Spagnolo, Paolo/G-3401-2017; Saoulidou, Niki/AAA-2891-2020; Markina, Anastasia/E-3390-2012; Grassi, Marco/U-1260-2017; Hall-Wilton, Richard/U-6918-2019; Lazzizzera, Ignazio/E-9678-2015; Krammer, Manfred/A-6508-2010; Kumar, Dr Ashok/HNR-6570-2023; Wimpenny, Stephen J/K-8848-2013; Felcini, Marta A./S-2684-2019; Seixas, Joao C/F-5441-2013; dumanoglu, isa/C-3627-2018; Matorras, Francisco/I-4983-2015; Kumar, Amit/HMP-4853-2023; Shopova, Mariana Filipova/HKW-0149-2023; Rivero, Celso Martinez/V-6916-2017; Özdemir, Kadri/P-8058-2014; KUMAR, ASHOK/AAF-4055-2021; Krücker, Dirk/X-1844-2019; Gokbulut, Gul/G-2141-2018; Markowitz, Pete/AAC-3382-2020; Abdulsalam, Abdulla/B-3386-2019; MAESTRE, JUAN ALCARAZ/I-5763-2015; Ciulli, Vitaliano/ABC-5440-2020; HAJ AHMAD (Vael HAJAHMAD), Wael/E-6738-2016; Azarkin, Maxim/N-2578-2015; Tuominen, Eija M/A-5288-2017; Fanelli, Cristiano/AAS-1489-2020; Obertino, Maria Margherita/ABH-1662-2020; Fiorendi, Sara/ABA-3311-2021; de la Cruz, Begona/K-7552-2014; Duarte, Javier Mauricio/AAA-5414-2020; Juodagalvis, Andrius/F-6952-2018; LECOQ, Paul/S-7246-2019; Govoni, Pietro/K-9619-2016; Benaglia, Andrea/AGF-5495-2022; Mahmoud, Mohammed Attia/AAD-1249-2019; Josa, Isabel/K-5184-2014; Leonardo, Nuno T/M-6940-2016; Sanchez-Hernandez, Alberto/AAH-3473-2020; d'Enterria, David/W-7763-2019; Kumar, Amit/HJP-6588-2023; Lista, Luca/M-2912-2019; Mussgiller, Andreas/AAF-6933-2019; Mekterović, Darko/S-7270-2018; Tinoco Mendes, Andre David/D-4314-2011; Xie, Si/O-6830-2016; Ligabue, Franco/ABI-1681-2020; Gülmez, Erhan/P-9518-2015; cartiglia, nicolo/ABF-1008-2021; de Souza Sandro, Fonseca/ABB-8505-2020; My, Salvatore/I-5160-2015; Sen, Sercan/C-6473-2014; Ratnikov, Fedor/D-5137-2016; Cortabitarte, Rocio Vilar/P-8480-2014; TOPAKSU, Aysel KAYİS/B-8910-2018; Worm, Steven D/I-3575-2012; Trocino, Daniele/AGI-2155-2022; Colafranceschi, Stefano/M-1807-2016; Biino, Cristina/AAF-1585-2020; Amapane, Nicola/J-3683-2012; Gundacker, Stefan/FBD-4491-2022; García Abia, Pablo/K-3595-2017; Salt, Jose/F-4928-2016; gao, yi/HCI-8298-2022; D'Alessandro, Raffaello/P-4138-2019; Boimska, Bożena/Z-6088-2019; Rovelli, Tiziano/K-4432-2015; Ruiz, Alberto/E-4473-2011; Bahtiyar, Huseyin/AAA-1419-2021; Isildak, Bora/AAW-4994-2020; Belyaev, Alexander/F-6637-2015; Della Ricca, Giuseppe/B-6826-2013; khurshid, taimoor/AAA-5677-2021; Ulrich, Ralf/Q-3882-2019; Piperov, Stefan/Q-1980-2017; Theofilatos, Konstantinos/AAL-9162-2021; Malik, Sudhir/D-6621-2018; Fernandez Menendez, Javier/B-6550-2014; Redondo, Ignacio/K-8639-2014; Kyberd, Paul/P-1092-2014; Rolandi, Luigi (Gigi)/E-8563-2013; Azzi, Patrizia/H-5404-2012; Luo, Wuming/J-4263-2018; Gonzalez Suarez, Rebeca/L-6128-2014; Popov, Andrey/E-1052-2012 González Caballero, Isidro/0000-0002-8087-3199; Iorio, Alberto Orso Maria/0000-0002-3798-1135; Singh, J B/0000-0001-9029-2462; Bunichev, Viacheslav/0000-0003-4418-2072; SONMEZ, Nasuf/0000-0002-0969-127X; Grandi, Claudio/0000-0001-5998-3070; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; BARRILLON, Stephanie/0000-0002-7075-1673; Wulz, Claudia-Elisabeth E/0000-0001-9226-5812; de Paula Carvalho, Wagner/0000-0003-0738-6615; Tiras, Emrah/0000-0002-5628-7464; Gamsızkan, Halil/0000-0002-2578-6295; Gomez, Jaime Arturo/0000-0003-3958-9054; De Guio, Federico/0000-0001-5927-8865; Ferencek, Dinko/0000-0001-9116-1202; Volpe, Roberta/0000-0003-1782-2978; Grigelionis, Ignas/0000-0001-8383-3427; Puerta Pelayo, Jesus/0000-0001-7390-1457; Tinti, Gemma/0000-0003-1364-844X; Petkov, Peicho/0000-0002-0420-9480; Civinini, Carlo/0000-0002-4952-3799; Goh, Junghwan/0000-0002-1129-2083; Peris, Antonio Delgado/0000-0002-8511-7958; Zhu, Ren-Yuan/0000-0003-3091-7461; Shmatov, Sergei/0000-0001-5354-8350; Stahl, Achim/0000-0002-8369-7506; Arce, Pedro/0000-0003-3009-0484; Verwilligen, Piet/0000-0002-9285-8631; de gruttola, michele/0000-0003-4835-8688; TUVE', Cristina/0000-0003-0739-3153; Paktinat Mehdiabadi, Saeid/0000-0001-7303-0217; Mertzimekis, Theo J./0000-0001-9191-7903; Novaes, Sergio F./0000-0003-0471-8549; Calvo Alamillo, Enrique/0000-0002-1100-2963; Ligabue, Franco/0000-0002-1549-7107; Waltenberger, Wolfgang/0000-0002-6215-7228; Tytgat, Michael G./0000-0002-3990-2074; KIM, Tae Jeong/0000-0001-8336-2434; Heredia De La Cruz, Ivan/0000-0002-8133-6467; Acosta, Monica Vazquez/0000-0002-2409-9792; Ozturk, Sertac/0000-0001-6533-6144; Bedoya, Cristina F/0000-0001-8057-9152; Dahms, Torsten/0000-0003-4274-5476; Palencia Cortezon, Jose Enrique/0000-0001-8264-0287; Casarsa, Massimo/0000-0002-1353-8964; Bortignon, Pierluigi/0000-0002-5360-1454; Doğangün, Oktay/0000-0002-1255-2211; Santoro, Alberto F.S./0000-0002-0568-665X; Deniz, Muhammed/0000-0002-9781-8241; Hos, ilknur/0000-0002-7678-1101; Daskalakis, Georgios/0000-0001-6070-7698; Andreev, Vladimir F/0000-0002-5492-6920; Gozalez-Lopez, Oscar/0000-0002-4532-6464; Hill, Christopher/0000-0003-0059-0779; Montanari, Alessandro/0000-0003-2748-6373; Calligaris, Luigi/0000-0002-9951-9448; Hernández, José M./0000-0001-6436-7547; banerjee, swagato/0000-0001-8852-2409; MANJHVAR, AMIT KUMAR/0000-0002-9577-6295; Dudko, Lev/0000-0002-4462-3192; Akgun, Bora/0000-0001-8888-3562; Trocsanyi, Zoltan/0000-0002-2129-1279; Polatoz, Ayse/0000-0001-9516-0821; Bourilkov, Dimitri/0000-0003-0260-4935; Rabady, Dinyar/0000-0001-9239-0605; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Cepeda, Maria/0000-0002-6076-4083; Doroba, krzysztof/0000-0002-7818-2364; Ozkorucuklu, Suat/0000-0001-5153-9266; Ferguson, Thomas A/0000-0001-5822-3731; GULER, E GURPINAR/0000-0002-6172-0285; Kozhuharov, Venelin/0000-0002-0669-7799; Duarte-Campderrós, Jordi/0000-0003-0687-5214; Fanò, Livio/0000-0002-9007-629X; CAKIR, Altan/0000-0002-8627-7689; Vischia, Pietro/0000-0002-7088-8557; Pereira, Antonio Vilela/0000-0003-3177-4626; Verwilligen, Piet Omer J/0000-0002-9285-8631; Luukka, Panja-Riina/0000-0003-2340-4641; Fouz, Maria Cruz/0000-0003-2950-976X; Amapane, Nicola/0000-0001-9449-2509; Ramos, Juan Pablo Fernández/0000-0002-0122-313X; Scodellaro, Luca/0000-0002-4974-8330; Demortier, Luc/0000-0002-0499-4298; Calderon, Alicia/0000-0002-7205-2040; Mousa, Jehad/0000-0002-2978-2718; Giammanco, Andrea/0000-0001-9640-8294; Travaglini, Riccardo/0000-0002-5288-1407; 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Pastrone, Nadia/0000-0001-7291-1979 BMWF; FWF (Austria); FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPESP (Brazil); MEYS (Bulgaria); CERN; CAS; MoST; NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); MoER [SF0690030s09]; ERDF (Estonia); Academy of Finland; MEC; HIP (Finland); CEA; CNRS/IN2P3 (France); BMBF; DFG; HGF (Germany); GSRT (Greece); OTKA; NKTH (Hungary); DAE; DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF; WCU (Republic of Korea); LAS (Lithuania); CINVESTAV; CONACYT; SEP; UASLP-FAI (Mexico); MSI (New Zealand); PAEC (Pakistan); MSHE; NSC (Poland); FCT (Portugal); JINR (Armenia); JINR (Belarus); JINR (Georgia); JINR (Ukraine); JINR (Uzbekistan); MON; RosAtom; RAS; RFBR (Russia); MSTD (Serbia); SEIDI; CPAN (Spain); NSC (Taipei); ThEPCenter; IPST; NSTDA (Thailand); TUBITAK; TAEK (Tkey); NASU (Ukraine); STFC (United Kingdom); DOE; NSF (USA); Division Of Physics; Direct For Mathematical & Physical Scien [0969555] Funding Source: National Science Foundation; Division Of Physics; Direct For Mathematical & Physical Scien [1151640, 1211067, 1205960] Funding Source: National Science Foundation; STFC [ST/G502347/1, ST/L00609X/1, ST/F006748/1, ST/I000410/1, ST/I005912/1, ST/J005665/1, ST/F007094/1, ST/J004901/1, ST/I002200/1, ST/I505572/1, ST/K003844/1, ST/K001604/1, ST/H00081X/2] Funding Source: UKRI; Science and Technology Facilities Council [ST/K001604/1 MICE/UKNF, ST/K001604/1 GRIDPP, ST/K001604/1 LHCb Upgrades, ST/K001604/1 DMUK, ST/G502347/1, CMS, ST/F006748/1, ST/I002200/1, ST/I005912/1 GRIDPP, ST/L00609X/1 GRIDPP, ST/K001604/1 CMS Upgrade, ST/K001256/1, ST/K001604/1 SuperNEMO, ST/J005665/1, ST/K001604/1 LHCb, ST/L00609X/1, ST/I000410/1, ST/I505572/1, ST/J004901/1, ST/K003844/1, ST/K001604/1 T2K, ST/K001604/1, ST/F007094/1, ST/K003844/1 GRIDPP, GRIDPP, ST/I005912/1, ST/I003622/1 GRIDPP] Funding Source: researchfish BMWF; FWF (Austria)(Austrian Science Fund (FWF)); FNRS(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MEYS (Bulgaria); CERN; CAS(Chinese Academy of Sciences); MoST; NSFC (China)(National Natural Science Foundation of China (NSFC)); COLCIENCIAS (Colombia)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSES (Croatia); RPF (Cyprus); MoER; ERDF (Estonia); Academy of Finland(Academy of Finland); MEC; HIP (Finland); CEA(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF(Federal Ministry of Education & Research (BMBF)); DFG(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); OTKA(Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA)); NKTH (Hungary)(National Office for Research and Technology); DAE(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); NRF; WCU (Republic of Korea); LAS (Lithuania); CINVESTAV; CONACYT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); SEP; UASLP-FAI (Mexico); MSI (New Zealand); PAEC (Pakistan); MSHE; NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Armenia); JINR (Belarus); JINR (Georgia); JINR (Ukraine); JINR (Uzbekistan); MON; RosAtom; RAS(Russian Academy of SciencesRegione Sardegna); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); MSTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI; CPAN (Spain); NSC (Taipei); ThEPCenter; IPST; NSTDA (Thailand); TUBITAK(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Tkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE(United States Department of Energy (DOE)); NSF (USA)(National Science Foundation (NSF)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MEYS (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); MoER, SF0690030s09 and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Republic of Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MSI (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MON, RosAtom, RAS and RFBR (Russia); MSTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzurerland); NSC (Taipei); ThEPCenter, IPST and NSTDA (Thailand); TUBITAK and TAEK (Tkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). 26 35 35 1 105 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C APR 2013.0 73 4 2386 10.1140/epjc/s10052-013-2386-x 0.0 18 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 135MM Green Published, Green Submitted, Green Accepted, hybrid 2023-03-23 WOS:000318292200022 0
J Guo, JQ; Yang, L; Bie, RF; Yu, JG; Gao, Y; Shen, Y; Kos, A Guo, Junqi; Yang, Lan; Bie, Rongfang; Yu, Jiguo; Gao, Yuan; Shen, Yan; Kos, Anton An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring COMPUTER NETWORKS English Article Smart wearables; Physical fitness evaluation model; PPG signal; Advanced feature selection; XGBoost; Bayesian hyper-parameter optimization HEART-RATE; EXERCISE; PHOTOPLETHYSMOGRAPHY Thanks to the improvement of technologies such as Internet of Things, bio-sensing and data mining, smart wearable technologies have recently received increasing attention for teenagers' sport and health monitoring. Despite the powerful data-acquisition ability of the current wearable products on the market, they still suffer performance deficiency in valuable knowledge extraction due to the lack of accurate computational model and in-depth data analysis. Based on this, this paper proposes a machine learning based physical fitness evaluation model oriented to wearable running monitoring for teenagers, in which a variant of the gradient boosting machine (GBM) combined with advanced feature selection and Bayesian hyper-parameter optimization is employed to build a physical fitness evaluation model. To begin with, we design a special experimental paradigm for data acquisition based on a conventional running activity, in which a group of teenagers' photoplethysmography (PPG) signals in different testing stages are collected by a set of smartbands developed by ourselves. Next, PPG signals are processed in four steps which match with the four modules in the proposed model including signal preprocessing, physiological data estimation, feature engineering and classification modules. Firstly, the signal preprocessing module aims for suppressing noise and removing baseline drift in PPG signals by using a smoothness prior approach (SPA) and a median filter (MF), respectively. Secondly, the physiological data estimation module achieves conversion from PPG signals to physiological data such as heart rate (HR) and blood oxygen saturation (SpO(2)). Thirdly, the feature engineering module extracts from the physiological data a group of key features closely related to physical fitness statuses, and then implements a novel advanced feature selection scheme by using Pearson correlation and importance score ranking based sequential forward search (PC-ISR-SFS). Fourthly, the classification module utilizes an extreme gradient boosting (XGBoost) algorithm for classification of each teenager's physical fitness level, in which hyper-parameters are adaptively tuned with Bayesian optimization. Experimental results demonstrate that not only does the proposed model achieve higher evaluation accuracy than the existing reference models, but it also provides a promising solution to future physical fitness evaluation for teenagers through a machine-learning-model based intelligent computing instead of traditional-empirical-model based-manual. calculation. (C) 2019 Elsevier B.V. All rights reserved. [Guo, Junqi; Yang, Lan; Bie, Rongfang] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China; [Yu, Jiguo] Qufu Normal Univ, Sch Informat Sci & Engn, Beijing, Peoples R China; [Gao, Yuan] Acad Mil Sci PLA, Beijing, Peoples R China; [Gao, Yuan] Tsinghua Univ, State Key Lab Microwave & Digital Commun, Natl Lab Informat Sci & Technol, Beijing, Peoples R China; [Shen, Yan] Beijing Normal Univ, Business Sch, Beijing, Peoples R China; [Kos, Anton] Univ Liubljana, Fac Elect Engn, Trzaska 25, Ljubljana 1000, Slovenia Beijing Normal University; Qufu Normal University; Tsinghua University; Beijing Normal University Bie, RF (corresponding author), Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China. guojunqi@bnu.edu.cn; yanglan@mail.bnu.edu.cn; rfbie@bnu.edu.cn; jiguoyu@sina.com; yuangao08@tsinghua.edu.cn; shenyan@bnu.edu.cn; anton.kos@fe.uni-lj.si ; Yu, Jiguo/I-5242-2014 Gao, Yuan/0000-0001-8567-4888; Kos, Anton/0000-0001-6234-8561; Yu, Jiguo/0000-0001-6451-1158 National Natural Science Foundation of China [61401029]; Educational Big Data R&D and its Application -Major Big Data Engineering Project of National Development and Reform Commission; Beijing Advanced Innovation Center for Future Education [BJAICFE2016IR-004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Educational Big Data R&D and its Application -Major Big Data Engineering Project of National Development and Reform Commission; Beijing Advanced Innovation Center for Future Education This research is sponsored by National Natural Science Foundation of China (No. 61401029), Educational Big Data R&D and its Application -Major Big Data Engineering Project of National Development and Reform Commission 2017, and Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004). 44 62 65 6 54 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. MAR 14 2019.0 151 166 180 10.1016/j.comnet.2019.01.026 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HP5OF 2023-03-23 WOS:000461725700013 0
C Zhang, Y; Chen, HS; Zhao, YH; Liu, Q; Yin, DW Lang, J Zhang, Yuan; Chen, Hongshen; Zhao, Yihong; Liu, Qun; Yin, Dawei Learning Tag Dependencies for Sequence Tagging PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE English Proceedings Paper 27th International Joint Conference on Artificial Intelligence (IJCAI) JUL 13-19, 2018 Stockholm, SWEDEN Int Joint Conf Artifical Intelligence,Didi ChuXing,Xiaoi,Tencent,JD.com,Alibaba Grp,Baidu,Tieto,Adobe,Pwc,Artificial Intelligence Journal,Ericsson,Recorded Future,Songshu AI Learning,Microsoft,Node Pole,Nnaisense,Vinnova,IBM,Hitachi,Sondrel,Inovia ai,Figure Eight,Prowler.io,Nuance Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature. [Zhang, Yuan] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China; [Zhang, Yuan; Chen, Hongshen; Zhao, Yihong; Yin, Dawei] JD Com, Data Sci Lab, Beijing, Peoples R China; [Liu, Qun] Dublin City Univ, ADAPT Ctr, Sch Comp, Dublin, Ireland; [Zhang, Yuan] Univ Chinese Acad Sci, Beijing, Peoples R China Chinese Academy of Sciences; Institute of Computing Technology, CAS; Dublin City University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Zhang, Y (corresponding author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China. zhangyuan02@ict.ac.cn; chenhongshen@jd.com; ericzhao@jd.com; yindawei@acm.org 32 11 12 0 0 IJCAI-INT JOINT CONF ARTIF INTELL FREIBURG ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY 978-0-9992411-2-7 2018.0 4581 4587 7 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS7MQ 2023-03-23 WOS:000764175404102 0
J Lin, K; Li, YH; Deng, J; Pace, P; Fortino, G Lin, Kai; Li, Yihui; Deng, Jing; Pace, Pasquale; Fortino, Giancarlo Clustering-Learning-Based Long-Term Predictive Localization in 5G-Envisioned Internet of Connected Vehicles IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Roads; Prediction algorithms; Deep learning; Clustering algorithms; Correlation; Predictive models; Internet of Connected Vehicles; clustering learning; behavior analysis; vehicle localization Localization services play an important role in Internet of Connected Vehicles (IoCV) and vehicle predictive localization information can greatly improve traffic efficiency and reduce accidents. However, a huge amount of computing and communication overhead is required to obtain such information by traditional methods. In this work, we propose a Behavior-based Clustering Method (BCM) to analyze the behavioral correlation between vehicles and classify them into different clusters. Based on BCM results coupled with a deep learning model, we further propose a Clustering-learning-based Long-term Predictive Localization (CLPL) algorithm to predict vehicles' future location distribution. In the proposed CLPL algorithm, all the traffic roads are divided into consecutive small segments in order to pinpoint vehicles' precise current locations and to obtain long-term predictions. Extensive simulations, notably involving real dataset, have been carried out to evaluate BCM and CLPL in terms of several performance criteria including matching rates. The analysis of the results validated how the designed methods can predict vehicle location much more accurately than existing algorithms. [Lin, Kai; Li, Yihui] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China; [Deng, Jing] Univ North Carolina Greensboro, Dept Comp Sci, Greensboro, NC 27412 USA; [Pace, Pasquale; Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst DIMES, I-87036 Arcavacata Di Rende, Italy Dalian University of Technology; University of North Carolina; University of North Carolina Greensboro; University of Calabria Lin, K (corresponding author), Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China. link@dlut.edu.cn; 928982000@mail.dlut.edu.cn; jing.deng@uncg.edu; p.pace@dimes.unical.it; g.fortino@unical.it Pace, Pasquale/L-8666-2017; Pace, Pasquale/AAF-2126-2019; Fortino, Giancarlo/J-2950-2017 Pace, Pasquale/0000-0002-4159-2765; Pace, Pasquale/0000-0002-4159-2765; Fortino, Giancarlo/0000-0002-4039-891X Liaoning Province Higher Education Innovative Talent Support Program; National Natural Science Foundation of China [U1908214]; National Key Research and Development Program of China [2018YFB1600600]; Fundamental Research Funds for the Central Universities [DUT19JC22, DUT18RC(3)069] Liaoning Province Higher Education Innovative Talent Support Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the Liaoning Province Higher Education Innovative Talent Support Program, in part by the National Natural Science Foundation of China under Grant U1908214, in part by the National Key Research and Development Program of China under Grant 2018YFB1600600, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT19JC22 and Grant DUT18RC(3)069. 45 11 11 1 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. AUG 2021.0 22 8 5232 5246 10.1109/TITS.2020.2997472 0.0 15 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation TY8BD 2023-03-23 WOS:000684003100045 0
J Fu, HZ; Zhao, YT; Yap, PT; Schonlieb, CB; Frangi, AF Fu, Huazhu; Zhao, Yitian; Yap, Pew-Thian; Schonlieb, Carola-Bibiane; Frangi, Alejandro F. Guest Editorial Special Issue on Geometric Deep Learning in Medical Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING English Editorial Material In recent years, more and more attention has been devoted to geometric deep learning (GDL) and its applications to various problems in medical imaging. Unlike convolutional neural networks (CNNs) limited to 2-D/3-D grid-structured data, GDL can handle non-Euclidean data (i.e., graphs and manifolds) and is hence well-suited for medical imaging data such as structure-function connectivity networks, imaging genetics and omics, spatio-temporal anatomical representations, physics-informed GDL for optimal imaging sampling and acquisition, GDL in imaging inverse problems, etc. However, despite recent advances in GDL research, questions remain on how best to learn representations of non-Euclidean medical imaging data; how to convolve effectively on graphs; how to perform graph pooling/unpooling; how to handle heterogeneous data; and how to improve the interpretability of GDL. After discussing many other domain experts, we identify the need for a special issue that brings to the attention of the medical imaging community these interesting topics. [Fu, Huazhu] ASTAR, Inst High Performance Comp, Singapore 138632, Singapore; [Zhao, Yitian] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Beijing 100045, Peoples R China; [Yap, Pew-Thian] Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA; [Schonlieb, Carola-Bibiane] Univ Cambridge, DAMTP, Cambridge CB2 1TN, England; [Frangi, Alejandro F.] Univ Leeds, Computat Med & Royal Acad, Leeds LS2 9JT, England; [Frangi, Alejandro F.] Katholieke Univ Leuven, B-3000 Leuven, Belgium Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); Chinese Academy of Sciences; Ningbo Institute of Materials Technology and Engineering, CAS; University of North Carolina; University of North Carolina Chapel Hill; University of Cambridge; University of Leeds; KU Leuven Fu, HZ (corresponding author), ASTAR, Inst High Performance Comp, Singapore 138632, Singapore. hzfu@ieee.org; yitian.zhao@nimte.ac.cn; ptyap@med.unc.edu; cbs31@cam.ac.uk; a.frangi@leeds.ac.uk 19 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging FEB 2023.0 42 2 332 335 10.1109/TMI.2022.3231755 0.0 4 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging 9A6HB Bronze 2023-03-23 WOS:000934156000001 0
J Ruan, JF; Yan, Z; Dong, B; Zheng, QH; Qian, BY Ruan, Jianfei; Yan, Zheng; Dong, Bo; Zheng, Qinghua; Qian, Buyue Identifying suspicious groups of affiliated-transaction-based tax evasion in big data INFORMATION SCIENCES English Article Graph mining; Affiliated transaction; Tax evasion; Big data Affiliated-transaction-based tax evasion (ATTE) is a new strategy in tax evasion that is carried out via legal-like transactions between a group of companies that have heterogeneous, complex and covert interactive relationships to evade taxes. Existing studies cannot effectively detect ATTE behaviors since (i) they perform well only for determining the abnormal financial status of individuals and ineffectively address the interactive relationships among companies, (ii) they aim at detecting ATTE from the perspective of structural characteristics, which leads to a poor false-positive rate, and (iii) few of them perform well in most sectors of companies. Effectively detecting suspicious groups according to both structural characteristics of ATTE groups and business characteristics of ATTE means (BC-ATTEM) remains an open issue. In this paper, we propose an affiliated-parties interest-related network (APIRN) for modeling affiliated parties, interest-related relationships, and their properties for identifying ATTE. Then, we identify the behavioral patterns of ATTE via topological pattern abstraction from APIRN and theoretical inference of BC-ATTEM. Based on the above, we further propose a hybrid method, namely, 3TI, for identifying ATTE suspicious groups via three steps: tax rate differential detection, topological pattern matching and tax burden abnormality identification. Experimental tests that are based on two years of real-world tax data from a province in China demonstrate that 3TI can identify ATTE suspicious groups with higher accuracy and better generality than existing works. Moreover, we identify various interesting implications and provide useful guidance for ATTE inspection based on an analysis of our experimental results. (C) 2018 Elsevier Inc. All rights reserved. [Ruan, Jianfei; Zheng, Qinghua; Qian, Buyue] Xi An Jiao Tong Univ, MOE, Key Lab Intelligent Networks & Network Secur, Xian 710049, Shaanxi, Peoples R China; [Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Dong, Bo] Xi An Jiao Tong Univ, Sch Continuing Educ, Xian 710049, Shaanxi, Peoples R China; [Dong, Bo] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China Xi'an Jiaotong University; Xidian University; Aalto University; Xi'an Jiaotong University; Xi'an Jiaotong University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China. xjtu_jfruan@163.com; zyan@xidian.edu.cn; dong.bo@mail.xjtu.edu.cn; qhzheng@mail.xjtu.edu.cn; qianbuyue@xjtu.edu.cn yang, zheng/HGC-7753-2022; RUAN, JIANFEI/HKO-0993-2023 Yan, Zheng/0000-0002-9697-2108; Ruan, Jianfei/0000-0001-5250-3951 Fundamental Theory and Applications of Big Data with Knowledge Engineering under the National Key Research and Development Program of China [2016YFB1000903]; National Science Foundation of China [61502379, 61532015, 61672410, 61721002]; Academy of Finland [308087]; Academy of Finland (AKA) [308087] Funding Source: Academy of Finland (AKA) Fundamental Theory and Applications of Big Data with Knowledge Engineering under the National Key Research and Development Program of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work is sponsored by The Fundamental Theory and Applications of Big Data with Knowledge Engineering under the National Key Research and Development Program of China with Grant No. 2016YFB1000903, the National Science Foundation of China under Grant Nos. 61502379, 61532015, 61672410 and 61721002, and Academy of Finland (Grant No. 308087). 43 17 17 2 23 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. MAR 2019.0 477 508 532 10.1016/j.ins.2018.11.008 0.0 25 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science HI9GP Green Submitted 2023-03-23 WOS:000456763600034 0
J Kuang, YX; Wu, Q; Wang, Y; Dey, N; Shi, FQ; Crespo, RG; Sherratt, RS Kuang, Yuxiang; Wu, Qun; Wang, Ying; Dey, Nilanjan; Shi, Fuqian; Gonzalez Crespo, Ruben; Sherratt, R. Simon Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks APPLIED SOFT COMPUTING English Article AlexNet; Deep convolutional neural network; Acoustic signals; Affective computing; Short time Fourier transform WAVELET PACKET; RECOGNITION; SPEECH; IMAGE; CNN; ARCHITECTURE; FEATURES; ALEXNET Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication through the above modalities in the past decades. In addition to facial expressions, changes in the level of sound, strength, weakness, and turbulence will also convey affective. Extracting affective feature parameters from the acoustic signals have been widely applied in customer service, education, and the medical field. In this research, an improved AlexNet-based deep convolutional neural network (A-DCNN) is presented for acoustic signal recognition. Firstly, preprocessed on signals using simplified inverse filter tracking (SIFT) and short-time Fourier transform (STFT), Mel frequency Cepstrum (MFCC) and waveform-based segmentation were deployed to create the input for the deep neural network (DNN), which was applied widely in signals preprocess for most neural networks. Secondly, acoustic signals were acquired from the public Ryerson Audio Visual Database of Affective Speech and Song (RAVDESS) affective speech audio system. Through the acoustic signal preprocessing tools, the basic features of the kind of sound signals were calculated and extracted. The proposed DNN based on improved AlexNet has a 95.88% accuracy on classifying eight affective of acoustic signals. By comparing with some linear classifications, such as decision table (DT) and Bayesian inference (BI) and other deep neural networks, such as AlexNet+SVM, recurrent convolutional neural network (R-CNN), etc., the proposed method achieves high effectiveness on theaccuracy (A), sensitivity (S1), positive predictive (PP), and f1-score (F1). Acoustic signals affective recognition and classification can be potentially applied in industrial product design through measuring consumers' affective responses to products; by collecting relevant affective sound data to understand the popularity of the product, and furthermore, to improve the product design and increase the market responsiveness. (C) 2020 Elsevier B.V. All rights reserved. [Kuang, Yuxiang] Jiangxi Univ Finance & Econ, Arts Coll, Nanchang, Jiangxi, Peoples R China; [Wu, Qun] Zhejiang Sci Tech Univ, Inst Universal Design, Hangzhou, Peoples R China; [Wang, Ying] Zhejiang Sci Tech Univ, Dept Ind Design, Coll Art & Design, Hangzhou 310018, Peoples R China; [Dey, Nilanjan] Techno Int New Town, Dept Informat Technol, Kolkata, W Bengal, India; [Shi, Fuqian] Rutgers State Univ, Canc Inst New Jersey, New Brunswick, NJ 08903 USA; [Gonzalez Crespo, Ruben] Univ Int La Rioja, Dept Comp Sci & Technol, Logrono, Spain; [Sherratt, R. Simon] Univ Reading, Dept Biomed Engn, Reading RG6 6AY, Berks, England Jiangxi University of Finance & Economics; Zhejiang Sci-Tech University; Zhejiang Sci-Tech University; Rutgers State University New Brunswick; Rutgers State University Medical Center; Rutgers Cancer Institute of New Jersey; Universidad Internacional de La Rioja (UNIR); University of Reading Wu, Q; Wang, Y (corresponding author), Xiasha High Tech Educ Zone, 928,2nd St, Hangzhou, Zhejiang, Peoples R China. wuq@zstu.edu.cn; winered@zstu.edu.cn Gonzalez Crespo, Ruben/P-8601-2018 Gonzalez Crespo, Ruben/0000-0001-5541-6319 57 8 8 0 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. DEC 2020.0 97 A 106775 10.1016/j.asoc.2020.106775 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science PL1DK Green Accepted 2023-03-23 WOS:000602871300015 0
J Koch, W; Bonfanti, M; Eisenbrandt, P; Nandi, A; Fu, BN; Bowman, J; Tannor, D; Burghardt, I Koch, Werner; Bonfanti, Matteo; Eisenbrandt, Pierre; Nandi, Apurba; Fu, Bina; Bowman, Joel; Tannor, David; Burghardt, Irene Two-layer Gaussian-based MCTDH study of the S-1 <- S-0 vibronic absorption spectrum of formaldehyde using multiplicative neural network potentials JOURNAL OF CHEMICAL PHYSICS English Article QUANTUM DYNAMICS; MOLECULAR-DYNAMICS; HERZBERG-TELLER; PHOTOFRAGMENTATION; SPECTROSCOPY; WAVEPACKETS The absorption spectrum of the vibronically allowed S-1((1)A(2)) <- S-0((1)A(1)) transition of formaldehyde is computed by combining multiplicative neural network (NN) potential surface fits, based on multireference electronic structure data, with the two-layer Gaussian-based multiconfiguration time-dependent Hartree (2L-GMCTDH) method. The NN potential surface fit avoids the local harmonic approximation for the evaluation of the potential energy matrix elements. Importantly, the NN surface can be constructed so as to be physically well-behaved outside the domain spanned by the ab initio data points. A comparison with experimental results shows spectroscopic accuracy of the converged surface and 2L-GMCTDH quantum dynamics. Published under license by AIP Publishing. [Koch, Werner; Tannor, David] Weizmann Inst Sci, Rehovot, Israel; [Koch, Werner; Bonfanti, Matteo; Eisenbrandt, Pierre; Burghardt, Irene] Goethe Univ Frankfurt, Frankfurt, Germany; [Nandi, Apurba; Bowman, Joel] Emory Univ, Atlanta, GA 30322 USA; [Fu, Bina] Dalian Inst Chem Phys, Dalian, Peoples R China Weizmann Institute of Science; Goethe University Frankfurt; Emory University; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS Koch, W (corresponding author), Weizmann Inst Sci, Rehovot, Israel.;Koch, W (corresponding author), Goethe Univ Frankfurt, Frankfurt, Germany. werner.koch@weizmann.ac.il; burghardt@chemie.uni-frankfurt.de Bonfanti, Matteo/B-1094-2011 Bonfanti, Matteo/0000-0002-6361-5163; NANDI, APURBA/0000-0002-6191-5584 German-Israeli Foundation for Scientific Research and Development; GIF [I-1337-302.5/2016]; Alexander von Humboldt Foundation German-Israeli Foundation for Scientific Research and Development(German-Israeli Foundation for Scientific Research and Development); GIF(German-Israeli Foundation for Scientific Research and Development); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation) We gratefully acknowledge various discussions with Eli Pollak. We thank the German-Israeli Foundation for Scientific Research and Development for support of this project under Grant No. GIF I-1337-302.5/2016. M.B. acknowledges fellowship support by the Alexander von Humboldt Foundation. 43 11 11 0 9 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0021-9606 1089-7690 J CHEM PHYS J. Chem. Phys. AUG 14 2019.0 151 6 64121 10.1063/1.5113579 0.0 13 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics IR5DE 2023-03-23 WOS:000481452400045 0
J Guo, JW; Cheng, Y; Wang, DX; Tao, F; Pickl, S Guo, Jingwei; Cheng, Ying; Wang, Dongxu; Tao, Fei; Pickl, Stefan Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH English Article; Early Access Dataspace; industrial Big Data; smart decision-making; data analytics; smart manufacturing DATA-MANAGEMENT; DATABASES; MAPPINGS Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many advantages such as low cost of adaptation and convenience for local database, some methods are hard to manage variable data sources and discover proper range of data for smart decision-making. Therefore, Dataspace is considered in this article to be a feasible and effective method. From the relation-defined perspective of utilisation of industrial Big Data, the contribution is a novel industrial Dataspace design with static structure and working flow paths for smart manufacturing. In design, the industrial Dataspace platform has been proposed to accommodate smart manufacturing characteristics with the intelligence of pay-as-you-go, like harnessing distributed heterogenous data from industrial enterprises, understanding industrial data by ontology or knowledge, corelating the data with smart applications, and enabling related decisions. A further analytical case in Surface Mounting Technology manufacturing of welding procedure is provided to illustrate the execution of customisation, focused and related decision support, and system evolution within industrial Dataspace. [Guo, Jingwei; Cheng, Ying; Wang, Dongxu; Tao, Fei] Beihang Univ, Sch Automat Sci & Elect Engn, F105,New Main Bldg,37 Xueyuan Rd, Beijing, Peoples R China; [Pickl, Stefan] Univ Bundeswehr Munchen, Fak Informat, Munich, Germany Beihang University; Bundeswehr University Munich Cheng, Y (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, F105,New Main Bldg,37 Xueyuan Rd, Beijing, Peoples R China. ycheng@buaa.edu.cn Tao, Fei/F-8944-2012 Tao, Fei/0000-0002-9020-0633 National Key R&D Program of China [2019YFB1705402]; Beijing Science Fund for Distinguished Young Scholars [JQ19011]; National Natural Science Foundation of China [52005024]; Fundamental Research Funds for the Central Universities [YWF-21-BJ-J-1182] National Key R&D Program of China; Beijing Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work is partly supported by National Key R&D Program of China [grant number 2019YFB1705402], Beijing Science Fund for Distinguished Young Scholars [grant number JQ19011], National Natural Science Foundation of China [grant number 52005024], and Fundamental Research Funds for the Central Universities [grant number YWF-21-BJ-J-1182]. 58 5 5 8 53 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0020-7543 1366-588X INT J PROD RES Int. J. Prod. Res. 10.1080/00207543.2021.1955996 0.0 JUL 2021 16 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science TS7UL 2023-03-23 WOS:000679852100001 0
J Yang, LH; Liu, J; Ye, FF; Wang, YM; Nugent, C; Wang, H; Martinez, L Yang, Long-Hao; Liu, Jun; Ye, Fei-Fei; Wang, Ying-Ming; Nugent, Chris; Wang, Hui; Martinez, Luis Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme KNOWLEDGE-BASED SYSTEMS English Article Rule-based system; Uncertainty; Belief rule base; Knowledge representation; Information fusion; Explainable AI EVIDENTIAL REASONING APPROACH; EXPERT-SYSTEM; OPTIMIZATION; PARAMETER; METHODOLOGY; REPRESENTATION; GENERATION; ALGORITHM; TREE Advancement and application of rule-based expert systems have been a key research area in explainable artificial intelligence (XAI) because the rule-base is one of the most common and natural explainable frameworks for knowledge representation. The present work aims to design a novel rule based system, called Cumulative Belief Rule-Based System (CBRBS), by establishing efficient rule-base modeling and inference procedures, where the rule-base modeling procedure includes the generation of cumulative belief rules via numeric data transformation and extended belief rule integration, and the rule-base inference procedure includes the inference of cumulative belief rules via consistent rule activation and activated rule integration. All these procedures enable CBRBS to better achieve the balance of explainability, high-efficiency (or computing complexity), and accuracy to fit with different application scenarios, as well as overcome the limitations of classical rule-based systems. Extensive experiments based on the well-known pipeline leak detection problem and open-source classification problems are conducted to illustrate the feature and advantage of the CBRBS over other classical rule-based systems and some commonly used classifiers. (C)& nbsp;2021 Elsevier B.V. All rights reserved. [Yang, Long-Hao; Wang, Ying-Ming] Fuzhou Univ, Decis Sci Inst, Fuzhou 350108, Peoples R China; [Ye, Fei-Fei] Fujian Normal Univ, Sch Publ Adm, Fuzhou 350117, Peoples R China; [Yang, Long-Hao; Liu, Jun; Ye, Fei-Fei; Nugent, Chris; Wang, Hui] Ulster Univ, Sch Comp, Jordanstown Campus, Newtownabbey BT37 0QB, North Ireland; [Martinez, Luis] Univ Jaen, Dept Comp Sci, Jaen, Spain Fuzhou University; Fujian Normal University; Ulster University; Universidad de Jaen Ye, FF (corresponding author), Fujian Normal Univ, Sch Publ Adm, Fuzhou 350117, Peoples R China.;Ye, FF (corresponding author), Ulster Univ, Sch Comp, Jordanstown Campus, Newtownabbey BT37 0QB, North Ireland. 946755430@qq.com wang, ying/GQY-5077-2022; Martinez, Luis/A-1746-2009; Wang, Ying/HJI-2509-2023; wang, yan/GSE-6489-2022; wang, yingying/GRS-3058-2022; Yang, Long-Hao/H-4430-2017 Martinez, Luis/0000-0003-4245-8813; Nugent, Chris/0000-0003-0882-7902; Yang, Long-Hao/0000-0003-2239-1447 National Natural Sci-ence Foundation of China [72001043, 61773123]; Humanities and Social Science Foundation of the Ministry of Education of China [20YJC630188]; National Science Foun-dation of Fujian Province of China [2020J05122]; Chengdu International Science Cooperation Project [2020-GH02-00064-HZ] National Natural Sci-ence Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Science Foundation of the Ministry of Education of China(Ministry of Education, China); National Science Foun-dation of Fujian Province of China; Chengdu International Science Cooperation Project Acknowledgments This research was supported by the National Natural Sci-ence Foundation of China (Nos. 72001043 and 61773123) , the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188) , the National Science Foun-dation of Fujian Province of China (No. 2020J05122) , and the Chengdu International Science Cooperation Project (No. 2020-GH02-00064-HZ) . 68 5 5 22 35 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAR 15 2022.0 240 107805 10.1016/j.knosys.2021.107805 0.0 JAN 2022 26 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1A8JC Green Submitted 2023-03-23 WOS:000791994900002 0
J Tao, Y; Muller, JP; Xiong, S; Conway, SJ Tao, Yu; Muller, Jan-Peter; Xiong, Siting; Conway, Susan J. MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning REMOTE SENSING English Article 3D mapping; digital terrain model; DTM; topography; small-scale; high-resolution; Mars; deep learning; HiRISE; HRSC; ExoMars; Oxia Planum LASER ALTIMETER; DERIVATION; SURFACE; SHAPE The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25-50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to theMADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover's landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images. [Tao, Yu; Muller, Jan-Peter] Univ Coll London, Dept Space & Climate Phys, Imaging Grp, Mullard Space Sci Lab, Holmbury St Mary, Dorking RH5 6NT, Surrey, England; [Xiong, Siting] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Key Lab Geoenvironm Monitoring Great Bay Area, Minist Nat Resources, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [Conway, Susan J.] Univ Nantes, CNRS, UMR 6112, Lab Planetol & Geodynam, F-44300 Nantes, France University of London; University College London; Shenzhen University; Ministry of Natural Resources of the People's Republic of China; Shenzhen University; Shenzhen University; Shenzhen University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); Nantes Universite Tao, Y (corresponding author), Univ Coll London, Dept Space & Climate Phys, Imaging Grp, Mullard Space Sci Lab, Holmbury St Mary, Dorking RH5 6NT, Surrey, England. yu.tao@ucl.ac.uk; j.muller@ucl.ac.uk; xiongsiting@szu.edu.cn; susan.conway@univ-nantes.fr ; Conway, Susan/C-4354-2018 Muller, Jan-Peter/0000-0002-5077-3736; Conway, Susan/0000-0002-0577-2312; Tao, Yu/0000-0001-9170-6655 UKSA Aurora programme [ST/S001891/1]; STFC MSSL Consolidated Grant [ST/K000977/1]; French Space Agency CNES; Shenzhen Scientific Research and Development Funding Programme [JCYJ20190808120005713]; China Postdoctoral Science Foundation [2019M663073] UKSA Aurora programme; STFC MSSL Consolidated Grant(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); French Space Agency CNES(Centre National D'etudes Spatiales); Shenzhen Scientific Research and Development Funding Programme; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The research leading to these results is receiving funding from the UKSA Aurora programme (2018-2021) under grant no. ST/S001891/1 as well as partial funding from the STFC MSSL Consolidated Grant ST/K000977/1. S.J.C. is grateful to the French Space Agency CNES for supporting her HiRISE related work. S.X. has received funding from the Shenzhen Scientific Research and Development Funding Programme (grant No. JCYJ20190808120005713) and China Postdoctoral Science Foundation (grant No. 2019M663073). 76 6 6 8 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2021.0 13 21 4220 10.3390/rs13214220 0.0 40 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology ZD8JU Green Submitted, gold 2023-03-23 WOS:000758442300001 0
J Xu, XY; Yang, H Xu, Xiangyang; Yang, Hao Vision Measurement of Tunnel Structures with Robust Modelling and Deep Learning Algorithms SENSORS English Article tunnel inspection; 3D modeling; crack detection; camera array; robust modelling FINITE-ELEMENT MODEL; DEFORMATION ANALYSIS; LASER; INSPECTION The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring. [Xu, Xiangyang] Soochow Univ, Sch Rail Transit, Suzhou 215006, Peoples R China; [Yang, Hao] Jiangsu Univ Sci & Technol, Sch Civil Engn & Architecture, Zhenjiang 212003, Jiangsu, Peoples R China; [Yang, Hao] Politecn Torino, Dept Mech & Aerosp Engn, I-10129 Turin, Italy Soochow University - China; Jiangsu University of Science & Technology; Polytechnic University of Turin Yang, H (corresponding author), Jiangsu Univ Sci & Technol, Sch Civil Engn & Architecture, Zhenjiang 212003, Jiangsu, Peoples R China.;Yang, H (corresponding author), Politecn Torino, Dept Mech & Aerosp Engn, I-10129 Turin, Italy. X.Y.Xu@suda.edu.cn; yanghao99yanghao@163.com Yang, Hao/AAB-1198-2020 Yang, Hao/0000-0001-7883-9808 National Natural Science Foundation of China [U1934209] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work described in this paper was supported by a grant from the National Natural Science Foundation of China (Project No. U1934209). 31 30 30 15 70 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2020.0 20 17 4945 10.3390/s20174945 0.0 15 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation NP3LL 32882882.0 Green Accepted, gold 2023-03-23 WOS:000570080500001 0
J Lin, Y; Li, ST; Fang, LY; Ghamisi, P Lin, Yun; Li, Shutao; Fang, Leyuan; Ghamisi, Pedram Multispectral Change Detection With Bilinear Convolutional Neural Networks IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Feature extraction; Training; Image color analysis; Convolutional neural networks; Remote sensing; Indexes; Kernel; Bilinear convolutional neural networks (BCNNs) model; change detection; multispectral images IMAGE Recently, deep learning has been demonstrated to be an effective tool to detect changes in bitemporal remote sensing images. However, most existing methods based on deep learning obtain the ultimate change map by analyzing the difference image (DI) or the stacked feature vectors of input images, which cannot sufficiently capture the relationship between the two input images to obtain the change information. In this letter, a new method named bilinear convolutional neural networks (BCNNs) is proposed to detect changes in bitemporal multispectral images. The model can be trained end to end with two symmetric convolutional neural networks (CNNs), which are capable of learning the feature representation from bitemporal images and utilizing the relations between the two input images by a linear outer product operation in an effective way. Specifically, two sets of patches obtained from two multispectral images of different times are first input into two CNNs to extract deep features, respectively. Then, the matrix outer product is applied on the output feature maps to obtain the combined bilinear features. Finally, the ultimate change detected result can be produced by applying the softmax classifier on the combined features. Experimental results on real multispectral data sets demonstrate the superiority of the proposed method over several well-known change-detection approaches. [Lin, Yun; Li, Shutao; Fang, Leyuan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China; [Lin, Yun; Li, Shutao; Fang, Leyuan] Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany Hunan University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Fang, LY (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China. linyun@hnu.edu.cn; shutao_li@hnu.edu.cn; fangleyuan@gmail.com; p.ghamisi@gmail.com Ghamisi, Pedram/ABD-5419-2021 Fang, Leyuan/0000-0003-2351-4461; Li, Shutao/0000-0002-0585-9848 National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]; National Natural Science Foundation [61922029]; Science and Technology Plan Project Fund of Hunan Province [CX2018B171, 2017RS3024, 2018TP1013]; Science and Technology Talents Program of Hunan Association for Science and Technology [2017TJ-Q09] National Natural Science Fund of China for International Cooperation and Exchanges; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Plan Project Fund of Hunan Province; Science and Technology Talents Program of Hunan Association for Science and Technology This work was supported in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, in part by the National Natural Science Foundation under Grant 61922029, in part by the Science and Technology Plan Project Fund of Hunan Province under Grant CX2018B171, Grant 2017RS3024, and Grant 2018TP1013, and in part by the Science and Technology Talents Program of Hunan Association for Science and Technology under Grant 2017TJ-Q09. 19 15 15 1 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. OCT 2020.0 17 10 1757 1761 10.1109/LGRS.2019.2953754 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology NU7GL 2023-03-23 WOS:000573808500020 0
C Zhang, S; Ng, WWY; Zhang, JJ; Nugent, CD Ochoa, SF; Singh, P; Bravo, J Zhang, Shuai; Ng, Wing W. Y.; Zhang, Jianjun; Nugent, Chris D. Human Activity Recognition Using Radial Basis Function Neural Network Trained via a Minimization of Localized Generalization Error UBIQUITOUS COMPUTING AND AMBIENT INTELLIGENCE, UCAMI 2017 Lecture Notes in Computer Science English Proceedings Paper 11th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) NOV 07-10, 2017 Philadelphia, PA Villanova Univ,MAmI Res Lab Human activity recognition; Data uncertainty; Localized generation error; Radial basis function neural network SELECTION Human activity recognition is a crucial component of applications in the areas of pervasive computing in healthcare. Human activity recognition approaches which adopt a data-driven approach are challenged by handling uncertainty in the data. These uncertainties arise due to sensor unreliability, natural noise and variance introduced by those performing the underlying activities. In this paper we propose an approach to human activity recognition based on Radial Basis Function Neural Networks (RBFNN) trained via a minimization of Localized Generalization Error in an effort to minimize the effects of uncertainty in the data. The proposed approach minimizes the generalization error taking into consideration both the training error and the stochastic sensitivity measure, which subsequently results in an improved generalization capability and improved tolerance to the uncertainty in the data. The approached developed was evaluated using data collected from the IESim smart environment simulation tool. Eleven activities were performed in a simulated environment, with uncertainty in the data stemming from user variance in completing the activities and the sensor placements in the environment. Classification accuracy of 98.86% was achieved demonstrating that the proposed RBFNN approach is robust to minor differences in unseen samples, many of which are caused by data uncertainty, following training which offers good generalization capability. [Zhang, Shuai; Nugent, Chris D.] Ulster Univ, Sch Comp & Math, Newtownabbey BT37 0QB, Co Antrim, North Ireland; [Ng, Wing W. Y.; Zhang, Jianjun] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China Ulster University; South China University of Technology Zhang, S (corresponding author), Ulster Univ, Sch Comp & Math, Newtownabbey BT37 0QB, Co Antrim, North Ireland.;Ng, WWY (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China. s.zhang@ulster.ac.uk; wingng@ieee.org; jjzhangscut@gmail.com; cd.nugent@ulster.ac.uk Zhang, Shuai/0000-0002-4821-6404; Ng, Wing W. Y./0000-0003-0783-3585 Research Challenge Fund by Ulster University; National Natural Science Foundation of China [61572201]; Fundamental Research Funds for the Central Universities [2017ZD052]; Invest Northern Ireland [RD0513853] Research Challenge Fund by Ulster University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Invest Northern Ireland This work was supported by both the Research Challenge Fund by Ulster University, the National Natural Science Foundation of China under Grant 61572201 and the Fundamental Research Funds for the Central Universities (2017ZD052). Invest Northern Ireland is acknowledged for partially supporting this project under the Competence Centre Programs Grant RD0513853 -Connected Health Innovation Centre. 17 4 4 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-67585-5; 978-3-319-67584-8 LECT NOTES COMPUT SC 2017.0 10586 498 507 10.1007/978-3-319-67585-5_50 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ1IG 2023-03-23 WOS:000576663600050 0
J Michailidis, P; Pelitaris, P; Korkas, C; Michailidis, I; Baldi, S; Kosmatopoulos, E Michailidis, Panagiotis; Pelitaris, Paschalis; Korkas, Christos; Michailidis, Iakovos; Baldi, Simone; Kosmatopoulos, Elias Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study ENERGIES English Article building energy-management systems; domestic automation; centralized building optimization and control; energy-sustainable buildings; HVAC control; IoT ARTIFICIAL-INTELLIGENCE; CONTROL AGENTS; FUZZY; COMFORT The existing literature on energy saving focuses on large-scale buildings, wherein the energy-saving potential is substantially larger than smaller-scale buildings. However, the research intensity is significantly less for small-scale deployments and their capacities to regulate energy use individually, directly and without depreciating users' comfort and needs. The current research effort focused on energy saving and user satisfaction, concerning a low-cost-yet technically sophisticated-methodology for controlling conventional residential HVAC units through cheap yet reliable actuation and sensing and auxiliary IoT equipment. The basic ingredients of the proposed experimental methodology involve a conventional A/C unit, an Arduino microcontroller, typical wireless IoT sensors and actuators, a configured graphical environment and a sophisticated, model-free, optimization-and-control algorithm (PCAO) that portrays the ground basis for achieving improved performance results in comparison with conventional methods. The main goal of this study was to produce a system that would adequately and expeditiously achieve energy savings by utilizing minimal hardware/equipment (affordability). The system was designed to be easily expandable in terms of new units or thermal equipment (expandability) and also to be autonomous, requiring zero user interventions at the experimental site (automation). The real-life measurements were collected over two different seasonal periods of the year (winter, summer) and concerned a conventional apartment in the city of Xanthi, Northern Greece, where summers and winters exhibit quite diverse climate characteristics. The final results revealed the increased efficiency of PCAO's optimization in comparison with a conventional rule-based control strategy (RBC), as concerns energy savings and user satisfaction. [Michailidis, Panagiotis; Pelitaris, Paschalis; Korkas, Christos; Michailidis, Iakovos; Kosmatopoulos, Elias] Democritus Univ Thrace, Polytech Sch Xanthi, Elect & Comp Engn Dept, Xanthi 67100, Greece; [Michailidis, Panagiotis; Korkas, Christos; Michailidis, Iakovos; Kosmatopoulos, Elias] Ctr Res & Technol Hellas CERTH, Informat Technol Inst ITI, Thessaloniki 57001, Greece; [Baldi, Simone] Southeast Univ, Sch Math, Jiulonghu Campus, Nanjijng 211189, Peoples R China; [Baldi, Simone] Delft Univ Technol, Ctr Syst & Control, NL-2628 CD Delft, Netherlands Democritus University of Thrace; Centre for Research & Technology Hellas; Southeast University - China; Delft University of Technology Michailidis, P (corresponding author), Democritus Univ Thrace, Polytech Sch Xanthi, Elect & Comp Engn Dept, Xanthi 67100, Greece.;Michailidis, P (corresponding author), Ctr Res & Technol Hellas CERTH, Informat Technol Inst ITI, Thessaloniki 57001, Greece. panosmih@iti.gr; pascpeli@ee.duth.gr; chriskorkas@iti.gr; michaild@iti.gr; S.Baldi@tudelft.nl; kosmatop@iti.gr ; Baldi, Simone/C-6796-2015; MICHAILIDIS, IAKOVOS/O-1109-2018 Michailidis, Panagiotis/0000-0001-7148-7010; Baldi, Simone/0000-0001-9752-8925; Korkas, Christos/0000-0002-2491-2659; MICHAILIDIS, IAKOVOS/0000-0001-7295-8806 European Commission [H2020-EU.2.1.5.2, 768735] European Commission(European CommissionEuropean Commission Joint Research Centre) FundingThe research leading to these results was partially funded by the European Commission H2020-EU.2.1.5.2.-Technologies enabling energy-efficient systems and energy-efficient buildings with a low environmental impact (Grant agreement ID: 768735) PLUG-N-HARVEST-https://www.plug-n-harvest.eu/, accessed on 22 November 2021. 28 2 2 0 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies DEC 2021.0 14 23 7910 10.3390/en14237910 0.0 25 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels XV2DF gold, Green Published 2023-03-23 WOS:000734758500001 0
J Zhang, R; Li, GY; Wunderlich, T; Wang, L Zhang, Rui; Li, Guangyun; Wunderlich, Thomas; Wang, Li A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Precise boundary recovery; Semantic segmentation; DCNNs; 2D images; 3D point clouds Precise localization of semantic segmentation is attracting increasing attention, and salient performances are dominated by deep learning-based methods, especially deep convolutional neural networks (DCNNs). However, the outputs from the final layer of DCNNs are not sufficiently localized for accurate object boundaries due to their invariance properties, which makes precise boundary recovery of semantic segmentation an academically challenging question. Both 2D and 3D objects suffer from the same problem. Considering this, this paper conducts a comprehensive survey of precise boundary recovery for semantic segmentation, focusing mainly on 2D images and 3D point clouds. Firstly, we formulate the problem of potential boundary recovery for semantic segmentation based on DCNNs, elaborate on the terminology as well as background concepts in this field. Then, we categorize boundary recovery methods into four strategies according to their techniques and network architectures to discuss how they obtain accurate boundaries of semantic segmentation. Next, publicly available datasets on which they have been assessed are argued. To compare these datasets, we design diagrams based on five indicators to help researchers judge which are the ones that best suit their tasks. Moreover, we further compare and analyze the performance of all the reviewed methods through experimental results. Finally, current challenges and prospective research issues are discussed extensively. [Zhang, Rui] North China Univ Water Resources & Elect Power, Zhengzhou 450045, Peoples R China; [Li, Guangyun; Wang, Li] PLA Informat Engn Univ, Dept Geospatial Informat, Zhengzhou 450001, Peoples R China; [Zhang, Rui; Wunderlich, Thomas] Tech Univ Munich, Chair Geodesy, D-80333 Munich, Germany North China University of Water Resources & Electric Power; PLA Information Engineering University; Technical University of Munich Li, GY (corresponding author), PLA Informat Engn Univ, Dept Geospatial Informat, Zhengzhou 450001, Peoples R China. 599154503@qq.com National Natural Science Foundation of China (NSFC) [42071454]; Science and Technology Research Projects of Science and Technology Department in Henan Province, China [192102210265, 202102210141]; China Scholarship Council (CSC) National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Science and Technology Research Projects of Science and Technology Department in Henan Province, China; China Scholarship Council (CSC)(China Scholarship Council) This study is undertaken with the financial support of the National Natural Science Foundation of China (NSFC) (Grant No. 42071454) and the Science and Technology Research Projects of Science and Technology Department in Henan Province, China (No. 192102210265 and 202102210141). Rui Zhang also acknowledges the financial support provided by China Scholarship Council (CSC). 57 7 8 8 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-8432 1872-826X INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. OCT 2021.0 102 102411 10.1016/j.jag.2021.102411 0.0 12 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing UA3TC gold 2023-03-23 WOS:000685083300002 0
J Yan, J; Pan, XY; Li, LD; Li, CS; Cui, P; Ma, C Yan, Junchi; Pan, Xiaoyong; Li, Liangda; Li, Changsheng; Cui, Peng; Ma, Chao IEEE ACCESS SPECIAL SECTION: SEQUENTIAL DATA MODELING AND ITS EMERGING APPLICATIONS IEEE ACCESS English Editorial Material With the tremendous advance of technologies in data collection and storage, sequential data are becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as time series/video frames, biological data, and event data. It bears important practical utility for learning and understanding the dynamic behavior as well as the causality relationships across sequences, and it also calls for robust models to handle noisy and incomplete sequence data in real-world settings. Thus, machine-learning-based methods can be applied to efficiently analyze and model these sequential data. [Yan, Junchi; Ma, Chao] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China; [Pan, Xiaoyong] Univ Ghent, Dept Elect & Informat Syst, B-9000 Ghent, Belgium; [Li, Liangda] Yahoo Res, Sunnyvale, CA 94089 USA; [Li, Changsheng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China; [Cui, Peng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China Shanghai Jiao Tong University; Ghent University; University of Electronic Science & Technology of China; Tsinghua University Yan, J (corresponding author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. Yan, Junchi/0000-0001-9639-7679 0 0 0 0 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 210167 210170 10.1109/ACCESS.2020.3036537 0.0 4 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PA9ZB gold 2023-03-23 WOS:000595986400001 0
J Ullah, A; Elahi, H; Sun, ZY; Khatoon, A; Ahmad, I Ullah, Asad; Elahi, Hassan; Sun, Zhaoyun; Khatoon, Amna; Ahmad, Ishfaq Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING English Article Road Cracks; AlexNet; ResNet18; SqueezeNet; Deep Learning; Convolution Architectures Road cracks are caused due to the abundant usage of the roads, heavy traffic, and increased prerequisite of transportation. So road maintenance is an important aspect of such a huge number of vehicles on the roads to have safety measures and continuity. Besides the traffic, bad weather is also contributing its part in creating road cracks. In the proposed research automatic road cracks have been detected, it looks simple apparently but the intensity, and complexity of the background make it a challenging task. In this challenging task, the contrast of the processed image, complexity of different kinds of crack recognition, assembly of proper database images, elapsed time, and approximate classification, etc., are processed to get the optimum results. Deep learning has multiple neural networks among them few networks are used like AlexNet, ResNet18, and SqueezeNet for the data recognition mainly for the huge database having the optimum results in minimum throughput. In the accomplished research 4333 images with eight diverse road cracks classes are used. The classified images are processed by utilizing three different networks through a supervised dataset. Dataset is built by mixing the university resources-collected images and some online datasets. The best expected result is gained after proper training and tested along with classification. In this experiment, the training and testing images were kept the same in epoch and iteration. But the ResNet18 was superlative with an accuracy of 85.20%. [Ullah, Asad; Sun, Zhaoyun; Khatoon, Amna] Changan Univ Xian, Sch Informat Engn, Xian 710064, Peoples R China; [Elahi, Hassan] Sapienza Univ Rome, Dept Mech & Aerosp Engn, Rome, Italy; [Ahmad, Ishfaq] Univ Architecture & Technol, Sch Control Sci, Xian, Peoples R China Chang'an University; Sapienza University Rome Ullah, A (corresponding author), Changan Univ Xian, Sch Informat Engn, Xian 710064, Peoples R China. 2018024901@chd.edu.cn; hassan.elahi@uniroma1.it; zhaoyunsun@126.com; 2018024900@chd.edu.cn; ahmadishfaq583@yahoo.com Khatoon, Amna/ABD-3394-2021 Khatoon, Amna/0000-0002-6929-3935 Chang'An University China, Shaanxi, Xi'an, Beilin Chang'An University China, Shaanxi, Xi'an, Beilin This research was funded by Chang'An University China, Shaanxi, Xi'an, Beilin, 2nd Ring Rd South East Section as the author is associate with the institution. 50 10 10 7 16 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2193-567X 2191-4281 ARAB J SCI ENG Arab. J. Sci. Eng. FEB 2022.0 47 2 2397 2417 10.1007/s13369-021-06182-6 0.0 OCT 2021 21 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics ZC4ZZ 2023-03-23 WOS:000705769900006 0
C Polo, A; Salucci, M; Verzura, S; Zhou, ZK; Massa, A IEEE Polo, Alessandro; Salucci, Marco; Verzura, Stefano; Zhou, Zhenkun; Massa, Andrea Real-Time CSI-Based Wireless Gesture Recognition for Human-Machine Interaction 2021 10TH INTERNATIONAL CONFERENCE ON MODERN CIRCUITS AND SYSTEMS TECHNOLOGIES (MOCAST) English Proceedings Paper 10th International Conference on Modern Circuits and Systems Technologies (MOCAST) JUL 05-07, 2021 Thessaloniki, GREECE IEEE, Greece Sect,Aristotle Univ Thessaloniki, Dept Phys,IEEE Greece CASS SSCS Chapter,Chua Memristor Ctr,Aristotle Univ Thessaloniki, Elect Lab Wireless Sensing; Gesture Recognition; WiFi; Human-Machine Interaction; Channel State Information ANTENNA The study and the design of novel methodologies and techniques for user's activity and gesture recognition is of great interest and a hot topic in human-computer interactions. Hand gesture recognition techniques based on computer-vision have yielded impressive results, but they involve users' privacy concerns, therefore other sensing approaches are of interest. In this work, a novel machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed. Such an approach has been preliminary validated in a real house environment with a classification accuracy of 98%. [Polo, Alessandro; Salucci, Marco; Massa, Andrea] Univ Trento, CNIT, Res Unit, Trento, Italy; [Verzura, Stefano] Huawei Technol, Segrate, Italy; [Zhou, Zhenkun] Huawei Technol, Shenzhen, Peoples R China; [Massa, Andrea] ELEDIA Res Ctr ELEDIA UESTC UESTC, Chengdu, Peoples R China; [Massa, Andrea] Tsinghua Univ, ELEDIA Res Ctr ELEDIA TSINGHUA, Beijing, Peoples R China University of Trento; Huawei Technologies; Huawei Technologies; Tsinghua University Polo, A (corresponding author), Univ Trento, CNIT, Res Unit, Trento, Italy. alessandro.polo.1@unitn.it; marco.salucci@unitn.it; stefano.verzura@huawei.com; zhouzhenkun@huawei.com; andrea.massa@unitn.it Salucci, Marco/S-8654-2016; Massa, Andrea/F-3276-2013; Polo, Alessandro/G-2892-2017; Zhou, Zhekun/G-5281-2011 Salucci, Marco/0000-0002-6948-8636; Massa, Andrea/0000-0002-8429-8937; Polo, Alessandro/0000-0002-8190-2377; Zhou, Zhekun/0000-0002-0710-2128 Hangzhou Research Center; Huawei Innovation Research Program (HIRP) grant Hangzhou Research Center; Huawei Innovation Research Program (HIRP) grant This work benefited from the research activities carried out within the Huawei Innovation Research Program (HIRP) grant in cooperation with the Hangzhou Research Center. 22 0 0 4 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-1847-8 2021.0 10.1109/MOCAST52088.2021.9493383 0.0 4 Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT8GA 2023-03-23 WOS:000853082400048 0
J Liu, JJ; Ye, YD; Shen, CL; Wang, YM; Erdelyi, R Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdelyi, Robert A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA ASTROPHYSICAL JOURNAL English Article solar-terrestrial relations; Sun: coronal mass ejections (CMEs) CORONAL MASS EJECTIONS; SOLAR-CYCLE 23; FLUX-ROPE; INTERPLANETARY SHOCKS; MAGNETIC CLOUDS; EIT WAVES; MODEL; HELIOSPHERE; ORIENTATION; EVOLUTION Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of similar to 5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA. [Liu, Jiajia; Erdelyi, Robert] Univ Sheffield, Solar Phys & Space Plasma Res Ctr SP2RC, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England; [Ye, Yudong] Chinese Acad Sci, Natl Space Sci Ctr, State Key Lab Space Weather, SIGMA Weather Grp, Beijing 100190, Peoples R China; [Ye, Yudong] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Shen, Chenglong; Wang, Yuming] Univ Sci & Technol China, Dept Geophys & Planetary Sci, CAS Key Lab Geospace Environm, Hefei 230026, Anhui, Peoples R China; [Shen, Chenglong; Wang, Yuming] Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China; [Erdelyi, Robert] Eotvos Lorand Univ, Dept Astron, Pazmany P Setany 1-A, H-1117 Budapest, Hungary University of Sheffield; Chinese Academy of Sciences; National Space Science Center, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Eotvos Lorand University Liu, JJ (corresponding author), Univ Sheffield, Solar Phys & Space Plasma Res Ctr SP2RC, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England. jj.liu@sheffield.ac.uk erdelyi, robertus/AAQ-3262-2020; Liu, Jiajia/AAX-7996-2020; Wang, Yuming/A-8968-2012; shen, Chenglong/C-7588-2013 erdelyi, robertus/0000-0003-3439-4127; Liu, Jiajia/0000-0003-2569-1840; Wang, Yuming/0000-0002-8887-3919; shen, Chenglong/0000-0002-3577-5223; Ye, Yudong/0000-0002-1854-8459 Science and Technology Facility Council (STFC), UK [ST/M000826/1]; Royal Society (UK); NSFC [41574165, 41774178]; STFC [ST/J001430/1, ST/L006316/1, ST/M000826/1] Funding Source: UKRI Science and Technology Facility Council (STFC), UK(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Royal Society (UK)(Royal Society of London); NSFC(National Natural Science Foundation of China (NSFC)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) The SOHO LASCO CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA. J.L. appreciates discussions with Dr. Xin Huang (National Astronomical Observatories, Chinese Academy of Sciences). We thank Dr. Manolis K. Georgoulis (Research Center for Astronomy and Applied Mathematics, Academy of Athens) for his useful advice in improving this paper. J.L. and R.E. acknowledge the support (grant number ST/M000826/1) received by the Science and Technology Facility Council (STFC), UK. R.E. is grateful for the support received from the Royal Society (UK). Y.W. is supported by grants 41574165 and 41774178 from NSFC. 84 35 36 0 28 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. MAR 10 2018.0 855 2 109 10.3847/1538-4357/aaae69 0.0 10 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics FZ1UI Green Submitted, Green Accepted, Bronze 2023-03-23 WOS:000427362600025 0
J Gao, W; Darvishan, A; Toghani, M; Mohammadi, M; Abedinia, O; Ghadimi, N Gao, Wei; Darvishan, Ayda; Toghani, Mohammad; Mohammadi, Mohsen; Abedinia, Oveis; Ghadimi, Noradin Different states of multi-block based forecast engine for price and load prediction INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Load and price; Feature selection; Intelligent algorithm; Multi-block forecast engine TERM ELECTRIC-LOAD; NEURAL-NETWORK; WAVELET TRANSFORM; TIME-SERIES; OPTIMIZATION; ALGORITHM; MARKET; MODEL This work proposes different prediction models based on multi-block forecast engine for load and price forecast in electricity market. Due to high correlation of load and price signals, the density of this reaction can affect the demand curve and shift it in market. Furthermore, to improve the operation and planning improvement in the power system, an accurate prediction model can play an important role. So, in this paper, a complex prediction approach is presented based on feature selection, and multi-stage forecast engine. The forecast engine is comprised of multi-block neural network (NN) and optimized by an intelligent algorithm to increase the training mechanism and forecasting abilities. Moreover, different models of multi-block forecast engine are presented in this paper to choose the effective model. In other words, different combinations of NN are tested in the same prediction condition to show their abilities. The proposed model is tested over real-world engineering test cases through comparison with other prediction methods. Obtained results demonstrate the validity of the proposed model. [Gao, Wei] Yunnan Normal Univ, Sch Informat Sci & Technol, Kunming 650500, Yunnan, Peoples R China; [Darvishan, Ayda] Univ Houston, Dept Ind Engn, Houston, TX 77004 USA; [Toghani, Mohammad] Islamic Azad Univ, Dept Mech, Najafabad Branch, Najafabad, Iran; [Mohammadi, Mohsen] Payame Noor Univ, Dept Elect Engn, Tehran, Iran; [Abedinia, Oveis] Budapest Univ Technol & Econ, Budapest, Hungary; [Ghadimi, Noradin] Islamic Azad Univ, Ardabil Branch, Young Researchers & Elite Club, Ardebil, Iran Yunnan Normal University; University of Houston System; University of Houston; Islamic Azad University; Payame Noor University; Budapest University of Technology & Economics; Islamic Azad University Gao, W (corresponding author), Yunnan Normal Univ, Sch Informat Sci & Technol, Kunming 650500, Yunnan, Peoples R China. gaowei@ynnu.edu.cn Ghadimi, Noradin/AAF-3603-2019 47 174 174 8 71 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. JAN 2019.0 104 423 435 10.1016/j.ijepes.2018.07.014 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering GW6WX 2023-03-23 WOS:000447106500040 0
J Tomaz, ID; Colaco, FHG; Sarfraz, S; Pimenov, DY; Gupta, MK; Pintaude, G Tomaz, Italo do Valle; Colaco, Fernando Henrique Gruber; Sarfraz, Shoaib; Pimenov, Danil Yu.; Gupta, Munish Kumar; Pintaude, Giuseppe Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Artificial neural network; Genetic algorithm; Pulsed GTAW; Multi-objective optimization; Quality characteristics BEAD GEOMETRY; MULTIOBJECTIVE OPTIMIZATION; PROCESS PARAMETERS; WEAR-RESISTANCE; PREDICTION; STEEL; MICROSTRUCTURE; SELECTION; GTAW; DILUTION Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25 degrees, wire feed pulse frequency = 8 Hz. The determination coefficient (R-2) and RMSE value of all response parameters are satisfactory, and the R-2 of all the data remained higher than 0.65. [Tomaz, Italo do Valle] Inst Fed Fluminense, Lab Ensaios Mat LEMat, Estr Cabo Frio Buzios S-N Baia Formosa, Cabo Frio, RJ, Brazil; [Tomaz, Italo do Valle] Waterford Inst Technol, Dept Engn Technol, SEAM Res Ctr, Waterford X91TX03, Ireland; [Colaco, Fernando Henrique Gruber] Inst Fed Santa Catarina, Rua Imigrantes,44 Rau, Jaragua Do Sul, SC, Brazil; [Colaco, Fernando Henrique Gruber; Pintaude, Giuseppe] Univ Tecnol Fed Parana, Campus Curitiba Rua Deputado Heitor Alencar Furta, BR-81280340 Curitiba, Parana, Brazil; [Sarfraz, Shoaib] Cranfield Univ, Mfg Dept, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England; [Pimenov, Danil Yu.; Gupta, Munish Kumar] South Ural State Univ, Dept Automated Mech Engn, Lenin Prosp 76, Chelyabinsk 454080, Russia; [Gupta, Munish Kumar] Shandong Univ, Key Lab High Efficiency & Clean Mech Manufacture, Minist Educ, Sch Mech Engn, Jinan, Peoples R China Universidade Federal Fluminense; South East Technological University (SETU); Instituto Federal de Santa Catarina (IFSC); Universidade Tecnologica Federal do Parana; Cranfield University; South Ural State University; Shandong University Sarfraz, S (corresponding author), Cranfield Univ, Mfg Dept, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England. italo.tomaz@iff.edu.br; fernandogruber@ifsc.edu.br; shoaib.sarfraz@cranfield.ac.uk; danil_u@rambler.ru; munishguptanit@gmail.com; giuseppepintaude@gmail.com Pimenov, Danil Yu./D-9048-2013; Pintaude, Giuseppe/E-8563-2010 Pimenov, Danil Yu./0000-0002-5568-8928; Pintaude, Giuseppe/0000-0001-8215-4481; Colaco, Fernando/0000-0001-5485-5635; Tomaz, Italo/0000-0003-0441-9477; Sarfraz, Shoaib/0000-0002-1373-2054 CNPq [308416/2017-1]; IFSC [05/2015/PROPPI] CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); IFSC G. Pintaude acknowledges CNPq for the grant through Project no. 308416/2017-1. Authors thank IFSC through Application no. 05/2015/PROPPI. They also thank CMCM-UTFPR for the facilities in the characterization of materials. 43 27 27 2 10 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. APR 2021.0 113 11-12 3569 3583 10.1007/s00170-021-06846-5 0.0 MAR 2021 15 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering RG7MQ Green Published, hybrid 2023-03-23 WOS:000625608000001 0
J Ramos-Casals, M; Acar-Denizli, N; Vissink, A; Brito-Zeron, P; Li, XM; Carubbi, F; Priori, R; Toplak, N; Baldini, C; Faugier-Fuentes, E; Kruize, AA; Mandl, T; Tomiita, M; Gandolfo, S; Hashimoto, K; Hernandez-Molina, G; Hofauer, B; Mendieta-Zeron, S; Rasmussen, A; Sandhya, P; Sene, D; Trevisani, VFM; Isenberg, D; Sundberg, E; Pasoto, SG; Sebastian, A; Suzuki, Y; Retamozo, S; Xu, B; Giacomelli, R; Gattamelata, A; Bizjak, M; Bombardieri, S; Loor-Chavez, RE; Hinrichs, A; Olsson, P; Bootsma, H; Lieberman, SM Ramos-Casals, Manuel; Acar-Denizli, Nihan; Vissink, Arjan; Brito-Zeron, Pilar; Li, Xiaomei; Carubbi, Francesco; Priori, Roberta; Toplak, Natasa; Baldini, Chiara; Faugier-Fuentes, Enrique; Kruize, Aike A.; Mandl, Thomas; Tomiita, Minako; Gandolfo, Saviana; Hashimoto, Kunio; Hernandez-Molina, Gabriela; Hofauer, Benedikt; Mendieta-Zeron, Samara; Rasmussen, Astrid; Sandhya, Pulukool; Sene, Damien; Trevisani, Virginia Fernandes Moca; Isenberg, David; Sundberg, Erik; Pasoto, Sandra G.; Sebastian, Agata; Suzuki, Yasunori; Retamozo, Soledad; Xu, Bei; Giacomelli, Roberto; Gattamelata, Angelica; Bizjak, Masa; Bombardieri, Stefano; Loor-Chavez, Richard-Eduardo; Hinrichs, Anneline; Olsson, Peter; Bootsma, Hendrika; Lieberman, Scott M. Sjogren Big Data Consortium Childhood-onset of primary Sjogren's syndrome: phenotypic characterization at diagnosis of 158 children RHEUMATOLOGY English Article Sjogren's syndrome; epidemiology; autoimmune diseases; paediatrics; childhood CLASSIFICATION CRITERIA; CLINICAL-FEATURES; PAROTITIS; CONSENSUS; AGE Objectives To characterize the phenotypic presentation at diagnosis of childhood-onset primary SS. Methods The Big Data Sjogren Project Consortium is an international, multicentre registry using worldwide data-sharing cooperative merging of pre-existing clinical SS databases from the five continents. For this study, we selected those patients in whom the disease was diagnosed below the age of 19 years according to the fulfilment of the 2002/2016 classification criteria. Results Among the 12 083 patients included in the Sjogren Big Data Registry, 158 (1.3%) patients had a childhood-onset diagnosis (136 girls, mean age of 14.2 years): 126 (80%) reported dry mouth, 111 (70%) dry eyes, 52 (33%) parotid enlargement, 118/122 (97%) positive minor salivary gland biopsy and 60/64 (94%) abnormal salivary US study, 140/155 (90%) positive ANA, 138/156 (89%) anti-Ro/La antibodies and 86/142 (68%) positive RF. The systemic EULAR Sjogren's syndrome disease activity index (ESSDAI) domains containing the highest frequencies of active patients included the glandular (47%), articular (26%) and lymphadenopathy (25%) domains. Patients with childhood-onset primary SS showed the highest mean ESSDAI score and the highest frequencies of systemic disease in 5 (constitutional, lymphadenopathy, glandular, cutaneous and haematological) of the 12 ESSDAI domains, and the lowest frequencies in 4 (articular, pulmonary, peripheral nerve and CNS) in comparison with patients with adult-onset disease. Conclusions Childhood-onset primary SS involves around 1% of patients with primary SS, with a clinical phenotype dominated by sicca features, parotid enlargement and systemic disease. Age at diagnosis plays a key role in modulating the phenotypic expression of the disease. [Ramos-Casals, Manuel] Hosp Clin Barcelona, Dept Autoimmune Dis, ICMiD, Barcelona, Spain; [Ramos-Casals, Manuel; Brito-Zeron, Pilar; Retamozo, Soledad] IDIBAPS CELLEX, Lab Autoimmune Dis Josep Font, Sjogren Syndrome Res Grp AGAUR, Barcelona, Spain; [Ramos-Casals, Manuel] Univ Barcelona, Dept Med, Barcelona, Spain; [Acar-Denizli, Nihan] Univ Politecn Cataluna, Dept Stat & Operat Res, Barcelona, Spain; [Vissink, Arjan] Univ Groningen, Univ Med Ctr Groningen, Dept Oral & Maxillofacial Surg, Groningen, Netherlands; [Brito-Zeron, Pilar] Hosp CIMA Sanitas, Dept Med, Autoimmune Dis Unit, Barcelona, Spain; [Li, Xiaomei; Xu, Bei] Anhui Prov Hosp, Dept Rheumatol & Immunol, Hefei, Peoples R China; [Carubbi, Francesco; Giacomelli, Roberto] Univ Aquila, Sch Med, Clin Unit Rheumatol, Laquila, Italy; [Priori, Roberta; Gattamelata, Angelica] Sapienza Univ Rome, Dept Internal Med & Med Specialties, Rheumatol Clin, Rome, Italy; [Toplak, Natasa; Bizjak, Masa] Univ Childrens Hosp Ljubljana, Univ Med Ctr Ljubljana, Med Fac Ljubljana, Ljubljana, Slovenia; [Baldini, Chiara; Bombardieri, Stefano] Univ Pisa, Rheumatol Unit, Pisa, Italy; [Faugier-Fuentes, Enrique; Loor-Chavez, Richard-Eduardo] Hosp Infantil Mexico Dr Federico Gomez, Ciudad De Mexico, Mexico; [Kruize, Aike A.; Hinrichs, Anneline] Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands; [Mandl, Thomas; Olsson, Peter] Lund Univ, Skane Univ Hosp Malmo, Dept Rheumatol, Lund, Sweden; [Tomiita, Minako] Shimoshizu Natl Hosp, Natl Hosp Org, Dept Pediat, Yotsukaido, Japan; [Gandolfo, Saviana] Univ Hosp Santa Maria Misericordia, Dept Med & Biol Sci, Clin Rheumatol, Udine, Italy; [Hashimoto, Kunio] Nagasaki Univ, Grad Sch Biomed Sci, Dept Pediat Pediat Allergy & Rheumatol, Nagasaki, Japan; [Hernandez-Molina, Gabriela] Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Immunol & Rheumatol Dept, Mexico City, DF, Mexico; [Hofauer, Benedikt] Tech Univ Munich, Otorhinolaryngol Head & Neck Surg, Munich, Germany; [Mendieta-Zeron, Samara] Hosp Materno Infantil ISSEMyM, Toluca, Mexico; [Rasmussen, Astrid] Oklahoma Med Res Fdn, Genes & Human Dis Res Program, 825 NE 13th St, Oklahoma City, OK 73104 USA; [Sandhya, Pulukool] Christian Med Coll & Hosp, Dept Clin Immunol & Rheumatol, Vellore, Tamil Nadu, India; [Sene, Damien] Univ Paris VII, Hop Lariboisiere, AP HP, Serv Med Interne 2, Paris, France; [Trevisani, Virginia Fernandes Moca] Univ Fed Sao Paulo, Sao Paulo, Brazil; [Isenberg, David] UCL, Ctr Rheumatol, Div Med, London, England; [Sundberg, Erik] Karolinska Inst, Astrid Lindgrens Children Hosp, Pediat Rheumatol, Stockholm, Sweden; [Sundberg, Erik] Karolinska Univ Hosp, Stockholm, Sweden; [Pasoto, Sandra G.] Univ Sao Paulo HCFMUSP, Hosp Clin, Fac Med, Rheumatol Div, Sao Paulo, Brazil; [Sebastian, Agata] Wroclaw Med Univ, Dept Rheumatol & Internal Med, Wroclaw, Poland; [Suzuki, Yasunori] Kanazawa Univ Hosp, Div Rheumatol, Kanazawa, Ishikawa, Japan; [Retamozo, Soledad] Inst Modelo Cardiol Privado SRL, Cordoba, Argentina; [Retamozo, Soledad] Inst Univ Ciencias Biomed Cordoba IUCBC, Cordoba, Argentina; [Bootsma, Hendrika] Univ Groningen, Univ Med Ctr Groningen, Dept Rheumatol & Clin Immunol, Groningen, Netherlands; [Lieberman, Scott M.] Univ Iowa, Carver Coll Med, Stead Family Dept Pediat, Iowa City, IA USA University of Barcelona; Hospital Clinic de Barcelona; University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS; University of Barcelona; Universitat Politecnica de Catalunya; University of Groningen; University of L'Aquila; Sapienza University Rome; University Medical Centre Ljubljana; University of Pisa; Hospital Infantil de Mexico Federico Gomez; Utrecht University; Utrecht University Medical Center; Lund University; Skane University Hospital; Hospital Santa Maria della Misericordia; Nagasaki University; Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran - Mexico; Technical University of Munich; Oklahoma Medical Research Foundation; Christian Medical College & Hospital (CMCH) Vellore; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Lariboisiere-Fernand-Widal - APHP; Universidade Federal de Sao Paulo (UNIFESP); University of London; University College London; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; Universidade de Sao Paulo; Wroclaw Medical University; Kanazawa University; University of Groningen; University of Iowa Ramos-Casals, M (corresponding author), Hosp Clin Barcelona, Serv Malalties Autoimmunes Sistem, C Villarroel 170, Barcelona 08036, Spain. mramos@clinic.cat Giacomelli, Roberto/AAB-9962-2022; Acar-Denizli, Nihan/D-5954-2019; Lieberman, Scott M/GQP-2355-2022; Li, Xiaomei/HNI-0635-2023; PASOTO, SANDRA/E-1213-2012; sene, damien/R-3970-2017; Sebastian, Agata/AAX-9590-2020; Hofauer, Benedikt/HDN-5761-2022; Ramos-Casals, Manuel/HJH-4887-2023; Retamozo, Soledad/AFT-7300-2022; Rasmussen, Astrid/A-4878-2013 Acar-Denizli, Nihan/0000-0002-0012-8632; sene, damien/0000-0001-8640-4580; Sebastian, Agata/0000-0001-6332-8714; Ramos-Casals, Manuel/0000-0001-5709-6734; Rasmussen, Astrid/0000-0001-7744-2948; Gandolfo, Saviana/0000-0001-9406-3632; Lieberman, Scott/0000-0003-4082-7582 Fondo de Investigaciones Sanitarias [INT15/00085]; Hospital Clinic Barcelona (Ajut per a la Recerca Josep Font) Fondo de Investigaciones Sanitarias(Instituto de Salud Carlos III); Hospital Clinic Barcelona (Ajut per a la Recerca Josep Font) This work was supported by Grants Fondo de Investigaciones Sanitarias (INT15/00085 to M.R.-C.) and Hospital Clinic Barcelona (Ajut per a la Recerca Josep Font to P.B.-Z.). 34 0 0 1 2 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1462-0324 1462-0332 RHEUMATOLOGY RHEUMATOLOGY OCT 2 2021.0 60 10 4558 4567 10.1093/rheumatology/keab032 0.0 MAR 2021 10 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology YW0SM Green Published 2023-03-23 WOS:000753131900023 0
C Gong, QY; Chen, Y; He, XL; Li, F; Xiao, Y; Hui, P; Wang, X; Fu, XM Sun, Y; Lu, T; Xie, X; Gao, L; Fan, H Gong, Qingyuan; Chen, Yang; He, Xinlei; Li, Fei; Xiao, Yu; Hui, Pan; Wang, Xin; Fu, Xiaoming Identification of Influential Users in Emerging Online Social Networks Using Cross-site Linking COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2018 Communications in Computer and Information Science English Proceedings Paper 13th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW) AUG 18-19, 2018 Guilin, PEOPLES R CHINA China Comp Federat, Tech Comm Cooperat Comp,Guilin Univ Technol Influential users; Emerging online social networks; Cross-site linking Online social networks (OSNs) have become a commodity in our daily-life. Besides the dominant platforms such as Facebook and Twitter, several emerging OSNs have been launched recently, where users may generate less activity data than on dominant ones. Identifying influential users is critical for the advertisement and the initial development of the emerging OSNs. In this work, we investigate the identification of potential influential users in these emerging OSNs. We build a supervised machine learning-based system by leveraging the widely adopted cross-site linking function, which could overcome the limitations of referring to the user data of a single OSN. Based on the collected real data from Twitter (a dominant OSN) and Medium (an emerging OSN), we show that our system is able to achieve an F1-score of 0.701 and an AUC of 0.755 in identifying influential users on Medium using the Twitter data only. [Gong, Qingyuan; Chen, Yang; He, Xinlei; Li, Fei; Wang, Xin] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Gong, Qingyuan; Chen, Yang; He, Xinlei; Li, Fei; Wang, Xin] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Gong, Qingyuan; Chen, Yang; He, Xinlei; Li, Fei; Wang, Xin] Chinese Acad Sci, Inst Software, SKLCS, Beijing, Peoples R China; [Xiao, Yu] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, CSE Dept, Clear Water Bay, Hong Kong, Peoples R China; [Fu, Xiaoming] Univ Gottingen, Inst Comp Sci, Gottingen, Germany Fudan University; Fudan University; Chinese Academy of Sciences; Institute of Software, CAS; Aalto University; University of Helsinki; Hong Kong University of Science & Technology; University of Gottingen Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Chen, Y (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China.;Chen, Y (corresponding author), Chinese Acad Sci, Inst Software, SKLCS, Beijing, Peoples R China. gongqingyuan@fudan.edu.cn; chenyang@fudan.edu.cn; yu.xiao@aalto.fi; panhui@cs.helsinki.fi; xinw@fudan.edu.cn; fu@cs.uni-goettingen.de Hui, Pan/AAK-6660-2020; Fu, Xiaoming/AAD-2828-2022; Fu, Xiaoming/B-7208-2016 Hui, Pan/0000-0001-6026-1083; Fu, Xiaoming/0000-0002-8012-4753; Fu, Xiaoming/0000-0002-8012-4753 National Natural Science Foundation of China [61602122, 71731004]; Natural Science Foundation of Shanghai [16ZR1402200]; Shanghai Pujiang Program [16PJ1400700]; EU FP7 IRSES MobileCloud project [612212]; Lindemann Foundation [12-2016]; Research Grants Council of Hong Kong [26211515, 16214817] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Pujiang Program(Shanghai Pujiang Program); EU FP7 IRSES MobileCloud project; Lindemann Foundation; Research Grants Council of Hong Kong(Hong Kong Research Grants Council) This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), EU FP7 IRSES MobileCloud project (No. 612212) and Lindemann Foundation (No. 12-2016), Projects 26211515 and 16214817 from the Research Grants Council of Hong Kong. Yang Chen is the corresponding author. 22 0 0 0 0 SPRINGER-VERLAG SINGAPORE PTE LTD SINGAPORE 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE 1865-0929 1865-0937 978-981-13-3044-5; 978-981-13-3043-8 COMM COM INF SC 2019.0 917 331 341 10.1007/978-981-13-3044-5_24 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BQ4LY 2023-03-23 WOS:000590395800024 0
C Ren, XW; Ren, PJ; Chen, BD; Min, T; Zheng, NN IEEE Ren, Xiaowei; Ren, Pengju; Chen, Badong; Min, Tai; Zheng, Nanning Hardware Implementation of KLMS Algorithm using FPGA PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 06-11, 2014 Beijing, PEOPLES R CHINA IEEE KERNEL; ENTROPY Fast and accurate machine learning algorithms are needed in many physical applications. However, the learning efficiency is badly subjected to the intensive computation. Knowing that hardware implementation could speed up computation effectively, we use a FPGA hardware platform to implement an on-line kernel learning algorithm, namely the kernel least mean square (KLMS) which adopts the simple survival kernel as the Mercer kernel. By using an on-line quantization method and pipeline technology, the requirement of hardware resources and computation burden can be reduced significantly and the data processing speed can be accelerated apparently without losing accuracy. Finally, a 128-way parallel FPGA platform which works at 200MHz is implemented. It could achieve an average speedup of 6553 versus Matlab running on a 3GHz Intel(R) Core(TM) i5-2320 CPU. [Ren, Xiaowei; Ren, Pengju; Chen, Badong; Zheng, Nanning] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, 28 Xianning West Rd, Xian 710049, Peoples R China; [Min, Tai] IMEC, B-3001 Leuven, Belgium Xi'an Jiaotong University; IMEC Ren, XW (corresponding author), Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, 28 Xianning West Rd, Xian 710049, Peoples R China. renxiaowei66@gmail.com; pengjuren@gmail.com; chenbd@mail.xjtu.edu.cn; tmdfz@hotmail.com; nnzheng@mail.xjtu.edu.cn Ren, Xiaowei/AAU-7277-2020 24 7 7 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-4799-1484-5 IEEE IJCNN 2014.0 2276 2281 6 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BE4BF 2023-03-23 WOS:000371465702054 0
C Verwilst, M; Zizakic, N; Gu, LC; Pizurica, A IEEE Verwilst, Maxim; Zizakic, Nina; Gu, Lingchen; Pizurica, Aleksandra Deep image hashing based on twin-bottleneck hashing with variational autoencoders IEEE MMSP 2021: 2021 IEEE 23RD INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP) IEEE International Workshop on Multimedia Signal Processing English Proceedings Paper 23rd IEEE International Workshop on Multimedia Signal Processing (IEEE MMSP) OCT 06-08, 2021 Tampere, FINLAND IEEE,IEEE Signal Proc Soc, Multimedia Signal Proc Tech Comm Image hashing; deep hashing; content-based image retrieval; variational autoencoders; unsupervised deep learning NEAREST-NEIGHBOR; QUANTIZATION; ALGORITHMS With the ever-increasing availability of data, the need for efficient and accurate image retrieval methods has become larger and larger. Deep hashing has proven to be a promising solution, by defining a hash function to convert the data into a manageable lower-dimensional representation. In this paper, we apply recent insights from the field of variational autoencoders to the field of deep image hashing, thus achieving an improvement over the current state of the art as shown by experimental evaluation. The code used in this paper is open-source and available on GitHub (https://github.com/maximverwilst/deepimagehashing-VAE). [Verwilst, Maxim; Zizakic, Nina; Gu, Lingchen; Pizurica, Aleksandra] Univ Ghent, TELIN, Grp Artificial Intelligence & Sparse Modelling GA, Ghent, Belgium; [Gu, Lingchen] Shandong Univ, Sch Informat Sci & Engn, Qingdao, Peoples R China Ghent University; Shandong University Verwilst, M (corresponding author), Univ Ghent, TELIN, Grp Artificial Intelligence & Sparse Modelling GA, Ghent, Belgium. maxim.verwilst@ugent.be; nina.zizakic@ugent.be; lingchengu@mail.sdu.edu.cn; aleksandra.pizurica@ugent.be Pizurica, Aleksandra/AAG-4687-2021; Gu, Lingchen/GZG-8349-2022; Pizurica, Aleksandra/K-6866-2015 Pizurica, Aleksandra/0000-0002-9322-4999 Flemish Government (AI Research Program) Flemish Government (AI Research Program) This research received funding from the Flemish Government (AI Research Program). 49 0 0 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2163-3517 978-1-6654-3287-0 IEEE INT WORKSH MULT 2021.0 10.1109/MMSP53017.2021.9733464 0.0 6 Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology BT1UI Green Published 2023-03-23 WOS:000803057000012 0
C Chen, JY; Guo, H; Hu, W; He, JJ; Wang, YH; Wen, Y IEEE Chen, JiaYou; Guo, Hong; Hu, Wei; He, JuanJuan; Wang, YongHao; Wen, Yuan Research on Plant Disease Recognition Based on Deep Complementary Feature Classification Network 2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 11-14, 2020 ELECTR NETWORK IEEE,IEEE Syst Man & Cybernet Soc,IEEE Brain,Intheon,Guger Technologies Plant disease identification; Deep complementary features; DeepLabv3+; CRF; Bi-GRU Traditional convolutional neural network classification models often only focus on the most distinguishing feature regions of the image and ignore the weaker feature regions. However, the image position distribution of plant diseases is very uneven. If we use convolutional neural network for plant disease recognition, there will be insufficient feature response, which will cause recognition errors. Aiming at such problems, we have designed a deep complementary feature classification network. First, the network uses DeepLabv3+ and Conditional Random Field (CRF) to generate disease part detection frames in a weakly supervised manner and combines semantic segmentation to extract disease object instances. Then we designed Complementary Feature Part Generation Models. Finally, it uses a bidirectional Gated Recurrent Unit (Bi-GRU) to perform the classification and recognition of the complementary features described above. We performed experiments on the PlantVillage dataset. The experimental results show that the proposed network recognition accuracy is 99.21%, which is 4.2% higher than the baseline model xception-65 used. We also performed experiments on the grape disease data set that we created. The accuracy of the proposed network recognition is 93.46%, which is 7.2% higher than the baseline model xception-65. In addition, compared with the better algorithms for plant disease identification in recent years, the accuracy performance has also been improved. [Chen, JiaYou; Guo, Hong; Hu, Wei; He, JuanJuan] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China; [Wang, YongHao] Birmingham City Univ, Coll Comp & Digital Technol, Birmingham, W Midlands, England; [Wen, Yuan] Trinity Coll Dublin, Dublin, Ireland Wuhan University of Science & Technology; Birmingham City University; Trinity College Dublin Chen, JY (corresponding author), Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China. jiayou828@vip.qq.com; guohong@wust.edu.cn; huwei@wust.edu.cn; hejuanjuan@wust.edu.cn; yonghao.wang@bcu.ac.uk; weny@tcd.ie Wang, Yonghao/0000-0002-4924-2508 Key Project of Hubei Provincial Department of Education [D20181103, 61702383] Key Project of Hubei Provincial Department of Education This work was sponsored by Key Project of Hubei Provincial Department of Education under Granted No. D20181103.; National Fund : No.61702383 43 0 0 0 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-7281-8526-2 IEEE SYS MAN CYBERN 2020.0 1685 1692 8 Computer Science, Cybernetics; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS1DD 2023-03-23 WOS:000687430601110 0
C Yin, ZY; Liu, JX; Krueger, M; Gao, HJ IEEE Yin, Zuyu; Liu, Jianxing; Krueger, Minjia; Gao, Huijun Introduction of SVM Algorithms and Recent Applications about Fault Diagnosis and Other Aspects PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) IEEE International Conference on Industrial Informatics INDIN English Proceedings Paper 13th IEEE International Conference on Industrial Informatics (INDIN) JUL 22-24, 2015 Cambridge, UNITED KINGDOM IEEE,IEEE Ind Elect Soc (IES),Anglia Ruskin Univ,Inst of Engn Techn SUPPORT VECTOR MACHINE; DECOMPOSITION METHOD; CLASSIFICATION; IDENTIFICATION; PREDICTION; SYSTEM; FRAMEWORK; FUSION Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods will be introduced. Finally, we discuss fault diagnosis and other aspects' recent applications and the directions we should research in the future. [Yin, Zuyu; Liu, Jianxing; Gao, Huijun] Harbin Inst Technol, Sch Astronaut, Heilongjiang 150001, Peoples R China; [Krueger, Minjia] Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany Harbin Institute of Technology; University of Duisburg Essen Yin, ZY (corresponding author), Harbin Inst Technol, Sch Astronaut, Heilongjiang 150001, Peoples R China. 1499542217@qq.com Liu, Jianxing/C-6153-2017 Liu, Jianxing/0000-0002-2201-3887 80 4 6 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1935-4576 978-1-4799-6649-3 IEEE INTL CONF IND I 2015.0 550 555 6 Computer Science, Artificial Intelligence; Engineering, Industrial; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BF2BU 2023-03-23 WOS:000380453900077 0
J Jentzen, A; Riekert, A Jentzen, Arnulf; Riekert, Adrian A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND PHYSIK English Article Artificial intelligence; Neural networks; Stochastic gradient descent; Non-convex optimization In this article we study the stochastic gradient descent (SGD) optimization method in the training of fully connected feedforward artificial neural networks with ReLU activation. The main result of this work proves that the risk of the SGD process converges to zero if the target function under consideration is constant. In the established convergence result the considered artificial neural networks consist of one input layer, one hidden layer, and one output layer (with d is an element of N neurons on the input layer, H is an element of N neurons on the hidden layer, and one neuron on the output layer). The learning rates of the SGD process are assumed to be sufficiently small, and the input data used in the SGD process to train the artificial neural networks is assumed to be independent and identically distributed. [Jentzen, Arnulf] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen, Peoples R China; [Jentzen, Arnulf] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China; [Jentzen, Arnulf; Riekert, Adrian] Univ Munster, Appl Math, Inst Anal & Numer, Munster, Germany Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen; University of Munster Jentzen, A (corresponding author), Chinese Univ Hong Kong, Sch Data Sci, Shenzhen, Peoples R China.;Jentzen, A (corresponding author), Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China.;Jentzen, A (corresponding author), Univ Munster, Appl Math, Inst Anal & Numer, Munster, Germany. ajentzen@cuhk.edu.cn; ariekert@uni-muenster.de Riekert, Adrian/0000-0002-1458-3388 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2044-390685587]; Shenzhen Research Institute of Big Data [T00120220001] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy(German Research Foundation (DFG)); Shenzhen Research Institute of Big Data This work has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC 2044-390685587, MathematicsMunster: DynamicsGeometry-Structure. This project has been partially supported by the startup fund project of Shenzhen Research Institute of Big Data under grant No. T00120220001. 47 1 1 1 2 SPRINGER INT PUBL AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0044-2275 1420-9039 Z ANGEW MATH PHYS Z. Angew. Math. Phys. OCT 2022.0 73 5 10.1007/s00033-022-01716-w 0.0 30 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics 3P7AB hybrid, Green Submitted 2023-03-23 WOS:000837686600001 0
J Li, F; Zhang, HX; Wang, J; Liu, Y; Gao, LL; Xu, X; Sanin, C; Szczerbicki, E Li, Fei; Zhang, Haoxi; Wang, Juan; Liu, Yong; Gao, Lulu; Xu, Xiang; Sanin, Cesar; Szczerbicki, Edward Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA CYBERNETICS AND SYSTEMS English Article Deep learning; knowledge representation; neural knowledge DNA; set of experience knowledge structure; vehicle intrusion detection In this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands based on learning from past valid driving behavior data on a simulator. [Li, Fei; Zhang, Haoxi; Wang, Juan; Liu, Yong; Gao, Lulu; Xu, Xiang] Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Sichuan, Peoples R China; [Sanin, Cesar] Univ Newcastle, Sch Mech Engn, Newcastle, NSW, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Li, F (corresponding author), Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Sichuan, Peoples R China. lifei@cuit.edu.cn Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912; Szczerbicki, Edward/0000-0001-7794-2862 Scientific Research Foundation of Sichuan Province [2016GZ0343] Scientific Research Foundation of Sichuan Province This work was supported by the Scientific Research Foundation of Sichuan Province as a part of the Project 2016GZ0343. 13 0 0 2 17 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. 2018.0 49 5-6 SI 412 419 10.1080/01969722.2017.1418788 0.0 8 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science HC7OZ 2023-03-23 WOS:000451992300011 0
C Yin, H; Huang, XL; Zhang, HY; Gao, XZ Chen, J; Zhao, Q Yin Hang; Huang Xianlin; Zhang Hongyang; Gao Xiao-Zhi Stabilization of Distributed Parameter Hopfield Neural Networks Based on Operator Spectral Theory PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 Chinese Control Conference English Proceedings Paper 35th Chinese Control Conference (CCC) JUL 27-29, 2016 Chengdu, PEOPLES R CHINA Chinese Assoc Automat, Tech Comm Control Theory,Syst Engn Soc China,SW Jiaotong Univ,Chinese Acad Sci, Acad Math & Syst Sci,China Soc Indu & Appl Math,Univ Elect Sci & Technol China,Sichuan Univ,Asian Control Assocn,IEEE Control Syst Soc,Inst Control Robot & Syst,SocInstrument & Control Engineers,Sichuan Soc Automat & Instrument Stability; Operator Spectral Theory; Reaction-Diffusion System; Hopfield Neural Network In this paper, we analyze the stability of distributed Hopfield neural networks. Distributed parameter Hopfield neural networks model is established by PDEs so state space of the controlled system belongs infinity dimensions, which is different from ODEs and DAEs models. Reaction-Diffusion Hopfield neural networks model is a new class of net systems which exist widely in control science, intelligent computation, cells of neurology and biology mathematica. Most of papers about Hopfield neural networks apply average Lyapunov function and M-matrix theory to study stability. Now, we use operator spectral theory to obtain stability of the system, which does not need complex calculation. At last, we make some simulations verify our criterions. [Yin Hang; Huang Xianlin; Zhang Hongyang] Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150001, Peoples R China; [Gao Xiao-Zhi] Aalto Univ, Sch Elect Engn, Dept Elect Engn & Automat, Aalto 00076, Finland Harbin Institute of Technology; Aalto University Yin, H (corresponding author), Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150001, Peoples R China. 13B904008@hit.edu.cn; xiao.z.gao@gmail.com National Natural Science Foundation (NNSF) China [51275107] National Natural Science Foundation (NNSF) China(National Natural Science Foundation of China (NSFC)) This work is supported by National Natural Science Foundation (NNSF) of China under Grant 51275107. 10 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-2927 978-9-8815-6391-0 CHIN CONTR CONF 2016.0 1447 1452 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BG4GL 2023-03-23 WOS:000388809501122 0
C Fang, S; Yue, Y; He, LY; Du, K; Tian, YH; Huang, TJ IEEE Fang, Shu; Yue, Yang; He, Liuyuan; Du, Kai; Tian, Yonghong; Huang, Tiejun Image Saliency Analysis based on Retina Simulation 2017 IEEE THIRD INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM 2017) English Proceedings Paper IEEE 3rd International Conference on Multimedia Big Data (BigMM) APR 19-21, 2017 Laguna Hills, CA IEEE,IEEE Comp Soc Visual Saliency; Retina Simulation; Neural Spike Trains MAP In this paper, we provide an insight of visual saliency modeling from the perspective of simulating visual information manipulation process in human vision system. For humans, visual stimuli are converted into spike trains by retina, and the spike signals are then transferred to brain areas for further analysis. Therefore, we propose to estimate image saliency based on a retina neural network, which is built with realistic morphology and electrophysics data. Then, we analyze the correlation between the spike trains generated by retina and image saliency. The experimental results show the effectiveness of retinal spike trains in image saliency analysis. [Fang, Shu; Yue, Yang; He, Liuyuan; Tian, Yonghong; Huang, Tiejun] Peking Univ, Sch EE&CS, Natl Engn Lab Video Technol, Beijing, Peoples R China; [Fang, Shu; Yue, Yang; He, Liuyuan; Tian, Yonghong; Huang, Tiejun] Cooperat Medianet Innovat Ctr, Beijing, Peoples R China; [Du, Kai] Karolinska Inst, Dept Neurosci, Stockholm, Sweden Peking University; Karolinska Institutet Tian, YH; Huang, TJ (corresponding author), Peking Univ, Sch EE&CS, Natl Engn Lab Video Technol, Beijing, Peoples R China.;Tian, YH; Huang, TJ (corresponding author), Cooperat Medianet Innovat Ctr, Beijing, Peoples R China.;Du, K (corresponding author), Karolinska Inst, Dept Neurosci, Stockholm, Sweden. sfang@pku.edu.cn; yueyang999@pku.edu.cn; liyhe@pku.edu.cn; Kai.Du@ki.se; yhtian@pku.edu.cn; tjhuang@pku.edu.cn National Basic Research Program of China [2015CB351806]; National Natural Science Foundation of China [61390515, 61425025]; Beijing Municipal Commission of Science and Technology [Z151100000915070] National Basic Research Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Commission of Science and Technology This work is partially supported by the National Basic Research Program of China under grant 2015CB351806, the National Natural Science Foundation of China under contract No. 61390515, and No. 61425025, and Beijing Municipal Commission of Science and Technology under contract No. Z151100000915070,. 32 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-6549-3 2017.0 142 145 10.1109/BigMM.2017.66 0.0 4 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ2RS 2023-03-23 WOS:000419853200027 0
J Zeng, WZ; Zhang, DY; Fang, YH; Wu, JW; Huang, JS Zeng, Wenzhi; Zhang, Dongying; Fang, Yuanhao; Wu, Jingwei; Huang, Jiesheng Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data JOURNAL OF APPLIED REMOTE SENSING English Article salinity; Landsat; hyperspectral data; remote sensing; Hetao Irrigation District REFLECTANCE SPECTROSCOPY; SALT TOLERANCE; METHODS PLSR; MOISTURE; LANDSAT; SATELLITE; MODEL; CLASSIFICATION; CONTINUITY; NETWORKS This study explored three techniques for estimating the soil salt content from Landsat data. First, the 127 items of in situ measured hyperspectral reflectance data were collected and resampled to the spectral resolution of the reflectance bands of Landsat 5 and Landsat 8, respectively. Second, 12 soil salt indices (SSI) summarized from previous literature were determined based on the simulated Landsat bands. Third, 127 measurement groups with Landsat bands and SSI were randomly divided into training (102) and testing subgroups (25). Three techniques including partial least square regression (PLSR), support vector machine (SVM), and deep learning (DL) were used to establish a soil salinity model using SSI and the simulated Landsat bands as independent variables (IV), respectively. Results indicated that PLSR with SSI performed best for both simulated Landsat 5 and Landsat 8 data. Compared with PLSR, SVM underestimated soil salt content, whereas DL obtained centralized simulations and failed to capture the lower and upper observations. We recommend the PLSR model with SSI as IV to estimate soil salt content because it can identify >66% moderate-to-high-saline soils, which indicates its great potential for soil salt monitoring in arid or semiarid regions. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) [Zeng, Wenzhi; Wu, Jingwei; Huang, Jiesheng] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China; [Zeng, Wenzhi] Univ Bonn, Crop Sci Grp, Inst Crop Sci & Resource Conservat INRES, Bonn, Germany; [Zeng, Wenzhi] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Jiangsu, Peoples R China; [Zhang, Dongying] Zhengzhou Univ, Sch Water Conservancy & Environm, Zhengzhou, Henan, Peoples R China; [Fang, Yuanhao] Hohai Univ, Dept Hydrol & Water Resources, Nanjing, Jiangsu, Peoples R China Wuhan University; University of Bonn; Hohai University; Zhengzhou University; Hohai University Zeng, WZ (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China.;Zeng, WZ (corresponding author), Univ Bonn, Crop Sci Grp, Inst Crop Sci & Resource Conservat INRES, Bonn, Germany.;Zeng, WZ (corresponding author), Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Jiangsu, Peoples R China. zengwenzhi1989@whu.edu.cn Zeng, Wen/GPS-8058-2022; Huang, Jie/GWQ-5005-2022 State Natural Science Fund of China [51609175, 51790533, 51379151]; Natural Science Fund of Hubei Province, China [ZRMS2017000430]; Major Program of National Science and Technology Support Plan of China [2017YFC0403304, 2016YFC0501304]; Fundamental Research Funds for the Central Universities [2042016kf0043, 2042016kf1034]; China Postdoctoral Science Foundation [2017T100579]; State Key Laboratory of Water Resources and Hydropower Engineering Science [2015NSG01] State Natural Science Fund of China; Natural Science Fund of Hubei Province, China; Major Program of National Science and Technology Support Plan of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); State Key Laboratory of Water Resources and Hydropower Engineering Science This work was made possible by support from the State Natural Science Fund of China (Grant Nos. 51609175, 51790533, and 51379151), the Natural Science Fund of Hubei Province, China (Grant No. ZRMS2017000430), the Major Program of National Science and Technology Support Plan of China (Grant Nos. 2017YFC0403304 and 2016YFC0501304), the Fundamental Research Funds for the Central Universities (Grant Nos. 2042016kf0043 and 2042016kf1034), China Postdoctoral Science Foundation (Grant No. 2017T100579), and the Open Research Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science (2015NSG01). Many thanks to Professor Yonggen Zhang and Dr. Hongya Zhang for improving the English language of the paper. 67 17 17 8 160 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JAN 22 2018.0 12 22204 10.1117/1.JRS.12.022204 0.0 16 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology FT5EF 2023-03-23 WOS:000423176100001 0
J Tong, LS; Kauer, J; Chen, X; Chu, KQ; Dou, H; Smith, ZJ Tong, Lieshu; Kauer, Josef; Chen, Xi; Chu, Kaiqin; Dou, Hu; Smith, Zachary J. Screening of nutritional and genetic anemias using elastic light scattering LAB ON A CHIP English Article IRON-DEFICIENCY ANEMIA; INDEPENDENT RISK-FACTOR; HEMOGLOBIN CONCENTRATION; BETA-THALASSEMIA; YOUNG-CHILDREN; RANDOMIZED-TRIAL; CHINESE CHILDREN; CELL-VOLUME; DIFFERENTIATION; PREVALENCE Anemia affects more than 1/4 of the world's population, mostly concentrated in low-resource areas, and carries serious health risks. Yet current screening methods are inadequate due to their inability to separate iron deficiency anemia (IDA) from genetic anemias such as thalassemia trait (TT), thus preventing targeted supplementation of oral iron. Here we present an accurate approach to diagnose anemia and anemia type using measures of pediatric red cell morphology determined through machine learning applied to optical light scattering measurements. A partial least squares model shows that our system can accurately extract mean cell volume, red cell size heterogeneity, and mean cell hemoglobin concentration with high accuracy. These clinical parameters (or the raw data itself) can be submitted to machine learning algorithms such as quadratic discriminants or support vector machines to classify a patient into healthy, IDA, or TT. A clinical trial conducted on 268 Chinese children, of which 49 had IDA and 24 had TT, shows >98% sensitivity and specificity for diagnosing anemia, with 81% sensitivity and 86% specificity for discriminating IDA and TT. The majority of the misdiagnoses are IDA patients with particularly severe anemia, possibly requiring hospital care. Therefore, in a screening paradigm where anyone testing positive for TT is sent to the hospital for gold-standard diagnosis and care, we maximize patient benefit while minimizing use of scarce resources. [Tong, Lieshu; Chu, Kaiqin; Smith, Zachary J.] Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei, Anhui, Peoples R China; [Kauer, Josef] Beuth Hsch Tech Berlin, Berlin, Germany; [Kauer, Josef] Charite Univ Med Berlin, NeuroCure Clin Res Ctr, Berlin, Germany; [Kauer, Josef] Humboldt Univ, Freie Univ Berlin, Berlin, Germany; [Kauer, Josef] Berlin Inst Hlth, Berlin, Germany; [Chen, Xi; Dou, Hu] Chongqing Med Univ, Chongqing Int Sci & Technol Cooperat Ctr Child De, Key Lab Pediat Chongqing,Childrens Hosp,Minist Ed, Dept Clin Lab,Key Lab Child Dev & Disorders, Chongqing, Peoples R China; [Chen, Xi] Chongqing Med Univ, Childrens Hosp, Ctr Clin Mol Med, Chongqing, Peoples R China Chinese Academy of Sciences; University of Science & Technology of China, CAS; Berliner Hochschule fur Technik; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health; Chongqing Medical University; Chongqing Medical University Smith, ZJ (corresponding author), Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei, Anhui, Peoples R China.;Dou, H (corresponding author), Chongqing Med Univ, Chongqing Int Sci & Technol Cooperat Ctr Child De, Key Lab Pediat Chongqing,Childrens Hosp,Minist Ed, Dept Clin Lab,Key Lab Child Dev & Disorders, Chongqing, Peoples R China. 375009808@qq.com; zsmith@ustc.edu.cn Smith, Zachary/S-2211-2019; Kauer-Bonin, Josef/N-3004-2018 Smith, Zachary/0000-0002-7946-7863; Kauer-Bonin, Josef/0000-0001-7207-9027 Ministry of Science and Technology of the People's Republic of China National Key Research and Development Program [2016YFA0201303]; 1000 Young Talents Global Recruitment Plan; Chongqing Municipal Science and Technology Commission [cstc2017shmsA130083] Ministry of Science and Technology of the People's Republic of China National Key Research and Development Program; 1000 Young Talents Global Recruitment Plan; Chongqing Municipal Science and Technology Commission Z. J. S. acknowledges funding from The Ministry of Science and Technology of the People's Republic of China National Key Research and Development Program (2016YFA0201303), and the 1000 Young Talents Global Recruitment Plan. H. D. and Z. J. S. acknowledge funding support from the Chongqing Municipal Science and Technology Commission (award cstc2017shmsA130083). 49 3 3 2 15 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1473-0197 1473-0189 LAB CHIP Lab Chip NOV 7 2018.0 18 21 10.1039/c8lc00377g 0.0 10 Biochemical Research Methods; Chemistry, Multidisciplinary; Chemistry, Analytical; Nanoscience & Nanotechnology; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry; Science & Technology - Other Topics; Instruments & Instrumentation GY2BO 30264831.0 hybrid 2023-03-23 WOS:000448343100005 0
J Azarafza, M; Nanehkaran, YA; Rajabion, L; Akgun, H; Rahnamarad, J; Derakhshani, R; Raoof, A Azarafza, Mohammad; Nanehkaran, Yaser A.; Rajabion, Lila; Akgun, Haluk; Rahnamarad, Jafar; Derakhshani, Reza; Raoof, Amir Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran ENGINEERING GEOLOGY English Article Rock slope engineering; Q-slope system; Python; Slope stability; Artificial intelligence techniques; Iran MASS RATING SMR; GEOLOGICAL STRENGTH INDEX; RMR The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between Q-slope and slope stability degree using case studies of sedimentary rock slopes from 10 regions of Iran. To this end, we have investigated 200 areas from these regions, gathered the necessary geo-technical data, have classified the slopes from a Q-slope perspective, and have estimated their stability relationships. Based on artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, Decision tree, Random-forest, Multilayer perceptron, AdaBoost, Naive Bayes and Quadratic discriminant analysis, the relationships and classifications were implemented and revised in the Python high-level programming language. According to the results of the controlled learning models, the Q-slope equation for Iran has indicated that the stability-instability class distributions are limited to two linear states. These limits refer to the B-Line (lower limit) as beta = 11.9log(10)(Q(number)) + 46.3 and the U-Line (upper limit) as beta = 17.2log(10)(Q(number)) 1+ 54.1. We present the modified Q-slope equation (beta) to correct the primary relation for sedimentary rock slopes in Iran. To this end, the beta-relation from Bar and Barton (2017) that is illustrated by Eq. (2) was modified and refined by the U-line and B-line relations as presented by Eqs. (3) and (4). [Azarafza, Mohammad] Univ Isfahan, Dept Geol, Esfahan, Iran; [Nanehkaran, Yaser A.] Xiamen Univ, Dept Comp Sci, Xiamen, Fujian, Peoples R China; [Rajabion, Lila] Univ South Florida Sarasota Manatee, Dept Informat Technol, Sarasota, FL USA; [Akgun, Haluk] METU, Dept Geol Engn, Geotechnol Unit, Ankara, Turkey; [Rahnamarad, Jafar] Islamic Azad Univ, Dept Geol, Zahedan Branch, Zahedan, Iran; [Derakhshani, Reza] Shahid Bahonar Univ Kerman, Dept Geol, Kerman, Iran; [Derakhshani, Reza; Raoof, Amir] Univ Utrecht, Dept Earth Sci, Utrecht, Netherlands University of Isfahan; Xiamen University; Middle East Technical University; Islamic Azad University; Shahid Bahonar University of Kerman (SBUK); Utrecht University Rahnamarad, J (corresponding author), Islamic Azad Univ, Dept Geol, Zahedan Branch, Zahedan, Iran. m.azarafza.geotech@gmail.com; yaser@stu.xmu.edu.cn; Lrajabion@sar.usf.edu; hakgun@metu.edu.tr; rahnamarad@iauzah.ac.ir; r.derakhshani@uu.nl; a.raoof@uu.nl Derakhshani, Reza/P-1194-2019; Nanehkaran, Yaser Ahangari/AAN-6150-2021; Azarafza, Mohammad/AAP-2136-2020; Akgun, Haluk/ABA-2218-2020 Derakhshani, Reza/0000-0001-7499-4384; Nanehkaran, Yaser Ahangari/0000-0002-8055-3195; Azarafza, Mohammad/0000-0001-7777-3800; AKGUN, Professor of Geological Engineering, HALUK/0000-0003-0404-5647 71 20 20 5 36 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0013-7952 1872-6917 ENG GEOL Eng. Geol. JAN 2020.0 264 105349 10.1016/j.enggeo.2019.105349 0.0 12 Engineering, Geological; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology KM2XG 2023-03-23 WOS:000513985500015 0
J Liu, C; Sun, DH; Yang, Z; Liu, XY; Yang, HY; Huang, JJ Liu, Chao; Sun, Dihua; Yang, Zheng; Liu, Xiaoyang; Yang, Hongyu; Huang, Junjian Stabilization of switched neural networks with time-varying delay via bumpless transfer control ASIAN JOURNAL OF CONTROL English Article bumpless transfer; delay; stabilization; switched neural network H-INFINITY CONTROL; STABILITY ANALYSIS; NEUTRAL-TYPE; SYSTEMS; DESIGN This paper investigates the stabilization of switched neural networks with time-varying delay. In order to overcome the drawback that the classical switching state feedback controller may generate the bumps at switching time, a new switching feedback controller which can smooth effectively the bumps is proposed. According to mode-dependent average dwell time, new exponential stabilization results are deduced for switched neural networks under the proposed feedback controller. Based on a simple corollary, the procedures which are used to calculate the feedback control gain matrices are also obtained. Two simple numerical examples are employed to demonstrate the effectiveness of the proposed results. [Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Yang, Hongyu] Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China; [Liu, Chao; Sun, Dihua] Hubei Univ, Hubei Key Lab Appl Math, Wuhan, Peoples R China; [Yang, Zheng] Univ Helsinki, Dept Comp Sci, Gustaf Hallstromin Katu 2b, Helsinki, Finland; [Huang, Junjian] Chongqing Univ Educ, Key Lab Machine Percept & Childrens Intelligence, Chongqing, Peoples R China Chongqing University of Technology; Hubei University; University of Helsinki; Chongqing University of Education Liu, C (corresponding author), Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China. xiuwenzheng2000@163.com Yang, Zheng/AAE-6121-2021; Huang, Jian/GYJ-5213-2022 National Natural Science Foundation of China [61503052, 61872051, 61503050, 61603065, 61573075]; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN201801120, KJQN201801104, KJ1709206]; Opening Foundation of Hubei Key Laboratory of Applied Mathematics (Hubei University) [HBAM201805]; National Key R&D Program of China [2016YFB0100904]; China Postdoctoral Science Foundation [2017M612911]; 2018 Chongqing Science and Technology Commission Technology Innovation and Application Demonstration (Social Livelihood General) Project [cstc2018jscx-msybX0049] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific and Technological Research Program of Chongqing Municipal Education Commission; Opening Foundation of Hubei Key Laboratory of Applied Mathematics (Hubei University); National Key R&D Program of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); 2018 Chongqing Science and Technology Commission Technology Innovation and Application Demonstration (Social Livelihood General) Project National Natural Science Foundation of China, Grant/Award Number: 61503052, 61872051, 61503050, 61603065 and 61573075; Scientific and Technological Research Program of Chongqing Municipal Education Commission, Grant/Award Number: KJQN201801120, KJQN201801104 and KJ1709206; Opening Foundation of Hubei Key Laboratory of Applied Mathematics (Hubei University), Grant/Award Number: HBAM201805; National Key R&D Program of China, Grant/Award Number: 2016YFB0100904; China Postdoctoral Science Foundation, Grant/Award Number: 2017M612911; 2018 Chongqing Science and Technology Commission Technology Innovation and Application Demonstration (Social Livelihood General) Project, Grant/Award Number: cstc2018jscx-msybX0049 31 1 1 4 24 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1561-8625 1934-6093 ASIAN J CONTROL Asian J. Control MAR 2020.0 22 2 1008 1020 10.1002/asjc.2061 0.0 13 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems KQ7LK Green Published 2023-03-23 WOS:000517101700032 0
J Luo, H; Luo, MX; Wang, K; Xu, T; Zhu, GH Luo, Hui; Luo, Ming-Xing; Wang, Kai; Xu, Tao; Zhu, GuoHuai Quark jet versus gluon jet: fully-connected neural networks with high-level features SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY English Article standard model simulation; quark-gluon jets; machine learning Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the bene fit that essentially no physics input is required. Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks (FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum p(TJ) bins; trained on a wide region of p(TJ) is an element of [200, 1000] GeV. We find their performances are almost the same. The performance is better when the p(TJ) is larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic (SIC) from detector level data would at most decrease by 15%. We also test the performance of FNNs with the full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets. [Luo, Hui] China Acad Engn Phys, Grad Sch, Beijing 100193, Peoples R China; [Luo, Hui; Luo, Ming-Xing; Wang, Kai; Xu, Tao; Zhu, GuoHuai] Zhejiang Univ, Zhejiang Inst Modern Phys, Hangzhou 310027, Zhejiang, Peoples R China; [Luo, Hui; Luo, Ming-Xing; Wang, Kai; Xu, Tao; Zhu, GuoHuai] Zhejiang Univ, Dept Phys, Hangzhou 310027, Zhejiang, Peoples R China; [Luo, Hui] Univ Hamburg, Inst Theoret Phys 2, Luruper Chaussee 149, D-22761 Hamburg, Germany Chinese Academy of Engineering Physics; Zhejiang University; Zhejiang University; University of Hamburg Luo, H (corresponding author), China Acad Engn Phys, Grad Sch, Beijing 100193, Peoples R China.;Luo, H (corresponding author), Zhejiang Univ, Zhejiang Inst Modern Phys, Hangzhou 310027, Zhejiang, Peoples R China.;Luo, H (corresponding author), Zhejiang Univ, Dept Phys, Hangzhou 310027, Zhejiang, Peoples R China.;Luo, H (corresponding author), Univ Hamburg, Inst Theoret Phys 2, Luruper Chaussee 149, D-22761 Hamburg, Germany. hluo@gscaep.ac.cn Wang, Kai/B-9604-2013 Wang, Kai/0000-0003-1661-689X German Science Foundation (DFG); Recruitment Program of Global Youth Experts of China; National Natural Science Foundation of China [11135006, 11275168, 11422544, 11375151, 11535002]; Zhejiang University Fundamental Research Funds for the Central Universities [2017QNA3007] German Science Foundation (DFG)(German Research Foundation (DFG)); Recruitment Program of Global Youth Experts of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang University Fundamental Research Funds for the Central Universities Hui Luo is supported by the German Science Foundation (DFG) within the Collaborative Research Center 676 Particles, Strings and the Early Universe and the Recruitment Program of Global Youth Experts of China. Other authors are supported in part by the National Natural Science Foundation of China (Grant Nos. 11135006, 11275168, 11422544, 11375151, and 11535002), and the Zhejiang University Fundamental Research Funds for the Central Universities (Grant No. 2017QNA3007). We gratefully thank Andrew Larkoski for helpful discussions, and thank Gavin Salam and Torbjorn Sjostrand for clarifying technical details. 63 11 11 0 6 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-7348 1869-1927 SCI CHINA PHYS MECH Sci. China-Phys. Mech. Astron. SEP 2019.0 62 9 991011 10.1007/s11433-019-9390-8 0.0 11 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics IF3UT Green Submitted 2023-03-23 WOS:000473009300001 0
J Ruan, JG; Wu, CC; Liang, ZW; Liu, K; Li, B; Li, WH; Li, TY Ruan, Jiageng; Wu, Changcheng; Liang, Zhaowen; Liu, Kai; Li, Bin; Li, Weihan; Li, Tongyang The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode ENERGY English Article DDPG; Discrete -continuous hybrid actions; Regenerative braking; Hierarchical structure SYSTEM Machine learning (ML)-based methods have attracted great attention in the multi-objective optimization prob-lems, which is the key challenge in the energy management strategy (EMS) of the multi-power hybrid system. Our recently published research in this journal verified the effectiveness and feasibility of a Deep Deterministic Policy Gradient (DDPG)-based EMS in the charge-sustaining (CS) stage of a multi-mode plug-in hybrid vehicle (PHEV). However, the application of ML-based-EMS in the charge-depletion (CD) stage and the regenerative braking mode of PHEV are still missing. This study proposes a discrete-continuous hybrid actions-based hier-archical EMS to optimally distribute the dual-motor driving force in battery electric driving and regenerative braking. In the upper layer of EMS, DDPG is trained to learn the torque distribution principles of dual-motor operation to achieve better energy efficiency without losing dynamic performance. Meanwhile, the total recoverable braking torque is also determined by the upper layer EMS considering the braking demand, me-chanical and electrical braking system conditions, vehicle safety, and the provisions of law. In the lower level of EMS, the driving mode is determined under the guidance of energy consumption optimization. The verified results show that the proposed EMS outperforms other deep reinforcement learning (DRL)-based hierarchical and non-hierarchical EMSs. [Ruan, Jiageng; Wu, Changcheng; Li, Tongyang] Beijing Univ Technol, Coll Intelligent Machinery, Dept Mat & Mfg, Beijing 100020, Peoples R China; [Liang, Zhaowen] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China; [Liang, Zhaowen; Liu, Kai] Beijing Foton AUV New Energy Bus Co Ltd, Beijing 102200, Peoples R China; [Li, Bin] Changan Univ, Sch Automobile, Xian 710021, Shaanxi, Peoples R China; [Li, Weihan] Rhein Westfal TH Aachen, Inst Power Elect & Elect Drives ISEA, Jaegerstr 17-19, D-52066 Aachen, Germany Beijing University of Technology; Beijing Institute of Technology; Chang'an University; RWTH Aachen University Ruan, JG (corresponding author), Beijing Univ Technol, Coll Intelligent Machinery, Dept Mat & Mfg, Beijing 100020, Peoples R China. ruanjiageng@bjut.edu.cn National Key Research and Devel- opment Program of China [2022YFE0102700] National Key Research and Devel- opment Program of China This work was supported by the National Key Research and Devel- opment Program of China under Grant No. 2022YFE0102700. 34 0 0 5 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy APR 15 2023.0 269 126792 10.1016/j.energy.2023.126792 0.0 JAN 2023 13 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 8N0IP 2023-03-23 WOS:000924837800001 0
J Malosso, C; Zhang, LF; Car, R; Baroni, S; Tisi, D Malosso, Cesare; Zhang, Linfeng; Car, Roberto; Baroni, Stefano; Tisi, Davide Viscosity in water from first-principles and deep-neural-network simulations NPJ COMPUTATIONAL MATERIALS English Article MOLECULAR-DYNAMICS SIMULATIONS; 1ST PRINCIPLES SIMULATIONS; DENSITY-FUNCTIONAL THEORY; STOKES-EINSTEIN RELATION; LIQUID WATER; DIFFUSION-COEFFICIENTS; IRREVERSIBLE-PROCESSES; SUPERCOOLED WATER; MECHANICAL THEORY; TRANSPORT We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments. [Malosso, Cesare; Baroni, Stefano; Tisi, Davide] SISSA Scuola Int Super Studi Avanzati, I-34136 Trieste, Italy; [Zhang, Linfeng; Car, Roberto] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA; [Zhang, Linfeng] DP Technol, Beijing 100080, Peoples R China; [Car, Roberto] Princeton Univ, Dept Phys, Dept Chem, Princeton, NJ 08544 USA; [Car, Roberto] Princeton Univ, Princeton Inst Sci & Technol Mat, Princeton, NJ 08544 USA; [Baroni, Stefano] CNR Ist Officina Mat, SISSA Unit, I-34136 Trieste, Italy International School for Advanced Studies (SISSA); Princeton University; Princeton University; Princeton University; Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); International School for Advanced Studies (SISSA) Baroni, S (corresponding author), SISSA Scuola Int Super Studi Avanzati, I-34136 Trieste, Italy.;Baroni, S (corresponding author), CNR Ist Officina Mat, SISSA Unit, I-34136 Trieste, Italy. baroni@sissa.it Malosso, Cesare/GVT-2845-2022; Baroni, Stefano/F-2982-2011 Tisi, Davide/0000-0001-7229-6101; Baroni, Stefano/0000-0002-3508-6663 EU through the MAX Centre of Excellence for supercomputing applications [824143]; Italian MUR, through the PRIN grant FERMAT; Computational Chemical Sciences Center Chemistry in Solution and at Interfaces - US Department of Energy [DE-SC0019394] EU through the MAX Centre of Excellence for supercomputing applications; Italian MUR, through the PRIN grant FERMAT(Ministry of Education, Universities and Research (MIUR)); Computational Chemical Sciences Center Chemistry in Solution and at Interfaces - US Department of Energy(United States Department of Energy (DOE)) C.M., S.B., and D.T. are grateful to Federico Grasselli, Paolo Pegolo, and Riccardo Bertossa for enlightening discussions throughout the completion of this work. This work was partially funded by the EU through the MAX Centre of Excellence for supercomputing applications (Project No. 824143) and the Italian MUR, through the PRIN grant FERMAT. The work at Princeton University was supported by the Computational Chemical Sciences Center Chemistry in Solution and at Interfaces funded by the US Department of Energy under Award No. DE-SC0019394. 106 5 5 17 29 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2057-3960 NPJ COMPUT MATER npj Comput. Mater. JUL 1 2022.0 8 1 139 10.1038/s41524-022-00830-7 0.0 10 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science 2P5QD gold, Green Submitted 2023-03-23 WOS:000819794500004 0
J Duan, HT; Ma, RH; Simis, SGH; Zhang, YZ Duan, Hongtao; Ma, Ronghua; Simis, Stefan G. H.; Zhang, Yuanzhi Validation of MERIS Case-2 Water Products in Lake Taihu, China GISCIENCE & REMOTE SENSING English Article DISSOLVED ORGANIC-MATTER; ATMOSPHERIC CORRECTION; SCATTERING PROPERTIES; LIGHT-ABSORPTION; ALGORITHM; PHYTOPLANKTON; RETRIEVAL; PARTICLES; MODEL; OCEAN Three artificial neural network (ANN) processors available as plug-in modules for the Basic ERS & ENVISAT (A) ATSR and MERIS Toolbox (BEAM) were validated in Lake Taihu, China. Mean deviations of reflectance derived from Lakes\Boreal and Lakes\Eutrophic were 10-90%, while reflectance from the -FUB-WeW processor showed larger errors. All processors showed underestimates of chlorophyll a (Chl-a), total suspended matter (TSM), and phytoplankton pigment absorption, while particulate scattering values were severely overestimated. None of the readily available MERIS processors is currently able to separate atmospheric and water-leaving radiance over Lake Taihu, while the retrieval of phytoplankton biomass through ANN processors shows promise. [Duan, Hongtao; Ma, Ronghua] Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Jiangsu, Peoples R China; [Simis, Stefan G. H.] Ctr Marine Res, Finnish Environm Inst SYKE, Helsinki 00560, Finland; [Zhang, Yuanzhi] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Hong Kong, Peoples R China Chinese Academy of Sciences; Nanjing Institute of Geography & Limnology, CAS; Finnish Environment Institute; Chinese University of Hong Kong Ma, RH (corresponding author), Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Jiangsu, Peoples R China. htduan@niglas.ac.cn; rhma@niglas.ac.cn Duan, Hongtao/B-7210-2011 Duan, Hongtao/0000-0002-1985-2292; Simis, Stefan/0000-0002-6296-9146 Chinese Academy of Sciences [KZCX2-EW-QN308, KZCX2-YW-QN311]; National Natural Science Foundation of China [41171271, 41171273]; National Projects of International Cooperation and Exchanges of the Ministry of Science and Technology of China [2010DFA90960]; Hong Kong Research Grants Council (RGC) General Research Fund (GRF) [CUHK459210]; Hong Kong Innovation & Technology Fund (ITF) [GHP/002/11GD]; ESA-MOST Dragon Cooperation Programm [10561] Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Projects of International Cooperation and Exchanges of the Ministry of Science and Technology of China; Hong Kong Research Grants Council (RGC) General Research Fund (GRF)(Hong Kong Research Grants Council); Hong Kong Innovation & Technology Fund (ITF); ESA-MOST Dragon Cooperation Programm The authors would like to thank Steven Arthur Loiselle (University of Siena, Italy) for his valuable suggestions to improve this manuscript. Financial support was provided by the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-EW-QN308 and KZCX2-YW-QN311), the National Natural Science Foundation of China (41171271 and 41171273), the National Projects of International Cooperation and Exchanges of the Ministry of Science and Technology of China (2010DFA90960), Hong Kong Research Grants Council (RGC) General Research Fund (GRF) Project (CUHK459210), and Hong Kong Innovation & Technology Fund (ITF) Project (GHP/002/11GD). We thank ESA-MOST Dragon Cooperation Programme (ID_10561) and the Scientific Data Sharing Platform for Lake and Watershed (SDSPLW), Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS) for providing us with MERIS images and in situ data, and thank the anonymous reviewers for their extensive comments and suggestions that helped improve this manuscript. 46 13 14 2 34 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1548-1603 1943-7226 GISCI REMOTE SENS GISci. Remote Sens. NOV-DEC 2012.0 49 6 873 894 10.2747/1548-1603.49.6.873 0.0 22 Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Remote Sensing 047II 2023-03-23 WOS:000311832800005 0
J Zhang, HX; Li, F; Wang, J; Zhou, Y; Sanin, C; Szczerbicki, E Zhang, Haoxi; Li, Fei; Wang, Juan; Zhou, Yang; Sanin, Cesar; Szczerbicki, Edward Experience-Based Cognition for Driving Behavioral Fingerprint Extraction CYBERNETICS AND SYSTEMS English Article Deep learning; knowledge representation; neural knowledge DNA; set of experience knowledge structure; vehicle intrusion detection MODEL; KNOWLEDGE; INTERNET With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver's driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver's pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver's driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks). [Zhang, Haoxi; Li, Fei; Wang, Juan; Zhou, Yang] Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China; [Sanin, Cesar] Univ Newcastle, Sch Mech Engn, Newcastle, NSW, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Sch Cybersecur, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China. haoxi@cuit.edu.cn Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912 Sichuan Science and Technology Program [2019YFH0185] Sichuan Science and Technology Program This research was supported by Sichuan Science and Technology Program under the grant number 2019YFH0185. 27 2 2 2 8 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. FEB 17 2020.0 51 2 SI 103 114 10.1080/01969722.2019.1705547 0.0 JAN 2020 12 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science KG6FX 2023-03-23 WOS:000508103700001 0
J Scutari, G; Facchinei, F; Lampariello, L; Sardellitti, S; Song, P Scutari, Gesualdo; Facchinei, Francisco; Lampariello, Lorenzo; Sardellitti, Stefania; Song, Peiran Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Interference networks; multicast beamforming; nonconvex optimization; statistical learning; successive convex approximation TRANSCEIVER DESIGN; SELECTION; ALGORITHMS; REGRESSION In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of) the rate profile maximization in MIMO interference broadcast networks; 2) the max-min fair multicast multigroup beamforming problem in a multicell environment; and 3) a general nonconvex constrained bi-criteria formulation for k-sparse variable selection in statistical learning; the two criteria are a nonconvex loss objective function, measuring the fitness of the model to data, and the latter is a nonconvex sparsity-inducing constraint in the general form of difference-ofconvex (DC) functions, which allows to accomodate in a unified fashion convex and nonconvex surrogates of the lo function. The proposed algorithms outperform current state-of-the-art schemes for 1)-3) both theoretically and numerically. For instance, they are the first distributed schemes for the class of problems 1) and 2); and they also lead to subproblems enjoying closed form solutions. [Scutari, Gesualdo] Purdue Univ, Sch Ind Engn, W Lafayette, IN 47907 USA; [Facchinei, Francisco] Univ Roma La Sapienza, Dept Comp Control & Management Engn, I-00184 Rome, Italy; [Lampariello, Lorenzo] Univ Rome Tre, Dept Business Studies, I-00154 Rome, Italy; [Sardellitti, Stefania] Univ Roma La Sapienza, Dept Informat Elect & Telecommun, I-00184 Rome, Italy; [Song, Peiran] Beijing Informat Sci & Technol Univ, Sch Informat & Commun Engn, Beijing 100101, Peoples R China Purdue University System; Purdue University; Purdue University West Lafayette Campus; Sapienza University Rome; Roma Tre University; Sapienza University Rome; Beijing Information Science & Technology University Scutari, G (corresponding author), Purdue Univ, Sch Ind Engn, W Lafayette, IN 47907 USA. gscutari@purdue.edu; facchinei@diag.uniroma1.it; lorenzo.lampariello@uniroma3.it; stefania.sardellitti@uniroma1.it; peiransong@bistu.edu.cn LAMPARIELLO, Lorenzo/0000-0003-4177-3598; Sardellitti, Stefania/0000-0002-4749-2933 USA National Science Foundation [CIF 1632599, CIF 1564044, 1555850]; Office of Naval Research [N00014-16-1-2244]; Direct For Computer & Info Scie & Enginr; Division of Computing and Communication Foundations [1632599, 1564044] Funding Source: National Science Foundation; Div Of Electrical, Commun & Cyber Sys; Directorate For Engineering [1555850] Funding Source: National Science Foundation USA National Science Foundation(National Science Foundation (NSF)); Office of Naval Research(Office of Naval Research); Direct For Computer & Info Scie & Enginr; Division of Computing and Communication Foundations(National Science Foundation (NSF)NSF - Directorate for Computer & Information Science & Engineering (CISE)); Div Of Electrical, Commun & Cyber Sys; Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)) The work of G. Scutari was supported in part by the USA National Science Foundation under Grant CIF 1632599, Grant CIF 1564044, and CAREER Award 1555850; and in part by the Office of Naval Research under the Grant N00014-16-1-2244. 46 41 42 1 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. APR 15 2017.0 65 8 1945 1960 10.1109/TSP.2016.2637314 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering EN2GE 2023-03-23 WOS:000395827100002 0
J Jerbi, H; Alharbi, H; Omri, M; Ladhar, L; Simos, TE; Mourtas, SD; Katsikis, VN Jerbi, Houssem; Alharbi, Hadeel; Omri, Mohamed; Ladhar, Lotfi; Simos, Theodore E.; Mourtas, Spyridon D.; Katsikis, Vasilios N. Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations MATHEMATICS English Article zeroing neural networks; hyperpower iterations; algebraic Riccati equations; dynamical system H-INFINITY-CONTROL; MATRIX; PERFORM; SYSTEMS; DESIGN; PAIRS One of the most often used approaches for approximating various matrix equation problems is the hyperpower family of iterative methods with arbitrary convergence order, whereas the zeroing neural network (ZNN) is a type of neural dynamics intended for handling time-varying problems. A family of ZNN models that correlate with the hyperpower iterative methods is defined on the basis of the analogy that was discovered. These models, known as higher-order ZNN models (HOZNN), can be used to find real symmetric solutions of time-varying algebraic Riccati equations. Furthermore, a noise-handling HOZNN (NHOZNN) class of dynamical systems is introduced. The traditional ZNN and HOZNN dynamic flows are compared theoretically and numerically. [Jerbi, Houssem] Univ Hail, Coll Engn, Dept Ind Engn, Hail 1234, Saudi Arabia; [Alharbi, Hadeel] Univ Hail, Coll Comp Sci & Engn, Dept Comp Engn, Hail 1234, Saudi Arabia; [Omri, Mohamed] King Abdulaziz Univ, Deanship Sci Res DSR, Jeddah 21589, Saudi Arabia; [Ladhar, Lotfi] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Simos, Theodore E.] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan; [Simos, Theodore E.] Ulyanovsk State Tech Univ, Lab Interdisciplinary Problems Energy Prod, 32 Severny Venetz St, Ulyanovsk 432027, Russia; [Simos, Theodore E.] Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China; [Simos, Theodore E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi 67100, Greece; [Mourtas, Spyridon D.; Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Dept Econ Math Informat & Stat Econometr, Sofokleous 1 St, Athens 10559, Greece; [Mourtas, Spyridon D.] Siberian Fed Univ, Lab Hybrid Methods Modelling & Optimizat Complex S, Prosp Svobodny 79, Krasnoyarsk 660041, Russia University Ha'il; University Ha'il; King Abdulaziz University; King Abdulaziz University; China Medical University Taiwan; China Medical University Hospital - Taiwan; Ulyanovsk State Technical University; Democritus University of Thrace; National & Kapodistrian University of Athens; Siberian Federal University Simos, TE (corresponding author), China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan.;Simos, TE (corresponding author), Ulyanovsk State Tech Univ, Lab Interdisciplinary Problems Energy Prod, 32 Severny Venetz St, Ulyanovsk 432027, Russia.;Simos, TE (corresponding author), Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China.;Simos, TE (corresponding author), Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi 67100, Greece. simost@susu.ru JERBI, Houssem/AEL-1686-2022; Alharbi, Hadeel/ADE-2018-2022; Mourtas, Spyridon D./AAB-3651-2022; Katsikis, Vasilios/AAD-7216-2019 JERBI, Houssem/0000-0003-1816-3767; Mourtas, Spyridon D./0000-0002-8299-9916; Katsikis, Vasilios/0000-0002-8208-9656 Institutional Fund Projects [IFPIP 509-135-1443]; Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia Institutional Fund Projects; Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia This research work was funded by Institutional Fund Projects under grant no. (IFPIP 509-135-1443). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia. 37 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics DEC 2022.0 10 23 4490 10.3390/math10234490 0.0 16 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 6Y7MM gold 2023-03-23 WOS:000897274800001 0
J Hu, XH; Cammann, H; Meyer, HA; Jung, K; Lu, HB; Leva, N; Magheli, A; Stephan, C; Busch, J Hu, Xin-Hai; Cammann, Henning; Meyer, Hellmuth-A; Jung, Klaus; Lu, Hong-Biao; Leva, Natalia; Magheli, Ahmed; Stephan, Carsten; Busch, Jonas Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score ASIAN JOURNAL OF ANDROLOGY English Article artificial neural network; prostate cancer; recurrence ARTIFICIAL NEURAL-NETWORK; SURGICAL MARGINS STATUS; PATHOLOGICAL STAGE; CANCER DETECTION; DIAGNOSIS; FAILURE; IDENTIFICATION; PROGRESSION; CATEGORIES; PROGNOSIS Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always < 0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS >= 7. [Hu, Xin-Hai; Meyer, Hellmuth-A; Jung, Klaus; Lu, Hong-Biao; Magheli, Ahmed; Stephan, Carsten; Busch, Jonas] Charite, Dept Urol, D-13353 Berlin, Germany; [Cammann, Henning] Charite, Inst Med Informat, D-13353 Berlin, Germany; [Jung, Klaus; Stephan, Carsten] Berlin Inst Urol Res, Berlin, Germany; [Lu, Hong-Biao] Changzhou 2 Peoples Hosp, Dept Urol, Changzhou, Peoples R China; [Leva, Natalia] Stanford Univ, Sch Med, Stanford, CA 94305 USA Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Stanford University Busch, J (corresponding author), Charite, Dept Urol, D-13353 Berlin, Germany. jonas.busch@charite.de Jung, Klaus/Z-2122-2018 Jung, Klaus/0000-0001-9797-5362; Stephan, Carsten/0000-0002-3270-258X Berliner Sparkassenstiftung Medizin [89838600] Berliner Sparkassenstiftung Medizin This study was supported by a grant of the Berliner Sparkassenstiftung Medizin (Grant No. 89838600). 32 9 9 0 4 WOLTERS KLUWER MEDKNOW PUBLICATIONS MUMBAI WOLTERS KLUWER INDIA PVT LTD , A-202, 2ND FLR, QUBE, C T S NO 1498A-2 VILLAGE MAROL, ANDHERI EAST, MUMBAI, 400059, INDIA 1008-682X 1745-7262 ASIAN J ANDROL Asian J. Androl. NOV-DEC 2014.0 16 6 897 901 10.4103/1008-682X.129940 0.0 5 Andrology; Urology & Nephrology Science Citation Index Expanded (SCI-EXPANDED) Endocrinology & Metabolism; Urology & Nephrology AS8WQ 25130472.0 gold, Green Published, Green Submitted 2023-03-23 WOS:000344526300022 0
J Craig, P; Ng, R; Tefsen, B; Linsen, S; Liu, Y; Hendel, J Craig, Paul; Ng, Ruben; Tefsen, Boris; Linsen, Sam; Liu, Yu; Hendel, Joshua Information Visualisation for Antibiotic Detection Biochip Design and Testing PROCESSES English Article information visualisation; machine learning; bioinformatics UNCERTAINTY VISUALIZATION; CANCER Biochips are engineered substrates that have different spots that change colour according to biochemical reactions. These spots can be read together to detect different analytes (such as different types of antibiotic, pathogens, or biological agents). While some chips are designed so that each spot on its own can detect a particular analyte, chip designs that use a combination of spots to detect different analytes can be more efficient and detect a larger number of analytes with a smaller number of spots. These types of chip can, however, be more difficult to design, as an efficient and effective combination of biosensors needs to be selected for the chip. These need to be able to differentiate between a range of different analytes so the values can be combined in a way that demonstrates the confidence that a particular analyte is present or not. The study described in this paper examines the potential for information visualisation to support the process of designing and reading biochips by developing and evaluating applications that allow biologists to analyse the results of experiments aimed at detecting candidate bio-sensors (to be used as biochip spots) and examining how biosensors can combine to identify different analytes. Our results demonstrate the potential of information visualisation and machine learning techniques to improve the design of biochips. [Craig, Paul; Ng, Ruben; Liu, Yu; Hendel, Joshua] Xian Jiaotong Liverpool Univ, Dept Comp, Suzhou 215123, Peoples R China; [Tefsen, Boris] Univ Utrecht, Dept Mol Microbiol, Padualaan 8, NL-3584 CH Utrecht, Netherlands; [Tefsen, Boris] Ronin Inst, Montclair, NJ 07043 USA; [Tefsen, Boris] Xian Jiaotong Liverpool Univ, Dept Biol Sci, Suzhou 215123, Peoples R China; [Linsen, Sam] SquaredAnt Network Technol Co Ltd, Hangzhou 311199, Peoples R China Xi'an Jiaotong-Liverpool University; Utrecht University; Xi'an Jiaotong-Liverpool University Craig, P (corresponding author), Xian Jiaotong Liverpool Univ, Dept Comp, Suzhou 215123, Peoples R China. p.craig@xjtlu.edu.cn Tefsen, Boris/J-1503-2012 Tefsen, Boris/0000-0001-6668-217X Xi'an Jiaotong-Liverpool University Key Program Special Fund [KSF-E-10] Xi'an Jiaotong-Liverpool University Key Program Special Fund The work presented in this paper was supported by Xi'an Jiaotong-Liverpool University Key Program Special Fund project KSF-E-10. 47 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-9717 PROCESSES Processes DEC 2022.0 10 12 2680 10.3390/pr10122680 0.0 18 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 7J4AV gold 2023-03-23 WOS:000904524300001 0
J Zhang, WP; Kumar, M; Ding, WP; Li, XJ; Yu, JF Zhang, Weiping; Kumar, Mohit; Ding, Weiping; Li, Xiujuan; Yu, Junfeng Variational learning of deep fuzzy theoretic nonparametric model NEUROCOMPUTING English Article Fuzzy membership function; Nonparametric modeling; Variational optimization; Gaussian process REGRESSION; GENERATION; INFERENCE; SIGNALS; SYSTEMS Nonparametric approaches are a good choice for function approximation as they are flexible, robust to overfitting, and provide well calibrated predictive uncertainty. The nonparametric form of fuzzy modeling can be promising, however, not analytically studied to compete with the Gaussian processes-based Bayesian framework in statistics and machine learning. In this work, a fuzzy modeling framework is built for data analysis of both supervised and unsupervised learning task, as a deterministic fuzzy analogous against the probabilistic Gaussian process, which defines a fuzzy membership over possible membership functions and is updated in light of data via the rules of variational inference. A fast variational inference framework has been suggested to study the propagation of the uncertainty through the layers of the deep model which can jointly infers the inducing inputs and the hyperparameters. The maximization problem is analytically solved using variational optimization with derived lower bound. We provide the sufficient number of experiments to support our argument that the proposed approach works well in practice for the problem which has the requirements to update the model dynamically. In addition, the application potential of the proposed methodology in data representation learning is also investigated, the ``auxiliary inducing points are used to express general features of tasks with smaller datasets. Nevertheless, this study offers new contents to the theory of nonparametric model and presents the potential for the design of practical fuzzy machine learning method. (c) 2022 Elsevier B.V. All rights reserved. [Zhang, Weiping] Northwestern Polytech Univ, Sch Software, Taicang 215400, Peoples R China; [Kumar, Mohit] Univ Rostock, Fac Comp Sci & Elect Engn, D-18051 Rostock, Germany; [Ding, Weiping] Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China; [Li, Xiujuan; Yu, Junfeng] Zhejiang Univ, Binhai Ind Technol Res Inst, Tianjin 300301, Peoples R China Northwestern Polytechnical University; University of Rostock; Nantong University; Zhejiang University Ding, WP (corresponding author), Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China. ding.wp@ntu.edu.cn National Natural Science Foundation of China [NSFC: 61300167, 61662045, 61976120]; Natural Science Foundation of Jiangsu Province [BK20191445]; Natural Science Key Foundation of Jiangsu Education Department [21KJA510004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Natural Science Key Foundation of Jiangsu Education Department This work was supported partially by National Natural Science Foundation of China (NSFC: 61300167, 61662045, 61976120), partially by the Natural Science Foundation of Jiangsu Province (BK20191445), and partially by the Natural Science Key Foundation of Jiangsu Education Department (21KJA510004). 48 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 28 2022.0 506 128 145 10.1016/j.neucom.2022.07.029 0.0 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7X4KX 2023-03-23 WOS:000914170300011 0
J Tao, YL; Wang, Y; Wang, XY; Tian, GH; Zhang, SM Tao, Yilei; Wang, Ying; Wang, Xinyu; Tian, Guohang; Zhang, Shumei Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China LAND English Article human perception; human activity density; visual characteristics; urban planning; streetscape URBAN DESIGN QUALITIES; WALKING Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel's semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities. [Tao, Yilei; Tian, Guohang; Zhang, Shumei] Henan Agr Univ, Coll Landscape Architecture & Art, Zhengzhou 450002, Peoples R China; [Wang, Ying] Zhengzhou Univ Ind Technol, Coll Architecture & Engn, Zhengzhou 450002, Peoples R China; [Wang, Xinyu] Hungarian Univ Agr & Life Sci, Doctoral Sch Landscape Architecture & Landscape E, Technol, H-1114 Budapest, Hungary; [Tian, Guohang; Zhang, Shumei] Henan Prov Dept Sci & Technol, Henan Prov Joint Int Res Lab Landscape Architectu, Zhengzhou 450002, Peoples R China Henan Agricultural University; Hungarian University of Agriculture & Life Sciences Zhang, SM (corresponding author), Henan Agr Univ, Coll Landscape Architecture & Art, Zhengzhou 450002, Peoples R China.;Zhang, SM (corresponding author), Henan Prov Dept Sci & Technol, Henan Prov Joint Int Res Lab Landscape Architectu, Zhengzhou 450002, Peoples R China. yleitao@stu.henau.edu.cn; qixian951212@163.com; wang.xinyu@phd.uni-mate.hu; tgh@henau.edu.cn; meishu_zhang83@henau.edu.cn Tao, Yilei/0000-0003-1582-0207; zhang, shumei/0000-0002-5052-6997 cultural ecosystem services in the campuses of Henan Agricultural University [30501193]; Humanities and Social Sciences Research Projects Foundation of Henan Educational Committee [2019-ZZJH-422]; Programme of Introducing Talents of Discipline to Universities [CXJD2021004] cultural ecosystem services in the campuses of Henan Agricultural University; Humanities and Social Sciences Research Projects Foundation of Henan Educational Committee; Programme of Introducing Talents of Discipline to Universities(Ministry of Education, China - 111 Project) This study was funded by the cultural ecosystem services in the campuses of Henan Agricultural University (30501193), the Humanities and Social Sciences Research Projects Foundation of Henan Educational Committee (2019-ZZJH-422), and the Programme of Introducing Talents of Discipline to Universities (CXJD2021004). 48 3 3 21 38 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-445X LAND-BASEL Land MAR 2022.0 11 3 400 10.3390/land11030400 0.0 19 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology 0C1KU gold 2023-03-23 WOS:000775081000001 0
J Xie, HL; Wang, XP; van der Hooft, JJJ; Medema, MH; Chen, ZY; Yue, XF; Zhang, Q; Li, PW Xie, Huali; Wang, Xiupin; van der Hooft, Justin J. J.; Medema, Marnix H.; Chen, Zhi-Yuan; Yue, Xiaofeng; Zhang, Qi; Li, Peiwu Fungi population metabolomics and molecular network study reveal novel biomarkers for early detection of aflatoxigenic Aspergillus species JOURNAL OF HAZARDOUS MATERIALS English Article Aflatoxins; Biomarkers; Early detection; Metabolomics; Machine learning MASS-SPECTROMETRY; BIOSYNTHESIS; FLAVUS; IDENTIFICATION; CONTAMINATION; ACID Mycotoxins threaten global food safety, public health and cause huge socioeconomic losses. Early detection is an effective preventive strategy, yet efficient biomarkers for early detection of aflatoxigenic Aspergillus species are lacking. Here, we proposed to use untargeted metabolomics and machine learning to mine biomarkers of aflatoxigenic Aspergillus species. We systematically delineated metabolic differences across 568 extensive field sampling A. flavus and performed biomarker analysis. Versicolorin B, 11-hydroxy-O-methylsterigmatocystin et.al metabolites shown a high correlation (from 0.71 to 0.95) with strains aflatoxin-producing capacity. Molecular networking analysis deciphered the connection of aflatoxins and biomarkers as well as potential emerging mycotoxins. We then developed a model using the biomarkers as variables to discern aflatoxigenic Aspergillus species with 97.8% accuracy. A validation dataset and metabolome from other 16 fungal isolates confirmed the robustness and specificity of these biomarkers. We further demonstrated the solution feasibility in agricultural products by early detection of biomarkers, which predicted aflatoxin contamination risk 35-47 days in advance. A developed operable decision rule by the XGBoost algorithm help regulators to intuitively assess the risk prioritization with 87.2% accuracy. Our research provides novel insights into global food safety risk assessment which will be crucial for early prevention and control of mycotoxins. [Xie, Huali; Wang, Xiupin; Yue, Xiaofeng; Zhang, Qi; Li, Peiwu] Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430061, Peoples R China; [Xie, Huali; Zhang, Qi; Li, Peiwu] Minist Agr, Key Lab Detect Aflatoxins, Wuhan, Peoples R China; [Xie, Huali; Wang, Xiupin; Yue, Xiaofeng; Zhang, Qi; Li, Peiwu] Minist Agr, Lab Risk Assessment Oilseeds Prod Wuhan, Wuhan 430061, Peoples R China; [Wang, Xiupin; Zhang, Qi; Li, Peiwu] Minist Agr, Qual Inspect & Test Ctr Oilseeds Prod, Wuhan 430061, Peoples R China; [Zhang, Qi; Li, Peiwu] Hubei Hongshan Lab, Wuhan, Peoples R China; [Xie, Huali; van der Hooft, Justin J. J.; Medema, Marnix H.] Wageningen Univ, Bioinfomat Grp, NL-6708 PB Wageningen, Netherlands; [Chen, Zhi-Yuan] Louisiana State Univ Agr Ctr, Dept Plant Pathol & Crop Physiol, Baton Rouge, LA 70803 USA Chinese Academy of Agricultural Sciences; Oil Crops Research Institute, CAAS; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Wageningen University & Research; Louisiana State University System; Louisiana State University Zhang, Q; Li, PW (corresponding author), Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430061, Peoples R China. zhangqi01@caas.cn; lipeiwu@caas.cn Medema, Marnix H./AAR-3138-2021; van der Hooft, Justin J.J./N-1694-2013 Medema, Marnix H./0000-0002-2191-2821; van der Hooft, Justin J.J./0000-0002-9340-5511 National Key Research and Development Program of China, China [2017YFC1601200]; National Natural Sciences Foundation of China, China [32030085]; Agricultural Science and Technology Innovation Program of CAAS, China [CAAS-ZDRW202011]; project of Major Project of Hubei Provincial Technical Innovation, China [2018ABA081] National Key Research and Development Program of China, China; National Natural Sciences Foundation of China, China(National Natural Science Foundation of China (NSFC)); Agricultural Science and Technology Innovation Program of CAAS, China; project of Major Project of Hubei Provincial Technical Innovation, China We thank Qiuyu Yu and Tao Li for assistance with storage of the strains used in this work, as well as for helpful discussions in data analysis. This work was supported by the project of National Key Research and Development Program of China (2017YFC1601200), China, the project of National Natural Sciences Foundation of China (32030085), China, the project of Agricultural Science and Technology Innovation Program of CAAS (CAAS-ZDRW202011), China, and the project of Major Project of Hubei Provincial Technical Innovation (2018ABA081), China. 41 6 6 29 122 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3894 1873-3336 J HAZARD MATER J. Hazard. Mater. FEB 15 2022.0 424 A 127173 10.1016/j.jhazmat.2021.127173 0.0 SEP 2021 13 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology WB0EN 34597924.0 Green Published 2023-03-23 WOS:000703254600004 0
J Yin, RC; Wu, HP; Li, M; Cheng, Y; Song, YT; Handroos, H Yin, Ruochen; Wu, Huapeng; Li, Ming; Cheng, Yong; Song, Yuntao; Handroos, Heikki RGB-D-Based Robotic Grasping in Fusion Application Environments APPLIED SCIENCES-BASEL English Article clustering; deep neural networks; fusion application environment; point cloud; planar extraction; RGB-D message; robotic grasping Although deep neural network (DNN)-based robot grasping has come a long way, the uncertainty of predicted results has prevented DNN-based approaches from meeting the stringent requirements of some industrial scenarios. To prevent these uncertainties from affecting the behavior of the robot, we break down the whole process into instance segmentation, clustering and planar extraction, which means we add some traditional approaches between the output of the instance segmentation network and the final control decision. We have experimented with challenging environments, and the results show that our approach can cope well with the challenging environment and achieve more stable and superior results than end-to-end grasping networks. [Yin, Ruochen; Cheng, Yong; Song, Yuntao] Chinese Acad Sci, Inst Plasma Phys Chinese Acad Sci ASIPP, Hefei 230031, Peoples R China; [Yin, Ruochen] Univ Sci & Technol China, Hefei 230026, Peoples R China; [Yin, Ruochen; Wu, Huapeng; Li, Ming; Handroos, Heikki] LUT Univ, Sch Energy Syst, Lab Intelligent Machines, Lappeenranta 53850, Finland; [Cheng, Yong] Hefei Comprehens Natl Sci Ctr, Inst Energy, Hefei 230031, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Science & Technology of China, CAS Yin, RC; Song, YT (corresponding author), Chinese Acad Sci, Inst Plasma Phys Chinese Acad Sci ASIPP, Hefei 230031, Peoples R China.;Yin, RC (corresponding author), Univ Sci & Technol China, Hefei 230026, Peoples R China.;Yin, RC (corresponding author), LUT Univ, Sch Energy Syst, Lab Intelligent Machines, Lappeenranta 53850, Finland. ruochen.yin@lut.fi; huapeng.wu@lut.fi; ming.li@lut.fi; chengyong@ipp.ac.cn; songyt@ipp.ac.cn; heikki.handroos@lut.fi Yin, Ruochen/0000-0002-0751-3138 European Union via the Euratom Research and Training Programme [101052200-EUROfusion]; Comprehensive Research Facility for Fusion Technology Program of China [2018-000052-73-01-001228]; University Synergy Innovation Program of Anhui Province [GXXT-2020-010] European Union via the Euratom Research and Training Programme; Comprehensive Research Facility for Fusion Technology Program of China; University Synergy Innovation Program of Anhui Province This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No. 101052200-EUROfusion). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. This work also supported by the Comprehensive Research Facility for Fusion Technology Program of China Under Contract No. 2018-000052-73-01-001228 and the University Synergy Innovation Program of Anhui Province with Grant No. GXXT-2020-010. 20 1 1 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel AUG 2022.0 12 15 7573 10.3390/app12157573 0.0 11 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 3R6IX gold 2023-03-23 WOS:000839015000001 0
J Hu, RZ; Chen, B; Xu, JZ; van Kaick, O; Deussen, O; Huang, H Hu, Ruizhen; Chen, Bin; Xu, Juzhan; van Kaick, Oliver; Deussen, Oliver; Huang, Hui Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS English Article Shape; Shape measurement; Task analysis; Coordinate measuring machines; Reinforcement learning; Recurrent neural networks; Optimization; Star glyph set; coordinate ordering; reinforcement learning; shape context METRICS We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set. To find the optimal coordinate order for the given set, we train a neural network using reinforcement learning to reward orderings with high silhouette coefficients. The network consists of an encoder and a decoder with an attention mechanism. The encoder employs a recurrent neural network (RNN) to encode input shape and class information, while the decoder together with the attention mechanism employs another RNN to output a sequence with the new coordinate order. In addition, we introduce a neural network to efficiently estimate the similarity between shape context descriptors, which allows to speed up the computation of silhouette coefficients and thus the training of the axis ordering network. Two user studies demonstrate that the orders provided by our method are preferred by users for perceiving class separation. We tested our model on different settings to show its robustness and generalization abilities and demonstrate that it allows to order input sets with unseen data size, data dimension, or number of classes. We also demonstrate that our model can be adapted to coordinate ordering of other types of plots such as RadViz by replacing the proposed shape-aware silhouette coefficient with the corresponding quality metric to guide network training. [Hu, Ruizhen; Chen, Bin; Xu, Juzhan; Huang, Hui] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [van Kaick, Oliver] Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada; [Deussen, Oliver] Univ Konstanz, Dept Comp & Informat Sci, D-78464 Constance, Germany; [Deussen, Oliver] SIAT Shenzhen, Shenzhen 518055, Peoples R China Shenzhen University; Carleton University; University of Konstanz Huang, H (corresponding author), Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China. ruizhen.hu@gmail.com; codeb.box@gmail.com; juzhan.xu@gmail.com; Oliver.vanKaick@carleton.ca; oliver.deussen@uni-konstanz.de; hhzhiyan@gmail.com Deussen, Oliver/HKF-2004-2023 Huang, Hui/0000-0003-3212-0544; van Kaick, Oliver/0000-0001-9869-6832 NSFC [61872250, U2001206, 61902254]; GD Talent Program [2019JC05X328]; GD Science and Technology Program [2020A0505100064, 2015A030312015]; DEGP Key Project [2018KZDXM058]; Shenzhen Innovation Program [JCYJ20180305125709986]; NSERC [2015-05407]; DFG [422037984]; National Engineering Laboratory for Big Data System Computing Technology; Guangdong Laboratory ofArtificial Intelligence and Digital Economy (SZ) NSFC(National Natural Science Foundation of China (NSFC)); GD Talent Program; GD Science and Technology Program; DEGP Key Project; Shenzhen Innovation Program; NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); DFG(German Research Foundation (DFG)); National Engineering Laboratory for Big Data System Computing Technology; Guangdong Laboratory ofArtificial Intelligence and Digital Economy (SZ) The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported in part by NSFC under Grants 61872250, U2001206, 61902254, GD Talent Program under Grant 2019JC05X328, GD Science and Technology Program under Grants 2020A0505100064, 2015A030312015, DEGP Key Project under Grant 2018KZDXM058, Shenzhen Innovation Program (JCYJ20180305125709986), NSERC (2015-05407), DFG (422037984), National Engineering Laboratory for Big Data System Computing Technology, and Guangdong Laboratory ofArtificial Intelligence and Digital Economy (SZ). 26 2 2 0 10 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1077-2626 1941-0506 IEEE T VIS COMPUT GR IEEE Trans. Vis. Comput. Graph. JUN 1 2021.0 27 6 3034 3047 10.1109/TVCG.2021.3052167 0.0 14 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science SC2CP 33460381.0 Green Submitted 2023-03-23 WOS:000650486500002 0
J Long, ZG; Sioutis, M; Li, QQ; Meng, H; Li, HC Long, Zhiguo; Sioutis, Michael; Li, Qingqian; Meng, Hua; Li, Heng-Chao Approximating Region Boundaries Based on Qualitative and Quantitative Information IEEE INTELLIGENT SYSTEMS English Article Intelligent systems; Social networking (online); Shape; Roads; Natural languages; Probabilistic logic; Partitioning algorithms Approximating regions is a topic that can have important applications in artificial intelligence whenever uncertain, incomplete, or inconsistent/contradictory spatial information is involved. This article devises a new method to generate region approximations based on rough qualitative direction and distance information. The main idea is first to give a suitability score to smaller regions, cells, that are obtained by partitioning the area of interest, and then to identify candidates to form an approximation by evaluating score contribution ratios under a certain threshold. This article designs a novel mechanism that compares the actual information of cells with the provided rough information in order to calculate suitability scores, and proposes to exploit a regressor model that can predict a threshold given certain suitability scores. Experimental results show that, given a good threshold, this new method can approximate target regions effectively, and that good thresholds can be reliably obtained through a trained regressor. [Long, Zhiguo; Li, Qingqian; Meng, Hua; Li, Heng-Chao] Southwest Jiaotong Univ, Chengdu 611756, Peoples R China; [Sioutis, Michael] Univ Bamberg, D-96047 Bamberg, Germany; [Long, Zhiguo; Li, Qingqian] Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence, Chengdu, Peoples R China; [Meng, Hua] Southwest Jiaotong Univ, Sch Math, Chengdu, Peoples R China; [Li, Heng-Chao] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Peoples R China Southwest Jiaotong University; Otto Friedrich University Bamberg; Southwest Jiaotong University; Southwest Jiaotong University; Southwest Jiaotong University Meng, H (corresponding author), Southwest Jiaotong Univ, Chengdu 611756, Peoples R China. zhiguolong@swjtu.edu.cn; michail.sioutis@uni-bamberg.de; lqq123@my.swjtu.edu.cn; menghua@swjtu.edu.cn; hcli@swjtu.edu.cn National Natural Science Foundation of China [61806170]; Humanities and Social Sciences Fund of Ministry of Education [18XJC72040001]; National Key Research and Development Program of China [2019YFB1706104]; Fundamental Research Funds for the Central Universities [2682018CX25] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Sciences Fund of Ministry of Education; National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61806170,in part by the Humanities and Social Sciences Fund of Ministry of Education under Grant 18XJC72040001, in part by the National Key Research and Development Program of China under Grant 2019YFB1706104, and in part by the Fundamental Research Funds for the Central Universities under Grant 2682018CX25. 11 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1541-1672 1941-1294 IEEE INTELL SYST IEEE Intell. Syst. NOV 1 2022.0 37 6 38 47 10.1109/MIS.2022.3204561 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 9P2XL 2023-03-23 WOS:000944152000006 0
J Yuan, YX; Lei, L; Vu, TX; Chang, Z; Chatzinotas, S; Sun, SM Yuan, Yaxiong; Lei, Lei; Vu, Thang X.; Chang, Zheng; Chatzinotas, Symeon; Sun, Sumei Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS English Article Heuristic algorithms; Task analysis; Satellites; Low earth orbit satellites; Fading channels; Resource management; Dynamic scheduling; LEO satellites; resource scheduling; reinforcement learning; meta-critic learning; dynamic environment NETWORKS; 5G; MANAGEMENT Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. [Yuan, Yaxiong; Vu, Thang X.; Chatzinotas, Symeon] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust, L-1855 Kirchberg, Luxembourg; [Lei, Lei] Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China; [Chang, Zheng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China; [Chang, Zheng] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland; [Sun, Sumei] Agcy Sci Technol & Res, Inst Infocomm Res, Singapore 138632, Singapore University of Luxembourg; Xi'an Jiaotong University; University of Electronic Science & Technology of China; University of Jyvaskyla; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R) Lei, L (corresponding author), Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China. yaxiong.yuan@uni.lu; thang.vu@uni.lu; zheng.chang@jyu.fi; sunsm@i2r.a-star.edu.sg Chang, Zheng/G-2873-2018; Sun, Sumei/F-5376-2018; Chatzinotas, Symeon/D-4191-2015 Chang, Zheng/0000-0003-3766-820X; Sun, Sumei/0000-0002-1701-8122; Chatzinotas, Symeon/0000-0001-5122-0001 European Research Council (ERC) Project Actively Enhanced Cognition based Framework for Design of Complex Systems (AGNOSTIC) [742648]; Luxembourg National Research Fund (FNR) [C21/IS/16193290, C17/IS/11632107, C19/IS/13696663]; FNR Bilateral Project Learning-Assisted Optimization for Resource and Security Management in Slicing-Based 5G Networks (LARGOS) [12173206] European Research Council (ERC) Project Actively Enhanced Cognition based Framework for Design of Complex Systems (AGNOSTIC)(European Research Council (ERC)); Luxembourg National Research Fund (FNR)(Luxembourg National Research Fund); FNR Bilateral Project Learning-Assisted Optimization for Resource and Security Management in Slicing-Based 5G Networks (LARGOS) This work was supported in part by the European Research Council (ERC) Project Actively Enhanced Cognition based Framework for Design of Complex Systems (AGNOSTIC) under Grant 742648; in part by the Luxembourg National Research Fund (FNR) CORE projects: Leveraging AI to Empower the Next Generation of Satellite Communications (SmartSpace) under Grant C21/IS/16193290, Resource Optimization for integrated Satellite-5G nETworks with non-orThogonal multiple Access (ROSETTA) under Grant C17/IS/11632107, Resource Optimization for Next Generation of Flexible SATellite Payloads (FlexSAT) under Grant C19/IS/13696663; and in part by FNR Bilateral Project Learning-Assisted Optimization for Resource and Security Management in Slicing-Based 5G Networks (LARGOS) under Grant 12173206. 45 1 1 10 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1276 1558-2248 IEEE T WIREL COMMUN IEEE Trans. Wirel. Commun. NOV 2022.0 21 11 9582 9595 10.1109/TWC.2022.3178171 0.0 14 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 6C4RP Green Published, Green Submitted, hybrid 2023-03-23 WOS:000882003900051 0
J Li, QP; Mou, LC; Liu, QJ; Wang, YH; Zhu, XX Li, Qingpeng; Mou, Lichao; Liu, Qingjie; Wang, Yunhong; Zhu, Xiao Xiang HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Convolutional neural network (CNN); multiscale analysis; object detection; optical image; remote sensing SATELLITE IMAGES; AUTOMATIC DETECTION; OBJECT DETECTION; URBAN SPRAWL; SAR IMAGES; SHAPE; ALGORITHM; SALIENCY Ship detection is an important and challenging task in remote sensing applications. Most methods utilize specially designed hand-crafted features to detect ships, and they usually work well only on one scale, which lack generalization and impractical to identify ships with various scales from multiresolution images. In this paper, we propose a novel deep feature-based method to detect ships in very high-resolution optical remote sensing images. In our method, a regional proposal network is used to generate ship candidates from feature maps produced by a deep convolutional neural network. To efficiently detect ships with various scales, a hierarchical selective filtering layer is proposed to map features in different scales to the same scale space. The proposed method is an end-to-end network that can detect both inshore and offshore ships ranging from dozens of pixels to thousands. We test our network on a large ship data set which will be released in the future, consisting of Google Earth images, GaoFen-2 images, and unmanned aerial vehicle data. Experiments demonstrate high precision and robustness of our method. Further experiments on aerial images show its good generalization to unseen scenes. [Li, Qingpeng; Liu, Qingjie; Wang, Yunhong] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China; [Mou, Lichao; Zhu, Xiao Xiang] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany; [Mou, Lichao; Zhu, Xiao Xiang] Tech Univ Munich, Signal Proc Earth Observat, D-80333 Munich, Germany Beihang University; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich Liu, QJ (corresponding author), Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China. liqingpeng@buaa.edu.cn; lichao.mou@dlr.de; qingjie.liu@buaa.edu.cn; yhwang@buaa.edu.cn; xiao.zhu@dlr.de Zhu, Xiao Xiang/ABE-7138-2020 Zhu, Xiao Xiang/0000-0001-5530-3613; Li, Qingpeng/0000-0002-7401-0928 National Natural Science Foundation of China [61601011]; China Scholarship Council; European Research Council through the European Union [ERC-2016-StG-714087]; Helmholtz Association [VH-NG-1018]; Bavarian Academy of Sciences and Humanities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); European Research Council through the European Union; Helmholtz Association(Helmholtz Association); Bavarian Academy of Sciences and Humanities This work was supported in part by the National Natural Science Foundation of China under Grant 61601011, in part by the China Scholarship Council, in part by the European Research Council through the European Union's Horizon 2020 Research and Innovation Programme (So2Sat) under Grant ERC-2016-StG-714087, in part by the Helmholtz Association under the framework of the Young Investigators Group, SiPEO, under Grant VH-NG-1018, and in part by the Bavarian Academy of Sciences and Humanities in the framework of Junges Kolleg. 58 112 121 11 93 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing DEC 2018.0 56 12 7147 7161 10.1109/TGRS.2018.2848901 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology HC2GT 2023-03-23 WOS:000451621000023 0
J Wang, Y; Wang, JJ; Hermann, A; Liu, C; Gao, H; Tosatti, E; Wang, HT; Xing, DY; Sun, J Wang, Yong; Wang, Junjie; Hermann, Andreas; Liu, Cong; Gao, Hao; Tosatti, Erio; Wang, Hui-Tian; Xing, Dingyu; Sun, Jian Electronically Driven 1D Cooperative Diffusion in a Simple Cubic Crystal PHYSICAL REVIEW X English Article MOLECULAR-DYNAMICS; HIGH-PRESSURE; PHASE; CALCIUM; WATER; STABILIZATION; TRANSITIONS; STATES Atomic diffusion is a spontaneous process and significantly influences properties of materials, such as fracture toughness, creep-fatigue properties, thermal conductivity, thermoelectric properties, etc. Here, using extensive molecular dynamics simulations based on both ab initio and machine-learning potentials, we demonstrate that an atomic one dimensional cooperative diffusion exists in the simple cubic high-pressure finite-temperature phase of calcium in the premelting regime, where some atoms diffuse cooperatively as chains or even rings, while others remain in the solid state. This intermediate regime is triggered by anharmonicity of the system at high temperature and is stabilized by the competition between the internal energy minimization and the entropy maximization, and has close connections with the unique electronic structures of simple cubic Ca as an electride with a pseudogap. This cooperative diffusion regime explains the abnormal enhancement of the melting line of Ca under high pressure and suggests that the cooperative chain melting is a much more common high-temperature feature among metals under extreme conditions than hitherto thought. The microscopic electronic investigations of these systems combining ab initio and machine-learning data point out the direction for further understanding of other metallic systems such as the glass transition, liquid metals, etc. [Wang, Yong; Wang, Junjie; Liu, Cong; Gao, Hao; Wang, Hui-Tian; Xing, Dingyu; Sun, Jian] Nanjing Univ, Sch Phys, Natl Lab Solid State Microstruct, Nanjing 210093, Peoples R China; [Wang, Yong; Wang, Junjie; Liu, Cong; Gao, Hao; Wang, Hui-Tian; Xing, Dingyu; Sun, Jian] Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China; [Hermann, Andreas] Univ Edinburgh, Ctr Sci Extreme Condit, Sch Phys & Astron, Peter Guthrie Tait Rd, Edinburgh EH9 3FD, Midlothian, Scotland; [Hermann, Andreas] Univ Edinburgh, SUPA, Sch Phys & Astron, Peter Guthrie Tait Rd, Edinburgh EH9 3FD, Midlothian, Scotland; [Tosatti, Erio] Int Sch Adv Studies SISSA, Via Bonomea 265, I-34136 Trieste, Italy; [Tosatti, Erio] CNR IOM Democritos, Via Bonomea 265, I-34136 Trieste, Italy; [Tosatti, Erio] Abdus Salam Int Ctr Theoret Phys ICTP, Str Costiera 11, I-34151 Trieste, Italy Nanjing University; Nanjing University; University of Edinburgh; University of Edinburgh; International School for Advanced Studies (SISSA); Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); Abdus Salam International Centre for Theoretical Physics (ICTP) Sun, J (corresponding author), Nanjing Univ, Sch Phys, Natl Lab Solid State Microstruct, Nanjing 210093, Peoples R China.;Sun, J (corresponding author), Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China. jiansun@nju.edu.cn Hermann, Andreas/E-8687-2010; Sun, Jian/A-9893-2010; Wang, Hui-Tian/B-8406-2009 Hermann, Andreas/0000-0002-8971-3933; Sun, Jian/0000-0001-6172-9100; Liu, Cong/0000-0002-5969-9202; Junjie, Wang/0000-0003-1411-3324; Wang, Yong/0000-0001-8676-1641; Tosatti, Erio/0000-0002-5219-7728; Gao, Hao/0000-0003-1597-7829 National Key R&D Program of China [2016YFA0300404]; National Natural Science Foundation of China [11974162, 11834006]; Fundamental Research Funds for the Central Universities; ERC [8344023]; Italian Ministry of University and Research through PRIN UTFROM [20178PZCB5]; UK Materials and Molecular Modelling Hub - EPSRC [EP/P020194]; UKCP consortium - EPSRC [EP/P022561/1]; EPSRC [EP/P022561/1] Funding Source: UKRI National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); ERC(European Research Council (ERC)European Commission); Italian Ministry of University and Research through PRIN UTFROM; UK Materials and Molecular Modelling Hub - EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); UKCP consortium - EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) J. S. gratefully acknowledges financial support from the National Key R&D Program of China (Grant No. 2016YFA0300404), the National Natural Science Foundation of China (Grants No. 11974162 and No. 11834006), and the Fundamental Research Funds for the Central Universities. The calculations were carried out using supercomputers at the High Performance Computing Center of Collaborative Innovation Center of Advanced Microstructures, the high-performance supercomputing center of Nanjing University, Tianhe-2 at NSCC-Guangzhou. E. T. acknowledges the support of ERC Advanced Grant No. 8344023 ULTRADISS and in part, by the Italian Ministry of University and Research through PRIN UTFROM 20178PZCB5. A. H. acknowledges support from the UK Materials and Molecular Modelling Hub, which is funded by EPSRC (EP/P020194), and the UKCP consortium funded by EPSRC Grant No. EP/P022561/1. 75 14 15 6 44 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2160-3308 PHYS REV X Phys. Rev. X JAN 11 2021.0 11 1 11006 10.1103/PhysRevX.11.011006 0.0 14 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics PQ7OT Green Published, gold 2023-03-23 WOS:000606733200001 0
J Nakoneczny, SJ; Bilicki, M; Pollo, A; Asgari, M; Dvornik, A; Erben, T; Giblin, B; Heymans, C; Hildebrandt, H; Kannawadi, A; Kuijken, K; Napolitano, NR; Valentijn, E Nakoneczny, S. J.; Bilicki, M.; Pollo, A.; Asgari, M.; Dvornik, A.; Erben, T.; Giblin, B.; Heymans, C.; Hildebrandt, H.; Kannawadi, A.; Kuijken, K.; Napolitano, N. R.; Valentijn, E. Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4 ASTRONOMY & ASTROPHYSICS English Article methods: data analysis; methods: observational; catalogs; surveys; quasars: general; large-scale structure of Universe ACTIVE GALACTIC NUCLEI; DIGITAL-SKY-SURVEY; TARGET SELECTION; HALO MASSES; KIDS-SQUAD; MID-IR; WISE; CLASSIFICATION; SDSS; CATALOG We present a catalog of quasars with their corresponding redshifts derived from the photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine learning (ML) models, using optical ugri and near-infrared ZYJHK(s) bands, on objects known from Sloan Digital Sky Survey (SDSS) spectroscopy. We define inference subsets from the 45 million objects of the KiDS photometric data limited to 9-band detections, based on a feature space built from magnitudes and their combinations. We show that projections of the high-dimensional feature space on two dimensions can be successfully used, instead of the standard color-color plots, to investigate the photometric estimations, compare them with spectroscopic data, and efficiently support the process of building a catalog. The model selection and fine-tuning employs two subsets of objects: those randomly selected and the faintest ones, which allowed us to properly fit the bias versus variance trade-off. We tested three ML models: random forest (RF), XGBoost (XGB), and artificial neural network (ANN). We find that XGB is the most robust and straightforward model for classification, while ANN performs the best for combined classification and redshift. The ANN inference results are tested using number counts, Gaia parallaxes, and other quasar catalogs that are external to the training set. Based on these tests, we derived the minimum classification probability for quasar candidates which provides the best purity versus completeness trade-off: p(QSO(cand)) > 0.9 for r<22 and p(QSO(cand)) > 0.98 for 2210 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. [Broderick, David T. J.; Waite, David W.; Taylor, Michael W.] Univ Auckland, Sch Biol Sci, Auckland, New Zealand; [Marsh, Robyn L.; Chang, Anne B.] Charles Darwin Univ, Menzies Sch Hlth Res, Child Hlth Div, Darwin, NT, Australia; [Camargo, Carlos A.; Hasegawa, Kohei] Massachusetts Gen Hosp, Dept Emergency Med, Boston, MA 02114 USA; [Camargo, Carlos A.; Hasegawa, Kohei] Harvard TH Chan Sch Publ Hlth, Dept Epidmiol, Boston, MA USA; [Camargo, Carlos A.; Hasegawa, Kohei; Mansbach, Jonathan M.] Harvard Med Sch, Boston, MA 02115 USA; [Cardenas, Paul] Univ San Francisco Quito, Inst Microbiol, Colegio Ciencias Biol & Ambientales, Quito, Ecuador; [Chang, Anne B.] Queensland Childrens Hosp, Dept Resp & Sleep Med, Brisbane, Qld, Australia; [Chang, Anne B.] Queensland Univ Technol, Australian Ctr Hlth Serv Innovat, Brisbane, Qld, Australia; [Cookson, William O. C.] Imperial Coll London, Natl Heart & Lung Inst, London, England; [Cookson, William O. C.; Cuthbertson, Leah] Royal Brompton & Harefield NHS Fdn Trust, London, England; [Dai, Wenkui] Peking Univ Shenzhen Hosp, Dept Obstet & Gynecol, Shenzhen, Peoples R China; [Everard, Mark L.] Univ Western Australia, Sch Med, Perth, WA, Australia; [Gervaix, Alain] Univ Hosp Geneva, Fac Med, Dept Pediat Gynecol & Obstet, Geneva, Switzerland; [Harris, J. Kirk; Zemanick, Edith T.] Univ Colorado, Sch Med, Dept Pediat, Aurora, CO USA; [Hoffman, Lucas R.] Seattle Childrens Hosp, Seattle, WA USA; [Hoffman, Lucas R.] Univ Washington, Dept Pediat & Microbiol, Seattle, WA 98195 USA; [Hong, Soo-Jong] Univ Ulsan, Coll Med, Dept Pediat,Asan Med Ctr, Childhood Asthma Atopy Ctr,Humidifier Disinfectan, Seoul, South Korea; [Josset, Laurence] Hosp Civils Lyon, Lyon, France; [Kelly, Matthew S.] Duke Univ, Div Pediat Infect Dis, Durham, NC USA; [Kim, Bong-Soo] Hallym Univ, Multidisciplinary Genome Inst, Dept Life Sci, Chunchon, South Korea; [Kong, Yong] Yale Univ, Dept Biostat, Yale Sch Publ Hlth, New Haven, CT USA; [Li, Shuai C.] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China; [Li, Shuai C.] City Univ Hong Kong, Dept Biomed Engn, Kowloon, Hong Kong, Peoples R China; [Mansbach, Jonathan M.] Boston Childrens Hosp, Dept Pediat, Boston, MA USA; [Mejias, Asuncion; Ramilo, Octavio] Ohio State Univ, Coll Med, Dept Pediat,Nationwide Childrens Hosp, Div Pediat Infect Dis,Ctr Vaccines & Immun,Abigai, Columbus, OH 43210 USA; [O'Toole, George A.] Geisel Sch Med Dartmouth, Dept Microbiol & Immunol, Hanover, NH USA; [Paalanen, Laura] Finnish Inst Hlth & Welf THL, Helsinki, Finland; [Perez-Losada, Marcos] George Washington Univ, Dept Biostat & Bioinformat, Computat Biol Inst, Milken Inst Sch Publ Hlth, Washington, DC USA; [Perez-Losada, Marcos] Univ Porto, Ctr Invest Biodiversidade & Recursos Genet, CIBIO InBIO, Campus Agr Vairao, Vairao, Portugal; [Pettigrew, Melinda M.] Yale Sch Publ Hlth, Dept Epidemiol Microbial Dis, New Haven, CT USA; [Pichon, Maxime] CHU Poitiers, Infect Agents Dept, Poitiers, France; [Pichon, Maxime] Univ Poitiers, INSERM U1070, Poitiers, France; [Ruokolainen, Lasse] Univ Helsinki, Dept Biosci, Helsinki, Finland; [Sakwinska, Olga] Nestle Res, Lausanne, Switzerland; [Seed, Patrick C.] Northwestern Univ, Dept Pediat, Feinberg Sch Med, Chicago, IL 60611 USA; [van der Gast, Christopher J.] Manchester Metropolitan Univ, Dept Life Sci, Manchester, Lancs, England; [Wagner, Brandie D.] Univ Colorado, Colorado Sch Publ Hlth, Dept Biostat & Informat, Aurora, CO USA; [Yi, Hana] Korea Univ, Sch Biosyst & Biomed Sci, Seoul, South Korea; [Zheng, Yuejie] Shenzhen Childrens Hosp, Shenzhen, Peoples R China; [Pillarisetti, Naveen] Starship Childrens Hosp, Auckland, New Zealand University of Auckland; Charles Darwin University; Menzies School of Health Research; Harvard University; Massachusetts General Hospital; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Queensland University of Technology (QUT); Imperial College London; Royal Brompton & Harefield NHS Foundation Trust; Peking University; University of Western Australia; University of Geneva; University of Colorado System; University of Colorado Anschutz Medical Campus; Seattle Children's Hospital; University of Washington; University of Washington Seattle; University of Ulsan; Asan Medical Center; CHU Lyon; Duke University; Hallym University; Yale University; City University of Hong Kong; City University of Hong Kong; Harvard University; Boston Children's Hospital; University System of Ohio; Ohio State University; Nationwide Childrens Hospital; Research Institute at Nationwide Children's Hospital; Dartmouth College; George Washington University; Universidade do Porto; Yale University; CHU Poitiers; Universite de Poitiers; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Poitiers; University of Helsinki; Nestle SA; Northwestern University; Feinberg School of Medicine; Manchester Metropolitan University; Colorado School of Public Health; University of Colorado System; University of Colorado Anschutz Medical Campus; Korea University; Starship Children's Hospital Taylor, MW (corresponding author), Univ Auckland, Sch Biol Sci, Auckland, New Zealand. mw.taylor@auckland.ac.nz Camargo, Carlos A./C-2145-2008; Pichon, Maxime/V-2505-2018; Cookson, William/HHC-1790-2022; Harris, Jonathan Kirk/HPF-2570-2023; Chang, Anne B/GPW-7525-2022; Perez-Losada, Marcos/I-4407-2013; Josset, Laurence/A-7960-2015; Kong, Yong/B-5105-2014 Camargo, Carlos A./0000-0002-5071-7654; Pichon, Maxime/0000-0002-3732-6894; Chang, Anne B/0000-0002-1331-3706; Perez-Losada, Marcos/0000-0002-2585-4657; Josset, Laurence/0000-0002-7158-1186; Ruokolainen, Lasse/0000-0003-0951-9100; Marsh, Robyn/0000-0002-8507-6586; Taylor, Michael/0000-0002-0463-7813; Kong, Yong/0000-0002-2881-5274; Cardenas, Paul/0000-0001-9626-4489; Everard, Mark/0000-0003-1571-261X 58 1 1 3 9 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-302X FRONT MICROBIOL Front. Microbiol. DEC 23 2021.0 12 711134 10.3389/fmicb.2021.711134 0.0 14 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology YJ7SX 35002989.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000744731000001 0
C Rouces, J; de Melo, G; Hose, K Sack, H; Blomqvist, E; DAquin, M; Ghidini, C; Ponzetto, SP; Lange, C Rouces, Jacobo; de Melo, Gerard; Hose, Katja Heuristics for Connecting Heterogeneous Knowledge via FrameBase SEMANTIC WEB: LATEST ADVANCES AND NEW DOMAINS Lecture Notes in Computer Science English Proceedings Paper 13th European Semantic Web Conference (ESWC) MAY 29-JUN 02, 2016 Heraklion, GREECE EuTravel,Byte,Entropy,HOBBIT,Springer ALIGNMENT With recent advances in information extraction techniques, various large-scale knowledge bases covering a broad range of knowledge have become publicly available. As no single knowledge base covers all information, many applications require access to integrated knowledge from multiple knowledge bases. Achieving this, however, is challenging due to differences in knowledge representation. To address this problem, this paper proposes to use linguistic frames as a common representation and maps heterogeneous knowledge bases to the FrameBase schema, which is formed by a large inventory of these frames. We develop several methods to create complex mappings from external knowledge bases to this schema, using text similarity measures, machine learning, and different heuristics. We test them with different widely used large-scale knowledge bases, YAGO2s, Freebase and WikiData. The resulting integrated knowledge can then be queried in a homogeneous way. [Rouces, Jacobo; Hose, Katja] Aalborg Univ, Aalborg, Denmark; [de Melo, Gerard] Tsinghua Univ, Beijing, Peoples R China Aalborg University; Tsinghua University Rouces, J (corresponding author), Aalborg Univ, Aalborg, Denmark. jrg@es.aau.dk; gdm@demelo.org; khose@cs.aau.dk de Melo, Gerard/P-7886-2014 de Melo, Gerard/0000-0002-2930-2059; Hose, Katja/0000-0001-7025-8099 26 6 6 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-34129-3; 978-3-319-34128-6 LECT NOTES COMPUT SC 2016.0 9678 20 35 10.1007/978-3-319-34129-3_2 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BF0MX 2023-03-23 WOS:000379122700002 0
J Lv, ZH; Qiao, L; Yang, S; Li, JH; Lv, HB; Picciall, F Lv, Zhihan; Qiao, Liang; Yang, Shuo; Li, Jinhua; Lv, Haibin; Picciall, Francesco Memory-augmented neural networks based dynamic complex image segmentation in digital twins for self-driving vehicle PATTERN RECOGNITION English Article Deep learning; Image segmentation; Memory-augmented neural networks; LSTM; Self-driving, Digital twins With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function alpha= 0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing. (C) 2022 The Authors. Published by Elsevier Ltd. [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden; [Yang, Shuo] Kyushu Inst Technol, Dept Mech & Control Engn, Kitakyushu, Japan; [Lv, Haibin] North China Sea Offshore Engn Survey Inst, Minist Nat Resources, North Sea Bur, Qingdao, Peoples R China; [Picciall, Francesco] Univ Naples Feder II, Dept Matemath & Applicat Renato Caccioppoli, Naples, Italy; [Qiao, Liang; Li, Jinhua] Qingdao Univ, Sch Comp Sci, Qingdao, Peoples R China Uppsala University; Kyushu Institute of Technology; Ministry of Natural Resources of the People's Republic of China; Qingdao University Lv, ZH (corresponding author), Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden. zhihan.lyu@speldesign.uu.se; leonqiaoove@gmail.com; dlmz1shuoy@gmail.com; lijh@qdu.edu.cn; lvhaibinsoa@gmail.com; francesco.piccialli@unina.it Lv, Zhihan/GLR-6000-2022; Lv, Zhihan/I-3187-2014 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Qiao, Liang/0000-0002-8188-886X National Natural Science Founda- tion of China [61902203]; Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province [2019JZZY020101] National Natural Science Founda- tion of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province This work was supported by National Natural Science Founda- tion of China (No. 61902203) and Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101) . 39 0 0 11 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. DEC 2022.0 132 108956 10.1016/j.patcog.2022.108956 0.0 AUG 2022 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5B1EK Green Published, hybrid 2023-03-23 WOS:000863318300012 0
J Wang, ST; Gao, JS; Lu, FQ; Wang, F; You, ZY; Huang, MD; Fang, WH; Liu, XF; Li, YL; Liu, Y Wang, Shuting; Gao, Jianshu; Lu, Fuqi; Wang, Fang; You, Zhongyuan; Huang, Meidong; Fang, Weihai; Liu, Xiufeng; Li, Yunliang; Liu, Ying Human motion recognition by a shoes-floor triboelectric nanogenerator and its application in fall detection NANO ENERGY English Article TENG; Human movement monitor; Wireless data transmission ENERGY; SENSOR With the advent of the Internet of Things (IoT), various IoT devices have been widely used, and in recent years, the rapid development of smart sensors as wearable electronic devices has increasingly promoted human -computer integration. Traditional methods using solid-state power supplies suffer from limited battery life, high maintenance costs and environmental pollution. In this work, we propose a simple yet powerful battery-free human motion sensing system that can recognize human motion through a shoes-ground comprised natural triboelectric nanogenerator (TENG), with a conductive PVA-PEDOT:PSS hydrogel as the signal collection component. In addition, we develop an artificial intelligence (AI)-based fall detection system based on the TENG. To this end, we extend TENG with a custom Bluetooth module to transmit the collected signals to the cloud and develop an anomaly detection AI algorithm to detect fall accidents during walking in real time and send instant messages for notification. We experimentally evaluate the proposed TENG fall detection system and conclude that the human sensing-based TENG system has a wide range of potential in wearable electronic devices, and also provides a viable reference for other applications related to human motion detection. [Wang, Shuting; Wang, Fang; You, Zhongyuan; Fang, Weihai; Liu, Ying] Beijing Normal Univ, Dept Chem, Beijing City, Peoples R China; [Gao, Jianshu; Lu, Fuqi; Huang, Meidong] Tianjin Normal Univ, Coll Phys & Mat Sci, 393 Binshuixi Rd, Tianjin 300387, Peoples R China; [Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark; [Li, Yunliang] Chinese Acad Sci Beijing, Natl Lab Condensed Matter Phys Inst Phys, Beijing 100190, Peoples R China; [Li, Yunliang] Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China Beijing Normal University; Tianjin Normal University; Technical University of Denmark; Songshan Lake Materials Laboratory Fang, WH; Liu, Y (corresponding author), Beijing Normal Univ, Dept Chem, Beijing City, Peoples R China.;Liu, XF (corresponding author), Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark.;Li, YL (corresponding author), Chinese Acad Sci Beijing, Natl Lab Condensed Matter Phys Inst Phys, Beijing 100190, Peoples R China. fangwh@bnu.edu.cn; xiuli@dtu.dk; yunliangli@iphy.ac.cn; 11112017013@bnu.edu.cn Liu, Xiufeng/0000-0001-5133-6688 National Natural Science Foundation of China [21873014] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Acknowledgements The work is supported by the National Natural Science Foundation of China (No. 21873014) . 46 0 0 31 31 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2211-2855 2211-3282 NANO ENERGY Nano Energy APR 2023.0 108 108230 10.1016/j.nanoen.2023.108230 0.0 FEB 2023 10 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics 8Y3MW 2023-03-23 WOS:000932603900001 0
J Zhang, TJ; Deng, LJ; Huang, TZ; Chanussot, J; Vivone, G Zhang, Tian-Jiang; Deng, Liang-Jian; Huang, Ting-Zhu; Chanussot, Jocelyn; Vivone, Gemine A Triple-Double Convolutional Neural Network for Panchromatic Sharpening IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Pansharpening; Spatial resolution; Convolutional neural networks; Convolution; Kernel; Image resolution; Feature extraction; Deep convolutional neural networks (CNNs); multi-resolution analysis (MRA); multi-scale feature extraction; multispectral (MS) image fusion; pansharpening; remote sensing; triple-double network (TDNet) IMAGE FUSION TECHNIQUE; REMOTE-SENSING IMAGES; INTENSITY MODULATION; RESOLUTION IMAGES Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet. [Zhang, Tian-Jiang] Univ Elect Sci & Technol China, Yingcai Honors Coll, Chengdu 611731, Sichuan, Peoples R China; [Deng, Liang-Jian; Huang, Ting-Zhu] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Lab Jean Kuntzmann, INRIA, CNRS,Grenoble INP, F-38000 Grenoble, France; [Vivone, Gemine] CNR IMAA, Natl Res Council, Inst Methodol Environm Anal, I-85050 Tito, Italy University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR) Deng, LJ (corresponding author), Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China. zhangtianjinguestc@163.com; liangjian.deng@uestc.edu.cn; tingzhuhuang@126.com; jocelyn.chanussot@grenoble-inp.fr; gemine.vivone@imaa.cnr.it VIVONE, Gemine/ABE-5121-2020; huang, ting/GRR-3141-2022 VIVONE, Gemine/0000-0001-9542-0638; Chanussot, Jocelyn/0000-0003-4817-2875; Huang, Ting-Zhu/0000-0001-7766-230X National Natural Science Foundation of China [12171072, 61702083]; Key Projects of Applied Basic Research in Sichuan Province [2020YJ0216]; National Key Research and Development Program of China [2020YFA0714001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Projects of Applied Basic Research in Sichuan Province; National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 12171072 and Grant 61702083, in part by the Key Projects of Applied Basic Research in Sichuan Province under Grant 2020YJ0216, and in part by the National Key Research and Development Program of China under Grant 2020YFA0714001. 50 16 16 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3155655 0.0 MAR 2022 14 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering ZR5FV 35263264.0 Green Submitted 2023-03-23 WOS:000767809500001 0
J Chen, C; Ma, JT; Susilo, Y; Liu, Y; Wang, ML Chen, Cynthia; Ma, Jingtao; Susilo, Yusak; Liu, Yu; Wang, Menglin The promises of big data and small data for travel behavior (aka human mobility) analysis TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES English Review Big data; Small data; Human mobility; Travel behavior; Transportation planning RESIDENTIAL SELF-SELECTION; OF-THE-ART; PROSPECT-THEORY; INTRAPERSONAL VARIABILITY; BUILT ENVIRONMENT; UTILITY-THEORY; REGRET THEORY; PHONE DATA; PATTERNS; TIME The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called travel behavior analysis or human mobility analysis). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields. (C) 2016 The Authors. Published by Elsevier Ltd. [Chen, Cynthia] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA; [Ma, Jingtao] Traff Technol Serv, Portland, OR USA; [Susilo, Yusak] KTH, Royal Inst Technol, Stockholm, Sweden; [Liu, Yu] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China; [Wang, Menglin] Cambridge Systemat, Chicago, IL USA University of Washington; University of Washington Seattle; Royal Institute of Technology; Peking University Chen, C (corresponding author), Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA. qzchen@uw.edu Liu, Yu/D-4022-2012; Susilo, Yusak O/M-3707-2013 Liu, Yu/0000-0002-0016-2902; Susilo, Yusak O/0000-0001-7124-7164 NSF (National Science Foundation) [CMMI 1200275]; NIH (National Institute of Health) [1R01GM108731-01A1]; Div Of Civil, Mechanical, & Manufact Inn; Directorate For Engineering [1200275] Funding Source: National Science Foundation NSF (National Science Foundation)(National Science Foundation (NSF)); NIH (National Institute of Health)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Div Of Civil, Mechanical, & Manufact Inn; Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)) Funding for this research is provided by a NSF (National Science Foundation) Grant (CMMI 1200275) and a NIH (National Institute of Health) grant (1R01GM108731-01A1) to Cynthia Chen. The authors sincerely thank the help of Xiangyang Guan, a PhD student in the Department of Civil and Environmental Engineering, University of Washington (Seattle), for his help in creating the figures. 140 278 287 26 205 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0968-090X 1879-2359 TRANSPORT RES C-EMER Transp. Res. Pt. C-Emerg. Technol. JUL 2016.0 68 285 299 10.1016/j.trc.2016.04.005 0.0 15 Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Transportation DQ5YF 27182125.0 Green Accepted, hybrid 2023-03-23 WOS:000379280500019 0
J Zhu, SL; Ptak, M; Yaseen, ZM; Dai, JY; Sivakumar, B Zhu, Senlin; Ptak, Mariusz; Yaseen, Zaher Mundher; Dai, Jiangyu; Sivakumar, Bellie Forecasting surface water temperature in lakes: A comparison of approaches JOURNAL OF HYDROLOGY English Article Lake surface water temperature; Air2water; Artificial neural network; Wavelet transform; Non-linear regression; Polish lakes ARTIFICIAL NEURAL-NETWORK; AIR-TEMPERATURE; WAVELET TRANSFORM; CLIMATE-CHANGE; PREDICTION; MODEL; SIMULATION; TIME; DECOMPOSITION; HYDROLOGY Accurate water temperature forecasting in lake systems is important for environmental impact assessment and fisheries management, among others. In this study, two models are developed and applied for water temperature forecasting in lake systems: (1) the multi-layer perceptron neural network (MLPNN) model; and (2) the wavelet transform and MLPNN integrated model (WT_MLPNN). The models are applied to forecast daily lake surface water temperature (LSWT) of eight lowland Polish lakes. Long-term daily LSWT from eight lakes and daily air temperatures from seven meteorological stations are used for daily LSWT forecasting. The results from the two models are compared with those obtained from two other widely used models: the physically-statistically based hybrid air2water model and a non-linear regression model (S-curve). The modelling results show that the air2water model performs the best, followed by the WT_MLPNN, MLPNN, and the non-linear regression model. Overall, the air2water, WT_MLPNN, and MLPNN models reproduce well the seasonal and inter-annual variations of the LSWT dynamics in the eight lakes. The non-linear regression model, although providing the lowest accuracy, can still provide good preliminary estimates of the LSWT for the eight lakes. The outcomes of the present research can provide references for forecasting lake surface water temperature and sustainable management of lake ecosystems. [Zhu, Senlin; Dai, Jiangyu] Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210029, Peoples R China; [Zhu, Senlin] Univ Houston, Cullen Coll Engn, Dept Civil & Environm Engn, Houston, TX 77204 USA; [Ptak, Mariusz] Adam Mickiewicz Univ, Dept Hydrol & Water Management, Krygowskiego 10, PL-61680 Poznan, Poland; [Yaseen, Zaher Mundher] Ton Duc Thang Univ, Fac Civil Engn, Sustainable Dev Civil Engn Res Grp, Ho Chi Minh City, Vietnam; [Sivakumar, Bellie] Indian Inst Technol, Dept Civil Engn, Mumbai 400076, Maharashtra, India Nanjing Hydraulic Research Institute; University of Houston System; University of Houston; Adam Mickiewicz University; Ton Duc Thang University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay Zhu, SL; Dai, JY (corresponding author), Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210029, Peoples R China.;Zhu, SL (corresponding author), Univ Houston, Cullen Coll Engn, Dept Civil & Environm Engn, Houston, TX 77204 USA. slzhu@nhri.cn; marp114@wp.pl; yaseen@tdtu.edu.vn; jydai@nhri.cn; b.sivakumar@iitb.ac.in Ptak, Mariusz/O-3217-2015; Yaseen, Zaher Mundher/G-7029-2018 Ptak, Mariusz/0000-0003-1225-1686; Yaseen, Zaher Mundher/0000-0003-3647-7137 Project of the National Natural Science Foundation of China [51679146]; Young Elite Scientists Sponsorship Program by CAST [2017QNRC001]; China Postdoctoral Science Foundation [2018M640499] Project of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Young Elite Scientists Sponsorship Program by CAST; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors acknowledge the Institute of Meteorology and Water Management-National Research Institute in Poland for providing the data used in this study. This study was supported by the Project of the National Natural Science Foundation of China (51679146), the Young Elite Scientists Sponsorship Program by CAST (2017QNRC001), and the China Postdoctoral Science Foundation (2018M640499). The authors would like to thank the two anonymous reviewers for their constructive comments and useful suggestions on an earlier version of this manuscript. 55 39 39 10 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. JUN 2020.0 585 124809 10.1016/j.jhydrol.2020.124809 0.0 10 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources MD8PK 2023-03-23 WOS:000544230000066 0
J Karimi, HR; Lu, YY Karimi, Hamid Reza; Lu, Yanyang Guidance and control methodologies for marine vehicles: A survey CONTROL ENGINEERING PRACTICE English Article Offshore mechatronics systems; Marine robotic vehicles; Guidance and control; Disturbances; Uncertainties OUTPUT-FEEDBACK CONTROL; SLIDING MODE CONTROL; AUTONOMOUS UNDERWATER VEHICLES; DYNAMIC SURFACE CONTROL; FOLLOWER FORMATION CONTROL; FAULT-TOLERANT CONTROL; NEURAL-NETWORK CONTROL; TRAJECTORY TRACKING CONTROL; PATH-FOLLOWING CONTROL; ADAPTIVE-CONTROL Offshore mechatronics systems engineering has recently received high attentions in various sectors such as energy, transportation, etc. Specifically, offshore robotic vehicles have become challenging topics in terms of design, guidance, control and maintenance aspects. Based on these reasons, in this article, some recent developments on the guidance and control methodologies for marine robotic vehicles are surveyed. The application-oriented methodologies under consideration mainly include fuzzy-based control design approach, neural network-based control design scheme, dynamic surface control strategy, feedback control technique as well as sliding model control method, for instance. With the help of these methodologies, the developments on various guidance and robust control issues are reviewed in great detail. In practical engineering, guidance and control design problems are mainly addressed for the maneuvering, path following, trajectory tracking, formation control and consensus. In particular, the state of art of the solution to guidance and control issues for marine robotic vehicles under different control methodologies is addressed respectively. Finally, some latest research results in filtering and control design developments are introduced, some conclusions are drawn and several possible future research directions based on the latest results are pointed out. [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Lu, Yanyang] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China Polytechnic University of Milan; Donghua University Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. hamidreza.karimi@polimi.it Italian Ministry of Education, University and Research through the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0'' Italian Ministry of Education, University and Research through the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0'' This work was partially funded by the Italian Ministry of Education, University and Research through the Project Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0''. 225 40 40 15 71 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0967-0661 1873-6939 CONTROL ENG PRACT Control Eng. Practice JUN 2021.0 111 104785 10.1016/j.conengprac.2021.104785 0.0 MAR 2021 14 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering UO7ZY 2023-03-23 WOS:000694912300004 0
J Jiang, J; Li, W; Wen, Z; Bie, YF; Schwarz, H; Zhang, CH Jiang, Jun; Li, Wei; Wen, Zhe; Bie, Yifan; Schwarz, Harald; Zhang, Chaohai Series Arc Fault Detection Based on Random Forest and Deep Neural Network IEEE SENSORS JOURNAL English Article Feature extraction; Circuit faults; Time-domain analysis; Sensors; Harmonic analysis; Electrodes; Power harmonic filters; Series arc fault; Random Forest; DNN; online detection KALMAN FILTER; TRANSFORM Series arc is prone to cause fire accidents, but its occurrences induced by different load types and connections make the detection challengeable. This paper proposes a series arc fault detection and location algorithm for multi-load circuit topology, especially for branch arc faults and nonlinear power loads. Several typical loads of paralleling connected are considered to measure the current changes caused by the arcing phenomenon at different positions. Different aspects of arc features are extracted by time-domain, frequency-domain, and wavelet packet energy analysis. A feature selection method based on random forest (RF) is adopted to determine the specific feature sets according to the reduction of Gini impurity. The integrated top ten features with a high correlation to the arc were selected for different combinations of loads and are input into the deep neural network (DNN) for calculating and training. Eventually, a comprehensive arc detection model with the function of detection and location determination for different load types is obtained. It proves that the proposed RF-DNN-based arc detection algorithm can identify and protect the series arc faults in multi-load scenarios efficiently and accurately to meet the practical needs. [Jiang, Jun; Li, Wei; Bie, Yifan; Zhang, Chaohai] Nanjing Univ Aeronaut & Astronaut, Ctr More Elect Aircraft Power Syst, Nanjing 211106, Peoples R China; [Wen, Zhe] State Grid Zhoukou Power Supply Co, Zhoukou 466000, Peoples R China; [Schwarz, Harald] Brandenburg Univ Technol Cottbus Senftenberg, Dept Energy Distribut & High Voltage Engn, Fac 3, D-03046 Cottbus, Germany Nanjing University of Aeronautics & Astronautics; Brandenburg University of Technology Cottbus Zhang, CH (corresponding author), Nanjing Univ Aeronaut & Astronaut, Ctr More Elect Aircraft Power Syst, Nanjing 211106, Peoples R China. jiangjun0628@163.com; ccvn16@163.com; nuaawenzhe@163.com; byf960326@163.com; harald.schwarz@b-tu.de; zch852@126.com Natural Science Foundation of Jiangsu Province [BK20170786]; Jiangsu Planned Projects for Postdoctoral Research Funds; Science and Technology Project of State Grid Corporation of China [J2019008] Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Jiangsu Planned Projects for Postdoctoral Research Funds; Science and Technology Project of State Grid Corporation of China This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20170786 and in part by the Jiangsu Planned Projects for Postdoctoral Research Funds and the Science and Technology Project of State Grid Corporation of China under Grant J2019008. The associate editor coordinating the review of this article and approving it for publication was Prof. Ruqiang Yan. (Corresponding author: Chaohai Zhang.) 30 13 13 12 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. AUG 1 2021.0 21 15 17171 17179 10.1109/JSEN.2021.3082294 0.0 9 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics TS3GB 2023-03-23 WOS:000679541000079 0
C Zhou, ZY; Xiong, F; Yu, HJ; Xu, C; Mumtaz, S; Rodriguez, J; Tariq, M IEEE Zhou, Zhenyu; Xiong, Fei; Yu, Houjian; Xu, Chen; Mumtaz, Shahid; Rodriguez, Jonathan; Tariq, Muhammad Trajectory-based Reliable Content Distribution in D2D-based Cooperative Vehicular Networks: A Coalition Formation Approach 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) / Workshop on Integrating UAVs into 5G MAY 20-24, 2018 Kansas City, MO IEEE,Sprint,Qualcomm,Cisco,Huawei,Natl Instruments,Nokia,Comput Res Assoc Women,GARMIN,Juniper Networks,NSF D2D-V2V communication; reliable content distribution; cooperative vehicular networks; vehicle trajectory prediction; QoS; coalition formation game; big data PERFORMANCE ANALYSIS; NAVIGATION; SYSTEMS; D2D In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. [Zhou, Zhenyu; Xiong, Fei; Yu, Houjian; Xu, Chen] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing, Peoples R China; [Mumtaz, Shahid; Rodriguez, Jonathan] Inst Telecomunicacoes, P-1049001 Aveiro, Portugal; [Rodriguez, Jonathan] Univ South Wales, Pontypridd CF37 1DL, M Glam, Wales; [Tariq, Muhammad] NUCES, Dept Elect Engn, Peshawar, Pakistan North China Electric Power University; Universidade de Aveiro; University of South Wales Zhou, ZY (corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, Beijing, Peoples R China. Tariq, Muhammad/P-7695-2019; Yu, Houjian/GRF-1157-2022; Mumtaz, Dr shahid/V-3603-2019; Rodriguez, Jonathan/F-6055-2010 Tariq, Muhammad/0000-0003-1296-2058; Mumtaz, Dr shahid/0000-0001-6364-6149; Rodriguez, Jonathan/0000-0001-9829-0955 National Science Foundation of China (NSFC) [61601180]; Fundamental Research Funds for the Central Universities [2016MS17, 2017MS13]; Beijing Natural Science Foundation [4174104]; Beijing Outstanding Young Talent [2016000020124G081] National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Beijing Outstanding Young Talent This work was partially supported by the National Science Foundation of China (NSFC) under Grant Numbers 61601180, the Fundamental Research Funds for the Central Universities under Grant Numbers 2016MS17, 2017MS13; supported by Beijing Natural Science Foundation (4174104); Beijing Outstanding Young Talent under Grant Number 2016000020124G081. 20 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-5386-3180-5 IEEE ICC 2018.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BO5ZI 2023-03-23 WOS:000519271304027 0
J Sun, BR; Gong, L; Shim, J; Jang, K; Park, BB; Wang, HN; Hu, J Sun, Bingrong; Gong, Lin; Shim, Jisup; Jang, Kitae; Park, B. Brian; Wang, Hongning; Hu, Jia A human-centric machine learning based personalized route choice prediction in navigation systems JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS English Article; Early Access Intelligent transportation system; machine learning; personalized choice prediction; route choice; traveler information system TRAVELER INFORMATION; BEHAVIOR; MULTICRITERIA; MODEL; PREFERENCES; NETWORK Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers' route choice preferences and do not adequately accommodate drivers' heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers' preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers' common aspects of route choice preferences and yet adapts to each driver's own preference. In addition, any evolvement of individual drivers' preferences can be simultaneously integrated to update the common preference for further individual drivers' preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers' navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies. [Sun, Bingrong; Park, B. Brian] Univ Virginia, Dept Engn Syst & Environm, Charlottesville, VA 22903 USA; [Sun, Bingrong; Park, B. Brian] Univ Virginia, Link Lab, Charlottesville, VA 22903 USA; [Gong, Lin; Wang, Hongning] Univ Virginia, Dept Comp Sci, Charlottesville, VA 22903 USA; [Shim, Jisup] Delft Univ Technol, Dept Transport & Planning, Delft, Netherlands; [Jang, Kitae] Korea Adv Inst Sci & Technol, Grad Sch Mobil, Daejeon, South Korea; [Hu, Jia] Tongji Univ, Minist Educ, Key Lab Rd & Traff Engn, Shanghai 200092, Peoples R China; [Hu, Jia] Tongji Univ, Inst Adv Study, Shanghai, Peoples R China University of Virginia; University of Virginia; University of Virginia; Delft University of Technology; Korea Advanced Institute of Science & Technology (KAIST); Tongji University; Tongji University Hu, J (corresponding author), Tongji Univ, Minist Educ, Key Lab Rd & Traff Engn, Shanghai 200092, Peoples R China. jh8dn@virginia.edu Park, B. Brian/0000-0003-4597-6368; Sun, Bingrong/0000-0003-2806-0200; Shim, Jisup/0000-0002-5054-946X DGIST R&D Program of the Ministry of Science and ICT [18-EE-01]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF2020R1A6A3A03037304] DGIST R&D Program of the Ministry of Science and ICT; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education(National Research Foundation of KoreaMinistry of Education (MOE), Republic of KoreaNational Research Council for Economics, Humanities & Social Sciences, Republic of Korea) This work was supported by the DGIST R&D Program of the Ministry of Science and ICT (18-EE-01). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2020R1A6A3A03037304), and authors are grateful to Kakao Mobility Corp. for their data used in this paper. 37 0 0 6 9 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1547-2450 1547-2442 J INTELL TRANSPORT S J. Intell. Transport. Syst. 10.1080/15472450.2022.2069499 0.0 APR 2022 13 Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Transportation 2Y9XQ 2023-03-23 WOS:000826240700001 0
J Palansooriya, KN; Li, J; Dissanayake, PD; Suvarna, M; Li, LY; Yuan, XZ; Sarkar, B; Tsang, DCW; Rinklebe, J; Wang, XN; Ok, YS Palansooriya, Kumuduni N.; Li, Jie; Dissanayake, Pavani D.; Suvarna, Manu; Li, Lanyu; Yuan, Xiangzhou; Sarkar, Binoy; Tsang, Daniel C. W.; Rinklebe, Joerg; Wang, Xiaonan; Ok, Yong Sik Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning ENVIRONMENTAL SCIENCE & TECHNOLOGY English Article machine learning models; heavy metal; soil remediation; graphical user interface; biochar MICROBIAL COMMUNITY ABUNDANCE; PYROLYSIS TEMPERATURE; MINING SOIL; BIOAVAILABILITY; WASTE; PB; AVAILABILITY; ADSORPTION; BIOMASS; CD Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils. [Palansooriya, Kumuduni N.; Dissanayake, Pavani D.; Yuan, Xiangzhou; Ok, Yong Sik] Korea Univ, Korea Biochar Res Ctr, APRU Sustainable Waste Management Program, Seoul 02841, South Korea; [Palansooriya, Kumuduni N.; Dissanayake, Pavani D.; Yuan, Xiangzhou; Ok, Yong Sik] Korea Univ, Div Environm Sci & Ecol Engn, Seoul 02841, South Korea; [Li, Jie; Suvarna, Manu; Li, Lanyu] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore; [Dissanayake, Pavani D.] Coconut Res Inst, Soils & Plant Nutr Div, Lunuwila 61150, Sri Lanka; [Sarkar, Binoy] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Tsang, Daniel C. W.] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Rinklebe, Joerg] Univ Wuppertal, Inst Fdn Engn Water & Waste Management, Sch Architecture & Civil Engn, Lab Soil & Groundwater Management, D-42285 Wuppertal, Germany; [Rinklebe, Joerg] Sejong Univ, Dept Environm Energy & Geoinformat, Seoul 05006, South Korea; [Wang, Xiaonan] Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China Korea University; Korea University; National University of Singapore; Lancaster University; Hong Kong Polytechnic University; University of Wuppertal; Sejong University; Tsinghua University Ok, YS (corresponding author), Korea Univ, Korea Biochar Res Ctr, APRU Sustainable Waste Management Program, Seoul 02841, South Korea.;Ok, YS (corresponding author), Korea Univ, Div Environm Sci & Ecol Engn, Seoul 02841, South Korea.;Wang, XN (corresponding author), Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China. wangxiaonan@tsinghua.edu.cn; yongsikok@korea.ac.kr Rinklebe, Joerg/Y-2398-2019; Tsang, Dan/E-5442-2012; Li, Jie/GNP-6986-2022; li, lanyu/GSN-6045-2022; Wang, Xiaonan/T-1102-2017; Yuan, Xiangzhou/I-3671-2018; Sarkar, Binoy/D-1573-2011 Rinklebe, Joerg/0000-0001-7404-1639; Tsang, Dan/0000-0002-6850-733X; Li, Jie/0000-0001-7969-4997; Wang, Xiaonan/0000-0001-9775-2417; Yuan, Xiangzhou/0000-0002-6480-3983; Sarkar, Binoy/0000-0002-4196-1225; Dissanayake, Pavani/0000-0002-6934-5037 Rural Development Administration, the Republic of Korea [PJ01475801]; National Research Foundation of Korea (NRF) - Korea government (MSIT) [2021R1A2C2011734]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2021R1A6A1A10045235] Rural Development Administration, the Republic of Korea(Rural Development Administration (RDA), Republic of Korea); National Research Foundation of Korea (NRF) - Korea government (MSIT)(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of KoreaMinistry of Science & ICT (MSIT), Republic of Korea); Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education(National Research Foundation of KoreaMinistry of Education (MOE), Republic of KoreaNational Research Council for Economics, Humanities & Social Sciences, Republic of Korea) This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475801) from Rural Develop-ment Administration, the Republic of Korea. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2011734) . This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A10045235) . The authors acknowledge the National Research Foundation, Prime Minister?s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program. 78 27 28 232 421 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0013-936X 1520-5851 ENVIRON SCI TECHNOL Environ. Sci. Technol. APR 5 2022.0 56 7 4187 4198 10.1021/acs.est.1c08302 0.0 12 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology 2E2VJ 35289167.0 Green Accepted, hybrid 2023-03-23 WOS:000812088500001 0
J Yu, Y; Zhu, HL; Wang, LW; Pedrycz, W Yu, Ying; Zhu, Huilin; Wang, Lewei; Pedrycz, Witold Dense crowd counting based on adaptive scene division INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Crowd counting; Granular computing; Density map; Feature extraction; Dilated convolution PEOPLE With the rapid development of computer vision and artificial intelligence, crowd counting has attracted significant attention from researchers and many well-known methods were proposed. However, due to interocclusions, perspective distortion, and uneven crowd distribution, crowd counting is still a highly challenging task in crowd analysis. Motivated by granular computing, a novel end-to-end crowd counting network (GrCNet) is proposed to enable the problem of crowd counting to be conceptualized at different levels of granularity, and to map problem into computationally tractable subproblems. It shows that by adaptively dividing the image into granules and then feeding the granules into different counting subnetworks separately, the scale variation range of image is narrowed and the the adaptability of counting algorithm to different scenarios is improved. Experiments on four well-known crowd counting benchmark datasets indicate that GrCNet achieves state-of-the-art counting performance and high robustness in dense crowd counting. [Yu, Ying; Zhu, Huilin; Wang, Lewei] East China Jiaotong Univ, Coll Software, Nanchang 330013, Jiangxi, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2G7, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland East China Jiaotong University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Yu, Y (corresponding author), East China Jiaotong Univ, Coll Software, Nanchang 330013, Jiangxi, Peoples R China. yuyingjx@163.com National Natural Science Foundation of China [61563016, 61762036]; Natural Science Foundation of Jiangxi Provincial [20181BAB202023, 20171BAB202012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangxi Provincial The authors would like to thank the editors for their kindly help and the anonymous referees for their valuable comments and helpful suggestions. The work is partially supported by the National Natural Science Foundation of China (Serial No. 61563016, 61762036), the Natural Science Foundation of Jiangxi Provincial (Serial No. 20181BAB202023, 20171BAB202012). 31 3 3 3 17 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. APR 2021.0 12 4 931 942 10.1007/s13042-020-01212-5 0.0 SEP 2020 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science QX9HH 2023-03-23 WOS:000573747000001 0
J Yang, YX; Gao, ZK; Wang, XM; Li, YL; Han, JW; Marwan, N; Kurths, J Yang, Yu-Xuan; Gao, Zhong-Ke; Wang, Xin-Min; Li, Yan-Li; Han, Jing-Wei; Marwan, Norbert; Kurths, Juergen A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG CHAOS English Article CLASSIFICATION; FEATURES; MUSIC; BRAIN; RESPONSES; SIGNALS; GAMMA; PLOTS Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interaction. In this study, we contribute a novel channel-frequency convolutional neural network (CFCNN), combined with recurrence quantification analysis (RQA), for the robust recognition of electroencephalogram (EEG) signals collected from different emotion states. We employ movie clips as the stimuli to induce happiness, sadness, and fear emotions and simultaneously measure the corresponding EEG signals. Then the entropy measures, obtained from the RQA operation on EEG signals of different frequency bands, are fed into the novel CFCNN. The results indicate that our system can provide a high emotion recognition accuracy of 92.24% and a relatively excellent stability as well as a satisfactory Kappa value of 0.884, rendering our system particularly useful for the emotion recognition task. Meanwhile, we compare the performance of the entropy measures, extracted from each frequency band, in distinguishing the three emotion states. We mainly find that emotional features extracted from the gamma band present a considerably higher classification accuracy of 90.51% and a Kappa value of 0.858, proving the high relation between emotional process and gamma frequency band. Published by AIP Publishing. [Yang, Yu-Xuan; Gao, Zhong-Ke; Wang, Xin-Min; Li, Yan-Li; Han, Jing-Wei] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China; [Marwan, Norbert; Kurths, Juergen] Potsdam Inst Climate Impact Res, Telegraphenberg A31, D-14473 Potsdam, Germany; [Kurths, Juergen] Humboldt Univ, Dept Phys, D-12489 Berlin, Germany Tianjin University; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin Gao, ZK (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China. zhongkegao@tju.edu.cn Marwan, Norbert/D-9576-2011; Gao, Zhong-Ke/Y-5325-2019; 王, 新/HHS-1436-2022 Marwan, Norbert/0000-0003-1437-7039; Gao, Zhong-Ke/0000-0002-9551-202X; Kurths, Juergen/0000-0002-5926-4276 National Natural Science Foundation of China [61473203, 61873181]; Natural Science Foundation of Tianjin, China [16JCYBJC18200] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Tianjin, China(Natural Science Foundation of Tianjin) This work was supported by the National Natural Science Foundation of China under Grant Nos. 61473203 and 61873181, and the Natural Science Foundation of Tianjin, China, under Grant No. 16JCYBJC18200. 60 35 36 5 77 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1054-1500 1089-7682 CHAOS Chaos AUG 2018.0 28 8 85724 10.1063/1.5023857 0.0 8 Mathematics, Applied; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics GS5ZY 30180618.0 2023-03-23 WOS:000443760700058 0
C Li, SC; Li, K; Kacher, I; Taira, Y; Yanatori, B; Sato, I Krzhizhanovskaya, VV; Zavodszky, G; Lees, MH; Dongarra, JJ; Sloot, PMA; Brissos, S; Teixeira, J Li, SuChang; Li, Kan; Kacher, Ilyes; Taira, Yuichiro; Yanatori, Bungo; Sato, Imari ArtPDGAN: Creating Artistic Pencil Drawing with Key Map Using Generative Adversarial Networks COMPUTATIONAL SCIENCE - ICCS 2020, PT VII Lecture Notes in Computer Science English Proceedings Paper 20th Annual International Conference on Computational Science (ICCS) JUN 03-05, 2020 Amsterdam, NETHERLANDS Univ Amsterdam,NTU Singapore,Univ Tennessee Generative adversarial networks; Deep learning; Artistic pencil drawing A lot of researches focus on image transfer using deep learning, especially with generative adversarial networks (GANs). However, no existing methods can produce high quality artistic pencil drawings. First, artists do not convert all the details of the photos into the drawings. Instead, artists tend to use strategies to magnify some special parts of the items and cut others down. Second, the elements in artistic drawings may not be located precisely. What's more, the lines may not relate to the features of the items strictly. To address above challenges, we propose ArtPDGAN, a novel GAN based framework that combines an image-to-image network to generate key map. And then, we use the key map as an important part of input to generate artistic pencil drawings. The key map can show the key parts of the items to guide the generator. We use a paired and unaligned artistic drawing dataset containing high-resolution photos of items and corresponding professional artistic pencil drawings to train ArtPDGAN. Results of our experiments show that the proposed framework performs excellently against existing methods in terms of similarity to artist's work and user evaluations. [Li, SuChang; Li, Kan] Beijing Inst Technol, Beijing, Peoples R China; [Kacher, Ilyes] Qwant Res, Paris, France; [Taira, Yuichiro; Yanatori, Bungo] Tokyo Univ Arts, Tokyo, Japan; [Sato, Imari] Natl Inst Informat, Tokyo, Japan Beijing Institute of Technology; Tokyo University of the Arts; Research Organization of Information & Systems (ROIS); National Institute of Informatics (NII) - Japan Li, K (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. likan@bit.edu.cn Rajak, Vivek Kumar/0000-0003-2806-1553 Beijing Natural Science Foundation [L181010, 4172054]; National Basic Research Program of China [2013CB329605] Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Basic Research Program of China(National Basic Research Program of China) This research was supported by Beijing Natural Science Foundation (No. L181010, 4172054), and National Basic Research Program of China (No. 2013CB329605). 31 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-50436-6; 978-3-030-50435-9 LECT NOTES COMPUT SC 2020.0 12143 285 298 10.1007/978-3-030-50436-6_21 0.0 14 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT6HJ Bronze 2023-03-23 WOS:000841686400021 0
J Dai, LL; Lu, H; Hua, DZ; Liu, XH; Chen, HM; Glowacz, A; Krolczyk, G; Li, ZX Dai, Lili; Lu, He; Hua, Dezheng; Liu, Xinhua; Chen, Hongming; Glowacz, Adam; Krolczyk, Grzegorz; Li, Zhixiong A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm SUSTAINABILITY English Article production scheduling; hybrid genetic algorithm; artificial intelligence; sustainable design; discrete manufacturing Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling. [Dai, Lili; Lu, He] Lianyungang Normal Coll, Inst Smart Mat & Appl Technol, Lianyungang 222006, Peoples R China; [Lu, He; Hua, Dezheng; Liu, Xinhua; Chen, Hongming] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 211006, Jiangsu, Peoples R China; [Glowacz, Adam] AGH Univ Sci & Technol, Dept Automat Control & Robot, PL-30059 Krakow, Poland; [Krolczyk, Grzegorz; Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea China University of Mining & Technology; AGH University of Science & Technology; Opole University of Technology; Yonsei University Liu, XH (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 211006, Jiangsu, Peoples R China.;Li, ZX (corresponding author), Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland.;Li, ZX (corresponding author), Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea. liuxinhua@cumt.edu.cn; zhixiong.li@yonsei.ac.kr Glowacz, Adam/I-1024-2019; Krolczyk, Grzegorz/D-6709-2013; Glowacz, Adam/N-6462-2013; Li, Zhixiong/G-8418-2018 Glowacz, Adam/0000-0003-0546-7083; Krolczyk, Grzegorz/0000-0002-2967-1719; Glowacz, Adam/0000-0003-0546-7083; Li, Zhixiong/0000-0002-7265-0008 Natural Science Foundation of the Jiangsu Higher Education Institution of China [22KJD460004]; National Science Foundation of China [51975568]; Natural Science Foundation of Jiangsu Province [BK20191341]; Qing Lan Project for Excellent Young Key Teachers of Colleges and Universities of Jiangsu Province [2022] Natural Science Foundation of the Jiangsu Higher Education Institution of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project for Excellent Young Key Teachers of Colleges and Universities of Jiangsu Province The authors acknowledge the support from the Natural Science Foundation of the Jiangsu Higher Education Institution of China (22KJD460004), National Science Foundation of China (No. 51975568), Natural Science Foundation of Jiangsu Province (No. BK20191341), Qing Lan Project for Excellent Young Key Teachers of Colleges and Universities of Jiangsu Province (2022) in carrying out this research are gratefully acknowledged. 31 2 2 22 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability SEP 2022.0 14 18 11747 10.3390/su141811747 0.0 15 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 4U3LI gold 2023-03-23 WOS:000858699300001 0
J Zhang, C; Zhang, D; Zhang, MY; Mao, WG Zhang, Chi; Zhang, Di; Zhang, Mingyang; Mao, Wengang Data-driven ship energy efficiency analysis and optimization model for route planning in ice-covered Arctic waters OCEAN ENGINEERING English Article Ship energy efficiency; Optimization model; Route planning; Arctic navigation ANT COLONY OPTIMIZATION; PREDICTION; RISK; SIMULATION; ALGORITHM; SYSTEM; SPEED; FAULT As increasing numbers of merchant ships navigate in the Arctic waters, more energy efficient navigation in the Arctic is needed for both economic and environmental purposes. This paper aims to provide a comprehensive analysis of energy efficiency of ice-going ships. Firstly, a data-driven model based on neural network theory is developed to predict the energy efficiency of ships in the Arctic. Then, a route with optimum energy efficiency is presented, intended to cut costs and be as environmentally friendly as possible. Finally, a case study is carried out to analyze the performance of a case study ship sailing at various environmental conditions. The results indicate that when planning the route for such an Arctic ship, it would be shortsighted only to focus on distance rather than energy efficiency. Moreover, the model shows good agreement between the simulation and real-life navigation. It is expected that the construction of this multi-angle, energy efficiency optimization model could help to improve Arctic navigation. [Zhang, Chi; Zhang, Di] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Hubei, Peoples R China; [Zhang, Chi; Zhang, Di] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan, Hubei, Peoples R China; [Zhang, Mingyang] Aalto Univ, Dept Mech Engn, Sch Engn, Espoo, Finland; [Mao, Wengang] Chalmers Univ Technol, Dept Mech & Maritime Sci, Gothenburg, Sweden Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; Aalto University; Chalmers University of Technology Zhang, D (corresponding author), Wuhan Univ Technol, 1178 Heping Ave, Wuhan 430063, Hubei, Peoples R China. zhangdi@whut.edu.cn Zhang, Di/F-3431-2017; Zhang, Mingyang/AEC-5093-2022 Zhang, Mingyang/0000-0001-5820-2789; Zhang, Chi/0000-0003-4992-1639; Zhang, Di/0000-0001-8790-9206; Mao, Wengang/0000-0002-7126-1254 National Science Foundation of China [51579203, 51711530033]; EU H2020-MCSA-RISE project RESET - Reliability and Safety Engineering and Technology for large maritime engineering systems [730888] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU H2020-MCSA-RISE project RESET - Reliability and Safety Engineering and Technology for large maritime engineering systems The authors would like to thank the anonymous reviewers for their constructive suggestions which comprehensively improve the paper. This study was supported by the National Science Foundation of China under Grant No. 51579203 and 51711530033, and the EU H2020-MCSA-RISE project RESET - Reliability and Safety Engineering and Technology for large maritime engineering systems under Grant No. 730888. 45 45 47 19 62 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. AUG 15 2019.0 186 106071 10.1016/j.oceaneng.2019.05.053 0.0 12 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography IU5KQ Green Submitted 2023-03-23 WOS:000483626500030 0
J Ozdemir, V; Springer, S; Yildirim, A; Bicer, S; Kendirci, A; Sardas, S; Kilic, H; Hekim, N; Kunej, T; Arga, KY; Dzobo, K; Wang, W; Geanta, M; Brand, A; Bayram, M Ozdemir, Vural; Springer, Simon; Yildirim, Arif; Bicer, Sehmus; Kendirci, Asligul; Sardas, Semra; Kilic, Hakan; Hekim, Nezih; Kunej, Tanja; Arga, Kazim Yalcin; Dzobo, Kevin; Wang, Wei; Geanta, Marius; Brand, Angela; Bayram, Mustafa Thanatechnology and the Living Dead: New Concepts in Digital Transformation and Human-Computer Interaction OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY English Review human-computer interaction; innovation policy; critical thanatechnology studies; e-death; the right to nonparticipation; industry 5; 0 In a digital society, shall we be the authors of our own experience, not only during our lifetime but also after we die? We ask this question because dying and bereavement have become even harder, and much less private, in the digital age. New big data-driven digital industries and technologies are on the rise, with promises of interactive 3D avatars and storage of digital memories of the deceased, so they can continue to exist online as the living dead in a digital afterlife. Famous rock and roll icons like Roy Orbison, Frank Zappa, Ronnie James Dio, and Amy Winehouse have famously been turned into holograms that can once again give live performances on the touring circuit, often pulling in large audiences. Death studies, dying, and grief have become virtual in the 21st century. We live in truly unprecedented times for human-computer interactions. Thanatology is the scientific study of death, dying, loss, and grief. In contrast to the biological study of biological aging (cellular senescence) and programmed cell death (apoptosis), thanatology employs multiple professional lenses, medical, psychological, physical, spiritual, ethical, descriptive, and normative. In 1997, Carla Sofka introduced the term thanatechnology as technological mechanisms such as interactive videodiscs and computer programs that are used to access information or aid in learning about thanatology topics. Onward to 2021, the advent of social media, the Internet of Things, and sensors that digitize and archive nearly every human movement and experience are taking thanatechnology, and by extension, digital transformation, to new heights. For example, what happens to digital remains of persons once they cease to exist physically? This article offers a critical study and snapshot of this nascent field, and the un-disciplinary sociotechnical issues digital thanatechnologies raise in relation to big data. We also discuss how best to critically govern this new frontier in systems science and the digital society. We suggest that new policy narratives such as (1) the right to nonparticipation in relation to information and communication technologies and (2) the planetary public goods deserve further attention to democratize thanatechnology and big data. To the extent that systems science often depends on data from online platforms, for example, in times of pandemics and ecological crises, critical thanatechnology studies, introduced in this article, is a timely and essential field of scholarship with broad importance for systems science and planetary health. [Ozdemir, Vural] OMICS, New Rochelle, NY 10801 USA; [Springer, Simon] Univ Newcastle, Sch Environm & Life Sci, Ctr Urban & Reg Studies, Discipline Geog & Environm Studies,Fac Sci, Callaghan, NSW 2308, Australia; [Yildirim, Arif] Canakkale Sekiz Mart Univ, Fac Commun, Dept Journalism, Canakkale, Turkey; [Bicer, Sehmus] Canakkale Sekiz Mart Univ, Sch Grad Studies, Dept Media & Cultural Studies, Canakkale, Turkey; [Kendirci, Asligul] Ascot Sci Pharmaceut & Clin Trial Innovat, Istanbul, Turkey; [Kendirci, Asligul; Sardas, Semra] Istinye Univ, Fac Pharm, Istanbul, Turkey; [Kilic, Hakan] Danube Univ Krems, Dept Migrat & Globalizat, Krems, Austria; [Hekim, Nezih] Biruni Univ, Dept Mol Biol & Genet, Istanbul, Turkey; [Kunej, Tanja] Univ Ljubljana, Biotech Fac, Dept Anim Sci, Ljubljana, Slovenia; [Arga, Kazim Yalcin] Marmara Univ, Fac Engn, Dept Bioengn, Istanbul, Turkey; [Dzobo, Kevin] Univ Cape Town, Div Med Biochem, Cape Town, South Africa; [Dzobo, Kevin] Univ Cape Town, Inst Infect Dis & Mol Med, Dept Integrat Biomed Sci, Fac Hlth Sci, Cape Town, South Africa; [Wang, Wei] Capital Med Univ, Key Municipal Lab Clin Epidemiol, Beijing, Peoples R China; [Wang, Wei] Edith Cowan Univ, Ctr Precis Hlth, Joondalup, Australia; [Wang, Wei] Shandong First Med Univ, Sch Publ Hlth, Tai An, Shandong, Peoples R China; [Wang, Wei] Shandong Acad Med Sci, Tai An, Shandong, Peoples R China; [Wang, Wei] Shantou Univ, Coll Med, Affiliated Hosp 1, Shantou, Peoples R China; [Geanta, Marius] Ctr Innovat Med, Bucharest, Romania; [Geanta, Marius] KOL Med Media, Bucharest, Romania; [Geanta, Marius; Brand, Angela] United Nations Univ, Maastricht Econ & Social Res Inst Innovat & Techn, Maastricht, Netherlands; [Brand, Angela] Maastricht Univ, Fac Hlth Med & Life Sci, Maastricht, Netherlands; [Brand, Angela] Manipal Acad Higher Educ, Dept Publ Hlth Genom, Manipal, India; [Brand, Angela] Manipal Acad Higher Educ, Manipal Sch Life Sci, Publ Hlth Genom, Manipal, India; [Bayram, Mustafa] Gaziantep Univ, Fac Engn, Dept Food Engn, Gaziantep, Turkey University of Newcastle; Canakkale Onsekiz Mart University; Canakkale Onsekiz Mart University; Istinye University; Danube University Krems; Biruni University; University of Ljubljana; Marmara University; University of Cape Town; University of Cape Town; Capital Medical University; Edith Cowan University; Shandong First Medical University & Shandong Academy of Medical Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences; University of Jinan; Shantou University; Maastricht University; Maastricht University; Manipal Academy of Higher Education (MAHE); Manipal Academy of Higher Education (MAHE); Gaziantep University Ozdemir, V (corresponding author), OMICS, New Rochelle, NY 10801 USA.;Springer, S (corresponding author), Univ Newcastle, Sch Environm & Life Sci, Ctr Urban & Reg Studies, Discipline Geog & Environm Studies,Fac Sci, Callaghan, NSW 2308, Australia. OJIB@liebertpub.com; simonspringer@gmail.com Arga, Kazım Yalçın/HGB-8786-2022; Geanta, Marius/HOH-9666-2023; YILDIRIM, Arif/J-5821-2018; Geanta, Marius/AAD-9204-2022; Kunej, Tanja/G-5957-2017; liu, li/HGC-0900-2022 YILDIRIM, Arif/0000-0002-4446-4865; Kunej, Tanja/0000-0002-0465-1762; KILIC, Hakan/0000-0001-9495-9703; wang, wei/0000-0002-1430-1360; Springer, Simon/0000-0002-0064-7440; Arga, Kazim Yalcin/0000-0002-6036-1348; Bicer, Sehmus/0000-0001-6114-3265 34 3 3 6 29 MARY ANN LIEBERT, INC NEW ROCHELLE 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA 1536-2310 1557-8100 OMICS OMICS JUL 1 2021.0 25 7 401 407 10.1089/omi.2021.0100 0.0 JUN 2021 7 Biotechnology & Applied Microbiology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Genetics & Heredity TI7IC 34191613.0 2023-03-23 WOS:000668301900001 0
J Guo, H; Zhang, W; Nie, XY; Dong, X; Sun, ZW; Zhou, B; Wang, ZK; Richter, M Guo, Hao; Zhang, Wei; Nie, Xiangyu; Dong, Xue; Sun, Zhiwei; Zhou, Bo; Wang, Zhenkan; Richter, Mattias High-speed planar imaging of OH radicals in turbulent flames assisted by deep learning APPLIED PHYSICS B-LASERS AND OPTICS English Article BURST-MODE LASER; SIMULTANEOUS VISUALIZATION; COMBUSTION; TEMPERATURE; PLIF; ZONE; CH2O High-speed planar imaging of key combustion species, like hydroxyl radicals (OH), is crucial for understanding the complex chemistry-turbulence interactions in turbulent flames. However, conducting high-speed (kHz) diagnostics is challenging due to the requirements on advanced optical system, including both fast lasers and cameras. In this paper, we report a computational imaging method to artificially achieve higher diagnosing rates based on experimental data at relatively low rates. Sequencies of planar laser-induced fluorescence (PLIF) of OH recorded at 100 kHz in a turbulent flame were first down sampled to 50 kHz, 33.3 kHz and 20 kHz, respectively, and then used as a data source to train several networks. The accuracies of the models were assessed by comparing the predicted images with those from laser measurements. It was found that, among the models tested, convolutional long short-term memory network (CONV-LSTM) can provide the best predictions and is reliable in predicting consecutive images with higher repetition rates. The model can also generate consecutive OH-PLIF images at 200 kHz based on the 100 kHz experimental data. This work sheds light on the hybrid of deep learning-based computational methods with conventional high-speed laser diagnosing techniques, which can potentially increase the temporal resolution of planar optical measurement in turbulent flows, and significantly reduce the computational timing. [Guo, Hao; Zhang, Wei; Nie, Xiangyu; Dong, Xue] Shanghai Jiao Tong Univ, China UK Low Carbon Coll, Shanghai 200240, Peoples R China; [Sun, Zhiwei] Univ Adelaide, Sch Mech Engn & Ctr Energy Technol CET, Adelaide, SA 5005, Australia; [Zhou, Bo] Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen, Peoples R China; [Wang, Zhenkan; Richter, Mattias] Lund Univ, Div Combust Phys, POB 118, S-22100 Lund, Sweden Shanghai Jiao Tong University; University of Adelaide; Southern University of Science & Technology; Lund University Dong, X (corresponding author), Shanghai Jiao Tong Univ, China UK Low Carbon Coll, Shanghai 200240, Peoples R China. xue.dong@sjtu.edu.cn Richter, Mattias/0000-0002-9914-7218 National Natural Science Foundation of China [52006137]; Shanghai Sailing Program [19YF1423400] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Sailing Program This research was financially supported by the National Natural Science Foundation of China under Grant No. 52006137, as well as Shanghai Sailing Program, under Grant No. 19YF1423400. 60 0 0 3 6 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0946-2171 1432-0649 APPL PHYS B-LASERS O Appl. Phys. B-Lasers Opt. MAR 2022.0 128 3 52 10.1007/s00340-021-07742-2 0.0 13 Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics ZB4RH 2023-03-23 WOS:000756830500001 0
C Xu, JH; Guo, CY; Liu, QJ; Qin, J; Wang, YH; Liu, L IEEE Xu, Jiehao; Guo, Chengyu; Liu, Qingjie; Qin, Jie; Wang, Yunhong; Liu, Li DHA: Supervised Deep Learning to Hash with an Adaptive Loss Function 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) IEEE International Conference on Computer Vision Workshops English Proceedings Paper IEEE/CVF International Conference on Computer Vision (ICCV) OCT 27-NOV 02, 2019 Seoul, SOUTH KOREA IEEE,IEEE Comp Soc,CVF BINARY-CODES Hashing, which refers to the binary embedding of high-dimensional data, has been an effective solution for fast nearest neighbor retrieval in large-scale databases due to its computational and storage efficiency. Recently, deep learning to hash has been attracting increasing attention since it has shown great potential in improving retrieval quality by leveraging the strengths of deep neural networks. In this paper, we consider the problem of supervised hashing and propose an effective model (i.e., DHA), which is able to generate compact and discriminative binary codes while preserving semantic similarities of original data with an adaptive loss function. The key idea is that we scale and shift the loss function to avoid the saturation of gradients during training, and simultaneously adjust the loss to adapt to different levels of similarities of data. We evaluate the proposed DHA on three widely-used benchmarks, i.e., NUS-WIDE, CIFAR-10, and MS COCO. The state-of-the-art image retrieval performance clearly shows the effectiveness of our method in learning discriminative hash codes for nearest neighbor retrieval. [Xu, Jiehao; Liu, Qingjie; Wang, Yunhong] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China; [Guo, Chengyu; Liu, Li] Natl Univ Def Technol, Coll Syst Engn, Changsha, Peoples R China; [Qin, Jie] Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland Beihang University; National University of Defense Technology - China; University of Oulu Liu, QJ (corresponding author), Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China. qingjie.liu@buaa.edu.cn Foundation for Innovative Research Groups through the National Natural Science Foundation of China [61421003] Foundation for Innovative Research Groups through the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Foundation for Innovative Research Groups through the National Natural Science Foundation of China under Grant 61421003. 36 3 4 0 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2473-9936 978-1-7281-5023-9 IEEE INT CONF COMP V 2019.0 3054 3062 10.1109/ICCVW.2019.00368 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BP4UH Green Accepted 2023-03-23 WOS:000554591603018 0
J Lv, JQ; Zheng, XD; Pawlak, M; Mo, WK; Miskowicz, M Lv, Jiaqing; Zheng, Xiaodong; Pawlak, Miroslaw; Mo, Weike; Miskowicz, Marek Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms RENEWABLE ENERGY English Article Interval forecast; Multivariate estimation; Nonparametric density estimation; Sparse modeling; Wind power prediction SPEED; GENERATION; REGRESSION; FRAMEWORK; INTERVALS; SELECTION; SYSTEMS; MODEL In this paper, a sparse machine learning technique is applied to predict the next-hour wind power. The hourly wind power prediction values within a few future hours can be obtained by meteorological/ physical methods, and such values are often broadcast and available for many wind generators. Our model takes into consideration those available forecast values, together with the real-time observations of the past hours, as well as the values in all the power generators in nearby locations. Such a model is consisted of features of high dimensions, and is solved by the sparse technique. We demonstrate our method using the realistic wind power data that belongs to the IEEE 118-bus test system named NREL118. The modeling result shows that our approach leads to better prediction accuracy comparing to several other competing methods, and our results improves from the broadcast values obtained by meteorological/physical methods. Apart from that, we apply a novel nonparametric density estimation approach to give the probabilistic band of prediction, which is demonstrated by the 25% and 75% confidence interval of the prediction. The coverage rate is compared with that yielded from quantile regression. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Lv, Jiaqing; Pawlak, Miroslaw; Miskowicz, Marek] AGH Univ Sci & Technol, Krakow, Poland; [Zheng, Xiaodong; Mo, Weike] South China Univ Technol, Guangzhou, Peoples R China; [Pawlak, Miroslaw] Univ Manitoba, Winnipeg, MB, Canada; [Pawlak, Miroslaw] Univ Social Sci, Informat Technol Inst, Warsaw, Poland AGH University of Science & Technology; South China University of Technology; University of Manitoba; University of Social Sciences Zheng, XD (corresponding author), South China Univ Technol, Guangzhou, Peoples R China. z.xiaodong@mail.scut.edu.cn Zheng, Xiaodong/AAY-6656-2021 Zheng, Xiaodong/0000-0002-7993-9963 Polish National Center of Science [DEC2018/31/B/ST7/03874]; National Natural Science Foundation of China [51937005] Polish National Center of Science; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work of Jiaqing Lv was supported by the Polish National Center of Science under Grant DEC2018/31/N/ST7/03977. The work of Xiaodong Zheng and Weike Mo was supported by the National Natural Science Foundation of China under Grant 51937005. The work of Mirosaw Pawlak was supported by the Polish National Center of Science under grant DEC2017/27/B/ST7/03082. The work of Marek Mis?kowicz was supported by the Polish National Center of Science under grant DEC2018/31/B/ST7/03874. 53 10 10 3 15 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-1481 1879-0682 RENEW ENERG Renew. Energy NOV 2021.0 177 181 192 10.1016/j.renene.2021.05.123 0.0 JUN 2021 12 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Energy & Fuels TY1MJ hybrid 2023-03-23 WOS:000683548000016 0
J Li, Z; Liu, XH; Incecik, A; Gupta, MK; Kroclzyk, GM; Gardoni, P Li, Zhixiong; Liu, Xihao; Incecik, Atilla; Gupta, Munish Kumar; Kroclzyk, Grzegorz M.; Gardoni, Paolo A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors JOURNAL OF MANUFACTURING PROCESSES English Article Machining; Tool wear; Audio signal processing; Intelligent detection; Sensors SUPPORT VECTOR MACHINE; COMPONENT ANALYSIS; NEURAL-NETWORKS; VISION; SYSTEM; ONLINE; SEPARATION; VIBRATION; SIGNALS Tool wear is an important parameter in the machining because the production, cost and performance is highly depend upon its performance. Therefore, the monitoring of cutting tool wear plays an important role in mechanical machining processes. With this aim, the present work deals with the application of novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. The tool wear data during machining was extracted with an audio denoising technique combined with Fast Fourier Transform (FFT) and bandpass filters and dependent component analysis (DCA). Then, the ensemble convolutional neural networks (CNN) detection model was trained and audio signals were converted into audio images with different algorithms. Finally, the results confirm that this novel method is very accurate to predict the tool wear values under different cutting conditions. [Li, Zhixiong; Gupta, Munish Kumar; Kroclzyk, Grzegorz M.] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Liu, Xihao] Ocean Univ China, Sch Engn, Qingdao 266001, Peoples R China; [Incecik, Atilla] Univ Strathclyde, Dept Naval Architecture Ocean & Marine Engn, Glasgow G1 1XQ, Scotland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Gardoni, Paolo] Univ Illinois, Dept Civil & Environm Engn, Champaign, IL USA Opole University of Technology; Ocean University of China; University of Strathclyde; Yonsei University; University of Illinois System; University of Illinois Urbana-Champaign Gupta, MK (corresponding author), Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland. zhixiong.li@yonsei.ac.kr; 21190931169@stu.ouc.edu.cn; atilla.incecik@strath.ac.uk; munishguptanit@gmail.com; g.krolczyk@po.opole.pl; gardoni@illinois.edu Krolczyk, Grzegorz/D-6709-2013 Krolczyk, Grzegorz/0000-0002-2967-1719 Fundamental Research Funds for the Central Universities [201912036]; Taishan Scholar of Shandong, China [2020/37/K/ST8/02748]; Narodowego Centrum Nauki, Poland Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Taishan Scholar of Shandong, China; Narodowego Centrum Nauki, Poland Acknowledgement The authors would like to express sincere thanks to Dr. R. Liu at Rochester Institute of Technology and Dr. D. Wu at University of Central Florida for sharing the experimental datasets. This work is supported by Fundamental Research Funds for the Central Universities (No. 201912036) , Taishan Scholar of Shandong, China (No. tsqn201812025) , Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748) . 44 5 5 16 25 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1526-6125 2212-4616 J MANUF PROCESS J. Manuf. Process. JUL 2022.0 79 233 249 10.1016/j.jmapro.2022.04.066 0.0 MAY 2022 17 Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Engineering 1Q6LU 2023-03-23 WOS:000802797100003 0
J Youngblood, MW; Duran, D; Montejo, JD; Li, C; Omay, SB; Ozduman, K; Sheth, AH; Zhao, AY; Tyrtova, E; Miyagishima, DF; Fomchenko, EI; Hong, CS; Clark, VE; Riche, M; Peyre, M; Boetto, J; Sohrabi, S; Koljaka, S; Baranoski, JF; Knight, J; Zhu, HD; Pamir, MN; Avsar, T; Kilic, T; Schramm, J; Timmer, M; Goldbrunner, R; Gong, Y; Bayri, Y; Amankulor, N; Hamilton, RL; Bilguvar, K; Tikhonova, I; Tomak, PR; Huttner, A; Simon, M; Krischek, B; Kalamarides, M; Erson-Omay, EZ; Moliterno, J; Gunel, M Youngblood, Mark W.; Duran, Daniel; Montejo, Julio D.; Li, Chang; Omay, Sacit Bulent; Ozduman, Koray; Sheth, Amar H.; Zhao, Amy Y.; Tyrtova, Evgeniya; Miyagishima, Danielle F.; Fomchenko, Elena, I; Hong, Christopher S.; Clark, Victoria E.; Riche, Maximilien; Peyre, Matthieu; Boetto, Julien; Sohrabi, Sadaf; Koljaka, Sarah; Baranoski, Jacob F.; Knight, James; Zhu, Hongda; Pamir, M. Necmettin; Avsar, Timucin; Kilic, Turker; Schramm, Johannes; Timmer, Marco; Goldbrunner, Roland; Gong, Ye; Bayri, Yasar; Amankulor, Nduka; Hamilton, Ronald L.; Bilguvar, Kaya; Tikhonova, Irina; Tomak, Patrick R.; Huttner, Anita; Simon, Matthias; Krischek, Boris; Kalamarides, Michel; Erson-Omay, E. Zeynep; Moliterno, Jennifer; Guenel, Murat Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas JOURNAL OF NEUROSURGERY English Article meningioma; precision medicine; genomics; clinical correlations; machine learning; oncology MUTATIONS; AKT1; GRADE; SMO; EPENDYMOMAS; EXPRESSION; GERMLINE; TRAF7; KLF4 OBJECTIVE Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts. METHODS Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher's exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features. RESULTS Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among mutation unknown samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor's underlying driver mutation. CONCLUSIONS Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures. [Youngblood, Mark W.; Duran, Daniel; Montejo, Julio D.; Li, Chang; Omay, Sacit Bulent; Sheth, Amar H.; Zhao, Amy Y.; Tyrtova, Evgeniya; Miyagishima, Danielle F.; Fomchenko, Elena, I; Hong, Christopher S.; Sohrabi, Sadaf; Koljaka, Sarah; Huttner, Anita; Erson-Omay, E. Zeynep; Moliterno, Jennifer; Guenel, Murat] Yale Sch Med, Yale Program Brain Tumor Res, New Haven, CT USA; [Youngblood, Mark W.; Duran, Daniel; Montejo, Julio D.; Li, Chang; Omay, Sacit Bulent; Sheth, Amar H.; Zhao, Amy Y.; Tyrtova, Evgeniya; Miyagishima, Danielle F.; Fomchenko, Elena, I; Hong, Christopher S.; Sohrabi, Sadaf; Koljaka, Sarah; Tomak, Patrick R.; Erson-Omay, E. Zeynep; Moliterno, Jennifer; Guenel, Murat] Yale Sch Med, Dept Neurosurg, New Haven, CT USA; [Youngblood, Mark W.; Miyagishima, Danielle F.; Knight, James; Bilguvar, Kaya; Guenel, Murat] Yale Sch Med, Dept Genet, New Haven, CT USA; [Guenel, Murat] Yale Sch Med, Dept Neurosci, New Haven, CT USA; [Duran, Daniel] Univ Mississippi, Med Ctr, Dept Neurosurg, Jackson, MS 39216 USA; [Montejo, Julio D.] Dartmouth Hitchcock Med Ctr, Sect Neurosurg, Lebanon, NH 03766 USA; [Li, Chang] Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha, Peoples R China; [Li, Chang] Cent South Univ, Xiangya Hosp 3, Changsha, Peoples R China; [Ozduman, Koray; Pamir, M. Necmettin] Acibadem Mehmet Ali Aydinlar Univ, Sch Med, Dept Neurosurg, Istanbul, Turkey; [Clark, Victoria E.] Massachusetts Gen Hosp, Dept Neurosurg, Boston, MA 02114 USA; [Riche, Maximilien; Peyre, Matthieu; Boetto, Julien; Kalamarides, Michel] Hop Univ Pitie Salpetriere, AP HP, Dept Neurosurg, Paris, France; [Riche, Maximilien; Peyre, Matthieu; Boetto, Julien; Kalamarides, Michel] Sorbonne Univ, Paris, France; [Baranoski, Jacob F.] St Josephs Hosp, Dept Neurosurg, Barrow Neurol Inst, Phoenix, AZ USA; [Knight, James; Bilguvar, Kaya; Tikhonova, Irina; Guenel, Murat] Yale Univ, Yale Ctr Genome Anal, West Campus, Orange, CT USA; [Zhu, Hongda; Gong, Ye] Fudan Univ, Huashan Hosp, Shanghai Med Coll, Dept Neurosurg, Shanghai, Peoples R China; [Avsar, Timucin] BAU Fac Med, Dept Med Biol, Istanbul, Turkey; [Kilic, Turker] Bahcesehir Univ, Sch Med, Dept Neurosurg, Istanbul, Turkey; [Schramm, Johannes; Simon, Matthias] Univ Bonn, Med Sch, Bonn, Germany; [Timmer, Marco; Goldbrunner, Roland; Krischek, Boris] Univ Hosp Cologne, Ctr Neurosurg, Cologne, Germany; [Bayri, Yasar] Marmara Univ, Dept Neurosurg, Sch Med, Istanbul, Turkey; [Amankulor, Nduka; Hamilton, Ronald L.] Univ Pittsburgh, Dept Neurosurg, Med Ctr, Pittsburgh, PA USA; [Huttner, Anita] Yale Sch Med, Dept Pathol, New Haven, CT USA; [Simon, Matthias] Bethel Clin, Dept Neurosurg, Bielefeld, Germany Yale University; Yale University; Yale University; Yale University; University of Mississippi; University of Mississippi Medical Center; Dartmouth College; Central South University; Central South University; Acibadem University; Harvard University; Massachusetts General Hospital; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Pitie-Salpetriere - APHP; UDICE-French Research Universities; Sorbonne Universite; UDICE-French Research Universities; Sorbonne Universite; Barrow Neurological Institute; St. Joseph's Hospital and Medical Center; Yale University; Fudan University; Bahcesehir University; University of Bonn; University of Cologne; Marmara University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Yale University Gunel, M (corresponding author), Yale Sch Med, New Haven, CT 06510 USA. gunel@yale.edu Amankulor, Nduka/CAJ-3564-2022; Boetto, Julien/AAG-5777-2021; AVSAR, TIMUCIN/W-2624-2019 Boetto, Julien/0000-0003-2572-8594; AVSAR, TIMUCIN/0000-0001-8841-4811; Youngblood, Mark/0000-0002-1044-8015; Sohrabi, Sadaf/0000-0002-1200-3335; Duran, Daniel/0000-0001-6888-252X; Miyagishima, Danielle/0000-0003-3257-5469; Peyre, Matthieu/0000-0001-8470-9516 Gregory M. Kiez and Mehmet Kutman Foundation; Yale School of Medicine funds; NIH Medical Scientist Training Program grant [T32GM007205]; NIH/NCI National Research Service Award [F30CA213666] Gregory M. Kiez and Mehmet Kutman Foundation; Yale School of Medicine funds; NIH Medical Scientist Training Program grant(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NIH/NCI National Research Service Award We sincerely thank the patients and families who donated the samples that made this study possible. We would like to thank the tireless efforts of individuals in our laboratory and at the Yale Center for Genomic Analysis for assistance in sample processing and sequencing, including Chris Castaldi, Evelyn Ng, Jaime Miller, Rachel Hagan, Judith Janette, Erica Ryke, Lindsey Wirsing, Cynthia Zerillo, Guilin Wang, and Shrikant Mane. This study was supported by the Gregory M. Kiez and Mehmet Kutman Foundation and Yale School of Medicine funds (M.G.). M.W.Y., D.F. M., and V.E.C. are supported by an NIH Medical Scientist Training Program grant (no. T32GM007205). M.W.Y. is supported by NIH/NCI National Research Service Award (no. F30CA213666). 31 54 55 0 10 AMER ASSOC NEUROLOGICAL SURGEONS ROLLING MEADOWS 5550 MEADOWBROOK DRIVE, ROLLING MEADOWS, IL 60008 USA 0022-3085 1933-0693 J NEUROSURG J. Neurosurg. NOV 2020.0 133 5 1345 1354 10.3171/2019.8.JNS191266 0.0 10 Clinical Neurology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Surgery OM6AS 31653806.0 2023-03-23 WOS:000586105700009 0
J Dai, MJ; Zhang, CT; Hong, ZY; Wawrzynski, P; Trzcinski, T; Lin, XH Dai, Mingjun; Zhang, Chanting; Hong, Zhaoyan; Wawrzynski, Pawel; Trzcinski, Tomasz; Lin, Xiaohui Multiple Dimensional Encoding/Modulation Shift-and-Addition Design for Distributed Systems WIRELESS COMMUNICATIONS & MOBILE COMPUTING English Article REPAIR; CODES In distributed computing/storage/machine learning system, the method of encoding and decoding combing shift-and-addition (SA) and zigzag decoding (ZD) is proposed to solve the problem of high computational complexity. However, in each encoded packet, one element takes part in the encoding only once, so the obtained overhead is extremely high. In this work, based on the idea of multidimensional encoding/modulation, we propose to employ one element of the encoding process multiple times when constructing one encoded packet based on the Cauchy matrix, thereby leveraging the favourable properties of the code based on Cauchy matrix. The overhead is reduced from square to logarithmic in certain parameters. Compared with the overhead of the existing square computational complexity, it is greatly reduced. [Dai, Mingjun; Zhang, Chanting; Lin, Xiaohui] Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518000, Peoples R China; [Hong, Zhaoyan] Shenzhen Univ, Coll Math & Stat, Shenzhen 518000, Peoples R China; [Wawrzynski, Pawel; Trzcinski, Tomasz] Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland Shenzhen University; Shenzhen University; Warsaw University of Technology Hong, ZY (corresponding author), Shenzhen Univ, Coll Math & Stat, Shenzhen 518000, Peoples R China. 1546623526@qq.com National Key Research and Development Program [2019YFB1803305]; Natural Science Foundation of China [62071304]; Natural Science Foundation of Guangdong Province [2020A1515010381]; Basic Research Foundation of Shenzhen City [20200826152915001]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011219]; Natural Science Foundation of Shenzhen City [JCYJ20190808120415286]; Natural Science Foundation of Shenzhen University [00002501] National Key Research and Development Program; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Basic Research Foundation of Shenzhen City; Guangdong Basic and Applied Basic Research Foundation; Natural Science Foundation of Shenzhen City; Natural Science Foundation of Shenzhen University This study was supported by the National Key Research and Development Program under Grant 2019YFB1803305, the Natural Science Foundation of China (62071304), the Natural Science Foundation of Guangdong Province (2020A1515010381), the Basic Research Foundation of Shenzhen City (20200826152915001), the Guangdong Basic and Applied Basic Research Foundation (2022A1515011219), the Natural Science Foundation of Shenzhen City (JCYJ20190808120415286), and the Natural Science Foundation of Shenzhen University (00002501). 28 0 0 0 0 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1530-8669 1530-8677 WIREL COMMUN MOB COM Wirel. Commun. Mob. Comput. SEP 15 2022.0 2022 10.1155/2022/5615041 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 7B6PV gold 2023-03-23 WOS:000899253900006 0
J Xu, JC; Liu, JN; Zhang, DZ; Zhou, ZJ; Jiang, XY; Zhang, CP; Chen, XJ Xu, Jiangchang; Liu, Jiannan; Zhang, Dingzhong; Zhou, Zijie; Jiang, Xiaoyi; Zhang, Chenping; Chen, Xiaojun Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY English Article Mandible segmentation; Fully convolutional neural network; DenseASPP; Attention gates ORGANS Purpose In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At present, the mandible is commonly segmented by experienced doctors using manually or semi-automatic methods, which is time-consuming and has poor segmentation consistency. In addition, existing automatic segmentation methods still have problems such as region misjudgment, low accuracy, and time-consuming. Methods For these issues, an automatic mandibular segmentation method using 3d fully convolutional neural network based on densely connected atrous spatial pyramid pooling (DenseASPP) and attention gates (AG) was proposed in this paper. Firstly, the DenseASPP module was added to the network for extracting dense features at multiple scales. Thereafter, the AG module was applied in each skip connection to diminish irrelevant background information and make the network focus on segmentation regions. Finally, a loss function combining dice coefficient and focal loss was used to solve the imbalance among sample categories. Results Test results showed that the proposed network obtained a relatively good segmentation result, with a Dice score of 97.588 +/- 0.425%, Intersection over Union of 95.293 +/- 0.812%, sensitivity of 96.252 +/- 1.106%, average surface distance of 0.065 +/- 0.020 mm and 95% Hausdorff distance of 0.491 +/- 0.021 mm in segmentation accuracy. The comparison with other segmentation networks showed that our network not only had a relatively high segmentation accuracy but also effectively reduced the network's misjudgment. Meantime, the surface distance error also showed that our segmentation results were relatively close to the ground truth. Conclusion The proposed network has better segmentation performance and realizes accurate and automatic segmentation of the mandible. Furthermore, its segmentation time is 50.43 s for one CT scan, which greatly improves the doctor's work efficiency. It will have practical significance in cranio-maxillofacial surgery in the future. [Xu, Jiangchang; Zhang, Dingzhong; Chen, Xiaojun] Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Inst Biomed Mfg & Life Qual Engn, Room 805,Dongchuan Rd 800, Shanghai 200240, Peoples R China; [Liu, Jiannan; Zhou, Zijie; Zhang, Chenping] Shanghai Jiao Tong Univ, Sch Med, Shanghai Peoples Hosp 9, 639 ZhiZao JuRd, Shanghai 200011, Peoples R China; [Jiang, Xiaoyi] Univ Munster, Fac Math & Comp Sci, Munster, Germany; [Chen, Xiaojun] Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai, Peoples R China Shanghai Jiao Tong University; Shanghai Jiao Tong University; University of Munster; Shanghai Jiao Tong University Chen, XJ (corresponding author), Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Inst Biomed Mfg & Life Qual Engn, Room 805,Dongchuan Rd 800, Shanghai 200240, Peoples R China.;Zhang, CP (corresponding author), Shanghai Jiao Tong Univ, Sch Med, Shanghai Peoples Hosp 9, 639 ZhiZao JuRd, Shanghai 200011, Peoples R China.;Chen, XJ (corresponding author), Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai, Peoples R China. zhang.chenping@hotmail.com; xiaojunchen@sjtu.edu.cn Jiang, Xiaoyi/AAA-3532-2022 Jiang, Xiaoyi/0000-0001-7678-9528; Zhang, Dingzhong/0000-0002-5163-8122; Chen, Xiaojun/0000-0002-0298-4491; Xu, Jiangchang/0000-0003-3187-888X National Key Research and Development Program of China [2017YFB1302900]; National Natural Science Foundation of China [81971709, M-0019, 82011530141]; Foundation of Science and Technology Commission of Shanghai Municipality [19510712200, 20490740700]; Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research [ZH2018ZDA15, YG2019ZDA06, ZH2018QNA23]; 2020 Key Research project of Xiamen Municipal Government [3502Z20201030] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation of Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research; 2020 Key Research project of Xiamen Municipal Government This work was supported by grants from the National Key Research and Development Program of China (No. 2017YFB1302900), National Natural Science Foundation of China (81971709; M-0019; 82011530141), the Foundation of Science and Technology Commission of Shanghai Municipality (19510712200, 20490740700), Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research (ZH2018ZDA15; YG2019ZDA06; ZH2018QNA23), and 2020 Key Research project of Xiamen Municipal Government (3502Z20201030). 25 3 3 6 17 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1861-6410 1861-6429 INT J COMPUT ASS RAD Int. J. Comput. Assist. Radiol. Surg. OCT 2021.0 16 10 SI 1785 1794 10.1007/s11548-021-02447-5 0.0 JUL 2021 10 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging; Surgery Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging; Surgery WV1HC 34287750.0 2023-03-23 WOS:000677864900001 0
J Tsizh, M; Novosyadlyj, B; Holovatch, Y; Libeskind, NI Tsizh, Maksym; Novosyadlyj, Bohdan; Holovatch, Yurij; Libeskind, Noam I. Large-scale structures in the Lambda CDM Universe: network analysis and machine learning MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article software: data analysis; dark matter; large-scale structure of Universe DARK-MATTER HALOES; COSMIC WEB; GALAXY; CLASSIFICATION; FILAMENTS We perform an analysis of the cosmic web as a complex network, which is built on a Lambda cold dark matter (Lambda CDM) cosmological simulation. For each of nodes, which are in this case dark matter haloes formed in the simulation, we compute 10 network metrics, which characterize the role and position of a node in the network. The relation of these metrics to topological affiliation of the halo, i.e. to the type of large-scale structure, which it belongs to, is then investigated. In particular, the correlation coefficients between network metrics and topology classes are computed. We have applied different machine learning methods to test the predictive power of obtained network metrics and to check if one could use network analysis as a tool for establishing topology of the large-scale structure of the Universe. Results of such predictions, combined in the confusion matrix, show that it is not possible to give a good prediction of the topology of cosmic web (score is approximate to 70 per cent in average) based only on coordinates and velocities of nodes (haloes), yet network metrics can give a hint about the topological landscape of matter distribution. [Tsizh, Maksym; Novosyadlyj, Bohdan] Ivan Franko Natl Univ Lviv, Kyryla & Methodia Str 8, UA-79005 Lvov, Ukraine; [Novosyadlyj, Bohdan] Jilin Univ, Coll Phys, Qianjin Str 2699, Changchun 130012, Peoples R China; [Novosyadlyj, Bohdan] Jilin Univ, Int Ctr Future Sci, Qianjin Str 2699, Changchun 130012, Peoples R China; [Holovatch, Yurij] Natl Acad Sci Ukraine, Inst Condensed Matter Phys, UA-79011 Lvov, Ukraine; [Holovatch, Yurij] Leipzig Lorraine Lviv Coventry, Collaborat & Doctoral Coll Stat Phys Complex Syst, D-04109 Leipzig, Germany; [Holovatch, Yurij] Coventry Univ, Ctr Fluid & Complex Syst, Coventry CV1 5FB, W Midlands, England; [Libeskind, Noam I.] Leibniz Inst Astrophys Potsdam AIP, Sternwarte 16, D-14482 Potsdam, Germany; [Libeskind, Noam I.] Univ Lyon, UCB Lyon 1, CNRS IN2P3, IPN Lyon, F-69622 Lyon, France Ministry of Education & Science of Ukraine; Ivan Franko National University Lviv; Jilin University; Jilin University; National Academy of Sciences Ukraine; Institute for Condensed Matter Physics of NASU; Coventry University; Leibniz Institut fur Astrophysik Potsdam (AIP); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1 Tsizh, M; Novosyadlyj, B (corresponding author), Ivan Franko Natl Univ Lviv, Kyryla & Methodia Str 8, UA-79005 Lvov, Ukraine.;Novosyadlyj, B (corresponding author), Jilin Univ, Coll Phys, Qianjin Str 2699, Changchun 130012, Peoples R China.;Novosyadlyj, B (corresponding author), Jilin Univ, Int Ctr Future Sci, Qianjin Str 2699, Changchun 130012, Peoples R China. maksym.tsizh@lnu.edu.ua; bohdan.novosyadlyj@lnu.edu.ua Tsizh, Maksym/L-6082-2017 Tsizh, Maksym/0000-0002-4480-3185; Libeskind, Noam/0000-0002-6406-0016 State Fund for Fundamental Research of Ukraine [F76]; Ministry of Education and Science of Ukraine 'Formation and characteristics of elements of the structure of the multicomponent Universe, gamma radiation of supernova remnants and observations of variable stars' [0119U001544]; Project IDEXLYON at the University of Lyon under the Investments for the Future Program [ANR-16-IDEX-0005]; joint Sino-German DFG research project `The cosmic web and its impact on galaxy formation and alignment' [DFG-LI 2015/5-1] State Fund for Fundamental Research of Ukraine(State Fund for Fundamental Research (SFFR)); Ministry of Education and Science of Ukraine 'Formation and characteristics of elements of the structure of the multicomponent Universe, gamma radiation of supernova remnants and observations of variable stars'; Project IDEXLYON at the University of Lyon under the Investments for the Future Program; joint Sino-German DFG research project `The cosmic web and its impact on galaxy formation and alignment' This work was supported by the State Fund for Fundamental Research of Ukraine under the project F76 and the project of Ministry of Education and Science of Ukraine `Formation and characteristics of elements of the structure of the multicomponent Universe, gamma radiation of supernova remnants and observations of variable stars' (state registration number 0119U001544). NIL acknowledges financial support from the Project IDEXLYON at the University of Lyon under the Investments for the Future Program (ANR-16-IDEX-0005). NIL also acknowledges support from the joint Sino-German DFG research project `The cosmic web and its impact on galaxy formation and alignment' (DFG-LI 2015/5-1). Authors are thankful to Dr R. de Regt and Dr S. Apunevych for inspiring discussions. We also thank the anonymous referee for the useful comments to the first version of the manuscript. 44 7 7 1 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. JUN 2020.0 495 1 1311 1320 10.1093/mnras/staa1030 0.0 10 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics LW4GB Green Submitted 2023-03-23 WOS:000539101400097 0
C Zia, MF; Elbouchikhi, E; Benbouzid, M; Guerrero, JM IEEE Zia, Muhammad Fahad; Elbouchikhi, Elhoussin; Benbouzid, Mohamed; Guerrero, Josep M. Microgrid Transactive Energy Systems: A Perspective on Design, Technologies, and Energy Markets 45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019) IEEE Industrial Electronics Society English Proceedings Paper 45th Annual Conference of the IEEE Industrial Electronics Society (IECON) OCT 14-17, 2019 Lisbon, PORTUGAL IEEE,Univ Nova Lisboa,IEEE Ind Elect Soc Transactive energy; microgrid; prosumers; distributed ledger technology; blockchain; peer-to-peer energy trading; community-based energy market PEER-TO-PEER; DEMAND RESPONSE; BLOCKCHAIN; CHALLENGES; MANAGEMENT Prosumers concept has evolved with the technology advancements in renewable energy sources and intelligent responsive load devices. Digitalization paves the way for prosumers participation in energy market with the help of big data and distributed ledger technologies. Hence, power system is becoming more decentralized at distributed level, which consists of prosumers, consumers, and distributed energy resources-based microgrid systems. These microgrid participants require a transactive energy system for energy price signals-based smooth energy transactions among themselves. In this regard, this paper proposes a microgrid transactive energy system design and its functional layers. Blockchain-based transactive energy systems are also discussed. Finally, the peer-to-peer and community-based energy markets are presented. [Zia, Muhammad Fahad; Benbouzid, Mohamed] Univ Brest, CNRS, UMR 6027, IRDL, F-29238 Brest, France; [Elbouchikhi, Elhoussin] CNRS, UMR 6027, ISEN Yncrea Ouest, IRDL, F-29200 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Shanghai 201306, Peoples R China; [Guerrero, Josep M.] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Centre National de la Recherche Scientifique (CNRS); Universite de Bretagne Occidentale; Centre National de la Recherche Scientifique (CNRS); Universite de Bretagne Occidentale; Shanghai Maritime University; Aalborg University Zia, MF (corresponding author), Univ Brest, CNRS, UMR 6027, IRDL, F-29238 Brest, France. muhammadfahad.zia@univ-brest.fr; elhoussin.elbouchikhi@isen-ouest.yncrea.fr; mohamed.benbouzid@univ-brest.fr; joz@et.aau.dk Guerrero, Josep M./Y-2929-2019; Zia, Muhammad Fahad/K-5548-2019; Guerrero, Josep M./D-5519-2014 Zia, Muhammad Fahad/0000-0002-8654-6121; Guerrero, Josep M./0000-0001-5236-4592 54 20 20 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1553-572X 978-1-7281-4878-6 IEEE IND ELEC 2019.0 5795 5800 6 Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Engineering BO6SZ 2023-03-23 WOS:000522050605126 0
J Shen, T; Jiang, JH; Lin, W; Ge, JJ; Wu, P; Zhou, YJ; Zuo, CT; Wang, J; Yan, ZZ; Shi, KY Shen, Ting; Jiang, Jiehui; Lin, Wei; Ge, Jingjie; Wu, Ping; Zhou, Yongjin; Zuo, Chuantao; Wang, Jian; Yan, Zhuangzhi; Shi, Kuangyu Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control FRONTIERS IN NEUROSCIENCE English Article Parkinson's disease; Deep Belief Network; overlapping group LASSO; sparse representation; deep learning; early diagnose NONLINEAR DIMENSIONALITY REDUCTION; DIFFERENTIAL-DIAGNOSIS; FDG-PET; CLASSIFICATION As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80: 20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD. [Shen, Ting; Jiang, Jiehui; Zhou, Yongjin; Yan, Zhuangzhi] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai, Peoples R China; [Jiang, Jiehui] Shanghai Univ, Key Lab Specialty Fiber Opt & Opt Access Networks, Joint Int Res Lab Specialty Fiber Opt & Adv Commu, Shanghai, Peoples R China; [Lin, Wei] Anhui Med Univ, Hosp PLA 904, Dept Neurosurg, Wuxi, Peoples R China; [Ge, Jingjie; Wu, Ping; Zuo, Chuantao] Fudan Univ, Huashan Hosp, PET Ctr, Shanghai, Peoples R China; [Zuo, Chuantao] Fudan Univ, Inst Funct & Mol Med Imaging, Shanghai, Peoples R China; [Zuo, Chuantao] Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China; [Wang, Jian] Fudan Univ, Huashan Hosp, Dept Neurol, Shanghai, Peoples R China; [Shi, Kuangyu] Univ Hosp Bern, Dept Nucl Med, Bern, Switzerland; [Shi, Kuangyu] Tech Univ Munich, Dept Nucl Med, Munich, Germany Shanghai University; Shanghai University; Anhui Medical University; Fudan University; Fudan University; Fudan University; Fudan University; University of Bern; University Hospital of Bern; Technical University of Munich; University of Munich Jiang, JH (corresponding author), Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai, Peoples R China.;Jiang, JH (corresponding author), Shanghai Univ, Key Lab Specialty Fiber Opt & Opt Access Networks, Joint Int Res Lab Specialty Fiber Opt & Adv Commu, Shanghai, Peoples R China.;Zuo, CT (corresponding author), Fudan Univ, Huashan Hosp, PET Ctr, Shanghai, Peoples R China.;Zuo, CT (corresponding author), Fudan Univ, Inst Funct & Mol Med Imaging, Shanghai, Peoples R China.;Zuo, CT (corresponding author), Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China.;Wang, J (corresponding author), Fudan Univ, Huashan Hosp, Dept Neurol, Shanghai, Peoples R China. jiangjiehui@shu.edu.cn; zuochuantao@fudan.edu.cn; wangjian336@hotmail.com Jiang, Jiehui/ABA-5060-2020; Zuo, Chuantao/AAD-9940-2022 Shi, Kuangyu/0000-0002-8714-3084 National Natural Science Foundation of China [61603236, 81671239, 81361120393, 81401135, 81771483]; National Key Research and Development Program of China from Ministry of Science and Technology of China [2016YFC1306305, 2016YFC1306500, 2018YFC1707704]; Shanghai Technology and Science Key Project in Healthcare [17441902100]; Science and Technology Commission of Shanghai Municipality [17JC1401600]; Open Project Funding of Human Phenome Institute, Fudan University [HUPIKF2018203] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China from Ministry of Science and Technology of China; Shanghai Technology and Science Key Project in Healthcare; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Open Project Funding of Human Phenome Institute, Fudan University This study was supported by grants from the National Natural Science Foundation of China (Nos. 61603236, 81671239, 81361120393, 81401135, 81771483, and 81361120393), the National Key Research and Development Program of China (Nos. 2016YFC1306305, 2016YFC1306500 and 2018YFC1707704) from Ministry of Science and Technology of China, Shanghai Technology and Science Key Project in Healthcare (No. 17441902100), Science and Technology Commission of Shanghai Municipality (17JC1401600), and the Open Project Funding of Human Phenome Institute (No. HUPIKF2018203), Fudan University. 45 14 15 0 30 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-453X FRONT NEUROSCI-SWITZ Front. Neurosci. APR 29 2019.0 13 396 10.3389/fnins.2019.00396 0.0 12 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology HV7YX 31110472.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000466198700001 0
J Gan, CQ; Yang, YC; Zhu, QY; Jain, DK; Struc, V Gan, Chenquan; Yang, Yucheng; Zhu, Qingyi; Jain, Deepak Kumar; Struc, Vitomir DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition EXPERT SYSTEMS WITH APPLICATIONS English Article Dialogue emotion recognition; Contextual information; Fine-grained information; Hierarchical feature; Interactive fusion CLASSIFICATION To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively. [Gan, Chenquan; Zhu, Qingyi] Chongqing Univ Posts & Telecommun, Sch Cyber Secur & Informat Law, Chongqing 400065, Peoples R China; [Gan, Chenquan; Yang, Yucheng] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China; [Jain, Deepak Kumar] Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Cooperat Control U, Chongqing 400065, Peoples R China; [Struc, Vitomir] Univ Ljubljana, Fac Elect Engn, Trzaska Cesta 25, SI-1000 Ljubljana, Slovenia Chongqing University of Posts & Telecommunications; Chongqing University of Posts & Telecommunications; Chongqing University of Posts & Telecommunications; University of Ljubljana Struc, V (corresponding author), Univ Ljubljana, Fac Elect Engn, Trzaska Cesta 25, SI-1000 Ljubljana, Slovenia. gcq2010cqu@163.com; s190101138@stu.cqupt.edu.cn; zhuqy@cqupt.edu.cn; deepak@cqupt.edu.cn; vitomir.struc@fe.uni-lj.si ; Zhu, Qingyi/A-6494-2018; Struc, Vitomir/R-8164-2018 Jain, Dr.Deepak Kumar/0000-0002-3400-1613; Zhu, Qingyi/0000-0002-1168-1599; Struc, Vitomir/0000-0002-3385-5780; Gan, Chenquan/0000-0002-0453-5630 43 0 0 7 7 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. DEC 30 2022.0 210 118525 10.1016/j.eswa.2022.118525 0.0 AUG 2022 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 4V6GL Green Published 2023-03-23 WOS:000859573300010 0
J Li, QD; Wang, HF; Luo, YP; Li, Q; Deng, LC; Ting, YS Li, Qi-Da; Wang, Hai-Feng; Luo, Yang-Ping; Li, Qing; Deng, Li-Cai; Ting, Yuan-Sen Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES English Article STELLAR RADIAL-VELOCITIES; GALACTIC DISK; OPEN CLUSTER; MILKY-WAY; STARS; EVOLUTION; ROTATION; ASTEROSEISMOLOGY; PLANETS; WARP We present a catalog of 948,216 stars with mass labels and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training data set is crossmatched from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR5, and high-resolution asteroseismology data, mass, and age are predicted by the random forest (RF) method or a convex-hull algorithm. The stellar parameters with a high correlation with mass and age are extracted and the test data set shows that the median relative error of the prediction model for the mass of the large sample is 3%, and for the mass and age of RC stars is 4% and 7%. We also compare the predicted age of RC stars with recent works and find that the final uncertainty of the RC sample could reach 18% for age and 9% for mass; meanwhile, the final precision of the mass for the large sample with different types of stars could reach 13% without considering systematics. All of this implies that this method could be widely used in the future. Moreover, we explore the performance of different machine-learning methods for our sample, including Bayesian linear regression and the gradient-boosting decision tree (GBDT), multilayer perceptron, multiple linear regression, RF, and support vector regression methods. Finally, we find that the performance of a nonlinear model is generally better than that of a linear model, and the GBDT and RF methods are relatively better. [Li, Qi-Da; Wang, Hai-Feng; Luo, Yang-Ping; Li, Qing; Deng, Li-Cai] China West Normal Univ, Dept Astron, Nanchong 637002, Peoples R China; [Wang, Hai-Feng] Univ PSL, Observ Paris, GEPI, CNRS, Pl Jules Janssen, F-92195 Meudon, France; [Wang, Hai-Feng] Ctr Ric Enrico Fermi, CREF, Via Panisperna 89A, I-00184 Rome, Italy; [Ting, Yuan-Sen] Australian Natl Univ, Res Sch Comp Sci, Acton, ACT 2601, Australia China West Normal University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Cite; Universite PSL; Observatoire de Paris; Australian National University Wang, HF (corresponding author), China West Normal Univ, Dept Astron, Nanchong 637002, Peoples R China.;Wang, HF (corresponding author), Univ PSL, Observ Paris, GEPI, CNRS, Pl Jules Janssen, F-92195 Meudon, France.;Wang, HF (corresponding author), Ctr Ric Enrico Fermi, CREF, Via Panisperna 89A, I-00184 Rome, Italy. hfwang@bao.ac.cn ; Wang, Hai-Feng/O-9861-2018 Ting, Yuan-Sen/0000-0001-5082-9536; Wang, Hai-Feng/0000-0001-8459-1036 CNRS-K.C.Wong; China Manned Space Project [CMS-CSST-2021-B03, CMS-CSST2021-A08]; project Complexity in self-gravitating systems of the Enrico Fermi Research Center (Rome, Italy); National Key Basic R&D Program of China [2021YFA1600401]; National Natural Science Foundation of China (NSFC) [12173028]; Chinese Space Station Telescope project [CMS-CSST-2021-A10]; Sichuan Science and Technology Program [2020YFSY0034]; Sichuan Youth Science and Technology Innovation Research Team [21CXTD0038]; Major Science and Technology Project of Qinghai Province [2019ZJ-A10]; Innovation Team Funds of China West Normal [KCXTD2022-6]; National Development and Reform Commission CNRS-K.C.Wong; China Manned Space Project; project Complexity in self-gravitating systems of the Enrico Fermi Research Center (Rome, Italy); National Key Basic R&D Program of China(National Basic Research Program of China); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Chinese Space Station Telescope project; Sichuan Science and Technology Program; Sichuan Youth Science and Technology Innovation Research Team; Major Science and Technology Project of Qinghai Province; Innovation Team Funds of China West Normal; National Development and Reform Commission We would like to thank the anonymous referee for the very helpful and insightful comments. Thanks also for the helpful comments from Martin Lopez-Corredoira. H.F.W. is supported by the CNRS-K.C.Wong Fellow in France and we acknowledge the science research grants from the China Manned Space Project with Nos. CMS-CSST-2021-B03 and CMS-CSST2021-A08. H.F.W. also acknowledges the support from the project Complexity in self-gravitating systems of the Enrico Fermi Research Center (Rome, Italy). L.Y.P is supported by the National Key Basic R&D Program of China via 2021YFA1600401, the National Natural Science Foundation of China (NSFC) under grant 12173028, the Chinese Space Station Telescope project: CMS-CSST-2021-A10, the Sichuan Science and Technology Program (grant No. 2020YFSY0034), the Sichuan Youth Science and Technology Innovation Research Team (grant No. 21CXTD0038), the Major Science and Technology Project of Qinghai Province (grant No. 2019ZJ-A10), and the Innovation Team Funds of China West Normal (grant No. KCXTD2022-6).; H.F.W. is fighting for the plan Mapping the MilkyWay (Disk) Population Structures and Galactoseismology (MWDPSG) with large sky surveys in order to establish a theoretical framework in the future to unify the global picture of the disk structures and origins with a possible comprehensive distribution function. We pay our respects to elders, colleagues, and others for comments and suggestions, thanks to all of them. The Guo Shou Jing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by National Astronomical Observatories, Chinese Academy of Sciences. This work has also made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium).Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. 60 2 2 0 1 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0067-0049 1538-4365 ASTROPHYS J SUPPL S Astrophys. J. Suppl. Ser. SEP 1 2022.0 262 1 20 10.3847/1538-4365/ac81be 0.0 17 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 4A1DY Green Submitted, Green Published, gold 2023-03-23 WOS:000844852500001 0
J Xing, Y; Yue, JP; Guo, ZZ; Chen, Y; Hu, J; Trave, A Xing, Yin; Yue, Jianping; Guo, Zizheng; Chen, Yang; Hu, Jia; Trave, Anna Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China FRONTIERS IN EARTH SCIENCE English Article landslide susceptibility; random forest; integrated model; causal factor; GIS NORTHERN APENNINES; LOESS LANDSLIDE; NEURAL-NETWORKS; LAND-USE; HAZARD; CLASSIFICATION; PREDICTION; REGRESSION; ENSEMBLES; RAINFALL Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an optimal result. [Xing, Yin; Yue, Jianping; Hu, Jia] Hohai Univ, Sch Earth Sci & Engn, Nanjing, Peoples R China; [Guo, Zizheng] China Univ Geosci, Fac Engn, Wuhan, Peoples R China; [Chen, Yang] Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China; [Trave, Anna] Univ Barcelona, Fac Ciencies Terra, Barcelona, Spain Hohai University; China University of Geosciences; Wuhan University; University of Barcelona Guo, ZZ (corresponding author), China Univ Geosci, Fac Engn, Wuhan, Peoples R China. cuggzz@cug.edu.cn Xing, Yin/0000-0002-3328-0569 Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX20_0484]; Fundamental Research Funds for the Central Universities [B200203105]; National Key Research and Development Program of China [2018YFC1508603] Postgraduate Research and Practice Innovation Program of Jiangsu Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Key Research and Development Program of China This research was funded by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX20_0484) and the Fundamental Research Funds for the Central Universities (B200203105) and the National Key Research and Development Program of China (2018YFC1508603). 69 13 13 7 44 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-6463 FRONT EARTH SC-SWITZ Front. Earth Sci. AUG 17 2021.0 9 10.3389/feart.2021.722491 0.0 15 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology UM6XG gold, Green Published 2023-03-23 WOS:000693472800001 0
C Li, JK; Wang, T; Zhou, Y; Wang, ZY; Snoussi, H Liu, T; Zhao, Q Li, Jiakun; Wang, Tian; Zhou, Yi; Wang, Ziyu; Snoussi, Hichem Using Gabor Filter in 3D Convolutional Neural Networks for Human Action Recognition PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) Chinese Control Conference English Proceedings Paper 36th Chinese Control Conference (CCC) JUL 26-28, 2017 Dalian, PEOPLES R CHINA Chinese Acad Sci, Acad Math & Syst Sci,China Soc Ind & Appl Math,Liaoning Assoc Automat,Shenyang Univ Chem Technol,Liaoning Univ Sci & Technol,Asian Control Assoc,IEEE Control Syst Soc,Inst Control Robot & Syst,Soc Instrument & Control Engineers,Chinese Assoc Automat, Tech Comm Control Theory,Syst Engn Soc China,Dalian Univ Technol Action recognition; 3D CNNs; Gabor filter Human action recognition is an important topic in the field of computer vision. We use Gabor filter in 3D CNNs models in recognizing action. Convolutional neural networks (CNNs) are a type of deep learning models, which is an efficient recognition model and has a unique superiority in image processing. Three dimension convolutional neural networks can well analyze action from video data. Gabor filter is a special convolution kernel. Its performance in feature extraction is outstanding. We test out model by KTH dataset and achieve a well result. [Li, Jiakun; Wang, Tian] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Zhou, Yi] Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116026, Peoples R China; [Wang, Ziyu] Nanjing Dingtiger Informat Technol Co Ltd, Nanjing, Jiangsu, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, Inst Charles Delaunay, LM2S, UMR STMR 6279,CNRS, F-10004 Troyes, France Beihang University; Dalian Maritime University; Centre National de la Recherche Scientifique (CNRS); Universite de Technologie de Troyes Li, JK (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. lijiakun@buaa.edu.cn; wangtian@buaa.edu.cn; yi.zhou@dlmu.edu.cn; wangziyu@dingtiger.com; hichem.snoussi@utt.fr ANR AutoFerm project; Platform CAPSEC - Rgion Champagne-Ardenne; FEDER; Fundamental Research Funds for the Central Universities [YWF-14-RSC-102]; Aeronautical Science Foundation of China [2016ZC51022]; National Natural Science Foundation of China [U1435220, 61503017] ANR AutoFerm project(French National Research Agency (ANR)); Platform CAPSEC - Rgion Champagne-Ardenne; FEDER(European Commission); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Aeronautical Science Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is partially supported by the ANR AutoFerm project and the Platform CAPSEC funded by Rgion Champagne-Ardenne and FEDER, the Fundamental Research Funds for the Central Universities (YWF-14-RSC-102), Aeronautical Science Foundation of China (2016ZC51022), and the National Natural Science Foundation of China (U1435220, 61503017). 22 9 12 2 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-2927 978-9-8815-6393-4 CHIN CONTR CONF 2017.0 11139 11144 6 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering BK1PY 2023-03-23 WOS:000432015505003 0
C Li, RJ; Zhang, SY; Wan, B; He, XM IEEE COMP SOC Li, Rongjie; Zhang, Songyang; Wan, Bo; He, Xuming Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality. [Li, Rongjie; Zhang, Songyang; Wan, Bo; He, Xuming] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China; [He, Xuming] Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China; [Li, Rongjie; Zhang, Songyang] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Beijing, Peoples R China; [Li, Rongjie; Zhang, Songyang] Univ Chinese Acad Sci, Beijing, Peoples R China; [Wan, Bo] Katholieke Univ Leuven, Dept Elect Engn ESAT, Leuven, Belgium ShanghaiTech University; Chinese Academy of Sciences; Shanghai Institute of Microsystem & Information Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; KU Leuven Wan, B (corresponding author), ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China.;Wan, B (corresponding author), Katholieke Univ Leuven, Dept Elect Engn ESAT, Leuven, Belgium. lirj2@shanghaitech.edu.cn; zhangsy@shanghaitech.edu.cn; wanbo@shanghaitech.edu.cn; hexm@shanghaitech.edu.cn Shanghai NSF [18ZR1425100] Shanghai NSF This work was done when Bo Wan was a master student in ShanghaiTech University.This work was supported by Shanghai NSF Grant (No. 18ZR1425100). 63 20 20 1 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 11104 11114 10.1109/CVPR46437.2021.01096 0.0 11 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5UT Green Submitted 2023-03-23 WOS:000742075001030 0
J Lee, K; Kwan, MP Lee, Kangjae; Kwan, Mei-Po Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories TRANSACTIONS IN GIS English Article SUPPORT VECTOR MACHINE; PHYSICAL-ACTIVITY; BUILT ENVIRONMENT; RANDOM FOREST; GREEN SPACE; NEIGHBORHOOD ENVIRONMENT; GIS; WALKING; ADULTS; SIMULATION Past studies have failed to address the spatially and temporally varying impacts of environmental factors regarding the uncertain geographic context problem. This study seeks to provide an innovative framework to facilitate the understanding of spatially and temporally varying impacts of multiple contexts on individuals' travel modes using GIS and machine learning techniques. It adopts machine learning techniques to create likelihood maps to predict the spatiotemporal patterns of individual travel behaviors and uses explanatory tools to explore the spatially and temporally varying impacts. The most notable change at a local level in the spatial dimension was that assaults and offenses involving children turned out to be important in two selected communities in Chicago. Regarding the temporally varying impact, batteries, other offenses, and robberies showed negative associations with the walking prediction to some extent at the afternoon peak (5-7:59 p.m.) during weekdays. The proposed approach will enable meaningful interpretation of complex interactions between multiple environmental factors and individual travel behaviors to suggest policies in urban planning and design. [Lee, Kangjae] Univ Seoul, Dept Geoinformat, Seoul, South Korea; [Kwan, Mei-Po] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands University of Seoul; Chinese University of Hong Kong; Chinese University of Hong Kong; Utrecht University Lee, K (corresponding author), Univ Seoul, Dept Geoinformat, Seoul, South Korea. kasbiss@gmail.com Lee, Kangjae/0000-0002-2857-6496 80 1 1 1 11 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1361-1682 1467-9671 T GIS Trans. GIS JUN 2021.0 25 3 1301 1330 10.1111/tgis.12729 0.0 FEB 2021 30 Geography Social Science Citation Index (SSCI) Geography TF2GX 2023-03-23 WOS:000618052600001 0
C Zhou, LF; Ye, W; Wang, Z; Plant, C; Bohm, C Benslimane, D; Damiani, E; Grosky, WI; Hameurlain, A; Sheth, A; Wagner, RR Zhou, Linfei; Ye, Wei; Wang, Zhen; Plant, Claudia; Boehm, Christian Indexing Multiple-Instance Objects DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2017, PT II Lecture Notes in Computer Science English Proceedings Paper 28th International Conference on Database and Expert Systems Applications (DEXA) AUG 28-31, 2017 Lyon, FRANCE Jean Moulin Lyon 3 Univ,FAW Co,LIRIS Lab,Federat Informatique Lyon,Natl Ctr Sci Res,Agence Mathematiques Interact Avec Entreprise Soc,Claude Bernard Univ Lyon 1,Natl Inst Appl Sci Lyon,ERIC Lab,Univ Lyon,Univ Lumiere Lyon 2 DISTANCE As an actively investigated topic in machine learning, Multiple-Instance Learning (MIL) has many proposed solutions, including supervised and unsupervised methods. We introduce an indexing technique supporting efficient queries on Multiple-Instance (MI) objects. Our technique has a dynamic structure that supports efficient insertions and deletions and is based on an effective similarity measure for MI objects. Some MIL approaches have proposed their similarity measures for MI objects, but they either do not use all information or are time consuming. In this paper, we use two joint Gaussian based measures for MIL, Joint Gaussian Similarity (JGS) and Joint Gaussian Distance (JGD). They are based on intuitive definitions and take all the information into account while being robust to noise. For JGS, we propose the Instance based Index for querying MI objects. For JGD, metric trees can be directly used as the index because of its metric properties. Extensive experimental evaluations on various synthetic and real-world data sets demonstrate the effectiveness and efficiency of the similarity measures and the performance of the corresponding index structures. [Zhou, Linfei; Ye, Wei; Boehm, Christian] Ludwig Maximilians Univ Munchen, Munich, Germany; [Wang, Zhen] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China; [Plant, Claudia] Univ Vienna, Vienna, Austria University of Munich; University of Electronic Science & Technology of China; University of Vienna Bohm, C (corresponding author), Ludwig Maximilians Univ Munchen, Munich, Germany. zhou@dbs.ifi.lmu.de; ye@dbs.ifi.lmu.de; zhen.wang@std.uestc.edu.cn; claudia.plant@univie.ac.at; boehm@dbs.ifi.lmu.de Böhm, Christian/HDN-5925-2022; Ye, Wei/AAD-1688-2021 Ye, Wei/0000-0002-3784-7788 31 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-64471-4 LECT NOTES COMPUT SC 2017.0 10439 143 157 10.1007/978-3-319-64471-4_13 0.0 15 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL5PX 2023-03-23 WOS:000452448100013 0
C Karimzadeh, M; Aebi, R; de Souza, AM; Zhao, ZL; Braun, T; Sargento, S; Villas, L IEEE Karimzadeh, Mostafa; Aebi, Ryan; de Souza, Allan M.; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana; Villas, Leandro Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction 2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) IEEE Wireless Communications and Networking Conference English Proceedings Paper IEEE Wireless Communications and Networking Conference (WCNC) MAR 29-APR 01, 2021 Nanjing, PEOPLES R CHINA IEEE Trajectory prediction; Traffic flow prediction; Reinforcement Learning; Transfer Learning; Graph convolution; LSTM Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordEr tRaffIc convoluTiOn Rl-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art. [Karimzadeh, Mostafa; Aebi, Ryan; Zhao, Zhongliang; Braun, Torsten] Univ Bern, Inst Comp Sci, Bern, Switzerland; [Zhao, Zhongliang] Beihang Univ, Sch Elect Engn, Beijing, Peoples R China; [de Souza, Allan M.; Villas, Leandro] Univ Estadual Campinas, Campinas, SP, Brazil; [Sargento, Susana] Inst Telecomunicacoes Aveiro, Aveiro, Portugal University of Bern; Beihang University; Universidade Estadual de Campinas; Universidade de Aveiro Karimzadeh, M (corresponding author), Univ Bern, Inst Comp Sci, Bern, Switzerland. mostafa.karimzadeh@inf.unibe.ch; ryan.aebi@hotmail.com; allanms@lrc.ic.unicamp.br; zhaozl@buaa.edu.cn; torsten.braun@inf.unibe.ch; susana@ua.pt; leandro@lrc.ic.unicamp.br ; de Souza, Allan M./H-7010-2016 Braun, Torsten/0000-0001-5968-7108; de Souza, Allan M./0000-0002-5518-8392 20 7 7 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1525-3511 978-1-7281-9505-6 IEEE WCNC 2021.0 10.1109/WCNC49053.2021.9417511 0.0 6 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS2OB 2023-03-23 WOS:000704226500265 0
J Han, Z; Hong, DF; Gao, LR; Zhang, B; Huang, M; Chanussot, J Han, Zhu; Hong, Danfeng; Gao, Lianru; Zhang, Bing; Huang, Min; Chanussot, Jocelyn AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Computer architecture; Training; Hyperspectral imaging; Search problems; Kernel; Convolution; Task analysis; Affine parameter sharing (APS); autoencoder (AE); deep learning (DL); evolutionary algorithm (EA); hyperspectral unmixing (HU); neural architecture search (NAS) DIMENSIONALITY REDUCTION; COMPONENT ANALYSIS; MIXING MODEL; NETWORK; AUTOENCODERS; ALGORITHM; FRAMEWORK; GRAPH Due to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU). Among the DL approaches, autoencoders (AEs) have become a widely used and promising network architecture. However, these AE-based methods heavily rely on manual design and may not be a good fit for specific datasets. To unmix hyperspectral images more intelligently, we propose an automatic neural architecture search model for HU, AutoNAS for short, to determine the optimal network architecture by considering channel configurations and convolution kernels simultaneously. In AutoNAS, the self-supervised training mechanism based on hyperspectral images is first designed for generating the training samples of the supernet. Then, the affine parameter sharing strategy is adopted by applying different affine transformations on the supernet weights in the training phase, which enables finding the optimal channel configuration. Furthermore, on the basis of the obtained channel configuration, the evolutionary algorithm with additional computational constraints is introduced into networks to achieve flexible convolution kernel search by evaluating unmixing results of different architectures in the supernet. Extensive experiments conducted on four hyperspectral datasets demonstrate the effectiveness and superiority of the proposed AutoNAS in comparison with several state-of-the-art unmixing algorithms. [Han, Zhu] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Han, Zhu] Chinese Acad Sci, Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China; [Han, Zhu; Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Hong, Danfeng; Gao, Lianru; Huang, Min] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China; [Zhang, Bing; Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble Inst Technol Grenoble INP, CNRS, GIPSA Lab, F-38000 Grenoble, France Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China. hanzhu19@mails.ucas.ac.cn; hongdf@aircas.ac.cn; gaolr@aircas.ac.cn; huangmin@aircas.ac.cn; jocelyn@hi.is Han, Zhu/ABH-5886-2022 Han, Zhu/0000-0002-8602-864X; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [62161160336, 42030111]; MIAI@Grenoble Alpes [ANR-19-P3IA0003]; AXA Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MIAI@Grenoble Alpes; AXA Research Fund(AXA Research Fund) This work was supported in part by the National Natural Science Foundation of China under Grant 62161160336 and Grant 42030111, in part by MIAI@Grenoble Alpes under Grant ANR-19-P3IA0003, and in part by the AXA Research Fund. 82 3 3 8 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5532214 10.1109/TGRS.2022.3186480 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3Q9SX 2023-03-23 WOS:000838563600013 0
J Zhou, C; Tang, B; Ding, LY; Sekula, P; Zhou, Y; Zhang, ZK Zhou, Cheng; Tang, Bin; Ding, Lieyun; Sekula, Przemyslaw; Zhou, Ying; Zhang, Zekun Design and automated assembly of Planetary LEGO Brick for lunar in-situ construction AUTOMATION IN CONSTRUCTION English Article Lunar habitat; In-situ construction; Finite element; Deep Convolutional Neural Network (DCNN); Automated assembly; Planetary LEGO Brick CONVOLUTIONAL NEURAL-NETWORKS; REGOLITH SIMULANT; CONCRETE; VISION; ARCHITECTURES Prior studies of brick design for future lunar constructed facilities generated various possible solution ideas. This paper describes the proposed design and automated assembly of Planetary LEGO Brick for lunar in-situ construction. The finite element method was implemented to analyze the state of the proposed structure based on ABAQUS. A Deep Convolutional Neural Network (DCNN) with the Xception architecture was used to recognize six various brick types in the process of assembling a lunar habitat facility with the use of a mechanical arm in a laboratory setting. A novel scheme with a modular system is proposed for lunar in-situ construction using Planetary LEGO Brick. The designed lunar habitat can be used to protect the astronauts from the extreme environment on the moon and provide a place for them to rest. The results demonstrate that the design scheme of Planetary LEGO Brick provides a possible solution for lunar in-situ construction. The proposed design scheme of Planetary LEGO Brick provides a feasible way for the construction of a lunar habitat and the developed algorithm can be used for automated assembly of bricks and other building components. [Zhou, Cheng; Tang, Bin; Ding, Lieyun; Zhou, Ying; Zhang, Zekun] Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan 430074, Peoples R China; [Zhou, Cheng; Tang, Bin; Ding, Lieyun; Zhou, Ying; Zhang, Zekun] Hubei Engn Res Ctr Virtual Safe & Automated Const, Wuhan, Hubei, Peoples R China; [Sekula, Przemyslaw] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA; [Sekula, Przemyslaw] Univ Econ Katowice, Fac Informat & Commun, Katowice, Poland Huazhong University of Science & Technology; University System of Maryland; University of Maryland College Park; University of Economics in Katowice Zhou, Y (corresponding author), Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan 430074, Peoples R China. ying_zhou@hust.edu.cn ; Sekula, Przemyslaw/U-1413-2018 Zhou, Ying/0000-0002-3661-9265; Ding, Lieyun/0000-0002-9873-3776; Sekula, Przemyslaw/0000-0002-4599-1077 China's National Natural Science Foundation [71671072, 71732001]; National Key R&D Program of China [2018YFB1306905] China's National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China This work was partially supported by China's National Natural Science Foundation of under Grant Nos. (71671072 & 71732001). This research is also supported by the National Key R&D Program of China (No. 2018YFB1306905). The authors would like to thank Prof. Hanbin Luo for providing the laboratory facilities used in this research. Acknowledgements are also directed to Prof. Peter E. D. Love for providing thoughtful suggestions that have helped improve this paper substantially. The authors also gratefully acknowledge Mr. Rui Chen and Mr. Weiguang Jiang's support for related works. 68 6 6 20 61 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0926-5805 1872-7891 AUTOMAT CONSTR Autom. Constr. OCT 2020.0 118 103282 10.1016/j.autcon.2020.103282 0.0 25 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering NS0QZ 2023-03-23 WOS:000571966100001 0
C Xia, ZQ; Feng, XY; Hong, XP; Zhao, GY IEEE Xia, Zhaoqiang; Feng, Xiaoyi; Hong, Xiaopeng; Zhao, Guoying Spontaneous Facial Micro-expression Recognition via Deep Convolutional Network 2018 EIGHTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) International Conference on Image Processing Theory Tools and Applications English Proceedings Paper 8th International Conference on Image Processing Theory, Tools and Applications (IPTA) NOV 07-10, 2018 Xian, PEOPLES R CHINA IEEE UKRI Comp Chapter,IEEE Xian Sect,European Assoc Signal Proc,NW Polytechn Univ,NW Univ,Key Lab Aerosp Informat Percept & Photoectr Control Micro-Expression Recognition; Recurrent Convolutional Networks; Temporal Jittering; Motion Magnification The automatic recognition of spontaneous facial micro-expressions becomes prevalent as it reveals the actual emotion of humans. However, handcrafted features employed for recognizing micro-expressions are designed for general applications and thus cannot well capture the subtle facial deformations of micro-expressions. To address this problem, we propose an end-to-end deep learning framework to suit the particular needs of micro-expression recognition (MER). In the deep model, recurrent convolutional networks are utilized to learn the representation of subtle changes from image sequences. To guarantee the learning of deep model, we present a temporal jittering procedure to greatly enrich the training samples. Through performing the experiments on three spontaneous micro-expression datasets, i.e., SMIC, CASME, and CASME2, we verify the effectiveness of our proposed MER approach. [Xia, Zhaoqiang; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China; [Hong, Xiaopeng; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland Northwestern Polytechnical University; University of Oulu Xia, ZQ (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China. zxia@nwpu.edu.cn HONG, Xiaopeng/V-6078-2019; Zhao, Guoying/ABE-7716-2020; Xia, Zhaoqiang/AAC-4021-2019 HONG, Xiaopeng/0000-0002-0611-0636; Zhao, Guoying/0000-0003-3694-206X; Xia, Zhaoqiang/0000-0003-0630-3339 National Nature Science Foundation of China [61702419]; Natural Science Basic Research Plan in Shaanxi Province of China [2018JQ6090] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China This work is partly supported by the National Nature Science Foundation of China (No.61702419), and the Natural Science Basic Research Plan in Shaanxi Province of China (No.2018JQ6090). 30 0 0 1 12 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2154-512X 978-1-5386-6428-5 INT CONF IMAG PROC 2018.0 235 240 6 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BM2PI 2023-03-23 WOS:000461275700042 0
J Barry, TJ; Hallford, DJ; Hitchcock, C; Takano, K; Raes, F Barry, Tom J.; Hallford, David J.; Hitchcock, Caitlin; Takano, Keisuke; Raes, Filip The current state of memory Specificity Training (MeST) for emotional disorders CURRENT OPINION IN PSYCHOLOGY English Review Autobiographical memory; Episodic memory; Depression; Post-traumatic stress disorder; Schizophrenia OVERGENERAL AUTOBIOGRAPHICAL MEMORY; RANDOMIZED CONTROLLED-TRIAL; FUTURE THINKING; DEPRESSION; METAANALYSIS; INDUCTION; RETRIEVAL; SYMPTOMS; PLEASURE; YOUNG Memory Specificity Training (MeST) is an intervention developed from basic science that has found clinical utility. MeST uses cued recall exercises to target the difficulty that some people with emotional disorders have in recalling personally experienced events. MeST is simple enough to be delivered alongside traditional interventions or online by artificial intelligence. Currently, research indicates MeST's effects are immediate but short-lived, and there is limited research indicating its superiority over established interventions. Future investigations must establish the dosage and specific components of MeST that are necessary for clinically significant effects. Further, it must establish the secondary processes (e.g., problem-solving) that mediate between MeST-driven improvements in memory and symptoms. Similar interventions that build upon the idea of training autobiographical memory specificity are also emerging and warrant further investigation. [Barry, Tom J.] Univ Hong Kong, Fac Social Sci, Pok Fu Lam, Hong Kong, Peoples R China; [Barry, Tom J.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Psychol, London, England; [Hallford, David J.] Deakin Univ, Sch Psychol, Melbourne, Vic, Australia; [Hallford, David J.] Federat Univ, Sch Hlth & Life Sci, Mt Helen, Australia; [Hitchcock, Caitlin] Univ Cambridge, Med Res Council, Cognit & Brain Sci Unit, Cambridge, England; [Takano, Keisuke] Ludwig Maximilians Univ Munchen, Div Clin Psychol & Psychotherapy, Dept Psychol, Munich, Germany; [Raes, Filip] Katholieke Univ Leuven, Fac Psychol & Educ Sci, Leuven, Belgium University of Hong Kong; University of London; King's College London; Deakin University; Federation University Australia; UK Research & Innovation (UKRI); Medical Research Council UK (MRC); University of Cambridge; University of Munich; KU Leuven Barry, TJ (corresponding author), Fac Social Sci, Jockey Club Tower,Pok Fu Lam Rd, Hong Kong, Peoples R China. tjbarry@hku.hk Hallford, David John/AFK-8316-2022; Hallford, David/I-7260-2015 Hallford, David/0000-0003-1093-8345; Takano, Keisuke/0000-0003-0406-8654; Hitchcock, Caitlin/0000-0002-2435-0713; Barry, Tom/0000-0003-1042-3827 ESRC [ES/R010781/1] Funding Source: UKRI ESRC(UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)) 55 4 4 3 18 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-250X 2352-2518 CURR OPIN PSYCHOL Curr. Opin. Psychol. OCT 2021.0 41 28 33 10.1016/j.copsyc.2021.02.002 0.0 MAR 2021 6 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology WJ5MJ 33689992.0 Green Accepted 2023-03-23 WOS:000709088500005 0
J Chen, L; Gao, YJ; Song, X; Li, Z; Zhu, YF; Miao, XY; Jensen, CS Chen, Lu; Gao, Yunjun; Song, Xuan; Li, Zheng; Zhu, Yifan; Miao, Xiaoye; Jensen, Christian S. Indexing Metric Spaces for Exact Similarity Search ACM COMPUTING SURVEYS English Article Metric spaces; indexing and querying; metric similarity search NEAREST NEIGHBOR SEARCH; ACCESS METHODS; TREE; ALGORITHM; EFFICIENT; DISTANCE; RETRIEVAL; QUERIES; PERFORMANCE; SELECTION With the continued digitization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity, and variety. Many studies address volume or velocity, while fewer studies concern the variety. Metric spaces are ideal for addressing variety because they can accommodate any data as long as it can be equipped with a distance notion that satisfies the triangle inequality. To accelerate search in metric spaces, a collection of indexing techniques for metric data have been proposed. However, existing surveys offer limited coverage, and a comprehensive empirical study exists has yet to be reported. We offer a comprehensive survey of existing metric indexes that support exact similarity search: we summarize existing partitioning, pruning, and validation techniques used by metric indexes to support exact similarity search; we provide the time and space complexity analyses of index construction; and we offer an empirical comparison of their query processing performance. Empirical studies are important when evaluating metric indexing performance, because performance can depend highly on the effectiveness of available pruning and validation as well as on the data distribution, which means that complexity analyses often offer limited insights. This article aims at revealing strengths and weaknesses of different indexing techniques to offer guidance on selecting an appropriate indexing technique for a given setting, and to provide directions for future research on metric indexing. [Chen, Lu; Gao, Yunjun; Song, Xuan; Li, Zheng; Zhu, Yifan] Zhejiang Univ, Coll Comp Sci, 38 Zheda Rd, Hangzhou 310027, Peoples R China; [Miao, Xiaoye] Zhejiang Univ, Ctr Data Sci, 86 Yuhangtang Rd, Hangzhou 310058, Peoples R China; [Jensen, Christian S.] Aalborg Univ, Dept Comp Sci, Selma Lagerlofs Vej 300, DK-9220 Aalborg, Denmark Zhejiang University; Zhejiang University; Aalborg University Gao, YJ (corresponding author), Zhejiang Univ, Coll Comp Sci, 38 Zheda Rd, Hangzhou 310027, Peoples R China. luchen@zju.edu.cn; gaoyj@zju.edu.cn; xsong@zju.edu.cn; zhengli3401@zju.edu.cn; xtf_z@zju.edu.cn; miaoxy@zju.edu.cn; csj@cs.aau.dk Jensen, Christian Sondergaard/0000-0002-9697-7670; Zhu, Yifan/0000-0003-4555-6232; Miao, Xiaoye/0000-0002-8632-1539 NSFC [62102351, 62025206, 61972338]; Zhejiang Provincial Natural Science Foundation [LR21F020005]; DIREC center project NSFC(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation(Natural Science Foundation of Zhejiang Province); DIREC center project This work was supported in part by the NSFC under Grants No. (62102351, 62025206 and 61972338), the Zhejiang Provincial Natural Science Foundation under Grant No. LR21F020005 and the DIREC center project. 156 0 0 3 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. JUN 2023.0 55 6 128 10.1145/3534963 0.0 39 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6S8PS Green Submitted 2023-03-23 WOS:000893245700020 0
J Gu, HY; Wei, YA Gu, Haiyan; Wei, Yinan Environmental monitoring and landscape design of green city based on remote sensing image and improved neural network ENVIRONMENTAL TECHNOLOGY & INNOVATION English Article Remote sensing image; Neural network; Green city; Environmental monitoring CLIMATE-CHANGE; WATER; NEXUS; FOOD; ENERGY; OPTIMIZATION In some developed countries in Europe, cities are developing very rapidly, but the rapid development of these countries also has to go through the following stages, which are the three essential development processes of urbanization, counter-urbanization and re-urbanization. My country's development situation is relatively uneven. In the more developed areas of my country, the speed of urbanization is relatively rapid, such as cities near the Yangtze River Delta and the Pearl River Delta. But with the development of urbanization, these areas are also facing a very troublesome problem, that is, the balance between the development of the city and the environmental ecosystem. In order to monitor this situation, we will use remote sensing technology to conduct a comprehensive survey of the urban environmental ecosystem and analyze the impact and effect of the rapid development of the city on the changes in the ecosystem. This article will focus on the area around the Yangtze River Delta, and investigate the spatial patterns of urban environmental ecosystems by comparing this area with cities in developed regions such as Western Europe and North America. Under the conditions of different regions and different degrees of urbanization, the comprehensive situation of environmental elements in the city is investigated, and the environmental impact effects and the causes, effects and effects of environmental changes are more accurately studied. Combine the BP network model related to the nervous system in the experiment, and continuously optimize the model during the experiment. The experimental data selects environmental difference reports displayed in different years and different seasons to predict the air pollution value of a certain city. The reason why we use this method is that this model can improve the accuracy of statistical data and greatly simplify the unnecessary and complicated steps in the research process, and it can also reduce the error problems generated in the operation of the BP system, such as when we When entering a piece of data, the data obtained is often very different from the target data. Therefore, we will use the ant colony algorithm to solve the above problems, and add stereo and 3D technologies, which can monitor experiments in real time, greatly improve the speed of the experiment, and the 3D stereo technology can make the results more perceptual to meet the needs of the experiment. (C) 2021 Elsevier B.V. All rights reserved. [Gu, Haiyan] Yancheng Inst Technol, Yancheng City, Jiangsu, Peoples R China; Vilnius Gediminas Tech Univ, Sauletekio Al 25, LT-10225 Vilnius, Lithuania Yancheng Institute of Technology; Vilnius Gediminas Technical University Gu, HY (corresponding author), Yancheng Inst Technol, Yancheng City, Jiangsu, Peoples R China. guhaiyan@ycit.edu.cn 25 8 8 6 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-1864 ENVIRON TECHNOL INNO Environ. Technol. Innov. AUG 2021.0 23 101718 10.1016/j.eti.2021.101718 0.0 JUL 2021 13 Biotechnology & Applied Microbiology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Biotechnology & Applied Microbiology; Engineering; Environmental Sciences & Ecology UA2UQ 2023-03-23 WOS:000685019500006 0
J Han, Z; Hong, DF; Gao, LR; Yao, J; Zhang, B; Chanussot, J Han, Zhu; Hong, Danfeng; Gao, Lianru; Yao, Jing; Zhang, Bing; Chanussot, Jocelyn Multimodal Hyperspectral Unmixing: Insights From Attention Networks IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Laser radar; Feature extraction; Hyperspectral imaging; Data mining; Synthetic aperture radar; Data models; Estimation; Attention; autoencoder (AE); deep learning (DL); hyperspectral unmixing (HU); light detection and ranging (LiDAR); multimodality MIXING MODEL; LIDAR; FUSION Deep learning (DL) has aroused wide attention in hyperspectral unmixing (HU) owing to its powerful feature representation ability. As a representative of unsupervised DL approaches, autoencoder (AE) has been proven to be effective to better capture nonlinear components of hyperspectral images than the traditional model-driven linearized methods. However, only using hyperspectral images for unmixing fails to distinguish objects in complex scene, especially for different endmembers with similar materials. To overcome this limitation, we propose a novel multimodal unmixing network for hyperspectral images, called MUNet, by considering the height differences of light detection and ranging (LiDAR) data in a squeeze-and-excitation (SE)-driven attention fashion to guide the unmixing process, yielding performance improvement. MUNet is capable of fusing multimodal information and using the attention map derived by LiDAR to aid network that focuses on more discriminative and meaningful spatial information regarding scenes. Moreover, attribute profile (AP) is adopted to extract the geometrical structures of different objects to better model the spatial information of LiDAR. Experimental results on synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method compared with several state-of-the-art unmixing algorithms. The codes will be available at https://github.com/hanzhu97702/IEEE_TGRS_MUNet, contributing to the remote sensing community. [Han, Zhu; Zhang, Bing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Han, Zhu; Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Hong, Danfeng; Gao, Lianru; Yao, Jing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China. hanzhu19@mails.ucas.ac.cn; hongdf@aircas.ac.cn; gaolr@aircas.ac.cn; yaojing@aircas.ac.cn; zb@radi.ac.cn; jocelyn@hi.is Han, Zhu/ABH-5886-2022 Han, Zhu/0000-0002-8602-864X; Chanussot, Jocelyn/0000-0003-4817-2875; Yao, Jing/0000-0003-1301-9758 National Natural Science Foundation of China [62161160336, 42030111]; MIAI@Grenoble Alpes [ANR-19-P3IA-0003]; AXA Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MIAI@Grenoble Alpes; AXA Research Fund(AXA Research Fund) This work was supported in part by the National Natural Science Foundation of China under Grant 62161160336 and Grant 42030111, in part by MIAI@Grenoble Alpes under Grant ANR-19-P3IA-0003, and in part by the AXA Research Fund. 59 9 10 15 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5524913 10.1109/TGRS.2022.3155794 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 0J1AZ 2023-03-23 WOS:000779841600013 0
J Ahmed, I; Camacho, D; Jeon, G; Piccialli, F Ahmed, Imran; Camacho, David; Jeon, Gwanggil; Piccialli, Francesco Internet of health things driven deep learning-based system for non-invasive patient discomfort detection using time frame rules and pairwise keypoints distance feature SUSTAINABLE CITIES AND SOCIETY English Article Sustainable smart cities; Smart healthcare; Internet of health things; Deep learning; Non-invasive; Patient discomfort EPILEPTIC SEIZURES; RESPIRATORY RATE; ALGORITHM; TRACKING Nowadays, digitization has opened up various possibilities in the healthcare sector. Smart healthcare is becoming a field of remarkable transformations and growth in the new era of smart cities and digitally interconnected societies. In smart healthcare systems, multiple sensors, devices, and sources are interconnected through networks and collect valuable data that gives insights about the patient's health status and ongoing health trends. Internet of Health Things (IoHT) is a concept that represents the identifiable sensors, devices, and sources connected to the internet and that can be utilized in various automated patient monitoring applications to provide remote healthcare services. This article aims to provide an IoHT driven deep learning-based system for non-invasive patient discomfort detection. The developed system comprises of an RGB camera device to identify discomfort in a patient's body instead of utilizing any wearable sensors or devices. In order to identify movement and discomfort, the system utilizes the YOLOv3 model to detect the patient's body and the Alphapose method to extract different keypoints information of the patient's body. Applying association rules, the detected keypoints are then converted into six central body organs. To detect a patient's discomfort, pairwise distance is measured between detected keypoints, and finally, time frame rules are applied to set a threshold in order to analyze movements as either associated with normal or discomfort conditions. The patient's body is monitored continuously for multiple frames, based on which comfort and discomfort levels are distinguished. Different video sequences are used for experimental evaluations; results reveal the effectiveness of the developed system by achieving a True Positive Rate of 95% with a 6% False Positive Rate. [Ahmed, Imran] Inst Management Sci, Ctr excellence Informat Technol, Peshawar, Pakistan; [Camacho, David] Univ Politecn Madrid, Comp Syst Engn Dept, Madrid, Spain; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea; [Piccialli, Francesco] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy Universidad Politecnica de Madrid; Xidian University; Incheon National University; University of Naples Federico II Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China.;Jeon, G (corresponding author), Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea. imran.ahmed@imsciences.edu.pk; david.camacho@upm.es; ggjeon@gmail.com; francesco.piccialli@unina.it Ahmed, Imran/HDL-7255-2022 62 2 2 8 20 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. APR 2022.0 79 103672 10.1016/j.scs.2022.103672 0.0 JAN 2022 15 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels 0J2BG 2023-03-23 WOS:000779909900001 0
J Lu, P; Qin, YY; Li, ZB; Mondini, AC; Casagli, N Lu, Ping; Qin, Yuanyuan; Li, Zhongbin; Mondini, Alessandro C.; Casagli, Nicola Landslide mapping from multi-sensor data through improved change detection-based Markov random field REMOTE SENSING OF ENVIRONMENT English Article Landslide inventory mapping; Change detection; NDVI; Principal component analysis; Independent component analysis; Markov random field (MRF); Multi-sensor CLIMATE-CHANGE; SHALLOW LANDSLIDES; TIME-SERIES; RIVER-BASIN; SPATIAL-DISTRIBUTION; ENERGY MINIMIZATION; HAZARD ASSESSMENT; EARTHQUAKE; INVENTORY; IMAGES Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi sensor data. The results demonstrate that the improved delta NDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data. [Lu, Ping; Qin, Yuanyuan] Tongji Univ, Coll Surveying & Geoinformat, Siping Rd 1239, Shanghai, Peoples R China; [Li, Zhongbin] Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA; [Mondini, Alessandro C.] CNR IRPI, Via Madonna Alta 126, Perugia, Italy; [Casagli, Nicola] Univ Firenze, Dept Earth Sci, Via La Pira 4, Florence, Italy Tongji University; Michigan State University; Consiglio Nazionale delle Ricerche (CNR); Istituto di Ricerca per la Protezione Idrogeologica (IRPI-CNR); University of Florence Li, ZB (corresponding author), Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA. lizhong2@msu.edu Mondini, Alessandro/AAK-9138-2020; Casagli, Nicola/B-6882-2016 Mondini, Alessandro/0000-0002-4895-8272; Casagli, Nicola/0000-0002-8684-7848; Lu, Ping/0000-0002-0127-3911; Li, Zhongbin/0000-0002-7479-0266; Li, Zhongbin/0000-0003-2600-0589 National Key R&D Program of China [2017YFA0603100]; National Natural Science Foundation of China [41671413]; IPL project of the International Consortium on Landslides (ICL); Fundamental Research Funds for the Central Universities (Tongji University); Italian Civil Protection Department National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); IPL project of the International Consortium on Landslides (ICL); Fundamental Research Funds for the Central Universities (Tongji University); Italian Civil Protection Department This study was funded by the National Key R&D Program of China (2017YFA0603100), National Natural Science Foundation of China (No. 41671413), the IPL project of the International Consortium on Landslides (ICL) and the Fundamental Research Funds for the Central Universities (Tongji University). FORMOSAT-2 images were provided by NARLabs-NSPO under the FS2CFP program. Landslide inventory and characterization in Messina have been done by the Italian National Research Council and the University of Firenze with funding of the Italian Civil Protection Department. 81 77 79 19 107 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. SEP 15 2019.0 231 111235 10.1016/j.rse.2019.111235 0.0 17 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology IW0HV hybrid, Green Published 2023-03-23 WOS:000484643900027 0
J Xia, XH; Jiang, DM; Sahli, H Xia, Xiaohan; Jiang, Dongmei; Sahli, Hichem Learning Salient Segments for Speech Emotion Recognition Using Attentive Temporal Pooling IEEE ACCESS English Article Feature extraction; Hidden Markov models; Emotion recognition; Speech recognition; Computer architecture; Computational modeling; Support vector machines; Attentive temporal pooling; deep neural networks; hand-crafted audio features; speech emotion recognition DEEP NEURAL-NETWORK; AUDIO; MODEL; FEATURES In the temporal process of expressing the emotions, some intervals embed more salient emotion information than others. In this paper, by introducing an attentive temporal pooling module into the deep neural network (DNN) architecture, we present a simple but effective speech emotion recognition (SER) framework, which is able to automatically highlight the emotionally salient segments while suppressing the influence of less relevant ones. For an input speech utterance, the extracted feature sequence of hand-crafted low-level descriptors (LLDs) are evenly split into several overlapping temporal segments, and the segment-level features are computed by performing functionals on the LLDs of each segment. These segment-level features are then input into a DNN model outputting the emotion probabilities as well as the more condensed representation of each segment. An attentive temporal pooling module, consisting of an auxiliary DNN and a Gaussian Mixture Model (GMM), is proposed to learn the emotional saliency weights of different temporal segments from the condensed representations, which are then assigned to the segment-level emotion probabilities for the final utterance-level prediction. Notably, the attentive temporal pooling module and the DNN architecture for feature abstraction can be jointly trained using only the utterance-level labels, while without any frame-level or segment-level supervisory information. Experimental results on the three public released emotion datasets RML, EMO-DB, and IEMOCAP show that the proposed framework obtains state-of-the-art performance on SER. [Xia, Xiaohan; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint AVSP Res Lab, Xian 710072, Peoples R China; [Xia, Xiaohan; Jiang, Dongmei] Northwestern Polytech Univ, Shaanxi Key Lab Speech & Image Informat Proc, Xian 710072, Peoples R China; [Xia, Xiaohan; Jiang, Dongmei] Peng Cheng Lab, Shenzhen 518055, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel VUB, Dept Elect & Informat ETRO, VUB NPU Joint AVSP Lab, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Northwestern Polytechnical University; Peng Cheng Laboratory; Vrije Universiteit Brussel; IMEC Jiang, DM (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint AVSP Res Lab, Xian 710072, Peoples R China.;Jiang, DM (corresponding author), Northwestern Polytech Univ, Shaanxi Key Lab Speech & Image Informat Proc, Xian 710072, Peoples R China.;Jiang, DM (corresponding author), Peng Cheng Lab, Shenzhen 518055, Peoples R China. jiangdm@nwpu.edu.cn Sahli, Hichem/0000-0002-1774-2970; Jiang, Dongmei/0000-0002-6238-8499 Shaanxi Provincial Key Research and Development Program [2017KW-ZD-14]; 111 Project [B16039]; Vrije Universiteit Brussel Interdisciplinary Research Program through the EMO-App Project Shaanxi Provincial Key Research and Development Program; 111 Project(Ministry of Education, China - 111 Project); Vrije Universiteit Brussel Interdisciplinary Research Program through the EMO-App Project This work was supported in part by the Shaanxi Provincial Key Research and Development Program under Grant 2017KW-ZD-14, in part by the 111 Project under Grant B16039, and in part by the Vrije Universiteit Brussel Interdisciplinary Research Program through the EMO-App Project. 47 3 4 1 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 151740 151752 10.1109/ACCESS.2020.3014733 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications NG7LA gold 2023-03-23 WOS:000564161000001 0
J Lin, C; Hu, RF; Sun, FF; Liang, WW Lin, Chen; Hu, Ruofei; Sun, FangFang; Liang, Weiwei Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression JOURNAL OF CLINICAL LABORATORY ANALYSIS English Article adrenocortical carcinoma; ferroptosis; LASSO; machine learning; prognosis model PROGRAMMED CELL-DEATH; SENSITIVITY; PACKAGE Background This study aimed to find ferroptosis-related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model. Methods We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis-related genes were screened by univariate regression analysis, machine-learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis-related gene-based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival-associated ferroptosis-related genes. Results Seventy genes were identified as survival-associated ferroptosis-related genes. The prognostic model was constructed with 17 ferroptosis-related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1-, 3-, and 5- year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems. Conclusion We constructed a prognosis model of ACC based on ferroptosis-related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development. [Lin, Chen] Zhejiang Univ, Sch Med, Affiliated Hosp 2, Dept Breast Surg, Hangzhou, Peoples R China; [Hu, Ruofei] Tech Univ Madrid, Lifestyle Supporting Technol Grp, Madrid, Spain; [Sun, FangFang] Zhejiang Univ, Key Lab Canc Prevent & Intervent, China Natl Minist Educ, Canc Inst,Sch Med,Affiliated Hosp 2, Hangzhou, Peoples R China; [Liang, Weiwei] Zhejiang Univ, Sch Med, Affiliated Hosp 2, Dept Endocrinol, Hangzhou, Peoples R China Zhejiang University; Universidad Politecnica de Madrid; Zhejiang University; Zhejiang University Liang, WW (corresponding author), Zhejiang Univ, Sch Med, Affiliated Hosp 2, Dept Endocrinol, Hangzhou, Peoples R China. helenliangww@zju.edu.cn Lin, Chen/0000-0001-6783-215X Zhejiang Provincial Natural Science Foundation of China [LQ20H160021] Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This study was funded by Zhejiang Provincial Natural Science Foundation of China [grant number LQ20H160021] 43 0 0 2 6 WILEY HOBOKEN 111 RIVER ST, HOBOKEN, NJ 07030 USA 0887-8013 1098-2825 J CLIN LAB ANAL J. Clin. Lab. Anal. JUN 2022.0 36 6 e24465 10.1002/jcla.24465 0.0 MAY 2022 10 Medical Laboratory Technology Science Citation Index Expanded (SCI-EXPANDED) Medical Laboratory Technology 1V8TT 35500219.0 Green Accepted 2023-03-23 WOS:000789607000001 0
J Ding, KY; Wang, JJ; Zhou, YX; Tian, H; Lu, L; Mazzarello, R; Jia, CL; Zhang, W; Rao, F; Ma, E Ding, Keyuan; Wang, Jiangjing; Zhou, Yuxing; Tian, He; Lu, Lu; Mazzarello, Riccardo; Jia, Chunlin; Zhang, Wei; Rao, Feng; Ma, Evan Phase-change heterostructure enables ultralow noise and drift for memory operation SCIENCE English Article PLANE-WAVE; CRYSTALLIZATION; NETWORK; COHP Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)-based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost. [Ding, Keyuan; Rao, Feng] Shenzhen Univ, Coll Mat Sci & Engn, Shenzhen 518060, Peoples R China; [Ding, Keyuan] Shenzhen Univ, Coll Optoelect Engn, Key Lab Optoelect Devices & Syst, Minist Educ & Guangdong Prov, Shenzhen 518060, Peoples R China; [Wang, Jiangjing; Zhou, Yuxing; Zhang, Wei] Xi An Jiao Tong Univ, Ctr Adv Mat Performance Nanoscale, State Key Lab Mech Behav Mat, Xian 710049, Shaanxi, Peoples R China; [Wang, Jiangjing] Yulin Univ, Sch Chem & Chem Engn, Yulin 719000, Peoples R China; [Tian, He] Zhejiang Univ, Ctr Electron Microscopy, Sch Mat Sci & Engn, State Key Lab Silicon Mat, Hangzhou 310027, Zhejiang, Peoples R China; [Lu, Lu; Jia, Chunlin] Xian Jiaetong Univ, Sch Microelect, State Key Lab Mech Behav Mat, Xian 710049, Shaanxi, Peoples R China; [Mazzarello, Riccardo] Rhein Westfal TH Aachen, JARA FIT, Inst Theoret Solid State Phys, D-52074 Aachen, Germany; [Mazzarello, Riccardo] Rhein Westfal TH Aachen, JARA HPC, D-52074 Aachen, Germany; [Jia, Chunlin] Forschungszentrum Julich, Ernst Ruska Ctr Microscopy & Spect Electrons, D-52425 Julich, Germany; [Rao, Feng] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, State Key Lab Funct Mat Informat, Shanghai 200050, Peoples R China; [Ma, Evan] Johns Hopkins Univ, Dept Mat Sci & Engn, Baltimore, MD 21218 USA Shenzhen University; Shenzhen University; Xi'an Jiaotong University; Yulin University; Zhejiang University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Research Center Julich; Chinese Academy of Sciences; Shanghai Institute of Microsystem & Information Technology, CAS; Johns Hopkins University Rao, F (corresponding author), Shenzhen Univ, Coll Mat Sci & Engn, Shenzhen 518060, Peoples R China.;Zhang, W (corresponding author), Xi An Jiao Tong Univ, Ctr Adv Mat Performance Nanoscale, State Key Lab Mech Behav Mat, Xian 710049, Shaanxi, Peoples R China.;Rao, F (corresponding author), Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, State Key Lab Funct Mat Informat, Shanghai 200050, Peoples R China.;Ma, E (corresponding author), Johns Hopkins Univ, Dept Mat Sci & Engn, Baltimore, MD 21218 USA. wzhangO@mail.xjtu.edu.cn; fengrao@szu.edu.cn; ema@jhu.edu Zhou, Yuxing/AAA-3958-2020; xjtu, campnano/Q-1904-2015; Mazzarello, Riccardo/G-4502-2014; Wang, Jiangjing/AAD-2136-2021; Tian, He/AAS-7880-2020; Zhang, Wei/P-3546-2019; Zhang, Wei/I-3541-2012 Zhou, Yuxing/0000-0001-8061-3867; Mazzarello, Riccardo/0000-0003-2319-375X; Wang, Jiangjing/0000-0001-7327-8159; Tian, He/0000-0003-2108-3100; Ma, Evan/0000-0002-7468-4340; Zhang, Wei/0000-0002-0720-4781; Rao, Feng/0000-0002-6922-5393; Ding, Keyuan/0000-0002-4234-4341 National Natural Science Foundation of China [11474249, 61622408, 61774123]; Major Provincial Basic Research Program of Guangdong [2017KZDXM070]; Science and Technology Foundation of Shenzhen [JCYJ20180507182248605]; 111 Project 2.0 [BP2018008]; U.S. Department of Energy (DOE) DOE-BES-DMSE [DE-FG02-16ER46056]; National 973 Program of China [2015CB654901]; Deutsche Forschungsgemeinschaft (DFG) [SFB 917]; National Supercomputing Center TianHe-2 (A) in Guangzhou; HPC platform; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies at Xi'an Jiaotong University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Provincial Basic Research Program of Guangdong; Science and Technology Foundation of Shenzhen; 111 Project 2.0; U.S. Department of Energy (DOE) DOE-BES-DMSE(United States Department of Energy (DOE)); National 973 Program of China(National Basic Research Program of China); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); National Supercomputing Center TianHe-2 (A) in Guangzhou; HPC platform; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies at Xi'an Jiaotong University F.R. thanks the National Natural Science Foundation of China (61622408), the Major Provincial Basic Research Program of Guangdong (2017KZDXM070), and the Science and Technology Foundation of Shenzhen (JCYJ20180507182248605). W.Z. thanks the support of National Natural Science Foundation of China (61774123) and 111 Project 2.0 (BP2018008). E.M. is supported at Johns Hopkins University by U.S. Department of Energy (DOE) DOE-BES-DMSE under grant DE-FG02-16ER46056. H.T. thanks the National 973 Program of China(2015CB654901) and the National Natural Science Foundation of China (11474249). RM. and C.J. acknowledge funding from Deutsche Forschungsgemeinschaft (DFG) within SFB 917 (Nanoswitches). The authors also acknowledge the support by National Supercomputing Center TianHe-2 (A) in Guangzhou and by the HPC platform and the International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies at Xi'an Jiaotong University. 60 178 181 58 399 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 0036-8075 1095-9203 SCIENCE Science OCT 11 2019.0 366 6462 210 + 10.1126/science.aay0291 0.0 25 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics JD5HS 31439757.0 2023-03-23 WOS:000490014700037 0
J Zhang, Q; Zhang, ZY; Lu, T; Yu, YT; Penuelas, J; Zhu, YG; Qian, HF Zhang, Qi; Zhang, Zhenyan; Lu, Tao; Yu, Yitian; Penuelas, Josep; Zhu, Yong-Guan; Qian, Haifeng Gammaproteobacteria, a core taxon in the guts of soil fauna, are potential responders to environmental concentrations of soil pollutants MICROBIOME English Article Gut microbiota; Pesticide; Indicator taxa; Machine learning; Soil invertebrate; Antibiotic resistance genes MICROBIOTA; DIVERSITY; COMMUNITIES; SUCCESSION; OXYTETRACYCLINE; ANTIBIOTICS; POPULATIONS; RESISTANCE; RESISTOME; SURFACE Background: The ubiquitous gut microbiotas acquired from the environment contribute to host health. The gut microbiotas of soil invertebrates are gradually assembled from the microecological region of the soil ecosystem which they inhabit, but little is known about their characteristics when the hosts are under environmental stress. The rapid development of high-throughput DNA sequencing in the last decade has provided unprecedented insights and opportunities to characterize the gut microbiotas of soil invertebrates. Here, we characterized the core, transient, and rare bacterial taxa in the guts of soil invertebrates using the core index (CI) and developed a new theory of global microbial diversity of soil ecological microregions. Results: We found that the Gammaproteobacteria could respond indiscriminately to the exposure to environmental concentrations of soil pollutants and were closely associated with the physiology and function of the host. Meanwhile, machine-learning models based on metadata calculated that Gammaproteobacteria were the core bacteria with the highest colonization potential in the gut, and further identified that they were the best indicator taxon of the response to environmental concentrations of soil pollution. Gammaproteobacteria also closely correlated with the abundance of antibiotic resistance genes. Conclusions: Our results determined that Gammaproteobacteria were an indicator taxon in the guts of the soil invertebrates that responded to environmental concentrations of soil pollutants, thus providing an effective theoretical basis for subsequent assessments of soil ecological risk. The results of the physiological and biochemical analyses of the host and the microbial-community functions, and the antibiotic resistance of Gammaproteobacteria, provide new insights for evaluating global soil ecological health. [Zhang, Qi; Zhang, Zhenyan; Lu, Tao; Yu, Yitian; Qian, Haifeng] Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China; [Penuelas, Josep] CSIC, Global Ecol Unit CREAF CSIC UAB, Barcelona 08193, Catalonia, Spain; [Penuelas, Josep] CREAF, Campus Univ Autonoma Barcelona, Barcelona 08193, Catalonia, Spain; [Zhu, Yong-Guan] Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen 361021, Spain; [Zhu, Yong-Guan] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China Zhejiang University of Technology; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Consejo Superior de Investigaciones Cientificas (CSIC); Autonomous University of Barcelona; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); University of Barcelona; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES) Qian, HF (corresponding author), Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China. hfqian@zjut.edu.cn CAS, KLUEH/T-5743-2019; Penuelas, Josep/D-9704-2011; Zhu, Yong-Guan/A-1412-2009; Lu, Tao/Y-3535-2018; Qian, Haifeng/A-7791-2012 Penuelas, Josep/0000-0002-7215-0150; Zhu, Yong-Guan/0000-0003-3861-8482; Lu, Tao/0000-0002-2030-3813; Qian, Haifeng/0000-0003-0807-9991; Zhang, Zhenyan/0000-0002-9122-2391 National Natural Science Foundation of China [21976161, 21777144, 41907210] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Funding was provided by the National Natural Science Foundation of China (21976161, 21777144, and 41907210). 57 19 20 32 149 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2049-2618 MICROBIOME Microbiome SEP 30 2021.0 9 1 196 10.1186/s40168-021-01150-6 0.0 17 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology UZ8PL 34593032.0 Green Published, gold 2023-03-23 WOS:000702461200001 0
J Wang, Q; Xie, XY; Shahrour, I; Huang, Y Wang, Qiang; Xie, Xiongyao; Shahrour, Isam; Huang, Yu Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions AUTOMATION IN CONSTRUCTION English Article Shield tunneling; Slurry pressure; Prediction; Fluctuations; Denoising method; Long short-term memory; Deep learning; Mixed ground VARIATIONAL MODE DECOMPOSITION; NEURAL-NETWORKS Tunnel face stability constitutes a significant challenge for shield tunneling in urban areas. The use of a slurry pressure balanced shield machine in mixed ground conditions containing mudstone is generally disturbed by clogging, which results in noises and large fluctuations in the tunnel face pressure. These fluctuations add difficulties to the prediction of the slurry pressure. This paper proposes a denoising method to overcome this difficulty. This method is combined with variational mode decomposition and detrended fluctuation analysis. The method is coupled with cross-correlation analysis (CCA) and a long short-term memory (LSTM) network to predict the tunnel face pressure using both tunneling parameters and geological data as input. The paper proposes a predictive strategy that separates the trend component and fluctuation component from the denoised tunneling data via CCA. Two LSTM-based predictors are presented and combined for the development of a new modeling strategy. The performances of the proposed strategy are illustrated through an application to the Nanning metro. The LSTM model with the proposed strategy gave excellent results in both mudstone and round gravel grounds with an overall R2 value of 0.9974. The paper also presents a comparison of the proposed model with some traditional models as well as a discussion on the importance of input features in different ground conditions. [Wang, Qiang; Xie, Xiongyao; Shahrour, Isam; Huang, Yu] Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China; [Wang, Qiang; Xie, Xiongyao; Huang, Yu] Tongji Univ, Minist Educ, Key Lab Geotech & Underground Engn, Shanghai 200092, Peoples R China; [Shahrour, Isam] Lille Univ, Lab Genie Civil & Geoenvironm, F-59000 Lille, France Tongji University; Tongji University; Universite de Lille - ISITE; Universite de Lille Xie, XY (corresponding author), Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China. xiexiongyao@tongji.edu.cn xie, xiongyao/AAH-3550-2022; xie, xiongyao/AFM-0926-2022 National Key R&D Program of China [2019YFC0605100, 2019YFC0605103]; National Natural Science Foundation of China [52038008]; Shanghai Science and Technology Development Funds [20DZ1202004]; China Railway 16 Bureau Group Beijing Metro Engineering Construction Co., Ltd. National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Science and Technology Development Funds; China Railway 16 Bureau Group Beijing Metro Engineering Construction Co., Ltd. This work was supported by the National Key R&D Program of China under grant 2019YFC0605100 and 2019YFC0605103, National Natural Science Foundation of China under grant 52038008, and Shanghai Science and Technology Development Funds under grant 20DZ1202004. Thanks also go to China Railway 16 Bureau Group Beijing Metro Engineering Construction Co., Ltd. for providing the data about the section from Bai-cang-ling station to Railway station of Nanning metro line 1. 48 1 2 8 73 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0926-5805 1872-7891 AUTOMAT CONSTR Autom. Constr. AUG 2021.0 128 103741 10.1016/j.autcon.2021.103741 0.0 MAY 2021 16 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering SV1BW 2023-03-23 WOS:000663561700004 0
C Wassermann, S; Seufert, M; Casas, P; Gang, L; Li, K Assoc Comp Machinery Wassermann, Sarah; Seufert, Michael; Casas, Pedro; Gang, Li; Li, Kuang I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic INTERNET-QOE'19: PROCEEDINGS OF THE 4TH INTERNET-QOE WORKSHOP: QOE-BASED ANALYSIS AND MANAGEMENT OF DATA COMMUNICATION NETWORKS English Proceedings Paper 4th Internet-QoE Workshop - QoE-Based Analysis and Management of Data Communication Networks OCT 21, 2019 Los Cabos, MEXICO ACM SIGMOBILE,Assoc Comp Machinery Network Monitoring; QoE; HTTP Adaptive Video Streaming; Encrypted Traffic We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing in particular on video-resolution analysis. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict different video resolution levels in a fine-grained scale, ranging from 144p to 1080p resolution, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict video resolution with very high accuracy, and in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction. [Wassermann, Sarah; Casas, Pedro] Austrian Inst Technol, Seibersdorf, Austria; [Seufert, Michael] Univ Wurzburg, Wurzburg, Germany; [Gang, Li; Li, Kuang] Huawei Technol, Shenzhen, Peoples R China Austrian Institute of Technology (AIT); University of Wurzburg; Huawei Technologies Wassermann, S (corresponding author), Austrian Inst Technol, Seibersdorf, Austria. Seufert, Michael/AAR-7846-2021 Seufert, Michael/0000-0002-5036-5206 16 8 8 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6927-5 2019.0 1 6 10.1145/3349611.3355549 0.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BO5ND 2023-03-23 WOS:000518379400001 0
C Manate, B; Fortis, F; Moore, P IEEE Manate, Bogdan; Fortis, Florin; Moore, Philip Applying the Prometheus methodology for an Internet of Things architecture 2014 IEEE/ACM 7TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC) International Conference on Utility and Cloud Computing English Proceedings Paper IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC) DEC 08-11, 2014 London, UNITED KINGDOM IEEE,IEEE Comp Soc,ACM,TCSC IEEE,Sigarch Cloud Computing; Cloud Governance; multi-agent system; agent oriented methodology MULTIAGENT SYSTEMS; GAIA METHODOLOGY The rapid expansion of the Internet of Things (IoT) will generate a diverse range of data types that needs to be handled, processed and stored. This paper aims to create a multi-agent system that suits the needs introduced by the IoT expansion, thus being able to oversee the Big Data collection and processing and also to maintain the semantic links between the data sources and data consumers. In order to build a complex agent oriented architecture, we have assessed the existing agent oriented methodologies searching for the best solution that is not bound to a specific programming language of framework, and it is flexible enough to be applied in such a divers domain like IoT. [Manate, Bogdan; Fortis, Florin] West Univ Timisoara, Fac Math & Informat, Dept Comp Sci, Bvd V Parvan 4, Timisoara, Romania; [Moore, Philip] Lanzhou Univ, Lanzhou 730000, Peoples R China West University of Timisoara; Lanzhou University Manate, B (corresponding author), West Univ Timisoara, Fac Math & Informat, Dept Comp Sci, Bvd V Parvan 4, Timisoara, Romania. bogdan.manate@info.uvt.ro; fortis@info.uvt.ro; ptmbcu@gmail.com Fortis, Teodor-Florin/J-4173-2012; Moore, Philip/F-3981-2019 Fortis, Teodor-Florin/0000-0002-3143-8908; Moore, Philip/0000-0003-3874-8981 31 8 8 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2373-6860 978-1-4799-7881-6 INT CONF UTIL CLOUD 2014.0 435 442 8 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BF3NE 2023-03-23 WOS:000380558700048 0
J Chen, G; Chen, JN; Lienen, M; Conradt, J; Rohrbein, F; Knoll, AC Chen, Guang; Chen, Jieneng; Lienen, Marten; Conradt, Joerg; Roehrbein, Florian; Knoll, Alois C. FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition FRONTIERS IN NEUROSCIENCE English Article representation learning; neuromorphic vision; continuous gesture recognition; mixture density autoencoder; recurrent neural network; hidden markov model REAL-TIME; VISION; LONG A neuromorphic vision sensors is a novel passive sensing modality and frameless sensors with several advantages over conventional cameras. Frame-based cameras have an average frame-rate of 30 fps, causing motion blur when capturing fast motion, e.g., hand gesture. Rather than wastefully sending entire images at a fixed frame rate, neuromorphic vision sensors only transmit the local pixel-level changes induced by the movement in a scene when they occur. This leads to advantageous characteristics, including low energy consumption, high dynamic range, a sparse event stream and low response latency. In this study, a novel representation learning method was proposed: Fixed Length Gists Representation (FLGR) learning for event-based gesture recognition. Previous methods accumulate events into video frames in a time duration (e.g., 30 ms) to make the accumulated image-level representation. However, the accumulated-frame-based representation waives the friendly event-driven paradigm of neuromorphic vision sensor. New representation are urgently needed to fill the gap in non-accumulated-frame-based representation and exploit the further capabilities of neuromorphic vision. The proposed FLGR is a sequence learned from mixture density autoencoder and preserves the nature of event-based data better. FLGR has a data format of fixed length, and it is easy to feed to sequence classifier. Moreover, an RNN-HMM hybrid was proposed to address the continuous gesture recognition problem. Recurrent neural network (RNN) was applied for FLGR sequence classification while hidden Markov model (HMM) is employed for localizing the candidate gesture and improving the result in a continuous sequence. A neuromorphic continuous hand gestures dataset (Neuro ConGD Dataset) was developed with 17 hand gestures classes for the community of the neuromorphic research. Hopefully, FLGR can inspire the study on the event-based highly efficient, high-speed, and high-dynamic-range sequence classification tasks. [Chen, Guang] Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China; [Chen, Guang; Lienen, Marten; Roehrbein, Florian; Knoll, Alois C.] Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany; [Chen, Jieneng] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China; [Conradt, Joerg] KTH Royal Inst Technol, Dept Computat Sci & Technol, Stockholm, Sweden Tongji University; Technical University of Munich; Tongji University; Royal Institute of Technology Chen, G (corresponding author), Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Chen, G; Knoll, AC (corresponding author), Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany. guang@in.tum.de; knoll@in.tum.de Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X; Conradt, Jorg/0000-0001-5998-9640 German Research Foundation (DFG); Technical University of Munich (TUM); Shanghai Automotive Industry Sci-Tech Development Program [1838]; European Union's Horizon 2020 Research and Innovation Program [785907]; Fundamental Research Funds for the Central Universities of Tongji German Research Foundation (DFG)(German Research Foundation (DFG)); Technical University of Munich (TUM); Shanghai Automotive Industry Sci-Tech Development Program; European Union's Horizon 2020 Research and Innovation Program; Fundamental Research Funds for the Central Universities of Tongji This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. The research leading to these results has partially received funding from the Shanghai Automotive Industry Sci-Tech Development Program under Grant Agreement No. 1838, from the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No. 785907 (HBP SGA2), and from the Fundamental Research Funds for the Central Universities of Tongji. 44 9 9 2 16 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-453X FRONT NEUROSCI-SWITZ Front. Neurosci. FEB 12 2019.0 13 73 10.3389/fnins.2019.00073 0.0 13 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology HL1PZ 30809114.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000458472700001 0
J Chen, YH; Visnjic, I; Parida, V; Zhang, ZG Chen, Yihua; Visnjic, Ivanka; Parida, Vinit; Zhang, Zhengang On the road to digital servitization - The (dis)continuous interplay between business model and digital technology INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT English Article Digital servitization; Business model innovation; Digital business model; Value creation; Value driver; Servitization; Digitalization VALUE CO-CREATION; PUNCTUATED EQUILIBRIUM; FIRM PERFORMANCE; MARKET STRATEGY; SERVICE; CAPABILITY; MANAGEMENT; INNOVATION; DIVERSIFICATION; TRANSFORMATION Purpose The authors seek to understand the process of digital servitization as a shift of manufacturing companies from the provision of standard products and services to smart solutions. Specifically, the authors focus on changes in the business model (i.e. the value proposition, the value delivery system and the value capture mechanism) for digital servitization. Design/methodology/approach The authors examine a Chinese air conditioner manufacturer, Gree, who became the global leader with their smart solutions. These solutions included performance-based contracts underpinned by artificial intelligence (AI)-powered air conditioners that automatically adjust to environmental changes and are capable of remote monitoring and servicing thanks to its Internet of things (IoT) technology. Findings To successfully offer smart solution value propositions, a manufacturer needs an ecosystem value delivery system composed of suppliers, distributors, partners and customers. Once the ecosystem relationships are well aligned, the manufacturer gains value with multiple value capture mechanisms (i.e. efficiency, accountability, shared customer value and novelty). To arrive at this point, a manufacturer has to pass through different stages that are characterized by both discontinuous and continuous interplay between business models and digital technologies. At the beginning of each stage, new value propositions and value delivery systems are first discontinuously created and then enabled with digital technology. As a result, new value capture mechanisms are activated. Meanwhile, the elements of the existing business model are continuously improved. Research limitations/implications By combining process-perspective and business-model lenses, the authors offer nuanced insights into how digital servitization unfolds. Practical implications Executives can obtain insights into the business model elements, they need to change over the course of digital servitization and how to manage the process. Originality/value A longitudinal case study of a traditional manufacturer that has achieved stellar success through digital servitization business models development. [Chen, Yihua; Zhang, Zhengang] South China Univ Technol, Sch Business Adm, Guangzhou, Peoples R China; [Visnjic, Ivanka] Ramon Llull Univ, ESADE Business Sch, Barcelona, Spain; [Parida, Vinit] Lulea Univ Technol, Entrepreneurship & Innovat, Lulea, Sweden; [Parida, Vinit] Univ Vaasa, Sch Management, Vaasa, Finland South China University of Technology; Universitat Ramon Llull; Escuela Superior de Administracion y Direccion de Empresas (ESADE); Lulea University of Technology; University of Vaasa Visnjic, I (corresponding author), Ramon Llull Univ, ESADE Business Sch, Barcelona, Spain. payihuachen@mail.scut.edu.cn; ivanka.visnjic@esade.edu; vinit.parida@ltu.se; adgzhang@scut.edu.cn Parida, Vinit/ABA-5703-2021; Visnjic, Ivanka/S-8346-2016 Parida, Vinit/0000-0003-3255-414X; Visnjic, Ivanka/0000-0002-1628-2634; Chen, Yihua/0000-0001-8227-554X Major Project of National Social Science Fund of China [18ZDA062]; Spanish Ministry of Science, Innovation and Universities [101022-A-100]; Vinnova; PiiA Process Industrial IT and Automation Major Project of National Social Science Fund of China; Spanish Ministry of Science, Innovation and Universities(Spanish Government); Vinnova(Vinnova); PiiA Process Industrial IT and Automation The authors gratefully acknowledge the help and excellent comments provided by the International Journal of Operations & Production Management editorial team and Servitization 2.0 Special Issue Editors: Rodrigo Rabetino, Marko Kohtamaki, Christian Kowalkowski, Tim S. Baines and Rui Sousa. Yihua Chen and Zhengang Zhang acknowledge financial support from the Major Project of National Social Science Fund of China (No. 18ZDA062). Ivanka Visnjic acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities, Reference: PGC2018-2010;101022-A-100 SERSISTEMICS. Vinit Parida acknowledges financial support from Vinnova and PiiA Process Industrial IT and Automation. The authors are also very grateful for the insightful and constructive comments from the anonymous reviewers. 97 29 29 47 201 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0144-3577 1758-6593 INT J OPER PROD MAN Int. J. Oper. Prod. Manage. AUG 13 2021.0 41 5 SI 694 722 10.1108/IJOPM-08-2020-0544 0.0 JUN 2021 29 Management Social Science Citation Index (SSCI) Business & Economics UA9VC hybrid, Green Published 2023-03-23 WOS:000658380000001 0
J Jiang, ZS; Li, JZ; Yang, Y; Mu, LQ; Wei, CX; Yu, XQ; Pianetta, P; Zhao, KJ; Cloetens, P; Lin, F; Liu, YJ Jiang, Zhisen; Li, Jizhou; Yang, Yang; Mu, Linqin; Wei, Chenxi; Yu, Xiqian; Pianetta, Piero; Zhao, Kejie; Cloetens, Peter; Lin, Feng; Liu, Yijin Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes NATURE COMMUNICATIONS English Article X-RAY TOMOGRAPHY; ELECTROCHEMICAL PROPERTIES; PHASE; MICROSTRUCTURE; ELECTRODES; NANOSCALE; TRANSPORT; KNOWLEDGE; NMC The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles' evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode's microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity. Developing understanding of degradation phenomena in nickel rich cathodes is under intense investigation. Here the authors use learning-assisted statistical analysis and experiment-informed mathematical modelling to resolve the microstructure of a Ni-rich NMC composite cathode. [Jiang, Zhisen; Wei, Chenxi; Pianetta, Piero; Liu, Yijin] SLAC Natl Accelerator Lab, Stanford Synchrotron Radiat Lightsource, Menlo Pk, CA 94025 USA; [Li, Jizhou] Stanford Univ, Howard Hughes Med Inst, Stanford, CA 94305 USA; [Yang, Yang; Cloetens, Peter] European Synchrotron Radiat Facil, Grenoble 38000, France; [Yang, Yang] Brookhaven Natl Lab, Natl Synchrotron Light Source 2, Upton, NY 11973 USA; [Mu, Linqin; Lin, Feng] Virginia Tech, Dept Chem, Blacksburg, VA 24061 USA; [Yu, Xiqian] Chinese Acad Sci, Inst Phys, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100190, Peoples R China; [Zhao, Kejie] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47906 USA Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Howard Hughes Medical Institute; Stanford University; European Synchrotron Radiation Facility (ESRF); United States Department of Energy (DOE); Brookhaven National Laboratory; Virginia Polytechnic Institute & State University; Chinese Academy of Sciences; Purdue University System; Purdue University; Purdue University West Lafayette Campus Liu, YJ (corresponding author), SLAC Natl Accelerator Lab, Stanford Synchrotron Radiat Lightsource, Menlo Pk, CA 94025 USA.;Cloetens, P (corresponding author), European Synchrotron Radiat Facil, Grenoble 38000, France.;Lin, F (corresponding author), Virginia Tech, Dept Chem, Blacksburg, VA 24061 USA.;Zhao, KJ (corresponding author), Purdue Univ, Sch Mech Engn, W Lafayette, IN 47906 USA. kjzhao@purdue.edu; Cloetens@esrf.fr; fenglin@vt.edu; liuyijin@slac.stanford.edu Cloetens, Peter/AAJ-6131-2020; Pianetta, Piero/GLS-9094-2022; LI, Jizhou/I-4197-2013; NanoImaging, ID16A/AAJ-7861-2020; Yu, Xiqian/B-5574-2014; Liu, Yijin/O-2640-2013 Cloetens, Peter/0000-0002-4129-9091; Pianetta, Piero/0000-0002-4834-6014; LI, Jizhou/0000-0002-7399-1349; Yu, Xiqian/0000-0001-8513-518X; Liu, Yijin/0000-0002-8417-2488 U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]; National Science Foundation [DMR-1832613, CBET-1912885, CMMI-1726392, DMR-1832707]; DOE Vehicle Technologies Program (VTP) within the Applied Battery Research (ABR) for Transportation Program; National Key RAMP;D Program of China [2016YFB0100100]; National Natural Science Foundation of China [51822211, 11574281]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [51421002] U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences(United States Department of Energy (DOE)); National Science Foundation(National Science Foundation (NSF)); DOE Vehicle Technologies Program (VTP) within the Applied Battery Research (ABR) for Transportation Program(United States Department of Energy (DOE)); National Key RAMP;D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The hard X-ray phase contrast tomography was conducted at the Nano-Imaging beamline ID16A-NI at the ESRF, Grenoble, France. F.L. acknowledges support from the National Science Foundation under the Grants DMR-1832613 and CBET-1912885. K.Z. is grateful for the support by the National Science Foundation through the Grants CMMI-1726392 and DMR-1832707. The NMC powders were produced at the U.S. Department of Energy's (DOE) CAMP (Cell Analysis, Modeling and Prototyping) Facility, Argonne National Laboratory. The CAMP Facility is fully supported by the DOE Vehicle Technologies Program (VTP) within the core funding of the Applied Battery Research (ABR) for Transportation Program. The work done at IOP was supported by funding from National Key R&D Program of China (Grant No. 2016YFB0100100), National Natural Science Foundation of China (Grant Nos. 51822211 and 11574281), and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51421002). The engineering support from D. Van Campen, D. Day, and V. Borzenets for the TXM experiment at beamline 6-2C of SSRL is gratefully acknowledged. 50 111 113 44 183 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. MAY 8 2020.0 11 1 2310 10.1038/s41467-020-16233-5 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics LO9IB 32385347.0 Green Published, gold, Green Submitted 2023-03-23 WOS:000533936800034 0
J Li, T; Jiang, ZH; Treut, HL; Li, LR; Zhao, LL; Ge, LL Li, Tong; Jiang, Zhihong; Treut, Herve Le; Li, Laurent; Zhao, Lilong; Ge, Lingling Machine learning to optimize climate projection over China with multi-model ensemble simulations ENVIRONMENTAL RESEARCH LETTERS English Article ensemble-processing strategy; climate extremes; observational constraint; random forest; China regional climate 1.5 DEGREES-C; PRECIPITATION CHANGES; MODEL PERFORMANCE; CMIP5; TEMPERATURE; 2-DEGREES-C; EXTREMES; IMPACTS; INDEXES The multi-model ensemble approach is generally considered as the best way to explore the advantage and to avoid the weakness of each individual model, and ultimately to achieve the best climate projection. But the design of an optimal strategy and its practical implementation still constitutes a challenge. Here we use the random forest (RF) algorithm (from the category of machine learning) to explore the information offered by the multi-model ensemble simulations within the Coupled Model Intercomparison Project Phase 6. Our objective is to achieve a more reliable climate projection (mean climate and extremes) over China. RF is furthermore compared to two other ensemble-processing strategies of different nature, one is the basic arithmetic mean (AM), and another is the linear regression across the ensemble members. Our results indicate that RF effectively enhances the capability in capturing spatial climate characteristics. Regions with complex topography, such as the Tibetan Plateau and its periphery, show the most significant improvements. RF projects less future warming but enhanced wet conditions across China. It also produces larger spatial variability and more small-scale features. The most obvious increase of precipitation is in the northern part and the periphery of the Tibetan Plateau. The projected changes in RF for strong precipitation are almost twice higher than in AM, while in the northwestern area, weaker increases of precipitation are projected by RF, which indicates larger spatial inhomogeneity of its projection. [Li, Tong; Zhao, Lilong] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Joint Int Res Lab Climate & Environm Change, Nanjing 210044, Peoples R China; [Jiang, Zhihong] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Minist Educ, Key Lab Meteorol Disaster, Nanjing 210044, Peoples R China; [Treut, Herve Le; Li, Laurent] Sorbonne Univ, Ecole Normale Super, Ecole Polytech, Lab Meteorol Dynam,IPSL,CNRS, F-75005 Paris, France; [Ge, Lingling] Nanjing Univ Informat Sci & Technol, Sch Informat & Control, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Peoples R China Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Universite PSL; Ecole Normale Superieure (ENS); Nanjing University of Information Science & Technology Jiang, ZH (corresponding author), Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Minist Educ, Key Lab Meteorol Disaster, Nanjing 210044, Peoples R China. zhjiang@nuist.edu.cn Li, Laurent/X-3278-2019 Li, Laurent/0000-0002-3855-3976 National Key Research and Development Program of China [2017YFA0603804, 2018YFC1507704]; Postgraduate Research and Practice Innovation Program of Government of Jiangsu Province [KYCX21_0940] National Key Research and Development Program of China; Postgraduate Research and Practice Innovation Program of Government of Jiangsu Province We would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP6. We thank the climate modeling groups for producing simulations and making their model outputs available. This work is supported by the National Key Research and Development Program of China (Grant 2017YFA0603804 and Grant 2018YFC1507704), and the Postgraduate Research and Practice Innovation Program of Government of Jiangsu Province (KYCX21_0940). 54 5 6 6 41 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-9326 ENVIRON RES LETT Environ. Res. Lett. SEP 2021.0 16 9 94028 10.1088/1748-9326/ac1d0c 0.0 13 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences UI1SD gold, Green Submitted 2023-03-23 WOS:000690395100001 0
J Brazier, T; Cherif, E; Martin, JF; Gilles, A; Blanchet, S; Zhao, YH; Combe, M; McCairns, RJS; Gozlan, RE Brazier, Thomas; Cherif, Emira; Martin, Jean-Francois; Gilles, Andre; Blanchet, Simon; Zhao, Yahui; Combe, Marine; McCairns, R. J. Scott; Gozlan, Rodolphe E. The influence of native populations' genetic history on the reconstruction of invasion routes: the case of a highly invasive aquatic species BIOLOGICAL INVASIONS English Article Invasion pathways; Aquatic biological invasion; Demographic inference; Population modeling; Population genomics; Approximate bayesian computation FRESH-WATER FISH; APPROXIMATE BAYESIAN COMPUTATION; CYPRINID PSEUDORASBORA-PARVA; OUTBREEDING DEPRESSION; AMBROSIA-ARTEMISIIFOLIA; CRYPTIC INVASION; GENOMIC DATA; 1ST RECORD; R-PACKAGE; ADMIXTURE Insufficient data on the origins of the first introduced propagule and the initial stages of invasion complicate the reconstruction of a species' invasion history. Phylogeography of the native area profoundly shapes the genomic patterns of the propagules on which subsequent demographic processes of the invasion are based. Thus, a better understanding of this aspect helps to disentangle native and invasive histories. Here, we used genomic data together with clustering methods, explicit admixture tests combined with ABC models and Machine Learning algorithms, to compare patterns of genetic structure and gene flow of native and introduced populations, and infer the most likely invasion pathways of the highly invasive freshwater fish Pseudorasbora parva. This species is the vector of a novel lethal fungal-like pathogen (Sphaerothecum destruens) that is responsible for the decline of several fish species in Europe. We found that the current genetic structuring in the native range of P. parva has been shaped by waves of gene flow from populations in southern and northern China. Furthermore, our results strongly suggest that the genetic diversity of invasive populations results from recurrent global invasion pathways of admixed native populations. Our study also illustrates how the combination of admixture tests, ABC and Machine Learning can be used to detect high-resolution demographic signatures and reconstruct an integrative biological invasion history. [Brazier, Thomas; McCairns, R. J. Scott] IFREMER, Inst Agro, INRAE, DECOD Ecosyst Dynam & Sustainabil, Rennes, France; [Brazier, Thomas] Univ Rennes, UMR 6553, CNRS, ECOBIO Ecosyst Biodivers Evolut, Rennes, France; [Cherif, Emira; Combe, Marine; Gozlan, Rodolphe E.] Univ Montpellier, CNRS, ISEM, IRD, Montpellier, France; [Martin, Jean-Francois] Univ Montpellier, CIRAD, INRA, Montpellier SupAgro,IRD, Montpellier, France; [Gilles, Andre] Aix Marseille Univ, INRAE, UMR Recover 1467, Ctr St Charles, 3 Pl Victor Hugo, F-13331 Marseille, France; [Blanchet, Simon] CNRS, Stn Ecol Theor & Expt SETE, Moulis, France; [Zhao, Yahui] Chinese Acad Sci, Inst Zool, Beijing 100101, Peoples R China Ifremer; INRAE; Institut Agro; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; CIRAD; INRAE; Institut Agro; Montpellier SupAgro; Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; INRAE; UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; Institute of Zoology, CAS Cherif, E (corresponding author), Univ Montpellier, CNRS, ISEM, IRD, Montpellier, France. emira.cherif@ird.fr CHERIF, Emira/K-1587-2016; Martin, Jean-Francois/M-9426-2017; McCairns, R.J. Scott/E-4458-2015 CHERIF, Emira/0000-0001-9365-7500; Zhao, Yahui/0000-0002-4615-596X; Blanchet, Simon/0000-0002-3843-589X; Martin, Jean-Francois/0000-0001-9176-4476; Brazier, Thomas/0000-0001-5990-7545; McCairns, R.J. Scott/0000-0002-0392-7413 Agence Nationale de la Recherche [GENESIS ANR-AF 13-ADAP-0005-001]; project PROBIS (Biodiversa); IRD postdoctoral fellowship; ANR Bioadapt Agence Nationale de la Recherche(French National Research Agency (ANR)); project PROBIS (Biodiversa); IRD postdoctoral fellowship; ANR Bioadapt(French National Research Agency (ANR)) This study is part of the project GENESIS ANR-AF 13-ADAP-0005-001 supported by the Agence Nationale de la Recherche. S.B. was co-funded by the project PROBIS (Biodiversa) and E.C. was funded by an IRD postdoctoral fellowship. R.E.G., A.G., J.F.M. and S.B. received funding for this research from ANR Bioadapt. 137 1 1 10 28 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1387-3547 1573-1464 BIOL INVASIONS Biol. Invasions AUG 2022.0 24 8 2399 2420 10.1007/s10530-022-02787-6 0.0 APR 2022 22 Biodiversity Conservation; Ecology Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology 3O2IR Green Submitted 2023-03-23 WOS:000778057100001 0
J Li, X; Zhu, RC; Ye, H; Jiang, CX; Benslimane, A Li, Xin; Zhu, Rongchen; Ye, Han; Jiang, Chunxiao; Benslimane, Abderrahim MetaInjury: Meta-learning framework for reusing the risk knowledge of different construction accidents SAFETY SCIENCE English Article Occupational injury; Small sample learning; Machine learning; Prediction; Decision support; Risk analysis ALGORITHM SELECTION; SYSTEMS; SET In recent years, many scholars have used data mining algorithms to discover the laws related to the prevention of occupational injuries in the construction industry. Using accident injury records to model occupational risk is a promising direction (for example, predict injury risk from accident frequency and severity). However, the records of specific accident injury data are relatively limited, bringing great difficulties for people to obtain risk knowledge and establish an effective accident consequence prediction model. This article proposes a metalearning framework called Meta-Injury, which can help safety managers share risk knowledge and predict the risk of work-related injuries in various construction industry accidents. When facing small sample data of a new accident type, we first calculate the document vector of the accident description and compare its center vector with vectors in the Meta-knowledge database to find the type of accident most similar. Then, we correspond the meta-features with the best machine learning algorithms on the data set to implement the recommendation of accident prediction algorithms. Through finding the most similar cases and the recommended algorithm, important accident risk factors and accident assessment rules can be shared by safety managers to realize effective risk management. Moreover, specific small sample accident consequences can be predicted by the recommended algorithm. Finally, we verify the method's effectiveness in four different small sample accident data. The results show that the MetaInjury framework can provide theoretical support for preventing small sample accidents and injury reduction in the construction industry. [Li, Xin; Zhu, Rongchen; Ye, Han] Peoples Publ Secur Univ China, Sch Informat Technol & Network Secur, Beijing, Peoples R China; [Jiang, Chunxiao] Tsinghua Univ, Sch Informat Sci & Technol, Beijing, Peoples R China; [Benslimane, Abderrahim] Univ Avignon, LIA CERI, Avignon, France People's Public Security University of China; Tsinghua University; Avignon Universite Li, X (corresponding author), Peoples Publ Secur Univ China, Sch Informat Technol & Network Secur, Beijing, Peoples R China. lixin@ppsuc.edu.cn; zhurongchen@stu.ppsuc.edu.cn; yehan@stu.ppsuc.edu.cn; jchx@tsinghua.edu.cn; abderrahim.benslimane@univ-avignon.fr 71 6 6 9 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-7535 1879-1042 SAFETY SCI Saf. Sci. AUG 2021.0 140 SI 105315 10.1016/j.ssci.2021.105315 0.0 MAY 2021 12 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Operations Research & Management Science SF1KW 2023-03-23 WOS:000652522700027 0
C Li, HJ; Leung, KS; Wong, MH; Ballester, PJ Ortuno, F; Rojas, I Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J. The Use of Random Forest to Predict Binding Affinity in Docking BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2015), PT II Lecture Notes in Bioinformatics English Proceedings Paper 3rd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) APR 15-17, 2015 Grenada, SPAIN Univ Granada, CITIC,BioMed Cent,E Hlth Business Dev BULL S A,Univ Granada, Fac Sci, Dept Comp Architecture & Comp Technol Molecular docking; scoring functions; random forest; chemical informatics; structural bioinformatics SCORING FUNCTIONS; LIGAND COMPLEXES; ACCURACY Docking is a structure-based computational tool that can be used to predict the strength with which a small ligand molecule binds to a macromolecular target. Such binding affinity prediction is crucial to design molecules that bind more tightly to a target and thus are more likely to provide the most efficacious modulation of the target's biochemical function. Despite intense research over the years, improving this type of predictive accuracy has proven to be a very challenging task for any class of method. New scoring functions based on non-parametric machine-learning regression models, which are able to exploit effectively much larger volumes of experimental data and circumvent the need for a predetermined functional form, have become the most accurate to predict binding affinity of diverse protein-ligand complexes. In this focused review, we describe the inception and further development of RF-Score, the first machine-learning scoring function to achieve a substantial improvement over classical scoring functions at binding affinity prediction. RF-Score employs Random Forest (RF) regression to relate a structural description of the complex with its binding affinity. This overview will cover adequate benchmarking practices, studies exploring optimal intermolecular features, further improvements and RF-Score software availability including a user-friendly docking webserver and a standalone software for rescoring docked poses. Some work has also been made on the application of RF-Score to the related problem of virtual screening. Comprehensive retrospective virtual screening studies of RF-based scoring functions constitute now one of the next research steps. [Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Sha Tin, Hong Kong, Peoples R China; [Ballester, Pedro J.] INSERM U1068, Canc Res Ctr Marseille, F-13009 Marseille, France; [Ballester, Pedro J.] Inst Paoli Calmettes, F-13009 Marseille, France; [Ballester, Pedro J.] Aix Marseille Univ, F-13284 Marseille, France; [Ballester, Pedro J.] CNRS UMR7258, F-13009 Marseille, France Chinese University of Hong Kong; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; UNICANCER; Institut Paoli-Calmette (IPC); UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB) Li, HJ (corresponding author), Chinese Univ Hong Kong, Dept Comp Sci & Engn, Sha Tin, Hong Kong, Peoples R China. pedro.ballester@inserm.fr Ballester, Pedro/A-1148-2008; Wong, Man Hon/GOH-0560-2022 Ballester, Pedro/0000-0002-4078-743X; A*MIDEX grant - French Government Investissements d'Avenir program [ANR-11-IDEX-0001-02]; Direct Grant from the Chinese University of Hong Kong; GRF Grant from the Research Grants Council of Hong Kong SAR [414413] A*MIDEX grant - French Government Investissements d'Avenir program(French National Research Agency (ANR)); Direct Grant from the Chinese University of Hong Kong; GRF Grant from the Research Grants Council of Hong Kong SAR This work has been carried out thanks to the support of the A*MIDEX grant (no ANR-11-IDEX-0001-02) funded by the French Government Investissements d'Avenir program, the Direct Grant from the Chinese University of Hong Kong and the GRF Grant (Project Reference 414413) from the Research Grants Council of Hong Kong SAR. 17 3 3 0 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-16480-9; 978-3-319-16479-3 LECT N BIOINFORMAT Lect. Notes Bioinforma. 2015.0 9044 238 247 10 Biochemical Research Methods; Computer Science, Information Systems; Engineering, Biomedical; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Computer Science; Engineering; Mathematical & Computational Biology BI3DY 2023-03-23 WOS:000410784400024 0
J Halme, E; Pellikka, P; Mottus, M Halme, Eelis; Pellikka, Petri; Mottus, Matti Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Hyperspectral imaging; Sentinel-2; Forest structure variables; Machine learning; Support vector regression; Gaussian process regression SUPPORT VECTOR REGRESSION; GAUSSIAN PROCESS REGRESSION; RETRIEVAL Three-quarters of Finland's land surface area is filled with forests, which compose a great part of the country's biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices,.such as forestry.It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400-1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data. [Halme, Eelis; Mottus, Matti] VIT Tech Res Ctr Finland, POB 1000, FI-02044 Espoo, Finland; [Pellikka, Petri] Univ Helsinki, Dept Geosci & Geog, POB 64, FI-00014 Helsinki, Finland; [Pellikka, Petri] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China VTT Technical Research Center Finland; University of Helsinki; Wuhan University Halme, E (corresponding author), VIT Tech Res Ctr Finland, POB 1000, FI-02044 Espoo, Finland. eelis.halme@vtt.fi; petri.pellikka@helsinki.fi; matti.mottus@vtt.fi Mõttus, Matti/A-4130-2009 Mõttus, Matti/0000-0002-2745-1966; Halme, Eelis/0000-0002-7517-6663 Academy of Finland [311928, 317387]; Academy of Finland (AKA) [317387, 311928] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) Funding was supported by the Academy of Finland grants 311928 and 317387. We are grateful to Titta Majasalmi and Miina Rautiainen (Aalto University) for providing the in situ measurements from Hyytiala. In addition, we thank the Finnish Forest Centre for the additional and more detailed information on the forest resource data. 33 26 26 12 47 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-8432 1872-826X INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. NOV 2019.0 83 101942 10.1016/j.jag.2019.101942 0.0 9 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing JA1JV hybrid, Green Published 2023-03-23 WOS:000487574200026 0
J Hitouri, S; Varasano, A; Mohajane, M; Ijlil, S; Essahlaoui, N; Ali, SA; Essahlaoui, A; Pham, QB; Waleed, M; Palateerdham, SK; Teodoro, AC Hitouri, Sliman; Varasano, Antonietta; Mohajane, Meriame; Ijlil, Safae; Essahlaoui, Narjisse; Ali, Sk Ajim; Essahlaoui, Ali; Quoc Bao Pham; Waleed, Mirza; Palateerdham, Sasi Kiran; Teodoro, Ana Claudia Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article gully erosion; hybrid machine learning; frequency ratio; Mediterranean area SOIL-EROSION; CERTAINTY FACTOR; RANDOM FOREST; LAND-USE; GIS; PREDICTION; MODEL; BASIN; REGRESSION; ENTROPY Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naive Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of where and why gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area. [Hitouri, Sliman] Univ Ibn Tofail, Fac Sci, Dept Geol, Geosci Lab, BP 133, Kenitra 14000, Morocco; [Varasano, Antonietta; Mohajane, Meriame] CNR, Construct Technol Inst, ITC CNR, I-70124 Bari, Italy; [Ijlil, Safae; Essahlaoui, Narjisse; Essahlaoui, Ali] Moulay Ismail Univ, Fac Sci, Dept Geol, Geoengn & Environm Lab,Water Sci & Environm Engn, Meknes 50050, Morocco; [Ali, Sk Ajim] Aligarh Muslim Univ AMU, Fac Sci, Dept Geog, Aligarh 202002, Uttar Pradesh, India; [Quoc Bao Pham] Thu Dau Mot Univ, Inst Appl Technol, Thu Dau Mot City 820000, Vietnam; [Waleed, Mirza] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China; [Palateerdham, Sasi Kiran] Univ Roma La Sapienza, Sch Aerosp Engn, I-00138 Rome, Italy; [Teodoro, Ana Claudia] Univ Porto, Fac Sci, Earth Sci Inst ICT, P-4169007 Porto, Portugal; [Teodoro, Ana Claudia] Univ Porto, Fac Sci, Dept Geosci Environm & Land Planning, P-4169007 Porto, Portugal Ibn Tofail University of Kenitra; Consiglio Nazionale delle Ricerche (CNR); Istituto per le Tecnologie della Costruzione (ITC-CNR); Moulay Ismail University of Meknes; Aligarh Muslim University; Thu Dau Mot University; Hong Kong Baptist University; Sapienza University Rome; Universidade do Porto; Universidade do Porto Varasano, A (corresponding author), CNR, Construct Technol Inst, ITC CNR, I-70124 Bari, Italy. sliman.hitouri@uit.ac.ma; varasano@itc.cnr.it; mohajane@itc.cnr.it; safae.ijlil@edu.umi.ac.ma; essahlaoui.narjisse@gmail.com; skajimali.saa@gmail.com; a.essahlaoui@umi.ac.ma; phamphambaoquoc@tdmu.edu.vn; mirzawaleed197@gmail.com; sasikiran.palateerdham@uniroma1.it; amteodor@fc.up.pt MOHAJANE, Meriame/AAW-9028-2020; Ali, Sk Ajim/AAN-6448-2020; Mohajane, Meiame/GRS-4002-2022; Waleed, Mirza/GSD-9289-2022; ALI, ESSAHLAOUI/AAD-1441-2020; Varasano, Antonietta/P-9438-2018; Teodoro, Ana/B-5840-2011 MOHAJANE, Meriame/0000-0002-0019-6862; Ali, Sk Ajim/0000-0001-7488-5591; ALI, ESSAHLAOUI/0000-0003-1112-1783; Varasano, Antonietta/0000-0003-4173-5241; Waleed, Mirza/0000-0003-0006-2490; IJLIL, Safae/0000-0001-8089-0290; Teodoro, Ana/0000-0002-8043-6431 108 3 3 4 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. JUL 2022.0 11 7 401 10.3390/ijgi11070401 0.0 23 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physical Geography; Remote Sensing 3H2PQ gold 2023-03-23 WOS:000831882500001 0
C Cai, HY; Kaloorazi, ME; Chen, J; Chen, W; Richard, C IEEE Cai, Haoyuan; Kaloorazi, Maboud E.; Chen, Jie; Chen, Wei; Richard, Cedric ONLINE DOMINANT GENERALIZED EIGENVECTORS EXTRACTION VIA A RANDOMIZED METHOD 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) European Signal Processing Conference English Proceedings Paper 28th European Signal Processing Conference (EUSIPCO) JAN 18-22, 2021 ELECTR NETWORK European Assoc Signal Proc Randomized algorithms; dominant generalized eigenvectors; online algorithms; fast subspace tracking ADAPTIVE ALGORITHMS The generalized Hermitian eigendecomposition problem is ubiquitous in signal and machine learning applications. Considering the need of processing streaming data in practice and restrictions of existing methods, this paper is concerned with fast and efficient generalized eigenvectors tracking. We first present a computationally efficient algorithm based on randomization termed alternate-projections randomized eigenvalue decomposition (APR-EVD) to solve a standard eigenvalue problem. By exploiting rank-1 strategy, two online algorithms based on APR-EVD are developed for the dominant generalized eigenvectors extraction. Numerical examples show the practical applicability and efficacy of the proposed online algorithms. [Cai, Haoyuan; Kaloorazi, Maboud E.; Chen, Jie] Northwestern Polytech Univ, Sch Marine Sci & Technol, Ctr Intelligent Acoust & Immers Commun CIAIC, Xian, Peoples R China; [Chen, Wei] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China; [Richard, Cedric] Univ Cote dAzur, Nice, France Northwestern Polytechnical University; Beijing Jiaotong University; UDICE-French Research Universities; Universite Cote d'Azur Chen, J (corresponding author), Northwestern Polytech Univ, Sch Marine Sci & Technol, Ctr Intelligent Acoust & Immers Commun CIAIC, Xian, Peoples R China. haoyuan.cai@mail.nwpu.edu.cn; kaloorazi@nwpu.edu.cn; dr.jie.chen@ieee.org; weich@bjtu.edu.cn; cedric.richard@unice.fr NSFC [61671382, 61811530283]; 111 project [B18041] NSFC(National Natural Science Foundation of China (NSFC)); 111 project(Ministry of Education, China - 111 Project) dThis work was supported in part by NSFC grants 61671382 and 61811530283, and 111 project (B18041). 26 1 1 2 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2076-1465 978-9-0827-9705-3 EUR SIGNAL PR CONF 2021.0 2353 2357 5 Acoustics; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology BR1KB Green Submitted 2023-03-23 WOS:000632622300474 0
J Wu, XQ; Peng, JS; Boscolo, S; Zhang, Y; Finot, C; Zeng, HP Wu, Xiuqi; Peng, Junsong; Boscolo, Sonia; Zhang, Ying; Finot, Christophe; Zeng, Heping Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers LASER & PHOTONICS REVIEWS English Article breathers; mode locking; ultrafast fiber lasers MODE-LOCKING; DISSIPATIVE SOLITONS; DYNAMICS; EXPLOSIONS Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a specific mode-locked regime generally involves adjusting multiple control parameters, in connection with a wide range of accessible pulse dynamics. Machine-learning tools have recently shown promising for the design of smart lasers that can tune themselves to desired operating states. Yet, machine-learning algorithms are mainly designed to target regimes of parameter-invariant, stationary pulse generation, while the intelligent excitation of evolving pulse patterns in a laser remains largely unexplored. Breathing solitons exhibiting periodic oscillatory behavior, emerging as ubiquitous mode-locked regime of ultrafast fiber lasers, are attracting considerable interest by virtue of their connection with a range of important nonlinear dynamics, such as exceptional points, and the Fermi-Pasta-Ulam paradox. Here, an evolutionary algorithm is implemented for the self-optimization of the breather regime in a fiber laser mode-locked through a four-parameter nonlinear polarization evolution. Depending on the specifications of the merit function used for the optimization procedure, various breathing-soliton states are obtained, including single breathers with controllable oscillation period and breathing ratio, and breather molecular complexes with a controllable number of elementary constituents. This work opens up a novel avenue for exploration and optimization of complex dynamics in nonlinear systems. [Wu, Xiuqi; Peng, Junsong; Zhang, Ying; Zeng, Heping] East China Normal Univ, State Key Lab Precis Spect, Shanghai 200062, Peoples R China; [Peng, Junsong; Zeng, Heping] East China Normal Univ, Chongqing Inst, Chongqing 401120, Peoples R China; [Boscolo, Sonia] Aston Univ, Aston Inst Photon Technol, Birmingham B4 7ET, W Midlands, England; [Finot, Christophe] Univ Bourgogne Franche Comte, CNRS, UMR 6303, Lab Interdisciplinaire Carnot Bourgogne, F-21078 Dijon, France East China Normal University; East China Normal University; Aston University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); Universite de Bourgogne; Universite de Technologie de Belfort-Montbeliard (UTBM) Peng, JS; Zeng, HP (corresponding author), East China Normal Univ, State Key Lab Precis Spect, Shanghai 200062, Peoples R China.;Peng, JS; Zeng, HP (corresponding author), East China Normal Univ, Chongqing Inst, Chongqing 401120, Peoples R China. jspeng@lps.ecnu.edu.cn; hpzeng@phy.ecnu.edu.cn Peng, Junsong/0000-0002-1432-5880; Boscolo, Sonia/0000-0001-5388-2893 National Key Research and Development Program [2018YFB0407100]; National Natural Science Fund of China [11621404, 11561121003, 11727812, 61775059, 11704123]; Key Project of Shanghai Education Commission [2017-01-07-00-05-E00021]; Science and Technology Innovation Program of Basic Science Foundation of Shanghai [18JC1412000]; Shanghai Rising-Star Program; National Key Laboratory Foundation of China [6142411196307]; UK Engineering and Physical Sciences Research Council [EP/S003436/1] National Key Research and Development Program; National Natural Science Fund of China(National Natural Science Foundation of China (NSFC)); Key Project of Shanghai Education Commission; Science and Technology Innovation Program of Basic Science Foundation of Shanghai; Shanghai Rising-Star Program; National Key Laboratory Foundation of China; UK Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) Y This research was funded by the National Key Research and Development Program (2018YFB0407100), the National Natural Science Fund of China (11621404, 11561121003, 11727812, 61775059, and 11704123), Key Project of Shanghai Education Commission (2017-01-07-00-05-E00021), Science and Technology Innovation Program of Basic Science Foundation of Shanghai (18JC1412000), Shanghai Rising-Star Program, and National Key Laboratory Foundation of China (6142411196307). S.B. acknowledges support from the UK Engineering and Physical Sciences Research Council (EP/S003436/1). 59 17 18 24 70 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1863-8880 1863-8899 LASER PHOTONICS REV Laser Photon. Rev. FEB 2022.0 16 2 2100191 10.1002/lpor.202100191 0.0 DEC 2021 10 Optics; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics YT6IH Green Accepted, Green Submitted 2023-03-23 WOS:000729432600001 0
J Kang, JS; Lee, C; Song, W; Choo, W; Lee, S; Lee, S; Han, YM; Bassi, C; Salvia, R; Marchegiani, G; Wolfgang, CL; He, J; Blair, AB; Kluger, MD; Su, GH; Kim, SC; Song, KB; Yamamoto, M; Higuchi, R; Hatori, T; Yang, CY; Yamaue, H; Hirono, S; Satoi, S; Fujii, T; Hirano, S; Lou, WH; Hashimoto, Y; Shimizu, Y; Del Chiaro, M; Valente, R; Lohr, M; Choi, DW; Choi, SH; Heo, JS; Motoi, F; Matsumoto, I; Lee, WJ; Kang, CM; Shyr, YM; Wang, SE; Han, HS; Yoon, YS; Besselink, MG; van Huijgevoort, NCM; Sho, M; Nagano, H; Kim, SG; Honda, G; Yang, YM; Yu, HC; Do Yang, J; Chung, JC; Nagakawa, Y; Il Seo, H; Choi, YJ; Byun, Y; Kim, H; Kwon, W; Park, T; Jang, JY Kang, Jae Seung; Lee, Chanhee; Song, Wookyeong; Choo, Wonho; Lee, Seungyeoun; Lee, Sungyoung; Han, Youngmin; Bassi, Claudio; Salvia, Roberto; Marchegiani, Giovanni; Wolfgang, Cristopher L.; He, Jin; Blair, Alex B.; Kluger, Michael D.; Su, Gloria H.; Kim, Song Cheol; Song, Ki-Byung; Yamamoto, Masakazu; Higuchi, Ryota; Hatori, Takashi; Yang, Ching-Yao; Yamaue, Hiroki; Hirono, Seiko; Satoi, Sohei; Fujii, Tsutomu; Hirano, Satoshi; Lou, Wenhui; Hashimoto, Yasushi; Shimizu, Yasuhiro; Del Chiaro, Marco; Valente, Roberto; Lohr, Matthias; Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok; Motoi, Fuyuhiko; Matsumoto, Ippei; Lee, Woo Jung; Kang, Chang Moo; Shyr, Yi-Ming; Wang, Shin-E; Han, Ho-Seong; Yoon, Yoo-Seok; Besselink, Marc G.; van Huijgevoort, Nadine C. M.; Sho, Masayuki; Nagano, Hiroaki; Kim, Sang Geol; Honda, Goro; Yang, Yinmo; Yu, Hee Chul; Do Yang, Jae; Chung, Jun Chul; Nagakawa, Yuichi; Il Seo, Hyung; Choi, Yoo Jin; Byun, Yoonhyeong; Kim, Hongbeom; Kwon, Wooil; Park, Taesung; Jang, Jin-Young Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning SCIENTIFIC REPORTS English Article VALIDATION; GUIDELINES; NOMOGRAM Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability. [Kang, Jae Seung; Han, Youngmin; Choi, Yoo Jin; Byun, Yoonhyeong; Kim, Hongbeom; Kwon, Wooil; Jang, Jin-Young] Seoul Natl Univ, Dept Surg, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea; [Kang, Jae Seung; Han, Youngmin; Choi, Yoo Jin; Byun, Yoonhyeong; Kim, Hongbeom; Kwon, Wooil; Jang, Jin-Young] Seoul Natl Univ, Canc Res Inst, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea; [Lee, Chanhee; Song, Wookyeong; Choo, Wonho; Park, Taesung] Seoul Natl Univ, Dept Stat, 56-1 Shill Dong, Seoul 151747, South Korea; [Lee, Chanhee; Song, Wookyeong; Choo, Wonho; Park, Taesung] Seoul Natl Univ, Interdisciplinary Program Biostat, 56-1 Shill Dong, Seoul 151747, South Korea; [Lee, Seungyeoun] Sejong Univ, Dept Math & Stat, Seoul, South Korea; [Lee, Sungyoung] Seoul Natl Univ Hosp, Ctr Precis Med, Seoul, South Korea; [Bassi, Claudio; Salvia, Roberto; Marchegiani, Giovanni] Univ Verona Hosp Trust, Pancreas Inst, Dept Gen & Pancreat Surg, Verona, Italy; [Wolfgang, Cristopher L.; He, Jin; Blair, Alex B.] Johns Hopkins Univ, Sch Med, Dept Surg, Baltimore, MD 21205 USA; [Kluger, Michael D.] Columbia Univ, Coll Phys & Surg, Dept Surg, Div Gastrointestinal & Endocrine Surg, New York, NY USA; [Su, Gloria H.] Columbia Univ, Dept Pathol & Cell Biol, Med Ctr, New York, NY USA; [Kim, Song Cheol; Song, Ki-Byung] Univ Ulsan, Asan Med Ctr, Dept Surg, Coll Med, Seoul, South Korea; [Yamamoto, Masakazu; Higuchi, Ryota; Hatori, Takashi] Tokyo Womens Med Univ, Inst Gastroenterol, Dept Surg, Tokyo, Japan; [Hatori, Takashi] Int Univ Hlth & Welf, Dept Surg, Mita Hosp, Tokyo, Japan; [Yang, Ching-Yao] Natl Taiwan Univ Hosp, Dept Surg, Taipei, Taiwan; [Yang, Ching-Yao] Natl Taiwan Hosp, Taipei, Taiwan; [Yamaue, Hiroki; Hirono, Seiko] Wakayama Med Univ, Sch Med, Dept Surg 2, Wakayama, Japan; [Satoi, Sohei] Kansai Med Univ, Dept Surg, Osaka, Japan; [Fujii, Tsutomu] Nagoya Univ, Grad Sch Med, Dept Gastroenterol Surg Surg 2, Nagoya, Aichi, Japan; [Fujii, Tsutomu] Univ Toyama, Acad Assembly, Fac Med, Dept Surg & Sci, Toyama, Japan; [Hirano, Satoshi] Hokkaido Univ, Fac Med, Dept Gastroenterol Surg 2, Sapporo, Hokkaido, Japan; [Lou, Wenhui] Fudan Univ, Zhongshan Hosp, Dept Pancreat Surg, Shanghai, Peoples R China; [Hashimoto, Yasushi] Hiroshima Univ, Inst Biomed & Hlth Sci, Dept Surg, Hiroshima, Japan; [Hashimoto, Yasushi] Hiroshima Mem Hosp, Dept Surg, Hiroshima, Japan; [Shimizu, Yasuhiro] Aichi Canc Ctr Hosp, Gastroenterol Surg, Nagoya, Aichi, Japan; [Del Chiaro, Marco; Valente, Roberto] Karolinska Univ Hosp, Ctr Digest Dis, Karolinska Inst,Div Surg,Pancreat Surg Unit, Dept Clin Sci Intervent & Technol CLINTEC, Stockholm, Sweden; [Del Chiaro, Marco; Valente, Roberto] Univ Colorado, Dept Surg, Anschutz Med Campus, Denver, CO USA; [Lohr, Matthias] Karolinska Inst, Dept Clin Sci Intervent & Technol CLINTEC, Stockholm, Sweden; [Lohr, Matthias] Karolinska Univ Hosp, Dept Digest Dis, Stockholm, Sweden; [Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok] Sungkyunkwan Univ, Dept Surg, Sch Med, Seoul, South Korea; [Motoi, Fuyuhiko] Tohoku Univ, Dept Surg, Tohoku, Japan; [Matsumoto, Ippei] Kobe Univ, Dept Surg, Grad Sch Med, Kobe, Hyogo, Japan; [Matsumoto, Ippei] Kindai Univ, Fac Med, Dept Surg, Osaka, Japan; [Lee, Woo Jung; Kang, Chang Moo] Yonsei Univ, Severance Hosp, Coll Med, Yonsei Canc Ctr,Pancreaticobiliary Canc Clin, Seoul, South Korea; [Shyr, Yi-Ming; Wang, Shin-E] Taipei Vet Gen Hosp, Dept Surg, Taipei, Taiwan; [Shyr, Yi-Ming; Wang, Shin-E] Natl Yang Ming Univ, Taipei, Taiwan; [Han, Ho-Seong; Yoon, Yoo-Seok] Seoul Natl Univ, Coll Med, Dept Surg, Bundang Hosp, Seoul, South Korea; [Besselink, Marc G.] Univ Amsterdam, Canc Ctr Amsterdam, Dept Surg, Amsterdam UMC, Amsterdam, Netherlands; [van Huijgevoort, Nadine C. M.] Univ Amsterdam, Dept Gastroenterol & Hepatol, Amsterdam UMC, Amsterdam Gastroenterol Endocrinol Metab, Amsterdam, Netherlands; [Sho, Masayuki] Nara Med Univ, Dept Surg, Nara, Japan; [Nagano, Hiroaki] Osaka Univ, Dept Surg, Grad Sch Med, Osaka, Japan; [Nagano, Hiroaki] Yamaguchi Univ, Gastroenterol Breast & Endocrine Surg, Yamaguchi, Japan; [Kim, Sang Geol] Kyungpook Natl Univ, Dept Surg, Daegu, South Korea; [Honda, Goro] Komagome Hosp, Tokyo Metropolitan Canc & Infect Dis Ctr, Dept Surg, Tokyo, Japan; [Yang, Yinmo] Peking Univ First Hosp, Dept Gen Surg, Beijing, Peoples R China; [Yu, Hee Chul; Do Yang, Jae] Jeonbuk Natl Univ, Dept Surg, Med Sch, Jeonju, South Korea; [Chung, Jun Chul] Soonchunhyang Univ, Dept Surg, Asan, South Korea; [Nagakawa, Yuichi] Tokyo Med Univ, Dept Gastrointestinal & Pediat Surg, Tokyo, Japan; [Il Seo, Hyung] Pusan Natl Univ, Dept Surg, Pusan, South Korea Seoul National University (SNU); Seoul National University (SNU); Seoul National University (SNU); Seoul National University (SNU); Sejong University; Seoul National University (SNU); Seoul National University Hospital; University of Verona; Azienda Ospedaliera Universitaria Integrata Verona; Johns Hopkins University; Columbia University; Columbia University; University of Ulsan; Asan Medical Center; Tokyo Women's Medical University; International University of Health & Welfare; National Taiwan University; National Taiwan University Hospital; Wakayama Medical University; Kansai Medical University; Nagoya University; University of Toyama; Hokkaido University; Fudan University; Hiroshima University; Aichi Cancer Center; Karolinska Institutet; Karolinska University Hospital; University of Colorado System; University of Colorado Anschutz Medical Campus; University of Colorado Denver; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; Sungkyunkwan University (SKKU); Tohoku University; Kobe University; Kindai University (Kinki University); Yonsei University; Yonsei University Health System; Taipei Veterans General Hospital; National Yang Ming Chiao Tung University; Seoul National University (SNU); Seoul National University Hospital; University of Amsterdam; Vrije Universiteit Amsterdam; University of Amsterdam; Nara Medical University; Osaka University; Yamaguchi University; Kyungpook National University; Tokyo Metropolitan Cancer & Infectious Diseases Center Komagome Hospital; Peking University; Jeonbuk National University; Soonchunhyang University; Tokyo Medical University; Pusan National University Jang, JY (corresponding author), Seoul Natl Univ, Dept Surg, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea.;Jang, JY (corresponding author), Seoul Natl Univ, Canc Res Inst, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea.;Park, T (corresponding author), Seoul Natl Univ, Dept Stat, 56-1 Shill Dong, Seoul 151747, South Korea.;Park, T (corresponding author), Seoul Natl Univ, Interdisciplinary Program Biostat, 56-1 Shill Dong, Seoul 151747, South Korea. tspark@stats.snu.ac.kr; jangjy4@snu.ac.kr Salvia, Roberto/J-1773-2018; MATSUMOTO, IPPEI/AFK-9075-2022; Besselink, Marc G/R-4268-2019; Kang, Jae Seung Seung/AAX-5664-2021; Kang, Jae Seung/J-5363-2012; Marchegiani, Giovanni/J-1823-2018; Fujii, Tsutomu/AAN-4890-2021; Lee, Sungyoung/L-3433-2018 Besselink, Marc G/0000-0003-2650-9350; Kang, Jae Seung Seung/0000-0001-6587-9579; Marchegiani, Giovanni/0000-0002-6824-4533; Fujii, Tsutomu/0000-0003-0625-1911; Kang, Chang Moo/0000-0002-5382-4658; Lee, Chanhee/0000-0001-9643-7159; Lee, Sungyoung/0000-0003-3458-1440; Lee, Woo jung/0000-0001-9273-261X; YANG, CHING-YAO/0000-0001-6312-3719; Valente, Roberto/0000-0002-6785-2326; Motoi, Fuyuhiko/0000-0001-7601-8395; Lohr, Matthias/0000-0002-7647-198X Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health & Welfare, Republic of Korea [HI16C2037]; Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation - Ministry of Science and ICT [NRF-2017M3C9A5031591] Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health & Welfare, Republic of Korea; Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation - Ministry of Science and ICT This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037) and the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation funded by the Ministry of Science and ICT (NRF-2017M3C9A5031591). 28 6 6 0 7 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep NOV 18 2020.0 10 1 20140 10.1038/s41598-020-76974-7 0.0 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics PA6CT 33208887.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000595721900060 0
C Boukir, S; Feng, W IEEE COMP SOC Boukir, Sarnia; Feng, Wei Boundary bagging to address training data issues in ensemble classification 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 25th International Conference on Pattern Recognition (ICPR) JAN 10-15, 2021 ELECTR NETWORK Int Assoc Pattern Recognit,IEEE Comp Soc,Italian Assoc Comp Vis Pattern Recognit & Machine Learning NOISE DETECTION; MARGIN The characteristics of training data is a fundamental consideration when constructing any supervised classifier. Class mislabelling and imbalance are major training data issues that often adversely affect machine learning algorithms, including ensembles. This work proposes extended bagging algorithms to better handle noisy and multi-class imbalanced classification tasks. These algorithms upgrade the sampling procedure by taking benefit of the confidence in ensemble classification outcome. The underlying idea is that a bagging ensemble learning algorithm can achieve greater performance if it is allowed to choose the data from which it learns. The effectiveness of the proposed methods is demonstrated in performing classification on 10 various data sets. [Boukir, Sarnia] Bordeaux INP, G&E Lab, EA 4592, F-33600 Pessac, France; [Feng, Wei] Xidian Univ, Sch Elect Engn, Dept Remote Sensing Sci & Technol, Xian 710071, Peoples R China Xidian University Boukir, S (corresponding author), Bordeaux INP, G&E Lab, EA 4592, F-33600 Pessac, France. sboukir@ipb.fr; wfeng@xidian.edu.cn 31 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1051-4651 978-1-7281-8808-9 INT C PATT RECOG 2021.0 9975 9981 10.1109/ICPR48806.2021.9413055 0.0 7 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology BS0DY 2023-03-23 WOS:000681331402064 0
J Mo, DM; Wong, WK; Liu, XJ; Ge, Y Mo, Dongmei; Wong, Wai Keung; Liu, Xianjing; Ge, Yao Concentrated hashing with neighborhood embedding for image retrieval and classification INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Similarity search; Hashing; Image retrieval; Classification; Deep learning NEAREST-NEIGHBOR; ALGORITHMS; MODEL Hashing learning is efficient for large-scale image retrieval by using the nearest neighbor search with binary codes instead of continuous representations. With the success of deep neural networks in related tasks such as data representation, recent hashing methods based on deep learning can further improve image retrieval quality and classification accuracy. However, most existing methods are primarily designed to maximize the performance of retrieval based on linear scan of hash codes which is still time-consuming on large-scale datasets. Fortunately, Hamming space retrieval is an alternative as it is less time-consuming by retrieving data points that are within a Hamming ball with a given Hamming radius, but few works focus on that. In this paper, we propose a concentrated hashing method with neighborhood embedding (CHNE) for efficient and effective image retrieval and classification. By integrating Cauchy cross-entropy and pair-wise weighted similarity loss, CHNE can enable similar data pairs with smaller Hamming distance and dissimilar data pairs with larger Hamming distance. In addition, existing hashing methods are usually designed for retrieval, thus the performance of classification using the binary codes is not guaranteed. To tackle this problem, we jointly minimize the regression quantization and neighborhood structure reconstruction errors in the loss function to improve the classification accuracy. The proposed end-to-end deep hashing method can be optimized by back-propagation in a standard manner. Experimental results on several datasets demonstrate that the proposed method can improve the performance of retrieval and classification. Due to its generality, the proposed method is expected to be useful for image retrieval and classification in broader areas. [Mo, Dongmei; Wong, Wai Keung] Hong Kong Polytech Univ, Inst Text & Clothing, Hung Hom, Hong Kong, Peoples R China; [Wong, Wai Keung] Lab Artificial Intelligence Design, Hong Kong, Peoples R China; [Liu, Xianjing] Erasmus MC Univ Med Ctr, Dept Radiol & Nucl Med, Rotterdam, Netherlands; [Ge, Yao] Chinese Univ Hong Kong, Dept Elect Engn, Sha Tin, Hong Kong, Peoples R China Hong Kong Polytechnic University; Erasmus University Rotterdam; Erasmus MC; Chinese University of Hong Kong Wong, WK (corresponding author), Hong Kong Polytech Univ, Inst Text & Clothing, Hung Hom, Hong Kong, Peoples R China.;Wong, WK (corresponding author), Lab Artificial Intelligence Design, Hong Kong, Peoples R China. dongmei.mo@connect.polyu.hk; calvin.wong@polyu.edu.hk; x.liu.1@erasmusmc.nl Wong, Wai Keung/0000-0002-5214-7114; LIU, Xianjing/0000-0002-2554-5168 Laboratory for Artificial Intelligence in Design under InnoHK Research Clusters, Hong Kong SAR Government [RP3-4] Laboratory for Artificial Intelligence in Design under InnoHK Research Clusters, Hong Kong SAR Government This research is supported by Laboratory for Artificial Intelligence in Design (Project Code: RP3-4) under InnoHK Research Clusters, Hong Kong SAR Government. 60 1 1 3 15 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. JUN 2022.0 13 6 1571 1587 10.1007/s13042-021-01466-7 0.0 JAN 2022 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0U0EG 2023-03-23 WOS:000737098400001 0
J Mao, RC; Bao, Y; Huang, Z; Liu, Q; Liu, G Mao, Ruichang; Bao, Yi; Huang, Zhou; Liu, Qiance; Liu, Gang High-Resolution Mapping of the Urban Built Environment Stocks in Beijing ENVIRONMENTAL SCIENCE & TECHNOLOGY English Article MATERIAL FLOW-ANALYSIS; BUILDING MATERIAL STOCKS; RESIDENTIAL BUILDINGS; LIFE-CYCLE; CHINA; CONSTRUCTION; DEMOLITION; INFRASTRUCTURE; EMISSIONS; ACCUMULATION Improving our comprehension of the weight and spatial distribution of urban built environment stocks is essential for informing urban resource, waste, and environmental management, but this is often hampered by inaccuracy and inconsistency of the typology and material composition data of buildings and infrastructure. Here, we have integrated big data mining and analytics techniques and compiled a local material composition database to address these gaps, for a detailed characterization of the quantity, quality, and spatial distribution (in 500 m X 500 m grids) of the urban built environment stocks in Beijing in 2018. We found that 3621 megatons (140 ton/cap) of construction materials were accumulated in Beijing's buildings and infrastructure, equaling to 1141 Mt of embodied greenhouse gas emissions. Buildings contribute the most (63% of total, roughly half in residential and half in nonresidential) to the total stock and the subsurface stocks account for almost half. Spatially, the belts between 3 and 7 km from city center (approximately 5 t/m(2)) and commercial grids (approximately 8 t/m(2)) became the densest. Correlation analyses between material stocks and socioeconomic factors at a high resolution reveal an inverse relationship between building and road stock densities and suggest that Beijing is sacrificing skylines for space in urban expansion. Our results demonstrate that harnessing emerging big data and analytics (e.g., point of interest data and web crawling) could help realize more spatially refined characterization of built environment stocks and highlight the role of such information and urban planning in urban resource, waste, and environmental strategies. [Bao, Yi; Huang, Zhou] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China; [Bao, Yi; Huang, Zhou] Peking Univ, Beijing Key Lab Spatial Informat Integrat & Appl, Beijing, Peoples R China; [Mao, Ruichang; Liu, Gang] Univ Southern Denmark, SDU Life Cycle Engn, Dept Chem Engn Biotechnol & Environm Technol, Odense 5230, Denmark; [Liu, Qiance; Liu, Gang] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China Peking University; Peking University; University of Southern Denmark; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Huang, Z (corresponding author), Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China.;Huang, Z (corresponding author), Peking Univ, Beijing Key Lab Spatial Informat Integrat & Appl, Beijing, Peoples R China.;Liu, G (corresponding author), Univ Southern Denmark, SDU Life Cycle Engn, Dept Chem Engn Biotechnol & Environm Technol, Odense 5230, Denmark.;Liu, G (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China. huangzhou@pku.edu.cn; gli@kbm.sdu.dk yi, Bao/HCH-2605-2022; Mao, Ruichang/AAO-6111-2020; Huang, Zhou/D-8911-2011 yi, Bao/0000-0002-2175-2688; Mao, Ruichang/0000-0001-5465-565X; Liu, Gang/0000-0002-7613-1985; Liu, Qiance/0000-0003-0532-8550 National Natural Science Foundation of China [71991484, 41728002, 41771425]; China Scholarship Council [201708440214]; Danish Council for Independent Research [611100555B] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Danish Council for Independent Research(Det Frie Forskningsrad (DFF)) This work is funded by the National Natural Science Foundation of China (71991484, 41728002, and 41771425), China Scholarship Council (201708440214), and the Danish Council for Independent Research (CityWeight; 611100555B). 69 33 33 17 76 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0013-936X 1520-5851 ENVIRON SCI TECHNOL Environ. Sci. Technol. MAY 5 2020.0 54 9 5345 5355 10.1021/acs.est.9b07229 0.0 11 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology LK1XK 32275823.0 2023-03-23 WOS:000530651900009 0
J Chen, M; Hernandez, A Chen, M.; Hernandez, A. Towards an Explainable Model for Sepsis Detection Based on Sensitivity Analysis IRBM English Article Intensive care unit (ICU); Random forest; Explainable AI; Sensitivity analysis INTERNATIONAL CONSENSUS DEFINITIONS Objectives: Sepsis is a life-threatening condition which is responsible for a high proportion of intrahospital deaths and related healthcare costs each year. Early detection and treatment of sepsis episodes is critical, since an early treatment may highly improve prognosis. This study proposed an original method to increase the interpretability of a set of machine learning models for the early detection of sepsis onset. Material and methods: Open data from the electronic medical records of 40,336 patients monitored in intensive care units (ICU), provided by the PhysioNet/Computing in Cardiology Challenge 2019 is used in this paper. We proposed a method including data preprocessing, feature engineering, model construction and tuning, as well as an original interpretability analysis method for the final stage. Results: A total of 24 models were developed and analyzed. The best model, based on 142 features achieved a 0.4274 utility score. The best compact model integrates only 20 selected features, and provided a utility score of 0.3862. Meanwhile, the proposed sensitivity analysis method allows for the identification of the most relevant markers to early detect the onset of sepsis, as well as their interdependence and relative importance on the final decision. Conclusion: A set of optimized machine-learning models were proposed for predicting sepsis early in a real-time way with high performance, and interpretable information including the most significant biomarkers were analyzed through novel interpretability method. (C) 2021 AGBM. Published by Elsevier Masson SAS. All rights reserved. [Chen, M.] Southeast Univ, Sch Biomed Sci & Med Engn, Lab Med Imaging & Artificial Intelligence, Nanjing, Peoples R China; [Hernandez, A.] Univ Rennes, INSERM, LTSI UMR 1099, Rennes, France Southeast University - China; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes Hernandez, A (corresponding author), Univ Rennes, INSERM, LTSI UMR 1099, Rennes, France. alfredo.hernandez@inserm.fr Chen, Meng/0000-0002-2386-7649 35 0 0 5 11 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1959-0318 1876-0988 IRBM IRBM FEB 2022.0 43 1 75 86 10.1016/j.irbm.2021.05.006 0.0 JAN 2022 12 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering YQ6UN Green Submitted 2023-03-23 WOS:000749442600009 0
J Tadesse, MG; Chen, Y; Wang, LC; Nierstrasz, V; Loghin, C Tadesse, Melkie Getnet; Chen, Yan; Wang, Lichuan; Nierstrasz, Vincent; Loghin, Carmen Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems FIBERS AND POLYMERS English Article ANFIS; ANN; Mechanical properties; Total hand value; Tactile comfort SENSORY EVALUATION; NEURAL-NETWORKS; FUZZY-LOGIC Subjective and objective evaluations of the handle of textile materials are very important to describe its tactile comfort for next-to-skin goods. In this paper, the applicability of artificial neural-network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modeling approaches for the prediction of the psychological perceptions of functional fabrics from mechanical properties were investigated. Six distinct functional fabrics were evaluated using human subjects for their tactile score and total hand values (THV) using tactile and comfort-based fabric touch attributes. Then, the measurement of mechanical properties of the same set of samples using KES-FB was performed. The RMSE values for ANN and ANFIS predictions were 0.014 and 0.0122 and are extremely lower than the variations of the perception scores of 0.644 and 0.85 for ANN and ANFIS, respectively with fewer prediction errors. The observed results indicated that the predicted tactile score and THV are almost very close to the actual output obtained using the human judgment. Fabric objective measurement technology, therefore, provides reliable measurement approaches for functional fabric quality inspection, control, and design specification. [Tadesse, Melkie Getnet; Loghin, Carmen] Gheorghe Asachi Tech Univ Iasi, Fac Text Leather & Ind Management, 53 D Mangeron Blvd, Iasi 700050, Romania; [Tadesse, Melkie Getnet; Nierstrasz, Vincent] Univ Boras, Text Mat Technol, Dept Text Technol, Fac Text Engn & Business, SE-50190 Boras, Sweden; [Tadesse, Melkie Getnet; Chen, Yan; Wang, Lichuan] Soochow Univ, Coll Text & Clothing Engn, Suzhou 215006, Peoples R China University of Boras; Soochow University - China Tadesse, MG (corresponding author), Gheorghe Asachi Tech Univ Iasi, Fac Text Leather & Ind Management, 53 D Mangeron Blvd, Iasi 700050, Romania.;Tadesse, MG (corresponding author), Univ Boras, Text Mat Technol, Dept Text Technol, Fac Text Engn & Business, SE-50190 Boras, Sweden.;Tadesse, MG (corresponding author), Soochow Univ, Coll Text & Clothing Engn, Suzhou 215006, Peoples R China. melkie.tadesse@hb.se Loghin, Maria Carmen N/E-6341-2010; Loghin, Maria Carmen/N-4821-2019 Loghin, Maria Carmen/0000-0002-2317-7680; Tadesse, Melkie Getnet/0000-0002-0781-319X; Nierstrasz, Vincent/0000-0002-4369-9304 Erasmus Mundus Joint Doctorate Programme SMDTex-Sustainable Management and Design for Textile [n_2015-1594/001-001-EMJD] Erasmus Mundus Joint Doctorate Programme SMDTex-Sustainable Management and Design for Textile This work was financially supported by Erasmus Mundus Joint Doctorate Programme SMDTex-Sustainable Management and Design for Textile (Grant Number n_2015-1594/001-001-EMJD). 32 9 10 3 21 KOREAN FIBER SOC SEOUL KOREA SCIENCE TECHNOLOGY CTR #501 635-4 YEOGSAM-DONG, KANGNAM-GU, SEOUL 135-703, SOUTH KOREA 1229-9197 1875-0052 FIBER POLYM Fiber. Polym. JAN 2019.0 20 1 199 209 10.1007/s12221-019-8301-9 0.0 11 Materials Science, Textiles; Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Polymer Science HL6RY hybrid 2023-03-23 WOS:000458864500024 0
J Chen, PP; Dong, W; Lu, XD; Kaymak, U; He, KL; Huang, ZX Chen, Peipei; Dong, Wei; Lu, Xudong; Kaymak, Uzay; He, Kunlun; Huang, Zhengxing Deep representation learning for individualized treatment effect estimation using electronic health records JOURNAL OF BIOMEDICAL INFORMATICS English Article Individualized treatment effect estimation; Counterfactual inference; Deep representation learning; Multi-task learning; K-Nearest neighbors PROPENSITY SCORE METHODS; DOUBLY ROBUST ESTIMATION; 2013 ACCF/AHA GUIDELINE; ASSOCIATION TASK-FORCE; CONTROLLED-TRIALS; AMERICAN-COLLEGE; HEART-FAILURE; OUTCOMES; MANAGEMENT; COVARIATE Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased estimators of the treatment effects. Although valuable, learning a balanced representation is sometimes directly opposed to the objective of learning an effective and discriminative model for ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly adopts multi-task deep learning to extract both outcome-predictive and treatment-specific latent representations from Electronic Health Records (EHR), by jointly performing the outcome prediction and treatment category classification. Thereafter, we estimate counterfactual outcomes by KNN based on the learned hidden representations. We validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the estimation of heterogeneous effect (PEHE) and average treatment effect (ATE), respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score respectively, on HF dataset. The results demonstrate that the proposed model achieves competitive performance over state-of-the-art models. In addition, the results reveal several findings which are consistent with existing medical domain knowledge, and discover certain suggestive hypotheses that could be validated through further investigations in the clinical domain. [Chen, Peipei; Lu, Xudong; Kaymak, Uzay; Huang, Zhengxing] Zhejiang Univ, Coll Biomed Engn & Instrumental Sci, Zhou Yiqing Bldg 510,Zheda Rd 38, Hangzhou 310008, Peoples R China; [Chen, Peipei; Lu, Xudong; Kaymak, Uzay] Eindhoven Univ Technol, Sch Ind Engn, Eindhoven, Netherlands; [Dong, Wei; He, Kunlun] Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Fuxing Rd 28, Beijing 100853, Peoples R China Zhejiang University; Eindhoven University of Technology; Chinese People's Liberation Army General Hospital Huang, ZX (corresponding author), Zhejiang Univ, Coll Biomed Engn & Instrumental Sci, Zhou Yiqing Bldg 510,Zheda Rd 38, Hangzhou 310008, Peoples R China.;He, KL (corresponding author), Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Fuxing Rd 28, Beijing 100853, Peoples R China. kunlunhe@plagh.org; zhengxing.h@gmail.com Kaymak, Uzay/A-3364-2008 Kaymak, Uzay/0000-0002-4500-9098; He, Kunlun/0000-0002-3335-5700 National Key Research and Development Program of China [2016YFC1300303]; National Natural Science Foundation of China [61672450]; Philips Research under the Brain Bridge Project National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Philips Research under the Brain Bridge Project This work is partially supported by the National Key Research and Development Program of China under Grant No. 2016YFC1300303, the National Natural Science Foundation of China under Grant No 61672450, and Philips Research under the Brain Bridge Project. 69 8 8 2 17 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1532-0464 1532-0480 J BIOMED INFORM J. Biomed. Inform. DEC 2019.0 100 103303 10.1016/j.jbi.2019.103303 0.0 11 Computer Science, Interdisciplinary Applications; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Medical Informatics LD0DD 31610264.0 Green Published, Bronze 2023-03-23 WOS:000525702900009 0
J Zheng, YH; Wang, QY; Danca, MF Zheng, Yanhong; Wang, Qingyun; Danca, Marius-F. Noise induced complexity: patterns and collective phenomena in a small-world neuronal network COGNITIVE NEURODYNAMICS English Article Small-world neural network; Pattern; Synchronization; Coherence resonance COHERENCE RESONANCE; SYNCHRONIZATION; ORDER The effects of noise on patterns and collective phenomena are studied in a small-world neuronal network with the dynamics of each neuron being described by a two-dimensional Rulkov map neuron. It is shown that for intermediate noise levels, noise-induced ordered patterns emerge spatially, which supports the spatiotemporal coherence resonance. However, the inherent long range couplings of small-world networks can effectively disrupt the internal spatial scale of the media at small fraction of long-range couplings. The temporal order, characterized by the autocorrelation of a firing rate function, can be greatly enhanced by the introduction of small-world connectivity. There exists an optimal fraction of randomly rewired links, where the temporal order and synchronization can be optimized. [Zheng, Yanhong; Wang, Qingyun] Beihang Univ, Dept Dynam & Control, Beijing 100191, Peoples R China; [Zheng, Yanhong] Fujian Normal Univ, Sch Math & Comp Sci, Fuzhou 350007, Peoples R China; [Danca, Marius-F.] Avram Iancu Univ, Dept Math & Comp Sci, Cluj Napoca 400380, Romania; [Danca, Marius-F.] Romanian Inst Sci & Technol, Cluj Napoca 400487, Romania Beihang University; Fujian Normal University; Avram Iancu University of Cluj-Napoca, Romania; Romanian Institute of Science & Technology Wang, QY (corresponding author), Beihang Univ, Dept Dynam & Control, Beijing 100191, Peoples R China. nmqingyun@163.com Danca, Marius-F./A-4572-2011; Wang, Qingyun/G-2309-2010; wang, qinyu/GYA-0366-2022 Danca, Marius-F./0000-0001-7699-8709; Wang, Qingyun/0000-0003-2931-4663; National Natural Science Foundation of China [11102041, 11172017]; Research Fund for the Doctoral Program of Higher Education [20121102110014] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Fund for the Doctoral Program of Higher Education(Research Fund for the Doctoral Program of Higher Education of China (RFDP)) This work was supported by the National Natural Science Foundation of China (Nos. 11102041 and 11172017) and the Research Fund for the Doctoral Program of Higher Education (No. 20121102110014). 26 14 14 1 29 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1871-4080 1871-4099 COGN NEURODYNAMICS Cogn. Neurodynamics APR 2014.0 8 2 143 149 10.1007/s11571-013-9257-x 0.0 7 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology AC7TG 24624233.0 Green Accepted, Green Submitted, Green Published 2023-03-23 WOS:000332734200005 0
J Zhou, X; Ye, QH; Yang, XL; Chen, JK; Ma, HQ; Xia, J; Del Ser, J; Yang, G Zhou, Xi; Ye, Qinghao; Yang, Xiaolin; Chen, Jiakun; Ma, Haiqin; Xia, Jun; Del Ser, Javier; Yang, Guang AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus NEURAL COMPUTING & APPLICATIONS English Article; Early Access Normal pressure hydrocephalus; Machine learning; Computed tomography; Magnetic resonance imaging; Ventricular volume; Intracranial volume; Medical AI; AI-based diagnosis CEREBROSPINAL-FLUID; SEGMENTATION; PREVALENCE; GUIDELINES; MANAGEMENT; DEMENTIA Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 +/- 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 +/- 3.8, respectively. The whole process takes 3.4 +/- 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 +/- 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 +/- 3.8, respectively. The whole process took 1.9 +/- 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles. [Zhou, Xi; Yang, Xiaolin; Chen, Jiakun; Ma, Haiqin; Xia, Jun] Shenzhen Univ, Affiliated Hosp 1, Shenzhen Peoples Hosp 2, Dept Radiol, 3002 SunGang Rd West, Shenzhen 518035, Guangdong, Peoples R China; [Ye, Qinghao] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA; [Del Ser, Javier] Univ Basque Country, UPV EHU, Bilbao 48013, Spain; [Del Ser, Javier] Basque Res & Technol Alliance BRTA, TECNALIA, Derio 48160, Spain; [Yang, Guang] Royal Brompton Hosp, London, England; [Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London, England Shenzhen University; University of California System; University of California San Diego; University of Basque Country; Royal Brompton Hospital; Imperial College London Xia, J (corresponding author), Shenzhen Univ, Affiliated Hosp 1, Shenzhen Peoples Hosp 2, Dept Radiol, 3002 SunGang Rd West, Shenzhen 518035, Guangdong, Peoples R China.;Yang, G (corresponding author), Royal Brompton Hosp, London, England.;Yang, G (corresponding author), Imperial Coll London, Natl Heart & Lung Inst, London, England. xiajun@email.szu.edu.cn; g.yang@imperial.ac.uk Yang, Guang/S-5032-2016 Yang, Guang/0000-0001-7344-7733 Project of Shenzhen International Cooperation Foundation [GJHZ20180926165402083]; National Natural Science Foundation of China [82171913]; Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE [IT1294-19]; British Heart Foundation [TG/18/5/34111, PG/16/78/32402]; Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology); European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavir [H2020-JTI-IMI2 101005122]; AI for Health Imaging Award CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management [H2020-SC1-FA-DTS-2019-1 952172]; MRC [MC/PC/21013]; UK Research and Innovation Future Leaders Fellowship [MR/V023799/1] Project of Shenzhen International Cooperation Foundation; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE; British Heart Foundation(British Heart Foundation); Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology); European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavir; AI for Health Imaging Award CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); UK Research and Innovation Future Leaders Fellowship(UK Research & Innovation (UKRI)) The author is very grateful to Mengyao Xu and Yibo Xu for their guidance on the experimental design of this study, Chuming Xu and Xiaolian Li for their sincere help in data collection, Weiwen Zhou and Fengping Huang for their suggestion on data analysis. This study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083), in part by the National Natural Science Foundation of China (grant number 82171913), in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology), in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics [H2020-JTI-IMI2 101005122], in part by the AI for Health Imaging Award CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management [H2020-SC1-FA-DTS-2019-1 952172], in part by the MRC (MC/PC/21013), and in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1]. 37 2 2 7 19 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. 10.1007/s00521-022-07048-0 0.0 FEB 2022 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZH9RT 35228779.0 hybrid, Green Published, Green Submitted 2023-03-23 WOS:000761267100001 0
J Huang, HF; Zhu, LJ; Zhu, WF; Lin, T; Los, LI; Yao, CP; Chen, XJ; Chen, HY Huang, Haifan; Zhu, Liangjiu; Zhu, Weifang; Lin, Tian; Los, Leonoor Inge; Yao, Chenpu; Chen, Xinjian; Chen, Haoyu Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema FRONTIERS IN MEDICINE English Article diabetic macular edema; diabetic retinopathy; optical coherence tomography; deep learning algorithm; hyperreflective dots ASSOCIATION; PREVALENCE; FOCI; RANIBIZUMAB; MICROGLIA; OUTCOMES Purpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME).& nbsp;Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study. Two types of HRDs, hard exudates and small HRDs (hypothesized to be activated microglia), were identified and labeled independently by two raters. An algorithm using deep learning technology was developed based on input (in total 2,560 OCT b scans) of manual labeling and differentiation of HRDs from rater 1. 4-fold cross-validation was used to train and validate the algorithm. Dice coefficient, intraclass coefficient (ICC), correlation coefficient, and Bland-Altman plot were used to evaluate agreement of the output parameters between two methods (either between two raters or between one rater and proposed algorithm).& nbsp;Results: The Dice coefficients of total HRDs, hard exudates, and small HRDs area of the algorithm were 0.70 +/- 0.10, 0.72 +/- 0.11, and 0.46 +/- 0.06, respectively. The correlations between rater 1 and proposed algorithm (range: 0.95-0.99, all p < 0.001) were stronger than the correlations between the two raters (range: 0.84-0.96, all p < 0.001) for all parameters. The ICCs were higher for all the parameters between rater 1 and proposed algorithm (range: 0.972-0.997) than those between the two raters (range: 0.860-0.953).& nbsp;Conclusions: Our proposed algorithm is a good tool to detect and quantify HRDs and can provide objective and repeatable information of OCT for DME patients in clinical practice and studies. [Huang, Haifan; Lin, Tian; Chen, Haoyu] Shantou Univ, Joint Shantou Int Eye Ctr, Shantou, Peoples R China; [Huang, Haifan; Lin, Tian; Chen, Haoyu] Chinese Univ Hong Kong, Shantou, Peoples R China; [Huang, Haifan; Los, Leonoor Inge] Univ Groningen, Univ Med Ctr Groningen, Dept Ophthalmol, Groningen, Netherlands; [Zhu, Liangjiu; Zhu, Weifang; Yao, Chenpu; Chen, Xinjian] Soochow Univ, Sch Elect & Informat Engn, Suzhou, Peoples R China Shantou University; University of Groningen; Soochow University - China Chen, HY (corresponding author), Shantou Univ, Joint Shantou Int Eye Ctr, Shantou, Peoples R China.;Chen, HY (corresponding author), Chinese Univ Hong Kong, Shantou, Peoples R China.;Chen, XJ (corresponding author), Soochow Univ, Sch Elect & Informat Engn, Suzhou, Peoples R China. xjchen@suda.edu.cn; drchenhaoyu@gmail.com Chen, Haoyu/A-7432-2013 Chen, Haoyu/0000-0003-0676-4610; Lin, Tian/0000-0002-6415-5381; Huang, Haifan/0000-0002-6310-9332 Guangdong Province Science and Technology Special Fund Project Supporting Directed Training of Ph.D. [20200405]; Shantou Science and Technology Program [190917085269835]; National Key R&D Program of China [2018YFA0701700] Guangdong Province Science and Technology Special Fund Project Supporting Directed Training of Ph.D.; Shantou Science and Technology Program; National Key R&D Program of China Funding. This work was supported by Guangdong Province Science and Technology Special Fund Project Supporting Directed Training of Ph.D. Students (No. 20200405) under grant; and Shantou Science and Technology Program (190917085269835), National Key R&D Program of China (2018YFA0701700) under grant. 30 4 4 1 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-858X FRONT MED-LAUSANNE Front. Med. AUG 18 2021.0 8 688986 10.3389/fmed.2021.688986 0.0 11 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine XF6GV 34485331.0 gold, Green Published 2023-03-23 WOS:000724168500001 0
J Goliber, S; Black, T; Catania, G; Lea, JM; Olsen, H; Cheng, D; Bevan, S; Bjork, A; Bunce, C; Brough, S; Carr, JR; Cowton, T; Gardner, A; Fahrner, D; Hill, E; Joughin, I; Korsgaard, NJ; Luckman, A; Moon, T; Murray, T; Sole, A; Wood, M; Zhang, EZ Goliber, Sophie; Black, Taryn; Catania, Ginny; Lea, James M.; Olsen, Helene; Cheng, Daniel; Bevan, Suzanne; Bjork, Anders; Bunce, Charlie; Brough, Stephen; Carr, J. Rachel; Cowton, Tom; Gardner, Alex; Fahrner, Dominik; Hill, Emily; Joughin, Ian; Korsgaard, Niels J.; Luckman, Adrian; Moon, Twila; Murray, Tavi; Sole, Andrew; Wood, Michael; Zhang, Enze TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications CRYOSPHERE English Article TERMINATING OUTLET GLACIERS; MULTI-DECADAL RETREAT; TIDEWATER GLACIER; CALVING FRONT; SURFACE ELEVATION; JAKOBSHAVN ISBRAE; ICE MELANGE; DYNAMICS; FLUCTUATIONS; DISCHARGE Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 +/- 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of similar to 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. [Goliber, Sophie; Catania, Ginny] Univ Texas Austin, Dept Geol Sci, Austin, TX 78712 USA; [Goliber, Sophie; Catania, Ginny; Olsen, Helene] Univ Texas Austin, Inst Geophys, Austin, TX 78712 USA; [Black, Taryn] Univ Washington, Dept Earth & Space Sci, Seattle, WA 98195 USA; [Black, Taryn; Joughin, Ian] Univ Washington, Polar Sci Ctr, Appl Phys Lab, Seattle, WA 98195 USA; [Lea, James M.; Brough, Stephen; Fahrner, Dominik] Univ Liverpool, Dept Geog & Planning, Liverpool, Merseyside, England; [Cheng, Daniel] Univ Calif Irvine, Dept Comp Sci, Irvine, CA USA; [Bevan, Suzanne; Luckman, Adrian; Murray, Tavi] Swansea Univ, Coll Sci, Geog Dept, Swansea, W Glam, Wales; [Bjork, Anders] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark; [Bunce, Charlie; Carr, J. Rachel] Newcastle Univ, Sch Geog Polit & Sociol, Newcastle Upon Tyne, Tyne & Wear, England; [Bunce, Charlie] Univ Edinburgh, Sch Geosci, Edinburgh, Midlothian, Scotland; [Cowton, Tom] Univ St Andrews, Sch Geog & Sustainable Dev, St Andrews, Fife, Scotland; [Gardner, Alex; Wood, Michael] CALTECH, Jet Prop Lab, Pasadena, CA USA; [Fahrner, Dominik] Univ Liverpool, Inst Risk & Uncertainty, Liverpool, Merseyside, England; [Hill, Emily] Univ Northumbria, Dept Geog & Environm Sci, Newcastle Upon Tyne, Tyne & Wear, England; [Korsgaard, Niels J.] Geol Survey Denmark & Greenland, Ostervoldgade 10, DK-1350 Copenhagen K, Denmark; [Moon, Twila] Univ Colorado, Natl Snow & Ice Data Ctr, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA; [Sole, Andrew] Univ Sheffield, Dept Geog, Sheffield, S Yorkshire, England; [Zhang, Enze] Chinese Univ Hong Kong, Earth Syst Sci Programme, Hong Kong, Peoples R China University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Liverpool; University of California System; University of California Irvine; Swansea University; University of Copenhagen; Newcastle University - UK; University of Edinburgh; University of St Andrews; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); University of Liverpool; Northumbria University; Geological Survey Of Denmark & Greenland; University of Colorado System; University of Colorado Boulder; University of Sheffield; Chinese University of Hong Kong Goliber, S (corresponding author), Univ Texas Austin, Dept Geol Sci, Austin, TX 78712 USA.;Goliber, S (corresponding author), Univ Texas Austin, Inst Geophys, Austin, TX 78712 USA. sgoliber@utexas.edu Korsgaard, Niels Jákup/D-8256-2013; Lea, James/HNI-3615-2023; Bjork, Anders Anker/B-1625-2013; Lea, James/N-5699-2015 Korsgaard, Niels Jákup/0000-0002-8700-7023; Hill, Emily/0000-0003-3175-3163; Cowton, Tom/0000-0003-1668-7372; Black, Taryn/0000-0002-7836-3309; Bjork, Anders Anker/0000-0002-4919-792X; Brough, Stephen/0000-0002-6581-6081; Lea, James/0000-0003-1885-0858; Bevan, Suzanne/0000-0003-2649-2982 NASA Earth and Space Sciences fellowship [18-EARTH18F-323]; Programme for Monitoring of the Greenland Ice Sheet (PROMICE); UKRI Future Leaders Fellowship [MR/S017232/1]; EPSRC; ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments [EP/L015927/1]; Leverhulme Trust Research Leadership scheme [F/00391/J]; UK NERC [NE/G010366/1] NASA Earth and Space Sciences fellowship; Programme for Monitoring of the Greenland Ice Sheet (PROMICE); UKRI Future Leaders Fellowship(UK Research & Innovation (UKRI)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments; Leverhulme Trust Research Leadership scheme; UK NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) We acknowledge support from a NASA Earth and Space Sciences fellowship to Sophie Goliber (18-EARTH18F-323) and terminus tracers everywhere. At the University of Texas this includes Lucero Casteneda, Gene Hsu, David Peters, Andrea Jimenez, and Mason Fried. Niels J. Korsgaard was supported by the Programme for Monitoring of the Greenland Ice Sheet (PROMICE). Michael Wood was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by the Universities Space Research Association under contract with NASA. James M. Lea is supported by a UKRI Future Leaders Fellowship (grant no. MR/S017232/1). Dominik Fahrner acknowledges support for this study through the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments (grant no. EP/L015927/1). Tavi Murray is funded by the Leverhulme Trust Research Leadership scheme F/00391/J and the UK NERC NE/G010366/1. Landsat images used in figures were downloaded from USGS Earth Explorer https://earthexplorer.usgs.gov/ (last access: May 2021). 74 2 2 3 6 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1994-0416 1994-0424 CRYOSPHERE Cryosphere AUG 12 2022.0 16 8 3215 3233 10.5194/tc-16-3215-2022 0.0 19 Geography, Physical; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology 3S5BF Green Submitted, gold, Green Accepted 2023-03-23 WOS:000839610600001 0
J Xing, J; Chen, C; Lu, QY; Cai, X; Yu, AJ; Xu, Y; Xia, XL; Sun, Y; Xiao, J; Huang, LY Xing, Jie; Chen, Chao; Lu, Qinyang; Cai, Xun; Yu, Aijun; Xu, Yi; Xia, Xiaoling; Sun, Yue; Xiao, Jing; Huang, Lingyun Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Feature extraction; Ultrasonic imaging; Imaging; Task analysis; Breast cancer; Ultrasound breast imaging reporting and data system; breast mass classification; attention mechanism; deep learning; BI-RADS stratification estimation Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity. In this study, we aim to incorporate the instructive information provided by breast imaging reporting and data system (BI-RADS) within DL-based classification. A novel DL-based BI-RADS Vector-Attention Network (BVA Net) that trains with both texture information and decoded information from BI-RADS stratifications was proposed for the task. Three baseline models, pre-trained DenseNet-121, ResNet-50 and Residual-Attention Network (RA Net) were included for comparison. Experiments were conducted on a large scale private main dataset and two public datasets, UDIAT and BUSI. On the main dataset, BVA Net outperformed other models, in terms of AUC (area under the receiver operating curve, 0.908), ACC (accuracy, 0.865), sensitivity (0.812) and precision (0.795). BVA Net also achieved the high AUC (0.87 and 0.882) and ACC (0.859 and 0.843), on UDIAT and BUSI. Moreover, we proposed a method that integrates both BVA Net binary classification and BI-RADS stratification estimation, called integrated classification. The introduction of integrated classification helped improving the overall sensitivity while maintaining a high specificity. [Xing, Jie; Yu, Aijun] Chinese Acad Sci, Inst Canc Res & Basic Med Sci, Canc Hosp, Zhejiang Canc Hosp, Hangzhou 310022, Peoples R China; [Chen, Chao; Cai, Xun; Xiao, Jing; Huang, Lingyun] PingAn Technol, Shenzhen, Peoples R China; [Lu, Qinyang; Xia, Xiaoling] Donghua Univ, Dept Comp Sci, Shanghai 200051, Peoples R China; [Cai, Xun] Chinese Univ Hong Kong, Dept Biomed Engn, Hong Kong, Peoples R China; [Xu, Yi] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China; [Sun, Yue] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 Eindhoven, Netherlands Chinese Academy of Sciences; Zhejiang Cancer Hospital; Donghua University; Chinese University of Hong Kong; Shanghai Jiao Tong University; Eindhoven University of Technology Huang, LY (corresponding author), PingAn Technol, Shenzhen, Peoples R China. xingjie@zjcc.org.cn; chenchao721@pingan.com.cn; 2181818@mail.dhu.edu.cn; 1155130148@link.cuhk.edu.hk; yuaj@zjcc.org.cn; xuyi@sjtu.edu.cn; SherlySha@dhu.edu.cn; Y.sun1@tue.nl; xiaojing661@pingan.com.cn; huanglingyun691@pingan.com.cn chen, chao/HLX-5841-2023 chen, chao/0000-0001-8592-8606; Xing, Jie/0000-0002-5689-9268 Shanghai Key Lab of Digital Media Processing and Transmission [STCSM 18DZ2270700]; Medical Health Science and Technology Project of Zhejiang Provincial Health Commission [2019PY022] Shanghai Key Lab of Digital Media Processing and Transmission; Medical Health Science and Technology Project of Zhejiang Provincial Health Commission This work was supported in part by the Shanghai Key Lab of Digital Media Processing and Transmission (STCSM 18DZ2270700) and in part by Medical Health Science and Technology Project of Zhejiang Provincial Health Commission 2019PY022. 32 11 11 8 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. JUN 2021.0 25 6 2058 2070 10.1109/JBHI.2020.3034804 0.0 13 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics SN5TP 33119515.0 2023-03-23 WOS:000658352100020 0
J Wong, JK; Mayberg, HS; Wang, DD; Richardson, RM; Halpern, CH; Krinke, L; Arlotti, M; Rossi, L; Priori, A; Marceglia, S; Gilron, R; Cavanagh, JF; Judy, JW; Miocinovic, S; Devergnas, AD; Sillitoe, RV; Cernera, S; Oehrn, CR; Gunduz, A; Goodman, WK; Petersen, EA; Bronte-Stewart, H; Raike, RS; Malekmohammadi, M; Greene, D; Heiden, P; Tan, HL; Volkmann, J; Voon, V; Li, LM; Sah, P; Coyne, T; Silburn, PA; Kubu, CS; Wexler, A; Chandler, J; Provenza, NR; Heilbronner, SR; Luciano, MS; Rozell, CJ; Fox, MD; de Hemptinne, C; Henderson, JM; Sheth, SA; Okun, MS Wong, Joshua K. K.; Mayberg, Helen S. S.; Wang, Doris D. D.; Richardson, R. Mark; Halpern, Casey H. H.; Krinke, Lothar; Arlotti, Mattia; Rossi, Lorenzo; Priori, Alberto; Marceglia, Sara; Gilron, Ro'ee; Cavanagh, James F. F.; Judy, Jack W. W.; Miocinovic, Svjetlana; Devergnas, Annaelle D. D.; Sillitoe, Roy V. V.; Cernera, Stephanie; Oehrn, Carina R. R.; Gunduz, Aysegul; Goodman, Wayne K. K.; Petersen, Erika A. A.; Bronte-Stewart, Helen; Raike, Robert S. S.; Malekmohammadi, Mahsa; Greene, David; Heiden, Petra; Tan, Huiling; Volkmann, Jens; Voon, Valerie; Li, Luming; Sah, Pankaj; Coyne, Terry; Silburn, Peter A. A.; Kubu, Cynthia S. S.; Wexler, Anna; Chandler, Jennifer; Provenza, Nicole R. R.; Heilbronner, Sarah R. R.; Luciano, Marta San; Rozell, Christopher J. J.; Fox, Michael D. D.; de Hemptinne, Coralie; Henderson, Jaimie M. M.; Sheth, Sameer A. A.; Okun, Michael S. S. Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation FRONTIERS IN HUMAN NEUROSCIENCE English Article deep brain stimulation (DBS); artificial intelligence; neuroethics; Parkinson's disease; dystonia; interventional psychiatry; adaptive DBS; epilepsy SUBTHALAMIC STIMULATION; NONMOTOR SYMPTOMS; DOUBLE-BLIND; ELECTROCORTICOGRAPHY; EXPERIENCE; DISORDERS; TRIAL The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging DBS technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the trough of disillusionment. DBS for depression was considered as re-emerging and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS. [Wong, Joshua K. K.; de Hemptinne, Coralie] Univ Florida, Fixel Inst Neurol Dis, Dept Neurol, Gainesville, FL 32601 USA; [Mayberg, Helen S. S.] Icahn Sch Med Mt Sinai, New York, NY USA; [Wang, Doris D. D.; Cernera, Stephanie; Oehrn, Carina R. R.] Univ Calif San Francisco, Weill Inst Neurosci, Dept Neurol Surg, San Francisco, CA USA; [Richardson, R. Mark] Massachusetts Gen Hosp, Dept Neurosurg, Boston, MA USA; [Richardson, R. Mark] Harvard Med Sch, Boston, MA USA; [Halpern, Casey H. H.] Univ Penn, Penn Hosp, Perelman Sch Med, Dept Neurosurg,Richards Med Res Lab, Philadelphia, PA USA; [Krinke, Lothar; Arlotti, Mattia; Rossi, Lorenzo; Priori, Alberto; Marceglia, Sara] Newronika, Goose Creek, SC USA; [Krinke, Lothar] West Virginia Univ, Dept Neurosci, Morgantown, WV USA; [Gilron, Ro'ee] Rune Labs, San Francisco, CA USA; [Cavanagh, James F. F.] Stanford Univ, Dept Neurosurg, Albuquerque, CA USA; Univ Florida, Dept Elect & Comp Engn, Gainesville, FL USA; [Miocinovic, Svjetlana; Devergnas, Annaelle D. D.] Emory Univ, Sch Med, Dept Neurol, Atlanta, GA USA; [Sillitoe, Roy V. V.] Baylor Coll Med, Dept Neurosci, Houston, TX USA; [Gunduz, Aysegul] Univ Florida, J Crayton Pruitt Family Dept Biomed Engn, Gainesville, FL USA; [Goodman, Wayne K. K.] Baylor Coll Med, Dept Psychiat & Behav Sci, Houston, TX USA; [Petersen, Erika A. A.] Univ Arkansas Med Sci, Dept Neurosurg, Little Rock, AR USA; [Bronte-Stewart, Helen] Stanford Univ, Dept Neurol & Neurol Sci, Sch Med, Stanford, CA USA; [Raike, Robert S. S.] Medtronic Inc, Restorat Therapies Grp Implantables Res & Core Tec, Minneapolis, MN USA; [Malekmohammadi, Mahsa] Boston Sci Neuromodulat Corp, Valencia, CA USA; [Greene, David] NeuroPace Inc, Mountain View, CA USA; [Heiden, Petra] Univ Cologne, Univ Hosp Cologne, Fac Med, Dept Stereotact & Funct Neurosurg, Cologne, Germany; [Tan, Huiling] Univ Oxford, Brain Network Dynam Unit, Nuffield Dept Clin Neurosci, MRC, Oxford, England; [Volkmann, Jens] Univ Wurzburg, Dept Neurol, Wurzburg, Germany; [Voon, Valerie] Univ Cambridge, Dept Psychiat, Cambridge, England; [Li, Luming] Tsinghua Univ, Natl Engn Res Ctr Neuromodulat, Sch Aerosp Engn, Beijing, Peoples R China; [Sah, Pankaj; Coyne, Terry; Silburn, Peter A. A.] Univ Queensland, Queensland Brain Inst, St Lucia, Qld, Australia; [Kubu, Cynthia S. S.] Cleveland Clin, Dept Neurol, Cleveland, OH USA; [Wexler, Anna] Univ Penn, Dept Med Eth & Hlth Policy, Philadelphia, PA USA; [Chandler, Jennifer] Univ Ottawa, Fac Law, Ctr Hlth Law Policy & Eth, Ottawa, ON, Canada; [Provenza, Nicole R. R.] Baylor Coll Med, Dept Neurosurg, Houston, TX USA; [Heilbronner, Sarah R. R.] Univ Minnesota, Dept Neurosci, Minneapolis, MN USA; [Luciano, Marta San] Univ Calif San Francisco, Weill Inst Neurosci, Dept Neurol, San Francisco, CA USA; [Rozell, Christopher J. J.] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA USA; [Fox, Michael D. D.] Brigham & Womens Hosp, Ctr Brain Circuit Therapeut, Dept Neurol Psychiat Radiol & Neurosurg, Boston, MA USA State University System of Florida; University of Florida; Icahn School of Medicine at Mount Sinai; University of California System; University of California San Francisco; Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; University of Pennsylvania; Pennsylvania Medicine; West Virginia University; State University System of Florida; University of Florida; Emory University; Baylor College of Medicine; State University System of Florida; University of Florida; Baylor College of Medicine; University of Arkansas System; University of Arkansas Medical Sciences; Stanford University; Medtronic; University of Cologne; University of Oxford; University of Wurzburg; University of Cambridge; Tsinghua University; University of Queensland; Cleveland Clinic Foundation; University of Pennsylvania; University of Ottawa; Baylor College of Medicine; University of Minnesota System; University of Minnesota Twin Cities; University of California System; University of California San Francisco; University System of Georgia; Georgia Institute of Technology; Harvard University; Brigham & Women's Hospital Wong, JK (corresponding author), Univ Florida, Fixel Inst Neurol Dis, Dept Neurol, Gainesville, FL 32601 USA. joshua.wong@neurology.ufl.edu Henderson, Jaimie/0000-0002-3276-2267 NIH [R25NS108939, K23NS097576, R01NS119301, R01NS127435, P50HD103555, UH3NS100544, R01NS096008, UH3NS100549, 5K23NS099441, UH3NS103550, R01DC014034, U01DC017844, UH2NS095548, U01NS098968, R01 NR014852, UH3 NS103550, R01MH119382]; Hope for Depression Research Foundation; Michael J. Fox Foundation; Burroughs-Wellcome Career Award for Medical Scientists; UCSF Weill Institute for Neurosciences; Thiemann Foundation; NSF [MC_UU_00003/2]; Medical Research Council UK [MR/V00655X/1, MR/P012272/1, CRC-TRR 295]; NIHR, Oxford Biomedical Research Centre; NIH BRAIN; Rosetrees Trust; Medtronic [KL2TR001429]; Deutsche Forschungsgemeinschaft; University of Queensland through the Queensland Brain Institute; Canadian Institutes of Health Research within European Commission; Parkinson's Foundation; NIH, Parkinson's Foundation; Parkinson Alliance; Smallwood Foundation; Bachmann-Strauss Foundation; Tourette Syndrome Association; UF Foundation; [1553482]; [R01NS100874] NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Hope for Depression Research Foundation; Michael J. Fox Foundation; Burroughs-Wellcome Career Award for Medical Scientists(Burroughs Wellcome Fund); UCSF Weill Institute for Neurosciences; Thiemann Foundation; NSF(National Science Foundation (NSF)); Medical Research Council UK(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); NIHR, Oxford Biomedical Research Centre(National Institute for Health Research (NIHR)); NIH BRAIN(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Rosetrees Trust(Rosetrees Trust); Medtronic(Medtronic); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); University of Queensland through the Queensland Brain Institute; Canadian Institutes of Health Research within European Commission(Canadian Institutes of Health Research (CIHR)); Parkinson's Foundation; NIH, Parkinson's Foundation(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Parkinson Alliance; Smallwood Foundation; Bachmann-Strauss Foundation; Tourette Syndrome Association; UF Foundation; ; JW research was supported by NIH KL2TR001429. HM research was supported by NIH BRAIN UH3 NS103550 and Hope for Depression Research Foundation. DW research was supported by Michael J. Fox Foundation, Burroughs-Wellcome Career Award for Medical Scientists, and UCSF Weill Institute for Neurosciences. JaC research was funded by R01MH119382. SMi research was supported by NIH K23NS097576. RS research was funded by NIH R01NS100874, R01NS119301, R01NS127435, and P50HD103555. CO research was funded by the Thiemann Foundation and by NIH UH3NS100544. AG research was supported by NIH R01NS096008 and NSF Career Award 1553482. WG acknowledges grant funding from NIH UH3NS100549. HT research was supported by the Medical Research Council UK (MC_UU_00003/2, MR/V00655X/1, and MR/P012272/1), the NIHR, Oxford Biomedical Research Centre, and the Rosetrees Trust. JV gratefully acknowledges grant support by the Deutsche Forschungsgemeinschaft within the framework of CRC-TRR 295 (Retune). PaS, TC, and PeS research was funded by the University of Queensland through the Queensland Brain Institute in partnership with Medtronic. JeC work was supported by the Canadian Institutes of Health Research within the funding framework Network of European Funding for Neuroscience Research (NEURON) under the ERA-NET scheme of the European Commission. ML research was supported by NIH 5K23NS099441. CR research was supported by NIH UH3NS103550. JH research was supported by NIH R01DC014034, U01DC017844, UH2NS095548, and U01NS098968. MO served as a consultant for the Parkinson's Foundation and has received research grants from NIH, Parkinson's Foundation, the Michael J. Fox Foundation, the Parkinson Alliance, Smallwood Foundation, the Bachmann-Strauss Foundation, the Tourette Syndrome Association, and the UF Foundation, DBS research was supported by: NIH R01 NR014852 and R01NS096008, and PI of the NIH R25NS108939 Training Grant. 110 0 0 2 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5161 FRONT HUM NEUROSCI Front. Hum. Neurosci. JAN 27 2023.0 16 1084782 10.3389/fnhum.2022.1084782 0.0 19 Neurosciences; Psychology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychology 9K4RI 36819295.0 Green Accepted, gold 2023-03-23 WOS:000940856800001 0
J Cao, HR; Shao, HD; Zhong, X; Deng, QW; Yang, XK; Xuan, JP Cao, Hongru; Shao, Haidong; Zhong, Xiang; Deng, Qianwang; Yang, Xingkai; Xuan, Jianping Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds JOURNAL OF MANUFACTURING SYSTEMS English Article Unsupervised domain-share CNN; Fault transfer diagnosis; Time-varying speeds; Cauchy kernel-induced maximum mean difference; Adjustable and segmented factors CONVOLUTIONAL NEURAL-NETWORK; KERNEL WIDTH; OPTIMIZATION; DRIVEN The existing deep transfer learning-based intelligent fault diagnosis studies for machinery mainly consider steady speed scenarios, and there exists a problem of low diagnosis efficiency. In order to overcome these limitations, an unsupervised domain-share convolutional neural network (CNN) is proposed for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. First, a Cauchy kernel-induced maximum mean discrepancy based on unbiased estimation is developed for improving the efficiency and robustness of feature adaptation. Secondly, an unsupervised domain-share CNN is constructed to simultaneously extract the domain invariant features from the source domain and the target domain. Finally, adjustable and segmented balance factors are designed to flexibly weigh the distribution-adaptation loss and cross-entropy loss to improve diagnosis accuracy and transferability. The proposed method analyzes raw vibration signals collected from bearings and gears under different rotating speeds. Results of case studies show that the proposed method can achieve higher diagnosis accuracy, faster convergence, and better robustness than the reported methods, which demonstrates its potential applications in machine fault transfer diagnosis from a steady speed condition to a time-varying speed condition. [Cao, Hongru; Shao, Haidong; Zhong, Xiang; Deng, Qianwang] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China; [Shao, Haidong] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Yang, Xingkai] Univ Alberta, Dept Mech Engn, Edmonton, AB T6G 1H9, Canada; [Xuan, Jianping] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China Hunan University; Lulea University of Technology; University of Alberta; Huazhong University of Science & Technology Shao, HD; Zhong, X (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China. hdshao@hnu.edu.cn; zx5587@hnu.edu.cn Shao, Haidong/AAE-5814-2022 Shao, Haidong/0000-0002-8018-1774; deng, qianwang/0000-0003-1779-6634 National Key Research and Development of China [2020YFB1712100]; National Natural Science Foundation of China [51905160]; Natural Science Fund for Excellent Young Scholars of Hunan Province [2021JJ20017]; Fundamental Research Funds for the Central Universities [531118010335] National Key Research and Development of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fund for Excellent Young Scholars of Hunan Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) Acknowledgements This research is supported by the National Key Research and Development of China (No. 2020YFB1712100) , the National Natural Science Foundation of China (No. 51905160) , the Natural Science Fund for Excellent Young Scholars of Hunan Province (No. 2021JJ20017) , and the Fundamental Research Funds for the Central Universities (No. 531118010335) . 44 66 66 121 159 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-6125 1878-6642 J MANUF SYST J. Manuf. Syst. JAN 2022.0 62 186 198 10.1016/j.jmsy.2021.11.016 0.0 13 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 0P5ZQ 2023-03-23 WOS:000784306700006 0
J Yang, YC; Sun, DW; Wang, NN Yang, Yi-Chao; Sun, Da-Wen; Wang, Nan-Nan Rapid detection of browning levels of lychee pericarp as affected by moisture contents using hyperspectral imaging COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Litchi; Moisture content (MC); Back-propagation neural network (BP-NN); Radial basis function support vector regression (RBF-SVR); Browning levels SUCCESSIVE PROJECTIONS ALGORITHM; NEAR-INFRARED SPECTROSCOPY; QUALITY EVALUATION; COMPUTER VISION; NONDESTRUCTIVE MEASUREMENT; MULTIVARIATE-ANALYSIS; DESORPTION ISOTHERMS; LITCHI FRUIT; WATER-LOSS; BEEF Lychee is an important tropical and subtropical fruit. However, the quality of lychee fruit changes easily after harvest and it is difficult to control the process. One of the most significant factors impacting lychee quality seriously is enzymatic browning, which is commonly affected by moisture loss of pericarp during storage. As an emerging technique, hyperspectral imaging (HSI) carries many unique advantages compared to conventional detection methods, providing an innovative tool for quality evaluation of many fruits. The current study focused on exploring the relationship between browning levels of lychee and moisture contents (MC) of pericarp, and developing calibration models for determining browning degree of lychee based on the MC prediction of pericarp using HSI technique. Two sets of optimal wavelengths were selected using regression coefficients (RC) from partial least squares regression (PLSR) and successive projections algorithm (SPA), respectively. Calibration models for determining browning levels of lychee were developed using PLSR, back-propagation neural network (BP-NN) and radial basis function support vector regression (RBF-SVR) algorithms and their performances were compared. The results demonstrated that the RBF-SVR model based on the optimal wavelengths selected by RC had the best performance with coefficients of determination R-2 of 0.946 and 0.948, and root mean square error (RMSE) of 0.80% and 0.83% for training and testing sets, respectively, showing browning levels of lychee could be determined by this approach. Finally, the visualization map of lychee with different browning levels was created and distribution of browning degree in a lychee was observed by examining color variation among pixels in the map. (C) 2015 Elsevier B.V. All rights reserved. [Yang, Yi-Chao; Sun, Da-Wen; Wang, Nan-Nan] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Wang, Nan/GRY-3150-2022; Sun, Da-Wen/B-1899-2012; Wang, Nan/HLV-7836-2023 Sun, Da-Wen/0000-0002-3634-9963; Guangdong Province Government (China); National Key Technologies RD Program [2014BAD08B09, 2015BAD19B03]; International S&T Cooperation Programme of China [2015DFA71150]; International S&T Cooperation Projects of Guangdong Province [2013B051000010] Guangdong Province Government (China); National Key Technologies RD Program(National Key Technology R&D Program); International S&T Cooperation Programme of China; International S&T Cooperation Projects of Guangdong Province The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by the National Key Technologies R&D Program (2014BAD08B09, 2015BAD19B03), the International S&T Cooperation Programme of China (2015DFA71150), and the International S&T Cooperation Projects of Guangdong Province (2013B051000010). Special thanks to Dr. Lijun You, Dr. Dan Liu, Dr. Hongbin Pu and Mr. Rana Muhammad Aadil from South China University of Technology for their kind suggestions. 69 27 29 3 68 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. APR 2015.0 113 203 212 10.1016/j.compag.2015.02.008 0.0 10 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science CH0TV 2023-03-23 WOS:000353736200019 0
C Giri, C; Harale, N; Thomassey, S; Zeng, XY Liu, J; Lu, J; Xu, Y; Martinez, L; Kerre, EE Giri, Chandadevi; Harale, Nitin; Thomassey, Sebastien; Zeng, Xianyi Analysis of consumer emotions about fashion brands: An exploratory study DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT World Scientific Proceedings Series on Computer Engineering and Information Science English Proceedings Paper 13th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS) AUG 21-24, 2018 Belfast, IRELAND Ulster Univ,Belfast City Council,Tourism NI,IEEE UK & I Sect,IEEE Syst Man & Cybernet Soc,SW Jiaotong Univ,Chinese Soc Log, Natl Assoc Non Class Log & Computat,IEEE Syst Man & Cybernet Soc Ireland Chapter,European Soc Fuzzy Log & Technol,Int Fuzzy Syst Assoc Sentiment analysis; fashion industry; big data; Twitter Fashion products are characterized by high variability in terms of rapidly changing consumer preferences. Consumers express their emotions on social networks such as Twitter, Facebook and Instagram. The main objective of this paper is to explore Twitter data for recognizing customer sentiments about fashion brands and to analyze their overall perception towards the brands. Two brands, Zara and Levis, are considered and users' tweets related to these brands are analyzed using text mining and Naive Bayes classifier. The results from this study suggest that social media such as Twitter can serve to be the repository of consumer sentiments and opinions. Sentiment analysis of the tweets can indicate fashion trend and thereby enable fashion brand companies to quickly respond to the ever changing consumer demands. [Giri, Chandadevi; Harale, Nitin; Thomassey, Sebastien; Zeng, Xianyi] Ensait, Gemtex, Roubaix, France; [Giri, Chandadevi] Univ Boras, SE-50190 Boras, Sweden; [Giri, Chandadevi] Coll Text & Clothing Engn, Suzhou 21506, Peoples R China Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); University of Boras Giri, C (corresponding author), Ensait, Gemtex, Roubaix, France.;Giri, C (corresponding author), Univ Boras, SE-50190 Boras, Sweden.;Giri, C (corresponding author), Coll Text & Clothing Engn, Suzhou 21506, Peoples R China. thomassey, sebastien/AAZ-9320-2020 thomassey, sebastien/0000-0002-5556-7173 8 3 3 1 1 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE 978-981-3273-22-1 WD SCI P COMP ENG 2018.0 11 1567 1574 8 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Operations Research & Management Science BM7MX 2023-03-23 WOS:000468160600195 0
J Li, JN; Pimentel, P; Szengel, A; Ehlke, M; Lamecker, H; Zachow, S; Estacio, L; Doenitz, C; Ramm, H; Shi, HC; Chen, XJ; Matzkin, F; Newcombe, V; Ferrante, E; Jin, Y; Ellis, DG; Aizenberg, MR; Kodym, O; Spanel, M; Herout, A; Mainprize, JG; Fishman, Z; Hardisty, MR; Bayat, A; Shit, S; Wang, BM; Liu, Z; Eder, M; Pepe, A; Gsaxner, C; Alves, V; Zefferer, U; von Campe, G; Pistracher, K; Schafer, U; Schmalstieg, D; Menze, BH; Glocker, B; Egger, J Li, Jianning; Pimentel, Pedro; Szengel, Angelika; Ehlke, Moritz; Lamecker, Hans; Zachow, Stefan; Estacio, Laura; Doenitz, Christian; Ramm, Heiko; Shi, Haochen; Chen, Xiaojun; Matzkin, Franco; Newcombe, Virginia; Ferrante, Enzo; Jin, Yuan; Ellis, David G.; Aizenberg, Michele R.; Kodym, Oldrich; Spanel, Michal; Herout, Adam; Mainprize, James G.; Fishman, Zachary; Hardisty, Michael R.; Bayat, Amirhossein; Shit, Suprosanna; Wang, Bomin; Liu, Zhi; Eder, Matthias; Pepe, Antonio; Gsaxner, Christina; Alves, Victor; Zefferer, Ulrike; von Campe, Gord; Pistracher, Karin; Schaefer, Ute; Schmalstieg, Dieter; Menze, Bjoern H.; Glocker, Ben; Egger, Jan AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design IEEE TRANSACTIONS ON MEDICAL IMAGING English Article Skull; Shape; Implants; Three-dimensional displays; Cranial; Image reconstruction; Biomedical imaging; Volumetric shape completion; shape inpainting; skull reconstruction; shape prior; statistical shape model; deep learning; cranioplasty RECONSTRUCTION The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi. [Li, Jianning; Jin, Yuan; Eder, Matthias; Pepe, Antonio; Gsaxner, Christina; Schmalstieg, Dieter] Graz Univ Technol, Inst Comp Graph & Vis, A-8010 Graz, Austria; [Li, Jianning; Jin, Yuan; Eder, Matthias; Pepe, Antonio; Gsaxner, Christina; Egger, Jan] Comp Algorithms Med Lab, Graz, Austria; [Pimentel, Pedro; Szengel, Angelika; Ehlke, Moritz; Lamecker, Hans; Zachow, Stefan; Ramm, Heiko] 1000shapes GmbH, D-12247 Berlin, Germany; [Zachow, Stefan] Zuse Inst Berlin ZIB, D-14195 Berlin, Germany; [Estacio, Laura] Univ Catolica San Pablo, Dept Comp Sci, Arequipa 04001, Peru; [Doenitz, Christian] Univ Med Ctr Regensburg, Dept Neurosurg, D-93053 Regensburg, Germany; [Shi, Haochen] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Chen, Xiaojun] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China; [Matzkin, Franco; Ferrante, Enzo] Univ Nacl Litoral, Inst Signals Syst & Computat Intelligence, CONICET, RA-3000 Santa Fe, Argentina; [Newcombe, Virginia] Univ Cambridge, Univ Div Anaesthesia, Cambridge CB2 0QQ, England; [Ellis, David G.; Aizenberg, Michele R.] Univ Nebraska Med Ctr, Dept Neurosurg, Omaha, NE 68198 USA; [Kodym, Oldrich; Spanel, Michal; Herout, Adam] Brno Univ Technol, Dept Comp Graph & Multimedia, Brno 61200, Czech Republic; [Mainprize, James G.; Fishman, Zachary; Hardisty, Michael R.] Sunnybrook Res Inst, Toronto, ON M4N 3M5, Canada; [Mainprize, James G.] Calavera Surg Design Inc, Toronto, ON M4N 3M5, Canada; [Hardisty, Michael R.] Univ Toronto, Div Orthopaed Surg, Toronto, ON M5T 1P5, Canada; [Bayat, Amirhossein; Shit, Suprosanna; Menze, Bjoern H.] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Wang, Bomin; Liu, Zhi] Shandong Univ, Sch Informat Sci & Engn, Qingdao 266000, Peoples R China; [Egger, Jan] Med Univ Graz, Dept Oral & Maxillofacial Surg, A-8036 Graz, Austria; [Alves, Victor] Univ Minho, Ctr ALGORITMI, P-4710057 Braga, Portugal; [Zefferer, Ulrike; Schaefer, Ute] Med Univ Graz, Res Unit Expt Neurotraumatol, A-8036 Graz, Austria; [von Campe, Gord; Pistracher, Karin] Med Univ Graz, Dept Neurosurg, A-8036 Graz, Austria; [Menze, Bjoern H.] Univ Zurich, Dept Quantitat Biomed, CH-8006 Zurich, Switzerland; [Glocker, Ben] Imperial Coll London, Biomed Image Anal Grp, Dept Comp, London SW7 2AZ, England Graz University of Technology; Zuse Institute Berlin; Universidad Catolica San Pablo; University of Regensburg; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of the Littoral; University of Cambridge; University of Nebraska System; University of Nebraska Medical Center; Brno University of Technology; University of Toronto; Sunnybrook Health Science Center; Sunnybrook Research Institute; University of Toronto; Technical University of Munich; Shandong University; Medical University of Graz; Universidade do Minho; Medical University of Graz; Medical University of Graz; University of Zurich; Imperial College London Li, JN (corresponding author), Graz Univ Technol, Inst Comp Graph & Vis, A-8010 Graz, Austria.;Li, JN; Egger, J (corresponding author), Comp Algorithms Med Lab, Graz, Austria. jianning.li@icg.tugraz.at; pedro.fpimentel@gmail.com; angelika.szengel@1000shapes.com; mehlke@mailbox.org; hans.lamecker@1000shapes.com; stefan.zachow@1000shapes.com; laura.estacio@ucsp.edu.pe; christian.doenitz@klinik.uni-regensburg.de; heiko.ramm@1000shapes.com; jcsyshc@sjtu.edu.cn; xiaojunchen@sjtu.edu.cn; fmatzkin@sinc.unl.edu.ar; vfjn2@cam.ac.uk; eferrante@sinc.unl.edu.ar; kingdongyang@gmail.com; david.ellis@unmc.edu; maizenberg@unmc.edu; ikodym@fit.vut.cz; spanel@fit.vutbr.cz; herout@fit.vutbr.cz; james.mainprize@sri.utoronto.ca; zachary.fishman@utoronto.ca; m.hardisty@utoronto.ca; amir.bayat@tum.de; suprosanna.shit@tum.de; 201712354@mail.sdu.edu.cn; liuzhi@sdu.edu.cn7; m.h.eder@student.tugraz.at; antonio.pepe@tugraz.at; gsaxner@tugraz.at; valves@di.uminho.pt; ulrike.fasching@medunigraz.at; gord.von-campe@medunigraz.at; karin.pistracher@medunigraz.at; ute.schaefer@medunigraz.at; schmalstieg@tugraz.at; bjoern.menze@tum.de; b.glocker@imperial.ac.uk; jan.egger@medunigraz.at Alves, Victor/D-1319-2009; Spanel, Michal/G-9639-2016; Shit, Suprosanna/AET-7423-2022 Alves, Victor/0000-0003-1819-7051; Shit, Suprosanna/0000-0003-4435-7207; Menze, Bjoern/0000-0003-4136-5690; Aizenberg, Michele/0000-0001-6689-8907; Lamecker, Hans/0000-0001-6320-2564; Chen, Xiaojun/0000-0002-0298-4491; Ellis, David/0000-0002-3718-6836; Estacio Cerquin, Laura Jovani/0000-0003-0678-2157; Jin, Yuan/0000-0001-8695-1525; Newcombe, Virginia/0000-0001-6044-9035; Gsaxner, Christina/0000-0002-2227-3523 Clinical Additive Manufacturing for Medical Applications (CAMed) through COMET K-Project - Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) [871132]; Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Styrian Business Promotion Agency (SFG); Austrian Science Fund (FWF) [KLI 678-B31]; TU Graz Lead Project (Mechanics, Modeling and Simulation of Aortic Dissection) Clinical Additive Manufacturing for Medical Applications (CAMed) through COMET K-Project - Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT); Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Styrian Business Promotion Agency (SFG); Austrian Science Fund (FWF)(Austrian Science Fund (FWF)); TU Graz Lead Project (Mechanics, Modeling and Simulation of Aortic Dissection) This work was supported in part by the Clinical Additive Manufacturing for Medical Applications (CAMed) through COMET K-Project 871132, which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT), in part by the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), in part by the Styrian Business Promotion Agency (SFG), in part by the Austrian Science Fund (FWF) under Grant KLI 678-B31, and in part by the TU Graz Lead Project (Mechanics, Modeling and Simulation of Aortic Dissection). 56 4 5 2 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging SEP 2021.0 40 9 2329 2342 10.1109/TMI.2021.3077047 0.0 14 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging UK8IN 33939608.0 2023-03-23 WOS:000692208500013 0
J Su, J; Gao, LH; Li, W; Xia, Y; Cao, N; Wang, RC Su, Jia; Gao, Lihui; Li, Wei; Xia, Yu; Cao, Ning; Wang, Ruichao Fast Face Tracking-by-Detection Algorithm for Secure Monitoring APPLIED SCIENCES-BASEL English Article Internet of Things; secure monitoring; face tracking; tracking-by-detection; correlation filter; convolution neural network OBJECT; NETWORKS This work proposes a fast face tracking-by-detection (FFTD) algorithm that can perform tracking, face detection and discrimination tasks. On the basis of using the kernelized correlation filter (KCF) as the basic tracker, multitask cascade convolutional neural networks (CNNs) are used to detect the face, and a new tracking update strategy is designed. The update strategy uses the tracking result modified by detector to update the filter model. When the tracker drifts or fails, the discriminator module starts the detector to correct the tracking results, which ensures the out-of-view object can be tracked. Through extensive experiments, the proposed FFTD algorithm is shown to have good robustness and real-time performance for video monitoring scenes. [Su, Jia; Gao, Lihui; Li, Wei; Xia, Yu] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China; [Cao, Ning] Wuxi Vocat Coll Sci & Technol, Sch Internet Things & Software Technol, Wuxi 214028, Jiangsu, Peoples R China; [Cao, Ning] Chuzhou Univ, Sch Comp & Informat Engn, Chuzhou 239000, Peoples R China; [Wang, Ruichao] Univ Coll Dublin, Coll Social Sci & Law, Dublin 4, Ireland Hebei University of Science & Technology; Chuzhou University; University College Dublin Cao, N (corresponding author), Wuxi Vocat Coll Sci & Technol, Sch Internet Things & Software Technol, Wuxi 214028, Jiangsu, Peoples R China.;Cao, N (corresponding author), Chuzhou Univ, Sch Comp & Informat Engn, Chuzhou 239000, Peoples R China. 2010005@wxsc.edu.cn Cao, Ning/P-6652-2017 gao, li hui/0000-0001-8632-9197 key laboratory of equipment pre-research [6142A010301]; science and technology support plan of Hebei Province [18210803D] key laboratory of equipment pre-research; science and technology support plan of Hebei Province This work was supported by key laboratory of equipment pre-research (No. 6142A010301), science and technology support plan of Hebei Province (No. 18210803D). 47 3 4 0 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel SEP 2019.0 9 18 3774 10.3390/app9183774 0.0 17 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics JC2OB gold, Green Submitted 2023-03-23 WOS:000489115200134 0
C Fang, Y; Wang, CH; Ma, QQ; Gu, CY; O'Neill, M; Liu, WQ IEEE Fang, Yue; Wang, Chenghua; Ma, Qingqing; Gu, Chongyan; O'Neill, Maire; Liu, Weiqiang Attacking Arbiter PUFs Using Various Modeling Attack Algorithms: A Comparative Study 2018 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2018) English Proceedings Paper 14th IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) OCT 26-30, 2018 Chengdu, PEOPLES R CHINA IEEE,IEEE Circuits & Syst Soc,Inst Integrated Circuits & Syst,Univ Elect Sci & Technol China,Chengdu Univ Informat Technol,Publishing House Elect Ind,Profess Conf Oranizer Co,Digilent,OUNG Acad Physical Unclonable Functions; Machine Learning; Modeling Attacks In this paper, we investigate the effectiveness of four different modeling attack algorithms, including Logistic Regression (LR), Naive Bayes, AdaBoost and Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES), on attacking arbiter physical unclonable functions (APUFs). A comparison of experimental results using theses algorithms is presented. The results show that the performance of the algorithms is related to the number of training data, the noise level involved in the APUF design and the number of stages in the generation of each bit response. It is found that the mainstream LR and CMA-ES are worse for a small number of data compared with Naive Bayes and AdaBoost. [Fang, Yue; Wang, Chenghua; Ma, Qingqing; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, CEIE, Nanjing 211106, Jiangsu, Peoples R China; [Gu, Chongyan; O'Neill, Maire] Queens Univ Belfast, CSIT, ECIT, Belfast BT3 9DT, Antrim, North Ireland Nanjing University of Aeronautics & Astronautics; Queens University Belfast Fang, Y (corresponding author), Nanjing Univ Aeronaut & Astronaut, CEIE, Nanjing 211106, Jiangsu, Peoples R China. yuef@nuaa.edu.cn; chwang@nuaa.edu.cn; martin@nuaa.edu.cn; cgu01@qub.ac.uk; m.oneill@ecit.qub.ac.uk; liuweiqiang@nuaa.edu.cn GU, CHONGYAN/ABD-5437-2021; Liu, Weiqiang/T-5716-2019 GU, CHONGYAN/0000-0002-3028-8004; National Natural Science Foundation of China [61771239]; Natural Science Foundatoin of Jiangsu Province [BK20151477]; Six Talent Peaks Project of Jiangsu Province [XYDXX-009]; EPSRC [EP/R007187/1, EP/N508664/1, EP/K004379/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundatoin of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Six Talent Peaks Project of Jiangsu Province; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work has been supported by grants from National Natural Science Foundation of China (61771239), Natural Science Foundatoin of Jiangsu Province (BK20151477) and Six Talent Peaks Project of Jiangsu Province (XYDXX-009). 9 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-8240-1 2018.0 394 397 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BL9TO Green Accepted 2023-03-23 WOS:000458319800096 0
J Chatzopoulos, D; Bermejo, C; ul Haq, E; Li, Y; Hui, P Chatzopoulos, Dimitris; Bermejo, Carlos; ul Haq, Ehsan; Li, Yong; Hui, Pan D2D Task Offloading: A Dataset-Based Q&A IEEE COMMUNICATIONS MAGAZINE English Article D2D interactions are promoted both in the context of traffic and computation offloading. Applications with functionalities that are executed in parallel in more than one mobile device have been proposed while the idea of using another mobile device as a relay is going to be part of 5G. In this work, we first present the main types of D2D applications, then we discuss the characteristics of task offloading and the quality of experience in D2D ecosystems. After that, we list five popular questions regarding the ability of other users to be helpful in terms of resources, connectivity, availability, incentives, and security. To answer the listed questions, we use two mobile big data datasets, one experimental study, and arguments from our experience and the literature. [Chatzopoulos, Dimitris] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Bermejo, Carlos; ul Haq, Ehsan] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China; [Hui, Pan] Hong Kong Univ Sci & Technol, HKUST DT Syst & Media Lab, Hong Kong, Peoples R China; [Hui, Pan] Univ Helsinki, Data Sci, Helsinki, Finland; [Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; University of Helsinki; Tsinghua University Chatzopoulos, D (corresponding author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China. dcab@cse.ust.hk; cbf@connect.ust.hk; euhaq@connect.ust.hk; liyong07@tsinghua.edu.cn; panhui@cse.ust.hk Chatzopoulos, Dimitris/AAV-8599-2020; li, yong/HDN-3885-2022; Hui, Pan/AAK-6660-2020 Chatzopoulos, Dimitris/0000-0002-4765-5085; Hui, Pan/0000-0001-6026-1083; Haq, Ehsan Ul/0000-0002-4578-5302 Research Grants Council of Hong Kong [26211515, 16214817] Research Grants Council of Hong Kong(Hong Kong Research Grants Council) This research has been supported in part by projects 26211515 and 16214817 from the Research Grants Council of Hong Kong. 15 13 13 1 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. FEB 2019.0 57 2 102 107 10.1109/MCOM.2018.1700873 0.0 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications HM5RZ 2023-03-23 WOS:000459534200017 0
J Luo, YL; Wan, L; Liu, JX; Harkin, J; Cao, Y Luo, Yuling; Wan, Lei; Liu, Junxiu; Harkin, Jim; Cao, Yi An Efficient, Low-Cost Routing Architecture for Spiking Neural Network Hardware Implementations NEURAL PROCESSING LETTERS English Article Spiking neural networks; Networks-on-chip; Routing arbitration ALGORITHM; CHIPS The basic processing units in brain are neurons and synapses that are interconnected in a complex pattern and show many surprised information processing capabilities. The researchers attempt to mimic this efficiency and build artificial neural systems in hardware device to emulate the key information processing principles of the brain. However, the neural network hardware system has a challenge of interconnecting neurons and synapses efficiently. An efficient, low-cost routing architecture (ELRA) is proposed in this paper to provide a communication infrastructure for the hardware spiking neuron networks (SNN). A dynamic traffic arbitration strategy is employed in ELRA, where the traffic status weights of input ports are calculated in real-time according to the channel traffic statuses and the port with the largest traffic status weight is given a high priority to forward packets. This strategy enables the router to serve congested ports preferentially, which can balance the overall network traffic loads. Experimental results show the feasibility of ELRA under various traffic scenarios, and the hardware synthesis result using SAED 90nm technology demonstrates it has a low hardware area overhead which maintains scalability for large-scale SNN hardware implementations. [Luo, Yuling; Wan, Lei; Liu, Junxiu] Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China; [Harkin, Jim] Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine BT48 7JL, Londonderry, North Ireland; [Cao, Yi] Univ Surrey, Surrey Business Sch, Dept Business Transformat & Sustainable Enterpris, Guildford GU2 7XH, Surrey, England Guangxi Normal University; Ulster University; University of Surrey Liu, JX (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China. junxiu6@163.com Harkin, Jim/F-8958-2013 Harkin, Jim/0000-0001-7484-8205; Cao, Yi/0000-0002-5087-8861 National Natural Science Foundation of China [61603104, 61661008]; Guangxi Natural Science Foundation [2015GXNSFBA139256, 2016GXNSFCA380017]; Overseas 100 Talents Programme of Guangxi Higher Education; Research Project of Guangxi University of China [KY2016YB059]; Guangxi Key Lab of Multi-source Information Mining and Security [MIMS15-07]; Doctoral Research Foundation of Guangxi Normal University; Guangxi Centre of Humanities and Social SciencesEcological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone [ZX2016030]; Innovation Project of Guangxi Graduate Education [YCSZ2016034] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Programme of Guangxi Higher Education; Research Project of Guangxi University of China; Guangxi Key Lab of Multi-source Information Mining and Security; Doctoral Research Foundation of Guangxi Normal University; Guangxi Centre of Humanities and Social SciencesEcological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone; Innovation Project of Guangxi Graduate Education This research was supported by the National Natural Science Foundation of China under Grants 61603104 and 61661008, the Guangxi Natural Science Foundation under Grants 2015GXNSFBA139256 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Programme of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining and Security under Grant MIMS15-07, the Doctoral Research Foundation of Guangxi Normal University, the Research Project of Guangxi Centre of Humanities and Social SciencesEcological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone (ZX2016030), and the Innovation Project of Guangxi Graduate Education (YCSZ2016034). 22 9 9 0 6 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1370-4621 1573-773X NEURAL PROCESS LETT Neural Process. Lett. DEC 2018.0 48 3 SI 1777 1788 10.1007/s11063-018-9797-5 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HC0BN Green Submitted 2023-03-23 WOS:000451460500030 0
J Ren, PJ; Chen, ZM; Ren, ZC; Wei, FR; Nie, LQ; Ma, J; De Rijke, M Ren, Pengjie; Chen, Zhumin; Ren, Zhaochun; Wei, Furu; Nie, Liqiang; Ma, Jun; De Rijke, Maarten Sentence Relations for Extractive Summarization with Deep Neural Networks ACM TRANSACTIONS ON INFORMATION SYSTEMS English Article Extractive summarization; sentence relations; neural network; attentive pooling Sentence regression is a type of extractive summarization that achieves state-of-the-art performance and is commonly used in practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to represent each sentence. In this article, we study the use of sentence relations, e.g., Contextual Sentence Relations (CSR), Title Sentence Relations (TSR), and Query Sentence Relations (QSR), so as to improve the performance of sentence regression. CSR, TSR, and QSR refer to the relations between a main body sentence and its local context, its document title, and a given query, respectively. We propose a deep neural network model, Sentence Relation-based Summarization (SRSum), that consists of five sub-models, PriorSum, CSRSum, TSRSum, QSRSum, and SFSum. PriorSum encodes the latent semantic meaning of a sentence using a bi-gram convolutional neural network. SFSum encodes the surface information of a sentence, e.g., sentence length, sentence position, and so on. CSRSum TSRSum, and QSRSum are three sentence relation sub-models corresponding to CSR, TSR, and QSR, respectively. CSRSum evaluates the ability of each sentence to summarize its local contexts. Specifically, CSRSum applies a CSR-based word-level and sentence-level attention mechanism to simulate the context-aware reading of a human reader, where words and sentences that have anaphoric relations or local summarization abilities are easily remembered and paid attention to. TSRSum evaluates the semantic closeness of each sentence with respect to its title, which usually reflects the main ideas of a document. TSRSum applies a TSR-based attention mechanism to simulate people's reading ability with the main idea (title) in mind. QSRSum evaluates the relevance of each sentence with given queries for the query-focused summarization. QSRSum applies a QSR-based attention mechanism to simulate the attentive reading of a human reader with some queries in mind. The mechanism can recognize which parts of the given queries are more likely answered by a sentence under consideration. Finally as a whole, SRSum automatically learns useful latent features by jointly learning representations of query sentences, content sentences, and title sentences as well as their relations. We conduct extensive experiments on six benchmark datasets, including generic multi-document summarization and query-focused multi-document summarization. On both tasks, SRSum achieves comparable or superior performance compared with state-of-the-art approaches in terms of multiple ROUGE metrics. [Ren, Pengjie; Chen, Zhumin; Nie, Liqiang; Ma, Jun] Shandong Univ, Jinan, Shandong, Peoples R China; [Ren, Zhaochun] JD Com, Data Sci Lab, Beijing, Peoples R China; [Wei, Furu] Microsoft Res Asia, Beijing, Peoples R China; [De Rijke, Maarten] Univ Amsterdam, Amsterdam, Netherlands Shandong University; Microsoft; Microsoft Research Asia; University of Amsterdam Ren, PJ (corresponding author), Shandong Univ, Jinan, Shandong, Peoples R China. jay.ren@outlook.com; chenzhumin@sdu.edu.cn; renzhaochun@jd.com; fuwei@microsoft.com; nieliqiang@gmail.com; majun@sdu.edu.cn; derijke@uva.nl Ren, Zhaochun/0000-0002-9076-6565; de Rijke, Maarten/0000-0002-1086-0202 Natural Science Foundation of China [61672324, 61672322]; Natural Science Foundation of Shandong province [2016ZRE27468]; Fundamental Research Funds of Shandong University; Ahold Delhaize; Amsterdam Data Science; Bloomberg Research Grant program; Elsevier; European Community's Seventh Framework Programme [FP7/2007-2013, 312827]; Google Faculty Research Awards program; Microsoft Research Ph.D. program; Netherlands Institute for Sound and Vision; Netherlands Organisation for Scientific Research (NWO) [CI-14-25, 652.002.001, 612.001.551, 652.001.003]; Yandex Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong province(Natural Science Foundation of Shandong Province); Fundamental Research Funds of Shandong University; Ahold Delhaize; Amsterdam Data Science; Bloomberg Research Grant program; Elsevier; European Community's Seventh Framework Programme(European Commission); Google Faculty Research Awards program(Google Incorporated); Microsoft Research Ph.D. program(Microsoft); Netherlands Institute for Sound and Vision; Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); Yandex This article is supported by the Natural Science Foundation of China (No. 61672324, No. 61672322), the Natural Science Foundation of Shandong province (No. 2016ZRE27468), the Fundamental Research Funds of Shandong University, Ahold Delhaize, Amsterdam Data Science, the Bloomberg Research Grant program, Elsevier, the European Community's Seventh Framework Programme (No. FP7/2007-2013) under grant agreement nr 312827 (VOX-Pol), the Google Faculty Research Awards program, the Microsoft Research Ph.D. program, the Netherlands Institute for Sound and Vision, the Netherlands Organisation for Scientific Research (NWO) under Projects No. CI-14-25, No. 652.002.001, No. 612.001.551, No. 652.001.003, and Yandex. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. 69 29 30 0 12 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1046-8188 1558-2868 ACM T INFORM SYST ACM Trans. Inf. Syst. OCT 2018.0 36 4 39 10.1145/3200864 0.0 32 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science HK4KO 2023-03-23 WOS:000457900100005 0
J Ye, Z; Kutas, M; George, MS; Sereno, MI; Ling, F; Munte, TF Ye, Zheng; Kutas, Marta; George, Marie St.; Sereno, Martin I.; Ling, Feng; Muente, Thomas F. Rearranging the world: Neural network supporting the processing of temporal connectives NEUROIMAGE English Article Temporal conjunction; Caudate nucleus; Prefrontal cortex; fMRI; Independent component analysis NONTHALAMIC SUBCORTICAL LESIONS; INDEPENDENT COMPONENT ANALYSIS; BASAL GANGLIA CIRCUITS; PREFRONTAL CORTEX; WORKING-MEMORY; COMPREHENSION; SENTENCE; FMRI; SEQUENCE; TIME Temporal connectives (before/after) give us the freedom to describe a sequence of events in different orders. Studies have suggested that 'before-initiating' sentences, in which events are expressed in an order inconsistent with their actual order of occurrence, might need additional computation(s) during comprehension. The results of independent component analysis suggest that these computations are supported by a neural network connecting the bilateral caudate nucleus with the right middle frontal gyrus, left precentral gyrus, bilateral parietal lobule and inferior temporal gyrus. Among those regions, the caudate nucleus and the left middle frontal gyrus showed greater activations for 'before' than 'after' sentences. The functional network observed in this study may support sequence learning and processing in a general sense. (C) 2011 Elsevier Inc. All rights reserved. [Ye, Zheng; Muente, Thomas F.] Med Univ Lubeck, Dept Neurol, D-23538 Lubeck, Germany; [Ye, Zheng; Ling, Feng] Capital Med Univ, Xuanwu Hosp, Dept Neurosurg, Beijing, Peoples R China; [Kutas, Marta] Univ Calif San Diego, Dept Cognit Sci, San Diego, CA 92103 USA; [George, Marie St.] San Diego City Coll, Dept Psychol, San Diego, CA USA; [Sereno, Martin I.] Birkbeck Coll London, Sch Psychol, London, England University of Lubeck; Capital Medical University; University of California System; University of California San Diego; University of London; Birkbeck University London Munte, TF (corresponding author), Med Univ Lubeck, Dept Neurol, D-23538 Lubeck, Germany. ling-feng@vip.163.com; Thomas.muente@neuro.uni-luebeck.de Ye, Zheng/F-7819-2013; Münte, Thomas/C-2077-2014; Sereno, Martin I/F-7657-2012; Ye, Zheng/V-9575-2019 Ye, Zheng/0000-0003-1912-6992; Sereno, Martin I/0000-0002-7598-7829; Ye, Zheng/0000-0003-1912-6992 China Scholarship Council; DFG; BMBF; German Center for Neurodegenerative Diseases (DZNE); NICHD [HD66214]; NIA [AG08313]; NIMH [MH081990] China Scholarship Council(China Scholarship Council); DFG(German Research Foundation (DFG)); BMBF(Federal Ministry of Education & Research (BMBF)); German Center for Neurodegenerative Diseases (DZNE)(Helmholtz Association); NICHD(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)); NIA(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); NIMH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)) ZY was supported by a fellowship of the China Scholarship Council. TFM is supported by grants of the DFG, the BMBF and the German Center for Neurodegenerative Diseases (DZNE). MK and some of the research described herein were supported by grants from NICHD (HD66214) and NIA (AG08313). MS was supported by NIMH (MH081990). 42 18 18 0 9 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage FEB 15 2012.0 59 4 3662 3667 10.1016/j.neuroimage.2011.11.039 0.0 6 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging 903BG 22146750.0 Green Accepted 2023-03-23 WOS:000301090100061 0
C Dong, XS; Zhou, JH; Hu, B; Riekki, J; Xiong, G; Wang, FY; Zhu, FH IEEE Dong, Xisong; Zhou, Jiehan; Hu, Bin; Riekki, Jukka; Xiong, Gang; Wang, Feiyue; Zhu, Fenghua A Framework of Future Innovative Urban Transport 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) English Proceedings Paper 19th IEEE International Conference on Intelligent Transportation Systems (ITSC) NOV 01-04, 2016 Rio de Janeiro, BRAZIL IEEE Rapidly growing urbanization is putting more pressure on the well-being of citizens and the environment. Applying the latest ICT technology to address urban transport challenges is a key strategy to release the new innovation powerhouse of urbanization. This paper introduces a framework of future Innovative Urban Transport (IUT), to support next generation urban transport, based on IoT, 4G, Big Data, Cloud Computing and other novel ICT technologies. We present the construction of the framework, its reference architecture, and contribution to the environmental and socially important impacts. IUT aims to advance the existing smart city transport, to foster urban innovations that maximize the well-being of citizens while minimizing the negative effects like traffic congestion, air pollution high resource congestion, and traffic accidents. [Dong, Xisong; Hu, Bin] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China; [Zhou, Jiehan; Riekki, Jukka] Univ Oulu, Ctr Ubiquitous Comp, Oulu, Finland; [Xiong, Gang] Chinese Acad Sci, Cloud Comp Ctr, Dongguan, Peoples R China; [Wang, Feiyue] Qingdao Acad Intelligent Ind, Qingdao, Peoples R China; [Zhu, Fenghua] Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing, Peoples R China Chinese Academy of Sciences; Institute of Automation, CAS; University of Oulu; Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Automation, CAS Hu, B (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China. xisong.dong@ia.ac.cn; jiehan.zhou@ee.ouu.fi; binhu@ia.ac.cn; jukka.riekki@oulu.fi; xionggang@casc.ac.cn; feiyue.wang@qaii.ac.cn; zhuth@casc.ac.cn Hu, Bin/HGV-2207-2022; Zhou, Jiehan/C-5314-2018 Zhou, Jiehan/0000-0002-4026-1649 Natural Science Foundation of China [61233001, 71232006, 61533019, 91520301, 61304201]; Guangdong Province Science & Technology Department projects [2014A010103004, 2014B010118001, 2014B090902001, 2016B090918004]; Dongguan's Innovation Talents Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Province Science & Technology Department projects; Dongguan's Innovation Talents This work is supported in part by Natural Science Foundation of China (61233001, 71232006, 61533019, 91520301, 91520301, and 61304201); Guangdong Province Science & Technology Department projects (2014A010103004, 2014B010118001, 2014B090902001, and 2016B090918004); Dongguan's Innovation Talents Project (Xiong Gang). 13 15 15 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-1889-5 2016.0 19 23 5 Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Transportation BG8CR 2023-03-23 WOS:000392215500004 0
J Ma, RX; Wu, F; Lu, ZH; Zhong, WM; Wu, QL; Wan, JG; Xie, CS Ma, Ruixiang; Wu, Fei; Lu, Zhonghai; Zhong, Wenmin; Wu, Qiulin; Wan, Jiguang; Xie, Changsheng BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS English Article Reliability; Three-dimensional displays; Error correction codes; Predictive models; Threshold voltage; Logic gates; Adaptation models; Endurance; failure; machine learning; NAND flash; prediction MEMORY; PERFORMANCE; RETENTION; ENDURANCE; OPTIMIZATION; REFRESH nand flash-based storage devices have gained a lot of popularity in recent years. Unfortunately, flash blocks suffer from limited endurance. For guaranteeing flash reliability, flash manufactures also prescribe a specified number of program and erase (P/E) cycles to define the endurance of flash blocks within the same chip. To extend the service lifetime of a flash-based device, existing works also assume that flash blocks have the same endurance and take P/E-based wear-leveling algorithms which evenly distribute P/E cycle across flash blocks in the controller. However, many studies indicate flash blocks exhibit a wide endurance difference due to the fabrication process. The endurance of flash blocks is limited by the weakest block. Thus, the traditional P/E-based block retirement mechanism makes flash blocks underutilized. To best excavate the endurance of all blocks and improve the reliability of flash devices, we present BlockHammer, a process variation aware proactive failure prediction scheme. BlockHammer takes process variation and blocks similarity into consideration, it consists of a block classifier and a block lifetime predictor. Using machine learning technology, we first establish a block classifier to classify flash blocks into different classes. Based on the classification results, we then establish the block lifetime prediction model for different classes. Flash blocks belonging to the same class are assigned the same model. To verify the effectiveness of BlockHammer, we collect block data from a real nand flash-based testing platform by emulating the true application scenario of nand flash. We compare the predicted value and the tested value, the experimental results show the proposed proactive failure scheme can achieve more than 92% accuracy for flash blocks. Therefore, the block failure point can be accurately predicted using BlockHammer in advance, which greatly enhance the reliability of nand flash. [Ma, Ruixiang; Wu, Fei; Wu, Qiulin; Wan, Jiguang; Xie, Changsheng] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China; [Ma, Ruixiang; Wu, Fei; Wu, Qiulin; Wan, Jiguang; Xie, Changsheng] Huazhong Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518000, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Sch Informat & Commun Technol, S-16440 Kista, Sweden; [Zhong, Wenmin] Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Peoples R China Huazhong University of Science & Technology; Huazhong University of Science & Technology; Royal Institute of Technology; Huazhong University of Science & Technology Wu, F (corresponding author), Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China. ruixiang@hust.edu.cn; wufei@hust.edu.cn; zhonghai@kth.se; zhongwenmin@hust.edu.cn; qiulin_wu@hust.edu.cn; jgwan@hust.edu.cn; cs_xie@hust.edu.cn Lu, Zhonghai/0000-0003-0061-3475; Ma, Ruixiang/0000-0001-8829-408X; wu, fei/0000-0001-9746-4714 National Key Research and Development Program of China [2018YFB10033005]; NSFC [61821003, 61872413, U1709220, 61902137]; Key-Area Research and Development Program of Guangdong Province [2019B010107001]; Shenzhen Basic Research Project [JCYJ20170307160135308, JCYJ20170818162129916]; 111 Project [B07038]; China Postdoctoral Science Foundation National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Key-Area Research and Development Program of Guangdong Province; Shenzhen Basic Research Project; 111 Project(Ministry of Education, China - 111 Project); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB10033005, in part by NSFC under Grant 61821003, Grant 61872413, Grant U1709220, and Grant 61902137, in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010107001, in part by the Shenzhen Basic Research Project under Grant JCYJ20170307160135308 and Grant JCYJ20170818162129916, in part by the 111 Project under Grant B07038, and in part by the Project Funded by China Postdoctoral Science Foundation. 46 7 7 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0070 1937-4151 IEEE T COMPUT AID D IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. DEC 2020.0 39 12 4563 4574 10.1109/TCAD.2020.2981025 0.0 12 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OV3JT 2023-03-23 WOS:000592111400020 0
J Ding, L; Guo, HT; Liu, SC; Mou, LC; Zhang, J; Bruzzone, L Ding, Lei; Guo, Haitao; Liu, Sicong; Mou, Lichao; Zhang, Jing; Bruzzone, Lorenzo Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Semantics; Convolutional neural networks; Task analysis; Feature extraction; Image segmentation; Data models; Correlation; Change detection (CD); convolutional neural network (CNN); remote sensing; semantic change detection (SCD); semantic segmentation (SS) UNSUPERVISED CHANGE DETECTION; LAND-COVER TRANSITIONS; CHANGE VECTOR ANALYSIS; CHANGE DETECTION NETWORK; FUSION; MAD Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting bi-temporal semantic reasoning network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improve the segmentation of both semantic categories and the changed areas. The codes in this article are accessible at https://github.com/ggsDing/Bi-SRNet. [Ding, Lei; Guo, Haitao] PLA Strateg Force Informat Engn Univ, Zhengzhou 450001, Peoples R China; [Liu, Sicong] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Mou, Lichao] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wsslling, Germany; [Mou, Lichao] Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany; [Zhang, Jing; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy Tongji University; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich; University of Trento Guo, HT (corresponding author), PLA Strateg Force Informat Engn Univ, Zhengzhou 450001, Peoples R China. dinglei14@outlook.com; ghtgjp2002@163.com; sicongliu.rs@gmail.com; lichao.mou@dlr.de; jing.zhang-1@unitn.it; lorenzo.bruzzone@unitn.it Ding, Lei/AAK-2573-2021; Liu, Sicong/J-7094-2013; Bruzzone, Lorenzo/A-2076-2012 Ding, Lei/0000-0003-0653-8373; Liu, Sicong/0000-0003-1612-4844; Bruzzone, Lorenzo/0000-0002-6036-459X National Natural Science Foundation of China [41876105, 41671410, 42071324]; China Scholarship Council [201703170123] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported in part by the National Natural Science Foundation of China under Grant 41876105, Grant 41671410, and Grant 42071324; and in part by the China Scholarship Council under Grant 201703170123. 63 10 10 29 58 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5620014 10.1109/TGRS.2022.3154390 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 0J1AZ Green Submitted 2023-03-23 WOS:000779841600021 0
J Xiong, ZJ; Sun, DW; Dai, Q; Han, Z; Zeng, XA; Wang, L Xiong, Zhenjie; Sun, Da-Wen; Dai, Qiong; Han, Zhong; Zeng, Xin-An; Wang, Lu Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat FOOD ANALYTICAL METHODS English Article Hyperspectral imaging; Nondestructive; Chicken meat; Springiness; Successful projections algorithm; Partial least squares regression; Artificial neural network INFRARED REFLECTANCE SPECTROSCOPY; MOISTURE TRANSFER CHARACTERISTICS; SUCCESSIVE PROJECTIONS ALGORITHM; ISOTHERM EQUATIONS; NONDESTRUCTIVE DETERMINATION; CHEMICAL-COMPOSITION; QUALITY ATTRIBUTES; CFD SIMULATION; COLOR; SELECTION Springiness is an important quality characteristic of chicken meat, related with muscle structure and contents of biochemical components, and has great influence on eating quality of chicken meat. Traditional methods for springiness evaluation including manual inspection and instrumental measurement are tedious, time-consuming, and destructive. This study implemented a smart and promising nondestructive technique, i.e., hyperspectral imaging, for rapid prediction of springiness of fresh chicken meat. Hyperspectral images of tested samples with different springiness levels were acquired, and their spectral data were extracted in the spectral range of 400-1,000 nm. Two calibration methods, namely, partial least squares regression (PLSR) and artificial neural network (ANN), were respectively used to correlate the extracted spectra of chicken meat samples with the reference springiness values estimated by a twice-compression method. Successful projections algorithm (SPA) as a popular wavelength selection tool was applied, and ten optimal wavelengths (416, 458, 581, 637, 696, 722, 740, 754, 773, and 973 nm) were finally selected. Based on the selected optimal wavelengths, optimized SPA-PLSR and SPA-ANN model were established, respectively. By comparing with the results of two optimized models, the SPA-PLSR model showed better prediction results with high correlation coefficient (R (p)) of 0.84 and low root mean square error by prediction (RMSEP) of 0.159. Finally, an image processing algorithm was developed to transfer the SPA-PLSR model to each pixel in chicken meat for visualizing their springiness distribution. The results from the current study indicated that hyperspectral imaging could be a rapid and nondestructive tool for prediction of springiness of chicken meat. [Xiong, Zhenjie; Sun, Da-Wen; Dai, Qiong; Han, Zhong; Zeng, Xin-An; Wang, Lu] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, Dublin 4, Ireland South China University of Technology; University College Dublin Han, Z (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie; fezhonghan@scut.edu.cn Sun, Da-Wen/B-1899-2012; WANG, LU/AAO-9121-2020 Sun, Da-Wen/0000-0002-3634-9963; HAN, ZHONG/0000-0003-0475-9463 Guangdong Province Government (China) through program Leading Talent of Guangdong Province; National Key Technologies RD Program [2014BAD08B09] Guangdong Province Government (China) through program Leading Talent of Guangdong Province; National Key Technologies RD Program(National Key Technology R&D Program) The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by the National Key Technologies R&D Program (2014BAD08B09). Special thanks to Nannan Wang from South China University of Technology for her kind suggestions. 84 42 47 4 68 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1936-9751 1936-976X FOOD ANAL METHOD Food Anal. Meth. FEB 2015.0 8 2 380 391 10.1007/s12161-014-9853-3 0.0 12 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology AY6MT 2023-03-23 WOS:000347681100012 0
J Xiong, ZH; Xia, Q; Hu, ZQ; Huang, N; Bian, C; Zheng, YF; Vesal, S; Ravikumar, N; Maier, A; Yang, X; Heng, PA; Ni, D; Li, CZ; Tong, QQ; Si, WX; Puybareau, E; Khoudli, Y; Geraud, T; Chen, C; Bai, WJ; Rueckert, D; Xu, LC; Zhuang, XH; Luo, XZ; Jia, S; Sermesant, M; Liu, YS; Wang, KQ; Borra, D; Masci, A; Corsi, C; de Vente, C; Veta, M; Karim, R; Preetha, CJ; Engelhardt, S; Qiao, MY; Wang, YY; Tao, Q; Nunez-Garcia, M; Camara, O; Savioli, N; Lamata, P; Zhao, JC Xiong, Zhaohan; Xia, Qing; Hu, Zhiqiang; Huang, Ning; Bian, Cheng; Zheng, Yefeng; Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas; Yang, Xin; Heng, Pheng-Ann; Ni, Dong; Li, Caizi; Tong, Qianqian; Si, Weixin; Puybareau, Elodie; Khoudli, Younes; Geraud, Thierry; Chen, Chen; Bai, Wenjia; Rueckert, Daniel; Xu, Lingchao; Zhuang, Xiahai; Luo, Xinzhe; Jia, Shuman; Sermesant, Maxime; Liu, Yashu; Wang, Kuanquan; Borra, Davide; Masci, Alessandro; Corsi, Cristiana; de Vente, Coen; Veta, Mitko; Karim, Rashed; Preetha, Chandrakanth Jayachandran; Engelhardt, Sandy; Qiao, Menyun; Wang, Yuanyuan; Tao, Qian; Nunez-Garcia, Marta; Camara, Oscar; Savioli, Nicolo; Lamata, Pablo; Zhao, Jichao A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging MEDICAL IMAGE ANALYSIS English Article Left atrium; Convolutional neural networks; Late gadolinium-enhanced magnetic resonance imaging; Image segmentation AUTOMATIC SEGMENTATION; MRI Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community. (C) 2020 Elsevier B.V. All rights reserved. [Xiong, Zhaohan; Zhao, Jichao] Univ Auckland, Auckland Bioengn Inst, Private Bag 92019, Auckland 1142, New Zealand; [Xia, Qing] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China; [Hu, Zhiqiang] Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China; [Huang, Ning] SenseTime Inc, Shenzhen, Peoples R China; [Bian, Cheng; Zheng, Yefeng] Tencent Jarvis Lab, Shenzhen, Peoples R China; [Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas] Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, Erlangen, Germany; [Yang, Xin; Heng, Pheng-Ann] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Ni, Dong] Shenzhen Univ, Sch Biomed Engn, Hlth Sci Ctr, Natl Reg Key Technol Engn Lab Med Ultrasound, Shenzhen, Peoples R China; [Li, Caizi; Tong, Qianqian] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China; [Si, Weixin] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Virtual Real & Human Interact Te, Shenzhen, Peoples R China; [Puybareau, Elodie; Khoudli, Younes; Geraud, Thierry] EPITA Res & Dev Lab, Paris, France; [Chen, Chen; Bai, Wenjia; Rueckert, Daniel] Imperial Coll London, Dept Comp, London, England; [Xu, Lingchao] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai, Peoples R China; [Zhuang, Xiahai; Luo, Xinzhe] Fudan Univ, Sch Data Sci, Shanghai, Peoples R China; [Jia, Shuman; Sermesant, Maxime] Univ Cote Azur, INRIA, Epione Team, Sophia Antipolis, France; [Liu, Yashu; Wang, Kuanquan] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China; [Borra, Davide; Masci, Alessandro; Corsi, Cristiana] Univ Bologna, Dept Elect Elect & Informat Engn, Cesena, Italy; [de Vente, Coen; Veta, Mitko] Eindhoven Univ Technol, Dept Biomed Engn, Eindhoven, Netherlands; [Karim, Rashed] Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England; [Preetha, Chandrakanth Jayachandran] Univ Magdeburg, Fac Elect Engn & Informat Technol, Magdeburg, Germany; [Engelhardt, Sandy] Heidelberg Univ Hosp, Dept Internal Med 3, Heidelberg, Germany; [Qiao, Menyun; Wang, Yuanyuan] Fudan Univ, Ctr Biomed Engn, Shanghai, Peoples R China; [Tao, Qian] Leiden Univ Med Ctr, Dept Radiol, Leiden, Netherlands; [Nunez-Garcia, Marta; Camara, Oscar] Univ Pompeu Fabra, Physense, Dept Informat & Commun Technol, Barcelona, Spain; [Savioli, Nicolo; Lamata, Pablo] Kings Coll London, Dept Bioengn, London, England University of Auckland; Beihang University; Peking University; University of Erlangen Nuremberg; Chinese University of Hong Kong; Shenzhen University; Wuhan University; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Imperial College London; Shanghai Jiao Tong University; Fudan University; Inria; UDICE-French Research Universities; Universite Cote d'Azur; Harbin Institute of Technology; University of Bologna; Eindhoven University of Technology; University of London; King's College London; Otto von Guericke University; Ruprecht Karls University Heidelberg; Fudan University; Leiden University; Leiden University Medical Center (LUMC); Pompeu Fabra University; University of London; King's College London Zhao, JC (corresponding author), Univ Auckland, Auckland Bioengn Inst, Private Bag 92019, Auckland 1142, New Zealand. j.zhao@auckland.ac.nz Borra, Davide/G-3547-2017; Zhuang, Xiahai/AAH-6334-2019; Luo, Xinzhe/HLG-0755-2023; Lv, Yuanjie/AER-0767-2022; Maier, Andreas/AAV-6505-2021; Chen, Chen/AAM-1881-2021; wangwangwang, yuanyaun/HHN-6432-2022; Wang, Yuan/HHC-1520-2022; Lamata, Pablo/AAE-7884-2020; Wang, Yu/GZL-9655-2022; bai, wenjia/HPE-4360-2023; Luo, Xinzhe/AGT-1221-2022; Bai, Wenjia/ABH-6023-2020 Borra, Davide/0000-0003-3791-8555; Zhuang, Xiahai/0000-0003-4351-4979; Luo, Xinzhe/0000-0003-2822-1633; Maier, Andreas/0000-0002-9550-5284; Chen, Chen/0000-0002-3525-9755; Lamata, Pablo/0000-0002-3097-4928; Luo, Xinzhe/0000-0003-2822-1633; Bai, Wenjia/0000-0003-2943-7698; Zheng, Yefeng/0000-0003-2195-2847; Nunez-Garcia, Marta/0000-0001-9349-3059; Xia, Qing/0000-0002-0328-7882; xiong, zhaohan/0000-0002-2537-6149; bian, cheng/0000-0003-3498-8283; Ravikumar, Nishant/0000-0003-0134-107X; Vesal, Sulaiman/0000-0001-6156-9338 Health Research Council of New Zealand [16/385] Health Research Council of New Zealand(Health Research Council of New Zealand) The authors would like to thank Nvidia, MedTech CoRE New Zealand, and Arterys for providing prizes for the winners of the 2018 LA Segmentation Challenge. Z.X. and J.Z. are grateful for Nvidia for donating Titan-X Pascal GPU for algorithm development and testing, and the NIH/NIGMS Center for Integrative Biomedical Computing (CIBC) at the University of Utah for providing the LGEMRI dataset. This work was funded by the Health Research Council of New Zealand [#16/385]. 45 50 51 10 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JAN 2021.0 67 101832 10.1016/j.media.2020.101832 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging PF2LE 33166776.0 Green Published, Green Submitted, Green Accepted 2023-03-23 WOS:000598891600005 0
J Zhou, N; Zhan, XX; Lin, S; Yang, SH; Liu, C; Sun, GQ; Zhang, ZK Zhou, Nan; Zhan, Xiu-Xiu; Lin, Song; Yang, Shang-Hui; Liu, Chuang; Sun, Gui-Quan; Zhang, Zi-Ke Information diffusion on communication networks based on Big Data analysis ELECTRONIC LIBRARY English Article Big data; Communication networks; Information carriers; Information diffusion MEDIA; OPTIMIZATION; EMERGENCE Purpose - Information carriers (including mass media and We-Media) play important roles in information diffusion on social networks. The purpose of this paper is to investigate changes in the dissemination of information combing with data analysis. Design/methodology/approach - This work analyzed nearly 200 years of coverage of different information carriers during different periods of human society, from the period of only mouth-to-mouth communication to the period of modern society. Information diffusion models are built to illustrate how the information dynamic changes with time and combined box office data of several movies to predict the process of information diffusion. In addition, a metric is defined to identify which information would become news in the future. Findings - Results show that with the development of information carriers, information spreads faster and wider nowadays. The correctness of the metric proposed has been validated. Research limitations/implications - The structure of social networks influences the dissemination of information. There are an enormous number of factors that influence the formation of hotspots. Practical implications - The results and conclusion of this work will benefit by predicting the evolution of information carriers. The metric proposed will aid in searching hot news in the future. Originality/value - This work may shed some light on a better understanding of information diffusion, spreading not only on social networks but also on the carriers used for the information spreading. [Zhou, Nan; Lin, Song; Yang, Shang-Hui; Liu, Chuang; Zhang, Zi-Ke] Hangzhou Normal Univ, Hangzhou, Zhejiang, Peoples R China; [Zhan, Xiu-Xiu] Delft Univ Technol, Delft, Netherlands; [Sun, Gui-Quan] Shanxi Univ, Taiyuan, Shanxi, Peoples R China Hangzhou Normal University; Delft University of Technology; Shanxi University Zhang, ZK (corresponding author), Hangzhou Normal Univ, Hangzhou, Zhejiang, Peoples R China. zhangzike@gmail.com chuang, liu/AAH-1377-2021; Zhan, Xiuxiu/AAZ-2944-2020; Sun, Gui-Quan/AAE-7769-2019; Sun, Gui-Quan/Y-3606-2019 Sun, Gui-Quan/0000-0002-1831-1603 Natural Science Foundation of China [61673151, 11671241]; Zhejiang Provincial Natural Science Foundation of China [LY14A05001]; Zhejiang Qianjiang Talents Project [QJC1302001]; Zhejiang Xinmiao Talents Program [2016R423069]; EU FP7 [611272] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); Zhejiang Qianjiang Talents Project; Zhejiang Xinmiao Talents Program; EU FP7(European Commission) This work was partially supported by Natural Science Foundation of China (Grant Nos 61673151 and 11671241), Zhejiang Provincial Natural Science Foundation of China (Grant No. LY14A05001), Zhejiang Qianjiang Talents Project (QJC1302001), Zhejiang Xinmiao Talents Program (2016R423069) and the EU FP7 Grant 611272 (project GROWTHCOM). 57 4 5 2 35 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0264-0473 1758-616X ELECTRON LIBR Electron. Libr. 2017.0 35 4 SI 745 757 10.1108/EL-09-2016-0194 0.0 13 Information Science & Library Science Social Science Citation Index (SSCI) Information Science & Library Science FJ6MB 2023-03-23 WOS:000412870800008 0
J Bai, BG; Bai, YH; Wang, GQ; Bao, XY; Wang, HJ Bai, Baogang; Bai, Yuhe; Wang, Guoqing; Bao, Xiaoyu; Wang, Huijie ANN/GA-ANN modeling study on operating parameter prediction for waste-to-energy plant BIOMASS CONVERSION AND BIOREFINERY English Article; Early Access Municipal solid waste; Incineration; Artificial neural network; Genetic algorithm MUNICIPAL SOLID-WASTE; FLUIDIZED-BED; SEWAGE-SLUDGE; HEATING VALUE; INCINERATION; CHINA; GRATE Automatic combustion control (ACC) system is the main control system of the current municipal solid waste (MSW) incineration power plant. In this work, the artificial neural network (ANN) model was first used for prediction and guiding of operation of the incineration system. Results showed that BP-ANN model was an effective tool for operation parameter prediction, especially the crucial parameters of steam mass flow and furnace temperature. The Log-Sigmoid function was more suitable to be used as the transfer function in hidden layer. Comparison of the input parameter showed that furnace temperature, grate speed, primary air, and secondary air are the most important and relevant parameters for prediction of steam mass flow in MSW incineration system. The genetic algorithm (GA) could be used to optimize the initial weight and threshold values of the BP-ANN network, which largely improved the prediction accuracy. Impact weight analysis results show that furnace temperature and secondary have the largest influence on the steam flow. With regard to furnace temperature, secondary air, primary air, and oxygen concentration in exhaust gas play the leading roles. The largest error for steam mass flow prediction of the real-time operation was lower than 8%. [Bai, Baogang; Wang, Huijie] Wenzhou Business Coll, Sch Informat & Technol, Wenzhou 325000, Zhejiang, Peoples R China; [Bai, Baogang] Zhejiang Engn Res Ctr Intelligent Med, Wenzhou 325000, Zhejiang, Peoples R China; [Bai, Baogang] 1st Affiliate Hosp WMU, Sch Med 1, Sch Informat & Engn, Wenzhou 325000, Zhejiiang, Peoples R China; [Bai, Yuhe] Ecole Polytech, Inst Polytech Paris, Paris, France; [Wang, Guoqing] Zhejiang Sox Technol Co Ltd, Wenzhou 325000, Zhejiang, Peoples R China; [Bao, Xiaoyu] Informat Ctr, Lucheng Dist Environm Sanitat Management Off, Wenzhou 325000, Zhejiang, Peoples R China Bai, BG (corresponding author), Wenzhou Business Coll, Sch Informat & Technol, Wenzhou 325000, Zhejiang, Peoples R China.;Bai, BG (corresponding author), Zhejiang Engn Res Ctr Intelligent Med, Wenzhou 325000, Zhejiang, Peoples R China.;Bai, BG (corresponding author), 1st Affiliate Hosp WMU, Sch Med 1, Sch Informat & Engn, Wenzhou 325000, Zhejiiang, Peoples R China.;Bai, YH (corresponding author), Ecole Polytech, Inst Polytech Paris, Paris, France. baibaogang0@sina.com; yuhe.bai@polytechnique.edu 34 0 0 3 3 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2190-6815 2190-6823 BIOMASS CONVERS BIOR Biomass Convers. Biorefinery 10.1007/s13399-022-03687-4 0.0 DEC 2022 16 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 7I7LH 2023-03-23 WOS:000904067100002 0
J Liu, ZB; Shao, JF; Xu, WY; Zhang, Y; Chen, HJ Liu, Zaobao; Shao, Jianfu; Xu, Weiya; Zhang, Yu; Chen, Hongjie Prediction of elastic compressibility of rock material with soft computing techniques APPLIED SOFT COMPUTING English Article Soft computing; Relevance vector machine; Mechanical parameter; Porous material; Artificial neural network; Support vector machine PHYSICAL-PROPERTIES; SANDSTONES; MODELS; SET Mechanical and physical properties of sandstone are interesting scientifically and have great practical significance as well as their relations to the mineralogy and pore features. These relations are however highly nonlinear and cannot be easily formulated by conventional methods. This paper investigates the potential of the technique named as the relevance vector machine (RVM) for prediction of the elastic compressibility of sandstone based on its characteristics of physical properties. Based on the fact that the hyper-parameters may have effects on the RVM performance, an iteration method is proposed in this paper to search for optimal hyper-parameter value so that it can produce best predictions. Also, the qualitative sensitivity of the physical properties is investigated by the backward regression analysis. Meanwhile, the hyper-parameter effect of the RVM approach is discussed in the prediction of the elastic compressibility of sandstone. The predicted results of the RVM demonstrate that hyper-parameter values have evident effects on the RVM performance. Comparisons on the results of the RVM, the artificial neural network and the support vector machine prove that the proposed strategy is feasible and reliable for prediction of the elastic compressibility of sandstone based on its physical properties. (C) 2014 Elsevier B.V. All rights reserved. [Liu, Zaobao; Shao, Jianfu; Zhang, Yu] Hohai Univ, Geotech Res Inst, Nanjing 210098, Jiangsu, Peoples R China; [Liu, Zaobao; Shao, Jianfu; Xu, Weiya; Chen, Hongjie] Univ Lille 1, Lab Mech Lille, F-59655 Villeneuve Dascq, France Hohai University; Universite de Lille - ISITE; Centrale Lille; Universite de Lille Liu, ZB (corresponding author), Hohai Univ, Geotech Res Inst, Nanjing 210098, Jiangsu, Peoples R China. zaobao.liu@polytech-lille.fr Liu, Zaobao/R-6767-2016; Liu, Zaobao/ACA-9819-2022; Liu, Zaobao/AAF-2751-2020; shao, jianfu/N-2447-2018 Liu, Zaobao/0000-0002-2047-5463; Liu, Zaobao/0000-0002-2047-5463; Liu, Zaobao/0000-0002-2047-5463; shao, jianfu/0000-0002-6632-8207 China Key Basic Research Program (973 Program) [2011CB013504]; China Natural Science Foundation [11272114] China Key Basic Research Program (973 Program)(National Basic Research Program of China); China Natural Science Foundation(National Natural Science Foundation of China (NSFC)) Financial support from China Key Basic Research Program (973 Program, No. 2011CB013504) and the China Natural Science Foundation (No. 11272114) is acknowledged. The authors are grateful to the anonymous reviewers and the editors for their insightful suggestions. 35 14 14 0 34 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. SEP 2014.0 22 118 125 10.1016/j.asoc.2014.05.009 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science AK8VL 2023-03-23 WOS:000338706600011 0
J Borowski, M; Zyczkowski, P; Cheng, JW; Luczak, R; Zwolinska, K Borowski, Marek; Zyczkowski, Piotr; Cheng, Jianwei; Luczak, Rafal; Zwolinska, Klaudia The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions-Electricity Prediction Using ANN ENERGIES English Article methane capture and utilisation; cogeneration; coal mine methane (CMM); greenhouse gas reduction; production forecast; electrical energy; artificial neural network (ANN) VENTILATION AIR METHANE; MINE METHANE; DRAINAGE Greenhouse gases such as carbon dioxide and methane cause global warming and consequently climate change. Great efforts are being made to reduce greenhouse gas emissions with the objective of addressing this problem, hence the popularity of technologies conductive to reducing greenhouse gas emissions. CO(2)emissions can be reduced by improving the thermal efficiency of combustion engines, for example, by using cogeneration systems. Coal mine methane (CMM) emerges due to mining activities as methane released from the coal and surrounding rock strata. The amount of methane produced is primarily influenced by the productivity of the coal mine and the gassiness of the coal seam. The gassiness of the formation around the coal seam and geological conditions are also important. Methane can be extracted to the surface using methane drainage installations and along with ventilation air. The large amounts of methane captured by methane drainage installations can be used for energy production. This article presents a quarterly summary of the hourly values of methane capture, its concentration in the methane-air mixture, and electricity production in the cogeneration system for electricity and heat production. On this basis, neural network models have been proposed in order to predict electricity production based on known values of methane capture, its concentration, pressure, and parameters determining the time and day of the week. A prediction model has been established on the basis of a multilayer perceptron network (MLP). [Borowski, Marek; Zyczkowski, Piotr; Luczak, Rafal; Zwolinska, Klaudia] AGH Univ Sci & Technol, Fac Min & Geoengn, PL-30059 Krakow, Poland; [Cheng, Jianwei] China Univ Min & Technol, Key Lab Gas & Fire Control Coal Mines, Xuzhou 221116, Jiangsu, Peoples R China AGH University of Science & Technology; China University of Mining & Technology Borowski, M (corresponding author), AGH Univ Sci & Technol, Fac Min & Geoengn, PL-30059 Krakow, Poland. borowski@agh.edu.pl; zycz@agh.edu.pl; Cheng.Jianwei@cumt.edu.cn; rluczak@agh.edu.pl; kzwolinska@agh.edu.pl Cheng, Jianwei/O-9241-2015; Zwolińska, Klaudia/ABC-4352-2020; Borowski, Marek/A-8978-2017; Luczak, Rafal/F-9998-2018; Zyczkowski, Piotr/T-1961-2017 Cheng, Jianwei/0000-0003-4915-6295; Zwolińska, Klaudia/0000-0003-4581-3489; Borowski, Marek/0000-0003-4736-4824; Luczak, Rafal/0000-0001-6897-8679; Zyczkowski, Piotr/0000-0003-1573-9943 26 4 5 2 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies SEP 2020.0 13 17 4429 10.3390/en13174429 0.0 20 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels NR5JZ Green Published, gold 2023-03-23 WOS:000571600100001 0
C Li, PF; Lu, H; Zheng, Q; Li, SJ; Pan, G IEEE Li, Pengfei; Lu, Hua; Zheng, Qian; Li, Shijian; Pan, Gang HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement 2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) IEEE International Conference on Data Engineering English Proceedings Paper IEEE 36th International Conference on Data Engineering (ICDE) APR 20-24, 2020 Dallas, TX IEEE,IEEE Comp Soc This study explores the problem of co-location judgement, i.e., to decide whether two Twitter users are co-located at some point-of-interest (POI). We extract novel features, named HisRect, from users' historical visits and recent tweets: The former has impact on where a user visits in general, whereas the latter gives more hints about where a user is currently. To alleviate the issue of data scarcity, a semi-supervised learning (SSL) framework is designed to extract HisRect features. Moreover, we use an embedding neural network layer to decide co-location based on the difference between two users' HisRect features. Extensive experiments on real Twitter data suggest that our HisRect features and SSL framework are highly effective at deciding co-locations. [Li, Pengfei; Zheng, Qian; Li, Shijian; Pan, Gang] Zhejiang Univ, Dept Comp Sci & Technol, Hangzhou, Peoples R China; [Lu, Hua] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark; [Pan, Gang] Zhejiang Univ, State Key Lab CAD & CG, Hangzhou, Peoples R China Zhejiang University; Aalborg University; Zhejiang University Li, PF (corresponding author), Zhejiang Univ, Dept Comp Sci & Technol, Hangzhou, Peoples R China. pfl@zju.edu.cn; luhua@cs.aau.dk; csqianzheng@gmail.com; shijianli@zju.edu.cn; gpan@zju.edu.cn Lu, Hua/0000-0003-1199-6678 7 0 0 0 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1084-4627 978-1-7281-2903-7 PROC INT CONF DATA 2020.0 2034 2035 10.1109/ICDE48307.2020.00236 0.0 2 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ3CR Green Published 2023-03-23 WOS:000584252700229 0
J Hu, P; Aghajanirefah, H; Anvari, A; Nehdi, ML Hu, Pan; Aghajanirefah, Hamidreza; Anvari, Arsalan; Nehdi, Moncef L. L. Combining Artificial Neural Network and Seeker Optimization Algorithm for Predicting Compression Capacity of Concrete-Filled Steel Tube Columns BUILDINGS English Article CCFST columns; axial compression capacity; composite structures; neural network; seeker optimization algorithm AXIAL-COMPRESSION; SEARCH ALGORITHM; STRENGTH; BEHAVIOR Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study proposes a novel methodology based on computational intelligence for estimating the compression capacity of CCFST. Accordingly, a conventional artificial neural network (ANN) is hybridized with a metaheuristic algorithm called the seeker optimization algorithm (SOA). Utilizing information such as the column's length, compressive strength of ultra-high-strength concrete, and the diameter, thickness, yield stress, and ultimate stress of the steel tube, the capacity of the column is predicted through non-linear calculations. In addition to the SOA, the future search algorithm (FSA) and social ski driver (SSD) are used as comparative benchmarks. The prediction results showed that the SOA-ANN can learn and predict the compression capacity pattern with high accuracy (relative error < 2.5% and correlation > 0.99). Also, this model outperformed both benchmark hybrids (i.e., FSA-ANN and SSD-ANN). Apart from accuracy, the configuration of the SOA-ANN is simpler owing to the smaller population recruited for the optimization task. An explicit formula for the proposed model is developed, which, owing to its observed efficiency, can be reliably applied to CCFST columns for the early estimation of the compression capacity. [Hu, Pan] Tech Univ Munich, Sch Civil & Architectural Engn, D-80333 Munich, Germany; [Hu, Pan] Wuhan Municipal Construct Grp Co Ltd, Wuhan 430023, Peoples R China; [Aghajanirefah, Hamidreza] Islamic Azad Univ, Fac Engn, Dept Civil Engn, Qazvin Branch, Qazvin 3419915195, Iran; [Anvari, Arsalan] Islamic Azad Univ, Fac Civil Engn, Sci & Res Branch, Engn & Management, Tehran 1477893855, Iran; [Nehdi, Moncef L. L.] McMaster Univ, Dept Civil Engn, Hamilton, ON L8S 4L8, Canada Technical University of Munich; Islamic Azad University; Islamic Azad University; McMaster University Nehdi, ML (corresponding author), McMaster Univ, Dept Civil Engn, Hamilton, ON L8S 4L8, Canada. nehdim@mcmaster.ca Research on Key Hydraulic Technology of Bionic Fishway Construction [CX2017Z19]; action plan for stability and sustainability protection of water ecosystem in the Three Gorges Reservoir Area Research on Key Hydraulic Technology of Bionic Fishway Construction; action plan for stability and sustainability protection of water ecosystem in the Three Gorges Reservoir Area This work was supported by Research on Key Hydraulic Technology of Bionic Fishway Construction, Grant/Award Number: CX2017Z19; Countermeasures and action plan for stability and sustainability protection of water ecosystem in the Three Gorges Reservoir Area. 64 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-5309 BUILDINGS-BASEL BUILDINGS-BASEL FEB 2023.0 13 2 391 10.3390/buildings13020391 0.0 15 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 9H6TK gold 2023-03-23 WOS:000938962800001 0
J Guo, HX; Li, YJ; Shang, J; Gu, MY; Huang, YY; Bing, G Guo Haixiang; Li Yijing; Shang, Jennifer; Gu Mingyun; Huang Yuanyue; Bing, Gong Learning from class-imbalanced data: Review of methods and applications EXPERT SYSTEMS WITH APPLICATIONS English Review Rare events; lmbalanced data; Machine learning; Data mining SUPPORT VECTOR MACHINE; COST-SENSITIVE CLASSIFIER; CARD FRAUD DETECTION; OIL-SPILL DETECTION; FEATURE-SELECTION; DATA-SETS; NEURAL-NETWORKS; CAUSE IDENTIFICATION; LOGISTIC-REGRESSION; CHURN PREDICTION Rare events, especially those that could potentially negatively impact society, often require humans' decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from, the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields. (C) 2016 Elsevier Ltd. All rights reserved. [Guo Haixiang; Li Yijing; Gu Mingyun; Huang Yuanyue] China Univ Geosci, Coll Econ & Management, Wuhan 430074, Peoples R China; [Guo Haixiang; Li Yijing] China Univ Geosci, Res Ctr Digital Business Management, Wuhan 430074, Peoples R China; [Guo Haixiang] China Univ Geosci WUHAN, Mineral Resource Strategy & Policy Res Ctr, Wuhan 43007, Peoples R China; [Shang, Jennifer] Univ Pittsburgh, Joseph M Katz Grad Sch Business, Pittsburgh, PA 15260 USA; [Bing, Gong] Univ Politecn Madrid, ETS Ind Engn, Dept Ind Engn Business Adm & Stat, C Jose Gutierrez Abascal,2, Madrid 20086, Spain China University of Geosciences; China University of Geosciences; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Universidad Politecnica de Madrid Guo, HX; Li, YJ (corresponding author), China Univ Geosci, Coll Econ & Management, Wuhan 430074, Peoples R China.;Guo, HX; Li, YJ (corresponding author), China Univ Geosci, Res Ctr Digital Business Management, Wuhan 430074, Peoples R China.;Guo, HX (corresponding author), China Univ Geosci WUHAN, Mineral Resource Strategy & Policy Res Ctr, Wuhan 43007, Peoples R China.;Shang, J (corresponding author), Univ Pittsburgh, Joseph M Katz Grad Sch Business, Pittsburgh, PA 15260 USA. faterdumk0732@sina.com; liyijing024@hotmail.com; shang@katz.pitt.edu; 550312686@qq.com; huangyuanyue1991@126.com; gongbing1112@gmail.com Li, Yijing/C-3410-2015 Li, Yijing/0000-0003-1104-7069 National Natural Science Foundation of China [71103163, 71573237]; New Century Excellent Talents in University of China [NCET-13-1012]; Research Foundation of Humanities and Social Sciences of Ministry of Education of China [15YjA630019]; Special Funding for Basic Scientific Research of Chinese Central University [CUG120111, CUG110411, G2012002A, CUG140604, CUG160605]; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan) [H2015004B] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); New Century Excellent Talents in University of China(Program for New Century Excellent Talents in University (NCET)); Research Foundation of Humanities and Social Sciences of Ministry of Education of China; Special Funding for Basic Scientific Research of Chinese Central University; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan) This research has been supported by National Natural Science Foundation of China under Grant No.71103163, No.71573237; New Century Excellent Talents in University of China under Grant No. NCET-13-1012; Research Foundation of Humanities and Social Sciences of Ministry of Education of China No.15YjA630019; Special Funding for Basic Scientific Research of Chinese Central University under Grant No.CUG120111, CUG110411, G2012002A, CUG140604, CUG160605; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan) under Grant No. H2015004B. 300 914 956 85 707 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. MAY 1 2017.0 73 220 239 10.1016/j.eswa.2016.12.035 0.0 20 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Operations Research & Management Science EL4ZX 2023-03-23 WOS:000394632200016 0
J Gan, JY; Xiang, L; Zhai, YK; Mai, CY; He, GH; Zeng, JY; Bai, ZF; Labati, RD; Piuri, V; Scott, F Gan, Junying; Xiang, Li; Zhai, Yikui; Mai, Chaoyun; He, Guohui; Zeng, Junying; Bai, Zhenfeng; Labati, Ruggero Donida; Piuri, Vincenzo; Scott, Fabio 2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning IEEE ACCESS English Article Facial beauty prediction; multi-task transfer learning; multi-input multi-output network; multi-task loss weight automatic learning strategy ATTRACTIVENESS Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multi-task loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23;. Our 2M BeautyNet structure is suitable for multiple inputs of different databases. [Gan, Junying; Xiang, Li; Zhai, Yikui; Mai, Chaoyun; He, Guohui; Zeng, Junying; Bai, Zhenfeng] Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China; [Zhai, Yikui; Labati, Ruggero Donida; Piuri, Vincenzo; Scott, Fabio] Univ Milan, Departimento Informat, I-20133 Milan, Italy Wuyi University; University of Milan Zhai, YK (corresponding author), Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China.;Zhai, YK (corresponding author), Univ Milan, Departimento Informat, I-20133 Milan, Italy. yikuizhai@163.com ; Piuri, Vincenzo/A-3970-2012 Zeng, Junying/0000-0002-7559-0637; Piuri, Vincenzo/0000-0003-3178-8198; Zhai, Yikui/0000-0003-0154-9743; XIANG, LI/0000-0002-6577-9532; Scotti, Fabio/0000-0002-4277-3701 National Natural Science Foundation of China [61771347]; Guangdong Basic and Applied Basic Research Foundation [2019A1515010716]; Characteristic Innovation Project of Guangdong Province [2017KTSCX181]; Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province [2018KZDXM073] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Characteristic Innovation Project of Guangdong Province; Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province This work was supported in part by the National Natural Science Foundation of China under Grant 61771347, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515010716, in part by the Characteristic Innovation Project of Guangdong Province under Grant 2017KTSCX181, and in part by the Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province under Grant 2018KZDXM073. 51 8 9 6 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 20245 20256 10.1109/ACCESS.2020.2968837 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications LC5SJ gold, Green Submitted 2023-03-23 WOS:000525390100035 0
J Rafiq, MS; Xie, JS; Arif, M; Barra, P Rafiq, Muhammad Sajid; Jianshe, Xie; Arif, Muhammad; Barra, Paola Intelligent query optimization and course recommendation during online lectures in E-learning system JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING English Article Intelligent query optimization; E-learning; Education; Lectures; Text mining; Optimization; Evaluation VANETS; COMMUNICATION; ARCHITECTURE; WORDS; MODEL This article explores the possibility of disaggregating query/question information in e-learning system online lectures or course recommendations. Information arrangement includes reading, parsing and classification of inquiry/question messages. Data extraction is a kind of shallow content processing. It finds a set of predefined applicable content in the feature language archives and performs common language processing through artificial intelligence strategies. During online lectures, many problems emerged in the listener's minds, and the development of query optimization systems is of great significance to the evaluation of problems in online lectures. The results shows that our proposed method improve the classification of action verbs to a more accurate level. Later, we evaluated our proposed method, and measured a very high macro average for all one-sixth of the cognitive domain. We also provide the analytical examination in which we compared the designed method with the state of the art methods. The results indicate that the proposed method outperform the traditional methods [Rafiq, Muhammad Sajid] Guangzhou Univ, Sch Educ, Guangzhou 510006, Peoples R China; [Jianshe, Xie] Guangzhou Univ, Sch Publ Policy & Management, Guangzhou 510006, Peoples R China; [Arif, Muhammad] Guangzhou Univ, Sch Comp Sci, Guangzhou 510006, Peoples R China; [Barra, Paola] Univ Salerno, Dept Comp Sci, I-84084 Fisciano, SA, Italy Guangzhou University; Guangzhou University; Guangzhou University; University of Salerno Xie, JS (corresponding author), Guangzhou Univ, Sch Publ Policy & Management, Guangzhou 510006, Peoples R China.;Arif, M (corresponding author), Guangzhou Univ, Sch Comp Sci, Guangzhou 510006, Peoples R China. msrafiq@e.gzhu.edu.cn; xjs20090618@gzhu.edu.cn; arifmuhammad36@hotmail.com; pbarra@unisa.it Arif, Muhammad/J-8700-2019 Barra, Paola/0000-0002-7692-0626 66 5 5 1 16 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-5137 1868-5145 J AMB INTEL HUM COMP J. Ambient Intell. Humaniz. Comput. NOV 2021.0 12 11 10375 10394 10.1007/s12652-020-02834-x 0.0 JAN 2021 20 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Telecommunications UT9GM 2023-03-23 WOS:000611482000001 0
J Zhao, XF; Lamperti, G; Ouyang, DT; Tong, XR Zhao, Xiangfu; Lamperti, Gianfranco; Ouyang, Dantong; Tong, Xiangrong Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata COMPLEXITY English Article DISTRIBUTED FAULT-DIAGNOSIS; FAILURE DIAGNOSIS; FRAMEWORK; ROBUST In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth. [Zhao, Xiangfu; Tong, Xiangrong] Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China; [Lamperti, Gianfranco] Univ Brescia, Dept Informat Engn, I-25123 Brescia, Italy; [Ouyang, Dantong] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China Yantai University; University of Brescia; Jilin University Zhao, XF; Tong, XR (corresponding author), Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China. xiangfuzhao@gmail.com; gianfranco.lamperti@unibs.it; ouyangdantong@163.com; txr@ytu.edu.cn Zhao, Xiangfu/AAP-7918-2020 Zhao, Xiangfu/0000-0001-5870-5730; Tong, Xiangrong/0000-0003-4855-3723; LAMPERTI, Gian Franco/0000-0002-1915-6932 National Natural Science Foundation of China [61972360] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Grant no. 61972360). 50 0 0 1 5 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1076-2787 1099-0526 COMPLEXITY Complexity FEB 18 2020.0 2020 4306261 10.1155/2020/4306261 0.0 17 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Science & Technology - Other Topics KT3LV Green Published, gold 2023-03-23 WOS:000518919200001 0
J Samat, A; Gamba, P; Liu, SC; Li, EZ; Miao, ZL; Abuduwaili, J Samat, Alim; Gamba, Paolo; Liu, Sicong; Li, Erzhu; Miao, Zelang; Abuduwaili, Jilili Fuzzy multiclass active learning for hyperspectral image classification IET IMAGE PROCESSING English Article fuzzy set theory; learning (artificial intelligence); hyperspectral imaging; image classification; possibility theory; remote sensing; geophysical image processing; fuzzy multiclass active learning; hyperspectral image classification; possibility theory; fuzzy sets; fuzzy logic; active learning problems; fuzzy multiclass AL algorithms; fuzzy C-order ambiguity The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios' classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C-order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state-of-the-art AL methods are evaluated, such as breaking ties, margin sampling, and multi-class level uncertainty, with or without diversity criteria such as angle-based diversity (ABD), clustering-based diversity (CBD), and enhanced clustering-based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones. [Samat, Alim; Abuduwaili, Jilili] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; [Samat, Alim; Abuduwaili, Jilili] Chinese Acad Sci, CAS Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China; [Gamba, Paolo] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy; [Liu, Sicong] Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China; [Li, Erzhu] Jiangsu Normal Univ, Dept Geog Informat Sci, Xuzhou, Jiangsu, Peoples R China; [Miao, Zelang] Cent S Univ, Sch Geosci & Info Phys, Changsha, Hunan, Peoples R China; [Abuduwaili, Jilili] Univ Chinese Acad Sci, Beijing, Peoples R China Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Pavia; Tongji University; Jiangsu Normal University; Central South University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Samat, A (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China.;Samat, A (corresponding author), Chinese Acad Sci, CAS Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China. alim_smt@ms.xjb.ac.cn Liu, Sicong/J-7094-2013; Samat, Alim/H-5309-2019; Miao, Zelang/K-4774-2019; Gamba, Paolo E/G-1959-2010 Liu, Sicong/0000-0003-1612-4844; Samat, Alim/0000-0002-9091-6033; Gamba, Paolo E/0000-0002-9576-6337; ji, li li/0000-0001-8483-1554 China Postdoctoral Science Foundation [2016M592872]; National Natural Science Foundation of China [41601440, 41601354, 41471098]; Xinjiang Uyghur Autonomous Region High Level Talents Introduction Project [Y648031] China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Xinjiang Uyghur Autonomous Region High Level Talents Introduction Project This work was partially supported by a Project funded by the China Postdoctoral Science Foundation [2016M592872]; the National Natural Science Foundation of China [41601440, 41601354, 41471098]; and the Xinjiang Uyghur Autonomous Region High Level Talents Introduction Project [Y648031]. 20 2 2 0 24 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. JUL 2018.0 12 7 1095 1101 10.1049/iet-ipr.2017.0784 0.0 7 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology GJ4CI 2023-03-23 WOS:000435301200004 0
J Shi, HH; Wang, L; Scherer, R; Wozniak, M; Zhang, PC; Wei, W Shi, Hanhong; Wang, Lei; Scherer, Rafal; Wozniak, Marcin; Zhang, Pengchao; Wei, Wei Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network IEEE ACCESS English Article Load modeling; Load forecasting; Feature extraction; Predictive models; Correlation; Prediction algorithms; Neural networks; Short-term load forecasting; distance correlation coefficient; adabelief; temporal convolution network; gated recurrent unit; attention mechanism To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper, a hybrid neural network short-term load forecasting model based on temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed. Firstly, the correlation between meteorological features and load is measured with the distance correlation coefficient, and the fixed-length sliding time window method is used to reconstruct the features. Next, temporal convolutional network is adopted to extract the hidden historical information and time relationship including meteorological features, electricity price, etc., and a better-performing gated recurrent unit is utilized for perdition. Furthermore, the state-of-the-art AdaBelief optimizer and Attention mechanism are utilized to enhance the prediction accuracy and efficiency. The effectiveness and superiority of the proposed model are verified by load and weather data from Spain and PJM power system data. Short-term load forecasting results in different periods and comprehensive comparisons with the performance of different models show that the proposed model can provide accurate load forecasting results rather quickly. The highlights of this paper are that temporal convolutional network and gated recurrent unit are combined for load forecasting for the first time, and the forecasting performance is improved by the novel optimizer AdaBelief and feature selection based on distance correlation coefficient. [Shi, Hanhong; Wang, Lei; Zhang, Pengchao] Shaanxi Univ Technol, Shaanxi Key Lab Ind Automat, Hanzhong 723001, Peoples R China; [Wang, Lei; Wei, Wei] Xian Univ Technol, Shaanxi Key Lab Network Comp & Secur Technol, Xian 710048, Peoples R China; [Scherer, Rafal] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland; [Wozniak, Marcin] Silesian Tech Univ, Fac Appl Math, PL-44100 Gliwice, Poland Shaanxi University of Technology; Xi'an University of Technology; Technical University Czestochowa; Silesian University of Technology Wang, L (corresponding author), Shaanxi Univ Technol, Shaanxi Key Lab Ind Automat, Hanzhong 723001, Peoples R China. leiwang@xaut.edu.cn Scherer, Rafal/F-6745-2012; Wei, Wei/ABB-8665-2021; wei, wei/HHR-8613-2022; Wozniak, Marcin/L-6640-2013 Scherer, Rafal/0000-0001-9592-262X; Wei, Wei/0000-0002-8751-9205; Wozniak, Marcin/0000-0002-9073-5347 National Natural Science Foundation of China [61773314]; Shaanxi Provincial Natural Science Basic Research Program [2019JZ-11]; Scientific Research Project of the Education Department of the Shaanxi Provincial Government [19JC011]; Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-036]; Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data [IPBED7] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shaanxi Provincial Natural Science Basic Research Program; Scientific Research Project of the Education Department of the Shaanxi Provincial Government; Key Research and Development Program of Shaanxi Province; Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data This work was supported in part by the National Natural Science Foundation of China under Grant 61773314, in part by the Shaanxi Provincial Natural Science Basic Research Program under Grant 2019JZ-11, in part by the Scientific Research Project of the Education Department of the Shaanxi Provincial Government under Grant 19JC011, in part by the Key Research and Development Program of Shaanxi Province under Grant 2018ZDXM-GY-036, and in part by the Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data under Grant IPBED7. 54 12 12 14 54 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 66965 66981 10.1109/ACCESS.2021.3076313 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RZ0ZH gold 2023-03-23 WOS:000648328000001 0
J Song, SJ; Ge, LF; Zhang, M Song, Shoujun; Ge, Lefei; Zhang, Man Data-Reconstruction-Based Modeling of SRM With Few Flux-Linkage Samples From Torque-Balanced Measurement IEEE TRANSACTIONS ON ENERGY CONVERSION English Article Switched reluctance machine; modeling; flux linkage characteristics; few samples; data reconstruction; characteristic description; support vector machine; back-propagation neural network SWITCHED RELUCTANCE MOTORS; SIMULATION; GENERATOR This paper proposes a combined modeling method for switched reluctance machine (SRM) with few samples of flux linkage characteristics measured without rotor clamping devices and position sensors. The proposed method mainly consists of two steps, namely data reconstruction and characteristic description. In data reconstruction, the entire flux linkage characteristics are obtained by training support vector machine (SVM) with the measured few samples. The characteristics from the trained SVM are consistent well with those from the rotor-clamping measurement. In characteristic description, back-propagation neural network (BPNN) is adopted to describe the reconstructed flux linkage characteristics and calculated static torque characteristics. On this basis, the simulation model of the SRM prototype is built in MATLAB. The results from simulation under both motoring and generating mode are compared with those from experiments, and good agreements can be found, which prove the effectiveness of the proposed modeling method. To further demonstrate the accuracy and application of the entire flux linkage characteristics obtained from data reconstruction, BPNN is used again to build the mapping from flux linkage and phase current to rotor position for rotor position estimation, and some results are presented. The applicability of the proposed method to different SRM topologies is discussed as well. [Song, Shoujun; Ge, Lefei] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China; [Zhang, Man] Elect Engn Lab Paris, F-91190 Paris, France Northwestern Polytechnical University; UDICE-French Research Universities; Universite Paris Saclay Song, SJ; Ge, LF (corresponding author), Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China.;Zhang, M (corresponding author), Elect Engn Lab Paris, F-91190 Paris, France. sunnyway@nwpu.edu.cn; gelefei1120@163.com; zrh9707@163.com National Natural Science Foundation of China [51107100]; Natural Science Fundamental Research Plan of Shaanxi Province [2015JM5157]; Fundamental Research Funds for the Central Universities [3102015ZY053]; Aeronautical Science Foundation of China [2015ZB53018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fundamental Research Plan of Shaanxi Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Aeronautical Science Foundation of China This work was supported by the National Natural Science Foundation of China under Grant 51107100, the Natural Science Fundamental Research Plan of Shaanxi Province under Grant 2015JM5157, the Fundamental Research Funds for the Central Universities under Grant 3102015ZY053, and the Aeronautical Science Foundation of China under Grant 2015ZB53018. Paper no. TEC-00715-2014. 23 32 32 2 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8969 1558-0059 IEEE T ENERGY CONVER IEEE Trans. Energy Convers. JUN 2016.0 31 2 424 435 10.1109/TEC.2016.2517924 0.0 12 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering DN5KO 2023-03-23 WOS:000377106100002 0
J Roldan, JB; Maldonado, D; Aguilera-Pedregosa, C; Moreno, E; Aguirre, F; Romero-Zaliz, R; Garcia-Vico, AM; Shen, YQ; Lanza, M Roldan, Juan B.; Maldonado, David; Aguilera-Pedregosa, Cristina; Moreno, Enrique; Aguirre, Fernando; Romero-Zaliz, Rocio; Garcia-Vico, Angel M.; Shen, Yaqing; Lanza, Mario Spiking neural networks based on two-dimensional materials NPJ 2D MATERIALS AND APPLICATIONS English Article MEMRISTOR; MEMORY The development of artificial neural networks using memristors is gaining a lot of interest among technological companies because it can reduce the computing time and energy consumption. There is still no memristor, made of any material, capable to provide the ideal figures-of-merit required for the implementation of artificial neural networks, meaning that more research is required. Here we present the use of multilayer hexagonal boron nitride based memristors to implement spiking neural networks for image classification. Our study indicates that the recognition accuracy of the network is high, and that can be resilient to device variability if the number of neurons employed is large enough. There are very few studies that present the use of a two-dimensional material for the implementation of synapses of different features; in our case, in addition to a study of the synaptic characteristics of our memristive devices, we deal with complete spiking neural network training and inference processes. [Roldan, Juan B.; Maldonado, David; Aguilera-Pedregosa, Cristina] Univ Granada, Fac Ciencias, Dept Elect & Tecnol Comp, Avd Fuentenueva S-N, Granada 18071, Spain; [Moreno, Enrique] Czech Tech Univ, Fac Elect Engn, Dept Electromagnet Field, CTU, Prague 16627 6, Dejvice, Czech Republic; [Aguirre, Fernando; Lanza, Mario] King Abdullah Univ Sci & Technol KAUST, Phys Sci & Engn Div, Thuwal 239556900, Saudi Arabia; [Romero-Zaliz, Rocio; Garcia-Vico, Angel M.] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada 18071, Spain; [Shen, Yaqing] Soochow Univ, Collaborat Innovat Ctr Suzhou Nano Sci & Technol, Inst Funct Nano & Soft Mat FUNSOM, Suzhou 215123, Peoples R China University of Granada; Czech Technical University Prague; King Abdullah University of Science & Technology; University of Granada; Soochow University - China Roldan, JB (corresponding author), Univ Granada, Fac Ciencias, Dept Elect & Tecnol Comp, Avd Fuentenueva S-N, Granada 18071, Spain.;Lanza, M (corresponding author), King Abdullah Univ Sci & Technol KAUST, Phys Sci & Engn Div, Thuwal 239556900, Saudi Arabia. jroldan@ugr.es; mario.lanza@kaust.edu.sa Maldonado, David/AAP-2903-2020; Lanza, Mario/AAE-3986-2019; Roldan Aranda, Juan Bautista/C-6844-2012; Moreno Perez, Enrique/M-7793-2017 Maldonado, David/0000-0002-3475-9440; Lanza, Mario/0000-0003-4756-8632; Aguirre, Fernando Leonel/0000-0001-7793-1194; Roldan Aranda, Juan Bautista/0000-0003-1662-6457; Moreno Perez, Enrique/0000-0002-5304-1311; Aguilera-Pedregosa, Cristina/0000-0001-7204-5441; Shen, Yaqing/0000-0002-2325-5618 Ministry of Science and Technology of China [2018YFE0100800]; National Natural Science Foundation of China [61874075]; Collaborative Innovation Centre of Suzhou Nano Science Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; 111 Project from the State Administration of Foreign Experts Affairs of China; Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain); European Regional Development Fund (ERDF) [A-TIC-117-UGR18, B-TIC-624-UGR20, IE2017-5414]; Spanish Ministry of Science, Innovation and Universities; ERDF fund [RTI2018-098983-B-I00]; King Abdullah University of Science and Technology Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Collaborative Innovation Centre of Suzhou Nano Science Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; 111 Project from the State Administration of Foreign Experts Affairs of China; Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain)(Junta de Andalucia); European Regional Development Fund (ERDF)(European Commission); Spanish Ministry of Science, Innovation and Universities(Spanish Government); ERDF fund; King Abdullah University of Science and Technology(King Abdullah University of Science & Technology) This work has been supported by the Ministry of Science and Technology of China (2018YFE0100800), the National Natural Science Foundation of China (grant no. 61874075), the Collaborative Innovation Centre of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the 111 Project from the State Administration of Foreign Experts Affairs of China. We also acknowledge the Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain) and European Regional Development Fund (ERDF) under projects A-TIC-117-UGR18, B-TIC-624-UGR20 and IE2017-5414, as well as the Spanish Ministry of Science, Innovation and Universities and ERDF fund under project RTI2018-098983-B-I00. M.L. acknowledges generous support from the King Abdullah University of Science and Technology. 34 0 0 18 18 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2397-7132 NPJ 2D MATER APPL npj 2D Mater. Appl. SEP 9 2022.0 6 1 63 10.1038/s41699-022-00341-5 0.0 7 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science; Physics 4L1UA Green Published, gold 2023-03-23 WOS:000852419000002 0
J Lee, S; Zhou, JD; Chung, CT; Lee, ROY; Bazoukis, G; Letsas, KP; Wong, WT; Wong, ICK; Mok, NS; Liu, T; Zhang, QP; Tse, G Lee, Sharen; Zhou, Jiandong; Chung, Cheuk To; Lee, Rebecca On Yu; Bazoukis, George; Letsas, Konstantinos P.; Wong, Wing Tak; Wong, Ian Chi Kei; Mok, Ngai Shing; Liu, Tong; Zhang, Qingpeng; Tse, Gary Comparing the Performance of Published Risk Scores in Brugada Syndrome: A Multi-center Cohort Study CURRENT PROBLEMS IN CARDIOLOGY English Review REPORT EMERGING CONCEPTS; PREVIOUS CARDIAC-ARREST; ARRHYTHMIC EVENTS; STRATIFICATION; INDIVIDUALS; MANAGEMENT; STIMULATION; SURVIVAL; MODEL; LONG The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. This retrospective cohort study included consecutive patients diagnosed with BrS from January 1, 1997 to June 20, 2020 from Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. Two novel risk risk scores and 7 machine learning-based models (random survival forest, Ada boost classifier, Gaussian nayve Bayes, light gradient boosting machine, random forest classifier, gradient boosting classifier and decision tree classifier) were developed. The area under the receiver operator characteristic curve (AUC) [95% confidence intervals] was compared between the different models. This study included 548 consecutive BrS patients (7% female, age at diagnosis: 50 +/- 16 years, follow-up: 84 +/- 55 months). For the whole cohort, the score developed by Sieira et al showed the best performance (AUC: 0.806 [0.747-0.865]). A novel risk score was developed using the Sieira score and additional variables significant on univariable Cox regression (AUC: 0.855 [0.808-0.901]). A simpler score based on non-invasive results only showed a statistically comparable AUC (0.784 [0.724-0.845]), improved using random survival forests (AUC: 0.942 [0.913-0.964]). For the intermediate risk subgroup (N = 274), a gradient boosting classifier model showed the best performance (AUC: 0.814 [0.791-0.832]). A simple risk score based on clinical and electrocardiographic variables showed a good performance for predicting VT/VF, improved using machine learning. [Lee, Sharen; Chung, Cheuk To; Lee, Rebecca On Yu] China UK Collaborat, Cardiac Electrophysiol Unit, Cardiovasc Analyt Grp, Hong Kong, Peoples R China; [Zhou, Jiandong; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China; [Bazoukis, George; Letsas, Konstantinos P.] Evangelismos Gen Hosp Athens, Cardiol Dept 2, Arrhythmia Unit, Lab Cardiac Electrophysiol, Athens, Greece; [Wong, Wing Tak] Chinese Univ Hong Kong, Sch Life Sci, Hong Kong, Peoples R China; [Wong, Ian Chi Kei] Univ Hong Kong, Li Ka Shing Fac Med, Ctr Safe Medicat Practice & Res, Dept Pharmacol & Pharm, Hong Kong, Peoples R China; [Wong, Ian Chi Kei] UCL, Sch Pharm, London, England; [Mok, Ngai Shing] Princess Margaret Hosp, Hong Kong Hosp Author, Dept Med & Geriatr, Hong Kong, Peoples R China; [Liu, Tong; Tse, Gary] Tianjin Med Univ, Hosp 2, Tianjin Inst Cardiol,Dept Cardiol, Tianjin Key Lab Ion Mol Funct Cardiovasc Dis, Tianjin, Peoples R China; [Tse, Gary] Kent & Medway Med Sch, Canterbury, Kent, England City University of Hong Kong; Chinese University of Hong Kong; University of Hong Kong; University of London; University College London; University of London School of Pharmacy; Tianjin Medical University Tse, G (corresponding author), Tianjin Med Univ, Hosp 2, Tianjin Inst Cardiol,Dept Cardiol, Tianjin Key Lab Ion Mol Funct Cardiovasc Dis, Tianjin, Peoples R China.;Tse, G (corresponding author), Kent & Medway Med Sch, Canterbury, Kent, England. Wong, Wing Tak Jack/Q-4512-2018; Tse, Gary/P-1171-2015 Wong, Wing Tak Jack/0000-0002-4514-3780; Zhou, Jiandong/0000-0003-3780-9033; Tse, Gary/0000-0001-5510-1253 52 1 1 2 2 MOSBY-ELSEVIER NEW YORK 360 PARK AVENUE SOUTH, NEW YORK, NY 10010-1710 USA 0146-2806 1535-6280 CURR PROB CARDIOLOGY Curr. Probl. Cardiol. DEC 2022.0 47 12 101381 10.1016/j.cpcardiol.2022.101381 0.0 21 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology 5K4YW 36058344.0 Green Published, hybrid 2023-03-23 WOS:000869734400008 0
J Ropelewska, E; Cai, X; Zhang, Z; Sabanci, K; Aslan, MF Ropelewska, Ewa; Cai, Xiang; Zhang, Zhan; Sabanci, Kadir; Aslan, Muhammet Fatih Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels AGRICULTURE-BASEL English Article plum kernel images; texture parameters; discrimination; algorithms; performance metrics VARIETY Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars 'Emper', 'Kalipso', and 'Polinka', and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for 'Emper' and 'Kalipso' to 99% for 'Polinka'. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the 'Kalipso' kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels. [Ropelewska, Ewa] Natl Inst Hort Res, Fruit & Vegetable Storage & Proc Dept, Konstytucji 3 Maja 1-3, PL-96100 Skierniewice, Poland; [Cai, Xiang] Lomonosov Moscow State Univ, Fac Biol, Leninskie Gory 1, Moscow 119991, Russia; [Cai, Xiang] Shenzhen MSU BIT Univ, Dept Ecol, Sch Biol, Dayun Newtown 1, Shenzhen 518172, Peoples R China; [Zhang, Zhan] Shenzhen MSU BIT Univ, Univ Off, Dayun Newtown 1, Shenzhen 518172, Peoples R China; [Sabanci, Kadir; Aslan, Muhammet Fatih] Karamanoglu Mehmetbey Univ, Dept Elect & Elect Engn, TR-70100 Karaman, Turkey Lomonosov Moscow State University; Shenzhen MSU-BIT University; Shenzhen MSU-BIT University; Karamanoglu Mehmetbey University Ropelewska, E (corresponding author), Natl Inst Hort Res, Fruit & Vegetable Storage & Proc Dept, Konstytucji 3 Maja 1-3, PL-96100 Skierniewice, Poland. ewa.ropelewska@inhort.pl; caixiang@smbu.edu.cn; zhangz@smbu.edu.cn; kadirsabanci@kmu.edu.tr; mfatihaslan@kmu.edu.tr Aslan, Muhammet Fatih/V-8019-2017; Ropelewska, Ewa/R-2482-2018 Ropelewska, Ewa/0000-0001-8891-236X; Zhang, Zhan/0000-0001-8366-9807; Cai, Xiang/0000-0001-7265-6867; ASLAN, Muhammet Fatih/0000-0001-7549-0137 Youth Innovative Talents Project of Guangdong Province, China; Shenzhen MSU-BIT University, China [2021WQNCX084] Youth Innovative Talents Project of Guangdong Province, China; Shenzhen MSU-BIT University, China This research was partially funded by the Youth Innovative Talents Project of Guangdong Province, China, the funder: Shenzhen MSU-BIT University, China, the funding number: 2021WQNCX084. 32 3 3 1 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-0472 AGRICULTURE-BASEL Agriculture-Basel FEB 2022.0 12 2 285 10.3390/agriculture12020285 0.0 12 Agronomy Science Citation Index Expanded (SCI-EXPANDED) Agriculture ZI2EK gold, Green Published 2023-03-23 WOS:000761439900001 0
J Xiao, XL; Lv, JG; Chang, YH; Chen, JZ; He, HW Xiao, Xuelian; Lv, Jianguo; Chang, Yuhua; Chen, Jinzhou; He, Hongwen Adaptive Sliding Mode Control Integrating with RBFNN for Proton Exchange Membrane Fuel Cell Power Conditioning APPLIED SCIENCES-BASEL English Article proton exchange membrane fuel cell (PEMFC); DC; DC buck converter; adaptive backstepping sliding mode control (SMC); RBFNN estimation; power conditioning DC-DC CONVERTER; NEURAL-NETWORK; PREDICTIVE CONTROL; STABILITY ANALYSIS; ENERGY MANAGEMENT; BUCK CONVERTER; DESIGN; SYSTEM Proton exchange membrane fuel cells (PEMFC) are considered a promising solution for renewable energy application. To meet industrial requirements, the power source consisting of PEMFC is required to be power regulator to generate a stable and desired current and/or voltage under various working conditions. In this article, the adaptive sliding mode control integrating with the radial basis function neural network (RBFNN) approach for DC/DC buck converter-based PEMFC is presented to address perturbations from inner parameters as well as external disturbances in terms of power conditioning. Sliding mode control (SMC) and backstepping schemes are integrated to tackle the nonlinear and coupled outputs resulting in large control errors and slow response caused by PEMFC characteristics. To accurately estimate the parametric uncertainties and disturbance injections, such as buck converter parameter varying and PEMFC operation point changing, the RBFNN adaptive law is developed according to the defined Lyapunov and Gaussian functions overcoming the limitations of non-/linear parameter estimating. Simulations and experiments on the PEMFC power supply prototype governed by the DS1104 board are carried out. The comparative results indicate that the proposed RBFNN estimation associated with the backstepping SMC can reduce up to 7.5% overshoot and smooth PEMFC voltage and inductor current when disturbance changes in a voltage regulation experiment. Thus, the proposed method can regulate the current or voltage of a PEMFC power supply with robustness, adaptivity, and no chattering phenomenon. [Xiao, Xuelian; Lv, Jianguo] China North Vehicle Res Inst, Beijing 100072, Peoples R China; [Chang, Yuhua] Warsaw Univ Technol, Fac Automot & Construct Machinery Engn, PL-00661 Warsaw, Poland; [Chen, Jinzhou; He, Hongwen] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China Warsaw University of Technology; Beijing Institute of Technology He, HW (corresponding author), Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China. xiaoxuelian@aliyun.com; lucan@noveri.com.cn; yuhua.chang@pw.edu.pl; 3120215291@bit.edu.cn; hwhebit@bit.edu.cn He, Hongwen/Y-2860-2019 Chen, Jinzhou/0000-0003-2300-6141; , Hongwen/0000-0003-2874-1858 43 2 2 5 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel MAR 2022.0 12 6 3132 10.3390/app12063132 0.0 20 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 0D5AJ gold 2023-03-23 WOS:000776007600001 0
J Parra-Ullauri, JM; Garcia-Dominguez, A; Bencomo, N; Zheng, CG; Zhen, C; Boubeta-Puig, J; Ortiz, G; Yang, SF Parra-Ullauri, Juan Marcelo; Garcia-Dominguez, Antonio; Bencomo, Nelly; Zheng, Changgang; Zhen, Chen; Boubeta-Puig, Juan; Ortiz, Guadalupe; Yang, Shufan Event-driven temporal models for explanations-ETeMoX: explaining reinforcement learning SOFTWARE AND SYSTEMS MODELING English Article Temporal models; Complex event processing; Artificial intelligence; Explainable reinforcement learning; Event-driven monitoring Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system's reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows. [Parra-Ullauri, Juan Marcelo; Garcia-Dominguez, Antonio] Aston Univ, Engn & Appl Sci Sch, Birmingham, W Midlands, England; [Bencomo, Nelly] Univ Durham, Dept Comp Sci, Durham, England; [Zheng, Changgang] Univ Oxford, Dept Engn Sci, Oxford, England; [Zhen, Chen] Univ Sci & Technol China, Comp Sci & Technol, Hefei, Peoples R China; [Boubeta-Puig, Juan; Ortiz, Guadalupe] Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain; [Yang, Shufan] Edinburgh Napier Univ, Sch Comp, Edinburgh, Midlothian, Scotland; [Yang, Shufan] Univ Glasgow, Ctr Med & Ind Ultrason, Glasgow, Lanark, Scotland Aston University; Durham University; University of Oxford; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universidad de Cadiz; Edinburgh Napier University; University of Glasgow Parra-Ullauri, JM (corresponding author), Aston Univ, Engn & Appl Sci Sch, Birmingham, W Midlands, England. j.parra-ullauri@aston.ac.uk; a.garcia-dominguez@aston.ac.uk; nelly.bencomo@durham.ac.uk; changgang.zheng@eng.ox.ac.uk; cz2016@mail.ustc.edu.cn; juan.boubeta@uca.es; guadalupe.ortiz@uca.es; s.yang@napier.ac.uk Ortiz, Guadalupe/K-8601-2014; Bencomo, Nelly/GZL-1379-2022; Zheng, Changgang/AFS-4697-2022; Boubeta-Puig, Juan/L-9429-2014 Ortiz, Guadalupe/0000-0002-5121-6341; Zheng, Changgang/0000-0003-1894-722X; Boubeta-Puig, Juan/0000-0002-8989-7509; Parra-Ullauri, Juan Marcelo/0000-0003-1801-3494 The Leverhulme Trust Fellowship QuantUn: quantification of uncertainty using Bayesian surprises [RF-2019-548/9]; EPSRC Research Project [EP/T017627/1]; Royal Society of Edinburgh project A Reinforcement Learning Based Resource Management System for Long Term Care for Elderly People [961_Yang]; Spanish Ministry of Science and Innovation; European Regional Development Funds under project FAME [RTI2018-093608-B-C33]; Research Plan from the University of Cadiz; Grupo Ener-getico de Puerto Real S.A. under project GANGES [IRTP03_UCA] The Leverhulme Trust Fellowship QuantUn: quantification of uncertainty using Bayesian surprises; EPSRC Research Project(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society of Edinburgh project A Reinforcement Learning Based Resource Management System for Long Term Care for Elderly People; Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); European Regional Development Funds under project FAME; Research Plan from the University of Cadiz; Grupo Ener-getico de Puerto Real S.A. under project GANGES This work has been partially sponsored by The Leverhulme Trust Fellowship QuantUn: quantification of uncertainty using Bayesian surprises (Grant No. RF-2019-548/9), the EPSRC Research Project Twenty20Insight (Grant No. EP/T017627/1), The Royal Society of Edinburgh project A Reinforcement Learning Based Resource Management System for Long Term Care for Elderly People (Grant No. 961_Yang), the Spanish Ministry of Science and Innovation and the European Regional Development Funds under project FAME (Grant No. RTI2018-093608-B-C33], and the Research Plan from the University of Cadiz and Grupo Ener-getico de Puerto Real S.A. under project GANGES (Grant No. IRTP03_UCA). 77 4 4 2 10 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1619-1366 1619-1374 SOFTW SYST MODEL Softw. Syst. Model. JUN 2022.0 21 3 1091 1113 10.1007/s10270-021-00952-4 0.0 DEC 2021 23 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0U0DM Green Accepted, Green Published, hybrid 2023-03-23 WOS:000731367600001 0
J Kang, Q; Fan, QW; Zurada, JM; Huang, TW Kang, Qian; Fan, Qinwei; Zurada, Jacek M.; Huang, Tingwen A pruning algorithm with relaxed conditions for high-order neural networks based on smoothing group L1/2 regularization and adaptive momentum KNOWLEDGE-BASED SYSTEMS English Article Sigma-pi-sigma neural network; Smoothing group L1; 2 regularization; Adaptive momentum; Convergence L-1/2 REGULARIZATION; CONVERGENCE ANALYSIS; LEARNING ALGORITHM; GRADIENT-METHOD; REGRESSION; STABILITY; SELECTION; DISCRETE; PENALTY; GROWTH To enhance the sparseness of the network, improve its generalization ability and accelerate its training, we propose a novel pruning approach for sigma-pi-sigma neural network (SPSNN) under the relaxed condition by adding smoothing group L1/2 regularization and adaptive momentum. The main strength of this method is that it can prune both the redundant nodes between groups in the network, and also the redundant weights of the non-redundant nodes within the group, so as to achieve the sparseness of the network. Another strength is that the non-smooth absolute value function in the traditional L1/2 regularization method is replaced by a smooth function. This reduces the oscillations of learning and enables us to more effectively prove the convergence of the proposed algorithm. Finally, the numerical simulation results demonstrate the effectiveness of the proposed algorithm.(c) 2022 Elsevier B.V. All rights reserved. [Kang, Qian; Fan, Qinwei] Xian Polytech Univ, Sch Sci, Xian 710048, Peoples R China; [Zurada, Jacek M.] Univ Louisville, Elect & Comp Engn, Louisville, KY 40292 USA; [Zurada, Jacek M.] Univ Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland; [Huang, Tingwen] Texas A&M Univ Qatar, Sci Program, Doha, Qatar Xi'an Polytechnic University; University of Louisville; University of Social Sciences; Qatar Foundation (QF); Texas A&M University Qatar Fan, QW (corresponding author), Xian Polytech Univ, Sch Sci, Xian 710048, Peoples R China. kangqian0373@126.com; qinweifan@xpu.edu.cn; jmzura02@louisville.edu; tingwen.huang@qatar.tamu.edu Fan, Qinwei/0000-0002-1017-3496 Special Science Research Plan of the Education Bureau of Shaanxi Province of China [18JK0344]; Natural Science Foundation Guidance Project of Liaoning Province [2019-ZD-0128]; Natural Science Basic Research Plan in Shaanxi Province of China [2021JM-446] Special Science Research Plan of the Education Bureau of Shaanxi Province of China; Natural Science Foundation Guidance Project of Liaoning Province; Natural Science Basic Research Plan in Shaanxi Province of China This work was supported by Special Science Research Plan of the Education Bureau of Shaanxi Province of China (No. 18JK0344), Natural Science Foundation Guidance Project of Liaoning Province (No. 2019-ZD-0128), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2021JM-446). 49 0 0 7 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. DEC 5 2022.0 257 109858 10.1016/j.knosys.2022.109858 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5I8CB 2023-03-23 WOS:000868577000015 0
J Fan, AL; Yang, J; Yang, L; Liu, WQ; Vladimir, N Fan, Ailong; Yang, Jian; Yang, Liu; Liu, Weiqin; Vladimir, Nikola Joint optimisation for improving ship energy efficiency considering speed and trim control TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT English Article Inland river ship; Ship energy efficiency; Speed-trim joint optimisation; Computational fluid dynamics; Artificial neural networks PREDICTION; DEPLOYMENT Operational optimisation is one of the most important methods for reducing energy consumption and emissions related to ships. In this study, a speed-trim joint optimisation method is proposed. First, the propulsion power for various operating conditions was estimated by numerical simulation, two-dimensional method, and ship-engine-propeller principle. Second, the obtained data, consisting of ship power, draft, trim angle, and speed, were further expanded to form a joint optimisation decision database using the artificial neural network method. Subsequently, a dynamic programming algorithm was used to calculate the optimal speed for each segment of the voyage. The optimal trim angle corresponding to the speed was obtained based on the joint optimisation decision database. Finally, a 7500 ton inland bulk carrier was chosen as the case ship to validate the proposed method. The results showed that the method could reduce the total ship power consumption by up to 7.64%. [Fan, Ailong; Yang, Liu] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [Yang, Jian; Liu, Weiqin] Wuhan Univ Technol, Sch Naval Architecture Ocean & Energy Power Engn, Wuhan, Peoples R China; [Fan, Ailong] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan, Peoples R China; [Vladimir, Nikola] Univ Zagreb, Fac Mech Engn & Naval Architecture, Zagreb, Croatia Wuhan University of Technology; Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; University of Zagreb; University of Zagreb Faculty of Mechanical Engineering & Naval Architecture Yang, L (corresponding author), Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China. yang.liu@whut.edu.cn National Natural Science Foundation of China [51809202]; Green Intelligent Inland Ship Innovation Programme of China [MC-202002-C01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Green Intelligent Inland Ship Innovation Programme of China This work is supported by the National Natural Science Foundation of China (51809202) and Green Intelligent Inland Ship Innovation Programme of China (MC-202002-C01). 58 2 2 2 2 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1361-9209 1879-2340 TRANSPORT RES D-TR E Transport. Res. Part D-Transport. Environ. DEC 2022.0 113 103527 10.1016/j.trd.2022.103527 0.0 NOV 2022 19 Environmental Studies; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Transportation 7A7AN 2023-03-23 WOS:000898604300003 0
J Chen, ZH; Wang, ML; De Wilde, RL; Feng, RF; Su, MQ; Torres-de la Roche, LA; Shi, WJ Chen, Zihao; Wang, Maoli; De Wilde, Rudy Leon; Feng, Ruifa; Su, Mingqiang; Torres-de la Roche, Luz Angela; Shi, Wenjie A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype FRONTIERS IN IMMUNOLOGY English Article TNBC (triple negative breast cancer); immune checkpoint blockade; immune subtype; web server; TCGA CLASS DISCOVERY; STATISTICS; SIGNATURES; BLOCKADE Background Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. Methods Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. Results A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. Conclusion TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed. [Chen, Zihao] Univ Freiburg, Dept Urol, Freiburg, Germany; [Wang, Maoli] Fudan Univ, Dept Breast Surg, Obstet & Gynecol Hosp, Shanghai, Peoples R China; [De Wilde, Rudy Leon; Torres-de la Roche, Luz Angela; Shi, Wenjie] Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany; [Feng, Ruifa] Guilin Med Univ, Breast Ctr, Affiliated Hosp 2, Guilin, Peoples R China; [Su, Mingqiang] Southwest Med Univ, Zigong Hosp, Dept Urol, Zigong, Peoples R China University of Freiburg; Fudan University; Pius-Hospital Oldenburg; Guilin Medical University; Southwest Medical University Torres-de la Roche, LA; Shi, WJ (corresponding author), Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany. Luz-Angela.Torres-de-la-Roche@Pius-Hospital.de; wenjie.shi@uni-oldenburg.de Chen, zihao/HGE-3792-2022; Torres-de la Roche, Luz Angela/L-4281-2013 Torres-de la Roche, Luz Angela/0000-0003-1237-4192; chen, zihao/0000-0003-4027-1158; chen, zihao/0000-0002-7690-8651 University Medicine Oldenburg University Medicine Oldenburg This study was supported by the University Medicine Oldenburg, Carl von Ossietzky University Oldenburg. 59 12 12 1 8 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. SEP 17 2021.0 12 749459 10.3389/fimmu.2021.749459 0.0 14 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology WV5OM 34603338.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000717286400001 0
J Zhou, T; Geng, YJ; Ji, C; Xu, XR; Wang, H; Pan, JJ; Bumberger, J; Haase, D; Lausch, A Zhou, Tao; Geng, Yajun; Ji, Cheng; Xu, Xiangrui; Wang, Hong; Pan, Jianjun; Bumberger, Jan; Haase, Dagmar; Lausch, Angela Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images SCIENCE OF THE TOTAL ENVIRONMENT English Article Soil organic carbon; C:N ratio; Sentinel; Landsat; Machine learning; Digital soil mapping REMOTE-SENSING DATA; TOPOGRAPHIC WETNESS INDEX; RANDOM FOREST MODELS; LAND-USE; TOTAL NITROGEN; SPATIAL-DISTRIBUTION; AGRICULTURAL SOILS; SENSED DATA; 8 OLI; STOCKS Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery. (C) 2020 Elsevier B.V. All rights reserved. [Zhou, Tao; Haase, Dagmar; Lausch, Angela] Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany; [Zhou, Tao; Haase, Dagmar; Lausch, Angela] UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, Permoserstr 15, D-04318 Leipzig, Germany; [Geng, Yajun; Xu, Xiangrui; Pan, Jianjun] Nanjing Agr Univ, Coll Resources & Environm Sci, Weigang 1, Nanjing 210095, Peoples R China; [Ji, Cheng] Jiangsu Acad Agr Sci, Inst Agr Resource & Environm Sci, Zhongling St 50, Nanjing 210014, Peoples R China; [Wang, Hong] Anhui Sci & Technol Univ, Coll Resource & Environm, Donghua Rd 9, Chuzhou 233100, Peoples R China; [Bumberger, Jan] UFZ Helmholtz Ctr Environm Res, Dept Monitoring & Explorat Technol, Permoserstr 15, D-04318 Leipzig, Germany Humboldt University of Berlin; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ); Nanjing Agricultural University; Jiangsu Academy of Agricultural Sciences; Anhui Science & Technology University; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ) Zhou, T (corresponding author), Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany.;Geng, YJ (corresponding author), Nanjing Agr Univ, Coll Resources & Environm Sci, Weigang 1, Nanjing 210095, Peoples R China. tao.zhou@ufz.de; 2017203042@njau.edu.cn bumberger, jan/AHE-8005-2022 Lausch, Angela/0000-0002-4490-7232; Zhou, Tao/0000-0003-1216-0709 China Scholarship Council China Scholarship Council(China Scholarship Council) Wethank the European Soil Data Centre, the European Space Agency and the U.S. Geological Survey for their open data policies. First author acknowledges the financial support provided by the China Scholarship Council. 140 49 51 16 136 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. FEB 10 2021.0 755 2 142661 10.1016/j.scitotenv.2020.142661 0.0 16 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology PI0JJ 33059134.0 2023-03-23 WOS:000600786700067 0
J Han, J; Zhang, ZX; Mascolo, C; Andre, E; Tao, JH; Zhao, ZP; Schuller, BW Han, Jing; Zhang, Zixing; Mascolo, Cecilia; Andre, Elisabeth; Tao, Jianhua; Zhao, Ziping; Schuller, Bjoern W. Deep Learning for Mobile Mental Health: Challenges and recent advances IEEE SIGNAL PROCESSING MAGAZINE English Article DEPRESSION Mental health plays a key role in everyone's day-to-day lives, impacting our thoughts, behaviors, and emotions. Also, over the past years, given their ubiquitous and affordable characteristics, the use of smartphones and wearable devices has grown rapidly and provided support within all aspects of mental health research and care-from screening and diagnosis to treatment and monitoring-and attained significant progress in improving remote mental health interventions. While there are still many challenges to be tackled in this emerging cross-disciplinary research field, such as data scarcity, lack of personalization, and privacy concerns, it is of primary importance that innovative signal processing and deep learning (DL) techniques are exploited. In particular, recent advances in DL can help provide a key enabling technology for the development of next-generation user-centric mobile mental health applications. In this article, we briefly introduce the basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight the conventional technologies involved. We also describe several major challenges and various DL technologies that have potential for strongly contributing to dealing with these issues, and we discuss other problems to be addressed via research collaboration across multiple disciplines. [Han, Jing; Mascolo, Cecilia] Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0FD, England; [Zhang, Zixing] Imperial Coll London, Dept Comp, London, England; [Andre, Elisabeth] Augsburg Univ, Human Ctr Artificial Intelligence, D-86159 Augsburg, Germany; [Tao, Jianhua] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China; [Zhao, Ziping] Tianjin Normal Univ, Comp Sci, Tianjin 300387, Peoples R China; [Zhao, Ziping; Schuller, Bjoern W.] Univ Augsburg, Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany; [Schuller, Bjoern W.] Imperial Coll London, Dept Comp, Artificial Intelligence, London SW7 2AZ, England University of Cambridge; Imperial College London; Chinese Academy of Sciences; Institute of Automation, CAS; Tianjin Normal University; University of Augsburg; Imperial College London Han, J (corresponding author), Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0FD, England. jh2298@cam.ac.uk; zixing.zhang@tum.de; cm542@cam.ac.uk; andre@informatik.uni-augsburg.de; jhtao@nlpr.ia.ac.cn; zhaoziping@tjnu.edu.cn; bjoern.schuller@imperial.ac.uk Andre, Elisabeth/0000-0002-2367-162X Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth; National Natural Science Foundation of China [62071330, 61702370]; Key Program of the National Natural Science Foundation of China [61831022] Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This article has been partially funded by the Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth, the National Natural Science Foundation of China (grants 62071330 and 61702370), and the Key Program of the National Natural Science Foundation of China (grant 61831022). 30 1 1 1 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-5888 1558-0792 IEEE SIGNAL PROC MAG IEEE Signal Process. Mag. NOV 2021.0 38 6 96 105 10.1109/MSP.2021.3099293 0.0 10 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering WN4CT Green Submitted 2023-03-23 WOS:000711718500018 0
J Liu, SS; Wu, B; Chen, K; Liu, WQ; O'Neill, M; Lombardi, F Liu, Shanshan; Wu, Bi; Chen, Ke; Liu, Weiqiang; O'Neill, Maire; Lombardi, Fabrizio Special Issue on Circuits and Systems for Emerging Computing Paradigms IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS English Editorial Material AS Dennard's law is coming to an end, on-chip power consumption reduction and throughput improvement due to technology scaling pose serious challenges; workloads of today's applications (such as AI, big data, and the IoT) have also reached extremely high levels of complex computation. Power dissipation has become the fundamental barrier to scale computing performance across all technology platforms. Computation at nanoscales requires innovative approaches. [Liu, Shanshan] New Mexico State Univ, Klipsch Sch Elect & Comp Engn, Las Cruces, NM 88003 USA; [Wu, Bi; Chen, Ke; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 210016, Peoples R China; [O'Neill, Maire] Queens Univ Belfast, Ctr Secure Informat Technol, Belfast BT7 1NN, Antrim, North Ireland; [Lombardi, Fabrizio] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA New Mexico State University; Nanjing University of Aeronautics & Astronautics; Queens University Belfast; Northeastern University Liu, SS (corresponding author), New Mexico State Univ, Klipsch Sch Elect & Comp Engn, Las Cruces, NM 88003 USA. ssliu@nmsu.edu; wubi_sl@nuaa.edu.cn; chen.ke@nuaa.edu.cn; liuweiqiang@nuaa.edu.cn; m.oneill@ecit.qub.ac.uk; lombardi@ece.neu.edu Wu, Bi/0000-0001-9972-0478; Lombardi, Fabrizio/0000-0003-3152-3245; Liu, Weiqiang/0000-0001-8398-8648 8 0 0 7 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-8328 1558-0806 IEEE T CIRCUITS-I IEEE Trans. Circuits Syst. I-Regul. Pap. JUL 2022.0 69 7 2653 2654 10.1109/TCSI.2022.3176060 0.0 2 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 2O1TK Bronze 2023-03-23 WOS:000818848800004 0
J Hu, DAL; Zhang, YK; Zhu, JF; Liu, QG; Chen, Y Hu, Dianlin; Zhang, Yikun; Zhu, Jianfeng; Liu, Qiegen; Chen, Yang TRANS-Net: Transformer-Enhanced Residual-Error AlterNative Suppression Network for MRI Reconstruction IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Image reconstruction; Magnetic resonance imaging; Neural networks; Imaging; Reconstruction algorithms; Transformers; Convolutional neural networks; Deep prior; magnetic resonance imaging (MRI) imaging; residual image domain; transformer; unrolled reconstruction method ACCELERATED DYNAMIC MRI; IMAGE-RECONSTRUCTION; NEURAL-NETWORK; DECOMPOSITION; MANIFOLD Since deep priors could exploit more intrinsic features than handcrafted prior knowledge, unrolled reconstruction methods significantly improve image quality for fast magnetic resonance imaging (MRI) reconstruction with the combination of iterative optimization and deep neural network-based regularization terms. One popular way for unrolled methods is to employ the regularization penalty on the reconstructed result within the image space. Dissimilarly, in this article, we innovatively propose a reconstruction framework termed Transformer-enhanced Residual-error AlterNative Suppression Network (TRANS-Net) for MRI reconstruction to extend the unrolled methods. Unlike some existing model-based algorithms, TRANS-Net additionally deploys the regularization term on the error maps in the residual image domain, which greatly emphasizes the high-frequency information contained in the MR images. Besides, to further explore the global spatial correlation of the reconstructed image, the transformer module is adopted to enlarge the receptive field of deep priors regularized on the reconstructed images for better tissue recovery. The quantitative and qualitative results show that the proposed TRANS-Net method has superior performance to the state-of-the-art reconstruction algorithms in anatomical structure restoration and perceptual detail preservation. [Hu, Dianlin; Zhang, Yikun] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Hu, Dianlin; Zhang, Yikun] Southeast Univ, Key Lab Comp Network & Informat Integrat, Nanjing 210096, Peoples R China; [Hu, Dianlin; Zhang, Yikun] Ctr Rech Informat Biomed Sino Francais LIA CRIBs, F-35000 Rennes, France; [Zhu, Jianfeng] Xingaoyi Med Equipment Co Ltd, Yuyao 315400, Peoples R China; [Liu, Qiegen] Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China Southeast University - China; Southeast University - China; Universite de Rennes; Nanchang University; Southeast University - China; Southeast University - China Liu, QG (corresponding author), Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China. dianlin@seu.edu.cn; yikun@seu.edu.cn; zhujf@china-mri.com; liuqiegen@ncu.edu.cn; chenyang.list@seu.edu.cn Yikun, Zhang/HNI-6209-2023 Zhang, Yikun/0000-0002-4048-4869; Jianfeng, Zhu/0000-0003-2611-8886 Science and Technology Program of Guangdong [2018B030333001]; State Key Project of Research and Development Plan [2022YFC2401600]; National Natural Science Foundation of China [T2225025]; Key Research and Development Programs in Jiangsu Province of China [BE2021703, BE2022768] Science and Technology Program of Guangdong; State Key Project of Research and Development Plan; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Programs in Jiangsu Province of China This work was supported in part by the Science and Technology Program of Guangdong under Grant 2018B030333001, in part by the State Key Project of Research and Development Plan under Grant 2022YFC2401600, in part by the National Natural Science Foundation of China under Grant T2225025, and in part by the Key Research and Development Programs in Jiangsu Province of China under Grant BE2021703 and Grant BE2022768. The Associate Editor coordinating the review process was Dr. M. Shamim Hossain. 55 0 0 9 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 2517913 10.1109/TIM.2022.3205684 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 4S3TM Green Submitted 2023-03-23 WOS:000857367400028 0
J Jain, D; Nambirajan, A; Chen, G; Geisinger, K; Hiroshima, K; Layfield, L; Minami, Y; Moreira, AL; Motoi, N; Papotti, M; Rekhtman, N; Russell, PA; Prince, SS; Schmitt, F; Yatabe, Y; Eppenberger-Castori, S; Bubendorf, L Jain, Deepali; Nambirajan, Aruna; Chen, Gang; Geisinger, Kim; Hiroshima, Kenzo; Layfield, Lester; Minami, Yuko; Moreira, Andre L.; Motoi, Noriko; Papotti, Mauro; Rekhtman, Natasha; Russell, Prudence A.; Prince, Spasenija Savic; Schmitt, Fernando; Yatabe, Yasushi; Eppenberger-Castori, Serenella; Bubendorf, Lukas IASLC Pathology Comm NSCLC Subtyping in Conventional Cytology: Results of the International Association for the Study of Lung Cancer Cytology Working Group Survey to Determine Specific Cytomorphologic Criteria for Adenocarcinoma and Squamous Cell Carcinoma JOURNAL OF THORACIC ONCOLOGY English Article Non-small cell lung carcinoma; Cytology; Subtyping; Machine learning; Regression tree; IASLC OF-AMERICAN-PATHOLOGISTS; BIOPSY; P63; SPECIMENS Introduction: Accurate subtyping of NSCLC into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples reveal higher rates of classification failures, that is, subtyping as non-small cell carcinoma-not otherwise specified (NSCC-NOS), as compared with histology specimens. This study aims to identify specific algorithms on the basis of known cytomorphologic features that aid accurate and successful subtyping of NSCLC on cytology. Methods: A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees. Results: From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates. Conclusions: Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology. (C) 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. [Jain, Deepali; Nambirajan, Aruna] All India Inst Med Sci, Dept Pathol, New Delhi, India; [Chen, Gang] Fudan Univ, Zhongshan Hosp, Dept Pathol, Shanghai, Peoples R China; [Geisinger, Kim] Joint Pathol Ctr, Silver Spring, MD USA; [Hiroshima, Kenzo] Tokyo Womens Med Univ, Dept Pathol, Yachiyo Med Ctr, Yachiyo, Japan; [Layfield, Lester] Univ Missouri, Dept Pathol & Anat Sci, Columbia, MO USA; [Minami, Yuko] Natl Hosp Org, Ibaraki Higashi Natl Hosp, Dept Pathol, Ibaraki, Japan; [Moreira, Andre L.] New York Univ Langone Hlth, Dept Pathol, New York, NY USA; [Motoi, Noriko; Yatabe, Yasushi] Natl Canc Ctr, Dept Diagnost Pathol, Tokyo, Japan; [Motoi, Noriko] Saitama Canc Ctr, Dept Pathol, Saitama, Japan; [Papotti, Mauro] Univ Turin, Dept Oncol, Turin, Italy; [Rekhtman, Natasha] Mem Sloan Kettering Canc Ctr, Dept Pathol, 1275 York Ave, New York, NY 10021 USA; [Russell, Prudence A.] St Vincents Hosp, Anat Pathol Dept, Fitzroy, Vic, Australia; [Russell, Prudence A.] Univ Melbourne, Fitzroy, Vic, Australia; [Prince, Spasenija Savic; Eppenberger-Castori, Serenella; Bubendorf, Lukas] Univ Hosp Basel, Inst Med Genet & Pathol, Basel, Switzerland; [Schmitt, Fernando] Univ Porto, Med Fac, RISE Cintesis, Porto, Portugal All India Institute of Medical Sciences (AIIMS) New Delhi; Fudan University; Tokyo Women's Medical University; University of Missouri System; University of Missouri Columbia; New York University; National Cancer Center - Japan; University of Turin; Memorial Sloan Kettering Cancer Center; St Vincent's Hospital Melbourne; University of Melbourne; University of Basel; Universidade do Porto Jain, D (corresponding author), All India Inst Med Sci, Dept Pathol, New Delhi, India. deepalijain76@gmail.com Berezowska, Sabina/0000-0001-5442-9791 24 1 1 0 0 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1556-0864 1556-1380 J THORAC ONCOL J. Thorac. Oncol. JUN 2022.0 17 6 793 805 10.1016/j.jtho.2022.02.013 0.0 MAY 2022 13 Oncology; Respiratory System Science Citation Index Expanded (SCI-EXPANDED) Oncology; Respiratory System 3Z2DG 35331963.0 Bronze 2023-03-23 WOS:000844229200019 0
J Monshizadeh, M; Khatri, V; Kantola, R; Yan, Z Monshizadeh, Mehrnoosh; Khatri, Vikramajeet; Kantola, Raimo; Yan, Zheng A deep density based and self-determining clustering approach to label unknown traffic JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Intrusion detection; Data mining; Machine Learning; Network security; Network traffic Analyzing non-labeled data is a major concern in the field of intrusion detection as the attack clusters are continuously evolving which are unknown for the system. Many studies have been conducted on different techniques such as clustering to solve this issue. Consequently, in this paper the clustering techniques are applied based on the packets' similarity to categorize unknown traffic. After clustering is done by the proposed architecture, the security investigator analyzes one packet from each cluster (instead of thousands of packets) and generalize the result of analysis to all packets belonging to the cluster. The proposed architecture, namely Associated Density Based Clustering (ADBC) applies multiple unsupervised algorithms and a co-association matrix to detect attack clusters of any shape as long as they have density-connected elements. Furthermore, the architecture automatically determines the best number of clusters in order to categorize non-labeled data. The performance of proposed architecture is evaluated based on the various metrics, while its generalization capability is tested with three publicly available datasets. [Monshizadeh, Mehrnoosh] Nokia Bell Labs, Nozay, France; [Monshizadeh, Mehrnoosh; Kantola, Raimo; Yan, Zheng] Aalto Univ, Dept Comnet, Aalto, Finland; [Khatri, Vikramajeet] Nok Bell Labs, Espoo, Finland; [Yan, Zheng] Xidian Univ, State Key Lab ISN, Xian, Peoples R China; [Monshizadeh, Mehrnoosh] Aalto Univ, Dept Comnet, Aalto, Finland Nokia Corporation; Aalto University; Xidian University; Aalto University Monshizadeh, M (corresponding author), Aalto Univ, Dept Comnet, Aalto, Finland. mehrnoosh.monshizadeh@nokia-bell-labs.com; vikramajeet.khatri@nokia-bell-labs.com; raimo.kantola@aalto.fi; zheng.yan@aalto.fi Kantola, Raimo A/G-2277-2013 45 1 1 4 4 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 1095-8592 J NETW COMPUT APPL J. Netw. Comput. Appl. NOV 2022.0 207 103513 10.1016/j.jnca.2022.103513 0.0 18 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5M4RF hybrid, Green Published 2023-03-23 WOS:000871083700002 0
J Yu, CJ; Li, S; Ghista, D; Gao, ZF; Zhang, HY; Del Ser, J; Xu, L Yu, Chengjin; Li, Shuang; Ghista, Dhanjoo; Gao, Zhifan; Zhang, Heye; Del Ser, Javier; Xu, Lin Multi-level multi-type self-generated knowledge fusion for cardiac ultrasound segmentation INFORMATION FUSION English Article Multi-level and Multi-type Self-Generated (MM-SG); Deep Neural Networks (DNNs); SLIC (Simple Linear Iterative Clustering) algorithm; Dual Closed-loop Network (DCLNet); Full convolution network (FCN); Anatomically constrained neural network (ACNN) Most existing works on cardiac echocardiography segmentation require a large number of ground-truth labels to appropriately train a neural network; this, however, is time consuming and laborious for physicians. Self -supervision learning is one of the potential solutions to address this challenge by deeply exploiting the raw data. However, existing works mainly exploit single type/level of pretext task. In this work, we propose fusion of the multi-level and multi-type self-generated knowledge. We obtain multi-level information of sub-anatomical structures in ultrasound images via a superpixel method. Subsequently, we fuse various types of information generated through multi-types of pretext tasks. In the end, we transfer the learned knowledge to our downstream task. In the experimental studies, we have demonstrated the prove the effectiveness of this method through the cardiac ultrasound segmentation task. The results show that the performance of our proposed method for echocardiography segmentation matches the performance of fully supervised methods without requiring a high amount of labeled data. [Yu, Chengjin] Zhejiang Univ, Coll Opt Sci & Engn, Hangzhou 310058, Peoples R China; [Xu, Lin] Gen Hosp Southern Theatre Command, Dept Geriatr Cardiol, Guangzhou 510010, Peoples R China; [Li, Shuang; Xu, Lin] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China; [Xu, Lin] Southern Med Univ, Clin Med Coll 1, Guangzhou 510010, Peoples R China; [Ghista, Dhanjoo] Univ 2020 Fdn, Northborough, MA 01532 USA; [Gao, Zhifan; Zhang, Heye] Sun Yat sen Univ, Sch Biomed Engn, Shenzhen 518107, Peoples R China; [Del Ser, Javier] TECNALIA, Basque Res & Technol Alliance BRTA, Derio 48160, Spain; [Del Ser, Javier] Univ Basque Country UPV EHU, Bilbao 48013, Spain Zhejiang University; Guangdong University of Technology; Southern Medical University - China; Sun Yat Sen University; University of Basque Country Xu, L (corresponding author), Gen Hosp Southern Theatre Command, Dept Geriatr Cardiol, Guangzhou 510010, Peoples R China.;Xu, L (corresponding author), Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China.;Xu, L (corresponding author), Southern Med Univ, Clin Med Coll 1, Guangzhou 510010, Peoples R China.;Del Ser, J (corresponding author), TECNALIA, Basque Res & Technol Alliance BRTA, Derio 48160, Spain.;Del Ser, J (corresponding author), Univ Basque Country UPV EHU, Bilbao 48013, Spain. javier.delser@tecnalia.com; xxgnk_xlin@126.com National Natural Science Foundation of China [U21A20522, 61976222, 62101491, 62271511]; General Logistics Department of PLA [BLB19J005]; Department of Education of the Basque Government, Spain [IT1456-22]; Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation); Guangdong Natural Science Funds for Distinguished Young Scholar, China [2019B151502031]; National Youth Talent Support Program, China [RC2020-01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); General Logistics Department of PLA; Department of Education of the Basque Government, Spain; Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation); Guangdong Natural Science Funds for Distinguished Young Scholar, China; National Youth Talent Support Program, China This work was supported by the National Natural Science Foundation of China (No. U21A20522, No. 61976222, No. 62101491, No. 62271511), the General Logistics Department of PLA (No. BLB19J005), the Consolidated Research Group MATHMODE (IT1456-22) from the Department of Education of the Basque Government, Spain, the ``Red Cervera'' Programme (AI4ES project) from the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation), the Guangdong Natural Science Funds for Distinguished Young Scholar, China (2019B151502031) and the National Youth Talent Support Program, China (RC2020-01). 62 0 0 17 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion APR 2023.0 92 1 12 10.1016/j.inffus.2022.11.004 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6Q9HV 2023-03-23 WOS:000891921400001 0
J Zhou, T; Geng, YJ; Chen, J; Pan, JJ; Haase, D; Lausch, A Zhou, Tao; Geng, Yajun; Chen, Jie; Pan, Jianjun; Haase, Dagmar; Lausch, Angela High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms SCIENCE OF THE TOTAL ENVIRONMENT English Article Soil organic carbon; Soil total nitrogen; Sentinel-1; Sentinel-2; Digital soil mapping; Machine learning REMOTE-SENSING DATA; ARTIFICIAL NEURAL-NETWORKS; SYNTHETIC-APERTURE RADAR; SUPPORT VECTOR MACHINE; RANDOM FOREST MODELS; CLASSIFICATION-TREE; SPATIAL-DISTRIBUTION; TOPSOIL PROPERTIES; LUCAS SOIL; STOCKS Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil health and play a key role in the global carbon and nitrogen cycles. High-resolution radar Sentinel-1 and multispectral Sentinel-2 images have the potential to investigate soil spatial distribution information over a large area, although Sentinel-1 and Sentinel-2 data have rarely been combined to map either SOC or STN content. In this study, we applied machine learning techniques to map both SOC and STN content in the southern part of Central Europe using digital elevation model (DEM) derivatives, multi-temporal Sentinel-1 and Sentinel-2 data, and evaluated the potential of different remote sensing sensors (Sentinel-1 and Sentinel-2) to predict SOC and STN content. Four machinelearners including random forest (RF), boosted regression trees (BRT), support vector machine (SVM) and Bagged CART were used to construct predictive models of SOC and STN contents based on 179 soil samples and different combinations of environmental covariates. The performance of these models was evaluated based on a 10-fold cross-validationmethod by three statistical indicators. Overall, the BRT model performed better than RF, SVMand Bagged CART, and thesemodels yielded similar spatial distribution patterns of SOC and STN. Our results showed that multi-source sensor methods provided more accurate predictions of SOC and STN contents than individual sensors. The application of radar Sentinel-1 and multispectral Sentinel-2 images proved useful for predicting SOC and STN. A combination of Sentinel-1/2-derived predictors and DEMderivatives yielded the highest prediction accuracy. The prediction accuracy changedwith andwithout the Sentinel-1/2-derived predictors, with the R-2 for estimating both SOC and STN content using the BRT model increasing by 12.8% and 18.8%, respectively. Topographic variables were the main explanatory variables for SOC and STN predictions, where elevation was assigned as the variable with the most importance by the models. The results of this study illustrate the potential of free high-resolution radar Sentinel-1 andmultispectral Sentinel-2 data as inputwhen developing SOC and STN prediction models. (C) 2020 Elsevier B.V. All rights reserved. [Zhou, Tao; Haase, Dagmar; Lausch, Angela] Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany; [Zhou, Tao; Haase, Dagmar; Lausch, Angela] UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, Permoserstr 15, D-04318 Leipzig, Germany; [Geng, Yajun; Chen, Jie; Pan, Jianjun] Nanjing Agr Univ, Coll Resources & Environm Sci, Weigang 1, Nanjing 210095, Peoples R China Humboldt University of Berlin; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ); Nanjing Agricultural University Zhou, T (corresponding author), Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany. tao.zhou@ufz.de Chen, Jie/0000-0003-4647-3025; Zhou, Tao/0000-0003-1216-0709 China Scholarship Council China Scholarship Council(China Scholarship Council) Tao Zhou thanks the China Scholarship Council for its financial support. We are very grateful for the data support of the ESDAC. 138 54 58 27 174 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. AUG 10 2020.0 729 138244 10.1016/j.scitotenv.2020.138244 0.0 13 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology LU0DU 32498148.0 2023-03-23 WOS:000537434800006 0
C Koike, T; Qian, K; Schuller, BW; Yamamoto, Y IEEE Koike, Tomoya; Qian, Kun; Schuller, Bjorn W.; Yamamoto, Yoshiharu Transferring Cross-Corpus Knowledge: An Investigation on Data Augmentation for Heart Sound Classification 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0% the sensitivity and by 7.9% the specificity on average across 6 databases, which is a significant difference on 4 out of these (p < .05 by one-tailed z-test). [Koike, Tomoya; Qian, Kun; Yamamoto, Yoshiharu] Univ Tokyo, Grad Sch Educ, Educ Physiol Lab, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1130033, Japan; [Qian, Kun] Beijing Inst Technol, Inst Engn Med, Grp Audit Intelligent Med AIM, 5 Zhongguancun South St, Beijing 100081, Peoples R China; [Schuller, Bjorn W.] Imperial Coll London, GLAM Grp Language Audio & Mus, 180 Queens Gate,Huxley Bldg, London SW7 2AZ, England; [Schuller, Bjorn W.] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, Eichleitnerstr 30, D-86159 Augsburg, Germany University of Tokyo; Beijing Institute of Technology; Imperial College London; University of Augsburg Koike, T (corresponding author), Univ Tokyo, Grad Sch Educ, Educ Physiol Lab, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1130033, Japan. tommy@p.utokyo.ac.jp; qiantum@hotmail.com; schuller@ieee.org; yamamoto@p.utokyo.ac.jp BIT Teli Young Fellow Program from the Beijing Institute of Technology, China; JSPS Postdoctoral Fellowship for Research in Japan from the Japan Society for the Promotion of Science (JSPS), Japan [P19081]; Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan [19F19081, 20H00569]; Grants-in-Aid for Scientific Research [20H00569, 19F19081] Funding Source: KAKEN BIT Teli Young Fellow Program from the Beijing Institute of Technology, China; JSPS Postdoctoral Fellowship for Research in Japan from the Japan Society for the Promotion of Science (JSPS), Japan; Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was partially supported by the BIT Teli Young Fellow Program from the Beijing Institute of Technology, China, the JSPS Postdoctoral Fellowship for Research in Japan (ID No. P19081) from the Japan Society for the Promotion of Science (JSPS), Japan, and the Grants-in-Aid for Scientific Research (No. 19F19081 and No. 20H00569) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Kun Qian is the Corresponding author. 24 1 1 4 12 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 1976 1979 10.1109/EMBC46164.2021.9629714 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34891674.0 Green Submitted 2023-03-23 WOS:000760910502002 0
J Li, J; Liu, YG; Zhang, YJ; Lei, ZZ; Chen, Z; Li, G Li, Jie; Liu, Yonggang; Zhang, Yuanjian; Lei, Zhenzhen; Chen, Zheng; Li, Guang Data-driven based eco-driving control for plug-in hybrid electric vehicles JOURNAL OF POWER SOURCES English Article Eco-driving; Neural network; Energy management; Data-driven; Plug-in hybrid electric vehicle ENERGY MANAGEMENT; STRATEGIES With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural network-based dynamic programming is exploited to optimize the vehicle velocity by merging the datadriven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark. [Li, Jie; Liu, Yonggang] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Li, Jie; Liu, Yonggang] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China; [Zhang, Yuanjian] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AG, Antrim, North Ireland; [Lei, Zhenzhen] Chongqing Univ Sci & Technol, Sch Mech & Power Engn, Chongqing 401331, Peoples R China; [Chen, Zheng] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China; [Chen, Zheng; Li, Guang] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England Chongqing University; Chongqing University; Queens University Belfast; Chongqing University of Science & Technology; Kunming University of Science & Technology; University of London; Queen Mary University London Liu, YG (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China.;Chen, Z (corresponding author), Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China. lijiecqu2015@163.com; andylyg@umich.edu; y.zhang@qub.ac.uk; 2010048@cqust.edu.cn; chen@kust.edu.cn; g.li@qmul.ac.uk Zhang, Yuanjian/HKN-4832-2023 Zhang, Yuanjian/0000-0001-5563-8480; chen, zheng/0000-0002-1634-7231 National Natural Science Foundation of China [52002046]; Chongqing Fundamental Research and Frontier Exploration Project [CSTC2019JCYJMSO0642]; Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201901539]; EUfunded Marie SkodowskaCurie Individual Fellowships Project [845102] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chongqing Fundamental Research and Frontier Exploration Project; Science and Technology Research Program of Chongqing Municipal Education Commission; EUfunded Marie SkodowskaCurie Individual Fellowships Project The work is funded by the National Natural Science Foundation of China (No. 52002046) in part, Chongqing Fundamental Research and Frontier Exploration Project (No. CSTC2019JCYJMSO0642) in part, Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201901539) in part, and the EUfunded Marie SkodowskaCurie Individual Fellowships Project (No. 845102) in part. Any opinions expressed in this paper are solely those of the authors and do not represent those of the sponsors. 42 18 18 22 71 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-7753 1873-2755 J POWER SOURCES J. Power Sources JUN 30 2021.0 498 229916 10.1016/j.jpowsour.2021.229916 0.0 APR 2021 12 Chemistry, Physical; Electrochemistry; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels; Materials Science SF3KA Green Submitted 2023-03-23 WOS:000652657000003 0
J Pfister, H; Dotti, M; Laigle, C; Dubois, Y; Volonteri, M Pfister, Hugo; Dotti, Massimo; Laigle, Clothilde; Dubois, Yohan; Volonteri, Marta Real galaxy mergers from galaxy pair catalogues MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article galaxies: evolution; galaxies: kinematics and dynamics; methods: observational SUPERMASSIVE BLACK-HOLES; ULTRALUMINOUS INFRARED GALAXIES; COSMOLOGICAL SIMULATIONS; MAJOR MERGERS; EXTRA LIGHT; EVOLUTION; GROWTH; DARK; MATTER; DISSIPATION Mergers of galaxies are extremely violent events shaping their evolution. Such events are thought to trigger starbursts and, possibly, black hole accretion. Nonetheless, it is still not clear how to know the fate of a galaxy pair from the data available at a given time, limiting our ability to constrain the exact role of mergers. In this paper we use the light-cone of the HORIZON-AGN simulation, for which we know the fate of each pair, to test three selection processes aiming at identifying true merging pairs. We find that the simplest one (selecting objects within two thresholds on projected distance d and redshift difference Delta z) gives similar results than the most complex one (based on a neural network analysing d, Delta z, redshift of the primary, masses/star formation rates/aspect ratio of both galaxies). Our best thresholds are d(th) similar to 100 kpc and Delta z(th) similar to 10(-3), in agreement with recent results. [Pfister, Hugo] Univ Copenhagen, Niels Bohr Inst, DARK, DK-2100 Copenhagen, Denmark; [Pfister, Hugo] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Dotti, Massimo] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy; [Dotti, Massimo] Ist Nazl Fis Nucl, Sez Milano Bicocca, Piazza Sci 3, I-20126 Milan, Italy; [Laigle, Clothilde; Dubois, Yohan; Volonteri, Marta] Sorbonne Univ, Inst Astrophys Paris, CNRS, UMR 7095, 98 Bis Bd Arago, F-75014 Paris, France University of Copenhagen; Niels Bohr Institute; University of Hong Kong; University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Sorbonne Universite Pfister, H (corresponding author), Univ Copenhagen, Niels Bohr Inst, DARK, DK-2100 Copenhagen, Denmark.;Pfister, H (corresponding author), Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China. hugo.pfister@nbi.ku.dk Pfister, Hugo/0000-0003-0841-5182 Danish National Research Foundation [DNRF132]; European Research Council [614199] Danish National Research Foundation(Danmarks Grundforskningsfond); European Research Council(European Research Council (ERC)European Commission) HP is indebted to the Danish National Research Foundation (DNRF132) for support. HP, MV, and YD acknowledge support from the European Research Council (Project no. 614199, 'BLACK'). This work has made use of the Horizon Cluster hosted by the Institut d'Astrophysique de Paris; we thank Stephane Rouberol for running smoothly this cluster for us. HP thanks A. Szenicer for insightful discussions. We thank the referee, Jon Loveday, for carefully reading the manuscript and providing useful comments. 68 3 3 0 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. MAR 2020.0 493 1 922 929 10.1093/mnras/staa227 0.0 8 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics KS2QY Green Published, Green Submitted 2023-03-23 WOS:000518156100066 0
J Huang, L; Asteris, PG; Koopialipoor, M; Armaghani, DJ; Tahir, MM Huang, Lei; Asteris, Panagiotis G.; Koopialipoor, Mohammadreza; Armaghani, Danial Jahed; Tahir, M. M. Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength APPLIED SCIENCES-BASEL English Article invasive weed optimization; artificial neural network; hybrid model; Brazilian tensile strength ARTIFICIAL BEE COLONY; UNIAXIAL COMPRESSIVE STRENGTH; IMPERIALIST COMPETITIVE ALGORITHM; SUPPORT VECTOR MACHINE; NEURAL-NETWORK; GENETIC ALGORITHM; BEARING CAPACITY; PENETRATION RATE; SHEAR-STRENGTH; PERFORMANCE In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design. [Huang, Lei] Inner Mongolia Agr Univ, Water Conservancy & Civil Engn Coll, Hohhot 010018, Peoples R China; [Asteris, Panagiotis G.] Sch Pedag & Technol Educ, Computat Mech Lab, Athens 14121, Greece; [Koopialipoor, Mohammadreza] Amirkabir Univ Technol, Fac Civil & Environm Engn, Tehran 15914, Iran; [Armaghani, Danial Jahed] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Tahir, M. M.] Univ Teknol Malaysia, UTM Construct Res Ctr, ISIIC, Fac Engn,Sch Civil Engn, Skudai 81310, Malaysia Inner Mongolia Agricultural University; ASPETE - School of Pedagogical & Technological Education; Amirkabir University of Technology; Duy Tan University; Universiti Teknologi Malaysia Asteris, PG (corresponding author), Sch Pedag & Technol Educ, Computat Mech Lab, Athens 14121, Greece.;Armaghani, DJ (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. lei_huang@yeah.net; panagiotisasteris@gmail.com; mr.koopialipoor@aut.ac.ir; danialjahedarmaghani@duytan.edu.vn; mahmoodtahir@utm.my Koopialipoor, Mohammadreza/Q-7630-2019; Asteris, Panagiotis G./U-3798-2017 Asteris, Panagiotis G./0000-0002-7142-4981 National Natural Science Foundation of China [51969023]; High-level Talents Research Start-up Fund Project of Inner Mongolia Agricultural University; Inner Mongolia Autonomous Region Natural Science Foundation of China [2018MS05005]; Inner Mongolia Autonomous Region Higher Education Science Research Project of China [NJZY18064] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High-level Talents Research Start-up Fund Project of Inner Mongolia Agricultural University; Inner Mongolia Autonomous Region Natural Science Foundation of China; Inner Mongolia Autonomous Region Higher Education Science Research Project of China This research was supported by the National Natural Science Foundation of China (No. 51969023), High-level Talents Research Start-up Fund Project of Inner Mongolia Agricultural University (No. NDYB2018-60), Inner Mongolia Autonomous Region Natural Science Foundation of China (NO. 2018MS05005), and Inner Mongolia Autonomous Region Higher Education Science Research Project of China (No. NJZY18064). 101 72 72 1 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel DEC 2019.0 9 24 5372 10.3390/app9245372 0.0 18 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics KS1AC gold 2023-03-23 WOS:000518042000107 0
C Tang, XJ; Qiu, J; Zhang, WB; Toure, I; Zhang, ML; Messina, E; Xie, XP; Wang, XB; Yu, S Baru, C; Huan, J; Khan, L; Hu, XH; Ak, R; Tian, Y; Barga, R; Zaniolo, C; Lee, K; Ye, YF Tang, Xuejiao; Qiu, Jiong; Zhang, Wenbin; Toure, Ibrahim; Zhang, Mingli; Messina, Enza; Xie, Xueping; Wang, Xuebing; Yu, Sheng The Internet of Responsibilities - Connecting Human Responsibilities using Big Data and Blockchain 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) IEEE International Conference on Big Data English Proceedings Paper IEEE International Conference on Big Data (Big Data) DEC 09-12, 2019 Los Angeles, CA IEEE Comp Soc,IEEE,Baidu,Very,Ankura Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations. [Tang, Xuejiao] Leibniz Univ Hannover, Hannover, Germany; [Qiu, Jiong; Wang, Xuebing; Yu, Sheng] Hangzhou Quanshi Software Co Ltd, Hangzhou, Zhejiang, Peoples R China; [Zhang, Wenbin] Univ Maryland Baltimore Cty, Baltimore, MD USA; [Toure, Ibrahim] Doninya Inc, Vista, CA USA; [Zhang, Mingli] McGill Univ, Montreal, PQ, Canada; [Messina, Enza] Univ Milano Bicocca, Milan, Italy; [Xie, Xueping] Hangzhou Dianzi Univ, Hangzhou, Zhejiang, Peoples R China Leibniz University Hannover; University System of Maryland; University of Maryland Baltimore County; McGill University; University of Milano-Bicocca; Hangzhou Dianzi University Qiu, J (corresponding author), Hangzhou Quanshi Software Co Ltd, Hangzhou, Zhejiang, Peoples R China. xuejiao.tang@stud.uni-halmover.de; colin_qiu@hotmail.com; wenbinzhang@umbc.cdu; ibrahim.iba.toure@gmail.com; mingli.zhang@mcgill.ca; enza.messina@unimib.it; xxp@hdu.edu.cn; wangxuebing1109@qq.com; yusheng01291@qq.com Messina, Vincenzina/AAB-4097-2021; Messina, Vincenzina/AAB-4123-2021 Messina, Vincenzina/0000-0002-4062-0824; Messina, Vincenzina/0000-0002-4062-0824 6 0 0 2 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2639-1589 978-1-7281-0858-2 IEEE INT CONF BIG DA 2019.0 6226 6228 3 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP4WK 2023-03-23 WOS:000554828706100 0
J Liu, Y; Xi, DG; Li, ZL; Shi, CX Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Shi, Chun-Xiang Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images ADVANCES IN METEOROLOGY English Article MESOSCALE CONVECTIVE COMPLEXES; ARTIFICIAL NEURAL-NETWORK; STORM INITIATION; METEOSAT IMAGERY; INFRARED IMAGERY; DEEP CONVECTION; LIFE-CYCLE; PART I; SYSTEMS; CLASSIFICATION This paper presents an automated method to track cumulonimbus (Cb) clouds based on cloud classification and characterizes Cb behavior from FengYun-2C (FY-2C). First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. Second, a cross-correlation-based approach is used to track Cb patches within an image sequence. Third, 7 pixel parameters and 19 cloud patch parameters of Cb are derived. To assess the performance of the proposed method, 8 cases exhibiting different life stages and the temporal evolution of a single case are analyzed. The results show that (1) the proposed method is capable of locating and tracking Cb until dissipation and can account for the eventual splitting or merging of clouds; (2) compared to traditional brightness temperature (TB) thresholds-based cloud tracking methods, the proposed method reduces the uncertainty stemming from TB thresholds by classifying clouds with multichannel data in an advanced manner; and (3) the configuration and developmental stages of Cb that the method identifies are close to reality, suggesting that the characterization of Cb can provide detailed insight into the study of the motion and development of thunderstorms. [Liu, Yu] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Liu, Yu] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China; [Xi, Du-Gang] PLA Informat Engn Univ, Zhengzhou 450001, Peoples R China; [Li, Zhao-Liang] Chinese Acad Agr Sci, Minist Agr, Key Lab Agriinformat, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China; [Li, Zhao-Liang] CNRS, UdS, ICube, F-67412 Illkirch Graffenstaden, France; [Shi, Chun-Xiang] China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; PLA Information Engineering University; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; China Meteorological Administration Li, ZL (corresponding author), Chinese Acad Agr Sci, Minist Agr, Key Lab Agriinformat, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. lizl@igsnrr.ac.cn National Natural Science Foundation of China [41301379, 41231170, 41101326]; China Postdoctoral Science Foundation [2013M541029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by the National Natural Science Foundation of China under Grants 41301379, 41231170, and 41101326 and China Postdoctoral Science Foundation under Grant 2013M541029. The authors would like to thank NSMC (National Satellite Meteorological Center in Beijing) for providing FY2C satellite data. The authors greatly appreciate the careful and insightful suggestions and comments of reviewers which helped to improve the paper and data analysis. 51 5 6 1 16 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-9309 1687-9317 ADV METEOROL Adv. Meteorol. 2014.0 2014 478419 10.1155/2014/478419 0.0 18 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences AY9CX gold, Green Submitted 2023-03-23 WOS:000347848300001 0
J Yu, Y; Qin, XJ; Hussain, S; Hou, WY; Weis, T Yu, Yang; Qin, Xiangju; Hussain, Shabir; Hou, Weiyan; Weis, Torben Pedestrian Counting Based on Piezoelectric Vibration Sensor APPLIED SCIENCES-BASEL English Article vibration signal; pedestrian counting; pattern recognition; deep learning; privacy protection; piezoelectric sensor REAL-TIME; GEOPHONE Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods. [Yu, Yang; Weis, Torben] Univ Duisburg Essen, Distributed Syst Grp, D-47057 Duisburg, Germany; [Yu, Yang; Hussain, Shabir; Hou, Weiyan] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China; [Qin, Xiangju] Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki 00014, Finland University of Duisburg Essen; Zhengzhou University; University of Helsinki Hou, WY (corresponding author), Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China. yang.yu@uni-due.de; xiangju.qin@helsinki.fi; shabir@gs.zzu.edu.cn; houwy@zzu.edu.cn; torben.weis@uni-due.de Yu, Yang/AAG-7823-2021; Hussain, Shabir/GQB-0376-2022 Yu, Yang/0000-0003-1895-9722; Hussain, Shabir/0000-0002-8225-3647 45 5 5 4 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel FEB 2022.0 12 4 1920 10.3390/app12041920 0.0 16 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics ZQ1AM Green Published, gold 2023-03-23 WOS:000766845300001 0
J Li, KX; You, L; Wang, JH; Gao, XQ; Tsinos, CG; Chatzinotas, S; Ottersten, B Li, Ke-Xin; You, Li; Wang, Jiaheng; Gao, Xiqi; Tsinos, Christos G.; Chatzinotas, Symeon; Ottersten, Bjorn Downlink Transmit Design for Massive MIMO LEO Satellite Communications IEEE TRANSACTIONS ON COMMUNICATIONS English Article Satellites; Low earth orbit satellites; Precoding; Massive MIMO; Satellite antennas; Satellite broadcasting; Doppler shift; LEO satellite communications; massive MIMO; DL transmit design; DL precoding; machine learning CONSTELLATION; CHALLENGES; WIRELESS; SYSTEMS This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes. [Li, Ke-Xin; You, Li; Wang, Jiaheng; Gao, Xiqi] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China; [Tsinos, Christos G.; Chatzinotas, Symeon; Ottersten, Bjorn] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, L-2721 Luxembourg, Luxembourg Southeast University - China; University of Luxembourg Gao, XQ (corresponding author), Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China. likexin3488@seu.edu.cn; lyou@seu.edu.cn; jhwang@seu.edu.cn; xqgao@seu.edu.cn; chtsinos@gmail.com; symeon.chatzinotas@uni.lu; bjorn.ottersten@uni.lu Chatzinotas, Symeon/D-4191-2015; Ottersten, Bjorn/G-1005-2011 Chatzinotas, Symeon/0000-0001-5122-0001; Ottersten, Bjorn/0000-0003-2298-6774; Li, Ke-Xin/0000-0002-2337-2427 National Key Research and Development Program of China [2018YFB1801103]; Jiangsu Province Basic Research Project [BK20192002]; National Natural Science Foundation of China [61801114, 61801113, 61975234, 61960206006, 61971136, 61971130, 61720106003]; Fundamental Research Funds for the Central Universities; China Scholarship Council; Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu [BK20212001]; Luxembourg National Research Fund (FNR) National Key Research and Development Program of China; Jiangsu Province Basic Research Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council); Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu; Luxembourg National Research Fund (FNR)(Luxembourg National Research Fund) This work of Ke-Xin Li, Li You, and Xiqi Gao was supported by the National Key Research and Development Program of China under Grant 2018YFB1801103, the Jiangsu Province Basic Research Project under Grant BK20192002, the National Natural Science Foundation of China under Grants 61801114, 61801113, 61975234, 61960206006, and 61971136, and the Fundamental Research Funds for the Central Universities. The work of Ke-Xin Li was supported in part by the China Scholarship Council. The work of Jiaheng Wang was supported by the National Natural Science Foundation of China under Grants 61971130 and 61720106003, and the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20212001. The work of Christos G. Tsinos, Symeon Chatzinotas, and Bjorn Ottersten was supported by the Luxembourg National Research Fund (FNR) under the projects MEGALEO and DISBUS. The associate editor coordinating the review of this article and approving it for publication was L. Sanguinetti. 45 8 8 5 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0090-6778 1558-0857 IEEE T COMMUN IEEE Trans. Commun. FEB 2022.0 70 2 1014 1028 10.1109/TCOMM.2021.3131573 0.0 15 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications ZB4PY Green Submitted 2023-03-23 WOS:000756827000026 0
J Liu, J; Dey, N; Crespo, RG; Shi, FQ; Liu, CJ Liu, Jie; Dey, Nilanjan; Gonzalez Crespo, Ruben; Shi, Fuqian; Liu, Chanjuan Inadequate dataset learning for major depressive disorder MRI semantic classification IET IMAGE PROCESSING English Article ALZHEIMERS-DISEASE Predicting patients with major depression (MDD) is currently a difficult task. Magnetic resonance imaging (MRI) data analysis may provide insight into individual patient responses, allowing for more customized treatment decisions. Due to the absence of brain MRI data for MDD patients, a transfer learning (TL) method developed is used using calculation criteria. Combining an Inception-v3 neural network with a typical pre-trained neural network, the move learning-based Inception-v3 was proposed for the classification of MDD MRI datasets. An experiment was performed on the classification of eight semantic emotions (defined by IMAPS). Compared to other methods, the proposed method performs high efficiency for 90-10% and 80-20% (positive and negative classes), normal (N), unnormal (UN), and average/total sets, and for 70-30%, accuracy (A) is 92.90%, area under the curve (AUC) is 94.23%, and average precision score (APS) is 95.75%. Individual patients' responses to emotional stimulation can be predicted using the proposed methods, which can provide guidance in diagnosis and prognosis. [Liu, Jie; Liu, Chanjuan] Wenzhou Med Univ, Dept Informat, Wenzhou 325035, Peoples R China; [Dey, Nilanjan] JIS Univ, Dept Comp Sci & Engn, Kolkata, India; [Gonzalez Crespo, Ruben] Univ Int La Rioja, Dept Comp Sci & Technol, Logroho, Spain; [Shi, Fuqian] Rutgers State Univ, Rutgers Canc Inst New Jersey, New Brunswick, NJ USA Wenzhou Medical University; Universidad Internacional de La Rioja (UNIR); Rutgers State University New Brunswick; Rutgers State University Medical Center; Rutgers Cancer Institute of New Jersey Liu, CJ (corresponding author), Wenzhou Med Univ, Dept Informat, Wenzhou 325035, Peoples R China. chanjuan.liu1988@gmail.com ; Gonzalez Crespo, Ruben/P-8601-2018 Liu, Chanjuan/0000-0001-6938-4805; Gonzalez Crespo, Ruben/0000-0001-5541-6319 34 0 0 10 21 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. MAY 2022.0 16 6 1648 1656 10.1049/ipr2.12437 0.0 FEB 2022 9 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology 0G6KN 2023-03-23 WOS:000755054000001 0
J Wang, K; Xu, SP; Chen, CM; Islam, SKH; Hassan, MM; Savaglio, C; Pace, P; Aloi, G Wang, Ke; Xu, Ship Peng; Chen, Chien-Ming; Islam, S. K. Hafizul; Hassan, Mohammad Mehedi; Savaglio, Claudio; Pace, Pasquale; Aloi, Gianluca A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain IEEE NETWORK English Article Tools; Collaborative work; Safety; Consensus protocol; Servers; Artificial intelligence; Secure storage Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission, and management. Therefore, how to build a distributed trust system for cooperative learning in edge computing is a complex and challenging engineering problem. Block-chain can help realize collaborative learning in a distributed way without the need for third-party audit. Also, the consensus protocol plays a key technology role. In this article, a trusted consensus scheme for multi-party collaborative learning of edge AI is proposed. To improve system security, reputation-based rights are used to support fast removal of abnormal nodes. With the method of reputation rewards and punishments, the reputations of the nodes participating in the consensus process are scored and recorded on the chain according to their behavior. Nodes who commit malicious actions will be punished by reducing their reputation values. Nodes are rated according to their reputation value, and those with higher reputation rates are acknowledged with more credits and rights in consensus. Since the reputation module is loadable on X-BFT protocols, it is easy to enforce in real applications. The experimental results show that the probability of the attacker being chosen as the leader of XR-BFT is 87.5 percent lower than that of the X-BFT protocols, thus enabling our protocol to achieve trust collaborative learning in edge AI computing with more safety and efficiency. [Wang, Ke] Jinan Univ, Guangzhou, Peoples R China; [Xu, Ship Peng] Harbin Inst Technol, Harbin, Peoples R China; [Chen, Chien-Ming] Shandong Univ Sci & Technol, Qingdao, Peoples R China; [Islam, S. K. Hafizul] Inst Informat Technol Kalyani, Kalyani, W Bengal, India; [Hassan, Mohammad Mehedi] King Saud Univ, Res Chair Pervas & Mobile Comp, Riyadh, Saudi Arabia; [Savaglio, Claudio] ICAR CNR Inst, Arcavacata Di Rende, Italy; [Pace, Pasquale] Univ Calabria, DIMES, Telecommun, Commenda Di Rende, Italy; [Aloi, Gianluca] Univ Calabria, Commenda Di Rende, Italy Jinan University; Harbin Institute of Technology; Shandong University of Science & Technology; King Saud University; University of Calabria; University of Calabria Hassan, MM (corresponding author), King Saud Univ, Res Chair Pervas & Mobile Comp, Riyadh, Saudi Arabia. Chen, Chien-Ming/W-2133-2019; Pace, Pasquale/AAF-2126-2019; Savaglio, Claudio/D-2519-2017; Hassan, Mohammad Mehedi/D-4946-2016; Pace, Pasquale/L-8666-2017; Aloi, Gianluca/ABE-4944-2020; Hassan, Mohammad/GZA-7507-2022 Chen, Chien-Ming/0000-0002-6502-472X; Pace, Pasquale/0000-0002-4159-2765; Savaglio, Claudio/0000-0001-5092-0823; Hassan, Mohammad Mehedi/0000-0002-3479-3606; Pace, Pasquale/0000-0002-4159-2765; Aloi, Gianluca/0000-0002-4688-1021; Hassan, Mohammad/0000-0002-1712-0004 Deanship of Scientific Research at King Saud University; Italian MIUR, PRIN [CUP H24I17000070001] Deanship of Scientific Research at King Saud University(King Saud University); Italian MIUR, PRIN(Ministry of Education, Universities and Research (MIUR)Research Projects of National Relevance (PRIN)) The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. This work has also been partially supported by the Italian MIUR, PRIN 2017 Project Fluidware (CUP H24I17000070001). 14 3 3 3 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. JAN-FEB 2021.0 35 1 204 210 10.1109/MNET.011.2000249 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RO6NJ 2023-03-23 WOS:000641159800031 0
J Kostmann, M; Hardle, WK Kostmann, Michael; Haerdle, Wolfgang K. Forecasting in Blockchain-Based Local Energy Markets ENERGIES English Article blockchain; local energy market; smart contract; smart meter; short-term energy forecasting; machine learning; least absolute shrinkage and selection operator (LASSO); long short-term memory (LSTM); prediction errors; market mechanism; market simulation Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. Blockchain-based LEMs provide a decentralized market to local energy consumer and prosumers. They implement a market mechanism in the form of a smart contract without the need for a central authority coordinating the market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households' energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs. [Kostmann, Michael] Humboldt Univ, Sch Business & Econ, Spandauer Str 1, D-10178 Berlin, Germany; [Haerdle, Wolfgang K.] Humboldt Univ, Sch Business & Econ, Ladislaus von Bortkiewicz Chair Stat, Unter Linden 6, D-10099 Berlin, Germany; [Haerdle, Wolfgang K.] Xiamen Univ, Wang Yanan Inst Studies Econ, 422 Siming Rd, Xiamen 361005, Fujian, Peoples R China; [Haerdle, Wolfgang K.] Charles Univ Prague, Dept Math & Phys, Ke Karlovu 2027-3, Prague 12116 2, Czech Republic Humboldt University of Berlin; Humboldt University of Berlin; Xiamen University; Charles University Prague Kostmann, M (corresponding author), Humboldt Univ, Sch Business & Econ, Spandauer Str 1, D-10178 Berlin, Germany. michael.kostmann@hu-berlin.de Sureth, Michael/0000-0003-0219-9538; Hardle, Wolfgang Karl/0000-0001-5600-3014 51 8 8 2 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies JUL 2 2019.0 12 14 2718 10.3390/en12142718 0.0 27 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Energy & Fuels IN9LB gold, Green Submitted, Green Published 2023-03-23 WOS:000478999400078 0
J Wang, XY; Zhang, GX; Neri, F; Jiang, T; Zhao, JB; Gheorghe, M; Ipate, F; Lefticaru, R Wang, Xueyuan; Zhang, Gexiang; Neri, Ferrante; Jiang, Tao; Zhao, Junbo; Gheorghe, Marian; Ipate, Florentin; Lefticaru, Raluca Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots INTEGRATED COMPUTER-AIDED ENGINEERING English Article Membrane computing; membrane controller; PID; trajectory tracking; nonholonomic wheeled mobile robot WAVELET NEURAL-NETWORK; SLIDING-MODE; DYNAMIC CONTROL; OPTIMIZATION; ALGORITHM; SYSTEMS; ONLINE This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts' knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach. [Wang, Xueyuan; Zhang, Gexiang; Zhao, Junbo] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China; [Wang, Xueyuan] Southwest Univ Sci & Technol, Sch Informat Engn, Mianyang, Sichuan, Peoples R China; [Neri, Ferrante] De Montfort Univ, Ctr Computat Intelligence, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England; [Neri, Ferrante] Univ Jyvaskyla, Dept Math Informat Technol, Jyvaskyla, Finland; [Jiang, Tao] Chengdu Univ Informat Technol, Control Engn Coll, Chengdu, Sichuan, Peoples R China; [Gheorghe, Marian] Univ Bradford, Fac Engn & Informat, Bradford BD7 1DP, W Yorkshire, England; [Ipate, Florentin; Lefticaru, Raluca] Univ Bucharest, Fac Math & Comp Sci, Bucharest, Romania Southwest Jiaotong University; Southwest University of Science & Technology - China; De Montfort University; University of Jyvaskyla; Chengdu University of Information Technology; University of Bradford; University of Bucharest Zhang, GX (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China. zhgxdylan@126.com; fneri@dmu.ac.uk Gheorghe, Marian/GRX-9731-2022; Zhao, Junbo/R-8341-2019; Ipate, Florentin/D-4565-2011; Lefticaru, Raluca/C-4071-2011; Zhang, Gexiang/B-7097-2012; Neri, Ferrante/U-7036-2019 Gheorghe, Marian/0000-0002-2409-4959; Zhao, Junbo/0000-0002-8498-9666; Ipate, Florentin/0000-0001-8777-3425; Lefticaru, Raluca/0000-0001-5289-0162; Neri, Ferrante/0000-0002-6100-6532; Zhang, Gexiang/0000-0001-8034-0977 National Natural Science Foundation of China [61170016, 61373047]; Romanian National Authority for Scientific Research; CNCS-UEFISCDI [PN-II-ID-PCE-2011-3-0688] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Romanian National Authority for Scientific Research; CNCS-UEFISCDI The work of XW and GZ is supported by the National Natural Science Foundation of China (61170016, 61373047). The work of MG, Fl and RL was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688). 54 68 69 4 62 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1069-2509 1875-8835 INTEGR COMPUT-AID E Integr. Comput.-Aided Eng. 2016.0 23 1 15 30 10.3233/ICA-150503 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering DB0PY Green Accepted 2023-03-23 WOS:000368211200002 0
J Chen, ZY; Mauricio, A; Li, WH; Gryllias, K Chen, Zhuyun; Mauricio, Alexandre; Li, Weihua; Gryllias, Konstantinos A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Fault diagnosis; Cyclic Spectral Coherence; Convolutional Neural Networks; Rolling element bearings; Group normalization ROTATING MACHINERY; FEATURE-EXTRACTION; AUTOENCODER Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have recently gained increasing attention due to theirs powerful feature learning capacity. However, one of the critical challenges lies in how to embed domain diagnosis knowledge into DL to obtain suitable features that correlate well with the health conditions and to generate better predictors. In this paper, a novel DL-based fault diagnosis method, based on 2D map representations of Cyclic Spectral Coherence (CSCoh) and Convolutional Neural Networks (CNN), is proposed to improve the recognition performance of rolling element bearing faults. Firstly, the 2D CSCoh maps of vibration signals are estimated by cyclic spectral analysis to provide bearing discriminative patterns for specific type of faults. The motivation for using CSCoh-based preprocessing scheme is that the valuable health condition information can be revealed by exploiting the second-order cyclostationary behavior of bearing vibration signals. Thus, the difficulty of feature learning in deep diagnosis model is reduced by leveraging domain-related diagnosis knowledge. Secondly, a CNN model is constructed to learn high-level feature representations and conduct fault classification. More specifically, Group Normalization (GN) is employed in CNN to normalize the feature maps of network, which can reduce the internal covariant shift induced by data distribution discrepancy. The proposed method is tested and evaluated on two experimental datasets, including data category imbalances and data collected under different operating conditions. Experimental results demonstrate that the proposed method can achieve high diagnosis accuracy under different datasets and present better generalization ability, compared to state-of-the-art fault diagnosis techniques. (C) 2020 Elsevier Ltd. All rights reserved. [Chen, Zhuyun; Li, Weihua] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Peoples R China; [Mauricio, Alexandre; Gryllias, Konstantinos] Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300, B-3001 Leuven, Belgium; [Mauricio, Alexandre; Gryllias, Konstantinos] Dynam Mech & Mechatron Syst, Flanders Make, Belgium South China University of Technology; KU Leuven Li, WH (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Peoples R China. whlee@scut.edu.cn LI, Weihua/AAE-6294-2022; Gryllias, Konstantinos/F-4989-2017; chen, zhuyun/AFV-7930-2022 Gryllias, Konstantinos/0000-0002-8703-8938; Mauricio, Alexandre/0000-0001-9327-5625; Li, Weihua/0000-0002-7493-1399 National Natural Science Foundation of China [51875208]; National Key Research and Development Program of China [2018YFB1702400] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work is partially supported by the National Natural Science Foundation of China (Grant No. 51875208) and National Key Research and Development Program of China (Grant No. 2018YFB1702400). 45 182 186 53 310 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. JUN 2020.0 140 106683 10.1016/j.ymssp.2020.106683 0.0 16 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering LE4WX Green Published 2023-03-23 WOS:000526721400030 0
J An, FX; Stach, SM; Smail, I; Swinbank, AM; Almaini, O; Simpson, C; Hartley, W; Maltby, DT; Ivison, RJ; Arumugam, V; Wardlow, JL; Cooke, EA; Gullberg, B; Thomson, AP; Chen, CC; Simpson, JM; Geach, JE; Scott, D; Dunlop, JS; Farrah, D; van der Werf, P; Blain, AW; Conselice, C; Michalowski, M; Chapman, SC; Coppin, KEK An, Fang Xia; Stach, S. M.; Smail, Ian; Swinbank, A. M.; Almaini, O.; Simpson, C.; Hartley, W.; Maltby, D. T.; Ivison, R. J.; Arumugam, V.; Wardlow, J. L.; Cooke, E. A.; Gullberg, B.; Thomson, A. P.; Chen, Chian-Chou; Simpson, J. M.; Geach, J. E.; Scott, D.; Dunlop, J. S.; Farrah, D.; van der Werf, P.; Blain, A. W.; Conselice, C.; Michalowski, M.; Chapman, S. C.; Coppin, K. E. K. A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS ASTROPHYSICAL JOURNAL English Article observations; galaxies: evolution; galaxies: formation; galaxies: high-redshift; galaxies: starburst; submillimeter: galaxies DEEP-FIELD-SOUTH; DEGREE EXTRAGALACTIC SURVEY; ALMA SPECTROSCOPIC SURVEY; PARKES SELECTED REGIONS; STAR-FORMING GALAXIES; MU-M; MIDINFRARED COUNTERPARTS; REDSHIFT DISTRIBUTION; BOLOMETER CAMERA; INFRARED COLORS We describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S-870 mu m greater than or similar to 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey's UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machinelearning method is able to successfully recover similar to 85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of similar to 100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 mu m ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3 sigma ALMA detection. The median peak flux density of these galaxies is S-870 mu m, = (0.61 +/- 0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs. [An, Fang Xia] Chinese Acad Sci, Purple Mt Observ, 8 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China; [An, Fang Xia] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [An, Fang Xia; Stach, S. M.; Smail, Ian; Swinbank, A. M.; Wardlow, J. L.; Cooke, E. A.; Gullberg, B.] Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England; [Almaini, O.; Hartley, W.; Maltby, D. T.; Conselice, C.] Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England; [Simpson, C.] Northern Operat Ctr, Gemini Observ, 670 N Aohuku Pl, Hilo, HI 96720 USA; [Ivison, R. J.; Arumugam, V.; Chen, Chian-Chou] European Southern Observ, Karl Schwarzschild Str 2, Garching, Germany; [Ivison, R. J.; Arumugam, V.; Dunlop, J. S.] Univ Edinburgh, Inst Astron, Royal Observ, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland; [Thomson, A. P.] Univ Manchester, Oxford Rd, Manchester M13 9PL, Lancs, England; [Simpson, J. M.] Acad Sinica, Inst Astron & Astrophys, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan; [Geach, J. E.; Coppin, K. E. K.] Univ Hertfordshire, Sch Phys Astron & Math, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England; [Scott, D.] Univ British Columbia, Dept Phys & Astron, 6224 Agr Rd, Vancouver, BC V6T 1Z1, Canada; [Farrah, D.] Univ Hawaii, 2505 Correa Rd, Honolulu, HI 96822 USA; [Farrah, D.] Virginia Polytech Inst & State Univ, Dept Phys, MC 0435,910 Drillfield Dr, Blacksburg, VA 24061 USA; [van der Werf, P.] Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands; [Blain, A. W.] Univ Leicester, Dept Phys & Astron, Univ Rd, Leicester LE1 7RH, Leics, England; [Michalowski, M.] Adam Mickiewicz Univ, Fac Phys, Astron Observ Inst, Ul Sloneczna 36, PL-60286 Poznan, Poland; [Chapman, S. C.] Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS B3H 3J5, Canada Chinese Academy of Sciences; Nanjing Institute of Astronomical Optics & Technology, NAOC, CAS; Purple Mountain Observatory, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Durham University; University of Nottingham; European Southern Observatory; University of Edinburgh; University of Manchester; Academia Sinica - Taiwan; University of Hertfordshire; University of British Columbia; University of Hawaii System; Virginia Polytechnic Institute & State University; Leiden University; Leiden University - Excl LUMC; University of Leicester; Adam Mickiewicz University; Dalhousie University An, FX (corresponding author), Chinese Acad Sci, Purple Mt Observ, 8 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China.;An, FX (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;An, FX (corresponding author), Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England. fangxiaan@pmo.ac.cn Smail, Ian/AAL-9018-2020; Cooke, Elizabeth/HNP-0923-2023; CHEN, Chian-Chou (TC) 陳建州/ABB-2819-2020; Conselice, Christopher J/B-4348-2013; Michałowski, Michał/AAQ-4789-2021; Wardlow, Julie/C-9903-2015 Smail, Ian/0000-0003-3037-257X; CHEN, Chian-Chou (TC) 陳建州/0000-0002-3805-0789; Almaini, Omar/0000-0001-9328-3991; Thomson, Alasdair/0000-0003-2002-6913; swinbank, mark/0000-0003-1192-5837; An, Fangxia/0000-0001-7943-0166; Stach, Stuart/0000-0003-1122-6948; Farrah, Duncan/0000-0003-1748-2010; Ivison, Rob/0000-0001-5118-1313; Wardlow, Julie/0000-0003-2376-8971; /0000-0002-0729-2988; Gullberg, Bitten/0000-0002-4671-3036; Michalowski, Michal/0000-0001-9033-4140 China Scholarship Council; STFC [ST/P000541/1]; ERC Advanced Grant DUSTYGAL [321334]; Royal Society/Wolfson Merit Award; National Key Research and Development Program of China [2017YFA0402703]; NSFC [11773076]; Ernest Rutherford Fellowship; Royal Society; Canada Foundation for Innovation; STFC [ST/R000972/1, ST/L000598/1, ST/L000695/1, 1653119, ST/R000905/1, ST/L005042/1, ST/M001008/1, ST/P000541/1, ST/P004784/1] Funding Source: UKRI China Scholarship Council(China Scholarship Council); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ERC Advanced Grant DUSTYGAL; Royal Society/Wolfson Merit Award(Royal Society of London); National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Ernest Rutherford Fellowship; Royal Society(Royal Society of London); Canada Foundation for Innovation(Canada Foundation for InnovationCGIAR); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) F.X.A. acknowledges support from the China Scholarship Council for studying two years at Durham University. All Durham coauthors acknowledge STFC support through grant ST/P000541/1. I.R.S., B.G., and E.A.C. acknowledge the ERC Advanced Grant DUSTYGAL (321334). I.R.S. also acknowledges a Royal Society/Wolfson Merit Award. F.X.A. also acknowledges support from the National Key Research and Development Program of China (No. 2017YFA0402703) and NSFC grant (11773076). J.L.W. acknowledges the support of an Ernest Rutherford Fellowship. J.E.G. acknowledges the Royal Society. F.X.A acknowledges Ryley Hill for helpful discussions about the machine-learning algorithms. We thank the staff at UKIRT for their efforts in ensuring the success of the UDS project. The James Clerk Maxwell Telescope has historically been operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom, the National Research Council of Canada, and the Netherlands Organisation for Scientific Research. Additional funds for the construction of SCUBA-2 were provided by the Canada Foundation for Innovation. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2012.1.00090.S, 2015.1.01528.S, and 2016.1.00434.S. ALMA is a partnership of the ESO (representing its member states), NSF (USA), and NINS (Japan), together with the NRC (Canada), NSC and ASIAA (Taiwan), andKASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by the ESO, AUI/NRAO, and NAOJ. 88 10 10 1 5 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. AUG 1 2018.0 862 2 101 10.3847/1538-4357/aacdaa 0.0 18 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics GO5GC Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000440045800002 0
J Guo, ZX; Liao, WZ; Xiao, YF; Veelaert, P; Philips, W Guo, Zhixin; Liao, Wenzhi; Xiao, Yifan; Veelaert, Peter; Philips, Wilfried An Occlusion-Robust Feature Selection Framework in Pedestrian Detection SENSORS English Article pedestrian detection; feature selection; occlusion handling; deep learning GENE SELECTION; MACHINE Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned model. Current solutions typically exploit dimension reduction techniques. In this paper, we propose a simple but effective feature selection framework for pedestrian detection. Moreover, we introduce occluded pedestrian samples into the training process and combine it with a new feature selection criterion, which enables improved performances for occlusion handling problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our method over the state-of-art methods, especially for the occluded pedestrians. [Guo, Zhixin; Liao, Wenzhi; Veelaert, Peter; Philips, Wilfried] Univ Ghent, Interuniv Microelekt Ctr IMEC, Dept Telecommun & Informat Proc, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium; [Xiao, Yifan] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China Ghent University; Shandong University Guo, ZX (corresponding author), Univ Ghent, Interuniv Microelekt Ctr IMEC, Dept Telecommun & Informat Proc, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium. zhixin.guo@ugent.be; Wenzhi.Liao@UGent.be; xiaoyifran@163.com; Peter.Veelaert@UGent.be; Wilfried.Philips@UGent.be Veelaert, Peter/0000-0003-4746-9087 Chinese Scholarship Council (CSC) [201606220043]; IMEC Chinese Scholarship Council (CSC)(China Scholarship Council); IMEC This work is supported by the Chinese Scholarship Council (CSC), and the grant number is 201606220043. It is also supported by the project Cooperative radar-video sensing for urban environments from IMEC. 49 3 3 1 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUL 2018.0 18 7 2272 10.3390/s18072272 0.0 18 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GQ0UF 30011869.0 Green Accepted, gold, Green Submitted, Green Published 2023-03-23 WOS:000441334300294 0
J Jiang, JW; Zhang, HY; Dai, CX; Zhao, QJ; Feng, H; Ji, ZL; Ganchev, I Jiang, Jiawen; Zhang, Haiyang; Dai, Chenxu; Zhao, Qingjuan; Feng, Hao; Ji, Zhanlin; Ganchev, Ivan Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization IEEE ACCESS English Article Decoding; Natural language processing; Data models; Computational modeling; Task analysis; Semantics; Encoding; Natural language processing (NLP); automatic text summarization (ATS); sequenceto-sequence (Seq2Seq) model; attention mechanism; bidirectional LSTM (Bi-LSTM); pointer network; coverage mechanism; mixed learning objective (MLO) function The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for the Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated summaries lack accuracy and coherence, while the computational power involved, and performance achieved, could not easily meet the current needs. This paper proposes four novel ATS models with a Sequence-to-Sequence (Seq2Seq) structure, utilizing an attention-based bidirectional Long Short-Term Memory (LSTM), with added enhancements for increasing the correlation between the generated text summary and the source text, and solving the problem of out-of-vocabulary (OOV) words, suppressing the repeated words, and preventing the spread of cumulative errors in generated text summaries. Experiments conducted on two public datasets confirmed that the proposed ATS models achieve indeed better performance than the baselines and some of the state-of-the-art models considered. [Jiang, Jiawen; Dai, Chenxu; Feng, Hao; Ji, Zhanlin] North China Univ Sci & Technol, Coll Artificial Intelligence, Dept Comp Sci, Tangshan 063009, Peoples R China; [Zhang, Haiyang] Univ Sheffield, Dept Comp Sci, Sheffield S10 2TN, S Yorkshire, England; [Zhao, Qingjuan] Beihang Univ, Dept Comp Sci & Engn, Beijing 100191, Peoples R China; [Ji, Zhanlin; Ganchev, Ivan] Univ Limerick, Telecommun Res Ctr TRC, Limerick V94 T9PX, Ireland; [Ganchev, Ivan] Plovdiv Univ Paisii Hilendarski, Dept Comp Syst, Plovdiv 4000, Bulgaria; [Ganchev, Ivan] Bulgarian Acad Sci BAS, Inst Math & Informat IMI, Sofia 1040, Bulgaria North China University of Science & Technology; University of Sheffield; Beihang University; University of Limerick; Plovdiv University Ji, ZL (corresponding author), North China Univ Sci & Technol, Coll Artificial Intelligence, Dept Comp Sci, Tangshan 063009, Peoples R China.;Ji, ZL; Ganchev, I (corresponding author), Univ Limerick, Telecommun Res Ctr TRC, Limerick V94 T9PX, Ireland.;Ganchev, I (corresponding author), Plovdiv Univ Paisii Hilendarski, Dept Comp Syst, Plovdiv 4000, Bulgaria.;Ganchev, I (corresponding author), Bulgarian Acad Sci BAS, Inst Math & Informat IMI, Sofia 1040, Bulgaria. zhanlin.ji@ncst.edu.cn; ivan.ganchev@ul.ie Ganchev, Ivan/D-4973-2012 Ganchev, Ivan/0000-0003-0535-7087 National Key Research and Development Program of China [2017YFE0135700]; Science and Education for Smart Growth Operational Program of Bulgaria (2014-2020) [BG05M2OP001-1.001-0003]; European Union through the European Structural and Investment Funds National Key Research and Development Program of China; Science and Education for Smart Growth Operational Program of Bulgaria (2014-2020); European Union through the European Structural and Investment Funds This work was supported in part by the National Key Research and Development Program of China under Grant No. 2017YFE0135700 and in part by Grant No. BG05M2OP001-1.001-0003 ~nanced by the Science and Education for Smart Growth Operational Program of Bulgaria (2014~2020) and co-~nanced by the European Union through the European Structural and Investment Funds. 35 3 3 8 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 123660 123671 10.1109/ACCESS.2021.3110143 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications UQ4TE gold 2023-03-23 WOS:000696056800001 0
C Ni, F; Liu, JQ; Wei, F; Zhu, CD; Xie, SX IEEE Ni, F.; Liu, J. Q.; Wei, F.; Zhu, C. D.; Xie, S. X. Phase Identification in Distribution Systems by Data Mining Methods 2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2) English Proceedings Paper IEEE Conference on Energy Internet and Energy System Integration (EI2) NOV 26-28, 2017 Beijing, PEOPLES R CHINA IEEE,IEEE Power & Energy Soc,Tsinghua Univ,SEE,Energy Internet Res Inst,Beijing Assoc Sci & Technol,Tsinghua Berkeley Shenzhen Inst Phase identification; distribution system; data mining; Clustering; K-means; GMM Data mining is one of the statistical means that extracts useful information from an extremely large set of raw data. Therefore, data mining methods are under vigorous development and are commonly used in artificial intelligence fields such as image processing and robot industry. There has also been recently applications of data mining in electric power industry, such as classification, clustering and forecasting. In this research work, clustering techniques are adopted to identify the phase connectivity in power systems. Supported by smart meter data obtained from end-users on the low-voltage (LV) feeder, phase identification is properly discussed in this paper. Firstly, the LV network model is modeled using simulation tool OpenDSS. Secondly, the phase identification algorithm of the LV network is developed in Matlab by using K-means clustering as well as the Gaussian Mixture Model (GMM) clustering. Finally, the IEEE European Low Voltage Test Feeder is used to verify the proposed method. Results indicate that these two methods enable phase identification to realize its goals, which is to precisely address the active loads as well as the correlated phase of corresponding load. [Ni, F.; Liu, J. Q.; Wei, F.; Zhu, C. D.] Tellhow Sci Tech Co Ltd, Energy Internet Div, Shanghai, Peoples R China; [Xie, S. X.] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands Eindhoven University of Technology Ni, F (corresponding author), Tellhow Sci Tech Co Ltd, Energy Internet Div, Shanghai, Peoples R China. fei.ni@meinergy.cn; junqi.liu@meinergy.cn; feng.wei@meinergy.cn; changdong.zhu@meinergy.cn; s.xie@student.tue.nl Ni, Fei/0000-0002-7175-9036 10 4 4 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-1427-3 2017.0 6 Energy & Fuels; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Energy & Fuels; Engineering BJ7UN 2023-03-23 WOS:000427701303017 0
J Mwase, C; Jin, Y; Westerlund, T; Tenhunen, H; Zou, Z Mwase, Christine; Jin, Yi; Westerlund, Tomi; Tenhunen, Hannu; Zou, Zhuo Communication-efficient distributed AI strategies for the IoT edge FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Review Distributed AI/ML; Communication efficient AI/ML; Fog/edge computing; Edge ML; IIoT STOCHASTIC GRADIENT DESCENT; ALGORITHMS The impact that artificial intelligence (AI) has made across several industries in today's society is clearly seen in applications ranging from medical diagnosis to customer service chatbots, to financial trading. It is also evident that AI has a huge role to play in emerging and future applications and will be increasingly used in mission-critical and time-sensitive applications such as remote surgeries, cybersecurity and self-driving cars. To satisfy the latency, security and privacy requirements that such applications require, AI which has gained its merit by utilising resource-heavy cloud infrastructure, needs to perform well in resource-constrained environments at the network edge. To address this need, this paper characterises the cloud-to-thing continuum and provides an architecture for enabling AI in fully edge-based scenarios. In addition, the paper provides strategies to tackle the communication inefficiencies that arise from the distributed nature of fully edge-based scenarios. Performance improvements exhibited by these strategies in state-of-the art research is presented, as well as directions where further advancements can be made. The material is presented in a simple manner to catalyse the understanding and hence the participation of multidisciplinary researchers in addressing this challenge.(C) 2022 Elsevier B.V. All rights reserved. [Mwase, Christine; Jin, Yi; Zou, Zhuo] Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Westerlund, Tomi; Tenhunen, Hannu] Univ Turku, Fac Technol, Turku, Finland Fudan University; University of Turku Zou, Z (corresponding author), Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China. zhuo@fudan.edu.cn Westerlund, Tomi/I-2167-2019 Westerlund, Tomi/0000-0002-1793-2694; Tenhunen, Hannu/0000-0003-1959-6513 National Natural Sci-ence Foundation of China [92164301, 62011530132]; (NSFC-STINT project); Shanghai Municipal Sci-ence and Technology Major Project [2021SHZDZX0103, 2018SHZDZX01]; Shanghai Platform for Neuromorphic [17DZ2260900] National Natural Sci-ence Foundation of China(National Natural Science Foundation of China (NSFC)); (NSFC-STINT project); Shanghai Municipal Sci-ence and Technology Major Project; Shanghai Platform for Neuromorphic Acknowledgements This work was supported in part by the National Natural Sci-ence Foundation of China under Grant 92164301 and 62011530132 (NSFC-STINT project) , and Shanghai Municipal Sci-ence and Technology Major Project No. 2021SHZDZX0103 and No. 2018SHZDZX01, and in part by the Shanghai Platform for Neuromorphic and AI Chip under Grant 17DZ2260900. 75 1 1 10 19 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUN 2022.0 131 292 308 10.1016/j.future.2022.01.013 0.0 FEB 2022 17 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0L6IT 2023-03-23 WOS:000781577100021 0
J Wang, HT; Pei, ZA; Xu, LF; Xu, T; Bezerianos, A; Sun, Y; Li, JH Wang, Hongtao; Pei, Zian; Xu, Linfeng; Xu, Tao; Bezerianos, Anastasios; Sun, Yu; Li, Junhua Performance Enhancement of P300 Detection by Multiscale-CNN IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Electroencephalogram (EEG); event-related potential (ERP); multiscale convolutional neural network (MS-CNN); subject-specific; subject-unspecific BCI-COMPETITION-III; EEG; POTENTIALS; FEATURES The P300-based spelling system is one of the most popular brain-computer interface applications. Its success largely depends on performance, including the information transmission rate (ITR) and detection rate (i.e., accuracy). To achieve good performance, we proposed a multiscale convolutional neural network (MS-CNN) model that consists of seven layers. First, an upfront data set was used to train the MS-CNN, aiming to obtain a subject-unspecific model (universal model) for P300 detection. Second, this universal model was adapted by a portion of data derived from a subject to update the model to obtain a subject-specific model by incorporating a transfer learning technique. We applied the proposed model in the brain-computer interface (BCI) Controlled Robot Contest at the 2019 World Robot Conference, and our performance was the best among the teams in the contest. In the contest, ten healthy young subjects were randomly assigned by the contest committee to assess the model. Our model achieved the best P300 detection performance (higher accuracy with less repetition time). The ITR for the subject-unspecific case was 13.49 hits/min, while the ITR for the subject-specific case was 12.13 bits/min when the repetitions were fewer than six. It is believed that our method may pave a promising path for taking a further step toward efficient implementation of the P300-based spelling system. [Wang, Hongtao; Pei, Zian; Xu, Linfeng; Xu, Tao; Li, Junhua] Wuyi Univ, Fac Intelligent Mfg, Jiangmen 529020, Peoples R China; [Bezerianos, Anastasios] Univ Patras Greece, Dept Med Phys, Patras 26500, Greece; [Sun, Yu] Zhejiang Univ, Key Lab Biomed Engn, Minist Educ China, Hangzhou 310027, Peoples R China; [Li, Junhua] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England Wuyi University; Ministry of Education, China; Zhejiang University; University of Essex Wang, HT; Li, JH (corresponding author), Wuyi Univ, Fac Intelligent Mfg, Jiangmen 529020, Peoples R China.;Sun, Y (corresponding author), Zhejiang Univ, Key Lab Biomed Engn, Minist Educ China, Hangzhou 310027, Peoples R China. nushongtaowang@qq.com; yusun@zju.edu.cn; juhalee.bcmi@gmail.com Bezerianos, Tasos/AAC-2499-2022 Bezerianos, Tasos/0000-0002-8199-6000; Wang, Hongtao/0000-0002-6564-5753; SUN, Yu/0000-0002-6666-8586 Technology Development Project of Guangdong Province [2020ZDZX3018]; Special Fund for Science and Technology of Guangdong Province [2020182]; Wuyi University [2019WGALH16]; Hong Kong and Macao Joint Research and Development Project [2019WGALH16]; Science Foundation for Young Teachers of Wuyi University [2018td01]; Jiangmen Brain-like Computation and Hybrid Intelligence RD Center [[2018] 359, [2019]26]; National Natural Science Foundation of China [61806149, 81801785]; Guangdong Basic and Applied Basic Research Foundation [2020A1515010991] Technology Development Project of Guangdong Province; Special Fund for Science and Technology of Guangdong Province; Wuyi University; Hong Kong and Macao Joint Research and Development Project; Science Foundation for Young Teachers of Wuyi University; Jiangmen Brain-like Computation and Hybrid Intelligence RD Center; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation This work was supported by the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province under Grant 2020ZDZX3018, in part by the Special Fund for Science and Technology of Guangdong Province under Grant 2020182, in part by the Wuyi University and Hong Kong and Macao Joint Research and Development Project under Grant 2019WGALH16, in part by the Science Foundation for Young Teachers of Wuyi University under Grant 2018td01, in part by the Jiangmen Brain-like Computation and Hybrid Intelligence R&D Center under Grant [2018] 359 and Grant [2019]26, in part by the National Natural Science Foundation of China under Grant 61806149 and Grant 81801785, and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515010991. The Associate Editor coordinating the review process was Yuhua Cheng. (Hongtao Wang and Zian Pei contributed equally to this work.) 55 5 5 6 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70 2507012 10.1109/TIM.2021.3067943 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation YR3DC Green Accepted 2023-03-23 WOS:000749874100001 0
J Fang, J; Liu, CL; Simos, TE; Famelis, IT Fang, Jie; Liu, Chenglian; Simos, T. E.; Famelis, I. Th. Neural Network Solution of Single-Delay Differential Equations MEDITERRANEAN JOURNAL OF MATHEMATICS English Article Neural networks; delay differential equations VANISHED PHASE-LAG; RUNGE-KUTTA PAIRS; INITIAL-VALUE PROBLEMS; NUMEROV-TYPE METHODS; P-STABLE METHOD; NUMERICAL-SOLUTION; HIGH-ORDER; FITTED MODIFICATIONS; SOLVING ORDINARY; NYSTROM METHODS Following the ideas of Lagaris et al. (IEEE Trans Neural Netw 9(5):987-1000, 1998), we use Neural Networks to solve approximatively first-order single-delay differential equations and systems. We apply the proposed novel methodology to various problems with constant delay terms and the resulted continuous solutions prove to be very efficient. This is the case not only for nonstiff problems but for equations with stiffness too. [Fang, Jie] Fujian Normal Univ, Sch Elect & Informat Engn, Fuqing Branch, Fuqing 350300, Peoples R China; [Liu, Chenglian] Neusoft Inst Guangdong, Dept Comp Sci & Technol, Foshan 528228, Peoples R China; [Simos, T. E.] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China; [Simos, T. E.] South Ural State Univ, 76 Lenin Ave, Chelyabinsk 454080, Russia; [Simos, T. E.] Ural Fed Univ, Grp Modern Computat Methods, 19 Mira St, Ekaterinburg 620002, Russia; [Simos, T. E.] Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Dongtong Rd 705, Neijiang 641100, Peoples R China; [Simos, T. E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi, Greece; [Famelis, I. Th.] Univ West Attica, Dept Elect & Elect Engn, MicroSENSES Lab, Athens, Greece Fujian Normal University; Shandong University of Science & Technology; South Ural State University; Ural Federal University; Neijiang Normal University; Democritus University of Thrace; University of West Attica Simos, TE (corresponding author), Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China.;Simos, TE (corresponding author), South Ural State Univ, 76 Lenin Ave, Chelyabinsk 454080, Russia.;Simos, TE (corresponding author), Ural Fed Univ, Grp Modern Computat Methods, 19 Mira St, Ekaterinburg 620002, Russia.;Simos, TE (corresponding author), Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Dongtong Rd 705, Neijiang 641100, Peoples R China.;Simos, TE (corresponding author), Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi, Greece. tsimos.conf@gmail.com Liu, Chenglian/P-6985-2019; Famelis, Ioannis/AAP-5476-2021; FAMELIS, Ioannis/GWV-1255-2022 Liu, Chenglian/0000-0002-9086-9740; Famelis, Ioannis/0000-0003-0352-9404; 51 33 33 1 19 SPRINGER BASEL AG BASEL PICASSOPLATZ 4, BASEL, 4052, SWITZERLAND 1660-5446 1660-5454 MEDITERR J MATH Mediterr. J. Math. FEB 2020.0 17 1 30 10.1007/s00009-019-1452-5 0.0 15 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics KI4OC 2023-03-23 WOS:000511328700006 0
J Huang, XX; Li, YF; Tian, Z; Ye, QH; Ke, Q; Fan, DL; Mao, GQ; Chen, AF; Liu, JG Huang, Xinxing; Li, Yifan; Tian, Zhan; Ye, Qinghua; Ke, Qian; Fan, Dongli; Mao, Ganquan; Chen, Aifang; Liu, Junguo Evaluation of short-term streamflow prediction methods in Urban river basins PHYSICS AND CHEMISTRY OF THE EARTH English Article Streamflow simulation; Neural network models; SOBEK model; Urban rivers ARTIFICIAL NEURAL-NETWORKS; HYDROLOGICAL MODEL; FLOOD RISK; RUNOFF; RAINFALL; CHINA; PERFORMANCE; SIMULATION; CATCHMENT; LEVEL Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management. [Huang, Xinxing; Fan, Dongli] Shanghai Inst Technol, Sch Chem & Environm Engn, Shanghai 201418, Peoples R China; [Huang, Xinxing; Li, Yifan; Tian, Zhan; Mao, Ganquan; Chen, Aifang; Liu, Junguo] Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China; [Ye, Qinghua] Deltares, Delft, Netherlands; [Ke, Qian] Delft Univ Technol, Fac Civil Engn & Geosci, Dept Hydraul Engn, Delft, Netherlands Shanghai Institute of Technology; Southern University of Science & Technology; Deltares; Delft University of Technology Tian, Z; Liu, JG (corresponding author), Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China. tianz@sustech.edu.cn; liujg@sustech.edu.cn Li, Yifan/HGA-6843-2022; Chen, Aifang/AAG-6613-2019; Chen, Aifang/AET-2075-2022; Liu, Junguo/B-3021-2012; LI, yi/HKO-0480-2023 Li, Yifan/0000-0002-3249-1778; Chen, Aifang/0000-0002-3237-3122; Liu, Junguo/0000-0002-5745-6311; Li, Yifan/0000-0002-0708-0192; Tian, Zhan/0000-0002-4520-0036; ye, qinghua/0000-0002-8779-7707; Ke, Qian/0000-0002-3111-994X National Key R& D Program of China [2018YFE0206200]; National Natural Science Foundation of China [51761135024]; High-level Special Funding of the Southern University of Science and Technology [G02296302, G02296402] National Key R& D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High-level Special Funding of the Southern University of Science and Technology This work was supported by the National Key R& D Program of China (2018YFE0206200) , the National Natural Science Foundation of China (Grant no. 51761135024) and the High-level Special Funding of the Southern University of Science and Technology (Grant no. G02296302, G02296402) . We would like to show our gratitude to Honglong Yang in Meteorological Bureau of Shenzhen. We also would like to show our gratitude to Meteorological Bureau of Shenzhen, Huadong Engineering Corporation Limited and Ecological Environment Bureau of Shenzhen. 69 8 8 2 23 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1474-7065 1873-5193 PHYS CHEM EARTH Phys. Chem. Earth OCT 2021.0 123 103027 10.1016/j.pce.2021.103027 0.0 MAY 2021 12 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences; Water Resources UA9FU 2023-03-23 WOS:000685460800003 0
J Gong, QY; Chen, Y; Yu, XL; Xu, C; Guo, ZC; Xiao, Y; Ben Abdesslem, F; Wang, X; Hui, P Gong, Qingyuan; Chen, Yang; Yu, Xiaolong; Xu, Chao; Guo, Zhichun; Xiao, Yu; Ben Abdesslem, Fehmi; Wang, Xin; Hui, Pan Exploring the power of social hub services WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS English Article Online social networks; Social hub services; Measurement; Social influence; Machine learning Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user's social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter. [Gong, Qingyuan; Chen, Yang; Yu, Xiaolong; Xu, Chao; Guo, Zhichun; Wang, Xin] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Gong, Qingyuan; Chen, Yang; Yu, Xiaolong; Xu, Chao; Guo, Zhichun; Wang, Xin] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Xiao, Yu] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Ben Abdesslem, Fehmi] RISE SICS, Kista, Sweden; [Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, CSE Dept, Hong Kong, Peoples R China Fudan University; Fudan University; Aalto University; RISE Research Institutes of Sweden; University of Helsinki; Hong Kong University of Science & Technology Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Chen, Y (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China. chenyang@fudan.edu.cn; yu.xiao@aalto.fi; fehmi.ben.abdesslem@ri.se; xinw@fudan.edu.cn; panhui@cs.helsinki.fi Hui, Pan/AAK-6660-2020 Hui, Pan/0000-0001-6026-1083; Xiao, Yu/0000-0002-4517-3779 National Natural Science Foundation of China [61602122, 71731004]; Natural Science Foundation of Shanghai [16ZR1402200]; Shanghai Pujiang Program [16PJ1400700] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Pujiang Program(Shanghai Pujiang Program) This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700). 43 2 2 0 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1386-145X 1573-1413 WORLD WIDE WEB World Wide Web NOV 2019.0 22 6 SI 2825 2852 10.1007/s11280-018-0633-7 0.0 28 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science JY3MK 2023-03-23 WOS:000504322400025 0
J Li, XT; Li, C; Rahaman, MM; Sun, HZ; Li, XQ; Wu, J; Yao, YD; Grzegorzek, M Li, Xintong; Li, Chen; Rahaman, Md Mamunur; Sun, Hongzan; Li, Xiaoqi; Wu, Jian; Yao, Yudong; Grzegorzek, Marcin A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches ARTIFICIAL INTELLIGENCE REVIEW English Review Whole-slide image analysis; Computer-aided diagnosis; Feature extraction; Image segmentation; Image classification; Object detection DIAGNOSTICALLY RELEVANT REGIONS; CONVOLUTIONAL NEURAL-NETWORKS; RANDOM FORESTS; DIGITAL PATHOLOGY; AUTOMATED-ANALYSIS; NEAREST-NEIGHBOR; TEXTURE ANALYSIS; SVM CLASSIFIER; BREAST-CANCER; CELL-NUCLEI With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted. [Li, Xintong; Li, Chen; Rahaman, Md Mamunur; Li, Xiaoqi; Wu, Jian] Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang 110169, Peoples R China; [Sun, Hongzan] China Med Univ, Shengjing Hosp, Shenyang 110122, Peoples R China; [Yao, Yudong] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA; [Grzegorzek, Marcin] Univ Lubeck, Inst Med Informat, Lubeck, Germany Northeastern University - China; China Medical University; Stevens Institute of Technology; University of Lubeck Li, C (corresponding author), Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang 110169, Peoples R China. lichen201096@hotmail.com Rahaman, Md Mamunur/AAS-5300-2021 Rahaman, Md Mamunur/0000-0003-2268-2092; Li, Chen/0000-0003-1545-8885 National Natural Science Foundation of China [61806047] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by National Natural Science Foundation of China (No. 61806047). We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. We also thank B.E. Xiaoming Zhou, B.E. Jinghua Zhang and B.E. Jining Li, for their Important technical supports. 267 21 22 26 79 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. AUG 2022.0 55 6 4809 4878 10.1007/s10462-021-10121-0 0.0 JAN 2022 70 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2W8KB Green Submitted 2023-03-23 WOS:000750286100001 0
J Liu, J; Li, MC; Chen, YF; Islam, SMN; Crespi, N Liu, Jia; Li, Mingchu; Chen, Yuanfang; Islam, Sardar M. N.; Crespi, Noel Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks SENSORS English Article artificial intelligence algorithm; truth inference; channel estimation; message passing MULTIUSER MASSIVE MIMO With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs. [Liu, Jia; Li, Mingchu] Dalian Univ Technol, Sch Software Technol, Dalian 116620, Peoples R China; [Liu, Jia; Li, Mingchu] Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software, Dalian 116620, Peoples R China; [Chen, Yuanfang] Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou 310018, Peoples R China; [Islam, Sardar M. N.] Victoria Univ, Inst Sustainable Ind & Liveable Cities, Melbourne, Vic 14428, Australia; [Crespi, Noel] Telecom SudParis, Inst Polytech Paris, Inst Mines Telecom, F-91011 Evry, France Dalian University of Technology; Dalian University of Technology; Hangzhou Dianzi University; Victoria University; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Chen, YF (corresponding author), Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou 310018, Peoples R China. Jialiudlut@gmail.com; mingchul@dlut.edu.cn; chenyuanfang@hdu.edu.cn; Sardar.Islam@vu.edu.au; noel.crespi@mines-telecom.fr Crespi, Noel/ABE-7052-2020 Crespi, Noel/0000-0003-2962-192X; Islam, Sardar/0000-0001-9451-7390; Chen, Yuanfang/0000-0002-1557-087X; ISLAM, MAUNG NOOR/0000-0001-9975-4889; Liu, Jia/0000-0003-4284-2313 National Natural Science Foundation of China [61802097]; Project of Qianjiang Talent [QJD1802020]; Key Research & Development Plan of Zhejiang Province [2019C01012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of Qianjiang Talent; Key Research & Development Plan of Zhejiang Province This work was supported by the National Natural Science Foundation of China (Grant No. 61802097), the Project of Qianjiang Talent (Grant No. QJD1802020), and the Key Research & Development Plan of Zhejiang Province (Grant No. 2019C01012). 35 0 0 2 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors OCT 2020.0 20 20 5939 10.3390/s20205939 0.0 19 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation OL8LL 33096632.0 Green Submitted, gold, Green Accepted 2023-03-23 WOS:000585584000001 0
C Zhao, DB; Bu, L; Alippi, C; Wei, QL Zhao, Dongbin; Bu, Li; Alippi, Cesare; Wei, Qinglai A Kolmogorov-Smirnov Test to Detect Changes in Stationarity in Big Data IFAC PAPERSONLINE English Proceedings Paper 20th World Congress of the International-Federation-of-Automatic-Control (IFAC) JUL 09-14, 2017 Toulouse, FRANCE Int Federat Automat Control,Continental Automot,Occitanie Reg,Toulouse Metropole,CNES,Univ Toulouse III,Paul Sabatier,Inria,CNRS,OPTITRACK,MDPI,ISAE Supaero,iCODE,EECI,Int Journal Automat & Comp,IEEE CAA Journal Automatica Sinica,Moveo change detection test; LSDD; KS test The paper proposes an effective change detection test for online monitoring data streams by inspecting the least squares density difference (LSDD) features extracted from two non-overlapped windows. The first window contains samples associated with the pre-change probability distribution function (pdf) and the second one with the post-change one (that differs from the former if a change in stationarity occurs). This method can detect changes by also controlling the false positive rate. However, since the window sizes is fixed after the test has been configured (it has to be small to reduce the execution time), the method may fail to detect changes with small magnitude which need more samples to reach the requested level of confidence. In this paper, we extend our work to the Big Data framework by applying the Kolmogorov-Smirnov test (KS test) to infer changes. Experiments show that the proposed method is effective in detecting changes. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [Zhao, Dongbin; Bu, Li; Wei, Qinglai] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Zhao, Dongbin; Bu, Li; Wei, Qinglai] Univ Chinese Acad Sci, Beijing 100190, Peoples R China; [Alippi, Cesare] Politecn Milan, Milan, Italy; [Alippi, Cesare] Univ Svizzera Italiana, Lugano, Switzerland Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Polytechnic University of Milan; Universita della Svizzera Italiana Zhao, DB (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.;Zhao, DB (corresponding author), Univ Chinese Acad Sci, Beijing 100190, Peoples R China. dongbin.zhao@ia.ac.cn; bulipolly@gmail.com; cesare.alippi@polimi.it; qinglai.wei@ia.ac.cn National Natural Science Foundation of China [61573353, 61533017, 61603382] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grants No. 61573353, No.61533017, and No. 61603382. 18 4 4 1 6 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2017.0 50 1 14260 14265 10.1016/j.ifacol.2017.08.1821 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems FU6LX gold 2023-03-23 WOS:000423965200365 0
J Ma, ZJ; Mei, G; Cuomo, S; Piccialli, F Ma, Zhengjing; Mei, Gang; Cuomo, Salvatore; Piccialli, Francesco Heterogeneous data fusion considering spatial correlations using graph convolutional networks and its application in air quality prediction JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES English Article Heterogeneous data sources; Data fusion; Radial basis functions (RBF); Deep learning; Graph convolutional networks (GCN) PM2.5 Models that predict the future state of certain observations are commonly developed by utilizing heterogeneous data. Most traditional prediction models tend to ignore inconsistencies and imperfections in heterogeneous data, and they are also limited in their ability to consider spatial correlations among monitoring points, as well as predict for the entire study area simultaneously. As a solution, this paper proposes a deep learning method to fuse heterogeneous data collected from multiple monitoring points by exploiting graph convolutional networks (GCNs), thus perform prediction for certain observations. The effectiveness of this approach was evaluated by applying it to a prediction scenario for air quality. As the fundamental idea behind the proposed method, (1) the collected heterogeneous data is fused according to the coordinates of the monitoring points considering its spatial correlations, and (2) the prediction considers global information as opposed to local information. According to an air quality prediction scenario, (1) the fused data obtained by the RBF-based fusion method are reasonable and reliable; (2) the fusion significantly increases predictive model performance; and (3) the STGCN model enhanced by the fusion achieves the highest performance. This approach can be similarly applied in scenarios involving continuous heterogeneous data collected from scattered multiple monitoring points in the study area. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Ma, Zhengjing; Mei, Gang] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China; [Cuomo, Salvatore; Piccialli, Francesco] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy China University of Geosciences; University of Naples Federico II Mei, G (corresponding author), China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China.;Piccialli, F (corresponding author), Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy. gang.mei@cugb.edu.cn; francesco.piccialli@unina.it Mei, Gang/C-9124-2016; Cuomo, Salvatore/Q-1365-2016 Mei, Gang/0000-0003-0026-5423; Cuomo, Salvatore/0000-0003-4128-2588; ma, mazhengjing/0000-0003-0044-945X 41 2 2 13 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1319-1578 2213-1248 J KING SAUD UNIV-COM J. King Saud Univ.-Comput. Inf. Sci. JUN 2022.0 34 6 B 3433 3447 10.1016/j.jksuci.2022.04.003 0.0 15 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2U2YD gold, Green Submitted 2023-03-23 WOS:000823025000007 0
C Jurado, PG; Liang, XY; Chatterjee, S IEEE Jurado, Pol Grau; Liang, Xinyue; Chatterjee, Saikat DETERMINISTIC TRANSFORM BASED WEIGHT MATRICES FOR NEURAL NETWORKS 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 22-27, 2022 Singapore, SINGAPORE Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Randomized neural networks; multilayer neural network; deterministic transforms; weight matrices We propose to use deterministic transforms as weight matrices for several feedforward neural networks. The use of deterministic transforms helps to reduce the computational complexity in two ways: (1) matrix-vector product complexity in forward pass, helping real time complexity, and (2) fully avoiding backpropagation in the training stage. For each layer of a feedforward network, we propose two unsupervised methods to choose the most appropriate deterministic transform from a set of transforms (a bag of well-known transforms). Experimental results show that the use of deterministic transforms is as good as traditional random matrices in the sense of providing similar classification performance. [Jurado, Pol Grau; Chatterjee, Saikat] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, Stockholm, Sweden; [Liang, Xinyue] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China Royal Institute of Technology; Ocean University of China Jurado, PG (corresponding author), KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, Stockholm, Sweden. polgrauj@gmail.com; xinyuel92@163.com; sach@kth.se 23 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-6654-0540-9 INT CONF ACOUST SPEE 2022.0 4528 4532 10.1109/ICASSP43922.2022.9747256 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering BT9UB 2023-03-23 WOS:000864187904163 0
J Jiang, MF; Yan, B; Li, Y; Zhang, JC; Li, TQ; Ke, W Jiang, Mingfeng; Yan, Bin; Li, Yang; Zhang, Jucheng; Li, Tieqiang; Ke, Wei Image Classification of Alzheimer's Disease Based on External-Attention Mechanism and Fully Convolutional Network BRAIN SCIENCES English Article Alzheimer's disease; fully convolutional network; external-attention mechanism; double normalization; image classification MULTICLASS DIAGNOSIS Automatic and accurate classification of Alzheimer's disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer's disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer's disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods. [Jiang, Mingfeng; Yan, Bin; Li, Yang] Zhejiang Sci Tech Univ, Sch Informat Sci & Technol, Hangzhou 310018, Peoples R China; [Zhang, Jucheng] Zhejiang Univ, Affiliated Hosp 2, Sch Med, Dept Clin Engn, Hangzhou 310019, Peoples R China; [Li, Tieqiang] Karolinska Inst, Dept Clin Sci Intervent & Technol, S-17177 Stockholm, Sweden; [Ke, Wei] Macao Polytech Inst, Sch Appl Sci, Macau 999078, Peoples R China Zhejiang Sci-Tech University; Zhejiang University; Karolinska Institutet; Macao Polytechnic University Li, Y (corresponding author), Zhejiang Sci Tech Univ, Sch Informat Sci & Technol, Hangzhou 310018, Peoples R China. m.jiang@zstu.edu.cn; yan0106air@163.com; dr.yangli@outlook.com; jucheng@zju.edu.cn; tie-qiang.li@ki.se; wke@ipm.edu.mo Zhang, Jucheng/GWU-5212-2022 Li, Yang/0000-0001-9678-1320; Li, Tie-Qiang/0000-0002-4866-5904; Ke, Wei/0000-0003-0952-0961; Zhang, Jucheng/0000-0001-5398-5091 National Natural Science Foundation of China [61672466, 62011530130, 62101497]; Joint Fund of Zhejiang Provincial Natural Science Foundation [LSZ19F010001]; Key Research and Development Program of Zhejiang Province [2020C03060]; 521 Talents project of Zhejiang Sci-Tech University; Science Foundation of Zhejiang Sci-Tech University [19032111-Y] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Joint Fund of Zhejiang Provincial Natural Science Foundation; Key Research and Development Program of Zhejiang Province; 521 Talents project of Zhejiang Sci-Tech University(Zhejiang Sci-Tech University); Science Foundation of Zhejiang Sci-Tech University(Zhejiang Sci-Tech University) This research was funded by the National Natural Science Foundation of China (61672466, 62011530130, 62101497), Joint Fund of Zhejiang Provincial Natural Science Foundation (LSZ19F010001), the Key Research and Development Program of Zhejiang Province (2020C03060), the 521 Talents project of Zhejiang Sci-Tech University, and this work is also supported by Science Foundation of Zhejiang Sci-Tech University under Grant No. 19032111-Y. 42 2 2 18 32 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3425 BRAIN SCI Brain Sci. MAR 2022.0 12 3 319 10.3390/brainsci12030319 0.0 17 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 0D1ON 35326275.0 Green Accepted, gold 2023-03-23 WOS:000775772100001 0
J Yang, L; Jiang, DM; Sahli, C Yang, Le; Jiang, Dongmei; Sahli, Hichem Feature Augmenting Networks for Improving Depression Severity Estimation From Speech Signals IEEE ACCESS English Article Depression estimation; audio features; data augmentation; deep convolutional generative adversarial network; spatial domain; frequency domain; deep learning aspect ENHANCEMENT; DOMAIN Depression disorder has become one of the major psychological diseases endangering human health. Researcher in the affective computing community is supporting the development of reliable depression severity estimation system, from multiple modalities (speech, face, text), to assist doctors in their diagnosis. However, the limited amount of annotated data has become the main bottleneck restricting the study on depression screening, especially when deep learning models are used. To alleviate this issue, in this work we propose to use Deep Convolutional Generative Adversarial Network (DCGAN) for features augmentation to improve depression severity estimation from speech. To the best of our knowledge, this approach is the first attempt to apply the Generative Adversarial Network for depression severity estimation from speech. Besides, to measure the quality of the augmented features, we propose three different measurement criteria, characterizing the spatial, frequency and representation learning of the augmented features. Finally, the augmented features are used to train depression estimation models. Experiments are carried out on speech signals from the Audio Visual Emotion Challenge (AVEC2016) depression dataset, and the relationship between the model performance and data size is explored. Our experimental results show that: 1) The combination of the three proposed evaluation criteria can effectively and comprehensively evaluate the quality of the augmented features. 2) When increasing the size of the augmented data, the performance of depression severity estimation gradually improves and the model converges to a certain stable state. 3) The proposed DCGAN based data augmentation approach effectively improves the performance of depression severity estimation, with the root mean square error (RMSE) reduced to 5.520 and mean absolute error (MAE) reduced to 4.634, which is better than most of the state of the art results on AVEC 2016. [Yang, Le; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint Audio Visual Signal Proc AVSP Res L, Shaanxi Key Lab Speech & Image Informat Proc,Natl, Xian 710072, Peoples R China; [Yang, Le; Jiang, Dongmei] Peng Cheng Lab, Shenzhen 518055, Peoples R China; [Sahli, Hichem] VUB, VUB NPU Joint Audio Visual Signal Proc AVSP Res L, Dept Elect & Informat ETRO, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Peng Cheng Laboratory; Vrije Universiteit Brussel; IMEC Jiang, DM (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint Audio Visual Signal Proc AVSP Res L, Shaanxi Key Lab Speech & Image Informat Proc,Natl, Xian 710072, Peoples R China.;Jiang, DM (corresponding author), Peng Cheng Lab, Shenzhen 518055, Peoples R China. jiangdm@nwpu.edu.cn Yang, Le/HHZ-4011-2022 Jiang, Dongmei/0000-0002-6238-8499; Sahli, Hichem/0000-0002-1774-2970 Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14]; VUB Interdisciplinary Research Program through the EMO-App Project Shaanxi Provincial International Science and Technology Collaboration Project; VUB Interdisciplinary Research Program through the EMO-App Project This work was supported in part by the Shaanxi Provincial International Science and Technology Collaboration Project under Grant 2017KW-ZD-14, and in part by the VUB Interdisciplinary Research Program through the EMO-App Project. 59 10 11 1 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 24033 24045 10.1109/ACCESS.2020.2970496 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LC5XR gold 2023-03-23 WOS:000525405500006 0
J Li, JH; Wang, JZ; Wang, SK; Qi, W; Zhang, LB; Hu, YB; Su, H Li, Jiehao; Wang, Junzheng; Wang, Shoukun; Qi, Wen; Zhang, Longbin; Hu, Yingbai; Su, Hang Neural Approximation-based Model Predictive Tracking Control of Non-holonomic Wheel-legged Robots INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS English Article Model predictive control; neural approximation; nonholonomic system; tracking control; wheel-legged robot NETWORK; DESIGN This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot's dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability. [Li, Jiehao; Wang, Junzheng; Wang, Shoukun] Beijing Inst Technol, State Key Lab Intelligent Control & Decis Complex, Beijing, Peoples R China; [Qi, Wen; Su, Hang] Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy; [Zhang, Longbin] KTH Royal Inst Technol, BioMEx Ctr, SE-10044 Stockholm, Sweden; [Zhang, Longbin] KTH Royal Inst Technol, KTH Mech, SE-10044 Stockholm, Sweden; [Hu, Yingbai] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany Beijing Institute of Technology; Polytechnic University of Milan; Royal Institute of Technology; Royal Institute of Technology; Technical University of Munich Su, H (corresponding author), Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy. jiehao.li@bit.edu.cn; wangjz@bit.edu.cn; bitwsk@bit.edu.cn; wen.qi@polimi.it; longbin@kth.se; yingbai.hu@tum.de; hang.su@polimi.it Li, Jiehao/AAH-9089-2021; qi, wen/AAE-5175-2022 Nation Natural Science Foundation of China [61773060]; China Scholarship Council [201906030066] Nation Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This study was supported by the Nation Natural Science Foundation of China under Grant 61773060, and China Scholarship Council under Grant 201906030066. 25 20 20 9 49 INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS SEOUL SUSEO HYUNDAI-VENTUREVILLE 723, BAMGOGAE-RO 1-GIL 10, GANGNAM-GU, SEOUL, SOUTH KOREA 1598-6446 2005-4092 INT J CONTROL AUTOM Int. J. Control Autom. Syst. JAN 2021.0 19 1 372 381 10.1007/s12555-019-0927-2 0.0 SEP 2020 10 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems PR7IZ 2023-03-23 WOS:000569970900021 0
J Huang, ZK; Argyroudis, SA; Pitilakis, K; Zhang, DM; Tsinidis, G Huang, Zhongkai; Argyroudis, Sotirios A.; Pitilakis, Kyriazis; Zhang, Dongmei; Tsinidis, Grigorios Fragility assessment of tunnels in soft soils using artificial neural networks UNDERGROUND SPACE English Article Circular tunnels; Fragility curves; Artificial neural network; Numerical study; Probabilistic seismic demand model SEISMIC VULNERABILITY; DAMAGE; PARAMETERS; DESIGN Recent earthquakes have shown that tunnels are prone to damage, posing a major threat to safety and having major cascading and socioeconomic impacts. Therefore, reliable models are needed for the seismic fragility assessment of underground structures and the quantitative evaluation of expected losses. Based on previous researches, this paper presented a probabilistic framework based on an artificial neural network (ANN), aiming at the development of fragility curves for circular tunnels in soft soils. Initially, a twodimensional incremental dynamic analysis of the nonlinear soil-tunnel system was performed to estimate the response of the tunnel under ground shaking. The effects of soil-structure-interaction and the ground motion characteristics on the seismic response and the fragility of tunnels were adequately considered within the proposed framework. An ANN was employed to develop a probabilistic seismic demand model, and its results were compared with the traditional linear regression models. Fragility curves were generated for various damage states, accounting for the associated uncertainties. The results indicate that the proposed ANN-based probabilistic framework can results in reliable fragility models, having similar capabilities as the traditional approaches, and a lower computational cost is required. The proposed fragility models can be adopted for the risk analysis of typical circular tunnel in soft soils subjected to seismic loading, and they are expected to facilitate decision-making and risk management toward more resilient transport infrastructure. [Huang, Zhongkai; Zhang, Dongmei] Tongji Univ, Dept Geotech Engn, Key Lab Geotech & Underground Engn, Minist Educ, Shanghai, Peoples R China; [Argyroudis, Sotirios A.] Brunel Univ London, Dept Civil & Environm Engn, Uxbridge UB8 3PH, Middx, England; [Pitilakis, Kyriazis; Tsinidis, Grigorios] Aristotle Univ Thessaloniki, Dept Civil Engn, Thessaloniki, Greece Tongji University; Brunel University; Aristotle University of Thessaloniki Argyroudis, SA (corresponding author), Brunel Univ London, Dept Civil & Environm Engn, Uxbridge UB8 3PH, Middx, England. sotirios.argyroudis@brunel.ac.uk Tsinidis, Grigorios/GRR-7136-2022 Tsinidis, Grigorios/0000-0001-6045-1039; Argyroudis, Sotirios/0000-0002-8131-3038 National Natural Science Foundation of China [52108381, 52090082, 41772295, 51978517]; Innovation Program of Shanghai Municipal Education Commission [2019-01-07-00-07-456 E00051]; key innovation team program of innovation talents promotion plan by MOST of China [2016RA4059] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Program of Shanghai Municipal Education Commission(Innovation Program of Shanghai Municipal Education Commission); key innovation team program of innovation talents promotion plan by MOST of China This work was financially supported by National Natu-ral Science Foundation of China (Grant Nos. 52108381, 52090082, 41772295, 51978517) , Innovation Program of Shanghai Municipal Education Commission (Grant No. 2019-01-07-00-07-456 E00051) and key innovation team program of innovation talents promotion plan by MOST of China (No. 2016RA4059) . 60 11 11 22 45 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGCHENG DISTRICT 100717, PEOPLES R CHINA 2096-2754 2467-9674 UNDERGR SPACE Undergr. Space APR 2022.0 7 2 242 253 10.1016/j.undsp.2021.07.007 0.0 MAR 2022 12 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering 0F5NB gold, Green Published 2023-03-23 WOS:000777405100007 0
J Ni, B; Liu, ZY; Cai, XT; Nappi, M; Wan, SH Ni, Bo; Liu, Zhiyuan; Cai, Xiantao; Nappi, Michele; Wan, Shaohua Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model NEURAL COMPUTING & APPLICATIONS English Article; Early Access Uterine fibroids; Ultrasound image; Deformable contour; Deep Siamese network; Shape similarity ACTIVE CONTOURS; THRESHOLDING ALGORITHM; CONVOLUTIONAL NETWORK; SHAPE MODEL; TRACKING Deformable contours are widely applied in medical image segmentation, which are usually derived from appearance cues in medical images. However, the performance of deformed contour is suppressed in ultrasonic image segmentation by the weak, misleading boundaries and the complex shapes of lesion regions. In this paper, a novel deformable contour model is proposed for segmenting ultrasound image sequences, which aims to utilize the powerful ability of deep learning network in learning of image features to help the deformable contour model resist weaknessses of ultrasound images. The deep learning network is designed as a densely connected siamese architecture. It trains a contrastive loss that serves as a boundary searching metric of a deformable contour to segment ultrasound image sequences. In this network, the densely residual blocks and the attention focused blocks are designed to make the network efficiently propagate features and focus on the lesion region, and the feature memory module stores and generates the prior features to aid the evolution of a deformable contour. Moreover, for resisting the impact of misleading or weak boundary, the shape similarity of lesion regions is used to as a shape prior and integrated into the framework of deformable contour to constrain the change of contours. The experimental results for the clinical ultrasound image sequences demonstrate that compared to the state-of-the-art methods, the proposed method can provide more accurate results in HIFU ultrasound images. [Ni, Bo; Cai, Xiantao] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China; [Ni, Bo; Liu, Zhiyuan] Hubei Polytech Univ, Sch Comp Sci, Huangshi 435003, Hubei, Peoples R China; [Nappi, Michele] Univ Salerno, Fisciano, Italy; [Wan, Shaohua] Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China Wuhan University; Hubei Polytechnic University; University of Salerno; University of Electronic Science & Technology of China Wan, SH (corresponding author), Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China. nb@hbpu.edu.cn; 68568224@qq.com; Caixiantao@whu.edu.cn; mnappi@unisa.it; shwanhust@zuel.edu.cn Wan, Shaohua/B-9243-2014 Wan, Shaohua/0000-0001-7013-9081 National Natural Science Foundation of China [62172438]; fundamental research funds for the central universities [31412111303, 31512111310]; State Key Laboratory for Novel Software Technology, Nanjing University [KFKT2019B17] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); fundamental research funds for the central universities(Fundamental Research Funds for the Central Universities); State Key Laboratory for Novel Software Technology, Nanjing University(Nanjing University) This work was supported in part by the National Natural Science Foundation of China (No.62172438), the fundamental research funds for the central universities (31412111303, 31512111310) and by the open project from the State Key Laboratory for Novel Software Technology, Nanjing University, under Grant No.KFKT2019B17. 72 7 7 3 13 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. 10.1007/s00521-022-07054-2 0.0 MAR 2022 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZS7RY 2023-03-23 WOS:000768660400005 0
J Li, L; Xia, ZQ; Jiang, XY; Roli, F; Feng, XY Li, Lei; Xia, Zhaoqiang; Jiang, Xiaoyue; Roli, Fabio; Feng, Xiaoyi CompactNet: learning a compact space for face presentation attack detection NEUROCOMPUTING English Article Face PAD; Biometrics; Compact space; Deep learning SPOOFING DETECTION; LIVENESS DETECTION; IMAGE QUALITY Face presentation attack detection (PAD) has become a clear and present threat for face recognition systems and many countermeasures have been proposed to mitigate it. In these countermeasures, some of them use the features directly extracted from well-known color spaces (e.g., RGB, HSV and YCbCr) to distinguish the fake face images from the genuine (live) ones. However, the existing color spaces have been originally designed for displaying the visual content of images or videos with high fidelity and are not well suited for directly discriminating the live and fake face images. Therefore, in this paper, we propose a deep-learning system, called CompactNet, for learning a compact space tailored for face PAD. More specifically, the proposed CompactNet does not directly extract the features in existing color spaces, but inputs the color face image into a layer-by-layer progressive space generator. Then, under the optimization of the points-to-center triplet loss function, the generator learns a compact space with small intra-class distance, large inter-class distance and a safe interval between different classes. Finally, the feature of the image in compact space is extracted by a pre-trained feature extractor and used for image classification. Reported experiments on three publicly available face PAD databases, namely, the Replay-Attack, the OULU-NPU and the HKBU-MARs V1, show that CompactNet separates very well the two classes of fake and genuine faces and significantly outperforms the state-of-the-art methods for PAD. (C) 2020 Elsevier B.V. All rights reserved. [Li, Lei] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan, Peoples R China; [Xia, Zhaoqiang; Jiang, Xiaoyue; Roli, Fabio; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China; [Roli, Fabio] Univ Cagliari, Cagliari, Italy Shandong University of Finance & Economics; Northwestern Polytechnical University; University of Cagliari Xia, ZQ; Feng, XY (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China. zxia@nwpu.edu.cn; fengxiao@nwpu.edu.cn Bueno, Regis Cortez/AAG-3852-2020 Bueno, Regis Cortez/0000-0002-2923-4930; ROLI, FABIO/0000-0003-4103-9190; Li, Lei/0000-0003-4498-6126 National Natural Science Foundation of China [61702419]; Natural Science Basic Research Plan of Shaanxi Province of China [2018JQ6090] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan of Shaanxi Province of China This work is partly supported by the National Natural Science Foundation of China (No. 61702419), and the Natural Science Basic Research Plan of Shaanxi Province of China (No. 2018JQ6090). 67 7 8 2 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 7 2020.0 409 191 207 10.1016/j.neucom.2020.05.017 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science NE4BU 2023-03-23 WOS:000562543100016 0
J Li, G; Wang, F; Quan, SS Li Gang; Wang Fang; Quan Sishi The impact of COVID-19 on the protection of rural traditional village JOURNAL OF INTELLIGENT & FUZZY SYSTEMS English Article Big data; Huizhou traditional village cultural resources; cultural heritage; mining applications; COVID-19 BIG-DATA COVID-19's significant impact on economic and social life has rightfully garnered the attention of citizens and policymakers alike. In response to the pandemic, governments have issued strict guidelines and restrictions to shut down some cities and many rural villages in China. With no cure or vaccine on the horizon, governments are working to mitigate the damage of the lockdowns on rural cultural village. Over the past two decades, rural village has been negatively impacted by terrorism, lack of funding and loss of population. COVID-19 has had similar effect, but in an incredibly short period of time. During the control period of COVID-19, traditional data are widely used in village protection and renewal. Collect and sort out the original data of Huizhou culture to prepare for the subsequent calculation. After the data is ready, the data is processed as the basis of mining its potential application value. In this paper, the key words of Huizhou cultural resources are summarized. The data analysis platform is established. This paper analyzes people's preference for Huizhou cultural resources. To better realize the more effective and far-reaching development and exploitation of Huizhou cultural resources. [Li Gang] Wuhan Donghu Univ, Wuhan 430212, Hubei, Peoples R China; [Wang Fang] Wuchang Univ Technol, Wuhan, Hubei, Peoples R China; [Quan Sishi] Siemens Transformer Wuhan Co Ltd, Munich, Germany Wuhan Donghu University; Wuchang University of Technology Li, G (corresponding author), Wuhan Donghu Univ, Wuhan 430212, Hubei, Peoples R China. lg325@tom.com 13 1 1 2 19 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1064-1246 1875-8967 J INTELL FUZZY SYST J. Intell. Fuzzy Syst. 2020.0 39 6 8685 8693 10.3233/JIFS-189264 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PF7MC Bronze 2023-03-23 WOS:000599232800055 0
J Wang, X; Wang, H; Huang, TW; Kurths, J Wang, Xin; Wang, Hui; Huang, Tingwen; Kurths, Jurgen Neural-Network-Based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case IEEE TRANSACTIONS ON CYBERNETICS English Article Multi-agent systems; Artificial neural networks; Space vehicles; Disturbance observers; Backstepping; Target tracking; Adaptive control; Composite disturbance observer; event-triggered; state observer; tracking control STRICT-FEEDBACK SYSTEMS; CONSENSUS TRACKING; SYNCHRONIZATION; SPACECRAFT This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach. [Wang, Xin] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligen, Chongqing 400715, Peoples R China; [Wang, Hui] Chongqing Normal Univ, Sch Math Sci, Chongqing 401331, Peoples R China; [Huang, Tingwen] Texas A&M Univ Qatar, Dept Sci, Doha, Qatar; [Kurths, Jurgen] Potsdam Inst Climate Impact Res, Res Domain Transdisciplinary Concepts & Methods, D-14473 Potsdam, Germany; [Kurths, Jurgen] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany Southwest University - China; Chongqing Normal University; Qatar Foundation (QF); Texas A&M University Qatar; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin Huang, TW (corresponding author), Texas A&M Univ Qatar, Dept Sci, Doha, Qatar. xinwangswu@163.com; tingwen.huang@qatar.tamu.edu; juergen.kurths@pik-potsdam.de Huang, Tingwen/K-1367-2013 Huang, Tingwen/0000-0001-9610-846X Natural Science Foundation Project of Chongqing CSTC [cstc2019jcyj-msxm2319]; Qatar National Research Fund (a member of Qatar Foundation) through NPRP [NPRP 7-1482-1-278] Natural Science Foundation Project of Chongqing CSTC(Natural Science Foundation of ChongqingNatural Science Foundation Project of CQ CSTC); Qatar National Research Fund (a member of Qatar Foundation) through NPRP(Qatar National Research Fund (QNRF)) This work was supported in part by the Natural Science Foundation Project of Chongqing CSTC under Grant cstc2019jcyj-msxm2319, and in part by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant NPRP 7-1482-1-278. This article was recommended by Associate Editor W. Yu. 59 6 6 7 54 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JAN 2023.0 53 1 138 150 10.1109/TCYB.2021.3086495 0.0 JUL 2021 13 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 8Q1ND 34236976.0 2023-03-23 WOS:000732079700001 0
J Huang, J; Ding, W; Zhang, JR; Li, Z; Shu, T; Kuosmanen, P; Zhou, GQ; Zhou, C; Yu, G Huang, Jian; Ding, Wen; Zhang, Jiarun; Li, Zhao; Shu, Ting; Kuosmanen, Pekka; Zhou, Guanqun; Zhou, Chuan; Yu, Gang Variational deep embedding-based active learning for the diagnosis of pneumonia FRONTIERS IN NEUROROBOTICS English Article pneumonia diagnosis; active learning; variational autoencoders; brain-like computing; human-centric computing Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia. [Huang, Jian; Yu, Gang] Zhejiang Univ, Sch Med, Childrens Hosp, Dept Data & Informat, Hangzhou, Peoples R China; [Huang, Jian; Kuosmanen, Pekka; Yu, Gang] Sino Finland Joint AI Lab Child Hlth Zhejiang Prov, Hangzhou, Peoples R China; [Huang, Jian; Ding, Wen; Yu, Gang] Natl Clin Res Ctr Child Hlth, Hangzhou, Peoples R China; [Ding, Wen] Zhejiang Univ, Childrens Hosp, Sch Med, Dept Res & Educ, Hangzhou, Peoples R China; [Zhang, Jiarun] Univ Calif San Diego, Dept Comp Sci & Engn, San Diego, CA USA; [Li, Zhao; Yu, Gang] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China; [Shu, Ting] Natl Hlth Commiss, Natl Inst Hosp Adm, Beijing, Peoples R China; [Kuosmanen, Pekka] Avaintec Oy, Helsinki, Finland; [Zhou, Guanqun] JancsiTech, Hangzhou, Peoples R China; [Zhou, Chuan] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China Zhejiang University; Zhejiang University; University of California System; University of California San Diego; Zhejiang University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS Yu, G (corresponding author), Zhejiang Univ, Sch Med, Childrens Hosp, Dept Data & Informat, Hangzhou, Peoples R China.;Yu, G (corresponding author), Sino Finland Joint AI Lab Child Hlth Zhejiang Prov, Hangzhou, Peoples R China.;Yu, G (corresponding author), Natl Clin Res Ctr Child Hlth, Hangzhou, Peoples R China.;Yu, G (corresponding author), Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China.;Shu, T (corresponding author), Natl Hlth Commiss, Natl Inst Hosp Adm, Beijing, Peoples R China. nctingting@126.com; yugbme@zju.edu.cn National Key R&D Program of China; National Natural Science Foundation of China; [2019YFE0126200]; [62076218] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; Funding This work was partially supported by the National Key R&D Program of China (No. 2019YFE0126200) and the National Natural Science Foundation of China (No. 62076218). 25 0 0 1 1 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics NOV 25 2022.0 16 1059739 10.3389/fnbot.2022.1059739 0.0 7 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology 6U3UT 36506818.0 gold, Green Accepted 2023-03-23 WOS:000894294800001 0
J Chen, YF; Wang, Y; Ren, PJ; Wang, M; de Rijke, M Chen, Yifan; Wang, Yang; Ren, Pengjie; Wang, Meng; de Rijke, Maarten Bayesian feature interaction selection for factorization machines ARTIFICIAL INTELLIGENCE English Article Factorization machine; Bayesian variable selection; Feature interaction selection VARIABLE SELECTION; LINEAR-REGRESSION; MODEL Factorization machines are a generic supervised method for a wide range of tasks in the field of artificial intelligence, such as prediction, inference, etc., which can effectively model feature interactions. However, handling combinations of features is expensive due to the exponential growth of feature interactions with the order. In nature, not all feature interactions are equally useful for prediction. Recently, a large number of methods that perform feature interaction selection have attracted great attention because of their effectiveness at filtering out useless feature interactions. Current feature interaction selection methods suffered from the following limitations: (1) they assume that all users share the same feature interactions; and (2) they select pairwise feature interactions only. In this paper, we propose novel Bayesian variable selection methods, targeting feature interaction selection for factorization machines, which effectively reduce the number of interactions. We study personalized feature interaction selection to account for individual preferences, and further extend the model to investigate higher-order feature interaction selection on higher-order factorization machines. We provide empirical evidence for the advantages of the proposed Bayesian feature interaction selection methods using different prediction tasks. (C) 2021 Elsevier B.V. All rights reserved. [Chen, Yifan] Natl Univ Def Technol, Changsha, Peoples R China; [Wang, Yang; Wang, Meng] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei, Peoples R China; [Wang, Yang; Wang, Meng] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei, Peoples R China; [Ren, Pengjie; de Rijke, Maarten] Univ Amsterdam, Amsterdam, Netherlands; [de Rijke, Maarten] Ahold Delhaize, Zaandam, Netherlands National University of Defense Technology - China; Hefei University of Technology; Hefei University of Technology; University of Amsterdam Wang, Y (corresponding author), Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei, Peoples R China. yfchen@nudt.edu.cn; yangwang@hfut.edu.cn; p.ren@uva.nl; eric.mengwang@gmail.com; derijke@uva.nl de Rijke, Maarten/0000-0002-1086-0202 National Natural Science Foundation of China (NSFC) [61806035, 62172136, U1936217, 61725203, 61732008]; Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research This research was partially supported by the National Natural Science Foundation of China (NSFC) under grants 61806035, 62172136, U1936217, 61725203, 61732008, and by the Hybrid Intelligence Center, a 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, https://hybridintelligence-centre.nl. 82 8 8 13 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0004-3702 1872-7921 ARTIF INTELL Artif. Intell. JAN 2022.0 302 103589 10.1016/j.artint.2021.103589 0.0 SEP 2021 31 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UZ0BM 2023-03-23 WOS:000701878300004 0
C Jin, HL; Hou, L; Li, JZ; Dong, TS Assoc Computat Linguist Jin, Hailong; Hou, Lei; Li, Juanzi; Dong, Tiansi Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE English Proceedings Paper Conference on Empirical Methods in Natural Language Processing / 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) NOV 03-07, 2019 Hong Kong, HONG KONG Google,Facebook,Apple,ASAPP,Salesforce,Huawei,Baidu,Deepmind,Amazon,PolyAI,Naver,ByteDance,Megagon Labs,Zhuiyi,Verisk,MI This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods. [Jin, Hailong; Hou, Lei; Li, Juanzi] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Jin, Hailong; Hou, Lei; Li, Juanzi] Tsinghua Univ, KIRC, Inst Artificial Intelligence, Beijing, Peoples R China; [Jin, Hailong; Hou, Lei; Li, Juanzi] Beijing Natl Res Ctr Informat Sci & Technol, Beijing, Peoples R China; [Dong, Tiansi] Univ Bonn, B IT, Bonn, Germany Tsinghua University; Tsinghua University; University of Bonn Hou, L (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.;Hou, L (corresponding author), Tsinghua Univ, KIRC, Inst Artificial Intelligence, Beijing, Peoples R China.;Hou, L (corresponding author), Beijing Natl Res Ctr Informat Sci & Technol, Beijing, Peoples R China. jinh115@mails.tsinghua.edu.cn; houlei@tsinghua.edu.cn; lijuanzi@tsinghua.edu.cn; dongt@bit.uni-bonn.de Hou, Lei/0000-0002-8907-3526 National Key Research and Development Program of China [2017YFB1002101]; NSFC key projects [U1736204, 6153301]; Alibaba; THUNUS NExT Co-Lab National Key Research and Development Program of China; NSFC key projects; Alibaba; THUNUS NExT Co-Lab This work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key projects (U1736204, 6153301), a research fund by Alibaba, and THUNUS NExT Co-Lab. 25 13 14 1 1 ASSOC COMPUTATIONAL LINGUISTICS-ACL STROUDSBURG 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA 978-1-950737-90-1 2019.0 4969 4978 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BT8OE 2023-03-23 WOS:000854193305015 0
J Sun, C; Wu, CZ; Chu, DF; Lu, ZJ; Tan, J; Wang, JY Sun, Chuan; Wu, Chaozhong; Chu, Duanfeng; Lu, Zhenji; Tan, Jian; Wang, Jianyu A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE English Article Driving data; vehicle driving risk; data-driven; belief rule-base; ADAS EVIDENTIAL REASONING APPROACH; DRIVER ASSISTANCE SYSTEMS; BEHAVIOR; NETWORKS This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field. [Sun, Chuan; Tan, Jian; Wang, Jianyu] Huanggang Normal Univ, Coll Transportat, Huanggang 438000, Peoples R China; [Wu, Chaozhong; Chu, Duanfeng] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan 430063, Hubei, Peoples R China; [Lu, Zhenji] Delft Univ Technol, Fac Mech Maritime & Mat Engn, Mekelweg 2, NL-2628 CD Delft, Netherlands Huanggang Normal University; Wuhan University of Technology; Delft University of Technology Sun, C (corresponding author), Huanggang Normal Univ, Coll Transportat, Huanggang 438000, Peoples R China. sunchuan33@foxmail.com; wucz@whut.edu.cn; chudf@whut.edu.cn; z.lu@tudelft.nl; 2529011569@qq.com; 191253016@qq.com National Undergraduate Training Program for Innovation and Entrepreneurship [201710514011]; National Key Technology R&D Program of China [2014BAG01B03]; Hubei Provincial Natural Science Foundation of China [2018CFC863]; National Natural Science Foundation of China [51675390]; Truck Merging Support-a Step towards Autonomous Driving (HTSM Automotive) [12831] National Undergraduate Training Program for Innovation and Entrepreneurship; National Key Technology R&D Program of China(National Key Technology R&D Program); Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Truck Merging Support-a Step towards Autonomous Driving (HTSM Automotive) This study is sponsored by the National Undergraduate Training Program for Innovation and Entrepreneurship (201710514011); the National Key Technology R&D Program of China (2014BAG01B03); Hubei Provincial Natural Science Foundation of China (2018CFC863); the National Natural Science Foundation of China (51675390); and Truck Merging Support-a Step towards Autonomous Driving (HTSM Automotive No. 12831). The authors would like to thank the China Scholarship Council. 32 2 2 5 50 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-0014 1793-6381 INT J PATTERN RECOGN Int. J. Pattern Recognit. Artif. Intell. NOV 2018.0 32 11 1850037 10.1142/S0218001418500374 0.0 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science GO1GM Green Published 2023-03-23 WOS:000439697800004 0
J Tian, Z; Yu, ZW; Li, YF; Ke, Q; Liu, JG; Luo, HY; Tang, YD Tian, Zhan; Yu, Ziwei; Li, Yifan; Ke, Qian; Liu, Junguo; Luo, Hongyan; Tang, Yingdong Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China FRONTIERS IN ENVIRONMENTAL SCIENCE English Article rainfall-runoff pollution; rainfall characteristics; EMC; integrated learning methods; ANN WATER-QUALITY; COMPUTATIONAL INTELLIGENCE; RANDOM FOREST; MODEL Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25-49.9 mm/day) and two times lower under moderate rain (10-24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities. [Tian, Zhan; Yu, Ziwei; Li, Yifan; Liu, Junguo] Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China; [Tian, Zhan] Pengcheng Lab, Shenzhen, Peoples R China; [Ke, Qian] Delft Univ Technol, Delft, Netherlands; [Luo, Hongyan] Meteorol Bur Shenzhen Municipal, Shenzhen, Peoples R China; [Tang, Yingdong] PowerChina Huadong Engn Corp Ltd, Hangzhou, Peoples R China Southern University of Science & Technology; Delft University of Technology; POWERCHINA Huadong Engineering Corporation Limited Liu, JG (corresponding author), Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China. junguo.liu@gmail.com LI, yi/HKO-0480-2023; Li, Yifan/HGA-6843-2022 Li, Yifan/0000-0002-3249-1778; Ke, Qian/0000-0002-3111-994X 43 0 0 14 19 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-665X FRONT ENV SCI-SWITZ Front. Environ. Sci. JUN 22 2022.0 10 887446 10.3389/fenvs.2022.887446 0.0 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 2R7GI Green Published, gold 2023-03-23 WOS:000821276200001 0
J Li, Y; Van Oosterom, P; Ge, Y; Zhang, XX; Baart, F Li, Yong; Van Oosterom, Peter; Ge, Ying; Zhang, Xiaoxiang; Baart, Fedor A CNN-LSTM Method for the Morphology Evolution Prediction of Beach Mega-Nourishment IEEE ACCESS English Article Sea measurements; Morphology; Engines; Correlation; Feature extraction; Biological system modeling; Data models; Mega-nourishment; long short-term memory; convolutional neural network; nourishment morphology evolution prediction BIDIRECTIONAL LSTM; NEURAL-NETWORKS; IMPACTS; RECOGNITION; SEQUENCE; EROSION; PILOT Sand nourishment is widely adopted as an effective soft approach to provide long-term coastal safety, protect the ecology environment, and promote tourism and recreation. With the increase in frequency and expenses in beach nourishment worldwide, an adequate prediction of morphology evolution is greatly desired for coastline management. Based on detailed monitoring data of the mega-nourishment Sand Engine, this article integrates a convolutional neural network (CNN) and long short-term memory (LSTM) to predict the nourishment morphology evolution. The historical surveyed data are transformed into sequence grids, which are input into the CNN to obtain the spatial features of beach nourishment. The CNN is constructed by performing the convolutional and pooling operation on the historical data, which can extract actual spatial features and reduce network complexity. The output of the CNN is input to LSTM to learn the temporal relationship to predict future nourishment terrain using past time-series features. Finally, the LSTM output is decoded by the fully connected layer to obtain the prediction result. The complex spatiotemporal correlations among the input data are identified through effective training of the proposed model. The major contribution of this article is to propose a data-driven model that combines CNN and LSTM for the morphology evolution prediction of beach nourishment, and validate the effectiveness of the proposed model by comparing with the performances of other popular methods in predicting the nourishment changes. [Li, Yong; Ge, Ying] Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Peoples R China; [Van Oosterom, Peter] Delft Univ Technol TU Delft, GIS Technol Sect, Fac Architecture & Built Environm, NL-2628 BL Delft, Netherlands; [Zhang, Xiaoxiang] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Baart, Fedor] Deltares, Dept Software Data & Innovat, NL-2629 HV Delft, Netherlands Hohai University; Hohai University; Deltares Li, Y (corresponding author), Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Peoples R China.;Zhang, XX (corresponding author), Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China. liyong@hhu.edu.cn; xiaoxiang@hhu.edu.cn National Key Research and Development Program by Ministry of Science and Technology of China [2016YFA0601504]; National Natural Science Foundation of China [41977394]; Natural Science Foundation of Jiangsu Province of China [BK20161504]; Fundamental Research Funds for the Central Universities [2018B18514] National Key Research and Development Program by Ministry of Science and Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province of China(Natural Science Foundation of Jiangsu Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development Program approved by Ministry of Science and Technology of China under Grant 2016YFA0601504, in part by the National Natural Science Foundation of China under Grant 41977394, in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20161504, and in part by the Fundamental Research Funds for the Central Universities under Grant 2018B18514. 40 0 0 3 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 184512 184523 10.1109/ACCESS.2020.3030119 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications OI8WC gold, Green Published 2023-03-23 WOS:000583550700001 0
J Khan, JA; Yasin, A; Fatima, R; Vasan, D; Khan, AA; Khan, AW Khan, Javed Ali; Yasin, Affan; Fatima, Rubia; Vasan, Danish; Khan, Arif Ali; Khan, Abdul Wahid Valuating requirements arguments in the online user's forum for requirements decision-making: The CrowdRE-VArg framework SOFTWARE-PRACTICE & EXPERIENCE English Article argumentation; machine learning; natural language processing; new features; Reddit forum; requirements SOFTWARE; ACCEPTABILITY; SYSTEMS User forums enable a large population of crowd-users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end-user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user-generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd-based requirements engineering by valuation argumentation (CrowdRE-VArg) approach that analyzes the end-users discussion in the Reddit forum and identifies conflict-free new features, design alternatives, or issues, and reach a rationale-based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd-users on the run by finding a settlement that satisfies the involved crowd-users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE-VArg approach is illustrated through a sample crowd-users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE-VArg approach. The results demonstrate that the proposed CrowdRE-VArg approach works as a proof-of-concept and automatically identifies prioritized requirements-related information for software engineers. [Khan, Javed Ali] Univ Sci & Technol, Dept Software Engn, Bannu, Pakistan; [Yasin, Affan; Fatima, Rubia; Vasan, Danish] Tsinghua Univ, Sch Software, Beijing, Peoples R China; [Khan, Arif Ali] Univ Oulu, Empir Software Engn Res Unit M3S, Oulu, Finland; [Khan, Abdul Wahid] Univ Sci & Technol, Dept Comp Sci, Bannu, Pakistan University of Science & Technology Bannu; Tsinghua University; University of Oulu; University of Science & Technology Bannu Khan, JA (corresponding author), Univ Sci & Technol, Dept Software Engn, Bannu, Pakistan. engr_javed501@yahoo.com khan, Arif/HMV-3165-2023; Khan, Javed/HJH-7672-2023; Yasin, Affan/GON-8035-2022 Yasin, Affan/0000-0002-0166-2239 77 0 0 16 17 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0038-0644 1097-024X SOFTWARE PRACT EXPER Softw.-Pract. Exp. DEC 2022.0 52 12 2537 2573 10.1002/spe.3137 0.0 AUG 2022 37 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6A1NL 2023-03-23 WOS:000840376700001 0
J Rana, P; Bhardwaj, A; Makkar, V; Pop, I; Gupta, G Rana, Puneet; Bhardwaj, Anuj; Makkar, Vinita; Pop, Ioan; Gupta, Gaurav Critical points and stability analysis in MHD radiative non-Newtonian nanoliquid transport phenomena with artificial neural network prediction MATHEMATICAL METHODS IN THE APPLIED SCIENCES English Article; Early Access artificial neural network; heat source; sink; non-Newtonian nanoliquid; numerical solution; radiation; stability analysis CONVECTED MAXWELL FLUID; LIE GROUP-ANALYSIS; HEAT-TRANSFER; SLIP-FLOW; THERMAL-RADIATION; STRETCHING SHEET; HYBRID NANOFLUID; CYLINDER; SURFACE In the present framework, flow and thermal transport behavior of non-Newtonian viscoelastic fluid induced by stretching/shrinking of the horizontal sheet under the influence of Lorentz force, volumetric heat source/sink, and radiation (assuming optically thick medium) has been investigated. Multiple solutions (Branches) have been predicted numerically using Lie symmetry transformation and Runge Kutta Dormand Prince (RKDP) algorithm-based Shooting method for the different controlling parameters, stretching/shrinking (gamma c<-1)$$ \left({\gamma}_c<-1<\gamma <\infty \right) $$, and suction (alpha c<<)$$ \left({\alpha}_c<\alpha <\infty \right) $$ with substantial impact with UCM parameter, beta >0$$ \beta >0 $$. Some of the results have also been compared with MATLAB built-in solvers to validate our in-house code. The deviations in critical (turning) points (beta c,gamma c)$$ \left({\beta}_c,{\gamma}_c\right) $$ have been noticed for different values of M$$ M $$ and alpha $$ \alpha $$. The temporal stability analysis is performed for these parameters (beta,gamma)$$ \left(\beta, \gamma \right) $$, and the upper (lower) branch is only physically feasible (infeasible). The combined effect on Nusselt number and skin-friction coefficient is also depicted in the form of contour plots. The outcomes from artificial neural network (ANN) using Levenberg-Marquardt algorithm give a preferred estimate for current numerical simulation. In ANN prediction, the optimal number of neurons in the hidden layer is used, having minimum value of RMSE and RMRE with maximum R2$$ {R}<^>2 $$ for different cases of non Newtonian stretching/shrinking sheet. Finally, the results conclude that the applied ANN model can accurately predict the output results. [Rana, Puneet; Gupta, Gaurav] Wenzhou Kean Univ, Coll Sci & Technol, Sch Math Sci, Wenzhou 325060, Zhejiang, Peoples R China; [Bhardwaj, Anuj] Jaypee Inst Informat Technol, Dept Math, Noida 201309, India; [Makkar, Vinita] GD Goenka Univ, Sch Engn & Sci, Dept Basic & Appl Sci, Gurgaon 122103, Haryana, India; [Pop, Ioan] Babes Bolyai Univ, Dept Math, Cluj Napoca 400084, Romania Wenzhou-Kean University; Jaypee Institute of Information Technology (JIIT); GD Goenka University; Babes Bolyai University from Cluj Rana, P (corresponding author), Wenzhou Kean Univ, Coll Sci & Technol, Sch Math Sci, Wenzhou 325060, Zhejiang, Peoples R China. puneetranaiitr@gmail.com 51 0 0 0 0 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0170-4214 1099-1476 MATH METHOD APPL SCI Math. Meth. Appl. Sci. 10.1002/mma.8907 0.0 DEC 2022 21 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics 7I1NE 2023-03-23 WOS:000903660700001 0
J Dai, HQ; Chen, YY; Dai, WQ; Hu, Z; Xie, FX; Xu, WQ; Cui, ZJ; Wei, X; Chen, ZH; Yang, BB; Zhang, WL; Wei, W; Guo, RQ; Zhang, GQ Dai, Hanqing; Chen, Yuanyuan; Dai, Wenqing; Hu, Zhe; Xie, Fengxian; Xu, Wenqian; Cui, Zhongjie; Wei, Xian; Chen, Zhihao; Yang, Bobo; Zhang, Wanlu; Wei, Wei; Guo, Ruiqian; Zhang, Guoqi Investigating the Electrochemical Performance of Smart Self-Powered Bionic Skin Fragment Based on Bioelectricity Generation ADVANCED MATERIALS TECHNOLOGIES English Article bionic skins; flexibility; reparable surface; self-powered functions ELECTRIC-EEL Human skin, with beneficial elasticity and reparability, could sense the changes of the external environment through different receptors. Imitating these mechanical properties and perception of bionic skin with power supply function is an enormous challenge in the field of Internet of things and artificial intelligence. Herein, the neural signal transmission of human skin is imitated to create a smart self-powered bionic skin fragment integrating skin and power supply functions. Unlike the traditional bionic skin in essence, it can intelligently perceive the outside world by using anion-selective and cation-selective gels to control exchangeable anions and cations to realize the change of resting current and action current, and it can maintain the relatively stable self-powered current of 0.5 mu A for nearly 2.2 h. Moreover, its mechanisms of current and voltage changes are systematically investigated. These results reveal that it can be applied to the synchronous transmission of signals for the next-generation neurologically integrated soft engineering systems such as bionic sensors, or prosthetic devices in hybrids of living and nonliving systems. [Dai, Hanqing; Chen, Yuanyuan; Hu, Zhe; Xie, Fengxian; Cui, Zhongjie; Wei, Xian; Chen, Zhihao; Yang, Bobo; Zhang, Wanlu; Guo, Ruiqian] Fudan Univ, Acad Engn & Technol, Inst Elect Light Sources, Shanghai 200433, Peoples R China; [Dai, Wenqing] Shanghai Univ Engn Sci, Coll Mech & Automot Engn, Shanghai 201620, Peoples R China; [Xu, Wenqian; Wei, Wei] Nanjing Univ Posts & Telecommun, Coll Elect & Opt Engn, Nanjing 210023, Peoples R China; [Zhang, Guoqi] Delft Univ Technol, Dept Microelect, NL-2628 CD Delft, Netherlands Fudan University; Shanghai University of Engineering Science; Nanjing University of Posts & Telecommunications; Delft University of Technology Guo, RQ (corresponding author), Fudan Univ, Acad Engn & Technol, Inst Elect Light Sources, Shanghai 200433, Peoples R China.;Wei, W (corresponding author), Nanjing Univ Posts & Telecommun, Coll Elect & Opt Engn, Nanjing 210023, Peoples R China.;Zhang, GQ (corresponding author), Delft Univ Technol, Dept Microelect, NL-2628 CD Delft, Netherlands. weiwei@njupt.edu.cn; rqguo@fudan.edu.cn; g.q.zhang@tudelft.nl Chen, Yuanyuan/GXG-2130-2022 Cui, Zhongjie/0000-0002-6335-0513; Dai, Hanqing/0000-0001-5383-8702; Dai, Wenqing/0000-0003-3357-4687 National Natural Science Foundation of China [61675049, 61377046]; outstanding doctoral research promotion program of Fudan University [SSH6281011/003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); outstanding doctoral research promotion program of Fudan University Financial support from the National Natural Science Foundation of China (no. 61675049 and 61377046) and the outstanding doctoral research promotion program of Fudan University (SSH6281011/003) is gratefully acknowledged. 25 2 2 8 40 WILEY HOBOKEN 111 RIVER ST, HOBOKEN, NJ 07030 USA 2365-709X ADV MATER TECHNOL-US Adv. Mater. Technol. MAR 2021.0 6 3 2000848 10.1002/admt.202000848 0.0 JAN 2021 7 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science QU5CD 2023-03-23 WOS:000611682100001 0
C Comerford, L; Beer, M; Lu, NW IEEE Comerford, Liam; Beer, Michael; Lu, Naiwei Revealing Prediction Uncertainty in Artificial Neural Network Based Reconstruction of Missing Data in Stochastic Process Records utilizing Extreme Learning Machines 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) English Proceedings Paper IEEE Symposium Series on Computational Intelligence (IEEE SSCI) NOV 27-DEC 01, 2017 Honolulu, HI IEEE,IEEE Computat Intelligence Soc artificial neural networks; extreme learning machines; missing data; prediction uncertainty TIME-SERIES ANALYSIS; SPECTRUM ESTIMATION SUBJECT; POWER SPECTRUM; GENETIC ALGORITHM; SPACED DATA An artificial neural network (ANN) based approach is developed for reconstructing gappy stochastic process records. ANNs can be utilized to capture stochastic patterns in process records in an average sense, however individual predictions of missing data can be unreliable. This is both due to the inherent uncertainty introduced by the stochastic process itself, and to uncertainties relating to ANN parameters. Here, instead of relying on single network predictions, an ensemble of networks are trained on the data, the outputs of which are used to estimate statistical distributions for random variables which replace deterministic predictions. In order for this approach to be computationally viable, single-layer feed-forward networks, initialized with random weights are trained via fast, non-iterative least-squares fitting, popularly known as Extreme Learning Machine (ELM) networks. Examples are provided with real data to demonstrate both network prediction effectiveness and uncertainties revealed by varying network parameters. [Comerford, Liam; Beer, Michael] Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany; [Beer, Michael] Univ Liverpool, Inst Risk & Uncertainty, Liverpool, Merseyside, England; [Beer, Michael] Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China; [Beer, Michael] Tongji Univ, Shanghai Inst Disaster Prevent & Relief, Shanghai 200092, Peoples R China; [Lu, Naiwei] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha, Hunan, Peoples R China Leibniz University Hannover; University of Liverpool; Tongji University; Tongji University; Changsha University of Science & Technology Comerford, L (corresponding author), Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany. comerford@irz.uni-hannover.de; beer@irz.uni-hannover.de; lunaiweide@163.com Lu, Naiwei/ABB-3082-2020 25 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-2726-6 2017.0 871 877 7 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ8IF 2023-03-23 WOS:000428251400120 0
J Zhu, J; Ma, XD; Lin, ZH; De Meo, P Zhu, Jia; Ma, Xiaodong; Lin, Zhihao; De Meo, Pasquale A quantum-like approach for text generation from knowledge graphs CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY English Article; Early Access data mining; knowledge-based vision; machine learning; natural language processing; text analysis Recent text generation methods frequently learn node representations from graph-based data via global or local aggregation, such as knowledge graphs. Since all nodes are connected directly, node global representation encoding enables direct communication between two distant nodes while disregarding graph topology. Node local representation encoding, which captures the graph structure, considers the connections between nearby nodes but misses out onlong-range relations. A quantum-like approach to learning better-contextualised node embeddings is proposed using a fusion model that combines both encoding strategies. Our methods significantly improve on two graph-to-text datasets compared to state-of-the-art models in various experiments. [Zhu, Jia; Ma, Xiaodong] Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zhejia, Jinhua, Peoples R China; [Lin, Zhihao] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China; [De Meo, Pasquale] Univ Messina, DICAM Dept, Messina, Italy Zhejiang Normal University; South China Normal University; University of Messina Ma, XD (corresponding author), Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zhejia, Jinhua, Peoples R China. felicity@m.scnu.edu.cn Ma, Xiaodong/0000-0003-1585-5020 National Natural Science Foundation of China [62077015]; Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province; Key Researh and Development Program of Zhejiang Province [2021C03141]; National Key R&D Program of China Under. Province [2022YFC3303600] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province; Key Researh and Development Program of Zhejiang Province; National Key R&D Program of China Under. Province National Natural Science Foundation of China, Grant/Award Number: 62077015; The Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province; Key Researh and Development Program of Zhejiang Province, Grant/Award Number: 2021C03141;The National Key R & D Program of China Under. Province,Grant/Award Number: 2022YFC3303600 36 0 0 2 2 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2468-6557 2468-2322 CAAI T INTELL TECHNO CAAI T. Intell. Technol. 10.1049/cit2.12178 0.0 FEB 2023 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8M3VW gold 2023-03-23 WOS:000924398000001 0
J Xu, HS; Angrisano, A; Gaglione, S; Hsu, LT Xu, Haosheng; Angrisano, Antonio; Gaglione, Salvatore; Hsu, Li-Ta Machine learning based LOS/NLOS classifier and robust estimator for GNSS shadow matching SATELLITE NAVIGATION English Article GNSS; Shadow matching; NLOS and machine learning; Robust estimation GPS Global Navigation Satellites Systems (GNSS) is frequently used for positioning services in various applications, e.g., pedestrian and vehicular navigation. However, it is well-known that GNSS positioning performs unreliably in urban environments. GNSS shadow matching is a method of improving accuracy in the cross-street direction. Initial position and classification of observed satellite visibility between line-of-sight (LOS) and non-line- of-sight (NLOS) are essential for its performance. For the conventional LOS/NLOS classification, the classifiers are based on a single feature, extracted from raw GNSS measurements, such as signal noise ratio, pseudorange, elevation angle, etc. Especially in urban canyons, these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding buildings. Besides, the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban areas. In our study, shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine (SVM). The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5% in urban scenarios. An important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be misclassified. By solving this problem, the SVM classification shows the potential of about 90% classification accuracy for various urban cases. With the help of these approaches, the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction; these performances are suitable for urban positioning. [Xu, Haosheng; Hsu, Li-Ta] Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China; [Angrisano, Antonio] Univ Benevento G Fortunato, Benevento, Italy; [Gaglione, Salvatore] Univ Naples Parthenope, Naples, Italy Hong Kong Polytechnic University; Universita Telematica Giustino Fortunato; Parthenope University Naples Hsu, LT (corresponding author), Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China. lt.hsu@polyu.edu.hk Hong Kong PolyU startup fund on the project [1-ZVKZ] Hong Kong PolyU startup fund on the project The authors acknowledge the support of The Hong Kong PolyU startup fund on the project 1-ZVKZ, Navigation for Autonomous Driving Vehicle using Sensor Integration. 24 17 18 3 4 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-9291 2662-1363 SATELLITE NAVIG Satell. Navig. 2020.0 1 1 15 10.1186/s43020-020-00016-w 0.0 12 Engineering, Aerospace; Remote Sensing; Telecommunications Emerging Sources Citation Index (ESCI) Engineering; Remote Sensing; Telecommunications VK7NC gold 2023-03-23 WOS:000751601600015 0
J Wang, YX; Hou, M; Plataniotis, KN; Kwong, S; Leung, H; Tunstel, E; Rudas, IJ; Trajkovic, L Wang, Yingxu; Hou, Ming; Plataniotis, Konstantinos N.; Kwong, Sam; Leung, Henry; Tunstel, Edward; Rudas, Imre J.; Trajkovic, Ljiljana Towards a theoretical framework of autonomous systems underpinned by intelligence and systems sciences IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Article AAutonomous systems (AS); cognitive systems; computational intelligence; engineering paradigms; intelligence science; intelligent mathematics ARTIFICIAL-INTELLIGENCE; REFERENCE MODEL; BRAIN Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence, cognition, computer, and systems sciences. This paper explores the intelligent and mathematical foundations of autonomous systems. It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A hierarchical intelligence model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering. Emerging paradigms of autonomous systems including brain-inspired systems, cognitive robots, and autonomous knowledge learning systems are described. Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities. [Wang, Yingxu] Univ Calgary, Int Inst Cognit Informat & Cognit Comp, Dept Elect & Comp Engn, Schulich Sch Engn, Calgary, AB T2N 1N4, Canada; [Wang, Yingxu] Univ Calgary, Hotchkiss Brain Inst, Calgary, AB T2N 1N4, Canada; [Hou, Ming] Def Res & Dev Canada, Toronto Res Ctr, Toronto, ON M3K 2C9, Canada; [Plataniotis, Konstantinos N.] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada; [Kwong, Sam] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Leung, Henry] Univ Calgary, Schulich Sch Engn, Dept Elect & Comp Engn, Calgary, AB T2N 1N4, Canada; [Tunstel, Edward] United Technol Res Ctr, Autonomous & Intelligent Syst Dept, E Hartford, CT USA; [Rudas, Imre J.] Obuda Univ, Univ Res & Innovat Ctr EKIK, H-1034 Budapest, Hungary; [Trajkovic, Ljiljana] Simon Fraser Univ, Sch Engn Sci, Burnaby, BC V5A 1S6, Canada University of Calgary; University of Calgary; Defence Research & Development Canada; University of Toronto; City University of Hong Kong; University of Calgary; Raytheon Technologies; Obuda University; Simon Fraser University Hou, M (corresponding author), Def Res & Dev Canada, Toronto Res Ctr, Toronto, ON M3K 2C9, Canada. yingxu@ucalgary.ca; ming.hou@drdc-rddc.gc.ca; kostas@ece.utoronto.ca; cssamk@cityu.edu.hk; leungh@ucalgary.ca; tunstel@ieee.org; rudas@uni-obuda.hu; ljilja@cs.sfu.ca Kwong, Sam/C-9319-2012 Kwong, Sam/0000-0001-7484-7261; Wang, Yingxu/0000-0003-0445-3632; , Ming/0000-0003-2621-5539 Department of National Defence's Innovation for Defence Excellence and Security (IDEaS) Program, Canada, through the Project of AutoDefence: Towards Trustworthy Technologies for Autonomous Human-Machine Systems; NSERC; IEEE SMC Society Technical Committee on Brain-Inspired Systems (TCBCS) Department of National Defence's Innovation for Defence Excellence and Security (IDEaS) Program, Canada, through the Project of AutoDefence: Towards Trustworthy Technologies for Autonomous Human-Machine Systems; NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); IEEE SMC Society Technical Committee on Brain-Inspired Systems (TCBCS) This work was supported in part by the Department of National Defence's Innovation for Defence Excellence and Security (IDEaS) Program, Canada, through the Project of AutoDefence: Towards Trustworthy Technologies for Autonomous Human-Machine Systems, NSERC, and the IEEE SMC Society Technical Committee on Brain-Inspired Systems (TCBCS). 36 7 7 4 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. JAN 2021.0 8 1 52 63 10.1109/JAS.2020.1003432 0.0 12 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems OZ2BY 2023-03-23 WOS:000594739100004 0
C Troia, S; Sheng, G; Alvizu, R; Maier, GA; Pattavina, A IEEE Troia, Sebastian; Sheng, Gao; Alvizu, Rodolfo; Maier, Guido Alberto; Pattavina, Achille Identification of Tidal-Traffic Patterns in Metro-Area Mobile Networks via Matrix Factorization Based Model 2017 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) International Conference on Pervasive Computing and Communications English Proceedings Paper IEEE International Conference on Pervasive Computing and Communications (PerCom) MAR 13-17, 2017 HI IEEE Non-Negative Matrix Factorization (NMF); Collective NMF (C-NMF); Machine Learning; Point of Interest (POI); Call Detail Record (CDR) Due to the highly predictable daily movements of citizens in urban areas, mobile traffic shows repetitive patterns with spatio-temporal variations. This phenomenon is known as Tidal Effect analogy to the rise and fall of the sea levels. Recognizing and defining traffic load patterns at the base station thus plays a vital role in traffic engineering, network design and load balancing since it represents an important solution for the Internet Service Providers (ISPs) that face network congestion problems or over-provisioning of the link capacity. Previous works have dealt with the classification and identification of patterns through the use of techniques, which inspect the flow of data of a particular application. But they assume prior knowledge on the stream of data packets, making the trend identification much inefficient. Recent methods based on machine learning techniques build their classification models based on sample data collected at certain points of the network with high accuracy. Therefore, in this paper, we address the problem by applying matrix factorization based models on real-world datasets, identifying typical patterns from data streams, which frequently occur in the network, without investigating the type of flows. For that, we propose a Collective Non-negative Matrix Factorization based model combining multi-source data, such as point of interests attributes, traffic data and base station information, identifying the basic patterns of those areas of the city that present the same type of attributes. The experimental results show the effectiveness of our proposed approach compared with the baselines. [Troia, Sebastian; Alvizu, Rodolfo; Maier, Guido Alberto; Pattavina, Achille] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy; [Sheng, Gao] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China Polytechnic University of Milan; Beijing University of Posts & Telecommunications Troia, S (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy. EU FP7 IRSES Marie Curie MobileCloud project EU FP7 IRSES Marie Curie MobileCloud project This work was supported by the EU FP7 IRSES Marie Curie MobileCloud project that has goal to foster research collaborations between Chinese and European university in Mobile Cloud Computing. The data-set of POIs was provided by the municipality of Milan and we thank them for their cooperation and commitment. 11 2 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2474-2503 978-1-5090-4338-5 INT CONF PERVAS COMP 2017.0 5 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BI3KB 2023-03-23 WOS:000411208400067 0
J Wang, Y; Wei, X; Tang, X; Shen, H; Ding, L Wang, Yong; Wei, Xian; Tang, Xuan; Shen, Hao; Ding, Lu CNN tracking based on data augmentation KNOWLEDGE-BASED SYSTEMS English Article Visual tracking; Data augmentation; Convolutional neural network VISUAL TRACKING; OBJECT TRACKING; FILTER Correlation filter based tracking methods have aroused increasing attention due to the appealing performance on tracking benchmark datasets. For each frame, a filter is trained to separate the object from its background. Considering that the object always undergoes challenging situations, the trained filter should consider both external and internal distractions. In this paper, we propose a data augmentation based robust visual tracking algorithm to better generalize the training data. Specifically, data augmentation technique is utilized to generate training samples to improve the robustness of the training filter. Then hierarchical convolutional neural network (CNN) features are utilized to encode the target and the augmented sample. Different from previous work, we exploit to use a hash matrix to reduce the dimension of the CNN features. Next, the correlation filter tracking method is employed. The tracking results of multiple hash features are combined to locate the target. Extensive experiments on five large scale datasets show that the proposed method achieves comparable results to state-of-the-art trackers. (C) 2020 Elsevier B.V. All rights reserved. [Wang, Yong] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada; [Wei, Xian; Tang, Xuan] Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Peoples R China; [Shen, Hao] Tech Univ Munich, Munich, Germany; [Shen, Hao] fortiss GmbH, Munich, Germany; [Ding, Lu] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai, Peoples R China University of Ottawa; Chinese Academy of Sciences; Fujian Institute of Research on the Structure of Matter, CAS; Technical University of Munich; fortiss; Shanghai Jiao Tong University Wei, X (corresponding author), Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Peoples R China. xian.wei@fjirsm.ac.cn shen, hao/HGF-0551-2022 National Science Found for Young Scholars [61806186]; State Key Laboratory of Robotics and System (HIT) [SKLRS-2019-KF-15]; CAS Pioneer Hundred Talents Program (Type C) [2017-122]; German Research Foundation (DFG) [KL 2189/9-1] National Science Found for Young Scholars; State Key Laboratory of Robotics and System (HIT); CAS Pioneer Hundred Talents Program (Type C); German Research Foundation (DFG)(German Research Foundation (DFG)) This work was partially supported by National Science Found for Young Scholars under Grant No. 61806186, State Key Laboratory of Robotics and System (HIT) with No. SKLRS-2019-KF-15, CAS Pioneer Hundred Talents Program (Type C) under Grant No. 2017-122 and the German Research Foundation (DFG) under Grant No. KL 2189/9-1. 71 11 12 2 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. APR 22 2020.0 194 105594 10.1016/j.knosys.2020.105594 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LP8QD 2023-03-23 WOS:000534581300046 0
J Zhang, M; Sun, QY; Garme, K; Burman, M; Zhou, L Zhang, Meng; Sun, Qianyang; Garme, Karl; Burman, Magnus; Zhou, Li Analysis of inland waterway ship performance in ice: Operation Time Window OCEAN ENGINEERING English Article Ice-going ships; Inland waterway; Artificial neural network; Ice resistance; Operation time window; Statistical analysis RESISTANCE; SIMULATION; NETWORKS; CHANNEL; MODEL Inland waterway (IWW) shipping is a sustainable opportunity to reduce traffic on, in many times very congested, roads and railways. This is especially true for cities and urban areas. However, for an operator, the ship Oper-ation Time Window (OTW) is important in order to predict possible business cases, especially for regions with long-term winter seasons with icy conditions. The OTW indicates the probable number of navigable days for the ship. The operability is in relation with ship speed, ice thickness, whereas the ship resistance is of significant relevance. This study proposes a model to investigate the possibility of a certain operating condition for ice-going ships based on an Artificial Neural Network (ANN) model and a statistical model. To demonstrate the proposed method for calculating the ship OTW of an IWW, a case study is performed. Ice condition in Lake Ma center dot laren (in Sweden) and an IWW ship designed to maximise its dimension restrictions are used for this case. The Radial Basis Function-Particle swarm optimization (RBF-PSO) ANN model is used to predict ice resistance in level ice con-ditions. Given the ice resistance prediction, a statistical analysis is further conducted regarding to the ice thickness distribution and the operational ship speed distribution to obtain ship OTW. Comparisons are made between semi-empirical ice resistance prediction methods and the ANN model. The influence of different ship speed distribution profiles is investigated by performing a parametric study. The OTW model can be used to evaluate ship operational scenarios in ice-covered waters for ship designers and owners. [Zhang, Meng; Garme, Karl; Burman, Magnus] KTH Royal Inst Technol, Dept Engn Mech, S-10044 Stockholm, Sweden; [Sun, Qianyang; Zhou, Li] Jiangsu Univ Sci & Technol, Dept Naval Architecture & Ocean Engn, Zhenjiang 212003, Peoples R China Royal Institute of Technology; Jiangsu University of Science & Technology Zhang, M (corresponding author), KTH Royal Inst Technol, Dept Engn Mech, S-10044 Stockholm, Sweden. mengzh@kth.se zhou, li/AAU-6808-2020 Burman, Magnus/0000-0002-1187-4796; Zhang, Meng/0000-0002-1743-0686; Garme, Karl/0000-0002-9110-9401 Sjofartsverket (Swedish Maritime Administration) [16-00778]; Trafikverket (Swedish Transport Administration) [TRV 2017/64978]; National Natural Science Foundation of China [51809124, 51911530156]; Swedish Foundation for International Cooperation in Research and Higher Education [Dnr: CH 2018-7827] Sjofartsverket (Swedish Maritime Administration); Trafikverket (Swedish Transport Administration); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Swedish Foundation for International Cooperation in Research and Higher Education This research was funded by Sjofartsverket (Swedish Maritime Administration): Dnr 16-00778, Trafikverket (Swedish Transport Administration): TRV 2017/64978 and National Natural Science Foundation of China, grant number 51809124 and 51911530156. The work was also supported under Swedish Foundation for International Cooperation in Research and Higher Education with grant number Dnr: CH 2018-7827. 55 0 0 3 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. NOV 1 2022.0 263 112409 10.1016/j.oceaneng.2022.112409 0.0 15 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 4Z3US hybrid 2023-03-23 WOS:000862138800003 0
J Kang, J; Wang, ZR; Zhu, RX; Xia, JS; Sun, X; Fernandez-Beltran, R; Plaza, A Kang, Jian; Wang, Zhirui; Zhu, Ruoxin; Xia, Junshi; Sun, Xian; Fernandez-Beltran, Ruben; Plaza, Antonio DisOptNet: Distilling Semantic Knowledge From Optical Images for Weather-Independent Building Segmentation IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Image segmentation; Buildings; Radar polarimetry; Optical imaging; Optical sensors; Adaptive optics; Synthetic aperture radar; Building extraction; deep learning; knowledge distillation; missing modality; semantic segmentation; synthetic aperture radar (SAR); transfer learning RESOLUTION SAR IMAGES; FOOTPRINT EXTRACTION; CLASSIFICATION; DATASET Synthetic aperture radar (SAR) images provide all-weather and all-time capabilities for Earth observation, which becomes highly beneficial in the field of intelligent remote sensing (RS) image interpretation. Due to these advantages, SAR images have been widely exploited in automatic building segmentation tasks under poor weather conditions, especially when disasters happen. However, compared to optical images, the semantics inherent to SAR images are less rich and interpretable due to factors such as speckle noise and imaging geometry. In this scenario, most state-of-the-art methods are focused on designing advanced network architectures or loss functions for building footprint extraction. However, few works have been oriented toward improving segmentation performance through knowledge transfer from optical images. In this article, we propose a novel method based on the DisOptNet network, which can distill the useful semantic knowledge from optical images into a network only trained with SAR data. Specifically, we first analyze the multilevel feature discrepancies between multiple stages of the networks pretrained on the two image modalities. We observe that feature discrepancies start to increase as the encoding stage gradually changes from low level to high level. Based on such observation, we reuse the early stage features and construct parallel convolutional neural network (CNN) branches that are responsible for capturing high-level domain-specific knowledge for each image modality. The optical branch is aimed at mimicking feature generation at the optical pretrained network given the input SAR images. Then, an aggregation module is introduced to calibrate and fuse the features from different modalities while generating the building segments. Extensive experiments were conducted on a large-scale multisensor all-weather building segmentation dataset with state-of-the-art methods used for comparison. Our experimental results validate the effectiveness of DisOptNet, which demonstrates great potential in the task of weather-independent building footprint generation under real scenarios. The codes of this article will be made publicly available at https://github.com/jiankang1991/TGRS_DisOptNet. [Kang, Jian] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China; [Wang, Zhirui; Sun, Xian] Chinese Acad Sci, Inst Elect, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China; [Wang, Zhirui; Sun, Xian] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China; [Zhu, Ruoxin] Xian Res Inst Surveying & Mapping, State Key Lab Geoinformat Engn, Xian 710054, Peoples R China; [Xia, Junshi] RIKEN Ctr Adv Intelligence Project AIP, Geoinformat Unit, Tokyo 1030027, Japan; [Fernandez-Beltran, Ruben] Univ Murcia, Dept Comp Sci & Syst, Murcia 30100, Spain; [Sun, Xian] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10003, Spain Soochow University - China; Chinese Academy of Sciences; Chinese Academy of Sciences; RIKEN; University of Murcia; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidad de Extremadura Wang, ZR (corresponding author), Chinese Acad Sci, Inst Elect, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China.;Wang, ZR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China. jiankang@suda.edu.cn; zhirui1990@126.com; ruoxin.zhu@tum.de; junshi.xia@riken.jp; sunxian@mailie.ac.cn; rufernan@um.es; aplaza@unex.es Plaza, Antonio/C-4455-2008; Fernandez-Beltran, Ruben/GLT-5907-2022; Xia, Junshi/ABD-4116-2020; kang, jian/V-3055-2019 Plaza, Antonio/0000-0002-9613-1659; Xia, Junshi/0000-0002-5586-6536; kang, jian/0000-0001-6284-3044; Sun, Xian/0000-0002-0038-9816 Natural Science Foundation of China [62101371, 62076241]; Jiangsu Province Science Foundation for Youths [BK20210707]; Ministry of Science, Innovation and Universities of Spain [RTI2018-098651-B-C54, PID2019-110315RB-I00]; Valencian Government of Spain [GV/2020/167]; European Fund for Economic and Regional Development (FEDER)-Junta de Extremadura [GR18060]; European Union through the H2020 EOXPOSURE Project [734541] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Province Science Foundation for Youths; Ministry of Science, Innovation and Universities of Spain(Spanish Government); Valencian Government of Spain; European Fund for Economic and Regional Development (FEDER)-Junta de Extremadura; European Union through the H2020 EOXPOSURE Project This work was supported in part by the Natural Science Foundation of China under Grant 62101371 and Grant 62076241; in part by the Jiangsu Province Science Foundation for Youths under Grant BK20210707; in part by the Ministry of Science, Innovation and Universities of Spain (APRISA) under Grant RTI2018-098651-B-C54 and Grant PID2019-110315RB-I00; in part by the Valencian Government of Spain under Grant GV/2020/167; in part by the European Fund for Economic and Regional Development (FEDER)-Junta de Extremadura under Grant GR18060; and in part by the European Union through the H2020 EOXPOSURE Project under Grant 734541. 57 13 13 6 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 4706315 10.1109/TGRS.2022.3165209 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 0P4VQ 2023-03-23 WOS:000784219700022 0
J Wei, H; Zhang, QH; Gu, Y Wei, Hao; Zhang, Qinghua; Gu, Yu Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network MEASUREMENT SCIENCE AND TECHNOLOGY English Article bearing; rotating machinery; remaining useful life prediction; deep learning; temporal convolution network NEURAL-NETWORKS; MACHINE Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance of rotating machinery. However, the effectiveness of many existing RUL prediction methods depends on expert experience and signal processing algorithms, which limiting the application of these methods in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting of a multi-scale attention-based dilated causal convolutional (MADCC) module and a multi-layer temporal convolutional network (MTCN) to predict the RUL of bearings using raw vibration data. First, the MADCC module extracts multi-scale temporal features of the bearing degradation process (BDP) and provides fused feature vectors (FFVs) containing comprehensive BDP information for the MTCN module. Subsequently, the MTCN module mines deep temporal dependencies hidden in the FFV to predict the RUL of bearings. Ablation experiments are conducted to analyze the contribution of the framework's components. Three evaluation metrics (root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R (2))) are used to verify the effectiveness of the proposed framework and other state-of-the-art methods on two public bearing datasets. The experimental results show that the proposed framework achieves the lowest RMSE and MAE and the highest R (2), demonstrating excellent performance and potential for RUL prediction of bearings. [Wei, Hao] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Wei, Hao; Zhang, Qinghua; Gu, Yu] Guangdong Univ Petrochem Technol, Sch Automat, Maoming 525000, Peoples R China; [Gu, Yu] Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China; [Gu, Yu] Goethe Univ, Inst Inorgan & Analyt Chem, Dept Chem, Max Von Laue Str 9, D-60438 Frankfurt, Germany Beijing University of Chemical Technology; Guangdong University of Petrochemical Technology; Beijing University of Chemical Technology; Goethe University Frankfurt Gu, Y (corresponding author), Guangdong Univ Petrochem Technol, Sch Automat, Maoming 525000, Peoples R China.;Gu, Y (corresponding author), Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China.;Gu, Y (corresponding author), Goethe Univ, Inst Inorgan & Analyt Chem, Dept Chem, Max Von Laue Str 9, D-60438 Frankfurt, Germany. guyu@mail.buct.edu.cn National Natural Science Foundation of China [61876059, 61933013] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank the National Natural Science Foundation of China (Grant Nos. 61876059 and 61933013) and the IEEE Reliability Society and FEMTO-ST Institute. 40 0 0 23 23 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0957-0233 1361-6501 MEAS SCI TECHNOL Meas. Sci. Technol. APR 1 2023.0 34 4 45107 10.1088/1361-6501/acb0e9 0.0 16 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 8D2YW 2023-03-23 WOS:000918165900001 0
J Zeraatpisheh, M; Garosi, Y; Owliaie, HR; Ayoubi, S; Taghizadeh-Mehrjardi, R; Scholten, T; Xu, M Zeraatpisheh, Mojtaba; Garosi, Younes; Owliaie, Hamid Reza; Ayoubi, Shamsollah; Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas; Xu, Ming Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates CATENA English Article Soil organic carbon; Spatial prediction; Environmental covariates; Time-series vegetation indices; Machine learning; Uncertainty DATA-MINING TECHNIQUES; LAND-USE CHANGE; TOPOGRAPHY; NITROGEN; MATTER; REGION; FOREST; MANAGEMENT; FERTILITY; NORTHEAST In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0-20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R-2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R-2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R-2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content. [Zeraatpisheh, Mojtaba; Xu, Ming] Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China; [Zeraatpisheh, Mojtaba; Xu, Ming] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China; [Garosi, Younes; Ayoubi, Shamsollah] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 8415683111, Iran; [Owliaie, Hamid Reza] Univ Yasuj, Coll Agr, Yasuj, Iran; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, SFB 1070 ResourceCultures, Gartenstr 29, D-72074 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, DFG Cluster Excellence Machine Learning, Tubingen, Germany Henan University; Henan University; Isfahan University of Technology; Yasouj University; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen Xu, M (corresponding author), Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China.;Xu, M (corresponding author), Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China. mojtaba.zeraatpisheh@henu.edu.cn; owliaie@yu.ac.ir; ayoubi@cc.iut.ac.ir; ruhollah.taghizadeh-mehrjardi@mnf.uni-tuebingen.de; thomas.scholten@uni-tuebingen.de; mingxu@henu.edu.cn Zeraatpisheh, Mojtaba/V-1244-2018; Owliaie, Hamidreza/AAE-1179-2022; Taghizadeh-Mehrjardi, Ruhollah/H-3682-2013 Zeraatpisheh, Mojtaba/0000-0001-7209-0744; Taghizadeh-Mehrjardi, Ruhollah/0000-0002-4620-6624 National Key Research and Development Program of China [2017YFA0604302, 2018YFA0606500]; Isfahan University of Technology; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC [2064/1, 390727645, SFB 1070, 215859406] National Key Research and Development Program of China; Isfahan University of Technology; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC(German Research Foundation (DFG)) Mojtaba Zeraatpisheh's postdoctoral program at Henan University, China, has been supported by the National Key Research and Development Program of China, grant numbers 2017YFA0604302 and 2018YFA0606500. Younes Garosi would like to acknowledge the support from the Isfahan University of Technology. Ruhollah Taghizadeh-Mehrjardi and Thomas Scholten have been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC number 2064/1 -Project number 390727645, and the collaborative research center SFB 1070 `ResourceCultures' -Project number 215859406. The authors appreciate Dr. Xunhe Zhang for his help in Google earth engine visualization. We are thankful to Mr. Steven Xu, a native English speaker, for proofreading our manuscript. 93 30 30 20 106 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0341-8162 1872-6887 CATENA Catena JAN 2022.0 208 105723 10.1016/j.catena.2021.105723 0.0 SEP 2021 14 Geosciences, Multidisciplinary; Soil Science; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Agriculture; Water Resources UX0ZD 2023-03-23 WOS:000700576300013 0
J Li, YX; Ma, CY; Liao, SY; Qi, SW; Meng, SH; Cai, WX; Dai, WE; Cao, R; Dong, XN; Kramer, BK; Yun, C; Hocher, B; Hong, XP; Liu, DZ; Tang, D; He, JQ; Yin, LH; Dai, Y Li, Yixi; Ma, Chiyu; Liao, Shengyou; Qi, Suwen; Meng, Shuhui; Cai, Wanxia; Dai, Weier; Cao, Rui; Dong, Xiangnan; Kramer, Bernhard K.; Yun, Chen; Hocher, Berthold; Hong, Xiaoping; Liu, Dongzhou; Tang, Donge; He, Jingquan; Yin, Lianghong; Dai, Yong Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus FRONTIERS IN IMMUNOLOGY English Article machine learning; biomarker; immune cell; disease exacerbation; disease diagnosis INDUCIBLE GENE-EXPRESSION; L-SELECTIN LEVELS; RHEUMATOID-ARTHRITIS; INTERFERON; PATHOGENESIS; ASSOCIATION; SIGNATURE; MANIFESTATIONS; NEPHRITIS; SERUM IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge. MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers. ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq. DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets. [Li, Yixi; Cao, Rui; Dong, Xiangnan; Yin, Lianghong] Jinan Univ, Affiliated Hosp 1, Inst Nephrol & Blood Purificat, Guangzhou, Peoples R China; [Li, Yixi; Ma, Chiyu; Liao, Shengyou; Qi, Suwen; Meng, Shuhui; Cai, Wanxia; Tang, Donge; He, Jingquan; Dai, Yong] Jinan Univ, Clin Med Coll 2, Shenzhen Peoples Hosp, Clin Med Res Ctr, Shenzhen, Peoples R China; [Dai, Weier] Univ Texas Austin, Coll Nat Sci, Austin, TX 78712 USA; [Kramer, Bernhard K.; Hocher, Berthold] Heidelberg Univ, Univ Med Ctr Mannheim, Dept Med 5, Heidelberg, Germany; [Yun, Chen] Charite Univ Med Berlin, Dept Nephrol, Berlin, Germany; [Hocher, Berthold] Hunan Normal Univ, Sch Med, Key Lab Study & Discovery Small Targeted Mol Huna, Changsha, Peoples R China; [Hocher, Berthold] Reprod & Genet Hosp China Int Trust & Investment, Changsha, Peoples R China; [Hocher, Berthold] Inst Med Diagnost IMD, Berlin, Germany; [Hong, Xiaoping; Liu, Dongzhou] Jinan Univ, Clin Med Coll 2, Shenzhen Peoples Hosp, Dept Rheumatol & Immunol, Shenzhen, Peoples R China; [Yin, Lianghong] Guangzhou Enttxs Med Prod Co Ltd, Guangzhou, Peoples R China Jinan University; Jinan University; University of Texas System; University of Texas Austin; Ruprecht Karls University Heidelberg; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Hunan Normal University; Jinan University Yin, LH (corresponding author), Jinan Univ, Affiliated Hosp 1, Inst Nephrol & Blood Purificat, Guangzhou, Peoples R China.;Tang, D; He, JQ; Dai, Y (corresponding author), Jinan Univ, Clin Med Coll 2, Shenzhen Peoples Hosp, Clin Med Res Ctr, Shenzhen, Peoples R China.;Yin, LH (corresponding author), Guangzhou Enttxs Med Prod Co Ltd, Guangzhou, Peoples R China. donge66@126.com; astroh@163.com; yin-yun@126.com; daiyong22@aliyun.com science and technology plan of Shenzhen; Shenzhen Key Medical Discipline Construction Fund; Key Research and Development Program of Guangdong Province; Sanming Project of Medicine in Shenzhen; National Natural Science Foundation of China; Guangdong Science and Technology Projects; [JCYJ20200109144218597]; [SZXK011]; [2019B020229001]; [SYJY201704]; [SYJY201705]; [81971464]; [2020A1313030112] science and technology plan of Shenzhen; Shenzhen Key Medical Discipline Construction Fund; Key Research and Development Program of Guangdong Province; Sanming Project of Medicine in Shenzhen; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Science and Technology Projects; ; ; ; ; ; ; Funding This project was supported by the science and technology plan of Shenzhen (No. JCYJ20200109144218597), Shenzhen Key Medical Discipline Construction Fund (No. SZXK011), the Key Research and Development Program of Guangdong Province (No.2019B020229001), Sanming Project of Medicine in Shenzhen (No. SYJY201704 and No. SYJY201705), the National Natural Science Foundation of China (No.81971464) and Guangdong Science and Technology Projects (No. 2020A1313030112). 60 0 0 3 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. NOV 29 2022.0 13 969509 10.3389/fimmu.2022.969509 0.0 19 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology 6Y0WT 36524113.0 gold, Green Accepted 2023-03-23 WOS:000896825600001 0
J Peng, XD; Shu, WW; Pan, CC; Ke, ZJ; Zhu, HQ; Zhou, X; Song, WW Peng, Xiangdong; Shu, Weiwei; Pan, Congcheng; Ke, Zejun; Zhu, Huaqiang; Zhou, Xiao; Song, William Wei DSCSSA: A Classification Framework for Spatiotemporal Features Extraction of Arrhythmia Based on the Seq2Seq Model With Attention Mechanism IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Arrhythmias classification; attention mechanism; bidirectional long short-term memory (Bi-ESTM); convolutional neural network (CNN); discrete wavelet transform (DWT); sequence to sequence (Seq2Seq); synthetic minority oversampling technique (SMOTE) ECG; DECOMPOSITION In the field of arrhythmia classification, classification accuracy has always been a research hotspot. However, the noises of electrocardiogram (ECG) signals, the class imbalance of ECG data, and the complexity of spatiotemporal features of ECG data are all important factors affecting the accuracy of ECG arrhythmias classification. In this article, a novel DSCSSA ECG arrhythmias classification framework is proposed. First, discrete wavelet transform (DWT) is used to denoise and reconstruct ECG signals to improve the feature extraction ability of ECG signals. Then, the synthetic minority oversampling technique (SMOTE) oversampling method is used to synthesize a new minority sample ECG signal to reduce the impact of ECG data imbalance on classification. Finally, a convolutional neural network (CNN) and sequence-to-sequence (Seq2Seq) classification model with attention mechanism based on bidirectional long short-term memory (Bi-LSTM) as the codec is used for arrhythmias classification, and the model can give corresponding weight according to the importance of heartbeat features and can improve the ability to extract and filter the spatiotemporal features of heartbeats. In the classification of five heartbeat types, including normal beat (N), supraventricular ectopic beat (5), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q), the proposed method achieved the overall accuracy (OA) value and Macro-F1 score of 99.28% and 95.70%, respectively, in public the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database. These methods are helpful to improve the effectiveness and clinical reference value of computer-aided ECG automatic classification diagnosis. [Peng, Xiangdong; Shu, Weiwei; Pan, Congcheng; Ke, Zejun; Zhu, Huaqiang; Zhou, Xiao] Jiangxi Univ Finance & Econ, Sch Software & Internet Things Engn, Nanchang 330013, Jiangxi, Peoples R China; [Song, William Wei] Dalarna Univ, Dept Informat Syst, S-79188 Falun, Sweden Jiangxi University of Finance & Economics; Dalarna University Peng, XD (corresponding author), Jiangxi Univ Finance & Econ, Sch Software & Internet Things Engn, Nanchang 330013, Jiangxi, Peoples R China. pengxiangdong@jxufe.edu.cn SONG, WILLIAM W./0000-0003-3681-8173; Zhou, Xiao/0000-0002-5418-7347; Shu, Weiwei/0000-0002-4394-8702; Peng, xiangdong/0000-0001-9929-6830 Science and Technology Project of Jiangxi Education Department [GJJ180263]; Natural Science Foundation Project of Jiangxi Province [20192BAB207003] Science and Technology Project of Jiangxi Education Department; Natural Science Foundation Project of Jiangxi Province This work was supported in part by the Science and Technology Project of Jiangxi Education Department under Grant GJJ180263 and in part by the Natural Science Foundation Project of Jiangxi Province under Grant 20192BAB207003. 52 0 0 36 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 2515112 10.1109/TIM.2022.3194906 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3V6WS 2023-03-23 WOS:000841802700009 0
J Wang, JT; Lovarelli, D; Rota, N; Shen, MX; Lu, MZ; Guarino, M Wang, Jintao; Lovarelli, Daniela; Rota, Nicola; Shen, Mingxia; Lu, Mingzhou; Guarino, Marcella The Potentialities of Machine Learning for Cow-Specific Milking: Automatically Setting Variables in Milking Machines ANIMALS English Article algorithms; dairy cows; detachment flow rate; milking time; pulsation ratio TEAT CONDITION; VACUUM LEVELS; DAIRY; FLOW; PERFORMANCE; ATTACHMENT; MASTITIS; SYSTEMS; HEALTH; PHASE Simple Summary In dairy farms, milking-related operations and procedures are often demanding, time-consuming, and directly affect farm economics. Therefore, milking operations need to be performed efficiently and effectively, with the proper pre-dipping and post-dipping operations, and with the avoidance of overmilking. Several studies have been carried out on milking operations and the parameters for shortening milking time without harming cows. The most important prerequisites for ensuring high-level milking conditions are the appropriate pulsation ratio and detachment flow rate. Both parameters were investigated in this study, where milking operations and parameters were recorded for three months on a dairy cattle farm in Northern Italy. A comparison was made between cows milked with a pulsation ratio of 60:40 vs. 65:35 and between cows milked with a detachment flow rate of 600 g/min vs. 800 g/min. Machine learning was used to achieve automatic adjustment of pulsation ratios and detachment flows for individual cows. The least-squares support vector machine (LSSVM) classification model based on the sparrow search algorithm (SSA) applied in this study outperformed other common machine learning models. Therefore, if implemented on milking machines, this could help to automatically vary the machine's settings based on cows' specific characteristics, for the benefit of cows' welfare. In dairy farming, milking-related operations are time-consuming and expensive, but are also directly linked to the farm's economic profit. Therefore, reducing the duration of milking operations without harming the cows is paramount. This study aimed to test the variation in different parameters of milking operations on non-automatic milking machines to evaluate their effect on a herd and finally reduce the milking time. Two trials were set up on a dairy farm in Northern Italy to explore the influence of the pulsation ratio (60:40 vs. 65:35 pulsation ratio) and that of the detachment flow rate (600 g/min vs. 800 g/min) on milking performance, somatic cell counts, clinical mastitis, and teats score. Moreover, the innovative aspect of this study relates to the development of an optimized least-squares support vector machine (LSSVM) classification model based on the sparrow search algorithm (SSA) to predict the proper pulsation ratio and detachment flow rate for individual cows within the first two minutes of milking. The accuracy and precision of this model were 92% and 97% for shortening milking time at different pulsation ratios, and 78% and 79% for different detachment rates. The implementation of this algorithm in non-automatic milking machines could make milking operations cow-specific. [Wang, Jintao; Shen, Mingxia; Lu, Mingzhou] Nanjing Agr Univ, Coll Engn, Lab Modern Facil Agr Technol & Equipment Engn, 40 Dianjiangtai Rd, Nanjing 210031, Peoples R China; [Lovarelli, Daniela; Guarino, Marcella] Univ Milan, Dept Environm Sci & Policy, Via G Celoria 2, I-20133 Milan, Italy; [Rota, Nicola] Agribovis Srl, Via B Luini 73, I-20821 Meda, Italy Nanjing Agricultural University; University of Milan Lovarelli, D (corresponding author), Univ Milan, Dept Environm Sci & Policy, Via G Celoria 2, I-20133 Milan, Italy. 2018212012@njau.edu.cn; daniela.lovarelli@unimi.it; nicola.rota@agribovis.it; mingxia@njau.edu.cn; lmz@njau.edu.cn; marcella.guarino@unimi.it Guarino, Marcella/K-8328-2017; Lovarelli, Daniela/Q-9547-2017 Guarino, Marcella/0000-0003-4131-0016; Lu, Mingzhou/0000-0002-7909-7962; Lovarelli, Daniela/0000-0002-0187-8515 43 1 1 3 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-2615 ANIMALS-BASEL Animals JUL 2022.0 12 13 1614 10.3390/ani12131614 0.0 14 Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Veterinary Sciences; Zoology 2Y0PM 35804513.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000825596600001 0
J Sajadi, P; Sang, YF; Gholamnia, M; Bonafoni, S; Mukherjee, S Sajadi, Payam; Sang, Yan-Fang; Gholamnia, Mehdi; Bonafoni, Stefania; Mukherjee, Saumitra Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms GEOSCIENCE LETTERS English Letter Feature selection technique; Landslide susceptibility; Machine learning algorithm; Spatial differencing; Qinghai-Tibetan Plateau SUPPORT VECTOR MACHINE; ANALYTICAL HIERARCHY PROCESS; ARTIFICIAL NEURAL-NETWORKS; LOGISTIC-REGRESSION; RANDOM FOREST; DECISION TREE; FREQUENCY RATIO; RIVER-BASIN; HAZARD ASSESSMENT; DEMPSTER-SHAFER Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naive Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage (Ds(d)), distance to faults (Ds(f)), drainage density (D-d), elevation (Elev), fault density (F-d), lithology, normalized difference vegetation index (NDVI), plan curvature (Pl(c)), profile curvature (Pr-c), slope (S degrees), stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. [Sajadi, Payam] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle Related Land Surface Proc, Beijing, Peoples R China; [Sang, Yan-Fang] Minist Emergency Management China, Key Lab Compound & Chained Nat Hazards Dynam, Beijing 100085, Peoples R China; [Gholamnia, Mehdi] Islamic Azad Univ, Sanandaj Branch, Dept Civil Engn, Sanandaj, Iran; [Bonafoni, Stefania] Univ Perugia, Dept Engn, Perugia, Italy; [Mukherjee, Saumitra] Jawaharlal Nehru Univ, Sch Environm Sci, New Delhi, India Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Islamic Azad University; University of Perugia; Jawaharlal Nehru University, New Delhi Sang, YF (corresponding author), Minist Emergency Management China, Key Lab Compound & Chained Nat Hazards Dynam, Beijing 100085, Peoples R China. sangyf@igsnrr.ac.cn sang, yan-fang/I-8277-2016 sang, yan-fang/0000-0001-6770-9311 Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [2019QZKK0903]; National Natural Science Foundation of China [41971040]; CAS Interdisciplinary Innovation Team [JCTD-2019-04] Second Tibetan Plateau Scientific Expedition and Research Program (STEP); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CAS Interdisciplinary Innovation Team This project was financially supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (No. 2019QZKK0903), the National Natural Science Foundation of China (No. 41971040), and the CAS Interdisciplinary Innovation Team (No. JCTD-2019-04). 155 1 1 12 41 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2196-4092 GEOSCI LETT Geosci. Lett. FEB 14 2022.0 9 1 9 10.1186/s40562-022-00218-x 0.0 25 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences YY9UP gold 2023-03-23 WOS:000755130700001 0
J Zhao, MX; Kluge, K; Papp, L; Grahovac, M; Yang, SM; Jiang, CT; Krajnc, D; Spielvogel, CP; Ecsedi, B; Haug, A; Wang, SW; Hacker, M; Zhang, WF; Li, X Zhao, Meixin; Kluge, Kilian; Papp, Laszlo; Grahovac, Marko; Yang, Shaomin; Jiang, Chunting; Krajnc, Denis; Spielvogel, Clemens P.; Ecsedi, Boglarka; Haug, Alexander; Wang, Shiwei; Hacker, Marcus; Zhang, Weifang; Li, Xiang Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma EUROPEAN RADIOLOGY English Article Lung adenocarcinoma; Positron-emission tomography; Biomarkers; Machine learning INTERNATIONAL ASSOCIATION; STAGE-I; CLASSIFICATION; CANCER; PREDICTION; SIGNATURE; FEATURES; SUBTYPES; IMPACT; IMAGES Objectives This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients. Methods: A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis. Results The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS. Conclusion ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy. [Zhao, Meixin; Jiang, Chunting; Zhang, Weifang] Peking Univ Third Hosp, Dept Nucl Med, 49 North Garden Rd, Beijing 100191, Peoples R China; [Kluge, Kilian; Grahovac, Marko; Spielvogel, Clemens P.; Haug, Alexander; Hacker, Marcus; Li, Xiang] Med Univ Vienna, Dept Biomed Imaging & Image Guided Therapy, Div Nucl Med, Wahringer Gurtel 18-20,Floor 3L, A-1090 Vienna, Austria; [Kluge, Kilian; Spielvogel, Clemens P.; Haug, Alexander] Christian Doppler Lab Appl Metabol CDLAM, Vienna, Austria; [Papp, Laszlo; Krajnc, Denis; Ecsedi, Boglarka] Med Univ Vienna, Ctr Med Phys & Biomed Engn, QIMP Team, Vienna, Austria; [Yang, Shaomin] Peking Univ, Hlth Sci Ctr, Dept Pathol, Beijing, Peoples R China; [Wang, Shiwei] Evom Med Technol Co Ltd, Shanghai, Peoples R China Medical University of Vienna; Medical University of Vienna; Peking University Zhang, WF (corresponding author), Peking Univ Third Hosp, Dept Nucl Med, 49 North Garden Rd, Beijing 100191, Peoples R China.;Li, X (corresponding author), Med Univ Vienna, Dept Biomed Imaging & Image Guided Therapy, Div Nucl Med, Wahringer Gurtel 18-20,Floor 3L, A-1090 Vienna, Austria. tsy1997@126.com; xiang.li@meduniwien.ac.at Hacker, Marcus/GRJ-2825-2022; Papp, Laszlo/AFT-0975-2022 Ecsedi, Boglarka/0000-0002-3592-8306; Krajnc, Denis/0000-0003-1786-4297; Papp, Laszlo/0000-0002-9049-9989; Li, Xiang/0000-0002-0016-6233; Kluge, Kilian/0000-0002-4224-7314; Grahovac, Marko/0000-0002-3323-778X National Major Science and Technology Projects of China (CN) [2016YFC0103705]; Key Clinical Project of Peking University Third Hospital [BYSYZD2019038] National Major Science and Technology Projects of China (CN); Key Clinical Project of Peking University Third Hospital This study was funded by the National Major Science and Technology Projects of China (CN) (2016YFC0103705) and the Key Clinical Project of Peking University Third Hospital (BYSYZD2019038). 38 1 1 3 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. OCT 2022.0 32 10 7056 7067 10.1007/s00330-022-08999-7 0.0 JUL 2022 12 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 4N3LT 35896836.0 2023-03-23 WOS:000830993000001 0
J Wang, C; Ferrando, M; Causone, F; Jin, X; Zhou, X; Shi, X Wang, Chao; Ferrando, Martina; Causone, Francesco; Jin, Xing; Zhou, Xin; Shi, Xing An innovative method to predict the thermal parameters of construction assemblies for urban building energy models BUILDING AND ENVIRONMENT English Article UBEM; Urban energy simulation; Non -archetype approaches; Construction assemblies; Thermal parameters; Machine learning IN-SITU MEASUREMENT; SIMULATION; STOCK; TRANSMITTANCE; OPTIMIZATION; GENERATION; RESISTANCE; SECTOR Thermal parameters of construction assemblies, e.g., the U-values of roofs, walls, floors, ground floors, windows and the solar heat gain coefficient (SHGC) of windows, are significant inputs for urban building energy models (UBEMs). However, estimating these values at the urban scale is difficult. A common practice to handle this issue is the use of archetypes. Additionally, there are three approaches in building-level studies, namely, 1) estimation based on technical documents, 2) in-situ measurements, and 3) prediction by machine learning. However, the lack of documentation or long-term testing of physical parameters restricts their applications at the urban scale. This paper presents a non-archetype approach that employs two learning algorithms, i.e., k-means and random forest classification (RFC), to predict thermal parameters of construction assemblies, with several urban & building (UB) factors selected as the inputs. The steps involve: 1) partitioning the thermal parameters in the dataset into k clusters using k-means, 2) assigning a Cluster_ID to the thermal parameters in cluster j and recording its centroid mu j, 3) training the RFC, with UB factors as inputs and the Cluster_ID as outputs, 4) predicting the Cluster_ID of thermal parameters of investigated buildings via the trained model, 5) using corresponding centroids as their final thermal parameters, based on the predicted Cluster_ID. As a pilot study, the developed approach has an acceptable result, with the R2 greater than 0.6 and even 0.8. In addition, the study also introduces approaches for acquiring UB factors at the urban scale and demonstrates a case study in Nanjing. [Wang, Chao; Jin, Xing; Zhou, Xin] Southeast Univ, Sch Architecture, Nanjing 210096, Peoples R China; [Ferrando, Martina; Causone, Francesco] Politecn Milan, Dept Energy, I-20156 Milan, Italy; [Shi, Xing] Minist Educ, Key Lab Ecol Energy Saving Study Dense Habitat, Shanghai 200092, Peoples R China; [Shi, Xing] Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China Southeast University - China; Polytechnic University of Milan; Tongji University Shi, X (corresponding author), Minist Educ, Key Lab Ecol Energy Saving Study Dense Habitat, Shanghai 200092, Peoples R China. 20101@tongji.edu.cn Zhou, Xin/AAA-3990-2022 Zhou, Xin/0000-0002-3135-0260; FERRANDO, MARTINA/0000-0003-4060-256X; Causone, Francesco/0000-0002-8694-7232 93 2 2 20 20 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-1323 1873-684X BUILD ENVIRON Build. Environ. OCT 2022.0 224 109541 10.1016/j.buildenv.2022.109541 0.0 SEP 2022 19 Construction & Building Technology; Engineering, Environmental; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 4U1ZZ 2023-03-23 WOS:000858601900002 0
J Xihua, Z; Goyal, SB; Tesfayohanis, M; Verma, C Xihua, Zhang; Goyal, S. B.; Tesfayohanis, Miretab; Verma, Chaman Blockchain-Based Privacy-Preserving Approach Using SVML for Encrypted Smart City Data in the Era of IR 4.0 JOURNAL OF NANOMATERIALS English Article CLASSIFICATION; FRAMEWORK; MACHINE The collection of amounts of useful data from various IoT devices through machine learning (ML) techniques has been extensively applied in lots of areas of the smart city. To improve the efficiency of machine learning, people realize efficient data classification by supporting the ML model, namely, the support vector machine (SVM) model, which is used in the realization of linear classification. However, data security and data protection are not addressed in SVM classifier training from multiple entity-labeled IoT data. In the existing solution, they depend on the implicit hypothesis that learning data can be dependable collected from complex data providers, which is often not the reality. To solve the question between the above problems, in this article, we put forward a secure support vector machine-a secure SECURE SVM training scheme for protecting the privacy on encrypted IoT data based on blockchain. Firstly, a secure and dependable data encryption sharing platform is established among complex data providers by using blockchain technology, which is recorded in a distributed ledger. Secondly, the homomorphic cryptosystem is used to design secure components, and the secure polynomial multiplication is improved to design a secure SVM training algorithm, which only needs two interactions in one iteration and does not need a trusted third party. Finally, the experimental results display that the plan ensures the confidentiality of sensitive data of each data provider and the confidentiality and validity of SVM model parameters of data analysts. [Xihua, Zhang] Baicheng Normal Univ, Baicheng 137000, Jilin, Peoples R China; [Xihua, Zhang; Goyal, S. B.] City Univ Petaling Jaya, Fac Informat Technol, Petaling Jaya 46100, Malaysia; [Tesfayohanis, Miretab] Dambi Dollo Univ, Dept Informat Technol, Dembi Dolo, Ethiopia; [Verma, Chaman] Eotvos Lorand Univ, Budapest, Hungary Baicheng Normal University; Eotvos Lorand University Tesfayohanis, M (corresponding author), Dambi Dollo Univ, Dept Informat Technol, Dembi Dolo, Ethiopia. 103718606@qq.com; drsbgoyal@gmail.com; miretab@dadu.edu.et; chaman@inf.elte.hu verma, chaman/A-5517-2018 verma, chaman/0000-0002-9925-112X; zhang, xihua/0000-0002-8733-6812 31 0 0 1 2 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-4110 1687-4129 J NANOMATER J. Nanomater. JUL 30 2022.0 2022 7463513 10.1155/2022/7463513 0.0 8 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 4M3VQ gold 2023-03-23 WOS:000853253600003 0
C Friess, S; Tino, P; Menzel, S; Sendhoff, B; Yao, X IEEE Friess, Stephen; Tino, Peter; Menzel, Stefan; Sendhoff, Bernhard; Yao, Xin Learning Transferable Variation Operators in a Continuous Genetic Algorithm 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) English Proceedings Paper IEEE Symposium Series on Computational Intelligence (SSCI) DEC 06-09, 2019 Xiamen, PEOPLES R CHINA IEEE,IEEE Computat Intelligence Soc,Xiamen Univ,Xi AN Univ Posts & Telecommunicat,Fujian Assoc Artificial Intelligence evolutionary computation; statistical learning; stochastic optimization; knowledge transfer; machine learning OPTIMIZATION The notion of experience has often been neglected within the domain of evolutionary computation while in machine learning a large variety of methods has emerged in the recent years under the umbrella of transfer learning. Notably, realizing experience-based methods suffers from a variety of conceptual key problems. The first one being in regards to what constitutes problem-similarity from an algorithm perspective and the second one being what constitutes the transferable experience by itself. Ideally, one would envision that a learning optimization algorithm could be expected to act similarly to a human-problem solver who tackles novel tasks initially without any preconceptions. Experience only comes into play until sufficient similarity to known problems is established. Our paper therefore has two aims. First, to outline existing related fields and methodologies and highlight their insufficiencies. Second, to make the case for experience-based optimization by a demonstration using a novel and statistics-based approach with a real-coded genetic algorithm as a case study. In this paper we do not claim to construct universal problem solvers, but instead propose that from an algorithm-specific-view, problem characteristics can be learned and harnessed to improve future performance of similarly-structured optimization tasks. [Friess, Stephen; Tino, Peter] Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England; [Menzel, Stefan; Sendhoff, Bernhard] Honda Res Inst Europe GmbH, D-63073 Offenbach, Germany; [Yao, Xin] Southern Univ Sci & Technol, Shenzhen, Peoples R China University of Birmingham; Honda Motor Company; Southern University of Science & Technology Friess, S (corresponding author), Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England. shf814@cs.bham.ac.uk; p.tino@cs.bham.ac.uk; stefan.menzel@honda-ri.de; bernhard.sendhoff@honda-ri.de; xiny@sustech.edu.cn Sendhoff, Bernhard/AAW-6486-2020 Sendhoff, Bernhard/0000-0002-1233-9584 European Union [766186]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X386]; Shenzhen Peacock Plan [KQTD2016112514355531]; Program for University Key Laboratory of Guangdong Province [2017KSYS008] European Union(European Commission); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Shenzhen Peacock Plan; Program for University Key Laboratory of Guangdong Province This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 766186. It was also supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008). 29 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-2485-8 2019.0 2027 2033 7 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP5GE Green Submitted 2023-03-23 WOS:000555467202017 0
J Xie, JJ; Zhong, YJ; Zhang, JG; Liu, S; Ding, CQ; Triantafyllopoulos, A Xie, Jiangjian; Zhong, Yujie; Zhang, Junguo; Liu, Shuo; Ding, Changqing; Triantafyllopoulos, Andreas A review of automatic recognition technology for bird vocalizations in the deep learning era ECOLOGICAL INFORMATICS English Review Bird vocalization; Automatic recognition; Pre -process; Feature extraction; Classification; Clustering ACOUSTIC SEQUENCES; CLASSIFICATION; IDENTIFICATION; SEGMENTATION; ENHANCEMENT; PRIMATES; IDENTITY; FEATURES; FUSION; ISLAND Birds are considered critical indicators of ecosystem condition. Automatic recording devices have emerged as a trending tool to assist field observations, contributing to biodiversity monitoring on large spatio-temporal scales. However, manually processing huge volumes of recordings is challenging. Consequently, there has been a growing interest in automatic bird vocalization recognition in recent years. Automatic bird vocalization recog-nition technology has advanced from classical pattern recognition to deep learning (DL), with significantly improved recognition performance. This paper reviews related works on DL-based automatic bird vocalization recognition technology in the last decade. In this review, we present the current state of research in the three key areas of pre-processing, feature extraction and recognition methods involved in automatic bird vocalization recognition. The related datasets, evaluation metrics and software are also summarized. Finally, existing chal-lenges along with opportunities for future work are highlighted. We conclude that, while DL-based automatic bird vocalization recognition has made recent advances in specific species, more robust denoising approaches, larger public datasets, and stronger generalization capabilities of feature extraction and recognition are required to achieve reliable and general bird recognition in the wild. We expect that this review will serve as a firm foundation for new researchers working in the field of DL-based automatic bird vocalization recognition tech-nologies, as well as become an insightful guide for computer science and ecology experts. [Xie, Jiangjian; Zhong, Yujie; Zhang, Junguo] Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China; [Xie, Jiangjian; Zhang, Junguo] Beijing Forestry Univ, Res Ctr Biodivers Intelligent Monitoring, Beijing 100083, Peoples R China; [Xie, Jiangjian; Liu, Shuo; Triantafyllopoulos, Andreas] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, D-86159 Augsburg, Germany; [Ding, Changqing] Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China Beijing Forestry University; Beijing Forestry University; University of Augsburg; Beijing Forestry University Ding, CQ (corresponding author), Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China. shyneforce@bjfu.edu.cn; zhongyujie@bjfu.edu.cn; zhangjunguo@bjfu.edu.cn; shuo.liu@uni-a.de; cqding@bjfu.edu.cn; andreas.triantafyllopoulos@uni-a.de Beijing Municipal Natural Science Foundation; Fundamental Research Funds for the Central Universities; National Natural Science Foun-dation of China; China Scholarship Council; [6214040]; [2021ZY70]; [32270554]; [202106515010] Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foun-dation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); ; ; ; Acknowledgements This work was jointly supported by the Beijing Municipal Natural Science Foundation (6214040) , the Fundamental Research Funds for the Central Universities (2021ZY70) , the National Natural Science Foun-dation of China (32270554) and China Scholarship Council (202106515010) . 162 0 0 7 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1574-9541 1878-0512 ECOL INFORM Ecol. Inform. MAR 2023.0 73 101927 10.1016/j.ecoinf.2022.101927 0.0 25 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 7V9YX 2023-03-23 WOS:000913170500002 0
C Ma, B; Wu, JS; Liu, W; Chiaraviglio, L; Ming, X IEEE Comp Soc Ma, Bo; Wu, Jinsong; Liu, William; Chiaraviglio, Luca; Ming, Xing Combating Hard or Soft Disasters with Privacy-Preserving Federated Mobile Buses-and-Drones based Networks 2020 IEEE 21ST INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE (IRI 2020) English Proceedings Paper 21st IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI) AUG 11-13, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,Soc Informat Reuse & Integrat Infectious disease surveillance; Federated Machine Learning; Privacy-Preserving(PP) EDGE INFRASTRUCTURE It is foreseeable the popularity of the mobile edge computing enabled infrastructure for wireless networks in the incoming fifth generation (5G) and future sixth generation (6G) wireless networks. Especially after a 'hard' disaster such as earthquakes or a 'soft' disaster such as COVID-19 pandemic, the existing telecommunication infrastructure, including wired and wireless networks, is often seriously compromised or with infectious disease risks and should-not-close-contact, thus cannot guarantee regular coverage and reliable communications services. These temporarily-missing communications capabilities are crucial to rescuers, health-carers, or affected or infected citizens as the responders need to effectively coordinate and communicate to minimize the loss of lives and property, where the 5G/6G mobile edge network helps. On the other hand, the federated machine learning (FML) methods have been newly developed to address the privacy leakage problems of the traditional machine learning held normally by one centralized organization, associated with the high risks of a single point of hacking. After detailing current state-of-the-art both in privacy-preserving, federated learning, and mobile edge communications networks for 'hard' and 'soft' disasters, we consider the main challenges that need to be faced. We envision a privacy-preserving federated learning enabled buses-and-drones based mobile edge infrastructure (ppFL-AidLife) for disaster or pandemic emergency communications. The ppFL-AidLife system aims at a rapidly deployable resilient network capable of supporting flexible, privacy-preserving and low-latency communications to serve large-scale disaster situations by utilizing the existing public transport networks, associated with drones to maximally extend their radio coverage to those hard-to-reach disasters or should-not-close-contact pandemic zones. [Ma, Bo; Liu, William; Ming, Xing] Auckland Univ Technol, Dept Informat Technol & Software Engn, Auckland, New Zealand; [Wu, Jinsong] Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin, Peoples R China; [Wu, Jinsong] Univ Chile, Dept Elect Engn, Santiago, Chile; [Chiaraviglio, Luca] Tor Vergata Univ Rome, Dept Elect Engn Technol & Software Engn, Rome, Italy Auckland University of Technology; Guilin University of Electronic Technology; Universidad de Chile; University of Rome Tor Vergata Ma, B (corresponding author), Auckland Univ Technol, Dept Informat Technol & Software Engn, Auckland, New Zealand. bo.ma@aut.ac.nz; wujs@ieee.org; william.liu@aut.ac.nz; luca.chiaraviglio@uniroma2.it; Xing.Ming@aut.ac.nz Ming, Xing/GQZ-6385-2022 32 5 5 1 4 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 978-1-7281-1054-7 2020.0 31 36 10.1109/IRI49571.2020.00013 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BR1TM Bronze 2023-03-23 WOS:000635425100005 0
J Xu, H; Zhang, W; Wang, Y Xu, Hui; Zhang, Wei; Wang, Yong Explore missing flow dynamics by physics-informed deep learning: The parameterized governing systems PHYSICS OF FLUIDS English Article NEURAL-NETWORKS; DATA-DRIVEN; TURBULENCE; MODEL Gaining and understanding flow dynamics have much importance in a wide range of disciplines, e.g., astrophysics, geophysics, biology, mechanical engineering, and biomedical engineering. For turbulent flows, local flow information such as velocity and its statistics, can be measured experimentally. Due to the poor fidelity or experimental limitations, some information may not be resolved in a region of interest. On the other hand, detailed flow features are described by the governing equations, e.g., the Navier-Stokes equations for viscous fluid, and can be resolved numerically, which is heavily dependent on the capability of either computing resources or modeling. Alternatively, we address this problem by employing the physics-informed deep learning and treat the governing equations as a parameterized constraint to recover the missing flow dynamics. We demonstrate that with limited data, no matter from experiment or others, the flow dynamics in the region where the required data are missing or not measured, can be reconstructed with the parameterized governing equations. Meanwhile, a richer dataset, with spatial distribution of the control parameter (e.g., eddy viscosity of turbulence modelings), can be obtained. The method provided in this paper may shed light on the data-driven scale-adaptive turbulent structure recovering and understanding of complex fluid physics and can be extended to other parameterized governing systems beyond fluid mechanics. Published under an exclusive license by AIP Publishing. https://doi.org/10.1063/5.0062377 [Xu, Hui] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China; [Zhang, Wei] Marine & Res Inst China, Sci & Technol Water Jet Prop Lab, Shanghai 200011, Peoples R China; [Wang, Yong] Max Planck Inst Dynam & Self Org, Lab Fluid Phys, Pattern Format & Biocomplex, D-37077 Gottingen, Germany Shanghai Jiao Tong University; Max Planck Society Wang, Y (corresponding author), Max Planck Inst Dynam & Self Org, Lab Fluid Phys, Pattern Format & Biocomplex, D-37077 Gottingen, Germany. yong.wang@ds.mpg.de Wang, Yong/E-2391-2013; Xu, Hui/E-1549-2011 Wang, Yong/0000-0001-8518-6745; Xu, Hui/0000-0001-8448-4425 National Numerical Wind Tunnel Project [NNW2019ZT4-B09]; National Natural Science Foundation of China [91852106, 91851127] National Numerical Wind Tunnel Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to acknowledge support from the National Numerical Wind Tunnel Project (Grant No. NNW2019ZT4-B09) and the National Natural Science Foundation of China (Grant Nos. 91852106 and 91851127). 28 17 17 7 25 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1070-6631 1089-7666 PHYS FLUIDS Phys. Fluids SEP 2021.0 33 9 95116 10.1063/5.0062377 0.0 10 Mechanics; Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Physics WW1AI Green Submitted 2023-03-23 WOS:000717657700002 0
C Yang, LH; Liu, J; Wang, YM; Wang, H; Martinez, L Denoeux, T; Lefevre, E; Liu, Z; Pichon, F Yang, Long-Hao; Liu, Jun; Wang, Ying-Ming; Wang, Hui; Martinez, Luis Improving Micro-Extended Belief Rule-Based System Using Activation Factor for Classification Problems BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2021) Lecture Notes in Artificial Intelligence English Proceedings Paper 6th International Conferences on Belief Functions (BELIEF) OCT 15-19, 2021 Shanghai, PEOPLES R CHINA Belief Funct & Applicat Soc Extended belief rule-based system; Activation factor; Classification; Rule inference The micro-extended belief rule-based system (Micro-EBRBS) is an advanced rule-based system and has shown its superior ability in solving big data problems. To overcome the activation rule incompleteness and inconsistency of Micro-EBRBS, a new concept, named activation factor (AF), is introduced to revise the calculation of individual matching degree and, furthermore, an AF-based inference (AFI) method is proposed for improving Micro-EBRBS. A comparative analysis study is conducted using three classification datasets. Results demonstrate that the proposed AFI method can not only improve the accuracy of Micro-EBRBS, but also reduce the number of failed data in the process of rule inference. [Yang, Long-Hao; Wang, Ying-Ming] Fuzhou Univ, Decis Sci Inst, Fuzhou, Peoples R China; [Wang, Ying-Ming] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China; [Yang, Long-Hao; Liu, Jun; Wang, Hui; Martinez, Luis] Ulster Univ, Sch Comp, Coleraine, Londonderry, North Ireland; [Yang, Long-Hao; Martinez, Luis] Univ Jaen, Dept Comp Sci, Jaen, Spain Fuzhou University; Fuzhou University; Ulster University; Universidad de Jaen Yang, LH (corresponding author), Fuzhou Univ, Decis Sci Inst, Fuzhou, Peoples R China.;Yang, LH (corresponding author), Ulster Univ, Sch Comp, Coleraine, Londonderry, North Ireland.;Yang, LH (corresponding author), Univ Jaen, Dept Comp Sci, Jaen, Spain. j.liu@ulster.ac.uk; h.wang@ulster.ac.uk; martin@ujaen.es Martinez, Luis/A-1746-2009; Wang, Ying/HJI-2509-2023; wang, yan/GSE-6489-2022; wang, yingying/GRS-3058-2022 Martinez, Luis/0000-0003-4245-8813; National Natural Science Foundation of China [72001043, 61773123]; Natural Science Foundation of Fujian Province of China [2020J05122]; Humanities and Social Science Foundation of the Ministry of Education of China [20YJC630188]; Chengdu International Science Cooperation Project [2020-GH02-00064-HZ]; Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project [PGC2018-099402-B-I00] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province); Humanities and Social Science Foundation of the Ministry of Education of China(Ministry of Education, China); Chengdu International Science Cooperation Project; Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project \ Supported by the National Natural Science Foundation of China (Nos. 72001043 and 61773123), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188), the Chengdu International Science Cooperation Project (No. 2020-GH02-00064-HZ), and the Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project PGC2018-099402-B-I00. 6 0 0 2 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-88601-1; 978-3-030-88600-4 LECT NOTES ARTIF INT 2021.0 12915 79 86 10.1007/978-3-030-88601-1_8 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematics, Applied; Logic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics; Science & Technology - Other Topics BS4HY 2023-03-23 WOS:000719386300008 0
J Li, L; Zhu, HG; Zhang, ZY; Zhao, L; Xu, L; Jonas, RA; Garway-Heath, DF; Jonas, JB; Wang, YX Li, Lei; Zhu, Haogang; Zhang, Zhenyu; Zhao, Liang; Xu, Liang; Jonas, Rahul A.; Garway-Heath, David F.; Jonas, Jost B.; Wang, Ya Xing Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation JMIR MEDICAL INFORMATICS English Article retinal nerve fiber layer thickness; radial basis neural network; neural network; glaucoma; optic nerve head; optical coherence tomography; myopia; optic nerve OPEN-ANGLE GLAUCOMA; DIABETIC-RETINOPATHY; LEARNING ALGORITHM; VISION IMPAIRMENT; AXIAL LENGTH; PREVALENCE; RISK; PERFORMANCE; PARAMETERS; BLINDNESS Background: Due to the axial elongation-associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field are not able to correctly differentiate glaucomatous lesions. It has been clinically challenging to detect glaucoma in highly myopic eyes. Objective: This study aimed to develop a neural network to adjust for the dependence of the peripapillary retinal nerve fiber layer (RNFL) thickness (RNFLT) profile on age, gender, and ocular biometric parameters and to evaluate the network's performance for glaucoma diagnosis, especially in high myopia. Methods: RNFLT with 768 points on the circumferential 3.4-mm scan was measured using spectral-domain optical coherence tomography. A fully connected network and a radial basis function network were trained for vertical (scaling) and horizontal (shift) transformation of the RNFLT profile with adjustment for age, axial length (AL), disc-fovea angle, and distance in a test group of 2223 nonglaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 nonglaucomatous participants. Results: By applying the RNFL compensation algorithm, the area under the receiver operating characteristic curve for detecting glaucoma increased from 0.70 to 0.84, from 0.75 to 0.89, from 0.77 to 0.89, and from 0.78 to 0.87 for eyes in the highest 10% percentile subgroup of the AL distribution (mean 26.0, SD 0.9 mm), highest 20% percentile subgroup of the AL distribution (mean 25.3, SD 1.0 mm), highest 30% percentile subgroup of the AL distribution (mean 24.9, SD 1.0 mm), and any AL (mean 23.5, SD 1.2 mm), respectively, in comparison with unadjusted RNFLT. The difference between uncompensated and compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2%, and 11.3% in the highest 10% percentile subgroup, highest 20% percentile subgroup, highest 30% percentile subgroup, and all eyes, respectively. Conclusions: In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the RNFLT profile for glaucoma detection particularly in myopic and highly myopic eyes. [Li, Lei; Zhu, Haogang; Zhang, Zhenyu] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing, Peoples R China; [Li, Lei; Zhao, Liang; Xu, Liang; Jonas, Jost B.; Wang, Ya Xing] Capital Univ Med Sci, Beijing Tongren Hosp, Beijing Inst Ophthalmol, Beijing Ophthalmol & Visual Sci Key Lab, 17 Hougou Lane, Beijing 100005, Peoples R China; [Zhu, Haogang; Garway-Heath, David F.] Moorfields Eye Hosp NHS Fdn Trust, NIHR Biomed Res Ctr Ophthalmol, UCL Inst Ophthalmol, London, England; [Jonas, Rahul A.] Univ Cologne, Fac Med, Dept Ophthalmol, Cologne, Germany; [Jonas, Rahul A.] Univ Cologne, Univ Hosp, Cologne, Germany; [Jonas, Jost B.] Heidelberg Univ, Med Fac Mannheim, Dept Ophthalmol, Mannheim, Germany Beihang University; University of London; University College London; Moorfields Eye Hospital NHS Foundation Trust; University of Cologne; University of Cologne; Ruprecht Karls University Heidelberg Wang, YX (corresponding author), Capital Univ Med Sci, Beijing Tongren Hosp, Beijing Inst Ophthalmol, Beijing Ophthalmol & Visual Sci Key Lab, 17 Hougou Lane, Beijing 100005, Peoples R China. yaxingw@gmail.com zhang, zhenyu/HOA-8440-2023; Jonas, Rahul A/N-9304-2017; wang, YA XING/K-9671-2016; Wang, Ying/HJI-2509-2023; Jonas, Rahul/HKE-7913-2023 Jonas, Rahul A/0000-0002-5389-3047; wang, YA XING/0000-0003-2749-7793; Jonas, Rahul/0000-0002-5389-3047; Garway-Heath, David/0000-0003-2907-6992; Garway-Heath, David/0000-0001-8611-1155; Jonas, Jost/0000-0003-2972-5227 National Natural Science Foundation of China [81570835]; State Key Laboratory of Software Development Environment, Beihang University, Beijing, China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Software Development Environment, Beihang University, Beijing, China This work was supported by the National Natural Science Foundation of China (number 81570835) and the State Key Laboratory of Software Development Environment, Beihang University, Beijing, China. 39 3 3 0 8 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA 2291-9694 JMIR MED INF JMIR Med. Inf. MAY 2021.0 9 5 e22664 10.2196/22664 0.0 13 Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Medical Informatics SL1FZ 34003137.0 gold, Green Accepted, Green Submitted 2023-03-23 WOS:000656664300020 0
J Wang, Y; Wei, X; Shen, H; Ding, L; Wan, JQ Wang, Yong; Wei, Xian; Shen, Hao; Ding, Lu; Wan, Jiuqing Robust fusion for RGB-D tracking using CNN features APPLIED SOFT COMPUTING English Article RGB-D tracking; Robust fusion; Hierarchical convolutional neural network; Correlation filter tracking DEEP CONVOLUTIONAL NETWORKS; VISUAL TRACKING; OBJECT TRACKING; MODEL; TIME Recently, RGB-D sensors have become popular. Many computer vision problems can be better dealt with depth data. It is a challenging problem to integrate depth data into a visual object tracker to address the problems such as scale change and occlusion. In this paper, we propose a robust fusion based RGB-D tracking method. Specifically, hierarchical convolutional neural network (CNN) features are first adopted to encode RGB and depth images separately. Next, target is tracked based on correlation filter tracking framework. Then the results of each CNN feature are localized according to the tracking results in a short period of time. Finally, the target is localized by jointly fusing the results of RGB and depth images. Model updating is finally carried out according to the differences between RGB and depth images. Experiments on the University of Birmingham RGB-D Tracking Benchmark (BTB) and the Princeton RGB-D Tracking Benchmark (PTB) achieve comparable results to state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved. [Wang, Yong] Sun Yat Sen Univ, Sch Aeronaut & Astronaut, Guangzhou, Guangdong, Peoples R China; [Wang, Yong] Univ Ottawa, Sch Elect & Comp Sci, Ottawa, ON, Canada; [Wei, Xian] Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Peoples R China; [Shen, Hao] Tech Univ Munich, Munich, Germany; [Shen, Hao] Fortiss GmbH, Munich, Germany; [Ding, Lu] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China; [Wan, Jiuqing] Beijing Univ Aeronaut & Astronaut, Dept Automat, Beijing, Peoples R China Sun Yat Sen University; University of Ottawa; Chinese Academy of Sciences; Fujian Institute of Research on the Structure of Matter, CAS; Technical University of Munich; fortiss; Shanghai Jiao Tong University; Beihang University Wei, X (corresponding author), Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Peoples R China. xian.wei@fjirsm.ac.cn shen, hao/HGF-0551-2022 , yong/0000-0001-6559-9550 National Science Found for Young Scholars China [61806186]; State Key Laboratory of Robotics and System(HIT) China [SKLRS-2019-KF-15]; program Construction of Fujian Research Institute on Intelligent Logistics Industry Technology'' China [2018H2001]; CAS Pioneer Hundred Talents Program [2017-122]; program 'Quanzhou Science and Technology Plan' [2019C112, 2019C011R, 2019STS08]; National Natural Science Foundation of China [61873015]; Beijing Municipal Natural Science Foundation China [4192031]; German Research Foundation (DFG) [KL 2189/9-1] National Science Found for Young Scholars China; State Key Laboratory of Robotics and System(HIT) China; program Construction of Fujian Research Institute on Intelligent Logistics Industry Technology'' China; CAS Pioneer Hundred Talents Program; program 'Quanzhou Science and Technology Plan'; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation China(Beijing Natural Science Foundation); German Research Foundation (DFG)(German Research Foundation (DFG)) This work was partially supported by National Science Found for Young Scholars China under Grant No. 61806186, State Key Laboratory of Robotics and System(HIT) China under Grant No. SKLRS-2019-KF-15, the program ``Construction of Fujian Research Institute on Intelligent Logistics Industry Technology'' China under Grant No. 2018H2001, CAS Pioneer Hundred Talents Program (Type C) under Grant No. 2017-122, the program 'Quanzhou Science and Technology Plan' under Grant No. 2019C112, No. 2019C011R and No. 2019STS08, the National Natural Science Foundation of China No. 61873015, Beijing Municipal Natural Science Foundation China No. 4192031 and the German Research Foundation (DFG) under Grant No. KL 2189/9-1. 46 10 10 1 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. JUL 2020.0 92 106302 10.1016/j.asoc.2020.106302 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science LT7OF 2023-03-23 WOS:000537255300014 0
J Gharleghi, R; Adikari, D; Ellenberger, K; Ooi, SY; Ellis, C; Chen, CM; Gao, RC; He, YT; Hussain, R; Lee, CY; Li, J; Ma, J; Nie, ZW; Oliveira, B; Qi, YL; Skandarani, Y; Vilaca, JL; Wang, XY; Yang, S; Sowmya, A; Beier, S Gharleghi, Ramtin; Adikari, Dona; Ellenberger, Katy; Ooi, Sze-Yuan; Ellis, Chris; Chen, Chung-Ming; Gao, Ruochen; He, Yuting; Hussain, Raabid; Lee, Chia-Yen; Li, Jun; Ma, Jun; Nie, Ziwei; Oliveira, Bruno; Qi, Yaolei; Skandarani, Youssef; Vilaca, Joao L.; Wang, Xiyue; Yang, Sen; Sowmya, Arcot; Beier, Susann Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge COMPUTERIZED MEDICAL IMAGING AND GRAPHICS English Article Coronary arteries; Image segmentation; Machine learning MODELS; ATLAS Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and recon-structing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and ar-tefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new auto-mated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications. [Gharleghi, Ramtin; Beier, Susann] Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW, Australia; [Adikari, Dona; Ellenberger, Katy; Ooi, Sze-Yuan] UNSW Sydney, Prince Wales Clin Sch Med, Sydney, NSW, Australia; [Adikari, Dona; Ellenberger, Katy; Ooi, Sze-Yuan] Prince Wales Hosp, Dept Cardiol, Sydney, NSW, Australia; [Ellis, Chris] Auckland City Hosp, Auckland, New Zealand; [Chen, Chung-Ming] Natl Taiwan Univ, Inst Biomed Engn, Taipei, Taiwan; [Gao, Ruochen; Li, Jun] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China; [Hussain, Raabid; Skandarani, Youssef] Univ Burgundy, ImViA Lab, Dijon, France; [He, Yuting; Qi, Yaolei] Southeast Univ, Nanjing, Jiangsu, Peoples R China; [Skandarani, Youssef] CASIS Inc, Dijon, France; [Lee, Chia-Yen] Natl United Univ, Dept Elect Engn, Miaoli, Miaoli County, Taiwan; [Ma, Jun] Nanjing Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Nie, Ziwei] Nanjing Univ, Nanjing, Jiangsu, Peoples R China; [Oliveira, Bruno; Vilaca, Joao L.] Polytech Inst Cavado & Ave, 2Ai Sch Technol, Barcelos, Portugal; [Oliveira, Bruno] Univ Minho, Sch Med, Life & Hlth Sci Res Inst ICVS, Braga, Portugal; [Oliveira, Bruno] Univ Minho, Algoritmi Ctr, Sch Engn, Guimaraes, Portugal; [Wang, Xiyue] Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China; [Yang, Sen] Sichuan Univ, Coll Biomed Engn, Chengdu, Peoples R China; [Sowmya, Arcot] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia University of New South Wales Sydney; University of New South Wales Sydney; Auckland City Hospital; National Taiwan University; Chinese Academy of Sciences; Institute of Computing Technology, CAS; Universite de Bourgogne; Southeast University - China; National United University; Nanjing University of Science & Technology; Nanjing University; Instituto Politecnico do Cavado e do Ave - IPCA; Universidade do Minho; Universidade do Minho; Sichuan University; Sichuan University; University of New South Wales Sydney Gharleghi, R (corresponding author), Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW, Australia. r.gharleghi@student.unsw.edu.au he, yuting/HLX-6751-2023; Wang, Xiyue/GXM-5641-2022; Vilaça, João L./HLH-0735-2023 Wang, Xiyue/0000-0002-3597-9090; Vilaça, João L./0000-0002-4196-5357; Yang, Sen/0000-0002-0639-4122; Gharleghi, Ramtin/0000-0001-7478-2920; Qi, Yaolei/0000-0002-8531-7386 Auckland Academic Health Alliance (AAHA); Auckland Medical Research Foundation (AMRF); Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER) [NORTE-010145-FEDER-000045]; FCT; European Social Found, through Programa Operacional Capital Humano (POCH) [SFRH/BD/136721/2018]; Fundação para a Ciência e a Tecnologia [SFRH/BD/136721/2018] Funding Source: FCT Auckland Academic Health Alliance (AAHA); Auckland Medical Research Foundation (AMRF); Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER); FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); European Social Found, through Programa Operacional Capital Humano (POCH); Fundação para a Ciência e a Tecnologia The authors thank Jane Liggins and Miriam Hayward, Intra Imaging for assisting with the collection of the challenge dataset. SB acknowledges the Auckland Academic Health Alliance (AAHA) and the Auckland Medical Research Foundation (AMRF) for their financial support and endorsement. The work of BO was funded in part by the project NORTE-010145-FEDER-000045?, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). BO and JV also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136721/2018. 35 3 3 6 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0895-6111 1879-0771 COMPUT MED IMAG GRAP Comput. Med. Imaging Graph. APR 2022.0 97 102049 10.1016/j.compmedimag.2022.102049 0.0 MAR 2022 8 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging 0U8HB 35334316.0 hybrid 2023-03-23 WOS:000787887200007 0
J Hu, F; Teichert, T; Deng, SL; Liu, Y; Zhou, GT Hu, Feng; Teichert, Thorsten; Deng, Shengli; Liu, Yong; Zhou, Guotao Dealing with pandemics: An investigation of the effects of COVID-19 on customers? evaluations of hospitality services TOURISM MANAGEMENT English Article COVID-19 pandemic; Service evaluation; Hotel reviews; Text mining; Sentiment analysis; Comparative importance?performance analysis IMPORTANCE-PERFORMANCE ANALYSIS; TOURISM DEMAND; BIG DATA; ONLINE; SATISFACTION; IMPACT; HOTELS; EXPECTATION; ANTECEDENTS; REPEAT The hospitality industry is highly vulnerable to pandemics. However, little is known about how pandemics alter travelers? evaluations of hospitality services. Therefore, this study investigates the changes in travelers? expectations and perceptions of hotel services during different stages of the novel coronavirus 2019 (COVID-19) pandemic. 98,163 Chinese hotel reviews were collected and scrutinized via text mining and sentiment analysis techniques to derive new implications for service optimization. The results reveal shifts in consumers? evaluations well beyond hygienic requirements. Insights obtained from this research can help guide hospitality practice in organizing its priorities during acute pandemic situations and adjusting to possibly longer-lasting shifts in consumer preferences. [Hu, Feng] Zhejiang Normal Univ, Coll Econ & Management, Jinhua 321004, Zhejiang, Peoples R China; [Hu, Feng; Teichert, Thorsten] Univ Hamburg, Chair Mkt & Innovat, D-20146 Hamburg, Germany; [Deng, Shengli] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China; [Liu, Yong] Aalto Univ, Dept Informat & Serv Econ, Sch Business, Helsinki, Finland; [Zhou, Guotao] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China Zhejiang Normal University; University of Hamburg; Wuhan University; Aalto University; Wuhan University Deng, SL (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China. hufeng@zjnu.cn; Thorsten.Teichert@uni-hamburg.de; victorydc@sina.com; yong.liu@aalto.fi; grant_zgt@163.com Gui, Yuliang/AAP-5071-2021 Liu, Yong/0000-0002-0228-8971 National Natural Science Foundation, PR China [71974149]; Wuhan University artificial intelligence project [2020AI021] National Natural Science Foundation, PR China(National Natural Science Foundation of China (NSFC)); Wuhan University artificial intelligence project This research is supported in part by National Natural Science Foundation, PR China (Grant No. 71974149) and Wuhan University artificial intelligence project (Grant No. 2020AI021). 73 38 38 18 176 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0261-5177 1879-3193 TOURISM MANAGE Tourism Manage. AUG 2021.0 85 104320 10.1016/j.tourman.2021.104320 0.0 MAR 2021 14 Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Social Sciences - Other Topics; Business & Economics RQ5QZ 34815612.0 Green Accepted 2023-03-23 WOS:000642474700002 0
J Shi, R; Bao, XW; Unger, K; Sun, J; Lu, S; Manapov, F; Wang, XB; Belka, C; Li, ML Shi, Run; Bao, Xuanwen; Unger, Kristian; Sun, Jing; Lu, Shun; Manapov, Farkhad; Wang, Xuanbin; Belka, Claus; Li, Minglun Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage I lung adenocarcinoma patients THERANOSTICS English Article Stage I lung adenocarcinoma; Hypoxia; Clinical outcomes; Genomic alterations; Machine learning ADJUVANT CHEMOTHERAPY; CANCER; EXPRESSION; PROLIFERATION; HETEROGENEITY; RESOURCE; SURVIVAL; REVEALS; MODEL Rationale: The current tumour-node-metastasis (TNM) staging system is insufficient for precise treatment decision-making and accurate survival prediction for patients with stage I lung adenocarcinoma (LUAD). Therefore, more reliable biomarkers are urgently needed to identify the high-risk subset in stage I patients to guide adjuvant therapy. Methods: This study retrospectively analysed the transcriptome profiles and clinical parameters of 1,400 stage I LUAD patients from 14 public datasets, including 13 microarray datasets from different platforms and 1 RNA-Seq dataset from The Cancer Genome Atlas ( TCGA). A series of bioinformatic and machine learning approaches were combined to establish hypoxia-derived signatures to predict overall survival (OS) and immune checkpoint blockade (ICB) therapy response in stage I patients. In addition, enriched pathways, genomic and copy number alterations were analysed in different risk subgroups and compared to each other. Results: Among various hallmarks of cancer, hypoxia was identified as a dominant risk factor for overall survival in stage I LUAD patients. The hypoxia-related prognostic risk score (HPRS) exhibited more powerful capacity of survival prediction compared to traditional clinicopathological features, and the hypoxia-related immunotherapeutic response score (HIRS) outperformed conventional biomarkers for ICB therapy. An integrated decision tree and nomogram were generated to optimize risk stratification and quantify risk assessment. Conclusions: In summary, the proposed hypoxia-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in stage I LUAD patients. [Shi, Run; Unger, Kristian; Sun, Jing; Manapov, Farkhad; Belka, Claus; Li, Minglun] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiat Oncol, Marchioninistr 15, D-81377 Munich, Germany; [Bao, Xuanwen] Zhejiang Univ, Affiliated Hosp 1, Coll Med, Dept Med Oncol, Hangzhou 310003, Peoples R China; [Unger, Kristian] Helmholtz Ctr Munich, German Res Ctr Environm Hlth GmbH, Res Unit Radiat Cytogenet, D-85764 Neuherberg, Germany; [Lu, Shun] Univ Elect Sci & Technol China, Sichuan Canc Hosp, Sch Med, Dept Radiotherapy, Chengdu 610041, Peoples R China; [Wang, Xuanbin] Hubei Univ Med, Renmin Hosp, Oncol Ctr, Lab Chinese Herbal Pharmacol,Hubei Key Lab Wudang, Shiyan 442000, Peoples R China University of Munich; Zhejiang University; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Electronic Science & Technology of China; Hubei University of Medicine Li, ML (corresponding author), Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiat Oncol, Marchioninistr 15, D-81377 Munich, Germany. minglun.li@med.uni-muenchen.de Li, Minglun/A-8086-2019; Lu, Shun/ABF-8341-2021; Wang, Xuanbin/AHE-9819-2022 Wang, Xuanbin/0000-0003-0769-2874; /0000-0002-6232-3783 China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) We greatly thank the China Scholarship Council (CSC) for supporting the work of Run Shi, Xuanwen Bao and Jing Sun. 53 25 25 1 10 IVYSPRING INT PUBL LAKE HAVEN PO BOX 4546, LAKE HAVEN, NSW 2263, AUSTRALIA 1838-7640 THERANOSTICS Theranostics 2021.0 11 10 5061 5076 10.7150/thno.56202 0.0 16 Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Research & Experimental Medicine QX0RP 33754044.0 Green Accepted, gold 2023-03-23 WOS:000629058400031 0
J Zhang, Q; Khetan, A; Sorkun, E; Niu, F; Loss, A; Pucher, I; Er, S Zhang, Qi; Khetan, Abhishek; Sorkun, Elif; Niu, Fang; Loss, Andrea; Pucher, Ilaria; Er, Suleyman Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries ENERGY STORAGE MATERIALS English Article Data-driven discovery; High-throughput virtual screening; Computational chemistry; Electrochemistry; Redox flow batteries HYDROLYSIS; ZINC A high-throughput virtual screening (HTVS)-guided experimental study is applied for the large-scale exploration of quinone-like anolytes for aqueous redox flow batteries (ARFBs). This includes the design of a focused virtual chemical library inspired by stable molecules, quantum chemical prediction of redox properties, machine learning prediction of aqueous solubility, automated search for commercial availability on vendor databases, and electrochemical characterization of the promising compounds. Screening in a chemical space of 3257 redox pairs led to 205 predicted candidates with higher solubility and lower redox potential than that of anthraquinone-2,7-disulfonic acid (AQDS) anolyte used in ARFBs. Through electrochemical studies on commercially available compounds, the molecules that show good performance in a practical ARFB setup are identified. Among them, indigo trisulfonate [Indigo-3(SO3H)] showed higher solubility, capacity retention, and coulombic efficiency than AQDS and its predecessors. The data-driven material design approach is therefore highly effective for the future explorations of electrochemical energy storage compounds. [Zhang, Qi; Khetan, Abhishek; Sorkun, Elif; Er, Suleyman] DIFFER Dutch Inst Fundamental Energy Res, De Zaale 20, NL-5612 AJ Eindhoven, Netherlands; [Zhang, Qi] Eindhoven Univ Technol, Dept Appl Phys, NL-5600 MB Eindhoven, Netherlands; [Niu, Fang; Loss, Andrea; Pucher, Ilaria] Green Energy Storage, Via Sommar 18, I-38123 Povo, Italy; [Zhang, Qi] Chinese Acad Sci, SIAT Shenzhen Inst Adv Technol, 1068 Xueyuan Rd, Shenzhen 518055, Peoples R China; [Khetan, Abhishek] Rhein Westfal TH Aachen, Fuel Sci Ctr, D-52062 Aachen, Germany Eindhoven University of Technology; Chinese Academy of Sciences; RWTH Aachen University Er, S (corresponding author), DIFFER Dutch Inst Fundamental Energy Res, De Zaale 20, NL-5612 AJ Eindhoven, Netherlands. s.er@differ.nl Khetan, Abhishek/Q-4245-2018 Khetan, Abhishek/0000-0003-2260-8271; Er, Suleyman/0000-0002-5005-3894; Sorkun, Elif/0000-0003-1693-6463 Dutch Research Council (NWO); Ministry of Economic Affairs [739.017.013]; initiative Computational Sciences for Energy Research of Shell; NWO [15CSTT05] Dutch Research Council (NWO)(Netherlands Organization for Scientific Research (NWO)); Ministry of Economic Affairs; initiative Computational Sciences for Energy Research of Shell; NWO(Netherlands Organization for Scientific Research (NWO)) This research received funding from the Dutch Research Council (NWO), through the COLORFLOW project partnership of DIFFER and Green Energy Storage, in the framework of the Materials for Sustainability program and from the Ministry of Economic Affairs in the framework of the PPS-Toeslagregeling grant no 739.017.013. S.E. acknowledges funding from the initiative Computational Sciences for Energy Research of Shell and NWO grant no 15CSTT05. This work was sponsored by NWO Exact and Natural Sciences for the use of supercomputer facilities. We thank Prof. ReneJanssen for proofreading the article. 52 5 5 17 31 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8297 2405-8289 ENERGY STORAGE MATER Energy Storage Mater. MAY 2022.0 47 167 177 10.1016/j.ensm.2022.02.013 0.0 FEB 2022 11 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science 2P4WJ Green Submitted, hybrid 2023-03-23 WOS:000819743100009 0
J Chen, C; Li, KL; Cheng, ZY; Piccialli, F; Hoi, SCH; Zeng, Z Chen, Cen; Li, Kenli; Cheng Zhongyao; Piccialli, Francesco; Hoi, Steven C. H.; Zeng, Zeng A Hybrid Deep Learning Based Framework for Component Defect Detection of Moving Trains IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Automatic defect detection; deep convolutional neural networks; railway system; train component defects; visual inspection INSPECTION; SYSTEM Defect detection of trains is of great significance for operation safety and maintenance efficiency for railway maintenance. Nowadays, China railway system utilizes high-speed line scan cameras to capture images of critical parts of moving trains. The visual inspection on the images still heavily relies on manual interpretation. To reduce the labor requirements, we propose a novel two-stage deep learning based framework for component defect detection of moving trains. The proposed framework is composed of two major successive stages: detecting train components by using our proposed hierarchical object detection scheme (HOD), and detecting component defects based on multiple neural networks and image processing methods. Our proposed HOD can effectively detect and localize train components from large to small in a hierarchical way. Furthermore, a gated feature fusion method that can extract and combine the hierarchical contextual features and spatial contexts is also proposed to improve the performance. To the best of our knowledge, it is the first time in the literature that component defect detection of moving trains is systematically analyzed. Extensive experiments on real images from China railway system have demonstrated that our framework outperforms the state-of-the-art baselines significantly. [Chen, Cen; Li, Kenli] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China; [Chen, Cen; Li, Kenli] Natl Super Comp Ctr Changsha, Changsha 410082, Peoples R China; [Chen, Cen; Cheng Zhongyao; Zeng, Zeng] ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore; [Piccialli, Francesco] Univ Naples Federico II, Dept Elect Engn & Informat Technol, I-80138 Naples, Italy; [Hoi, Steven C. H.] Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore Hunan University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); University of Naples Federico II; Singapore Management University Li, KL (corresponding author), Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China.;Li, KL (corresponding author), Natl Super Comp Ctr Changsha, Changsha 410082, Peoples R China.;Zeng, Z (corresponding author), ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore. chencen@hnu.edu.cn; lkl@hnu.edu.cn; cheng_zhongyao@i2r.a-star.edu.sg; francesco.piccialli@unina.it; chhoi@smu.edu.sg; zengz@i2r.a-star.edu.sg National Key Research and Development Program of China [2018YFB1003401]; National Natural Science Foundation of China [61902120]; Singapore China NRF-NSFC [NRF2016NRF-NSFC001-111]; Postdoctoral Science Foundation of China [2019M662768, 2019TQ0086]; International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China [61860206011] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Singapore China NRF-NSFC; Postdoctoral Science Foundation of China(China Postdoctoral Science Foundation); International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1003401, in part by the National Natural Science Foundation of China under Grant 61902120, in part by the Singapore China NRF-NSFC under Grant NRF2016NRF-NSFC001-111, in part by the Postdoctoral Science Foundation of China under Grant 2019M662768 and Grant 2019TQ0086, and in part by the International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China under Grant 61860206011. 46 3 3 5 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. APR 2022.0 23 4 3268 3280 10.1109/TITS.2020.3034239 0.0 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 0D7RG 2023-03-23 WOS:000776187400033 0
J Yu, FR; Li, BH; Sun, JF; Qi, J; De Wilde, RL; Torres-de la Roche, LA; Li, C; Ahmad, S; Shi, WJ; Li, XQ; Chen, ZH Yu, Fanrong; Li, Bihui; Sun, Jianfeng; Qi, Jing; De Wilde, Rudy Leon; Torres-de la Roche, Luz Angela; Li, Cheng; Ahmad, Sajjad; Shi, Wenjie; Li, Xiqing; Chen, Zihao PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules FRONTIERS IN MOLECULAR BIOSCIENCES English Article microRNA; small molecule; machine learning; web server; endometrial cancer SEQUENCE-BASED DESIGN; MICRORNAS; IDENTIFICATION; INFORMATION; THERAPY; CELLS; RNA Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules. [Yu, Fanrong] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Dept Obstet & Gynecol, Fengxian Dist Cent Hosp, Shanghai, Peoples R China; [Li, Bihui] Guilin Med Univ, Dept Oncol, Affiliated Hosp 2, Guilin, Peoples R China; [Sun, Jianfeng] Tech Univ Munich, Dept Bioinformat, Wissenschaftzentrum Weihenstephan, Freising Weihenstephan, Germany; [Qi, Jing] Heinrich Heine Univ Dusseldorf, Med Fac, Inst Transplantat Diagnost & Cell Therapeut, Moorenstr, Dusseldorf, Germany; [De Wilde, Rudy Leon; Torres-de la Roche, Luz Angela; Shi, Wenjie; Chen, Zihao] Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany; [Li, Cheng] Peking Univ, Beijing Jishuitan Hosp, Dept Orthopaed Surg, Clin Coll 4, Beijing, Peoples R China; [Ahmad, Sajjad] Abasyn Univ, Dept Hlth & Biol Sci, Peshawar, Pakistan; [Li, Xiqing] Zhengzhou Univ, Henan Prov Peoples Hosp, Oncol Dept, Peoples Hosp, Zhengzhou, Peoples R China Shanghai Jiao Tong University; Guilin Medical University; Technical University of Munich; Heinrich Heine University Dusseldorf; Pius-Hospital Oldenburg; Peking University; Zhengzhou University Chen, ZH (corresponding author), Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany.;Li, XQ (corresponding author), Zhengzhou Univ, Henan Prov Peoples Hosp, Oncol Dept, Peoples Hosp, Zhengzhou, Peoples R China. leeexiqing@gmail.com; zihaochenn@gmail.com Chen, zihao/HGE-3792-2022 Sun, Jianfeng/0000-0002-1274-5080 Scientific and Technological Innovation Act Program of Fengxian of Science and Technology Commission [.20211610] Scientific and Technological Innovation Act Program of Fengxian of Science and Technology Commission This study was supported by the Scientific and Technological Innovation Act Program of Fengxian of Science and Technology Commission (grant number.20211610). 49 2 2 2 4 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-889X FRONT MOL BIOSCI Front. Mol. Biosci. MAR 21 2022.0 9 817294 10.3389/fmolb.2022.817294 0.0 12 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 5E9VO 35386297.0 Green Accepted, gold 2023-03-23 WOS:000865970100001 0
C Cannavo, A; Bardella, C; Semeraro, L; De Lorenzis, F; Zhang, CY; Jiang, Y; Lamberti, F DePaolis, LT; Arpaia, P; Bourdot, P Cannavo, Alberto; Bardella, Christian; Semeraro, Lorenzo; De Lorenzis, Federico; Zhang, Congyi; Jiang, Ying; Lamberti, Fabrizio An Automatic 3D Scene Generation Pipeline Based on a Single 2D Image AUGMENTED REALITY, VIRTUAL REALITY, AND COMPUTER GRAPHICS Lecture Notes in Computer Science English Proceedings Paper 8th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics (SALENTO AVR) SEP 07-10, 2021 ELECTR NETWORK Univ Salento, Augmented & Virtual Real Lab Image-based modelling; Scene-object modelling; Machine learning In the last years, solutions were proposed in the literature to alleviate the complexity of using sophisticated graphic suites for 3D scene generation by leveraging automatic tools. The most common approach based on the processing of text descriptions, however, may not represent the ideal solution, e.g., for fast prototyping purposes. This paper proposes an alternative methodology able to extract information about the objects and the layout of the scene to be created from a single 2D image. Compared to previous works, experimental results reported in this work show improvements in terms of similarity between the 2D and 3D scenes. [Cannavo, Alberto; Bardella, Christian; Semeraro, Lorenzo; De Lorenzis, Federico; Lamberti, Fabrizio] Politecn Torino, Corso Duca Abruzzi 24, Turin, Italy; [Zhang, Congyi; Jiang, Ying] Univ Hong Kong, Pokfulam Rd, Hong Kong, Peoples R China Polytechnic University of Turin; University of Hong Kong Cannavo, A (corresponding author), Politecn Torino, Corso Duca Abruzzi 24, Turin, Italy. alberto.cannavo@polito.it; christian.bardella@polito.it; lorenzo.semeraro@polito.it; federico.delorenzis@polito.it; cyzhang@cs.hku.hk; yingjiang@connect.hku.hk; fabrizio.lamberti@polito.it Zhang, Congyi/0000-0002-4259-2863; Cannavo, Alberto/0000-0002-6884-9268 VR@POLITOinitiative VR@POLITOinitiative This work has been supported by VR@POLITOinitiative. 14 1 1 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-87595-4; 978-3-030-87594-7 LECT NOTES COMPUT SC 2021.0 12980 109 117 10.1007/978-3-030-87595-4_9 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS3IZ 2023-03-23 WOS:000711936100009 0
J Hamzehei, S; Akbarzadeh, O; Attar, H; Rezaee, K; Fasihihour, N; Khosravi, MR Hamzehei, Sahand; Akbarzadeh, Omid; Attar, Hani; Rezaee, Khosro; Fasihihour, Nazanin; Khosravi, Mohammad R. Predicting the total Unified Parkinson's Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS English Article Machine learning; Lineal regression; Linear least square; Conjugate gradient; Adam optimization algorithm; Ridge regression; Machine learning for healthcare; Parkinson's disease Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson's disease to assess the methods' performance and identify the essential features that may be used to predict the total Unified Parkinson's disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm's estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS. [Hamzehei, Sahand; Akbarzadeh, Omid; Fasihihour, Nazanin] Polytech Univ Turin, Dept Elect & Telecommun, Turin, Italy; [Attar, Hani] Zarqa Univ, Dept Energy Engn, Zarqa, Jordan; [Rezaee, Khosro] Meybod Univ, Dept Biomed Engn, Meybod, Iran; [Khosravi, Mohammad R.] Weifang Univ Sci & Technol, Weifang, Shandong, Peoples R China Polytechnic University of Turin; Zarqa University Rezaee, K (corresponding author), Meybod Univ, Dept Biomed Engn, Meybod, Iran. kh.rezaee@meybod.ac.ir 29 0 0 1 1 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2192-113X J CLOUD COMPUT-ADV S J. Cloud Comput.-Adv. Syst. Appl. JAN 21 2023.0 12 1 12 10.1186/s13677-022-00388-1 0.0 16 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8S2EP gold 2023-03-23 WOS:000928397600001 0
J Xue, Y; Wang, YK; Liang, JY; Slowik, A Xue, Yu; Wang, Yankang; Liang, Jiayu; Slowik, Adam A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE English Article NETWORKS Recently, convolutional neural networks (CNNs) have achieved great success in the field of artificial intelligence, including speech recognition, image recognition, and natural language processing. CNN architecture plays a key role in CNNs' performance. Most previous CNN architectures are hand-crafted, which requires designers to have rich expert domain knowledge. The trial-and-error process consumes a lot of time and computing resources. To solve this problem, researchers proposed the neural architecture search, which searches CNN architecture automatically, to satisfy different requirements. However, the blindness of the search strategy causes a 'loss of experience' in the early stage of the search process, and ultimately affects the results of the later stage. In this paper, we propose a self-adaptive mutation neural architecture search algorithm based on ResNet blocks and DenseNet blocks. The self-adaptive mutation strategy makes the algorithm adaptively adjust the mutation strategies during the evolution process to achieve better exploration. In addition, the whole search process is fully automatic, and users do not need expert knowledge about CNNs architecture design. In this paper, the proposed algorithm is compared with 17 state-of-the-art algorithms, including manually designed CNN and automatic search algorithms on CIFAR10 and CIFAR100. The results indicate that the proposed algorithm outperforms the competitors in terms of classification performance and consumes fewer computing resources. [Xue, Yu; Wang, Yankang] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China; [Liang, Jiayu] Tiangong Univ, Tianjin, Peoples R China; [Slowik, Adam] Koszalin Univ Technol, Koszalin, Poland Nanjing University of Information Science & Technology; Tiangong University; Koszalin University of Technology Xue, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China. xueyu@nuist.edu.cn National Natural Science Foundation of China [61876089, 61876185, 61902281]; Engineering Research Center of Digital Forensics, Ministry of Education; Priority Academic Program Development of Jiangsu Higher Education Institutions; Postgraduate Research AMP; Practice Innovation Program of Jiangsu Province [KYCX21_1019] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Engineering Research Center of Digital Forensics, Ministry of Education; Priority Academic Program Development of Jiangsu Higher Education Institutions; Postgraduate Research AMP; Practice Innovation Program of Jiangsu Province This work was partially supported by the National Natural Science Foundation of China (61876089, 61876185, 61902281), the Engineering Research Center of Digital Forensics, Ministry of Education, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_1019). 63 56 57 9 40 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-603X 1556-6048 IEEE COMPUT INTELL M IEEE Comput. Intell. Mag. AUG 2021.0 16 3 67 78 10.1109/MCI.2021.3084435 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science TL9VS 2023-03-23 WOS:000675201200008 0
J Wei, R; Xu, XL; Duan, YY; Zhang, NNN; Sun, J; Li, HQ; Li, YX; Li, YM; Zeng, C; Han, XM; Zhou, FQ; Huang, MH; Li, RZ; Zhuo, ZZ; Barkhof, F; Cole, JH; Liu, YO Wei, Ren; Xu, Xiaolu; Duan, Yunyun; Zhang, Ningnannan; Sun, Jie; Li, Haiqing; Li, Yuxin; Li, Yongmei; Zeng, Chun; Han, Xuemei; Zhou, Fuqing; Huang, Muhua; Li, Runzhi; Zhuo, Zhizheng; Barkhof, Frederik; Cole, James H.; Liu, Yaou Brain age gap in neuromyelitis optica spectrum disorders and multiple sclerosis JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY English Article neuroimmunology; MRI; multiple sclerosis; neuroradiology ATROPHY Objective To evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS). Methods This cohort study used data retrospectively collected from 6 tertiary neurological centres in China between 2009 and 2018. In total, 199 patients with NMOSD and 200 patients with RRMS were studied alongside 269 healthy controls. Clinical follow-up was available in 85 patients with NMOSD and 124 patients with RRMS (mean duration NMOSD=5.8 +/- 1.9 (1.9-9.9) years, RRMS=5.2 +/- 1.7 (1.5-9.2) years). Deep learning was used to learn 'brain age' from MRI scans in the healthy controls and estimate the brain age gap (BAG) in patients. Results A significantly higher BAG was found in the NMOSD (5.4 +/- 8.2 years) and RRMS (13.0 +/- 14.7 years) groups compared with healthy controls. A higher baseline disability score and advanced brain volume loss were associated with increased BAG in both patient groups. A longer disease duration was associated with increased BAG in RRMS. BAG significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD and RRMS. Conclusions There is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS. [Wei, Ren; Xu, Xiaolu; Duan, Yunyun; Zhuo, Zhizheng; Liu, Yaou] Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China; [Zhang, Ningnannan; Sun, Jie] Tianjin Med Univ, Dept Radiol, Gen Hosp, Tianjin, Peoples R China; [Zhang, Ningnannan; Sun, Jie] Tianjin Med Univ Gen Hosp, Tianjin Key Lab Funct Imaging, Tianjin, Peoples R China; [Li, Haiqing; Li, Yuxin] Fudan Univ, Dept Radiol, Huashan Hosp, Shanghai, Peoples R China; [Li, Yongmei; Zeng, Chun] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing, Peoples R China; [Han, Xuemei] Jilin Univ, Dept Neurol, China Japan Union Hosp, Changchun, Peoples R China; [Zhou, Fuqing; Huang, Muhua] Nanchang Univ, Dept Radiol, Affiliated Hosp 1, Nanchang, Jiangxi, Peoples R China; [Li, Runzhi] Beijing Tiantan Hosp, Dept Neurol, Beijing, Peoples R China; [Barkhof, Frederik] VU Univ Med Ctr Amsterdam, Dept Radiol & Nucl Med, Neurosci Campus Amsterdam, Amsterdam, Netherlands; [Barkhof, Frederik; Cole, James H.] UCL, Ctr Med Image Comp, Dept Comp Sci, London, England; [Cole, James H.] UCL, Dementia Res Ctr, Queen Sq Inst Neurol, London, England Capital Medical University; Tianjin Medical University; Tianjin Medical University; Fudan University; Chongqing Medical University; Jilin University; Nanchang University; Capital Medical University; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; University of London; University College London; University of London; University College London Liu, YO (corresponding author), Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China. yaouliu80@163.com Barkhof, Frederik/0000-0003-3543-3706; Xu, Xiaolu/0000-0001-9963-529X National Natural Science Foundation of China [81870958]; Beijing Municipal Natural Science Foundation for Distinguished Young Scholars [JQ20035]; Beijing Young Scholarship; Capital's Funds for Health Improvement and Research [CFH2022-1-2042]; NIHR Biomedical Research Centre at UCLH National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation for Distinguished Young Scholars; Beijing Young Scholarship; Capital's Funds for Health Improvement and Research; NIHR Biomedical Research Centre at UCLH This work was supported by the National Natural Science Foundation of China (No. 81870958), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), Beijing Young Scholarship and the Capital's Funds for Health Improvement and Research (CFH2022-1-2042). FB is supported by the NIHR Biomedical Research Centre at UCLH. 27 0 0 7 7 BMJ PUBLISHING GROUP LONDON BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND 0022-3050 1468-330X J NEUROL NEUROSUR PS J. Neurol. Neurosurg. Psychiatry JAN 2023.0 94 1 31 37 10.1136/jnnp-2022-329680 0.0 OCT 2022 7 Clinical Neurology; Psychiatry; Surgery Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychiatry; Surgery 7H4LZ 36216455.0 Green Submitted 2023-03-23 WOS:000866157100001 0
J Wan, WB; Wang, J; Zhang, YM; Li, J; Yu, H; Sun, JD Wan, Wenbo; Wang, Jun; Zhang, Yunming; Li, Jing; Yu, Hui; Sun, Jiande A comprehensive survey on robust image watermarking NEUROCOMPUTING English Article Image watermarking; Robustness; Deep learning; HDR image; Model watermarking DIGITAL BLIND WATERMARKING; QUALITY ASSESSMENT; DITHER MODULATION; INTERBLOCK PREDICTION; LEARNING FRAMEWORK; MULTI-WATERMARKING; NEURAL-NETWORKS; TRANSFORM; DOMAIN; DWT With the rapid development and popularity of the Internet, multimedia security has become a general essential concern. Especially, as manipulation of digital images gets much easier, the challenges it brings to authentication certification are increasing. As part of the solution, digital watermarking has made sig-nificant contributions to image content security and has attracted increasing attention. In this paper, we present a comprehensive review on digital image watermarking methods that were published in recent years illustrating the conventional schemes in different domains. We provide an overview of geometric invariant techniques and emerging watermarking methods for novel medias, such as depth image based rendering (DIBR), high dynamic range (HDR), screen content images (SCIs), and point cloud model. Particularly, as deep learning has achieved a great success in the field of image processing, and has also successfully been used in the field of digital watermarking, learning-based watermarking methods using various neural networks are summarized according to the utilization of neural networks in the single stage training (SST) and double stage training (DST). Finally, we provide an analysis and summary on those methods, and suggest some future research directions.(c) 2022 Elsevier B.V. All rights reserved. [Wan, Wenbo; Zhang, Yunming; Sun, Jiande] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China; [Wang, Jun] Univ Siena, Dept Informat Engn & Math, I-53100 Siena, Italy; [Li, Jing] Shandong Normal Univ, Sch Journalism & Commun, Jinan 250014, Peoples R China; [Li, Jing] Shandong Management Univ, Sch Intelligent Engn, Jinan 250357, Peoples R China; [Yu, Hui] Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2UP, Hants, England Shandong Normal University; University of Siena; Shandong Normal University; Shandong Management University; University of Portsmouth Sun, JD (corresponding author), Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China.;Yu, H (corresponding author), Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2UP, Hants, England. hui.yu@port.ac.uk; jiandesun@hotmail.com Yu, Hui/G-1115-2018 Yu, Hui/0000-0002-7655-9228 Scientific Research Lea-der Studio of Jinan [2021GXRC081]; Joint Project for Smart Computing of Shandong Natural Science Foundation [ZR2020LZH015]; Taishan Scholar Project of Shandong, China [ts20190924] Scientific Research Lea-der Studio of Jinan; Joint Project for Smart Computing of Shandong Natural Science Foundation; Taishan Scholar Project of Shandong, China This work is partially supported by the Scientific Research Lea-der Studio of Jinan (No. 2021GXRC081) , Joint Project for Smart Computing of Shandong Natural Science Foundation (ZR2020LZH015) and Taishan Scholar Project of Shandong, China (ts20190924) . 277 16 16 24 36 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUN 1 2022.0 488 226 247 10.1016/j.neucom.2022.02.083 0.0 MAR 2022 22 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0X4KP 2023-03-23 WOS:000789677800002 0
J Yao, PP; Shi, JC; Zhao, TJ; Lu, H; Al-Yaari, A Yao, Panpan; Shi, Jiancheng; Zhao, Tianjie; Lu, Hui; Al-Yaari, Amen Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index REMOTE SENSING English Article soil moisture; neural network; long time series; microwave vegetation index POLARIZATION DIFFERENCE INDEX; OPTICAL DEPTH RETRIEVAL; AMSR-E; BRIGHTNESS TEMPERATURES; DATA ASSIMILATION; SAR DATA; SMOS; VALIDATION; METHODOLOGY; ALGORITHM This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010-June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m(3)/m(3) and Bias = -0.0005 m(3)/m(3), particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003-2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.84 m(3)/m(3) and Bias = -0.002 m(3)/m(3). We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM). [Yao, Panpan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Yao, Panpan; Shi, Jiancheng; Zhao, Tianjie] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Shi, Jiancheng; Zhao, Tianjie; Lu, Hui] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China; [Lu, Hui] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China; [Al-Yaari, Amen] INRA, ISPA UMR1391, F-33140 Villenave Dornon, France Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Tsinghua University; INRAE Shi, JC (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.;Shi, JC (corresponding author), Joint Ctr Global Change Studies, Beijing 100875, Peoples R China. yaopp@radi.ac.cn; shijc@radi.ac.cn; zhaotj@radi.ac.cn; luhuil@tsinghua.edu.cn; amen.al-yaari@inra.fr Lu, Hui/H-4349-2011; Shi, Jiancheng/HIU-0908-2022; Al-Yaari, Amen/B-1444-2016 Lu, Hui/0000-0003-1640-239X; Al-Yaari, Amen/0000-0001-7530-6088; Yao, Panpan/0000-0002-5877-9569; zhao, tian jie/0000-0002-0914-599X National Key Basic Research Program [2015CB953701]; Key Research Program of Frontier Sciences, CAS [QYZDY-SSW-DQC011]; International S&T Cooperation Program of China [2016YFE0117300]; National Natural Science Foundation of China [41371354, 41301396]; Youth Innovation Promotion Association [2016061] National Key Basic Research Program(National Basic Research Program of China); Key Research Program of Frontier Sciences, CAS; International S&T Cooperation Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Innovation Promotion Association This work was supported in part by the National Key Basic Research Program (2015CB953701), Key Research Program of Frontier Sciences, CAS (Grant No. QYZDY-SSW-DQC011), International S&T Cooperation Program of China (2016YFE0117300), the National Natural Science Foundation of China (41371354, 41301396) and the Youth Innovation Promotion Association (No. 2016061). The authors would like to acknowledge CATDS for providing the SMOS L3 datasets. The authors thank the NSIDC and the JAXA for providing the AMSR-E/ AMSR2 TB and SM data, the NASA for providing the AMSRsm products and the ISMN for providing SCAN observations. 72 20 20 4 53 MDPI AG BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2017.0 9 1 35 10.3390/rs9010035 0.0 27 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology EM7LN Green Published, gold, Green Submitted 2023-03-23 WOS:000395492600035 0
C Xia, QL; Wang, MQ; Jiang, B; Hu, N; Wu, XY; Hou, WS; Nyberg, T IEEE Xia, Qing L.; Wang, Man Q.; Jiang, Bin; Hu, Ning; Wu, Xiao Y.; Hou, Wen S.; Nyberg, Tobias Infrared Laser Pulses Excite Action Potentials In Primary Cortex Neurons In Vitro 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) JUL 23-27, 2019 Berlin, GERMANY STIMULATION Infrared neural modulation (INM) has been well studied in the peripheral nervous system (PNS) for potential clinical applications. However, limited research has been conducted ou the central nervous systems (CNS). In this study, we aimed at investigating the feasibility of using pulsed infrared (IR) laser with a wavelength of 1940 nm to excite network activity of cultixated rat cortex neurons. We cultured rat cortex neurons, forming neural networks with spontaneous neural activity, on glass multi-electrode arrays (MEAs). Laser al p rp of (AM rip I, and a pulse rate of 10 Hz were used to Ntnaalat, the aLut nowork,, pqng rke Unties of an ititt o Mat Opt Pulse durations were Heim! from 200 ps to 1 ms. The spike rate was calculated to evaluate the change of the neural network activity during the IR stimuli and ilk corresponding frequency components of neural response were calculated to examine whether recorded spikes were evoked b ht, IR plata' or not. A temperature model was adapted from a previous study to estimate the temperature rise during laser pulsing. We observed that no IR II rad ation with a pulse duration of 800 is and 1 ms could excite neuronal action potentials. The temperature rose 18.5 and 23.9 C, for pulse daY Aorks of 800,is and 1 ms, respectively. Thus, in addition to previously shown inhibition of IR irrachntion with a wavelength of 1550 tun, we demonstrate an optical method that Ca a modulate neural network acth tix in vitro. The preliminary results from this paper also suggested that MEA recording technology coupled with I laser and microscope systems can be exploited as a new approach for future studies to understand mechanisms and characterize laser parameters of INM soul rn't. [Xia, Qing L.; Wang, Man Q.; Jiang, Bin; Hu, Ning; Wu, Xiao Y.; Hou, Wen S.] Chongqing Univ, Minist Educ, Bioengn Coll, Key Lab Biorheol Sci & Technol, Chongqing 400044, Peoples R China; [Nyberg, Tobias] KTH Royal Inst Technol, Dept Biomed Engn & Hlth Syst, Div Neuron Engn, Stockholm, Sweden Chongqing University; Royal Institute of Technology Hou, WS (corresponding author), Chongqing Univ, Minist Educ, Bioengn Coll, Key Lab Biorheol Sci & Technol, Chongqing 400044, Peoples R China. w.s.hou@cqu.edu.cn; tnybe@kth.se KTH-CSC Programme KTH-CSC Programme The authors are grateful for the supporting from KTH-CSC Programme. The first author would like to thank Zhouzhou and Kelu for giving critical comments. 21 1 1 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-5386-1311-5 IEEE ENG MED BIO 2019.0 5184 5187 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BP5OL 31947026.0 2023-03-23 WOS:000557295305145 0
J Ferrara, E; Fragale, L; Fortino, G; Song, W; Perra, C; Di Mauro, M; Liotta, A Ferrara, Enrico; Fragale, Luigi; Fortino, Giancarlo; Song, Wei; Perra, Cristian; Di Mauro, Mario; Liotta, Antonio An AI Approach to Collecting and Analyzing Human Interactions With Urban Environments IEEE ACCESS English Article Urban areas; Sensors; Data collection; Data analysis; Real-time systems; Data science; social science; smart city; data analysis; urban analytics; artificial intelligence; crowd sensing SMART CITY; INTERNET; FRAMEWORK; THINGS Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41 acceleration factor and a 75 reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizens well-being projects, and sociology, to mention but a few cases. [Ferrara, Enrico] Univ Derby, Data Sci Res Ctr, Derby DE22 1GB, England; [Fragale, Luigi; Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst, I-87036 Arcavacata Di Rende, Italy; [Song, Wei] Shanghai Ocean Univ, Coll Informat Technol, Shanghai 201306, Peoples R China; [Perra, Cristian] Univ Cagliari, Dept Elect & Elect Engn, UdR CNIT, I-09123 Cagliari, Italy; [Di Mauro, Mario] Univ Salerno, Dept Informat & Elect Engn & Appl Math, I-84084 Fisciano, Italy; [Liotta, Antonio] Edinburgh Napier Univ, Sch Comp, Edinburgh EH10 5DT, Midlothian, Scotland University of Derby; University of Calabria; Shanghai Ocean University; University of Cagliari; University of Salerno; Edinburgh Napier University Ferrara, E (corresponding author), Univ Derby, Data Sci Res Ctr, Derby DE22 1GB, England. e.ferrara@derby.ac.uk Fortino, Giancarlo/J-2950-2017; Ferrara, Enrico/AAB-8623-2021; Liotta, Antonio/G-9532-2014 Fortino, Giancarlo/0000-0002-4039-891X; Ferrara, Enrico/0000-0002-2580-9987; Song, Wei/0000-0002-0604-5563; Di Mauro, Mario/0000-0001-6574-2601; Liotta, Antonio/0000-0002-2773-4421 Natural Environment Research Council; ESRC; BBSRC; AHRC; Defra [NERC grant] [NE/N013565/1] Natural Environment Research Council(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)); ESRC(UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)); BBSRC(UK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC)); AHRC(UK Research & Innovation (UKRI)Arts & Humanities Research Council (AHRC)); Defra [NERC grant] The dataset used for the case study comes from the IWUN project, supported by the Natural Environment Research Council, ESRC, BBSRC, AHRC & Defra [NERC grant reference number NE/N013565/1]. The emulation framework development and data analysis were performed in collaboration between the Data Science Research Centre (www.derby.ac.uk/data-science) and the Joint Intellisensing Lab (www.thejil.com) 34 4 4 4 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 141476 141486 10.1109/ACCESS.2019.2943845 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications JN8QJ Green Published, Green Submitted, gold 2023-03-23 WOS:000497156000109 0
J Wang, MH; Li, QP; Gu, YC; Fang, LY; Zhu, XX Wang, Minghui; Li, Qingpeng; Gu, Yunchao; Fang, Leyuan; Zhu, Xiao Xiang SCAF-Net: Scene Context Attention-Based Fusion Network for Vehicle Detection in Aerial Imagery IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Vehicle detection; Feature extraction; Task analysis; Remote sensing; Computer architecture; Training; Testing; Attention-based model; deep learning; fusion network; remote sensing; vehicle detection In recent years, deep learning methods have achieved great success for vehicle detection tasks in aerial imagery. However, most existing methods focus only on extracting latent vehicle target features, and rarely consider the scene context as vital prior knowledge. In this letter, we propose a scene context attention-based fusion network (SCAF-Net), to fuse the scene context of vehicles into an end-to-end vehicle detection network. First, we propose a novel strategy, patch cover, to keep the original target and scene context information in raw aerial images of a large scale as much as possible. Next, we use an improved YOLO-v3 network as one branch of SCAF-Net, to generate vehicle candidates on each patch. Here, a novel branch for the scene context is utilized to extract the latent scene context of vehicles on each patch without any extra annotations. Then, these two branches above are concatenated together as a fusion network, and we apply an attention-based model to further extract vehicle candidates of each local scene. Finally, all vehicle candidates of different patches, are merged by global nonmax suppress (g-NMS) to output the detection result of the whole original image. Experimental results demonstrate that our proposed method outperforms the comparison methods with both high detection accuracy and speed. Our code is released at https://github.com/minghuicode/SCAF-Net. [Wang, Minghui; Gu, Yunchao] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China; [Li, Qingpeng; Fang, Leyuan] Hunan Univ, Sch Robot, State Lab Robot Visual Percept & Control Technol, Changsha 410082, Hunan, Peoples R China; [Li, Qingpeng; Fang, Leyuan] Hunan Univ, Coll Elect & Engn, Changsha 410082, Hunan, Peoples R China; [Zhu, Xiao Xiang] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany; [Zhu, Xiao Xiang] Tech Univ Munich, Signal Proc Earth Observat, D-80333 Munich, Germany Beihang University; Hunan University; Hunan University; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich Li, QP (corresponding author), Hunan Univ, Sch Robot, State Lab Robot Visual Percept & Control Technol, Changsha 410082, Hunan, Peoples R China. minghuiw@buaa.edu.cn; liqingpeng@hnu.edu.cn; guyunchao@buaa.edu.cn; leyuan_fang@hnu.edu.cn; xiao.zhu@dlr.de Fang, Leyuan/0000-0003-2351-4461; Li, Qingpeng/0000-0002-7401-0928; Zhu, Xiao Xiang/0000-0001-5530-3613 National Key Research and Development Program of China [2018YFB1308604]; National Natural Science Foundation of China [61672215, 61976086, 61890962]; Hunan Innovation Technology Investment Project [2019GK5061] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hunan Innovation Technology Investment Project This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1308604; in part by the National Natural Science Foundation of China under Grant 61672215, Grant 61976086, and Grant 61890962; and in part by Hunan Innovation Technology Investment Project under Grant 2019GK5061. 19 0 0 10 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 3508305 10.1109/LGRS.2021.3107281 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XP9AX 2023-03-23 WOS:000731151800048 0
J Ferreira, A; Bondi, L; Baroffio, L; Bestagini, P; Huang, JW; dos Santos, JA; Tubaro, S; Rocha, A Ferreira, Anselmo; Bondi, Luca; Baroffio, Luca; Bestagini, Paolo; Huang, Jiwu; dos Santos, Jefersson A.; Tubaro, Stefano; Rocha, Anderson Data-Driven Feature Characterization Techniques for Laser Printer Attribution IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Laser printer attribution; deep learning-based document provenance analysis; convolutional neural networks; multiple representation; multiple data SCANNER IDENTIFICATION; FORENSIC ANALYSIS; SECURITY Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem. [Ferreira, Anselmo; Huang, Jiwu] Shenzhen Univ, Shenzhen Key Lab Media Secur, Coll Informat Engn, Shenzhen 518060, Peoples R China; [Bondi, Luca; Baroffio, Luca; Bestagini, Paolo; Tubaro, Stefano] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy; [dos Santos, Jefersson A.] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil; [Rocha, Anderson] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil Shenzhen University; Polytechnic University of Milan; Universidade Federal de Minas Gerais; Universidade Estadual de Campinas Huang, JW (corresponding author), Shenzhen Univ, Shenzhen Key Lab Media Secur, Coll Informat Engn, Shenzhen 518060, Peoples R China. jwhuang@szu.edu.cn Bondi, Luca/L-4871-2015; dos Santos, Jefersson/HKW-4282-2023; Ferreira, Anselmo C.B/J-5210-2014 Bondi, Luca/0000-0003-3974-7542; dos Santos, Jefersson/0000-0002-8889-1586; Ferreira, Anselmo C.B/0000-0002-2196-7232; Bestagini, Paolo/0000-0003-0406-0222 NSFC [61332012, U1636202]; Shenzhen Research and Development Program [JCYJ20160328144421330]; Brazilian National Council for Scientific and Technological Development [477662/2013-7, 304352/2012-8, 449638/2014-6, 304472/2015-8]; Sao Paulo Research Foundation-FAPESP [2015/19222-9]; DejaVu Project, Minas Gerais Research Foundation-FAPEMIG [APQ-00768-14]; Brazilian Coordination for the Improvement of Higher Level Education Personnel through the DeepEyes Project [99999.002341/2015-08]; Microsoft Research NSFC(National Natural Science Foundation of China (NSFC)); Shenzhen Research and Development Program; Brazilian National Council for Scientific and Technological Development(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Sao Paulo Research Foundation-FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); DejaVu Project, Minas Gerais Research Foundation-FAPEMIG; Brazilian Coordination for the Improvement of Higher Level Education Personnel through the DeepEyes Project; Microsoft Research(Microsoft) This work was supported in part by the NSFC under Grant 61332012 and Grant U1636202, in part by the Shenzhen Research and Development Program under Grant JCYJ20160328144421330, in part by the Brazilian National Council for Scientific and Technological Development under Grant 477662/2013-7, Grant 304352/2012-8, Grant 449638/2014-6, and Grant 304472/2015-8, the Sao Paulo Research Foundation-FAPESP under Grant 2015/19222-9, in part by the DejaVu Project, Minas Gerais Research Foundation-FAPEMIG under Grant APQ-00768-14, in part by the Brazilian Coordination for the Improvement of Higher Level Education Personnel through the DeepEyes Project under Grant 99999.002341/2015-08, and in part by the Microsoft Research. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Xinpeng Zhang. (Corresponding author: Jiwu Huang.) 52 28 28 0 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. AUG 2017.0 12 8 1860 1873 10.1109/TIFS.2017.2692722 0.0 14 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering EU9EJ Green Submitted 2023-03-23 WOS:000401340500008 0
J Li, Q; Xue, ZB; Wu, YH; Zeng, XY Li, Qing; Xue, Zhebin; Wu, Yuhan; Zeng, Xianyi The Status Quo and Prospect of Sustainable Development of Smart Clothing SUSTAINABILITY English Article smart clothing; sustainability; design; production; supply chain LIFE-CYCLE; MARKETING ACTIVITIES; GARMENT DESIGN; SOCIAL MEDIA; FASHION; TECHNOLOGIES; MANAGEMENT; POLLUTION; SELECTION; TEXTILES With the booming development of the Internet and AI (Artificial Intelligence), smart clothing has emerged to meet consumers' personalized needs in healthcare, work, entertainment, etc., and has rapidly become a hotspot in the clothing industry and research field. However, as smart clothing gets popular, sustainability issues are becoming increasingly prominent during its development and circulation. To explore the status quo of the sustainable development of smart clothing, from the perspective of the industry chain, this paper discusses its challenges during raw material supply, design, manufacturing, storage, logistics and recycling. Based on these challenges and the characteristics of smart clothing and the future trend of the apparel industry, some countermeasures are put forward from three aspects: design, raw material and supply chain management. This review aims to arouse the reflection of practitioners and provide feasible suggestions for the healthy and lasting development of the apparel industry, also hoping to offer references for other industries. [Li, Qing; Xue, Zhebin] Soochow Univ, Coll Text & Clothing Engn, Suzhou 215021, Peoples R China; [Wu, Yuhan] Jiangnan Univ, Sch Text & Clothing, Wuxi 214021, Jiangsu, Peoples R China; [Zeng, Xianyi] GEMTEX, Ecole Natl Super Arts & Ind Text, F-59056 Roubaix, France Soochow University - China; Jiangnan University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT) Xue, ZB (corresponding author), Soochow Univ, Coll Text & Clothing Engn, Suzhou 215021, Peoples R China. 20204215031@stu.suda.edu.cn; zhebin.xue@hotmail.com; yuhan.wu@jiangnan.edu.cn; xianyi.zeng@ensait.fr Zeng, Xianyi/0000-0002-3236-6766 National Natural Science Foundation of China [61503154] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) FundingThis research was funded by the National Natural Science Foundation of China, grant number 61503154. 131 2 2 33 78 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JAN 2022.0 14 2 990 10.3390/su14020990 0.0 27 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology YM0QB gold 2023-03-23 WOS:000746286800001 0
J Zhang, LB; Li, ZJ; Hu, YB; Smith, C; Farewik, EMG; Wang, RL Zhang, Longbin; Li, Zhijun; Hu, Yingbai; Smith, Christian; Farewik, Elena M. Gutierrez; Wang, Ruoli Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article Adaptive moment estimation (Adam); hill-type muscle model; musculotendon kinematics; OpenSim MUSCLE FORCES; PREDICTION; MOMENTS; EXOSKELETONS; COORDINATION; KINEMATICS; DYNAMICS; TOOLBOX; WALKING In recent decades, there has been an increasing interest in the use of robotic powered exoskeletons to assist patients with movement disorders in rehabilitation and daily life. Providing assistive torque that compensates for the user's remaining muscle contributions is a growing and challenging field within exoskeleton control. In this article, ankle joint torques were estimated using electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in seven movement tasks, including fast walking, slow walking, self-selected speed walking, and isokinetic dorsi/plantar flexion at 60 similar to / s and 90 degrees / s. In each method, EMG signals and ankle joint angles were used as input, the models were trained with data from 3-D motion analysis, and ankle joint torques were predicted. Six cases using different motion trials as calibration (for the NMS model)/training (for the ANN) were devised, and the agreement between the predicted and measured ankle joint torques was computed. We found that the NMS model could overall better predict ankle joint torques from EMG and angle data than the ANN model with some exceptions; the ANN predicted ankle joint torques with better agreement when trained with data from the same movement. The NMS model predicted ankle joint torque best when calibrated with trials during which EMG reached maximum levels, whereas the ANN predicted well when trained with many trials and types of movements. In addition, the ANN prediction may become less reliable when predicting unseen movements. Detailed comparative studies of methods to predict ankle joint torque are crucial for determining strategies for exoskeleton control. Note to Practitioners-In exoskeleton control for strength augmentation applied in military, industry, and healthcare applications, providing assistive torque that compensates for the user's remaining muscle contributions, is a challenging problem. This article predicted the ankle joint torques by electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in different movements. To the best of our knowledge, this is the first study comparing joint torque prediction performance of EMG-driven model to ANN. In the EMG-driven NMS model, mathematical equations were formulated to reproduce the transformations from EMG signal generation and joint angles to musculotendon forces and joint torques. A three-layer ANN was constructed with an adaptive moment estimation (Adam) optimization method to learn the relationships between the inputs (EMG signals and joint angles) and the outputs (joint torques). In the experiments, we estimated ankle joint torques in gait and isokinetic movements and compared the performance of methods to predict ankle joint torque, relating to how the methods have been calibrated/trained. The detailed analysis of the methods' performance in predicting ankle joint torque can significantly contribute to determining which model to choose, and under which circumstances, and, thus, be of great benefit for exoskeleton rehabilitation controller design. [Zhang, Longbin; Farewik, Elena M. Gutierrez; Wang, Ruoli] KTH Royal Inst Technol, KTH BioMEx Ctr, Dept Engn Mech, KTH MoveAbil Lab, SE-10044 Stockholm, Sweden; [Li, Zhijun] Univ Sci & Technol China, Dept Automat, Hefei 230060, Peoples R China; [Hu, Yingbai] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Smith, Christian] KTH Royal Inst Technol, Robot Percept & Learning Lab, SE-10044 Stockholm, Sweden; [Farewik, Elena M. Gutierrez; Wang, Ruoli] Karolinska Inst, Dept Womens & Childrens Hlth, SE-10044 Stockholm, Sweden Royal Institute of Technology; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Technical University of Munich; Royal Institute of Technology; Karolinska Institutet Wang, RL (corresponding author), KTH Royal Inst Technol, KTH BioMEx Ctr, Dept Engn Mech, KTH MoveAbil Lab, SE-10044 Stockholm, Sweden. longbin@Kth.se; zjli@ieee.org; yingbai.hu@tum.de; ccs@kth.se; lanie@kth.se; ruoli@kth.se Smith, Christian/C-5228-2013; Farewik, Elena M. Gutierrez/AAU-9832-2020; Gutierrez Farewik, Elena/G-6945-2012 Smith, Christian/0000-0003-2078-8854; Zhang, Longbin/0000-0001-8785-5885; Wang, Ruoli/0000-0002-2232-5258; Gutierrez Farewik, Elena/0000-0001-5417-5939; Hu, Yingbai/0000-0003-2452-3570 Promobilia Foundation [18200, 18014]; Swedish Research Council [2018-00750, 2018-04902]; China Scholarship Council (CSC); Zhejiang Lab's International Talent Fund for Young Professionals Promobilia Foundation; Swedish Research Council(Swedish Research Council); China Scholarship Council (CSC)(China Scholarship Council); Zhejiang Lab's International Talent Fund for Young Professionals This work was supported in part by the Promobilia Foundation under Grant 18200 and Grant 18014 and in part by the Swedish Research Council under Grant 2018-00750 and Grant 2018-04902. The work of Longbin Zhang was supported by the China Scholarship Council (CSC). The work of Yingbai Hu was supported by the Zhejiang Lab's International Talent Fund for Young Professionals. 48 19 19 15 55 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. APR 2021.0 18 2 564 573 10.1109/TASE.2020.3033664 0.0 10 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems RK6KA hybrid 2023-03-23 WOS:000638401500017 0
J Karimi, HR; Du, H; Lin, J; Zeng, X Karimi, H. R.; Du, H.; Lin, J.; Zeng, X. Guest Editorial: Focused Section on Reliability Design and Resilient Control for Intelligent Mechatronic Systems (RDRC-IMS) IEEE-ASME TRANSACTIONS ON MECHATRONICS English Editorial Material Intelligent Mechatronic Systems (IMS), such as intelligent vehicles/robots/transportation systems, are generally complex due to the integrations of artificial intelligence and multidisciplinary features taken from mechanical engineering, electrical engineering, and control engineering. This integrated complexity leads to challenges in reliability modeling and reliability testing due to different and complex failure modes. To achieve reliability requirements, reliability design and resilient control are critical for the development of IMS. With the advances in information and network techniques, it is opportunistic to exploit them for the benefit of reliability design and the resilient control. The main focus of this Focused Section will be on the new techniques in reliability modeling, reliability analysis, reliability design, fault and failure detection, signal processing, and resilient control of IMS. [Karimi, H. R.] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Du, H.] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong, NSW 2522, Australia; [Lin, J.] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100083, Peoples R China; [Zeng, X.] Ford Motor Co, Dearborn, MI 48126 USA Polytechnic University of Milan; University of Wollongong; Beihang University; Ford Motor Company Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. hrkarimi@ieee.org; hdu@uow.edu.au; linjing@buaa.edu.cn; zeng.195@buckeyemail.osu.edu Zeng, Xiangrui/AAC-7485-2021 Zeng, Xiangrui/0000-0001-5425-9219 0 0 0 2 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1083-4435 1941-014X IEEE-ASME T MECH IEEE-ASME Trans. Mechatron. DEC 2019.0 24 6 2437 2440 10.1109/TMECH.2019.2953396 0.0 4 Automation & Control Systems; Engineering, Manufacturing; Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering KB6SF 2023-03-23 WOS:000506621800001 0
J Han, YG; Zhou, YQ; Li, HB; Gong, ZY; Liu, ZY; Wang, H; Wang, B; Ye, XJ; Liu, Y Han, Yaguang; Zhou, Yiqin; Li, Haobo; Gong, Zhenyu; Liu, Ziye; Wang, Huan; Wang, Bo; Ye, Xiaojian; Liu, Yi Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection FRONTIERS IN IMMUNOLOGY English Article weighted gene co-expression network analysis (WGCBA); recursive feature elimination (RFE); ankylosing spondylitis (AS); mRNAs biomarkers; support vector machine GENE SELECTION; OSTEOBLAST DIFFERENTIATION; CANCER CLASSIFICATION; SVM-RFE; EXPRESSION; SIGNATURE Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study aimed to investigate credible AS-specific mRNA biomarkers from the whole blood of AS patients by analyzing an mRNA expression profile (GSE73754) downloaded Gene Expression Omnibus, which includes AS and healthy control blood samples. Weighted gene co-expression network analysis was performed and revealed three mRNA modules associated with AS. By performing gene set enrichment analysis, the functional annotations of these modules revealed immune biological processes that occur in AS. Several feature mRNAs were identified by analyzing the hubs of the protein-protein interaction network, which was based on the intersection between differentially expressed mRNAs and mRNA modules. A machine learning-based feature selection method, SVM-RFE, was used to further screen out 13 key feature mRNAs. After verifying by qPCR, IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1 and Cxcr6 were found to be significant for AS diagnosis. Among them, Cxcr6, IL17RA and Lrrfip1 were correlated with severity of AS symptoms. In conclusion, our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS. [Han, Yaguang; Zhou, Yiqin; Li, Haobo; Liu, Ziye; Wang, Huan; Wang, Bo; Liu, Yi] Naval Med Univ, Shanghai Changzheng Hosp, Dept Orthopaed, Shanghai, Peoples R China; [Zhou, Yiqin] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Radiol, Shanghai, Peoples R China; [Gong, Zhenyu] Tech Univ Munich, Dept Neurosurg, Klinikum Rechts Isar, Munich, Germany; [Ye, Xiaojian; Liu, Yi] Shanghai Jiao Tong Univ, Tongren Hosp, Dept Orthopaed, Sch Med, Shanghai, Peoples R China Naval Medical University; Shanghai University of Traditional Chinese Medicine; Technical University of Munich; Shanghai Jiao Tong University Wang, B; Liu, Y (corresponding author), Naval Med Univ, Shanghai Changzheng Hosp, Dept Orthopaed, Shanghai, Peoples R China.;Ye, XJ; Liu, Y (corresponding author), Shanghai Jiao Tong Univ, Tongren Hosp, Dept Orthopaed, Sch Med, Shanghai, Peoples R China. dr.wangbo@vip.163.com; yexj2002@163.com; ley582196589@163.com National Key R&D Program of China; [2020YFC2008404] National Key R&D Program of China; Funding This study was supported by the National Key R&D Program of China (2020YFC2008404). 46 0 0 4 4 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. SEP 12 2022.0 13 956027 10.3389/fimmu.2022.956027 0.0 11 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology 4W9KF 36172367.0 gold 2023-03-23 WOS:000860472900001 0
J Fu, B; Wei, XX Fu, Bo; Wei, Xinxin An Intelligent Analysis Method for Human-Induced Vibration of Concrete Footbridges INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS English Article Machine learning; serviceability; footbridge; social force model; human-induced vibration; gradient boosting regression tree HIGH-STRENGTH CONCRETE; ELASTIC-MODULUS; COMPRESSIVE STRENGTH; PREDICTION; SERVICEABILITY; MODEL It is essential to reliably predict the human-induced vibrations in serviceability design of footbridges to ensure the vibration levels to be within the acceptable comfort limits. The human-induced structural responses are dependent on the dynamic properties of structures and human-induced excitations. For concrete footbridges, the elastic modulus of concrete is a vital parameter for determining the dynamic structural properties. To this end, a two-stage machine learning (ML)-based method is first proposed for modeling the elastic modulus of concrete. At the first stage, the ensemble algorithm, i.e. the gradient boosting regression tree (GBRT), is used to predict the compressive strength by selecting eight parameters, including concrete ingredients and curing time, as the inputs. At the second stage, the elastic modulus of concrete is modeled by using the GBRT method with the compressive strength as the input. Pedestrian crowd-induced load is the most common and crucial design load for footbridges. To consider the inter- and intra-subject variability in walking parameters and induced forces among persons in a crowd, a load model is developed by associating a modified social force model with a walking force model. By integrating the two submodels of structure and excitation, an intelligent analysis method for human-induced vibration is finally developed. A concrete footbridge with typical box cross-section subjected to human-induced excitation is analysed to illustrate the application of the proposed method. [Fu, Bo] Changan Univ, Sch Civil Engn, Xian 710061, Peoples R China; [Fu, Bo] Xian Univ Architecture & Technol, State Key Lab Green Bldg Western China, Xian 710055, Peoples R China; [Wei, Xinxin] Ruhr Univ Bochum, Univ Str 150, D-44780 Bochum, Germany Chang'an University; Xi'an University of Architecture & Technology; Ruhr University Bochum Wei, XX (corresponding author), Ruhr Univ Bochum, Univ Str 150, D-44780 Bochum, Germany. wei_xinxin@hotmail.com Fu, Bo/0000-0003-3597-1504 National Natural Science Foundation of China [51908048]; Young Talent Fund of University Association for Science and Technology in Shaanxi, China [20200412]; Natural Science Foundation of Shaanxi Province [2019JQ-021]; Fundamental Research Funds for the Central Universities, CHD [300102289301]; Open Project of State Key Laboratory of Green Building in Western China [LSKF202007] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Young Talent Fund of University Association for Science and Technology in Shaanxi, China; Natural Science Foundation of Shaanxi Province(Natural Science Foundation of Shaanxi Province); Fundamental Research Funds for the Central Universities, CHD; Open Project of State Key Laboratory of Green Building in Western China The authors greatly appreciate the National Natural Science Foundation of China (Grant No. 51908048), the Young Talent Fund of University Association for Science and Technology in Shaanxi, China (Grant No. 20200412), the Natural Science Foundation of Shaanxi Province (Grant No. 2019JQ-021), the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102289301), and the Open Project of State Key Laboratory of Green Building in Western China (Grant No. LSKF202007). 47 8 8 5 19 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0219-4554 1793-6764 INT J STRUCT STAB DY Int. J. Struct. Stab. Dyn. JAN 2021.0 21 1 2150013 10.1142/S0219455421500139 0.0 26 Engineering, Civil; Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics QG6FR 2023-03-23 WOS:000617679700014 0
J Kolodziejczyk, F; Mortazavi, B; Rabczuk, T; Zhuang, XY Kolodziejczyk, Felix; Mortazavi, Bohayra; Rabczuk, Timon; Zhuang, Xiaoying Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries' thermal management INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER English Article Thermal management; Li-ion battery; Machine learning; Neural networks; Finite element method HIGH-POWER; PARAFFIN; MICROSTRUCTURE; DESIGN; CELL; SIMULATION; UNIFORMITY; GRAPHENE; SYSTEM; PACKS In this work, we develop a combined convolutional neural networks (CNNs) and finite element method (FEM) to examine the effective thermal properties of composite phase change materials (CPCMs) consisting of paraffin and copper foam. In this approach, first the CPCM microstructures are modeled using FEM and next the image dataset with corresponding thermal properties is created. The image dataset is subsequently used to train and test the CNN's performance, which is then compared with the performance of a popular network architecture for image classification tasks. The predicted thermal properties are employed to define the properties of the CPCM material of a battery pack. The heat generation and electrochemical response of a Li-ion cell during the charging/discharging is simulated by developing Newman's battery model. Thermal management is achieved by the latent heat of paraffin, with copper foam for enhancing the thermal conductivity. The multiscale model is finally developed using FEM to investigate the effectiveness of the thermal management of the battery pack. In these models the thermal properties estimated by the FEM and the CNN are employed to define the CPCM materials properties of a battery pack. Our results confirm that the model developed on the basis of a CNN can evaluate the effectiveness of the battery pack's thermal management system with an excellent accuracy in comparison with the original FEM models. (C) 2021 Elsevier Ltd. All rights reserved. [Kolodziejczyk, Felix] Leibniz Univ Hannover, Inst Continuum Mech, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra; Zhuang, Xiaoying] Leibniz Univ Hannover, Inst Photon, Dept Math & Phys, Computat Sci & Simulat Technol, Appelstr 11, D-30167 Hannover, Germany; [Rabczuk, Timon; Zhuang, Xiaoying] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Tongji University Kolodziejczyk, F (corresponding author), Leibniz Univ Hannover, Inst Continuum Mech, Appelstr 11, D-30167 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Inst Photon, Dept Math & Phys, Computat Sci & Simulat Technol, Appelstr 11, D-30167 Hannover, Germany. felixkolodziejczyk@web.de; bohayra.mortazavi@gmail.com Rabczuk, Timon/A-3067-2009; Zhuang, Xiaoying/G-4754-2011 Rabczuk, Timon/0000-0002-7150-296X; Zhuang, Xiaoying/0000-0001-6562-2618 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122) [390833453]; cluster system team at the Leibniz Universitat of Hannover Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122)(German Research Foundation (DFG)); cluster system team at the Leibniz Universitat of Hannover B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). Authors also acknowledge the support of the cluster system team at the Leibniz Universitat of Hannover. 55 26 26 13 40 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0017-9310 1879-2189 INT J HEAT MASS TRAN Int. J. Heat Mass Transf. JUN 2021.0 172 121199 10.1016/j.ijheatmasstransfer.2021.121199 0.0 MAR 2021 11 Thermodynamics; Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Engineering; Mechanics RO6GW Green Submitted 2023-03-23 WOS:000641142400056 0
C Liu, M; Zhao, YC; Chu, F; Zheng, FF; Chu, CB Liu, Ming; Zhao, Yecheng; Chu, Feng; Zheng, Feifeng; Chu, Chengbin New MDP Model and Learning Algorithm for Bus Scheduling Problem with Conditional Signal Priority IFAC PAPERSONLINE English Proceedings Paper 10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM) JUN 22-24, 2022 Nantes, FRANCE Int Federat Automat Control, Tech Comm 5 2 Management & Control Mfg & Logist,Int Federat Automat Control, Tech Comm 1 3 Discrete Event & Hybrid Syst,Int Federat Automat Control, Tech Comm 3 2 Computat Intelligence Control,Int Federat Automat Control, Tech Comm 5 1 Mfg Plant Control,Int Federat Automat Control, Tech Comm 7 4 Transportat Syst,Int Federat Automat Control, Tech Comm 9 1 Econ, Business, & Financial Syst Bus scheduling; smart transportation; signal priority; Markov decision process; artificial intelligence algorithm; deep reinforcement learning; bus driving process RELIABILITY Buses are expected to be punctual, but deviation from the schedule is a common occurrence. Transit signal priority (TSP) and conditional signal priority (CSP) are methods to help the bus perform the schedule better by giving traffic signal priority to the bus. Markov decision process (MDP) is suitable for modeling such sequential decision process. In this article, we point out the Markov property of the system based on the analysis of the bus driving process. We model the process of bus driving with CSP as a Markov decision process. Then, the Deep Q Network (DQN) algorithm is applied to solve this MDP model. To the best of our knowledge, this is the first time that the bus scheduling problem with CSP has been modeled as an MDP model and solved by a learning algorithm. Numerical experiments verify the applicability of the DQN algorithm to solve this MDP model. Copyright (C) 2022 The Authors. [Liu, Ming; Zhao, Yecheng] Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China; [Chu, Feng] Univ Evry, Univ Paris Saclay, IBISC, Evry, France; [Zheng, Feifeng] Donghua Univ, Glorious Sun Sch Business & Management, Shanghai, Peoples R China; [Chu, Chengbin] Univ Gustave Eiffel, ESIEE Paris, F-93162 Noisy Le Grand, France; [Chu, Chengbin] Lab GRETTIA COSYS, F-93162 Noisy Le Grand, France Tongji University; UDICE-French Research Universities; Universite Paris Saclay; Donghua University; Universite Gustave-Eiffel; ESIEE Paris Liu, M (corresponding author), Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China. mingliu@tongji.edu.cn; yechengzhao@tongji.edu.cn; feng.chu@univ-evry.fr; ffzheng@dhu.edu.cn; chengbin.chu@esiee.fr National Natural Science Foundation of China (NSFC) [72021002, 71771048, 71832001, 72071144, 71871159, 71890973] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 72021002, 71771048, 71832001, 72071144, 71871159, and 71890973. 11 0 0 5 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE OCT 2022.0 55 10 3160 3165 10.1016/j.ifacol.2022.10.215 0.0 OCT 2022 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems 6B9YM gold 2023-03-23 WOS:000881681700478 0
J Liu, BS; Zou, XC; Yi, S; Sneeuw, N; Li, JC; Cai, JQ Liu, Bingshi; Zou, Xiancai; Yi, Shuang; Sneeuw, Nico; Li, Jiancheng; Cai, Jianqiang Reconstructing GRACE-like time series of high mountain glacier mass anomalies REMOTE SENSING OF ENVIRONMENT English Article A statistical model; Reconstruction; Separation; Glaciers and snow; Hydrological signal SEA-LEVEL RISE; ASIA; BALANCES; PRECIPITATION; MELTWATER; BUDGET; SNOW; GULF High mountain glaciers (HMGs), called the water towers of the world, are vulnerable to the effects of climate change and thus are rapidly shrinking. Monitoring and evaluating large-scale glacier and snow (GS) mass changes are critical for humans and ecosystems. Although modern gravity satellites monitor GS on a global scale, the contemporary Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE FO) missions are only able to do so at coarse spatial resolutions and are restricted by their limited observation periods. Moreover, because of the complex mechanism behind a glacier's mass balance, existing reconstruction methods for GRACE-like mass anomalies, including machine learning approaches and statistical models, cannot be applied to HMGs. Here, we propose a precipitation and temperature data-driven statistical model combining hydrology and GS processes to reconstruct GRACE-like mass anomalies of HMGs, from which the hydrological and GS signals can be further separated. The prediction and reconstruction performance of this method was consistent with the GRACE/GRACE FO observations (the median correlation coefficient/Nash-Sutcliffe efficiency: similar to 0.92/0.85). Additionally, the separated GS signals agreed with the independent external data used for comparison. Compared with machine learning methods, this method better reconstructed the long-term trend of GRACE-like mass anomalies. Our study provides an acceptable method for expanding mass anomaly time series on HMGs, thereby assisting in the sustainable management and protection of water resources in their downstream areas. [Liu, Bingshi; Zou, Xiancai; Li, Jiancheng] Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China; [Yi, Shuang; Sneeuw, Nico; Cai, Jianqiang] Univ Stuttgart, Inst Geodesy, Stuttgart, Germany; [Zou, Xiancai; Li, Jiancheng] Wuhan Univ, Key Lab Geospace Environm & Geodesy, Minist Educ, Wuhan, Peoples R China; [Zou, Xiancai; Sneeuw, Nico; Li, Jiancheng; Cai, Jianqiang] Wuhan Univ, Joint Int Res Lab Modern Geodesy & Geodynam, Minist Educ, Wuhan, Peoples R China; [Yi, Shuang] Univ Chinese Acad Sci, Key Lab Computat Geodynam, Beijing, Peoples R China; [Li, Jiancheng] Hubei Luojia Lab, Wuhan, Peoples R China Wuhan University; University of Stuttgart; Wuhan University; Wuhan University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Liu, BS (corresponding author), Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China. bs.liu@whu.edu.cn Liu, Bingshi/0000-0003-4116-5605 National Natural Science Foundation of China [41874021, 42192530, 41721003]; DAAD Thematic Network Project [57421148] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); DAAD Thematic Network Project This work was supported by the National Natural Science Foundation of China [grant numbers 41874021, 42192530, 41721003] ; the DAAD Thematic Network Project [grant number 57421148] . The authors gratefully appreciate these publicly accessible data sets used in this research. Special thanks are due to editors and three anonymous re- viewers for their valuable comments for the improvement of the manuscript. 72 0 0 23 37 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. OCT 2022.0 280 113177 10.1016/j.rse.2022.113177 0.0 JUL 2022 22 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology 3S0XX 2023-03-23 WOS:000839328200002 0
J Markussen, T; Putterman, L; Wang, LJ Markussen, Thomas; Putterman, Louis; Wang, Liangjun Algorithmic Leviathan or Individual Choice: Choosing Sanctioning Regimes in the Face of Observational Error ECONOMICA English Article PUBLIC-GOODS; PUNISHMENT; ENFORCEMENT; PROVISION Laboratory experiments are a promising tool for studying how competing institutional arrangements perform and what determines preferences between them. Reliance on enforcement by peers versus formal authorities is a key example. That people incur costs to punish free riders is a well-documented departure from non-behavioural game-theoretic predictions, but how robust is peer punishment to informational problems? We report experimental evidence that reluctance to personally impose punishment when choices are reported unreliably may tip the scales towards rule-based and algorithmic formal enforcement even when observation by the centre is equally prone to error. We provide new and consonant evidence from treatments in which information quality differs for authority versus peers, and confirmatory patterns in both binary decision and quasi-continuous decision variants. Since the role of formal authority is assumed by a computer in our experiment, our findings are also relevant to the question of willingness to entrust machines to make morally fraught decisions, a choice increasingly confronting humans in the age of artificial intelligence. [Markussen, Thomas] Univ Copenhagen, Copenhagen, Denmark; [Putterman, Louis] Brown Univ, Providence, RI 02912 USA; [Wang, Liangjun] Zhejiang Univ Technol, Hangzhou, Peoples R China University of Copenhagen; Brown University; Zhejiang University of Technology Markussen, T (corresponding author), Univ Copenhagen, Copenhagen, Denmark. Markussen, Thomas/0000-0002-9731-4107 National Natural Science Foundation of China [71473225]; Soft Science Foundation of Zhejiang [2021C25006]; Independent Research Fund Denmark (DFF/FSE) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Soft Science Foundation of Zhejiang; Independent Research Fund Denmark (DFF/FSE) We would like to thank the National Natural Science Foundation of China (grant no.71473225), the Soft Science Foundation of Zhejiang (grant no.2021C25006) and Independent Research Fund Denmark (DFF/FSE) for funding. We thank Xinyi Zhang for assistance in checking translation of instructions, and Vicky Zhang and Kristin Petersmann for help in checking proofs. We are grateful to seminar participants at the University of Copenhagen, University of Gothenburg, at the Seventh Biennial Social Dilemmas Conference held at University of Massachusetts, Amherst in June 2017, and at the North America regional meeting of the Economic Science Association in Antigua, Guatemala in 2018, and to Attila Ambrus, Laura Gee, James Walker, Noam Yuchtman and two anonymous reviewers for helpful comments. 53 0 0 2 2 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0013-0427 1468-0335 ECONOMICA Economica JAN 2023.0 90 357 315 338 10.1111/ecca.12443 0.0 OCT 2022 24 Economics Social Science Citation Index (SSCI) Business & Economics 6Z9YX 2023-03-23 WOS:000863002500001 0
J Safaei-Farouji, M; Thanh, HV; Dai, ZX; Mehbodniya, A; Rahimi, M; Ashraf, U; Radwan, AE Safaei-Farouji, Majid; Hung Vo Thanh; Dai, Zhenxue; Mehbodniya, Abolfazl; Rahimi, Mohammad; Ashraf, Umar; Radwan, Ahmed E. Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project JOURNAL OF CLEANER PRODUCTION English Article CO2 geological sequestration; Geological CO2 storage; Machine learning; Random forest CO2 STORAGE; ENERGY-CONSUMPTION; SEQUESTRATION; SIMULATION; CAPACITY; OPTIMIZATION; INJECTION Carbon geological sequestration (CGS) in saline aquifers is an effective carbon utilization approach to decrease the effect of greenhouse gases on the atmosphere. However, the GCS project's fundamental problem remains accurate prediction of carbon trapping efficiency in storage formations. As a result, four machine learning (ML) models were explored and compared in this study to improve the prediction of carbon storage performance in saline aquifers. The adaptive neuro-fuzzy inference system (ANFIS), extra tree (ET), random forest (RF), and radial basis function (RBF) models were used. These were considered for mapping the link between carbon trapping efficiency and influencing factors. A total of 1868 field simulation data points for carbon residual (RTI) and solubility trapping (STI) were extracted from published literature to develop the ML models. The four ML outcomes were assessed and compared using graphical analysis and statistical metrics. The results of the four models, ranked from strongest to weakest, were RF > ANFIS > RBF > ET. The RF scheme achieved the R-2 values of 0.995 and 0.965 and MAE values of 0.0074 and 0.0086 for RTI and STI, respectively. Therefore, an RF robust model is proposed to evaluate the carbon trapping effectiveness in saline aquifers. Simultaneously, the relevancy factor was applied to evaluate the sensitivity of each input parameter. The sensitivity analysis indicated that depth and post injection time were the most influential factors in residual and solubility trapping performance. The findings of this study could help us better understand the potential of ML for predicting carbon trapping efficiencies. The best RF model could serve as a template to quickly predict the performance of carbon storage. [Safaei-Farouji, Majid] Univ Tehran, Coll Sci, Sch Geol, Tehran, Iran; [Hung Vo Thanh] Seoul Natl Univ, Sch Earth & Environm Sci, 1 Gwanak Ro, Seoul, South Korea; [Dai, Zhenxue] Jilin Univ, Coll Construct Engn, Changchun 130026, Peoples R China; [Mehbodniya, Abolfazl] Kuwait Coll Sci & Technol KCST, Dept Elect & Commun Engn, Kuwait, Kuwait; [Rahimi, Mohammad] Ferdowsi Univ Mashhad, Fac Agr, Dept Biosyst Engn, Mashhad, Razavi Khorasan, Iran; [Ashraf, Umar] Yunnan Univ, Sch Ecol & Environm Sci, Inst Ecol Res & Pollut Control Plateau Lakes, Kunming 650500, Yunnan, Peoples R China; [Radwan, Ahmed E.] Jagiellonian Univ, Fac Geog & Geol, Inst Geol Sci, Gronostajowa 3a, PL-30387 Krakow, Poland University of Tehran; Seoul National University (SNU); Jilin University; Kuwait College Science & Technology; Ferdowsi University Mashhad; Yunnan University; Jagiellonian University Thanh, HV (corresponding author), Seoul Natl Univ, Sch Earth & Environm Sci, 1 Gwanak Ro, Seoul, South Korea. vothanhhung198090@gmail.com Vo, Thanh Hung/AFJ-5727-2022; Ashraf, Umar/ABB-7906-2021; Radwan, Ahmed E./M-1034-2017; Mehbodniya, Abolfazl/ABD-6134-2021 Vo, Thanh Hung/0000-0002-7094-9380; Ashraf, Umar/0000-0003-2402-3605; Radwan, Ahmed E./0000-0002-3011-5832; Rahimi, Mohammad/0000-0002-9562-1445; Mehbodniya, Abolfazl/0000-0002-0945-512X BrainKorea21 (BK21) FOUR Postdoctoral Fellowship BrainKorea21 (BK21) FOUR Postdoctoral Fellowship The authors would like to express their gratitude to the BrainKorea21 (BK21) FOUR Postdoctoral Fellowship for their financial support. 87 7 7 2 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. OCT 20 2022.0 372 133778 10.1016/j.jclepro.2022.133778 0.0 15 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 7T8VL 2023-03-23 WOS:000911719600004 0
J Ottino-Gonzalez, J; Uhlmann, A; Hahn, S; Cao, ZP; Cupertino, RB; Schwab, N; Allgaier, N; Alia-Klein, N; Ekhtiari, H; Fouche, JP; Goldstein, RZ; Li, CSR; Lochner, C; London, ED; Luijten, M; Masjoodi, S; Momenan, R; Oghabian, MA; Roos, A; Stein, DJ; Stein, EA; Veltman, DJ; Verdejo-Garcia, A; Zhang, S; Zhao, M; Zhong, N; Jahanshad, N; Thompson, PM; Conrod, P; Mackey, S; Garavan, H Ottino-Gonzalez, Jonatan; Uhlmann, Anne; Hahn, Sage; Cao, Zhipeng; Cupertino, Renata B.; Schwab, Nathan; Allgaier, Nicholas; Alia-Klein, Nelly; Ekhtiari, Hamed; Fouche, Jean-Paul; Goldstein, Rita Z.; Li, Chiang-Shan R.; Lochner, Christine; London, Edythe D.; Luijten, Maartje; Masjoodi, Sadegh; Momenan, Reza; Oghabian, Mohammad Ali; Roos, Annerine; Stein, Dan J.; Stein, Elliot A.; Veltman, Dick J.; Verdejo-Garcia, Antonio; Zhang, Sheng; Zhao, Min; Zhong, Na; Jahanshad, Neda; Thompson, Paul M.; Conrod, Patricia; Mackey, Scott; Garavan, Hugh White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group DRUG AND ALCOHOL DEPENDENCE English Article Addiction; DTI; FA; Myelin; Machine learning CIGARETTE-SMOKING; TOBACCO SMOKING; INTEGRITY; SMOKERS; BRAIN; CLASSIFICATION; DISORDER; IMPACT; ADULTS; VOLUME Background: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. Methods: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. Results: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). Conclusions: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine. [Ottino-Gonzalez, Jonatan; Hahn, Sage; Cao, Zhipeng; Cupertino, Renata B.; Schwab, Nathan; Allgaier, Nicholas; Mackey, Scott; Garavan, Hugh] Univ Vermont, Coll Med, Dept Psychiat, Burlington, VT 05401 USA; [Uhlmann, Anne] Tech Univ Dresden, Dept Child & Adolescent Psychiat & Psychotherapy, Dresden, Germany; [Alia-Klein, Nelly; Goldstein, Rita Z.] Icahn Sch Med Mt Sinai, Dept Psychiat & Neurosci, New York, NY 10029 USA; [Ekhtiari, Hamed] Univ Tehran, Inst Cognit Sci Studies, Tehran, Iran; [Ekhtiari, Hamed] Univ Tehran Med Sci, Iranian Natl Ctr Addict Studies, Tehran, Iran; [Fouche, Jean-Paul] Stellenbosch Univ, Dept Psychiat, SA MRC Genom & Brain Disorders Unit, Stellenbosch, South Africa; [Lochner, Christine; Roos, Annerine] Stellenbosch Univ, Dept Psychiat, SA MRC Unit Risk & Resilience Mental Disorders, Stellenbosch, South Africa; [Li, Chiang-Shan R.; Zhang, Sheng] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA; [London, Edythe D.] Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90024 USA; [Luijten, Maartje] Radboud Univ Nijmegen, Behav Sci Inst, Nijmegen, Netherlands; [Masjoodi, Sadegh; Oghabian, Mohammad Ali] Univ Tehran Med Sci, Res Ctr Mol & Cellular Imaging, Neuroimaging & Anal Grp, Tehran, Iran; [Momenan, Reza] NIAAA, Clin Neuroimaging Res Core, NIH, Bethesda, MD USA; [Roos, Annerine; Stein, Dan J.] Univ Cape Town, SA MRC Unit Risk & Resilience Mental Disorders, Dept Psychiat, Cape Town, South Africa; [Roos, Annerine; Stein, Dan J.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa; [Stein, Elliot A.] NIDA, Neuroimaging Res Branch, Intramural Res Program, Baltimore, MD USA; [Veltman, Dick J.] Amsterdam UMC Locat VUMC, Dept Psychiat, Amsterdam, Netherlands; [Verdejo-Garcia, Antonio] Monash Univ, Sch Psychol Sci, Melbourne, Vic, Australia; [Verdejo-Garcia, Antonio] Monash Univ, Turner Inst Brain & Mental Hlth, Melbourne, Vic, Australia; [Zhao, Min; Zhong, Na] Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Sch Med, Shanghai, Peoples R China; [Jahanshad, Neda; Thompson, Paul M.] Univ Southern Calif, Keck Sch Med, Stevens Inst Neuroimaging & Informat, San Diego, CA USA; [Conrod, Patricia] Univ Montreal, Dept Psychiat, Montreal, PQ, Canada University of Vermont; Technische Universitat Dresden; Icahn School of Medicine at Mount Sinai; University of Tehran; Tehran University of Medical Sciences; Stellenbosch University; Stellenbosch University; Yale University; University of California System; University of California Los Angeles; Radboud University Nijmegen; Tehran University of Medical Sciences; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA); University of Cape Town; University of Cape Town; National Institutes of Health (NIH) - USA; NIH National Institute on Drug Abuse (NIDA); Monash University; Monash University; Shanghai Jiao Tong University; University of Southern California; Universite de Montreal Ottino-Gonzalez, J (corresponding author), Univ Vermont, Dept Psychiat, UVM Med Ctr, 1 South Prospect St, Burlington, VT 05401 USA. jottinog@uvm.edu Stein, Dan/A-1752-2008; Cao, Zhipeng/AAA-3563-2022; Ottino-Gonzalez, Jonatan/T-6823-2019; Roos, Annerine/B-5861-2011; Cupertino, Renata B/L-2684-2015 Stein, Dan/0000-0001-7218-7810; Cao, Zhipeng/0000-0003-2624-2182; Ottino-Gonzalez, Jonatan/0000-0003-2910-9926; Roos, Annerine/0000-0001-6487-3159; Cupertino, Renata B/0000-0002-3452-0632; Uhlmann, Anne/0000-0002-1753-7811 National Institutes on Drug Abuse (NIDA) [1R01DA047119-01, R21DA034954, 1R01DA041528, 1R01DA047851, 1R01DA048301, R01AA021449, R01DA023248, K25DA040032, R01DA020726]; Marjorie Greene Family Trust; UCLA contract [20063287]; Philip Morris USA; National Institutes on Alcohol Abuse and Alcoholism (NIAAA) , Division of Intramural Clinical and Biological Research [ZIA-AA000123]; VIDI grant from Netherlands Organization for Scientific Research (NWO) [016.08.322]; Career Development Fellowship from the Australian Medical Research Future fund [MRF1141214]; National Key Research and Development Program of China [2017YFC1310400]; National Nature Science Foundation of China [81771436]; Shanghai Municipal Health and Family Planning Commission [2018YQ045]; Thomas P. and Katherine K. Pike Chair in Addiction Studies National Institutes on Drug Abuse (NIDA)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA)); Marjorie Greene Family Trust; UCLA contract; Philip Morris USA; National Institutes on Alcohol Abuse and Alcoholism (NIAAA) , Division of Intramural Clinical and Biological Research; VIDI grant from Netherlands Organization for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); Career Development Fellowship from the Australian Medical Research Future fund; National Key Research and Development Program of China; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Health and Family Planning Commission; Thomas P. and Katherine K. Pike Chair in Addiction Studies Dr Garavan was funded (1R01DA047119-01) by the NationalInstitutes on Drug Abuse (NIDA) . Dr Goldstein received financial sup-port from NIDA (R21DA034954, 1R01DA041528, 1R01DA047851, and 1R01DA048301) . Dr Li was supported with funds from NIDA (R01AA021449, R01DA023248, and K25DA040032) . NIDA (R01DA020726) , the Thomas P. and Katherine K. Pike Chair in Addic-tion Studies, the Endowment from the Marjorie Greene Family Trust, and UCLA contract 20063287 with Philip Morris USA funded Dr London. Dr Momenan and the Clinical NeuroImaging Research Core was sup-ported (ZIA-AA000123) by the National Institutes on Alcohol Abuse and Alcoholism (NIAAA) , Division of Intramural Clinical and Biological Research. Dr Luijten and Dr Veltman received funds from VIDI grant 016.08.322 from Netherlands Organization for Scientific Research (NWO) , awarded to Ingmar H.A. Franken. Dr Verdejo-Garcia was sup-ported by the Career Development Fellowship from the Australian Medical Research Future fund (MRF1141214) . Dr Zhao was funded by the National Key Research and Development Program of China (2017YFC1310400) , the National Nature Science Foundation of China (81771436) , and the Shanghai Municipal Health and Family Planning Commission (2018YQ045) . Authors have no more financial aspects to disclose. 87 4 4 2 5 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0376-8716 1879-0046 DRUG ALCOHOL DEPEN Drug Alcohol Depend. JAN 1 2022.0 230 109185 10.1016/j.drugalcdep.2021.109185 0.0 NOV 2021 10 Substance Abuse; Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Substance Abuse; Psychiatry XJ7UJ 34861493.0 Green Accepted 2023-03-23 WOS:000726987600002 0
C Kos, A; Wei, Y; Tomazic, S; Umek, A Bie, R; Sun, Y; Yu, J Kos, Anton; Wei, Yu; Tomazic, Sago; Umek, Anton The role of science and technology in sport 2017 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS Procedia Computer Science English Proceedings Paper 6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) OCT 19-21, 2017 Qufu Normal Univ, Sch Informat Sci & Engn, Shandong, PEOPLES R CHINA Qufu Normal Univ, Sch Informat Sci & Engn motor learning; sensors; sport activity signal acquisition; biofeedback system; sport data analysis; smart sport equipment BIOFEEDBACK; RECOGNITION; SMARTPHONE Wearable devices measuring some physical or physiological quantity of an individual have already become a part of daily life for many people. While such simple devices output mainly the statistical values of measured quantities or count events, demands in sport are more stringent. Quantities of interest must be measured in wider range, with greater precision, and with higher sampling frequency. We present a short introduction to motor learning in sport and its needs for technology back-up. We present properties and limitations of various sensors used for sport activity signal acquisition, means of communication, and properties and limitations of communication channels. We shed some light on the analysis of various aspects of sport activity signal and data processing. We present timing, spatial, and computational power constraints of processing. Attention is given also to the state of the art data processing techniques such as machine learning and data mining. In conclusion we present some technological trends and challenges in sport, such as Internet of Things, smart sport equipment, and real-time biofeedback systems and applications. Copyright (C) 2018 Elsevier Ltd. All rights reserved. [Kos, Anton; Tomazic, Sago; Umek, Anton] Univ Ljubljana, Fac Elect Engn, Trzaska Cesta 25, Ljubljana 1000, Slovenia; [Wei, Yu] Capital Univ Phys Educ & Sports, Comp Teaching & Res Sect, 11 Beisanhuan West Rd, Beijing, Peoples R China University of Ljubljana; CAPITAL UNIVERSITY OF PHYSICAL EDUCATION AND SPORTS Wei, Y (corresponding author), Capital Univ Phys Educ & Sports, Comp Teaching & Res Sect, 11 Beisanhuan West Rd, Beijing, Peoples R China. weiyu@cupes.edu.cn Tomazic, Saso/A-2018-2008; Umek, Anton/AAG-2730-2020 Tomazic, Saso/0000-0002-2968-8879 Slovenian Research Agency [P2-0246] Slovenian Research Agency(Slovenian Research Agency - Slovenia) The authors acknowledge the support from the Slovenian Research Agency (research core funding No. P2-0246). 13 8 8 1 12 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-0509 PROCEDIA COMPUT SCI 2018.0 129 489 495 10.1016/j.procs.2018.03.029 0.0 7 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BK3OK gold 2023-03-23 WOS:000435296700079 0
J Liang, JM; Shen, SQ; Li, M Liang, Jin-Min; Shen, Shu-Qian; Li, Ming Quantum Algorithms and Circuits for Linear Equations with Infinite or No Solutions INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS English Article Quantum algorithm; Linear equations; Quantum circuits Linear equations with infinite or no solutions play key roles in machine learning and optimization. However, the existed quantum algorithms cannot be applied directly for these classes of equations. In this paper, based on the modification of algorithm (Wossnig et al., Phys. Rev. Lett. 120, 050502 2017), we describe a quantum algorithm to compute the minimal norm solution or minimum norm least-squares solution for equations with infinite or no solutions, respectively. It can be shown that the presented algorithm can achieve an exponential speedup over the best classical algorithm. Furthermore, the corresponding quantum circuit is designed on a quantum computer. [Liang, Jin-Min; Shen, Shu-Qian; Li, Ming] China Univ Petr, Coll Sci, Qingdao 266580, Shandong, Peoples R China; [Li, Ming] Max Planck Inst Math Sci, D-04103 Leipzig, Germany China University of Petroleum; Max Planck Society Shen, SQ (corresponding author), China Univ Petr, Coll Sci, Qingdao 266580, Shandong, Peoples R China. sqshen@upc.edu.cn Li, Ming/GLV-0336-2022 Liang, Jin-Min/0000-0002-8210-1504 Natural Science Foundation of Shandong Province [ZR2016AM23]; Fundamental Research Funds for the Central Universities [18CX02035A] Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors thank Dr. Bo-Jia Duan for her invaluable suggestions. This work is supported by the Natural Science Foundation of Shandong Province (ZR2016AM23) and the Fundamental Research Funds for the Central Universities (18CX02035A). 25 2 2 0 8 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0020-7748 1572-9575 INT J THEOR PHYS Int. J. Theor. Phys. AUG 2019.0 58 8 2632 2640 10.1007/s10773-019-04151-2 0.0 9 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics IJ5AN 2023-03-23 WOS:000475915300021 0
J Zhu, B; Niu, YG; Xiao, J; Baesens, B Zhu, Bing; Niu, Yongge; Xiao, Jin; Baesens, Bart A new transferred feature selection algorithm for customer identification NEURAL COMPUTING & APPLICATIONS English Article Customer identification; Feature selection; Class imbalance; Absolute rarity; Transfer learning; Group method of data handling CHURN PREDICTION; NEURAL-NETWORK; MODEL; CRM Class imbalance brings great challenges to feature selection in customer identification, and most of the current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural networks. The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application using data from a cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially in terms of the predictive accuracy of the minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish high-value customers from low-value ones. [Zhu, Bing; Niu, Yongge; Xiao, Jin] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China; [Baesens, Bart] Katholieke Univ Leuven, Dept Decis Sci & Informat Management, B-3000 Leuven, Belgium; [Baesens, Bart] Univ Southampton, Sch Management, Southampton SO17 1BJ, Hants, England Sichuan University; KU Leuven; University of Southampton Niu, YG (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China. zhubing1866@hotmail.com; niuyongge@gmail.com; xiaojin@scu.edu.cn; bart.baesens@kuleuven.be Zhu, Bing/0000-0003-0610-9317 Natural Science Foundation of China [71401115, 71471124, 71172196, 71572120]; MOE (Ministry of Education in China) Youth Project of Humanities and Social Sciences [13YJC630249]; Specialized Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China [20120181120074]; Foundation of Sichuan University [skqy201507]; China Scholarship Council (CSC) Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MOE (Ministry of Education in China) Youth Project of Humanities and Social Sciences; Specialized Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China; Foundation of Sichuan University; China Scholarship Council (CSC)(China Scholarship Council) This work is supported by Natural Science Foundation of China (Grant Nos. 71401115, 71471124, 71172196 and 71572120), MOE (Ministry of Education in China) Youth Project of Humanities and Social Sciences (Grant No. 13YJC630249), Specialized Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China (Grant No. 20120181120074), and Foundation of Sichuan University (skqy201507). Bing Zhu is supported by postdoctoral fellowships from the China Scholarship Council (CSC). 45 4 4 3 32 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. SEP 2017.0 28.0 9 SI 2593 2603 10.1007/s00521-016-2214-y 0.0 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FC7SV Green Accepted 2023-03-23 WOS:000407043400018 0
C Pan, C; Pradel, M Cadar, C; Zhang, X Pan, Cong; Pradel, Michael Continuous Test Suite Failure Prediction ISSTA '21: PROCEEDINGS OF THE 30TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS English Proceedings Paper 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) JUL 11-17, 2021 ELECTR NETWORK ACM SIGSOFT,Assoc Comp Machinery,Google,AWS,Dragon Testing,Microsoft Res,Facebook,KBR,NASA,JetBeans continuous test suite failure prediction; continuous integration; cost model; machine learning TEST-CASE PRIORITIZATION; SELECTION; MINIMIZATION Continuous integration advocates to run the test suite of a project frequently, e.g., for every code change committed to a shared repository. This process imposes a high computational cost and sometimes also a high human cost, e.g., when developers must wait for the test suite to pass before a change appears in the main branch of the shared repository. However, only 4% of all test suite invocations turn a previously passing test suite into a failing test suite. The question arises whether running the test suite for each code change is really necessary. This paper presents continuous test suite failure prediction, which reduces the cost of continuous integration by predicting whether a particular code change should trigger the test suite at all. The core of the approach is a machine learning model based on features of the code change, the test suite, and the development history. We also present a theoretical cost model that describes when continuous test suite failure prediction is worthwhile. Evaluating the idea with 15k test suite runs from 242 open-source projects shows that the approach is effective at predicting whether running the test suite is likely to reveal a test failure. Moreover, we find that our approach improves the AUC over baselines that use features proposed for just-in-time defect prediction and test case failure prediction by 13.9% and 2.9%, respectively. Overall, continuous test suite failure prediction can significantly reduce the cost of continuous integration. [Pan, Cong] Beihang Univ, Key Lab Reliabil & Environm Engn Technol, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Pradel, Michael] Univ Stuttgart, Dept Comp Sci, Stuttgart, Germany Beihang University; University of Stuttgart Pan, C (corresponding author), Beihang Univ, Key Lab Reliabil & Environm Engn Technol, Sch Reliabil & Syst Engn, Beijing, Peoples R China. cong_pan@buaa.edu.cn; michael@binaervarianz.de Pan, Cong/0000-0002-4955-7761 China Sponsorship Council; European Research Council (ERC) [851895]; German Research Foundation within the ConcSys project; German Research Foundation within the Perf4JS project China Sponsorship Council; European Research Council (ERC)(European Research Council (ERC)European Commission); German Research Foundation within the ConcSys project; German Research Foundation within the Perf4JS project This work has been supported by the China Sponsorship Council, by the European Research Council (ERC, grant agreement 851895), and by the German Research Foundation within the ConcSys and Perf4JS projects. 56 2 2 2 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8459-9 2021.0 553 565 10.1145/3460319.3464840 0.0 13 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS4UR 2023-03-23 WOS:000722628800045 0
J Sun, X; Yu, YB; Liang, Y; Dong, JY; Plant, C; Bohm, C Sun, Xin; Yu, Yongbo; Liang, Yao; Dong, Junyu; Plant, Claudia; Bohm, Christian Fusing attributed and topological global-relations for network embedding INFORMATION SCIENCES English Article Network data; Deep learning; Feature representation; Transfer behaviors; Network embedding; Attributed network SOCIAL NETWORKS Network embedding aims to learn a vector for each node while preserves inherent properties of the network. Topological structure and node attributes are both critical for understanding the network formulation. This paper focuses on making the topological and attributed properties complement each other. We propose FATNet for integrating the global relations of the topology and attributes into robust representations. Specifically, nonlocal attribute relations are proposed to capture long-distance dependencies for enriching the topological structure. Meanwhile, we design attributes smoothing filter to preserve the critical attribute values, while interpolating global topology relations via high-order proximity. These relations provide reasonable principles to fuse the structure and attribute for network embedding. Extensive experiments are carried out with five real-world datasets on four downstream tasks, including node classification, link prediction, node clustering and graph visualization. Experiments have shown that the FATNet can achieve superior performance in most cases. (C) 2021 Elsevier Inc. All rights reserved. [Sun, Xin; Yu, Yongbo; Liang, Yao; Dong, Junyu] Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Peoples R China; [Plant, Claudia] Univ Vienna, Fac Comp Sci, Vienna, Austria; [Plant, Claudia] Univ Vienna, Data Sci, Vienna, Austria; [Bohm, Christian] Ludwig Maximilians Univ Munchen, Munich, Germany Ocean University of China; University of Vienna; University of Vienna; University of Munich Sun, X (corresponding author), Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Peoples R China. sunxin@ouc.edu.cn; yuyongbo@stu.ouc.edu.cn; liangyao@stu.ouc.edu.cn; dongjunyu@ouc.edu.cn; claudia.plant@univie.ac.at; boehm@ifi.lmu.de Böhm, Christian/HDN-5925-2022 National Natural Science Foundation of China [61971388, U1706218, 41576011] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank the associated editor and the anonymous referees for their careful revision. This work is supported in part by the National Natural Science Foundation of China (No. 61971388, U1706218, 41576011). 49 5 5 3 10 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. MAY 2021.0 558.0 76 90 10.1016/j.ins.2021.01.012 0.0 FEB 2021 15 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science RF4QF 2023-03-23 WOS:000634824100005 0
J Baker, JS; Supriya, R; Dutheil, F; Gao, Y Baker, Julien S.; Supriya, Rashmi; Dutheil, Frederic; Gao, Yang Obesity: Treatments, Conceptualizations, and Future Directions for a Growing Problem BIOLOGY-BASEL English Article obesity; prebiotic; probiotic; pharmacology; anti obesity drugs; future direction WEIGHT MANAGEMENT; BARIATRIC SURGERY; HEALTH Simple Summary The pyramid of interventions in the management of obesity include, diet restriction, diet therapy, physical exercise, anti-obesity drugs, prebiotics, probiotics, bariatric surgery, and cognitive behavioral strategies. Behavioral strategies that are being applied by predominantly private medical groups focusing on psychological assessments is appreciable; however, they are not replicated by all medical establishments, especially non-private health care institutions. This multifaceted disease has many treatment options; however, these options are not being fully implemented, especially in poor areas globally. Despite the upsurge in the market and expenditures in anti-obesity treatments currently available in economically stable areas of the world, obesity is still increasing at epidemic levels. The aim of this paper is to briefly highlight the anti-obesity treatments used presently and their inefficiency in isolation as individual treatments for obesity due to multifactorial causes. This report emphasizes that even focusing on psychological screening may not be enough to control this epidemic without further government and community cooperation. This report also presents novel strategies and radical thinking to include and amend successful interventions. Furthermore, we present details for incorporating web-based counseling and artificial intelligence to make treatments feasible, accessible, and cost-effective for all populations. We hope this paper will stimulate further debate and increase awareness in relation to the obesity problem and treatment strategies. Interventions in obesity management include nutritional selection, diet restriction, and physical exercise, followed by cognitive behavioral strategies, pharmacology, and surgery towards the tapered treatment end of the obesity pyramid of interventions. Calorie restriction, regular exercise, and several weight reducing drugs, including probiotic and prebiotic use, are increasing in the market as potential anti-obesity treatments all over the world. Despite these efforts, obesity is increasing and is at epidemic levels. We propose here that there should be a multicomponent individual specific treatment approach for treating this multifactorial pathogenesis, incorporating psychological assessment as a first step that may help to reduce the prevalence of this alarming epidemic. We also believe that focusing on psychological screening may not be enough to control this epidemic without government and community cooperation and intervention. Additionally, we suggest that it is imperative to take advantage of the developments in web-based counseling and artificial intelligence expansion in combination with available anti-obesity treatments to make treatments feasible, accessible, and cost-effective for populations of all ages. The purpose of this paper is to increase awareness and stimulate debate in relation to this growing problem. [Baker, Julien S.; Supriya, Rashmi; Gao, Yang] Hong Kong Baptist Univ, Dept Sport Phys Educ & Hlth, Ctr Hlth & Exercise Sci Res, Kowloon Tong, Hong Kong 999077, Peoples R China; [Dutheil, Frederic] Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS,Physiol & Psychosocial Stress, LaPSCo,Occupat & Environm Med, F-63000 Clermont Ferrand, France Hong Kong Baptist University; Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA); CHU Clermont Ferrand Supriya, R (corresponding author), Hong Kong Baptist Univ, Dept Sport Phys Educ & Hlth, Ctr Hlth & Exercise Sci Res, Kowloon Tong, Hong Kong 999077, Peoples R China. jsbaker@hkbu.edu.hk; rashmisupriya@hkbu.edu.hk; fdutheil@chu-clermontferrand.fr; gaoyang@hkbu.edu.hk ; Dutheil, Frederic/O-9707-2014 Gao, Yang (Gemma)/0000-0003-0439-4804; Supriya, Rashmi/0000-0003-2995-1910; Baker, Julien/0000-0002-9093-7897; Dutheil, Frederic/0000-0002-1468-6029 Hong Kong Research Grants Council Postdoctoral Fellowship Scheme [PDFS2021-2H01] Hong Kong Research Grants Council Postdoctoral Fellowship Scheme FundingThis research received no external funding. 28 4 4 8 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-7737 BIOLOGY-BASEL Biology-Basel FEB 2022.0 11.0 2 160 10.3390/biology11020160 0.0 11 Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics ZK2ZT 35205027.0 Green Accepted, gold 2023-03-23 WOS:000762862900001 0
J Liu, S; Liu, DY; Srivastava, G; Polap, D; Wozniak, M Liu, Shuai; Liu, Dongye; Srivastava, Gautam; Polap, Dawid; Wozniak, Marcin Overview and methods of correlation filter algorithms in object tracking COMPLEX & INTELLIGENT SYSTEMS English Article Artificial intelligence; Object tracking; Correlation filter algorithms; Dataset; Template update strategy VISUAL TRACKING An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed. [Liu, Shuai] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha, Peoples R China; [Liu, Shuai] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha, Peoples R China; [Liu, Dongye] Inner Mongolia Univ, Coll Comp Sci, Hohhot, Peoples R China; [Srivastava, Gautam] Brandon Univ, Dept Math & Comp Sci, Brandon, MB, Canada; [Polap, Dawid; Wozniak, Marcin] Silesian Tech Univ, Inst Math, Gliwice, Poland; [Srivastava, Gautam] China Med Univ, Res Ctr Interneual Comp, Taichung, Taiwan Hunan Normal University; Hunan Normal University; Inner Mongolia University; Brandon University; Silesian University of Technology; China Medical University Taiwan Srivastava, G (corresponding author), Brandon Univ, Dept Math & Comp Sci, Brandon, MB, Canada.;Srivastava, G (corresponding author), China Med Univ, Res Ctr Interneual Comp, Taichung, Taiwan. liushuai@hunnu.edu.cn; cs_ldy@mail.imu.edu.cn; srivastavag@brandonu.ca; dawid.polap@polsl.pl; marcin.wozniak@polsl.pl Połap, Dawid/D-1547-2017; Woźniak, Marcin/L-6640-2013; Liu, Shuai/P-3939-2017; Srivastava, Gautam/N-5668-2019 Połap, Dawid/0000-0003-1972-5979; Woźniak, Marcin/0000-0002-9073-5347; Liu, Shuai/0000-0001-9909-0664; Srivastava, Gautam/0000-0001-9851-4103 Key Scientific Research Projects of Department of Education of Hunan Province [19A312]; Hunan Provincial Science and Technology Project Foundation [2018TP1018, 2018RS3065]; National Natural Science Foundationof China [61502254]; Open Project Program of the State Key Lab of CAD&CG, Zhejiang University [A1926] Key Scientific Research Projects of Department of Education of Hunan Province; Hunan Provincial Science and Technology Project Foundation; National Natural Science Foundationof China(National Natural Science Foundation of China (NSFC)); Open Project Program of the State Key Lab of CAD&CG, Zhejiang University This work was supported in part by the Key Scientific Research Projects of Department of Education of Hunan Province (19A312), Hunan Provincial Science and Technology Project Foundation (2018TP1018, 2018RS3065), National Natural Science Foundationof China under Grant 61502254, Open Project Program of the State Key Lab of CAD&CG under Grant A1926, Zhejiang University. 36 86 86 11 35 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2199-4536 2198-6053 COMPLEX INTELL SYST COMPLEX INTELL. SYST. AUG 2021.0 7.0 4 SI 1895 1917 10.1007/s40747-020-00161-4 0.0 JUN 2020 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science TZ2CZ gold 2023-03-23 WOS:000539167000001 0
J Bernard, J; Chang, TW; Popescu, E; Graf, S Bernard, Jason; Chang, Ting-Wen; Popescu, Elvira; Graf, Sabine Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms EXPERT SYSTEMS WITH APPLICATIONS English Article Intelligent tutoring systems; Distance education and telelearning; Interactive learning environments; Computational intelligence PARTICLE SWARM; GENETIC ALGORITHMS; OPTIMIZATION; CONVERGENCE; STRATEGIES; NETWORKS; SYSTEMS Identifying students' learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student's learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students' behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions. (C) 2017 Elsevier Ltd. All rights reserved. [Bernard, Jason; Graf, Sabine] Athabasca Univ, Sch Comp & Informat Syst, 1200 10011-109 St, Edmonton, AB T5J 3S8, Canada; [Chang, Ting-Wen] Beijing Normal Univ, Smart Learning Inst, 19 Xinjiekou Outer St, Beijing 1008700875, Peoples R China; [Popescu, Elvira] Univ Craiova, Fac Automat Comp & Elect, Str Alexandru loan Cuza 13, Craiova 200585, Romania Athabasca University; Beijing Normal University; University of Craiova Bernard, J (corresponding author), Athabasca Univ, Sch Comp & Informat Syst, 1200 10011-109 St, Edmonton, AB T5J 3S8, Canada. c.j.bernard@ieee.org; tingwenchang@bnu.edu.cn; popescu_elvira@software.ucv.ro; sabineg@athabascau.ca Popescu, Elvira/A-2220-2013 Popescu, Elvira/0000-0002-6575-4212; Graf, Sabine/0000-0002-9898-4145 Athabasca University; Alberta Innovates-Technology Futures; Alberta Innovation & Advanced Education; NSERC; Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI [PN-II-RU-TE-2014-42604]; Smart Learning Institute of Beijing Normal University Athabasca University; Alberta Innovates-Technology Futures; Alberta Innovation & Advanced Education; NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI; Smart Learning Institute of Beijing Normal University The authors wish to acknowledge the support of Athabasca University, Alberta Innovates - Technology Futures, Alberta Innovation & Advanced Education, NSERC and the Smart Learning Institute of Beijing Normal University. This work was also supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-42604. 56 59 61 4 86 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. JUN 1 2017.0 75.0 94 108 10.1016/j.eswa.2017.01.021 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Operations Research & Management Science EM3OL 2023-03-23 WOS:000395224400007 0
J Zhang, ZF; Pan, JW; Luo, WL; Ramakrishnan, KR; Singh, HK Zhang, Zhifang; Pan, Jingwen; Luo, Weili; Ramakrishnan, Karthik Ram; Singh, Hemant Kumar Vibration-based delamination detection in curved composite plates COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING English Article Curved plate; Delamination detection; Structural health monitoring; Natural frequency; Surrogate model; Genetic algorithm; Artificial neural network DAMAGE IDENTIFICATION; BEAMS; PREDICTION Delamination is one of common damages in fibre-reinforced composite laminates. It is known to cause changes in the vibration characteristics of the laminates, which allows for the use of vibration-based indicators for assessing structural health conditions and identifying potential risk of catastrophic failures. This paper presents a vibration-based approach to assess delamination in fibre reinforced composite curved plates through frequency shifts as indicative parameters. Two algorithms based on computational intelligence, namely artificial neural network (ANN) and surrogate assisted genetic algorithm (SAGA), were employed as inverse algorithms for predicting the location, size and interface of the delamination. The validation of the two algorithms is realized numerically through finite element (FE) studies conducted using a structured selection of parameters of genetic algorithms, number of frequency shifts and database size. Experimental modal testing was conducted using scanning laser vibrometer on five carbon fibre reinforced polymer (CFRP) curved plates. Among these, one was intact and the other four were manufactured with artificially induced laminations. It was confirmed that the two inverse algorithms were able to reasonably predict delamination parameters in curved composite plates. Among the two, SAGA's performance was observed to be better. A sensitivity analysis was further conducted by adding artificial noise to the numerical data to simulate measurement errors for investigating the influence of noise on the accuracy of the two algorithms. [Zhang, Zhifang; Pan, Jingwen] Guangzhou Univ, Guangzhou Univ Tamkang Univ Joint Res Ctr Engn St, Guangzhou 510006, Guangdong, Peoples R China; [Luo, Weili] Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Guangdong, Peoples R China; [Ramakrishnan, Karthik Ram] Tampere Univ Technol, Lab Mat Sci, FIN-33101 Tampere, Finland; [Singh, Hemant Kumar] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia Guangzhou University; Guangzhou University; Tampere University; University of New South Wales Sydney Luo, WL (corresponding author), Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Guangdong, Peoples R China. zfzhang@gzhu.edu.cn; wlluo@gzhu.edu.cn; karthik.ramakrishnan@tut.fi; h.singh@adfa.edu.au Singh, Hemant K/B-9861-2015; Zhang, Zhifang/Q-8889-2019 Singh, Hemant K/0000-0003-1653-232X; National Natural Science Funds of China [51508118, 51808147]; Natural Science Foundation of Guangdong Province, China [2016A030310261]; Science and Technology Planning Project of Guangdong Province, China [2016B050501004] National Natural Science Funds of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); Science and Technology Planning Project of Guangdong Province, China The research has been supported by National Natural Science Funds of China (Grant No. 51508118, 51808147); Natural Science Foundation of Guangdong Province, China (Grant No. 2016A030310261); Science and Technology Planning Project of Guangdong Province, China (Grant No. 2016B050501004). 28 25 26 8 52 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1359-835X 1878-5840 COMPOS PART A-APPL S Compos. Pt. A-Appl. Sci. Manuf. APR 2019.0 119.0 261 274 10.1016/j.compositesa.2019.02.002 0.0 14 Engineering, Manufacturing; Materials Science, Composites Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science HP1DA 2023-03-23 WOS:000461404400026 0
J Kim, S; Wang, Y; Boon, C Kim, Sunghoon; Wang, Ying; Boon, Corine Sixty years of research on technology and human resource management: Looking back and looking forward HUMAN RESOURCE MANAGEMENT English Article Artificial intelligence; Human resource management; HR analytics; information systems; technology INFORMATION-TECHNOLOGY; TECHNICAL PROFESSIONALS; CHANGING WORK; E-HRM; ORGANIZATION; RECRUITMENT; FUTURE; PERSPECTIVE; EXPERIENCE; ANALYTICS Technology has changed the way we work and how companies manage their employees. This article reviews 60 years of research on the relationship between technology and human resource management, as represented in Human Resource Management. Based on 154 articles, we identify recurring and evolving patterns of research on technology across three time periods (separated by the advent of the personal computer in 1977 and by the popularization of consumer internet services in 1997), three perspectives on technology (tool, proxy, and ensemble view of technology), and three thematic streams (the impact of technology on jobs and organizations, the utilization of technology in HR activities, and the management of technology workers). Drawing on patterns of research that emerged in the past, we provide suggestions for future HR research on newly arriving technology. [Kim, Sunghoon] Univ Sydney, Sch Business, Sydney, NSW, Australia; [Wang, Ying] Beijing Inst Technol, Sch Management & Econ, 5 Zhongguancun South St, Beijing 100081, Peoples R China; [Boon, Corine] Univ Amsterdam, Amsterdam Business Sch, Amsterdam, Netherlands University of Sydney; Beijing Institute of Technology; University of Amsterdam Wang, Y (corresponding author), Beijing Inst Technol, Sch Management & Econ, 5 Zhongguancun South St, Beijing 100081, Peoples R China. ltzlltzl@126.com Wang, Ying/AAH-9972-2021 Wang, Ying/0000-0003-1850-9393; Kim, Sunghoon/0000-0002-4374-9332 158 19 19 34 144 WILEY PERIODICALS, INC SAN FRANCISCO ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA 0090-4848 1099-050X HUM RESOUR MANAGE-US Hum. Resour. Manage. JAN 2021.0 60.0 1 SI 229 247 10.1002/hrm.22049 0.0 NOV 2020 19 Psychology, Applied; Management Social Science Citation Index (SSCI) Psychology; Business & Economics PY5SR 2023-03-23 WOS:000590179600001 0
J Cheng, TY; Li, N; Conselice, CJ; Aragon-Salamanca, A; Dye, S; Metcalf, RB Cheng, Ting-Yun; Li, Nan; Conselice, Christopher J.; Aragon-Salamanca, Alfonso; Dye, Simon; Metcalf, Robert B. Identifying strong lenses with unsupervised machine learning using convolutional autoencoder MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article gravitational lensing: strong; techniques: image processing; methods: statistical STRONG GRAVITATIONAL LENSES; SELF-ORGANIZING MAPS; DARK-MATTER; MORPHOLOGICAL CLASSIFICATION; MAXIMUM-LIKELIHOOD; GALAXY CLUSTERS; STAR-FORMATION; CONSTRAINTS; FIELD; SDSS In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up similar to 63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 +/- 0.48 per cent in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique. [Cheng, Ting-Yun; Li, Nan; Conselice, Christopher J.; Aragon-Salamanca, Alfonso; Dye, Simon] Univ Nottingham, Sch Phys & Astron, Univ Pk, Nottingham NG7 2RD, England; [Li, Nan] Natl Astron Observ, CAS Key Lab Space Astron & Technol, Beijing 100012, Peoples R China; [Metcalf, Robert B.] Univ Bologna, Dipanimento Fis & Astron, Via Gobetti 93-2, I-40129 Bologna, Italy; [Metcalf, Robert B.] INAF Osservatotio Astron Bologna, Via Ranzani 1, I-40127 Bologna, Italy University of Nottingham; Chinese Academy of Sciences; National Astronomical Observatory, CAS; University of Bologna; Istituto Nazionale Astrofisica (INAF) Cheng, TY (corresponding author), Univ Nottingham, Sch Phys & Astron, Univ Pk, Nottingham NG7 2RD, England. ting-yun.cheng@nottingham.ac.uk ; METCALF, ROBERT BENTON/D-1237-2017 Aragon-Salamanca, Alfonso/0000-0001-8215-1256; Cheng, Ting-Yun/0000-0001-8670-4495; Dye, Simon/0000-0002-1318-8343; METCALF, ROBERT BENTON/0000-0003-3167-2574 UK Science and Technology Facilities Council (STFC); UK STFC Rutherford Fellowship; University of Nottingham; STFC [ST/L004771/1] Funding Source: UKRI UK Science and Technology Facilities Council (STFC)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); UK STFC Rutherford Fellowship; University of Nottingham; STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) The authors acknowledges the support by the UK Science and Technology Facilities Council (STFC). SD is supported by a UK STFC Rutherford Fellowship. T-YC gives a thank to the support of the Vice-Chancellor's Scholarship from the University of Nottingham, and discussions with Bobby Clement. 122 30 30 0 4 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. MAY 2020.0 494.0 3 3750 3765 10.1093/mnras/staa1015 0.0 16 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics LR7NO Green Accepted, Green Submitted 2023-03-23 WOS:000535882100055 0
J Kang, ZQ; Catal, C; Tekinerdogan, B Kang, Ziqiu; Catal, Cagatay; Tekinerdogan, Bedir Product failure detection for production lines using a data-driven model EXPERT SYSTEMS WITH APPLICATIONS English Article Machine learning; Production lines; Data analytics; Data mining; Product failure detection FAULT-DETECTION; DIAGNOSIS; QUALITY For a healthy production line, it is essential to ensure a low failure rate of products. Product quality in production lines can be inspected using several techniques at the end of a production process, including a manual inspection. Different methods are applied to inspect the product quality at the end of the production process and sometimes during the production. This is often done using manual inspection, but this is less efficient, expensive, and time-consuming. Machine learning algorithms have the potential for evaluating and predicting product quality in a production line. In this paper, a novel product failure detection model that applies ANOVA (Analysis of Variance) feature selection method, Min-Max Scaling normalization method, mean imputation technique, Random Forest classification algorithm, a data sampling technique, and Grid Search parameter optimization approach is pro-posed and validated. For the comparison of the proposed model, several experiments have been performed using five classification algorithms, including RUSBoosted Tree. Experimental results demonstrated that the proposed model using the Random Forest algorithm, ANOVA feature selection, and sampling method achieves the best performance among other models and detects the faulty products effectively. It was also shown that the RUS-Boosted Tree algorithm can be considered by practitioners for building the faulty product prediction model when data sampling and feature selection techniques are not integrated into the prediction model. [Kang, Ziqiu; Tekinerdogan, Bedir] Wageningen Univ & Res, Informat Technol Grp, Wageningen, Netherlands; [Kang, Ziqiu] Chinese Acad Agr Sci, Inst Urban Agr, Chengdu 610213, Peoples R China; [Catal, Cagatay] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar Wageningen University & Research; Chinese Academy of Agricultural Sciences; Institute of Urban Agriculture, CAAS; Qatar University Catal, C (corresponding author), Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar. ziqiu.kang@wur.nl; ccatal@qu.edu.qa; bedir.tekinerdogan@wur.nl 44 0 0 12 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. SEP 15 2022.0 202.0 117398 10.1016/j.eswa.2022.117398 0.0 APR 2022 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 1T7RW 2023-03-23 WOS:000804926300006 0
J Wang, C; Gong, YF; Deng, FC; Ding, EM; Tang, J; Codling, G; Challis, JK; Green, D; Wang, J; Chen, QL; Xie, YW; Su, S; Yang, ZL; Raine, J; Jones, PD; Tang, S; Giesy, JP Wang, Chao; Gong, Yufeng; Deng, Fuchang; Ding, Enmin; Tang, Jie; Codling, Garry; Challis, Jonathan K.; Green, Derek; Wang, Jing; Chen, Qiliang; Xie, Yuwei; Su, Shu; Yang, Zilin; Raine, Jason; Jones, Paul D.; Tang, Song; Giesy, John P. Remodeling of Arctic char (Salvelinus alpinus) lipidome under a stimulated scenario of Arctic warming GLOBAL CHANGE BIOLOGY English Article Arctic warming; chronic temperature shifts; dynamic alterations; glycerolipids; glycerophospholipids; lipidomics; machine learning FATTY-ACID-COMPOSITION; TROUT SALMO-TRUTTA; WATER TEMPERATURE; OXIDATIVE STRESS; BROWN TROUT; HOMEOVISCOUS ADAPTATION; FUNCTIONAL ROLES; MEMBRANE-LIPIDS; CLIMATE-CHANGE; METABOLISM Arctic warming associated with global climate change poses a significant threat to populations of wildlife in the Arctic. Since lipids play a vital role in adaptation of organisms to variations in temperature, high-resolution mass-spectrometry-based lipidomics can provide insights into adaptive responses of organisms to a warmer environment in the Arctic and help to illustrate potential novel roles of lipids in the process of thermal adaption. In this study, we studied an ecologically and economically important species-Arctic char (Salvelinus alpinus)-with a detailed multi-tissue analysis of the lipidome in response to chronic shifts in temperature using a validated lipidomics workflow. In addition, dynamic alterations in the hepatic lipidome during the time course of shifts in temperature were also characterized. Our results showed that early life stages of Arctic char were more susceptible to variations in temperature. One-year-old Arctic char responded to chronic increases in temperature with coordinated regulation of lipids, including headgroup-specific remodeling of acyl chains in glycerophospholipids (GP) and extensive alterations in composition of lipids in membranes, such as less lyso-GPs, and more ether-GPs and sphingomyelin. Glycerolipids (e.g., triacylglycerol, TG) also participated in adaptive responses of the lipidome of Arctic char. Eight-week-old Arctic char exhibited rapid adaptive alterations of the hepatic lipidome to stepwise decreases in temperature while showing blunted responses to gradual increases in temperature, implying an inability to adapt rapidly to warmer environments. Three common phosphatidylethanolamines (PEs) (PE 36:6|PE 16:1_20:5, PE 38:7|PE 16:1_22:6, and PE 40:7|PE 18:1_22:6) were finally identified as candidate lipid biomarkers for temperature shifts via machine learning approach. Overall, this work provides additional information to a better understanding of underlying regulatory mechanisms of the lipidome of Arctic organisms in the face of near-future warming. [Wang, Chao; Deng, Fuchang; Ding, Enmin; Tang, Song] Natl Inst Environm Hlth, Chinese Ctr Dis Control & Prevent, China CDC Key Lab Environm & Populat Hlth, Beijing, Peoples R China; [Wang, Chao; Gong, Yufeng; Tang, Jie; Codling, Garry; Challis, Jonathan K.; Green, Derek; Chen, Qiliang; Xie, Yuwei; Su, Shu; Yang, Zilin; Raine, Jason; Jones, Paul D.; Giesy, John P.] Univ Saskatchewan, Toxicol Ctr, Saskatoon, SK, Canada; [Tang, Jie] Anhui Agr Univ, Sch Resources & Environm, Hefei, Anhui, Peoples R China; [Codling, Garry] Masaryk Univ, Res Ctr Contaminants Environm, Brno, Czech Republic; [Wang, Jing] Ocean Univ China, Coll Marine Life Sci, MOE Key Lab Marine Genet & Breeding, Qingdao, Peoples R China; [Chen, Qiliang] Chongqing Normal Univ, Coll Life Sci, Chongqing, Peoples R China; [Tang, Song] Nanjing Med Univ, Sch Publ Hlth, Ctr Global Hlth, Nanjing, Peoples R China; [Giesy, John P.] Univ Saskatchewan, Dept Vet Biomed Sci, Saskatoon, SK, Canada; [Giesy, John P.] Baylor Univ, Dept Environm Sci, Waco, TX 76798 USA; [Giesy, John P.] Nanjing Univ, Sch Environm, State Key Lab Pollut Control & Resource Reuse, Nanjing, Peoples R China Chinese Center for Disease Control & Prevention; National Institute of Environmental Health, Chinese Center for Disease Control & Prevention; University of Saskatchewan; Anhui Agricultural University; Masaryk University Brno; Ocean University of China; Chongqing Normal University; Nanjing Medical University; University of Saskatchewan; Baylor University; Nanjing University Gong, YF (corresponding author), Univ Saskatchewan, Toxicol Ctr, Saskatoon, SK, Canada. yufeng.gong@usask.ca Xie, Yuwei/CAI-7227-2022 Xie, Yuwei/0000-0001-5652-6413; Giesy, John/0000-0003-1931-9962; Wang, Chao/0000-0002-5056-9903; Gong, Yufeng/0000-0002-1396-527X; Raine, Jason/0000-0003-3177-4175; Tang, Song/0000-0003-3219-1422 Banting Postdoctoral Fellowship; Canada First Research Excellence Fund; Young Scholar Scientific Research Foundation of China CDC [2018A201]; Oversea Research Training Program of NIEH, China CDC; NSERC Doctoral Scholarship Funding [PGSD 3-504735-2017]; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement [839243]; Global Water Futures Program; Fisheries and Oceans Canada's National Contaminants Advisory Group Banting Postdoctoral Fellowship; Canada First Research Excellence Fund; Young Scholar Scientific Research Foundation of China CDC; Oversea Research Training Program of NIEH, China CDC; NSERC Doctoral Scholarship Funding; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement; Global Water Futures Program; Fisheries and Oceans Canada's National Contaminants Advisory Group Banting Postdoctoral Fellowship; Canada First Research Excellence Fund; Young Scholar Scientific Research Foundation of China CDC, Grant/Award Number: 2018A201; Oversea Research Training Program of NIEH, China CDC; NSERC Doctoral Scholarship Funding, Grant/Award Number: PGSD 3-504735-2017; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement, Grant/Award Number: 839243; Global Water Futures Program; Fisheries and Oceans Canada's National Contaminants Advisory Group 84 0 0 7 18 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1354-1013 1365-2486 GLOBAL CHANGE BIOL Glob. Change Biol. JUL 2021.0 27.0 14 3282 3298 10.1111/gcb.15638 0.0 MAY 2021 17 Biodiversity Conservation; Ecology; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology SR3ZQ 33837644.0 2023-03-23 WOS:000646003100001 0
J Concas, S; La Cava, SM; Orru, G; Cuccu, C; Gao, J; Feng, XY; Marcialis, GL; Roli, F Concas, Sara; La Cava, Simone Maurizio; Orru, Giulia; Cuccu, Carlo; Gao, Jie; Feng, Xiaoyi; Marcialis, Gian Luca; Roli, Fabio Analysis of Score-Level Fusion Rules for Deepfake Detection APPLIED SCIENCES-BASEL English Article deepfake; detection; fusion FACE MANIPULATION; NEURAL-NETWORK Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach. [Concas, Sara; La Cava, Simone Maurizio; Orru, Giulia; Cuccu, Carlo; Marcialis, Gian Luca] Univ Cagliari, Dept Elect & Elect Engn, I-09124 Cagliari, Italy; [Gao, Jie; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China; [Roli, Fabio] Univ Genoa, Dept Informat Bioengn Robot & Syst Engn, I-16126 Genoa, Italy University of Cagliari; Northwestern Polytechnical University; University of Genoa Marcialis, GL (corresponding author), Univ Cagliari, Dept Elect & Elect Engn, I-09124 Cagliari, Italy. sara.concas90c@unica.it; simonem.lac@unica.it; giulia.orru@unica.it; c.cuccu7@studenti.unica.it; jie_gao@mail.nwpu.edu.cn; fengxiao@nwpu.edu.cn; marcialis@unica.it; fabio.roli@unige.it Orrù, Giulia/U-5760-2019 Orrù, Giulia/0000-0002-7802-2483; ROLI, FABIO/0000-0003-4103-9190; LA CAVA, SIMONE MAURIZIO/0000-0002-6344-1845; Gao, Jie/0000-0003-2823-2682; Marcialis, Gian Luca/0000-0002-8719-9643; Concas, Sara/0000-0001-8114-0686 Italian Ministry of Education, University and Research (MIUR) [CUP: F74I19000370001] Italian Ministry of Education, University and Research (MIUR)(Ministry of Education, Universities and Research (MIUR)) This work was supported by the Italian Ministry of Education, University and Research (MIUR) within the PRIN2017-BullyBuster-A framework for bullying and cyberbullying action detection by computer vision and artificial intelligence methods and algorithms (CUP: F74I19000370001). 54 0 0 9 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel AUG 2022.0 12.0 15 7365 10.3390/app12157365 0.0 21 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 3T4IF Green Published, gold 2023-03-23 WOS:000840239300001 0
C Wu, XT; Feng, XY; Boutellaa, E; Hadid, A IEEE Wu, Xiaoting; Feng, Xiaoyi; Boutellaa, Elhocine; Hadid, Abdenour Kinship Verification using Color Features and Extreme Learning Machine 2018 IEEE 3RD INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP) English Proceedings Paper IEEE 3rd International Conference on Signal and Image Processing (ICSIP) JUL 13-15, 2018 SE Univ, Shenzhen, PEOPLES R CHINA IEEE SE Univ kinship verification; color features; extreme learning machine Kinship verification from faces is a challenging task that is attracting an increasing attention in the recent years. The proposed methods so far are not robust enough to predict the kin between persons via facial appearance only. The initial studies using deep convolutional neural networks (CNN) have not shown their full potential as well, mainly due to limited training data. To mitigate this problem, we propose a new approach to kinship verification based on color features and extreme learning machines (ELM). While ELM aims to deal with small size training sets, color features are proven to provide significant enhancement over gray-scale counterparts. We evaluate our proposed method on three benchmark and publicly available kinship databases, namely KinFaceW-I, KinFaceW-II and TSKinFace. The obtained results compares favorably against some state-of-the-art methods including those based on deep learning. [Wu, Xiaoting; Boutellaa, Elhocine; Hadid, Abdenour] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Wu, Xiaoting; Feng, Xiaoyi; Hadid, Abdenour] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China University of Oulu; Northwestern Polytechnical University Hadid, A (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland.;Hadid, A (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China. xiaoting.wu@oulu.fi; fengxiao@nwpu.edu.cn; elhocine.boutellaa@oulu.fi; abdenour.hadid@oulu.fi China Scholarship Council; Academy of Finland China Scholarship Council(China Scholarship Council); Academy of Finland(Academy of Finland) This work was partially funded by China Scholarship Council and Academy of Finland. 21 4 4 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-6396-7 2018.0 187 191 5 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Imaging Science & Photographic Technology BM5FP Green Accepted 2023-03-23 WOS:000464883600036 0
J Li, S; Hendrich, N; Liang, HZ; Ruppel, P; Zhang, CS; Zhang, JW Li, Shuang; Hendrich, Norman; Liang, Hongzhuo; Ruppel, Philipp; Zhang, Changshui; Zhang, Jianwei A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision IEEE TRANSACTIONS ON CYBERNETICS English Article; Early Access Robots; Pose estimation; Cameras; Robot sensing systems; Manipulators; Robot vision systems; Machine vision; Active vision; deep neural network; dexterous hand-arm system; hand pose estimation; vision-based teleoperation RECOGNITION; ALGORITHMS Markerless vision-based teleoperation that leverages innovations in computer vision offers the advantages of allowing natural and noninvasive finger motions for multifingered robot hands. However, current pose estimation methods still face inaccuracy issues due to the self-occlusion of the fingers. Herein, we develop a novel vision-based hand-arm teleoperation system that captures the human hands from the best viewpoint and at a suitable distance. This teleoperation system consists of an end-to-end hand pose regression network and a controlled active vision system. The end-to-end pose regression network (Transteleop), combined with an auxiliary reconstruction loss function, captures the human hand through a low-cost depth camera and predicts joint commands of the robot based on the image-to-image translation method. To obtain the optimal observation of the human hand, an active vision system is implemented by a robot arm at the local site that ensures the high accuracy of the proposed neural network. Human arm motions are simultaneously mapped to the slave robot arm under relative control. Quantitative network evaluation and a variety of complex manipulation tasks, for example, tower building, pouring, and multitable cup stacking, demonstrate the practicality and stability of the proposed teleoperation system. [Li, Shuang; Hendrich, Norman; Liang, Hongzhuo; Ruppel, Philipp; Zhang, Jianwei] Univ Hamburg, Dept Informat, Tech Aspects Multimodal Syst Grp, D-20146 Hamburg, Germany; [Zhang, Changshui] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Automat, State Key Lab Intelligent Technol & Syst, Beijing 100190, Peoples R China University of Hamburg; Tsinghua University Li, S (corresponding author), Univ Hamburg, Dept Informat, Tech Aspects Multimodal Syst Grp, D-20146 Hamburg, Germany. shuang.li@uni-hamburg.de Ruppel, Philipp/0000-0002-1336-8483; Hendrich, Norman/0000-0003-0499-886X; Liang, Hongzhuo/0000-0002-6870-9898 German Research Foundation (DFG) [DFG TRR-169/NSFC 62061136001]; National Science Foundation of China (NSFC) through Project Crossmodal Learning [DFG TRR-169/NSFC 62061136001]; European Project Ultracept [778062] German Research Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC) through Project Crossmodal Learning(National Natural Science Foundation of China (NSFC)); European Project Ultracept This work was supported in part by the German Research Foundation (DFG) and the National Science Foundation of China (NSFC) through Project Crossmodal Learning under Grant DFG TRR-169/NSFC 62061136001, and in part by the European Project Ultracept under Grant 778062. 49 0 0 32 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. 10.1109/TCYB.2022.3207290 0.0 SEP 2022 12 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 5D6RK 36179009.0 hybrid 2023-03-23 WOS:000865066000001 0
C Sun, PH; Lan, JL; Guo, ZH; Zhang, D; Chen, XF; Hu, YX; Liu, Z IEEE Sun, Penghao; Lan, Julong; Guo, Zehua; Zhang, Di; Chen, Xianfu; Hu, Yuxiang; Liu, Zhi DeepMigration: Flow Migration for NFV with Graph-based Deep Reinforcement Learning ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond JUN 07-11, 2020 ELECTR NETWORK IEEE,Huawei,ZTE,Qualcomm Network Function Virtualization; Flow Migration; Deep Reinforcement Learning; Graph Neural Network Network Function Virtualization (NFV) enables flexible deployment of network services as applications. Network operators expect to use a limited number of Network Function (NF) instances to handle the fluctuating traffic load and provide network services. However, it is a big challenge to guarantee the Quality of Service (QoS) under the unpredictable network traffic while minimizing the processing resources. One typical solution is to realize NF scale-out, scale-in and load balancing by elastically migrating the related traffic flows with Software-Defined Networking (SDN). However, it is difficult to optimally migrate flows since many real-time statuses of NF instances should be considered to make accurate decisions. In this paper, we propose DeepMigration to solve the problem by efficiently and dynamically migrating traffic flows among different NF instances. DeepMigration is a Deep Reinforcement Learning (DRL)-based solution coupled with Graph Neural Network (GNN). By taking advantages of the graph-based relationship deduction ability from our customized GNN and the self-evolution ability from the experience training of DRL, DeepMigration can accurately model the cost (e.g., migration latency) and the benefit (e.g., reducing the number of NF instances) of flow migration among different NF instances and generate dynamic and effective flow migration policies to improve the QoS. Experiment results show that DeepMigration requires less migration cost and saves up to 71.6% of the computation time than existing solutions. [Sun, Penghao; Lan, Julong; Hu, Yuxiang] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou, Peoples R China; [Guo, Zehua] Beijing Inst Technol, Beijing, Peoples R China; [Zhang, Di] Zhengzhou Univ, Zhengzhou, Peoples R China; [Chen, Xianfu] VTT Tech Res Ctr Finland, Oulu, Finland; [Liu, Zhi] Shizuoka Univ, Shizuoka, Japan PLA Information Engineering University; Beijing Institute of Technology; Zhengzhou University; VTT Technical Research Center Finland; Shizuoka University Guo, ZH (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. Liu, Zhi/AAE-5698-2020 Liu, Zhi/0000-0003-0537-4522 National Key Research and Development Plan [2017YFB0803204]; National Natural Science Fund of China [61521003, 61872382]; Beijing Institute of Technology Research Fund Program for Young Scholars National Key Research and Development Plan; National Natural Science Fund of China(National Natural Science Foundation of China (NSFC)); Beijing Institute of Technology Research Fund Program for Young Scholars This paper is supported by the National Key Research and Development Plan under Grant Number 2017YFB0803204, the National Natural Science Fund of China under Grant Numbers 61521003 and 61872382, and the Beijing Institute of Technology Research Fund Program for Young Scholars. The corresponding author is Zehua Guo. 28 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-7281-5089-5 IEEE ICC 2020.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BQ5PR 2023-03-23 WOS:000606970300094 0
J Capossio, JP; Fabani, MP; Roman, MC; Zhang, X; Baeyens, J; Rodriguez, R; Mazza, G Pablo Capossio, Juan; Paula Fabani, Maria; Celia Roman, Maria; Zhang, Xin; Baeyens, Jan; Rodriguez, Rosa; Mazza, German Zero-Waste Watermelon Production through Nontraditional Rind Flour: Multiobjective Optimization of the Fabrication Process PROCESSES English Article watermelon rind; by-product; waste valorization; multiobjective optimization; neural network modeling DIETARY FIBER Watermelon is a fruit produced around the world. Unfortunately, about half of it-the rind-is usually discarded as waste. To transform such waste into a useful product like flour, a thermal treatment is needed. The drying temperature for the rind that produces flour with the best characteristics is most important. A multiobjective optimization (MOO) procedure was applied to define the optimum drying temperature for the rind flour fabrication to be used in bakery products. A neural network model of the fabrication process was developed with the drying temperature as input and five process indicators as outputs. The group of process indicators comprised acidity, pH, water-holding capacity (WHC), oil-holding capacity (OHC), and batch time. Those indicators represent conflicting objectives that are to be balanced by the MOO procedure using the weighted distance method. The MOO process showed that the temperature interval from 67.3 degrees C to 73.1 degrees C holds the compromise solutions for the conflicting indicators based on the stakeholder's preferences. Optimum indicator were 0.12-0.19 g malic acid/100 g dwb (acidity), 5.7-5.8 (pH), 8.93-9.08 g H2O/g dwb (WHC), 1.46-1.56 g oil/g dwb (OHC), and 128-139 min (drying time). [Pablo Capossio, Juan; Mazza, German] Inst Invest & Desarrollo Ingn Proc, PROBIEN, Biotecnol & Energias Alternat, CONICET,UNCo, RA-8300 Neuquen, Argentina; [Paula Fabani, Maria] Univ Nacl San Juan, Fac Ingn, Inst Biotecnol, J5400ARL, San Juan, Argentina; [Celia Roman, Maria; Rodriguez, Rosa] Univ Nacl San Juan, Fac Ingn, Inst Ingn Quim, Grp Vinculado,PROBIEN,CONICET,UNCo, J5400ARL, San Juan, Argentina; [Zhang, Xin] Beijing Acad Food Sci, Beijing 100068, Peoples R China; [Baeyens, Jan] Katholieke Univ Leuven, Proc & Environm Technol Lab, Dept Chem Engn, B-2860 St Katelijne Waver, Belgium; [Baeyens, Jan] Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Universidad Nacional de San Juan; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Universidad Nacional de San Juan; KU Leuven; Beijing University of Chemical Technology Mazza, G (corresponding author), Inst Invest & Desarrollo Ingn Proc, PROBIEN, Biotecnol & Energias Alternat, CONICET,UNCo, RA-8300 Neuquen, Argentina.;Baeyens, J (corresponding author), Katholieke Univ Leuven, Proc & Environm Technol Lab, Dept Chem Engn, B-2860 St Katelijne Waver, Belgium.;Baeyens, J (corresponding author), Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China. jan.baeyens@kuleuven.be; german.mazza@probien.gob.ar Rodriguez, Rosa Ana/0000-0003-1252-4752; Mazza, German/0000-0002-1362-8521; Roman, Maria Celia/0000-0002-7422-386X National Scientific and Technical Research Council, CONICET, Argentina [22920150100067, 11220200100950CO]; Universidad Nacional de San Juan, Argentina [1054/18]; Universidad Nacional del Comahue, Neuquen, Argentina [2022-04/I260]; FONCYT-ANPCyT (National Agency for Scientific and Technological Promotion, Argentina) [PICT 2017-1390, PICT 2019-01810]; San Juan Province, Argentina [Nffi 0272-SECITI-2019] National Scientific and Technical Research Council, CONICET, Argentina(Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)); Universidad Nacional de San Juan, Argentina; Universidad Nacional del Comahue, Neuquen, Argentina; FONCYT-ANPCyT (National Agency for Scientific and Technological Promotion, Argentina); San Juan Province, Argentina This researchwas funded by the National Scientific and Technical Research Council, CONICET, Argentina [grant number PUE PROBIEN 22920150100067 and PIP 2021-2023-No. 11220200100950CO]; Universidad Nacional de San Juan, Argentina [grant number PDTS Res. 1054/18]; Universidad Nacional del Comahue, Neuquen, Argentina [grant number PIN 2022-04/I260]; FONCYT-ANPCyT (National Agency for Scientific and Technological Promotion, Argentina) [grant numbers PICT 2017-1390 and PICT 2019-01810]; San Juan Province, Argentina [grant number IDEA -Res. Nffi 0272-SECITI-2019]. 44 1 1 1 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-9717 PROCESSES Processes OCT 2022.0 10.0 10 1984 10.3390/pr10101984 0.0 20 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 5T7VE gold 2023-03-23 WOS:000876068200001 0
J Ivanovic, M; Budimac, Z; Radovanovic, M; Kurbalija, V; Dai, WH; Badica, C; Colhon, M; Ninkovic, S; Mitrovic, D Ivanovic, Mirjana; Budimac, Zoran; Radovanovic, Milos; Kurbalija, Vladimir; Dai, Weihui; Badica, Costin; Colhon, Mihaela; Ninkovic, Srdan; Mitrovic, Dejan Emotional Agents - State of the Art and Applications COMPUTER SCIENCE AND INFORMATION SYSTEMS English Article artificial intelligence; intelligent agents; design; emotion detection SPEECH RECOGNITION; SYSTEM; RESOURCES In last decade, intensive research on emotional intelligence has advanced significantly from its theoretical basis, analytical studies and processing technology to exploratory applications in a wide range of real-life domains. This paper brings new insights in the field of emotional, intelligent software agents. The first part is devoted to an overview of the state-of-the-art in emotional intelligence research with emphasis on emotional agents. A wide range of applications in different areas like modeling emotional agents, aspects of learning in emotional environments, interactive emotional systems and so on are presented. After that we suggest a systematic order of research steps with the idea of proposing an adequate framework for several possible real-life applications of emotional agents. We recognize that it is necessary to apply specific methods for dynamic data analysis in order to identify and discover new knowledge from available emotional information and data sets. The last part of the paper discusses research activities for designing an agent-based architecture, in which agents are capable of reasoning about and displaying some kind of emotions based on emotions detected in human speech, as well as online documents. [Ivanovic, Mirjana; Budimac, Zoran; Radovanovic, Milos; Kurbalija, Vladimir; Mitrovic, Dejan] Univ Novi Sad, Fac Sci, Dept Math & Informat, Novi Sad 21000, Serbia; [Dai, Weihui] Fudan Univ, Sch Management, Shanghai 200433, Peoples R China; [Badica, Costin; Colhon, Mihaela] Univ Craiova, Dept Comp & Informat Technol, Craiova, Romania; [Colhon, Mihaela] Univ Craiova, Dept Comp Sci, Craiova, Romania; [Ninkovic, Srdan] Univ Novi Sad, Clin Ctr Vojvodina, Dept Orthoped Surg & Traumatol, Novi Sad 21000, Serbia University of Novi Sad; Fudan University; University of Craiova; University of Craiova; University of Novi Sad Ivanovic, M (corresponding author), Univ Novi Sad, Fac Sci, Dept Math & Informat, Novi Sad 21000, Serbia. mira@dmi.uns.ac.rs; zjb@dmi.uns.ac.rs; radacha@dmi.uns.ac.rs; kurba@dmi.uns.ac.rs; whdai@fudan.edu.cn; cbadica@software.ucv.ro; mcolhon@inf.ucv.ro; srdjan.ninkovic@yahoo.com; dejan@dmi.uns.ac.rs Colhon, Mihaela/AAD-8649-2019; Badica, Costin/B-8006-2012; Radovanović, Miloš/ABB-7403-2022; Colhon, Mihaela/C-4492-2011; Ivanovic, M./AAE-7711-2020; Colhon, Mihaela/AAD-8646-2019 Colhon, Mihaela/0000-0001-5332-2220; Badica, Costin/0000-0001-8480-9867; Colhon, Mihaela/0000-0001-5332-2220; Colhon, Mihaela/0000-0001-5332-2220; Budimac, Zoran/0000-0001-5688-6320; Dai, Weihui/0000-0002-9426-5303; Radovanovic, Milos/0000-0003-2225-7803; Kurbalija, Vladimir/0000-0002-9599-4495; Ivanovic, Mirjana/0000-0003-1946-0384 Ministry of Education, Science and Technological Development of Serbia [OI174023, 2-9/2013] Ministry of Education, Science and Technological Development of Serbia(Ministry of Education, Science & Technological Development, Serbia) This work was supported by the Ministry of Education, Science and Technological Development of Serbia, through project no. OI174023: Intelligent techniques and their integration into wide-spectrum decision support, and by bilateral project no. 2-9/2013: Emotional Intelligence and Applications Based on Multi-Agent Systems. 89 15 16 0 31 COMSIS CONSORTIUM NOVI SAD UNIV NOVI SAD, FAC TECH SCI, TRG DOSITEJA OBRADOVICA 6, NOVI SAD, 21000, SERBIA 1820-0214 COMPUT SCI INF SYST Comput. Sci. Inf. Syst. NOV 2015.0 12.0 4 SI 1121 1148 10.2298/CSIS141026047I 0.0 28 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science CY0WG Green Submitted, gold 2023-03-23 WOS:000366127000001 0
J Deng, WX; Liao, Q; Zhao, LJ; Guo, DK; Kuang, GY; Hu, DW; Liu, L Deng, Wanxia; Liao, Qing; Zhao, Lingjun; Guo, Deke; Kuang, Gangyao; Hu, Dewen; Liu, Li Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Feature extraction; Task analysis; Image reconstruction; Training; Image coding; Deep learning; Data mining; Domain adaptation; deep learning; transfer learning; unsupervised learning; semisupervised learning DEEP; KERNEL Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled source domain with a different but related distribution. Many existing approaches typically learn a domain-invariant representation space by directly matching the marginal distributions of the two domains. However, they ignore exploring the underlying discriminative features of the target data and align the cross-domain discriminative features, which may lead to suboptimal performance. To tackle these two issues simultaneously, this paper presents a Joint Clustering and Discriminative Feature Alignment (JCDFA) approach for UDA, which is capable of naturally unifying the mining of discriminative features and the alignment of class-discriminative features into one single framework. Specifically, in order to mine the intrinsic discriminative information of the unlabeled target data, JCDFA jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data, where the classification of the source domain can guide the clustering learning of the target domain to locate the object category. We then conduct the cross-domain discriminative feature alignment by separately optimizing two new metrics: 1) an extended supervised contrastive learning, i.e., semi-supervised contrastive learning 2) an extended Maximum Mean Discrepancy (MMD), i.e., conditional MMD, explicitly minimizing the intra-class dispersion and maximizing the inter-class compactness. When these two procedures, i.e., discriminative features mining and alignment are integrated into one framework, they tend to benefit from each other to enhance the final performance from a cooperative learning perspective. Experiments are conducted on four real-world benchmarks (e.g., Office-31, ImageCLEF-DA, Office-Home and VisDA-C). All the results demonstrate that our JCDFA can obtain remarkable margins over state-of-the-art domain adaptation methods. Comprehensive ablation studies also verify the importance of each key component of our proposed algorithm and the effectiveness of combining two learning strategies into a framework. [Deng, Wanxia; Zhao, Lingjun; Kuang, Gangyao] Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Hunan, Peoples R China; [Liao, Qing] Harbin Inst Technol, Dept Comp Sci & Technol, Shenzhen 518055, Peoples R China; [Guo, Deke; Liu, Li] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China; [Hu, Dewen] Natl Univ Def Technol, Coll Intelligent Sci, Changsha 410073, Hunan, Peoples R China; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90570, Finland National University of Defense Technology - China; Harbin Institute of Technology; National University of Defense Technology - China; National University of Defense Technology - China; University of Oulu Liu, L (corresponding author), Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China. wanxiadeng@163.com; liaoqing@hit.edu.cn; nudtzlj@163.com; guodeke@gmail.com; kuanggangyao@nudt.edu.cn; dwhu@nudt.edu.cn; li.liu@oulu.fi Zhao, Lingjun/HPE-8072-2023; Hu, Dewen/AAN-8511-2020 Liu, li/0000-0002-2011-2873; Guo, Deke/0000-0003-4894-5540 Academy of Finland [331883]; National Natural Science Foundation of China [61872379, 62022091, 71701205] Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Academy of Finland under Grant 331883; and in part by the National Natural Science Foundation of China under Grant 61872379, Grant 62022091, and Grant 71701205. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Weiyao Lin. 71 7 7 10 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2021.0 30.0 7842 7855 10.1109/TIP.2021.3109530 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering UR3QC 34506283.0 Green Accepted 2023-03-23 WOS:000696665500003 0
J Zhu, HY; Tiwari, P; Zhang, YZ; Gupta, D; Alharbi, M; Nguyen, TG; Dehdashti, S Zhu, Hongyin; Tiwari, Prayag; Zhang, Yazhou; Gupta, Deepak; Alharbi, Meshal; Nguyen, Tri Gia; Dehdashti, Shahram SwitchNet: A modular neural network for adaptive relation extraction COMPUTERS & ELECTRICAL ENGINEERING English Article Relation extraction; Modular neural network; Information flow; Joint optimization; Entity pair This paper presents a portable toolkit, SwitchNet, for extracting relations from textual input. We summarize four data protocols for relation extraction tasks, including relation classification, relation extraction, triple extraction, and distant supervision relation extraction. This neural architecture is modular, so it can take as input data at different stages of the information extraction process (simple text, text and entities or entity pairs as relation candidates) and compute the rest of the process (named entity recognition and relation classification). We systematically design four information flows to integrate the above protocols by sharing network building blocks and switching different information flows. This framework can extract multiple triples (subject, predicate, object) in one pass. This framework enhances the use of relation classification models in end-to-end triple extraction by inferring pairs of entities of interest and using the shared representation mechanism. [Zhu, Hongyin] Inspur Elect Informat Ind Co Ltd, Jinan 250101, Peoples R China; [Zhu, Hongyin] State Key Lab High end Server & Storage Technol, Beijing 100085, Peoples R China; [Tiwari, Prayag] Halmstad Univ, Sch Informat Technol, Halmstad, Sweden; [Zhang, Yazhou] Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450002, Peoples R China; [Gupta, Deepak] Maharaja Agrasen Inst Technol, Delhi, India; [Zhang, Yazhou] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Sci, POB 151, Alkharj 11942, Saudi Arabia; [Alharbi, Meshal] FPT Univ, Danang 50509, Vietnam; [Nguyen, Tri Gia; Dehdashti, Shahram] Queensland Univ Technol, Sch Informat Syst, Brisbane 4000, Australia; [Dehdashti, Shahram] Tech Univ Munich, Dept Elect & Comp Engn, D-80333 Munich, Germany Inspur; Halmstad University; Zhengzhou University of Light Industry; Maharaja Agrasen Institute of Technology; Prince Sattam Bin Abdulaziz University; FPT University; Queensland University of Technology (QUT); Technical University of Munich Zhu, HY (corresponding author), Inspur Elect Informat Ind Co Ltd, Jinan 250101, Peoples R China.;Zhu, HY (corresponding author), State Key Lab High end Server & Storage Technol, Beijing 100085, Peoples R China.;Tiwari, P (corresponding author), Halmstad Univ, Sch Informat Technol, Halmstad, Sweden. zhuhongyin@inspur.com; prayag.tiwari@ieee.org; yzzhang@zzuli.edu.cn; deepakgupta@mait.ac.in; Mg.alharbi@psau.edu.sa; tri@ieee.org; shahram.dehdashti@gmail.com Tiwari, Prayag/N-6261-2017; Alharbi, Meshal/GQP-6226-2022; Nguyen, Tri Gia/A-9189-2018; Zhu, Hongyin/HJA-5160-2022 Tiwari, Prayag/0000-0002-2851-4260; Nguyen, Tri Gia/0000-0003-4578-9925; Zhu, Hongyin/0000-0001-5786-7594; zhu, hong yin/0000-0002-4223-5209 25 0 0 12 12 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. DEC 2022.0 104.0 B 108445 10.1016/j.compeleceng.2022.108445 0.0 NOV 2022 22 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 6Y2FP hybrid 2023-03-23 WOS:000896916300006 0
J Wang, M; Jayaraman, PP; Solaiman, E; Chen, LY; Li, Z; Jun, S; Georgakopoulos, D; Ranjan, R Wang, Meisong; Jayaraman, Prem Prakash; Solaiman, Ellis; Chen, Lydia Y.; Li, Zheng; Jun, Song; Georgakopoulos, Dimitrios; Ranjan, Rajiv A multi-layered performance analysis for cloud-based topic detection and tracking in Big Data applications FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Cloud-based TDT; Big Data; Performance analysis; Cloud computing MAPREDUCE; NETWORKS In the era of the Internet of Things and social media; communities, governments, and corporations are increasingly eager to exploit new technological innovations in order to track and keep up to date with important new events. Examples of such events include the news, health related incidents, and other major occurrences such as earthquakes and landslides. This area of research commonly referred to as Topic Detection and Tracking (TDT) is proving to be an important component of the current generation of Internet-based applications, where it is of critical importance to have early detection and timely response to important incidents such as those mentioned above. The advent of Big data though beneficial to TDT applications also brings about the enormous challenge of dealing with data variety, velocity and volume (3Vs). A promising solution is to employ Cloud Computing, which enables users to access powerful and scalable computational and storage resources in a pay-as-you-go fashion. However, the efficient use of Cloud resources to boost the performance of mission critical applications employing TDT is still an open topic that has not been fully and effectively investigated. An important prerequisite is to build a performance analysis capable of capturing and explaining specific factors (for example; CPU, Memory, I/O, Network, Cloud Platform Service, and Workload) that influence the performances of TDT applications in the cloud. Within this paper, our main contribution, is that we present a multi-layered performance analysis for big data TDT applications deployed in a cloud environment. Our analysis captures factors that have an important effect on the performance of TDT applications. The novelty of our work is that it is a first kind of vertical analysis on infrastructure, platform and software layers. We identify key parameters and metrics in each cloud layer (including Infrastructure, Software, and Platform layers), and establish the dependencies between these metrics across the layers. We demonstrate the effectiveness of the proposed analysis via experimental evaluations using real-world datasets obtained from Twitter. (C) 2018 Elsevier B.V. All rights reserved. [Wang, Meisong] Australian Natl Univ, Sch Comp Sci, Canberra, ACT, Australia; [Jayaraman, Prem Prakash; Georgakopoulos, Dimitrios] Swinburne Univ Technol, Fac Sci Engn & Technol, Melbourne, Vic, Australia; [Solaiman, Ellis; Ranjan, Rajiv] Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne, Tyne & Wear, England; [Chen, Lydia Y.] IBM Corp, Zurich Res Lab, Zurich, Switzerland; [Li, Zheng] Lund Univ, Dept Elect & Informat Technol, Lund, Sweden; [Jun, Song; Ranjan, Rajiv] Chinese Univ Geosci, Dept Comp Sci, Wuhan, Hubei, Peoples R China Australian National University; Swinburne University of Technology; Newcastle University - UK; International Business Machines (IBM); Lund University; China University of Geosciences Solaiman, E (corresponding author), Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne, Tyne & Wear, England. deanmeisong@gmail.com; pjayaraman@swin.edu.au; ellis.solaiman@ncl.ac.uk; yic@zurich.ibm.com; zheng.li@eit.lth.se; songjun@cug.edu.cn; dgeorgakopoulos@swin.edu.au; rranjans@gmail.com Ranjan, Rajiv/F-4700-2011; Li, Zheng/R-5781-2016 Ranjan, Rajiv/0000-0002-6610-1328; Li, Zheng/0000-0002-9704-7651; Jayaraman, Prem Prakash/0000-0003-4500-3443; Georgakopoulos, Dimitrios/0000-0001-7880-2140; song, Jun/0000-0003-3820-7632 SNSF NRP75, Switzerland project [407540_167266] SNSF NRP75, Switzerland project This work is partly funded by SNSF NRP75, Switzerland project 407540_167266. 27 8 8 0 51 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. OCT 2018.0 87.0 580 590 10.1016/j.future.2018.01.047 0.0 11 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GM3IH Green Accepted, Green Published 2023-03-23 WOS:000437997500048 0
J Gao, BW; Chen, NH; Blaschke, T; Wu, CSQ; Chen, JY; Xu, YC; Yang, XP; Du, ZH Gao, Bowen; Chen, Ninghua; Blaschke, Thomas; Wu, Chase Q.; Chen, Jianyu; Xu, Yaochen; Yang, Xiaoping; Du, Zhenhong Automated Characterization of Yardangs Using Deep Convolutional Neural Networks REMOTE SENSING English Article aeolian landform; yardang; morphological characteristic; deep learning; Mask R-CNN; Google Earth imagery QAIDAM BASIN; SCENE CLASSIFICATION; UAV IMAGES; EXTRACTION; FEATURES; SEGMENTATION; CHINA The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform. [Gao, Bowen; Chen, Ninghua; Xu, Yaochen; Yang, Xiaoping; Du, Zhenhong] Zhejiang Univ, Sch Earth Sci, Key Lab Geosci Big Data & Deep Resource Zhejiang, Hangzhou 310027, Peoples R China; [Blaschke, Thomas] Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; [Wu, Chase Q.] New Jersey Inst Technol, Dept Comp Sci, Newark, NJ 07102 USA; [Chen, Jianyu] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China Zhejiang University; Salzburg University; New Jersey Institute of Technology; Ministry of Natural Resources of the People's Republic of China Chen, NH (corresponding author), Zhejiang Univ, Sch Earth Sci, Key Lab Geosci Big Data & Deep Resource Zhejiang, Hangzhou 310027, Peoples R China. gaobw@zju.edu.cn; geo316@zju.edu.cn; thomas.blaschke@sbg.ac.at; chase.wu@njit.edu; chenjianyu@sio.org.cn; 11838005@zju.edu.cn; xpyang@zju.edu.cn; duzhenhong@zju.edu.cn Blaschke, Thomas/F-3342-2011; Xu, Yaochen/AAK-2677-2021; Blaschke, Thomas/AAR-9037-2021; Gao, Bowen/AAK-2638-2021; Wu, Chase/R-8962-2017 Blaschke, Thomas/0000-0002-1860-8458; Blaschke, Thomas/0000-0002-1860-8458; Gao, Bowen/0000-0002-3109-1772; Yang, Xiaoping/0000-0001-8702-1813; Wu, Chase/0000-0002-8218-1209 National Key Research and Development Program of China [2016YFC1400903]; National Natural Science Foundation of China [41372205, U1609202]; Fundamental Research Funds for the Central Universities [2019QNA3013]; State Scientific Survey Project of China [2017FY101001] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Scientific Survey Project of China This research was funded by the National Key Research and Development Program of China under Grant No. 2016YFC1400903, the National Natural Science Foundation of China under Grant No. 41372205 and U1609202, the Fundamental Research Funds for the Central Universities under Grant No. 2019QNA3013, and the State Scientific Survey Project of China under Grant No. 2017FY101001. 69 2 2 8 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2021.0 13.0 4 733 10.3390/rs13040733 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology QQ3JI gold 2023-03-23 WOS:000624420100001 0
C Huang, ZF; Yi, K IEEE Huang, Zengfeng; Yi, Ke The Communication Complexity of Distributed epsilon-Approximations 2014 55TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS 2014) Annual IEEE Symposium on Foundations of Computer Science English Proceedings Paper 55th Annual IEEE Symposium on Foundations of Computer Science (FOCS) OCT 18-21, 2014 Microsoft Res New England, Philadelphia, PA IEEE,IEEE Comp Soc,Alcatel Lucent,IEEE Tech Comm Math Fdn Comp,Bell Labs,Ctr Discrete Math & Theoret Comp Sci Microsoft Res New England epsilon-approximations; communication complexity; discrepancy; distributed data BOUNDS Data summarization is an effective approach to dealing with the big data problem. While data summarization problems traditionally have been studied is the streaming model, the focus is starting to shift to distributed models, as distributed/parallel computation seems to be the only viable way to handle today's massive data sets. In this paper, we study epsilon-approximations, a classical data summary that, intuitively speaking, preserves approximately the density of the underlying data set over a certain range space. We consider the problem of computing epsilon-approximations for a data set which is held jointly by k players, and give general communication upper and lower bounds that hold for any range space whose discrepancy is known. [Huang, Zengfeng] Aarhus Univ, MADALGO, DK-8000 Aarhus C, Denmark; [Yi, Ke] HKUST, Dept CSE, Hong Kong, Hong Kong, Peoples R China Aarhus University; Hong Kong University of Science & Technology Huang, ZF (corresponding author), Aarhus Univ, MADALGO, DK-8000 Aarhus C, Denmark. zhuang@cs.au.dk; yike@cse.ust.hk Huang, Zengfeng/0000-0003-2671-7483 25 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0272-5428 978-1-4799-6517-5 ANN IEEE SYMP FOUND 2014.0 591 600 10.1109/FOCS.2014.69 0.0 10 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BE0MY 2023-03-23 WOS:000366324300061 0
J Zhao, KW; Hohmann, A; Chang, Y; Zhang, B; Pion, J; Gao, BH Zhao, Kewei; Hohmann, Andreas; Chang, Yu; Zhang, Bei; Pion, Johan; Gao, Binghong Physiological, Anthropometric, and Motor Characteristics of Elite Chinese Youth Athletes From Six Different Sports FRONTIERS IN PHYSIOLOGY English Article talent; youth sport; discriminant analysis; neural network; test validity DRY-LAND STRENGTH; TALENT IDENTIFICATION; PERFORMANCE-CHARACTERISTICS; MUSCLE STRENGTH; MALE JUDO; BASKETBALL; VOLLEYBALL; FEMALE; RELIABILITY; POWER Several talent selection programs in elite sport schools are based on motor diagnostics for the purpose of recommending or transferring promising talents to general groups of sports; game sports, combat sports or endurance sports, and to more concrete sports such as gymnastics, skiing, or tennis. However, the predictive value of such testing is unclear. This study evaluated the concurrent validity of physiological performance prerequisites, body dimensions, as well as specific motor performances. The sample consisted of N = 97 youth athletes from all ninth grade classes of a Shanghai Elite Sport school belonging to six different sports including basketball (n = 7), fencing (n = 23), judo (n = 20), swimming (n = 10), table tennis (n = 15), and volleyball (n = 22). The performance diagnosis took place between September 2016 and March 2017, and comprised five physiological measurements of the heart rate at rest, vital capacity, systolic and diastolic blood pressure, and hemoglobin concentration in the blood, eighteen anthropometric parameters, and two motor tests on back strength and complex reaction speed. The aim of the study was to investigate whether U15 age group athletes participating in six different sports already at this age show a sport specific anthropometric, motor performance, and physiological profile which is in line with the specific requirements of each of the sports. A discriminant analysis and a Neural Network (Multilayer Perceptron) were used to test whether it is possible to discriminate between athletes of the six sports and to assign each individual of the Under-15 athletes to his own sport on the basis of a unique profile of the morphological, motor, and physiological prerequisites. All diagnostic methods exhibited medium to high validity to discriminate between the six different sports. The relevance of the eighteen body dimensions, five physiological measures, and two motor tests for talent identification was confirmed. [Zhao, Kewei; Gao, Binghong] Shanghai Univ Sport, Sch Phys Educ & Sport Training, Shanghai, Peoples R China; [Hohmann, Andreas] Univ Bayreuth, Dept Sport Sci, Bayreuth, Germany; [Chang, Yu; Zhang, Bei] Shanghai Sports Sch, Shanghai, Peoples R China; [Pion, Johan] HAN Univ Appl Sci, Fac Educ, Hogesch Arnhem & Nijmegen, Inst Sport & Exercise Studies, Nijmegen, Netherlands Shanghai University of Sport; University of Bayreuth Gao, BH (corresponding author), Shanghai Univ Sport, Sch Phys Educ & Sport Training, Shanghai, Peoples R China. gaobinghong@126.com zhao, kewei/T-1825-2019 Zhao, Kewei/0000-0001-5394-162X; Pion, Johan/0000-0002-2633-9120 Shanghai Key Lab of Human Performance of Shanghai University of Sport [11DZ2261100]; Science and Technology Commission of Shanghai Municipality [18DZ1200600] Shanghai Key Lab of Human Performance of Shanghai University of Sport; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) This work was supported by Shanghai Key Lab of Human Performance of Shanghai University of Sport, grant no. 11DZ2261100. And also supported by directed research grants from Science and Technology Commission of Shanghai Municipality, grant no. 18DZ1200600. 63 15 15 16 44 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-042X FRONT PHYSIOL Front. Physiol. APR 12 2019.0 10.0 405 10.3389/fphys.2019.00405 0.0 12 Physiology Science Citation Index Expanded (SCI-EXPANDED) Physiology HT3JS 31105576.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000464460700001 0
J Li, YF; Shi, Q; He, L; Cai, RL; Meng, LL; Li, J; Plaza, A Li, Yunfei; Shi, Qian; He, Lin; Cai, Runlin; Meng, Liangli; Li, Jun; Plaza, Antonio Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Remote sensing; Earth; Artificial satellites; Spatial resolution; Image resolution; Spatiotemporal phenomena; Task analysis; Landsat-8; local normalization; remote sensing image fusion; Sentinel-2 SPATIOTEMPORAL FUSION; IMAGES; CLASSIFICATION; BIOMASS Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager and Sentinel-2 Multispectral Imager observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the image series with spatial resolution of 30 m, which cannot meet the data demand of some applications. Therefore, it is necessary to fuse Landsat-8 and Sentinel-2 images to provide 10-m image series. Currently, there are three means to achieve that, including the area-to-point regression kriging fusion approach (ATPRK), spatiotemporal fusion methods, and deep-learning-based fusion models. However, the ATPRK and spatiotemporal fusion methods suffer from the limited fusion performance, while the deep-learning-based fusion models are hardware dependent, i.e., requiring the graphics processing units, which may not be satisfied sometimes. To address these issues, in this article, we develop a new pixel-wise local normalization-based fusion method (LN-FM) for fusing Sentinel-2 and Landsat-8 images. The newly proposed LN-FM is compared to the ATPRK and three representative spatiotemporal fusion methods in experiments, which use imagery collected from both a rural area and an urban area. The experimental results demonstrate that the newly developed LN-FM exhibits excellent qualitative and quantitative performance, as well as remarkable spatial, spectral, and pixel distribution fidelity. Furthermore, this approach is fast, which may improve its applicability [Li, Yunfei; Shi, Qian; Cai, Runlin; Meng, Liangli] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China; [He, Lin] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China; [Li, Jun] China Univ Geosci, Sch Comp Sci, Wuhan 430078, Peoples R China; [Li, Jun] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Hyperspect Comp Lab, Dept Technol Comp & Commun, E-10071 Caceres, Spain Sun Yat Sen University; South China University of Technology; China University of Geosciences; China University of Geosciences; Universidad de Extremadura Li, YF (corresponding author), Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China. liyf18213483@163.com; helin@scut.edu.cn; cairlin5@mail2.sysu.edu.cn; menglli5@mail2.sysu.edu.cn; lijuncug@cug.edu.cn; aplaza@unex.es Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Cai, Runlin/0000-0002-4971-6187; Li, Yunfei/0000-0003-1734-5008 National Natural Science Foundation of China [T2225019, 42030111, 61976234, 62071184]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011615, 2019A1515011057, 202002030402]; Guangzhou Science and Technology Program [202002030395] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Guangzhou Science and Technology Program This work was supported in part by the National Natural Science Foundation of China under Grant T2225019, Grant 42030111, Grant 61976234, Grant and Grant 62071184; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011615, Grant 2019A1515011057, and Grant 202002030402; and in part by the Guangzhou Science and Technology Program under Grant 202002030395 61 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 7359 7374 10.1109/JSTARS.2022.3200713 0.0 16 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 4N2SY gold 2023-03-23 WOS:000853871300003 0
J Cheng, X; Zhang, YJ; Liu, HS; Wollherr, D; Buss, M Cheng, Xin; Zhang, Yajun; Liu, Huashan; Wollherr, Dirk; Buss, Martin Adaptive neural backstepping control for flexible-joint robot manipulator with bounded torque inputs NEUROCOMPUTING English Article Flexible-joint robot; Bounded input; Singular perturbation; Backstepping control; Saturated neural network approximator FEEDBACK TRACKING CONTROL; PROPORTIONAL DERIVATIVE CONTROL; MECHANICAL SYSTEMS; NONLINEAR-SYSTEMS; ROBUST-CONTROL; FINITE-TIME; NETWORK; STABILIZATION; COMPENSATION; OPTIMIZATION Aiming at tracking control with bounded torque inputs of the flexible-joint robot manipulators, we propose a generalized saturated adaptive controller based on backstepping control, singular perturbation decoupling and neural networks. First, by using the singular perturbation theory, the full-order rigid flexible dynamics of the robot manipulator is decoupled into a slow subsystem and a fast subsystem. Second, saturated sub-controller by backstepping method is proposed for the slow subsystem, where the projection-type parameter adaptation and a class of saturation functions are applied to make the torque inputs bounded, and a saturated neural network approximator is involved to simplify the control law and to compensate for the uncertain nonlinearity. Third, for fast subsystem, a new filtered tracking error of the elastic torque is used in the fast control law to make the boundary layer subside quickly. In addition, explicit but strict stability analysis is given for the system. Finally, comparisons indicate that the proposed controller results in a more satisfactory tracking performance with keeping the control inputs bounded within the given range all the time and superior anti-disturbance capability. (c) 2021 Elsevier B.V. All rights reserved. [Cheng, Xin; Zhang, Yajun; Liu, Huashan] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China; [Liu, Huashan] Minist Educ, Engn Res Ctr Digitized Text & Apparel Technol, Shanghai 201620, Peoples R China; [Wollherr, Dirk; Buss, Martin] Tech Univ Munich, Inst Automat Control Engn LSR, D-80333 Munich, Germany Donghua University; Technical University of Munich Liu, HS (corresponding author), Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China.;Liu, HS (corresponding author), Minist Educ, Engn Res Ctr Digitized Text & Apparel Technol, Shanghai 201620, Peoples R China. hsliu@dhu.edu.cn zhang, yu/HNS-5948-2023; Liu, Huashan/C-3639-2008; Zhang, Y J/HLG-1022-2023 Liu, Huashan/0000-0002-8209-4922; Shanghai Rising-Star Program [19QA1400400]; Natural Science Foundation of Shanghai [21ZR1401100]; Fundamental Research Funds for the Central Universities [2232019G-09] Shanghai Rising-Star Program; Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was sponsored in part by the Shanghai Rising-Star Program under Grant 19QA1400400, the Natural Science Foundation of Shanghai under Grant 21ZR1401100 and the Fundamental Research Funds for the Central Universities under Grant 2232019G-09. 50 14 14 9 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 11 2021.0 458.0 70 86 10.1016/j.neucom.2021.06.013 0.0 JUN 2021 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UJ8WL 2023-03-23 WOS:000691559800007 0
J Fei, ZG; Xu, XJ; Georgiadis, A Fei, Zhigen; Xu, Xiaojie; Georgiadis, Anthimos Short-arc measurement and fitting based on the bidirectional prediction of observed data MEASUREMENT SCIENCE AND TECHNOLOGY English Article short-arc fitting; radial basis function neural network (RBFNN); time series prediction ARTIFICIAL NEURAL-NETWORKS; LEVENBERG-MARQUARDT METHOD; TIME-SERIES PREDICTION; MODEL; ALGORITHM To measure a short arc is a notoriously difficult problem. In this study, the bidirectional prediction method based on the Radial Basis Function Neural Network (RBFNN) to the observed data distributed along a short arc is proposed to increase the corresponding arc length, and thus improve its fitting accuracy. Firstly, the rationality of regarding observed data as a time series is discussed in accordance with the definition of a time series. Secondly, the RBFNN is constructed to predict the observed data where the interpolation method is used for enlarging the size of training examples in order to improve the learning accuracy of the RBFNN's parameters. Finally, in the numerical simulation section, we focus on simulating how the size of the training sample and noise level influence the learning error and prediction error of the built RBFNN. Typically, the observed data coming from a 5 degrees short arc are used to evaluate the performance of the Hyper method known as the 'unbiased fitting method of circle' with a different noise level before and after prediction. A number of simulation experiments reveal that the fitting stability and accuracy of the Hyper method after prediction are far superior to the ones before prediction. [Fei, Zhigen] Zhengzhou Univ Light Ind, Electromech Engn Coll, Zhengzhou 450002, Peoples R China; [Xu, Xiaojie] Henan Polytech Univ, Phys & Chem Coll, Jiaozuo 454000, Peoples R China; [Georgiadis, Anthimos] Leuphana Univ, Inst Prod & Proc Innovat PPI, D-21339 Luneburg, Germany Zhengzhou University of Light Industry; Henan Polytechnic University; Leuphana University Luneburg Fei, ZG (corresponding author), Zhengzhou Univ Light Ind, Electromech Engn Coll, Zhengzhou 450002, Peoples R China. 20959414@qq.com Henan Education Department [13A460377]; Precision Manufacturing Key Laboratory in Henan [PMTE201304A]; Henan Science and Technology Department; Zhengzhou University of Light Industry Henan Education Department; Precision Manufacturing Key Laboratory in Henan; Henan Science and Technology Department; Zhengzhou University of Light Industry The authors would like to express their sincere gratitude to the supporters who funded this project. They are the Henan Education Department (grant no. 13A460377), the Precision Manufacturing Key Laboratory in Henan (grant no. PMTE201304A), the Henan Science and Technology Department, and the Doctor Fund of Zhengzhou University of Light Industry. 37 4 4 0 9 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0957-0233 1361-6501 MEAS SCI TECHNOL Meas. Sci. Technol. FEB 2016.0 27.0 2 25013 10.1088/0957-0233/27/2/025013 0.0 9 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation DH1VO 2023-03-23 WOS:000372573200014 0
J Wang, C; Qiang, X; Fan, HR; Wu, T; Chen, YL Wang, Chong; Qiang, Xin; Fan, Haoran; Wu, Tao; Chen, Yuli Novel data-driven method for non-probabilistic uncertainty analysis of engineering structures based on ellipsoid model COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING English Article Non-probabilistic uncertainty; Fuzzy equivalence relation; Data preprocessing procedure; Multi-ellipsoid model; Back propagation artificial neural network RELIABILITY-BASED OPTIMIZATION; PARAMETRIC CONVEX MODEL; HEAT-TRANSFER; INTERVAL; PROPAGATION; IDENTIFICATION; CONSTRUCTION; PERFORMANCE; SYSTEM For the various engineering structures with non-probabilistic uncertain parameters, the ellipsoid modeling is a momentous analysis method considering parameter cross-dependency. To efficiently procure a more precise analysis results based on ellipsoid model, this paper proposes a novel data-driven strategy for uncertainty quantification and propagation. Firstly, a similarity distribution-based identification method is presented to eliminate the data deviation caused by outliers. For the problem with dispersed samples, a fuzzy equivalence relation-based clustering method is subsequently introduced, where the number of clusters is adaptively determined by the prior similarity levels. On the basis of the above data preprocessing procedure, a more precise multi-ellipsoid modeling framework is constructed, in which the statistical information of existing samples is utilized to derive the explicit expression of ellipsoid models. Meanwhile, to improve the computational efficiency of uncertainty propagation analysis under the multi-ellipsoid model, the back propagation artificial neural network (BPANN) is constructed as the surrogate model of the original time-consuming simulation model. Eventually, two numerical examples are provided to investigate the effectiveness of the proposed methods.(c) 2022 Elsevier B.V. All rights reserved. [Wang, Chong; Qiang, Xin; Fan, Haoran; Chen, Yuli] Beihang Univ, Inst Solid Mech, Beijing 100191, Peoples R China; [Wu, Tao] Tech Univ Dresden, Holbeinstr 3, D-01307 Dresden, Germany Beihang University; Technische Universitat Dresden Wang, C (corresponding author), Beihang Univ, Inst Solid Mech, Beijing 100191, Peoples R China. chongwang.buaa@gmail.com Chen, Yuli/C-1103-2008 Chen, Yuli/0000-0003-1880-3103; Qiang, Xin/0000-0001-7182-0944 National Natural Science Foundation of PR China [12002015, 12132001]; Overseas High-Level Talents Plan of PR China; Young Talent Support Plan of Beihang University National Natural Science Foundation of PR China(National Natural Science Foundation of China (NSFC)); Overseas High-Level Talents Plan of PR China; Young Talent Support Plan of Beihang University Acknowledgments This work was supported by the National Natural Science Foundation of PR China (No. 12002015, No. 12132001) , the Overseas High-Level Talents Plan of PR China, and the Young Talent Support Plan of Beihang University. 56 1 1 17 30 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0045-7825 1879-2138 COMPUT METHOD APPL M Comput. Meth. Appl. Mech. Eng. MAY 1 2022.0 394.0 114889 10.1016/j.cma.2022.114889 0.0 APR 2022 22 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics; Mechanics 1I9QS 2023-03-23 WOS:000797562600007 0
J Lv, JM; Sun, QH; Li, Q; Moreira-Matias, L Lv, Jianming; Sun, Qinghui; Li, Qing; Moreira-Matias, Luis Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Trajectory; Public transportation; Prediction algorithms; Predictive models; Clustering algorithms; Hidden Markov models; Convolutional neural networks; Trajectory; multi-scale; convolutional neural network (CNN) Precise destination prediction from partial trajectories have a huge potential impact on intelligent location-based approaches. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in a single scale, fail to capture diverse and rich two-dimensional patterns of trajectories in different spatial scales. Meanwhile, most models treat each portion of a trajectory equally in terms of contributing to final destination prediction. This is in conflict with our observation that there exists some albeit small local areas playing much more important roles for destination prediction than the others. To address these problems, we propose a novel prediction algorithm T-CONV, which models trajectories as two-dimensional images, and then feed them into a convolutional neural network (CNN) architecture to extract multi-scale patterns for precise destination prediction. Furthermore, we propose a method to extract regions with different relevance for final output of T-CONV, and further explore the local patterns of important regions by integrating multi-scope local-enhancement areas based on attention mechanism. The comprehensive experiments based on two large-scale real taxi trajectory datasets show that T-CONV can achieve higher accuracy than the state-of-the-art methods, demonstrating the strength of the multi-scale and multi-scope feature extraction mechanisms in trajectory mining. [Lv, Jianming; Sun, Qinghui] South China Univ Technol, Dept Comp Sci & Engn, Guangzhou 510006, Peoples R China; [Li, Qing] Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China; [Moreira-Matias, Luis] NEC Labs Europe GmbH, D-69115 Heidelberg, Germany South China University of Technology; Hong Kong Polytechnic University Lv, JM (corresponding author), South China Univ Technol, Dept Comp Sci & Engn, Guangzhou 510006, Peoples R China. jmlv@scut.edu.cn; csqli@comp.polyu.edu.hk; luis.moreira.matias@gmail.com Li, Qing/H-4100-2011 Li, Qing/0000-0003-3370-471X; Moreira-Matias, Luis/0000-0003-3863-6357; Lv, Jianming/0000-0003-3691-4755 NSFC [61876065, U1611461]; Natural Science Foundation of Guangdong Province, China [2018A0303130022]; Science and Technology Program of Guangzhou, China [201904010200]; Research Grants Council of the Hong Kong Special Administrative Region, China [C1031-18G]; Science and Technology Program of Guangdong Province, China [2016A010101012]; CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China [CASNDST201703] NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); Science and Technology Program of Guangzhou, China; Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council); Science and Technology Program of Guangdong Province, China; CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China This work was supported in part by the NSFC under Grant 61876065 and Grant U1611461, in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2018A0303130022, in part by the Science and Technology Program of Guangzhou, China, 201904010200, in part by a collaborative research fund by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant C1031-18G, in part by the Science and Technology Program of Guangdong Province, China, under Grant 2016A010101012, and in part by the CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, under Grant CASNDST201703. The Associate Editor for this paper was A. Che. 26 16 18 6 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. AUG 2020.0 21.0 8 3184 3195 10.1109/TITS.2019.2924903 0.0 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation MT3ZM 2023-03-23 WOS:000554907200004 0
J Fu, CH; Zhang, YQ; Huang, ZY; Duan, R; Xie, ZW Fu, Changhong; Zhang, Yinqiang; Huang, Ziyuan; Duan, Ran; Xie, Zongwu Part-Based Background-Aware Tracking for UAV With Convolutional Features IEEE ACCESS English Article Visual object tracking; unmanned aerial vehicle (UAV); convolutional neural network; background-aware correlation filter; part-based strategy; Gaussian process regression VISUAL TRACKING; OBJECT TRACKING In recent years, visual tracking is a challenging task in UAV applications. The standard correlation filter (CF) has been extensively applied for UAV object tracking. However, the CF-based tracker severely suffers from boundary effects and cannot effectively cope with object occlusion, which results in suboptimal performance. Besides, it is still a tough task to obtain an appearance model precisely with hand-crafted features. In this paper, a novel part-based tracker is proposed for the UAV. With successive cropping operations, the tracking object is separated into several parts. More specially, the background-aware correlation filters with different cropping matrices are applied. To estimate the translation and scale variation of the tracking object, a structured comparison, and a Bayesian inference approach are proposed, which jointly achieve a coarse-to-fine strategy. Moreover, an adaptive mechanism is used to update the local appearance model of each part with a Gaussian process regression method. To construct a better appearance model, features extracted from the convolutional neural network are utilized instead of hand-crafted features. Through extensive experiments, the proposed tracker reaches competitive performance on 123 challenging UAV image sequences and outperforms other 20 popular state-of-the-art visual trackers in terms of overall performance and different challenging attributes. [Fu, Changhong] Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China; [Zhang, Yinqiang] Tech Univ Munich, Dept Mech Engn, D-80333 Munich, Germany; [Huang, Ziyuan] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China; [Duan, Ran] Hong Kong Polytech Univ, Adapt Robot Controls Lab, Hong Kong, Peoples R China; [Xie, Zongwu] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Heilongjiang, Peoples R China Tongji University; Technical University of Munich; Tongji University; Hong Kong Polytechnic University; Harbin Institute of Technology Fu, CH (corresponding author), Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China.;Xie, ZW (corresponding author), Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Heilongjiang, Peoples R China. changhongfu@tongji.edu.cn; xiezongwu@hit.edu.cn Fu, Changhong/AAN-2859-2020 Fu, Changhong/0000-0002-9897-6022; Zhang, Yinqiang/0000-0002-5423-1774 State Key Laboratory of Robotics and System (HIT) [SKLRS-2018-KF-03] State Key Laboratory of Robotics and System (HIT) This work was supported by the State Key Laboratory of Robotics and System (HIT) under Grant SKLRS-2018-KF-03. 50 5 6 1 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 79997 80010 10.1109/ACCESS.2019.2922703 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IH6JH Green Submitted, gold 2023-03-23 WOS:000474601000001 0
J Liew, SL; Zavaliangos-Petropulu, A; Jahanshad, N; Lang, CE; Hayward, KS; Lohse, KR; Juliano, JM; Assogna, F; Baugh, LA; Bhattacharya, AK; Bigjahan, B; Borich, MR; Boyd, LA; Brodtmann, A; Buetefisch, CM; Byblow, WD; Cassidy, JM; Conforto, AB; Craddock, RC; Dimyan, MA; Dula, AN; Ermer, E; Etherton, MR; Fercho, KA; Gregory, CM; Hadidchi, S; Holguin, JA; Hwang, DH; Jung, S; Kautz, SA; Khlif, MS; Khoshab, N; Kim, B; Kim, H; Kuceyeski, A; Lotze, M; MacIntosh, BJ; Margetis, JL; Mohamed, FB; Piras, F; Ramos-Murguialday, A; Richard, G; Roberts, P; Robertson, AD; Rondina, JM; Rost, NS; Sanossian, N; Schweighofer, N; Seo, NJ; Shiroishi, MS; Soekadar, SR; Spalletta, G; Stinear, CM; Suri, A; Tang, WKW; Thielman, GT; Vecchio, D; Villringer, A; Ward, NS; Werden, E; Westlye, LT; Winstein, C; Wittenberg, GF; Wong, KA; Yu, CS; Cramer, SC; Thompson, PM Liew, Sook-Lei; Zavaliangos-Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S.; Lohse, Keith R.; Juliano, Julia M.; Assogna, Francesca; Baugh, Lee A.; Bhattacharya, Anup K.; Bigjahan, Bavrina; Borich, Michael R.; Boyd, Lara A.; Brodtmann, Amy; Buetefisch, Cathrin M.; Byblow, Winston D.; Cassidy, Jessica M.; Conforto, Adriana B.; Craddock, R. Cameron; Dimyan, Michael A.; Dula, Adrienne N.; Ermer, Elsa; Etherton, Mark R.; Fercho, Kelene A.; Gregory, Chris M.; Hadidchi, Shahram; Holguin, Jess A.; Hwang, Darryl H.; Jung, Simon; Kautz, Steven A.; Khlif, Mohamed Salah; Khoshab, Nima; Kim, Bokkyu; Kim, Hosung; Kuceyeski, Amy; Lotze, Martin; MacIntosh, Bradley J.; Margetis, John L.; Mohamed, Feroze B.; Piras, Fabrizio; Ramos-Murguialday, Ander; Richard, Genevieve; Roberts, Pamela; Robertson, Andrew D.; Rondina, Jane M.; Rost, Natalia S.; Sanossian, Nerses; Schweighofer, Nicolas; Seo, Na Jin; Shiroishi, Mark S.; Soekadar, Surjo R.; Spalletta, Gianfranco; Stinear, Cathy M.; Suri, Anisha; Tang, Wai Kwong W.; Thielman, Gregory T.; Vecchio, Daniela; Villringer, Arno; Ward, Nick S.; Werden, Emilio; Westlye, Lars T.; Winstein, Carolee; Wittenberg, George F.; Wong, Kristin A.; Yu, Chunshui; Cramer, Steven C.; Thompson, Paul M. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke HUMAN BRAIN MAPPING English Review big data; lesions; MRI; neuroinformatics; stroke HUMAN CEREBRAL-CORTEX; UPPER-LIMB FUNCTION; ISCHEMIC-STROKE; CORE RECOMMENDATIONS; LESION IDENTIFICATION; FUNCTIONAL OUTCOMES; CORTICAL THICKNESS; REHABILITATION; SEGMENTATION; RELIABILITY The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided. [Liew, Sook-Lei; Holguin, Jess A.; Margetis, John L.] Univ Southern Calif, Chan Div Occupat Sci & Occupat Therapy, Los Angeles, CA 90007 USA; [Liew, Sook-Lei; Zavaliangos-Petropulu, Artemis; Jahanshad, Neda; Bigjahan, Bavrina; Kim, Hosung; Thompson, Paul M.] Univ Southern Calif, USC Stevens Neuroimaging & Informat Inst, Keck Sch Med, Dept Neurol, Los Angeles, CA 90007 USA; [Liew, Sook-Lei] Univ Southern Calif, Dept Biomed Engn, Los Angeles, CA 90007 USA; [Liew, Sook-Lei; Zavaliangos-Petropulu, Artemis; Juliano, Julia M.] Univ Southern Calif, Grad Program Neurosci, Los Angeles, CA 90007 USA; [Zavaliangos-Petropulu, Artemis; Jahanshad, Neda; Thompson, Paul M.] Univ Southern Calif, Keck Sch Med, USC Stevens Neuroimaging & Informat Inst, Imaging Genet Ctr, Los Angeles, CA 90007 USA; [Lang, Catherine E.] Washington Univ, Sch Med, Program Phys Therapy, St Louis, MO USA; [Hayward, Kathryn S.] Univ Melbourne, Dept Physiotherapy, Parkville, Vic, Australia; [Hayward, Kathryn S.] Univ Melbourne, Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia; [Hayward, Kathryn S.] Univ Melbourne, NHMRC Ctr Res Excellence Stroke Rehabil & Brain R, Parkville, Vic, Australia; [Lohse, Keith R.] Univ Utah, Dept Hlth Kinesiol & Recreat, Salt Lake City, UT USA; [Lohse, Keith R.] Univ Utah, Dept Phys Therapy & Athlet Training, Salt Lake City, UT USA; [Assogna, Francesca; Piras, Fabrizio; Spalletta, Gianfranco; Vecchio, Daniela] IRCCS Santa Lucia Fdn, Dept Clin & Behav Neurol, Lab Neuropsychiat, Rome, Italy; [Baugh, Lee A.; Fercho, Kelene A.] Univ South Dakota, Sanford Sch Med, Div Basic Biomed Sci, Vermillion, SD USA; [Baugh, Lee A.] Sioux Falls VA Hlth Care Syst, Sioux Falls, SD USA; [Bhattacharya, Anup K.] Washington Univ, Sch Med, Mallinckrodt Inst Radiol, St Louis, MO USA; [Bigjahan, Bavrina; Hwang, Darryl H.] Univ Southern Calif, Dept Radiol, Keck Sch Med, Los Angeles, CA 90007 USA; [Borich, Michael R.; Buetefisch, Cathrin M.] Emory Univ, Dept Rehabil Med, Atlanta, GA 30322 USA; [Boyd, Lara A.] Univ British Columbia, Fac Med, Dept Phys Therapy, Vancouver, BC, Canada; [Boyd, Lara A.] Djavad Mowafaghian Ctr Brain Hlth, Vancouver, BC, Canada; [Brodtmann, Amy; Khlif, Mohamed Salah; Werden, Emilio] Univ Melbourne, Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia; [Buetefisch, Cathrin M.] Emory Univ, Dept Neurol, Atlanta, GA 30322 USA; [Byblow, Winston D.] Univ Auckland, Ctr Brain Res, Dept Exercise Sci, Auckland, New Zealand; [Cassidy, Jessica M.] Univ N Carolina, Dept Allied Hlth Sci, Div Phys Therapy, Chapel Hill, NC 27515 USA; [Conforto, Adriana B.] Univ Sao Paulo, Hosp Clin, Neurol Clin Div, Sao Paulo, Brazil; [Conforto, Adriana B.] Hosp Israelita Albert Einstein, Sao Paulo, Brazil; [Craddock, R. Cameron; Dula, Adrienne N.] Univ Texas Austin, Dell Med Sch, Dept Diagnost Med, Austin, TX 78712 USA; [Dimyan, Michael A.; Ermer, Elsa] Univ Maryland, Sch Med, Dept Neurol & Neurorehabil, Baltimore, MD 21201 USA; [Dimyan, Michael A.] VA Maryland Hlth Care Syst, Baltimore, MD USA; [Dula, Adrienne N.] Univ Texas Austin, Dell Med Sch, Dept Neurol, Austin, TX 78712 USA; [Etherton, Mark R.] Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA; [Etherton, Mark R.] Harvard Med Sch, J Philip Kistler Stroke Res Ctr, Boston, MA 02115 USA; [Fercho, Kelene A.] Civil Aerosp Med Inst, Fed Aviat Adm, Oklahoma City, OK USA; [Gregory, Chris M.; Kautz, Steven A.; Seo, Na Jin] Med Univ South Carolina, Dept Hlth Sci & Res, Charleston, SC 29425 USA; [Hadidchi, Shahram] Wayne State Univ, Detroit Med Ctr, Dept Radiol, Detroit, MI USA; [Hadidchi, Shahram] Wayne State Univ, Dept Internal Med, Detroit Med Ctr, Detroit, MI 48202 USA; [Jung, Simon] Univ Bern, Dept Neurol, Bern, Switzerland; [Kautz, Steven A.; Seo, Na Jin] Ralph H Johnson VA Med Ctr, Charleston, SC USA; [Khoshab, Nima] Univ Calif Irvine, Dept Anat & Neurobiol, Irvine, CA 92717 USA; [Kim, Bokkyu] SUNY Upstate Med Univ, Dept Phys Therapy Educ, Syracuse, NY 13210 USA; [Kim, Bokkyu] Univ Southern Calif, Div Biokinesiol & Phys Therapy, Los Angeles, CA 90007 USA; [Kuceyeski, Amy] Weill Cornell Med, Dept Radiol, New York, NY USA; [Kuceyeski, Amy] Weill Cornell Med, Brain & Mind Res Inst, New York, NY USA; [Lotze, Martin] Univ Greifswald, Sch Med, Ctr Diagnost Radiol, Funct Imaging Unit, Greifswald, Germany; [MacIntosh, Bradley J.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada; [MacIntosh, Bradley J.] Sunnybrook Res Inst, Brain Sci Program, Phys Sci Platform, Toronto, ON, Canada; [Mohamed, Feroze B.] Thomas Jefferson Univ, Dept Radiol, Philadelphia, PA 19107 USA; [Ramos-Murguialday, Ander] BRTA, TECNALIA, Neurotechnol Lab, Derio, Spain; [Ramos-Murguialday, Ander] Univ Tubingen, Inst Med Psychol & Behav Neurobiol, Tubingen, Germany; [Richard, Genevieve; Westlye, Lars T.] Univ Oslo, Dept Psychol, Oslo, Norway; [Richard, Genevieve; Westlye, Lars T.] Oslo Univ Hosp, Div Mental Hlth & Addict, NORMENT, Oslo, Norway; [Richard, Genevieve] Univ Oslo, Inst Clin Med, Oslo, Norway; [Roberts, Pamela] Cedars Sinai, Dept Phys Med & Rehabil, Los Angeles, CA USA; [Robertson, Andrew D.] Univ Waterloo, Dept Kinesiol, Waterloo, ON, Canada; [Robertson, Andrew D.] Univ Waterloo, Schlegel UW Res Inst Aging, Waterloo, ON, Canada; [Rondina, Jane M.] UCL, UCL Queen Sq Inst Neurol, Dept Clin & Movement Neurosci, London, England; [Rost, Natalia S.] Harvard Med Sch, Massachusetts Gen Hosp, Dept Neurol, Stroke Div, Boston, MA 02115 USA; [Sanossian, Nerses] Univ Southern Calif, Div Neurocrit Care & Stroke, Dept Neurol, Keck Sch Med, Los Angeles, CA 90007 USA; [Schweighofer, Nicolas; Winstein, Carolee] Univ Southern Calif, Div Biokinesiol & Phys Therapy, Los Angeles, CA 90007 USA; [Seo, Na Jin] Med Univ South Carolina, Dept Hlth Profess, Div Occupat Therapy, Charleston, SC 29425 USA; [Shiroishi, Mark S.] Univ Southern Calif, Keck Sch Med, Dept Radiol, Div Neuroradiol, Los Angeles, CA 90007 USA; [Soekadar, Surjo R.] Charite Univ Med Berlin, Clin Neurotechnol Lab, Dept Psychiat & Psychotherapy, Berlin, Germany; [Soekadar, Surjo R.] Univ Tubingen, Dept Psychiat & Psychotherapy, Appl Neurotechnol Lab, Tubingen, Germany; [Spalletta, Gianfranco] Baylor Coll Med, Menninger Dept Psychiat & Behav Sci, Div Neuropsychiat, Houston, TX 77030 USA; [Stinear, Cathy M.] Univ Auckland, Dept Med, Auckland, New Zealand; [Suri, Anisha] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA USA; [Tang, Wai Kwong W.] Chinese Univ Hong Kong, Dept Psychiat, Hong Kong, Peoples R China; [Thielman, Gregory T.] Univ Sci, Phys Therapy & Neurosci, Philadelphia, PA USA; [Thielman, Gregory T.] Samson Coll, Quezon City, Philippines; [Villringer, Arno] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany; [Villringer, Arno] Univ Hosp Leipzig, Dept Cognit Neurol, Leipzig, Germany; [Villringer, Arno] Charite Univ Med Berlin, Ctr Stroke Res, Berlin, Germany; [Ward, Nick S.] UCL, UCL Queen Sq Inst Neurol, London, England; [Winstein, Carolee] Univ Southern Calif, Dept Neurol, Los Angeles, CA 90007 USA; [Wittenberg, George F.] Univ Pittsburgh, Dept Neurol, Pittsburgh, PA 15260 USA; [Wittenberg, George F.] Univ Dr Campus, Dept Vet Affairs, Pittsburgh, PA USA; [Wong, Kristin A.] Univ Texas Austin, Dept Phys Med & Rehabil, Dell Med Sch, Austin, TX 78712 USA; [Yu, Chunshui] Tianjin Med Univ, Gen Hosp, Dept Radiol, Tianjin, Peoples R China; [Yu, Chunshui] Tianjin Med Univ, Gen Hosp, Tianjin Key Lab Funct Imaging, Tianjin, Peoples R China; [Cramer, Steven C.] Univ Calif Los Angeles, Dept Neurol, Los Angeles, CA 90024 USA; [Cramer, Steven C.] Calif Rehabil Inst, Los Angeles, CA 90024 USA University of Southern California; University of Southern California; University of Southern California; University of Southern California; University of Southern California; Washington University (WUSTL); University of Melbourne; Florey Institute of Neuroscience & Mental Health; University of Melbourne; University of Melbourne; Utah System of Higher Education; University of Utah; Utah System of Higher Education; University of Utah; IRCCS Santa Lucia; University of South Dakota; Washington University (WUSTL); University of Southern California; Emory University; University of British Columbia; Florey Institute of Neuroscience & Mental Health; University of Melbourne; Emory University; University of Auckland; University of North Carolina; University of North Carolina Chapel Hill; Universidade de Sao Paulo; Hospital Israelita Albert Einstein; University of Texas System; University of Texas Austin; University System of Maryland; University of Maryland Baltimore; University of Texas System; University of Texas Austin; Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; Medical University of South Carolina; Detroit Medical Center; Wayne State University; Detroit Medical Center; Wayne State University; University of Bern; US Department of Veterans Affairs; Veterans Health Administration (VHA); Ralph H Johnson VA Medical Center; University of California System; University of California Irvine; State University of New York (SUNY) System; State University of New York (SUNY) Upstate Medical Center; University of Southern California; Cornell University; Weill Cornell Medicine; Cornell University; Weill Cornell Medicine; Ernst Moritz Arndt Universitat Greifswald; University of Toronto; University of Toronto; Sunnybrook Health Science Center; Sunnybrook Research Institute; Jefferson University; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; University of Oslo; University of Oslo; University of Oslo; Cedars Sinai Medical Center; University of Waterloo; University of Waterloo; University of London; University College London; Harvard University; Harvard Medical School; Massachusetts General Hospital; University of Southern California; University of Southern California; Medical University of South Carolina; University of Southern California; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Eberhard Karls University of Tubingen; Baylor College of Medicine; University of Auckland; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Chinese University of Hong Kong; Max Planck Society; Leipzig University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; University of London; University College London; University of Southern California; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Texas System; University of Texas Austin; Tianjin Medical University; Tianjin Medical University; University of California System; University of California Los Angeles Liew, SL (corresponding author), Univ Southern Calif, 2025 Zonal Ave, Los Angeles, CA 90033 USA. sliew@usc.edu Soekadar, Surjo/AAA-3801-2020; Vecchio, Daniela/AAC-2024-2022; Kautz, Steven/ABD-5364-2020; Robertson, Andrew Donald/H-7199-2019; Holguin, Jess/GYJ-5583-2022; Jahanshad, Neda/ABD-5944-2021; Kautz, Steven A/I-4164-2012; Hwang, Darryl Hwa/M-7221-2019; Thompson, Paul M/C-4194-2018; Hayward, Kate/AAU-7253-2020; Lotze, Martin/D-2964-2012; Fercho, Kelene/AAV-1699-2021; Suri, Anisha/AAA-3685-2022; Boyd, Lara/CAF-0372-2022; Thielman, Gregory/GLU-3997-2022; Liew, Sook-Lei/ABH-1939-2020; Dula, Adrienne/AFZ-2921-2022; Schweighofer, Nicolas/A-3675-2008; piras, fabrizio/M-7902-2016 Soekadar, Surjo/0000-0003-1280-5538; Vecchio, Daniela/0000-0001-8428-7376; Kautz, Steven/0000-0003-3151-8235; Robertson, Andrew Donald/0000-0002-9095-9877; Kautz, Steven A/0000-0003-3151-8235; Hwang, Darryl Hwa/0000-0002-1111-9176; Thompson, Paul M/0000-0002-4720-8867; Hayward, Kate/0000-0001-5240-3264; Lotze, Martin/0000-0003-4519-4956; Suri, Anisha/0000-0001-5308-2079; Liew, Sook-Lei/0000-0001-5935-4215; Dula, Adrienne/0000-0002-4809-6115; Schweighofer, Nicolas/0000-0003-3362-6088; Kim, Bokkyu/0000-0003-4245-373X; piras, fabrizio/0000-0003-3566-5494; Winstein, Carolee/0000-0001-9789-4626; Ward, Nick/0000-0002-7688-9649; Seo, Na Jin/0000-0001-6446-5905; Yu, Chunshui/0000-0001-5648-5199; Margetis, John/0000-0002-0958-3594; Villringer, Arno/0000-0003-2604-2404; Tang, Wai Kwong/0000-0002-2616-6864; Baugh, Lee/0000-0001-6227-497X; Conforto, Adriana/0000-0001-7869-3490 112 22 22 5 23 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. JAN 2022.0 43.0 1 SI 129 148 10.1002/hbm.25015 0.0 APR 2020 20 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging XP3FD 32310331.0 Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000527682300001 0
C Li, Q; Chalvatzaki, G; Peters, J; Wang, Y IEEE Li, Qin; Chalvatzaki, Georgia; Peters, Jan; Wang, Yong Directed Acyclic Graph Neural Network for Human Motion Prediction 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 30-JUN 05, 2021 Xian, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,So Univ Sci & Technol,Harbin Inst Technol,Xian Jiaotong Univ,Univ Toronto,Nat Sci & Engn Res Council Canada,Chinese Univ Hong Kong,DJI,Biomimet Intelligence & Robot,Toyota Res Inst,Baidu,Mech Mind Robot Technologies,Facebook AI,Nokov,Manycore,Franka Emika,Zhejiang Univ, Huzhou Inst,Jing Tian,ATI Ind Automat,Amber,Direct Drive Tech,Inceptio,Flexiv,Deeproute AI,Unitree,Waymo,Kuka,Pudu Robot,Deep Robot,Nvidia,MathWorks INTENTION INFERENCE; MODEL Human motion prediction is essential in human-robot interaction. Current research mostly considers the joint dependencies but ignores the bone dependencies and their relationship in the human skeleton, thus limiting the prediction accuracy. To address this issue, we represent the human skeleton as a directed acyclic graph with joints as vertexes and bones as directed edges. Then, we propose a novel directed acyclic graph neural network (DA-GNN) that follows the encoder-decoder structure. The encoder is stacked by multiple encoder blocks, each of which includes a directed acyclic graph computational operator (DA-GCO) to update joint and bone attributes based on the relationship between joint and bone dependencies in the observed human states, and a temporal update operator (TUO) to update the temporal dynamics of joints and bones in the same observation. After progressively implementing the above update process, the encoder outputs the final update result, fed into the decoder. The decoder includes a directed acyclic graph-based gated recurrent unit (DAG-GRU) and a multi-layered perceptron (MLP) to predict future human states sequentially. To the best of our knowledge, this is the first time to introduce the relationship between bone and joint dependencies in human motion prediction. Our experimental evaluations on two datasets, CMU Mocap and Human 3.6m, prove that DA-GNN outperforms current models. Finally, we showcase the efficacy of DA-GNN in a realistic HRI scenario. [Li, Qin; Wang, Yong] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Chalvatzaki, Georgia; Peters, Jan] Tech Univ Darmstadt, Intelligent Autonomous Syst, Hochschulstr 10, D-64289 Darmstadt, Germany Central South University; Technical University of Darmstadt Wang, Y (corresponding author), Cent South Univ, Sch Automat, Changsha 410083, Peoples R China. liqin6@csu.edu.cn; georgia@robot-learning.de; ywang@csu.edu.cn RoboTrust; Skills4Robots RoboTrust; Skills4Robots The work of Chalvatzaki and Peters has been supported by the projects RoboTrust and Skills4Robots. 49 4 4 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-9077-8 IEEE INT CONF ROBOT 2021.0 3197 3204 10.1109/ICRA48506.2021.9561540 0.0 8 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BS7PC 2023-03-23 WOS:000765738802087 0
J Zeng, H; Li, XF; Borghini, G; Zhao, Y; Arico, P; Di Flumeri, G; Sciaraffa, N; Zakaria, W; Kong, WZ; Babiloni, F Zeng, Hong; Li, Xiufeng; Borghini, Gianluca; Zhao, Yue; Arico, Pietro; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Zakaria, Wael; Kong, Wanzeng; Babiloni, Fabio An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction SENSORS English Article cross-subject prediction; Domain-Adversarial Neural Network (DANN); electroencephalogram (EEG); Generative Adversarial Networks (GAN) DRIVER FATIGUE; PATTERN Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively transfering the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain-computer interaction (BCI). [Zeng, Hong; Li, Xiufeng; Zhao, Yue; Kong, Wanzeng] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China; [Zeng, Hong; Kong, Wanzeng] Key Lab Brain Machine Collaborat Intelligence Zhe, Hangzhou 310018, Peoples R China; [Zeng, Hong; Borghini, Gianluca; Arico, Pietro; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Babiloni, Fabio] Univ Roma La Sapienza, Ind NeuroSci Lab, I-00161 Rome, Italy; [Zakaria, Wael] Ain Shams Univ, Dept Math Comp Sci, Fac Sci, Cairo 11435, Egypt Hangzhou Dianzi University; Sapienza University Rome; Egyptian Knowledge Bank (EKB); Ain Shams University Babiloni, F (corresponding author), Univ Roma La Sapienza, Ind NeuroSci Lab, I-00161 Rome, Italy. jivon@hdu.edu.cn; lixiufengcn@hdu.edu.cn; gianluca.borghini@uniroma1.it; zhaoyue@hdu.edu.cn; pietro.arico@uniroma1.it; gianluca.diflumeri@uniroma1.it; nicolina.sciaraffa@uniroma1.it; wael.zakaria@sci.asu.edu.eg; kongwanzeng@hdu.edu.cn; fabio.babiloni@uniroma1.it Di Flumeri, Gianluca/I-7196-2019; Zakaria, Wael/AAU-7837-2021; Babiloni, Fabio/E-5169-2015 Di Flumeri, Gianluca/0000-0003-4426-051X; Zakaria, Wael/0000-0001-6718-0980; Li, XiuFeng/0000-0002-3830-5534; Babiloni, Fabio/0000-0002-4962-176X; Arico, Pietro/0000-0002-3831-6620; Kong, Wanzeng/0000-0002-0113-6968; Borghini, Gianluca/0000-0001-8560-5671 National Key R&D Program of China [2017YFE0118200]; NSFC [62076083]; National International Joint Research Center for Brain-Machine Collaborative Intelligence [2017B01020]; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province [2020E10010]; Fundamental Research Funds for the Provincial Universities of Zhejiang [GK209907299001-008]; Graduate Scientific Research Foundation of Hangzhou Dianzi University [CXJJ2020086]; Industrial Neuroscience Laboratory of University of Rome La Sapienza National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); National International Joint Research Center for Brain-Machine Collaborative Intelligence; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province; Fundamental Research Funds for the Provincial Universities of Zhejiang; Graduate Scientific Research Foundation of Hangzhou Dianzi University; Industrial Neuroscience Laboratory of University of Rome La Sapienza This work was supported by the National Key R&D Program of China with grant No. 2017YFE0118200, also supported by NSFC with grant No. 62076083. The authors also thank the National International Joint Research Center for Brain-Machine Collaborative Intelligence with grant No. 2017B01020, Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province with grant No. 2020E10010, Fundamental Research Funds for the Provincial Universities of Zhejiang with grant No. GK209907299001-008, the Graduate Scientific Research Foundation of Hangzhou Dianzi University with grant No.CXJJ2020086, and Industrial Neuroscience Laboratory of University of Rome La Sapienza. 40 13 13 22 85 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2021.0 21.0 7 2369 10.3390/s21072369 0.0 20 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation RL3GW 33805522.0 gold, Green Accepted 2023-03-23 WOS:000638866700001 0
J Li, BW; Xiong, DL; Huang, K; Jiang, XW; Yao, H; Chen, JJ; Claesen, L Li, Bowen; Xiong, Dongliang; Huang, Kai; Jiang, Xiaowen; Yao, Hao; Chen, Junjian; Claesen, Luc Sample-Wise Dynamic Precision Quantization for Neural Network Acceleration IEICE ELECTRONICS EXPRESS English Article; Early Access convolutional neural networks; dynamic quantization; hardware accelerators Quantization is a well-known method for deep neural networks (DNNs) compression and acceleration. In this work, we propose the Sample-Wise Dynamic Precision (SWDP) quantization scheme, which can switch the bit-width of weights and activations in the model according to the task difficulty of input samples at runtime. Using low-precision networks for easy input images brings advantages in terms of computational and energy efficiency. We also propose an adaptive hardware design for the efficient implementation of our SWDP networks. The experimental results on various networks and datasets demonstrate that our SWDP achieves an average of 3.3x speedup and 3.0x energy saving over the bit-level dynamically composable architecture BitFusion. [Li, Bowen; Xiong, Dongliang; Huang, Kai; Jiang, Xiaowen] Zhejiang Univ, Sch Micronano Elect, Hangzhou 310030, Peoples R China; [Yao, Hao; Chen, Junjian] China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510670, Peoples R China; [Claesen, Luc] Univ Hasselt, Engn Technol Elect ICT Dept, Diepenbeek, Belgium Zhejiang University; China Southern Power Grid; Hasselt University Xiong, DL (corresponding author), Zhejiang Univ, Sch Micronano Elect, Hangzhou 310030, Peoples R China. xiongdl@zju.edu.cn National Key R&D Program of China [2020YFB0906000, 2020YFB0906001] National Key R&D Program of China This work is supported by the National Key R&D Program of China (2020YFB0906000, 2020YFB0906001) . 30 0 0 1 6 IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS TOKYO KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN 1349-2543 IEICE ELECTRON EXPR IEICE Electron. Express 20220229 10.1587/elex.19.20220229 0.0 JUL 2022 6 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 2Z0UY Green Published, gold 2023-03-23 WOS:000826303400001 0
J Alirezazadeh, P; Dornaika, F; Moujahid, A Alirezazadeh, Pendar; Dornaika, Fadi; Moujahid, Abdelmalik Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval SENSORS English Article cross-domain fashion retrieval; margin-based loss function; adaptive margin; deep learning; discriminative analysis Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset-(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval-confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance. [Alirezazadeh, Pendar; Dornaika, Fadi] Univ Basque Country, Dept Informat, Donostia San Sebastian 20008, Spain; [Dornaika, Fadi] Henan Univ, Sch Comp & Informat Engn, Kaifeng 475000, Peoples R China; [Dornaika, Fadi] Ikerbasque, Basque Fdn Sci, Plaza Euskadi 5, Bilbao 48009, Spain; [Moujahid, Abdelmalik] Univ Basque Country, Dept Math, Bilbao 48080, Spain University of Basque Country; Henan University; Basque Foundation for Science; University of Basque Country Dornaika, F (corresponding author), Univ Basque Country, Dept Informat, Donostia San Sebastian 20008, Spain.;Dornaika, F (corresponding author), Henan Univ, Sch Comp & Informat Engn, Kaifeng 475000, Peoples R China.;Dornaika, F (corresponding author), Ikerbasque, Basque Fdn Sci, Plaza Euskadi 5, Bilbao 48009, Spain. pendar.alirezazadeh@ehu.eus; fadi.dornaika@ehu.eus; abdelmalik.moujahid@ehu.eus Moujahid, Abdelmalik/A-7626-2012 Moujahid, Abdelmalik/0000-0002-4971-3613; Dornaika, Fadi/0000-0001-6581-9680 34 2 2 6 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2022.0 22.0 7 2660 10.3390/s22072660 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 0M2XK 35408276.0 gold, Green Published 2023-03-23 WOS:000782023200001 0
J Liu, S; Mallol-Ragolta, A; Yan, TH; Qian, K; Parada-Cabaleiro, E; Hu, B; Schuller, BW Liu, Shuo; Mallol-Ragolta, Adria; Yan, Tianhao; Qian, Kun; Parada-Cabaleiro, Emilia; Hu, Bin; Schuller, Bjoern W. Capturing Time Dynamics From Speech Using Neural Networks for Surgical Mask Detection IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Face recognition; Spectrogram; Neural networks; Task analysis; Feature extraction; COVID-19; Acoustics; Face mask detection; convolutional transformer network; convolutional recurrent neural network; multi-head attention; audio processing AIRBORNE TRANSMISSION; DATA AUGMENTATION; FACE MASKS The importance of detecting whether a person wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Networks (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand. [Liu, Shuo; Mallol-Ragolta, Adria] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbein, D-86159 Augsburg, Germany; [Yan, Tianhao] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150009, Peoples R China; [Qian, Kun; Hu, Bin] Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China; [Parada-Cabaleiro, Emilia] Johannes Kepler Univ Linz, Inst Computat Percept, A-4040 Linz, Austria; [Schuller, Bjoern W.] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, D-86159 Augsburg, Germany; [Schuller, Bjoern W.] Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England University of Augsburg; Harbin Engineering University; Beijing Institute of Technology; Johannes Kepler University Linz; University of Augsburg; Imperial College London Liu, S (corresponding author), Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbein, D-86159 Augsburg, Germany.;Qian, K; Hu, B (corresponding author), Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China. shuo.liu@uni-a.de; adria.mallol-ragolta@uni-a.de; 1825109095@hrbeu.edu.cn; qian@bit.edu.cn; emilia.parada-cabaleiro@jku.at; bh@bit.edu.cn; bjoern.schuller@imperial.ac.uk Parada-Cabaleiro, Emilia/GXF-2079-2022 Parada-Cabaleiro, Emilia/0000-0003-1843-3632; Liu, Shuo/0000-0001-8133-8588; Hu, Bin/0000-0003-3514-5413; Schuller, Bjorn/0000-0002-6478-8699; Mallol-Ragolta, Adria/0000-0001-6855-485X European Union [826506]; China Scholarship Council (CSC) [202006680068]; Beijing Institute of Technology, China European Union(European Commission); China Scholarship Council (CSC)(China Scholarship Council); Beijing Institute of Technology, China This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant agreement 826506 (sustAGE), in part by the China Scholarship Council (CSC) under Grant 202006680068, and in part by the BIT Teli Young Fellow Programme from the Beijing Institute of Technology, China. 61 0 0 5 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. AUG 2022.0 26.0 8 4291 4302 10.1109/JBHI.2022.3173128 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics 3V7PB 35522639.0 2023-03-23 WOS:000841851000067 0
J Su, YC; Marinoni, A; Li, J; Plaza, J; Gamba, P Su, Yuanchao; Marinoni, Andrea; Li, Jun; Plaza, Javier; Gamba, Paolo Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Deep learning; hyperspectral remote sensing; nonnegative sparse autoencoder (NNSAE); unmixing ALGORITHM As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance. [Su, Yuanchao; Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Ctr Integrated Geog Informat Anal, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Plaza, Javier] Univ Extremadura, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Escuela Politecn, E-10071 Caceres, Spain; [Marinoni, Andrea; Gamba, Paolo] Univ Pavia, Dipartimento Ingn Ind & Informaz, I-27100 Pavia, Italy Sun Yat Sen University; Universidad de Extremadura; University of Pavia Li, J (corresponding author), Sun Yat Sen Univ, Sch Geog & Planning, Ctr Integrated Geog Informat Anal, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China. suych3@mail2.sysu.edu.cn; lijun48@mail.sysu.edu.cn Su, Yuanchao/ABD-6108-2021; Plaza, Javier/C-4452-2008; Plaza, Antonio/C-4455-2008; Li, Jun/AAD-7233-2020; Gamba, Paolo E/G-1959-2010; Li, Jun/E-6840-2019; Marinoni, Andrea/F-4610-2017 Su, Yuanchao/0000-0002-4776-0862; Plaza, Javier/0000-0002-2384-9141; Plaza, Antonio/0000-0002-9613-1659; Li, Jun/0000-0003-1613-9448; Gamba, Paolo E/0000-0002-9576-6337; Li, Jun/0000-0003-1613-9448; Marinoni, Andrea/0000-0001-6789-0915 National Natural Science Foundation of China [61771496]; National Key Research and Development Program of China [2017YFB0502900]; Guangdong Provincial Natural Science Foundation [2016A030313254] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province) This work was supported in part by the National Natural Science Foundation of China under Grant 61771496, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by Guangdong Provincial Natural Science Foundation under Grant 2016A030313254. (Corresponding author: Jun Li.) 13 60 62 2 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. SEP 2018.0 15.0 9 1427 1431 10.1109/LGRS.2018.2841400 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology GR9FB 2023-03-23 WOS:000443051700024 0
J Pandey, M; Janizadeh, S; Norouzi, A; Ali, S; Ahmadi, K; Linh, NTT; Bhunia, GS; Pham, QB; Anh, DT Pandey, Manish; Janizadeh, Saeid; Norouzi, Akbar; Ali, Shoaib; Ahmadi, Kourosh; Linh, Nguyen Thi Thuy; Bhunia, Gouri Sankar; Pham, Quoc Bao; Anh, Duong Tran Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm ADVANCES IN SPACE RESEARCH English Article Flood susceptibility; Genetic algorithm; XGBoost hybrid; Tafresh watershed TOPOGRAPHIC WETNESS INDEX; SUPPORT VECTOR MACHINE; GENETIC ALGORITHM; SPATIAL PREDICTION; DRAINAGE DENSITY; NEURAL-NETWORK; SOIL-MOISTURE; WORLD MAP; CLASSIFICATION; IRAN Flood is the most common natural hazard that causing unprecedented loss of life and property in the world. In recent years, flood damage has increased due to human intervention and land use and climate changes. The purpose of this study is to predict flood susceptibility in Tafresh watershed in Markazi Province, Iran based on K-nearest neighbor (KNN), Extremely Gradient Boosting (XGB) machine learning models and evaluate the performance of hybrid genetic algorithm (GA) optimization method and XGB model. For this purpose, 14 independent variables affecting flood susceptibility were prepared, also, 227 flood locations were identified as independent variables based on available information and field survey. In order to evaluate the efficiency of the models, receiver operating characteristic (ROC) parameters were used. Evaluating the efficiency of the models based on the AUCs of testing dataset showed the higher efficiency of the GA-XGB hybrid model in modeling flood susceptibility in the Tafresh watershed is compared to KNN and XGB models, and the AUC in KNN, XGB, and GA-XGB models are 0.82, 0.85, and 0.87, respectively. So using the genetic algorithm as an optimizer for determining the best parameters in the XGB model increases efficiency in this model. The results of determining the relative importance of independent variables in flood susceptibility modeling showed that the independent variables considered in each model have different effects. Distance from road and distance from river in all three models had significant importance in modeling flood hazard in the Tafresh watershed. The optimization method in this study can be used as a powerful method in other spatial modeling studies. The results of this study also support management programs to reduce flood risks in the study area. (C)& nbsp;2022 COSPAR. Published by Elsevier B.V. All rights reserved. [Linh, Nguyen Thi Thuy] Thuyloi Univ, 175 Tay Son, Hanoi, Vietnam; [Pandey, Manish] Tarbiat Modares Univ TMU, Fac Nat Resource & Marine Sci, 14115-111, Tehran, Iran; [Pandey, Manish] Chandigarh Univ, Univ Ctr Res & Dev UCRD, Mohali 140413, Punjab, India; [Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115-111, Iran; [Bhunia, Gouri Sankar] TPF Gentisa Euroestudios SL Gurgaon, Gurgaon, Haryana, India; [Norouzi, Akbar] Shahrekord Univ, Fac Nat Resources & Earth Sci, Dept Nat Engn, shahrekord, Iran; [Norouzi, Akbar] East Azarbaijan Reg Water Co, Expert Water Resource Khoda Afarin Cty, Tabriz, Iran; [Ali, Shoaib] Northeast Agr Univ, Sch Water Conservancy & Civil Engn, Harbin 150030, Peoples R China; [Pham, Quoc Bao] Univ Siles Katowice, Inst Earth Sci, Fac Nat Sci, Bedzinska St 60, PL-41200 Sosnowiec, Poland; [Anh, Duong Tran] HUTECH Univ, Ward 25, Dien Bien Phu, 475A, Binh Thanh Dist, Ho Chi Minh City, Vietnam; [Ahmadi, Kourosh] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Forestry, 14115-111, Tehran, Iran Thuyloi University; Tarbiat Modares University; Chandigarh University; Tarbiat Modares University; Shahrekord University; Northeast Agricultural University - China; Ho Chi Minh City University of Technology (HUTECH); Vietnam National University Hochiminh City; Tarbiat Modares University Anh, DT (corresponding author), HUTECH Univ, Ward 25, Dien Bien Phu, 475A, Binh Thanh Dist, Ho Chi Minh City, Vietnam. ta.duong@hutech.edu.vn Tran Anh, Duong/M-6789-2019; Pandey, Manish/J-6825-2019; NTT, Linh/AAJ-2377-2020 Tran Anh, Duong/0000-0002-6775-0055; Pandey, Manish/0000-0001-8291-2043; NTT, Linh/0000-0001-8631-9941 123 8 8 6 6 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. MAY 1 2022.0 69.0 9 3301 3318 10.1016/j.asr.2022.02.027 0.0 MAR 2022 18 Engineering, Aerospace; Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences 0Y4FC 2023-03-23 WOS:000790346200007 0
J AlShorman, O; Masadeh, M; Bin Heyat, MB; Akhtar, F; Almahasneh, H; Ashraf, GM; Alexiou, A AlShorman, Omar; Masadeh, Mahmoud; Bin Heyat, Md Belal; Akhtar, Faijan; Almahasneh, Hossam; Ashraf, Ghulam Md; Alexiou, Athanasios Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection JOURNAL OF INTEGRATIVE NEUROSCIENCE English Article Automatic detection; Brain; Electroencephalogram; Fast fourier transform; Frontal lobe; Machine learning; Stress; University students DEVELOPMENTAL DYSLEXIA; BRAIN; CONNECTIVITY; DISORDER; SIGNAL; IMPACT Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical effects. Moreover, hormones, physiological effects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using different mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is appl ied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. After that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications. [AlShorman, Omar] Najran Univ, Coll Engn, Najran 55461, Saudi Arabia; [Masadeh, Mahmoud] Yarmouk Univ, Comp Engn Dept, Irbid 21163, Jordan; [Bin Heyat, Md Belal] Shenzhen Univ, Coll Comp Sci & Software Engn, IoT Res Ctr, Shenzhen 518060, Guangdong, Peoples R China; [Bin Heyat, Md Belal] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Sichuan, Peoples R China; [Bin Heyat, Md Belal; Alexiou, Athanasios] Novel Global Community Educ Fdn, Hebersham, NSW 2770, Australia; [Akhtar, Faijan] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China; [Almahasneh, Hossam] Dubai Taxi Corp, Dubai 2647, U Arab Emirates; [Ashraf, Ghulam Md] King Abdulaziz Univ, King Fahd Med Res Ctr, Preclin Res Unit, Jeddah 21589, Saudi Arabia; [Ashraf, Ghulam Md] King Abdulaziz Univ, Fac Appl Med Sci, Dept Med Lab Technol, Jeddah 21589, Saudi Arabia; [Alexiou, Athanasios] AFNP Med Austria, A-2770 Vienna, Austria Najran University; Yarmouk University; Shenzhen University; University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; King Abdulaziz University; King Abdulaziz University Masadeh, M (corresponding author), Yarmouk Univ, Comp Engn Dept, Irbid 21163, Jordan.;Bin Heyat, MB (corresponding author), Shenzhen Univ, Coll Comp Sci & Software Engn, IoT Res Ctr, Shenzhen 518060, Guangdong, Peoples R China.;Bin Heyat, MB (corresponding author), Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Sichuan, Peoples R China.;Bin Heyat, MB; Alexiou, A (corresponding author), Novel Global Community Educ Fdn, Hebersham, NSW 2770, Australia.;Alexiou, A (corresponding author), AFNP Med Austria, A-2770 Vienna, Austria. mahmoud.s@yu.edu.jo; belalheyat@gmail.com; alextha@yahoo.gr Bin Heyat, Belal/AAT-8607-2021; Alexiou, Athanasios/AAT-8491-2021; Ashraf, Ghulam Md/H-9485-2012 Bin Heyat, Belal/0000-0001-5307-9582; Alexiou, Athanasios/0000-0002-2206-7236; Ashraf, Ghulam Md/0000-0002-9820-2078; Masadeh, Mahmoud Saleh/0000-0001-7447-1276 Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia [KEP-1-141-41]; DSR Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia; DSR This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. KEP-1-141-41. The authors, therefore, acknowledge with thanks DSR technical and financial support. 72 7 7 11 15 IMR PRESS ROBINSON 112 ROBINSON RD, ROBINSON, SINGAPORE 0219-6352 1757-448X J INTEGR NEUROSCI J. Integr. Neurosci. JAN 28 2022.0 21.0 1 10.31083/j.jin2101020 0.0 11 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 0G6EN 35164456.0 gold 2023-03-23 WOS:000778136200017 0
J Fang, PJ; Cai, YF; Chen, L; Wang, H; Li, YC; Sotelo, MA; Li, ZX Fang, Peijun; Cai, Yingfeng; Chen, Long; Wang, Hai; Li, Yicheng; Sotelo, Miguel Angel; Li, Zhixiong A high-performance neural network vehicle dynamics model for trajectory tracking control PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING English Article; Early Access Autonomous driving; vehicle dynamics modeling; long-short term memory neural network; trajectory tracking control; simulation analysis PREDICTIVE CONTROL; SYSTEM Traditional models of vehicle dynamics engineered from physical principles are usually simplified and assumed, resulting in the model cannot accurately reflect the actual dynamic characteristics of the vehicle under some working conditions, affecting the control accuracy and even safety. In view of this, inspired by the single-track model, this paper uses the data-driven methods to establish a new high-performance time-delay feedback neural network vehicle dynamics model. The feedback connection of a network can describe complex dynamics. The multi-time-step input of the state and control can include highly nonlinear and strong coupling characteristics of a vehicle. The test results of modeling accuracy show that the proposed model can achieve higher vehicle dynamics prediction accuracy than nonlinear vehicle model. Different from the traditional vehicle dynamics model, the proposed model has long-term memory cells, which can implicitly predict coefficient of friction and can be applied to different road conditions. Then, the trajectory tracking control algorithm is designed based on the proposed vehicle model. According to the steady-state steering assumption, the feedforward front wheel steering angle is calculated, and the steady-state sideslip angle is integrated into the steering feedback according to the reference path to realize the reference trajectory tracking control. Finally, Simulink/CarSim is used to conduct the simulation analysis under the double lane change conditions to evaluate the proposed control algorithm. The analysis results show that the control algorithm based on the proposed model can achieve an accurate tracking control effect of a vehicle at medium and high speeds, providing high-accuracy track tracking and good lateral stability of intelligent vehicles. [Fang, Peijun; Cai, Yingfeng; Chen, Long; Li, Yicheng] Jiangsu Univ, Automot Engn Res Inst, 301 Xuefu Rd, Zhenjiang 212013, Jiangsu, Peoples R China; [Wang, Hai] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang, Jiangsu, Peoples R China; [Sotelo, Miguel Angel] Univ Alcala, Dept Comp Engn, Alcala De Henares, Madrid, Spain; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea Jiangsu University; Jiangsu University; Universidad de Alcala; Yonsei University Cai, YF (corresponding author), Jiangsu Univ, Automot Engn Res Inst, 301 Xuefu Rd, Zhenjiang 212013, Jiangsu, Peoples R China. caicaixiao0304@126.com National Natural Science Foundation of China [U20A20333, 61906076, 51875255]; Key Research and Development Program of Jiangsu Province [BE2019010-2, BE2020083-2, BE2020083-3]; Australia ARC DECRA [DE190100931]; Natural Science Foundation of Jiangsu Province [BK20190853]; China Postdoctoral Science Foundation [2020T130258] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Jiangsu Province; Australia ARC DECRA(Australian Research Council); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by the National Natural Science Foundation of China (U20A20333, 61906076, 51875255), Key Research and Development Program of Jiangsu Province (BE2019010-2, BE2020083-2, BE2020083-3), Australia ARC DECRA (DE190100931), Natural Science Foundation of Jiangsu Province (BK20190853), China Postdoctoral Science Foundation (2020T130258). 36 1 1 16 24 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0954-4070 2041-2991 P I MECH ENG D-J AUT Proc. Inst. Mech. Eng. Part D-J. Automob. Eng. 9544070221095660 10.1177/09544070221095660 0.0 MAY 2022 15 Engineering, Mechanical; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 0Z9AR 2023-03-23 WOS:000791362800001 0
J Jida, SN; Hetet, JF; Chesse, P; Guadie, A Jida, Solomon Neway; Hetet, Jean-Francois; Chesse, Pascal; Guadie, Awoke Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia JOURNAL OF ENVIRONMENTAL SCIENCES English Article Addis Ababa; Artificial neural network; PM prediction; Roadside emission AIR-QUALITY; TRAFFIC EMISSIONS; POLLUTION; PERFORMANCE; HEALTH; ENVIRONMENT; PREDICTION; PM2.5 Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM2.5) and coarse particle (PM10) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM2.5 concentration is 13%-144% and 58%-241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM10 results also exceeded the AQI (54%-65%) and WHO (8%-395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM2.5 and PM10 exhaust emission correlation coefficient were predicted to be (R-2 = 0.775) and (R-2 = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM2.5 (R-2 = 0.943) and PM10 (R-2 = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings. (C) 2020 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. [Jida, Solomon Neway; Hetet, Jean-Francois; Chesse, Pascal] Ecole Cent Nantes, Res Lab Hydrodynam Energet & Atmospher Environm L, F-44300 Nantes, France; [Jida, Solomon Neway] Arba Minch Univ, Fac Mech & Prod Engn, Inst Technol, Dept Mech Engn, Arba Minch 21, Ethiopia; [Guadie, Awoke] Chinese Acad Sci, Key Lab Environm Biotechnol, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China; [Guadie, Awoke] Arba Minch Univ, Coll Nat Sci, Dept Biol, Arba Minch 21, Ethiopia Nantes Universite; Ecole Centrale de Nantes; Arba Minch University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Arba Minch University Jida, SN (corresponding author), Ecole Cent Nantes, Res Lab Hydrodynam Energet & Atmospher Environm L, F-44300 Nantes, France.;Jida, SN (corresponding author), Arba Minch Univ, Fac Mech & Prod Engn, Inst Technol, Dept Mech Engn, Arba Minch 21, Ethiopia. solomonnewayjida@gmail.com Guadie, Awoke/AAF-7252-2021 Jida, Solomon Neway/0000-0002-2607-6551; Guadie, Awoke/0000-0003-2753-4216 59 6 6 2 42 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1001-0742 1878-7320 J ENVIRON SCI J. Environ. Sci. MAR 2021.0 101.0 428 439 10.1016/j.jes.2020.08.018 0.0 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology PF2TY 33334536.0 Green Published, Bronze 2023-03-23 WOS:000598914600035 0
J Adhikari, P; Jawad, B; Rao, P; Podgornik, R; Ching, WY Adhikari, Puja; Jawad, Bahaa; Rao, Praveen; Podgornik, Rudolf; Ching, Wai-Yim Delta Variant with P681R Critical Mutation Revealed by Ultra-Large Atomic-Scale Ab Initio Simulation: Implications for the Fundamentals of Biomolecular Interactions VIRUSES-BASEL English Article SARS-CoV-2; spike-protein; delta variant; interatomic interaction; amino-acid-amino-acid bond pair; machine learning ELECTRONIC POPULATION ANALYSIS; SPIKE-PROTEIN; RECEPTOR-BINDING; CHARGE-DISTRIBUTION; SARS-COV-2; RECOMBINATION; MECHANISMS; NETWORK; VIRUSES; DOMAIN The SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing for the virus to invade human cells much faster and with an increased efficiency. In particular, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain, including the effect of double mutation, and compare the results with the wild type. We have recently developed a method of calculating the amino-acid-amino-acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant finding is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations. [Adhikari, Puja; Jawad, Bahaa; Ching, Wai-Yim] Univ Missouri, Dept Phys & Astron, Kansas City, MO 64110 USA; [Jawad, Bahaa] Univ Technol Baghdad, Dept Appl Sci, Baghdad 10066, Iraq; [Rao, Praveen] Univ Missouri, Dept Hlth Management & Informat, Dept Elect Engn & Comp Sci, Columbia, MO 65212 USA; [Podgornik, Rudolf] Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China; [Podgornik, Rudolf] Univ Chinese Acad Sci, Kavli Inst Theoret Sci, Beijing 100049, Peoples R China; [Podgornik, Rudolf] Chinese Acad Sci, Inst Phys, CAS Key Lab Soft Matter Phys, Beijing 100090, Peoples R China; [Podgornik, Rudolf] Univ Chinese Acad Sci, Wenzhou Inst, Wenzhou 325000, Peoples R China; [Podgornik, Rudolf] Univ Ljubljana, Fac Math & Phys, Dept Phys, SI-1000 Ljubljana, Slovenia University of Missouri System; University of Missouri Kansas City; University of Technology- Iraq; University of Missouri System; University of Missouri Columbia; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Ljubljana Ching, WY (corresponding author), Univ Missouri, Dept Phys & Astron, Kansas City, MO 64110 USA. paz67@umkc.edu; bajrmd@mail.umkc.edu; praveen.rao@missouri.edu; rudipod@gmail.com; chingw@umkc.edu Jawad, Bahaa/GNM-5385-2022; Ching, Wai-Yim/B-4686-2009; Podgornik, Rudolf/C-6209-2008 Jawad, Bahaa/0000-0001-6252-3999; Ching, Wai-Yim/0000-0001-7738-8822; Podgornik, Rudolf/0000-0002-3855-4637 National Science Foundation of USA [RAPID DMR/CMMT2028803, RAPID CISE/CNS2034247] National Science Foundation of USA(National Science Foundation (NSF)) This project is funded partly by the National Science Foundation of USA: RAPID DMR/CMMT2028803. P.R. was partly funded by the National Science Foundation of USA: RAPID CISE/CNS2034247. 81 4 4 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4915 VIRUSES-BASEL Viruses-Basel MAR 2022.0 14.0 3 465 10.3390/v14030465 0.0 19 Virology Science Citation Index Expanded (SCI-EXPANDED) Virology 0B0BW 35336872.0 gold, Green Submitted, Green Accepted 2023-03-23 WOS:000774310300001 0
J Guan, FQ; Sun, Y; Qi, XZ; Hu, Y; Yu, G; Zhang, JW Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying; Yu, Gang; Zhang, Jianwei State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation SENSORS English Article pedicle drilling; acoustic emission signal; frequency distribution; neural network SIGNAL ANALYSIS; AUDIO SIGNAL; SYSTEM; FIXATION; SPINE; PARAMETERS; SURGERY; DAMAGE; ROBOT Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation. [Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Guan, Fengqing; Sun, Yu; Yu, Gang] Harbin Inst Technol Shenzhen, Shenzhen 518052, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Dept Informat, D-22527 Hamburg, Germany Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Harbin Institute of Technology; University of Hamburg Qi, XZ; Hu, Y (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China. fq.guan@siat.ac.cn; yu.sun@siat.ac.cn; xz.qi@siat.ac.cn; ying.hu@siat.ac.cn; gangyu@hit.edu.cn; zhang@informatik.uni-hamburg.de Hu, Ying/GQZ-8100-2022; zhang, jian/HPD-1712-2023 Xiaozhi, Qi/0000-0001-6010-3974 National Natural Science Foundation of China [U1613224, U1713221, 61573336, 61603374]; Shenzhen Fundamental Research Funds [JCYJ20150529143500954, JCYJ20160428144135222, JCYJ20160608153218487, JCYJ20170307170252420]; China Postdoctoral Science Foundation [2018M631003]; Shenzhen Key Laboratory Project [ZDSYS201707271637577] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Fundamental Research Funds; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Shenzhen Key Laboratory Project This work is financially supported by the National Natural Science Foundation of China (Grant Nos. U1613224, U1713221, 61573336 and 61603374), in part by Shenzhen Fundamental Research Funds (Grant Nos. JCYJ20150529143500954, JCYJ20160428144135222, JCYJ20160608153218487 and JCYJ20170307170252420), and in part by China Postdoctoral Science Foundation (Grant No. 2018M631003) and Shenzhen Key Laboratory Project (Grant No. ZDSYS201707271637577). 28 11 11 8 25 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2018.0 18.0 5 1484 10.3390/s18051484 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GJ7PP 29747395.0 gold, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000435580300183 0
J Elshafei, B; Pena, A; Xu, D; Ren, J; Badger, J; Pimenta, FM; Giddings, D; Mao, XR Elshafei, Basem; Pena, Alfredo; Xu, Dong; Ren, Jie; Badger, Jake; Pimenta, Felipe M.; Giddings, Donald; Mao, Xuerui A hybrid solution for offshore wind resource assessment from limited onshore measurements APPLIED ENERGY English Article Artificial neural network; Gaussian process regression; Spatiotemporal data fusion; Wind resource assessment In wind resource assessments, which are critical to the pre-construction of wind farms, measurements by LiDARs or masts are a source of high-fidelity data, but are expensive and scarce in space and time, particularly for offshore sites. On the other hand, numerical simulations, using for example the Weather Research and Forecasting (WRF) model, generate temporally and spatially continuous data with relatively low-fidelity. A hybrid approach is proposed here to combine the merit of measurements and simulations for the assessment of offshore wind. Firstly a temporal data fusion using deep Multi Fidelity Gaussian Process Regression (MFGPR) is performed to combine the intermittent measurement and the continuous simulation data at an onshore location. Then a spatial data fusion using a neural network with Non-linear Autoregression (NAR) and Nonlinear Autoregression with external input (NARX) are conducted to project the wind from onshore to offshore. The numerical and measured wind speeds along the west coast of Denmark were used to evaluate the method. We show that the proposed data fusion technique using a gappy onshore measurement results in accurate offshore wind resource assessment within a 2% margin error. [Elshafei, Basem; Ren, Jie; Giddings, Donald; Mao, Xuerui] Univ Nottingham, Fac Engn, Nottingham NG7 2RD, England; [Pena, Alfredo; Badger, Jake] Tech Univ Denmark, DTU Wind Energy, Roskilde, Denmark; [Xu, Dong] Xinjiang Goldwind Sci & Technol Co Ltd, Urumqi, Peoples R China; [Pimenta, Felipe M.] Univ Fed Santa Catarina, Ctr Ciencias Fis & Matemat, Campus Trindade, BR-88010970 Florianopolis, SC, Brazil University of Nottingham; Technical University of Denmark; Universidade Federal de Santa Catarina (UFSC) Mao, XR (corresponding author), Univ Nottingham, Fac Engn, Nottingham NG7 2RD, England. xuerui.mao@nottingham.ac.uk Pimenta, Felipe Mendonça/D-7050-2012; Peña, Alfredo/AFF-8214-2022 Pimenta, Felipe Mendonça/0000-0002-0748-9182; Peña, Alfredo/0000-0002-7900-9651; Ren, Jie/0000-0001-8448-4361; Mao, Xuerui/0000-0002-8452-5773; Badger, Jake/0000-0001-7360-2019 European Union [777717, 828799] European Union(European Commission) This project has received funding from the European Union Horizon 2020 research and innovation programme under the Marie SkodowskaCurie grant agreement No 777717 and the future and emerging technologies programme with agreement No. 828799. 22 2 2 2 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy SEP 15 2021.0 298.0 117245 10.1016/j.apenergy.2021.117245 0.0 JUN 2021 9 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering TM9HP 2023-03-23 WOS:000675858100005 0
J Pham, BT; Phong, TV; Nguyen, HD; Qi, CC; Al-Ansari, N; Amini, A; Ho, LS; Tuyen, TT; Yen, HPH; Ly, HB; Prakash, I; Bui, DT Binh Thai Pham; Tran Van Phong; Huu Duy Nguyen; Qi, Chongchong; Al-Ansari, Nadhir; Amini, Ata; Lanh Si Ho; Tran Thi Tuyen; Hoang Phan Hai Yen; Hai-Bang Ly; Prakash, Indra; Dieu Tien Bui A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naive Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping WATER English Article flash flood; kernel logistic regression; radial basis function network; multinomial naive Bayes; logistic model tree; machine learning; Vietnam MACHINE LEARNING-METHODS; LANDSLIDE SUSCEPTIBILITY; RISK-ASSESSMENT; SPATIAL PREDICTION; RANDOM FOREST; HAZARD; AREA; GROUNDWATER; ALGORITHMS; VULNERABILITY Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983-0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management. [Binh Thai Pham; Hai-Bang Ly] Univ Transport Technol, Hanoi 100000, Vietnam; [Tran Van Phong] Vietnam Acad Sci & Technol, Inst Geol Sci, 84 Chua Lang St, Hanoi 100000, Vietnam; [Huu Duy Nguyen] VNU Univ Sci, Fac Geog, 334 Nguyen Trai, Hanoi 100000, Vietnam; [Qi, Chongchong] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China; [Al-Ansari, Nadhir] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Amini, Ata] AREEO, Kurdistan Agr & Nat Resources Res & Educ Ctr, Sanandaj 6617715175, Iran; [Lanh Si Ho] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Tran Thi Tuyen] Vinh Univ, Sch Agr & Resources, Dept Resource & Environm Management, Nghe An 470000, Vietnam; [Hoang Phan Hai Yen] Vinh Univ, Sch Social Educ, Dept Geog, Nghe An 470000, Vietnam; [Prakash, Indra] Govt Gujarat, Dept Sci & Technol, Bhaskarcharya Inst Space Applicat & GeoInformat, Gandhinagar 382002, India; [Dieu Tien Bui] Univ South Eastern Norway, Dept Business & IT, Geog Informat Syst Grp, N-3674 Notodden, Norway Vietnam Academy of Science & Technology (VAST); Vietnam National University Hanoi; Central South University; Lulea University of Technology; Duy Tan University; Vinh University; Vinh University; University College of Southeast Norway Al-Ansari, N (corresponding author), Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden.;Ho, LS (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam.;Yen, HPH (corresponding author), Vinh Univ, Sch Social Educ, Dept Geog, Nghe An 470000, Vietnam. binhpt@utt.edu.vn; tphong1617@gmail.com; huuduy151189@gmail.com; chongchong.qi@gmail.com; nadhir.alansari@ltu.se; a.amini@areeo.ac.ir; hosilanh@duytan.edu.vn; ttt.dhv@gmail.com; yenhphdhv@gmail.com; banglh@utt.edu.vn; indra52prakash@gmail.com; dieu.t.bui@usn.no Qi, Chongchong/K-1167-2019; PHAM, BINH THAI/H-8316-2018; Amini, Ata/S-1629-2016; Prakash, Indra/M-8265-2017 Qi, Chongchong/0000-0001-5189-1614; PHAM, BINH THAI/0000-0001-9707-840X; Amini, Ata/0000-0001-9358-185X; Prakash, Indra/0000-0002-4309-0187; Al-Ansari, Nadhir/0000-0002-6790-2653 Vinh University, Vietnam in Nghe An Province, Vietnam Vinh University, Vietnam in Nghe An Province, Vietnam This study was financially supported by the research fund of Vinh University, Vietnam in Nghe An Province, Vietnam. 110 49 50 11 39 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water JAN 2020.0 12.0 1 239 10.3390/w12010239 0.0 21 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources KU6TM gold, Green Published 2023-03-23 WOS:000519847200239 0
J Xiao, HX; Zhang, F; He, AS; Liu, P; Yan, F; Miao, LJ; Yang, ZP Xiao, Haixia; Zhang, Feng; He, Ianshan; Liu, Pu; Yan, Fei; Miao, Lijuan; Yang, Zhipeng Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning EARTH AND SPACE SCIENCE English Article classification; ice crystals; deep convolutional neural network; ice clouds; transfer learning OPTICAL-PROPERTIES; RETRIEVAL; GROWTH Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single-scattering properties of ice crystal particles. The single-scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked-eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL-ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL-ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high-precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. [Xiao, Haixia; Zhang, Feng; Liu, Pu; Yang, Zhipeng] Nanjing Univ Informat Sci & Technol, CIC FEMD, Minist Educ KLME, Key Lab Meteorol Disaster, Nanjing, Jiangsu, Peoples R China; [He, Ianshan] Shanghai Meteorol Serv, Shanghai Key Lab Meteorol & Hlth, Shanghai, Peoples R China; [Yan, Fei] Hebei Prov Weather Modificat Off, Wuhan, Hubei, Peoples R China; [Miao, Lijuan] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Sch Geog Sci, Nanjing, Jiangsu, Peoples R China; [Miao, Lijuan] Leibniz Inst Agr Dev Transit Econ, Dept Struct Dev Farms & Rural Areas, Halle, Germany Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Leibniz Institut fur Agrarentwicklung in Transformationsokonomien (IAMO) Zhang, F (corresponding author), Nanjing Univ Informat Sci & Technol, CIC FEMD, Minist Educ KLME, Key Lab Meteorol Disaster, Nanjing, Jiangsu, Peoples R China. fengzhang@nuist.edu.cn , IAMO/G-2328-2012; Change, Structural/W-8306-2019 , IAMO/0000-0001-7922-9665; Change, Structural/0000-0002-2459-4646; Xiao, Haixia/0000-0001-8680-9086 National Key R&D Program of China [2018YFC1507002]; National Natural Science Foundation of China [41675003, 91637101] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is financially supported by the National Key R&D Program of China (2018YFC1507002) and National Natural Science Foundation of China (41675003 and 91637101). Our database is available online at this site (https://doi.org/10.7910/DVN/517LJZ). 33 22 22 3 25 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2333-5084 EARTH SPACE SCI Earth Space Sci. OCT 2019.0 6.0 10 1877 1886 10.1029/2019EA000636 0.0 OCT 2019 10 Astronomy & Astrophysics; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Geology JO1ZX gold, Green Submitted 2023-03-23 WOS:000490458100001 0
J Wang, X; Wang, MZ; Liu, XJ; Zhu, LT; Shi, SY; Glade, T; Chen, MZ; Xie, YJ; Wu, YG; He, YF Wang, Xing; Wang, Meizhen; Liu, Xuejun; Zhu, Litao; Shi, Shuaiyi; Glade, Thomas; Chen, Mingzheng; Xie, Yujia; Wu, Yiguang; He, Yufeng Near-infrared surveillance video-based rain gauge JOURNAL OF HYDROLOGY English Article Rainfall observation; Surveillance camera; Near-infrared light video; All-weather; Convolutional neural network GEOMETRICAL-OPTICS APPROXIMATION; SIZE DISTRIBUTION; SCATTERING; SHAPE; MODEL; CLOUD Widespread surveillance cameras that continuously record rainfall information offer an opportunity for high-spatiotemporal resolution rainfall inversion. Surveillance video-based rainfall data estimation has become one of the most promising methods. However, existing relevant studies have focused on rainfall observations during the day. Little attention has been given to developing a surveillance camera-based rain gauge that works at night. Generally, ordinary surveillance cameras actively emit near-infrared light (NIR) to supplement the insufficient illumination of a surveillance scenario, providing an essential prerequisite for rainfall observations at night. In this paper, an NIR-surveillance video-based rain gauge (NIR-VRG) was constructed. First, combining the mete-orological and microphysical characteristics of raindrops with the camera imaging principle, the abilities of different NIR-surveillance cameras to capture raindrops during different rainfall scenarios were discussed, providing the theoretical basis for subsequent work; second, a tensor-based algorithm was proposed for rain streak extraction from NIR surveillance video; and finally, a one-dimensional convolutional neural network (1D CNN)-based regression algorithm was proposed and used to build a mapping relationship between the extracted rain streaks and the rainfall intensity (RI). Experimental results on synthetic rainy videos showed that the proposed rain streak extraction algorithm achieves robust performance in a light breeze (speed approximately 3 m/s) and still works in a gentle breeze (speed approximately 5 m/s). Moreover, experiments during various rainfall scenarios show that the designed NIR-VRG measures rainfall information with high accuracy. The relative error of the RI and cumulative rainfall ranged from 8.86 % to 84.84 % and 7.82 % to 30.70 % respectively. The NIR-VRG fills the gap of rainfall observations at night by using surveillance cameras and provides a reference for constructing an all-weather rainfall observation network. [Wang, Xing; Wang, Meizhen; Liu, Xuejun; Zhu, Litao; Chen, Mingzheng; Wu, Yiguang; He, Yufeng] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China; [Wang, Xing; Wang, Meizhen; Liu, Xuejun; Zhu, Litao; Chen, Mingzheng; Wu, Yiguang; He, Yufeng] State Key Lab Cultivat Base Geog Environm Evolut J, Nanjing 210023, Peoples R China; [Wang, Xing; Wang, Meizhen; Liu, Xuejun; Zhu, Litao; Chen, Mingzheng; Wu, Yiguang; He, Yufeng] Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China; [Shi, Shuaiyi] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China; [Wang, Xing; Glade, Thomas] Univ Vienna, Dept Geog & Reg Res, A-1010 Vienna, Austria; [Xie, Yujia] Nanjing Univ Finance & Econ, Coll Informat Engn, Nanjing 210023, Jiangsu, Peoples R China; [Wang, Meizhen] Nanjng Normal Univ, Sch Geog, Nanjng, Peoples R China Nanjing Normal University; Chinese Academy of Sciences; University of Vienna; Nanjing University of Finance & Economics Wang, MZ (corresponding author), Nanjng Normal Univ, Sch Geog, Nanjng, Peoples R China. wangmeizhen@njnu.edu.cn He, Yufeng/0000-0002-7411-5981 National Key R&D Program of China [2021YFE0112300]; National Natural Science Foundation of China (NSFC) [41771420, 41801305, 41971364]; State Scholarship Fund from the China Scholarship Council (CSC) [202006860047, 201906860035]; Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX20_1180]; Priority Academic Program Development of Jiangsu Higher Education Institutions [164320H116] National Key R&D Program of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); State Scholarship Fund from the China Scholarship Council (CSC); Postgraduate Research and Practice Innovation Program of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions This research was funded by the National Key R & D Program of China (2021YFE0112300) , the National Natural Science Foundation of China (NSFC) (No. 41771420, No. 41801305 and No. 41971364) , the State Scholarship Fund from the China Scholarship Council (CSC) (No.202006860047 and No.201906860035) , the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX20_1180) , and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No. 164320H116) . 64 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. MAR 2023.0 618.0 129173 10.1016/j.jhydrol.2023.129173 0.0 FEB 2023 21 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources 9A8AR 2023-03-23 WOS:000934274600001 0
J Winkel, DJ; Tong, A; Lou, B; Kamen, A; Comaniciu, D; Disselhorst, JA; Rodriguez-Ruiz, A; Huisman, H; Szolar, D; Shabunin, I; Choi, MH; Xing, PY; Penzkofer, T; Grimm, R; von Busch, H; Boll, DT Winkel, David J.; Tong, Angela; Lou, Bin; Kamen, Ali; Comaniciu, Dorin; Disselhorst, Jonathan A.; Rodriguez-Ruiz, Alejandro; Huisman, Henkjan; Szolar, Dieter; Shabunin, Ivan; Choi, Moon Hyung; Xing, Pengyi; Penzkofer, Tobias; Grimm, Robert; von Busch, Heinrich; Boll, Daniel T. A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate Results of a Multireader, Multicase Study INVESTIGATIVE RADIOLOGY English Article prostatic neoplasms; deep learning; computer-aided diagnosis; magnetic resonance imaging CANCER; GRADE Objective The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. Materials and Methods We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss kappa), and average reading time without DL-CAD assistance were evaluated. Results The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS >= 4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss kappa) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS >= 3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). Conclusions Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time. [Winkel, David J.; Boll, Daniel T.] Univ Hosp Basel, Dept Radiol, CH-4011 Basel, Switzerland; [Tong, Angela] NYU Langone Hlth, Dept Radiol, New York, NY USA; [Lou, Bin; Kamen, Ali; Comaniciu, Dorin] Siemens Healthineers, Digital Technol & Innovat, Princeton, NJ USA; [Disselhorst, Jonathan A.] Siemens Healthcare AG Adv Clin Imaging Technol, Lausanne, Vaud, Switzerland; [Rodriguez-Ruiz, Alejandro] ScreenPoint Med, Nijmegen, Netherlands; [Huisman, Henkjan] Radboud Univ Nijmegen, Dept Radiol, Med Ctr, Nijmegen, Netherlands; [Szolar, Dieter] Diagnostikum Graz, Graz, Austria; [Shabunin, Ivan] Patero Clin, Moscow, Russia; [Choi, Moon Hyung] Catholic Univ Korona, Eunpyeong St Marys Hosp, Seoul, South Korea; [Xing, Pengyi] Changhai Hosp Shanghai, Radiol Dept, Shanghai, Peoples R China; [Penzkofer, Tobias] Charite Univ Med Berlin, Berlin, Germany; [Penzkofer, Tobias] Berlin Inst Hlth, Berlin, Germany; [Grimm, Robert; von Busch, Heinrich] Siemens Healthineers Diagnost Imaging, Erlangen, Germany University of Basel; NYU Langone Medical Center; Siemens AG; Radboud University Nijmegen; Naval Medical University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Berlin Institute of Health Winkel, DJ (corresponding author), Univ Hosp Basel, Dept Radiol, CH-4011 Basel, Switzerland. davidjean.winkel@usb.ch xing, peng/HDM-6843-2022; Penzkofer, Tobias/AFN-1192-2022 Penzkofer, Tobias/0000-0001-9591-8575; Tong, Angela/0000-0002-4733-3414 Swiss Society of Radiology; Research Fund Junior Researchers of the University Hospital Basel [3MS1034] Swiss Society of Radiology; Research Fund Junior Researchers of the University Hospital Basel D.J.W. receives research support from the Swiss Society of Radiology and the Research Fund Junior Researchers of the University Hospital Basel (grant no. 3MS1034). 39 24 24 3 13 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA 0020-9996 1536-0210 INVEST RADIOL Invest. Radiol. OCT 2021.0 56.0 10 605 613 10.1097/RLI.0000000000000780 0.0 9 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging US1SZ 33787537.0 Green Published 2023-03-23 WOS:000697217100001 0
J Hussain, S; Keung, J; Khan, AA; Ahmad, A; Cuomo, S; Piccialli, F; Jeon, G; Akhunzada, A Hussain, Shahid; Keung, Jacky; Khan, Arif Ali; Ahmad, Awais; Cuomo, Salvatore; Piccialli, Francesco; Jeon, Gwanggil; Akhunzada, Adnan Implications of deep learning for the automation of design patterns organization JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING English Article Design patterns; Deep learning; Feature set; Performance; Classifiers FEATURE-SELECTION; INFORMATION GAIN; ALGORITHM; MODELS Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier's performance in terms of organization of design patterns. (C) 2017 Elsevier Inc. All rights reserved. [Hussain, Shahid; Keung, Jacky; Khan, Arif Ali] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Ahmad, Awais] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan, South Korea; [Cuomo, Salvatore; Piccialli, Francesco] Univ Naples Federico II, Naples, Italy; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea; [Akhunzada, Adnan] Comsats Inst Informat Technol, Islamabad, Pakistan City University of Hong Kong; Yeungnam University; University of Naples Federico II; Incheon National University; COMSATS University Islamabad (CUI) Ahmad, A (corresponding author), Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan, South Korea.;Jeon, G (corresponding author), Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea. Shussain7-c@my.cityu.edu.hk; jacky.keung@cityu.edu.hk; aliakhan2-c@my.cityu.edu.hk; aahmad.marwat@gmail.com; salvatore.cuomo@unina.it; francesco.piccialli@unina.it; gjeon@inu.ac.kr; a.qureshi@comsats.edu.pk Akhunzada, Adnan/N-7917-2017; Keung, Jacky/ABA-7089-2020; Khan, Arif Ali/ABG-2862-2020; Hussain, Shahid/AAP-5065-2021; Ahmad, Awais/AAA-4504-2019; Piccialli, Francesco/ABC-2457-2020; khan, Arif/HMV-3165-2023; Cuomo, Salvatore/Q-1365-2016; Keung, Jacky/X-4693-2019 Akhunzada, Adnan/0000-0001-8370-9290; Khan, Arif Ali/0000-0002-8479-1481; Hussain, Shahid/0000-0003-4826-3339; Piccialli, Francesco/0000-0002-5179-2496; Cuomo, Salvatore/0000-0003-4128-2588; Keung, Jacky/0000-0002-3803-9600 General Research Fund of the Research Grants Council of Hong Kong [11208017, 11214116]; research funds of City University of Hong Kong [7004683, 7004474]; NRF of Korea Grant - Korean Government [2015R1D1A1A01058171] General Research Fund of the Research Grants Council of Hong Kong; research funds of City University of Hong Kong; NRF of Korea Grant - Korean Government This work is supported in part by the General Research Fund of the Research Grants Council of Hong Kong (No. 11208017 and 11214116), the research funds of City University of Hong Kong (No. 7004683 and 7004474), and the NRF of Korea Grant funded by the Korean Government (2015R1D1A1A01058171). 44 24 24 0 16 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0743-7315 1096-0848 J PARALLEL DISTR COM J. Parallel Distrib. Comput. JUL 2018.0 117.0 256 266 10.1016/j.jpdc.2017.06.022 0.0 11 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GG7UN 2023-03-23 WOS:000432903500022 0
C Wang, SS; Chen, ZX; Yan, QB; Ji, K; Wang, L; Yang, B; Conti, M IEEE Wang, Shanshan; Chen, Zhenxiang; Yan, Qiben; Ji, Ke; Wang, Lin; Yang, Bo; Conti, Mauro Deep and Broad Learning based Detection of Android Malware via Network Traffic 2018 IEEE/ACM 26TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS) English Proceedings Paper 26th IEEE/ACM International Symposium on Quality of Service (IWQoS) JUN 04-06, 2018 Banff, CANADA IEEE,ACM,IEEE Commun Soc In recent years, the scale and diversity of malicious software on mobile networks are constantly increasing, thereby causing considerable danger to users' property and personal privacy. In this study, we devise a method that uses the URLs visited by applications to identify malicious apps. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of the input automatically and distribute soft attention weights to focus on different features of input. Multiple views preserve rich semantic information from input for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method has a certain timeliness. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance. [Wang, Shanshan; Chen, Zhenxiang; Ji, Ke; Wang, Lin; Yang, Bo] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Shandong, Peoples R China; [Yan, Qiben] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA; [Conti, Mauro] Univ Padua, Dept Math, Padua, Italy University of Jinan; University of Nebraska System; University of Nebraska Lincoln; University of Padua Chen, ZX (corresponding author), Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Shandong, Peoples R China. czx@ujn.edu.cn Yan, Qiben/AAZ-3002-2020; Conti, Mauro/F-9145-2012 Yan, Qiben/0000-0001-6272-7668; Conti, Mauro/0000-0002-3612-1934 National Natural Science Foundation of China [61672262, 61573166, 61472164, 61572230, 61702218, 61702216]; Natural Science Foundation of Shandong Province [ZR2012FM010, ZR2017BF001]; Shandong Provincial Key RD Program [2016GGX101001]; CERNET Next Generation Internet Technology Innovation Project [NGII20160404]; NSF [CNS-1566388] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Shandong Provincial Key RD Program; CERNET Next Generation Internet Technology Innovation Project; NSF(National Science Foundation (NSF)) This work was supported by the National Natural Science Foundation of China under Grants No. 61672262, No. 61573166, No. 61472164, No. 61572230, No. 61702218 and No. 61702216, the Natural Science Foundation of Shandong Province under Grants No. ZR2012FM010 and No. ZR2017BF001, the Shandong Provincial Key R&D Program under Grant No. 2016GGX101001, CERNET Next Generation Internet Technology Innovation Project under Grant No. NGII20160404. This work is also supported in part by NSF grant CNS-1566388. 16 8 8 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-2542-2 2018.0 6 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM2RH 2023-03-23 WOS:000461354300024 0
C Zhang, XC; Li, C; Chen, KL; Chrysostomou, D; Yang, HJ IEEE Comp Soc Zhang, Xiaochun; Li, Chen; Chen, Kuan-Lin; Chrysostomou, Dimitrios; Yang, Hongji Stock Prediction with Stacked-LSTM Neural Networks 2021 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2021) IEEE International Conference on Software Quality, Reliability and Security Companion English Proceedings Paper 21st IEEE International Conference on Software Quality, Reliability and Security (QRS) DEC 06-10, 2021 Hainan, PEOPLES R CHINA IEEE,IEEE Comp Soc Stacked Long Short Term Memory; Deep Learning; Time Series; Over-fitting TIME-SERIES This paper explores a stacked long-teen and short-term memory (LSTM) model for non-stationary financial time series in stock price prediction. The proposed LSTM is designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, build time series with different days for network input, and then add early-stopping, rectified linear units (Relu) activation function to avoid over-fitting during the training stage. Finally, save trained parameters state and new batch size for testing. The results suggest that the developed stacked LSTM produces better predictive power and generalization. [Zhang, Xiaochun] Anhui Univ Financial & Econ, Sch Management Sci & Engn, Hefei, Peoples R China; [Li, Chen; Chen, Kuan-Lin; Chrysostomou, Dimitrios] Aalborg Univ, Dept Mat & Prod, Aalborg, Denmark; [Yang, Hongji] Univ Leicester, Sch Comp & Math Sci, Leicester, Leics, England Anhui University of Finance & Economics; Aalborg University; University of Leicester Zhang, XC (corresponding author), Anhui Univ Financial & Econ, Sch Management Sci & Engn, Hefei, Peoples R China. zhang_xc@qq.com; cl@mp.aau.dk; klc@mp.aau.dk; dimi@mp.aau.dk; hongji.yang@leicester.ac.uk LI, Chen/AAF-8336-2019; Chrysostomou, Dimitrios/H-7077-2013 LI, Chen/0000-0001-6249-8957; Chen, Kuan-Lin/0000-0002-9658-0147; Chrysostomou, Dimitrios/0000-0002-6114-8944 National Natural Science Foundation of China [31601224]; National Social Science Fund Project [18BFX079]; Natural Science Foundation of the Education Department of Anhui Province [KJ2019A0662, KJ2020A0012]; program of School Scientific Research of Anhui University of Finance and Economics [ACKYC20049]; H2020-WIDESPREAD project [857061] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Social Science Fund Project; Natural Science Foundation of the Education Department of Anhui Province(Natural Science Foundation of Anhui Province); program of School Scientific Research of Anhui University of Finance and Economics; H2020-WIDESPREAD project This study was supported partially by the National Natural Science Foundation of China (Grant no.31601224), National Social Science Fund Project(Grant No.18BFX079), the Natural Science Foundation of the Education Department of Anhui Province (Grant No.KJ2019A0662, Grant No.KJ2020A0012), the program of School Scientific Research of Anhui University of Finance and Economics (Grant No.ACKYC20049) and the H2020-WIDESPREAD project no. 857061 Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing -R2P2. 18 0 0 4 6 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2693-938X 2693-9371 978-1-6654-7836-6 IEEE I C SQRS-C 2021.0 1119 1125 10.1109/QRS-C55045.2021.00166 0.0 7 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT2HW 2023-03-23 WOS:000808814500154 0
C Chen, JQ; Tandon, N; de Melo, G IEEE Chen, Jiaqiang; Tandon, Niket; de Melo, Gerard Neural Word Representations from Large-Scale Commonsense Knowledge 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1 English Proceedings Paper IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) DEC 06-09, 2015 Singapore, SINGAPORE IEEE,DataSpark,IEEE Comp Soc,Web Intelligence Consortium,Assoc Comp Machinery,Memet Comp Soc,Joint NTU UBC Res Ctr Excellence Act Living Elderly,Singapore Management Univ, Living Analyt Res Ctr,Nanyang Tech Univ There has recently been a surge of research on neural network-inspired algorithms to produce numerical vector representations of words, based on contextual information. In this paper, we present an approach to improve such word embeddings by first mining cognitively salient word relationships from text and then using stochastic gradient descent to jointly optimize the embeddings to reflect this information, in addition to the regular contextual information captured by the word2vec CBOW objective. Our findings show that this new training regime leads to vectors that better reflect commonsense information about words. [Chen, Jiaqiang; de Melo, Gerard] Tsinghua Univ, IIIS, Beijing, Peoples R China; [Tandon, Niket] Max Planck Inst Informat, Saarbrucken, Germany Tsinghua University; Max Planck Society Chen, JQ (corresponding author), Tsinghua Univ, IIIS, Beijing, Peoples R China. cjq0707@gmail.com; ntandon@mpi-inf.mpg.de; gdm@demelo.org de Melo, Gerard/P-7886-2014 de Melo, Gerard/0000-0002-2930-2059 China 973 Program [2011CBA00300, 2011CBA00301]; NSFC [61033001, 61361136003, 61450110088] China 973 Program(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)) This research was supported by China 973 Program Grants 2011CBA00300, 2011CBA00301, and NSFC Grants 61033001, 61361136003, 61450110088. 13 8 9 1 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 978-1-4673-9618-9 2015.0 225 228 10.1109/WI-IAT.2015.150 0.0 4 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BG9GF 2023-03-23 WOS:000393162800037 0
J Wang, L; Huang, WB; Li, YP; Evans, J; He, SL Wang, Lei; Huang, Wenbin; Li, Yuanpeng; Evans, Julian; He, Sailing Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game SCIENTIFIC REPORTS English Article BIODIVERSITY; PROMOTES; DYNAMICS Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as single AI) to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as multi-AI), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called focus length (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents. [Wang, Lei; Huang, Wenbin; Li, Yuanpeng; Evans, Julian; He, Sailing] Zhejiang Univ, Natl Engn Res Ctr Opt Instruments, Ctr Opt & Electromagnet Res, Hangzhou 310058, Peoples R China; [Huang, Wenbin; Li, Yuanpeng; He, Sailing] Zhejiang Univ, Ningbo Res Inst, Ningbo 315100, Peoples R China; [He, Sailing] Royal Inst Technol, Sch Elect Engn, Dept Electromagnet Engn, S-10044 Stockholm, Sweden Zhejiang University; Zhejiang University; Royal Institute of Technology He, SL (corresponding author), Zhejiang Univ, Natl Engn Res Ctr Opt Instruments, Ctr Opt & Electromagnet Res, Hangzhou 310058, Peoples R China.;He, SL (corresponding author), Zhejiang Univ, Ningbo Res Inst, Ningbo 315100, Peoples R China.;He, SL (corresponding author), Royal Inst Technol, Sch Elect Engn, Dept Electromagnet Engn, S-10044 Stockholm, Sweden. Sailing@kth.se He, Sailing/C-2438-2009 He, Sailing/0000-0002-3401-1125 National Key Research and Development Program of China [2018YFC1407506]; National Natural Science Foundation of China [11621101]; Fundamental Research Funds for the Central Universities; Royal Institute of Technology National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Royal Institute of Technology This work was partially supported by the National Key Research and Development Program of China (No. 2018YFC1407506), the National Natural Science Foundation of China (No. 11621101), and the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform). We thank Shuo Li for excellent assistance in arranging the experiments. Open access funding provided by Royal Institute of Technology. 23 4 4 1 11 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep AUG 17 2020.0 10.0 1 13873 10.1038/s41598-020-70544-7 0.0 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics NF8JF 32807813.0 Green Accepted, Green Submitted, gold 2023-03-23 WOS:000563538400023 0
J Zhao, TS; Zhao, C; Xu, WY; Liu, YN; Gao, H; Mitrovic, IZ; Lim, EG; Yang, L; Zhao, CZ Zhao, Tianshi; Zhao, Chun; Xu, Wangying; Liu, Yina; Gao, Hao; Mitrovic, Ivona Z.; Lim, Eng Gee; Yang, Li; Zhao, Ce Zhou Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti3C2Tx MXenes Floating Gate ADVANCED FUNCTIONAL MATERIALS English Article image recognition; MXenes; neuromorphic computing; photoelectric plasticity; synaptic transistors THERMAL-STABILITY; MEMORY; TRANSISTORS; ARRAY The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks. [Zhao, Tianshi; Zhao, Chun; Lim, Eng Gee; Zhao, Ce Zhou] Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China; [Zhao, Tianshi; Mitrovic, Ivona Z.] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England; [Xu, Wangying] Shenzhen Univ, Coll Mat Sci & Engn, Shenzhen 518060, Peoples R China; [Liu, Yina] Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou 215123, Peoples R China; [Gao, Hao] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands; [Yang, Li] Xian Jiaotong Liverpool Univ, Dept Chem, Suzhou 215123, Peoples R China Xi'an Jiaotong-Liverpool University; University of Liverpool; Shenzhen University; Xi'an Jiaotong-Liverpool University; Eindhoven University of Technology; Xi'an Jiaotong-Liverpool University Zhao, C (corresponding author), Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China.;Yang, L (corresponding author), Xian Jiaotong Liverpool Univ, Dept Chem, Suzhou 215123, Peoples R China. Chun.Zhao@xjtlu.edu.cn; Li.Yang@xjtlu.edu.cn lim, enggee/0000-0003-0199-7386; Zhao, Chun/0000-0002-4783-960X; Mitrovic, Ivona/0000-0003-4816-8905 Natural Science Foundation of China [61828401]; Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program [19KJB510059]; Natural Science Foundation of Jiangsu Province of China [BK20180242]; Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation [SYG201924]; Key Program Special Fund in XJTLU [KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07, KSF-A-18]; British Council UKIERI project [IND/CONT/G/17-18/18] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program; Natural Science Foundation of Jiangsu Province of China(Natural Science Foundation of Jiangsu Province); Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation; Key Program Special Fund in XJTLU; British Council UKIERI project This research was funded in part by the Natural Science Foundation of China (61828401), Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (19KJB510059), Natural Science Foundation of Jiangsu Province of China (BK20180242), the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924), and the Key Program Special Fund in XJTLU (KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07, KSF-A-18). The author Ivona Z. Mitrovic acknowledges the British Council UKIERI project no. IND/CONT/G/17-18/18). 75 26 27 28 126 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1616-301X 1616-3028 ADV FUNCT MATER Adv. Funct. Mater. NOV 2021.0 31.0 45 2106000 10.1002/adfm.202106000 0.0 AUG 2021 11 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics WP7FY 2023-03-23 WOS:000682719100001 0
J Labus, S; Altmann, MM; Huisman, H; Tong, A; Penzkofer, T; Choi, MH; Shabunin, I; Winkel, DJ; Xing, PY; Szolar, DH; Shea, SM; Grimm, R; von Busch, H; Kamen, A; Herold, T; Baumann, C Labus, Sandra; Altmann, Martin M.; Huisman, Henkjan; Tong, Angela; Penzkofer, Tobias; Choi, Moon Hyung; Shabunin, Ivan; Winkel, David J.; Xing, Pengyi; Szolar, Dieter H.; Shea, Steven M.; Grimm, Robert; von Busch, Heinrich; Kamen, Ali; Herold, Thomas; Baumann, Clemens A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists EUROPEAN RADIOLOGY English Article Prostatic neoplasms; Multiparametric magnetic resonance imaging; Deep learning; Neoplasm grading; ROC curve DIAGNOSIS SYSTEM; CANCER; ACCURACY; PIRADS; BIOPSY Objectives To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. Methods In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. Results In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG >= 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG >= 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG >= 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG >= 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). Conclusions DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. [Labus, Sandra; Altmann, Martin M.; Herold, Thomas; Baumann, Clemens] Helios Klinikum Berlin Buch, Dept Radiol, Schwanebecker Ch 50, D-13125 Berlin, Germany; [Huisman, Henkjan] Radboud Univ Nijmegen, Med Ctr, Nijmegen, Netherlands; [Tong, Angela] NYU, Dept Radiol, Grossman Sch Med, New York, NY USA; [Penzkofer, Tobias] Charite Univ Med Berlin, Berlin, Germany; [Choi, Moon Hyung] Catholic Univ Korea, Eunpyeong St Marys Hosp, Seoul, South Korea; [Shabunin, Ivan] Patero Clin, Moscow, Russia; [Winkel, David J.] Univ Hosp Basel, Dept Radiol, Basel, Switzerland; [Xing, Pengyi] Changhai Hosp, Dept Radiol, Shanghai, Peoples R China; [Szolar, Dieter H.] Diagnostikum Graz Sud West, Graz, Austria; [Shea, Steven M.] Loyola Univ Med Ctr, Maywood, IL 60153 USA; [Grimm, Robert; von Busch, Heinrich] Siemens Healthcare, Diagnost Imaging, Erlangen, Germany; [Kamen, Ali] Siemens Healthineers, Digital Technol & Innovat, Princeton, NJ USA Helios Kliniken; Radboud University Nijmegen; New York University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Catholic University of Korea; University of Basel; Naval Medical University; Loyola University Chicago; Siemens AG; Siemens Germany; Siemens AG Labus, S (corresponding author), Helios Klinikum Berlin Buch, Dept Radiol, Schwanebecker Ch 50, D-13125 Berlin, Germany. sandra.labus@helios-gesundheit.de xing, peng/HDM-6843-2022 Shea, Steven/0000-0002-0360-4884; Labus, Sandra/0000-0002-3725-1204 Helios Kliniken GmbH [:2020_0064] Helios Kliniken GmbH This study was supported by Helios Kliniken GmbH (GrandID:2020_0064). 31 1 1 1 1 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. JAN 2023.0 33.0 1 64 76 10.1007/s00330-022-08978-y 0.0 JUL 2022 13 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 7U5QK 35900376.0 2023-03-23 WOS:000832453900001 0
J Fan, AL; Yang, J; Yang, L; Wu, D; Vladimir, N Fan, Ailong; Yang, Jian; Yang, Liu; Wu, Da; Vladimir, Nikola A review of ship fuel consumption models OCEAN ENGINEERING English Review Ship energy efficiency; Fuel consumption model; Energy consumption prediction; Machine learning; Knowledge map SPEED OPTIMIZATION; CONTAINER SHIP; EMISSIONS; PREDICTION; SYSTEM; DESIGN; FUSION; ROUTE; OIL The ship fuel consumption (SFC) model is crucial for research on ship energy efficiency simulation, optimisation, and carbon emission prediction. In this study, the bibliometric tool CiteSpace was used to conduct a literature review on SFC models. Based on the review, it was concluded that the current SFC models can be classified into three types: white, black, and grey boxes. Considering the different types of SFC models, the advantages and disadvantages, accuracy improvement methods, and verification methods were analysed. Furthermore, the influencing factors of the SFC models were investigated. Based on the top-down and bottom-up modelling methods, appropriate applications of the SFC models were analysed. Furthermore, the SFC models suitable for different operation stages of ships were classified based on the degree of data availability. Finally, the persisting problems in SFC models were summarised, and corresponding solutions were proposed. In addition, future research directions for SFC models were also proposed. This study can serve as a reference for research on ship energy efficiency improvement and carbon emission forecasting. [Fan, Ailong; Yang, Liu] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [Yang, Jian] Wuhan Univ Technol, Sch Naval Architecture Ocean & Energy Power Engn, Wuhan, Peoples R China; [Wu, Da] Wuhan Univ Technol, Intelligent Transportat Res Syst Ctr, Wuhan, Peoples R China; [Vladimir, Nikola] Univ Zagreb, Fac Mech Engn & Naval Architecture, Zagreb, Croatia Wuhan University of Technology; Wuhan University of Technology; Wuhan University of Technology; University of Zagreb; University of Zagreb Faculty of Mechanical Engineering & Naval Architecture Yang, L (corresponding author), Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China. yang.liu@whut.edu.cn /0000-0002-4898-5203 National Natural Science Foundation of China [51809202]; Green Intelligent Inland Ship Innovation Programme of China [MC-202002-C01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Green Intelligent Inland Ship Innovation Programme of China This work is supported by the National Natural Science Foundation of China (51809202) and Green Intelligent Inland Ship Innovation Programme of China (MC-202002-C01) . 139 8 8 61 61 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. NOV 15 2022.0 264.0 112405 10.1016/j.oceaneng.2022.112405 0.0 SEP 2022 17 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 5A7HF 2023-03-23 WOS:000863053800001 0
C Shi, YJ; Shen, WM; Wang, LH; Longo, F; Nicoletti, L; Padovano, A Longo, F; Affenzeller, M; Padovano, A Shi, Yanjun; Shen, Weiming; Wang, Lihui; Longo, Francesco; Nicoletti, Letizia; Padovano, Antonio A Cognitive Digital Twins Framework for Human-Robot Collaboration 3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING Procedia Computer Science English Proceedings Paper 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM) NOV 17-19, 2021 Upper Austria Univ Appl Sci, Hagenberg Campus, Linz, AUSTRIA Upper Austria Univ Appl Sci, Hagenberg Campus Digital twin; human-robot collaboration; human cyber-physical system; 5G communication network; edge-cloud collaboration INTELLIGENCE; CONTEXT This paper proposes a cognitive digital twin framework for smart manufacturing, and especially for human-robot-collaboration cases. The proposed framework comprises three layers (field, edge, and cloud layers) based on the 5G communication network. In the field layer, the physical twin's data from the physical machine and human operators are transmitted through the edge layer and then to the cloud layer to virtualize the digital twin. The cloud layer generates inference model generation by deep learning training and updates the inference model in the edge layer to make the field's machine smart. Especially, human operators' models are built based on the multimodal fusion in the cloud layer for cognitive function. Also, edge-cloud collaborative computing is presented to implement the proposed framework. Finally, the study is validated with a human-robot-collaboration case involving 5G edge computing. (C) 2022 The Authors. Published by Elsevier B.V. [Shi, Yanjun] Dalian Univ Technol, Dept Mech Engn, Dalian, Peoples R China; [Shen, Weiming] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Peoples R China; [Wang, Lihui] Royal Inst Technol KTH, Dept Prod Engn, Stockholm, Sweden; [Longo, Francesco; Padovano, Antonio] Univ Calabria, Dept Mech Energy & Management Engn DIMEG, Arcavacata Di Rende, Italy; [Nicoletti, Letizia] CAL TEK Srl, Arcavacata Di Rende, Italy Dalian University of Technology; Huazhong University of Science & Technology; Royal Institute of Technology; University of Calabria Shi, YJ (corresponding author), Dalian Univ Technol, Dept Mech Engn, Dalian, Peoples R China. Shen, Weiming/B-7400-2013; SHI, YAN/HNI-1042-2023; Wang, Lihui/O-3907-2014; shen, Weiming/HJZ-2337-2023 Shen, Weiming/0000-0001-5204-7992; Wang, Lihui/0000-0001-8679-8049; 19 2 2 16 31 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-0509 PROCEDIA COMPUT SCI 2022.0 200.0 1867 1874 10.1016/j.procs.2022.01.387 0.0 MAR 2022 8 Computer Science, Theory & Methods; Engineering, Industrial; Engineering, Manufacturing Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS8UA gold 2023-03-23 WOS:000777601300190 0
J Shi, TY; Boutry, N; Xu, YC; Geraud, T Shi, Tianyi; Boutry, Nicolas; Xu, Yongchao; Geraud, Thierry Local Intensity Order Transformation for Robust Curvilinear Object Segmentation IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Image segmentation; Transforms; Object segmentation; Retina; Biomedical imaging; Learning systems; Blood vessels; Curvilinear structure; segmentation; local intensity order; deep learning; generalizability BLOOD-VESSEL SEGMENTATION; RETINAL IMAGES; TREE; FEATURES; FILTER Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://github.com/TY-Shi/LIOT. [Shi, Tianyi] Huazhong Univ Sci & Technol HUST, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China; [Boutry, Nicolas; Geraud, Thierry] Lab Rech & Dev EPITA LRDE, F-94276 Paris, France; [Xu, Yongchao] Wuhan Univ, Sch Comp Sci, Wuhan 430070, Peoples R China Huazhong University of Science & Technology; Wuhan University Xu, YC (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan 430070, Peoples R China. shitianyihust@hust.edu.cn; nicolas.boutry@lrde.epita.fr; yongchao.xu@whu.edu.cn; thierry.geraud@lrde.epita.fr Shi, Tianyi/AAQ-2319-2020 Shi, Tianyi/0000-0002-8186-9183 National Key Research and Development Program of China [2018AAA0100400]; National Natural Science Foundation of China [61872417, 61936003, 62176186]; Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST) National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST) The work of Yongchao Xu was supported in part by the National Key Research and Development Program of China under Grant 2018AAA0100400, in part by the National Natural Science Foundation of China under Grant 61936003 and Grant 62176186, and in part by the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST). The work of Tianyi Shi was supported in part by the National Natural Science Foundation of China under Grant 61872417. 78 4 4 10 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2022.0 31.0 2557 2569 10.1109/TIP.2022.3155954 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering ZY2TY 35275816.0 Green Submitted 2023-03-23 WOS:000772442800005 0
J Yang, LB; Huang, R; Huang, JF; Lin, T; Wang, LM; Mijiti, R; Wei, PL; Tang, C; Shao, J; Li, QZ; Du, X Yang, Lingbo; Huang, Ran; Huang, Jingfeng; Lin, Tao; Wang, Limin; Mijiti, Ruzemaimaiti; Wei, Pengliang; Tang, Chao; Shao, Jie; Li, Qiangzi; Du, Xin Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Agriculture; Data models; Satellites; Deep learning; Radar polarimetry; Remote sensing; Data augmentation; feature visualization; generalization ability; temporal feature-based segmentation (TFBS); time-series images CONVOLUTIONAL NEURAL-NETWORKS; FOOD SECURITY; DEEP; CLASSIFICATION; SENTINEL-1; EXTRACTION; CLOUD; SIZE Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different growth stages of crops under all weather conditions. Such time-series SAR images can provide an abundance of temporal and spatial features for use in large-scale crop mapping and analysis. In this study, we propose a temporal feature-based segmentation (TFBS) model for accurate crop mapping using time-series SAR images. This model first extracts deep-seated temporal features and then learns the spatial context of the extracted temporal features for crop mapping. The results indicate that the TFBS model significantly outperforms traditional long short-term memory (LSTM), U-network, and convolutional LSTM models in crop mapping based on time-series SAR images. TFBS demonstrates better generalizability than other models in the study area, which makes it more transferable, and the results show that data augmentation can significantly improve this generalizability. The visualization of the temporal features extracted by the TFBS shows that there is a high degree of intraclass homogeneity among rice fields and interclass heterogeneity between rice fields and other features. TFBS also achieved the highest accuracy of the four deep learning models for multicrop classification in the study area. This study presents a feasible way of producing high-accuracy large-scale crop maps based on the proposed model. [Yang, Lingbo; Huang, Jingfeng; Mijiti, Ruzemaimaiti; Wei, Pengliang; Tang, Chao] Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China; [Huang, Ran] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China; [Lin, Tao] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China; [Wang, Limin] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China; [Shao, Jie] Univ Toulouse, Inst Rech Informat Toulouse IRIT, CNRS, F-31062 Toulouse, France; [Li, Qiangzi; Du, Xin] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China Zhejiang University; Hangzhou Dianzi University; Zhejiang University; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Institut National Polytechnique de Toulouse; Universite Toulouse 1 Capitole; Universite de Toulouse - Jean Jaures; Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS Huang, JF (corresponding author), Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China.;Wang, LM (corresponding author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. yanglingbo@zju.edu.cn; ran_huang@hdu.edu.cn; hjf@zju.edu.cn; lintao1@zju.edu.cn; wanglimin01@caas.cn; ruzi_mamat@zju.edu.cn; weipengliang@zju.edu.cn; xueshu@zju.edu.cn; shaojie@mail.bnu.edu.cn; liqz@radi.ac.cn; duxin@radi.ac.cn Lin, Tao/AAB-3023-2020 Lin, Tao/0000-0001-9721-5363; Tang, Chao/0000-0001-9199-6728; Huang, Jingfeng/0000-0003-4627-6021; Shao, Jie/0000-0001-7793-2031 Eramus+ Programme of the European Union [598838-EPP-1-2018-EL-EPPKA2-CBHE-JP]; National Major Project of China [09-Y20A05-9001-17/18] Eramus+ Programme of the European Union; National Major Project of China This work was supported in part by the Eramus+ Programme of the European Union under Grant 598838-EPP-1-2018-EL-EPPKA2-CBHE-JP and in part by the National Major Project of China under Grant 09-Y20A05-9001-17/18. (Corresponding authors: Jingfeng Huang; Limin Wang.) 83 14 14 24 52 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3099522 0.0 AUG 2021 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YL6FH 2023-03-23 WOS:000732752700001 0
J Ji, XY; Yan, QY; Huang, D; Wu, B; Xu, XJ; Zhang, AB; Liao, GL; Zhou, JX; Wu, MH Ji, Xiaoyuan; Yan, Qiuyu; Huang, Dong; Wu, Bo; Xu, Xiaojing; Zhang, Aibin; Liao, Guanglan; Zhou, Jianxin; Wu, Menghuai Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition JOURNAL OF MATERIALS PROCESSING TECHNOLOGY English Article Convolutional neural networks; Selective search; Defect detection; Classification; Casting X-ray flaw detection is a key link in the detection of internal defects in titanium alloy castings which are used for most important components in aeroengines. However, the existing manual defect detection methods from the X-ray images have common drawbacks such as unstable artificial recognition, misdetection, misjudgment, fails of quantitative analysis, huge workload, and low-quality inspection efficiency. To avoid these drawbacks, this paper proposes a new artificial intelligent (AI) method to detect and recognize the aerospace titanium casting defects from the X-ray images. It includes the target defect positioning method named as filtered selective search algorithm (FSS) and the defect classification method named as evenly distributed convolutional neural network (ED-CNN). In the target positioning step, through statistical analysis of defect characteristics, a filtered selective search algorithm is built with two filters (size and edge curvature). In this way, the FSS algorithm can position the defects with almost 100 % of accuracy, hence avoid missed detection and false detection. In the target classification step, an ED-CNN is constructed with a similar structure of the same number of layers in each feature extraction stage, and its entire architecture is evenly distributed. Compared with other three classic high-performance convolutional neural network models (AlexNet, VGG16 and VGG19), the ED-CNN model has the best performance. The ED-CNN model was tested with 324 targets from 50 original images, a classification accuracy of nearly 90 % was obtained for low density holes, porosity, linear defects, high density inclusions and casting structure. The FSS/ED-CNN method of two phases defect detection proposed in this paper can achieve accurate positioning and high accurate classification of typical defect targets, and is expected to solve the common drawbacks of manual defect detection. The newly-proposed FSS/ED-CNN method has important research significance and engineering value. [Ji, Xiaoyuan; Yan, Qiuyu; Huang, Dong; Wu, Bo; Xu, Xiaojing; Zhou, Jianxin] Huazhong Univ Sci & Technol, Sch Mat Sci & Engn, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Hubei, Peoples R China; [Yan, Qiuyu] CISDI Informat Technol Co Ltd, Chongqing 401122, Peoples R China; [Zhang, Aibin] Beijing Baimu High Tech Co Ltd Aeronaut Mat, Beijing 100095, Peoples R China; [Liao, Guanglan] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Peoples R China; [Wu, Menghuai] Univ Leoben, Dept Met, Leoben, Austria Huazhong University of Science & Technology; Huazhong University of Science & Technology; University of Leoben Zhou, JX (corresponding author), Huazhong Univ Sci & Technol, Sch Mat Sci & Engn, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Hubei, Peoples R China.;Wu, MH (corresponding author), Univ Leoben, Dept Met, Leoben, Austria. zhoujianxin@hust.edu.cn; menghuai.wu@unileoben.ac.at Wu, Menghuai/HGD-3482-2022 Wu, Menghuai/0000-0003-0321-412X; HUANG, Dong/0000-0002-4275-3006 National Natural Science Foundation of China [51905188, 51775205]; National Key Research and Development Program of China: Network Cooperative Manufacturing and Intelligent Factory [2020YFB1710100]; Investment Casting Collaborating Laboratory on Advanced Aeronautical Lightweight Alloy Materials of Huazhong University of Science Technology; AECC Beijing Institute of Aeronautical Materials National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China: Network Cooperative Manufacturing and Intelligent Factory; Investment Casting Collaborating Laboratory on Advanced Aeronautical Lightweight Alloy Materials of Huazhong University of Science Technology; AECC Beijing Institute of Aeronautical Materials We gratefully acknowledge the support of the National Natural Science Foundation of China (No. 51905188, No. 51775205), the National Key Research and Development Program of China: Network Cooperative Manufacturing and Intelligent Factory (No. 2020YFB1710100), Investment Casting Collaborating Laboratory on Advanced Aeronautical Lightweight Alloy Materials of Huazhong University of Science & Technology and AECC Beijing Institute of Aeronautical Materials. 25 10 10 9 75 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0924-0136 1873-4774 J MATER PROCESS TECH J. Mater. Process. Technol. JUN 2021.0 292.0 117064 10.1016/j.jmatprotec.2021.117064 0.0 JAN 2021 12 Engineering, Industrial; Engineering, Manufacturing; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science QH8PY 2023-03-23 WOS:000618535600013 0
J Sun, JR; Wu, Z; Yu, ZC; Chen, HJ; Du, CP; Xu, L; Zhong, J; Feng, J; Coatrieux, G; Coatrieux, JL; Chen, Y Sun, Jiarui; Wu, Zhan; Yu, Zechen; Chen, Huanji; Du, Changping; Xu, Liang; Zhong, Jian; Feng, Juan; Coatrieux, Gouenou; Coatrieux, Jean-Louis; Chen, Yang Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article X-ray imaging; Imaging; Image recognition; Protocols; Torso; Computed tomography; Diagnostic radiography; X-ray imaging automation; exposure region recognition; computer vision; video analysis; deep learning RADIOGRAPHY; SYSTEM The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer. [Sun, Jiarui; Yu, Zechen; Chen, Huanji; Du, Changping] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China; [Wu, Zhan] Southeast Univ, Sch Cyberspace Secur, Nanjing 211189, Jiangsu, Peoples R China; [Xu, Liang; Zhong, Jian; Feng, Juan] United Imaging Healthcare, XR IC Dept, Shenyang 201807, Peoples R China; [Coatrieux, Gouenou] IMT Atlantique, INSERM, LaTIM UMR1101, F-29000 Brest, France; [Coatrieux, Jean-Louis] Univ Rennes 1, Lab Traitement Signal & Image, F-35000 Rennes, France; [Coatrieux, Jean-Louis] Ctr Rech Informat Biomed Sinofrancais, F-35042 Rennes, France; [Coatrieux, Jean-Louis] Natl Inst Hlth & Med Res, F-35000 Rennes, France; [Chen, Yang] Southeast Univ, Lab Image Sci & Technol, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Sch Comp Sci & Engn, Nanjing 210096, Peoples R China Southeast University - China; Southeast University - China; IMT - Institut Mines-Telecom; IMT Atlantique; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; Universite de Rennes; Institut National de la Sante et de la Recherche Medicale (Inserm); Southeast University - China; Southeast University - China Chen, Y (corresponding author), Southeast Univ, Lab Image Sci & Technol, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China. zzu_sjr@163.com; zhanwubusheng1994@foxmail.com; 220191708@seu.edu.cn; 513748165@qq.com; 220191716@seu.edu.cn; skytiger00000@163.com; sau_zhongjian@163.com; 13352433355@163.com; gouenou.coatrieux@telecom-bretagne.eu; jean-louis.coatrieux@univ-rennes1.fr; chenyang.list@seu.edu.cn Wu, Zhan/ABX-1238-2022 Wu, Zhan/0000-0002-3914-0102; Coatrieux, Jean Louis/0000-0001-8381-1770; Sun, Jiarui/0000-0002-8007-4602 Science and Technology Program of Jiangsu Province [BE2021703]; State's Key Project of Research and Development Plan [2021ZD0113202, 2017YFA0104302]; National Natural Science Foundation [61871117]; Science and Technology Program of Guangdong Province [2018B030333001] Science and Technology Program of Jiangsu Province; State's Key Project of Research and Development Plan; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Guangdong Province This work was supported in part by the Science and Technology Program of Jiangsu Province (No. BE2021703), in part by the State's Key Project of Research and Development Plan under Grants 2021ZD0113202 and 2017YFA0104302, in part by the National Natural Science Foundation under Grant 61871117, and in part by Science and Technology Program of Guangdong Province (2018B030333001). 48 1 1 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. SEP 2022.0 26.0 9 4359 4370 10.1109/JBHI.2022.3172369 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics 4K9FX 35503854.0 Green Submitted 2023-03-23 WOS:000852247000005 0
J Zhao, JP; Datcu, M; Zhang, ZH; Xiong, HL; Yu, WX Zhao, Juanping; Datcu, Mihai; Zhang, Zenghui; Xiong, Huilin; Yu, Wenxian Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Scattering; Radar polarimetry; Matrix decomposition; Radar imaging; Synthetic aperture radar; Task analysis; < italic xmlns:ali=http:; www; niso; org; schemas; ali; 1; 0; xmlns:mml=http:; www; w3; org; 1998; Math; MathML xmlns:xlink=http:; www; w3; org; 1999; xlink xmlns:xsi=http:; www; w3; org; 2001; XMLSchema-instance> H <; italic >-< italic xmlns:ali=http:; www; niso; org; schemas; ali; 1; 0; xmlns:mml=http:; www; w3; org; 1998; Math; MathML xmlns:xlink=http:; www; w3; org; 1999; xlink xmlns:xsi=http:; www; w3; org; 2001; XMLSchema-instance> A <; italic >-mu target decomposition; complex-valued convolutional neural networks (CNNs); contrastive-regulated objective cost; data augmentation; physical scattering signatures; polarimetric synthetic aperture radar (PolSAR) DECOMPOSITION; MODEL Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottiers H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Centers (DLR)s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification. [Zhao, Juanping; Zhang, Zenghui; Xiong, Huilin; Yu, Wenxian] Shanghai Jiao Tong Univ, Dept Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Zhao, Juanping; Datcu, Mihai] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany Shanghai Jiao Tong University; Helmholtz Association; German Aerospace Centre (DLR) Datcu, M (corresponding author), German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany. juanpingzhao@sjtu.edu.cn; mihai.datcu@dlr.de; zenghui.zhang@sjtu.edu.cn; hlxiong@sjtu.edu.cn; wxyu@sjtu.edu.cn zhang, zenghui/GVS-8983-2022 zhang, zenghui/0000-0002-1238-8538 National Natural Science Foundation of China [U1830103]; China Scholarship Council (CSC) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC)(China Scholarship Council) The authors would like to thank Dr. R. Scheiber at the German Aerospace Center's (DLR)'s Microwaves and Radar Institute for providing the F-SAR data. The authors would like to acknowledge the National Natural Science Foundation of China under Grant U1830103 and the China Scholarship Council (CSC) for a scholarship. They would also like to thank the editors and the anonymous reviewers for their careful reviews. 45 17 19 0 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing DEC 2019.0 57.0 12 10116 10135 10.1109/TGRS.2019.2931620 0.0 20 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology KA3MB 2023-03-23 WOS:000505701800046 0
J Ni, H; Chanussot, J; Niu, XN; Tang, H; Guan, HY Ni, Huan; Chanussot, Jocelyn; Niu, Xiaonan; Tang, Hong; Guan, Haiyan Reverse Difference Network for Highlighting Small Objects in Aerial Images ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article difference; semantic segmentation; convolutional networks; attention; deep learning SEMANTIC SEGMENTATION; AGGREGATION The large-scale variation issue in high-resolution aerial images significantly lowers the accuracy of segmenting small objects. For a deep-learning-based semantic segmentation model, the main reason is that the deeper layers generate high-level semantics over considerably large receptive fields, thus improving the accuracy for large objects but ignoring small objects. Although the low-level features extracted by shallow layers contain small-object information, large-object information has predominant effects. When the model, using low-level features, is trained, the large objects push the small objects aside. This observation motivates us to propose a novel reverse difference mechanism (RDM). The RDM eliminates the predominant effects of large objects and highlights small objects from low-level features. Based on the RDM, a novel semantic segmentation method called the reverse difference network (RDNet) is designed. In the RDNet, a detailed stream is proposed to produce small-object semantics by enhancing the output of RDM. A contextual stream for generating high-level semantics is designed by fully accumulating contextual information to ensure the accuracy of the segmentation of large objects. Both high-level and small-object semantics are concatenated when the RDNet performs predictions. Thus, both small- and large-object information is depicted well. Two semantic segmentation benchmarks containing vital small objects are used to fully evaluate the performance of the RDNet. Compared with existing methods that exhibit good performance in segmenting small objects, the RDNet has lower computational complexity and achieves 3.9-18.9% higher accuracy in segmenting small objects. [Ni, Huan; Guan, Haiyan] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Chanussot, Jocelyn] Grenoble Alpes Univ, Inst Engn Grenoble INP, Jean Kuntzmann Lab LJK, CNRS,INRIA, F-38000 Grenoble, France; [Niu, Xiaonan] China Geol Survey, Nanjing Ctr, Nanjing 210016, Peoples R China; [Tang, Hong] Beijing Normal Univ, Fac Geog Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China Nanjing University of Information Science & Technology; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; China Geological Survey; Beijing Normal University Niu, XN (corresponding author), China Geol Survey, Nanjing Ctr, Nanjing 210016, Peoples R China. nih2015@yeah.net; jocelyn.chanussot@grenoble-inp.fr; niuxiaonan@mail.cgs.gov.cn; tanghong@bnu.edu.cn; guanhy.nj@nuist.edu.cn Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [41901310, 41971414, 41801384] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China (Grant No. 41901310, 41971414, and 41801384). 54 0 0 8 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. SEP 2022.0 11.0 9 494 10.3390/ijgi11090494 0.0 25 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physical Geography; Remote Sensing 4Q9MV gold 2023-03-23 WOS:000856399500001 0
J Ouyang, RC; Costa, AR; Cassidy, CK; Otwinowska, A; Williams, VCJ; Latka, A; Stansfeld, PJ; Drulis-Kawa, Z; Briers, Y; Pelt, DM; Brouns, SJJ; Briegel, A Ouyang, Ruochen; Costa, Ana Rita; Cassidy, C. Keith; Otwinowska, Aleksandra; Williams, Vera C. J.; Latka, Agnieszka; Stansfeld, Phill J.; Drulis-Kawa, Zuzanna; Briers, Yves; Pelt, Daniel M.; Brouns, Stan J. J.; Briegel, Ariane High-resolution reconstruction of a Jumbo-bacteriophage infecting capsulated bacteria using hyperbranched tail fibers NATURE COMMUNICATIONS English Article CRYO-EM STRUCTURE; 3-DIMENSIONAL STRUCTURE; MOLECULAR-DYNAMICS; PROTEIN STRUCTURES; PREDICTION; IMPLEMENTATION; VISUALIZATION; TAILSPIKE; APPARATUS; SHEATH The Klebsiella jumbo myophage phi Kp24 displays an unusually complex arrangement of tail fibers interacting with a host cell. In this study, we combine cryo-electron microscopy methods, protein structure prediction methods, molecular simulations, microbiological and machine learning approaches to explore the capsid, tail, and tail fibers of phi Kp24. We determine the structure of the capsid and tail at 4.1 angstrom and 3.0 angstrom resolution. We observe the tail fibers are branched and rearranged dramatically upon cell surface attachment. This complex configuration involves fourteen putative tail fibers with depolymerase activity that provide phi Kp24 with the ability to infect a broad panel of capsular polysaccharide (CPS) types of Klebsiella pneumoniae. Our study provides structural and functional insight into how phi Kp24 adapts to the variable surfaces of capsulated bacterial pathogens, which is useful for the development of phage therapy approaches against pan-drug resistant K. pneumoniae strains. The jumbo contractile bacteriophage, Kp24, has been isolated from clinical strains of Klebsiella pneumonia. Here, the authors present structural and functional insight into the capsid, tail and tail fibres and how this impacts infectivity. [Ouyang, Ruochen] Xi An Jiao Tong Univ, Sch Phys, MOE Key Lab Nonequilibrium Synth & Modulat Conden, Xianning West Rd 28, Xian 710049, Peoples R China; [Ouyang, Ruochen; Williams, Vera C. J.; Briegel, Ariane] Leiden Univ, Inst Biol, Dept Microbial Sci, Sylviusweg 72, NL-2333 BE Leiden, Netherlands; [Costa, Ana Rita; Brouns, Stan J. J.] Delft Univ Technol, Dept Bionanosci, Van der Maasweg 9, NL-2629 HZ Delft, Netherlands; [Costa, Ana Rita; Brouns, Stan J. J.] Kavli Inst Nanosci, Delft, Netherlands; [Cassidy, C. Keith] Univ Oxford, Dept Biochem, Oxford, England; [Otwinowska, Aleksandra; Latka, Agnieszka; Drulis-Kawa, Zuzanna] Univ Wroclaw, Dept Pathogen Biol & Immunol, Przybyszewskiego 63-77, PL-51148 Wroclaw, Poland; [Latka, Agnieszka; Briers, Yves] Univ Ghent, Dept Biotechnol, Valentin Vaerwyckweg 1, B-9000 Ghent, Belgium; [Stansfeld, Phill J.] Univ Warwick, Sch Life Sci, Coventry CV4 7AL, W Midlands, England; [Stansfeld, Phill J.] Univ Warwick, Dept Chem, Coventry CV4 7AL, W Midlands, England; [Pelt, Daniel M.] Leiden Univ, Leiden Inst Adv Comp Sci, Niels Bohrweg 1, NL-2333 CA Leiden, Netherlands Xi'an Jiaotong University; Leiden University; Leiden University - Excl LUMC; Delft University of Technology; Delft University of Technology; University of Oxford; University of Wroclaw; Ghent University; University of Warwick; University of Warwick; Leiden University; Leiden University - Excl LUMC Briegel, A (corresponding author), Leiden Univ, Inst Biol, Dept Microbial Sci, Sylviusweg 72, NL-2333 BE Leiden, Netherlands. a.briegel@biology.leidenuniv.nl Łątka, Agnieszka/GZM-5169-2022; Stansfeld, Phill/C-6213-2011 Łątka, Agnieszka/0000-0003-4492-497X; Cassidy, Keith/0000-0001-8106-1187; Pelt, Daniel/0000-0002-8253-0851; Ouyang, Ruochen/0000-0003-1628-8152; Drulis-Kawa, Zuzanna/0000-0002-4733-4660; Stansfeld, Phill/0000-0001-8800-7669 Dutch Research Council (NWO) [184.034.014]; MRC; Wellcome; National Science Centre, Poland [UMO-2017/26/M/NZ1/00233, UMO-2020/38/E/NZ8/00432]; Research Foundation-Flanders (FWO) [1240021N]; NWO [016.Veni.192.235]; NWO with VICI grant [VI.C.182.027]; European Research Council (ERC) CoG [101003229]; China Scholarship Council (CSC) [201906280465] Dutch Research Council (NWO)(Netherlands Organization for Scientific Research (NWO)); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Wellcome; National Science Centre, Poland(National Science Centre, Poland); Research Foundation-Flanders (FWO)(FWO); NWO(Netherlands Organization for Scientific Research (NWO)); NWO with VICI grant; European Research Council (ERC) CoG(European Research Council (ERC)); China Scholarship Council (CSC)(China Scholarship Council) This work is part of the research program National Roadmap for Large-Scale Research Infrastructure 2017-2018 with project number 184.034.014, which is financed in part by the Dutch Research Council (NWO). C.K.C. and P.J.S. are funded by BBSRC, MRC, and Wellcome. The depolymerase experiments and analyses were supported by National Science Centre, Poland, with grant numbers UMO-2017/26/M/NZ1/00233 to Z.D.-K., A.L., and UMO-2020/38/E/NZ8/00432 to A.O. A.L. holds a junior postdoctoral fellowship of the Research Foundation-Flanders (FWO) with grant number 1240021N. D.M.P. is supported by NWO with grant number 016.Veni.192.235. S.J.J.B. is supported by the NWO with VICI grant VI.C.182.027 and the European Research Council (ERC) CoG with grant no. 101003229. R.O. was supported by the China Scholarship Council (CSC) with project number 201906280465. We thank Jamie Depelteau and Adam Sidi Mabrouk for cryo-EM sample preparation help; Wen Yang and Willem Noteborn for cryo-EM data collection; Ludovic Renault and Frederic Bonnet for cryo-EM data processing help; Boris Estrada-Bonilla for technical support; Xiao Zhang for help with the extraction of the network results and Lei Zhang for critical feedback on the manuscript. The K. pneumoniae reference strains used in this study (CIP labeled strains) were obtained from the Collection de l'Institut Pasteur (CIP, Paris, France). 76 0 0 11 11 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. NOV 24 2022.0 13.0 1 7241 10.1038/s41467-022-34972-5 0.0 16 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 6M5KE 36433970.0 Green Published, gold, Green Submitted 2023-03-23 WOS:000888905000024 0
J Varotsos, CA; Krapivin, VF; Xue, Y; Soldatov, V; Voronova, T Varotsos, Costas A.; Krapivin, Vladimir F.; Xue, Yong; Soldatov, Vladimir; Voronova, Tatiana COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness SAFETY SCIENCE English Article COVID-19; Decision making; Big data; Prognosis; Trend; Model; Vaccination; Simulation The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario. [Varotsos, Costas A.; Voronova, Tatiana] Natl & Kapodistrian Univ Athens, Dept Environm Phys & Meteorol, Bldg PHYS 5, GR-15784 Athens, Greece; [Krapivin, Vladimir F.; Soldatov, Vladimir] Russian Acad Sci, Kotelnikovs Inst Radioengn & Elect, Fryazino Branch, Vvedensky 1, Fryazino 141190, Moscow Region, Russia; [Xue, Yong] Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China; [Xue, Yong] Univ Derby, Coll Sci & Engn, Dept Elect Comp & Math, Kedleston Rd, Derby DE22 1GB, England National & Kapodistrian University of Athens; Russian Academy of Sciences; China University of Mining & Technology; University of Derby Varotsos, CA (corresponding author), Natl & Kapodistrian Univ Athens, Dept Environm Phys & Meteorol, Bldg PHYS 5, GR-15784 Athens, Greece.;Xue, Y (corresponding author), Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China.;Xue, Y (corresponding author), Univ Derby, Coll Sci & Engn, Dept Elect Comp & Math, Kedleston Rd, Derby DE22 1GB, England. covar@phys.uoa.gr Varotsos, Costas/H-6257-2013 Varotsos, Costas/0000-0001-7215-3610 29 4 4 3 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-7535 1879-1042 SAFETY SCI Saf. Sci. OCT 2021.0 142.0 105370 10.1016/j.ssci.2021.105370 0.0 JUN 2021 7 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science TM9XK 34108816.0 Green Accepted 2023-03-23 WOS:000675899400018 0
J She, L; Zhang, HKK; Li, ZQ; de Leeuw, G; Huang, B She, Lu; Zhang, Hankui K.; Li, Zhengqiang; de Leeuw, Gerrit; Huang, Bo Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations REMOTE SENSING English Article aerosol optical depth (AOD); Himawari-8; deep neural network PM2.5 CONCENTRATIONS; RANDOM-FOREST; LAND; VALIDATION Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R-2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R-2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed. [She, Lu] Ningxia Univ, Coll Resources & Environm Sci, Yinchuan 750021, Ningxia, Peoples R China; [Zhang, Hankui K.] South Dakota State Univ, Geospatial Sci Ctr Excellence, Geog & Geospatial Sci, Brookings, SD 57007 USA; [Li, Zhengqiang; de Leeuw, Gerrit] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [de Leeuw, Gerrit] Royal Netherlands Meteorol Inst KNMI, R&D Satellite Observat, NL-3730 AE De Bilt, Netherlands; [Huang, Bo] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China Ningxia University; South Dakota State University; Chinese Academy of Sciences; Royal Netherlands Meteorological Institute; Chinese University of Hong Kong Zhang, HKK (corresponding author), South Dakota State Univ, Geospatial Sci Ctr Excellence, Geog & Geospatial Sci, Brookings, SD 57007 USA. shelu_whu@nxu.edu.cn; hankui.zhang@sdstate.edu; lizq@radi.ac.cn; gerrit.de.leeuw@knmi.nl; bohuang@cuhk.edu.hk li, zhengqiang/C-5678-2013; de Leeuw, Gerrit/AAI-3270-2020; Huang, Bo/H-9874-2014 li, zhengqiang/0000-0002-7795-3630; de Leeuw, Gerrit/0000-0002-1649-6333; Zhang, Hankui/0000-0003-4470-3616; Huang, Bo/0000-0002-5063-3522; She, Lu/0000-0002-5413-9150 Open Fund of the State Key Laboratory of Remote Sensing Science [OFSLRSS201912]; Science and Technology Department of Ningxia [2019BEB04009] Open Fund of the State Key Laboratory of Remote Sensing Science; Science and Technology Department of Ningxia This work was supported in part by Open Fund of the State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201912). We gratefully acknowledge the support from the Science and Technology Department of Ningxia under grant No. 2019BEB04009. 77 16 16 7 31 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. DEC 2020.0 12.0 24 4125 10.3390/rs12244125 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology PL5XG gold 2023-03-23 WOS:000603193900001 0
J Chen, Z; Su, CC; Chen, AF Chen, Zhuo; Su, Chris Chao; Chen, Anfan Top-down or Bottom-up? A Network Agenda-setting Study of Chinese Nationalism on Social Media JOURNAL OF BROADCASTING & ELECTRONIC MEDIA English Article OPINION LEADERS; DISCOURSE; TWITTER The extent to which nationalist discourse is transferred between the authority and the people remains unclear. Taking Chinese nationalism on Weibo as a research context, this study differentiated Weibo accounts into (1) individual users, (2) influencers, and (3) organizational accounts, and it applied a network agenda-setting (NAS) approach to analyze how nationalist issues are transferred among different actors. Based on large-scale data, supervised machine learning was employed to identify issue categories, while semantic network analysis was completed to construct issue networks. The results indicate that media agenda influences individuals' agenda while the construction of nationalism follows a bottom-up direction. [Chen, Zhuo] Natl Univ Singapore, Commun & New Media, Singapore, Singapore; [Su, Chris Chao] Univ Copenhagen, Copenhagen, Denmark; [Chen, Anfan] Univ Sci & Technol China, Dept Sci Commun & Sci Educ, Hefei, Anhui, Peoples R China National University of Singapore; University of Copenhagen; Chinese Academy of Sciences; University of Science & Technology of China, CAS Chen, Z (corresponding author), Natl Univ Singapore, Commun & New Media, Singapore, Singapore. Chen, Anfan/AAR-4988-2021 Su, Chris Chao/0000-0003-4591-1062; Chen, Zhuo/0000-0002-5607-0046 47 27 27 20 79 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0883-8151 1550-6878 J BROADCAST ELECTRON J. Broadcast. Electron. Media JUL 3 2019.0 63.0 3 512 533 10.1080/08838151.2019.1653104 0.0 22 Communication; Film, Radio, Television Social Science Citation Index (SSCI) Communication; Film, Radio & Television IZ7PL 2023-03-23 WOS:000487288000008 0
C Cheng, L; Murphy, J; Liu, QZ; Hao, CL; Theodoropoulos, G Aldinucci, M; Padovani, L; Torquati, M Cheng, Long; Murphy, John; Liu, Qingzhi; Hao, Chunliang; Theodoropoulos, Georgios Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling EURO-PAR 2018: PARALLEL PROCESSING Lecture Notes in Computer Science English Proceedings Paper 24th International European Conference on Parallel and Distributed Computing (Euro-Par) AUG 27-31, 2018 Turin, ITALY Univ Turin, Comp Sci Dept Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current techniques since it always incurs a significant cost on network communication. Various advanced approaches have been proposed to improve the performance, however, most of them just focus on data skew handling, and algorithms designed specifically for communication reduction have received less attention. Moreover, although the state-of-the-art technique can minimize network traffic, it provides fine-grained optimal schedules for all individual join keys, which could result in obvious overhead. In this paper, we propose a new approach called LAS (Lightweight Locality-Aware Scheduling), which targets reducing network communication for large distributed joins in an efficient and effective manner. We present the detailed design and implementation of LAS, and conduct an experimental evaluation using large data joins. Our results show that LAS can effectively reduce scheduling overhead and achieve comparable performance on network reduction compared to the state-of-the-art. [Cheng, Long; Murphy, John] Univ Coll Dublin, PEL, Dublin, Ireland; [Cheng, Long; Liu, Qingzhi] Eindhoven Univ Technol, Eindhoven, Netherlands; [Hao, Chunliang] Chinese Acad Sci, Inst Software, Beijing, Peoples R China; [Theodoropoulos, Georgios] Southern Univ Sci & Technol, Shenzhen, Peoples R China University College Dublin; Eindhoven University of Technology; Chinese Academy of Sciences; Institute of Software, CAS; Southern University of Science & Technology Cheng, L (corresponding author), Univ Coll Dublin, PEL, Dublin, Ireland.;Cheng, L (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. long.cheng@ucd.ie ; Cheng, Long/ADJ-1122-2022 Theodoropoulos, Georgios/0000-0002-7448-5886; Cheng, Long/0000-0003-1638-059X European Union [799066] European Union(European Commission) Part of this work was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 799066. The computations were performed on the Inspur TS10K Cluster at the High Performance Computing Center in SUSTech. 13 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-96983-1; 978-3-319-96982-4 LECT NOTES COMPUT SC 2018.0 11014.0 293 305 10.1007/978-3-319-96983-1_21 0.0 13 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT7UL Green Submitted 2023-03-23 WOS:000851042300020 0
J Kim, MK; Cremers, B; Liu, JY; Zhang, JH; Wang, JQ Kim, Moon Keun; Cremers, Bart; Liu, Jiying; Zhang, Jianhua; Wang, Junqi Prediction and correlation analysis of ventilation performance in a residential building using artificial neural network models based on data-driven analysis SUSTAINABLE CITIES AND SOCIETY English Article Artificial neural network; Ventilation performance prediction; Natural ventilation; Mechanical ventilation; Environmental elements ELECTRICITY CONSUMPTION; ENERGY-CONSUMPTION; CO2 CONCENTRATIONS; FEEDFORWARD; VALIDATION; SHANGHAI; CLIMATES; EXPOSURE; SYSTEMS; ANN This study investigates approaches to evaluate prediction and correlation how significantly mechanical and natural ventilation rate and local weather conditions affect the actual ventilation performance of a residential building using Artificial Neural Network (ANN) algorithms: Feedforward networks and Layer recurrent neural networks. In order to evaluate the ventilation performance in a residential building, an impact factor was determined for these measured datasets. This study selected two residential apartments in Switzerland and measured indoor carbon dioxide concentration and volatile organic compound levels, facade opening ratio, mechanical ventilation rates, and indoor temperature and humidity ratio between July 2019 and June 2020. The results described that ANN models illustrate performance in predicting ventilation performance and indoor air quality using mechanical and natural ventilation systems in a residential apartment. Both algorithms have presented relatively lower average error rates, 3.36- 6.12 % in the analysis results. The results presented that the two ANN models using the Levenberg-Marquardt Back Propagation (LMBP) algorithm have good agreements with actual data measured. The accuracy differences were 0.18-1.89 for the average error rates, 0.13-0.78 for the Coefficient of Variation of the Root Mean Square Error (CVRMSE) and 0.07-0.35 for the Normalized Mean Bias Error (NMBE). Through impact factor analysis, mechanical ventilation system mainly dominates the impact of indoor ventilation performance, and other surrounding environments also had significantly affected the resi-dential building. However, the natural ventilation system has limitations to largely influence the ventilation performance in the building because occupants have difficulties adjusting ventilation rates in extreme weather conditions or early morning and nighttime. And these elements could not affect indoor air quality independently. These ANN methods are helpful in analyzing input parameters how each element factor can influence indoor air quality in a residential building. The proposed ANN methods can utilize to predict the performance as reliable approaches. [Kim, Moon Keun] Oslo Metropolitan Univ, Dept Civil Engn & Energy Technol, N-0130 Oslo, Norway; [Cremers, Bart] Zehnder Grp Zwolle BV, Lingen str 2, NL-8028 PM Zwolle, Netherlands; [Liu, Jiying] Shandong Jianzhu Univ, Sch Thermal Engn, Jinan 250101, Peoples R China; [Zhang, Jianhua] Oslo Metropolitan Univ, Dept Comp Sci, N-0130 Oslo, Norway; [Wang, Junqi] Southeast Univ, Sch Architecture, 2 Sipailou, Nanjing 210096, Peoples R China; [Wang, Junqi] Suzhou Univ Sci & Technol, Jiangsu Key Lab Intelligent Bldg Energy Efficiency, Suzhou 215009, Jiangsu, Peoples R China Oslo Metropolitan University (OsloMet); Shandong Jianzhu University; Oslo Metropolitan University (OsloMet); Southeast University - China; Suzhou University of Science & Technology Kim, MK (corresponding author), Oslo Metropolitan Univ, Dept Civil Engn & Energy Technol, N-0130 Oslo, Norway. zhang, jian/HPD-1712-2023; Liu, Jiying/T-4645-2018; WANG, Junqi/I-9462-2019 Liu, Jiying/0000-0001-7385-6959; WANG, Junqi/0000-0001-8843-7781; Kim, Moon Keun/0000-0001-9614-5412 Natural Science Foundation of Shandong Province [ZR2021ME199] Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work was supported by Zehnder Group AG and Oslo Metro-politan University. This work was also supported by Natural Science Foundation of Shandong Province (ZR2021ME199) . 65 2 2 5 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. AUG 2022.0 83.0 103981 10.1016/j.scs.2022.103981 0.0 JUN 2022 13 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels 2T8ET hybrid, Green Published 2023-03-23 WOS:000822702000006 0
J Luo, ZX; Chen, W; Lei, N; Guo, Y; Zhao, T; Liu, J; Gu, XF Luo, Zhongxuan; Chen, Wei; Lei, Na; Guo, Yang; Zhao, Tong; Liu, Jiakun; Gu, Xianfeng The Singularity Set of Optimal Transportation Maps COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS English Article upper envelope; convex hull; power diagram; weighted Delaunay triangulation; secondary polytope; normal Frechet distance; obliqueness; curvature REARRANGEMENT; ALGORITHMS Optimal transportation plays an important role in many engineering fields, especially in deep learning. By the Brenier theorem, computing optimal transportation maps is reduced to solving Monge-Ampere equations, which in turn is equivalent to constructing Alexandrov polytopes. Furthermore, the regularity theory of Monge-Ampere equation explains mode collapsing issue in deep learning. Hence, computing and studying the singularity sets of OT maps become important. In this work, we generalize the concept of medial axis to power medial axis, which describes the singularity sets of optimal transportation maps. Then we propose a computational algorithm based on variational approach using power diagrams. Furthermore, we prove that when the measures are changed homotopically, the corresponding singularity sets of the optimal transportation maps are homotopy equivalent as well. Furthermore, we generalize the Frechet distance concept and utilize the obliqueness condition to give a sufficient condition for the existence of singularities of optimal transportation maps between planar domains. The condition is formulated using the boundary curvature. [Luo, Zhongxuan] Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China; [Chen, Wei] Dalian Univ Technol, Sch Software Technol, Dalian 116620, Peoples R China; [Lei, Na] Dalian Univ Technol, DUT RU ISE, Dalian 116620, Peoples R China; [Guo, Yang; Gu, Xianfeng] SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA; [Zhao, Tong] INRIA Sophia Antipolis & Telecom Paris, Paris, France; [Liu, Jiakun] Univ Wollongong, Sch Math & Appl Stat, Wollongong, NSW 2522, Australia Dalian University of Technology; Dalian University of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Wollongong Lei, N (corresponding author), Dalian Univ Technol, DUT RU ISE, Dalian 116620, Peoples R China. nalei@dlut.edu.cn Liu, Jiakun/F-6198-2019 Liu, Jiakun/0000-0003-4409-4187 National Natural Science Foundation of China [61720106005, 61772105, 61936002]; Fundamental Research Funds for the Central Universities [DUT20TD107, DUT20JC32]; National Science Foundation [CMMI-1762287]; Ford University Research Program [2017-9198R]; National Institute of Health [R21EB029733, R01LM012434]; Australian Research Council [DP170100929, DP200101084]; Australian Research Council [DP200101084] Funding Source: Australian Research Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Science Foundation(National Science Foundation (NSF)); Ford University Research Program; National Institute of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Australian Research Council(Australian Research Council); Australian Research Council(Australian Research Council) This research of Luo, Chen, and Lei was partially supported by the National Natural Science Foundation of China under Grant nos. 61720106005, 61772105, 61936002, the Fundamental Research Funds for the Central Universities (DUT20TD107, DUT20JC32). The work of Guo and Gu was supported by the National Science Foundation (CMMI-1762287), the Ford University Research Program (URP no. 2017-9198R), the National Institute of Health (R21EB029733, R01LM012434). The research of Liu was supported by the Australian Research Council DP170100929 and DP200101084. 19 0 0 1 1 PLEIADES PUBLISHING INC NEW YORK PLEIADES HOUSE, 7 W 54 ST, NEW YORK, NY, UNITED STATES 0965-5425 1555-6662 COMP MATH MATH PHYS+ Comput. Math. Math. Phys. AUG 2022.0 62.0 8 1313 1330 10.1134/S0965542522080097 0.0 18 Mathematics, Applied; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics 4L4WO 2023-03-23 WOS:000852630600008 0
J Zaffino, P; Pernelle, G; Mastmeyer, A; Mehrtash, A; Zhang, HT; Kikinis, R; Kapur, T; Spadea, MF Zaffino, Paolo; Pernelle, Guillaume; Mastmeyer, Andre; Mehrtash, Alireza; Zhang, Hongtao; Kikinis, Ron; Kapur, Tina; Spadea, Maria Francesca Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy PHYSICS IN MEDICINE AND BIOLOGY English Article brachytherapy; gynecologic cancer; medical image segmentation; image guided surgery; convolutional neural networks; catheter segmentation CERVIX CANCER BRACHYTHERAPY; LOCALLY ADVANCED-CARCINOMA; INTERSTITIAL BRACHYTHERAPY; LOCALIZATION; ULTRASOUND; RECOMMENDATIONS; CONTOUR; TERMS; TOOL External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 +/- 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 +/- 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy. [Zaffino, Paolo; Spadea, Maria Francesca] Magna Graecia Univ Catanzaro, Dept Expt & Clin Med, I-88100 Catanzaro, Italy; [Pernelle, Guillaume] Imperial Coll London, Bioengn Dept, London SW7 2AZ, England; [Mastmeyer, Andre] Univ Lubeck, Inst Med Informat, D-23562 Lubeck, Germany; [Mehrtash, Alireza; Kikinis, Ron; Kapur, Tina] Harvard Med Sch, Dept Radiol, Brigham & Womens Hosp, Boston, MA 02115 USA; [Zhang, Hongtao] Hebei Gen Hosp, Oncol Dept, Shijiazhuang 050051, Hebei, Peoples R China; [Kikinis, Ron] Univ Bremen, Dept Comp Sci, Fraunhofer MEVIS, D-28359 Bremen, Germany Magna Graecia University of Catanzaro; Imperial College London; University of Lubeck; Harvard University; Brigham & Women's Hospital; Harvard Medical School; University of Bremen Zaffino, P (corresponding author), Magna Graecia Univ Catanzaro, Dept Expt & Clin Med, I-88100 Catanzaro, Italy. p.zaffino@unicz.it Mastmeyer, Andre/J-4379-2017; Spadea, Maria Francesca/AHI-0454-2022; Zaffino, Paolo/T-3761-2019 Mastmeyer, Andre/0000-0003-0561-8363; Zaffino, Paolo/0000-0002-0219-0157; zhang, hongtao/0000-0002-1572-0719 Nvidia GPU grant 2018; DFG: GS-CMLS; NIH [P41EB015898, P41EB015902, U24CA180918]; [MA 6791/1-1] Nvidia GPU grant 2018; DFG: GS-CMLS(German Research Foundation (DFG)); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); This work was supported by the following grants: Nvidia GPU grant 2018, DFG: GS-CMLS, and MA 6791/1-1 (A Mastmeyer), NIH grants P41EB015898 (T Kapur, R Kikinis), P41EB015902 and U24CA180918 (R Kikinis). 50 36 36 2 15 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0031-9155 1361-6560 PHYS MED BIOL Phys. Med. Biol. AUG 2019.0 64.0 16 165008 10.1088/1361-6560/ab2f47 0.0 11 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging IS0PD 31272095.0 Green Accepted 2023-03-23 WOS:000481852600008 0
J Shang, P; Zhu, MQ; Wang, Y; Zheng, XY; Wu, XJ; Zhu, J; Feng, JC; Zhang, HL Shang, Pei; Zhu, Mingqin; Wang, Ying; Zheng, Xiangyu; Wu, Xiujuan; Zhu, Jie; Feng, Jiachun; Zhang, Hong-Liang Axonal variants of Guillain-Barre syndrome: an update JOURNAL OF NEUROLOGY English Review Axonal GBS; Acute motor axonal neuropathy; Acute motor and sensory axonal neuropathy; Guillain-Barre syndrome CAMPYLOBACTER-JEJUNI INFECTION; CERVICAL-BRACHIAL VARIANT; MILLER FISHER SYNDROME; ZIKA VIRUS-INFECTION; ANTIGANGLIOSIDE ANTIBODIES; INFLUENZA VACCINATION; CLINICAL-FEATURES; NERVE ULTRASOUND; INTERFERON-BETA; RABBIT MODEL Axonal variants of Guillain-Barre syndrome (GBS) mainly include acute motor axonal neuropathy, acute motor and sensory axonal neuropathy, and pharyngeal-cervical-brachial weakness. Molecular mimicry of human gangliosides by a pathogen's lipooligosaccharides is a well-established mechanism for Campylobacter jejuni-associated GBS. New triggers of the axonal variants of GBS (axonal GBS), such as Zika virus, hepatitis viruses, intravenous administration of ganglioside, vaccination, and surgery, are being identified. However, the pathogenetic mechanisms of axonal GBS related to antecedent bacterial or viral infections other than Campylobacter jejuni remain unknown. Currently, autoantibody classification and serial electrophysiology are cardinal approaches to differentiate axonal GBS from the prototype of GBS, acute inflammatory demyelinating polyneuropathy. Newly developed technologies, including metabolite analysis, peripheral nerve ultrasound, and feature selection via artificial intelligence are facilitating more accurate diagnosis of axonal GBS. Nevertheless, some key issues, such as genetic susceptibilities, remain unanswered and moreover, current therapies bear limitations. Although several therapies have shown considerable benefits to experimental animals, randomized controlled trials are still needed to validate their efficacy. [Shang, Pei; Zhu, Mingqin; Wang, Ying; Zheng, Xiangyu; Wu, Xiujuan; Zhu, Jie; Feng, Jiachun] Jilin Univ, Hosp 1, Dept Neurol, Xinmin St 71, Changchun 130021, Peoples R China; [Zhu, Jie] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden; [Zhang, Hong-Liang] Natl Nat Sci Fdn China, Dept Life Sci, Shuangqing Rd 83, Beijing 100085, Peoples R China Jilin University; Karolinska Institutet; National Natural Science Foundation of China (NSFC) Feng, JC (corresponding author), Jilin Univ, Hosp 1, Dept Neurol, Xinmin St 71, Changchun 130021, Peoples R China.;Zhang, HL (corresponding author), Natl Nat Sci Fdn China, Dept Life Sci, Shuangqing Rd 83, Beijing 100085, Peoples R China. fengjcfrank@qq.com; drzhl@hotmail.com Shang, Pei/ABC-9913-2020; Zhang, Hongliang/AAN-3077-2020 Shang, Pei/0000-0003-4345-2165; Zhang, Hongliang/0000-0001-9205-5559 National Key R&D Program of China [2017YFC0110304]; National Natural Science Foundation of China [31600820, 81771257] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work was supported by grants from the National Key R&D Program of China (No. 2017YFC0110304 to JF), and the National Natural Science Foundation of China to (No. 31600820 to MZ and No. 81771257 to JF). 161 35 36 3 17 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0340-5354 1432-1459 J NEUROL J. Neurol. JUL 2021.0 268.0 7 2402 2419 10.1007/s00415-020-09742-2 0.0 MAR 2020 18 Clinical Neurology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology SV9TF 32140865.0 2023-03-23 WOS:000518303800006 0
J Wang, XC; Klemes, JJ; Dong, XB; Fan, WG; Xu, ZH; Wang, YT; Varbanov, PS Wang, Xue-Chao; Klemes, Jiri Jaromir; Dong, Xiaobin; Fan, Weiguo; Xu, Zihan; Wang, Yutao; Varbanov, Petar Sabev Air pollution terrain nexus: A review considering energy generation and consumption RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Review Air pollution terrain nexus; Air pollutants; Terrain; Energy generation; Energy consumption; Air pollutants modelling; Air pollutants sampling GREENHOUSE-GAS EMISSIONS; COMPLEX TERRAIN; NEURAL-NETWORK; ECOSYSTEM SERVICES; HAZE POLLUTION; REGIONAL HAZE; PREMATURE MORTALITY; MODEL SIMULATIONS; CLIMATE-CHANGE; SO2 EMISSIONS Air pollution caused by energy generation and consumption is both a global as well as localised issue. It contributes to global warming along with degradation in human health, ecosystem health, local and global sustainable development. About 75% of the global GHG emissions, 66% of NOx emissions and most of the PM emissions are from energy sectors. Energy sectors have also been reported as the main contributors (90%) of SO2 emission in China. Among various influential factors (like energy utilisation, the intensity of economic activities, climatic conditions), the terrain is one of the key elements for the formation and dispersion of air pollution. The relationship between air pollution and terrain requires to be more deeply studied. The main contributions of this review include: 1) Summarising the main sources and types of air pollutants as well as the emission characteristics of different sector groups; 2) Proposing the mechanism of air pollution terrain nexus; 3) Reviewing modelling and experiments approaches for air pollution terrain nexus simulation; 4) Highlighting the existing limitations and challenges. This review provides a better understanding of the air pollution terrain nexus. It can contribute to mitigating the air pollution problems. [Wang, Xue-Chao; Klemes, Jiri Jaromir; Varbanov, Petar Sabev] Brno Univ Technol, Sustainable Proc Integrat Lab, Fac Mech Engn, SPIL,NETME Ctr,VUT Brno,Tech 2896 2, Brno 61669, Czech Republic; [Dong, Xiaobin; Fan, Weiguo; Xu, Zihan] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; [Wang, Yutao] Fudan Univ, Dept Environm Sci & Engn, Shanghai Key Lab Atmospher Particle Pollut & Prev, Shanghai 200438, Peoples R China; [Varbanov, Petar Sabev] Fudan Univ, Dept Environm Sci & Engn, Tyndall Ctr, Shanghai 200438, Peoples R China Brno University of Technology; Beijing Normal University; Fudan University; Fudan University Wang, XC (corresponding author), Brno Univ Technol, Sustainable Proc Integrat Lab, Fac Mech Engn, SPIL,NETME Ctr,VUT Brno,Tech 2896 2, Brno 61669, Czech Republic. wang@fme.vutbr.cz Varbanov, Petar Sabev/B-8954-2009; Klemes, Jiri Jaromir/B-7291-2009; Wang, Xue-Chao/J-5860-2019; Wang, Yutao/P-4878-2019 Varbanov, Petar Sabev/0000-0001-5261-1645; Klemes, Jiri Jaromir/0000-0002-7450-7029; Wang, Xue-Chao/0000-0003-4431-0900; Wang, Yutao/0000-0001-8297-8579 EU [CZ.02.1.01/0.0/0.0/15_003/0000456]; Czech Republic Operational Programme Research and Development, Education; Fudan University; International S&T Cooperation Program of China [YS2017YFGH000562] EU(European Commission); Czech Republic Operational Programme Research and Development, Education; Fudan University; International S&T Cooperation Program of China This work is supported by the EU supported project Sustainable Process Integration Laboratory - SPIL (CZ.02.1.01/0.0/0.0/15_003/0000456), by Czech Republic Operational Programme Research and Development, Education under a collaboration agreement with Beijing Normal University and Fudan University; and the International S&T Cooperation Program of China (YS2017YFGH000562). 122 96 99 22 129 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. MAY 2019.0 105.0 71 85 10.1016/j.rser.2019.01.049 0.0 15 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels HN3YL 2023-03-23 WOS:000460121000006 0
J Zhou, FF; Yin, XJ; Hu, R; Houssein, A; Gastinger, S; Martin, B; Li, SS; Prioux, J Zhou, Fenfen; Yin, Xiaojian; Hu, Rui; Houssein, Aya; Gastinger, Steven; Martin, Brice; Li, Shanshan; Prioux, Jacques Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model NUTRIENTS English Article energy expenditure; age; respiratory magnetometer plethysmography; physical activities; temporal convolutional networks model RIB CAGE; HEART-RATE; EXERCISE; CALORIMETRY; VALIDATION; INTENSITY; DISTANCES; MOVEMENT; VALIDITY; SENSORS Purpose: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model. Methods: Twenty-three healthy subjects in three groups (nine adults (A), eight post-pubertal (PP) males and six pubertal (P) females) first sat or stood for six minutes and then performed a maximal graded test on a bicycle ergometer until exhaustion. We measured energy expenditure, oxygen uptake, ventilatory thresholds 1 and 2 and maximal oxygen uptake. The respiratory magnetometer plethysmography system measured four chest and abdomen distances using magnetometers sensors. We trained the models to predict energy expenditure based on the temporal convolutional networks model. Results: The respiratory magnetometer plethysmography system provided accurate energy expenditure estimation in groups A (R-2 = 0.98), PP (R-2 = 0.98) and P (R-2 = 0.97). The temporal convolutional networks model efficiently estimates energy expenditure under sitting, standing and high levels of exercise intensities. Conclusion: Our results proved the respiratory magnetometer plethysmography system's effectiveness in estimating energy expenditure for different age populations across various intensities of physical activity. [Zhou, Fenfen; Yin, Xiaojian] East China Normal Univ, Coll Phys Educ & Hlth, Sino French Joint Res Ctr Sport Sci, Shanghai 200241, Peoples R China; [Zhou, Fenfen; Houssein, Aya; Prioux, Jacques] Ecole Normale Super Rennes, Dept Sci Sport & Educ Phys, F-35170 Bruz, France; [Zhou, Fenfen; Houssein, Aya; Gastinger, Steven; Martin, Brice; Prioux, Jacques] Univ Rennes 2, Lab Mouvement Sport Sante EA 7404, F-35170 Bruz, France; [Hu, Rui] Chinese Univ Hong Kong, Dept Sports Sci & Phys Educ, Hong Kong 999077, Peoples R China; [Li, Shanshan] Jianghai Polytech Coll, Dept Econ & Management, Yangzhou 225101, Jiangsu, Peoples R China East China Normal University; Ecole Normale Superieure de Rennes (ENS Rennes); Universite Rennes 2; Chinese University of Hong Kong Zhou, FF (corresponding author), East China Normal Univ, Coll Phys Educ & Hlth, Sino French Joint Res Ctr Sport Sci, Shanghai 200241, Peoples R China.;Zhou, FF (corresponding author), Ecole Normale Super Rennes, Dept Sci Sport & Educ Phys, F-35170 Bruz, France.;Zhou, FF (corresponding author), Univ Rennes 2, Lab Mouvement Sport Sante EA 7404, F-35170 Bruz, France. fenfen.zhou@ens-rennes.fr Zhou, Fenfen/GXM-6958-2022 Zhou, Fenfen/0000-0002-3317-642X Chinese Scholarship Council [CSC] [201806140220] Chinese Scholarship Council [CSC](China Scholarship Council) The Ph.D. thesis of Fenfen Zhou is funded by the Chinese Scholarship Council [CSC NO. 201806140220]. 61 0 0 6 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6643 NUTRIENTS Nutrients OCT 2022.0 14.0 19 4190 10.3390/nu14194190 0.0 15 Nutrition & Dietetics Science Citation Index Expanded (SCI-EXPANDED) Nutrition & Dietetics 5H8TM 36235842.0 gold 2023-03-23 WOS:000867945300001 0
J Hu, BF; Xue, J; Zhou, Y; Shao, S; Fu, ZY; Li, Y; Chen, SC; Qi, L; Shi, Z Hu, Bifeng; Xue, Jie; Zhou, Yin; Shao, Shuai; Fu, Zhiyi; Li, Yan; Chen, Songchao; Qi, Lin; Shi, Zhou Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning ENVIRONMENTAL POLLUTION English Article Heavy metals; Soil-crop ecosystems; Machine learning; Random forest; Bioaccumulation factor; Controlling factors PEARL RIVER DELTA; RISK-ASSESSMENT; CADMIUM ACCUMULATION; AGRICULTURAL SOILS; WASTE-WATER; RICE; CHINA; BIOAVAILABILITY; VEGETABLES; CONTAMINATION The prediction and identification of the factors controlling heavy metal transfer in soil-crop ecosystems are of critical importance. In this study, random forest (RF), gradient boosted machine (GBM), and generalised linear (GLM) models were compared after being used to model and identify prior factors that affect the transfer of heavy metals (HMs) in soil-crop systems in the Yangtze River Delta, China, based on 13 covariates with 1822 pairs of soil-crop samples. The mean bioaccumulation factors (BAFs) for all crops followed the order Cd > Zn > As > Cu > Ni > Hg > Cr > Pb. The RF model showed the best prediction ability for the BAFs of HMs in soil-crop ecosystems, followed by GBM and GLM. The R2 values of the RF models for the BAFs of Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb were 0.84, 0.66, 0.59, 0.58, 0.58, 0.51, 0.30, and 0.17, respectively. The primary controlling factor in soil-to-crop transfer of all HMs under study was plant type, followed by soil heavy metal content and soil organic materials. The model used herein could be used to assist the prediction of heavy metal contents in crops based on heavy metal contents in soil and other covariates, and can significantly reduce the cost, labour, and time requirements involved with laboratory analysis. It can also be used to quantify the importance of variables and identify potential control factors in heavy metal bioaccumulation in soil-crop ecosystems. (C) 2020 Elsevier Ltd. All rights reserved. [Hu, Bifeng; Xue, Jie; Zhou, Yin; Shao, Shuai; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Key Lab Environm Remediat & Ecol Hlth, Minist Educ, Hangzhou 310058, Peoples R China; [Hu, Bifeng] INRAE, Unite Rech Sci Sol, F-45075 Orleans, France; [Hu, Bifeng] Orleans Univ, Sci Terre & Univers, F-45067 Orleans, France; [Zhou, Yin; Li, Yan] Zhejiang Univ, Sch Publ Affairs, Inst Land Sci & Property, Hangzhou 310058, Peoples R China; [Fu, Zhiyi] Zhejiang Univ, Sch Earth Sci, Hangzhou 310058, Peoples R China; [Chen, Songchao] INRAE, US 1106, InfoSol, F-4075 Orleans, France; [Qi, Lin] Ningbo Planting Management Stn, Ningbo 315012, Zhejiang, Peoples R China Zhejiang University; INRAE; Zhejiang University; Zhejiang University; INRAE Shi, Z (corresponding author), Zhejiang Univ, Coll Environm & Resource Sci, Key Lab Environm Remediat & Ecol Hlth, Minist Educ, Hangzhou 310058, Peoples R China. bifeng.hu@inra.fr; shizhou@zju.edu.cn Zhou, yixin/HGE-2329-2022; shi, Zhou/M-7845-2019; Chen, Songchao/S-7982-2019; Hu, Bifeng/G-2190-2018; jie, xue/HLP-6332-2023 shi, Zhou/0000-0003-3914-5402; Chen, Songchao/0000-0003-1245-0482; jie, xue/0000-0002-0340-5594; Hu, Bifeng/0000-0001-9353-0307 National Key Research and Development Program of China [2018YFC1800105]; Key Research and Development Project of Zhejiang Province, China [2015C02011]; China Scholarship Council [201706320317] National Key Research and Development Program of China; Key Research and Development Project of Zhejiang Province, China; China Scholarship Council(China Scholarship Council) This work was supported by the National Key Research and Development Program of China (2018YFC1800105) and the Key Research and Development Project of Zhejiang Province, China (2015C02011). We also acknowledge the support received by Bifeng Hu from the China Scholarship Council (under grant agreement No. 201706320317) for 3 years' Ph.D. study in INRA and Orleans University. 80 82 85 55 247 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0269-7491 1873-6424 ENVIRON POLLUT Environ. Pollut. JUL 2020.0 262.0 114308 10.1016/j.envpol.2020.114308 0.0 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology LO3JG 32155557.0 2023-03-23 WOS:000533524300103 0
J Fan, QW; Kang, Q; Zurada, JM Fan, Qinwei; Kang, Qian; Zurada, Jacek M. Convergence analysis for sigma-pi-sigma neural network based on some relaxed conditions INFORMATION SCIENCES English Article Sigma-Pi-sigma neural network; Batch algorithm; Boundedness; Convergence SMOOTHING L-1/2 REGULARIZATION; ONLINE GRADIENT-METHOD; DETERMINISTIC CONVERGENCE; ALGORITHM; MOMENTUM; PRODUCT; SUM The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H-infinity control scheme. Instead of considering the conventional network transmission delay, the paper introduces the probability distribution based network transmission delay, which is more compatible with real time-simulations. Due to distributed transmission delay, the closed-loop network con-trol system has become distributed delay system. To make use of full information about the membership function, the Lyapunov function candidate is designed as fuzzy-membership-dependent Lyapunov function. A switching concept with respect to the rate of changes in membership functions is introduced to design the optimal control gain matrices for the considered system. The main advantage of the fuzzy-membership-dependent Lyapunov function is that it can ensure the less conservatism of the result without increasing the number of decision variables. To validate the effectiveness of the proposed approach, two kinds are real-time systems are taken into account, one is nonlinear mass-spring-damper model, followed by an industrial example, say, surface mounted permanent mag-net synchronous motor model. The corresponding simulation results show the superiority of the proposed results over the existing works. (C) 2021 Elsevier Inc. All rights reserved. [Fan, Qinwei; Kang, Qian] Xian Polytech Univ, Sch Sci, Xian 710048, Peoples R China; [Zurada, Jacek M.] Univ Louisville, Dept Elect & Comp Engn, Louisville, KY 40292 USA; [Zurada, Jacek M.] Spoleczna Akad Nauk, PL-90011 Lodz, Poland Xi'an Polytechnic University; University of Louisville Fan, QW (corresponding author), Xian Polytech Univ, Sch Sci, Xian 710048, Peoples R China. qinweifan@xpu.edu.cn; kangqian0373@126.com; jmzura02@louisville.edu Fan, Qinwei/0000-0002-1017-3496 65th China Postdoctoral Science Foundation [2019M652837]; Natural Science Foundation Guidance Project of Liaoning Province [2019-ZD-0128]; Natural Science Basic Research Plan in Shaanxi Province of China [2021JM-446]; Graduate Innovation Fund of Xi'an Polytechnic University [chx2021030] 65th China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation Guidance Project of Liaoning Province; Natural Science Basic Research Plan in Shaanxi Province of China; Graduate Innovation Fund of Xi'an Polytechnic University This work was supported by The 65th China Postdoctoral Science Foundation (No. 2019M652837) . The Natural Science Foundation Guidance Project of Liaoning Province (No. 2019-ZD-0128) . Natural Science Basic Research Plan in Shaanxi Province of China (No. 2021JM-446) . The Graduate Innovation Fund of Xi'an Polytechnic University (No. chx2021030). 37 5 6 3 18 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. MAR 2022.0 585.0 70 88 10.1016/j.ins.2021.11.044 0.0 NOV 2021 19 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science XK9JB 2023-03-23 WOS:000727771200005 0
J Guo, QQ; Fang, XY; Wang, LB; Zhang, EM Guo, Qingqing; Fang, Xianyong; Wang, Linbo; Zhang, Enming Polyp Segmentation of Colonoscopy Images by Exploring the Uncertain Areas IEEE ACCESS English Article Image segmentation; Transformers; Colonoscopy; Feature extraction; Deep learning; Cancer; Uncertainty; Polyp segmentation; deep learning; colonoscopy image; colorectal cancer NETWORK Colorectal cancer is one of the leading causes of death worldwide. Polyps are early symptoms of colorectal cancer and prone to malignant transformation. Polyp segmentation of colonoscopy images can help diagnosis. However, existing studies on polyp segmentation of colonoscopy images face two main difficulties: blurry polyp boundaries, close resemblances between polyps and surrounding tissues. The former may lead to partial segmentations, while the latter can result in false positive segmentations. This paper proposes a new polyp segmentation framework to tackle the two challenges. In this method, an uncertainty region based module called Uncertainty eXploration (UnX) is introduced to get the complete polyp region while eliminating the interferences from the backgrounds. Specifically, it refines the feature maps with ternary guidance masks by dividing the initial guidance maps into three types: foreground, background and uncertain region, so that the uncertain areas are highlighted for more foreground objects while the backgrounds are forcefully suppressed to avoid interferences of tissues in background. Taking UnX as side supervision to the transformer encoder based backbone stages, the proposed method can mine the boundary areas from the uncertainty regions gradually and obtain robust polyp segmentation finally. Moreover, a new module called Feature Enhancement (FeE) is also incorporated in the framework to enhance the discrimination for images with significant variation of sizes and shapes of polyps. FeE can supply multi-scale features to the global oriented transformer features. Experiments on five polyp segmentation benchmark datasets of colonoscopy images, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, show the superior performances of our proposed method. Especially, for ETIS, the most challenging among the five datasets, our method achieves 7.7% and 5.6% improvements in mDSC (mean Dice Similarity Coefficient) and mIoU (mean Intersection over Union) respectively in comparison with the state-of-the-arts methods. [Guo, Qingqing; Fang, Xianyong; Wang, Linbo] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China; [Zhang, Enming] Lund Univ, Dept Clin Sci, Islet Pathophysiol, Diabet Ctr, S-21428 Malmo, Sweden Anhui University; Lund University Fang, XY (corresponding author), Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China. fangxianyong@ahu.edu.cn Natural Science Foundation of Anhui Province, China [2108085MF210] Natural Science Foundation of Anhui Province, China(Natural Science Foundation of Anhui Province) This work was supported by the Natural Science Foundation of Anhui Province, China, under Grant 2108085MF210. 61 1 1 10 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2022.0 10.0 52971 52981 10.1109/ACCESS.2022.3175858 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 1P4RD gold 2023-03-23 WOS:000801996900001 0
C Pathan, MS; Jain, M; Nag, A; Al Skaif, T; Dev, S IEEE Pathan, Muhammad Salman; Jain, Mayank; Nag, Avishek; Al Skaif, Tarek; Dev, Soumyabrata Analyzing Impact of Time on Early Detection of Rainfall Event 2021 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS 2021) English Proceedings Paper Photonics and Electromagnetics Research Symposium (PIERS) NOV 21-25, 2021 Hangzhou, PEOPLES R CHINA IEEE,Zhejiang Univ, Electromagnet Acad,Coll Informat Sci & Elect Engn,Zhejiang Key Lab Adv Microelectron Intelligent Syst & Applicat,Zhejiang Univ, Univ Illinois Urbana Champaign Inst,Natl Engn Res Ctr Opt Instruments,Shanghai Ideaoptics Corp Ltd,IEEE Geoscience & Remote Sensing Soc,IEEE Antennas & Propagat Soc,IEEE Photon Soc,Electromagnet Acad Rainfall is a critical feature of a climatic system, which has a chaotic impact on agriculture, water resource management and biological systems. An early and accurate prediction of rainfall is a very important task and has vital effects on human life. However rainfall prediction is a challenging task in meteorology. Rainfall data mostly have high inconstancy and irregular patterns which are rare in other time series data. The rainfall data changes greatly with time, so the time factor has a high importance in such time series data. In order to develop efficient forecast models, one should deeply analyze the effect of time on prediction accuracy. Therefore, in this paper, we analyze how early we can predict a rainfall event with accuracy. We have performed the experiments on a 5-year daily rainfall data obtained from National Oceanic and Atmospheric Administration (NOAA) at 1-, 2-, 3-, and 4-day prior forecasting horizons using a machine learning technique to observe the trends in the prediction accuracy. Furthermore, we have also identified some most important input features from the dataset which plays a major role in the prediction of a rainfall event. Our results conclude that average wind speed and minimum temperature are most important weather variables in classifying rainfall events. We also observe that the forecasting error gradually increases with increasing lead times. [Pathan, Muhammad Salman; Jain, Mayank; Dev, Soumyabrata] ADAPT SFI Res Ctr, Dublin, Ireland; [Pathan, Muhammad Salman; Jain, Mayank; Dev, Soumyabrata] Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland; [Nag, Avishek] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin, Ireland; [Al Skaif, Tarek] Wageningen Univ & Res, Informat Technol Grp, Wageningen, Netherlands; [Dev, Soumyabrata] Beijing Dublin Int Coll, Beijing, Peoples R China University College Dublin; University College Dublin; Wageningen University & Research Pathan, MS (corresponding author), ADAPT SFI Res Ctr, Dublin, Ireland.;Pathan, MS (corresponding author), Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland. Pathan, Muhammad Salman/AAJ-1458-2020; Jain, Mayank/GLR-8004-2022 Pathan, Muhammad Salman/0000-0002-0210-3121; Jain, Mayank/0000-0003-2941-5579 European Union [801522]; Science Foundation Ireland; European Regional Development Fund through the ADAPT Centre for Digital Content Technology [13/RC/2106 P2] European Union(European Commission); Science Foundation Ireland(Science Foundation Ireland); European Regional Development Fund through the ADAPT Centre for Digital Content Technology This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie SkAlodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology grant number 13/RC/2106 P2. 13 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-7247-7 2021.0 1985 1989 10.1109/PIERS53385.2021.9695104 0.0 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT0ZS 2023-03-23 WOS:000795902300326 0
J Al Hajj, H; Lamard, M; Conze, PH; Roychowdhury, S; Hu, XW; Marsalkaite, G; Zisimopoulos, O; Dedmari, MA; Zhao, FQ; Prellberg, J; Sahu, M; Galdran, A; Araujo, T; Vo, DM; Panda, C; Dahiya, N; Kondo, S; Bian, ZB; Vandat, A; Bialopetravicius, J; Flouty, E; Qiu, CH; Dill, S; Mukhopadhyay, A; Costa, P; Aresta, G; Ramamurthys, S; Lee, SW; Campilho, A; Zachow, S; Xia, SR; Conjeti, S; Stoyanov, D; Armaitis, J; Heng, PA; Macready, WG; Cochener, B; Quellec, G Al Hajj, Hassan; Lamard, Mathieu; Conze, Pierre-Henri; Roychowdhury, Soumali; Hu, Xiaowei; Marsalkaite, Gabija; Zisimopoulos, Odysseas; Dedmari, Muneer Ahmad; Zhao, Fenqiang; Prellberg, Jonas; Sahu, Manish; Galdran, Adrian; Araujo, Teresa; Duc My Vo; Panda, Chandan; Dahiya, Navdeep; Kondo, Satoshi; Bian, Zhengbing; Vandat, Arash; Bialopetravicius, Jonas; Flouty, Evangello; Qiu, Chenhui; Dill, Sabrina; Mukhopadhyay, Anirban; Costa, Pedro; Aresta, Guilherme; Ramamurthys, Senthil; Lee, Sang-Woong; Campilho, Aurelio; Zachow, Stefan; Xia, Shunren; Conjeti, Sailesh; Stoyanov, Danail; Armaitis, Jogundas; Heng, Pheng-Ann; Macready, William G.; Cochener, Beatrice; Quellec, Gwenole CATARACTS: Challenge on automatic tool annotation for cataRACT surgery MEDICAL IMAGE ANALYSIS English Article Cataract surgery; Video analysis; Deep learning; Challenge SURGICAL-INSTRUMENTS; NEEDLE TRACKING; POSE ESTIMATION; LOCALIZATION; RECOGNITION; NETWORKS; MODEL; ROBOT Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. (C) 2018 Elsevier B.V. All rights reserved. [Al Hajj, Hassan; Lamard, Mathieu; Conze, Pierre-Henri; Cochener, Beatrice; Quellec, Gwenole] Inserm, UMR 1101, F-29200 Brest, France; [Lamard, Mathieu; Cochener, Beatrice] Univ Bretagne Occidentale, F-29200 Brest, France; [Conze, Pierre-Henri] UBL, IMT Atlantique, LaTIM UMR 1101, F-29200 Brest, France; [Roychowdhury, Soumali; Bian, Zhengbing; Vandat, Arash; Macready, William G.] D Wave Syst Inc, Burnaby, BC V5G 4M9, Canada; [Hu, Xiaowei; Heng, Pheng-Ann] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Marsalkaite, Gabija; Bialopetravicius, Jonas; Armaitis, Jogundas] UAB, Oxipit, LT-10224 Vilnius, Lithuania; [Zisimopoulos, Odysseas; Flouty, Evangello; Stoyanov, Danail] Digital Surg Ltd, London EC1V 2QY, England; [Stoyanov, Danail] UCL, Gower St, London WC1E 6BT, England; [Dedmari, Muneer Ahmad; Conjeti, Sailesh] Tech Univ Munich, Fac Informat, Chair Comp Aided Med Procedures, D-85748 Garching, Germany; [Conjeti, Sailesh] German Ctr Neurodegenrat Dis DZNE, D-53127 Bonn, Germany; [Zhao, Fenqiang; Qiu, Chenhui; Xia, Shunren] Zhejiang Univ, Minist Educ, Key Lab Biomed Engn, Hangzhou 310000, Zhejiang, Peoples R China; [Prellberg, Jonas] Carl von Ossietzky Univ Oldenburg, Dept Informat, D-26129 Oldenburg, Germany; [Sahu, Manish; Dill, Sabrina; Zachow, Stefan] Zuse Inst Berlin, Dept Visual Data Anal, D-14195 Berlin, Germany; [Mukhopadhyay, Anirban] Tech Univ Darmstadt, Dept Comp Sci, D-64283 Darmstadt, Germany; [Araujo, Teresa; Aresta, Guilherme; Campilho, Aurelio] Univ Porto, Fac Engn, P-4200465 Porto, Portugal; [Galdran, Adrian; Araujo, Teresa; Costa, Pedro; Aresta, Guilherme; Campilho, Aurelio] INESC TEC Inst Engn Sistemas & Comp Tecnol & Cien, P-4200465 Porto, Portugal; [Duc My Vo; Lee, Sang-Woong] Gachon Univ, 1342 Seongnamdaero, Seongnam 13120, South Korea; [Panda, Chandan] Epsilon, Bengaluru 560045, Karnataka, India; [Dahiya, Navdeep; Ramamurthys, Senthil] Georgia Tech, Lab Computat Comp Vis, Atlanta, GA 30332 USA; [Kondo, Satoshi] Konica Minolta Inc, Osaka 5698503, Japan; [Cochener, Beatrice] CHRU Brest, Serv Ophtalmol, F-29200 Brest, France Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; Universite de Bretagne Occidentale; IMT - Institut Mines-Telecom; IMT Atlantique; Institut National de la Sante et de la Recherche Medicale (Inserm); Chinese University of Hong Kong; University of London; University College London; Technical University of Munich; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE); Zhejiang University; Carl von Ossietzky Universitat Oldenburg; Zuse Institute Berlin; Technical University of Darmstadt; Universidade do Porto; INESC TEC; Gachon University; University System of Georgia; Georgia Institute of Technology; Konica Minolta Inc.; CHU Brest; Universite de Bretagne Occidentale Quellec, G (corresponding author), Inserm, UMR 1101, F-29200 Brest, France. gwenole.quellec@inserm.fr Mukhopadhyay, Anirban/AAR-9364-2020; Zhao, Fenqiang/AAJ-2807-2021; Quellec, Gwenole/L-9946-2015; Conze, Pierre-Henri/AAE-9248-2020; Dahiya, Navdeep/AAJ-9062-2020; Bialopetravičius, Jonas/ABD-3001-2020; Lee, Sang-Woong/ABF-6191-2020; Kondo, Satoshi/AAY-9642-2021; Sahu, Manish/AAN-5925-2021; Stoyanov, Danail/V-1043-2019; Campilho, Aurelio/M-4869-2013; Araujo, Teresa/F-5629-2016 Quellec, Gwenole/0000-0003-1669-7140; Conze, Pierre-Henri/0000-0003-2214-3654; Lee, Sang-Woong/0000-0001-8117-6566; Kondo, Satoshi/0000-0002-4941-4920; Sahu, Manish/0000-0003-2158-823X; Stoyanov, Danail/0000-0002-0980-3227; Dahiya, Navdeep/0000-0002-6275-6845; Campilho, Aurelio/0000-0002-5317-6275; Aresta, Guilherme/0000-0002-4225-2156; Costa, Pedro/0000-0002-9528-1292; Heng, Pheng Ann/0000-0003-3055-5034; Hu, Xiaowei/0000-0002-5708-7018; Bialopetravicius, Jonas/0000-0001-7603-6382; Araujo, Teresa/0000-0001-9687-528X FCT [SFRH/BD/120435/2016, SFRH/BD/122365/2016]; National Funds through the FCT Fundacao para a Ciencia e a Tecnologia [CMUP-ERI/TIC/0028/2014]; North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement [Analytics/NORTE-01-0145-FEDER-000016]; European Regional Development Fund (ERDF); German Federal Ministry of Education and Research (BMBF) under the Project BIOPASS [165V7257] FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); National Funds through the FCT Fundacao para a Ciencia e a Tecnologia(Fundacao para a Ciencia e a Tecnologia (FCT)); North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement; European Regional Development Fund (ERDF)(European Commission); German Federal Ministry of Education and Research (BMBF) under the Project BIOPASS(Federal Ministry of Education & Research (BMBF)) Teresa Aratijo is funded by the FCT grant contract SFRH/BD/122365/2016. Guilherme Aresta is funded by the FCT grant contract SFRH/BD/120435/2016. Adrian Galdran is with ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and National Funds through the FCT Fundacao para a Ciencia e a Tecnologia within project CMUP-ERI/TIC/0028/2014. Aurelio Campilho is with project NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Manish Sahu, Sabrina Dill, Anirban Mukhopadhyay and Stefan Zachow are funded by German Federal Ministry of Education and Research (BMBF) under the Project BIOPASS (grant Nr. 165V7257) 96 26 26 0 18 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. FEB 2019.0 52.0 24 41 10.1016/j.media.2018.11.008 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging HJ9IR 30468970.0 Green Submitted 2023-03-23 WOS:000457512600003 0
J Li, J; Zhang, YW; Chen, Q; Pan, ZH; Chen, J; Sun, MX; Wang, JF; Li, YX; Ye, Q Li, Jing; Zhang, Yuwei; Chen, Qing; Pan, Zhenhua; Chen, Jun; Sun, Meixiu; Wang, Junfeng; Li, Yingxin; Ye, Qing Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath FRONTIERS IN ONCOLOGY English Article breath analysis; lung cancer; machine learning; PTR-TOF-MS; screening DIAGNOSIS ObjectivesLung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer. Materials and methodsWe enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver-operating characteristic (ROC) curve (AUC). ResultsSix features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941-0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718-0.823), and sensitivity of 67.7% and specificity of 73.0%. ConclusionOur results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting. [Li, Jing; Sun, Meixiu; Li, Yingxin] Chinese Acad Med Sci, Inst Biomed Engn, Laser Med Lab, Tianjin, Peoples R China; [Li, Jing; Sun, Meixiu; Li, Yingxin] Peking Union Med Coll, Tianjin, Peoples R China; [Zhang, Yuwei; Ye, Qing] Nankai Univ, Sch Phys & TEDA Appl Phys, Key Lab Weak Light Nonlinear Photon, Minist Educ, Tianjin, Peoples R China; [Chen, Qing] Tianjin Med Univ, Canc Inst & Hosp, Dept Cardiopulm Funct, Natl Clin Res Ctr, Tianjin, Peoples R China; [Pan, Zhenhua; Chen, Jun] Tianjin Med Univ, Tianjin Lung Canc Inst, Tianjin Key Lab Lung Canc Metastasis & Tumor Micro, Gen Hosp, Tianjin, Peoples R China; [Wang, Junfeng] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands Chinese Academy of Medical Sciences - Peking Union Medical College; Institute of BioMedical Engineering - CAMS; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; Nankai University; Tianjin Medical University; Tianjin Medical University; Utrecht University; Utrecht University Medical Center Sun, MX (corresponding author), Chinese Acad Med Sci, Inst Biomed Engn, Laser Med Lab, Tianjin, Peoples R China.;Sun, MX (corresponding author), Peking Union Med Coll, Tianjin, Peoples R China.;Wang, JF (corresponding author), Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands. sunmx@bme.cams.cn; j.wang5@uu.nl Chinese Academy of Medical Sciences Initiative for Innovative Medicine; [2018-I2M-AI-012] Chinese Academy of Medical Sciences Initiative for Innovative Medicine; Funding This work was supported by the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (2018-I2M-AI-012). 35 0 0 5 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. SEP 20 2022.0 12.0 975563 10.3389/fonc.2022.975563 0.0 11 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 5F4MR 36203414.0 gold, Green Accepted 2023-03-23 WOS:000866291800001 0
J Liang, RL; Bai, YQ; Lin, HX Liang, Renli; Bai, Yanqin; Lin, Hai Xiang An inexact splitting method for the subspace segmentation from incomplete and noisy observations JOURNAL OF GLOBAL OPTIMIZATION English Article Subspace segmentation; Low rank representation; Inexact augmented Lagrange multiplier method LOW-RANK; ALGORITHM; FACTORIZATION; RECOVERY Subspace segmentation is a fundamental issue in computer vision and machine learning, which segments a collection of high-dimensional data points into their respective low-dimensional subspaces. In this paper, we first propose a model for segmenting the data points from incomplete and noisy observations. Then, we develop an inexact splitting method for solving the resulted model. Moreover, we prove the global convergence of the proposed method. Finally, the inexact splitting method is implemented on the clustering problems in synthetic and benchmark data, respectively. Numerical results demonstrate that the proposed method is computationally efficient, robust as well as more accurate compared with the state-of-the-art algorithms. [Liang, Renli; Bai, Yanqin] Shanghai Univ, Dept Math, Shanghai, Peoples R China; [Lin, Hai Xiang] Delft Univ Technol, Dept Appl Math, Delft, Netherlands Shanghai University; Delft University of Technology Bai, YQ (corresponding author), Shanghai Univ, Dept Math, Shanghai, Peoples R China. yqbai@t.shu.edu.cn Liang, Renli/AAS-8162-2021 Lin, Hai Xiang/0000-0002-1653-4854 China Scholarship Council China Scholarship Council(China Scholarship Council) The authors would like to thank the financial support from the China Scholarship Council. 34 2 2 1 4 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-5001 1573-2916 J GLOBAL OPTIM J. Glob. Optim. FEB 2019.0 73.0 2 411 429 10.1007/s10898-018-0684-4 0.0 19 Operations Research & Management Science; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science; Mathematics HK4AC 2023-03-23 WOS:000457860400008 0
J Dubost, C; Humbert, P; Oudre, L; Labourdette, C; Vayatis, N; Vidal, PP Dubost, Clement; Humbert, Pierre; Oudre, Laurent; Labourdette, Christophe; Vayatis, Nicolas; Vidal, Pierre-Paul Quantitative assessment of consciousness during anesthesia without EEG data JOURNAL OF CLINICAL MONITORING AND COMPUTING English Article Anesthesia; Depth of anesthesia; Data base; Machine learning; Prediction BISPECTRAL INDEX; GENERAL-ANESTHESIA; ELECTROENCEPHALOGRAM; DEPTH; CLASSIFICATION; AUTOREGULATION; PROPOFOL; ENTROPY; SURGERY Assessing the depth of anesthesia (DoA) is a daily challenge for anesthesiologists. The best assessment of the depth of anesthesia is commonly thought to be the one made by the doctor in charge of the patient. This evaluation is based on the integration of several parameters including epidemiological, pharmacological and physiological data. By developing a protocol to record synchronously all these parameters we aim at having this evaluation made by an algorithm. Our hypothesis was that the standard parameters recorded during anesthesia (without EEG) could provide a good insight into the consciousness level of the patient. We developed a complete solution for high-resolution longitudinal follow-up of patients during anesthesia. A Hidden Markov Model (HMM) was trained on the database in order to predict and assess states based on four physiological variables that were adjusted to the consciousness level: Heart Rate (HR), Mean Blood Pressure (MeanBP) Respiratory Rate (RR), and AA Inspiratory Concentration (AAFi) all without using EEG recordings. Patients undergoing general anesthesia for hernial inguinal repair were included after informed consent. The algorithm was tested on 30 patients. The percentage of error to identify the actual state among Awake, LOC, Anesthesia, ROC and Emergence was 18%. This protocol constitutes the very first step on the way towards a multimodal approach of anesthesia. The fact that our first classifier already demonstrated a good predictability is very encouraging for the future. Indeed, this first model was merely a proof of concept to encourage research ways in the field of machine learning and anesthesia. [Dubost, Clement] Begin Mil Hosp, St Mande, France; [Dubost, Clement; Humbert, Pierre; Oudre, Laurent; Labourdette, Christophe; Vayatis, Nicolas; Vidal, Pierre-Paul] Univ Paris Saclay, ENS Paris Saclay, CNRS, Ctr Borelli, F-91190 Gif Sur Yvette, France; [Oudre, Laurent] Univ Paris 13, L2TI, Villetaneuse, France; [Vidal, Pierre-Paul] Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou, Zhejiang, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Universite Paris 13; Hangzhou Dianzi University Dubost, C (corresponding author), Begin Mil Hosp, St Mande, France.;Dubost, C (corresponding author), Univ Paris Saclay, ENS Paris Saclay, CNRS, Ctr Borelli, F-91190 Gif Sur Yvette, France. clement.dubost@hotmail.fr 49 3 3 2 6 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1387-1307 1573-2614 J CLIN MONIT COMPUT J. Clin. Monitor. Comp. OCT 2021.0 35.0 5 993 1005 10.1007/s10877-020-00553-4 0.0 JUL 2020 13 Anesthesiology Science Citation Index Expanded (SCI-EXPANDED) Anesthesiology WE9GF 32661827.0 2023-03-23 WOS:000548262500001 0
J Legrand, F; Eychene, JM; Audiffren, J; Klein, A; Labourdette, C; Nicolai, A; Sandron, F; Vidal, PP Legrand, Florian; Eychene, Jean-Marc; Audiffren, Julien; Klein, Armelle; Labourdette, Christophe; Nicolai, Alice; Sandron, Frederic; Vidal, Pierre-Paul The 5P program, personalized and participatory primary prevention pathway: Rational and design of a clinical trial in general practice CONTEMPORARY CLINICAL TRIALS COMMUNICATIONS English Article Comprehensive geriatric assessment; Clinical trial; Disability; Frail elderly; General practice; Machine learning; Physical exercise OLDER-ADULTS; DISABILITY; FRAILTY; HEALTH The aging of the population is leading to an increase in the number of people with loss of autonomy, placing a strain on the health care system. Its prevention at early stages such as the frailty stage would allow an improvement in the quality of life of seniors while limiting health care expenses. The Atout Age prevention program set up by the health public authorities of Reunion Is land for retired people and the new frailty assessment tools based on mathematical machine learning algorithms could improve the ambulatory care of senior citizens. At present, referral care remains hospital with comprehensive geriatric assessment and there is a lack of evidence of the effectiveness of a prevention pathway for loss of autonomy in primary care. For these reasons, the 5P program Personalized and Participative Primary Prevention Pathway has been started in order to obtain scientific evidence. In this article, we present the objectives, design and first results, used in the 5P program up to the implementation of a clinical trial in general practice. The program is articulated in 3 phases. A first phase to evaluate the acceptability of innovative screening tools for frailty. A second pilot phase evaluates the feasibility of a large-scale ambulatory clinical trial in general practice. The last phase described in this article, is a multisite, pseudo-randomized, controlled clinical trial measuring the impact of the Atout Age workshops on the physical performance and the quality of life of seniors compared with their usual ambulatory follow-up [Legrand, Florian; Klein, Armelle; Sandron, Frederic] Univ Paris, IRD Ceped UMR 196, 45 Rue St Peres, F-75006 Paris, France; [Legrand, Florian] Univ Reun, CHU Reun, Dept Univ Med Gen, UFR Sante,Site IES, Terre St BP 350, F-97448 St Pierre, France; [Eychene, Jean-Marc] CHU Site Sud St Joseph, 2 Rue Mere Theresa,Bp 4, St Joseph, France; [Eychene, Jean-Marc; Audiffren, Julien; Labourdette, Christophe; Nicolai, Alice; Vidal, Pierre-Paul] Univ Paris Saclay, Ctr Borelli, ENS Paris Saclay, CNRS,UMR 9010, Paris, France; [Vidal, Pierre-Paul] Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310018, Peoples R China UDICE-French Research Universities; Universite Paris Cite; CHU Reunion; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; Hangzhou Dianzi University Legrand, F (corresponding author), Univ Reun, Dept Univ Med Gen, Site IES, UFR Sante,CHU Reun, Terre St BP 350, F-9744 St Pierre, France. florian.legrand@univ-reunion.fr Legrand, Florian/0000-0001-5257-6317; Audiffren, Julien/0000-0003-4321-2575; Klein, Armelle/0000-0001-5749-3529 Caisse ge nerale de securite sociale (CGSS), La Reunion; Caisse nationale d'assurance vieillesse (CNAV), France; Agence regionale de sante (ARS) Ocean Indien, La Reunion; Fonds MUTA La Reunion; IDEX Sorbonne Paris cite, programme dynamique du vieillir, France Caisse ge nerale de securite sociale (CGSS), La Reunion(General Electric); Caisse nationale d'assurance vieillesse (CNAV), France; Agence regionale de sante (ARS) Ocean Indien, La Reunion; Fonds MUTA La Reunion; IDEX Sorbonne Paris cite, programme dynamique du vieillir, France This work was supported by the Caisse ge nerale de securite sociale (CGSS), La Reunion, the Caisse nationale d'assurance vieillesse (CNAV), France, the Agence regionale de sante (ARS) Ocean Indien, La Reunion, the Fonds MUTA La Reunion, the IDEX Sorbonne Paris cite, programme dynamique du vieillir, France. 39 0 0 1 4 ELSEVIER INC SAN DIEGO 525 B STREET, STE 1900, SAN DIEGO, CA 92101-4495 USA 2451-8654 CONT CLIN TRIAL COMM Contemp. Clin. Trials Commun. JUN 2021.0 22.0 100786 10.1016/j.conctc.2021.100786 0.0 JUN 2021 6 Medicine, Research & Experimental Emerging Sources Citation Index (ESCI) Research & Experimental Medicine TE9MH 34159279.0 Green Accepted, gold 2023-03-23 WOS:000670329900009 0
J Lokoc, J; Kovalcik, G; Munzer, B; Schoffmann, K; Bailer, W; Gasser, R; Vrochidis, S; Nguyen, PA; Rujikietgumjorn, S; Barthel, KU Lokoc, Jakub; Kovalcik, Gregor; Muenzer, Bernd; Schoeffmann, Klaus; Bailer, Werner; Gasser, Ralph; Vrochidis, Stefanos; Phuong Anh Nguyen; Rujikietgumjorn, Sitapa; Barthel, Kai Uwe Interactive Search or Sequential Browsing? A Detailed Analysis of the Video Browser Showdown 2018 ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS English Article Interactive video retrieval; video browsing; content-based methods; evaluation campaigns OF-THE-ART; RETRIEVAL; USER This work summarizes the findings of the 7th iteration of the Video Browser Showdown (VBS) competition organized as a workshop at the 24th International Conference on Multimedia Modeling in Bangkok. The competition focuses on video retrieval scenarios in which the searched scenes were either previously observed or described by another person (i.e., an example shot is not available). During the event, nine teams competed with their video retrieval tools in providing access to a shared video collection with 600 hours of video content. Evaluation objectives, rules, scoring, tasks, and all participating tools are described in the article. In addition, we provide some insights into how the different teams interacted with their video browsers, which was made possible by a novel interaction logging mechanism introduced for this iteration of the VBS. The results collected at the VBS evaluation server confirm that searching for one particular scene in the collection when given a limited time is still a challenging task for many of the approaches that were showcased during the event. Given only a short textual description, finding the correct scene is even harder. In ad hoc search with multiple relevant scenes, the tools were mostly able to find at least one scene, whereas recall was the issue for many teams. The logs also reveal that even though recent exciting advances in machine learning narrow the classical semantic gap problem, user-centric interfaces are still required to mediate access to specific content. Finally, open challenges and lessons learned are presented for future VBS events. [Lokoc, Jakub; Kovalcik, Gregor] Charles Univ Prague, Fac Math & Phys, Malostranske Nam 25, CR-11800 Prague, Czech Republic; [Muenzer, Bernd; Schoeffmann, Klaus] Klagenfurt Univ, Inst Informat Technol, Klagenfurt, Austria; [Bailer, Werner] JOANNEUM RES, Inst Informat & Commun Technol, Steyrergasse 17, A-8010 Graz, Austria; [Gasser, Ralph] Univ Basel, Dept Math & Comp Sci, CH-4051 Basel, Switzerland; [Vrochidis, Stefanos] Ctr Res & Technol Hellas, Informat Technol Inst, 6th Klm Charilaou Thermi Rd, Thessaloniki 57001, Greece; [Phuong Anh Nguyen] City Univ Hong Kong, Dept Comp Sci, Kowloon Tong, Tatchee Ave, Hong Kong, Peoples R China; [Rujikietgumjorn, Sitapa] Natl Elect & Comp Technol Ctr, Khlong Nueng, Thailand; [Barthel, Kai Uwe] HTW Berlin, Visual Comp Grp, Wilhelminenhofstr 75a, D-12459 Berlin, Germany Charles University Prague; University of Klagenfurt; University of Basel; Centre for Research & Technology Hellas; City University of Hong Kong; National Science & Technology Development Agency - Thailand; National Electronics & Computer Technology Center (NECTEC) Lokoc, J (corresponding author), Charles Univ Prague, Fac Math & Phys, Malostranske Nam 25, CR-11800 Prague, Czech Republic. lokoc@ksi.mff.cuni.cz; gregor.kovalcik@gmail.com; bernd@itec.aau.at; ks@itec.aau.at; werner.bailer@joanneum.at; ralph.gasser@unibas.ch; stefanos@iti.gr; panguyen2-c@my.city.edu.hk; sitapa.ruj@nectec.or.th; Kai-Uwe.Barthel@HTW-Berlin.de Nguyen, Phuong Anh/AAQ-4427-2021 Nguyen, Phuong Anh/0000-0003-1289-3785; Vrochidis, Stefanos/0000-0002-2505-9178; Watcharapinchai (Rujikietgumjorn), Sitapa/0000-0001-6691-1243; Gasser, Ralph Marc Philipp/0000-0002-3016-1396 Czech Science Foundation (GACR) [17-22224S]; Universitat Klagenfurt; European Regional Development Fund; Carinthian Economic Promotion Fund (KWF) [KWF 20214 u. 3520/26336/38165]; Horizon 2020 Research and Innovation Programme V4Design [779962]; Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 11250716]; CHIST-ERA project IMOTION; Swiss National Science Foundation (SNSF) [20CH21_151571]; Lakeside Labs GmbH, Klagenfurt, Austria Czech Science Foundation (GACR)(Grant Agency of the Czech Republic); Universitat Klagenfurt; European Regional Development Fund(European Commission); Carinthian Economic Promotion Fund (KWF); Horizon 2020 Research and Innovation Programme V4Design; Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council); CHIST-ERA project IMOTION; Swiss National Science Foundation (SNSF)(Swiss National Science Foundation (SNSF)); Lakeside Labs GmbH, Klagenfurt, Austria This work was supported by Czech Science Foundation (GACR) project no. 17-22224S. Parts of this work were supported by Universitat Klagenfurt and Lakeside Labs GmbH, Klagenfurt, Austria, and funding from the European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF) under grant KWF 20214 u. 3520/26336/38165. Part of this research received funding from the Horizon 2020 Research and Innovation Programme V4Design, under grant agreement no. 779962. Parts of this work were supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11250716). Work on VITRIVR was partly supported by the CHIST-ERA project IMOTION, with contributions from the Swiss National Science Foundation (SNSF, contract no. 20CH21_151571). 47 34 34 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1551-6857 1551-6865 ACM T MULTIM COMPUT ACM Trans. Multimed. Comput. Commun. Appl. FEB 2019.0 15.0 1 29 10.1145/3295663 0.0 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HM9JB 2023-03-23 WOS:000459798800013 0
J Li, XJ; Cheng, XW; Chen, WT; Chen, G; Liu, SW Li, Xianju; Cheng, Xinwen; Chen, Weitao; Chen, Gang; Liu, Shengwei Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms REMOTE SENSING English Article 3 GORGES AREA; LAND-COVER CLASSIFICATION; DEEP-SEATED LANDSLIDES; HAZARD ASSESSMENT; TRIGGERED LANDSLIDES; ORIENTED ANALYSIS; FEATURE-SELECTION; SPATIAL-PATTERNS; SCALE PARAMETER; IKONOS IMAGERY For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% +/- 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain. [Li, Xianju; Cheng, Xinwen; Chen, Gang] China Univ Geosci, Fac Informat Engn, Wuhan 430074, Peoples R China; [Chen, Weitao] China Univ Geosci, Fac Comp Sci, Wuhan 430074, Peoples R China; [Chen, Weitao] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China; [Chen, Weitao] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 Enschede, Netherlands; [Liu, Shengwei] China Aero Geophys Survey & Remote Sensing Ctr La, Inst Remote Sensing Method, Beijing 100083, Peoples R China China University of Geosciences; China University of Geosciences; China University of Geosciences Chen, WT (corresponding author), China Univ Geosci, Fac Comp Sci, Wuhan 430074, Peoples R China. uandlixianju@gmail.com; chxw377@126.com; wtchen@cug.edu.cn; chen88gang@gmail.com; lsw6002@163.com Li, Xianju/0000-0001-7785-2541; Chen, Weitao/0000-0002-6272-1618 Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUGL150417]; China Geological Survey [2010200082]; China Scholarship Council [201406415051]; Natural Science Foundation of Hubei Province of China [2011CDB350] Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(Fundamental Research Funds for the Central Universities); China Geological Survey(China Geological Survey); China Scholarship Council(China Scholarship Council); Natural Science Foundation of Hubei Province of China(Natural Science Foundation of Hubei Province) This research was jointly supported by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUGL150417), the China Geological Survey (No. 2010200082), the China Scholarship Council (No. 201406415051), and the Natural Science Foundation of Hubei Province of China (No.2011CDB350). The authors also thank the China Aero Geophysical Survey and Remote Sensing Center for Land and Resources for providing the LiDar data used in the study. 101 100 103 5 103 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2015.0 7.0 8 9705 9726 10.3390/rs70809705 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology CQ7XF gold, Green Submitted 2023-03-23 WOS:000360818800011 0
J Dobrolinska, M; van der Werf, N; Greuter, M; Jiang, BB; Slart, R; Xie, XQ Dobrolinska, Magdalena; van der Werf, Niels; Greuter, Marcel; Jiang, Beibei; Slart, Riemer; Xie, Xueqian Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors BMC MEDICAL IMAGING English Article Artifacts; Artificial intelligence; Imaging phantoms; X-ray computed tomography COMPUTED-TOMOGRAPHY; CALCIUM SCORES; HEART-RATE; CALCIFICATION; GUIDELINES; PROGNOSIS; SCANS Background Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance. Methods Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models. Results The accuracy of the CNN classification was 90.2 +/- 3.1%, 90.6 +/- 3.5%, and 90.1 +/- 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P < 0.001). The classification accuracy in all three CNN architectures was not affected by CT system, radiation dose or image reconstruction method (all P > 0.05). Conclusions The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores. [Dobrolinska, Magdalena; Greuter, Marcel; Slart, Riemer] Univ Groningen, Univ Med Ctr Groningen, Dept Radiol, Med Imaging Ctr, POB 30 001, NL-9700 RB Groningen, Netherlands; [Dobrolinska, Magdalena; Greuter, Marcel; Slart, Riemer] Univ Groningen, Univ Med Ctr Groningen, Dept Nucl Med, Med Imaging Ctr, POB 30 001, NL-9700 RB Groningen, Netherlands; [Dobrolinska, Magdalena; Greuter, Marcel; Slart, Riemer] Univ Groningen, Univ Med Ctr Groningen, Dept Mol Imaging, Med Imaging Ctr, POB 30 001, NL-9700 RB Groningen, Netherlands; [Dobrolinska, Magdalena] Med Univ Silesia, Div Cardiol & Struct Heart Dis, Ziolowa 45-47, PL-40635 Katowice, Poland; [van der Werf, Niels] Univ Utrecht, Univ Med Ctr Utrecht, Dept Radiol, Heidelberglaan 100, NL-3584 CX Utrecht, Netherlands; [van der Werf, Niels] Erasmus Univ, Dept Radiol & Nucl Med, Erasmus Med Ctr Rotterdam, Postbus 2040, NL-3000 CA Rotterdam, Netherlands; [Jiang, Beibei; Xie, Xueqian] Shanghai Jiao Tong Univ, Shanghai Gen Hosp, Dept Radiol, Sch Med, Haining Rd 100, Shanghai 200080, Peoples R China; [Slart, Riemer] Univ Twente, Fac Sci & Technol, Dept Biomed Photon Imaging, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands; [Greuter, Marcel] Univ Twente, Dept Robot & Mechatron, Fac Elect Engn Math & Comp Sci, POB 217, NL-7500 AE Enschede, Netherlands University of Groningen; University of Groningen; University of Groningen; Medical University Silesia; Utrecht University; Utrecht University Medical Center; Erasmus University Rotterdam; Shanghai Jiao Tong University; University of Twente; University of Twente Xie, XQ (corresponding author), Shanghai Jiao Tong Univ, Shanghai Gen Hosp, Dept Radiol, Sch Med, Haining Rd 100, Shanghai 200080, Peoples R China. xiexueqian@hotmail.com Xie, Xueqian/AAE-2292-2022 Dobrolinska, Magdalena/0000-0002-7997-5884; Slart, Riemer/0000-0002-5565-1164; Xie, Xueqian/0000-0002-6669-0097; Greuter, Marcel/0000-0001-5721-9159 Ministry of Science and Technology of China [2016YFE0103000]; National Natural Science Foundation of China [81971612, 81471662]; Shanghai Jiao Tong University [ZH2018ZDB10] Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Jiao Tong University This study was sponsored by Ministry of Science and Technology of China (project no. 2016YFE0103000), National Natural Science Foundation of China (81971612 and 81471662), Shanghai Jiao Tong University (ZH2018ZDB10). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. 39 1 1 0 1 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2342 BMC MED IMAGING BMC Med. Imag. OCT 19 2021.0 21.0 1 151 10.1186/s12880-021-00680-7 0.0 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging WJ2GD 34666714.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000708866000002 0
C Jha, SK; Paramasivam, P; Pan, ZQ; Wang, JW Sun, X; Pan, Z; Bertino, E Jha, Sunil Kr; Paramasivam, Parimala; Pan, Zhaoqing; Wang, Jinwei Decision Stump and StackingC-Based Hybrid Algorithm for Healthcare Data Classification CLOUD COMPUTING AND SECURITY, PT V Lecture Notes in Computer Science English Proceedings Paper 4th International Conference on Cloud Computing and Security (ICCCS) JUN 08-10, 2018 Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Haikou, PEOPLES R CHINA Nanjing Univ Informat Sci & Technol,Springer,Hainan Univ,IEEE Nanjing Chapter,ACM China,Michigan State Univ,Taiwan Cheng Kung Univ,Taiwan Dong Hwa Univ,Providence Univ,Nanjing Univ Aeronaut & Astronaut,State Key Lab Integrated Serv Networks,Tech Sci Press,Natl Nat Sci Fdn China Nanjing Univ Informat Sci & Technol, Sch Comp & Software Decision stump; StackingC; Classifier combination; Healthcare data mining RECOGNITION SYSTEM; TREE CLASSIFIER; NEURAL-NETWORK; REDUCTION; PROGNOSIS; DIAGNOSIS; MODEL The healthcare data analytics is a demanding task at present due to enormous amount and diversity of information. Several algorithms were developed to analyze healthcare data with the objective to develop a non-invasive, unbiased and robust prediction system. The present study proposed a hybrid algorithm for healthcare data mining by using the Decision stump (DS), StackingC (SC), and voting methods. Five benchmarked healthcare datasets related to cancer, diabetes, hyperthyroid, lymphography have been selected for the analysis and validation. The hybrid algorithm DS-SC results in 0.31%-30.05% improvement in classification accuracy with DS and SC methods, individually. [Jha, Sunil Kr; Pan, Zhaoqing; Wang, Jinwei] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China; [Paramasivam, Parimala] Univ Informat Technol & Management, IT Fundamentals & Educ Technol Applicat, Chair Math, PL-35225 Rzeszow, Poland Nanjing University of Information Science & Technology; University of Information Technology & Management Rzeszow Jha, SK (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China. 002891@nuist.edu.cn Jha, Sunil/AAZ-7636-2020 Jha, Sunil/0000-0002-6955-1244 Startup Foundation for Introducing Talent of NUIST Startup Foundation for Introducing Talent of NUIST This work is supported by The Startup Foundation for Introducing Talent of NUIST. The authors acknowledge reviewers for their appreciated comments and suggestions. 48 0 0 0 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-00018-9; 978-3-030-00017-2 LECT NOTES COMPUT SC 2018.0 11067.0 205 216 10.1007/978-3-030-00018-9_19 0.0 12 Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BN3AQ 2023-03-23 WOS:000478658700019 0
C Hu, YB; Su, H; Chen, G; Ferrigno, G; De Momi, E; Knoll, A IEEE Hu, Yingbai; Su, Hang; Chen, Guang; Ferrigno, Giancarlo; De Momi, Elena; Knoll, Alois Hierarchical optimization Control of Redundant Manipulator for Robot-assisted Minimally Invasive Surgery 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) OCT 24-JAN 24, 2020-2021 ELECTR NETWORK IEEE,RSJ SAFETY For the time varying optimization problem, the tracking error cannot converge to zero at the finite time because of the optimal solution changing over time. This paper proposes a novel varying parameter recurrent neural network (VPRNN) based hierarchical optimization of a 7-DoF surgical manipulator for Robot-Assisted Minimally Invasive Surgery (RAMIS), which guarantees task tracking, Remote Center of Motion (RCM) and manipulability index optimization. A theoretically grounded hierarchical optimization framework based is introduced to control multiple tasks based on their priority. Finally, the effectiveness of the proposed control strategy is demonstrated with both simulation and experimental results. The results show that the proposed VPRNN-based method can optimal three tasks at the same time and have better performance than previous work. [Hu, Yingbai; Chen, Guang; Knoll, Alois] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Su, Hang; Ferrigno, Giancarlo; De Momi, Elena] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy; [Chen, Guang] Tongji Univ, Coll Automot Engn, Shanghai 201804, Peoples R China Technical University of Munich; Polytechnic University of Milan; Tongji University Chen, G (corresponding author), Tech Univ Munich, Dept Informat, D-85748 Munich, Germany.;Chen, G (corresponding author), Tongji Univ, Coll Automot Engn, Shanghai 201804, Peoples R China. yingbai.hu@tum.de; hang.su@polimi.it; guangchen@tongji.edu.cn; giancarlo.ferrigno@polimi.it; elena.demomi@polimi.it; knoll@in.tum.de European Commission Horizon 2020 research and innovation program, under the project SMARTsurg [732515]; Human Brain Project SGA2 [785907] European Commission Horizon 2020 research and innovation program, under the project SMARTsurg; Human Brain Project SGA2 This work was supported in part by the European Commission Horizon 2020 research and innovation program, under the project SMARTsurg, grant agreement No. 732515 and in part by the Human Brain Project SGA2, under the Specific Grant Agreement No. 785907. Guang Chen is the corresponding author. 21 4 4 5 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-7281-6212-6 IEEE INT C INT ROBOT 2020.0 2929 2934 10.1109/IROS45743.2020.9341389 0.0 6 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering; Robotics BS3PB 2023-03-23 WOS:000714033801047 0
C Guo, CL; Szemenyei, M; Hu, YT; Wang, WL; Zhou, W; Yi, YG IEEE Guo, Changlu; Szemenyei, Marton; Hu, Yangtao; Wang, Wenle; Zhou, Wei; Yi, Yugen CHANNEL ATTENTION RESIDUAL U-NET FOR RETINAL VESSEL SEGMENTATION 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) English Proceedings Paper IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) JUN 06-11, 2021 ELECTR NETWORK IEEE,Inst Elect & Elect Engineers, Signal Proc Soc Retinal vessel segmentation; CAR-UNet; Efficient Channel Attention; residual structure NETWORK Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the skip connections in the traditional U-shaped networks, instead of simply copying the feature maps of the contracting path to the corresponding expansive path. On the other hand, we propose a Channel Attention Double Residual Block (CADRB), which integrates MECA into a residual structure as a core structure to construct the proposed CAR-UNet. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE. [Guo, Changlu; Szemenyei, Marton] Budapest Univ Technol & Econ, Budapest, Hungary; [Hu, Yangtao] Hosp Peoples Liberat Army Joint Logist Support Fo, Nanchang, Jiangxi, Peoples R China; [Wang, Wenle; Yi, Yugen] Jiangxi Normal Univ, Nanchang, Jiangxi, Peoples R China; [Zhou, Wei] Chinese Acad Sci, Shenyang Inst Comp Technol, Shenyang, Peoples R China Budapest University of Technology & Economics; Jiangxi Normal University; Chinese Academy of Sciences Guo, CL (corresponding author), Budapest Univ Technol & Econ, Budapest, Hungary.;Yi, YG (corresponding author), Jiangxi Normal Univ, Nanchang, Jiangxi, Peoples R China. China Scholarship Council; Stipendium Hungaricum Scholarship; National Natural Science Foundation of China [62062040]; Chinese Postdoctoral Science Foundation [2019M661117] China Scholarship Council(China Scholarship Council); Stipendium Hungaricum Scholarship; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by the China Scholarship Council, the Stipendium Hungaricum Scholarship, the National Natural Science Foundation of China under Grants 62062040, and Chinese Postdoctoral Science Foundation 2019M661117. 20 11 11 2 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-7605-5 2021.0 1185 1189 10.1109/ICASSP39728.2021.9414282 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology BS2OF Green Submitted 2023-03-23 WOS:000704288401086 0
J Rybalkin, V; Sudarshan, C; Weis, C; Lappas, J; Wehn, N; Cheng, L Rybalkin, Vladimir; Sudarshan, Chirag; Weis, Christian; Lappas, Jan; Wehn, Norbert; Cheng, Li Efficient Hardware Architectures for 1D-and MD-LSTM Networks JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY English Article Long short-term memory; LSTM; MD-LSTM; 2D-LSTM; FPGA; DRAM; Processing-in-memory; PIM; Optical character recognition; OCR; MNIST; DIBCO; Zynq; Image binarization; Hardware architecture; Deep learning NEURAL-NETWORK; MEMORY Recurrent Neural Networks, in particular One-dimensional and Multidimensional Long Short-Term Memory (1D-LSTM and MD-LSTM) have achieved state-of-the-art classification accuracy in many applications such as machine translation, image caption generation, handwritten text recognition, medical imaging and many more. However, high classification accuracy comes at high compute, storage, and memory bandwidth requirements, which make their deployment challenging, especially for energy-constrained platforms such as portable devices. In comparison to CNNs, not so many investigations exist on efficient hardware implementations for 1D-LSTM especially under energy constraints, and there is no research publication on hardware architecture for MD-LSTM. In this article, we present two novel architectures for LSTM inference: a hardware architecture for MD-LSTM, and a DRAM-based Processing-in-Memory (DRAM-PIM) hardware architecture for 1D-LSTM. We present for the first time a hardware architecture for MD-LSTM, and show a trade-off analysis for accuracy and hardware cost for various precisions. We implement the new architecture as an FPGA-based accelerator that outperforms NVIDIA K80 GPU implementation in terms of runtime by up to 84x and energy efficiency by up to 1238x for a challenging dataset for historical document image binarization from DIBCO 2017 contest, and a well known MNIST dataset for handwritten digits recognition. Our accelerator demonstrates highest accuracy and comparable throughput in comparison to state-of-the-art FPGA-based implementations of multilayer perceptron for MNIST dataset. Furthermore, we present a new DRAM-PIM architecture for 1D-LSTM targeting energy efficient compute platforms such as portable devices. The DRAM-PIM architecture integrates the computation units in a close proximity to the DRAM cells in order to maximize the data parallelism and energy efficiency. The proposed DRAM-PIM design is 16.19 x more energy efficient as compared to FPGA implementation. The total chip area overhead of this design is 18 % compared to a commodity 8 Gb DRAM chip. Our experiments show that the DRAM-PIM implementation delivers a throughput of 1309.16 GOp/s for an optical character recognition application. [Rybalkin, Vladimir; Sudarshan, Chirag; Weis, Christian; Lappas, Jan; Wehn, Norbert] Tech Univ Kaiserslautern, Kaiserslautern, Germany; [Cheng, Li] Huawei Technol Co Ltd, Chengdu, Peoples R China University of Kaiserslautern; Huawei Technologies Rybalkin, V (corresponding author), Tech Univ Kaiserslautern, Kaiserslautern, Germany. rybalkin@eit.uni-kl.de; sudarshan@eit.uni-kl.de; weis@eit.uni-kl.de; lappas@eit.uni-kl.de; wehn@eit.uni-kl.de; licheng56@huawei.com Wehn, Norbert/0000-0002-9010-086X Future and Emerging Technologies (FET) programme within the European Unions Horizon 2020 research and innovation programme [732631]; InnoProm program of the state Rhineland-Palatinate, Germany Future and Emerging Technologies (FET) programme within the European Unions Horizon 2020 research and innovation programme; InnoProm program of the state Rhineland-Palatinate, Germany This work was initiated in the context of a cooperation with Huawei. The project OPRECOMP acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the European Unions Horizon 2020 research and innovation programme, under grant agreement No.732631 (http://www.oprecomp.eu).Partially this work was supported by the InnoProm program of the state Rhineland-Palatinate, Germany. The training was partly performed on High Performance Computer 'Elwetritsch' at the TU Kaiserslautern. We also acknowledge Cadence and Synopsys for the design and evaluation of ASIC circuits. 57 5 5 2 13 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1939-8018 1939-8115 J SIGNAL PROCESS SYS J. Signal Process. Syst. Signal Image Video Technol. NOV 2020.0 92.0 11 SI 1219 1245 10.1007/s11265-020-01554-x 0.0 JUL 2020 27 Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OH0UY hybrid 2023-03-23 WOS:000545070500001 0
J Wang, YR; Wang, PC; Adams, LC; Sheybani, ND; Qu, LQ; Sarrami, AH; Theruvath, AJ; Gatidis, S; Ho, TA; Zhou, Q; Pribnow, A; Thakor, AS; Rubin, D; Daldrup-Link, HE Wang, Yan-Ran (Joyce); Wang, Pengcheng; Adams, Lisa Christine; Sheybani, Natasha Diba; Qu, Liangqiong; Sarrami, Amir Hossein; Theruvath, Ashok Joseph; Gatidis, Sergios; Ho, Tina; Zhou, Quan; Pribnow, Allison; Thakor, Avnesh S.; Rubin, Daniel; Daldrup-Link, Heike E. Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING English Article; Early Access PET restoration; Whole-body PET imaging; Transformer model; CNN; Deep learning IMAGE-RECONSTRUCTION; RADIATION; PET; CHILDREN Purpose To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose F-18-FDG PET data over the entire dose reduction spectrum.Methods In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg F-18-FDG dose. The comparisons were performed upon two independent cohorts - (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tubingen University - in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis.Results nFor low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887-0.910) for EDSR, 0.893 (0.881-0.905) for EDSR-ViT, 0.873 (0.859-0.887) for GAN, 0.885 (0.873-0.898) for U-Net, and 0.910 (0.900-0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%.Conclusion Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques. [Wang, Yan-Ran (Joyce); Adams, Lisa Christine; Sarrami, Amir Hossein; Theruvath, Ashok Joseph; Ho, Tina; Zhou, Quan; Daldrup-Link, Heike E.] Stanford Univ, Sch Med, Dept Radiol, 725 Welch Rd, Stanford, CA 94304 USA; [Wang, Yan-Ran (Joyce); Sheybani, Natasha Diba; Qu, Liangqiong; Rubin, Daniel] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94304 USA; [Wang, Pengcheng] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei, Peoples R China; [Gatidis, Sergios] Univ Hosp Tuebingen, Dept Diagnost & Intervent Radiol, Tubingen, Germany; [Pribnow, Allison; Thakor, Avnesh S.; Rubin, Daniel; Daldrup-Link, Heike E.] Stanford Univ, Lucile Packard Childrens Hosp, Dept Pediat Pediat Oncol, Stanford, CA 94304 USA Stanford University; Stanford University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Lucile Packard Children's Hospital (LPCH); Stanford University Wang, YR; Daldrup-Link, HE (corresponding author), Stanford Univ, Sch Med, Dept Radiol, 725 Welch Rd, Stanford, CA 94304 USA.;Wang, YR (corresponding author), Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94304 USA.;Daldrup-Link, HE (corresponding author), Stanford Univ, Lucile Packard Childrens Hosp, Dept Pediat Pediat Oncol, Stanford, CA 94304 USA. wangyanran100@gmail.com; heiked@stanford.edu Zhou, Quan/ADF-9242-2022 Zhou, Quan/0000-0002-9351-5614 National Cancer Institute of the US National Institutes of Health [R01CA269231]; Andrew McDonough B + Foundation National Cancer Institute of the US National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Andrew McDonough B + Foundation This study was supported by a grant from the National Cancer Institute of the US National Institutes of Health, grant number R01CA269231, and the Andrew McDonough B + Foundation. 51 0 0 5 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1619-7070 1619-7089 EUR J NUCL MED MOL I Eur. J. Nucl. Med. Mol. Imaging 10.1007/s00259-022-06097-w 0.0 JAN 2023 14 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 7V8TW 36633614.0 2023-03-23 WOS:000913086400001 0
J Zecchetto, S; Zanchetta, A Zecchetto, Stefano; Zanchetta, Andrea Structure of High-Resolution SAR Winds Over the Venice Lagoon Area IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Coastal areas; deep residual network; lagoon; synthetic aperture radar (SAR); wind field GEOPHYSICAL MODEL FUNCTION; SEA; RETRIEVAL; IMAGES; SPEED; SCATTEROMETER; POLARIZATION; EXTRACTION; FIELDS; VIEW The spatial characteristics at mesoscale gamma of the strongest sea surface winds blowing on the Adriatic Sea and Venice Lagoon, Italy, have been investigated through 500 m x 500 m wind fields extracted, without external information, from the C-band Sentinel-1 synthetic aperture radar (SAR) images interferometric wide (IW) swath ground range detected high resolution (GRDH) level 1 (L1) by a deep learning method based on a residual neural network (ResNet). The derived SAR wind directions have been used to retrieve the wind speed using the C_SARMOD2 geophysical model function (GMF). The resulting wind fields have been compared with European Centre for Medium-range Weather Forecasts (ECMWF) and in situ data: the wind direction bias vertical bar beta(theta)vertical bar <= 1 degrees indicates a very good agreement and the value of RMS difference against in situ winds of 16 degrees results smaller than those reported in the literature; the wind speed bias beta(w) = -1.5 ms I with respect to in situ data indicates an underestimation of SAR speed. However, the innovation brought by ResNet results is evident in the study of the spatial structure of the wind fields, showing unprecedented details. The spatial gradients of wind direction and speed obtained (approximate to 6 degrees/km and 0.5 ms(-1)/km, respectively) are well consistent with those derived from previous experimental campaigns. The maps of the atmospheric wind stress curl derived from the ResNet wind fields exhibit the presence of contiguous cells with positive and negative values roughly elongated in the wind direction, not reproducible with such profusion of details by any other existing datasets. ResNet wind fields provide exhaustive coverage of the Venice lagoon, allowing to study their spatial structure: under northeasterly winds, the wind speed increases on average from northern to southern lagoon by 30% in agreement with a case study reported in the literature. Local differences of ResNet wind direction with respect to ECMWF (+/- 30 degrees maximum) generate local differences of wind speed as large as +/- 2 ms(-1). [Zecchetto, Stefano; Zanchetta, Andrea] CNR, Ist Sci Polari, I-35127 Padua, Italy; [Zecchetto, Stefano] Persian Gulf Univ, Dept Elect Engn, Bushehr 7516913817, Iran; [Zanchetta, Andrea] Hong Kong Baptist Univ, Dept Mus, Hong Kong, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Polari (ISP-CNR); Persian Gulf University; Hong Kong Baptist University Zecchetto, S (corresponding author), CNR, Ist Sci Polari, I-35127 Padua, Italy. stefano.zecchetto@cnr.it Zanchetta, Andrea/0000-0001-9377-3923 Italian Space Agency (ASI) [ASI DC-UOT-2019-017, 2021-4-U.0 (CUP F65F21000070005)] Italian Space Agency (ASI)(Agenzia Spaziale Italiana (ASI)) This work was supported in part by the Italian Space Agency (ASI), Contract Call ASI DC-UOT-2019-017, under Project APPLICAVEMARS, 2021-4-U.0 (CUP F65F21000070005). 48 1 1 2 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 4207209 10.1109/TGRS.2022.3170705 0.0 9 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 1D7GB 2023-03-23 WOS:000793964300033 0
J Rasheed, I; Asif, M; Khan, WU; Ihsan, A; Ullah, K; Ali, MS Rasheed, Iftikhar; Asif, Muhammad; Khan, Wali Ullah; Ihsan, Asim; Ullah, Kalim; Ali, Md Sadek Blockchain-Based Trust Verification and Streaming Service Awareness for Big Data-Driven 5G and Beyond Vehicle-to-Everything (V2X) Communication WIRELESS COMMUNICATIONS & MOBILE COMPUTING English Article EFFICIENT; FRAMEWORK; SYSTEMS; NETWORK; SECURE Vigorous security and transparency with minimum latency and a high data rate are some of the key requirements for 5G and beyond vehicular networks especially in case of big data-driven vehicle-to-everything (V2X) communication applications which plays an important role in enabling advance intelligent transportation systems (ITS) and Internet of Vehicle (IoV) based network. The trace-ability in blockchain makes it one of the vital options, back-boned by the decentralized network to ensure independent security and privacy by exploiting the hash of a previous transaction with the time stamp and announcing it publicly for rejection of possible adversary attack. Therefore, in this work, we have opted for blockchains for trusted authentication and service awareness in 5G and beyond vehicle-to-everything (V2X) communication-based vehicular networks to stream blocks by certifying privacy and security. Here, we have considered blockchain as intermediary trusted agent to witness communication between a vehicle node and edge node, where edge node also uploads microservices along with calling figure of microservices to the blockchain. Furthermore, to ensure vehicle privacy with joint verification of vehicular network and server, we have devised a mechanism in which every vehicle nodes anonymously ask for services from the edge server along with signing an individual's identity. Moreover, based on generated vehicle requests and service awareness, we devised an improved edge cache scheme for the prior compilation of services at each edge. In comparison to the previous state-of-art work, the result shows that our proposed work handles the requests more efficiently to achieve a better service request cache hit rate generated by vehicles at the edge server. [Rasheed, Iftikhar] Islamia Univ Bahawalpur, Dept Informat & Commun Engn, Bahawalpur, Pakistan; [Asif, Muhammad] Shenzhen Univ, Coll Elect & Informat Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China; [Khan, Wali Ullah] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, L-1855 Luxembourg, Luxembourg; [Ihsan, Asim] Shanghai Jiao Tong Univ, Dept Informat & Commun Engn, Shanghai 200240, Peoples R China; [Ullah, Kalim] Univ Sci & Technol Bannu, Dept Elect Engn, Khyber Pakhtunkhwa, Pakistan; [Ali, Md Sadek] Islamic Univ, Commun Res Lab, Dept Informat & Commun Technol, Kushtia 7003, Bangladesh Islamia University of Bahawalpur; Shenzhen University; University of Luxembourg; Shanghai Jiao Tong University; Islamic University Ali, MS (corresponding author), Islamic Univ, Commun Res Lab, Dept Informat & Commun Technol, Kushtia 7003, Bangladesh. sadek@ice.iu.ac.bd Asif, Muhammad/0000-0002-9699-1675; Rasheed, Iftikhar/0000-0003-1408-8528; Khan, Wali Ullah/0000-0003-1485-5141 42 0 0 6 10 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1530-8669 1530-8677 WIREL COMMUN MOB COM Wirel. Commun. Mob. Comput. APR 1 2022.0 2022.0 7357820 10.1155/2022/7357820 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 1M5IU gold 2023-03-23 WOS:000800003400003 0
J Li, CK; Xu, ZZ; Ho, J; Lakhani, I; Liu, YZ; Bazoukis, G; Liu, T; Wong, WT; Cheng, SH; Chan, MTV; Zhang, L; Gin, T; Wong, MCS; Wong, ICK; Wu, WKK; Zhang, QP; Tse, G Li, Christien Kh; Xu, Zhongzhi; Ho, Jeffery; Lakhani, Ishan; Liu, Ying Zhi; Bazoukis, George; Liu, Tong; Wong, Wing Tak; Cheng, Shuk Han; Chan, Matthew T., V; Zhang, Lin; Gin, Tony; Wong, Martin C. S.; Wong, Ian Chi Kei; Wu, William Ka Kei; Zhang, Qingpeng; Tse, Gary Association of NPAC score with survival after acute myocardial infarction ATHEROSCLEROSIS English Article Cardiovascular; Heart disease; Mortality; Myocardial infarction; Neutrophil-to-lymphocyte ratio TO-LYMPHOCYTE RATIO; LONG-TERM MORTALITY; PREDICTIVE-VALUE; NEUTROPHIL; SUBSETS; RISK Background and aims: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. Methods: Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n = 1747) and an independent tertiary care center (validation cohort, n = 1276), with index acute myocardial infarction between January 2013 and December 2017, as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. Results: Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant for 90-day mortality (Hosmer-Lemeshow test, p = 0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic = 0.63). Judicious application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. Conclusions: The NPAC score comprises four items from routine laboratory parameters to basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gatekeeper to facilitate clinical decision-making. [Li, Christien Kh] Newcastle Univ, Fac Med, Newcastle Upon Tyne, Tyne & Wear, England; [Ho, Jeffery; Liu, Ying Zhi; Chan, Matthew T., V; Zhang, Lin; Gin, Tony; Wu, William Ka Kei] Chinese Univ Hong Kong, Li Ka Shing Inst Hlth Sci, Dept Anesthesia & Intens Care, Hong Kong, Peoples R China; [Lakhani, Ishan] Li Ka Shing Inst Hlth Sci, Lab Cardiovasc Physiol, Hong Kong, Peoples R China; [Bazoukis, George] Evangelismos Gen Hosp Athens, Dept Cardiol 2, Lab Cardiac Electrophysiol, Athens, Greece; [Liu, Tong; Tse, Gary] Tianjin Med Univ, Tianjin Inst Cardiol, Tianjin Key Lab Ion Mol Funct Cardiovasc Dis, Dept Cardiol,Hosp 2, Tianjin 300211, Peoples R China; [Wong, Wing Tak; Tse, Gary] Chinese Univ Hong Kong, Sch Life Sci, Hong Kong, Peoples R China; [Cheng, Shuk Han] City Univ Hong Kong, Coll Vet Med & Life Sci, Dept Biomed Sci, Kowloon Tong,Kowloon, Hong Kong, Peoples R China; [Wong, Martin C. S.] Chinese Univ Hong Kong, Fac Med, Jockey Club Sch Publ Hlth & Primary Care, Hong Kong, Peoples R China; [Zhang, Lin; Wu, William Ka Kei] Chinese Univ Hong Kong, State Key Lab Digest Dis, Inst Digest Dis, Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China; [Zhang, Lin; Wu, William Ka Kei] Chinese Univ Hong Kong, Shenzhen Res Inst, Hong Kong, Peoples R China; [Wong, Ian Chi Kei] Univ Hong Kong, Li Ka Shing Fac Med, Dept Pharmacol & Pharm, Hong Kong, Peoples R China; [Wong, Ian Chi Kei] UCL, London, England; [Xu, Zhongzhi; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China Newcastle University - UK; Chinese University of Hong Kong; Tianjin Medical University; Chinese University of Hong Kong; City University of Hong Kong; Chinese University of Hong Kong; Chinese University of Hong Kong; Chinese University of Hong Kong; University of Hong Kong; University of London; University College London; City University of Hong Kong Wu, WKK (corresponding author), Chinese Univ Hong Kong, Li Ka Shing Inst Hlth Sci, Dept Anesthesia & Intens Care, Hong Kong, Peoples R China.;Zhang, QP (corresponding author), City Univ Hong Kong, Sch Data Sci, Kowloon Tong, Hong Kong, Peoples R China.;Tse, G (corresponding author), Chinese Univ Hong Kong, Hong Kong, Peoples R China. wukakei@cuhk.edu.hk; qingpeng.zhang@cityu.edu.hk; gary.tse@doctors.org.uk Tse, Gary/P-1171-2015; Bazoukis, George/AAM-4006-2020; wu, william/HII-5817-2022; Wong, Wing Tak Jack/Q-4512-2018; GIN, Tony/J-2521-2013; Chan, Matthew/J-2884-2013; Zhang, Qingpeng/D-4682-2011; Wu, William K.K./A-3277-2009; Wong, Martin/L-4568-2014; ZHANG, Lin/O-9109-2015; Ho, Jeffery/AAL-9394-2020 Tse, Gary/0000-0001-5510-1253; Wong, Wing Tak Jack/0000-0002-4514-3780; GIN, Tony/0000-0001-7283-6761; Chan, Matthew/0000-0002-3574-7855; Zhang, Qingpeng/0000-0002-6819-0686; Wu, William K.K./0000-0002-5662-5240; Wong, Martin/0000-0001-7706-9370; ZHANG, Lin/0000-0003-1634-3780; Ho, Jeffery/0000-0003-3357-2729; Cheng, Shuk Han/0000-0002-5822-7238 Croucher Foundation, Hong Kong Croucher Foundation, Hong Kong Gary Tse was supported by the Croucher Foundation, Hong Kong. 30 18 18 1 9 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0021-9150 1879-1484 ATHEROSCLEROSIS Atherosclerosis MAY 2020.0 301.0 30 36 10.1016/j.atherosclerosis.2020.03.004 0.0 7 Cardiac & Cardiovascular Systems; Peripheral Vascular Disease Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology LT6KM 32304975.0 Green Submitted 2023-03-23 WOS:000537177600005 0
J Li, C; Cabrera, D; Sancho, F; Sanchez, RV; Cerrada, M; de Oliveira, JV Li, Chuan; Cabrera, Diego; Sancho, Fernando; Sanchez, Rene-Vinicio; Cerrada, Mariela; de Oliveira, Jose Valente One-Shot Fault Diagnosis of Three-Dimensional Printers Through Improved Feature Space Learning IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Fault diagnosis; Feature extraction; Three-dimensional displays; Printers; Training; Aerospace electronics; Convolution; Deep learning; fault diagnosis; one-shot learning; three-dimensional (3-D) printer STOCHASTIC RESONANCE; ALGORITHMS; NETWORK Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based approach is proposed for multiclass classification of signals coming from a feature space created only from healthy condition signals and one single sample for each faulty class. First, a transformation mapping between the input signal space and a feature space is learned through a bidirectional generative adversarial network. Next, the identification of different health condition regions in this feature space is carried out by means of a single input signal per fault. The method is applied to three fault diagnosis problems of a three-dimensional printer and outperforms other methods in the literature. [Li, Chuan] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China; [Cabrera, Diego] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China; [Cabrera, Diego; Sanchez, Rene-Vinicio; Cerrada, Mariela] Univ Politecn Salesiana, GIDTEC Grp, Cuenca 010102, Ecuador; [Sancho, Fernando] Univ Seville, Dept Comp Sci & Artificial Intelligence, Seville 41012, Spain; [de Oliveira, Jose Valente] Univ Algarve, 139 Algarve, P-8005 Algarve, Portugal Chongqing Technology & Business University; Chongqing Technology & Business University; Universidad Politecnica Salesiana; University of Sevilla; Universidade do Algarve Cabrera, D (corresponding author), Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China. chuanli@ctbu.edu.cn; dcabrera@ups.edu.ec; fsancho@us.es; rsanchezl@ups.edu.ec; mcerrada@ups.edu.ec; jvo@ualg.pt Cerrada, Mariela/AFL-9564-2022; Valente de Oliveira, Jose/B-6426-2008 Cerrada, Mariela/0000-0003-4379-8836; Sanchez, Rene Vinicio/0000-0003-0395-9228; Cabrera, Diego/0000-0003-1023-871X; Valente de Oliveira, Jose/0000-0001-5337-5699 GIDTEC Research Group of Universidad Politecnica Salesiana; National Natural Science Foundation of China [51775112]; Science and Technology Partnership Program [KY201802006]; Chongqing Natural Science Foundation [cstc2019jcyj-zdxmX0013]; CTBU Project [KFJJ2018107, KFJJ2018075] GIDTEC Research Group of Universidad Politecnica Salesiana; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Partnership Program; Chongqing Natural Science Foundation(Natural Science Foundation of Chongqing); CTBU Project This work was supported in part by GIDTEC Research Group of Universidad Politecnica Salesiana, in part by the National Natural Science Foundation of China under Grant 51775112, in part by the Science and Technology Partnership Program under Grant KY201802006, in part by the Chongqing Natural Science Foundation under Grant cstc2019jcyj-zdxmX0013, and in part by the CTBU Project under Grant KFJJ2018107 and Grant KFJJ2018075. 41 6 6 5 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. SEP 2021.0 68.0 9 8768 8776 10.1109/TIE.2020.3013546 0.0 9 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation SV7ME 2023-03-23 WOS:000664002600098 0
J Tlili, A; Huang, RH; Shehata, B; Liu, DJ; Zhao, JL; Metwally, AHS; Wang, HH; Denden, M; Bozkurt, A; Lee, LH; Beyoglu, D; Altinay, F; Sharma, RC; Altinay, Z; Li, ZS; Liu, JH; Ahmad, F; Hu, Y; Salha, S; Abed, M; Burgos, D Tlili, Ahmed; Huang, Ronghuai; Shehata, Boulus; Liu, Dejian; Zhao, Jialu; Metwally, Ahmed Hosny Saleh; Wang, Huanhuan; Denden, Mouna; Bozkurt, Aras; Lee, Lik-Hang; Beyoglu, Dogus; Altinay, Fahriye; Sharma, Ramesh C.; Altinay, Zehra; Li, Zhisheng; Liu, Jiahao; Ahmad, Faizan; Hu, Ying; Salha, Soheil; Abed, Mourad; Burgos, Daniel Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis SMART LEARNING ENVIRONMENTS English Review Metaverse; Cyber worlds; Virtual worlds; Avatar; Artificial intelligence; Digital twin; Mirror world VIRTUAL WORLD; EXPERIENCES; STUDENTS; HYBRID; SYSTEM The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse. [Tlili, Ahmed; Huang, Ronghuai; Shehata, Boulus; Liu, Dejian; Zhao, Jialu; Metwally, Ahmed Hosny Saleh; Wang, Huanhuan; Li, Zhisheng; Liu, Jiahao; Ahmad, Faizan; Hu, Ying] Beijing Normal Univ, Smart Learning Inst, Beijing, Peoples R China; [Denden, Mouna; Abed, Mourad] Univ Polytech Hauts France, CNRS, LAMIH, UMR 8201, F-59313 Valenciennes, France; [Denden, Mouna; Abed, Mourad] INSA Hauts France, F-59313 Valenciennes, France; [Bozkurt, Aras] Anadolu Univ, Open Educ Fac, Distance Educa Dept, TR-26470 Eskisehir, Turkey; [Bozkurt, Aras] Univ South Africa UNISA, Dept English Studies, Coll Human Sci, ZA-0003 Pretoria, South Africa; [Lee, Lik-Hang] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Beyoglu, Dogus; Altinay, Fahriye; Altinay, Zehra] Near East Univ, Inst Grad Studies, Societal Res & Dev Ctr, Fac Educ, Nicosia, North Cyprus, Turkey; [Sharma, Ramesh C.] Dr BR Ambedkar Univ Delhi, Delhi, India; [Salha, Soheil] Najah Natl Univ, Fac Educ Sci & Teachers Training, Nablus, Palestine; [Burgos, Daniel] Univ Int La Rioja UNIR, Res Inst Innovat & Technol Educ UNIR iTED, Logrono 26006, Spain; [Ahmad, Faizan] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad, Pakistan Beijing Normal University; Centre National de la Recherche Scientifique (CNRS); Universite Polytechnique Hauts-de-France; Anadolu University; University of South Africa; Korea Advanced Institute of Science & Technology (KAIST); Near East University; Ambedkar University Delhi; An Najah National University; Universidad Internacional de La Rioja (UNIR); COMSATS University Islamabad (CUI) Denden, M (corresponding author), Univ Polytech Hauts France, CNRS, LAMIH, UMR 8201, F-59313 Valenciennes, France. mouna.denden91@gmail.com Metwally, Ahmed Hosny Saleh/P-8004-2019; BOZKURT, Aras/O-3654-2017 Metwally, Ahmed Hosny Saleh/0000-0002-9545-5870; BOZKURT, Aras/0000-0002-4520-642X; Lee, Lik Hang/0000-0003-1361-1612; Wang, Huanhuan/0000-0001-8979-2769 71 16 16 135 177 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2196-7091 SMART LEARN ENVIRON Smart Learn. Env. JUL 6 2022.0 9.0 1 24 10.1186/s40561-022-00205-x 0.0 31 Education & Educational Research Emerging Sources Citation Index (ESCI) Education & Educational Research 2S1YS Green Published, gold 2023-03-23 WOS:000821596000001 0
J Cheng, XG; Hu, F; Yang, B; Wang, FM; Olofsson, T Cheng, Xiaogang; Hu, Fei; Yang, Bin; Wang, Faming; Olofsson, Thomas Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study INDOOR AIR English Article contactless measurements; deep learning; pose estimation; sleep posture; sleep quality; sleep thermal comfort Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy. [Cheng, Xiaogang; Hu, Fei] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Peoples R China; [Cheng, Xiaogang; Yang, Bin; Olofsson, Thomas] Umea Univ, Dept Appl Phys & Elect, Umea, Sweden; [Yang, Bin] Tianjing Chengjian Univ, Sch Energy & Safety Engn, Tianjin, Peoples R China; [Wang, Faming] Katholieke Univ Leuven, Dept Biosyst BIOSYST, Leuven, Belgium; [Yang, Bin] Tianjin Chengjian Univ, Sch Energy & Safety Engineeing, Tianjin, Peoples R China Nanjing University of Posts & Telecommunications; Umea University; Tianjin Chengjian University; KU Leuven; Tianjin Chengjian University Yang, B (corresponding author), Tianjin Chengjian Univ, Sch Energy & Safety Engineeing, Tianjin, Peoples R China. binyang@tcu.edu.cn Yang, Bin/GQA-9324-2022; Wang, Faming/B-7799-2010 Yang, Bin/0000-0002-1579-7978; Wang, Faming/0000-0002-2945-4685; Yang, Bin/0000-0003-4015-199X Social Development Project of Jiangsu Key RD Program; National Natural Science Foundation of China; Ministry of Industry and Information Technology of China; Jiangsu Postdoctoral Science Foundation; [BE2022680]; [52278119]; [61972214]; [2021-R-43]; [1601039B] Social Development Project of Jiangsu Key RD Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Industry and Information Technology of China; Jiangsu Postdoctoral Science Foundation(China Postdoctoral Science Foundation); ; ; ; ; ACKNOWLEDGMENTS The project was financially supported by the Social Development Project of Jiangsu Key R&D Program (BE2022680), the National Natural Science Foundation of China (No. 52278119, No. 61972214), the Ministry of Industry and Information Technology of China (No. 2021-R-43), and Jiangsu Postdoctoral Science Foundation (1601039B). The authors appreciate Hui Zhang from University of California Berkeley for her suggestion. 48 0 0 16 16 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0905-6947 1600-0668 INDOOR AIR Indoor Air DEC 2022.0 32.0 12 e13175 10.1111/ina.13175 0.0 12 Construction & Building Technology; Engineering, Environmental; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Public, Environmental & Occupational Health 7F5II 36567523.0 2023-03-23 WOS:000901880600001 0
J Li, XS; Yuan, ZY; Zhao, JH; Du, B; Liao, XY; Humar, I Li, Xiaosa; Yuan, Zhiyong; Zhao, Jianhui; Du, Bo; Liao, Xiangyun; Humar, Iztok Edge-Learning-Enabled Realistic Touch and Stable Communication for Remote Haptic Display IEEE NETWORK English Article Heuristic algorithms; Neural networks; Prediction algorithms; Real-time systems; Haptic interfaces; Magnetic fields; Sustainable development As the basis of Tactile Internet, remote haptic display has been made possible with the development of ultra-reliable low-latency communication in 5G. In this study, edge learning is employed to enable realistic haptic display and stable remote communication. We propose a double-loop control algorithm, which merges decoupling and PID neural network, for magnetic field generation of the electromagnetic haptic device. In addition, a supervised bidirectional LSTM network is constructed for online haptic prediction during remote interaction, thus complementing the missing haptic data on account of time delay and packet loss in network communications. Experiments have been conducted on the built remote haptic display system, where data streams from sensors are gathered, stored, and forwarded in real time. The results show that dynamic and accurate haptic display is achieved through our magnetic field control algorithm for the haptic device, and the error of haptic prediction by step is less than 0.01N. The conclusion is that the service sustainability of remote haptic display can be guaranteed by edge learning effectively. [Li, Xiaosa; Yuan, Zhiyong; Zhao, Jianhui; Du, Bo] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China; [Liao, Xiangyun] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Machine Vis & Virtual Real, Beijing, Peoples R China; [Humar, Iztok] Univ Ljubljana, Fac Elect Engn FE, Ljubljana, Slovenia Wuhan University; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; University of Ljubljana Yuan, ZY (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China. xiaosa_li@whu.edu.cn; zhiyongyuan@whu.edu.cn; jianhuizhao@whu.edu.cn; gunspace@163.com; xyunliao@gmail.com; iztok.humar@fe.uni-lj.si Li, Xiaosa/0000-0001-5285-2502 Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) [2019AEA170]; Natural Science Foundation of China [62073248, 61902386]; Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University [ZNJC201926]; Natural Science Foundation of Guangdong [2018A030313100]; Slovenian Research Agency [P2-0246] Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University; Natural Science Foundation of Guangdong(National Natural Science Foundation of Guangdong Province); Slovenian Research Agency(Slovenian Research Agency - Slovenia) This work was supported by the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant No. 2019AEA170, the Natural Science Foundation of China under Grant Nos. 62073248 and No. 61902386, the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University under Grant No. ZNJC201926, and the Natural Science Foundation of Guangdong under Grant No. 2018A030313100. Dr. Humar acknowledges financial support from the Slovenian Research Agency (research core funding No. P2-0246). 16 3 3 6 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. JAN-FEB 2021.0 35.0 1 141 147 10.1109/MNET.011.2000255 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RO6NJ 2023-03-23 WOS:000641159800022 0
J Akbariforouz, M; Zhao, Q; Chen, KW; Baghbanan, A; Dehnavi, RN; Zheng, CM Akbariforouz, Mohammadreza; Zhao, Qi; Chen, Kewei; Baghbanan, Alireza; Dehnavi, Roohollah Narimani; Zheng, Chunmiao Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES English Article Squeezing; Statistical processing methods; Regression analyses; Neural network; Empirical equations TIME-DEPENDENT BEHAVIOR; TUNNEL CONVERGENCE; PREDICTION; MODEL; SUPPORT; DEFORMATION; MANAGEMENT; STRENGTH The time-dependent deformation of rocks due to stress released by excavation is referred to as squeezing. Accurate evaluation of the squeezing at the design stage can dramatically reduce technical problems and the financial costs of underground structures. Although various methods are presented to predict tunnel squeezing at the preliminary stage, being site-specific and incorporating incomplete databases are deficiencies of the available procedures. In this study, based on a comprehensive literature review, we prepared a database of tunnel squeezing for soft rocks, including possible effective parameters. Statistical processing methods such as univariate, reduction, and cleaning were employed to improve the statistical quality of the database. The statistically-processed datasets were also validated based on various scales such as accuracy, convergence, and usefulness. Significant predictors of squeezing are recognized as the ratio of strength to stress and the rock mass classification system. New squeezing criteria were developed using binary and multi-class regression methods to predict the squeezing occurrence and intensity of soft rocks. The results are confirmed by a Multilayer Perceptron Feed-Forward Neural Network and are compared to well-known empirical equations. The developed equations are more accurate comparing the empirical equations used to predict the squeezing of soft rocks. This methodology can be utilized at the design stage for another database to predict squeezing rocks for topographic-stress and tectonic-stress-based cases. [Akbariforouz, Mohammadreza; Zheng, Chunmiao] EIT Inst Adv Study, Ningbo, Peoples R China; [Akbariforouz, Mohammadreza; Zhao, Qi] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Hong Kong, Peoples R China; [Chen, Kewei; Zheng, Chunmiao] Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Peoples R China; [Baghbanan, Alireza] Isfahan Univ Technol IUT, Dept Min Engn, Esfahan, Iran; [Dehnavi, Roohollah Narimani] Univ Leoben, Min Engn & Mineral Econ, Leoben, Austria Hong Kong Polytechnic University; Southern University of Science & Technology; Isfahan University of Technology; University of Leoben Akbariforouz, M (corresponding author), EIT Inst Adv Study, Ningbo, Peoples R China.;Akbariforouz, M; Zhao, Q (corresponding author), Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Hong Kong, Peoples R China. mo2022.akbariforouz@connect.polyu.hk; qi.qz.zhao@polyu.edu.hk Zheng, Chunmiao/I-5257-2014 Zheng, Chunmiao/0000-0001-5839-1305 National Natural Science Foundation of China [41890852]; FCE Startup Fund for New Recruits at the Hong Kong Polytechnic University [P0034042]; Council of the Hong Kong Special Administrative Region, China [PolyU 25220021] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); FCE Startup Fund for New Recruits at the Hong Kong Polytechnic University; Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council) We thank the editor and reviewers whose valuable comments and suggestions have led us to improve this manuscript. M. Akbariforouz is supported by the National Natural Science Foundation of China (No. 41890852) , awarded to Chunmiao Zheng. Qi Zhao is supported by the FCE Startup Fund for New Recruits at the Hong Kong Polytechnic University (P0034042) and the Early Career Scheme of the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 25220021) . 76 0 0 3 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1365-1609 1873-4545 INT J ROCK MECH MIN Int. J. Rock Mech. Min. Sci. MAR 2023.0 163.0 105306 10.1016/j.ijrmms.2022.105306 0.0 JAN 2023 15 Engineering, Geological; Mining & Mineral Processing Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mining & Mineral Processing 8O1SG 2023-03-23 WOS:000925621300001 0
J Zhang, CC; Oh, SK; Fu, ZW; Pedrycz, W Zhang, Congcong; Oh, Sung-Kwun; Fu, Zunwei; Pedrycz, Witold Self-organized hybrid fuzzy neural networks driven with the aid of probability-based node selection and enhanced input strategy NEUROCOMPUTING English Article Self-organized hybrid fuzzy neural; networks (SHFNN); Enhanced input strategy; Probability-based node selection; Norm regularization DESIGN; IDENTIFICATION; ALGORITHM; SYSTEMS Polynomial neural network (PNN) is a flexible self-organizing network model, and there have been lots of improvements to it. However, various and improved models of PNN do not properly balance their perfor-mance and efficiency. In this study, we present novel self-organized hybrid fuzzy neural networks (SHFNN). The purpose of this study is to develop a new design methodology of constructing a hybrid fuzzy model through enhanced input strategy (EIS) and probability-based node selection (PNS) to reinforce the performance of the model without sacrificing efficiency. C2-norm regularization (C2) is utilized to mitigate overfitting as well as enhance generalization capability. The key points of SHFNN can be summarized as follows. First, a hybrid network structure is constructed by combining fuzzy radial basis function neural networks (FRBFNN) and PNN. Second, we propose a probability-based node selection strategy for node (neuron) selection. Third, an enhanced input strategy (EIS) is proposed to design the enhanced input set by merging the general candidate input set (GCIS) and the original candidate input set (OCIS). The regularization coefficient estimation method based on C2 is used in conjunction with EIS and PNS to improve the performance and enhance the stability of the model. The effectiveness of SHFNN is validated using synthetic and machine learning dataset. The experimental results show that the prediction accuracy of the proposed model is improved by 4-22% when compared with some methods reported in the literature. Statistical analysis is also considered to demonstrate the superiority of the proposed model. (c) 2020 Elsevier B.V. All rights reserved. [Zhang, Congcong] Univ Suwon, Dept Comp, San 2-2 Wau Ri, Hwaseong Si 445743, Gyeonggi Do, South Korea; [Oh, Sung-Kwun; Fu, Zunwei] Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Shandong, Peoples R China; [Oh, Sung-Kwun] Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland Suwon University; Linyi University; Suwon University; University of Alberta; King Abdulaziz University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Fu, ZW (corresponding author), Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Shandong, Peoples R China.;Oh, SK (corresponding author), Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea. Korea Electric Power Corporation, South Korea [R19XO01-18]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, South Korea [NRF-2017R1D1A1B03032333] Korea Electric Power Corporation, South Korea; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, South Korea This work was supported by Korea Electric Power Corporation. (Grant number : R19XO01-18), South Korea and also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032333), South Korea. 41 5 5 1 3 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing DEC 5 2020.0 417.0 471 489 19 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science OS8IJ 2023-03-23 WOS:000590401100003 0
J Wei, LQ; Wu, GR; Bi, MH; Baeken, C Wei, Luqing; Wu, Guo-Rong; Bi, Minghua; Baeken, Chris Effective connectivity predicts cognitive empathy in cocaine addiction: a spectral dynamic causal modeling study BRAIN IMAGING AND BEHAVIOR English Article social cognition; cocaine dependence; effective connectivity; fantasy empathy VENTRAL TEGMENTAL AREA; WHITE-MATTER; FUNCTIONAL CONNECTIVITY; HUMAN BRAIN; SOCIAL COGNITION; FEAR RECOGNITION; REWARD SYSTEM; DRUG-USE; AMYGDALA; REACTIVITY Social cognition plays a crucial role in the development and treatment of cocaine dependence. However, studies investigating social cognition, such as empathy and its underlying neural basis, are lacking. To explore the neural interactions among reward and memory circuits, we applied effective connectivity analysis on resting-state fMRI data collected from cocaine-dependent subjects. The relationship between effective connectivity within these two important circuits and empathy ability - evaluated with the Interpersonal Reactivity Index (IRI) - was assessed by machine learning algorithm using multivariate regression analysis. In accordance with the neurocircuitry disruptions of cocaine addiction, the results showed that cocaine-dependent subjects relative to healthy controls had altered resting state effective connectivity between parts of the memory and reward systems. Furthermore, effective connectivity between the memory and reward system could predict the fantasy empathy (FE) subscale scores in cocaine dependence. Overall, our findings provide further evidence for the neural substrates of social cognition in cocaine-dependent patients. These new insights could be useful for the development of new treatment programs for this substance dependency disorder. [Wei, Luqing] Jiangxi Normal Univ, Sch Psychol, Nanchang, Jiangxi, Peoples R China; [Wu, Guo-Rong; Bi, Minghua] Southwest Univ, Fac Psychol, Key Lab Cognit & Personal, Chongqing, Peoples R China; [Wu, Guo-Rong; Baeken, Chris] Univ Ghent, Ghent Expt Psychiat GHEP Lab, Ghent, Belgium; [Baeken, Chris] Univ Ghent, Dept Psychiat & Med Psychol, Ghent, Belgium; [Baeken, Chris] Vrije Univ Brussel VUB, Univ Ziekenhuis Brussel UZBrussel, Dept Psychiat, Laarbeeklaan 101, B-1090 Brussels, Belgium; [Baeken, Chris] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands Jiangxi Normal University; Southwest University - China; Ghent University; Ghent University; University Hospital Brussels; Vrije Universiteit Brussel; Eindhoven University of Technology Wu, GR (corresponding author), Southwest Univ, Fac Psychol, Key Lab Cognit & Personal, Chongqing, Peoples R China.;Wu, GR (corresponding author), Univ Ghent, Ghent Expt Psychiat GHEP Lab, Ghent, Belgium. guorongwu@swu.edu.cn Baeken, Chris/D-6790-2013 National Natural Science Foundation of China [61876156, 31900764]; Fundamental Research Funds for the Central Universities [SWU116074]; Ghent University Multidisciplinary Research Partnership The integrative neuroscience of behavioral control; Ghent University [BOF16/GOA/017] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Ghent University Multidisciplinary Research Partnership The integrative neuroscience of behavioral control; Ghent University(Ghent University) This work was supported by the National Natural Science Foundation of China (Grant No. 61876156, 31900764), the Fundamental Research Funds for the Central Universities (Grant No. SWU116074), the Ghent University Multidisciplinary Research Partnership The integrative neuroscience of behavioral control, and a BOF16/GOA/017 grant for a Concerted Research Action of Ghent University. 79 5 5 1 12 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1931-7557 1931-7565 BRAIN IMAGING BEHAV Brain Imaging Behav. JUN 2021.0 15.0 3 1553 1561 10.1007/s11682-020-00354-y 0.0 JUL 2020 9 Neuroimaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology TJ9UM 32710329.0 2023-03-23 WOS:000552181000002 0
C Liu, FH; Huang, XL; Chen, YY; Suykens, JAK Banerjee, A; Fukumizu, K Liu, Fanghui; Huang, Xiaolin; Chen, Yingyi; Suykens, Johan A. K. Fast learning in reproducing kernel Krein spaces via signed measures 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) Proceedings of Machine Learning Research English Proceedings Paper 24th International Conference on Artificial Intelligence and Statistics (AISTATS) APR 13-15, 2021 ELECTR NETWORK POSITIVE-DEFINITE FUNCTIONS In this paper, we attempt to solve a long-lasting open question for non-positive definite (non-PD) kernels in machine learning community: can a given non-PD kernel be decomposed into the difference of two PD kernels (termed as positive decomposition)? We cast this question as a distribution view by introducing the signed measure, which transforms positive decomposition to measure decomposition: a series of non-PD kernels can be associated with the linear combination of specific finite Borel measures. In this manner, our distribution-based framework provides a sufficient and necessary condition to answer this open question. Specifically, this solution is also computationally implementable in practice to scale non-PD kernels in large sample cases, which allows us to devise the first random features algorithm to obtain an unbiased estimator. Experimental results on several benchmark datasets verify the effectiveness of our algorithm over the existing methods. [Liu, Fanghui; Chen, Yingyi; Suykens, Johan A. K.] Katholieke Univ Leuven, ESAT STADIUS, Leuven, Belgium; [Huang, Xiaolin] Shanghai Jiao Tong Univ, Inst Med Robot, Dept Automat, Shanghai, Peoples R China KU Leuven; Shanghai Jiao Tong University Liu, FH (corresponding author), Katholieke Univ Leuven, ESAT STADIUS, Leuven, Belgium. fanghui.liu@kuleuven.be; xiaolin.huang@sjtu.edu.cn Suykens, Johan/C-9781-2014 Suykens, Johan/0000-0002-8846-6352; Chen, Yingyi/0000-0002-5571-9050 European Research Council under the European Union's Horizon 2020 research and innovation program/ERC Advanced Grant [787960]; Research Council KU Leuven [C14/18/068]; Flemish Government: FWO [GOA4917N]; Flemish Government (AI Research Program); Ford KU Leuven Research Alliance Project (Stability analysis and performance improvement of deep reinforcement learning algorithms) [KUL0076]; EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization), Leuven; AI Institute; National Natural Science Foundation of China [61977046] European Research Council under the European Union's Horizon 2020 research and innovation program/ERC Advanced Grant; Research Council KU Leuven(KU Leuven); Flemish Government: FWO(FWO); Flemish Government (AI Research Program); Ford KU Leuven Research Alliance Project (Stability analysis and performance improvement of deep reinforcement learning algorithms); EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization), Leuven; AI Institute; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program/ERC Advanced Grant E-DUALITY (787960). This paper re~ects only the authors' views and the Union is not liable for any use that may be made of the contained information. This work was supported in part by Research Council KU Leuven: Optimization frameworks for deep kernel machines C14/18/068; Flemish Government: FWO projects: GOA4917N (Deep Restricted Kernel Machines: Methods and Foundations), PhD/Postdoc grant. This research received funding from the Flemish Government (AI Research Program). This work was supported in part by Ford KU Leuven Research Alliance Project KUL0076 (Stability analysis and performance improvement of deep reinforcement learning algorithms), EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization), Leuven.AI Institute; and in part by the National Natural Science Foundation of China 61977046. Corresponding author: Fanghui Liu(fanghui.liu@kuleuven.be) and Xiaolin Huang (xiaolin.huang@sjtu.edu.cn). 63 2 2 0 1 MICROTOME PUBLISHING BROOKLINE 31 GIBBS ST, BROOKLINE, MA 02446 USA 2640-3498 PR MACH LEARN RES 2021.0 130.0 388 + 11 Computer Science, Artificial Intelligence; Mathematics, Applied; Statistics & Probability Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BR6DC 2023-03-23 WOS:000659893800044 0
J Liu, T; Chen, T; Niu, RQ; Plaza, A Liu, Tong; Chen, Tao; Niu, Ruiqing; Plaza, Antonio Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Terrain factors; Reservoirs; Convolutional neural networks; Soil; Indexes; Training; Rivers; Deep neural network; dense convolutional neural network (DenseNet); feature selection; landslide detection; Three Gorges Reservoir (TGR) FULLY CONVOLUTIONAL DENSENET; LOGISTIC-REGRESSION; SUSCEPTIBILITY MAPS; RISK-ASSESSMENT; DECISION TREE; RANDOM FOREST; NETWORKS; CLASSIFIER; HAZARD; AREAS Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this article, we constructed an accurate LDM model based on convolutional neural networks, residual neural networks, and dense convolutional neural networks (DenseNets) that considers ZY-3 high spatial resolution (HSR) data and conditioning factors (CFs). In this article, 19 factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the evaluation metrics, which exhibited Kappa coefficient improvements of 0.01-0.04 and ACC improvements of 0.02-0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment. [Liu, Tong; Chen, Tao; Niu, Ruiqing] China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China; [Chen, Tao] Geomat Technol & Applicat Key Lab Qinghai Prov, Xining 810001, Peoples R China; [Chen, Tao] Beijing Key Lab Urban Spatial Informat Engn, Beijing 100038, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Caceres 10071, Spain China University of Geosciences; Universidad de Extremadura Chen, T (corresponding author), China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China. zhinian@cug.edu.cn; taochen@cug.edu.cn; niuruiqing@cug.edu.cn; aplaza@unex.es Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; chen, tao/0000-0001-6965-1256 National Natural Science Foundation of China [62071439, 61871259]; Opening Foundation of Qilian Mountain National Park Research Center (Qinghai) [GKQ2019-01]; Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering [20210209]; Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province [QHDX-2019-01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Foundation of Qilian Mountain National Park Research Center (Qinghai); Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering; Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province This work was in part by the National Natural Science Foundation of China under Grant 62071439 and Grant 61871259; in part by the Opening Foundation of Qilian Mountain National Park Research Center (Qinghai) under Grant GKQ2019-01, in part by the Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering, under Grant 20210209, and in part by the Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province, under Grant QHDX-2019-01. 87 13 13 16 55 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 11417 11428 10.1109/JSTARS.2021.3117975 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology XA2YM gold 2023-03-23 WOS:000720519100011 0
J Yang, DD; Martinez, C; Visuna, L; Khandhar, H; Bhatt, C; Carretero, J Yang, Dandi; Martinez, Cristhian; Visuna, Lara; Khandhar, Hardev; Bhatt, Chintan; Carretero, Jesus Detection and analysis of COVID-19 in medical images using deep learning techniques SCIENTIFIC REPORTS English Article The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19. [Yang, Dandi] Beijing Electromech Engn Inst, Beijing 100074, Peoples R China; [Martinez, Cristhian; Visuna, Lara; Carretero, Jesus] Carlos III Univ Madrid, Dept Comp Sci & Engn, Madrid 28911, Spain; [Khandhar, Hardev; Bhatt, Chintan] Charotar Univ Sci & Technol CHARUSAT, CSPIT, U&PU Patel Dept Comp Engn, Changa, India Charotar University of Science & Technology - Charusat Carretero, J (corresponding author), Carlos III Univ Madrid, Dept Comp Sci & Engn, Madrid 28911, Spain. jesus.carretero@uc3m.es Visuna, Lara/0000-0002-2944-6693; Carretero, Jesus/0000-0002-1413-4793 Innovative Medicines Innitiative 2 Joint Undertaking (JU) [853989]; European Union's Horizon 2020 research and innovation programme; EFPIA; Global Alliance for TB Drug Development non profit organisation; Bill & Melinda Gates Foundation; University of Dundee Innovative Medicines Innitiative 2 Joint Undertaking (JU); European Union's Horizon 2020 research and innovation programme; EFPIA; Global Alliance for TB Drug Development non profit organisation; Bill & Melinda Gates Foundation(Bill & Melinda Gates FoundationCGIAR); University of Dundee The research leading to these results received funding from the Innovative Medicines Innitiative 2 Joint Undertaking (JU) under grant agreement No 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non profit organisation, Bill & Melinda Gates Foundation and University of Dundee. 38 25 25 2 10 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep OCT 4 2021.0 11.0 1 19638 10.1038/s41598-021-99015-3 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics WB5OV 34608186.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000703622500046 0
C Wu, CR; Chen, SC; Sheng, GR; Roussel, P; Denby, B IEEE Wu, Chengrui; Chen, Shicheng; Sheng, Guorui; Roussel, Pierre; Denby, Bruce PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) English Proceedings Paper IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) APR 15-20, 2018 Calgary, CANADA Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc convolutional neural networks; motion detection; unsupervised learning; speech production; ultrasound; tongue; silent speech interface A 3-dimensional convolutional neural network is trained on unlabeled ultrasound video to predict an upcoming tongue image from previous ones. The network obtains results superior to those of simpler predictors and provides a starting point for exploiting the higher-level representation of the tongue learned by the system in a variety of applications in speech research. This work is believed to be the first application of convolutional neural networks to unlabeled ultrasound video for the purpose of predicting tongue movement. [Wu, Chengrui; Chen, Shicheng; Sheng, Guorui; Denby, Bruce] Tianjin Univ, Tianjin, Peoples R China; [Roussel, Pierre] Inst Langevin, Paris, France Tianjin University; UDICE-French Research Universities; Universite Paris Cite Wu, CR (corresponding author), Tianjin Univ, Tianjin, Peoples R China. wcrtju01@tju.edu.cn; coder.chen.shi.cheng@gmail.com; shengguorui@outlook.com; pierre.roussel@espci.fr; denby@ieee.org China Ministry of Education 985 Foundation [060-0903071001] China Ministry of Education 985 Foundation Partial funding for this work was provided by the China Ministry of Education 985 Foundation via grant number 060-0903071001. The authors wish to thank the reviewers for many useful suggestions for improving the article. 17 3 3 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-4658-8 2018.0 5764 5768 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BL0QY 2023-03-23 WOS:000446384605184 0
J Xue, ZH; Xu, Q; Zhang, MX Xue, Zhaohui; Xu, Qi; Zhang, Mengxue Local Transformer With Spatial Partition Restore for Hyperspectral Image Classification IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Transformers; Convolutional neural networks; Image restoration; Feature extraction; Convolution; Licenses; Correlation; Convolutional neural network (CNN); hyperspectral image (HSI) classification; spatial attention; transformer Convolutional neural network (CNN) has exhibited enormous potentials in hyperspectral image (HSI) classification owing to excellent locally modeling ability. Although excellent performance of CNN-based methods has been witnessed, there still have some limitations of their internal network backbone. On the one hand, modeling long-distance context dependencies is an inborn defect, which leads to receptive field limitation and insufficient feature capture in HSI. On the other hand, CNN-based methods usually need various sample distribution to train and cannot infer dynamically, which may not capture the inherent changes of HSI data well. To overcome the abovementioned issues, we propose a novel local transformer with spatial partition restore network (SPRLT-Net) for HSI classification. First, local transformer is introduced to obtain the spatial attention weights dynamically by measuring the similarity between related pixel pairs. Second, a spatial partition restore (SPR) module is designed to split the input patch into several overlapping continuous subpatches as sequential. With the obtained attention weights at hand, the SPR module restores the sequential to the original patch. Finally, a fully connected layer is used for classification. SPRLT-Net can capture global context dependencies, and the dynamical attention weights can adapt the inherent changes of HSI spatial pixels. Experimental results based on spatially disjoint samples and randomly selected samples of five benchmark datasets demonstrate that SPRLT-Net outperforms the other state-of-the-art methods in terms of classification accuracy, generalization performance, and computational complexity. [Xue, Zhaohui; Xu, Qi] Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China; [Xue, Zhaohui; Xu, Qi] Hohai Univ, Jiangsu Prov Engn Res Ctr Water Resources & Envir, Nanjing 211100, Peoples R China; [Zhang, Mengxue] Univ Valencia, Image Proc Lab, Valencia 46980, Spain Hohai University; Hohai University; University of Valencia Xue, ZH (corresponding author), Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China. zhaohui.xue@hhu.edu.cn; hhuxuqi@163.com; meng_xue_zhang@163.com Xue, Zhaohui/K-3413-2014 Xue, Zhaohui/0000-0001-6253-2967; Xue, Zhaohui/0000-0002-1672-317X; Zhang, Mengxue/0000-0002-8587-4334 National Natural Science Foundation of China [41971279]; Fundamental Research Funds for the Central Universities [B200202012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 41971279 and in part by the Fundamental Research Funds for the Central Universities under Grant B200202012. 55 6 6 12 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 4307 4325 10.1109/JSTARS.2022.3174135 0.0 19 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 2A3HI gold 2023-03-23 WOS:000809396100001 0
J Zhou, XY; Qi, W; Ovur, SE; Zhang, LB; Hu, YB; Su, H; Ferrigno, G; De Momi, E Zhou, Xuanyi; Qi, Wen; Ovur, Salih Ertug; Zhang, Longbin; Hu, Yingbai; Su, Hang; Ferrigno, Giancarlo; De Momi, Elena A novel muscle-computer interface for hand gesture recognition using depth vision JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING English Article Depth vision; Hand gesture recognition; Muscle computer interface; Clustering; Classification CLASSIFICATION; PROTOTYPE Muscle computer Interface (muCI), one of the widespread human-computer interfaces, has been widely adopted for the identification of hand gestures by using the electrical activity of muscles. Although multi-modal theory and machine learning algorithms have made enormous progress in muCI over the last decades, the processing of the collecting and labeling large data sets creates a high workload and leads to time-consuming implementations. In this paper, a novel muCI was developed to integrate the advantages of EMG signals and depth vision, which could be used to automatically label the cluster of EMG data collected using depth vision. A three layers hierarchical k-medoids approach was designed to extract and label the clustering feature of ten hand gestures. A multi-class linear discriminant analysis algorithm was applied to build the hand gesture classifier. The results showed that the proposed algorithm had high accuracy and the muCI performed well, which could automatically label the hand gesture in all experiments. The proposed muCI can be utilized for hand gesture recognition without labeling the data in advance and has the potential for robot manipulation and virtual reality applications. [Zhou, Xuanyi] Cent South Univ, State Key Lab High Performance Complex Mfg, Changsha 410083, Peoples R China; [Qi, Wen; Ovur, Salih Ertug; Su, Hang; Ferrigno, Giancarlo; De Momi, Elena] Politecn Milan, DEIB, Piazza Leonardo da Vinci 32, I-20133 Milan, MI, Italy; [Zhang, Longbin] KTH Royal Inst Technol, Dept Mech, MoveAbil Lab, S-10044 Stockholm, Sweden; [Hu, Yingbai] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany Central South University; Polytechnic University of Milan; Royal Institute of Technology; Technical University of Munich Su, H (corresponding author), Politecn Milan, DEIB, Piazza Leonardo da Vinci 32, I-20133 Milan, MI, Italy. hang.su@polimi.it qi, wen/AAE-5175-2022; OVUR, Salih Ertug Ertug/AAW-6009-2021; DE MOMI, ELENA/D-7375-2016 DE MOMI, ELENA/0000-0002-8819-2734 European Unions Horizon 2020 research and innovation program under SMARTsurg project [732515] European Unions Horizon 2020 research and innovation program under SMARTsurg project This work was supported by the European Unions Horizon 2020 research and innovation program under SMARTsurg project Grant Agreement No. 732515. 48 18 18 2 24 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-5137 1868-5145 J AMB INTEL HUM COMP J. Ambient Intell. Humaniz. Comput. NOV 2020.0 11.0 11 SI 5569 5580 10.1007/s12652-020-01913-3 0.0 MAR 2020 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications OQ9QX 2023-03-23 WOS:000522181000001 0
C Ferreira, JP; Ribeiro, B; Vieira, A; Coimbra, AP; Crisostomo, MM; Boucas, C; Liu, T; Pinheiro, JP Botto-Tobar, M; Vizuete, MZ; Torres-Carrion, P; Leon, SM; Vasquez, GP; Durakovic, B Ferreira, Joao P.; Ribeiro, Bernardete; Vieira, Alexandra; Coimbra, A. Paulo; Crisostomo, Manuel M.; Boucas, Cesar; Liu, Tao; Pinheiro, Joao Pascoa Knee Injured Recovery Analysis Using Extreme Learning Machine APPLIED TECHNOLOGIES (ICAT 2019), PT II Communications in Computer and Information Science English Proceedings Paper 1st International Conference on Applied Technologies (ICAT) DEC 03-09, 2019 Univ Fuerzas Armadas, Quito, ECUADOR Univ Tecnica Particular Loja,Univ Tecnica del Norte,GDEON,Univ Tecnica Estatal Quevedo,Univ Tecnica Particular Loja, Inclusive Human Comp Interact Res Grp,GIISIC,Carrera Ingn Telecomunicaciones,IEEE UG,Asociac Software Libre Ecuador,Univ Amer Univ Fuerzas Armadas MLP; ELM; MSVR; Knee pattern; GVS GAIT ANALYSIS; PATTERN; ASYMMETRIES; PREDICTION The physiotherapists analyse gait patterns to recognize normal and pathological gait movements. The gait patterns are affected by the characteristics of the individual (gender, age, weight and height) and the walking speed. In this paper, a gait analysis system to evaluate the severity of gait pathology is proposed. The Machine Learning (ML) algorithm can generate reference knee patterns for specific individuals. Gait index are used to compare the patterns generated by the ELM and patterns of the patients who suffered a surgical knee reconstruction. Two gait index are compared: The Gait Variable Score (GVS) and the Global Index (G(Index)) developed by the authors. The G(Index) classified 7 patients as not recovery, corroborating with the opinion of physiotherapists, while the GVS only classified 2 as not recovered. The proposed gait analysis system using the Extreme Learning Machine (ELM) and the G(Index) can be useful tool for physiotherapy team in the gait pathology diagnosis and evaluation of future pathologies. [Ferreira, Joao P.] Super Inst Engn Coimbra, Dept Elect Engn, Coimbra, Portugal; [Ferreira, Joao P.; Vieira, Alexandra; Coimbra, A. Paulo; Crisostomo, Manuel M.; Boucas, Cesar] Univ Coimbra, Dept Elect & Comp Engn, ISR, Coimbra, Portugal; [Ribeiro, Bernardete; Vieira, Alexandra] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal; [Liu, Tao] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Sch Mech Engn, Hangzhou 310027, Peoples R China; [Pinheiro, Joao Pascoa] Coimbra Univ Hosp, Rehabil Med Dept, Coimbra, Portugal Universidade de Coimbra; Universidade de Coimbra; Universidade de Coimbra; Zhejiang University; Universidade de Coimbra; Centro Hospitalar e Universitario de Coimbra (CHUC) Ferreira, JP (corresponding author), Super Inst Engn Coimbra, Dept Elect Engn, Coimbra, Portugal.;Ferreira, JP (corresponding author), Univ Coimbra, Dept Elect & Comp Engn, ISR, Coimbra, Portugal. ferreira@isec.pt Ferreira, Joao Paulo/E-3816-2016; Ribeiro, Bernardete/A-8010-2016 Ferreira, Joao Paulo/0000-0003-0143-9421; Ribeiro, Bernardete/0000-0002-9770-7672 Fundacao para a Ciencia e Tecnologia (FCT) [SFRH/BD/132408/2017, PTDC/EEI-AUT/5141/2014]; COMPETE 2020 program [PTDC/EEI-AUT/5141/2014]; Fundação para a Ciência e a Tecnologia [SFRH/BD/132408/2017, PTDC/EEI-AUT/5141/2014] Funding Source: FCT Fundacao para a Ciencia e Tecnologia (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); COMPETE 2020 program; Fundação para a Ciência e a Tecnologia The Fundacao para a Ciencia e Tecnologia (FCT) is gratefully acknowledged for funding this work with the grants SFRH/BD/132408/2017 and PTDC/EEI-AUT/5141/2014 (Automatic Adaptation of a Humanoid Robot Gait to Different Floor-Robot Friction Coefficients). The authors also acknowledge the COMPETE 2020 program for the financial support with the PTDC/EEI-AUT/5141/2014. 41 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1865-0929 1865-0937 978-3-030-42520-3; 978-3-030-42519-7 COMM COM INF SC 2020.0 1194.0 65 79 10.1007/978-3-030-42520-3_6 0.0 15 Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT4NR 2023-03-23 WOS:000832292000006 0
J Huang, SZ; Zhang, X; Chen, NC; Ma, HL; Fu, P; Dong, JZ; Gu, XH; Nam, WH; Xu, L; Rab, G; Niyogi, D Huang, Shuzhe; Zhang, Xiang; Chen, Nengcheng; Ma, Hongliang; Fu, Peng; Dong, Jianzhi; Gu, Xihui; Nam, Won-Ho; Xu, Lei; Rab, Gerhard; Niyogi, Dev A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio-Temporal Continuity WATER RESOURCES RESEARCH English Article surface soil moisture; point-surface data fusion; deep learning IN-SITU MEASUREMENTS; MODEL; SMOS; TEMPERATURE; PRODUCTS; SMAP; DYNAMICS; LANDSAT; EVAPOTRANSPIRATION; ASSIMILATION Surface soil moisture (SSM) has a considerable impact on land-atmosphere exchanges of water and energy fluxes. However, due to the inherent deficiencies of remotely sensed data (e.g., cloud contamination in thermal remote sensing and coarse resolutions for microwave remote sensing), none of the current algorithms alone can provide daily and seamless field-scale (30 m) SSM information. To fill the gap, we proposed a novel SSM fusion framework to Generate high Resolution, Accurate, Seamless data using Point-Surface fusion (GRASPS) based on remotely sensed, reanalysis, and in-situ data sets. First, 30 m seamless continuous SSM correlated variables (land surface temperature, NDVI, and albedo) were downscaled by enhanced spatial and temporal adaptive reflectance fusion model. Then, downscaled auxiliary variables and other background variables were input into a deep learning model to produce 30 m daily and seamless SSM fields. To further improve the SSM estimation accuracy, a pixel classification-based bias correction method was developed. The GRASPS method was validated over an in situ soil moisture sensor network (HOAL network) in Austria. The average Pearson correlation coefficient, root mean square error (RMSE), unbiased RMSE (ubRMSE), bias, and mean absolute error (MAE) over all validation sites achieved 0.78, 0.048, 0.033, -0.001, and 0.041 m(3)m(-3) respectively. After bias correction, RMSE, ubRMSE, bias, and MAE decreased by 13%, 7%, 22%, and 18%, respectively. The proposed method maximizes the potential of data fusion and deep learning in generating field-scale seamless SSM, which is promising for fine-scale studies and applications in agricultural, hydrological, and environmental domains. [Huang, Shuzhe; Ma, Hongliang; Xu, Lei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China; [Zhang, Xiang; Chen, Nengcheng] China Univ Geosci Wuhan, Sch Geog & Informat Engn, Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China; [Chen, Nengcheng] Hubei Luojia Lab, Wuhan, Peoples R China; [Fu, Peng] Harrisburg Univ, Ctr Environm Energy & Econ, Harrisburg, PA USA; [Dong, Jianzhi] Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin, Peoples R China; [Gu, Xihui] China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan, Peoples R China; [Gu, Xihui] Univ Oxford, Sch Geog & Environm, Oxford, England; [Nam, Won-Ho] Hankyong Natl Univ, Sch Social Safety & Syst Engn, Inst Agr Environm Sci, Natl Agr Water Res Ctr, Anseong, South Korea; [Rab, Gerhard] Univ Technol Vienna, Inst Hydraul Engn & Water Resources Management, Vienna, Austria; [Rab, Gerhard] Fed Agcy Water Management, Inst Land & Water Management Res, Petzenkirchen, Austria; [Niyogi, Dev] Univ Texas Austin, Dept Geol Sci, Jackson Sch Geosci, Austin, TX USA; [Niyogi, Dev] Univ Texas Austin, Dept Civil Architecture & Environm Engn, Austin, TX USA Wuhan University; China University of Geosciences; Tianjin University; China University of Geosciences; University of Oxford; Hankyong National University; Technische Universitat Wien; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin Zhang, X (corresponding author), China Univ Geosci Wuhan, Sch Geog & Informat Engn, Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China. zhangxiang76@cug.edu.cn Ma, Hongliang/Z-2638-2019; Niyogi, Dev/H-6326-2013; Zhang, Xiang/H-3158-2013; NAM, WONHO/B-3489-2014 Ma, Hongliang/0000-0002-8260-8030; Niyogi, Dev/0000-0002-1848-5080; Fu, Peng/0000-0003-1568-1518; Rab, Gerhard/0000-0002-2271-275X; Xu, Lei/0000-0002-6454-2963; Zhang, Xiang/0000-0002-1017-742X; Chen, Nengcheng/0000-0002-3521-9972; Gu, Xihui/0000-0002-1924-9282; NAM, WONHO/0000-0002-9671-6569 Hubei Provincial Natural Science Foundation of China [2020CFB615]; National Natural Science Foundation of China Program [41890822, 41801339]; Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS202114]; National Natural Science Foundation of China [52179021] Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China Program(National Natural Science Foundation of China (NSFC)); Open Fund of State Key Laboratory of Remote Sensing Science; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This study was supported by grants from Hubei Provincial Natural Science Foundation of China (2020CFB615), the National Natural Science Foundation of China Program (41890822, 41801339), and Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202114). Jianzhi Dong is funded by National Natural Science Foundation of China (52179021). Besides, the authors would like to thank Gerhard Rab for the support with the in-situ data collection for the HOAL catchment. Dev Niyogi acknowledges the John E. Brick Elliott endowment at the University of Texas at Austin. 102 4 4 24 51 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0043-1397 1944-7973 WATER RESOUR RES Water Resour. Res. MAY 2022.0 58.0 5 e2021WR030827 10.1029/2021WR030827 0.0 26 Environmental Sciences; Limnology; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Marine & Freshwater Biology; Water Resources 1G3US 2023-03-23 WOS:000795776600001 0
J Yuan, S; Zhang, Y; Tang, J; Hall, W; Cabota, JB Yuan, Sha; Zhang, Yu; Tang, Jie; Hall, Wendy; Cabota, Juan Bautista Expert finding in community question answering: a review ARTIFICIAL INTELLIGENCE REVIEW English Review Expert finding; Matrix factorization; Deep learning; Ensemble learning MATRIX FACTORIZATION; RECOMMENDATION; MODEL The rapid development of Community Question Answering (CQA) satisfies users' quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA. [Yuan, Sha; Tang, Jie] Tsinghua Univ, Dept Comp Sci & Technol, Knowledge Engn Lab, Beijing, Peoples R China; [Zhang, Yu] Chinese Acad Med Sci, Inst Med Informat, Peking Union Med Coll, Beijing, Peoples R China; [Cabota, Juan Bautista] Univ Valencia, Dept Comp Sci, Valencia, Spain; [Hall, Wendy] Univ Southampton, Elect & Comp Sci, Southampton, Hants, England Tsinghua University; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; University of Valencia; University of Southampton Tang, J (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Knowledge Engn Lab, Beijing, Peoples R China. yuansha@mail.tsinghua.edu.cn; zhang.yu@imicams.ac.cn; jietang@tsinghua.edu.cn; wh@ecs.soton.ac.uk; jcabota@gmail.com Hall, Wendy/0000-0003-4327-7811 NSFC for Distinguished Young Scholar [61825602]; National Natural Science Foundation of China [61806111]; National High Technology Research and Development Program of China (863 Program) [2015AA124102] NSFC for Distinguished Young Scholar; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National High Technology Research and Development Program of China (863 Program)(National High Technology Research and Development Program of China) This work is supported by the NSFC for Distinguished Young Scholar (61825602), National Natural Science Foundation of China (61806111), and the National High Technology Research and Development Program of China (863 Program) (2015AA124102). 86 39 40 7 44 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. FEB 2020.0 53.0 2 843 874 10.1007/s10462-018-09680-6 0.0 32 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science KL2RC Green Accepted, Green Submitted 2023-03-23 WOS:000513275100002 0
J Liu, J; Tiwari, P; Nguyen, TG; Gupta, D; Band, SS Liu, Jing; Tiwari, Prayag; Tri Gia Nguyen; Gupta, Deepak; Band, Shahab S. Multi-scale local-global architecture for person re-identification SOFT COMPUTING English Article Person re-identification; Multi-scale local-global architecture; Attention mechanism; Deep learning ATTENTION; NETWORK With the emergence of deep learning method, which has been driven a great success for the field of person re-identification (re-ID). However, the existing works mainly focus on first-order attention (i.e., spatial and channels attention) statistics to model the valuable information for person re-ID. On the other hand, most existing methods operate data points respectively, which ignores discriminative patterns to some extent. In this paper, we present an automated framework named multi-scale local-global for person re-ID. The framework consists of two components. The first component is that a high-order attention module is adopted to learn high-order attention patterns to model the subtle differences among pedestrians and to generate the informative attention features. On the other hand, a novel architecture named spectral feature transformation is designed to make for the optimization of group wise similarities. Furthermore, we fuse the components together to form an ensemble model for person re-ID. Extensive experiments were conducted on the three benchmark datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, showing the superiority of the proposed method. [Liu, Jing] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China; [Liu, Jing] Weinan Normal Univ, Sch Comp Sci, Weinan 714099, Peoples R China; [Tiwari, Prayag] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Tri Gia Nguyen] FPT Univ, Danang 50509, Vietnam; [Gupta, Deepak] Maharaja Agrasen Inst Technol, Delhi, India; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan Northwestern Polytechnical University; Weinan Normal University; Aalto University; FPT University; Maharaja Agrasen Institute of Technology; National Yunlin University Science & Technology Tiwari, P (corresponding author), Aalto Univ, Dept Comp Sci, Espoo, Finland.;Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan. liujing8318@mail.nwpu.edu.cn; prayag.tiwari@aalto.fi; tri@ieee.org; deepakgupta@mait.ac.in; shamshirbands@yuntech.edu.tw S. Band, Shahab/ABB-2469-2020; Nguyen, Tri Gia/A-9189-2018; Tiwari, Prayag/N-6261-2017 S. Band, Shahab/0000-0001-6109-1311; Nguyen, Tri Gia/0000-0003-4578-9925; Tiwari, Prayag/0000-0002-2851-4260 Aalto University; Academy of Finland [336033, 315896]; Business Finland [884/31/2018]; EU H2020 [101016775]; Academy of Finland (AKA) [315896] Funding Source: Academy of Finland (AKA) Aalto University; Academy of Finland(Academy of Finland); Business Finland; EU H2020; Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) Open Access funding provided by Aalto University. This work was supported by the Academy of Finland (Grants 336033, 315896), Business Finland (Grant 884/31/2018), and EU H2020 (Grant 101016775). 61 1 1 7 11 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1432-7643 1433-7479 SOFT COMPUT Soft Comput. AUG 2022.0 26.0 16 SI 7967 7977 10.1007/s00500-022-06859-6 0.0 MAR 2022 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2Y7RD hybrid, Green Published 2023-03-23 WOS:000763380000003 0
J An, QL; Tao, ZR; Xu, XW; El Mansori, M; Chen, M An, Qinglong; Tao, Zhengrui; Xu, Xingwei; El Mansori, Mohamed; Chen, Ming A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network MEASUREMENT English Article Tool condition monitoring; Long short-term memory network; Convolutional neural network; Remaining useful life; Cyber-physical system This paper introduces a hybrid model that incorporates a convolutional neural network (CNN) with a stacked bi-directional and uni-directional LSTM (SBULSTM) network, named CNN-SBULSTM, to address sequence data in the task of tool remaining useful life (RUL) prediction. In the CNN-SBULSTM network, CNN is firstly utilized for local feature extraction and dimension reduction. Then SBULSTM network is designed to denoise and encode the temporal information. Finally, multiple fully connected layers are built on the top of the CNN-SBULSTM network to add non-linearity to the output, and one regression layer is utilized to generate the target RUL. The cyber-physical system (CPS) is used to collect the internal controller signals and the external sensor signals during milling process. The proposed hybrid model and several other published methods are applied to the datasets acquired from milling experiments. The comparison and analysis results indicate that the integrated framework is applicable to track the tool wear evolution and predict its RUL with the average prediction accuracy reaching up to 90%. (C) 2020 Elsevier Ltd. All rights reserved. [An, Qinglong; Tao, Zhengrui; Xu, Xingwei; Chen, Ming] Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China; [El Mansori, Mohamed] Arts & Metiers ParisTech, MSMP EA 7350, Rue St Domin,BP 508, F-51006 Chalons Sur Marne, France; [El Mansori, Mohamed] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77840 USA Shanghai Jiao Tong University; Arts et Metiers Institute of Technology; Texas A&M University System; Texas A&M University College Station An, QL (corresponding author), Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China. qlan@sjtu.edu.cn Chen, Ming/GVU-8412-2022; Tao, Zhengrui/ABA-1147-2021 Tao, Zhengrui/0000-0003-1510-6855 National Key R&D Program of China [2018YFB2002200] National Key R&D Program of China This research is supported by National Key R&D Program of China (2018YFB2002200). 24 86 90 47 222 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement MAR 15 2020.0 154.0 107461 10.1016/j.measurement.2019.107461 0.0 15 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation KQ7GW 2023-03-23 WOS:000517088600002 0
C Do, TH; Luo, X; Nguyen, DM; Deligiannis, N IEEE Tien Huu Do; Luo, Xiao; Duc Minh Nguyen; Deligiannis, Nikos RUMOUR DETECTION VIA NEWS PROPAGATION DYNAMICS AND USER REPRESENTATION LEARNING 2019 IEEE DATA SCIENCE WORKSHOP (DSW) English Proceedings Paper IEEE Data Science Workshop (DSW) JUN 02-05, 2019 Minneapolis, MN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus becomes important to early detect them. Many methods have been introduced to detect rumours using the content or the social context of news. However, most existing methods ignore or do not explore effectively the propagation pattern of news in social media, including the sequence of interactions of social media users with news across time. In this work, we propose a novel method for rumour detection based on deep learning. Our method leverages the propagation process of the news by learning the users' representation and the temporal interrelation of users' responses. Experiments conducted on Twitter and Weibo datasets demonstrate the state-of-the-art performance of the proposed method. [Tien Huu Do; Luo, Xiao; Duc Minh Nguyen; Deligiannis, Nikos] Vrije Univ Brussel, Pl Laan 2, B-1050 Brussels, Belgium; [Tien Huu Do; Duc Minh Nguyen; Deligiannis, Nikos] IMEC, Kapeldreef 75, B-3001 Leuven, Belgium; [Luo, Xiao] CRRC Zhuzhou Inst Co Ltd, 412001 Shidai Rd, Zhuzhou, Hunan, Peoples R China Vrije Universiteit Brussel; IMEC; CRRC Corporation Do, TH (corresponding author), Vrije Univ Brussel, Pl Laan 2, B-1050 Brussels, Belgium.;Do, TH (corresponding author), IMEC, Kapeldreef 75, B-3001 Leuven, Belgium. Deligiannis, Nikos/ABH-2381-2020 Deligiannis, Nikos/0000-0001-9300-5860; Do Huu, Tien/0000-0002-7346-5496; Nguyen, Duc Minh/0000-0002-0367-564X 35 7 7 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-0708-0 2019.0 196 200 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematics BN5KS Green Submitted 2023-03-23 WOS:000483469300038 0
J Long, B; Huang, LJ; Sun, HB; Chen, Y; Victor, F; Chong, KT Long, Bo; Huang, LiJun; Sun, HongBin; Chen, Yong; Victor, Freret; Chong, Kil To An intelligent dc current minimization method for transformerless grid-connected photovoltaic inverters ISA TRANSACTIONS English Article DC current injection; Power quality; Adaptive-back-propagation neural network; Three-phase grid-connected inverter CURRENT INJECTION; CONTROL STRATEGY Due to the scaling and zero-drift of current sensor errors, unbalanced grid voltages, tolerance of power switching devices, and asymmetry of PWM gate driving pulses, transformerless grid-connected inverters usually have certain amount of dc components injected to the ac grid. Therefore, power quality of the grid is degraded. Many efforts, such as using blocking capacitors, a dc current feedback control method, and a voltage dc component feedback control method, etc., have been introduced to minimize the dc injection. This paper proposes an intelligent control strategy of dc current injection suppression to the grid by utilizing adaptive-back-propagation (ABP) neural network PID controller. The performance of the proposed scheme is evaluated and compared with the traditional and existing method. Finally, the control scheme is verified on a 2-kW three-phase grid-connected inverter in the laboratory. (C) 2018 Published by Elsevier Ltd on behalf of ISA. [Long, Bo; Huang, LiJun; Chen, Yong] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Inst Elect Vehicle Driving Syst & Safety Technol, Chengdu, Sichuan, Peoples R China; [Sun, HongBin] Tsinghua Univ, Sch Elect Engn, Beijing, Peoples R China; [Victor, Freret] Natl Coll Elect & Applicat, Cergy Pontoise, French, France; [Chong, Kil To] Chonbuk Natl Univ, Sch Elect Engn, Jonjue, South Korea University of Electronic Science & Technology of China; Tsinghua University; Jeonbuk National University Long, B (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Inst Elect Vehicle Driving Syst & Safety Technol, Chengdu, Sichuan, Peoples R China. longbo_1978@uestc.edu.cn; jun_uestc@std.uestc.edu.cn; shb@tsinghua.edu.cn; ychencd@uestc.edu.cn; Victor.freret@ensea.fr; kitchong@jbnu.ac.kr Long, Bo/AGX-8013-2022 chen, yong/0000-0003-4580-8655; Long, Bo/0000-0003-2953-6362 Fundamental Research Funds for the Central Universities of China [ZYGX2016J115]; State Key Laboratory of Control and Simulation of Power System Generation Equipment, China [SKLD17KM09]; Tsinghua University, China; Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), China [2007DA10512716415]; Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF); Korea government (MISIT) [2017M3C7A1044815] Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); State Key Laboratory of Control and Simulation of Power System Generation Equipment, China; Tsinghua University, China; Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), China; Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF); Korea government (MISIT)(Korean Government) This work was supported by the Fundamental Research Funds for the Central Universities of China (NO. ZYGX2016J115), State Key Laboratory of Control and Simulation of Power System Generation Equipment, China (SKLD17KM09), Tsinghua University, China, Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), China (No.2007DA10512716415), and by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) & funded by the Korea government (MISIT) (No. 2017M3C7A1044815). The authors would like to thank all the reviewers for their advices and suggestions on improving this paper. 20 10 12 5 33 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-0578 1879-2022 ISA T ISA Trans. MAY 2019.0 88.0 268 279 10.1016/j.isatra.2018.12.005 0.0 12 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation HZ3AG 30554893.0 2023-03-23 WOS:000468719300023 0
J Zhang, DM; Zeng, L; Cao, KH; Wang, MX; Peng, SZ; Zhang, Y; Zhang, YG; Klein, JO; Wang, Y; Zhao, WS Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS English Article; Proceedings Paper IEEE International Symposium on Circuits and Systems (ISCAS) MAY 24-27, 2015 Lisbon, PORTUGAL IEEE All Spin Artificial Neural Network (ASANN); artificial synaptic device; compound spintronic device; magnetic tunnel junction (MTJ); post-CMOS non-volatile memory (NVM) technologies; resistive RAM (RRAM) NEUROMORPHIC NETWORK; CROSSBAR; DEVICE; MEMORY; ANISOTROPY Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work. [Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Zhao, Weisheng] Beihang Univ, SIC, Beijing 100191, Peoples R China; [Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Zhao, Weisheng] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Klein, Jacques-Olivier] Univ Paris 11, IEF, Bat 425, F-91405 Orsay, France; [Wang, Yu] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China Beihang University; Beihang University; UDICE-French Research Universities; Universite Paris Saclay; Tsinghua University Zhao, WS (corresponding author), Beihang Univ, SIC, Beijing 100191, Peoples R China. deming.zhang@buaa.edu.cn; zenglang@buaa.edu.cn; kaihua.cao@buaa.edu.cn; wangmxbj@163.com; shouzhong.peng@buaa.edu.cn; yz@buaa.edu.cn; zhangyouguang@vip.sina.com; Jacques-olivier.klein@u-psud.fr; yu-wang@mail.tsinghua.edu.cn; weisheng.zhao@buaa.edu.cn KLEIN, Jacques-Olivier/B-3014-2010; Zhang, Yue/D-4205-2014; KLEIN, Jacques-Olivier/HKN-6702-2023; WANG, Yu/B-7985-2011 KLEIN, Jacques-Olivier/0000-0002-6923-5276; Zhang, Yue/0000-0001-6893-7199; KLEIN, Jacques-Olivier/0000-0002-6923-5276; WANG, Yu/0000-0001-6108-5157 34 70 70 6 61 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4545 1940-9990 IEEE T BIOMED CIRC S IEEE Trans. Biomed. Circuits Syst. AUG 2016.0 10.0 4 SI 828 836 10.1109/TBCAS.2016.2533798 0.0 9 Engineering, Biomedical; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering DT4IA 27214913.0 2023-03-23 WOS:000381442200005 0
J NguyenVan, D; Lu, SJ; Tian, SX; Ouarti, N; Mokhtari, M Dinh NguyenVan; Lu, Shijian; Tian, Shangxuan; Ouarti, Nizar; Mokhtari, Mounir A pooling based scene text proposal technique for scene text reading in the wild PATTERN RECOGNITION English Article Scene text proposal; Pooling based grouping; Scene text detection; Scene text reading; Scene text spotting NEURAL-NETWORK; RECOGNITION; LOCALIZATION Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding. In this paper, we propose a novel scene text proposal technique aiming for accurate reading texts in scenes. Inspired by the pooling layer in the deep neural network architecture, a pooling based scene text proposal technique is developed. A novel score function is designed which exploits the histogram of oriented gradients and is capable of ranking the proposals according to their probabilities of being text. An end-to-end scene text reading system has also been developed by incorporating the proposed scene text proposal technique where false alarms elimination and words recognition are performed simultaneously. Extensive experiments over several public datasets show that the proposed technique can handle multi-orientation and multi-language scene texts and obtains outstanding proposal performance. The developed end-to-end systems also achieve very competitive scene text spotting and reading performance. (C) 2018 Elsevier Ltd. All rights reserved. [Dinh NguyenVan; Ouarti, Nizar] Univ Paris 06, Sorbonne Univ, 4 Pl Jussieu, F-75252 Paris 05, France; [Lu, Shijian] Nanyang Technol Univ, Nanyang Ave, Singapore 639798, Singapore; [Tian, Shangxuan] Tencent Co LTD, Gaoxinnanyi Ave,Southern Dist Hitech Pk, Shenzhen 518057, Peoples R China; [Mokhtari, Mounir] Inst Mines Telecom, 37-39 Rue Dareau, F-75014 Paris, France; [Dinh NguyenVan; Ouarti, Nizar; Mokhtari, Mounir] CNRS, Image & Pervas Access Lab, UMI 2955, I2R, 1 Fusionopolis Way,21-01 Connexis South Tower, Singapore 138632, Singapore UDICE-French Research Universities; Sorbonne Universite; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Tencent; IMT - Institut Mines-Telecom; IMT Atlantique; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R) NguyenVan, D (corresponding author), Univ Paris 06, Sorbonne Univ, 4 Pl Jussieu, F-75252 Paris 05, France. nguyendinh1987@gmail.com; Shijian.Lu@ntu.edu.sg; sxtian@tencent.com; nizar.ouarti@ipal.cnrs.fr; Mounir.Mokhtari@mines-telecom.fr Lu, Shijian/AAU-4831-2021 Lu, Shijian/0000-0002-6766-2506 Sorbonne University - University Pierre; Marie CURIE in Paris, France; Image & Pervasive Access Lab, CNRS, in Singapore [UMI2955] Sorbonne University - University Pierre; Marie CURIE in Paris, France; Image & Pervasive Access Lab, CNRS, in Singapore This work is funded by Sorbonne University - University Pierre and Marie CURIE in Paris, France and Image & Pervasive Access Lab, UMI2955, CNRS, in Singapore. 65 11 11 1 16 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. MAR 2019.0 87.0 118 129 10.1016/j.patcog.2018.10.012 0.0 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering HE4LK Green Submitted 2023-03-23 WOS:000453338200010 0
J Li, Y; Wang, XY; Pang, JZ; Zhu, AD Li, Yue; Wang, Xiaoyu; Pang, Jingzhou; Zhu, Anding Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES English Article Decision trees; Boosting; Complexity theory; Radio frequency; Optimization; Hardware; Data models; Behavioral modeling; boosting; decision tree; digital predistortion (DPD); machine learning; power amplifier (PA) LINEARIZATION; COMPENSATION; REDUCTION; VOLTERRA; DESIGN In this article, we propose a new behavioral modeling approach, called boosted model tree, to characterize and compensate for the complex nonlinear distortions induced by wideband high-efficiency radio frequency power amplifiers. With the proposed model, the input data are classified into different zones by decision trees and each zone is assigned separate submodels. We also employ a model boosting technique to build multiple parallel tree structures that jointly model the desired nonlinear behavior. By designing dedicated optimization procedures, both tree structures and submodel coefficients can be efficiently identified. It is demonstrated that the combination of piecewise and parallel structures provides a powerful and hardware-efficient way to model nonlinear memory effect and cross terms. Based on the experimental results, the proposed method can achieve improved linearization performance with low hardware complexity under challenging wideband predistortion scenarios. [Li, Yue; Wang, Xiaoyu; Zhu, Anding] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin D04 V1W8, Ireland; [Pang, Jingzhou] Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China University College Dublin; Chongqing University Li, Y (corresponding author), Univ Coll Dublin, Sch Elect & Elect Engn, Dublin D04 V1W8, Ireland. yue.li@ucd.ie; xiaoyu.wang1@ucdconnect.ie; jingzhou.pang@cqu.edu.cn; anding.zhu@ucd.ie wang, xiaoyu/HJP-6901-2023 Zhu, Anding/0000-0002-8911-0905; Wang, Xiaoyu/0000-0002-6674-8885; Li, Yue/0000-0003-0419-7958 Science Foundation Ireland [16/IA/4449, 17/NSFC/4850, 13/RC/2077_P2] Science Foundation Ireland(Science Foundation Ireland) Manuscript received March 28, 2021; revised April 27, 2021; accepted April 28, 2021. Date of publication May 27, 2021; date of current version September 2, 2021. This work was supported in part by the Science Foundation Ireland under Grant 16/IA/4449, Grant 17/NSFC/4850, and Grant 13/RC/2077_P2. 48 8 8 3 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9480 1557-9670 IEEE T MICROW THEORY IEEE Trans. Microw. Theory Tech. SEP 2021.0 69.0 9 3976 3988 10.1109/TMTT.2021.3081096 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering UK8QY hybrid 2023-03-23 WOS:000692231600007 0
C Quan, YH; Dong, SX; Feng, W; Dauphin, G; Zhao, GP; Wang, Y; Xing, MD IEEE Quan, Yinghui; Dong, Shuxian; Feng, Wei; Dauphin, Gabriel; Zhao, Guoping; Wang, Yong; Xing, Mengdao SPECTRAL-SPATIAL FEATURE EXTRACTION BASED CNN FOR HYPERSPECTRAL IMAGE CLASSIFICATION IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) SEP 26-OCT 02, 2020 ELECTR NETWORK Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc Hyperspectral image; spectral-spatial fusion; classification; convolutional neural network ROTATION FOREST Convolutional neural networks (CNN) can automatically learn features from the hyperspectral image data, which could avoid the difficulty of manually extracting features. However, the number of training set for the classification of hyperspectral images is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. In this paper, a spectral-spatial feature (SSF) extraction based CNN method is proposed for an accurate classification with a small training set. Experimental results based on two standard hyperspectral images demonstrate the effectiveness of the proposed method. [Quan, Yinghui; Dong, Shuxian; Feng, Wei; Wang, Yong] Xidian Univ, Sch Elect Engn, Dept Remote Sensing Sci & Technol, Xian 710071, Peoples R China; [Dauphin, Gabriel] Univ Paris XIII, Inst Galilee, Lab Informat Proc & Transmiss, L2TI, St Denis, France; [Zhao, Guoping] Shaan Xi Acad Forestry, Key Lab State Forestry Adm Soil Land Water Conser, Xian 710082, Peoples R China; [Xing, Mengdao] Xidian Univ, Acad Adv Interdisciplinary Res, Xian 710071, Peoples R China Xidian University; Xidian University Feng, W (corresponding author), Xidian Univ, Sch Elect Engn, Dept Remote Sensing Sci & Technol, Xian 710071, Peoples R China. yhquan@mail.xidian.edu.cn; wfeng@xidian.edu.cn xing, mengdao/A-6449-2019 xing, mengdao/0000-0002-4084-0915; Dauphin, Gabriel/0000-0002-0677-6702 National Natural Science Foundation of China [61772397]; National Key R&D Program of China [2016YFE0200400]; Fundamental Research Funds for the Central Universities, Innovation Fund of Xidian University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Fundamental Research Funds for the Central Universities, Innovation Fund of Xidian University This work is supported by the National Natural Science Foundation of China (61772397), National Key R&D Program of China (2016YFE0200400), Fundamental Research Funds for the Central Universities, Innovation Fund of Xidian University. 11 3 3 5 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-7281-6374-1 INT GEOSCI REMOTE SE 2020.0 485 488 10.1109/IGARSS39084.2020.9323629 0.0 4 Computer Science, Artificial Intelligence; Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Optics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Environmental Sciences & Ecology; Geology; Remote Sensing; Optics BR6WH 2023-03-23 WOS:000664335300112 0
J Segler, MHS; Waller, MP Segler, Marwin H. S.; Waller, Mark P. Modelling Chemical Reasoning to Predict and Invent Reactions CHEMISTRY-A EUROPEAN JOURNAL English Article; Proceedings Paper 8th Munster Symposium on Cooperative Effects in Chemistry MAY 12, 2017 Munster, GERMANY artificial intelligence; augmented scientific discovery; computational chemistry; graph theory; organic chemistry ORGANIC-CHEMISTRY; CLASSIFICATION; DESIGN; VALIDATION; REACTIVITY; DISCOVERY; NETWORKS; DATABASE; TOOL The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. [Segler, Marwin H. S.; Waller, Mark P.] Westfal Wilhelms Univ Munster, Inst Organ Chem, Corrensstr 40, D-48149 Munster, Germany; [Segler, Marwin H. S.; Waller, Mark P.] Westfal Wilhelms Univ Munster, Ctr Multiscale Theory & Computat, Corrensstr 40, D-48149 Munster, Germany; [Waller, Mark P.] Shanghai Univ, Dept Phys, Shangda Rd 99, Shanghai 200444, Peoples R China; [Waller, Mark P.] Shanghai Univ, Int Ctr Quantum & Mol Struct, Shangda Rd 99, Shanghai 200444, Peoples R China University of Munster; University of Munster; Shanghai University; Shanghai University Waller, MP (corresponding author), Westfal Wilhelms Univ Munster, Inst Organ Chem, Corrensstr 40, D-48149 Munster, Germany.;Waller, MP (corresponding author), Westfal Wilhelms Univ Munster, Ctr Multiscale Theory & Computat, Corrensstr 40, D-48149 Munster, Germany.;Waller, MP (corresponding author), Shanghai Univ, Dept Phys, Shangda Rd 99, Shanghai 200444, Peoples R China.;Waller, MP (corresponding author), Shanghai Univ, Int Ctr Quantum & Mol Struct, Shangda Rd 99, Shanghai 200444, Peoples R China. m.waller@uni-muenster.de Deutsche Forschungsgemeinschaft [SFB 858] Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)) This work was supported by the Deutsche Forschungsgemeinschaft (SFB 858). We thank D. Evans (RELX Intellectual Properties SA) as well as J. Swienty-Busch and S. Radestock (Elsevier Information Systems GmbH) for the reaction dataset and ChemAxon for an academic software license. 56 102 104 1 77 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0947-6539 1521-3765 CHEM-EUR J Chem.-Eur. J. MAY 2 2017.0 23.0 25 6118 6128 10.1002/chem.201604556 0.0 11 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Chemistry ET8UZ 27862477.0 Green Submitted 2023-03-23 WOS:000400580000038 0
J Gao, XZ; Gao, FL; Liu, J; Li, YJ; Wan, PB; Yu, ZZ; Li, XF Gao, Xuan-Zhi; Gao, Fu-Lin; Liu, Ji; Li, Yongji; Wan, Pengbo; Yu, Zhong-Zhen; Li, Xiaofeng Self-Powered Resilient Porous Sensors with Thermoelectric Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) and Carbon Nanotubes for Sensitive Temperature and Pressure Dual-Mode Sensing ACS APPLIED MATERIALS & INTERFACES English Article; Early Access pressure sensing; temperature sensing; piezoresistive effect; melamine sponges; Seebeck coefficien PERFORMANCE; COMPOSITES; AEROGELS; STRAIN Portable and wearable dual-mode sensors that can simultaneously detect multiple stimuli are essential for emerging artificial intelligence applications, and most efforts are devoted to exploring pressure sensing devices. It is still challenging to integrate temperature and pressure sensing functions into one sensor without the requirement for complex decoupling processes. Herein, we develop a self-powered and multifunctional dual-mode sensor by dip-coating melamine sponge with both poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and carboxylated single-walled carbon nanotubes (CNTs). By integrating thermoelectric and conductive PEDOT:PSS/CNT components with the hydrophilic and resilient porous sponge, the resultant sensor is efficient in independently detecting temperature and pressure changes. The temperature and pressure stimuli can be independently converted to voltage and electrical resistance signals on the basis of the Seebeck and piezoresistive effects, respectively. The sensor exhibits a high Seebeck coefficient of 35.9 mu V K-1 with a minimum temperature detection limit of 0.4 K and a pressure sensitivity of -3.35% kPa(-1) with a minimum pressure detection limit of 4 Pa. Interestingly, the sensor can also be self-powered upon illumination. These multifunctionalities make the sensor a promising tool for applications in electronic skin, soft robots, solar energy conversion, and personal health monitoring. [Gao, Xuan-Zhi; Gao, Fu-Lin; Li, Xiaofeng] Beijing Univ Chem Technol, Beijing Key Lab Adv Funct Polymer Composites, Beijing 100029, Peoples R China; [Gao, Xuan-Zhi; Li, Yongji; Wan, Pengbo] Beijing Univ Chem Technol, Coll Mat Sci & Engn, State Key Lab Organ Inorgan Composites, Beijing 100029, Peoples R China; [Liu, Ji] Trinity Coll Dublin, Sch Chem, CRANN, Dublin D2, Ireland; [Liu, Ji] Trinity Coll Dublin, AMBER, Dublin D2, Ireland; [Yu, Zhong-Zhen] Beijing Univ Chem Technol, Coll Mat Sci & Engn, State Key Lab Organ Inorgan Composites, Beijing 100029, Peoples R China Beijing University of Chemical Technology; Beijing University of Chemical Technology; Trinity College Dublin; Trinity College Dublin; Beijing University of Chemical Technology Li, XF (corresponding author), Beijing Univ Chem Technol, Beijing Key Lab Adv Funct Polymer Composites, Beijing 100029, Peoples R China.;Liu, J (corresponding author), Trinity Coll Dublin, Sch Chem, CRANN, Dublin D2, Ireland.;Liu, J (corresponding author), Trinity Coll Dublin, AMBER, Dublin D2, Ireland.;Yu, ZZ (corresponding author), Beijing Univ Chem Technol, Coll Mat Sci & Engn, State Key Lab Organ Inorgan Composites, Beijing 100029, Peoples R China. ji.liu@tcd.ie; yuzz@mail.buct.edu.cn; xfli@mail.buct.edu.cn Yu, Zhong-Zhen/M-2252-2018 Yu, Zhong-Zhen/0000-0001-8357-3362 National Natural Science Foundation of China [51773008, 52090034]; Fundamental Research Funds for the Central Universities [XK1802] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) ? ACKNOWLEDGMENTS Financial support from the National Natural Science Foundation of China (51773008 and 52090034) and the Fundamental Research Funds for the Central Universities (XK1802) is gratefully acknowledged. 59 2 2 38 38 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1944-8244 1944-8252 ACS APPL MATER INTER ACS Appl. Mater. Interfaces 10.1021/acsami.2c12892 0.0 SEP 2022 9 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 5E1WH 36112650.0 2023-03-23 WOS:000865418700001 0
J van Geffen, F; Heim, B; Brieger, F; Geng, RW; Shevtsova, IA; Schulte, L; Stuenzi, SM; Bernhardt, N; Troeva, EI; Pestryakova, LA; Zakharov, ES; Pflug, B; Herzschuh, U; Kruse, S van Geffen, Femke; Heim, Birgit; Brieger, Frederic; Geng, Rongwei; Shevtsova, Iuliia A.; Schulte, Luise; Stuenzi, Simone M.; Bernhardt, Nadine; Troeva, Elena, I; Pestryakova, Luidmila A.; Zakharov, Evgenii S.; Pflug, Bringfried; Herzschuh, Ulrike; Kruse, Stefan SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches EARTH SYSTEM SCIENCE DATA English Article CLIMATE-CHANGE The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen-evergreen transition zone in Central Yakutia and the tundra-taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, ). The dataset includes structure-from-motion (SfM) point clouds and red-green-blue (RGB) and red-green-near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, ). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch ( and ) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, ). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. Larix gmeliniiLarix cajanderiDataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, ). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra-taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions. [van Geffen, Femke; Heim, Birgit; Brieger, Frederic; Geng, Rongwei; Shevtsova, Iuliia A.; Schulte, Luise; Stuenzi, Simone M.; Bernhardt, Nadine; Herzschuh, Ulrike; Kruse, Stefan] Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst AWI, Res Unit Potsdam, Bremerhaven, Germany; [van Geffen, Femke; Shevtsova, Iuliia A.; Schulte, Luise; Herzschuh, Ulrike] Univ Potsdam, Inst Biochem & Biol, Potsdam, Germany; [Brieger, Frederic] Carleton Univ, Dept Geog & Environm Studies, Ottawa, ON, Canada; [Geng, Rongwei] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; [Geng, Rongwei] Univ Chinese Acad Sci, Beijing, Peoples R China; [Stuenzi, Simone M.] Humboldt Univ, Geog Dept, Berlin, Germany; [Bernhardt, Nadine] Julius Kuhn Inst Bundesforschungsinst Kulturpflan, Quedlinburg, Germany; [Troeva, Elena, I; Zakharov, Evgenii S.] Russian Acad Sci, Inst Biol Problems Cryolithozone, Siberian Branch, Yakutsk, Russia; [Pestryakova, Luidmila A.; Zakharov, Evgenii S.] North Eastern Fed Univ Yakutsk, Inst Nat Sci NEFU, Yakutsk, Russia; [Pflug, Bringfried] German Aerosp Ctr DLR, Berlin, Germany; [Herzschuh, Ulrike] Univ Potsdam, Inst Environm Sci & Geog, Potsdam, Germany Helmholtz Association; Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research; University of Potsdam; Carleton University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Humboldt University of Berlin; Julius Kuhn-Institut; Institute for Biological Problems of Cryolithozone; Russian Academy of Sciences; North-Eastern Federal University in Yakutsk; Helmholtz Association; German Aerospace Centre (DLR); University of Potsdam van Geffen, F; Kruse, S (corresponding author), Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst AWI, Res Unit Potsdam, Bremerhaven, Germany.;van Geffen, F (corresponding author), Univ Potsdam, Inst Biochem & Biol, Potsdam, Germany. femke.van.geffen@awi.de; stefan.kruse@awi.de Zakharov, Evgeny/S-8642-2016; Shevtsova, Iuliia/AFO-7746-2022; Troeva, Elena/Q-9286-2016; Heim, Birgit/B-2815-2017 Zakharov, Evgeny/0000-0003-4982-0301; Shevtsova, Iuliia/0000-0002-6287-9431; Troeva, Elena/0000-0002-8016-830X; Stuenzi, Simone Maria/0000-0002-6071-289X; Schulte, Luise/0000-0002-0428-8107; Bernhardt, Nadine/0000-0003-0716-6284; Pestryakova, Luidmila/0000-0001-5347-4478; Heim, Birgit/0000-0003-2614-9391 ERC [772852] ERC(European Research Council (ERC)European Commission) This research has been supported by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852). 59 0 0 5 5 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1866-3508 1866-3516 EARTH SYST SCI DATA Earth Syst. Sci. Data NOV 11 2022.0 14.0 11 4967 4994 10.5194/essd-14-4967-2022 0.0 28 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences 6I8XI gold, Green Submitted, Green Accepted 2023-03-23 WOS:000886413700001 0
C Ripon, KSN; Ali, LE; Siddique, N; Ma, J IEEE Ripon, Kazi Shah Nawaz; Ali, Lasker Ershad; Siddique, Nazmul; Ma, Jinwen Convolutional Neural Network based Eye Recognition from Distantly Acquired Face Images for Human Identification 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 14-19, 2019 Budapest, HUNGARY Human identification; Eye recognition; KELM; CNN; Distantly acquired image EXTREME LEARNING-MACHINE; IRIS RECOGNITION; ZERNIKE MOMENTS; BIOMETRICS; PALMPRINT; SELECTION; FEATURES; FUSION Eye image recognition from a face image acquired at a distant is a promising physical biometric technique to use for human identification. This contemporary field of research depends on image preprocessing, feature extraction, and reliable classification techniques. In this work, we separate eye images from an image of the entire face of a subject and then extract features from these eye images utilizing a convolutional neural network (CNN) model. In general, CNN models convolve images in different layers to extract effective features and then use the softmax function to produce a probability output in the final layer. In our approach, we use CNN features and a kernel extreme learning machine (KELM) classifier instead of softmax to modify the original CNN model. The modified CNN-KELM model has been verified using the publicly available CASIA.v4 distance image database. The experimental results demonstrate that our proposed approach obtains a satisfactory recognition result when compared with several current state-of-the-art human identification approaches. [Ripon, Kazi Shah Nawaz] Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway; [Ali, Lasker Ershad] Khulna Univ, Math Discipline, Khulna, Bangladesh; [Siddique, Nazmul] Univ Ulster, Sch Comp Engn & Intelligent Syst, Coleraine, Londonderry, North Ireland; [Ma, Jinwen] Peking Univ, Dept Informat Sci, Beijing, Peoples R China Norwegian University of Science & Technology (NTNU); Khulna University; Ulster University; Peking University Ripon, KSN (corresponding author), Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway. ksripon@ieee.org; ershad@math.ku.ac.bd; nh.siddique@ulster.ac.uk; jwma@math.pku.edu.cn Ripon, Kazi Shah Nawaz/N-7873-2014 Ripon, Kazi Shah Nawaz/0000-0002-6551-9714 43 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-7281-1985-4 IEEE IJCNN 2019.0 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO9HA 2023-03-23 WOS:000530893803122 0
J van Helvoort, EM; Jansen, MP; Marijnissen, ACA; Kloppenburg, M; Blanco, FJ; Haugen, IK; Berenbaum, F; Bay-Jensen, ACC; Ladel, C; Lalande, A; Larkin, J; Loughlin, J; Mobasheri, A; Weinans, HH; Widera, P; Bacardit, J; Welsing, PMJ; Lafeber, FPJG van Helvoort, Eefje M.; Jansen, Mylene P.; Marijnissen, Anne C. A.; Kloppenburg, Margreet; Blanco, Francisco J.; Haugen, Ida K.; Berenbaum, Francis; Bay-Jensen, Anne-Christine C.; Ladel, Christoph; Lalande, Agnes; Larkin, Jonathan; Loughlin, John; Mobasheri, Ali; Weinans, Harrie H.; Widera, Pawel; Bacardit, Jaume; Welsing, Paco M. J.; Lafeber, Floris P. J. G. Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort RHEUMATOLOGY English Article Knee osteoarthritis; clinical trials and methods; study design; biomarkers HAND OSTEOARTHRITIS; HIP Objectives. The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods. Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Delta) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results. Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Delta and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Delta and regression KOOS pain, respectively). Conclusion. The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. [van Helvoort, Eefje M.; Jansen, Mylene P.; Marijnissen, Anne C. A.; Welsing, Paco M. J.; Lafeber, Floris P. J. G.] Univ Utrecht, Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands; [Kloppenburg, Margreet] Leiden Univ Med Ctr, Dept Rheumatol, Leiden, Netherlands; [Kloppenburg, Margreet] Leiden Univ Med Ctr, Dept Clin Epidmiol, Leiden, Netherlands; [Blanco, Francisco J.] Univ A Coruna, Dept Fisiotherapia & Med, Ctr Invest CICA, Grp Invest Reumatol GIR INIBIC Complejo Hosp, Coruna, Spain; [Haugen, Ida K.] Diakonhjemmet Hosp, Div Rheumatol & Res, Oslo, Norway; [Berenbaum, Francis] St Antoine Hosp, AP HP, Dept Rheumatol, Paris, France; [Berenbaum, Francis] Sorbonne Univ, Ctr Rech St, INSERM, Paris, France; [Bay-Jensen, Anne-Christine C.] Nord Biosci AS, Herlev, Denmark; [Ladel, Christoph] BioBone BV, Amsterdam, Netherlands; [Lalande, Agnes] Inst Res Int Serv, Suresnes, France; [Larkin, Jonathan] GlaxoSmithKline USA, Collegeville, PA USA; [Loughlin, John] Newcastle Univ, Biosci Inst, Newcastle Upon Tyne, Tyne & Wear, England; [Mobasheri, Ali] Univ Oulo, Fac Med, Res Unit Med Imaging, Phys & Technol, Oulu, Finland; [Mobasheri, Ali] State Res Inst Ctr Innovat Med, Dept Regenerat Med, Vilnius, Lithuania; [Mobasheri, Ali] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Joint Surg, Guangzhou, Peoples R China; [Mobasheri, Ali] World Hlth Org Collaborating Ctr Publ Hlth Aspect, Liege, Belgium; [Mobasheri, Ali; Weinans, Harrie H.] Univ Utrecht, Univ Med Ctr Utrecht, Dept Orthped, Utrecht, Netherlands; [Widera, Pawel; Bacardit, Jaume] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England Utrecht University; Utrecht University Medical Center; Leiden University; Leiden University Medical Center (LUMC); Leiden University; Leiden University Medical Center (LUMC); Universidade da Coruna; Diakonhjemmet Hospital; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Saint-Antoine - APHP; UDICE-French Research Universities; Sorbonne Universite; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; Nordic Bioscience; GlaxoSmithKline; Newcastle University - UK; State Research Institute Centre for Innovative Medicine; Sun Yat Sen University; World Health Organization; Utrecht University; Utrecht University Medical Center; Newcastle University - UK van Helvoort, EM (corresponding author), Univ Utrecht, Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands.;van Helvoort, EM (corresponding author), UMC Utrecht, Rheumatol & Clin Immunol, Postbus 85500,internal mail G02 232, NL-3508 GA Utrecht, Netherlands. E.M.vanHelvoort-3@umcutrecht.nl Bay-Jensen, Anne-Christine/AEQ-7281-2022; Ladel, Christoph/ABG-3165-2021; Jansen, Mylène/AAE-1410-2022 Bay-Jensen, Anne-Christine/0000-0001-7952-9297; Jansen, Mylène/0000-0003-1929-6350; Loughlin, John/0000-0003-2018-3361 Innovative Medicines Initiative Joint Undertaking from the European Union's Seventh Framework Programme (FP7/2007-2013) [115770]; EFPIA companies' Innovative Medicines Initiative Joint Undertaking from the European Union's Seventh Framework Programme (FP7/2007-2013); EFPIA companies' This work was supported by the Innovative Medicines Initiative Joint Undertaking under Grant Agreement no. 115770, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. See www.imi.europa.eu and www.approachproject.eu. 18 2 2 1 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1462-0324 1462-0332 RHEUMATOLOGY RHEUMATOLOGY DEC 23 2022.0 62.0 1 147 157 10.1093/rheumatology/keac292 0.0 MAY 2022 11 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology 8I9ST 35575381.0 Green Published 2023-03-23 WOS:000803754700001 0
J An, FX; Simpson, JM; Smail, I; Swinbank, AM; Ma, C; Liu, DZ; Lang, P; Schinnerer, E; Karim, A; Magnelli, B; Leslie, S; Bertoldi, F; Chen, CC; Geach, JE; Matsuda, Y; Stach, SM; Wardlow, JL; Gullberg, B; Ivison, RJ; Ao, Y; Coogan, RT; Thomson, AP; Chapman, SC; Wang, R; Wang, WH; Yang, Y; Asquith, R; Bourne, N; Coppin, K; Hine, NK; Ho, LC; Hwang, HS; Kato, Y; Lacaille, K; Lewis, AJR; Oteo, I; Scholtz, J; Sawicki, M; Smith, D An, Fang Xia; Simpson, J. M.; Smail, Ian; Swinbank, A. M.; Ma, Cong; Liu, Daizhong; Lang, P.; Schinnerer, E.; Karim, A.; Magnelli, B.; Leslie, S.; Bertoldi, F.; Chen, Chian-Chou; Geach, J. E.; Matsuda, Y.; Stach, S. M.; Wardlow, J. L.; Gullberg, B.; Ivison, R. J.; Ao, Y.; Coogan, R. T.; Thomson, A. P.; Chapman, S. C.; Wang, R.; Wang, Wei-Hao; Yang, Y.; Asquith, R.; Bourne, N.; Coppin, K.; Hine, N. K.; Ho, L. C.; Hwang, H. S.; Kato, Y.; Lacaille, K.; Lewis, A. J. R.; Oteo, I.; Scholtz, J.; Sawicki, M.; Smith, D. Multi-wavelength Properties of Radio- and Machine-learning-identified Counterparts to Submillimeter Sources in S2COSMOS ASTROPHYSICAL JOURNAL English Article DEEP-FIELD-SOUTH; COSMOLOGY LEGACY SURVEY; STAR-FORMING GALAXIES; DEGREE EXTRAGALACTIC SURVEY; ALMA SPECTROSCOPIC SURVEY; PARKES SELECTED REGIONS; HIGH-REDSHIFT; LENSING CLUSTERS; SOURCE CATALOG; MOLECULAR GAS We identify multi-wavelength counterparts to 1147 submillimeter sources from the S2COSMOS SCUBA-2 survey of the COSMOS field by employing a recently developed radio+machine-learning method trained on a large sample of Atacama Large Millimeter/submillimeter Array (ALMA)-identified submillimeter galaxies (SMGs), including 260 SMGs identified in the AS2COSMOS pilot survey. In total, we identify 1222 optical/near-infrared (NIR)/radio counterparts to the 897 S2COSMOS submillimeter sources with S-850 > 1.6 mJy, yielding an overall identification rate of (78 +/- 9)%. We find that (22 +/- 5)% of S2COSMOS sources have multiple identified counterparts. We estimate that roughly 27% of these multiple counterparts within the same SCUBA-2 error circles very likely arise from physically associated galaxies rather than line-of-sight projections by chance. The photometric redshift of our radio+machine-learning-identified SMGs ranges from z = 0.2 to 5.7 and peaks at z = 2.3 +/- 0.1. The AGN fraction of our sample is (19 +/- 4)%, which is consistent with that of ALMA SMGs in the literature. Comparing with radio/NIR-detected field galaxy population in the COSMOS field, our radio+machine-learning-identified counterparts of SMGs have the highest star formation rates and stellar masses. These characteristics suggest that our identified counterparts of S2COSMOS sources are a representative sample of SMGs at z less than or similar to 3. We employ our machine-learning technique to the whole COSMOS field and identified 6877 potential SMGs, most of which are expected to have submillimeter emission fainter than the confusion limit of our S2COSMOS surveys (S-850 mu m less than or similar to 1.5 mJy). We study the clustering properties of SMGs based on this statistically large sample, finding that they reside in high-mass dark matter halos ((1.2 +/- 0.3) x 10(13) h(-1) M-circle dot), which suggests that SMGs may be the progenitors of massive ellipticals we see in the local universe. [An, Fang Xia; Simpson, J. M.; Smail, Ian; Swinbank, A. M.; Stach, S. M.; Gullberg, B.; Scholtz, J.] Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England; [An, Fang Xia] Univ Western Cape, Interuniv Inst Data Intens Astron, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa; [An, Fang Xia; Ao, Y.] Univ Western Cape, Dept Phys & Astron, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa; [An, Fang Xia; Wang, Wei-Hao] Chinese Acad Sci, Purple Mt Observ, 10 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China; [Simpson, J. M.] Acad Sinica, Inst Astron & Astrophys, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan; [Ma, Cong] South African Radio Astron Observ, 2 Fir St,Observ 7925, Western Cape, South Africa; [Ma, Cong] Univ Cape Town, Dept Math & Appl Math, Cross Campus Rd,Rondebosch 7700, Western Cape, South Africa; [Ma, Cong] African Inst Math Sci, 6-8 Melrose Rd, ZA-7945 Muizenberg, Western Cape, South Africa; [Liu, Daizhong; Lang, P.; Schinnerer, E.; Leslie, S.] Max Planck Inst Astron, Konigstuhl 17, D-69117 Heidelberg, Germany; [Karim, A.; Magnelli, B.; Bertoldi, F.; Oteo, I.] Univ Bonn, Argelander Inst Astron, Hugel 71, D-53121 Bonn, Germany; [Chen, Chian-Chou; Ivison, R. J.] European Southern Observ, Karl Schwarzschild Str 2, Garching, Germany; [Geach, J. E.; Kato, Y.] Univ Hertfordshire, Sch Phys Astron & Math, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England; [Matsuda, Y.] Natl Astron Observ Japan, 2-21-1 Osawa, Mitaka, Tokyo 1818588, Japan; [Matsuda, Y.] Grad Univ Adv Studies SOKENDAI, Mitaka, Tokyo 1818588, Japan; [Wardlow, J. L.; Bourne, N.; Lewis, A. J. R.; Oteo, I.] Univ Lancaster, Phys Dept, Lancaster LA1 4YB, England; [Ivison, R. J.] Univ Edinburgh, Royal Observ, Inst Astron, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland; [Coogan, R. T.] Univ Sussex, Dept Phys & Astron, Astron Ctr, Brighton BN1 9QH, E Sussex, England; [Thomson, A. P.] Univ Manchester, Oxford Rd, Manchester M13 9PL, Lancs, England; [Chapman, S. C.] Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS B3H 3J5, Canada; [Wang, R.; Ho, L. C.] Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China; [Yang, Y.; Hwang, H. S.] Korea Astron & Space Sci Inst, 776 Daedeokdae Ro, Daejeon 34055, South Korea; [Asquith, R.] Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England; [Coppin, K.; Hine, N. K.; Smith, D.] Univ Hertfordshire, Sci & Technol Res Inst, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England; [Ho, L. C.] Peking Univ, Dept Astron, Beijing 100087, Peoples R China; [Lacaille, K.] McMaster Univ, Dept Phys & Astron, Hamilton, ON L8S 4M1, Canada; [Sawicki, M.] St Marys Univ, Dept Phys & Astron, Halifax, NS B3H 3C3, Canada Durham University; University of the Western Cape; University of the Western Cape; Chinese Academy of Sciences; Nanjing Institute of Astronomical Optics & Technology, NAOC, CAS; Purple Mountain Observatory, CAS; Academia Sinica - Taiwan; University of Cape Town; Max Planck Society; University of Bonn; European Southern Observatory; University of Hertfordshire; National Institutes of Natural Sciences (NINS) - Japan; National Astronomical Observatory of Japan (NAOJ); Graduate University for Advanced Studies - Japan; Lancaster University; University of Edinburgh; University of Sussex; University of Manchester; Dalhousie University; Peking University; Korea Astronomy & Space Science Institute (KASI); University of Nottingham; University of Hertfordshire; Peking University; McMaster University; Saint Marys University - Canada An, FX (corresponding author), Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England.;An, FX (corresponding author), Univ Western Cape, Interuniv Inst Data Intens Astron, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa.;An, FX (corresponding author), Univ Western Cape, Dept Phys & Astron, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa.;An, FX (corresponding author), Chinese Acad Sci, Purple Mt Observ, 10 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China. fangxia@idia.ac.za Wang, Wei-Hao/ABD-9942-2020; Smail, Ian/AAL-9018-2020; CHEN, Chian-Chou (TC) 陳建州/ABB-2819-2020; Liu, Daizhong/HNP-2407-2023; HWANG, Ho Seong/AAS-6010-2020 Wang, Wei-Hao/0000-0003-2588-1265; Smail, Ian/0000-0003-3037-257X; CHEN, Chian-Chou (TC) 陳建州/0000-0002-3805-0789; HWANG, Ho Seong/0000-0003-3428-7612; swinbank, mark/0000-0003-1192-5837; Liu, Daizhong/0000-0001-9773-7479; Karim, Alexander/0000-0002-8414-9579; An, Fangxia/0000-0001-7943-0166; Schinnerer, Eva/0000-0002-3933-7677; Ivison, Rob/0000-0001-5118-1313; Ma, Cong/0000-0001-6109-2365; Magnelli, Benjamin/0000-0002-6777-6490; /0000-0002-0729-2988; Sawicki, Marcin/0000-0002-7712-7857; Smith, Daniel/0000-0001-9708-253X China Scholarship Council; STFC [ST/P000541/1]; ERC [DUSTYGAL 321334]; STFC Ernest Rutherford Fellowship [ST/P004784/2]; SKA/SARAO/NRF [96692]; JSPS [17H04831, 17KK0098, 19H00697]; National Key RAMP;D Program of China [2017YFA0402700]; Science and Technology Facilities Council of the United Kingdom; Canadian Space Agency; [ST/M007634/1]; [ST/M003019/1]; [ST/N005856/1]; Grants-in-Aid for Scientific Research [17H04831, 19H00697, 17KK0098] Funding Source: KAKEN; STFC [ST/P000541/1, ST/P004784/1, ST/P000649/1, ST/R000905/1, ST/P004784/2, ST/L005042/1, ST/R001464/1] Funding Source: UKRI China Scholarship Council(China Scholarship Council); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ERC(European Research Council (ERC)European Commission); STFC Ernest Rutherford Fellowship(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); SKA/SARAO/NRF; JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); National Key RAMP;D Program of China; Science and Technology Facilities Council of the United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Canadian Space Agency(Canadian Space Agency); ; ; ; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) F.X.A. acknowledges support from the China Scholarship Council for two years of study at Durham University. All Durham co-authors acknowledge STFC support through grant ST/P000541/1. I.R.S. and B.G. acknowledge the ERC Advanced Grant DUSTYGAL 321334. J.L.W. acknowledges support from an STFC Ernest Rutherford Fellowship (ST/P004784/2). C.M. gratefully acknowledges the support of SKA/SARAO/NRF (grant No. 96692). Y.M. acknowledges support from the JSPS grants 17H04831, 17KK0098, and 19H00697.r The James Clerk Maxwell Telescope is operated by the East Asian Observatory on behalf of The National Astronomical Observatory of Japan; Academia Sinica Institute of Astronomy and Astrophysics; the Korea Astronomy and Space Science Institute; the Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments, budgeted from the Ministry of Finance (MOF) of China and administrated by the Chinese Academy of Sciences(CAS), as well as the National Key R&D Program of China (No. 2017YFA0402700). Additional funding support is provided by the Science and Technology Facilities Council of the United Kingdom and participating universities in the United Kingdom and Canada (ST/M007634/1, ST/M003019/1, ST/N005856/1). The James Clerk Maxwell Telescope has historically been operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom, the National Research Council of Canada, and the Netherlands Organization for Scientific Research and data from observations undertaken during this period of operation is used in this manuscript. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00090.S, 2015.1.01528.S, 2016.1.00434. S, and 2016.1.00463.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. 139 16 16 1 8 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. NOV 20 2019.0 886.0 1 48 10.3847/1538-4357/ab4d53 0.0 18 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics JR0ZK Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000499364000001 0
J Razavi, S; Jakeman, A; Saltelli, A; Prieur, C; Iooss, B; Borgonovo, E; Plischke, E; Lo Piano, S; Iwanaga, T; Becker, W; Tarantola, S; Guillaume, JHA; Jakeman, J; Gupta, H; Melillo, N; Rabitti, G; Chabridon, V; Duan, QY; Sun, XF; Smith, S; Sheikholeslami, R; Hosseini, N; Asadzadeh, M; Puy, A; Kucherenko, S; Maier, HR Razavi, Saman; Jakeman, Anthony; Saltelli, Andrea; Prieur, Clementine; Iooss, Bertrand; Borgonovo, Emanuele; Plischke, Elmar; Lo Piano, Samuele; Iwanaga, Takuya; Becker, William; Tarantola, Stefano; Guillaume, Joseph H. A.; Jakeman, John; Gupta, Hoshin; Melillo, Nicola; Rabitti, Giovanni; Chabridon, Vincent; Duan, Qingyun; Sun, Xifu; Smith, Stefan; Sheikholeslami, Razi; Hosseini, Nasim; Asadzadeh, Masoud; Puy, Arnald; Kucherenko, Sergei; Maier, Holger R. The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support ENVIRONMENTAL MODELLING & SOFTWARE English Article Sensitivity analysis; Mathematical modeling; Machine learning; Uncertainty quantification; Decision making; Model validation and verification; Model robustness; Policy support MULTICRITERIA DECISION-ANALYSIS; GLOBAL SENSITIVITY; NEURAL-NETWORKS; POLYNOMIAL CHAOS; SOBOL INDEXES; UNCERTAINTY ANALYSIS; DEEP UNCERTAINTY; EVOLUTIONARY ALGORITHMS; ENVIRONMENTAL-MODELS; BAYESIAN-APPROACH Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society. [Razavi, Saman; Sheikholeslami, Razi; Hosseini, Nasim] Univ Saskatchewan, Global Inst Water Secur, Dept Civil Geol & Environm Engn, Sch Environm & Sustainabil, Saskatoon, SK, Canada; [Jakeman, Anthony; Iwanaga, Takuya; Guillaume, Joseph H. A.; Sun, Xifu] Australian Natl Univ, Inst Water Futures, Fenner Sch Environm & Soc, Sydney, NSW, Australia; [Saltelli, Andrea] Univ Oberta Catalunya UOC, Open Evidence Res, Barcelona, Spain; [Prieur, Clementine] Univ Grenoble Alpes, CNRS, INRIA, Grenoble INP,LJK, F-38000 Grenoble, France; [Iooss, Bertrand; Chabridon, Vincent] Dept PRISME, EDF R&D, Chatou, France; [Borgonovo, Emanuele] SINCLAIR AI Lab, Saclay, France; [Plischke, Elmar; Puy, Arnald] Bocconi Univ, Dept Decis Sci, I-20136 Milan, Italy; [Lo Piano, Samuele; Smith, Stefan] Tech Univ Clausthal, Inst Disposal Res, Clausthal Zellerfeld, Germany; [Tarantola, Stefano] Univ Reading, Sch Built Environm, Reading, Berks, England; [Jakeman, John] Joint Res Ctr JRC, European Commiss, Ispra, Italy; [Gupta, Hoshin] Sandia Natl Labs, Optimizat & Uncertainty Quantificat, POB 5800, Albuquerque, NM 87185 USA; [Melillo, Nicola] Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ 85721 USA; [Rabitti, Giovanni] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy; [Duan, Qingyun] Heriot Watt Univ, Maxwell Inst Math Sci, Dept Actuarial Math & Stat, Edinburgh, Midlothian, Scotland; [Sheikholeslami, Razi] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Asadzadeh, Masoud] Univ Oxford, Sch Geog & Environm, Environm Change Inst, South Parks Rd, Oxford OX1 3QY, England; [Puy, Arnald] Univ Manitoba, Dept Civil Engn, Winnipeg, MB, Canada; [Puy, Arnald] Princeton Univ, Dept Ecol & Evolutionary Biol, M31 Guyot Hall, Princeton, NJ 08544 USA; [Puy, Arnald] Univ Bergen, Ctr Study Sci & Humanities, Parkveien 9, N-5020 Bergen, Norway; [Kucherenko, Sergei] Imperial Coll London, Dept Chem Engn, London, England; [Maier, Holger R.] Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA, Australia University of Saskatchewan; Global Institute for Water Security; Australian National University; UOC Universitat Oberta de Catalunya; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Electricite de France (EDF); Bocconi University; TU Clausthal; University of Reading; European Commission Joint Research Centre; EC JRC ISPRA Site; United States Department of Energy (DOE); Sandia National Laboratories; University of Arizona; University of Pavia; Heriot Watt University; University of Edinburgh; Hohai University; University of Oxford; University of Manitoba; Princeton University; University of Bergen; Imperial College London; University of Adelaide Razavi, S (corresponding author), Univ Saskatchewan, Global Inst Water Secur, Dept Civil Geol & Environm Engn, Sch Environm & Sustainabil, Saskatoon, SK, Canada. saman.razavi@usask.ca Kucherenko, Sergei/HHD-0446-2022; Guillaume, Joseph/I-6019-2014; Gupta, Hoshin V/CAG-5323-2022; Maier, Holger R/B-9639-2008; Gupta, Hoshin V/D-1642-2010; Jakeman, John/AAV-6772-2021; Melillo, Nicola/AAH-4505-2021; SUN, XIFU/AAA-6253-2022; Jakeman, Anthony John/P-6786-2014; Duan, Qingyun/C-7652-2011; Razavi, Saman/L-3725-2013; Lo Piano, Samuele/N-1469-2017 Kucherenko, Sergei/0000-0003-1593-8264; Guillaume, Joseph/0000-0001-6854-8708; Gupta, Hoshin V/0000-0001-9855-2839; Maier, Holger R/0000-0002-0277-6887; Gupta, Hoshin V/0000-0001-9855-2839; Jakeman, John/0000-0002-3517-337X; SUN, XIFU/0000-0001-6396-6206; Jakeman, Anthony John/0000-0001-5282-2215; Duan, Qingyun/0000-0001-9955-1512; Razavi, Saman/0000-0003-1870-5810; Iwanaga, Takuya/0000-0001-8173-0870; Puy, Arnald/0000-0001-9469-2156; Iooss, Bertrand/0000-0001-6988-2126; Lo Piano, Samuele/0000-0002-2625-483X French research association on stochastic methods for the analysis of numerical codes (MASCOTNUM), Open Evidence Research at Universitat Oberta de Catalunya; Joint Research Centre of the European Commission; University of Bergen (Norway); French CERFACS, Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique; Integrated modeling Program for Canada (IMPC) under the framework of Global Water Futures (GWF); National Socio-Environmental Synthesis Center of the United States from the National Science Foundation [DBI-1639145]; U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]; U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program; Australian Research Council [DE190100317]; Marie Sklodowska-Curie Global Fellowship [792178]; Australian Government Research Training Program (AGRTP) Scholarship; ANU Hilda-John Endowment Fund; Australian Research Council [DE190100317] Funding Source: Australian Research Council French research association on stochastic methods for the analysis of numerical codes (MASCOTNUM), Open Evidence Research at Universitat Oberta de Catalunya; Joint Research Centre of the European Commission; University of Bergen (Norway); French CERFACS, Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique; Integrated modeling Program for Canada (IMPC) under the framework of Global Water Futures (GWF); National Socio-Environmental Synthesis Center of the United States from the National Science Foundation; U.S. Department of Energy's National Nuclear Security Administration(National Nuclear Security Administration); U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program; Australian Research Council(Australian Research Council); Marie Sklodowska-Curie Global Fellowship; Australian Government Research Training Program (AGRTP) Scholarship(Australian Government); ANU Hilda-John Endowment Fund; Australian Research Council(Australian Research Council) This perspective paper is the outcome of a one-day workshop called The Future of Sensitivity Analysis, which was held as a satellite event to the Ninth International Conference on Sensitivity Analysis of Model Output (SAMO) , October 28?31, 2019 in Barcelona, Spain. We are thankful to the sponsors of this event, including the French research association on stochastic methods for the analysis of numerical codes (MASCOTNUM), Open Evidence Research at Universitat Oberta de Catalunya, the Joint Research Centre of the European Commission, the University of Bergen (Norway), and the French CERFACS, Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique. The financial and logistic support to Saman Razavi, including underwriting open-access publication fees, by the Integrated modeling Program for Canada (IMPC) under the framework of Global Water Futures (GWF) is acknowledged. Furthermore, part of the efforts leading to this paper was supported by The National Socio-Environmental Synthesis Center of the United States under funding received from the National Science Foundation DBI-1639145. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. John Jakeman's work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. Joseph Guillaume received funding from an Australian Research Council Discovery Early Career Award (project no. DE190100317) . Arnald Puy worked on this paper on a Marie Sklodowska-Curie Global Fellowship, grant number 792178. Takuya Iwanaga is supported through an Australian Government Research Training Program (AGRTP) Scholarship and the ANU Hilda-John Endowment Fund. We would like to thank Dan Ames, Editor-in-Chief, for insightful comments and encouragement. 348 102 103 22 78 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1364-8152 1873-6726 ENVIRON MODELL SOFTW Environ. Modell. Softw. MAR 2021.0 137.0 104954 10.1016/j.envsoft.2020.104954 0.0 JAN 2021 22 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Environmental Sciences & Ecology; Water Resources QT3YN Green Published, Green Accepted, Green Submitted, hybrid 2023-03-23 WOS:000626525700001 0
J Mehryar, E; Ding, WM; Hemmat, A; Hassan, M; Talha, Z; Kafashan, J; Huang, HY Mehryar, Esmaeil; Ding, Weimin; Hemmat, Abbas; Hassan, Muhammad; Talha, Zahir; Kafashan, Jalal; Huang, Hongying Modeling and Multiresponse Optimization for Anaerobic Codigestion of Oil Refinery Wastewater and Chicken Manure by Using Artificial Neural Network and the Taguchi Method BIOMED RESEARCH INTERNATIONAL English Article METHANE PRODUCTION; CO-DIGESTION; FOOD WASTE; BIOGAS; INHIBITION; ENHANCEMENT; PYROLYSIS; SLUDGE To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L-9 (3(4)) Taguchi's orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36 degrees C temperature, 30 min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76 mL/gVS, 151.95 mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained. [Mehryar, Esmaeil; Ding, Weimin; Talha, Zahir] Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China; [Mehryar, Esmaeil; Hemmat, Abbas] Isfahan Univ Technol, Coll Agr, Dept Biosyst Engn, Esfahan 8415683111, Iran; [Hassan, Muhammad] Natl Univ Sci & Technol, US Pakistan Ctr Adv Studies Energy, Islamabad 44000, Pakistan; [Kafashan, Jalal] Katholieke Univ Leuven, Div Mech Biostat & Sensors MeBioS, Kasteelpk Arenberg 30, B-3001 Heverlee, Belgium; [Kafashan, Jalal] Agr Res Educ & Extens Org, Agr Engn Res Inst, Dept Mech Engn Agromachinery & Mechanisat, POB 31585-845, Karaj, Iran; [Huang, Hongying] Jiangsu Acad Agr Sci, Inst Agr Resources & Environm, Nanjing 210014, Jiangsu, Peoples R China Nanjing Agricultural University; Isfahan University of Technology; National University of Sciences & Technology - Pakistan; KU Leuven; Jiangsu Academy of Agricultural Sciences Ding, WM (corresponding author), Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China. wmding@njau.edu.cn Mehryar, Esmaeil/ACP-5682-2022 Talha, Zahir/0000-0002-7047-2391; Hassan, Muhammad/0000-0002-4273-6863 National Science and Technology Support Program [2013BAD08B04]; Ministry of Science, Research and Technology of the IR Iranian government National Science and Technology Support Program; Ministry of Science, Research and Technology of the IR Iranian government This study was sponsored by the National Science and Technology Support Program (2013BAD08B04). The first author of the paper would like to express his appreciation to the Ministry of Science, Research and Technology of the IR Iranian government for providing Ph.D. scholarship to him. 25 7 7 0 7 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 2314-6133 2314-6141 BIOMED RES INT Biomed Res. Int. 2017.0 2017.0 2036737 10.1155/2017/2036737 0.0 15 Biotechnology & Applied Microbiology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Research & Experimental Medicine FR0FV 29441352.0 gold, Green Accepted, Green Submitted, Green Published 2023-03-23 WOS:000418737700001 0
J Liu, HY; Lu, GB; Wang, YJ; Kasabov, N Liu, Hengyuan; Lu, Guibin; Wang, Yangjun; Kasabov, Nikola Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai AEROSOL AND AIR QUALITY RESEARCH English Article Air pollutant prediction; PM2.5 hourly concentration; Seasonality; Evolving spiking neural networks; Time series clustering AIR-POLLUTION; PARTICULATE MATTER; TIME-SERIES; PM10; ENSEMBLE In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models. [Liu, Hengyuan; Lu, Guibin] Shanghai Univ, Sch Econ, Shanghai 200444, Peoples R China; [Wang, Yangjun] Shanghai Univ, Sch Environm & Chem Engn, Shanghai 200444, Peoples R China; [Kasabov, Nikola] Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland, New Zealand; [Kasabov, Nikola] Ulster Univ, Intelligent Syst Res Ctr, Derry, Londonderry, North Ireland Shanghai University; Shanghai University; Auckland University of Technology; Ulster University Lu, GB (corresponding author), Shanghai Univ, Sch Econ, Shanghai 200444, Peoples R China. guibinlu@163.com National Natural Science Foundation of China [41105102]; National Key R&D Program of China [2018YFC0213600] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China The work was financially supported by the National Natural Science Foundation of China (No. 41105102) and the National Key R&D Program of China (No. 2018YFC0213600). 32 1 1 3 15 TAIWAN ASSOC AEROSOL RES-TAAR TAICHUNG COUNTY CHAOYANG UNIV TECH, DEPT ENV ENG & MGMT, PROD CTR AAQR, NO 168, JIFONG E RD, WUFONG TOWNSHIP, TAICHUNG COUNTY, 41349, TAIWAN 1680-8584 2071-1409 AEROSOL AIR QUAL RES Aerosol Air Qual. Res. FEB 2021.0 21.0 2 200247 10.4209/aaqr.2020.05.0247 0.0 15 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology QU9IO Green Published, gold 2023-03-23 WOS:000627593600005 0
J Liao, HJ; Zhou, ZY; Zhao, XW; Zhang, L; Mumtaz, S; Jolfaei, A; Ahmed, SH; Bashir, AK Liao, Haijun; Zhou, Zhenyu; Zhao, Xiongwen; Zhang, Lei; Mumtaz, Shahid; Jolfaei, Alireza; Ahmed, Syed Hassan; Bashir, Ali Kashif Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT IEEE INTERNET OF THINGS JOURNAL English Article Context awareness; edge computing; Industrial Internet of Things (IIoT); Lyapunov optimization; machine learning; matching theory; resource allocation SMALL-CELL NETWORKS; MULTIARMED BANDIT; ACCESS; MANAGEMENT; CHANNEL Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework. [Liao, Haijun; Zhou, Zhenyu; Zhao, Xiongwen] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China; [Liao, Haijun; Zhou, Zhenyu; Zhao, Xiongwen] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China; [Zhang, Lei] State Grid Corp China, Shandong Elect Power Res Inst, Jinan 25001, Peoples R China; [Mumtaz, Shahid] Inst Telecomunicacoes, P-1049001 Aveiro, Portugal; [Jolfaei, Alireza] Macquarie Univ, Dept Comp, Sydney, NSW 2113, Australia; [Ahmed, Syed Hassan] Georgia Southern Univ, Dept Elect & Comp Sci, Statesboro, GA 30460 USA; [Bashir, Ali Kashif] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England North China Electric Power University; North China Electric Power University; State Grid Corporation of China; Universidade de Aveiro; Macquarie University; University System of Georgia; Georgia Southern University; Manchester Metropolitan University Zhou, ZY (corresponding author), North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China.;Zhou, ZY (corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China. haijun_liao@ncepu.edu.cn; zhenyu_zhou@ncepu.edu.cn; zhaoxw@ncepu.edu.cn; 18660130685@163.com; smumtaz@av.it.pt; alireza.jolfaei@mq.edu.au; sh.ahmed@ieee.org; dr.alikashif.b@ieee.org Mumtaz, Dr shahid/V-3603-2019; Ahmed, Syed/GSN-7305-2022; Jolfaei, Alireza/GQH-6907-2022; Bashir, Ali Kashif/R-4015-2019; Shah, Syed Hassan/E-5058-2014 Mumtaz, Dr shahid/0000-0001-6364-6149; Bashir, Ali Kashif/0000-0003-2601-9327; Jolfaei, Alireza/0000-0001-7818-459X; Shah, Syed Hassan/0000-0002-1381-5095; Zhao, Xiongwen/0000-0001-9421-4795; Zhou, Zhenyu/0000-0002-3344-4463 National Natural Science Foundation of China [61971189]; Science and Technology Project of State Grid Corporation of China [SGSDDK00KJJS1900405]; Exploration Project of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) [LAPS2019-12]; European Regional Development Fund (FEDER) through the Competitiveness and Internationalization (COMPETE 2020); Regional Operational Program of the Agarve (2020); Fundao para a cilncia e Tecnologia; i-Five: Extenso do acesso de espectro dinmico para rdio 5G [POCI-01-0145-FEDER-030500] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Project of State Grid Corporation of China; Exploration Project of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University); European Regional Development Fund (FEDER) through the Competitiveness and Internationalization (COMPETE 2020); Regional Operational Program of the Agarve (2020); Fundao para a cilncia e Tecnologia; i-Five: Extenso do acesso de espectro dinmico para rdio 5G This work was supported in part by the National Natural Science Foundation of China under Grant 61971189, in part by the Science and Technology Project of State Grid Corporation of China under Grant SGSDDK00KJJS1900405, in part by the Exploration Project of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) under Grant LAPS2019-12, in part by the European Regional Development Fund (FEDER) through the Competitiveness and Internationalization (COMPETE 2020), in part by the Regional Operational Program of the Agarve (2020), in part by Fundao para a cilncia e Tecnologia, and in part by the i-Five: Extenso do acesso de espectro dinmico para rdio 5G under Grant POCI-01-0145-FEDER-030500. 34 152 156 13 102 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAY 2020.0 7.0 5 4260 4277 10.1109/JIOT.2019.2963371 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LS0EE Green Accepted 2023-03-23 WOS:000536066300051 0
J Talari, S; Shafie-Khah, M; Osorio, GJ; Wang, F; Heidari, A; Catalao, JPS Talari, Saber; Shafie-Khah, Miadreza; Osorio, Gerardo J.; Wang, Fei; Heidari, Alireza; Catalao, Joao P. S. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators SUSTAINABILITY English Article bivariate ARIMA; hybrid method; price forecasting; wind generator; wavelet transform NEURAL-NETWORK; WAVELET TRANSFORM; MARGINAL PRICE; ARIMA MODELS; PREDICTION Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators. [Talari, Saber; Shafie-Khah, Miadreza; Osorio, Gerardo J.; Catalao, Joao P. S.] Univ Beira Interior, C MAST, P-6201001 R Fonte Do Lameiro, Covilha, Portugal; [Wang, Fei] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Baoding 071003, Peoples R China; [Wang, Fei] Univ Illinois, Dept Elect & Comp Engn, Urbana, IL 61801 USA; [Heidari, Alireza] Univ New South Wales, Sch Elect Engn & Telecommun, Australian Energy Res Inst, Sydney, NSW 2052, Australia; [Catalao, Joao P. S.] Univ Porto, INESC TEC, Fac Engn, P-4200465 Oporto, Portugal; [Catalao, Joao P. S.] Univ Lisbon, INESC ID, Inst Super Tecn, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal Universidade da Beira Interior; North China Electric Power University; University of Illinois System; University of Illinois Urbana-Champaign; University of New South Wales Sydney; INESC TEC; Universidade do Porto; INESC-ID; Universidade de Lisboa; Instituto Superior Tecnico Catalao, JPS (corresponding author), Univ Beira Interior, C MAST, P-6201001 R Fonte Do Lameiro, Covilha, Portugal.;Catalao, JPS (corresponding author), Univ Porto, INESC TEC, Fac Engn, P-4200465 Oporto, Portugal.;Catalao, JPS (corresponding author), Univ Lisbon, INESC ID, Inst Super Tecn, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal. saber.talari@ubi.pt; miadreza@ubi.pt; gjosilva@gmail.com; ncepu_wangfei@sina.com; alireza.heidari@unsw.edu.au; catalao@ubi.pt Osório, Gerardo/C-3616-2014; Shafie-khah, Miadreza/AAE-1539-2021; Catalão, João P. S./I-3927-2012; Talari, Saber/AAO-7784-2020; Shafie-khah, Miadreza/AAS-6882-2020 Osório, Gerardo/0000-0001-8328-9708; Catalão, João P. S./0000-0002-2105-3051; Talari, Saber/0000-0003-1368-781X; Shafie-khah, Miadreza/0000-0003-1691-5355 FEDER funds through COMPETE; Portuguese funds through FCT [SAICT-PAC/0004/2015-POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013]; EU [309048] FEDER funds through COMPETE; Portuguese funds through FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); EU(European Commission) Joao P. S. Catalao acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015-POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013, and also funding from the EU 7th Framework Programme FP7/2007-2013 under GA No. 309048. 35 6 6 1 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability NOV 2017.0 9.0 11 2065 10.3390/su9112065 0.0 13 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology FO4EH Green Submitted, gold 2023-03-23 WOS:000416793400145 0
C Lian, YC; Bisazza, A; Verhoef, T Assoc Computat Linguist Lian, Yuchen; Bisazza, Arianna; Verhoef, Tessa The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021) English Proceedings Paper Conference on Empirical Methods in Natural Language Processing (EMNLP) NOV 07-11, 2021 Punta Cana, DOMINICAN REP LEAST EFFORT; WORD-ORDER; EVOLUTION; EMERGENCE Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language. [Lian, Yuchen] Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian, Peoples R China; [Lian, Yuchen; Verhoef, Tessa] Leiden Univ, Leiden Inst Adv Comp Sci, Leiden, Netherlands; [Bisazza, Arianna] Univ Groningen, Ctr Language & Cognit, Groningen, Netherlands Xi'an Jiaotong University; Leiden University; Leiden University - Excl LUMC; University of Groningen Lian, YC (corresponding author), Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian, Peoples R China.;Lian, YC (corresponding author), Leiden Univ, Leiden Inst Adv Comp Sci, Leiden, Netherlands. y.lian@liacs.leidenuniv.nl; a.bisazza@rug.nl; t.verhoef@liacs.leidenuniv.nl Verhoef, Tessa/0000-0002-1219-3730 China Scholarship Council [CSC 201906280463]; Netherlands Organization for Scientific Research (NWO) [639021646] China Scholarship Council(China Scholarship Council); Netherlands Organization for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)) Lian was partly funded by the China Scholarship Council (CSC 201906280463). Bisazza was partly funded by the Netherlands Organization for Scientific Research (NWO project 639021646). 32 0 0 0 0 ASSOC COMPUTATIONAL LINGUISTICS-ACL STROUDSBURG 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA 978-1-955917-09-4 2021.0 10121 10129 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BT9HF 2023-03-23 WOS:000860727004018 0
J Chen, BT; Wan, JF; Shu, L; Li, P; Mukherjee, M; Yin, BX Chen, Baotong; Wan, Jiafu; Shu, Lei; Li, Peng; Mukherjee, Mithun; Yin, Boxin Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges IEEE ACCESS English Article Smart factory; big data; cloud computing; cyber-physical systems; industrial Internet of Things SERVICE-ORIENTED ARCHITECTURE; CYBER-PHYSICAL SYSTEMS; RECONFIGURATION; COMMUNICATION; OPTIMIZATION; AUTOMATION; MANAGEMENT; ALGORITHM; INTERNET; DESIGN Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved. [Chen, Baotong; Wan, Jiafu] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China; [Shu, Lei] Nanjing Agr Univ, Sch Engn, Nanjing 210014, Jiangsu, Peoples R China; [Shu, Lei] Univ Lincoln, Sch Engn, Lincoln, England; [Li, Peng] Ostwestfalen Lippe Univ Appl Sci, Inst Ind IT, D-32657 Lemgo, Germany; [Mukherjee, Mithun] Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China; [Yin, Boxin] Jiangxi Univ Sci & Technol, Sch Elect Engn & Automat, Ganzhou 314000, Peoples R China South China University of Technology; Nanjing Agricultural University; University of Lincoln; Guangdong University of Petrochemical Technology; Jiangxi University of Science & Technology Wan, JF (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China. jiafuwan2017@163.com Wan, Jiafu/I-3059-2016; Shu, Lei/HHT-0694-2022; Chen, Baotong/L-6294-2018; Chen, Baotong/AAV-2923-2020 Wan, Jiafu/0000-0001-9188-4179; Shu, Lei/0000-0002-6700-9347; Chen, Baotong/0000-0003-2324-4916; National Key Research and Development Project [2017YFE0101000]; Key Program of Natural Science Foundation of Guangdong Province, China [2017B030311008]; Maoming Engineering Research Center of Industrial Internet of Things [517018]; National Natural Science Foundation of China [61401107] National Key Research and Development Project; Key Program of Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of China (NSFC)); Maoming Engineering Research Center of Industrial Internet of Things; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Project under Grant 2017YFE0101000, in part by the Key Program of Natural Science Foundation of Guangdong Province, China, under Grant 2017B030311008, in part by the Maoming Engineering Research Center of Industrial Internet of Things under Grant 517018, and in part by the National Natural Science Foundation of China under Grant 61401107. 94 473 481 57 367 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 6505 6519 10.1109/ACCESS.2017.2783682 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications FZ3IH gold 2023-03-23 WOS:000427480200001 0
J Lin, CC; Zhao, GS; Yang, ZR; Yin, AH; Wang, XM; Guo, L; Chen, HB; Ma, ZH; Zhao, L; Luo, HY; Wang, TX; Ding, BC; Pang, XW; Chen, QR Lin, Chengchuang; Zhao, Gansen; Yang, Zhirong; Yin, Aihua; Wang, Xinming; Guo, Li; Chen, Hanbiao; Ma, Zhaohui; Zhao, Lei; Luo, Haoyu; Wang, Tianxing; Ding, Bichao; Pang, Xiongwen; Chen, Qiren CIR-Net: Automatic Classification of Human Chromosome Based on Inception-ResNet Architecture IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS English Article; Proceedings Paper 18th Asia Pacific Bioinformatics Conference (APBC) AUG 18-20, 2020 ELECTR NETWORK Biological cells; Task analysis; Training; Genetics; Feature extraction; Asia; Bioinformatics; Chromosome classification; inception-ResNet; biomedical image analysis; chromosome image augmentation NEURAL NETWORKS Background: In medicine, karyotyping chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosome karyotyping is usually done by skilled cytologists manually, which requires experience, domain expertise, and considerable manual efforts. Therefore, automating the karyotyping process is a significant and meaningful task. Method: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. Results: The experimental results show that our proposed method achieves 95.98 percent classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5 percent (from 87.46 to 95.98 percent) classification accuracy comparing to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net. [Lin, Chengchuang; Zhao, Gansen; Zhao, Lei; Luo, Haoyu; Wang, Tianxing; Ding, Bichao; Pang, Xiongwen; Chen, Qiren] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China; [Yang, Zhirong] Norwegian Univ Sci & Technol, N-7491 Trondheim, Norway; [Yang, Zhirong] Aalto Univ, Espoo 02150, Finland; [Yin, Aihua; Guo, Li; Chen, Hanbiao] GuangdongWomen & Children Hosp, Med Genet Ctr & Maternal & Children Metab, Genet Key Lab, Guangzhou 510623, Peoples R China; [Wang, Xinming] Lakala Grp Bldg D1,Zhongguancun Yihao,Beiqing Rd, Beijing 100094, Peoples R China; [Ma, Zhaohui] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China; [Ma, Zhaohui] Guangdong Univ Foreign Studies, Sch Informat Sci & Technol, Guangzhou 510420, Peoples R China South China Normal University; Norwegian University of Science & Technology (NTNU); Aalto University; South China Normal University; Guangdong University of Foreign Studies Zhao, GS (corresponding author), South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.;Yang, ZR (corresponding author), Norwegian Univ Sci & Technol, N-7491 Trondheim, Norway.;Yang, ZR (corresponding author), Aalto Univ, Espoo 02150, Finland. chengchuang.lin@m.scnu.edu.cn; gzhao@m.scnu.edu.cn; zhirong.yang@ntnu.no; yinaiwa@vip.126.com; wangxinming@lakala.com; guoli3861@163.com; chenhanbiao2000@126.com; mazhaohui@gdufs.edu.cn; zhaolei@m.scnu.edu.cn; hluo@m.scnu.edu.cn; wangtianxing@m.scnu.edu.cn; dingbichao@m.scnu.edu.cn; augepang@gmail.com; 1248105154@qq.com XIONGWEN, PANG/A-7577-2011 National Key R & D Program of China [2018YFB1404402, 2018YFB1404400]; Guangdong Science and Technology Fund [2019B010137003, 2016B030305006, 2018A07071702, 201804010314]; Guangzhou Science & Technology Fund [201804010314, 2012224-12]; VeChain Foundation [SCNU-2018-01]; Academy of Finland [314177, 307929]; Research Council of Norway [287284] National Key R & D Program of China; Guangdong Science and Technology Fund; Guangzhou Science & Technology Fund; VeChain Foundation; Academy of Finland(Academy of Finland); Research Council of Norway(Research Council of Norway) This work was supported by the National Key R & D Program of China (2018YFB1404402 and 2018YFB1404400), Guangdong Science and Technology Fund (No.2019B010137003, No.2016B030305006, No.2018A07071702, and No.201804010314), Guangzhou Science & Technology Fund (No.201804010314 and No.2012224-12), and VeChain Foundation (No.SCNU-2018-01). Z. Yang was supported by Academy of Finland (projects 314177 and 307929) and The Research Council of Norway (project 287284). 28 12 12 7 18 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5963 1557-9964 IEEE ACM T COMPUT BI IEEE-ACM Trans. Comput. Biol. Bioinform. MAY-JUN 2022.0 19.0 3 1285 1293 10.1109/TCBB.2020.3003445 0.0 9 Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematics, Interdisciplinary Applications; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Computer Science; Mathematics 1V0RE 32750868.0 2023-03-23 WOS:000805807200005 0
C van Noord, K; Wang, WJ; Jiao, HL IEEE van Noord, Kris; Wang, Wenjin; Jiao, Hailong Insights of 3D Input CNN in EEG-based Emotion Recognition 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applications. In this paper, a reproducible framework is proposed for classifying human emotions based on EEG signals. The framework consists of extracting frequency-dependent features from raw EEG signals to form a three-dimensional EEG image which is classified by a convolutional neural network (CNN). The framework is used to show that the 3D input CNN outperforms conventional methods with two-dimensional input, using a public dataset. The implementation of the framework is publicly available to facilitate further work on this topic: https://github.com/KvanNoord/3D-CNN-EEG-Emotion-Classification. [van Noord, Kris; Wang, Wenjin; Jiao, Hailong] Eindhoven Univ Technol, Eindhoven, Netherlands; [Jiao, Hailong] Peking Univ, Beijing, Peoples R China Eindhoven University of Technology; Peking University Wang, WJ (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. wwang@tue.nl 9 1 1 3 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 212 215 10.1109/EMBC46164.2021.9631042 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34891274.0 2023-03-23 WOS:000760910500049 0
C Zhang, XW; Zheng, XL; Wu, DD Zhang, Xingwei; Zheng, Xiaolong; Wu, Desheng Dash Attacking DNN-based Intrusion Detection Models IFAC PAPERSONLINE English Proceedings Paper 3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS) DEC 03-05, 2020 Beijing, PEOPLES R CHINA Int Federat Automat Control, Tech Comm 9 1 Econ, Busines, & Financial Syst,Int Federat Automat Control, Tech Comm 9 2 Social Impact Automat,Int Federat Automat Control, Tech Comm 1 3 Discrete Event & Hybrid Syst,Int Federat Automat Control, Tech Comm 1 4 Stochastic Syst,Int Federat Automat Control, Tech Comm 1 5 Networked Syst,Int Federat Automat Control, Tech Comm 3 1 Comp Control,Int Federat Automat Control, Tech Comm 4 3 Robot,Int Federat Automat Control, Tech Comm 4 5 Human Machine Syst,Int Federat Automat Control, Tech Comm 7 3 Aerosp,Int Federat Automat Control, Tech Comm 9 5 Technol, Culture & Int Stabil public security; intrusion detection; deep neural networks; adversarial perturbations GAME; GO Intrusion detection plays an important role in public security domains Dynamic deep neural network(DNN)-based intrusion detection models have been demonstrated to show effective performance for timely detecting network intrusions. While DNN-based intrusion detection models have shown powerful performance, in this paper, we verify that they could be easily attacked by well-designed small adversarial perturbations. We design an effective procedure to employ commonly used adversarial perturbations for attacking well-trained DNN detection models on NSL-KDD dataset We further find that the performance of DNN models for recognizing real labels of abnormal data suffers more from attacks compared with that on normal samples. Copyright (C) 2020 The Authors. [Zhang, Xingwei; Zheng, Xiaolong] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Zhang, Xingwei; Zheng, Xiaolong] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China; [Wu, Desheng Dash] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China; [Wu, Desheng Dash] Stockholm Univ, Stockholm Business Sch, SE-10691 Stockholm, Sweden Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Stockholm University Zhang, XW (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.;Zhang, XW (corresponding author), Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China. zhangxingwei2019@ia.ac.cn; xiaolong.zheng@ia.ac.cn; dash@risklab.ca Ministry of Health of China [2017ZX10303401-002]; Natural Science Foundation of China [71472175, 71602184, 71621002]; National Key Research and Development Program of China [2017YFC1200302, 2016QY02D0305] Ministry of Health of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work is supported by the Ministry of Health of China under Grant No.: 2017ZX10303401-002, by the Natural Science Foundation of China under Grant No.: 71472175, 71602184, 71621002, by the National Key Research and Development Program of China under Grant No.: 2017YFC1200302 and 2016QY02D0305. 23 1 1 1 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53.0 5 SI 415 419 10.1016/j.ifacol.2021.04.118 0.0 5 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SL0EN gold 2023-03-23 WOS:000656589700072 0
J Chen, TC; Iliyasu, AM; Hanus, R; Salama, AS; Hirota, K Chen, Tzu-Chia; Iliyasu, Abdullah M.; Hanus, Robert; Salama, Ahmed S.; Hirota, Kaoru Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime ENERGIES English Article scale thickness; frequency characteristics; fast Fourier transform; multilayer perceptron neural network 3-PHASE FLOW One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale inside the pipes has always been one of the needs of the oil industry. In this research, a non-invasive, accurate, and reliable system is presented, which works based on the attenuation of gamma rays. A dual-energy gamma source (Am-241 and Ba-133 radioisotopes), a sodium iodide detector, and a steel pipe are used in the structure of the detection system. The configuration of the detection structure is such that the dual-energy source and the detector are directly opposite each other and on both sides of the steel pipe. In the steel pipe, a stratified flow regime consisting of gas, water, and oil in different volume percentages was simulated using Monte Carlo N Particle (MCNP) code. Seven scale thicknesses between 0 and 3 cm were simulated inside the tube. After the end of the simulation process, the received signals were labeled and transferred to the frequency domain usage of fast Fourier transform (FFT). Frequency domain signals were processed, and four frequency characteristics were extracted from them. The multilayer perceptron (MLP) neural network was used to obtain the relationship between the extracted frequency characteristics and the scale thickness. Frequency characteristics were defined as inputs and scale thickness in cm as the output of the neural network. The prediction of scale thickness with an RMSE of 0.13 and the use of only one detector in the structure of the detection system are among the advantages of this research. [Chen, Tzu-Chia] Ming Chi Univ Technol, Coll Management & Design, New Taipei 243303, Taiwan; [Iliyasu, Abdullah M.] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Al Kharj 11942, Saudi Arabia; [Iliyasu, Abdullah M.; Hirota, Kaoru] Tokyo Inst Technol, Sch Comp, Yokohama, Kanagawa 2268502, Japan; [Hanus, Robert] Rzeszow Univ Technol, Fac Elect & Comp Engn, PL-35959 Rzeszow, Poland; [Salama, Ahmed S.] Future Univ Egypt, Fac Engn & Technol, New Cairo 11835, Egypt; [Hirota, Kaoru] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China Ming Chi University of Technology; Prince Sattam Bin Abdulaziz University; Tokyo Institute of Technology; Rzeszow University of Technology; Egyptian Knowledge Bank (EKB); Future University in Egypt; Beijing Institute of Technology Chen, TC (corresponding author), Ming Chi Univ Technol, Coll Management & Design, New Taipei 243303, Taiwan.;Iliyasu, AM (corresponding author), Prince Sattam Bin Abdulaziz Univ, Coll Engn, Al Kharj 11942, Saudi Arabia.;Iliyasu, AM (corresponding author), Tokyo Inst Technol, Sch Comp, Yokohama, Kanagawa 2268502, Japan.;Hanus, R (corresponding author), Rzeszow Univ Technol, Fac Elect & Comp Engn, PL-35959 Rzeszow, Poland. tzuchiachen1688@gmail.com; a.iliyasu@psau.edu.sa; rohan@prz.edu.pl Iliyasu, Abdullah/D-8194-2016; Hanus, Robert/E-1468-2015 Iliyasu, Abdullah/0000-0002-4964-6609; Hanus, Robert/0000-0001-8595-1478; Chen, Tzu-Chia/0000-0002-2942-7839; Hirota, Kaoru/0000-0001-5347-6182 Deputyship for Research and Innovation of the Saudi Ministry of Education [IF-PSAU-2021/01/18316]; Minister of Education and Science of the Republic of Poland within the Regional Initiative of Excellence program for the years 2019-2023 [027/RID/2018/19] Deputyship for Research and Innovation of the Saudi Ministry of Education; Minister of Education and Science of the Republic of Poland within the Regional Initiative of Excellence program for the years 2019-2023 The authors acknowledge funding from the Deputyship for Research and Innovation of the Saudi Ministry of Education via its funding for the PSAU Advanced Computational Intelligence and Intelligent Systems Engineering (ACIISE) Research Group, Project Number IF-PSAU-2021/01/18316, and from the Minister of Education and Science of the Republic of Poland within the Regional Initiative of Excellence program for the years 2019-2023 (project number 027/RID/2018/19, amount granted 11,999,900 PLN). 36 0 0 1 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies OCT 2022.0 15.0 20 7564 10.3390/en15207564 0.0 11 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 5O8TO gold 2023-03-23 WOS:000872738600001 0
J Zhai, YK; Zhou, W; Sun, B; Li, JW; Ke, QR; Ying, ZL; Gan, JY; Mai, CY; Labati, RD; Piuri, V; Scotti, F Zhai, Yikui; Zhou, Wenlve; Sun, Bing; Li, Jingwen; Ke, Qirui; Ying, Zilu; Gan, Junying; Mai, Chaoyun; Labati, Ruggero Donida; Piuri, Vincenzo; Scotti, Fabio Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Synthetic aperture radar; Task analysis; Radar polarimetry; Neural networks; Training; Target recognition; Training data; Batch instance discrimination (BID); deep learning; dynamic-weighted variance loss (DWV loss); small sample synthetic aperture radar (SAR) automatic target recognition (ATR); weakly contrastive learning (WCL) NETWORK In recent years, impressive performance of deep learning technology has been recognized in synthetic aperture radar (SAR) automatic target recognition (ATR). Since a large amount of annotated data are required in this technique, it poses a trenchant challenge to the issue of obtaining a high recognition rate through less labeled data. To overcome this problem, inspired by the contrastive learning, we proposed a novel framework named batch instance discrimination and feature clustering (BIDFC). In this framework, different from that of the objective of general contrastive learning methods, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Consequently, our flexible framework is equipped with adjustable distance between embedding, which we term as weakly contrastive learning. Technically, instance labels are assigned to the unlabeled data in per batch, and random augmentation and training are performed few times on these augmented data. Meanwhile, a novel dynamic-weighted variance loss (DWV loss) function is also posed to cluster the embedding of enhanced versions for each sample. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data. Even though a linear evaluation is performed on the same training data, the accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in OpenSarShip database, indicating that our method can be generalized to other data sets. Our code is available at: https://github.com/Wenlve-Zhou/BIDFC-master. [Zhai, Yikui; Zhou, Wenlve; Ke, Qirui; Ying, Zilu; Gan, Junying; Mai, Chaoyun] Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China; [Sun, Bing; Li, Jingwen] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Labati, Ruggero Donida; Piuri, Vincenzo; Scotti, Fabio] Univ Milan, Dept Informat, I-20133 Milan, Italy Wuyi University; Beihang University; University of Milan Sun, B (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China. yikuizhai@163.com; wenlvezhou@163.com; bingsun@buaa.edu.cn; lijingwen@buaa.edu.cn; 17683921638@163.com; ziluy@163.com; junyinggan@163.com; maichao-yun@foxmail.com; ruggero.donida@unimi.it; vincenzo.piuri@unimi.it; fabio.scotti@unimi.it Donida Labati, Ruggero/L-4524-2013; Piuri, Vincenzo/A-3970-2012 Donida Labati, Ruggero/0000-0002-2636-086X; Zhai, Yikui/0000-0003-0154-9743; Zhou, Wenlve/0000-0002-7500-5581; Piuri, Vincenzo/0000-0003-3178-8198; Scotti, Fabio/0000-0002-4277-3701 National Natural Science Foundation [61771347, 61672335]; Open Project of Guizhou Province Public Big Data Key Laboratory [2019BDKFJJ015]; Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province [2018KZDXM073, 2019KZDZX1017, 2020ZDZX3031]; Guangdong Basic and Applied Basic Research Foundation [2019A1515010716]; Archives Scientific Research Project of Guangdong Provincial Archives Bureau [YDK2302019]; Jiangmen Science and Technology Project [2019030102070008984] National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Open Project of Guizhou Province Public Big Data Key Laboratory; Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province; Guangdong Basic and Applied Basic Research Foundation; Archives Scientific Research Project of Guangdong Provincial Archives Bureau; Jiangmen Science and Technology Project This work was supported in part by the National Natural Science Foundation under Grant 61771347 and Grant 61672335; in part by the Open Project of Guizhou Province Public Big Data Key Laboratory under Grant 2019BDKFJJ015; in part by the Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province under Grant 2018KZDXM073, Grant 2019KZDZX1017, and Grant 2020ZDZX3031; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515010716; in part by the Archives Scientific Research Project of Guangdong Provincial Archives Bureau under Grant YDK2302019; and in part by the Jiangmen Science and Technology Project under Grant 2019030102070008984. 51 5 5 3 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5204317 10.1109/TGRS.2021.3066195 0.0 17 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XP1FR 2023-03-23 WOS:000730619400024 0
J Bian, HY; Qin, X; Wu, YM; Yi, ZG; Liu, SR; Wang, Y; Brites, CDS; Carlos, LD; Liu, XG Bian, Hongyu; Qin, Xian; Wu, Yiming; Yi, Zhigao; Liu, Sirui; Wang, Yu; Brites, Carlos D. S.; Carlos, Luis D.; Liu, Xiaogang Multimodal Tuning of Synaptic Plasticity Using Persistent Luminescent Memitters ADVANCED MATERIALS English Article image memorization; persistent luminescence; photonic memory; synaptic plasticity MEMORY; METAPLASTICITY; NETWORKS; STORAGE; DEVICE Mimicking memory processes, including encoding, storing, and retrieving information, is critical for neuromorphic computing and artificial intelligence. Synaptic behavior simulations through electronic, magnetic, or photonic devices based on metal oxides, 2D materials, molecular complex and phase change materials, represent important strategies for performing computational tasks with enhanced power efficiency. Here, a special class of memristive materials based on persistent luminescent memitters (termed as a portmanteau of memory and emitter) with optical characteristics closely resembling those of biological synapses is reported. The memory process and synaptic plasticity can be successfully emulated using such memitters under precisely controlled excitation frequency, wavelength, pulse number, and power density. The experimental and theoretical data suggest that electron-coupled trap nucleation and propagation through clustering in persistent luminescent memitters can explain experience-dependent plasticity. The use of persistent luminescent memitters for multichannel image memorization that allows direct visualization of subtle changes in luminescence intensity and realization of short-term and long-term memory is also demonstrated. These findings may promote the discovery of new functional materials as artificial synapses and enhance the understanding of memory mechanisms. [Bian, Hongyu; Qin, Xian; Wu, Yiming; Yi, Zhigao; Liu, Sirui; Liu, Xiaogang] Natl Univ Singapore, Dept Chem, Singapore 117543, Singapore; [Wang, Yu; Liu, Xiaogang] Shenzhen Univ, SZU NUS Collaborat Ctr, Shenzhen 518060, Peoples R China; [Wang, Yu; Liu, Xiaogang] Shenzhen Univ, Int Collaborat Lab 2D Mat Optoelect Sci & Technol, Minist Educ, Inst Microscale Optoelect, Shenzhen 518060, Peoples R China; [Brites, Carlos D. S.; Carlos, Luis D.] Univ Aveiro, Dept Phys, CICECO Aveiro Inst Mat, Phantom G, P-3810193 Aveiro, Portugal; [Liu, Xiaogang] Natl Univ Singapore Suzhou Res Inst, Ctr Funct Mat, Suzhou 215123, Peoples R China National University of Singapore; Shenzhen University; Shenzhen University; Universidade de Aveiro; National University of Singapore Qin, X; Liu, XG (corresponding author), Natl Univ Singapore, Dept Chem, Singapore 117543, Singapore.;Liu, XG (corresponding author), Shenzhen Univ, SZU NUS Collaborat Ctr, Shenzhen 518060, Peoples R China.;Liu, XG (corresponding author), Shenzhen Univ, Int Collaborat Lab 2D Mat Optoelect Sci & Technol, Minist Educ, Inst Microscale Optoelect, Shenzhen 518060, Peoples R China.;Carlos, LD (corresponding author), Univ Aveiro, Dept Phys, CICECO Aveiro Inst Mat, Phantom G, P-3810193 Aveiro, Portugal.;Liu, XG (corresponding author), Natl Univ Singapore Suzhou Res Inst, Ctr Funct Mat, Suzhou 215123, Peoples R China. chmqinx@nus.edu.sg; lcarlos@ua.pt; chmlx@nus.edu.sg LIU, XIAOGANG/A-8038-2011; Wang, Yu/N-4119-2018; Bian, Hongyu/HJY-0500-2023; Carlos, Luís Dias/B-2869-2009; Brites, Carlos/B-7314-2008 LIU, XIAOGANG/0000-0003-2517-5790; Wang, Yu/0000-0001-5971-6597; Bian, Hongyu/0000-0002-0231-5466; Carlos, Luís Dias/0000-0003-4747-6535; Wu, Yiming/0000-0002-1201-5053; Brites, Carlos/0000-0001-9636-2628 Ministry of Education, Singapore [MOE2017-T2-2-110]; Agency for Science, Technology, and Research (A*STAR) under its AME program [A1883c0011, A1983c0038]; National Research Foundation, the Prime Minister's Office of Singapore [NRF-NRFI05-2019-0003]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [OSR-2018-CRG7-3736]; National Natural Science Foundation of China [21771135, 21871071]; FCT/MEC [UIDB/50011/2020, UIDP/50011/2020]; FEDER under the PT2020 Partnership Agreement Ministry of Education, Singapore(Ministry of Education, Singapore); Agency for Science, Technology, and Research (A*STAR) under its AME program; National Research Foundation, the Prime Minister's Office of Singapore; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); FCT/MEC(Fundacao para a Ciencia e a Tecnologia (FCT)); FEDER under the PT2020 Partnership Agreement The authors thank the Ministry of Education, Singapore (MOE2017-T2-2-110), Agency for Science, Technology, and Research (A*STAR) under its AME program (Grant No. A1883c0011 and A1983c0038), National Research Foundation, the Prime Minister's Office of Singapore under its NRF Investigatorship Programme (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, and the National Natural Science Foundation of China (21771135, 21871071). C.D.S.B. and L.C. acknowledge the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 and UIDP/50011/2020, financed by Portuguese funds through the FCT/MEC and when appropriate co-financed by DFEDER under the PT2020 Partnership Agreement. The authors acknowledge Professor X. Chen and Dr. M. A. Hernandez-Rodriguez for helpful discussion. 51 11 11 28 153 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0935-9648 1521-4095 ADV MATER Adv. Mater. JUN 2022.0 34.0 25 SI 2101895 10.1002/adma.202101895 0.0 JUN 2021 7 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics 2H8DG 34145646.0 2023-03-23 WOS:000663208400001 0
J Cai, ZP; De, S; Kedziora, M; Wang, CK Cai, Zhipeng; De, Suparna; Kedziora, Michal; Wang, Chaokun Guest Editorial Special Issue on Graph-Powered Machine Learning for Internet of Things IEEE INTERNET OF THINGS JOURNAL English Editorial Material Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph data continuously, which stimulates fertilization and making use of advanced graph-powered methods on the diverse, dynamic, and large-scale graph IoT data. [Cai, Zhipeng] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA; [De, Suparna] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England; [Kedziora, Michal] Wroclaw Univ Sci & Technol, Fac Comp Sci & Management, PL-50370 Wroclaw, Poland; [Wang, Chaokun] Tsinghua Univ, Sch Software, Beijing 100190, Peoples R China University System of Georgia; Georgia State University; University of Surrey; Wroclaw University of Science & Technology; Tsinghua University Cai, ZP (corresponding author), Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA. zcai@gsu.edu; s.de@surrey.ac.uk; michal.kedziora@pwr.edu.pl; chaokun@tsinghua.edu.cn Wang, Chao/GXF-8353-2022 0 0 0 2 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. JUN 15 2022.0 9.0 12 9102 9105 10.1109/JIOT.2022.3164812 0.0 4 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 1Y4FB Green Submitted, Bronze 2023-03-23 WOS:000808096100006 0
J Khider, D; Emile-Geay, J; McKay, NP; Gil, Y; Garijo, D; Ratnakar, V; Alonso-Garcia, M; Bertrand, S; Bothe, O; Brewer, P; Bunn, A; Chevalier, M; Comas-Bru, L; Csank, A; Dassie, E; DeLong, K; Felis, T; Francus, P; Frappier, A; Gray, W; Goring, S; Jonkers, L; Kahle, M; Kaufman, D; Kehrwald, NM; Martrat, B; McGregor, H; Richey, J; Schmittner, A; Scroxton, N; Sutherland, E; Thirumalai, K; Allen, K; Arnaud, F; Axford, Y; Barrows, T; Bazin, L; Birch, SEP; Bradley, E; Bregy, J; Capron, E; Cartapanis, O; Chiang, HW; Cobb, KM; Debret, M; Dommain, R; Du, J; Dyez, K; Emerick, S; Erb, MP; Falster, G; Finsinger, W; Fortier, D; Gauthier, N; George, S; Grimm, E; Hertzberg, J; Hibbert, F; Hillman, A; Hobbs, W; Huber, M; Hughes, ALC; Jaccard, S; Ruan, J; Kienast, M; Konecky, B; Le Roux, G; Lyubchich, V; Novello, VF; Olaka, L; Partin, JW; Pearce, C; Phipps, SJ; Pignol, C; Piotrowska, N; Poli, MS; Prokopenko, A; Schwanck, F; Stepanek, C; Swann, GEA; Telford, R; Thomas, E; Thomas, Z; Truebe, S; von Gunten, L; Waite, A; Weitzel, N; Wilhelm, B; Williams, J; Winstrup, M; Zhao, N; Zhou, Y Khider, D.; Emile-Geay, J.; McKay, N. P.; Gil, Y.; Garijo, D.; Ratnakar, V; Alonso-Garcia, M.; Bertrand, S.; Bothe, O.; Brewer, P.; Bunn, A.; Chevalier, M.; Comas-Bru, L.; Csank, A.; Dassie, E.; DeLong, K.; Felis, T.; Francus, P.; Frappier, A.; Gray, W.; Goring, S.; Jonkers, L.; Kahle, M.; Kaufman, D.; Kehrwald, N. M.; Martrat, B.; McGregor, H.; Richey, J.; Schmittner, A.; Scroxton, N.; Sutherland, E.; Thirumalai, K.; Allen, K.; Arnaud, F.; Axford, Y.; Barrows, T.; Bazin, L.; Birch, S. E. Pilaar; Bradley, E.; Bregy, J.; Capron, E.; Cartapanis, O.; Chiang, H-W; Cobb, K. M.; Debret, M.; Dommain, R.; Du, J.; Dyez, K.; Emerick, S.; Erb, M. P.; Falster, G.; Finsinger, W.; Fortier, D.; Gauthier, Nicolas; George, S.; Grimm, E.; Hertzberg, J.; Hibbert, F.; Hillman, A.; Hobbs, W.; Huber, M.; Hughes, A. L. C.; Jaccard, S.; Ruan, J.; Kienast, M.; Konecky, B.; Le Roux, G.; Lyubchich, V; Novello, V. F.; Olaka, L.; Partin, J. W.; Pearce, C.; Phipps, S. J.; Pignol, C.; Piotrowska, N.; Poli, M-S; Prokopenko, A.; Schwanck, F.; Stepanek, C.; Swann, G. E. A.; Telford, R.; Thomas, E.; Thomas, Z.; Truebe, S.; von Gunten, L.; Waite, A.; Weitzel, N.; Wilhelm, B.; Williams, J.; Winstrup, M.; Zhao, N.; Zhou, Y. PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data PALEOCEANOGRAPHY AND PALEOCLIMATOLOGY English Article standards; FAIR; paleoclimate; paleoceanography; data; best practices TECHNICAL NOTE; FRAMEWORK; CLIMATE The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches. [Khider, D.; Gil, Y.; Garijo, D.; Ratnakar, V] Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90007 USA; [Khider, D.; Emile-Geay, J.] Univ Southern Calif, Dept Earth Sci, Los Angeles, CA 90007 USA; [McKay, N. P.; Kaufman, D.; Erb, M. P.] No Arizona Univ, Sch Earth & Sustainabil, Flagstaff, AZ 86011 USA; [Alonso-Garcia, M.] Univ Salamanca, Dept Geol, Salamanca, Spain; [Bertrand, S.] Univ Ghent, Renard Ctr Marine Geol, Ghent, Belgium; [Bothe, O.] Helmholtz Zentrum Geesthacht, Geesthacht, Germany; [Brewer, P.] Lab Tree Ring Res, Tucson, AZ USA; [Bunn, A.] Western Washington Univ, Bellingham, WA 98225 USA; [Chevalier, M.] Univ Lausanne, Lausanne, Switzerland; [Comas-Bru, L.] Univ Coll Dublin, Sch Earth Sci, Belfied, Ireland; [Comas-Bru, L.] Univ Reading, Sch Archaeol Geog & Environm Sci, Reading, Berks, England; [Csank, A.] Univ Nevada, Dept Geog, Reno, NV 89557 USA; [Dassie, E.] Bordeaux Univ, CNRS, Bordeaux, France; [DeLong, K.] Louisiana State Univ, Baton Rouge, LA 70803 USA; [Felis, T.; Jonkers, L.] Univ Bremen, MARUM Ctr Marine Environm Sci, Bremen, Germany; [Francus, P.] Inst Natl Rech Sci, Quebec City, PQ, Canada; [Frappier, A.] Skidmore Coll, Geosiences, Saratoga Springs, NY 12866 USA; [Gray, W.; Bazin, L.] IPSL, LSCE, Gif Sur Yvette, France; [Goring, S.; Williams, J.] Univ Wisconsin, Dept Geog, Madison, WI 53706 USA; [Kahle, M.] Univ Freiburg, Phys Geog, Freiburg, Germany; [Kehrwald, N. M.] US Geol Survey, Geosci & Environm Change Sci Ctr, Box 25046, Denver, CO 80225 USA; [Martrat, B.] Spanish Council Sci Res, Inst Environm Assessment & Water Res, Dept Environm Chem, Barcelona, Spain; [Martrat, B.] Univ Cambridge, Dept Earth Sci, Cambridge, England; [McGregor, H.; Barrows, T.] Univ Wollongong, Sch Earth Atmospher & Life Sci, Wollongong, NSW, Australia; [Richey, J.] US Geol Survey, St Petersburg, FL USA; [Schmittner, A.; Du, J.] Oregon State Univ, Coll Earth Ocean & Atmospher Sci, Corvallis, OR 97331 USA; [Scroxton, N.] Univ Coll Dublin, Sch Earth Sci, Dublin, Ireland; [Sutherland, E.] US Forest Serv, Rocky Mt Res Stn, Jemez Pueblo, NM USA; [Thirumalai, K.] Univ Arizona, Dept Geosci, Tucson, AZ 85721 USA; [Allen, K.] Univ Melbourne, Dept Forest & Ecosyst Sci, Richmond, Vic, Australia; [Arnaud, F.; Pignol, C.] Univ Grenoble Alpes, Univ Savoie Mt Blanc, CNRS, EDYTEM, Chambery, France; [Axford, Y.] Northwestern Univ, Dept Earth & Planetary Sci, Evanston, IL USA; [Birch, S. E. Pilaar] Univ Georgia, Dept Geog, Athens, GA 30602 USA; [Bradley, E.] Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA; [Bregy, J.] Indiana Univ, Dept Geog, Bloomington, IN 47405 USA; [Capron, E.] Univ Copenhagen, Niels Bohr Inst, Phys Ice Climate & Earth, Copenhagen, Denmark; [Cartapanis, O.; Jaccard, S.] Univ Bern, Inst Geol Sci, Bern, Switzerland; [Chiang, H-W] Natl Taiwan Univ, Dept Geosci, Taipei, Taiwan; [Cobb, K. M.] Georgia Tech, Sch Earth & Atmospher Sci, Atlanta, GA USA; [Debret, M.] Univ Rouen Normandie, Mont St Aignan, France; [Dommain, R.] Univ Potsdam, Inst Geosci, Potsdam, Germany; [Dyez, K.] Univ Michigan, Earth & Environm Sci, Ann Arbor, MI 48109 USA; [Emerick, S.; Novello, V. F.] Univ Sao Paulo, Inst Geociencias, Lab Sistemas Carst, Sao Paulo, Brazil; [Falster, G.] Univ Adelaide, Adelaide, SA, Australia; [Finsinger, W.] Univ Montpellier, CNRS, ISEM, Montpellier, France; [Fortier, D.] Univ Montreal, Dept Geog, Montreal, PQ, Canada; [Gauthier, Nicolas] Arizona State Univ, Shcool Human Evolut & Social Change, Tempe, AZ USA; [George, S.] Univ Reading, Dept Meteorol, NCAS, Reading, Berks, England; [Grimm, E.] Univ Minnesota, Dept Earth Sci, Minneapolis, MN USA; [Hertzberg, J.] Old Dominion Univ, Dept Ocean Earth & Atmospher Sci, Norfolk, VA USA; [Hibbert, F.] Australian Natl Univ, Res Sch Earth Sci, Canberra, ACT, Australia; [Hillman, A.] Univ Louisiana Lafayette, Sch Geosci, Lafayette, LA 70504 USA; [Hobbs, W.] Univ Tasmania, Antarctic Climate & Ecosyst Cooperat Res Ctr, Hobart, Tas, Australia; [Huber, M.] Purdue Univ, Earth Atmospher & Planetary Sci Dept, W Lafayette, IN 47907 USA; [Hughes, A. L. C.] Univ Manchester, Dept Geog, Sch Environm Educ & Dev, Manchester, Lancs, England; [Hughes, A. L. C.] Univ Bergen, Dept Earth Sci, Bergen, Norway; [Hughes, A. L. C.] Bjerknes Ctr Climate Res, Bergen, Norway; [Ruan, J.] Sun Yat Sen Univ, Sch Earth Sci & Engn, Guangzhou, Guangdong, Peoples R China; [Kienast, M.] Dalhousie Univ, Dept Oceanog, Halifax, NS, Canada; [Konecky, B.] Washington Univ, Earth & Planetary Sci, St Louis, MO 63110 USA; [Le Roux, G.] Univ Toulouse, CNRS, EcoLab UMR 5245, Toulouse, France; [Lyubchich, V] Univ Maryland, Ctr Environm Sci, Cambridge, MD USA; [Olaka, L.] Univ Nairobi, Geol Dept, Nairobi, Kenya; [Partin, J. W.] Univ Texas Austin, Inst Geophys, Austin, TX USA; [Pearce, C.] Aarhus Univ, Dept Geosci, Aarhus, Denmark; [Phipps, S. J.] Univ Tasmania, Inst Marine & Antarctic Studies, Hobart, Tas, Australia; [Piotrowska, N.] Silesian Tech Univ, Inst Phys CSE, Gliwice, Poland; [Poli, M-S] Eastern Michigan Univ, Dept Geog & Geol, Ypsilanti, MI 48197 USA; [Prokopenko, A.] Univ Cologne, Inst Geol & Mineral, Cologne, Germany; [Schwanck, F.] Univ Fed Rio Grande do Sul, Ctr Polar & Climat, Porto Alegre, RS, Brazil; [Stepanek, C.] Alfred Wegener Inst, Helmholtz Ctr Polar & Marine Res, Bremerhaven, Germany; [Swann, G. E. A.] Univ Nottingham, Sch Geog, Nottingham, England; [Telford, R.] Bergen Univ, Dept Biol Sci, Bergen, Norway; [Thomas, E.] British Antarctic Survey, Cambridge, England; [Thomas, Z.] UNSW, Sch Biol Earth & Environm Sci, Sydney, NSW, Australia; [Truebe, S.] Arizona State Pk & Trails, Benson, AZ USA; [von Gunten, L.] PAGES Int Project Off, Bern, Switzerland; [Waite, A.] ANGARI Fdn, W Palm Beach, FL USA; [Weitzel, N.] Heidelberg Univ, Inst Environm Phys, Heidelberg, Germany; [Wilhelm, B.] Univ Grenoble Alpes, CNRS, IRD, INP,IGE, Grenoble, France; [Winstrup, M.] Univ Copenhagen, Danish Meteorol Inst, Copenhagen, Denmark; [Zhao, N.] Max Planck Inst Chem, Mainz, Germany; [Zhou, Y.] Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USA University of Southern California; University of Southern California; Northern Arizona University; University of Salamanca; Ghent University; Helmholtz Association; Helmholtz-Zentrum Geesthacht - Zentrum fur Material- und Kustenforschung; Western Washington University; University of Lausanne; University College Dublin; University of Reading; Nevada System of Higher Education (NSHE); University of Nevada Reno; Centre National de la Recherche Scientifique (CNRS); Louisiana State University System; Louisiana State University; University of Bremen; University of Quebec; Institut national de la recherche scientifique (INRS); Skidmore College; UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); University of Wisconsin System; University of Wisconsin Madison; University of Freiburg; United States Department of the Interior; United States Geological Survey; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro de Investigacion y Desarrollo Pascual Vila (CID-CSIC); CSIC - Instituto de Diagnostico Ambiental y Estudios del Agua (IDAEA); University of Cambridge; University of Wollongong; United States Department of the Interior; United States Geological Survey; Oregon State University; University College Dublin; United States Department of Agriculture (USDA); United States Forest Service; University of Arizona; University of Melbourne; Centre National de la Recherche Scientifique (CNRS); Universite de Savoie; Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Northwestern University; University System of Georgia; University of Georgia; University of Colorado System; University of Colorado Boulder; Indiana University System; Indiana University Bloomington; University of Copenhagen; Niels Bohr Institute; University of Bern; National Taiwan University; University System of Georgia; Georgia Institute of Technology; Universite de Rouen Normandie; University of Potsdam; University of Michigan System; University of Michigan; Universidade de Sao Paulo; University of Adelaide; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; Universite de Montreal; Arizona State University; Arizona State University-Tempe; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC National Centre for Atmospheric Science; University of Reading; University of Minnesota System; University of Minnesota Twin Cities; Old Dominion University; Australian National University; University of Louisiana Lafayette; Antarctic Climate & Ecosystems Cooperative Research Centre (ACE CRC); University of Tasmania; Purdue University System; Purdue University; Purdue University West Lafayette Campus; University of Manchester; University of Bergen; Bjerknes Centre for Climate Research; Sun Yat Sen University; Dalhousie University; Washington University (WUSTL); Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Institut National Polytechnique de Toulouse; University System of Maryland; University of Maryland Center for Environmental Science; University of Nairobi; University of Texas System; University of Texas Austin; Aarhus University; University of Tasmania; Silesian University of Technology; Eastern Michigan University; University of Cologne; Universidade Federal do Rio Grande do Sul; Helmholtz Association; Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research; University of Nottingham; University of Bergen; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC British Antarctic Survey; University of New South Wales Sydney; Ruprecht Karls University Heidelberg; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Institut de Recherche pour le Developpement (IRD); Danish Meteorological Institute DMI; University of Copenhagen; Max Planck Society; Columbia University Khider, D (corresponding author), Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90007 USA.;Khider, D (corresponding author), Univ Southern Calif, Dept Earth Sci, Los Angeles, CA 90007 USA. khider@usc.edu Axford, Yarrow/N-4151-2014; Finsinger, Walter/A-7937-2011; Felis, Thomas/G-6670-2011; Du, Jianghui/I-6570-2017; DeLong, Kristine/B-7500-2008; Falster, Georgina/AEN-9770-2022; Huber, Matthew/A-7677-2008; Pignol, cecile/AAC-2872-2020; Arnaud, Fabien/F-7003-2012; Stepanek, Christian/ABA-9210-2021; Chevalier, Manuel/W-6949-2019; Alonso-Garcia, Montserrat/AAB-5338-2021; Swann, George/B-2794-2011; Hobbs, Will/P-8094-2019; Barrows, Timothy T/E-8471-2011; Thomas, Elizabeth/ADF-3660-2022; Lyubchich, Vyacheslav/M-9516-2016; Novello, Valdir F./P-5824-2015; Kahle, Michael/Z-2242-2019; Zhou, Yuxin/AGX-7253-2022; Telford, Richard/AAD-2249-2019; Thomas, Zoë A/D-1656-2016; Ruan, Jiaoyang/AAE-7410-2020; Bertrand, Sebastien/G-9744-2011; Allen, Kathryn/AAH-8535-2019; Dyez, Kelsey/AAI-8110-2021; Bothe, Oliver/J-2975-2012; Hibbert, Fiona/ABA-8606-2021; Cartapanis, Olivier/AAM-9779-2021; Hughes, Anna/J-5628-2015; Pearce, Christof/M-4852-2013; Emile-Geay, Julien/B-1102-2010; Waite, Amanda/A-1675-2011; Winstrup, Mai/M-5844-2014; Emerick, Suellyn/H-1725-2018; Jaccard, Samuel/G-3447-2014; Kehrwald, Natalie/A-3848-2013; Martrat, Belen/I-5952-2015; McGregor, Helen/I-1479-2013 Axford, Yarrow/0000-0002-8033-358X; Finsinger, Walter/0000-0002-8297-0574; Felis, Thomas/0000-0003-1417-9657; Du, Jianghui/0000-0002-3386-9314; DeLong, Kristine/0000-0001-6320-421X; Falster, Georgina/0000-0001-8567-7413; Huber, Matthew/0000-0002-2771-9977; Pignol, cecile/0000-0003-4411-8898; Arnaud, Fabien/0000-0002-8706-9902; Chevalier, Manuel/0000-0002-8183-9881; Alonso-Garcia, Montserrat/0000-0002-0241-2178; Swann, George/0000-0002-4750-9504; Hobbs, Will/0000-0002-2061-0899; Barrows, Timothy T/0000-0003-2614-7177; Thomas, Elizabeth/0000-0002-3010-6493; Lyubchich, Vyacheslav/0000-0001-7936-4285; Novello, Valdir F./0000-0002-0120-3745; Kahle, Michael/0000-0001-8571-2821; Zhou, Yuxin/0000-0002-3523-8524; Telford, Richard/0000-0001-9826-3076; Thomas, Zoë A/0000-0002-2323-4366; Bertrand, Sebastien/0000-0003-0374-4040; Allen, Kathryn/0000-0002-8403-4552; Dyez, Kelsey/0000-0001-8347-3089; Bothe, Oliver/0000-0002-6257-8786; Hibbert, Fiona/0000-0002-3686-6514; Cartapanis, Olivier/0000-0001-8542-6884; Hughes, Anna/0000-0001-8584-5202; Gray, William/0000-0001-5608-7836; Pearce, Christof/0000-0002-4866-3204; Schwanck, Franciele/0000-0003-1046-8080; Hertzberg, Jennifer/0000-0001-6437-5977; Emile-Geay, Julien/0000-0001-5920-4751; Williams, Joseph/0000-0003-4521-4902; Gil, Yolanda/0000-0001-8465-8341; Waite, Amanda/0000-0002-1863-666X; Partin, Judson/0000-0003-0315-5545; McKay, Nicholas/0000-0003-3598-5113; Poli, Maria-Serena/0000-0001-6470-032X; Dommain, Rene/0000-0003-4547-8983; Winstrup, Mai/0000-0002-4794-4004; Khider, Deborah/0000-0001-7501-8430; George, Steve/0000-0002-0396-0299; Stepanek, Christian/0000-0002-3912-6271; Zhao, Ning/0000-0002-1936-8978; Emerick, Suellyn/0000-0002-4285-0113; Bregy, Joshua/0000-0002-3483-595X; Jaccard, Samuel/0000-0002-5793-0896; von Gunten, Lucien/0000-0003-0425-2881; Chiang, Hong-Wei/0000-0002-5274-594X; Kehrwald, Natalie/0000-0002-9160-2239; Dassie, Emilie/0000-0002-3927-2690; Gauthier, Nicolas/0000-0002-2225-5827; Scroxton, Nick/0000-0003-2315-9199; Martrat, Belen/0000-0001-9904-9178; McGregor, Helen/0000-0002-4031-2282; Garijo, Daniel/0000-0003-0454-7145 National Science Foundation through the EarthCube Program [ICER-1541029]; PAGES; NERC [bas0100034] Funding Source: UKRI National Science Foundation through the EarthCube Program; PAGES; NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) Code and data to reproduce the figures of this article are available on GitHub and released on Zenodo (doi:10.5281/zenodo.3165019). Definition of properties and recommendations are summarized here: http://wiki.linked.earth/PaCTS_v1.0. This work was supported by the National Science Foundation through the EarthCube Program with Grant ICER-1541029. Feedback solicitation on the standard was facilitated by the Past Global Changes (PAGES) organization. The 2016 workshop on Paleoclimate Data Standards was hosted by the World Data Service for Paleoclimatology (WDS/NOAA-Paleo), and the participation of international attendees was made possible by a PAGES travel grant. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 51 19 20 2 24 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2572-4517 2572-4525 PALEOCEANOGR PALEOCL Paleoceanogr. Paleoclimatology OCT 2019.0 34.0 10 1570 1596 10.1029/2019PA003632 0.0 OCT 2019 27 Geosciences, Multidisciplinary; Oceanography; Paleontology Science Citation Index Expanded (SCI-EXPANDED) Geology; Oceanography; Paleontology JP2FY hybrid, Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000492497000001 0
J Yang, ZS; Jiang, WS; Lin, YP; Elberink, SO Yang, Zhishuang; Jiang, Wanshou; Lin, Yaping; Elberink, Sander Oude Using Training Samples Retrieved from a Topographic Map and Unsupervised Segmentation for the Classification of Airborne Laser Scanning Data REMOTE SENSING English Article automatic training samples generation; unsupervised segmentation; graph convolutional neural network; ALS point clouds POINT CLOUDS; LIDAR DATA; URBAN; IMAGES The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs. [Yang, Zhishuang; Jiang, Wanshou] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China; [Lin, Yaping; Elberink, Sander Oude] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7514 AE Enschede, Netherlands Wuhan University; University of Twente Jiang, WS (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China. tcyzs@whu.edu.cn; jws@whu.edu.cn; y.lin@utwente.nl; s.j.oudeelberink@utwente.nl Elberink, Sander Oude/D-3829-2009 Elberink, Sander Oude/0000-0002-4511-2095; Jiang, Wanshou/0000-0002-3162-0566 China Scholarship Council [201906270225]; National Key R&D Program of China [2018YFB0504800, 2018YFB0504801] China Scholarship Council(China Scholarship Council); National Key R&D Program of China This research was funded by China Scholarship Council, grant number 201906270225. This work was supported in part by the National Key R&D Program of China, grant number 2018YFB0504800 (2018YFB0504801). 38 4 4 2 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2020.0 12.0 5 877 10.3390/rs12050877 0.0 18 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology LL4XB Green Published, gold 2023-03-23 WOS:000531559300132 0
J Ni, YQ; Lohinai, Z; Heshiki, Y; Dome, B; Moldvay, J; Dulka, E; Galffy, G; Berta, J; Weiss, GJ; Sommer, MOA; Panagiotou, G Ni, Yueqiong; Lohinai, Zoltan; Heshiki, Yoshitaro; Dome, Balazs; Moldvay, Judit; Dulka, Edit; Galffy, Gabriella; Berta, Judit; Weiss, Glen J.; Sommer, Morten O. A.; Panagiotou, Gianni Distinct composition and metabolic functions of human gut microbiota are associated with cachexia in lung cancer patients ISME JOURNAL English Article PERMEABILITY; SUPPRESSES; ACTIVATION; PLASMA Cachexia is associated with decreased survival in cancer patients and has a prevalence of up to 80%. The etiology of cachexia is poorly understood, and limited treatment options exist. Here, we investigated the role of the human gut microbiome in cachexia by integrating shotgun metagenomics and plasma metabolomics of 31 lung cancer patients. The cachexia group showed significant differences in the gut microbial composition, functional pathways of the metagenome, and the related plasma metabolites compared to non-cachectic patients. Branched-chain amino acids (BCAAs), methylhistamine, and vitamins were significantly depleted in the plasma of cachexia patients, which was also reflected in the depletion of relevant gut microbiota functional pathways. The enrichment of BCAAs and 3-oxocholic acid in non-cachectic patients were positively correlated with gut microbial species Prevotella copri and Lactobacillus gasseri, respectively. Furthermore, the gut microbiota capacity for lipopolysaccharides biosynthesis was significantly enriched in cachectic patients. The involvement of the gut microbiome in cachexia was further observed in a high-performance machine learning model using solely gut microbial features. Our study demonstrates the links between cachectic host metabolism and specific gut microbial species and functions in a clinical setting, suggesting that the gut microbiota could have an influence on cachexia with possible therapeutic applications. [Ni, Yueqiong; Heshiki, Yoshitaro; Panagiotou, Gianni] Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Jena, Germany; [Lohinai, Zoltan; Dome, Balazs; Moldvay, Judit; Berta, Judit] Natl Koranyi Inst Pulmonol, Budapest, Hungary; [Heshiki, Yoshitaro; Panagiotou, Gianni] Univ Hong Kong, Sch Biol Sci, Kadoorie Biol Sci Bldg, Hong Kong, Peoples R China; [Dulka, Edit; Galffy, Gabriella] Cty Hosp Torokbalint, Torokbalint, Hungary; [Weiss, Glen J.] MiRanost Consulting, Oro Valley, AZ USA; [Sommer, Morten O. A.] Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, Kongens, Denmark; [Panagiotou, Gianni] Univ Hong Kong, Li Ka Shing Fac Med, Dept Microbiol, Hong Kong, Peoples R China Hans Knoll Institute (HKI); University of Hong Kong; Technical University of Denmark; University of Hong Kong Panagiotou, G (corresponding author), Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Jena, Germany.;Panagiotou, G (corresponding author), Univ Hong Kong, Sch Biol Sci, Kadoorie Biol Sci Bldg, Hong Kong, Peoples R China.;Panagiotou, G (corresponding author), Univ Hong Kong, Li Ka Shing Fac Med, Dept Microbiol, Hong Kong, Peoples R China. gianni.panagiotou@leibniz-hki.de Sommer, Morten Otto Alexander/0000-0003-4005-5674; Ni, Yueqiong/0000-0003-1285-7354; Dome, Balazs/0000-0001-8799-8624 DFG [EXC 2051, 390713860]; Novo Nordisk Foundation [NNF10CC1016517, NNF17CO0028232]; LCFA-BMS/IASLC Young Investigator Scholarship Award; Hungarian National Research, Development and Innovation Office (OTKA) [124652, 129664, KH130356, KNN121510, K129065, NAP2-2017-1.2.1-NKP-002]; Austrian Science FUND (FWF) [I3977, I4677] DFG(German Research Foundation (DFG)); Novo Nordisk Foundation(Novo Nordisk FoundationNovocure Limited); LCFA-BMS/IASLC Young Investigator Scholarship Award; Hungarian National Research, Development and Innovation Office (OTKA)(Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA)); Austrian Science FUND (FWF)(Austrian Science Fund (FWF)) GP would like to thank DFG under Germany's Excellence Strategy-EXC 2051-Project ID 390713860. MOAS acknowledges funding from: The Novo Nordisk Foundation under grant agreements: NNF10CC1016517 and NNF17CO0028232. ZL was supported by the 2018 LCFA-BMS/IASLC Young Investigator Scholarship Award. ZL, BD, and JM acknowledge funding from the Hungarian National Research, Development and Innovation Office (OTKA #124652 and OTKA #129664, ZL; KH130356 and KNN121510, BD; K129065 and NAP2-2017-1.2.1-NKP-002 projects, JM). BD was supported by the Austrian Science FUND (FWF I3977 and I4677). 62 24 25 12 39 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1751-7362 1751-7370 ISME J ISME J. NOV 2021.0 15.0 11 3207 3220 10.1038/s41396-021-00998-8 0.0 MAY 2021 14 Ecology; Microbiology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Microbiology WK0FM 34002024.0 Green Published, hybrid 2023-03-23 WOS:000651336600001 0
J Cheng, L; Xin, HH; Groves, RM; Veljkovic, M Cheng, Lu; Xin, Haohui; Groves, Roger M.; Veljkovic, Milan Acoustic emission source location using Lamb wave propagation simulation and artificial neural network for I-shaped steel girder CONSTRUCTION AND BUILDING MATERIALS English Article Source location; Lamb waves; Time of arrival (TOA); Finite element analysis; Artificial neural network (ANN); Composite structures Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures. (C) 2020 The Author(s). Published by Elsevier Ltd. [Cheng, Lu; Xin, Haohui; Veljkovic, Milan] Delft Univ Technol, Fac Civil Engn & Geosci, Steel & Composite Struct Grp, Delft, Netherlands; [Xin, Haohui] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Dept Civil Engn, Xian, Peoples R China; [Groves, Roger M.] Delft Univ Technol, Fac Aerosp Engn, Aerosp Nondestruct Testing Lab, Delft, Netherlands Delft University of Technology; Xi'an Jiaotong University; Delft University of Technology Xin, HH (corresponding author), Delft Univ Technol, Fac Civil Engn & Geosci, Steel & Composite Struct Grp, Delft, Netherlands.;Xin, HH (corresponding author), Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Dept Civil Engn, Xian, Peoples R China. H.Xin@tudelft.nl Groves, Roger/0000-0001-9169-9256; Veljkovic, Milan/0000-0003-4791-2341 CSC Scholarship Council [201806060122] CSC Scholarship Council The first author wishes to express her gratitude for the financial support of the CSC Scholarship Council under grant number 201806060122. 67 14 14 6 22 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. MAR 1 2021.0 273.0 121706 10.1016/j.conbuildmat.2020.121706 0.0 FEB 2021 17 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science QB5DQ hybrid, Green Published 2023-03-23 WOS:000614160800039 0
C Peng, W; Hong, XP; Zhao, GY IEEE Peng, Wei; Hong, Xiaopeng; Zhao, Guoying VIDEO ACTION RECOGNITION VIA NEURAL ARCHITECTURE SEARCHING 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) IEEE International Conference on Image Processing ICIP English Proceedings Paper 26th IEEE International Conference on Image Processing (ICIP) SEP 22-25, 2019 Taipei, TAIWAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Automated machine learning; neural architecture search; video action recognition Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to evaluate each architecture candidate is still heavy. To alleviate this issue, we, for the video input, introduce a temporal segment approach to reduce the computational cost without losing global video information. For the architecture, we explore in an efficient search space by introducing pseudo 3D operators. Experiments show that, our architecture outperforms popular neural architectures, under the training from scratch protocol, on the challenging UCF101 dataset, surprisingly, with only around one percentage of parameters of its manual-design counterparts. [Peng, Wei; Hong, Xiaopeng; Zhao, Guoying] Univ Oulu, Ctr Machine Vision & Signal Anal, Oulu, Finland; [Hong, Xiaopeng] Xi An Jiao Tong Univ, Xian, Peoples R China University of Oulu; Xi'an Jiaotong University Zhao, GY (corresponding author), Univ Oulu, Ctr Machine Vision & Signal Anal, Oulu, Finland. Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X Academy of Finland ICT 2023 project [313600]; Tekes Fidipro program [1849/31/2015]; Business Finland project [3116/31/2017]; Infotech Oulu; National Natural Science Foundation of China [61772419] Academy of Finland ICT 2023 project; Tekes Fidipro program(Finnish Funding Agency for Technology & Innovation (TEKES)); Business Finland project; Infotech Oulu; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Academy of Finland ICT 2023 project (Grant No. 313600), Tekes Fidipro program (Grant No. 1849/31/2015) and Business Finland project (Grant No. 3116/31/2017), Infotech Oulu, and the National Natural Science Foundation of China (Grants No. 61772419). As well, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. 21 22 25 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1522-4880 978-1-5386-6249-6 IEEE IMAGE PROC 2019.0 11 15 5 Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Imaging Science & Photographic Technology BO6RM Green Accepted, Green Submitted 2023-03-23 WOS:000521828600003 0
J Huang, P; He, P; Tian, SK; Ma, MR; Feng, P; Xiao, HL; Mercaldo, F; Santone, A; Qin, J Huang, Pan; He, Peng; Tian, Sukun; Ma, Mingrui; Feng, Peng; Xiao, Hualiang; Mercaldo, Francesco; Santone, Antonella; Qin, Jing A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images IEEE TRANSACTIONS ON MEDICAL IMAGING English Article Visually interpretable; model fusion; tumor grading; laryngeal cancer; histopathology images SEGMENTATION; P63; CLASSIFICATION; TRANSFORMER The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding's inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at https://github.com/Baron-Huang/ViT-AMCNet. [Huang, Pan; He, Peng; Feng, Peng] Chongqing Univ, Key Lab Optoelect Technol & Syst, Minist Educ, Coll Optoelect Engn, Chongqing 400044, Peoples R China; [Tian, Sukun] Peking Univ Sch, Hosp Stomatol, Fac Prosthodont, Ctr Digital Dent, Beijing 100082, Peoples R China; [Tian, Sukun] Technol forComputerized Dent Minist Hlth, Res Ctr Engn, Natl Engn Res Ctr Oral Biomat & Digital Med Device, Beijing Key Lab Digital Stomatol, Beijing 100081, Peoples R China; [Tian, Sukun] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China; [Ma, Mingrui] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China; [Xiao, Hualiang] Army Med Univ, Daping Hosp, Dept Pathol, Chongqing 400044, Peoples R China; [Mercaldo, Francesco; Santone, Antonella] Univ Molise, Dept Med, Hlth Sci Vincenzo Tiberio, I-86100 Campobasso, Italy; [Qin, Jing] Hong Kong Polytech Univ, Sch Nursing, Hong Kong, Peoples R China Chongqing University; Peking University; Shandong University; Xinjiang University; Army Medical University; University of Molise; Hong Kong Polytechnic University Feng, P (corresponding author), Chongqing Univ, Key Lab Optoelect Technol & Syst, Minist Educ, Coll Optoelect Engn, Chongqing 400044, Peoples R China.;Tian, SK (corresponding author), Peking Univ Sch, Hosp Stomatol, Fac Prosthodont, Ctr Digital Dent, Beijing 100082, Peoples R China.;Xiao, HL (corresponding author), Army Med Univ, Daping Hosp, Dept Pathol, Chongqing 400044, Peoples R China. panhuang@cqu.edu.cn; penghe@cqu.edu.cn; sukhum169@hotmail.com; mmrwin007@163.com; coe-fp@cqu.edu.cn; dpbl_xhl@163.com; francesco.mercaldo@unimol.it; antonella.santone@unimol.it; harry.qin@polyu.edu.hk Huang, Pan/AAC-5550-2021 Huang, Pan/0000-0001-8158-2628; Tian, Sukun/0000-0001-8289-5968; FENG, PENG/0000-0002-7420-2161 National Key Research and Development Program of China [2019YFC0605203]; National Natural Science Foundation of China [52105265]; Innovation and Technology Fund under Hong Kong Innovation and Technology Commission [ITS/180/20FP]; Chongqing Basic Research and Frontier Exploration Project [cstc2020jcyj-msxmX0553]; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN201904007] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation and Technology Fund under Hong Kong Innovation and Technology Commission; Chongqing Basic Research and Frontier Exploration Project; Scientific and Technological Research Program of Chongqing Municipal Education Commission This work was supported in partby the National Key Research and Development Program of China under Grant 2019YFC0605203, in part by the National Natural Science Foundation of China under Grant 52105265, in part by the Innovation and Technology Fund under Hong Kong Innovation and Technology Commission under Project ITS/180/20FP, in part by the Chongqing Basic Research and Frontier Exploration Project under Grant cstc2020jcyj-msxmX0553, and in part by the Scientific and Technological Research Program of Chongqing Municipal Education Commissionunder Grant KJQN201904007. 53 1 1 26 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging JAN 2023.0 42.0 1 15 28 10.1109/TMI.2022.3202248 0.0 14 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging 7N2FQ 36018875.0 2023-03-23 WOS:000907160700002 0
J Liao, BL; Hua, C; Cao, XW; Katsikis, VN; Li, S Liao, Bolin; Hua, Cheng; Cao, Xinwei; Katsikis, Vasilios N.; Li, Shuai Complex Noise-Resistant Zeroing Neural Network for Computing Complex Time-Dependent Lyapunov Equation MATHEMATICS English Article complex time-dependent Lyapunov equation; zeroing neural network (ZNN); complex linear noise; complex quadratic noise; noise-suppression ZNN; DYNAMICS; CONVERGENCE; PARAMETER; MODEL Complex time-dependent Lyapunov equation (CTDLE), as an important means of stability analysis of control systems, has been extensively employed in mathematics and engineering application fields. Recursive neural networks (RNNs) have been reported as an effective method for solving CTDLE. In the previous work, zeroing neural networks (ZNNs) have been established to find the accurate solution of time-dependent Lyapunov equation (TDLE) in the noise-free conditions. However, noises are inevitable in the actual implementation process. In order to suppress the interference of various noises in practical applications, in this paper, a complex noise-resistant ZNN (CNRZNN) model is proposed and employed for the CTDLE solution. Additionally, the convergence and robustness of the CNRZNN model are analyzed and proved theoretically. For verification and comparison, three experiments and the existing noise-tolerant ZNN (NTZNN) model are introduced to investigate the effectiveness, convergence and robustness of the CNRZNN model. Compared with the NTZNN model, the CNRZNN model has more generality and stronger robustness. Specifically, the NTZNN model is a special form of the CNRZNN model, and the residual error of CNRZNN can converge rapidly and stably to order 10(-5) when solving CTDLE under complex linear noises, which is much lower than order 10(-1) of the NTZNN model. Analogously, under complex quadratic noises, the residual error of the CNRZNN model can converge to 2 parallel to A parallel to(F)/zeta(3) quickly and stably, while the residual error of the NTZNN model is divergent. [Liao, Bolin; Hua, Cheng] Jishou Univ, Coll Comp Sci & Engn, Jishou 416000, Peoples R China; [Cao, Xinwei] Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Sofokleous 1 St, Athens 10559, Greece; [Li, Shuai] Swansea Univ, Sch Engn, Swansea SA2 8PP, W Glam, Wales Jishou University; Jiangnan University; National & Kapodistrian University of Athens; Swansea University Cao, XW (corresponding author), Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China. xcao.fudan@gmail.com ; Katsikis, Vasilios/AAD-7216-2019 Li, Shuai/0000-0001-8316-5289; Liao, Bolin/0000-0001-9036-2723; Katsikis, Vasilios/0000-0002-8208-9656 National Natural Science Foundation of China [62066015]; Natural Science Foundation of Hunan Province of China [2020JJ4511]; Research Foundation of Education Bureau of Hunan Province, China [20A396] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province of China(Natural Science Foundation of Hunan Province); Research Foundation of Education Bureau of Hunan Province, China This work was supported in part by the National Natural Science Foundation of China under Grant No. 62066015, the Natural Science Foundation of Hunan Province of China under Grant No. 2020JJ4511, and the Research Foundation of Education Bureau of Hunan Province, China, under Grant No. 20A396. 41 2 2 2 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics AUG 2022.0 10.0 15 2817 10.3390/math10152817 0.0 17 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 3S8AI Green Published, gold 2023-03-23 WOS:000839812100001 0
C Wu, Y; Li, SS; Li, L; Li, MZ; Ming, L; Arvanitis, KG; Georgieva, C; Sigrimis, N Wu Yong; Li Shuaishuai; Li Li; Li Minzan; Ming, Li; Arvanitis, K. G.; Georgieva, Cs; Sigrimis, N. Smart Sensors from Ground to Cloud and Web Intelligence IFAC PAPERSONLINE English Proceedings Paper 6th International-Federation-of-Automatic-Control (IFAC) Conference on Bio-Robotics (BIOROBOTICS) JUL 13-15, 2018 Beijing, PEOPLES R CHINA Int Federat Automat Control internet of things; ubiquitous computing; embedded intelligence; precision irrigation With the development of sensing, wireless communication, and Internet technologies, we are now living in a world that is filled with various smart things the Internet of Things (TOT). This paper introduces and prospects an emerging research area the Embedded Intelligence (El). This field aims at revealing the individual behaviours, spatial contexts, as well as social patterns and urban dynamics by mining the digital traces left by people while interacting with Internet of smart things (cameras, smart cars, smart cards, etc). In the agricultural sector we add mining of existing technology (books, articles, blue prints) to generate high depth ontologies or metadata and knowledge extraction from Big Data for reasoning and making pro-active decisions using open data information. We include intelligent data analysis to discover new knowledge from data records. The paper discusses the research history, characteristics, general architecture, major applications, and research issues of EI and exemplifies an application of the team in IOT smart irrigation. The purpose of presenting it as a Technology Roadmap (TRM) was to clarify the challenges and opportunities in the general area of Intelligent Building Technologies (IBT) and more specifically to smart greenhouses, smart irrigation, or smart crop management. The understanding of smart agriculture is important to be identified by the scientific community as a significant issue because it is needed for nations to develop and adopt these emerging technologies. Global interoperability is of key importance, and so it is a common understanding of values such as privacy and security, based on open, fair and transparent international standards. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [Wu Yong; Li Shuaishuai; Li Li] China Agr Univ, Minist Educ, Key Lab Modern Precis Agr Syst Integrat Res, Beijing 100083, Peoples R China; [Li Minzan] China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr, Beijing 100083, Peoples R China; [Ming, Li] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Arvanitis, K. G.; Sigrimis, N.] Agr Univ Athens, Dept Nat Resources Management & Agr Engn, GR-11855 Athens, Greece; [Georgieva, Cs] Univ Ruse, Dept Automat & Mechatron, Ruse 7017, Bulgaria China Agricultural University; China Agricultural University; Ministry of Agriculture & Rural Affairs; Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Agricultural University of Athens; University of Ruse Wu, Y (corresponding author), China Agr Univ, Minist Educ, Key Lab Modern Precis Agr Syst Integrat Res, Beijing 100083, Peoples R China. takeyourcap@foxmail.com; takeyourcap@foxmail.com; takeyourcap@foxmail.com; limz@cau.edu.cn; lim@nercita.org.cn; ns@aua.gr Georgieva, Tsvetelina/AAJ-6539-2021; Arvanitis, Kostas G./L-3539-2016; Ming, Li/B-4439-2015 Georgieva, Tsvetelina/0000-0002-4055-0897; Arvanitis, Kostas G./0000-0002-5778-6644; Traceability and Early warning system for supply chain of Agricultural Product: complementarities between EU and China [TEAP:PIRSES-GA-2013-612659]; National Key Research and Development Program of China [2016YED0201003]; Yunnan Academician Expert Workstation [2015IC16] Traceability and Early warning system for supply chain of Agricultural Product: complementarities between EU and China; National Key Research and Development Program of China; Yunnan Academician Expert Workstation This research was supported by the Traceability and Early warning system for supply chain of Agricultural Product: complementarities between EU and China (TEAP:PIRSES-GA-2013-612659), the National Key Research and Development Program of China (2016YED0201003) and the Yunnan Academician Expert Workstation (Wang Maohua, Grant No. 2015IC16). The authors wish to thank Acad. Prof. Wang Maohua (member of CAE), the leader of the team for solar greenhouse closed cultivation system. 13 9 10 1 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2018.0 51.0 17 31 38 10.1016/j.ifacol.2018.08.057 0.0 8 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems GT4ZQ gold 2023-03-23 WOS:000444516000008 0
J Chen, Y; Hu, JY; Xiao, Y; Li, X; Hui, P Chen, Yang; Hu, Jiyao; Xiao, Yu; Li, Xiang; Hui, Pan Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global Scale IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS English Article Twitter; Publishing; Distributed databases; Sociology; Statistics; Systematics; IEEE Foundation; Data-driven study; location-based social networks (LBSNs); machine learning; social graph analysis; social influence; tips ONLINE SOCIAL NETWORKS; POPULARITY; POWER Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this article, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found several unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected component, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users and then look into the tip contents from the perspectives of tip venues, temporal patterns, and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to the profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users and are constructive for different relevant entities, including LBSN service providers, Internet service providers, and third-party application providers. [Chen, Yang; Hu, Jiyao] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Chen, Yang; Hu, Jiyao] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [Chen, Yang; Hu, Jiyao] Peng Cheng Lab, Shenzhen 518066, Peoples R China; [Xiao, Yu] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Li, Xiang] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki 00100, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China Fudan University; Fudan University; Peng Cheng Laboratory; Aalto University; Fudan University; University of Helsinki; Hong Kong University of Science & Technology Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China. chenyang@fudan.edu.cn; jyhu@cs.duke.edu; yu.xiao@aalto.fi; lix@fudan.edu.cn; panhui@cs.helsinki.fi li, xiang/GWM-6319-2022; Li, Xiang/AAB-4134-2020; Hu, Jiyao/AEC-8080-2022; HU, Jiyao/AAD-8703-2022 Li, Xiang/0000-0002-6482-2535; HU, Jiyao/0000-0001-5840-9552; Xiao, Yu/0000-0002-4517-3779 National Natural Science Foundation of China [71731004, 61602122]; CERNET Innovation Project [NGII20190105]; Project PCL Future Greater-Bay Area Network Facilities for LargeScale Experiments and Applications [LZC0019]; Research Grants Council of Hong Kong [16214817]; 5GEAR project from the Academy of Finland; FIT project from the Academy of Finland National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CERNET Innovation Project; Project PCL Future Greater-Bay Area Network Facilities for LargeScale Experiments and Applications; Research Grants Council of Hong Kong(Hong Kong Research Grants Council); 5GEAR project from the Academy of Finland; FIT project from the Academy of Finland This work was supported by the National Natural Science Foundation of China under Grant 71731004 and Grant 61602122, in part by the CERNET Innovation Project under Grant NGII20190105, in part by the Project PCL Future Greater-Bay Area Network Facilities for LargeScale Experiments and Applications under Grant LZC0019, in part by the Research Grants Council of Hong Kong under Grant 16214817, and in part by the 5GEAR project and FIT project from the Academy of Finland. 70 7 7 0 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-924X IEEE T COMPUT SOC SY IEEE Trans. Comput. Soc. Syst. AUG 2020.0 7.0 4 1019 1032 10.1109/TCSS.2020.2992294 0.0 14 Computer Science, Cybernetics; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science MW9MK Green Accepted 2023-03-23 WOS:000557355100016 0
J Ran, R; Deng, LJ; Jiang, TX; Hu, JF; Chanussot, J; Vivone, G Ran, Ran; Deng, Liang-Jian; Jiang, Tai-Xiang; Hu, Jin-Fan; Chanussot, Jocelyn; Vivone, Gemine GuidedNet: A General CNN Fusion Framework via High-Resolution Guidance for Hyperspectral Image Super-Resolution IEEE TRANSACTIONS ON CYBERNETICS English Article; Early Access Image reconstruction; Task analysis; Superresolution; Pansharpening; Hyperspectral imaging; Spatial resolution; Training; Convolutional neural network (CNN); high-resolution guidance; hyperspectral image super-resolution (HISR); image fusion; pansharpening; single-image super-resolution (SISR) INJECTION; NETWORK; FACTORIZATION Hyperspectral image super-resolution (HISR) is about fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI). Recently, convolutional neural network (CNN)-based techniques have been extensively investigated for HISR yielding competitive outcomes. However, existing CNN-based methods often require a huge amount of network parameters leading to a heavy computational burden, thus, limiting the generalization ability. In this article, we fully consider the characteristic of the HISR, proposing a general CNN fusion framework with high-resolution guidance, called GuidedNet. This framework consists of two branches, including 1) the high-resolution guidance branch (HGB) that can decompose the high-resolution guidance image into several scales and 2) the feature reconstruction branch (FRB) that takes the low-resolution image and the multiscaled high-resolution guidance images from the HGB to reconstruct the high-resolution fused image. GuidedNet can effectively predict the high-resolution residual details that are added to the upsampled HSI to simultaneously improve spatial quality and preserve spectral information. The proposed framework is implemented using recursive and progressive strategies, which can promote high performance with a significant network parameter reduction, even ensuring network stability by supervising several intermediate outputs. Additionally, the proposed approach is also suitable for other resolution enhancement tasks, such as remote sensing pansharpening and single-image super-resolution (SISR). Extensive experiments on simulated and real datasets demonstrate that the proposed framework generates state-of-the-art outcomes for several applications (i.e., HISR, pansharpening, and SISR). Finally, an ablation study and more discussions assessing, for example, the network generalization, the low computational cost, and the fewer network parameters, are provided to the readers. The code link is: https://github.com/Evangelion09/GuidedNet. [Ran, Ran; Deng, Liang-Jian; Hu, Jin-Fan] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China; [Jiang, Tai-Xiang] Southwestern Univ Finance & Econ, FinTech Innovat Ctr, Sch Econ Informat Engn, FinancialIntelligence & Financial Engn Res Key Lab, Chengdu 610074, Sichuan, Peoples R China; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100045, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Inria, CNRS, Grenoble INP, F-38000 Grenoble, France; [Vivone, Gemine] CNR, Inst Methodol Environm Anal, CNR IMAA, I-85050 Potenza, Italy University of Electronic Science & Technology of China; Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR) Deng, LJ (corresponding author), Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China. ranran@std.uestc.edu.cn; liangjian.deng@uestc.edu.cn; taixiangjiang@gmail.com; hujf0206@163.com; jocelyn.chanussot@grenoble-inp.fr; gemine.vivone@imaa.cnr.it Vivone, Gemine/0000-0001-9542-0638; Chanussot, Jocelyn/0000-0003-4817-2875 NSFC [12271083, 62203089, 12001446]; Natural Science Foundation of Sichuan Province [2022NSFSC0501, 2022NSFSC0507, 2022NSFSC1798]; Key Projects of Applied Basic Research in Sichuan Province [2020YJ0216]; National Key Research and Development Program of China [2020YFA0714001] NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Sichuan Province; Key Projects of Applied Basic Research in Sichuan Province; National Key Research and Development Program of China This work was supported in part by NSFC under Grant 12271083, Grant 62203089, and Grant 12001446; in part by the Natural Science Foundation of Sichuan Province under Grant 2022NSFSC0501, Grant 2022NSFSC0507, and Grant 2022NSFSC1798; in part by the Key Projects of Applied Basic Research in Sichuan Province under Grant 2020YJ0216; and in part by the National Key Research and Development Program of China under Grant 2020YFA0714001 70 0 0 9 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. 10.1109/TCYB.2023.3238200 0.0 FEB 2023 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 9C0OX 2023-03-23 WOS:000935128100001 0
J Xie, R; Chen, Y; Lin, SH; Zhang, TY; Xiao, Y; Wang, X Xie, Rong; Chen, Yang; Lin, Shihan; Zhang, Tianyong; Xiao, Yu; Wang, Xin Understanding Skout users' mobility patterns on a global scale: a data-driven study WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS English Article Human mobility; Skout; Global city network; PageRank Location-based social apps, such as Skout, have been widely used by millions of users for sharing their location information. In this work, we collected all the location information published by over 1.2 million Skout users during December 2012 and June 2016. Based on the collected information, we model the inter-city mobility of Skout users with a global city network, and analyze the evolution of the network based on its structural characteristics. Moreover, we look into Skout users' mobility patterns by discovering the most popular inter-city routes, destinations, and tightly connected city groups, and analyze the impact on the mobility patterns from geographical distances, languages and cultures. Finally, we leverage machine learning techniques to build a model for identifying the most influential cities in the world according to the Skout data. The results are able to assist individuals, governors and business leaders in making better decisions regarding traveling, immigrating, measuring city improvements and cooperation with cities. [Xie, Rong; Chen, Yang; Lin, Shihan; Zhang, Tianyong; Wang, Xin] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Xie, Rong; Chen, Yang; Lin, Shihan; Zhang, Tianyong; Wang, Xin] Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai, Peoples R China; [Xie, Rong; Chen, Yang; Lin, Shihan; Zhang, Tianyong; Wang, Xin] Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China; [Xiao, Yu] Aalto Univ, Dept Commun & Networking, Espoo, Finland Fudan University; Xidian University; Aalto University Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Chen, Y (corresponding author), Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai, Peoples R China.;Chen, Y (corresponding author), Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China. xieronglucy@fudan.edu.cn; chenyang@fudan.edu.cn; shlin15@fudan.edu.cn; tyzhang14@fudan.edu.cn; yu.xiao@aalto.fi; xinw@fudan.edu.cn zhang, tian/GZK-6001-2022 Xiao, Yu/0000-0002-4517-3779 National Natural Science Foundation of China [61602122, 71731004]; Natural Science Foundation of Shanghai [16ZR1402200]; Shanghai Pujiang Program [16PJ1400700]; Academy of Finland [268096]; Academy of Finland (AKA) [268096] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Pujiang Program(Shanghai Pujiang Program); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), Academy of Finland (No. 268096). 33 3 3 0 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1386-145X 1573-1413 WORLD WIDE WEB World Wide Web NOV 2019.0 22.0 6 SI 2655 2673 10.1007/s11280-018-0551-8 0.0 19 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science JY3MK 2023-03-23 WOS:000504322400018 0
J Tao, Y; Han, YB; Liu, WX; Peng, L; Wang, Y; Kadam, S; Show, PL; Ye, XS Tao, Yang; Han, Yongbin; Liu, Wangxin; Peng, Lu; Wang, Yue; Kadam, Shekhar; Show, Pau Loke; Ye, Xiaosong Parametric and phenomenological studies about ultrasound-enhanced biosorption of phenolics from fruit pomace extract by waste yeast ULTRASONICS SONOCHEMISTRY English Article Ultrasound; Fruit pomace; Yeast biosorption; Phenolics; Neural network; Diffusional model GRAPE POMACE; SACCHAROMYCES-CEREVISIAE; SURFACE-DIFFUSION; MASS-TRANSFER; ADSORPTION MECHANISM; ASSISTED EXTRACTION; EXPERIMENTAL-DESIGN; AQUEOUS-SOLUTION; POLYPHENOLS; OPTIMIZATION In this work, sonication (20-kHz) was conducted to assist the biosorption of phenolics from blueberry pomace extracts by brewery waste yeast biomass. The adsorption capacity of yeast increased markedly under ultrasonic fields. After sonication at 394.2 W/L and 40 degrees C for 120 min, the adsorption capacity was increased by 62.7% compared with that under reciprocating shaking. An artificial neural network was used to model and visualize the effects of different parameters on yeast biosorption capacity. Both biosorption time and acoustic energy density had positive influences on yeast biosorption capacity, whereas no clear influence of temperature on biosorption process was observed. Regarding the mechanism of ultrasound-enhanced biosorption process, the amino and carboxyl groups in yeast were considered to be associated with the yeast biosorption property. Meanwhile, ultrasound promoted the decline of the structure order of yeast cells induced by phenolic uptake. The interactions between yeast cells and phenolics were also affected by the structures of phenolics. Moreover, the mass transfer process was simulated by a surface diffusional model considering the ultrasound-induced yeast cell disruption. The modeling results showed that the external mass transfer coefficient in liquid phase and the surface diffusion coefficient under sonication at 394.2 W/L and 40 degrees C were 128.5% and 74.3% higher than that under reciprocating shaking, respectively. [Tao, Yang; Han, Yongbin; Liu, Wangxin; Peng, Lu; Wang, Yue] Nanjing Agr Univ, Coll Food Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China; [Kadam, Shekhar] Kerry Global Technol & Innovat Ctr, Naas W91W923, Kildare, Ireland; [Show, Pau Loke] Univ Nottingham, Dept Chem & Environm Engn, Fac Engn, Malaysia Campus,Jalan Broga, Semenyih 43500, Malaysia; [Ye, Xiaosong] Zhihai Postgrad Working Stn, Zhenjiang 212000, Jiangsu, Peoples R China Nanjing Agricultural University; University of Nottingham Malaysia Han, YB (corresponding author), Nanjing Agr Univ, Coll Food Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China. hanyongbin@njau.edu.cn Pau Loke, Show/A-7953-2015 Pau Loke, Show/0000-0002-0913-5409 National Natural Science Foundation of China [31701616]; Natural Science Foundation of Jiangsu, China [BK20170714]; Fundamental Research Funds for the Central Universities [KJQN201824]; Shuangchuang Talent Project, Jiangsu, China; Jiangsu Agriculture Science and Technology Innovation Fund [CX(18)2017] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu, China(Natural Science Foundation of Jiangsu Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Shuangchuang Talent Project, Jiangsu, China; Jiangsu Agriculture Science and Technology Innovation Fund This work was funded by the National Natural Science Foundation of China (Grant No. 31701616), the Natural Science Foundation of Jiangsu, China (Grant No. BK20170714), the Fundamental Research Funds for the Central Universities (Grant No. KJQN201824), Shuangchuang Talent Project, Jiangsu, China and Jiangsu Agriculture Science and Technology Innovation Fund (Grant No. CX(18)2017). 61 21 21 1 30 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1350-4177 1873-2828 ULTRASON SONOCHEM Ultrason. Sonochem. APR 2019.0 52.0 193 204 10.1016/j.ultsonch.2018.11.018 0.0 12 Acoustics; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Chemistry HX6JF 30514598.0 2023-03-23 WOS:000467509200023 0
C Yang, BW; Wu, C; Sigg, S; Zhang, YX IEEE Yang, Bowen; Wu, Chao; Sigg, Stephan; Zhang, Yaoxue CoCo (Context v.s. Content): Behavior-Inspired Social Media Recommendation for Mobile Apps 2016 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper 59th Annual IEEE Global Communications Conference (IEEE GLOBECOM) DEC 04-08, 2016 Washington, DC IEEE,Natl Instruments,AT & T,Huawei,Intel,Qualcomm,Nokia,Samsung,Keysight Technologies,Rohde & Schwarz Exponential growth of media generated in online social networks demands effective recommendation to improve the efficiency of media access especially for mobile users. In particular, content, objective quality or general popularity are less decisive for the prediction of user-click behavior than friendship-conditioned patterns. Existing recommender systems however, rarely consider user behavior in real-life. By means of a large-scale data-driven analysis over real-life mobile Twitter traces from 15,144 users over a period of one year, we reveal the importance of social closeness related behavior features. This paper proposes CoCo, the first-ever behavior-inspired mobile social media recommender system to improve the media access experience. CoCo exploits representative behavior features via a latent bias based machine learning approach. Our comprehensive evaluation through trace-driven emulations on the Android app exposes a superior accuracy of 72.3%, with a small additional daily energy consumption of 1.3% and a monthly data overhead of 9.1MB. [Yang, Bowen; Wu, Chao] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Sigg, Stephan] Aalto Univ, Dept Communicat & Networking, Espoo, Finland; [Zhang, Yaoxue] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China Tsinghua University; Aalto University; Central South University Yang, BW (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China. Sigg, Stephan/M-2866-2015 Sigg, Stephan/0000-0001-6118-3355 Tsinghua University Initiative Scientific Research Program Tsinghua University Initiative Scientific Research Program This work is supported by Tsinghua University Initiative Scientific Research Program. 19 2 2 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 978-1-5090-1328-9 IEEE GLOB COMM CONF 2016.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BH6NX 2023-03-23 WOS:000401963301022 0
J Chi, HR; Wu, CK; Huang, NF; Tsang, KF; Radwan, A Chi, Hao Ran; Wu, Chung Kit; Huang, Nen-Fu; Tsang, Kim-Fung; Radwan, Ayman A Survey of Network Automation for Industrial Internet-of-Things Toward Industry 5.0 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Artificial intelligence (AI); industrial Internet-of-things; industry 5.0; network automation SMALL-CELL ALLOCATION; ON-DEMAND DEPLOYMENT; EDGE; 6G; CHALLENGES; SYSTEMS; OPTIMIZATION; ARCHITECTURE; MANAGEMENT; FUTURE Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies for IIoT in 5G based Industry 4.0 and summary of challenges for next-generation network automation regarding the stricter network requirements of 6G based Industry 5.0. Therefore, in this article, we conduct a comprehensive overview of the state-of-the-art network automation technologies, standardizations, and corresponding impact on IIoT of Industry 4.0. We also forecast the next-generation network automation development toward 6G based Industry 5.0. This article provides blueprint of the next-generation network automation, meanwhile conducting comprehensive overview of the SoA network automation technologies in Industry 4.0, which gains high referable value for the researchers in the relative domain. [Chi, Hao Ran; Radwan, Ayman] Univ Aveiro, Inst Telecomun, P-3810193 Aveiro, Portugal; [Wu, Chung Kit; Tsang, Kim-Fung] City Univ Hong Kong, Kowloon Tong, Hong Kong, Peoples R China; [Huang, Nen-Fu] Natl Tsing Hua Univ, Hsinchu 300, Taiwan Universidade de Aveiro; City University of Hong Kong; National Tsing Hua University Radwan, A (corresponding author), Univ Aveiro, Inst Telecomun, P-3810193 Aveiro, Portugal. ytchr@av.it.pt; chungkwu4-c@my.cityu.edu.hk; nfhuang@cs.nthu.edu.tw; ee330015@cityu.edu.hk; aradwan@ua.pt FCT/MEC; European Regional Devel-opment Fund (FEDER) [UIDB/50008/2020-UIDP/50008/2020]; FEDER; Regional Operational Programme of Centre [072082] FCT/MEC(Fundacao para a Ciencia e a Tecnologia (FCT)); European Regional Devel-opment Fund (FEDER)(European Commission); FEDER(European Commission); Regional Operational Programme of Centre This work was supported in part by FCT/MEC through nationalfunds and when applicable co-funded by European Regional Devel-opment Fund (FEDER) PT2020 Partnership Agreement under Grant UIDB/50008/2020-UIDP/50008/2020, in part by the FEDER, through theRegional Operational Programme of Lisbon (POR LISBOA 2020), andin part by the Regional Operational Programme of Centre (CENTRO2020) of the Portugal 2020 Framework [Project Safe-Home with Nr.072082 (CENTRO-01-0247-FEDER-072082)]. 60 2 2 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2023.0 19.0 2 2065 2077 10.1109/TII.2022.3215231 0.0 13 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 8Q1HM 2023-03-23 WOS:000926964700091 0
J Ma, J; Naas, SA; Sigg, S; Lyu, XX Ma, Jing; Naas, Si-Ahmed; Sigg, Stephan; Lyu, Xixiang Privacy-preserving federated learning based on multi-key homomorphic encryption INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS English Article federated learning; IoT; multi-key homomorphic encryption; privacy protection; smart healthcare SECURE; NOISE With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all participating devices is required. Our scheme prevents privacy leakage from publicly shared model updates in federated learning and is resistant to collusion between k < N - 1 participating devices and the server. The evaluation demonstrates that the scheme outperforms other innovations in communication and computational cost while preserving model accuracy. [Ma, Jing; Lyu, Xixiang] Xidian Univ, Sch Cyber Engn, Xian 710071, Shaanxi, Peoples R China; [Naas, Si-Ahmed; Sigg, Stephan] Aalto Univ, Dept Commun & Networking, Espoo, Uusimaa, Finland Xidian University; Aalto University Lyu, XX (corresponding author), Xidian Univ, Sch Cyber Engn, Xian 710071, Shaanxi, Peoples R China. xxlv@mail.xidian.edu.cn ; Sigg, Stephan/M-2866-2015 Ma, Jing/0000-0002-5217-3910; naas, si-ahmed/0000-0002-4019-938X; Sigg, Stephan/0000-0001-6118-3355 China National Science Foundation [62072356]; National Key Research and Development Program of Shaanxi [2019ZDLGY12-08] China National Science Foundation(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of Shaanxi China National Science Foundation, Grant/Award Number: 62072356; The National Key Research and Development Program of Shaanxi, Grant/Award Number: 2019ZDLGY12-08 51 14 14 31 89 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 0884-8173 1098-111X INT J INTELL SYST Int. J. Intell. Syst. SEP 2022.0 37.0 9 5880 5901 10.1002/int.22818 0.0 JAN 2022 22 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 3K1XR Green Submitted 2023-03-23 WOS:000743082400001 0
J Bruce, E; Zhao, SR; Egala, SB; Amoah, J; Du, Y; Huang, R; Tai, Y Bruce, Emmanuel; Zhao Shurong; Egala, Sulemana Bankuoru; Amoah, John; Du Ying; Huang Rui; Tai Lyu Social Media Usage and SME Firms' Sustainability: An Introspective Analysis from Ghana SUSTAINABILITY English Article social media usage; SMEs; sustainability; manufacturing; Ghana PLS-SEM; NETWORKING SITES; VALUE CREATION; BIG DATA; COMMUNICATION; CAPABILITIES; ANTECEDENTS; PERSPECTIVE; INNOVATION; INTENSITY Social media is gradually transforming diverse business ecosystems due to the limitless capabilities they offer. Given this, emerging businesses across the globe are leveraging this innovation to improve their operations. While the literature on the usage of social media by small and medium enterprises (SMEs) is still emerging, the outcomes from existing research have not been coherent. Amid this, limited empirical evidence has been adduced on the affordance of the technology for the SME ecosystem in developing economies, particularly Ghana. Following this, this study aims to fill this research gap by investigating the impact of social media usage on the long-term sustainability of SMEs, specifically in Ghana. Using empirical data from 424 respondents who are representatives of manufacturing SMEs in Ghana, using SmartPLS techniques, the study confirmed that, generally, social media usage does not only have a positive impact on SMEs but drastically drives their motivations towards resilience and sustainability. The results further revealed a positive and significant effect of social media usage value creation, business connections and opportunities on SMEs' sustainability. This study contributes to knowledge of social media usage and sustainability from a developing country's perspective. This study offers several implications for theory and practice. [Bruce, Emmanuel; Egala, Sulemana Bankuoru] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China; [Bruce, Emmanuel; Zhao Shurong; Egala, Sulemana Bankuoru; Du Ying; Huang Rui] Univ Elect Sci & Technol China, Ctr West Africa Studies, Chengdu 611731, Peoples R China; [Zhao Shurong; Du Ying; Huang Rui; Tai Lyu] Univ Elect Sci & Technol China, Sch Publ Affairs & Adm, Chengdu 611731, Peoples R China; [Amoah, John] Tomas Bata Univ Zlin, Fac Management & Econ, Dept Business Adm, Mostni 5139, Zlin 76001, Czech Republic; [Tai Lyu] Univ Elect Sci & Technol China, Sch Int Educ, Chengdu 611371, Peoples R China University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; Tomas Bata University Zlin; University of Electronic Science & Technology of China Bruce, E (corresponding author), Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China.;Bruce, E; Zhao, SR (corresponding author), Univ Elect Sci & Technol China, Ctr West Africa Studies, Chengdu 611731, Peoples R China.;Zhao, SR (corresponding author), Univ Elect Sci & Technol China, Sch Publ Affairs & Adm, Chengdu 611731, Peoples R China. kinbuki100@outlook.com; shurz2015@ustc.edu.cn; sbegala@ubids.edu.gh; amoah@utb.cz; 202021160302@std.uestc.edu.cn; hr1999ht@163.com; cocolu@uestc.edu.cn Egala, Sulemana Bankuoru/ABA-7841-2021 Egala, Sulemana Bankuoru/0000-0002-1070-5480; AMOAH, JOHN/0000-0002-3558-2077; Bruce, Emmanuel/0000-0002-7811-0264 Doctoral Research Innovation Fund Project of CWAS of UESTC [CXJJ2021122504] Doctoral Research Innovation Fund Project of CWAS of UESTC This research received external funding from Doctoral Research Innovation Fund Project of CWAS of UESTC accredited by Ministry of Education, China. Project No (CXJJ2021122504). 99 3 3 10 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability AUG 2022.0 14.0 15 9433 10.3390/su14159433 0.0 17 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 3R6NS Green Published, gold 2023-03-23 WOS:000839027500001 0
J Comes, MC; Filippi, J; Mencattini, A; Casti, P; Cerrato, G; Sauvat, A; Vacchelli, E; De Ninno, A; Di Giuseppe, D; D'Orazio, M; Mattei, F; Schiavoni, G; Businaro, L; Di Natale, C; Kroemer, G; Martinelli, E Comes, M. C.; Filippi, J.; Mencattini, A.; Casti, P.; Cerrato, G.; Sauvat, A.; Vacchelli, E.; De Ninno, A.; Di Giuseppe, D.; D'Orazio, M.; Mattei, F.; Schiavoni, G.; Businaro, L.; Di Natale, C.; Kroemer, G.; Martinelli, E. Multi-scale generative adversarial network for improved evaluation of cell-cell interactions observed in organ-on-chip experiments NEURAL COMPUTING & APPLICATIONS English Article Biomedical application; Deep learning; Generative adversarial network (GAN); Time-lapse microscopy; Video prediction CANCER Organs On a Chip (OOCs) represent a sophisticated approach for exploring biological mechanisms and developing therapeutic agents. In conjunction with high-quality time-lapse microscopy (TLM), OOCs allow for the visualization of reconstituted complex biological processes, such as multi-cell-type migration and cell-cell interactions. In this context, increasing the frame rate is desirable to reconstruct accurately cell-interaction dynamics. However, a trade-off between high resolution and carried information content is required to reduce the overall data volume. Moreover, high frame rates increase photobleaching and phototoxicity. As a possible solution for these problems, we report a new hybrid-imaging paradigm based on the integration of OOC/TLMs with a Multi-scale Generative Adversarial Network (GAN) predicting interleaved video frames with the aim to provide high-throughput videos. We tested the performance of the predictive capability of GAN on synthetic videos, as well as on real OOC experiments dealing with tumor-immune cell interactions. The proposed approach offers the possibility to acquire a reduced number of high-quality TLM images without any major loss of information on the phenomena under investigation. [Comes, M. C.; Filippi, J.; Mencattini, A.; Casti, P.; Di Giuseppe, D.; D'Orazio, M.; Di Natale, C.; Martinelli, E.] Univ Roma Tor Vergata, Dept Elect Engn, Via Politecn 1, I-00133 Rome, Italy; [Comes, M. C.; Filippi, J.; Mencattini, A.; Casti, P.; Di Giuseppe, D.; D'Orazio, M.; Martinelli, E.] Univ Roma Tor Vergata, Interdisciplinary Ctr Adv Studies Lab Chip & Orga, I-00133 Rome, Italy; [Cerrato, G.; Sauvat, A.; Vacchelli, E.; Kroemer, G.] Sorbonne Univ, Univ Paris Diderot, Univ Sorbonne Paris Cite,INSERM U1138,Ctr Rech Co, Univ Paris Descartes,Equipe Labellisee Ligue Cont, Paris, France; [Cerrato, G.; Sauvat, A.; Vacchelli, E.; Kroemer, G.] Inst Gustave Roussy, Metabol & Cell Biol Platforms, Villejuif, France; [Kroemer, G.] Hop Europeen Georges Pompidou, AP HP, Pole Biol, Paris, France; [Kroemer, G.] Chinese Acad Sci, Suzhou Inst Syst Biol, Suzhou, Peoples R China; [Kroemer, G.] Karolinska Inst, Karolinska Univ Hosp, Dept Womens & Childrens Hlth, Stockholm, Sweden; [De Ninno, A.; Businaro, L.] Italian Natl Res Council, Inst Photon & Nanotechnol, Via Cineto Romano 42, I-00156 Rome, Italy; [Mattei, F.; Schiavoni, G.] Ist Super Sanita, Dept Oncol & Mol Med, Viale Regina Elena 299, I-00161 Rome, Italy University of Rome Tor Vergata; University of Rome Tor Vergata; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; UNICANCER; Gustave Roussy; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Europeen Georges-Pompidou - APHP; Chinese Academy of Sciences; Karolinska Institutet; Karolinska University Hospital; Consiglio Nazionale delle Ricerche (CNR); Istituto di Fotonica e Nanotecnologie (IFN-CNR); Istituto Superiore di Sanita (ISS) Martinelli, E (corresponding author), Univ Roma Tor Vergata, Dept Elect Engn, Via Politecn 1, I-00133 Rome, Italy.;Martinelli, E (corresponding author), Univ Roma Tor Vergata, Interdisciplinary Ctr Adv Studies Lab Chip & Orga, I-00133 Rome, Italy. martinelli@ing.uniroma2.it Comes, Maria Colomba/AAE-4797-2021; Schiavoni, Giovanna/T-4418-2017; KROEMER, Guido/B-4263-2013; De Ninno, Adele/M-8087-2017; DOrazio, Michele/HKP-1217-2023; Kroemer, Guido/AAY-9859-2020; Filippi, Joanna/AAC-7607-2022 Comes, Maria Colomba/0000-0003-2903-4389; Schiavoni, Giovanna/0000-0001-9135-0556; KROEMER, Guido/0000-0002-9334-4405; De Ninno, Adele/0000-0003-3139-2820; Ligue contre le Cancer (equipe labellisee); Agence National de la Recherche (ANR) - Projets blancs; ANR; Association pour la recherche sur le cancer (ARC); Canceropole Ile-de-France; Chancelerie des universites de Paris (Legs Poix), Fondation pour la Recherche Medicale (FRM); European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China [GDW20171100085, GDW20181100051]; Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; LabEx Immuno-Oncology; RHU Torino Lumiere; Seerave Foundation; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); SIRIC Cancer Research and Personalized Medicine (CARPEM) Ligue contre le Cancer (equipe labellisee); Agence National de la Recherche (ANR) - Projets blancs(French National Research Agency (ANR)); ANR(French National Research Agency (ANR)); Association pour la recherche sur le cancer (ARC)(Fondation ARC pour la Recherche sur le Cancer); Canceropole Ile-de-France(Region Ile-de-France); Chancelerie des universites de Paris (Legs Poix), Fondation pour la Recherche Medicale (FRM); European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China; Institut National du Cancer (INCa)(Institut National du Cancer (INCA) France); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation(Leducq Foundation); LabEx Immuno-Oncology; RHU Torino Lumiere; Seerave Foundation; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); SIRIC Cancer Research and Personalized Medicine (CARPEM) GK is supported by the Ligue contre le Cancer (equipe labellisee); Agence National de la Recherche (ANR) - Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Canceropole Ile-de-France; Chancelerie des universites de Paris (Legs Poix), Fondation pour la Recherche Medicale (FRM); a donation by Elior; European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China (GDW20171100085 and GDW20181100051), Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology; the RHU Torino Lumiere; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and the SIRIC Cancer Research and Personalized Medicine (CARPEM). 58 9 9 4 24 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. APR 2021.0 33.0 8 3671 3689 10.1007/s00521-020-05226-6 0.0 JUL 2020 19 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RA7BC 2023-03-23 WOS:000554058400002 0
C Deng, BY; Jouvet, D; Laprie, Y; Steiner, I; Sini, A IEEE Deng, Boyuan; Jouvet, Denis; Laprie, Yves; Steiner, Ingmar; Sini, Aghilas TOWARDS CONFIDENCE MEASURES ON FUNDAMENTAL FREQUENCY ESTIMATIONS 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) MAR 05-09, 2017 New Orleans, LA IEEE,Inst Elect & Elect Engineers, Signal Proc Soc Pitch; fundamental frequency; estimation; confidence measures SPEECH RECOGNITION; NEURAL-NETWORKS The fundamental frequency is one of the prosodic parameters, and many algorithms have been developed for estimating the fundamental frequency of speech signals. Most of them provide good results on good quality speech signals, but their performance degrades when dealing with noisy signals. Moreover, although some provide a probability for the voicing decision, none of them indicate how reliable the estimated fundamental frequency is. In this paper, we investigate the computation of a confidence (or reliability) measure on the estimated fundamental frequency values. A neural network based approach is proposed for computing the posterior probability that the estimated fundamental frequency is correct. Experiments are conducted on the PTDB-TUG pitch-tracking database, using three fundamental frequency estimation algorithms. [Deng, Boyuan; Jouvet, Denis; Laprie, Yves; Sini, Aghilas] Inria, LORIA, Speech Grp, F-54600 Villers Les Nancy, France; [Deng, Boyuan; Jouvet, Denis; Laprie, Yves; Sini, Aghilas] Univ Lorraine, LORIA, UMR 7503, F-54600 Villers Les Nancy, France; [Deng, Boyuan; Jouvet, Denis; Laprie, Yves; Sini, Aghilas] LORIA, CNRS, UMR 7503, F-54600 Villers Les Nancy, France; [Deng, Boyuan] Tencent, Shenzhen, Peoples R China; [Steiner, Ingmar] Saarland Univ, Saarbrucken, Germany; [Steiner, Ingmar] DFKI GmbH, Saarbrucken, Germany Inria; Universite de Lorraine; Universite de Lorraine; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Tencent; Saarland University; German Research Center for Artificial Intelligence (DFKI) Deng, BY (corresponding author), Inria, LORIA, Speech Grp, F-54600 Villers Les Nancy, France.;Deng, BY (corresponding author), Univ Lorraine, LORIA, UMR 7503, F-54600 Villers Les Nancy, France.;Deng, BY (corresponding author), LORIA, CNRS, UMR 7503, F-54600 Villers Les Nancy, France.;Deng, BY (corresponding author), Tencent, Shenzhen, Peoples R China. bryandeng@tencent.com; denis.jouvet@loria.fr; yves.laprie@loria.fr; ingmar.steiner@dfki.de; aghilas.sini@loria.fr Steiner, Ingmar/GRX-7213-2022 Steiner, Ingmar/0000-0001-6415-5915 CPER LCHN (Contrat Plan Etat Region Langues, Connaissances et Humanites Numeriques) CPER LCHN (Contrat Plan Etat Region Langues, Connaissances et Humanites Numeriques) This work was carried out in the framework of the Prosod-Corpus operation supported by the CPER LCHN (Contrat Plan Etat Region Langues, Connaissances et Humanites Numeriques). 18 3 3 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-4117-6 INT CONF ACOUST SPEE 2017.0 5605 5609 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BI7JW Green Submitted 2023-03-23 WOS:000414286205153 0
C Sun, W; Zhou, AJ; Stuijkt, S; Nelson, A; Wijnhoven, R; Li, HS; Corporaal, H Ranzato, M; Beygelzimer, A; Dauphin, Y; Liang, PS; Vaughan, JW Sun, Wei; Zhou, Aojun; Stuijkt, Sander; Nelson, Andrew; Wijnhoven, Rob; Li, Hongsheng; Corporaal, Henk DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) Advances in Neural Information Processing Systems English Proceedings Paper 35th Conference on Neural Information Processing Systems (NeurIPS) DEC 06-14, 2021 ELECTR NETWORK Neural pruning is a widely-used compression technique for Deep Neural Networks (DNNs). Recent innovations in Hardware Architectures (e.g. Nvidia Ampere Sparse Tensor Core) and N:M fine-grained Sparse Neural Network algorithms (i.e. every M-weights contains N non-zero values) reveal a promising research line of neural pruning. However, the existing N:M algorithms only address the challenge of how to train N:M sparse neural networks in a uniform fashion (i.e. every layer has the same N:M sparsity) and suffer from a significant accuracy drop for high sparsity (i.e. when sparsity > 80%). To tackle this problem, we present a novel technique - DominoSearch to find mixed N:M sparsity schemes from pre-trained dense deep neural networks to achieve higher accuracy than the uniform-sparsity scheme with equivalent complexity constraints (e.g. model size or FLOPs). For instance, for the same model size with 2.1M parameters (87.5% sparsity), our layer-wise N:M sparse ResNet18 outperforms its uniform counterpart by 2.1% top-1 accuracy, on the large-scale ImageNet dataset. For the same computational complexity of 227M FLOPs, our layer-wise sparse ResNet18 outperforms the uniform one by 1.3% top-1 accuracy. Furthermore, our layer-wise fine-grained N:M sparse ResNet50 achieves 76.7% top-1 accuracy with 5.0M parameters. This is competitive to the results achieved by layer-wise unstructured sparsity that is believed to be the upper-bound of Neural Network pruning with respect to the accuracy-sparsity trade-off. We believe that our work can build a strong baseline for further sparse DNN research and encourage future hardware-algorithm co-design work. Our code and models are publicly available at https://github.com/NM-sparsity/DominoSearch. [Sun, Wei; Stuijkt, Sander; Nelson, Andrew; Corporaal, Henk] Eindhoven Univ Technol, Eindhoven, Netherlands; [Wijnhoven, Rob] ViNotion BV, Eindhoven, Netherlands; [Li, Hongsheng] CUHK Sensetime Joint Lab, CUHK, Hong Kong, Peoples R China Eindhoven University of Technology; Chinese University of Hong Kong Sun, W (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. w.sun@tue.nl; aojunzhou@gmail.com; s.stuijk@tue.nl; a.nelson@tue.nl; rob.wijnhoven@vinotion.nl; hsli@ee.cuhk.edu.hk; h.corporaal@tue.nl Dutch Research Council (NWO) Perspectief program [ZERO-ARM P3] Dutch Research Council (NWO) Perspectief program We thanks to all anonymous reviewers for their feedback on improving this work. This work is funded by the Dutch Research Council (NWO) Perspectief program ZERO-ARM P3. We thanks for the computation resources provided by Sensetime Research and CUHK-Sensetime Joint Lab. 39 1 1 0 0 NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) LA JOLLA 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA 1049-5258 ADV NEUR IN 2021.0 34.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU4JS 2023-03-23 WOS:000901616408022 0
J Pan, XY; Hu, XH; Zhang, YH; Feng, KY; Wang, SP; Chen, L; Huang, T; Cai, YD Pan, Xiaoyong; Hu, Xiaohua; Zhang, Yu Hang; Feng, Kaiyan; Wang, Shao Peng; Chen, Lei; Huang, Tao; Cai, Yu Dong Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection GENES English Article atrioventricular septal defect; Down syndrome; self-normalizing neural network; Monte Carlo feature selection; random forest CONGENITAL HEART-DISEASE; COPY-NUMBER VARIATION; MENTAL-RETARDATION; SCIENTIFIC STATEMENT; PROTEIN INTERACTIONS; GENE; EXPRESSION; PREDICTION; CLASSIFICATION; PHOSPHODIESTERASE Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew's correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles. [Pan, Xiaoyong; Wang, Shao Peng; Cai, Yu Dong] Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; [Pan, Xiaoyong] Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands; [Hu, Xiaohua] Fudan Univ, Sch Life Sci, Dept Biostat & Computat Biol, Shanghai 200438, Peoples R China; [Zhang, Yu Hang; Huang, Tao] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; [Feng, Kaiyan] Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China Shanghai University; Erasmus University Rotterdam; Erasmus MC; Fudan University; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Shanghai Maritime University Cai, YD (corresponding author), Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China.;Chen, L (corresponding author), Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China. x.pan@erasmusmc.nl; xhhu@fudan.edu.cn; zhangyh825@163.com; addland@126.com; wsptfb@163.com; lchen@shmtu.edu.cn; huangtao@sibs.ac.cn; caiyudong@staff.shu.edu.cn Zhang, Yu-Hang/AAQ-9012-2021; Chen, Lei/A-2275-2011; Zhang, Yu-Hang/O-9774-2015; Chen, Lei/HMD-2646-2023 Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3718-9268; cai, yudong/0000-0001-5664-7979 National Natural Science Foundation of China [31371335, 31701151, 31571343]; Natural Science Foundation of Shanghai [17ZR1412500, 16ZR1403100]; Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); key Laboratory of Stem Cell Biology of Chinese Academy of Sciences This study was supported by the National Natural Science Foundation of China (31371335, 31701151, 31571343), Natural Science Foundation of Shanghai (17ZR1412500, 16ZR1403100), Shanghai Sailing Program, the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703). 88 28 29 1 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4425 GENES-BASEL Genes APR 2018.0 9.0 4 208 10.3390/genes9040208 0.0 17 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity GJ3HH 29649131.0 Green Accepted, Green Submitted, gold, Green Published 2023-03-23 WOS:000435182200036 0
J Niu, JJ; Chen, DG; Li, JH; Wang, H Niu, Jiaojiao; Chen, Degang; Li, Jinhai; Wang, Hui Fuzzy Rule-Based Classification Method for Incremental Rule Learning IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Task analysis; Fuzzy sets; Numerical models; Lattices; Granular computing; Learning systems; Faces; Fuzzy concept lattice; fuzzy granular rule; granular reduct; incremental learning; rule-based classification ATTRIBUTE REDUCTION THEORY; DECISION; CLASSIFIERS; COMPLEXITY; ALGORITHM; SELECTION; SYSTEM Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance. [Niu, Jiaojiao] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China; [Chen, Degang] North China Elect Power Univ, Sch Math & Phys, Beijing 102206, Peoples R China; [Li, Jinhai] Kunming Univ Sci & Technol, Data Sci Res Ctr, Kunming 650500, Yunnan, Peoples R China; [Li, Jinhai] Kunming Univ Sci & Technol, Fac Sci, Kunming 650500, Yunnan, Peoples R China; [Wang, Hui] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5BN, Antrim, North Ireland North China Electric Power University; North China Electric Power University; Kunming University of Science & Technology; Kunming University of Science & Technology; Queens University Belfast Chen, DG (corresponding author), North China Elect Power Univ, Sch Math & Phys, Beijing 102206, Peoples R China. njjiao_92@163.com; wscz973@163.com; jhlixjtu@163.com; h.wang@uwl.ac.uk Wang, Hui/HMU-9512-2023 Wang, Hui/0000-0003-2633-6015; Li, Jinhai/0000-0002-5206-9304 National Key R&D Program of China [2020YFB1707800]; National Natural Science Foundation of China [11971211] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1707800 and in part by the National Natural Science Foundation of China under Grant 11971211. (Corresponding author: Degang Chen.) 75 2 2 30 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. SEP 2022.0 30.0 9 3748 3761 10.1109/TFUZZ.2021.3128061 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4F1GT Green Accepted 2023-03-23 WOS:000848264000031 0
J Cao, J; Zhang, Z; Tao, FL; Zhang, LL; Luo, YC; Han, JC; Li, ZY Cao, Juan; Zhang, Zhao; Tao, Fulu; Zhang, Liangliang; Luo, Yuchuan; Han, Jichong; Li, Ziyue Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China REMOTE SENSING English Article machine learning (ML); multi-source data; yield prediction; winter wheat LEAF-AREA INDEX; RIDGE-REGRESSION; LANDSAT TM; CROP; SATELLITE; SUSCEPTIBILITY; PERFORMANCE; PATTERNS; MODELS; PHOTOSYNTHESIS Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R-2: 0.68 similar to 0.75), with the most individual contributions from climate (similar to 0.53), followed by VIs (similar to 0.45), and SC factors (similar to 0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.similar to Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions. [Cao, Juan; Zhang, Zhao; Zhang, Liangliang; Luo, Yuchuan; Han, Jichong; Li, Ziyue] Beijing Normal Univ Beijing, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, MEM, Beijing 100875, Peoples R China; [Cao, Juan; Zhang, Zhao; Zhang, Liangliang; Luo, Yuchuan; Han, Jichong; Li, Ziyue] Beijing Normal Univ Beijing, Fac Geog Sci, MoE Key Lab Environm Change & Nat Hazards, Beijing 100875, Peoples R China; [Tao, Fulu] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; [Tao, Fulu] Nat Resources Inst Finland Luke, FI-00790 Helsinki, Finland Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Natural Resources Institute Finland (Luke) Zhang, Z (corresponding author), Beijing Normal Univ Beijing, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, MEM, Beijing 100875, Peoples R China.;Zhang, Z (corresponding author), Beijing Normal Univ Beijing, Fac Geog Sci, MoE Key Lab Environm Change & Nat Hazards, Beijing 100875, Peoples R China. caojuan@mail.bnu.edu.cn; zhangzhao@bnu.edu.cn; taofl@igsnrr.ac.cn; zhangliangliang@mail.bnu.edu.cn; luo_yuchuan@mail.bnu.edu.cn; 201921051199@mail.bnu.edu.cn; 201821051202@mail.bnu.edu.cn Luo, Yuchuan/GSN-5985-2022; Han, Jichong/HLG-1244-2023 Tao, F/0000-0001-8574-0080 National Natural Science Foundation of China [41977405, 41621061, 31561143003]; Academy of Finland [316172]; Academy of Finland (AKA) [316172] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This study was funded by the National Natural Science Foundation of China (41977405, 41621061, 31561143003). F.T was partly supported by the Academy of Finland through projects AI-CropPro (decision no. 316172). 75 32 35 15 55 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2020.0 12.0 5 750 10.3390/rs12050750 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology LL4XB gold 2023-03-23 WOS:000531559300005 0
J Zhu, GB; Ma, Y; Li, ZX; Malekian, R; Sotelo, M Zhu, Guibing; Ma, Yong; Li, Zhixiong; Malekian, Reza; Sotelo, M. Adaptive Neural Output Feedback Control for MSVs With Predefined Performance IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Adaptive neural network; marine surface vehicle; output feedback; predefined performance; uncertainty FOLLOWER FORMATION CONTROL; SURFACE VESSELS; TRACKING CONTROL; LEARNING CONTROL; IDENTIFICATION; SHIPS; RANGE In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which are subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network-based (NN-based) state observer is constructed. To guarantee the transient and steady-state tracking performance of the system, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is characterized by a user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method in combination with the adaptive NN technique and vector-backstepping design tool, an adaptive neural output-feedback trajectory tracking control scheme with predefined performance is developed. With regard to the developed control scheme, uncertainties can be reconstructed only by utilizing the position and heading of the MSVs. Independent designs of the state observer and the controller can be achieved, and the position tracking error can be guaranteed to fall into a predefined residual set in the user-defined time frame and remain in the above set. A rigorous stability analysis validates that all signals in the closed-loop trajectory tracking control system for MSVs are uniformly ultimately bounded. Simulation results verify the effectiveness of the developed adaptive neural output-feedback trajectory tracking control scheme. [Zhu, Guibing] Zhejiang Ocean Univ, Marine Coll, Zhoushan 316022, Peoples R China; [Ma, Yong] Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China; [Li, Zhixiong] Ocean Univ China, Sch Engn, Qingdao 266110, Peoples R China; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Malekian, Reza] Malmo Univ, Dept Comp Sci & Media Technol, S-21119 Malmo, Sweden; [Sotelo, M.] Univ Alcala, Dept Comp Engn, Madrid 28806, Spain Zhejiang Ocean University; Hubei Key Laboratory of Inland Shipping Technology; Wuhan University of Technology; Ocean University of China; Yonsei University; Malmo University; Universidad de Alcala Li, ZX (corresponding author), Ocean Univ China, Sch Engn, Qingdao 266110, Peoples R China.;Li, ZX (corresponding author), Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea. zhuguibing2003@163.com; myongdl@whut.edu.cn; zhixiong_li@uow.edu.au; reza.malekian@ieee.org; miguel.sotelo@uah.es Zhu, Guibing/HHZ-2170-2022; Li, Zhixiong/G-8418-2018; Sotelo, Miguel Angel/A-8663-2013 Zhu, Guibing/0000-0002-8267-9437; Li, Zhixiong/0000-0002-7265-0008; Ma, Yong/0000-0003-0418-9210; Sotelo, Miguel Angel/0000-0001-8809-2103 National Science Foundation of China [52022073, 62073251, 51911540478, 51579202, 61903174]; Natural Science Foundation of Zhejiang Province [LY21E090005]; Excellent Youth Foundation of Hubei Scientific Committee [2020CFA055]; High level talents cultivation project in transport industry [2019-011]; National key RD plan [2018YFC1407400]; Major Scientific and Technological Innovation Project of Shandong Province [2019JZZY010820] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Excellent Youth Foundation of Hubei Scientific Committee; High level talents cultivation project in transport industry; National key RD plan; Major Scientific and Technological Innovation Project of Shandong Province This work was supported in part by the National Science Foundation of China under Grants 52022073, 62073251, 51911540478, 51579202, and 61903174, in part by Natural Science Foundation of Zhejiang Province under Grant LY21E090005, in part by the Excellent Youth Foundation of Hubei Scientific Committee under Grant 2020CFA055, in part by the High level talents cultivation project in transport industry (2019-011), in part by National key R&D plan under Grant 2018YFC1407400, and in part by the Major Scientific and Technological Innovation Project of Shandong Province under Grant 2019JZZY010820. 42 33 34 13 60 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. APR 2021.0 70.0 4 2994 3006 10.1109/TVT.2021.3063687 0.0 13 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation RX7PO 2023-03-23 WOS:000647411800006 0
J Liu, XL; Dumontier, C; Hu, P; Liu, C; Yeung, W; Mao, Z; Ho, V; Thoral, PJ; Kuo, PC; Hu, J; Li, DY; Cao, DS; Mark, RG; Zhou, FH; Zhang, ZB; Celi, LA Liu, Xiaoli; Dumontier, Clark; Hu, Pan; Liu, Chao; Yeung, Wesley; Mao, Zhi; Ho, Vanda; Thoral, Patrick J.; Kuo, Po-Chih; Hu, Jie; Li, Deyu; Cao, Desen; Mark, Roger G.; Zhou, Feihu; Zhang, Zhengbo; Celi, Leo Anthony Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES English Article; Early Access International multicenter; Interpretable models; Machine learning; Mortality; Multiple organ dysfunction syndrome HOSPITAL MORTALITY; CARE; DELIRIUM; ILLNESS; ADULTS; ICU Background Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. Methods The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (>= 80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. Results Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. Conclusions Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making. [Liu, Xiaoli; Li, Deyu; Zhang, Zhengbo] Beihang Univ, Sch Biol Sci & Med Engn, Key Lab Biomech & Mechanobiol, Minist Educ,Beijing Adv Innovat Ctr Biomed Engn, Beijing, Peoples R China; [Liu, Xiaoli; Yeung, Wesley; Kuo, Po-Chih; Mark, Roger G.; Celi, Leo Anthony] MIT, Lab Computat Physiol, Inst Med Engn & Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Liu, Xiaoli; Zhang, Zhengbo] Gen Hosp PLA, Ctr Artificial Intelligence Med, 28 Fuxing Rd, Beijing 100853, Peoples R China; [Dumontier, Clark] VA Boston Healthcare Syst, New England Geriatr Res Educ & Clin Ctr, Boston, MA USA; [Dumontier, Clark] Brigham & Womens Hosp, Div Aging, 75 Francis St, Boston, MA 02115 USA; [Hu, Pan] 920 Hosp Joint Logist Support Force Chinese PLA, Dept Anesthesiol, Kunming, Yunnan, Peoples R China; [Hu, Pan; Liu, Chao; Mao, Zhi; Hu, Jie; Zhou, Feihu] Gen Hosp PLA, Med Ctr 1, Dept Crit Care Med, Beijing, Peoples R China; [Yeung, Wesley] Natl Univ Singapore Hosp, Dept Med, Singapore, Singapore; [Ho, Vanda] Natl Univ Singapore Hosp, Dept Med, Div Geriatr Med, Singapore, Singapore; [Thoral, Patrick J.] Amsterdam UMC, Dept Intens Care Med, Amsterdam, Netherlands; [Kuo, Po-Chih] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu, Taiwan; [Cao, Desen] Gen Hosp PLA, Dept Biomed Engn, Beijing, Peoples R China; [Zhou, Feihu] Gen Hosp PLA, Elderly Ctr, Beijing, Peoples R China; [Celi, Leo Anthony] Beth Israel Deaconess Med Ctr, Dept Med, Boston, MA 02215 USA; [Celi, Leo Anthony] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA USA Beihang University; Massachusetts Institute of Technology (MIT); Geriatric Research Education & Clinical Center; US Department of Veterans Affairs; Veterans Health Administration (VHA); Harvard University; VA Boston Healthcare System; Harvard University; Brigham & Women's Hospital; National University of Singapore; National University of Singapore; University of Amsterdam; National Tsing Hua University; Harvard University; Beth Israel Deaconess Medical Center; Harvard University; Harvard T.H. Chan School of Public Health Zhang, ZB (corresponding author), Gen Hosp PLA, Ctr Artificial Intelligence Med, 28 Fuxing Rd, Beijing 100853, Peoples R China. zhangzhengbo@301hospital.com.cn Liu, Xiaoli/HGE-7614-2022; liu, xiao/HKE-9880-2023; liu, xiao/HMD-7454-2023 Ho, Vanda/0000-0002-6413-4636 National Natural Science Foundation of China [62171471]; Big Data Research and Development Project of Chinese PLA General Hospital [2018MBD-009]; National Clinical Research Center for Geriatric Diseases of China [NCRCG-PLAGH-2017008]; National Institutes of Health [NIBIB R01 EB017205]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Big Data Research and Development Project of Chinese PLA General Hospital; National Clinical Research Center for Geriatric Diseases of China; National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); China Scholarship Council(China Scholarship Council) This work had been carried out when X.L. was at the Laboratory for Computational Physiology, Massachusetts Institute of Technology as a visiting student with the support of the China Scholarship Council. The study was funded by the National Natural Science Foundation of China (62171471) and Big Data Research and Development Project of Chinese PLA General Hospital (2018MBD-009), and National Clinical Research Center for Geriatric Diseases of China (NCRCG-PLAGH-2017008). L.A.C. is funded by the National Institutes of Health through NIBIB R01 EB017205. 47 0 0 7 14 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 1079-5006 1758-535X J GERONTOL A-BIOL J. Gerontol. Ser. A-Biol. Sci. Med. Sci. 10.1093/gerona/glac107 0.0 JUN 2022 9 Geriatrics & Gerontology; Gerontology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Geriatrics & Gerontology 2O9EN 35657011.0 2023-03-23 WOS:000819353600001 0
J Yang, SM; Wang, J; Deng, B; Azghadi, MR; Linares-Barranco, B Yang, Shuangming; Wang, Jiang; Deng, Bin; Azghadi, Mostafa Rahimi; Linares-Barranco, Bernabe Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Neuromorphics; Neurons; Fault tolerant systems; Fault tolerance; Task analysis; Context modeling; Brain modeling; Brain inspired; context-dependent learning; fault tolerant; neuromorphic computing; spiking neural network (SNN) EFFICIENT FPGA IMPLEMENTATION; HIPPOCAMPAL-NEURONS; NEURAL-NETWORK; DYNAMICS; MEMORY; ALGORITHM; NUCLEUS; DESIGN; SYSTEM; CORTEX Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning. [Yang, Shuangming; Wang, Jiang; Deng, Bin] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China; [Azghadi, Mostafa Rahimi] James Cook Univ, Coll Sci & Engn, Townsville, Qld 4814, Australia; [Linares-Barranco, Bernabe] Microelect Inst Seville, Seville 41092, Spain Tianjin University; James Cook University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Sevilla (IMS-CNM); University of Sevilla Yang, SM (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China. yangshuangming@tju.edu.cn; mostafa.rahimiazghadi@jcu.edu; bernabe@imse-cnm.csic.es Azghadi, Mostafa Rahimi/P-9944-2016; Linares-Barranco, Bernabe/H-6886-2012 Azghadi, Mostafa Rahimi/0000-0001-7975-3985; Linares-Barranco, Bernabe/0000-0002-1813-4889 National Natural Science Foundation of China [62071324, 62006170]; China Postdoctoral Science Foundation [2020M680885] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 62071324 and Grant 62006170, and in part by the China Postdoctoral Science Foundation under Grant 2020M680885. 64 63 63 14 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. DEC 2022.0 33.0 12 7126 7140 10.1109/TNNLS.2021.3084250 0.0 JUN 2021 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 6S5MN 34115596.0 2023-03-23 WOS:000732414600001 0
J Irvine, N; Nugent, C; Zhang, S; Wang, H; Ng, WWY Irvine, Naomi; Nugent, Chris; Zhang, Shuai; Wang, Hui; Ng, Wing W. Y. Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments SENSORS English Article human activity recognition; neural networks; ensemble neural networks; model conflict resolution; smart environments MULTIPLE CLASSIFIER SYSTEMS; DATA MINING CONCEPTS; TRACKING; FEATURES; MODELS In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value. [Irvine, Naomi; Nugent, Chris; Zhang, Shuai; Wang, Hui] Ulster Univ, Sch Comp, Newtownabbey BT37 0QB, Co Antrim, North Ireland; [Ng, Wing W. Y.] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510640, Peoples R China Ulster University; South China University of Technology Irvine, N (corresponding author), Ulster Univ, Sch Comp, Newtownabbey BT37 0QB, Co Antrim, North Ireland. irvine-n2@ulster.ac.uk; cd.nugent@ulster.ac.uk; s.zhang@ulster.ac.uk; h.wang@ulster.ac.uk; wingng@ieee.org Wang, Hui/HMU-9512-2023 Ng, Wing W. Y./0000-0003-0783-3585; Wang, Hui/0000-0003-2633-6015; Irvine, Naomi/0000-0002-8439-7054; Nugent, Chris/0000-0003-0882-7902 Northern Ireland Department for the Economy (DfE) PhD scholarship; DfE PhD scholarship Northern Ireland Department for the Economy (DfE) PhD scholarship; DfE PhD scholarship This research was supported through a Northern Ireland Department for the Economy (DfE) PhD scholarship. The APC was funded through the DfE PhD scholarship. 82 31 32 1 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JAN 2020.0 20.0 1 216 10.3390/s20010216 0.0 26 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation KH2QT 31905991.0 gold, Green Accepted, Green Submitted, Green Published 2023-03-23 WOS:000510493100216 0
J Chen, Y; Yue, WZ; La Rosa, D Chen, Yang; Yue, Wenze; La Rosa, Daniele Which communities have better accessibility to green space? An investigation into environmental inequality using big data LANDSCAPE AND URBAN PLANNING English Article Environmental inequality; Green space accessibility; Residential community; Amap API ECOSYSTEM SERVICES; URBAN PARKS; ACCESS; GIS; SHANGHAI; JUSTICE; EQUITY; CITIES; PREFERENCES; DISPARITIES Green space accessibility is widely acknowledged as a crucial aspect of a livable environment and human well-being. Whether green space accessibility is equitable among communities is increasingly considered as an issue of environmental justice. Therefore, this study focuses on the possible environmental inequality of green space accessibility that can be found among residential communities in the context of Chinese booming housing market. The case study of Shanghai, China was conducted with the use of big data. A real-time navigation route measurement based on Amap application programming interface (AAPI) was developed to calculate green space accessibility, and housing price was used to indicate dwellers' socioeconomic status. Bivariate Moran's I, multiple regression, and spatial lag regression were adopted to explore inequality of green space accessibility among residential communities. The results reveal a spatial inequality of green space accessibility between communities in central portion of the city and those in peri-urban areas. We further found a spatial mismatch between green space accessibility and housing price. Environmental inequality is evident within the inner and middle ring road wherein wealthier communities benefit more from green space accessibility than disadvantaged communities. We attribute these findings to spatial restructuring and green gentrification process in Shanghai. The findings can inform planners and policymakers to determine where and how to implement greening strategies and to gain awareness to prevent environmental inequality. [Chen, Yang; Yue, Wenze] Zhejiang Univ, Dept Land Management, Hangzhou 310058, Peoples R China; [La Rosa, Daniele] Univ Catania, Dept Civil Engn & Architecture, I-95100 Catania, Italy Zhejiang University; University of Catania Yue, WZ (corresponding author), Zhejiang Univ, Dept Land Management, Hangzhou 310058, Peoples R China. herochen945@163.com; wzyue@zju.edu.cn; dlarosa@darc.unict.it La Rosa, Daniele/A-8331-2012 La Rosa, Daniele/0000-0002-3975-1405; Yue, Wenze/0000-0002-7533-3294 National Natural Science Foundation of China [41871169, 41671533]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work has been supported by the National Natural Science Foundation of China (No. 41871169 and 41671533), the Fundamental Research Funds for the Central Universities. We give our thanks to the editor and reviewers for their valuable comments and suggestions. We also thank Dr. Liu Yong and Dr. Wu Jiayu for their comments on the revised version of this paper. 74 63 68 69 376 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0169-2046 1872-6062 LANDSCAPE URBAN PLAN Landsc. Urban Plan. DEC 2020.0 204.0 103919 10.1016/j.landurbplan.2020.103919 0.0 10 Ecology; Environmental Studies; Geography; Geography, Physical; Regional & Urban Planning; Urban Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geography; Physical Geography; Public Administration; Urban Studies OC8BV 2023-03-23 WOS:000579382500005 0
J Hussain, S; Yu, Y; Ayoub, M; Khan, A; Rehman, R; Wahid, JA; Hou, WY Hussain, Shabir; Yu, Yang; Ayoub, Muhammad; Khan, Akmal; Rehman, Rukhshanda; Wahid, Junaid Abdul; Hou, Weiyan IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19 APPLIED SCIENCES-BASEL English Article COVID-19 rapid screening; face mask detection; IoT; deep learning The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19. [Hussain, Shabir; Yu, Yang; Wahid, Junaid Abdul; Hou, Weiyan] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China; [Hussain, Shabir; Ayoub, Muhammad] NCBA&E, Dept Comp Sci, Rahim Yar Khan 64200, Pakistan; [Yu, Yang] Univ Duisburg Essen, Distributed Syst Grp, D-47050 Duisburg, Germany; [Khan, Akmal] Islamia Univ Bahawalpur, Dept Data Sci, Bahawalpur 63100, Pakistan; [Rehman, Rukhshanda] Islamia Univ Bahawalpur, Dept Zool, Bahawalpur 63100, Pakistan Zhengzhou University; University of Duisburg Essen; Islamia University of Bahawalpur; Islamia University of Bahawalpur Hou, WY (corresponding author), Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China. shabir@gs.zzu.edu.cn; yang.yu@uni-due.de; ayoubncbae@gmail.com; akmal.shahbaz@iub.edu.pk; rukhshandazoologist@gmail.com; junaid.a.wahid@gs.zzu.edu.cn; iewyhou@zzu.edu.cn Hussain, Shabir/GQB-0376-2022; Yu, Yang/AAG-7823-2021; Ayoub, Muhammad/HNB-3652-2023 Khan, Akmal/0000-0003-3636-8053; Hussain, Shabir/0000-0002-8225-3647; Wahid, Junaid/0000-0003-1067-7691; Yu, Yang/0000-0003-1895-9722 43 21 22 3 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel APR 2021.0 11.0 8 3495 10.3390/app11083495 0.0 27 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics RS8FG gold 2023-03-23 WOS:000644008200001 0
J Oveneke, MC; Gonzalez, I; Enescu, V; Jiang, DM; Sahli, H Oveneke, Meshia Cedric; Gonzalez, Isabel; Enescu, Valentin; Jiang, Dongmei; Sahli, Hichem Leveraging the Bayesian Filtering Paradigm for Vision-Based Facial Affective State Estimation IEEE TRANSACTIONS ON AFFECTIVE COMPUTING English Article Facial expressions; affective state estimation; probabilistic reasoning; multiple instance regression; Hausdorff distance; sparse Gaussian processes; variational inference; regularized least-squares; Kalman filtering CORE AFFECT; EMOTION; EXPRESSIONS Estimating a person's affective state from facial information is an essential capability for social interaction. Automatizing such a capability has therefore increasingly driven multidisciplinary research for the past decades. At the heart of this issue are very challenging signal processing and artificial intelligence problems driven by the inherent complexity of human affect. We therefore propose a principled framework for designing automated systems capable of continuously estimating the human affective state from an incoming stream of images. First, we model human affect as a dynamical system and define the affective state in terms of valence, arousal and their higher-order derivatives. We then pose the affective state estimation problem as a Bayesian filtering problem and provide a solution based on Kalman filtering (KF) for probabilistic reasoning over time, combined with multiple instance sparse Gaussian processes (MI-SGP) for inferring affect-related measurements from image sequences. We quantitatively and qualitatively evaluate our proposed framework on the AVEC 2012 and AVEC 2014 benchmark datasets and obtain state-of-the-art results using the baseline features as input to our MI-SGP-KF model. We therefore believe that leveraging the Bayesian filtering paradigm can pave the way for further enhancing the design of automated systems for affective state estimation. [Oveneke, Meshia Cedric; Gonzalez, Isabel; Enescu, Valentin; Sahli, Hichem] VUB, Dept Elect Informat ETRO, VUB NPU Joint AVSP Res Lab, Pl Laan 2, B-1050 Brussels, Belgium; [Jiang, Dongmei] NPU, Shaanxi Key Lab Speech & Image Informat Proc, VUB NPU Joint AVSP Res Lab, Youyo Xilu 127, Xian 710072, Shaanxi, Peoples R China; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium Vrije Universiteit Brussel; Northwestern Polytechnical University; IMEC Oveneke, MC (corresponding author), VUB, Dept Elect Informat ETRO, VUB NPU Joint AVSP Res Lab, Pl Laan 2, B-1050 Brussels, Belgium. mcovenek@etrovub.be; igonzale@etrovub.be; venescu@etrovub.be; jiangdm@nwpu.edu.cn; hsahli@etrovub.be Sahli, Hichem/0000-0002-1774-2970 Agency for Innovation by Science and Technology in Flanders (IWT) [131814]; VUB Interdisciplinary Research Program through the EMO-App project; National Natural Science Foundation of China [61273265] Agency for Innovation by Science and Technology in Flanders (IWT)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); VUB Interdisciplinary Research Program through the EMO-App project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the Agency for Innovation by Science and Technology in Flanders (IWT)-PhD grant nr. 131814, the VUB Interdisciplinary Research Program through the EMO-App project and the National Natural Science Foundation of China (grant 61273265). 53 5 5 0 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3045 IEEE T AFFECT COMPUT IEEE Trans. Affect. Comput. OCT-DEC 2018.0 9.0 4 463 477 10.1109/TAFFC.2016.2643661 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science HC6NJ Bronze 2023-03-23 WOS:000451918200006 0
J Rashad, A; Kamel, S; Jurado, F; Abdel-Nasser, M; Mahmoud, K Rashad, Ahmed; Kamel, Salah; Jurado, Francisco; Abdel-Nasser, Mohamed; Mahmoud, Karar ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms ELECTRIC POWER COMPONENTS AND SYSTEMS English Article squirrel cage induction generators; doubly fed induction generators; combined wind farm; STATCOM; artificial neural network; multiobjective genetic algorithm; whale optimization algorithm RIDE-THROUGH ENHANCEMENT; POWER-SYSTEM; STABILITY ENHANCEMENT; DFIG; OPTIMIZATION; TURBINES; SVC; GA Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA. [Rashad, Ahmed; Kamel, Salah; Abdel-Nasser, Mohamed; Mahmoud, Karar] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan, Egypt; [Rashad, Ahmed; Jurado, Francisco] Univ Jaen, Dept Elect Engn, Jaen, Spain; [Kamel, Salah] Chongqing Univ, Coll Elect Engn, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing, Peoples R China Egyptian Knowledge Bank (EKB); Aswan University; Universidad de Jaen; Chongqing University Jurado, F (corresponding author), Univ Jaen, Dept Elect Engn, EPS Linares, Jaen 23700, Spain. fjurado@ujaen.es Mahmoud, Karar/H-9716-2018; Abdel-Nasser, Mohamed/H-4321-2015; Jurado, Francisco/F-7546-2011 Mahmoud, Karar/0000-0002-6729-6809; Abdel-Nasser, Mohamed/0000-0002-1074-2441; Jurado, Francisco/0000-0001-8122-7415 45 16 16 1 4 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1532-5008 1532-5016 ELECTR POW COMPO SYS Electr. Power Compon. Syst. JAN 20 2019.0 47.0 1-2 10 26 10.1080/15325008.2019.1570052 0.0 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering HZ3JT 2023-03-23 WOS:000468745300002 0
J Zhang, ZY; Cheng, HX; Hong, XM; Di Narzo, AF; Franzen, O; Peng, SN; Ruusalepp, A; Kovacice, JC; Bjorkegren, JLM; Wang, XB; Hao, K Zhang, Zhongyang; Cheng, Haoxiang; Hong, Xiumei; Di Narzo, Antonio F.; Franzen, Oscar; Peng, Shouneng; Ruusalepp, Arno; Kovacice, Jason C.; Bjorkegren, Johan L. M.; Wang, Xiaobin; Hao, Ke EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data NUCLEIC ACIDS RESEARCH English Article GENOME-WIDE ASSOCIATION; HIDDEN-MARKOV MODEL; STRUCTURAL VARIATION; COMPLEX TRAITS; DISCOVERY; POLYMORPHISM; PLEIOTROPY; MULTIPLE; DISEASES The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases. [Zhang, Zhongyang; Cheng, Haoxiang; Di Narzo, Antonio F.; Peng, Shouneng; Bjorkegren, Johan L. M.; Hao, Ke] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA; [Zhang, Zhongyang; Cheng, Haoxiang; Di Narzo, Antonio F.; Peng, Shouneng; Bjorkegren, Johan L. M.; Hao, Ke] Icahn Sch Med Mt Sinai, Icahn Inst Genom & Multiscale Biol, New York, NY 10029 USA; [Hong, Xiumei; Wang, Xiaobin] Johns Hopkins Univ Bloomberg Sch Publ Hlth, Ctr Early Life Origins Dis, Dept Populat Family & Reprod Hlth, Baltimore, MD 21205 USA; [Franzen, Oscar; Bjorkegren, Johan L. M.] Karolinska Univ Jukhuset, Integrated Cardio Metab Ctr, Karolinska Inst, Huddinge, Sweden; [Ruusalepp, Arno] Tartu Univ Hosp, Dept Cardiac Surg, Tartu, Estonia; [Kovacice, Jason C.] Icahn Sch Med Mt Sinai, Cardiovasc Res Ctr, New York, NY 10029 USA; [Wang, Xiaobin] Johns Hopkins Univ, Sch Med, Dept Pediat, Div Gen Pediat & Adolescent Med, Baltimore, MD 21205 USA; [Hao, Ke] Tongji Univ, Peoples Hosp 10, Shanghai 200072, Peoples R China; [Hao, Ke] Tongji Univ, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China Icahn School of Medicine at Mount Sinai; Icahn School of Medicine at Mount Sinai; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Karolinska Institutet; Icahn School of Medicine at Mount Sinai; Johns Hopkins University; Tongji University; Tongji University Hao, K (corresponding author), Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA.;Hao, K (corresponding author), Icahn Sch Med Mt Sinai, Icahn Inst Genom & Multiscale Biol, New York, NY 10029 USA.;Hao, K (corresponding author), Tongji Univ, Peoples Hosp 10, Shanghai 200072, Peoples R China.;Hao, K (corresponding author), Tongji Univ, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China. ke.hao@mssm.edu Bjorkegren, Johan/HMV-5160-2023 Kovacic, Jason/0000-0003-4555-769X; Ruusalepp, Arno/0000-0001-5816-8613 National Institutes of Health [1R41DA042464-01, 1U01HD079068, 1R01ES029212-01, R01HL125863]; National Natural Science Foundation of China [91643201, 21876134]; Ministry of Science and Technology of China [2016YFC0206507]; Transatlantic Networks of Excellence Award fromFoundation Leducq [12CVD02]; Bunning Family Food Allergy Project/Food Allergy Initiative; Sacks Family Foundation Fund; Food Allergy Research and Education (FARE); National Institute of Allergy and Infectious Diseases [U01AI090727, R56AI080627, R21AI088609]; Icahn School of Medicine at Mount Sinai Internal Research Fund National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of China(Ministry of Science and Technology, China); Transatlantic Networks of Excellence Award fromFoundation Leducq; Bunning Family Food Allergy Project/Food Allergy Initiative; Sacks Family Foundation Fund; Food Allergy Research and Education (FARE); National Institute of Allergy and Infectious Diseases(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Allergy & Infectious Diseases (NIAID)); Icahn School of Medicine at Mount Sinai Internal Research Fund National Institutes of Health [1R41DA042464-01, 1U01HD079068, 1R01ES029212-01, R01HL125863]; National Natural Science Foundation of China [91643201, 21876134]; Ministry of Science and Technology of China [2016YFC0206507]; Transatlantic Networks of Excellence Award fromFoundation Leducq [12CVD02]. The FA study is supported in part by the Bunning Family Food Allergy Project/Food Allergy Initiative, Sacks Family Foundation Fund, Food Allergy Research and Education (FARE) and the National Institute of Allergy and Infectious Diseases [U01AI090727, R56AI080627 and R21AI088609, PI, Xiaobin Wang]. Funding for open access charge: Icahn School of Medicine at Mount Sinai Internal Research Fund. 43 12 12 2 12 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0305-1048 1362-4962 NUCLEIC ACIDS RES Nucleic Acids Res. APR 23 2019.0 47.0 7 e39 10.1093/nar/gkz068 0.0 13 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology HY2PU 30722045.0 gold, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000467965900003 0
J He, CX; Duan, L; Zheng, HR; Song, LL; Huang, ML He, Chengxin; Duan, Lei; Zheng, Huiru; Song, Linlin; Huang, Menglin An explainable framework for drug repositioning from disease information network NEUROCOMPUTING English Article Drug repositioning; Graph neural network; Interpretable prediction; Disease information network PREDICTION; DOPAMINE; MATRIX; GENE Exploring efficient and high-accuracy computational drug repositioning methods has become a popular and attractive topic in drug development. This technology can systematically identify potential drugdisease interactions, which could greatly alleviate the pressures from the high cost and long period taken by traditional drug research and discovery. However, plenty of current computational drug repositioning approaches lack interpretability in predicting drug-disease associations, which will not be friendly to their subsequent in-depth research. To this end, we hereby propose a novel computational framework, called EDEN, for exploring explainable drug repositioning from the disease information network (DIN). EDEN is a graph neural network framework that learns the local semantics and global structure of the DIN, and models the drugdisease associations into the DIN by maximizing the mutual information of both and an end-to-end manner. In this way, the learned biomedical entity and link embeddings are enabled to retain the ability to drug repositioning with the semantical structure of external knowledge, thereby making interpretation possible. Meanwhile, we also propose a matching score based on the final embeddings to generate the predictive drug repositioning explanation. Empirical results on the real-world dataset show that EDEN outperforms other state-of-the-art baselines on most of the metrics. Further studies reveal the effectiveness of the explainability of our approach. CO 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [He, Chengxin; Duan, Lei; Huang, Menglin] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China; [He, Chengxin; Duan, Lei] Sichuan Univ, Med X Ctr Informat, Chengdu 610065, Peoples R China; [Zheng, Huiru] Ulster Univ, Sch Comp, Coleraine BT37 0QB, North Ireland; [Song, Linlin] Sichuan Univ, Dept Ultrasound, West China Hosp, Chengdu 610041, Peoples R China; [Song, Linlin] Sichuan Univ, Frontiers Sci Ctr Dis Related Mol Network, West China Hosp, Chengdu 610041, Peoples R China Sichuan University; Sichuan University; Ulster University; Sichuan University; Sichuan University Duan, L (corresponding author), Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China.;Zheng, HR (corresponding author), Ulster Univ, Sch Comp, Coleraine BT37 0QB, North Ireland. leiduan@scu.edu.cn; h.zheng@ulster.ac.uk National Natural Science Foundation of China [61972268]; Sichuan Science and Technology Program [2020YFG0034]; Med-X Center for Informatics Funding Project of SCU [YGJC001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program; Med-X Center for Informatics Funding Project of SCU This work was supported in part by the National Natural Science Foundation of China (61972268) , the Sichuan Science and Technology Program (2020YFG0034) , and the Med-X Center for Informatics Funding Project of SCU (YGJC001) . 60 1 1 11 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 28 2022.0 511.0 247 258 10.1016/j.neucom.2022.09.063 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4S2DD Green Published, hybrid 2023-03-23 WOS:000857257300003 0
J Liu, JJ; Fang, WL; Love, PED; Hartmann, T; Luo, HB; Wang, LL Liu, Jiajing; Fang, Weili; Love, Peter E. D.; Hartmann, Timo; Luo, Hanbin; Wang, Lulu Detection and location of unsafe behaviour in digital images: A visual grounding approach ADVANCED ENGINEERING INFORMATICS English Article Computer vision; Deep learning; Natural language processing; Visual grounding DEEP; WORKERS; FALLS Using computer vision and deep learning (e.g., Convolutional Neural Networks) to automatically recognise unsafe behaviour from digital images can help managers identify and respond quickly to such actions and mitigate an adverse event. However, there has been a tendency for computer vision studies in construction to focus solely on detecting unsafe behaviour (i.e., object detection) or the regions of interest with pre-defined labels. Moreover, such approaches have been unable to consider rich semantic information among multiple unsafe actions in a digital image. The research we present in this paper uses a safety rule query to determine and locate several unsafe behaviours in a digital image by employing a visual grounding approach. Our approach consists of: (1) visual and text feature extraction, (2) recursive sub-query, and (3) generation of the bounding box. We validate our approach by conducting an experiment to demonstrate it is effectiveness. The results from an experimental study demonstrate an average precision, recall, and F1-score were 0.55, 0.85, and 0.65, respectively, suggesting our approach can accurately identify and locate different types of unsafe behaviours from digital images acquired from a construction site. [Liu, Jiajing; Luo, Hanbin; Wang, Lulu] Huazhong Univ Sci & Technol, Sch Civil Engn & Hydraul Engn, Wuhan 430074, Hubei, Peoples R China; [Fang, Weili; Hartmann, Timo] Tech Univ Berlin, Dept Civil & Bldg Syst, Gustav Meyer Allee 25, D-13156 Berlin, Germany; [Love, Peter E. D.] Curtin Univ, Sch Civil & Mech Engn, GPO Box U1987, Perth, WA 6845, Australia Huazhong University of Science & Technology; Technical University of Berlin; Curtin University Fang, WL (corresponding author), Tech Univ Berlin, Dept Civil & Bldg Syst, Gustav Meyer Allee 25, D-13156 Berlin, Germany. weili.fang@protonmail.com Alexander von Humboldt Stiftung/Foundation; National Natural Science Foundation of China [51978302, 71732001, U21A20151] Alexander von Humboldt Stiftung/Foundation(Alexander von Humboldt Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to acknowledge the financial support pro- vided by Alexander von Humboldt Stiftung/Foundation, the National Natural Science Foundation of China (Grant No. 51978302, 71732001, U21A20151) . We also would like to thank the three anonymous re- viewers and Mr Pei Yan for their constructive and insightful comments on earlier versions of this manuscript. 58 1 1 26 26 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1474-0346 1873-5320 ADV ENG INFORM Adv. Eng. Inform. AUG 2022.0 53.0 101688 10.1016/j.aei.2022.101688 0.0 JUL 2022 11 Computer Science, Artificial Intelligence; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5A0LH 2023-03-23 WOS:000862587700002 0
J Yeung, AWK; Kulnik, ST; Parvanov, ED; Fassl, A; Eibensteiner, F; Volkl-Kernstock, S; Kletecka-Pulker, M; Crutzen, R; Gutenberg, J; Hoppchen, I; Niebauer, J; Smeddinck, JD; Willschke, H; Atanasov, AG Yeung, Andy Wai Kan; Kulnik, Stefan Tino; Parvanov, Emil D.; Fassl, Anna; Eibensteiner, Fabian; Voelkl-Kernstock, Sabine; Kletecka-Pulker, Maria; Crutzen, Rik; Gutenberg, Johanna; Hoeppchen, Isabel; Niebauer, Josef; Smeddinck, Jan David; Willschke, Harald; Atanasov, Atanas G. Research on Digital Technology Use in Cardiology: Bibliometric Analysis JOURNAL OF MEDICAL INTERNET RESEARCH English Article cardiovascular; heart; hypertension; atrial fibrillation; cardiopulmonary resuscitation; electrocardiography; photoplethysmography; wearable device; digital health; mHealth; cardiology; cardiac; health application 3-DIMENSIONAL DOPPLER-ECHOCARDIOGRAPHY; HEALTH-CARE; QUANTIFIED SELF; STROKE VOLUME; BIG-DATA; MEDICINE; INTERVENTIONS; LEGAL; COMPUTER; HISTORY Background: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area. Objective: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field. Methods: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization. Results: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues. Conclusions: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth. [Yeung, Andy Wai Kan] Univ Hong Kong, Fac Dent, Div Oral & Maxillofacial Radiol, Appl Oral Sci & Community Dent Care, Hong Kong, Peoples R China; [Yeung, Andy Wai Kan; Parvanov, Emil D.; Fassl, Anna; Eibensteiner, Fabian; Voelkl-Kernstock, Sabine; Kletecka-Pulker, Maria; Willschke, Harald; Atanasov, Atanas G.] Med Univ Vienna, Ludwig Boltzmann Inst Digital Hlth & Patient Safe, Spitalgasse 23,Bauteil 86,2 Stock, A-1090 Vienna, Austria; [Kulnik, Stefan Tino; Crutzen, Rik; Gutenberg, Johanna; Hoeppchen, Isabel; Niebauer, Josef; Smeddinck, Jan David] Ludwig Boltzmann Inst Digital Hlth & Prevent, Salzburg, Austria; [Parvanov, Emil D.] Med Univ Varna, Dept Translat Stem Cell Biol, Res Inst, Varna, Bulgaria; [Eibensteiner, Fabian] Med Univ Vienna, Comprehens Ctr Pediat, Dept Pediat & Adolescent Med, Div Pediat Nephrol & Gastroenterol, Vienna, Austria; [Kletecka-Pulker, Maria] Univ Vienna, Inst Eth & Law Med, Vienna, Austria; [Crutzen, Rik; Gutenberg, Johanna] Maastricht Univ, Care & Publ Hlth Res Inst, Dept Hlth Promot, Maastricht, Netherlands; [Hoeppchen, Isabel] Paris Lodron Univ Salzburg, Ctr Human Comp Interact, Salzburg, Austria; [Niebauer, Josef] Paracelsus Med Univ Salzburg, Univ Inst Sports Med Prevent & Rehabil, Salzburg, Austria; [Niebauer, Josef] REHA Zentrum Salzburg, Salzburg, Austria; [Willschke, Harald] Med Univ Vienna, Dept Anaesthesia Intens Care Med & Pain Med, Vienna, Austria; [Atanasov, Atanas G.] Polish Acad Sci, Inst Genet & Anim Biotechnol, Jastrzebiec, Poland University of Hong Kong; Medical University of Vienna; Medical University Varna; Medical University of Vienna; University of Vienna; Maastricht University; Maastricht University Medical Centre (MUMC); Salzburg University; Paracelsus Private Medical University; Medical University of Vienna; Institute of Genetics & Animal Biotechnology, Polish Academy of Sciences; Polish Academy of Sciences Atanasov, AG (corresponding author), Med Univ Vienna, Ludwig Boltzmann Inst Digital Hlth & Patient Safe, Spitalgasse 23,Bauteil 86,2 Stock, A-1090 Vienna, Austria. atanas.atanasov@univie.ac.at Yeung, Andy Wai Kan/M-4332-2019; Atanasov, Atanas/C-5535-2013 Yeung, Andy Wai Kan/0000-0003-3672-357X; Teufel, Anna/0000-0001-6850-6532; Willschke, Harald/0000-0002-9093-7563; Gutenberg, Johanna/0000-0002-6130-701X; Hoppchen, Isabel/0000-0002-7725-766X; Eibensteiner, Fabian/0000-0001-5012-3082; Atanasov, Atanas/0000-0003-2545-0967 127 9 9 24 29 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA 1438-8871 J MED INTERNET RES J. Med. Internet Res. MAY 11 2022.0 24.0 5 e36086 10.2196/36086 0.0 17 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics 1N3WW 35544307.0 Green Accepted, gold 2023-03-23 WOS:000800590500001 0
S Xu, XL; Resconi, G; Xu, GL Nguyen, NT; Kowalczyk, R Xu, Xiaolin; Resconi, Germano; Xu, Guanglin Dynamic Database by Inconsistency and Morphogenetic Computing TRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE XXII Lecture Notes in Computer Science English Article; Book Chapter Entity-Relationship model; Morphogenetic computing; Dynamical process; Inconsistency; Compensation REPRESENTATION; MODEL Since Peter Chen published the article Entity-Relationship Modeling in 1976, Entity-Relationship database has become a hot spot for research. With the advent of the big data, it appears that Entity-Relationship database is substituted for attribute reduction map structure. In the big data we have no evidence of the relationship but only of attributes and maps. In this paper we give an attribute representation of the relationship. In fact we assume that any entity can be in two different attributes (states) with two different values. One is the attribute that sends a message that we denote as e(1) and the other is to receive the message that we denote as e(2). The values of the attributes are the names of the entities. A relationship is a superposition ae(1) + be(2) of the two states. When we change the values of the states we change the database. When we change the two states in the same way we have isomorphism among database, and when we change the two states in different way we have isomorphism with distortion (homotopic transformation). Given a set of independent data base we can generate (compute) all the other data base in a dynamical way. In this way we can reduce the database that we must memorize. Because we are interested in the generation of the form (morphology) of database we denote this new model of computation as morphogenetic computing. [Xu, Xiaolin] Shanghai Second Polytech Univ, Coll Int Vocat Educ, Shanghai, Peoples R China; [Resconi, Germano] Catholic Univ, Dept Math & Phys, Brescia, Italy; [Xu, Guanglin] Shanghai Lixin Univ Commerce, Coll Math & Informat, Shanghai, Peoples R China Shanghai Polytechnic University; Catholic University of the Sacred Heart; Shanghai Lixin University of Accounting & Finance Resconi, G (corresponding author), Catholic Univ, Dept Math & Phys, Brescia, Italy. xlxu2001@163.com; resconi@speedyposta.it; glxu@outlook.com Resconi, Germano/AAE-7652-2020 11 0 0 0 0 SPRINGER INT PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 978-3-662-49619-0; 978-3-662-49618-3 LECT NOTES COMPUT SC 2016.0 9655.0 189 204 10.1007/978-3-662-49619-0_10 0.0 10.1007/978-3-662-49619-0 16 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Book Citation Index – Science (BKCI-S) Computer Science BF5FQ 2023-03-23 WOS:000381943200011 0
J Li, Y; Chen, SY; Rapakoulia, T; Kuwahara, H; Yip, KY; Gao, X Li, Yu; Chen, Siyuan; Rapakoulia, Trisevgeni; Kuwahara, Hiroyuki; Yip, Kevin Y.; Gao, Xin Deep learning identifies and quantifies recombination hotspot determinants BIOINFORMATICS English Article MEIOTIC RECOMBINATION; PRDM9; LANDSCAPE; TRANSCRIPTION; INSTABILITY; CROSSOVERS; REVEALS; MOTIFS Motivation: Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we propose a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes and species. Results: RHSNet can significantly outperform other sequence-based methods on multiple datasets across different species, sexes and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification and GC content. Further cross-sex, cross-population and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs. [Li, Yu; Yip, Kevin Y.] Chinese Univ Hong Kong CUHK, Dept Comp Sci & Engn CSE, Hong Kong 999077, Peoples R China; [Li, Yu; Chen, Siyuan; Kuwahara, Hiroyuki; Gao, Xin] King Abdullah Univ Sci & Technol KAUST, Comp Sci Program, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia; [Li, Yu; Chen, Siyuan; Kuwahara, Hiroyuki; Gao, Xin] King Abdullah Univ Sci & Technol, KAUST Computat Biosci Res Ctr CBRC, Thuwal 239556900, Saudi Arabia; [Li, Yu] CUHK Shenzhen Res Inst, Shenzhen 518057, Peoples R China; [Rapakoulia, Trisevgeni] Max Planck Inst Mol Genet, D-14195 Berlin, Germany King Abdullah University of Science & Technology; King Abdullah University of Science & Technology; Max Planck Society Li, Y (corresponding author), Chinese Univ Hong Kong CUHK, Dept Comp Sci & Engn CSE, Hong Kong 999077, Peoples R China.;Li, Y; Gao, X (corresponding author), King Abdullah Univ Sci & Technol KAUST, Comp Sci Program, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia.;Li, Y; Gao, X (corresponding author), King Abdullah Univ Sci & Technol, KAUST Computat Biosci Res Ctr CBRC, Thuwal 239556900, Saudi Arabia.;Li, Y (corresponding author), CUHK Shenzhen Res Inst, Shenzhen 518057, Peoples R China. liyu@cse.cuhk.edu.hk; xin.gao@kaust.edu.sa Gao, Xin/D-5487-2013 Gao, Xin/0000-0002-7108-3574; Yip, Kevin/0000-0001-5516-9944; Chen, Siyuan/0000-0002-2382-2248; Li, Yu/0000-0002-3664-6722 KAUST Office of Sponsored Research (OSR) [BAS/1/1624-01, FCC/1/1976-23-01, FCC/1/1976-2601, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/447301-01, URF/1/4098-01-01, REI/1/4742-01-01] KAUST Office of Sponsored Research (OSR) This work was supported by the KAUST Office of Sponsored Research (OSR) under award numbers [BAS/1/1624-01, FCC/1/1976-23-01, FCC/1/1976-2601, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/447301-01, URF/1/4098-01-01, REI/1/4742-01-01]. 44 0 0 2 4 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics APR 23 2022.0 38.0 10 2683 2691 10.1093/bioinformatics/btac234 0.0 APR 2022 9 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics 1F5JR 35561158.0 Green Published, Green Accepted, Green Submitted, hybrid 2023-03-23 WOS:000786506200001 0
J Waseem, M; Lin, ZZ; Ding, Y; Wen, FS; Liu, SY; Palu, I Waseem, Muhammad; Lin, Zhenzhi; Ding, Yi; Wen, Fushuan; Liu, Shengyuan; Palu, Ivo Technologies and Practical Implementations of Air-conditioner Based Demand Response JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY English Article Load management; Power demand; Smart grids; Heat sinks; Air-conditioner (AC); commercial load; cooling demand; demand response (DR); energy consumption; residen-tialload DIRECT LOAD CONTROL; MODEL-PREDICTIVE CONTROL; THERMOSTATICALLY CONTROLLED LOADS; ARTIFICIAL NEURAL-NETWORK; ENERGY-CONSUMPTION; CONTROL STRATEGY; THERMAL COMFORT; COMMERCIAL BUILDINGS; SIDE MANAGEMENT; HVAC SYSTEMS Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand of end-users. Demand response (DR) has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies. The percentage of the air-conditioner (AC) load over the total load demand in a building is usually very high. Therefore, controlling the power demand of ACs is one of significant measures for implementing DR. In this paper, the increasing development of ACs, and their impacts on power demand are firstly introduced, with an overview of possible DR programs. Then, a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out. Next, comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented. Finally, the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems. [Waseem, Muhammad; Lin, Zhenzhi; Ding, Yi; Wen, Fushuan; Liu, Shengyuan] Zhejiang Univ, Sch Elect Engn, Hangzhou 310027, Peoples R China; [Wen, Fushuan; Palu, Ivo] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, EE-19086 Tallinn, Estonia Zhejiang University; Tallinn University of Technology Lin, ZZ (corresponding author), Zhejiang Univ, Sch Elect Engn, Hangzhou 310027, Peoples R China. mwaseem@zju.edu.cn; zhenzhi.lin@gmail.com; yiding@zju.edu.cn; fushuan.wen@gmail.com; eelsy@zju.edu.cn; ivo.palu@taltech.ee Wen, Fushuan/D-2470-2019; Wen, Fushuan/ABG-4218-2020; Waseem, M./AAU-5241-2021 Wen, Fushuan/0000-0002-6838-2602; Wen, Fushuan/0000-0002-6838-2602; Waseem, M./0000-0002-0923-1476 National Key R&D Program of China [2016YFB0900100]; National Natural Science Foundation of China [51777185]; Natural Science Foundation of Zhejiang Province [LY17E070003] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province) This work was jointly supported by National Key R&D Program of China (No. 2016YFB0900100), National Natural Science Foundation of China (No. 51777185), and Natural Science Foundation of Zhejiang Province (No. LY17E070003). 278 26 26 15 36 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2196-5625 2196-5420 J MOD POWER SYST CLE J. Mod. Power Syst. Clean Energy NOV 2021.0 9.0 6 1395 1413 10.35833/MPCE.2019.000449 0.0 19 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering XD5JA gold 2023-03-23 WOS:000722743900016 0
J Patel, CI; Labana, D; Pandya, S; Modi, K; Ghayvat, H; Awais, M Patel, Chirag I.; Labana, Dileep; Pandya, Sharnil; Modi, Kirit; Ghayvat, Hemant; Awais, Muhammad Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences SENSORS English Article moving object detection; surveillance system; support vector machines; histogram of oriented gradient; meta-cognitive neural network (MCNN); multiple kernel learning DESCRIPTORS Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors. [Patel, Chirag I.; Labana, Dileep] Parul Univ, Parul Inst Technol, Comp Sci & Engn, Vadodara 391760, India; [Pandya, Sharnil] Symbiosis Int Deemed Univ, Symbiosis Ctr Appl Artificial Intelligence, Pune 412115, Maharashtra, India; [Pandya, Sharnil] Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune 412115, Maharashtra, India; [Modi, Kirit] Sankalchand Patel Univ, Sankalchand Patel Coll Engn, Visnagar 384315, India; [Ghayvat, Hemant] Linnaeus Univ, Fac Technol, Comp Sci Dept, S-35195 Vaxjo, Sweden; [Awais, Muhammad] Fudan Univ, Sch Informat Sci & Technol, Dept Elect Engn, Ctr Intelligent Med Elect, Shanghai 200433, Peoples R China Parul University; Symbiosis International University; Symbiosis International University; Symbiosis Institute of Technology (SIT); Linnaeus University; Fudan University Patel, CI (corresponding author), Parul Univ, Parul Inst Technol, Comp Sci & Engn, Vadodara 391760, India. chirag.patel8781@paruluniversity.ac.in; dileepkumar.labana@gujgov.edu.in; sharnil.pandya@sitpune.edu.in; kjmodi.fet@spu.ac.in; hemant.ghayvat@lnu.se; 17110720061@fudan.edu.cn Patel, Chirag/AAE-3975-2019; ghayvat, hemant/E-5873-2015; MODI, KIRITKUMAR/AAU-4424-2021; Pandya, Sharnil/P-7479-2019; Institute of Technology, Symbiosis Institute of Technology Symbiosis/AAC-5693-2019 Patel, Chirag/0000-0001-8280-1140; MODI, KIRITKUMAR/0000-0001-6462-059X; Pandya, Sharnil/0000-0002-4507-1844; 97 25 27 3 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2020.0 20.0 24 7299 10.3390/s20247299 0.0 32 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation PL6ML 33353248.0 Green Accepted, gold, Green Submitted 2023-03-23 WOS:000603233400001 0
J Liang, XN; Li, GX; Zhang, H; Nolan, E; Chen, FD Liang, Xiaoning; Li, Guoxin; Zhang, Hao; Nolan, Eimear; Chen, Fadong Firm performance and marketing analytics in the Chinese context: A contingency model JOURNAL OF BUSINESS RESEARCH English Article Marketing Analytics; Market Agility; Inter-departmental Coordination; Success Traps; Market Turbulence BIG DATA ANALYTICS; DYNAMIC CAPABILITIES; INTELLIGENCE; KNOWLEDGE; AMBIDEXTERITY; PERSISTENCE; MANAGEMENT; CREATION; AGILITY; SUCCESS Despite the growing prevalence of marketing analytics, concerns exist regarding how to realize the full potential of business analytics initiatives. This study explores how and under what conditions a firm's deployment of marketing analytics can influence its business performance. Analyzing survey data collected from 238 Chinese firms, this study confirms that marketing analytics positively influences a firm's market agility, leading to increased firm performance. Moreover, inter-departmental coordination and market turbulence are found to strengthen the positive effect of marketing analytics on market agility, while success traps hinder such effect. The findings suggest that firms need to align their marketing analytical activities with their internal practices and external market environment. [Liang, Xiaoning; Nolan, Eimear] Univ Dublin, Trinity Coll Dublin, Trinity Business Sch, D02 H308, Dublin 2, Ireland; [Li, Guoxin] Harbin Inst Technol, Sch Management, 13 Fayuan St, Harbin 150006, Peoples R China; [Zhang, Hao] Northeastern Univ, Sch Business Adm, Dept Mkt, Shenyang, Peoples R China; [Chen, Fadong] Zhejiang Univ, Sch Management, Hangzhou 310058, Peoples R China; [Chen, Fadong] Zhejiang Univ, Neuromanagement Lab, Hangzhou 310058, Peoples R China Trinity College Dublin; Harbin Institute of Technology; Northeastern University - China; Zhejiang University; Zhejiang University Zhang, H (corresponding author), Northeastern Univ, Sch Business Adm, Dept Mkt, Shenyang, Peoples R China. xiaoning.liang@tcd.ie; liguoxin@hit.edu.cn; hzhang@mail.neu.edu.cn; eimear.nolan@tcd.ie; fadongchen@zju.edu.cn Raharisoa, Emérancia/HOH-8694-2023 Nolan, Eimear/0000-0001-9282-8873; Zhang, Hao/0000-0002-0345-1814 National Natural Science Foundation of China, China [71831005, 72172030, 71803174]; Trinity Arts and Social Sciences Benefaction Fund, Ireland [RLIA020] National Natural Science Foundation of China, China(National Natural Science Foundation of China (NSFC)); Trinity Arts and Social Sciences Benefaction Fund, Ireland We would like to thank the associate editor, Professor Charles Dennis, and three anonymous reviewers for their constructive comments and feedback. We are also grateful for Professor Paul Coughlan, Professor Natasha Evers, and Ms Laura Kidd from Trinity College Dublin for proofreading our manuscript. This project is supported by the National Natural Science Foundation of China, China (Nos. 71831005, 72172030, & 71803174) and Trinity Arts and Social Sciences Benefaction Fund, Ireland (No. RLIA020). 54 4 4 16 30 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0148-2963 1873-7978 J BUS RES J. Bus. Res. MAR 2022.0 141.0 589 599 10.1016/j.jbusres.2021.11.061 0.0 JAN 2022 11 Business Social Science Citation Index (SSCI) Business & Economics 0F9CQ 2023-03-23 WOS:000777653400019 0
J Jiang, XY; Simon, M; Yang, Y; Denzler, J Jiang, Xiaoyan; Simon, Marcel; Yang, Yuan; Denzler, Joachim Multi-marker tracking for large-scale X-ray stereo video data SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Multi-object tracking; Data association; Directed acyclic graph; X-ray; Big data ASSOCIATION Analyzing large amounts of video data is one of the key challenges in the trend towards big data. In the field of medical research, for example, to analyze infected cardiac movements, stereo X-ray sequences of beating animal hearts implanted with radiopaque markers are recorded. As manual annotation of exact marker positions in large-scale recordings is time-consuming and infeasible, research on automatic tracking of multiple markers is a crucial task. We propose an efficient two-stage graph-based data association approach to tackle this problem. Difficulties of the sequences like 2D occlusions, low contrast, inhomogeneous movement, and inaccurate detections, are considered in the framework. Reconstructed 3D observations are modeled and connected using a weighted directed acyclic graph to obtain tracklets with high confidence via shortest path extraction. Afterwards, tracklets are linked into longer tracks by a tracklet graph in a similar manner while global features are adopted. The approach is validated on eight X-ray cardiac datasets of beating sheep hearts with various diseases. Outperforming standard tracking approaches, e.g. particle filter, the experimental results show a high accuracy comparable to human experts and efficiency in the meantime. The proposed approach is generic and can be directly applied to other video data as well. (C) 2017 Elsevier B.V. All rights reserved. [Jiang, Xiaoyan] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai, Peoples R China; [Simon, Marcel; Denzler, Joachim] Friedrich Schiller Univ Jena, Comp Vis Grp, Jena, Germany; [Yang, Yuan] Southeast Univ, Sch Instrument Sci & Engn, Nanjing, Jiangsu, Peoples R China Shanghai University of Engineering Science; Friedrich Schiller University of Jena; Southeast University - China Jiang, XY (corresponding author), Room 908,Longteng Rd 333, Shanghai 201620, Peoples R China. xiaoyan.jiang@outlook.com; marcel.simon@uni-jena.de; yangyuancsi@163.com; joachim.denzler@uni-jena.de National Natural Science Foundation of China (NSFC) [61461021]; Young Teacher Development Program of Shanghai [ZZGCD16008]; Local Colleges Faculty Construction of Shanghai [15590501300] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Young Teacher Development Program of Shanghai; Local Colleges Faculty Construction of Shanghai The work is supported by the following projects: National Natural Science Foundation of China (NSFC), Nr.: 61461021; Young Teacher Development Program of Shanghai, Nr.: ZZGCD16008; Local Colleges Faculty Construction of Shanghai, Nr.: 15590501300. 41 2 2 0 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. NOV 2017.0 59.0 SI 140 149 10.1016/j.image.2017.06.009 0.0 10 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering FO1FC 2023-03-23 WOS:000416501100016 0
J Lam, JH; Li, Y; Zhu, LZ; Umarov, R; Jiang, HL; Heliou, A; Sheong, FK; Liu, TY; Long, YK; Li, YF; Fang, L; Altman, RB; Chen, W; Huang, XH; Gao, X Lam, Jordy Homing; Li, Yu; Zhu, Lizhe; Umarov, Ramzan; Jiang, Hanlun; Heliou, Amelie; Sheong, Fu Kit; Liu, Tianyun; Long, Yongkang; Li, Yunfei; Fang, Liang; Altman, Russ B.; Chen, Wei; Huang, Xuhui; Gao, Xin A deep learning framework to predict binding preference of RNA constituents on protein surface NATURE COMMUNICATIONS English Article HUMAN ARGONAUTE-2; STRUCTURAL BASIS; RECOGNITION; DNA; TARGETS; SITES; SPECIFICITIES; MECHANISM; INSIGHT Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNA compete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins. [Lam, Jordy Homing; Li, Yu; Umarov, Ramzan; Long, Yongkang; Gao, Xin] King Abdullah Univ Sci & Technol, Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia; [Lam, Jordy Homing; Zhu, Lizhe; Sheong, Fu Kit; Huang, Xuhui] Hong Kong Univ Sci & Technol, Dept Chem, Hong Kong, Peoples R China; [Zhu, Lizhe] Chinese Univ Hong Kong Shenzhen, Sch Life & Hlth Sci, Warshel Inst Computat Biol, Shenzhen 518172, Guangdong, Peoples R China; [Jiang, Hanlun] Univ Washington, Dept Biochem, Seattle, WA 98195 USA; [Jiang, Hanlun] Univ Washington, Inst Prot Design, Seattle, WA 98195 USA; [Heliou, Amelie] Ecole Polytech, Lab Informat, Dept Comp Sci, Palaiseau, France; [Liu, Tianyun; Altman, Russ B.] Stanford Univ, Dept Med, Stanford, CA 94305 USA; [Liu, Tianyun; Altman, Russ B.] Stanford Univ, Dept Genet, Stanford, CA 94305 USA; [Liu, Tianyun; Altman, Russ B.] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA; [Long, Yongkang; Li, Yunfei; Fang, Liang; Chen, Wei] Southern Univ Sci & Technol, Dept Biol, Shenzhen 518055, Guangdong, Peoples R China; [Huang, Xuhui] Hong Kong Univ Sci & Technol, Div Biomed Engn, Hong Kong, Peoples R China; [Huang, Xuhui] Hong Kong Univ Sci & Technol, State Key Lab Mol Neurosci, Hong Kong, Peoples R China; [Huang, Xuhui] Hong Kong Univ Sci & Technol, Hong Kong Branch, Chinese Natl Engn Res Ctr Tissue Restorat & Recon, Hong Kong, Peoples R China; [Huang, Xuhui] Hong Kong Univ Sci & Technol, Inst Adv Study, Hong Kong, Peoples R China; [Huang, Xuhui] HKUST Shenzhen Res Inst, Hitech Pk, Shenzhen 518057, Peoples R China King Abdullah University of Science & Technology; Hong Kong University of Science & Technology; Chinese University of Hong Kong, Shenzhen; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Institut Polytechnique de Paris; Stanford University; Stanford University; Stanford University; Southern University of Science & Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology Gao, X (corresponding author), King Abdullah Univ Sci & Technol, Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia.;Huang, XH (corresponding author), Hong Kong Univ Sci & Technol, Dept Chem, Hong Kong, Peoples R China.;Chen, W (corresponding author), Southern Univ Sci & Technol, Dept Biol, Shenzhen 518055, Guangdong, Peoples R China.;Huang, XH (corresponding author), Hong Kong Univ Sci & Technol, Div Biomed Engn, Hong Kong, Peoples R China.;Huang, XH (corresponding author), Hong Kong Univ Sci & Technol, State Key Lab Mol Neurosci, Hong Kong, Peoples R China.;Huang, XH (corresponding author), Hong Kong Univ Sci & Technol, Hong Kong Branch, Chinese Natl Engn Res Ctr Tissue Restorat & Recon, Hong Kong, Peoples R China.;Huang, XH (corresponding author), Hong Kong Univ Sci & Technol, Inst Adv Study, Hong Kong, Peoples R China.;Huang, XH (corresponding author), HKUST Shenzhen Res Inst, Hitech Pk, Shenzhen 518057, Peoples R China. zhulizhe@chk.edu.cn; chenw@sustech.edu.cn; xuhuihuang@ust.hk; xin.gao@kaust.edu.sa Li, Yu/AAE-2502-2021; chen, wei/AFV-4999-2022; Huang, Xuhui/ABE-3207-2021; Fang, Liang/V-5019-2018; Gao, Xin/D-5487-2013 Li, Yu/0000-0002-3664-6722; Huang, Xuhui/0000-0002-7119-9358; Fang, Liang/0000-0003-4502-1756; Gao, Xin/0000-0002-7108-3574; Zhu, Lizhe/0000-0001-8252-7807; Long, Yongkang/0000-0003-0953-7325 KAUST [BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, FCS/1/4102-02-01, URF/1/3007-01]; Hong Kong Research Grant Council [HKUST C6009-15G, AoE/M-09/12, AoE/P705/16]; Innovation and Technology Commission [ITCPD/17-9, ITCCNERC14SC01]; Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government [KQTD20180411143432337, JCYJ20170307105752508]; Center for Computational Science and Engineering of Southern University of Science and Technology KAUST(King Abdullah University of Science & Technology); Hong Kong Research Grant Council(Hong Kong Research Grants Council); Innovation and Technology Commission; Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government; Center for Computational Science and Engineering of Southern University of Science and Technology We are grateful to Wei Wang for helpful discussions. Figure 1 was created by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST). This work was supported by grants from KAUST to X.G. (BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and FCS/1/4102-02-01) and funding from the KAUST to X.G. and X.H. (URF/1/3007-01). The Hong Kong Research Grant Council (HKUST C6009-15G, AoE/M-09/12, and AoE/P705/16) and Innovation and Technology Commission (ITCPD/17-9 and ITCCNERC14SC01) to X.H.; L.F., Y.F.L., and W.C. were supported by Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government (No. KQTD20180411143432337 and JCYJ20170307105752508). Part of bioinformatics analysis was supported by the Center for Computational Science and Engineering of Southern University of Science and Technology. 59 37 37 5 30 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. OCT 30 2019.0 10.0 4941 10.1038/s41467-019-12920-0 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics JI2EF 31666519.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000493275600015 0
C Alesiani, F; Yu, SJ; Yu, X Bailey, J; Miettinen, P; Koh, YS; Tao, D; Wu, X Alesiani, Francesco; Yu, Shujian; Yu, Xi Gated Information Bottleneck for Generalization in Sequential Environments 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021) IEEE International Conference on Data Mining English Proceedings Paper 21st IEEE International Conference on Data Mining (IEEE ICDM) DEC 07-10, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,US Natl Sci Fdn,Univ Auckland, Sch Comp Sci,IEEE Tech Comm Intelligent Informat,Google,Two Sigma Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution (OOD) detection. Meanwhile, we also establish the connection between ID theory and invariant causal representation learning, and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization (IRM) fails. [Alesiani, Francesco] NEC Labs Europe, Heidelberg, Germany; [Yu, Shujian] UiT Arctic Univ Norway, Tromso, Norway; [Yu, Shujian] Xi An Jiao Tong Univ, Xian, Peoples R China; [Yu, Xi] Univ Florida, Gainesville, FL 32611 USA NEC Corporation; UiT The Arctic University of Tromso; Xi'an Jiaotong University; State University System of Florida; University of Florida Alesiani, F (corresponding author), NEC Labs Europe, Heidelberg, Germany. Francesco.Alesiani@neclab.eu; yusj9011@gmail.com YU, XI/AGY-5521-2022 YU, XI/0000-0002-2029-1680; Alesiani, Francesco/0000-0003-4413-7247 46 2 2 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1550-4786 978-1-6654-2398-4 IEEE DATA MINING 2021.0 1 10 10.1109/ICDM51629.2021.00010 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS9GC Green Submitted 2023-03-23 WOS:000780454100001 0
J Abbasi, AA; Shamshirband, S; Al-qaness, MAA; Abbasi, A; AL-Jallad, NT; Mosavi, A Abbasi, Aaqif Afzaal; Shamshirband, Shahab; Al-qaness, Mohammed A. A.; Abbasi, Almas; AL-Jallad, Nashat T.; Mosavi, Amir Resource-Aware Network Topology Management Framework ACTA POLYTECHNICA HUNGARICA English Article cloud computing; big data; fog computing; software-defined; networking; network management; resource management; topology Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network's data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework. [Abbasi, Aaqif Afzaal] Fdn Univ, Dept Software Engn, Islamabad 44000, Pakistan; [Shamshirband, Shahab] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh 758307, Vietnam; [Shamshirband, Shahab] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh 758307, Vietnam; [Al-qaness, Mohammed A. A.] Wuhan Univ, Sch Comp Sci, Bayi Rd 299, Wuhan 430072, Peoples R China; [Abbasi, Almas] Int Islamic Univ, Dept Comp Sci, Islamabad 44000, Pakistan; [AL-Jallad, Nashat T.] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430079, Peoples R China; [AL-Jallad, Nashat T.] Palestine Tech Univ, Sch Comp Sci, Tulkarem 44864, Palestine; [Mosavi, Amir] Oxford Brookes Univ, Sch Built Environm, 5 Jack Straws Lane, Oxford OX3 0BP, England; [Mosavi, Amir] Obuda Univ, Kando Kalman Fac Elect Engn, Becsi Ut 94-96, H-1034 Budapest, Hungary; [Mosavi, Amir] Bauhaus Univ Weimar, Inst Struct Mech, Marienstr 15, D-99423 Weimar, Germany; [Mosavi, Amir] J Selye Univ, Dept Math & Informat, Hradna Str 21, Komarno 94501, Slovakia Ton Duc Thang University; Ton Duc Thang University; Wuhan University; International Islamic University, Pakistan; Wuhan University of Technology; Oxford Brookes University; Obuda University; Bauhaus-Universitat Weimar; J. Selye University Shamshirband, S (corresponding author), Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh 758307, Vietnam.;Shamshirband, S (corresponding author), Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh 758307, Vietnam. aagif.afzaal@fui.edu.pk; shahaboddin.shamshirnand@tdtu.edu.vn; alqaness@whu.edu.cn; almas.abbasi@iiu.edu.pk; jallad@whut.edu.cn; a.mosavi@brookes.ac.uk Abbasi, Aaqif Afzaal/AFG-9482-2022; S.Band, Shahab/ABI-7388-2020; Mosavi, Amir/I-7440-2018; Al-qaness, Mohammed/ABE-7552-2020 Abbasi, Aaqif Afzaal/0000-0002-9982-1321; S.Band, Shahab/0000-0002-8963-731X; Mosavi, Amir/0000-0003-4842-0613; Al-qaness, Mohammed/0000-0002-6956-7641 Hungarian State; European Union [EFOP-3.6.1-16-2016-00010, 20171.3.1-VKE-2017-00025] Hungarian State; European Union(European Commission) We acknowledge the financial support of this work by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project and the 20171.3.1-VKE-2017-00025 project. 24 3 3 0 1 BUDAPEST TECH BUDAPEST BECSI UT 96-B, BUDAPEST, H-1034, HUNGARY 1785-8860 ACTA POLYTECH HUNG Acta Polytech. Hung. 2020.0 17.0 4 89 101 13 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering LG6NM 2023-03-23 WOS:000528215300005 0
J Zhou, XS; Li, L; Chen, LQ; Liu, YQ; Cui, YF; Zhang, Y; Zhang, T Zhou, Xisheng; Li, Long; Chen, Longqian; Liu, Yunqiang; Cui, Yifan; Zhang, Yu; Zhang, Ting Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China FORESTS English Article urban forest; Sentinel-2A; LSMA; SVM TREE SPECIES CLASSIFICATION; GROWING STOCK VOLUME; LANDSAT-TM DATA; HYPERSPECTRAL DATA; TEMPERATE FOREST; NEURAL-NETWORK; GREEN SPACE; LAVA FLOWS; LIDAR DATA; VEGETATION Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination. [Zhou, Xisheng; Li, Long; Chen, Longqian; Liu, Yunqiang; Cui, Yifan; Zhang, Ting] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Daxue Rd 1, Xuzhou 221116, Jiangsu, Peoples R China; [Zhou, Xisheng; Li, Long; Chen, Longqian; Liu, Yunqiang; Cui, Yifan; Zhang, Ting] China Univ Min & Technol, Engn Res Ctr, Minist Educ Mine Ecol Restorat, Daxue Rd 1, Xuzhou 221116, Jiangsu, Peoples R China; [Li, Long] Vrije Univ Brussel, Dept Geog, Earth Syst Sci, Pl Laan 2, B-1050 Brussels, Belgium; [Zhang, Yu] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Shanghai Rd 101, Xuzhou 221116, Jiangsu, Peoples R China China University of Mining & Technology; China University of Mining & Technology; Vrije Universiteit Brussel; Jiangsu Normal University Li, L (corresponding author), China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Daxue Rd 1, Xuzhou 221116, Jiangsu, Peoples R China.;Li, L (corresponding author), China Univ Min & Technol, Engn Res Ctr, Minist Educ Mine Ecol Restorat, Daxue Rd 1, Xuzhou 221116, Jiangsu, Peoples R China.;Li, L (corresponding author), Vrije Univ Brussel, Dept Geog, Earth Syst Sci, Pl Laan 2, B-1050 Brussels, Belgium. xisheng.zhou@cumt.edu.cn; long.li@cumt.edu.cn; chenlq@cumt.edu.cn; yunqiang.liu@cumt.edu.cn; yifan.cui@cumt.edu.cn; yuzhang@cumt.edu.cn; js_t@163.com Zhang, Yu/GPW-6909-2022 Zhang, Yu/0000-0002-3613-8613; Li, Long/0000-0001-7763-1108; Zhou, Xisheng/0000-0003-3532-1333 Fundamental Research Funds for the Central Universities [2018ZDPY07] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was supported by the Fundamental Research Funds for the Central Universities (Grant No.: 2018ZDPY07). 79 12 12 6 34 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4907 FORESTS Forests JUN 2019.0 10.0 6 478 10.3390/f10060478 0.0 19 Forestry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Forestry II6LI Green Published, gold 2023-03-23 WOS:000475305000018 0
J Zhang, HX; Li, F; Wang, J; Wang, ZL; Shi, L; Zhao, J; Sanin, C; Szczerbicki, E Zhang, Haoxi; Li, Fei; Wang, Juan; Wang, Zuli; Shi, Lei; Zhao, Jun; Sanin, Cesar; Szczerbicki, Edward Adding Intelligence to Cars Using the Neural Knowledge DNA CYBERNETICS AND SYSTEMS English Article Decisional DNA; deep learning; knowledge representation; neural networks; set of experience knowledge structure DECISIONAL DNA; INFORMATION; EXPERIENCE; SET In this paper, we propose a Neural Knowledge DNA (NK-DNA)-based framework that is capable of learning from the car's daily operations and reusing such learned knowledge in future tasks. The NK-DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers' classification based on their driving behaviors. The experimental data are collected via smartphone sensors. The initial results are presented, and the direction for our future research is defined. [Zhang, Haoxi; Li, Fei; Wang, Juan; Wang, Zuli; Shi, Lei; Zhao, Jun] Chengdu Univ Informat Technol, Coll Informat Secur Engn, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China; [Sanin, Cesar] Univ Newcastle, Sch Mech Engn, Newcastle, NSW, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Coll Informat Secur Engn, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China. haoxi@cuit.edu.cn Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912; Szczerbicki, Edward/0000-0001-7794-2862 Scientific Research Foundation of CUIT [KYTZ201422] Scientific Research Foundation of CUIT This work was supported as part of the Project KYTZ201422 by the Scientific Research Foundation of CUIT. 15 5 5 0 8 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. 2017.0 48.0 3 SI 267 273 10.1080/01969722.2016.1276780 0.0 7 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science EO7QM 2023-03-23 WOS:000396885200009 0
J Bogris, A; Mesaritakis, C; Deligiannidis, S; Li, P Bogris, Adonis; Mesaritakis, Charis; Deligiannidis, Stavros; Li, Pu Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS English Article Laser modes; Semiconductor lasers; Laser feedback; Cavity resonators; Optical feedback; Neuromorphics; Fabry-Perot; Optical communication; recurrent neural networks; laser modes; dispersive channels; neural network hardware CHAOS GENERATION; PERFORMANCE; WAVELENGTH; EQUATION We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of modes multiplies the number of virtual nodes and offers the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged to enhance classification performance. [Bogris, Adonis; Deligiannidis, Stavros] Univ West Attica, Dept Informat & Comp Engn, Athens 12243, Greece; [Mesaritakis, Charis] Univ Aegean, Dept Informat & Commun Syst Engn, Karlovassi Samos 83200, Greece; [Li, Pu] Taiyuan Univ Technol, Minist Educ, Key Lab Adv Transducers & Intelligent Control Sys, Taiyuan 030024, Peoples R China University of West Attica; University of Aegean; Taiyuan University of Technology Bogris, A (corresponding author), Univ West Attica, Dept Informat & Comp Engn, Athens 12243, Greece. abogris@uniwa.gr; cmesar@aegean.gr; sdeligiannid@uniwa.gr; lipu8603@126.com Bogris, Adonis/ABE-8583-2020; Deligiannidis, Stavros/AAB-9917-2020; Bogris, Adonis/O-4808-2018 Mesaritakis, Charis/0000-0002-0072-3116; Bogris, Adonis/0000-0002-5740-6285; Deligiannidis, Stavros/0000-0003-2786-2308 H2020 Project NEoteRIC [871330]; General Secretariat for Research and Technology (GSRT) [2247]; Hellenic Foundation for Research and Innovation (HFRI) H2020 Project NEoteRIC; General Secretariat for Research and Technology (GSRT)(Greek Ministry of Development-GSRT); Hellenic Foundation for Research and Innovation (HFRI) This work was supported in part by the H2020 Project NEoteRIC (871330), in part by the Hellenic Foundation for Research and Innovation (HFRI), and in part by the General Secretariat for Research and Technology (GSRT), under Grant 2247. 27 20 20 4 119 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1077-260X 1558-4542 IEEE J SEL TOP QUANT IEEE J. Sel. Top. Quantum Electron. MAR-APR 2021.0 27.0 2 7500307 10.1109/JSTQE.2020.3011879 0.0 7 Engineering, Electrical & Electronic; Quantum Science & Technology; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physics; Optics NF8DW Green Submitted 2023-03-23 WOS:000563524400001 0
J Hu, ZM; Dakos, V; Rieterk, M Hu, Zhongmin; Dakos, Vasili; Rieterk, Max Using functional indicators to detect state changes in terrestrial ecosystems TRENDS IN ECOLOGY & EVOLUTION English Review EARLY-WARNING SIGNALS; WATER-USE EFFICIENCY; PRIMARY PRODUCTIVITY; CATASTROPHIC SHIFTS; CARBON-DIOXIDE; SLOWING-DOWN; PRECIPITATION; VARIABILITY; GRADIENT; MODELS Indicators to predict ecosystem state change are urgently needed to cope with the degradation of ecosystem services caused by global change. With the development of new technologies for measuring ecosystem function with fine spatiotemporal resolution over broad areas, we are in the era of 'big data'. However, it is unclear how large, emerging datasets can be used to anticipate ecosystem state change. We propose the construction of indicators based on functional variables (flows) and state variables (pools) to predict future ecosystem state changes. The indicators identified here may be useful signals for doing so. In addition, functional indicators have explicit ecological meanings that can identify the ecological mechanism that is causing state changes, and can thus be used to improve ecosystem models. [Hu, Zhongmin] Hainan Univ, Key Lab Agroforestry Environm Proc & Ecol Regulat, Haikou 570228, Peoples R China; [Hu, Zhongmin] Southern Marine Sci & Engn Guangdong Lab, Zhuhai 519082, Guangdong, Peoples R China; [Dakos, Vasili] Univ Montpellier, Ecole Prat Hautes Etud EPHE, Inst Sci Evolut Montpellier ISEM, Inst Rech Dev IRD,Ctr Natl Rech Sci CNRS, Montpellier, France; [Rieterk, Max] Univ Utrecht, Copernicus Inst Sustainable Dev, NL-3508 TC Utrecht, Netherlands Hainan University; Southern Marine Science & Engineering Guangdong Laboratory; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; Utrecht University Hu, ZM (corresponding author), Hainan Univ, Key Lab Agroforestry Environm Proc & Ecol Regulat, Haikou 570228, Peoples R China.;Hu, ZM (corresponding author), Southern Marine Sci & Engn Guangdong Lab, Zhuhai 519082, Guangdong, Peoples R China. huzm@hainanu.edu.cn Hu, Zhongmin/0000-0002-4801-5370 National Natural Science Foundation of China [2017YFA0604801]; National Key R&D Program of China [2019QZKK0405]; Second Tibetan Plateau Scientific Expedition and Research Program [ZDYF2022SHFZ042]; Key R&D Program of Hainan [KYQD (ZR) 21096]; start-up fund of Hainan University; [31922053] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Second Tibetan Plateau Scientific Expedition and Research Program; Key R&D Program of Hainan; start-up fund of Hainan University; This research was jointly supported by the National Natural Science Foundation of China (grant no. 31922053) , National Key R & D Program of China (grant no. 2017YFA0604801) , Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2019QZKK0405) , Key R & D Program of Hainan (grant no. ZDYF2022SHFZ042) , and the start-up fund of Hainan University [no. KYQD (ZR) 21096] . The authors appreciate Dr Andrea Stephens and three anonymous reviewers for their constructive comments on earlier versions of this manuscript. 50 0 0 23 23 ELSEVIER SCIENCE LONDON LONDON 84 THEOBALDS RD, LONDON WC1X 8RR, ENGLAND 0169-5347 1872-8383 TRENDS ECOL EVOL Trends Ecol. Evol. DEC 2022.0 37.0 12 1036 1045 10.1016/j.tree.2022.07.011 0.0 NOV 2022 10 Ecology; Evolutionary Biology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Evolutionary Biology; Genetics & Heredity 6P3KG 36008160.0 2023-03-23 WOS:000890830800005 0
J Chi, H; Peng, GG; Yang, JY; Zhang, JH; Song, GB; Xie, XX; Strohmer, DF; Lai, GC; Zhao, SY; Wang, R; Yang, F; Tian, G Chi, Hao; Peng, Gaoge; Yang, Jinyan; Zhang, Jinhao; Song, Guobin; Xie, Xixi; Strohmer, Dorothee Franziska; Lai, Guichuan; Zhao, Songyun; Wang, Rui; Yang, Fang; Tian, Gang Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma FRONTIERS IN ENDOCRINOLOGY English Article sphingolipid metabolism; UVM; tumor microenvironment; immunotherapy; predictive signature ANTIGEN EXPRESSION; OVARIAN-CANCER; CLASS-I; CELLS; MICROENVIRONMENT; GANGLIOSIDES; ASSOCIATION; ACTIVATION; PREDICTION; MORTALITY BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM. MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals. Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy. Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies. [Chi, Hao; Peng, Gaoge; Wang, Rui] Southwest Med Univ, Clin Med Coll, Luzhou, Peoples R China; [Yang, Jinyan; Zhang, Jinhao; Song, Guobin; Xie, Xixi] Southwest Med Univ, Sch Stomatol, Luzhou, Peoples R China; [Strohmer, Dorothee Franziska] Ludwig Maximilians Univ Munchen, Dept Gen Visceral & Transplant Surg, Munich, Germany; [Lai, Guichuan] Chongqing Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Chongqing, Peoples R China; [Zhao, Songyun] Nanjing Med Univ, Wuxi Peoples Hosp, Dept Neurosurg, Wuxi, Peoples R China; [Yang, Fang] Charite Univ Med Berlin, Dept Ophthalmol, Campus Virchow Klinikum, Berlin, Germany; [Tian, Gang] Southwest Med Univ, Affiliated Hosp, Dept Lab Med, Luzhou, Peoples R China Southwest Medical University; Southwest Medical University; University of Munich; Chongqing Medical University; Jiangnan University; Nanjing Medical University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Southwest Medical University Yang, F (corresponding author), Charite Univ Med Berlin, Dept Ophthalmol, Campus Virchow Klinikum, Berlin, Germany.;Tian, G (corresponding author), Southwest Med Univ, Affiliated Hosp, Dept Lab Med, Luzhou, Peoples R China. yang.fang@charite.de; tiangang@swmu.edu.cn Chi, Hao/GOE-4749-2022; Zhao, Songyun/GXI-0381-2022 Chi, Hao/0000-0002-5210-0770; Zhao, Songyun/0000-0003-0542-1085 85 0 0 1 1 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-2392 FRONT ENDOCRINOL Front. Endocrinol. DEC 8 2022.0 13.0 1056310 10.3389/fendo.2022.1056310 0.0 18 Endocrinology & Metabolism Science Citation Index Expanded (SCI-EXPANDED) Endocrinology & Metabolism 7C5HO 36568076.0 gold 2023-03-23 WOS:000899844000001 0
J Hu, XS; Jiang, JC; Cao, DP; Egardt, B Hu, Xiaosong; Jiang, Jiuchun; Cao, Dongpu; Egardt, Bo Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Bayesian inference; electric vehicle; energy storage; health monitoring; lithium-ion battery; machine learning LITHIUM-ION BATTERIES; ENERGY-STORAGE SYSTEMS; DATA-DRIVEN DESIGN; STATE-OF-CHARGE; MANAGEMENT; CAPACITY; POWER; SAFETY Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery state-of-health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis. [Hu, Xiaosong; Jiang, Jiuchun] Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Beijing 100044, Peoples R China; [Hu, Xiaosong] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA; [Cao, Dongpu] Cranfield Univ, Ctr Automot Engn, Cranfield MK43 0AL, Beds, England; [Egardt, Bo] Chalmers Univ Technol, Dept Signals & Syst, S-41296 Gothenburg, Sweden Beijing Jiaotong University; University of California System; University of California Berkeley; Cranfield University; Chalmers University of Technology Hu, XS; Jiang, JC (corresponding author), Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Beijing 100044, Peoples R China.;Hu, XS (corresponding author), Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA. xiaosonghu@ieee.org; jcjiang@bjtu.edu.cn; d.cao@cranfield.ac.uk; bo.egardt@chalmers.se Cao, Dongpu/0000-0003-2541-5272 UK EPSRC FUTURE Vehicles project [EP/I038586/1]; EPSRC [EP/I038586/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/I038586/1] Funding Source: researchfish UK EPSRC FUTURE Vehicles project(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) The work of D. Cao was supported by the UK EPSRC FUTURE Vehicles project under Grant EP/I038586/1. (Corresponding authors: Xiaosong Hu and Jiuchun Jiang.) 51 315 329 32 337 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. APR 2016.0 63.0 4 2645 2656 10.1109/TIE.2015.2461523 0.0 12 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation DH2VT 2023-03-23 WOS:000372645900066 0
C Fan, R; Wang, Y; Qiao, L; Yao, RW; Han, P; Zhang, WD; Pitas, I; Liu, M IEEE Fan, Rui; Wang, Yuan; Qiao, Lei; Yao, Ruiwen; Han, Peng; Zhang, Weidong; Pitas, Ioannis; Liu, Ming PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS & TECHNIQUES (IST 2019) IEEE International Conference on Imaging Systems and Techniques English Proceedings Paper IEEE International Conference on Imaging Systems and Techniques (IST) . IEEE International School on Imaging DEC 08-10, 2019 Abu Dhabi, U ARAB EMIRATES IEEE,IEEE UAE Sect,IEEE Instrumentat & Measurement Soc Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optimizing the v-disparity map using dynamic programming. This linear model is then utilized to reduce the redundant information in the left and right road images. The right image is also transformed into the left perspective view, which greatly enhances the road surface similarity between the two images. Finally, the processed stereo images and their disparity maps are concatenated to create a set of 3D images, which are then utilized to train our neural network. The experimental results illustrate that our network achieves a maximum F1-measure of approximately 91.19%, when analyzing the images from the KITTI road dataset. [Fan, Rui; Wang, Yuan; Liu, Ming] Hong Kong Univ Sci & Technol, Robot Inst, Hong Kong, Peoples R China; [Qiao, Lei; Yao, Ruiwen; Han, Peng; Zhang, Weidong] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China; [Pitas, Ioannis] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki, Greece Hong Kong University of Science & Technology; Shanghai Jiao Tong University; Aristotle University of Thessaloniki Fan, R (corresponding author), Hong Kong Univ Sci & Technol, Robot Inst, Hong Kong, Peoples R China. eeruifan@ust.hk; ywangeq@connect.ust.hk; qiaolei2008114106@gmail.com; yaoruiwen88@foxmail.com; han_ipac@sjtu.edu.cn; wdzhang@sjtu.edu.cn; pitas@csd.auth.gr; eelium@ust.hk Qiao, Lei/AGM-5592-2022 Qiao, Lei/0000-0001-9922-7595 National Natural Science Foundation of China [U1713211]; Research Grant Council of Hong Kong SAR Government, China [11210017, 21202816]; Shenzhen Science, Technology and Innovation Commission (SZSTI) [JCYJ20160428154842603] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Grant Council of Hong Kong SAR Government, China(Hong Kong Research Grants Council); Shenzhen Science, Technology and Innovation Commission (SZSTI) This work was supported by the National Natural Science Foundation of China, under grant No. U1713211, the Research Grant Council of Hong Kong SAR Government, China, under Project No. 11210017, No. 21202816, and the Shenzhen Science, Technology and Innovation Commission (SZSTI) under grant JCYJ20160428154842603, awarded to Prof. Ming Liu. 31 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2471-6162 978-1-7281-3868-8 IEEE CONF IMAGING SY 2019.0 5 Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Imaging Science & Photographic Technology BP5GH 2023-03-23 WOS:000555473200066 0
J Xiong, PY; Jahanshahi, H; Alcaraz, R; Chu, YM; Gomez-Aguilar, JF; Alsaadi, FE Xiong, Pei-Ying; Jahanshahi, Hadi; Alcaraz, Raul; Chu, Yu-Ming; Gomez-Aguilar, J. F.; Alsaadi, Fawaz E. Spectral Entropy Analysis and Synchronization of a Multi-Stable Fractional-Order Chaotic System using a Novel Neural Network-Based-Free Mode CHAOS SOLITONS & FRACTALS English Article Chaos; Fractional calculus; Hidden attractors; Self-excited system; Robust control; Intelligent control technique; Disturbance observer ATTRACTORS; CONTROLLER; INFECTION An immense body of research has focused on chaotic systems, mainly because of their interesting applications in a wide variety of fields. A comprehensive understanding and synchronization of chaotic systems play pivotal roles in practical applications. To this end, the present study investigates a multi stable fractional-order chaotic system. Firstly, some dynamical features of the system are described, and the chaotic behaviour of the system is verified. Then, both spectral entropy and spectral Min-Entropy are computed, and the phenomenon of multi-stability is shown. Besides, the combination of a new chattering-free robust sliding mode controller with a neural network observer is proposed for the synchronization of the fractional-order system. With the neural network estimator, unknown functions of the system are obtained, and the effects of disturbances are completely taken into account. Also, based on the Lyapunov stability theorem, the asymptotical stability of the closed-loop system is confirmed. Lastly, the proposed control technique is applied to the fractional-order system. Numerical results demonstrate the chattering-free and effective performance of the proposed control method for uncertain systems in the presence of unknown time-varying external disturbances. (c) 2021 Elsevier Ltd. All rights reserved. [Xiong, Pei-Ying] Hunan City Univ, Sch Sci, Yiyang 413000, Peoples R China; [Jahanshahi, Hadi] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada; [Alcaraz, Raul] Univ Castilla La Mancha UCLM, Res Grp Elect Biomed & Telecommun Engn, Cuenca 16071, Spain; [Chu, Yu-Ming] Huzhou Univ, Dept Math, Huzhou 313000, Peoples R China; [Chu, Yu-Ming] Changsha Univ Sci & Technol, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Peoples R China; [Gomez-Aguilar, J. F.] Tecnol Nacl Mexico, CONACyT, CENIDET, Interior Internado Palmira S-N, Cuernavaca 62490, Morelos, Mexico; [Gomez-Aguilar, J. F.] Univ Tecnol Mexico, UNITEC MEXICO, Campus Linea, Mexico City, DF, Mexico; [Alsaadi, Fawaz E.] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah, Saudi Arabia Hunan City University; University of Manitoba; Universidad de Castilla-La Mancha; Huzhou University; Changsha University of Science & Technology; King Abdulaziz University Chu, YM (corresponding author), Huzhou Univ, Dept Math, Huzhou 313000, Peoples R China.;Chu, YM (corresponding author), Changsha Univ Sci & Technol, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Peoples R China.;Gomez-Aguilar, JF (corresponding author), Tecnol Nacl Mexico, CONACyT, CENIDET, Interior Internado Palmira S-N, Cuernavaca 62490, Morelos, Mexico.;Gomez-Aguilar, JF (corresponding author), Univ Tecnol Mexico, UNITEC MEXICO, Campus Linea, Mexico City, DF, Mexico. xiongpeiyang@hncu.edu.cn; jahanshahi.hadi90@gmail.com; raul.alcaraz@uclm.es; chuyuming@zjhu.edu.cn; jose.ga@cenidet.tecnm.mx; fesalsaadi@kau.edu.sa Jahanshahi, Hadi/L-5815-2019; Gómez Aguilar, José Francisco/I-7027-2019; Alsaadi, Fawaz E./GLT-2606-2022; Alcaraz, Raul/L-7592-2014 Gómez Aguilar, José Francisco/0000-0001-9403-3767; Alsaadi, Fawaz E./0000-0003-0041-3158; Alcaraz, Raul/0000-0002-0942-3638 Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [FP-106-42]; National Natural Science Foundation of China [11971142, 11401192, 61673169, 11701176, 11626101, 11601485]; CONACyT: Catedras CONACyT para jovenes investigadores 2014; SNICONACyT Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CONACyT: Catedras CONACyT para jovenes investigadores 2014; SNICONACyT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)) The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia funded this project, under grant no. (FP-106-42). Additionally, the research was supported by the National Natural Science Foundation of China (Grant Nosnos. 11971142, 11401192, 61673169, 11701176, 11626101, 11601485). J.F. Gomez Aguilar acknowledges the support provided by CONACyT: Catedras CONACyT para jovenes investigadores 2014 and SNICONACyT. 63 62 62 10 42 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-0779 1873-2887 CHAOS SOLITON FRACT Chaos Solitons Fractals MAR 2021.0 144.0 110576 10.1016/j.chaos.2020.110576 0.0 JAN 2021 12 Mathematics, Interdisciplinary Applications; Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics RB9ET 2023-03-23 WOS:000632408500014 0
C Wang, MX; Lu, ZD; Li, H; Liu, Q Yang, Q; Wooldridge, M Wang, Mingxuan; Lu, Zhengdong; Li, Hang; Liu, Qun Syntax-Based Deep Matching of Short Texts PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI) English Proceedings Paper 1st International Workshop on Social Influence Analysis / 24th International Joint Conference on Artificial Intelligence (IJCAI) JUL 25-31, 2015 Buenos Aires, ARGENTINA Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts. We propose a new approach to the problem, called Deep Match Tree (DEEPMATCH tree), under a general setting. The approach consists of two components, 1) a mining algorithm to discover patterns for matching two short-texts, defined in the product space of dependency trees, and 2) a deep neural network for matching short texts using the mined patterns, as well as a learning algorithm to build the network having a sparse structure. We test our algorithm on the problem of matching a tweet and a response in social media, a hard matching problem proposed in [Wang et al., 2013], and show that DEEPMATCH tree can outperform a number of competitor models including one without using dependency trees and one based on word-embedding, all with large margins. [Wang, Mingxuan; Liu, Qun] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China; [Lu, Zhengdong; Li, Hang] Huawei Technol, Noahs Ark Lab, Shenzhen, Peoples R China; [Liu, Qun] Dublin City Univ, Ctr Next Generat Localisat, Dublin, Ireland Chinese Academy of Sciences; Institute of Computing Technology, CAS; Huawei Technologies; Dublin City University Wang, MX (corresponding author), Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China. China National 973 project [2014CB340301]; Science Foundation Ireland, ADAPT Centre at Dublin City University [12/CE/I2267, 13/RC/2106] China National 973 project(National Basic Research Program of China); Science Foundation Ireland, ADAPT Centre at Dublin City University(Science Foundation Ireland) This work is supported in part by China National 973 project 2014CB340301. Qun Liu's work is partially supported by the Science Foundation Ireland (Grant 12/CE/I2267 and 13/RC/2106) as part of the ADAPT Centre at Dublin City University. 19 27 32 1 1 IJCAI-INT JOINT CONF ARTIF INTELL FREIBURG ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY 978-1-57735-738-4 2015.0 1354 1361 8 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK8FW 2023-03-23 WOS:000442637801062 0
J Haridas, S; Albert, R; Binder, M; Bloem, J; LaButti, K; Salamov, A; Andreopoulos, B; Baker, SE; Barry, K; Bills, G; Bluhm, BH; Cannon, C; Castanera, R; Culley, DE; Daum, C; Ezra, D; Gonzalez, JB; Henrissat, B; Kuo, A; Liang, C; Lipzen, A; Lutzoni, F; Magnuson, J; Mondo, SJ; Nolan, M; Ohm, RA; Pangilinan, J; Park, HJ; Ramirez, L; Alfaro, M; Sun, H; Tritt, A; Yoshinaga, Y; Zwiers, LH; Turgeon, BG; Goodwin, SB; Spatafora, JW; Crous, PW; Grigoriev, IV Haridas, S.; Albert, R.; Binder, M.; Bloem, J.; LaButti, K.; Salamov, A.; Andreopoulos, B.; Baker, S. E.; Barry, K.; Bills, G.; Bluhm, B. H.; Cannon, C.; Castanera, R.; Culley, D. E.; Daum, C.; Ezra, D.; Gonzalez, J. B.; Henrissat, B.; Kuo, A.; Liang, C.; Lipzen, A.; Lutzoni, F.; Magnuson, J.; Mondo, S. J.; Nolan, M.; Ohm, R. A.; Pangilinan, J.; Park, H-J; Ramirez, L.; Alfaro, M.; Sun, H.; Tritt, A.; Yoshinaga, Y.; Zwiers, L-H; Turgeon, B. G.; Goodwin, S. B.; Spatafora, J. W.; Crous, P. W.; Grigoriev, I., V 101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens STUDIES IN MYCOLOGY English Article Fungal evolution; Genome-based prediction; Machine-learning; New taxa FUNGI; TOOL; PHYLOGENY; VIRULENCE; PROTEIN Dothideomycetes is the largest class of kingdom Fungi and comprises an incredible diversity of lifestyles, many of which have evolved multiple times. Plant pathogens represent a major ecological niche of the class Dothideomycetes and they are known to infect most major food crops and feedstocks for biomass and biofuel production. Studying the ecology and evolution of Dothideomycetes has significant implications for our fundamental understanding of fungal evolution, their adaptation to stress and host specificity, and practical implications with regard to the effects of climate change and on the food, feed, and livestock elements of the agro-economy. In this study, we present the first large-scale, whole-genome comparison of 101 Dothideomycetes introducing 55 newly sequenced species. The availability of whole-genome data produced a high-confidence phylogeny leading to reclassification of 25 organisms, provided a clearer picture of the relationships among the various families, and indicated that pathogenicity evolved multiple times within this class. We also identified gene family expansions and contractions across the Dothideomycetes phylogeny linked to ecological niches providing insights into genome evolution and adaptation across this group. Using machine-learning methods we classified fungi into lifestyle classes with >95 % accuracy and identified a small number of gene families that positively correlated with these distinctions. This can become a valuable tool for genome-based prediction of species lifestyle, especially for rarely seen and poorly studied species. [Haridas, S.; Albert, R.; LaButti, K.; Salamov, A.; Andreopoulos, B.; Barry, K.; Castanera, R.; Daum, C.; Kuo, A.; Lipzen, A.; Mondo, S. J.; Nolan, M.; Ohm, R. A.; Pangilinan, J.; Sun, H.; Tritt, A.; Yoshinaga, Y.; Grigoriev, I., V] Lawrence Berkeley Natl Lab, US Dept Energy Joint Genome Inst, Berkeley, CA 94720 USA; [Albert, R.; Grigoriev, I., V] Univ Calif Berkeley, Dept Plant & Microbial Biol, Berkeley, CA 94720 USA; [Binder, M.; Bloem, J.; Zwiers, L-H; Crous, P. W.] Westerdijk Fungal Biodivers Inst, Utrecht, Netherlands; [Baker, S. E.; Culley, D. E.; Magnuson, J.] Pacific Northwest Natl Lab, Funct & Syst Biol Grp, Environm Mol Sci Div, Earth & Biol Sci Directorate, Richland, WA 99352 USA; [Bills, G.] Univ Texas Hlth Sci Ctr Houston, Houston, TX 77030 USA; [Bluhm, B. H.] Univ Arkansas, Fayelletville, AR USA; [Cannon, C.] Texas Tech Univ, Lubbock, TX 79409 USA; [Castanera, R.; Ramirez, L.; Alfaro, M.] Univ Publ Navarra, Inst Multidisciplinary Res Appl Biol IMAB UPNA, Navarra, Spain; [Ezra, D.] Agr Res Org, Volcani Ctr, Rishon Letsiyon, Israel; [Gonzalez, J. B.; Park, H-J; Turgeon, B. G.] Cornell Univ, Sect Plant Pathol & Plant Microbe Biol, Sch Integrat Plant Sci, Ithaca, NY USA; [Henrissat, B.] Aix Marseille Univ, CNRS, Marseille, France; [Henrissat, B.] INRA, Marseille, France; [Henrissat, B.] King Abdulaziz Univ, Dept Biol Sci, Jeddah, Saudi Arabia; [Liang, C.] Qingdao Agr Univ, Coll Agron & Plant Protect, Qingdao, Shandong, Peoples R China; [Lutzoni, F.] Duke Univ, Dept Biol, Durham, NC USA; [Mondo, S. J.] Colorado State Univ, Bioagr Sci & Pest Management Dept, Ft Collins, CO 80523 USA; [Ohm, R. A.; Crous, P. W.] Univ Utrecht, Fac Sci, Dept Biol, Microbiol, Utrecht, Netherlands; [Goodwin, S. B.] ARS, USDA, 915 W State St, W Lafayette, IN USA; [Spatafora, J. W.] Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA; [Castanera, R.] Ctr Res Agr Genom CSIC IRTA UAB UB, Campus UAB,Edif Crag, Barcelona 08193, Spain; [Liang, C.] Qingdao Agr Univ, Coll Plant Hlth & Med, Qingdao, Shandong, Peoples R China United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; United States Department of Energy (DOE); Pacific Northwest National Laboratory; University of Texas System; University of Texas Health Science Center Houston; University of Arkansas System; University of Arkansas Fayetteville; Texas Tech University System; Texas Tech University; Universidad Publica de Navarra; VOLCANI INSTITUTE OF AGRICULTURAL RESEARCH; Cornell University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Aix-Marseille Universite; INRAE; King Abdulaziz University; Qingdao Agricultural University; Duke University; Colorado State University; Utrecht University; United States Department of Agriculture (USDA); Oregon State University; Autonomous University of Barcelona; Consejo Superior de Investigaciones Cientificas (CSIC); Centre de Recerca en Agrigenomica (CRAG); IRTA; Qingdao Agricultural University Grigoriev, IV (corresponding author), Lawrence Berkeley Natl Lab, US Dept Energy Joint Genome Inst, Berkeley, CA 94720 USA.;Grigoriev, IV (corresponding author), Univ Calif Berkeley, Dept Plant & Microbial Biol, Berkeley, CA 94720 USA.;Crous, PW (corresponding author), Westerdijk Fungal Biodivers Inst, Utrecht, Netherlands.;Crous, PW (corresponding author), Univ Utrecht, Fac Sci, Dept Biol, Microbiol, Utrecht, Netherlands. p.crous@wi.knaw.nl; ivgrigoriev@lbl.gov Ramírez, Lucía/K-3845-2014; Ohm, Robin A/I-6689-2016; liang, chen/GZK-8923-2022; Goodwin, Steve/ABB-1862-2021; Baker, Scott E./ABB-1635-2020; Alfaro, Manuel/K-4559-2014; Castanera, Raúl/D-4823-2019; Barry, Kerrie Whitelaw/AAA-5500-2020; Henrissat, Bernard/J-2475-2012 Ramírez, Lucía/0000-0002-0023-4240; liang, chen/0000-0002-0139-8140; Baker, Scott E./0000-0001-5085-3106; Castanera, Raúl/0000-0002-3772-7727; Barry, Kerrie Whitelaw/0000-0002-8999-6785; Lipzen, Anna/0000-0003-2293-9329; Ohm, Robin A./0000-0003-2548-0074; Goodwin, Stephen/0000-0001-5708-9729; Henrissat, Bernard/0000-0002-3434-8588; Bills, Gerald/0000-0003-2352-8417; Alfaro, Manuel/0000-0002-1130-1904 Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; NSF [DEB-1354625, IOS-1456958]; JGI Community Sequencing Project [662] Office of Science of the U.S. Department of Energy(United States Department of Energy (DOE)); NSF(National Science Foundation (NSF)); JGI Community Sequencing Project The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges, that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. R.A. and I.V.G. were supported by the NSF grants DEB-1354625 and IOS-1456958. We thank Daniele Armaleo who provided materials for sequencing. Sequencing of Dothideomycetes genomes were conducted as part of the 1000 Fungal Genomes Project supported by the JGI Community Sequencing Project #662. 54 80 84 12 39 CENTRAALBUREAU SCHIMMELCULTURE UTRECHT PO BOX 85167, 3508 AD UTRECHT, NETHERLANDS 0166-0616 1872-9797 STUD MYCOL Stud. Mycol. JUN 2020.0 96 141 153 10.1016/j.simyco.2020.01.003 0.0 13 Mycology Science Citation Index Expanded (SCI-EXPANDED) Mycology MZ0AG 32206138.0 Green Accepted, Green Published, gold, Green Submitted 2023-03-23 WOS:000558784000003 0
J Lamperti, G; Zhao, XF Lamperti, Gianfranco; Zhao, Xiangfu Diagnosis of Active Systems by Semantic Patterns IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Artificial intelligence; decision support systems; discrete-event systems (DESs); fault diagnosis; intelligent systems DISCRETE-EVENT SYSTEMS; MODEL-BASED DIAGNOSIS; FAILURE DIAGNOSIS; FAULT-DIAGNOSIS A gap still exists between complex discrete-event systems (DESs) and the effectiveness of the state-of-the-art diagnosis techniques, where faults are defined at component levels and diagnoses incorporate the occurrences of component faults. All these approaches to diagnosis are context-free, in as much diagnosis is anchored to components, irrespective of the context in which they are embedded. By contrast, since complex DESs are naturally organized in hierarchies of contexts, different diagnosis rules are to be defined for different contexts. Diagnosis rules are specified based on associations between context-sensitive faults and regular expressions, called semantic patterns. Since the alphabets of such regular expressions are stratified, so that the semantic patterns of a context are defined based on the interface symbols of its subcontexts only, separation of concerns is achieved, and the expressive power of diagnosis is enhanced. This new approach to diagnosis is bound to seemingly contradictory but nonetheless possible scenarios: a DES can be normal despite the faulty behavior of a number of its components; also, it can be faulty despite the normal behavior of all its components. [Lamperti, Gianfranco] Univ Brescia, Dept Informat Engn, I-25123 Brescia, Italy; [Zhao, Xiangfu] Zhejiang Normal Univ, Coll Math Phys & Informat Engn, Hangzhou 321004, Zhejiang, Peoples R China University of Brescia; Zhejiang Normal University Lamperti, G (corresponding author), Univ Brescia, Dept Informat Engn, I-25123 Brescia, Italy. lamperti@ing.unibs.it; xiangfuzhao@zjnu.cn Zhao, Xiangfu/AAP-7918-2020 Zhao, Xiangfu/0000-0001-5870-5730; LAMPERTI, Gian Franco/0000-0002-1915-6932 NSFC [61003101]; Zhejiang Provincial Natural Science Foundation [Y1100191] NSFC(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation(Natural Science Foundation of Zhejiang Province) This work was supported in part by the NSFC under Grant 61003101 and in part by the Zhejiang Provincial Natural Science Foundation under Grant Y1100191. This paper was recommended by Associate Editor F. Y. Wang. 42 21 21 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. AUG 2014.0 44.0 8 1028 1043 10.1109/TSMC.2013.2296277 0.0 16 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science AP7TB hybrid 2023-03-23 WOS:000342278500005 0
J Zhang, Y; Cai, YC; Roca, X; Kwoh, CK; Fullwood, MJ Zhang, Yu; Cai, Yichao; Roca, Xavier; Kwoh, Chee Keong; Fullwood, Melissa Jane Chromatin loop anchors predict transcript and exon usage BRIEFINGS IN BIOINFORMATICS English Article gene expression; transcript; exon usage; machine learning; ChIA-PET; chromatin loop anchors; histone modifications; alternative splicing Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, the locations of chromatin loops in the genome identified by techniques such as Chromatin Interaction Analysis with Paired End Tag sequencing (ChIA-PET) have never been used for prediction tasks. Here, we developed machine learning models to investigate if ChIA-PET could contribute to transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data. We developed different Gradient Boosting Trees models according to the different tasks with the integrated datasets from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage. [Zhang, Yu] Shandong Univ, Jinan, Peoples R China; [Zhang, Yu] Nanyang Technol Univ, Singapore, Singapore; [Cai, Yichao] Huazhong Univ Sci & Technol, Wuhan, Peoples R China; [Cai, Yichao] Natl Univ Singapore, Singapore, Singapore; [Cai, Yichao] Canc Sci Inst Singapore, Singapore, Singapore; [Roca, Xavier] Autonomous Univ Barcelona, Barcelona, Spain; [Roca, Xavier; Fullwood, Melissa Jane] Nanyang Technol Univ, Sch Biol Sci, 60 Nanyang Dr, Singapore 637551, Singapore; [Kwoh, Chee Keong] Univ London, Imperial Coll Sci Technol & Med, London, England; [Kwoh, Chee Keong] Nanyang Technol Univ NTU, Sch Comp Engn, Singapore, Singapore; [Fullwood, Melissa Jane] Natl Univ Singapore, Canc Sci Inst Singapore, 14 Med Dr, Singapore 117599, Singapore; [Fullwood, Melissa Jane] ASTAR, Inst Mol & Cell Biol, Singapore, Singapore Shandong University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Huazhong University of Science & Technology; National University of Singapore; Autonomous University of Barcelona; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Imperial College London; University of London; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; National University of Singapore; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Molecular & Cell Biology (IMCB) Fullwood, MJ (corresponding author), Nanyang Technol Univ, Sch Biol Sci, 60 Nanyang Dr, Singapore 637551, Singapore.;Fullwood, MJ (corresponding author), Natl Univ Singapore, Canc Sci Inst Singapore, 14 Med Dr, Singapore 117599, Singapore.;Fullwood, MJ (corresponding author), ASTAR, Inst Mol & Cell Biol, 61 Biopolis Dr, Singapore 138673, Singapore.;Kwoh, CK (corresponding author), Nanyang Technol Univ, Sch Comp Sci & Engn, 50 Nanyang Ave, Singapore 639798, Singapore. asckkwoh@ntu.edu.sg; mfullwood@ntu.edu.sg Roca, Xavier/C-5838-2011 Roca, Xavier/0000-0003-3853-5273; ZHANG, Yu/0000-0003-4952-8095; Cai, Yichao/0000-0002-5142-4167 National Research Foundation (NRF) Singapore through an NRF Fellowship [NRF-NRFF2012-054]; Nanyang Technological University; RNA Biology Center at the Cancer Science Institute of Singapore, NUS, under the Singapore Ministry of Education Academic Research Fund Tier 3 [MOE2014-T3-1-006]; National Research Foundation Competitive Research Programme [NRF-CRP17-2017-02]; National Research Foundation Singapore; Singapore Ministry of Education under its Research Centres of Excellence initiative; Ministry of Education Tier II grant [T2EP30120-0020]; Advancing Creativity and Excellence (ACE) - Nanyang Technological University [NTU-ACE2019-03] National Research Foundation (NRF) Singapore through an NRF Fellowship(National Research Foundation, Singapore); Nanyang Technological University(Nanyang Technological University); RNA Biology Center at the Cancer Science Institute of Singapore, NUS, under the Singapore Ministry of Education Academic Research Fund Tier 3; National Research Foundation Competitive Research Programme; National Research Foundation Singapore(National Research Foundation, Singapore); Singapore Ministry of Education under its Research Centres of Excellence initiative(Ministry of Education, Singapore); Ministry of Education Tier II grant; Advancing Creativity and Excellence (ACE) - Nanyang Technological University National Research Foundation (NRF) Singapore through an NRF Fellowship (NRF-NRFF2012-054 to M.J.F); Nanyang Technological University start-up funds to M.J.F.; RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 to D.T. as lead PI with M.J.F. as co-investigator (MOE2014-T3-1-006); National Research Foundation Competitive Research Programme grant to V.T. as lead PI and M.J.F. as co-PI (NRF-CRP17-2017-02); National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative; Ministry of Education Tier II grant to M.J.F. (T2EP30120-0020); Advancing Creativity and Excellence (ACE) grant awarded by Nanyang Technological University to M.J.F. as PI, C.K.K. as co-PI and X.R. as co-PI (NTU-ACE2019-03). 39 0 0 0 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. NOV 2021.0 22.0 6 bbab254 10.1093/bib/bbab254 0.0 JUL 2021 11 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology XT0ZO 34263910.0 hybrid, Green Published, Green Accepted 2023-03-23 WOS:000733325700124 0
J Ahmed, I; Din, S; Jeon, G; Piccialli, F; Fortino, G Ahmed, Imran; Din, Sadia; Jeon, Gwanggil; Piccialli, Francesco; Fortino, Giancarlo Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Article Collaborative robotics; deep learning; object detection and tracking; top view; video surveillance VISUAL TRACKING Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines. [Ahmed, Imran] Inst Management Sci, Ctr Excellence Informat Technol, Peshawar 25000, Pakistan; [Din, Sadia] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan, South Korea; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea; [Piccialli, Francesco] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, I-80138 Naples, Italy; [Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst, I-87036 Arcavacata Di Rende, CS, Italy Yeungnam University; Xidian University; Incheon National University; University of Naples Federico II; University of Calabria Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China. imran.ahmed@imsciences.edu.pk; saadia.deen@gmail.com; gjeon@inu.ac.kr; francesco.piccialli@unina.it; g.fortino@unical.it Fortino, Giancarlo/J-2950-2017; Ahmed, Imran/HDL-7255-2022 Fortino, Giancarlo/0000-0002-4039-891X; Framework of International Cooperation Program [2019K1A3A1A8011295711] Framework of International Cooperation Program This work was supported by the Framework of International Cooperation Program managed by the National Research Foundation of Korea (2019K1A3A1A8011295711). 101 26 26 6 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. JUL 2021.0 8.0 7 1253 1270 10.1109/JAS.2020.1003453 0.0 18 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems SN5YM 2023-03-23 WOS:000658364800004 0
J Javadpour, A; Abadi, AMH; Rezaei, S; Zomorodian, M; Rostami, AS Javadpour, Amir; Abadi, Ali Majed Hossein; Rezaei, Samira; Zomorodian, Mozhdeh; Rostami, Ali Shokouhi Improving load balancing for data-duplication in big data cloud computing networks CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS English Article Cloud computing; Data duplication; Accessibility; Reliability; Workload balance; Response time DATA REPLICATION; AWARE REPLICATION; RELIABILITY; PERFORMANCE; STRATEGY Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture. [Javadpour, Amir] Guangzhou Univ, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China; [Abadi, Ali Majed Hossein] Univ Isfahan, Dept Comp Sci, Esfahan, Iran; [Rezaei, Samira] Univ Groningen, Bernoulli Inst Math & Comp Sci, Groningen, Netherlands; [Zomorodian, Mozhdeh] Islamic Azad Univ Khomeini Shahr, Dept Comp Sci & Elect Engn, Khomeini Shahr, Iran; [Rostami, Ali Shokouhi] Univ Sistan & Baluchestan, Dept Commun Engn, Zahedan, Iran Guangzhou University; University of Isfahan; University of Groningen; Islamic Azad University; University of Sistan & Baluchestan Javadpour, A (corresponding author), Guangzhou Univ, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China. a_javadpour@e.gzhu.edu.cn Javadpour, Amir/AEM-2932-2022 Javadpour, Amir/0000-0002-4932-1660 National Natural Science Foundation of China [61632009, 61472451]; Guangdong Provincial Natural Science Foundation [2017A030308006]; High-Level Talents Program of Higher Education in Guangdong Province [2016ZJ01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); High-Level Talents Program of Higher Education in Guangdong Province This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 & 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006 and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01. 47 10 10 0 2 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1386-7857 1573-7543 CLUSTER COMPUT Cluster Comput. AUG 2022.0 25.0 4 2613 2631 10.1007/s10586-021-03312-5 0.0 JUN 2021 19 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2Y0FN 2023-03-23 WOS:000666848400001 0
J Gomez-Hernandez, JJ; Xu, T Jaime Gomez-Hernandez, J.; Xu, Teng Contaminant Source Identification in Aquifers: A Critical View MATHEMATICAL GEOSCIENCES English Article Simulation-optimization; Backward tracking; Bayesian approach; Machine learning; Surrogate models; Heuristic approaches POLLUTION SOURCE IDENTIFICATION; SIMULTANEOUS PARAMETER-ESTIMATION; MONITORING NETWORK DESIGN; ENSEMBLE KALMAN FILTER; AIR-FORCE-BASE; HYDRAULIC CONDUCTIVITY; GROUNDWATER POLLUTION; RELEASE HISTORY; SIMULATION; TRANSIENT Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers. [Jaime Gomez-Hernandez, J.] Univ Politecn Valencia, Inst Water & Environm Engn, Valencia, Spain; [Xu, Teng] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China Universitat Politecnica de Valencia; Hohai University Gomez-Hernandez, JJ (corresponding author), Univ Politecn Valencia, Inst Water & Environm Engn, Valencia, Spain. jgomez@upv.es; teng.xu@hhu.edu.cn Gómez-Hernández, J. Jaime/J-6315-2013 Gómez-Hernández, J. Jaime/0000-0002-0720-2196; Xu, Teng/0000-0002-0207-9061 Spanish Ministry of Science and Innovation [PID2019-109131RB-I00]; Fundamental Research Funds for the Central Universities [B200201015]; Jiangsu Specially-Appointed Professor Program from Jiangsu Provincial Department of Education [B19052] Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Jiangsu Specially-Appointed Professor Program from Jiangsu Provincial Department of Education The first author wishes to acknowledge the financial contribution of the Spanish Ministry of Science and Innovation through Project No. PID2019-109131RB-I00, and the second author acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program from Jiangsu Provincial Department of Education (B19052). 48 10 10 6 27 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1874-8961 1874-8953 MATH GEOSCI Math Geosci. FEB 2022.0 54.0 2 437 458 10.1007/s11004-021-09976-4 0.0 SEP 2021 22 Geosciences, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Geology; Mathematics YU9RH Green Published, hybrid 2023-03-23 WOS:000701012700001 0
J Golilarz, NA; Gao, H; Pirasteh, S; Yazdi, M; Zhou, JL; Fu, Y Golilarz, Noorbakhsh Amiri; Gao, Hui; Pirasteh, Saied; Yazdi, Mohammad; Zhou, Junlin; Fu, Yan Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain REMOTE SENSING English Article adaptive; enhanced AGGD; image de-noising; optimization algorithm; remote sensing; TNN; AVIRIS sensor; HYDICE sensor; ROSIS sensor LOCAL ANOMALY DETECTION; WEIGHTED MEDIAN FILTER; NEURAL-NETWORK; REMOVAL; SPARSE; TRANSFORM; ALGORITHM; SCALE The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [-sigma(n), sigma(n)], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods. [Golilarz, Noorbakhsh Amiri; Gao, Hui; Zhou, Junlin; Fu, Yan] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China; [Pirasteh, Saied] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 611756, Peoples R China; [Yazdi, Mohammad] Univ Lisbon, Inst Super Tecn, P-1049001 Lisbon, Portugal University of Electronic Science & Technology of China; Southwest Jiaotong University; Universidade de Lisboa; Instituto Superior Tecnico Gao, H (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China. noorbakhsh.amiri@std.uestc.edu.cn; huigao@uestc.edu.cn; sapirasteh@swjtu.edu.cn; mohammad.yazdi@tecnico.ulisboa.pt; jlzhou@uestc.edu.cn; fuyan@uestc.edu.cn Yazdi, Mohammad/AAS-2345-2021; Golilarz, Noorbakhsh Amiri/AAU-8412-2021 Yazdi, Mohammad/0000-0002-6714-5285; Pirasteh, Saied/0000-0002-3177-037X National Natural Science Foundation of China [61673085] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Natural Science Foundation of China, Grant No. 61673085. 54 8 8 2 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2021.0 13.0 1 101 10.3390/rs13010101 0.0 16 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology PP9NN gold 2023-03-23 WOS:000606180400001 0
J Cheng, C; Fu, J; Su, H; Ren, LQ Cheng, Chao; Fu, Jun; Su, Hang; Ren, Luquan Recent Advancements in Agriculture Robots: Benefits and Challenges MACHINES English Review agriculture robot; complex environments; robot navigation; robot manipulation DESIGN; SYSTEM; VISION In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots. [Cheng, Chao; Fu, Jun; Ren, Luquan] Jilin Univ, Key Lab Bion Engn, Minist Educ, Changchun 130022, Peoples R China; [Cheng, Chao; Fu, Jun; Su, Hang; Ren, Luquan] Jilin Univ, Weihai Inst Bion, Weihai 264402, Peoples R China; [Su, Hang] Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy Jilin University; Jilin University; Polytechnic University of Milan Fu, J (corresponding author), Jilin Univ, Key Lab Bion Engn, Minist Educ, Changchun 130022, Peoples R China.;Fu, J (corresponding author), Jilin Univ, Weihai Inst Bion, Weihai 264402, Peoples R China. fu_jun@jlu.edu.cn su, hang/0000-0002-6877-6783 148 1 1 19 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-1702 MACHINES Machines JAN 2023.0 11.0 1 48 10.3390/machines11010048 0.0 24 Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 7Y8OH gold, Green Published 2023-03-23 WOS:000915130800001 0
J Sirico, DG; Miccio, L; Wang, Z; Memmolo, P; Xiao, W; Che, LP; Xin, L; Pan, F; Ferraro, P Sirico, Daniele Gaetano; Miccio, Lisa; Wang, Zhe; Memmolo, Pasquale; Xiao, Wen; Che, Leiping; Xin, Lu; Pan, Feng; Ferraro, Pietro Compensation of aberrations in holographic microscopes: main strategies and applications APPLIED PHYSICS B-LASERS AND OPTICS English Article PHASE-CONTRAST MICROSCOPY; DIGITAL HOLOGRAPHY; SINGLE-SHOT; PIXEL SUPERRESOLUTION; RECONSTRUCTION; POLYNOMIALS; CURVATURE; FRAMEWORK; TRACKING; REMOVAL Digital holography is a technique that provides a non-invasive, label-free, quantitative, and high-resolution imaging employable in biological and science of matter fields, but not only. In the last decade, digital holography (DH) has undergone very significant signs of progress that made it one of the most powerful metrology tools. However, one of the most important issues to be afforded and solved for obtaining quantitative phase information about the analyzed specimen is related to phase aberrations. Sources of aberrations can be diverse, and several strategies have been developed and tested to make DH a reliable optical system with submicron resolution. This paper reviews the most effective and robust methods to remove or compensate phase aberrations in retrieved quantitative phase imaging by DH. Different strategies are presented and discussed in detail on how to remove or compensate for such disturbing aberrations. Among the various methods improvements in the optical setups are considered the numerical algorithms, the hybrid methods, and the very recent Artificial Intelligence (AI) approaches to compensate for all aberrations which affect the setups to improve the imaging quality and the accuracy of the reconstruction images' procedures. [Sirico, Daniele Gaetano; Miccio, Lisa; Wang, Zhe; Memmolo, Pasquale; Ferraro, Pietro] Inst Appl Sci & Intelligent Syst ISASI, CNR, Via Campi Flegrei 34, Pozzuoli, NA, Italy; [Sirico, Daniele Gaetano; Wang, Zhe] Univ Naples Federico II, Dept Chem Mat & Prod Engn, Piazzale Tecchio 80, I-80125 Naples, Italy; [Xiao, Wen; Che, Leiping; Xin, Lu; Pan, Feng] Beihang Univ, Key Lab Precis Optomechatron Technol, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Applicate e Sistemi Intelligenti Eduardo Caianiello (ISASI-CNR); University of Naples Federico II; Beihang University Miccio, L; Ferraro, P (corresponding author), Inst Appl Sci & Intelligent Syst ISASI, CNR, Via Campi Flegrei 34, Pozzuoli, NA, Italy.;Pan, F (corresponding author), Beihang Univ, Key Lab Precis Optomechatron Technol, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China. lisa.miccio@isasi.cnr.it; panfeng@buaa.edu.cn; pietro.ferraro@cnr.it Ferraro, Pietro/0000-0002-0158-3856 107 3 3 9 17 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0946-2171 1432-0649 APPL PHYS B-LASERS O Appl. Phys. B-Lasers Opt. APR 2022.0 128.0 4 78 10.1007/s00340-022-07798-8 0.0 19 Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics 0J3LM hybrid 2023-03-23 WOS:000780007100002 0
J Cheng, L; Mi, ZF; Wei, YM; Wang, SD; Hubacek, K Cheng, Lu; Mi, Zhifu; Wei, Yi-Ming; Wang, Shidong; Hubacek, Klaus Dirty skies lower subjective well-being JOURNAL OF CLEANER PRODUCTION English Article Subjective well-being; Air pollution; Big data; Sentiment analysis FINE PARTICULATE MATTER; AMBIENT AIR-POLLUTION; HAPPINESS DATA; HEALTH; LIFE; VALUATION; WEATHER; OUTDOOR; QUALITY; IMPACT Self-reported life satisfaction of China's population has not improved as much as expected during the economic boom, which was accompanied by a significant decline in environmental performance. Is environmental pollution the culprit for the lagging subjective well-being? To explore this issue, this paper adopts sentiment analysis to construct a real-time daily subjective well-being metric at the city level based on the big data of online search traces. Using daily data from 13 Chinese cities centred on Beijing between August 2014 and December 2019, we look at the corelation between subjective well-being and air pollution and the heterogeneity in this relationship based on two separate identification strategies. We find that air pollutants are negatively correlated with subjective well-being, and well-being tends to decline more from pollution during hot seasons. In addition, residents in wealthier regions tend to be more sensitive to air pollution. This result may be explained by the differences in the subjective perception of air pollution and personal preferences at different levels of income. These findings provide information about concerns of the public, thereby helping the government to take appropriate actions to respond to the dynamics of subjective well-being. [Cheng, Lu; Mi, Zhifu] UCL, Bartlett Sch Sustainable Construct, London WC1E 7HB, England; [Wei, Yi-Ming] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100081, Peoples R China; [Wang, Shidong] Univ Oxford, Regents Pk Coll, Pusey St, Oxford OX1 2LB, England; [Hubacek, Klaus] Univ Groningen, Energy Sustainabil Res Inst Groningen, Integrated Res Energy Environm & Soc, NL-9747 AG Groningen, Netherlands University of London; University College London; Beijing Institute of Technology; University of Oxford; University of Groningen Mi, ZF (corresponding author), UCL, Bartlett Sch Sustainable Construct, London WC1E 7HB, England. z.mi@ucl.ac.uk Cheng, Lu/0000-0002-2503-2522; Mi, Zhifu/0000-0001-8106-0694; Wang, Shidong/0000-0002-6034-1208 63 0 0 14 14 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. DEC 10 2022.0 378.0 134380 10.1016/j.jclepro.2022.134380 0.0 OCT 2022 10 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 5T5KL Green Published, hybrid 2023-03-23 WOS:000875905100001 0
J Ding, YC; Ban, Y; Martin-Guerrero, JD; Solano, E; Casanova, J; Chen, X Ding, Yongcheng; Ban, Yue; Martin-Guerrero, Jose D.; Solano, Enrique; Casanova, Jorge; Chen, Xi Breaking adiabatic quantum control with deep learning PHYSICAL REVIEW A English Article DYNAMICS In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speedup by pulse control. Here, we select the single-component control of qubits, resembling the ubiquitous two-level Landau-Zener problem for gate operation. We aim at obtaining fast and robust digital pulses by combining the STA and DRL algorithm. In particular, we find that DRL leads to robust digital quantum control with the operation time bounded by quantum speed limits dictated by STA. In addition, we demonstrate that robustness against systematic errors can be achieved by DRL without any input from STA. Our results introduce a general framework of digital quantum control, leading to a promising enhancement in quantum information processing. [Ding, Yongcheng; Solano, Enrique; Chen, Xi] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Solano, Enrique; Chen, Xi] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Ban, Yue; Solano, Enrique; Casanova, Jorge; Chen, Xi] Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Ban, Yue] Shanghai Univ, Coll Mat Sci & Engn, Shanghai 200444, Peoples R China; [Martin-Guerrero, Jose D.] Univ Valencia, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain; [Solano, Enrique; Casanova, Jorge] Ikerbasque, Basque Fdn Sci, Plaza Euskadi 5, Bilbao 48009, Spain; [Solano, Enrique] IQM, Nymphenburgerstr 86, D-80636 Munich, Germany Shanghai University; Shanghai University; University of Basque Country; Shanghai University; University of Valencia; Basque Foundation for Science Ding, YC; Chen, X (corresponding author), Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China.;Ding, YC; Chen, X (corresponding author), Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China.;Ding, YC; Casanova, J; Chen, X (corresponding author), Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain.;Martin-Guerrero, JD (corresponding author), Univ Valencia, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain.;Casanova, J (corresponding author), Ikerbasque, Basque Fdn Sci, Plaza Euskadi 5, Bilbao 48009, Spain. jonzen.ding@gmail.com; jose.d.martin@uv.es; jcasanovamar@gmail.com; xchen@shu.edu.cn Martín-Guerrero, José D./L-8146-2014; Chen, Xi/ABE-6575-2020; Ding, Yongcheng/AAQ-5097-2021; Ban, Yue/AAR-9240-2021; chen, xi/GXH-3653-2022; Casanova, Jorge/A-6293-2019 Martín-Guerrero, José D./0000-0001-9378-0285; Chen, Xi/0000-0003-4221-4288; Ding, Yongcheng/0000-0002-6008-0001; Ban, Yue/0000-0003-1764-4470; Casanova, Jorge/0000-0002-6110-671X NSFC [12075145]; STCSM [2019SHZDZX01-ZX04, 18010500400, 18ZR1415500]; HiQ [YBN2019115204]; QMiCS [820505]; OpenSuperQ of the EU Flagship on Quantum Technologies [820363]; Spanish Government [PGC2018-095113-B-I00]; Basque Government [IT986-16]; EU FET Open Grant Quromorphic [828826]; EPIQUS [899368]; Ramn y Cajal program [RYC-2017-22482, RYC2018-025197-I]; Spanish MICINN [EUR2020-112117]; UPV/EHU through the grant EHUrOPE; Program for Eastern Scholar NSFC(National Natural Science Foundation of China (NSFC)); STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); HiQ; QMiCS; OpenSuperQ of the EU Flagship on Quantum Technologies; Spanish Government(Spanish Government); Basque Government(Basque Government); EU FET Open Grant Quromorphic; EPIQUS; Ramn y Cajal program; Spanish MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); UPV/EHU through the grant EHUrOPE; Program for Eastern Scholar This work is partially supported from NSFC (12075145), STCSM (2019SHZDZX01-ZX04, 18010500400, and 18ZR1415500), Program for Eastern Scholar, HiQ funding for developing STA&QAOA (YBN2019115204), QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies, Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE), Basque Government IT986-16, EU FET Open Grant Quromorphic (828826), as well as EPIQUS (899368). X.C. acknowledges Ramn y Cajal program (RYC-2017-22482). J.C. acknowledges the Ramon y Cajal program (RYC2018-025197-I) and the EUR2020-112117 project of the Spanish MICINN, as well as support from the UPV/EHU through the grant EHUrOPE. 73 16 16 2 19 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9926 2469-9934 PHYS REV A Phys. Rev. A APR 9 2021.0 103.0 4 L040401 10.1103/PhysRevA.103.L040401 0.0 6 Optics; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics RV8MG Green Submitted 2023-03-23 WOS:000646080500009 0
J Cai, JL; Zhang, LK; Fang, B; Lv, WX; Zhang, BS; Finocchio, G; Xiong, R; Liang, SH; Zeng, ZM Cai, Jialin; Zhang, Like; Fang, Bin; Lv, Wenxing; Zhang, Baoshun; Finocchio, Giovanni; Xiong, Rui; Liang, Shiheng; Zeng, Zhongming Sparse neuromorphic computing based on spin-torque diodes APPLIED PHYSICS LETTERS English Article We report on the sparse neuromorphic computing based on spin-torque diodes (STDs). The rectification characteristics of STDs have been investigated in the absence and presence of d.c. bias currents. While the injection locking phenomenon is observed in our devices, the output functions versus the d.c. bias currents mimic artificial neurons with sparse representations. Furthermore, we construct a neural network with STD neurons to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database, with a produced accuracy of up to 92.7%. The results suggest that STDs have potential to be building blocks for the realization of a biologically plausible neuromorphic computing system. [Cai, Jialin; Zhang, Like; Fang, Bin; Lv, Wenxing; Zhang, Baoshun; Zeng, Zhongming] Chinese Acad Sci, Suzhou Inst Nanotech & Nanobion, Key Lab Multifunct Nanomat & Smart Syst, Suzhou 215123, Jiangsu, Peoples R China; [Cai, Jialin; Zhang, Like; Lv, Wenxing; Zhang, Baoshun; Zeng, Zhongming] Univ Sci & Technol China, Sch Nano Technol & Nano Bion, Hefei 230026, Anhui, Peoples R China; [Finocchio, Giovanni] Univ Messina, Dept Math & Comp Sci Phys Sci & Earth Sci, I-98166 Messina, Italy; [Xiong, Rui] Wuhan Univ, Sch Phys & Technol, Minist Educ, Key Lab Artificial Micro & Nanostruct, Wuhan 430072, Hubei, Peoples R China; [Liang, Shiheng] Hubei Univ, Dept Phys, Wuhan 430062, Hubei, Peoples R China Chinese Academy of Sciences; Suzhou Institute of Nano-Tech & Nano-Bionics, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Messina; Wuhan University; Hubei University Zeng, ZM (corresponding author), Chinese Acad Sci, Suzhou Inst Nanotech & Nanobion, Key Lab Multifunct Nanomat & Smart Syst, Suzhou 215123, Jiangsu, Peoples R China.;Zeng, ZM (corresponding author), Univ Sci & Technol China, Sch Nano Technol & Nano Bion, Hefei 230026, Anhui, Peoples R China. zmzeng2012@sinano.ac.cn JiaLin, Cai/W-1262-2019; Liang, Shiheng/W-7938-2019; Zeng, Zhongming/AAL-7444-2021 JiaLin, Cai/0000-0001-9462-5586; Zhang, Like/0000-0002-0869-3417; Xiong, Rui/0000-0003-0468-6014; Finocchio, Giovanni/0000-0002-1043-3876; Liang, Shiheng/0000-0002-2133-2659 Executive Programme of Scientific and Technological Cooperation Between Italy and China - Ministero degli Affari Esteri e della Cooperazione Internazionale [CN16GR09, 2016YFE0104100]; National Science Foundation of China [51761145025, 11804370]; National Postdoctoral Program for Innovative Talents [BX201700275]; China Postdoctoral Science Foundation [2017M621858] Executive Programme of Scientific and Technological Cooperation Between Italy and China - Ministero degli Affari Esteri e della Cooperazione Internazionale; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by the Executive Programme of Scientific and Technological Cooperation Between Italy and China for the years 2016-2018 (code CN16GR09, 2016YFE0104100) titled Nanoscale broadband spin-transfer-torque microwave detector funded by the Ministero degli Affari Esteri e della Cooperazione Internazionale. This work was supported in part by the National Science Foundation of China (Nos. 51761145025 and 11804370), the National Postdoctoral Program for Innovative Talents (No. BX201700275), and China Postdoctoral Science Foundation Funded Project (No. 2017M621858). 35 15 16 1 39 AMER INST PHYSICS MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0003-6951 1077-3118 APPL PHYS LETT Appl. Phys. Lett. MAY 13 2019.0 114.0 19 192402 10.1063/1.5090566 0.0 4 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics IB3FG 2023-03-23 WOS:000470152800011 0
J Rashidi, MM; Parsa, AB; Shamekhi, L; Nazari, F; Ali, M Rashidi, M. M.; Parsa, A. Basiri; Shamekhi, L.; Nazari, F.; Ali, M. Exergetic optimisation of a multi-stage compression transcritical refrigeration cycle INTERNATIONAL JOURNAL OF EXERGY English Article CO2 refrigerant; COP; coefficient of performance; exergy efficiency; Artificial neural network; particle swarm optimisation algorithm PERFORMANCE Thermodynamic analyses as well as optimisation studies based on maximum cooling coefficient of performance (COP) as thermal efficiency and optimum values of exergy efficiency and total input work of a three-stage compression transcritical CO2 cycle have been presented. A new triple-stage configuration of compression is presented, which uses two intercoolers to enhance the performance. For parametric optimisation, COP and total input work are selected as objective functions, which are calculated for different values of outlet pressure of first and second compressors. For optimisation, a procedure based on artificial neural network and particle swarm optimisation (PSO) algorithm is proposed. The procedure includes two stages. According to the parametric analysis data, in the first stage two different multilayer perceptron neural networks are trained. In the next stage, two distinct PSO algorithms are used to optimise total input work and thermal efficiency. [Rashidi, M. M.] Tongji Univ, Shanghai Key Lab Vehicle Aerodynam & Vehicle Ther, 4800 Cao An Rd, Shanghai 201804, Peoples R China; [Rashidi, M. M.] ENN Tongji Clean Energy Inst Adv Studies, Shanghai, Peoples R China; [Parsa, A. Basiri; Nazari, F.] Islamic Azad Univ, Young Researchers & Elite Club, Hamedan Branch, Hamadan 1574365181, Iran; [Shamekhi, L.] Univ Paris 10, LTIE, GTE, EA 4415, 50 Rue Sevres, F-92410 Ville Davray, France; [Ali, M.] King Saud Univ, Dept Mech Engn, Coll Engn, POB 800, Riyadh 11421, Saudi Arabia Tongji University; Islamic Azad University; Universite Paris Nanterre; King Saud University Shamekhi, L (corresponding author), Univ Paris 10, LTIE, GTE, EA 4415, 50 Rue Sevres, F-92410 Ville Davray, France. mm_rashidi@tongji.edu.cn; amirbparsa@yahoo.com; leila.shamekhi@gmail.com; amirbparsa@yahoo.com; mali@ksu.edu.sa Rashidi, Mohammad Mehdi/P-2692-2014; Ali, Mohamed/E-3986-2014 Rashidi, Mohammad Mehdi/0000-0002-6309-8688; Ali, Mohamed/0000-0001-8149-8098; Ebaid Ali Ahmed, Mohamed/0000-0001-9817-8306 Deanship of Scientific Research at king Saud University [RGP-VPP-080] Deanship of Scientific Research at king Saud University(King Saud University) The authors extend their appreciation to the Deanship of Scientific Research at king Saud University for funding this work through the research group project No. RGP-VPP-080. 36 17 18 0 21 INDERSCIENCE ENTERPRISES LTD GENEVA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND 1742-8297 1742-8300 INT J EXERGY Int. J. Exergy 2016.0 20.0 1 22 47 10.1504/IJEX.2016.076677 0.0 26 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels DN8VD 2023-03-23 WOS:000377357000002 0
C Franzoni, V; Li, YX; Mengoni, P ACM Franzoni, Valentina; Li, Yuanxi; Mengoni, Paolo A Path-based Model for Emotion Abstraction on Facebook using Sentiment Analysis and Taxonomy Knowledge 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017) English Proceedings Paper IEEE/WIC/ACM International Conference on Web Intelligence (WI) AUG 23-26, 2017 Leipzig, GERMANY IEEE,WIC,ACM word similarity; semantic distance; artificial intelligence; data mining; collective knowledge; knowledge discovery; emotional abstraction; sentiment analysis Each term in a short text can potentially convey emotional meaning. Facebook comments and shared posts often convey human biases, which play a pivotal role in information spreading and content consumption. Such bias is at the basis of human-generated content, and capable of conveying contexts which shape the opinion of users through the social media flow of information. Starting from the observation that a separation in topic clusters, i.e. sub-contexts, spontaneously occur if evaluated by human common sense, this work introduces a process for automated extraction of sub-context in Facebook. Basing on emotional abstraction and valence, the automated extraction is exploited through a class of path-based semantic similarity measures and sentiment analysis. Experimental results are obtained using validated clustering techniques on such features, on the domain of information security, over a sample of over 9 million page users. An additional expert evaluation of clusters in tag clouds confirms that the proposed automated algorithm for emotional abstraction clusters Facebook comments compatibly with human common sense. The baseline methods rely on the robust notion of collective concept similarity. [Franzoni, Valentina] Sapienza Univ Rome, Dept Comp Control & Management Engn, Rome, Italy; [Li, Yuanxi] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Mengoni, Paolo] Univ Florence, Dept Math & Comp Sci, Florence, Italy Sapienza University Rome; Hong Kong Baptist University; University of Florence Franzoni, V (corresponding author), Sapienza Univ Rome, Dept Comp Control & Management Engn, Rome, Italy. franzoni@dis.uniroma1.it; csyxli@comp.hkbu.edu.hk; paolo.mengoni@unifi.it Franzoni, Valentina/AAC-7783-2021 Franzoni, Valentina/0000-0002-2972-7188; Li, Yuanxi/0000-0003-0333-6039 30 21 20 0 7 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-4951-2 2017.0 947 952 10.1145/3106426.3109420 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ6US 2023-03-23 WOS:000426965100121 0
J Mijumbi, R; Asthana, A; Koivunen, M; Fu, HY; Zhu, QJ Mijumbi, Rashid; Asthana, Abhaya; Koivunen, Markku; Haiyong, Fu; Zhu, Qinjun Design, implementation, and evaluation of learning algorithms for dynamic real-time network monitoring INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT English Article FLOW Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state-of-the-art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning-based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms. [Mijumbi, Rashid] Nokia Bell Labs, Software & Syst Reliabil Engn, Dublin, Ireland; [Asthana, Abhaya] Nokia Bell Labs, Software & Syst Reliabil Engn, Westford, MA USA; [Koivunen, Markku] Nokia, Nokia Solut & Networks, Espoo, Finland; [Haiyong, Fu; Zhu, Qinjun] Nokia, Nokia Solut & Networks, Chengdu, Peoples R China Nokia Corporation; Nokia Corporation; Nokia Bell Labs; Nokia Corporation; Nokia Finland Mijumbi, R (corresponding author), Blanchardstown Business & Technol Pk, Clyde House,Snugborough Rd, Dublin D15 Y6NT, Ireland. rashidmijumbi@gmail.com joy, shone/AAR-3728-2020 Mijumbi, Rashid/0000-0002-0797-9034 25 3 3 1 1 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1055-7148 1099-1190 INT J NETW MANAG Int. J. Netw. Manag. JUL 2021.0 31.0 4 e2108 10.1002/nem.2108 0.0 MAR 2020 23 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications TD8FP 2023-03-23 WOS:000522336900001 0
J Sun, T; Barrio, R; Jiang, H; Cheng, LZ Sun, Tao; Barrio, Roberto; Jiang, Hao; Cheng, Lizhi Local Linear Convergence of a Primal-Dual Algorithm for the Augmented Convex Models JOURNAL OF SCIENTIFIC COMPUTING English Article Convex optimization; Primal-dual algorithm; Augmented convex model; Partial smoothness; Local linear convergence BREGMAN; L(1)-MINIMIZATION Convex optimization has become an essential technique in many different disciplines. In this paper, we consider the primal-dual algorithm minimizing augmented models with linear constraints, where the objective function consists of two proper closed convex functions; one is the square of a norm and the other one is a gauge function being partly smooth relatively to an active manifold. Examples of this situation can be found in signal processing, optimization, statistics and machine learning literature. We present a unified framework to understand the local convergence behaviour of the primal-dual algorithm for these augmented models. This result explains some numerical results shown in the literature on local linear convergence of the algorithm. [Sun, Tao] Natl Univ Def Technol, Coll Sci, Changsha 410073, Hunan, Peoples R China; [Barrio, Roberto] Univ Zaragoza, IUMA & CoDy Grp, Dept Matemat Aplicada, Pedro Cerbuna 12, E-50009 Zaragoza, Spain; [Jiang, Hao] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China; [Cheng, Lizhi] Natl Univ Def Technol, Coll Sci, Changsha 410073, Hunan, Peoples R China; [Cheng, Lizhi] Natl Univ Def Technol, State Key Lab High Performance Computat, Changsha 410073, Hunan, Peoples R China National University of Defense Technology - China; University of Zaragoza; National University of Defense Technology - China; National University of Defense Technology - China; National University of Defense Technology - China Sun, T (corresponding author), Natl Univ Def Technol, Coll Sci, Changsha 410073, Hunan, Peoples R China. nudtsuntao@163.com; rbarrio@unizar.es; haojiang@nudt.edu.cn; clzcheng@nudt.edu.cn Barrio, Roberto/L-4983-2014 Barrio, Roberto/0000-0002-8089-343X National Natural Science Foundation of Hunan Province, China [13JJ2001]; Science Project of National University of Defense Technology [JC120201]; National Science Foundation of China [61402495]; Spanish Research Projects [MTM2012-31883, MTM2015-64095-P]; University of Zaragoza/CUD Project [UZCUD2015-CIE-05]; European Social Fund and Diputacion General de Aragon [E48] National Natural Science Foundation of Hunan Province, China(Natural Science Foundation of Hunan Province); Science Project of National University of Defense Technology; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Research Projects(Spanish Government); University of Zaragoza/CUD Project; European Social Fund and Diputacion General de Aragon The authors are really indebted to the editors and anonymous referees for their useful suggestions and help to improve the quality of the manuscript. H.J. and L.C. have been supported during this research by the National Natural Science Foundation of Hunan Province, China (13JJ2001), the Science Project of National University of Defense Technology (JC120201) and the National Science Foundation of China (No. 61402495). R.B. has been supported during this research by the Spanish Research Projects MTM2012-31883 and MTM2015-64095-P, the University of Zaragoza/CUD Project UZCUD2015-CIE-05 and the European Social Fund and Diputacion General de Aragon (Grant E48). 23 2 2 0 7 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0885-7474 1573-7691 J SCI COMPUT J. Sci. Comput. DEC 2016.0 69.0 3 1301 1315 10.1007/s10915-016-0235-4 0.0 15 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics EB5TP 2023-03-23 WOS:000387443600014 0
C Zhang, YB; Peng, Y; Adebisi, B; Gui, G; Gacanin, H; Sari, H IEEE Zhang, Yibin; Peng, Yang; Adebisi, Bamidele; Gui, Guan; Gacanin, Haris; Sari, Hikmet Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network 2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL) IEEE Vehicular Technology Conference Proceedings English Proceedings Paper IEEE 96th Vehicular Technology Conference (VTC-Fall) SEP 26-29, 2022 London, ECUADOR IEEE Specific emitter identification; malicious access; multiscale convolution neural network; radio frequency fingerprints The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods. [Zhang, Yibin; Peng, Yang; Gui, Guan; Sari, Hikmet] NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China; [Adebisi, Bamidele] Manchester Metropolitan Univ, Dept Engn, Manchester, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, Aachen, Germany Nanjing University of Posts & Telecommunications; Manchester Metropolitan University; RWTH Aachen University Zhang, YB (corresponding author), NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China. Adebisi, Bamidele/0000-0001-9071-9120 15 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2577-2465 978-1-6654-5468-1 IEEE VTS VEH TECHNOL 2022.0 10.1109/VTC2022-Fall57202.2022.10013023 0.0 5 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications; Transportation BU6QI 2023-03-23 WOS:000927580600327 0
J Peng, S; Canessa, G; Allen-Zhao, Z Peng, Shen; Canessa, Gianpiero; Allen-Zhao, Zhihua Chance constrained conic-segmentation support vector machine with uncertain data ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE English Article; Early Access Support vector machines; Conic-segmentation; Chance constraint; Kernelization CLASSIFICATION Support vector machines (SVM) is one of the well known supervised machine learning model. The standard SVM models are dealing with the situation where the exact values of the data points are known. This paper studies the SVM model when the data set contains uncertain or mislabelled data points. To ensure the small probability of misclassification for the uncertain data, a chance constrained conic-segmentation SVM model is proposed for multiclass classification. Based on the data set, a mixed integer programming formulation for the chance constrained conic-segmentation SVM is derived. Kernelization of chance constrained conic-segmentation SVM model is also exploited for nonlinear classification. The geometric interpretation is presented to show how the chance constrained conic-segmentation SVM works on uncertain data. Finally, experimental results are presented to demonstrate the effectiveness of the chance constrained conic-segmentation SVM for both artificial and real-world data. [Peng, Shen; Allen-Zhao, Zhihua] Xidian Univ, Sch Math & Stat, Xian 710126, Peoples R China; [Peng, Shen; Canessa, Gianpiero] KTH Royal Inst Technol, Dept Math, Lindstedtsvagen 25, SE-10044 Stockholm, Sweden Xidian University; Royal Institute of Technology Peng, S (corresponding author), Xidian Univ, Sch Math & Stat, Xian 710126, Peoples R China.;Peng, S (corresponding author), KTH Royal Inst Technol, Dept Math, Lindstedtsvagen 25, SE-10044 Stockholm, Sweden. pengshen@xidian.edu.cn; canessa@kth.se; allenzhaozh@gmail.com 26 0 0 4 4 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1012-2443 1573-7470 ANN MATH ARTIF INTEL Ann. Math. Artif. Intell. 10.1007/s10472-022-09822-1 0.0 JAN 2023 23 Computer Science, Artificial Intelligence; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics 7X6UL Green Submitted 2023-03-23 WOS:000914334900001 0
J Wang, WP; He, C; Wang, Z; Hramov, A; Fan, DG; Yuan, MM; Luo, X; Kurths, J Wang, Weiping; He, Chang; Wang, Zhen; Hramov, Alexander; Fan, Denggui; Yuan, Manman; Luo, Xiong; Kurths, Juergen Dynamic analysis of synaptic loss and synaptic compensation in the process of associative memory ability decline in Alzheimer's disease APPLIED MATHEMATICS AND COMPUTATION English Article Associative memory neural network; Synaptic plasticity; Synapse loss; Synapse compensation NEURONAL NETWORK; NEURAL-NETWORKS; SYNCHRONIZATION; DELAYS The cognitive decline caused by Alzheimer's disease (AD) has a great impact on the life of patients and their families. Modern medicine has shown that loss of synaptic function is one of the causes of AD, and synaptic compensation compensates for cognitive abilities of the human brain. However, there are no studies on the internal mechanism of synaptic loss and synaptic compensation affecting human cognitive ability. In order to solve this problem, we propose here a three-layer neural network with multiple associative memory abilities, which is one of the main cognitive abilities. Based on synaptic plasticity, models of synaptic loss and synaptic compensation are established to study the pathogenesis of the degeneration of associative memory and explore feasible treatment approaches by setting different degrees of loss and compensation. Our simulation results show that the model can describe the associative memory ability at different stages of AD, which is of great significance for paramedics to determine the stage of disease and develop effective treatment strategies. (c) 2021 Elsevier Inc. All rights reserved. [Wang, Weiping; He, Chang; Yuan, Manman; Luo, Xiong] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Wang, Weiping; He, Chang; Yuan, Manman; Luo, Xiong] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China; [Wang, Weiping; He, Chang; Yuan, Manman; Luo, Xiong] Beijing Univ Sci & Technol, Shunde Grad Sch, Guangzhou 528399, Peoples R China; [Wang, Zhen] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China; [Hramov, Alexander] Innopolis Univ, Ctr Technol inRobot & Mechatron Components, Univ St 1, Innopolis 420500, Russia; [Hramov, Alexander] Saratov State Med Univ, Bolshaya Kazachia St 112, Saratov 410012, Russia; [Fan, Denggui] Univ Sci & Technol Beijing, Sch Math & Phys, Beijing 100083, Peoples R China; [Kurths, Juergen] Humboldt Univ, Inst Phys, D-10099 Berlin, Germany; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany University of Science & Technology Beijing; University of Science & Technology Beijing; Northwestern Polytechnical University; Innopolis University; Razumovsky Saratov State Medical University; University of Science & Technology Beijing; Humboldt University of Berlin; Potsdam Institut fur Klimafolgenforschung Wang, WP (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.;Wang, WP (corresponding author), Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.;Wang, WP (corresponding author), Beijing Univ Sci & Technol, Shunde Grad Sch, Guangzhou 528399, Peoples R China.;Wang, Z (corresponding author), Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China. weipingwangjt@ustb.edu.cn; zhenwang0@gmail.com Hramov, Alexander E/C-5600-2008 Hramov, Alexander E/0000-0003-2787-2530 National Natural Science Foundation of China [81961138010, U1803263, 11931015, U1836106]; Fundamental Research Funds for theCentral Universities [FRF-TP-19-005A3, QNXM20210036, FRF-IDRY-20-022]; Technological Innovation Foundation of Shunde Graduate School, USTB [BK19BF006, BK20BF010]; National Natural Science Foundation [62025602]; Fok Ying-Tong Education Foundation of China [171105]; Key Area RAMP;D Program of Guangdong Province [2019B010137004]; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province [2020TD-013] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for theCentral Universities(Fundamental Research Funds for the Central Universities); Technological Innovation Foundation of Shunde Graduate School, USTB; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Fok Ying-Tong Education Foundation of China; Key Area RAMP;D Program of Guangdong Province; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province This work was supported in part by the National Natural Science Foundation of China under Grant 81961138010, U1803263, 11931015 and Grant U1836106, in part by the Fundamental Research Fundsfor theCentral Universities under Grant FRF-TP-19-005A3, QNXM20210036, and Grant FRF-IDRY-20-022, in part by the Technological Innovation Foundation of Shunde Graduate School, USTB, under Grant BK19BF006 and Grant BK20BF010, in part by National Natural Science Foundation for Distinguished Young Scholars under Grant 62025602, in part by Fok Ying-Tong Education Foundation of China under Grant 171105, in part by Key Area R&D Program of Guangdong Province under Grant 2019B010137004, in part by Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province under Grant 2020TD-013. 32 1 1 3 28 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0096-3003 1873-5649 APPL MATH COMPUT Appl. Math. Comput. NOV 1 2021.0 408.0 126372 10.1016/j.amc.2021.126372 0.0 JUN 2021 15 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics SU0GF 2023-03-23 WOS:000662817600004 0
J Susai, SR; Mongan, D; Healy, C; Cannon, M; Cagney, G; Wynne, K; Byrne, JF; Markulev, C; Schafer, MR; Berger, M; Mossaheb, N; Schlogelhofer, M; Smesny, S; Hickie, IB; Berger, GE; Chen, EYH; de Haan, L; Nieman, DH; Nordentoft, M; Riecher-Rossler, A; Verma, S; Street, R; Thompson, A; Yung, AR; Nelson, B; McGorry, PD; Focking, M; Amminger, GP; Cotter, D Susai, Subash Raj; Mongan, David; Healy, Colm; Cannon, Mary; Cagney, Gerard; Wynne, Kieran; Byrne, Jonah F.; Markulev, Connie; Schafer, Miriam R.; Berger, Maximus; Mossaheb, Nilufar; Schlogelhofer, Monika; Smesny, Stefan; Hickie, Ian B.; Berger, Gregor E.; Chen, Eric Y. H.; de Haan, Lieuwe; Nieman, Dorien H.; Nordentoft, Merete; Riecher-Rossler, Anita; Verma, Swapna; Street, Rebekah; Thompson, Andrew; Yung, Alison Ruth; Nelson, Barnaby; McGorry, Patrick D.; Focking, Melanie; Amminger, G. Paul; Cotter, David Machine learning based prediction and the influence of complement - Coagulation pathway proteins on clinical outcome: Results from the NEURAPRO trial BRAIN BEHAVIOR AND IMMUNITY English Article Clinical high risk; Functional outcome; Prediction models; Schizophrenia; Psychosis ULTRA-HIGH RISK; SUPPLEMENTATION LOWERS INFLAMMATION; BEHAVIORAL CASE-MANAGEMENT; 1ST-EPISODE PSYCHOSIS; FUNCTIONAL OUTCOMES; SCHIZOPHRENIA RISK; INDIVIDUALS; CONVERSION; OMEGA-3-FATTY-ACIDS; NEUROCOGNITION Background: Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR. Materials and methods: We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome. Results and conclusion: A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients. [Susai, Subash Raj; Mongan, David; Healy, Colm; Cannon, Mary; Byrne, Jonah F.; Focking, Melanie; Cotter, David] RCSI Univ Med & Hlth Sci, Dept Psychiat, Dublin, Ireland; [Cagney, Gerard; Wynne, Kieran] Univ Coll Dublin, Conway Inst, Sch Biomol & Biomed Sci, Dublin, Ireland; [Markulev, Connie; Schafer, Miriam R.; Street, Rebekah; Thompson, Andrew; Yung, Alison Ruth; Nelson, Barnaby; McGorry, Patrick D.; Amminger, G. Paul] Univ Melbourne, Ctr Youth Mental Hlth, Melbourne, Vic, Australia; [Markulev, Connie; Schafer, Miriam R.; Street, Rebekah; Thompson, Andrew; Nelson, Barnaby; McGorry, Patrick D.; Amminger, G. Paul] Orygen, 35 Poplar Rd, Parkville 3052, Australia; [Berger, Maximus] Univ Bern, Dept Psychiat & Psychotherapy, Bern, Switzerland; [Mossaheb, Nilufar] Med Univ Vienna, Dept Psychiat & Psychotherapy, Vienna, Austria; [Schlogelhofer, Monika] BioPsyC Biopsychosocial Corp Non Profit Assoc Res, Vienna, Austria; [Smesny, Stefan] Jena Univ Hosp, Dept Psychiat & Psychotherapy, Jena, Germany; [Hickie, Ian B.] Univ Sydney, Brain & Mind Ctr, Sydney, NSW, Australia; [Berger, Gregor E.] Canton Zurich, Child & Adolescent Psychiat Serv, Zurich, Switzerland; [Chen, Eric Y. H.] Univ Hong Kong, Dept Psychiat, Hong Kong, Peoples R China; [de Haan, Lieuwe; Nieman, Dorien H.] Acad Med Ctr, Dept Psychiat, Amsterdam, Netherlands; [Nordentoft, Merete] Copenhagen Univ Hosp, Dept Clin Med, Mental Hlth Ctr Copenhagen, Copenhagen, Denmark; [Riecher-Rossler, Anita] Univ Basel, Fac Med, Basel, Switzerland; [Verma, Swapna] Inst Mental Hlth, Singapore, Singapore; [Yung, Alison Ruth] Deakin Univ, Inst Mental & Phys Hlth & Clin Translat IMPACT, Geelong, Australia; [Yung, Alison Ruth] Univ Manchester, Sch Hlth Sci, Manchester, Lancs, England; [Wynne, Kieran] Univ Coll Dublin, Syst Biol Ireland, Dublin, Ireland University College Dublin; Orygen, The National Centre of Excellence in Youth Mental Health; University of Melbourne; Orygen, The National Centre of Excellence in Youth Mental Health; University of Bern; Medical University of Vienna; Friedrich Schiller University of Jena; University of Sydney; University of Hong Kong; University of Amsterdam; Academic Medical Center Amsterdam; University of Copenhagen; University of Basel; University of Geneva; Deakin University; University of Manchester; University College Dublin Susai, SR; Cotter, D (corresponding author), RCSI Univ Med & Hlth Sci, Dept Psychiat, Dublin, Ireland. subashrajsusai@rcsi.ie; drcotter@rcsi.ie McGorry, Patrick/O-4115-2019; Foecking, Melanie/G-9491-2011 WYNNE, KIERAN/0000-0001-5759-5285; Mongan, David/0000-0001-7931-9636; Cagney, Gerard/0000-0001-7189-9496; Foecking, Melanie/0000-0002-6299-2952; Berger, Gregor/0000-0003-2030-141X; Byrne, Jonah/0000-0002-7046-0630 Irish Health Research Board research [HRB ILP POR 2019-005]; Health Research Board (HRB) [HRB/HRA/PHR/2015-1293]; Science Foundation Ireland (SFI) [413 16/RC/3948]; European Regional Development Fund; Orygen, Australia; Wellcome Trust; Health Research Board [203930/B/16/Z]; Health and Social 418 Care, Research and Development Division, Northern Ireland; NHMRC Senior Research Fellowship [220438Z/20/Z]; University of Melbourne Dame Kate Campbell Fellowship; 417 Health Service Executive National Doctors Training and Planning [1137687]; Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme; Wellcome Flagship Innovations Award; MRFF RART Fellowship [18/RI/5702] Irish Health Research Board research; Health Research Board (HRB); Science Foundation Ireland (SFI)(Science Foundation Ireland); European Regional Development Fund(European Commission); Orygen, Australia; Wellcome Trust(Wellcome Trust); Health Research Board; Health and Social 418 Care, Research and Development Division, Northern Ireland; NHMRC Senior Research Fellowship(National Health and Medical Research Council (NHMRC) of Australia); University of Melbourne Dame Kate Campbell Fellowship; 417 Health Service Executive National Doctors Training and Planning; Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme; Wellcome Flagship Innovations Award; MRFF RART Fellowship This work was supported by an Irish Health Research Board research grant to DC (HRB ILP POR 2019-005) . This publication has emanated from research supported by Health Research Board (HRB) [to 411 DC, MF] under grant number HRB/HRA/PHR/2015-1293. The research was funded in 412 part by a research grant from Science Foundation Ireland (SFI) under Grant Number 413 16/RC/3948 and co-funded under the European Regional Development Fund and by 414 Future Neuro industry partners. The immune marker study was supported by Orygen, Australia. 415 DM is a Fellow on the Irish Clinical Academic Training (ICAT) Programme which is supported 416 by the Wellcome Trust and the Health Research Board (Grant Number 203930/B/16/Z) , the 417 Health Service Executive National Doctors Training and Planning and the Health and Social 418 Care, Research and Development Division, Northern Ireland. JFB was supported by a Wellcome Flagship Innovations Award (220438Z/20/Z) . BN was supported by an NHMRC Senior Research Fellowship (1137687) and a University of Melbourne Dame Kate Campbell Fellowship. MB was supported by a MRFF RART Fellowship. Research reported in this publication was supported by The Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme, reference 18/RI/5702. 102 0 0 0 1 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0889-1591 1090-2139 BRAIN BEHAV IMMUN Brain Behav. Immun. JUL 2022.0 103.0 50 60 10.1016/j.bbi.2022.03.013 0.0 APR 2022 11 Immunology; Neurosciences; Psychiatry Science Citation Index Expanded (SCI-EXPANDED) Immunology; Neurosciences & Neurology; Psychiatry 1G9UN 35341915.0 2023-03-23 WOS:000796192600007 0
J Zhao, XD; Tao, R; Li, W; Li, HC; Du, Q; Liao, WZ; Philips, W Zhao, Xudong; Tao, Ran; Li, Wei; Li, Heng-Chao; Du, Qian; Liao, Wenzhi; Philips, Wilfried Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Laser radar; Hyperspectral imaging; Convolution; Probability distribution; Convolutional neural network (CNN); hyperspectral image (HSI); multisensor data fusion; hierarchical random walk IMAGE CLASSIFICATION; WAVE-FORM; FUSION Earth observation using multisensor data is drawing increasing attention. Fusing remotely sensed hyperspectral imagery and light detection and ranging (LiDAR) data helps to increase application performance. In this article, joint classification of hyperspectral imagery and LiDAR data is investigated using an effective hierarchical random walk network (HRWN). In the proposed HRWN, a dual-tunnel convolutional neural network (CNN) architecture is first developed to capture spectral and spatial features. A pixelwise affinity branch is proposed to capture the relationships between classes with different elevation information from LiDAR data and confirm the spatial contrast of classification. Then in the designed hierarchical random walk layer, the predicted distribution of dual-tunnel CNN serves as global prior while pixelwise affinity reflects the local similarity of pixel pairs, which enforce spatial consistency in the deeper layers of networks. Finally, a classification map is obtained by calculating the probability distribution. Experimental results validated with three real multisensor remote sensing data demonstrate that the proposed HRWN significantly outperforms other state-of-the-art methods. For example, the two branches CNN classifier achieves an accuracy of 88.91 on the University of Houston campus data set, while the proposed HRWN classifier obtains an accuracy of 93.61, resulting in an improvement of approximately 5. [Zhao, Xudong; Tao, Ran; Li, Wei] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China; [Zhao, Xudong; Liao, Wenzhi; Philips, Wilfried] Univ Ghent, IMEC Res Grp, Image Proc & Interpretat, B-9000 Ghent, Belgium; [Li, Heng-Chao] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Liao, Wenzhi] Flemish Inst Technol Res VITO, Sustainable Mat Management, B-2400 Mol, Belgium Beijing Institute of Technology; Ghent University; Southwest Jiaotong University; Mississippi State University; VITO Tao, R (corresponding author), Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China. zhaoxudong@bit.edu.cn; rantao@bit.edu.cn; liwei089@ieee.org; lihengchao_78@163.com; du@ece.msstate.edu; wenzhi.liao@vito.be; wilfried.philips@ugent.be LI, WEI/ABD-5001-2021; du, qian/GYJ-7090-2022; Du, Qian/AAB-8840-2022 Liao, Wenzhi/0000-0002-2183-0324; Zhao, Xudong/0000-0002-6942-136X National Natural Science Foundation of China [61421001, 91638201, 61922013, 61571033, U1833203]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported in part by the National Natural Science Foundation of China under Grant 61421001, Grant 91638201, Grant 61922013, Grant 61571033, and Grant U1833203. The work of Xudong Zhao was supported by the China Scholarship Council. 53 97 99 16 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing OCT 2020.0 58.0 10 7355 7370 10.1109/TGRS.2020.2982064 0.0 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology NU8YK 2023-03-23 WOS:000573923100044 0
J Zhao, H; Liu, SC; Du, Q; Bruzzone, L; Zheng, YJ; Du, KC; Tong, XH; Xie, H; Ma, XL Zhao, Hui; Liu, Sicong; Du, Qian; Bruzzone, Lorenzo; Zheng, Yongjie; Du, Kecheng; Tong, Xiaohua; Xie, Huan; Ma, Xiaolong GCFnet: Global Collaborative Fusion Network for Multispectral and Panchromatic Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Remote sensing; Collaboration; Training; Spatial resolution; Image classification; Decoding; Classification; deep learning (DL); feature fusion; global collaborative fusion; remote sensing Among various multimodal remote sensing data, the pairing of multispectral (MS) and panchromatic (PAN) images is widely used in remote sensing applications. This article proposes a novel global collaborative fusion network (GCFnet) for joint classification of MS and PAN images. In particular, a global patch-free classification scheme based on an encoder-decoder deep learning (DL) network is developed to exploit context dependencies in the image. The proposed GCFnet is designed based on a novel collaborative fusion architecture, which mainly contains three parts: 1) two shallow-to-deep feature fusion branches related to individual MS and PAN images; 2) a multiscale cross-modal feature fusion branch of the two images, where an adaptive loss weighted fusion strategy is designed to calculate the total loss of two individual and the cross-modal branches; and 3) a probability weighted decision fusion strategy for the fusion of the classification results of three branches to further improve the classification performance. Experimental results obtained on three real datasets covering complex urban scenarios confirm the effectiveness of the proposed GCFnet in terms of higher accuracy and robustness compared to existing methods. By utilizing both sampled and non-sampled position data in the feature extraction process, the proposed GCFnet can achieve excellent performance even in a small sample-size case. [Zhao, Hui; Liu, Sicong; Zheng, Yongjie; Du, Kecheng; Tong, Xiaohua; Xie, Huan] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy; [Tong, Xiaohua] Chinese Acad Surveying & Mapping, Inst Cartog & Geog Informat Syst, Beijing 100036, Peoples R China Tongji University; Mississippi State University; University of Trento; Chinese Academy of Surveying & Mapping Liu, SC (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. zhaohui@tongji.edu.cn; sicong.liu@tongji.edu.cn; du@ece.msstate.edu; lorenzo.bruzzone@unitn.it; yongjie.Zheng@outlook.com; kecheng_du@163.com; xhtong@tongji.edu.cn; huanxie@tongji.edu.cn; maxiaolong@casm.ac.cn Zheng, yongjie/HKO-1755-2023; Bruzzone, Lorenzo/A-2076-2012; Liu, Sicong/J-7094-2013 Zheng, yongjie/0000-0002-7030-6713; Bruzzone, Lorenzo/0000-0002-6036-459X; Liu, Sicong/0000-0003-1612-4844 National Key Research and Development Program of China [2018YFB0505000]; National Natural Science Foundation of China [42071324, 42001387]; Shanghai Rising-Star Program [21QA1409100] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Rising-Star Program This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0505000, in part by the National Natural Science Foundation of China under Grant 42071324 and Grant 42001387, and in part by the Shanghai Rising-Star Program under Grant 21QA1409100. 32 2 2 8 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5632814 10.1109/TGRS.2022.3215020 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 6A6HH 2023-03-23 WOS:000880753700004 0
J Silvestro, PC; Pignatti, S; Pascucci, S; Yang, H; Li, ZH; Yang, GJ; Huang, WJ; Casa, R Silvestro, Paolo Cosmo; Pignatti, Stefano; Pascucci, Simone; Yang, Hao; Li, Zhenhai; Yang, Guijun; Huang, Wenjiang; Casa, Raffaele Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models REMOTE SENSING English Article leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO) FAO CROP MODEL; PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORK ESTIMATION; SIMULATION-MODEL; AREA; VEGETATION; VARIABLES; FCOVER; FAPAR; LAI Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models' specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. [Silvestro, Paolo Cosmo; Casa, Raffaele] Univ Tuscia, DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy; [Pignatti, Stefano; Pascucci, Simone] CNR, Inst Methodol Environm Anal, IMAA, Via Fosso Cavaliere 100, I-00133 Rome, Italy; [Yang, Hao; Li, Zhenhai; Yang, Guijun] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Huang, Wenjiang] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China Tuscia University; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS Casa, R (corresponding author), Univ Tuscia, DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy. p.c.silvestro@unitus.it; stefano.pignatti@cnr.it; simone.pascucci@cnr.it; yangh@nercita.org.cn; lizh323@126.com; yanggj@nercita.org.cn; huangwj@radi.ac.cn; rcasa@unitus.it Li, Zhenhai/AAU-3396-2020; huang, wenjiang/AAM-6844-2021; Morano, Stefano Pignatti/A-4640-2008; Pascucci, Simone/I-8172-2019 Li, Zhenhai/0000-0001-9878-3274; Morano, Stefano Pignatti/0000-0002-0587-8926; Pascucci, Simone/0000-0002-8311-8615; Yang, Guijun/0000-0002-6425-8321; CASA, Raffaele/0000-0003-3091-7680; Silvestro, Paolo Cosmo/0000-0001-7375-2906; Yang, Hao/0000-0001-8506-7295 European Space Agency (ESA); Ministry of Science and Technology (MOST) of the People's Republic of China, in the context of the Dragon-3 Programme [10448]; Young Scientist Dragon 3 Grant from ESA; University of Tuscia PhD scholarship European Space Agency (ESA)(European Space AgencyEuropean Commission); Ministry of Science and Technology (MOST) of the People's Republic of China, in the context of the Dragon-3 Programme; Young Scientist Dragon 3 Grant from ESA; University of Tuscia PhD scholarship The authors acknowledge with gratitude funding from the European Space Agency (ESA) and the Ministry of Science and Technology (MOST) of the People's Republic of China, in the context of the Dragon-3 Programme, project ID 10448 Farmland Drought. Paolo Cosmo Silvestro was supported during his PhD work by co-funding from a Young Scientist Dragon 3 Grant from ESA and from a University of Tuscia PhD scholarship. Paolo Cosmo Silvestro is thankful to NERCITA for hosting him in China during 2014 and 2016. The authors are grateful to Fred Baret and Marie Weiss (INRA Avignon, France) for the provision of the Matlab code of the ANN algorithm and for their kind assistance; to CESBIO and Benoit Duchemin for providing the SAFY code and to Dirk Raes and FAO for providing the command-line version of Aquacrop. 56 64 66 6 48 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAY 2017.0 9.0 5 509 10.3390/rs9050509 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology EW5VD gold, Green Published, Green Submitted 2023-03-23 WOS:000402573700112 0
J Ren, R; Wu, DD; Liu, TX Ren, Rui; Wu, Desheng Dash; Liu, Tianxiang Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine IEEE SYSTEMS JOURNAL English Article Day-of-week effect; decision making; sentiment analysis; stock markets; text mining INVESTOR SENTIMENT Investor sentiment plays an important role on the stock market. User-generated textual content on the Internet provides a precious source to reflect investor psychology and predicts stock prices as a complement to stock market data. This paper integrates sentiment analysis into a machine learning method based on support vector machine. Furthermore, we take the day-of-week effect into consideration and construct more reliable and realistic sentiment indexes. Empirical results illustrate that the accuracy of forecasting the movement direction of the SSE 50 Index can be as high as 89.93% with a rise of 18.6% after introducing sentiment variables. And, meanwhile, our model helps investors make wiser decisions. These findings also imply that sentiment probably contains precious information about the asset fundamental values and can be regarded as one of the leading indicators of the stock market. [Ren, Rui; Wu, Desheng Dash; Liu, Tianxiang] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China; [Wu, Desheng Dash] Stockholm Univ, Stockholm Business Sch, SE-10691 Stockholm, Sweden Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Stockholm University Wu, DD (corresponding author), Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China. renrui115@mails.ucas.ac.cn; dash@risklab.ca; 201518009443015@mails.ucas.ac.cn Ren, Rui/0000-0003-3362-8083 Ministry of Science and Technology of China [2016YFC0503606]; National Natural Science Foundation of China [71471055, 91546102]; Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project [QYZDB-SSW-SYS021]; CAS Strategic Research and Decision Support System Development [GHJ-ZLZX-2017-36] Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project; CAS Strategic Research and Decision Support System Development This work was supported in part by the Ministry of Science and Technology of China under Grant 2016YFC0503606, in part by the National Natural Science Foundation of China under Grant 71471055 and Grant 91546102, in part by the Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project under Grant QYZDB-SSW-SYS021, and in part by the CAS Strategic Research and Decision Support System Development under Grant GHJ-ZLZX-2017-36. 28 71 72 11 142 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-8184 1937-9234 IEEE SYST J IEEE Syst. J. MAR 2019.0 13.0 1 760 770 10.1109/JSYST.2018.2794462 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Operations Research & Management Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Operations Research & Management Science; Telecommunications HM7ZH 2023-03-23 WOS:000459697700072 0
C Hrovat, A; Guam, K; Kocevska, T; Javornik, T IEEE Hrovat, Andrej; Guam, Ke; Kocevska, Teodora; Javornik, Tomaz 3D Indoor Environment Charactirazation based on Radio Scanning: Initial Idea and Methodolgy 2019 23RD INTERNATIONAL CONFERENCE ON APPLIED ELECTROMAGNETICS AND COMMUNICATIONS (ICECOM 2019) English Proceedings Paper 23rd International Conference on Applied Electromagnetics and Communications (ICECOM) SEP 30-OCT 02, 2019 Dubrovnik, CROATIA IEEE, Croatia Sect,MTT Croatia Chapter,AP Croatia Chapter,GRSS AESS Croatia Joint Chapter,Fac Elect Engn & Comp,Univ Zagreb,Univ Dubrovnik,AMTA,Italian AMTA Node,Korema optimization; environment scanning; eletromagnetic properties; radio environment characterization SIMULTANEOUS LOCALIZATION In this paper a novel idea of estimating the 3D geometry and building material of indoor environments is proposed. The idea is based on assumption that the received radio signal is distorted due to interaction with the surrounding objects and thus includes the signature of the radio environment. We describe the methodology how to apply radio environment signatures for characterization of geometry and building material of indoor environments applying machine learning tools using ray tracing simulations and ultra-wide band communication technology. The idea is based on the assumption that the emerging broadband wireless systems applies multiple antenna and is able to estimate the channel impulse response for each transmit-receive antenna pair. [Hrovat, Andrej; Javornik, Tomaz] Jozef Stefan Insitute, Dept Commun Syst, Ljubljana, Slovenia; [Guam, Ke] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China; [Kocevska, Teodora] Univ Skopje, Fac Elect Engn & Informat Technol, Skopje, North Macedonia Beijing Jiaotong University; Saints Cyril & Methodius University of Skopje Hrovat, A (corresponding author), Jozef Stefan Insitute, Dept Commun Syst, Ljubljana, Slovenia. andrej.hrovat@ijs.si; ke.guan.bjtu@qq.com; teodora@feit.ukim.edu.mk; tomaz.javornik@ijs.si Hrovat, Andrej/0000-0001-5220-875X Slovenian Research Agency [P2-0016]; COST Action [CA15104] Slovenian Research Agency(Slovenian Research Agency - Slovenia); COST Action(European Cooperation in Science and Technology (COST)) This work was funded by the Slovenian Research Agency under the grants no. P2-0016 and partially carried out in the scope of the COST Action CA15104. 32 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-5514-2 2019.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BQ8ZB 2023-03-23 WOS:000623436600046 0
J Qian, JP; Cheng, XG; Yang, B; Li, Z; Ren, JC; Olofsson, T; Li, HB Qian, Junpeng; Cheng, Xiaogang; Yang, Bin; Li, Zhe; Ren, Junchi; Olofsson, Thomas; Li, Haibo Vision-Based Contactless Pose Estimation for Human Thermal Discomfort ATMOSPHERE English Article thermal discomfort; machine learning; computer vision; human centered intelligent buildings TEMPERATURE Real-time and effective human thermal discomfort detection plays a critical role in achieving energy efficient control of human centered intelligent buildings because estimation results can provide effective feedback signals to heating, ventilation and air conditioning (HVAC) systems. How to detect occupant thermal discomfort is a challenge. Unfortunately, contact or semi-contact perception methods are inconvenient in practical application. From the contactless perspective, a kind of vision-based contactless human discomfort pose estimation method was proposed in this paper. Firstly, human pose data were captured from a vision-based sensor, and corresponding human skeleton information was extracted. Five thermal discomfort-related human poses were analyzed, and corresponding algorithms were constructed. To verify the effectiveness of the algorithms, 16 subjects were invited for physiological experiments. The validation results show that the proposed algorithms can recognize the five human poses of thermal discomfort. [Qian, Junpeng; Cheng, Xiaogang; Ren, Junchi; Li, Haibo] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Cheng, Xiaogang; Li, Haibo] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden; [Yang, Bin; Li, Zhe] Xian Univ Architecture & Technol, Sch Bldg Serv Sci & Engn, Xian 710055, Peoples R China; [Yang, Bin; Olofsson, Thomas] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden Nanjing University of Posts & Telecommunications; Royal Institute of Technology; Xi'an University of Architecture & Technology; Umea University Cheng, XG (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China.;Cheng, XG (corresponding author), KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden. qianjp1995@163.com; chengxg@njupt.edu.cn; yangbin@xauat.edu.cn; lizhe19@xauat.edu.cn; 1217012303@njupt.edu.cn; thomas.olofsson@umu.se; haiboli@kth.se Yang, Bin/GQA-9324-2022 Yang, Bin/0000-0002-1579-7978; Olofsson, Thomas/0000-0002-8704-8538 International Postdoctoral Fellowship Program from China Postdoctoral Council [20160022]; National Natural Science Foundation of China [61401236]; Jiangsu Postdoctoral Science Foundation [1601039B]; Key Research Project of Jiangsu Science and Technology Department [BE2016001-3] International Postdoctoral Fellowship Program from China Postdoctoral Council; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Key Research Project of Jiangsu Science and Technology Department This research was funded by the International Postdoctoral Fellowship Program from China Postdoctoral Council, grant number 20160022; the National Natural Science Foundation of China, grant number 61401236; the Jiangsu Postdoctoral Science Foundation, grant number 1601039B; the Key Research Project of Jiangsu Science and Technology Department, grant number BE2016001-3. 24 10 10 3 18 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4433 ATMOSPHERE-BASEL Atmosphere APR 2020.0 11.0 4 376 10.3390/atmos11040376 0.0 11 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences LW9WE gold, Green Published 2023-03-23 WOS:000539492200063 0
J Tong, Z; Liu, Y; Ma, HT; Zhang, JD; Lin, B; Bao, XW; Xu, XT; Gu, CH; Zheng, Y; Liu, LL; Fang, WJ; Deng, SG; Zhao, P Tong, Zhou; Liu, Yu; Ma, Hongtao; Zhang, Jindi; Lin, Bo; Bao, Xuanwen; Xu, Xiaoting; Gu, Changhao; Zheng, Yi; Liu, Lulu; Fang, Weijia; Deng, Shuiguang; Zhao, Peng Development, Validation and Comparison of Artificial Neural Network Models and Logistic Regression Models Predicting Survival of Unresectable Pancreatic Cancer FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY English Article artificial neural network; logistic regression; unresectable pancreatic cancer; survival; prognosis LONG-TERM SURVIVAL; DUCTAL ADENOCARCINOMA; PROSTATE-CANCER; ALBUMIN; PANCREATICODUODENECTOMY; CLASSIFICATION; MALIGNANCY; NOMOGRAMS; MARKERS; RATIO Background: Prediction models for the overall survival of pancreatic cancer remain unsatisfactory. We aimed to explore artificial neural networks (ANNs) modeling to predict the survival of unresectable pancreatic cancer patients. Methods: Thirty-two clinical parameters were collected from 221 unresectable pancreatic cancer patients, and their prognostic ability was evaluated using univariate and multivariate logistic regression. ANN and logistic regression (LR) models were developed on a training group (168 patients), and the area under the ROC curve (AUC) was used for comparison of the ANN and LR models. The models were further tested on the testing group (53 patients), and k-statistics were used for accuracy comparison. Results: We built three ANN models, based on 3, 7, and 32 basic features, to predict 8 month survival. All 3 ANN models showed better performance, with AUCs significantly higher than those from the respective LR models (0.811 vs. 0.680, 0.844 vs. 0.722, 0.921 vs. 0.849, all p < 0.05). The ability of the ANN models to discriminate 8 month survival with higher accuracy than the respective LR models was further confirmed in 53 consecutive patients. Conclusion: We developed ANN models predicting the 8 month survival of unresectable pancreatic cancer patients. These models may help to optimize personalized patient management. [Tong, Zhou; Liu, Yu; Zheng, Yi; Liu, Lulu; Fang, Weijia; Zhao, Peng] Zhejiang Univ, Affiliated Hosp 1, Dept Med Oncol, Sch Med, Hangzhou, Peoples R China; [Ma, Hongtao; Zhang, Jindi; Lin, Bo; Deng, Shuiguang] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China; [Bao, Xuanwen] Tech Univ Munich, Munich, Germany; [Xu, Xiaoting] Tai He Peoples Hosp, Dept Med Oncol, Fuyang, Peoples R China; [Gu, Changhao] Cangnan Tradit Chinese Med Hosp, Internal Med, Wenzhou, Peoples R China; [Fang, Weijia] Zhejiang Prov Key Lab Pancreat Dis, Hangzhou, Peoples R China Zhejiang University; Zhejiang University; Technical University of Munich Zhao, P (corresponding author), Zhejiang Univ, Affiliated Hosp 1, Dept Med Oncol, Sch Med, Hangzhou, Peoples R China. zhaop@zju.edu.cn Deng, Shuiguang/0000-0001-5015-6095 National Natural Science Foundation of China [81472346]; Natural Science Foundation of Zhejiang Province [LY20H160033]; Educational Science Plan of Zhejiang Province [2020SCG200] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Educational Science Plan of Zhejiang Province This work was supported by the National Natural Science Foundation of China (81472346), the Natural Science Foundation of Zhejiang Province (LY20H160033). Educational Science Plan of Zhejiang Province (2020SCG200). 45 11 11 1 8 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-4185 FRONT BIOENG BIOTECH Front. Bioeng. Biotechnol. MAR 13 2020.0 8.0 196 10.3389/fbioe.2020.00196 0.0 11 Biotechnology & Applied Microbiology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Science & Technology - Other Topics LD0EQ 32232040.0 Green Published, gold 2023-03-23 WOS:000525706800001 0
J Chen, W; Liu, Y; Pu, N; Wang, WP; Liu, L; Lew, MS Chen, Wei; Liu, Yu; Pu, Nan; Wang, Weiping; Liu, Li; Lew, Michael S. Feature Estimations Based Correlation Distillation for Incremental Image Retrieval IEEE TRANSACTIONS ON MULTIMEDIA English Article Task analysis; Correlation; Data models; Modeling; Training; Context modeling; Image retrieval; Incremental learning; fine-grained image retrieval; correlations distillation; feature estimation KNOWLEDGE Deep learning for fine-grained image retrieval in an incremental context is less investigated. In this paper, we explore this task to realize the model's continuous retrieval ability. That means, the model enables to perform well on new incoming data and reduce forgetting of the knowledge learned on preceding old tasks. For this purpose, we distill semantic correlations knowledge among the representations extracted from the new data only so as to regularize the parameters updates using the teacher-student framework. In particular, for the case of learning multiple tasks sequentially, aside from the correlations distilled from the penultimate model, we estimate the representations for all prior models and further their semantic correlations by using the representations extracted from the new data. To this end, the estimated correlations are used as an additional regularization and further prevent catastrophic forgetting over all previous tasks, and it is unnecessary to save the stream of models trained on these tasks. Extensive experiments demonstrate that the proposed method performs favorably for retaining performance on the already-trained old tasks and achieving good accuracy on the current task when new data are added at once or sequentially. [Chen, Wei; Pu, Nan; Lew, Michael S.] Leiden Univ, Leiden Inst Adv Comp Sci, NL-2311 EZ Leiden, Netherlands; [Liu, Yu] Dalian Univ Technol, DUT RU Int Sch Informat Sci & Engn, Dalian 116024, Peoples R China; [Wang, Weiping; Liu, Li] NUDT, Coll Syst Engn, Changsha 410073, Peoples R China; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland Leiden University; Leiden University - Excl LUMC; Dalian University of Technology; National University of Defense Technology - China; University of Oulu Chen, W (corresponding author), Leiden Univ, Leiden Inst Adv Comp Sci, NL-2311 EZ Leiden, Netherlands. w.chen@liacs.leidenuniv.nl; liuyu8824@dlut.edu.cn; n.pu@liacs.leidenuniv.nl; wangwp@nudt.edu.cn; li.liu@oulu.fi; m.s.k.lew@liacs.leidenuniv.nl Liu, li/0000-0002-2011-2873; CHEN, Wei/0000-0001-7875-4548; Pu, Nan/0000-0002-2179-8301 National Natural Science Foundation of China [71701205, 62022091]; China Scholarship Council (CSC) [201703170183]; LIACS Media Laboratory at Leiden University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC)(China Scholarship Council); LIACS Media Laboratory at Leiden University This work was supported in part by LIACS Media Laboratory at Leiden University, China Scholarship Council (CSC No. 201703170183), and in part by the National Natural Science Foundation of China under Grants 71701205 and 62022091. 57 2 2 4 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia 2022.0 24.0 1844 1856 10.1109/TMM.2021.3073279 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 0I7PA 2023-03-23 WOS:000779607000003 0
J He, ZT; Chen, YY; Zhang, D; Yin, WT; Karimi, HR He, Zhitao; Chen, Yongyi; Zhang, Dan; Yin, Wutao; Karimi, Hamid Reza A new intelligent ECG recognition approach based on CNN and improved ALO-SVM SIGNAL IMAGE AND VIDEO PROCESSING English Article; Early Access Electrocardiogram; Convolutional neural network; Lifting wavelet; Support vector machine; Antlion optimization algorithm CLASSIFICATION; ALGORITHM Cardiovascular disease is one of the most common diseases, which seriously threatens people's life and health. Therefore, cardiovascular disease prevention becomes one of the most attractive research topics in health care system design. Intelligent recognition of electrocardiogram (ECG) signals represents an effective method for rapid diagnosis and the evaluation of cardiovascular diseases in medicine. Realization and efficiency of the classification of ECG signals in real time play major roles in the detection of cardiovascular diseases. This paper is concerned with the proposition of an intelligent ECG signal recognition method based on a convolutional neural network (CNN) and support vector machines (SVM) with an improved antlion algorithm (ALO). First, the ECG signal is denoised and pre-processed by lifting the wavelet. Subsequently, CNN is used to extract the signal characteristics of the denoising signal, and the extracted signal characteristics are used as the input characteristics of the SVM. Finally, an improved ALO algorithm is used to optimize the relevant input functions of the SVM to achieve a better signal classification. In our algorithm, the performance is enhanced by improving the threshold estimation method of the lifting wavelet, to improve the filtering effect. The proposed CNN architecture is tested with multi-lead ECG signals from the MIT-BIH ECG signal data set. The results display that the method has obtained an average accuracy, sensitivity, and specificity values of 99.97% 99.97%, and 99.99%,respectively. Compared with the existing results, the proposed approach has a better recognition performance. [He, Zhitao; Chen, Yongyi; Zhang, Dan] Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Zhejiang, Peoples R China; [Yin, Wutao] Wuxi Bozhixin Technol Co Ltd, Wuxi 214000, Jiangsu, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy Zhejiang University of Technology; Polytechnic University of Milan Zhang, D (corresponding author), Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Zhejiang, Peoples R China. danzhang@zjut.edu.cn National Key R D Funding [2018YFE0206900]; National Natural Science Foundation of China [61873237] National Key R D Funding; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R &D Funding under Grant 2018YFE0206900, and the National Natural Science Foundation of China under Grant 61873237. 25 0 0 12 16 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 1863-1703 1863-1711 SIGNAL IMAGE VIDEO P Signal Image Video Process. 10.1007/s11760-022-02300-5 0.0 AUG 2022 8 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Imaging Science & Photographic Technology 3X2TF Green Submitted 2023-03-23 WOS:000842893800001 0
J Zhao, HR; Sun, X; Gao, F; Dong, JY Zhao, Haoran; Sun, Xin; Gao, Feng; Dong, Junyu Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification REMOTE SENSING English Article knowledge distillation; imaging science; scene classification; geosciences; convolutional neural network NETWORK Remote sensing image (RSI) scene classification aims to identify the semantic categories of remote sensing images based on their contents. Owing to the strong learning capability of deep convolutional neural networks (CNNs), RSI scene classification methods based on CNNs have drawn much attention and achieved remarkable performance. However, such outstanding deep neural networks are usually computationally expensive and time-consuming, making them impossible to apply on resource-constrained edge devices, such as the embedded systems used on drones. To tackle this problem, we introduce a novel pair-wise similarity knowledge distillation method, which could reduce the model complexity while maintaining satisfactory accuracy, to obtain a compact and efficient deep neural network for RSI scene classification. Different from the existing knowledge distillation methods, we design a novel distillation loss to transfer the valuable discriminative information, which could reduce the within-class variations and restrain the between-class similarity, from the cumbersome model to the compact model. This method could obtain the compact student model with higher performance compared with existing knowledge distillation methods in RSI scene classification. To be specific, we distill the probability outputs between sample pairs with the same label and match the probability outputs between the teacher and student models. Experiments on three public benchmark datasets for RSI scene classification, i.e., AID, UCMerced, and NWPU-RESISC datasets, verify that the proposed method could effectively distill the knowledge and result in a higher performance. [Zhao, Haoran; Sun, Xin; Gao, Feng; Dong, Junyu] Ocean Univ China, Coll Informat Sci & Engn, Haide Coll, Qingdao 266100, Peoples R China; [Zhao, Haoran; Sun, Xin; Gao, Feng; Dong, Junyu] Ocean Univ China, Inst Adv Ocean Study, Qingdao 266100, Peoples R China; [Sun, Xin] Tech Univ Munich, Dept Aerosp & Geodesy, D-80333 Munich, Germany Ocean University of China; Ocean University of China; Technical University of Munich Sun, X (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Haide Coll, Qingdao 266100, Peoples R China.;Sun, X (corresponding author), Ocean Univ China, Inst Adv Ocean Study, Qingdao 266100, Peoples R China.;Sun, X (corresponding author), Tech Univ Munich, Dept Aerosp & Geodesy, D-80333 Munich, Germany. zhaohaoran@stu.ouc.edu.cn; sunxin1984@ieee.org; gaofeng@ouc.edu.cn; dongjunyu@ouc.edu.cn Sun, Xin/0000-0003-1870-9037; Gao, Feng/0000-0002-1825-328X; Zhao, Haoran/0000-0003-2842-3526 National Natural Science Foundation of China [61971388, U1706218, 41576011]; Alexander von Humboldt Foundation; Key Natural Science Foundation of Shandong Province [ZR2018ZB0852] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Key Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This research was funded by the National Natural Science Foundation of China, grant numbers 61971388, U1706218 and 41576011, Alexander von Humboldt Foundation and the Key Natural Science Foundation of Shandong Province (grant number ZR2018ZB0852). 43 2 2 2 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAY 2022.0 14.0 10 2483 10.3390/rs14102483 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 1Q5KX gold 2023-03-23 WOS:000802727100001 0
J Lu, F; Chen, G; Li, ZJ; Zhang, LJ; Liu, YL; Qu, SQ; Knoll, A Lu, Fan; Chen, Guang; Li, Zhijun; Zhang, Lijun; Liu, Yinlong; Qu, Sanqing; Knoll, Alois MoNet: Motion-Based Point Cloud Prediction Network IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Point cloud compression; Feature extraction; Pipelines; Three-dimensional displays; Predictive models; Trajectory; Correlation; Point cloud; prediction; autonomous driving Predicting the future can significantly improve the safety of intelligent vehicles, which is a key component in autonomous driving. 3D point clouds can accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene. Therefore, prediction of 3D point clouds has great significance for intelligent vehicles, which can be utilized for numerous further applications. However, due to point clouds are unordered and unstructured, point cloud prediction is challenging and has not been deeply explored in current literature. In this paper, we propose a novel motion-based neural network named MoNet. The key idea of the proposed MoNet is to integrate motion features between two consecutive point clouds into the prediction pipeline. The introduction of motion features enables the model to more accurately capture the variations of motion information across frames and thus make better predictions for future motion. In addition, content features are introduced to model the spatial content of individual point clouds. A recurrent neural network named MotionRNN is proposed to capture the temporal correlations of both features. Moreover, an attention-based motion align module is proposed to address the problem of missing motion features in the inference pipeline. Extensive experiments on two large-scale outdoor LiDAR point cloud datasets demonstrate the performance of the proposed MoNet. Moreover, we perform experiments on applications using the predicted point clouds and the results indicate the great application potential of the proposed method. [Lu, Fan; Chen, Guang; Zhang, Lijun; Qu, Sanqing] Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China; [Li, Zhijun] Univ Sci & Technol China, Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Dept Automat, Hefei 230052, Peoples R China; [Liu, Yinlong; Knoll, Alois] Tech Univ Munich, Chair Robot, Artificial Intelligence & Real Time Syst, D-80333 Munich, Germany Tongji University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Technical University of Munich Chen, G (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China. guangchen@tongji.edu.cn Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X National Natural Science Foundation of China [61906138]; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project [31815005]; European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement (Human Brain Project SGA3) [945539]; Shanghai Rising Star Program [21QC1400900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project; European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement (Human Brain Project SGA3); Shanghai Rising Star Program This work was supported in part by the National Natural Science Foundation of China under Grant 61906138, in part by the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project under Grant 31815005, in part by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement (Human Brain Project SGA3) under Grant 945539, and in part by the Shanghai Rising Star Program under Grant 21QC1400900. 55 0 0 5 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. AUG 2022.0 23.0 8 13794 13804 10.1109/TITS.2021.3128424 0.0 DEC 2021 11 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 4A1EQ Green Submitted 2023-03-23 WOS:000734079000001 0
J Wang, RF; Wang, MM; Zhang, ZJ; Hu, T; Xing, JW; He, ZJ; Liu, XG Wang, Renfeng; Wang, Mengmeng; Zhang, Zhengjia; Hu, Tian; Xing, Jiawen; He, Zhanjun; Liu, Xiuguo Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China REMOTE SENSING English Article local climate zone; geodetector; thermal environment LAND-SURFACE TEMPERATURE; YANGTZE-RIVER DELTA; HEAT-ISLAND; METROPOLITAN CITY; CLASSIFICATION; LANDSCAPE; INDEX; WATER; METHODOLOGY; MORPHOLOGY The urban morphology has impacts on the urban thermal environment, which has drawn extensive attention1520]This study explored the relationship between the urban morphology and spatial variation of land surface temperature (LST) in Wuhan by using the local climate zone (LCZ) and seven natural and social factors. A deep learning model (light LCZ model) was used to generate LCZ map in Wuhan, and a geographic detector model was utilized to explore the driving mechanism of LST spatial differentiation. The results show that the LST difference between LCZ classes in summer is greater than that in winter, and the LST of the built-up classes (LCZ 1-10) are significantly higher than that of the vegetation classes in summer. Among the six residential building classes (i.e., LCZ 1-6), LCZ 1 is characterized by compact and high buildings and has the largest average LST. Building density and height have a warming effect, and the building density has a stronger effect than the height. Compared with other natural and social factors, LCZ has the largest explanatory power for LST spatial differentiation in the main urban area and surrounding areas of Wuhan, with explanatory power (q) values reaching 0.660 (summer) and 0.316 (winter). The types of interaction for all pairwise cases are mutual and nonlinear. The strongest interaction is MNDWI-NDBI combination (0.780) in summer and LCZ-NDBI combination (0.460) in winter. [Wang, Renfeng; Wang, Mengmeng; Zhang, Zhengjia; Xing, Jiawen; He, Zhanjun; Liu, Xiuguo] China Univ Geosci Wuhan, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China; [Wang, Mengmeng; Zhang, Zhengjia; He, Zhanjun] Wuchang Univ Technol, Artificial Intelligence Sch, Wuhan 430223, Peoples R China; [Hu, Tian] LIST, Dept ERIN, Remote Sensing & Nat Resources Modeling, L-2450 Luxembourg, Luxembourg China University of Geosciences; Wuchang University of Technology; Luxembourg Institute of Science & Technology Wang, MM (corresponding author), China Univ Geosci Wuhan, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.;Wang, MM (corresponding author), Wuchang Univ Technol, Artificial Intelligence Sch, Wuhan 430223, Peoples R China. rf_wang@cug.edu.cn; wangmm@cug.edu.cn; zhangzj@cug.edu.cn; tian.hu@list.lu; xingjiawen@cug.edu.cn; hezj@cug.edu.cn; liuxiuguo@cug.edu.cn zhang, zhengjia/GWA-0586-2022 Liu, Xiuguo/0000-0002-0045-9642 Open Fund of Key Laboratory of National Geographical Census and Monitoring, Ministry of Natural Resources [2022NGCM06]; National Natural Science Foundation of China [61801443, 41801348]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19090300] Open Fund of Key Laboratory of National Geographical Census and Monitoring, Ministry of Natural Resources; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences) This research was funded by the Open Fund of Key Laboratory of National Geographical Census and Monitoring, Ministry of Natural Resources, grant number 2022NGCM06, the National Natural Science Foundation of China, grant numbers 61801443 and 41801348, and the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDA19090300. 79 8 8 25 53 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2022.0 14.0 5 1067 10.3390/rs14051067 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology ZT6KY gold 2023-03-23 WOS:000769264000001 0
J Xie, CL; Zhuang, XX; Niu, ZM; Ai, RX; Lautrup, S; Zheng, SJ; Jiang, YH; Han, RY; Sen Gupta, T; Cao, SQ; Lagartos-Donate, MJ; Cai, CZ; Xie, LM; Caponio, D; Wang, WW; Schmauck-Medina, T; Zhang, JY; Wang, HL; Lou, GF; Xiao, XL; Zheng, WH; Palikaras, K; Yang, G; Caldwell, KA; Caldwell, GA; Shen, HM; Nilsen, H; Lu, JH; Fang, EF Xie, Chenglong; Zhuang, Xu-Xu; Niu, Zhangming; Ai, Ruixue; Lautrup, Sofie; Zheng, Shuangjia; Jiang, Yinghui; Han, Ruiyu; Sen Gupta, Tanima; Cao, Shuqin; Lagartos-Donate, Maria Jose; Cai, Cui-Zan; Xie, Li-Ming; Caponio, Domenica; Wang, Wen-Wen; Schmauck-Medina, Tomas; Zhang, Jianying; Wang, He-ling; Lou, Guofeng; Xiao, Xianglu; Zheng, Wenhua; Palikaras, Konstantinos; Yang, Guang; Caldwell, Kim A.; Caldwell, Guy A.; Shen, Han-Ming; Nilsen, Hilde; Lu, Jia-Hong; Fang, Evandro F. Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow NATURE BIOMEDICAL ENGINEERING English Article INTERFERENCE COMPOUNDS PAINS; BLOOD-BRAIN-BARRIER; ASSAY INTERFERENCE; C. ELEGANS; A-BETA; LIFE-SPAN; KAEMPFEROL; NAD(+); MODEL; NEURONS A reduced removal of dysfunctional mitochondria is common to aging and age-related neurodegenerative pathologies such as Alzheimer's disease (AD). Strategies for treating such impaired mitophagy would benefit from the identification of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds. From a library of naturally occurring compounds, the workflow allowed us to identify 18 small molecules, and among them two potent mitophagy inducers (Kaempferol and Rhapontigenin). In nematode and rodent models of AD, we show that both mitophagy inducers increased the survival and functionality of glutamatergic and cholinergic neurons, abrogated amyloid-beta and tau pathologies, and improved the animals' memory. Our findings suggest the existence of a conserved mechanism of memory loss across the AD models, this mechanism being mediated by defective mitophagy. The computational-experimental screening and validation workflow might help uncover potent mitophagy modulators that stimulate neuronal health and brain homeostasis. [Xie, Chenglong] Wenzhou Med Univ, Dept Neurol, Affiliated Hosp 1, Wenzhou, Peoples R China; [Xie, Chenglong; Ai, Ruixue; Lautrup, Sofie; Han, Ruiyu; Sen Gupta, Tanima; Cao, Shuqin; Lagartos-Donate, Maria Jose; Caponio, Domenica; Schmauck-Medina, Tomas; Zhang, Jianying; Wang, He-ling; Lou, Guofeng; Nilsen, Hilde; Fang, Evandro F.] Univ Oslo, Dept Clin Mol Biol, Lorenskog, Norway; [Xie, Chenglong; Ai, Ruixue; Lautrup, Sofie; Han, Ruiyu; Sen Gupta, Tanima; Cao, Shuqin; Lagartos-Donate, Maria Jose; Caponio, Domenica; Schmauck-Medina, Tomas; Zhang, Jianying; Wang, He-ling; Lou, Guofeng; Nilsen, Hilde; Fang, Evandro F.] Akershus Univ Hosp, Lorenskog, Norway; [Xie, Chenglong] Wenzhou Med Univ, Inst Aging, Wenzhou, Peoples R China; [Xie, Chenglong] Oujiang Lab, Wenzhou, Zhejiang, Peoples R China; [Xie, Chenglong] Key Lab Alzheimers Dis Zhejiang Prov, Wenzhou, Peoples R China; [Zhuang, Xu-Xu; Cai, Cui-Zan; Xie, Li-Ming; Lu, Jia-Hong] Univ Macau, Inst Chinese Med Sci, State Key Lab Qual Res Chinese Med, Taipa, Macao, Peoples R China; [Niu, Zhangming] Aladdin Healthcare Technol Ltd, London, England; [Niu, Zhangming; Jiang, Yinghui; Xiao, Xianglu] MindRank Al Ltd, Hangzhou, Zhejiang, Peoples R China; [Zheng, Shuangjia] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China; [Wang, Wen-Wen] Wenzhou Med Univ, Ctr Tradit Chinese Med, Affiliated Hosp 2, Wenzhou, Peoples R China; [Wang, Wen-Wen] Wenzhou Med Univ, Yuying Childrens Hosp, Wenzhou, Peoples R China; [Zheng, Wenhua; Shen, Han-Ming] Univ Macau, Fac Hlth Sci, Taipa, Macao, Peoples R China; [Palikaras, Konstantinos] Natl & Kapodistrian Univ Athens, Sch Med, Dept Physiol, Athens, Greece; [Yang, Guang] Royal Brompton Hosp, Cardiovasc Res Ctr, London, England; [Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London, England; [Caldwell, Kim A.; Caldwell, Guy A.] Univ Alabama, Dept Biol Sci, Tuscaloosa, AL USA; [Caldwell, Kim A.; Caldwell, Guy A.] Univ Alabama Birmingham, Dept Neurol, Ctr Neurodegenerat & Expt Therapeut, Nathan Shock Ctr Res Basic Biol, UAB Stn, Birmingham, AL 35294 USA; [Caldwell, Kim A.; Caldwell, Guy A.] Univ Alabama Birmingham, Dept Neurobiol, Ctr Neurodegenerat & Expt Therapeut, Nathan Shock Ctr Res Basic Biol Aging, Birmingham, AL USA; [Shen, Han-Ming] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Physiol, Singapore, Singapore; [Nilsen, Hilde; Fang, Evandro F.] Norwegian Ctr Hlth Ageing NO Age, Oslo, Norway; [Fang, Evandro F.] Zhengzhou Univ, Affiliated Hosp 1, Dept Geriatr, Zhengzhou, Peoples R China Wenzhou Medical University; University of Oslo; University of Oslo; Wenzhou Medical University; University of Macau; Sun Yat Sen University; Wenzhou Medical University; Wenzhou Medical University; University of Macau; Athens Medical School; National & Kapodistrian University of Athens; Royal Brompton Hospital; Imperial College London; University of Alabama System; University of Alabama Tuscaloosa; University of Alabama System; University of Alabama Birmingham; University of Alabama System; University of Alabama Birmingham; National University of Singapore; Zhengzhou University Fang, EF (corresponding author), Univ Oslo, Dept Clin Mol Biol, Lorenskog, Norway.;Fang, EF (corresponding author), Akershus Univ Hosp, Lorenskog, Norway.;Lu, JH (corresponding author), Univ Macau, Inst Chinese Med Sci, State Key Lab Qual Res Chinese Med, Taipa, Macao, Peoples R China.;Fang, EF (corresponding author), Norwegian Ctr Hlth Ageing NO Age, Oslo, Norway.;Fang, EF (corresponding author), Zhengzhou Univ, Affiliated Hosp 1, Dept Geriatr, Zhengzhou, Peoples R China. jiahonglu@um.edu.mo; e.f.fang@medisin.uio.no LU, Jia-Hong/X-1395-2019 LU, Jia-Hong/0000-0002-1147-125X; Wang, He-Ling/0000-0002-8920-1487; Zhang, Jianying/0000-0001-8161-7536; Zhuang, Xu-Xu/0000-0002-2938-5923; Shen, Han-Ming/0000-0001-7369-5227; Schmauck-Medina, Tomas/0000-0003-4618-0628 Research Council of Norway [MYRG201900129-ICMS, 2019098]; National Natural Science Foundation of China [262175, 277813]; Akershus University Hospital [81971327]; Civitan Norges Forskningsfond for Alzheimers sykdom [269901, 261973]; Science and Technology Development Fund, Macau SAR [281931, TO01000215]; University of Macau [0128/2019/A3, 024/2017/AMJ]; British Heart Foundation [302483]; Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology) [PG/16/78/32402]; European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award `DRAGON: rapiD [H2020-JTI-IMI2 101005122, H2020-SC1-FA-DTS-2019-1 952172]; UK Research and Innovation Future Leaders Fellowship; National Science Foundation of China [MR/V023799/1]; Wenzhou City Committee of Science and Technology [81600977]; Natural Science Foundation of Zhejiang Province [Y20180137]; China Scholarship Council [Y19H090059]; Collaborative Innovation Award from the Howard Hughes Medical Institute Research Council of Norway(Research Council of Norway); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Akershus University Hospital; Civitan Norges Forskningsfond for Alzheimers sykdom; Science and Technology Development Fund, Macau SAR; University of Macau; British Heart Foundation(British Heart Foundation); Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology); European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award `DRAGON: rapiD; UK Research and Innovation Future Leaders Fellowship(UK Research & Innovation (UKRI)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Wenzhou City Committee of Science and Technology; Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); China Scholarship Council(China Scholarship Council); Collaborative Innovation Award from the Howard Hughes Medical Institute This project was supported by HELSE SOR-OST (Nos. 2017056, 2020001 and 2021021), the Research Council of Norway (Nos. 262175 and 277813), the National Natural Science Foundation of China (No. 81971327), Akershus University Hospital (Nos. 269901 and 261973), the Civitan Norges Forskningsfond for Alzheimers sykdom (for a 3-year Ph.D. Fellowship, No. 281931) and the Czech Republic-Norway KAPPA programme (with Martin Vyhnalek, No. TO01000215) to E.F.F. The project was partially supported by the Science and Technology Development Fund, Macau SAR (Grants No. 0128/2019/A3 and 024/2017/AMJ), the University of Macau grants (Grant No. MYRG201900129-ICMS) awarded to J.H.L. H.N. was supported by grants from HELSE SOR-OST (No. 2019098) and the Research Council of Norway (No. 302483). G.Y. was supported by the British Heart Foundation (Project No. PG/16/78/32402), the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology), the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award `DRAGON: rapiD and secuRe AI imaging-based diaGnosis, stratification, fOllow-up and preparedness for coronavirus paNdemics' (H2020-JTI-IMI2 101005122), the AI for Health Imaging Award `CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management' (H2020-SC1-FA-DTS-2019-1 952172) and the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1). C.X. was supported by the National Science Foundation of China (No. 81600977), Wenzhou City Committee of Science and Technology (Y20180137) and the Natural Science Foundation of Zhejiang Province (Y19H090059). R.A. and H.W. were funded by the China Scholarship Council (http://www.csc.edu.cn/); the funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Support for some of the transgenic nematode models used came from a Collaborative Innovation Award from the Howard Hughes Medical Institute to G.A.C. We thank B. Olaisen (KCL) and D. J. Patrick-Brown (UiO) for reading and comments on the manuscript. Cartoon images in Fig. 2a and Supplementary Fig. 4a were generated using BioRender via subscription through the Fang laboratory. 96 46 48 58 118 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2157-846X NAT BIOMED ENG Nat. Biomed. Eng JAN 2022.0 6.0 1 76 + 10.1038/s41551-021-00819-5 0.0 JAN 2022 31 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering YL7ZM 34992270.0 hybrid, Green Published, Green Accepted 2023-03-23 WOS:000739783800003 0
J Bing, ZS; Baumann, I; Jiang, ZY; Huang, K; Cai, CX; Knoll, A Bing, Zhenshan; Baumann, Ivan; Jiang, Zhuangyi; Huang, Kai; Cai, Caixia; Knoll, Alois Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle FRONTIERS IN NEUROROBOTICS English Article spiking neural network; R-STDP; supervised learning; end-to-end control; autonomous locomotion NEURAL-NETWORK; MOBILE ROBOT; NAVIGATION; MODEL; STDP Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios. [Bing, Zhenshan; Baumann, Ivan; Jiang, Zhuangyi; Cai, Caixia; Knoll, Alois] Tech Univ Munich, Chair Robot Artificial Intelligence & Embedded Sy, Dept Informat, Munich, Germany; [Huang, Kai] Sun Yat Sen Univ, Dept Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Huang, Kai] Peng Cheng Lab, Shenzhen, Peoples R China Technical University of Munich; Sun Yat Sen University; Peng Cheng Laboratory Cai, CX (corresponding author), Tech Univ Munich, Chair Robot Artificial Intelligence & Embedded Sy, Dept Informat, Munich, Germany. ccxtum@gmail.com Bing, Zhenshan/AAF-7965-2020; Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) [720270]; Chinese Scholarship Council; German Research Foundation (DFG); Technical University of Munich (TUM) European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020); Chinese Scholarship Council(China Scholarship Council); German Research Foundation (DFG)(German Research Foundation (DFG)); Technical University of Munich (TUM) The research leading to these results received funding from the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under grant agreement no. 720270 (The Human Brain Project, HBP) and the Chinese Scholarship Council. Meanwhile, it was also supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. 51 15 15 2 11 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics MAY 3 2019.0 13.0 18 10.3389/fnbot.2019.00018 0.0 17 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology HW9BL 31130854.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000466985500001 0
J Ibragimov, B; Toesca, DAS; Chang, DT; Yuan, YX; Koong, AC; Xing, L Ibragimov, Bulat; Toesca, Diego A. S.; Chang, Daniel T.; Yuan, Yixuan; Koong, Albert C.; Xing, Lei Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning -based portal vein segmentation NEUROCOMPUTING English Article Toxicity prediction; Primary liver cancer; SBRT; Deep learning PHASE-I; RADIOTHERAPY; HEAD; TRIALS; ORGANS; TUMORS Purpose: To develop a framework for automated prediction of hepatobiliary (HB) toxicity after liver stereotactic body radiation therapy (SBRT). Materials and methods: A newly recognized toxicity type, named central or HB liver toxicity, had been reported, manifestation of which strongly correlates with the dose delivered to portal vein (PV) during SBRT. We propose a novel framework for automated HB toxicity prediction by combining deep learningbased auto-segmentation, PV anatomy analysis and the previously reported HB toxicity model. For validation of the framework, an IBR approved representative database of 72 patients treated with SBRT from primary (37) and metastatic (35) liver cancer was assembled. Each case included a pre-treatment CT, manual segmentations of tumor and PV, approved treatment plan, and the record of acute and late posttreatment toxicities. Performance of the developed framework was evaluated by quantitative comparison against manual predictions of HB toxicity, as well as post-treatment toxicity follow-ups. Results: The manual and automated predictions of HB toxicity were in agreement for 94% cases using either V-BED10 30 = 45 cc or V-BED10 40 = 37 cc dosimetric predictors. When compared to post-treatment follow-ups for primary liver cancer, the proposed automated framework made 86% and 83% correct predictions in comparison to 83% and 80% correct manual predictions using V(BED10)30 = 45 cc or V(BED10)40 >= 37 cc, respectively. Conclusion: The proposed framework automates the HB toxicity prediction with the accuracy similar to manual analysis-based HB toxicity prediction. The strategy is quite general and extendable to the automated prediction of toxicities of other organs. (c) 2019 Published by Elsevier B.V. [Ibragimov, Bulat; Toesca, Diego A. S.; Chang, Daniel T.; Xing, Lei] Stanford Univ, Dept Radiat Oncol, 875 Blake Wilbur Dr, Palo Alto, CA 94305 USA; [Yuan, Yixuan] City Univ Hong Kong, Dept Elect Engn, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China; [Koong, Albert C.] Univ Texas MD Anderson Canc Ctr, Dept Radiat Oncol, 1515 Holcombe Blvd, Houston, TX 77030 USA; [Ibragimov, Bulat] Univ Copenhagen, Dept Univ Comp Sci, Univ Pk 5, DK-2100 Copenhagen, Denmark Stanford University; City University of Hong Kong; University of Texas System; UTMD Anderson Cancer Center; University of Copenhagen Xing, L (corresponding author), Stanford Univ, Dept Radiat Oncol, 875 Blake Wilbur Dr, Palo Alto, CA 94305 USA. bulat@stanford.edu Toesca, Diego/R-3983-2017 Toesca, Diego/0000-0003-3012-7021; Xing, Lei/0000-0003-2536-5359; Chang, Daniel/0000-0003-2760-1554; Yuan, Yixuan/0000-0002-0853-6948; Ibragimov, Bulat/0000-0001-7739-7788 National Cancer Institute [5R01 CA176553]; Varian Medical Systems, Inc. National Cancer Institute(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Varian Medical Systems, Inc. The research was supported by the National Cancer Institute under grant 5R01 CA176553 and grant from Varian Medical Systems, Inc. 26 5 5 2 11 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUN 7 2020.0 392.0 181 188 10.1016/j.neucom.2018.11.112 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LN1KE 2023-03-23 WOS:000532703500003 0
J Gholami, H; Mohammadifar, A; Malakooti, H; Esmaeilpour, Y; Golzari, S; Mohammadi, F; Li, Y; Song, YG; Kaskaoutis, DG; Fitzsimmons, KE; Collins, AL Gholami, Hamid; Mohammadifar, Aliakbar; Malakooti, Hossein; Esmaeilpour, Yahya; Golzari, Shahram; Mohammadi, Fariborz; Li, Yue; Song, Yougui; Kaskaoutis, Dimitris G.; Fitzsimmons, Kathryn Elizabeth; Collins, Adrian L. Integrated modelling for mapping spatial sources of dust in central Asia-An important dust source in the global atmospheric system ATMOSPHERIC POLLUTION RESEARCH English Article Dust spatial mapping; Effective factors; Deep learning; Hybrid copula-gcForest model; Gaussian copula model; Model sensitivity; Central asia JAZMURIAN BASIN; SOUTHWEST ASIA; WESTERN CHINA; MINERAL DUST; CASPIAN SEA; TIAN-SHAN; TRANSPORT; EROSION; DESERT; REGION Spatial mapping of dust sources in arid and semi-arid regions is necessary to mitigate on-site and off-site impacts. In this study, we apply a novel integrated modelling approach including leave one feature out (LOFO) - as a technique for feature selection -, deep learning (DL) models (gcForest and bidirectional long short-term memory (Bi-LSTM)), game theory (GT) and a Gaussian copula-based multivariate (GCBM) model for mapping dust sources in Central Asia (CA). Eight factors (precipitation, cation exchange capacity, bulk density, wind speed, slope, silt content, lithology and coarse fragment content) were selected by LOFO as effective for controlling dust emissions, and were used in the novel modelling process. Six statistical indicators were utilized to assess the performance of the two DL models and a hybrid copula-gcForest model, while a sensitivity analysis of the models was also carried out. The hybrid copula-gcForest model was identified as the most accurate, predicting that 16%, 7.1%, 9.5% and 67.4% of the study area is grouped at low, moderate, high and very high susceptibility classes for dust emissions, respectively. Based on permutation feature importance measure (PFIM) and Shapely Additive exPlanations (SHAP), bulk density, precipitation and coarse fragment content were evaluated as the three most important factors with the highest contributions to the predictive model output. The study area suffers from intense wind erosion and the generated spatial maps of dust sources may be helpful for mitigating and controlling dust phenomena in CA. [Gholami, Hamid; Mohammadifar, Aliakbar; Esmaeilpour, Yahya] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran; [Malakooti, Hossein] Univ Hormozgan, Fac Marine Sci & Technol, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Dept Elect & Comp Engn, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Deep Learning Res Grp, Bandar Abbas, Hormozgan, Iran; [Mohammadi, Fariborz] Univ Hormozgan, Minab Higher Educ Ctr, Dept Agr, Bandar Abbas, Iran; [Mohammadi, Fariborz] Univ Hormozgan, Hormoz Res Ctr, Hormozgan, Iran; [Li, Yue; Song, Yougui] Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China; [Li, Yue; Song, Yougui] CAS Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China; [Li, Yue] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Kaskaoutis, Dimitris G.] Natl Observ Athens, Inst Environm Res & Sustainable Dev, Athens 15784, Greece; [Kaskaoutis, Dimitris G.] Univ Crete, Dept Chem, Environm Chem Proc Lab, Iraklion 71003, Greece; [Fitzsimmons, Kathryn Elizabeth] Max Planck Inst Evolutionary Anthropol, Dept Human Evolut, Deutsch Pl 6, D-04103 Leipzig, Germany; [Collins, Adrian L.] Rothamsted Res, Sustainable Agr Sci Dept, Okehampton EX20 2SB, Devon, England University of Hormozgan; University of Hormozgan; University of Hormozgan; University of Hormozgan; University of Hormozgan; University of Hormozgan; Chinese Academy of Sciences; Institute of Earth Environment, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National Observatory of Athens; University of Crete; Max Planck Society; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Rothamsted Research Gholami, H (corresponding author), Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran. hgholami@hormozgan.ac.ir Song, yougui/I-8813-2014; Fitzsimmons, Kathryn/U-3565-2018; Esmaeilpour, Yahya/N-9343-2015; Collins, Adrian/V-5938-2018 Song, yougui/0000-0003-0064-3260; Fitzsimmons, Kathryn/0000-0002-9337-0793; Esmaeilpour, Yahya/0000-0002-9816-899X; Li, Yue/0000-0001-7553-6772; Gholami, Hamid/0000-0002-7280-8880; Collins, Adrian/0000-0001-8790-8473 Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran; Institute of Earth Environment, Chinese Academy of Sciences; project PANhellenic infrastructure for Atmospheric Composition and climatE change, PANACEA - Operational Programme Competitiveness, Entrepreneurship and Innovation (NSRF 2014-2020) [MIS 5021516]; UKRI-BBSRC (UK Research and Innovation-Biotechnology and Biological Sciences Research Council) [BBS/E/C/000I0330]; BBSRC [BBS/E/C/000I0330] Funding Source: UKRI Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran; Institute of Earth Environment, Chinese Academy of Sciences(Chinese Academy of Sciences); project PANhellenic infrastructure for Atmospheric Composition and climatE change, PANACEA - Operational Programme Competitiveness, Entrepreneurship and Innovation (NSRF 2014-2020); UKRI-BBSRC (UK Research and Innovation-Biotechnology and Biological Sciences Research Council)(UK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC)); BBSRC(UK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC)) The authors would like to thank the Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran, for supporting this joint research project. The research was also supported by a collaborative agreement on fingerprinting sources of aeolian dust and climate and environmental change in the core zone of the Silk Road economic belt based on loess deposits between the University of Hormozgan and the Institute of Earth Environment, Chinese Academy of Sciences. D.G.K. acknowledges support from the project PANhellenic infrastructure for Atmospheric Composition and climatE change, PANACEA (MIS 5021516), which is implemented under the Action ``Reinforcement of the Research and Innovation Infrastructure, funded by the Operational Programme ``Competitiveness, Entrepreneurship and Innovation (NSRF 2014-2020). The contribution of ALC was funded by UKRI-BBSRC (UK Research and Innovation-Biotechnology and Biological Sciences Research Council) grant award BBS/E/C/000I0330. 91 14 14 2 22 TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP BUCA DOKUZ EYLUL UNIV, DEPT ENVIRONMENTAL ENGINEERING, TINAZTEPE CAMPUS, BUCA, IZMIR 35160, TURKEY 1309-1042 ATMOS POLLUT RES Atmos. Pollut. Res. SEP 2021.0 12.0 9 101173 10.1016/j.apr.2021.101173 0.0 AUG 2021 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology US8MT 2023-03-23 WOS:000697680600003 0
J Lin, JH; Rosasco, L; Zhou, DX Lin, Junhong; Rosasco, Lorenzo; Zhou, Ding-Xuan Iterative Regularization for Learning with Convex Loss Functions JOURNAL OF MACHINE LEARNING RESEARCH English Article CLASSIFICATION; CONSISTENCY We consider the problem of supervised learning with convex loss functions and propose a new form of iterative regularization based on the subgradient method. Unlike other regularization approaches, in iterative regularization no constraint or penalization is considered, and generalization is achieved by (early) stopping an empirical iteration. We consider a nonparametric setting, in the framework of reproducing kernel Hilbert spaces, and prove finite sample bounds on the excess risk under general regularity conditions. Our study provides a new class of efficient regularized learning algorithms and gives insights on the interplay between statistics and optimization in machine learning. [Lin, Junhong; Zhou, Ding-Xuan] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China; [Rosasco, Lorenzo] Univ Genoa, DIBRIS, Via Dodecaneso 35, I-16146 Genoa, Italy; [Rosasco, Lorenzo] Ist Italiano Tecnol, Lab Computat & Stat Learning, Bldg 46-5155,77 Massachusetts Ave, Cambridge, MA 02139 USA; [Rosasco, Lorenzo] MIT, Bldg 46-5155,77 Massachusetts Ave, Cambridge, MA 02139 USA City University of Hong Kong; University of Genoa; Massachusetts Institute of Technology (MIT) Lin, JH (corresponding author), City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China. JHLIN5@HOTMAIL.COM; LROSASCO@MIT.EDU; MAZHOU@CITYU.EDU.HK Lin, Junhong/M-9045-2016; Zhou, Ding-Xuan/B-3160-2013 Lin, Junhong/0000-0002-4507-9424; Zhou, Ding-Xuan/0000-0003-0224-9216 Research Grants Council of Hong Kong [CityU 104012]; National Natural Science Foundation of China [11461161006]; FIRB [RBFR12M3AC]; Center for Minds, Brains and Machines (CBMM); NSF STC award [CCF-1231216] Research Grants Council of Hong Kong(Hong Kong Research Grants Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); FIRB(Ministry of Education, Universities and Research (MIUR)Fund for Investment in Basic Research (FIRB)); Center for Minds, Brains and Machines (CBMM); NSF STC award(National Science Foundation (NSF)) The work described in this paper is supported partially by the Research Grants Council of Hong Kong [Project No. CityU 104012] and by National Natural Science Foundation of China under Grant 11461161006. LR is supported by the FIRB project RBFR12M3AC and the Center for Minds, Brains and Machines (CBMM), funded by NSF STC award CCF-1231216. JL is now within LCSL, MIT & Istituto Italiano di Tecnologia. The authors would like to thank the referees and Dr. Yunlong Feng for their valuable comments. 47 0 0 1 1 MICROTOME PUBL BROOKLINE 31 GIBBS ST, BROOKLINE, MA 02446 USA 1532-4435 J MACH LEARN RES J. Mach. Learn. Res. 2016.0 17.0 77 38 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science EH1KF 2023-03-23 WOS:000391524800001 0
J Chen, SY; Gu, CS; Lin, CN; Hariri-Ardebili, MA Chen, Siyu; Gu, Chongshi; Lin, Chaoning; Hariri-Ardebili, Mohammad Amin Prediction of arch dam deformation via correlated multi-target stacking APPLIED MATHEMATICAL MODELLING English Article Multi-target regression; Maximum correlated stacking of single-target; Machine learning; Prediction; Dam health monitoring SUPPORT VECTOR REGRESSION; EXTREME LEARNING-MACHINE; MODEL; ERROR; TEMPERATURE; RELIABILITY; ENSEMBLES; RISK Majority of the existing dam deformation monitoring models focus on the prediction of individual displacement, and ignore the spatial correlation of data. In this study, we propose a method dealing with multi-target prediction called the Maximum Correlated Stacking of Single-Target. The proposed method can provide reliable predictions of multi-target simultaneously, while fully exploiting the internal relationships between target variables via the strategy of targets stacking. Moreover, it can be coupled with different existing baseline models for the prediction and anomaly detection of arch dam deformation. Jinping-I arch dam is taken as a case study, where the monitoring displacement of 23 different points are analyzed and modeled simultaneously. Three kernel-based machine learning algorithms (i.e., support vector machine, relevance vector machine, and kernel extreme learning machine) and the partial least squares regression are adopted as baseline models for multi-target regression methods. Compared with the single-target regression and two state-of-the-art multi-target regression methods, the simulated results reveal the higher accuracy of the proposed method. Furthermore, model performance is validated in terms of anomaly detection capability, where two progressive anomalous scenarios (i.e., anomalies of single or multiple points) are investigated. The proposed method can be adapted for the health monitoring of other infrastructures in which multiple responses (e.g., displacement, temperature, or stress) need to be predicted simultaneously. (C) 2020 Elsevier Inc. All rights reserved. [Chen, Siyu; Gu, Chongshi; Lin, Chaoning] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing, Peoples R China; [Gu, Chongshi] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China; [Gu, Chongshi] Hohai Univ, Natl Engn Res Ctr Water Resources Efficient Utili, Nanjing, Peoples R China; [Chen, Siyu; Hariri-Ardebili, Mohammad Amin] Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO 80309 USA; [Hariri-Ardebili, Mohammad Amin] Univ Maryland, College Pk, MD 20742 USA; [Lin, Chaoning] Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands Hohai University; Hohai University; Hohai University; University of Colorado System; University of Colorado Boulder; University System of Maryland; University of Maryland College Park; Delft University of Technology Hariri-Ardebili, MA (corresponding author), Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO 80309 USA. sychenhhu@163.com; csgu@hhu.edu.cn; linchaoning@hhu.edu.cn; mohammad.haririardebili@colorado.edu Chen, Siyu/K-8902-2019; lin, chaoning/AAW-3324-2021 Chen, Siyu/0000-0003-1070-9761; lin, chaoning/0000-0001-5615-308X National Key R&D Program of China [2018YFC0407104]; National Natural Science Foundation of China [51739003, 51909173]; Fundamental Research Funds for the Central Universities [B200203065]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_0478]; Basic Research Project Funded of National Key Laboratory [20195025912]; China Scholarship Council [201906710106] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Postgraduate Research & Practice Innovation Program of Jiangsu Province; Basic Research Project Funded of National Key Laboratory; China Scholarship Council(China Scholarship Council) This study is financially supported by National Key R&D Program of China (2018YFC0407104), National Natural Science Foundation of China (Grant nos. 51739003, 51909173), the Fundamental Research Funds for the Central Universities (B200203065), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant KYCX20_0478), Basic Research Project Funded of National Key Laboratory (20195025912). The first author is supported by China Scholarship Council (Grant no. 201906710106). 58 21 23 12 82 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0307-904X 1872-8480 APPL MATH MODEL Appl. Math. Model. MAR 2021.0 91.0 1175 1193 10.1016/j.apm.2020.10.028 0.0 19 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics; Mechanics PP6RE 2023-03-23 WOS:000605986800004 0
J Zhu, SY; Li, XY; Xu, J; Cheng, TH; Zhang, XY; Wang, HM; Wang, YP; Miao, J Zhu, Songyan; Li, Xiaoying; Xu, Jian; Cheng, Tianhai; Zhang, Xingying; Wang, Hongmei; Wang, Yapeng; Miao, Jing Neural network aided fast pointing information determination approach for occultation payloads from in-flight measurements: Algorithm design and assessment ADVANCES IN SPACE RESEARCH English Article Occultation observation; Pointing information; N-2 absorption; Tangential strides; Triangular iteration; LSTM TROPOSPHERIC NO2; DISTRIBUTIONS; TEMPERATURE; VALIDATION; OMI Pointing information is decisive to solving precise profile retrieval issues from occultation measurements. Research regarding stratospheric O-3 hole in Antarctic and surface O-3 pollution would significantly benefit from massive occultation measurements. A neural network aided pointing information determination approach, in terms of tangent heights, is proposed to address issues requiring fast and easy-to-use determined tangent heights. The geometrical triangular iteration (GTI) algorithm in this work is based on N-2 absorption microwindows, and several treatments (e.g., tangential stride generator and triangular-net optimization) are adopted. In addition, LSTM is employed to reduce time consumption and increase accuracy. Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) in-flight measurements are used to assess this approach. The comparison between the proposed algorithm and eight official products indicates a promising performance. Correlation coefficient for each orbit is greater than 0.99. The processing time is about 16.6 min per orbit with an average cost of similar to 0.06. The introduction of LSTM technique demonstrates an approximate 28.49% better result, with less computation time. It costed less than 30 s to determine eight orbit tangent heights. In general, although minor issues remain, this LSTM-aided GTI algorithm is applicable in industry. (C) 2019 COSPAR. Published by Elsevier Ltd. [Zhu, Songyan] China Ctr Resources Satellite Data & Applicat, Beijing 100830, Peoples R China; [Zhu, Songyan; Li, Xiaoying; Cheng, Tianhai; Wang, Hongmei; Wang, Yapeng; Miao, Jing] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China; [Xu, Jian] German Aerosp Ctr DLR, Remote Sensing Technol Inst, Oberpfaffenhofen, Germany; [Zhang, Xingying] China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China; [Wang, Hongmei; Wang, Yapeng; Miao, Jing] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Helmholtz Association; German Aerospace Centre (DLR); China Meteorological Administration; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Li, XY (corresponding author), Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China. soonyenju@outlook.com; lixy01@radi.ac.cn; jian.xu@DLR.de; chength@radi.ac.cn; zxy@cma.gov.cn; wwhongmei@126.com; wangy-p@radi.ac.cn; miaojing16@mails.ucas.ac.cn Cheng, Tianhai/H-1113-2013; Xu, Jian/AAP-5179-2021 Cheng, Tianhai/0000-0001-7889-9579; Xu, Jian/0000-0003-2348-125X State Key Laboratory of Remote Sensing Science; State Key Laboratory of Digital Earth Science; National Natural Science Foundation of China [41571345]; Special Foundation for Free Exploration of State Laboratory of Remote Sensing Science [Y1Y00202KZ]; National Key Research and Development Program of China [2018YFB050490303]; Major Projects of High Resolution Earth Observation System [32-Y20A18-9001-15-17-1]; Canadian Space Agency (CSA) State Key Laboratory of Remote Sensing Science; State Key Laboratory of Digital Earth Science; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Special Foundation for Free Exploration of State Laboratory of Remote Sensing Science; National Key Research and Development Program of China; Major Projects of High Resolution Earth Observation System; Canadian Space Agency (CSA)(Canadian Space Agency) This work was supported by the State Key Laboratories of Remote Sensing Science and Digital Earth Science, the General Program of National Natural Science Foundation of China (Grant No. 41571345), the Special Foundation for Free Exploration of State Laboratory of Remote Sensing Science (Grant No. Y1Y00202KZ), the National Key Research and Development Program of China (Grant No. 2018YFB050490303), and the Major Projects of High Resolution Earth Observation System (Grant No. 32-Y20A18-9001-15-17-1). The ACE-FTS data were provided by ACE-FTS team. ACE, also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency (CSA). The ARTS forward model is downloaded from the website http://www.radiativetransfer.org/. 37 2 2 2 16 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. APR 15 2019.0 63.0 8 2323 2336 10.1016/j.asr.2019.01.041 0.0 14 Engineering, Aerospace; Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences HQ8QO Green Accepted, hybrid 2023-03-23 WOS:000462690600001 0
J Cohen, Y; Faccio, M; Pilati, F; Yao, XF Cohen, Yuval; Faccio, Maurizio; Pilati, Francesco; Yao, Xifan Design and management of digital manufacturing and assembly systems in the Industry 4.0 era INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Editorial Material Industry 4.0; Smart manufacturing; Factory of the Future; Assembly line; Cyber-physical production system; Internet of Things; Artificial Intelligence; Additive manufacturing; Predictive maintenance; Digitalization; Digital Twin; Framework; Editorial; Review AUGMENTED REALITY; INTERNET; INFORMATION; PICKING; THINGS The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given. [Cohen, Yuval] Tel Aviv Afeka Coll Engn, Dept Ind Engn, Mivtza Kadesh St 38, Tel Aviv, Israel; [Faccio, Maurizio] Univ Padua, Dept Management & Engn, Stradella San Nicola 3, I-36100 Vicenza, Italy; [Pilati, Francesco] Univ Trento, Dept Ind Engn, Via Sommar 9, I-38123 Trento, Italy; [Yao, Xifan] South China Univ Technol Guangzhou, Sch Mech & Automot Engn, Guangzhou 510640, Guangdong, Peoples R China University of Padua; University of Trento; South China University of Technology Pilati, F (corresponding author), Univ Trento, Dept Ind Engn, Via Sommar 9, I-38123 Trento, Italy. francesco.pilati@unitn.it Yao, Xifan/ABB-1074-2021; Cohen, Yuval/R-2804-2016 Yao, Xifan/0000-0002-2712-0411; Cohen, Yuval/0000-0002-8225-6960; PILATI, FRANCESCO/0000-0002-6085-0985 74 55 55 12 110 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. DEC 2019.0 105.0 9 SI 3565 3577 10.1007/s00170-019-04595-0 0.0 13 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering JT2MK Bronze 2023-03-23 WOS:000500829700001 0
J Chen, WB; Chen, WZ; Liu, HX; Wang, YQ; Bi, CL; Gu, Y Chen, Wenbai; Chen, Weizhao; Liu, Huixiang; Wang, Yiqun; Bi, Chunli; Gu, Yu A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation MATHEMATICS English Article DCNN-BiLSTM; domain adaptation; MMD; fine-tuning; C-MAPSS; cross-working; small sample USEFUL LIFE ESTIMATION To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network-bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions. [Chen, Wenbai; Chen, Weizhao; Liu, Huixiang; Wang, Yiqun] Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100101, Peoples R China; [Bi, Chunli] China Acad Informat & Commun Technol, Beijing 100191, Peoples R China; [Gu, Yu] Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China; [Gu, Yu] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Gu, Yu] Goethe Univ, Inst Inorgan & Analyt Chem, Dept Chem, Max Von Laue Str 9, D-60438 Frankfurt, Germany Beijing Information Science & Technology University; China Academy of Information & Communication Technology; Guangdong University of Petrochemical Technology; Beijing University of Chemical Technology; Goethe University Frankfurt Chen, WB (corresponding author), Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100101, Peoples R China. chenwb@bistu.edu.cn; chenwz312@163.com; liuhx@bistu.edu.cn; wangyiqun@bistu.edu.cn; bichunli@caict.ac.cn; guyufrankfurt@163.com Major Project of Scientific and Technological Innovation 2030 [2021ZD0113603]; Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University [QXTCP A202102]; General Project of Beijing Municipal Education Commission Scientific Research Program [KM202011232023] Major Project of Scientific and Technological Innovation 2030; Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University; General Project of Beijing Municipal Education Commission Scientific Research Program This research was funded by The Major Project of Scientific and Technological Innovation 2030 (2021ZD0113603), The Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University (QXTCP A202102), and The General Project of Beijing Municipal Education Commission Scientific Research Program (KM202011232023). 25 8 8 20 38 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics APR 2022.0 10.0 7 1022 10.3390/math10071022 0.0 14 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 0L2GR gold 2023-03-23 WOS:000781299000001 0
J Chen, XL; Liu, CY; Proietti, R; Li, ZH; Yoo, SJB Chen, Xiaoliang; Liu, Che-Yu; Proietti, Roberto; Li, Zhaohui; Yoo, S. J. Ben Automating Optical Network Fault Management with Machine Learning IEEE COMMUNICATIONS MAGAZINE English Article FAILURE IDENTIFICATION; LOCALIZATION Effective fault management is essential for quality of service assurance in optical networks. Conventional fault management designs for optical networks mainly rely on threshold-based rules, which can hardly characterize the complex fault patterns therein. This article discusses the application of machine learning (ML) in actuating an automated optical network fault management architecture. The architecture is built on advanced optical performance monitoring (OPM) techniques and software-defined-networking-enabled programmable network control and management. With the viability of abundant OPM data, the architecture employs various ML models to automatically learn fault patterns and thereby realize data-driven cognitive fault management. We review the state of the art on fault detection, identification, and localization based on such an architecture, focusing specifically on soft failures. To overcome the applicability and scalability issues encountered by existing soft failure detection designs, we present a hybrid learning solution that combines the merits of supervised, unsupervised, and cooperative ML. In particular, we present a self-taught mechanism that self-learns fault patterns with unsupervised learning and then trains supervised learning classifiers with the learned patterns for online detection. We further introduce a broker-plane-aided federated learning framework to enable collaborative training of classifiers from multiple network domains while complying with domain privacy constraints. Performance evaluations show that the hybrid learning design can achieve high fault detection rates with negligible false alarms using less than 10 abnormal data samples (similar to 0.1 percent of the scale of normal samples) for training. [Chen, Xiaoliang; Li, Zhaohui] Sun Yat Sen Univ, Guangzhou, Peoples R China; [Li, Zhaohui] Southern Marine Sci & Engn Guangdong Lab, Zhuhai, Peoples R China; [Liu, Che-Yu; Yoo, S. J. Ben] Univ Calif Davis, Davis, CA 95616 USA; [Proietti, Roberto] Politecn Torino, Turin, Italy Sun Yat Sen University; Southern Marine Science & Engineering Guangdong Laboratory; University of California System; University of California Davis; Polytechnic University of Turin Li, ZH (corresponding author), Sun Yat Sen Univ, Guangzhou, Peoples R China. xlichen@ucdavis.edu; cyliu@ucdavis.edu; roberto.proietti@polito.it; lzhh88@mail.sysu.edu.cn; sbyoo@ucdavis.edu Key-Area Research and Development Program of Guangdong Province [2020B0101080002]; NSFC Project [U2001601, ICE-T:RC 1836921]; NSF award [62035018] Key-Area Research and Development Program of Guangdong Province; NSFC Project(National Natural Science Foundation of China (NSFC)); NSF award(National Science Foundation (NSF)) This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2020B0101080002, NSFC Project U2001601, NSF award # ICE-T:RC 1836921, and NSFC Project 62035018. 15 1 1 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. DEC 2022.0 60.0 12 88 94 10.1109/MCOM.003.2200110 0.0 7 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 7J5RD 2023-03-23 WOS:000904638500018 0
C Song, YX; Cheng, C; Fei, YJ; Li, XQ; Liu, QC; Yu, CB IEEE Song, Yaoxian; Cheng, Chun; Fei, Yuejiao; Li, Xiangqing; Liu, Qingchen; Yu, Changbin 2.5D Image-based Robotic Grasping 2019 AUSTRALIAN & NEW ZEALAND CONTROL CONFERENCE (ANZCC) English Proceedings Paper Australian and New Zealand Control Conference (ANZCC) NOV 27-29, 2019 Auckland, NEW ZEALAND We consider the problem of robotic grasping by 2.5D image data sampling from a real sensor. We design an encoder-decoder neural network to predict grasping policy in real-time which enhances the robustness for the policy generation at different observation heights by fusing depth image and RGB image. We propose an open-loop algorithm to realize robotic grasp operation and evaluate our method in a physical robotic system. The result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0. [Song, Yaoxian; Cheng, Chun; Fei, Yuejiao; Li, Xiangqing; Yu, Changbin] Westlake Univ, Sch Engn, Hangzhou, Peoples R China; [Song, Yaoxian; Cheng, Chun] Fudan Univ, Westlake Inst Adv Study, Shanghai, Peoples R China; [Liu, Qingchen] Tech Univ Munich, Chair Informat Oriented Control, Munich, Germany Westlake University; Fudan University; Technical University of Munich Song, YX (corresponding author), Westlake Univ, Sch Engn, Hangzhou, Peoples R China. songyaoxian@westlake.edu.cn; chengchun@westlake.edu.cn; feiyuejiao@westlake.edu.cn; lixiangqing@westlake.edu.cn; qingchen.liu@tum.de; yuchangbin@westlake.edu.cn Liu, Qingchen/0000-0002-5892-3591 National Science Foundation China (NSFC) [61761136005]; Australian Research Council(ARC) [DP-160104500, DP-190100887] National Science Foundation China (NSFC)(National Natural Science Foundation of China (NSFC)); Australian Research Council(ARC)(Australian Research Council) This work is supported by National Science Foundation China (NSFC) 61761136005, and Australian Research Council(ARC) DP-160104500 and DP-190100887. 21 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-1786-7 2019.0 264 269 6 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BP3XO Green Submitted 2023-03-23 WOS:000549940000053 0
J Ai, H; Xin, YY; Luo, YJ; Gu, RL; Xu, PF Ai, Hui; Xin, Yuanyuan; Luo, Yue-jia; Gu, Ruolei; Xu, Pengfei Volume of motor area predicts motor impulsivity EUROPEAN JOURNAL OF NEUROSCIENCE English Article brain volume; motor impulsiveness; prediction analysis FUNCTIONAL CONNECTIVITY; INDIVIDUAL-DIFFERENCES; NEURAL BASIS; SCALE; EXTENT Impulsivity is a personality trait associated with many maladaptive behaviors. Trait impulsivity is typically divided into three different dimensions, including attentional impulsiveness, motor impulsiveness, and non-planning impulsiveness. In the present study, we examined the neuroanatomical basis of the multidimensional impulsivity trait. Eighty-four healthy participants were studied with structural magnetic resonance imaging. Multiple regression analyses revealed that the score of motor impulsiveness was negatively correlated with gray matter volumes of the right supplementary motor area and paracentral lobule. A machine-learning-based prediction analysis indicated that decreased gray matter volumes of the supplementary motor area and paracentral lobule strongly predicted the decrease in motor impulsiveness control. Our findings provide insights into the predictive role of motor brain structures in motor impulsivity and inhibition control. [Ai, Hui; Xin, Yuanyuan; Luo, Yue-jia; Xu, Pengfei] Shenzhen Univ, Ctr Brain Disorders & Cognit Sci, Shenzhen Key Lab Affect & Social Neurosci, Shenzhen, Peoples R China; [Xin, Yuanyuan; Xu, Pengfei] Univ Groningen, Univ Med Ctr Groningen, Dept Neurosci, Groningen, Netherlands; [Luo, Yue-jia; Xu, Pengfei] Shenzhen Inst Neurosci, Ctr Emot & Brain, Shenzhen, Peoples R China; [Luo, Yue-jia] Kunming Univ Sci & Technol, Med Sch, Kunming, Yunnan, Peoples R China; [Gu, Ruolei] Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China; [Gu, Ruolei] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China Shenzhen University; University of Groningen; Kunming University of Science & Technology; Chinese Academy of Sciences; Institute of Psychology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Gu, RL (corresponding author), Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China.;Xu, PF (corresponding author), Shenzhen Univ, Shenzhen Key Lab Affect & Social Neurosci, Shenzhen, Peoples R China. gurl@psych.ac.cn; xupf@szu.edu.cn Gu, Ruolei/H-6946-2019 Gu, Ruolei/0000-0001-8722-2172 National Natural Science Foundation of China [31500920, 31530031, 31571124, 31671133, 31700959, 31871137, 81471376]; Shenzhen Peacock Program [827-000235, KQTD2015033016104926]; Natural Science Foundation of Shenzhen University [8530300000275]; Zhujiang Talent Project for Postdoctoral researchers, Guangdong young Innovative Talent Project [2016KQNCX149]; Shenzhen Science and Technology Research Funding Program [JCYJ20170412164413575, JCYJ20170307155304424] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Peacock Program; Natural Science Foundation of Shenzhen University; Zhujiang Talent Project for Postdoctoral researchers, Guangdong young Innovative Talent Project; Shenzhen Science and Technology Research Funding Program National Natural Science Foundation of China, Grant/Award Number: 31500920, 31530031, 31571124, 31671133, 31700959, 31871137 and 81471376; Shenzhen Peacock Program, Grant/Award Number: 827-000235 and KQTD2015033016104926; Natural Science Foundation of Shenzhen University, Grant/Award Number: 8530300000275; Zhujiang Talent Project for Postdoctoral researchers, Guangdong young Innovative Talent Project, Grant/Award Number: 2016KQNCX149; Shenzhen Science and Technology Research Funding Program, Grant/Award Number: JCYJ20170412164413575 and JCYJ20170307155304424 39 6 6 0 13 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0953-816X 1460-9568 EUR J NEUROSCI Eur. J. Neurosci. JUN 2019.0 49.0 11 1470 1476 10.1111/ejn.14339 0.0 7 Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology IM1IM 30636081.0 2023-03-23 WOS:000477742100009 0
J Zhang, YK; Hu, DL; Lyu, TL; Quan, GT; Xiang, J; Coatrieux, G; Luo, SH; Chen, Y Zhang, Yikun; Hu, Dianlin; Lyu, Tianling; Quan, Guotao; Xiang, Jun; Coatrieux, Gouenou; Luo, Shouhua; Chen, Yang SPIE-DIR: Self-Prior Information Enhanced Deep Iterative Reconstruction Using Two Complementary Limited-Angle Scans for DECT IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Image reconstruction; Computed tomography; Deep learning; Iterative algorithms; X-ray imaging; Reconstruction algorithms; Neural networks; Deep iterative reconstruction; dual-energy computed tomography; generative adversarial network; limited-angle; self-prior information DUAL-ENERGY CT; GENERATIVE ADVERSARIAL NETWORK; PHOTON-COUNTING DETECTOR; COMPUTED-TOMOGRAPHY; IMAGE-RECONSTRUCTION; DOMAIN; RADIATION; NET; OPTIMIZATION; ANGIOGRAPHY Dual-energy computed tomography (DECT) can simultaneously provide the anatomical structure and material-specific information of the scanned object, having many applications in industry and medicine. Different from conventional CT, DECT acquires two attenuation measurements of the same object at two different X-ray spectra, resulting in apparent redundant information. This article exploits this kind of redundancy to develop the self-prior information enhanced deep iterative reconstruction (SPIE-DIR) algorithm for limited-angle DECT. Unlike the routine practice in model-based deep learning (DL) algorithms, the SPIE-DIR method simultaneously performs constraints in the projection, residual, and image domains, corresponding to three modules: projection inpainting, residual correction, and image refinement. During this stage, the prior image and prior projection derived from two complementary limited-angle scans are used to improve the algorithm performance. Besides, to avoid the blurring effect caused by minimizing the Euclidean distance, the Wasserstein generative adversarial network with gradient penalty is adopted to enhance the visual perception of the generated results. Experiments on the simulated data and real rat data have demonstrated that the proposed SPIE-DIR algorithm has the potential to obtain high-quality DECT images from two limited-angle scans. Furthermore, visual and quantitative assessments have shown the promising performance of SPIE-DIR in artifact removal, structural fidelity, CT number preservation, and visual perception enhancement. [Zhang, Yikun; Hu, Dianlin; Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Lyu, Tianling] Zhejiang Lab, Hangzhou 311121, Peoples R China; [Quan, Guotao] United Imaging Healthcare Ltd Co, CT RPA Dept, Shanghai 201807, Peoples R China; [Xiang, Jun] United Imaging Healthcare Ltd Co, Xray Dept, Shanghai 201807, Peoples R China; [Coatrieux, Gouenou] IMT Atlantique, LaTIM UMR1101, INSERM, F-29000 Brest, France; [Luo, Shouhua] Southeast Univ, Dept Biomed Engn, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China Southeast University - China; Zhejiang Laboratory; IMT - Institut Mines-Telecom; IMT Atlantique; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; Southeast University - China; Southeast University - China Chen, Y (corresponding author), Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China. yikun@seu.edu.cn; dianlin@seu.edu.cn; lvfucius@gmail.com; guotao.quan@united-imaging.com; jun.xiang@united-imaging.com; gouenou.coatrieux@telecom-bretagne.eu; luoshouhua@seu.edu.cn; chenyang.list@seu.edu.cn Zhang, Yikun/0000-0002-4048-4869 State Key Project of Research and Development Plan [2022YFC2401600]; National Natural Science Foundation of China [T2225025]; Key Research and Development Programs in Jiangsu Province of China [BE2021703, BE2022768]; China Scholarship Council [202006090314] State Key Project of Research and Development Plan; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Programs in Jiangsu Province of China; China Scholarship Council(China Scholarship Council) This work was supported in part by the State Key Project of Research and Development Plan under Grant 2022YFC2401600,in part by the National Natural Science Foundation of China under Grant T2225025, in part by the Key Research and Development Programs in Jiangsu Province of China under Grant BE2021703 and Grant BE2022768, and in part by the China Scholarship Council under Grant 202006090314. 87 0 0 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2023.0 72.0 4500212 10.1109/TIM.2022.3227549 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 7W1SM 2023-03-23 WOS:000913293900094 0
J Nourani, M; Alali, N; Samadianfard, S; Band, SS; Chau, KW; Shu, CM Nourani, Meysam; Alali, Najeh; Samadianfard, Saeed; Band, Shahab S.; Chau, Kwok-wing; Shu, Chi-Min Comparison of machine learning techniques for predicting porosity of chalk JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING English Article Porosity; Chalk; Hand-held X-ray fluorescence; Random forest; Multilayer perceptron; Random forest optimized by genetic algorithm; Multilayer perceptron optimized by genetic algorithm MULTILAYER PERCEPTRON; RANDOM FOREST; GENETIC ALGORITHM; DIAGENESIS; MODELS; OPTIMIZATION; CARBON; SHALE Precise and fast estimation of porosity is a vital element of reservoir characterization. A new technology for fast and reliable porosity prediction of chalk samples is presented by applying machine learning methods and X-ray fluorescence (XRF) elemental analysis. Input parameters of prediction models are based on rapid and accurate elemental analysis of chalk samples obtained from Hand-held X-ray fluorescence (HH-XRF) measurements. The intelligent models, including Random Forest (RF), Multilayer perceptron (MLP), Random Forest integrated by Genetic Algorithm (GA-RF) and Multilayer Perceptron integrated by Genetic Algorithm (GA-MLP), are trained and tested based on samples consisting of outcrop chalk samples from Rordal and Stevns Klint (ST) and core samples from Ekofisk Formation in the North Sea. Results are evaluated by sustainability index (SI), determination coefficient (R-2), correlation coefficient (CC), and Willmott's Index of agreement (WI). Results indicate that the combination of GA-RF intelligent method with XRF elemental analysis successfully provides an accurate model by 0.99, 0.02, 0.995 and 0.99 respectively for CC, SI, WI and R-2, respectively. [Nourani, Meysam] Geol Survey Denmark & Greenland GEUS, Reservoir Geol Dept, Copenhagen, Denmark; [Alali, Najeh] Al Ayen Univ, Coll Petr Engn, Thi Gar 64001, Iraq; [Samadianfard, Saeed] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz, Iran; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; [Chau, Kwok-wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Shu, Chi-Min] Natl Yunlin Univ Sci & Technol, Grad Sch Engn Sci & Technol, Touliu 64002, Yunlin, Taiwan Geological Survey Of Denmark & Greenland; University of Tabriz; National Yunlin University Science & Technology; Hong Kong Polytechnic University; National Yunlin University Science & Technology Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan. shamshirbands@yuntech.edu.tw Alali, Najeh/K-6240-2019; S.Band, Shahab/AAD-3311-2021; Chau, Kwok-wing/E-5235-2011; Shu, Chi-Min/AHE-7272-2022; Samadianfard, Saeed/ABF-1097-2021; S. Band, Shahab/ABB-2469-2020; Nourani, Meysam/L-5068-2016 Alali, Najeh/0000-0002-9958-2412; Chau, Kwok-wing/0000-0001-6457-161X; Shu, Chi-Min/0000-0001-9455-6162; Samadianfard, Saeed/0000-0002-6876-7182; S. Band, Shahab/0000-0001-6109-1311; Nourani, Meysam/0000-0002-1923-1648 77 8 8 8 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0920-4105 1873-4715 J PETROL SCI ENG J. Pet. Sci. Eng. FEB 2022.0 209.0 109853 10.1016/j.petrol.2021.109853 0.0 JAN 2022 9 Energy & Fuels; Engineering, Petroleum Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering YF2UI 2023-03-23 WOS:000741667900002 0
J Sun, QQ; Bourennane, S; Liu, XF Sun, Qiaoqiao; Bourennane, Salah; Liu, Xuefeng Multi-size and multi-model framework based on progressive growing and transfer learning for small target feature extraction and classification INTERNATIONAL JOURNAL OF REMOTE SENSING English Article IMAGE CLASSIFICATION; NEURAL-NETWORKS Many deep learning-based models have been designed to extract features for hyperspectral classification. The high-dimensional target data in hyperspectral images (HSIs) can be mapped to corresponding low-dimensional features by the deep learning models. However, for most models, the input size for different targets is always same while different targets often perform differently with the same input size, especially when there are small targets. In order to solve this problem and balance different targets, a novel multi-size and multi-model framework based on three-dimensional convolutional autoencoder, called 3D-M(2)CAE, is proposed in this paper. The establishment and optimization of the framework rely on progressive growing and transfer learning. For each observed target, three 3D-CAEs with progressive input sizes and layers are used to obtain features, which can improve the feature robustness to spatial variation. In order to reduce the amount of calculation and save training time, three three-dimensional convolutional autoencoders (3D-CAEs) are constructed and trained in a progressive way with the help of transfer learning. The weights of the middle layers of the latter 3D-CAE are transferred from the former optimized 3D-CAE, which speeds up and facilitates network training. Benefiting from this training method, the features of the same target from different sizes are obtained in a more efficient way. Moreover, the proposed framework has great robustness to different targets and the obtained features can provide more information for subsequent processing. The effectiveness of the proposed method is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great potential in improving the classification results of small targets. [Sun, Qiaoqiao; Bourennane, Salah] Aix Marseille Univ, Inst Fresnel, Cent Marseille, CNRS, Marseille, France; [Sun, Qiaoqiao; Liu, Xuefeng] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Aix-Marseille Universite; Qingdao University of Science & Technology Bourennane, S (corresponding author), Aix Marseille Univ, Inst Fresnel, Cent Marseille, CNRS, Marseille, France. salah.bourennane@fresnel.fr /0000-0003-2383-369X China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) The first author would like to thank the China Scholarship Council (CSC) for a Ph.D. grant. All authors would like to thank the reviewers and editors for their careful reading and helpful comments which improve the quality of this paper. 37 2 2 2 11 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. NOV 2 2021.0 42.0 21 8145 8164 10.1080/01431161.2021.1973688 0.0 20 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology WA5JO 2023-03-23 WOS:000702921500001 0
J Dreissigacker, C; Sharma, R; Messenger, C; Zhao, RN; Prix, R Dreissigacker, Christoph; Sharma, Rahul; Messenger, Chris; Zhao, Ruining; Prix, Reinhard Deep-learning continuous gravitational waves PHYSICAL REVIEW D English Article We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the WEAVE pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an easy search using T = 10(5) s of data, and a harder search using T = 10(6) s. The detection probability p(det) is measured on a signal population for which matched filtering achieves P-det = 90% in Gaussian noise. In the easiest test case (T = 10(5) s at 20 Hz) the DNN achieves p(det) similar to 88%, corresponding to a loss in sensitivity depth of similar to 5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until p(det )similar to 13% and a loss of similar to 66% in sensitivity depth in the hardest case (T = 10(6) s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search. [Dreissigacker, Christoph; Sharma, Rahul; Prix, Reinhard] Albert Einstein Inst, Max Planck Inst Gravitat Phys, D-30167 Hannover, Germany; [Dreissigacker, Christoph; Sharma, Rahul; Prix, Reinhard] Leibniz Univ Hannover, D-30167 Hannover, Germany; [Sharma, Rahul] Birla Inst Technol & Sci, Pilani 333031, Rajasthan, India; [Messenger, Chris] Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow G12 8QQ, Lanark, Scotland; [Zhao, Ruining] Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100101, Peoples R China; [Zhao, Ruining] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Zhao, Ruining] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China Max Planck Society; Leibniz University Hannover; Birla Institute of Technology & Science Pilani (BITS Pilani); University of Glasgow; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Beijing Normal University Dreissigacker, C (corresponding author), Albert Einstein Inst, Max Planck Inst Gravitat Phys, D-30167 Hannover, Germany.;Dreissigacker, C (corresponding author), Leibniz Univ Hannover, D-30167 Hannover, Germany. christoph.dreissigacker@aei.mpg.de Dreissigacker, Christoph/0000-0001-5859-185X; Messenger, Chris/0000-0001-7488-5022; ZHAO, Ruining/0000-0003-4936-4959 Science and Technology Research Council [ST/L000946/1]; European Cooperation in Science and Technology (COST) action [CA17137]; STFC [ST/N005422/1, Gravitational Waves] Funding Source: UKRI Science and Technology Research Council; European Cooperation in Science and Technology (COST) action(European Cooperation in Science and Technology (COST)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank Sinead Walsh, Maria Alessandra Papa, Marlin Schafer and the AEI CW group for helpful comments. All WEAVE Monte Carlo simulations and all DNN training and testing were performed on the ATLAS computing cluster of the Albert-Einstein Institute in Hannover. C.M. is supported by the Science and Technology Research Council (Grant No. ST/L000946/1) and the European Cooperation in Science and Technology (COST) action CA17137. 49 44 45 2 9 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D AUG 7 2019.0 100.0 4 44009 10.1103/PhysRevD.100.044009 0.0 11 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics IN9XA Green Published, hybrid, Green Accepted, Green Submitted 2023-03-23 WOS:000479032600004 0
C You, WZ; Alexandre, S; Ichchou, M; Abdel, Z; Zhong, XP Belhaq, M You, Weizhen; Alexandre, Saidi; Ichchou, Mohamed; Abdel, Zine; Zhong, Xiaopin Reliability modeling and prediction of passive controlled structures through Random Forest INTERNATIONAL CONFERENCE ON STRUCTURAL NONLINEAR DYNAMICS AND DIAGNOSIS (CSNDD 2018) MATEC Web of Conferences English Proceedings Paper International Conference on Structural Nonlinear Dynamics and Diagnosis (CSNDD) JUN 25-27, 2018 Tangier, MOROCCO OPTIMIZATION Reliability prediction plays a significant role in risk assessment of engineering structures. Mathematically, the prediction task can be seen as a classification (regression) procedure. In this aspect, machine learning methods have recently shown their superior performance over others in various research domains. Random forest (RF) is distinguished for its robustness and high accuracy in modeling and prediction work. However, its application in the area of structural reliability has not been widely explored. This study aims to explore the feasibility of RF as well as examine its performance in modeling and prediction of structure reliability in passive control mode. A numerical example is introduced in the simulation part to evaluate performance of the proposed method in different perspectives. [You, Weizhen; Alexandre, Saidi; Ichchou, Mohamed; Abdel, Zine] Ecole Cent Lyon, LTDS, F-69134 Ecully, France; [Zhong, Xiaopin] Shenzhen Univ, Shenzhen 518060, Peoples R China Ecole Centrale de Lyon; Shenzhen University Ichchou, M (corresponding author), Ecole Cent Lyon, LTDS, F-69134 Ecully, France. mohamed.ichchou@ec-lyon.fr 8 0 0 2 2 E D P SCIENCES CEDEX A 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A, FRANCE 2261-236X MATEC WEB CONF 2018.0 241.0 1023 10.1051/matecconf/201824101023 0.0 3 Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Mathematics BP9TZ gold 2023-03-23 WOS:000570880700023 0
J Wang, H; Chen, YY; Cai, YF; Chen, L; Li, YC; Sotelo, MA; Li, ZX Wang, Hai; Chen, Yanyan; Cai, Yingfeng; Chen, Long; Li, Yicheng; Sotelo, Miguel Angel; Li, Zhixiong SFNet-N: An Improved SFNet Algorithm for Semantic Segmentation of Low-Light Autonomous Driving Road Scenes IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Image segmentation; Semantics; Lighting; Meteorology; Roads; Autonomous vehicles; Annotations; Semantic segmentation; deep learning; low visible light; autonomous driving In recent years, considerable progress has been made in semantic segmentation of images with favorable environments. However, the environmental perception of autonomous driving under adverse weather conditions is still very challenging. In particular, the low visibility at nighttime greatly affects driving safety. In this paper, we aim to explore image segmentation in low-light scenarios, thereby expanding the application range of autonomous vehicles. The segmentation algorithms for road scenes based on deep learning are highly dependent on the volume of images with pixel-level annotations. Considering the scarcity of labeled large-scale nighttime data, we performed synthetic data collection and data style transfer using images acquired in daytime based on the autonomous driving simulation platform and generative adversarial network, respectively. In addition, we also proposed a novel nighttime segmentation framework (SFNET-N) to effectively recognize objects in dark environments, aiming at the boundary blurring caused by low semantic contrast in low-illumination images. Specifically, the framework comprises a light enhancement network which introduces semantic information for the first time and a segmentation network with strong feature extraction capability. Extensive experiments with Dark Zurich-test and Nighttime Driving-test datasets show the effectiveness of our method compared with existing state-of-the art approaches, with 56.9% and 57.4% mIoU (mean of category-wise intersection-over-union) respectively. Finally, we also performed real-vehicle verification of the proposed models in road scenes of Zhenjiang city with poor lighting. The datasets are available at https://github.com/pupu-chenyanyan/semantic-segmentation-on-nightime. [Wang, Hai; Chen, Yanyan] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [Cai, Yingfeng; Chen, Long; Li, Yicheng] Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China; [Sotelo, Miguel Angel] Univ Alcala, Dept Comp Engn, Alcala De Henares 28801, Madrid, Spain; [Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea Jiangsu University; Jiangsu University; Universidad de Alcala; Opole University of Technology; Yonsei University Cai, YF (corresponding author), Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China. wanghai1019@163.com; 1912128283@qq.com; caicaixiao0304@126.com; chenlong@ujs.edu.cn; liyucheng070@163.com; miguel.sotelo@uah.es; zhixiong.li@yonsei.ac.kr Sotelo, Miguel Angel/A-8663-2013; Li, Zhixiong/G-8418-2018 Sotelo, Miguel Angel/0000-0001-8809-2103; Wang, Hai/0000-0002-9136-8091; Cai, Yingfeng/0000-0002-0633-9887; Li, Zhixiong/0000-0002-7265-0008 National Natural Science Foundation of China [U20A20333, 52072160, 51875255]; Key Research and Development Program of Jiangsu Province [BE2019010-2, BE2020083-3]; Jiangsu Province's Six Talent Peaks [TD-GDZB-022]; Norwegian Financial Mechanism; Narodowego Centrum Nauki [2020/37/K/ST8/02748]; Natural Science Foundation of Jiangsu Province [BK20190853]; China Postdoctoral Science Foundation [2020T130258] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Jiangsu Province; Jiangsu Province's Six Talent Peaks; Norwegian Financial Mechanism; Narodowego Centrum Nauki; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant U20A20333, Grant 52072160, and Grant 51875255; in part by the Key Research and Development Program of Jiangsu Province under Grant BE2019010-2 and Grant BE2020083-3; in part by the Jiangsu Province's Six Talent Peaks under Grant TD-GDZB-022; in part by the Norwegian Financial Mechanism 2014-2021 with Narodowego Centrum Nauki under Grant 2020/37/K/ST8/02748; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20190853; and in part by the China Postdoctoral Science Foundation under Grant 2020T130258. 54 18 18 25 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. NOV 2022.0 23.0 11 21405 21417 10.1109/TITS.2022.3177615 0.0 MAY 2022 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 6A6GZ 2023-03-23 WOS:000809363800001 0
J Gao, Y; Liu, HZ; Jiang, L; Yang, CF; Yin, XD; Coatrieux, JL; Chen, Y Gao, Yuan; Liu, Hongzhi; Jiang, Liang; Yang, Chunfeng; Yin, Xindao; Coatrieux, Jean-Louis; Chen, Yang CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Article Rib fracture; Deep learning; Contralateral module; Contextual module; Edge enhanced module BLUNT CHEST TRAUMA; MANAGEMENT; MORBIDITY; MORTALITY; CT; RISK Rib fracture is a common disease that requires prompt treatment. This study focuses on developing a rib fracture diagnosis deep learning method using contralateral, contextual, and edge enhanced modules and evaluating its detection performance. A novel rib fracture diagnosis method was designed, named CCE-Net. To evaluate the performance of this method, 1639 digital radiography (DR) images were enrolled. Fracture features were extracted for three modules: contralateral, contextual, and edge enhanced modules. These modules can be used to identify fracture features in rib DR images, imitating the experience of broad-certificated radiologists. The contralateral module assists in diagnosing rib fractures by comparing the difference between the detected target region and the contralateral region. The contextual module helps to aid rib fracture detection by extracting contextual features. The edge enhanced module improves the accuracy of fracture detection by enhancing the edge information of the rib bone. The head of this two-stage detection network uses the multi-path fusion mechanism as the main architecture to integrate and utilize the above modules. The qualitative results show that with the ground truth of rib fracture as the evaluation standard, CCE-Net can achieve a better visual effect of fracture detection than other methods. The quantitative results show that CCE-Net can achieve the best performance in various detection indicators include AP50 0.911, AP75 0.794, AP25 0.913, and Recall 0.934. Experimental results show that CCE-Net can acquire the excellent ability of rib fracture diagnosis. We invasion that this approach will be applied to clinical study. [Gao, Yuan; Liu, Hongzhi; Yang, Chunfeng; Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Gao, Yuan; Liu, Hongzhi; Yang, Chunfeng; Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China; [Jiang, Liang; Yin, Xindao] Nanjing Med Univ, Nanjing Hosp 1, Dept Radiol, Nanjing 210006, Peoples R China; [Coatrieux, Jean-Louis] Univ Rennes 1, INSERM, Ctr Rech Informat Biomed Sino Francais, F-35042 Rennes, France Southeast University - China; Southeast University - China; Nanjing Medical University; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China.;Jiang, L (corresponding author), Nanjing Med Univ, Nanjing Hosp 1, Dept Radiol, Nanjing 210006, Peoples R China. Jiangliang0402@163.com; chenyang.list@seu.edu.cn Gao, Yuan/0000-0001-5351-6922 Postgraduate Research & Practice Innovation Program of Jiangsu Province [SJCX21_0635]; Xinghuo Talent Program of Nanjing First Hospital; State's Key Project of Research and Development Plan [2017YFA0104302, 2017YFC0109202, 2017YFC0107900]; National Natural Science Foundation [81530060, 61871117]; Science and Technology Program of Guangdong [2018B030333001] Postgraduate Research & Practice Innovation Program of Jiangsu Province; Xinghuo Talent Program of Nanjing First Hospital; State's Key Project of Research and Development Plan; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Guangdong This work was supported in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grants SJCX21_0635; in part by the Xinghuo Talent Program of Nanjing First Hospital; in part by the State's Key Project of Research and Development Plan under Grants 2017YFA0104302, 2017YFC0109202, and 2017YFC0107900, in part by the National Natural Science Foundation under Grants 81530060 and 61871117, in part by the Science and Technology Program of Guangdong under Grant 2018B030333001. 48 1 1 7 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control MAY 2022.0 75.0 103620 10.1016/j.bspc.2022.103620 0.0 MAR 2022 10 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0G7PY Green Submitted 2023-03-23 WOS:000778233800004 0
J Tien, KJ; Wang, MZ; Adachi, I; Aihara, H; Asner, DM; Aulchenko, V; Aushev, T; Bakich, AM; Bala, A; Bhuyan, B; Bozek, A; Bracko, M; Browder, TE; Chang, P; Chekelian, V; Chen, A; Chen, P; Cheon, BG; Chilikin, K; Chistov, R; Cho, IS; Cho, K; Chobanova, V; Choi, Y; Cinabro, D; Dalseno, J; Danilov, M; Dolezal, Z; Drasal, Z; Dutta, D; Eidelman, S; Farhat, H; Fast, JE; Ferber, T; Gaur, V; Ganguly, S; Gillard, R; Goh, YM; Golob, B; Haba, J; Hayashii, H; Horii, Y; Hoshi, Y; Hou, WS; Hsiung, YB; Huschle, M; Hyun, HJ; Iijima, T; Ishikawa, A; Itoh, R; Iwasaki, Y; Julius, T; Kah, DH; Kang, JH; Kato, E; Kawasaki, T; Kichimi, H; Kiesling, C; Kim, DY; Kim, HJ; Kim, JB; Kim, JH; Kim, YJ; Klucar, J; Ko, BR; Kodys, P; Korpar, S; Krizan, P; Krokovny, P; Kronenbitter, B; Kuhr, T; Kumita, T; Kuzmin, A; Kwon, YJ; Lee, SH; Li, J; Li, Y; Libby, J; Liu, C; Liu, Y; Liventsev, D; Lukin, P; Miyabayashi, K; Miyata, H; Mohanty, GB; Moll, A; Mussa, R; Nakano, E; Nakao, M; Natkaniec, Z; Nayak, M; Nedelkovska, E; Ng, C; Nisar, NK; Nishida, S; Nitoh, O; Ogawa, S; Okuno, S; Olsen, SL; Ostrowicz, W; Oswald, C; Park, CW; Park, H; Park, HK; Pedlar, TK; Pestotnik, R; Petric, M; Piilonen, LE; Ritter, M; Rohrken, M; Rostomyan, A; Sahoo, H; Saito, T; Sakai, Y; Sandilya, S; Santel, D; Santelj, L; Sanuki, T; Sato, Y; Savinov, V; Schneider, O; Schnell, G; Schwanda, C; Semmler, D; Senyo, K; Seon, O; Sevior, ME; Shapkin, M; Shen, CP; Shibata, TA; Shiu, JG; Sibidanov, A; Sohn, YS; Sokolov, A; Stanic, S; Staric, M; Steder, M; Sumihama, M; Sumiyoshi, T; Tanida, K; Tatishvili, G; Teramoto, Y; Uchida, M; Uehara, S; Uglov, T; Unno, Y; Uno, S; Urquijo, P; Vahsen, SE; Van Hulse, C; Vanhoefer, P; Varner, G; Varvell, KE; Vinokurova, A; Vorobyev, V; Wagner, MN; Wang, CH; Wang, P; Watanabe, M; Watanabe, Y; Williams, KM; Won, E; Yamaoka, J; Yamashita, Y; Yashchenko, S; Zhang, ZP; Zhilich, V; Zhulanov, V; Zupanc, A Tien, K. -J.; Wang, M. -Z.; Adachi, I.; Aihara, H.; Asner, D. M.; Aulchenko, V.; Aushev, T.; Bakich, A. M.; Bala, A.; Bhuyan, B.; Bozek, A.; Bracko, M.; Browder, T. E.; Chang, P.; Chekelian, V.; Chen, A.; Chen, P.; Cheon, B. G.; Chilikin, K.; Chistov, R.; Cho, I. -S.; Cho, K.; Chobanova, V.; Choi, Y.; Cinabro, D.; Dalseno, J.; Danilov, M.; Dolezal, Z.; Drasal, Z.; Dutta, D.; Eidelman, S.; Farhat, H.; Fast, J. E.; Ferber, T.; Gaur, V.; Ganguly, S.; Gillard, R.; Goh, Y. M.; Golob, B.; Haba, J.; Hayashii, H.; Horii, Y.; Hoshi, Y.; Hou, W. -S.; Hsiung, Y. B.; Huschle, M.; Hyun, H. J.; Iijima, T.; Ishikawa, A.; Itoh, R.; Iwasaki, Y.; Julius, T.; Kah, D. H.; Kang, J. H.; Kato, E.; Kawasaki, T.; Kichimi, H.; Kiesling, C.; Kim, D. Y.; Kim, H. J.; Kim, J. B.; Kim, J. H.; Kim, Y. J.; Klucar, J.; Ko, B. R.; Kodys, P.; Korpar, S.; Krizan, P.; Krokovny, P.; Kronenbitter, B.; Kuhr, T.; Kumita, T.; Kuzmin, A.; Kwon, Y. -J.; Lee, S. -H.; Li, J.; Li, Y.; Libby, J.; Liu, C.; Liu, Y.; Liventsev, D.; Lukin, P.; Miyabayashi, K.; Miyata, H.; Mohanty, G. B.; Moll, A.; Mussa, R.; Nakano, E.; Nakao, M.; Natkaniec, Z.; Nayak, M.; Nedelkovska, E.; Ng, C.; Nisar, N. K.; Nishida, S.; Nitoh, O.; Ogawa, S.; Okuno, S.; Olsen, S. L.; Ostrowicz, W.; Oswald, C.; Park, C. W.; Park, H.; Park, H. K.; Pedlar, T. K.; Pestotnik, R.; Petric, M.; Piilonen, L. E.; Ritter, M.; Roehrken, M.; Rostomyan, A.; Sahoo, H.; Saito, T.; Sakai, Y.; Sandilya, S.; Santel, D.; Santelj, L.; Sanuki, T.; Sato, Y.; Savinov, V.; Schneider, O.; Schnell, G.; Schwanda, C.; Semmler, D.; Senyo, K.; Seon, O.; Sevior, M. E.; Shapkin, M.; Shen, C. P.; Shibata, T. -A.; Shiu, J. -G.; Sibidanov, A.; Sohn, Y. -S.; Sokolov, A.; Stanic, S.; Staric, M.; Steder, M.; Sumihama, M.; Sumiyoshi, T.; Tanida, K.; Tatishvili, G.; Teramoto, Y.; Uchida, M.; Uehara, S.; Uglov, T.; Unno, Y.; Uno, S.; Urquijo, P.; Vahsen, S. E.; Van Hulse, C.; Vanhoefer, P.; Varner, G.; Varvell, K. E.; Vinokurova, A.; Vorobyev, V.; Wagner, M. N.; Wang, C. H.; Wang, P.; Watanabe, M.; Watanabe, Y.; Williams, K. M.; Won, E.; Yamaoka, J.; Yamashita, Y.; Yashchenko, S.; Zhang, Z. P.; Zhilich, V.; Zhulanov, V.; Zupanc, A. Belle Collaboration Evidence for semileptonic B- -> p(p)over-barl(-)(nu)over-bar(l) decays PHYSICAL REVIEW D English Article We find evidence for the semileptonic baryonic decay B- -> p (p) over barl(-)(nu) over bar (l) (l = e, mu), based on a data sample of 772 million B (B) over bar pairs collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy electron-positron collider. A neural-network based hadronic B-meson tagging method is used in this study. The branching fraction of B- -> p (p) over barl(-)(nu) over bar (l) is measured to be (5.8(-2.1)(+2.4)(stat) +/- 0.9(syst)) x 10(-6) with a significance of 3.2 sigma s, where lepton universality is assumed. We also estimate the corresponding upper limit: B(B- -> p (p) over barl(-)(nu) over bar (l)) < 9.6 x 10(-6) at the 90% confidence level. This measurement helps constrain the baryonic transition form factor in B decays. [Schnell, G.; Van Hulse, C.] Univ Basque Country UPV EHU, Bilbao 48080, Spain; [Oswald, C.; Urquijo, P.] Univ Bonn, D-53115 Bonn, Germany; [Aulchenko, V.; Eidelman, S.; Krokovny, P.; Kuzmin, A.; Lukin, P.; Vinokurova, A.; Vorobyev, V.; Zhilich, V.; Zhulanov, V.] Budker Inst Nucl Phys SB RAS, Novosibirsk 630090, Russia; [Aulchenko, V.; Eidelman, S.; Krokovny, P.; Kuzmin, A.; Lukin, P.; Vinokurova, A.; Vorobyev, V.; Zhilich, V.; Zhulanov, V.] Novosibirsk State Univ, Novosibirsk 630090, Russia; [Dolezal, Z.; Drasal, Z.; Kodys, P.] Charles Univ Prague, Fac Math & Phys, CR-12116 Prague, Czech Republic; [Liu, Y.; Santel, D.] Univ Cincinnati, Cincinnati, OH 45221 USA; [Ferber, T.; Rostomyan, A.; Steder, M.; Yashchenko, S.] DESY, D-22607 Hamburg, Germany; [Semmler, D.; Wagner, M. N.] Univ Giessen, D-35392 Giessen, Germany; [Sumihama, M.] Gifu Univ, Gifu 5011193, Japan; [Cheon, B. G.; Goh, Y. M.; Unno, Y.] Hanyang Univ, Seoul 133791, South Korea; [Browder, T. E.; Sahoo, H.; Vahsen, S. E.; Varner, G.; Yamaoka, J.] Univ Hawaii, Honolulu, HI 96822 USA; [Adachi, I.; Haba, J.; Itoh, R.; Iwasaki, Y.; Kichimi, H.; Liventsev, D.; Nakao, M.; Nishida, S.; Sakai, Y.; Uehara, S.; Uno, S.] KEK, High Energy Accelerator Res Org, Tsukuba, Ibaraki 3050801, Japan; [Schnell, G.] Ikerbasque, Bilbao 48011, Spain; [Bhuyan, B.; Dutta, D.] Indian Inst Technol Guwahati, Gauhati 781039, Assam, India; [Libby, J.; Nayak, M.] Indian Inst Technol, Madras 600036, Tamil Nadu, India; [Wang, P.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China; [Schwanda, C.] Inst High Energy Phys, A-1050 Vienna, Austria; [Shapkin, M.; Sokolov, A.] Inst High Energy Phys, Protvino 142281, Russia; [Mussa, R.] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; [Aushev, T.; Chilikin, K.; Chistov, R.; Danilov, M.; Uglov, T.] Inst Theoret & Expt Phys, Moscow 117218, Russia; [Bracko, M.; Golob, B.; Klucar, J.; Korpar, S.; Krizan, P.; Pestotnik, R.; Petric, M.; Santelj, L.; Staric, M.] Jozef Stefan Inst, Ljubljana 1000, Slovenia; [Okuno, S.; Watanabe, Y.] Kanagawa Univ, Yokohama, Kanagawa 2218686, Japan; [Huschle, M.; Kronenbitter, B.; Kuhr, T.; Roehrken, M.; Zupanc, A.] Karlsruhe Inst Technol, Inst Expt Kernphys, D-76131 Karlsruhe, Germany; [Cho, K.; Kim, J. H.; Kim, Y. J.] Korea Inst Sci & Technol Informat, Taejon 305806, South Korea; [Kim, J. B.; Ko, B. R.; Lee, S. -H.; Won, E.] Korea Univ, Seoul 136713, South Korea; [Hyun, H. J.; Kah, D. H.; Kim, H. J.; Park, H.; Park, H. K.] Kyungpook Natl Univ, Taegu 702701, South Korea; [Schneider, O.] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland; [Golob, B.; Krizan, P.] Univ Ljubljana, Fac Math & Phys, Ljubljana 1000, Slovenia; [Pedlar, T. K.] Luther Coll, Decorah, IA 52101 USA; [Bracko, M.; Korpar, S.] Univ Maribor, SLO-2000 Maribor, Slovenia; [Chekelian, V.; Chobanova, V.; Dalseno, J.; Kiesling, C.; Moll, A.; Nedelkovska, E.; Ritter, M.; Vanhoefer, P.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [Julius, T.; Sevior, M. E.] Univ Melbourne, Sch Phys, Melbourne, Vic 3010, Australia; [Danilov, M.] Moscow Phys Engn Inst, Moscow 115409, Russia; [Uglov, T.] Moscow Inst Phys & Technol, Dolgoprudnyi 141700, Moscow Region, Russia; [Iijima, T.; Seon, O.; Shen, C. P.] Nagoya Univ, Grad Sch Sci, Nagoya, Aichi 4648602, Japan; [Horii, Y.; Iijima, T.] Nagoya Univ, Kobayashi Maskawa Inst, Nagoya, Aichi 4648602, Japan; [Hayashii, H.; Miyabayashi, K.] Nara Womens Univ, Nara 6308506, Japan; [Chen, A.] Natl Cent Univ, Chungli 32054, Taiwan; [Wang, C. H.] Natl United Univ, Miaoli 36003, Taiwan; [Tien, K. -J.; Wang, M. -Z.; Chang, P.; Chen, P.; Hou, W. -S.; Hsiung, Y. B.; Shiu, J. -G.] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan; [Bozek, A.; Natkaniec, Z.; Ostrowicz, W.] H Niewodniczanski Inst Nucl Phys, PL-31342 Krakow, Poland; [Yamashita, Y.] Nippon Dent Univ, Niigata 9518580, Japan; [Kawasaki, T.; Miyata, H.; Watanabe, M.] Niigata Univ, Niigata 9502181, Japan; [Stanic, S.] Univ Nova Gorica, Nova Gorica 5000, Slovenia; [Nakano, E.; Teramoto, Y.] Osaka City Univ, Osaka 5588585, Japan; [Asner, D. M.; Fast, J. E.; Tatishvili, G.] Pacific NW Natl Lab, Richland, WA 99352 USA; [Bala, A.] Panjab Univ, Chandigarh 160014, India; [Savinov, V.] Univ Pittsburgh, Pittsburgh, PA 15260 USA; [Liu, C.; Zhang, Z. P.] Univ Sci & Technol China, Hefei 230026, Peoples R China; [Li, J.; Olsen, S. L.; Tanida, K.] Seoul Natl Univ, Seoul 151742, South Korea; [Kim, D. Y.] Soongsil Univ, Seoul 156743, South Korea; [Choi, Y.; Park, C. W.] Sungkyunkwan Univ, Suwon 440746, South Korea; [Bakich, A. M.; Sibidanov, A.; Varvell, K. E.] Univ Sydney, Sch Phys, Sydney, NSW 2006, Australia; [Gaur, V.; Mohanty, G. B.; Nisar, N. K.; Sandilya, S.] Tata Inst Fundamental Res, Bombay 400005, Maharashtra, India; [Dalseno, J.; Moll, A.] Tech Univ Munich, Excellence Cluster Universe, D-85748 Garching, Germany; [Ogawa, S.] Toho Univ, Funabashi, Chiba 2748510, Japan; [Hoshi, Y.] Tohoku Gakuin Univ, Tagajo, Miyagi 9858537, Japan; [Ishikawa, A.; Kato, E.; Saito, T.; Sanuki, T.; Sato, Y.] Tohoku Univ, Sendai, Miyagi 9808578, Japan; [Aihara, H.; Ng, C.] Univ Tokyo, Dept Phys, Tokyo 1130033, Japan; [Shibata, T. -A.; Uchida, M.] Tokyo Inst Technol, Tokyo 1528550, Japan; [Kumita, T.; Sumiyoshi, T.] Tokyo Metropolitan Univ, Tokyo 1920397, Japan; [Nitoh, O.] Tokyo Univ Agr & Technol, Tokyo 1848588, Japan; [Li, Y.; Piilonen, L. E.; Williams, K. M.] Virginia Polytech Inst & State Univ, CNP, Blacksburg, VA 24061 USA; [Cinabro, D.; Farhat, H.; Ganguly, S.; Gillard, R.] Wayne State Univ, Detroit, MI 48202 USA; [Senyo, K.] Yamagata Univ, Yamagata 9908560, Japan; [Cho, I. -S.; Kang, J. H.; Kwon, Y. -J.; Sohn, Y. -S.] Yonsei Univ, Seoul 120749, South Korea University of Basque Country; University of Bonn; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Charles University Prague; University System of Ohio; University of Cincinnati; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Justus Liebig University Giessen; Gifu University; Hanyang University; University of Hawaii System; High Energy Accelerator Research Organization (KEK); Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Guwahati; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Austrian Academy of Sciences; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Istituto Nazionale di Fisica Nucleare (INFN); National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Kanagawa University; Helmholtz Association; Karlsruhe Institute of Technology; Korea Institute of Science & Technology Information (KISTI); Korea Institute of Science & Technology (KIST); Korea University; Kyungpook National University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Ljubljana; University of Maribor; Max Planck Society; University of Melbourne; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Moscow Institute of Physics & Technology; Nagoya University; Nagoya University; Nara Womens University; National Central University; National United University; National Taiwan University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Nippon Dental University; Niigata University; University of Nova Gorica; Osaka Metropolitan University; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Panjab University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Seoul National University (SNU); Soongsil University; Sungkyunkwan University (SKKU); University of Sydney; Tata Institute of Fundamental Research (TIFR); Technical University of Munich; University of Munich; Toho University; Tohoku Gakuin University; Tohoku University; University of Tokyo; Tokyo Institute of Technology; Tokyo Metropolitan University; Tokyo University of Agriculture & Technology; Virginia Polytechnic Institute & State University; Wayne State University; Yamagata University; Yonsei University Tien, KJ (corresponding author), Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan. Kuzmin, Alexander/AAR-8369-2021; ISHIKAWA, Akimasa/AAG-9668-2020; Chobanova, Veronika/AAA-5282-2020; Chistov, Ruslan N/B-4893-2014; Haba, Junji/AAI-3849-2020; Natkaniec, Zbigniew/AAP-2995-2021; Uglov, Timofey V/B-2406-2014; Van Hulse, Charlotte/AAD-8064-2021; Pestotnik, Rok/A-3626-2008; Križan, Peter/L-7809-2016; Chistov, Ruslan/R-5574-2019; Aushev, Tagir/AAN-9735-2020; Tetsuro, Kumita/R-7771-2017; Kodys, Peter/P-2636-2017; Sandilya, Saurabh/AAR-2477-2021; Won, Eunil/S-7182-2019; Ishikawa, Akimasa/G-6916-2012; Dalseno, Jeremy/AAA-5567-2020; Aihara, Hiroaki/F-3854-2010; Schneider, Olivier P/R-9566-2018; Won, Eunil/AAF-9815-2019; Vorobyev, Vitaly/H-9799-2018; Danilov, Mikhail/C-5380-2014; Krokovny, Pavel/G-4421-2016; Chilikin, Kirill/B-4402-2014; Ostrowicz, Waclaw/Y-3938-2018; Sahoo, Himansu S/GQZ-8361-2022; Sumiyoshi, Takayuki/Q-5536-2017; Fast, James/AAA-4844-2019; Dolezal, Zdenek/K-6861-2017; Drasal, Zbynek/Q-7165-2017 Kuzmin, Alexander/0000-0002-7011-5044; ISHIKAWA, Akimasa/0000-0002-3561-5633; Chobanova, Veronika/0000-0002-1353-6002; Chistov, Ruslan N/0000-0003-1439-8390; Haba, Junji/0000-0002-1018-219X; Natkaniec, Zbigniew/0000-0003-0486-9291; Uglov, Timofey V/0000-0002-4944-1830; Križan, Peter/0000-0002-4967-7675; Chistov, Ruslan/0000-0003-1439-8390; Aushev, Tagir/0000-0002-6347-7055; Kodys, Peter/0000-0002-8644-2349; Sandilya, Saurabh/0000-0002-4199-4369; Won, Eunil/0000-0002-4245-7442; Dalseno, Jeremy/0000-0003-3288-4683; Aihara, Hiroaki/0000-0002-1907-5964; Schneider, Olivier P/0000-0002-6014-7552; Vorobyev, Vitaly/0000-0002-6660-868X; Danilov, Mikhail/0000-0001-9227-5164; Krokovny, Pavel/0000-0002-1236-4667; Chilikin, Kirill/0000-0001-7620-2053; Ostrowicz, Waclaw/0000-0003-1434-4608; Sumiyoshi, Takayuki/0000-0002-0486-3896; Fast, James/0000-0003-2258-701X; Tanida, Kiyoshi/0000-0002-8255-3746; Ritter, Martin/0000-0001-6507-4631; Piilonen, Leo/0000-0001-6836-0748; Zilic, Viktor/0000-0002-0907-5565; Liventsev, Dmitri/0000-0003-3416-0056; Golob, Bostjan/0000-0001-9632-5616; Nishida, Shohei/0000-0001-6373-2346; URQUIJO, PHILLIP/0000-0002-0887-7953; Dolezal, Zdenek/0000-0002-5662-3675; Vahsen, Sven/0000-0003-1685-9824; Ferber, Torben/0000-0002-6849-0427; Schnell, Gunar/0000-0002-7336-3246; Mussa, Roberto/0000-0002-0294-9071; Sevior, Martin/0000-0002-4824-101X; Park, HyangKyu/0000-0002-6966-1689; WANG, MIN-ZU/0000-0002-0979-8341; CHANG, PAO-TI/0000-0003-4064-388X; Varvell, Kevin/0000-0003-1017-1295; Libby, James/0000-0002-1219-3247; Hayashii, Hisaki/0000-0002-5138-5903; Drasal, Zbynek/0000-0001-9951-4834; Browder, Thomas E/0000-0001-7357-9007; Bozek, Andrzej/0000-0002-5915-1319; HSIUNG, YEE/0000-0003-4801-1238; Dutta, Deepanwita/0000-0002-1191-3978; Rostomyan, Armine/0000-0003-1839-8152 MEXT (Japan); JSPS (Japan); Nagoya's TLPRC (Japan); ARC (Australia); DIISR (Australia); NSFC (China); MSMT (Czechia); CZF (Germany); DFG (Germany); VS (Germany); DST (India); INFN (Italy); MEST (Korea); NRF (Korea); GSDC of KISTI (Korea); WCU (Korea); MNiSW (Poland); NCN (Poland); MES (Russia); RFAAE (Russia); ARRS (Slovenia); IKERBASQUE (Spain); UPV/EHU (Spain); NSC (Taiwan); MOE (Taiwan); DOE (USA); NSF (USA) MEXT (Japan)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS (Japan)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Nagoya's TLPRC (Japan); ARC (Australia)(Australian Research Council); DIISR (Australia)(Australian GovernmentDepartment of Industry, Innovation and Science); NSFC (China)(National Natural Science Foundation of China (NSFC)); MSMT (Czechia)(Ministry of Education, Youth & Sports - Czech Republic); CZF (Germany); DFG (Germany)(German Research Foundation (DFG)); VS (Germany); DST (India)(Department of Science & Technology (India)); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MEST (Korea)(Ministry of Education, Science & Technology (MEST), Republic of Korea); NRF (Korea)(National Research Foundation of Korea); GSDC of KISTI (Korea)(Korea Institute of Science & Technology Information (KISTI)); WCU (Korea); MNiSW (Poland)(Ministry of Science and Higher Education, Poland); NCN (Poland); MES (Russia)(Ministry of Education and Science, Russian Federation); RFAAE (Russia); ARRS (Slovenia)(Slovenian Research Agency - Slovenia); IKERBASQUE (Spain); UPV/EHU (Spain); NSC (Taiwan)(Ministry of Science and Technology, Taiwan); MOE (Taiwan)(Ministry of Education, Taiwan); DOE (USA)(United States Department of Energy (DOE)); NSF (USA)(National Science Foundation (NSF)) We thank the KEKB group for excellent operation of the accelerator; the KEK cryogenics group for efficient solenoid operations; and the KEK computer group, the NII, and PNNL/EMSL for valuable computing and SINET4 network support. We acknowledge support from MEXT, JSPS and Nagoya's TLPRC (Japan); ARC and DIISR (Australia); NSFC (China); MSMT (Czechia); CZF, DFG, and VS (Germany); DST (India); INFN (Italy); MEST, NRF, GSDC of KISTI, and WCU (Korea); MNiSW and NCN (Poland); MES and RFAAE (Russia); ARRS (Slovenia); IKERBASQUE and UPV/EHU (Spain); SNSF (Switzerland); NSC and MOE (Taiwan); and DOE and NSF (USA). 24 8 8 0 27 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D JAN 9 2014.0 89.0 1 11101 10.1103/PhysRevD.89.011101 0.0 6 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics AB5PP Green Submitted, Green Published 2023-03-23 WOS:000331841100001 0
C Wang, XM; Chen, W; Xia, JZ; Chen, ZX; Xu, DS; Wu, XY; Xu, ML; Schreck, T IEEE Comp Soc Wang, Xumeng; Chen, Wei; Xia, Jiazhi; Chen, Zexian; Xu, Dongshi; Wu, Xiangyang; Xu, Mingliang; Schreck, Tobias ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data 2020 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY (VAST 2020) IEEE Conference on Visual Analytics Science and Technology English Proceedings Paper 15th IEEE Conference on Visual Analytics Science and Technology (VAST) OCT 25-30, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,IEEE VGTC Temporal data; data analysis; reasoning; problem solving; and decision making; machine learning techniques DETECTING CONCEPT DRIFT; OF-THE-ART; VISUALIZATION Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach. [Wang, Xumeng; Chen, Wei; Chen, Zexian] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Peoples R China; [Xia, Jiazhi] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China; [Xu, Dongshi] China Jiliang Univ, Coll Informat Engn, Hangzhou, Peoples R China; [Wu, Xiangyang] Hangzhou Dianzi Univ, Inst Graph & Image, Hangzhou, Peoples R China; [Xu, Mingliang] Zhengzhou Univ, Zhengzhou, Peoples R China; [Schreck, Tobias] Graz Univ Technol, Graz, Austria Zhejiang University; Central South University; China Jiliang University; Hangzhou Dianzi University; Zhengzhou University; Graz University of Technology Wang, XM (corresponding author), Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Peoples R China. wangxumeng@zju.edu.cn; chenwei@cad.zju.edu.cn; xiajiazhi@csu.edu.cn; zexianchen@zju.edu.cn; p1703085223@stu.cjlu.edu.cn; wuxy@hdu.edu.cn; iexumingliang@zzu.edu.cn; tobias.schreck@cgv.tugraz.at 王, 叙萌/GZK-9445-2022 National Natural Science Foundation of China [61772456, 61761136020, 61972122, 61872389]; Open Project Program of State Key Lab of CADCG [A1903]; FFG [854184]; Austrian COMET Program Competence Centers for Excellent Technologies under Austrian Federal Ministry of Transport, Innovation and Technology; Austrian Federal Ministry for Digital and Economic Affairs; Province of Upper Austria; Province of Styria National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Project Program of State Key Lab of CADCG; FFG; Austrian COMET Program Competence Centers for Excellent Technologies under Austrian Federal Ministry of Transport, Innovation and Technology; Austrian Federal Ministry for Digital and Economic Affairs; Province of Upper Austria; Province of Styria This work was supported by National Natural Science Foundation of China (61772456, 61761136020, 61972122, 61872389) and Open Project Program of State Key Lab of CAD&CG (A1903). This work has partially been supported by the FFG, Contract No. 854184: Pro2Future is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria. COMET is managed by the Austrian Research Promotion Agency FFG. 51 7 7 1 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2325-9442 978-1-7281-8009-0 IEEE CONF VIS ANAL 2020.0 1 11 10.1109/VAST50239.2020.00006 0.0 11 Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology BS4RD 2023-03-23 WOS:000722239900001 0
C Zhang, MY; Li, Y; Sun, FN; Guo, DS; Hui, P Bailey, J; Miettinen, P; Koh, YS; Tao, D; Wu, X Zhang, Mingyang; Li, Yong; Sun, Funing; Guo, Diansheng; Hui, Pan Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021) IEEE International Conference on Data Mining English Proceedings Paper 21st IEEE International Conference on Data Mining (IEEE ICDM) DEC 07-10, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,US Natl Sci Fdn,Univ Auckland, Sch Comp Sci,IEEE Tech Comm Intelligent Informat,Google,Two Sigma Spatio-temporal Model; Traffic Prediction; Graph Neural Network Traffic prediction is a crucial task in many real-world applications. The task is challenging due to the implicit and dynamic spatio-temporal dependencies among traffic data. On the one hand, the spatial dependencies among traffic flows are latent and fluctuate with environmental conditions. On the other hand, the temporal dependencies among traffic flows also vary significantly over time and locations. In this paper, we propose Adaptive Spatio-Temporal Convolutional Network (ASTCN) to tackle these challenges. First, we propose a spatial graph learning module that learns the dynamic spatial relations among traffic data based on multiple influential factors. Furthermore, we design an adaptive temporal convolution module that captures complex temporal traffic dependencies with environment-aware dynamic filters. We conduct extensive experiments on three real-world traffic datasets. The results demonstrate that the proposed ASTCN consistently outperforms state-of-the-arts. [Zhang, Mingyang; Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China; [Sun, Funing; Guo, Diansheng] Tencent Corp, Shenzhen, Peoples R China; [Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki, Finland Hong Kong University of Science & Technology; Tsinghua University; Tencent; University of Helsinki Zhang, MY (corresponding author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China. mzhangbj@connect.ust.hk Guo, Diansheng/0000-0003-3483-4153 FIT project [318927]; Academy of Finland [325570]; Academy of Finland (AKA) [325570] Funding Source: Academy of Finland (AKA) FIT project; Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported by the 5GEAR project (Decision No. 318927) and the FIT project (Decision No. 325570) funded by the Academy of Finland. 21 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1550-4786 978-1-6654-2398-4 IEEE DATA MINING 2021.0 1475 1480 10.1109/ICDM51629.2021.00191 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS9GC Green Published 2023-03-23 WOS:000780454100182 0
J Agarwal, V; Akyilmaz, O; Shum, CK; Feng, W; Yang, TY; Forootan, E; Syed, TH; Haritashya, UK; Uz, M Agarwal, Vibhor; Akyilmaz, Orhan; Shum, C. K.; Feng, Wei; Yang, Ting-Yi; Forootan, Ehsan; Syed, Tajdarul Hassan; Haritashya, Umesh K.; Uz, Metehan Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California SCIENCE OF THE TOTAL ENVIRONMENT English Article Machine learning; Groundwater; GRACE; Remote sensing SAN-JOAQUIN VALLEY; CLIMATE-CHANGE; WATER; DEPLETION; DROUGHT; ERROR; MODEL; INDIA California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled ground-water level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical dis-placements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 +/- 1.2 km3 yr-1 and-9.8 +/- 1.7 km3 yr-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater deple-tion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal. [Agarwal, Vibhor] Coll Wooster, Dept Earth Sci, Wooster, OH 44691 USA; [Agarwal, Vibhor; Haritashya, Umesh K.] Univ Dayton, Dept Geol & Environm Geosci, Dayton, OH USA; [Agarwal, Vibhor; Shum, C. K.] Ohio State Univ, Sch Earth Sci, Div Geodet Sci, Columbus, OH USA; [Akyilmaz, Orhan; Uz, Metehan] Istanbul Tech Univ, Dept Geomatic Engn, Istanbul, Southern Ethiop; [Shum, C. K.; Feng, Wei] Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Beijing, Peoples R China; [Feng, Wei] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou, Peoples R China; [Yang, Ting-Yi] Polaris Geospatial Serv, Columbus, OH USA; [Forootan, Ehsan] Aalborg Univ, Dept Planning, Aalborg, Denmark; [Syed, Tajdarul Hassan] Indian Inst Technol Kanpur, Dept Earth Sci, Kanpur, India College of Wooster; University of Dayton; University System of Ohio; Ohio State University; Chinese Academy of Sciences; Sun Yat Sen University; Aalborg University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur Agarwal, V (corresponding author), Coll Wooster, Dept Earth Sci, Wooster, OH 44691 USA. agarwal.282@osu.edu Haritashya, Umesh/0000-0001-9527-954X OSU Fellowship; NSF's Partnerships for Innovation Program [80NSSC20K0494]; NASA's Earth Surface Interior Focus Area Program [119Y176]; Danmarks Frie Forskningsfond under DANSk-LSM project; Scientific and Technological Research Council of Turkey - TUEBITAK; [2044704] OSU Fellowship; NSF's Partnerships for Innovation Program; NASA's Earth Surface Interior Focus Area Program; Danmarks Frie Forskningsfond under DANSk-LSM project; Scientific and Technological Research Council of Turkey - TUEBITAK; Vibhor Agarwal received OSU Fellowship and salary as Teaching Assistant while a PhD student at OSU. Orhan Akyilmaz and Metehan Uz is partially supported by the Scientific and Technological Research Council of Turkey - TUEBITAK (119Y176). C.K. Shum is partially supported by NSF's Partnerships for Innovation Program (2044704), and NASA's Earth Surface Interior Focus Area Program (80NSSC20K0494). Ehsan Forootan is supported by the Danmarks Frie Forskningsfond [10.46540/2035-00247B] under DANSk-LSM project. 87 0 0 21 21 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. MAR 20 2023.0 865.0 161138 10.1016/j.scitotenv.2022.161138 0.0 JAN 2023 14 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 8I8NO 36586696.0 Green Published 2023-03-23 WOS:000921985700001 0
J Mi, C; Huang, SF; Zhang, YJ; Zhang, ZW; Postolache, O Mi, Chao; Huang, Shifeng; Zhang, Yujie; Zhang, Zhiwei; Postolache, Octavian Design and Implementation of 3-D Measurement Method for Container Handling Target JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article automated container terminals; container handling; container attitude; three-dimensional measurement In the process of automatic container terminal loading and unloading, the three-dimensional attitude of the container affects the security of loading and unloading operations, so the three-dimensional attitude positioning of the container is very important. In this paper, a visual non-contact measurement method is used to realize the real-time orientation of the three-dimensional attitude of the container. First, the container corner is coarsely positioned by a small-scale deep learning network. Secondly, the precise position of the container keyhole is obtained by the secondary positioning of the container corner through the traditional image processing algorithm, and the container posture is measured in three dimensions by combining the physical motion model of the container during loading and unloading. After testing, unlike previous measurement methods, the measurement accuracy of this method met the requirements of automatic loading and unloading of container terminals, and the measurement time met the requirements of real-time measurement. [Mi, Chao] Shanghai Maritime Univ, Container Supply Chain Technol Engn Res Ctr, Minist Educ, Shanghai 201306, Peoples R China; [Huang, Shifeng] Shanghai Maritime Univ, Logist Sci & Engn Res Inst Sch, Shanghai 201306, Peoples R China; [Zhang, Yujie] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China; [Zhang, Zhiwei] Shanghai SMUVis Smart Technol Ltd, Shanghai 201306, Peoples R China; [Postolache, Octavian] Univ Lisbon, Inst Telecomunicacoes ISCTE Inst, P-1649026 Lisbon, Portugal Shanghai Maritime University; Shanghai Maritime University; Shanghai Maritime University; Universidade de Lisboa Postolache, O (corresponding author), Univ Lisbon, Inst Telecomunicacoes ISCTE Inst, P-1649026 Lisbon, Portugal. opostolache@lx.it.pt Postolache, Octavian Adrian/ADC-7853-2022 Postolache, Octavian Adrian/0000-0001-5055-6347; MI, Chao/0000-0002-0809-0624 EDD of China [80912020104]; Science and Technology Commission of Shanghai Municipality [22ZR1427700]; China (Shanghai) Pilot Free Trade Zone Lingang Special Area Administration [SH-LG-GK-2020-21] EDD of China; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); China (Shanghai) Pilot Free Trade Zone Lingang Special Area Administration This research was supported by the EDD of China (No. 80912020104), the Science and Technology Commission of Shanghai Municipality (No. 22ZR1427700), China (Shanghai) Pilot Free Trade Zone Lingang Special Area Administration (No. SH-LG-GK-2020-21). 32 1 1 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. DEC 2022.0 10.0 12 1961 10.3390/jmse10121961 0.0 19 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 7G3NG Green Published, gold 2023-03-23 WOS:000902435000001 0
C Li, YX; Liu, ML; Drossos, K; Virtanen, T IEEE Li, Yanxiong; Liu, Mingle; Drossos, Konstantinos; Virtanen, Tuomas SOUND EVENT DETECTION VIA DILATED CONVOLUTIONAL RECURRENT NEURAL NETWORKS 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing MAY 04-08, 2020 Barcelona, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Signal Proc Soc Sound event detection; dilated convolutional recurrent neural network; temporal context CLASSIFICATION Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely a CRNN with a dilated convolutional kernel, as the classifier for the task of SED. We investigate the effectiveness of dilation operations which provide a CRNN with expanded receptive fields to capture long temporal context without increasing the amount of CRNN's parameters. Compared to the classifier of the baseline CRNN, the classifier of the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound Event 2016 and the TUT Sound Event 2017, respectively. [Li, Yanxiong; Liu, Mingle] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China; [Drossos, Konstantinos; Virtanen, Tuomas] Tampere Univ, Audio Res Grp, Tampere, Finland South China University of Technology; Tampere University Li, YX (corresponding author), South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China. eeyxli@scut.edu.cn; mingle96@foxmail.com; konstantinos.drossos@tuni.fi; tuomas.virtanen@tuni.fi Drosos, Konstantinos/0000-0002-3605-7127 national natural science foundation of China [61771200, 6191101570]; project of international science and technology collaboration of Guangdong [2019A050509001]; European Unions H2020 Framework Programme through ERC [637422] national natural science foundation of China(National Natural Science Foundation of China (NSFC)); project of international science and technology collaboration of Guangdong; European Unions H2020 Framework Programme through ERC This work was partly supported by the national natural science foundation of China (61771200, 6191101570), the project of international science and technology collaboration of Guangdong (2019A050509001), and the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND. 23 12 12 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-6631-5 INT CONF ACOUST SPEE 2020.0 286 290 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BQ7HU Green Submitted 2023-03-23 WOS:000615970400058 0
C Wang, J; Gao, XZ; Tanskanen, JMA; Guo, P IEEE Wang, Jing; Gao, X. Z.; Tanskanen, Jarno M. A.; Guo, Ping Epileptic EEG Signal Classification with ANFIS based on Harmony Search Method PROCEEDINGS OF THE 2012 EIGHTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2012) English Proceedings Paper 8th International Conference on Computational Intelligence and Security (CIS) NOV 17-18, 2012 Guangzhou, PEOPLES R CHINA Xidian Univ,Beijing Normal Univ,IEEE CPS Harmony Search (HS) method; Electroencephalogram; Seizure detection; Adaptive Neuro-Fuzzy Inference System (ANFIS); signal classification ARTIFICIAL NEURAL-NETWORK; FUZZY INFERENCE SYSTEM; AUTOMATIC DETECTION; OPTIMIZATION In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments. [Wang, Jing; Guo, Ping] Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China; [Gao, X. Z.] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, Espoo, Finland; [Tanskanen, Jarno M. A.] Tampere Univ Technol, Dept Biomed Engn, Tampere, Finland; [Wang, Jing] Peking Univ Hlth Sci Ctr, Sch Fdn Educ, Beijing, Peoples R China Beijing Normal University; Aalto University; Tampere University; Peking University Wang, J (corresponding author), Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China. wang_jing@bjmu.edu.cn; xiao-zhi.gao@aalto.fi; tanskanen@ieee.org; pguo@ieee.org Guo, Peng/GWC-0572-2022; guo, peng/AAG-4052-2019; GUO, Ping/AAG-2160-2019; Gao, Xiao-Zhi/Q-3038-2016; GUO, Ping/A-3482-2015 GUO, Ping/0000-0002-7122-1084; Gao, Xiao-Zhi/0000-0002-0078-5675; GUO, Ping/0000-0002-7122-1084; Tanskanen, Jarno/0000-0001-7183-318X National Natural Science Foundation of China; Academy of Finland [135225, 135220]; National Natural Science Foundation of China [60911130513]; Academy of Finland (AKA) [135220] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) The research work in this paper was fully supported by the grants from a joint call by the National Natural Science Foundation of China and Academy of Finland. J. Wang's and P. Guo's research work was funded by the National Natural Science Foundation of China ( Project No 60911130513). X. Z. Gao's work was funded by the Academy of Finland under Grant 135225, and J. M. A. Tanskanen's by the Academy of Finland under Grant 135220. 25 6 6 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-0-7695-4896-8; 978-1-4673-4725-9 2012.0 690 694 10.1109/CIS.2012.159 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BEU99 2023-03-23 WOS:000318241200151 0
J Li, MX; Palchykov, V; Jiang, ZQ; Kaski, K; Kertesz, J; Micciche, S; Tumminello, M; Zhou, WX; Mantegna, RN Li, Ming-Xia; Palchykov, Vasyl; Jiang, Zhi-Qiang; Kaski, Kimmo; Kertesz, Janos; Micciche, Salvatore; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N. Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data NEW JOURNAL OF PHYSICS English Article complex networks; social systems; statistically validated networks; mobile call records; 3-motifs PREDICTABILITY; PATTERNS Big data open up unprecedented opportunities for investigating complex systems, including society. In particular, communication data serve as major sources for computational social sciences, but they have to be cleaned and filtered as they may contain spurious information due to recording errors as well as interactions, like commercial and marketing activities, not directly related to the social network. The network constructed from communication data can only be considered as a proxy for the network of social relationships. Here we apply a systematic method, based on multiple-hypothesis testing, to statistically validate the links and then construct the corresponding Bonferroni network, generalized to the directed case. We study two large datasets of mobile phone records, one from Europe and the other from China. For both datasets we compare the raw data networks with the corresponding Bonferroni networks and point out significant differences in the structures and in the basic network measures. We show evidence that the Bonferroni network provides a better proxy for the network of social interactions than the original one. Using the filtered networks, we investigated the statistics and temporal evolution of small directed 3-motifs and concluded that closed communication triads have a formation time scale, which is quite fast and typically intraday. We also find that open communication triads preferentially evolve into other open triads with a higher fraction of reciprocated calls. These stylized facts were observed for both datasets. [Li, Ming-Xia; Jiang, Zhi-Qiang; Zhou, Wei-Xing] E China Univ Sci & Technol, Sch Sci, Sch Business, Shanghai 200237, Peoples R China; [Li, Ming-Xia; Jiang, Zhi-Qiang; Zhou, Wei-Xing] E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China; [Palchykov, Vasyl; Kaski, Kimmo; Kertesz, Janos] Aalto Univ, Dept Biomed Engn & Computat Sci, FI-00076 Aalto, Finland; [Palchykov, Vasyl] Natl Acad Sci Ukraine, Inst Condensed Matter Phys, UA-79011 Lvov, Ukraine; [Palchykov, Vasyl] Leiden Univ, Inst Lorentz, NL-2300 RA Leiden, Netherlands; [Kertesz, Janos; Mantegna, Rosario N.] Cent European Univ, Ctr Network Sci, H-1051 Budapest, Hungary; [Micciche, Salvatore; Mantegna, Rosario N.] Univ Palermo, Dipartimento Fis & Chim, I-90128 Palermo, Italy; [Tumminello, Michele] Univ Palermo, Dipartimento Sci Econ Aziendali & Stat, I-90128 Palermo, Italy; [Mantegna, Rosario N.] Cent European Univ, Dept Econ, H-1051 Budapest, Hungary East China University of Science & Technology; East China University of Science & Technology; Aalto University; National Academy of Sciences Ukraine; Institute for Condensed Matter Physics of NASU; Leiden University; Leiden University - Excl LUMC; Central European University; University of Palermo; University of Palermo; Central European University Li, MX (corresponding author), E China Univ Sci & Technol, Sch Sci, Sch Business, Shanghai 200237, Peoples R China. rn.mantegna@gmail.com Li, Ming-Xia/I-1035-2016; Zhou, Wei-Xing/B-1250-2008; Palchykov, Vasyl/H-2629-2012; Miccichè, Salvatore/B-6575-2008; Tumminello, Michele/G-4492-2011; Mantegna, Rosario N/H-3652-2014; Kaski, Kimmo K K/G-8067-2012; Jiang, Zhi-Qiang/B-5746-2008; Kaski, Kimmo/AAO-4320-2021 Li, Ming-Xia/0000-0002-0932-4584; Zhou, Wei-Xing/0000-0002-8952-8228; Miccichè, Salvatore/0000-0003-3779-592X; Tumminello, Michele/0000-0001-6045-6761; Mantegna, Rosario N/0000-0003-4177-171X; Jiang, Zhi-Qiang/0000-0003-4013-1753; Kaski, Kimmo/0000-0002-3805-9687 National Natural Science Foundation of China [11205057]; Humanities and Social Sciences Fund of the Ministry of Education of China [09YJCZH040]; PhD Programs Foundation of the Ministry of Education of China [20120074120028]; Fok Ying Tong Education Foundation [132013]; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China [11205057]; Humanities and Social Sciences Fund of the Ministry of Education of China [09YJCZH040]; PhD Programs Foundation of the Ministry of Education of China [20120074120028]; Fok Ying Tong Education Foundation [132013]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Sciences Fund of the Ministry of Education of China(Ministry of Education, China); PhD Programs Foundation of the Ministry of Education of China(Ministry of Education, China); Fok Ying Tong Education Foundation(Fok Ying Tung Education Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Sciences Fund of the Ministry of Education of China(Ministry of Education, China); PhD Programs Foundation of the Ministry of Education of China(Ministry of Education, China); Fok Ying Tong Education Foundation(Fok Ying Tung Education Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors thank L Barabasi for the European data. This work was partially supported by the National Natural Science Foundation of China (11205057), the Humanities and Social Sciences Fund of the Ministry of Education of China (09YJCZH040), the PhD Programs Foundation of the Ministry of Education of China (20120074120028), the Fok Ying Tong Education Foundation (132013), and the Fundamental Research Funds for the Central Universities. 35 40 41 1 37 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1367-2630 NEW J PHYS New J. Phys. AUG 21 2014.0 16.0 83038 10.1088/1367-2630/16/8/083038 0.0 24 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics AP2RO Green Published, Green Submitted, gold 2023-03-23 WOS:000341922200002 0
J Shehu, Y Shehu, Yekini Convergence Results of Forward-Backward Algorithms for Sum of Monotone Operators in Banach Spaces RESULTS IN MATHEMATICS English Article Inclusion problem; 2-uniformly convex Banach space; forward-backward algorithm; weak convergence; strong convergence SPLITTING METHOD; HILBERT-SPACES; INEQUALITIES; CONVEX; THEOREMS; ZERO; WEAK It is well known that many problems in image recovery, signal processing, and machine learning can be modeled as finding zeros of the sum of maximal monotone and Lipschitz continuous monotone operators. Many papers have studied forward-backward splitting methods for finding zeros of the sum of two monotone operators in Hilbert spaces. Most of the proposed splitting methods in the literature have been proposed for the sum of maximal monotone and inverse-strongly monotone operators in Hilbert spaces. In this paper, we consider splitting methods for finding zeros of the sum of maximal monotone operators and Lipschitz continuous monotone operators in Banach spaces. We obtain weak and strong convergence results for the zeros of the sum of maximal monotone and Lipschitz continuous monotone operators in Banach spaces. Many already studied problems in the literature can be considered as special cases of this paper. [Shehu, Yekini] IST Austria, Campus 1, A-3400 Klosterneuburg, Austria; [Shehu, Yekini] Zhejiang Normal Univ, Dept Math, Jinhua 321004, Zhejiang, Peoples R China Institute of Science & Technology - Austria; Zhejiang Normal University Shehu, Y (corresponding author), IST Austria, Campus 1, A-3400 Klosterneuburg, Austria.;Shehu, Y (corresponding author), Zhejiang Normal Univ, Dept Math, Jinhua 321004, Zhejiang, Peoples R China. yekini.shehu@unn.edu.ng Shehu, Yekini/J-6631-2013 Shehu, Yekini/0000-0001-9224-7139 Institute of Science and Technology (IST Austria) Institute of Science and Technology (IST Austria) Open access funding provided by Institute of Science and Technology (IST Austria). 53 25 25 0 2 SPRINGER BASEL AG BASEL PICASSOPLATZ 4, BASEL, 4052, SWITZERLAND 1422-6383 1420-9012 RESULTS MATH Results Math. DEC 2019.0 74.0 4 138 10.1007/s00025-019-1061-4 0.0 24 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics IF7BW hybrid, Green Submitted 2023-03-23 WOS:000473237500002 0
J Liu, J; Cao, LC; Stoykova, E; Ferraro, P; Memmolo, P; Blanche, PA Liu, Juan; Cao, Liangcai; Stoykova, Elena; Ferraro, Pietro; Memmolo, Pasquale; Blanche, Pierre-Alexandre Digital Holography and 3D Imaging 2020: introduction to the feature issue JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION English Editorial Material This feature issue of JOSA A and Applied Optics is dedicated to the fourteenth OSA Topical Meeting Digital Holography and 3D Imaging held 22-26 June 2020 in a virtual meeting. The conference, taking place every year, is a focal point for global technical interchange in the field of digital holography and 3D imaging, providing premier opportunities for people working in the field to present their new advances in research and development. Papers presented at the meeting highlight current research in digital holography and three-dimensional imaging, including interferometry, phase microscopy, phase retrieval, novel holographic processes, 3D and novel holographic displays, integral imaging, computer-generated holograms, compressive holography, 3D holographic display, AR display, full-field tomography, specific image and signal processing, and holography with various light sources, including coherent to incoherent and x-ray to terahertz waves. Techniques of digital holography and of 3D imaging have numerous applications, such as the state-of-the-art technological developments that are currently underway and stimulate further novel applications of digital holography and 3D imaging in biomedicine, deep learning, and scientific and industrial metrologies. (c) 2021 Optical Society of America [Liu, Juan] Beijing Inst Technol, Beijing, Peoples R China; [Cao, Liangcai] Tsinghua Univ, Beijing, Peoples R China; [Stoykova, Elena] Bulgarian Acad Sci, Sofia, Bulgaria; [Ferraro, Pietro] CNR, Inst Intelligent Syst, Bari, Italy; [Memmolo, Pasquale] CNR, ISASI, Biella, Italy; [Blanche, Pierre-Alexandre] Univ Arizona, Tucson, AZ 85721 USA Beijing Institute of Technology; Tsinghua University; Bulgarian Academy of Sciences; Consiglio Nazionale delle Ricerche (CNR); Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Applicate e Sistemi Intelligenti Eduardo Caianiello (ISASI-CNR); University of Arizona Liu, J (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. juanliu@bit.edu.cn Ferraro, Pietro/A-5288-2009 Ferraro, Pietro/0000-0002-0158-3856 0 2 2 4 21 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1084-7529 1520-8532 J OPT SOC AM A J. Opt. Soc. Am. A-Opt. Image Sci. Vis. FEB 1 2021.0 38.0 2 DH1 DH2 10.1364/JOSAA.419210 0.0 2 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics QD8WM 33690531.0 2023-03-23 WOS:000615792300001 0
C Pathan, MS; Wu, JT; Lee, YH; Yan, JZ; Dev, S IEEE Pathan, Muhammad Salman; Wu, Jiantao; Lee, Yee Hui; Yan, Jianzhuo; Dev, Soumyabrata Analyzing the Impact of Meteorological Parameters on Rainfall Prediction 2021 IEEE USNC-URSI RADIO SCIENCE MEETING (JOINT WITH AP-S SYMPOSIUM) USNC-URSI Radio Science Meeting - Joint with AP-S SYMPOSIUM English Proceedings Paper IEEE International Symposium on Antennas and Propagation / USNC-URSI Radio Science Meeting (IEEE APS/URSI) DEC 04-10, 2021 Singapore, SINGAPORE Inst Elect & Elect Engineers,Natl Acad Sci, Board Int Sci Org, U S Natl Comm Int Union Radio Sci,Int Union Radio Sci,IEEE Antennaa & Propagat Soc,Int Union Radio Sci, U S Natl Comm Rainfall is a climatic factor that affects many human activities like agriculture, construction, and forestry. Rainfall is dependent on various meteorological features and its prediction is a very complex task due to the dynamic climatic nature. A detailed study of different climatic features associated with the occurrence of rainfall should be made in order to understand the influence of each parameter in the context of rainfall. In this paper, we propose a methodical approach to analyze the affect of various parameters on rainfall. Our study uses 5 years of meteorological data from a weather station located in the United States. The correlation and interdependence among the collected meteorological features were obtained. Additionally, we identified the most important meteorological features for rainfall prediction using a machine learning-based feature selection technique. [Pathan, Muhammad Salman; Wu, Jiantao; Dev, Soumyabrata] Univ Coll Dublin, ADAPT SFI Res Ctr, Sch Comp Sci, Dublin, Ireland; [Lee, Yee Hui] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore; [Yan, Jianzhuo] Beijing Univ Technol, Fac Informat Technol, Minist Educ, Beijing, Peoples R China University College Dublin; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Beijing University of Technology Dev, S (corresponding author), Univ Coll Dublin, ADAPT SFI Res Ctr, Sch Comp Sci, Dublin, Ireland. soumyabrata.dev@ucd.ie Lee, Yee Hui/A-4984-2011 Lee, Yee Hui/0000-0001-6452-9606; Pathan, Muhammad Salman/0000-0002-0210-3121 European Union [801522]; Science Foundation Ireland; European Regional Development Fund through the ADAPT Centre for Digital Content Technology [13/RC/2106_P2] European Union(European Commission); Science Foundation Ireland(Science Foundation Ireland); European Regional Development Fund through the ADAPT Centre for Digital Content Technology This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology grant number 13/RC/2106_P2. 8 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2572-3804 2573-3036 978-1-946815-10-1 USNC-URSI RADIO SCI 2021.0 99 100 2 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BT9KY Green Submitted 2023-03-23 WOS:000861555600050 0
J Sun, H; Yao, TD; Su, FG; He, ZH; Tang, GQ; Li, N; Zheng, BW; Huang, JH; Meng, FC; Ou, TH; Chen, DL Sun, He; Yao, Tandong; Su, Fengge; He, Zhihua; Tang, Guoqiang; Li, Ning; Zheng, Bowen; Huang, Jingheng; Meng, Fanchong; Ou, Tinghai; Chen, Deliang Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins JOURNAL OF HYDROMETEOROLOGY English Article Precipitation; Hydrology; Hydrologic models UPSTREAM RIVER-BASINS; UPPER INDUS BASIN; QUANTITATIVE ASSESSMENT; TIBETAN PLATEAU; CLIMATE-CHANGE; MELT; VARIABILITY; CALIBRATION; WESTERLIES; RUNOFF Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km x 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951-2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%-50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance StatementThe Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km x 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951-2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations. [Sun, He; Yao, Tandong; Su, Fengge; Li, Ning; Zheng, Bowen; Huang, Jingheng] Chinese Acad Sci, Inst Tibetan Plateau Res TPESER, State Key Lab Tibetan Plateau Earth Syst Environm, Beijing, Peoples R China; [Yao, Tandong; Su, Fengge] CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China; [Yao, Tandong; Su, Fengge; Li, Ning; Zheng, Bowen; Huang, Jingheng] Univ Chinese Acad Sci, Beijing, Peoples R China; [He, Zhihua] Univ Saskatchewan, Ctr Hydrol, Saskatoon, SK, Canada; [Tang, Guoqiang] Univ Saskatchewan, Ctr Hydrol, Canmore, AB, Canada; [Meng, Fanchong] North China Univ Water Resources & Elect Power, Coll Geosci & Engn, Zhengzhou, Peoples R China; [Ou, Tinghai; Chen, Deliang] Univ Gothenburg, Dept Earth Sci, Reg Climate Grp g, Gothenburg, Sweden Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Saskatchewan; University of Saskatchewan; North China University of Water Resources & Electric Power; University of Gothenburg Sun, H (corresponding author), Chinese Acad Sci, Inst Tibetan Plateau Res TPESER, State Key Lab Tibetan Plateau Earth Syst Environm, Beijing, Peoples R China. sunhe@itpcas.ac.cn He, Zhihua/0000-0002-0183-693X National Natural Science Foundation of China [42201140]; Second Tibetan Plateau Scientific Expedition and Research (STEP) Program [2019QZKK0201]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA20100300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Second Tibetan Plateau Scientific Expedition and Research (STEP) Program; Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences) This study was financially supported by the National Natural Science Foundation of China (42201140), the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0201), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20100300). We acknowledge that the Fig. 1 in this study was previously published under the terms of the Creative Commons Attribution 4.0 license in Sun et al. (2021b). Author contributions: He Sun: conceptualization, formal analysis, investigation, methodology, resources, visualization, writing draft; Tandong Yao and Fengge Su: conceptualization, resources, visualization, funding acquisition, writing (review and editing); Zhihua He and Guoqiang Tang: formal analysis and writing (review and editing); Ning Li, Bowen Zheng, Jingheng Huang, and Fanchong Meng: formal analysis, methodology, writing (review and editing); Tinghai Ou and Deliang Chen: writing (review and editing) and providing WRF-9km data. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 47 0 0 9 9 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693, UNITED STATES 1525-755X 1525-7541 J HYDROMETEOROL J. Hydrometeorol. OCT 2022.0 23.0 10 1663 1679 10.1175/JHM-D-22-0015.1 0.0 17 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences 5Y4EI 2023-03-23 WOS:000879236000008 0
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J.; Shulga, E.; Sicho, P.; Sickles, A. M.; Haddad, E. Sideras; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj; Oliveira, M. V. Silva; Silverstein, S. B.; Simion, S.; Simoniello, R.; Simpson-allsop, C. J.; Simsek, S.; Sinervo, P.; Sinetckii, V; Singh, S.; Sinha, S.; Sioli, M.; Siral, I; Sivoklokov, S. Yu; Sjolin, J.; Skaf, A.; Skorda, E.; Skubic, P.; Slawinska, M.; Sliwa, K.; Smakhtin, V; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, E. A.; ; ATLAS Collaboration Measurements of Higgs bosons decaying to bottom quarks from vvector boson fusion production with the ATLAS experiment at root s=13 TeV EUROPEAN PHYSICAL JOURNAL C English Article BROKEN SYMMETRIES; GAUGE The paper presents a measurement of the Standard Model Higgs Boson decaying to b-quark pairs in the vector boson fusion (VBF) production mode. A sample corresponding to 126 fb(-1) of root s = 13 TeVproton-proton collision data, collected with the ATLAS experiment at the Large Hadron Collider, is analyzed utilizing an adversarial neural network for event classification. The signal strength, defined as the ratio of the measured signal yield to that predicted by the Standard Model for VBF Higgs production, is measured to be 0.95(-0.36)(+0.38), corresponding to an observed (expected) significance of 2.6 (2.8) standard deviations from the background only hypothesis. The results are additionally combined with an analysis of Higgs bosons decaying to b-quarks, produced via VBF in association with a photon. [Duvnjak, D.; Jackson, P.; Kong, A. X. Y.; Oliver, J. L.; Ruggeri, T. A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Biswal, J. P.; Gingrich, D. M.; Pinfold, J. 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M.; Sciolla, G.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Popa, S.] Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Chitan, A.; Ciubotaru, D. A.; Dobre, M.; Ducu, O. A.; Dumitriu, A. E.; Geanta, A. A.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Pietreanu, D.; Renda, M.; Rotaru, M.; Stoicea, G.; Tarna, G.; Tudorache, A.; Tudorache, V; Vasile, M. E.] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Agheorghiesei, C.; Postolache, P.] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Phys Dept, Cluj Napoca, Romania; [Hobincu, R.] Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Babal, D.; Bartos, P.; Blazek, T.; Dubovsky, M.; Hyrych, S.; Majersky, O.; Racko, M.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Murin, P.; Smiesko, J.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Dept Subnucl Phys, Inst Expt Phys, Kosice, Slovakia; [Abidi, S. H.; Assamagan, K.; Barone, G.; Begel, M.; Benoit, M.; Boye, D.; Brost, E.; Cavaliere, V; Chen, H.; D'amen, G.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Iakovidis, G.; Kalderon, C. W.; Klimentov, A.; Lancon, E.; Lanni, F.; Lee, C. A.; Lynn, D.; Ma, H.; Maeno, T.; Matakias, D.; Mc Ginn, C.; Nagle, J. L.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Ouellette, J.; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Roloff, J.; Seidlitz, B. D.; Serfon, C.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Tricoli, A.; Undrus, A.; Weber, C.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Daneri, M. F.; Otero Y Garzon, G.; Piegaia, R.; Toscani, M.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Bawa, H. S.; Gao, Y. S.; Grimm, K.; Moss, J.; Parker, A. J.] Calif State Univ Fresno, Fresno, CA 93740 USA; [Batley, J. R.; Brandt, O.; Chapman, J. D.; Cowley, J. W.; Fawcett, W. J.; Henkelmann, L.; Hill, J. C.; Hommels, L. B. A. H.; Lester, C. G.; Lin, C. Y.; Malone, C.; Noel, D. L.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson; Tombs, R.; Ward, C. P.; Williams, S.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Keaveney, J. M.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; iThemba Labs, Western Cape, South Africa; [Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; Univ Philippines Diliman, Natl Inst Phys, Quezon City, Philippines; Univ South Africa, Dept Phys, Pretoria, South Africa; [Christopher, L. D.; Dahbi, S.; Jimenez, Y. Hernandez; Jivan, H.; Kar, D.; Kumar, M.; Mdhluli, J. E.; Garcia, B. R. Mellado; Mokgatitswane, G.; Ruan, X.; Shrif, E. M.; Haddad, E. Sideras; Sinha, S.; von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Keller, J. S.; Klein, C.; Koffas, T.; Laurier, A.; Miller, L. S.; Naseri, M.; Oakham, F. G.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chadi, Z.; El Moussaouy, A.; Imam, H.; Zerradi, S.] Univ Hassan 2, Fac Sci Ain Chock, Reseau Univ Phys Hautes Energies, Casablanca, Morocco; [Gouighri, M.] Univ Ibn Tofail, Fac Sci, Kenitra, Morocco; Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA, Marrakech, Morocco; [Assahsah, J.; Derkaoui, J. E.; Ouchrif, M.] Moroccan Fdn Adv Sci Innovat & Res MAScIR, Rabat, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; El Jarrari, H.; Fassi, F.; Hamdaoui, H.; Ngair, B.; Tayalati, Y.; Zaazoua, M.] Univ Mohamed Premier, Fac Sci, LPMR, Oujda, Morocco; Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Abud, A. Abed; Ahmad, A.; Alderweireldt, S.; Aleksa, M.; Allaire, C.; Amelung, C.; Aranzabal, N.; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Bielski, R.; Bisanz, T.; Boyd, J.; Brenner, L.; Calace, N.; Camarda, S.; Camincher, C.; Carli, T.; Catinaccio, A.; Cattai, A.; Coimbra, A. E. C.; Czodrowski, P.; Dachs, F.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Dittus, F.; Ducu, O. A.; Dudarev, A.; Duehrssen, M.; Dyndal, M.; Ellis, N.; Elsing, M.; Falke, S.; Farthouat, P.; Francis, D.; Froidevaux, D.; Gabrielli, A.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Hawkings, R. J.; Heinrich, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Jenni, P.; Jovicevic, J.; Kiehn, M.; Klimek, P.; Klimpel, F.; Klioutchnikova, T.; Koehler, N. M.; Krasznahorkay, A.; Kuehn, S.; Kuwertz, E. S.; Lassnig, M.; Miotto, G. Lehmann; Longo, L.; Marzin, A.; Meehan, S.; Meng, L.; Berlingen, J. Montejo; Morley, A. K.; Morvaj, L.; Moschovakos, P.; Nairz, A. M.; Nessi, M.; Palestini, S.; Pauly, T.; Pernegger, H.; Perrella, S.; Petersen, B. A.; Pontecorvo, L.; Astigarraga, M. E. Pozo; Queitsch-Maitland, M.; Raymond, M.; Rembser, C.; Rizzi, C.; Roe, S.; Rummler, A.; Saimpert, M.; Salzburger, A.; Pineda, A. Sanchez; Schlenker, S.; Schovancova, J.; Sharma, A.; Sidiropoulou, O.; Oliveira, M. V. Silva; Sanchez, C. A. Solans; Spigo, G.; Stewart, G. A.; Stockton, M. C.; Ten Kate, H.; Tuna, A. N.; Unal, G.; Vafeiadis, T.; Vallier, A.; Vandelli, W.; Schroeder, T. Vazquez; Vuillermet, R.; Wanotayaroj, C.; Wells, P. S.; Wengler, T.; Wenig, S.; Wilkens, H. G.; Young, C. J. S.; Zambito, S.; Zwalinski, L.; ATLAS Collaboration] CERN, Geneva, Switzerland; [Gardner, R. W.; Hank, M. D.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Liu, J. K. K.; Miller, D. W.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Smith, E. A.; Tosciri, C.; Vukotic, I; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Agaras, M. N.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Morales, F. A. Jimenez; Madar, R.; Megy, T.; Nibigira, E.; Rustige, L.; Santoni, C.; Vazeille, F.] Univ Clermont Auvergne, IN2P3, CNRS, LPC, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Chen, J.; Cole, B.; Emerman, A.; Gilbert, B. J.; Gonski, J. L.; Hu, Q.; Kahn, A.; Kennedy, K. E.; Mahon, D. J.; Mohapatra, S.; Parsons, J. A.; Tuts, P. M.; Williams, D. M.; Yin, P.] Columbia Univ, Nevis Lab, Irvington, NY USA; [AbouZeid, O. S.; Camplani, A.; Dam, M.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Nielsen, D. S.; Petersen, T. C.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Carducci, G.; Crosetti, G.; Malito, D.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Capua, M.; Carducci, G.; Crosetti, G.; Gnesi, I; Malito, D.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Collegato Cosenza, Frascati, Italy; [Betti, A.; Deiana, A. M.; Leney, K. J. C.; McKay, M. A.; Milke, C. D.; Narayan, R.; Parajuli, S.; Sekula, S. J.; Stroynowski, R.; Wang, P.; Ye, J.] Southern Methodist Univ, Phys Dept, Dallas, TX USA; [Ferguson, S. W.; Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Phys Dept, Richardson, TX 75083 USA; [Fanourakis, G.; Geralis, T.; Prapa, M. M.; Stavropoulos, G.] Natl Ctr Sci Res Demokritos, Aghia Paraskevi, Greece; [Andrean, S. Y.; Backman, F.; Navarro, L. Barranco; Bohm, C.; Clement, C.; Hellman, S.; Lou, X.; Milstead, D. A.; Moa, T.; Nelson, M. E.; Pasuwan, P.; Sanchez, L. Pereira; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Strubig, A.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Andrean, S. Y.; Backman, F.; Navarro, L. Barranco; Clement, C.; Hellman, S.; Lou, X.; Milstead, D. A.; Moa, T.; Nelson, M. E.; Pasuwan, P.; Sanchez, L. Pereira; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Strubig, A.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Amoroso, S.; Bella, L. Aperio; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Bloch, I; Braren, F.; Brendlinger, K.; Brueers, B.; Burgard, C. D.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Diaz, Y. Delabat; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Gillwald, N. E. K.; Gregor, I. M.; Grevtsov, K.; Guida, A.; Gupta, R.; Heim, S.; Heinemann, B.; Helary, L.; Hiller, K. H.; Issever, C.; Jacobs, R. M.; Katzy, J.; Kitali, V; Kuechler, D.; Kuechler, J. T.; Kuhl, T.; Leight, W. A.; Li, X.; Li, Y.; Liberatore, M.; Linss, A.; Liu, Y.; Lobodzinska, E. M.; Meloni, F.; Moder, P.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Pownall, G.; Rauch, D. M.; Renardi, A.; Richter, S.; Rifki, O.; Rimoldi, M.; Rivadeneira, P.; Robin, M.; Rossi, E.; Rossini, L.; Iglesias, J. A. Sabater; Sander, C. O.; Schmitt, S.; Seitz, C.; South, D.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Thompson, E. A.; Tsai, F.; Valery, L.; Von Ahnen, J.; Wongel, A. F.; Worm, S. D.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Hamburg, Germany; [Amoroso, S.; Bella, L. Aperio; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Bloch, I; Braren, F.; Brendlinger, K.; Brueers, B.; Burgard, C. D.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Diaz, Y. Delabat; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Gillwald, N. E. K.; Gregor, I. M.; Grevtsov, K.; Guida, A.; Gupta, R.; Heim, S.; Heinemann, B.; Helary, L.; Hiller, K. H.; Issever, C.; Jacobs, R. M.; Katzy, J.; Kitali, V; Kuechler, D.; Kuechler, J. T.; Kuhl, T.; Leight, W. A.; Li, X.; Li, Y.; Liberatore, M.; Linss, A.; Liu, Y.; Lobodzinska, E. M.; Meloni, F.; Moder, P.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Pownall, G.; Rauch, D. M.; Renardi, A.; Richter, S.; Rifki, O.; Rimoldi, M.; Rivadeneira, P.; Robin, M.; Rossi, E.; Rossini, L.; Iglesias, J. A. Sabater; Sander, C. O.; Schmitt, S.; Seitz, C.; South, D.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Thompson, E. A.; Tsai, F.; Valery, L.; Von Ahnen, J.; Wongel, A. F.; Worm, S. D.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Zeuthen, Germany; [Dado, T.; Dungs, S.; Erdmann, J.; Freundlich, E. M.; Gessner, G.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Rustige, L.; Sedlaczek, K.; Weingarten, J.; Wendland, B.; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Berthold, A.; Bittrich, C.; Herrmann, T.; Hils, M.; Horn, P.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Maerker, M.; Ramos, J. Manjarres; Nag, A. Nag; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wiel, C.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Beacham, J. B.; Davis, D. R.; Eggleston, M. G.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Pachal, K.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Carter, T. M.; Clark, P. J.; Farrington, S. M.; Giannelli, M. Faucci; Gao, Y.; Hamity, G. N.; Hasib, A.; Heath, M. P.; Leonidopoulos, C.; Martin, V. J.; Palazzo, S.; Sogaard, A.; Takeva, E. P.; Themistokleous, N.; Villhauer, E. M.; Vishwakarma, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Frascati, Italy; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Argyropoulos, S.; Becherer, F.; Gargiulo, S.; Guth, M.; Heidegger, C.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hohn, D.; Honig, J. C.; Jakobs, K.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Kuprash, O.; Landgraf, U.; Lang, V. S.; Losle, A.; Magerl, V; Parzefall, U.; Plesanovs, V; Rodriguez, A. Rodriguez; Roland, B.; Rottler, B.; Ruhr, F.; Rurikova, Z.; Sammel, D.; Sauerburger, F.; Schleicher, K. E.; Scholer, P. G.; Schumacher, M.; Sperlich, D.; Weiser, C.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wollrath, J.; Zanzi, D.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Abeling, K.; Achkar, B.; Bindi, M.; Bokan, P.; Buschmann, E.; Dreyer, T.; Gerlach, L. O.; Gama, R. Goncalves; Grosse-Knetter, J.; Kawamura, G.; Kirchhoff, A.; Lai, S.; Lange, J. C.; Mobius, S.; Niemeyer, M.; Oerdek, S.; Peiffer, T.; Pokharel, I; Quadt; Shabalina, E.; Skaf, A.; Sohns, F.; Veatch, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Adorni, S.; Akilli, E.; Amrouche, C. S.; Antel, C.; Clark, A.; Della Volpe, D.; Dubreuil, A.; Ferrere, D.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Lionti, A. L.; Mermod, P.; Nessi, M.; Nindhito, H. R.; Pandini, C. E.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Gagliardi, G.; Lapertosa, A.; Parodi, F.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Gagliardi, G.; Gemme, C.; Lapertosa, A.; Morettini, P.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Caforio, D.; Dueren, M.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Bates, R. L.; Blue, A.; Borbely, A. G.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P. J.; Buttar, C. M.; Callea, G.; Connelly, I. A.; Cunningham, W. R.; Doyle, A. T.; Fabbri, F.; Gray, C.; Howarth, J.; Jamieson, J.; Mince, L.; O'Shea, V; Owen, M.; Ravina, B.; Rennie, A. G.; Robson, A.; Spiteri, D. P.; Warrack, N.; Wraight, K.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; De Regie, J. B. De Vivie; Delsart, P. A.; Genest, M. H.; Hulsken, R.; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Quintero, D. M. Portillo; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Grenoble INP, IN2P3, CNRS,LPSC, Grenoble, France; [Asbah, N. A.; Bullard, B. A.; Franklin, M.; Huth, J.; Lee, L.; Morii, M.; Bolanos, G. Rabanal; Wang, A. M.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Chen, Y.; Gao, J.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, M. Y.; Liu, X.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Su, X.; Wang, C.; Wang, R.; Wang, T.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xie, X.; Xu, H.; Xu, H.; Yang, S.; Yang, X.; Yang, Z.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Peoples R China; [Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Chen, Y.; Gao, J.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, M. Y.; Liu, X.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Su, X.; Wang, C.; Wang, R.; Wang, T.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xie, X.; Xu, H.; Xu, H.; Yang, S.; Yang, X.; Yang, Z.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, D.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Tariq, K.; Wang, S.; Xu, Z.; Yuan, R.; Zhang, X.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, D.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Tariq, K.; Wang, S.; Xu, Z.; Yuan, R.; Zhang, X.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Peoples R China; [Brahimi, N.; Dong, B.; Faraj, M.; Guo, J.; Hu, S.; Li, C-Q; Li, F.; Li, J.; Li, L.; Li, S.; Liu, K.; Mittal, M.; Nishu, N.; Sampsonidou, D.; Su, W.; Wang, C.; Yan, J.; Yang, H. J.; Zhang, X.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, Key Lab Particle Astrophys & Cosmol MOE, Shanghai, Peoples R China; [Brahimi, N.; Duan, Y.; Kato, C.; Li, C-Q; Li, S.; Liu, K.; Sampsonidou, D.; Su, W.; Yang, H. J.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Bartels, F.; Dunford, M.; Franchino, S.; Klassen, M.; Mkrtchyan, T.; Napolitano, F.; Ott, P. S.; Rassloff, D. F.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Czurylo, M. M.; Dittmeier, S. J.; Krishnan, A.; Schoening, A.; Vigani, L.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Cheng, H. C.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Wang, J.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Hernandez, D. Paredes; Peng, C.; Tam, K. C.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Xiang, J.; Yang, T.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Xiang, J.; Yang, T.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Agapopoulou, C.; Al Khoury, K.; Atmani, H.; Bassalat, A.; Delgove, D.; Delporte, C.; Duflot, L.; Escalier, M.; Fayard, L.; Fournier, D.; Ghosh, A.; Grivaz, J-F; Hohov, D.; Iconomidou-Fayard, L.; Kotsokechagia, A.; Lounis, A.; Makovec, N.; Morange, N.; Perego, M. M.; Petroff, P.; Puzo, P.; Rousseau, D.; Sacerdoti, S.; Schaffer, A. C.; Serin, L.; Simion, S.; Tanaka, R.; Trofymov, A.; Varouchas, D.; Zerwas, D.; Zhang, Z.] Univ Paris Saclay, IJCLab, IN2P3, CNRS, F-91405 Orsay, France; [Calfayan, P.; Evans, H.; Forland, B. C.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Meyer, C.; Myers, G.; Palacino, G.; Roland, C. P. A.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Magro, J.; Panizzo, G.; Pinamonti, M.; Pineda, A. Sanchez; Sbrizzi, A.; Serkin, L.; Soualah, R.] Ist Nazl Fis Nucl, Sez Trieste, Grp Collegato Udine, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Magro, J.; Panizzo, G.; Pinamonti, M.; Pineda, A. Sanchez; Sbrizzi, A.; Soualah, R.] Univ Udine, Dipartimento Politecn Ingn & Architettura, Udine, Italy; [Chiodini, G.; Gorini, E.; Gravili, F. G.; Primavera, M.; Reale, M.; Schioppa, E. J.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, Lecce, Italy; [Gorini, E.; Gravili, F. G.; Reale, M.; Schioppa, E. J.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Citterio, M.; D'Auria, S.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Meroni, C.; Mungo, D. P.; Murrone, A.; Perini, L.; Ragusa, F.; Resconi, S.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.] Ist Nazl Fis Nucl, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; D'Auria, S.; Fanti, M.; Lazzaroni, M.; Mungo, D. P.; Murrone, A.; Perini, L.; Ragusa, F.; Stabile, A.] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V; Lavorgna, M.; Massarotti, P.; Merola, L.; Rossi, E.; Sekhniaidze, G.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Pezzotti, L.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Farina, E. M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Pezzotti, L.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; Di Gregorio, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Mastrandrea, P.; Roda, C.; Scuri, F.; Verducci, M.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; Di Gregorio, G.; Francavilla, P.; Mastrandrea, P.; Roda, C.; Verducci, M.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Bruscino, N.; Chomont, A. R.; Corradi, M.; De Pedis, D.; De Salvo, A.; Di Bello, F. A.; Falciano, S.; Francescato, S.; Frattari, G.; Gauzzi, P.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Longarini, I; Luci, C.; Luminari, L.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rosati, S.; Sabetta, L.; Tehrani, F. Safai; Vannicola, D.; Vari, R.; Veneziano, S.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Bruscino, N.; Chomont, A. R.; Corradi, M.; Di Bello, F. A.; Francescato, S.; Frattari, G.; Gauzzi, P.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Longarini, I; Luci, C.; Policicchio, A.; Sabetta, L.; Vannicola, D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Camelia, E. Alunno; Caltabiano, A.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Liberti, B.; Loffredo, S.; Marcoccia, L.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Camelia, E. Alunno; Caltabiano, A.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Loffredo, S.; Marcoccia, L.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Biglietti, M.; Camerlingo, M. T.; D'Amico, V; Di Micco, B.; Di Nardo, R.; Farilla, A.; Iodice, M.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.] Ist Nazl Fis Nucl, Sez Roma Tre, Rome, Italy; [Camerlingo, M. T.; D'Amico, V; Di Micco, B.; Di Nardo, R.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Cristoforetti, M.; Di Luca, A.; Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] INFN TIFPA, Trento, Italy; [Cristoforetti, M.; Di Luca, A.; Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Guest, D.; Khoo, T. J.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Bret, M. Cano; Ghosh, A.; Mallik, U.] Univ Iowa, Iowa City, IA USA; [Chen, B.; Chen, C. H.; Cochran, J.; Heidorn, W. D.; Kang, S.; Krumnack, N.; Lebedev, A.; Lee, S.; Liu, B.; Prell, S.; Yoshihara, K.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M., V; Dedovich, D., V; Demichev, M.; Gongadze, A.; Gostkin, M., I; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V; Kulchitsky, Y.; Ladygin, E.; Lyubushkin, V; Lyubushkina, T.; Malyukov, S.; Mineev, M.; Plotnikova, E.; Potrap, I. N.; Prokoshin, F.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Tsiareshka, P., V; Turchikhin, S.; Yeletskikh, I; Zhemchugov, A.; Zimine, N., I] Joint Inst Nucl Res, Dubna, Russia; [Ferraz, V. Araujo; Cerqueira, A. S.; de Andrade Filho, L. Manhaes; Peralva, B. S.] Univ Fed Juiz de Fora UFJF, Dept Engn Eletr, Juiz De Fora, Brazil; [Coutinho, Y. Amaral; Pereira, R. Araujo; Begalli, M.; Caloba, L. P.; Da Fonseca Pinto, J., V; Freund, W. S.; Gaspar, P.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Chinese Academy of Sciences; Institute of High Energy Physics, CAS; 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University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; New York University; Ochanomizu University; University System of Ohio; Ohio State University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Universidade de Lisboa; Instituto Superior Tecnico; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Andres Bello; Universidad de Tarapaca; Universidad Tecnica Federico Santa Maria; Universidade Federal de Sao Joao del-Rei; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; City University of New York (CUNY) System; Peking University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; Autonomous University of Barcelona; University of Aegean; University of Louisville; Ben Gurion University; California State University System; California State University East Bay; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; University of Fribourg; University of Udine; Lomonosov Moscow State University; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Bulgarian Academy of Sciences; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Istanbul University; Moscow Institute of Physics & Technology; An Najah National University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aad, G (corresponding author), Aix Marseille Univ, IN2P3, CNRS, CPPM, Marseille, France. 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YILDIZ, Hatice DURAN/0000-0003-3469-6045; Snesarev, Andrei/0000-0002-9067-8362; Brahimi, Nihal/0000-0003-0992-3509; Francavilla, Paolo/0000-0003-1164-6870; Adiguzel, Aytul/0000-0001-6644-0517; Simsek, Sinem/0000-0002-9650-3846; Ye, Sheng/0000-0003-3229-0746; Napolitano, Fabrizio/0000-0002-8686-5923; Valero, Alberto/0000-0002-9776-5880; Ippolito, Valerio/0000-0001-5126-1620; Fabbri, Laura/0000-0002-4002-8353; Burdin, Sergey/0000-0003-4831-4132; Jiménez Peña, Javier/0000-0002-8705-628X; Kroll, Jiri/0000-0001-6215-3326; Popa, Stefan/0000-0001-9275-4536; Olesya, Kuchinskaya/0000-0002-3664-2465; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Martinez-Agullo, Pablo/0000-0001-8925-9518; Lefebvre, Benoit/0000-0001-8212-6624; Ohm, Christian/0000-0002-8015-7512; Castro, Nuno/0000-0001-8491-4376; Chizhov, Mihail/0000-0001-5841-3316; Wolters, Helmut/0000-0002-9588-1773; Zanzi, Daniele/0000-0002-1222-7937; Schioppa, Enrico Junior/0000-0002-1369-9944; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; 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Balek, Petr/0000-0002-0942-1966; Kersevan, Borut/0000-0002-4529-452X; Portales, Louis/0000-0002-9860-9185; Allport, Philip/0000-0001-7303-2570; Chudoba, Jiri/0000-0002-6425-2579; Goossens, Luc/0000-0002-2536-4498; Abidi, Syed Haider/0000-0002-8496-9294; Jeanneau, Fabien/0000-0002-6360-6136; Starz, Steffen/0000-0002-2908-3909; Krieter, Ferdinand/0000-0002-7675-8024; Hays, Chris/0000-0003-2371-9723; Grieco, Chiara/0000-0002-3955-4399; Petersen, Troels/0000-0003-0221-3037; Ould-Saada, Farid/0000-0002-9404-835X; Siral, Ismet/0000-0003-4554-1831; Krasnopevtsev, Dimitrii/0000-0002-6356-372X; Betti, Alessandra/0000-0003-0839-9311; Pizzini, Alessio/0000-0001-8891-1842; Groh, Sabrina/0000-0002-3001-3545; White, Martin/0000-0001-5474-4580; Sanchez, Javier/0000-0001-9913-310X; Dittus, Fridolin/0000-0002-1760-8237; Gonzalez Suarez, Rebeca/0000-0002-6126-7230; Rozen, Yoram/0000-0001-6969-0634; Lobodzinska, Ewelina Maria/0000-0001-9012-3431; Becherer, Fabian/0000-0003-0562-4616; Ellert, Mattias/0000-0001-5265-3175 ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; ANID, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, China; Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia, Russian Federation; NRCKI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, USA; NSF, USA; BCKDF, Canada; CANARIE, Canada; ComputeCanada, Canada; CRC, Canada; IVADO, Canada; Beijing Municipal Science & Technology Commission, China; COST, European Union; ERC, European Union; ERDF, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex, France; ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme, Greece; Aristeia programme, Greece; EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; Canton of Geneva, Switzerland; Horizon 2020, European Union; Thales programme, Greece; Science and Technology Facilities Council [ST/S000879/1] Funding Source: researchfish ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; ANID, Chile; CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, China; Hong Kong SAR, China; ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; JINR; MES of Russia, Russian Federation(Russian Federation); NRCKI, Russian Federation(Russian Federation); MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MICINN, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, USA(United States Department of Energy (DOE)); NSF, USA(National Science Foundation (NSF)); BCKDF, Canada; CANARIE, Canada; ComputeCanada, Canada; CRC, Canada; IVADO, Canada; Beijing Municipal Science & Technology Commission, China(Beijing Municipal Science & Technology Commission); COST, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France(French National Research Agency (ANR)); Investissements d'Avenir Idex, France(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme, Greece; Aristeia programme, Greece; EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society, United Kingdom; Leverhulme Trust, United Kingdom(Leverhulme Trust); Canton of Geneva, Switzerland; Horizon 2020, European Union; Thales programme, Greece(Thales Group); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO, CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRCKI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, USA. In addition, individual groups and members have received support from BCKDF, CANARIE, ComputeCanada, CRC and IVADO, Canada; Beijing Municipal Science & Technology Commission, China; COST, ERC, ERDF, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [69]. 68 9 9 3 25 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C JUN 2021.0 81.0 6 537 10.1140/epjc/s10052-021-09192-8 0.0 32 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics TT6MD Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000680459800003 0
C Liu, YK; Wang, LH; Wang, XV Onori, M; Wang, L; Wang, XV; Ji, W Liu, Yongkui; Wang, Lihui; Wang, Xi Vincent Cloud manufacturing: latest advancements and future trends PROCEEDINGS OF THE 8TH SWEDISH PRODUCTION SYMPOSIUM (SPS 2018) Procedia Manufacturing English Proceedings Paper 8th Swedish Production Symposium (SPS) MAY 16-18, 2018 Stockholm, SWEDEN Cloud manufacturing; Latest advancements; Future trends SERVICE COMPOSITION; PREDICTIVE MAINTENANCE; OPTIMAL CONFIGURATION; BIG DATA; FRAMEWORK; INTEROPERABILITY; SELECTION; DESIGN; CUSTOMIZATION; OPTIMIZATION Cloud manufacturing as a new manufacturing paradigm has been attracted a large amount of research interest. More than eight hundred papers have been published on cloud manufacturing. Several review articles have been published, and the latest paper that presented the overall research status of cloud manufacturing was published online in April 2015. However, during the past more than two years, many new papers have been published. In order to speed up research on cloud manufacturing and provide insights to future research, this paper presents the latest research advancements of cloud manufacturing and identifies future trends in this particular area. (C) 2018 The Authors. Published by Elsevier B.V. [Liu, Yongkui; Wang, Lihui; Wang, Xi Vincent] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden; [Liu, Yongkui] Xidian Univ, Sch Mechano Elect Engn, Ctr Complex Syst, Xian 710071, Peoples R China Royal Institute of Technology; Xidian University Liu, YK (corresponding author), KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden.;Liu, YK (corresponding author), Xidian Univ, Sch Mechano Elect Engn, Ctr Complex Syst, Xian 710071, Peoples R China. Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049 112 24 25 1 9 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 2351-9789 PROCEDIA MANUF 2018.0 25.0 62 73 10.1016/j.promfg.2018.06.058 0.0 12 Automation & Control Systems; Engineering, Manufacturing Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BP3KJ gold 2023-03-23 WOS:000547903500009 0
J Zhang, XJ; Kendrick, KM; Zhou, HF; Zhan, Y; Feng, JF Zhang, Xuejuan; Kendrick, Keith M.; Zhou, Haifu; Zhan, Yang; Feng, Jianfeng A Computational Study on Altered Theta-Gamma Coupling during Learning and Phase Coding PLOS ONE English Article INFERIOR TEMPORAL CORTEX; NEURONAL SYNCHRONIZATION; SPIKING NEURONS; WORKING-MEMORY; SYNAPTIC BASIS; OSCILLATIONS; NETWORKS; DYNAMICS; RHYTHM; HIPPOCAMPUS There is considerable interest in the role of coupling between theta and gamma oscillations in the brain in the context of learning and memory. Here we have used a neural network model which is capable of producing coupling of theta phase to gamma amplitude firstly to explore its ability to reproduce reported learning changes and secondly to memory-span and phase coding effects. The spiking neural network incorporates two kinetically different GABAA receptor-mediated currents to generate both theta and gamma rhythms and we have found that by selective alteration of both NMDA receptors and GABAA, slow receptors it can reproduce learning-related changes in the strength of coupling between theta and gamma either with or without coincident changes in theta amplitude. When the model was used to explore the relationship between theta and gamma oscillations, working memory capacity and phase coding it showed that the potential storage capacity of short term memories, in terms of nested gamma-subcycles, coincides with the maximal theta power. Increasing theta power is also related to the precision of theta phase which functions as a potential timing clock for neuronal firing in the cortex or hippocampus. [Zhang, Xuejuan; Zhou, Haifu; Feng, Jianfeng] Zhejiang Normal Univ, Dept Math, Jinhua, Peoples R China; [Kendrick, Keith M.] Univ Elect Sci & Technol China, Key Lab Neuroinformat, Social Cognit & Affect Neurosci Grp, Sch Life Sci & Technol, Chengdu 610054, Peoples R China; [Zhan, Yang] European Mol Biol Lab, Mouse Biol Unit, Monterotondo, Italy; [Feng, Jianfeng] Fudan Univ, Ctr Computat Syst Biol, Shanghai 200433, Peoples R China; [Zhan, Yang] EMBL EBI, Cambridge, England; [Feng, Jianfeng] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England Zhejiang Normal University; University of Electronic Science & Technology of China; European Molecular Biology Laboratory (EMBL); Fudan University; European Molecular Biology Laboratory (EMBL); University of Warwick Zhang, XJ (corresponding author), Zhejiang Normal Univ, Dept Math, Jinhua, Peoples R China. jianfeng.feng@warwick.ac.uk Kendrick, Keith/AFL-0909-2022 Kendrick, Keith/0000-0002-0371-5904; Zhan, Yang/0000-0002-1150-6344; feng, jianfeng/0000-0002-9328-5732 CARMEN e-science project; EPSRC [EP/E002331/1]; EU; Natural Science Foundation of China (NSFC) [10971196]; Royal Society; Zhejiang Innovation Project [T200905]; FANEDD in China CARMEN e-science project; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EU(European Commission); Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Royal Society(Royal Society of London); Zhejiang Innovation Project; FANEDD in China JF is a Royal Society Wolfson Research Award Holder and was partially supported by CARMEN e-science project (www.carmen.org.uk) funded by the EPSRC (EP/E002331/1) and an EU grant BION. XZ was partially supported by the Natural Science Foundation of China (NSFC) (Nos. 10971196), Zhejiang Innovation Project (Grant No. T200905), and the FANEDD in China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study. 39 4 5 2 27 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JUN 21 2012.0 7.0 6 e36472 10.1371/journal.pone.0036472 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 964KY 22737207.0 Green Published, Green Submitted, gold 2023-03-23 WOS:000305695100002 0
C Chen, Z; Li, W; Gryllias, K Desmet, W; Pluymers, B; Moens, D; Rottiers, W Chen, Z.; Li, W.; Gryllias, K. Gearbox fault diagnosis based on Convolutional Neural Networks PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING (ISMA2018) / INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (USD2018) English Proceedings Paper International Conference on Noise and Vibration Engineering (ISMA) / International Conference on Uncertainty in Structural Dynamics (USD) SEP 17-19, 2018 Leuven, BELGIUM KU Leuven, Dept Mech Engn ROTATING MACHINERY; MODE DECOMPOSITION A major challenge in mechanical intelligent diagnosis process lies in how to full exploit potential information and extract robust features to achieve more reliable diagnosis results. In real environment, machines are usually running under variable speed and load conditions and the measured signals present a complex amplitude and frequency signature, making fault identification a difficult task. In this paper, Wavelet transform is firstly utilized to convert the time signals into (2D) time-frequency images to obtain good representative features. Then a deep learning method based on CNN is constructed to automatically learn discriminative features from inputs, which obstacles the deficiencies of manual feature extraction and selection stage. The proposed method is evaluated on datasets with gear and bearing faults acquired under variable speed operating conditions. Experimental results show that the proposed method has improved the performance of gearbox fault classification and outperforms other methods. [Chen, Z.; Li, W.] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Guangdong, Peoples R China; [Chen, Z.; Gryllias, K.] Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300 B, B-3001 Heverlee, Belgium; [Gryllias, K.] Flanders Make, Dynam Mech & Mechatron Syst, Celestijnenlaan 300 B, B-3001 Heverlee, Belgium South China University of Technology; KU Leuven Li, W (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Guangdong, Peoples R China. whlee@scut.edu.cn Gryllias, Konstantinos/F-4989-2017; LI, Weihua/AAE-6294-2022 Gryllias, Konstantinos/0000-0002-8703-8938 24 3 3 0 3 KATHOLIEKE UNIV LEUVEN, DEPT WERKTUIGKUNDE HEVERLEE CELESTIJNENLAAN 300B, HEVERLEE, B-3001, BELGIUM 978-90-73802-99-5 2018.0 941 953 13 Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM6QO 2023-03-23 WOS:000467299101021 0
J Akpolat, AN; Habibi, MR; Baghaee, HR; Dursun, E; Kuzucuoglu, AE; Yang, YH; Dragicevic, T; Blaabjerg, F Akpolat, Alper Nabi; Habibi, Mohammad Reza; Baghaee, Hamid Reza; Dursun, Erkan; Kuzucuoglu, Ahmet Emin; Yang, Yongheng; Dragicevic, Tomislav; Blaabjerg, Frede Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control IEEE TRANSACTIONS ON ENERGY CONVERSION English Article Microgrids; Voltage control; Batteries; Load modeling; Energy management; Artificial neural networks; Training; Artificial neural network (ANN); battery energy storage system (BESS); DC microgrids; model predictive controller (MPC); photovoltaics (PVs) VOLTAGE; CONVERTERS; OPERATION; SYSTEM; MANAGEMENT; DESIGN Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids. [Akpolat, Alper Nabi; Dursun, Erkan; Kuzucuoglu, Ahmet Emin] Marmara Univ, Fac Technol, Dept Elect Elect Engn, TR-34854 Istanbul, Turkey; [Habibi, Mohammad Reza; Blaabjerg, Frede] Aalborg Univ, AAU Energy, DK-9220 Aalborg, Denmark; [Baghaee, Hamid Reza] Amirkabir Univ Technol, Dept Elect Engn, Tehran 15914, Iran; [Yang, Yongheng] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China; [Dragicevic, Tomislav] Tech Univ Denmark, Ctr Elect Power & Energy, Dept Elect Engn, DK-2800 Lyngby, Denmark Marmara University; Aalborg University; Amirkabir University of Technology; Zhejiang University; Technical University of Denmark Akpolat, AN (corresponding author), Marmara Univ, Fac Technol, Dept Elect Elect Engn, TR-34854 Istanbul, Turkey. alper.nabi@marmara.edu.tr; mre@energy.aau.dk; hrbaghaee@aut.ac.ir; erkandursun@marmara.edu.tr; kuzucuoglu@marmara.edu.tr; yang_yh@zju.edu.cn; tomdr@elektro.dtu.dk; fbl@energy.aau.dk Yang, Yongheng/N-7735-2015; Blaabjerg, Frede/A-5008-2008 Yang, Yongheng/0000-0002-1488-4762; Blaabjerg, Frede/0000-0001-8311-7412 Marmara University-BAPKO [FEN-C-DRP-150218-0063] Marmara University-BAPKO(Marmara University) This work was supported in part by Marmara University-BAPKO with Project No. FEN-C-DRP-150218-0063 and Scholarship Commission YOKYUDAB. 46 3 3 4 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8969 1558-0059 IEEE T ENERGY CONVER IEEE Trans. Energy Convers. JUN 2022.0 37.0 2 999 1010 10.1109/TEC.2021.3118664 0.0 12 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 1M8OG Green Published 2023-03-23 WOS:000800225300028 0
J Li, RQ; Zhao, X; Moens, MF Li, Ruiqi; Zhao, Xiang; Moens, Marie-Francine A Brief Overview of Universal Sentence Representation Methods: A Linguistic View ACM COMPUTING SURVEYS English Article Sentence embedding; universal representation; deep learning; representation learning TEXT; ASSOCIATION How to transfer the semantic information in a sentence to a computable numerical embedding form is a fundamental problem in natural language processing. An informative universal sentence embedding can greatly promote subsequent natural language processing tasks. However, unlike universal word embeddings, a widely accepted general-purpose sentence embedding technique has not been developed. This survey summarizes the current universal sentence-embedding methods, categorizes them into four groups from a linguistic view, and ultimately analyzes their reported performance. Sentence embeddings trained from words in a bottom-up manner are observed to have different, nearly opposite, performance patterns in downstream tasks compared to those trained from logical relationships between sentences. By comparing differences of training schemes in and between groups, we analyze possible essential reasons for different performance patterns. We additionally collect incentive strategies handling sentences from other models and propose potentially inspiring future research directions. [Li, Ruiqi] Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A,Box 03-65, B-3001 Heverlee, Belgium; [Li, Ruiqi; Zhao, Xiang] Natl Univ Def Technol, Coll Syst Engn, Deya Rd 109, Changsha 410073, Peoples R China; [Moens, Marie-Francine] Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A,Box 04-25, B-3001 Heverlee, Belgium KU Leuven; National University of Defense Technology - China; KU Leuven Li, RQ (corresponding author), Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A,Box 03-65, B-3001 Heverlee, Belgium.;Li, RQ; Zhao, X (corresponding author), Natl Univ Def Technol, Coll Syst Engn, Deya Rd 109, Changsha 410073, Peoples R China. ruiqi.li1993@outlook.com; xiangzhao@nudt.edu.cn; sien.moens@cs.kuleuven.be NSFC [61872446]; NSF of Hunan Province [2019JJ20024]; Science and Technology Innovation Program of Hunan Province [2020RC4046]; European Research Council Advanced Grant [H2020-ERC-2017-ADG 788506] NSFC(National Natural Science Foundation of China (NSFC)); NSF of Hunan Province; Science and Technology Innovation Program of Hunan Province; European Research Council Advanced Grant(European Research Council (ERC)) This work is partially supported by NSFC under grant No. 61872446, NSF of Hunan Province under grant No. 2019JJ20024, and the Science and Technology Innovation Program of Hunan Province under grant No. 2020RC4046, and the European Research Council Advanced Grant H2020-ERC-2017-ADG 788506. 163 0 0 7 7 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. APR 2023.0 55.0 3 56 10.1145/3482853 0.0 42 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6J6LA 2023-03-23 WOS:000886932500015 0
C Yang, KY; Gao, YJ; Ma, R; Chen, L; Wu, S; Chen, G IEEE Yang, Keyu; Gao, Yunjun; Ma, Rui; Chen, Lu; Wu, Sai; Chen, Gang DBSCAN-MS: Distributed Density-Based Clustering in Metric Spaces 2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2019) IEEE International Conference on Data Engineering English Proceedings Paper IEEE 35th International Conference on Data Engineering (ICDE) APR 08-11, 2019 Macau, PEOPLES R CHINA IEEE,IEEE Comp Soc MR-DBSCAN; ALGORITHM DBSCAN is one of important density-based clustering methods, which has a wide range of applications in machine learning and data mining, to name but a few. However, the rapid growing volume and variety of data nowadays challenges traditional DBSCAN, and thus, distributed DBSCAN in metric spaces is required. In this paper, we propose DBSCAN-MS, a distributed density-based clustering in metric spaces. To ensure load balancing, we present a k-d tree based partitioning approach. It utilizes pivots to map the data in metric spaces to vector spaces, and employs k-d tree partitioning technique to equally divide the data. To avoid unnecessary computation and communication cost, we propose a framework that divides data into partitions, find out local DBSCAN result, and merge local result based on a merging graph. In addition, the pivot filtering and the sliding window techniques are also used in the framework for pruning. Extensive experiments with both real and synthetic datasets demonstrate the efficiency and scalability of our proposed DBSCAN-MS. [Yang, Keyu; Gao, Yunjun; Ma, Rui; Wu, Sai; Chen, Gang] Zhejiang Univ, Coll Comp Sci, Hangzhou, Zhejiang, Peoples R China; [Chen, Lu] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark; [Gao, Yunjun] Zhejiang Univ, Joint Inst Frontier Technol, Alibaba, Hangzhou, Zhejiang, Peoples R China Zhejiang University; Aalborg University; Alibaba Group; Zhejiang University Yang, KY (corresponding author), Zhejiang Univ, Coll Comp Sci, Hangzhou, Zhejiang, Peoples R China. kyyang@zju.edu.cn; gaoyj@zju.edu.cn; ruima@zju.edu.cn; luchen@cs.aau.dk; wusai@zju.edu.cn; cg@zju.edu.cn National Key R&D Program of China [2018YFB1004003]; 973 Program [2015CB352502]; NSFC [61522208, 61661146001]; NSFC-Zhejiang Joint Fund [U1609217] National Key R&D Program of China; 973 Program(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)); NSFC-Zhejiang Joint Fund This work was supported in part by the National Key R&D Program of China Under Grant No. 2018YFB1004003, the 973 Program under Grant No. 2015CB352502, NSFC Grant Nos. 61522208 and 61661146001, and NSFC-Zhejiang Joint Fund under Grant No. U1609217. Yunjun Gao is the corresponding author of the work. 30 7 8 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1084-4627 978-1-5386-7474-1 PROC INT CONF DATA 2019.0 1346 1357 10.1109/ICDE.2019.00122 0.0 12 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN2TF 2023-03-23 WOS:000477731600115 0
J Yue, NH; Caini, M; Li, LL; Zhao, Y; Li, Y Yue, Naihua; Caini, Mauro; Li, Lingling; Zhao, Yang; Li, Yu A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions APPLIED ENERGY English Article RDPG; A3C; LSTM; CNN; Multi vectors prediction; Gymnasiums ENERGY PERFORMANCE; DECISION-MAKING; NEURAL-NETWORK; DESIGN; OPTIMIZATION; CONSUMPTION; REGRESSION; TEMPERATURE; SUPPORT; MODELS Building performance simulation (BPS) is essential for testing energy demand and indoor environment quality of different building designs. However, software for BPS is computationally intensive and impractical to run thousands even millions of simulations for performance analysis and optimization. Especially for the large space buildings, which usually have complex forms and high energy consumption. The computational problem could be overcome by the adoption of metamodels. Most correlational research focuses on single performance prediction of a particular building, which makes the model less robust when applied to multi vectors prediction for different buildings under multiple weather conditions. This paper leveraged six metamodels, which include recurrent deterministic policy gradient (RDPG), asynchronous advantage actor-critic (A3C), long short-term memory (LSTM), convolution neural network (CNN), artificial neural network (ANN) and support vector regression (SVR), to predict hourly-based multi performance vectors of gymnasiums under various design parameters and multiple weather conditions. Six metamodels are trained and tested on a large scale of datasets simulated by EnergyPlus over four gymnasium cases in different cities of China. The accuracy, efficiency, ease-ofuse, robustness and interpretability of the models are investigated. To conduct a fair and detailed comparison, a methodological approach using grid searches for model settings selection assisted by sensitivity analysis is pursued. Principal component analysis (PCA) is also adopted to interpret the work process of the metamodels. The comparison showed that the RDPG model provides the most accurate prediction results with R-2 converges at 0.993, 0.982 and 0.941 for energy, temperature and CO2, respectively. LSTM model is more efficient than RDPG, and suitable for users who need emphasis on both time and accuracy. ANN is suitable for users with limited time and require models of ease-of-use and robustness. SVR and ANN could be used for the automatically co-simulation with BPS software. In future research, the influence of occupant behavior also should be investigated. [Yue, Naihua] Qingdao Univ Technol, Sch Architecture & Urban Planning, 11 Fushun Rd, Qingdao 266033, Shandong, Peoples R China; [Yue, Naihua] Minist Educ, Engn Res Ctr Concrete Technol Marine Environm, 777 Jialingjiang Rd, Qingdao 266520, Shandong, Peoples R China; [Caini, Mauro] Univ Padua, Dept Civil Environm & Architectural Engn, Via 8 Febbraio, I-35122 Padua 2, Italy; [Li, Lingling; Zhao, Yang; Li, Yu] Harbin Inst Technol, Sch Architecture, 66 Xidazhi St, Harbin 150000, Peoples R China; [Zhao, Yang; Li, Yu] Natl Univ Singapore, Coll Design & Engn, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore Qingdao University of Technology; University of Padua; Harbin Institute of Technology; National University of Singapore Yue, NH (corresponding author), Qingdao Univ Technol, Sch Architecture & Urban Planning, 11 Fushun Rd, Qingdao 266033, Shandong, Peoples R China. yuenaihua@qut.edu.cn National Natural Science Foundation of China Project [51178130, 51678176] National Natural Science Foundation of China Project(National Natural Science Foundation of China (NSFC)) The research was supported by the National Natural Science Foundation of China Project (No. 51178130 and 51678176) . The assistance of Mr. Wen Yuan and Mr. Jacopo Sabbatini during the site measurements and program coding process is greatly appreciated. 64 0 0 5 5 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy FEB 15 2023.0 332.0 120481 10.1016/j.apenergy.2022.120481 0.0 DEC 2022 27 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 7N0FK 2023-03-23 WOS:000907023000001 0
C Xu, Z; Figueiredo, MAT; Yuan, XM; Studer, C; Goldstein, T IEEE Xu, Zheng; Figueiredo, Mario A. T.; Yuan, Xiaoming; Studer, Christoph; Goldstein, Tom Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUL 21-26, 2017 Honolulu, HI IEEE,IEEE Comp Soc,CVF ALTERNATING DIRECTION METHOD; FACE RECOGNITION; SELECTION; MULTIPLIERS; PENALTY; MODELS; IMAGE Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user. We propose an adaptive method that automatically tunes the key algorithm parameters to achieve optimal performance without user oversight. Inspired by recent work on adaptivity, the proposed adaptive relaxed ADMM (ARADMM) is derived by assuming a Barzilai-Borwein style linear gradient. A detailed convergence analysis of ARADMM is provided, and numerical results on several applications demonstrate fast practical convergence. [Xu, Zheng; Goldstein, Tom] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA; [Figueiredo, Mario A. T.] Univ Lisbon, Inst Telecomun, Inst Super Tecn, Lisbon, Portugal; [Yuan, Xiaoming] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China; [Studer, Christoph] Cornell Univ, Sch Elect & Comp Engn, Ithaca, NY USA University System of Maryland; University of Maryland College Park; Universidade de Lisboa; Instituto Superior Tecnico; Hong Kong Baptist University; Cornell University Xu, Z (corresponding author), Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA. xuzh@cs.umd.edu Figueiredo, Mario/C-5428-2008 Figueiredo, Mario/0000-0002-0970-7745; Yuan, Xue-Ming/0000-0003-1575-0130 US Office of Naval Research [N00014-17-1-2078]; US National Science Foundation (NSF) [CCF-1535902]; Fundacao para a Ciencia e Tecnologia [UID/EEA/5008/2013]; General Research Fund from Hong Kong Research Grants Council [HKBU-12313516]; Xilinx Inc.; US NSF [ECCS-1408006, CCF-1535897]; CAREER [CCF-1652065] US Office of Naval Research(Office of Naval Research); US National Science Foundation (NSF)(National Science Foundation (NSF)); Fundacao para a Ciencia e Tecnologia(Fundacao para a Ciencia e a Tecnologia (FCT)); General Research Fund from Hong Kong Research Grants Council; Xilinx Inc.; US NSF(National Science Foundation (NSF)); CAREER(National Science Foundation (NSF)) TG and ZX were supported by the US Office of Naval Research under grant N00014-17-1-2078 and by the US National Science Foundation (NSF) under grant CCF-1535902. MF was partially supported by the Fundacao para a Ciencia e Tecnologia, grant UID/EEA/5008/2013. XY was supported by the General Research Fund from Hong Kong Research Grants Council under grant HKBU-12313516. CS was supported in part by Xilinx Inc., and by the US NSF under grants ECCS-1408006, CCF-1535897, and CAREER CCF-1652065. 57 17 18 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-5386-0457-1 PROC CVPR IEEE 2017.0 7234 7243 10.1109/CVPR.2017.765 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ1XG Green Accepted, Green Submitted 2023-03-23 WOS:000418371407036 0
J Song, WR; Afgan, MS; Yun, YH; Wang, H; Cui, JC; Gu, WL; Hou, ZY; Wang, Z Song, Weiran; Afgan, Muhammad Sher; Yun, Yong-Huan; Wang, Hui; Cui, Jiacheng; Gu, Weilun; Hou, Zongyu; Wang, Zhe Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis EXPERT SYSTEMS WITH APPLICATIONS English Article Domain knowledge; Laser-induced breakdown spectroscopy; Machine learning; Multivariate analysis; Quantitative analysis; Regression PARTIAL LEAST-SQUARES; EXTREME LEARNING-MACHINE; COAL Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission spectroscopic technique for multi-elemental analysis and has the advantages of real-time multi-element measurement, minimal sample preparation and remote detection. However, the quality of LIBS data can be low due to matrix effects and signal uncertainty which hinders the wide application of LIBS. Recent studies attempt to improve the performance of LIBS quantitative analysis using linear and nonlinear multivariate analysis models. Linear models can easily present how key variables contribute to the prediction but suffer from performance degradation if data has a high degree of nonlinearity. Nonlinear models tend to have good performance, but they lack simple and intuitive explanations for the contribution of variables and are prone to overfitting. Moreover, nonlinear models used in LIBS quantitative analysis, such as support vector regression (SVR) and kernel extreme learning machine (K-ELM), are not designed to incorporate domain knowledge of spectral data. In this work, a new machine learning algorithm is proposed, namely spectral knowledge-based regression (SKR), which integrates linear and nonlinear models to improve the performance of LIBS quantitative analysis. The linear model is knowledge-driven and built on key variables correlated with analyte composition. The nonlinear model is data-driven and transforms the input data into a kernel matrix. The proposed SKR is tested on 4 LIBS datasets and outperforms 5 baseline methods on 12 of 18 quantification tasks. Moreover, it intuitively explains the contribution of key variables towards prediction and has the same low computational complexity as ridge regression These results demonstrate that SKR inherits the high accuracy of nonlinear modelling and the simple variable interpretability of linear models. Therefore, it can serve as a promising method for improving the accuracy and reliability of LIBS quantitative analysis. [Song, Weiran; Afgan, Muhammad Sher; Cui, Jiacheng; Gu, Weilun; Hou, Zongyu; Wang, Zhe] Tsinghua Univ, Dept Energy & Power Engn, State Key Lab Power Syst, Int Joint Lab Low Carbon Clean Energy Innovat, Beijing 100084, Peoples R China; [Yun, Yong-Huan] Hainan Univ, Coll Food Sci & Engn, Haikou 570228, Hainan, Peoples R China; [Wang, Hui] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5BN, Antrim, North Ireland Tsinghua University; Hainan University; Queens University Belfast Wang, Z (corresponding author), Tsinghua Univ, Dept Energy & Power Engn, State Key Lab Power Syst, Int Joint Lab Low Carbon Clean Energy Innovat, Beijing 100084, Peoples R China. song-w@ulster.ac.uk; sherafgan@tsinghua.edu.cn; yunyonghuan@hainanu.edu.cn; h.wang@qub.ac.uk; cjc20@mails.tsinghua.edu.cn; gwl19@mails.tsinghua.edu.cn; houzy@tsinghua.edu.cn; zhewang@tsinghua.edu.cn Song, Weiran/GMX-1935-2022; Gu, Weilun/GNH-2556-2022; Hou, Zongyu/L-9293-2016; Wang, Hui/HMU-9512-2023; Wang, Zhe/E-1705-2011 Song, Weiran/0000-0003-2120-4216; Gu, Weilun/0000-0002-2841-0031; Hou, Zongyu/0000-0002-6141-4149; Wang, Zhe/0000-0001-6857-7672; Wang, Hui/0000-0003-2633-6015 National Natural Science Foundation of China [51906124]; Shanxi Province Science and Technology Plan, China [20201101013]; Scientific Research Funds of Hainan University, China [KYQD(ZR)-1844] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanxi Province Science and Technology Plan, China; Scientific Research Funds of Hainan University, China The research was supported by the National Natural Science Foundation of China (No. 51906124), Shanxi Province Science and Technology Plan, China (20201101013) and the Scientific Research Funds of Hainan University, China (KYQD(ZR)-1844). 38 5 5 6 6 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 1 2022.0 205.0 117756 10.1016/j.eswa.2022.117756 0.0 JUN 2022 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 3I8HQ 2023-03-23 WOS:000832950900007 0
J Xu, XH; Arshad, MA; Ali, U; Mahmood, A Xu, Xianhang; Arshad, Mohd Anuar; Ali, Ubaid; Mahmood, Arshad The GM-BP Neural Network Prediction Model for International Competitiveness of Computer Information Service Industry ALGORITHMS English Article computer information service industry; international competitiveness; BP neural network; grey relational degree; high-quality development The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the critical period of global economic transformation and development, the use of mathematical models to predict its international competitiveness will help scientifically evaluate the development level of the industry and accelerate the adaptation to the needs of the fourth industrial revolution. In this article, a prediction model is proposed for the international competitiveness of the computer information service industry. First, we used the Revealed Comparative Advantage (RCA) index to measure the international competitiveness of the computer information service industry. Furthermore, based on the characteristics of the industry and high-quality development theory, we constructed the evaluation indicator system of influencing factors and used the grey relational analysis method to screen key indicators. Then, we combined the Grey model and BP neural network algorithm to construct the GM-BP prediction model. Finally, China is used as an example to predict the international competitiveness of its computer information service industry, and suggestions are made for industrial development. The results show that the grey relational analysis method can genuinely reflect the impact of different aspects on the international competitiveness of China's computer information service industry and better determine the key indicators of influencing factors. The GM-BP model has minor errors and excellent simulation results and can accurately predict the future status of international competitiveness. The applicability and reliability of the model are reasonable. [Xu, Xianhang] Chongqing Inst Engn, Sch Innovat & Entrepreneurship, Chongqing 400056, Peoples R China; [Xu, Xianhang; Arshad, Mohd Anuar; Ali, Ubaid] Univ Sains Malaysia, Sch Management, Gelugor 11800, Malaysia; [Ali, Ubaid] COMSATS Univ Islamabad, Management Sci Dept, Abbottabad 22010, Pakistan; [Mahmood, Arshad] Univ Wuppertal, Sch Business & Econom, D-42119 Wuppertal, Germany Chongqing Institute of Engineering; Universiti Sains Malaysia; COMSATS University Islamabad (CUI); University of Wuppertal Xu, XH (corresponding author), Chongqing Inst Engn, Sch Innovat & Entrepreneurship, Chongqing 400056, Peoples R China.;Xu, XH; Arshad, MA (corresponding author), Univ Sains Malaysia, Sch Management, Gelugor 11800, Malaysia. xuxianhang@cqie.edu.cn; anuar_arshad@usm.my; Ubaid@cuiatd.edu.pk; arshad@usm.my ali, ubaid/HNP-2975-2023 XU, XIANHANG/0000-0003-3717-5771 Humanistic and Social Science Research Program of Chongqing Municipal Education Commission [20SKGH276]; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN202101902] Humanistic and Social Science Research Program of Chongqing Municipal Education Commission; Scientific and Technological Research Program of Chongqing Municipal Education Commission FundingThis work is supported by the Humanistic and Social Science Research Program of Chongqing Municipal Education Commission (Grant No. 20SKGH276) and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202101902). 39 0 0 1 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4893 ALGORITHMS Algorithms NOV 2021.0 14.0 11 308 10.3390/a14110308 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Emerging Sources Citation Index (ESCI) Computer Science XH4DM gold 2023-03-23 WOS:000725387100001 0
J Chen, Y; Guizani, M; Zhang, Y; Wang, L; Crespi, N; Lee, GM; Wu, T Chen, Yuanfang; Guizani, Mohsen; Zhang, Yan; Wang, Lei; Crespi, Noel; Lee, Gyu Myoung; Wu, Ting When Traffic Flow Prediction and Wireless Big Data Analytics Meet IEEE NETWORK English Article NETWORKS In this article, we verify whether or not prediction performance can be improved by fitting the actual data to optimize the parameter values of a prediction model. Traffic flow prediction is an important research issue for solving the traffic congestion problem in an ITS. Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. The verification in this article is conducted by comparing the optimized and the normal time series prediction models. With the verification, we can learn that the era of big data is here and will become an important aspect for the study of traffic flow prediction to solve the congestion problem. Experimental results of a case study are provided to verify the existence of the performance improvement in the prediction, while the research challenges of this data-analytics-based prediction are presented and discussed. [Chen, Yuanfang; Wu, Ting] Hangzhou Dianzi Univ, Hangzhou, Zhejiang, Peoples R China; [Wu, Ting] Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou, Zhejiang, Peoples R China; [Guizani, Mohsen] Univ Idaho, Moscow, ID 83843 USA; [Zhang, Yan] Univ Oslo, Oslo, Norway; [Wang, Lei] Dalian Univ Technol, Sch Software, Dalian, Peoples R China; [Crespi, Noel; Lee, Gyu Myoung] Telecom SudParis, Inst Mines, Evry, France Hangzhou Dianzi University; Hangzhou Dianzi University; Idaho; University of Idaho; University of Oslo; Dalian University of Technology; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Wu, T (corresponding author), Hangzhou Dianzi Univ, Hangzhou, Zhejiang, Peoples R China.;Wu, T (corresponding author), Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou, Zhejiang, Peoples R China.;Zhang, Y (corresponding author), Univ Oslo, Oslo, Norway. Guizani, Mohsen/AAX-4534-2021; wang, lei/U-2378-2019; Crespi, Noel/ABE-7052-2020; Zhang, Yan/AFK-8566-2022 Guizani, Mohsen/0000-0002-8972-8094; Crespi, Noel/0000-0003-2962-192X; National Science Foundation of China [61802097] National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Science Foundation of China (Grant No. 61802097). 15 12 13 6 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. MAY-JUN 2019.0 33.0 3 161 167 10.1109/MNET.2018.1800134 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IB1KE Green Accepted 2023-03-23 WOS:000470023300022 0
J Jia, P; Wu, XB; Li, ZY; Li, B; Wang, WH; Liu, Q; Popowicz, A; Cai, DM Jia, Peng; Wu, Xuebo; Li, Zhengyang; Li, Bo; Wang, Weihua; Liu, Qiang; Popowicz, Adam; Cai, Dongmei Point spread function estimation for wide field small aperture telescopes with deep neural networks and calibration data MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article methods: numerical; techniques: image processing; telescopes IDENTIFICATION; CLASSIFICATION; MODEL The point spread function (PSF) reflects states of a telescope and plays an important role in the development of data-processing methods, such as PSF-based astrometry, photometry, and image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view is hard, because aberrations induced by the optical system are quite complex and the signal-to-noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN)-based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentring). Then, we use these data to train a DNN (Tel-Net). After training, the Tel-Net can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that the Tel-Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the field of view (FoV). Its results are significantly more precise than results obtained by the compared classic method - inverse distance weight interpolation. Our method provides foundations for developing deep neural network-based data-processing methods for WFSATs, which require strong prior information of PSFs. [Jia, Peng; Wu, Xuebo; Wang, Weihua; Liu, Qiang; Cai, Dongmei] Taiyuan Univ Technol, Coll Phys & Optoelect, Taiyuan 030024, Peoples R China; [Jia, Peng] Univ Durham, Dept Phys, Durham DH1 3LE, England; [Li, Zhengyang; Li, Bo] Nanjing Inst Astron Opt & Technol CAS, Nanjing 210042, Jiangsu, Peoples R China; [Popowicz, Adam] Silesian Tech Univ, Dept Elect Elect Engn & Microelect, Akad 16, PL-44100 Gliwice, Poland Taiyuan University of Technology; Durham University; Silesian University of Technology Jia, P (corresponding author), Taiyuan Univ Technol, Coll Phys & Optoelect, Taiyuan 030024, Peoples R China.;Jia, P (corresponding author), Univ Durham, Dept Phys, Durham DH1 3LE, England.;Li, ZY (corresponding author), Nanjing Inst Astron Opt & Technol CAS, Nanjing 210042, Jiangsu, Peoples R China.;Popowicz, A (corresponding author), Silesian Tech Univ, Dept Elect Elect Engn & Microelect, Akad 16, PL-44100 Gliwice, Poland. robinmartin20@gmail.com; zyli@niaot.ac.cn; adam.popowicz@polsl.pl Jia, Peng/J-9617-2014 Jia, Peng/0000-0001-6623-0931 National Natural Science Foundation of China (NSFC) [11503018]; NSFC [U1631133, U1931207]; Chinese Academy of Sciences (CAS) [U1631133, U1931207]; French National Research Agency (ANR) [ANR-19-CE310011]; Shanxi Province Science Foundation for Youths [201901D211081]; Research and Development Program of Shanxi [201903D121161]; Shanxi Scholarship Council of China; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [2019L0225]; [02/140/RGJ21/0012]; [BK-225/RAu-11/2021] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS)(Chinese Academy of Sciences); French National Research Agency (ANR)(French National Research Agency (ANR)); Shanxi Province Science Foundation for Youths; Research and Development Program of Shanxi; Shanxi Scholarship Council of China; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi; ; Authors would like to thank the reviewer, Dr. Morgan Robert, for his kind suggestions that improved this paper a lot. Peng Jia would like to thank Dr. Alastair Basden from Durham University and Professor Rongyu Sun from Purple Mountain Observatory who provided very helpful suggestions for this paper. This work is supported by the National Natural Science Foundation of China (NSFC) (11503018) and the Joint Research Fund in Astronomy (U1631133, U1931207) under cooperative agreement between the NSFC and the Chinese Academy of Sciences (CAS). Adam Popowicz was supported by grants: 02/140/RGJ21/0012 and BK-225/RAu-11/2021. The authors acknowledge the French National Research Agency (ANR) to support this work through the ANR APPLY (grant ANR-19-CE310011) coordinated by B. Neichel. This work is also supported by the Shanxi Province Science Foundation for Youths (201901D211081), Research and Development Program of Shanxi (201903D121161), Research Project Supported by Shanxi Scholarship Council of China, and the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2019L0225). 44 1 1 2 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. AUG 2021.0 505.0 4 4717 4725 10.1093/mnras/stab1461 0.0 JUN 2021 9 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics US2QC Green Submitted 2023-03-23 WOS:000697277900001 0
J Luo, SH; Song, YD; Lewis, FL; Garrappa, R Luo, Shaohua; Song, Yongduan; Lewis, Frank L.; Garrappa, Roberto Neuroadaptive Optimal Fixed-Time Synchronization and Its Circuit Realization for Unidirectionally Coupled FO Self-Sustained Electromechanical Seismograph Systems IEEE TRANSACTIONS ON CYBERNETICS English Article; Early Access Synchronization; Backstepping; Oscillators; Nonlinear dynamical systems; Analog circuits; Capacitors; Detectors; Chaos synchronization; circuit implementation; interval type-2 fuzzy neural network (IT2FNN); optimal fixed-time control; unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems NEURAL-NETWORKS; CHAOS; DYNAMICS; STABILITY This article investigates the neuroadaptive optimal fixed-time synchronization and its circuit realization along with dynamical analysis for unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems under subharmonic and superharmonic oscillations. The synchronization model of the coupled FO seismograph system is established based on drive and response seismic detectors. The dynamical analysis reveals this coupled system generating transient chaos and homoclinic/heteroclinic oscillations. The test results of the constructed equivalent analog circuit further testify its complex nonlinear dynamics. Then, a neuroadaptive optimal fixed-time synchronization controller integrated with the FO hyperbolic tangent tracking differentiator (HTTD), interval type-2 fuzzy neural network (IT2FNN) with transformation, and prescribed performance function (PPF) together with the constraint condition is developed in the backstepping recursive design. Furthermore, it is proved that all signals of this closed-loop system are bounded, and the tracking errors fall into a trap of the prescribed constraint along with the minimized cost function. Extensive studies confirm the effectiveness of the proposed scheme. [Luo, Shaohua] Guizhou Univ, State Key Lab Publ Big Date, Guiyang 550025, Peoples R China; [Luo, Shaohua; Lewis, Frank L.] Univ Texas Arlington, UTA Res Inst, Ft Worth, TX 76118 USA; [Song, Yongduan] Chongqing Univ, Chongqing Key Lab Intelligent Unmanned Syst, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400044, Peoples R China; [Song, Yongduan] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China; [Garrappa, Roberto] Univ Bari, Dept Math, I-70126 Bari, Italy Guizhou University; Chongqing University; Chongqing University; Universita degli Studi di Bari Aldo Moro Song, YD (corresponding author), Chongqing Univ, Chongqing Key Lab Intelligent Unmanned Syst, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400044, Peoples R China.;Song, YD (corresponding author), Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China. shluo@gzu.edu.cn; ydsong@cqu.edu.cn; lewis@uta.edu; roberto.garrappa@uniba.it Garrappa, Roberto/O-4807-2018 Garrappa, Roberto/0000-0002-7881-0885 National Natural Science Foundation of China [52065008, 61860206008, 61773081]; Science and Technology Planning Project of Guizhou Province [[2020]1Y274]; Chongqing Technology Innovation and Application Development Special Project [cstc2019jscx-fxydX0092]; Foundation for Innovative Research Groups of Universities in Chongqing [CXQTP20035]; Open Research Fund of Education Department of Guizhou Province [KYzhi[2019]041] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Planning Project of Guizhou Province; Chongqing Technology Innovation and Application Development Special Project; Foundation for Innovative Research Groups of Universities in Chongqing; Open Research Fund of Education Department of Guizhou Province This work was supported in part by the National Natural Science Foundation of China under Grant 52065008, Grant 61860206008, and Grant 61773081; in part by the Science and Technology Planning Project of Guizhou Province under Grant [2020]1Y274; in part by the Chongqing Technology Innovation and Application Development Special Project under Grant cstc2019jscx-fxydX0092; in part by the Foundation for Innovative Research Groups of Universities in Chongqing under Grant CXQTP20035; and in part by the Open Research Fund of Education Department of Guizhou Province under Grant KYzhi[2019]041. 48 1 1 9 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. 10.1109/TCYB.2021.3121069 0.0 NOV 2021 13 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science XT2VK 34731084.0 hybrid 2023-03-23 WOS:000733451600001 0
J Kang, Y; Cai, Z; Tan, CW; Huang, Q; Liu, HF Kang, Yue; Cai, Zhao; Tan, Chee-Wee; Huang, Qian; Liu, Hefu Natural language processing (NLP) in management research: A literature review JOURNAL OF MANAGEMENT ANALYTICS English Review natural language processing; literature review; sentiment analysis; machine learning SOCIAL MEDIA; TEXTUAL ANALYSIS; SEARCH ENGINES; SENTIMENT; IMPACT; INFORMATION; ADAPTATION; ANALYTICS; EVOLUTION; PATTERNS Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in management research, there is neither a comprehensive review of extant literature on such applications, nor is there a detailed walkthrough on how it can be employed as an analytical technique. To this end, we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple disciplines. We describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and disadvantages. In so doing, we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires. [Kang, Yue; Huang, Qian; Liu, Hefu] Univ Sci & Technol China, Sch Management, Hefei, Peoples R China; [Cai, Zhao] Univ Nottingham Ningbo China, Nottingham Univ Business Sch China, Ningbo, Peoples R China; [Tan, Chee-Wee] Copenhagen Business Sch, Dept Digitalizat, Copenhagen, Denmark Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Nottingham Ningbo China; Copenhagen Business School Cai, Z (corresponding author), Univ Nottingham Ningbo China, Nottingham Univ Business Sch China, Ningbo, Peoples R China. zhao.cai@nottingham.edu.cn Cai, Zhao/AAD-3347-2021; Liu, Hefu/Q-1463-2017 Liu, Hefu/0000-0003-3854-7821; Tan, Chee-Wee/0000-0003-4245-541X; Cai, Zhao/0000-0001-8705-4164 National Natural Science Foundation of China [71571177,71622009,71801204,71921001,71971202] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China: [Grant Number 71571177,71622009,71801204,71921001,71971202]. 91 58 58 28 105 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 2327-0012 2327-0039 J MANAG ANAL J. Manag. Anal. APR 2 2020.0 7.0 2 139 172 10.1080/23270012.2020.1756939 0.0 34 Business; Management; Social Sciences, Mathematical Methods Social Science Citation Index (SSCI) Business & Economics; Mathematical Methods In Social Sciences LS6VM 2023-03-23 WOS:000536519700001 0
J Jhamb, S; Enekvist, M; Liang, XD; Zhang, XP; Dam-Johansen, K; Kontogeorgis, GM Jhamb, Spardha; Enekvist, Markus; Liang, Xiaodong; Zhang, Xiangping; Dam-Johansen, Kim; Kontogeorgis, Georgios M. A review of computer-aided design of paints and coatings CURRENT OPINION IN CHEMICAL ENGINEERING English Review LIQUID-LIQUID EQUILIBRIA; SERVICE LIFE PREDICTION; PRODUCT DESIGN; MIXTURE DESIGN; SYSTEMATIC METHODOLOGY; POLYMER-SOLUTIONS; MODEL; BINARY There is an immense potential for the computer-aided tools in the design of paints and coatings. Significant advances have been made, involving also the use of thermodynamic and in general property models for the study and theoretical formulation of these products. Algorithms and tools based on such models enable the formulation chemist to speed up the design process, by allowing them to focus their experimental efforts on a selected number of reliable constituents for the coating formulation. Even though model-based methods and tools can save resources and time required for the design, service life prediction and formulation of new products, the experimental validation cannot be done away with; as certain interactions in these complex systems can be accounted for only by using practical design procedures. Machine learning algorithms can, however, be used to improve the accuracy of predictive methods, if sufficient data on observed anomalies from physicochemical based theoretical predictions, is available. [Jhamb, Spardha; Enekvist, Markus; Liang, Xiaodong; Kontogeorgis, Georgios M.] Tech Univ Denmark, CERE, Dept Chem & Biochem Engn, Bldg 229,Soltofts Plads 229, DK-2800 Lyngby, Denmark; [Jhamb, Spardha; Enekvist, Markus; Liang, Xiaodong; Dam-Johansen, Kim; Kontogeorgis, Georgios M.] Tech Univ Denmark, CoaST, Dept Chem & Biochem Engn, Bldg 229,Soltofts Plads 229, DK-2800 Lyngby, Denmark; [Zhang, Xiangping] Chinese Acad Sci, Beijing Key Lab Ionic Liquids Clean Proc, CAS Key Lab Green Proc & Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China Technical University of Denmark; Technical University of Denmark; Chinese Academy of Sciences Kontogeorgis, GM (corresponding author), Tech Univ Denmark, CERE, Dept Chem & Biochem Engn, Bldg 229,Soltofts Plads 229, DK-2800 Lyngby, Denmark.;Kontogeorgis, GM (corresponding author), Tech Univ Denmark, CoaST, Dept Chem & Biochem Engn, Bldg 229,Soltofts Plads 229, DK-2800 Lyngby, Denmark. gk@kt.dtu.dk ; Liang, Xiaodong/B-4104-2018 Dam-Johansen, Kim/0000-0003-1658-6176; Enekvist, Markus/0000-0002-2205-5796; Liang, Xiaodong/0000-0002-2007-546X; Jhamb, Spardha/0000-0002-6450-9733 DTU Chemical Engineering; Hempel Foundation DTU Chemical Engineering; Hempel Foundation Financial support from DTU Chemical Engineering and the Hempel Foundation to CoaST (The Hempel Foundation Coatings Science and Technology Centre) is acknowledged. 74 10 10 2 23 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2211-3398 CURR OPIN CHEM ENG Curr. Opin. Chem. Eng. MAR 2020.0 27.0 SI 107 120 10.1016/j.coche.2019.12.005 0.0 14 Biotechnology & Applied Microbiology; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Engineering ME3WO Green Submitted 2023-03-23 WOS:000544589400014 0
J Chen, A; Li, C; Zou, SJ; Rahaman, MM; Yao, YD; Chen, HY; Yang, HC; Zhao, P; Hu, WM; Liu, WL; Grzegorzek, M Chen, Ao; Li, Chen; Zou, Shuojia; Rahaman, Md Mamunur; Yao, Yudong; Chen, Haoyuan; Yang, Hechen; Zhao, Peng; Hu, Weiming; Liu, Wanli; Grzegorzek, Marcin SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis BIOCYBERNETICS AND BIOMEDICAL ENGINEERING English Article Open dataset; Medical image; Microscopic image; Microscopic video; Computer-aided sperm analysis INFERTILITY Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional kNN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: https://github.com/Demozsj/Detection-Sperm. (c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved. [Chen, Ao; Li, Chen; Zou, Shuojia; Rahaman, Md Mamunur; Chen, Haoyuan; Yang, Hechen; Zhao, Peng; Hu, Weiming; Liu, Wanli] Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang 110169, Peoples R China; [Yao, Yudong] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA; [Grzegorzek, Marcin] Univ Lubeck, Inst Med Informat, Lubeck, Germany Northeastern University - China; Stevens Institute of Technology; University of Lubeck Li, C (corresponding author), Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang 110169, Peoples R China. lichen@bmie.neu.edu.cn Chen, Ao/GVT-7295-2022; Rahaman, Md Mamunur/AAS-5300-2021 Rahaman, Md Mamunur/0000-0003-2268-2092; chen, ao/0000-0002-0989-2530 National Natural Science Foundation of China [61806047] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China(No.61806047). We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. 52 10 10 12 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0208-5216 BIOCYBERN BIOMED ENG Biocybern. Biomed. Eng. JAN-MAR 2022.0 42.0 1 204 214 10.1016/j.bbe.2021.12.010 0.0 11 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0P6DK 2023-03-23 WOS:000784316500012 0
J Ding, RX; Palomares, I; Wang, XQ; Yang, GR; Liu, BS; Dong, YC; Herrera-Viedma, E; Herrera, F Ding, Ru-Xi; Palomares, Ivan; Wang, Xueqing; Yang, Guo-Rui; Liu, Bingsheng; Dong, Yucheng; Herrera-Viedma, Enrique; Herrera, Francisco Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective INFORMATION FUSION English Article Large-scale decision making; Group decision making; Consensus reaching processes; Behaviour management; Subgroup clustering; Artificial Intelligence; Preference modelling PERSONALIZED INDIVIDUAL SEMANTICS; LINGUISTIC REPRESENTATION MODEL; CONSENSUS REACHING PROCESS; NONCOOPERATIVE BEHAVIORS; TERM SETS; PREFERENCE; INFORMATION; AGGREGATION; SYSTEMS; MAKERS The last decade witnessed tremendous developments in social media and e-democracy technologies. A fundamental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM research was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior management, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided. [Ding, Ru-Xi; Wang, Xueqing; Yang, Guo-Rui; Liu, Bingsheng] Tianjin Univ, Coll Management & Econ, Tianjin, Peoples R China; [Ding, Ru-Xi] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China; [Ding, Ru-Xi; Herrera-Viedma, Enrique; Herrera, Francisco] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain; [Palomares, Ivan] Univ Bristol, Sch Comp Sci, Bristol, Avon, England; [Palomares, Ivan] Alan Turing Inst, London, England; [Liu, Bingsheng] Chongqing Univ, Sch Publ Affairs, Chongqing, Peoples R China; [Dong, Yucheng] Sichuan Univ, Business Sch, Chengdu, Peoples R China; [Herrera-Viedma, Enrique] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah, Saudi Arabia; [Herrera, Francisco] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia Tianjin University; Beijing Institute of Technology; University of Granada; University of Bristol; Chongqing University; Sichuan University; King Abdulaziz University; King Abdulaziz University Palomares, I (corresponding author), Univ Bristol, Sch Comp Sci, Bristol, Avon, England.;Palomares, I (corresponding author), Alan Turing Inst, London, England. dingruxi@tju.edu.cn; i.palomares@bristol.ac.uk; wxq@tju.edu.cn; yangguorui@tju.edu.cn; liubingsheng@tju.edu.cn; ycdong@scu.edu.cn; viedma@decsai.ugr.es; herrera@decsai.ugr.es Palomares, Iván/AAR-6496-2020; Dong, Yucheng/J-1668-2017 Palomares, Iván/0000-0003-4335-9139; Dong, Yucheng/0000-0002-0028-6796; Yang, Guo-Rui/0000-0001-8189-0946 National Natural Science Foundation of China [71772136, 71722004]; Spanish Ministry of Economy and Competitiveness [TIN2016-75850-R]; FEDER funds National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Economy and Competitiveness(Spanish Government); FEDER funds(European Commission) We appreciate the financial support of the National Natural Science Foundation of China (Grant Nos. 71772136 and 71722004) and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds. 122 105 108 87 239 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion JUL 2020.0 59.0 84 102 10.1016/j.inffus.2020.01.006 0.0 19 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science KZ9GO 2023-03-23 WOS:000523567600006 0
C Liu, HC; van Oosterom, P; Hu, CF; Wang, W Kim, JH; Kim, HS; Yoo, DG; Jung, D; Song, CG Liu, Haicheng; van Oosterom, Peter; Hu, Chengfang; Wang, Wen Managing large multidimensional array hydrologic datasets: a case study comparing NetCDF and SciDB 12TH INTERNATIONAL CONFERENCE ON HYDROINFORMATICS (HIC 2016) - SMART WATER FOR THE FUTURE Procedia Engineering English Proceedings Paper 12th International Conference on Hydroinformatics (HIC) - Smart Water for the Future AUG 21-26, 2016 SOUTH KOREA Incheon Metropolitan Govt,Korea Tourism Org,Smart Water Grid Res Grp benchmark; NetCDF; SciDB; chunked storage structure; hydrologic dataset Management of large hydrologic datasets including storage, structuring, indexing and query is one of the crucial challenges in the era of big data. This research originates from a specific data query problem: time series extraction at specific locations takes a long time when a large multidimensional dataset is stored in non-chunked NetCDF classic or 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file based solutions and a multidimensional (MD) array database management system (DBMS) applying chunked storage structure are benchmarked to determine the best solution for storing and querying large hydrologic datasets. To achieve this, expert consultancy was conducted to establish benchmark sets. To guarantee a fair benchmark test environment, HydroNET-4 system was utilized and adapters for NetCDF files and SciDB were developed to manage and query data. In final benchmark tests, effect of data storage configurations such as chunk size and compression on query performance is also explored. Results indicate that SciDB arrays utilizing small chunk sizes show favorable performance. However with current implementation of SciDB, large numbers of small chunks cause huge overload of main memory which constraints SciDB's scalability. Compression of SciDB can either have negative or no effect on query performance, while it causes significant query degradation to NetCDF-4 solution. The research illustrates that for big hydrologic array data management, the properly chunked NetCDF-4 solution without compression is in general more efficient than the SciDB DBMS. So under current big data environment, traditionally adopted file-based hydroinformatic solutions can still be applicable after proper updating. (C) 2016 The Authors. Published by Elsevier Ltd. [Liu, Haicheng; Hu, Chengfang] CRSRI, Dept Spatial Informat, Sect Digital Basins, 289 HuangPu Rd, Wuhan 430010, Peoples R China; [van Oosterom, Peter] Delft Univ Technol, Fac Architecture & Built Environm, Dept OTB, Sect GIS Technol, Julianalaan 134, NL-2628 BL Delft, Netherlands; [Wang, Wen] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China Delft University of Technology; Hohai University Liu, HC (corresponding author), CRSRI, Dept Spatial Informat, Sect Digital Basins, 289 HuangPu Rd, Wuhan 430010, Peoples R China. liuhaicheng@mail.crsri.cn Wang, Wen/A-3729-2013 8 7 7 0 5 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1877-7058 PROCEDIA ENGINEER 2016.0 154.0 207 214 10.1016/j.proeng.2016.07.449 0.0 8 Water Resources Conference Proceedings Citation Index - Science (CPCI-S) Water Resources BF9PK gold, Green Published 2023-03-23 WOS:000385793200028 0
J Zhang, SQ; Zeng, T; Hu, B; Zhang, YH; Feng, KY; Chen, L; Niu, ZB; Li, JH; Huang, T; Cai, YD Zhang, Shiqi; Zeng, Tao; Hu, Bin; Zhang, Yu-Hang; Feng, Kaiyan; Chen, Lei; Niu, Zhibin; Li, Jianhao; Huang, Tao; Cai, Yu-Dong Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY English Article methylation signature; dys-methylated pattern; cell line; rule; classification FOCAL ADHESION PROTEIN; NOVO DNA METHYLATION; TRICELLULIN EXPRESSION; MUC5AC HYPOMETHYLATION; COLORECTAL-CANCER; FEATURE-SELECTION; CPG METHYLATION; COLON-CANCER; GENE; GENOME DNA methylation is an essential epigenetic modification for multiple biological processes. DNA methylation in mammals acts as an epigenetic mark of transcriptional repression. Aberrant levels of DNA methylation can be observed in various types of tumor cells. Thus, DNA methylation has attracted considerable attention among researchers to provide new and feasible tumor therapies. Conventional studies considered single-gene methylation or specific loci as biomarkers for tumorigenesis. However, genome-scale methylated modification has not been completely investigated. Thus, we proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns. This study contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues. [Zhang, Shiqi; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China; [Zhang, Shiqi] Univ Copenhagen, Dept Biostat, Copenhagen, Denmark; [Zeng, Tao] Shanghai Res Ctr Brain Sci & Brain Inspired Intel, Shanghai, Peoples R China; [Hu, Bin; Li, Jianhao] Guangdong Acad Agr Sci, Inst Anim Sci, State Key Lab Livestock & Poultry Breeding, Guangdong Publ Lab Anim Breeding & Nutr,Guangdong, Guangzhou, Peoples R China; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai, Peoples R China; [Feng, Kaiyan] Guangdong AIB Polytech, Dept Comp Sci, Guangzhou, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China; [Niu, Zhibin] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China Shanghai University; University of Copenhagen; Guangdong Academy of Agricultural Sciences; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; Shanghai Institutes for Biological Sciences, CAS; Shanghai Maritime University; Tianjin University Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai, Peoples R China. tohuangtao@126.com; cai_yud@126.com Zeng, Tao/A-1711-2012; Zhang, Yu-Hang/AAQ-9012-2021; Zhang, Yu-Hang/O-9774-2015 Zeng, Tao/0000-0002-0295-3994; Zhang, Yu-Hang/0000-0003-3825-0796; Zhang, Yu-Hang/0000-0003-3825-0796; Niu, Zhibin/0000-0002-5171-7648 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Key R&D Program of China [2018YFC0910403]; National Natural Science Foundation of China [31701151, 61701298]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Key-Area Research and Development Program of Guangdong Province [2018B020203003]; Guangzhou science and technology planning project [201707020007] Shanghai Municipal Science and Technology Major Project; National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Key-Area Research and Development Program of Guangdong Province; Guangzhou science and technology planning project This research was supported by the Shanghai Municipal Science and Technology Major Project [2017SHZDZX01], National Key R&D Program of China [2018YFC0910403], National Natural Science Foundation of China [31701151, 61701298], Natural Science Foundation of Shanghai [17ZR1412500], Shanghai Sailing Program [16YF1413800], the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245], the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703], Key-Area Research and Development Program of Guangdong Province (2018B020203003), Guangzhou science and technology planning project (201707020007). 109 9 9 1 6 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-4185 FRONT BIOENG BIOTECH Front. Bioeng. Biotechnol. MAY 26 2020.0 8.0 507 10.3389/fbioe.2020.00507 0.0 13 Biotechnology & Applied Microbiology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Science & Technology - Other Topics LY5VK 32528944.0 gold, Green Published 2023-03-23 WOS:000540596900001 0
C Yao, S; Yang, F; Cheng, YM; Mozerov, MG IEEE Comp Soc Yao, Shun; Yang, Fei; Cheng, Yongmei; Mozerov, Mikhail G. 3D Shapes Local Geometry Codes Learning with SDF 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) IEEE International Conference on Computer Vision Workshops English Proceedings Paper 18th IEEE/CVF International Conference on Computer Vision (ICCV) OCT 11-17, 2021 ELECTR NETWORK IEEE,CVF,IEEE Comp Soc A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics. [Yao, Shun; Yang, Fei; Cheng, Yongmei] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China; [Yao, Shun; Yang, Fei; Cheng, Yongmei; Mozerov, Mikhail G.] Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona, Spain Northwestern Polytechnical University; Autonomous University of Barcelona; Centre de Visio per Computador (CVC) Yao, S (corresponding author), Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China.;Yao, S (corresponding author), Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona, Spain. shunyao@cvc.uab.cat; fyang@cvc.uab.cat; chengym@nwpu.edu.cn; mozerov@cvc.uab.cat Mozerov, Mikhail/AAN-5362-2021 Spanish project (MINECO/FEDER) [TIN2015-65464-R, TIN2016-79717-R]; COST (European Cooperation in Science and Technology) [IC1307]; Chinese Scholarship Council (CSC) [201906290108] Spanish project (MINECO/FEDER)(Spanish Government); COST (European Cooperation in Science and Technology)(European Cooperation in Science and Technology (COST)); Chinese Scholarship Council (CSC)(China Scholarship Council) This work has been supported by the Spanish project TIN2015-65464-R, TIN2016-79717-R (MINECO/FEDER) and the COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language), supported by COST (European Cooperation in Science and Technology). We acknowledge the CERCA Programme of Generalitat de Catalunya. Shun Yao acknowledges the Chinese Scholarship Council (CSC) grant No.201906290108. We also acknowledge the generous GPU support from Nvidia. 26 3 3 2 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2473-9936 978-1-6654-0191-3 IEEE INT CONF COMP V 2021.0 2110 2117 10.1109/ICCVW54120.2021.00239 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5QB Green Submitted 2023-03-23 WOS:000739651102022 0
J Sun, XD; Zhao, ZX; Zhang, S; Liu, JT; Yang, XT; Zhou, C Sun, Xudong; Zhao, Zhenxi; Zhang, Song; Liu, Jintao; Yang, Xinting; Zhou, Chao Image Super-Resolution Reconstruction Using Generative Adversarial Networks Based on Wide-Channel Activation IEEE ACCESS English Article Super-resolution; residual block; relativistic average discriminator; generative adversarial network; perceived quality CONVOLUTIONAL NEURAL-NETWORK; DEEP; MODEL; FISH In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6 similar to 9 using a 1 x 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine. [Sun, Xudong; Zhao, Zhenxi] East China Jiaotong Univ, Sch Mechatron Engn, Nanchang 330013, Jiangxi, Peoples R China; [Zhao, Zhenxi; Zhang, Song; Liu, Jintao; Yang, Xinting; Zhou, Chao] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Zhao, Zhenxi; Zhang, Song; Liu, Jintao; Yang, Xinting; Zhou, Chao] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Zhao, Zhenxi; Zhang, Song; Liu, Jintao; Yang, Xinting; Zhou, Chao] Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China; [Liu, Jintao] Univ Almeria, Sch Engn, Almeria 04120, Spain East China Jiaotong University; Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Universidad de Almeria Zhou, C (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.;Zhou, C (corresponding author), Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.;Zhou, C (corresponding author), Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. supperchao@hotmail.com Yang, Xinting/HKV-1450-2023; Sun, Xudong/AAA-6281-2019 Zhao, zhenxi/0000-0001-8056-484X; Jintao, Liu/0000-0002-4681-8583; Sun, Xudong/0000-0001-6743-1348; Zhou, Chao/0000-0001-6528-3257 National Key Technology Research and Development Program of China [2019YFD0901004]; National Natural Science Foundation of China [31960497]; Youth Research Fund of Beijing Academy of Agricultural and Forestry Sciences [QNJJ202014]; Beijing Natural Science Foundation [4184089] National Key Technology Research and Development Program of China(National Key Technology R&D ProgramNational High Technology Research and Development Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Research Fund of Beijing Academy of Agricultural and Forestry Sciences; Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work was supported in part by the National Key Technology Research and Development Program of China under Grant 2019YFD0901004, in part by the National Natural Science Foundation of China under Grant 31960497, in part by the Youth Research Fund of Beijing Academy of Agricultural and Forestry Sciences under Grant QNJJ202014, and in part by the Beijing Natural Science Foundation under Grant 4184089. 46 5 5 0 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 33838 33854 10.1109/ACCESS.2020.2974759 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications NL7QG gold 2023-03-23 WOS:000567604800065 0
J de Melo, G; Kacimi, M; Varde, AS de Melo, Gerard; Kacimi, Mouna; Varde, Aparna S. Dissertation Research Problems in Data Management and Related Areas SIGMOD RECORD English Article Wikipedia; IR Evaluation; Exploratory Search; Query Processing; Recommender Systems; Rule Mining Databases and related fields such as Information Retrieval, Data Mining and Knowledge Management offer many topics of interest for dissertation research. Specific areas include, for instance, big data, social networks, Web question answering and interactive knowledge discovery. In this article, we provide a summary and critique of research problems presented in these and related areas at a workshop on dissertation proposals and early doctoral research. [de Melo, Gerard] Tsinghua Univ, IIIS, Beijing 100084, Peoples R China; [Kacimi, Mouna] Free Univ Bozen Bolzano, Fac Comp Sci, Bozen Bolzano, Italy; [Varde, Aparna S.] Montclair State Univ, Dept Comp Sci, Montclair, NJ USA Tsinghua University; Free University of Bozen-Bolzano; Montclair State University de Melo, G (corresponding author), Tsinghua Univ, IIIS, Beijing 100084, Peoples R China. gdm@demelo.org; Mouna.Kacimi@unibz.it; vardea@montclair.edu de Melo, Gerard/P-7886-2014; Kacimi, Mouna/AAO-4168-2021 de Melo, Gerard/0000-0002-2930-2059; China 973 Program [2011CBA00300, 2011CBA00301]; NSFC [61033001, 61361136003, 61450110088]; CSAM (College of Science and Math) Dean's Office at Montclair State University, NJ, USA China 973 Program(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)); CSAM (College of Science and Math) Dean's Office at Montclair State University, NJ, USA Gerard de Melo's research is supported by China 973 Program Grants 2011CBA00300, 2011CBA00301, and NSFC Grants 61033001, 61361136003, 61450110088.; Aparna Varde's participation in PIKM was supported by funds from the CSAM (College of Science and Math) Dean's Office at Montclair State University, NJ, USA. 4 2 2 2 7 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0163-5808 1943-5835 SIGMOD REC Sigmod Rec. DEC 2015.0 44.0 4 53 56 10.1145/2935694.2935707 0.0 4 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science DL5WL 2023-03-23 WOS:000375707600007 0
J Abbas, A; Khan, MA; Latif, S; Ajaz, M; Shah, AA; Ahmad, J Abbas, Adeel; Khan, Muazzam A.; Latif, Shahid; Ajaz, Maria; Shah, Awais Aziz; Ahmad, Jawad A New Ensemble-Based Intrusion Detection System for Internet of Things ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING English Article Intrusion detection; IoT; Machine learning; Security; Anomaly detection; Ensemble learning The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model's performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios. [Abbas, Adeel; Khan, Muazzam A.; Ajaz, Maria] Quaid I Azam Univ, Dept Comp Sci, Islamabad, Pakistan; [Khan, Muazzam A.] Pakistan Acad Sci, Islamabad, Pakistan; [Latif, Shahid] Fudan Univ, Sch Informat Sci & Engn, Shanghai, Peoples R China; [Shah, Awais Aziz] Politecn Bari, Dept Elect & Informat Engn DEI, Bari, Italy; [Ahmad, Jawad] Edinburgh Napier Univ, Sch Comp, Edinburgh, Midlothian, Scotland Quaid I Azam University; Fudan University; Politecnico di Bari; Edinburgh Napier University Shah, AA (corresponding author), Politecn Bari, Dept Elect & Informat Engn DEI, Bari, Italy. aabbas@cs.qau.edu.pk; muazzam.khattak@qau.edu.pk; lshalid19@fudan.edu.cn; mariaajaz@cs.qau.edu.pk; awais.shah@poliba.it; J.Ahmad@napier.ac.uk Latif, Shahid/ABD-5640-2020; Abbas, Adeel/ABB-1711-2021; Khattak, Muazzam A. Khan/AAF-1494-2020 Latif, Shahid/0000-0002-6368-2729; Khattak, Muazzam A. Khan/0000-0001-6140-1201; Shah, Awais/0000-0002-1656-739X; Abbas, Adeel/0000-0002-6720-7968 Politecnico di Bari within the CRUI-CARE Agreement Politecnico di Bari within the CRUI-CARE Agreement Open access funding provided by Politecnico di Bari within the CRUI-CARE Agreement. 60 13 13 1 8 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2193-567X 2191-4281 ARAB J SCI ENG Arab. J. Sci. Eng. FEB 2022.0 47.0 2 1805 1819 10.1007/s13369-021-06086-5 0.0 AUG 2021 15 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics ZC4ZZ hybrid, Green Published 2023-03-23 WOS:000690814100001 0
J Khan, MI; Sutanto, MH; Khan, K; Iqbal, M; Bin Napiah, M; Zoorob, SE; Klemes, JJ; Bokhari, A; Rafiq, W Khan, Muhammad Imran; Sutanto, Muslich Hartadi; Khan, Kaffayatullah; Iqbal, Mudassir; Bin Napiah, Madzlan; Zoorob, Salah E.; Klemes, Jiri Jaromir; Bokhari, Awais; Rafiq, Waqas Effective use of recycled waste PET in cementitious grouts for developing sustainable semi-flexible pavement surfacing using artificial neural network (ANN) JOURNAL OF CLEANER PRODUCTION English Article Irradiated waste PET; Artificial neural network; Compressive strength; Flow value; Prediction model; Semi-flexible pavement CONCRETE; OPTIMIZATION; STRENGTH; EMISSIONS; BEHAVIOR; MODELS The effective way of recycling waste polyethylene terephthalate (PET) by exposing it to gamma rays, is adopted in formulating the compositions of cementitious grouts for semi-flexible pavement surfaces. The ordinary Portland cement in grouts was partially replaced by regular PET (2.5%-10%) and irradiated PET (2.5%-10%) combined with fly ash (FA) and silica fume (SF). The physical, mechanical, and microstructural characteristics of grouts were investigated, and the results achieved from regular and irradiated PET substituted cement gouts were used to develop single hidden layers (SHLs) and two hidden layers (THLs) neural network models. The test results show that replacing cement with regular PET or irradiated PET caused a significant increase in the flow value of cement grouts. When using regular PET, compressive strength is significantly reduced at all curing ages (53-78% at 1 d, 24%-46% at 7 d, and 23%-36% at 28 d). Some of the strength was restored when irradiated PET was used (the recovery is 20-30% at 1 d, 17-24% at 7 d, and 7-12% at 28 d). The pozzolanic characteristics of FA and SF led to an increase in compressive strength. The microstructural analysis by FESEM-EDX and XRD confirms that the irradiated PET causes densification and refinement of microstructure. The statistical evaluation using R, MAE, RMSE, and RSE reflects a close agreement of actual values to the predicted results for the developed ANN models. The proximal values of objective functions to zero represents no overfitting of the trained models. The value of R for all the developed models is > 0.91, which depicts a strong correlation between experimental and predicted results of flow and compressive strength. It is possible to conclude that with the irradiation process, more waste PET can be recycled than regular PET while still achieving similar strength properties, thus providing a sustainable solution to the recycling of waste PET. [Khan, Muhammad Imran; Sutanto, Muslich Hartadi; Bin Napiah, Madzlan; Rafiq, Waqas] Univ Teknol PETRONAS, Dept Civil & Environm Engn, Seri Iskandar 32610, Perak, Malaysia; [Khan, Kaffayatullah] King Faisal Univ KFU, Coll Engn, Dept Civil & Environm Engn, Al Hufuf 31982, Al Ahsa, Saudi Arabia; [Iqbal, Mudassir] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai Key Lab Digital Maintenance Bldg & Infra, Shanghai 200240, Peoples R China; [Iqbal, Mudassir] Univ Engn & Technol, Dept Civil Engn, Peshawar, Pakistan; [Zoorob, Salah E.] Kuwait Inst Sci Res, Construct & Bldg Mat Program, Kuwait, Kuwait; [Klemes, Jiri Jaromir; Bokhari, Awais] Brno Univ Technol, Fac Mech Engn, Sustainable Proc Integrat Lab, SPIL,NETME Ctr,VUT Brno, Tech 2896-2, Brno 61600, Czech Republic; [Bokhari, Awais] COMSATS Univ Islamabad CUI, Dept Chem Engn, Lahore Campus,Def Rd Rawind Rd, Lahore 54000, Punjab, Pakistan Universiti Teknologi Petronas; King Faisal University; Shanghai Jiao Tong University; University of Engineering & Technology Peshawar; Kuwait Institute for Scientific Research; Brno University of Technology; COMSATS University Islamabad (CUI) Khan, MI (corresponding author), Univ Teknol PETRONAS, Dept Civil & Environm Engn, Seri Iskandar 32610, Perak, Malaysia. muhammad_17007177@utp.edu.my Bokhari, Dr. Awais/ABF-6997-2020; Klemes, Jiri Jaromir/B-7291-2009; Iqbal, Mudassir/GPS-7678-2022; Khan, Muhammad Imran/P-7619-2016 Klemes, Jiri Jaromir/0000-0002-7450-7029; Khan, Muhammad Imran/0000-0002-7185-0622; /0000-0001-7994-4642 Universiti Teknologi PETRONAS, Malaysia; project Sustainable Process Integration Laboratory-SPIL - EU CZ Operational Programme Research and Development, Education, Priority1: Strengthening capacity for quality research [CZ.02.1.01/0.0/0.0/15_003/0000456] Universiti Teknologi PETRONAS, Malaysia; project Sustainable Process Integration Laboratory-SPIL - EU CZ Operational Programme Research and Development, Education, Priority1: Strengthening capacity for quality research The authors would like to acknowledge the support of Universiti Teknologi PETRONAS, Malaysia by providing financial and laboratory facilities and the support from the project Sustainable Process Integration Laboratory-SPIL funded by EU CZ Operational Programme Research and Development, Education, Priority1: Strengthening capacity for quality research (Grant No. CZ.02.1.01/0.0/0.0/15_003/0000456). 73 24 24 21 29 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. MAR 15 2022.0 340.0 130840 10.1016/j.jclepro.2022.130840 0.0 FEB 2022 16 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 0P7YL 2023-03-23 WOS:000784444900006 0
J Li, JJ; Zhang, P; Qu, LY; Sun, T; Duan, YY; Wu, MH; Weng, JY; Li, ZH; Gong, XD; Liu, X; Wang, YZ; Jia, WQ; Su, XR; Yue, Q; Li, JR; Zhang, ZQ; Barkhof, F; Huang, RY; Chang, K; Sair, H; Ye, CY; Zhang, LW; Zhuo, ZZ; Liu, YO Li, Junjie; Zhang, Peng; Qu, Liying; Sun, Ting; Duan, Yunyun; Wu, Minghao; Weng, Jinyuan; Li, Zhaohui; Gong, Xiaodong; Liu, Xing; Wang, Yongzhi; Jia, Wenqing; Su, Xiaorui; Yue, Qiang; Li, Jianrui; Zhang, Zhiqiang; Barkhof, Frederik; Huang, Raymond Y.; Chang, Ken; Sair, Haris; Ye, Chuyang; Zhang, Liwei; Zhuo, Zhizheng; Liu, Yaou Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging JOURNAL OF MAGNETIC RESONANCE IMAGING English Article; Early Access diffuse midline glioma; H3 K27M mutation; magnetic resonance imaging; deep learning GRADE GLIOMAS; DIFFERENTIATION; SUBGROUPS; FEATURES; IDH Background: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG.Purpose: This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. Study Type: Retrospective and prospective.Population: For diffuse midline brain gliomas, 341 patients from Center-1 (27 +/- 19 years, 184 males), 42 patients from Center-2 (33 +/- 19 years, 27 males) and 35 patients (37 +/- 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 +/- 15 years, 80 males).Field Strength/Sequence: 5T and 3T, T2-weighted turbo spin echo imaging. Assessment: Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve.Statistical Tests: Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value < 0.05 was considered statistically significant.Results: In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specific-ities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%.Data Conclusion: In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. [Li, Junjie; Qu, Liying; Sun, Ting; Duan, Yunyun; Wu, Minghao; Zhuo, Zhizheng; Liu, Yaou] Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China; [Zhang, Peng; Wang, Yongzhi; Jia, Wenqing; Zhang, Liwei] Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China; [Weng, Jinyuan; Gong, Xiaodong] Neusoft Grp Ltd, Dept Med Imaging Prod, Shenyang, Peoples R China; [Li, Zhaohui] BioMind Inc, Beijing, Peoples R China; [Liu, Xing] Capital Med Univ, Beijing Tiantan Hosp, Dept Pathol, Beijing, Peoples R China; [Su, Xiaorui; Yue, Qiang] Sichuan Univ, West China Hosp, Dept Radiol, Chengdu, Peoples R China; [Li, Jianrui; Zhang, Zhiqiang] Nanjing Univ, Med Sch, Affiliated Jinling Hosp, Dept Diagnost Radiol, Nanjing, Peoples R China; [Barkhof, Frederik] UCL Inst Neurol & Healthcare Engn, London, England; [Barkhof, Frederik] Univ Amsterdam, Med Ctr, Dept Radiol & Nucl Med, Amsterdam, Netherlands; [Huang, Raymond Y.] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiol, Boston, MA USA; [Chang, Ken] Harvard Med Sch, Massachusetts Gen Hosp, Martinos Ctr Biomed Imaging, Dept Radiol, Boston, MA USA; [Sair, Haris] Johns Hopkins Univ, Sch Med, Dept Radiol & Radiol Sci, Baltimore, MD USA; [Ye, Chuyang] Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China Capital Medical University; Capital Medical University; Capital Medical University; Sichuan University; Nanjing University; University of London; University College London; University of Amsterdam; Vrije Universiteit Amsterdam; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Harvard University; Harvard Medical School; Massachusetts General Hospital; Johns Hopkins University; Beijing Institute of Technology Liu, YO (corresponding author), 119,West Southern 4th Ring Rd, Beijing 100070, Peoples R China. liuyaou@bjtth.org Li, Junjie/0000-0002-6410-5296; Barkhof, Frederik/0000-0003-3543-3706 National Science Foundation of China [81870958, 81571631]; Beijing Municipal Natural Science Foundation for Distinguished Young Scholars [JQ20035]; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority [XTYB201831]; NIHR Biomedical Research Center at UCLH National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation for Distinguished Young Scholars; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority; NIHR Biomedical Research Center at UCLH We acknowledged all the colleagues who help the patient recruitment and MR imaging. This work was supported by the National Science Foundation of China (Nos. 81870958 and 81571631), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), Beijing Youth Scholar and the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (No. XTYB201831). Frederik Barkhof was supported by the NIHR Biomedical Research Center at UCLH. 39 0 0 0 0 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1053-1807 1522-2586 J MAGN RESON IMAGING J. Magn. Reson. Imaging 10.1002/jmri.28606 0.0 JAN 2023 12 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 8I9BZ 36692205.0 2023-03-23 WOS:000922023300001 0
J Afnan, MAM; Khan, KS; Mol, B Afnan, Masoud A. M.; Khan, Khalid Saeed; Mol, Ben Willem Generating translatable evidence to improve patient care: the contribution of human factors REPRODUCTIVE BIOMEDICINE ONLINE English Editorial Material Big data; Human factors; RCT; Translational evidence FROZEN EMBRYOS; FRESH; WOMEN Generating translatable evidence to improve patient care has proved challenging in reproductive medicine, with many 'add-on' treatments in routine assisted conception clinical practice that have not been reliably tested. This has consequences for patient care; specifically, IVF pregnancy rates have not improved. A change of culture is required in our profession, from indiscriminately applying the latest 'add-on' to large-scale participation in generating reliable translatable evidence. [Afnan, Masoud A. M.] Qingdao United Family Hosp, Dept Obstet & Gynaecol, Qingdao, Peoples R China; [Khan, Khalid Saeed] Univ Granada, Prevent Med & Publ Hlth, Granada, Spain; [Mol, Ben Willem] Monash Univ, Dept Obstet & Gynaecol, Clayton, Vic, Australia University of Granada; Monash University Afnan, MAM (corresponding author), Qingdao United Family Hosp, Dept Obstet & Gynaecol, Qingdao, Peoples R China. masoud.afnan@ufh.com.cn Khan, Khalid S/AAT-8824-2020 Khan, Khalid S/0000-0001-5084-7312; Mol, Ben Willem/0000-0001-8337-550X 27 5 5 1 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1472-6483 1472-6491 REPROD BIOMED ONLINE Reprod. Biomed. Online SEP 2020.0 41.0 3 353 356 10.1016/j.rbmo.2020.04.025 0.0 4 Obstetrics & Gynecology; Reproductive Biology Science Citation Index Expanded (SCI-EXPANDED) Obstetrics & Gynecology; Reproductive Biology NL5II 32553553.0 Green Accepted, Green Published 2023-03-23 WOS:000567448900002 0
C Huan, YX; Qin, YF; You, YT; Zheng, LR; Zou, Z Bhatia, K; Alioto, M; Zhao, D; Marshall, A; Sridhar, R Huan, Yuxiang; Qin, Yifan; You, Yantian; Zheng, Lirong; Zou, Zhuo A Multiplication Reduction Technique with Near-Zero Approximation for Embedded Learning in IoT Devices 2016 29TH IEEE INTERNATIONAL SYSTEM-ON-CHIP CONFERENCE (SOCC) English Proceedings Paper 29th IEEE International System-on-Chip Conference (SOCC) SEP 06-09, 2016 Seattle, WA IEEE,CYPRESS,Intel,Microsoft,IEEE Circuits & Syst Soc LOGIC This paper presents a multiplication reduction technique through near-zero approximation, enabling embedded learning in resource-constrained IoT devices. The intrinsic resilience of neural network and the sparsity of data are identified and utilized. Based on the analysis of leading zero counting and adjustable threshold, intentional approximation is applied to reduce near-zero multiplications. By setting the threshold of the multiplication result to 2(-5) and employing ReLU as the neuron activation function, the sparsity of the CNN model can reach 75% with negligible loss in accuracy when recognizing the MNIST data set. Corresponding hardware implementation has been designed and simulated in UMC 65nm process. It can achieve more than 70% improvement of energy efficiency with only 0.37% area overhead of a 256 Multiply-Accumulator array. [Huan, Yuxiang; Qin, Yifan; Zheng, Lirong; Zou, Zhuo] Fudan Univ, Shanghai, Peoples R China; [Huan, Yuxiang; You, Yantian; Zheng, Lirong; Zou, Zhuo] Royal Inst Technol, Stockholm, Sweden Fudan University; Royal Institute of Technology Huan, YX (corresponding author), Fudan Univ, Shanghai, Peoples R China.;Huan, YX (corresponding author), Royal Inst Technol, Stockholm, Sweden. ZHENG, Lirong/ABF-9274-2020 ZHENG, Lirong/0000-0001-9588-0239 NSFC [61571137]; JSSF [BK20150164]; Science and Technology Commission of Shanghai Municipality of China [14510711500]; Swedish Research Council through VR NSFC(National Natural Science Foundation of China (NSFC)); JSSF; Science and Technology Commission of Shanghai Municipality of China(Science & Technology Commission of Shanghai Municipality (STCSM)); Swedish Research Council through VR This work was supported in part by NSFC project (61571137), JSSF project (BK20150164), Science and Technology Commission of Shanghai Municipality Project (No. 14510711500) of China, and Swedish Research Council through VR program. We thank all the group members in this program for their efforts and suggestions. 21 2 3 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-1367-8 2016.0 102 107 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BH8RK 2023-03-23 WOS:000403576000018 0
C Wang, BQ; Lu, ZH; Chen, SG ACM Wang, Boqian; Lu, Zhonghai; Chen, Shenggang ANN Based Admission Control for On-Chip Networks PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC) English Proceedings Paper 56th ACM/EDAC/IEEE Design Automation Conference (DAC) JUN 02-06, 2019 Las Vegas, NV ACM,EDAC,IEEE We propose an admission control method in Network-on-Chip (NoC) with a centralized Artificial Neural Network (ANN) admission controller, which can improve system performance by predicting the most appropriate injection rate of each node via the network performance information. In the online control process, a data preprocessing unit is applied to simplify the ANN architecture and make the prediction results more accurate. Based on the preprocessed information, the ANN predictor determines the control strategy and broadcasts it to each node where the admission control will be applied. Compared to the previous work, our method builds up a high-fidelity model between the network status and the injection rate regulation. The full-system simulation results show that our proposed method can enhance application performance by 17.8% on average and up to 23.8%. [Wang, Boqian; Lu, Zhonghai] KTH Royal Inst Technol, Stockholm, Sweden; [Wang, Boqian; Chen, Shenggang] Natl Univ Def Technol, Changsha, Hunan, Peoples R China Royal Institute of Technology; National University of Defense Technology - China Wang, BQ (corresponding author), KTH Royal Inst Technol, Stockholm, Sweden. boqian@kth.se; zhonghai@kth.se; shec@kth.se Lu, Zhonghai/AAP-2154-2020 23 3 3 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6725-7 2019.0 10.1145/3316781.3317772 0.0 6 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN4JZ Bronze 2023-03-23 WOS:000482058200046 0
J Yang, L; Chen, X; Perlaza, SM; Zhang, JS Yang, Lei; Chen, Xu; Perlaza, Samir M.; Zhang, Junshan Special Issue on Artificial-Intelligence-Powered Edge Computing for Internet of Things IEEE INTERNET OF THINGS JOURNAL English Editorial Material Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments. [Yang, Lei] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA; [Chen, Xu] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China; [Perlaza, Samir M.] INRIA, Ctr Rech Sophia Antipolis Mediterranee, F-06902 Sophia Antipolis, France; [Zhang, Junshan] Arizona State Univ, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA Nevada System of Higher Education (NSHE); University of Nevada Reno; Sun Yat Sen University; Inria; Arizona State University; Arizona State University-Tempe Yang, L (corresponding author), Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA. Chen, Xu/GXW-3072-2022 Yang, Lei/0000-0002-5176-003X 0 8 8 2 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. OCT 2020.0 7.0 10 9224 9226 10.1109/JIOT.2020.3019948 0.0 3 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications OA2LX Bronze 2023-03-23 WOS:000577624800001 0
C Bermejo, C; Chatzopoulos, D; Hui, P ASSOC COMP MACHINERY Bermejo, Carlos; Chatzopoulos, Dimitris; Hui, Pan EyeShopper: Estimating Shoppers' Gaze using CCTV Cameras MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA English Proceedings Paper 28th ACM International Conference on Multimedia (MM) OCT 12-16, 2020 ELECTR NETWORK ACM SIGMM,Assoc Comp Machinery Retail Stores; Indoor Location; Camera; Gaze Estimator; Convolutional Neural Networks TRACKING Recent advances in machine and deep learning allow for enhanced retail analytics by applying object detection techniques. However, existing approaches either require laborious installation processes to function or lack precision when the customers turn their back in the installed cameras. In this paper, we present EyeShopper, an innovative system that tracks the gaze of shoppers when facing away from the camera and provides insights about their behavior in physical stores. EyeShopper is readily deployable in existing surveillance systems and robust against low-resolution video inputs. At the same time, its accuracy is comparable to state-of-the-art gaze estimation frameworks that require high-resolution and continuous video inputs to function. Furthermore, EyeShopper is more robust than state-of-the-art gaze tracking techniques for back head images. Extensive evaluation with different real video datasets and a synthetic dataset we produced shows that EyeShopper estimates with high accuracy the gaze of customers. [Bermejo, Carlos; Chatzopoulos, Dimitris; Hui, Pan] HKUST, Hong Kong, Peoples R China; [Hui, Pan] Univ Helsinki, Helsinki, Finland Hong Kong University of Science & Technology; University of Helsinki Bermejo, C (corresponding author), HKUST, Hong Kong, Peoples R China. cbf@cse.ust.hk; dcab@cse.ust.hk; panhui@cse.ust.hk Chatzopoulos, Dimitris/AAV-8599-2020 Chatzopoulos, Dimitris/0000-0002-4765-5085 Research Grants Council of Hong Kong [16214817]; FIT projects from Academy of Finland Research Grants Council of Hong Kong(Hong Kong Research Grants Council); FIT projects from Academy of Finland This research has been supported in part by project 16214817 from the Research Grants Council of Hong Kong,and the 5GEAR and FIT projects from Academy of Finland. 57 6 6 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7988-5 2020.0 2765 2774 10.1145/3394171.3413683 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BT2LS 2023-03-23 WOS:000810735002092 0
J Wu, HW; Bagherzadeh, SA; D'Orazio, A; Habibollahi, N; Karimipour, A; Goodarzi, M; Bach, QV Wu, Huawei; Bagherzadeh, Seyed Amin; D'Orazio, Annunziata; Habibollahi, Navid; Karimipour, Arash; Goodarzi, Marjan; Quang-Vu Bach Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS English Article Fe3O4/liquid paraffin; Thermo-physical properties; Multi objective genetic algorithm; Optimization MOLECULAR-DYNAMICS SIMULATION; WALL CARBON NANOTUBES; THERMOPHYSICAL PROPERTIES; RHEOLOGICAL BEHAVIOR; SENSITIVITY-ANALYSIS; NATURAL-CONVECTION; SLIP VELOCITY; NANOFLUID; TEMPERATURE; VISCOSITY This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop. The results showed that the proposed model of multi objective optimization of GA Pareto optimal front, quantified the trade-offs to handle 2 fitness functions of the considered non-Newtonian pipe flow. (C) 2019 Elsevier B.V. All rights reserved. [Wu, Huawei] Hubei Univ Arts & Sci, Hubei Key Lab Power Syst Design & Test Elect Vehi, Xiangyang 441053, Peoples R China; [Wu, Huawei] Hubei Univ Arts & Sci, Sch Automot & Traff Engn, Xiangyang 441053, Peoples R China; [Bagherzadeh, Seyed Amin; Habibollahi, Navid; Karimipour, Arash] Islamic Azad Univ, Dept Mech Engn, Najafabad Branch, Najafabad, Iran; [D'Orazio, Annunziata] Sapienza Univ Roma, Dipartimento Ingn Astronaut Elettr & Energet, Via Eudossiana 18, I-00184 Rome, Italy; [Goodarzi, Marjan; Quang-Vu Bach] Ton Duc Thang Univ, Sustainable Management Nat Resources & Environm R, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam Hubei University of Arts & Science; Hubei University of Arts & Science; Islamic Azad University; Sapienza University Rome; Ton Duc Thang University Goodarzi, M (corresponding author), Ton Duc Thang Univ, Sustainable Management Nat Resources & Environm R, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam. huawei.wuhb@gmail.com; marjan.goodarzi@tdtu.edu.vn Karimipour, Arash/A-3478-2019; Goodarzi, Marjan/F-8848-2013; Bach, Quang-Vu/I-3160-2015; Vu, Bach Quang/M-8809-2019 Karimipour, Arash/0000-0001-7596-7134; Goodarzi, Marjan/0000-0002-1285-0593; Bach, Quang-Vu/0000-0002-4176-3974; Vu, Bach Quang/0000-0002-2205-6887; Wu, Huawei/0000-0003-3305-362X; Bagherzadeh, Seyed Amin/0000-0001-6061-7967 Technical Innovation Project of Hubei Province, China [2017AAA133]; Hubei Superior and Distinctive Discipline Group of Mechatronics and Automobiles, China [XKQ2018002] Technical Innovation Project of Hubei Province, China; Hubei Superior and Distinctive Discipline Group of Mechatronics and Automobiles, China This research is partially supported by the Technical Innovation Project of Hubei Province, China (No. 2017AAA133), Hubei Superior and Distinctive Discipline Group of Mechatronics and Automobiles, China (No. XKQ2018002). 44 35 35 2 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-4371 1873-2119 PHYSICA A Physica A DEC 1 2019.0 535.0 122409 10.1016/j.physa.2019.122409 0.0 11 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics JQ1YP 2023-03-23 WOS:000498749000007 0
C Bifet, A; Maniu, S; Qian, JF; Tian, GJ; He, C; Fan, W Cui, P; Dy, J; Aggarwal, C; Zhou, ZH; Tuzhilin, A; Xiong, H; Wu, X Bifet, Albert; Maniu, Silviu; Qian, Jianfeng; Tian, Guangjian; He, Cheng; Fan, Wei StreamDM: Advanced Data Mining in Spark Streaming 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) English Proceedings Paper IEEE 15th International Conference on Data Mining Workshops (ICDMW) NOV 14-17, 2015 Atlantic, NJ Baidu,Stony Brook Univ,Pinnacle,NSF,IEEE,Rutgers,KD Nuggets,Cisco,Drawbridge,IEEE Comp Soc stream mining; Spark Streaming; open-source; software Real-time analytics are becoming increasingly important due to the large amount of data that is being created continuously. Drawing from our experiences at Huawei Noah's Ark Lab, we present and demonstrate here StreamDM, a new open source data mining and machine learning library, designed on top of Spark Streaming, an extension of the core Spark API that enables scalable stream processing of data streams. StreamDM is designed to be easily extended and used, either practitioners, developers, or researchers, and is the first library to contain advanced stream mining algorithms for Spark Streaming. [Bifet, Albert] Telecom ParisTech, Paris, France; [Maniu, Silviu] Univ Paris 11, Orsay, France; [Qian, Jianfeng; Tian, Guangjian; He, Cheng] Huawei Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China; [Fan, Wei] Baidu Big Data Lab, Sunnyvale, CA USA IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Huawei Technologies; Baidu Bifet, A (corresponding author), Telecom ParisTech, Paris, France. bifet@telecom-paristech.fr; maniu@lri.fr; jianfeng.qian@outlook.com; tian.guangjian@huawei.com; hecheng@huawei.com; wei.fan@gmail.com Bifet, Albert/E-4984-2017 Bifet, Albert/0000-0002-8339-7773; Maniu, Silviu/0000-0002-8623-1533 10 23 24 1 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-8493-3 2015.0 1608 1611 10.1109/ICDMW.2015.140 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BF3MK Green Submitted 2023-03-23 WOS:000380556700226 0
J Lin, FF; Zhang, X; Li, XZ; Sun, CJ; Cai, WJ; Zhang, Z Lin, Fanfan; Zhang, Xin; Li, Xinze; Sun, Changjiang; Cai, Wenjian; Zhang, Zhe Automatic Triple Phase-Shift Modulation for DAB Converter With Minimized Power Loss IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS English Article; Proceedings Paper 12th Annual IEEE Energy Conversion Congress and Exposition (IEEE ECCE) SEP 23-OCT 15, 2020 Detroit, MI IEEE,IEEE Ind Applicat Soc,IEEE Power Elect Soc,MathWorks,ROHM Semiconductor Modulation; Bridge circuits; Table lookup; Optimization; Harmonic analysis; Real-time systems; Phase modulation; Dual active bridge (DAB); fuzzy inference system (FIS); neural network (NN); particle swarm optimization (PSO); power loss; triple phase-shift (TPS) DUAL-ACTIVE-BRIDGE; DC-DC CONVERTER; EFFICIENCY; STRESS; TRANSFORMER; STRATEGY; SCHEME Currently, triple phase-shift (TPS) modulation has attracted more and more attention of researchers as an advanced modulation strategy for dual active bridge converter (DAB). Since it has three degrees of freedom, it can realize better performance both in soft switching ranges and power efficiency. However, how to choose these three degrees of freedom to realize optimal power efficiency of DAB converter becomes a concern for researchers. Generally, there exist two difficulties to apply efficiency-oriented TPS modulation. The first difficulty lies in the analysis process in which the main task is to figure out the relationships between modulation parameters and power loss. The three modulation parameters in TPS bring difficulties in analysis and deduction process, which suffers from high computational burden and low accuracy. Additionally, the second difficulty lies in the real-time realization of TPS modulation. If a look-up table is applied to store the optimized modulation parameters, it is highly likely that its discrete nature will result in unsatisfactory modulation performance. Therefore, this article proposes an efficiency-oriented automatic TPS (ATPS) modulation approach, which utilizes neural network, particle swarm optimization, and fuzzy inference system, respectively, in its three stages. The proposed ATPS is able to mitigate labor in computational burden with a highly automatic fashion. Finally, this proposed ATPS has been validated with 1-kW hardware experiments. [Lin, Fanfan] Nanyang Technol Univ, ERI N Interdisciplinary Grad Program, Singapore 639798, Singapore; [Zhang, Xin] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China; [Zhang, Xin] Zhejiang Univ, Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 310058, Peoples R China; [Li, Xinze; Cai, Wenjian] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Sun, Changjiang] Nanyang Technol Univ, Rolls Royce NTU Corp Lab, Singapore 639798, Singapore; [Zhang, Zhe] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Zhejiang University; Zhejiang University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Technical University of Denmark Zhang, X (corresponding author), Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China. fanfan001@e.ntu.edu.sg; zhangxin_ieee@163.com; xinze001@e.ntu.edu.sg; changjiang.sun@ntu.edu.sg; ewjcai@ntu.edu.sg; zz@elektro.dtu.dk ZHNAG, XIN/HLX-8630-2023; SUN, CHANG/GXM-3680-2022; Zhang, Zhe/D-2434-2014 ZHNAG, XIN/0000-0001-5647-2777; li, xinze/0000-0003-3513-209X; Zhang, Zhe/0000-0001-8407-3167; Lin, Fanfan/0000-0002-5562-2478 NSFC [52177198]; Cao Guangbiao High-tech Development Fund [2020QN012]; Delta Research and Education [DREG2021002] NSFC(National Natural Science Foundation of China (NSFC)); Cao Guangbiao High-tech Development Fund; Delta Research and Education This work was supported in part by NSFC under Grant 52177198, in part by Cao Guangbiao High-tech Development Fund under Grant 2020QN012, and in part by Delta Research and Education under Grant DREG2021002. 36 3 3 1 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0093-9994 1939-9367 IEEE T IND APPL IEEE Trans. Ind. Appl. MAY-JUN 2022.0 58.0 3 3840 3851 10.1109/TIA.2021.3136501 0.0 12 Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering 1L4SE Green Published 2023-03-23 WOS:000799279300082 0
J Kang, MZ; Wang, XJ; Wang, HY; Hua, J; de Reffye, P; Wang, FY Kang, Mengzhen; Wang, Xiujuan; Wang, Haoyu; Hua, Jing; de Reffye, Philippe; Wang, Fei-Yue The Development of AgriVerse: Past, Present, and Future IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article; Early Access Computational modeling; Biological systems; Biomass; Biological system modeling; Production; Plants (biology); Metaverse; Agricultural metaverse (AgriVerse); agriculture cyber-physical-social system (CPSS); artificial systems; computational experiments; and parallel execution (ACP); decentralized science (DeSci); knowledge-and-data driven modeling; parallel agriculture; plant model PARALLEL INTELLIGENCE; OPTIMIZATION PROBLEMS; MATHEMATICAL-MODELS; SYSTEMS; METAVERSES; ARCHITECTURE; AGRICULTURE; ALGORITHMS; SIMULATION; BLOCKCHAIN Agricultural metaverse (AgriVerse) aims to optimize the production chain by saving costs, increasing efficiencies, and breaking information silos, in order to achieve sustainable agriculture. While AgriVerse is featured by the virtual-real interaction of the agriculture-related processes based on heterogeneous data, knowledge, and models, the link between AgriVerse and the intensively studied plant modeling is vague. This article presents briefly the research contents of plant modeling, analyzes the ongoing transition at the age of artificial intelligence (AI), and envisions future AgriVerse with the support of the agricultural foundation model, the decentralized agricultural organization (DAO) and the decentralized science (DeSci) of the plant model. Three AgriVerse application scenarios are presented. The opportunities and challenges of AgriVerse are discussed. This work is expected to identify the key research issues of AgriVerse and bring practitioners of diverse backgrounds together into the AgriVerse community. [Kang, Mengzhen; Wang, Xiujuan; Wang, Haoyu; Hua, Jing; Wang, Fei-Yue] Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Wang, Xiujuan] Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China; [de Reffye, Philippe] Univ Montpellier, AMAP, CIRAD, CNRS,INRAE,IRD, F-34398 Montpellier, France; [Wang, Fei-Yue] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Wang, Fei-Yue] Macau Univ Sci & Technol, Macao Inst Syst Engn, Macau, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; Centre National de la Recherche Scientifique (CNRS); CIRAD; INRAE; Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Macau University of Science & Technology Wang, FY (corresponding author), Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China. mengzhen.kang@ia.ac.cn; xiujuan.wang@ia.ac.cn; haoyu.wang@ia.ac.cn; jing.hua@ia.ac.cn; ph.dereffye@gmail.com; feiyue.wang@ia.ac.cn 67 0 0 24 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. 10.1109/TSMC.2022.3230830 0.0 JAN 2023 10 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 7S2IV 2023-03-23 WOS:000910583400001 0
J Li, RR; Chen, XL; Balezentis, T; Streimikiene, D; Niu, ZY Li, Ranran; Chen, Xueli; Balezentis, Tomas; Streimikiene, Dalia; Niu, Zhiyong Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application NEURAL COMPUTING & APPLICATIONS English Article Sample entropy; Multi-objective sine cosine algorithm; Least squares support vector machine; Variational mode decomposition; Multi-step forecasting ARTIFICIAL NEURAL-NETWORK; TIME-SERIES; MULTIOBJECTIVE OPTIMIZATION; APPROXIMATE ENTROPY; COMPLEXITY ANALYSIS; SAMPLE ENTROPY; PREDICTION; ALGORITHM; SYSTEM; CONSUMPTION Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting. [Li, Ranran] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China; [Chen, Xueli] Chinese Acad Social Sci, Beijing 100732, Peoples R China; [Balezentis, Tomas; Streimikiene, Dalia] Lithuanian Inst Agr Econ, Vilnius, Lithuania; [Niu, Zhiyong] Shanghai Univ Finance & Econ, Shanghai 200433, Peoples R China Hebei University of Technology; Chinese Academy of Social Sciences; Lithuanian Institute of Agrarian Economics; Shanghai University of Finance & Economics Niu, ZY (corresponding author), Shanghai Univ Finance & Econ, Shanghai 200433, Peoples R China. niu.zhiyong@mail.shufe.edu.cn Li, Ranran/Z-2312-2019; Balezentis, Tomas/D-8609-2015 Li, Ranran/0000-0003-0284-2730; Balezentis, Tomas/0000-0002-3906-1711; niu, zhiyong/0000-0001-8386-6851 59 12 12 3 26 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. JAN 2021.0 33.0 1 SI 301 320 10.1007/s00521-020-04996-3 0.0 MAY 2020 20 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PZ0JG 2023-03-23 WOS:000533495100001 0
J Ji, W; Liu, XQ; Qi, HJ; Liu, XN; Lin, CN; Li, TC Ji, Wei; Liu, Xiaoqing; Qi, Huijun; Liu, Xunnan; Lin, Chaoning; Li, Tongchun Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm MATHEMATICAL PROBLEMS IN ENGINEERING English Article DAMS; MODEL During the long-term operating period, the mechanical parameters of hydraulic structures and foundation deteriorated gradually because of the environmental factors. In order to evaluate the overall safety and durability, these parameters should be calculated by some accurate analysis methods, which are hindered by slow computational efficiency and optimization performance. The improved deep Q-network (DQN) algorithm combined with the deep neural network (DNN) surrogate model was proposed in this paper to ameliorate the above problems. Through the study cases of different zoning in the dam body and the actual engineering foundation, it is shown that the improved DQN algorithm has a good application effect on inversion analysis of material mechanical parameters in this paper. [Ji, Wei; Liu, Xiaoqing; Liu, Xunnan; Lin, Chaoning; Li, Tongchun] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210098, Jiangsu, Peoples R China; [Qi, Huijun] Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China; [Lin, Chaoning] Delft Univ Technol, Fac Technol Policy & Management, NL-2628 BX Delft, Netherlands Hohai University; Hohai University; Delft University of Technology Liu, XQ (corresponding author), Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210098, Jiangsu, Peoples R China.;Qi, HJ (corresponding author), Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China. lxqhhu@163.com; qihuijun@hhu.edu.cn liu, xiao/HMD-7454-2023; li, tong/HPC-6702-2023; lin, chaoning/AAW-3324-2021 lin, chaoning/0000-0001-5615-308X; ji, Ji/0000-0001-8712-5460; Qi, Huijun/0000-0001-6595-5308 National Key Research and Development Plan [2018YFC0407102]; Fundamental Research Funds for the Central Universities [SN: B200202180]; National Natural Science Foundation of China [SN: 52009035] National Key Research and Development Plan; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was greatly supported by the National Key Research and Development Plan (no. 2018YFC0407102), the Fundamental Research Funds for the Central Universities (SN: B200202180), and the National Natural Science Foundation of China (SN: 52009035). 36 2 2 7 21 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. DEC 28 2020.0 2020.0 6404819 10.1155/2020/6404819 0.0 20 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics PU7NL gold, Green Published 2023-03-23 WOS:000609487900003 0
J Zhao, Y; Feng, HL; Gallinari, P Zhao, Yu; Feng, Huali; Gallinari, Patrick Embedding Learning with Triple Trustiness on Noisy Knowledge Graph ENTROPY English Article knowledge graph; embedding learning; cross entropy; noise detection; triple trustiness Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume that all facts in existing KGs are completely correct and ignore noise issues, which brings about potentially serious errors. To address this issue, in this paper we propose a novel approach to learn embeddings with triple trustiness on KGs, which takes possible noises into consideration. Specifically, we calculate the trustiness value of triples according to the rich and relatively reliable information from large amounts of entity type instances and entity descriptions in KGs. In addition, we present a cross-entropy based loss function for model optimization. In experiments, we evaluate our models on KG noise detection, KG completion and classification. Through extensive experiments on three datasets, we demonstrate that our proposed model can learn better embeddings than all baselines on noisy KGs. [Zhao, Yu; Feng, Huali] Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Sichuan, Peoples R China; [Gallinari, Patrick] Univ Paris 06, LIP6, F-75252 Paris, France Southwestern University of Finance & Economics - China; UDICE-French Research Universities; Sorbonne Universite Zhao, Y; Feng, HL (corresponding author), Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Sichuan, Peoples R China. zhaoyu@swufe.edu.cn; fenghuali0723@163.com; patrick.gallinari@lip6.fr National Natural Science Foundation of China [61906159]; Sichuan Science and Technology Program [2018JY0607]; Fundamental Research Funds for the Central Universities [JBK1901030, JBK1801080] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was funded in part by the National Natural Science Foundation of China under Grant No. 61906159, in part by the Sichuan Science and Technology Program under Grant No. 2018JY0607, in part by the Fundamental Research Funds for the Central Universities under Grant No. JBK1901030, No. JBK1801080. 56 4 5 8 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy NOV 2019.0 21.0 11 1083 10.3390/e21111083 0.0 16 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics JV1QZ Green Accepted, Green Submitted, gold 2023-03-23 WOS:000502145000056 0
J Jia, YH; Jiang, QL; Sun, HD; Liu, PP; Hu, DH; Pei, YZ; Liu, WS; Crispin, X; Fabiano, S; Ma, YG; Cao, Y Jia, Yanhua; Jiang, Qinglin; Sun, Hengda; Liu, Peipei; Hu, Dehua; Pei, Yanzhong; Liu, Weishu; Crispin, Xavier; Fabiano, Simone; Ma, Yuguang; Cao, Yong Wearable Thermoelectric Materials and Devices for Self-Powered Electronic Systems ADVANCED MATERIALS English Review processing strategies; self-powered electronic systems; thermoelectric materials; wearable devices HUMAN-BODY HEAT; HIGH-PERFORMANCE; CONDUCTING POLYMER; THIN-FILM; CARBON NANOTUBES; ORGANIC SEMICONDUCTORS; SEEBECK COEFFICIENT; THERMAL-CONDUCTIVITY; ENERGY; TEMPERATURE The emergence of artificial intelligence and the Internet of Things has led to a growing demand for wearable and maintenance-free power sources. The continual push toward lower operating voltages and power consumption in modern integrated circuits has made the development of devices powered by body heat finally feasible. In this context, thermoelectric (TE) materials have emerged as promising candidates for the effective conversion of body heat into electricity to power wearable devices without being limited by environmental conditions. Driven by rapid advances in processing technology and the performance of TE materials over the past two decades, wearable thermoelectric generators (WTEGs) have gradually become more flexible and stretchable so that they can be used on complex and dynamic surfaces. In this review, the functional materials, processing techniques, and strategies for the device design of different types of WTEGs are comprehensively covered. Wearable self-powered systems based on WTEGs are summarized, including multi-function TE modules, hybrid energy harvesting, and all-in-one energy devices. Challenges in organic TE materials, interfacial engineering, and assessments of device performance are discussed, and suggestions for future developments in the area are provided. This review will promote the rapid implementation of wearable TE materials and devices in self-powered electronic systems. [Jia, Yanhua; Jiang, Qinglin; Liu, Peipei; Hu, Dehua; Ma, Yuguang; Cao, Yong] South China Univ Technol, Inst Polymer Optoelect Mat & Devices, State Key Lab Luminescent Mat & Devices, Guangzhou 510640, Peoples R China; [Sun, Hengda] Donghua Univ, Coll Mat Sci & Engn, State Key Lab Modificat Chem Fibers & Polymer Mat, Shanghai 201620, Peoples R China; [Pei, Yanzhong] Tongji Univ, Sch Mat Sci & Engn, Minist Educ, Key Lab Adv Civil Engn Mat, Shanghai 201804, Peoples R China; [Liu, Weishu] Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China; [Crispin, Xavier; Fabiano, Simone] Linkoping Univ, Dept Sci & Technol, Lab Organ Elect, SE-60174 Norrkoping, Sweden South China University of Technology; Donghua University; Tongji University; Southern University of Science & Technology; Linkoping University Jiang, QL; Ma, YG (corresponding author), South China Univ Technol, Inst Polymer Optoelect Mat & Devices, State Key Lab Luminescent Mat & Devices, Guangzhou 510640, Peoples R China.;Fabiano, S (corresponding author), Linkoping Univ, Dept Sci & Technol, Lab Organ Elect, SE-60174 Norrkoping, Sweden. jiangql@scut.edu.cn; simone.fabiano@liu.se; ygma@scut.edu.cn Pei, Yanzhong/E-4708-2011; Jiang, Qinglin/AAL-2882-2021; Pei, Yanzhong/M-7840-2019; Fabiano, Simone/F-4954-2012 Pei, Yanzhong/0000-0003-1612-3294; Fabiano, Simone/0000-0001-7016-6514 National Natural Science Foundation of China [51521002, 21334002]; Open Fund of the State Key Laboratory of Luminescent Materials and Devices (South China University of Technology) [2019-skllmd-01]; Knut and Alice Wallenberg Foundation (Wallenberg Wood Science Center); Swedish Research Council [2016-06146]; Advanced Functional Materials Center at Linkoping University [2009-00971] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund of the State Key Laboratory of Luminescent Materials and Devices (South China University of Technology); Knut and Alice Wallenberg Foundation (Wallenberg Wood Science Center); Swedish Research Council(Swedish Research Council); Advanced Functional Materials Center at Linkoping University Y.J. and Q.J. contributed equally to this work. This work was supported by the National Natural Science Foundation of China (51521002 and 21334002), and by the Open Fund of the State Key Laboratory of Luminescent Materials and Devices (South China University of Technology, 2019-skllmd-01). In Sweden, the work was supported by the Knut and Alice Wallenberg Foundation (Wallenberg Wood Science Center), the Swedish Research Council (2016-06146), the Advanced Functional Materials Center at Linkoping University (2009-00971). 284 90 90 164 582 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0935-9648 1521-4095 ADV MATER Adv. Mater. OCT 2021.0 33.0 42 2102990 10.1002/adma.202102990 0.0 SEP 2021 46 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics WI8EU 34486174.0 2023-03-23 WOS:000692743700001 0
J Behrens, T; Rossel, RAV; Kerry, R; MacMillan, R; Schmidt, K; Lee, JW; Scholten, T; Zhu, AX Behrens, Thorsten; Rossel, Raphael A. Viscarra; Kerry, Ruth; MacMillan, Robert; Schmidt, Karsten; Lee, Juhwan; Scholten, Thomas; Zhu, A-Xing The relevant range of scales for multi-scale contextual spatial modelling SCIENTIFIC REPORTS English Article RANDOM FORESTS; SOIL; GEOSTATISTICS; VARIOGRAM; PATTERNS Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling. [Behrens, Thorsten] Eberhard Karls Univ Tuebingen, Cluster Excellence Machine Learning New Perspecti, Maria Linden Str 6, D-72076 Tubingen, Germany; [Behrens, Thorsten; Schmidt, Karsten; Scholten, Thomas] Eberhard Karls Univ Tuebingen, Dept Geosci Soil Sci & Geomorphol, Ruemelinstr 19-23, D-72070 Tubingen, Germany; [Rossel, Raphael A. Viscarra; Lee, Juhwan] Curtin Univ, Fac Sci & Engn, Sch Mol & Life Sci, Soil & Landscape Sci, GPO Box U1987, Perth, WA 6845, Australia; [Kerry, Ruth] Brigham Young Univ, Dept Geog, 690 Spencer W Kimball Tower, Provo, UT 84602 USA; [MacMillan, Robert] LandMapper Environm Solut Inc, 702-250 Douglas St, Victoria, BC, Canada; [Zhu, A-Xing] Univ Wisconsin, Dept Geog, Madison, WI 53706 USA; [Zhu, A-Xing] Chinese Acad Sci, State Key Lab Environm Informat Syst, Inst Geog Sci & Nat Resources, Beijing 100101, Peoples R China Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Curtin University; Brigham Young University; University of Wisconsin System; University of Wisconsin Madison; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Behrens, T (corresponding author), Eberhard Karls Univ Tuebingen, Cluster Excellence Machine Learning New Perspecti, Maria Linden Str 6, D-72076 Tubingen, Germany.;Behrens, T (corresponding author), Eberhard Karls Univ Tuebingen, Dept Geosci Soil Sci & Geomorphol, Ruemelinstr 19-23, D-72070 Tubingen, Germany. thorsten.behrens@uni-tuebingen.de Viscarra Rossel, Raphael A./B-4061-2011; Schmidt, Karsten/G-8190-2015 Viscarra Rossel, Raphael A./0000-0003-1540-4748; Schmidt, Karsten/0000-0003-0337-3024; Lee, Juhwan/0000-0002-7967-2955; Behrens, Thorsten/0000-0002-9287-9395; Kerry, Ruth/0000-0001-9174-0167 German Research Foundation (DFG) under the Pedoscale project [BE 4023/3-1] German Research Foundation (DFG) under the Pedoscale project(German Research Foundation (DFG)) This research was funded by the German Research Foundation (DFG) under the Pedoscale project (BE 4023/3-1). We are very grateful to the Federal Geological Survey of Rhineland Palatinate for providing the Rhine-Hesse dataset. We are very grateful to the Federal Geological Survey of Rhineland Palatinate for providing the RhineHesse dataset and to Jose A.M. Dematte for providing the Brazilian dataset. 42 11 11 0 9 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep OCT 15 2019.0 9.0 14800 10.1038/s41598-019-51395-3 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics JD6WE 31616033.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000490121400018 0
J He, J; Chen, SH; Shahrour, I He, Ji; Chen, Sheng-hong; Shahrour, Isam Numerical estimation and prediction of stress-dependent permeability tensor for fractured rock masses INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES English Article Fractured rock; Permeability tensor; Hydro-mechanical coupling; Composite element method; Discrete fracture network; Artificial neural network HYDRAULIC CONDUCTIVITY TENSOR; COMPOSITE ELEMENT MODEL; POROUS-MEDIA; FLUID-FLOW; FIELD DETERMINATION; ANISOTROPIC MEDIA; DEFORMATION; REV; NETWORKS; APERTURE The numerical experiment based on the Composite Element Method (CEM) is extended to study the stress-dependent permeability tensor for fractured rock masses. The CEM allows rock meshing regardless of fractures, and the fractures are automatically formulated with an explicit representation. An equivalent filled fracture model is introduced into the CEM to realize a hydro-mechanical coupling simulation for the numerical experiment. This model assumes the hydraulic properties both along and normal to the fracture plane dependent on the fracture normal stress with an exponential relation. Large numbers of rock specimens with different sizes and orientations are modeled and their hydro-mechanical coupling behaviors under various stress states are simulated by the CEM. From the simulated results, the stress-dependent permeability tensors are calculated from the flow rates through the rock specimen boundaries. In order to predict the permeability tensor for any in situ stress state, a load/permeability database is developed using the Artificial Neural Network (ANN). The database establishes the mapping from the stress state to the permeability tensor, based on the powerful data modeling capacity of the ANN. Inputting any in situ stress state into the database, the database output is the relevant permeability tensor predicted. The above algorithms are verified by comparison with an analytical solution and successfully used in a stochastic fracture system. (c) 2012 Elsevier Ltd. All rights reserved. [He, Ji; Chen, Sheng-hong; Shahrour, Isam] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Chen, Sheng-hong; Shahrour, Isam] Univ Lille 1, Lab Genie Civil & Geoenvironm, F-59650 Villeneuve Dascq, France Wuhan University; Universite de Lille - ISITE; Universite de Lille He, J (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. jihechn@gmail.com Shahrour, isam/A-6865-2008; Shahrour, Isam/I-7151-2019 Shahrour, Isam/0000-0001-7279-8005 National Science Foundation of China [NSFC: 51079109] National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is granted by the National Science Foundation of China (NSFC: 51079109). 32 18 21 11 79 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1365-1609 1873-4545 INT J ROCK MECH MIN Int. J. Rock Mech. Min. Sci. APR 2013.0 59.0 70 79 10.1016/j.ijrmms.2012.12.001 0.0 10 Engineering, Geological; Mining & Mineral Processing Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mining & Mineral Processing 114GD 2023-03-23 WOS:000316728200008 0
C Nebout, A; Wei, WJ; Liu, Z; Huang, LJ; Le Meur, O IEEE Nebout, Alexis; Wei, Weijie; Liu, Zhi; Huang, Lijin; Le Meur, Olivier PREDICTING SALIENCY MAPS FOR ASD PEOPLE 2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW) IEEE International Conference on Multimedia and Expo Workshops English Proceedings Paper IEEE International Conference on Multimedia and Expo (IEEE ICME) JUL 08-12, 2019 Shanghai, PEOPLES R CHINA IEEE,IEEE Signal Proc Soc,IEEE Commun Soc,IEEE Circuit & Syst Soc,IEEE Comp Soc,Kuaishou,JD.COM,MEGVII,iQIYI,Microsoft,DiDi,Alibaba,Horizon Robot,Baidu,Lenovo Res,Unisound,SenseTime,Jiangmen saliency map; ASD; prediction; visual attention INDIVIDUALS; PATTERNS; AUTISM This paper presents a novel saliency prediction model for children with autism spectrum disorder (ASD). We design a new convolution neural network and train it with a new ASD dataset. Among the contributions, we can cite the coarse-to-fine architecture as well as the loss function which embeds a regularization term. We also discuss about some data augmentation methods for ASD dataset. Experimental results show that the proposed model performs better than 6 models, one supervised model finetuned with the ASD dataset. Contrary to control people, our results hint that no center bias apply in visuall attention for autistic children. [Nebout, Alexis; Le Meur, Olivier] Univ Rennes 1, CNRS, IRISA, Rennes, France; [Wei, Weijie; Liu, Zhi; Huang, Lijin] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai, Peoples R China; [Wei, Weijie; Liu, Zhi; Huang, Lijin] Shanghai Univ, Sch Commun & Informat Engn, Shanghai, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite de Rennes; Shanghai University; Shanghai University Nebout, A (corresponding author), Univ Rennes 1, CNRS, IRISA, Rennes, France. alexis.nebout@irisa.fr; codename1995@shu.edu.cn; liuzhisjtu@163.com; hyrx@live.cn; olemeur@irisa.fr LIU, Zhi/D-4518-2012 LIU, Zhi/0000-0002-8428-1131; Wei, Weijie/0000-0002-5952-5341 National Natural Science Foundation of China [61771301] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant No. 61771301. 25 11 11 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2330-7927 978-1-5386-9214-1 IEEE INT CONF MULTI 2019.0 629 632 10.1109/ICMEW.2019.00121 0.0 4 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO0EW Green Submitted 2023-03-23 WOS:000490430500114 0
J Wilf, P; Zhang, SP; Chikkerur, S; Little, SA; Wing, SL; Serre, T Wilf, Peter; Zhang, Shengping; Chikkerur, Sharat; Little, Stefan A.; Wing, Scott L.; Serre, Thomas Computer vision cracks the leaf code PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA English Article leaf architecture; leaf venation; computer vision; sparse coding; paleobotany CLASSIFICATION; ARCHITECTURE; SHAPE Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. [Wilf, Peter; Little, Stefan A.] Penn State Univ, Dept Geosci, University Pk, PA 16802 USA; [Zhang, Shengping; Serre, Thomas] Brown Univ, Dept Cognit Linguist & Psychol Sci, Brown Inst Brain Sci, Providence, RI 02912 USA; [Zhang, Shengping] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Shandong, Peoples R China; [Chikkerur, Sharat] Microsoft, Azure Machine Learning, Cambridge, MA 02142 USA; [Little, Stefan A.] Univ Paris 11, Lab Ecol Systemat & Evolut, Bat 425, F-91405 Orsay, France; [Wing, Scott L.] Smithsonian Inst, Dept Paleobiol, Natl Museum Nat Hist, Washington, DC 20013 USA Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Brown University; Harbin Institute of Technology; Microsoft; AgroParisTech; UDICE-French Research Universities; Universite Paris Saclay; Smithsonian Institution; Smithsonian National Museum of Natural History Wilf, P (corresponding author), Penn State Univ, Dept Geosci, University Pk, PA 16802 USA.;Zhang, SP; Serre, T (corresponding author), Brown Univ, Dept Cognit Linguist & Psychol Sci, Brown Inst Brain Sci, Providence, RI 02912 USA.;Zhang, SP (corresponding author), Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Shandong, Peoples R China. pwilf@psu.edu; s.zhang@hit.edu.cn; thomas_serre@brown.edu Wing, Scott/0000-0002-2954-8905 David and Lucile Packard Foundation; National Science Foundation [IIS-1252951]; Defense Advanced Research Projects Agency Young Investigator Award [N66001-14-1-4037]; Office of Naval Research Grant [N000141110743]; Brown Center for Computation and Visualization; National Natural Science Foundation of China [61300111, 61133003] David and Lucile Packard Foundation(The David & Lucile Packard Foundation); National Science Foundation(National Science Foundation (NSF)); Defense Advanced Research Projects Agency Young Investigator Award(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Office of Naval Research Grant(Office of Naval Research); Brown Center for Computation and Visualization; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank A. Young and J. Kissell for image preparations, the anonymous reviewers for helpful comments, Y. Guo for software, A. Rozo for book scanning, and D. Erwin for assistance with the Axelrod collection. We acknowledge financial support from the David and Lucile Packard Foundation (P. W.); National Science Foundation Early Career Award IIS-1252951, Defense Advanced Research Projects Agency Young Investigator Award N66001-14-1-4037, Office of Naval Research Grant N000141110743, and the Brown Center for Computation and Visualization (T. S.); and National Natural Science Foundation of China Grant 61300111 and Key Program Grant 61133003 (to S.Z.). 38 86 90 3 63 NATL ACAD SCIENCES WASHINGTON 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA 0027-8424 1091-6490 P NATL ACAD SCI USA Proc. Natl. Acad. Sci. U. S. A. MAR 22 2016.0 113.0 12 3305 3310 10.1073/pnas.1524473113 0.0 6 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics DH0QH 26951664.0 Bronze, Green Accepted, Green Published 2023-03-23 WOS:000372488200058 0
J Yuan, YX; Qin, WJ; Ibragimov, B; Zhang, GL; Han, B; Meng, MQH; Xing, L Yuan, Yixuan; Qin, Wenjian; Ibragimov, Bulat; Zhang, Guanglei; Han, Bin; Meng, Max Q-H; Xing, Lei Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article Deep learning; Image recognition; Task analysis; Data models; Biomedical imaging; Shape; Feature extraction; Category sensitive (CS) loss; densely connected convolutional network (DenseNet); polyp image classification; unbalanced discriminant (UD) loss WIRELESS CAPSULE ENDOSCOPY; CLASSIFICATION; IMAGES Automatic polyp recognition in endoscopic images is challenging because of the low contrast between polyps and the surrounding area, the fuzzy and irregular polyp borders, and varying imaging light conditions. In this article, we propose a novel densely connected convolutional network with unbalanced discriminant (UD) loss and category sensitive (CS) loss (DenseNet-UDCS) for the task. We first utilize densely connected convolutional network (DenseNet) as the basic framework to conduct end-to-end polyp recognition task. Then, the proposed dual constraints, UD loss and CS loss, are simultaneously incorporated into the DenseNet model to calculate discriminative and suitable image features. The UD loss in our network effectively captures classification errors from both majority and minority categories to deal with the strong data imbalance of polyp images and normal ones. The CS loss imposes the ratio of intraclass and interclass variations in the deep feature learning process to enable features with large interclass variation and small intraclass compactness. With the joint supervision of UD loss and CS loss, a robust DenseNet-UDCS model is trained to recognize polyps from endoscopic images. The experimental results achieved polyp recognition accuracy of 93.19%, showing that the proposed DenseNet-UDCS can accurately characterize the endoscopic images and recognize polyps from the images. In addition, our DenseNet-UDCS model is superior in detection accuracy in comparison with state-of-the-art polyp recognition methods. Note to Practitioners-Wireless capsule endoscopy (WCE) is a crucial diagnostic tool for polyp detection and therapeutic monitoring, thanks to its noninvasive, user-friendly, and nonpainful properties. A challenge in harnessing the enormous potential of the WCE to benefit the gastrointestinal (GI) patients is that it requires clinicians to analyze a huge number of images (about 50 000 images for each patient). We propose a novel automatic polyp recognition scheme, namely, DenseNet-UDCS model, by addressing practical image unbalanced problem and small interclass variances and large intraclass differences in the data set. The comprehensive experimental results demonstrate superior reliability and robustness of the proposed model compared to the other polyp recognition approaches. Our DenseNet-UDCS model can be further applied in the clinical practice to provide valuable diagnosis information for GI disease recognition and precision medicine. [Yuan, Yixuan; Meng, Max Q-H] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Qin, Wenjian] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing 518055, Peoples R China; [Ibragimov, Bulat] Univ Copenhagen, Dept Machine Learning & Med Imaging, DK-2100 Copenhagen, Denmark; [Ibragimov, Bulat] Univ Copenhagen, Dept Comp Sci, DK-2100 Copenhagen, Denmark; [Zhang, Guanglei] Beihang Univ, Sch Biol Sci & Med Engn, Beijing Adv Innovat Ctr Biomed Engn, Beijing 100191, Peoples R China; [Han, Bin; Xing, Lei] Stanford Univ, Dept Radiat Oncol, Stanford, CA 94305 USA City University of Hong Kong; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; University of Copenhagen; University of Copenhagen; Beihang University; Stanford University Yuan, YX (corresponding author), City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China. yxyuan.ee@cityu.edu.hk; qinwenjian09@mails.ucas.edu.cn; bulat@di.ku.dk; guangleizhang@buaa.edu.cn; hanbin@stanford.edu; max@ee.cuhk.edu.hk; lei@stanford.edu Zhang, Guanglei/GPX-8904-2022; meng, meng/GWZ-7461-2022 Zhang, Guanglei/0000-0002-2617-9673; Ibragimov, Bulat/0000-0001-7739-7788; Yuan, Yixuan/0000-0002-0853-6948; Meng, Max Q.-H./0000-0002-5255-5898; Xing, Lei/0000-0003-2536-5359 Sichuan Provincial Science and Technology Department Applied Basic Research Project [2019JY0632]; CityU Start-Up Fund [7200580] Sichuan Provincial Science and Technology Department Applied Basic Research Project; CityU Start-Up Fund This work was supported in part by Sichuan Provincial Science and Technology Department Applied Basic Research Project 2019JY0632 and in part by CityU Start-Up Fund 7200580. 48 17 17 3 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. APR 2020.0 17.0 2 574 583 10.1109/TASE.2019.2936645 0.0 10 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems LH3FP 2023-03-23 WOS:000528673100003 0
J You, X; Yan, XP; Liu, JL; Li, SJ; Negenborn, RR You, Xu; Yan, Xinping; Liu, Jialun; Li, Shijie; Negenborn, Rudy R. Adaptive neural sliding mode control for heterogeneous ship formation keeping considering uncertain dynamics and disturbances OCEAN ENGINEERING English Article Heterogeneous formation; Formation control; Adaptive control; Neural networks; Heterogeneous dynamics DESIGN; VEHICLES This paper investigates the formation keeping problem of heterogeneous ships with underactuated inputs, uncertain dynamics, and environmental disturbances. The control objective is to make the heterogeneous followers keep the desired formation while tracking a leader. To solve the problem effectively, a novel virtual leader-follower formation scheme considering the ship heterogeneity is proposed by utilizing the backstepping method, adaptive neural network, and adaptive control law. The stability of the formation control system is proved based on Lyapunov's direct method where all tracking errors are guaranteed to be uniformly ultimately bounded. Finally, simulations and comparisons are conducted to verify the effectiveness of the proposed control law. [You, Xu; Li, Shijie] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [You, Xu; Yan, Xinping; Liu, Jialun] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Peoples R China; [You, Xu; Yan, Xinping; Liu, Jialun] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan, Peoples R China; [Negenborn, Rudy R.] Delft Univ Technol, Dept Maritime & Transport Technol, Delft, Netherlands Wuhan University of Technology; Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; Delft University of Technology Liu, JL (corresponding author), Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Peoples R China. jialunliu@whut.edu.cn ; Negenborn, Rudy/M-5184-2015; Yan, Xinping/F-3627-2017 Liu, Jialun/0000-0002-0802-8986; Negenborn, Rudy/0000-0001-9784-1225; You, Xu/0000-0002-4141-8222; Yan, Xinping/0000-0002-2265-2689 National Natural Science Foundation of China [62003250]; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [SML2021SP101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Supported by the National Natural Science Foundation of China (62003250) , and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2021SP101) . 41 0 0 10 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. NOV 1 2022.0 263.0 112268 10.1016/j.oceaneng.2022.112268 0.0 12 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 4Z3FU 2023-03-23 WOS:000862100000001 0
J Qin, HF; El-Yacoubi, MA; Li, YT; Liu, CW Qin, Huafeng; El-Yacoubi, Mounim A.; Li, Yantao; Liu, Chongwen Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-Vein Identification IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Veins; Training; Biometrics (access control); Image recognition; Gallium nitride; Face recognition; Training data; Single training sample per person; data augmentation; multi-scale and multi-direction GAN; palm-vein identification; convolutional neural network (CNN) DEEP REPRESENTATION; RECOGNITION; FINGER; AUTHENTICATION Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we propose a single-sample-per-person (SSPP) palm-vein identification approach, where only a single sample per class is enrolled in the gallery set for training. Our approach, named MSMDGAN + CNN, consists of a multi-scale and multi-direction generative adversarial network (MSMDGAN) for data augmentation and a convolutional neural network (CNN) for palm-vein identification. First, a novel data augmentation approach, MSMDGAN, is developed to learn the internal distribution of patches in a single image. The proposed MSMDGAN consists of multiple fully convolutional GANs, each of which is responsible for learning the patch distribution within an image at a different scale and at a different direction. Second, given the resulting augmented data by MSMDGAN, we design a CNN for single sample palm-vein recognition. The experimental results on two public hand-vein databases demonstrate that MSMDGAN is able to generate realistic and diverse samples, which, in turn, improves the stability of the CNN. In terms of accuracy, MSMDGAN + CNN outperforms other representative approaches and achieves state-of-the-art recognition results. [Qin, Huafeng; Liu, Chongwen] Chongqing Technol & Business Univ, Sch Comp Sci & Informat Engn, Chongqing Engn Lab Detect Control & Integrated Sy, Chongqing 400067, Peoples R China; [El-Yacoubi, Mounim A.] Inst Polytech Paris, CNRS, Telecom SudParis, SAMOVAR, F-91120 Palaiseau, France; [Li, Yantao] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China Chongqing Technology & Business University; Centre National de la Recherche Scientifique (CNRS); Chongqing University Li, YT (corresponding author), Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China. qinhuafengfeng@163.com; mounim.el_yacoubi@telecom-sudparis.eu; yantaoli@cqu.edu.cn qin, huafeng/AAR-9728-2021; Yacoubi, Mounîm A. M.A. EL/C-1348-2017 Yacoubi, Mounîm A. M.A. EL/0000-0002-7383-0588; qin, huafeng/0000-0003-4911-0393 National Natural Science Foundation of China [61976030, 62072061, U20A20176]; Natural Science Foundation Project of Chongqing [cstc2018jcyjAX0095]; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN201900848] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation Project of Chongqing(Natural Science Foundation of Chongqing); Scientific and Technological Research Program of Chongqing Municipal Education Commission This work was supported in part by the National Natural Science Foundation of China under Grant 61976030, Grant 62072061, and Grant U20A20176, in part by the Natural Science Foundation Project of Chongqing under Grant cstc2018jcyjAX0095, and in part by the Scientific and Technological Research Program of Chongqing Municipal Education Commission under Grant KJQN201900848. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Raymond Veldhuis. 70 6 6 11 61 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2021.0 16.0 2652 2666 10.1109/TIFS.2021.3059340 0.0 15 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering QY0IQ 2023-03-23 WOS:000629724400001 0
J Lu, L; Pestourie, R; Yao, WJ; Wang, ZC; Verdugo, F; Johnson, SG Lu, Lu; Pestourie, Raphael; Yao, Wenjie; Wang, Zhicheng; Verdugo, Francesc; Johnson, Steven G. PHYSICS-INFORMED NEURAL NETWORKS WITH HARD CONSTRAINTS FOR INVERSE DESIGN\ast SIAM JOURNAL ON SCIENTIFIC COMPUTING English Article inverse design; topology optimization; partial differential equations; physics-informed neural networks; penalty method; augmented Lagrangian method TOPOLOGY OPTIMIZATION Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is an important form of inverse design, where one optimizes a designed geometry to achieve targeted properties parameterized by the materials at every point in a design region. This optimization is challenging, because it has a very high dimensionality and is usually constrained by partial differential equations (PDEs) and additional inequalities. Here, we propose a new deep learning method-physics-informed neural networks with hard constraints (hPINNs)---for solving topology optimization. hPINN leverages the recent development of PINNs for solving PDEs, and thus does not require a large dataset (generated by numerical PDE solvers) for training. However, all the constraints in PINNs are soft constraints, and hence we impose hard constraints by using the penalty method and the augmented Lagrangian method. We demonstrate the effectiveness of hPINN for a holography problem in optics and a fluid problem of Stokes flow. We achieve the same objective as conventional PDE-constrained optimization methods based on adjoint methods and numerical PDE solvers, but find that the design obtained from hPINN is often smoother for problems whose solution is not unique. Moreover, the implementation of inverse design with hPINN can be easier than that of conventional methods because it exploits the extensive deep-learning software infrastructure. [Lu, Lu] Univ Penn, Dept Chem & Biomol Engn, Philadelphia, PA 19104 USA; [Pestourie, Raphael; Yao, Wenjie; Johnson, Steven G.] MIT, Dept Math, Cambridge, MA 02139 USA; [Wang, Zhicheng] Dalian Univ Technol, Lab Ocean Energy Utilizat, Minist Educ, Dalian 116024, Peoples R China; [Verdugo, Francesc] Ctr Int Metodes Numer Engn, Esteve Terradas 5, Barcelona 08860, Spain University of Pennsylvania; Massachusetts Institute of Technology (MIT); Dalian University of Technology; Universitat Politecnica de Catalunya; Centre Internacional de Metodes Numerics en Enginyeria (CIMNE) Lu, L (corresponding author), Univ Penn, Dept Chem & Biomol Engn, Philadelphia, PA 19104 USA. lulu1@seas.upenn.edu; raphael.pestourie@gmail.com; jayyao@mit.edu; zhicheng_wang@brown.edu; fverdugo@cimne.upc.edu; stevenj@math.mit.edu MIT-IBM Watson AI Laboratory [2415]; Spanish Ministry of Economy and Competitiveness through the ''Severo Ochoa Programme for Centers of Excellence in R\D [CEX2018-000797-S] MIT-IBM Watson AI Laboratory(International Business Machines (IBM)); Spanish Ministry of Economy and Competitiveness through the ''Severo Ochoa Programme for Centers of Excellence in R\D(Spanish Government) This work was supported by the MIT-IBM Watson AI Laboratory (challenge 2415). The work of the fifth author was supported by the Spanish Ministry of Economy and Competitiveness through the ''Severo Ochoa Programme for Centers of Excellence in R\&D (CEX2018-000797-S). \dagger Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA (lulu1@seas.upenn.edu). 66 34 34 44 70 SIAM PUBLICATIONS PHILADELPHIA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA 1064-8275 1095-7197 SIAM J SCI COMPUT SIAM J. Sci. Comput. 2021.0 43.0 6 B1105 B1132 10.1137/21M1397908 0.0 28 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics XY1LK Green Published, Green Submitted 2023-03-23 WOS:000736742800001 0
J Ren, JY; Yu, L; Jiang, YL; Barbot, JP; Sun, H Ren, Junying; Yu, Lei; Jiang, Yulun; Barbot, Jean-Pierre; Sun, Hong A Dynamical System With Fixed Convergence Time for Sparse Recovery IEEE ACCESS English Article Sparse recovery; dynamical system; fixed-time convergence RECURRENT NEURAL-NETWORK; THRESHOLDING ALGORITHM; STABILITY The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog circuits, which is usually faster. In this paper, we focus on the continuous method and propose a fixed-time convergence dynamical system. Compared with the existing system, it dynamically allocates the exponent according to time-varying elements of the system state, avoiding possible mismatches between the fixed exponent and some elements. [Ren, Junying; Yu, Lei; Jiang, Yulun; Sun, Hong] Wuhan Univ, Sch Elect & Informat, Wuhan 430072, Hubei, Peoples R China; [Barbot, Jean-Pierre] ENSEA, Quartz EA 7393, F-95014 Cergy Pontoise, France Wuhan University Yu, L (corresponding author), Wuhan Univ, Sch Elect & Informat, Wuhan 430072, Hubei, Peoples R China. ly.wd@whu.edu.cn Yu, Lei/0000-0002-7329-4631 National Natural Science Foundation of China [61871297, 61771014, 61401315] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in the National Natural Science Foundation of China under Grant 61871297, 61771014, and 61401315. 26 0 0 1 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 21844 21850 10.1109/ACCESS.2019.2897488 0.0 7 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HO1FV gold 2023-03-23 WOS:000460651800001 0
J Pourghasemi, HR; Sadhasivam, N; Amiri, M; Eskandari, S; Santosh, M Pourghasemi, Hamid Reza; Sadhasivam, Nitheshnirmal; Amiri, Mahdis; Eskandari, Saeedeh; Santosh, M. Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques NATURAL HAZARDS English Article Partial least square; Landslides; Functional discriminant analysis; Mixture discriminant analysis; Boosted regression tree; Generalized linear model BOOSTED REGRESSION TREE; 3 GORGES RESERVOIR; LOGISTIC-REGRESSION; SPATIAL PREDICTION; RANDOM FOREST; FREQUENCY RATIO; DARJEELING HIMALAYA; HAZARD ASSESSMENT; DECISION TREE; MODELS Landslides pose a serious risk to human life and the natural environment. Here, we compare machine learning algorithms including the generalized linear model (GLM), mixture discriminant analysis (MDA), boosted regression tree (BRT), and functional discriminant analysis (FDA) to evaluate the landslide exposure regions in Fars Province, comprising an area of approximately 7% of Iran. Initially, an aggregate of 179 historical landslide occurrences was prepared and partitioned. Subsequently, ten landslide conditioning factors (LCFs) were generated. The partial least squares algorithm was utilized to assess the significance of the LCFs with the help of a training dataset which indicated that distance from road had the maximum significance in forecasting landslides, followed by altitude (Al), lithological units, and slope degree. Finally, the LSMs generated using BRT, GLM, MDA, and FDA were validated and compared using cut-off reliant and independent validation measures. The results of the validation metrics showed that GLM and BRT had an AUC of 0.908, while FDA and MDA had AUCs of 0.858 and 0.821, respectively. The results from our case study can be utilized to develop strategies and plans to minimize the loss of human lives and the natural environment. [Pourghasemi, Hamid Reza] Shiraz Univ, Dept Nat Resources & Environm Engn, Coll Agr, Shiraz, Iran; [Sadhasivam, Nitheshnirmal] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Hengelosestr 99, NL-7514 AE Enschede, Netherlands; [Amiri, Mahdis] Gorgan Univ Agr Sci & Nat Resources, Dept Watershed & Arid Zone Management, Gorgan 49189434, Golestan, Iran; [Eskandari, Saeedeh] Agr Res Educ & Extens Org AREEO, Res Inst Forests & Rangelands, Forest Res Div, Tehran, Iran; [Santosh, M.] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China; [Santosh, M.] Univ Adelaide, Dept Earth Sci, Adelaide, SA 5005, Australia Shiraz University; University of Twente; China University of Geosciences; University of Adelaide Pourghasemi, HR (corresponding author), Shiraz Univ, Dept Nat Resources & Environm Engn, Coll Agr, Shiraz, Iran. hr.pourghasemi@shirazu.ac.ir Pourghasemi, Hamid Reza/G-9926-2014; Eskandari, Saeedeh/C-7081-2017 Pourghasemi, Hamid Reza/0000-0003-2328-2998; Eskandari, Saeedeh/0000-0002-0099-600X College of Agriculture, Shiraz University [98GRC1M271143] College of Agriculture, Shiraz University The study was supported by the College of Agriculture, Shiraz University (Grant No. 98GRC1M271143). 110 14 14 3 20 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0921-030X 1573-0840 NAT HAZARDS Nat. Hazards AUG 2021.0 108.0 1 1291 1316 10.1007/s11069-021-04732-7 0.0 APR 2021 26 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences; Water Resources TQ9PP 2023-03-23 WOS:000642822900001 0
J Wei, D; Moser, G Wei, Dan; Moser, Gustavo HUMAN ARBITRATORS (THE UNDISPUTED CHAMPION) V ROBOTS (THE CHALLENGER) HONG KONG LAW JOURNAL English Article JUDGMENT; CHOICE; PREFERENCES; HEURISTICS Empirical studies suggest that robots could outperform humans in commoditised legal tasks which have traditionally been assigned to, and undertaken by, humans. We should therefore not be surprised to witness robotised arbitrations in the near future. That prediction, however, raises a number of questions: Can we rely on a decision delivered by a source with which we have had no human interaction previously? Do we appreciate and actually seek emotionally detached decisions after all? More importantly, are we emotionally prepared to task a machine with fact-finding-and-resolution skills that may outperform us? This article proposes a thought-provoking look into the strengths and weaknesses of robots and humans and aims (i) to raise awareness to the effects of artificial intelligence (AI) in international arbitration; and (ii) to draw to the readers' attention that this is an opportune occasion to embrace AI as a tool to develop and perfect dispute resolution mechanisms and the approach to problem-solving. [Wei, Dan] Univ Macau, Fac Law, Taipa, Macao, Peoples R China; [Wei, Dan] Int Acad Comparat Law IACL, Paris, France; [Wei, Dan] China Int Econ & Trade Arbitrat Commiss CIETAC, Beijing, Peoples R China; [Wei, Dan] World Trade Ctr Macau Arbitrat Ctr, Macau, Peoples R China; [Moser, Gustavo] London Court Int Arbitrat LCIA, London, England; [Moser, Gustavo] Swiss Int Law Sch SiLS, London, England University of Macau Wei, D (corresponding author), Univ Macau, Fac Law, Taipa, Macao, Peoples R China.;Wei, D (corresponding author), Int Acad Comparat Law IACL, Paris, France.;Wei, D (corresponding author), China Int Econ & Trade Arbitrat Commiss CIETAC, Beijing, Peoples R China.;Wei, D (corresponding author), World Trade Ctr Macau Arbitrat Ctr, Macau, Peoples R China. wei, dan/AAW-2126-2021 60 0 0 0 3 SWEET MAXWELL LTD ANDOVER NORTH WAY, ANDOVER SP10 5BE, HANTS, ENGLAND 0378-0600 HONG KONG LAW J Hong Kong Law J. 2020.0 50.0 1 215 241 27 Law Social Science Citation Index (SSCI) Government & Law MN4HG 2023-03-23 WOS:000550804000013 0
J Chen, MZ; Saad, W; Yin, CC; Debbah, M Chen, Mingzhe; Saad, Walid; Yin, Changchuan; Debbah, Merouane Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS English Article; Proceedings Paper IEEE Global Telecommunications Conference (IEEE GLOBECOM) DEC 04-08, 2016 Washington, DC IEEE CRAN; mobility; caching; echo state networks; machine learning EFFECTIVE CAPACITY; WIRELESS; QUALITY; ENERGY In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering. [Chen, Mingzhe; Yin, Changchuan] Beijing Univ Posts & Telecommun, Beijing Lab Adv Informat Network, Beijing 100876, Peoples R China; [Chen, Mingzhe; Yin, Changchuan] Beijing Univ Posts & Telecommun, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China; [Saad, Walid] Virginia Polytech Inst & State Univ, Elect & Comp Engn Dept, Wireless VT, Blacksburg, VA 24061 USA; [Debbah, Merouane] Univ Paris Saclay, Cent Supelec, Large Networks & Syst Grp, F-91192 Gif Sur Yvette, France; [Debbah, Merouane] Huawei France Res & Dev Ctr, Math & Algorithm Sci Lab, F-91192 Paris, France Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Virginia Polytechnic Institute & State University; UDICE-French Research Universities; Universite Paris Saclay; Huawei Technologies Chen, MZ (corresponding author), Beijing Univ Posts & Telecommun, Beijing Lab Adv Informat Network, Beijing 100876, Peoples R China.;Chen, MZ (corresponding author), Beijing Univ Posts & Telecommun, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China. chenmingzhe@bupt.edu.cn; walids@vt.edu; ccyin@ieee.org; merouane.debbah@huawei.com Saad, Walid/C-7978-2018; Chen, Mingzhe/U-3377-2019; Debbah, Merouane/B-6261-2011; Yin, Changchuan/AAB-7284-2021 Saad, Walid/0000-0003-2247-2458; Chen, Mingzhe/0000-0003-2570-703X; National Natural Science Foundation of China [61671086, 61629101]; ERC Starting through MORE (Advanced Mathematical Tools for Complex Network Engineering) [305123]; U.S. National Science Foundation [IIS-1633363, CNS-1460316, CNS-1513697] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ERC Starting through MORE (Advanced Mathematical Tools for Complex Network Engineering); U.S. National Science Foundation(National Science Foundation (NSF)) This work was supported in part by the National Natural Science Foundation of China under Grant 61671086, and Grant 61629101, in part by the ERC Starting through MORE (Advanced Mathematical Tools for Complex Network Engineering) under Grant 305123, and in part by the U.S. National Science Foundation under Grant IIS-1633363, Grant CNS-1460316, and Grant CNS-1513697. This paper was presented at the IEEE GLOBECOM Workshops Washington, DC, USA, December 2016 [1]. The associate editor coordinating the review of this paper and approving it for publication was T. Melodia. 35 123 126 1 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1276 1558-2248 IEEE T WIREL COMMUN IEEE Trans. Wirel. Commun. JUN 2017.0 16.0 6 3520 3535 10.1109/TWC.2017.2683482 0.0 16 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications EX8JK hybrid, Green Submitted 2023-03-23 WOS:000403495400034 0
J Huang, YB; Nicholson, D; Huang, BQ; Cassar, N Huang, Yibin; Nicholson, David; Huang, Bangqin; Cassar, Nicolas Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations GLOBAL BIOGEOCHEMICAL CYCLES English Article NET COMMUNITY PRODUCTION; TRIPLE-ISOTOPE COMPOSITION; ORGANIC-CARBON PRODUCTION; OXYGEN ISOTOPES; EQUATORIAL PACIFIC; METABOLIC BALANCE; IN-VITRO; OCEAN; MODEL; PHYTOPLANKTON Approximately half of global primary production occurs in the ocean. While the large-scale variability in net primary production (NPP) has been extensively studied, ocean gross primary production (GPP) has thus far received less attention. In this study, we derived two satellite-based GPP models by training machine learning algorithms (Random Forest) with light-dark bottle incubations (GPP(LD)) and the triple isotopes of dissolved oxygen (GPP(17 Delta)). The two algorithms predict global GPPs of 9.2 +/- 1.3 x 10(15) and 15.1 +/- 1.05 x 10(15) mol O-2 yr(-1) for GPP(LD) and GPP(17 Delta), respectively. The projected GPP distributions agree with our understanding of the mechanisms regulating primary production. Global GPP(17 Delta) was higher than GPP(LD) by an average factor of 1.6 which varied meridionally. The discrepancy between GPP(17 Delta) and GPP(LD) simulations can be partly explained by the known biases of each methodology. After accounting for some of these biases, the GPP(17 Delta) and GPP(LD) converge to 9.5 similar to 12.6 x 10(15) mol O-2 yr(-1), equivalent to 103 similar to 150 Pg C yr(-1). Our results suggest that global oceanic GPP is 1.5-2.2 fold larger than oceanic NPP and comparable to GPP on land. [Huang, Yibin; Cassar, Nicolas] Duke Univ, Nicholas Sch Environm, Div Earth & Ocean Sci, Durham, NC 27708 USA; [Nicholson, David] Woods Hole Oceanog Inst, Dept Marine Chem & Geochem, Woods Hole, MA 02543 USA; [Huang, Yibin; Huang, Bangqin] Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen, Peoples R China; [Huang, Yibin; Huang, Bangqin] Xiamen Univ, Fujian Prov Key Lab Coastal Ecol & Environm Studi, Xiamen, Peoples R China; [Cassar, Nicolas] Inst Univ Europeen Mer IUEM, Lab Sci Environm Marin LEMAR, Brest, France Duke University; Woods Hole Oceanographic Institution; Xiamen University; Xiamen University; Centre National de la Recherche Scientifique (CNRS); Ifremer; Institut de Recherche pour le Developpement (IRD) Cassar, N (corresponding author), Duke Univ, Nicholas Sch Environm, Div Earth & Ocean Sci, Durham, NC 27708 USA.;Cassar, N (corresponding author), Inst Univ Europeen Mer IUEM, Lab Sci Environm Marin LEMAR, Brest, France. Nicolas.Cassar@duke.edu Huang, Bangqin/0000-0003-2279-6241; Nicholson, David/0000-0003-2653-9349 Laboratoire d'Excellence LabexMER [ANR10-LABX-19]; French government under the program Investissements d'Avenir; National Key and Development Program of China [2016YFA0601201]; China NSF [41890803, U1805241]; Chinese State Scholarship Fund [201806310052]; NASA [OBB NNX16AR48 G, 80NSSC17K0663]; Woods Hole Oceanographic Institution Laboratoire d'Excellence LabexMER; French government under the program Investissements d'Avenir(French National Research Agency (ANR)); National Key and Development Program of China; China NSF(National Natural Science Foundation of China (NSFC)); Chinese State Scholarship Fund; NASA(National Aeronautics & Space Administration (NASA)); Woods Hole Oceanographic Institution N. Cassar was supported by the Laboratoire d'Excellence LabexMER (ANR10-LABX-19) and co-funded by a grant from the French government under the program Investissements d'Avenir. Y. Huang and B. Huang were supported by grants from the National Key and Development Program of China (No.2016YFA0601201) and China NSF (No.41890803, U1805241). Y. Huang was also partly supported by Chinese State Scholarship Fund to study at Duke University as a joint Ph. D student (No. 201806310052). D. Nicholson was supported by NASA OBB NNX16AR48 G and NASA 80NSSC17K0663 and an Early Career Award from the Woods Hole Oceanographic Institution. 103 7 7 7 50 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0886-6236 1944-9224 GLOBAL BIOGEOCHEM CY Glob. Biogeochem. Cycle MAR 2021.0 35.0 3 e2020GB006718 10.1029/2020GB006718 0.0 18 Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences RG0IV Green Published 2023-03-23 WOS:000635221000009 0
J Sun, JF; Ru, JL; Ramos-Mucci, L; Qi, F; Chen, ZH; Chen, SY; Cribbs, AP; Deng, L; Wang, X Sun, Jianfeng; Ru, Jinlong; Ramos-Mucci, Lorenzo; Qi, Fei; Chen, Zihao; Chen, Suyuan; Cribbs, Adam P. P.; Deng, Li; Wang, Xia DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES English Article drug discovery; miRNAs; small molecule compounds; regulatory effect prediction; deep learning TARGETING MICRORNAS; MIRNA ASSOCIATIONS; CANCER; NETWORKS; BIOGENESIS; SIGNATURES; DATABASE; GENES Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs. [Sun, Jianfeng; Wang, Xia] Northwest A&F Univ, Coll Anim Sci & Technol, Yangling 712100, Peoples R China; [Sun, Jianfeng; Ramos-Mucci, Lorenzo; Cribbs, Adam P. P.] Univ Oxford, Botnar Res Ctr, Nuffield Dept Orthoped Rheumatol & Musculoskeletal, Oxford OX3 7LD, England; [Ru, Jinlong; Deng, Li] German Res Ctr Environm Hlth, Inst Virol, Helmholtz Ctr Munich, D-85764 Neuherberg, Germany; [Ru, Jinlong; Deng, Li] Tech Univ Munich, Chair Prevent Microbial Dis, Sch Life Sci Weihenstephan, D-85354 Freising Weihenstephan, Germany; [Qi, Fei] Huaqiao Univ, Inst Genom, Sch Med, Xiamen 362021, Peoples R China; [Chen, Zihao] Biolife Biotechnol Ltd, Dept Computat Biol Drug Discovery, Zhumadian 463200, Peoples R China; [Chen, Suyuan] Leibniz Inst Analyt Wissensch ISAS eV, Otto Hahn Str 6b, D-44227 Dortmund, Germany; [Wang, Xia] Univ Arizona, Dept Mol & Cellular Biol, Tucson, AZ 85721 USA Northwest A&F University - China; University of Oxford; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich; Huaqiao University; Dortmund University of Technology; Leibniz Institut fur Analytische Wissenschaften (ISAS); University of Arizona Wang, X (corresponding author), Northwest A&F Univ, Coll Anim Sci & Technol, Yangling 712100, Peoples R China.;Deng, L (corresponding author), German Res Ctr Environm Hlth, Inst Virol, Helmholtz Ctr Munich, D-85764 Neuherberg, Germany.;Deng, L (corresponding author), Tech Univ Munich, Chair Prevent Microbial Dis, Sch Life Sci Weihenstephan, D-85354 Freising Weihenstephan, Germany.;Wang, X (corresponding author), Univ Arizona, Dept Mol & Cellular Biol, Tucson, AZ 85721 USA. li.deng@helmholtz-muenchen.de; xiawang@nwafu.edu.cn ; Deng, Li/A-7233-2015 Ru, Jinlong/0000-0002-6757-6018; Deng, Li/0000-0003-0225-0663 Chinese Universities Scientific Fund [2452022255]; German Research Foundation [SPP2330, 464797012, DE 2360/6-1]; European Research Council [ERC StG 803077]; National Natural Science Foundation of China [32000462]; Scientific Research Funds of Huaqiao University [22BS114] Chinese Universities Scientific Fund; German Research Foundation(German Research Foundation (DFG)); European Research Council(European Research Council (ERC)European Commission); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific Research Funds of Huaqiao University This study is supported by the Chinese Universities Scientific Fund (2452022255) to X.W., the German Research Foundation (SPP2330, Project No. 464797012 and DE 2360/6-1), and the European Research Council Starting grant (ERC StG 803077) to L.D., F.Q. is supported by the National Natural Science Foundation of China (Grant No. 32000462) and the Scientific Research Funds of Huaqiao University (Grant No. 22BS114). 76 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1422-0067 INT J MOL SCI Int. J. Mol. Sci. FEB 2023.0 24.0 3 1878 10.3390/ijms24031878 0.0 23 Biochemistry & Molecular Biology; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry 8U0ZU 36768205.0 Green Accepted, gold 2023-03-23 WOS:000929680800001 0
J Wang, R; Hao, YX; Yu, Q; Chen, M; Humar, I; Fortino, G Wang, Rui; Hao, Yixue; Yu, Qiao; Chen, Min; Humar, Iztok; Fortino, Giancarlo Depression Analysis and Recognition Based on Functional Near-Infrared Spectroscopy IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Depression; Feature extraction; Brain modeling; Data models; Manuals; Correlation; Task analysis; Depression recognition; fNIRS; Feature extraction; Channel correlation VERBAL FLUENCY TASK; PREFRONTAL CORTEX; CLASSIFICATION; SCHIZOPHRENIA; OXYGENATION; ACTIVATION; DIAGNOSIS Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression. [Wang, Rui; Hao, Yixue; Yu, Qiao; Chen, Min] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China; [Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst, I-87036 Arcavacata Di Rende, CS, Italy; [Humar, Iztok] Univ Ljubljana, Fac Elect Engn, Ljubljana 1000, Slovenia Huazhong University of Science & Technology; University of Calabria; University of Ljubljana Hao, YX; Chen, M (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China.;Humar, I (corresponding author), Univ Ljubljana, Fac Elect Engn, Ljubljana 1000, Slovenia. ruiwang2020@hust.edu.cn; yixuehao@hust.edu.cn; qiaoyu_epic@hust.edu.cn; minchen2020@hust.edu.cn; iztok.humar@fe.uni-lj.si; g.fortino@unical.it Fortino, Giancarlo/J-2950-2017; Hao, Yixue/H-8549-2017 Fortino, Giancarlo/0000-0002-4039-891X; Hao, Yixue/0000-0001-7296-2522 National Key R&D Program of China [2018YFC1314600]; National Natural Science Foundation of China [61802138]; Fundamental Research Funds for the Central Universities [2021XXJS107]; COGITO Project - Italian NOP Research and Innovation (2014-2020) [H56C18000100005]; Fluidware Project - Italian PRIN 2017 [H24I17000070001]; Slovenian Research Agency [P2-0246]; Technology Innovation Project of Hubei Province of China [2019AHB061] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); COGITO Project - Italian NOP Research and Innovation (2014-2020); Fluidware Project - Italian PRIN 2017; Slovenian Research Agency(Slovenian Research Agency - Slovenia); Technology Innovation Project of Hubei Province of China This work was supported in part by the National Key R&D Program of China under Grant 2018YFC1314600. The work of Yixue Hao was supported by the National Natural Science Foundation of China under Grant 61802138, the Fundamental Research Funds for the Central Universities under Grant 2021XXJS107. The work of Giancarlo Fortino was supported by the COGITO Project, funded by Italian NOP Research and Innovation (2014-2020) -code H56C18000100005, and the Fluidware Project funded by Italian PRIN 2017 -code H24I17000070001. Dr. Humar would like to acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0246). This work was supported in part by the Technology Innovation Project of Hubei Province of China under Grant 2019AHB061. 41 4 4 9 47 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. DEC 2021.0 25.0 12 4289 4299 10.1109/JBHI.2021.3076762 0.0 11 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Mathematical & Computational Biology; Medical Informatics XL4UU 33929968.0 2023-03-23 WOS:000728140900010 0
J Saftoiu, A; Vilmann, P; Dietrich, CF; Iglesias-Garcia, J; Hocke, M; Seicean, A; Ignee, A; Hassan, H; Streba, CT; Ioncica, AM; Gheonea, DI; Ciurea, T Saftoiu, Adrian; Vilmann, Peter; Dietrich, Christoph F.; Iglesias-Garcia, Julio; Hocke, Michael; Seicean, Andrada; Ignee, Andre; Hassan, Hazem; Streba, Costin Teodor; Ioncica, Ana Maria; Gheonea, Dan Ionut; Ciurea, Tudorel Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos) GASTROINTESTINAL ENDOSCOPY English Article LOW MECHANICAL INDEX; ENDOSCOPIC ULTRASOUND; TRANSABDOMINAL ULTRASOUND; CONVENTIONAL ULTRASOUND; ULTRASONOGRAPHY; CANCER; TUMORS; LESIONS; QUANTIFICATION; PERFUSION Background: The role of EUS with contrast agents can be expanded through the use of time-intensity curve (TIC) analysis and computer-aided interpretation. Objective: To validate the use of parameters derived from TIC analysis in an artificial neural network (ANN) classification model designed to diagnose pancreatic carcinoma (PC) and chronic pancreatitis (CP). Setting: Prospective, multicenter, observational trialdendoscopy units from Romania, Denmark, Germany, and Spain. Patients: A total of 167 consecutive patients with PC or CP. Interventions: Contrast-enhanced harmonic EUS (CEH-EUS) and EUS-guided FNA (EUS-FNA), TIC analysis, and ANN processing. Main Outcome Measurements: Sensitivity, specificity, positive and negative predictive values (PPV, NPV) for EUS-FNA, CEH-EUS, and the ANN. Results: After excluding all of the recordings that did not meet the technical and procedural criteria, 112 cases of PC and 55 cases of CP were included. EUS-FNA was performed in 129 patients, and the diagnosis was confirmed by surgery (n Z 15) or follow-up (n = 23) in the remaining cases. Its sensitivity and specificity were 84.82% and 100%, respectively, whereas the PPV and NPV were 100% and 76.63%, respectively. The sensitivity of realtime quantitative assessment of CEH-EUS was 87.5%, specificity 92.72%, PPV 96.07%, and NPV 78.46%. Peak enhancement, wash-in area under the curve, wash-in rate, and the wash-in perfusion index were significantly different between the groups. No significant differences were found between rise time, mean transit time, and time to peak. For the ANN, sensitivity was 94.64%, specificity 94.44%, PPV 97.24%, and NPV 89.47%. Limitations: Only PC and CP lesions were included. Conclusion: Parameters obtained through TIC analysis can differentiate between PC and CP cases and can be used in an automated computer-aided diagnostic system with good diagnostic results. [Saftoiu, Adrian; Streba, Costin Teodor; Ioncica, Ana Maria; Gheonea, Dan Ionut; Ciurea, Tudorel] Univ Med & Pharm, Res Ctr Gastroenterol & Hepatol Craiova, Craiova, Romania; [Saftoiu, Adrian; Vilmann, Peter; Hassan, Hazem] Copenhagen Univ Hosp, Endoscopy Dept, Herlev, Denmark; [Dietrich, Christoph F.] Zhengzhou Univ, Affiliated Hosp 1, Sino German Res Ctr Ultrasound Med, Zhengzhou 450052, Peoples R China; [Dietrich, Christoph F.; Ignee, Andre] Caritas Krankenhaus Bad, Med D 2, Mergentheim, Germany; [Iglesias-Garcia, Julio] Univ Hosp Santiago de Compostela, Dept Gastroenterol, Coruna, Spain; [Hocke, Michael] Hosp Meiningen, Internal Med 2, Meiningen, Germany; [Seicean, Andrada] Univ Med & Pharm Iuliu Hatieganu, Reg Inst Gastroenterol & Hepatol, Cluj Napoca, Romania University of Medicine & Pharmacy of Craiova; University of Copenhagen; Zhengzhou University; Caritas Hospital Bad Mergentheim; Complexo Hospitalario Universitario de Santiago de Compostela; Iuliu Hatieganu University of Medicine & Pharmacy; Regional Institute of Gastroenterology & Hepatology Streba, CT (corresponding author), Res Ctr Gastroenterol & Hepatol, Craiova, Romania. Streba, Costin Teodor T/C-4196-2011; Vilmann, Peter/AAJ-8401-2020; Ciurea, Tudorel/G-3226-2016; Saftoiu, Adrian/C-2792-2011; Ignee, André/G-7729-2019; Vilmann, Peter/AAP-2730-2021; Ignee, André/AAH-3333-2019; Dietrich, Christoph/AAB-7514-2020; Gheonea, Dan Ionut/C-3578-2012 Streba, Costin Teodor T/0000-0001-9590-3197; Saftoiu, Adrian/0000-0001-7993-8269; Ignee, André/0000-0002-2388-2945; Ciurea, Tudorel/0009-0009-9860-0795 Bracco Suisse SA (Geneva, Switzerland) [CEH-EUS-001]; National Research Council-UEFISCDI [PN-II-ID-PCE-2011-3-0589]; research grant Minimal Invasive Assessment of Angiogenesis in Pancreatic Cancer Based on Imaging Methods and Molecular Techniques (Angio-PAC), Ideas Programme [164/2011] Bracco Suisse SA (Geneva, Switzerland); National Research Council-UEFISCDI; research grant Minimal Invasive Assessment of Angiogenesis in Pancreatic Cancer Based on Imaging Methods and Molecular Techniques (Angio-PAC), Ideas Programme The study Contrast Enhanced Harmonic Endoscopic Ultrasound (CEH-EUS) in Focal Pancreatic Masses was supported by Bracco Suisse SA (CEH-EUS-001; Geneva, Switzerland), who provided a trial version of the VueBox quantification software after reviewing the study protocol. The study was partially supported by the research grant Minimal Invasive Assessment of Angiogenesis in Pancreatic Cancer Based on Imaging Methods and Molecular Techniques (Angio-PAC), Ideas Programme, 164/2011, National Research Council-UEFISCDI, PN-II-ID-PCE-2011-3-0589. All other authors disclosed no financial relationships relevant to this article. 54 91 94 1 12 MOSBY-ELSEVIER NEW YORK 360 PARK AVENUE SOUTH, NEW YORK, NY 10010-1710 USA 0016-5107 1097-6779 GASTROINTEST ENDOSC Gastrointest. Endosc. JUL 2015.0 82.0 1 59 69 10.1016/j.gie.2014.11.040 0.0 11 Gastroenterology & Hepatology Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology CK4GR 25792386.0 2023-03-23 WOS:000356182600009 0
J Abudinen, F; Akopov, N; Aloisio, A; Babu, V; Banerjee, S; Bauer, M; Bennett, JV; Bernlochner, FU; Bessner, M; Bettarini, S; Bilka, T; Bilokin, S; Biswas, D; Bodrov, D; Borah, J; Bracko, M; Branchini, P; Budano, A; Campajola, M; Casarosa, G; Cecchi, C; Cheaib, R; Chekelian, V; Chen, C; Chen, YQ; Cho, HE; Cunliffe, S; De Nardo, G; De Pietro, G; de Sangro, R; Dey, S; Di Canto, A; Di Capua, F; Dong, TV; Dujany, G; Ecker, P; Eliachevitch, M; Ferber, T; Forti, F; Ganiev, E; Gaz, A; Gelb, M; Gemmler, J; Godang, R; Goldenzweig, P; Graziani, E; Hara, K; Hershenhorn, A; Higuchi, T; Hill, EC; Hohmann, M; Humair, T; Inguglia, G; Junkerkalefeld, H; Karl, R; Kato, Y; Keck, T; Kiesling, C; Kim, CH; Kohani, S; Komarov, I; Kraetzschmar, TMG; Krizan, P; Krohn, JF; Kuhr, T; Kumar, J; Kumara, K; Kurz, S; Lacaprara, S; La Licata, C; Laurenza, M; Lautenbach, K; Lee, SC; Lieret, K; Li Gioi, L; Liu, QY; Longo, S; Maggiora, M; Manoni, E; Marinas, C; Martini, A; Meier, F; Merola, M; Metzner, F; Milesi, M; Miyabayashi, K; Mohanty, GB; Mueller, F; Murphy, C; Oxford, ER; Park, SH; Passeri, A; Pham, F; Piilonen, LE; Pokharel, S; Prim, MT; Pulvermacher, C; Rados, P; Ritter, M; Rostomyan, A; Sandilya, S; Santelj, L; Sato, Y; Schwartz, AJ; Sevior, ME; Soffer, A; Spataro, S; Stroili, R; Sutcliffe, W; Tagnani, D; Takizawa, M; Tamponi, U; Tenchini, F; Torassa, E; Urquijo, P; Vitale, L; Yusa, Y; Zani, L; Zhou, QD; Zlebcik, R; Zupanc, A Abudinen, F.; Akopov, N.; Aloisio, A.; Babu, V; Banerjee, Sw; Bauer, M.; Bennett, J., V; Bernlochner, F. U.; Bessner, M.; Bettarini, S.; Bilka, T.; Bilokin, S.; Biswas, D.; Bodrov, D.; Borah, J.; Bracko, M.; Branchini, P.; Budano, A.; Campajola, M.; Casarosa, G.; Cecchi, C.; Cheaib, R.; Chekelian, V; Chen, C.; Chen, Y. Q.; Cho, H-E; Cunliffe, S.; De Nardo, G.; De Pietro, G.; de Sangro, R.; Dey, S.; Di Canto, A.; Di Capua, F.; Dong, T., V; Dujany, G.; Ecker, P.; Eliachevitch, M.; Ferber, T.; Forti, F.; Ganiev, E.; Gaz, A.; Gelb, M.; Gemmler, J.; Godang, R.; Goldenzweig, P.; Graziani, E.; Hara, K.; Hershenhorn, A.; Higuchi, T.; Hill, E. C.; Hohmann, M.; Humair, T.; Inguglia, G.; Junkerkalefeld, H.; Karl, R.; Kato, Y.; Keck, T.; Kiesling, C.; Kim, C-H; Kohani, S.; Komarov, I; Kraetzschmar, T. M. G.; Krizan, P.; Krohn, J. F.; Kuhr, T.; Kumar, J.; Kumara, K.; Kurz, S.; Lacaprara, S.; La Licata, C.; Laurenza, M.; Lautenbach, K.; Lee, S. C.; Lieret, K.; Li Gioi, L.; Liu, Q. Y.; Longo, S.; Maggiora, M.; Manoni, E.; Marinas, C.; Martini, A.; Meier, F.; Merola, M.; Metzner, F.; Milesi, M.; Miyabayashi, K.; Mohanty, G. B.; Mueller, F.; Murphy, C.; Oxford, E. R.; Park, S-H; Passeri, A.; Pham, F.; Piilonen, L. E.; Pokharel, S.; Prim, M. T.; Pulvermacher, C.; Rados, P.; Ritter, M.; Rostomyan, A.; Sandilya, S.; Santelj, L.; Sato, Y.; Schwartz, A. J.; Sevior, M. E.; Soffer, A.; Spataro, S.; Stroili, R.; Sutcliffe, W.; Tagnani, D.; Takizawa, M.; Tamponi, U.; Tenchini, F.; Torassa, E.; Urquijo, P.; Vitale, L.; Yusa, Y.; Zani, L.; Zhou, Q. D.; Zlebcik, R.; Zupanc, A. B-flavor tagging at Belle II EUROPEAN PHYSICAL JOURNAL C English Article CP ASYMMETRIES; BHABHA SCATTERING; ISOSPIN ANALYSIS; VIOLATION We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom (B) mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic B decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of 62.8 fb(-1), collected at the gamma(4S) resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be epsilon(eff) = (30.0 +/- 1.2(stat) +/- 0.4(syst))% for a category-based algorithm and epsilon(eff) = (28.8 +/- 1.2(stat) +/- 0.4(syst))% for a deep-learning-based algorithm. [Lautenbach, K.; Zani, L.] Aix Marseille Univ, CPPM, CNRS IN2P3, F-13288 Marseille, France; [Di Canto, A.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Kumar, J.; Oxford, E. R.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Bilka, T.; Zlebcik, R.] Charles Univ Prague, Fac Math & Phys, Prague 12116, Czech Republic; [Babu, V; Cheaib, R.; Cunliffe, S.; Karl, R.; Komarov, I; Kurz, S.; Liu, Q. Y.; Longo, S.; Martini, A.; Rados, P.; Rostomyan, A.] DESY, D-22607 Hamburg, Germany; [Meier, F.] Duke Univ, Durham, NC 27708 USA; [Dong, T., V] Duy Tan Univ, Inst Theoret & Appl Res ITAR, Hanoi 100000, Vietnam; [Hara, K.] Grad Univ Adv Studies SOKENDAI, Hayama, Kanagawa 2400193, Japan; [Cho, H-E; Kim, C-H] Hanyang Univ, Dept Phys, Seoul 04763, South Korea; [Cho, H-E; Kim, C-H] Hanyang Univ, Inst Nat Sci, Seoul 04763, South Korea; [Hara, K.; Park, S-H] High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki 3050801, Japan; [Takizawa, M.] High Energy Accelerator Res Org KEK, KEK Theory Ctr, J PARC Branch, Tsukuba, Ibaraki 3050801, Japan; [Bodrov, D.] Higher Sch Econ HSE, Moscow 101000, Russia; [Chen, Y. Q.] Hunan Normal Univ, Changsha 410081, Peoples R China; [Borah, J.] Indian Inst Technol Guwahati, Gauhati 781039, Assam, India; [Sandilya, S.] Indian Inst Technol Hyderabad, Hyderabad 502285, Telangana, India; [Inguglia, G.] Inst High Energy Phys, A-1050 Vienna, Austria; [Marinas, C.] Inst Fis Corpuscular, Paterna 46980, Spain; [de Sangro, R.] Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Italy; [Aloisio, A.; Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.] Ist Nazl Fis Nucl, Sez Napoli, I-80126 Naples, Italy; [Gaz, A.; Lacaprara, S.; Stroili, R.; Torassa, E.] Ist Nazl Fis Nucl, Sez Padova, I-35131 Padua, Italy; [Cecchi, C.; Manoni, E.] Ist Nazl Fis Nucl, Sez Perugia, I-06123 Perugia, Italy; [Bettarini, S.; Casarosa, G.; Forti, F.; Tenchini, F.] Ist Nazl Fis Nucl, Sez Pisa, I-56127 Pisa, Italy; [Branchini, P.; Budano, A.; De Pietro, G.; Graziani, E.; Laurenza, M.; Passeri, A.; Tagnani, D.] Ist Nazl Fis Nucl, Sez Roma Tre, I-00146 Rome, Italy; [Maggiora, M.; Spataro, S.; Tamponi, U.] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; [Abudinen, F.; Ganiev, E.; Vitale, L.] Ist Nazl Fis Nucl, Sez Trieste, I-34127 Trieste, Italy; [Bauer, M.; Ecker, P.; Ferber, T.; Gelb, M.; Gemmler, J.; Goldenzweig, P.; Keck, T.; Metzner, F.; Pulvermacher, C.] Karlsruher Inst Technol, Inst Expt Teilchenphys, D-76131 Karlsruhe, Germany; [Lee, S. C.] Kyungpook Natl Univ, Daegu 41566, South Korea; [Bodrov, D.] Russian Acad Sci, PN Lebedev Phys Inst, Moscow 119991, Russia; [Bilokin, S.; Kuhr, T.; Lieret, K.; Ritter, M.] Ludwig Maximilians Univ Munchen, D-80539 Munich, Germany; [Chen, C.] Iowa State Univ, Ames, IA 50011 USA; [Chekelian, V; Humair, T.; Kiesling, C.; Kraetzschmar, T. M. G.; Li Gioi, L.; Mueller, F.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [Kato, Y.; Zhou, Q. D.] Nagoya Univ, Grad Sch Sci, Nagoya, Aichi 4648602, Japan; [Kato, Y.; Zhou, Q. D.] Nagoya Univ, Kobayashi Maskawa Inst, Nagoya, Aichi 4648602, Japan; [Zhou, Q. D.] Nagoya Univ, Inst Adv Res, Nagoya, Aichi 4648602, Japan; [Miyabayashi, K.] Nara Womens Univ, Nara 6308506, Japan; [Yusa, Y.] Niigata Univ, Niigata 9502181, Japan; [Takizawa, M.] RIKEN, Meson Sci Lab, Cluster Pioneering Res, Saitama 3510198, Japan; [Takizawa, M.] Showa Pharmaceut Univ, Tokyo 1948543, Japan; [Bracko, M.; Krizan, P.; Santelj, L.; Zupanc, A.] J Stefan Inst, Ljubljana 1000, Slovenia; [Mohanty, G. B.] Tata Inst Fundamental Res, Mumbai 400005, Maharashtra, India; [Dey, S.; Soffer, A.] Tel Aviv Univ, Sch Phys & Astron, IL-69978 Tel Aviv, Israel; [Sato, Y.] Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan; [Aloisio, A.; Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.] Univ Napoli Federico II, Dipartimento Sci Fis, I-80126 Naples, Italy; [Gaz, A.; Stroili, R.] Univ Padua, Dipartimento Fis & Astron, I-35131 Padua, Italy; [Cecchi, C.] Univ Perugia, Dipartimento Fis, I-06123 Perugia, Italy; [Bettarini, S.; Casarosa, G.; Forti, F.; Tenchini, F.] Univ Pisa, Dipartimento Fis, I-56127 Pisa, Italy; [Laurenza, M.] Univ Roma Tre, Dipartimento Matemat & Fis, I-00146 Rome, Italy; [Maggiora, M.; Spataro, S.] Univ Torino, Dipartimento Fis, I-10125 Turin, Italy; [Ganiev, E.; Vitale, L.] Univ Trieste, Dipartimento Fis, I-34127 Trieste, Italy; [Dujany, G.] Univ Strasbourg, IPHC, CNRS, UMR 7178, F-67037 Strasbourg, France; [Bernlochner, F. U.; Eliachevitch, M.; Junkerkalefeld, H.; Prim, M. T.; Sutcliffe, W.] Univ Bonn, D-53115 Bonn, Germany; [Hershenhorn, A.; Hill, E. C.] Univ British Columbia, Vancouver, BC V6T 1Z1, Canada; [Schwartz, A. J.] Univ Cincinnati, Cincinnati, OH 45221 USA; [Bessner, M.; Kohani, S.] Univ Hawaii, Honolulu, HI 96822 USA; [Krizan, P.; Santelj, L.] Univ Ljubljana, Fac Math & Phys, Ljubljana 1000, Slovenia; [Banerjee, Sw; Biswas, D.] Univ Louisville, Louisville, KY 40292 USA; [Bracko, M.] Univ Maribor, Maribor 2000, Slovenia; [Hohmann, M.; Krohn, J. F.; Milesi, M.; Pham, F.; Sevior, M. E.; Urquijo, P.] Univ Melbourne, Sch Phys, Parkville, Vic 3010, Australia; [Bennett, J., V; Pokharel, S.] Univ Mississippi, University, MS 38677 USA; [Godang, R.] Univ S Alabama, Mobile, AL 36688 USA; [Higuchi, T.; La Licata, C.; Murphy, C.] Univ Tokyo, Kavli Inst Phys & Math Universe WPI, Kashiwa, Chiba 2778583, Japan; [Piilonen, L. E.] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA; [Kumara, K.] Wayne State Univ, Detroit, MI 48202 USA; [Akopov, N.] Alikhanyan Natl Sci Lab, Yerevan 0036, Armenia Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; United States Department of Energy (DOE); Brookhaven National Laboratory; Carnegie Mellon University; Charles University Prague; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Duke University; Duy Tan University; Graduate University for Advanced Studies - Japan; Hanyang University; Hanyang University; High Energy Accelerator Research Organization (KEK); High Energy Accelerator Research Organization (KEK); HSE University (National Research University Higher School of Economics); Hunan Normal University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Guwahati; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Hyderabad; Austrian Academy of Sciences; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Helmholtz Association; Karlsruhe Institute of Technology; Kyungpook National University; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; University of Munich; Iowa State University; Max Planck Society; Nagoya University; Nagoya University; Nagoya University; Nara Womens University; Niigata University; RIKEN; Showa Pharmaceutical University; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Tata Institute of Fundamental Research (TIFR); Tel Aviv University; Tohoku University; University of Naples Federico II; University of Padua; University of Perugia; University of Pisa; Roma Tre University; University of Turin; University of Trieste; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; University of Bonn; University of British Columbia; University System of Ohio; University of Cincinnati; University of Hawaii System; University of Ljubljana; University of Louisville; University of Maribor; University of Melbourne; University of Mississippi; University of South Alabama; University of Tokyo; Virginia Polytechnic Institute & State University; Wayne State University Abudinen, F (corresponding author), Ist Nazl Fis Nucl, Sez Trieste, I-34127 Trieste, Italy. Bodrov, Denis/AAH-3024-2021; Forti, Francesco/H-3035-2011; Campajola, Marcello/V-1689-2019; Marinas, Carlos/AAB-9328-2021; Torassa, Ezio/I-1788-2012 Bodrov, Denis/0000-0001-5279-4787; Forti, Francesco/0000-0001-6535-7965; Campajola, Marcello/0000-0003-2518-7134; Marinas, Carlos/0000-0003-1903-3251; Borah, Jyotirmoi/0000-0003-2990-1913; Pokharel, Saroj/0000-0002-3367-738X; Junkerkalefeld, Henrik/0000-0003-3987-9895; Hill, Ewan/0000-0002-1725-7414; Martini, Alberto/0000-0003-1161-4983; Abudinen, Fernando/0000-0002-6737-3528; Cecchi, Claudia/0000-0002-2192-8233; Cunliffe, Sam/0000-0003-0167-8641; Torassa, Ezio/0000-0003-2321-0599; De Pietro, Giacomo/0000-0001-8442-107X; Gaz, Alessandro/0000-0001-6754-3315; Metzner, Felix/0000-0002-0128-264X; Tagnani, Diego/0000-0003-0124-5088; Dong, Thanh/0000-0003-3043-1939; Miyabayashi, Kenkichi/0000-0003-4352-734X; banerjee, swagato/0000-0001-8852-2409; Zhou, Qidong/0000-0001-5968-6359; Bennett, Jake/0000-0002-5440-2668 Science Committee of the Republic of Armenia [20TTCG-1C010]; Australian Research Council [DP180102629, DP170102389, DP170102204, DP150103061, FT130100303, FT130100018, FT120100745]; Austrian Federal Ministry of Education, Science and Research, Austrian Science Fund [P 31361-N36]; Horizon 2020 ERC Starting Grant [947006]; Natural Sciences and Engineering Research Council of Canada; Compute Canada; CANARIE; Chinese Academy of Sciences and research [QYZDJ-SSW-SLH011]; National Natural Science Foundation of China [11521505, 11575017, 11675166, 11761141009, 11705209, 11975076]; LiaoNing Revitalization Talents Program [XLYC1807135]; Shanghai Municipal Science and Technology Committee [19ZR1403000]; Shanghai Pujiang Program [18PJ1401000]; CAS Center for Excellence in Particle Physics (CCEPP); Ministry of Education, Youth and Sports of the Czech Republic [LTT17020]; Charles University grants [SVV 260448, GAUK 404316]; European Research Council [PIEF-GA-2013-622527]; Horizon 2020 ERC Advanced Grants [267104, 884719]; Horizon 2020 ERC Consolidator Grant [819127]; Horizon 2020 Marie Sklodowska-Curie grant agreement [700525]; Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 grant [822070]; L'Institut National de Physique Nucleaire et de Physique des Particules (IN2P3) du CNRS (France); BMBF (Germany); DFG (Germany); HGF (Germany); MPG (Germany); AvH Foundation (Germany); Department of Atomic Energy (India) [RTI 4002]; Department of Science and Technology (India); Israel Science Foundation [2476/17]; United States-Israel Binational Science Foundation [2016113]; Israel Ministry of Science grant [3-16543]; Istituto Nazionale di Fisica Nucleare; research grants BELLE2; Japan Society for the Promotion of Science [16H03968, 16H03993, 16H06492, 16K05323, 17H01133, 17H05405, 18K03621, 18H03710, 18H05226, 19H00682, 26220706, 26400255]; National Institute of Informatics, and Science Information NETwork 5 (SINET5); Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan; National Research Foundation (NRF) of Korea [2016R1D1A1B01010135, 2016R1D1A1B02012900, 2018R1A2B3003643, 2018R1A6A1A06024970, 2018R1D1A1B07047294, 2019K1A3A7A09033840, 2019R1I1A3A01058933]; Radiation Science Research Institute; Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information; KREONET/GLORIAD; Universiti Malaya RU grant; Akademi Sains Malaysia; Ministry of Education Malaysia; Frontiers of Science Program [FOINS-296, CB-221329, CB-236394, CB-254409, CB-180023]; SEP-CINVESTAV research grant (Mexico) [237]; Polish Ministry of Science and Higher Education; National Science Center; Ministry of Science and Higher Education of the Russian Federation [14.W03.31.0026]; HSE University Basic Research Program, Moscow; University of Tabuk (Saudi Arabia) [S-0256-1438, S-0280-1439]; Slovenian Research Agency [J1-9124, P1-0135]; Agencia Estatal de Investigacion, Spain [FPA2014-55613-P, FPA2017-84445-P]; Generalitat Valenciana [CIDEGENT/2018/020]; Ministry of Science and Technology and research [MOST106-2112-M-002-005-MY3, MOST107-2119-M-002-035-MY3]; Ministry of Education (Taiwan); Thailand Center of Excellence in Physics; TUBITAK ULAKBIM (Turkey); Ministry of Education and Science of Ukraine; US National Science Foundation and research [PHY-1807007, PHY-1913789]; US Department of Energy and research [DE-AC06-76RLO1830, DE-SC0007983, DE-SC0009824, DE-SC0009973, DE-SC0010007, DE-SC0010073, DE-SC0010118, DE-SC0010504, DE-SC0011784, DE-SC0012704, DE-SC0021274]; Vietnam Academy of Science and Technology (VAST) [DL0000.05/21-23]; Australian Research Council [FT120100745] Funding Source: Australian Research Council; Grants-in-Aid for Scientific Research [16H03968, 17H05405, 16H03993, 18H03710, 18K03621, 17H01133, 18H05226, 26400255, 19H00682, 16K05323] Funding Source: KAKEN Science Committee of the Republic of Armenia; Australian Research Council(Australian Research Council); Austrian Federal Ministry of Education, Science and Research, Austrian Science Fund; Horizon 2020 ERC Starting Grant; Natural Sciences and Engineering Research Council of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)CGIAR); Compute Canada; CANARIE; Chinese Academy of Sciences and research; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); LiaoNing Revitalization Talents Program; Shanghai Municipal Science and Technology Committee; Shanghai Pujiang Program(Shanghai Pujiang Program); CAS Center for Excellence in Particle Physics (CCEPP); Ministry of Education, Youth and Sports of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Charles University grants; European Research Council(European Research Council (ERC)European Commission); Horizon 2020 ERC Advanced Grants; Horizon 2020 ERC Consolidator Grant; Horizon 2020 Marie Sklodowska-Curie grant agreement; Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 grant; L'Institut National de Physique Nucleaire et de Physique des Particules (IN2P3) du CNRS (France); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); MPG (Germany)(Max Planck Society); AvH Foundation (Germany)(Alexander von Humboldt Foundation); Department of Atomic Energy (India)(Department of Atomic Energy (DAE)); Department of Science and Technology (India)(Department of Science & Technology (India)); Israel Science Foundation(Israel Science Foundation); United States-Israel Binational Science Foundation(US-Israel Binational Science Foundation); Israel Ministry of Science grant; Istituto Nazionale di Fisica Nucleare(Istituto Nazionale di Fisica Nucleare (INFN)); research grants BELLE2; Japan Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); National Institute of Informatics, and Science Information NETwork 5 (SINET5); Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); National Research Foundation (NRF) of Korea(National Research Foundation of Korea); Radiation Science Research Institute; Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information; KREONET/GLORIAD; Universiti Malaya RU grant; Akademi Sains Malaysia(Universiti Sains Malaysia); Ministry of Education Malaysia(Ministry of Education, Malaysia); Frontiers of Science Program; SEP-CINVESTAV research grant (Mexico); Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); National Science Center(National Science Centre, Poland); Ministry of Science and Higher Education of the Russian Federation; HSE University Basic Research Program, Moscow; University of Tabuk (Saudi Arabia); Slovenian Research Agency(Slovenian Research Agency - Slovenia); Agencia Estatal de Investigacion, Spain(Spanish Government); Generalitat Valenciana(Center for Forestry Research & Experimentation (CIEF)); Ministry of Science and Technology and research; Ministry of Education (Taiwan)(Ministry of Education, Taiwan); Thailand Center of Excellence in Physics; TUBITAK ULAKBIM (Turkey); Ministry of Education and Science of Ukraine; US National Science Foundation and research; US Department of Energy and research(United States Department of Energy (DOE)); Vietnam Academy of Science and Technology (VAST); Australian Research Council(Australian Research Council); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) We thank the SuperKEKB group for the excellent operation of the accelerator; the KEK cryogenics group for the efficient operation of the solenoid; the KEK computer group for on-site computing support; and the raw-data centers at BNL, DESY, GridKa, IN2P3, and INFN for off-site computing support. This work was supported by the following funding sources: Science Committee of the Republic of Armenia Grant No. 20TTCG-1C010; Australian Research Council and research grant Nos. DP180102629, DP170102389, DP170102204, DP150103061, FT130100303, FT130100018, and FT120100745; Austrian Federal Ministry of Education, Science and Research, Austrian Science Fund No. P 31361-N36, and Horizon 2020 ERC Starting Grant no. 947006 InterLeptons; Natural Sciences and Engineering Research Council of Canada, Compute Canada and CANARIE; Chinese Academy of Sciences and research grant No. QYZDJ-SSW-SLH011, National Natural Science Foundation of China and research grant Nos. 11521505, 11575017, 11675166, 11761141009, 11705209, and 11975076, LiaoNing Revitalization Talents Program under contract No. XLYC1807135, Shanghai Municipal Science and Technology Committee under contract No. 19ZR1403000, Shanghai Pujiang Program under Grant No. 18PJ1401000, and the CAS Center for Excellence in Particle Physics (CCEPP); the Ministry of Education, Youth and Sports of the Czech Republic under Contract No. LTT17020 and Charles University grants SVV 260448 and GAUK 404316; European Research Council, 7th Framework PIEF-GA-2013-622527, Horizon 2020 ERC Advanced Grants No. 267104 and 884719, Horizon 2020 ERC Consolidator Grant No. 819127, Horizon 2020 Marie Sklodowska-Curie grant agreement No. 700525 'NIOBE,' and Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 grant agreement No. 822070 (European grants); L'Institut National de Physique Nucleaire et de Physique des Particules (IN2P3) du CNRS (France); BMBF, DFG, HGF, MPG, and AvH Foundation (Germany); Department of Atomic Energy under Project Identification No. RTI 4002 and Department of Science and Technology (India); Israel Science Foundation grant No. 2476/17, United States-Israel Binational Science Foundation grant No. 2016113, and Israel Ministry of Science grant No. 3-16543; Istituto Nazionale di Fisica Nucleare and the research grants BELLE2; Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research grant Nos. 16H03968, 16H03993, 16H06492, 16K05323, 17H01133, 17H05405, 18K03621, 18H03710, 18H05226, 19H00682, 26220706, and 26400255, the National Institute of Informatics, and Science Information NETwork 5 (SINET5), and the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan; National Research Foundation (NRF) of Korea Grant Nos. 2016R1D1A1B01010135, 2016R1D1A1B02012900, 2018R1A2B3003643, 2018R1A6A1A06024970, 2018R1D1A1B07047294, 2019K1A3A7A09033840, and 2019R1I1A3A01058933, Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Universiti Malaya RU grant, Akademi Sains Malaysia and Ministry of Education Malaysia; Frontiers of Science Program contracts FOINS-296, CB-221329, CB-236394, CB-254409, and CB-180023, and SEP-CINVESTAV research grant 237 (Mexico); the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Science and Higher Education of the Russian Federation, Agreement 14.W03.31.; 0026, and the HSE University Basic Research Program, Moscow; University of Tabuk research grants S-0256-1438 and S-0280-1439 (Saudi Arabia); Slovenian Research Agency and research grant Nos. J1-9124 and P1-0135; Agencia Estatal de Investigacion, Spain grant Nos. FPA2014-55613-P and FPA2017-84445-P, and CIDEGENT/2018/020 of Generalitat Valenciana; Ministry of Science and Technology and research grant Nos. MOST106-2112-M-002-005-MY3 and MOST107-2119-M-002-035-MY3, and the Ministry of Education (Taiwan); Thailand Center of Excellence in Physics; TUBITAK ULAKBIM (Turkey); Ministry of Education and Science of Ukraine; the US National Science Foundation and research grant Nos. PHY-1807007 and PHY-1913789, and the US Department of Energy and research grant Nos. DE-AC06-76RLO1830, DE-SC0007983, DE-SC0009824, DE-SC0009973, DE-SC0010007, DE-SC0010073, DE-SC0010118, DE-SC0010504, DE-SC0011784, DE-SC0012704, DE-SC0021274; and the Vietnam Academy of Science and Technology (VAST) under grant DL0000.05/21-23. 60 0 0 1 6 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C APR 1 2022.0 82.0 4 283 10.1140/epjc/s10052-022-10180-9 0.0 29 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 0F1XB Green Accepted, Green Submitted, gold 2023-03-23 WOS:000777159100005 0
J Yang, MY; Liao, WT; Li, XB; Cao, YP; Rosenhahn, B Yang, Michael Ying; Liao, Wentong; Li, Xinbo; Cao, Yanpeng; Rosenhahn, Bodo Vehicle Detection in Aerial Images PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING English Article The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection. [Yang, Michael Ying] Univ Twente, ITC Fac, Scene Understanding Grp, Enschede, Netherlands; [Liao, Wentong; Li, Xinbo; Rosenhahn, Bodo] Leibniz Univ Hannover, Inst Informat Proc, Hannover, Germany; [Cao, Yanpeng] Zhejiang Univ, Sch Mech Engn, Hangzhou, Zhejiang, Peoples R China University of Twente; Leibniz University Hannover; Zhejiang University Cao, YP (corresponding author), Zhejiang Univ, Sch Mech Engn, Hangzhou, Zhejiang, Peoples R China. caoyp@zju.edu.cn Yang, Michael/AAC-6698-2019 Yang, Michael/0000-0002-0649-9987 DFG (German Research Foundation) [YA 351/2-1, RO 4804/2-1] DFG (German Research Foundation)(German Research Foundation (DFG)) The work is funded by DFG (German Research Foundation) YA 351/2-1 and RO 4804/2-1. The authors gratefully acknowledge the support. We thank Slagboom en Peeters for providing the aerial images. 35 22 22 2 24 AMER SOC PHOTOGRAMMETRY BETHESDA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 USA 0099-1112 2374-8079 PHOTOGRAMM ENG REM S Photogramm. Eng. Remote Sens. APR 2019.0 85.0 4 297 304 10.14358/PERS.85.4.297 0.0 8 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology HU8IX Green Published, Green Submitted 2023-03-23 WOS:000465532000008 0
C Ji, ZL; Ganchev, I; O'Droma, M; Zhao, QJ IEEE Ji, Zhanlin; Ganchev, Ivan; O'Droma, Mairtin; Zhao, Qingjuan A Push-Notification Service for Use in the UCWW 2014 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC) English Proceedings Paper 6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) OCT 10-12, 2014 Shanghai, PEOPLES R CHINA IEEE Comp Soc,IEEE,IEEE CS Tech Comm Simulat,IEEE Task Force Networked Mobile Syst,Beijing Jiaotong Univ,Univ Louisville,AT & T,INFOBEYOND,HUAWEI,Comrise,HP,Tech Mahindra UCWW; ABC&S; Push Notification; Android; Distributed Platform This paper proposes a push-notification service for use in the emerging ubiquitous consumer wireless world (UCWW). It follows the client/server design pattern and is developed based on the extensible messaging and presence protocol (XMPP). On the client side, a mobile application collects user behavior information and periodically sends it (over HTTP) to the UCWW cloud in order to identify the 'best' instance of a service that this user might be interested in. When such service instance is found, a corresponding notification is generated by the cloud and pushed (over XMPP) to a notification manager operating on the user's mobile device. On the server side, a distributed platform is designed for 'big data' processing and services management. The provided solution is flexible and scalable, and could be easily integrated into the UCWW cloud for the always best connected and best served (ABC&S) paradigm realization. [Ji, Zhanlin; Ganchev, Ivan; O'Droma, Mairtin] Univ Limerick, Telecommun Res Ctr, Limerick, Ireland; [Ji, Zhanlin; Zhao, Qingjuan] Hebei United Univ, Coll Informat, Beijing, Peoples R China University of Limerick; North China University of Science & Technology Ji, ZL (corresponding author), Univ Limerick, Telecommun Res Ctr, Limerick, Ireland. Zhanlin.Ji@ul.ie; Ivan.Ganchev@ul.ie; Mairtin.ODroma@ul.ie; QingjuanZhao@163.com O'Droma, Máirtín/D-2743-2015; Ji, Zhanlin/AAW-5661-2021; Ganchev, Ivan/D-4973-2012 O'Droma, Máirtín/0000-0002-1672-1668; Ganchev, Ivan/0000-0003-0535-7087; ji, zhanlin/0000-0003-3527-3773 15 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-6236-5 2014.0 318 322 10.1109/CyberC.2014.88 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BD3XF 2023-03-23 WOS:000360282600056 0
J Pedersen, J; Munoz-Gomez, R; Huang, JN; Sun, HZ; Tu, WW; Guyon, I Pedersen, Joseph; Munoz-Gomez, Rafael; Huang, Jiangnan; Sun, Haozhe; Tu, Wei-Wei; Guyon, Isabelle LTU Attacker for Membership Inference ALGORITHMS English Article machine learning; privacy attacks; membership inference We address the problem of defending predictive models, such as machine learning classifiers (Defender models), against membership inference attacks, in both the black-box and white-box setting, when the trainer and the trained model are publicly released. The Defender aims at optimizing a dual objective: utility and privacy. Privacy is evaluated with the membership prediction error of a so-called Leave-Two-Unlabeled LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each, giving the strongest possible attack scenario. We prove that, under certain conditions, even a naive LTU Attacker can achieve lower bounds on privacy loss with simple attack strategies, leading to concrete necessary conditions to protect privacy, including: preventing over-fitting and adding some amount of randomness. This attack is straightforward to implement against any model trainer, and we demonstrate its performance against MemGaurd. However, we also show that such a naive LTU Attacker can fail to attack the privacy of models known to be vulnerable in the literature, demonstrating that knowledge must be complemented with strong attack strategies to turn the LTU Attacker into a powerful means of evaluating privacy. The LTU Attacker can incorporate any existing attack strategy to compute individual privacy scores for each training sample. Our experiments on the QMNIST, CIFAR-10, and Location-30 datasets validate our theoretical results and confirm the roles of over-fitting prevention and randomness in the algorithms to protect against privacy attacks. [Pedersen, Joseph] Rensselaer Polytech Inst, Dept Ind & Syst Engn, Troy, NY 12180 USA; [Munoz-Gomez, Rafael; Huang, Jiangnan; Sun, Haozhe; Guyon, Isabelle] Paris Saclay Univ, LISN, INRIA, CNRS, 3 Rue Joliot Curie Batiment Breguet, F-91190 Gif Sur Yvette, France; [Tu, Wei-Wei] 4Paradigm, 66 Qinghe Middle St, Beijing 100089, Peoples R China; [Tu, Wei-Wei; Guyon, Isabelle] ChaLearn, 397 Schimke Rd, Alpine County, CA 95223 USA Rensselaer Polytechnic Institute; Centre National de la Recherche Scientifique (CNRS); Inria; UDICE-French Research Universities; Universite Paris Saclay Pedersen, J (corresponding author), Rensselaer Polytech Inst, Dept Ind & Syst Engn, Troy, NY 12180 USA. joseph.m.pedersen@gmail.com; rafaelmugo07@gmail.com; jiangnan2217@gmail.com; sunhaozhe275940200@gmail.com; tuww.cn@gmail.com; guyon@chalearn.org 涂, 威威/GRF-5615-2022 涂, 威威/0000-0002-2407-0252; HUANG, Jiangnan/0000-0003-1585-1185; Guyon, Isabelle/0000-0002-9266-1783 ANR (Agence Nationale de la Recherche, National Agency for Research) under AI chair of excellence HUMANIA [ANR-19-CHIA-0022] ANR (Agence Nationale de la Recherche, National Agency for Research) under AI chair of excellence HUMANIA(French National Research Agency (ANR)) This work is funded in part by the ANR (Agence Nationale de la Recherche, National Agency for Research) under AI chair of excellence HUMANIA, grant number ANR-19-CHIA-0022. 35 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4893 ALGORITHMS Algorithms JUL 2022.0 15.0 7 254 10.3390/a15070254 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Emerging Sources Citation Index (ESCI) Computer Science 3J1RL Green Submitted, gold 2023-03-23 WOS:000833179800001 0
J Zhou, L; Sun, LL; Li, ZC; Li, WP; Cao, N; Higgs, R Zhou, Li; Sun, Lili; Li, Zhaochan; Li, Weipeng; Cao, Ning; Higgs, Russell Study on a storage location strategy based on clustering and association algorithms SOFT COMPUTING English Article Cluster; Association rules; Class-based storage; Order picking; Picking efficiency CLOUD In this paper, we study the improvement of a storage location strategy through the use of big data technology, including data collection, cluster analysis and association analysis, to improve order picking efficiency. A clustering algorithm is used to categorize the types of goods in orders. Classification is performed based on the turnover of goods, value, sales volume, favorable commodity ratings, whether free shipping is provided and whether cash on delivery is supported. An association algorithm is used to determine the relationships among goods by studying the habits of consumers who buy them. A method for improving the class-based storage strategy is proposed. The picking distance of the improved storage strategy is compared with that of the traditional strategy via simulation experiments. The picking efficiency is shown to be enhanced by the improved strategy. [Zhou, Li; Sun, Lili; Li, Zhaochan; Li, Weipeng] Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China; [Cao, Ning] Qingdao Binhai Univ, Coll Informat Engn, Qingdao, Peoples R China; [Higgs, Russell] Univ Coll Dublin, Sch Math & Stat, Dublin, Ireland Beijing Wuzi University; University College Dublin Cao, N (corresponding author), Qingdao Binhai Univ, Coll Informat Engn, Qingdao, Peoples R China. zhoulibit@126.com; sunlilibjwy@126.com; zhaochanlibwz@126.com; walter1900@126.com; ning.cao2008@hotmail.com; russell.higgs@ucd.ie Sun, lili/GQH-2057-2022; Cao, Ning/P-6652-2017 Higgs, Russell/0000-0002-0896-6788 37 16 16 5 12 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1432-7643 1433-7479 SOFT COMPUT Soft Comput. APR 2020.0 24.0 8 SI 5499 5516 10.1007/s00500-018-03702-9 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science KY1TA hybrid 2023-03-23 WOS:000522353100003 0
C Wang, XY; Lee, B; Qiao, YS IEEE Wang, Xueyuan; Lee, Brian; Qiao, Yuansong Experimental Evaluation of Memory Configurations of Hadoop in Docker Environments 2016 27TH IRISH SIGNALS AND SYSTEMS CONFERENCE (ISSC) English Proceedings Paper 27th Irish Signals and Systems Conference (ISSC) JUN 21-22, 2016 Ulster Univ, Derry, NORTH IRELAND IEEE,Rohde & Schwarz,Xilinix,ExOrdo,Microelectron Circuits Ctr Ireland,VMForensics ltd,Visit Derry,MIDAS,AWR Grp,Natl Instruments,Ulster Univ Ulster Univ Hadoop; configuration; Docker; memory MAPREDUCE Hadoop is widely used in these days for big data analytics. Docker, a new container technology, is hot nowadays, and is the new QuickStart option for Apache Hadoop. It is a trend to build Hadoop cluster in Docker environments in clouds or clusters. However, how to make better use of hardware resources and improve Hadoop performance in Docker environments is a challenge for users. In this paper we study memory configurations of Hadoop in Docker environments, and analyse the performance of Hadoop while altering Hadoop's memory configurations. We select two different applications (a CPU-intensive application - WordCount and a memory-intensive application - TeraSort) and measure their resource usages in terms of CPU and memory footprint. Extensive tests have been executed, the test results show that appropriate customization of memory configurations improves performance compared to the default memory parameter settings. [Wang, Xueyuan] East China Univ Technol, Sch Software, Nanchang, Jiangxi, Peoples R China; [Wang, Xueyuan; Lee, Brian; Qiao, Yuansong] Athlone Inst Technol, Software Res Inst, Dublin Rd, Athlone, Co Westmeath, Ireland East China University of Technology; Technological University of the Shannon: Midlands Midwest Wang, XY (corresponding author), East China Univ Technol, Sch Software, Nanchang, Jiangxi, Peoples R China.;Wang, XY (corresponding author), Athlone Inst Technol, Software Res Inst, Dublin Rd, Athlone, Co Westmeath, Ireland. xwang@ait.ie; blee@ait.ie; ysqiao@research.ait.ie Qiao, Yuansong/A-1140-2017 Qiao, Yuansong/0000-0002-1543-1589 17 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-3409-3 2016.0 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BG7GF 2023-03-23 WOS:000391254900010 0
J Niu, K; Zhang, FS; Wang, XZ; Lv, Q; Luo, HT; Zhang, DQ Niu, Kai; Zhang, Fusang; Wang, Xuanzhi; Lv, Qin; Luo, Haitong; Zhang, Daqing Understanding WiFi Signal Frequency Features for Position-Independent Gesture Sensing IEEE TRANSACTIONS ON MOBILE COMPUTING English Article Sensors; Wireless fidelity; Training; Gesture recognition; Time-frequency analysis; Feature extraction; Mobile computing; Time frequency feature; position-independent; gesture recognition; contactless sensing; WiFi sensing Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96 percent recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions. [Niu, Kai; Wang, Xuanzhi; Zhang, Daqing] Peking Univ, Key Lab High Confidence Software Technol, Minist Educ, Beijing 100871, Peoples R China; [Niu, Kai; Wang, Xuanzhi; Zhang, Daqing] Peking Univ, Sch Elect Engn & Comp Sci, Dept Comp Sci & Technol, Beijing 100871, Peoples R China; [Zhang, Fusang] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China; [Lv, Qin] Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA; [Luo, Haitong] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 102206, Peoples R China; [Zhang, Daqing] Inst Polytech Paris, Telecom SudParis, F-91011 Evey, France Peking University; Peking University; Chinese Academy of Sciences; Institute of Software, CAS; University of Colorado System; University of Colorado Boulder; Beijing University of Posts & Telecommunications Zhang, DQ (corresponding author), Peking Univ, Key Lab High Confidence Software Technol, Minist Educ, Beijing 100871, Peoples R China.;Zhang, DQ (corresponding author), Peking Univ, Sch Elect Engn & Comp Sci, Dept Comp Sci & Technol, Beijing 100871, Peoples R China. xjtunk@pku.edu.cn; zhangfusang@otcaix.iscas.ac.cn; xuanzhiwang@stu.pku.edu.cn; qin.lv@colorado.edu; ivanccc@bupt.edu.cn; dqzhang@sei.pku.edu.cn NSFC A3 Project [62061146001]; PKUBaidu Fund [2019BD005]; EU CHIST-ERA RadioSense Project; NSFC [61802373]; Youth Innovation Promotion Association, Chinese Academy of Sciences [2020109] NSFC A3 Project; PKUBaidu Fund; EU CHIST-ERA RadioSense Project; NSFC(National Natural Science Foundation of China (NSFC)); Youth Innovation Promotion Association, Chinese Academy of Sciences This work was supported by the NSFC A3 Project 62061146001, the Project 2019BD005 supported by PKUBaidu Fund, the EU CHIST-ERA RadioSense Project, the NSFC Grant 61802373, the Youth Innovation Promotion Association, Chinese Academy of Sciences (No. 2020109). 40 8 8 9 9 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1536-1233 1558-0660 IEEE T MOBILE COMPUT IEEE. Trans. Mob. Comput. NOV 1 2022.0 21.0 11 4156 4171 10.1109/TMC.2021.3063135 0.0 16 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 5C5UR Green Published 2023-03-23 WOS:000864325200025 0
J Zhang, JH; Akinsolu, MO; Liu, B; Vandenbosch, GAE Zhang, Jiahao; Akinsolu, Mobayode O.; Liu, Bo; Vandenbosch, Guy A. E. Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION English Article Isolators; Mutual coupling; Topology; Optimization; Microstrip antenna arrays; Frequency reconfigurability; mutual coupling reduction; optimization; reconfigurable antenna; surrogate modeling EFFICIENT GLOBAL OPTIMIZATION; SOFT SURFACES; MIMO ANTENNAS; REDUCTION; EBG An automatic artificial intelligence (AI)-driven design procedure for mutual coupling reduction and a novel isolator are proposed for a frequency reconfigurable antenna array. The design process is driven and expedited by the parallel surrogate model-assisted differential evolution for antenna synthesis (PSADEA) method. The reconfigurable array element can switch its operation between the 2.5 GHz ISM band and the 3.4 GHz WiMAX band. By introducing the proposed isolator, the mutual coupling in the higher and lower band is reduced by 8 and 7 dB, respectively. The reconfigurable array was prototyped, and measurements agree well with simulations, verifying the validity of the proposed concept. Although used for a specific antenna in this communication, the proposed AI-driven design strategy is generic and can easily be employed for other array topologies. [Zhang, Jiahao; Vandenbosch, Guy A. E.] Katholieke Univ Leuven, ESAT TELEMIC Res Div, Dept Elect Engn, B-3001 Leuven, Belgium; [Zhang, Jiahao] Natl Key Lab Sci & Technol Vessel Integrated Powe, Wuhan 430000, Peoples R China; [Akinsolu, Mobayode O.] Wrexham Glyndwr Univ, Fac Arts Sci & Technol, Wrexham LL11 2AW, Wales; [Liu, Bo] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland KU Leuven; University of Glasgow Zhang, JH (corresponding author), Katholieke Univ Leuven, ESAT TELEMIC Res Div, Dept Elect Engn, B-3001 Leuven, Belgium. jiahao.z@hotmail.com Akinsolu, Mobayode O./ABD-9725-2020 Akinsolu, Mobayode O./0000-0003-1164-5501; Liu, Bo/0000-0002-3093-4571 18 16 16 8 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-926X 1558-2221 IEEE T ANTENN PROPAG IEEE Trans. Antennas Propag. MAR 2021.0 69.0 3 1831 1836 10.1109/TAP.2020.3012792 0.0 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications QT2LE Green Accepted 2023-03-23 WOS:000626422500065 0
J Zhang, ZY; Zhang, Q; Wang, TZ; Xu, NH; Lu, T; Hong, WJ; Penuelas, J; Gillings, M; Wang, MX; Gao, WW; Qian, HF Zhang, Zhenyan; Zhang, Qi; Wang, Tingzhang; Xu, Nuohan; Lu, Tao; Hong, Wenjie; Penuelas, Josep; Gillings, Michael; Wang, Meixia; Gao, Wenwen; Qian, Haifeng Assessment of global health risk of antibiotic resistance genes NATURE COMMUNICATIONS English Article MICROBIOME; SYSTEM Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health. Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health. Here, Zhang et al. analyze 4572 metagenomic samples to illustrate the global patterns of ARG distribution in diverse habitats. They quantitatively evaluate the health risk to humans of 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. With the machine learning, they map the antibiotic resistance threats in global marine habitats. [Zhang, Zhenyan; Zhang, Qi; Xu, Nuohan; Lu, Tao; Qian, Haifeng] Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China; [Wang, Tingzhang; Hong, Wenjie; Wang, Meixia; Gao, Wenwen] Key Lab Microbial Technol & Bioinformat Zhejiang, Hangzhou 310012, Peoples R China; [Penuelas, Josep] Global Ecol Unit CREAF CSIC UAB, CSIC, Barcelona 08193, Catalonia, Spain; [Penuelas, Josep] CREAF, Campus Univ Autonoma Barcelona, Barcelona 08193, Catalonia, Spain; [Gillings, Michael] Macquarie Univ, Fac Sci & Engn, ARC Ctr Excellence Synthet Biol, Sydney, NSW 2109, Australia Zhejiang University of Technology; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Consejo Superior de Investigaciones Cientificas (CSIC); Autonomous University of Barcelona; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); University of Barcelona; Macquarie University Qian, HF (corresponding author), Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China. hfqian@zjut.edu.cn Penuelas, Josep/D-9704-2011; Qian, Haifeng/A-7791-2012; Lu, Tao/Y-3535-2018 Penuelas, Josep/0000-0002-7215-0150; Qian, Haifeng/0000-0003-0807-9991; Lu, Tao/0000-0002-2030-3813; Zhang, Zhenyan/0000-0002-9122-2391 National Natural Science Foundation of China [21976161, 21777144, 41907210]; Fundacion Ramon Areces grant ELEMENTAL-CLIMATE; Spanish Government grant [PID2019110521GB-I00]; Catalan Government grants [AGAUR-2020PANDE00117, SGR 2017-1005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundacion Ramon Areces grant ELEMENTAL-CLIMATE; Spanish Government grant(Spanish Government); Catalan Government grants We appreciate Nayfach et al. (2020)28 for their work on the genomic catalog of Earth's microbiomes and its publication as a public resource, and Almeida (2021)40 for their work on the human gut genome and its publication as a public resource. We also acknowledge all the PIs for uploading the metagenomic samples as precious public resources. All the PIs' name were listed in Supplementary Data 1. H.F.Q. was financially supported by the National Natural Science Foundation of China (21976161 and 21777144). T.L. was financially supported by the National Natural Science Foundation of China (41907210). Josep Penuelas was financially supported by the Fundacion Ramon Areces grant ELEMENTAL-CLIMATE, the Spanish Government grant PID2019110521GB-I00, and the Catalan Government grants AGAUR-2020PANDE00117 and SGR 2017-1005. 65 56 58 221 448 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. MAR 23 2022.0 13.0 1 1553 10.1038/s41467-022-29283-8 0.0 11 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics ZY4RV 35322038.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000772575300023 0
J Ting, DSW; Cheung, CY; Nguyen, Q; Sabanayagam, C; Lim, G; Lim, ZW; Tan, GSW; Soh, YQ; Schmetterer, L; Wang, YX; Jonas, JB; Varma, R; Li Lee, M; Hsu, W; Lamoureux, E; Cheng, CY; Wong, TY Ting, Daniel S. W.; Cheung, Carol Y.; Nguyen, Quang; Sabanayagam, Charumathi; Lim, Gilbert; Lim, Zhan Wei; Tan, Gavin S. W.; Soh, Yu Qiang; Schmetterer, Leopold; Wang, Ya Xing; Jonas, Jost B.; Varma, Rohit; Li Lee, Mong; Hsu, Wynne; Lamoureux, Ecosse; Cheng, Ching-Yu; Wong, Tien Yin Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study NPJ DIGITAL MEDICINE English Article GLOBAL PREVALENCE; SCREENING-PROGRAM; CLASSIFICATION; EPIDEMIOLOGY; VALIDATION; MANAGEMENT; FREQUENCY; GAME; CARE; GO In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors - 10 retinal specialists/ophthalmologists and 7 professional graders). The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision-threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1%; DLS: 16.1%, 6.4%, 3.7%). Both assessment methods identified similar risk factors (with comparable AUCs), including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p > 0.05). The total time taken for DLS to evaluate DR from 93,293 fundus photographs was similar to 1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of Al for future epidemiology or clinical trials for DR grading in the global communities. [Ting, Daniel S. W.; Nguyen, Quang; Sabanayagam, Charumathi; Tan, Gavin S. W.; Soh, Yu Qiang; Schmetterer, Leopold; Lamoureux, Ecosse; Cheng, Ching-Yu; Wong, Tien Yin] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Ting, Daniel S. W.; Sabanayagam, Charumathi; Cheng, Ching-Yu] Natl Univ Singapore, Duke NUS Med Sch, Singapore, Singapore; [Cheung, Carol Y.] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China; [Lim, Gilbert; Lim, Zhan Wei; Li Lee, Mong; Hsu, Wynne] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Schmetterer, Leopold] Nanyang Technol Univ, Lee Kong Chian Sch Med, Dept Ophthalmol, Singapore, Singapore; [Schmetterer, Leopold] Med Univ Vienna, Dept Clin Pharmacol, Vienna, Austria; [Schmetterer, Leopold] Med Univ Vienna, Ctr Med Phys & Biomed Engn, Vienna, Austria; [Wang, Ya Xing; Jonas, Jost B.] Capital Med Univ, Beijing Tongren Hosp, Beijing Tongren Eye Ctr, Beijing Inst Ophthalmol,Beijing Key Lab Ophthalmo, Beijing, Peoples R China; [Jonas, Jost B.] Heidelberg Univ, Dept Ophthalmol, Med Fac Mannheim, Mannheim, Germany; [Varma, Rohit] Univ Southern Calif Gayle, Los Angeles, CA USA; [Varma, Rohit] Edward Roski Eye Inst, Los Angeles, CA USA National University of Singapore; Singapore National Eye Center; National University of Singapore; Chinese University of Hong Kong; National University of Singapore; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Medical University of Vienna; Medical University of Vienna; Capital Medical University; Ruprecht Karls University Heidelberg Ting, DSW; Wong, TY (corresponding author), Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.;Ting, DSW (corresponding author), Natl Univ Singapore, Duke NUS Med Sch, Singapore, Singapore. daniel.ting.s.w@singhealth.com.sg; wong.tien.yin@singhealth.com.sg Wang, Ying/HJI-2509-2023; Tan, Gavin/HNS-5461-2023; Lamoureux, Ecosse/Z-5482-2019; Cheung, Carol/AAF-1101-2020; Nguyen, Duc Quang/AHC-6430-2022; Cheung, Carol Y./G-7895-2016; Wong, Tien Yin/AAC-9724-2020; Sabanayagam, Charumathi/C-1294-2011; wang, YA XING/K-9671-2016; Cheng, Ching-Yu/Y-2229-2019; Lim, Gilbert/N-7322-2017 Cheung, Carol/0000-0002-9672-1819; Nguyen, Duc Quang/0000-0001-6919-5211; Wong, Tien Yin/0000-0002-8448-1264; Sabanayagam, Charumathi/0000-0002-4042-4719; wang, YA XING/0000-0003-2749-7793; Cheng, Ching-Yu/0000-0003-0655-885X; Schmetterer, Leopold/0000-0002-7189-1707; Lim, Gilbert/0000-0002-5381-9250 National Medical Research Council Singapore, Ministry of Health, Singapore; Tanoto Foundation; National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant) [NHIC-I2D-1409022]; Health Service Research Grant; SingHealth Foundation Research Grant [SHF/FG648S/2015]; NMRC, MOH [0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, STaR/2013, CG/SERI/2010]; Biomedical Research Council [08/1/35/19/550, 09/1/35/19/616]; Singapore Diabetic Retinopathy Program (SiDRP); MOH, Singapore [AIC/RPDD/SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912]; National Medical Research Council (NMRC) Large Collaborative Grant (LCG) National Medical Research Council Singapore, Ministry of Health, Singapore(Ministry of Health-SingaporeNational Medical Research Council, Singapore); Tanoto Foundation; National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant)(Ministry of Health-SingaporeNational Medical Research Council, Singapore); Health Service Research Grant; SingHealth Foundation Research Grant(SingHealth); NMRC, MOH; Biomedical Research Council(Agency for Science Technology & Research (A*STAR)); Singapore Diabetic Retinopathy Program (SiDRP); MOH, Singapore(Ministry of Health-Singapore); National Medical Research Council (NMRC) Large Collaborative Grant (LCG)(National Medical Research Council, Singapore) The Singapore Ocular Reading Center (Haslina Hamzah, Jin Y. Ho) has helped coordinated the data transfer and management of the study. This study was supported by the National Medical Research Council Singapore, Ministry of Health, Singapore and Tanoto Foundation. This project received funding from National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022); Health Service Research Grant; SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation. For Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, & STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550, 09/1/35/19/616). The Singapore Diabetic Retinopathy Program (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912). The Diabetes study in Nephro-pathy And other Microvascular cOmplications (DYNAMO) received funding from National Medical Research Council (NMRC) Large Collaborative Grant (LCG). 51 31 32 1 11 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2398-6352 NPJ DIGIT MED npj Digit. Med. APR 10 2019.0 2.0 24 10.1038/s41746-019-0097-x 0.0 8 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics HW6GY 31304371.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000466788700001 0
J Mesin, L; Porcu, P; Russu, D; Farina, G; Borzi, L; Zhang, W; Guo, YZ; Olmo, G Mesin, Luca; Porcu, Paola; Russu, Debora; Farina, Gabriele; Borzi, Luigi; Zhang, Wei; Guo, Yuzhu; Olmo, Gabriella A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson's Disease SENSORS English Article Parkinson's disease; Freezing of Gait; multi-modal analysis; inertial sensors; electroencephalogram (EEG); skin conductance (SC); machine learning; support vector machine (SVM); k-nearest neighbor (kNN) PREDICTION; FALLS Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson's disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2-3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living. [Mesin, Luca; Porcu, Paola; Russu, Debora] Politecn Torino, Dept Elect & Telecommun, Corso Duca Abruzzi 24, I-10129 Turin, Italy; [Farina, Gabriele] Azienda Osped Univ Sassari, Neurol Unit, Viale San Pietro 10, I-07100 Sassari, Italy; [Borzi, Luigi; Olmo, Gabriella] Politecn Torino, Dept Control & Comp Engn, Corso Duca Abruzzi 24, I-10129 Turin, Italy; [Zhang, Wei] Capital Med Univ, Beijing Inst Geriatr, Dept Neurol Neurobiol & Geriatr, Xuanwu Hosp, Beijing 100053, Peoples R China; [Zhang, Wei] Xuzhou Med Univ, Dept Neurol, Affiliated Hosp, Xuzhou 221006, Jiangsu, Peoples R China; [Guo, Yuzhu] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China Polytechnic University of Turin; University of Sassari; Polytechnic University of Turin; Capital Medical University; Xuzhou Medical University; Beihang University Olmo, G (corresponding author), Politecn Torino, Dept Control & Comp Engn, Corso Duca Abruzzi 24, I-10129 Turin, Italy. luca.mesin@polito.it; paola.porcu@studenti.polito.it; debora.russu@studenti.polito.it; gabriele.farina@gmail.com; luigi.borzi@polito.it; wzhang_pd@hotmail.com; yuzhuguo@buaa.edu.cn; gabriella.olmo@polito.it Borzì, Luigi/AAG-5036-2021 Borzì, Luigi/0000-0003-0875-6913; Zhang, Wei/0000-0002-3169-4442; Mesin, Luca/0000-0002-8239-2348; Russu, Debora/0000-0003-4495-7544; Porcu, Paola/0000-0001-7337-6939; OLMO, Gabriella/0000-0002-3670-9412; Guo, Yuzhu/0000-0002-8588-5172 National Natural Science Foundation of China [61876015]; Beijing Natural Science Foundation, China [4202040] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation, China(Beijing Natural Science Foundation) National Natural Science Foundation of China (Grant No. 61876015), and the Beijing Natural Science Foundation, China (Grant No. 4202040). 41 2 2 2 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2022.0 22.0 7 2613 10.3390/s22072613 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 0L7VS 35408226.0 gold 2023-03-23 WOS:000781678400001 0
J Chen, YY; Hu, SX; Shen, X; Ai, CR; Suykens, JAK Chen, Yingyi; Hu, Shell Xu; Shen, Xi; Ai, Chunrong; Suykens, Johan A. K. Compressing Features for Learning With Noisy Labels IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Noise measurement; Training; Principal component analysis; Kernel; Deep learning; Biological system modeling; Benchmark testing; Bias variance decomposition; compression; deep learning; information sorting; label noise Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that are corrupted, and hence can hardly generalize to clean datasets. In this article, we focus on the problem of learning with noisy labels and introduce compression inductive bias to network architectures to alleviate this overfitting problem. More precisely, we revisit one classical regularization named Dropout and its variant Nested Dropout. Dropout can serve as a compression constraint for its feature dropping mechanism, while Nested Dropout further learns ordered feature representations with respect to feature importance. Moreover, the trained models with compression regularization are further combined with co-teaching for performance boost. Theoretically, we conduct bias variance decomposition of the objective function under compression regularization. We analyze it for both single model and co-teaching. This decomposition provides three insights: 1) it shows that overfitting is indeed an issue in learning with noisy labels; 2) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; and 3) it gives explanations on the performance boost brought by incorporating compression regularization into co-teaching. Experiments show that our simple approach can have comparable or even better performance than the state-of-the-art methods on benchmarks with real-world label noise including Clothing1M and ANIMAL-10N. Our implementation is available at https://yingyichen-cyy.github.io/ CompressFeatNoisyLabels/. [Chen, Yingyi; Suykens, Johan A. K.] Katholieke Univ Leuven, Dept Elect Engn ESAT, STADIUS, B-3001 Leuven, Belgium; [Hu, Shell Xu] Samsung AI Ctr, Cambridge, England; [Shen, Xi] Tencent AI Lab, Shenzhen, Peoples R China; [Ai, Chunrong] Chinese Univ Hong Kong, Sch Management & Econ, Shenzhen, Peoples R China KU Leuven; Tencent; Chinese University of Hong Kong, Shenzhen Chen, YY (corresponding author), Katholieke Univ Leuven, Dept Elect Engn ESAT, STADIUS, B-3001 Leuven, Belgium.;Shen, X (corresponding author), Tencent AI Lab, Shenzhen, Peoples R China. yingyi.chen@esat.kuleuven.be; shell.hu@samsung.com; xi.shen@enpc.fr; chunrongai@cuhk.edu.cn; johan.suykens@esat.kuleuven.be Suykens, Johan/C-9781-2014 Suykens, Johan/0000-0002-8846-6352; Chen, Yingyi/0000-0002-5571-9050 European Research Council under the European Union [787960]; Research Council KU Leuven [C14/18/068]; Research Foundation-Flanders (FWO) [GOA4917N]; Flemish Government (AI Research Program); EU European Research Council under the European Union(European Research Council (ERC)); Research Council KU Leuven(KU Leuven); Research Foundation-Flanders (FWO)(FWO); Flemish Government (AI Research Program); EU(European Commission) This work was jointly supported by the European Research Council under the European Union's Horizon 2020 research and innovation program/ERC Advanced Grant E-DUALITY (787960), Research Council KU Leuven: Optimization framework for deep kernel machines C14/18/068, The Research Foundation-Flanders (FWO) projects: GOA4917N (Deep Restricted kernel Machines: Methods and Foundations), Ph.D./Postdoctoral grant, the Flemish Government (AI Research Program), EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI-Integrating Reasoning, Learning and Optimization), Leuven.AI Institute. 65 0 0 4 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3186930 0.0 JUL 2022 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 2Y7JG 35802546.0 Green Submitted, Green Accepted 2023-03-23 WOS:000826067800001 0
J Feijoo, C; Kwon, Y; Bauer, JM; Bohlin, E; Howell, B; Jain, R; Potgieter, P; Vu, K; Whalley, J; Xia, J Feijoo, Claudio; Kwon, Youngsun; Bauer, Johannes M.; Bohlin, Erik; Howell, Bronwyn; Jain, Rekha; Potgieter, Petrus; Vu, Khuong; Whalley, Jason; Xia, Jun Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy TELECOMMUNICATIONS POLICY English Article Artificial intelligence; Human well-being; Decentralisation; Protectionism; Techno-nationalism; Fragmentation; Technology diplomacy; International collaborative governance COLLABORATIVE GOVERNANCE The field of artificial intelligence (AI) is experiencing a period of intense progress due to the consolidation of several key technological enablers. AI is already deployed widely and has a high impact on work and daily life activities. The continuation of this process will likely contribute to deep economic and social changes. To realise the tremendous benefits of AI while mitigating undesirable effects will require enlightened responses by many stakeholders. Varying national institutional, economic, political, and cultural conditions will influence how AI will affect convenience, efficiency, personalisation, privacy protection, and surveillance of citizens. Many expect that the winners of the AI development race will dominate the coming decades economically and geopolitically, potentially exacerbating tensions between countries. Moreover, nations are under pressure to protect their citizens and their interests-and even their own political stability-in the face of possible malicious or biased uses of AI. On the one hand, these different stressors and emphases in AI development and deployment among nations risk a fragmentation between world regions that threatens technology evolution and collaboration. On the other hand, some level of differentiation will likely enrich the global AI ecosystem in ways that stimulate innovation and introduce competitive checks and balances through the decentralisation of AI development. International cooperation, typically orchestrated by intergovernmental and nongovernmental organisations, private sector initiatives, and by academic researchers, has improved common welfare and avoided undesirable outcomes in other technology areas. Because AI will most likely have more fundamental effects on our lives than other recent technologies, stronger forms of cooperation that address broader policy and governance challenges in addition to regulatory and technological issues may be needed. At a time of great challenges among nations, international policy coordination remains a necessary instrument to tackle the ethical, cultural, economic, and political repercussions of AI. We propose to advance the emerging concept of technology diplomacy to facilitate the global alignment of AI policy and governance and create a vibrant AI innovation system. We argue that the prevention of malicious uses of AI and the enhancement of human welfare create strong common interests across jurisdictions that require sustained efforts to develop better, mutually beneficial approaches. We hope that new technology diplomacy will facilitate the dialogues necessary to help all interested parties develop a shared understanding and coordinate efforts to utilise AI for the benefit of humanity, a task whose difficulty should not be underestimated. [Feijoo, Claudio] Tech Univ Madrid, Madrid, Spain; [Feijoo, Claudio] Tongji Univ, Shanghai, Peoples R China; [Kwon, Youngsun] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Bauer, Johannes M.] Michigan State Univ, E Lansing, MI 48824 USA; [Bohlin, Erik] Chalmers Univ Technol, Gothenburg, Sweden; [Howell, Bronwyn] Victoria Univ Wellington, Wellington, New Zealand; [Jain, Rekha] Indian Inst Management Ahmedabad, Ahmadabad, Gujarat, India; [Potgieter, Petrus] Univ South Africa, Pretoria, South Africa; [Vu, Khuong] Natl Univ Singapore, Singapore, Singapore; [Whalley, Jason] Northumbria Univ, Newcastle Upon Tyne, Tyne & Wear, England; [Xia, Jun] Beijing Univ Posts & Telecommun, Beijing, Peoples R China Universidad Politecnica de Madrid; Tongji University; Korea Advanced Institute of Science & Technology (KAIST); Michigan State University; Chalmers University of Technology; Victoria University Wellington; Indian Institute of Management (IIM System); Indian Institute of Management Ahmedabad; University of South Africa; National University of Singapore; Northumbria University; Beijing University of Posts & Telecommunications Feijoo, C (corresponding author), Tech Univ Madrid, Madrid, Spain. claudio.feijoo@upm.es; yokwon@kaist.ac.kr; bauerj@msu.edu; erik.bohlin@chalmers.se; bronwyn.howell@vuw.ac.nz; rekha@iima.ac.in; php@grensnut.com; vuminhkhuong@nus.edu.sg; jason.whalley@northumbria.ac.uk; xiajun@bupt.edu.cn Feijoo, Claudio/AAR-7878-2020; Xia, Jun/AAT-7630-2020 Potgieter, Petrus/0000-0003-1785-761X; Feijoo, Claudio/0000-0002-9499-7790; Xia, Jun/0000-0003-4856-7770 65 25 26 9 42 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0308-5961 1879-3258 TELECOMMUN POLICY Telecommun. Policy JUL 2020.0 44.0 6 101988 10.1016/j.telpol.2020.101988 0.0 14 Communication; Information Science & Library Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Communication; Information Science & Library Science; Telecommunications LT9YF 32377031.0 Green Published, Green Accepted, hybrid 2023-03-23 WOS:000537420300012 0
C Ismail, A; Saidi, L; Sayadi, M; Benbouzid, M IEEE Ismail, Adla; Saidi, Lotfi; Sayadi, Mounir; Benbouzid, Mohamed Power IGBT Remaining Useful Life Estimation Using Neural Networks based Feature Reduction 2020 6TH IEEE INTERNATIONAL ENERGY CONFERENCE (ENERGYCON) IEEE International Energy Conference English Proceedings Paper 6th IEEE International Energy Conference (IEEE ENERGYCON) - Energy Transition for Developing Smart Sustainable Cities SEP 28-OCT 01, 2020 IEEE, ELECTR NETWORK IEEE Reg 8,IEEE Africa Council,IEEE Tunisia Sect,IEEE Power & Energy Soc,IEEE Power Elect Soc,IEEE Ind Applicat Soc,IEEE Ind Elect Soc IEEE Data-based approach; IGBT; Feedforward Neural Network; Prognostic; Power converter; Remaining useful life; Time-domain feature; Wind energy system Power converters (PCVs) are a fundamental part of wind energy systems (WESs). Generally, the PCVs are subjected to high failure frequency rate and consequently to the system downtime due to its structural complexity. Therefore, the main propose of this study is to develop an understanding prognostic approach dedicated to WES PCVs. The proposed method investigated the Insulated Gate Bipolar Transistor (IGBT) failure in PCVs. Therefore, it integrates the data-based methodology. Usually, data-based approaches based on a recorded data base. Therefore, in this paper, we use the collector-emitter voltage as a precursor signal. In this work, the prediction of the Remaining Useful Life (RUL) of the IGBT device is achieved through the Feedforward Neural Network (FFNN) technique. Moreover, we used the time-domain analysis to extract useful information used as an input in the health indicator generation task. In this paper, the health indicator generation task is achieved using the Principal Component Analysis (PCA) technique. The advantages of the proposed prognostic method in term of RUL prediction are illustrated using a real IGBT accelerated ageing database provided from NASA Ames laboratory Prognostics Center of Excellence. [Ismail, Adla; Saidi, Lotfi; Sayadi, Mounir] Univ Tunis, Lab Signal Image & Energy Mastery SIME, LR 13ES03, ENSIT, Tunis 1008, Tunisia; [Saidi, Lotfi] Univ Sousse, Dept Elect & Comp Engn, ESSTHS, Sousse 4011, Tunisia; [Saidi, Lotfi; Benbouzid, Mohamed] Univ Brest, UMR CNRS 6027 IRDL, F-29238 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Shanghai, Peoples R China Universite de Tunis; Universite de Sousse; Universite de Bretagne Occidentale; Shanghai Maritime University Ismail, A (corresponding author), Univ Tunis, Lab Signal Image & Energy Mastery SIME, LR 13ES03, ENSIT, Tunis 1008, Tunisia. 24 1 2 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-4322 978-1-7281-2956-3 IEEE INT ENER CONF 2020.0 137 142 6 Energy & Fuels; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Energy & Fuels; Engineering BR3CB 2023-03-23 WOS:000644437800025 0
J Lopez-Belmonte, J; Segura-Robles, A; Cho, WC; Parra-Gonzalez, ME; Moreno-Guerrero, AJ Lopez-Belmonte, Jesus; Segura-Robles, Adrian; Cho, William C.; Elena Parra-Gonzalez, Maria; Moreno-Guerrero, Antonio J. What does literature teach about digital pathology? A bibliometric study in Web of Science IJERI-INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH AND INNOVATION English Article Bibliometric analysis; digital pathology; scientific mapping; scientific production; web of science Digital pathology (DIPA) has become an effective discipline that generates a graphic environment to diagnose and interpret the pathological information of people. When analyzing the existing literature on DIPA, a knowledge gap was produced by not reporting a study that has bibliometrically analyzed the publications on the subject. The objective of this study is to analyze the scientific production and performance achieved by the term digital pathology in the Web of Science (WoS) database. For this, a methodology based on bibliometrics has been carried out, complemented by the scientific mapping technique to search, recorder, analyze and predict the scientific literature on the state of question. We have worked with an analysis unit of 1222 documents reported from WoS database. The results show that there is no research topic in the field of study of DIPA that stands out from the rest. A conceptual gap can be observed in the thematic development, given that there is no theme that is repeated in all periods, where the connections are more thematic than conceptual. There are key documents for different topics. The main themes have been very different over the years like telepathology and artificial intelligence. [Lopez-Belmonte, Jesus; Segura-Robles, Adrian; Elena Parra-Gonzalez, Maria; Moreno-Guerrero, Antonio J.] Univ Granada, Granada, Spain; [Cho, William C.] Queen Elizabeth Hosp, Hong Kong, Peoples R China University of Granada Lopez-Belmonte, J (corresponding author), Univ Granada, Granada, Spain. jesuslopez@ugr.es; adrianseg@ugr.es; williamcscho@gmail.com; elenaparra@ugr.es; ajmoreno@ugr.es Segura, Adrián/B-4963-2019; Moreno-Guerrero, Antonio-José/P-5859-2018 Segura, Adrián/0000-0003-0753-7129; Moreno-Guerrero, Antonio-José/0000-0003-3191-2048 37 1 1 2 9 UNIV PABLO OLAVIDE, FAC CIENCIAS SOCIALES SEVILLA CTRA UTRERA KM 1, SEVILLA, 41013, SPAIN 2386-4303 IJERI-INT J EDUC RES IJERI-Int. J. Educ. Res. Innov. 2021.0 16 106 121 10.46661/ijeri.4918 0.0 16 Education & Educational Research Emerging Sources Citation Index (ESCI) Education & Educational Research XV4AV Green Published, gold 2023-03-23 WOS:000734887500006 0
J Yu, S; Huang, CJ; Tang, JS; Jia, ZA; Wang, YT; Ke, ZJ; Liu, W; Liu, X; Zhou, ZQ; Cheng, ZD; Xu, JS; Wu, YC; Zhao, YY; Xiang, GY; Li, CF; Guo, GC; Sentis, G; Munoz-Tapia, R Yu, Shang; Huang, Chang-Jiang; Tang, Jian-Shun; Jia, Zhih-Ahn; Wang, Yi-Tao; Ke, Zhi-Jin; Liu, Wei; Liu, Xiao; Zhou, Zong-Quan; Cheng, Ze-Di; Xu, Jin-Shi; Wu, Yu-Chun; Zhao, Yuan-Yuan; Xiang, Guo-Yong; Li, Chuan-Feng; Guo, Guang-Can; Sentis, Gael; Munoz-Tapia, Ramon Experimentally detecting a quantum change point via the Bayesian inference PHYSICAL REVIEW A English Article PHASE Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the point where the change took place. In this Rapid Communication, we build a pseudo-on-demand single-photon source to prepare the photon sequences, and consider a learning agent that applies Bayesian inference on experimental data to solve this problem. This learning machine adjusts the measurement over each photon according to the past experimental results and finds the change position in an online fashion. Our results show that the local-detection success probability can be largely improved by using such a machine-learning technique. This protocol provides a tool for improvement in many applications where a sequence of identical quantum states is required. [Yu, Shang; Huang, Chang-Jiang; Tang, Jian-Shun; Jia, Zhih-Ahn; Wang, Yi-Tao; Ke, Zhi-Jin; Liu, Wei; Liu, Xiao; Zhou, Zong-Quan; Cheng, Ze-Di; Xu, Jin-Shi; Wu, Yu-Chun; Zhao, Yuan-Yuan; Xiang, Guo-Yong; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Anhui, Peoples R China; [Yu, Shang; Huang, Chang-Jiang; Tang, Jian-Shun; Jia, Zhih-Ahn; Wang, Yi-Tao; Ke, Zhi-Jin; Liu, Wei; Liu, Xiao; Zhou, Zong-Quan; Cheng, Ze-Di; Xu, Jin-Shi; Wu, Yu-Chun; Zhao, Yuan-Yuan; Xiang, Guo-Yong; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China; [Sentis, Gael] Univ Siegen, Nat Wissensch Tech Fak, D-57068 Siegen, Germany; [Munoz-Tapia, Ramon] Univ Autonoma Barcelona, Dept Fis, Fis Teor Informacio & Fenomens Quant, Bellaterra 08193, Barcelona, Spain Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universitat Siegen; Autonomous University of Barcelona Tang, JS; Xiang, GY; Li, CF (corresponding author), Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Anhui, Peoples R China.;Tang, JS; Xiang, GY; Li, CF (corresponding author), Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China.;Sentis, G (corresponding author), Univ Siegen, Nat Wissensch Tech Fak, D-57068 Siegen, Germany.;Munoz-Tapia, R (corresponding author), Univ Autonoma Barcelona, Dept Fis, Fis Teor Informacio & Fenomens Quant, Bellaterra 08193, Barcelona, Spain. tjs@ustc.edu.cn; gyxiang@ustc.edu.cn; cfli@ustc.edu.cn; gael.sentis@uni-siegen.de; Ramon.Munoz@uab.cat Munoz Tapia, Ramon/F-7332-2016; Sentís, Gael/AAB-4005-2019 Munoz Tapia, Ramon/0000-0002-3048-9236; Sentís, Gael/0000-0002-4982-6570; Jia, Zhian/0000-0001-8588-173X; Liu, Xiao/0000-0002-3264-7939 National Key Research and Development Program of China [2016YFA0302700]; National Natural Science Foundation of China [11674304, 61327901, 61490711, 11774334, 11774335, 11474267, 11325419, 11574291, 91321313]; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences [QYZDY-SSW-SLH003]; Anhui Initiative in Quantum Information Technologies [AHY020100, AHY060300]; Youth Innovation Promotion Association of Chinese Academy of Sciences [2017492]; Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences [YJKYYQ20170032]; Fundamental Research Funds for the Central Universities [WK2470000026]; Spanish MINECO [FIS2013-40627-P, FIS2016-80681-P]; ERC Consolidator Grant [683107/TempoQ]; DFG National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences of the Chinese Academy of Sciences; Anhui Initiative in Quantum Information Technologies; Youth Innovation Promotion Association of Chinese Academy of Sciences; Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Spanish MINECO(Spanish Government); ERC Consolidator Grant(European Research Council (ERC)); DFG(German Research Foundation (DFG)) This work is supported by the National Key Research and Development Program of China (No. 2016YFA0302700), the National Natural Science Foundation of China (Grants No. 11674304, No. 61327901, No. 61490711, No. 11774334, No. 11774335, No. 11474267, No. 11325419, No. 11574291, and No. 91321313), the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (Grant No. QYZDY-SSW-SLH003), Anhui Initiative in Quantum Information Technologies (AHY020100, AHY060300), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2017492), the Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences (No. YJKYYQ20170032), and the Fundamental Research Funds for the Central Universities (No. WK2470000026). R.M.T. acknowledges the Spanish MINECO through Contracts No. FIS2013-40627-P and No. FIS2016-80681-P. G.S. acknowledges financial support from the ERC Consolidator Grant No. 683107/TempoQ, and the DFG. 34 3 3 0 19 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9926 2469-9934 PHYS REV A Phys. Rev. A OCT 15 2018.0 98.0 4 40301 10.1103/PhysRevA.98.040301 0.0 6 Optics; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics GW9GO Green Submitted 2023-03-23 WOS:000447291200001 0
J He, L; Zhu, JW; Li, J; Plaza, A; Chanussot, J; Li, B He, Lin; Zhu, Jiawei; Li, Jun; Plaza, Antonio; Chanussot, Jocelyn; Li, Bo HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Convolutional neural networks (CNN); hyperspectral (HS) image; pansharpening; spectral prediction FUSION TECHNIQUE; IMAGE FUSION; MODULATION Hyperspectral (HS) pansharpening intends to synthesize a HS image with a registered panchromatic image, to generate an enhanced image with simultaneous high spectral resolution and high spatial resolution. However, the spectral range gap between the two kinds of images and the need to resolve details for many continuous narrow bands make the technique prone to spectral distortion and spatial blurring. To mitigate the problems, we propose a new HS pansharpening framework via spectrally predictive convolutional neural networks (HyperPNN). In our proposed HyperPNN, spectrally predictive structure is introduced to strengthen the spectral prediction capability of a pansharpening network. Following the concept of the proposed HyperPNN, two specific pansharpening convolutional neural network (CNN) models, i.e., HyperPNN1 and HyperPNN2, are designed. Experimental results from three datasets suggest the excellent performance of our CNN-based HS pansharpening methods. [He, Lin; Zhu, Jiawei] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain; [Chanussot, Jocelyn] Univ Grenoble Alpes, INRIA, CNRS, LJK,Grenoble INP, F-38000 Grenoble, France; [Li, Bo] Beihang Univ, Sch Comp Sci & Engn, Beijing Key Lab Digital Media, Beijing 100191, Peoples R China; [Li, Bo] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China South China University of Technology; Sun Yat Sen University; Universidad de Extremadura; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Beihang University; Beihang University He, L (corresponding author), South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.;Li, J (corresponding author), Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China. helin@scut.edu.cn; zjw7342@163.com; lijun48@mail.sysu.edu.cn; aplaza@unex.es; jocelyn.chanussot@gipsa-lab.grenoble-inp.fr; boli@buaa.edu.cn Li, Bo/AAA-8968-2020; Plaza, Antonio/C-4455-2008; Li, Jun/AAD-7233-2020 Li, Bo/0000-0002-7294-6888; Plaza, Antonio/0000-0002-9613-1659; Li, Jun/0000-0003-1613-9448; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [61571195, 61771496, 61633010, 61836003]; Guangdong Provincial Natural Science Foundation [2016A030313254, 2016A030313516, 2017A030313382]; National Key Research and Development Program of China [2017YFB0502900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grants 61571195, 61771496, 61633010, and 61836003, in part by the Guangdong Provincial Natural Science Foundation under Grants 2016A030313254, 2016A030313516, and 2017A030313382, and in part by the National Key Research and Development Program of China under Grant 2017YFB0502900. (Corresponding authors: Lin He; Jun Li.) 34 47 48 5 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. AUG 2019.0 12.0 8 SI 3092 3100 10.1109/JSTARS.2019.2917584 0.0 9 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology JA0UZ 2023-03-23 WOS:000487530100042 0
J Zhang, XY; Xie, GG; Wang, X; Zhang, PH; Li, YBA; Salamatian, K Zhang, Xinyi; Xie, Gaogang; Wang, Xin; Zhang, Penghao; Li, Yanbiao; Salamatian, Kave Fast Online Packet Classification With Convolutional Neural Network IEEE-ACM TRANSACTIONS ON NETWORKING English Article Packet classification; Software Defined Networking (SDN); Open vSwitch (OVS) ALGORITHMS; CHALLENGES Packet classification is a critical component in network appliances. Software Defined Networking and cloud computing update the rulesets frequently for flexible policy configuration. Tuple Space Search (TSS), implemented in Open vSwitch (OVS), achieves fast rule updating at the sacrifice of the classification rate. In TSS, each tuple is managed by a hash table and classifying a packet needs to go through all hash tables. Merging tuples can reduce the number of hash tables, but inevitably increases the hash conflicts that may even worsen the classification performance in some cases. No existing algorithm meets the need of both fast packet classification and online rule updating. In this paper, we propose Convolutional Neural Network (CNN)-based Range Partition (CRP) to achieve fast packet classification and online update simultaneously. CRP exploits CNN-based image recognition to quickly partition tuples into range spaces upon the change of ruleset distribution, which reduces hash operations while avoiding rule overlapping caused by hashing many rules to the same location of the hash table. Experimental results demonstrate that CRP achieves 3.2x classification speed and 4.2x update speed on average compared with state-of-the-art algorithms. We also implement CRP in OVS. The throughput of CRP-OVS is 10x that of native OVS. [Zhang, Xinyi; Zhang, Penghao] Chinese Acad Sci, Inst Comp Technol ICT, Beijing 100190, Peoples R China; [Zhang, Xinyi; Xie, Gaogang; Zhang, Penghao; Li, Yanbiao] Univ Chinese Acad Sci UCAS, Sch Comp Sci & Technol, Beijing 100049, Peoples R China; [Xie, Gaogang; Li, Yanbiao] Chinese Acad Sci, Comp Network Informat Ctr CNIC, Beijing 100190, Peoples R China; [Wang, Xin] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA; [Salamatian, Kave] Univ Savoie Mt Blanc, LISTIC, F-73000 Chambery, France Chinese Academy of Sciences; Institute of Computing Technology, CAS; Chinese Academy of Sciences; Computer Network Information Center, CAS; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook Xie, GG (corresponding author), Chinese Acad Sci, Comp Network Informat Ctr CNIC, Beijing 100190, Peoples R China. zhangxinyi@ict.ac.cn; xie@cnic.cn; x.wang@stonybrook.edu; zhangpenghao@ict.ac.cn; lybmath@cnic.cn; kave.salamatian@univ-smb.fr Wang, Xin/0000-0001-8639-3818; Salamatian, Kave/0000-0001-5557-9134 National Key Research and Development Program of China [2019YFB1802800]; National Science Fund for Distinguished Young Scholars [61725206] National Key Research and Development Program of China; National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars) This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1802800 and in part by the National Science Fund for Distinguished Young Scholars under Grant 61725206. (Corresponding author: Gaogang Xie.) 45 1 1 2 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6692 1558-2566 IEEE ACM T NETWORK IEEE-ACM Trans. Netw. DEC 2021.0 29.0 6 2765 2778 10.1109/TNET.2021.3100114 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications XP8ZE 2023-03-23 WOS:000731147300031 0
J Song, Q; Xu, F; Zhu, XX; Jin, YQ Song, Qian; Xu, Feng; Zhu, Xiao Xiang; Jin, Ya-Qiu Learning to Generate SAR Images With Adversarial Autoencoder IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Radar polarimetry; Training; Synthetic aperture radar; Task analysis; Target recognition; Generative adversarial networks; Generators; Adversarial autoencoder (AAE); deep learning (DL); few-shot learning (FSL); image representation; synthetic aperture radar (SAR) Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due to scattering scintillation. Thus, data-driven SAR target recognition is considered a typical few-shot learning (FSL) task. This article first reviews the key issues of FSL and provides a definition of the FSL task. A novel adversarial autoencoder (AAE) is then proposed as an SAR representation and generation network. It consists of a generator network that decodes target knowledge to SAR images and an adversarial discriminator network that not only learns to discriminate ``fake'' generated images from real ones but also encodes the input SAR image back to target knowledge. The discriminator employs progressively expanding convolution layers and a corresponding layer-by-layer training strategy. It uses two cyclic loss functions to enforce consistency between the inputs and outputs. Moreover, rotated cropping is introduced as a mechanism to address the challenge of representing the target orientation. The moving and stationary Target recognition (MSTAR) 7-target dataset is used to evaluate the AAE's performance, and the results demonstrate its ability to generate SAR images with aspect angular diversity. Using only 90 training samples with at least 25 degrees of orientation interval, the trained AAE is able to generate the remaining 1748 samples of other orientation angles with an unprecedented level of fidelity. Thus, it can be used for data augmentation in SAR target recognition FSL tasks. Our experimental results show that the AAE could boost the test accuracy by 5.77%. [Song, Qian; Xu, Feng; Jin, Ya-Qiu] Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai 200433, Peoples R China; [Song, Qian; Zhu, Xiao Xiang] Remote Sensing Technol Inst, German Aerosp Ctr, D-82234 Wessling, Germany; [Zhu, Xiao Xiang] Tech Univ Munich, Data Sci Earth Observ, D-80333 Munich, Germany Fudan University; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich Xu, F (corresponding author), Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai 200433, Peoples R China. fengxu@fudan.edu.cn XU, Feng/A-4582-2010 XU, Feng/0000-0002-7015-1467; Zhu, Xiao Xiang/0000-0001-5530-3613; Song, Qian/0000-0003-2746-6858 Natural Science Foundation of China [61991422, 61822107] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Natural Science Foundation of China under Grant 61991422 and Grant 61822107. 33 3 3 6 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3086817 0.0 JUN 2021 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG3HV Green Accepted 2023-03-23 WOS:000733476000001 0
J Makarious, MB; Leonard, HL; Vitale, D; Iwaki, H; Sargent, L; Dadu, A; Violich, I; Hutchins, E; Saffo, D; Bandres-Ciga, S; Kim, JJ; Song, YJ; Maleknia, M; Bookman, M; Nojopranoto, W; Campbell, RH; Hashemi, SH; Botia, JA; Carter, JF; Craig, DW; Van Keuren-Jensen, K; Morris, HR; Hardy, JA; Blauwendraat, C; Singleton, AB; Faghri, F; Nalls, MA Makarious, Mary B.; Leonard, Hampton L.; Vitale, Dan; Iwaki, Hirotaka; Sargent, Lana; Dadu, Anant; Violich, Ivo; Hutchins, Elizabeth; Saffo, David; Bandres-Ciga, Sara; Kim, Jonggeol Jeff; Song, Yeajin; Maleknia, Melina; Bookman, Matt; Nojopranoto, Willy; Campbell, Roy H.; Hashemi, Sayed Hadi; Botia, Juan A.; Carter, John F.; Craig, David W.; Van Keuren-Jensen, Kendall; Morris, Huw R.; Hardy, John A.; Blauwendraat, Cornelis; Singleton, Andrew B.; Faghri, Faraz; Nalls, Mike A. Multi-modality machine learning predicting Parkinson's disease NPJ PARKINSONS DISEASE English Article SMELL IDENTIFICATION TEST; HEALTH; RISK; UNIVERSITY; DIAGNOSIS; ONSET Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available. [Makarious, Mary B.; Leonard, Hampton L.; Iwaki, Hirotaka; Sargent, Lana; Bandres-Ciga, Sara; Kim, Jonggeol Jeff; Song, Yeajin; Blauwendraat, Cornelis; Singleton, Andrew B.; Faghri, Faraz; Nalls, Mike A.] NIA, Neurogenet Lab, NIH, Bethesda, MD 20892 USA; [Makarious, Mary B.; Morris, Huw R.; Hardy, John A.] UCL Queen Sq Inst Neurol, Dept Clin & Movement Neurosci, London, England; [Makarious, Mary B.; Morris, Huw R.; Hardy, John A.] UCL, UCL Movement Disorders Ctr, London, England; [Leonard, Hampton L.; Vitale, Dan; Iwaki, Hirotaka; Sargent, Lana; Singleton, Andrew B.; Faghri, Faraz; Nalls, Mike A.] NIH, Ctr Alzheimers & Related Dementias, Bethesda, MD 20814 USA; [Leonard, Hampton L.; Vitale, Dan; Iwaki, Hirotaka; Song, Yeajin; Faghri, Faraz; Nalls, Mike A.] Data Tecn Int LLC, Glen Echo, MD 20812 USA; [Leonard, Hampton L.] German Ctr Neurodegenerat Dis DZNE, Tubingen, Germany; [Sargent, Lana] Virginia Commonwealth Univ, Sch Nursing, Richmond, VA USA; [Sargent, Lana] Virginia Commonwealth Univ, Sch Pharm, Geriatr Pharmacotherapy Program, Richmond, VA USA; [Dadu, Anant; Campbell, Roy H.; Hashemi, Sayed Hadi] Univ Illinois, Dept Comp Sci, Urbana, IL USA; [Violich, Ivo; Craig, David W.] Univ Southern Calif, Inst Translat Genom, Los Angeles, CA USA; [Hutchins, Elizabeth; Van Keuren-Jensen, Kendall] Translat Genom Res Inst TGen, Neurogen Div, Phoenix, AZ USA; [Saffo, David] Northeastern Univ, Coll Comp Sci, Boston, MA 02115 USA; [Kim, Jonggeol Jeff] Queen Mary Univ London, Wolfson Inst Prevent Med, Prevent Neurol Unit, London, England; [Maleknia, Melina] Georgia Inst Technol, Atlanta, GA USA; [Bookman, Matt; Nojopranoto, Willy] Verily Life Sci, San Francisco, CA USA; [Botia, Juan A.] UCL Queen Sq Inst Neurol, Dept Mol Neurosci, London, England; [Botia, Juan A.] Univ Murcia, Dept Ingn Informac & Comun, Murcia, Spain; [Carter, John F.] ModelOp, Chicago, IL USA; [Hardy, John A.] UK Dementia Res Inst, London, England; [Hardy, John A.] Dept Neurodegenerat Dis, London, England; [Hardy, John A.] Reta Lila Weston Inst, London, England; [Hardy, John A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Hong Kong, Peoples R China National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA); University of London; University College London; University of London; University College London; National Institutes of Health (NIH) - USA; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE); Virginia Commonwealth University; Virginia Commonwealth University; University of Illinois System; University of Illinois Urbana-Champaign; University of Southern California; Translational Genomics Research Institute; Northeastern University; University of London; Queen Mary University London; University System of Georgia; Georgia Institute of Technology; University of London; University College London; University of Murcia; University of London; University College London; Hong Kong University of Science & Technology Faghri, F; Nalls, MA (corresponding author), NIA, Neurogenet Lab, NIH, Bethesda, MD 20892 USA.;Faghri, F; Nalls, MA (corresponding author), NIH, Ctr Alzheimers & Related Dementias, Bethesda, MD 20814 USA.;Faghri, F; Nalls, MA (corresponding author), Data Tecn Int LLC, Glen Echo, MD 20812 USA. faraz@datatecnica.com; mike@datatecnica.com Singleton, Andrew B/C-3010-2009; Ciga, Sara Bandres/AAA-5036-2020; Dadu, Anant/GWM-5115-2022; Craig, David Wesley/K-6961-2019; Hardy, John/C-2451-2009 Craig, David Wesley/0000-0003-2040-1955; Hardy, John/0000-0002-3122-0423; Makarious, Mary/0000-0002-7978-1051 Intramural Research Program of the National Institute on Aging; National Institute of Neurological Disorders and Stroke [Z01-AG000949-02]; Celgene; GSK; Michael J. Fox Foundation for Parkinson's Research; National Institute of Neurological Disorders and Stroke; Pfizer; Sanofi; Verily; National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health; NINDS [U01NS082134, U01NS082157, U01NS082151, U01NS082137, U01NS082148, U01NS082133]; Aligning Science against Parkinson's (ASAP) initiative Intramural Research Program of the National Institute on Aging(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); National Institute of Neurological Disorders and Stroke(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); Celgene(Bristol-Myers SquibbCelgene Corporation); GSK(GlaxoSmithKline); Michael J. Fox Foundation for Parkinson's Research; National Institute of Neurological Disorders and Stroke(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); Pfizer(Pfizer); Sanofi; Verily; National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health; NINDS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); Aligning Science against Parkinson's (ASAP) initiative This work was supported in part by the Intramural Research Program of the National Institute on Aging and the National Institute of Neurological Disorders and Stroke (project number Z01-AG000949-02). Data used in the preparation of this article were obtained from the AMP PD Knowledge Platform. For up-to-date information on the study, please visit. AMP PD-a public-private partnership-is managed by the FNIH and funded by Celgene, GSK, the Michael J. Fox Foundation for Parkinson's Research, the National Institute of Neurological Disorders and Stroke, Pfizer, Sanofi, and Verily. Clinical data and biosamples used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI), and the Parkinson's Disease Biomarkers Program (PDBP). PPMI-a public-private partnership-is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including full names of all of the PPMI funding partners found . The PPMI Investigators have not participated in reviewing the data analysis or content of the manuscript. For up-to-date information on the study, visit. The Parkinson's Disease Biomarker Program (PDBP) consortium is supported by the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health. A full list of PDBP investigators can be found. The PDBP Investigators have not participated in reviewing the data analysis or content of the manuscript. PDBP sample and clinical data collection is supported under grants by NINDS: U01NS082134, U01NS082157, U01NS082151, U01NS082137, U01NS082148, and U01NS082133. A portion of the resources used in the preparation of this article were obtained from Global Parkinson's Genetics Program (GP2). GP2 is funded by the Aligning Science against Parkinson's (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson's Research . For a complete list of GP2 members, see. The workflow diagram was created with BioRender.com. 63 2 2 5 10 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2373-8057 NPJ PARKINSONS DIS npj Parkinsons Dis. APR 1 2022.0 8.0 1 35 10.1038/s41531-022-00288-w 0.0 13 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 0F1IN 35365675.0 Green Submitted, gold, Green Accepted, Green Published 2023-03-23 WOS:000777121300001 0
J Ahmed, I; Ahmad, M; Rodrigues, JJPC; Jeon, G; Din, S Ahmed, Imran; Ahmad, Misbah; Rodrigues, Joel J. P. C.; Jeon, Gwanggil; Din, Sadia A deep learning-based social distance monitoring framework for COVID-19 SUSTAINABLE CITIES AND SOCIETY English Article Deep learning; Social distancing; COVID-19; Transfer learning; Overhead view; Person detection; YOLOv3 TRACKING The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. In the current situation, as there are no vaccines available; therefore, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. The purpose of this work is, therefore, to provide a deep learning platform for social distance tracking using an overhead perspective. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is also implemented to increase the accuracy of the model. In this way, the detection algorithm uses a pre-trained algorithm that is connected to an extra trained layer using an overhead human data set. The detection model identifies peoples using detected bounding box information. Using the Euclidean distance, the detected bounding box centroid's pairwise distances of people are determined. To estimate social distance violations between people, we used an approximation of physical distance to pixel and set a threshold. A violation threshold is established to evaluate whether or not the distance value breaches the minimum social distance threshold. In addition, a tracking algorithm is used to detect individuals in video sequences such that the person who violates/ crosses the social distance threshold is also being tracked. Experiments are carried out on different video sequences to test the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model. The accuracy of 92% and 98% achieved by the detection model without and with transfer learning, respectively. The tracking accuracy of the model is 95%. [Ahmed, Imran; Ahmad, Misbah] Inst Management Sci, Ctr Excellence Informat Technol, 1-A,Sect E-5,Phase 7, Peshawar, Pakistan; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Postgrad Program Elect Engn PPGEE, BR-64049550 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea; [Din, Sadia] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan, South Korea Universidade Federal do Piaui; Instituto de Telecomunicacoes; Xidian University; Incheon National University; Yeungnam University Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China.;Jeon, G (corresponding author), Incheon Natl Univ, Dept Embedded Syst Engn, Incheon, South Korea. imran.ahmed@imsciences.edu.pk; misbahahmad4872@gmail.com; joeljr@ieee.org; gjeon@inu.ac.kr; saadia.deen@gmail.com Rodrigues, Joel J. P. C./A-8103-2013; Ahmed, Imran/HDL-7255-2022 Rodrigues, Joel J. P. C./0000-0001-8657-3800; Ahmad, Misbah/0000-0001-7013-0159 FCT/MCTES through national funds; EU funds [UIDB/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5] FCT/MCTES through national funds(Fundacao para a Ciencia e a Tecnologia (FCT)); EU funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work is partially supported by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020; and by Brazilian National Council for Scientific and Technological Development (CNPq) via Grant No. 309335/2017-5. 37 31 31 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. FEB 2021.0 65.0 102571 10.1016/j.scs.2020.102571 0.0 JAN 2021 12 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels QE0OU Green Accepted 2023-03-23 WOS:000615907800002 0
C He, J; Cheng, YJ; Wang, M; Xie, JY; Xie, W; Su, R; Yuan, SD; Cui, Y Tetko, IV; Kurkova, V; Karpov, P; Theis, F He, Jun; Cheng, Yongjing; Wang, Min; Xie, Jingyu; Xie, Wei; Su, Rui; Yuan, Shandong; Cui, Yao An Ensemble Model for Winning a Chinese Machine Reading Comprehension Competition ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: TEXT AND TIME SERIES, PT IV Lecture Notes in Computer Science English Proceedings Paper 28th International Conference on Artificial Neural Networks (ICANN) SEP 17-19, 2019 Tech Univ Munchen, Klinikum Rechts Isar, Munich, GERMANY Tech Univ Munchen, Klinikum Rechts Isar MRC; RNN; BIDAF; VNET; MwAN; Deep learning To facilitate the application of machine reading comprehension, the 28th Research Institute of China Electronics Technology Group Corporation organized a Chinese machine reading comprehension competition, namely the LES Cup Challenge, in October 2018. The competition introduces a big dataset of long articles and improperly labelled data, therefore challenges the state-of-the-art methods in this area. We proposed an ensemble model of four novel recurrent neural networks, which ranked on the top 2% over more than 250 teams (97 teams successfully submitted results) mainly from top universities and AI companies of China, and won the third prize (3000 USD) of the competition. [He, Jun; Cheng, Yongjing; Wang, Min; Xie, Wei; Su, Rui; Yuan, Shandong] Natl Univ Def Technol, Coll Informat & Commun, Wuhan 430010, Peoples R China; [Xie, Jingyu] Tech Univ Munich, Dept Elect & Comp Engn, D-80333 Munich, Germany; [Cui, Yao] Bur Publ Secur, Wuhan Police Headquarter, Wuhan 430010, Peoples R China National University of Defense Technology - China; Technical University of Munich He, J (corresponding author), Natl Univ Def Technol, Coll Informat & Commun, Wuhan 430010, Peoples R China. hejun_nudt@nudt.edu.cn; ge57quq@mytum.de; 94025708@qq.com He, Jun/0000-0001-5201-4445 15 0 0 0 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-30490-4; 978-3-030-30489-8 LECT NOTES COMPUT SC 2019.0 11730.0 3 8 10.1007/978-3-030-30490-4_1 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP3AW 2023-03-23 WOS:000546015300001 0
J Brito-Zeron, P; Acar-Denizli, N; Ng, WF; Zeher, M; Rasmussen, A; Mandl, T; Seror, R; Li, X; Baldini, C; Gottenberg, JE; Danda, D; Quartuccio, L; Priori, R; Hernandez-Molina, G; Armagan, B; Kruize, AA; Kwok, SK; Kvarnstrom, M; Praprotnik, S; Sene, D; Bartoloni, E; Solans, R; Rischmueller, M; Suzuki, Y; Isenberg, DA; Valim, V; Wiland, P; Nordmark, G; Fraile, G; Bootsma, H; Nakamura, T; Giacomelli, R; Devauchelle-Pensec, V; Knopf, A; Bombardieri, M; Trevisani, VF; Hammenfors, D; Pasoto, SG; Retamozo, S; Gheita, TA; Atzeni, F; Morel, J; Vollenveider, C; Horvath, IF; Sivils, KL; Olsson, P; De Vita, S; Sanchez-Guerrero, J; Kilic, L; Wahren-Herlenius, M; Mariette, X; Ramos-Casals, M Brito-Zeron, P.; Acar-Denizli, N.; Ng, W. F.; Zeher, M.; Rasmussen, A.; Mandl, T.; Seror, R.; Li, X.; Baldini, C.; Gottenberg, J. -E.; Danda, D.; Quartuccio, L.; Priori, R.; Hernandez-Molina, G.; Armagan, B.; Kruize, A. A.; Kwok, S. -K.; Kvarnstrom, M.; Praprotnik, S.; Sene, D.; Bartoloni, E.; Solans, R.; Rischmueller, M.; Suzuki, Y.; Isenberg, D. A.; Valim, V.; Wiland, P.; Nordmark, G.; Fraile, G.; Bootsma, H.; Nakamura, T.; Giacomelli, R.; Devauchelle-Pensec, V.; Knopf, A.; Bombardieri, M.; Trevisani, V. -F; Hammenfors, D.; Pasoto, S. G.; Retamozo, S.; Gheita, T. A.; Atzeni, F.; Morel, J.; Vollenveider, C.; Horvath, I-F.; Sivils, K. L.; Olsson, P.; De Vita, S.; Sanchez-Guerrero, J.; Kilic, L.; Wahren-Herlenius, M.; Mariette, X.; Ramos-Casals, M. Sjogren Big Data Consortium How immunological profile drives clinical phenotype of primary Sjogren's syndrome at diagnosis: analysis of 10,500 patients (Sjogren Big Data Project) CLINICAL AND EXPERIMENTAL RHEUMATOLOGY English Article primary Sjogren's syndrome; salivary gland biopsy; Ro/La autoantibodies; hypocomplementaemia; cryoglobulinaemia; ESSDAI DISEASE-ACTIVITY; CLASSIFICATION CRITERIA; ANTI-LA; LOWER PREVALENCE; MORTALITY; MANIFESTATIONS; CONSENSUS; CRYOGLOBULINEMIA; PREDICTORS; ANTIBODIES Objective. To evaluate the influence of the main immunological markers on the disease phenotype at diagnosis in a large international cohort of patients with primary Sjogren's syndrome (SjS). Methods. The Big Data Sjogren Project Consortium is an international, multicentre registry created in 2014. As a first step, baseline clinical information from leading centres on clinical research in SjS of the 5 continents was collected. The centres shared a harmonised data architecture and conducted cooperative online efforts in order to refine collected data under the coordination of a big data statistical team. Inclusion criteria were the fulfillment of the 2002 classification criteria. Immunological tests were carried out using standard commercial assays. Results. By January 2018, the participant centres had included 10,500 valid patients from 22 countries. The cohort included 9,806 (93%) women and 694 (7%) men, with a mean age at diagnosis of primary SjS of 53 years, mainly White (78%) and included from European countries (71%). The frequency of positive immunological markers at diagnosis was 79.3% for ANA, 73.2% for anti-Ro, 48.6% for RF, 45.1% for anti-La, 13.4% for low C3 levels, 14.5% for low C4 levels and 7.3% for cryoglobulins. Positive autoantibodies (ANA, Ro, La) correlated with a positive result in salivary gland biopsy, while hypocomplementaemia and especially cryoglobulinaemia correlated with systemic activity (mean ESSDAI score of 17.7 for ctyoglobulins, 11.3 for low C3 and 9.2 for low C4, in comparison with 3.8 for negative markers). The immunological markers with a great number of statistically-significant associations (p<0.001) in the organ-by-organ ESSDAI evaluation were cryoglobulins (9 domains), low C3 (8 domains), anti-La (7 domains) and low C4 (6 domains). Conclusion. We confirm the strong influence of immunological markers on the phenotype of primary SjS at diagnosis in the largest multi-ethnic international cohort ever analysed, with a greater influence for cryoglobulinaemic-related markers in comparison with Ro/La autoantibodies and ANA. Immunological patterns play a central role in the phenotypic expression of the disease already at the time of diagnosis, and may guide physicians to design a specific personalised management during the follow-up of patients with primary SjS. [Brito-Zeron, P.] Hosp CIMA Sanitas, Dept Med, Autoimmune Dis Unit, Barcelona, Spain; [Brito-Zeron, P.; Ramos-Casals, M.] Univ Barcelona, Sjogrens Syndrome Res Grp AGAUR, Lab Autoimmune Dis Josep Font,Hosp Clin, IDIBAPS,CELLEX,Dept Autoimmune Dis,ICMiD, Barcelona, Spain; [Acar-Denizli, N.] Mimar Sinan Fine Arts Univ, Fac Sci & Letters, Dept Stat, Istanbul, Turkey; [Ng, W. F.] Newcastle Univ, Inst Cellular Med, Newcastle Upon Tyne, Tyne & Wear, England; [Zeher, M.; Horvath, I-F.] Univ Debrecen, Fac Med, Div Clin Immunol, Debrecen, Hungary; [Rasmussen, A.; Sivils, K. L.] Oklahoma Med Res Fdn, Arthrit & Clin Immunol Res Program, 825 NE 13th St, Oklahoma City, OK 73104 USA; [Mandl, T.; Olsson, P.] Lund Univ, Skane Univ Hosp Malmo, Dept Rheumatol, Malmo, Sweden; [Seror, R.; Mariette, X.] Univ Paris Sud, Hop Univ Paris Sud, AP HP, Ctr Immunol Viral Infect & Autoimmune Dis,INSERM, Paris, France; [Li, X.] Anhui Prov Hosp, Dept Rheumatol & Immunol, Hefei, Anhui, Peoples R China; [Baldini, C.] Univ Pisa, Rheumatol Unit, Pisa, Italy; [Gottenberg, J. -E.] Univ Strasbourg, Strasbourg Univ Hosp, CNRS, Dept Rheumatol, Strasbourg, France; [Danda, D.] Christian Med Coll & Hosp, Dept Clin Immunol & Rheumatol, Vellore, Tamil Nadu, India; [Quartuccio, L.; De Vita, S.] Univ Hosp Santa Maria della Misericordia, Dept Med Area DAME, Clin Rheumatol, Udine, Italy; [Priori, R.] Sapienza Univ Rome, Rheumatol Clin, Dept Internal Med & Med Specialties, Rome, Italy; [Hernandez-Molina, G.; Sanchez-Guerrero, J.] Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Dept Rheumatol & Immunol, Mexico City, DF, Mexico; [Armagan, B.; Kilic, L.] Hacettepe Univ, Fac Med, Dept Internal Med, Ankara, Turkey; [Kruize, A. A.] Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands; [Kwok, S. -K.] Catholic Univ Korea, Seoul St Marys Hosp, Seoul, South Korea; [Kvarnstrom, M.; Wahren-Herlenius, M.] Karolinska Inst, Dept Med, Rheumatol Unit, Stockholm, Sweden; [Kvarnstrom, M.; Wahren-Herlenius, M.] Karolinska Univ Hosp, Stockholm, Sweden; [Praprotnik, S.] Univ Med Ctr, Dept Rheumatol, Ljubljana, Slovenia; [Sene, D.] Paris Diderot Univ, Lariboisiere Hosp, AP HP, Dept Internal Med, Paris, France; [Bartoloni, E.] Univ Perugia, Dept Med, Rheumatol Unit, Perugia, Italy; [Solans, R.] Hosp Valle De Hebron, Dept Internal Med, Barcelona, Spain; [Rischmueller, M.] Univ Western Australia, Sch Med, Dept Rheumatol, Crawley, Australia; [Suzuki, Y.] Kanazawa Univ Hosp, Div Rheumatol, Kanazawa, Ishikawa, Japan; [Isenberg, D. A.] UCL, Div Med, Ctr Rheumatol, London, England; [Valim, V.] Univ Espirito Santo, Dept Med, Vitoria, Brazil; [Valim, V.] Univ Hosp HUCAM EBSERH, Vitoria, Brazil; [Wiland, P.] Wroclaw Med Hosp, Dept Rheumatol & Internal Med, Wroclaw, Poland; [Nordmark, G.] Uppsala Univ, Dept Med Sci, Rheumatol, Uppsala, Sweden; [Fraile, G.] Hosp Ramon & Cajal, Dept Internal Med, Madrid, Spain; [Bootsma, H.] Univ Groningen, Univ Med Ctr Groningen, Dept Rheumatol & Clin Immunol, Groningen, Netherlands; [Nakamura, T.] Nagasaki Univ, Grad Sch Biomed Sci, Dept Radiol & Canc Biol, Nagasaki, Japan; [Giacomelli, R.] Univ Aquila, Sch Med, Clin Unit Rheumatol, Laquila, Italy; [Devauchelle-Pensec, V.] Brest Univ Hosp, Dept Rheumatol, Brest, France; [Knopf, A.] Tech Univ Munich, Klinikum Rechts Isar, Otorhinolaryngol Head & Neck Surg, Munich, Germany; [Bombardieri, M.] Queen Mary Univ London, Ctr Expt Med & Rheumatol, London, England; [Trevisani, V. -F] Univ Fed Sao Paulo, Sao Paulo, Brazil; [Hammenfors, D.] Univ Bergen, Dept Clin Sci, Bergen, Norway; [Hammenfors, D.] Haukeland Hosp, Dept Rheumatol, Bergen, Norway; [Pasoto, S. G.] Univ Sao Paulo, Fac Med, Hosp Clin, Div Rheumatol, Sao Paulo, Brazil; [Retamozo, S.] Univ Nacl Cordoba, Consejo Nacl Invest Cient & Tecn CONICET, Inst Invest Ciencias Salud INICSA, Hosp Privado Univ Cordoba,IUCBC, Cordoba, Argentina; [Gheita, T. A.] Cairo Univ, Kasr Al Ainy Sch Med, Dept Rheumatol, Cairo, Egypt; [Atzeni, F.] IRCCS Galeazzi Orthopaed Inst, Milan, Italy; [Atzeni, F.] Univ Messina, Rheumatol Unit, Messina, Italy; [Morel, J.] Montpellier Univ Hosp, Dept Rheumatol, Montpellier, France; [Morel, J.] Univ Montpellier, Montpellier, France; [Vollenveider, C.] German Hosp, Buenos Aires, DF, Argentina University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS; Mimar Sinan Guzel Sanatlar University; Newcastle University - UK; University of Debrecen; Oklahoma Medical Research Foundation; Lund University; Skane University Hospital; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Paul-Brousse - APHP; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; University of Pisa; Centre National de la Recherche Scientifique (CNRS); CHU Strasbourg; UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Christian Medical College & Hospital (CMCH) Vellore; Sapienza University Rome; Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran - Mexico; Hacettepe University; Utrecht University; Utrecht University Medical Center; Catholic University of Korea; Seoul St. Mary's Hospital; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; University Medical Centre Ljubljana; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Lariboisiere-Fernand-Widal - APHP; University of Perugia; Hospital Universitari Vall d'Hebron; University of Western Australia; Kanazawa University; University of London; University College London; Wroclaw Medical University; Uppsala University; Hospital Universitario Ramon y Cajal; University of Groningen; Nagasaki University; University of L'Aquila; CHU Brest; Technical University of Munich; University of London; Queen Mary University London; Universidade Federal de Sao Paulo (UNIFESP); University of Bergen; University of Bergen; Haukeland University Hospital; Universidade de Sao Paulo; Egyptian Knowledge Bank (EKB); Cairo University; IRCCS Istituto Ortopedico Galeazzi; University of Messina; Universite de Montpellier; CHU de Montpellier; Universite de Montpellier; Hospital Aleman; University of Buenos Aires Ramos-Casals, M (corresponding author), Hosp Clin Barcelona, Serv Malalties Autoimmunes Sistem, C Villarroel 170, Barcelona 08036, Spain. mramos@clinic.ub.es Armagan, Berkan/HNQ-2829-2023; Gheita, Tamer/HHC-8095-2022; Wahren-Herlenius, Marie/AAN-6942-2021; Retamozo, Soledad/AFT-7300-2022; Trevisani, Virginia/AAI-5225-2020; sene, damien/R-3970-2017; Hernandez-Molina, Gabriela/ABC-9915-2020; Mandl, Thomas/AAS-1548-2020; SOUNDRARAJAN, JOYSON/I-1779-2019; Ramos-Casals, Manuel/HJH-4887-2023; danda, debashish/K-2650-2013; Bartoloni, Elena/R-5842-2019; Quartuccio, Luca/AAC-4803-2022; Giacomelli, Roberto/AAB-9962-2022; Acar-Denizli, Nihan/D-5954-2019; Rasmussen, Astrid/A-4878-2013; PASOTO, SANDRA/E-1213-2012 Gheita, Tamer/0000-0002-1155-9729; Wahren-Herlenius, Marie/0000-0002-0915-7245; Trevisani, Virginia/0000-0002-7180-6285; sene, damien/0000-0001-8640-4580; Mandl, Thomas/0000-0002-7143-7088; Ramos-Casals, Manuel/0000-0001-5709-6734; danda, debashish/0000-0002-2121-0942; Acar-Denizli, Nihan/0000-0002-0012-8632; Rasmussen, Astrid/0000-0001-7744-2948; 34 60 60 1 11 CLINICAL & EXPER RHEUMATOLOGY PISA VIA SANTA MARIA 31, 56126 PISA, ITALY 0392-856X 1593-098X CLIN EXP RHEUMATOL Clin. Exp. Rheumatol. 2018.0 36.0 3 112.0 S102 S112 11 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology GV9NS 2023-03-23 WOS:000446486100015 0
C Zhao, W; Tao, T; Zio, E Zio, E; Baraldi, P; Pierucci, S; Klemes, JJ Zhao, Wei; Tao, Tao; Zio, Enrico Parameters Tuning in Support Vector Regression for Reliability Forecasting 2013 PROGNOSTICS AND HEALTH MANAGEMENT CONFERENCE (PHM) Chemical Engineering Transactions English Proceedings Paper 4th IEEE International Conference on Prognostics and System Health Management (PHM) SEP 08-11, 2013 Milan, ITALY IEEE SYSTEMS RELIABILITY; SELECTION; MACHINES The recent and promising machine learning technique called support vector machine (SVM) has become a hot research subject in time series forecasting, since proposed from Statistic Learning Theory by Vapnik. As an important application of time series forecasting, reliability prediction by analyzing the historical time series data of system condition to predict the future system behaviour and/or diagnose the possible system fault, has been solved successfully by SVM with high forecasting accuracy. For this, the critical problem is the selection of SVM parameters. Many methods have been proposed, such as genetic algorithm, particle swarm optimization and analytic selection; but there is no generally structured way, yet. In this paper, the capability of SVM to perform function fitting and reliability forecasting based on different methods is investigated by experimenting on both artificial and real-world data. A comparison of the methods is offered on criteria of prediction accuracy and robustness. Finally, an attempt is made to obtain a comparative optimal parameter selection method. [Zhao, Wei; Tao, Tao] Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China; [Zio, Enrico] European Fdn New Energy Eect, Chair Syst Sci & Energet Challenge, Paris, France; [Zio, Enrico] Politecn Milan, Dipartimento Energia, Milan, Italy Beihang University; UDICE-French Research Universities; Universite Paris Saclay; Polytechnic University of Milan Zhao, W (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China. zhaowei203@buaa.edu.cn Zio, Enrico/0000-0002-7108-637X 15 9 9 0 3 AIDIC SERVIZI SRL MILANO VIA GIUSEPPE COLOMBO 81/A, MILANO, MI 20133, ITALY 2283-9216 978-88-95608-24-2 CHEM ENGINEER TRANS 2013.0 33.0 523 528 10.3303/CET1333088 0.0 6 Engineering, Multidisciplinary; Engineering, Chemical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BA8BZ 2023-03-23 WOS:000337960300087 0
J Tonon, N; Petersen, HA; Martin, MA; Asmuss, P; Baxter, S; Bayatmakou, M; Behnke, O; Martinez, AB; Bhattacharya, S; Bin Anuar, AA; Borras, K; Brunner, D; Campbell, A; Cardini, A; Cheng, C; Colombina, F; Rodriguez, SC; Silva, GC; Danilov, V; De Silva, M; Didukh, L; Eckerlin, G; Eckstein, D; Banos, LIE; Filatov, O; Gallo, E; Geiser, A; Giraldi, A; Grohsjean, A; Guthoff, M; Jafari, A; Jomhari, NZ; Jung, H; Kasem, A; Kasemann, M; Kaveh, H; Kleinwort, C; Krucker, D; Lange, W; Lidrych, J; Lipka, K; Lohmann, W; Mankel, R; Melzer-Pellmann, IA; Morentin, MM; Metwally, J; Meyer, AB; Meyer, M; Mnich, J; Mussgiller, A; Otarid, Y; Adan, DP; Pitzl, D; Raspereza, A; Lopes, BR; Rubenach, J; Saggio, A; Saibel, A; Savitskyi, M; Scham, M; Scheurer, V; Schutze, P; Schwanenberger, C; Singh, A; Ricardo, RES; Stafford, D; Van De Klundert, M; Walsh, R; Walter, D; Wen, Y; Wichmann, K; Wiens, L; Wissing, C; Wuchterl, S; Bakhshiansohi, H; Tumasyan, A; Adam, W; Andrejkovic, JW; Bergauer, T; Chatterjee, S; Dragicevic, M; 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M.; Bonham, B.; Das, P.; Dezoort, G.; Elmer, P.; Frankenthal, A.; Greenberg, B.; Haubrich, N.; Higginbotham, S.; Kalogeropoulos, A.; Kopp, G.; Kwan, S.; Lange, D.; Marlow, D.; Mei, K.; Ojalvo, I; Olsen, J.; Stickland, D.; Tully, C.; Norberg, S.; Bakshi, A. S.; Barnes, V. E.; Chawla, R.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Karmarkar, S.; Kondratyev, D.; Liu, M.; Negro, G.; Neumeister, N.; Paspalaki, G.; Peng, C. C.; Piperov, S.; Purohit, A.; Schulte, J. F.; Stojanovic, M.; Thieman, J.; Wang, F.; Xiao, R.; Xie, W.; Dolen, J.; Parashar, N.; Baty, A.; Decaro, M.; Dildick, S.; Ecklund, K. M.; Freed, S.; Gardner, P.; Geurts, F. J. M.; Li, W.; Padley, B. P.; Redjimi, R.; Shi, W.; Leiton, A. G. Stahl; Zhang, L.; Bodek, A.; de Barbaro, P.; Demina, R.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Hindrichs, O.; Khukhunaishvili, A.; Ranken, E.; Taus, R.; Chiarito, B.; Chou, J. P.; Gandrakota, A.; Gershtein, Y.; Halkiadakis, E.; Hart, A.; Heindl, M.; Karacheban, O.; Laflotte, I; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thayil, S. A.; Thomas, S.; Wang, H.; Acharya, H.; Delannoy, A. G.; Fiorendi, S.; Spanier, S.; Bouhali, O.; Dalchenko, M.; Delgado, A.; Eusebi, R.; Gilmore, J.; Huang, T.; Luo, S.; Malhotra, S.; Mueller, R.; Overton, D.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; Hegde, V; Kunori, S.; Lamichhane, K.; Mengke, T.; Muthumuni, S.; Peltola, T.; Volobouev, I; Whitbeck, A.; Appelt, E.; Greene, S.; Gurrola, A.; Johns, W.; Melo, A.; Ni, H.; Padeken, K.; Romeo, F.; Sheldon, P.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Cox, B.; Cummings, G.; Hakala, J.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, A.; Neu, C.; Tannenwald, B.; White, S.; Wolfe, E.; Poudyal, N.; Black, K.; Bose, T.; Caillol, C.; Dasu, S.; De Bruyn, I; Everaerts, P.; Fienga, F.; Galloni, C.; He, H.; Herndon, M.; Herve, A.; Hussain, U.; Lanaro, A.; Loeliger, A.; Loveless, R.; Sreekala, J. Madhusudanan; Mallampalli, A.; Mohammadi, A.; Pinna, D.; Savin, A.; Shang, V; Smith, W. H.; Teague, D.; Trembath-Reichert, S.; Vetens, W. CMS Collaboration Probing effective field theory operators in the associated production of top quarks with a Z boson in multilepton final states at root s=13 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron scattering (experiments); Top physics A search for new top quark interactions is performed within the framework of an effective field theory using the associated production of either one or two top quarks with a Z boson in multilepton final states. The data sample corresponds to an integrated luminosity of 138 fb(-1) of proton-proton collisions at root s = 13 TeV collected by the CMS experiment at the LHC. Five dimension-six operators modifying the electroweak interactions of the top quark are considered. Novel machine-learning techniques are used to enhance the sensitivity to effects arising from these operators. Distributions used for the signal extraction are parameterized in terms of Wilson coefficients describing the interaction strengths of the operators. All five Wilson coefficients are simultaneously fit to data and 95% confidence level intervals are computed. All results are consistent with the SM expectations. [Tonon, N.; Petersen, H. Aarup; Martin, M. Aldaya; Asmuss, P.; Baxter, S.; Bayatmakou, M.; Behnke, O.; Martinez, A. Bermudez; Bhattacharya, S.; Bin Anuar, A. A.; Borras, K.; Brunner, D.; Campbell, A.; Cardini, A.; Cheng, C.; Colombina, F.; Rodriguez, S. Consuegra; Silva, G. Correia; Danilov, V; De Silva, M.; Didukh, L.; Eckerlin, G.; Eckstein, D.; Banos, L. I. Estevez; Filatov, O.; Gallo, E.; Geiser, A.; Giraldi, A.; Grohsjean, A.; Guthoff, M.; Jafari, A.; Jomhari, N. Z.; Jung, H.; Kasem, A.; Kasemann, M.; Kaveh, H.; Kleinwort, C.; Kruecker, D.; Lange, W.; Lidrych, J.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I-A; Morentin, M. Mendizabal; Metwally, J.; Meyer, A. B.; Meyer, M.; Mnich, J.; Mussgiller, A.; Otarid, Y.; Adan, D. Perez; Pitzl, D.; Raspereza, A.; Lopes, B. Ribeiro; Rubenach, J.; Saggio, A.; Saibel, A.; Savitskyi, M.; Scham, M.; Scheurer, V; Schuetze, P.; Schwanenberger, C.; Singh, A.; Ricardo, R. E. Sosa; Stafford, D.; Van De Klundert, M.; Walsh, R.; Walter, D.; Wen, Y.; Wichmann, K.; Wiens, L.; Wissing, C.; Wuchterl, S.; Bakhshiansohi, H.] DESY, Hamburg, Germany; [Tumasyan, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Del Valle, A. Escalante; Fruhwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I; Paulitsch, P.; Pitters, F. M.; Schieck, J.; Schofbeck, R.; Schwarz, D.; Templ, S.; Waltenberger, W.; Wulz, C-E] Inst Hochenergiephys, Vienna, Austria; [Chekhovsky, V; Litomin, A.; Makarenko, V] Inst Nucl Problems, Minsk, BELARUS; [Darwish, M. R.; De Wolf, E. A.; Janssen, T.; Kello, T.; Lelek, A.; Sfar, H. Rejeb; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.] Univ Antwerp, Antwerp, Belgium; [Blekman, F.; Bols, E. S.; D'Hondt, J.; Delcourt, M.; El Faham, H.; Lowette, S.; Moortgat, S.; Morton, A.; Muller, D.; Sahasransu, A. R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Stylianou, N.] Vrije Univ Brussel, Brussels, Belgium; [Kello, T.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I; Moureaux, L.; Petre, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Wezenbeek, L.; Gao, X.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Knolle, J.; Lambrecht, L.; Mestdach, G.; Niedziela, M.; Roskas, C.; Samalan, A.; Skovpen, K.; Tytgat, M.; Vermassen, B.; Vit, M.] Univ Ghent, Ghent, Belgium; [Benecke, A.; Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Jaffel, K.; Jain, Sa; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Tran, T. T.; Vischia, P.; Wertz, S.] Catholic Univ Louvain, Louvain La Neuve, Belgium; [Alves, G. A.; Hensel, C.; Moraes, A.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Junior, W. L. Alda; Gallo Pereira, M. Alves; Ferreira Filho, M. Barroso; Malbouisson, H. Brandao; Carvalho, W.; Chinellato, J.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; De Souza, S. Fonseca; Figueiredo, D. Matos; Herrera, C. Mora; Amarilo, K. Mota; Mundim, L.; Nogima, H.; Teles, P. Rebello; Santoro, A.; Silva Do Amaral, S. M.; Sznajder, A.; Thiel, M.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.] Univ Estadual Paulista, Sao Paulo, Brazil; [Gregores, E. M.; Mercadante, P. G.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Antchev, G.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Dimitrov, A.; Ivanov, T.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.] Univ Sofia, Sofia, Bulgaria; [Cheng, T.; Javaid, T.; Mittal, M.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bauer, G.; Dozen, C.; Hu, Z.; Martins, J.; Wang, Y.; Yi, K.] Tsinghua Univ, Dept Phys, Beijing, Peoples R China; [Chapon, E.; Chen, G. M.; Chen, H. S.; Chen, M.; Iemmi, F.; Kapoor, A.; Leggat, D.; Liao, H.; Liu, Z-A; Milosevic, V; Monti, F.; Sharma, R.; Tao, J.; Thomas-Wilsker, J.; Wang, J.; Zhang, H.; Zhao, J.] Inst High Energy Phys, Beijing, Peoples R China; [Agapitos, A.; An, Y.; Ban, Y.; Chen, C.; Levin, A.; Li, Q.; Lyu, X.; Mao, Y.; Qian, S. J.; Wang, D.; Wang, Q.; Xiao, J.] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing, Peoples R China; [Lu, M.; You, Z.] Sun Yat Sen Univ, Guangzhou, Peoples R China; [Gao, X.; Okawa, H.] Fudan Univ, Inst Modern Phys, Shanghai, Peoples R China; [Gao, X.; Okawa, H.] Fudan Univ, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai, Peoples R China; [Lin, Z.; Xiao, M.] Zhejiang Univ, Hangzhou, Peoples R China; [Avila, C.; Cabrera, A.; Florez, C.; Fraga, J.] Univ Los Andes, Bogota, Colombia; [Mejia Guisao, J.; Ramirez, F.; Ruiz Alvarez, J. D.; Salazar Gonzalez, C. A.] Univ Antioquia, Medellin, Colombia; [Giljanovic, D.; Godinovic, N.; Lelas, D.; Puljak, I] Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; [Antunovic, Z.; Kovac, M.; Sculac, T.] Univ Split, Fac Sci, Split, Croatia; [Brigljevic, V; Ferencek, D.; Majumder, D.; Roguljic, M.; Starodumov, A.; Susa, T.] Inst Rudjer Boskovic, Zagreb, Croatia; [Attikis, A.; Christoforou, K.; Erodotou, E.; Ioannou, A.; Kole, G.; Kolosova, M.; Konstantinou, S.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Saka, H.] Univ Cyprus, Nicosia, Cyprus; [Finger, M.; Jr, M. Finger; Kveton, A.] Charles Univ Prague, Prague, Czech Republic; [Ayala, E.] Escuela Politec Nacl, Quito, Ecuador; [Jarrin, E. Carrera] Univ San Francisco Quito, Quito, Ecuador; [Abdalla, H.; Assran, Y.] Egyptian Network High Energy Phys, Acad Sci Res & Technol Arab Republ Egypt, Cairo, Egypt; [Lotfy, A.; Mahmoud, M. A.] Fayoum Univ, Ctr High Energy Phys CHEP FU, Al Fayyum, Egypt; [Bhowmik, S.; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Nandan, S.; Nielsen, C.; Pata, J.; Raidal, M.; Tani, L.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Bharthuar, S.; Brucken, E.; Garcia, F.; Havukainen, J.; Kim, M. S.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Lehti, S.; Linden, T.; Lotti, M.; Martikainen, L.; Myllymaki, M.; Ott, J.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Luukka, P.; Petrow, H.; Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Amendola, C.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Lenzi, B.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Savoy-Navarro, A.; Titov, M.; Yu, G. B.; Mijuskovic, J.] Univ Paris Saclay, IRFU, CEA, Gif Sur Yvette, France; [Ahuja, S.; Beaudette, F.; Bonanomi, M.; Perraguin, A. Buchot; Busson, P.; Cappati, A.; Charlot, C.; Davignon, O.; Diab, B.; Falmagne, G.; Ghosh, S.; de Cassagnac, R. Granier; Hakimi, A.; Kucher, I; Motta, J.; Nguyen, M.; Ochando, C.; Paganini, P.; Rembser, J.; Salerno, R.; Sarkar, U.; Sauvan, J. B.; Sirois, Y.; Tarabini, A.; Zabi, A.; Zghiche, A.] Ecole Polytech, Lab Leprince Ringuet, Inst Polytech Paris, CNRS IN2P3, Palaiseau, France; [Agram, J-L; Andrea, J.; Apparu, D.; Bloch, D.; Bourgatte, G.; Brom, J-M; Chabert, E. C.; Collard, C.; Darej, D.; Fontaine, J-C; Goerlach, U.; Grimault, C.; Le Bihan, A-C; Nibigira, E.; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; [Asilar, E.; Beauceron, S.; Bernet, C.; Boudoul, G.; Camen, C.; Carle, A.; Chanon, N.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Laktineh, I. B.; Lattaud, H.; Lesauvage, A.; Lethuillier, M.; Mirabito, L.; Perries, S.; Shchablo, K.; Sordini, V; Torterotot, L.; Touquet, G.; Vander Donckt, M.; Viret, S.] Inst Phys 2 Infinis Lyon IP2I, Villeurbanne, France; [Adamov, G.; Lomidze, I; Tsamalaidze, Z.; Mestvirishvili, A.] Georgian Tech Univ, Tbilisi, Georgia; [Botta, V; Feld, L.; Klein, K.; Lipinski, M.; Meuser, D.; Pauls, A.; Roewert, N.; Schulz, J.; Teroerde, M.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Borras, K.; Kasem, A.; Ghosh, S.; Dodonova, A.; Eliseev, D.; Erdmann, M.; Fackeldey, P.; Fischer, B.; Hebbeker, T.; Hoepfner, K.; Ivone, F.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Mocellin, G.; Mondal, S.; Mukherjee, S.; Noll, D.; Novak, A.; Pook, T.; Pozdnyakov, A.; Rath, Y.; Reithler, H.; Roemer, J.; Schmidt, A.; Schuler, S. C.; Sharma, A.; Vigilante, L.; Wiedenbeck, S.; Zaleski, S.; Coubez, X.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Dziwok, C.; Fluegge, G.; Ahmad, W. Haj; Hlushchenko, O.; Kress, T.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.; Ziemons, T.; Zotz, A.] Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; [Gallo, E.; Schwanenberger, C.; Aggleton, R.; Albrecht, S.; Bein, S.; Benato, L.; Connor, P.; De Leo, K.; Eich, M.; Feindt, F.; Frohlich, A.; Garbers, C.; Garutti, E.; Gunnellini, P.; Hajheidari, M.; Haller, J.; Hinzmann, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kramer, T.; Kutzner, V; Lange, J.; Lange, T.; Lobanov, A.; Malara, A.; Nigamova, A.; Rodriguez, K. J. Pena; Rieger, O.; Schleper, P.; Schroeder, M.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Tews, A.; Zoi, I] Univ Hamburg, Hamburg, Germany; [Bechtel, J.; Brommer, S.; Butz, E.; Caspart, R.; Chwalek, T.; De Boer, W.; Dierlamm, A.; Droll, A.; El Morabit, K.; Faltermann, N.; Giffels, M.; Gosewisch, J. O.; Gottmann, A.; Hartmann, F.; Heidecker, C.; Husemann, U.; Keicher, P.; Koppenhoefer, R.; Maier, S.; Metzler, M.; Mitra, S.; Muller, Th; Neukum, M.; Nuernberg, A.; Quast, G.; Rabbertz, K.; Rauser, J.; Savoiu, D.; Schnepf, M.; Seith, D.; Shvetsov, I; Simonis, H. J.; Ulrich, R.; Van der Linden, J.; Von Cube, R. F.; Wassmer, M.; Weber, M.; Wieland, S.; Wolf, R.; Wozniewski, S.; Wunsch, S.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Stakia, A.] NCSR Demokritos, Inst Nucl & Particle Phys INPP, Aghia Paraskevi, Greece; [Diamantopoulou, M.; Karasavvas, D.; Karathanasis, G.; Kontaxakis, P.; Koraka, C. K.; Manousakis-Katsikakis, A.; Panagiotou, A.; Papavergou, I; Saoulidou, N.; Theofilatos, K.; Tziaferi, E.; Vellidis, K.; Vourliotis, E.; Sphicas, P.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Bakas, G.; Kousouris, K.; Papakrivopoulos, I; Tsipolitis, G.; Zacharopoulou, A.] Natl Tech Univ Athens, Athens, Greece; [Adamidis, K.; Bestintzanos, I; Evangelou, I; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Manthos, N.; Papadopoulos, I; Strologas, J.] Univ Ioannina, Ioannina, Greece; [Csanad, M.; Farkas, K.; Gadallah, M. M. A.; Lokos, S.; Major, P.; Mandal, K.; Mehta, A.; Pasztor, G.; Radl, A. J.; Suranyi, O.; Veres, G., I; Trocsanyi, Z. L.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Csorgo, T.; Nemes, F.] Wigner Res Ctr Phys, Budapest, Hungary; [Horvath, D.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Teyssier, D.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Trocsanyi, Z. L.; Bartok, M.; Karancsi, J.; Raics, P.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Lokos, S.; Csorgo, T.; Nemes, F.; Novak, T.] MATE Inst Technol, Karoly Robert Campus, Gyongyos, Hungary; [Komaragiri, J. R.; Kumar, D.; Panwar, L.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Kar, C.; Mal, P.; Mishra, T.; Bindhu, V. K. Muraleedharan Nair; Nayak, A.; Saha, P.; Sur, N.; Swain, S. K.; Vats, D.] HBNI, Natl Inst Sci Educ & Res, Bhubaneswar, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V; Chaudhary, G.; Chauhan, S.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, M.; Kaur, S.; Kumari, P.; Meena, M.; Sandeep, K.; Singh, J. B.; Virdi, A. K.] Panjab Univ, Chandigarh, India; [Ahmed, A.; Bhardwaj, A.; Choudhary, B. C.; Gola, M.; Keshri, S.; Kumar, A.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, A.] Univ Delhi, Delhi, India; [Bhattacharya, S.; Bharti, M.; Bhattacharya, R.; Bhowmik, D.; Dutta, S.; Gomber, B.; Maity, M.; Palit, P.; Rout, P. K.; Saha, G.; Sahu, B.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] HBNI, Saha Inst Nucl Phys, Kolkata, India; [Sharma, A.; Behera, P. K.; Behera, S. C.; Kalbhor, P.; Muhammad, A.; Pradhan, R.; Pujahari, P. R.; Sikdar, A. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Dutta, D.; Jha, V; Kumar, V; Mishra, D. K.; Naskar, K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Dugad, S.; Kumar, M.] Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; [Mukherjee, S.; Banerjee, S.; Chudasama, R.; Guchait, M.; Karmakar, S.; Kumar, S.; Majumder, G.; Mazumdar, K.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Alpana, K.; Dube, S.; Kansal, B.; Laha, A.; Pandey, S.; Rane, A.; Rastogi, A.; Sharma, S.] Indian Inst Sci Educ & Res IISER, Pune, Maharashtra, India; [Jafari, A.; Bakhshiansohi, H.; Khazaie, E.; Zeinali, M.] Isfahan Univ Technol, Dept Phys, Esfahan, Iran; [Chenarani, S.; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi] Inst Res Fundamental Sci IPM, Tehran, Iran; [Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Aruta, C.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Fiore, L.; Gelmi, A.; Gul, M.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Margjeka, I; Mastrapasqua, V; Merlin, J. A.; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Pugliese, G.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Simone, F. M.; Venditti, R.; Verwilligen, P.] Ist Nazl Fis Nucl, Sez Bari, Bari, Italy; [Abbrescia, M.; Aruta, C.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Gelmi, A.; Ince, M.; Lezki, S.; Margjeka, I; Mastrapasqua, V; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Selvaggi, G.; Simone, F. M.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Cuffiani, M.; Dallavalle, G. M.; Diotalevi, T.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Giommi, L.; Grandi, C.; Guiducci, L.; Lo Meo, S.; Lunerti, L.; Marcellini, S.; Masetti, G.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Campanini, R.; Capiluppi, P.; Castro, A.; Cuffiani, M.; Diotalevi, T.; Fanfani, A.; Giommi, L.; Guiducci, L.; Lunerti, L.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Cassese, A.; Ceccarelli, R.; Ciulli, V; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.] Ist Nazl Fis Nucl, Sez Firenze, Florence, Italy; [Ceccarelli, R.; Ciulli, V; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Seidita, R.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Bozzo, M.; Mulargia, R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Brivio, F.; Cetorelli, F.; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Lucchini, M. T.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Ist Nazl Fis Nucl, Sez Milano Bicocca, Milan, Italy; [Brivio, F.; Cetorelli, F.; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Lucchini, M. T.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Carnevali, F.; Cavallo, N.; De Iorio, A.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Carnevali, F.; De Iorio, A.; Iorio, A. O. M.; Lista, L.; Sciacca, C.; Layer, L.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Layer, L.; Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Re, V; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.; Fallavollita, F.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Riccardi, C.; Vitulo, P.; Fallavollita, F.] Univ Pavia, Pavia, Italy; [Asenov, P.; Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Piccinelli, A.; Rossi, A.; Santocchia, A.; Tedeschi, T.; Magherini, M.] Univ Perugia, Perugia, Italy; [Azzurri, P.; Bagliesi, G.; Bertacchi, V; Bianchini, L.; Boccali, T.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; D'Amante, V; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Parolia, S.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Bossini, E.; Ciocci, M. A.; Messineo, A.; Parolia, S.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [D'Amante, V; Di Domenico, M. R.; Turini, N.] Univ Siena, Siena, Italy; [Barria, P.; Campana, M.; Cavallari, F.; Del Re, D.; Di Marco, E.; Diemoz, M.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Campana, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ruspa, M.; Shchelina, K.; Siviero, F.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.; Vagnerini, A.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Vagnerini, A.; Rumerio, P.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Candelise, V; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Kim, J.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.; Kamon, T.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Francois, B.; Kim, T. J.; Park, J.] Hanyang Univ, Seoul, South Korea; [Lee, K.; Park, J.; Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K. S.; Lim, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Kim, J.; Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Lee, S.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I] Seoul Natl Univ, Seoul, South Korea; [Jang, W.; Kang, D. Y.; Kang, Y.; Kim, S.; Ko, B.; Lee, J. S. H.; Lee, Y.; Park, I. C.; Roh, Y.; Ryu, M. S.; Song, D.; Watson, I. J.; Yang, S.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Lee, H.; Lee, Y.; Choi, M.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Beyrouthy, T.; Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Kuwait; [Torims, T.; Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; De Oliveira, A. Carvalho Antunes; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.] Vilnius Univ, Vilnius, Lithuania; [Bin Norjoharuddeen, N.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Leon Coello, M.; Murillo Quijada, J. A.; Sehrawat, A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I; Lopez-Fernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez-Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M., I; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Gorski, M.; Kazana, M.; Szleper, M.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Faccioli, P.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Niknejad, T.; Pisano, M.; Seixas, J.; Toldaiev, O.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Finger, M.; Jr, M. Finger; Tsamalaidze, Z.; Afanasiev, S.; Budkouski, D.; Golutvin, I; Gorbunov, I; Karjavine, V; Korenkov, V; Lanev, A.; Malakhov, A.; Matveev, V; Palichik, V; Perelygin, V; Savina, M.; Seitova, D.; Shalaev, V; Shmatov, S.; Shulha, S.; Smirnov, V; Teryaev, O.; Voytishin, N.; Yuldashev, B. S.; Zarubin, A.; Zhizhin, I] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V; Ivanov, Y.; Kim, V; Kuznetsova, E.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirpichnikov, D.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Toropin, A.; Musienko, Y.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Nikitenko, A.; Popov, V; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys & Technol, Moscow, Russia; [Matveev, V; Chadeeva, M.; Oskin, A.; Parygin, P.; Popova, E.; Rusinov, V; Selivanova, D.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Chadeeva, M.; Andreev, V; Azarkin, M.; Dremin, I; Kirakosyan, M.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Belyaev, A.; Boos, E.; Bunichev, V; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V; Korneeva, N.; Lokhtin, I; Obraztsov, S.; Perfilov, M.; Savrin, V; Volkov, P.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Dimova, T.; Kardapoltsev, L.; Kozyrev, A.; Ovtin, I; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Elumakhov, D.; Kachanov, V; Konstantinov, D.; Mandrik, P.; Petrov, V; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Natl Res Ctr, Inst High Energy Phys, Kurchatov Inst, Protvino, Russia; [Babaev, A.; Okhotnikov, V] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borshch, V; Ivanchenko, V; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I; Barrio Luna, M.; Bedoya, Cristina F.; Carrillo Montoya, C. A.; Cepeda, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Leon Holgado, J.; Moran, D.; Tobar, A. Navarro; Perez Dengra, C.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Sanchez Navas, S.; Gomez, L. Urda; Willmott, C.] Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid, Spain; [de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Ramon Alvarez, C.; Rodriguez Bouza, V; Soto Rodriguez, A.; Trapote, A.; Trevisani, N.; Vico Villalba, C.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Matorras-Cuevas, Pablo; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I; Vizan Garcia, J. M.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Jayananda, M. K.; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, D. D. C.] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Sharma, A.; Stahl, A.; Hartmann, F.; Sphicas, P.; Valsecchi, D.; Meola, S.; Paolucci, P.; Fallavollita, F.; Aarrestad, T. K.; Abbaneo, D.; Alimena, J.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chhibra, S. S.; Cipriani, M.; Cristella, L.; D'Enterria, D.; Dabrowski, A.; David, A.; De Roeck, A.; Defranchis, M. M.; Deile, M.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fasanella, D.; Florent, A.; Franzoni, G.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Haranko, M.; Hegeman, J.; Innocente, V; James, T.; Janot, P.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Lintuluoto, A.; Long, K.; Lourenco, C.; Maier, B.; Malgeri, L.; Mallios, S.; Mannelli, M.; Marini, A. C.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Pitt, M.; Qu, H.; Quast, T.; Rabady, D.; Racz, A.; Gutierrez, G. Reales; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schafer, C.; Schwick, C.; Selvaggi, M.; Silva, P.; Snoeys, W.; Summers, S.; Tatar, K.; Tavolaro, V. R.; Treille, D.; Tropea, P.; Tsirou, A.; Van Onsem, G. P.; Wanczyk, J.; Wozniak, K. A.; Zeuner, W. D.; Virdee, T.] European Org Nucl Res, CERN, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Noehte, L.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Androsov, K.; Backhaus, M.; Berger, P.; Calandri, A.; Chernyavskaya, N.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Eble, F.; Gedia, K.; Glessgen, F.; Espinosa, T. A. Gomez; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A-M; Manzoni, R. A.; Marchese, L.; Perez, C. Martin; Meinhard, M. T.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Stampf, V; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Caminada, L.; Amsler, C.; Bartschi, P.; Botta, C.; Brzhechko, D.; Canelli, M. F.; Cormier, K.; De Wit, A.; Del Burgo, R.; Heikkila, J. K.; Huwiler, M.; Jin, W.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Liechti, S. P.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I; Reimers, A.; Robmann, P.; Cruz, S. Sanchez; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Ceard, L.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W-S; Li, Y. Y.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Wu, H. Y.; Yazgan, E.; Yu, P. R.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dumanoglu, I; Eskut, E.; Guler, Y.; Guler, E. Gurpinar; Hos, I; Isik, C.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Ukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Cerci, S.; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Kharkov Inst Phys & Technol, Natl Sci Ctr, Kharkov, Ukraine; [Stylianou, N.; Anthony, D.; Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Krikler, B.; Paramesvaran, S.; El Nasr-Storey, S. Seif; Smith, V. J.; Pass, K. Walkingshaw; White, R.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Bell, K. W.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Cooke, C.; Ellis, K., V; Harder, K.; Harper, S.; Holmberg, M. L.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Nikitenko, A.; Virdee, T.; Bainbridge, R.; Bloch, P.; Bonomally, S.; Borg, J.; ; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Egyptian Knowledge Bank (EKB); Fayoum University; National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Georgian Technical University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Hungarian University of Agriculture & Life Sciences; Indian Institute of Science (IISC) - Bangalore; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; Isfahan University of Technology; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; University of Siena; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Kyung Hee University; Sejong University; Seoul National University (SNU); University of Seoul; Yonsei University; Sungkyunkwan University (SKKU); American University of Middle East; Riga Technical University; Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; University of Montenegro; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; AGH University of Science & Technology; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade Federal de Mato Grosso do Sul; Nanjing Normal University; Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Suez University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Erzincan Binali Yildirim University; Brandenburg University of Technology Cottbus; Helmholtz Association; Research Center Julich; Egyptian Knowledge Bank (EKB); Assiut University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Sharif University of Technology; University of Science & Technology of Mazandaran; Italian National Agency New Technical Energy & Sustainable Economics Development; Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); Academy of Sciences of Uzbekistan; Peter the Great St. Petersburg Polytechnic University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Eastern University, Sri Lanka; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Sirnak University; Near East University; Konya Technical University; Istanbul University - Cerrahpasa; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Egyptian Knowledge Bank (EKB); Ain Shams University; Bingol University; Sinop University; Erciyes University; Qatar Foundation (QF); Texas A&M University Qatar Tonon, N (corresponding author), DESY, Hamburg, Germany. Bortignon, Pierluigi/AAF-6561-2020; Tsirou, Andromachi/HHC-6869-2022; Dudko, Lev/D-7127-2012; Bunichev, Viacheslav/E-2285-2012; Lokos, Sandor/A-4798-2019; Gozalez-Lopez, Oscar/AAH-3533-2019; Kim, Victor T/L-1648-2013; Di Florio, Adriano/ABA-3631-2020; Parygin, Pavel/C-8908-2016; Norjoharuddeen, Nurfikri/S-3109-2018; Chenarani, Shirin/AAO-8918-2021; Benussi, Luigi/O-9684-2014; SIMSEK, Ali Eren/AFO-3050-2022; Tcherniaev, Evgueni/G-3453-2016; Snoeys, Walter J M/K-8259-2015; Blekman, Freya/ABD-7916-2020; Lokhtin, Igor/D-7004-2012; Watson, Ian James/AAH-1423-2022; Vai, Ilaria/AAT-5836-2020; Amapane, Nicola/HDN-9630-2022; Konecki, Marcin/G-4164-2015; Lowette, Steven/HKV-3341-2023; Fienga, Francesco/HKE-1204-2023; Özdemir, Kadri/P-8058-2014; Ambrozas, Marijus/F-5551-2018; Androsov, Konstantin/HJA-2332-2022; Strologas, John/GWR-2036-2022; Guler, Yalcin/AAG-7833-2019; Benaglia, Andrea/AGF-5495-2022; Casarsa, Massimo/L-3623-2018; Moscatelli, Francesco/AEL-1878-2022; Shopova, Mariana Filipova/HKW-0149-2023; Dallavalle, Gaetano Marco/AFW-4654-2022; Schwarz, Dennis/GYA-4479-2022; De Iorio, Agostino/HJH-7155-2023; Pellecchia, Antonello/HNB-9377-2023; Legger, Federica/HNC-2568-2023; Fouz, Maria Cruz/AAF-1105-2019; TUVE', Cristina/P-3933-2015; Ciangottini, Diego/HJH-5914-2023; de Souza Sandro, Fonseca/ABB-8505-2020; Iorio, Alberto Orso Maria/GXA-0233-2022; Sguazzoni, Giacomo/J-4620-2015; Demiroglu, Zuhal Seyma/A-5168-2016; Watson, Ian James/AFL-9360-2022; Hos, ilknur/T-1165-2019; kaya, mithat/D-8062-2011; Gil Da Silveira, Gustavo/N-7279-2014; Selvaggi, Michele/CAJ-2129-2022; Lezki, Samet/AAG-8277-2019; Joo, Changwoo/ABI-4034-2020; Kirpichnikov, Dmitry/U-9605-2019; Novaes, Sergio F./D-3532-2012; Puerta Pelayo, Jesus/L-1632-2014; Bermudez Martinez, Armando/HHN-4625-2022; Aarrestad, Thea/GLV-3031-2022; Zorbilmez, Caglar/ABH-1905-2020; Sunar Cerci, Deniz/AHE-1706-2022; Ivanchenko, Vladimir N/L-5254-2017; M, F/HGF-3166-2022; Giommi, Luca/AGV-7154-2022; Tinoco Mendes, Andre David/D-4314-2011; Duarte, Javier Mauricio/AAA-5414-2020; 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Sarica, Ulascan/0000-0002-1557-4424; CECCARELLI, RUDY/0000-0003-3232-9380; Shi, Kun/0000-0001-9833-5674; Donega, Mauro/0000-0001-9830-0412; Buontempo, Salvatore/0000-0001-9526-556X; Primavera, Federica/0000-0001-6253-8656; Dewanjee, Ram Krishna/0000-0001-6645-6244; Covarelli, Roberto/0000-0003-1216-5235; Vila, Ivan/0000-0002-6797-7209; D'Alessandro, Raffaello/0000-0001-7997-0306; Silvestris, Lucia/0000-0002-8985-4891; Seitova, Diana/0000-0003-2522-7642; Bharthuar, Shudhashil/0000-0001-5871-9622; Malawski, Maciej/0000-0001-6005-0243; Flix Molina, Jose/0000-0003-2688-8047; Alessandro, Calandri/0000-0001-7774-0099; Monteno, Marco/0000-0002-3521-6333; Colaleo, Anna/0000-0002-0711-6319; Csanad, Mate/0000-0002-3154-6925; Grunewald, Martin/0000-0002-5754-0388; Meyer, Arnd/0000-0001-9598-6623; Meschi, Emilio/0000-0003-4502-6151; Fasanella, Daniele/0000-0002-2926-2691; Padula, Sandra S./0000-0003-3071-0559; Dimova, Tatyana/0000-0002-9560-0660; Grzanka, Leszek/0000-0002-3599-854X; Aly, Reham/0000-0001-6808-1335; gennai, simone/0000-0001-5269-8517; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Haddad, Yacine/0000-0003-4916-7752; Manthos, Nikolaos/0000-0003-3247-8909; Navarria, Francesco Luigi/0000-0001-7961-4889; Gulmez, Erhan/0000-0002-6353-518X; Heath, Helen/0000-0001-6576-9740; Tapper, Alexander/0000-0003-4543-864X; Lotfy Abdelfattah Ahmad, Ahmad/0000-0003-4681-0079; Garcia, Francisco/0000-0002-4023-7964; Brommer, Sebastian/0000-0001-8988-2035; Boimska, Bozena/0000-0002-4200-1541 BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia); RFBR (Russia); NRC KI (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (U.S.A.); NSF (U.S.A.); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 (European Union) [675440, 724704, 752730, 758316, 765710, 824093]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG) [390833306, 400140256 -GRK2497]; Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; Latvian Council of Science; Ministry of Science and Higher Education (Poland); National Science Center (Poland) [Opus 2014/15/B/ST2/03998, 2015/19/B/ST2/02861]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [07232020-0041]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.) BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MoST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (U.S.A.)(United States Department of Energy (DOE)); NSF (U.S.A.)(National Science Foundation (NSF)); Marie-Curie program (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); Horizon 2020 (European Union); COST Action (European Union); Leventis Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); Beijing Municipal Science AMP; Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Hungarian Academy of Sciences (Hungary)(Hungarian Academy of Sciences); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); Latvian Council of Science(Latvian Ministry of Education and Science); Ministry of Science and Higher Education (Poland)(Ministry of Science and Higher Education, Poland); National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306, and under project number 400140256 -GRK2497; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no.; 07232020-0041 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 85 0 0 8 31 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. DEC 13 2021.0 12 83 10.1007/JHEP12(2021)083 0.0 55 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics XO5EP Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000730208800004 0
C Yang, S; Zheng, LH; Chen, XYL; Zabawa, L; Zhang, M; Wang, MJ IEEE Yang, Si; Zheng, Lihua; Chen, Xieyuanli; Zabawa, Laura; Zhang, Man; Wang, Minjuan Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022) English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-24, 2022 New Orleans, LA IEEE,CVF,IEEE Comp Soc Deep learning; Transfer learning; Computer vision; Instance segmentation; Plant phenotyping SEMANTIC SEGMENTATION; AGRICULTURE; SOFTWARE; SHAPE The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to rapidly generate a synthetic in-vitro soybean pods dataset with plenty of annotated samples. The in-vitro soybean pods samples are overlapped to simulate the frequently physically touching of on-branch soybean pods. Then, we design a two-step transfer learning. In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset). In the second step, transferring from simulation to reality is performed by finetuning on a few real-world mature soybean plant samples. The experimental results show the effectiveness of the proposed two-step transfer learning method, such that AP50 was 0.80 for the real-world mature soybean plant test dataset, which is higher than that of direct adaptation and its AP50 was 0.77. Furthermore, the visualizations of in-situ segmentation results of on-branch soybean pods show that our method performs better than other methods, especially when soybean pods overlap densely. [Yang, Si; Zheng, Lihua; Zhang, Man; Wang, Minjuan] China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China; [Yang, Si; Zheng, Lihua; Zhang, Man; Wang, Minjuan] China Agr Univ, Minist Educ, Key Lab Smart Agr Syst, Beijing, Peoples R China; [Yang, Si; Chen, Xieyuanli; Zabawa, Laura] Univ Bonn, Inst Geodesy & Geoinformat, Bonn, Germany China Agricultural University; China Agricultural University; University of Bonn Wang, MJ (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China.;Wang, MJ (corresponding author), China Agr Univ, Minist Educ, Key Lab Smart Agr Syst, Beijing, Peoples R China. yangsi@cau.edu.cn; zhenglh@cau.edu.cn; xieyuanli.chen@igg.uni-bonn.de; zabawa@igg.uni-bonn.de; minjuan@cau.edu.cn Chen, Xieyuanli/AAH-6401-2020 Chen, Xieyuanli/0000-0003-0955-6681; Yang, Si/0000-0002-3254-8834 National Natural Science Foundation of China [31971786]; China Agriculture Research System [CARS-04]; National Key R&D Program of China [2017YFD0101400]; Chinese Universities Scientific Fund [2021TC111] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Agriculture Research System; National Key R&D Program of China; Chinese Universities Scientific Fund This work was supported jointly by National Natural Science Foundation of China (31971786), the China Agriculture Research System(CARS-04), the National Key R&D Program of China (2017YFD0101400) and the Chinese Universities Scientific Fund(2021TC111). All of the mentioned support is gratefully acknowledged. In addition, thanks for all the help of the teachers and students of the related universities. 32 3 3 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-8739-9 2022.0 1665 1674 10.1109/CVPRW56347.2022.00173 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT9KZ 2023-03-23 WOS:000861612701068 0
J Wang, HT; Xu, LF; Bezerianos, A; Chen, CQ; Zhang, ZG Wang, Hongtao; Xu, Linfeng; Bezerianos, Anastasios; Chen, Chuangquan; Zhang, Zhiguo Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Attention-based multiscale convolutional neural network (CNN); driving fatigue; dynamical graph convolution network (GCN); electroencephalography (EEG); spatiotemporal structure MENTAL FATIGUE; EEG; PERFORMANCE; NETWORKS Electroencephalography (EEG) signals have been proven to be one of the most predictive and reliable indicators for estimating driving fatigue state. However, how to make full use of EEG data for driving fatigue detection remains a challenge. Many existing methods include a time-consuming manual process or tedious parameter tunings for feature extraction, which is inconvenient to train and implement. On the other hand, most models ignore or manually determine EEG connectivity features between different channels, thus failing to thoroughly exploit the intrinsic interchannel relations for classification. In this article, we introduce a new attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN) model, aiming to conquer these two issues in a unified end-to-end model. AMCNN-DGCN starts with attention-based multiscale temporal convolutions to automatically learn frequency filters to extract the salient pattern from raw EEG data. Subsequently, AMCNN-DGCN uses dynamical graph convolutional networks (DGCNs) to learn spatial filters, in which the adjacency matrix is adaptively determined in a data-driven way to exploit the intrinsic relationship between channels effectively. With the temporal-spatial structure, AMCNN-DCCN can capture highly discriminative features. To verify the effectiveness of AMCNN-DGCN, we conduct a simulated fatigue driving environment to collect EEG signals from 29 healthy subjects (male/female = 17/12 and age = 23.28 +/- 2.70 years) through a remote wireless cap with 24 channels. The results demonstrate that our proposed model outperforms six widely used competitive EEG models with high accuracy of 95.65%. Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix. [Wang, Hongtao; Xu, Linfeng; Chen, Chuangquan] Wuyi Univ, Fac Intelligent Mfg, Jiangmen 529020, Peoples R China; [Bezerianos, Anastasios] Natl Univ Singapore, N1 Inst, Singapore 117456, Singapore; [Bezerianos, Anastasios] Univ Patras, Dept Med Phys, Patras 26500, Greece; [Zhang, Zhiguo] Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen 518060, Peoples R China Wuyi University; National University of Singapore; University of Patras; Shenzhen University Chen, CQ (corresponding author), Wuyi Univ, Fac Intelligent Mfg, Jiangmen 529020, Peoples R China.;Zhang, ZG (corresponding author), Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen 518060, Peoples R China. chenchuangquan87@163.com; zgzhang@szu.edu.cn wang, hongtao/AAH-1233-2020; Bezerianos, Tasos/AAC-2499-2022; Wang, Hongtao/AAH-5528-2021 wang, hongtao/0000-0002-6564-5753; Bezerianos, Tasos/0000-0002-8199-6000; Zhang, Zhiguo/0000-0001-7992-7965; Xu, LinFeng/0000-0002-6188-8424 Special Projects in Key Fields by Technology Development Project of Guangdong Province [2020ZDZX3018]; Special Fund for Science and Technology of Guangdong Province [2020182]; Wuyi University and Hong Kong & Macao joint Research and Development Project [2019WGALH16]; Science Foundation for Young Teachers of Wuyi University [2018td01]; Jiangmen Brain-like Computation and Hybrid Intelligence Research and Development Center [[2018]359, [2019]26]; Scientific Research Startup Funds for High-Level Talents of Wuyi University [2020AL006]; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging [SZD201909]; Science, Technology and Innovation Commission of Shenzhen Municipality Technology Fund [JCYJ20170818093322718]; National Natural Science Foundation [81871443] Special Projects in Key Fields by Technology Development Project of Guangdong Province; Special Fund for Science and Technology of Guangdong Province; Wuyi University and Hong Kong & Macao joint Research and Development Project; Science Foundation for Young Teachers of Wuyi University; Jiangmen Brain-like Computation and Hybrid Intelligence Research and Development Center; Scientific Research Startup Funds for High-Level Talents of Wuyi University; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging; Science, Technology and Innovation Commission of Shenzhen Municipality Technology Fund; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province under Grant 2020ZDZX3018, in part by the Special Fund for Science and Technology of Guangdong Province under Grant 2020182, in part by the Wuyi University and Hong Kong & Macao joint Research and Development Project under Grant 2019WGALH16, in part by the Science Foundation for Young Teachers of Wuyi University under Grant 2018td01, in part by the Jiangmen Brain-like Computation and Hybrid Intelligence Research and Development Center under Grant [2018]359 and Grant [2019]26, in part by the Scientific Research Startup Funds for High-Level Talents of Wuyi University under Grant 2020AL006, in part by the Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging under Grant SZD201909, in part by the Science, Technology and Innovation Commission of Shenzhen Municipality Technology Fund under Grant JCYJ20170818093322718, and in part by the National Natural Science Foundation of China under Grant 81871443. (Hongtao Wang and Linfeng Xu contributed equally to this work.) The Associate Editor coordinating the review process was Dr. Jing Lei. 37 18 18 24 102 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70.0 2504811 10.1109/TIM.2020.3047502 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation RH5QW 2023-03-23 WOS:000636274000017 0
J Huang, J; Liu, K; Xu, ML; Perc, M; Li, XL Huang, Ju; Liu, Kang; Xu, Mingliang; Perc, Matjaz; Li, Xuelong Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Detectors; Anomaly detection; Image reconstruction; Hyperspectral imaging; Training; Optics; Dictionaries; Anomaly detection; background purification; extended attribute profile (EAP); hyperspectral image (HSI); sparse representation (SR); stacked autoencoder (SAE) Hyperspectral anomaly detection has attracted extensive interests for its wide use in military and civilian fields, and three main categories of detection methods have been developed successively over past few decades, including statistical model-based, representation-based, and deep-learning-based methods. Most of these algorithms are essentially trying to construct proper background profiles, which describe the characteristics of background and then identify the pixels that do not conform to the profiles as anomalies. Apparently, the crucial issue is how to build an accurate background profile; however, the background profiles constructed by existing methods are not accurate enough. In this article, a novel and universal background purification framework with extended morphological attribute profiles is proposed. It explores the spatial characteristic of image and removes suspect anomaly pixels from the image to obtain a purified background. Moreover, three detectors with this framework covering different categories are also developed. The experiments implemented on four real hyperspectral images demonstrate that the background purification framework is effective, universal, and suitable. Furthermore, compared with other popular algorithms, the detectors with the framework perform well in terms of accuracy and efficiency. [Huang, Ju; Liu, Kang] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Shaanxi Key Lab Ocean Opt, Xian 710119, Peoples R China; [Huang, Ju; Liu, Kang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Xu, Mingliang] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China; [Perc, Matjaz] Univ Maribor, Fac Nat Sci & Math, Maribor 2000, Slovenia; [Li, Xuelong] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect, Xian 710072, Peoples R China; [Li, Xuelong] Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Intelligent Interact & Applicat, Xian 710072, Peoples R China Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Zhengzhou University; University of Maribor; Northwestern Polytechnical University; Northwestern Polytechnical University Li, XL (corresponding author), Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect, Xian 710072, Peoples R China. juhuang_iopen@163.com; liukang@opt.ac.cn; iexumingliang@zzu.edu.cn; matjaz.perc@gmail.com; li@nwpu.edu.cn Perc, Matjaz/A-5148-2009 Perc, Matjaz/0000-0002-3087-541X; ju, huang/0000-0001-8191-9809 Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDY-SSW-JSC044]; National Natural Science Foundation of China [61871470] Key Research Program of Frontier Sciences, Chinese Academy of Sciences; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Manuscript received May 11, 2021; revised July 22, 2021; accepted August 1, 2021. Date of publication August 10, 2021; date of current version August 27, 2021. This work was supported in part by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences under Grant QYZDY-SSW-JSC044 and in part by the National Natural Science Foundation of China under Grant 61871470. 45 6 6 2 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 8113 8124 10.1109/JSTARS.2021.3103858 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology UI2JZ gold 2023-03-23 WOS:000690441600011 0
J Hu, XM; Zhou, JH; Li, Y; Wang, YK; Guo, J; Sack, I; Chen, WB; Yan, FH; Li, RK; Wang, CY Hu, Xumei; Zhou, Jiahao; Li, Yan; Wang, Yikun; Guo, Jing; Sack, Ingolf; Chen, Weibo; Yan, Fuhua; Li, Ruokun; Wang, Chengyan Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model CANCERS English Article Ki-67; hepatocellular carcinoma (HCC); conventional MRI (cMRI); magnetic resonance elastography (MRE); deep learning combined radiomics (DLCR) ELASTOGRAPHY; STIFFNESS; DENSITY Simple Summary This study aimed to explore the added value of magnetic resonance elastography (MRE) in the prediction of Ki-67 expression in hepatocellular carcinoma (HCC) using a deep learning combined radiomics (DLCR) model. A total of 108 histopathology-proven HCC patients who underwent preoperative MRI and MR elastography were included. All the patients were divided into training and validation cohorts. An independent cohort including 43 patients was included for testing. A DLCR model was proposed to predict the expression of Ki-67 with conventional MRI (cMRI) as inputs. The images of shear wave speed (c-map) and phase angle (phi-map) derived from MRE were also fed into the DLCR model. Experimental results show that both c and phi values were ranked within the top six features for Ki-67 prediction with random forest selection, which revealed the value of MRE-based viscosity for the assessment of the tumor proliferation status in HCC. The model with all modalities (MRE, AFP, and cMRI) as inputs achieved the highest AUC of 0.90 +/- 0.03 (CI: 0.89-0.91) in the validation cohort. The same finding was observed in the independent testing cohort with an AUC of 0.83 +/- 0.03 (CI: 0.82-0.84). MRE-based c and phi-maps can serve as important parameters to assess the tumor proliferation status in HCC. This study aimed to explore the added value of viscoelasticity measured by magnetic resonance elastography (MRE) in the prediction of Ki-67 expression in hepatocellular carcinoma (HCC) using a deep learning combined radiomics (DLCR) model. This retrospective study included 108 histopathology-proven HCC patients (93 males; age, 59.6 +/- 11.0 years) who underwent preoperative MRI and MR elastography. They were divided into training (n = 87; 61.0 +/- 9.8 years) and testing (n = 21; 60.6 +/- 10.1 years) cohorts. An independent validation cohort including 43 patients (60.1 +/- 11.3 years) was included for testing. A DLCR model was proposed to predict the expression of Ki-67 with cMRI, including T2W, DW, and dynamic contrast enhancement (DCE) images as inputs. The images of the shear wave speed (c-map) and phase angle (phi-map) derived from MRE were also fed into the DLCR model. The Ki-67 expression was classified into low and high groups with a threshold of 20%. Both c and phi values were ranked within the top six features for Ki-67 prediction with random forest selection, which revealed the value of MRE-based viscosity for the assessment of tumor proliferation status in HCC. When comparing the six CNN models, Xception showed the best performance for classifying the Ki-67 expression, with an AUC of 0.80 +/- 0.03 (CI: 0.79-0.81) and accuracy of 0.77 +/- 0.04 (CI: 0.76-0.78) when cMRI were fed into the model. The model with all modalities (MRE, AFP, and cMRI) as inputs achieved the highest AUC of 0.90 +/- 0.03 (CI: 0.89-0.91) in the validation cohort. The same finding was observed in the independent testing cohort, with an AUC of 0.83 +/- 0.03 (CI: 0.82-0.84). The shear wave speed and phase angle improved the performance of the DLCR model significantly for Ki-67 prediction, suggesting that MRE-based c and phi-maps can serve as important parameters to assess the tumor proliferation status in HCC. [Hu, Xumei; Wang, Chengyan] Fudan Univ, Human Phenome Inst, Shanghai 201203, Peoples R China; [Zhou, Jiahao; Li, Yan; Wang, Yikun; Yan, Fuhua; Li, Ruokun] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Radiol, Shanghai 200025, Peoples R China; [Guo, Jing; Sack, Ingolf] Charite Univ Med Berlin, Dept Radiol, D-10117 Berlin, Germany; [Chen, Weibo] Philips Healthcare, Shanghai 200070, Peoples R China Fudan University; Shanghai Jiao Tong University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Philips; Philips Healthcare Wang, CY (corresponding author), Fudan Univ, Human Phenome Inst, Shanghai 201203, Peoples R China.;Li, RK (corresponding author), Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Radiol, Shanghai 200025, Peoples R China. 21212030009@m.fudan.edu.cn; zjh01e31@rjh.com.cn; ly40730@rjh.com.cn; wyk01g67@rjh.com.cn; jing.guo@charite.de; ingolf.sack@charite.de; weibo.chen@philips.com; yfh11655@rjh.com.cn; lrk12113@rjh.com.cn; wangcy@fudan.edu.cn Guo, Jing/0000-0002-1170-8157; Sack, Ingolf/0000-0003-2460-1444; Zhou, Jiahao/0000-0003-0790-5729 37 2 2 4 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6694 CANCERS Cancers JUN 2022.0 14.0 11 2575 10.3390/cancers14112575 0.0 11 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 1Z6TM 35681558.0 Green Accepted, gold 2023-03-23 WOS:000808954300001 0
J Zotti, C; Luo, ZM; Lalande, A; Jodoin, PM Zotti, Clement; Luo, Zhiming; Lalande, Alain; Jodoin, Pierre-Marc Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Cardiac MRI segmentation; convolutional neural networks; shape prior FULLY-AUTOMATIC SEGMENTATION; LEFT-VENTRICLE; HEART; MODEL; REGRESSION In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. With its multiresolution grid architecture, the network learns both high and low-level features useful to register the shape prior as well as accurately localize the borders of the cardiac regions. Experimental results obtained on the Automatic Cardiac Diagnostic Challenge Medical Image Computing and Computer Assisted Intervention (ACDC-MICCAI) 2017 dataset show that our model segments multislices CMRI (left and right ventricle contours) in 0.18 s with an average Dice coefficient of 0.91 and an average 3-D Hausdorff distance of 9.5 mm. [Zotti, Clement; Luo, Zhiming; Jodoin, Pierre-Marc] Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ J1K 2R1, Canada; [Lalande, Alain] Univ Burgundy, Le2i Lab, CNRS, FRE 2005, F-21079 Dijon, France; [Lalande, Alain] Univ Hosp Dijon, MRI Dept, F-21079 Dijon, France; [Luo, Zhiming] Xiamen Univ, Dept Cognit Sci, Xiamen 361005, Peoples R China University of Sherbrooke; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Information Sciences & Technologies (INS2I); Universite de Bourgogne; CHU Dijon Bourgogne; Xiamen University Zotti, C (corresponding author), Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ J1K 2R1, Canada. clement.zotti@usherbrooke.ca; zhiming.luo@usherbrooke.ca; alain.lalande@u-bourgogne.fr; pierre-marc.jodoin@usherbrooke.ca Lalande, Alain/T-1050-2017 Lalande, Alain/0000-0002-7970-366X; Luo, Zhiming/0000-0002-3411-9582 Conseil franco-quebecois de cooperation universitaire (CFQCU) [2015-FQ-186778] Conseil franco-quebecois de cooperation universitaire (CFQCU) This research was funded by the Conseil franco-quebecois de cooperation universitaire (CFQCU), Nb: 2015-FQ-186778. 60 88 91 3 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. MAY 2019.0 23.0 3 1119 1128 10.1109/JBHI.2018.2865450 0.0 10 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics HX0DT 30113903.0 2023-03-23 WOS:000467060400023 0
J Cui, SZ; Wang, DJ; Yin, YQ; Fan, X; Dhamotharan, L; Kumar, A Cui, Shaoze; Wang, Dujuan; Yin, Yunqiang; Fan, Xin; Dhamotharan, Lalitha; Kumar, Ajay Carbon trading price prediction based on a two-stage heterogeneous ensemble method ANNALS OF OPERATIONS RESEARCH English Article; Early Access Carbon trading; Ensemble learning; Empirical mode decomposition; Variational mode decomposition; Particle swarm optimization VARIATIONAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINES; OPTIMIZATION; NETWORK; ARIMA Several countries have formulated carbon-neutral plans in dealing with global warming, which have also derived various carbon trading markets. All parties involved in carbon trading aim to obtain the maximum benefit from it, and this requires participants to accurately judge the carbon trading price. This study then proposes a two-stage heterogeneous ensemble method for predicting carbon trading prices. To accurately capture the characteristics of the time series data, we extracted four feature sets based on the lag length, moving average, variational mode decomposition, and empirical mode decomposition methods. Subsequently, four algorithms, linear regression, neural network, random forest, and XGBoost, constructed the first-layer model. We used a neural network algorithm to build the second-layer model to enhance the predictive model fit. Moreover, we used the particle swarm optimization algorithm to optimize the crucial parameters involved in the model. Extensive numerical experiments were conducted on carbon trading data from the Beijing carbon trading market in the past five years (2016-2021), and showed that our proposed method is superior to other popular methods such as LightGBM, support vector machine, and k-nearest neighbor. [Cui, Shaoze] Dalian Univ Technol, Sch Econ & Management, Dalian, Peoples R China; [Wang, Dujuan; Fan, Xin] Sichuan Univ, Business Sch, Chengdu, Peoples R China; [Yin, Yunqiang] Univ Elect Sci & Technol China, Sch Econ & Management, Chengdu, Peoples R China; [Dhamotharan, Lalitha] Univ Exeter, Ctr Simulat Analyt & Modelling CSAM, Business Sch, Exeter EX4 4PU, Devon, England; [Kumar, Ajay] EMLYON Business Sch, Ecully, France Dalian University of Technology; Sichuan University; University of Electronic Science & Technology of China; University of Exeter; EMLYON Business School Yin, YQ (corresponding author), Univ Elect Sci & Technol China, Sch Econ & Management, Chengdu, Peoples R China. yinyq@uestc.edu.cn Cui, Shaoze/GVU-8637-2022 National Natural Science Foundation of China [72171161, 71971041, 71871148, 71533001]; Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province [2020JDJQ0035]; Major Program of National Social Science Foundation of China [20ZD084]; Sichuan University to Building aWorld-class University [SKSYL2021-08]; China Scholarship Council [202006060162] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province; Major Program of National Social Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan University to Building aWorld-class University; China Scholarship Council(China Scholarship Council) This paper was supported in part by the National Natural Science Foundation of China underGrant Numbers 72171161, 71971041, 71871148, and 71533001; by theChina Scholarship Council under Grant Number 202006060162, by the Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province under Grant number 2020JDJQ0035; and by the Major Program of National Social Science Foundation of China under Grant 20 & ZD084; and by Sichuan University to Building aWorld-class University under Grant No. SKSYL2021-08. 39 0 0 11 21 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0254-5330 1572-9338 ANN OPER RES Ann. Oper. Res. 10.1007/s10479-022-04821-1 0.0 JUL 2022 25 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science 2X2EL 2023-03-23 WOS:000825022800002 0
J Dubey, R; Gunasekaran, A; Childe, SJ; Luo, ZW; Wamba, SF; Roubaud, D; Foropon, C Dubey, Rameshwar; Gunasekaran, Angappa; Childe, Stephen J.; Luo, Zongwei; Wamba, Samuel Fosso; Roubaud, David; Foropon, Cyril Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour JOURNAL OF CLEANER PRODUCTION English Article Sustainable consumption; Sustainable production; Collaboration; Inter-organizational fit; Resource complementarity; Sustainable operations PARTIAL LEAST-SQUARES; SUPPLY CHAIN; DYNAMIC CAPABILITIES; ORGANIZATION DESIGN; MANAGEMENT RESEARCH; DATA SCIENCE; PLS-SEM; OPERATIONS; COMPLEMENTARITY; COMMITMENT The organizations engaged in sustainable development programmes are increasingly paying serious attention towards synergetic relationships between focal firms and their partners to achieve the goal of sustainable consumption and production (SCP) via big data and predictive analytics (BDPA). The study examines the role of BDPA in collaborative performance (CP) among the partners engaged in sustainable development programme to achieve the goal of SCP. The study further investigates the contingent effect of organization fit on the impact of BDPA on CP. We used variance based structural equation modelling (PLS SEM) to test research hypotheses using a sample of 190 respondents working in auto-components manufacturing organizations in India drawn from the ACMA and Dun & Bradstreet databases. The results indicate that BDPA has a significant positive impact on the CP among partners and the organizational compatibility and resource complementarity have positive moderating effects on the path joining BDPA and CP. The study contributes to the understanding of BDPA and collaboration literature in the context of sustainable development. These findings extend the dynamic capability view (DCV) to create a better understanding of contemporary applications of big data and predictive analytics capability, while also providing theoretically grounded directions to managers who seek to use information processing technologies to continuously improve the collaboration in supply chain networks. We have also noted some of the limitations of our study and identified numerous further research directions. (C) 2018 Elsevier Ltd. All rights reserved. [Dubey, Rameshwar; Roubaud, David; Foropon, Cyril] Montpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier, France; [Gunasekaran, Angappa] Calif State Univ Bakersfield, Sch Business & Publ Adm, 9001 Stockdale Highway, Bakersfield, CA 93311 USA; [Childe, Stephen J.] Plymouth Univ, Plymouth Business Sch, Plymouth PL4 8AA, Devon, England; [Luo, Zongwei] Southern Univ Sci & Technol China, Comp Sci & Engn Dept, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, Peoples R China; [Wamba, Samuel Fosso] Toulouse Univ, Toulouse Business Sch, 20 Blvd Lascrosses, F-31068 Toulouse, France Montpellier Business School; Universite de Montpellier; California State University System; California State University Bakersfield; University of Plymouth; Southern University of Science & Technology; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; TBS Education Luo, ZW (corresponding author), Southern Univ Sci & Technol China, Comp Sci & Engn Dept, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, Peoples R China. r.dubey@montpellier-bs.com; agunasekaran@csub.edu; stephen.childe@plymouth.ac.uk; luozw@sustc.edu.cn; s.fosso-wamba@tbs-education.fr; d.roubaud@montpellier-bs.com; c.foropon@rnontpellier-bs.com Foropon, Cyril/AAF-5640-2019; Childe, Stephen J/ABE-8243-2020; ROUBAUD, DAVID/ABA-8123-2020; Luo, Zongwei/N-1985-2015; Dubey, Rameshwar/AAT-1348-2020; Dubey, Rameshwar/U-7022-2018; Wamba, Samuel Fosso/AAB-4953-2019 Foropon, Cyril/0000-0003-3083-0016; Childe, Stephen J/0000-0002-9476-4209; ROUBAUD, DAVID/0000-0003-3827-6187; Luo, Zongwei/0000-0001-9322-959X; Dubey, Rameshwar/0000-0002-3913-030X; Wamba, Samuel Fosso/0000-0002-1073-058X National Natural Science Foundation of China [6171101169]; Guangdong Natural Science Foundation [2015A030313782]; Guangdong Education Bureau Fund [2017KTSCX166]; Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ20170817112037041] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Natural Science Foundation(National Natural Science Foundation of Guangdong Province); Guangdong Education Bureau Fund; Science and Technology Innovation Committee Foundation of Shenzhen This work is partially supported by National Natural Science Foundation of China 6171101169, Guangdong Natural Science Foundation 2015A030313782, Guangdong Education Bureau Fund 2017KTSCX166, the Science and Technology Innovation Committee Foundation of Shenzhen JCYJ20170817112037041. 114 75 76 6 76 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. SEP 20 2018.0 196.0 1508 1521 10.1016/j.jclepro.2018.06.097 0.0 14 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology GT2XU 2023-03-23 WOS:000444364400129 0
J Pakdaman, F; Yu, L; Hashemi, MR; Ghanbari, M; Gabbouj, M Pakdaman, Farhad; Yu, Li; Hashemi, Mahmoud Reza; Ghanbari, Mohammad; Gabbouj, Moncef SVM based approach for complexity control of HEVC intra coding SIGNAL PROCESSING-IMAGE COMMUNICATION English Article HEVC; Complexity control; Intra coding; SVM; Machine learning; Video compression CU SIZE DECISION; MODE DECISION; ALGORITHM; PREDICTION; PARTITION; SELECTION The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVMbased techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%. [Pakdaman, Farhad; Gabbouj, Moncef] Tampere Univ, Dept Comp Sci, POB 553, Tampere, Finland; [Pakdaman, Farhad; Hashemi, Mahmoud Reza; Ghanbari, Mohammad] Univ Tehran, Sch Elect & Comp Engn, Coll Engn, POB 14395-515, Tehran, Iran; [Yu, Li] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China; [Ghanbari, Mohammad] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England Tampere University; University of Tehran; Nanjing University of Information Science & Technology; University of Essex Hashemi, MR (corresponding author), Univ Tehran, Sch Elect & Comp Engn, Coll Engn, POB 14395-515, Tehran, Iran. rhashemi@ut.ac.ir Pakdaman, Farhad/L-1457-2019; Gabbouj, Moncef/G-4293-2014; Hashemi, Mahmoud Reza/H-2172-2011 Pakdaman, Farhad/0000-0001-6526-3811; Gabbouj, Moncef/0000-0002-9788-2323; Hashemi, Mahmoud Reza/0000-0002-3518-9195 Business Finland [IMD4072] Business Finland This work has been funded in part by Business Finland, within Project IMD4072. 51 5 5 2 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. APR 2021.0 93.0 116177 10.1016/j.image.2021.116177 0.0 FEB 2021 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QV5KX 2023-03-23 WOS:000628011400001 0
C Li, Z; Hu, CY; Zhang, Y; Guo, SQ ACM Li, Zheng; Hu, Chengyu; Zhang, Yang; Guo, Shanqing How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN 35TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE (ACSA) English Proceedings Paper 35th Annual Computer Security Applications Conference (ACSA) DEC 09-13, 2019 San Juan, PREF_CTRY_STATE Appl Comp Secur Associates,Natl Sci Fdn,Charles Koch Inst intellectual property protection; neural networks; blind watermark; security and privacy Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep neural networks is computationally expensive and requires both human and intellectual resources. Therefore, it is necessary to protect the intellectual property of the model and externally verify the ownership of the model. However, previous studies either fail to defend against the evasion attack or have not explicitly dealt with fraudulent claims of ownership by adversaries. Furthermore, they can not establish a clear association between the model and the creator's identity. To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility. Our framework accepts ordinary samples and the exclusive logo as inputs, outputting newly generated samples as watermarks, which are almost indistinguishable from the origin, and infuses these watermarks into DNN models by assigning specific labels, leaving the backdoor as the basis for our copyright claim. We evaluated our IPP framework on two benchmark datasets and 15 popular deep learning models. The results show that our framework successfully verifies the ownership of all the models without a noticeable impact on their primary task. Most importantly, we are the first to successfully design and implement a blind-watermark based framework, which can achieve state-of-art performances on undetectability against evasion attack and unforgeability against fraudulent claims of ownership. Further, our framework shows remarkable robustness and establishes a clear association between the model and the author's identity. [Li, Zheng] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China; [Hu, Chengyu; Guo, Shanqing] Shandong Univ, Sch Cyber Sci & Technol, Minist Educ, Key Lab Cryptol Technol & Informat Secur, Jinan, Peoples R China; [Zhang, Yang] CISPA Helmholtz Ctr Informat Secur, Saarland Informat Campus, Saarbrucken, Germany Shandong University; Shandong University Hu, CY; Guo, SQ (corresponding author), Shandong Univ, Sch Cyber Sci & Technol, Minist Educ, Key Lab Cryptol Technol & Informat Secur, Jinan, Peoples R China. zheng.li@mail.sdu.edu.cn; hcy@sdu.edu.cn; yang.zhang@cispa.saarland; guoshanqing@sdu.edu.cn Li, Zheng/0000-0002-4466-7523 National Natural Science Foundation of China [91546203]; Key Science Technology Project of Shandong Province [2015G GX101046]; Major Scientific and Technological Innovation Projects of Shandong Province, China [2018CXGC0708, 2017CXGC0704] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Science Technology Project of Shandong Province; Major Scientific and Technological Innovation Projects of Shandong Province, China We would like to thank the anonymous reviewers for their comments on previous drafts of this paper. This work is partially supported by National Natural Science Foundation of China (91546203), the Key Science Technology Project of Shandong Province (2015G GX101046), Major Scientific and Technological Innovation Projects of Shandong Province, China (2018CXGC0708, 2017CXGC0704). 30 28 28 2 11 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7628-0 2019.0 126 137 10.1145/3359789.3359801 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP1RJ Green Submitted 2023-03-23 WOS:000540643900010 0
C Cui, ZH; Bao, CC; Nielsen, JK; Christensen, MG IEEE Cui, Zihao; Bao, Changchun; Nielsen, Jesper Kjoer; Christensen, Mads Groesboll AUTOREGRESSIVE PARAMETER ESTIMATION WITH DNN-BASED PRE-PROCESSING 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing MAY 04-08, 2020 Barcelona, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Signal Proc Soc Auto-regressive model; Levinson-Durbin recursion; DNN; generalized analysis-by-synthesis SPEECH; SERIES In this paper, a method for estimating the autoregressive parameters from a signal segment is proposed. The method is based on a deep neural network (DNN) in combination with the classical Levinson-Durbin recursion (LDR). The DNN acts as a pre-processor for the LDR and can be trained on different metrics commonly encountered in speech processing using a generalized analysis-by-synthesis (GABS) structure where the LDR acts as the encoder. Unlike end-to-end data-driven approaches, this structure ensures that the DNN is easy to train and initialize since the DNN only has to learn a simple mapping. The results confirm this and show that the proposed method produces an AR-spectrum that efficiently represents the speech spectrum in terms of the Itakura-Saito divergence, Kullback-Leibler divergence, log-spectral distortion, and speech distortion. [Cui, Zihao; Bao, Changchun] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China; [Nielsen, Jesper Kjoer; Christensen, Mads Groesboll] Aalborg Univ, Audio Anal Lab, CREATE, Aalborg, Denmark Beijing University of Technology; Aalborg University Cui, ZH (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China. cuizihao@emails.bjut.edu.cn; baochch@bjut.edu.cn; jkn@create.aau.dk; mgc@create.aau.dk Nielsen, Jesper Kjaer/0000-0002-2586-7925; Christensen, Mads Graesboll/0000-0003-3586-7969 National Natural Science Foundation of China [61831019, 61471014, 61231015]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported by the National Natural Science Foundation of China (Grant No.61831019, No.61471014 and No. 61231015) and in part by the China Scholarship Council. 25 2 3 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-6631-5 INT CONF ACOUST SPEE 2020.0 6759 6763 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BQ7HU Green Submitted 2023-03-23 WOS:000615970407005 0
S Xiao, Y; Zhai, QX; Zhang, H; Chen, W; Hill, C Doyle, M; McClements, DJ Xiao, Yue; Zhai, Qixiao; Zhang, Hao; Chen, Wei; Hill, Colin Gut Colonization Mechanisms of Lactobacillus and Bifidobacterium: An Argument for Personalized Designs ANNUAL REVIEW OF FOOD SCIENCE AND TECHNOLOGY, VOL 12, 2021 Annual Review of Food Science and Technology English Review; Book Chapter gut colonization; probiotics; diet; natural history; microbiome; personalized intervention JOHNSONII STRAIN NCC533; IN-VIVO; BACTERIOCIN PRODUCTION; BIFIDUM PRL2010; REUTERI; HOST; MICROBIOTA; ENGRAFTMENT; PLANTARUM; GENES Lactobacillus and Bifidobacterium spp. are best understood for their applications as probiotics, which are often transient, but as commensals it is probable that stable colonization in the gut is important for their beneficial roles. Recent research suggests that the establishment and persistence of strains of Lactobacillus and Bifidobacterium in the gut are species- and strain-specific and affected by natural history, genomic adaptability, and metabolic interactions of the bacteria and the microbiome and immune aspects of the host but also regulated by diet. This provides new perspectives on the underlying molecular mechanisms. With an emphasis on host-microbe interaction, this review outlines how the characteristics of individual Lactobacillus and Bifidobacterium bacteria, the host genotype and microbiome structure, diet, and host-microbe coadaptation during bacterial gut transition determine and influence the colonization process. The diet-tuned and personally tailored colonization can be achieved via a machine learning prediction model proposed here. [Xiao, Yue; Zhai, Qixiao; Zhang, Hao; Chen, Wei] Jiangnan Univ, State Key Lab Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China; [Xiao, Yue; Zhai, Qixiao; Zhang, Hao; Chen, Wei] Jiangnan Univ, Sch Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China; [Zhang, Hao; Chen, Wei] Jiangnan Univ, Natl Engn Res Ctr Funct Food, Wuxi 214122, Jiangsu, Peoples R China; [Zhai, Qixiao] Jiangnan Univ, Int Joint Res Lab Probiot, Wuxi 214122, Jiangsu, Peoples R China; [Zhang, Hao] Jiangnan Univ, Inst Food Biotechnol, Yangzhou 225004, Jiangsu, Peoples R China; [Chen, Wei] Beijing Technol & Business Univ, Beijing Adv Innovat Ctr Food Nutr & Human Hlth, Beijing 100048, Peoples R China; [Hill, Colin] Univ Coll Cork, Sch Microbiol, Cork T12 YN60, Ireland; [Hill, Colin] Univ Coll Cork, APC Microbiome Inst, Cork T12 YN60, Ireland Jiangnan University; Jiangnan University; Jiangnan University; Jiangnan University; Jiangnan University; Beijing Technology & Business University; University College Cork; University College Cork Xiao, Y; Zhai, QX (corresponding author), Jiangnan Univ, State Key Lab Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China.;Xiao, Y; Zhai, QX (corresponding author), Jiangnan Univ, Sch Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China.;Zhai, QX (corresponding author), Jiangnan Univ, Int Joint Res Lab Probiot, Wuxi 214122, Jiangsu, Peoples R China. xiaoyuejndx@sina.com; zhaiqixiao@sina.com; zhanghao@jiangnan.edu.cn; chenwei66@jiangnan.edu.cn; c.hill@ucc.ie Hill, Colin/A-5611-2012; Chen, Wei/GZK-7348-2022 Hill, Colin/0000-0002-8527-1445; National Natural Science Foundation of China Key Program [31530056]; National First-Class Discipline Program of Food Science and Technology [JUFSTR20180102]; BBSRC Newton Fund Joint Centre Award; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province National Natural Science Foundation of China Key Program(National Natural Science Foundation of China (NSFC)); National First-Class Discipline Program of Food Science and Technology; BBSRC Newton Fund Joint Centre Award(UK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC)); Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province This work was supported by the National Natural Science Foundation of China Key Program (31530056), the National First-Class Discipline Program of Food Science and Technology (JUFSTR20180102), a BBSRC Newton Fund Joint Centre Award, and the Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province. 98 16 17 10 76 ANNUAL REVIEWS PALO ALTO 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0897 USA 1941-1413 1941-1421 ANNU REV FOOD SCI T Annu. Rev. Food Sci. Technol. 2021.0 12.0 213 233 10.1146/annurev-food-061120-014739 0.0 21 Food Science & Technology Book Citation Index – Science (BKCI-S); Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology BR2LZ 33317320.0 2023-03-23 WOS:000637940300012 0
C Xu, X; Lu, JH; Wang, W Bailey, J; Khan, L; Washio, T; Dobbie, G; Huang, JZ; Wang, R Xu, Xin; Lu, Jiaheng; Wang, Wei Incremental Hierarchical Clustering of Stochastic Pattern-Based Symbolic Data ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT II Lecture Notes in Artificial Intelligence English Proceedings Paper 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) APR 19-22, 2016 Univ Auckland, Auckland, NEW ZEALAND Auckland Tourism Events & Econ Dev,BECA Univ Auckland Symbolic data analysis; Stochastic pattern; Incremental learning; Hierarchical clustering; Emitter parameter analysis Classic data analysis techniques generally assume that variables have single values only. However, the data complexity during the age of big data has gone beyond the classic framework such that variable values probably take the form of a set of stochastic measurements instead. We refer to the above case as the stochastic pattern-based symbolic data where each measurement set is an instance of an underlying stochastic pattern. In such a case, non existing classic data analysis approaches, such as the crystal item or fuzzy region ones, could apply yet. For this reason, we put forward a novel Incremental Hierarchical Clustering algorithm for stochastic Pattern-based Symbolic Data (IHCPSD). IHCPSD is robust to overlapping and missing measurements and well adapted for incremental learning. Experiments on synthetic and application on real-life emitter parameter data have validated its effectiveness. [Xu, Xin] NRIEE, Sci & Technol Informat Syst Engn Lab, Nanjing, Jiangsu, Peoples R China; [Lu, Jiaheng] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Wang, Wei] Nanjing Univ, State Key Lab Novel Software & Technol, Nanjing, Jiangsu, Peoples R China University of Helsinki; Nanjing University Xu, X (corresponding author), NRIEE, Sci & Technol Informat Syst Engn Lab, Nanjing, Jiangsu, Peoples R China. flora.xin.xu@gmail.com; jiahenglu@gmail.com; ww@nju.edu.cn Lu, Jiaheng/T-7826-2019; Wang, Wei/AAB-7397-2019 Wang, Wei/0000-0002-9882-2090; Lu, Jiaheng/0000-0003-2067-454X 14 1 1 0 4 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 978-3-319-31750-2; 978-3-319-31749-6 LECT NOTES ARTIF INT 2016.0 9652.0 156 167 10.1007/978-3-319-31750-2_13 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG4NW 2023-03-23 WOS:000389019600013 0
J Polykovskiy, D; Zhebrak, A; Sanchez-Lengeling, B; Golovanov, S; Tatanov, O; Belyaev, S; Kurbanov, R; Artamonov, A; Aladinskiy, V; Veselov, M; Kadurin, A; Johansson, S; Chen, HM; Nikolenko, S; Aspuru-Guzik, A; Zhavoronkov, A Polykovskiy, Daniil; Zhebrak, Alexander; Sanchez-Lengeling, Benjamin; Golovanov, Sergey; Tatanov, Oktai; Belyaev, Stanislav; Kurbanov, Rauf; Artamonov, Aleksey; Aladinskiy, Vladimir; Veselov, Mark; Kadurin, Artur; Johansson, Simon; Chen, Hongming; Nikolenko, Sergey; Aspuru-Guzik, Alan; Zhavoronkov, Alex Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models FRONTIERS IN PHARMACOLOGY English Article generative models; drug discovery; deep learning; benchmark; distribution learning DESIGN; DISCOVERY; LIBRARIES; SMILES; DRUGS Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at . [Polykovskiy, Daniil; Zhebrak, Alexander; Aladinskiy, Vladimir; Veselov, Mark; Kadurin, Artur; Nikolenko, Sergey; Zhavoronkov, Alex] Insil Med Hong Kong Ltd, Pak Shek Kok, Hong Kong, Peoples R China; [Sanchez-Lengeling, Benjamin] Harvard Univ, Chem & Chem Biol Dept, Cambridge, MA 02138 USA; [Golovanov, Sergey; Tatanov, Oktai; Belyaev, Stanislav; Kurbanov, Rauf; Artamonov, Aleksey; Nikolenko, Sergey] Neuromat OU, Tallinn, Estonia; [Johansson, Simon; Chen, Hongming] AstraZeneca, R&D, DiscoverySci, Mol AI, Gothenburg, Sweden; [Nikolenko, Sergey] Natl Res Univ Higher Sch Econ, Comp Sci Dept, St Petersburg, Russia; [Aspuru-Guzik, Alan] Univ Toronto, Dept Chem, Chem Phys Theory Grp, Toronto, ON, Canada; [Aspuru-Guzik, Alan] Univ Toronto, Dept Comp Sci, Toronto, ON, Canada; [Aspuru-Guzik, Alan] Vector Inst Artificial Intelligence, CIFAR AI Chair, Toronto, ON, Canada; [Aspuru-Guzik, Alan] Canadian Inst Adv Res CIFAR, Toronto, ON, Canada Harvard University; AstraZeneca; HSE University (National Research University Higher School of Economics); University of Toronto; University of Toronto; Canadian Institute for Advanced Research (CIFAR) Polykovskiy, D; Nikolenko, S; Zhavoronkov, A (corresponding author), Insil Med Hong Kong Ltd, Pak Shek Kok, Hong Kong, Peoples R China.;Nikolenko, S (corresponding author), Neuromat OU, Tallinn, Estonia.;Nikolenko, S (corresponding author), Natl Res Univ Higher Sch Econ, Comp Sci Dept, St Petersburg, Russia.;Aspuru-Guzik, A (corresponding author), Univ Toronto, Dept Chem, Chem Phys Theory Grp, Toronto, ON, Canada.;Aspuru-Guzik, A (corresponding author), Univ Toronto, Dept Comp Sci, Toronto, ON, Canada.;Aspuru-Guzik, A (corresponding author), Vector Inst Artificial Intelligence, CIFAR AI Chair, Toronto, ON, Canada.;Aspuru-Guzik, A (corresponding author), Canadian Inst Adv Res CIFAR, Toronto, ON, Canada. daniil@insilico.com; snikolenko@gmail.com; alan@aspuru.com; alex@insilico.com Kadurin, Artur/ABF-9143-2021; Nikolenko, Sergey/AAU-3615-2020; Zhavoronkov, Alex/HCI-9762-2022; Kurbanov, Rauf/AAQ-7119-2020 Kadurin, Artur/0000-0003-1482-9365; Nikolenko, Sergey/0000-0001-7787-2251; Zhavoronkov, Alex/0000-0001-7067-8966; Kurbanov, Rauf/0000-0002-5153-0373 74 116 116 19 48 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1663-9812 FRONT PHARMACOL Front. Pharmacol. DEC 18 2020.0 11.0 565644 10.3389/fphar.2020.565644 0.0 10 Pharmacology & Pharmacy Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy PN7LH 33390943.0 Green Submitted, gold, Green Published, Green Accepted 2023-03-23 WOS:000604656000001 0
J Wang, SH; Zhang, YD; Yang, M; Liu, B; Ramirez, J; Gorriz, JM Wang, Shui-Hua; Zhang, Yu-Dong; Yang, Ming; Liu, Bin; Ramirez, Javier; Manuel Gorriz, Juan Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression INTEGRATED COMPUTER-AIDED ENGINEERING English Article Unilateral sensorineural hearing loss; dual-tree complex wavelet transform; kernel principal component analysis; multinomial logistic regression; double-density dual-tree complex wavelet transform; magnetic resonance imaging; alexNet PRINCIPAL COMPONENT ANALYSIS; PATHOLOGICAL BRAIN DETECTION; SUPPORT VECTOR MACHINE; NEURAL-NETWORK; ROBUST; OPTIMIZATION; CLASSIFICATION; REGISTRATION; METHODOLOGY; UNIT AIM: Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology changes within brain structure. Traditional manual method may ignore this change. METHOD: In this work, we developed a novel method, based on the double-density dual-tree complex (DDDTCWT), and radial basis function kernel principal component analysis (RKPCA) and multinomial logistic regression (MLR) for the magnetic resonance imaging scanning. We first used DDDTCWT to extract features. Afterwards, we used RKPCA to reduce feature dimensionalities. Finally, MLR was employed to be the classifier. RESULT: The 10 times of 10-fold stratified cross validation showed our method achieved an overall accuracy of 96.44 +/- 0.88%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.67 +/- 2.72%, 96.67 +/- 3.51%, and 96.00 +/- 4.10%, respectively. CONCLUSION: Our method performed better than both raw and improved AlexNet, and eight state-of-the-art methods via a stringent statistical 10 x 10-fold stratified cross validation. The MLR gives better classification performance than decision tree, support vector machine, and back-propagation neural network. [Wang, Shui-Hua; Zhang, Yu-Dong] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Henan, Peoples R China; [Wang, Shui-Hua] Nanjing Univ, Med Sch, Affiliated Hosp, Dept Rheumatol & Immunol,Nanjing Drum Tower Hosp, Nanjing 210008, Jiangsu, Peoples R China; [Zhang, Yu-Dong] Univ Leicester, Dept Informat, Leicester, Leics, England; [Yang, Ming] Nanjing Med Univ, Childrens Hosp, Dept Radiol, Nanjing 210008, Jiangsu, Peoples R China; [Liu, Bin] Southeast Univ, Zhong Da Hosp, Dept Radiol, Nanjing 210009, Jiangsu, Peoples R China; [Ramirez, Javier] Univ Granada, Dept Signal Theory Networking & Commun, Granada, Spain; [Manuel Gorriz, Juan] Univ Cambridge, Dept Psychiat, Robinson Way, Cambridge, England; [Wang, Shui-Hua] Univ Leicester, Dept Math, Leicester, Leics, England Henan Polytechnic University; Nanjing University; University of Leicester; Nanjing Medical University; Southeast University - China; University of Granada; University of Cambridge; University of Leicester Zhang, YD (corresponding author), Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Henan, Peoples R China.;Zhang, YD (corresponding author), Univ Leicester, Dept Informat, Leicester, Leics, England. yudongzhang@ieee.org Wang, Shuihua/G-7326-2016; Ramírez, Javier/B-1836-2012; Gorriz, Juan Manuel/C-2385-2012; Zhang, Yudong/I-7633-2013 Wang, Shuihua/0000-0003-4713-2791; Ramírez, Javier/0000-0002-6229-2921; Gorriz, Juan Manuel/0000-0001-7069-1714; Zhang, Yudong/0000-0002-4870-1493 Natural Science Foundation of China [61602250]; Natural Science Foundation of Jiangsu Province [BK20150983, BK20150982]; Program of Natural Science Research of Jiangsu Higher Education Institutions [16KJB520025]; MIN ECO [TEC2015-64718-R]; Salvador de Madariaga Mobility Grants 2017; Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia, Spain) [P11-TIC-7103] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Program of Natural Science Research of Jiangsu Higher Education Institutions; MIN ECO; Salvador de Madariaga Mobility Grants 2017; Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia, Spain) This paper was supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025), and the MIN ECO under the TEC2015-64718-R project, the Salvador de Madariaga Mobility Grants 2017 and the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia, Spain) under the Excellence Project P11-TIC-7103. 59 52 52 0 19 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1069-2509 1875-8835 INTEGR COMPUT-AID E Integr. Comput.-Aided Eng. 2019.0 26.0 4 411 426 10.3233/ICA-190605 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IY8ZK Green Submitted 2023-03-23 WOS:000486683200007 0
J Bai, X; Yang, MK; Lyu, PY; Xu, YC; Luo, JB Bai, Xiang; Yang, Mingkun; Lyu, Pengyuan; Xu, Yongchao; Luo, Jiebo Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification IEEE ACCESS English Article Scene text; fine-grained classification; convolutional neural networks; attention mechanism; product search RECOGNITION; REPRESENTATION Text in natural images contains rich semantics that is often highly relevant to objects or scene. In this paper, we focus on the problem of fully exploiting scene text for visual understanding. The main idea is combining word representations and deep visual features in a globally trainable deep convolutional neural network. First, the recognized words are obtained by a scene text reading system. Next, we combine the word embedding of the recognized words and the deep visual features into a single representation that is optimized by a convolutional neural network for fine-grained image classification. In our framework, the attention mechanism is adopted to compute the relevance between each recognized word and the given image, which further enhances the recognition performance. We have performed experiments on two datasets: context dataset and drink bottle dataset, which are proposed for fine-grained classification of business places and drink bottles, respectively. The experimental results consistently demonstrate that the proposed method of combining textual and visual cues significantly outperforms classification with only visual representation. Moreover, we have shown that the learned representation improves the retrieval performance on the drink bottle images by a large margin, making it potentially powerful in product search. [Bai, Xiang; Yang, Mingkun; Lyu, Pengyuan; Xu, Yongchao] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Hubei, Peoples R China; [Xu, Yongchao] EPITA Res & Dev Lab, F-94270 Le Kremlin Bicetre, France; [Luo, Jiebo] Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA Huazhong University of Science & Technology; University of Rochester Xu, YC (corresponding author), Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Hubei, Peoples R China.;Xu, YC (corresponding author), EPITA Res & Dev Lab, F-94270 Le Kremlin Bicetre, France. yongchaoxu@hust.edu.cn Luo, Jiebo/AAI-7549-2020; Xu, Yongchao/F-2080-2019 Xu, Yongchao/0000-0002-7253-3151; Lyu, Pengyuan/0000-0003-3153-8519; Yang, Mingkun/0000-0002-6078-6100; Luo, Jiebo/0000-0002-4516-9729 National Key Research and Development Program of China [2018YFB1004600]; NSFC [61703171, 61573160]; Natural Science Foundation of Hubei Province of China [2018CFB199]; National Program for Support of Top-Notch Young Professionals; Program for HUST Academic Frontier Youth Team National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province of China(Natural Science Foundation of Hubei Province); National Program for Support of Top-Notch Young Professionals; Program for HUST Academic Frontier Youth Team This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1004600, in part by NSFC under Grant 61703171 and Grant 61573160, and in part by the Natural Science Foundation of Hubei Province of China under Grant 2018CFB199. The work of X. Bai was supported by the National Program for Support of Top-Notch Young Professionals and in part by the Program for HUST Academic Frontier Youth Team. 89 28 32 0 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 66322 66335 10.1109/ACCESS.2018.2878899 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HD2PR gold, Green Submitted 2023-03-23 WOS:000452353100001 0
J Zhan, M; Yu, Q; Wang, J Zhan, Ming; Yu, Qin; Wang, Ji Effectively organizing hashtags on Instagram: a study of library-related captions INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL English Article SOCIAL MEDIA; IMPACT; TWITTER; COMMUNITY; FACEBOOK; POLITICS; ADOPTION Introduction. Little scientific attention has been paid to outlining the communicating function of hashtags on Instagram. The present study aims to fill this gap by analysing hashtag organization. Practical insights into user engagement, community building, and big data utilisation in the context of libraries are discussed. Method. To achieve a valid result, 2.5 million captions on Instagram with library-related content were collected. Analysis. Quantitative analyses were carried out with the statistical add-in packages in Python 3.0. Results. For captions with one hashtag included, putting it in the middle results in more likesand comments; for captions composed of more than 80% of hashtags, it is advisable to use more words than having a caption full of hashtags; for captions composed of less than 80% of hashtags, placing all hashtags consecutively at the beginning is not recommended as this is more likely to result in zero comments. Conclusion. It is concluded that placing hashtag(s) in the front of the caption should be avoided; it is not wise to use as many hashtags as possible. More effort should be made to better organize hashtags in captions because their location has an influence on attracting comments and likes. [Zhan, Ming] Abo Akad Univ, Dept Informat Studies, Turku, Finland; [Zhan, Ming] Granditude, Stockholm, Sweden; [Yu, Qin] Ericsson AB, Stockholm, Sweden; [Wang, Ji] Liaoning Univ, Business Sch, Shenyang, Liaoning, Peoples R China Abo Akademi University; Ericsson; Liaoning University Zhan, M (corresponding author), Abo Akad Univ, Dept Informat Studies, Turku, Finland. 65 1 1 2 15 UNIV SHEFFIELD DEPT INFORMATION STUDIES SHEFFIELD UNIV SHEFFIELD, WESTERN BANK, SHEFFIELD S10 2TN, S YORKS, ENGLAND 1368-1613 INFORM RES Inf. Res. JUN 2020.0 25.0 2 858 21 Information Science & Library Science Social Science Citation Index (SSCI) Information Science & Library Science MD1FB 2023-03-23 WOS:000543719700006 0
J Ding, YC; Martin-Guerrero, JD; Song, YJ; Magdalena-Benedicto, R; Chen, X Ding, Yongcheng; Martin-Guerrero, Jose D.; Song, Yujing; Magdalena-Benedicto, Rafael; Chen, Xi Active learning for the optimal design of multinomial classification in physics PHYSICAL REVIEW RESEARCH English Article QUANTUM; MODEL Optimal design for model training is a critical topic in machine learning. Active learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze its capability as an assistant for the design of experiments, extracting maximum information for learning with the minimal cost in fidelity loss, or reducing total operation costs of labeling in the laboratory. We present two typical applications as quantum information retrieval in qutrits and phase boundary prediction in many-body physics. For an equivalent multinomial classification problem, we achieve the correct rate of 99% with less than 2% of samples labeled. We reckon that active-learning-inspired physics experiments will remarkably save budget without loss of accuracy. [Ding, Yongcheng; Song, Yujing] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Song, Yujing] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China; [Ding, Yongcheng; Chen, Xi] Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Ding, Yongcheng] ProQuam Co Ltd, Shanghai 200444, Peoples R China; [Martin-Guerrero, Jose D.; Magdalena-Benedicto, Rafael] Univ Valencia, IDAL, Elect Engn Dept, ETSE UV, Avinguda Univ S-N, Valencia 46100, Spain; [Chen, Xi] Univ Basque Country UPV EHU, EHU Quantum Ctr, Barrio Sarriena S-N, Leioa 48940, Biscay, Spain Shanghai University; Shanghai University; University of Basque Country; University of Valencia; University of Basque Country Ding, YC (corresponding author), Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China.;Ding, YC (corresponding author), Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China.;Ding, YC (corresponding author), Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain.;Ding, YC (corresponding author), ProQuam Co Ltd, Shanghai 200444, Peoples R China. jonzen.ding@gmail.com; jose.d.martin@uv.es; chenxi1979cn@gmail.com Chen, Xi/ABE-6575-2020; Martín-Guerrero, José D./L-8146-2014; chen, xi/GXH-3653-2022 Chen, Xi/0000-0003-4221-4288; Martín-Guerrero, José D./0000-0001-9378-0285; Ding, Yongcheng/0000-0002-6008-0001 NSFC [12075145]; STCSM [2019SHZDZX01-ZX04, 20DZ2290900]; SMAMR [2021-40]; Pro-gram for Eastern Scholar, QMiCS [820505]; OpenSuperQ of the EU Flagship on Quantum Technologies; EU FET Open Grant Quromor-phic [828826]; EPIQUS [899368]; Spanish Government (MCIU/AEI/FEDER, UE) [PGC2018-095113-B-I00]; Basque Government [IT986-16]; Ramon y Cajal pro-gram [RYC-2017-22482] NSFC(National Natural Science Foundation of China (NSFC)); STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); SMAMR; Pro-gram for Eastern Scholar, QMiCS; OpenSuperQ of the EU Flagship on Quantum Technologies; EU FET Open Grant Quromor-phic; EPIQUS; Spanish Government (MCIU/AEI/FEDER, UE)(Spanish Government); Basque Government(Basque Government); Ramon y Cajal pro-gram(Spanish Government) This work is partially supported from NSFC (Grant No. 12075145) , STCSM (Grants No. 2019SHZDZX01-ZX04 and No. 20DZ2290900) , SMAMR (Grant No. 2021-40) , Pro-gram for Eastern Scholar, QMiCS (Grant No. 820505) and OpenSuperQ (Grant No. 820363) of the EU Flagship on Quantum Technologies, EU FET Open Grant Quromor-phic (Grant No. 828826) and EPIQUS (Grant No. 899368), Spanish Government Grant No. PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE) , and Basque Government Grant No. IT986-16. X.C. acknowledges the Ramon y Cajal pro-gram (RYC-2017-22482). 70 0 0 7 9 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2643-1564 PHYS REV RES Phys. Rev. Res. MAR 21 2022.0 4.0 1 13213 10.1103/PhysRevResearch.4.013213 0.0 16 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics 0J1AU gold, Green Submitted 2023-03-23 WOS:000779841100001 0
J Liu, RL; Romero, SV; Oregi, I; Osaba, E; Villar-Rodriguez, E; Ban, Y Liu, Ruilin; V. Romero, Sebastian; Oregi, Izaskun; Osaba, Eneko; Villar-Rodriguez, Esther; Ban, Yue Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States ENTROPY English Article digital quantum simulation; quantum circuit learning; coherent state SYSTEMS Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources-such as numbers of quantum gates, layers and iterations-are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes. [Liu, Ruilin] Shanghai Univ, Sch Mat Sci & Engn, Shanghai 200444, Peoples R China; [V. Romero, Sebastian; Oregi, Izaskun; Osaba, Eneko; Villar-Rodriguez, Esther; Ban, Yue] Basque Res & Technol Alliance BRTA, TECNALIA, Derio 48160, Spain; [Oregi, Izaskun] Univ EUNEIZ, Fac New Interact Technol, Vitoria 01013, Spain Shanghai University Ban, Y (corresponding author), Basque Res & Technol Alliance BRTA, TECNALIA, Derio 48160, Spain. ybanxc@gmail.com Ban, Yue/AAR-9240-2021; Osaba, Eneko/K-2171-2014 Ban, Yue/0000-0003-1764-4470; Osaba, Eneko/0000-0001-7863-9910; Villar Rodriguez, Esther/0000-0003-3343-3737; Oregi, Izaskun/0000-0002-3950-1668; V. Romero, Sebastian/0000-0002-4675-4452 QUANTEK project (ELKARTEK program from the Basque Government) [KK-2021/00070]; project BRTA QUANTUM: Hacia una especializacion armonizada en tecnologias cuanticas en BRTA [KK-2022/00041] QUANTEK project (ELKARTEK program from the Basque Government); project BRTA QUANTUM: Hacia una especializacion armonizada en tecnologias cuanticas en BRTA This research is funded by the QUANTEK project (ELKARTEK program from the Basque Government, expedient No. KK-2021/00070), the project BRTA QUANTUM: Hacia una especializacion armonizada en tecnologias cuanticas en BRTA (expedient No. KK-2022/00041). 36 1 1 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy NOV 2022.0 24.0 11 1529 10.3390/e24111529 0.0 11 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics 6B3ZC 36359621.0 Green Submitted, gold, Green Published, Green Accepted 2023-03-23 WOS:000881274100001 0
J Chen, ZH; Xu, ZZ; Xu, C; Soto, J; Markl, V; Qian, WN; Zhou, AY Chen, Zihao; Xu, Zhizhen; Xu, Chen; Soto, Juan; Markl, Volker; Qian, Weining; Zhou, Aoying HyMAC: A Hybrid Matrix Computation System PROCEEDINGS OF THE VLDB ENDOWMENT English Article; Proceedings Paper 47th International Conference on Very Large Data Bases (VLDB) AUG 16-20, 2021 Copenhagen, DENMARK Distributed matrix computation is common in large-scale data processing and machine learning applications. Iterative-convergent algorithms involving matrix computation share a common property: parameters converge non-uniformly. This property can be exploited to avoid redundant computation via incremental evaluation. Unfortunately, existing systems that support distributed matrix computation, like SystemML, do not employ incremental evaluation. Moreover, incremental evaluation does not always outperform classical matrix computation, which we refer to as a full evaluation. To leverage the benefit of increments, we propose a new system called HyMAC, which performs hybrid plans to balance the trade-off between full and incremental evaluation at each iteration. In this demonstration, attendees will have an opportunity to experience the effect that full, incremental, and hybrid plans have on iterative algorithms. [Chen, Zihao; Xu, Zhizhen; Xu, Chen; Qian, Weining; Zhou, Aoying] East China Normal Univ, DaSE, Shanghai, Peoples R China; [Soto, Juan; Markl, Volker] Tech Univ Berlin, DIMA, Berlin, Germany; [Chen, Zihao; Xu, Zhizhen; Xu, Chen; Qian, Weining; Zhou, Aoying] Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China East China Normal University; Technical University of Berlin Xu, C (corresponding author), East China Normal Univ, DaSE, Shanghai, Peoples R China.;Xu, C (corresponding author), Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China. zhchen@stu.ecnu.edu.cn; zhizhxu@stu.ecnu.edu.cn; cxu@dase.ecnu.edu.cn; juan.soto@tu-berlin.de; volker.markl@tu-berlin.de; wnqian@dase.ecnu.edu.cn; ayzhou@dase.ecnu.edu.cn Chen, zihao/HGE-3792-2022 Soto, Juan/0000-0002-5821-4533 National Natural Science Foundation of China [61902128]; Shanghai Sailing Program [19YF1414200]; German Federal Ministry of Education and Research (BMBF) as BIFOLD [01IS18025A] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Sailing Program; German Federal Ministry of Education and Research (BMBF) as BIFOLD(Federal Ministry of Education & Research (BMBF)) This work was supported by the National Natural Science Foundation of China (No. 61902128), Shanghai Sailing Program (No. 19YF1414200), and the German Federal Ministry of Education and Research (BMBF) as BIFOLD (funding mark 01IS18025A). 5 1 1 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 2150-8097 PROC VLDB ENDOW Proc. VLDB Endow. AUG 2021.0 14.0 12 2699 2702 10.14778/3476311.3476323 0.0 4 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science YH1AX 2023-03-23 WOS:000742908500012 0
C Yu, JSD; Baumann, P IEEE Yu, Jinsongdi; Baumann, Peter Serving Spatio-Temporal Grids: How Standards Help 2015 23RD INTERNATIONAL CONFERENCE ON GEOINFORMATICS International Conference on Geoinformatics English Proceedings Paper 23rd International Conference on Geoinformatics (Geoinformatics) JUN 19-21, 2015 China Univ Geosciences, Wuhan, PEOPLES R CHINA Int Assoc Chinese Profess Geogr Informat Sci,China Univ Geosciences, Natl Engn ResCtr Geogr Informat Sci,Ind Alliance Technol Innovat Strategy Geog Informat Syst,Chinese Soc Surveying Mapping & Geoinformat,China Assoc Geog Informat Serv,China Informat Assoc, Nat Resources & Geospatial lnformat Comm,Wuhan Zondy Cyber Grp Co Ltd China Univ Geosciences Earth Science Systems; interoperability; OGe WeS2.0 The global Earth Science Systems (ESS) cooperation requires both flexible and interoperable Web Service support built on large varieties of Earth Observation archives. Given the complexity and dynamics of each observation and the large number of disciplines involved, Open GIS Consortium (OGC) proposed a modular standardization approach to facilitate ESS data retrieval and analytics. Its latest Web Coverage Service (WCS) 2.0 standard follows the approach and presents as an entry point to build ESS information exchange blocks for the improvement of global Earth Science Systems cooperation in the coming Big Data Era [Yu, Jinsongdi] Fuzhou Univ, Spatial Informat Res Ctr Fujian, Fuzhou, Peoples R China; [Baumann, Peter] Jacobs Univ, Large Scale Sci Informat Syst, Bremen, Germany Fuzhou University; Jacobs University Yu, JSD (corresponding author), Fuzhou Univ, Spatial Informat Res Ctr Fujian, Fuzhou, Peoples R China. vyx350@126.com Baumann, Peter/B-1198-2018 Baumann, Peter/0000-0003-3860-4726 10 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-024X INT CONF GEOINFORM 2015.0 6 Computer Science, Information Systems; Engineering, Electrical & Electronic; Geosciences, Multidisciplinary Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Geology BF4OB 2023-03-23 WOS:000381472200134 0
J Huang, JX; Obracht-Prondzynska, H; Kamrowska-Zaluska, D; Sun, YM; Li, LS Huang, Jianxiang; Obracht-Prondzynska, Hanna; Kamrowska-Zaluska, Dorota; Sun, Yiming; Li, Lishuai The image of the City on social media: A comparative study using Big Data and Small Data methods in the Tri-City Region in Poland LANDSCAPE AND URBAN PLANNING English Article Kevin Lynch; City image; Social media analytics; Tri-City Poland The Image of the City by Kevin Lynch is a landmark planning theory of lasting influence; its scientific rigor and relevance in the digital age were in dispute. The rise of social media and other digital technologies offers new opportunities to study the perception of urban environments. Questions remain as to whether social media analytics can provide a reliable measure of perceived city images? If yes, what implication does it hold for urban planners? This paper describes a study on the perception of city images using a combination of big data and small data methods in the Tri-City Region in Poland. The aims were to 1) test the hypothesis whether social media analytics can elicit Lynchian elements of city image in consistency with conventional methods, and 2) develop and evaluate social media-based indicators of Imageability for planning practice. Geo-tagged images and texts were collected from Instagram and Twitter, two popular social media platforms in Poland. Text-Mining, Image Processing, Clustering Analysis, Kernel Density Estimation, and Sentiment Analysis were used. Results were compared with benchmarks constructed from official GIS database, questionnaire responses and sketch maps. District, landmark, and path identified on social media were in good agreements with benchmarks, less so for edge and node. Two social media-based indicators have influenced the perception of a place: Instagramability, the frequency of a place captured on Instagram, was linked to its perception as an architectural landmark and tourist attraction, while Twitterability, the frequency of a place mentioned on Twitter by name, was linked to its perceived niceness and relevance to everyday life of communities. Methods developed in this study have theoretical and practical implications for urban planners. [Huang, Jianxiang; Sun, Yiming] Univ Hong Kong, Fac Architecture, Dept Urban Planning & Design, Pok Fu Lam Rd, Hong Kong, Peoples R China; [Obracht-Prondzynska, Hanna; Kamrowska-Zaluska, Dorota] Gdansk Univ Technol, Fac Architecture, Dept Urban Design & Reg Planning, 11-12 Gabriela Narutowicza St, PL-80233 Gdansk, Poland; [Li, Lishuai] City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China University of Hong Kong; Fahrenheit Universities; Gdansk University of Technology; City University of Hong Kong Li, LS (corresponding author), City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China. Huang, Jianxiang/G-4331-2013; Kamrowska-Zaluska, Dorota/AAM-1392-2020; Huang, Jianxiang/K-6400-2015; Li, Lishuai/K-8472-2014; Obracht-Prondzynska, Hanna/O-4740-2016 Kamrowska-Zaluska, Dorota/0000-0002-3083-6576; Huang, Jianxiang/0000-0002-0027-3944; Li, Lishuai/0000-0002-0990-5119; Obracht-Prondzynska, Hanna/0000-0003-0621-1020 Hong Kong Research Grants Council the General Research Fund [11209717]; Hong Kong Research Grants Council Theme-Based Research Scheme [T32-101/15-R]; National Natural Science Foundation of China [51978594]; Faculty of Architecture, the University of Hong Kong Hong Kong Research Grants Council the General Research Fund; Hong Kong Research Grants Council Theme-Based Research Scheme(Hong Kong Research Grants Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Faculty of Architecture, the University of Hong Kong(University of Hong Kong) The work was partially funded by the Hong Kong Research Grants Council the General Research Fund under Project 11209717 and the Theme-Based Research Scheme under Grant T32-101/15-R, the National Natural Science Foundation of China under Project 51978594, and the SEED grant from the Faculty of Architecture, the University of Hong Kong. We thank Mr. Yipeng Guo for reviewing social media contents and we thank Ms. Yuming Cui for her able assistance in GIS-based road network analysis. 72 35 37 65 188 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0169-2046 1872-6062 LANDSCAPE URBAN PLAN Landsc. Urban Plan. FEB 2021.0 206.0 103977 10.1016/j.landurbplan.2020.103977 0.0 19 Ecology; Environmental Studies; Geography; Geography, Physical; Regional & Urban Planning; Urban Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geography; Physical Geography; Public Administration; Urban Studies PC7EY hybrid 2023-03-23 WOS:000597161200001 0
C Tan, W; Zhang, JJ; Xiang, P; Zhou, HX; Thiton, W Schelkens, P; Kozacki, T Tan, Wei; Zhang, Jiajia; Xiang, Pei; Zhou, Huixin; Thiton, William Infrared and visible image fusion via NSST and PCNN in multiscale morphological gradient domain OPTICS, PHOTONICS AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS VI Proceedings of SPIE English Proceedings Paper Conference on Optics, Photonics and Digital Technologies for Imaging Applications VI APR 06-10, 2020 ELECTR NETWORK SPIE,Eurometropole,CNRS,ICube,Univ Strasbourg Infrared and visible image fusion; non-subsampled shearlet transform; pulse-coupled neural network; multi-scale morphological gradient PERFORMANCE; TRANSFORM With the development of sensor technologies, imaging technology is developing more rapidly. What followed was the widespread use of image processing technology in many kinds of applications. For instance, image processing technology has been widely used in video surveillance, medical diagnosis, remote sensing detection and object tracking. As a sub-field of image processing technology, image fusion is the one of most studied technology. The aim of image fusion is to acquire an integrated image that contains more information. This integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. In all kinds of image fusion, infrared (IR) and visible (VIS) image fusion is one of the most valuable multi-source image fusion. When imaging the same scene using both IR and VIS imaging system, more information can be obtained, but more redundant information is generated. The IR sensor acquires the thermal radiation information of the object in a scene, so the object can also be detected when the lighting conditions are poor. The image acquired by VIS light sensors has more spectral information, clearer texture details, and higher spatial resolution. Thus, the scene can be described more completely by integrating the IR and VIS images into one image. Meanwhile, the scene can be readily understood by observers, and the information of the scene can be easily perceived. In this paper, an effective IR and VIS image fusion via non-subsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. First, low frequency sub-image and high frequency sub-images are obtained through NSST. Then, the low frequency sub-image and high frequency sub-images are fused via a MSMG domain PCNN (MSMG-PCNN) strategy. Finally, the fused image is reconstructed by inverse NSST. Experimental results demonstrate that the proposed MSMG-PCNN-NSST algorithm performs effectively in most cases by qualitative and quantitative evaluation. [Tan, Wei; Zhang, Jiajia; Xiang, Pei; Zhou, Huixin] Xidian Univ, Sch Phys & Optoelect Engn, Xian, Peoples R China; [Tan, Wei; Thiton, William] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark Xidian University; University of Copenhagen Tan, W (corresponding author), Xidian Univ, Sch Phys & Optoelect Engn, Xian, Peoples R China.;Tan, W (corresponding author), Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark. twtanwei1992@163.com Tan, Wei/C-9415-2019; Thitøn, William/AAV-5179-2020 Tan, Wei/0000-0003-0024-8009; Thitøn, William/0000-0003-3681-3452 China Scholarship Council [CSC201906960047]; 111 Project [B17035] China Scholarship Council(China Scholarship Council); 111 Project(Ministry of Education, China - 111 Project) This paper is supported by China Scholarship Council (CSC201906960047) and 111 Project (B17035). We thank TNO providing their dataset freely.20 20 7 7 4 8 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-3479-4 PROC SPIE 2021.0 11353.0 113531E 10.1117/12.2551830 0.0 7 Optics; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Optics; Imaging Science & Photographic Technology BR8GQ 2023-03-23 WOS:000671891800037 0
J Nan, Y; Tang, P; Zhang, GY; Zeng, CH; Liu, ZH; Gao, ZF; Zhang, HY; Yang, G Nan, Yang; Tang, Peng; Zhang, Guyue; Zeng, Caihong; Liu, Zhihong; Gao, Zhifan; Zhang, Heye; Yang, Guang Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network IEEE TRANSACTIONS ON MEDICAL IMAGING English Article Semantic segmentation; unsupervised learning; unsupervised segmentation; deep mixture models; tissue segmentation INSTANCE SEGMENTATION; IMAGE SEGMENTATION; MUTUAL INFORMATION; MODEL; NET Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixel-wise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value > 0.05) compared to the fully supervised U-Net. [Nan, Yang; Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London SW3 6LY, England; [Tang, Peng] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany; [Zhang, Guyue] Zhejiang Inst Standardizat, Hangzhou 310008, Peoples R China; [Zeng, Caihong; Liu, Zhihong] Nanjing Univ, Natl Clin Res Ctr Kidney Dis, Nanjing 210093, Peoples R China; [Gao, Zhifan; Zhang, Heye] Sun Yat Sen Univ, Sch Biomed Engn, Guangzhou 510275, Peoples R China; [Yang, Guang] Royal Brompton Hosp, London SW3 6NP, England; [Yang, Guang] Kings Coll London, Sch Biomed Engn & Imaging Sci, London WC2R 2LS, England Imperial College London; Technical University of Munich; Nanjing University; Sun Yat Sen University; Royal Brompton Hospital; University of London; King's College London Nan, Y; Yang, G (corresponding author), Imperial Coll London, Natl Heart & Lung Inst, London SW3 6LY, England.;Yang, G (corresponding author), Royal Brompton Hosp, London SW3 6NP, England.;Yang, G (corresponding author), Kings Coll London, Sch Biomed Engn & Imaging Sci, London WC2R 2LS, England. y.nan20@imperial.ac.uk; peng.tang@tum.de; guyuezhang13@fudan.edu.cn; zengcaihong@nju.edu.cn; liuzhihong@nju.edu.cn; gaozhifan@gmail.com; zhangheye@mail.sysu.edu.cn; g.yang@imperial.ac.uk ; Yang, Guang/S-5032-2016 Gao, Zhifan/0000-0002-1576-4439; Yang, Guang/0000-0001-7344-7733; TANG, Peng/0000-0003-4099-6677 European Research Council Innovative Medicines Initiative [H2020-JTI-IMI2 101005122]; AI for Health Imaging Award [H2020-SC1-FA-DTS-2019-1 952172]; Medical Research Council [MC/PC/21013]; Royal Society [IEC\NSFC\211235]; NVIDIA Academic Hardware Grant Program; UK Research and Innovation Future Leaders Fellowship [MR/V023799/1] European Research Council Innovative Medicines Initiative; AI for Health Imaging Award; Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Royal Society(Royal Society of London); NVIDIA Academic Hardware Grant Program; UK Research and Innovation Future Leaders Fellowship(UK Research & Innovation (UKRI)) This work was supported in part by the European Research Council Innovative Medicines Initiative under Grant (DRAGON, H2020-JTI-IMI2 101005122), in part by the AI for Health Imaging Award under Grant (CHAIMELEON, H2020-SC1-FA-DTS-2019-1 952172), in part by the Medical Research Council under Grant MC/PC/21013, in part by the Royal Society under Grant IEC\NSFC\211235, in part by the NVIDIA Academic Hardware Grant Program, and in part by the UK Research and Innovation Future Leaders Fellowship under Grant MR/V023799/1. (Corresponding authors: Yang Nan; Guang Yang.) 55 0 0 5 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging DEC 2022.0 41.0 12 3799 3811 10.1109/TMI.2022.3195123 0.0 13 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging 7N4PV 35905069.0 Green Submitted 2023-03-23 WOS:000907324600027 0
J Louka, AM; Tsagkaris, C; Christoforou, P; Khan, A; Alexiou, F; Simou, P; Haranas, I; Gkigkitzis, I; Zouganelis, G; Jha, NK; Uddin, MS; Shen, BR; Kamal, MA; Ashraf, GM; Alexiou, A Louka, Anna Maria; Tsagkaris, Christos; Christoforou, Panagiotis; Khan, Andleeb; Alexiou, Filia; Simou, Panagiota; Haranas, Ioannis; Gkigkitzis, Ioannis; Zouganelis, Georgios; Jha, Niraj Kumar; Uddin, Md Sahab; Shen, Bairong; Kamal, Mohammad A.; Ashraf, Ghulam Md; Alexiou, Athanasios Current Trends of Computational Tools in Geriatric Medicine and Frailty Management FRONTIERS IN BIOSCIENCE-LANDMARK English Review aging; artificial intelligence; bioinformatics; brain -computer interface; dementia; expert systems; frailty; geriatrics CELL-CENTERED DATABASE; DIABETIC-RETINOPATHY; ALZHEIMERS-DISEASE; HEALTH; DEMENTIA; INSTRUMENTS; DIAGNOSTICS; DYNAMICS; ONTOLOGY; ENTROPY While frailty corresponds to a multisystem failure, geriatric assessment can recognize multiple pathophysiological lesions and age changes. Up to now, a few frailty indexes have been introduced, presenting definitions of psychological problems, dysregulations in nutritional intake, behavioral abnormalities, and daily functions, genetic, environmental, and cardiovascular comorbidities. The geriatric evaluation includes a vast range of health professionals; therefore, we describe a broad range of applications and frailty scales-biomarkers to investigate and formulate the relationship between frailty lesions, diagnosis, monitoring, and treatment. Additionally, artificial intel-ligence applications and computational tools are presented, targeting a more efficacy individualized geriatric management of healthy aging. [Louka, Anna Maria] Univ Thessaly, Fac Med, Larisa 41500, Greece; [Louka, Anna Maria; Tsagkaris, Christos] Novel Global Community Educ Fdn, Hebersham, NSW 2770, Australia; [Tsagkaris, Christos] Univ Crete, Fac Med, Iraklion 28403, Greece; [Christoforou, Panagiotis] Natl Univ Athens, Med Sch, Dept Pathophysiol, Athens 11527, Greece; [Khan, Andleeb] Jazan Univ, Coll Pharm, Dept Pharmacol & Toxicol, Jazan 45142, Saudi Arabia; [Alexiou, Filia; Simou, Panagiota] Novel Global Community Educ Fdn, Dept Social Studies & Arts, Hebersham, NSW 2770, Australia; [Alexiou, Filia] Univ West Att, Dept Business Adm, Athens 12243, Greece; [Haranas, Ioannis] Wilfrid Laurier Univ, Dept Phys & Comp Sci, Waterloo, ON N2L 3C5, Canada; [Gkigkitzis, Ioannis] Northern Virginia Community Coll, Dept Math, 21200 Campus Dr, Sterling, VA 20164 USA; [Zouganelis, Georgios] Univ Derby, Coll Life & Nat Sci, Sch Human Sci, Derby DE22 1GB, England; [Jha, Niraj Kumar] Sharda Univ, Sch Engn & Technol SET, Dept Biotechnol, Greater Noida 201310, Uttar Pradesh, India; [Jha, Niraj Kumar] Uttaranchal Univ, Sch Appl & Life Sci SALS, Dept Biotechnol, Dehra Dun 248007, India; [Uddin, Md Sahab] Southeast Univ, Dept Pharm, Dhaka 1213, Bangladesh; [Uddin, Md Sahab] Pharmakon Neurosci Res Network, Dhaka 1341, Bangladesh; [Shen, Bairong; Kamal, Mohammad A.] Sichuan Univ, West China Hosp, Inst Syst Genet, West China Sch Nursing,Frontiers Sci Ctr Dis Relat, Chengdu 610041, Sichuan, Peoples R China; [Kamal, Mohammad A.] King Abdulaziz Univ, King Fahd Med Res Ctr, Jeddah 21589, Saudi Arabia; [Kamal, Mohammad A.] Daffodil Int Univ, Fac Allied Hlth Sci, Dept Pharm, Dhaka 1341, Bangladesh; [Kamal, Mohammad A.] Enzymoics, Peterlee Pl, Hebersham, NSW 2770, Australia; [Ashraf, Ghulam Md] King Abdulaziz Univ, King Fahd Med Res Ctr, Preclin Res Unit, Jeddah 22254, Saudi Arabia; [Ashraf, Ghulam Md] King Abdulaziz Univ, Fac Appl Med Sci, Dept Med Lab Technol, Jeddah 22254, Saudi Arabia; [Alexiou, Athanasios] Novel Global Community Educ Fdn, Dept Sci & Engn, Hebersham, NSW 2770, Australia; [Alexiou, Athanasios] AFNP Med Austria, A-1030 Vienna, Austria University of Thessaly; University of Crete; National & Kapodistrian University of Athens; Jazan University; Wilfrid Laurier University; University of Derby; Sharda University; Uttaranchal University; Sichuan University; King Abdulaziz University; Daffodil International University; King Abdulaziz University; King Abdulaziz University Ashraf, GM (corresponding author), King Abdulaziz Univ, King Fahd Med Res Ctr, Preclin Res Unit, Jeddah 22254, Saudi Arabia.;Ashraf, GM (corresponding author), King Abdulaziz Univ, Fac Appl Med Sci, Dept Med Lab Technol, Jeddah 22254, Saudi Arabia.;Alexiou, A (corresponding author), Novel Global Community Educ Fdn, Dept Sci & Engn, Hebersham, NSW 2770, Australia.;Alexiou, A (corresponding author), AFNP Med Austria, A-1030 Vienna, Austria. ashraf.gm@gmail.com; alextha@yahoo.gr Khan, Andleeb/AAE-4252-2020; Kamal, Mohammad Amjad/J-2918-2014; Alexiou, Athanasios/AAT-8491-2021 Khan, Andleeb/0000-0001-6125-3309; Kamal, Mohammad Amjad/0000-0003-0088-0565; Alexiou, Athanasios/0000-0002-2206-7236 Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia [RG-18-130-43] Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia Funding This work was funded by the Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia under grant no. (RG-18-130-43) . The authors, therefore, acknowledge with thanks DSR technical and fi- nancial support. 97 1 1 2 2 IMR PRESS ROBINSON 112 ROBINSON RD, ROBINSON, SINGAPORE 2768-6701 2768-6698 FRONT BIOSCI-LANDMRK Front. Biosci. JUL 29 2022.0 27.0 8 232 10.31083/j.fbl2708232 0.0 11 Biochemistry & Molecular Biology; Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Cell Biology 5A3VW 36042184.0 gold 2023-03-23 WOS:000862818600006 0
J Li, XP; Wang, YD; Ruiz, R Li, Xiaoping; Wang, Yadi; Ruiz, Ruben A Survey on Sparse Learning Models for Feature Selection IEEE TRANSACTIONS ON CYBERNETICS English Article Feature extraction; Mathematical model; Data models; Computational modeling; Correlation; Analytical models; Optimization; Feature selection; group sparse; high-dimensional data; sparse learning models NONCONCAVE PENALIZED LIKELIHOOD; VARIABLE SELECTION; GROUP LASSO; REGRESSION SHRINKAGE; LOGISTIC-REGRESSION; EFFICIENT METHODS; DIVERGING NUMBER; GENE SELECTION; ADAPTIVE LASSO; ELASTIC-NET Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and improve result comprehensibility. Various methods have been proposed to identify informative features from high-dimensional data by removing redundant and irrelevant features to improve classification accuracy. In this article, we systematically survey existing sparse learning models for feature selection from the perspectives of individual sparse feature selection and group sparse feature selection, and analyze the differences and connections among various sparse learning models. Promising research directions and topics on sparse learning models are analyzed. [Li, Xiaoping; Wang, Yadi] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China; [Li, Xiaoping; Wang, Yadi] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 211189, Peoples R China; [Ruiz, Ruben] Univ Politecn Valencia, Grp Sistemas Optimizac Aplicada, Valencia 46022, Spain Southeast University - China; Southeast University - China; Universitat Politecnica de Valencia Wang, YD (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China. xpli@seu.edu.cn; yadiwang@seu.edu.cn; rruiz@eio.upv.es National Key Research and Development Program of China [2017YFB1400800]; National Natural Science Foundation of China [61872077, 61832004]; Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization [RTI2018-094940-B-I00]; FEDER funds National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization; FEDER funds(European Commission) This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1400800, and in part by the National Natural Science Foundation of China under Grant 61872077 and Grant 61832004. The work of Ruben Ruiz was supported in part by the Spanish Ministry of Science, Innovation, and Universities, through the Project OPTEP-Port Terminal Operations Optimization under Grant RTI2018-094940-B-I00 financed with FEDER funds. This article was recommended by Associate Editor X. Li. 151 65 67 75 127 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. MAR 2022.0 52.0 3 1642 1660 10.1109/TCYB.2020.2982445 0.0 19 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 1G5BW 32386172.0 2023-03-23 WOS:000795863600020 0
J Cao, CJ; Li, CD; Yang, Q; Liu, Y; Qu, T Cao, Cejun; Li, Congdong; Yang, Qin; Liu, Yang; Qu, Ting A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters JOURNAL OF CLEANER PRODUCTION English Article Relief distribution; Sustainable disaster supply chain; Victims' perceived satisfaction; Multi-objective programming model; Genetic algorithm LOGISTICS DISTRIBUTION; RESOURCE-ALLOCATION; EMERGENCY RESPONSE; BIG DATA; OPTIMIZATION; MANAGEMENT; RESILIENCE; OPERATIONS; EFFICIENCY; FRAMEWORK To save lives and reduce suffering of victims, the focus of this paper is to design the strategies of relief distribution regarding beneficiary perspective on sustainability. This problem is formulated as a multi objective mixed-integer nonlinear programming model to maximize the lowest victims' perceived satisfaction, and minimize respectively the largest deviation on victims' perceived satisfaction for all demand points and sub-phases. Then, genetic algorithm is proposed to solve this mathematical model. To validate the proposed methodologies, a case study from Wenchuan earthquake is illustrated. Computational results demonstrate genetic algorithm here can achieve the trade-off between solution quality and computation time for relief distribution with the concern of sustainability. Furthermore, it indicates that the methodology provides the tools for decision-makers to optimize the structure of relief distribution network and inventory, as well as alleviate the suffering of victims. Increasingly, this paper expects to not only validate the proposed model and method, but also highlight the importance and urge of considering beneficiary perspective on sustainability into relief distribution problem. (C) 2017 Elsevier Ltd. All rights reserved. [Cao, Cejun; Li, Congdong] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China; [Li, Congdong] Jinan Univ, Sch Management, Guangzhou 510632, Guangdong, Peoples R China; [Cao, Cejun; Liu, Yang; Qu, Ting] Jinan Univ, Inst Phys Internet, Zhuhai Campus, Zhuhai 519070, Peoples R China; [Yang, Qin] Sichuan Normal Univ, Sch Business, Chengdu 610101, Sichuan, Peoples R China; [Liu, Yang] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden; [Liu, Yang] Univ Vaasa, Dept Prod, Vaasa 65200, Finland Tianjin University; Jinan University; Jinan University; Sichuan Normal University; Linkoping University; University of Vaasa Li, CD (corresponding author), Jinan Univ, Sch Management, Guangzhou 510632, Guangdong, Peoples R China.;Liu, Y (corresponding author), Jinan Univ, Inst Phys Internet, Zhuhai Campus, Zhuhai 519070, Peoples R China. licd@jnu.edu.cn; yang.liu@liu.se Liu, Yang/C-8320-2013; yang, liu/GVU-8760-2022 Liu, Yang/0000-0001-8006-3236; National Natural Science Foundation of China [71772075, 71672074, 71401118]; Twelfth Five-year Planning Project of Philosophy and Social Science in Guangdong Province [GD15CGL07]; Department of Education Project of Sichuan Province [14ZA0026] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Twelfth Five-year Planning Project of Philosophy and Social Science in Guangdong Province; Department of Education Project of Sichuan Province This research is supported by the projects including National Natural Science Foundation of China, Grant Numbers 71772075, 71672074 and 71401118, Twelfth Five-year Planning Project of Philosophy and Social Science in Guangdong Province Grant Number GD15CGL07, and the Department of Education Project of Sichuan Province Grant Number 14ZA0026. 58 105 115 13 147 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. FEB 10 2018.0 174.0 1422 1435 10.1016/j.jclepro.2017.11.037 0.0 14 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology FV6YD Green Submitted 2023-03-23 WOS:000424727100127 0
J Liao, WL; Yang, DC; Wang, YS; Ren, X Liao, Wenlong; Yang, Dechang; Wang, Yusen; Ren, Xiang Fault diagnosis of power transformers using graph convolutional network CSEE JOURNAL OF POWER AND ENERGY SYSTEMS English Article Power transformer; fault diagnosis; graph convolutional network; similarity metrics Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy. [Liao, Wenlong] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Yang, Dechang] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China; [Wang, Yusen] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden; [Ren, Xiang] North China Elect Power Res Inst Co Ltd, Beijing 100045, Peoples R China Aalborg University; China Agricultural University; Royal Institute of Technology Yang, DC (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. Yangdechang@cau.edu.cn Yang, Dechang/AIC-3407-2022 China Scholarship Council China Scholarship Council(China Scholarship Council) This manuscript is supported by the China Scholarship Council. The authors are very grateful for their help. 31 25 26 34 107 CHINA ELECTRIC POWER RESEARCH INST BEIJING 15, QINGHE XIAOYING DONG LU, HAIDIAN-QU, BEIJING, 100192, PEOPLES R CHINA 2096-0042 CSEE J POWER ENERGY CSEE J. Power Energy Syst. MAR 2021.0 7.0 2 241 249 10.17775/CSEEJPES.2020.04120 0.0 9 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering RF7XB gold, Green Submitted 2023-03-23 WOS:000635052100004 0
J Lei, XX; Chen, W; Avand, M; Janizadeh, S; Kariminejad, N; Shahabi, H; Costache, R; Shahabi, H; Shirzadi, A; Mosavi, A Lei, Xinxiang; Chen, Wei; Avand, Mohammadtaghi; Janizadeh, Saeid; Kariminejad, Narges; Shahabi, Hejar; Costache, Romulus; Shahabi, Himan; Shirzadi, Ataollah; Mosavi, Amir GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran REMOTE SENSING English Article machine learning; GIS; gully erosion; susceptibility mapping; head-cut erosion; Iran KERNEL LOGISTIC-REGRESSION; FUZZY INFERENCE SYSTEM; DATA-MINING TECHNIQUES; REMOTE-SENSING DATA; NAIVE BAYES TREE; FREQUENCY RATIO; SPATIAL PREDICTION; RANDOM FORESTS; DECISION TREE; METAHEURISTIC OPTIMIZATION In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility. [Lei, Xinxiang; Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Avand, Mohammadtaghi; Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115111, Iran; [Kariminejad, Narges] Gorgan Univ Agr Sci & Nat Resources, Dept Watershed & Arid Zone Management, Gorgan 49189434, Golestan, Iran; [Shahabi, Hejar] Univ Tabriz, Dept Remote Sensing, Tabriz 5166616471, Iran; [Shahabi, Hejar] Univ Tabriz, GIS, Tabriz 5166616471, Iran; [Costache, Romulus] Univ Bucharest, Res Inst, 90-92 Sos Panduri,5th Dist, Bucharest 013686, Romania; [Costache, Romulus] Natl Inst Hydrol & Water Management, Bucuresti Ploiesti Rd 97E,1st Dist, Bucharest 013686, Romania; [Shahabi, Himan] Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran; [Shahabi, Himan] Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj 6617715175, Iran; [Shirzadi, Ataollah] Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj 6617715175, Iran; [Mosavi, Amir] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Xi'an University of Science & Technology; Ministry of Natural Resources of the People's Republic of China; Tarbiat Modares University; University of Tabriz; University of Tabriz; University of Bucharest; University of Kurdistan; University of Kurdistan; University of Kurdistan; Duy Tan University Mosavi, A (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. 19209071003@stu.xust.edu.cn; chenwei0930@xust.edu.cn; Mt.avand@modares.ac.ir; janizadehsaeid@modares.ac.ir; n.karimi@gay.ac.ir; h.shahabi94@ms.tabrizu.ac.ir; romulus.costache@icub.unibuc.ro; h.shahabi@uok.ac.ir; a.shirzadi@uok.ac.ir; amirhoseinmosavi@duytan.edu.vn avand, mohammadtaghi/AAG-6319-2019; Chen, Wei/ABB-8669-2020; Costache, Romulus/GVU-1762-2022; Kariminejad, Narges/HGT-8082-2022; Shirzadi, Ataollah/AAX-9800-2020; Costache, Romulus/O-2843-2019; Shahabi, Himan/J-1591-2017; Mosavi, Amir/I-7440-2018 avand, mohammadtaghi/0000-0001-7196-5051; Chen, Wei/0000-0002-5825-1422; Costache, Romulus/0000-0002-6876-8572; Shahabi, Himan/0000-0001-5091-6947; Mosavi, Amir/0000-0003-4842-0613; Shirzadi, Ataollah/0000-0001-9668-8687; Shahabi, Hejar/0000-0002-3275-8436; Shirzadi, Ataollah/0000-0003-1666-1180; Lei, Xinxiang/0000-0001-7927-831X Innovation Capability Support Program of Shaanxi [2020KJXX-005] Innovation Capability Support Program of Shaanxi This study was supported by the Innovation Capability Support Program of Shaanxi (Program No 2020KJXX-005). 140 56 57 1 18 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2020.0 12.0 15 2478 10.3390/rs12152478 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology MZ5QA gold 2023-03-23 WOS:000559177400001 0
C Hong, DF; Yao, J; Chanussot, J; Zhu, XX IEEE Hong, Danfeng; Yao, Jing; Chanussot, Jocelyn; Zhu, Xiao Xiang UNSUPERVISED HYPERSPECTRAL EMBEDDING BY LEARNING A DEEP REGRESSION NETWORK IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) SEP 26-OCT 02, 2020 ELECTR NETWORK Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc Deep learning; hyperspectral; manifold embedding; regression; remote sensing; unsupervised FRAMEWORK This work presents a novel hyperspectral embedding technique by learning a deep regression network in an unsupervised fashion, which aims at reducing the computational complexity and storage-costing of traditional manifold embedding methods as well as improving the representation ability of spectral signatures effectively. The proposed method attempts to learn an explicit and unified nonlinear mapping from all patch-wise correspondences of original hyperspectral data and dimension-reduced products generated by some existing manifold learning approaches. This process can be well performed by means of a deep regression model. The learned model is not only capable of locally capturing the manifold structure of the whole hyperspectral image from densely patch-based random sampling but also better applicable to high-efficient out-of-sample inference. Experimental results conducted on the real hyperspectral data demonstrate the effectiveness and superiority of the proposed hyperspectral embedding technique. [Hong, Danfeng; Yao, Jing; Zhu, Xiao Xiang] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany; [Yao, Jing] Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Peoples R China; [Yao, Jing; Zhu, Xiao Xiang] Tech Univ Munich TUM, Signal Proc Earth Observat SiPEO, Munich, Germany; [Chanussot, Jocelyn] Univ Grenoble Alpes, INRIA, Grenoble INP, CNRS,LJK, Grenoble, France Helmholtz Association; German Aerospace Centre (DLR); Xi'an Jiaotong University; Technical University of Munich; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria Hong, DF (corresponding author), German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany. Hong, Danfeng/U-6082-2019 Hong, Danfeng/0000-0002-3212-9584 25 1 1 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-7281-6374-1 INT GEOSCI REMOTE SE 2020.0 2049 2052 10.1109/IGARSS39084.2020.9323251 0.0 4 Computer Science, Artificial Intelligence; Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Optics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Environmental Sciences & Ecology; Geology; Remote Sensing; Optics BR6WH 2023-03-23 WOS:000664335302025 0
J Popoola, SI; Adebisi, B; Hammoudeh, M; Gui, G; Gacanin, H Popoola, Segun, I; Adebisi, Bamidele; Hammoudeh, Mohammad; Gui, Guan; Gacanin, Haris Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks IEEE INTERNET OF THINGS JOURNAL English Article Botnet; Feature extraction; Internet of Things; Dimensionality reduction; Protocols; Support vector machines; Decision trees; Autoencoder; botnet detection; dimensionality reduction; Internet of Things (IoT); long short-term memory (LSTM) INTRUSION DETECTION SYSTEM; ENERGY MANAGEMENT; IOT; SMART; REDUCTION; CLASSIFICATION; FRAMEWORK; LSTM Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained Internet-of-Things (IoT) devices. In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE). In order to classify network traffic samples correctly, we analyze the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep bidirectional long short-term memory (BLSTM). Extensive experiments are performed with the BoT-IoT data set to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92-27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model underfitting and overfitting. It also achieves good generalisation ability in binary and multiclass classification scenarios. [Popoola, Segun, I; Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester M1 5GD, Lancs, England; [Hammoudeh, Mohammad] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M1 5GD, Lancs, England; [Gui, Guan] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Gacanin, Haris] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, D-52062 Aachen, Germany Manchester Metropolitan University; Manchester Metropolitan University; Nanjing University of Posts & Telecommunications; RWTH Aachen University Adebisi, B (corresponding author), Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester M1 5GD, Lancs, England. s.popoola@mmu.ac.uk; b.adebisi@mmu.ac.uk; m.hammoudeh@mmu.ac.uk; guiguan@njupt.edu.cn; harisg@ice.rwth-aachen.de Hammoudeh, Mohammad/A-5797-2012; Popoola, Segun I./U-8485-2019; Gui, Guan/AAG-3593-2019 Hammoudeh, Mohammad/0000-0003-1058-0996; Popoola, Segun I./0000-0002-3941-5903; Gui, Guan/0000-0001-7428-4980; Adebisi, Bamidele/0000-0001-9071-9120 Cyraatek Ltd., U.K.; Faculty of Science & Engineering, Manchester Metropolitan University; ENERGY-IQ Project, a U.K.-Canada Power Forward Smart Grid Demonstrator Project - Department for Business, Energy and Industrial Strategy [7454460]; Nigerian Intelligent Clean Energy Marketplace Project - Department for International Development Cyraatek Ltd., U.K.; Faculty of Science & Engineering, Manchester Metropolitan University; ENERGY-IQ Project, a U.K.-Canada Power Forward Smart Grid Demonstrator Project - Department for Business, Energy and Industrial Strategy; Nigerian Intelligent Clean Energy Marketplace Project - Department for International Development This work was supported in part by the Cyraatek Ltd., U.K., the Faculty of Science & Engineering, Manchester Metropolitan University; in part by the ENERGY-IQ Project, a U.K.-Canada Power Forward Smart Grid Demonstrator Project funded by the Department for Business, Energy and Industrial Strategy under Grant 7454460; and in part by the Nigerian Intelligent Clean Energy Marketplace Project funded by the Department for International Development. 89 38 38 11 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAR 15 2021.0 8.0 6 4944 4956 10.1109/JIOT.2020.3034156 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications QT4PB Green Accepted 2023-03-23 WOS:000626569700065 0
J Xia, Y; Wang, C; Xu, YS; Zang, Y; Liu, WQ; Li, J; Stilla, U Xia, Yan; Wang, Cheng; Xu, Yusheng; Zang, Yu; Liu, Weiquan; Li, Jonathan; Stilla, Uwe RealPoint3D: Generating 3D Point Clouds from a Single Image of Complex Scenarios REMOTE SENSING English Article point cloud generation; single image; indoor scenario; deep learning; 3D reconstruction RECONSTRUCTION Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. However, they require objects to be captured with absolutely clean backgrounds and fixed viewpoints, which highly limits their application in the real environment. To guide 3D point cloud generation, we propose a novel network, RealPoint3D, to integrate prior 3D shape knowledge into the network. Taking additional 3D information, RealPoint3D can handle 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, provided a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image, together with the retrieved shape model, is fed into RealPoint3D to generate a fine-grained 3D point cloud. We evaluated the proposed RealPoint3D on the ShapeNet dataset and ObjectNet3D dataset for the 3D point cloud generation. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance. Furthermore, our proposed framework works well for real images in complex backgrounds (the image has the remaining objects in addition to the reconstructed object, and the reconstructed object may be occluded or truncated) with various viewing angles. [Xia, Yan; Xu, Yusheng; Stilla, Uwe] Tech Univ Munich, Photogrammetry & Remote Sensing, D-80333 Munich, Germany; [Xia, Yan; Wang, Cheng; Zang, Yu; Liu, Weiquan] Xiamen Univ, Sch Informat, Fujian Key Lab Sensing & Comp, Xiamen 361005, Fujian, Peoples R China; [Li, Jonathan] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada Technical University of Munich; Xiamen University; University of Waterloo Wang, C (corresponding author), Xiamen Univ, Sch Informat, Fujian Key Lab Sensing & Comp, Xiamen 361005, Fujian, Peoples R China. xiayan@stu.xmu.edu.cn; cwang@xmu.edu.cn; yusheng.xu@tum.de; zangyu7@xmu.edu.cn; wqliu1026@163.com; junli@uwaterloo.ca; stilla@tum.de Wang, Cheng/A-9472-2012; Li, Jonathan/AAA-7712-2021; Stilla, Uwe/H-1534-2011 Stilla, Uwe/0000-0002-1184-0924; LI, Jonathan/0000-0001-7899-0049 Natural Science Foundation of China [U1605254]; China Scholarship Council [201906310130]; German Research Foundation (DFG); Technical University of Munich within the funding program Open Access Publishing Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); German Research Foundation (DFG)(German Research Foundation (DFG)); Technical University of Munich within the funding program Open Access Publishing This research was funded by Natural Science Foundation of China grant number U1605254. The first author Yan Xia was supported by the China Scholarship Council (No. 201906310130) for his PhD study at Technical University of Munich. This work was supported by the German Research Foundation (DFG) and the Technical University of Munich within the funding program Open Access Publishing 35 6 6 8 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2 2019.0 11.0 22 2644 10.3390/rs11222644 0.0 14 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology JV3RV Green Submitted, gold 2023-03-23 WOS:000502284300049 0
J Krefting, D; Jansen, C; Penzel, T; Han, F; Kantelhardt, JW Krefting, Dagmar; Jansen, Christoph; Penzel, Thomas; Han, Fang; Kantelhardt, Jan W. Age and gender dependency of physiological networks in sleep PHYSIOLOGICAL MEASUREMENT English Article polysomnography; time delay stability; physiological network; sleep; age; gender HEART; EEG Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics. We find moderate dependencies of the network on gender. Significantly higher link strengths up to 13% are found in women for some links in different frequency bands of central and occipital regions in REM and light sleep (N2). Higher link strengths are found in men consistently in cardio-cerebral links in N2, but not significant. Age dependency is more pronounced. In particular a significant overall weakening of the network with age is found for wakefulness and non-REM sleep stages. The largest overall decrease is observed in N2 with 0.017 per decade. For individual links decrease rates up to 0.08 per decade are found, in particular for intra-brain links in non-REM sleep. Many of them show a significant decrease with age. Non-linear regression employing an artificial neural network can predict the age with a mean absolute error (MAE) of about five years, suggesting that an age-resolution of about a decade would be appropriate in normative data for physiological networks. [Krefting, Dagmar; Jansen, Christoph] Univ Appl Sci Berlin, Ctr Biomed Image & Informat Proc, Berlin, Germany; [Penzel, Thomas] Charite, Sleep Med Ctr, Berlin, Germany; [Penzel, Thomas] St Annes Univ Hosp Brno, Int Clin Res Ctr, Brno, Czech Republic; [Han, Fang] Peking Univ, Dept Pulm Med, Peoples Hosp, Beijing, Peoples R China; [Kantelhardt, Jan W.] Martin Luther Univ Halle Wittenberg, Inst Phys, Halle, Saale, Germany Technical University of Berlin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; St Anne's University Hospital Brno (FNUSA-ICRC); Peking University; Martin Luther University Halle Wittenberg Krefting, D (corresponding author), Univ Appl Sci Berlin, Ctr Biomed Image & Informat Proc, Berlin, Germany. dagmar.krefting@htw-berlin.de Penzel, Thomas/M-2344-2014; Kantelhardt, Jan/AAF-7955-2019 Penzel, Thomas/0000-0002-4304-0112; Kantelhardt, Jan/0000-0002-9293-0293; Krefting, Dagmar/0000-0002-7238-5339 German Ministry of Economic Affairs and Energy (ZIM Project BeCRF) [KF3470401BZ4]; Chinese-German Center for Science Promotion [GZ 1189] German Ministry of Economic Affairs and Energy (ZIM Project BeCRF); Chinese-German Center for Science Promotion The work is supported by the German Ministry of Economic Affairs and Energy (ZIM Project BeCRF, Grant number KF3470401BZ4) and the Chinese-German Center for Science Promotion (Grant No GZ 1189). 27 6 6 0 11 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0967-3334 1361-6579 PHYSIOL MEAS Physiol. Meas. MAY 2017.0 38.0 5 959 975 10.1088/1361-6579/aa614e 0.0 17 Biophysics; Engineering, Biomedical; Physiology Science Citation Index Expanded (SCI-EXPANDED) Biophysics; Engineering; Physiology ET5AM 28212113.0 2023-03-23 WOS:000400296200001 0
C Meng, QJ; Bai, WJ; Liu, TR; O'Regan, DP; Rueckert, D Wang, L; Dou, Q; Fletcher, PT; Speidel, S; Li, S Meng, Qingjie; Bai, Wenjia; Liu, Tianrui; O'Regan, Declan P.; Rueckert, Daniel Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 18-22, 2022 Singapore, SINGAPORE MICCAI Soc Mesh; Differentiable rasterizer; Multi-view images DEFORMATION; REGISTRATION; FRAMEWORK 3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation. The differentiability of the rasterizer enables us to train the method end-to-end. One advantage of the proposed method is that by tracking the motion of each vertex, it is able to keep the vertex correspondence of 3D meshes between time frames, which is important for quantitative assessment of the cardiac function on the mesh. We evaluate the proposed method on CMR images acquired from the UK Biobank study. Experimental results show that the proposed method quantitatively and qualitatively outperforms both conventional and learning-based cardiac motion tracking methods. [Meng, Qingjie; Bai, Wenjia; Rueckert, Daniel] Imperial Coll London, Dept Comp, BioMedIA, London, England; [Bai, Wenjia] Imperial Coll London, Data Sci Inst, London, England; [Bai, Wenjia] Imperial Coll London, Dept Brain Sci, London, England; [Liu, Tianrui] Natl Univ Def Technol, Changsha, Peoples R China; [O'Regan, Declan P.] Imperial Coll London, MRC London Inst Med Sci, London, England; [Rueckert, Daniel] Tech Univ Munich, Klinikum Rechts Isar, Munich, Germany Imperial College London; Imperial College London; Imperial College London; National University of Defense Technology - China; Imperial College London; Technical University of Munich Meng, QJ (corresponding author), Imperial Coll London, Dept Comp, BioMedIA, London, England. q.meng16@imperial.ac.uk bai, wenjia/HPE-4360-2023 UK Biobank Resource [40616]; British Heart Foundation [RG/19/6/34387, RE/18/4/34215]; Medical Research Council [MCA658-5QEB0]; National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre; EPSRC [EP/W01842X/1] UK Biobank Resource; British Heart Foundation(British Heart Foundation); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research has been conducted using the UK Biobank Resource under application number 40616. This work is supported by the British Heart Foundation (RG/19/6/34387, RE/18/4/34215); Medical Research Council (MCA658-5QEB0); National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre. W. Bai was supported by EPSRC DeepGeM Grant (EP/W01842X/1). 34 0 0 3 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-16446-0; 978-3-031-16445-3 LECT NOTES COMPUT SC 2022.0 13436.0 248 258 10.1007/978-3-031-16446-0_24 0.0 11 Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging BU0DK Green Submitted 2023-03-23 WOS:000867434800024 0
J Liu, XT; Zhang, TF; Yang, T; Liu, XL; Song, X; Yang, Y; Li, N; Rignanese, GM; Li, YW; Wen, XD Liu, Xiaotong; Zhang, Tianfu; Yang, Tao; Liu, Xiulei; Song, Xin; Yang, Yong; Li, Ning; Rignanese, Gian-Marco; Li, Yongwang; Wen, Xiaodong Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept JOURNAL OF PHYSICAL CHEMISTRY A English Article MOLECULES Traditionally, chemistry problems are solved by means of a deductive approach. The question to be addressed is typically related to the value of a property that is either measured experimentally, computed using quantum-chemistry software, or (more recently) predicted using a machine-learned model. In this paper, we demonstrate that an inductive approach can be adopted using End-to-End (E2E) machine learning. This approach is illustrated for tackling the following chemistry problems: (i) determine the fully coordinated (FC) and undercoordinated (UC) atoms in a molecule with one missing atom, (ii) identify the type of atom that is missing in such an incomplete molecule, and (iii) predict the direction of a reaction between two molecules according to an existing dataset. The E2E approach leads to accuracies higher than 99%, 98%, and 93% for these three problems, respectively. Finally, in order to achieve such accuracies, a descriptor for the molecules, called bag of clusters, is introduced and compared with a series previously proposed descriptors, highlighting a series of advantages. [Liu, Xiaotong; Zhang, Tianfu; Yang, Yong; Li, Yongwang; Wen, Xiaodong] Chinese Acad Sci, Inst Coal Chem, State Key Lab Coal Convers, Taiyuan 030001, Peoples R China; [Liu, Xiaotong; Zhang, Tianfu; Song, Xin; Yang, Yong; Li, Yongwang; Wen, Xiaodong] Synfuels China Co Ltd, Natl Energy Ctr Coal Liquids, Beijing 101400, Peoples R China; [Liu, Xiaotong] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Liu, Xiaotong; Yang, Tao; Liu, Xiulei; Li, Ning; Li, Yongwang; Wen, Xiaodong] Beijing Informat Sci & Technol Univ, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100101, Peoples R China; [Rignanese, Gian-Marco] UCLouvain, B-1348 Louvain La Neuve, Belgium Chinese Academy of Sciences; Institute of Coal Chemistry, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Beijing Information Science & Technology University; Universite Catholique Louvain Wen, XD (corresponding author), Chinese Acad Sci, Inst Coal Chem, State Key Lab Coal Convers, Taiyuan 030001, Peoples R China.;Wen, XD (corresponding author), Synfuels China Co Ltd, Natl Energy Ctr Coal Liquids, Beijing 101400, Peoples R China.;Wen, XD (corresponding author), Beijing Informat Sci & Technol Univ, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100101, Peoples R China.;Rignanese, GM (corresponding author), UCLouvain, B-1348 Louvain La Neuve, Belgium. gian-marco.rignanese@uclouvain.be; wxd@sxicc.ac.cn zhang, tian/GZK-6001-2022; Rignanese, Gian-Marco/A-7435-2008 Rignanese, Gian-Marco/0000-0002-1422-1205 National Natural Science Foundation of China [21972157, 21872164]; Key R&D Plan of Beijing Municipal Science and Technology Commission [Z181100005118014]; Synfuels China, Co. Ltd. National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key R&D Plan of Beijing Municipal Science and Technology Commission; Synfuels China, Co. Ltd. The authors are grateful for financial support from the National Natural Science Foundation of China (21972157 and 21872164) and the Key R&D Plan of Beijing Municipal Science and Technology Commission (Z181100005118014) as well as funding support from Synfuels China, Co. Ltd. We also thank Prof. Roald Hoffmann (Cornell University) for providing us with valuable comments on the work and acknowledge the Innovation Foundation of the Institute of Coal Chemistry, Chinese Academy of Sciences, the Hundred-Talent Program of the Chinese Academy of Sciences, the Shanxi Hundred-Talent Program, and the National Thousand Young Talents Program of China. 43 7 7 1 12 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1089-5639 1520-5215 J PHYS CHEM A J. Phys. Chem. A OCT 22 2020.0 124.0 42 8866 8873 10.1021/acs.jpca.0c06319 0.0 8 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics OM3VB 33045834.0 2023-03-23 WOS:000585953200020 0
J Wang, Y; Gui, G; Gacanin, H; Adebisi, B; Sari, H; Adachi, F Wang, Yu; Gui, Guan; Gacanin, Haris; Adebisi, Bamidele; Sari, Hikmet; Adachi, Fumiyuki Federated Learning for Automatic Modulation Classification Under Class Imbalance and Varying Noise Condition IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Automatic modulation classification (AMC); deep learning (DL); federated learning (FL); class imbalance NETWORKS; IDENTIFICATION; SYSTEMS; DESIGN Automatic modulation classification (AMC) is a promising technology for identifying modulation types, and deep learning (DL)-based AMC is one of its main research directions. Conventional DL-based AMC methods are centralized solutions (i.e., CentAMC), which are trained on abundant data collected from local clients and stored in the server and generally have advanced performance, but their major problem is the risk of data leakage. Besides, if DL-based AMC is only trained with the data from their corresponding clients, it may exhibit weak performance. Thus, a federated learning (FL)-based AMC (FedeAMC) is proposed under the condition of class imbalance and noise varying. Its advantage is low risk of data leakage without severe performance loss, because data and training are in each local client, while only knowledge (i.e., gradient or model weight), rather than data, is shared with the server. In addition, there is generally class imbalance problem in each local client, and balanced cross entropy is introduced as loss function for solving this problem. Simulation results demonstrated that average accuracy gap between FedeAMC and CentAMC is less than 2%. [Wang, Yu; Gui, Guan; Sari, Hikmet] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany; [Adebisi, Bamidele] Manchester Metropolitan Univ, Dept Engn, Fac Sci & Engn, Manchester M1 5GD, Lancs, England; [Adachi, Fumiyuki] Tohoku Univ, Res Org Elect Commun, Sendai, Miyagi 9808577, Japan Nanjing University of Posts & Telecommunications; RWTH Aachen University; Manchester Metropolitan University; Tohoku University Gui, G (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China. 1018010407@njupt.edu.cn; guiguan@njupt.edu.cn; harisg@ieee.org; b.adebisi@mmu.ac.uk; hikmet@njupt.edu.cn; adachi@ecei.tohoku.ac.jp Gui, Guan/AAG-3593-2019 Gui, Guan/0000-0001-7428-4980; Adebisi, Bamidele/0000-0001-9071-9120; Sari, Hikmet/0000-0001-8114-6164 National Science and Technology Major Project of the Ministry of Science and Technology of China [TC190A3WZ-2]; Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]; Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106] National Science and Technology Major Project of the Ministry of Science and Technology of China; Summit of the Six Top Talents Program of Jiangsu; Program for High-Level Entrepreneurial and Innovative Team; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China(Ministry of Education, China) This work was supported by in part by the Project Funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant TC190A3WZ-2, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. 53 15 15 5 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. MAR 2022.0 8.0 1 86 96 10.1109/TCCN.2021.3089738 0.0 11 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications ZP7VX 2023-03-23 WOS:000766629000010 0
J Zhang, MH; Yan, TX; Wang, W; Jia, XX; Wang, J; Klemes, JJ Zhang, Menghang; Yan, Tingxiang; Wang, Wei; Jia, Xuexiu; Wang, Jin; Klemes, Jirf Jaromfr Energy-saving design and control strategy towards modern sustainable greenhouse: A review RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Review Greenhouses; Energy efficiency; Renewable technology; Control strategy; Life cycle assessment; Environmental footprint. (word count: 15722) LIFE-CYCLE ASSESSMENT; DRIVEN NATURAL VENTILATION; GLOBAL SOLAR-RADIATION; ASSISTED HEAT-PUMP; ECONOMIC-ANALYSIS; TOMATO PRODUCTION; PERFORMANCE ANALYSIS; THERMAL PERFORMANCE; TEMPERATURE CONTROL; COVERING MATERIALS A greenhouse is an energy-intensive sector with substantial greenhouse gas emissions and extensive energy consumption. Energy-saving greenhouse strategies become particularly important on the premise of ensuring effective crop production to achieve sustainable energy development. This paper aims to deliver a comprehensive review on crucial energy-saving strategies from greenhouse design to operational stage. This contribution analyses effective energy-saving methods for greenhouse design considering greenhouse structures, ventilation and lighting systems. It details the energy-saving operation of greenhouses by summarising renewable energy technologies and integration systems, including photovoltaic modules, solar collectors, heat pumps and other integrated modules. These environment-friendly technologies achieve the purpose of environment protection and energy conservation of greenhouse. The research findings reveal that more than half of the energy is saved through appropriate greenhouse renovation. Control strategies for improving the energy efficiency of the greenhouse in aspects of monitoring system management and control algorithms have been discussed as well. The neural network combined with other control algorithms is a suitable approach to solve nonlinear control problems with a good control accuracy. In the final part, the life cycle environmental impacts and environmental footprints assessment of greenhouse is discussed. Life cycle assessment of modern integrated greenhouse is expected to be further studied. This review provides valuable insights and suggestions for the design and transformation of modern sustainable greenhouses. [Zhang, Menghang; Yan, Tingxiang] Hebei Univ Technol, Sch Energy & Environm Engn, Tianjin 300401, Peoples R China; [Wang, Wei] Hebei Univ Technol, Innovat & Entrepreneurial Ctr, Tianjin 300401, Peoples R China; [Jia, Xuexiu; Wang, Jin; Klemes, Jirf Jaromfr] Brno Univ Technol VUT Brno, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab, Technicka 2896-2, Brno 616 69, Czech Republic Hebei University of Technology; Hebei University of Technology; Brno University of Technology Wang, J (corresponding author), Brno Univ Technol VUT Brno, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab, Technicka 2896-2, Brno 616 69, Czech Republic. 202021303004@stu.hebut.edu.cn; 202121301021@stu.hebut.edu.cn; 2013084@hebut.edu.cn; jia@fme.vutbr.cz; wang.j@fme.vutbr.cz; jiri.klemes@vutbr.cz wang, jin/F-2166-2014; Klemes, Jiri Jaromir/B-7291-2009 wang, jin/0000-0003-4513-761X; Klemes, Jiri Jaromir/0000-0002-7450-7029 National Natural Science Foundation of China [52176067]; Natural Science Foundation of Hebei Province of China [E2021202163]; Special Project of Science and Technology Winter Olympics in the Hebei Technology Innovation Guidance Plan [21474501D]; Project of Innovation Ability Training for Postgraduate Students of Education Department of Hebei Province [CXZZSS2021046]; European Union projects Sustainable Process Integration Laboratory - SPIL [CZ.02.1.01/0.0/0.0/15_003/0000456]; Czech Republic Operational Programme Research and Development, Education; Hebei University of Technology Horizon 2020 research and innovation programme Renewable energy system for residential building heating and electricity production - RESHeat [956255] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Special Project of Science and Technology Winter Olympics in the Hebei Technology Innovation Guidance Plan; Project of Innovation Ability Training for Postgraduate Students of Education Department of Hebei Province; European Union projects Sustainable Process Integration Laboratory - SPIL; Czech Republic Operational Programme Research and Development, Education; Hebei University of Technology Horizon 2020 research and innovation programme Renewable energy system for residential building heating and electricity production - RESHeat This work is supported by the National Natural Science Foundation of China (Grant No. 52176067) , the Natural Science Foundation of Hebei Province of China (Grant No. E2021202163) , the Special Project of Science and Technology Winter Olympics in the Hebei Technology Innovation Guidance Plan (Grant No. 21474501D) , and the Project of Innovation Ability Training for Postgraduate Students of Education Department of Hebei Province (Grant No. CXZZSS2021046) . The authors also acknowledged the funding and support by the European Union projects Sustainable Process Integration Laboratory - SPIL, funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research operated by the Czech Ministry of Education, Youth and Sport under a collaboration agreement with Hebei University of Technology Horizon 2020 research and innovation programme Renewable energy system for residential building heating and electricity production - RESHeat, Grant Agreement #956255) . 184 1 1 54 76 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. AUG 2022.0 164.0 112602 10.1016/j.rser.2022.112602 0.0 MAY 2022 22 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels 1T7XZ 2023-03-23 WOS:000804942200001 0
J Li, JJ; Li, ZJ; Chen, F; Bicchi, A; Sun, Y; Fukuda, T Li, Junjun; Li, Zhijun; Chen, Fei; Bicchi, Antonio; Sun, Yu; Fukuda, Toshio Combined Sensing, Cognition, Learning, and Control for Developing Future Neuro-Robotics Systems: A Survey IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS English Article Cognition; learning; motion control; neuro-robotics systems (NRSs); sensory feedback END-POINT STIFFNESS; MOBILE ROBOT; DEVELOPMENTAL ROBOTICS; HUMANOID ROBOT; MAP FORMATION; HUMAN BRAIN; DESIGN; MODEL; HAND; SELF Neuro-robotics systems (NRSs) is the current state-of-the-art research with the strategic alliance of neuroscience and robotics. It endows the next generation of robots with embodied intelligence to identify themselves and interact with humans and environments naturally. Therefore, it needs to study the interaction of recent breakthroughs in brain neuroscience, robotics, and artificial intelligence where smarter robots could be developed by employing neural mechanisms and understanding brain functions. Recently, more sophisticated neural mechanisms of perception, cognition, learning, and control have been decoded, which investigate how to define and develop the brain for future robots. In this paper, a comprehensive survey is summarized by recent achievements in neuro-robotics, and some potential directions for the development of future neuro-robotics are discussed. [Li, Junjun] South China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China; [Li, Zhijun] Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China; [Chen, Fei; Bicchi, Antonio] Ist Italiano Tecnol, Dept Adv Robot, I-16163 Genoa, Italy; [Bicchi, Antonio] Univ Pisa, Fac Engn, Interdept Res Ctr E Piaggio, I-56126 Pisa, Italy; [Sun, Yu] Univ Toronto, Mech & Ind Engn, Toronto, ON M5S 3G8, Canada; [Fukuda, Toshio] Beijing Inst Technol, Sch Mechatron, Beijing 100081, Peoples R China; [Fukuda, Toshio] Nagoya Univ, Meijo Univ, Dept Mechatron Engn, Nagoya, Aichi 4688502, Japan South China University of Technology; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Istituto Italiano di Tecnologia - IIT; University of Pisa; University of Toronto; Beijing Institute of Technology; Meijo University; Nagoya University Li, ZJ (corresponding author), Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China. lijunjunyanyan@163.com; zjli@ieee.org; feichen@iit.it; bicchi@centropiaggio.unipi.it; sun@mie.utoronto.ca; t.fukuda@ieee.org Sun, Yu/0000-0001-7895-0741 National Natural Science Foundation of China [61573147, 61625303, 61751310]; Guangdong Science and Technology Research Collaborative Innovation Projects [2014B090901056, 2015B020214003, 2016A020220003]; Guangdong Science and Technology Plan Project (Application Technology Research Foundation) [2015B020233006]; Anhui Science and Technology Major Program [17030901029]; National Key Research and Development Program of China [2017YFB1302302] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Science and Technology Research Collaborative Innovation Projects; Guangdong Science and Technology Plan Project (Application Technology Research Foundation); Anhui Science and Technology Major Program; National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 61573147, Grant 61625303, and Grant 61751310; in part by the Guangdong Science and Technology Research Collaborative Innovation Projects under Grant 2014B090901056, Grant 2015B020214003, and Grant 2016A020220003; in part by the Guangdong Science and Technology Plan Project (Application Technology Research Foundation) under Grant 2015B020233006; in part by the Anhui Science and Technology Major Program under Grant 17030901029; and in part by the National Key Research and Development Program of China under Grant 2017YFB1302302. 156 13 13 3 56 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2379-8920 2379-8939 IEEE T COGN DEV SYST IEEE Trans. Cogn. Dev. Syst. JUN 2019.0 11.0 2 SI 148 161 10.1109/TCDS.2019.2897618 0.0 14 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Robotics; Neurosciences & Neurology IC6YR 2023-03-23 WOS:000471119200002 0
J Wu, PP; Wang, RH; Lin, H; Zhang, FL; Tu, J; Sun, M Wu, Pingping; Wang, Ruihao; Lin, Han; Zhang, Fanlong; Tu, Juan; Sun, Miao Automatic depression recognition by intelligent speech signal processing: A systematic survey CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY English Review; Early Access DETECTING DEPRESSION; CLINICAL DEPRESSION; RATING-SCALE; SEVERITY; VALIDITY; INDICATORS; INVENTORY Depression has become one of the most common mental illnesses in the world. For better prediction and diagnosis, methods of automatic depression recognition based on speech signal are constantly proposed and updated, with a transition from the early traditional methods based on hand-crafted features to the application of architectures of deep learning. This paper systematically and precisely outlines the most prominent and up-to-date research of automatic depression recognition by intelligent speech signal processing so far. Furthermore, methods for acoustic feature extraction, algorithms for classification and regression, as well as end to end deep models are investigated and analysed. Finally, general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition. [Wu, Pingping; Lin, Han] Nanjing Audit Univ, Sch Engn Audit, Jiangsu Key Lab Publ Project Audit, Nanjing 211815, Peoples R China; [Wang, Ruihao; Zhang, Fanlong] Nanjing Audit Univ, Sch Informat Engn, Nanjing, Peoples R China; [Tu, Juan] Nanjing Univ, Sch Phys, Key Lab Modern Acoust MOE, Nanjing 210093, Peoples R China; [Sun, Miao] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, Delft, Netherlands Nanjing Audit University; Nanjing Audit University; Nanjing University; Delft University of Technology Lin, H (corresponding author), Nanjing Audit Univ, Sch Engn Audit, Jiangsu Key Lab Publ Project Audit, Nanjing 211815, Peoples R China.;Tu, J (corresponding author), Nanjing Univ, Sch Phys, Key Lab Modern Acoust MOE, Nanjing 210093, Peoples R China. linhan@nau.edu.cn; juantu@nju.edu.cn Lin, Han/GRF-1081-2022; Lin, Han/X-5326-2019 Lin, Han/0000-0001-5136-5059 National Natural Science Foundation of China [61701243, 71771125]; Major Project of Natural Science Foundation of Jiangsu Education Department [19KJA180002, 21KJA630001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Project of Natural Science Foundation of Jiangsu Education Department National Natural Science Foundation of China, Grant/Award Numbers: 61701243, 71771125; Major Project of Natural Science Foundation of Jiangsu Education Department, Grant/Award Numbers: 19KJA180002, 21KJA630001 102 2 2 24 29 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2468-6557 2468-2322 CAAI T INTELL TECHNO CAAI T. Intell. Technol. 10.1049/cit2.12113 0.0 JUN 2022 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2G8AG Green Published 2023-03-23 WOS:000813810000001 0
C Delvaux, J; Gu, D; Verbauwhede, I IEEE Delvaux, Jeroen; Gu, Dawu; Verbauwhede, Ingrid Upper Bounds on The Min-Entropy of RO Sum, Arbiter, Feed-Forward Arbiter, and S-ArbRO PUFs PROCEEDINGS OF THE 2016 IEEE ASIAN HARDWARE ORIENTED SECURITY AND TRUST SYMPOSIUM (ASIANHOST 2016) English Proceedings Paper IEEE Asian Hardware-Oriented Security and Trust (AsianHOST) DEC 19-20, 2016 Yilan, TAIWAN IEEE,IEEE Comp Soc,IEEE Test Technol Tech Council,Raith America,Huawei,Minist Sci & Technol Taiwan The focus and novelty of this work is the derivation of tight upper bounds on the min-entropy of several physically unclonable funcions (PUFs), i.e., Ring Oscillator Sum, Arbiter, Feed-Forward Arbiter, and S-ArbRO PUFs. This constrains their usability for the fuzzy extraction of a secret key, as an alternative to storing keys in non-volatile memory. For example, it is shown that an ideal Arbiter PUF with 6 4 stages cannot provide more than 1 9 7 bits of min-entropy. At Financial Cryptography 2012, Van Herrewege et al. assume that 1 7 8 5 bits of min-entropy can be extracted, which renders their 128-bit key generator instantly insecure. We also derive upper bounds that comply with non-ideal PUFs, attributed to, e.g., manufacturing in silicon. As a side contribution hereby, we refute the claim that S-ArbRO PUFs are highly resistant against machine learning. [Delvaux, Jeroen; Gu, Dawu] Katholieke Univ Leuven, ESAT COSIC, Leuven, Belgium; [Delvaux, Jeroen; Verbauwhede, Ingrid] IMinds, Ghent, Belgium; [Delvaux, Jeroen; Gu, Dawu] Shanghai Jiao Tong Univ, CSE LoCCS, Shanghai, Peoples R China KU Leuven; IMEC; Shanghai Jiao Tong University Delvaux, J (corresponding author), Katholieke Univ Leuven, ESAT COSIC, Leuven, Belgium.;Delvaux, J (corresponding author), IMinds, Ghent, Belgium.;Delvaux, J (corresponding author), Shanghai Jiao Tong Univ, CSE LoCCS, Shanghai, Peoples R China. jdelvaux@esat.kuleuven.be; dwgu@sjtu.edu.cn; iverbauw@esat.kuleuven.be Verbauwhede, Ingrid/W-8616-2019 Verbauwhede, Ingrid/0000-0002-0879-076X European Union's Horizon research and innovation programme [644052]; ERC [695305]; national major development program for fundamental research of China (973 Plan) [2013CB338004]; IWT-Flanders grant [SBO 121552] European Union's Horizon research and innovation programme; ERC(European Research Council (ERC)European Commission); national major development program for fundamental research of China (973 Plan); IWT-Flanders grant The authors greatly appreciate the support received. The European Union's Horizon 2020 research and innovation programme under grant number 644052 (HECTOR) and the ERC Advanced Grant 695305. The Research Council of KU Leuven through C16/15/058, and the Hercules Foundation through AKUL/11/19. The national major development program for fundamental research of China (973 Plan) under grant number 2013CB338004. Jeroen Delvaux is funded by IWT-Flanders grant number SBO 121552. The authors thank Steven Delvaux for pointing out that (9) can be bounded via an inscribed hypersphere. 11 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-5701-6 2016.0 6 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BH6CZ 2023-03-23 WOS:000401663200023 0
J Yip, MYT; Lim, G; Lim, ZW; Nguyen, QD; Chong, CCY; Yu, M; Bellemo, V; Xie, YC; Lee, XQ; Hamzah, H; Ho, JY; Tan, TE; Sabanayagam, C; Grzybowski, A; Tan, GSW; Hsu, W; Lee, ML; Wong, TY; Ting, DSW Yip, Michelle Y. T.; Lim, Gilbert; Lim, Zhan Wei; Nguyen, Quang D.; Chong, Crystal C. Y.; Yu, Marco; Bellemo, Valentina; Xie, Yuchen; Lee, Xin Qi; Hamzah, Haslina; Ho, Jinyi; Tan, Tien-En; Sabanayagam, Charumathi; Grzybowski, Andrzej; Tan, Gavin S. W.; Hsu, Wynne; Lee, Mong Li; Wong, Tien Yin; Ting, Daniel S. W. Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy NPJ DIGITAL MEDICINE English Article RETINAL IMAGES; DISEASES; TECHNOLOGY; PREVALENCE; VALIDATION; ALGORITHM; DATASET; LESIONS; RISK; AGE Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p < 0.001). The DL performance performed better when there were increased number of fields (dataset 1: 2-field vs 1-field-AUC 0.936 vs 0.908, p < 0.001; dataset 2: 7-field vs 2-field vs 1-field, AUC 0.949 vs 0.911 vs 0.895). DL performed better in the pseudophakic than phakic eyes (AUC 0.918 vs 0.833, p < 0.001). Various image-related factors play more significant roles than technical factors in determining the diagnostic performance, suggesting the importance of having robust training and testing datasets for DL training and deployment in the real-world settings. [Yip, Michelle Y. T.; Lim, Gilbert; Nguyen, Quang D.; Chong, Crystal C. Y.; Yu, Marco; Bellemo, Valentina; Xie, Yuchen; Lee, Xin Qi; Hamzah, Haslina; Ho, Jinyi; Tan, Tien-En; Sabanayagam, Charumathi; Tan, Gavin S. W.; Wong, Tien Yin; Ting, Daniel S. W.] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Yip, Michelle Y. T.; Sabanayagam, Charumathi; Tan, Gavin S. W.; Wong, Tien Yin; Ting, Daniel S. W.] Duke NUS Med Sch, Singapore, Singapore; [Lim, Gilbert; Lim, Zhan Wei; Hsu, Wynne; Lee, Mong Li] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Grzybowski, Andrzej] Univ Warmia & Mazury, Dept Ophthalmol, Olsztyn, Poland; [Grzybowski, Andrzej] Fdn Ophthalmol Dev, Inst Res Ophthalmol, Poznan, Poland; [Ting, Daniel S. W.] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China National University of Singapore; Singapore National Eye Center; National University of Singapore; National University of Singapore; University of Warmia & Mazury; Sun Yat Sen University Ting, DSW (corresponding author), Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.;Ting, DSW (corresponding author), Duke NUS Med Sch, Singapore, Singapore.;Ting, DSW (corresponding author), Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China. daniel.ting45@gmail.com Nguyen, Duc Quang/AHC-6430-2022; Tan, Gavin/HNS-5461-2023; Wong, Tien Yin/AAC-9724-2020; Sabanayagam, Charumathi/C-1294-2011; Lim, Gilbert/N-7322-2017 Nguyen, Duc Quang/0000-0001-6919-5211; Wong, Tien Yin/0000-0002-8448-1264; Sabanayagam, Charumathi/0000-0002-4042-4719; Grzybowski, Andrzej/0000-0002-3724-2391; Bidwai, Pooja Vishal/0000-0002-3077-4395; Lim, Gilbert/0000-0002-5381-9250; Yu, Marco/0000-0002-2825-8914 National Medical Research Council Singapore, Ministry of Health, Singapore; Tanoto Foundation; National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant) [NHIC-I2D-1409022]; Health Service Research Grant; SingHealth Foundation Research Grant [SHF/FG648S/2015]; NMRC, MOH [0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, STaR/2013, CG/SERI/2010]; Biomedical Research Council [08/1/35/19/550, 09/1/35/19/616]; MOH, Singapore [AIC/RPDD/ SIDRP/SERI/FY2013/0018, AIC/HPD/FY2016/0912] National Medical Research Council Singapore, Ministry of Health, Singapore(Ministry of Health-SingaporeNational Medical Research Council, Singapore); Tanoto Foundation; National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant)(Ministry of Health-SingaporeNational Medical Research Council, Singapore); Health Service Research Grant; SingHealth Foundation Research Grant(SingHealth); NMRC, MOH; Biomedical Research Council(Agency for Science Technology & Research (A*STAR)); MOH, Singapore(Ministry of Health-Singapore) This study was supported by the National Medical Research Council Singapore, Ministry of Health, Singapore and Tanoto Foundation. This project received funding from National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022); Health Service Research Grant; SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation. For Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, & STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550, 09/1/35/19/616). The Singapore Diabetic Retinopathy Program (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/ SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912). The NMRC (Singapore), MOH (Singapore), NHIC (Singapore), and Tanoto Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. 65 16 16 0 3 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2398-6352 NPJ DIGIT MED npj Digit. Med. MAR 23 2020.0 3.0 1 40 10.1038/s41746-020-0247-1 0.0 12 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics KX6JG Green Published, gold, Green Accepted 2023-03-23 WOS:000521984900002 0
J Li, HC; Hu, WS; Li, W; Li, J; Du, Q; Plaza, A Li, Heng-Chao; Hu, Wen-Shuai; Li, Wei; Li, Jun; Du, Qian; Plaza, Antonio A(3)CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Attention mechanism; convolutional long short-term memory; feature extraction; fusion; multisource remote sensing data classification; transfer learning EXTINCTION PROFILES; FEATURE-EXTRACTION; FUSION The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this article, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel A(3)CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral, and multiscale attention mechanisms are first designed for HSI and LiDAR data in order to learn spectral- and spatial-enhanced feature representations and to represent multiscale information for different classes. In the designed fusion network, a novel composite attention learning mechanism (combined with a three-level fusion strategy) is used to fully integrate the features in these two data sources. Finally, inspired by the idea of transfer learning, a novel stepwise training strategy is designed to yield a final classification result. Our experimental results, conducted on several multisource remote sensing data sets, demonstrate that the newly proposed dual-channel A(3) CLNN exhibits better feature representation ability (leading to more competitive classification performance) than other state-of-the-art methods. [Li, Heng-Chao; Hu, Wen-Shuai] Southwest Jiaotong Univ, Sichuan Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Peoples R China; [Li, Wei] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10060, Spain Southwest Jiaotong University; Beijing Institute of Technology; Sun Yat Sen University; Mississippi State University; Universidad de Extremadura Hu, WS (corresponding author), Southwest Jiaotong Univ, Sichuan Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Peoples R China. lihengchao_78@163.com; wshu@my.swjtu.edu.cn LI, WEI/ABD-5001-2021; Plaza, Antonio/C-4455-2008; du, qian/GYJ-7090-2022 Plaza, Antonio/0000-0002-9613-1659; Hu, Wenshuai/0000-0002-4757-2765 National Natural Science Foundation of China [61871335]; Fundamental Research Funds for the Central Universities [2682020XG02, 2682020ZT35] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61871335 and in part by the Fundamental Research Funds for the Central Universities under Grant 2682020XG02 and 2682020ZT35. 57 25 25 38 90 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. FEB 2022.0 33.0 2 747 761 10.1109/TNNLS.2020.3028945 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YU4LL 33085622.0 Green Submitted 2023-03-23 WOS:000752016400026 0
C Chen, CY; Ruiz, MT; Lataire, E; Delefortrie, G; Mansuy, M; Mei, TL; Vantorre, M ASME Chen, Changyuan; Ruiz, Manases Tello; Lataire, Evert; Delefortrie, Guillaume; Mansuy, Marc; Mei, Tianlong; Vantorre, Marc SHIP MANOEUVRING MODEL PARAMETER IDENTIFICATION USING INTELLIGENT MACHINE LEARNING METHOD AND THE BEETLE ANTENNAE SEARCH ALGORITHM PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 7B Proceedings of the ASME International Conference on Ocean Offshore and Arctic Engineering English Proceedings Paper 38th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2019) JUN 09-14, 2019 Univ Strathclyde, Glasgow, SCOTLAND ASME, Ocean Offshore & Arctic Engn Div,Univ Strathclyde, Dept Naval Architecture Ocean & Marine Engn Univ Strathclyde ship motions model; NLSSVM; BAS; parameter identification In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion's model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data. [Chen, Changyuan; Ruiz, Manases Tello; Lataire, Evert; Delefortrie, Guillaume; Mansuy, Marc; Mei, Tianlong; Vantorre, Marc] Univ Ghent, Ghent, Belgium; [Delefortrie, Guillaume] Flanders Hydraul Res, Antwerp, Belgium; [Mei, Tianlong] Shanghai Jiao Tong Univ, Shanghai, Peoples R China Ghent University; Flanders Hydraulics Research (FHR); Shanghai Jiao Tong University Chen, CY (corresponding author), Univ Ghent, Ghent, Belgium. Changyuan.Chen@UGent.be Lataire, Evert/AGZ-6878-2022 Lataire, Evert/0000-0002-3551-6686; Vantorre, Marc/0000-0002-2645-6394 China Scholarships Council [201706570007]; CSC-grant in Ghent University [01SC8418] China Scholarships Council; CSC-grant in Ghent University(Ghent University) This work was supported by the program of China Scholarships Council (No.201706570007) and Special Research Fund -Cofunding for Chinese candidates holding a CSC-grant in Ghent University (01SC8418). 18 4 4 0 5 AMER SOC MECHANICAL ENGINEERS NEW YORK THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA 2153-4772 978-0-7918-5885-1 P ASME INT C OCEAN 2019.0 UNSP V07BT06A028 9 Engineering, Ocean; Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BO3YL 2023-03-23 WOS:000513308900028 0
C Buso, A; Scharff, RBN; Doubrovski, EL; Wu, J; Wang, CCL; Vink, P IEEE Buso, A.; Scharff, R. B. N.; Doubrovski, E. L.; Wu, J.; Wang, C. C. L.; Vink, P. Soft Robotic Module for Sensing and Controlling Contact Force 2020 3RD IEEE INTERNATIONAL CONFERENCE ON SOFT ROBOTICS (ROBOSOFT) English Proceedings Paper 3rd IEEE International Conference on Soft Robotics (RoboSoft) JUL 15, 2020 New Haven, CT IEEE,Yale Univ ACTUATION; DESIGN; STATE; SKIN This work presents a soft robotic module that can sense and control contact forces. The module is composed of a foam spring encapsulated by a pneumatic bellow that can be inflated to increase its stiffness. Optical sensors and a light source are integrated inside the soft pneumatic module. Changes in shape of the module lead to a variation in light reflectivity, which is captured by the optical sensors. These shape measurements are combined with air pressure measurements to predict the contact force through a machine learning model. Using these predictions, a closed-loop control of the contact force was implemented. The modules can be applied to realize pressure distribution control in support devices such as seats and mattresses. The presented method is robust and low-cost, can measure both shape and contact force, and does not require (rigid) sensors to be present at the movable contact interface between the support device and the user. [Buso, A.; Scharff, R. B. N.; Doubrovski, E. L.; Wu, J.; Vink, P.] Delft Univ Technol, Dept Design Engn, NL-2628 CE Delft, Netherlands; [Wang, C. C. L.] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China Delft University of Technology; Chinese University of Hong Kong Vink, P (corresponding author), Delft Univ Technol, Dept Design Engn, NL-2628 CE Delft, Netherlands. p.vink@tudelft.nl Doubrovski, Zjenja/GRJ-9889-2022; Wang, Charlie C. L./B-3730-2010 vink, Peter/0000-0001-9985-3369; Scharff, Rob/0000-0001-7996-4790; Buso, Alice/0000-0002-5973-7205; Wang, Charlie C. L./0000-0003-4406-8480 32 4 4 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-6570-7 2020.0 70 75 6 Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Robotics BQ6BJ 2023-03-23 WOS:000610491800011 0
J Zheng, JQ; Du, J; Wang, BH; Klemes, JJ; Liao, Q; Liang, YT Zheng, Jianqin; Du, Jian; Wang, Bohong; Klemes, Jiri Jaromir; Liao, Qi; Liang, Yongtu A hybrid framework for forecasting power generation of multiple renewable energy sources RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Article Renewable energy system; Power generation; Forecasting; Hybrid framework; Long short-term memory WIND-SPEED; TIME-SERIES; LEARNING-STRATEGY; SOLAR; MODEL; SELECTION; MACHINE; DESIGN; SYSTEM; LSTM The accurate power generation forecast of multiple renewable energy sources is significant for the power scheduling of renewable energy systems. However, previous studies focused more on the prediction of a single energy source, ignoring the relationship among different energy sources, and failing to predict accurate power generation for all energy sources simultaneously. This paper proposes a hybrid framework for the power gen-eration forecast of multiple renewable energy sources to overcome deficiencies. A Convolutional Neural Network (CNN) is developed to extract the local correlations among multiple energy sources, the Attention-based Long Short-Term Memory (A-LSTM) network is developed to capture the nonlinear time-series characteristics of weather conditions and individual energy, and the Auto-Regression model is applied to extract the linear time -series characteristics of each energy source. The accuracy and practicality of the proposed method are verified by taking a renewable energy system as an example. The results show that the hybrid framework is more accurate than other advanced models, such as artificial neural networks and decision trees. Mean absolute errors of the proposed method are reduced by 13.4%, 22.9%, and 27.1% for solar PV, solar thermal, and wind power compared with A-LSTM. The sensitivity analysis has been conducted to test the effectiveness of each component of the proposed hybrid framework to prove the significance of energy correlation patterns with higher accuracy and stability compared with the other two patterns. [Zheng, Jianqin; Du, Jian; Liao, Qi; Liang, Yongtu] China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China; [Wang, Bohong] Zhejiang Ocean Univ, Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S, Zhejiang Key Lab Petrochem Environm Pollut Control, 1 Haida South Rd, Zhoushan 316022, Peoples R China; [Klemes, Jiri Jaromir] Brno Univ Technol VUT BRNO, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab SPIL, Tech 2896-2, Brno 61669, Czech Republic China University of Petroleum; Zhejiang Ocean University; Brno University of Technology Liang, YT (corresponding author), China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China.;Wang, BH (corresponding author), Zhejiang Ocean Univ, Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S, Zhejiang Key Lab Petrochem Environm Pollut Control, 1 Haida South Rd, Zhoushan 316022, Peoples R China. wangbh@zjou.edu.cn; liangyt21st@cup.edu.cn Wang, Bohong/M-2379-2019; Klemes, Jiri Jaromir/B-7291-2009 Wang, Bohong/0000-0003-1206-475X; Klemes, Jiri Jaromir/0000-0002-7450-7029 National Natural Science Foundation of China [51874325]; EU [CZ.02.1.01/ 0.0/0.0/15_003/0000456, 956255]; Science Foundation of Zhejiang Ocean University [11025092122]; Scientific Research Fund of Zhejiang Provincial Education Department [Y202250605] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU(European Commission); Science Foundation of Zhejiang Ocean University; Scientific Research Fund of Zhejiang Provincial Education Department This work was part of the Program Study on Optimisation and Supply-side Reliability of Oil Product Supply Chain Logistics System funded under the National Natural Science Foundation of China, grant number 51874325. Additional funding comes from EU project Sus-tainable Process Integration Laboratory - SPIL, project No. CZ.02.1.01/ 0.0/0.0/15_003/0000456 funded by EU CZ Operational Programme Research, Development and Education, Priority 1: Strengthening ca-pacity for quality research, innovation programme Renewable energy system for residential building heating and electricity production - RESHeat, Grant Agreement # 956255), Science Foundation of Zhejiang Ocean University (11025092122), and Scientific Research Fund of Zhejiang Provincial Education Department (Y202250605). The authors are grateful to all study participants. 64 1 1 25 25 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. FEB 2023.0 172.0 113046 10.1016/j.rser.2022.113046 0.0 14 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels 6Q5GQ 2023-03-23 WOS:000891641900004 0
J Hu, S; d'Ambrosio, S; Finesso, R; Manelli, A; Marzano, MR; Mittica, A; Ventura, L; Wang, HC; Wang, YY Hu, Song; d'Ambrosio, Stefano; Finesso, Roberto; Manelli, Andrea; Marzano, Mario Rocco; Mittica, Antonio; Ventura, Loris; Wang, Hechun; Wang, Yinyan Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine ENERGIES English Article model-based; control; diesel engine; ANN; physics-based model; semi-empirical model EXHAUST EMISSIONS; GASOLINE-ENGINE; PERFORMANCE; PREDICTION; CALIBRATION A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 mu s when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications. [Hu, Song; d'Ambrosio, Stefano; Finesso, Roberto; Manelli, Andrea; Marzano, Mario Rocco; Mittica, Antonio; Ventura, Loris] Politecn Torino, Dept Energy, Corso Duca Abruzzi 24, I-10129 Turin, Italy; [Hu, Song; Wang, Hechun; Wang, Yinyan] Harbin Engn Univ, Coll Power & Energy Engn, Harbin 150001, Heilongjiang, Peoples R China Polytechnic University of Turin; Harbin Engineering University Finesso, R (corresponding author), Politecn Torino, Dept Energy, Corso Duca Abruzzi 24, I-10129 Turin, Italy. roberto.finesso@polito.it Wang, Yin/HCI-9352-2022 Hu, Song/0000-0003-1867-5869; d'Ambrosio, Stefano/0000-0001-7444-303X China Scholarship Council [201706680011]; Marine Low-Speed Engine Project-Phase I [CDG01-KT0302]; FPT China Scholarship Council(China Scholarship Council); Marine Low-Speed Engine Project-Phase I; FPT FPT is kindly acknowledged for its support in the activities. This work was also supported by the China Scholarship Council (Grant No. 201706680011) and Marine Low-Speed Engine Project-Phase I (CDG01-KT0302). The authors would like to thank Omar Marello for his efforts in discussing and carrying out this research. 54 10 10 2 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies SEP 2019.0 12.0 18 3423 10.3390/en12183423 0.0 41 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels JC2IR Green Submitted, gold 2023-03-23 WOS:000489101200019 0
J He, M; Gong, YS; Li, YQ; Mauri, T; Fumagalli, F; Bozzola, M; Cesana, G; Latini, R; Pesenti, A; Ristagno, G He, Mi; Gong, Yushun; Li, Yongqin; Mauri, Tommaso; Fumagalli, Francesca; Bozzola, Marcella; Cesana, Giancarlo; Latini, Roberto; Pesenti, Antonio; Ristagno, Giuseppe Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests CRITICAL CARE English Article Defibrillation; Ventricular fibrillation; Predictive features; Combination; Cardiac arrest VENTRICULAR-FIBRILLATION; COUNTERSHOCK SUCCESS; AMPLITUDE; FREQUENCY; CLASSIFICATION; PARAMETERS; SHOCK Introduction: Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain. Methods: A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients. Results: Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeaturebased methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered. Conclusions: In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance. [He, Mi; Gong, Yushun; Li, Yongqin] Third Mil Med Univ, Sch Biomed Engn, Chongqing 400038, Peoples R China; [He, Mi; Gong, Yushun; Li, Yongqin] Chongqing Univ, Chongqing 400038, Peoples R China; [Mauri, Tommaso; Pesenti, Antonio] Fdn IRCCS Ca Granda Osped Maggiore Policlin, Dept Anesthesia Crit Care & Emergency, I-20122 Milan, Italy; [Fumagalli, Francesca; Latini, Roberto; Ristagno, Giuseppe] IRCCS Ist Ric Farmacol Mario Negri, I-20156 Milan, Italy; [Bozzola, Marcella; Pesenti, Antonio] AREU, I-20124 Milan, Italy; [Cesana, Giancarlo] Univ Milano Bicocca, Dept Stat & Quantitat Methods, Res Ctr Publ Hlth, I-20126 Milan, Italy Army Medical University; Chongqing University; IRCCS Ca Granda Ospedale Maggiore Policlinico; Istituto di Ricerche Farmacologiche Mario Negri IRCCS; University of Milano-Bicocca Li, YQ (corresponding author), Third Mil Med Univ, Sch Biomed Engn, 30 Gaotanyan Main St, Chongqing 400038, Peoples R China. leeoken@gmail.com Fumagalli, Francesca/AAC-5078-2022; Mauri, Tommaso/O-5955-2017; Latini, Roberto/AAB-1410-2020; Pesenti, Antonio/H-7483-2012 Fumagalli, Francesca/0000-0002-9749-4478; Mauri, Tommaso/0000-0002-2709-8162; Latini, Roberto/0000-0002-3729-4650; Pesenti, Antonio/0000-0003-2593-729X; Ristagno, Giuseppe/0000-0002-9235-4820; HE, Mi/0000-0001-9983-8863 grant Ricerca Indipendente from Regione Lombardia, Italy; National Nature Science Foundation of China [NSFC81271656, NSFC41301397] grant Ricerca Indipendente from Regione Lombardia, Italy; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) The database creation was supported by a grant Ricerca Indipendente 2010 from Regione Lombardia, Italy. The study was supported in part, by the National Nature Science Foundation of China (NSFC81271656 and NSFC41301397). 31 20 20 0 10 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1466-609X 1364-8535 CRIT CARE Crit. Care DEC 10 2015.0 19.0 425 10.1186/s13054-015-1142-z 0.0 9 Critical Care Medicine Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine CY0NM 26652159.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000366102800001 0
J Zhang, JX; Jia, L; Menenti, M; Hu, GC Zhang, Jingxiao; Jia, Li; Menenti, Massimo; Hu, Guangcheng Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study REMOTE SENSING English Article automatic glacier facies mapping; Random Forest; Landsat; Parlung Zangbo basin DEBRIS-COVERED GLACIERS; SURFACE TEMPERATURE RETRIEVAL; TIBETAN PLATEAU; SATELLITE DATA; LANDSAT IMAGERY; RANDOM FORESTS; INVENTORY DATA; MASS-BALANCE; AREA CHANGES; RIVER-BASIN Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km(2)) were 88.3% distributed at lower elevations compared to larger size glaciers (1 km(2)). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20 degrees and 30 degrees, and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin. [Zhang, Jingxiao; Jia, Li; Menenti, Massimo; Hu, Guangcheng] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Zhang, Jingxiao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Menenti, Massimo] Delft Univ Technol, Dept Geosci & Remote Sensing, Stevinweg 1, NL-2628 CN Delft, Netherlands Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Delft University of Technology Jia, L (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. zhangjx@radi.ac.cn; jiali@radi.ac.cn; m.menenti@radi.ac.cn; hugc@radi.ac.cn li, jia/GVT-7587-2022; Jingxiao, Zhang/AAA-5777-2020; Jia, li/GPW-8015-2022 hu, guang cheng/0000-0003-2940-8336; Jia, Li/0000-0002-3108-8645; Zhang, Jingxiao/0000-0002-4392-636X; Menenti, Massimo/0000-0001-9176-4556 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030203]; International Partnership Program of the Chinese Academy of Sciences [131C11KYSB20160061]; National Natural Science Foundation of China [91737205]; SAFEA Long-Term-Projects of the 1000 Talent Plan for High-Level Foreign Experts [WQ20141100224] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); International Partnership Program of the Chinese Academy of Sciences; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); SAFEA Long-Term-Projects of the 1000 Talent Plan for High-Level Foreign Experts This research was jointly supported by the project funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19030203), the International Partnership Program of the Chinese Academy of Sciences (Grant No. 131C11KYSB20160061), the National Natural Science Foundation of China (Grant No. 91737205), and the SAFEA Long-Term-Projects of the 1000 Talent Plan for High-Level Foreign Experts (Grant No. WQ20141100224). 100 31 32 6 31 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2 2019.0 11.0 4 452 10.3390/rs11040452 0.0 38 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology HO2SL Green Submitted, gold 2023-03-23 WOS:000460766100082 0
J Zhao, YN; Ji, PY; Chen, F; Ji, GL; Jha, SK Zhao, Yingnan; Ji, Peiyuan; Chen, Fei; Ji, Guanlan; Jha, Sunil Kumar Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction INTELLIGENT AUTOMATION AND SOFT COMPUTING English Article Wind speed prediction; gated recurrent unit; squeeze-and-excitation networks; variational mode decomposition; attention mechanism NEURAL-NETWORK; DECOMPOSITION; MACHINE; ERROR This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and- excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for short-term prediction on the actual wind power dataset prove that VCGA is superior to prior algorithms. [Zhao, Yingnan; Ji, Peiyuan; Chen, Fei; Ji, Guanlan] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Jha, Sunil Kumar] Univ Informat Technol & Management Rzeszow, IT Fundamentals & Educ Technol Applicat, PL-100031 Rzeszow, Voivodeship, Poland Nanjing University of Information Science & Technology; University of Information Technology & Management Rzeszow Zhao, YN (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China. zh_yingnan@126.com undergraduate training program for innovation and entrepreneurship of NUIST [XJDC202110300239] undergraduate training program for innovation and entrepreneurship of NUIST This paper is supported by the undergraduate training program for innovation and entrepreneurship of NUIST (XJDC202110300239). 42 0 0 8 8 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1079-8587 2326-005X INTELL AUTOM SOFT CO Intell. Autom. Soft Comput. 2022.0 34.0 2 1001 1016 10.32604/iasc.2022.027710 0.0 16 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 1W1AL hybrid 2023-03-23 WOS:000806512400008 0
J Monshizadeh, M; Khatri, V; Gamdou, M; Kantola, R; Yan, Z Monshizadeh, Mehrnoosh; Khatri, Vikramajeet; Gamdou, Marah; Kantola, Raimo; Yan, Zheng Improving Data Generalization With Variational Autoencoders for Network Traffic Anomaly Detection IEEE ACCESS English Article Feature extraction; Random forests; Classification algorithms; Telecommunication traffic; Anomaly detection; Measurement; Vegetation; Anomaly detection; data mining; feature selection; machine learning; security Deep generative models have increasingly become popular in different domains such as image processing, though, they hardly appear in the cybersecurity arena. While the main application of these models is dimensionality reduction, marginally they have been utilized for overcoming challenges such as data generalization and overfitting issues inherited from feature selection methods. To solve the mentioned challenges, we propose a combined architecture comprising a Conditional Variational AutoEncoder (CVAE) and a Random Forest (RF) classifier to automatically learn similarity among input features, provide data distribution in order to extract discriminative features from original features, and finally classify various types of attacks. CVAE introduces the labels of traffic packets into a latent space in order to better learn the changes of input samples and distinguish the data characteristics of each class. It avoids the confusion between classes while learning the whole data distribution. Compared with feature selection mechanisms such as Support Vector Machine Online (SVMo) by considering various evaluation metrics, the proposed architecture demonstrates considerable improvement in terms of performance. To verify the versatility of the proposed architecture, two publicly available datasets have been used in experiments. [Monshizadeh, Mehrnoosh] Nokia Bell Labs, F-91620 Nozay, France; [Monshizadeh, Mehrnoosh; Kantola, Raimo; Yan, Zheng] Aalto Univ, Dept Comnet, Espoo 02150, Finland; [Khatri, Vikramajeet] Nokia Bell Labs, Espoo 02610, Finland; [Gamdou, Marah] Paris Saclay Univ, Cent Supelec Engn Sch, F-91190 Gif Sur Yvette, France; [Yan, Zheng] Xidian Univ, State Key Lab Integrated Serv Network, Xian 710071, Peoples R China Nokia Corporation; Aalto University; Nokia Corporation; Nokia Finland; UDICE-French Research Universities; Universite Paris Saclay; Xidian University Monshizadeh, M (corresponding author), Nokia Bell Labs, F-91620 Nozay, France.;Monshizadeh, M (corresponding author), Aalto Univ, Dept Comnet, Espoo 02150, Finland. mehrnoosh.monshizadeh@nokia-bell-labs.com Kantola, Raimo A/G-2277-2013; Gamdou, Marah/AAO-9545-2021; zheng, yan/GQY-6668-2022; yang, zheng/HGC-7753-2022; Khatri, Vikramajeet/B-2212-2018 Gamdou, Marah/0000-0003-2893-9856; Khatri, Vikramajeet/0000-0002-3386-8952 30 3 4 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9.0 56893 56907 10.1109/ACCESS.2021.3072126 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RP7YI gold, Green Published 2023-03-23 WOS:000641940400001 0
J Bellizio, F; Cremer, JL; Sun, MY; Strbac, G Bellizio, Federica; Cremer, Jochen L.; Sun, Mingyang; Strbac, Goran A causality based feature selection approach for data-driven dynamic security assessment ELECTRIC POWER SYSTEMS RESEARCH English Article Decision trees; Feature selection; Markov blanket; Power systems operation; Dynamic security assessment SYSTEM; MANAGEMENT; ONLINE The integration of renewable energy sources increases the operational uncertainty of electric power systems and can lead to more frequent dynamic phenomena. The use of classifiers from machine learning is promising to include dynamics in the security assessment of the power system. The training of these classifiers is typically performed offline on synthetically generated operating conditions (OCs) that are similar to real-time operation. However, the uncertainty in the generated OCs and the classifier's inaccuracy is larger the longer the time between offline and real-time operation. Moving the classifier training closer to real-time operation is an important step forward to reduce inaccurate predictions and improve reliability. In this paper, a novel causality-based feature selection approach for an online dynamic security assessment (DSA) framework is proposed. The key novelty is to use the system's physics to learn the causal structure between the features and then select the features based on this causal structure. The proposed approach results in faster computations, is more robust and more interpretable. Moreover, classifiers can be trained closer to real-time operation which enhances the predictive performance. Through a case study using transient stability on the IEEE 68-bus system, the proposed method reduces computational time by 75% in comparison to state of the art feature selection techniques. The proposed workflow showed superior performance in accuracy and robustness against uncertainty compared to conventional machine learning approaches for DSA. The computational benefit was also projected to a dataset of the French transmission system where the approach has the potential to achieve computational savings of up-to two orders of magnitudes. [Bellizio, Federica; Strbac, Goran] Imperial Coll London, London SW7 2BU, England; [Cremer, Jochen L.] Delft Univ Technol, Mekelweg 5, NL-2628 CD Delft, Netherlands; [Sun, Mingyang] Zhejiang Univ, 38 Zheda Rd, Hangzhou, Zhejiang, Peoples R China Imperial College London; Delft University of Technology; Zhejiang University Sun, MY (corresponding author), Zhejiang Univ, 38 Zheda Rd, Hangzhou, Zhejiang, Peoples R China. f.bellizio18@imperial.ac.uk; j.l.cremer@tudelft.nl; mingyangsun@zju.edu.cn; g.strbac@imperial.ac.uk Bellizio, Federica/0000-0002-7351-1704; Cremer, Jochen Lorenz/0000-0001-9284-5083 National Natural Science Foundation of China [62103371]; Engineering and Physical SciencesResearch Council (UK) under the Integrated Development of LowCarbon Energy Systems programme [EP/R045518/1]; TU Delft AI Labs Programme National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Engineering and Physical SciencesResearch Council (UK) under the Integrated Development of LowCarbon Energy Systems programme; TU Delft AI Labs Programme This work is supported by the National Natural Science Foundation of China under grant 62103371, the Engineering and Physical SciencesResearch Council (UK) under the Integrated Development of LowCarbon Energy Systems programme (EP/R045518/1), and by the TU Delft AI Labs Programme (NL). We are thankful to colleagues from Reseau de Transport d'Electricite who provided expertise that greatly assisted the research. We also thank the reviewers for their insightful thoughts and discussion in the review process. 44 5 6 6 15 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7796 1873-2046 ELECTR POW SYST RES Electr. Power Syst. Res. DEC 2021.0 201.0 107537 10.1016/j.epsr.2021.107537 0.0 SEP 2021 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering WE5FR hybrid, Green Published 2023-03-23 WOS:000705651900001 0
J Tang, XZ; Li, JF; Liu, MN; Liu, W; Hong, HY Tang, Xianzhe; Li, Jiufeng; Liu, Minnan; Liu, Wei; Hong, Haoyuan Flood susceptibility assessment based on a novel random Naive Bayes method: A comparison between different factor discretization methods CATENA English Article Random Naive Bayes; Repeatedly random sampling method; Most accurate classifier; Factor discretization methods SUPPORT VECTOR MACHINES; FUZZY INFERENCE SYSTEM; WEIGHTS-OF-EVIDENCE; LANDSLIDE SUSCEPTIBILITY; SPATIAL PREDICTION; LOGISTIC-REGRESSION; STATISTICAL-MODELS; RANDOM SUBSPACE; RANDOM FORESTS; RISK Random Naive Bayes (RNB) is a machine learning method that uses the Random Forest (RF) structure to optimize Naive Bayes (NB). It is interesting to see whether RNB could optimize NB and achieve satisfied assessment results like RF in the flood susceptibility assessment study. RNB has rarely been used in study of using machine learning methods to spatially analyze natural disasters, and thus it was selected as the analysis method. Based on the data feasibility, 12 spatial factors that affect the occurrence and spatial distribution of floods were selected. To avoid the influence of subjective equal-interval classification method, natural breaks and quantile method were used to discretize factors with continuous values, respectively. Here, a recently proposed repeatedly random sampling method was adopted to select negative samples for RNB to generate a most accurate classifier (MAC) that was employed to compute the probability of flood occurrence in the study area. Consequently, this paper adopted the integrated framework of GIS and RNB to spatially assess the flood susceptibility using the Wanan County in China as an instance. The results demonstrated that when integrated with the repeatedly random sampling method, the MAC-based flood susceptibility maps corresponding to different factor discretization methods were similar, meaning this framework can effectively avoid the effects caused by different factor discretization methods. Also, to testify the classification performance of RNB, RF and NB were chosen to compare the classification performance with it. The results indicated the classification performance in the order of RF > RNB > NB. This means RNB is able to achieve better classification performance than NB, but it exists limitations when compared with traditional strong classifiers like RF. The findings of this paper proved that RNB is a feasible approach for natural hazard susceptibility assessment. [Tang, Xianzhe] Osaka Univ, Grad Sch Engn, Div Sustainable Energy & Environm Engn, 2-1 Younadaoka, Suita, Osaka 5650871, Japan; [Li, Jiufeng] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China; [Liu, Minnan] Zhongkai Univ Agr & Engn, Coll Hort & Landscape Architecture, Guangzhou 510225, Guangdong, Peoples R China; [Liu, Wei] South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China; [Hong, Haoyuan] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan] Univ Vienna, Dept Geog & Reg Res, A-1010 Vienna, Austria Osaka University; Nanjing University; Zhongkai University of Agriculture & Engineering; South China Normal University; Nanjing Normal University; University of Vienna Hong, HY (corresponding author), Univ Vienna, Dept Geog & Reg Res, A-1010 Vienna, Austria. hong_haoyuan@outlook.com Hong, Haoyuan/C-8455-2014 Hong, Haoyuan/0000-0001-6224-069X; Tang, Xianzhe/0000-0002-9779-3494; , Wei Liu/0000-0003-0324-1267 International Partnership Program of Chinese Academy of Sciences [115242KYSB20170022]; China Scholarship Council [201906860029] International Partnership Program of Chinese Academy of Sciences; China Scholarship Council(China Scholarship Council) This research was supported by the International Partnership Program of Chinese Academy of Sciences (Grant No. 115242KYSB20170022). The authors acknowledge the joint PhD scholarship awarded to Haoyuan Hong (NO. 201906860029) supported by the China Scholarship Council. We express our sincere thanks to Adrian L. Collins, Editor-in-Chief of the Catena and anonymous reviewers, as their valuable comments and suggestions greatly improved the quality of the paper. 72 46 47 7 76 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0341-8162 1872-6887 CATENA Catena JUL 2020.0 190.0 104536 10.1016/j.catena.2020.104536 0.0 14 Geosciences, Multidisciplinary; Soil Science; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Agriculture; Water Resources LC4VT 2023-03-23 WOS:000525324600020 0
J Liang, W; Jin, J; Daly, I; Sun, H; Wang, XY; Cichocki, A Liang, Wei; Jin, Jing; Daly, Ian; Sun, Hao; Wang, Xingyu; Cichocki, Andrzej Novel channel selection model based on graph convolutional network for motor imagery COGNITIVE NEURODYNAMICS English Article; Early Access Brain-computer interface(BCI); Motor imagery(MI); Graph convolutional neural network (GCN); Channel selection SINGLE-TRIAL EEG; FUNCTIONAL CONNECTIVITY Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly. [Liang, Wei; Jin, Jing; Sun, Hao; Wang, Xingyu] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China; [Jin, Jing] East China Univ Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China; [Daly, Ian] Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester, Essex, England; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol, Moscow 143026, Russia; [Cichocki, Andrzej] Polish Acad Sci, Syst Res Inst, Warsaw, Poland; [Cichocki, Andrzej] Nicolaus Copernicus Univ, Dept Informat, Torun, Poland East China University of Science & Technology; East China University of Technology; University of Essex; Skolkovo Institute of Science & Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Nicolaus Copernicus University Jin, J (corresponding author), East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.;Jin, J (corresponding author), East China Univ Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China. jinjingat@gmail.com Jin, Jing/0000-0002-6133-5491 National Natural Science Foundation of China [62176090]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX]; Program of Introducing Talents of Discipline to Universities through the 111 Project [B17017]; Shanghai Municipal Education Commission [19SG25]; Shanghai Education Development Foundation [19SG25]; Ministry of Education and Science of the Russian Federation [14.756.31.0001]; Polish National Science Center [UMO-2016/20/W/NZ4/00354] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Science and Technology Major Project; Program of Introducing Talents of Discipline to Universities through the 111 Project; Shanghai Municipal Education Commission(Shanghai Municipal Education Commission (SHMEC)); Shanghai Education Development Foundation; Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Polish National Science Center This work was supported by the Grant National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX, in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; in part by the ShuGuang Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation under Grant 19SG25; in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001, and in part by the Polish National Science Center under Grant UMO-2016/20/W/NZ4/00354. 50 0 0 22 22 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1871-4080 1871-4099 COGN NEURODYNAMICS Cogn. Neurodynamics 10.1007/s11571-022-09892-1 0.0 OCT 2022 14 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 5E5XE 2023-03-23 WOS:000865696500002 0
J Zhang, MY; Zhang, D; Fu, SS; Kujala, P; Hirdaris, S Zhang, Mingyang; Zhang, Di; Fu, Shanshan; Kujala, Pentti; Hirdaris, Spyros A predictive analytics method for maritime traffic flow complexity estimation in inland waterways RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Traffic safety; Maritime operations; Inland waterways; Artificial intelligence; Lempel-Ziv algorithm; TOPSIS MISS SHIP COLLISIONS; DECISION-MAKING; RISK-ASSESSMENT; MODEL; NETWORK; INFRASTRUCTURE; PREDICTABILITY; CONGESTION; SIMULATION; NAVIGATION Maritime traffic flow complexity is the factor that presents in most existing maritime safety analysis methods. It is considered as one of the main influencing factors affecting maritime safety. It can be estimated quantitatively through the analysis of traffic data. To explore maritime ship traffic in more detail, a predictive analytics method utilizing the Lempel-Ziv algorithm and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for traffic safety management is proposed. The Lempel-Ziv algorithm quantifies the entropy for the evaluation of the irregularity and unpredictability of ship travel time sequences, and TOPSIS ranks complexity. The results presented utilize Automatic Identification System (AIS) data corresponding to complex inland waterway traffic scenarios encountered in the Yangtze River. They show that high complexity implies ship travel time sequences are neither periodic nor stochastic, but dependent on the evolution patterns of traffic encounters. A correlation analysis of traffic flow complexity with the number of maritime accidents is carried out, showing that higher traffic flow complexity may result in more unwanted events. It is therefore concluded that the proposed method may help to (1) differentiate traffic flow complexity accurately, indicating that the higher the complexity value is, the higher the irregularity and unpredictability of maritime traffic flow are, (2) provide helpful reference for optimizing traffic management over the life cycle of fleet operations as well as maritime safety management of high traffic flow complexity areas. [Zhang, Mingyang; Kujala, Pentti; Hirdaris, Spyros] Aalto Univ, Sch Engn, Dept Mech Engn, Maritime Technol, Espoo, Finland; [Zhang, Di] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [Zhang, Di; Hirdaris, Spyros] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan, Peoples R China; [Zhang, Di] Inland Port & Shipping Ind Res Co Ltd Guangdong P, Guangzhou, Peoples R China; [Fu, Shanshan] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China Aalto University; Wuhan University of Technology; National Engineering Research Center for Water Transport Safety; Wuhan University of Technology; Shanghai Maritime University Zhang, D (corresponding author), Wuhan Univ Technol, 1178 Heping Ave, Wuhan 430063, Hubei, Peoples R China. mingyang.0.zhang@aalto.fi; zhangdi@whut.edu.cn; ssfu@shmtu.edu.cn; pentti.kujala@aalto.fi; spyros.hirdaris@aalto.fi Kujala, Pentti JS/B-3780-2011; Vukša, Srđan/T-4521-2017; Hirdaris, Spyros/B-7333-2009; Zhang, Mingyang/AEC-5093-2022 Vukša, Srđan/0000-0003-3185-3308; Hirdaris, Spyros/0000-0002-4700-6325; Zhang, Mingyang/0000-0001-5820-2789; Fu, Shanshan/0000-0003-2495-0572 National Key Technologies Research & Development Program [2017YFC0804900, 2017YFC08 04904, 2017YFE0118000]; Hubei Provincial Natural Science Foundation of China [2019CFA039, 2021EH007]; Marie Sklodowska-Curie grant [730888]; Aalto University National Key Technologies Research & Development Program(National Key Technology R&D Program); Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Marie Sklodowska-Curie grant; Aalto University This research is supported by the National Key Technologies Research & Development Program (2017YFC0804900, 2017YFC08 04904, 2017YFE0118000), the Hubei Provincial Natural Science Foundation of China (2019CFA039,2021EH007), the Marie Sklodowska-Curie grant agreement No. 730888 (RESET) and Aalto University where the first author reads for his Ph.D. degree in Maritime Technology and big data analytics. 81 36 36 37 72 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. APR 2022.0 220.0 108317 10.1016/j.ress.2021.108317 0.0 JAN 2022 18 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science ZG6CC 2023-03-23 WOS:000760343700032 0
J Zhang, K; Li, YW; Zuo, WM; Zhang, L; Van Gool, L; Timofte, R Zhang, Kai; Li, Yawei; Zuo, Wangmeng; Zhang, Lei; Van Gool, Luc; Timofte, Radu Plug-and-Play Image Restoration With Deep Denoiser Prior IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Image restoration; Task analysis; Noise reduction; Noise level; Learning systems; Training; Optimization; Denoiser prior; image restoration; convolutional neural network; half quadratic splitting; plug-and-play RANDOM-FIELD; SPARSE Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR. [Zhang, Kai; Li, Yawei; Van Gool, Luc; Timofte, Radu] Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland; [Zuo, Wangmeng] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China; [Zhang, Lei] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Van Gool, Luc] Katholieke Univ Leuven, B-3000 Leuven, Belgium Swiss Federal Institutes of Technology Domain; ETH Zurich; Harbin Institute of Technology; Hong Kong Polytechnic University; KU Leuven Timofte, R (corresponding author), Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland. kai.zhang@vision.ee.ethz.ch; yawei.li@vision.ee.ethz.ch; cswmzuo@gmail.com; cslzhang@comp.polyu.edu.hk; vangool@vision.ee.ethz.ch; radu.timofte@vision.ee.ethz.ch Timofte, Radu/H-4438-2011; Timofte, Radu/AAK-6022-2021; Zhang, Kai/GPK-2574-2022; Zhang, Kai/ABF-3950-2020 Timofte, Radu/0000-0002-1478-0402; Zhang, Kai/0000-0002-6319-3722; Li, Yawei/0000-0002-8948-7892 ETH Zurich Fund OK; Huawei; Amazon AWS; NVIDIA; National Natural Science Foundation of China [U19A2073] ETH Zurich Fund OK; Huawei(Huawei Technologies); Amazon AWS; NVIDIA; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the ETH Zurich Fund OK, in part by Huawei, in part by Amazon AWS, in part by NVIDIA, and in part by the National Natural Science Foundation of China under Grant U19A2073. 95 75 75 82 109 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. OCT 1 2022.0 44.0 10 6360 6376 10.1109/TPAMI.2021.3088914 0.0 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4N2UL 34125670.0 Green Accepted, Green Submitted 2023-03-23 WOS:000853875300038 0
J Ren, JL; Liu, YC; Shi, XQ; Shan, GC; Tang, MM; Kaun, CC; Dou, KP Ren, Jinlong; Liu, Yingchao; Shi, Xingqiang; Shan, Guangcun; Tang, Mingming; Kaun, Chaocheng; Dou, Kunpeng Flexoelectricity Driven Fano Resonance in Slotted Carbon Nanotubes for Decoupled Multifunctional Sensing RESEARCH English Article GRAPHENE; ELECTRON; THERMOELECTRICITY; TRANSPORT Multifunctionality, interference-free signal readout, and quantum effect are important considerations for flexible sensors equipped within a single unit towards further miniaturization. To address these criteria, we present the slotted carbon nanotube (CNT) junction features tunable Fano resonance driven by flexoelectricity, which could serve as an ideal multimodal sensory receptor. Based on extensive ab initio calculations, we find that the effective Fano factor can be used as a temperature-insensitive extrinsic variable for sensing the bending strain, and the Seebeck coefficient can be used as a strain-insensitive intrinsic variable for detecting temperature. Thus, this dual-parameter permits simultaneous sensing of temperature and strain without signal interference. We further demonstrate the applicability of this slotted junction to ultrasensitive chemical sensing which enables precise determination of donor-type, acceptor-type, and inert molecules. This is due to the enhancement or counterbalance between flexoelectric and chemical gating. Flexoelectric gating would preserve the electron-hole symmetry of the slotted junction whereas chemical gating would break it. As a proof-of-concept demonstration, the slotted CNT junction provides an excellent quantum platform for the development of multistimuli sensation in artificial intelligence at the molecular scale. [Ren, Jinlong; Liu, Yingchao; Dou, Kunpeng] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China; [Shi, Xingqiang] Hebei Univ, Coll Phys Sci & Technol, Inst Life Sci & Green Dev, Key Lab Opt Elect Informat & Mat Hebei Prov, Baoding 071002, Peoples R China; [Shan, Guangcun] Beihang Univ, Inst Precis Instrument & Quantum Sensing, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China; [Shan, Guangcun] Saarland Univ, Inst Expt Phys, D-66123 Saarbrucken, Germany; [Tang, Mingming] China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China; [Kaun, Chaocheng] Acad Sinica, Res Ctr Appl Sci, Taipei 11529, Peoples R China Ocean University of China; Hebei University; Beihang University; Saarland University; China University of Petroleum; Chinese Academy of Sciences Dou, KP (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China.;Shi, XQ (corresponding author), Hebei Univ, Coll Phys Sci & Technol, Inst Life Sci & Green Dev, Key Lab Opt Elect Informat & Mat Hebei Prov, Baoding 071002, Peoples R China.;Shan, GC (corresponding author), Beihang Univ, Inst Precis Instrument & Quantum Sensing, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China.;Shan, GC (corresponding author), Saarland Univ, Inst Expt Phys, D-66123 Saarbrucken, Germany. shixq20hbu@hbu.edu.cn; gcshan@buaa.edu.cn; doukunpeng@ouc.edu.cn Kaun, Chao-Cheng/E-1311-2011; Kaun, Chao-Cheng/AAT-9116-2021; SHI, Xingqiang/D-8625-2011 Kaun, Chao-Cheng/0000-0002-5400-4758; Kaun, Chao-Cheng/0000-0002-5400-4758; SHI, Xingqiang/0000-0003-2029-1506 Natural Science Foundation of China [41877499, 21771017, 42072163]; Natural Science Foundation of Shandong Province [ZR2019BA003, ZR201807100384]; Ocean University of China [3002000-861901206002]; Natural Science Foundation of Hebei Province of China [A2021201001]; Advanced Talents Incubation Program of the Hebei University [521000981390] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Ocean University of China; Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Advanced Talents Incubation Program of the Hebei University General: K. Dou thanks C. J. Lambert for insightful discussions and thank C. h. Chang for the help of model analysis. This work was supported by the Natural Science Foundation of China (Grant nos. 41877499, 21771017, and 42072163) , the Natural Science Foundation of Shandong Province (Grant nos. ZR2019BA003 and ZR201807100384) , the Ocean University of China (Grant no. 3002000-861901206002) . X. Shi acknowledges the Natural Science Foundation of Hebei Province of China (no. A2021201001) and the Advanced Talents Incubation Program of the Hebei University (521000981390) . 38 0 0 7 28 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 2639-5274 RESEARCH-CHINA Research DEC 29 2021.0 2021.0 9821905 10.34133/2021/9821905 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics YA3ZF 35047827.0 gold 2023-03-23 WOS:000738274700001 0
J Xu, Y; Yao, JG; Jacobsen, HA; Guan, HB Xu, Yu; Yao, Jianguo; Jacobsen, Hans-Arno; Guan, Haibing Enabling Cloud Applications to Negotiate Multiple Resources in a Cost-Efficient Manner IEEE TRANSACTIONS ON SERVICES COMPUTING English Article Cloud computing; Pricing; Optimization; Resource management; Yarn; Learning (artificial intelligence); Jacobian matrices; Cloud scheduler; multi-resource; cost efficiency; reinforcement learning FRAMEWORK; MODELS Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously. [Xu, Yu; Yao, Jianguo; Guan, Haibing] Shanghai Jiao Tong Univ, Shanghai Key Lab Scalable Comp & Syst, Shanghai 200240, Peoples R China; [Jacobsen, Hans-Arno] Tech Univ Munich, Middleware Syst Res Grp, D-85748 Munich, Germany Shanghai Jiao Tong University; Technical University of Munich Yao, JG (corresponding author), Shanghai Jiao Tong Univ, Shanghai Key Lab Scalable Comp & Syst, Shanghai 200240, Peoples R China. vatiminxuyu@sjtu.edu.cn; jianguo.yao@sjtu.edu.cn; jacobsen@in.tum.de; hbguan@sjtu.edu.cn guan, haibing/G-8142-2011 guan, haibing/0000-0002-4714-7400 Program for NSFC [61772339, 61525204]; STCSM project [16QA1402200]; Alexander von Humboldt Fellowship Program for NSFC(National Natural Science Foundation of China (NSFC)); STCSM project(Science & Technology Commission of Shanghai Municipality (STCSM)); Alexander von Humboldt Fellowship(Alexander von Humboldt Foundation) This work was supported in part by the Program for NSFC (No.61772339, 61525204), the STCSM project (No.16QA1402200), and Alexander von Humboldt Fellowship. The corresponding author is Prof. Yao, Jianguo. 42 0 0 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1939-1374 IEEE T SERV COMPUT IEEE Trans. Serv. Comput. MAR-APR 2021.0 14.0 2 413 425 10.1109/TSC.2018.2814045 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science RK6JI hybrid 2023-03-23 WOS:000638399700002 0
C Yu, B; Zhang, BY; An, PC; Xu, LS; Xue, MR; Hu, J IEEE Yu, Bin; Zhang, Biyong; An, Pengcheng; Xu, Lisheng; Xue, Mengru; Hu, Jun An Unobtrusive Stress Recognition System for the Smart Office 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) JUL 23-27, 2019 Berlin, GERMANY HEART-RATE-VARIABILITY This paper presents a novel approach to monitor office workers' behavioral patterns and heart rate variability. We integrated an EMFi sensor into a chair to measure the pressure changes caused by a user's body movements and heartbeat. Then, we employed machine learning methods to develop a classification model through which different work behaviors (body moving, typing, talking and browsing) could be recognized from the sensor data. Subsequently, we developed a BCG processing method to process the data recognized as 'browsing' and further calculate heart rate variability. The results show that the developed model achieved classification accuracies of up to 91% and the HRV could be calculated effectively with an average error of 5.77ms, By combining these behavioral and physiological measures, the proposed approach portrays work-related stress in a more comprehensive manner and could contribute an unobtrusive early stress detection system for future smart offices. [Yu, Bin; Zhang, Biyong; An, Pengcheng; Xue, Mengru; Hu, Jun] Eindhoven Univ Technol, Ind Design, TU E, NL-5612 AZ Eindhoven, Netherlands; [Xu, Lisheng] Northeastern Univ, Sino Dutch Biomed & Informat Engn, Shenyang 110169, Peoples R China Eindhoven University of Technology; Northeastern University - China Yu, B (corresponding author), Eindhoven Univ Technol, Ind Design, TU E, NL-5612 AZ Eindhoven, Netherlands. b.yu@tue.nl Xu, Lisheng/AAX-7181-2020; An, Pengcheng/AAA-3510-2020 Xu, Lisheng/0000-0001-8360-3605; An, Pengcheng/0000-0002-7705-2031; Hu, Jun/0000-0003-2714-6264 Netherlands Organization for Scientific Research (Nwo), Creative Industries -Knowledge Innovation Mapping [KI. 18.001] Netherlands Organization for Scientific Research (Nwo), Creative Industries -Knowledge Innovation Mapping(Netherlands Organization for Scientific Research (NWO)) Research supported by Netherlands Organization for Scientific Research (Nwo), Creative Industries -Knowledge Innovation Mapping (KI. 18.001). 17 3 3 2 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-5386-1311-5 IEEE ENG MED BIO 2019.0 1326 1329 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BP5OL 31946137.0 2023-03-23 WOS:000557295301175 0
J Elhag, M; Bahrawi, J; Boteva, S Elhag, Mohamed; Bahrawi, Jarbou; Boteva, Silvena Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments OPEN GEOSCIENCES English Article daily evapotranspiration; desirability function; neural network analysis; SEBS ARTIFICIAL NEURAL-NETWORKS; REGIONAL EVAPOTRANSPIRATION; ENERGY-BALANCE; WATER-QUALITY; SURFACE; MODEL; OPTIMIZATION; PREDICTION; VEGETATION; ALGORITHM The reliable quantification of daily evapotranspiration (ET) over vast croplands is a quest in many scholarly works aimed at the precise practice of water resources management. Remote sensing-based empirical and nonempirical models were developed to overcome large-scale quantification issues, which are usually experienced when using conventional approaches for the estimation of ET. The surface energy balance system (SEBS) model was used to quantify the daily ET in the arid/semiarid over Wadi Ad-Dwaser, Saudi. SEBS input variables are parametrically sensitive and climatic dependent, and the model input/output dependencies are of high comprehensibility; therefore, the optimization analysis of SEBS input/ output parameters is the target of the current research. SEBS inputs reciprocal inconsistencies were determined using the artificial neural network analysis, while the output dependencies on the daily ET estimation were mapped. Results demonstrated that the temperature and relative humidity are the most sensitive parameters to be considered in the routine crop monitoring procedure. SEBS output thematic maps showed the robust proportional correlation between the daily ET and the conducted temperature map. Moreover, the estimated daily ET was inversely correlated with the estimated cold sensible heat fluxes. The findings suggest systematic monitoring and forecasting procedures for efficient water-saving management plans in Saudi Arabia. [Elhag, Mohamed; Bahrawi, Jarbou] King Abdulaziz Univ, Fac Meteorol Environm & Arid Land Agr, Dept Hydrol & Water Resources Management, Jeddah 21589, Saudi Arabia; [Elhag, Mohamed] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100094, Peoples R China; [Elhag, Mohamed] German Univ Technol Oman, Fac Sci, Dept Appl Geosci, Muscat 1816, Oman; [Boteva, Silvena] Sofia Univ St Kl Ohridski, Dept Ecol & Nat Conservat, Fac Biol, 8 Dragan Tsankov Blvd, Sofia 1164, Bulgaria King Abdulaziz University; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; University of Sofia Elhag, M (corresponding author), King Abdulaziz Univ, Fac Meteorol Environm & Arid Land Agr, Dept Hydrol & Water Resources Management, Jeddah 21589, Saudi Arabia.;Elhag, M (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100094, Peoples R China.;Elhag, M (corresponding author), German Univ Technol Oman, Fac Sci, Dept Appl Geosci, Muscat 1816, Oman. melhag@kau.edu.sa Elhag, Mohamed/L-6386-2019; Elhag, Mohamed/J-4419-2013 Elhag, Mohamed/0000-0001-9048-0084; Elhag, Mohamed/0000-0001-9048-0084; Boteva, Silvena/0000-0002-3928-7489 Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah [G-65-155-1440] Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (G-65-155-1440). The authors, therefore, acknowledge with thanks, DSR technical and financial support. 66 3 3 1 6 DE GRUYTER POLAND SP Z O O WARSAW BOGUMILA ZUGA 32A STR, 01-811 WARSAW, MAZOVIA, POLAND 2391-5447 OPEN GEOSCI Open Geosci. MAR 17 2021.0 13.0 1 321 334 10.1515/geo-2020-0141 0.0 14 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology RL5PD gold 2023-03-23 WOS:000639024000001 0
J Wen, B; Li, GL; Wright, JC; Du, CQ; Feng, Q; Xu, X; Choudhary, JS; Wang, J Wen, Bo; Li, Guilin; Wright, James C.; Du, Chaoqin; Feng, Qiang; Xu, Xun; Choudhary, Jyoti S.; Wang, Jun The OMSSAPercolator: An automated tool to validate OMSSA results PROTEOMICS English Article Bioinformatics; MS; Peptide identification; Shotgun proteomics SENSITIVE PEPTIDE IDENTIFICATION; MASS-SPECTROMETRY; STATISTICAL-MODEL; MASCOT PERCOLATOR; SEARCH; ACCURATE; DATABASE; MS/MS Protein identification by MS/MS is an important technique in proteome studies. The Open Mass Spectrometry Search Algorithm (OMSSA) is an open-source search engine that can be used to identify MS/MS spectra acquired in these experiments. Here, we present a software tool, termed OMSSAPercolator, which interfaces OMSSA with Percolator, a post-search machine learning method for rescoring database search results. We demonstrate that it outperforms the standard OMSSA scoring scheme, and provides reliable significant measurements. OMSSAPercolator is programmed using JAVA and can be readily used as a standalone tool or integrated into existing data analysis pipelines. OMSSAPercolator is freely available and can be downloaded at . [Wen, Bo; Li, Guilin; Du, Chaoqin; Feng, Qiang; Xu, Xun; Wang, Jun] BGI Shenzhen, Shenzhen 518083, Peoples R China; [Wright, James C.; Choudhary, Jyoti S.] Wellcome Trust Sanger Inst, Cambridge, England; [Feng, Qiang; Wang, Jun] Univ Copenhagen, Dept Biol, Copenhagen, Denmark Beijing Genomics Institute (BGI); Wellcome Trust Sanger Institute; University of Copenhagen Wang, J (corresponding author), BGI Shenzhen, Shenzhen 518083, Peoples R China. wangj@genomics.cn Wright, James/HGB-7234-2022; Xu, Xun/AAI-6763-2020; Wen, Bo/I-1259-2019; Choudhary, Jyoti/S-1232-2017; Wang, Jun/C-8434-2016; Wang, Jun/B-9503-2016 Wright, James/0000-0001-6950-4328; Xu, Xun/0000-0002-5338-5173; Wen, Bo/0000-0003-2261-3150; Choudhary, Jyoti/0000-0003-0881-5477; Wang, Jun/0000-0002-8540-8931; Wang, Jun/0000-0002-2113-5874 National High Technology Research and Development Program of China 863 Program [2012AA020202]; ShenZhen Engineering Laboratory for Proteomics National High Technology Research and Development Program of China 863 Program(National High Technology Research and Development Program of China); ShenZhen Engineering Laboratory for Proteomics This work was supported by the National High Technology Research and Development Program of China 863 Program (NO. 2012AA020202) and ShenZhen Engineering Laboratory for Proteomics. 13 14 14 0 13 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1615-9853 1615-9861 PROTEOMICS Proteomics MAY 2014.0 14.0 9 1011 1014 10.1002/pmic.201300393 0.0 4 Biochemical Research Methods; Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology AE8ZC 24504981.0 2023-03-23 WOS:000334290600008 0
J Li, BY; Jang, I; Riphagen, J; Almaktoum, R; Yochim, KM; Ances, BM; Bookheimer, SY; Salat, DH Li, Binyin; Jang, Ikbeom; Riphagen, Joost; Almaktoum, Randa; Yochim, Kathryn Morrison; Ances, Beau M.; Bookheimer, Susan Y.; Salat, David H. Alzheimer's Dis Neuroimaging Initi Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort HUMAN BRAIN MAPPING English Article aging; Alzheimer's disease; classifier; machine learning SURFACE-BASED ANALYSIS; CORTICAL THICKNESS; AGE; BETA; SEGMENTATION; DIAGNOSIS; DEMENTIA; TAU; PET Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical at-risk individuals has unique challenges. We examined whether age-correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross-sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP-A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP-A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD-like from the HCP-A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross-validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD-specific biomarkers and worse cognition. In an independent HCP-A cohort, 8.8% were identified as AD-like, and they trended toward worse cognition. An AD risk score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder. [Li, Binyin] Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Neurol, Sch Med, Shanghai 200025, Peoples R China; [Li, Binyin] Shanghai Jiao Tong Univ, Ruijin Hosp, Inst Neurol, Sch Med, Shanghai 200025, Peoples R China; [Li, Binyin; Jang, Ikbeom; Riphagen, Joost; Almaktoum, Randa; Yochim, Kathryn Morrison; Salat, David H.] Harvard Med Sch, Massachusetts Gen Hosp, HMS Athinoula A Martinos Ctr Biomed Imaging, MGH,MIT, Charlestown, MA USA; [Li, Binyin; Salat, David H.] Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02115 USA; [Riphagen, Joost] Maastricht Univ, Alzheimer Ctr Limburg, Sch Mental Hlth & Neurosci, Fac Hlth Med & Life Sci, Maastricht, Netherlands; [Ances, Beau M.] Washington Univ, Dept Psychol & Brain Sci, St Louis, MO USA; [Bookheimer, Susan Y.] Univ Calif Los Angeles, David Geffen Sch Med, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90095 USA; [Salat, David H.] VA Boston Healthcare Syst, Neuroimaging Res Vet Ctr, Boston, MA USA Shanghai Jiao Tong University; Shanghai Jiao Tong University; Harvard University; Massachusetts General Hospital; Massachusetts Institute of Technology (MIT); Harvard University; Harvard Medical School; Massachusetts General Hospital; Maastricht University; Washington University (WUSTL); University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; US Department of Veterans Affairs; Veterans Health Administration (VHA); Harvard University; VA Boston Healthcare System Li, BY (corresponding author), Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Neurol, Sch Med, Shanghai 200025, Peoples R China.;Li, BY (corresponding author), Shanghai Jiao Tong Univ, Ruijin Hosp, Inst Neurol, Sch Med, Shanghai 200025, Peoples R China. libinyin@126.com Jang, Ikbeom/AFN-1062-2022 Jang, Ikbeom/0000-0002-6901-983X; Li, Binyin/0000-0003-1953-382X Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]; DOD ADNI (Department of Defense) [W81XWH-12-2-0012]; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd; Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; Transition Therapeutics; Canadian Institutes of Health Research; NIH; NIH Blueprint for Neuroscience Research; McDonnell Center for Systems Neuroscience at Washington University; Office of the Provost at Washington University; University of Minnesota Medical School; Shanghai Rising-Star Program [21QA1405800]; [U01AG052564]; [U01AG052564-S1] Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); DOD ADNI (Department of Defense); National Institute on Aging(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); National Institute of Biomedical Imaging and Bioengineering(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Biomedical Imaging & Bioengineering (NIBIB)); AbbVie(AbbVie); Alzheimer's Association(Alzheimer's Association); Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen(Biogen); Bristol-Myers Squibb Company(Bristol-Myers Squibb); CereSpir, Inc.; Cogstate(CogState Limited); Eisai Inc.(Eisai Co Ltd); Elan Pharmaceuticals, Inc.; Eli Lilly and Company(Eli Lilly); EuroImmun; F. Hoffmann-La Roche Ltd(Hoffmann-La Roche); Genentech, Inc.(Roche HoldingGenentech); Fujirebio; GE Healthcare(General ElectricGE Healthcare); IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC(Johnson & JohnsonJohnson & Johnson USA); Lumosity; Lundbeck(Lundbeck Corporation); Merck Co., Inc.(Merck & Company); Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation(Novartis); Pfizer Inc.(Pfizer); Piramal Imaging; Servier(Servier); Takeda Pharmaceutical Company(Takeda Pharmaceutical Company Ltd); Transition Therapeutics; Canadian Institutes of Health Research(Canadian Institutes of Health Research (CIHR)); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NIH Blueprint for Neuroscience Research(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); McDonnell Center for Systems Neuroscience at Washington University; Office of the Provost at Washington University; University of Minnesota Medical School; Shanghai Rising-Star Program; ; We are grateful to all research participants and would like to thank our anonymous reviewers for their thoughtful comments on the manuscript. Data collection and sharing for ADNI in the study was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The HCP-A dataset reported in this study was supported by grants U01AG052564 and U01AG052564-S1 and by the 14 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, by the McDonnell Center for Systems Neuroscience at Washington University, by the Office of the Provost at Washington University, and by the University of Minnesota Medical School. This study was also supported by grants from Shanghai Rising-Star Program (21QA1405800). 46 4 4 1 6 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. DEC 1 2021.0 42.0 17 5535 5546 10.1002/hbm.25626 0.0 SEP 2021 12 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging WP8QZ 34582057.0 Green Published, Green Accepted 2023-03-23 WOS:000700853300001 0
J Berkhout, J; Heidergott, B; Lam, H; Peng, YJ Berkhout, Joost; Heidergott, Bernd; Lam, Henry; Peng, Yijie From Data to Stochastic Modeling and Decision Making: What Can We Do Better? ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH English Article Data analytics; Taylor series expansion; sensitivity analysis In the past decades we have witnessed a paradigm-shift from scarcity of data to abundance of data. Big data and data analytics have fundamentally reshaped many areas including operations research. In this paper, we discuss how to integrate data with the model-based analysis in a controlled way. Specifically, we consider techniques to quantify input uncertainty and the decision making under input uncertainty. Numerical experiments demonstrate that different ways in decision making may lead to significantly different outcomes in a maintenance problem. [Berkhout, Joost] Vrije Univ Amsterdam, Dept Math, NL-1081 HV Amsterdam, Netherlands; [Heidergott, Bernd] Vrije Univ Amsterdam, Dept Econometr & Operat Res, NL-1081 HV Amsterdam, Netherlands; [Lam, Henry] Columbia Univ, Dept Ind Engn & Operat Res, New York, NY 10027 USA; [Peng, Yijie] Peking Univ, Coll Engn, Dept Ind Engn & Management, Beijing 100871, Peoples R China Vrije Universiteit Amsterdam; Vrije Universiteit Amsterdam; Columbia University; Peking University Heidergott, B (corresponding author), Vrije Univ Amsterdam, Dept Econometr & Operat Res, NL-1081 HV Amsterdam, Netherlands. j2.berkhout@vu.nl; b.f.heidergott@vu.nl; henry.lam@columbia.edu; pengyijie@pku.edu.cn Heidergott, Bernd/0000-0002-3389-2311; Berkhout, Joost/0000-0001-5883-9683 National Natural Science Foundation of China (NSFC) [91846301, 71790615, 71571048, 71720107003, 71690232]; National Science Foundation (NSF) [CMMI-1542020, CAREER CMMI-1653339] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Science Foundation (NSF)(National Science Foundation (NSF)) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 91846301, 71790615, 71571048, 71720107003, and 71690232, by the National Science Foundation (NSF) under Grants CMMI-1542020 and CAREER CMMI-1653339. 21 0 0 0 15 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0217-5959 1793-7019 ASIA PAC J OPER RES Asia Pac. J. Oper. Res. DEC 2019.0 36.0 6 SI 1940012 10.1142/S0217595919400128 0.0 20 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science JV3LY Green Submitted 2023-03-23 WOS:000502269000005 0
J Wen, ZH; Wang, W; Khelif, A; Djafari-Rouhani, B; Jin, YB Wen, Zhihui; Wang, Wan; Khelif, Abdelkrim; Djafari-Rouhani, Bahram; Jin, Yabin A perspective on elastic metastructures for energy harvesting APPLIED PHYSICS LETTERS English Article Rapid advances have been made in the properties of metastructures, including phononic crystals, metamaterials, and metasurfaces, with respect to their robustness, high power density, and broadband behavior. This presents unique advantages for energy harvesting devices to meet the demands for technology upgrades in various applied science and engineering applications such as wireless sensors, MEMS, and wearable devices. We provide a systematic overview of the mechanisms underlying the above properties, considering different configurations of wave localization in elastic metastructures for energy harvesting purposes. The energy localization can result from cavity modes, topologically protected edge/cavity modes, chiral edge mode, focusing effects, among others. Finally, we give the prospects of energy harvesting metastructures such as the introduction of nonlinear design, time-modulation, machine learning inverse design, and nonlocal effects. Published under an exclusive license by AIP Publishing. [Wen, Zhihui; Wang, Wan; Jin, Yabin] Tongji Univ, Sch Aerosp Engn & Appl Mech, Shanghai 200092, Peoples R China; [Khelif, Abdelkrim] Univ Bourgogne Franche Comte, CNRS, Inst FEMTO ST, 15B Ave Montboucons, F-25030 Besancon, France; [Djafari-Rouhani, Bahram] Univ Lille, UMR CNRS 8520, Inst Elect Microelect & Nanotechnol, Dept Phys, F-59650 Villeneuve Dascq, France Tongji University; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Universite de Lille - ISITE; Centrale Lille; Universite de Lille; Universite Polytechnique Hauts-de-France Jin, YB (corresponding author), Tongji Univ, Sch Aerosp Engn & Appl Mech, Shanghai 200092, Peoples R China. 083623jinyabin@tongji.edu.cn Jin, Yabin/A-9555-2012 Jin, Yabin/0000-0002-6991-8827; Khelif, Abdelkrim/0000-0001-8712-824X; Wen, Zhihui/0000-0002-1252-1327 118 10 12 20 21 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0003-6951 1077-3118 APPL PHYS LETT Appl. Phys. Lett. JAN 10 2022.0 120.0 2 20501 10.1063/5.0078740 0.0 12 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics 0Z9FP 2023-03-23 WOS:000791375600001 0
J Sun, GL; Boateng, OROOWS; Huang, H; Jiang, W Sun, Guolin; Boateng, Gordon Owusu; Huang, Hu; Jiang, Wei A Reinforcement Learning Framework for Autonomous Cell Activation and Customized Energy-Efficient Resource Allocation in C-RANs KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS English Article reinforcement learning; autonomous cell activation; resource allocation; cloud radio access network USER ASSOCIATION; DOWNLINK Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes. [Sun, Guolin; Boateng, Gordon Owusu; Huang, Hu] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China; [Jiang, Wei] German Res Ctr Artificial Intelligence DFKI GmbH, Kaiserslautern, Germany; [Jiang, Wei] Tech Univ TU Kaiserslautern, Dept Elect & Informat Technol EIT, Kaiserslautern, Germany University of Electronic Science & Technology of China; German Research Center for Artificial Intelligence (DFKI) Sun, GL (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China. guolin.sun@uestc.edu.cn Jiang, Wei/ADI-7167-2022; Boateng, Gordon Owusu/U-2969-2019 Boateng, Gordon Owusu/0000-0002-7923-367X National Natural Science Research Foundation of China [61771098]; Science and Technology Planning project of Sichuan Province, China [2016GZ0075]; Fundamental Research Funds for the Central Universities [ZYGX2014J060]; ZTE Innovation Research Fund for Universities Program National Natural Science Research Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Planning project of Sichuan Province, China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); ZTE Innovation Research Fund for Universities Program This work is supported by National Natural Science Research Foundation of China, Grant, no. 61771098, by the Science and Technology Planning project of Sichuan Province, China, under grant, no. 2016GZ0075, by the Fundamental Research Funds for the Central Universities under grant, no. ZYGX2014J060, and the ZTE Innovation Research Fund for Universities Program 2016. 29 1 1 0 15 KSII-KOR SOC INTERNET INFORMATION GANGNAM-GU KOR SCI & TECHNOL CTR, 409 ON 4TH FLR, MAIN BLDG, 635-4 YEOKSAM 1-DONG, GANGNAM-GU, SEOUL 00000, SOUTH KOREA 1976-7277 KSII T INTERNET INF KSII Trans. Internet Inf. Syst. AUG 31 2019.0 13.0 8 3821 3841 10.3837/tiis.2019.08.001 0.0 21 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications IV1ZD gold 2023-03-23 WOS:000484076400001 0
J Wei, WZ; Shi, FK; Kolb, JF Wei, Wenzuo; Shi, Fukun; Kolb, Juergen F. Analysis of microstructural parameters of trabecular bone based on electrical impedance spectroscopy and deep neural networks BIOELECTROCHEMISTRY English Article Electrical impedance spectroscopy; Permittivity; Deep neural network; Microstructural parameters; Osseous tissue DIELECTRIC-PROPERTIES; INTERRELATIONSHIPS; PREDICTION; TISSUES The potential of electrical impedance spectroscopy (EIS) was demonstrated for the investigation of microstruc-tural properties of osseous tissue. Therefore, a deep neural network (DNN) was implemented for a sensitive assessment of different structural features that were derived on the basis of dielectric parameters, especially relative permittivities, recorded over a frequency range from 40 Hz to 5 MHz. The advantages of the developed method over conventional approaches, including equivalent circuit models (ECMs), linear regression and effective medium approximation (EMA), is the comprehensive quantification of bone morphologies by several microstructural parameters simultaneously, such as bone volume fraction (BV/TV), bone surface-volume-ratio (BS/BV), structure model index (SMI), trabecular number (Tb.N) and trabecular thickness (Tb.Th). The com-parison of predictions of the DNN with an analysis of mu CT-images confirmed a high accuracy for different microstructural parameters, which was indicated by corresponding Pearson correlation coefficients, especially for Tb.Th (r = 0.89) and BS/BV (r = 0.80). Concurrently, the approach was able to unambiguously discriminate anatomically similar bone regions (femoral head, greater trochanter and femoral neck) and therefore was capable to determine the morphological status of osseous tissue in detail. The classification was more discrim-inative than one based on classical linear discriminant analysis (LDA), due to the distinguishing features extracted by the DNN model. Accordingly, the method and model can serve as a potential tool for evaluating bone quality and bone status. [Wei, Wenzuo; Kolb, Juergen F.] Univ Rostock, Inst Phys, D-18059 Rostock, Germany; [Wei, Wenzuo; Kolb, Juergen F.] Univ Rostock, Dept Life Light & Matter, D-18059 Rostock, Germany; [Wei, Wenzuo; Kolb, Juergen F.] Leibniz Inst Plasma Sci & Technol INP, D-17489 Greifswald, Germany; [Shi, Fukun] Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, Suzhou 215163, Peoples R China; [Kolb, Juergen F.] Leibniz Inst Plasma Sci & Technol INP, D-17489 Greifswald, Germany University of Rostock; University of Rostock; Leibniz Institut fur Plasmaforschung und Technologie; Chinese Academy of Sciences; Suzhou Institute of Biomedical Engineering & Technology, CAS; Leibniz Institut fur Plasmaforschung und Technologie Kolb, JF (corresponding author), Leibniz Inst Plasma Sci & Technol INP, D-17489 Greifswald, Germany. juergen.kolb@inp-greifswald.de Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [SFB 1270/1,2 - 299150580] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 1270/1,2 - 299150580. 30 0 0 2 4 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1567-5394 1878-562X BIOELECTROCHEMISTRY Bioelectrochemistry DEC 2022.0 148.0 108232 10.1016/j.bioelechem.2022.108232 0.0 AUG 2022 10 Biochemistry & Molecular Biology; Biology; Biophysics; Electrochemistry Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Life Sciences & Biomedicine - Other Topics; Biophysics; Electrochemistry 4F7BD 35987060.0 2023-03-23 WOS:000848663600004 0
C Peltenburg, J; van Leeuwen, LTJ; Hoozemans, J; Fang, J; Al-Ars, Z; Hofstee, HP IEEE Comp Soc Peltenburg, Johan; van Leeuwen, Lars T. J.; Hoozemans, Joost; Fang, Jian; Al-Ars, Zaid; Hofstee, H. Peter Battling the CPU Bottleneck in Apache Parquet to Arrow Conversion Using FPGA 2020 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (ICFPT 2020) English Proceedings Paper 19th International Conference on Field-Programmable Technology (ICFPT) DEC 07-11, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,Intel,Xilinx FPGA; Accelerator; Apache Parquet; Apache Arrow In the domain of big data analytics, the bottleneck of converting storage-focused file formats to in-memory data structures has shifted from the bandwidth of storage to the performance of decoding and decompression software. Two widely used formats for big data storage and in-memory data are Apache Parquet and Apache Arrow, respectively. In order to improve the speed at which data can be loaded from disk to memory, we propose an FPGA accelerator design that converts Parquet files to Arrow in-memory data structures. We describe an extensible, publicly available, free and open-source implementation of the proposed converter that supports various Parquet file configurations. The performance of the converter is measured on an AWS EC2 Fl system and on a POWER9 system using the recently released OpenCAPI interface. A single instance of the converter can reach between 6 and 12 GB/s of end-to-end throughput, and shows up to a threefold improvement over the fastest single-thread CPU implementation. It has a low resource utilization (less than 5% for all types of FPGA resources). This allows scaling out the design to match the bandwidth of the coming generation of accelerator interfaces. The proposed design and implementation can be extended to support more of the many possible Parquet file configurations. [Peltenburg, Johan; van Leeuwen, Lars T. J.; Hoozemans, Joost; Fang, Jian; Al-Ars, Zaid; Hofstee, H. Peter] Delft Univ Technol, Accelerated Big Data Syst, Delft, Netherlands; [Fang, Jian] Natl Innovat Inst Def Technol, Beijing, Peoples R China; [Hofstee, H. Peter] IBM Corp, Armonk, NY USA Delft University of Technology; International Business Machines (IBM) Peltenburg, J (corresponding author), Delft Univ Technol, Accelerated Big Data Syst, Delft, Netherlands. j.w.peltenburg@tudelft.nl; hofstee@us.ibm.com Fang, Jian/0000-0002-1077-1859 ECSEL Joint Undertaking [H2020ECSEL-2017-2-783162]; Xilinx ECSEL Joint Undertaking; Xilinx This work is part of the FitOptiVis project [21] funded by the ECSEL Joint Undertaking under grant number H2020ECSEL-2017-2-783162. The authors thank Xilinx for their additional support. 21 1 1 1 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 978-1-6654-2302-1 2020.0 281 286 10.1109/ICFPT51103.2020.00048 0.0 6 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BR9JG Green Submitted 2023-03-23 WOS:000676387300039 0
J Yang, YY; Rossel, RAV; Li, S; Bissett, A; Lee, J; Shi, Z; Behrens, T; Court, L Yang, Yuanyuan; Rossel, Raphael A. Viscarra; Li, Shuo; Bissett, Andrew; Lee, Juhwan; Shi, Zhou; Behrens, Thorsten; Court, Leon Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions SOIL BIOLOGY & BIOCHEMISTRY English Article Soil bacteria; Bacteria diversity; Bacteria phyla; Machine learning; Vis-NIR spectra; Spectro-transfer function AUSTRALIA TERRESTRIAL CARBON; NEAR-INFRARED SPECTROSCOPY; ORGANIC-MATTER; REFLECTANCE SPECTROSCOPY; COMMUNITY COMPOSITION; FRACTIONS; PH; BIOGEOGRAPHY; UNCERTAINTY; TEMPERATURE Soil bacteria play a critical role in the functioning of ecosystems but are challenging to investigate. We developed state-factor models with machine learning to understand better and to predict the abundance of 10 dominant phyla and bacterial diversities in Australian soils, the latter expressed by the Chao and Shannon indices. In the models, we used proxies for the edaphic, climatic, biotic and topographic factors, which included soil properties, environmental variables, and the absorbance at visible-near infrared (vis-NIR) wavelengths. From a cross-validation with all observations (n = 681), we found that our models explained 43-73% of the variance in bacterial phyla abundance and diversity. The vis-NIR spectra, which represent the organic and mineral composition of soil, were prominent drivers of abundance and diversity in the models, as were changes in the soil-water balance, potential evapotranspiration, and soil nutrients. From independent validations, we found that spectr-otransfer functions could predict well the phyla Acidobacteria and Actinobacteria (R-2 > 0.7) as well as other dominant phyla and the Chao and Shannon diversities (R-2 > 0.5). Predictions of the phyla Firmicutes were the poorest (R-2 = 0.42). The vis-NIR spectra markedly improved the explanatory power and predictability of the models. [Yang, Yuanyuan; Rossel, Raphael A. Viscarra; Li, Shuo; Lee, Juhwan; Court, Leon] CSIRO Land & Water, GPO Box 1700, Canberra, ACT 2601, Australia; [Yang, Yuanyuan; Li, Shuo; Shi, Zhou] Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China; [Bissett, Andrew] CSIRO Oceans & Atmosphere, GPO BOX 1538, Hobart, Tas 7001, Australia; [Behrens, Thorsten] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Rumelinstr 19-23, D-72070 Tubingen, Germany Commonwealth Scientific & Industrial Research Organisation (CSIRO); Zhejiang University; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Eberhard Karls University of Tubingen Rossel, RAV (corresponding author), CSIRO Land & Water, GPO Box 1700, Canberra, ACT 2601, Australia. raphael.viscarra-rossel@csiro.au yang, yuanyuan/HPG-2020-2023; Bissett, Andrew/AFR-3887-2022; Court, Leon N/J-7138-2013; shi, Zhou/A-2283-2015; shi, Zhou/M-7845-2019; Viscarra Rossel, Raphael A./B-4061-2011 Bissett, Andrew/0000-0001-7396-1484; Court, Leon N/0000-0003-3673-9504; shi, Zhou/0000-0003-3914-5402; shi, Zhou/0000-0003-3914-5402; Viscarra Rossel, Raphael A./0000-0003-1540-4748; Lee, Juhwan/0000-0002-7967-2955; Behrens, Thorsten/0000-0002-9287-9395 Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS) Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS)(Australian GovernmentDepartment of Industry, Innovation and Science) We acknowledge the contribution of the Biomes of Australian Soil Environments (BASE) consortium in the generation of data used in this publication. DOI 10.1186/s13742-016-0126-5. The BASE project is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). 66 19 19 9 64 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0038-0717 SOIL BIOL BIOCHEM Soil Biol. Biochem. FEB 2019.0 129.0 29 38 10.1016/j.soilbio.2018.11.005 0.0 10 Soil Science Science Citation Index Expanded (SCI-EXPANDED) Agriculture HK1JL 2023-03-23 WOS:000457661000004 0
J Yang, JW; Jiang, LM; Luojus, K; Pan, JM; Lemmetyinen, J; Takala, M; Wu, SL Yang, Jianwei; Jiang, Lingmei; Luojus, Kari; Pan, Jinmei; Lemmetyinen, Juha; Takala, Matias; Wu, Shengli Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach CRYOSPHERE English Article REMOTE-SENSING DATA; PASSIVE MICROWAVE; WATER EQUIVALENT; DATA ASSIMILATION; RADIOMETER DATA; EMISSION MODEL; COVER; CLASSIFICATION; BOREAL; IMPLEMENTATION We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of similar to 4.4 and similar to 7.3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987-2018. The mean unbiased RMSE and bias were 7.1 and -0.05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and -1.20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (> 20 cm), with biases of -10.4, -8.9, and -34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai-Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of -0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC. [Yang, Jianwei; Jiang, Lingmei] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Fac Geog Sci, Beijing 100875, Peoples R China; [Luojus, Kari; Lemmetyinen, Juha; Takala, Matias] Finnish Meteorol Inst, POB 503, Helsinki 00101, Finland; [Pan, Jinmei] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Wu, Shengli] China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China Beijing Normal University; Finnish Meteorological Institute; Chinese Academy of Sciences; China Meteorological Administration Jiang, LM (corresponding author), Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Fac Geog Sci, Beijing 100875, Peoples R China. jiang@bnu.edu.cn Jiang, Lingmei/L-6099-2016; Lemmetyinen, Juha/B-3739-2016 Jiang, Lingmei/0000-0002-9847-9034; Lemmetyinen, Juha/0000-0003-4434-9696; Luojus, Kari/0000-0002-4066-6005; Wu, Shengli/0000-0002-5930-3183 Science and Technology Basic Resources Investigation Program of China [2017FY100502]; National Natural Science Foundation of China [41671334] Science and Technology Basic Resources Investigation Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research has been supported by the Science and Technology Basic Resources Investigation Program of China (grant no. 2017FY100502) and the National Natural Science Foundation of China (grant no. 41671334). 83 14 19 8 35 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1994-0416 1994-0424 CRYOSPHERE Cryosphere JUN 3 2020.0 14.0 6 1763 1778 10.5194/tc-14-1763-2020 0.0 16 Geography, Physical; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology LV4MH Green Submitted, gold 2023-03-23 WOS:000538410900001 0
J Hu, B; Yan, LF; Yang, Y; Yu, Y; Sun, Q; Zhang, J; Nan, HY; Han, Y; Hu, YC; Sun, YZ; Xiao, G; Tian, Q; Yue, C; Feng, JH; Zhai, LH; Zhao, D; Cui, GB; Welch, VL; Cornett, EM; Urits, I; Viswanath, O; Varrassi, G; Kaye, AD; Wang, W Hu, Bo; Yan, Lin-Feng; Yang, Yang; Yu, Ying; Sun, Qian; Zhang, Jin; Nan, Hai-Yan; Han, Yu; Hu, Yu-Chuan; Sun, Ying-Zhi; Xiao, Gang; Tian, Qiang; Yue, Cui; Feng, Jia-Hao; Zhai, Liang-Hao; Zhao, Di; Cui, Guang-Bin; Welch, Valerie Lockhart; Cornett, Elyse M.; Urits, Ivan; Viswanath, Omar; Varrassi, Giustino; Kaye, Alan D.; Wang, Wen Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images CUREUS JOURNAL OF MEDICAL SCIENCE English Article deep learning; transfer learning; magnetic resonance imaging (mri); prostate cancer CONVOLUTIONAL NEURAL-NETWORKS; COMPUTER-AIDED DETECTION; TESTS Purpose The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge. Materials and methods A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring). Results PTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size. Conclusion Deep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images. [Hu, Bo; Yan, Lin-Feng; Yang, Yang; Yu, Ying; Sun, Qian; Zhang, Jin; Nan, Hai-Yan; Han, Yu; Hu, Yu-Chuan; Sun, Ying-Zhi; Xiao, Gang; Tian, Qiang; Yue, Cui; Feng, Jia-Hao; Zhai, Liang-Hao; Cui, Guang-Bin; Wang, Wen] Fourth Mil Med Univ, Dept Radiol, Xian, Shaanxi, Peoples R China; [Zhao, Di] Chinese Acad Sci, Dept Comp Network Informat, Beijing, Peoples R China; [Welch, Valerie Lockhart] Louisiana State Univ LSU Hlth Shreveport, Dept Pathol, Shreveport, LA USA; [Cornett, Elyse M.] Louisiana State Univ LSU Hlth Shreveport, Dept Anaesthesiol, Shreveport, LA USA; [Urits, Ivan] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Dept Anesthesia Crit Care & Pain Med, Boston, MA 02115 USA; [Viswanath, Omar] Univ Arizona, Dept Pain Management, Phoenix, AZ USA; [Varrassi, Giustino] Paolo Procacci Fdn, Dept Res, Rome, Italy; [Kaye, Alan D.] Louisiana State Univ Hlth Shreveport, Dept Anesthesiol, Shreveport, LA USA Air Force Military Medical University; Chinese Academy of Sciences; Louisiana State University System; Louisiana State University Health Sciences Center at Shreveport; Louisiana State University System; Louisiana State University Health Sciences Center at Shreveport; Harvard University; Beth Israel Deaconess Medical Center; Harvard Medical School; University of Arizona; Louisiana State University System; Louisiana State University Health Sciences Center at Shreveport Wang, W (corresponding author), Fourth Mil Med Univ, Dept Radiol, Xian, Shaanxi, Peoples R China. wangwen@fmmu.edu.cn Varrassi, Giustino/H-8455-2019 Varrassi, Giustino/0000-0002-3822-2923 Tangdu Hospital Pathology Department; Paolo Procacci Foundation Tangdu Hospital Pathology Department; Paolo Procacci Foundation The authors would like to thank Dr. Cui Wu-Xun and Mrs. Xiu Si-Jie for the excellent work in helping collect the MRI images. They would like to thank the Tangdu Hospital Pathology Department for their support. The authors thank Mr. Xiao-Cheng Wei (MR research, GE Healthcare China) for his excellent support. They are also grateful to the Paolo Procacci Foundation for the support in editing and publishing this research. 30 1 1 1 8 CUREUS INC PALO ALTO PO BOX 61002, PALO ALTO, CA 94306 USA 2168-8184 CUREUS J MED SCIENCE Cureus J Med Sci MAR 25 2021.0 13.0 3 e14108 10.7759/cureus.14108 0.0 14 Medicine, General & Internal Emerging Sources Citation Index (ESCI) General & Internal Medicine RF6XU 33927922.0 Green Accepted, gold 2023-03-23 WOS:000634985900012 0
J Li, L; Wong, WCJ; Schwarz, B; Lau, TL Li, Lei; Wong, Wing Ching Jeremy; Schwarz, Benjamin; Lau, Tsz Lam Seismology Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science EARTH AND SPACE SCIENCE English Editorial Material seismological observations; data acquisition; data processing; ICON; machine learning; induced seismicity EARTHQUAKES; FRAMEWORK Seismology focuses on the study of earthquakes and associated phenomena to characterize seismic sources and Earth structure, which both are of immediate relevance to society. This article is composed of two independent views on the state of the integrated, coordinated, open, networked (ICON) principles (Goldman et al., 2021, ) in seismology and reflects on the opportunities and challenges of adopting them from a different angle. Each perspective focuses on a different topic. Section 1 deals with the integration of multiscale and multidisciplinary observations, focusing on integrated and open approaches, whereas Section 2 discusses computing and open-source algorithms, reflecting coordinated, networked, and open principles. In the past century, seismology has benefited from two co-existing technological advancements-The emergence of new, more capable sensory systems and affordable and distributed computing infrastructure. Integrating multiple observations is a crucial strategy to improve the understanding of earthquake hazards. However, current efforts in making big datasets available and manageable lack coherence, which makes it challenging to implement initiatives that span different communities. Building on ongoing advancements in computing, machine learning algorithms have been revolutionizing the way of seismic data processing and interpretation. A community-driven approach to code management offers open and networked opportunities for young scholars to learn and contribute to a more sustainable approach to seismology. Investing in new sensors, more capable computing infrastructure, and open-source algorithms following the ICON principles will enable new discoveries across the Earth sciences. [Li, Lei] Cent South Univ, Key Lab Metallogen Predict Nonferrous Met & Geol, Minist Educ, Changsha, Peoples R China; [Li, Lei] Cent South Univ, Sch Geosci & Infophys, Changsha, Peoples R China; [Wong, Wing Ching Jeremy; Lau, Tsz Lam] Chinese Univ Hong Kong, Hong Kong, Peoples R China; [Wong, Wing Ching Jeremy] Univ Calif San Diego, Scripps Inst Oceanog, San Diego, CA 92103 USA; [Schwarz, Benjamin] GFZ German Res Ctr Geosci, Potsdam, Germany Central South University; Central South University; Chinese University of Hong Kong; University of California System; University of California San Diego; Scripps Institution of Oceanography; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences Li, L (corresponding author), Cent South Univ, Key Lab Metallogen Predict Nonferrous Met & Geol, Minist Educ, Changsha, Peoples R China.;Li, L (corresponding author), Cent South Univ, Sch Geosci & Infophys, Changsha, Peoples R China. leileely@126.com Li, Lei/E-3024-2018 Li, Lei/0000-0002-3391-5608; Schwarz, Benjamin/0000-0001-9913-7311; Wong, Jeremy Wing Ching/0000-0001-5523-8145 National Natural Science Foundation of China [42004115]; Hunan Provincial Natural Science Foundation of China [2019JJ50762] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hunan Provincial Natural Science Foundation of China(Natural Science Foundation of Hunan Province) We thank the editor Kristy Tiampo for handling this paper, the reviewer A.-A. Gabriel and two anonymous reviewers for their helpful comments. L. Li. acted as a facilitator and point of contact with the special collection organizing team. We appreciate Dirk Gajewski, Zhigang Peng, Sujata R. Emani, and James C. Stegen for reviewing and proofreading the manuscript. The work is sponsored by the National Natural Science Foundation of China (Grant No. 42004115), Hunan Provincial Natural Science Foundation of China (Grant No. 2019JJ50762). 41 0 0 2 5 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2333-5084 EARTH SPACE SCI Earth Space Sci. MAR 2022.0 9.0 3 e2021EA002109 10.1029/2021EA002109 0.0 6 Astronomy & Astrophysics; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Geology 0F8MM Green Published 2023-03-23 WOS:000777609100003 0
J Du, HZ; Zhou, Y; Ma, YP; Wang, SW Du, Haizhou; Zhou, Yan; Ma, Yunpu; Wang, Shiwei Astrologer: Exploiting graph neural Hawkes process for event propagation prediction with spatio-temporal characteristics KNOWLEDGE-BASED SYSTEMS English Article Hawkes process; Graph neural network; Forecasting; Spatio-temporal events The prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as social network analysis. The data describing these phenomena are multidimensional asynchronous event data that affect each other and show complex dynamic patterns in the continuous-time domain. The study of these dynamic processes and the mining of their potential correlations provide a foundation for the application of event propagation forecasting. However, conventional forecasting methods often make strong assumptions about the generative processes of the event data that may or may not reflect the reality, and the strong parametric assumptions also restrict the expressive power of the respective processes. Therefore, it is difficult to capture both the temporal and spatial effects of past event sequences. Most of the existing methods capture the intensity function of the Hawkes processes conditioned only on the historical events while ignoring the spatial information and the influences among different events. In this work, we propose the Astrologer, a graph neural Hawkes process that can capture the propagation of events from historical events on graph. The underlying idea of Astrologer is to incorporate the conditional intensity function of the Hawkes processes with a graph convolutional recurrent neural network. Using both synthetic and real-world datasets, we show that, Astrologer can learn the dynamics of event propagation without knowing the actual parametric forms. Astrologer can also learn the dynamics and achieve better predictive performance than other parametric alternatives based on particular prior assumptions. (C) 2021 Elsevier B.V. All rights reserved. [Du, Haizhou; Zhou, Yan; Wang, Shiwei] Shanghai Univ Elect Power, Shanghai, Peoples R China; [Ma, Yunpu] Ludwig Maximilians Univ Munchen, Munich, Germany Shanghai University of Electric Power; University of Munich Du, HZ (corresponding author), Shanghai Univ Elect Power, Shanghai, Peoples R China. duhaizhou@shiep.edu.cn du, haizhou/AAE-6856-2022 37 2 3 1 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. SEP 27 2021.0 228.0 107247 10.1016/j.knosys.2021.107247 0.0 JUL 2021 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UB7AY 2023-03-23 WOS:000685995700003 0
J Sun, X; Shi, JY; Liu, LP; Dong, JY; Plant, C; Wang, XH; Zhou, HY Sun, Xin; Shi, Junyu; Liu, Lipeng; Dong, Junyu; Plant, Claudia; Wang, Xinhua; Zhou, Huiyu Transferring deep knowledge for object recognition in Low-quality underwater videos NEUROCOMPUTING English Article Deep learning; Transfer learning; Computer vision; Object detection; Underwater video analysis TRACKING In recent years, underwater video technologies allow us to explore the ocean in scientific and noninvasive ways, such as environmental monitoring, marine ecology studies, and fisheries management. However the low-light and high-noise scenarios pose great challenges for the underwater image and video analysis. We here propose a CNN knowledge transfer framework for underwater object recognition and tackle the problem of extracting discriminative features from relatively low contrast images. Even with the insufficient training set, the transfer framework can well learn a recognition model for the special underwater object recognition task together with the help of data augmentation. For better identifying objects from an underwater video, a weighted probabilities decision mechanism is introduced to identify the object from a series of frames. The proposed framework can be implemented for real-time underwater object recognition on autonomous underwater vehicles and video monitoring systems. To verify the effectiveness of our method, experiments on a public dataset are carried out. The results show that the proposed method achieves promising results for underwater object recognition on both test image datasets and underwater videos. (C) 2017 Elsevier B.V. All rights reserved. [Sun, Xin; Shi, Junyu; Liu, Lipeng; Dong, Junyu] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China; [Sun, Xin; Wang, Xinhua] Chinese Acad Sci, State Key Lab Appl Opt, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China; [Plant, Claudia] Univ Vienna, Fac Comp Sci, Vienna, Austria; [Zhou, Huiyu] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Ocean University of China; Chinese Academy of Sciences; Changchun Institute of Optics, Fine Mechanics & Physics, CAS; University of Vienna; Queens University Belfast Dong, JY (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China. sunxin1984@ieee.org; dongjunyu@ouc.edu.cn Wang, Xin/AAN-8435-2021; Wang, Xin/GYU-1129-2022; Zhou, Huiyu/O-2692-2014 Wang, Xin/0000-0002-4457-7376; Zhou, Huiyu/0000-0003-1634-9840; Sun, Xin/0000-0003-1870-9037; Plant, Claudia/0000-0001-5274-8123 National Natural Science Foundation of China [61401413, 41576011]; China Postdoctoral Science Foundation [2015T80749]; Natural Science Foundation of Shandong Province [ZR2014FQ023]; State Key Laboratory of Applied Optics; NVIDIA Academic Hardware Grant; EPSRC [EP/N011074/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/N011074/1] Funding Source: researchfish National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); State Key Laboratory of Applied Optics; NVIDIA Academic Hardware Grant; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work is supported by the National Natural Science Foundation of China (No. 61401413, 41576011), China Postdoctoral Science Foundation (No. 2015T80749), Natural Science Foundation of Shandong Province (No. ZR2014FQ023), Open Funding of State Key Laboratory of Applied Optics and NVIDIA Academic Hardware Grant. 46 47 48 4 90 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 31 2018.0 275.0 897 908 10.1016/j.neucom.2017.09.044 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FQ5AI Green Submitted, Green Accepted 2023-03-23 WOS:000418370200085 0
C Ren, B; Hou, B; Chanussot, J; Jiao, CZ; Zhang, XR IEEE Ren, Bo; Hou, Biao; Chanussot, Jocelyn; Jiao, Changzhe; Zhang, Xiangrong POLSAR LAND COVER CLASSIFICATION VIA TENSORIAL EMBEDDING METHODS 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Tensor embedding methods; feature extraction; polarimetric synthetic aperture radar (PolSAR) image; classification COMPONENT ANALYSIS In recent years, graph embedding has become a significant technique to deal with feature extraction and dimension reduction problems. Under the linearization and kernelization, it provides a unified framework in machine learning and other pattern recognition tasks. Polarimetric synthetic aperture (PolSAR) as a typical multi-channel sensor can obtain more geometrical and geophysical information. How to combine those polarimetric scattering signals and target decomposition features and explore the spatial information between pixels become a new research direction to address PolSAR data. In this paper, we utilize the tensorial embedding methods to extract the intrinsic features from a redundant feature space for the PolSAR land cover classification. The effectiveness of the proposed methods is demonstrated using AIRSAR Flevoland data set. [Ren, Bo; Hou, Biao; Jiao, Changzhe; Zhang, Xiangrong] Xidian Univ, Sch Artificial Intelligence, Xian, Peoples R China; [Chanussot, Jocelyn] Grenoble Inst Technol, Signal & Images Dept, Grenoble, France Xidian University; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble Ren, B (corresponding author), Xidian Univ, Sch Artificial Intelligence, Xian, Peoples R China. China Postdoctoral Science Foundation Funded Project [2018M633468]; China Scholarship Council [201806965045]; GIPSA-Lab, Grenoble Institute of Technology (Grenoble INP) China Postdoctoral Science Foundation Funded Project(China Postdoctoral Science Foundation); China Scholarship Council(China Scholarship Council); GIPSA-Lab, Grenoble Institute of Technology (Grenoble INP) This work is supported by China Postdoctoral Science Foundation Funded Project (Project No.:2018M633468). It is also supported by China Scholarship Council under grant No.201806965045. Many thanks to the support of GIPSA-Lab, Grenoble Institute of Technology (Grenoble INP). 11 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 1224 1227 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH 2023-03-23 WOS:000519270601123 0
J Wang, MY; Shen, LQ; Pasquale, LR; Boland, MV; Wellik, SR; De Moraes, CG; Myers, JS; Nguyen, TD; Ritch, R; Ramulu, P; Wang, H; Tichelaar, J; Li, D; Bex, PJ; Elze, T Wang, Mengyu; Shen, Lucy Q.; Pasquale, Louis R.; Boland, Michael, V; Wellik, Sarah R.; De Moraes, Carlos Gustavo; Myers, Jonathan S.; Nguyen, Thao D.; Ritch, Robert; Ramulu, Pradeep; Wang, Hui; Tichelaar, Jorryt; Li, Dian; Bex, Peter J.; Elze, Tobias Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma OPHTHALMOLOGY English Article INDEPENDENT COMPONENT ANALYSIS; STANDARD AUTOMATED PERIMETRY; OPTICAL COHERENCE TOMOGRAPHY; OPEN-ANGLE GLAUCOMA; QUALITY-OF-LIFE; DRIVING PERFORMANCE; PROGRESSION; DEFECTS; ABNORMALITIES; RELIABILITY Purpose: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. Design: Retrospective study. Participants: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. Methods: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). Main Outcome Measures: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. Results: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. Conclusions: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices. (C) 2019 by the American Academy of Ophthalmology [Wang, Mengyu; Wang, Hui; Tichelaar, Jorryt; Li, Dian; Elze, Tobias] Harvard Med Sch, Schepens Eye Res Inst, 20 Staniford St, Boston, MA 02114 USA; [Shen, Lucy Q.] Harvard Med Sch, Massachusetts Eye & Ear, Boston, MA 02114 USA; [Pasquale, Louis R.] Icahn Sch Med Mt Sinai, Eye & Vis Res Inst, New York, NY 10029 USA; [Pasquale, Louis R.] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02114 USA; [Boland, Michael, V; Ramulu, Pradeep] Johns Hopkins Univ, Sch Med, Wilmer Eye Inst, Baltimore, MD 21205 USA; [Wellik, Sarah R.] Univ Miami, Sch Med, Bascom Palmer Eye Inst, Miami, FL USA; [De Moraes, Carlos Gustavo] Columbia Univ, Med Ctr, Edward S Harkness Eye Inst, New York, NY USA; [Myers, Jonathan S.] Thomas Jefferson Univ, Wills Eye Hosp, Philadelphia, PA 19107 USA; [Nguyen, Thao D.] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA; [Ritch, Robert] New York Eye & Ear Infirm Mt Sinai, Einhorn Clin Res Ctr, New York, NY USA; [Wang, Hui] Jilin Univ Finance & Econ, Inst Psychol & Behav, Changchun, Peoples R China; [Bex, Peter J.] Northeastern Univ, Dept Psychol, Boston, MA 02115 USA; [Elze, Tobias] Max Planck Inst Math Sci, Complex Struct Biol & Cognit, Leipzig, Germany Harvard University; Harvard Medical School; Schepens Eye Research Institute; Harvard University; Harvard Medical School; Massachusetts Eye & Ear Infirmary; Icahn School of Medicine at Mount Sinai; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Johns Hopkins University; Johns Hopkins Medicine; Bascom Palmer Eye Institute; University of Miami; Columbia University; Jefferson University; Johns Hopkins University; New York Eye & Ear Infirmary of Mount Sinai; Jilin University of Finance & Economics; Northeastern University; Max Planck Society Elze, T (corresponding author), Harvard Med Sch, Schepens Eye Res Inst, 20 Staniford St, Boston, MA 02114 USA. tobias-elze@tobias-elze.de Nguyen, Thao/HNP-2729-2023; Tichelaar, Jorryt/ABD-4383-2021; Boland, Michael V/HJI-9286-2023; Li, Dian/AAQ-3482-2020; Nguyen, Thao/A-3391-2010 Boland, Michael V/0000-0003-2506-7095; Li, Dian/0000-0002-5374-7314; Ritch, Robert/0000-0002-9368-7547; Nguyen, Thao/0000-0002-0312-1583 National Institutes of Health, Bethesda, Maryland [R21 EY030142, R21 EY030631, R01 EY030575, R01 EY015473, K99 EY028631, R01 EY025253, P30 EY003790]; BrightFocus Foundation; Lions Foundation; Grimshaw-Gudewicz Foundation; Research to Prevent Blindness, Inc., New York, New York; Harvard Glaucoma Center of Excellence; Eleanor and Miles Shore Fellowship; Alice Adler Fellowship; Joseph Cohen Research Fund of the New York Eye and Ear Infirmary of Mount Sinai, New York, New York National Institutes of Health, Bethesda, Maryland(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); BrightFocus Foundation(BrightFocus Foundation); Lions Foundation; Grimshaw-Gudewicz Foundation; Research to Prevent Blindness, Inc., New York, New York(Research to Prevent Blindness (RPB)); Harvard Glaucoma Center of Excellence; Eleanor and Miles Shore Fellowship; Alice Adler Fellowship; Joseph Cohen Research Fund of the New York Eye and Ear Infirmary of Mount Sinai, New York, New York Supported by the National Institutes of Health, Bethesda, Maryland (grant nos.: R21 EY030142 [T.E.], R21 EY030631 [T.E.], R01 EY030575 [T.E.], R01 EY015473 [L.R.P.], K99 EY028631 [M.W.], R01 EY025253 [C.G.D.M.], P30 EY003790 [M.W., T.E., D.L., H.W.]); the BrightFocus Foundation (M.W., T.E.); the Lions Foundation (M.W., T.E.); the Grimshaw-Gudewicz Foundation (M.W., N.B, T.E.); Research to Prevent Blindness, Inc., New York, New York (M.W., T.E., and departmental grant [C.G.D.M.]); Harvard Glaucoma Center of Excellence (M.W., T.E., L.Q.S.); the Eleanor and Miles Shore Fellowship (L.Q.S.); the Alice Adler Fellowship (T.E.); and the Joseph Cohen Research Fund of the New York Eye and Ear Infirmary of Mount Sinai, New York, New York (R.R.). HUMAN SUBJECTS: Human subjects were included in this study. The human ethics committees at Massachusetts Eye and Ear, Wilmer Eye Institute, New York Eye and Ear Infirmary, Bascom Palmer Eye Institute, and Wills Eye Hospital approved the study. All research adhered to the tenets of the Declaration of Helsinki. The institutional review boards of all institutions approved this research. The institutional review boards waived the need for informed consent because of the retrospective and de-identified nature of the study. 51 21 21 0 7 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0161-6420 1549-4713 OPHTHALMOLOGY Ophthalmology JUN 2020.0 127.0 6 731 738 10.1016/j.ophtha.2019.12.004 0.0 8 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology MR8XV 32081491.0 Green Accepted 2023-03-23 WOS:000553874500006 0
J Wang, YL; Yang, P; Zhao, SY; Chevallier, J; Xiao, QT Wang, Yelin; Yang, Ping; Zhao, Shunyu; Chevallier, Julien; Xiao, Qingtai A hybrid intelligent framework for forecasting short-term hourly wind speed based on machine learning EXPERT SYSTEMS WITH APPLICATIONS English Article Hybrid prediction; Short-term; Hourly wind speed; Chaos; Machine learning EMPIRICAL MODE DECOMPOSITION; OPTIMIZATION ALGORITHM; WRF SIMULATION; PREDICTION; MULTISTEP; SPECTRUM With the development of wind power which is the great substitute for traditional energy, it is worth conducting an in-depth exploration of the hourly wind speed time series which is chaotic due to the complex weather. In this paper, the hybrid intelligent framework is proposed as an integrated prediction tool with high accuracy for signal pre-processing, data prediction, and result optimization of short-term hourly wind speed. Specifically, its excellent performance is guaranteed through adequate information extraction on three stages. The first stage is completed in the signal pre-processing module that the interference information is cleaned up from the hourly wind speed signal via de-noising. The second stage is conducted in the data prediction module that the hidden regular information is fully extracted via the decomposition method. The third stage is performed in the resulting optimization module that the residual information is recovered via error modification. For illustration, the performance of the proposed framework is evaluated through historical hourly wind speed, taken from publicly available Sotavento wind farms. The obtained experimental results indicate that de-noising is beneficial to capturing the real trend, but it may negatively impact short-term prediction accuracy. For the hybrid prediction model based on the empirical mode decomposition-based method, the de-noising mode integrating into the decomposition process is more effective than independent de-noising. The second stage is the key to improving the forecasting performance that, adopting the decomposition method, the average fitting performance is improved by 52.84% than the single models. Before the third stage, chaos test is necessary to determine whether there is a requirement of error modification. In summary, the proposed prediction framework can capture the complex characteristics for different short-term hourly wind speed time series, achieving greater performance than the other comparative models. [Wang, Yelin] Guangdong Univ Technol, Sch Ecol Environm & Resources, Guangzhou 510006, Guangdong, Peoples R China; [Yang, Ping; Zhao, Shunyu] Kunming Univ Sci & Technol, Fac Management & Econ, Kunming 650093, Yunnan, Peoples R China; [Chevallier, Julien] IPAG Business Sch, IPAG Lab, 184 bd St Germain, F-75006 Paris, France; [Chevallier, Julien] Univ Paris 8 LED, 2 Rue Liberte, F-93526 Saint Denis, France; [Xiao, Qingtai] Kunming Univ Sci & Technol, State Key Lab Complex Nonferrous Met Resource Clea, Kunming 650093, Yunnan, Peoples R China; [Xiao, Qingtai] Kunming Univ Sci & Technol, Fac Met & Energy Engn, Dept Energy & Power Engn, Kunming 650093, Yunnan, Peoples R China Guangdong University of Technology; Kunming University of Science & Technology; IPAG Business School; Kunming University of Science & Technology; Kunming University of Science & Technology Xiao, QT (corresponding author), Kunming Univ Sci & Technol, State Key Lab Complex Nonferrous Met Resource Clea, Kunming 650093, Yunnan, Peoples R China. xiaoqingtai2008@163.com Xiao, Qingtai/0000-0001-9982-7494 Natural Science Foundation of Yunnan Province, China [202101AU070031]; Yunnan Fundamental Research Project [202101BG070127]; Young Talent Training Program for Science and Technology Think Tank by China Association for Science and Technology [20220615ZZ07110003]; Young Elite Scientist Sponsorship Program by China Association for Science and Technology [YESS20210106] Natural Science Foundation of Yunnan Province, China(Natural Science Foundation of Yunnan Province); Yunnan Fundamental Research Project; Young Talent Training Program for Science and Technology Think Tank by China Association for Science and Technology; Young Elite Scientist Sponsorship Program by China Association for Science and Technology This research was financially supported by the Natural Science Foundation of Yunnan Province, China (No. 202101AU070031) , Yunnan Fundamental Research Project (No. 202101BG070127) , Young Talent Training Program for Science and Technology Think Tank by China Association for Science and Technology (No. 20220615ZZ07110003) , and Young Elite Scientist Sponsorship Program by China Association for Science and Technology (No. YESS20210106) . 49 0 0 31 31 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. MAR 1 2023.0 213.0 C 119223 10.1016/j.eswa.2022.119223 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 6P0ZJ 2023-03-23 WOS:000890664100006 0
J Zhang, R; Wang, YY; Hu, JL; Yang, W; Chen, J; Zhu, XX Zhang, Rui; Wang, Yuanyuan; Hu, Jingliang; Yang, Wei; Chen, Jie; Zhu, Xiao Xiang SAR4LCZ-Net: A Complex-Valued Convolutional Neural Network for Local Climate Zones Classification Using Gaofen-3 Quad-Pol SAR Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Synthetic aperture radar; Urban areas; Convolutional neural networks; Radar polarimetry; Remote sensing; Feature extraction; Sociology; Complex-valued convolutional neural networks (CNNs); local climate zones (LCZs); quad-pol synthetic aperture radar; urban land cover WUDAPT The recent local climate zones (LCZ) classification scheme provides spatially fine granular descriptions of inner urban morphology. It is universally applicable to cities worldwide and capable of supporting various urban studies. Although optical and dual-pol synthetic aperture radar (SAR) data continue to push the frontiers of this task, the potential of quad-pol SAR data for LCZ classification is not yet explored. In this article, we propose a novel complex-valued convolutional neural network (CNN), SAR4LCZ-Net, to tackle this challenge. SAR4LCZ-Net improves the state-of-the-art by exploiting two facts of this specific task: the semantic hierarchical structure of the LCZ classification scheme and the complex-valued nature of quad-pol SAR data. To validate the performance of our algorithm, we generate a Chinese Gaofen-3 quad-pol SAR dataset for LCZ which covers 31 cities around the world. Results show that the proposed SAR4LCZ-Net improves 2.4% on overall accuracy (OA) and 4.5% on average accuracy (AA) compared with the real-valued CNN with the same structure. Gaofen-3 quad-pol SAR data also showed its advantage over the dual-pol Sentinel-1 data. It enhanced 5.0% on OA and 7.2% on AA in LCZ classification, under a fair comparison with a model trained by Sentinel-1 of the same area. [Zhang, Rui; Yang, Wei; Chen, Jie] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Wang, Yuanyuan; Hu, Jingliang; Zhu, Xiao Xiang] Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany; [Wang, Yuanyuan; Zhu, Xiao Xiang] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany; [Yang, Wei; Chen, Jie] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China Beihang University; Technical University of Munich; Helmholtz Association; German Aerospace Centre (DLR) Zhu, XX (corresponding author), Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany.;Chen, J (corresponding author), Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China. zrui201@buaa.edu.cn; yuanyuan.wang@dlr.de; jingliang.hu@dlr.de; yangweigigi@sina.com; chenjie@buaa.edu.cn; xiaoxiang.zhu@dlr.de Wang, Yu/GZL-9655-2022; wangwangwang, yuanyaun/HHN-6432-2022; Wang, Yuan/GRF-3621-2022; Wang, Yuan/HHC-1520-2022; Lv, Yuanjie/AER-0767-2022 Wang, Yuanyuan/0000-0002-0586-9413; Zhang, Rui/0000-0002-7162-6751; Zhu, Xiao Xiang/0000-0001-5530-3613; Hu, Jingliang/0000-0002-0690-5260 National Natural Science Foundation of China [61861136008, 61701012]; Technical University of Munich-Beihang Doctoral Exchange Program; China Scholarship Council [CSC 202006020127]; European Research Council (ERC) under the European Union [ERC-2016-StG-714087]; Helmholtz Association; Helmholtz Excellent Professorship Data Science in Earth Observation-Big Data Fusion for Urban Research National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Technical University of Munich-Beihang Doctoral Exchange Program; China Scholarship Council(China Scholarship Council); European Research Council (ERC) under the European Union(European Research Council (ERC)); Helmholtz Association(Helmholtz Association); Helmholtz Excellent Professorship Data Science in Earth Observation-Big Data Fusion for Urban Research Manuscript received June 21, 2021; revised November 15, 2021 and December 12, 2021; accepted December 20, 2021. Date of publication December 23, 2021; date of current version March 8, 2022. This work was supported in part by the National Natural Science Foundation of China under Grant 61861136008 and Grant 61701012, in part by the Technical University of Munich-Beihang Doctoral Exchange Program and the China Scholarship Council under Grant CSC 202006020127, in part by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (acronym: So2Sat, project website: www.so2sat.eu) under Grant ERC-2016-StG-714087, in part by the Helmholtz Association through the framework of the Helmholtz Artificial Intelligence Cooperation Unit-Local Unit Munich Unit @Aeronautics, Space and Transport, and in part by the Helmholtz Excellent Professorship Data Science in Earth Observation-Big Data Fusion for Urban Research. (Corresponding authors: Jie Chen; Xiaoxiang Zhu.) 36 1 1 3 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 4408216 10.1109/TGRS.2021.3137911 0.0 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZP9VB hybrid 2023-03-23 WOS:000766762800044 0
J Xiong, LX; Ning, JJ; Dong, YH; Dai, MM Xiong, Li-Xin; Ning, Jia-Jun; Dong, Yun-He; Dai, Meng-Meng Simulation of the pollution abatement behavior of regional metal-related enterprises based on the interactive perspective of industrial agglomerations and emission reduction effects ENVIRONMENTAL GEOCHEMISTRY AND HEALTH English Article Metal-related enterprise agglomeration; Machine learning method; Water pollution simulation; Metal pollution RIVER-BASIN; ENVIRONMENTAL-REGULATIONS; CHINA POLLUTION; GREEN; MODEL; URBANIZATION; PERFORMANCE; INNOVATION; EFFICIENCY; ORIGIN A machine learning method was used to process a multiagent information database to study the spatial distribution characteristics of agglomerations of metal-related enterprises and to analyze the spatial and temporal differentiation characteristics of pollution reduction in metal-related enterprises. Based on the spatial distribution of enterprises and a simulation of their pollution reduction behaviors, the layout of 380 enterprises sample is optimized, and the direction of industrial transfer is planned to give full play to the pollution reduction effect of enterprise agglomeration. The results showed that (1) the metal-related enterprises in the Chang-Zhu-Tan urban agglomeration have obvious spatial heterogeneity and are mainly distributed in the district of Changsha, the Qingshuitang Industrial Zone, Liling city and the Qibaoshan Industrial Zone of Liuyang city, while the metal-related enterprises in Shaoshan city, Zhuzhou County and Liling city are scattered. (2) The pollution emission behaviors of enterprises differ in time and space, and the pollution concentrations are highest in industrial parks such as Qingshuitang and Zhubu Port. (3) There is an interactive relationship between the degree of enterprise agglomeration and the pollution reduction effect. The spatial positive coupling degree between the concentration of metal-related enterprises and the degree of metal-related pollution is significant, accounting for 94.96% of the study area. Low pollution-high agglomeration areas, high pollution-low agglomeration areas, high pollution-high agglomeration areas, and low pollution-low agglomeration area account for 1.01%, 4.03%, 2.87%, and 92.09% of the study area, respectively. Finally, based on the new development concept of dual circulation and the theory of a two-oriented society in the new era, the paper puts forward suggestions and policies for the sustainable development of industrial transfer. [Xiong, Li-Xin; Ning, Jia-Jun; Dong, Yun-He; Dai, Meng-Meng] Cent South Univ Forestry & Technol China, Business Sch, Changsha 410004, Peoples R China; [Xiong, Li-Xin] Univ Lille, Lab Genie Civil & Geoenvironm, F-59655 Lille, France Central South University of Forestry & Technology; Universite de Lille - ISITE; Universite de Lille Xiong, LX (corresponding author), Cent South Univ Forestry & Technol China, Business Sch, Changsha 410004, Peoples R China. xionglixin@csu.edu.cn Xiong, LiXin/0000-0003-2318-8301 National Natural Science Foundation of China [17CJY022]; National Scholarship Foundation [202008430028] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Scholarship Foundation This work was financially supported by the National Natural Science Foundation of China (17CJY022) and the National Scholarship Foundation (202008430028). 78 0 0 12 59 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-4042 1573-2983 ENVIRON GEOCHEM HLTH Environ. Geochem. Health MAR 2022.0 44.0 3 1081 1098 10.1007/s10653-021-01015-9 0.0 JUN 2021 18 Engineering, Environmental; Environmental Sciences; Public, Environmental & Occupational Health; Water Resources Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Environmental Sciences & Ecology; Public, Environmental & Occupational Health; Water Resources ZF6AT 34170458.0 2023-03-23 WOS:000666966200001 0
C Sun, YM; Meng, WZ; Li, WJ IEEE Sun, Yiming; Meng, Weizhi; Li, Wenjuan Designing In-Air Hand Gesture-based User Authentication System via Convex Hull 2022 19TH ANNUAL INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY & TRUST (PST) Annual Conference on Privacy Security and Trust-PST English Proceedings Paper 19th Annual International Conference on Privacy, Security and Trust (PST) AUG 23-24, 2022 ELECTR NETWORK Canadian Inst Cybersecur,Univ New Brunswick,Ignite Fredericton,IEEE User Authentication; Hand Gesture; In-Air Gesture; Convex Hull; Practical System With the rapid development of personal computers and mobile devices, it is very important to properly authenticate a user's identity to protect the information and data stored on these devices. Due to various privacy and security concerns, contactless authentication has received much attention, among which in-air gesture based authentication is one promising solution. Motivated by this observation, in this work, we develop and implement a real-time in-air hand gesture-based user authentication system, where users can define or select various gestures and generate their credentials. Our system can verify a user using a deep learning-enabled inference framework without the need of being trained by a powerful device. Different from the state-of-the-art, our system uses a method of convex hull to recognize the hand gesture. In our user study, we involve 20 participants to examine the system performance, and find that our system is viable and usable with a success rate of 95%. [Sun, Yiming; Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, SPTAGE Lab, Lyngby, Denmark; [Li, Wenjuan] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Peoples R China Technical University of Denmark; Hong Kong Polytechnic University Sun, YM (corresponding author), Tech Univ Denmark, Dept Appl Math & Comp Sci, SPTAGE Lab, Lyngby, Denmark. 20 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1712-364X 978-1-6654-7398-9 ANN CONF PRIV SECUR 2022.0 10.1109/PST55820.2022.9851982 0.0 5 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT9HW 2023-03-23 WOS:000861070900021 0
J Mishra, SK; Dahiya, S; Gangil, B; Ranakoti, L; Singh, T; Sharma, S; Boonyasopon, P; Rangappa, SM; Siengchin, S Mishra, Sandip Kumar; Dahiya, Sanjeev; Gangil, Brijesh; Ranakoti, Lalit; Singh, Tej; Sharma, Shubham; Boonyasopon, Pawinee; Rangappa, Sanjay Mavinkere; Siengchin, Suchart Mechanical, morphological, and tribological characterization of novel walnut shell-reinforced polylactic acid-based biocomposites and prediction based on artificial neural network BIOMASS CONVERSION AND BIOREFINERY English Article; Early Access Walnut shell filler; Polylactic acid (PLA); Mechanical properties; Biodegradation; ANN (artificial neural network) COMPOSITE Biocomposites are now being viewed as an almost perfect alternative to conventional nonbiodegradable polymers. In addition, the use of natural fillers further augments the value of biocomposites by reducing the overall cost. In the present investigation, biocomposite was prepared using a compression molding method with a walnut (WN) shell as a filler material and polylactic acid (PLA) as a matrix. The weight percentage of WN added in the matrix was varied as 0, 2.5, 5, 7.5, and 10. Composites were characterized for mechanical and wear behavior. Observation revealed that flexural strength, impact energy, and Vickers hardness of PLA showed improvement with WN reinforcement at all the wt. % and found to be maximum at 10 wt. %. However, a marginal reduction in tensile properties was observed at same weightage. Mass loss due to sliding wear was observed to be highest at 7.5 wt. % of WN which later reduced at 10 wt. %. Biodegradability results revealed that the composites showed several patches and material removal in the form of chips at the surfaces after 8 weeks of immersion in water. And the inclusion of WN has further enhanced biodegradability. In this study, the ANN model was developed to predict the material properties of different categories of materials when the percentage of WN was varied. [Mishra, Sandip Kumar; Dahiya, Sanjeev] Univ Technol, Sch Engn & Technol, Jaipur, Rajasthan, India; [Gangil, Brijesh] HNBGU, Sch Engn & Technol, Srinagar, Uttaranchal, India; [Ranakoti, Lalit] Graphic Era Deemed Be Univ, Dehra Dun, Uttaranchal, India; [Singh, Tej] Eotvos Lorand Univ, Savaria Inst Technol, Fac Informat, H-9700 Szombathely, Hungary; [Sharma, Shubham] Chandigarh Univ, Univ Ctr Res & Dev, Mech Engn Dept, Mohali 140413, India; [Sharma, Shubham] Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China; [Boonyasopon, Pawinee] King Mongkuts Univ Technol North Bangkok, Fac Architecture & Design, Dept Design Management & Business Dev, Bangkok, Thailand; [Rangappa, Sanjay Mavinkere; Siengchin, Suchart] King Mongkuts Univ Technol North Bangkok KMUTNB, Sirindhorn Int Thai German Grad Sch Engn TGGS, Dept Mat & Prod Engn, Nat Composites Res Grp Lab, Bangkok, Thailand Hemwati Nandan Bahuguna Garhwal University; Graphic Era University; Eotvos Lorand University; Chandigarh University; Qingdao University of Technology; King Mongkuts University of Technology North Bangkok; King Mongkuts University of Technology North Bangkok Sharma, S (corresponding author), Chandigarh Univ, Univ Ctr Res & Dev, Mech Engn Dept, Mohali 140413, India.;Sharma, S (corresponding author), Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China.;Rangappa, SM (corresponding author), King Mongkuts Univ Technol North Bangkok KMUTNB, Sirindhorn Int Thai German Grad Sch Engn TGGS, Dept Mat & Prod Engn, Nat Composites Res Grp Lab, Bangkok, Thailand. shubham543sharma@gmail.com; mcemrs@gmail.com King Mongkut's University of Technology North Bangkok [KMUTNB-66-KNOW-14] King Mongkut's University of Technology North Bangkok This research was funded by King Mongkut's University of Technology North Bangkok with Contract no. KMUTNB-66-KNOW-14. 45 0 0 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2190-6815 2190-6823 BIOMASS CONVERS BIOR Biomass Convers. Biorefinery 10.1007/s13399-022-03670-z 0.0 DEC 2022 12 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 8I8CU 2023-03-23 WOS:000921957600004 0
J Liu, Y; Guo, YM; Liu, L; Bakker, EM; Lew, MS Liu, Yu; Guo, Yanming; Liu, Li; Bakker, Erwin M.; Lew, Michael S. CycleMatch: A cycle-consistent embedding network for image-text matching PATTERN RECOGNITION English Article Image-text matching; Embedding; Deep neural networks; Late-fusion inference In numerous multimedia and multi-modal tasks from image and video retrieval to zero-shot recognition to multimedia question and answering, bridging image and text representations plays an important and in some cases an indispensable role. To narrow the modality gap between vision and language, prior approaches attempt to discover their correlated semantics in a common feature space. However, these approaches omit the intra-modal semantic consistency when learning the inter-modal correlations. To address this problem, we propose cycle-consistent embeddings in a deep neural network for matching visual and textual representations. Our approach named as CycleMatch can maintain both inter-modal correlations and intra-modal consistency by cascading dual mappings and reconstructed mappings in a cyclic fashion. Moreover, in order to achieve a robust inference, we propose to employ two late-fusion approaches: average fusion and adaptive fusion. Both of them can effectively integrate the matching scores of different embedding features, without increasing the network complexity and training time. In the experiments on cross-modal retrieval, we demonstrate comprehensive results to verify the effectiveness of the proposed approach. Our approach achieves state-of-the-art performance on two well-known multi modal datasets, Flickr30K and MSCOCO. (C) 2019 Elsevier Ltd. All rights reserved. [Liu, Yu; Bakker, Erwin M.; Lew, Michael S.] Leiden Univ, Dept Comp Sci, NL-2333 CA Leiden, Netherlands; [Guo, Yanming; Liu, Li] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 8000, Finland Leiden University; Leiden University - Excl LUMC; National University of Defense Technology - China; University of Oulu Lew, MS (corresponding author), Leiden Univ, Dept Comp Sci, NL-2333 CA Leiden, Netherlands. m.s.k.lew@liacs.leidenuniv.nl LIACS Media Lab at Leiden University [2006002026]; National Natural Science Foundation of China [61872379] LIACS Media Lab at Leiden University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the LIACS Media Lab at Leiden University under Grant 2006002026 and the National Natural Science Foundation of China under Grant 61872379. We are also grateful to the support of NVIDIA with the donation of GPU cards. 70 20 20 0 23 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. SEP 2019.0 93.0 365 379 10.1016/j.patcog.2019.05.008 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IE9MF Green Accepted 2023-03-23 WOS:000472697800028 0
J Li, YY Li, Yangyuan Stable Modeling on Resource Usage Parameters of MapReduce Application BRAIN-BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE English Article MapReduce application; resource bottleneck; resource usage parameters; multiple linear regression; stable modeling; minimum sampling time Currently, Hadoop MapReduce framework has been applied to many productive fields to analyze big data. MapReduce applications based on the MapReduce programming model are used to generate and process such huge data. Due to various computational purpose, MapReduce applications have different resource requirements. For specific applications, the resource bottleneck of the cloud computing platform must inevitably impact its executive performance. Therefore, identification of the bottleneck about the allocated resource for MapReduce applications is crucially needed from the viewpoint of either cloud operators or program developers. In this paper, we model the relationship of resource usage parameters of MapReduce applications using multiple linear regression methods and investigate the minimum sampling time for stable modeling. Based on the analysis, we propose the approach which can be used to build stable performance model to expose the bottleneck resource of Hadoop platform and give the effective optimization suggestion. [Li, Yangyuan] Budapest Univ Technol & Econ, Dept Networked Syst & Serv, Magyar Tudosok Korutja 2, H-1117 Budapest, Hungary; [Li, Yangyuan] Xian Siyuan Univ, Dept Comp Sci, Xian 710038, Shaanxi, Peoples R China Budapest University of Technology & Economics Li, YY (corresponding author), Budapest Univ Technol & Econ, Dept Networked Syst & Serv, Magyar Tudosok Korutja 2, H-1117 Budapest, Hungary.;Li, YY (corresponding author), Xian Siyuan Univ, Dept Comp Sci, Xian 710038, Shaanxi, Peoples R China. wlqsb@hotmail.com 28 0 0 0 0 EDUSOFT PUBLISHING BACAU 9 MAI STR 82, BACAU, 600065, ROMANIA 2067-3957 BRAIN-BROAD RES ARTI BRAIN-Broad Res. Artif. Intellect. Neurosci. MAY 2018.0 9.0 2 45 62 18 Neurosciences Emerging Sources Citation Index (ESCI) Neurosciences & Neurology GJ5CF 2023-03-23 WOS:000435398100004 0
J Su, YM; Hu, MM; Wang, YC; Zhang, HR; He, C; Wang, YW; Wang, DC; Wu, XH; Zhuang, YH; Hong, S; Trolle, D Su, Yuming; Hu, Mingming; Wang, Yuchun; Zhang, Haoran; He, Chao; Wang, Yanwen; Wang, Dianchang; Wu, Xinghua; Zhuang, Yanhua; Hong, Song; Trolle, Dennis Identifying key drivers of harmful algal blooms in a tributary of the Three Gorges Reservoir between different seasons: Causality based on data-driven methods ENVIRONMENTAL POLLUTION English Article Driving factor analysis; Data-driven; Machine-learning; Maximal information coefficient; Extra trees regression; Harmful algal blooms WATER-QUALITY; XIANGXI BAY; NUTRIENT LIMITATION; RELATIVE IMPORTANCE; EUTROPHICATION; LAKE; NITROGEN; PHYTOPLANKTON; PHOSPHORUS; CYANOBACTERIA Intense harmful algal blooms (HABs) can occur in the backwaters of tributaries supplying large-scale reservoirs. Due to the characteristics of process-based models and difficulties in modelling complex nonlinear processes, traditional models have difficulties disentangling the driving factors of HABs. In this study, we used data-driven methods (i.e., correlation analysis and machine-learning models) to identify the most important drivers of HABs in the Xiangxi River, a tributary of the Three Gorges Reservoir, China (2017-2018), for the dry season (from October to mid-April) and wet season (from April to September). We utilized the maximal information coefficient (MIC) combined with a time lag strategy and prior knowledge to quantitatively identify the driving variables of HABs. An extra trees regression (ETR) model was developed to assess the relative importance of causal variables driving algal blooms for the different periods. The results showed that water temperature was the most important driver for the duration of the study, followed by total nitrogen. Nitrogen had a stronger effect on algal blooms than phosphorus during both the wet and dry seasons. HABs were mainly affected by ammonia nitrogen in the wet season and by other forms of nitrogen in the dry season. In contrast, rather than the water temperature and nutrients, the operation of the Three Gorges Dam (difference between inflow and outflow discharge rate) was the most significant factor for algal blooms during the dry season, but its influence sharply declined during the wet season. This study showed that the key drivers of HABs can differ between seasons and suggests that HAB management should take seasonality into account. [Su, Yuming; He, Chao; Wang, Yanwen; Hong, Song] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China; [Su, Yuming; Hu, Mingming; Wang, Yuchun] China Inst Water Resources & Hydropower Res, Dept Water Ecol & Environm, Beijing 100038, Peoples R China; [Zhang, Haoran] Univ Washington, Dept Geog, Seattle, WA 98195 USA; [Wang, Dianchang; Wu, Xinghua] China Three Gorges Corp, Wuhan 430010, Peoples R China; [Zhuang, Yanhua] Chinese Acad Sci, Innovat Inst Geodesy & Geophys, Hubei Prov Engn Res Ctr Nonpoint Source Pollut Co, Wuhan 430077, Peoples R China; [Su, Yuming; Trolle, Dennis] Aarhus Univ, Dept Biosci, DK-8600 Silkeborg, Denmark Wuhan University; China Institute of Water Resources & Hydropower Research; University of Washington; University of Washington Seattle; China Three Gorges Corporation; Chinese Academy of Sciences; Aarhus University Hong, S (corresponding author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China. songhongpku@126.com Trolle, Dennis/0000-0001-7568-2411; Su, Yuming/0000-0002-1806-2571 National Natural Science Foundation of China [U2040211, U1802241, 92047204]; Project of China Three Gorges Corporation [201903144]; China Institute of Water Resources and Hydropower Research [SKL2020TS07]; water quality monitoring station for key tributaries of the Three Gorges Reservoir (Xiangxi river station) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of China Three Gorges Corporation; China Institute of Water Resources and Hydropower Research; water quality monitoring station for key tributaries of the Three Gorges Reservoir (Xiangxi river station) This study was funded by the National Natural Science Foundation of China (No. U2040211, No. U1802241, and No. 92047204), the Project of China Three Gorges Corporation (No. 201903144), and China Institute of Water Resources and Hydropower Research (SKL2020TS07). Thanks for the monitoring data supported by the water quality monitoring station for key tributaries of the Three Gorges Reservoir (Xiangxi river station). 61 6 6 26 71 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0269-7491 1873-6424 ENVIRON POLLUT Environ. Pollut. MAR 15 2022.0 297.0 118759 10.1016/j.envpol.2021.118759 0.0 JAN 2022 11 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology ZG3IQ 34971739.0 2023-03-23 WOS:000760152800008 0
J Liu, Q; Liu, BJ; Zhang, YH; Hu, TL; Lin, ZB; Liu, G; Wang, XJ; Ma, J; Wang, H; Jin, HY; Ambus, P; Amonette, JE; Xie, ZB Liu, Qi; Liu, Benjuan; Zhang, Yanhui; Hu, Tianlong; Lin, Zhibin; Liu, Gang; Wang, Xiaojie; Ma, Jing; Wang, Hui; Jin, Haiyang; Ambus, Per; Amonette, James E.; Xie, Zubin Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective GLOBAL CHANGE BIOLOGY English Article biochar; machine learning; N leaching; N2O; NH3; spatial variability CARBON; PYROLYSIS; DENITRIFICATION; METAANALYSIS; TEMPERATURE; NUTRITION; DYNAMICS; SYSTEM; IMPACT; FLUXES Biochar application to croplands has been proposed as a potential strategy to decrease losses of soil-reactive nitrogen (N) to the air and water. However, the extent and spatial variability of biochar function at the global level are still unclear. Using Random Forest regression modelling of machine learning based on data compiled from the literature, we mapped the impacts of different biochar types (derived from wood, straw, or manure), and their interactions with biochar application rates, soil properties, and environmental factors, on soil N losses (NH3 volatilization, N2O emissions, and N leaching) and crop productivity. The results show that a suitable distribution of biochar across global croplands (i.e., one application of <40 t ha(-1) wood biochar for poorly buffered soils, such as those characterized by soil pH<5, organic carbon<1%, or clay>30%; and one application of <80 t ha(-1) wood biochar, <40 t ha(-1) straw biochar, or <10 t ha(-1) manure biochar for other soils) could achieve an increase in global crop yields by 222-766 Tg yr(-1) (4%-16% increase), a mitigation of cropland N2O emissions by 0.19-0.88 Tg N yr(-1) (6%-30% decrease), a decline of cropland N leaching by 3.9-9.2 Tg N yr(-1) (12%-29% decrease), but also a fluctuation of cropland NH3 volatilization by -1.9-4.7 Tg N yr(-1) (-12%-31% change). The decreased sum of the three major reactive N losses amount to 1.7-9.4 Tg N yr(-1), which corresponds to 3%-14% of the global cropland total N loss. Biochar generally has a larger potential for decreasing soil N losses but with less benefits to crop production in temperate regions than in tropical regions. [Liu, Qi; Liu, Benjuan; Zhang, Yanhui; Hu, Tianlong; Lin, Zhibin; Liu, Gang; Wang, Xiaojie; Ma, Jing; Wang, Hui; Jin, Haiyang; Xie, Zubin] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Jiangsu, Peoples R China; [Liu, Qi; Liu, Benjuan; Zhang, Yanhui; Hu, Tianlong; Lin, Zhibin; Wang, Xiaojie; Ma, Jing; Wang, Hui; Jin, Haiyang] Univ Chinese Acad Sci, Beijing, Peoples R China; [Liu, Qi] Nanjing Forestry Univ, Coll Forestry, Nanjing, Jiangsu, Peoples R China; [Ambus, Per] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark; [Amonette, James E.] Pacific Northwest Natl Lab, Phys Sci Div, Richland, WA 99352 USA; [Amonette, James E.] Washington State Univ, Ctr Sustaining Agr & Nat Resources, Pullman, WA 99164 USA Chinese Academy of Sciences; Institute of Soil Science, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Nanjing Forestry University; University of Copenhagen; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Washington State University Xie, ZB (corresponding author), Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Jiangsu, Peoples R China.;Ambus, P (corresponding author), Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark.;Amonette, JE (corresponding author), Pacific Northwest Natl Lab, Phys Sci Div, Richland, WA 99352 USA. peam@ign.ku.dk; jim.amonette@pnnl.gov; zbxie@issas.ac.cn ; Ambus, Per/B-2514-2015 Jin, Haiyang/0000-0002-0357-9877; Ambus, Per/0000-0001-7580-524X Natural Science Foundation of China [NFSC-41171191]; Special Project on Agricultural Science and Technology [201503137]; Danish Agency for Science, Technology and Innovation [1370-00036B]; Special Project on the Basis of National Science and Technology of China: National Survey of Biological Nitrogen Fixation Resources in Paddies of China [2015FY110700]; Science and Technology Supporting Project of Jiangsu Province [BE2013451]; Blue Moon Fund USA; Science and Technology Supporting Project of China [2013BAD11B01] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Special Project on Agricultural Science and Technology; Danish Agency for Science, Technology and Innovation; Special Project on the Basis of National Science and Technology of China: National Survey of Biological Nitrogen Fixation Resources in Paddies of China; Science and Technology Supporting Project of Jiangsu Province; Blue Moon Fund USA; Science and Technology Supporting Project of China We gratefully acknowledge support for this research from the Natural Science Foundation of China (Grant no. NFSC-41171191), Special Project on Agricultural Science and Technology (Grant no. 201503137), the Danish Agency for Science, Technology and Innovation for financial support to Sino-Danish cooperation on biochar as a tool to mitigate climate change (No. 1370-00036B), Special Project on the Basis of National Science and Technology of China: National Survey of Biological Nitrogen Fixation Resources in Paddies of China (2015FY110700), the Science and Technology Supporting Project of China (2013BAD11B01), the Science and Technology Supporting Project of Jiangsu Province (BE2013451), and the Blue Moon Fund USA. 63 104 110 49 357 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1354-1013 1365-2486 GLOBAL CHANGE BIOL Glob. Change Biol. JUN 2019.0 25.0 6 2077 2093 10.1111/gcb.14613 0.0 17 Biodiversity Conservation; Ecology; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology HX5LE 30844112.0 2023-03-23 WOS:000467441900017 0
J Xing, JK; Luo, K; Pitsch, H; Wang, HO; Bai, Y; Zhao, CG; Fan, JR Xing, Jiangkuan; Luo, Kun; Pitsch, Heinz; Wang, Haiou; Bai, Yun; Zhao, Chunguang; Fan, Jianren Predicting kinetic parameters for coal devolatilization by means of Artificial Neural Networks PROCEEDINGS OF THE COMBUSTION INSTITUTE English Article Coal devolatilization; Artificial Neural Network (ANN); The CPD model; Heating rates; Two-step model PULVERIZED-COAL; NUMERICAL-SIMULATION; CONVERSION SUBMODELS; DESIGN APPLICATIONS; ELEVATED PRESSURES; PROCESS MODEL; PYROLYSIS; BEHAVIOR; FLAME The chemical percolation devolatilization (CPD) model has been shown to represent the devolatilization process of different coals and heating conditions with good accuracy. However, its use in computational fluid dynamics is limited because of its relatively high computational cost. Here, an Artificial Neural Network (ANN) based model for predicting coal devolatilization kinetics is developed based on a database constructed with the CPD model for a wide range of coals and heating rates. The heating rates and the information of ultimate and proximate analysis are chosen as inputs of the ANN model to consider the effects of coal types and heating conditions on coal devolatilization; the outputs are the kinetic parameters for the two-step kinetic model. The learning, validation, and application results show that the proposed ANN model has a competitive prediction capability on both the total volatile release and release rates when compared with the CPD model, but has obvious computational efficiency advantages. Furthermore, the relative impact of the coal type and heating rate on each kinetic parameter for coal devolatilization is quantitatively evaluated through the Garson equation. It is found that the heating rate has the strongest effect on the pre-exponential factor, while the coal types show significant influence on the activation energy and final yield of the two reactions in the two-step model. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved. [Xing, Jiangkuan; Luo, Kun; Wang, Haiou; Bai, Yun; Zhao, Chunguang; Fan, Jianren] Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China; [Pitsch, Heinz] Rhein Westfal TH Aachen, Inst Combust Technol, Templergraben 64, D-52056 Aachen, Germany Zhejiang University; RWTH Aachen University Luo, K (corresponding author), Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China. zjulk@zju.edu.cn Pitsch, Heinz/E-1082-2014; Xing, Jiangkuan/AAE-6356-2019; luo, kun/A-8039-2012 Pitsch, Heinz/0000-0001-5656-0961; luo, kun/0000-0003-3644-9400; Xing, Jiangkuan/0000-0002-2423-5627; Wang, Haiou/0000-0002-6480-2657 National Natural Science Foundation of China [51390493, 51390490]; Deutsche Forschungsgemeinschaft (DFG) [SFB/TRR 129] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)) This work is supported by the National Natural Science Foundation of China (Grant No: 51390493, 51390490). The authors are grateful to that. HP acknowledges financial support through the Deutsche Forschungsgemeinschaft (DFG) through SFB/TRR 129. 28 35 36 2 25 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1540-7489 1873-2704 P COMBUST INST Proc. Combust. Inst. 2019.0 37.0 3 2943 2950 10.1016/j.proci.2018.05.148 0.0 8 Thermodynamics; Energy & Fuels; Engineering, Chemical; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Engineering HI7IH 2023-03-23 WOS:000456628600034 0
J Wang, TL; Cao, JW; Pei, LY Wang, Tianlei; Cao, Jiuwen; Pei, Leiyu A Novel Underground Pipeline Surveillance System Based on Hybrid Acoustic Features IEEE SENSORS JOURNAL English Article Feature extraction; Spectrogram; Pipelines; Mel frequency cepstral coefficient; Surveillance; Neural networks; Underground pipeline surveillance; construction machinery classification; acoustic spectrogram; SBMS; IESF; PCNN MACHINE; RECOGNITION; ALGORITHM; INPUT; SCALE Underground pipeline network suffers severe damages caused by construction machines used in the rapid urbanization development and construction, leading to profound impact to people's life. An intelligent construction machinery classification (CMC) system thus becomes important to urban security and smart city engineering. Conventional methods are always problematic in one way or another. For instance, the one-dimensional speech features based recognition algorithms are usually less discriminative in the complex urban acoustic environment, and the two-dimensional spectrogram based convolution neural network (CNN) suffers a long model learning time and is less robust to the background noises. To address these deficiencies, a novel CMC system based on new hybrid acoustic features is presented. Two novel acoustic feature extraction methods are developed, where the first explores the concentrations of the Mel-frequency spectrogram (MFS) using the statistics of the binary MFS (SBMS) and the second characterizes the information entropy sequence feature of the binary MFS (IESF) based on the pulse-coupled neural network (PCNN) model. Then, the hybrid features of SBMS/IESF and the Linear Prediction Cepstral Coefficients (LPCC)/Mel-Frequency Cepstral Coefficients (MFCC) are further studied. A database consisting of 84,900 acoustic samples from 6 typical construction machines, horns of vehicles and other urban noises is recorded for experiment and performance validation. The comparisons with many state-of-the-art algorithms demonstrate the superiority of the proposed method. [Wang, Tianlei; Cao, Jiuwen; Pei, Leiyu] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China; [Wang, Tianlei; Cao, Jiuwen; Pei, Leiyu] Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Zhejiang, Peoples R China; [Cao, Jiuwen] Univ Wuppertal, Sch Elect Informat & Media Engn, D-42119 Wuppertal, Germany Hangzhou Dianzi University; Hangzhou Dianzi University; University of Wuppertal Cao, JW (corresponding author), Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China. tianlei.wang.cn@gmail.com; jwcao@hdu.edu.cn; peileiyu@foxmail.com Wang, Tianlei/0000-0002-4498-4326 NNSF of China [U1909209, 61503104]; K. C. Wong Education Foundation; Deutscher Akademischer Austauschdienst (DAAD), Germany NNSF of China(National Natural Science Foundation of China (NSFC)); K. C. Wong Education Foundation; Deutscher Akademischer Austauschdienst (DAAD), Germany(Deutscher Akademischer Austausch Dienst (DAAD)) This work was supported in part by the NNSF of China under Grant U1909209 and Grant 61503104, in part by the K. C. Wong Education Foundation, andin part by Deutscher Akademischer Austauschdienst (DAAD), Germany. The associate editor coordinating the review of this article and approving it for publication was Dr. Cynthia Furse. 42 5 5 2 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. JAN 15 2021.0 21.0 2 1040 1050 10.1109/JSEN.2020.3009112 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics PI2AU 2023-03-23 WOS:000600900300018 0
C Liu, FZ; Chen, ZP; Cui, YL; Liu, C; Li, XH; Gao, C ACM Liu, Fanzhen; Chen, Zhengpeng; Cui, Yali; Liu, Chen; Li, Xianghua; Gao, Chao A Hybrid Evolutionary Algorithm for Community Detection 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017) English Proceedings Paper IEEE/WIC/ACM International Conference on Web Intelligence (WI) AUG 23-26, 2017 Leipzig, GERMANY IEEE,WIC,ACM Complex networks; Community detection; Genetic algorithm; Physarum NETWORK; MODEL Evolutionary algorithm belongs to the behaviorism which is one of major approaches to artificial intelligence. Community detection is one of the important applications of the evolutionary algorithm. Detecting the community structure, an essential property for complex networks, can help us understand the inherent functions of real systems. It has been proved that genetic algorithm (GA) is feasible for community detection, and yet existing GA-based community detection algorithms still need improving in terms of their robustness and accuracy. A Physarum-based network model (PNM) with an intelligence of recognizing inter-community edges based on a kind of multi-headed slime mold, has been proposed in the phase of GA's initialization for optimization. In this paper, integrated with PNM after three operators of GA during the process of community detection, a novel genetic algorithm, called P-GACD, is proposed to improve the efficiency of GA for community detection. In addition, some experiments are implemented in five real-world networks to evaluate the performance of P-GACD. The results reveal that P-GACD shows an advantage in terms of the robustness and accuracy, contrasted with the existing algorithms. [Liu, Fanzhen; Chen, Zhengpeng; Li, Xianghua; Gao, Chao] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China; [Cui, Yali] Guangdong Coll Business & Technol, Guangzhou 526020, Guangdong, Peoples R China; [Liu, Chen] Pangang Grp Xichang Steel & Vanadium Co Ltd, Xichang 615032, Sichuan, Peoples R China; [Gao, Chao] Potsdam Inst Climate Impact Res PIK Potsdam, D-14473 Potsdam, Germany Southwest University - China Liu, FZ (corresponding author), Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China. liufanzhen@yeah.net; czp965227896@outlook.com; 52560507@foxmail.com; 292462254@foxmail.com; li_xianghua@163.com; cgao@swu.edu.cn Liu, Fanzhen/GPP-4403-2022 Liu, Fanzhen/0000-0002-4110-2893 Fundamental Research Funds for the Central Universities [XDJK2016A008, XDJK2016B029]; National Natural Science Foundation of China [61403315, 61402379]; CQ CSTC [cstc2015gjhz40002] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CQ CSTC(Natural Science Foundation Project of CQ CSTC) Dr. Chao Gao is the corresponding author of this paper. This work is supported by the National Natural Science Foundation of China (Nos. 61403315, 61402379), Fundamental Research Funds for the Central Universities (Nos. XDJK2016A008, XDJK2016B029), CQ CSTC (cstc2015gjhz40002). 26 3 3 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-4951-2 2017.0 469 475 10.1145/3106426.3106477 0.0 7 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ6US 2023-03-23 WOS:000426965100059 0
J Malinowska, A; Cui, XM; Salmi, EF; Hejmanowski, R Malinowska, Agnieszka; Cui, Ximin; Salmi, Ebrahim Fathi; Hejmanowski, Ryszard A novel fuzzy approach to gas pipeline risk assessment under influence of ground movement INTERNATIONAL JOURNAL OF COAL SCIENCE & TECHNOLOGY English Article Fuzzy logic; Gas pipeline; GIS; Horizontal strain; Land subsidence; Risk management LOGIC; IDENTIFICATION; PREDICTION; ALGORITHM; RANKING The gas transport infrastructure is frequently localized in areas subjected to anthropogenic movements and strains. The potential impact of the ground movements on the gas pipeline in the aspect of its damage can be properly assessed e.g. by predicting strains, taking into account the causes of terrain movement. On the other hand, the hazard is also related to technological factors like design of the pipeline. The presented method is based on artificial intelligence methods allowing for evaluation of probability of failure risk in gas supply pipeline sections. The Mamdani fuzzy inference was used in this study. Uncertainty of variables characterizing the resistance of the gas pipeline and predicted continuous deformations of ground surface were accounted for in the model by using triangular-shaped membership functions. Based on the surface deformations and gas pipeline resistance and the inference model one can make prediction when the gas pipeline is hazarded. There were estimated two the most hazarded parts for two pipelines. We proved that the proposed model can contribute to the protection, costoptimization of the designed pipelines and to the repairs of the existing gas pipelines. [Malinowska, Agnieszka; Hejmanowski, Ryszard] AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland; [Cui, Ximin] China Univ Min & Technol Beijing, D11 Xueyuan Rd, Beijing 100083, Peoples R China; [Salmi, Ebrahim Fathi] CSIRO, Hard Rock Min, Pullenvale, Qld 4069, Australia; [Salmi, Ebrahim Fathi] QCAT, Mining3, Pullenvale, Qld 4069, Australia AGH University of Science & Technology; China University of Mining & Technology; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Commonwealth Scientific & Industrial Research Organisation (CSIRO) Malinowska, A (corresponding author), AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland. amalin@agh.edu.pl Hejmanowski, Ryszard/B-4382-2010; Malinowska, Agnieszka/P-8585-2015 Hejmanowski, Ryszard/0000-0002-0269-0565; Malinowska, Agnieszka/0000-0002-8492-9073 National Science Centre [2011/01/D/ST10/06958] National Science Centre(National Science Centre, Poland) The research reported in this paper has been supported by a grant from the National Science Centre No. 2011/01/D/ST10/06958. 38 1 1 3 5 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2095-8293 2198-7823 INT J COAL SCI TECHN Int. J. Coal Sci. Technol. DEC 2022.0 9.0 1 47 10.1007/s40789-022-00511-2 0.0 11 Energy & Fuels; Mining & Mineral Processing Emerging Sources Citation Index (ESCI) Energy & Fuels; Mining & Mineral Processing 2G7KE Green Submitted, gold 2023-03-23 WOS:000813768200001 0
C Huang, J; Zhou, N; Chen, RQ Chen, R; Rong, C; Grigoras, D Huang, Jie; Zhou, Ning; Chen, Riqing Multi-agent Network and Cellular Automata based Resources Assignment for City Educational System 2016 15TH INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED COMPUTING (ISPDC) International Symposium on Parallel and Distributed Computing English Proceedings Paper 15th International Symposium on Parallel and Distributed Computing (ISPDC) JUL 08-10, 2016 Fujian Agr & Forestry Univ, Fuzhou, PEOPLES R CHINA Fuzhou Univ,Univ Coll Cork,Univ Stavanger,STC Cloud Comp,Intel,IEEE Comp Soc Fujian Agr & Forestry Univ SIMULATION; MODEL In the paper, a city educational resources assignment problem (CERAP) is raised. The combination of big data, multi-agent model and cellular automata (CA) methodologies are argued and developed. Then, the agent-based computational framework is discussed to the educational social science control (assignment) system. The proposed methodologies provides a powerful way to address nonlinearities, humanities, and especially interdisciplinary control problem. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work. The agent-based approach invites the interpretation of society as a distributed computational network, and in turn the interpretation of social dynamics as a type of computation. Then, the simulation tools are mentioned. Finally, a short discussion and conclusion end this paper. [Huang, Jie] Univ Groningen, Fac Math & Nat Sci, NL-9747 AG Groningen, Netherlands; [Zhou, Ning; Chen, Riqing] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Fujian, Peoples R China University of Groningen; Fujian Agriculture & Forestry University Huang, J (corresponding author), Univ Groningen, Fac Math & Nat Sci, NL-9747 AG Groningen, Netherlands. autohuangjie@gmail.com; zhouning200@gmail.com; riqing.chen@fafui.edu.cn Zhou, Ning/AAG-9151-2020 Zhou, Ning/0000-0002-7457-5048 Philosophy and Social Science Planning Project of Fujian Province of China [2014B182]; Natural Science Foundation of Fujian Province of China [2016J01286, 2016J01284]; Research Project of Fujian Institute of Education [2015ZD-08]; Research Foundation for Outstanding Young Scholars in the University of Fujian Province of China Philosophy and Social Science Planning Project of Fujian Province of China; Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province); Research Project of Fujian Institute of Education; Research Foundation for Outstanding Young Scholars in the University of Fujian Province of China The research is supported in part by the Philosophy and Social Science Planning Project of Fujian Province of China (grant No. 2014B182), the Natural Science Foundation of Fujian Province of China (grant No. 2016J01286, 2016J01284), Research Project of Fujian Institute of Education (grant No. 2015ZD-08), and the Research Foundation for Outstanding Young Scholars in the University of Fujian Province of China. 12 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2379-5352 978-1-5090-4152-7 INT SYMP PARA DISTR 2016.0 340 343 10.1109/ISPDC.2016.55 0.0 4 Computer Science, Hardware & Architecture Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI1HX Green Published 2023-03-23 WOS:000405941600051 0
J Sun, X; Liu, YH; Xu, MT; Chen, HL; Han, JW; Wang, KH Sun, Xin; Liu, Yanheng; Xu, Mantao; Chen, Huiling; Han, Jiawei; Wang, Kunhao Feature selection using dynamic weights for classification KNOWLEDGE-BASED SYSTEMS English Article Machine learning; Feature selection; Information criterion; Filter method; Classification THEORETIC FEATURE-SELECTION; MUTUAL INFORMATION; ALGORITHMS; CANCER; PREDICTION; RELEVANCE; TUMOR Feature selection aims at finding a feature subset that has the most discriminative information from the original feature set. In this paper, we firstly present a new scheme for feature relevance, interdependence and redundancy analysis using information theoretic criteria. Then, a dynamic weighting-based feature selection algorithm is proposed, which not only selects the most relevant features and eliminates redundant features, but also tries to retain useful intrinsic groups of interdependent features. The primary characteristic of the method is that the feature is weighted according to its interaction with the selected features. And the weight of features will be dynamically updated after each candidate feature has been selected. To verify the effectiveness of our method, experimental comparisons on six UCI data sets and four gene microarray datasets are carried out using three typical classifiers. The results indicate that our proposed method achieves promising improvement on feature selection and classification accuracy. (C) 2012 Elsevier B.V. All rights reserved. [Sun, Xin; Liu, Yanheng; Chen, Huiling; Han, Jiawei; Wang, Kunhao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China; [Sun, Xin; Xu, Mantao] Univ Eastern Finland, Sch Comp, FIN-80101 Joensuu, Finland; [Liu, Yanheng] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China; [Xu, Mantao] Shanghai Dianji Univ, Sch Elect Engn, Shanghai 200240, Peoples R China Jilin University; University of Eastern Finland; Jilin University; Shanghai Dianji University Liu, YH (corresponding author), Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China. sunxin1984@yahoo.com.cn; yhliu@jlu.edu.cn chen, huiling/J-2477-2013; Sun, Xin/HPE-1847-2023; Chen, Huiling/N-8510-2019 Chen, Huiling/0000-0002-7714-9693 Natural Science Foundation of China [60973136, 61073164, 61072146]; Erasmus Mundus External Cooperation Window's Project (EMECW): One-More-Step [2011-2581 001-001 EMA2]; Science Foundation for Youth of Jilin Province [201101033]; Shanghai Pujiang Talent Project [10PJ1404400]; Graduate Innovation Fund of Jilin University [20121106] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Erasmus Mundus External Cooperation Window's Project (EMECW): One-More-Step; Science Foundation for Youth of Jilin Province; Shanghai Pujiang Talent Project; Graduate Innovation Fund of Jilin University(Jilin University) We would like to express our sincere appreciation to the anonymous reviewers for their insightful comments, which have greatly aided us in improving the quality of the paper. This work is supported by the National Natural Science Foundation of China (Nos. 60973136, 61073164, 61072146), Erasmus Mundus External Cooperation Window's Project (EMECW): One-More-Step (No. 2011-2581 001-001 EMA2), Science Foundation for Youth of Jilin Province (No. 201101033), Shanghai Pujiang Talent Project (No. 10PJ1404400) and Graduate Innovation Fund of Jilin University (No. 20121106). 56 67 73 4 73 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JAN 2013.0 37.0 541 549 10.1016/j.knosys.2012.10.001 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 073SC 2023-03-23 WOS:000313761800048 0
J Song, SS; Santos, IR; Yu, HM; Wang, FM; Burnett, WC; Bianchi, TS; Dong, JY; Lian, EG; Zhao, B; Mayer, L; Yao, QZ; Yu, ZG; Xu, BC Song, Shasha; Santos, Isaac R.; Yu, Huaming; Wang, Faming; Burnett, William C.; Bianchi, Thomas S.; Dong, Junyu; Lian, Ergang; Zhao, Bin; Mayer, Lawrence; Yao, Qingzhen; Yu, Zhigang; Xu, Bochao A global assessment of the mixed layer in coastal sediments and implications for carbon storage NATURE COMMUNICATIONS English Article ORGANIC-CARBON; CONTINENTAL-SHELF; AMAZON RIVER; ACCUMULATION; MARINE; DYNAMICS; BURIAL; DEPTH; GEOCHRONOLOGIES; DEPOSITION The sediment-water interface in the coastal ocean is a highly dynamic zone controlling biogeochemical fluxes of greenhouse gases, nutrients, and metals. Processes in the sediment mixed layer (SML) control the transfer and reactivity of both particulate and dissolved matter in coastal interfaces. Here we map the global distribution of the coastal SML based on excess Pb-210 (Pb-210(ex)) profiles and then use a neural network model to upscale these observations. We show that highly dynamic regions such as large estuaries have thicker SMLs than most oceanic sediments. Organic carbon preservation and SMLs are inversely related as mixing stimulates oxidation in sediments which enhances organic matter decomposition. Sites with SML thickness >60 cm usually have lower organic carbon accumulation rates (<50 g C m(-2) yr(-1)) and total organic carbon/specific surface area ratios (<0.4 mg m(-2)). Our global scale observations reveal that reworking can accelerate organic matter degradation and reduce carbon storage in coastal sediments. The authors map the global distribution of the mixed layer in coastal ocean sediments, based on a neural network model. These observations reveal that mixing can accelerate organic matter degradation and reduce carbon storage in the coastal ocean. [Song, Shasha; Zhao, Bin; Yao, Qingzhen; Yu, Zhigang; Xu, Bochao] Ocean Univ China, Minist Educ, Key Lab Marine Chem Theory & Technol, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Qingdao 266100, Peoples R China; [Song, Shasha; Zhao, Bin; Yao, Qingzhen; Yu, Zhigang; Xu, Bochao] Qingdao Natl Lab Marine Sci & Technol, Lab Marine Ecol & Environm Sci, Qingdao 266100, Peoples R China; [Song, Shasha] Ocean Univ China, Coll Chem & Chem Engn, Qingdao 266100, Peoples R China; [Santos, Isaac R.] Univ Gothenburg, Dept Marine Sci, S-40530 Gothenburg, Sweden; [Santos, Isaac R.] Southern Cross Univ, Natl Marine Sci Ctr, Sch Environm Sci & Engn, Coffs Harbour, NSW 2450, Australia; [Yu, Huaming] Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao 266100, Peoples R China; [Yu, Huaming] Ocean Univ China, Sanya Oceanog Inst, Sanya 572000, Peoples R China; [Wang, Faming] Chinese Acad Sci, Key Lab Vegetat Restorat & Management Degraded Ec, Xiaoliang Res Stn Trop Coastal Ecosyst, Guangzhou 510650, Peoples R China; [Wang, Faming] Chinese Acad Sci, CAS Engn Lab Ecol Restorat Isl & Coastal Ecosyst, South China Bot Garden, Guangzhou 510650, Peoples R China; [Burnett, William C.] Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA; [Bianchi, Thomas S.] Univ Florida, Dept Geol Sci, Gainesville, FL 32611 USA; [Dong, Junyu] Ocean Univ China, Sch Comp Sci & Technol, Qingdao 266100, Peoples R China; [Lian, Ergang] Tongji Univ, State Key Lab Marine Geol, Shanghai 200092, Peoples R China; [Mayer, Lawrence] Univ Maine, Sch Marine Sci, Walpole, ME 04573 USA Ocean University of China; Qingdao National Laboratory for Marine Science & Technology; Ocean University of China; University of Gothenburg; Southern Cross University; Ocean University of China; Ocean University of China; Chinese Academy of Sciences; Chinese Academy of Sciences; South China Botanical Garden, CAS; State University System of Florida; Florida State University; State University System of Florida; University of Florida; Ocean University of China; Tongji University; University of Maine System; University of Maine Orono Xu, BC (corresponding author), Ocean Univ China, Minist Educ, Key Lab Marine Chem Theory & Technol, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Qingdao 266100, Peoples R China.;Xu, BC (corresponding author), Qingdao Natl Lab Marine Sci & Technol, Lab Marine Ecol & Environm Sci, Qingdao 266100, Peoples R China. xubc@ouc.edu.cn 许, 许博超/HMP-0149-2023; Wang, Faming/J-9342-2014; Bianchi, Thomas/L-4060-2014 许, 许博超/0000-0002-5069-3824; Wang, Faming/0000-0002-7543-6779; Bianchi, Thomas/0000-0002-3068-2933; Lian, Ergang/0000-0001-8155-6462 Natural Science Foundation of China [42130410, 41876075, U1906210] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank to Wenyuan Li for her assistance during the neural network building. We thank to Dr. Xiaoyong Duan, Limin Hu and Guangquan Chen for providing sediment information. Dr. Peter T. Harris and Miles Macmillan-Lawler are acknowledged for providing the global morphotypes data. This work was supported by Natural Science Foundation of China (NSFC grant 42130410 to Z.Y., 41876075 to B.X., and U1906210 to Q.Y.). 82 1 1 46 61 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. AUG 20 2022.0 13.0 1 4903 10.1038/s41467-022-32650-0 0.0 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 3X7GT 35987905.0 Green Accepted, gold 2023-03-23 WOS:000843206100002 0
C Ji, MQ; Gall, JR; Zheng, HT; Liu, YB; Fang, L IEEE Ji, Mengqi; Gall, Juergen; Zheng, Haitian; Liu, Yebin; Fang, Lu SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) IEEE International Conference on Computer Vision English Proceedings Paper 16th IEEE International Conference on Computer Vision (ICCV) OCT 22-29, 2017 Venice, ITALY IEEE,IEEE Comp Soc This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D convolutional network which is achieved by encoding the camera parameters together with the images in a 3D voxel representation. We evaluate SurfaceNet on the large-scale DTU benchmark. [Ji, Mengqi] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China; [Zheng, Haitian; Liu, Yebin; Fang, Lu] Tsinghua Univ, Beijing, Peoples R China; [Gall, Juergen] Univ Bonn, Bonn, Germany Hong Kong University of Science & Technology; Tsinghua University; University of Bonn Ji, MQ (corresponding author), Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China. mji@connect.ust.hk; luvision.net@gmail.com Ji, Mengqi/AAA-1232-2020; Liu, Yebin/L-7393-2019 Ji, Mengqi/0000-0002-9210-6851; Natural Science Foundation of China (NSFC) [61331015, 6152211, 61571259, 61531014]; National key foundation for exploring scientific instrument [2013YQ140517]; Shenzhen Fundamental Research fund [JCYJ20170307153051701]; DFG as part of the DFG Research Unit Mapping on Demand (MoD) [GA 1927/2-2, FOR 1505] Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National key foundation for exploring scientific instrument; Shenzhen Fundamental Research fund; DFG as part of the DFG Research Unit Mapping on Demand (MoD)(German Research Foundation (DFG)) The work has been supported in part by Natural Science Foundation of China (NSFC) under contract No. 61331015, 6152211, 61571259 and 61531014, in part by the National key foundation for exploring scientific instrument No.2013YQ140517, in part by Shenzhen Fundamental Research fund (JCYJ20170307153051701) and in part by the DFG project GA 1927/2-2 as part of the DFG Research Unit FOR 1505 Mapping on Demand (MoD). 33 158 163 6 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-5499 978-1-5386-1032-9 IEEE I CONF COMP VIS 2017.0 2326 2334 10.1109/ICCV.2017.253 0.0 9 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ4TW Green Submitted 2023-03-23 WOS:000425498402041 0
J Ni, JY; Li, XY; Amoroso, D; He, X; Feng, JS; Kan, EJ; Picozzi, S; Xiang, HJ Ni, J. Y.; Li, X. Y.; Amoroso, D.; He, X.; Feng, J. S.; Kan, E. J.; Picozzi, S.; Xiang, H. J. Giant Biquadratic Exchange in 2D Magnets and Its Role in Stabilizing Ferromagnetism of NiCl2 Monolayers PHYSICAL REVIEW LETTERS English Article SPIN; NANOTUBES; GAS Two-dimensional (2D) van der Waals (vdW) magnets provide an ideal platform for exploring, on the fundamental side, new microscopic mechanisms and for developing, on the technological side, ultra compact spintronic applications. So far, bilinear spin Hamiltonians have been commonly adopted to investigate the magnetic properties of 2D magnets, neglecting higher order magnetic interactions. However, we here provide quantitative evidence of giant biquadratic exchange interactions in monolayer NiX2 (X = Cl, Br and I), by combining first-principles calculations and the newly developed machine learning method for constructing Hamiltonian. Interestingly, we show that the ferromagnetic ground state within NiCl2 single layers cannot be explained by means of the bilinear Heisenberg Hamiltonian; rather, the nearest-neighbor biquadratic interaction is found to be crucial. Furthermore, using a three-orbitals Hubbard model, we propose that the giant biquadratic exchange interaction originates from large hopping between unoccupied and occupied orbitals on neighboring magnetic ions. On a general framework, our work suggests biquadratic exchange interactions to be important in 2D magnets with edge-shared octahedra. [Ni, J. Y.; Li, X. Y.; Feng, J. S.; Xiang, H. J.] Fudan Univ, Inst Computat Phys Sci, Minist Educ, Key Lab Computat Phys Sci, Shanghai 200433, Peoples R China; [Ni, J. Y.; Li, X. Y.; Feng, J. S.; Xiang, H. J.] Fudan Univ, Dept Phys, Shanghai 200433, Peoples R China; [Ni, J. Y.; Li, X. Y.; Xiang, H. J.] Shanghai Qi Zhi Inst, Shanghai 200030, Peoples R China; [Amoroso, D.; Picozzi, S.] Consiglio Nazl Ric CNR SPIN, Via Vestini 31, I-66100 Chieti, Italy; [He, X.] BIST, CSIC, Catalan Inst Nanosci & Nanotechnol ICN2, Campus UAB, Barcelona 08193, Spain; [Feng, J. S.] Hefei Normal Univ, Sch Phys & Mat Engn, Hefei 230601, Peoples R China; [Kan, E. J.] Nanjing Univ Sci & Technol, Dept Appl Phys, Nanjing 210094, Jiangsu, Peoples R China; [Kan, E. J.] Nanjing Univ Sci & Technol, Inst Energy & Microstruct, Nanjing 210094, Jiangsu, Peoples R China; [Xiang, H. J.] Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China Fudan University; Fudan University; Consiglio Nazionale delle Ricerche (CNR); Istituto Superconduttori, Materiali e Dispositivi Innovativi (SPIN-CNR); Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Catalan Institute of Nanoscience & Nanotechnology (ICN2); Consejo Superior de Investigaciones Cientificas (CSIC); Hefei Normal University; Nanjing University of Science & Technology; Nanjing University of Science & Technology Feng, JS (corresponding author), Fudan Univ, Inst Computat Phys Sci, Minist Educ, Key Lab Computat Phys Sci, Shanghai 200433, Peoples R China.;Feng, JS (corresponding author), Fudan Univ, Dept Phys, Shanghai 200433, Peoples R China.;Feng, JS (corresponding author), Hefei Normal Univ, Sch Phys & Mat Engn, Hefei 230601, Peoples R China. fjs@hfnu.edu.cn; hxiang@fudan.edu.cn Picozzi, Silvia/E-2374-2011; Xiang, Hongjun/I-4305-2016 Picozzi, Silvia/0000-0002-3232-788X; Xiang, Hongjun/0000-0002-9396-3214; Li, Xue-Yang/0000-0002-5060-4975; Amoroso, Danila/0000-0003-4385-800X; Kan, Erjun/0000-0003-0433-4190 NSFC [11825403, 11991061]; Program for Professor of Special Appointment (Eastern Scholar); Anhui Provincial Natural Science Foundation [1908085MA10]; Opening Foundation of State Key Laboratory of Surface Physics Fudan University [KF2019_07]; Nanoscience Foundries and Fine Analysis (NFFA-MIUR Italy); PRIN-2017 project TWEET: Towards Ferroelectricity in two dimen-sions [2017YCTB59]; EU H2020-NMBP-TO-IND-2018 project INTERSECT [814487]; Qing Nian Ba Jian Program NSFC(National Natural Science Foundation of China (NSFC)); Program for Professor of Special Appointment (Eastern Scholar); Anhui Provincial Natural Science Foundation(Natural Science Foundation of Anhui Province); Opening Foundation of State Key Laboratory of Surface Physics Fudan University; Nanoscience Foundries and Fine Analysis (NFFA-MIUR Italy); PRIN-2017 project TWEET: Towards Ferroelectricity in two dimen-sions; EU H2020-NMBP-TO-IND-2018 project INTERSECT; Qing Nian Ba Jian Program We thank Dr. C. Xu and Dr. P. Barone for interesting discussions. Work at Fudan is supported by NSFC (11825403, 11991061) , Program for Professor of Special Appointment (Eastern Scholar) , and Qing Nian Ba Jian Program. J. S. F. is supported by Anhui Provincial Natural Science Foundation (1908085MA10) , and the Opening Foundation of State Key Laboratory of Surface Physics Fudan University (Grant No. KF2019_07) . Work at CNR-SPIN supported by Nanoscience Foundries and Fine Analysis (NFFA-MIUR Italy) and by the PRIN-2017 project TWEET: Towards Ferroelectricity in two dimen-sions (IT-MIUR Grant No. 2017YCTB59) . X. H. thanks the support by the EU H2020-NMBP-TO-IND-2018 project INTERSECT (Grant No. 814487) . 59 16 16 30 82 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. DEC 10 2021.0 127.0 24 247204 10.1103/PhysRevLett.127.247204 0.0 7 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics XQ3AW 34951786.0 Green Published, Green Submitted 2023-03-23 WOS:000731422100009 0
J Segler, MHS; Preuss, M; Waller, MP Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. Planning chemical syntheses with deep neural networks and symbolic AI NATURE English Article ORGANIC-CHEMISTRY; KNOWLEDGE-BASE; SYSTEM; CLASSIFICATION; PREDICTION; REACTIVITY; DESIGN; ROUTE; RETROSYNTHESIS; DISCOVERY To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes. [Segler, Marwin H. S.] Westfalishe Wilhelms Univ, Inst Organ Chem, Munster, Germany; [Segler, Marwin H. S.] Westfalishe Wilhelms Univ, Ctr Multiscale Theory & Computat, Munster, Germany; [Segler, Marwin H. S.] BenevolentAI, London, England; [Preuss, Mike] Westfalische Wilhelms Univ Munster, European Res Ctr Informat Syst, Munster, Germany; [Waller, Mark P.] Shanghai Univ, Dept Phys, Shanghai, Peoples R China; [Waller, Mark P.] Shanghai Univ, Int Ctr Quantum & Mol Struct, Shanghai, Peoples R China University of Munster; Shanghai University; Shanghai University Segler, MHS (corresponding author), Westfalishe Wilhelms Univ, Inst Organ Chem, Munster, Germany.;Segler, MHS (corresponding author), Westfalishe Wilhelms Univ, Ctr Multiscale Theory & Computat, Munster, Germany.;Segler, MHS (corresponding author), BenevolentAI, London, England.;Waller, MP (corresponding author), Shanghai Univ, Dept Phys, Shanghai, Peoples R China.;Waller, MP (corresponding author), Shanghai Univ, Int Ctr Quantum & Mol Struct, Shanghai, Peoples R China. marwin.segler@wwu.de; waller@shu.edu.cn Preuss, Mike/0000-0003-4681-1346 75 757 777 70 824 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 0028-0836 1476-4687 NATURE Nature MAR 29 2018.0 555.0 7698 604 + 10.1038/nature25978 0.0 16 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics GA8UX 29595767.0 Green Submitted 2023-03-23 WOS:000428617600039 0
J Yang, C; Peng, T; Lan, SL; Shen, WM; Wang, LH Yang, Chen; Peng, Tao; Lan, Shulin; Shen, Weiming; Wang, Lihui Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds JOURNAL OF MANUFACTURING SYSTEMS English Article Dynamic service selection; Optimal service selection; Cloud manufacturing; Multi-cloud; Optimization model; Internet of things; Uncertainty OPTIMIZATION; UNCERTAINTY; INTERNET; SYSTEMS; THINGS With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model. [Yang, Chen; Peng, Tao] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China; [Lan, Shulin] Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China; [Shen, Weiming] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden Beijing Institute of Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Huazhong University of Science & Technology; Royal Institute of Technology Lan, SL (corresponding author), Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China. yangchen666@bit.edu.cn; buaa120414pt@163.com; lanshulin@ucas.ac.cn; wshen@ieee.org; lihuiw@kth.se shen, Weiming/HJZ-2337-2023; Shen, Weiming/B-7400-2013; Wang, Lihui/O-3907-2014 Shen, Weiming/0000-0001-5204-7992; Wang, Lihui/0000-0001-8679-8049 Beijing Institute of Technology Research Fund Program for Young Scholars, Beijing Institute of Technology, China Beijing Institute of Technology Research Fund Program for Young Scholars, Beijing Institute of Technology, China This work was supported by the Beijing Institute of Technology Research Fund Program for Young Scholars, Beijing Institute of Technology, China. 41 16 17 7 32 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-6125 1878-6642 J MANUF SYST J. Manuf. Syst. JUL 2020.0 56.0 213 226 10.1016/j.jmsy.2020.06.004 0.0 14 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science NO9HY 2023-03-23 WOS:000569798100001 0
C Zou, J; Huang, H; Guhmann, C IEEE Zou, Jie; Huang, Hua; Guehmann, Clemens Improved Sliding-Mode On-line Adaptive Position Control for AMT Clutch Systems Based On Neural Networks 2018 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) IEEE Intelligent Vehicles Symposium English Proceedings Paper IEEE Intelligent Vehicles Symposium (IV) JUN 26-30, 2018 Changshu, PEOPLES R CHINA IEEE Clutch; Sliding Mode Control; Neural Network; On-line Adaptive Evaluation criteria of automated transmission control algorithms are an essential research object under increasingly stringent emission requirements and demand for riding comfort. Model-based development and calibration can be employed to optimize the corresponding actuator control algorithm in the transmission control unit (TCU). In this paper, the challenge is to realize on-line adaptively optimization of the controller based on position trajectory. The clutch in an automated manual transmission system is taken as a case study and is considered as a nonlinear dynamic system with uncertainties and unknown external disturbances. A controller with accurate and rapid position trajectory tracking performance is crucial to finding out the correlation between the clutch position changes and the shift quality. Traditionally, the control parameters are manually tuned off-line via trial-and- error, which is time-consuming and often yield poor results. Besides, any controller with constant control parameters has limited ability to adapt to dynamic, real-world conditions. This study proposes a second-order sliding-mode position controller. The parameters are improved based on neural network, to control the clutch system to get better tracking performance on-line adaptive during the vehicle starting. Finally, the clutch controller is embedded into a Modelica (R) based vehicle model and verified through the Model-in-the-Loop(MiL) simulation. The simulation results demonstrate that the controller has better tracking performance and stronger robustness compared to the conventional controller and its parameters can be efficiently tuned online via neural networking as opposed to trial-and-error. [Zou, Jie] Chinese Acad Sci, Ctr Automot Elect, Shenzhen Inst Adv Technol, Beijing, Peoples R China; [Huang, Hua] Tech Univ Berlin, Berlin, Germany; [Huang, Hua] Schaeffler Technol AG & Co KG, Herzogenaurach, Germany; [Guehmann, Clemens] Tech Univ Berlin, Chair Elect Measurement & Diagnost Technol, Berlin, Germany Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Technical University of Berlin; Technical University of Berlin Zou, J (corresponding author), Chinese Acad Sci, Ctr Automot Elect, Shenzhen Inst Adv Technol, Beijing, Peoples R China. jie.zou@siat.ac.cn; hua.huang1985@hotmail.com; clemens.guehmann@tu-berlin.de 13 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-0587 978-1-5386-4452-2 IEEE INT VEH SYM 2018.0 174 178 5 Automation & Control Systems; Computer Science, Artificial Intelligence; Robotics; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Robotics; Transportation BS4IK 2023-03-23 WOS:000719424500030 0
J Ren, B; Hou, B; Chanussot, J; Jiao, LC Ren, Bo; Hou, Biao; Chanussot, Jocelyn; Jiao, Licheng PolSAR Feature Extraction Via Tensor Embedding Framework for Land Cover Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Matrix decomposition; Synthetic aperture radar; Task analysis; Scattering; Machine learning algorithms; Feature extraction; land cover classification; polarimetric synthetic aperture radar (PolSAR) image; tensor embedding framework POLARIMETRIC SAR DATA; UNSUPERVISED CLASSIFICATION; DISCRIMINANT-ANALYSIS; TARGET DECOMPOSITION; COMPONENT ANALYSIS; SCATTERING MODEL; TEXTURE ANALYSIS; MULTIFREQUENCY; REDUCTION; ICA Polarimetric synthetic aperture radar (PolSAR) as a typical multi-channel sensor can obtain refined geometrical and geophysical information. In the PolSAR land cover classification task, feature extraction is regarded as a critical step for the final classification. It can employ multi-modal features from the original scattering data, polarimetric target decomposition, and other transformation space. Then, how to efficiently combine multi-modal polarimetric information and extract discriminant features is an important challenge for PolSAR image processing. Graph embedding methods have become a significant technique to deal with feature extraction and dimensionality reduction (DR) problems in recent years. It provides a unified linearization framework in machine learning and other pattern recognition tasks. In this article, an extended tensor embedding framework is introduced to extract the intrinsic features for PolSAR land cover classification. First, each pixel is represented by a feature cube that is constructed by groups of polarimetric scattering signals and target decomposition features in a fixed size patch. Second, an intrinsic matrix is constructed to describe the original geometrical and statistical properties of the samples, and a penalty matrix is designed to represent some constraints. Third, the vector-based algorithms are transformed into tensor space in an unified framework and based on the pair of matrices to obtain the projection matrices in each mode by an iterative optimization process. The effectiveness of the proposed methods is demonstrated on three RADARSAT2 data sets covering the regions of Xi'an, San Francisco, and Flevoland, respectively. The visualization and quantification results show that the proposed method has superiority in land cover classification. [Ren, Bo; Hou, Biao; Jiao, Licheng] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, CNRS, INRIA, LJK,Grenoble INP, F-38000 Grenoble, France Ministry of Education, China; Xidian University; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria Hou, B (corresponding author), Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China. avcodec@hotmail.com Jiao, Licheng/0000-0003-3354-9617; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [61671350, 61836009]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005]; Key Research and Development Program in Shaanxi Province of China [2019ZDLGY03-05]; China Postdoctoral Science Foundation [2018M633468] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program in Shaanxi Province of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 61671350 and Grant 61836009, in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005, in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY03-05, and in part by the China Postdoctoral Science Foundation under Project 2018M633468. 59 11 12 4 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing APR 2020.0 58.0 4 2337 2351 10.1109/TGRS.2019.2948042 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology LV9FL 2023-03-23 WOS:000538748900007 0
J Liu, Y; Munteanu, CR; Kong, ZW; Ran, T; Sahagun-Ruiz, A; He, ZX; Zhou, CS; Tan, ZL Liu, Yong; Munteanu, Cristian R.; Kong, Zhiwei; Ran, Tao; Sahagun-Ruiz, Alfredo; He, Zhixiong; Zhou, Chuanshe; Tan, Zhiliang Identification of coenzyme-binding proteins with machine learning algorithms COMPUTATIONAL BIOLOGY AND CHEMISTRY English Article Coenzyme-binding; Protein sequence; Topological indices; Classification model; Random Forest SUPPORT VECTOR MACHINES; CRYSTAL-STRUCTURE; GLUTAMATE-DEHYDROGENASE; TOPOLOGICAL INDEXES; ESCHERICHIA-COLI; WEB SERVER; COFACTOR; SEQUENCE; NAD; RECOGNITION The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and beta-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. To simulate the properties of coenzyme-binding proteins, we created a dataset containing 2897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with Sequence to Star Network (S2SNet) application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches. [Liu, Yong; Kong, Zhiwei; Ran, Tao; He, Zhixiong; Zhou, Chuanshe; Tan, Zhiliang] Chinese Acad Sci, Inst Subtrop Agr, South Cent Expt Stn Anim Nutr & Feed Sci,Minist A, Key Lab Agroecol Proc Subtrop Reg,Natl Engn Lab P, Changsha 410125, Hunan, Peoples R China; [Liu, Yong; He, Zhixiong; Zhou, Chuanshe; Tan, Zhiliang] CICAPS, Hunan Coinnovat Ctr Anim Prod Safety, Changsha 410128, Hunan, Peoples R China; [Ran, Tao] Agr & Agri Food Canada, Lethbridge Res & Dev Ctr, Lethbridge, AB T1J 4B1, Canada; [Munteanu, Cristian R.] Univ A Coruna, Fac Comp Sci, RNASA IMEDIR, La Coruna, Spain; [Munteanu, Cristian R.] Univ Hosp Complex A Coruna CHUAC, Biomed Res Inst A Coruna INIBIC, La Coruna 15006, Spain; [Sahagun-Ruiz, Alfredo] Univ Nacl Autonoma Mexico, Dept Microbiol & Immunol, Fac Vet Med & Anim Sci, Univ 3000, Mexico City 04510, DF, Mexico; [Kong, Zhiwei] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Chinese Academy of Sciences; Institute of Subtropical Agriculture, CAS; Agriculture & Agri Food Canada; Universidade da Coruna; Complejo Hospitalario Universitario A Coruna; Universidade da Coruna; Instituto de Investigacion Biomedica de A Coruna (INIBIC); Universidad Nacional Autonoma de Mexico; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS He, ZX (corresponding author), Chinese Acad Sci, Inst Subtrop Agr, Changsha 410125, Hunan, Peoples R China. zxhe@isa.ac.cn Sahagún-Ruiz, Alfredo/AAE-2549-2020 He, Zhixiong/0000-0002-4802-3506; Munteanu, Cristian-Robert/0000-0002-5628-2268; Sahagun-Ruiz, Alfredo/0000-0002-9737-1945 National Key Research and Development plan of China [2016YFD0700201]; National Natural Science Foundation of China (NSFC) [31702141]; Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program [Y823042010]; General Directorate of Academic Personnel Affairs of the National Autonomous University of Mexico (DGAPA-PASPA of UNAM); General Directorate of Academic Personnel Affairs of the National Autonomous University of Mexico [PAPIIT-UNAM IN224717] National Key Research and Development plan of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program; General Directorate of Academic Personnel Affairs of the National Autonomous University of Mexico (DGAPA-PASPA of UNAM); General Directorate of Academic Personnel Affairs of the National Autonomous University of Mexico All authors give many thanks to the National Key Research and Development plan of China (2016YFD0700201), the National Natural Science Foundation of China (NSFC No. 31702141), and the Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program (Y823042010) for the joint support. Particularly, Dr. Y. Liu acknowledges to the General Directorate of Academic Personnel Affairs of the National Autonomous University of Mexico (DGAPA-PASPA of UNAM, and PAPIIT-UNAM IN224717) for the financial support of postdoctoral researches. In addition, all authors give thanks to Dr. E. Frank, M.A. Hall and I.H. Witten for providing a free machine learning application Weka to complete this work, to the S2SNet application for calculating the primary protein sequence star graph topological indices. 84 1 1 3 26 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1476-9271 1476-928X COMPUT BIOL CHEM Comput. Biol. Chem. APR 2019.0 79.0 185 192 10.1016/j.compbiolchem.2019.01.014 0.0 8 Biology; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science HR4NV 30851647.0 2023-03-23 WOS:000463124100021 0
J Cao, H; Chen, G; Li, ZJ; Hu, YB; Knoll, A Cao, Hu; Chen, Guang; Li, Zhijun; Hu, Yingbai; Knoll, Alois NeuroGrasp: Multimodal Neural Network With Euler Region Regression for Neuromorphic Vision-Based Grasp Pose Estimation IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Robots; Pose estimation; Neuromorphics; Vision sensors; Grasping; Cameras; Robot vision systems; Euler region regression sub-network (ERRN); grasp pose estimation; multimodal fusion; vision-based robotic manipulation Grasp pose estimation is a crucial procedure in robotic manipulation. Most of the current robot grasp manipulation systems are built on frame-based cameras like RGB-D cameras. However, the traditional frame-based grasp pose estimation methods have encountered challenges in scenarios such as low dynamic range and low power consumption. In this work, a neuromorphic vision sensor-dynamic and active-pixel vision sensor (DAVIS)-is introduced to the field of robotic grasp. DAVIS is an event-based bio-inspired vision sensor that records asynchronous streams of local pixel-level light intensity changes, called events. The strengths of DAVIS are it can provide high temporal resolution, high dynamic range, low power consumption, and no motion blur. We construct a neuromorphic vision-based robotic grasp dataset with 154 moving objects, named NeuroGrasp, which is the first RGB-Event multimodality grasp dataset (to the best of our knowledge). This dataset records both RGB frames and the corresponding event streams, providing frame data with rich color and texture information and event streams with high temporal resolution and high dynamic range. Based on the NeuroGrasp dataset, we further develop a multimodal neural network with a specific Euler region regression sub-network (ERRN) to perform grasp pose estimation. Combined with frame-based and event-based vision, the proposed method achieves better performance than the method that only takes RGB frames or event streams as input on the NeuroGrasp dataset. [Cao, Hu; Hu, Yingbai; Knoll, Alois] Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, D-80333 Munich, Germany; [Chen, Guang] Tongji Univ, Dept Comp Sci, Shanghai 201804, Peoples R China; [Chen, Guang] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China; [Li, Zhijun] Univ Sci & Technol China, Wearable Robot & Autonomous Syst Lab, Hefei 230022, Peoples R China Technical University of Munich; Tongji University; Tongji University; Chinese Academy of Sciences; University of Science & Technology of China, CAS Chen, G (corresponding author), Tongji Univ, Dept Comp Sci, Shanghai 201804, Peoples R China.;Chen, G (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China. guangchen@tongji.edu.cn Knoll, Alois/AAN-8417-2021; Cao, Hu/HJP-8442-2023 Knoll, Alois/0000-0003-4840-076X; Cao, Hu/0000-0001-8225-858X; Hu, Yingbai/0000-0003-2452-3570 Shanghai Municipal Science and Technology Major Project through the Zhejiang Lab; Shanghai Center for Brain Science and Brain-Inspired Technology [2018SHZDZX01]; National Natural Science Foundation of China [82072021, 61906138]; Shanghai Rising Star Program [21QC1400900]; European Union [945539] Shanghai Municipal Science and Technology Major Project through the Zhejiang Lab; Shanghai Center for Brain Science and Brain-Inspired Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Rising Star Program; European Union(European Commission) This work was supported in part by the Shanghai Municipal Science and Technology Major Project through the Zhejiang Lab and the Shanghai Center for Brain Science and Brain-Inspired Technology under Grant 2018SHZDZX01; in part by the National Natural Science Foundation of China under Grant 82072021 and Grant 61906138; in part by the Shanghai Rising Star Program under Grant 21QC1400900; and in part by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement 945539 (Human Brain Project SGA3). 36 5 5 10 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71.0 2511111 10.1109/TIM.2022.3179469 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 2D5JD 2023-03-23 WOS:000811582300007 0
C Liu, R; Cong, GL; Zheng, BL; Zheng, K; Su, H Cai, Y; Ishikawa, Y; Xu, J Liu, Rong; Cong, Guanglin; Zheng, Bolong; Zheng, Kai; Su, Han Location Prediction in Social Networks WEB AND BIG DATA (APWEB-WAIM 2018), PT II Lecture Notes in Computer Science English Proceedings Paper 2nd International Joint Conference on Asia-Pacific Web (APWeb) / Web-Age Information Management (WAIM) JUL 23-25, 2018 PEOPLES R CHINA User locations in social networks are needed in many applications which utilize location information to recommend local news and places of interest to users, as well as detect and alert emergencies around users. However, considering individual privacy, only a small portion users share their location on social networks. Thus, to predict the fine-grained locations of user tweets, we present a joint model containing three sub models: content-based model, social relationship based model and behavior habit based model. In the content-based model, we filter out those location-independent tweets and use deep learning algorithm to mine the relationship between semantics and locations. User trajectory similarity measure is used to build a social graph for users, and historical check-ins is used to provide users' daily activity habits. We conduct experiments using tweets collected from Shanghai during one year. The result shows that our joint model perform well, especially the content-based model. We find that our approach improves accuracy compared to the state-of-the-art location prediction algorithm. [Liu, Rong; Cong, Guanglin; Zheng, Kai; Su, Han] Univ Elect Sci & Technol China, Big Data Reaserch Ctr, Chengdu, Sichuan, Peoples R China; [Zheng, Bolong] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Zheng, Bolong] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark University of Electronic Science & Technology of China; Sun Yat Sen University; Aalborg University Su, H (corresponding author), Univ Elect Sci & Technol China, Big Data Reaserch Ctr, Chengdu, Sichuan, Peoples R China. lrong0913@gmail.com; nofloat@163.com; zblchris@gmail.com; kaizheng@uestc.edu.cn; hansu@uestc.edu.cn 15 1 1 0 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-96893-3; 978-3-319-96892-6 LECT NOTES COMPUT SC 2018.0 10988.0 151 165 10.1007/978-3-319-96893-3_12 0.0 15 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN4OG 2023-03-23 WOS:000482623700012 0
J Shi, SZ; Zhou, K; Zhao, JX; Mukherjee, S; Zhuang, PF Shi, Shuzhe; Zhou, Kai; Zhao, Jiaxing; Mukherjee, Swagato; Zhuang, Pengfei Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics PHYSICAL REVIEW D English Article DECONFINEMENT; SUPPRESSION Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks. The latter can be characterized by the temperature (T) dependent potential, with real (V-R(T, r)) and imaginary (V-I(T, r)) parts, as a function of the spatial separation (r). Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network, here, we obtain V-R(T, r) and V-I(T, r) in a model-independent fashion. The temperature dependence of V-R(T, r) was found to be very mild between T approximate to 0-334 MeV. For T = 151-334 MeV, V-I(T, r) shows a rapid increase with T and r, which is much larger than the perturbation-theory-based expectations. [Shi, Shuzhe] McGill Univ, Dept Phys, Montreal, PQ H3A 2T8, Canada; [Zhou, Kai] Frankfurt Inst Adv Studies, Ruth Moufang Str 1, D-60438 Frankfurt, Germany; [Zhao, Jiaxing; Zhuang, Pengfei] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China; [Mukherjee, Swagato] Brookhaven Natl Lab, Phys Dept, Upton, NY 11973 USA McGill University; Tsinghua University; United States Department of Energy (DOE); Brookhaven National Laboratory Shi, SZ (corresponding author), McGill Univ, Dept Phys, Montreal, PQ H3A 2T8, Canada.;Zhou, K (corresponding author), Frankfurt Inst Adv Studies, Ruth Moufang Str 1, D-60438 Frankfurt, Germany. shuzhe.shi@mcgill.ca; zhou@fias.uni-frankfurt.de Zhao, Jia-Xiang/GLT-3663-2022 Zhou, Kai/0000-0001-9859-1758; Mukherjee, Swagato/0000-0002-3824-1008 National Natural Science Foundation of China (NCFS) [11890712, 12075129]; Guangdong Major Project of Basic and Applied Basic Research [2020B0301030008]; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Quebec-Nature et technologies (FRQNT) through the Programmede Bourses d'Excellencepour Etudiants Etrangers (PBEEE) scholarship; Bundesministerium fur Bildung und Forschung (BMBF) under the Exploration of the Universe and Matter (ErUM)-Data project; AI grant at Frankfurt Institute for Advanced Studies (FIAS) of SAMSON AG, Frankfurt; NVIDIA Corporation; U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-SC0012704]; U.S. Department of Energy, Office of Science, Office of Nuclear Physics and Office of Advanced Scientific Computing Research National Natural Science Foundation of China (NCFS)(National Natural Science Foundation of China (NSFC)); Guangdong Major Project of Basic and Applied Basic Research; Natural Sciences and Engineering Research Council of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)CGIAR); Fonds de recherche du Quebec-Nature et technologies (FRQNT) through the Programmede Bourses d'Excellencepour Etudiants Etrangers (PBEEE) scholarship; Bundesministerium fur Bildung und Forschung (BMBF) under the Exploration of the Universe and Matter (ErUM)-Data project(Federal Ministry of Education & Research (BMBF)); AI grant at Frankfurt Institute for Advanced Studies (FIAS) of SAMSON AG, Frankfurt; NVIDIA Corporation; U.S. Department of Energy, Office of Science, Office of Nuclear Physics(United States Department of Energy (DOE)); U.S. Department of Energy, Office of Science, Office of Nuclear Physics and Office of Advanced Scientific Computing Research(United States Department of Energy (DOE)) The authors thank Min He, Matthew Heffernan, Rasmus Larsen, Simon Mak, Peter Petreczky, Ralf Rapp, Alexander Rothkopf, Michael Strickland, and Nan Su for many insightful discussions. This material is based upon work supported by: (i) The National Natural Science Foundation of China (NCFS) under Grants No. 11890712 and No. 12075129 and Guangdong Major Project of Basic and Applied Basic Research No. 2020B0301030008 (J. Z. and P. Z.); (ii) The Natural Sciences and Engineering Research Council of Canada (S. S.); (iii) The Fonds de recherche du Qu ' ebec-Nature et technologies (FRQNT) through the Programmede Bourses d'Excellencepour Etudiants Etrangers (PBEEE) scholarship (S. S.); (iv) TheBundesministerium fur Bildung und Forschung (BMBF) funding under the Exploration of the Universe and Matter (ErUM)-Data project and the AI grant at Frankfurt Institute for Advanced Studies (FIAS) of SAMSON AG, Frankfurt (K. Z.); (v) The GPU Grant of the NVIDIA Corporation (K. Z.); (vi) The U.S. Department of Energy, Office of Science, Office of Nuclear Physics through the Contract No. DESC0012704 (S. M.); (vii) The U.S. Department of Energy, Office of Science, Office of Nuclear Physics and Office of Advanced Scientific Computing Research, within the framework of Scientific Discovery through Advance Computing (SciDAC) award Computing the Properties of Matter with Leadership Computing Resources (S. M.). 76 7 7 3 7 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D JAN 21 2022.0 105.0 1 14017 10.1103/PhysRevD.105.014017 0.0 16 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics YP6PI Green Submitted, hybrid 2023-03-23 WOS:000748745200004 0
J Meng, HX; Yang, QQ; Zio, E; Xing, JD Meng, Huixing; Yang, Qiaoqiao; Zio, Enrico; Xing, Jinduo An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries PROCESS SAFETY AND ENVIRONMENTAL PROTECTION English Article Lithium-ion battery; Thermal runaway; Risk prediction; Dynamic Bayesian network; Support vector regression ELECTRIC VEHICLES; HUMAN RELIABILITY; SAFETY ANALYSIS; NEURAL-NETWORK; FAULT-TREE; SYSTEMS; ISSUES; MODEL; AHP The risk of thermal runaway in lithium-ion battery (LIB) attracts significant attention from domains of society, industry, and academia. However, the thermal runaway prediction in the framework of system safety requires further efforts. In this paper, we propose a methodology for dynamic risk prediction by integrating fault tree (FT), dynamic Bayesian network (DBN) and support vector regression (SVR). FT graphically describes the logic of mechanism of thermal runaway. DBN allows considering multiple states and uncertain inference for providing quantitative results of the risk evolution. SVR is subsequently utilized for predicting the risk from the DBN estimation. The proposed methodology can be applied for risk early warning of LIB thermal runaway. [Meng, Huixing; Yang, Qiaoqiao] Beijing Inst Technol, State Key Lab Explos Sci & Technol, Beijing 100081, Peoples R China; [Zio, Enrico] MINES ParisTech PSL Univ Paris, Ctr Rech Risques & Crises CRC, Paris, France; [Zio, Enrico] Politecn Milan, DOE, Milan, Italy; [Xing, Jinduo] Beijing Univ Civil Engn & Architecture, Sch Mech Elect & Vehicle Engn, Beijing 100044, Peoples R China; [Meng, Huixing] 5 South Zhongguancun St, Beijing 100081, Peoples R China Beijing Institute of Technology; UDICE-French Research Universities; Universite PSL; MINES ParisTech; Polytechnic University of Milan; Beijing University of Civil Engineering & Architecture Meng, HX (corresponding author), 5 South Zhongguancun St, Beijing 100081, Peoples R China. huixing.meng@bit.edu.cn; enrico.zio@polimi.it; xingjinduo@bucea.edu.cn National Natural Science Foundation of China [52004030]; Beijing Institute of Technology Research Fund Program for Young Scholars [XSQD- 202002007]; Exchange Program of High -end Foreign Experts of Ministry of Science and Technology, China [G2022178013L]; R & D Program of Beijing Municipal Education Commission [KM202310016003]; Open Research Fund Program of Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles [PGU2020K007] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Institute of Technology Research Fund Program for Young Scholars; Exchange Program of High -end Foreign Experts of Ministry of Science and Technology, China; R & D Program of Beijing Municipal Education Commission; Open Research Fund Program of Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles This work is supported by the National Natural Science Foundation of China (Grant No. 52004030) , the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. XSQD- 202002007) , Exchange Program of High -end Foreign Experts of Ministry of Science and Technology, China (Grant No. G2022178013L) , R & D Program of Beijing Municipal Education Commission (Grant No. KM202310016003) , and the Open Research Fund Program of Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles (Grant No. PGU2020K007) . 70 0 0 12 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0957-5820 1744-3598 PROCESS SAF ENVIRON Process Saf. Environ. Protect. MAR 2023.0 171.0 385 395 10.1016/j.psep.2023.01.021 0.0 JAN 2023 11 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 8L4QD 2023-03-23 WOS:000923767000001 0
J Acharya, S; Adam, J; Adamova, D; Adler, C; Adolfsson, J; Aggarwal, MM; Rinella, GA; Agnello, M; Agrawal, N; Ahammed, Z; Ahmad, N; Ahn, SU; Aiola, S; Akindinov, A; Al-Turany, M; Alam, SN; Antonczyk, D; Arend, A; Alba, JLB; Albuquerque, DSD; Aleksandrov, D; Alessandro, B; Molina, RA; Alici, A; Alkin, A; Alme, J; Alt, T; Altenkamper, L; Altsybeev, I; Prado, CAG; Andrei, C; Andreou, D; Andrews, HA; Andronic, A; Anguelov, V; Anson, C; Anticic, T; Antinori, F; Antonioli, P; Anwar, R; Aphecetche, L; Appelshauser, H; Arcelli, S; Arnaldi, R; Arnold, OW; Arsene, IC; Arslandok, M; Audurier, B; Augustinus, A; Averbeck, R; Azmi, MD; Badala, A; Baek, YW; Bagnasco, S; Bailhache, R; Bala, R; Baldisseri, A; Ball, M; Baral, RC; Barbano, AM; Barbera, R; Barile, F; Barioglio, L; Barnafoldi, GG; Barnby, LS; Barret, V; Bartalini, P; Barth, K; Bartos, D; Bartsch, E; Basile, M; Bastid, N; Basu, S; Bathen, B; Batigne, G; Batyunya, B; Batzing, PC; Baumann, C; Bearden, IG; Beck, H; Bedda, C; Behera, NK; Belikov, I; 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ALICE Collaboration The ALICE Transition Radiation Detector: Construction, operation, and performance NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT English Article Transition radiation detector; Multi-wire proportional drift chamber; Fibre/foam sandwich radiator; Xenon-based gas mixture; Tracking; Ionisation energy loss; dE/dx; TR; Electron-pion identification; Neural network; Trigger COLLIDING BEAM EXPERIMENT; ELECTRON IDENTIFICATION; DRIFT CHAMBERS; TRD PROTOTYPES; ENERGY-LOSS; GEV/C; COLLISIONS; PIONS The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection. (c) 2017 CERN for the benefit of the Authors. Published by Elsevier B.V. [Concas, M.] Politecnico Torino, Dipartimento DET, Turin, Italy; [Connors, M. E.] Georgia State Univ, Atlanta, GA 30303 USA; [de Cuveland, J.; Lesser, F.; Schneider, R.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Krawutschke, T.] Fachhochschule Koln, Cologne, Germany; [Malinina, L.] Moscow MV Lomonosov State Univ, DV Skobeltsyn Inst Nucl, Moscow, Russia; [Khan, M. 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Fusco; Meninno, E.; Pagano, P.; Virgili, T.] Univ Salerno, Dipartimento Fis ER Caianiello, Salerno, Italy; [De Caro, A.; De Gruttola, D.; De Pasquale, S.; Girard, M. Fusco; Meninno, E.; Pagano, P.; Virgili, T.] Grp Collegato INFN, Salerno, Italy; [Agnello, M.; Bufalino, S.; Grosa, F.; Ravasenga, I.] Dipartimento DISAT Politecn, Turin, Italy; [Agnello, M.; Bufalino, S.; Grosa, F.; Ravasenga, I.] Sezione Ist Nazl Fis Nucl, Turin, Italy; [Cortese, P.; Ramello, L.; Sitta, M.] Univ Piemonte Orientale, Dipartimento Sci & Innovaz Tecnol, Alessandria, Italy; [Cortese, P.; Ramello, L.; Sitta, M.] Ist Nazl Fis Nucl, Sez Torino, Alessandria, Italy; [Barile, F.; Bruno, G. E.; Colamaria, F.; Di Bari, D.; Mazzilli, M.; Trombetta, G.; Volpe, G.] Dipartimento Interateneo Fis M Merlin, Bari, Italy; [Barile, F.; Bruno, G. E.; Colamaria, F.; Di Bari, D.; Mazzilli, M.; Trombetta, G.; Volpe, G.] Sezione Ist Nazl Fis Nucl, Bari, Italy; [Adolfsson, J.; Christensen, C. H.; Oskarsson, A.; Silvermyr, D.; Stenlund, E.; Vislavicius, V.] Lund Univ, Div Expt High Energy Phys, Lund, Sweden; [Rinella, G. Aglieri; Andreou, D.; Augustinus, A.; Barth, K.; Bellini, F.; Berzano, D.; Betev, L.; Bock, F.; Bonora, M.; Buncic, P.; Caffarri, D.; Carena, W.; Chapeland, S.; Barroso, V. Chibante; Chochula, P.; Colella, D.; Costa, F.; Di Mauro, A.; Divia, R.; Dobrin, A.; Eulisse, G.; Festanti, A.; Floris, M.; Francescon, A.; Fuchs, U.; Gargiulo, C.; Giubellino, P.; Gonzalez, A. S.; Grigoras, C.; Grosse-Oetringhaus, J. F.; Haake, R.; Hillemanns, H.; Hristov, P.; Kalweit, A.; Karczmarczyk, P.; Keil, M.; Klein, J.; Kluge, A.; Knichel, M. L.; Kouzinopoulos, C.; Lakomov, I.; Laudi, E.; Mager, M.; Martinengo, P.; Pedreira, M. Martinez; Morsch, A.; Musa, L.; De Oliveira, R. A. Negrao; Niedziela, J.; Ohlson, A.; Pinazza, O.; Reidt, F.; Riedler, P.; Riegler, W.; Rohr, D.; Safarik, K.; Schukraft, J.; Schutz, Y.; Senyukov, S.; Shahoyan, R.; Sielewicz, K. M.; Simonetti, G.; Tauro, A.; Telesca, A.; Van Hoorne, J. W.; Vyvre, P. Vande; von Haller, B.; Vranic, D.; Wenzel, S. C.; Zampolli, C.; Zimmermann, M. B.] CERN, European Org Nucl Res, Geneva, Switzerland; [Arnold, O. W.; Bilandzic, A.; Chauvin, A.; Dahms, T.; Fabbietti, L.; Gasik, P.; Jahnke, C.; Mathis, A. M.; Doce, O. Vazquez; Vorobyev, I.] Tech Univ Munich, Excellence Cluster Universe, Munich, Germany; [Helstrup, H.; Hetland, K. F.; Kileng, B.; Nesbo, S. V.; Storetvedt, M. M.] Bergen Univ Coll, Fac Engn, Bergen, Norway; [Meres, M.; Pikna, M.; Sitar, B.; Strmen, P.; Szabo, A.; Szarka, I.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bielcik, J.; Broz, M.; Contreras, J. G.; Horak, D.; Lavicka, R.; Petracek, V.] Czech Tech Univ, Fac Nucl Sci & Phys Engn, Prague, Czech Republic; [Bombara, M.; Kravcakova, A.; Sefcik, M.; Tropp, L.; Vrlakova, J.] Safarik Univ, Fac Sci, Kosice, Slovakia; [Langoy, R.; Lien, J.] Buskerud & Vestfold Univ Coll, Fac Technol, Tonsberg, Norway; [Gorbunov, S.; Hutter, D.; Kirsch, S.; Kisel, I.; Krzewicki, M.; La Pointe, S. L.; Lehrbach, J.; Lindenstruth, V.; Rohr, D.] Goethe Univ Frankfurt, Frankfurt Inst Adv Studies, Frankfurt, Germany; [Kim, J. S.] Gangneung Wonju Natl Univ, Kangnung, South Korea; [Bhattacharjee, B.; Hussain, N.; Sarma, P.] Gauhati Univ, Dept Phys, Gauhati, India; [Ball, M.; Ketzer, B.; Muenning, K.; Ratza, V.] Rhein Friedrich Wilhelms Univ Bonn, Helmholtz Inst Strahlen & Kernphys, Bonn, Germany; [Brucken, E. J.; Litichevskyi, V.; Mieskolainen, M. M.; Orava, R.; Rasanen, S. S.; Saarinen, S.] HIP, Helsinki, Finland; [Okubo, T.; Sekihata, D.; Shigaki, K.; Sugitate, T.; Yamakawa, K.; Yano, S.] Hiroshima Univ, Hiroshima, Japan; [Agrawal, N.; Dash, S.; Dhankher, P.; Jadhav, M. B.; Meethaleveedu, G. Koyithatta; Kumar, J.; Kumar, S.; Naik, B.; Nandi, B. K.; Narayan, A.; Nayak, R.; Pandey, A. K.; Sahoo, B.; Sett, P.; Varma, R.] Indian Inst Technol, Mumbai, Maharashtra, India; [Akindinov, A.; De, S.; Khuntia, A.; Mishra, A. N.; Pareek, P.; Roy, A.; Sahoo, P.; Sahoo, R.; Thakur, D.; Tripathy, S.] Indian Inst Technol Indore, Indore, India; [Sumowidagdo, S.] Indonesian Inst Sci, Jakarta, Indonesia; [Bianchi, N.; Di Nezza, P.; Fantoni, A.; Gianotti, P.; Muccifora, V.; Reolon, A. R.; Ronchetti, F.; Spiriti, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Ricci, R. A.] Ist Nazl Fis Nucl, Lab Nazl Legnaro, Legnaro, Italy; [Barile, F.; Colella, D.; de Cataldo, G.; Elia, D.; Lenti, V.; Manzari, V.; Mastroserio, A.; Nappi, E.] Ist Nazl Fis Nucl, Sez Bari, Bari, Italy; [Alici, A.; Antonioli, P.; Cindolo, F.; Hatzifotiadou, D.; Margotti, A.; Nania, R.; Noferini, F.; Pinazza, O.; Preghenella, R.; Scapparone, E.; Wilkinson, J.; Williams, M. C. S.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Casula, E. A. R.; Cicalo, C.; Masoni, A.; Siddhanta, S.] Ist Nazl Fis Nucl, Sez Cagliari, Cagliari, Italy; [Badala, A.; Pappalardo, G. S.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy; [Antinori, F.; Dainese, A.; Di Ruzza, B.; Rossi, A.; Turrisi, R.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Mazzoni, M. A.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Alessandro, B.; Arnaldi, R.; Bagnasco, S.; Bruna, E.; Cerello, P.; Concas, M.; Morales, Y. Corrales; De Marco, N.; Feliciello, A.; Giubellino, P.; Oppedisano, C.; Paul, B.; Prino, F.; Scomparin, E.; Zaccolo, V.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Fragiacomo, E.; Grion, N.; Luparello, G.; Piano, S.; Rachevski, A.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Baek, Y. W.; Behera, N. K.; Cho, S.; Kim, M.; Kweon, M. J.; Park, J.; Yoon, J. H.] Inha Univ, Incheon, South Korea; [del Valle, Z. Conesa; Crkovska, J.; Espagnon, B.; Hadjidakis, C.; Suire, C.; Tarhini, M.] Univ Paris 11, CNRS, IN2P3, IPNO, Orsay, France; [Finogeev, D.; Furs, A.; Guber, F.; Karavichev, O.; Karavicheva, T.; Karpechev, E.; Konevskikh, A.; Kurepin, A.; Kurepin, A. B.; Maevskaya, A.; Pshenichnov, I.; Reshetin, A.; Shabanov, A.; Tikhonov, A.] Acad Sci, Inst Nucl Res, Moscow, Russia; [Bedda, C.; Caliva, A.; Doremalen, L. V. V.; Grelli, A.; Haque, M. R.; Jaelani, S.; Keijdener, D. L. D.; Leogrande, E.; Lodato, D. F.; Margutti, J.; Mischke, A.; Mohammadi, N.; Nooren, G.; Peitzmann, T.; Richert, T.; Sas, M. H. P.; Snellings, R. J. M.; Trzeciak, B. A.; Van Der Maarel, J.; van Leeuwen, M.; Veen, A. M.; Vigolo, S.; Wang, H.; Zhang, C.] Univ Utrecht, Inst Subat Phys, Utrecht, Netherlands; [Akindinov, A.; Kiselev, S.; Mal'Kevich, D.; Mikhaylov, K.; Nedosekin, A.; Sultanov, R.; Voloshin, K.; Zhigareva, N.] Inst Theoret & Expt Phys, Moscow, Russia; [Colella, D.; Kalinak, P.; Kralik, I.; Krivda, M.; Musinsky, J.; Vala, M.] Slovak Acad Sci, Inst Expt Phys, Kosice, Slovakia; [Hladky, J.; Mares, J.; Zavada, P.] Acad Sci Czech Republ, Inst Phys, Prague, Czech Republic; [Baral, R. C.; Sahoo, S.; Sahu, P. K.; Swain, S.] Inst Phys, Bhubaneswar, Orissa, India; [Danu, A.; Mitu, C. M.; Niculescu, M.; Ristea, C.; Sevcenco, A.; Stan, I.] ISS, Bucharest, Romania; [Engel, H.; Ramirez, A. Gomez; Kebschull, U.; Lara, C.; Martinez, J. A. L.] Goethe Univ Frankfurt, Inst Informat, Frankfurt, Germany; [Antonczyk, D.; Arend, A.; Alt, T.; Appelshaeuser, H.; Bailhache, R.; Bartsch, E.; Baumann, C.; Blume, C.; Book, J.; Bratrud, L.; Broker, T. A.; Buesching, H.; Dillenseger, P.; Doenigus, B.; Hartig, M.; Heckel, S. T.; Hellbaer, E.; Klein, C.; Kliemant, M.; Kramer, F.; Lehner, J.; Luettig, P.; Marquard, M.; Munzer, R. H.; Lezama, E. Perez; Peskov, V.; Pitz, N.; Pliquett, F.; Rascanu, B. T.; Reichelt, P.; Renfordt, R.; Sahlmuller, B.; Scheid, H. S.; Schmidt, N. V.; Sommer, W.; Toia, A.; Vargas, H.; Wegerle, D.; Wiechula, J.] Goethe Univ Frankfurt, Inst Kernphys, Frankfurt, Germany; [Bathen, B.; Bucher, D.; Cunqueiro, L.; Feldkamp, L.; Gatz, H.; Glasow, R.; Gremmler, S.; Gottschlag, H.; Grimm, H.; Heide, M.; Herrmann, F.; Klein-Boesing, C.; Klein-Boesing, M.; Kohn, M.; Luhder, J. R.; De Godoy, D. A. Moreira; Muehlheim, D.; Passfeld, A.; Poppenborg, H.; Santo, R.; Sicking, E.; Wessels, J. P.; Westerhoff, U.; Wilk, A.; Willems, G. A.] Westfal Wilhelms Univ Munster, Inst Kernphys, Munster, Germany; [Cuautle, E.; Iga Buitron, S. A.; Nellen, L.; Ortiz Velasquez, A.; Paic, G.; Rosas, E. D.; Rueda, O. V.] Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Mexico City, DF, Mexico; [Gay Ducati, M. B.; Pereira, L. G.; Pezzi, R. P.] Univ Fed Rio Grande do Sul, Inst Fis, Porto Alegre, RS, Brazil; [Alfaro Molina, R.; Gomez Coral, D. M.; Grabski, V.; Menchaca-Rocha, A.; Sandoval, A.; Serradilla, E.] Univ Nacl Autonoma Mexico, Inst Fis, Mexico City, DF, Mexico; [Baldisseri, A.; Borel, H.; Castellanos, J. Castillo; Feuillard, V. J. G.; Lardeux, A.; Panebianco, S.; Da Costa, H. Pereira; Rakotozafindrabe, A.] Univ Paris Saclay, CEA, IRFU, Saclay, France; [Buthelezi, Z.; Foertsch, S.; Marchisone, M.; Murray, S.; Senosi, K.] Natl Res Fdn, iThemba LABS, Somerset, NJ USA; [Batyunya, B.; Chernenko, S.; Fateev, O.; Grigoryan, S.; Kislov, E.; Malinina, L.; Mikhaylov, K.; Nomokonov, P.; Panebratsev, Y.; Pozdniakov, V.; Rogochaya, E.; Rumyantsev, B.; Shimansky, S.; Smykov, L.; Vodopyanov, A.; Yurevich, V.; Zanevski, Y.] JINR, Dubna, Russia; [Baek, Y. W.] Konkuk Univ, Seoul, South Korea; [Ahn, S. U.] Korea Inst Sci & Technol, Daejeon, South Korea; [Uysal, A. Karasu; Yalcin, S.] KTO Karatay Univ, Konya, Turkey; [Balbastre, G. Conesa; Faivre, J.; Furget, C.; Guernane, R.; Hassan, H.; Hosokawa, R.; Silvestre, C.; Vauthier, A.; Yokoyama, H.] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, Grenoble, France; [Bock, F.; Collu, A.; Fasel, M.; Greiner, L.; Jacak, B.; Jacobs, P. M.; Lai, Y. S.; Loizides, C.; Ploskon, M.] Lawrence Berkeley Natl Lab, Berkeley, CA USA; [Belyaev, V.; Bogdanov, A.; Grigoriev, V.; Ippolitov, M.; Kaplin, V.; Kondratyeva, N.; Loginov, V.; Melikyan, Y.; Peresunko, D.; Samsonov, V.; Selyuzhenkov, I.] Moscow Engn Phys Inst, Moscow, Russia; [Hamagaki, H.; Oyama, K.] Nagasaki Inst Appl Sci, Nagasaki, Japan; [Ganoti, P.; Roukoutakis, F.; Vasileiou, M.] Univ Athens, Phys Dept, Athens, Greece; [Deloff, A.; Kovalenko, O.; Kurashvili, P.; Nair, R.; Redlich, K.; Siemiarczuk, T.; Wilk, G.] Natl Ctr Nucl Studies, Warsaw, Poland; [Andrei, C.; Bartos, D.; Berceanu, I.; Bercuci, A.; Caragheorgheopol, G.; Catanescu, V.; Ciobanu, M.; Duta, V.; Giolu, G.; Herghelegiu, A.; Magureanu, C.; Petris, M.; Petrovici, M.; Pop, A.; Schiaua, C.; Tarzila, M. G.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Dash, A.; Jena, C.; Kundu, S.; Mohanty, B.; Nayak, K.; Singh, R.] HBNI, Natl Inst Sci Educ & Res, Jatni, India; [Rustamov, A.] Natl Nucl Res Ctr, Baku, Azerbaijan; [Aleksandrov, D.; Blau, D.; Fokin, S.; Ippolitov, M.; Manko, V.; Nikolaev, S.; Nikulin, S.; Nyanin, A.; Peresunko, D.; Ryabinkin, E.; Sibiriak, Y.; Silaeva, S.; Vasiliev, A.; Vinogradov, A.] Natl Res Ctr, Kurchatov Inst, Moscow, Russia; [Bearden, I. G.; Bourjau, C.; Chojnacki, M.; Christensen, C. H.; Gaardhoje, J. J.; Gajdosova, K.; Gulbrandsen, K.; Nielsen, B. S.; Pacik, V.; Pimentel, L. O. D. L.; Thoresen, F.; Zhou, Y.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Caffarri, D.; Christakoglou, P.; Deplano, C.; Khabanova, Z.; Kuijer, P. G.; Lehas, F.] Nikhef Natl Inst Subatomaire Fys, Amsterdam, Netherlands; [Barnby, L. S.; Lemmon, R. C.] STFC Daresbury Lab, Nucl Phys Grp, Daresbury, England; [Adamova, D.; Bielcikova, J.; Ferencei, J.; Krizek, F.; Kucera, V.; Kushpil, S.; Sumbera, M.; Vanat, T.] Acad Sci Czech Republ, Nucl Phys Inst, Prague, Czech Republic; [Cormier, T. M.; Fasel, M.; Poghosyan, M. G.; Read, K. F.; Schaefer, B.; Schmidt, N. V.; Stankus, P.] Oak Ridge Natl Lab, Oak Ridge, TN USA; [Berdnikov, Y.; Ivanov, V.; Khanzadeev, A.; Kryshen, E.; Malaev, M.; Nikulin, V.; Riabov, V.; Ryabov, Y.; Samsonov, V.; Zhalov, M.] Petersburg Nucl Phys Inst, Gatchina, Russia; [Adam, J.; Anson, C.; Cherney, M.; Seger, J. E.] Creighton Univ, Dept Phys, Omaha, NE 68178 USA; [Erhardt, F.; Jercic, M.; Planinic, M.; Poljak, N.; Simatovic, G.; Utrobicic, A.] Univ Zagreb, Fac Sci, Phys Dept, Zagreb, Croatia; [Aggarwal, M. M.; Bhati, A. K.; Duggal, A. K.; Kumar, L.; Parmar, S.; Rathee, D.; Sharma, A.; Sharma, N.] Panjab Univ, Phys Dept, Chandigarh, India; [Cleymans, J.; Dietel, T.; Mhlanga, S.; Whitehead, A. M.] Univ Cape Town, Phys Dept, Cape Town, South Africa; [Bala, R.; Bhasin, A.; Bhat, I. R.; Gupta, A.; Gupta, R.; Kour, M.; Kumar, A.; Mahajan, S.; Rajput, S.; Sambyal, S.; Sharma, A.; Sharma, M.] Univ Jammu, Phys Dept, Jammu, India; [Raniwala, R.; Raniwala, S.] Univ Rajasthan, Phys Dept, Jaipur, Rajasthan, India; [Hess, B. A.; Schmidt, H. R.; Schmidt, M.; Voelkl, M. A.] Eberhard Karls Univ Tubingen, Phys Inst, Tubingen, Germany; [Adler, C.; Anguelov, V.; Arslandok, M.; Beck, H.; Bock, F.; Danisch, M. C.; Deisting, A.; Emschermann, D.; Fleck, M.; Freuen, S.; Glassel, P.; Gottschalk, D.; Grajcarek, R.; Herrmann, N.; Karayan, L.; Kim, J.; Klingenmeyer, H.; Klewin, S.; Knichel, M. L.; Krumbhorn, D.; Leardini, L.; Lehmann, T.; Lohner, D.; Lu, X.; Ludolphs, W.; Mahmoud, T.; Perez, J. Mercado; Muecke, J.; Neher, M.; Oeschler, H.; Ohlson, A.; Pachmayer, Y.; Radomski, S.; Reischl, A.; Reygers, K.; Rusanov, I.; Schicker, R.; Schmidt, M. O.; Schmiederer, S.; Shukla, P.; Solveit, H.; Stachel, J.; Stiller, J.; Stockmeyer, M.; Tilsner, H.; Tsiledakis, G.; Voelkl, M. A.; Vulpescu, B.; Wang, Y.; Weiser, D. F.; Wilkinson, J.; Windelband, B.; Winn, M.; Xu, C.; Zimmermann, A.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Arnold, O. W.; Bilandzic, A.; Chauvin, A.; Dahms, T.; Fabbietti, L.; Gasik, P.; Hohlweger, B.; Mathis, A. M.; Mihaylov, D. L.; Doce, O. Vazquez; Vorobyev, I.] Tech Univ Munich, Phys Dept, Munich, Germany; [Scharenberg, R. P.; Srivastava, B. K.] Purdue Univ, W Lafayette, IN 47907 USA; [Al-Turany, M.; Andronic, A.; Averbeck, R.; Braun-Munzinger, P.; Daues, H.; Deisting, A.; Devismes, A.; Dubla, A.; Foka, P.; Frankenfeld, U.; Garabatos, C.; Giubellino, P.; Gronefeld, J. M.; Grosso, R.; Gutfleisch, M.; Hehner, J.; Hornung, S.; Huber, S.; Ivanov, M.; Bustamante, R. T. Jimenez; Karayan, L.; Kollegger, T.; Kreis, L.; Lippmann, C.; Malzacher, P.; Marin, A.; Martin, N. A.; Masciocchi, S.; Miskowiec, D.; Nicassio, M.; Onderwaater, J.; Park, W.; Sann, H.; Schmidt, C.; Schulze, R.; Schwarz, K.; Schweda, K.; Sedykh, S.; Selyuzhenkov, I.; Simon, R.; Sozzi, F.; Stelzer, H.; Vranic, D.; Weber, S. G.] GSI Helmholtzzentrum Schwerionenforschung GmbH, Res Div, Darmstadt, Germany; [Al-Turany, M.; Andronic, A.; Averbeck, R.; Braun-Munzinger, P.; Daues, H.; Deisting, A.; Devismes, A.; Dubla, A.; Foka, P.; Frankenfeld, U.; Garabatos, C.; Giubellino, P.; Gronefeld, J. M.; Grosso, R.; Gutfleisch, M.; Hehner, J.; Hornung, S.; Huber, S.; Ivanov, M.; Bustamante, R. T. Jimenez; Karayan, L.; Kollegger, T.; Kreis, L.; Lippmann, C.; Malzacher, P.; Marin, A.; Martin, N. A.; Masciocchi, S.; Miskowiec, D.; Nicassio, M.; Onderwaater, J.; Park, W.; Sann, H.; Schmidt, C.; Schulze, R.; Schwarz, K.; Schweda, K.; Sedykh, S.; Selyuzhenkov, I.; Simon, R.; Sozzi, F.; Stelzer, H.; Vranic, D.; Weber, S. G.] GSI Helmholtzzentrum Schwerionenforschung GmbH, ExtreMe Matter Inst EMMI, Darmstadt, Germany; [Anticic, T.] Rudjer Boskovic Inst, Zagreb, Croatia; [Budnikov, D.; Filchagin, S.; Ilkaev, R.; Kuryakin, A.; Mamonov, A.; Nazarenko, S.; Punin, V.; Tumkin, A.; Zaviyalov, N.] Russian Fed Nucl Ctr VNIIEF, Sarov, Russia; [Chattopadhyay, S.; Das, D.; Das, I.; Ghosh, J.; Islam, M. S.; Khan, P.; Roy, P.; Shaikh, W.; Sinha, T.] Saha Inst Nucl Phys, Kolkata, India; [Andrews, H. A.; Evans, D.; Graham, K. L.; Jones, P. G.; Jusko, A.; Krivda, M.; Lietava, R.; Baillie, O. Villalobos; Willsher, E.; Zardoshti, N.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Alba, J. L. B.; Calvo Villar, E.; Endress, E.; Gago, A. M.] Pontificia Univ Catol Peru, Secc Fis, Dept Ciencias, Lima, Peru; [Evdokimov, S.; Izucheev, V.; Kharlov, Y.; Kondratyuk, E.; Petrov, V.; Polichtchouk, B.; Sadovsky, S.; Shangaraev, A.] SSC IHEP NRC Kurchatov Inst, Protvino, Russia; [Buhler, P.; Capon, A. A.; Gruber, L.; Lehner, S.; Suzuki, K.; Weber, M.; Zmeskal, J.] Stefan Meyer Inst Subatomare Phys SMI, Vienna, Austria; [Aphecetche, L.; Audurier, B.; Batigne, G.; Erazmus, B.; Francisco, A.; Germain, M.; Garcia, G. Martinez; Masson, E.; Morreale, A.; Pillot, P.; Schutz, Y.; Shabetai, A.; Stocco, D.] Univ Nantes, CNRS, IN2P3, SUBATECH,IMT Atlant, Nantes, France; [Kobdaj, C.; Poonsawat, W.] Suranaree Univ Technol, Nakhon Ratchasima, Thailand; [Cabala, J.; Jadlovsky, J.; Kopcik, M.; Oravec, M.; Vala, M.; Voscek, D.] Tech Univ Kosice, Kosice, Slovakia; [Gotovac, S.; Loncar, P.; Mudnic, E.; Vickovic, L.] Tech Univ Split FESB, Split, Croatia; [Bhom, J.; Figiel, J.; Gladysz-Dziadus, E.; Gorlich, L.; Kielbowicz, M. M.; Kowalski, M.; Mayer, C.; Otwinowski, J.; Rybicki, A.; Sputowska, I.] Polish Acad Sci, Henryk Niewodniczanski Inst Nucl Phys, Krakow, Poland; [Blair, J. T.; Gauger, E. F.; Markert, C.; Thomas, D.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Beltran, L. G. E.; Galvan, C. D.; Leon Monzon, I.; Podesta-Lerma, P. L. M.; Torres, S. R.] Univ Autonoma Sinaloa, Culiacan, Mexico; [Alves Garcia Prado, C.; Bregant, M.; De, S.; de Conti, C.; Degenhardt, H. F.; Domenicis Gimenez, D.; Figueredo, M. A. S.; Lagana Fernandes, C.; Munhoz, M. G.; Natal da Luz, H.; Oliveira Da Silva, A. C.; Suaide, A. A. P.; Zanoli, H. J. C.] Univ Sao Paulo, Sao Paulo, Brazil; [Albuquerque, D. S. D.; Chinellato, D. D.; De Souza, R. D.; Takahashi, J.] Univ Estadual Campinas UNICAMP, Campinas, Brazil; [Cosentino, M. R.] Univ Fed ABC, Santo Andre, Brazil; [Anwar, R.; Bellwied, R.; Bianchi, L.; Jayarathna, P. H. S. Y.; Jena, S.; Knospe, A. G.; Myers, C. J.; Ng, F.; Pathak, S. P.; Pinsky, L.; Piyarathna, D. B.; Rana, D. B.; Timmins, A. R.; Umaka, E. N.] Univ Houston, Houston, TX USA; [Chang, B.; Kim, D. J.; Rak, J.; Slupecki, M.; Snellman, T. W.; Trzaska, W. H.; Vargyas, M.; Viinikainen, J.] Univ Jyvaskyla, Jyvaskyla, Finland; [Borri, M.; Chartier, M.; Hills, C.; Lindsay, S. W.; Norman, J.] Univ Liverpool, Liverpool, Merseyside, England; [Bertens, R. A.; Castro, A. J.; Hughes, C.; Matyja, A.; Mazer, J.; Nattrass, C.; Read, K. F.; Scott, R.; Sharma, N.; Sorensen, S.; Witt, W. E.] Univ Tennessee, Knoxville, TN USA; [Marchisone, M.; Vilakazi, Z.] Univ Witwatersrand, Johannesburg, South Africa; [Gunji, T.; Hayashi, S.; Morino, Y.; Murakami, H.; Sekiguchi, Y.; Terasaki, K.; Tsuji, T.; Watanabe, Y.] Univ Tokyo, Tokyo, Japan; [Busch, O.; Chujo, T.; Esumi, S.; Hosokawa, R.; Inaba, M.; Kim, B.; Konno, M.; Miake, Y.; Sakai, S.; Sakata, D.; Sano, M.; Tanaka, N.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Yokoyama, H.] Univ Tsukuba, Tsukuba, Ibaraki, Japan; [Barret, V.; Bastid, N.; Chowdhury, T.; Crochet, P.; Dupieux, P.; Feuillard, V. J. G.; Lopez, X.; Manso, F.; Porteboeuf-Houssais, S.; Rosnet, P.; Zhang, Z.] Univ Clermont Auvergne, CNRS, IN2P3, LPC, Clermont Ferrand, France; [Cheshkov, C.; Cheynis, B.; Ducroux, L.; Teyssier, B.; Tieulent, R.; Uras, A.] Univ Lyon 1, CNRS IN2P3, IPN Lyon, Lyon, France; [Belikov, I.; Hamon, J. C.; Hippolyte, B.; Kuhn, C.; Maire, A.; Rami, F.; Roy, C.; Schutz, Y.; Senyukov, S.] Univ Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France; [Boca, G.; Costanza, S.; Rotondi, A.] Univ Pavia, Pavia, Italy; [Bonomi, G.; Pagano, D.] Univ Brescia, Brescia, Italy; [Altsybeev, I.; Feofilov, G.; Kondratiev, V.; Kovalenko, V.; Vechernin, V.; Vinogradov, L.; Zarochentsev, A.] St Petersburg State Univ, V Fock Inst Phys, St Petersburg, Russia; [Acharya, S.; Ahammed, Z.; Alam, S. N.; Chandra, S.; Chattopadhyay, S.; Choudhury, S.; Dubey, A. K.; Ghosh, P.; Kar, S.; Khan, S. A.; Mitra, J.; Muhuri, S.; Nayak, T. K.; Patra, R. N.; Sadhu, S.; Saha, S. K.; Saini, J.; Sarkar, D.; Sarkar, N.; Sheikh, A. I.; Singaraju, R.; Singhal, V.; Thakur, S.] Ctr Variable Energy Cyclotron, Kolkata, India; [Graczykowski, L. K.; Jakubowska, M. J.; Janik, M. A.; Kisiel, A.; Myrcha, J. W.; Niedziela, J.; Oleniacz, J.; Pluta, J.; Rokita, P. S.; Zbroszczyk, H.] Warsaw Univ Technol, Warsaw, Poland; [Basu, S.; Bianchin, C.; Konyushikhin, M.; Llope, W. J.; Pan, J.; Pruneau, C. A.; Pujahari, P.; Putschke, J.; Saleh, M. A.; Voloshin, S. A.] Wayne State Univ, Detroit, MI USA; [Barnafoldi, G. G.; Bencedi, G.; Berenyi, D.; Biro, G.; Boldizsar, L.; Hamar, G.; Kiss, G.; Kofarago, M.; Levai, P.; Lowe, A.; Olah, L.; Pochybova, S.; Varga, D.; Vertesi, R.] Hungarian Acad Sci, Wigner Res Ctr Phys, Budapest, Hungary; [Aiola, S.; Caines, H.; Connors, M. E.; Ehlers, R. J.; Epple, E.; Harris, J. W.; Lapidus, K.; Lutz, T. H.; Majka, R. D.; Mulligan, J. D.; Oh, S.; Oliver, M. H.; Smirnov, N.] Yale Univ, New Haven, CT USA; [Kang, J. H.; Kim, D.; Kim, H.; Kim, M.; Kim, T.; Kwon, Y.; Lee, S.; Song, M.] Yonsei Univ, Seoul, South Korea; [Keidel, R.] Fachhochschule Worms, ZTT, Worms, Germany Polytechnic University of Turin; University System of Georgia; Georgia State University; Ruprecht Karls University Heidelberg; Lomonosov Moscow State University; Aligarh Muslim University; University of Wroclaw; Yerevan Physics Institute; Benemerita Universidad Autonoma de Puebla; National Academy of Sciences Ukraine; Bogolyubov Institute for Theoretical Physics; Department of Science & Technology (India); Bose Institute; Department of Science & Technology (India); Bose Institute; Russian Academy of Sciences; Budker Institute of Nuclear Physics; California State University System; California Polytechnic State University San Luis Obispo; Central China Normal University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Chicago State University; China Institute of Atomic Energy; COMSATS University Islamabad (CUI); Aligarh Muslim University; University System of Ohio; Ohio State University; Pusan National University; Sejong University; University of Oslo; University of Bergen; Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Cagliari; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; Istituto Nazionale di Fisica Nucleare (INFN); University of Salerno; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Lund University; European Organization for Nuclear Research (CERN); Technical University of Munich; Western Norway University of Applied Sciences; Comenius University Bratislava; Czech Technical University Prague; University of Pavol Jozef Safarik Kosice; University College of Southeast Norway; Goethe University Frankfurt; Kangnung Wonju National University; Gauhati University; Helmholtz Association; University of Bonn; Helsinki Institute of Physics; Hiroshima University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Indore; National Research & Innovation Agency of Indonesia (BRIN); Indonesian Institute of Sciences (LIPI); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Inha University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; Utrecht University; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Slovak Academy of Sciences; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Institute of Physics Bhubaneswar (IOPB); Institute of Atomic Physics - Romania; Institute of Space Science; National Institute for Laser, Plasma & Radiation Physics - Romania; Goethe University Frankfurt; Goethe University Frankfurt; University of Munster; Universidad Nacional Autonoma de Mexico; Universidade Federal do Rio Grande do Sul; Universidad Nacional Autonoma de Mexico; CEA; UDICE-French Research Universities; Universite Paris Saclay; Joint Institute for Nuclear Research - Russia; Konkuk University; Korea Institute of Science & Technology (KIST); KTO Karatay University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Nagasaki Institute of Applied Science; National & Kapodistrian University of Athens; Horia Hulubei National Institute of Physics & Nuclear Engineering; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); National Research Centre - Kurchatov Institute; University of Copenhagen; Niels Bohr Institute; STFC Daresbury Laboratory; Czech Academy of Sciences; Nuclear Physics Institute of the Czech Academy of Sciences; United States Department of Energy (DOE); Oak Ridge National Laboratory; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Creighton University; University of Zagreb; Panjab University; University of Cape Town; University of Jammu; University of Rajasthan; Eberhard Karls University of Tubingen; Ruprecht Karls University Heidelberg; Technical University of Munich; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Helmholtz Association; GSI Helmholtz-Center for Heavy Ion Research; Helmholtz Association; GSI Helmholtz-Center for Heavy Ion Research; Rudjer Boskovic Institute; Saha Institute of Nuclear Physics; University of Birmingham; Pontificia Universidad Catolica del Peru; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); IMT - Institut Mines-Telecom; IMT Atlantique; Nantes Universite; Suranaree University of Technology; Technical University Kosice; University of Split; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; University of Texas System; University of Texas Austin; Universidad Autonoma de Sinaloa; Universidade de Sao Paulo; Universidade Estadual de Campinas; Universidade Federal do ABC (UFABC); University of Houston System; University of Houston; University of Jyvaskyla; University of Liverpool; University of Tennessee System; University of Tennessee Knoxville; University of Witwatersrand; University of Tokyo; University of Tsukuba; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; University of Pavia; University of Brescia; Saint Petersburg State University; Variable Energy Cyclotron Centre; Warsaw University of Technology; Wayne State University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Yale University; Yonsei University Acharya, S (corresponding author), Ctr Variable Energy Cyclotron, Kolkata, India. Sharma, Natasha/AAY-3656-2021; Castellanos, Javier Castillo/G-8915-2013; Medina, Alberto Martín Gago/J-9320-2017; Sitar, Brano/AAG-1870-2019; Goerlich, Lidia/AAR-5457-2021; Chartier, Marielle/F-4055-2018; Raniwala, Rashmi/AAA-3747-2022; Gottschlag, Holger/AAF-9541-2020; Yano, Satoshi/ABC-6996-2021; Loncar, Petra/G-4837-2017; Usai, Gianluca/E-9604-2015; Bellini, Francesca/T-5470-2017; Lu, Xianguo/AAL-2016-2021; Reshetin, Andrey/AAZ-4491-2020; Vechernin, Vladimir V/AAF-8588-2021; Otwinowski, Jacek/W-1159-2018; Kalinak, Peter/AAZ-3841-2020; de Souza, Rafael Derradi/M-4791-2013; Broz, Michal/AAE-7300-2022; Dobrin, Alexandru/S-7451-2018; Trzaska, Wladyslaw Henryk/P-2225-2015; Kar, Somnath/P-5417-2017; Finogeev, Dmitry/AAZ-4597-2020; Barbera, Roberto/G-5805-2012; Feofilov, Grigory Aleksandrovich/ABC-6519-2021; Palni, Prabhakar/AAX-4648-2020; Rojas Torres, Solangel/AFV-5187-2022; Sakata, Dousatsu/AAW-9766-2021; Ferretti, Alessandro/F-4856-2013; Redlich, Krzysztof/AAH-3247-2020; Rossi, Andrea/AGH-4546-2022; Kravčáková, Adela/B-1001-2017; Selyuzhenkov, Ilya/AAD-6163-2020; Janik, Małgorzata Anna/HHN-7774-2022; Cunqueiro, Leticia/ABD-7165-2021; Snellings, Raimond/G-6158-2016; Costanza, Susanna/AAB-1822-2020; Sevcenco, Adrian/C-1832-2012; Peitzmann, Thomas/K-2206-2012; Riabov, Victor G/N-4331-2015; Haque, Md Rihan/W-5921-2018; de Albuquerque, Danilo Silva/Y-6630-2019; Jadlovsky, Jan/AAH-4050-2019; Kondratiev, Valery P/J-8574-2013; Arsene, Ionut-Cristian/AAS-4284-2020; Alkin, Anton/A-6627-2017; Ristea, Catalin/S-6934-2019; Bhom, Jihyun/V-5173-2018; Batigne, Guillaume/AAR-9308-2021; AGRAWAL, NEELIMA/G-5409-2018; giubellino, paolo/AAG-6848-2020; Buxton, James/GSN-9725-2022; Ducati, Maria Beatriz Gay/C-4930-2013; de Oliveira, Renato Aparecido Negrao/G-9133-2015; Kucera, Vit/G-8459-2014; Gasik, Piotr/AHA-6586-2022; Aiola, Salvatore/I-4136-2013; Selyuzhenkov, Ilya/AAH-8322-2019; Pereira, Luis Gustavo/C-3296-2009; Adamova, Dagmar/G-9789-2014; Nattrass, Christine E/J-6752-2016; Bhasin, Anju/AAS-4745-2021; Krizek, Filip/G-8967-2014; Colocci, Manuel/ABC-4305-2020; Alici, Andrea/ABC-4168-2020; Kisel, Ivan/H-7077-2017; POP, AMALIA/ABF-3823-2020; Kharlov, Yuri/D-2700-2015; Dietel, Thomas/AAS-7549-2021; Tripathy, Sushanta/AAP-9259-2021; Vodopyanov, Alexander/AAD-3296-2019; Munhoz, Marcelo Gameiro/C-7939-2013; Guber, Fedor/I-4271-2013; Uysal, Ayben Karasu/K-3981-2015; Mishra, Aditya Nath/AAD-9692-2020; Jakubowska, Monika J./J-6664-2018; Pezzi, Rafael P/F-7489-2010; Bonomi, Germano/G-4236-2010; Kowalski, Marek/X-1462-2018; Ramello, Luciano/F-9357-2013; Kumar, Lokesh/A-6154-2010; Vasiliev, Alexander N/N-4742-2017; Roy, Ashim/HLV-9248-2023; Siddhanta, Sabyasachi/AAB-3975-2019; Oleniacz, Janusz/V-5659-2018; Cosentino, Mauro R/L-2418-2014; Suleymanov, Mais/AAD-2670-2022; Kondratev, Valery/AAE-3051-2021; Vrlakova, Janka/B-1003-2017; Khuntia, Arvind/AAO-1695-2021; Pshenichnov, Igor A/A-4063-2008; Poljak, Nikola/AAI-4178-2020; Suaide, Alexandre A P/L-6239-2016; Kuijer, Paul/T-8836-2019; Yalcin Kuzu, Serpil/ABI-2571-2020; Kobdaj, Chinorat/AAR-6293-2020; Chinellato, David D/D-3092-2012; Colella, Domenico/AAA-4111-2021; Sadovsky, Sergey/B-6053-2017; Jacazio, Nicolò/HLW-2357-2023; Berdnikov, Yaroslav Aleksandrovich/U-3866-2019; Hernández, Mario Iván Martínez/F-4083-2010; Alexey, Bogdanov/AAI-1306-2019; Read, Kenneth F./AAI-4290-2020; Sumbera, Michal/O-7497-2014; Meninno, Elisa/AAJ-4478-2021; Nellen, Lukas/AAH-2388-2020; Ahn, Sang-Un/AAH-1014-2020; Di Ruzza, Benedetto/ABG-2800-2021; Malkevich, Dmitry Borisovich/ABC-6854-2020; Cleymans, Jean/O-2272-2019; Dahms, Torsten/A-8453-2015; soramel, francesca/GQB-3419-2022; Calivà, Alberto/GXN-0958-2022; Tellez, Arturo Fernandez/E-9700-2017; Vechernin, Vladimir/J-5832-2013; Klein-Bösing, Christian/H-1263-2019; Das, Indranil/AAB-4665-2019; Patra, Rajendra Nath/ABD-2348-2020; de Albuquerque, Danilo/C-2003-2016; Sugitate, Toru/X-1730-2019; Butt, Jamila/GXF-8837-2022; Bielcikova, Jana/AAB-4862-2020; Sputowska, Iwona A./D-2088-2014; Bearden, Ian G./AAH-6265-2019; Azmi, Mohd Danish/G-8245-2019; Siemiarczuk, Teodor/ABE-4469-2021; Behera, Nirbhay Kumar/AAG-3409-2019; rui, rinaldo/L-1926-2015; Barnby, Lee S/G-2135-2010; Zhou, You/ABH-2989-2021; Buehler, Paul/C-4851-2016; Christensen, Christian/AAR-2840-2021; Ferencei, Jozef/H-1308-2014; Nayak, Kishora/AAV-4969-2021; Graczykowski, Łukasz/O-7522-2015; Riggi, Francesco/AAF-6034-2019; Basu, Sumit/ABG-4301-2021; Planinic, Mirko/E-8085-2012; Hills, Christopher/E-5950-2019; Sputowska, Iwona/AAK-9108-2020; Paul, Biswarup/AAW-4752-2020; Petris, Mariana/W-9161-2019; Kisiel, Adam/O-8754-2015; Bielcikova, Jana/G-9342-2014; Preghenella, Roberto/AAX-7032-2020; Kovalenko, Vladimir/C-5709-2013; Andrea, Danu/T-7729-2019; Barnaföldi, Gergely Gábor/AAB-5738-2021; Peressounko, Dmitri/F-8568-2017; Sahin, Ozkan/AAH-6445-2021; Takahashi, Jun/B-2946-2012; da Luz, Hugo Natal/O-4412-2019; Kim, Beomkyu/AAM-7220-2020; da Luz, Hugo Natal/F-6460-2013; Nielsen, Borge S/C-3719-2015; Vazquez Doce, Oton/K-1668-2014; Floris, Michele/Q-7521-2016; Cabanillas Noris, Juan Carlos/B-7170-2018; De Pasquale, Salvatore/B-9165-2008; Gaardhoje, Jens-Jorgen/F-9008-2011; Das, Debasish/C-8842-2013; Vinogradov, Leonid/K-3047-2013; Akindinov, Alexander/J-2674-2016; Gulbrandsen, Kristjan/A-2032-2016; Rybicki, Andrzej/V-9697-2018; JENA, DEEPIKA/P-2873-2015; Silvermyr, David/V-9867-2017; Kurepin, Alexey/H-4852-2013; Jena, Satyajit/P-2409-2015 Castellanos, Javier Castillo/0000-0002-5187-2779; Medina, Alberto Martín Gago/0000-0002-0019-9692; Goerlich, Lidia/0000-0001-7792-2247; Chartier, Marielle/0000-0003-0578-5567; Gottschlag, Holger/0000-0001-8841-2576; Loncar, Petra/0000-0001-6486-2230; Usai, Gianluca/0000-0002-8659-8378; Bellini, Francesca/0000-0003-3498-4661; Lu, Xianguo/0000-0002-3077-1805; Vechernin, Vladimir V/0000-0003-1458-8055; Otwinowski, Jacek/0000-0002-5471-6595; Kalinak, Peter/0000-0002-0559-6697; de Souza, Rafael Derradi/0000-0002-2084-7001; Dobrin, Alexandru/0000-0003-4432-4026; Trzaska, Wladyslaw Henryk/0000-0003-0672-9137; Kar, Somnath/0000-0002-5987-9414; Barbera, Roberto/0000-0001-5971-6415; Feofilov, Grigory Aleksandrovich/0000-0003-3700-8623; Palni, Prabhakar/0000-0001-6201-2785; Ferretti, Alessandro/0000-0001-9084-5784; Redlich, Krzysztof/0000-0002-2629-1710; Kravčáková, Adela/0000-0002-1381-3436; Janik, Małgorzata Anna/0000-0001-9087-4665; Cunqueiro, Leticia/0000-0001-6764-5370; Snellings, Raimond/0000-0001-9720-0604; Costanza, Susanna/0000-0002-5860-585X; Sevcenco, Adrian/0000-0002-4151-1056; Peitzmann, Thomas/0000-0002-7116-899X; Haque, Md Rihan/0000-0001-7978-9638; de Albuquerque, Danilo Silva/0000-0002-8840-5648; Kondratiev, Valery P/0000-0002-0031-0741; Ristea, Catalin/0000-0002-9760-645X; Bhom, Jihyun/0000-0002-9709-903X; Batigne, Guillaume/0000-0001-8638-6300; AGRAWAL, NEELIMA/0000-0003-0348-9836; giubellino, paolo/0000-0002-1383-6160; Ducati, Maria Beatriz Gay/0000-0002-8450-5318; Gasik, Piotr/0000-0001-9840-6460; Aiola, Salvatore/0000-0001-6209-7627; Nattrass, Christine E/0000-0002-8768-6468; Alici, Andrea/0000-0003-3618-4617; Kisel, Ivan/0000-0002-4808-419X; Kharlov, Yuri/0000-0001-6653-6164; Dietel, Thomas/0000-0002-2065-6256; Tripathy, Sushanta/0000-0002-0061-5107; Munhoz, Marcelo Gameiro/0000-0003-3695-3180; Guber, Fedor/0000-0001-8790-3218; Uysal, Ayben Karasu/0000-0001-6297-2532; Mishra, Aditya Nath/0000-0002-3892-2719; Jakubowska, Monika J./0000-0001-9334-3798; Pezzi, Rafael P/0000-0002-0452-3103; Bonomi, Germano/0000-0003-1618-9648; Kowalski, Marek/0000-0002-7568-7498; Kumar, Lokesh/0000-0002-2746-9840; Roy, Ashim/0000-0002-5622-4260; Oleniacz, Janusz/0000-0003-2966-4903; Cosentino, Mauro R/0000-0002-7880-8611; Suleymanov, Mais/0000-0003-4231-0480; Kondratev, Valery/0000-0002-0031-0741; Vrlakova, Janka/0000-0002-5846-8496; Khuntia, Arvind/0000-0003-0996-8547; Pshenichnov, Igor A/0000-0003-1752-4524; Suaide, Alexandre A P/0000-0003-2847-6556; Kuijer, Paul/0000-0002-6987-2048; Yalcin Kuzu, Serpil/0000-0001-8905-8089; Kobdaj, Chinorat/0000-0001-7296-5248; Chinellato, David D/0000-0002-9982-9577; Jacazio, Nicolò/0000-0002-3066-855X; Hernández, Mario Iván Martínez/0000-0002-8503-3009; Sumbera, Michal/0000-0002-0639-7323; Nellen, Lukas/0000-0003-1059-8731; Di Ruzza, Benedetto/0000-0001-9925-5254; Dahms, Torsten/0000-0003-4274-5476; Tellez, Arturo Fernandez/0000-0003-0152-4220; Vechernin, Vladimir/0000-0003-1458-8055; Klein-Bösing, Christian/0000-0002-7285-3411; Das, Indranil/0000-0002-5437-2067; Patra, Rajendra Nath/0000-0003-0180-9883; de Albuquerque, Danilo/0000-0002-8840-5648; Butt, Jamila/0000-0002-5236-8697; Sputowska, Iwona A./0000-0002-7590-7171; Bearden, Ian G./0000-0003-2784-3094; Azmi, Mohd Danish/0000-0002-2501-6856; Siemiarczuk, Teodor/0000-0002-2014-5229; Behera, Nirbhay Kumar/0000-0002-1999-9876; rui, rinaldo/0000-0002-6993-0332; Barnby, Lee S/0000-0001-7357-9904; Zhou, You/0000-0002-7868-6706; Buehler, Paul/0000-0003-2049-1380; Christensen, Christian/0000-0002-1850-0121; Nayak, Kishora/0000-0003-1942-317X; Graczykowski, Łukasz/0000-0002-4442-5727; Basu, Sumit/0000-0003-0687-8124; Planinic, Mirko/0000-0001-6760-2514; Hills, Christopher/0000-0003-4647-4159; Sputowska, Iwona/0000-0002-7590-7171; Paul, Biswarup/0000-0002-1461-3743; Kisiel, Adam/0000-0001-8322-9510; Preghenella, Roberto/0000-0002-1539-9275; Kovalenko, Vladimir/0000-0001-6012-6615; Andrea, Danu/0000-0002-8899-3654; Barnaföldi, Gergely Gábor/0000-0001-9223-6480; Sahin, Ozkan/0000-0003-3940-7222; Takahashi, Jun/0000-0002-4091-1779; da Luz, Hugo Natal/0000-0003-1177-870X; da Luz, Hugo Natal/0000-0003-1177-870X; Giubilato, Piero/0000-0003-4358-5355; Rokita, Przemyslaw/0000-0002-4433-2133; Chowdhury, Tasnuva/0000-0002-2681-8105; Petta, Catia Maria Annunziata/0000-0002-2055-4196; Weber, Michael/0000-0001-5742-294X; Ronchetti, Federico/0000-0001-5245-8441; Rademakers, Fons/0000-0002-3571-9635; Zaviyalov, Nikolay/0000-0001-7898-9080; Nielsen, Borge S/0000-0002-0091-1934; Pachmayer, Yvonne/0000-0001-6142-1528; Rustamov, Anar/0000-0001-8678-6400; Crkovska, Jana/0000-0002-7946-7580; Norman, Jaime/0000-0002-3783-5760; Mieskolainen, Mikael/0000-0001-8893-7401; De Gruttola, Daniele/0000-0002-7055-6181; Barioglio, Luca/0000-0002-7328-9154; Vazquez Doce, Oton/0000-0001-6459-8134; Gorbunov, Sergey/0000-0001-9429-6444; Johann, Zmeskal/0000-0003-0815-0639; Teyssier, Boris/0000-0002-1707-2436; Altenkamper, Lucas/0000-0002-7695-8456; Scomparin, Enrico/0000-0001-9015-9610; Zhalov, Mikhail/0000-0003-0419-321X; Ramello, Luciano/0000-0003-2325-8680; Kielbowicz, Miroslaw/0000-0002-4403-9201; Khatun, Anisa/0000-0002-2724-668X; Sugitate, Toru/0000-0002-3784-5985; Cindolo, Federico/0000-0002-4255-7347; Blume, Christoph/0000-0002-6800-3465; Ahn, Sang Un/0000-0001-8847-489X; Nayak, Ranjit/0000-0001-6988-0606; Litichevskyi, Vladyslav/0000-0002-2574-241X; Guerzoni, Barbara/0000-0003-3187-7051; Siddhanta, Sabyasachi/0000-0002-0543-9245; Rodriguez Cahuantzi, Mario/0000-0002-9596-1060; Bock, Friederike/0000-0003-4185-2093; Alkin, Anton/0000-0002-2205-5761; morino, yuhei/0000-0002-8761-5811; Sadhu, Samrangy/0000-0002-6799-3903; Roed, Ketil/0000-0001-7803-9640; Lea, Ramona/0000-0001-5955-0769; Floris, Michele/0000-0003-0635-788X; 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Karczmarczyk, Przemyslaw/0000-0002-9057-9719; Bello Martinez, Hector/0000-0003-1315-5549; pruneau, claude/0000-0002-0458-538X; Hussain, Tahir/0000-0002-4951-5756; Mares, Jiri A./0000-0003-0194-9245; Masson, Erwann/0000-0002-5628-8926; Wegerle, Dominik/0000-0001-7442-1324; Germain, Marie/0000-0001-7382-1609; Vanat, Tomas/0000-0002-1777-6222; Grosa, Fabrizio/0000-0002-1469-9022; Rasanen, Sami/0000-0001-6792-7773; Puccio, Maximiliano/0000-0002-8118-9049; MITU, Ciprian Mihai/0000-0001-6522-6463; Petris, Mariana/0000-0003-2806-5941; Poljak, Nikola/0000-0002-4512-9620; Greiner, Leo/0000-0003-1476-6245; Berdnikov, Yaroslav/0000-0003-0309-5917; Figiel, Jan/0000-0002-7692-0079; Bhasin, Anju/0000-0002-3687-8179; Viinikainen, Jussi/0000-0003-2530-4265; Schweda, Kai/0000-0001-9935-6995; Carnesecchi, Francesca/0000-0001-9981-7536; Martinez Garcia, Gines/0000-0002-8657-6742; Margagliotti, Giacomo/0000-0003-1965-7953; Gaardhoje, Jens-Jorgen/0000-0001-6122-4698; Anson, Christopher/0000-0001-6244-4713; Gomez Ramirez, Andres/0000-0003-1529-4614; Murray, Sean/0000-0003-0548-588X; Hohlweger, Bernhard/0000-0001-6925-3469; Basile, Maurizio/0000-0002-1372-8063; Shigaki, Kenta/0000-0001-8416-8617; Christiansen, Peter/0000-0001-7066-3473; Winn, Michael/0000-0002-2207-0101; Lai, Yue Shi/0000-0002-5610-2327; ESPINOZA BELTRAN, LUCINA GABRIELA/0000-0002-9413-6069; Aphecetche, Laurent/0000-0001-7662-3878; Thakur, Dhananjaya/0000-0002-1540-242X; Kurashvili, Podist/0000-0002-0613-5278; Bombara, Marek/0000-0001-7333-224X; Zavada, Petr/0000-0002-8296-2128; Grigoryan, Smbat/0000-0002-0658-5949; Das, Debasish/0000-0002-2685-3111; Vinogradov, Leonid/0000-0001-9247-6230; Akindinov, Alexander/0000-0002-7388-3022; Hladky, Jan/0000-0002-7885-102X; Sakata, Dousatsu/0000-0002-8398-3334; Sarma, Pranjal/0000-0002-3191-4513; van Leeuwen, Marco/0000-0002-5222-4888; Pagano, Davide/0000-0003-0333-448X; Gulbrandsen, Kristjan/0000-0002-3809-4984; Pujahari, Prabhat Ranjan/0000-0002-0994-7212; Rybicki, Andrzej/0000-0003-3076-0505; Nair, Rahul Ramachandran/0000-0001-8326-9846; Bianchi, Antonio/0000-0003-0343-7497; Di Bari, Domenico/0000-0002-5559-8906; Bazo Alba, Jose Luis/0000-0001-9148-9101; Riabov, Victor/0000-0002-8142-6374; JENA, DEEPIKA/0000-0003-2112-0311; Bourjau, Christian/0000-0001-6745-5139; Jadhav, Manoj Bhanudas/0000-0002-5225-2803; Oppedisano, Chiara/0000-0001-6194-4601; Silvermyr, David/0000-0002-0526-5791; Sas, Mike/0000-0003-1419-2085; Pop, Amalia/0000-0003-0425-5724; Lemmon, Roy/0000-0002-1259-979X; Virgili, Tiziano/0000-0003-0471-7052; Kurepin, Alexey/0000-0002-1851-4136; Feliciello, Alessandro/0000-0001-5823-9733; Jaelani, Syaefudin/0000-0003-3958-9062; Jena, Satyajit/0000-0002-6220-6982; Read, Kenneth/0000-0002-3358-7667; Nikolaev, Sergey/0000-0003-1242-4866; Evans, Professor David/0000-0002-8427-322X; Chattopadhyay, Subhasis/0000-0003-1097-8806 Worldwide LHC Computing Grid (WLCG) collaboration; A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL); State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austria; Nationalstiftung fur Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, Azerbaijan; National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil; Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Financiadora de Estudos e Projetos (Finep), Brazil; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Brazil; Ministry of Science AMP; Technology of China (MSTC), China; National Natural Science Foundation of China (NSFC), China; Ministry of Education of China (MOEC), China; Ministry of Science, Education and Sport, Croatia; Croatian Science Foundation, Croatia; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; Danish Council for Independent Research | Natural Sciences, Denmark; Carlsberg Foundation, Denmark; Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat a l'Energie Atomique (CEA), France; Institut National de Physique Nucleaire et de Physique des Particules (IN2P3), France; Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF), Germany; GSI Helmholtzzentrum fur Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Greece; Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), New Delhi, India; Council of Scientific and Industrial Research (CSIR), New Delhi, India; Indonesian Institute of Science, Indonesia; Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology, Japan; Nagasaki Institute of Applied Science (IIST), Japan; Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnologia, through Fondo de Cooperacion Internacional en Ciencia y Tecnologia (FONCICYT), Mexico; Direccion General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Catolica del Peru, Peru; Ministry of Science and Higher Education, Poland; National Science Centre, Poland; Korea Institute of Science and Technology Information, Republic of Korea; National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Romania; Institute of Atomic Physics and Romanian National Agency for Science, Technology and Innovation, Romania; Joint Institute for Nuclear Research (JINR), Russia; Ministry of Education and Science of the Russian Federation, Russia; National Research Centre Kurchatov Institute, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Centro de Aplicaciones Tecnologicas y Desarrollo Nuclear (CEADEN), Cubaenergia, Cuba; Ministerio de Ciencia e Innovacion, Spain; Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), Spain; Swedish Research Council (VR), Sweden; Knut AMP; Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; National Science and Technology Development Agency (NSDTA), Thailand; Suranaree University of Technology (SUT), Thailand; Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF), United States of America; United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America; Direct For Mathematical & Physical Scien; Division Of Physics [1613118, 1625081] Funding Source: National Science Foundation; Science and Technology Facilities Council [ST/P005438/1, 1523365, ST/L005670/1, ST/M001598/1, GRIDPP, ST/J000108/1, 1657698, 1796881, 2017 STFC Nuclear Physics CG, ST/P004598/1, 2014 STFC Nuclear Physics CG, 1843572, ST/M001601/1] Funding Source: researchfish; Grants-in-Aid for Scientific Research [17H01122] Funding Source: KAKEN; STFC [1657698, ST/M001601/1] Funding Source: UKRI Worldwide LHC Computing Grid (WLCG) collaboration; A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL); State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austria; Nationalstiftung fur Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, Azerbaijan(Ministry of Transport, Communications and High Technologies of Azerbaijan Republic); National Nuclear Research Center, Azerbaijan(Ministry of Transport, Communications and High Technologies of Azerbaijan Republic); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Financiadora de Estudos e Projetos (Finep), Brazil(Financiadora de Inovacao e Pesquisa (Finep)); Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Ministry of Science AMP; Technology of China (MSTC), China; National Natural Science Foundation of China (NSFC), China(National Natural Science Foundation of China (NSFC)); Ministry of Education of China (MOEC), China(Ministry of Education, China); Ministry of Science, Education and Sport, Croatia(Ministry of Science, Education and Sports, Republic of Croatia); Croatian Science Foundation, Croatia; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; Danish Council for Independent Research | Natural Sciences, Denmark; Carlsberg Foundation, Denmark(Carlsberg Foundation); Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat a l'Energie Atomique (CEA), France(French Atomic Energy Commission); Institut National de Physique Nucleaire et de Physique des Particules (IN2P3), France; Centre National de la Recherche Scientifique (CNRS), France(Centre National de la Recherche Scientifique (CNRS)); Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF), Germany(Federal Ministry of Education & Research (BMBF)); GSI Helmholtzzentrum fur Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Greece(Greek Ministry of Development-GSRT); Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary(National Research, Development & Innovation Office (NRDIO) - Hungary); Department of Atomic Energy Government of India (DAE), New Delhi, India; Council of Scientific and Industrial Research (CSIR), New Delhi, India(Council of Scientific & Industrial Research (CSIR) - India); Indonesian Institute of Science, Indonesia(Ministry of Research and Technology of the Republic of Indonesia (RISTEK)); Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi; Istituto Nazionale di Fisica Nucleare (INFN), Italy(Istituto Nazionale di Fisica Nucleare (INFN)); Institute for Innovative Science and Technology, Japan; Nagasaki Institute of Applied Science (IIST), Japan; Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Consejo Nacional de Ciencia (CONACYT) y Tecnologia, through Fondo de Cooperacion Internacional en Ciencia y Tecnologia (FONCICYT), Mexico; Direccion General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); Research Council of Norway, Norway(Research Council of Norway); Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Catolica del Peru, Peru; Ministry of Science and Higher Education, Poland(Ministry of Science and Higher Education, Poland); National Science Centre, Poland(National Science Centre, Poland); Korea Institute of Science and Technology Information, Republic of Korea; National Research Foundation of Korea (NRF), Republic of Korea(National Research Foundation of Korea); Ministry of Education and Scientific Research, Romania(Ministry of Education and Research - Romania); Institute of Atomic Physics and Romanian National Agency for Science, Technology and Innovation, Romania; Joint Institute for Nuclear Research (JINR), Russia; Ministry of Education and Science of the Russian Federation, Russia(Ministry of Education and Science, Russian Federation); National Research Centre Kurchatov Institute, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Centro de Aplicaciones Tecnologicas y Desarrollo Nuclear (CEADEN), Cubaenergia, Cuba; Ministerio de Ciencia e Innovacion, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), Spain; Swedish Research Council (VR), Sweden(Swedish Research Council); Knut AMP; Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; National Science and Technology Development Agency (NSDTA), Thailand; Suranaree University of Technology (SUT), Thailand; Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); National Science Foundation of the United States of America (NSF), United States of America; United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America(United States Department of Energy (DOE)); Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: A. I.; r Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences and Nationalstiftung fur Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Universidade Federal do Rio Grande do Sul (UFRGS), Financiadora de Estudos e Projetos (Finep) and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Brazil; Ministry of Science & Technology of China (MSTC), National Natural Science Foundation of China (NSFC) and Ministry of Education of China (MOEC), China; Ministry of Science, Education and Sport and Croatian Science Foundation, Croatia; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the Carlsberg Foundation and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat a l'Energie Atomique (CEA) and Institut National de Physique Nucleaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF) and GSI Helmholtzzentrum fur Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE) and Council of Scientific and Industrial Research (CSIR), New Delhi, India; Indonesian Institute of Science, Indonesia; Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi and Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology, Nagasaki Institute of Applied Science (IIST), Japan Society for the Promotion of Science (JSPS) KAKENHI and Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnologia, through Fondo de Cooperacion Internacional en Ciencia y Tecnologia (FONCICYT) and Direccion General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Catolica del Peru, Peru; Ministry of Science and Higher Education and National Science Centre, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics and Romanian National Agency for Science, Technology and Innovation, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation and National Research Centre Kurchatov Institute, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Centro de Aplicaciones Tecnologicas y Desarrollo Nuclear (CEADEN), Cubaenergia, Cuba, Ministerio de Ciencia e Innovacion and Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), Spain; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Orga; nir zation for Nuclear Research, Switzerland; National Science and Technology Development Agency (NSDTA), Suranaree University of Technology (SUT) and Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America. 113 14 14 1 58 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0168-9002 1872-9576 NUCL INSTRUM METH A Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip. FEB 11 2018.0 881.0 88 127 10.1016/j.nima.2017.09.028 0.0 40 Instruments & Instrumentation; Nuclear Science & Technology; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation; Nuclear Science & Technology; Physics FQ7EG Green Published, Green Submitted, Green Accepted, hybrid 2023-03-23 WOS:000418525100013 0
J Crusiol, LGT; Sun, L; Sibaldelli, RNR; Felipe, V; Furlaneti, WX; Chen, R; Sun, Z; Wuyun, D; Chen, Z; Nanni, MR; Furlanetto, RH; Cezar, E; Nepomuceno, AL; Farias, JRB Crusiol, L. G. T.; Sun, Liang; Sibaldelli, R. N. R.; Felipe Junior, V; Furlaneti, W. X.; Chen, R.; Sun, Z.; Wuyun, D.; Chen, Z.; Nanni, M. R.; Furlanetto, R. H.; Cezar, E.; Nepomuceno, A. L.; Farias, J. R. B. Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods PRECISION AGRICULTURE English Article Yield prediction; Yield mapping; Partial least squares regression; Support vector regression; Multispectral image; Multitemporal data VEGETATION INDEX; TIME-SERIES; SPECTROSCOPY; PREDICTION; SENSOR; MAP Soybean crop plays an important role in world food production and food security, and agricultural production should be increased accordingly to meet the global food demand. Satellite remote sensing data is considered a promising proxy for monitoring and predicting yield. This research aimed to evaluate strategies for monitoring within-field soybean yield using Sentinel-2 visible, near-infrared and shortwave infrared (Vis/NIR/SWIR) spectral bands and partial least squares regression (PLSR) and support vector regression (SVR) methods. Soybean yield maps (over 500 ha) were recorded by a combine harvester with a yield monitor in 15 fields (3 farms) in Parana State, southern Brazil. Sentinel-2 images (spectral bands and 8 vegetation indices) across a cropping season were correlated to soybean yield. Information pooled across the cropping season presented better results compared to single images, with best performance of Vis/NIR/SWIR spectral bands under PLSR and SVR. At the grain filling stage, field-, farm- and global-based models were evaluated and presented similar trends compared to leaf-based hyperspectral reflectance collected at the Brazilian National Soybean Research Center. SVR outperformed PLSR, with a strong correlation between observed and predicted yield. For within-field soybean yield mapping, field-based SVR models (developed individually for each field) presented the highest accuracies. The results obtained demonstrate the possibility of developing within-field yield prediction models using Sentinel-2 Vis/NIR/SWIR bands through machine learning methods. [Crusiol, L. G. T.; Sun, Liang; Chen, R.; Sun, Z.; Wuyun, D.] Chinese Acad Agr Sci, CAAS CIAT Joint Lab Adv Technol Sustainable Agr, Instituteof Agr Resources & Reg Planning, Key Lab Agr Remote Sensing,Minist Agr, Beijing 100081, Peoples R China; [Crusiol, L. G. T.; Nanni, M. R.; Furlanetto, R. H.; Cezar, E.] Univ Estadual Maringa, Dept Agron, BR-87020900 Maringa, Parana, Brazil; [Sibaldelli, R. N. R.; Nepomuceno, A. L.; Farias, J. R. B.] Embrapa Soja Natl Soybean Res Ctr, Brazilian Agr Res Corp, BR-86001970 Londrina, Parana, Brazil; [Felipe Junior, V; Furlaneti, W. X.] Integrada Cooperat Agroind, BR-86010480 Londrina, Parana, Brazil; [Chen, Z.] Food & Agr Org United Nations, Digitalizat & Informat Div, I-00153 Rome, Italy Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Universidade Estadual de Maringa; Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA); Food & Agriculture Organization of the United Nations (FAO) Sun, L (corresponding author), Chinese Acad Agr Sci, CAAS CIAT Joint Lab Adv Technol Sustainable Agr, Instituteof Agr Resources & Reg Planning, Key Lab Agr Remote Sensing,Minist Agr, Beijing 100081, Peoples R China. luiscrusiol@gmail.com; sunliang@caas.cn; rubson.sibaldelli@embrapa.br; vanderlei.junior@ntegada.coop.br; wellington.furlaneti@integrada.coop.br; chenrq@mails.ccnu.edu.cn; sunzhengcaas@gmail.com; 82101181154@caas.cn; zhongxin.chen@fao.org; mrnanni@uem.br; renatohfurlanetto@hotmail.com; eccarpejani@gmail.com; alexandre.nepomuceno@embrapa.br; joserenato.farias@embrapa.br Nanni, Marcos Rafael/M-8829-2013 Nanni, Marcos Rafael/0000-0003-4854-2661; Sibaldelli, Rubson Natal R/0000-0002-8711-438X National Council for Scientific and Technological Development CNPq; Central Public-Interest Scientific Institution Basal Research Fund [Y2021GH18]; Innovation Project of Chinese Academy of Agricultural Sciences [G202120-5]; Talented Young Scientist ProgramChina Science and Technology Exchange Center [Brazil] [19-004] National Council for Scientific and Technological Development CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Central Public-Interest Scientific Institution Basal Research Fund; Innovation Project of Chinese Academy of Agricultural Sciences; Talented Young Scientist ProgramChina Science and Technology Exchange Center [Brazil] This work was supported by the National Council for Scientific and Technological DevelopmentCNPq; Central Public-Interest Scientific Institution Basal Research Fund [Y2021GH18]; Innovation Project of Chinese Academy of Agricultural Sciences [G202120-5]; and the Talented Young Scientist ProgramChina Science and Technology Exchange Center [Brazil-19-004]. 61 0 0 13 20 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1385-2256 1573-1618 PRECIS AGRIC Precis. Agric. JUN 2022.0 23.0 3 1093 1123 10.1007/s11119-022-09876-5 0.0 APR 2022 31 Agriculture, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Agriculture 1J3MK 2023-03-23 WOS:000784272500001 0
J Liao, HJ; Zhou, ZY; Liu, N; Zhang, Y; Xu, GY; Wang, ZT; Mumtaz, S Liao, Haijun; Zhou, Zhenyu; Liu, Nian; Zhang, Yan; Xu, Guangyuan; Wang, Zhenti; Mumtaz, Shahid Cloud-Edge-Device Collaborative Reliable and Communication-Efficient Digital Twin for Low-Carbon Electrical Equipment Management IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Cloud-edge-device collaboration; digital twin (DT); electrical equipment management; federated learning (FL); reliability and communication efficiency RESOURCE-ALLOCATION; IOT The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named C-3-FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that C-3-FLOW performs superior in loss function, communication efficiency, and carbon emission reduction. [Liao, Haijun; Zhou, Zhenyu; Liu, Nian] North China Elect Power Univ, Hebei Key Lab Power Internet Things Technol, Beijing 102206, Peoples R China; [Zhang, Yan] Univ Oslo, Dept Informat, N-0316 Oslo, Norway; [Zhang, Yan] Univ Oslo, Simula Metropolitan Ctr Digital Engn, N-0167 Oslo, Norway; [Xu, Guangyuan; Wang, Zhenti] Smart Grid Anhui Elect Power Co Ltd, Chuzhou Power Supply Co, Chuzhou, Peoples R China; [Mumtaz, Shahid] Univ Aveiro, Inst Telecomunicacoes, Campus Univ Santiago, P-3810193 Aveiro, Portugal; [Mumtaz, Shahid] Nottingham Trent Univ, Nottingham NG1 4BU, England North China Electric Power University; University of Oslo; University of Oslo; Universidade de Aveiro; Nottingham Trent University Zhou, ZY (corresponding author), North China Elect Power Univ, Hebei Key Lab Power Internet Things Technol, Beijing 102206, Peoples R China. haijun_liao@ncepu.edu.cn; zhenyu_zhou@ncepu.edu.cn; nianliu@ncepu.edu.cn; yanzhang@ieee.org; 18855005943@189.com; 18955000052@189.com; Shahid.mumtaz@ntu.ac.uk Science and Technology Project of State Grid Corporation of China [52094021N010 (5400-202199534A-0-5-ZN).] Science and Technology Project of State Grid Corporation of China This work was supported by the Science and Technology Project of State Grid Corporation of China underGrant 52094021N010 (5400-202199534A-0-5-ZN). 22 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2023.0 19.0 2 1715 1724 10.1109/TII.2022.3194840 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 8Q1HM Green Accepted 2023-03-23 WOS:000926964700057 0
J Liu, H; Dzitac, I; Guo, S Liu, H.; Dzitac, I.; Guo, S. Reduction of Conditional Factors in Causal Analysis INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL English Article factors space; causal analysis; reduction of factors; fuzzy logic FACTOR SPACE THEORY; DECISION-MAKING; GENERAL VIEW; REPRESENTATION Faced with a great number of conditional factors in big data causal analysis, the reduction algorithm put forward in this paper can reasonably reduce the number of conditional factors. Compared with the previous reduction methods, we take into consideration the influence of conditional factors on resulted factors, as well as the relationship among conditional factors themselves. The basic idea of the algorithm proposed in this paper is to establish the matrix of mutual deterministic degrees in between conditional factors. If a conditional factor f has a greater deterministic degree with respect to another conditional factor h, we will delete the factor h unless factor h has a greater deterministic degree with respect to f, then delete factor f in this case. With this reduction, we can ensure that the conditional factors participating in causal analysis are as irrelevant as possible. This is a reasonable requirement for causal analysis. [Liu, H.; Guo, S.] Liaoning Tech Univ, Inst Intelligence Engn & Math, Fuxing 123000, Peoples R China; [Liu, H.; Guo, S.] Liaoning Tech Univ, Coll Sci, Fuxing 123000, Peoples R China; [Dzitac, I.] Aurel Vlaicu Univ Arad, Elena Dragoi 2, Arad 310330, Romania; [Dzitac, I.] Agora Univ Oradea, P Ta Tineretului 8, Oradea 410526, Romania Liaoning Technical University; Liaoning Technical University; Aurel Vlaicu University of Arad Liu, H (corresponding author), Liaoning Tech Univ, Inst Intelligence Engn & Math, Fuxing 123000, Peoples R China.;Liu, H (corresponding author), Liaoning Tech Univ, Coll Sci, Fuxing 123000, Peoples R China. liuhaitao@lntu.edu.cn; ioan.dzitac@uav.ro Dzitac, Ioan/N-8695-2019; Dzitac, Ioan/T-2471-2019; Dzitac, Ioan/A-6169-2010; Dzitac, Ioan/A-8653-2010; Rossi, Jose Osvaldo/F-7391-2012 Dzitac, Ioan/0000-0002-5415-9936; Dzitac, Ioan/0000-0002-5415-9936; Dzitac, Ioan/0000-0002-3807-780X; LIU, Haitao/0000-0002-0517-4718; Rossi, Jose Osvaldo/0000-0002-1421-163X; da Luz, Lara Emanuele/0000-0001-5284-238X Natural Science Foundation of China [61350003, 11401284, 70621001, 70531040]; department of education of Liaoning Province [L2014133] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); department of education of Liaoning Province The authors specially thank Professor P. Z. Wang for his guidance and modification. This study was partially supported by the grants (Grant Nos. 61350003, 11401284, 70621001, 70531040) from the Natural Science Foundation of China, and the grant (Grant Nos. L2014133) from the department of education of Liaoning Province. 24 6 6 0 6 CCC PUBL-AGORA UNIV BIHOR PIATA TINERETULUI 8, ORADEA, JUD, BIHOR, 410526, ROMANIA 1841-9836 1841-9844 INT J COMPUT COMMUN Int. J. Comput. Commun. Control JUN 2018.0 13.0 3 383 390 10.15837/ijccc.2018.3.3252 0.0 8 Automation & Control Systems; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science GH2PB Green Submitted, gold 2023-03-23 WOS:000433242700006 0
J Salehi, E; Mohammadi, MR; Hemmati-Sarapardeh, A; Mahdavi, VR; Gentzis, T; Liu, B; Ostadhassan, M Salehi, Erfan; Mohammadi, Mohammad-Reza; Hemmati-Sarapardeh, Abdolhossein; Mahdavi, Vahid Reza; Gentzis, Thomas; Liu, Bo; Ostadhassan, Mehdi Modeling Interfacial Tension of N-2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection MINERALS English Article interfacial tension; CO2; N-2 mixture; n-alkanes; machine learning; flue gas injection; carbon dioxide sequestration MINIMUM MISCIBILITY PRESSURE; PARTICLE SWARM OPTIMIZATION; GAS INJECTION; SYSTEMS APPLICATION; CO2 INJECTION; OIL-RECOVERY; INTELLIGENCE; BRINE; HYDROCARBONS; PREDICTION The combustion of fossil fuels from the input of oil refineries, power plants, and the venting or flaring of produced gases in oil fields leads to greenhouse gas emissions. Economic usage of greenhouse and flue gases in conventional and unconventional reservoirs would not only enhance the oil and gas recovery but also offers CO2 sequestration. In this regard, the accurate estimation of the interfacial tension (IFT) between the injected gases and the crude oils is crucial for the successful execution of injection scenarios in enhanced oil recovery (EOR) operations. In this paper, the IFT between a CO2/N-2 mixture and n-alkanes at different pressures and temperatures is investigated by utilizing machine learning (ML) methods. To this end, a data set containing 268 IFT data was gathered from the literature. Pressure, temperature, the carbon number of n-alkanes, and the mole fraction of N-2 were selected as the input parameters. Then, six well-known ML methods (radial basis function (RBF), the adaptive neuro-fuzzy inference system (ANFIS), the least square support vector machine (LSSVM), random forest (RF), multilayer perceptron (MLP), and extremely randomized tree (extra-tree)) were used along with four optimization methods (colliding bodies optimization (CBO), particle swarm optimization (PSO), the Levenberg-Marquardt (LM) algorithm, and coupled simulated annealing (CSA)) to model the IFT of the CO2/N-2 mixture and n-alkanes. The RBF model predicted all the IFT values with exceptional precision with an average absolute relative error of 0.77%, and also outperformed all other models in this paper and available in the literature. Furthermore, it was found that the pressure and the carbon number of n-alkanes would show the highest influence on the IFT of the CO2/N-2 and n-alkanes, based on sensitivity analysis. Finally, the utilized IFT database and the area of the RBF model applicability were investigated via the leverage method. [Salehi, Erfan; Mohammadi, Mohammad-Reza; Hemmati-Sarapardeh, Abdolhossein] Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China; [Hemmati-Sarapardeh, Abdolhossein] Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman 7616914111, Iran; [Mahdavi, Vahid Reza] Jilin Univ, Coll Construct Engn, Changchun 130012, Peoples R China; [Gentzis, Thomas] Arak Univ Technol, Dept Civil & Geomech Engn, Arak 3818146763, Iran; [Liu, Bo; Ostadhassan, Mehdi] Core Labs Inc, 6316 Windfern Rd, Houston, TX 77040 USA; [Ostadhassan, Mehdi] Univ Kiel, Inst Geosci Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany; [Ostadhassan, Mehdi] Ferdowsi Univ Mashhad, Dept Geol, Mashhad 9177948974, Razavi Khorasan, Iran Northeast Petroleum University; Shahid Bahonar University of Kerman (SBUK); Jilin University; University of Kiel; Ferdowsi University Mashhad Hemmati-Sarapardeh, A (corresponding author), Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China.;Hemmati-Sarapardeh, A (corresponding author), Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman 7616914111, Iran.;Ostadhassan, M (corresponding author), Core Labs Inc, 6316 Windfern Rd, Houston, TX 77040 USA.;Ostadhassan, M (corresponding author), Univ Kiel, Inst Geosci Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany.;Ostadhassan, M (corresponding author), Ferdowsi Univ Mashhad, Dept Geol, Mashhad 9177948974, Razavi Khorasan, Iran. entezaremahdavi@gmail.com; mohammadi.mrm@gmail.com; aut.hemmati@aut.ac.ir; v.r.mahdavi@arakut.ac.ir; thomas.gentzis@corelab.com; liubo@nepu.edu.cn; mehdi.ostadhassan@nepu.edu.cn Liu, Bo/AAC-7509-2020; Sarapardeh, Abdolhossein Hemmati/M-5047-2019; Mohammadi, Mohammad Reza/AAZ-5153-2021 Liu, Bo/0000-0003-0136-7237; Sarapardeh, Abdolhossein Hemmati/0000-0002-5889-150X; Mohammadi, Mohammad Reza/0000-0002-6303-4246; Gentzis, Thomas/0000-0003-4592-9318; Ostadhassan, Mehdi/0000-0001-9235-4399 71 3 3 2 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-163X MINERALS-BASEL Minerals FEB 2022.0 12.0 2 252 10.3390/min12020252 0.0 24 Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing ZT2NR gold 2023-03-23 WOS:000768990700001 0
J Fuxi, S; Sina, N; Ahmadi, A; Malekshah, EH; Mahmoud, MZ; Aybar, HS Fuxi, Shi; Sina, Nima; Ahmadi, Amir; Malekshah, Emad Hasani; Mahmoud, Mustafa Z.; Aybar, Hikmet S. Effect of different pitches on the 3D helically coiled shell and tube heat exchanger filled with a hybrid nanofluid: Numerical study and artificial neural network modeling ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS English Article Heat exchanger effectiveness; Numerical study; Two-phase model; Turbulent flow; Artificial neural network; Sensitivity analysis FLOW CONDENSATION; TRANSFER ENHANCEMENT; SIDE; PERFORMANCE; EFFICIENCY; ENERGY The effects of using hybrid nanofluids and of helical coil pitch (lambda) in a 3D shell and tube heat exchanger (STHE) are investigated. The algorithm used in this study is Phase Coupled SIMPLE and the method used is Eulerian. Nanofluid flow with Reynolds (Re) numbers of 10,000, 15,000, and 20,000, nanoparticles with volume fractions (phi) of 2 and 4%, and lambda = 20, 25, 40, and 50 mm are investigated. The highest numbers related to the thermal index (Nu) and effectiveness occurred in the lambda = 20 mm and the maximum phi and Re. In the case of lambda = 20 mm, the maximum Nusselt number is 15.8%, 26%, and 45.3% more than that of 25, 40, and 50 mm, respectively. However, in the same case, in comparison between the phi = 4% and phi = 0, the Nu increases by 45.7%, 61.7%, and 76%. The present study shows that combining using hybrid nanofluids and changing the geometry of STHE, as an innovative approach can positively increase efficiency. Finally, the results are used for training an artificial neural network (ANN). In this regard, for finding the optimum neuron numbers in the hidden layer, the optimum feed-forward network is obtained to predict the efficiency of the material. [Fuxi, Shi] Northwest A&F Univ, Coll Mech & Elect Engn, Xianyang, Peoples R China; [Malekshah, Emad Hasani] Silesian Tech Univ, Dept Power Engn & Turbomachinery, PL-44100 Gliwice, Poland; [Mahmoud, Mustafa Z.] Prince Sattam bin Abdulaziz Univ, Coll Appl Med Sci Al Kharj, Dept Radiol & Med Imaging, Al Kharj 11942, Saudi Arabia; [Mahmoud, Mustafa Z.] Univ Canberra, Fac Hlth, Canberra, ACT, Australia; [Aybar, Hikmet S.] Eastern Mediterranean Univ, Dept Mech Engn, TRNC, Via Mersin 10, Famagusta, Turkey; [Aybar, Hikmet S.] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan Northwest A&F University - China; Silesian University of Technology; Prince Sattam Bin Abdulaziz University; University of Canberra; Eastern Mediterranean University; China Medical University Taiwan; China Medical University Hospital - Taiwan Malekshah, EH (corresponding author), Silesian Tech Univ, Dept Power Engn & Turbomachinery, PL-44100 Gliwice, Poland.;Aybar, HS (corresponding author), Eastern Mediterranean Univ, Dept Mech Engn, TRNC, Via Mersin 10, Famagusta, Turkey.;Aybar, HS (corresponding author), China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan. emad.hasani@polsl.pl; hikmet.aybar@emu.edu.tr Mahmoud, Prof. Mustafa Z./AAD-4833-2022 Mahmoud, Prof. Mustafa Z./0000-0003-2552-9165 Key Industry Innovation Chain (Group) Project of Shaanxi Province [2020ZDLNY07-05]; Key Research and Development Program of Shaanxi [2021NY-193] Key Industry Innovation Chain (Group) Project of Shaanxi Province; Key Research and Development Program of Shaanxi Acknowledgements This work is supported by Key Industry Innovation Chain (Group) Project of Shaanxi Province (2020ZDLNY07-05) , and Key Research and Development Program of Shaanxi (Program No. 2021NY-193) . 58 2 2 5 5 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0955-7997 1873-197X ENG ANAL BOUND ELEM Eng. Anal. Bound. Elem. OCT 2022.0 143.0 755 768 10.1016/j.enganabound.2022.07.018 0.0 AUG 2022 14 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics 5V6RI 2023-03-23 WOS:000877354800004 0
J Yang, XC; Nguyen, XC; Tran, QB; Nguyen, TTH; Ge, SB; Nguyen, DD; Nguyen, V; Le, PC; Rene, ER; Singh, P; Raizada, P; Ahamad, T; Alshehri, SM; Xia, CL; Kim, SY; Van Le, Q Yang, Xiaocui; Nguyen, X. Cuong; Tran, Quoc B.; Nguyen, T. T. Huyen; Ge, Shengbo; Nguyen, D. Duc; Nguyen, Van-Truc; Le, Phuoc-Cuong; Rene, Eldon R.; Singh, Pardeep; Raizada, Pankaj; Ahamad, Tansir; Alshehri, Saad M.; Xia, Changlei; Kim, Soo Young; Van Le, Quyet Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water ENVIRONMENTAL RESEARCH English Article Biochar; Pharmaceutical; Adsorption; Data mining; Machine learning PAPER-MILL SLUDGE; RICE STRAW; ADSORPTION; SORPTION; TETRACYCLINE; NORFLOXACIN; THERMODYNAMICS; ADSORBENTS; PYROLYSIS; IBUPROFEN A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal ad-sorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Qm) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Qm (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Qm and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K-nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R2 of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption. [Yang, Xiaocui] Nanjing Vocat Univ Ind Technol, Engn Training Ctr, Nanjing 210023, Jiangsu, Peoples R China; [Nguyen, X. Cuong; Tran, Quoc B.] Duy Tan Univ, Inst Res & Dev, Ctr Adv Chem, Da Nang 550000, Vietnam; [Nguyen, X. Cuong; Tran, Quoc B.] Duy Tan Univ, Fac Environm Chem Engn, Da Nang 550000, Vietnam; [Nguyen, T. T. Huyen; Le, Phuoc-Cuong] Univ Danang, Fac Environm, Univ Sci & Technol, Da Nang 550000, Vietnam; [Ge, Shengbo; Xia, Changlei] Nanjing Forestry Univ, Coll Mat Sci & Engn, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo, Int Innovat Ctr Forest Chem & Mat, Nanjing 210037, Jiangsu, Peoples R China; [Nguyen, D. Duc] Kyonggi Univ, Dept Environm Energy Engn, Suwon 442760, South Korea; [Nguyen, Van-Truc] Saigon Univ, Dept Environm Sci, Ho Chi Minh City 700000, Vietnam; [Rene, Eldon R.] IHE Delft Inst Water Educ, Dept Environm Engn & Water Technol, POB 3015, NL-2601 DA Delft, Netherlands; [Singh, Pardeep; Raizada, Pankaj] Shoolini Univ, Sch Adv Chem Sci, Solan 173212, Himachal Prades, India; [Ahamad, Tansir; Alshehri, Saad M.] King Saud Univ, Coll Sci, Dept Chem, Riyadh 11451, Saudi Arabia; [Kim, Soo Young; Van Le, Quyet] Korea Univ, Inst Green Mfg Technol, Dept Mat Sci & Engn, Seoul 02841, South Korea Nanjing Vocational University of Industry Technology; Duy Tan University; Duy Tan University; University of Danang; Nanjing Forestry University; Kyonggi University; Saigon University; IHE Delft Institute for Water Education; Shoolini University; King Saud University; Korea University Nguyen, XC (corresponding author), Duy Tan Univ, Inst Res & Dev, Ctr Adv Chem, Da Nang 550000, Vietnam.;Xia, CL (corresponding author), Nanjing Forestry Univ, Coll Mat Sci & Engn, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo, Int Innovat Ctr Forest Chem & Mat, Nanjing 210037, Jiangsu, Peoples R China.;Kim, SY; Van Le, Q (corresponding author), Korea Univ, Inst Green Mfg Technol, Dept Mat Sci & Engn, Seoul 02841, South Korea. nguyenxuancuong4@duytan.edu.vn; changlei.xia@njfu.edu.cn; sooyoungkim@korea.ac.kr; quyetkbk88@korea.ac.kr Kim, SOO YOUNG/B-4373-2015; Xia, Changlei/P-1475-2015; Ahamad, Tansir/H-8171-2015; Le, Quyet Van/GRS-9136-2022; Truc, Nguyen Van/AAR-2804-2020; Shengbo, Ge/ABG-8748-2022; Singh, Pardeep/AAI-7740-2020 Kim, SOO YOUNG/0000-0002-0685-7991; Xia, Changlei/0000-0002-7063-774X; Ahamad, Tansir/0000-0002-9400-5317; Le, Quyet Van/0000-0002-4313-301X; Truc, Nguyen Van/0000-0001-5217-7615; Shengbo, Ge/0000-0001-6666-8423; Singh, Pardeep/0000-0002-2181-8085; Nguyen, Xuan Cuong/0000-0002-1953-1456 Jiangsu Agricultural Science and Technology Innovation Fund [CX (22) 2045]; Natural Science Foundation of Jiangsu Province [BK20200775]; State Administration of Foreign Experts Affairs High-End Foreign Experts Project [G2021014041L]; Brain Pool Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [2020H1D3A1A04081409]; King Saud University, Riyadh, Saudi Arabia [RSP-2021/29]; Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF) Jiangsu Agricultural Science and Technology Innovation Fund; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); State Administration of Foreign Experts Affairs High-End Foreign Experts Project; Brain Pool Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of Korea); King Saud University, Riyadh, Saudi Arabia(King Saud University); Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF) This research is funded by the Jiangsu Agricultural Science and Technology Innovation Fund (CX (22) 2045) , Natural Science Foundation of Jiangsu Province [BK20200775] , and State Administration of Foreign Experts Affairs High-End Foreign Experts Project [G2021014041L] . This research was also supported Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant Number: 2020H1D3A1A04081409) . The authors thank the Researchers Supporting Project number [RSP-2021/29] , King Saud University, Riyadh, Saudi Arabia. Also, T. T. Huyen Nguyen was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF) , Institute of Big Data, code VINIF.2021.ThS.74. 78 4 4 43 45 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0013-9351 1096-0953 ENVIRON RES Environ. Res. NOV 2022.0 214.0 3 113953 10.1016/j.envres.2022.113953 0.0 AUG 2022 9 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Public, Environmental & Occupational Health 4X0NK 35934147.0 2023-03-23 WOS:000860548900005 0
J He, CM; Sun, J; Chen, YW; Wang, LC; Shi, S; Qiu, F; Wang, SQ; Tagesson, T He, Chunmei; Sun, Jia; Chen, Yuwen; Wang, Lunche; Shi, Shuo; Qiu, Feng; Wang, Shaoqiang; Tagesson, Torbern A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio: minimizing the effect of their correlation INTERNATIONAL JOURNAL OF DIGITAL EARTH English Article Leaf carotenoids content; leaf chlorophyll content; PROSPECT model; ratio of Car to Chl; vegetation index SPECTRAL INDEXES; OPTICAL-PROPERTIES; REFLECTANCE SPECTRA; INVERSION; PROSPECT; MODEL; LEAVES; VALIDATION; ALGORITHMS; SENESCENCE The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. [He, Chunmei; Sun, Jia; Chen, Yuwen; Wang, Lunche; Wang, Shaoqiang] China Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Reg Ecol & Environm Change, Wuhan, Peoples R China; [Sun, Jia; Tagesson, Torbern] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; [Shi, Shuo] Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan, Peoples R China; [Qiu, Feng] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing, Peoples R China; [Tagesson, Torbern] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark China University of Geosciences; Lund University; Wuhan University; Nanjing University; University of Copenhagen Sun, J (corresponding author), China Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Reg Ecol & Environm Change, Wuhan, Peoples R China.;Sun, J (corresponding author), Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden. sunjia@cug.edu.cn National Natural Science Foundation of China [42001314]; Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University [20R02]; Swedish National Space Agency [SNSA 2021-00144]; FORMAS [2021-00644] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University; Swedish National Space Agency; FORMAS(Swedish Research Council Formas) This study was supported by the National Natural Science Foundation of China (42001314) and the Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University (grant number 20R02). Torbern Tagesson was additionally funded by the Swedish National Space Agency (SNSA 2021-00144) and FORMAS (Dnr. 2021-00644). 67 0 0 12 12 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1753-8947 1753-8955 INT J DIGIT EARTH Int. J. Digit. Earth DEC 31 2023.0 16.0 1 272 288 10.1080/17538947.2023.2168772 0.0 17 Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Remote Sensing 8J1XG gold 2023-03-23 WOS:000922215500001 0
J Pan, Z; Gu, ZH; Jiang, XH; Zhu, GQ; Ma, D Pan, Zhe; Gu, Zonghua; Jiang, Xiaohong; Zhu, Guoquan; Ma, De A Modular Approximation Methodology for Efficient Fixed-Point Hardware Implementation of the Sigmoid Function IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Hardware; Taylor series; Newton method; Approximation error; Artificial neural networks; Quantization (signal); Input variables; Artificial neural networks (NNs); FPGA; hardware acceleration; Newton-Raphson (NR) method; sigmoid function NEURAL-NETWORK; PROCESSOR; DESIGN The sigmoid function is a widely used nonlinear activation function in neural networks. In this article, we present a modular approximation methodology for efficient fixed-point hardware implementation of the sigmoid function. Our design consists of three modules: piecewise linear (PWL) approximation as the initial solution, Taylor series approximation of the exponential function, and Newton-Raphson method-based approximation as the final solution. Its modularity enables the designer to flexibly choose the most appropriate approximation method for each module separately. Performance evaluation results indicate that our work strikes an appropriate balance among the objectives of approximation accuracy, hardware resource utilization, and performance. [Pan, Zhe; Jiang, Xiaohong; Ma, De] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China; [Gu, Zonghua] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Zhu, Guoquan] Res Ctr Intelligent Comp Syst, Artificial Intelligence Res Inst, Zhejiang Lab, Hangzhou 310058, Peoples R China Zhejiang University; Umea University; Zhejiang Laboratory Ma, D (corresponding author), Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China. panzhe@zju.edu.cn; zgu888@gmail.com; jiangxh@zju.edu.cn; zhugq@zhejianglab.com; made@zju.edu.cn Pan, Zhe/0000-0002-0355-8099; Gu, Zonghua/0000-0003-4228-2774; Zhu, Guoquan/0000-0001-8548-4857 National Key R&D Program of China [2018AAA0102100]; National Natural Science Foundation of China [U20A20220] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Key R&D Program of China Grant No. 2020AAA0109002, the National Natural Science Foundation of China under Grant U20A20220, and the National Key R&D Program of China under Grant 2018AAA0102100. 45 2 2 7 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. OCT 2022.0 69.0 10 10694 10703 10.1109/TIE.2022.3146573 0.0 10 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation 0Z1VH 2023-03-23 WOS:000790866600099 0
J Bi, HX; Yao, J; Wei, ZQ; Hong, DF; Chanussot, J Bi, Haixia; Yao, Jing; Wei, Zhiqiang; Hong, Danfeng; Chanussot, Jocelyn PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Feature extraction; Optimization; Speckle; Earth; Convolution; Training; Task analysis; Convolutional neural network (CNN); low-rank (LR) matrix factorization; Markov random field (MRF); mixture of Gaussian (MoG); polarimetric synthetic aperture radar (PolSAR) image classification Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method that removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via the Markov random field (MRF). Especially, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying a convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR data sets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency. [Bi, Haixia] Univ Bristol, Fac Engn, Bristol BS8 1UB, Avon, England; [Yao, Jing] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China; [Wei, Zhiqiang] Xian Elect & Engn Inst, Xian 710100, Peoples R China; [Hong, Danfeng] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China University of Bristol; Xi'an Jiaotong University; Helmholtz Association; German Aerospace Centre (DLR); Chinese Academy of Sciences Hong, DF (corresponding author), German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany. haixia.bi@bristol.ac.uk; jasonyao@stu.xjtu.edu.cn; zqwei@fudan.edu.cn; danfeng.hong@dlr.de; jocelyn@hi.is AXA Research Fund; China NSFC Project [61806162] AXA Research Fund(AXA Research Fund); China NSFC Project(National Natural Science Foundation of China (NSFC)) This work was supported in part by the AXA Research Fund and in part by the China NSFC Project under Contract 61806162. 24 1 1 2 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19.0 4005205 10.1109/LGRS.2020.3034700 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XP9AX Green Submitted, Green Accepted 2023-03-23 WOS:000731151800022 0
C Dimitrov, D; Baran, E; Fafalios, P; Yu, R; Zhu, XF; Zloch, M; Dietze, S ASSOC COMP MACHINERY Dimitrov, Dimitar; Baran, Erdal; Fafalios, Pavlos; Yu, Ran; Zhu, Xiaofei; Zloch, Matthaus; Dietze, Stefan TweetsCOV19-A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT English Proceedings Paper 29th ACM International Conference on Information and Knowledge Management (CIKM) OCT 19-23, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGIR,ACM SIGWEB Twitter; RDF; Entity Linking; Sentiment Analysis; Social Media Archives; COVID-19; Coronavirus Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods. With respect to the recent outbreak of the Coronavirus disease 2019 (COVID-19), online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution, and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection, or entity recognition. However, obtaining, archiving, and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning October 2019 - April 2020. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis and use cases of the corpus. [Dimitrov, Dimitar; Baran, Erdal; Yu, Ran; Zloch, Matthaus; Dietze, Stefan] GESIS Leibniz Inst Social Sci, Cologne, Germany; [Fafalios, Pavlos] FORTH ICS, Inst Comp Sci, Iraklion, Greece; [Zhu, Xiaofei] Chongqing Univ Technol, Chongqing, Peoples R China; [Dietze, Stefan] Heinrich Heine Univ Dusseldorf, Dusseldorf, Germany; [Dietze, Stefan] L3S Res Ctr, Hannover, Germany Leibniz Institut fur Sozialwissenschaften (GESIS); Chongqing University of Technology; Heinrich Heine University Dusseldorf; Leibniz University Hannover Dimitrov, D (corresponding author), GESIS Leibniz Inst Social Sci, Cologne, Germany. dimitar.dimitrov@gesis.org; erdal.baran@gesis.org; fafalios@ics.forth.gr; ran.yu@gesis.org; zxf@cqut.edu.cn; matthaeus.zloch@gesis.org; stefan.dietze@gesis.org Yu, Ran/0000-0002-1619-3164 29 18 18 2 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6859-9 2020.0 2991 2998 10.1145/3340531.3412765 0.0 8 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LW Green Submitted 2023-03-23 WOS:000749561302165 0
J Qin, C; Wang, S; Chen, C; Bai, WJ; Rueckert, D Qin, Chen; Wang, Shuo; Chen, Chen; Bai, Wenjia; Rueckert, Daniel Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior MEDICAL IMAGE ANALYSIS English Article Biomechanics-informed prior; Generative neural network; Myocardial motion tracking; Image registration; Latent space exploration DIFFEOMORPHIC IMAGE REGISTRATION; LEARNING FRAMEWORK; STRAIN ESTIMATION; MODEL Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method leverages a variational autoencoder-based generative model to learn a manifold for biomechanically plausible deformations. The motion tracking then can be performed via traversing the learnt manifold to search for the optimal transformations while considering the sequence information. The proposed method is validated on three public cardiac cine MRI datasets with com-prehensive evaluations. The results demonstrate that the proposed method can outperform other approaches, yielding higher motion tracking accuracy with reasonable volume preservation and better generalizability to varying data distributions. It also enables better estimates of myocardial strains, which indicates the potential of the method in characterizing spatiotemporal signatures for understanding cardiovascular diseases. [Qin, Chen; Chen, Chen] Imperial Coll London, Dept Elect & Elect Engn & I X, London, England; [Wang, Shuo] Fudan Univ, Digital Med Res Ctr, Sch Basic Med Sci, Shanghai, Peoples R China; [Chen, Chen; Rueckert, Daniel] Imperial Coll London, Dept Comp, Biomed Image Anal Grp, London, England; [Chen, Chen; Bai, Wenjia] Imperial Coll London, Dept Brain Sci, London, England; [Bai, Wenjia] Imperial Coll London, Data Sci Inst, London, England; [Rueckert, Daniel] Tech Univ Munich, Inst AI & Informat, Klinikum Rechts Isar, Munich, Germany Imperial College London; Fudan University; Imperial College London; Imperial College London; Imperial College London; Technical University of Munich Qin, C (corresponding author), Imperial Coll London, Dept Elect & Elect Engn & I X, London, England.;Wang, S (corresponding author), Fudan Univ, Digital Med Res Ctr, Sch Basic Med Sci, Shanghai, Peoples R China. c.qin15@imperial.ac.uk; shuowang@fudan.edu.cn bai, wenjia/HPE-4360-2023; Bai, Wenjia/B-3377-2017 Chen, Chen/0000-0002-3525-9755; Rueckert, Daniel/0000-0002-5683-5889; Bai, Wenjia/0000-0003-2943-7698; Wang, Shuo/0000-0002-2947-8783 EPSRC, UK [EP/W01842X/1]; ERC [884622]; Shanghai Sailing Programs of Shanghai Municipal Science and Technology Committee, China [22YF1409300]; UKRI Innovate UK Grant [104691]; [EP/P001009/1] EPSRC, UK(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); ERC(European Research Council (ERC)European Commission); Shanghai Sailing Programs of Shanghai Municipal Science and Technology Committee, China; UKRI Innovate UK Grant; This work was supported by EPSRC, UK Grant SmartHeart (EP/P001009/1) and ERC Grant Deep4MI (884622) . S. Wang was supported by the Shanghai Sailing Programs of Shanghai Municipal Science and Technology Committee, China (22YF1409300) . C. Chen was supported by UKRI Innovate UK Grant (No.104691) . W. Bai was supported by EPSRC, UK Grant DeepGeM (EP/W01842X/1) . 65 0 0 3 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JAN 2023.0 83.0 102682 10.1016/j.media.2022.102682 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging 6X9NT 36403311.0 Green Submitted 2023-03-23 WOS:000896734000003 0
J Nejad, HD; Nazari, M; Nazari, M; Mardan, MMS; Mohammadzadeh, A; Vu, MT; Mosavi, A Nejad, Hossein Darvishi; Nazari, Mostafa; Nazari, Mohsen; Mardan, Mohammad Mohsen Shah; Mohammadzadeh, Ardashir; Vu, Mai The; Mosavi, Amir Fuzzy State-Dependent Riccati Equation (FSDRE) Control of the Reverse Osmosis Desalination System With Photovoltaic Power Supply IEEE ACCESS English Article Desalination; Photovoltaic systems; Reverse osmosis; Solar power generation; Solar system; Fuzzy control; Mathematical models; Osmosis; Fuzzy control; maximum power point tracking; photovoltaic system; reverse osmosis desalination; SDRE control; artificial intelligence SLIDING MODE CONTROL; MAXIMUM POWER; POINT TRACKING; DESIGN; DRIVEN; CYCLE The two challenges facing human life are water and energy. Reverse osmosis (RO) desalination systems are popular owning to their unique advantages. However, robust performance and power supply are the two main challenges in this desalination system. This power is used to drive an induction motor that rotates a centrifugal pump to apply the required back pressure to the RO membrane. To solve these two challenges, a complete RO system powered by a photovoltaic (PV) system was considered, and for each subsystem, a robust controller was designed based on their dynamic models. A fuzzy controller optimized by the invasive weed algorithm (IWA) was designed to track the maximum power in the photovoltaic subsystem under different environmental conditions. A fuzzy-PID controller was used to control the motor-pump subsystem. Furthermore, it is focused on designing a robust controller with the ability to compensate for large set-point changes, reject external disturbances, and cope with parametric uncertainties, such as variations in feed water salinity. Hence, state-dependent Riccati equation control (SDRE) was used to control the reverse osmosis system. The simulation results for different scenarios show that the proposed controller performs well under different operating conditions and can remove the effects of disturbances on the system. [Nejad, Hossein Darvishi; Nazari, Mostafa; Nazari, Mohsen; Mardan, Mohammad Mohsen Shah] Shahrood Univ Technol, Fac Mech & Mechatron Engn, Shahrud, Iran; [Mohammadzadeh, Ardashir] Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China; [Vu, Mai The] Sejong Univ, Sch Intelligent Mechatron Engn, Seoul 05006, South Korea; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary; [Mosavi, Amir] Slovak Univ Technol Bratislava, Inst Informat Engn Automat & Math, Bratislava 81107, Slovakia; [Mosavi, Amir] Univ Publ Serv, Inst Informat Soc, H-1083 Budapest, Hungary Shahrood University of Technology; Shenyang University of Technology; Sejong University; Obuda University; Slovak University of Technology Bratislava; University of Public Service Nazari, M (corresponding author), Shahrood Univ Technol, Fac Mech & Mechatron Engn, Shahrud, Iran.;Vu, MT (corresponding author), Sejong Univ, Sch Intelligent Mechatron Engn, Seoul 05006, South Korea.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary.;Mosavi, A (corresponding author), Slovak Univ Technol Bratislava, Inst Informat Engn Automat & Math, Bratislava 81107, Slovakia.;Mosavi, A (corresponding author), Univ Publ Serv, Inst Informat Soc, H-1083 Budapest, Hungary. amir.mosavi@kvk.uni-obuda.hu; maithevu90@sejong.ac.kr; amir.mosavi@mailbox.tu-dresden.de Mosavi, Amir/I-7440-2018; Mohammadzadeh, Ardashir/AEN-2013-2022 Mosavi, Amir/0000-0003-4842-0613; Mohammadzadeh, Ardashir/0000-0001-5173-4563; Nazari, Mostafa/0000-0003-2223-8143 57 2 2 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2022.0 10.0 95585 95603 10.1109/ACCESS.2022.3204270 0.0 19 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 4O3DJ gold 2023-03-23 WOS:000854582900001 0
J Iorio, F; Knijnenburg, TA; Vis, DJ; Bignell, GR; Menden, MP; Schubert, M; Aben, N; Goncalves, E; Barthorpe, S; Lightfoot, H; Cokelaer, T; Greninger, P; van Dyk, E; Chang, H; de Silva, H; Heyn, H; Deng, XM; Egan, RK; Liu, QS; Mironenko, T; Mitropoulos, X; Richardson, L; Wang, JH; Zhang, TH; Moran, S; Sayols, S; Soleimani, M; Tamborero, D; Lopez-Bigas, N; Ross-Macdonald, P; Esteller, M; Gray, NS; Haber, DA; Stratton, MR; Benes, CH; Wessels, LFA; Saez-Rodriguez, J; McDermott, U; Garnett, MJ Iorio, Francesco; Knijnenburg, Theo A.; Vis, Daniel J.; Bignell, Graham R.; Menden, Michael P.; Schubert, Michael; Aben, Nanne; Goncalves, Emanuel; Barthorpe, Syd; Lightfoot, Howard; Cokelaer, Thomas; Greninger, Patricia; van Dyk, Ewald; Chang, Han; de Silva, Heshani; Heyn, Holger; Deng, Xianming; Egan, Regina K.; Liu, Qingsong; Mironenko, Tatiana; Mitropoulos, Xeni; Richardson, Laura; Wang, Jinhua; Zhang, Tinghu; Moran, Sebastian; Sayols, Sergi; Soleimani, Maryam; Tamborero, David; Lopez-Bigas, Nuria; Ross-Macdonald, Petra; Esteller, Manel; Gray, Nathanael S.; Haber, Daniel A.; Stratton, Michael R.; Benes, Cyril H.; Wessels, Lodewyk F. A.; Saez-Rodriguez, Julio; McDermott, Ultan; Garnett, Mathew J. A Landscape of Pharmacogenomic Interactions in Cancer CELL English Article DRUG-SENSITIVITY; IDENTIFICATION; GENE; MUTATIONS; DISCOVERY; PATTERNS Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations. [Iorio, Francesco; Menden, Michael P.; Schubert, Michael; Goncalves, Emanuel; Cokelaer, Thomas; Saez-Rodriguez, Julio] European Bioinformat Inst, European Mol Biol Lab, Wellcome Genome Campus, Cambridge CB10 1SA, England; [Iorio, Francesco; Bignell, Graham R.; Barthorpe, Syd; Lightfoot, Howard; Cokelaer, Thomas; Mironenko, Tatiana; Richardson, Laura; Soleimani, Maryam; Stratton, Michael R.; McDermott, Ultan; Garnett, Mathew J.] Wellcome Trust Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England; [Knijnenburg, Theo A.] Inst Syst Biol, Seattle, WA 98109 USA; [Knijnenburg, Theo A.; Vis, Daniel J.; Aben, Nanne; van Dyk, Ewald; Wessels, Lodewyk F. A.] Netherlands Canc Inst, Div Mol Carcinogenesis, Plesmanlaan 121, NL-1066 CX Amsterdam, Netherlands; [Menden, Michael P.; Saez-Rodriguez, Julio] Rhein Westfal TH Aachen, Joint Res Ctr Computat Biomed, Fac Med, D-52057 Aachen, Germany; [Aben, Nanne; Wessels, Lodewyk F. A.] Delft Univ Technol, Dept EEMCS, NL-2628 CD Delft, Netherlands; [Greninger, Patricia; Egan, Regina K.; Mitropoulos, Xeni; Haber, Daniel A.; Benes, Cyril H.] Harvard Med Sch, Massachusetts Gen Hosp, Ctr Canc Res, Charlestown, MA 02129 USA; [Chang, Han; de Silva, Heshani; Ross-Macdonald, Petra] Bristol Myers Squibb Res & Dev, Genetically Defined Dis & Genom, Hopewell, NJ 08534 USA; [Heyn, Holger; Moran, Sebastian; Sayols, Sergi; Esteller, Manel] Bellvitge Biomed Res Inst IDIBELL, PEBC, Barcelona CATALONIA, Spain; [Deng, Xianming; Liu, Qingsong; Wang, Jinhua; Zhang, Tinghu; Gray, Nathanael S.] Dana Farber Canc Inst, Dept Canc Biol, Boston, MA 02215 USA; [Deng, Xianming; Liu, Qingsong; Wang, Jinhua; Zhang, Tinghu; Gray, Nathanael S.] Harvard Med Sch, Dept Biol Chem & Mol Pharmacol, Boston, MA 02215 USA; [Tamborero, David; Lopez-Bigas, Nuria] IMIM Hosp del Mar Med Res Inst, Res Program Biomed Informat, Barcelona 08003, Spain; [Tamborero, David; Lopez-Bigas, Nuria] Univ Pompeu Fabra, Barcelona 08003, Spain; [Lopez-Bigas, Nuria; Esteller, Manel] ICREA, Barcelona 08010, Catalonia, Spain; [Esteller, Manel] Univ Barcelona, Sch Med, Dept Physiol Sci 2, Barcelona 08908, Catalonia, Spain; [Haber, Daniel A.] Howard Hughes Med Inst, Chevy Chase, MD 20815 USA; [Wessels, Lodewyk F. A.] Canc Genom Netherlands, Uppsalalaan 8, NL-3584 CT Utrecht, Netherlands; [Cokelaer, Thomas] Inst Pasteur, USR IP CNRS 3756, C3BI, Bioinformat & Biostat Hub, F-75015 Paris, France; [Deng, Xianming] Xiamen Univ, Sch Life Sci, Innovat Ctr Cell Signaling Network, Xiamen 361102, Peoples R China; [Sayols, Sergi] Inst Mol Biol, D-55128 Mainz, Germany European Molecular Biology Laboratory (EMBL); Wellcome Trust Sanger Institute; Institute for Systems Biology (ISB); Netherlands Cancer Institute; RWTH Aachen University; Delft University of Technology; Harvard University; Massachusetts General Hospital; Bristol-Myers Squibb; Institut d'Investigacio Biomedica de Bellvitge (IDIBELL); Harvard University; Dana-Farber Cancer Institute; Harvard University; Harvard Medical School; Institut Hospital del Mar d'Investigacions Mediques (IMIM); Hospital del Mar; Pompeu Fabra University; ICREA; University of Barcelona; Howard Hughes Medical Institute; UDICE-French Research Universities; Universite Paris Cite; Le Reseau International des Instituts Pasteur (RIIP); Institut Pasteur Paris; Xiamen University; Institute of Molecular Biology (IMB) McDermott, U; Garnett, MJ (corresponding author), Wellcome Trust Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England. um1@sanger.ac.uk; mg12@sanger.ac.uk tamborero, david/M-5283-2015; Iorio, Francesco/H-3284-2019; Iorio, Francesco/H-3282-2019; Lopez-Bigas, Nuria/F-6193-2011; Cokelaer, Thomas/AAN-8240-2020; Gasull, Martina/J-4076-2019; Moran, Sebastian/G-5293-2013; Saez-Rodriguez, Julio/H-7114-2019; Esteller, Manel/L-5956-2014 tamborero, david/0000-0002-7218-2806; Iorio, Francesco/0000-0001-7063-8913; Lopez-Bigas, Nuria/0000-0003-4925-8988; Cokelaer, Thomas/0000-0001-6286-1138; Moran, Sebastian/0000-0003-4192-8983; Saez-Rodriguez, Julio/0000-0002-8552-8976; Esteller, Manel/0000-0003-4490-6093; Deng, Xianming/0000-0002-9354-5864; Schubert, Michael/0000-0002-6862-5221; Goncalves, Emanuel/0000-0002-9967-5205; McDermott, Ultan/0000-0001-9032-4700; Sayols, Sergi/0000-0002-3877-4170; Heyn, Holger/0000-0002-3276-1889; Lightfoot, Howard/0000-0003-2082-9986 Wellcome Trust [086375, 102696]; European Bioinformatics Institute; Wellcome Trust Sanger Institute post-doctoral (ESPOD) program; National Cancer Institute [U24CA143835]; Netherlands Organization for Scientific Research; People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union [600388]; Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO); La Fundacio la Marato de TV3; European Research Council [268626]; Ministerio de Ciencia e Innovacion [SAF2011-22803]; Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence [PIE13/00022]; Spanish Cancer Research Network [RD12/0036/0039]; Health Department of the Catalan Government Generalitat de Catalunya [2014-SGR 633]; Science Department of the Catalan Government Generalitat de Catalunya [2014-SGR 633]; Cellex Foundation; Cancer Research UK Clinician Scientist Fellowship; AstraZeneca; ICREA Funding Source: Custom; Medical Research Council [1246915] Funding Source: researchfish Wellcome Trust(Wellcome Trust); European Bioinformatics Institute; Wellcome Trust Sanger Institute post-doctoral (ESPOD) program; National Cancer Institute(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Netherlands Organization for Scientific Research(Netherlands Organization for Scientific Research (NWO)); People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union; Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO); La Fundacio la Marato de TV3; European Research Council(European Research Council (ERC)European Commission); Ministerio de Ciencia e Innovacion(Ministry of Science and Innovation, Spain (MICINN)Instituto de Salud Carlos IIISpanish Government); Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence; Spanish Cancer Research Network; Health Department of the Catalan Government Generalitat de Catalunya(Generalitat de Catalunya); Science Department of the Catalan Government Generalitat de Catalunya; Cellex Foundation(Foundation CELLEX); Cancer Research UK Clinician Scientist Fellowship(Cancer Research UK); AstraZeneca(AstraZeneca); ICREA(ICREA); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) This work was funded by the Wellcome Trust (086375 and 102696). F.I. was supported by the European Bioinformatics Institute and Wellcome Trust Sanger Institute post-doctoral (ESPOD) program. T.A.K. was supported by the National Cancer Institute (U24CA143835) and the Netherlands Organization for Scientific Research. D.T. was supported by the People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union (FP7/2007-2013; 600388) and the Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO). N.L.-B. was supported by La Fundacio la Marato de TV3. M.E. was funded by the European Research Council (268626), the Ministerio de Ciencia e Innovacion (SAF2011-22803), the Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence (PIE13/00022), the Spanish Cancer Research Network (RD12/0036/0039), the Health and Science Departments of the Catalan Government Generalitat de Catalunya 2014-SGR 633, and the Cellex Foundation. U.M. was supported by a Cancer Research UK Clinician Scientist Fellowship. We thank Aiqing He for expression data and Ilya Shmulevich for assistance with the LOBICO framework. We thank P. Campbell, M. Ranzani, J. Brammeld, M. Petljak, F. Behan, C. Alsinet Armengol, H. Francies, V. Grinkevich, and A. Lilla Mupo for useful comments. P. R.-M., H. C., and H.d.S. are employees and shareholders of Bristol-Myers Squibb. Research in the M.J.G. lab is supported in part with funding from AstraZeneca. 34 929 943 15 130 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 0092-8674 1097-4172 CELL Cell JUL 28 2016.0 166.0 3 740 754 10.1016/j.cell.2016.06.017 0.0 15 Biochemistry & Molecular Biology; Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Cell Biology DS2ED 27397505.0 hybrid, Green Published, Green Accepted 2023-03-23 WOS:000380546500023 0
J Du, M; Ho, LC; Zhao, DY; Shi, JJ; Debattista, VP; Hernquist, L; Nelson, D Du, Min; Ho, Luis C.; Zhao, Dongyao; Shi, Jingjing; Debattista, Victor P.; Hernquist, Lars; Nelson, Dylan Identifying Kinematic Structures in Simulated Galaxies Using Unsupervised Machine Learning ASTROPHYSICAL JOURNAL English Article galaxies: fundamental parameters; galaxies: kinematics and dynamics; galaxies: structure; methods: numerical MILKY-WAY; ILLUSTRISTNG SIMULATIONS; STELLAR HALOES; DISC GALAXIES; ATLAS(3D) PROJECT; SECULAR EVOLUTION; BARRED GALAXIES; FINE-STRUCTURE; THICK DISC; BULGES Galaxies host a wide array of internal stellar components, which need to be decomposed accurately in order to understand their formation and evolution. While significant progress has been made with recent integral-field spectroscopic surveys of nearby galaxies, much can be learned from analyzing the large sets of realistic galaxies now available through state-of-the-art hydrodynamical cosmological simulations. We present an unsupervised machine-learning algorithm, named auto-GMM, based on Gaussian mixture models, to isolate intrinsic structures in simulated galaxies based on their kinematic phase space. For each galaxy, the number of Gaussian components allowed by the data is determined through a modified Bayesian information criterion. We test our method by applying it to prototype galaxies selected from the cosmological simulation IllustrisTNG. Our method can effectively decompose most galactic structures. The intrinsic structures of simulated galaxies can be inferred statistically by non-human supervised identification of galaxy structures. We successfully identify four kinds of intrinsic structures: cold disks, warm disks, bulges, and halos. Our method fails for barred galaxies because of the complex kinematics of particles moving on bar orbits. [Du, Min; Ho, Luis C.; Zhao, Dongyao; Shi, Jingjing] Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China; [Ho, Luis C.] Peking Univ, Sch Phys, Dept Astron, Beijing 100871, Peoples R China; [Debattista, Victor P.] Univ Cent Lancashire, Jeremiah Horrocks Inst, Preston PR1 2HE, Lancs, England; [Hernquist, Lars] Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA; [Nelson, Dylan] Max Planck Inst Astrophys, Karl Schwarzschild Str 1, D-85741 Garching, Germany Peking University; Peking University; University of Central Lancashire; Harvard University; Smithsonian Astrophysical Observatory; Smithsonian Institution; Max Planck Society Du, M (corresponding author), Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China. dumin@pku.edu.cn Du, Min/AGH-2683-2022; Nelson, Dylan/ABD-5217-2021; Shi, Jingjing/ABC-8121-2021 Du, Min/0000-0001-9953-0359; Nelson, Dylan/0000-0001-8421-5890; Shi, Jingjing/0000-0001-9879-4926; Debattista, Victor/0000-0001-7902-0116; Ho, Luis/0000-0001-6947-5846 National Science Foundation of China [11721303]; National Key R&D Program of China [2016YFA0400702]; National Postdoctoral Program for Innovative Talents from the China Postdoctoral Science Foundation [8201400810]; Postdoctoral Science Foundation of China from the China Postdoctoral Science Foundation [8201400927]; Peking University Boya Fellowship; STFC Consolidated grant [ST/R000786/1]; High-performance Computing Platform of Peking University, China; STFC [ST/R000786/1] Funding Source: UKRI National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; National Postdoctoral Program for Innovative Talents from the China Postdoctoral Science Foundation; Postdoctoral Science Foundation of China from the China Postdoctoral Science Foundation; Peking University Boya Fellowship; STFC Consolidated grant(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); High-performance Computing Platform of Peking University, China; STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) This work was supported by the National Science Foundation of China (11721303) and the National Key R&D Program of China (2016YFA0400702). M.D. is also supported by the grants National Postdoctoral Program for Innovative Talents (#8201400810) and Postdoctoral Science Foundation of China (#8201400927) from the China Postdoctoral Science Foundation. D.Y.Z. and J.S. acknowledge the support by the Peking University Boya Fellowship. V.P.D. was supported by STFC Consolidated grant ST/R000786/1. The TNG100 simulation used in this work, one of the flagship runs of the IllustrisTNG project, has been run on the HazelHen Cray XC40-system at the High Performance Computing Center Stuttgart as part of project GCS-ILLU of the Gauss centers for Supercomputing (GCS). The authors thanks all the IllustrisTNG team for making the IllustrisTNG data available to us prior to the public release. We thank the anonymous referee for valuable comments. We also thank Dandan Xu and Aura Obreja for constructive discussions. This work is highly supported by the High-performance Computing Platform of Peking University, China. The analysis was performed using Pynbody (Pontzen et al. 2013). 81 12 12 1 8 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. OCT 20 2019.0 884.0 2 129 10.3847/1538-4357/ab43cc 0.0 11 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics JU6IX Green Submitted, Green Accepted 2023-03-23 WOS:000501779300009 0
C Tian, K; Yin, Q; Zeng, LY; Wang, WY; McGrath, S IEEE Tian, Kun; Yin, Qian; Zeng, Liaoyuan; Wang, Wenyi; McGrath, Sean 3D Facial Expression Recognition Using Deep Feature Fusion CNN 2019 30TH IRISH SIGNALS AND SYSTEMS CONFERENCE (ISSC) English Proceedings Paper 30th Irish Signals and Systems Conference (ISSC) JUN 17-18, 2019 Maynooth Univ, Maynooth, IRELAND IEEE,IMEX,Analog Devices,Rohde & Schwarz,IEEE UK & Ireland Signal Proc Chapter,IEEE Computat Intelligence Soc UK & Ireland Maynooth Univ 3D facial expression recognition; Deep feature fusion CNN; Point cloud; Feature extraction FACE As an important way of human communication, facial expression not only reflects our mental activities but also provides useful information for human behavior research. Recently, 3D technology becomes promising method to achieve robust facial expression analysis. 3D face scans are robust to lighting and pose variations. In this paper, a novel deep feature fusion convolution neural network (CNN) is designed for 3D facial expression recognition (FER). Each 3D face scan is firstly represented as 2D facial attribute maps (including depth, normal, and shape index values). Then, we combine different of facial attribute maps to learn facial representations by fine-tuning a pre-trained deep feature fusion CNN subnet trained from a large-scale image dataset for universal visual tasks. Moreover, Global Average Pooling is utilized to reduce the number of parameters to decrease the effect of overfitting. The experiments are conducted on the Bosphorus database and the results demonstrate the effectiveness of the proposed method. [Tian, Kun; Yin, Qian; Wang, Wenyi] Univ Elect Sci & Technol China, Medialab, Chengdu, Peoples R China; [Zeng, Liaoyuan; McGrath, Sean] Univ Limerick, Marconi Shannon Ctr, Limerick, Ireland University of Electronic Science & Technology of China; University of Limerick Tian, K (corresponding author), Univ Elect Sci & Technol China, Medialab, Chengdu, Peoples R China. tiankun@std.uestc.edu.cn; candyyinqian@gmail.com; liaoyuan.zeng@gmail.com; wangwenyi@uestc.edu.cn; sean.mcgrath@ul.ie McGrath, Sean/HKO-3351-2023 Yin, Qian/0000-0002-9448-1639 research on efficient video coding algorithm for science and technology projects in Sichuan Province [2019YJ0166] research on efficient video coding algorithm for science and technology projects in Sichuan Province This work was supported by the research on efficient video coding algorithm for science and technology projects in Sichuan Province(2019YJ0166). 26 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-2800-9 2019.0 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BP0OP 2023-03-23 WOS:000535732000012 0
J Su, ZG; Hu, QH; Denoeux, T Su, Zhi-gang; Hu, Qinghua; Denoeux, Thierry A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Feature extraction; Rough sets; Runtime; Cluster computing; Fuzzy systems; Big Data; Training; Apache Spark; Big Data; Dempster-Shafer theory; feature selection; generalization error bound (GEB); neighborhood rough set (NRS) model; nonparametric classification ATTRIBUTE REDUCTION; NEAREST NEIGHBORS; BELIEF FUNCTIONS; ALGORITHM; MAPREDUCE; MODEL; RULE The Evidential K-Nearest Neighbor (EK-NN) classification rule provides a global treatment of imperfect knowledge in class labels, but still suffers from the curse of dimensionality as well as runtime and memory restrictions when performing nearest neighbors search, in particular for large and high-dimensional data. To avoid the curse of dimensionality, this article first proposes a rough evidential K-NN (REK-NN) classification rule in the framework of rough set theory. Based on a reformulated K-NN rough set model, REK-NN selects features and thus reduces complexity by minimizing a proposed neighborhood pignistic decision error rate, which considers both Bayes decision error and spatial information among samples in feature space. In contrast to existing rough set-based feature selection methods, REK-NN is a synchronized rule rather than a stepwise one, in the sense that feature selection and learning are performed simultaneously. In order to further handle data with large sample size, we derive a distributed REK-NN method and implement it in the Apache Spark. The theoretical analysis of the classifier generalization error bound is finally presented. It is shown that the distributed REK-NN achieves good performances while drastically reducing the number of features and consuming less runtime and memory. Numerical experiments conducted on real-world datasets validate our conclusions. [Su, Zhi-gang] Southeast Univ, Sch Energy & Environm, Nanjing 210096, Peoples R China; [Hu, Qinghua] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China; [Denoeux, Thierry] Univ Technol Compiegne, CNRS 7253, Compiegne, France; [Denoeux, Thierry] Inst Univ France, Paris, France Southeast University - China; Tianjin University; Picardie Universites; Universite de Technologie de Compiegne; Institut Universitaire de France Su, ZG (corresponding author), Southeast Univ, Sch Energy & Environm, Nanjing 210096, Peoples R China. zhigangsu@seu.edu.cn; huqinghua@tju.edu.cn; thierry.denoeux@utc.fr Hu, Qinghua/0000-0001-7765-8095; Denoeux, Thierry/0000-0002-0660-5436 National Natural Science Foundation of China [51676034, 51876035, 51976032] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Manuscript receivedMarch 4, 2020; revisedMay 12, 2020; accepted May 26, 2020. Date of publicationMay 29, 2020; date of current version August 4, 2021. This work was supported by the National Natural Science Foundation of China under Grant 51676034, Grant 51876035, and Grant 51976032. (Corresponding author: Zhi-gang Su.) 48 7 7 5 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. AUG 2021.0 29.0 8 2322 2335 10.1109/TFUZZ.2020.2998502 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering TU6HA Green Submitted 2023-03-23 WOS:000681134100024 0
C Yuan, JL; Wang, F; Shafie-khah, M; Zhen, Z; Catalao, JPS IEEE Yuan, Jinlu; Wang, Fei; Shafie-khah, Miadreza; Zhen, Zhao; Catalao, Joao P. S. A New Evaluation Metric Reflecting the Lead-Lag Scenarios in Wind Power Forecasting 2021 IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING (IAS) IEEE Industry Applications Society Annual Meeting English Proceedings Paper Annual Meeting of the IEEE-Industry-Applications-Society (IAS) OCT 10-14, 2021 Vancouver, CANADA IEEE Ind Applicat Soc Forecasting evaluation metric; lead-lag scenario; wind power forecasting; root mean square error; sign of error; moving average PREDICTION Wind power forecast evaluation matters greatly as wind power has an ever-increasing proportion in the power system. Generally speaking the forecasting result can be divided into lead-lag scenarios and common scenarios which depends on whether the wind process is predicted on time. During the lead-lag scenarios the errors usually change from large positive numbers to negative ones (or the opposite), especially in the both ends of the period. Compared with the common scenarios in the same value of root mean square error (RMSE), large changes in errors from positive to negative in a short time can cost nearly two times of spinning reserve but get the same assessment score. For power system the two scenarios should be evaluated differently, however, few metrics in the evaluation can indicate the lead-lag scenarios in that they dispose the errors ignoring the signs or time continuity of the errors, or analysis the errors in a macro-scale sight like 24 hours horizon scale. This paper proposes a new metric based on RMSE which detects the changes of signs of errors in a process of moving average. Except for normal advantages like objectivity, adaptability, unity, symmetry and stability, the new metric has the ability to reflect both the lead-lag scenarios and common scenarios. The new metric can be used in the evaluation of wind and solar power, load, price, demand response forecasting and the process of neural network parameter training. [Yuan, Jinlu; Wang, Fei] North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China; [Shafie-khah, Miadreza] Univ Vaasa, Dept Elect Engn, Sch Technol & Innovat, Vaasa 65200, Finland; [Zhen, Zhao] Tsinghua Univ, State Key Lab Power Syst, Dept Elect Engn, Beijing, Peoples R China; [Catalao, Joao P. S.] Univ Porto, Fac Engn, P-4200465 Porto, Portugal; [Catalao, Joao P. S.] INESC TEC, P-4200465 Porto, Portugal North China Electric Power University; University of Vaasa; Tsinghua University; Universidade do Porto; INESC TEC Yuan, JL (corresponding author), North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China. jinluyuan@ncepu.edu.cn; feiwang@ncepu.edu.cn; miadreza@gmail.com; georgiazhz@foxmail.com; catalao@fe.up.pt Catalão, João P. S./I-3927-2012 Catalão, João P. S./0000-0002-2105-3051 National Key R&D Program of China [2018YFB0904200]; State Grid Corporation of China [SGLNDKOOKJJS1800266] National Key R&D Program of China; State Grid Corporation of China This work is supported by the National Key R&D Program of China (Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption, 2018YFB0904200) and eponymous Complement S&T Program of State Grid Corporation of China (SGLNDKOOKJJS1800266). 30 0 0 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0197-2618 978-1-7281-6401-4 IEEE IND APPLIC SOC 2021.0 10.1109/IAS48185.2021.9677172 0.0 7 Engineering, Industrial; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT3PJ 2023-03-23 WOS:000821959500070 0
J Tang, YQ; Yu, FS; Pedrycz, W; Yang, XY; Wang, JY; Liu, SH Tang, Yuqing; Yu, Fusheng; Pedrycz, Witold; Yang, Xiyang; Wang, Jiayin; Liu, Shihu Building Trend Fuzzy Granulation-Based LSTM Recurrent Neural Network for Long-Term Time-Series Forecasting IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Market research; Time series analysis; Forecasting; Predictive models; Data models; Optimization methods; Microprocessors; Long short-term memory neural network; long-term forecasting; time series; trend fuzzy information granulation SUPPORT VECTOR MACHINES; INFORMATION GRANULES; MODEL; PREDICTION; ARMA The existing long-term time-series forecasting methods based on the neural networks suffer from multiple limitations, such as accumulated errors and diminishing temporal correlation, which compromise the prediction quality. To overcome these shortcomings, in this article, we build trend fuzzy granulation-based long short-term memory (LSTM) neural networks to carry out long-term forecasting, where data points with consistent trend characteristics, including trend change, fluctuation range, and trend persistence, are predicted in unison rather than individually. Noticing that these trend characteristics are more urgently needed than just magnitude information, a question on how to granulate a time series into a granular time series consists of meaningful granules containing trend information that comes to be a crucial step. Only if the established granules fit the varying patterns of time series at utmost, such a granulation can make sense for the improvement of the forecasting accuracy; thus, an optimization method of trend-oriented fuzzy granulation is proposed to meliorate the granulation results. With the built trend fuzzy granulation-based LSTM networks, the successive iterations of one-step forecasting are prevented, and the prediction errors of data within a granule will not further increase. This is the first attempt to build trend fuzzy granule-based LSTM to predict the trend characteristics. Experiments on publicly available time series show good performance of the proposed model. [Tang, Yuqing; Yu, Fusheng; Wang, Jiayin] Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R China; [Yu, Fusheng] Minnan Normal Univ, Sch Math & Stat, Zhangzhou 363000, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland; [Yang, Xiyang] Quanzhou Normal Univ, Key Lab Intelligent Comp & Informat Proc Fujian P, Quanzhou 362000, Peoples R China; [Liu, Shihu] Yunnan Minzu Univ, Sch Math & Comp Sci, Kunming 650500, Yunnan, Peoples R China Beijing Normal University; MinNan Normal University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Quanzhou Normal University; Yunnan Minzu University Yu, FS (corresponding author), Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R China. yuqingt689@163.com; yufusheng@bnu.edu.cn; wpedrycz@ualberta.ca; yangxiyang@139.com; wjy@bnu.edu.cn; liush02@126.com liu, shihu/0000-0002-4442-5057; Tang, Yuqing/0000-0002-5919-1804 National Natural Science Foundation of China [11971065, 61966039] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 11971065 and Grant 61966039 43 13 14 33 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. JUN 2022.0 30.0 6 1599 1613 10.1109/TFUZZ.2021.3062723 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 1T4KB 2023-03-23 WOS:000804698500013 0
J Ning, WC; Acharya, A; Sun, ZY; Ogbuehi, AC; Li, C; Hua, ST; Ou, QH; Zeng, MH; Liu, XQ; Deng, YP; Haak, R; Ziebolz, D; Schmalz, G; Pelekos, G; Wang, Y; Hu, XD Ning, Wanchen; Acharya, Aneesha; Sun, Zhengyang; Ogbuehi, Anthony Chukwunonso; Li, Cong; Hua, Shiting; Ou, Qianhua; Zeng, Muhui; Liu, Xiangqiong; Deng, Yupei; Haak, Rainer; Ziebolz, Dirk; Schmalz, Gerhard; Pelekos, George; Wang, Yang; Hu, Xianda Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis FRONTIERS IN GENETICS English Article deep learning; autoencoder (AE); periodontitis; immunosuppression genes; therapeutic targets; bioinformatics ADHESION MOLECULE-1 CD31; FACTOR-KAPPA-B; PORPHYROMONAS-GINGIVALIS; SIGNALING PATHWAYS; EXPRESSION; ROLES; CYCLOSPORINE; PATHOGENESIS; OVERGROWTH; MODELS Background Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three master immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis. [Ning, Wanchen] Ludwig Maximilians Univ Munchen, Dept Conservat Dent & Periodontol, Munich, Germany; [Acharya, Aneesha] Dr DY Patil Vidyapeeth, Dr DY Patil Dent Coll & Hosp, Pune, Maharashtra, India; [Acharya, Aneesha; Pelekos, George] Univ Hong Kong, Fac Dent, Hong Kong, Peoples R China; [Sun, Zhengyang] Tech Univ Chemnitz, Fac Mech Engn, Chemnitz, Germany; [Ogbuehi, Anthony Chukwunonso] Univ Munster, Fac Phys, Munster, Germany; [Li, Cong; Hua, Shiting; Ou, Qianhua; Zeng, Muhui; Haak, Rainer; Schmalz, Gerhard] Southern Med Univ, Zhujiang Hosp, Guangzhou, Peoples R China; [Liu, Xiangqiong; Deng, Yupei; Hu, Xianda] Beijing Tibetan Hosp, China Tibetol Res Ctr, Lab Cell & Mol Biol, Beijing, Peoples R China; [Ziebolz, Dirk] Univ Leipzig, Dept Cariol Endodontol & Periodontol, Leipzig, Germany; [Wang, Yang] Hubei Univ, Hubei Collaborat Innovat Ctr Green Transformat Bi, Sch Life Sci, State Key Lab Biocatalysis & Enzyme Engn, Wuhan, Peoples R China University of Munich; Dr DY Patil Vidyapeeth Pune; Dr D Y Patil Dental College & Hospital; University of Hong Kong; Technische Universitat Chemnitz; University of Munster; Southern Medical University - China; Leipzig University; Hubei University Pelekos, G (corresponding author), Univ Hong Kong, Fac Dent, Hong Kong, Peoples R China.;Hu, XD (corresponding author), Beijing Tibetan Hosp, China Tibetol Res Ctr, Lab Cell & Mol Biol, Beijing, Peoples R China.;Wang, Y (corresponding author), Hubei Univ, Hubei Collaborat Innovat Ctr Green Transformat Bi, Sch Life Sci, State Key Lab Biocatalysis & Enzyme Engn, Wuhan, Peoples R China. george74@hku.hk; yangwang@hubu.edu.cn; hellocean@hotmail.com Pelekos, George/AAA-3295-2022; wang, yang/GPK-8269-2022; Haak, Rainer/J-6535-2016; Acharya, Aneesha/H-9940-2019; Ning, Wanchen/AAW-8866-2021; Schmalz, Gerhard/ABF-2872-2021; Hu, Xianda/AAD-3730-2022 Pelekos, George/0000-0003-1917-1988; Haak, Rainer/0000-0002-3716-4231; Acharya, Aneesha/0000-0002-9319-6780; Liu, Xiangqiong/0000-0001-8942-9505; Ogbuehi, Anthony Chukwunonso/0000-0002-6396-3571; Ning, Wanchen/0000-0003-1075-997X; Deng, Yupei/0000-0002-6083-0087 63 8 8 7 23 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. MAR 12 2021.0 12.0 648329 10.3389/fgene.2021.648329 0.0 15 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity RC5XT 33777111.0 Green Accepted, gold 2023-03-23 WOS:000632874500001 0
J Li, X; Zhang, YZ; Tiwari, P; Song, DW; Hu, B; Yang, MH; Zhao, ZG; Kumar, N; Marttinen, P Li, Xiang; Zhang, Yazhou; Tiwari, Prayag; Song, Dawei; Hu, Bin; Yang, Meihong; Zhao, Zhigang; Kumar, Neeraj; Marttinen, Pekka EEG Based Emotion Recognition: A Tutorial and Review ACM COMPUTING SURVEYS English Review EEG; emotion recognition; Affective Computing; psychophysiological computing CONVOLUTIONAL NEURAL-NETWORKS; FEATURE-SELECTION; FACIAL EXPRESSIONS; MUSIC; CLASSIFICATION; ASYMMETRY; DEEP; SIGNALS; IDENTIFICATION; STIMULATION Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions. [Li, Xiang; Yang, Meihong; Zhao, Zhigang] Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan, Jinan, Peoples R China; [Zhang, Yazhou] Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou, Peoples R China; [Zhang, Yazhou] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China; [Tiwari, Prayag; Marttinen, Pekka] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Song, Dawei] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China; [Hu, Bin] Beijing Inst Technol, Inst Engn Med, Beijing, Peoples R China; [Kumar, Neeraj] Thapar Inst Engn & Technol Deemed Univ, Dept Comp Sci & Engn, Patiala, Punjab, India Qilu University of Technology; Zhengzhou University of Light Industry; Nanjing University; Aalto University; Beijing Institute of Technology; Beijing Institute of Technology; Thapar Institute of Engineering & Technology Tiwari, P; Marttinen, P (corresponding author), Aalto Univ, Dept Comp Sci, Espoo, Finland.;Song, DW (corresponding author), Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China.;Hu, B (corresponding author), Beijing Inst Technol, Inst Engn Med, Beijing, Peoples R China.;Kumar, N (corresponding author), Thapar Inst Engn & Technol Deemed Univ, Dept Comp Sci & Engn, Patiala, Punjab, India. xiangli@sdas.org; yzzhang@zzuli.edu.cn; prayag.tiwari@aalto.fi; dwsong@bit.edu.cn; bh@bit.edu.cn; yangmh@sdas.org; zhaozhg@sdas.org; neeraj.kumar@thapar.edu; pekka.marttinen@aalto.fi Tiwari, Prayag/N-6261-2017; Marttinen, Pekka/N-6234-2015 Tiwari, Prayag/0000-0002-2851-4260; Marttinen, Pekka/0000-0001-7078-7927 Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province [2019JZZY010108, 2019JZZY010113]; Natural Science Foundation of China [U1636203, 62006212]; Natural Science Foundation of Shandong Province; Natural Science Foundation of China; State Key Lab; Novel Software Technology in Nanjing University [KFKT2021B41]; Industrial Science and Technology Research Project of Henan Province [222102210031]; Academy of Finland [336033, 315896]; Business Finland [884/31/2018]; EU [101016775] Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Lab; Novel Software Technology in Nanjing University; Industrial Science and Technology Research Project of Henan Province; Academy of Finland(Academy of Finland); Business Finland; EU(European Commission) This work was supported in part by the Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province (grant No. 2019JZZY010108 and grant No. 2019JZZY010113), the Natural Science Foundation of China (grant No. U1636203 and grant No. 62006212), the Natural Science Foundation of Shandong Province (`Research on Crossdomain Emotion Recognition Based on Large-scale Pre-trained EEG Model'), the Natural Science Foundation of China (`Research on pre-trained EEG model for EEG-based cross-domain emotion recognition task'), the fund of State Key Lab. for Novel Software Technology in Nanjing University (grant No. KFKT2021B41), and the Industrial Science and Technology Research Project of Henan Province (grant No. 222102210031). This work was also supported by the Academy of Finland (grants 336033, 315896), Business Finland (grant 884/31/2018), and EU H2020 (grant 101016775). 231 7 7 72 72 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. MAY 2023.0 55.0 4 79 10.1145/3524499 0.0 57 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6P0SY Green Submitted 2023-03-23 WOS:000890647400015 0
J Rasheed, MW; Tang, JL; Sarwar, A; Shah, S; Saddique, N; Khan, MU; Khan, MI; Nawaz, S; Shamshiri, RR; Aziz, M; Sultan, M Rasheed, Muhammad Waseem; Tang, Jialiang; Sarwar, Abid; Shah, Suraj; Saddique, Naeem; Khan, Muhammad Usman; Khan, Muhammad Imran; Nawaz, Shah; Shamshiri, Redmond R.; Aziz, Marjan; Sultan, Muhammad Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review SUSTAINABILITY English Review surface soil moisture; volumetric-tensiometric methods; moisture sensors; remote sensing methods; deep learning methods TIME-DOMAIN REFLECTOMETRY; SURFACE-ENERGY BALANCE; REMOTE-SENSING DATA; WATER-CONTENT; HYDRAULIC CONDUCTIVITY; SPATIAL VARIABILITY; DIFFERENT SCALES; NEUTRON PROBE; FIELD; TEMPERATURE The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land-atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical-direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction. [Rasheed, Muhammad Waseem; Tang, Jialiang] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Surface Proc & Ecol Regulat, Chengdu 610229, Peoples R China; [Rasheed, Muhammad Waseem; Tang, Jialiang; Shah, Suraj] Univ Chinese Acad Sci UCAS, Coll Resources & Environm, Beijing 100049, Peoples R China; [Sarwar, Abid; Saddique, Naeem; Khan, Muhammad Imran] Univ Agr Faisalabad, Dept Irrigat & Drainage, Faisalabad 38000, Pakistan; [Khan, Muhammad Usman] Univ Agr Faisalabad, Dept Energy Syst Engn, Faisalabad 38000, Pakistan; [Nawaz, Shah] Univ Agr Faisalabad, Inst Soil & Environm Sci, Faisalabad 38000, Pakistan; [Shamshiri, Redmond R.] Leibniz Inst Agr Engn & Bioecon, Dept Engn Crop Prod, D-14469 Potsdam, Germany; [Aziz, Marjan] Barani Agr Res Inst, Dept Agr Engn, Chakwal 48800, Pakistan; [Sultan, Muhammad] Bahauddin Zakariya Univ, Dept Agr Engn, Multan 60800, Pakistan Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS; University of Agriculture Faisalabad; University of Agriculture Faisalabad; University of Agriculture Faisalabad; Leibniz Institut fur Agrartechnik und Biookonomie (ATB); Bahauddin Zakariya University Tang, JL (corresponding author), Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Surface Proc & Ecol Regulat, Chengdu 610229, Peoples R China.;Tang, JL (corresponding author), Univ Chinese Acad Sci UCAS, Coll Resources & Environm, Beijing 100049, Peoples R China.;Sarwar, A (corresponding author), Univ Agr Faisalabad, Dept Irrigat & Drainage, Faisalabad 38000, Pakistan.;Shamshiri, RR (corresponding author), Leibniz Inst Agr Engn & Bioecon, Dept Engn Crop Prod, D-14469 Potsdam, Germany. jltang@imde.ac.cn; abidsarwar@uaf.edu.pk; rshamshiri@atb-potsdam.de Sarwar, Abid/ABB-4023-2021; Sultan, Muhammad/AAE-7883-2020 Sarwar, Abid/0000-0001-7402-2825; Sultan, Muhammad/0000-0002-7301-5567; /0000-0002-1605-1895; Rasheed, Muhammad Waseem/0000-0002-3948-0529; Tang, Jialiang/0000-0001-8006-153X; Shamshiri, Redmond Ramin/0000-0002-5775-9654 Key Science and Technology Projects in Sichuan Province [2020JDR0404] Key Science and Technology Projects in Sichuan Province This research work funded by the Key Science and Technology Projects in Sichuan Province (2020JDR0404). 137 4 4 47 47 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability SEP 2022.0 14.0 18 11538 10.3390/su141811538 0.0 23 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 4R8HR gold 2023-03-23 WOS:000856998400001 0
J Papapicco, V; Chen, B; Munih, M; Davalli, A; Sacchetti, R; Gruppioni, E; Crea, S; Vitiello, N Papapicco, V; Chen, B.; Munih, M.; Davalli, A.; Sacchetti, R.; Gruppioni, E.; Crea, S.; Vitiello, N. A Classification Approach Based on Directed Acyclic Graph to Predict Locomotion Activities With One Inertial Sensor on the Thigh IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS English Article Locomotion mode classification; support vector machines; directed acyclic graph; inertial measurement unit; machine learning RECOGNITION; PROSTHESIS Current state-of-the-art locomotion mode classifiers for controlling robotic lower-limb prostheses rely on multiple sensors to achieve high accuracy, prediction performance, and robustness to both speed changes and subject-specific gait patterns. However, multiple sensors placed on different body parts usually entail discomfort and poor usability for the user. This paper presents an intention detection method that relies on the features extracted from an inertial measurement unit worn on the thigh and an online phase estimator. The algorithm classifies the locomotion mode of the upcoming stride among the three modes of ground-level walking, stair ascent, and stair descent. A two-stage classification process first distinguishes between transient and steady-state strides and then classifies the locomotion mode of the impending stride based on directed acyclic graphs of binary classifiers. The classification is performed at 75% or 85% of the previous stride phase, respectively for steady-state and transient strides. Data were gathered from 10 healthy subjects and processed offline. Feature design and selection were based on the data of all subjects, while the classification performance was assessed by leave-one-subject-out cross-validation. Results presented a median recognition accuracy of 98.7% for steady-state strides and 95.6% for transitions, suggesting that the method was inherently robust to variations in gait cadence, since all of the features were phase-based and not dependent on fixed time intervals. These results inform the design of control strategies for active transfemoral prostheses able to predict the user's locomotion intention during the next stride, using minimum sensors. [Papapicco, V; Crea, S.; Vitiello, N.] Scuola Super Sant Anna, BioRobot Inst, I-56127 Pisa, Italy; [Chen, B.] Tianjin Univ, Sch Mech Engn, Tianjin 300350, Peoples R China; [Munih, M.] Univ Ljubljana, Fac Elect Engn, Ljubljana 1000, Slovenia; [Davalli, A.; Sacchetti, R.; Gruppioni, E.] INAIL, Prosthesis Ctr, I-40054 Vigorso Di Budrio, Italy; [Crea, S.; Vitiello, N.] Scuola Super Sant Anna, Dept Excellence Robot & AI, I-56127 Pisa, Italy; [Crea, S.; Vitiello, N.] IRCCS Fdn Don Carlo Gnocchi, I-20148 Milan, Italy Scuola Superiore Sant'Anna; Tianjin University; University of Ljubljana; Istituto Nazionale per l'Assicurazione Contro gli Infortuni sul Lavoro (INAIL); Scuola Superiore Sant'Anna; IRCCS Fondazione Don Carlo Gnocchi Onlus Papapicco, V (corresponding author), Scuola Super Sant Anna, BioRobot Inst, I-56127 Pisa, Italy. vito.papapicco@santannapisa.it; baojun_chen@tju.edu.cn; marko.munih@robo.fe.uni-lj.si; a.davalli@inail.it; r.sacchetti@inail.it; e.gruppioni@inail.it; simona.crea@santannapisa.it; nicola.vitiello@santannapisa.it Papapicco, Vito/0000-0002-3525-177X; Davalli, Angelo/0000-0002-5007-8661; Munih, Marko/0000-0001-5289-4059; Gruppioni, Emanuele/0000-0003-0732-8378; Chen, Baojun/0000-0002-2840-7617 Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro (INAIL) [PPR-AI 1/2 MOTU] Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro (INAIL) This work was supported in part by the Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro (INAIL) through the Project PPR-AI 1/2 MOTU. 32 4 4 1 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2576-3202 IEEE T MED ROBOT BIO IEEE Trans. Med. Robotics Bionics MAY 2021.0 3.0 2 436 445 10.1109/TMRB.2021.3075096 0.0 10 Engineering, Biomedical; Robotics Emerging Sources Citation Index (ESCI) Engineering; Robotics 6X8OE 2023-03-23 WOS:000896666700015 0
J Yan, LQ; Han, J; Yue, YS; Zhang, L; Qian, YN Yan, Luqi; Han, Jin; Yue, Yishi; Zhang, Liu; Qian, Yannan Sentiment Analysis of Short Texts Based on Parallel DenseNet CMC-COMPUTERS MATERIALS & CONTINUA English Article Sentiment analysis; short texts; parallel DenseNet TWITTER Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks (CNNs). However, most of these CNN models focus only on learning local features while ignoring global features. In this paper, based on traditional densely connected convolutional networks (DenseNet), a parallel DenseNet is proposed to realize sentiment analysis of short texts. First, this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network. Second, this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block, and then sending the combined features into the final classifier. Last, a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed. [Yan, Luqi; Han, Jin] Nanjing Univ Informat Sci & Technol, Nanjing 210044, Peoples R China; [Yue, Yishi; Zhang, Liu] State Grid Hunan Elect Power Co Ltd, Res Inst, Changsha 410007, Peoples R China; [Qian, Yannan] Waterford Inst Technol, Waterford X91 K0EK, Ireland Nanjing University of Information Science & Technology; State Grid Corporation of China; South East Technological University (SETU) Han, J (corresponding author), Nanjing Univ Informat Sci & Technol, Nanjing 210044, Peoples R China. hjhaohj@126.com National Key R&D Program of China [2018YFB1003205]; National Natural Science Foundation of China [U1836208, U1536206, U1836110, 61602253, 61672294]; Startup Foundation for Introducing Talent of NUIST [1441102001002]; Jiangsu Basic Research ProgramsNatural Science Foundation [BK20181407]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Startup Foundation for Introducing Talent of NUIST; Jiangsu Basic Research ProgramsNatural Science Foundation; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China This work was supported by the National Key R&D Program of China under Grant Number 2018YFB1003205; by the National Natural Science Foundation of China under Grant Numbers U1836208, U1536206, U1836110, 61602253, and 61672294; by the Startup Foundation for Introducing Talent of NUIST (1441102001002) ; by the Jiangsu Basic Research ProgramsNatural Science Foundation under Grant Number BK20181407; by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund; and by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China. 25 2 2 5 17 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2021.0 69.0 1 51 65 10.32604/cmc.2021.016920 0.0 15 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science SO7CG gold 2023-03-23 WOS:000659131200010 0
J Wang, ZH; Xu, HT; Xia, L; Zou, ZJ; Soares, CG Wang, Zihao; Xu, Haitong; Xia, Li; Zou, Zaojian; Guedes Soares, C. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion OCEAN ENGINEERING English Article Nonparametric modeling; Support vector regression; System identification; Ship dynamics; Kernel method SYSTEM-IDENTIFICATION; NEURAL-NETWORK; DYNAMIC-MODEL; CATAMARAN A nonparametric identification method based on nu('nu')-support vector regression (nu-SVR) is proposed to establish robust models of ship maneuvering motion in an easy-to-operate way. Assisted by the kernel trick, the nonlinear model learns implicitly in high-dimensional feature space without a priori model structure. The nu-SVE controls the sparsity automatically, resulting in high efficiency. To improve the practicality, a parameter tuning scheme combining the hold-out validation and the simulation of dynamic processes is designed to avoid over-fitting. Taking the KVLCC2 ship as the study object, the experimental data from the SIMMAN database are used to evaluate the method. The selection and pre-processing of training data are discussed. The identified model shows good generalization performance in the prediction of multiple maneuvers not involved in the training set, verifying the effectiveness of the method. [Wang, Zihao; Xia, Li; Zou, Zaojian] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai, Peoples R China; [Wang, Zihao; Xu, Haitong; Guedes Soares, C.] Univ Lisbon, Ctr Marine Technol & Ocean Engn CENTEC, Inst Super Tecn, Lisbon, Portugal; [Zou, Zaojian] Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai, Peoples R China Shanghai Jiao Tong University; Universidade de Lisboa; Instituto Superior Tecnico; Shanghai Jiao Tong University Zou, ZJ (corresponding author), Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai, Peoples R China. zjzou@sjtu.edu.cn Soares, Carlos Guedes/AAW-7684-2020; Wang, Zihao/S-7875-2019; Xu, Haitong/R-7024-2018 Soares, Carlos Guedes/0000-0002-8570-4263; Wang, Zihao/0000-0002-0035-1789; Xu, Haitong/0000-0001-6701-929X National Natural Science Foundations of China [51979165] National Natural Science Foundations of China(National Natural Science Foundation of China (NSFC)) This work is financially supported by the National Natural Science Foundations of China [Grant number 51979165]. The authors are grateful to the Hamburg Ship Model Basin (HSVA) and Maritime Research Institute Netherlands (MARIN) for providing the experimental data. 41 28 29 3 13 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 OCEAN ENG Ocean Eng. NOV 15 2020.0 216.0 107994 10.1016/j.oceaneng.2020.107994 0.0 12 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography PC2UV 2023-03-23 WOS:000596863000050 0
C Liu, XW; Wei, YF; Gu, B; Song, M; Wang, XJ IEEE Liu, Xiaowei; Wei, Yifei; Gu, Bo; Song, Mei; Wang, XiaoJun Joint Power Control and User Association Strategy in Green HetNets Using Deep Q-Network with LSTM 2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (IEEE GLOBECOM) DEC 09-13, 2019 Waikoloa, HI IEEE,Huawei,Intel,ZTE,Google,Qualcomm,Natl Instruments,IEEE Commun Soc With the proliferation of users in wireless communication networks and the increase in traffic, heterogeneous networks have emerged. Minimizing the total energy consumption of base stations is an important issue for environmental reasons. Therefore, the concept of a green heterogeneous network was proposed. However, the deployment of a large number of small base stations also poses problems for user management. In order to better improve the decision-making and self-organizing ability of the network, the paper also proposes an user energy consumption prediction model based on Recurrent Neural Network. After that, the article also proposes a power control scheme through a Deep Reinforcement Learning algorithm with Long Short-Term Memory to improve the quality of user service. Finally, a joint optimization scheme of network power control and user management is realized. The experimental results show that the joint optimization effect using this method can converge to better solution and reduce energy consumption compared with traditional deep reinforcement learning. [Liu, Xiaowei; Wei, Yifei; Gu, Bo; Song, Mei] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China; [Wang, XiaoJun] Dublin City Univ, Sch Elect Engn, Dublin 9, Ireland Beijing University of Posts & Telecommunications; Dublin City University Liu, XW (corresponding author), Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China. liuxiaowei@bupt.edu.cn; weiyifei@bupt.edu.cn; gubo@bupt.edu.cn; songm@bupt.edu.cn; xiaojun.wang@dcu.ie National Natural Science Foundation of China [61571059] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China(No.61571059). 21 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-0962-6 IEEE GLOB COMM CONF 2019.0 6 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BP4GG 2023-03-23 WOS:000552238600108 0
C Franzoni, V; Milani, A; Biondi, G ACM Franzoni, Valentina; Milani, Alfredo; Biondi, Giulio SEMO: a Semantic Model for Emotion Recognition in Web Objects 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017) English Proceedings Paper IEEE/WIC/ACM International Conference on Web Intelligence (WI) AUG 23-26, 2017 Leipzig, GERMANY IEEE,WIC,ACM web document retrieval; semantic similarity measures; emotion recognition; affective data; sentiment analysis; artificial intelligence REPOSITORIES In this work, we present SEMO, a Semantic Model for Emotion Recognition, which enables users to detect and quantify the emotional load related to basic emotions hidden in short, emotionally rich sentences (e.g. news titles, tweets, captions). The idea of assessing the semantic similarity of concepts by looking at the occurrences and co-occurrences of terms describing them in pages indexed by a search engine can be directly extended to emotions, and to the words expressing them in different languages. The emotional content associated to a particular emotion for a term can thus be estimated using web-based similarity measures, e.g. Confidence, PMI, NGD and PMING, aggregating the distance computed by a model of emotions, e.g. Ekman, Plutchik and Lovheim. Emotions are ranked based on their similarity to the analyzed text, describing each sentence through a vector of values of emotion load, which form the Vector Space Model for the chosen emotion model and similarity measures. The model is tested comparing experimental results to a ground truth in literature. SEMO takes care of both the phases of data collection and data analysis, to produce knowledge to be used in application domains such as social robots, recommender systems, and human-machine interactive systems. [Franzoni, Valentina] Sapienza Univ Rome, Dept Comp Control & Management Engn, Rome, Italy; [Milani, Alfredo] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Biondi, Giulio] Univ Perugia, Dept Math & Comp Sci, Perugia, Italy Sapienza University Rome; Hong Kong Baptist University; University of Perugia Franzoni, V (corresponding author), Sapienza Univ Rome, Dept Comp Control & Management Engn, Rome, Italy. franzoni@dis.uniroma1.it; milani@comp.hkbu.edu.hk; giulio.biondi@studenti.unipg.it Biondi, Giulio/GWZ-8298-2022; Franzoni, Valentina/AAC-7783-2021 Biondi, Giulio/0000-0002-1854-2196; Franzoni, Valentina/0000-0002-2972-7188; MILANI, Alfredo/0000-0003-4534-1805 35 16 16 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-4951-2 2017.0 953 958 10.1145/3106426.3109417 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ6US 2023-03-23 WOS:000426965100122 0
C Cheng, R; Emrich, T; Kriegel, HP; Mamoulis, N; Renz, M; Trajcevski, G; Zufle, A IEEE Cheng, Reynold; Emrich, Tobias; Kriegel, Hans-Peter; Mamoulis, Nikos; Renz, Matthias; Trajcevski, Goce; Zuefle, Andreas Managing Uncertainty in Spatial and Spatio-temporal Data 2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) IEEE International Conference on Data Engineering English Proceedings Paper IEEE 30th International Conference on Data Engineering (ICDE) MAR 31-APR 04, 2014 Chicago, IL IEEE,Microsoft,Qatar Comp Res Inst,HERE Nokia,Purdue Univ, Cyber Ctr,NW Univ, McCormick Sch Engn,Google RANKING; QUERIES; SEARCH Location-related data has a tremendous impact in many applications of high societal relevance and its growing volume from heterogeneous sources is one true example of a Big Data [1]. An inherent property of any spatio-temporal dataset is uncertainty due to various sources of imprecision. This tutorial provides a comprehensive overview of the different challenges involved in managing uncertain spatial and spatio-temporal data and presents state-of-the-art techniques for addressing them. [Kriegel, Hans-Peter; Renz, Matthias; Zuefle, Andreas] Univ Munich, Inst Informat, D-81377 Munich, Germany; [Cheng, Reynold; Mamoulis, Nikos] Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Trajcevski, Goce] Northwestern Univ, Dept EECS, Evanston, IL USA; [Emrich, Tobias] Univ So Calif, Integrated Media Syst Ctr, Los Angeles, CA USA University of Munich; University of Hong Kong; Northwestern University; University of Southern California Cheng, R (corresponding author), Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. ckcheng@cs.hku.hk; emrich@dbs.ifi.lmu.de; kriegel@dbs.ifi.lmu.de; nikos@cs.hku.hk; renz@dbs.ifi.lmu.de; goce@eecs.northwestern.edu; zuefle@dbs.ifi.lmu.de Trajcevski, Goce/AAE-8444-2019; Trajcevski, Goce/B-7277-2009; /C-1836-2009 /0000-0003-3423-4895 58 12 13 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1084-4627 978-1-4799-3480-5 PROC INT CONF DATA 2014.0 1302 1305 4 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BC7YT 2023-03-23 WOS:000355411500133 0
C Helwegen, K; Widdicombe, J; Geiger, L; Liu, ZC; Cheng, KT; Nusselder, R Wallach, H; Larochelle, H; Beygelzimer, A; d'Alche-Buc, F; Fox, E; Garnett, R Helwegen, Koen; Widdicombe, James; Geiger, Lukas; Liu, Zechun; Cheng, Kwang-Ting; Nusselder, Roeland Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) Advances in Neural Information Processing Systems English Proceedings Paper 33rd Conference on Neural Information Processing Systems (NeurIPS) DEC 08-14, 2019 Vancouver, CANADA Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights cannot be treated analogously to weights in real-valued networks. Instead their main role is to provide inertia during training. We interpret current methods in terms of inertia and provide novel insights into the optimization of BNNs. We subsequently introduce the first optimizer specifically designed for BNNs, Binary Optimizer (Bop), and demonstrate its performance on CIFAR-10 and ImageNet. Together, the redefinition of latent weights as inertia and the introduction of Bop enable a better understanding of BNN optimization and open up the way for further improvements in training methodologies for BNNs. Code is available at: https://github.com/plumerai/rethinking-bnn-optimization. [Helwegen, Koen; Widdicombe, James; Geiger, Lukas; Nusselder, Roeland] Plumerai Res, Amsterdam, Netherlands; [Liu, Zechun; Cheng, Kwang-Ting] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China Hong Kong University of Science & Technology Helwegen, K (corresponding author), Plumerai Res, Amsterdam, Netherlands. koen@plumerai.com; james@plumerai.com; lukas@plumerai.com; zliubq@connect.ust.hk; timcheng@ust.hk; roeland@plumerai.com Cheng, Kwang-Ting Tim/0000-0002-3885-4912 36 21 21 0 0 NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) LA JOLLA 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA 1049-5258 ADV NEUR IN 2019.0 32.0 12 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP0ID 2023-03-23 WOS:000534424307054 0
C Tang, C; Xu, T; Chen, P; He, YB; Bezerianos, A; Wang, HT IEEE Tang, Cong; Xu, Tao; Chen, Peng; He, Yuebang; Bezerianos, Anastasios; Wang, Hongtao A Channel Selection Method for Event Related Potential Detection based on Random Forest and Genetic Algorithm 2020 CHINESE AUTOMATION CONGRESS (CAC 2020) Chinese Automation Congress English Proceedings Paper Chinese Automation Congress (CAC) NOV 06-08, 2020 Shanghai, PEOPLES R CHINA IEEE,CAA,IEEE Syst, Man, & Cybernet Soc Brain-computer interface (BCI); P300; random forest (RF); genetic algorithm (GA); channel selection P300 has been used in Brain-computer interface (BCI) as a typical visual event-related potential (ERP). However, electroencephalogram (EEG) analysis with a large number of channels will cause information redundancy and increase computational complexity. In this paper, a combination of random forest (RF) and genetic algorithm (GA) for EEG channel selection was proposed. Competition III Data set II was used for evaluation of the proposed improved genetic algorithm based on a six-layer batch normalized convolution neural network (BN-CNN). Experimental results show that the average accuracy of 76.1%, 85.7% and 96.9% were obtained between subject A and subject B in three conditions with data is superimposed for 5, 10, and 15 times respectively. In addition, the performance of RF-GACNN is superior that the GA-CNN and CNN. This RF and GA channel selection method would be a new method for event-related potential electroencephalogram signals analysis. [Tang, Cong; Xu, Tao; Chen, Peng; He, Yuebang; Wang, Hongtao] Univ Wuyi, Fac Intelligent Mfg, Jiangmen, Peoples R China; [Bezerianos, Anastasios] Univ Patras, Dept Med Phys, Patras, Greece University of Patras Wang, HT (corresponding author), Univ Wuyi, Fac Intelligent Mfg, Jiangmen, Peoples R China. nushongtaowang@qq.com wang, hongtao/AAH-1233-2020 wang, hongtao/0000-0002-6564-5753 Special Projects in Key Fields by Technology Development Project of Guangdong Province [2020ZDZX3018]; Special Fund for Science and Technology of Guangdong Province [2020182]; Science Foundation for Young Teachers of Wuyi University [2018td01]; Innovation and Entrepreneurship Project of Wuyi University [3344100104]; Hong Kong & Macao joint RD Project [2019WGALH16]; Jiangmen Brain-like Computation and Hybrid Intelligence RD Center [[2018]359, [2019]26]; Wuyi University and Hong Kong Special Projects in Key Fields by Technology Development Project of Guangdong Province; Special Fund for Science and Technology of Guangdong Province; Science Foundation for Young Teachers of Wuyi University; Innovation and Entrepreneurship Project of Wuyi University; Hong Kong & Macao joint RD Project; Jiangmen Brain-like Computation and Hybrid Intelligence RD Center; Wuyi University and Hong Kong This study was supported by Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province (No. 2020ZDZX3018), the Special Fund for Science and Technology of Guangdong Province (No. 2020182), the Science Foundation for Young Teachers of Wuyi University (No. 2018td01), Jiangmen Brain-like Computation and Hybrid Intelligence R&D Center ([2018]359, [2019]26), the Innovation and Entrepreneurship Project of Wuyi University under Grant 3344100104, and Wuyi University and Hong Kong & Macao joint R&D Project (No.2019WGALH16). 17 2 2 2 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2688-092X 2688-0938 978-1-7281-7687-1 CHIN AUTOM CONGR 2020.0 5419 5424 10.1109/CAC51589.2020.9327820 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BR9MR 2023-03-23 WOS:000678697005097 0
J Shu, ZG; Taleb, T Shu, Zhaogang; Taleb, Tarik A Novel QoS Framework for Network Slicing in 5G and Beyond Networks Based on SDN and NFV IEEE NETWORK English Article Quality of service; 5G mobile communication; Network slicing; Reliability; Cloud computing; Artificial intelligence; Heuristic algorithms Along with the development of 5G, NS plays an important role in the application of mobile networks to meet all kinds of personalized requirements. In terms of NS concept, network operators can vertically split a physical network into multiple logically separate networks to flexibly meet QoS requirements, which are mainly represented as higher bandwidth and lower latency. In this article, we propose a novel QoS framework of NS in 5G and beyond networks based on SDN and NFV to guarantee key QoS indicators for different application scenarios, such as eMBB, mMTC and URLLC. In this QoS framework, a 5G network is divided into three parts, RAN, TN and CN, to form three types of NS with different network resource allocation algorithms. The performance evaluation in the simulation environment of Mininet shows that the proposed QoS framework can steer different flows into different queues of OVS, schedule network resources for various NS types and provide reliable E2E QoS for users according to preconfigured QoS requirements. [Shu, Zhaogang] Fujian Agr & Forestry Univ, Comp & Informat Coll, Fuzhou, Fujian, Peoples R China; [Shu, Zhaogang] Fujian Agr & Forestry Univ, Key Lab Smart Agr & Forestry, Fuzhou, Fujian, Peoples R China; [Taleb, Tarik] Aalto Univ, Sch Elect Engn, Espoo, Finland; [Taleb, Tarik] Oulu Univ, Oulu, Finland; [Taleb, Tarik] Sejong Univ, Seoul, South Korea Fujian Agriculture & Forestry University; Fujian Agriculture & Forestry University; Aalto University; University of Oulu; Sejong University Shu, ZG (corresponding author), Fujian Agr & Forestry Univ, Comp & Informat Coll, Fuzhou, Fujian, Peoples R China.;Shu, ZG (corresponding author), Fujian Agr & Forestry Univ, Key Lab Smart Agr & Forestry, Fuzhou, Fujian, Peoples R China. zgshu@fafu.edu.cn; Tarik.Taleb@aalto.fi Taleb, Tarik/ABD-6339-2021 Shu, Zhaogang/0000-0002-7218-6321 CERNET innovation project of China [NGII20190102]; European Union's Horizon 2020 Research and Innovation Program through the MonB5G Project [871780]; Academy of Finland 6Genesis project [318927]; Academy of Finland CSN project [311654] CERNET innovation project of China; European Union's Horizon 2020 Research and Innovation Program through the MonB5G Project; Academy of Finland 6Genesis project; Academy of Finland CSN project This research work has partially received funding from the CERNET innovation project of China (No. NGII20190102). It was also partially supported by the European Union's Horizon 2020 Research and Innovation Program through the MonB5G Project under Grant No. 871780; by the Academy of Finland 6Genesis project under Grant No. 318927; and by the Academy of Finland CSN project under Grant No. 311654. 0 27 29 2 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. MAY-JUN 2020.0 34.0 3 256 263 10.1109/MNET.001.1900423 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LZ3TD Green Accepted 2023-03-23 WOS:000541150100035 0
J Mirzaei, M; Jafari, A; Gholamalifard, M; Azadi, H; Shooshtari, SJ; Moghaddam, SM; Gebrehiwot, K; Witlox, F Mirzaei, Mohsen; Jafari, Ali; Gholamalifard, Mehdi; Azadi, Hossein; Shooshtari, Sharif Joorabian; Moghaddam, Saghi Movahhed; Gebrehiwot, Kindeya; Witlox, Frank Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover LAND USE POLICY English Article Caspian Sea; Landscape metrics; Environmental risk mitigation; Nonpoint source pollution; Sustainable watershed management; River water quality SUPPORT VECTOR MACHINES; RIVER-BASIN; LANDSCAPE METRICS; CATCHMENT ATTRIBUTES; STREAM NITROGEN; NEURAL-NETWORK; USE PATTERNS; PREDICTION; HYDROLOGY; PROVINCE Understanding the relationship between rivers water quality (RWQ) and landscape metrics (LMs) is valuable for developing sustainable watershed management practices and pollution/environmental risk mitigation. To do so, the current study aimed to explore the relationship between RWQ and LMs by assessing 74 sub-basins within 2 million ha in Northern Iran. Principal component analyses were used to identify principal water quality parameters. Considering the effect of composition and configuration of the Land Use/Land Cover (LULC) on pollution loads, statistical models revealed that by increasing the mean of the Fractal Dimension Index of agricultural lands, the nitrate loads will increase. The results of this study can be especially used in the environmental impact/risk assessment of new industrial and residential applications. Also, the findings of the current study could provide a perfect source for calculations and choosing the best policy making decisions. [Mirzaei, Mohsen] Malayer Univ, Res Inst Grapes & Raisin RIGR, Grape Environm Sci Dept, Malayer, Iran; [Jafari, Ali] Shahrekord Univ, Fac Nat Resources & Earth Sci, Shahrekord, Iran; [Gholamalifard, Mehdi] Tarbiat Modares Univ, Fac Nat Resources, Dept Environm, Mazandaran, Iran; [Azadi, Hossein; Witlox, Frank] Univ Ghent, Dept Geog, Ghent, Belgium; [Shooshtari, Sharif Joorabian] Malayer Univ, Fac Nat Resources & Environm, Malayer, Iran; [Moghaddam, Saghi Movahhed] Shahid Beheshti Univ, Environm Sci Res Inst, Tehran, Iran; [Gebrehiwot, Kindeya] Mekelle Univ, Dept Land Resource Management & Environm Protect, Mekelle, Ethiopia; [Witlox, Frank] Univ Tartu, Dept Geog, Tartu, Estonia; [Witlox, Frank] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, Nanjing, Peoples R China Shahrekord University; Tarbiat Modares University; Ghent University; Shahid Beheshti University; Mekelle University; University of Tartu; Nanjing University of Aeronautics & Astronautics Gholamalifard, M (corresponding author), Tarbiat Modares Univ, Fac Nat Resources, Dept Environm, Mazandaran, Iran. m.gholamalifard@modares.ac.ir Movahhed Moghaddam, Saghi/GXH-9384-2022; Witlox, Frank/A-5143-2008; Joorabian Shooshtari, Sharif/ABC-7184-2020; Azadi, Hossein/E-2361-2011 Witlox, Frank/0000-0002-8966-6823; Joorabian Shooshtari, Sharif/0000-0002-6667-4230; Azadi, Hossein/0000-0002-5108-1993; Movahhed Moghaddam, Saghi/0000-0002-2809-9693; Mirzaei, Mohsen/0000-0002-8924-7143 71 20 21 7 16 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0264-8377 1873-5754 LAND USE POLICY Land Use Pol. JUN 2020.0 95.0 103766 10.1016/j.landusepol.2018.12.014 0.0 12 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology LU5VQ 2023-03-23 WOS:000537823200002 0
J Li, L; Chen, L; Dong, RH Li, Lei; Chen, Li; Dong, Ruihai CAESAR: context-aware explanation based on supervised attention for service recommendations JOURNAL OF INTELLIGENT INFORMATION SYSTEMS English Article Explainable recommendation; Context-aware recommender systems; Neural network; Multi-task learning Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users' satisfaction. However, little work has been done to provide explanations from the angle of a user's contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user's current contexts. [Li, Lei; Chen, Li] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China; [Dong, Ruihai] Univ Coll Dublin, Insight Ctr Data Analyt, Dublin, Ireland Hong Kong Baptist University; University College Dublin Li, L (corresponding author), Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China. csleili@comp.hkbu.edu.hk; lichen@comp.hkbu.edu.hk; ruihai.dong@ucd.ie 48 4 4 1 12 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-9902 1573-7675 J INTELL INF SYST J. Intell. Inf. Syst. AUG 2021.0 57.0 1 147 170 10.1007/s10844-020-00631-8 0.0 NOV 2020 24 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science TT7XP 2023-03-23 WOS:000592566600001 0
J He, F; Furones, AR; Landegren, N; Fuxe, J; Sarhan, D He, Fei; Furones, Andrea Rodgers; Landegren, Nils; Fuxe, Jonas; Sarhan, Dhifaf Sex dimorphism in the tumor microenvironment - From bench to bedside and back SEMINARS IN CANCER BIOLOGY English Article Sex dimorphism; Immune system; Cancer; Tumor microenvironment; Genderised cancer therapy PLASMACYTOID DENDRITIC CELLS; MENOPAUSAL HORMONE-THERAPY; BACILLUS-CALMETTE-GUERIN; ESTROGEN-RECEPTOR-ALPHA; INVASIVE BLADDER-CANCER; CYTOTOXIC T-CELLS; PERIPHERAL-BLOOD; LUNG ADENOCARCINOMAS; REPLACEMENT THERAPY; ANTIGEN-EXPRESSION Cancer represents a significant cause of death and suffering in both the developed and developing countries. Key underlying issues in the mortality of cancer are delayed diagnosis and resistance to treatments. However, improvements in biomarkers represent one important step that can be taken for alleviating the suffering caused by malignancy. Precision-based medicine is promising for revolutionizing diagnostic and treatment strategies for cancer patients worldwide. Contemporary methods, including various omics and systems biology approaches, as well as advanced digital imaging and artificial intelligence, allow more accurate assessment of tumor characteristics at the patient level. As a result, treatment strategies can be specifically tailored and adapted for individual and/or groups of patients that carry certain tumor characteristics. This includes immunotherapy, which is based on characterization of the immunosuppressive tumor microenvironment (TME) and, more specifically, the presence and activity of immune cell subsets. Unfortunately, while it is increasingly clear that gender strongly affects immune regulation and response, there is a knowledge gap concerning differences in sex-specific immune responses and how these contribute to the immunosuppressive TME and the response to immunotherapy. In fact, sex dimorphism is poorly understood in cancer progression and is typically ignored in current clinical practice. In this review, we aim to survey the available literature and highlight the existing knowledge gap in order to encourage further studies that would contribute to understanding both gender-biased immunosuppression in the TME and the driver of tumor progression towards invasive and metastatic disease. The review highlights the need to include sex optimized/genderized medicine as a new concept in future medicine cancer diagnostics and treatments. [He, Fei; Furones, Andrea Rodgers; Fuxe, Jonas; Sarhan, Dhifaf] Karolinska Inst, Dept Lab Med, Div Pathol, SE-14186 Stockholm, Sweden; [He, Fei] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Urol, Xian, Shaanxi, Peoples R China; [Furones, Andrea Rodgers] Radboud Inst Mol Life Sci, Tumor Immunol Dept, Nijmegen, Netherlands; [Landegren, Nils] Uppsala Univ, Dept Med Biochem & Microbiol, S-75123 Uppsala, Sweden; [Landegren, Nils] Karolinska Inst, Dept Med Solna, Ctr Mol Med, S-17176 Stockholm, Sweden Karolinska Institutet; Xi'an Jiaotong University; Uppsala University; Karolinska Institutet Sarhan, D (corresponding author), Karolinska Inst, Dept Lab Med, Div Pathol, SE-14186 Stockholm, Sweden. dhifaf.sarhan@ki.se He, Fei/0000-0003-2453-3648 Karolinska Institutet Funding, Sweden [2020-01829] Karolinska Institutet Funding, Sweden Karolinska Institutet Funding, Sweden, 2020-01829. 214 1 1 4 4 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1044-579X 1096-3650 SEMIN CANCER BIOL Semin. Cancer Biol. NOV 2022.0 86.0 3 166 179 10.1016/j.semcancer.2022.03.007 0.0 OCT 2022 14 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 6Y6RH 35278635.0 Green Published, hybrid 2023-03-23 WOS:000897219500014 0
C Li, W; Wang, SC; Lei, M; Siniscalchi, SM; Lee, CH IEEE Li, Wei; Wang, Sicheng; Lei, Ming; Siniscalchi, Sabato Marco; Lee, Chin-Hui IMPROVING AUDIO-VISUAL SPEECH RECOGNITION PERFORMANCE WITH CROSS-MODAL STUDENT-TEACHER TRAINING 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 12-17, 2019 Brighton, ENGLAND Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Audio-visual speech recognition; deep neural network; cross-modal training; student-teacher training; transfer learning; environmental-aware training In this paper, we propose a cross-modal student-teacher learning framework to make a full use of externally abundant acoustic data in addition to a given task-specific audio-visual training database for improving speech recognition performance under the low signal-to-noise-ratio ( SNR) and acoustic mismatch conditions. First, a teacher model is trained with large-sized audio-only databases. Next, a student, namely a deep neural network ( DNN) model, is trained on a small-sized audio-visual database to minimize the Kullback-Leibler ( KL) divergence between its output and the posterior distribution of the teacher. We evaluate the proposed approach in both matched and mismatch acoustic conditions for phone recognition with the NTCD-TIMIT database. Compared to the DNN recognition system trained with the original audio-visual data only, the proposed solution reduces the phone error rate ( PER) from 26.7% to 21.3% on a matched acoustic scenario. In the mismatch conditions, the PER is reduced from 47.9% to 42.9%. Moreover, we show that posteriors generated by the teacher contain environmental information, which enables our proposed student-teacher learning to work as an environmental-aware training and good PER reductions are observed in all SNR conditions. [Li, Wei; Wang, Sicheng; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Lei, Ming] Alibaba Grp, Machine Intelligence Technol, Beijing, Peoples R China; [Siniscalchi, Sabato Marco] Kore Univ Enna, Dept Comp Engn, Enna, Italy University System of Georgia; Georgia Institute of Technology; Alibaba Group; Universita Kore di ENNA Li, W (corresponding author), Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA. lee.wei@gatech.edu; sichengwang@gatech.edu; lm86501@alibaba-inc.com; marco.siniscalchi@unikore.it; chl@ece.gatech.edu Siniscalchi, Sabato Marco Marco/I-3423-2012 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507 China Scholarship Council; Italian NFR AULUS project China Scholarship Council(China Scholarship Council); Italian NFR AULUS project The first two authors performed this work while they were research interns at Alibaba iDST, Beijing, China, in summer 2018. The fourth author helped revising the work after re-joining the Kore University of Enna. The first author was also partially supported by a grant from the China Scholarship Council. The fourth author was partially supported by the Italian NFR AULUS project. 37 8 9 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-4799-8131-1 INT CONF ACOUST SPEE 2019.0 6560 6564 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BN4NP 2023-03-23 WOS:000482554006158 0
J Chen, XQ; Ling, J; Yang, YS; Zheng, HL; Xiong, PW; Postolache, O; Xiong, Y Chen, Xinqiang; Ling, Jun; Yang, Yongsheng; Zheng, Hailin; Xiong, Pengwen; Postolache, Octavian; Xiong, Yong Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction MATHEMATICAL PROBLEMS IN ENGINEERING English Article ANOMALY DETECTION; MARITIME; TRACKING Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base. [Chen, Xinqiang; Ling, Jun; Yang, Yongsheng] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China; [Chen, Xinqiang] Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China; [Chen, Xinqiang] Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China; [Zheng, Hailin] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China; [Xiong, Pengwen] Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Postolache, Octavian] ISCTE Inst Univ Lisboa, P-1649026 Lisbon, Portugal; [Postolache, Octavian] Inst Telecomunicacoes, P-1649026 Lisbon, Portugal; [Xiong, Yong] Hunan Lianzhi Technol Co Ltd, Changsha 410217, Peoples R China Shanghai Maritime University; Fudan University; Fudan University; Shanghai Maritime University; Nanchang University; Instituto Universitario de Lisboa; Instituto de Telecomunicacoes Xiong, PW (corresponding author), Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China. steven.xpw@ncu.edu.cn Postolache, Octavian/AAE-1343-2021; z, hl/GQY-9078-2022; Postolache, Octavian Adrian/ADC-7853-2022; z, h/HGB-1014-2022; Lang, Ming/HIK-0758-2022; XIONG, YONG/AAZ-4038-2020 Postolache, Octavian Adrian/0000-0001-5055-6347; Xiong, Pengwen/0000-0002-0623-8592 National Natural Science Foundation of China [51709167, 51579143, 61663027]; Shanghai Committee of Science and Technology, China [18040501700, 1829501100, 17595810300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Committee of Science and Technology, China(Shanghai Science & Technology Committee) This work was jointly supported by the National Natural Science Foundation of China (51709167, 51579143, and 61663027) and Shanghai Committee of Science and Technology, China (18040501700, 1829501100, and 17595810300). 31 12 12 12 45 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. AUG 17 2020.0 2020.0 7191296 10.1155/2020/7191296 0.0 9 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics NK7ZV gold 2023-03-23 WOS:000566952000014 0
C Gao, J; Li, M; Chen, L; Du, JH; Ma, RQ Tong, R; Zhang, Y; Lu, Y; Dong, M Gao, Jin; Li, Miao; Chen, Lei; Du, Jinhua; Ma, Rongqiang Information Entropy-Informed Sentence Representation for Question Classification 2017 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP) International Conference on Asian Language Processing English Proceedings Paper International Conference on Asian Language Processing (IALP) DEC 05-07, 2017 Natl Univ Singapore, Singapore, SINGAPORE Chinese & Oriental Languages Informat Proc Soc,IEEE,APSIPA,Singapore tourist board Natl Univ Singapore question classification; semantic representation; information entropy; question answering The traditional question classification methods generally employ a large number of features extracted from labelled questions to achieve good classification result. However, the high dimensionality of the feature space may lead to a higher training cost. In this paper, we propose a novel and effective question semantic representation-based method for question classification, avoiding the complicated feature extraction process. We first exploit the neural network-based language model to learn the distributed representation of words which can capture the semantic relations between words. We then introduce the information entropy to measure the importance of the word to question classification, i.e. use the information entropy to adjust the weight of the word. Subsequently, the question vectors are fed into an SVM classifier as semantic features to obtain the classification result. Experimental results demonstrate the effectiveness of our method with the improvement of 3.62% on open domain dataset and 650% on agricultural dataset over baseline. [Gao, Jin; Li, Miao; Chen, Lei; Ma, Rongqiang] Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China; [Gao, Jin] Univ Sci & Technol China, Hefei, Anhui, Peoples R China; [Du, Jinhua] Dublin City Univ, Sch Comp, ADAPT Ctr, Dublin, Ireland Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Dublin City University Gao, J (corresponding author), Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China.;Gao, J (corresponding author), Univ Sci & Technol China, Hefei, Anhui, Peoples R China. gaojin35@mail.ustc.edu.cn; mli@iim.ac.cn; alan.cl@163.com; geoveonedu@163.com National Natural Science Foundation of China [31771677, 61502445, 61572462] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work is supported by the National Natural Science Foundation of China under No. 31771677, 61502445, 61572462. 11 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-1962 2159-1970 978-1-5386-1981-0 INT CONF ASIAN LANG 2017.0 79 82 4 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ8KS 2023-03-23 WOS:000428370700019 0
C Qi, J; Hu, H; Wang, YN; Yang, CHH; Siniscalchi, SM; Lee, CH IEEE Qi, Jun; Hu, Hu; Wang, Yannan; Yang, Chao-Han Huck; Siniscalchi, Sabato Marco; Lee, Chin-Hui TENSOR-TO-VECTOR REGRESSION FOR MULTI-CHANNEL SPEECH ENHANCEMENT BASED ON TENSOR-TRAIN NETWORK 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing MAY 04-08, 2020 Barcelona, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Signal Proc Soc Tensor-Train network; speech enhancement; deep neural network; tensor-to-vector regression ALGORITHM We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recently emerged solution for compact representation of deep models with fully connected hidden layers. Thus TTN maintains DNN's expressive power yet involves a much smaller amount of trainable parameters. Furthermore, TTN can handle a multi-dimensional tensor input by design, which exactly matches the desired setting in multi-channel speech enhancement. We first provide a theoretical extension from DNN to TTN based regression. Next, we show that TTN can attain speech enhancement quality comparable with that for DNN but with much fewer parameters, e.g., a reduction from 27 million to only 5 million parameters is observed in a single-channel scenario. TTN also improves PESQ over DNN from 2.86 to 2.96 by slightly increasing the number of trainable parameters. Finally, in 8-channel conditions, a PESQ of 3.12 is achieved using 20 million parameters for TTN, whereas a DNN with 68 million parameters can only attain a PESQ of 3.06. [Qi, Jun; Hu, Hu; Yang, Chao-Han Huck; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Elect & Comp Engn, Atlanta, GA 30332 USA; [Siniscalchi, Sabato Marco] Univ Enna, Comp Engn Sch, Enna, Italy; [Wang, Yannan] Tencent Corp, Tencent Media Lab, Shenzhen, Guangdong, Peoples R China University System of Georgia; Georgia Institute of Technology; Universita Kore di ENNA; Tencent Qi, J (corresponding author), Georgia Inst Technol, Elect & Comp Engn, Atlanta, GA 30332 USA. 30 9 9 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-6631-5 INT CONF ACOUST SPEE 2020.0 7504 7508 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BQ7HU Green Submitted 2023-03-23 WOS:000615970407154 0
J Li, YY; Peng, YT; Luo, JQ; Cheng, YH; Veglianti, E Li, Yaya; Peng, Yongtao; Luo, Jianqiang; Cheng, Yihan; Veglianti, Eleonora Spatial-temporal variation characteristics and evolution of the global industrial robot trade: A complex network analysis PLOS ONE English Article WORLD-TRADE; FEATURES Industrial robots are a strategic future technology and an important part of the development of artificial intelligence, and they are a necessary means for the intelligent transformation of manufacturing industry. Based on global industrial robot trade data from 1998 to 2017, this paper applies the dynamic complex network analysis method to reveal the spatial and temporal variation characteristics and trade status evolution of the global industrial robot trade network. The results show that the global industrial robot network density has steadily increased, and the industrial robot trade has been characterized by 'diversification'. The number of major industrial robot exporters in the world is increasing, and the import market is increasingly diversified. The export market structure is relatively tight, the centrality of the global industrial robot trade network shows a downward trend, and the dissimilarity of the 'core-edge' clusters decreases year by year. The trade status of 'catch-up' countries represented by China has rapidly increased. However, Japan, Germany, and Italy are still in the central position of the industrial robot trade. Moreover, trade of the 'catch-up' countries' is dominated by imports, and exports of industrial robot products are insufficient. Finally, policy suggestions are provided according to the results. [Li, Yaya; Cheng, Yihan] Jiangsu Univ, Sch Finance & Econ, Zhenjiang, Jiangsu, Peoples R China; [Peng, Yongtao; Luo, Jianqiang] Jiangsu Univ, Sch Management, Zhenjiang, Jiangsu, Peoples R China; [Veglianti, Eleonora] Univ Uninettuno, Dept Econ, Rome, Italy Jiangsu University; Jiangsu University; UNINETTUNO Li, YY (corresponding author), Jiangsu Univ, Sch Finance & Econ, Zhenjiang, Jiangsu, Peoples R China. yizhi19881107@126.com Li, Yaya/GPC-6058-2022; Veglianti, Eleonora/GOE-4503-2022 Li, Yaya/0000-0002-1921-8478; Veglianti, Eleonora/0000-0002-9153-6745 National Natural Science Foundation of China [71704069, 71802099, 71772080]; China Postdoctoral Science Foundation [2018M642189]; Jiangsu Social Science Foundation [17GLC016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Jiangsu Social Science Foundation This work was supported from the National Natural Science Foundation of China, Grant No.71704069, China Postdoctoral Science Foundation, Grant No. 2018M642189 and Jiangsu Social Science Foundation, Grant No. 17GLC016 to YL. Yongtao Peng acknowledges support from the National Natural Science Foundation of China, Grant No.71802099. Jianqiang Luo acknowledges support from the National Natural Science Foundation of China, Grant No. 71772080. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 31 3 4 9 35 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One SEP 26 2019.0 14.0 9 e0222785 10.1371/journal.pone.0222785 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics KA7NQ 31557224.0 Green Published, gold 2023-03-23 WOS:000505985400001 0
J Liu, Q; Reed, M; Zhu, HY; Cheng, Y; Almeida, J; Fruhbeck, G; Ribeiro, R; Hu, PZ Liu, Qian; Reed, Madison; Zhu, Haiying; Cheng, Yan; Almeida, Joana; Fruhbeck, Gema; Ribeiro, Ricardo; Hu, Pingzhao Epigenome-wide DNA methylation and transcriptome profiling of localized and locally advanced prostate cancer: Uncovering new molecular markers GENOMICS English Article Localized prostate cancer; Locally advanced prostate cancer; DNA methylation; Transcriptome; Adipose tissue; Gene signature; Machine learning DISCOVERY; PROTEIN; PACKAGE Background: It has become increasingly important to identify molecular markers for accurately diagnosing prostate cancer (PCa) stages between localized PCa (LPC) and locally advanced PCa (LAPC). However, there is a lack of profiling both epigenome-wide DNA methylation and transcriptome for the same patients with PCa at different stages. This study aims to identify epitranscriptomic biomarkers screened in the periprostatic (PP) adipose tissue for predicting LPC and LAPC. Methods: We profiled gene expression and DNA methylation of 10 PCa patients' PP adipose tissue (4 LPC and 6 LAPC). Differential analysis was used to identify differentially methylated CpG sites and expressed genes. An integrative analysis of the microarray gene expression profiles and DNA methylation profiles was conducted using LASSO (least absolute shrinkage and selection operator) between each studied gene and the CpG sites in their promoter region. This epitranscriptomic signature was constructed by combining the association and differential analyses. The signature was then refined using the genetic mutation data of > 1500 primary PCa and metastasis PCa samples from 4 different studies. We determined genes that were the most significantly affected by mutations. Machine learning models were built to evaluate the classification ability of the identified signature using the gene expression profiles from three external cohorts. Results: From the LASSO-based association analysis, we identified 56 genes presenting significant anti-correlation between the expression level and the methylation level of at least one CpG site in the promoter region (p-value < 5 x 10(-8)). From the differential analysis, we detected 16,405 downregulated genes and 9485 genes containing at least one hypermethylated CpG site. We identified 30 genes that showed anti-correlation, down-regulation and hyper-methylation simultaneously. Using genetic mutation data, we determined that 6 of the 30 genes showed significant differences (adjusted p-value < 0.05) in mutation frequencies between the primary PCa and metastasis PCa samples. The identified 30 genes performed well in distinguishing PCa patients with metastasis from PCa patient without metastasis (area under the receiver operating characteristic curve (AUC) = 0.81). The gene signature also performed well in distinguishing PCa patients with high risk of progression from PCa patients with low risk of progression (AUC = 0.88). Conclusions: We established an integrative framework to identify differentially expressed genes with an aberrant methylation pattern on PP adipose tissue that may represent novel candidate molecular markers for distinguishing between LPC and LAPC. [Liu, Qian; Reed, Madison; Hu, Pingzhao] Univ Manitoba, Dept Biochem & Med Genet, Winnipeg, MB, Canada; [Zhu, Haiying; Hu, Pingzhao] Univ Toronto, Dalla Lana Sch Publ Hlth, Toronto, ON, Canada; [Cheng, Yan] Northwest Minzu Univ, Biomed Res Ctr, Key Lab Biotechnol & Bioengn State Ethn Affairs Co, Lanzhou 730030, Gansu, Peoples R China; [Cheng, Yan] Northwest Minzu Univ, Expt Ctr, Lanzhou 730030, Gansu, Peoples R China; [Cheng, Yan] Northwest Minzu Univ, Coll Med, Lanzhou 730030, Gansu, Peoples R China; [Almeida, Joana; Ribeiro, Ricardo] Univ Porto, Inst Invest & Inovacao Saude, Tumour & Microenvironm Interact Grp, i3S, Porto, Portugal; [Fruhbeck, Gema] Clin Univ Navarra, CIBERobn IdiSNA, Pamplona, Spain; [Ribeiro, Ricardo] Ctr Hosp Univ Porto, Dept Pathol, Porto, Portugal; [Ribeiro, Ricardo] S Martinho Hosp, Valongo, Portugal; [Hu, Pingzhao] CancerCare Manitoba Res Inst, CancerCare Manitoba, Winnipeg, MB, Canada; [Hu, Pingzhao] Univ Manitoba, Dept Biochem & Med Genet, Room 308 Basic Med Sci Bldg,745 Bannatyne Ave, Winnipeg, MB R3E 0J9, Canada University of Manitoba; University of Toronto; Northwest Minzu University; Northwest Minzu University; Northwest Minzu University; Universidade do Porto; i3S - Instituto de Investigacao e Inovacao em Saude, Universidade do Porto; CIBER - Centro de Investigacion Biomedica en Red; CIBEROBN; University of Navarra; CancerCare Manitoba Foundation; University of Manitoba Hu, PZ (corresponding author), Univ Manitoba, Dept Biochem & Med Genet, Room 308 Basic Med Sci Bldg,745 Bannatyne Ave, Winnipeg, MB R3E 0J9, Canada. pingzhao.hu@umanitoba.ca CancerCare Manitoba Foundation CancerCare Manitoba Foundation P.H. is the holder of Manitoba Medical Services Foundation (MMSF) Allen Rouse Basic Science Career Development Research Award. This research is partially funded by CancerCare Manitoba Foundation. 45 1 1 1 1 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0888-7543 1089-8646 GENOMICS Genomics SEP 2022.0 114.0 5 110474 10.1016/j.ygeno.2022.110474 0.0 SEP 2022 10 Biotechnology & Applied Microbiology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Genetics & Heredity 4V3JY 36057424.0 gold 2023-03-23 WOS:000859377900001 0
J Alsamhi, SH; Almalki, FA; Al-Dois, H; Ben Othman, S; Hassan, J; Hawbani, A; Sahal, R; Lee, B; Saleh, H Alsamhi, Saeed H.; Almalki, Faris A.; Al-Dois, Hatem; Ben Othman, Soufiene; Hassan, Jahan; Hawbani, Ammar; Sahal, Radyah; Lee, Brian; Saleh, Hager Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Review RESOURCE-MANAGEMENT; NEURAL-NETWORKS; RANDOM-ACCESS; INTERNET; IOT; ALGORITHMS; FUTURE; CLASSIFICATION; COMMUNICATION; INTELLIGENT The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works. [Alsamhi, Saeed H.; Lee, Brian] Athlone Inst Technol, Athlone, Ireland; [Alsamhi, Saeed H.] Ibb Univ, Ibb, Yemen; [Almalki, Faris A.] Taif Univ, Coll Comp & Informat Technol, Dept Comp Engn, POB 11099, At Taif 21944, Saudi Arabia; [Al-Dois, Hatem] Ibb Univ, Dept Elect Engn, Ibb, Yemen; [Ben Othman, Soufiene] Univ Sousse, Prince Lab Res, ISITCom, Hammam Sousse, Sousse, Tunisia; [Ben Othman, Soufiene] Sharda Univ, Tunisia & Sch Engn & Technol, Greater Noida, India; [Hassan, Jahan] Cent Queensland Univ, Sydney, NSW 2000, Australia; [Hawbani, Ammar] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Peoples R China; [Sahal, Radyah] Univ Coll Cork, Sch Comp Sci & Informat Technol, Cork, Ireland; [Saleh, Hager] South Valley Univ, Fac Comp & Artificial Intelligence, Hurghada, Egypt Technological University of the Shannon: Midlands Midwest; Taif University; Universite de Sousse; Sharda University; Central Queensland University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University College Cork; Egyptian Knowledge Bank (EKB); South Valley University Egypt Al-Dois, H (corresponding author), Ibb Univ, Dept Elect Engn, Ibb, Yemen. haldois@yahoo.com Hawbani, Ammar/S-3356-2019; Hassan, Jahan/AAB-4648-2022; Saleh, Hager/AES-9663-2022 Hawbani, Ammar/0000-0002-1069-3993; Saleh, Hager/0000-0001-6184-7107; Hager, Hager/0000-0002-7758-0811; Al-Dois, Hatem/0000-0001-6130-3716; Lee, Brian/0000-0002-8475-4074; Sahal, Radhya/0000-0002-8019-9069; Hassan, Dr Jahan/0000-0002-0939-2106; Alsamhi, Saeed/0000-0003-2857-6979 Science Foundation Ireland (SFI) [SFI/16/RC/3918]; Marie Sklodowska-Curie grant - European Regional Development Fund [847577]; Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia [TURSP-2020/265] Science Foundation Ireland (SFI)(Science Foundation Ireland); Marie Sklodowska-Curie grant - European Regional Development Fund; Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia This research has emanated from research supported by a research grant from Science Foundation Ireland (SFI) under grant no. SFI/16/RC/3918 (CONFIRM) and Marie Sklodowska-Curie grant agreement no. 847577 co-funded by the European Regional Development Fund. The authors are grateful to the Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia, for funding this project through Taif University Researchers Supporting Project (no. TURSP-2020/265). 227 17 17 4 19 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. SEP 20 2021.0 2021.0 6805151 10.1155/2021/6805151 0.0 23 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology WB1AV 34589123.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000703313000002 0
J Cai, XH; Chan, R; Morigi, S; Sgallari, F Cai, Xiaohao; Chan, Raymond; Morigi, Serena; Sgallari, Fiorella Vessel Segmentation in Medical Imaging Using a Tight-Frame-Based Algorithm SIAM JOURNAL ON IMAGING SCIENCES English Article tight-frame; wavelet transform; automatic image segmentation; medical imaging TUBULAR STRUCTURES; MRA IMAGES; WAVELET; TEXTURE; RESTORATION; EXTRACTION Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, and superresolution image restoration. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation such as model-based approaches, pattern recognition techniques, tracking-based approaches, and artificial intelligence-based approaches. In this paper, we propose applying the tight-frame approach to automatically identify tube-like structures in medical imaging, with the primary application of segmenting blood vessels in magnetic resonance angiography images. Our method iteratively refines a region that encloses the potential boundary of the vessels. At each iteration, we apply the tight-frame algorithm to denoise and smooth the potential boundary and sharpen the region. The cost per iteration is proportional to the number of pixels in the image. We prove that the iteration converges in a finite number of steps to a binary image whereby the segmentation of the vessels can be done straightforwardly. Numerical experiments on synthetic and real two-dimensional (2D) and three-dimensional (3D) images demonstrate that our method is more accurate when compared with some representative segmentation methods, and it usually converges within a few iterations. [Cai, Xiaohao; Chan, Raymond] Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong, Peoples R China; [Morigi, Serena; Sgallari, Fiorella] Univ Bologna, Dept Math CIRAM, Bologna, Italy Chinese University of Hong Kong; University of Bologna Cai, XH (corresponding author), Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong, Peoples R China. xhcai@math.cuhk.edu.hk; rchan@math.cuhk.edu.hk; serena.morigi@unibo.it; fiorella.sgallari@unibo.it Chan, Raymond/ABA-4510-2020 SGALLARI, Fiorella/0000-0002-9166-8879; , xiaohao/0000-0003-0924-2834 RGC [400510]; DAG [2060408] RGC(Hong Kong Research Grants Council); DAG This work is partially supported by RGC 400510 and DAG 2060408. 46 12 12 0 13 SIAM PUBLICATIONS PHILADELPHIA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA 1936-4954 SIAM J IMAGING SCI SIAM J. Imaging Sci. 2013.0 6.0 1 464 486 10.1137/110843472 0.0 23 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Mathematics, Applied; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics; Imaging Science & Photographic Technology 239NQ Green Submitted 2023-03-23 WOS:000326032900018 0
J Lv, Z; Mazurczyk, W; Wendzel, S; Song, H Lv, Z.; Mazurczyk, W.; Wendzel, S.; Song, H. Guest Editorial: Recent Advances in Cyber-Physical Security in Industrial Environments IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Editorial Material Informatics; Authentication; Cyber-physical systems; Cloud computing; Encryption; Big Data; Industrial Environment; Security; Cyber-Physical System Smart has gradually infiltrating all areas of people's daily life and the environments where we lead our life. The term of Smart Industrial Environment can be used to refer to each aspect of the industrial environments focused on the future, being smart vehicles, smart systems of transportation, smart devices (wearables and smartphones), smart services (such as just-in-time production pipelines adjusted to the requirements of the supply-chain), smart grids, smart factories and smart plants management utilizing information technology. It includes the inter-connection of all the smart technologies, involving every type of political and technological borders besides being a term that involves all the aspects. [Lv, Z.] Qingdao Univ, Qingdao 266071, Shandong, Peoples R China; [Mazurczyk, W.] Warsaw Univ Technol, PL-00661 Warsaw, Poland; [Wendzel, S.] Worms Univ Appl Sci, D-67549 Worms, Germany; [Song, H.] Embry Riddle Aeronaut Univ, Oklahoma City, OK 73135 USA Qingdao University; Warsaw University of Technology; Embry-Riddle Aeronautical University Lv, Z (corresponding author), Qingdao Univ, Qingdao 266071, Shandong, Peoples R China. Wendzel, Steffen/F-8906-2016; Lv, Zhihan/I-3187-2014; Song, Houbing/E-3628-2010; Wendzel, Steffen/AAN-3082-2021; Lv, Zhihan/GLR-6000-2022 Wendzel, Steffen/0000-0002-1913-5912; Lv, Zhihan/0000-0003-2525-3074; Song, Houbing/0000-0003-2631-9223; Wendzel, Steffen/0000-0002-1913-5912; Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China [61902203] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61902203. 14 3 3 1 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. DEC 2019.0 15.0 12 6468 6471 10.1109/TII.2019.2945971 0.0 4 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering JV3WE Bronze 2023-03-23 WOS:000502295800027 0
J Lu, YJ; Zheng, N; Ye, MT; Zhu, YH; Zhang, GD; Nazemi, E; He, J Lu, Yanjie; Zheng, Nan; Ye, Mingtao; Zhu, Yihao; Zhang, Guodao; Nazemi, Ehsan; He, Jie Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems DIAGNOSTICS English Article medical diagnostic radiology; air kerma; RBF neural network; radiology; X-ray beam FLOW REGIME; VOID FRACTION; NEURAL-NETWORKS; ATTENUATION; DENSITY The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV). [Lu, Yanjie; Ye, Mingtao; Zhang, Guodao] Hangzhou Dianzi Univ, Dept Digital Media Technol, Hangzhou 310018, Peoples R China; [Zheng, Nan] Wenzhou Med Univ, Coll Pharm, Wenzhou 325035, Peoples R China; [Zhu, Yihao] Zhejiang Ocean Univ, Sch Econ & Management, Zhoushan 316022, Peoples R China; [Nazemi, Ehsan] Univ Antwerp, Dept Phys, B-2610 Antwerp, Belgium; [He, Jie] First Peoples Hosp Fuyang Dist, Hangzhou 310000, Peoples R China Hangzhou Dianzi University; Wenzhou Medical University; Zhejiang Ocean University; University of Antwerp Zhang, GD (corresponding author), Hangzhou Dianzi Univ, Dept Digital Media Technol, Hangzhou 310018, Peoples R China.;Nazemi, E (corresponding author), Univ Antwerp, Dept Phys, B-2610 Antwerp, Belgium.;He, J (corresponding author), First Peoples Hosp Fuyang Dist, Hangzhou 310000, Peoples R China. guodaozhang@hdu.edu.cn; nazemi.ehsan@yahoo.com; hejie@wmuhospital.com nazemi, ehsan/I-7691-2019 nazemi, ehsan/0000-0001-5457-6943; Lu, Yanjie/0000-0001-7203-1445; Zhang, Guodao/0000-0002-6264-5854 Research Foundation of Hangzhou Dianzi University [KYS335622091, KYH333122029M] Research Foundation of Hangzhou Dianzi University The Research Foundation of Hangzhou Dianzi University (KYS335622091; KYH333122029M). 33 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-4418 DIAGNOSTICS Diagnostics JAN 2023.0 13.0 2 190 10.3390/diagnostics13020190 0.0 9 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine 7X8WZ 36673000.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000914476700001 0
J Wang, DY; Yuan, YA; Ben, YW; Luo, HY; Guo, HX Wang, Deyun; Yuan, Ying-An; Ben, Yawen; Luo, Hongyuan; Guo, Haixiang Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH English Article Municipal solid waste; Forecasting; Long short-term memory; Improved particle swarm optimization; Improved GM (1; 1); Scenario analysis TIME-SERIES ANALYSIS; GM(1,1) MODEL; PREDICTION; QUANTITY; OUTPUT; PSO Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing. [Wang, Deyun; Yuan, Ying-An; Ben, Yawen; Luo, Hongyuan; Guo, Haixiang] China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China; [Luo, Hongyuan] Univ Bourgogne Franche Comte, Nanomed Lab, UTBM, F-90010 Belfort, France China University of Geosciences; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Wang, DY (corresponding author), China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China. wang.deyun@hotmail.com Yuan, Ying-an/HKF-4253-2023 Yuan, Ying-an/0000-0002-3433-2607 Hubei Provincial Natural Science Foundation of China [2020CFB822]; National Social Science Foundation of China [21AZD074]; National Natural Science Foundation of China [71874165, 72074198]; National College Students' innovation and entrepreneurship training program [202110491031] Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Social Science Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National College Students' innovation and entrepreneurship training program This work was supported by Hubei Provincial Natural Science Foundation of China (Grant No. 2020CFB822), National Social Science Foundation of China (Grant No. 21AZD074), National Natural Science Foundation of China (Grant Nos.71874165, 72074198), National College Students' innovation and entrepreneurship training program (Grant No. 202110491031). 43 1 1 6 16 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0944-1344 1614-7499 ENVIRON SCI POLLUT R Environ. Sci. Pollut. Res. OCT 2022.0 29.0 46 69472 69490 10.1007/s11356-022-20438-0 0.0 MAY 2022 19 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 4W3GS 35567684.0 Green Accepted, Bronze 2023-03-23 WOS:000795608600001 0
J Wong, KL; Chou, KS; Tse, R; Tang, SK; Pau, G Wong, Kei Long; Chou, Ka Seng; Tse, Rita; Tang, Su-Kit; Pau, Giovanni A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation ELECTRONICS English Article synthetic data; lithium-ion battery; battery data; generative adversarial network; state-of-charge estimation; machine learning The recent success of machine learning has accelerated the development of data-driven lithium-ion battery state estimation and prediction. The lack of accessible battery operation data is one of the primary concerns with the data-driven approach. However, research on battery operation data augmentation is rare. When coping with data sparsity, one popular approach is to augment the dataset by producing synthetic data. In this paper, we propose a novel fusion method for synthetic battery operation data generation. It combines a generative, adversarial, network-based generation module and a state-of-charge estimator. The generation module generates battery operation features, namely the voltage, current, and temperature. The features are then fed into the state-of-charge estimator, which calculates the relevant state of charge. The results of the evaluation reveal that our method can produce synthetic data with distributions similar to the actual dataset and performs well in downstream tasks. [Wong, Kei Long; Chou, Ka Seng; Tse, Rita; Tang, Su-Kit] Macao Polytech Univ, Fac Appl Sci, Macau 999078, Peoples R China; [Chou, Ka Seng; Pau, Giovanni] Univ Bologna, Dept Comp Sci & Engn, I-40126 Bologna, Italy; [Pau, Giovanni] Technol Innovat Inst TII, Autonomous Robot Res Ctr, POB 9639, Abu Dhabi, U Arab Emirates; [Pau, Giovanni] Univ Calif Los Angeles, Samueli Comp Sci Dept, Los Angeles, CA 90095 USA University of Bologna; Technology Innovation Institute; University of California System; University of California Los Angeles Wong, KL (corresponding author), Macao Polytech Univ, Fac Appl Sci, Macau 999078, Peoples R China. keilong.wong@mpu.edu.mo Tang, Su-Kit/0000-0001-8104-7887 Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities [RP/ESCA-01/2020]; H2020 project titled European Bus Rapid Transit of 2030: Electrified, Automated, Connected, EBRT-Grant [101095882] Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities; H2020 project titled European Bus Rapid Transit of 2030: Electrified, Automated, Connected, EBRT-Grant This work was supported in part by the Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities (RP/ESCA-01/2020) and by the H2020 project titled European Bus Rapid Transit of 2030: Electrified, Automated, Connected, EBRT-Grant Agreement No. 101095882. 44 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics FEB 2023.0 12.0 3 657 10.3390/electronics12030657 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics 9A0YO Green Published, gold 2023-03-23 WOS:000933792700001 0
J Dar, MS; Akram, KB; Sohail, A; Arif, F; Zabihi, F; Yang, SY; Munir, S; Zhu, MF; Abid, M; Nauman, M Dar, M. S.; Akram, Khush Bakhat; Sohail, Ayesha; Arif, Fatima; Zabihi, Fatemeh; Yang, Shengyuan; Munir, Shamsa; Zhu, Meifang; Abid, M.; Nauman, Muhammad Heat induction in two-dimensional graphene-Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling RSC ADVANCES English Article MAGNETOTHERMAL PROPERTIES; NANOPARTICLES; OXIDE; CONDUCTIVITY; FERROFLUID; REDUCTION; THERAPY We report the synthesis and characterization of graphene functionalized with iron (Fe3+) oxide (G-Fe3O4) nanohybrids for radio-frequency magnetic hyperthermia application. We adopted the wet chemical procedure, using various contents of Fe3O4 (magnetite) from 0-100% for making two-dimensional graphene-Fe3O4 nanohybrids. The homogeneous dispersal of Fe3O4 nanoparticles decorated on the graphene surface combined with their biocompatibility and high thermal conductivity make them an excellent material for magnetic hyperthermia. The morphological and magnetic properties of the nanohybrids were studied using scanning electron microscopy (SEM) and a vibrating sample magnetometer (VSM), respectively. The smart magnetic platforms were exposed to an alternating current (AC) magnetic field of 633 kHz and of strength 9.1 mT for studying their hyperthermic performance. The localized antitumor effects were investigated with artificial neural network modeling. A neural net time-series model was developed for the assessment of the best nanohybrid composition to serve the purpose with an accuracy close to 100%. Six Nonlinear Autoregressive with External Input (NARX) models were obtained, one for each of the components. The assessment of the accuracy of the predicted results has been done on the basis of Mean Squared Error (MSE). The highest Mean Squared Error value was obtained for the nanohybrid containing 45% magnetite and 55% graphene (F(45)G(55)) in the training phase i.e., 0.44703, which is where the model achieved optimal results after 71 epochs. The F(45)G(55) nanohybrid was found to be the best for hyperthermia applications in low dosage with the highest specific absorption rate (SAR) and mean squared error values. [Dar, M. S.; Zabihi, Fatemeh; Yang, Shengyuan; Zhu, Meifang] Donghua Univ, Coll Mat Sci & Engn, Int Joint Lab Adv Fiber & Low Dimens Mat, State Key Lab Modicat Chem Fibers & Polymer Mat, Shanghai 201620, Peoples R China; [Dar, M. S.] Int Islamic Univ, Ctr Adv Elect & Photovolta Engn CAEPE, Islamabad, Pakistan; [Akram, Khush Bakhat; Munir, Shamsa] Natl Univ Technol NUTECH, Sch Appl Sci & Humanities, Main IJP Rd,Sect I-12, Islamabad, Pakistan; [Sohail, Ayesha; Arif, Fatima] COMSATS Univ Islamabad, Dept Math, Lahore Campus, Lahore 54000, Pakistan; [Abid, M.] COMSATS Univ Islamabad, Dept Mech Engn, Wah Campus,GT Rd, Wah Cantt, Pakistan; [Nauman, Muhammad] Inst Sci & Technol IST, Microscale Lab, Thermodynam Quantum Mat, Klosterneuburg, Austria Donghua University; International Islamic University, Pakistan; COMSATS University Islamabad (CUI); COMSATS University Islamabad (CUI); Institute of Science & Technology - Austria Dar, MS; Zhu, MF (corresponding author), Donghua Univ, Coll Mat Sci & Engn, Int Joint Lab Adv Fiber & Low Dimens Mat, State Key Lab Modicat Chem Fibers & Polymer Mat, Shanghai 201620, Peoples R China.;Dar, MS (corresponding author), Int Islamic Univ, Ctr Adv Elect & Photovolta Engn CAEPE, Islamabad, Pakistan. shamraiz@mail.dhu.edu.cn; zmf@dhu.edu.cn Dar, M. S./AFD-7580-2022; Nauman, Muhammad/AAG-9189-2019; Sohail, Ayesha/AAP-8462-2021 Dar, M. S./0000-0002-9782-6444; Nauman, Muhammad/0000-0002-2111-4846; Sohail, Ayesha/0000-0001-6835-6212 Higher Education Commission (HEC) Pakistan under start-up research grant program (SRGP) [2454] Higher Education Commission (HEC) Pakistan under start-up research grant program (SRGP) The research is funded by Higher Education Commission (HEC) Pakistan under start-up research grant program (SRGP) Project no. 2454. 54 3 3 3 14 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2046-2069 RSC ADV RSC Adv. JUN 30 2021.0 11.0 35 21702 21715 10.1039/d1ra03428f 0.0 14 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry SY1HC 35478795.0 Green Accepted, gold 2023-03-23 WOS:000665644000048 0
C Li, X; Ding, ML; Pizurica, A IEEE Li, Xian; Ding, Mingli; Pizurica, Aleksandra GROUP CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) IEEE International Conference on Image Processing ICIP English Proceedings Paper 26th IEEE International Conference on Image Processing (ICIP) SEP 22-25, 2019 Taipei, TAIWAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Group convolutional neural networks; multi-scale spectral feature extraction; hyperspectral image SPECTRAL-SPATIAL CLASSIFICATION Convolutional Neural Network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. The CNN model overfitting is a common issue in this domain due to limited amount of labelled training samples. In addition, making the full use of spectral information is still considered an open problem. In this paper, we propose a novel group 2D-CNN model for spectral-spatial classification. Specifically, we propose an original multi-scale spectral feature extraction approach based on a novel concept of multi-kernel depthwise convolution. Furthermore, we exploit for the first time shuffle operation on the group convolutions in HSI spectral-spatial feature extraction to effectively limit the amount of learning parameters. As a result, we design a small and efficient network for HSI classification. Experimental results on real data demonstrate favourable performance compared to the current state-of-the-art. [Li, Xian; Ding, Mingli] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin, Peoples R China; [Li, Xian; Pizurica, Aleksandra] Ghent Univ Imec, Dept Telecommun & Informat Proc, Ghent, Belgium Harbin Institute of Technology; Ghent University; IMEC Li, X (corresponding author), Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin, Peoples R China.;Li, X (corresponding author), Ghent Univ Imec, Dept Telecommun & Informat Proc, Ghent, Belgium. Pizurica, Aleksandra/AAG-4687-2021; Li, Xian/AAY-7826-2021; Pizurica, Aleksandra/K-6866-2015 Li, Xian/0000-0001-5714-3940; Pizurica, Aleksandra/0000-0002-9322-4999 China Scholarship Council; Research Foundation Flanders (FWO) [G.OA26.17N.] China Scholarship Council(China Scholarship Council); Research Foundation Flanders (FWO)(FWO) This work was supported by the China Scholarship Council and the Research Foundation Flanders (FWO) under the grant G.OA26.17N. 24 5 5 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1522-4880 978-1-5386-6249-6 IEEE IMAGE PROC 2019.0 639 643 5 Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Imaging Science & Photographic Technology BO6RM 2023-03-23 WOS:000521828600127 0
J Wang, DY; Liu, YL; Wu, Z; Fu, HX; Shi, Y; Guo, HX Wang, Deyun; Liu, Yanling; Wu, Zeng; Fu, Hongxue; Shi, Yong; Guo, Haixiang Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm ENERGIES English Article natural gas consumption forecasting; particle swarm optimization; wavelet neural networks; scenario analysis FORECASTING ENERGY-CONSUMPTION; DECOMPOSITION TECHNIQUE; MODE DECOMPOSITION; HYBRID MODEL; DEMAND; PREDICTION; MULTISTEP; MARKET; ARIMA; SVM Natural gas consumption has increased with an average annual growth rate of about 10% between 2012 and 2017. Total natural gas consumption accounted for 6.4% of consumed primary energy resources in 2016, up from 5.4% in 2012, making China the world's third-largest gas user. Therefore, accurately predicting natural gas consumption has become very important for market participants to organize indigenous production, foreign supply contracts and infrastructures in a better way. This paper first presents the main factors affecting China's natural gas consumption, and then proposes a hybrid forecasting model by combining the particle swarm optimization algorithm and wavelet neural network (PSO-WNN). In PSO-WNN model, the initial weights and wavelet parameters are optimized using PSO algorithm and updated through a dynamic learning rate to improve the training speed, forecasting precision and reduce fluctuation of WNN. The experimental results show the superiority of the proposed model compared with ANN and WNN based models. Then, this study conducts the scenario analysis of the natural gas consumption from 2017 to 2025 in China based on three scenarios, namely low scenario, reference scenario and high scenario, and the results illustrate that the China's natural gas consumption is going to be 342.70, 358.27, 366.42 million tce (standard tons coal equivalent) in 2020, and 407.01, 437.95, 461.38 million tce in 2025 under the low, reference and high scenarios, respectively. Finally, this paper provides some policy suggestions on natural gas exploration and development, infrastructure construction and technical innovations to promote a sustainable development of China's natural gas industry. [Wang, Deyun; Liu, Yanling; Wu, Zeng; Fu, Hongxue; Guo, Haixiang] China Univ Geosci, Sch Econ & Management, Wuhan 430074, Hubei, Peoples R China; [Wang, Deyun; Guo, Haixiang] China Univ Geosci, Mineral Resource Strategy & Policy Res Ctr, Wuhan 430074, Hubei, Peoples R China; [Wang, Deyun; Guo, Haixiang] Minist Land & Resources, Key Lab Land & Resources Strateg Studies, Wuhan 430074, Hubei, Peoples R China; [Wang, Deyun; Shi, Yong] UTBM, Univ Bourgogne Franche Comte, IRTES, Rue Thierry Mieg, F-90010 Belfort, France China University of Geosciences; China University of Geosciences; Ministry of Natural Resources of the People's Republic of China; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Wang, DY (corresponding author), China Univ Geosci, Sch Econ & Management, Wuhan 430074, Hubei, Peoples R China.;Wang, DY (corresponding author), China Univ Geosci, Mineral Resource Strategy & Policy Res Ctr, Wuhan 430074, Hubei, Peoples R China.;Wang, DY (corresponding author), Minist Land & Resources, Key Lab Land & Resources Strateg Studies, Wuhan 430074, Hubei, Peoples R China.;Wang, DY (corresponding author), UTBM, Univ Bourgogne Franche Comte, IRTES, Rue Thierry Mieg, F-90010 Belfort, France. wangdy@cug.edu.cn; yanlingliu@cug.edu.cn; wuzeng@cug.edu.cn; cherish@cug.edu.cn; yong.shi@utbm.fr; ghx@cug.edu.cn National Natural Science Foundation, China [71301153]; National Social Science foundation, China [13AZD078]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry National Natural Science Foundation, China(National Natural Science Foundation of China (NSFC)); National Social Science foundation, China; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry(Scientific Research Foundation for the Returned Overseas Chinese Scholars) The authors would like to acknowledge that this paper was supported by the National Natural Science Foundation, China (No. 71301153); the National Social Science foundation, China (No. 13AZD078); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. 30 16 17 2 24 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies APR 2018.0 11.0 4 825 10.3390/en11040825 0.0 16 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels GI7SV Green Published, Green Submitted, gold 2023-03-23 WOS:000434703400136 0
C Bifet, A; Zhang, JJ; Fan, W; He, C; Zhang, JF; Qian, JF; Holmes, G; Pfahringer, B ACM Bifet, Albert; Zhang, Jiajin; Fan, Wei; He, Cheng; Zhang, Jianfeng; Qian, Jianfeng; Holmes, Geoff; Pfahringer, Bernhard Extremely Fast Decision Tree Mining for Evolving Data Streams KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING English Proceedings Paper 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) AUG 13-17, 2017 Halifax, CANADA Assoc Comp Machinery,Assoc Comp Machinery SIGMOD,Assoc Comp Machinery SIGKDD Data Streams; Online Learning; Decision Trees; Classification Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources. [Bifet, Albert] Univ Paris Saclay, Telecom ParisTech, LTCI, F-75013 Paris, France; [Zhang, Jiajin; He, Cheng; Zhang, Jianfeng] HUAWEI Noahs Ark Lab, Hong Kong, Peoples R China; [Fan, Wei] Baidu Res Big Data Lab, Sunnyvale, CA USA; [Qian, Jianfeng] Columbia Univ, New York, NY USA; [Holmes, Geoff; Pfahringer, Bernhard] Univ Waikato, Hamilton, New Zealand IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Huawei Technologies; Baidu; Columbia University; University of Waikato Bifet, A (corresponding author), Univ Paris Saclay, Telecom ParisTech, LTCI, F-75013 Paris, France. Snow, Val O/O-8693-2016; Bifet, Albert/E-4984-2017 Snow, Val O/0000-0002-6911-8184; Bifet, Albert/0000-0002-8339-7773 24 39 41 0 4 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-4887-4 2017.0 1733 1742 10.1145/3097983.3098139 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL7XZ Green Published 2023-03-23 WOS:000455787300186 0
C Wang, X; Liu, XF; Chen, YX; Zhong, XY; Cheng, P Hartmann, S; Kung, J; Kotsis, G; Tjoa, AM; Khalil, I Wang, Xin; Liu, Xiufeng; Chen, Yuxiang; Zhong, Xueyan; Cheng, Ping View Selection for Graph Pattern Matching DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2020, PT II Lecture Notes in Computer Science English Proceedings Paper 31st International Conference on Database and Expert Systems Applications (DEXA) SEP 14-17, 2020 Comenius Univ Bratislava, ELECTR NETWORK Software Competence Ctr Hagenberg,JVU, Inst Telecooperaat,Informat Integrat & Web Based Applicat & Serv Comenius Univ Bratislava View-based techniques have been investigated on relational data, XML and graphs and proven effective for querying big data. While the pivot of using materialized views for query answering is view selection. Though explored for several years, the view selection problem for graph pattern matching has not been investigated. To this end, we investigate the problem in this paper. We first formalize the problem and show its intractability feature. We next propose an appropriate cost model and develop an effective algorithm to identify a set of view definitions under resource constraints, e.g., space storage and query processing cost, from a query workload. We finally verify the performance of the algorithms, and show that our view selection algorithm can identify a set of views that not only have low cost, e.g., storage space (below specified budget) and query processing cost but also can answer queries efficiently. [Wang, Xin; Chen, Yuxiang; Zhong, Xueyan] Southwest Petr Univ, Chengdu, Peoples R China; [Liu, Xiufeng] Tech Univ Denmark, Lyngby, Denmark; [Cheng, Ping] Sichuan ChangHong Elect Co Ltd, Mianyang, Sichuan, Peoples R China Southwest Petroleum University; Technical University of Denmark; Changhong Wang, X (corresponding author), Southwest Petr Univ, Chengdu, Peoples R China. xinwang.ed@gmail.com; xiuli@dtu.dk; cyx@swpu.edu.cn; zhongxueyan@sohu.com; ping.cheng@changhong.com Tomm, He/E-5457-2013 Liu, Xiufeng/0000-0001-5133-6688 32 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-59051-2; 978-3-030-59050-5 LECT NOTES COMPUT SC 2020.0 12392.0 93 110 10.1007/978-3-030-59051-2_7 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS3XM 2023-03-23 WOS:000716716900007 0
C Jammal, S; Tillo, T; Xiao, JM IEEE Jammal, Samer; Tillo, Tammam; Xiao, Jimin Multi-Resolution for Disparity Estimation with Convolutional Neural Networks 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017) Asia-Pacific Signal and Information Processing Association Annual Summit and Conference English Proceedings Paper 9th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC) DEC 12-15, 2017 Kuala Lumpur, MALAYSIA Disparity Estimation; Convolutional Neural Networks; Multi-Resolution; Up/down-sampling; Stereo Vision; Depth Estimation VISION Disparity estimation is the process of obtaining the depth information from the left and right views of a particular scene. A recent work based on convolutional neural network (CNN) has achieved state-of-the-art performance for the disparity estimation task. However, this network has some limitations for measuring small and large disparities, which compromises the accuracy of the obtained results. In this paper, a multi-resolution framework with a three-phase strategy to generate high quality disparity maps is proposed, which handles both small and large displacements and retains the details of the scene. The first phase up/down-samples the images to several different resolutions to improve the matching process between CNN feature maps where scaled information is obtained for objects with various sizes and distances. The second phase uses a deep CNN to estimate the disparity maps using the resampled versions, and each version is suitable for a specific range of disparities. Finally, the best fitting disparity map is adaptively selected. To the best of our knowledge, our framework is the first to exploit multiple resolutions of the stereo pair with convolutional neural network for disparity estimation. Significant performance gain is achieved with this proposed method, the mean absolute error is reduced to 3.40 from 5.66, the DispNetC performance for the Sintel dataset. [Jammal, Samer; Xiao, Jimin] Univ Liverpool, Liverpool, Merseyside, England; [Tillo, Tammam] Libera Univ Bolzano Hozen, Bolzano, Italy; Xian Jiaotong Liverpool Univ, Xian, Shaanxi, Peoples R China University of Liverpool; Free University of Bozen-Bolzano; Xi'an Jiaotong-Liverpool University Jammal, S (corresponding author), Univ Liverpool, Liverpool, Merseyside, England. Samer.Jammal@liverpool.ac.uk; Tammam.Tillo@unibz.it; Jimin.Xiao@xjtlu.edu.cn National Natural Science Foundation of China [61501379, 61210006]; Jiangsu Science and Technology Programme [BK20150375] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Science and Technology Programme This work was supported by the National Natural Science Foundation of China (NO. 61501379 and NO. 61210006) and the Jiangsu Science and Technology Programme (BK20150375). 25 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2309-9402 978-1-5386-1542-3 ASIAPAC SIGN INFO PR 2017.0 1756 1761 6 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ5GG 2023-03-23 WOS:000425879400321 0
J Liu, L; Fieguth, P; Guo, YL; Wang, XG; Pietikainen, M Liu, Li; Fieguth, Paul; Guo, Yulan; Wang, Xiaogang; Pietikainen, Matti Local binary features for texture classification: Taxonomy and experimental study PATTERN RECOGNITION English Article Texture classification; Local Binary Pattern; Rotation invariance; Noise robustness; Deep learning FACE RECOGNITION; PATTERNS; DESCRIPTOR; REPRESENTATION; COOCCURRENCE; FRAMEWORK; HISTOGRAM; SCALE Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered. (C) 2016 Elsevier Ltd. All rights reserved. [Liu, Li] Natl Univ Def Technol, Coll Informat Syst & Management, 109 Deya Rd, Changsha 410073, Hunan, Peoples R China; [Fieguth, Paul] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada; [Guo, Yulan] Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China; [Wang, Xiaogang] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China; [Pietikainen, Matti] Univ Oulu, Ctr Machine Vis Res, Oulu 90014, Finland National University of Defense Technology - China; University of Waterloo; National University of Defense Technology - China; Chinese University of Hong Kong; University of Oulu Liu, L (corresponding author), Natl Univ Def Technol, Coll Informat Syst & Management, 109 Deya Rd, Changsha 410073, Hunan, Peoples R China. liuli_nudt@nudt.edu.cn; pfieguth@uwaterloo.ca; yulan.guo@nudt.edu.cn; xgwang@ee.cuhk.edu.hk; matti.pietikainen@ee.oulu.fi Zhou, Pei/0000-0002-3885-6512 National Natural Science Foundation of China [61202336, 61602499, 61601481]; Open Project Program of the National Laboratory of Pattern Recognition [201407354] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Project Program of the National Laboratory of Pattern Recognition This work has been supported by the National Natural Science Foundation of China under contract numbers 61202336, 61602499 and 61601481 and by the Open Project Program of the National Laboratory of Pattern Recognition (201407354). 134 225 238 7 209 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. FEB 2017.0 62.0 135 160 10.1016/j.patcog.2016.08.032 0.0 26 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering EB5PS Green Accepted 2023-03-23 WOS:000387429100011 0
C Dasoulas, G; Dos Santos, L; Scaman, K; Virmaux, A Bessiere, C Dasoulas, George; Dos Santos, Ludovic; Scaman, Kevin; Virmaux, Aladin Coloring Graph Neural Networks for Node Disambiguation PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE English Proceedings Paper 29th International Joint Conference on Artificial Intelligence JAN 07-15, 2021 ELECTR NETWORK Int Joint Conf Artifical Intelligence ISOMORPHISM In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes 1. Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets. [Dasoulas, George; Dos Santos, Ludovic; Scaman, Kevin; Virmaux, Aladin] Huawei Noahs Ark Lab, Hong Kong, Peoples R China; [Dasoulas, George] Ecole Polytech, Paris, France Huawei Technologies Dasoulas, G (corresponding author), Huawei Noahs Ark Lab, Hong Kong, Peoples R China.;Dasoulas, G (corresponding author), Ecole Polytech, Paris, France. george.dasoulas1@huawei.com; ludovic.dos.santos@huawei.com; kevin.scaman@huawei.com; aladin.virmaux@huawei.com 42 9 9 0 0 IJCAI-INT JOINT CONF ARTIF INTELL FREIBURG ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY 978-0-9992411-6-5 2020.0 2126 2132 7 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS7MY 2023-03-23 WOS:000764196702035 0
C Luo, C; He, F; Ghezzi, C Li, X; Liu, Z; Yi, W Luo, Chen; He, Fei; Ghezzi, Carlo Inferring Software Behavioral Models with MapReduce DEPENDABLE SOFTWARE ENGINEERING: THEORIES, TOOLS, AND APPLICATIONS, SETTA 2015 Lecture Notes in Computer Science English Proceedings Paper 1st International Symposium on Dependable Software Engineering - Theories, Tools and Applications (SETTA) NOV 04-06, 2015 Nanjing, PEOPLES R CHINA Model inference; Parametric trace; Log analysis; MapReduce Software systems are often built without developing any explicit model and therefore research has been focusing on automatic inference of models by applying machine learning to execution logs. However, the logs generated by a real software system may be very large and the inference algorithm can exceed the capacity of a single computer. This paper focuses on inference of behavioral models and explores to use of MapReduce to deal with large logs. The approach consists of two distributed algorithms that perform trace slicing and model synthesis. For each job, a distributed algorithm using MapReduce is developed. With the parallel data processing capacity of MapReduce, the problem of inferring behavioral models from large logs can be efficiently solved. The technique is implemented on top of Hadoop. Experiments on Amazon clusters show efficiency and scalability of our approach. [Luo, Chen; He, Fei] Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing, Peoples R China; [Luo, Chen; He, Fei] Minist Educ, Key Lab Informat Syst Secur, Beijing, Peoples R China; [Luo, Chen; He, Fei] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China; [Ghezzi, Carlo] Politecn Milan, I-20133 Milan, Italy Tsinghua University; Tsinghua University; Polytechnic University of Milan He, F (corresponding author), Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing, Peoples R China. luoc13@mails.tsinghua.edu.cn; hefei@tsinghua.edu.cn; carlo.ghezzi@polimi.it 18 3 3 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-25942-0; 978-3-319-25941-3 LECT NOTES COMPUT SC 2015.0 9409.0 135 149 10.1007/978-3-319-25942-0_9 0.0 15 Computer Science, Software Engineering; Logic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Science & Technology - Other Topics BE6GH 2023-03-23 WOS:000374099200009 0
J Luo, SH; Lewis, FL; Song, YD; Garrappa, R Luo, Shaohua; Lewis, Frank L.; Song, Yongduan; Garrappa, Roberto Dynamical analysis and accelerated optimal stabilization of the fractional-order self-sustained electromechanical seismograph system with fuzzy wavelet neural network NONLINEAR DYNAMICS English Article FO self-sustained electromechanical seismograph system; Dynamical analysis; Accelerated optimal stabilization; Zero sum differential game; FWNN TRACKING CONTROL; BACKSTEPPING CONTROL; TIME-SYSTEMS; SYNCHRONIZATION; DESIGN This paper investigates dynamical analysis and accelerated optimal stabilization issues of the fractional-order (FO) self-sustained electromechanical seismograph system under energy mechanism. The FO equation governing this system with gyroscopic coupling is established. Its dynamical analysis, based on phase diagrams and Lyapunov exponents, shows that chaotic and periodic behaviors strongly depend on physical parameters and on the values of the FOs. In accelerated feedforward controller, a shaping behavior function (SBF) is used to accelerate tracking error convergence at controllable rate and time, a fuzzy wavelet neural network (FWNN) with transformation is employed to approximate unknown functions of system, and a tracking differentiator is set to solve issue from complexities of SBF and FO under the framework of FO backstepping. In optimal feedback controller, an adaptive dynamic programming strategy is proposed to deal with a zero sum differential game solution problem, wherein the FWNN is employed to approximate the solution of the constrained Hamilton-Jacobi-Isaacs equation online. Furthermore, it is testified that all signals of the closed-loop system are bounded by using barrier Lyapunov function and the constrained conditions are not violated along with the cost function being minimized. Numerical simulation proves the effectiveness and advantages of the proposed scheme. [Luo, Shaohua] Guizhou Univ, State Key Lab Publ Big Date, Guiyang 550025, Peoples R China; [Luo, Shaohua; Lewis, Frank L.] Univ Texas Arlington, UTA Res Inst, Arlington, TX 76118 USA; [Song, Yongduan] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China; [Garrappa, Roberto] Univ Bari, Dept Math, I-70126 Bari, Italy Guizhou University; University of Texas System; University of Texas Arlington; Chongqing University; Universita degli Studi di Bari Aldo Moro Song, YD (corresponding author), Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China. ydsong@cqu.edu.cn Garrappa, Roberto/O-4807-2018 Garrappa, Roberto/0000-0002-7881-0885 National Natural Science Foundation of China [52065008, 61860206008, 61773081]; Science and Technology Planning Project of Guizhou Province [[2020]1Y274]; Open Research Fund of Education Department of Guizhou Province [KYzhi[2019]041] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Planning Project of Guizhou Province; Open Research Fund of Education Department of Guizhou Province This project is supported by National Natural Science Foundation of China (Grant Nos. 52065008, 61860206008 and 61773081), Science and Technology Planning Project of Guizhou Province (No. [2020]1Y274) and Open Research Fund of Education Department of Guizhou Province (No. KYzhi[2019]041). 43 9 9 8 48 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. APR 2021.0 104.0 2 1389 1404 10.1007/s11071-021-06330-5 0.0 APR 2021 16 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics RW8HR 2023-03-23 WOS:000637667500001 0
J Ding, L; Lin, D; Lin, SF; Zhang, J; Cui, XJ; Wang, YB; Tang, H; Bruzzone, L Ding, Lei; Lin, Dong; Lin, Shaofu; Zhang, Jing; Cui, Xiaojie; Wang, Yuebin; Tang, Hao; Bruzzone, Lorenzo Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Transformers; Semantics; Image segmentation; Feature extraction; Task analysis; Convolutional neural networks; Context modeling; Convolutional neural network; remote sensing; semantic segmentation; vision transformer (ViT) Long-range contextual information is crucial for the semantic segmentation of high-resolution (HR) remote sensing images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a wide-context network (WiCoNet) for the semantic segmentation of HR RSIs. Apart from extracting local features with a conventional convolutional neural network (CNN), the WiCoNet has an extra context branch to aggregate information from a larger image area. Moreover, we introduce a context transformer to embed contextual information from the context branch and selectively project it onto the local features. The context transformer extends the vision transformer, an emerging kind of neural networks, to model the dual-branch semantic correlations. It overcomes the locality limitation of CNNs and enables the WiCoNet to see the bigger picture before segmenting the land-cover/land-use (LCLU) classes. Ablation studies and comparative experiments conducted on several benchmark datasets demonstrate the effectiveness of the proposed method. In addition, we present a new Beijing Land-Use (BLU) dataset. This is a large-scale HR satellite dataset with high-quality and fine-grained reference labels, which can facilitate future studies in this field. [Ding, Lei] PLA Strateg Force Informat Engn Univ, Zhengzhou 450001, Peoples R China; [Lin, Dong] Space Engn Univ, Beijing 102249, Peoples R China; [Lin, Dong] Xian Inst Surveying & Mapping, State Key Lab Geoinformat Engn, Xian 710054, Peoples R China; [Lin, Shaofu] Beijing Univ Technol, Fac Informat Technol, Beijing 100022, Peoples R China; [Zhang, Jing; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy; [Cui, Xiaojie] Beijing Inst Remote Sensing Informat, Beijing 100011, Peoples R China; [Wang, Yuebin] China Univ Geosci Beijing, Sch Land Sci & Technol, Beijing 100084, Peoples R China; [Tang, Hao] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, CH-8092 Zurich, Switzerland Aerospace Engineering University; Beijing University of Technology; University of Trento; China University of Geosciences; Swiss Federal Institutes of Technology Domain; ETH Zurich Lin, D (corresponding author), Space Engn Univ, Beijing 102249, Peoples R China. dinglei14@outlook.com; lindong_hb59@163.com; linshaofu@bjut.edu.cn; jing.zhang-1@unitn.it; cuixjgis@163.com; xxgcdxwyb@163.com; hao.tang@vision.ee.ethz.ch; lorenzo.biuzzone@unitn.it Ding, Lei/AAK-2573-2021; Bruzzone, Lorenzo/A-2076-2012 Ding, Lei/0000-0003-0653-8373; Bruzzone, Lorenzo/0000-0002-6036-459X; Lin, Shaofu/0000-0002-3653-6531; Wang, Yuebin/0000-0002-6978-4558; Tang, Hao/0000-0002-2077-1246 State Key Laboratory of Geo-Information Engineering [SKLGIE2019-Z-3-3]; China Scholarship Council [201703170123] State Key Laboratory of Geo-Information Engineering; China Scholarship Council(China Scholarship Council) This work was supported in part by the State Key Laboratory of Geo-Information Engineering under Grant SKLGIE2019-Z-3-3 and in part by the China Scholarship Council under Grant 201703170123. 49 15 15 21 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 4410313 10.1109/TGRS.2022.3168697 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 0Z1MU Green Submitted 2023-03-23 WOS:000790844500003 0
J Khalid, B; Bueh, C; Ghaffar, A Khalid, Bushra; Bueh, Cholaw; Ghaffar, Abdul Assessing the Factors of Dengue Transmission in Urban Environments of Pakistan ATMOSPHERE English Article dengue occurrence; factors; Pakistan; temperature; human population; traveling CLIMATE-CHANGE; RISK-FACTORS; ARABIAN SEA; MALARIA The rationale of this study is to highlight the significance of relationships of dengue transmission with climate and societal factors for four major cities in Pakistan (i.e., Islamabad, Rawalpindi, Lahore, and Karachi). These cities have been observed to report higher numbers of dengue cases in the last few years, with the highest number of cases reported during 2011. With careful consideration, the relationships of dengue transmission with climate factors, human population density, and traveling in the study areas have been taken into account. Regression model and generalized linear mixed model (GLM) with Markov chain Monte Carlo (MCMC) algorithm are computed to determine the relationships and random effects of different social (human population density, traveling) and climate (minimum-maximum temperatures, and rainfall) factors on dengue transmission. Neural network (NN) with multilayer perceptron algorithm is used to analyze the normalized importance of different covariates relative to dengue transmission. The results show that minimum temperature and rainfall, together with societal factors, significantly affecting the transmission of dengue in the study areas. The magnitude of these relationships is also shown by the results of the neural network. GLM also shows the significant random effects of minimum temperature, rainfall, human population density, and traveling on dengue transmission during the studied years (2009-2018). [Khalid, Bushra; Bueh, Cholaw] Chinese Acad Sci, Int Ctr Climate & Environm Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China; [Khalid, Bushra] Int Islamic Univ, Dept Environm Sci, Islamabad 46300, Pakistan; [Khalid, Bushra] Abdus Salam Int Ctr Theoret Phys, Earth Syst Phys, Trieste 34151, Italy; [Ghaffar, Abdul] Cholistan Univ Vet & Anim Sci, Fac BioSci, Dept Zool, Bahawalpur 63100, Pakistan Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; International Islamic University, Pakistan; Abdus Salam International Centre for Theoretical Physics (ICTP) Khalid, B; Bueh, C (corresponding author), Chinese Acad Sci, Int Ctr Climate & Environm Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China.;Khalid, B (corresponding author), Int Islamic Univ, Dept Environm Sci, Islamabad 46300, Pakistan.;Khalid, B (corresponding author), Abdus Salam Int Ctr Theoret Phys, Earth Syst Phys, Trieste 34151, Italy. kh_bushra@yahoo.com; bueh@lasg.iap.ac.cn; aghaffar@cuvas.edu.pk Bueh, Cholaw/0000-0002-6924-4383 CAS-TWAS Centre of Excellence; National Natural Science Foundation of China [41630424] CAS-TWAS Centre of Excellence; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is jointly funded by CAS-TWAS Centre of Excellence and the National Natural Science Foundation of China (grant no. 41630424). 35 3 3 2 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4433 ATMOSPHERE-BASEL Atmosphere JUN 2021.0 12.0 6 773 10.3390/atmos12060773 0.0 18 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences SY0ZR gold 2023-03-23 WOS:000665624100001 0
J Radoglou-Grammatikis, P; Rompolos, K; Sarigiannidis, P; Argyriou, V; Lagkas, T; Sarigiannidis, A; Goudos, S; Wan, SH Radoglou-Grammatikis, Panagiotis; Rompolos, Konstantinos; Sarigiannidis, Panagiotis; Argyriou, Vasileios; Lagkas, Thomas; Sarigiannidis, Antonios; Goudos, Sotirios; Wan, Shaohua Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article IEC Standards; Computer crime; Medical services; IP networks; Protocols; Intrusion detection; Informatics; Cybersecurity; IEC 60 870-5-104; Internet of Medical Things (IoMT); intrusion detection; machine learning (ML); reinforcement learning (RL); software defined networking (SDN) COUNTERMEASURES; SECURITY The rise of the Internet of Medical Things introduces the healthcare ecosystem in a new digital era with multiple benefits, such as remote medical assistance, real-time monitoring, and pervasive control. However, despite the valuable healthcare services, this progression raises significant cybersecurity and privacy concerns. In this article, we focus our attention on the IEC 60 870-5-104 protocol, which is widely adopted in industrial healthcare systems. First, we investigate and assess the severity of the IEC 60 870-5-104 cyberattacks by providing a quantitative threat model, which relies on Attack Defence Trees and Common Vulnerability Scoring System v3.1. Next, we introduce an intrusion detection and prevention system (IDPS), which is capable of discriminating and mitigating automatically the IEC 60 870-5-104 cyberattacks. The proposed IDPS takes full advantage of the machine learning (ML) and software defined networking (SDN) technologies. ML is used to detect the IEC 60 870-5-104 cyberattacks, utilizing 1) Transmission Control Protocol/Internet Protocol network flow statistics and 2) IEC 60 870-5-104 payload flow statistics. On the other side, the automated mitigation is transformed into a multiarmed bandit problem, which is solved through a reinforcement learning method called Thomson sampling and SDN. The evaluation analysis demonstrates the efficiency of the proposed IDPS in terms of intrusion detection accuracy and automated mitigation performance. The detection accuracy and the F1 score of the proposed IDPS reach 0.831 and 0.8258, respectively, while the mitigation accuracy is calculated at 0.923. [Radoglou-Grammatikis, Panagiotis; Rompolos, Konstantinos; Sarigiannidis, Panagiotis] Univ Western Macedonia, Dept Elect & Comp Engn, Kozani 50100, Greece; [Argyriou, Vasileios] Kingston Univ London, Dept Networks & Digital Media, Kingston KT1 2EE, England; [Lagkas, Thomas] Int Hellen Univ, Dept Comp Sci, Thermi 65404, Greece; [Sarigiannidis, Antonios] Sidroco Holdings Ltd, CY-1077 Nicosia, Cyprus; [Goudos, Sotirios] Aristotle Univ Thessaloniki, Sch Phys, Thessaloniki 54124, Greece; [Wan, Shaohua] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China University of Western Macedonia; Kingston University; Aristotle University of Thessaloniki; Zhongnan University of Economics & Law Wan, SH (corresponding author), Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China. pradoglou@uowm.gr; krobolos@uowm.gr; psarigiannidis@uowm.gr; vasileios.argyriou@kingston.ac.uk; tlagkas@cs.ihu.gr; asarigia@sidroco.com; sgoudo@physics.auth.gr; shwanhust@gmail.com Goudos, Sotirios K./HJA-6146-2022; Wan, Shaohua/B-9243-2014; Sarigiannidis, Panagiotis/O-5246-2017; Radoglou-Grammatikis, Panagiotis/AAF-4162-2021; Lagkas, Thomas/V-1461-2017 Goudos, Sotirios K./0000-0001-5981-5683; Wan, Shaohua/0000-0001-7013-9081; Sarigiannidis, Panagiotis/0000-0001-6042-0355; Radoglou-Grammatikis, Panagiotis/0000-0003-1605-9413; Lagkas, Thomas/0000-0002-0749-9794 European Union [833955] European Union(European Commission) This work was supported by the European Union's Horizon 2020 research and innovation program under Grant 833955. 21 24 24 17 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. MAR 2022.0 18.0 3 2041 2052 10.1109/TII.2021.3093905 0.0 12 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering XL5OS Green Submitted, hybrid 2023-03-23 WOS:000728195600063 0
J Zhang, RF; Kong, XF; Wang, HT; Zhang, SH; Legut, D; Sheng, SH; Srinivasan, S; Rajan, K; Germann, TC Zhang, R. F.; Kong, X. F.; Wang, H. T.; Zhang, S. H.; Legut, D.; Sheng, S. H.; Srinivasan, S.; Rajan, K.; Germann, T. C. An informatics guided classification of miscible and immiscible binary alloy systems SCIENTIFIC REPORTS English Article DIRECT SYNTHESIS CALORIMETRY; 5D TRANSITION-METALS; STANDARD ENTHALPIES; INTERMETALLIC COMPOUNDS; LANTHANIDE METALS; NOBLE-METALS; SOLID SOLUBILITY; THERMOCHEMISTRY; ELEMENTS; COPPER The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema's theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design. [Zhang, R. F.; Kong, X. F.; Zhang, S. H.; Sheng, S. H.] Beihang Univ, Sch Mat Sci & Engn, Beijing 100191, Peoples R China; [Zhang, R. F.; Kong, X. F.; Zhang, S. H.; Sheng, S. H.] Beihang Univ, Int Res Inst Multidisciplinary Sci, Beijing 100191, Peoples R China; [Wang, H. T.] Chinese Acad Sci, Inst Met Res, CAS Key Lab Nucl Mat & Safety Assessment, Shenyang 110016, Liaoning, Peoples R China; [Legut, D.] VSB Tech Univ Ostrava, Ctr IT4Innovat, CZ-70833 Ostrava, Czech Republic; [Legut, D.] VSB Tech Univ Ostrava, Nanotechnol Ctr, CZ-70833 Ostrava, Czech Republic; [Srinivasan, S.] Iowa State Univ, Inst Plant Sci, 2031 Roy J Carver Co Lab, Ames, IA 50011 USA; [Rajan, K.] SUNY Buffalo, Dept Mat Design & Innovat, 311 Bell Hall, Buffalo, NY 14260 USA; [Germann, T. C.] Los Alamos Natl Lab, Theoret Div, Los Alamos, NM 87545 USA Beihang University; Beihang University; Chinese Academy of Sciences; Institute of Metal Research, CAS; Technical University of Ostrava; Technical University of Ostrava; Iowa State University; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; United States Department of Energy (DOE); Los Alamos National Laboratory Zhang, RF (corresponding author), Beihang Univ, Sch Mat Sci & Engn, Beijing 100191, Peoples R China.;Zhang, RF (corresponding author), Beihang Univ, Int Res Inst Multidisciplinary Sci, Beijing 100191, Peoples R China. zrf@buaa.edu.cn Zhang, Ruifeng/AAH-1935-2020; Germann, Timothy C./ABF-5034-2021; Zhang, Ruifeng/D-3984-2009 Germann, Timothy C./0000-0002-6813-238X; Zhang, Ruifeng/0000-0002-9905-7271; Legut, Dominik/0000-0001-9185-9934; Kong, Xiangfei/0000-0001-6364-0767; Srinivasan, Srikant/0000-0001-8605-3952 National Natural Science Foundation of China (NFSC) [51471018, 51672015]; National Key Research and Development Program of China [2016YFC1102500, 2017YFB0702100]; National Thousand Young Talents Program of China; Fundamental Research Funds for the Central Universities; Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project IT4 Innovations National Supercomputing Center [LM2015070]; Grant Agency of the Czech Republic [17-23964 S] National Natural Science Foundation of China (NFSC)(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; National Thousand Young Talents Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project IT4 Innovations National Supercomputing Center; Grant Agency of the Czech Republic(Grant Agency of the Czech Republic) This work is supported by the National Natural Science Foundation of China (NFSC) with Nos. 51471018 and 51672015, National Key Research and Development Program of China (Nos. 2016YFC1102500 and 2017YFB0702100), National Thousand Young Talents Program of China and Fundamental Research Funds for the Central Universities. D. L. acknowledges support by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project IT4 Innovations National Supercomputing Center - LM2015070 and the project No. 17-23964 S of the Grant Agency of the Czech Republic. We would also like to thank Prof. Dr. S. Veprek for constructive comments and suggestions to the manuscript, and Prof. S. Curtarolo for his comments and providing online HTFP data. 51 24 24 4 49 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2045-2322 SCI REP-UK Sci Rep AUG 29 2017.0 7.0 9577 10.1038/s41598-017-09704-1 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics FE9OI 28851941.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000408532600013 0
C Qi, J; Hu, H; Wang, YN; Yang, CHH; Siniscalchi, SM; Lee, CH Int Speech Commun Assoc Qi, Jun; Hu, Hu; Wang, Yannan; Yang, Chao-Han Huck; Siniscalchi, Sabato Marco; Lee, Chin-Hui Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement INTERSPEECH 2020 Interspeech English Proceedings Paper Interspeech Conference OCT 25-29, 2020 Shanghai, PEOPLES R CHINA convolutional neural network; tensor-train network; tensor-to-vector regression; speech enhancement This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for speech enhancement. We find that a hybrid architecture, namely CNN-TT, is capable of maintaining a good quality performance with a reduced model parameter size. CNN-TT is composed of several convolutional layers at the bottom for feature extraction to improve speech quality and a tensor-train (TT) output layer on the top to reduce model parameters. We first derive a new upper bound on the generalization power of the convolutional neural network (CNN) based vector-to-vector regression models. Then, we provide experimental evidence on the Edinburgh noisy speech corpus to demonstrate that, in single-channel speech enhancement, CNN outperforms DNN at the expense of a small increment of model sizes. Besides, CNN-TT slightly outperforms the CNN counterpart by utilizing only 32% of the CNN model parameters. Besides, further performance improvement can be attained if the number of CNN-TT parameters is increased to 44% of the CNN model size. Finally, our experiments of multi-channel speech enhancement on a simulated noisy WSJ0 corpus demonstrate that our proposed hybrid CNN-TT architecture achieves better results than both DNN and CNN models in terms of better-enhanced speech qualities and smaller parameter sizes. [Qi, Jun; Hu, Hu; Yang, Chao-Han Huck; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Elect & Comp Engn, Atlanta, GA 30332 USA; [Siniscalchi, Sabato Marco] Univ Enna, Comp Engn Sch, Enna, Italy; [Wang, Yannan] Tencent Corp, Tencent Media Lab, Shenzhen, Guangdong, Peoples R China University System of Georgia; Georgia Institute of Technology; Universita Kore di ENNA; Tencent Qi, J (corresponding author), Georgia Inst Technol, Elect & Comp Engn, Atlanta, GA 30332 USA. 28 7 7 1 1 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2020.0 76 80 10.21437/Interspeech.2020-1900 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4QT Green Submitted 2023-03-23 WOS:000833594100016 0
C Wu, B; Li, KH; Huang, Z; Siniscalchi, SM; Yang, M; Lee, CH IEEE Wu, Bo; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Yang, Minglei; Lee, Chin-Hui A UNIFIED DEEP MODELING APPROACH TO SIMULTANEOUS SPEECH DEREVERBERATION AND RECOGNITION FOR THE REVERB CHALLENGE 2017 HANDS-FREE SPEECH COMMUNICATIONS AND MICROPHONE ARRAYS (HSCMA 2017) English Proceedings Paper Conference on Hands-Free Speech Communications and Microphone Arrays (HSCMA) MAR 01-03, 2017 San Francisco, CA IEEE Signal Proc Soc Signal space robustness; deep modeling; reverberant speech enhancement; robust speech recognition SUPPRESSION We propose a unified deep neural network (DNN) approach to achieve both high-quality enhanced speech and high-accuracy automatic speech recognition (ASR) simultaneously on the recent REverberant Voice Enhancement and Recognition Benchmark (RE-VERB) Challenge. These two goals are accomplished by two proposed techniques, namely DNN-based regression to enhance reverberant and noisy speech, followed by DNN-based multi-condition training that takes clean-condition, multi-condition and enhanced speech all into consideration. We first report on superior objective measures in enhanced speech to those listed in the 2014 REVERB Challenge Workshop. We then show that in clean-condition training, we obtain the best word error rate (WER) of 13.28% on the 1-channel REVERB simulated evaluation data with the proposed DNN-based pre-processing scheme. Similarly we attain a competitive single-system WER of 8.75% with the proposed multi-condition training strategy and the same less-discriminative log power spectrum features used in the enhancement stage. Finally by leveraging upon joint training with more discriminative ASR features and improved neural network based language models a state-of-the-art WER of 4.46% is attained with a single ASR system, and single-channel information. Another state-of-the-art WER of 4.10% is achieved through system combination. [Wu, Bo; Yang, Minglei] Xidian Univ, Natl Lab Radar Signal Proc, Xian, Shaanxi, Peoples R China; [Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Siniscalchi, Sabato Marco] Univ Enna Kore, I-94100 Enna, Italy Xidian University; University System of Georgia; Georgia Institute of Technology; Universita Kore di ENNA Wu, B (corresponding author), Xidian Univ, Natl Lab Radar Signal Proc, Xian, Shaanxi, Peoples R China. rambowu11@gmail.com; kehlekernel@gmail.com; huangzhenee@gmail.com; siniscalchi77.19@gmail.com; chl@ece.gatech.edu Siniscalchi, Sabato Marco Marco/I-3423-2012 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507 36 5 5 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-5925-6 2017.0 36 40 5 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BH8JK 2023-03-23 WOS:000403394000008 0
J Skach, J; Kiumarsi, B; Lewis, FL; Straka, O Skach, Jan; Kiumarsi, Bahare; Lewis, Frank L.; Straka, Ondrej Actor-Critic Off-Policy Learning for Optimal Control of Multiple-Model Discrete-Time Systems IEEE TRANSACTIONS ON CYBERNETICS English Article Actor-critic; adaptive self-organizing map (SOM); multiple-model; off-policy reinforcement learning (RL); optimal control OPTIMAL TRACKING CONTROL; LINEAR-SYSTEMS; ITERATION; NETWORK In this paper, motivated by human neurocognitive experiments, a model-free off-policy reinforcement learning algorithm is developed to solve the optimal tracking control of multiple-model linear discrete-time systems. First, an adaptive self-organizing map neural network is used to determine the system behavior from measured data and to assign a responsibility signal to each of system possible behaviors. A new model is added if a sudden change of system behavior is detected from the measured data and the behavior has not been previously detected. A value function is represented by partially weighted value functions. Then, the off-policy iteration algorithm is generalized to multiple-model learning to find a solution without any knowledge about the system dynamics or reference trajectory dynamics. The off-policy approach helps to increase data efficiency and speed of tuning since a stream of experiences obtained from executing a behavior policy is reused to update several value functions corresponding to different learning policies sequentially. Two numerical examples serve as a demonstration of the off-policy algorithm performance. [Skach, Jan; Straka, Ondrej] Univ West Bohemia, Dept Cybernet & NTIS New Technol Informat Soc, Fac Appl Sci, Plzen 30614, Czech Republic; [Kiumarsi, Bahare] Univ Texas Arlington, UTARI, Arlington, TX 76118 USA; [Lewis, Frank L.] Univ Texas Arlington, UTARI, Ft Worth, TX 76118 USA; [Lewis, Frank L.] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China University of West Bohemia Pilsen; University of Texas System; University of Texas Arlington; University of Texas System; University of Texas Arlington; Northeastern University - China Skach, J (corresponding author), Univ West Bohemia, Dept Cybernet & NTIS New Technol Informat Soc, Fac Appl Sci, Plzen 30614, Czech Republic. janskach@kky.zcu.cz; kiumarsi@uta.edu; lewis@uta.edu; straka30@kky.zcu.cz NSF [ECCS-1405173, IIS-1208623]; ONR [N00014-13-1-0562, N000141410718]; Czech Ministry of Education, Youth and Sports [LO1506]; Czech Science Foundation [GA 15-12068S]; Fulbright Program; Directorate For Engineering; Div Of Electrical, Commun & Cyber Sys [1405173] Funding Source: National Science Foundation NSF(National Science Foundation (NSF)); ONR(Office of Naval Research); Czech Ministry of Education, Youth and Sports(Ministry of Education, Youth & Sports - Czech Republic); Czech Science Foundation(Grant Agency of the Czech Republic); Fulbright Program; Directorate For Engineering; Div Of Electrical, Commun & Cyber Sys(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)) This work was supported in part by the NSF under Grant ECCS-1405173 and Grant IIS-1208623, in part by the ONR under Grant N00014-13-1-0562 and Grant N000141410718, in part by the Czech Ministry of Education, Youth and Sports under Project LO1506, in part by the Czech Science Foundation under Project GA 15-12068S, and in part by the Fulbright Program. This paper was recommended by Associate Editor G.-P. Liu. 49 37 37 6 45 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JAN 2018.0 48.0 1 29 40 10.1109/TCYB.2016.2618926 0.0 12 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science FQ3XI 27831897.0 hybrid 2023-03-23 WOS:000418291400003 0
C Chen, Y; Wang, T; Li, JK; Lv, XW; Snoussi, H Yang, J; Hu, Q; Cheng, MM; Wang, L; Liu, Q; Bai, X; Meng, D Chen, Yang; Wang, Tian; Li, Jiakun; Lv, Xiaowei; Snoussi, Hichem Human Action Recognition Based on Sub-data Learning COMPUTER VISION, PT III Communications in Computer and Information Science English Proceedings Paper 2nd CCF Chinese Conference on Computer Vision (CCCV) OCT 11-14, 2017 China Comp Federat, Tianjin, PEOPLES R CHINA China Comp Federat, Profess Comm Comp Vis,Civil Aviat Univ,Tianjin Univ,Nankai Univ,CCF Tech Comm Comp Vis,Megvii Face++,Sensetime,Isecure Technol,Ali A I Labs,Hiscene,Riseye,Tupu,Nvidia,Pingan Technol,Pinnacle,Vrview,Xilinx,Athena Eyes,Watrix Technol,Extreme Vis,Shanghai Acad Artificial Intelligence,Segway Robot,Percipio XYZ,AN China Comp Federat Action recognition; 3DCNN; Sub-data learning Human action recognizing nowadays plays a key role in varieties of computer vision applications while at the same time it's quite challenging for the requirement of accuracy and robustness. Most current computer vision methods focus on algorithms designing classifiers with handcrafted features which are complex and inflexible. To automatically extract both spatial and temporal features, in this paper we propose a method of human action recognition based on sub-data learning which combines the proposed 3D convolutional neural network (3DCNN) with the One-versus-One (OvO) algorithm. We also employ effective data augmentation to reduce overfitting. We evaluate our method on the KTH and UCF Sports dataset and achieve promising results. [Chen, Yang; Wang, Tian; Li, Jiakun] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; [Lv, Xiaowei] China Elect Technol Grp Corp, Informat Sci Acad, Beijing, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, CNRS, Inst Charles Delaunay, LM2S,UMR STMR 6279, Troyes, France Beihang University; China Electronics Technology Group; Centre National de la Recherche Scientifique (CNRS); Universite de Technologie de Troyes Wang, T (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China. youngcy1994@buaa.edu.cn; wangtian@buaa.edu.cn; lijiakun@buaa.edu.cn; lvxiaowei@cetc.com.cn; hichem.snoussi@utt.fr ANR AutoFerm project; FEDER; Fundamental Research Funds for the Central Universities [YWF-14-RSC-102]; Aeronautical Science Foundation of China [2016ZC51022]; National Natural Science Foundation of China [U1435220, 61503017]; Platform CAPSEC - Region Champagne-Ardenne ANR AutoFerm project(French National Research Agency (ANR)); FEDER(European Commission); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Aeronautical Science Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Platform CAPSEC - Region Champagne-Ardenne T. Wang-This work is partially supported by the ANR AutoFerm project and the Platform CAPSEC funded by Region Champagne-Ardenne and FEDER, the Fundamental Research Funds for the Central Universities (YWF-14-RSC-102), the Aeronautical Science Foundation of China (2016ZC51022), the National Natural Science Foundation of China (U1435220, 61503017). 27 0 0 0 1 SPRINGER-VERLAG SINGAPORE PTE LTD SINGAPORE 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE 1865-0929 1865-0937 978-981-10-7305-2; 978-981-10-7304-5 COMM COM INF SC 2017.0 773.0 617 626 10.1007/978-981-10-7305-2_52 0.0 10 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL3KM 2023-03-23 WOS:000449803000052 0
J Yao, QJ; Jahanshahi, H; Batrancea, LM; Alotaibi, ND; Rus, MI Yao, Qijia; Jahanshahi, Hadi; Batrancea, Larissa M.; Alotaibi, Naif D.; Rus, Mircea-Iosif Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning MATHEMATICS English Article synchronization; chaotic financial system; fixed-time control; neural network; output constraint BIFURCATION TOPOLOGICAL-STRUCTURE; GLOBAL COMPLICATED CHARACTER; ADAPTIVE SYNCHRONIZATION; NONLINEAR-SYSTEMS; LINEAR-CONTROL; FINITE-TIME; STABILIZATION; DYNAMICS; KIND This article addresses the challenging problem of fixed-time output-constrained synchronization for master-slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller. [Yao, Qijia] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China; [Jahanshahi, Hadi] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada; [Batrancea, Larissa M.] Babes Bolyai Univ, Dept Business, 7 Horea St, Cluj Napoca 400174, Romania; [Alotaibi, Naif D.] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Rus, Mircea-Iosif] Natl Inst Res & Dev Construct Urbanism & Sustaina, 117 Calea Floresti, Cluj Napoca 400524, Romania Beijing Institute of Technology; University of Manitoba; Babes Bolyai University from Cluj; King Abdulaziz University Jahanshahi, H (corresponding author), Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada.;Batrancea, LM (corresponding author), Babes Bolyai Univ, Dept Business, 7 Horea St, Cluj Napoca 400174, Romania. jahanshahi.hadi90@gmail.com; larissa.batrancea@ubbcluj.ro Rus, Mircea-Iosif/AAG-7577-2021 Rus, Mircea-Iosif/0000-0003-3334-5497; Jahanshahi, Hadi/0000-0001-7810-6479; Batrancea, Larissa/0000-0001-6254-2970; YAO, Qijia/0000-0001-7902-407X Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [FP-029-43] Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia The authors are grateful to the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia for funding this study under the grant no. FP-029-43. 40 5 5 10 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics OCT 2022.0 10.0 19 3682 10.3390/math10193682 0.0 14 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 5G8AD gold 2023-03-23 WOS:000867214100001 0
C Wang, ZL; Wang, JX; Zhao, HY; Yang, N; Fortino, G IEEE Wang, Zhelong; Wang, Jiaxin; Zhao, Hongyu; Yang, Ning; Fortino, Giancarlo CanoeSense: Monitoring Canoe Sprint Motion using Wearable Sensors 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 09-12, 2016 Budapest, HUNGARY IEEE wearable sensors; phase detection; motion measurement; machine learning; sport monitoring NETWORK This paper presents a monitoring system (CanoeSense) for canoe motion based on wearable Body Sensor Networks (BSNs). An effective motion segmentation method was applied to competitive sport, which can segment human motion phases automatically based on raw time series data that was acquired through wearable Inertial Measurement Units (IMUs). Orientation estimation algorithm was adopted to measure the attitude information of athletes' stroke motion of the canoe. By fusing the data of motion phases and attitude changes, the monitoring data may provide coach with a new performance monitoring method for improving coordination motions of two partners or adjusting the training plan in time. The experimental results showed that our system is able to simultaneously monitor motion phases and attitude changes of two athletes during training on the water. [Wang, Zhelong; Wang, Jiaxin; Zhao, Hongyu; Yang, Ning] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China; [Fortino, Giancarlo] Univ Calabria, DIMES, Via P Bucci,Cubo 41C, I-87036 Arcavacata Di Rende, CS, Italy Dalian University of Technology; University of Calabria Wang, JX (corresponding author), Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China. wangjx19890828@mail.dlut.edu.cn; g.fortino@unical.it Fortino, Giancarlo/J-2950-2017 Fortino, Giancarlo/0000-0002-4039-891X Water Sports Training Center in Liaoning Province Water Sports Training Center in Liaoning Province This work is supported by Water Sports Training Center in Liaoning Province. We would like to thank two athletes and their coach for their help of taking part in the experiment. 24 9 9 4 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-5090-1897-0 IEEE SYS MAN CYBERN 2016.0 644 649 6 Computer Science, Cybernetics; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BH7LC 2023-03-23 WOS:000402634700097 0
J Lin, ML; Liu, MM; Lek, S; Dong, LJ; Zhang, PJ; Gozlan, RE; Li, SH Lin, Mingli; Liu, Mingming; Lek, Sovan; Dong, Lijun; Zhang, Peijun; Gozlan, Rodolphe E.; Li, Songhai Modelling habitat suitability of the Indo-Pacific humpback dolphin using artificial neural network: The influence of shipping ECOLOGICAL INFORMATICS English Article Linear discriminant analysis; Estuary; Top-predator; Cetacean; Conservation SOUSA-CHINENSIS; CETACEAN-HABITAT; SPATIAL-DISTRIBUTION; AUSTRALIAN SNUBFIN; PREDATION RISK; ABUNDANCE; BEHAVIOR; SEA; PREFERENCES; WHALE The distribution of cetaceans is generally studied on the basis of their visual locations. However, the absence of observations does not exclude the presence of dolphins and not allow to distinguish habitats favourable to the species but where it would be currently absent due to anthropic disturbances. The modelling of ecological niches represents a powerful alternative choice and intensive computer modelling has been increasingly used to reveal the complexity of the relationships between cetaceans and their habitat. Here, we predicted the presence/ absence of the Indo-Pacific humpback dolphin (Sousa chinensis), an endangered species, using the artificial neural network model of back-propagation (BP-ANN) with eight environmental variables. The BP-ANN model had a higher success rate for correct prediction (74%) compared to linear discriminant analysis (67%), especially for the prediction of the presence of S. chinensis (63% to 31%), indicating its potential application in cetacean habitat research. In the model output map, three suitable habitats were predicted without S. chinensis sightings identified. However, only one was confirmed by subsequent field surveys, the other two being located in a strong shipping area. Therefore, we suggest that the traditional assessment of the baseline habitat based on visual sighting may miss the identification of some suitable habitats due to anthropogenic disturbance. We have also highlighted the importance of ecological modelling research for cetacean conservation. In addition, among the eight environmental variables studied, distance from shore, fish abundance and salinity proved to be the most important factors for the distribution of S. chinensis, indicating that coastal construction, sea recovery and overfishing would be key constraints for its conservation. [Lin, Mingli; Liu, Mingming; Dong, Lijun; Zhang, Peijun; Li, Songhai] Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Sanya 572000, Peoples R China; [Lin, Mingli; Lek, Sovan] Univ Paul Sabatier, UMR EDB 5174, CNRS, F-31062 Toulouse, France; [Gozlan, Rodolphe E.] Univ Montpellier, EPHE, CNRS, ISEM,IRD, F-34090 Montpellier, France Chinese Academy of Sciences; Institute of Deep-Sea Science & Engineering, CAS; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Ecole Nationale Formation Agronomique (ENSFEA); Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; UDICE-French Research Universities; Universite PSL; Ecole Pratique des Hautes Etudes (EPHE) Li, SH (corresponding author), Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Sanya 572000, Peoples R China. lish@idsse.ac.cn National Natural Science Foundation of China [41406182, 41306169, 41422604]; biodiversity investigation, observation and assessment program (2019-2023) of Ministry of Ecology and Environment of China; development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences [E072010101]; Ocean Park Conservation Foundation Hong Kong [AW02-1920] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); biodiversity investigation, observation and assessment program (2019-2023) of Ministry of Ecology and Environment of China; development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences; Ocean Park Conservation Foundation Hong Kong We are grateful to Dong Jiancheng, Xu Xiao, Li Kuan, Liu Mingzhong for their assistance with the field survey. This research was financially supported by the National Natural Science Foundation of China (41406182, 41306169 and 41422604), the biodiversity investigation, observation and assessment program (2019-2023) of Ministry of Ecology and Environment of China, the development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences (E072010101) and Ocean Park Conservation Foundation Hong Kong (AW02-1920). This work was performed under an Ethical Statement with the number of IDSSE-SYLL-MMMBL-01. 72 3 4 4 18 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1574-9541 1878-0512 ECOL INFORM Ecol. Inform. MAY 2021.0 62.0 101274 10.1016/j.ecoinf.2021.101274 0.0 MAR 2021 9 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology RM7PO 2023-03-23 WOS:000639851100008 0
J Liu, J; Cao, LC; Stoykova, E; Ferraro, P; Memmolo, P; Blanche, PA Liu, Juan; Cao, Liangcai; Stoykova, Elena; Ferraro, Pietro; Memmolo, Pasquale; Blanche, Pierre-Alexandre Digital Holography and 3D Imaging 2020: introduction to the feature issue APPLIED OPTICS English Editorial Material This feature issue of JOSA A and Applied Optics is dedicated to the fourteenth OSA Topical Meeting Digital Holography and 3D Imaging held 22-26 June 2020 in a virtual meeting. The conference, taking place every year, is a focal point for global technical interchange in the field of digital holography and 3D imaging, providing premier opportunities for people working in the field to present their new advances in research and development. Papers presented at the meeting highlight current research in digital holography and three-dimensional imaging, including interferometry, phase microscopy, phase retrieval, novel holographic processes, 3D and novel holographic displays, integral imaging, computer-generated holograms, compressive holography, 3D holographic display, AR display, full-field tomography, specific image and signal processing, and holography with various light sources, including coherent to incoherent and x-ray to terahertz waves. Techniques of digital holography and of 3D imaging have numerous applications, such as the state-of-the-art technological developments that are currently underway and have also stimulated further novel applications of digital holography and 3D imaging in biomedicine, deep learning, and scientific and industrial metrologies. (C) 2021 Optical Society of America [Liu, Juan] Beijing Inst Technol, Beijing, Peoples R China; [Cao, Liangcai] Tsinghua Univ, Beijing, Peoples R China; [Stoykova, Elena] Bulgarian Acad Sci, Sofia, Bulgaria; [Ferraro, Pietro] CNR, Inst Intelligent Syst, Bari, Italy; [Memmolo, Pasquale] CNR, ISASI, Biella, Italy; [Blanche, Pierre-Alexandre] Univ Arizona, Tucson, AZ 85721 USA Beijing Institute of Technology; Tsinghua University; Bulgarian Academy of Sciences; Consiglio Nazionale delle Ricerche (CNR); Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Applicate e Sistemi Intelligenti Eduardo Caianiello (ISASI-CNR); University of Arizona Liu, J (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. juanliu@bit.edu.cn Ferraro, Pietro/A-5288-2009 Ferraro, Pietro/0000-0002-0158-3856 0 0 0 3 23 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics FEB 1 2021.0 60.0 4 DH1 DH2 10.1364/AO.419209 0.0 2 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics QC1YC 33690383.0 2023-03-23 WOS:000614630300038 0
J Yue, L; Hu, D; Zhang, H; Wen, JH; Wu, Y; Li, W; Sun, L; Li, X; Wang, JH; Li, GJ; Wang, T; Shen, DG; Xiao, SF Yue, Ling; Hu, Dan; Zhang, Han; Wen, Junhao; Wu, Ye; Li, Wei; Sun, Lin; Li, Xia; Wang, Jinghua; Li, Guanjun; Wang, Tao; Shen, Dinggang; Xiao, Shifu Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment HUMAN BRAIN MAPPING English Article machine learning; MRI; prediction; subjective cognitive decline ALZHEIMERS-DISEASE; MEMORY DECLINE; CLINICAL PROGRESSION; EARLY-DIAGNOSIS; RISK; COMPLAINTS; DEMENTIA; ATROPHY; INDIVIDUALS; FRAMEWORK Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1-weighted structural MRI. A machine learning-based model was trained based on 198 baseline neuroimaging (morphometric) features and a battery of 25 clinical measurements to discriminate 24 progressive SCDs who converted to mild cognitive impairment (MCI) at follow-up from 52 stable SCDs. The SCD progression was satisfactorily predicted with the combined features. A history of stroke, a low education level, a low baseline MoCA score, a shrunk left amygdala, and enlarged white matter at the banks of the right superior temporal sulcus were found to favor the progression. This is to date the largest retrospective study of SCD-to-MCI conversion with the longest follow-up, suggesting predictable far-future cognitive decline for the risky populations with baseline measures only. These findings provide valuable knowledge to the future neuropathological studies of AD in its prodromal phase. [Yue, Ling; Li, Wei; Sun, Lin; Li, Xia; Wang, Jinghua; Li, Guanjun; Wang, Tao; Xiao, Shifu] Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Dept Geriatr Psychiat, Sch Med, Shanghai, Peoples R China; [Yue, Ling; Li, Wei; Sun, Lin; Li, Xia; Wang, Jinghua; Li, Guanjun; Wang, Tao; Xiao, Shifu] Shanghai Jiao Tong Univ, Alzheimers Dis & Related Disorders Ctr, Shanghai, Peoples R China; [Hu, Dan; Zhang, Han; Wu, Ye; Shen, Dinggang] Univ North Carolina Chapel Hill, Dept Radiol, Chapel Hill, NC 27599 USA; [Hu, Dan; Zhang, Han; Wu, Ye; Shen, Dinggang] Univ North Carolina Chapel Hill, BRIC, Chapel Hill, NC 27599 USA; [Wen, Junhao] Inserm U1127, ARAMIS Lab, ICM, Paris, France; [Wen, Junhao] CNRS R 7225, Paris, France; [Wen, Junhao] Sorbonne Univ, Paris, France; [Wen, Junhao] Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA; [Shen, Dinggang] Korea Univ, Dept Brain & Cognit Engn, Seoul, South Korea Shanghai Jiao Tong University; Shanghai Jiao Tong University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; University of Pennsylvania; Pennsylvania Medicine; Korea University Wang, T (corresponding author), Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Dept Geriatr Psychiat, Sch Med, Shanghai, Peoples R China.;Xiao, SF (corresponding author), Shanghai Jiao Tong Univ, Alzheimers Dis & Related Disorders Ctr, Shanghai, Peoples R China.;Shen, DG (corresponding author), Univ North Carolina Chapel Hill, Dept Radiol, Chapel Hill, NC 27599 USA.;Shen, DG (corresponding author), Univ North Carolina Chapel Hill, BRIC, Chapel Hill, NC 27599 USA. wtshhwy@163.com; dgshen@med.unc.edu; xiaoshifu@msn.com wang, tao/HLG-5649-2023; Shen, Dinggang/ABF-6812-2020 Shen, Dinggang/0000-0002-7934-5698; Wu, Ye/0000-0002-8643-4023 Shanghai Clinical Research Center for Mental Health [19MC1911100]; Shanghai Jiaotong University School of Medicine [CBXJ201815, YG2016MS38]; Shanghai Mental Health Center [2018-FX-05, 2020zd01, CRC2017ZD02]; Shanghai Municipal Human Resources Development Program [2017BR054]; China Ministry of Science and Technology [2009BAI77B03]; National Natural Science Foundation of China [81830059] Shanghai Clinical Research Center for Mental Health; Shanghai Jiaotong University School of Medicine; Shanghai Mental Health Center; Shanghai Municipal Human Resources Development Program; China Ministry of Science and Technology(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Shanghai Clinical Research Center for Mental Health, Grant/Award Number: 19MC1911100; Shanghai Jiaotong University School of Medicine, Grant/Award Numbers: CBXJ201815, YG2016MS38; Shanghai Mental Health Center, Grant/Award Numbers: 2018-FX-05, 2020zd01, CRC2017ZD02; Shanghai Municipal Human Resources Development Program, Grant/Award Number: 2017BR054; the China Ministry of Science and Technology, Grant/Award Number: 2009BAI77B03; the National Natural Science Foundation of China, Grant/Award Number: 81830059 51 13 13 1 19 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. JAN 2021.0 42.0 1 192 203 10.1002/hbm.25216 0.0 OCT 2020 12 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging PB1EJ 33030795.0 Green Submitted, Green Accepted, Green Published, gold 2023-03-23 WOS:000575964500001 0
J Kalaji, HM; Schansker, G; Brestic, M; Bussotti, F; Calatayud, A; Ferroni, L; Goltsev, V; Guidi, L; Jajoo, A; Li, PM; Losciale, P; Mishra, VK; Misra, AN; Nebauer, SG; Pancaldi, S; Penella, C; Pollastrini, M; Suresh, K; Tambussi, E; Yanniccari, M; Zivcak, M; Cetner, MD; Samborska, IA; Stirbet, A; Olsovska, K; Kunderlikova, K; Shelonzek, H; Rusinowski, S; Baba, W Kalaji, Hazem M.; Schansker, Gert; Brestic, Marian; Bussotti, Filippo; Calatayud, Angeles; Ferroni, Lorenzo; Goltsev, Vasilij; Guidi, Lucia; Jajoo, Anjana; Li, Pengmin; Losciale, Pasquale; Mishra, Vinod K.; Misra, Amarendra N.; Nebauer, Sergio G.; Pancaldi, Simonetta; Penella, Consuelo; Pollastrini, Martina; Suresh, Kancherla; Tambussi, Eduardo; Yanniccari, Marcos; Zivcak, Marek; Cetner, Magdalena D.; Samborska, Izabela A.; Stirbet, Alexandrina; Olsovska, Katarina; Kunderlikova, Kristyna; Shelonzek, Henry; Rusinowski, Szymon; Baba, Wojciech Frequently asked questions about chlorophyll fluorescence, the sequel PHOTOSYNTHESIS RESEARCH English Review Chl a fluorescence; Delayed fluorescence; Photochemical quenching; Energy partitioning; Area PHOTOSYNTHETIC ELECTRON-TRANSPORT; LIGHT-HARVESTING COMPLEX; QUANTITATIVE TRAIT LOCI; HORDEUM-VULGARE L.; STRONG CONTINUOUS ILLUMINATION; BUNDLE-SHEATH CHLOROPLASTS; RESTRICTED ENERGY-TRANSFER; XANTHOPHYLL CYCLE ACTIVITY; PHOTOSYSTEM-II HERBICIDES; SINGLET OXYGEN PRODUCTION Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F-V/F-M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge fromdifferent Chl a fluorescence analysis domains, yielding in several cases new insights. [Kalaji, Hazem M.; Cetner, Magdalena D.; Samborska, Izabela A.] Warsaw Univ Life Sci SGGW, Fac Agr & Biol, Dept Plant Physiol, Nowoursynowska 159, PL-02776 Warsaw, Poland; [Schansker, Gert] Wesemlinstr 58, CH-6006 Luzern, Switzerland; [Brestic, Marian; Zivcak, Marek] Slovak Univ Agr, Dept Plant Physiol, Tr A Hlinku 2, Nitra 94976, Slovakia; [Bussotti, Filippo; Pollastrini, Martina] Univ Florence, Dept Agr Food & Environm Sci, Piazzale Cascine 28, I-50144 Florence, Italy; [Calatayud, Angeles; Penella, Consuelo] Inst Valenciano Invest Agr, Dept Horticultura, Ctra Moncada Naquera Km 4-5, Valencia 46113, Spain; [Ferroni, Lorenzo; Pancaldi, Simonetta] Univ Ferrara, Dept Life Sci & Biotechnol, Corso Ercole I dEste 32, I-44121 Ferrara, Italy; [Goltsev, Vasilij] St Kliment Ohridski Univ Sofia, Fac Biol, Dept Biophys & Radiobiol, 8 Dr Tzankov Blvd, Sofia 1164, Bulgaria; [Guidi, Lucia] Dept Agr Food & Environm, Via Borghetto 80, I-56124 Pisa, Italy; [Jajoo, Anjana] Devi Ahilya Univ, Sch Life Sci, Indore 452001, Madhya Pradesh, India; [Li, Pengmin] Northwest A&F Univ, Coll Hort, State Key Lab Crop Stress Biol Arid Areas, Yangling 712100, Shaanxi, Peoples R China; [Losciale, Pasquale] Consiglio Ric Agr & Anal Econ Agr, Res Unit Agr Dry Environm, I-70125 Bari, Italy; [Mishra, Vinod K.] Doon PG Coll Agr Sci, Dept Biotechnol, Dehra Dun 248001, Uttarakhand, India; [Misra, Amarendra N.] Cent Univ Jharkhand, Ctr Life Sci, Ratu Lohardaga Rd, Ranchi 835205, Bihar, India; [Nebauer, Sergio G.] Univ Politecn Valencia, Dept Prod Vegetal, Camino Vera Sn, E-46022 Valencia, Spain; [Suresh, Kancherla] ICAR Indian Inst Oil Palm Res, Pedavegi 534450, Andhra Pradesh, India; [Tambussi, Eduardo] Univ Nacl La Plata, Consejo Nacl Invest Cient & Tecn, INFIVE, Inst Plant Physiol, Diagonal 113 495,CC 327, La Plata, Buenos Aires, Argentina; [Stirbet, Alexandrina] 204 Anne Burras Lane, Newport News, VA 23606 USA; [Olsovska, Katarina; Kunderlikova, Kristyna] Slovak Univ Agr, Dept Plant Physiol, A Hlinku 2, Nitra 94976, Slovakia; [Shelonzek, Henry] Univ Silesia, Fac Biol & Environm Protect, Dept Plant Anat & Cytol, Ul Jagiellonska 28, PL-40032 Katowice, Poland; [Rusinowski, Szymon] Inst Ecol Ind Areas, Kossutha 6, PL-40844 Katowice, Poland; [Baba, Wojciech] Jagiellonian Univ, Inst Bot, Dept Plant Ecol, Lubicz 46, PL-31512 Krakow, Poland Warsaw University of Life Sciences; Slovak University of Agriculture Nitra; University of Florence; Instituto Valenciano de Investigaciones Agrarias (IVIA); University of Ferrara; Bulgarian Academy of Sciences; Medical University Sofia; University of Sofia; Devi Ahilya University; Northwest A&F University - China; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Central University of Jharkhand; Universitat Politecnica de Valencia; Indian Council of Agricultural Research (ICAR); ICAR - Indian Institute of Oil Palm Research; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of La Plata; Slovak University of Agriculture Nitra; University of Silesia in Katowice; Institute for Ecology of Industrial Areas; Jagiellonian University Kalaji, HM (corresponding author), Warsaw Univ Life Sci SGGW, Fac Agr & Biol, Dept Plant Physiol, Nowoursynowska 159, PL-02776 Warsaw, Poland.;Schansker, G (corresponding author), Wesemlinstr 58, CH-6006 Luzern, Switzerland. hazem@kalaji.pl; gert.schansker@gmail.com; marian.brestic@uniag.sk; filippo.bussotti@unifi.it; calatayud_ang@gva.es; lorenzo.ferroni@unife.it; goltsev@gmail.com; lucia.guidi@unipi.it; anjanajajoo@hotmail.com; Lipm@nwsuaf.edu.cn; pasquale.losciale@crea.gov.it; mishravkbhu@gmail.com; misraan@yahoo.co.uk; sergonne@bvg.upv.es; simonetta.pancaldi@unife.it; penella_con@gva.es; martina.pollastrini@unifi.it; sureshkancherla@rediffmail.com; tambussi35@yahoo.es; marcosyanniccari@conicet.gov.ar; marek.zivcak@uniag.sk; stirbet@verizon.net; katarina.olsovska@uniag.sk; xkunderlikov@is.uniag.sk; shelonzek@gmail.com; rusinowski@ietu.katowice.pl; wojciech.baba12@gmail.com MISRA, AMARENDRA N./B-8378-2012; Kancherla, Suresh/AAO-6377-2020; Bąba, Wojciech/AFX-4233-2022; Schansker, Gert/AAI-5844-2020; Zivcak, Marek/B-2946-2015; Ferroni, Lorenzo/AAW-9342-2020; Misra, Amarendra Narayan Narayan/AAH-3996-2021; Pollastrini, Martina/Y-1046-2019; Brestic, Marian/A-8263-2012; Goltsev, Vasilij N/H-7423-2012; Losciale, Pasquale/AAE-6191-2021; Calatayud, Angeles/D-8110-2014; Kalaji, Mohamed Hazem/E-8086-2012; Ferroni, Lorenzo/AAA-5135-2021; , Martina/N-7989-2014; Nebauer, Sergio G/L-2904-2014 MISRA, AMARENDRA N./0000-0002-5565-8411; Zivcak, Marek/0000-0001-8165-3369; Misra, Amarendra Narayan Narayan/0000-0002-5565-8411; Pollastrini, Martina/0000-0003-0959-9489; Brestic, Marian/0000-0003-3470-6100; Goltsev, Vasilij N/0000-0003-4338-6671; Losciale, Pasquale/0000-0003-2274-3246; Calatayud, Angeles/0000-0002-1728-2273; Kalaji, Mohamed Hazem/0000-0002-3833-4917; Ferroni, Lorenzo/0000-0002-5694-2078; , Martina/0000-0003-0959-9489; Nebauer, Sergio G/0000-0001-7978-6680; Li, Pengmin/0000-0003-3514-0153; Baba, Wojciech/0000-0001-7428-0429; GUIDI, LUCIA/0000-0002-2472-720X; PENELLA, CONSUELO/0000-0001-7454-3022; Yanniccari, Marcos/0000-0003-0214-2048; Brestic, Marian/0000-0002-6597-7838; Stirbet, Alexandrina/0000-0001-5404-8040; Rusinowski, Szymon/0000-0003-4238-8763; Pancaldi, Simonetta/0000-0002-2077-3268 563 331 337 26 304 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0166-8595 1573-5079 PHOTOSYNTH RES Photosynth. Res. APR 2017.0 132.0 1 13 66 10.1007/s11120-016-0318-y 0.0 54 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences ES0YP 27815801.0 Green Submitted, Green Published, Green Accepted, hybrid 2023-03-23 WOS:000399255400002 0
J Wu, Y; Wu, YP; Guerrero, JM; Vasquez, JC Wu, Ying; Wu, Yanpeng; Guerrero, Josep M.; Vasquez, Juan C. A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Article Energy internet; Microgrids; Multi-energy systems; Standards; P2P; Energy trading; Energy transition; Digitalization; Decentralization; Electrification BIG DATA; POWER; FUTURE; TECHNOLOGIES; COMMUNICATION; FLEXIBILITY; INTEGRATION; RESOURCES; SECURITY; PRIVACY Energy Internet (EI) envisions a future energy system with sustainable concerns of efficiency, economy and environment by achieving flexibility of multi-energy-integrated physical space, digitalization of data-driven cyber space and interaction of customer-aware social space. To systemically understand how EI manages energy, data and information flows, a comprehensive investigation from multi-angle perspectives is presented in this paper. The core building blocks are categorized into three-layered framework: energy-oriented network, communication-oriented network and service-oriented management. Furthermore, this paper provides a multidisciplinary portrait of today's EI in terms of architecture, technologies, standards, services and platforms. Finally, open issues, future trends and challenges for development of sustainable EI are discussed. [Wu, Ying] Xian Shiyou Univ, Sch Comp Sci, Xian 710065, Shaanxi, Peoples R China; [Wu, Ying; Wu, Yanpeng; Guerrero, Josep M.; Vasquez, Juan C.] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Xi'an Shiyou University; Aalborg University Wu, Y (corresponding author), Xian Shiyou Univ, Sch Comp Sci, Xian 710065, Shaanxi, Peoples R China. yiw@energy.aau.dk; ywu@energy.aau.dk; joz@energy.aau.dk; juq@energy.aau.dk Guerrero, Josep M./D-5519-2014; Vasquez, Juan C./J-2247-2014 Guerrero, Josep M./0000-0001-5236-4592; Vasquez, Juan C./0000-0001-6332-385X; Wu, Ying/0000-0001-5602-3015 VILLUM FONDEN under the VILLUM Investigator Grant [25920]; Center for Research on Microgrids (CROM), AAU Talent Programme: The Energy Internet-Integrating IoT into the Smart Grid; National Natural Science Foundation of China [51707158, 61802301] VILLUM FONDEN under the VILLUM Investigator Grant(Villum Fonden); Center for Research on Microgrids (CROM), AAU Talent Programme: The Energy Internet-Integrating IoT into the Smart Grid; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920) : Center for Research on Microgrids (CROM) , AAU Talent Programme: The Energy Internet-Integrating IoT into the Smart Grid, National Natural Science Foundation of Chinaunder Grant 51707158 and 61802301. 107 18 18 7 25 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. OCT 2021.0 150.0 111409 10.1016/j.rser.2021.111409 0.0 JUL 2021 24 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels UR9JX 2023-03-23 WOS:000697057200004 0
J Wu, Z; Liao, HC; Lu, KY; Zavadskas, EK Wu, Zheng; Liao, Huchang; Lu, Keyu; Zavadskas, Edmundas Kazimieras Soft Computing Techniques and Their Applications in Intelligent Industrial Control Systems: A Survey INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL English Article soft computing; fuzzy logic; neural computing; genetic algorithm; intelligent industrial control system SLIDING-MODE CONTROL; NEURAL-NETWORK CONTROL; FUZZY-LOGIC CONTROL; PREDICTIVE CONTROL; ADAPTIVE-CONTROL; FAULT-DIAGNOSIS; DESIGN; DEFUZZIFICATION; ALGORITHM; SENSORS Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science. [Wu, Zheng; Liao, Huchang; Lu, Keyu] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China; [Zavadskas, Edmundas Kazimieras] Vilnius Gediminas Tech Univ, Inst Sustainable Construct, Vilnius, Lithuania Sichuan University; Vilnius Gediminas Technical University Liao, HC (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China. wuzheng0073@163.com; liaohuchang@scu.edu.cn; lukeyu2020@163.com; edmundas.zavadskas@vgtu.lt National Natural Science Foundation of China [71771156, 71971145] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work was supported by the National Natural Science Foundation of China (Nos. 71771156 and 71971145). 104 5 5 6 26 CCC PUBL-AGORA UNIV BIHOR PIATA TINERETULUI 8, ORADEA, JUD, BIHOR, 410526, ROMANIA 1841-9836 1841-9844 INT J COMPUT COMMUN Int. J. Comput. Commun. Control FEB 2021.0 16.0 1 SI 4142 10.15837/ijccc.2021.1.4142 0.0 28 Automation & Control Systems; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science QI0TV gold 2023-03-23 WOS:000618688900001 0
J Ge, RJ; Shen, TF; Zhou, Y; Liu, CY; Zhang, LB; Yang, BQ; Yan, Y; Coatrieux, JL; Chen, Y Ge, Rongjun; Shen, Tengfei; Zhou, Ying; Liu, Chengyu; Zhang, Libo; Yang, Benqiang; Yan, Ying; Coatrieux, Jean-Louis; Chen, Yang Convolutional squeeze-and-excitation network for ECG arrhythmia detection ARTIFICIAL INTELLIGENCE IN MEDICINE English Article Arrhythmia detection; 12-lead ECG; Se-residual convolutional module; Squeeze-and-excitation block NEURAL-NETWORK; FALSE ALARMS; CLASSIFICATION; REDUCTION Automatic detection of arrhythmia through an electrocardiogram (ECG) is of great significance for the prevention and treatment of cardiovascular diseases. In Convolutional neural network, the ECG signal is converted into multiple feature channels with equal weights through the convolution operation. Multiple feature channels can provide richer and more comprehensive information, but also contain redundant information, which will affect the diagnosis of arrhythmia, so feature channels that contain arrhythmia information should be paid attention to and given larger weight. In this paper, we introduced the Squeeze-and-Excitation (SE) block for the first time for the automatic detection of multiple types of arrhythmias with ECG. Our algorithm combines the residual convolutional module and the SE block to extract features from the original ECG signal. The SE block adaptively enhances the discriminative features and suppresses noise by explicitly modeling the interdependence between the channels, which can adaptively integrate information from different feature channels of ECG. The one-dimensional convolution operation over the time dimension is used to extract temporal information and the shortcut connection of the Se-Residual convolutional module in the proposed model makes the network easier to optimize. Thanks to the powerful feature extraction capabilities of the network, which can effectively extract discriminative arrhythmia features in multiple feature channels, so that no extra data preprocessing including denoising in other methods are need for our framework. It thus improves the working efficiency and keeps the collected biological information without loss. Experiments conducted with the 12-lead ECG dataset of the China Physiological Signal Challenge (CPSC) 2018 and the dataset of PhysioNet/Computing in Cardiology (CinC) Challenge 2017. The experiment results show that our model gains great performance and has great potential in clinical. [Ge, Rongjun; Shen, Tengfei; Zhou, Ying; Chen, Yang] Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China; [Ge, Rongjun] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China; [Chen, Yang] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 210096, Peoples R China; [Zhang, Libo; Yang, Benqiang; Yan, Ying] Gen Hosp Northern Theater Chinese Peoples Liberat, Dept Radiotherapy, Shenyang 110016, Peoples R China; [Liu, Chengyu] Southeast Univ, Sch Instrument Sci & Engn, Nanjing, Peoples R China; [Coatrieux, Jean-Louis] Sino Francais LIA CRIBs, Ctr Rech Informat Biomed, Rennes, France; [Coatrieux, Jean-Louis] INSERM, U1099, F-35000 Rennes, France; [Coatrieux, Jean-Louis] Univ Rennes 1, LTSI, F-35000 Rennes, France Southeast University - China; Nanjing University of Aeronautics & Astronautics; Southeast University - China; Southeast University - China; Southeast University - China; Universite de Rennes; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes; Universite de Rennes Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 210096, Peoples R China.;Liu, CY (corresponding author), Southeast Univ, Sch Instrument Sci & Engn, Nanjing, Peoples R China. chengyu@seu.edu.cn; chenyang.list@seu.edu.cn State's Key Project of Research and Development Plan [2017YFC0109202, 2018YFA0704102]; Fundamental Research Funds for the Central University [NS2021067]; National Natural Science Foundation [62101249, 61871117, 62171123, 81871444]; China Postdoctoral Science Foundation [2021TQ0149]; Science and Technology Program of Guangdong [2018B030333001] State's Key Project of Research and Development Plan; Fundamental Research Funds for the Central University; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Science and Technology Program of Guangdong This work was supported by the State's Key Project of Research and Development Plan (No. 2017YFC0109202, and 2018YFA0704102) ; the Fundamental Research Funds for the Central University (No. NS2021067) ; the National Natural Science Foundation (No. 62101249, 61871117, 62171123 and 81871444) ; the China Postdoctoral Science Foundation (No. 2021TQ0149) ; the Science and Technology Program of Guangdong (No. 2018B030333001) . 44 2 4 9 52 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0933-3657 1873-2860 ARTIF INTELL MED Artif. Intell. Med. NOV 2021.0 121.0 102181 10.1016/j.artmed.2021.102181 0.0 SEP 2021 11 Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Medical Informatics WH1CM 34763803.0 2023-03-23 WOS:000707425200001 0
J Quan, WZ; Wang, K; Yan, DM; Zhang, XP Quan, Weize; Wang, Kai; Yan, Dong-Ming; Zhang, Xiaopeng Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Image forensics; natural image; computer-generated image; convolutional neural network; robustness; local-to-global strategy; visualization CLASSIFICATION; DISCRIMINATION; SELECTION; GRAPHICS Distinguishing between natural images (NIs) and computer-generated (CG) images by naked human eyes is difficult. In this paper, we propose an effective method based on a convolutional neural network (CNN) for this fundamental image forensic problem. Having observed the rather limited performance of training existing CCNs from scratch or fine-tuning pretrained network, we design and implement a new and appropriate network with two cascaded convolutional layers at the bottom of a CNN. Our network can be easily adjusted to accommodate different sizes of input image patches while maintaining a fixed depth, a stable structure of CNN, and a good forensic performance. Considering the complexity of training CNNs and the specific requirement of image forensics, we introduce the so-called local-to-global strategy in our proposed network. Our CNN derives a forensic decision on local patches, and a global decision on a full-sized image can be easily obtained via simple majority voting. This strategy can also be used to improve the performance of existing methods that are based on hand-crafted features. Experimental results show that our method outperforms existing methods, especially in a challenging forensic scenario with NIs and CG images of heterogeneous origins. Our method also has good robustness against typical post-processing operations, such as resizing and JPEG compression. Unlike previous attempts to use CNNs for image forensics, we try to understand what our CNN has learned about the differences between NIs and CG images with the aid of adequate and advanced visualization tools. [Quan, Weize; Yan, Dong-Ming; Zhang, Xiaopeng] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China; [Quan, Weize; Yan, Dong-Ming; Zhang, Xiaopeng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Quan, Weize; Wang, Kai] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Yan, DM (corresponding author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. qweizework@gmail.com; kai.wang@gipsa-lab.grenoble-inp.fr; yandongming@gmail.com; xiaopeng.zhang@ia.ac.cn Yan, Dong-Ming/0000-0003-2209-2404 National Natural Science Foundation of China [61772523, 61620106003, 61331018]; French National Agency for Research through PERSYVAL-lab [ANR-11-LABX-0025-01]; DEFALS [ANR-16-DEFA-0003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); French National Agency for Research through PERSYVAL-lab; DEFALS This work was supported in part by the National Natural Science Foundation of China under Grant 61772523, Grant 61620106003, and Grant 61331018 and in part by the French National Agency for Research through PERSYVAL-lab under Grant ANR-11-LABX-0025-01 and through DEFALS under Grant ANR-16-DEFA-0003. 65 54 54 4 1573 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. NOV 2018.0 13.0 11 2772 2787 10.1109/TIFS.2018.2834147 0.0 16 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GH8HO 2023-03-23 WOS:000433909100005 0
J Hu, F; Li, HX; Liu, Y; Teichert, T Hu, Feng; Li, Hongxiu; Liu, Yong; Teichert, Thorsten Optimizing service offerings using asymmetric impact-sentiment- performance analysis INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT English Article User-generated content; Sentiment analysis; Improvement strategies; Attribute priority; Asymmetric impact-sentiment-performance analysis; Hotel chains CUSTOMER SATISFACTION; QUALITY ATTRIBUTES; 3-FACTOR THEORY; ONLINE REVIEWS; TOURIST SATISFACTION; HOTEL PERFORMANCE; SOCIAL MEDIA; BIG DATA; CLASSIFICATION; IDENTIFICATION Researchers refer to various theories to investigate the distinct relationships between importance, performance, and the (a)symmetric impact of service attributes on customer satisfaction (CS). However, a fully integrated model that would allow practitioners to automatically execute analyses to optimize their service offerings in a competitive landscape is missing. Previous studies widely rely on importance/performance ratings of predefined service attributes retrieved from closed-ended questionnaires, which can hardly capture the competitive landscape from the customers' perspective. This paper introduces a novel asymmetric impact-sentiment-performance analysis (AISPA) to address these gaps by performing automated opinion mining on online reviews. Customers' evaluations of three hotel chains serve as an example application. The impact-asymmetry of the hotel service attributes on CS, the attribute impact and performance are jointly visualized in a 3D grid. An elaborate understanding of service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future offerings. [Hu, Feng] Zhejiang Normal Univ, Xingzhi Coll, Jinhua 321004, Zhejiang, Peoples R China; [Hu, Feng; Teichert, Thorsten] Univ Hamburg, Chair Mkt & Innovat, Hamburg, Germany; [Li, Hongxiu] Tampere Univ, Dept Informat & Knowledge Management, Tampere, Finland; [Liu, Yong] Aalto Univ, Dept Informat & Serv Econ, Sch Business, Helsinki, Finland Zhejiang Normal University; University of Hamburg; Tampere University; Aalto University Hu, F (corresponding author), Zhejiang Normal Univ, Xingzhi Coll, Jinhua 321004, Zhejiang, Peoples R China.;Hu, F (corresponding author), Univ Hamburg, Chair Mkt & Innovat, Hamburg, Germany. hufeng@zjnu.cn; Hongxiu.li@tuni.fi; Yong.Liu@aalto.fi; Thorsten.Teichert@uni-hamburg.de Liu, Yong/AAY-8792-2020 Liu, Yong/0000-0002-0228-8971 National Natural Science Foundation, PR China [71974149] National Natural Science Foundation, PR China(National Natural Science Foundation of China (NSFC)) The authors have no conflicts of interest to disclose. This research is supported in part by National Natural Science Foundation, PR China (Grant No. 71974149). This article does not contain any studies with animals performed by any of the authors. 72 11 11 10 65 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-4319 1873-4693 INT J HOSP MANAG Int. J. Hosp. Manag. AUG 2020.0 89.0 102557 10.1016/j.ijhm.2020.102557 0.0 16 Hospitality, Leisure, Sport & Tourism Social Science Citation Index (SSCI) Social Sciences - Other Topics MO2DV 2023-03-23 WOS:000551344300025 0
J Yang, XY; Wallom, D; Waddington, S; Wang, JW; Shaon, A; Matthews, B; Wilson, M; Guo, YK; Guo, L; Blower, JD; Vasilakos, AV; Liu, KC; Kershaw, P Yang, Xiaoyu; Wallom, David; Waddington, Simon; Wang, Jianwu; Shaon, Arif; Matthews, Brian; Wilson, Michael; Guo, Yike; Guo, Li; Blower, Jon D.; Vasilakos, Athanasios V.; Liu, Kecheng; Kershaw, Philip Cloud computing in e-Science: research challenges and opportunities JOURNAL OF SUPERCOMPUTING English Article e-Science; e-Research; Informatics; Cloud computing; Semantic web; Grid computing; Workflow; Digital research; Big data SCIENTIFIC WORKFLOWS; DESIGN; MAPREDUCE; STORAGE; ASKALON; TOOL Service-oriented architecture (SOA), workflow, the Semantic Web, and Grid computing are key enabling information technologies in the development of increasingly sophisticated e-Science infrastructures and application platforms. While the emergence of Cloud computing as a new computing paradigm has provided new directions and opportunities for e-Science infrastructure development, it also presents some challenges. Scientific research is increasingly finding that it is difficult to handle big data using traditional data processing techniques. Such challenges demonstrate the need for a comprehensive analysis on using the above-mentioned informatics techniques to develop appropriate e-Science infrastructure and platforms in the context of Cloud computing. This survey paper describes recent research advances in applying informatics techniques to facilitate scientific research particularly from the Cloud computing perspective. Our particular contributions include identifying associated research challenges and opportunities, presenting lessons learned, and describing our future vision for applying Cloud computing to e-Science. We believe our research findings can help indicate the future trend of e-Science, and can inform funding and research directions in how to more appropriately employ computing technologies in scientific research. We point out the open research issues hoping to spark new development and innovation in the e-Science field. [Yang, Xiaoyu; Blower, Jon D.] Univ Reading, Reading E Sci Ctr, Reading, Berks, England; [Yang, Xiaoyu] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China; [Wallom, David] Univ Oxford, Oxford E Res Ctr, Oxford, England; [Waddington, Simon] Univ London, Kings Coll London, Ctr E Res, London SW3 6LX, England; [Wang, Jianwu] Univ Calif San Diego, San Diego Supercomp Ctr, San Diego, CA 92103 USA; [Shaon, Arif; Matthews, Brian; Wilson, Michael] Rutherford Appleton Lab, Dept Comp Sci, Didcot, Oxon, England; [Guo, Yike; Guo, Li] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London, England; [Vasilakos, Athanasios V.] Univ Western Macedonia, Dept Comp & Telecommun Engn, Florina, Greece; [Liu, Kecheng] Univ Reading, Informat Res Ctr, Reading, Berks, England; [Kershaw, Philip] Rutherford Appleton Lab, STFC, NCEO Ctr Environm Data Archival, Didcot OX11 0QX, Oxon, England University of Reading; Chinese Academy of Sciences; Computer Network Information Center, CAS; University of Oxford; University of London; King's College London; University of California System; University of California San Diego; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; University of Western Macedonia; University of Reading; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory Yang, XY (corresponding author), Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China. kxy@cnic.cn 郭, 伊可/GYD-3212-2022; Guo, Li/HFU-2288-2022; Vasilakos, Athanasios/J-2824-2017; Waddington, Simon/G-3778-2011; Wang, Jianwu/AAL-9943-2020; Matthews, Brian/I-4698-2014 Waddington, Simon/0000-0003-4970-4730; Wang, Jianwu/0000-0002-9933-1170; Blower, Jon/0000-0002-5014-1026; Liu, Kecheng/0000-0002-4642-957X; Guo, Li/0000-0003-1272-8480; Vasilakos, Athanasios/0000-0003-1902-9877; Kershaw, Philip/0000-0002-7646-291X; Matthews, Brian/0000-0002-3342-3160; Wallom, David/0000-0001-7527-3407; Shaon, Arif/0000-0002-5729-8632 EPSRC [EP/I03436X/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/I03436X/1] Funding Source: researchfish EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) 131 24 28 2 87 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0920-8542 1573-0484 J SUPERCOMPUT J. Supercomput. OCT 2014.0 70.0 1 408 464 10.1007/s11227-014-1251-5 0.0 57 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering AQ0JB Green Accepted 2023-03-23 WOS:000342468200025 0
C Colleman, S; Zhu, P; Sun, W; Verhelst, M IEEE Colleman, Steven; Zhu, Peter; Sun, Wei; Verhelst, Marian Optimizing Accelerator Configurability for Mobile Transformer Networks 2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA English Proceedings Paper IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era JUN 13-15, 2022 Incheon, SOUTH KOREA IEEE,CAS,Minist Sci & ICT,AI Semicond Forum,Incheon Metropolitan City,Incheon Tourism Org,Korea Tourism Org CNN; transformer; cross-layer; configurability; hardware modelling and optimization Transformers are increasingly used to process time series data. Their deployment in mobile devices is however challenging due to their large computational requirements. Hardware acceleration through custom neural network processors can reduce the resulting latency and energy footprint of the network. Yet, the large variety of different layer types in mobile transformers troubles the selection of the best accelerator architecture and hardware mapping. Specifically, the layer performance strongly depends on the spatial unrolling dimensions, i.e. the layer loop dimensions along which hardware parallelization is enabled. This paper will therefore research the best datapath organization, and required datapath flexibility to efficiently support mobile transformers. The MobileViT-S network is selected as the reference network for its wide variety in layer types. Results are explored across a wide range of accelerator area (datapath dimensions, memory size) and bandwidth constraints. [Colleman, Steven; Verhelst, Marian] KULeuven, Dept Elect Engn, MICAS ESAT, Leuven, Belgium; [Zhu, Peter; Sun, Wei] OPPO Elect, Dongguan, Peoples R China KU Leuven Colleman, S (corresponding author), KULeuven, Dept Elect Engn, MICAS ESAT, Leuven, Belgium. Verhelst, Marian/E-5739-2011 Verhelst, Marian/0000-0003-3495-9263 KU Leuven; Flemish Government (AI Research Program); EU ERC project Re-SENSE [ERC-2016-STG715037]; Oppo KU Leuven(KU Leuven); Flemish Government (AI Research Program); EU ERC project Re-SENSE; Oppo This work has been realized with support of KU Leuven, the Flemish Government (AI Research Program), the EU ERC project Re-SENSE under grant agreement ERC-2016-STG715037, and Oppo. 5 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-0996-4 2022.0 142 145 10.1109/AICAS54282.2022.9869945 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT9CO 2023-03-23 WOS:000859273200037 0
J Ghorbanzadeh, O; Crivellari, A; Tiede, D; Ghamisi, P; Lang, SF Ghorbanzadeh, Omid; Crivellari, Alessandro; Tiede, Dirk; Ghamisi, Pedram; Lang, Stefan Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning REMOTE SENSING English Article remote sensing; refugees; humanitarian operations; Africa CAMPS The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively. [Ghorbanzadeh, Omid; Tiede, Dirk; Lang, Stefan] Univ Salzburg, Dept Geoinformat Z GIS, Christian Doppler Lab Geospatial & EO based Humani, A-5020 Salzburg, Austria; [Ghorbanzadeh, Omid; Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, Landstr Hauptstr 5, A-1030 Vienna, Austria; [Crivellari, Alessandro] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Machine Learning Grp, Chemnitzer Str 40, D-09599 Freiberg, Germany Salzburg University; Southern University of Science & Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Ghorbanzadeh, O (corresponding author), Univ Salzburg, Dept Geoinformat Z GIS, Christian Doppler Lab Geospatial & EO based Humani, A-5020 Salzburg, Austria.;Ghorbanzadeh, O (corresponding author), Inst Adv Res Artificial Intelligence IARAI, Landstr Hauptstr 5, A-1030 Vienna, Austria. omid.ghorbanzadeh@iarai.ac.at Lang, Stefan/0000-0003-0619-0098; Ghorbanzadeh, Omid/0000-0002-9664-8770; Tiede, Dirk/0000-0002-5473-3344 67 0 0 1 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. DEC 2022.0 14.0 24 6382 10.3390/rs14246382 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 7H5IX gold, Green Published 2023-03-23 WOS:000903238200001 0
J Jangirala, S; Das, AK; Vasilakos, AV Jangirala, Srinivas; Das, Ashok Kumar; Vasilakos, Athanasios V. Designing Secure Lightweight Blockchain-Enabled RFID-Based Authentication Protocol for Supply Chains in 5G Mobile Edge Computing Environment IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Blockchain; 5G mobile communication; Artificial intelligence; Edge computing; Informatics; Supply chains; Protocols; Authentication; blockchain; 5G mobile edge computing; radio frequency identification (RFID); security; supplychain MUTUAL AUTHENTICATION; IOT; INTERNET; THINGS; MANAGEMENT; READER; CACHE Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols. [Jangirala, Srinivas] OP Jindal Global Univ, Jindal Global Business Sch, Comp Sci & Engn, Sonipat 131001, Haryana, India; [Das, Ashok Kumar] Int Inst Informat Technol, Ctr Secur Theory & Algorithm Res, Comp Sci & Engn, Hyderabad 500032, India; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden; [Vasilakos, Athanasios V.] Fuzhou Univ, Comp Sci & Technol, Fuzhou 350108, Fujian, Peoples R China O.P. Jindal Global University; International Institute of Information Technology Hyderabad; Lulea University of Technology; Fuzhou University Das, AK (corresponding author), Int Inst Informat Technol, Ctr Secur Theory & Algorithm Res, Comp Sci & Engn, Hyderabad 500032, India. sjangirala@jgu.edu.in; iitkgp.akdas@gmail.com; th.vasilakos@gmail.com Das, Ashok Kumar/U-2790-2019 Das, Ashok Kumar/0000-0002-5196-9589; Vasilakos, Athanasios/0000-0003-1902-9877 Ripple Centre of Excellence Scheme, Centre of Excellence (CoE) in Blockchain, IIIT Hyderabad, India [International Institute of Information Technology (IIIT)/R&D Office/Internal Projects/001/2019] Ripple Centre of Excellence Scheme, Centre of Excellence (CoE) in Blockchain, IIIT Hyderabad, India This work was supported by the Ripple Centre of Excellence Scheme, Centre of Excellence (CoE) in Blockchain (Sanction no. International Institute of Information Technology (IIIT)/R&D Office/Internal Projects/001/2019), IIIT Hyderabad, India. 57 95 95 19 182 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. NOV 2020.0 16.0 11 7081 7093 10.1109/TII.2019.2942389 0.0 13 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering MT3YP Green Submitted 2023-03-23 WOS:000554904700034 0
J Pang, SC; Zhang, Y; Song, T; Zhang, XD; Wang, X; Rodriguez-Paton, A Pang, Shanchen; Zhang, Ying; Song, Tao; Zhang, Xudong; Wang, Xun; Rodriguez-Paton, Alfonso AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction BRIEFINGS IN BIOINFORMATICS English Article DDI prediction; multidimensional feature; encoder; deep learning TORASEMIDE The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures. [Pang, Shanchen] Tongji Univ, Comp Software & Theory, Shanghai, Peoples R China; [Pang, Shanchen; Song, Tao; Wang, Xun] China Univ Petr, Qingdao, Peoples R China; [Zhang, Ying; Zhang, Xudong] China Univ Petr, Dept Comp Sci & Engn, Qingdao, Peoples R China; [Song, Tao] Huazhong Univ Sci & Technol, Wuhan, Peoples R China; [Song, Tao] Univ Politecn Madrid, Madrid, Spain; [Wang, Xun] Tsukuba Univ, Tsukuba, Ibaraki, Japan; [Wang, Xun] Chinese Acad Sci, Inst Comp Technol, China High Performance Comp Res Ctr, Beijing, Peoples R China; [Rodriguez-Paton, Alfonso] Univ Santiago de Compostela, Elect & Computat, Santiago De Compostela, Spain; [Rodriguez-Paton, Alfonso] Politecn Univ Madrid, Comp Sci, Madrid, Spain; [Rodriguez-Paton, Alfonso] Politecn Univ Madrid, Fac Informat, Madrid, Spain Tongji University; China University of Petroleum; China University of Petroleum; Huazhong University of Science & Technology; Universidad Politecnica de Madrid; University of Tsukuba; Chinese Academy of Sciences; Institute of Computing Technology, CAS; Universidade de Santiago de Compostela Song, T (corresponding author), China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China. t.song@upm.es 张, 旭东/GWR-2966-2022; Song, Tao/T-7360-2018; Wang, Xun/HOH-8824-2023 张, 旭东/0000-0003-2671-8275; Song, Tao/0000-0002-0130-3340; Natural Science Foundation of China [61873280, 61972416, 61873281]; Taishan Scholarship [tsqn201812029]; Natural Science Foundation of Shandong Province [2019GGX101067]; Foundation of Science and Technology Development of Jinan [201907116]; Fundamental Research Funds for the Central Universities [18CX02152A, 19CX05003A-6]; Intelligent Diagnosis of Early Lung Cancer Classification Based on Self-learning Pulsed Neural Membrane System and Its Clinical Application [05E19070003]; 3D Reconstruction and Accurate Volume Measurement of White Matter High Signal Based on Deep Learning and Its Clinical Application [05tc2107001]; Research on Topological Representation and ultra -low complexity computability of large-scale graph data [05E18070010]; TalentoComunidad de Madrid Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Taishan Scholarship; Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Foundation of Science and Technology Development of Jinan; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Intelligent Diagnosis of Early Lung Cancer Classification Based on Self-learning Pulsed Neural Membrane System and Its Clinical Application; 3D Reconstruction and Accurate Volume Measurement of White Matter High Signal Based on Deep Learning and Its Clinical Application; Research on Topological Representation and ultra -low complexity computability of large-scale graph data; TalentoComunidad de Madrid Natural Science Foundation of China (grant nos 61873280, 61972416, 61873281); Taishan Scholarship (grant no. tsqn201812029); Natural Science Foundation of Shandong Province (grant no. 2019GGX101067); Foundation of Science and Technology Development of Jinan (grant no. 201907116); Fundamental Research Funds for the Central Universities (grant no. 18CX02152A, 19CX05003A-6); Intelligent Diagnosis of Early Lung Cancer Classification Based on Self-learning Pulsed Neural Membrane System and Its Clinical Application (grant no. 05E19070003); 3D Reconstruction and Accurate Volume Measurement of White Matter High Signal Based on Deep Learning and Its Clinical Application (grant no. 05tc2107001); Research on Topological Representation and ultra -low complexity computability of large-scale graph data (grant no. 05E18070010); Juan de la Cierva and TalentoComunidad de Madrid. 52 15 15 7 23 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. JAN 17 2022.0 23.0 1 bbab545 10.1093/bib/bbab545 0.0 12 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology ZK5AS 34965586.0 2023-03-23 WOS:000763000800097 0
J Feng, QQ; Li, JL; Ping, XR; Van Calster, B Feng, Qunqiang; Li, Jialiang; Ping, Xingrun; Van Calster, Ben Hypervolume under ROC manifold for discrete biomarkers with ties JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION English Article Diagnostic medicine; discrete distribution; biomarkers; multi-category classification; hypervolume under ROC manifold MACHINE LEARNING-METHODS; PROBABILITY ESTIMATION; ACCURACY; RECLASSIFICATION; PERFORMANCE; REGRESSION; MODELS; TESTS Medical multi-category diagnostic problems may involve discrete biomarkers. Many traditional accuracy measures are based on the assumption that all biomarkers follow continuous distributions and consequently may underestimate the true discrimination ability of the discrete markers. In particular, we focus on Hypervolume Under ROC Manifold (HUM) in this paper and propose an extension of the familiar continuous version of HUM to incorporate discrete biomarkers with ties. Statistical estimation and inference procedures are proposed along with asymptotic properties. We carry out simulation studies to examine the performance of our proposed estimators for the new HUM measure. A real medical example is analysed to illustrate our methodology. [Feng, Qunqiang] Univ Sci & Technol China, Sch Management, Dept Stat & Finance, Hefei, Peoples R China; [Li, Jialiang] Natl Univ Singapore, Singapore, Singapore; [Li, Jialiang] Duke NUS Grad Med Sch, Singapore, Singapore; [Li, Jialiang] Singapore Eye Res Inst, Singapore, Singapore; [Ping, Xingrun] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Van Calster, Ben] Katholieke Univ Leuven, Leuven, Belgium Chinese Academy of Sciences; University of Science & Technology of China, CAS; National University of Singapore; National University of Singapore; National University of Singapore; Singapore National Eye Center; Shanghai Jiao Tong University; KU Leuven Feng, QQ (corresponding author), Univ Sci & Technol China, Sch Management, Dept Stat & Finance, Hefei, Peoples R China. fengqq@ustc.edu.cn Van Calster, Ben/F-9150-2010; Li, Jialiang/B-9132-2014 Van Calster, Ben/0000-0003-1613-7450; Li, Jialiang/0000-0002-9704-4135; Feng, Qunqiang/0000-0003-4680-0188 36 1 1 1 8 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0094-9655 1563-5163 J STAT COMPUT SIM J. Stat. Comput. Simul. DEC 12 2021.0 91.0 18 3864 3879 10.1080/00949655.2021.1954184 0.0 JUL 2021 16 Computer Science, Interdisciplinary Applications; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics XD9RM 2023-03-23 WOS:000675133600001 0
J Liu, XY; Wang, XY; Luo, YC; Wang, MJ; Chen, ZJ; Han, XY; Zhou, SJ; Wang, JH; Kong, J; Yu, HY; Wang, XB; Tang, XY; Guo, QY Liu, Xiaoya; Wang, Xueying; Luo, Yucheng; Wang, Meijuan; Chen, Zijian; Han, Xiaoyu; Zhou, Sijia; Wang, Jiahao; Kong, Jian; Yu, Hanry; Wang, Xiaobo; Tang, Xiaoying; Guo, Qiongyu A 3D Tumor-Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-Based Quantitative Analyses ADVANCED SCIENCE English Article; Early Access 3D drug release model; decellularized organ; extracellular matrix; hepatocellular carcinoma; transarterial chemoembolization DOXORUBICIN-ELUTING BEADS; HEPATOCELLULAR-CARCINOMA; TRANSARTERIAL CHEMOEMBOLIZATION; DC BEAD; DELIVERY; LIPIODOL; EMBOLIZATION; TISSUES; SAFETY; TACE Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first-line treatment option of unresectable HCC that employs drug-loaded embolic agents to occlude tumor-feeding arteries and concomitantly delivers chemotherapeutic drugs into the tumor, is still under fierce debate in terms of the treatment parameters. The models that can produce in-depth knowledge of the overall intratumoral drug release behavior are lacking. This study engineers a 3D tumor-mimicking drug release model, which successfully overcomes the substantial limitations of conventional in vitro models through utilizing decellularized liver organ as a drug-testing platform that uniquely incorporates three key features, i.e., complex vasculature systems, drug-diffusible electronegative extracellular matrix, and controlled drug depletion. This drug release model combining with deep learning-based computational analyses for the first time permits quantitative evaluation of all important parameters associated with locoregional drug release, including endovascular embolization distribution, intravascular drug retention, and extravascular drug diffusion, and establishes long-term in vitro-in vivo correlations with in-human results up to 80 d. This model offers a versatile platform incorporating both tumor-specific drug diffusion and elimination settings for quantitative evaluation of spatiotemporal drug release kinetics within solid tumors. [Liu, Xiaoya; Luo, Yucheng; Wang, Meijuan; Chen, Zijian; Han, Xiaoyu; Guo, Qiongyu] Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen Key Lab Smart Healthcare Engn, Guangdong Prov Key Lab Adv Biomat, Shenzhen 518055, Guangdong, Peoples R China; [Wang, Xueying; Tang, Xiaoying] Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Guangdong, Peoples R China; [Zhou, Sijia; Wang, Xiaobo] Univ Toulouse, Ctr Biol Integrat CBI, Dept Mol Cellular & Dev Biol MCD, F-31062 Toulouse, France; [Wang, Jiahao; Yu, Hanry] Natl Univ Singapore, Mechanobiol Inst, Singapore 117411, Singapore; [Kong, Jian] Jinan Univ, Southern Univ Sci & Technol, Clin Med Coll 2, Affiliated Hosp 1,Shenzhen Peoples Hosp,Dept Inter, Shenzhen 518020, Guangdong, Peoples R China; [Yu, Hanry] Natl Univ Singapore, Inst Digital Med, Dept Physiol, Singapore 117593, Singapore; [Yu, Hanry] Natl Univ Singapore, Mechanobiol Inst, Singapore 117593, Singapore; [Tang, Xiaoying] Southern Univ Sci & Technol, Jiaxing Res Inst, Jiaxing 314000, Zhejiang, Peoples R China; [Liu, Xiaoya] Shenzhen Childrens Hosp, Dept Pharm, Shenzhen 518026, Guangdong, Peoples R China Southern University of Science & Technology; Southern University of Science & Technology; Universite de Toulouse; National University of Singapore; Jinan University; Southern University of Science & Technology; National University of Singapore; National University of Singapore; Southern University of Science & Technology Guo, QY (corresponding author), Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen Key Lab Smart Healthcare Engn, Guangdong Prov Key Lab Adv Biomat, Shenzhen 518055, Guangdong, Peoples R China.;Tang, XY (corresponding author), Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Guangdong, Peoples R China.;Tang, XY (corresponding author), Southern Univ Sci & Technol, Jiaxing Res Inst, Jiaxing 314000, Zhejiang, Peoples R China. tangxy@sustech.edu.cn; guoqy@sustech.edu.cn Luo, Yucheng/0000-0003-1469-0746 Key-Area Research and Development Program of Guangdong Province [2019B010941001]; National Natural Science Foundation of China [81971764, 62071210]; Guangdong Research Fund [2019ZT08Y191]; Innovation Committee of Shenzhen Municipality [JCYJ20200109141638004, JCYJ20190809120205578, JCYJ20200925153847004, KCXFZ2020122117340001, RCYX20210609103056042]; Guangdong Provincial Key Laboratory of Advanced Biomaterials [2022B1212010003]; Scientifiques de la Fondation ARC [PJA 20171206526]; Shenzhen Key Laboratory of Smart Healthcare Engineering [ZDSYS20200811144003009]; Southern University of Science and Technology (SUSTech); SUSTech Core Research Facilities; Laboratory Animal Center of the SUSTech Key-Area Research and Development Program of Guangdong Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Research Fund; Innovation Committee of Shenzhen Municipality; Guangdong Provincial Key Laboratory of Advanced Biomaterials; Scientifiques de la Fondation ARC; Shenzhen Key Laboratory of Smart Healthcare Engineering; Southern University of Science and Technology (SUSTech); SUSTech Core Research Facilities; Laboratory Animal Center of the SUSTech X.L. and X.W. contributed equally to this work. This work was supported by the Key-Area Research and Development Program of Guangdong Province (2019B010941001, Q.G.), National Natural Science Foundation of China (81971764, Q.G.; 62071210, X.T.), Guangdong Research Fund (2019ZT08Y191, Q.G.), the Innovation Committee of Shenzhen Municipality (JCYJ20200109141638004, Q.G.; JCYJ20190809120205578, JCYJ20200925153847004, KCXFZ2020122117340001, and RCYX20210609103056042, X.T.), Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B1212010003, Q.G.), Scientifiques de la Fondation ARC (Grant No. PJA 20171206526, X.W.), Shenzhen Key Laboratory of Smart Healthcare Engineering (ZDSYS20200811144003009, Q.G.), and the Startup Funding from the Southern University of Science and Technology (SUSTech, Q.G.). The authors also appreciate the assistance from the SUSTech Core Research Facilities, and the support from the Laboratory Animal Center of the SUSTech. 68 0 0 5 5 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2198-3844 ADV SCI Adv. Sci. 10.1002/advs.202206195 0.0 FEB 2023 18 Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science 9E3NZ 36793129.0 gold 2023-03-23 WOS:000936698100001 0
J Zhang, CX; Yue, P; Tapete, D; Jiang, LC; Shangguan, BY; Huang, L; Liu, GC Zhang, Chenxiao; Yue, Peng; Tapete, Deodato; Jiang, Liangcun; Shangguan, Boyi; Huang, Li; Liu, Guangchao A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Change detection; Deep supervision network; Image fusion; High resolution remote sensing image; Image difference discrimination URBAN Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods. [Zhang, Chenxiao] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China; [Yue, Peng; Jiang, Liangcun; Shangguan, Boyi; Huang, Li; Liu, Guangchao] Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China; [Yue, Peng] Wuhan Univ, Hubei Prov Engn Ctr Intelligent Geoproc HPECIG, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China; [Yue, Peng] Collaborat Innovat Ctr Geospatial Technol, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China; [Tapete, Deodato] Italian Space Agcy ASI, Via Politecn Snc, I-00133 Rome, Italy Wuhan University; Wuhan University; Wuhan University; Agenzia Spaziale Italiana (ASI) Yue, P (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. pyue@whu.edu.cn Tapete, Deodato/AAB-7528-2021 Tapete, Deodato/0000-0002-7242-4473; Yue, Peng/0000-0003-3006-4542; Jiang, Liangcun/0000-0003-4994-7644 Major State Research Development Program of China [2017YFB0504103]; National Natural Science Foundation of China [41722109]; Hubei Provincial Natural Science Foundation of China [2018CFA053]; Wuhan Yellow Crane Talents (Science) Program Major State Research Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Wuhan Yellow Crane Talents (Science) Program We appreciate the reviewers and editors for their constructive comments that helped improve the quality of the paper. The work was supported by Major State Research Development Program of China (No. 2017YFB0504103), National Natural Science Foundation of China (No. 41722109), Hubei Provincial Natural Science Foundation of China (No. 2018CFA053), and Wuhan Yellow Crane Talents (Science) Program (2016). 43 195 201 43 201 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. AUG 2020.0 166.0 183 200 10.1016/j.isprsjprs.2020.06.003 0.0 18 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology MO1AQ 2023-03-23 WOS:000551268300015 0
J Jin, J; Zhu, AC; Wang, YQ; Wright, J Jin, Jing; Zhu, Aichun; Wang, Yuanqing; Wright, James A feature binding model in computer vision for object detection MULTIMEDIA TOOLS AND APPLICATIONS English Article Feature binding; Object detection; Computer vision; Occluded object; Human-machine interactive RECOGNITION; ATTENTION In this paper, the authors propose the Feature Binding (FB) strategy in computer vision, a method combined with the biological visual perception theory. Based on feature subspace, the proposed method refers to the biological model and binds features according to certain rules. All features bound in a group are taken as a whole. Besides, all groups with different weight coefficients according to different importance are used to determine the object and its location. The position of the object can be determined based on the calculation according to the corresponding criteria. Feature Binding can significantly enhance the accuracy of object detection and localization. Moreover, the method can accelerate object detection and resist external interference in the unbound feature subspace. Feature Binding has good accuracy not only for the whole object but also for the obscured object. It also has good robustness for different algorithms, which are based on features, including traditional methods and deep learning algorithms. The object positioning system can detect the partially occluded objects more accurately in practice. [Jin, Jing; Zhu, Aichun] Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing 211816, Peoples R China; [Wang, Yuanqing] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China; [Wright, James] Technol Univ Dublin, Sch Engn, Elect Engn, Dublin D24 FKT9, Ireland Nanjing Tech University; Nanjing University Jin, J (corresponding author), Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing 211816, Peoples R China. jing_jin@njtech.edu.cn Natural Science Foundation of Jiangsu Higher Education Institutions of China [19KJB520009] Natural Science Foundation of Jiangsu Higher Education Institutions of China This work was supported by the Natural Science Foundation of Jiangsu Higher Education Institutions of China (19KJB520009). 32 0 0 2 8 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2021.0 80.0 13 19377 19397 10.1007/s11042-021-10702-9 0.0 FEB 2021 21 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SG0SC 2023-03-23 WOS:000622245500001 0
J Zhong, XR; Luo, T; Deng, L; Liu, P; Hu, KJ; Lu, DH; Zheng, D; Luo, CX; Xie, YX; Li, JY; He, P; Pu, TJ; Ye, F; Bu, H; Fu, B; Zheng, H Zhong, Xiaorong; Luo, Ting; Deng, Ling; Liu, Pei; Hu, Kejia; Lu, Donghao; Zheng, Dan; Luo, Chuanxu; Xie, Yuxin; Li, Jiayuan; He, Ping; Pu, Tianjie; Ye, Feng; Bu, Hong; Fu, Bo; Zheng, Hong Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study JMIR MEDICAL INFORMATICS English Article breast cancer; prognosis; machine learning; prediction model REAL-WORLD DATA; ADJUVANT ONLINE; STAGE; SURVIVAL; WOMEN; RECURRENCE; RISK; DECISIONS; THERAPY; PROGRAM Background: Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. Objective: We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. Methods: This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. Results: The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. Conclusions: Our prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China. [Zhong, Xiaorong; Luo, Ting; Xie, Yuxin; He, Ping] Sichuan Univ, West China Hosp, Canc Ctr, Dept Head Neck & Mammary Gland Oncol, Chengdu, Peoples R China; [Deng, Ling; Zheng, Dan; Luo, Chuanxu; Zheng, Hong] Sichuan Univ, West China Hosp, Clin Res Ctr Breast, Lab Mol Diag Canc, 37 Guoxuexiang, Chengdu, Peoples R China; [Liu, Pei; Fu, Bo] Univ Elect Sci & Technol China, Big Data Res Ctr, Sch Comp Sci & Engn, Chengdu, Peoples R China; [Hu, Kejia; Lu, Donghao] Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden; [Li, Jiayuan] Sichuan Univ, West China Sch Publ Hlth, Dept Epidemiol & Biostat, Chengdu, Peoples R China; [Pu, Tianjie; Ye, Feng; Bu, Hong] Sichuan Univ, West China Hosp, Lab Pathol, Chengdu, Peoples R China Sichuan University; Sichuan University; University of Electronic Science & Technology of China; Karolinska Institutet; Sichuan University; Sichuan University Zheng, H (corresponding author), Sichuan Univ, West China Hosp, Clin Res Ctr Breast, Lab Mol Diag Canc, 37 Guoxuexiang, Chengdu, Peoples R China. hzheng@scu.edu.cn fu, bo/AAY-1952-2021; Pu, Tianjie/AAE-9454-2022; li, jia/GVT-7587-2022; Lu, Donghao/AAH-6445-2019; Pu, Tianjie/AFD-8574-2022 Lu, Donghao/0000-0002-4186-8661; Zheng, Dan/0000-0002-8066-328X; Luo, Chuanxu/0000-0002-2761-9394; Zhong, Xiaorong/0000-0003-1812-7213; Liu, Pei/0000-0002-3795-6140 key program of the Science and Technology Department of Sichuan Province [2017SZ0005]; 135 project for disciplines of excellence, West China Hospital, Sichuan University [ZYGD18012]; Post-Doctor Research Project, West China Hospital, Sichuan University [2019HXBH015] key program of the Science and Technology Department of Sichuan Province; 135 project for disciplines of excellence, West China Hospital, Sichuan University; Post-Doctor Research Project, West China Hospital, Sichuan University We thank Professor Paul Pharoah from the University of Cambridge for providing the PREDICT code and interpreting the validation results of PREDICT based on our data. The authors would like to thank AsiaEdit for providing linguistic assistance during the preparation of this manuscript. This work was supported by the key program of the Science and Technology Department of Sichuan Province (grant number: 2017SZ0005 to HZ), the 135 project for disciplines of excellence, West China Hospital, Sichuan University (grant number: ZYGD18012 to HB), and the Post-Doctor Research Project, West China Hospital, Sichuan University (grant number: 2019HXBH015 to LD). 45 8 8 1 12 JMIR PUBLICATIONS, INC TORONTO 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA 2291-9694 JMIR MED INF JMIR Med. Inf. NOV 2020.0 8.0 11 e19069 10.2196/19069 0.0 15 Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Medical Informatics PA5GG 33164899.0 Green Accepted, gold 2023-03-23 WOS:000595663400011 0
J Kou, L; Sysyn, M; Liu, JX; Nabochenko, O; Han, Y; Peng, D; Fischer, S Kou, Lei; Sysyn, Mykola; Liu, Jianxing; Nabochenko, Olga; Han, Yue; Peng, Dai; Fischer, Szabolcs Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory SUSTAINABILITY English Article contact fatigue; neural convolution; machine learning; turnout; switch; frog rail; rail surface; long short-term memory The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed. [Kou, Lei; Sysyn, Mykola; Nabochenko, Olga] Tech Univ Dresden, Inst Railway Syst & Publ Transport, D-01069 Dresden, Germany; [Liu, Jianxing] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu 610031, Peoples R China; [Han, Yue] China Railway Hohhot Grp Co Ltd, Hohhot 012000, Peoples R China; [Peng, Dai] China Acad Railway Sci, Infrastructure Inspection Res Inst, Beijing 100081, Peoples R China; [Fischer, Szabolcs] Szecheny Istvan Univ, Fac Architecture Civil & Transport Engn, Dept Transport Infrastructure & Water Resources En, H-9026 Gyor, Hungary Technische Universitat Dresden; Southwest Jiaotong University Sysyn, M (corresponding author), Tech Univ Dresden, Inst Railway Syst & Publ Transport, D-01069 Dresden, Germany.;Fischer, S (corresponding author), Szecheny Istvan Univ, Fac Architecture Civil & Transport Engn, Dept Transport Infrastructure & Water Resources En, H-9026 Gyor, Hungary. mykola.sysyn@tu-dresden.de; fischersz@sze.hu Kou, Lei/HLP-5021-2023; Fischer, Szabolcs/A-9957-2015 Kou, Lei/0000-0001-5372-6305; Fischer, Szabolcs/0000-0001-7298-9960; Sysyn, Mykola/0000-0001-6893-0018 36 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability DEC 2022.0 14.0 24 16565 10.3390/su142416565 0.0 17 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 7H3PI gold 2023-03-23 WOS:000903116600001 0
J Oldenbeuving, RM; Song, H; Schitter, G; Verhaegen, M; Klein, EJ; Lee, CJ; Offerhaus, HL; Boller, KJ Oldenbeuving, R. M.; Song, H.; Schitter, G.; Verhaegen, M.; Klein, E. J.; Lee, C. J.; Offerhaus, H. L.; Boller, K. -J. High precision wavelength estimation method for integrated optics OPTICS EXPRESS English Article DIODE-LASER; HIGH-RESOLUTION; WAVE-GUIDES; FIBER A novel and simple approach to optical wavelength measurement is presented in this paper. The working principle is demonstrated using a tunable waveguide micro ring resonator and single photodiode. The initial calibration is done with a set of known wavelengths and resonator tunings. The combined spectral sensitivity function of the resonator and photodiode at each tuning voltage was modeled by a neural network. For determining the unknown wavelengths, the resonator was tuned with a set of heating voltages and the corresponding photodiode signals were collected. The unknown wavelength was estimated, based on the collected photodiode signals, the calibrated neural networks, and an optimization algorithm. The wavelength estimate method provides a high spectral precision of about 8 pm (5.10(-6) at 1550 nm) in the wavelength range between 1549 nm to 1553 nm. A higher precision of 5 pm (3.10(-6)) is achieved in the range between 1550.3 nm to 1550.8 nm, which is a factor of five improved compared to a simple lookup of data. The importance of our approach is that it strongly simplifies the optical system and enables optical integration. The approach is also of general importance, because it may be applicable to all wavelength monitoring devices which show an adjustable wavelength response. (C) 2013 Optical Society of America [Oldenbeuving, R. M.; Lee, C. J.; Boller, K. -J.] Univ Twente, Laser Phys & Nonlinear Opt Grp, NL-7500 AE Enschede, Netherlands; [Oldenbeuving, R. M.; Lee, C. J.; Offerhaus, H. L.; Boller, K. -J.] MESA Res Inst Nanotechnol, NL-7500 AE Enschede, Netherlands; [Oldenbeuving, R. M.] Satrax BV, NL-7500 AL Enschede, Netherlands; [Song, H.] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Zhejiang, Peoples R China; [Song, H.; Verhaegen, M.] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands; [Song, H.] Zhejiang Univ, Ocean Coll, Hangzhou 310058, Zhejiang, Peoples R China; [Schitter, G.] Vienna Univ Technol, Automat & Control Inst, A-1040 Vienna, Austria; [Klein, E. J.] XiO Photon, NL-7500 BG Enschede, Netherlands; [Lee, C. J.] FOM Inst DIFFER, NL-3439 MN Nieuwegein, Netherlands; [Offerhaus, H. L.] Univ Twente, Opt Sci Grp, NL-7500 AE Enschede, Netherlands University of Twente; Zhejiang University; Delft University of Technology; Zhejiang University; Technische Universitat Wien; University of Twente Song, H (corresponding author), Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Zhejiang, Peoples R China. hongsong@zju.edu.cn Schitter, Georg/G-1764-2012; Lee, Chris/F-6743-2015; Offerhaus, Herman L/D-4848-2014; Song, Hong/O-9355-2017; Offerhaus, Herman/AAD-8742-2021; Song, Hong/AAW-4825-2021 Schitter, Georg/0000-0002-8746-5892; Lee, Chris/0000-0001-7020-2036; Offerhaus, Herman L/0000-0003-3697-5750; Offerhaus, Herman/0000-0003-3697-5750; Boller, Klaus-Jochen/0000-0001-6628-610X STW project Smart Optical Systems: Waveguide Based External Cavity Semiconductor Laser Arrays [10442]; Program for Zhejiang Leading Team of ST Innovation [2010R50036] STW project Smart Optical Systems: Waveguide Based External Cavity Semiconductor Laser Arrays; Program for Zhejiang Leading Team of ST Innovation This work is supported by the STW project Smart Optical Systems: Waveguide Based External Cavity Semiconductor Laser Arrays (project NO. 10442) and in part by the Program for Zhejiang Leading Team of S&T Innovation (project NO. 2010R50036). 22 3 3 0 27 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUL 15 2013.0 21.0 14 17042 17052 10.1364/OE.21.017042 0.0 11 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 183MK 23938552.0 Green Published, Green Submitted, gold 2023-03-23 WOS:000321819400080 0
J Gulati, A; Aujla, GS; Chaudhary, R; Kumar, N; Obaidat, M; Benslimane, A Gulati, Amuleen; Aujla, Gagangeet Singh; Chaudhary, Rajat; Kumar, Neeraj; Obaidat, Mohammad; Benslimane, Abderrahim DiLSe: Lattice-Based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING English Article Data dissemination; Internet; Lattices; Access control; Cryptography; Authentication; Fault tolerance; Blockchain; content centric networks; convolutional neural networks; data dissemination; Internet of Vehicles; lattice cryptography AUTHENTICATION SCHEME; THINGS With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,(1) 1. This article is an extended version of paper entitled Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles published in IEEE ICC, 20-24 May 2018, Kansas City, USA DiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics. [Gulati, Amuleen; Chaudhary, Rajat; Kumar, Neeraj] Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Patiala 147001, Punjab, India; [Kumar, Neeraj] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan; [Kumar, Neeraj] King Abdulaziz Univ, Jeddah, Saudi Arabia; [Aujla, Gagangeet Singh] Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England; [Aujla, Gagangeet Singh] Chandigarh Univ, Dept Comp Sci & Engn, Mohali 140413, India; [Obaidat, Mohammad] Univ Sharjah, Coll Comp & Informat, Sharjah 27272, U Arab Emirates; [Obaidat, Mohammad] Univ Jordan, Korea Adv Inst Sci & Technol, Amman 11942, Jordan; [Obaidat, Mohammad] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China; [Benslimane, Abderrahim] Univ Avignon, Dept Comp Sci & Engn, F-84029 Avignon, France Thapar Institute of Engineering & Technology; Asia University Taiwan; King Abdulaziz University; Newcastle University - UK; Chandigarh University; University of Sharjah; Korea Advanced Institute of Science & Technology (KAIST); University of Jordan; University of Science & Technology Beijing; Avignon Universite Kumar, N (corresponding author), Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Patiala 147001, Punjab, India.;Kumar, N (corresponding author), Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan. amuleengulati13@gmail.com; gagi_aujla82@yahoo.com; rajatlibran@gmail.com; neeraj.kumar@thapar.edu; msobaidat@gmail.com; abderrahim.benslimane@univ-avignon.fr Aujla, Gagangeet Singh/Q-9907-2019; Kumar, Neeraj/L-3500-2016 Aujla, Gagangeet Singh/0000-0002-2870-8938; Kumar, Neeraj/0000-0002-3020-3947; Chaudhary, Rajat/0000-0002-6554-918X; Obaidat, Mohammad S./0000-0002-1569-9657 35 15 15 5 16 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5971 1941-0018 IEEE T DEPEND SECURE IEEE Trans. Dependable Secur. Comput. NOV 1 2021.0 18.0 6 2520 2534 10.1109/TDSC.2019.2953841 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science WU7BW 2023-03-23 WOS:000716697500001 0
C Ge, CJ; Gu, IYH; Yang, J Ueda, N; Watanabe, S; Matsui, T; Chien, JT; Larsen, J Ge, Chenjie; Gu, Irene Yu-Hua; Yang, Jie HUMAN FALL DETECTION USING SEGMENT-LEVEL CNN FEATURES AND SPARSE DICTIONARY LEARNING 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING IEEE International Workshop on Machine Learning for Signal Processing English Proceedings Paper 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP) SEP 25-28, 2017 Int House Japan, Tokyo, JAPAN IEEE,IEEE Signal Proc Soc, Machine Learning Signal Proc Tech Comm Int House Japan Fall detection; CNN features; sparse dictionary learning; residual-based pooling; E-healthcare; assisted living This paper addresses issues in human fall detection from videos. Unlike using handcrafted features in the conventional machine learning, we extract features from Convolutional Neural Networks (CNNs) for human fall detection. Similar to many existing work using two stream inputs, we use a spatial CNN stream with raw image difference and a temporal CNN stream with optical flow as the inputs of CNN. Different from conventional two stream action recognition work, we exploit sparse representation with residual-based pooling on the CNN extracted features, for obtaining more discriminative feature codes. For characterizing the sequential information in video activity, we use the code vector from long-range dynamic feature representation by concatenating codes in segment-levels as the input to a SVM classifier. Experiments have been conducted on two public video databases for fall detection. Comparisons with six existing methods show the effectiveness of the proposed method. [Ge, Chenjie; Gu, Irene Yu-Hua] Chalmers Univ Technol, Dept Elect Engn, Gothenburg, Sweden; [Ge, Chenjie; Yang, Jie] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China Chalmers University of Technology; Shanghai Jiao Tong University Ge, CJ (corresponding author), Chalmers Univ Technol, Dept Elect Engn, Gothenburg, Sweden.;Ge, CJ (corresponding author), Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China. chenjie@chalmers.se; irenegu@chalmers.se; jieyang@sjtu.edu.cn Gu, Irene Yu-Hua/D-4044-2018 Gu, Irene Yu-Hua/0000-0003-4759-7038 STINT Joint Swedish-China Mobility Programme in Sweden [CH2015-6193]; NSFC China [61572315, 6151101179]; 863 Plan China [2015AA042308] STINT Joint Swedish-China Mobility Programme in Sweden; NSFC China(National Natural Science Foundation of China (NSFC)); 863 Plan China(National High Technology Research and Development Program of China) The project is partly supported by STINT Joint Swedish-China Mobility Programme in Sweden under the grant number CH2015-6193, NSFC China (No.61572315 and 6151101179) and 863 Plan China (No.2015AA042308). 21 7 7 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-0363 978-1-5090-6341-3 IEEE INT WORKS MACH 2017.0 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BJ4SM 2023-03-23 WOS:000425458700080 0
J Cichonska, A; Ravikumar, B; Allaway, RJ; Wan, FP; Park, S; Isayev, O; Li, SY; Mason, M; Lamb, A; Tanoli, Z; Jeon, M; Kim, S; Popova, M; Capuzzi, S; Zeng, JY; Dang, K; Koytiger, G; Kang, J; Wells, CI; Willson, TM; Oprea, TI; Schlessinger, A; Drewry, DH; Stolovitzky, G; Wennerberg, K; Guinney, J; Aittokallio, T; Tan, M; Huang, CH; Shih, ESC; Chen, TM; Wu, CH; Fang, WQ; Chen, JY; Hwang, MJ; Wang, XK; Ben Guebila, M; Shamsaei, B; Singh, S; Nguyen, T; Karimi, M; Wu, D; Wang, ZY; Shen, Y; Ozturk, H; Ozkirimli, E; Ozgur, A; Lim, H; Xie, L; Kanev, GK; Kooistra, AJ; Westerman, BA; Terzopoulos, P; Ntagiantas, K; Fotis, C; Alexopoulos, L; Boeckaerts, D; Stock, M; De Baets, B; Briers, Y; Luo, YA; Hu, HL; Peng, J; Dogan, T; Rifaioglu, AS; Atas, H; Atalay, RC; Atalay, V; Martin, MJ; Lee, J; Yun, S; Kim, B; Chang, B; Turu, G; Misak, A; Szalai, B; Hunyady, L; Lienhard, M; Prasse, P; Bachmann, I; Ganzlin, J; Barel, G; Herwig, R; Orsolic, D; Lucic, B; Stepanic, V; Smuc, T Cichonska, Anna; Ravikumar, Balaguru; Allaway, Robert J.; Wan, Fangping; Park, Sungjoon; Isayev, Olexandr; Li, Shuya; Mason, Michael; Lamb, Andrew; Tanoli, Ziaurrehman; Jeon, Minji; Kim, Sunkyu; Popova, Mariya; Capuzzi, Stephen; Zeng, Jianyang; Dang, Kristen; Koytiger, Gregory; Kang, Jaewoo; Wells, Carrow I.; Willson, Timothy M.; Oprea, Tudor I.; Schlessinger, Avner; Drewry, David H.; Stolovitzky, Gustavo; Wennerberg, Krister; Guinney, Justin; Aittokallio, Tero; Tan, Mehmet; Huang, Chih-Han; Shih, Edward S. C.; Chen, Tsai-Min; Wu, Chih-Hsun; Fang, Wei-Quan; Chen, Jhih-Yu; Hwang, Ming-Jing; Wang, Xiaokang; Ben Guebila, Marouen; Shamsaei, Behrouz; Singh, Sourav; Nguyen, Thin; Karimi, Mostafa; Wu, Di; Wang, Zhangyang; Shen, Yang; Ozturk, Hakime; Ozkirimli, Elif; Ozgur, Arzucan; Lim, Hansaim; Xie, Lei; Kanev, Georgi K.; Kooistra, Albert J.; Westerman, Bart A.; Terzopoulos, Panagiotis; Ntagiantas, Konstantinos; Fotis, Christos; Alexopoulos, Leonidas; Boeckaerts, Dimitri; Stock, Michiel; De Baets, Bernard; Briers, Yves; Luo, Yunan; Hu, Hailin; Peng, Jian; Dogan, Tunca; Rifaioglu, Ahmet S.; Atas, Heval; Atalay, Rengul Cetin; Atalay, Volkan; Martin, Maria J.; Lee, Junhyun; Yun, Seongjun; Kim, Bumsoo; Chang, Buru; Turu, Gabor; Misak, Adam; Szalai, Bence; Hunyady, Laszlo; Lienhard, Matthias; Prasse, Paul; Bachmann, Ivo; Ganzlin, Julia; Barel, Gal; Herwig, Ralf; Orsolic, Davor; Lucic, Bono; Stepanic, Visnja; Smuc, Tomislav IDG-DREAM Drug-Kinase Binding Crowdsourced mapping of unexplored target space of kinase inhibitors NATURE COMMUNICATIONS English Article DRUG; PHARMACOLOGY; PREDICTION; DISCOVERY; PACKAGE Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts. [Cichonska, Anna; Ravikumar, Balaguru; Tanoli, Ziaurrehman; Aittokallio, Tero] Univ Helsinki, Inst Mol Med Finland FIMM, Hi LIFE, Helsinki, Finland; [Cichonska, Anna; Aittokallio, Tero] Aalto Univ, Helsinki Inst Informat Technol HIIT, Dept Comp Sci, Espoo, Finland; [Cichonska, Anna] Univ Turku, Dept Comp, Turku, Finland; [Allaway, Robert J.; Mason, Michael; Lamb, Andrew; Dang, Kristen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA 98195 USA; [Wan, Fangping; Li, Shuya; Zeng, Jianyang] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing, Peoples R China; [Park, Sungjoon; Lamb, Andrew; Jeon, Minji; Kim, Sunkyu; Kang, Jaewoo; Lee, Junhyun; Yun, Seongjun; Kim, Bumsoo; Chang, Buru] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea; [Isayev, Olexandr; Popova, Mariya] Carnegie Mellon Univ, Dept Chem, 4400 5th Ave, Pittsburgh, PA 15213 USA; [Capuzzi, Stephen] Univ N Carolina, Lab Mol Modeling, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27515 USA; [Koytiger, Gregory] Immuneering Corp, Cambridge, MA USA; [Cichonska, Anna; Wells, Carrow I.; Willson, Timothy M.; Drewry, David H.] Univ N Carolina, Struct Genom Consortium, UNC Eshelman Sch Pharm, Chapel Hill, NC 27515 USA; [Oprea, Tudor I.] Univ New Mexico, Sch Med, Translat Informat Div, Albuquerque, NM 87131 USA; [Oprea, Tudor I.] Univ New Mexico, Sch Med, Ctr Comprehens Canc, Albuquerque, NM 87131 USA; [Schlessinger, Avner] Icahn Sch Med Mt Sinai, Dept Pharmacol Sci, New York, NY 10029 USA; [Stolovitzky, Gustavo] IBM Corp, TJ Watson Res Ctr, Yorktown Hts, NY USA; [Wennerberg, Krister] Univ Copenhagen, Biotech Res & Innovat Ctr BRIC, Copenhagen, Denmark; [Aittokallio, Tero] Univ Turku, Dept Math & Stat, Turku, Finland; [Aittokallio, Tero] Oslo Univ Hosp, Inst Canc Res, Oslo, Norway; [Aittokallio, Tero] Univ Oslo, Oslo Ctr Biostat & Epidemiol OCBE, Oslo, Norway; [Tan, Mehmet] TOBB Univ Econ & Technol, Dept Comp Engn, Ankara, Turkey; [Huang, Chih-Han; Shih, Edward S. C.; Chen, Tsai-Min; Wu, Chih-Hsun; Fang, Wei-Quan; Chen, Jhih-Yu; Hwang, Ming-Jing] Acad Sinica, Inst Biomed Sci, Taipei, Taiwan; [Wang, Xiaokang] Univ Calif Davis, Dept Biomed Engn, Davis, CA 95616 USA; [Ben Guebila, Marouen] Harvard Sch Publ Hlth, Dept Biostat, Boston, MA USA; [Shamsaei, Behrouz] Univ Cincinnati, Coll Med, Dept Environm Hlth, Cincinnati, OH 45267 USA; [Singh, Sourav] VIIT, Dept Comp Engn, Pune, Maharashtra, India; [Nguyen, Thin] Deakin Univ, Appl Artificial Intelligence Inst, Geelong, Vic, Australia; [Karimi, Mostafa; Wu, Di; Shen, Yang] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA; [Karimi, Mostafa] Microsoft, One Microsoft Way, Redmond, WA USA; [Wu, Di] Univ Penn, Philadelphia, PA 19104 USA; [Wang, Zhangyang] Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX 77843 USA; [Wang, Zhangyang] Univ Texas Austin, Austin, TX 78712 USA; [Ozturk, Hakime; Ozgur, Arzucan] Bogazici Univ, Dept Comp Engn, Istanbul, Turkey; [Ozkirimli, Elif] Bogazici Univ, Dept Chem Engn, Istanbul, Turkey; [Lim, Hansaim] CUNY, Grad Ctr, Dept Biochem, New York, NY USA; [Xie, Lei] CUNY, Dept Comp Sci, Hunter Coll, New York, NY 10021 USA; [Kanev, Georgi K.; Westerman, Bart A.] Canc Ctr Amsterdam CCA, Dept Neurosurg, Boelelaan 1117, Amsterdam, Netherlands; [Kooistra, Albert J.] Univ Copenhagen, Dept Drug Design & Pharmacol, Copenhagen, Denmark; [Terzopoulos, Panagiotis; Ntagiantas, Konstantinos; Fotis, Christos; Alexopoulos, Leonidas] Natl Tech Univ Athens, BioSys Lab, Athens, Greece; [Boeckaerts, Dimitri; Briers, Yves] Univ Ghent, Dept Biotechnol, Ghent, Belgium; [Stock, Michiel; De Baets, Bernard] Univ Ghent, Dept Data Anal & Math Modelling, Ghent, Belgium; [Luo, Yunan; Peng, Jian] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA; [Hu, Hailin] Tsinghua Univ, Sch Med, Beijing, Peoples R China; [Dogan, Tunca] Hacettepe Univ, Dept Comp & AI Engn, Ankara, Turkey; [Rifaioglu, Ahmet S.; Atalay, Volkan] METU, Dept Comp Engn, Ankara, Turkey; [Atas, Heval; Atalay, Rengul Cetin] METU, Grad Sch Informat, Dept Hlth Informat, KanSiL, Ankara, Turkey; [Martin, Maria J.] EMBL EBI, Cambridge, England; [Turu, Gabor; Misak, Adam; Szalai, Bence; Hunyady, Laszlo] Semmelweis Univ, Dept Physiol, Budapest, Hungary; [Lienhard, Matthias; Barel, Gal; Herwig, Ralf] Max Planck Inst Mol Genet, Dept Computat Mol Biol, Berlin, Germany; [Prasse, Paul; Ganzlin, Julia] Univ Potsdam, Dept Comp Sci, Potsdam, Germany; [Bachmann, Ivo] MicroDiscovery GmbH, Berlin, Germany; [Orsolic, Davor; Stepanic, Visnja; Smuc, Tomislav] Rudjer Boskovic Inst, Div Elect, Zagreb, Croatia; [Lucic, Bono] Rudjer Boskovic Inst, NMR Ctr, Zagreb, Croatia University of Helsinki; Aalto University; University of Helsinki; University of Turku; Tsinghua University; Korea University; Carnegie Mellon University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of New Mexico; University of New Mexico; Icahn School of Medicine at Mount Sinai; International Business Machines (IBM); University of Copenhagen; University of Turku; University of Oslo; University of Oslo; TOBB Ekonomi ve Teknoloji University; Academia Sinica - Taiwan; University of California System; University of California Davis; Harvard University; Harvard T.H. Chan School of Public Health; University System of Ohio; University of Cincinnati; Deakin University; Texas A&M University System; Texas A&M University College Station; Microsoft; University of Pennsylvania; Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin; Bogazici University; Bogazici University; City University of New York (CUNY) System; City University of New York (CUNY) System; Hunter College (CUNY); University of Copenhagen; National Technical University of Athens; Ghent University; Ghent University; University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University; Hacettepe University; Middle East Technical University; Middle East Technical University; European Molecular Biology Laboratory (EMBL); Semmelweis University; Max Planck Society; University of Potsdam; Rudjer Boskovic Institute; Rudjer Boskovic Institute Aittokallio, T (corresponding author), Univ Helsinki, Inst Mol Med Finland FIMM, Hi LIFE, Helsinki, Finland.;Aittokallio, T (corresponding author), Aalto Univ, Helsinki Inst Informat Technol HIIT, Dept Comp Sci, Espoo, Finland.;Guinney, J (corresponding author), Sage Bionetworks, Computat Oncol, Seattle, WA 98195 USA.;Wennerberg, K (corresponding author), Univ Copenhagen, Biotech Res & Innovat Ctr BRIC, Copenhagen, Denmark.;Aittokallio, T (corresponding author), Univ Turku, Dept Math & Stat, Turku, Finland.;Aittokallio, T (corresponding author), Oslo Univ Hosp, Inst Canc Res, Oslo, Norway.;Aittokallio, T (corresponding author), Univ Oslo, Oslo Ctr Biostat & Epidemiol OCBE, Oslo, Norway. krister.wennerberg@bric.ku.dk; jguinney@gmail.com; tero.aittokallio@helsinki.fi Allaway, Robert/ABE-1264-2021; Stepanic, Visnja/AFW-0865-2022; Isayev, Olexandr/B-7944-2008; Kooistra, Albert J./H-1135-2016; Dogan, Tunca/B-5274-2017; Stock, Michiel/E-4947-2018; Martín, María/HDL-9512-2022; Herwig, Ralf/AGK-3182-2022; Wennerberg, Krister/AFM-0539-2022; Lučić, Bono/J-3813-2012; Wennerberg, Krister/AAH-2919-2022; Ataş Güvenilir, Heval/AEP-0155-2022; Hwang, Ming-jing/E-9210-2012; Hu, Hailin/AFV-3910-2022; Lučić, Bono/L-7472-2013; Atalay, Volkan/M-2256-2016; Aittokallio, Tero/B-6583-2009 Allaway, Robert/0000-0003-3573-3565; Stepanic, Visnja/0000-0001-9518-4153; Isayev, Olexandr/0000-0001-7581-8497; Kooistra, Albert J./0000-0001-5514-6021; Dogan, Tunca/0000-0002-1298-9763; Stock, Michiel/0000-0003-0903-6061; Herwig, Ralf/0000-0002-9335-1760; Wennerberg, Krister/0000-0002-1352-4220; Lučić, Bono/0000-0001-7232-2007; Wennerberg, Krister/0000-0002-1352-4220; Ataş Güvenilir, Heval/0000-0003-3180-7010; Hwang, Ming-jing/0000-0002-9657-5663; Hu, Hailin/0000-0002-5768-4437; Lučić, Bono/0000-0001-7232-2007; Martin, Maria-Jesus/0000-0001-5454-2815; Atalay, Volkan/0000-0001-7850-0601; Wan, Fangping/0000-0003-1647-3278; Luo, Yunan/0000-0001-7728-6412; Cichonska, Anna/0000-0003-1072-8858; Mason, Michael/0000-0002-5652-7739; Willson, Timothy/0000-0003-4181-8223; CHEN, TSAI-MIN/0000-0002-1143-0677; Nguyen, Thin/0000-0003-3467-8963; Karimi, Mostafa/0000-0002-8329-9612; Aittokallio, Tero/0000-0002-0886-9769; Tanoli, Ziaurrehman/0000-0003-2435-9862 Academy of Finland [310507, 313267, 326238]; Cancer Research UK; Brain Tumour Charity [REF: C42454/A28596]; Helse SOr-Ost [2020026]; National Institutes of Health [1U24DK116204-01, U54OD020353, U24CA224370, U24TR002278, U01CA239108]; AbbVie; Bayer Pharma AG; Boehringer Ingelheim; Canada Foundation for Innovation; Eshelman Institute for Innovation; Genome Canada; Innovative Medicines Initiative [ULTRA-DD 115766]; Wellcome Trust; Janssen; Merck Kga; Merck Sharp Dohme; Novartis Pharma AG; Ontario Ministry of Economic Development and Innovation; Pfizer; Sao Paulo Research Foundation-FAPESP; Takeda; National Science Foundation (NSF) [CHE-1802789, CHE-2041108]; Eshelman Institute for Innovation (EII) awards; Molecular Sciences Software Institute (MolSSI) Software Fellowship; NVIDIA Graduate Fellowship Academy of Finland(Academy of Finland); Cancer Research UK(Cancer Research UK); Brain Tumour Charity; Helse SOr-Ost; National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); AbbVie(AbbVie); Bayer Pharma AG; Boehringer Ingelheim(Boehringer Ingelheim); Canada Foundation for Innovation(Canada Foundation for InnovationCGIAR); Eshelman Institute for Innovation; Genome Canada(Genome Canada); Innovative Medicines Initiative; Wellcome Trust(Wellcome Trust); Janssen(Johnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc); Merck Kga; Merck Sharp Dohme(Merck & Company); Novartis Pharma AG; Ontario Ministry of Economic Development and Innovation; Pfizer(Pfizer); Sao Paulo Research Foundation-FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Takeda(Takeda Pharmaceutical Company Ltd); National Science Foundation (NSF)(National Science Foundation (NSF)); Eshelman Institute for Innovation (EII) awards; Molecular Sciences Software Institute (MolSSI) Software Fellowship; NVIDIA Graduate Fellowship The authors thank the IDG Kinase Data and Resource Generation Center for generating new sets of target activity data for the Challenge Rounds 1 and 2, Olle Hansson (FIMM) for technical assistance with DrugTargetCommons platform, Tianduanyi Wang (FIMM) for his help with the baseline submissions, Anna Goldenberg (University of Toronto, Canada) and Chloe-Agathe Azencott (Institut Curie, France) for organizing the DREAM Idea Challenge, and Barbara Rieck and Ladan Naghavian for the bioactivity profiling at DiscoverX (Eurofins Corporation). T.A. acknowledges support from the Academy of Finland (grants 310507, 313267, 326238), Cancer Research UK and the Brain Tumour Charity (grant REF: C42454/A28596), and Helse SOr-Ost (grant No. 2020026). C.W., T.W., D.D. acknowledge support from the National Institutes of Health (1U24DK116204-01). The SGC is a registered charity that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada, Innovative Medicines Initiative (ULTRA-DD 115766), Wellcome Trust, Janssen, Merck Kga, Merck Sharp & Dohme, Novartis Pharma AG, Ontario Ministry of Economic Development and Innovation, Pfizer, Sao Paulo Research Foundation-FAPESP, and Takeda. O.I. acknowledges support from the National Science Foundation (NSF CHE-1802789 and CHE-2041108), and Eshelman Institute for Innovation (EII) awards. O.I. thanks the OpenEye Free Academic Licensing Program for providing a free academic license for their chemistry toolkit. M.P. acknowledges support from The Molecular Sciences Software Institute (MolSSI) Software Fellowship and NVIDIA Graduate Fellowship. We gratefully acknowledge the support and hardware donation from NVIDIA Corporation. J.G. acknowledges support from the National Institutes of Health (U54OD020353). T.I.O. acknowledges support from the National Institutes of Health (U24CA224370; U24TR002278; U01CA239108). 56 14 14 4 28 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. JUN 3 2021.0 12.0 1 3307 10.1038/s41467-021-23165-1 0.0 18 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics SS2LC 34083538.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000661571900005 0
C Liao, WZ; Ochoa, D; Gao, LR; Zhang, B; Philips, W IEEE Liao, Wenzhi; Ochoa, Daniel; Gao, Lianru; Zhang, Bing; Philips, Wilfried MORPHOLOGICAL ANALYSIS FOR BANANA DISEASE DETECTION IN CLOSE RANGE HYPERSPECTRAL REMOTE SENSING IMAGES 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Close range remote sensing; hyperspectral image; morphology; banana diseases Early detection of banana disease can limit the spread of disease, as well as reduce the treatment costs. However, the disease symptoms are so unapparent in the earlier stage that makes the labeled samples acquisition difficult and expensive. Meanwhile, it is much easier to obtain labeled samples at the late stage where the disease symptoms are obvious. In this paper, we exploit machine learning methods to use labeled samples from the late stage to train the model, then detect the banana disease in the earlier stage. Morphological openings and closings are utilized to extract the spectral-spatial features from banana leaves at both earlier and late stages, initial experimental results demonstrate significant improvements over using only spectral information. [Liao, Wenzhi; Philips, Wilfried] Univ Ghent, IMEC, Pietersnieuwstr 41, B-9000 Ghent, Belgium; [Ochoa, Daniel] Escuela Super Politecn Litoral, ESPOL, Guayaquil, Ecuador; [Gao, Lianru; Zhang, Bing] Chinese Acad Sci, RADI, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Zhang, Bing] Univ Chinese Acad Sci, Beijing 100094, Peoples R China Ghent University; IMEC; Escuela Superior Politecnica del Litoral; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Liao, WZ (corresponding author), Univ Ghent, IMEC, Pietersnieuwstr 41, B-9000 Ghent, Belgium. FWO project [G037115N] FWO project(FWO) This work was supported by the FWO project G037115N: Data fusion for image analysis in remote sensing. Wenzhi Liao is a postdoctoral fellow for Scientific Research in Flanders (FWO). 7 7 8 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 3697 3700 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH 2023-03-23 WOS:000519270603171 0
J Tham, YC; Goh, JHL; Anees, A; Lei, XF; Rim, TH; Chee, ML; Wang, YX; Jonas, JB; Thakur, S; Teo, ZL; Cheung, N; Hamzah, H; Tan, GSW; Husain, R; Sabanayagam, C; Wang, JJ; Chen, QY; Lu, ZY; Keenan, TD; Chew, EY; Tan, AG; Mitchell, P; Goh, RSM; Xu, XX; Liu, Y; Wong, TY; Cheng, CY Tham, Yih-Chung; Goh, Jocelyn Hui Lin; Anees, Ayesha; Lei, Xiaofeng; Rim, Tyler Hyungtaek; Chee, Miao-Li; Wang, Ya Xing; Jonas, Jost B.; Thakur, Sahil; Teo, Zhen Ling; Cheung, Ning; Hamzah, Haslina; Tan, Gavin S. W.; Husain, Rahat; Sabanayagam, Charumathi; Wang, Jie Jin; Chen, Qingyu; Lu, Zhiyong; Keenan, Tiarnan D.; Chew, Emily Y.; Tan, Ava Grace; Mitchell, Paul; Goh, Rick S. M.; Xu, Xinxing; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu Detecting visually significant cataract using retinal photograph-based deep learning NATURE AGING English Article SINGAPORE MALAY EYE; DIABETIC-RETINOPATHY; COST-EFFECTIVENESS; EPIDEMIOLOGY; METHODOLOGY; DISEASES; IMPAIRMENT; PREVALENCE; RATIONALE; ACUITY Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers. [Tham, Yih-Chung; Goh, Jocelyn Hui Lin; Rim, Tyler Hyungtaek; Chee, Miao-Li; Thakur, Sahil; Teo, Zhen Ling; Cheung, Ning; Hamzah, Haslina; Tan, Gavin S. W.; Husain, Rahat; Sabanayagam, Charumathi; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Tham, Yih-Chung; Rim, Tyler Hyungtaek; Cheung, Ning; Tan, Gavin S. W.; Husain, Rahat; Sabanayagam, Charumathi; Wang, Jie Jin; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu] Duke NUS Med Sch, Singapore, Singapore; [Tham, Yih-Chung; Wong, Tien Yin; Cheng, Ching-Yu] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Ophthalmol, Singapore, Singapore; [Anees, Ayesha; Lei, Xiaofeng; Goh, Rick S. M.; Xu, Xinxing; Liu, Yong] ASTAR, Inst High Performance Comp, Singapore, Singapore; [Wang, Ya Xing] Beijing Inst Ophthalmol, Beijing Ophthalmol & Visual Sci Key Lab, Beijing, Peoples R China; [Jonas, Jost B.] Ruprecht Karis Univ Heidelberg, Dept Ophthalmol, Med Fac, Mannheim, Germany; [Chen, Qingyu; Lu, Zhiyong] Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bethesda, MD 20894 USA; [Keenan, Tiarnan D.; Chew, Emily Y.] NEI, NIH, Bethesda, MD 20892 USA; [Tan, Ava Grace; Mitchell, Paul] Univ Sydney, Westmead Hosp, Ctr Vis Res, Westmead Inst Med Res,Dept Ophthalmol, Westmead, NSW, Australia; [Tan, Ava Grace] Univ Sydney, Natl Hlth Med Res Council Clin Trials Ctr, Sydney, NSW, Australia National University of Singapore; Singapore National Eye Center; National University of Singapore; National University of Singapore; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM); National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI); University of Sydney; Westmead Institute for Medical Research; University of Sydney Cheng, CY (corresponding author), Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.;Cheng, CY (corresponding author), Duke NUS Med Sch, Singapore, Singapore.;Cheng, CY (corresponding author), Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Ophthalmol, Singapore, Singapore. jost.jonas@medma.uniheidelberg.de; chingyu.cheng@duke-nus.edu.sg Tan, Gavin/HNS-5461-2023 Jonas, Jost/0000-0003-2972-5227 Intramural Research Program of National Library of Medicine, National Institutes of Health, USA; Agency for Science, Technology and Research (A*STAR) under its RIE2020 Health and Biomedical Sciences (HBMS) Industry Alignment Fund Pre-Positioning (IAF-PP) [H20c6a0031]; National Medical Research Council's Transition Award [NMRC/MOH/TA8nov-0002]; SingHealth Duke-NUS Academic Medicine [AM-NHIC/JMT010/2020, SRDUKAMR20M0]; National Medical Research Council's Senior Clinician Scientist Award [NMRC/CSASI/0012/2017] Intramural Research Program of National Library of Medicine, National Institutes of Health, USA; Agency for Science, Technology and Research (A*STAR) under its RIE2020 Health and Biomedical Sciences (HBMS) Industry Alignment Fund Pre-Positioning (IAF-PP); National Medical Research Council's Transition Award; SingHealth Duke-NUS Academic Medicine; National Medical Research Council's Senior Clinician Scientist Award We thank the professional graders of the SNEC Ocular Reading Centre, J. Ho and L. J. Lee, for their assistance. We also thank the Intramural Research Program of National Library of Medicine, National Institutes of Health, USA for their assistance and support. This project is supported by the Agency for Science, Technology and Research (A*STAR) under its RIE2020 Health and Biomedical Sciences (HBMS) Industry Alignment Fund Pre-Positioning (IAF-PP) grant no. H20c6a0031. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the A*STAR. Y.-C.T. was supported by the National Medical Research Council's Transition Award (no. NMRC/MOH/TA8nov-0002). T.H.R. was supported by the SingHealth Duke-NUS Academic Medicine (no. AM-NHIC/JMT010/2020(SRDUKAMR20M0)). C.-Y.C. was supported by the National Medical Research Council's Senior Clinician Scientist Award (no. NMRC/CSASI/0012/2017). These funders had no role in the study design, data collection, data analysis, decision to publish or preparation of the manuscript. 36 2 2 0 0 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-8465 NATURE AGING Nature Aging MAR 2022.0 2.0 3 264 271 10.1038/s43587-022-00171-6 0.0 8 Cell Biology; Geriatrics & Gerontology; Neurosciences Emerging Sources Citation Index (ESCI) Cell Biology; Geriatrics & Gerontology; Neurosciences & Neurology 8A9SZ hybrid 2023-03-23 WOS:000916575600016 0
J Guo, XY; Tiwari, P; Zhang, Y; Han, SG; Wang, YS; Ding, YJ Guo, Xiaoyi; Tiwari, Prayag; Zhang, Ying; Han, Shuguang; Wang, Yansu; Ding, Yijie Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins COMPUTERS IN BIOLOGY AND MEDICINE English Article Sequence classification; Biological sequence features; Random features; Sparse representation-based classifier PREDICTION MODEL; PSEAAC; IDENTIFICATION; RECOGNITION; INFORMATION; DPP DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based methods that have been used on a large scale for large-scale screening of DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparse representation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solve nonlinear classification problems. And L2,1-matrix norm is introduced to get sparse solution of model. To evaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRC achieves better performance in experimental results. [Guo, Xiaoyi; Wang, Yansu] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China; [Guo, Xiaoyi; Han, Shuguang; Ding, Yijie] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Quzhou 324000, Peoples R China; [Tiwari, Prayag] Halmstad Univ, Sch Informat Technol, Halmstad, Sweden; [Zhang, Ying] Beidahuang Ind Grp Gen Hosp, Harbin 150088, Peoples R China University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; Halmstad University Han, SG (corresponding author), Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Quzhou 324000, Peoples R China.;Tiwari, P (corresponding author), Halmstad Univ, Sch Informat Technol, Halmstad, Sweden.;Zhang, Y (corresponding author), Beidahuang Ind Grp Gen Hosp, Harbin 150088, Peoples R China. kerry.guoxiaoyi@163.com; prayag.tiwari@ieee.org; zhangying_biggh@163.com; shughan@uestc.edu.cn; Wangys_c@hotmail.com; wuxi_dyj@163.com Tiwari, Prayag/N-6261-2017 Tiwari, Prayag/0000-0002-2851-4260; Wang, Yansu/0000-0003-1274-4958 National Natural Science Foundation of China; China Postdoctoral Science Foundation; Municipal Government of Quzhou, China; [NSFC 62172076]; [2022T150095]; [2021D004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Municipal Government of Quzhou, China; ; ; Acknowledgments This work is supported in part by the National Natural Science Foundation of China (NSFC 62172076) , China Postdoctoral Science Foundation (No. 2022T150095) and the Municipal Government of Quzhou, China (Grant Number 2021D004) . 55 0 0 6 6 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0010-4825 1879-0534 COMPUT BIOL MED Comput. Biol. Med. DEC 2022.0 151.0 A 106268 10.1016/j.compbiomed.2022.106268 0.0 NOV 2022 8 Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology 7M8XM 36370585.0 2023-03-23 WOS:000906934200003 0
J Ali, S; Dmitrieva, M; Ghatwary, N; Bano, S; Polat, G; Temizel, A; Krenzer, A; Hekalo, A; Guo, YB; Matuszewski, B; Gridach, M; Voiculescu, I; Yoganand, V; Chavan, A; Raj, A; Nguyen, NT; Tran, DQ; Huynh, LD; Boutry, N; Rezvy, S; Chen, HJ; Choi, YH; Subramanian, A; Balasubramanian, V; Gao, XW; Hu, HY; Liao, YS; Stoyanov, D; Daul, C; Realdon, S; Cannizzaro, R; Lamarque, D; Tran-Nguyen, T; Bailey, A; Braden, B; East, JE; Rittscher, J Ali, Sharib; Dmitrieva, Mariia; Ghatwary, Noha; Bano, Sophia; Polat, Gorkem; Temizel, Alptekin; Krenzer, Adrian; Hekalo, Amar; Guo, Yun Bo; Matuszewski, Bogdan; Gridach, Mourad; Voiculescu, Irina; Yoganand, Vishnusai; Chavan, Arnav; Raj, Aryan; Nguyen, Nhan T.; Tran, Dat Q.; Huynh, Le Duy; Boutry, Nicolas; Rezvy, Shahadate; Chen, Haijian; Choi, Yoon Ho; Subramanian, Anand; Balasubramanian, Velmurugan; Gao, Xiaohong W.; Hu, Hongyu; Liao, Yusheng; Stoyanov, Danail; Daul, Christian; Realdon, Stefano; Cannizzaro, Renato; Lamarque, Dominique; Tran-Nguyen, Terry; Bailey, Adam; Braden, Barbara; East, James E.; Rittscher, Jens Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy ? MEDICAL IMAGE ANALYSIS English Article Endoscopy; Challenge; Artefact; Disease; Detection; Segmentation; Gastroenterology; Deep learning VALIDATION; DIAGNOSIS The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) [Ali, Sharib; Dmitrieva, Mariia; Rittscher, Jens] Univ Oxford, Inst Biomed Engn, Old Rd Campus, Oxford, England; [Ali, Sharib; Dmitrieva, Mariia; Rittscher, Jens] Univ Oxford, Big Data Inst, Old Rd Campus, Oxford, England; [Tran-Nguyen, Terry; Bailey, Adam; Braden, Barbara; East, James E.] Univ Oxford, John Radcliffe Hosp, Expt Med Div, Translat Gastroenterol Unit, Oxford, England; [Ali, Sharib; Bailey, Adam; Braden, Barbara; East, James E.] Oxford NIHR Biomed Res Ctr, Oxford, England; [Lamarque, Dominique] Univ Versailles St Quentin en Yvelines, Hop Ambroise Pare, Versailles, France; [Realdon, Stefano] Inst Onclol Veneto, IOV IRCCS, Padua, Italy; [Cannizzaro, Renato] CRO Ctr Riferimento Oncol IRCCS, Aviano, Italy; [Ghatwary, Noha] Arab Acad Sci & Technol, Comp Engn Dept, Alexandria, Egypt; [Bano, Sophia; Stoyanov, Danail] UCL, Wellcome EPSRC Ctr Intervent & Surg Sci WEISS, London, England; [Bano, Sophia; Stoyanov, Danail] UCL, Dept Comp Sci, London, England; [Daul, Christian] Univ Lorraine, CNRS, CRAN UMR 7039, Nancy, France; [Polat, Gorkem; Temizel, Alptekin] Middle East Tech Univ, Grad Sch Informat, Ankara, Turkey; [Krenzer, Adrian; Hekalo, Amar] Univ Wurzburg, Dept Artificial Intelligence & Knowledge Syst, Wurzburg, Germany; [Guo, Yun Bo; Matuszewski, Bogdan] Univ Cent Lancashire, Sch Engn, Preston, Lancs, England; [Voiculescu, Irina] Univ Oxford, Dept Comp Sci, Oxford, England; [Yoganand, Vishnusai] Mimyk Med Simulat Pvt Ltd, Indian Inst Sci, Bengaluru, India; [Chavan, Arnav; Raj, Aryan] Indian Inst Technol ISM, Dhanbad, Bihar, India; [Nguyen, Nhan T.; Tran, Dat Q.] Vingrp Big Data Inst VinBDI, Med Imaging Dept, Hanoi, Vietnam; [Huynh, Le Duy; Boutry, Nicolas] EPITA Res & Dev Lab LRDE, F-94270 Le Kremlin Bicetre, France; [Rezvy, Shahadate; Gao, Xiaohong W.] Middlesex Univ London, Sch Sci & Technol, London, England; [Chen, Haijian] Xiamen Univ, Sch Informat, Dept Comp Sci, Xiamen, Peoples R China; [Choi, Yoon Ho] Sungkyunkwan Univ, Samsung Adv Inst Hlth Sci & Tech SAIHST, Dept Hlth Sci & Tech, Seoul, South Korea; [Subramanian, Anand] Claritr India Pvt Ltd, Chennai, Tamil Nadu, India; [Balasubramanian, Velmurugan] Indian Inst Technol, Sch Med Sci & Technol, Kharagpur, W Bengal, India; [Hu, Hongyu; Liao, Yusheng] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Gridach, Mourad] Ibn Zohr Univ, Comp Sci HIT, Agadir, Morocco University of Oxford; University of Oxford; University of Oxford; University of Oxford; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Ambroise-Pare - APHP; UDICE-French Research Universities; Universite Paris Saclay; IRCCS Istituto Oncologico Veneto (IOV); Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC); University of London; University College London; University of London; University College London; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Middle East Technical University; University of Wurzburg; University of Central Lancashire; University of Oxford; Indian Institute of Science (IISC) - Bangalore; Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad; Middlesex University; Xiamen University; Sungkyunkwan University (SKKU); Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; Shanghai Jiao Tong University; Ibn Zohr University of Agadir Ali, S (corresponding author), Univ Oxford, Inst Biomed Engn, Old Rd Campus, Oxford, England.;Ali, S (corresponding author), Univ Oxford, Big Data Inst, Old Rd Campus, Oxford, England.;Ali, S (corresponding author), Oxford NIHR Biomed Res Ctr, Oxford, England. sharib.ali@eng.ox.ac.uk Temizel, Alptekin/E-5071-2016; Cannizzaro, Renato/V-3804-2018; Polat, Gorkem/AAL-5924-2021; Ali, Sharib/U-3807-2019; Temizel, Alptekin/D-1315-2010 Temizel, Alptekin/0000-0001-6082-2573; Cannizzaro, Renato/0000-0002-2020-222X; Polat, Gorkem/0000-0002-1499-3491; Ali, Sharib/0000-0003-1313-3542; Temizel, Alptekin/0000-0001-6082-2573; Rezvy, Shahadate/0000-0002-2684-7117; Hekalo, Amar/0000-0002-7325-9107; Bano, Sophia/0000-0003-1329-4565; Guo, Yunbo/0000-0001-6804-4884 National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC); NIHR BRC; EPSRC Seebibyte Programme Grant [EP/M013774/1]; EPSRC [EP/P012841/1, EP/M013774/1, EP/R004080/1] Funding Source: UKRI National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)(National Institute for Health Research (NIHR)); NIHR BRC; EPSRC Seebibyte Programme Grant(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) Acknowlgedgments The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) . The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. S. Ali, B. Braden, A. Bailey and J. E. East is supported by NIHR BRC and J. Rittscher by Ludwig Institute for Cancer Research and EPSRC Seebibyte Programme Grant (EP/M013774/1) . We want to acknowledge Karl Storz for cosponsoring the challenge workshop. We would also like to acknowledge the annotators and our EndoCV2020 challenge workshop proceedings reviewers and IEEE International Symposium on Biomedical Imaging 2020 challenge committee Michal Kozubek and Hans Johnson. 95 32 33 6 27 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. MAY 2021.0 70.0 102002 10.1016/j.media.2021.102002 0.0 FEB 2021 24 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging RM0FB 33657508.0 Green Published, Green Accepted, Green Submitted, hybrid 2023-03-23 WOS:000639337100006 0
J Zheng, M; Li, K Zheng, Min; Li, Kang New results on stability analysis of delayed recurrent neural networks based on the integral quadratic constraints approach TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL English Article Separation operators; integral quadratic constraints; recurrent neural networks; stability analysis TIME-VARYING DELAYS; INTERVAL; CRITERIA; SYNCHRONIZATION; SYSTEMS; ROBUSTNESS This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov-Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach. [Zheng, Min] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China; [Li, Kang] Queens Univ, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Shanghai University; Queens University Belfast Zheng, M (corresponding author), Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China. zhengmin203@163.com National Nature Science Foundation of China [61074032]; Shanghai Science Technology Commission [10JC1405000, 11ZR1413100] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Science Technology Commission(Science & Technology Commission of Shanghai Municipality (STCSM)) This work is supported by the National Nature Science Foundation of China (grant number 61074032) and Shanghai Science Technology Commission (grant numbers 10JC1405000 and 11ZR1413100). 33 2 2 0 20 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0142-3312 1477-0369 T I MEAS CONTROL Trans. Inst. Meas. Control AUG 2014.0 36.0 6 780 788 10.1177/0142331214521828 0.0 9 Automation & Control Systems; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Instruments & Instrumentation AQ6NA 2023-03-23 WOS:000342928800006 0
J Ivanov, V; Lv, C; Boulon, L; Hu, XS; Ongel, A Ivanov, Valentin; Lv, Chen; Boulon, Loic; Hu, Xiaosong; Ongel, Aybike Guest Editorial: Focused Section on Mechatronics in Road Mobility Systems IEEE-ASME TRANSACTIONS ON MECHATRONICS English Editorial Material Mechatronics; Roads; Mechanical power transmission; Actuators; Traction motors; Safety; Real-time systems ELECTRIC VEHICLES The road mobility system is undergoing a paradigm shift towards more sustainable, efficient, safer and smarter transportation. In this context, mechatronic systems requires an ever increasing combination of mechanical, electrical/electronic, control and information disciplines. In addition, emerging smart technologies, including artificial intelligence, cybernetics, internet of things, as well as high-performance computing and control, are changing the way in which road mobility systems interact with the society. Thus, future mobility becomes highly multidisciplinary and requires novel technologies and approaches of mechatronic systems to further improve its safety, sustainability and smartness. Within this context, the main goal of this Focused Section is to compile recent research and development efforts contributing to advances in mechatronics for road mobility systems. [Ivanov, Valentin] Tech Univ Ilmenau, D-98693 Ilmenau, Germany; [Lv, Chen] Nanyang Technol Univ, Singapore 639798, Singapore; [Boulon, Loic] Univ Quebec Trois Rivieres, Trois Rivieres, PQ G8Z 4M3, Canada; [Hu, Xiaosong] Chongqing Univ, Chongqing 400044, Peoples R China; [Ongel, Aybike] AI Business Sch, CH-8807 Freienbach, Switzerland Technische Universitat Ilmenau; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; University of Quebec; University of Quebec Trois Rivieres; Chongqing University Ivanov, V (corresponding author), Tech Univ Ilmenau, D-98693 Ilmenau, Germany. valentin.ivanov@tu-ilmenau.de; lyuchen@ntu.edu.sg; Loic.Boulon@uqtr.ca; xiaosonghu@ieee.org; aybike@gmail.com Lv, Chen/N-7055-2018; Hu, Xiao/GQI-0007-2022 Lv, Chen/0000-0001-6897-4512; Hu, Xiao/0000-0003-1128-4099 17 2 1 3 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1083-4435 1941-014X IEEE-ASME T MECH IEEE-ASME Trans. Mechatron. JUN 2021.0 26.0 3 1195 1200 10.1109/TMECH.2021.3080932 0.0 6 Automation & Control Systems; Engineering, Manufacturing; Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering TL9XM 2023-03-23 WOS:000675207800004 0
J Yuan, XL; Ochege, FU; De Maeyer, P; Kurban, A Yuan, Xiuliang; Uchenna Ochege, Friday; De Maeyer, Philippe; Kurban, Alishir Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning REMOTE SENSING English Article surface energy partitioning; latent heat fluxes; sensible heat fluxes; random forest TERRESTRIAL EVAPOTRANSPIRATION; SENSITIVITY-ANALYSIS; VAPOR-PRESSURE; SOIL-MOISTURE; ECOSYSTEMS; CLIMATE; FLUXES As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R-2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m(2) to 21.94 W/m(2) for the LE and from 12.05 W/m(2) to 22.34 W/m(2) for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982-2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies. [Yuan, Xiuliang; De Maeyer, Philippe] Univ Ghent, Dept Geog, B-9000 Ghent, Belgium; [Uchenna Ochege, Friday; De Maeyer, Philippe; Kurban, Alishir] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China; [Uchenna Ochege, Friday; Kurban, Alishir] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Peoples R China; [Uchenna Ochege, Friday; De Maeyer, Philippe; Kurban, Alishir] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Uchenna Ochege, Friday] Univ Port Harcourt, Dept Geog & Environm Management, PMB 5323, Port Harcourt, Rivers State, Nigeria; [De Maeyer, Philippe; Kurban, Alishir] Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium; [De Maeyer, Philippe; Kurban, Alishir] Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China Ghent University; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Port Harcourt; Ghent University Yuan, XL (corresponding author), Univ Ghent, Dept Geog, B-9000 Ghent, Belgium. xiuliang.yuan@ugent.be; uchenna.ochege@uniport.edu.ng; Philippe.DeMaeyer@UGent.be; alishir@ms.xjb.ac.cn Ochege, Friday Uchenna/AAF-6958-2021; Kurban, Alishir/AGK-9193-2022; De Maeyer, Philippe/F-2985-2011 Ochege, Friday Uchenna/0000-0002-6661-0966; Kurban, Alishir/0000-0001-9387-8127; Yuan, Xiuliang/0000-0001-9326-1485; De Maeyer, Philippe/0000-0001-8902-3855 Research Foundation -Flanders (FWO) [12T1419N]; Chinese Academy of Sciences President's International Fellowship Initiative (PIFI) [2017VCA0002]; Chinese Academy of Sciences-theWorld Academy of Sciences (CAS-TWAS) President's Fellowship Programme [2017A8010210001] Research Foundation -Flanders (FWO)(FWO); Chinese Academy of Sciences President's International Fellowship Initiative (PIFI); Chinese Academy of Sciences-theWorld Academy of Sciences (CAS-TWAS) President's Fellowship Programme This work was financially supported by the Research Foundation -Flanders (FWO) (Grant No. 12T1419N), the Chinese Academy of Sciences President's International Fellowship Initiative (PIFI, Grant No. 2017VCA0002) and the Chinese Academy of Sciences-theWorld Academy of Sciences (CAS-TWAS) President's Fellowship Programme (2017A8010210001). 67 0 0 3 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2020.0 12.0 22 3712 10.3390/rs12223712 0.0 18 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology OZ0BN gold, Green Published 2023-03-23 WOS:000594601900001 0
J Luo, XY; Bittremieux, W; Griss, J; Deutsch, EW; Sachsenberg, T; Levitsky, LI; Ivanov, MV; Bubis, JA; Gabriels, R; Webel, H; Sanchez, A; Bai, MZ; Kall, L; Perez-Riverol, Y Luo, Xiyang; Bittremieux, Wout; Griss, Johannes; Deutsch, Eric W.; Sachsenberg, Timo; Levitsky, Lev, I; Ivanov, Mark, V; Bubis, Julia A.; Gabriels, Ralf; Webel, Henry; Sanchez, Aniel; Bai, Mingze; Kall, Lukas; Perez-Riverol, Yasset A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics JOURNAL OF PROTEOME RESEARCH English Article mass spectrometry; clustering; spectral libraries; ProteomeXchange; big data; pride database; consensus spectra; benchmark PEPTIDE IDENTIFICATION; MASS; LIBRARIES Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-duster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark. [Luo, Xiyang] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Big Data B Intelligence, Chongqing 400065, Peoples R China; [Bittremieux, Wout] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, La Jolla, CA 92093 USA; [Griss, Johannes; Perez-Riverol, Yasset] European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Wellcome Genome Campus, Hinxton CB10 1SD, Cambs, England; [Griss, Johannes] Med Univ Vienna, Dept Dermatol, A-1090 Vienna, Austria; [Deutsch, Eric W.] Inst Syst Biol ISB, Seattle, WA 98109 USA; [Sachsenberg, Timo] Univ Tubingen, Dept Comp Sci, Appl Bioinformat, D-72076 Tubingen, Germany; [Levitsky, Lev, I; Ivanov, Mark, V; Bubis, Julia A.] Russian Acad Sci, VL Talrose Inst Energy Problems Chem Phys, NN Semenov Fed Res Ctr Chem Phys, Moscow 142432, Russia; [Gabriels, Ralf] VIB UGent Ctr Med Biotechnol, B-9000 Ghent, Belgium; [Gabriels, Ralf] Univ Ghent, Dept Biomol Med, B-9000 Ghent, Belgium; [Webel, Henry] Univ Copenhagen, Novo Nordisk Fdn Ctr Prot Res, DK-2200 Copenhagen, Denmark; [Sanchez, Aniel] Lund Univ, Skane Univ Hosp Malmo, Dept Translat Med, Sect Clin Chem, S-20502 Malmo, Sweden; [Bai, Mingze] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Big Data Bio Intelligence, Chongqing 400065, Peoples R China; [Kall, Lukas] Royal Inst Technol KTH, Sci Life Lab, Sch Engn Sci Chem Biotechnol & Hlth, S-17121 Solna, Sweden Chongqing University of Posts & Telecommunications; University of California System; University of California San Diego; European Molecular Biology Laboratory (EMBL); Medical University of Vienna; Eberhard Karls University of Tubingen; Russian Academy of Sciences; N.N. Semenov Federal Research Centre for Chemical Physics, Russian Academy of Sciences; Flanders Institute for Biotechnology (VIB); Ghent University; Ghent University; University of Copenhagen; Lund University; Skane University Hospital; Chongqing University of Posts & Telecommunications; Royal Institute of Technology Perez-Riverol, Y (corresponding author), European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Wellcome Genome Campus, Hinxton CB10 1SD, Cambs, England.;Kall, L (corresponding author), Royal Inst Technol KTH, Sci Life Lab, Sch Engn Sci Chem Biotechnol & Hlth, S-17121 Solna, Sweden. lukas.kall@scilifelab.se; yperez@ebi.ac.uk Perez-Riverol, Yasset/H-2873-2019; Gabriels, Ralf/AAU-5247-2021; Ivanov, Mark/J-3836-2014 Perez-Riverol, Yasset/0000-0001-6579-6941; Gabriels, Ralf/0000-0002-1679-1711; Deutsch, Eric/0000-0001-8732-0928; Ivanov, Mark/0000-0003-4762-2846; Bittremieux, Wout/0000-0002-3105-1359; Griss, Johannes/0000-0003-2206-9511; Bubis, Julia/0000-0002-4895-6146; Webel, Henry/0000-0001-8833-7617 Swedish Research Council [2017-04030] Swedish Research Council(Swedish Research Council) The authors would like to acknowledge the EuBIC-MS community that organized the EuBIC-MS Developer Meeting in January 2020,29 triggering the original discussions and implementations of this work. L.K. was supported by a grant from the Swedish Research Council (Grant 2017-04030) . 31 0 0 5 8 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1535-3893 1535-3907 J PROTEOME RES J. Proteome Res. JUN 3 2022.0 21.0 6 1566 1574 10.1021/acs.jproteome.2c00069 0.0 9 Biochemical Research Methods Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 2C0TP 35549218.0 Green Published, Green Submitted, Green Accepted 2023-03-23 WOS:000810591500018 0
J Gebremichael, T; Ledwaba, LPI; Eldefrawy, MH; Hancke, GP; Pereira, N; Gidlund, M; Akerberg, J Gebremichael, Teklay; Ledwaba, Lehlogonolo P. I.; Eldefrawy, Mohamed H.; Hancke, Gerhard P.; Pereira, Nuno; Gidlund, Mikael; Akerberg, Johan Security and Privacy in the Industrial Internet of Things: Current Standards and Future Challenges IEEE ACCESS English Article Protocols; Cryptography; Standards; Privacy; Internet of Things; Peer-to-peer computing; Industrial Internet of Things; IIoT; industrial networks; security and privacy ACCESS-CONTROL; BIG-DATA; SYSTEMS; ENERGY; COMMUNICATION; FRAMEWORK; TRUST; EDGE; COAP The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT. [Gebremichael, Teklay; Gidlund, Mikael] Mid Sweden Univ, Dept Informat Syst & Technol, S-85230 Sundsvall, Sweden; [Ledwaba, Lehlogonolo P. I.; Hancke, Gerhard P.] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Eldefrawy, Mohamed H.] Halmstad Univ, Sch Informat Technol, S-30118 Halmstad, Sweden; [Pereira, Nuno] Polytech Inst Porto, P-4200465 Porto, Portugal; [Akerberg, Johan] ABB Corp Res, S-72226 Vasteras, Sweden Mid-Sweden University; City University of Hong Kong; Halmstad University; Polytechnic Institute of Porto; ABB Hancke, GP (corresponding author), City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. gp.hancke@cityu.edu.hk Ledwaba, Lehlogonolo P.I./AAR-6732-2021; Pereira, Nuno/ACG-1223-2022; Eldefrawy, Mohamed/E-4086-2015; Gidlund, Mikael/L-7643-2018; Hancke, Gerhard/J-7187-2013 Ledwaba, Lehlogonolo P.I./0000-0002-7292-2835; Pereira, Nuno/0000-0001-6370-9373; Eldefrawy, Mohamed/0000-0001-6408-5152; Gidlund, Mikael/0000-0003-0873-7827; Hancke, Gerhard/0000-0002-2388-3542 STINT Foundation [IB2017-7022]; Industrial Technology Commission [PRP/036/19FX] STINT Foundation; Industrial Technology Commission This work was supported in part by the STINT Foundation under Grant IB2017-7022, and in part by the Partnership Research Program through the Industrial Technology Commission under Grant PRP/036/19FX. 103 26 26 10 39 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 152351 152366 10.1109/ACCESS.2020.3016937 0.0 16 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications NG7JY Green Submitted, gold 2023-03-23 WOS:000564158100001 0
C Liu, SS; Tang, XC; Reviriego, P; Liu, WQ; Tang, W; Lombardi, F IEEE Liu, Shanshan; Tang, Xiaochen; Reviriego, Pedro; Liu, Weiqiang; Tang, Wei; Lombardi, Fabrizio Low-Power k Nearest Neighbors Classifiers with Approximate Nanoscale Memories 2022 IEEE 22ND INTERNATIONAL CONFERENCE ON NANOTECHNOLOGY (NANO) IEEE International Conference on Nanotechnology English Proceedings Paper IEEE 22nd International Conference on Nanotechnology (NANO) JUL 06-08, 2022 Palma de Mallorca, SPAIN IEEE The implementation of machine learning classifiers in embedded devices poses significant challenges in terms of power consumption. In some classifiers, like for example the k nearest neighbors (kNNs), the memory accounts for a significant portion of power dissipation and thus, utilizing approximate memories is an attractive option to reduce it. In this paper, the use of approximate memories to implement kNNs classifiers is considered. Efficient schemes that reduce the impact of the errors introduced by the approximate memories on the classification result are proposed and evaluated. The results show that power dissipation of memory can be reduced by 43.75% (24.77%) when using SRAMs (DRAMs), while having a negligible impact on the classifier accuracy. [Liu, Shanshan; Tang, Xiaochen; Tang, Wei] New Mexico State Univ, Klipsch Sch Elect & Comp Engn, Las Cruces, NM 88001 USA; [Reviriego, Pedro] Univ Carlos III Madrid, Telemat Engn Dept, Madrid 28911, Spain; [Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China; [Lombardi, Fabrizio] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA New Mexico State University; Universidad Carlos III de Madrid; Nanjing University of Aeronautics & Astronautics; Northeastern University Liu, SS (corresponding author), New Mexico State Univ, Klipsch Sch Elect & Comp Engn, Las Cruces, NM 88001 USA. ssliu@nmsu.edu; hypnustang@nmsu.edu; revirieg@it.uc3m.es; liuweiqiang@nuaa.edu.cn; wtang@nmsu.edu; lombardi@ece.neu.edu Tang, Xiaochen/HMV-8551-2023 12 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1944-9399 1944-9380 978-1-6654-5225-0 IEEE CONF NANOTECH 2022.0 8 11 10.1109/NANO54668.2022.9928731 0.0 4 Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Science & Technology - Other Topics; Materials Science BU2TL 2023-03-23 WOS:000886541400002 0
J Peng, W; Shi, JG; Zhao, GY Peng, Wei; Shi, Jingang; Zhao, Guoying Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition IEEE SIGNAL PROCESSING LETTERS English Article Deconvolution; Convolution; Kernel; Skeleton; Task analysis; Covariance matrices; Correlation; Graph neural network; skeleton-based action recognition; over-smoothing Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-GCNs), skeleton-based human action recognition has gained promising success. However, the node interaction through message propagation does not always provide complementary information. Instead, it May even produce destructive noise and thus make learned representations indistinguishable. Inevitably, the graph representation would also become over-smoothing especially when multiple GCN layers are stacked. This paper proposes spatial-temporal graph deconvolutional networks (ST-GDNs), a novel and flexible graph deconvolution technique, to alleviate this issue. At its core, this method provides a better message aggregation by removing the embedding redundancy of the input graphs from either node-wise, frame-wise or element-wise at different network layers. Extensive experiments on three current most challenging benchmarks verify that ST-GDN consistently improves the performance and largely reduce the model size on these datasets. [Peng, Wei; Zhao, Guoying] Univ Oulu, Fac Informat Technol & Elect Engn, Oulu 90570, Finland; [Shi, Jingang] Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China University of Oulu; Xi'an Jiaotong University Zhao, GY (corresponding author), Univ Oulu, Fac Informat Technol & Elect Engn, Oulu 90570, Finland. ikerpeng@gmail.com; jingang.shi@oulu.fi; guoying.zhao@oulu.fi Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X; Peng, Wei/0000-0002-2892-5764 ICT2023 Project [328115]; Academy of Finland [316765]; Infotech Oulu; National Natural Science Foundation of China [62002283] ICT2023 Project; Academy of Finland(Academy of Finland); Infotech Oulu; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the ICT2023 Project under Grant 328115, in part by the Academy of Finland for Project MiGA under Grant 316765, in part by the Infotech Oulu, and also with the National Natural Science Foundation of China under Grant 62002283. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dezhong Peng. 40 19 20 4 53 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2021.0 28.0 244 248 10.1109/LSP.2021.3049691 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QE6GN Green Submitted, hybrid 2023-03-23 WOS:000616304000001 0
J Baronchelli, I; Scarlata, CM; Rodighiero, G; Rodriguez-Munoz, L; Bonato, M; Bagley, M; Henry, A; Rafelski, M; Malkan, M; Colbert, J; Dai, YS; Dickinson, H; Mancini, C; Mehta, V; Morselli, L; Teplitz, HI Baronchelli, I; Scarlata, C. M.; Rodighiero, G.; Rodriguez-Munoz, L.; Bonato, M.; Bagley, M.; Henry, A.; Rafelski, M.; Malkan, M.; Colbert, J.; Dai, Y. S.; Dickinson, H.; Mancini, C.; Mehta, V; Morselli, L.; Teplitz, H., I Identification of Single Spectral Lines through Supervised Machine Learning in a Large HST Survey (WISP): A Pilot Study for Euclid and WFIRST ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES English Article Spectroscopy; Algorithms; Maximum likelihood estimation; Spectral line identification; Redshift surveys STAR-FORMATION; REDSHIFT; SELECTION; GALAXIES Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a sample of single emission lines extracted from the WFC3 IR Spectroscopic Parallel survey, one of the closest existing analogs to future slitless surveys. Our automatic software integrates a spectral energy distribution (SED)-fitting strategy with additional independent sources of information. We calibrated it and tested it on a gold sample of securely identified objects with multiple lines detected. The algorithm correctly classifies real emission lines with an accuracy of 82.6%, whereas the accuracy of the SED-fitting technique alone is low (similar to 50%) due to the limited amount of photometric data available (<= 6 bands). While not specifically designed for the Euclid and WFIRST surveys, the algorithm represents an important precursor of similar algorithms to be used in these future missions. [Baronchelli, I; Rodighiero, G.; Rodriguez-Munoz, L.; Mancini, C.; Morselli, L.] Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy; [Scarlata, C. M.; Dickinson, H.; Mehta, V] Univ Minnesota, MN Inst Astrophys, 116 Church St SE, Minneapolis, MN 55455 USA; [Bonato, M.] INAF Ist Radioastron, Via Gobetti 101, I-40129 Bologna, Italy; [Bonato, M.] Italian ALMA Reg Ctr, Via Gobetti 101, I-40129 Bologna, Italy; [Bonato, M.] INAF, Osservatorio Astron Padova, Vicolo Osservatorio 5, I-35122 Padua, Italy; [Bagley, M.] Univ Texas Austin, Coll Nat Sci, 2515 Speedway, Austin, TX 78712 USA; [Henry, A.; Rafelski, M.] Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA; [Rafelski, M.] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA; [Malkan, M.] Univ Calif Los Angeles, Dept Phys & Astron, Phys & Astron Bldg 3-714, Los Angeles, CA 90095 USA; [Colbert, J.; Teplitz, H., I] CALTECH, IPAC, Mail Code 314-6,1200 E Calif Blvd, Pasadena, CA 91125 USA; [Dai, Y. S.] Chinese Acad Sci, South Amer Ctr Astron CASSACA, NAOC, 20A Datun Rd, Beijing 100101, Peoples R China; [Dickinson, H.] Open Univ, Sch Phys Sci, Walton Hall, Milton Keynes MK7 6AA, Bucks, England University of Padua; University of Minnesota System; University of Minnesota Twin Cities; Istituto Nazionale Astrofisica (INAF); Istituto Nazionale Astrofisica (INAF); University of Padua; University of Texas System; University of Texas Austin; Space Telescope Science Institute; Johns Hopkins University; University of California System; University of California Los Angeles; California Institute of Technology; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Open University - UK Baronchelli, I (corresponding author), Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy. ivano.baronchelli@unipd.it morselli, laura/ABF-6011-2021; Rodighiero, Giulia/U-7935-2017 Rodighiero, Giulia/0000-0002-9415-2296; Bagley, Micaela/0000-0002-9921-9218; Bonato, Matteo/0000-0001-9139-2342; Scarlata, Claudia/0000-0002-9136-8876; Malkan, Matthew/0000-0001-6919-1237; Baronchelli, Ivano/0000-0003-0556-2929; Henry, Alaina/0000-0002-6586-4446; mancini, chiara/0000-0002-4297-0561; Rodriguez Munoz, Lucia/0000-0002-0192-5131; Mehta, Vihang/0000-0001-7166-6035 NASA, through STScI program [HST-AR-14311.001-A]; NASA [NAS5-26555]; INAF under PRIN SKA/CTA FORECaST; Ministero degli Affari Esteri della Cooperazione Internazionale-Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza [ZA18GR02]; NASA, through the Space Telescope Science Institute (STScI) [HST-GO-14178.026-A] NASA, through STScI program; NASA(National Aeronautics & Space Administration (NASA)); INAF under PRIN SKA/CTA FORECaST(Ministry of Education, Universities and Research (MIUR)Istituto Nazionale Astrofisica (INAF)Research Projects of National Relevance (PRIN)); Ministero degli Affari Esteri della Cooperazione Internazionale-Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza; NASA, through the Space Telescope Science Institute (STScI) I.B. thanks Alvio Renzini, Alberto Franceschini, Paolo Cassata, and Andrea Grazian for the useful discussion about the thematics discussed in the paper. C.S. acknowledges financial support from NASA, through STScI program number HST-AR-14311.001-A. STScI is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. M.B. acknowledges support from INAF under PRIN SKA/CTA FORECaST and from the Ministero degli Affari Esteri della Cooperazione Internazionale-Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza ZA18GR02. M.R. acknowledges financial support from NASA, through a grant from the Space Telescope Science Institute (STScI), program number HST-GO-14178.026-A. 34 3 3 2 2 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0067-0049 1538-4365 ASTROPHYS J SUPPL S Astrophys. J. Suppl. Ser. JUL 2020.0 249.0 1 12 10.3847/1538-4365/ab9a3a 0.0 27 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics MQ1WO Green Submitted, Green Accepted 2023-03-23 WOS:000552689000001 0
J Ding, Y; Zhang, JY; Zhuang, WT; Gao, Z; Kuang, KM; Tian, D; Deng, C; Wu, HS; Chen, RX; Lu, GJ; Chen, G; Mendogni, P; Migliore, M; Kang, MW; Kanzaki, R; Tang, Y; Yang, JC; Shi, QL; Qiao, GB Ding, Yu; Zhang, Jingyu; Zhuang, Weitao; Gao, Zhen; Kuang, Kaiming; Tian, Dan; Deng, Cheng; Wu, Hansheng; Chen, Rixin; Lu, Guojie; Chen, Gang; Mendogni, Paolo; Migliore, Marcello; Kang, Min-Woong; Kanzaki, Ryu; Tang, Yong; Yang, Jiancheng; Shi, Qiuling; Qiao, Guibin Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies EUROPEAN RADIOLOGY English Article; Early Access Lung neoplasms; Nomograms; Autoantibodies; Artificial intelligence LUNG-CANCER; TUMOR-MARKERS; CLASSIFICATION; VALIDATION; BENIGN ObjectiveTo construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure. MethodsThis study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set. ResultsA diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851-0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of & LE; 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022). ConclusionsBased on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with & LE; 2 cm diameters. [Ding, Yu; Zhuang, Weitao; Tian, Dan; Deng, Cheng; Chen, Gang; Tang, Yong; Qiao, Guibin] Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Thorac Surg, 106 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China; [Ding, Yu; Gao, Zhen; Wu, Hansheng; Qiao, Guibin] Southern Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China; [Zhang, Jingyu; Shi, Qiuling] Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, 1 Med Coll Rd, Chongqing 400016, Peoples R China; [Kuang, Kaiming; Yang, Jiancheng] Dianei Technol, Shanghai, Peoples R China; [Wu, Hansheng] Shantou Univ Med Coll, Affiliated Hosp 1, Dept Thorac Surg, Shantou, Peoples R China; [Chen, Rixin] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Res Ctr Med Sci, Guangzhou, Peoples R China; [Lu, Guojie] Guangzhou Panyu Cent Hosp, Dept Thorac Surg, Guangzhou, Peoples R China; [Mendogni, Paolo] Fdn IRCCS Ca Granda Osped Maggiore Policlin, Thorac Surg & Lung Transplant Unit, Milan, Italy; [Migliore, Marcello] Univ Hosp Wales, Cardiothorac Dept, Thorac Surg, Cardiff, Wales; [Migliore, Marcello] Univ Catania, Univ Hosp Catania, Dept Surg & Med Specialties, Minimally Invas Surg & New Technol, Catania, Italy; [Kang, Min-Woong] Chungnam Natl Univ, Sch Med, Dept Thorac & Cardiovasc Surg, Daejeon, South Korea; [Kanzaki, Ryu] Osaka Univ, Grad Sch Med, Dept Gen Thorac Surg, Osaka, Japan; [Yang, Jiancheng] Ecole Polytech Fed Lausanne EPFL, Comp Vis Lab CVLab, Lausanne, Switzerland Guangdong Academy of Medical Sciences & Guangdong General Hospital; Southern Medical University - China; Chongqing Medical University; Guangdong Academy of Medical Sciences & Guangdong General Hospital; IRCCS Ca Granda Ospedale Maggiore Policlinico; Cardiff University; University of Catania; Chungnam National University; Osaka University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne Qiao, GB (corresponding author), Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Thorac Surg, 106 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China.;Qiao, GB (corresponding author), Southern Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China.;Shi, QL (corresponding author), Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, 1 Med Coll Rd, Chongqing 400016, Peoples R China. qshi@cqmu.edu.cn; guibinqiao@126.com Zhang, Jingyu/0000-0002-5057-5553 47 0 0 5 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. 10.1007/s00330-022-09317-x 0.0 DEC 2022 11 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 6W2SP 36480027.0 2023-03-23 WOS:000895582800001 0
J Cardenas-Lopez, FA; Lamata, L; Retamal, JC; Solano, E Cardenas-Lopez, F. A.; Lamata, L.; Retamal, J. C.; Solano, E. Multiqubit and multilevel quantum reinforcement learning with quantum technologies PLOS ONE English Article CIRCUITS; MEMORY; GATES We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations. [Cardenas-Lopez, F. A.; Retamal, J. C.] Univ Santiago Chile USACH, Dept Fis, Santiago, Chile; [Cardenas-Lopez, F. A.; Retamal, J. C.] Estn Cent, Ctr Dev Nanosci & Nanotechnol, Santiago, Chile; [Lamata, L.; Solano, E.] Univ Basque Country, UPV EHU, Dept Phys Chem, Bilbao, Spain; [Solano, E.] Basque Fdn Sci, IKERBASQUE, Bilbao, Spain; [Solano, E.] Shanghai Univ, Dept Phys, Shanghai, Peoples R China Universidad de Santiago de Chile; University of Basque Country; Basque Foundation for Science; Shanghai University Cardenas-Lopez, FA (corresponding author), Univ Santiago Chile USACH, Dept Fis, Santiago, Chile.;Cardenas-Lopez, FA (corresponding author), Estn Cent, Ctr Dev Nanosci & Nanotechnol, Santiago, Chile. francisco.cardenas@usach.cl Lamata, Lucas/B-2439-2009; Lamata, Lucas/CAG-6488-2022; Retamal, Juan Carlos/G-6425-2010 Lamata, Lucas/0000-0002-9504-8685; Lamata, Lucas/0000-0002-9504-8685; Retamal, Juan Carlos/0000-0002-7174-7879; SOLANO VILLANUEVA, ENRIQUE LEONIDAS/0000-0002-8602-1181; Cardenas, Francisco/0000-0002-2916-2826 CEDENNA basal grant [FB0807]; Direccion de Postgrado USACH; FONDECYT [1140194]; Spanish MINECO/FEDER [FIS2015-69983-P]; Basque Government [IT986-16]; Ramon y Cajal Grant [RYC-2012-11391] CEDENNA basal grant; Direccion de Postgrado USACH; FONDECYT(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT); Spanish MINECO/FEDER; Basque Government(Basque Government); Ramon y Cajal Grant(Spanish Government) We acknowledge support from CEDENNA basal grant No. FB0807 and Direccion de Postgrado USACH (FAC-L), FONDECYT under grant No. 1140194 (JCR), Spanish MINECO/FEDER FIS2015-69983-P and Basque Government IT986-16 (LL and ES), and Ramon y Cajal Grant RYC-2012-11391 (LL). 55 16 16 1 11 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JUL 19 2018.0 13.0 7 e0200455 10.1371/journal.pone.0200455 0.0 25 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics GN5RC 30024914.0 gold, Green Published, Green Submitted 2023-03-23 WOS:000439120000020 0
J Yang, XL; Zhang, XM; Zhang, PD; Bidegain, G; Dong, JY; Hu, CY; Li, M; Zhang, ZX; Guo, H Yang, Xiaolong; Zhang, Xiumei; Zhang, Peidong; Bidegain, Gorka; Dong, Jianyu; Hu, Chengye; Li, Min; Zhang, Zhixin; Guo, Hao Ensemble habitat suitability modeling for predicting optimal sites for eelgrass (Zostera marina) in the tidal lagoon ecosystem: Implications for restoration and conservation JOURNAL OF ENVIRONMENTAL MANAGEMENT English Article Ensemble model; Eelgrass; Habitat suitability; Site selection; Cross-validation SWAN-LAKE; CHINA IMPLICATIONS; WATER MOTION; AIR EXPOSURE; SEAGRASS; L.; REGRESSION; INDEX; SEDIMENT; JAPONICA Seagrass systems are in decline, mainly due to anthropogenic pressures and ongoing climate change. Imple-menting seagrass protection and restoration measures requires accurate assessment of suitable habitats. Commonly, such assessments have been performed using single-algorithm habitat suitability models, nearly always based on low environmental resolution information and short-term species data series. Here we address eelgrass (Zoostera marina) meadows' large-scale decline (>80%) in Shandong province (Yellow Sea, China) by developing an ensemble habitat model (EHM) to inform eelgrass conservation and restoration strategies in the Swan Lake (SL). For this, we applied a weighted EHM derived from ten single-algorithm models including profile, regression, classification, and machine learning methods to generate a high-resolution habitat suitability map. The EHM was constructed based on the predictive performances of each model, by combining a series of present-absent eelgrass datasets from recent years coupled with oceanographic and sediment data. The model was cross-validated with independent historical datasets, and a final habitat suitability map for conservation and restoration was generated. Our EHM scheme outperformed all single models in terms of habitat suitability, scoring similar to 0.95 for both true statistic skill (TSS) and area under the curve (AUC) performance criteria. Machine learning methods outperformed profile, regression and classification methods. Regarding model explanatory variables, overall, topographic characteristics such as depth (DEP) and seafloor slope (SSL) are the most significant factors determining the distribution of eelgrass. The EHM predicted that the overlapping area was almost 90% of the current eelgrass habitat. Using results from our EHM, a LOESS regression model for the relationship of the habitat suitability to both the biomass and density of Z. marina outperformed better than the classic Ordinary Least Squares regression model. The EHM is a promising tool for supporting eelgrass protection and restoration areas in temperate lagoons as data availability improves. [Yang, Xiaolong; Zhang, Xiumei; Hu, Chengye] Zhejiang Ocean Univ, Fishery Coll, Zhoushan 316022, Peoples R China; [Yang, Xiaolong; Guo, Hao] Natl Marine Environm Monitoring Ctr, State Environm Protect Key Lab Coastal Ecosyst, Dalian 116023, Peoples R China; [Zhang, Peidong; Dong, Jianyu] Ocean Univ China, Key Lab Mariculture, Minist Educ, Qingdao, Peoples R China; [Bidegain, Gorka] Univ Basque Country UPV EHU, Engn Sch Bilbao, Dept Appl Math, Ingeniero Torres Quevedo S N, Bilbao 48013, Spain; [Bidegain, Gorka] Univ Basque Country PiE UPV EHU, Res Ctr Expt Marine Biol & Biotechnol, Plentzia Marine Stn, Areatza Pasealekua, Plentzia 48620, Spain; [Li, Min] Ludong Univ, Inst Adv Study Coastal Ecol, Yantai 264025, Peoples R China; [Zhang, Zhixin] Chinese Acad Sci, South China Sea Inst Oceanol, CAS Key Lab Trop Marine Bioresources & Ecol, Guangzhou 510301, Peoples R China Zhejiang Ocean University; National Marine Environmental Monitoring Center; Ocean University of China; University of Basque Country; University of Basque Country; Ludong University; Chinese Academy of Sciences; South China Sea Institute of Oceanology, CAS Zhang, XM (corresponding author), Zhejiang Ocean Univ, Fishery Coll, Zhoushan 316022, Peoples R China. xiumei1227@163.com ; Bidegain, Gorka/N-8811-2016 Jian-Yu, Dong/0000-0003-0553-1655; Zhang, Zhixin/0000-0002-3457-2934; Bidegain, Gorka/0000-0002-2173-5140 National Natural Science Foundation of China [41906115]; Province Key Research and Development Program of Zhejiang [2021C02047]; Dalian high level talent innovation support program (Youth Science and Technology Star Project) [2021RQ094]; State Environmental Protection Key Laboratory of Coastal Ecosystem [202101]; National Natural Science Founda-tion of China [LGN18D060001, 42076100]; Basic public welfare research in Zhejiang Province [41906125] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Province Key Research and Development Program of Zhejiang; Dalian high level talent innovation support program (Youth Science and Technology Star Project); State Environmental Protection Key Laboratory of Coastal Ecosystem; National Natural Science Founda-tion of China(National Natural Science Foundation of China (NSFC)); Basic public welfare research in Zhejiang Province This work was supported by funds from the National Natural Science Foundation of China (Grant No. 41906115), the Province Key Research and Development Program of Zhejiang (2021C02047) , Dalian high level talent innovation support program (Youth Science and Technology Star Project) (2021RQ094) , State Environmental Protection Key Laboratory of Coastal Ecosystem (202101), the National Natural Science Foundation of China (Grant No. 42076100 and 41906125), and Basic public welfare research in Zhejiang Province (LGN18D060001). Many thanks to Institute of Oceanology, Chinese Academy of Sciences for providing environmental data, without which the modeling approach as implemented in this study would not be possible. We also express our gratitude to all the volunteers who participated in the field and laboratory work. 59 0 0 8 8 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0301-4797 1095-8630 J ENVIRON MANAGE J. Environ. Manage. MAR 15 2023.0 330.0 117108 10.1016/j.jenvman.2022.117108 0.0 DEC 2022 10 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 7Y2SZ 36584472.0 2023-03-23 WOS:000914737100001 0
J Sun, T; Barrio, R; Jiang, H; Cheng, LZ Sun, Tao; Barrio, Roberto; Jiang, Hao; Cheng, Lizhi Convergence rates of accelerated proximal gradient algorithms under independent noise NUMERICAL ALGORITHMS English Article Proximal gradient algorithm; Inexact algorithm; Independent noise; Convergence rates; Acceleration We consider an accelerated proximal gradient algorithm for the composite optimization with independent errors (errors little related with historical information) for solving linear inverse problems. We present a new inexact version of FISTA algorithm considering deterministic and stochastic noises. We prove some convergence rates of the algorithm and we connect it with the current existing catalyst framework for many algorithms in machine learning. We show that a catalyst can be regarded as a special case of the FISTA algorithm where the smooth part of the function vanishes. Our framework gives a more generic formulation that provides convergence results for the deterministic and stochastic noise cases and also to the catalyst framework. Some of our results provide simpler alternative analysis of some existing results in literature, but they also extend the results to more generic situations. [Sun, Tao] Natl Univ Def Technol, Dept Math, Changsha 410073, Hunan, Peoples R China; [Barrio, Roberto] Univ Zaragoza, Dept Matemat Aplicada, IUMA Grp, Pedro Cerbuna 12, E-50009 Zaragoza, Spain; [Barrio, Roberto] Univ Zaragoza, Dept Matemat Aplicada, CoDy Grp, Pedro Cerbuna 12, E-50009 Zaragoza, Spain; [Jiang, Hao] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China; [Cheng, Lizhi] Natl Univ Def Technol, Coll Sci, Changsha 410073, Hunan, Peoples R China; [Cheng, Lizhi] Natl Univ Def Technol, State Key Lab High Performance Computat, Changsha 410073, Hunan, Peoples R China National University of Defense Technology - China; University of Zaragoza; University of Zaragoza; National University of Defense Technology - China; National University of Defense Technology - China; National University of Defense Technology - China Sun, T (corresponding author), Natl Univ Def Technol, Dept Math, Changsha 410073, Hunan, Peoples R China. nudtsuntao@163.com; rbarrio@unizar.es; haojiang@nudt.edu.cn; clzcheng@nudt.edu.cn ; Barrio, Roberto/L-4983-2014 tao, sun/0000-0001-5024-1900; Barrio, Roberto/0000-0002-8089-343X National Science Foundation of China [61402495, 61571008]; National Natural Science Foundation of Hunan Province in China [2018JJ3616]; Spanish Research Project [MTM2015-64095-P] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of Hunan Province in China; Spanish Research Project(Spanish Government) We thank the unknown referees for suggestions on the improvement of the paper. H.J. and L.C. have been supported by the National Science Foundation of China (No. 61402495) and National Natural Science Foundation of Hunan Province in China (2018JJ3616). R.B. has been supported by the Spanish Research Project MTM2015-64095-P. T.S. has been supported by National Science Foundation of China (No.61571008). 47 3 3 0 4 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1017-1398 1572-9265 NUMER ALGORITHMS Numer. Algorithms JUN 2019.0 81.0 2 631 654 10.1007/s11075-018-0565-4 0.0 24 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics HZ1LA 2023-03-23 WOS:000468607000012 0
J Sadoune, N; Giudici, G; Liu, K; Pollet, L Sadoune, Nicolas; Giudici, Giuliano; Liu, Ke; Pollet, Lode Unsupervised interpretable learning of phases from many-qubit systems PHYSICAL REVIEW RESEARCH English Article QUANTUM STATES Experimental progress in qubit manufacturing calls for the development of new theoretical tools to analyze quantum data. We show how an unsupervised machine-learning technique can be used to understand short -range entangled many-qubit systems using data of local measurements. The method successfully constructs the phase diagram of a cluster-state model and detects the respective order parameters of its phases, including string order parameters. For the toric code subject to external magnetic fields, the machine identifies the explicit forms of its two stabilizers. Prior information of the underlying Hamiltonian or the quantum states is not needed; instead, the machine outputs their characteristic observables. Our work opens the door for a first-principles application of hybrid algorithms that aim at strong interpretability without supervision. [Sadoune, Nicolas; Giudici, Giuliano; Liu, Ke; Pollet, Lode] Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany; [Sadoune, Nicolas; Giudici, Giuliano; Liu, Ke; Pollet, Lode] Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany; [Pollet, Lode] Shanghai Jiao Tong Univ, Wilczek Quantum Ctr, Sch Phys & Astron, Shanghai 200240, Peoples R China University of Munich; Shanghai Jiao Tong University Sadoune, N (corresponding author), Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany.;Sadoune, N (corresponding author), Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany. Pollet, Lode C/F-6845-2010 Pollet, Lode C/0000-0002-7274-2842 FP7/ERC Consolidator Grant QSIMCORR [771891]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC-2111; Bavarian state government; Hightech Agenda Bayern Plus; [390814868] FP7/ERC Consolidator Grant QSIMCORR; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC-2111(German Research Foundation (DFG)); Bavarian state government; Hightech Agenda Bayern Plus; We acknowledge support from FP7/ERC Consolidator Grant QSIMCORR, No. 771891, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Grant No. 390814868. The research is part of the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus. Our simulations make use of the ?-SVM formulation [46] , the LIBSVM library [47,48] , and the iTensor library [49] . 48 0 0 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2643-1564 PHYS REV RES Phys. Rev. Res. FEB 6 2023.0 5.0 1 13082 10.1103/PhysRevResearch.5.013082 0.0 9 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics 9C0QW Green Submitted, gold 2023-03-23 WOS:000935133400002 0
J Wang, HH; Xu, H; Pooneeth, V; Gao, XZ Wang, Haihang; Xu, He; Pooneeth, Vishwanath; Gao, Xiao-Zhi A Novel One-Camera-Five-Mirror Three-Dimensional Imaging Method for Reconstructing the Cavitation Bubble Cluster in a Water Hydraulic Valve APPLIED SCIENCES-BASEL English Article three-dimensional imaging; optimization arrangement; cavitation bubble; water hydraulic valve HIGH-SPEED VISUALIZATION; RECTANGULAR CHANNEL; FLOW MEASUREMENT; STEREO VISION; DYNAMICS; TRACKING; PHOTOGRAPHY; RECOGNITION; HOLOGRAPHY; ALGORITHM In order to study the bubble morphology, a novel experimental and numerical approach was implemented in this research focusing on the analysis of a transparent throttle valve made by Polymethylmethacrylate (PMMA) material. A feature-based algorithm was written using the MATLAB software, allowing the 2D detection and three-dimensional (3D) reconstruction of bubbles: collapsing and clustered ones. The valve core, being an important part of the throttle valve, was exposed to cavitation; hence, to distinguish it from the captured frames, the faster region-based convolutional neural network (R-CNN) algorithm was used to detect its morphology. Additionally, the main approach grouping the above listed techniques was implemented using an optimized virtual stereo vision arrangement of one camera and five plane mirrors. The results obtained during this study validated the robust algorithms and optimization applied. [Wang, Haihang; Xu, He; Pooneeth, Vishwanath] Harbin Engn Univ, Coll Mech & Elect Engn, Harbin 150001, Heilongjiang, Peoples R China; [Gao, Xiao-Zhi] Univ Eastern Finland, Sch Comp, FI-70211 Kuopio, Finland Harbin Engineering University; University of Eastern Finland Xu, H (corresponding author), Harbin Engn Univ, Coll Mech & Elect Engn, Harbin 150001, Heilongjiang, Peoples R China. wanghaihang@hrbeu.edu.cn; railway_dragon@sohu.com; vpooneeth@ymail.com; xiao.z.gao@gmail.com Wang, Haihang/M-9123-2018 Wang, Haihang/0000-0002-5593-8237 Natural Science Foundation of China [51875113]; Natural Science Foundation of the Heilongjiang Province of China [F2016003]; Jinshan Talent Zhenjiang Manufacture 2025 Leading Talent Project; International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation; Jiangyan Planning Project in Yangzhong City Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of the Heilongjiang Province of China(Natural Science Foundation of Heilongjiang Province); Jinshan Talent Zhenjiang Manufacture 2025 Leading Talent Project; International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation; Jiangyan Planning Project in Yangzhong City This work was supported by the Natural Science Foundation of China under Grant 51875113, Natural Science Foundation of the Heilongjiang Province of China under Grant F2016003, Jinshan Talent Zhenjiang Manufacture 2025 Leading Talent Project, Jiangyan Planning Project in Yangzhong City, and the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation. 42 4 4 7 33 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel OCT 2018.0 8.0 10 1783 10.3390/app8101783 0.0 16 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics GY5WG Green Submitted, gold 2023-03-23 WOS:000448653700078 0
J Wang, X; Zhang, CG; Zhang, Y; Meng, XY; Zhang, ZY; Shi, X; Song, T Wang, Xun; Zhang, Chaogang; Zhang, Ying; Meng, Xiangyu; Zhang, Zhiyuan; Shi, Xin; Song, Tao IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES English Article scRNA-seq; batch effect; connected graphs; deep learning; GAN ATLAS There is a strong need to eliminate batch-specific differences when integrating single-cell RNA-sequencing (scRNA-seq) datasets generated under different experimental conditions for downstream task analysis. Existing batch correction methods usually transform different batches of cells into one preselected anchor batch or a low-dimensional embedding space, and cannot take full advantage of useful information from multiple sources. We present a novel framework, called IMGG, i.e., integrating multiple single-cell datasets through connected graphs and generative adversarial networks (GAN) to eliminate nonbiological differences between different batches. Compared with current methods, IMGG shows excellent performance on a variety of evaluation metrics, and the IMGG-corrected gene expression data incorporate features from multiple batches, allowing for downstream tasks such as differential gene expression analysis. [Wang, Xun; Zhang, Chaogang; Zhang, Ying; Meng, Xiangyu; Zhang, Zhiyuan; Shi, Xin; Song, Tao] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266555, Peoples R China; [Song, Tao] Politecn Univ Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain China University of Petroleum Song, T (corresponding author), China Univ Petr, Coll Comp Sci & Technol, Qingdao 266555, Peoples R China.;Song, T (corresponding author), Politecn Univ Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain. wangsyun@upc.edu.cn; s20070030@s.upc.edu.cn; zhangy9808@163.com; xiangyumeng@s.upc.edu.cn; flyeagle237@163.com; shix1104@163.com; t.song@upm.es Wang, Xun/HOH-8824-2023; Song, Tao/T-7360-2018 Song, Tao/0000-0002-0130-3340 29 3 3 3 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1422-0067 INT J MOL SCI Int. J. Mol. Sci. FEB 2022.0 23.0 4 2082 10.3390/ijms23042082 0.0 21 Biochemistry & Molecular Biology; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry ZL1MY 35216199.0 Green Accepted, gold 2023-03-23 WOS:000763446600001 0
J Feng, YB; Tang, TL; Chen, SY; Wu, YR Feng, Yangbo; Tang, Tinglong; Chen, Shengyong; Wu, Yirong AUTOMATED DEFECT DETECTION BASED ON TRANSFER LEARNING AND DEEP CONVOLUTION GENERATIVE ADVERSARIAL NETWORKS INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION English Article Defect detection; deep convolution generative adversarial network; transfer learning Machine learning and computer vision methods are widely used in defect detection. It is challenging to collect enough samples for training, and the collected samples are usually in unbalanced distribution. In this work, we use deep convolution generative adversarial networks (DCGANs) to augment the data and transfer learning method to avoid training from scratch. Particularly, only the instances of rare class are augmented using DCGAN so that the data distribution will be balanced, and transfer learning is adopted by fine-tuning the pre-trained VGG16 network which is improved by using focal loss function in our method. The proposed method is applied to detect penicillin bottle defects. Experiments show that the proposed method outperforms the traditional models. [Feng, Yangbo] Tianjin Univ Technol, Minist Educ, Key Lab Comp Vis & Syst, Tianjin, Peoples R China; [Tang, Tinglong] China Three Gorges Univ, Yichang, Peoples R China; [Chen, Shengyong] Univ Hamburg, Hamburg, Germany; [Chen, Shengyong] Tianjin Univ Technol, Tianjin, Peoples R China; [Chen, Shengyong] Zhejiang Univ Technol, Hangzhou, Zhejiang, Peoples R China Tianjin University of Technology; China Three Gorges University; University of Hamburg; Tianjin University of Technology; Zhejiang University of Technology Chen, SY (corresponding author), Tianjin Univ Technol, Tianjin, Peoples R China.;Chen, SY (corresponding author), Zhejiang Univ Technol, Hangzhou, Zhejiang, Peoples R China. ybfeng6@gmail.com; tangtinglong@ctgu.edu.cn; sy@ieee.org; yirongwu@hotmail.com Chen, S./H-3083-2011 National Key R&D Program of China [2018YFB1305200, 2016YBF0800403]; National Natural Science Foundation of China [U1509207]; Opening Foundation of Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology [TJUT-KLICNST-K20180002] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Foundation of Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology This work was supported by National Key R&D Program of China (2018YFB1305200, 2016YBF0800403), National Natural Science Foundation of China (U1509207), the Opening Foundation of Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology (TJUT-KLICNST-K20180002). 35 1 1 8 27 ACTA PRESS CALGARY 2509 DIEPPE AVE SW, BLDG B6, STE 101, CALGARY, AB T3E 7J9, CANADA 0826-8185 1925-7090 INT J ROBOT AUTOM Int. J. Robot. Autom. 2021.0 36.0 6 471 478 10.2316/J.2021.206-0735 0.0 8 Automation & Control Systems; Robotics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Robotics UI1NF 2023-03-23 WOS:000690382300010 0
C Flick, D; Gellrich, S; Filz, MA; Ji, L; Thiede, S; Herrmann, C IEEE Flick, Dominik; Gellrich, Sebastian; Filz, Marc-Andre; Ji, Li; Thiede, Sebastian; Herrmann, Christoph Conceptual Framework for manufacturing data preprocessing of diverse input sources 2019 IEEE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) IEEE International Conference on Industrial Informatics INDIN English Proceedings Paper 17th IEEE International Conference on Industrial Informatics (INDIN) JUL 22-25, 2019 Aalto Univ, FINLAND Inst Elect & Elect Engineers,Tampere Univ,Finnish Soc Automat,IEEE Ind Elect Soc Aalto Univ Diverse data sources; Manufacturing data preprocessing; Multivariate outlier detection of anomalies and noise; Outlier treatment values The manufacturing industry today is experiencing a never seen increase in available data. These data compromise a variety of different formats, semantics, and quality. It is often distributed in different data sources, e.g. sensor data from the production line, environmental data or machine tool parameters. Coming from the field of application the paper will discuss, within a conceptual framework, the possibilities of how to integrate the diverse existing data-sources and how to pre-process the data with high quality using advanced outlier detection algorithms and developing reasonable outlier treatment values by applying machine-learning methods. The result will be validated with real manufacturing data from an automotive use-case. [Flick, Dominik] Opel Automobile GmbH, Energy Performance Management EUSG, Russelsheim, Germany; [Gellrich, Sebastian; Filz, Marc-Andre; Thiede, Sebastian; Herrmann, Christoph] Tech Univ Carolo Wilhelmina Braunschweig, Inst Machine Tools & Prod Technol, Braunschweig, Germany; [Ji, Li] Bosch Rexroth Hydraul Co Ltd, Dept Mfg Operat & Engn, Beijing, Peoples R China Braunschweig University of Technology Flick, D (corresponding author), Opel Automobile GmbH, Energy Performance Management EUSG, Russelsheim, Germany. dominik.flick@opel-vauxhall.com; s.gellrich@tu-braunschweig.de; m.filz@tu-braunschweig.de; Li.J12@boschrexroth.com.cn; s.thiede@tu-braunschweig.de; c.herrmann@tu-braunschweig.de Thiede, Sebastian/ABD-5977-2020 Thiede, Sebastian/0000-0003-1102-2611 29 0 0 2 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1935-4576 978-1-7281-2927-3 IEEE INTL CONF IND I 2019.0 1041 1046 6 Computer Science, Hardware & Architecture; Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO8XJ 2023-03-23 WOS:000529510400154 0
J Bouguettaya, A; Singh, M; Huhns, M; Sheng, QZ; Dong, H; Yu, Q; Neiat, AG; Mistry, S; Benatallah, B; Medjahed, B; Ouzzani, M; Casati, F; Liu, XM; Wang, HB; Georgakopoulos, D; Chen, L; Nepal, S; Malik, Z; Erradi, A; Wang, Y; Blake, B; Dustdar, S; Leymann, F; Papazoglou, M Bouguettaya, Athman; Singh, Munindar; Huhns, Michael; Sheng, Quan Z.; Dong, Hai; Yu, Qi; Neiat, Azadeh Ghari; Mistry, Sajib; Benatallah, Boualem; Medjahed, Brahim; Ouzzani, Mourad; Casati, Fabio; Liu, Xumin; Wang, Hongbing; Georgakopoulos, Dimitrios; Chen, Liang; Nepal, Surya; Malik, Zaki; Erradi, Abdelkarim; Wang, Yan; Blake, Brian; Dustdar, Schahram; Leymann, Frank; Papazoglou, Michael A Service Computing Manifesto: The Next 10 Years COMMUNICATIONS OF THE ACM English Review WEB SERVICES; BIG DATA; ISSUES CURRENT IT ADVANCES can be likened in their effect and impact to the deep economic and societal transformations that were brought about by the industrial revolution. Recent years have seen the expansion of services in all sectors of the economy, building on the expansion of the Internet. We define service computing (alternatively termed service-oriented computing) as the discipline that seeks to develop computational abstractions, architectures, techniques, and tools to support services broadly. A service orientation seeks to transform physical, hardware and software assets into a paradigm in which users and assets establish on-demand interactions, binding resources and operations, providing an abstraction layer that shifts the focus from infrastructure and operations to services. [Bouguettaya, Athman] Univ Sydney, Sydney, NSW, Australia; [Singh, Munindar] North Carolina State Univ, Raleigh, NC 27695 USA; [Huhns, Michael] Univ South Carolina, Columbia, SC 29208 USA; [Sheng, Quan Z.; Wang, Yan] Macquarie Univ, N Ryde, NSW 2109, Australia; [Dong, Hai; Neiat, Azadeh Ghari; Mistry, Sajib] RMIT Univ, Melbourne, Vic, Australia; [Yu, Qi; Liu, Xumin] Rochester Inst Technol, Rochester, NY 14623 USA; [Benatallah, Boualem] Univ New South Wales, Sydney, NSW, Australia; [Medjahed, Brahim] Univ Michigan, Dearborn, MI 48128 USA; [Ouzzani, Mourad] Qatar Comp Res Inst, Ar Rayyan, Qatar; [Casati, Fabio] Univ Trento, Trento, Italy; [Wang, Hongbing] Southeast Univ, Dhaka, Bangladesh; [Georgakopoulos, Dimitrios] Swinburne Univ Technol, Hawthorn, Vic, Australia; [Chen, Liang] Sun Yat Sen Univ, Guangzhou, Guangdong, Peoples R China; [Nepal, Surya] CSIRO, Canberra, ACT, Australia; [Malik, Zaki] Texas A&M Univ, College Stn, TX 77843 USA; [Erradi, Abdelkarim] Qatar Univ, Doha, Qatar; [Blake, Brian] Univ Miami, Coral Gables, FL 33124 USA; [Dustdar, Schahram] TU Wein, Vienna, Austria; [Leymann, Frank] Univ Stuttgart, Stuttgart, Germany; [Papazoglou, Michael] Tilburg Univ, Tilburg, Netherlands University of Sydney; North Carolina State University; University of South Carolina System; University of South Carolina Columbia; Macquarie University; Royal Melbourne Institute of Technology (RMIT); Rochester Institute of Technology; University of New South Wales Sydney; University of Michigan System; University of Michigan; Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar; Qatar Computing Research Institute; University of Trento; Swinburne University of Technology; Sun Yat Sen University; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Texas A&M University System; Texas A&M University College Station; Qatar University; University of Miami; Technische Universitat Wien; University of Stuttgart; Tilburg University Bouguettaya, A (corresponding author), Univ Sydney, Sydney, NSW, Australia. athman.bouguettaya@sydney.edu.au Gomaa, Ahmed/I-6442-2017; Erradi, Abdelkarim/AAE-5614-2019; Mistry, Sajib/AAY-4148-2021; Dustdar, Schahram/G-9877-2015; Singh, Munindar P/L-8500-2013; Nepal, Surya/B-7523-2011; Sheng, Quan Z./B-8169-2008; Dong, Hai/E-9101-2010 Gomaa, Ahmed/0000-0003-0869-7888; Dustdar, Schahram/0000-0001-6872-8821; Singh, Munindar P/0000-0003-3599-3893; Nepal, Surya/0000-0002-3289-6599; Sheng, Quan Z./0000-0002-3326-4147; Dong, Hai/0000-0002-7033-5688; Bouguettaya, Athman/0000-0003-1254-8092; Wang, Yan/0000-0002-5344-1884; Papazoglou, Michael/0000-0002-2482-2282; Mistry, Sajib/0000-0001-7513-3789; Benatallah, Boualem/0000-0002-8805-1130; Malik, Zaki/0000-0003-3123-7687; Georgakopoulos, Dimitrios/0000-0001-7880-2140; yu, qi/0000-0002-0426-5407 Australian Research Council [LP120200305, DP150100149, DP160103595]; Qatar National Research Fund (a member of The Qatar Foundation) [NPRP 7-481-1-088, NPRP 9-224-1-049]; Australian Research Council [LP120200305] Funding Source: Australian Research Council Australian Research Council(Australian Research Council); Qatar National Research Fund (a member of The Qatar Foundation); Australian Research Council(Australian Research Council) This research was made possible by LP120200305, DP150100149, and DP160103595 grants from Australian Research Council and NPRP 7-481-1-088 and NPRP 9-224-1-049 grants from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. 25 136 143 0 14 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0001-0782 1557-7317 COMMUN ACM Commun. ACM APR 2017.0 60.0 4 64 72 10.1145/2983528 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science ER6NB 2023-03-23 WOS:000398920900025 0
J Jian, XD; Xia, Y; Lozano-Galant, JA; Sun, LM Jian, Xudong; Xia, Ye; Lozano-Galant, Jose A.; Sun, Limin Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges JOURNAL OF SENSORS English Article Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system. [Jian, Xudong; Sun, Limin] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai, Peoples R China; [Xia, Ye] Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China; [Lozano-Galant, Jose A.] Univ Castilla La Mancha, Dept Civil Engn, E-13071 Ciudad Real, Spain Tongji University; Tongji University; Universidad de Castilla-La Mancha Xia, Y (corresponding author), Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China. yxia@tongji.edu.cn Jian, Xudong/AAZ-2201-2021; Jian, Xudong/AAD-4756-2021 xia, ye/0000-0002-1548-0567; Lozano Galant, Jose Antonio/0000-0003-0741-0566; JIAN, Xudong/0000-0001-9973-9662 National Key R&D Program of China [2017YFC1500605]; Science and Technology Commission of Shanghai Municipality [17DZ1204300, 18DZ1201203]; Fundamental Research Funds for the Central Universities National Key R&D Program of China; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This paper is supported by the National Key R&D Program of China (2017YFC1500605), Science and Technology Commission of Shanghai Municipality (17DZ1204300 and 18DZ1201203), and the Fundamental Research Funds for the Central Universities. 37 29 33 13 64 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-725X 1687-7268 J SENSORS J. Sens. 2019.0 2019.0 3409525 10.1155/2019/3409525 0.0 15 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation IC5UC Green Published, gold 2023-03-23 WOS:000471033100001 0
C Liu, ZC; Herranz, L; Yang, F; Zhang, SP; Wan, S; Mrak, M; Blanch, MG IEEE Liu, Zhaocheng; Herranz, Luis; Yang, Fei; Zhang, Saiping; Wan, Shuai; Mrak, Marta; Blanch, Marc Gorriz Slimmable Video Codec 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022) English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-24, 2022 New Orleans, LA IEEE,CVF,IEEE Comp Soc Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression. [Liu, Zhaocheng; Wan, Shuai] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China; [Herranz, Luis; Yang, Fei] Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona 08193, Spain; [Mrak, Marta; Blanch, Marc Gorriz] BBC Res & Dev, White City Pl,201 Wood Lane, London, England; [Zhang, Saiping] Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Peoples R China Northwestern Polytechnical University; Autonomous University of Barcelona; Centre de Visio per Computador (CVC); Xidian University Liu, ZC (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China. liuzhaocheng@mail.nwpu.edu.cn MICINN, Spain [RYC2019-027020-I] MICINN, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government) L.H. acknowledges the support of the Ramon y Cajal grant RYC2019-027020-I (MICINN, Spain). 13 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-8739-9 2022.0 1742 1746 10.1109/CVPRW56347.2022.00183 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT9KZ 2023-03-23 WOS:000861612701078 0
J Luo, LB; Yin, HY; Cai, WT; Lees, M; Bin Othman, N; Zhou, SP Luo, Linbo; Yin, Haiyan; Cai, Wentong; Lees, Michael; Bin Othman, Nasri; Zhou, Suiping Towards a data-driven approach to scenario generation for serious games COMPUTER ANIMATION AND VIRTUAL WORLDS English Article scenario generation; serious games; virtual agents Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario's training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance.Copyright (c) 2014 John Wiley & Sons, Ltd. [Luo, Linbo] Xidian Univ, Sch Comp Sci & Technol, Xian, Peoples R China; [Luo, Linbo; Yin, Haiyan; Cai, Wentong] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore; [Lees, Michael] Univ Amsterdam, Sect Computat Sci, NL-1012 WX Amsterdam, Netherlands; [Bin Othman, Nasri] Inst High Performance Comp, Comp Sci Grp, Singapore, Singapore; [Zhou, Suiping] Middlesex Univ, Sch Sci & Technol, London N17 8HR, England Xidian University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; University of Amsterdam; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); Middlesex University Luo, LB (corresponding author), Xidian Univ, Sch Comp Sci & Technol, Xian, Peoples R China. lbluosg@gmail.com Cai, Wentong/A-3720-2011 Cai, Wentong/0000-0002-0183-3835 Singapore A*STAR SERC Complex Systems Programme research grant [1224504056] Singapore A*STAR SERC Complex Systems Programme research grant(Agency for Science Technology & Research (A*STAR)) Nasri Bin Othman is supported by the Singapore A*STAR SERC Complex Systems Programme research grant (1224504056). 17 9 9 2 19 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1546-4261 1546-427X COMPUT ANIMAT VIRT W Comput. Animat. Virtual Worlds MAY-AUG 2014.0 25.0 3-4 SI 395 404 10.1002/cav.1588 0.0 10 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science AJ2WD Green Accepted 2023-03-23 WOS:000337524300020 0
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A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H. -C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesaria, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherafati, N.; Sherman, A. D.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; ; ATLAS Collaboration Measurement of the production cross-section of a single top quark in association with a Z boson in proton-proton collisions at 13 TeV with the ATLAS detector PHYSICS LETTERS B English Article The production of a top quark in association with a Z boson is investigated. The proton-proton collision data collected by the ATLAS experiment at the LHC in 2015 and 2016 at a centre-of-mass energy of root s = 13 TeV are used, corresponding to an integrated luminosity of 36.1 fb(-1). Events containing three identified leptons (electrons and/or muons) and two jets, one of which is identified as a b-quark jet are selected. The major backgrounds are diboson, tt($)over-bar and Z + jets production. A neural network is used to improve the background rejection and extract the signal. The resulting significance is 4.2 sigma in the data and the expected significance is 5.4 sigma. The measured cross-section for tZq production is 600 +/- 170(stat.)+/- 140(syst.)fb. (C) 2018 The Author(s). Published by Elsevier B.V. [Duvnjak, D.; Jackson, P.; Petridis, A.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Fischer, A.; Jain, V.; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Dassoulas, J.; Dehghanian, N.; Gingrich, D. M.; Jabbar, S.; Moore, R. W.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Ciftci, A. K.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Grevtsov, K.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I.; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Mastrandrea, P.; Raspopov, S.; Sauvan, E.; Smart, B. H.; Todorova-Nova, S.; Vallier, A.; Wingerter-Seez, I.; Yatsenko, E.] CNRS IN2P3, LAPP, Annecy Le Vieux, France; [Berger, N.; Burger, A. M.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Grevtsov, K.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I.; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Mastrandrea, P.; Raspopov, S.; Sauvan, E.; Smart, B. H.; Todorova-Nova, S.; Vallier, A.; Wingerter-Seez, I.; Yatsenko, E.] Univ Savoie Mt Blanc, Annecy Le Vieux, France; [Blair, R. E.; Chekanov, S.; LeCompte, T.; Love, J.; Malon, D.; Metcalfe, J.; Paramonov, A.; Proudfoot, J.; Ryu, S.; Stanek, R. W.; van Gemmeren, P.; Wang, R.; Webster, J. S.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Cheu, E.; Delitzsch, C. M.; Johns, K. A.; Jones, S.; Lampl, W.; Lei, X.; Leone, R.; Loch, P.; Nayyar, R.; Rutherfoord, J. P.; Shupe, M. A.; Varnes, E. W.; Veeraraghavan, V.; Zhou, Y.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Bullock, D.; Darmora, S.; De, K.; Farbin, A.; Feremenga, L.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Kim, H. Y.; Ozturk, N.; Stradling, A. R.; Usai, G.; Vartapetian, A.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA; [Fassouliotis, D.; Gkialas, I.; Ioannou, P.; Kourkoumelis, C.; Papageorgiou, K.; Tsirintanis, N.] Natl & Kapodistrian Univ Athens, Dept Phys, Athens, Greece; [Alexopoulos, T.; Benekos, N.; Dris, M.; Gazis, E. N.; Gkountoumis, P.; Karakostas, K.; Karastathis, N.; Karentzos, E.; Koulouris, A.; Maltezos, S.; Moschovakos, P.; Panagiotopoulou, E. St.; Panagoulias, I.; Papadopoulou, Th. D.; Vlachos, S.; Zacharis, G.] Natl Tech Univ Athens, Dept Phys, Zografos, Greece; [Andeen, T.; Ilchenko, Y.; Narayan, R.; Nikiforou, N.; Onyisi, P. U. E.; Potti, H.; Webb, A. F.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Abdinov, O.; Ahmadov, F.; Huseynov, N.; Javadov, N.; Khalil-zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan; [Anjos, N.; Bosman, M.; Casado, M. P.; Casolino, M.; Cavallaro, E.; Cavalli-Sforza, M.; Fischer, C.; Forster, F. A.; Gerbaudo, D.; Gkougkousis, E. L.; Glatzer, J.; Grinstein, S.; Juste Rozas, A.; Korolkov, I.; Lange, J. C.; Paz, I. Lopez; Martinez, M.; Mir, L. M.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I.; Rizzi, C.; Rodina, Y.; Rodriguez Perez, A.; Terzo, S.; Tripiana, M. F.; Tsiskaridze, S.; Valery, L.; Vazquez Furelos, D.; Volpi, G.; Zaidan, R.] Barcelona Inst Sci & Technol, Inst Fis Altes Energies IFAE, Barcelona, Spain; [Agatonovic-Jovin, T.; Dimitrievska, A.; Krstic, J.; Sijacki, Dj.; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Dale, O.; Eigen, G.; Liebig, W.; Lipniacka, A.; Maeland, S.; Latour, B. Martin Dit; Stugu, B.; Yang, Z.; Zalieckas, J.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I.; Jeanty, L.; Krizka, K.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Nachman, B. P.; Griso, S. Pagan; Pranko, A.; Shapiro, M.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yang, H.; Yao, W-M.] Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA USA; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I.; Jeanty, L.; Krizka, K.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Nachman, B. P.; Griso, S. Pagan; Pranko, A.; Shapiro, M.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yang, H.; Yao, W-M.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Biedermann, D.; Dietrich, J.; Grancagnolo, S.; Herbert, G. H.; Hristova, I.; Kind, O. M.; Lacker, H.; Lohse, T.; Mergelmeyer, S.; Ng, Y. S.; Peri, F.; Rehnisch, L.; Schulz, H.; Sperlich, D.; Stamm, S.; zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Beck, H. P.; Ereditato, A.; Haug, S.; Meloni, F.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.; Weston, T. D.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Beck, H. P.; Ereditato, A.; Haug, S.; Meloni, F.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.; Weston, T. D.] Univ Bern, High Energy Phys Lab, Bern, Switzerland; [Allport, P. P.; Andari, N.; Baca, M. J.; Bracinik, J.; Briglin, D. L.; Broughton, J. H.; Casadei, D.; Charlton, D. G.; Daniells, A. C.; Foster, A. G.; Gonella, L.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Kendrick, J.; Levy, M.; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Reynolds, E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.; Worm, S. D.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Arik, M.; Gurbuz, S.; Istin, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkey; [Celebi, E.; Cetin, S. A.] Istanbul Bilgi Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Beddall, A. J.] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Ctr Invest, Bogota, Colombia; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, F.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giangiacomi, N.; Giorgi, F. M.; Manghi, F. Lasagni; Mengarelli, A.; Negrini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesaria, N.; Sidoti, A.; Sioli, M.; Spighi, R.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] INFN Sez Bologna, Bologna, Italy; [Alberghi, G. L.; Biondi, S.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, F.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giangiacomi, N.; Manghi, F. Lasagni; Mengarelli, A.; Romano, M.; Sbrizzi, A.; Semprini-Cesaria, N.; Sidoti, A.; Sioli, M.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Arslan, O.; Bandyopadhyay, A.; Bechtle, P.; Bernlochner, F. U.; Betti, A.; Brock, I.; Caudron, J.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Gaycken, G.; Ghneimat, M.; Gottardo, C. A.; Grefe, C.; Hageboeck, S.; Hansen, M. C.; Heer, S.; Hohn, D.; Huegging, F.; Jacobs, R. M.; Janssen, J.; Klingl, T.; Kostyukhin, V. V.; Kroseberg, J.; Krueger, H.; Lantzsch, K.; Lenz, T.; Liebal, J.; Melzer, A.; Moles-Valls, R.; Obermann, T.; Pohl, D.; Ricken, O.; Sarrazin, B.; Schildgen, L. K.; Schopf, E.; Schultens, M. J.; Sciandra, A.; Seema, P.; von Toerne, E.; Wagner, P.; Wermes, N.; Winter, B. T.; Wong, K. H. Yau; Yuen, S. P. Y.; Zhang, R.] Univ Bonn, Phys Inst, Bonn, Germany; [Ahlen, S. P.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Finelli, K. D.; Long, B. A.; Shank, J. T.; Sherman, A. D.; Yan, Z.; Yigitbasi, E.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Amundsen, G.; Barone, G.; Bensinger, J. R.; Bergsten, L. J.; Blocker, C.; Dhaliwal, S.; Dodsworth, D.; Goblirsch-Kolb, M.; Herde, H.; Loew, K. M.; O'connor, K.; Sciolla, G.; Venturini, A.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Amaral Coutinho, Y.; Araujo Ferraz, V.; Begalli, M.; Caloba, L. P.; Goncalves Gama, R.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio De Janeiro COPPE EE IF, Rio De Janeiro, Brazil; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.; Peralva, B. S.] Fed Univ Juiz de Fora UFJF, Elect Circuits Dept, Juiz De Fora, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Assamagan, K.; Begel, M.; Buttinger, W.; Chen, H.; Debbe, R.; Elmsheuser, J.; Ernst, M.; Gordon, H. A.; Hu, Q.; Lakovidis, G.; Klimentov, A.; Kouskoura, V.; Lancon, E.; Lanni, F.; Lee, C. A.; Liu, H.; Lynn, D.; Ma, H.; Maeno, T.; Mountricha, E.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Perepelitsa, D. V.; Pleier, M. -A.; Polychronakos, V.; Protopopescu, S.; Radeka, V.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Tulbure, T. T.] Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Caprini, I.; Caprini, M.; Chitan, A.; Ciubancan, M.; Constantinescu, S.; Dita, P.; Dita, S.; Dobre, M.; Ducu, O. A.; Dumitriu, A. E.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Pantea, D.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Agheorghiesei, C.] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Dept Phys, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ Timisoara, Timisoara, Romania; [Bossio Sola, J. D.; Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.; Sacerdoti, S.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Brunt, B. H.; Chapman, J. D.; Cottin, G.; Hill, J. C.; Lester, C. G.; Lin, C. Y.; Malone, C.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Yusuff, I.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Bellerive, A.; Chau, C. C.; Cree, G.; Di Valentino, D.; Gillberg, D.; Hunter, R. F. H.; Keller, J. S.; Koffas, T.; Nomidis, I.; Oakham, F. G.; Ruiz-Martinez, A.; Sherafati, N.; Vincter, M. G.; Weber, S. A.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Aleksa, M.; Gonzalez, B. Alvarez; Amoroso, S.; Bella, L. Aperio; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Beermann, T. A.; Beltramello, O.; Bortfeldt, J.; Boyd, J.; Butti, P.; Camarda, S.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Carrillo-Montoya, G. D.; Catinaccio, A.; Cattai, A.; Chelstowska, M. A.; Chisholm, A. S.; Conti, G.; Cortes-Gonzalez, A.; Czodrowski, P.; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Girolamo, B.; Di Nardo, R.; Dittus, F.; Dobos, D.; Dudarev, A.; Duhrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Faltova, J.; Farthouat, P.; Fassnacht, R.; Feng, E. J.; Francis, D.; Fressard-Batraneanu, S. M.; Froidevaux, D.; Gadatsch, S.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Gumpert, C.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Hrdinka, J.; Huhtinen, M.; Iengo, P.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Kuehn, S.; Langenberg, R. J.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Malyukov, S.; Manousos, A.; Mapelli, L.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Berlingen, J. Montejo; Llacer, M. Moreno; Morgenstern, S.; Morley, A. K.; Mornacchi, G.; Musheghyan, H.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pani, P.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Polifka, R.; Pommes, K.; Poveda, J.; Astigarraga, M. E. Pozo; Raymond, M.; Rembser, C.; Ristic, B.; Ritsch, E.; Roe, S.; Ruthmann, N.; Salzburger, A.; Schaepe, S.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sanchez, C. A. Solans; Spigo, G.; Staerz, S.; Stelzer, H. J.; Ten Kate, H.; Unal, G.; Vandelli, W.; Venturi, N.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Wotschack, J.; Young, C. J. S.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Toro, R. Camacho; Gardner, R. W.; Hildebrand, K.; Holmes, T. R.; Kim, Y. K.; Merritt, F. S.; Miller, D. W.; Olsson, M. J. R.; Oreglia, M. J.; Pilcher, J. E.; Saxon, J.; Schaefer, D.; Shochet, M. J.; Stark, G. H.; Swiatlowski, M.; Vukotic, I.; Wu, M.; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Blunier, S.; Diaz, M. A.; Garay Walls, F. M.; Lee, G. R.; Ochoa-Ricoux, J. P.; Olivares Pino, S. A.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Carquin, E.; Kuleshov, S.; Lopez, J. A.; Pezoa, R.; Prokoshin, F.; Salazar Loyola, J. E.; Araya, S. Tapia; Vasquez, G. A.; Viaux Maira, N.; White, R.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Ayoub, M. K.; da Costa, J. Barreiro Guimaraes; Bertella, C.; Cheng, H. J.; Fang, Y.; Pascual, J. A. Garcia; Han, S.; Huang, Y.; Kurth, M. G.; Li, Q.; Liang, Z.; Merino, J. Llorente; Lou, X.; Lyu, F.; Mansour, J. D.; Ouyang, Q.; Peng, C.; Ren, H.; Shan, L. Y.; Xu, D.; Zhang, Y.; Zhou, M.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Chen, S.; Jin, S.; Wang, C.; Wang, W.; Zhang, P.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Chen, X.; Xia, L.] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China; [Cheng, H. J.; Han, S.; Kurth, M. G.; Li, Q.; Peng, C.; Ren, H.; Zhang, Y.; Zhou, M.] Univ Chinese Acad Sci UCAS, Beijing, Peoples R China; [Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Guo, Y.; Han, L.; Jiang, Y.; Li, B.; Li, Changqiao; Li, Q.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y.; Ospanov, R.; Peng, H.; Wang, W.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Guo, Y.; Han, L.; Jiang, Y.; Li, B.; Li, Changqiao; Li, Q.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y.; Ospanov, R.; Peng, H.; Wang, W.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [Du, Y.; Feng, C.; Liu, J.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Sch Phys, Jinan, Shandong, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kondrashova, N.; Li, L.; Li, X.; Nishu, N.; Wang, Z.; Yang, H.; Zhou, N.] Shanghai Jiao Tong Univ, Dept Phys & Astron, Key Lab Particle Phys Astrophys & Cosmol, Minist Educ, Shanghai, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kondrashova, N.; Li, L.; Li, X.; Nishu, N.; Wang, Z.; Yang, H.; Zhou, N.] Shanghai Jiao Tong Univ, Shanghai Key Lab Particle Phys & Cosmol, Shanghai, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kondrashova, N.; Li, L.; Li, X.; Nishu, N.; Wang, Z.; Yang, H.; Zhou, N.] PKU CHEP, Shanghai, Peoples R China; [Angelidakis, S.; Boumediene, D.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Ganguly, S.; Gris, Ph.; Madar, R.; Marjanovic, M.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Senkin, S.; Vazeille, F.] Univ Clermont Auvergne, CNRS, IN2P3, LPC, Clermont Ferrand, France; [Alkire, S. P.; Angerami, A.; Brooijmans, G.; Carbone, R. M.; Clark, M. R.; Cole, B.; Havener, L. B.; Hughes, E. W.; Iordanidou, K.; Mohapatra, S.; Ochoa, I.; Parsons, J. A.; Smith, M. N. K.; Smith, R. W.; Tian, Y.; Tuts, P. M.; Wang, T.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Bajic, M.; Besjes, G. J.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Monk, J.; Petersen, T. C.; Stark, S. H.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Cairo, V. M.; Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] INFN Grp Collegato Cosenza, Lab Nazl Frascati, Arcavacata Di Rende, Italy; [Aloisio, A.; Cairo, V. M.; Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I.; Guzik, M. P.; Janus, P. A.; Kisielewska, D.; Kopernya, S.; Kowalski, T. Z.; Kremer, J. A.; Mindur, B.; Przybycien, M.; Zemla, A.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Bahmani, M.; Banas, E.; de Renstrom, P. A. Bruckman; Burka, K.; Chwastowski, J. J.; Czekierda, S.; Derendarz, D.; Dziedzic, B. S.; Godlewski, J.; Kaczmarska, A.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Slawinska, M.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Gupta, R.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Lo Sterzo, F.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Ye, J.; Zhao, X.; Zhou, L.] Southern Methodist Univ, Dept Phys, Dallas, TX USA; [Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Braren, F.; Brendlinger, K.; Britzger, D.; Bryngemark, L.; Daubney, T.; David, C.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Heim, S.; Heinemann, B.; Hiller, K. H.; Katzy, J.; Kitali, V.; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Madsen, A.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Robinson, J. E. M.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schaefer, R.; Schmitt, S.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Vishwakarma, A.; Wang, J.; Wanotayaroj, C.; Yap, Y. C.; Zakharchuk, N.] DESY, Hamburg, Germany; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Braren, F.; Brendlinger, K.; Britzger, D.; Bryngemark, L.; Daubney, T.; David, C.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Heim, S.; Heinemann, B.; Hiller, K. H.; Katzy, J.; Kitali, V.; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Madsen, A.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Robinson, J. E. M.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schaefer, R.; Schmitt, S.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Vishwakarma, A.; Wang, J.; Wanotayaroj, C.; Yap, Y. C.; Zakharchuk, N.] DESY, Zeuthen, Germany; [Burmeister, I.; Cinca, D.; Erdmann, J.; Gessner, G.; Goessling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.; Kupfer, T.; Nitsche, I.] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Hils, M.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Ramos, J. Manjarres; Novgorodova, O.; Siegert, F.; Socher, F.; Straessner, A.; Todt, S.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Li, S.; Oh, S. H.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bristow, T. M.; Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Mills, C.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Esposito, B.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Esposito, B.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Arnold, H.; Boehler, M.; Buehrer, F.; Buescher, D.; Cardillo, F.; Coniavitis, E.; Di Simone, A.; Gonella, G.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hirose, M.; Jakobs, K.; Javurek, T.; Javurkova, M.; Jenni, P.; Klapdor-Kleingrothaus, T.; Koeneke, K.; Kopp, A. K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V.; Megy, T. J.; Mogg, P.; Nagel, M.; Parzefall, U.; Ronzani, M.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schillo, C.; Schnoor, U.; Schumacher, M.; Ta, D.; Tornambe, P.; Tsiskaridze, V.; Weiser, C.; Wiik-Fuchs, L. A. M.; Zhang, L.; Zimmermann, S.] Albert Ludwigs Univ, Fak Math & Phys, Freiburg, Germany; [Akilli, E.; Ancu, L. S.; Benoit, M.; Calace, N.; Clark, A.; Coccaro, A.; della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Golling, T.; Gonzalez-Sevilla, S.; Lacobucci, G.; Jansky, R.; Katre, A.; Khoo, T. J.; Lanfermann, M. C.; Lionti, A. E.; March, L.; Mermod, P.; Nackenhorst, O.; Nessi, M.; Pandini, C. E.; Paolozzi, L.; Schramm, S.; Sfyrla, A.; Valente, M.; Wu, X.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Darbo, G.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Guido, E.; Lapertosa, A.; Miglioranzi, S.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] INFN Sez Genova, Genoa, Italy; [Barberis, D.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Guido, E.; Lapertosa, A.; Miglioranzi, S.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Jejelava, J.; Tskhadadze, E. G.] Iv Javakhishvili Tbilisi State Univ, E Andronikashvili Inst Phys, Tbilisi, Georgia; [Djobava, T.; Durglishvili, A.; Khubua, J.; Minashvili, I. A.; Mosidze, M.] Tbilisi State Univ, Inst High Energy Phys, Tbilisi, Georgia; [Dueren, M.; Heinz, C.; Kreutzfeldt, K.; Stenzel, H.] Justus Liebig Univ Giessen, Inst Phys 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Duncan, A. K.; Fenton, M. J.; Gray, C.; Gul, U.; Mullen, P.; O'Shea, V.; Owen, M.; Pollard, C. S.; Qin, G.; Quilty, D.; Robson, A.; Denis, R. D. St.; Stewart, G. A.; Thompson, A. S.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Beck, H. C.; Bindi, M.; Bisanz, T.; Bokan, P.; Brandt, G.; De Maria, A.; Drechsler, E.; Geisen, J.; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lemmer, B.; Mantoani, M.; Mchedlidze, G.; Mellenthin, J. D.; Nellist, C.; Pokharel, I.; Quadt, A.; Rieger, J.; Rosien, N. -A.; Rzehorz, G. F.; Shabalina, E.; Smith, J. W.; Stolte, P.; Torres, R. E. Ticse; Veatch, J.; Weingarten, J.] Georg August Univ, Phys Inst 2, Gottingen, Germany; [Berlendis, S.; Camincher, C.; Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, Grenoble, France; [Chan, S. K.; Clark, B. L.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Ippolito, V.; Lazovich, T.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Sun, S.; Tong, B.; Tuna, A. N.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Andrei, V.; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J. I.; Dunford, M.; Franchino, S.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kluge, E. -E.; Kugel, A.; Meier, K.; Zu Theenhausen, H. Meyer; Villar, D. I. Narrias; Sahinsoy, M.; Schultz-Coulon, H. -C.; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; de Lima, D. E. Ferreira; Giulini, M.; Kolb, M.; Lisovyi, M.; Schaetzel, S.; Schoening, A.; Sosa, D.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Bortolotto, V.; Chan, Y. L.; Chow, Y. S.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Lu, H.; Salvucci, A.; Tsang, K. W.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Lo, C. Y.; Orlando, N.; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, R.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Palacino, G.; Penwell, J.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Jakobsen, S.; Kneringer, E.; Lukas, W.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Liu, B.; Pluth, D.; Prell, S.; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gongadze, A.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Lyubushkin, V.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Simic, L.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] JINR Dubna, Joint Inst Nucl Res, Dubna, Russia; [Aoki, M.; Arai, Y.; Hanagaki, K.; Higashino, S.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Mizukami, A.; Nagai, R.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Nozaki, M.; Odaka, S.; Okuyama, T.; Sasaki, O.; Suzuki, S.; Takubo, Y.; Tanaka, S.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I.; Unno, Y.; Usui, J.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Kawade, K.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Shimizu, S.; Tanioka, R.; Wakamiya, K.; Yamane, F.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Akatsuka, S.; Kunigo, T.; Noguchi, Y.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto, Japan; [Kawagoe, K.; Kobayashi, D.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Res Ctr Adv Particle Phys, Fukuoka, Japan; [Kawagoe, K.; Kobayashi, D.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka, Japan; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Bertram, I. A.; Borissov, G.; Bouhova-Thacker, E. V.; Fox, H.; Grimm, K.; Henderson, R. C. W.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Muenstermann, D.; Parker, A. J.; Skinner, M. B.; Smizanska, M.; Walder, J.; Wharton, A. M.; Whitmore, B. W.] Univ Lancaster, Dept Phys, Lancaster, England; [Aliev, M.; Bachas, K.; Chiodini, G.; Gorini, E.; Longo, L.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] INFN Sez Lecce, Lecce, Italy; [Aliev, M.; Bachas, K.; Gorini, E.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Anders, J. K.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Gao, Y.; Gwilliam, C. B.; Hayward, H. S.; Jones, T. J.; Kay, E. F.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Maxfield, S. J.; Mehta, A.; Meng, L.; Rompotis, N.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool, Merseyside, England; [Cindro, V.; Filipcic, A.; Gorisek, A.; Hiti, B.; Kanjir, L.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Muskinja, M.; Sfiligoj, T.; Sokhrannyi, G.] Jozef Stefan Inst, Dept Expt Particle Phys, Ljubljana, Slovenia; [Cindro, V.; Filipcic, A.; Gorisek, A.; Hiti, B.; Kanjir, L.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Muskinja, M.; Sfiligoj, T.; Sokhrannyi, G.] Univ Ljubljana, Dept Phys, Ljubljana, Slovenia; [Armitage, L. J.; Bevan, A. J.; Blumenschein, U.; Bona, M.; Hays, J. M.; Landon, M. P. J.; Lewis, D.; Lloyd, S. L.; Millar, D. A.; Morris, J. D.; Nooney, T.; Rizvi, E.; Sandbach, R. L.; Stevenson, T. J.; Tahirovic, E.] Queen Mary Univ London, Sch Phys & Astron, London, England; [Ardell, R. E.; Berry, T.; Boisvert, V.; Bozson, A. J.; Brooks, T.; Cowan, G.; Gadomski, S.; George, S.; Gibson, S. M.; Kempster, J. J.; Kilby, C. R.; Vazquez, J. G. Panduro; Pastore, Fr.; Savage, G.; Sowden, B. C.; Spano, F.; Teixeira-Dias, R.; Thomas-Wilsker, J.] Royal Holloway Univ London, Dept Phys, Egham, Surrey, England; [Bell, A. S.; Butterworth, J. M.; Campanelli, M.; Christodoulou, V.; Davison, P.; Facini, G.; Falla, R. J.; Freeborn, D.; Gregersen, K.; Grout, Z. J.; Ortiz, N. G. Gutierrez; Gutschow, C.; Hesketh, G. G.; Jiggins, S.; Konstantinidis, N.; Korn, A.; Kucuk, H.; Leney, K. J. C.; Martyniuk, A. C.; McClymont, L. I.; Nurse, E.; Richter, S.; Scanlon, T.; Sherwood, P.; Simmons, B.; Vaidya, A.; Wardrope, D. R.; Waugh, B. M.] UCL, Dept Phys & Astron, London, England; [Greenwood, Z. D.; Grossi, G. C.; Jana, D. K.; Sawyer, L.; Wobisch, M.] Louisiana Tech Univ, Ruston, LA 71270 USA; [Beau, T.; Bernardi, G.; Bomben, M.; Calderini, G.; Crescioli, F.; D'eramo, L.; De Cecco, S.; Demilly, A.; Derue, F.; Ducourthial, A.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Quintero, D. M. Portillo; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Wang, R. -J.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bernardi, G.; Bomben, M.; Calderini, G.; Crescioli, F.; D'eramo, L.; De Cecco, S.; Demilly, A.; Derue, F.; Ducourthial, A.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Quintero, D. M. Portillo; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Wang, R. -J.] Univ Paris Diderot, Paris, France; [Beau, T.; Bernardi, G.; Bomben, M.; Calderini, G.; Crescioli, F.; D'eramo, L.; De Cecco, S.; Demilly, A.; Derue, F.; Ducourthial, A.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Quintero, D. M. Portillo; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Wang, R. -J.] CNRS IN2P3, Paris, France; [Akesson, T. P. A.; Bocchetta, S. S.; Doglioni, C.; Hedberg, V.; Jarlskog, G.; Kalderon, C. W.; Kellermann, E.; Konya, B.; Lytken, E.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; 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Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of New Mexico; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; University of Rome Tor Vergata; University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; CEA; University of California System; University of California Santa Cruz; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Comenius University Bratislava; Slovak Academy of Sciences; University of Cape Town; University of Johannesburg; University of Witwatersrand; Stockholm University; Oskar Klein Centre; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of British Columbia; University of Victoria; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; Academia Sinica - Taiwan; University of London; King's College London; Novosibirsk State University; University of Louisville; An Najah National University; California State University System; California State University Fresno; University of Fribourg; Autonomous University of Barcelona; Universidade do Porto; Moscow Institute of Physics & Technology; Parthenope University Naples; Peter the Great St. Petersburg Polytechnic University; City University of New York (CUNY) System; University of Aegean; ICREA; Ilia State University; Georgian Technical University; Ochanomizu University; Manhattan College; City University of New York (CUNY) System; California State University System; California State University Sacramento; Sun Yat Sen University; Bulgarian Academy of Sciences; Lomonosov Moscow State University; Stanford University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Giresun University; Universiti Malaya Aaboud, M (corresponding author), Univ Adelaide, Dept Phys, Adelaide, SA, Australia.;Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. Suster, Carl J E/L-3714-2016; Barreiro, Fernando/D-9808-2012; Price, Darren D/E-6162-2012; Della Pietra, Massimo/J-5008-2012; Lagouri, Theodota/AAC-7358-2021; Petrucci, Fabrizio/G-8348-2012; Introzzi, Gianluca/K-2497-2015; Maj, Klaudia/Q-2724-2019; Kaczmarska, Anna/B-2753-2019; Reznicek, Pavel/C-1989-2017; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Hamal, Petr/G-5540-2014; Giagu, Stefano/H-6455-2013; Benekos, Nektarios/I-9928-2017; Gabrielli, Alessandro/H-4931-2012; Kazanin, Vasily F./HHM-2056-2022; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Zhemchugov, Alexey/N-1717-2017; Dabrowski, Wladyslaw/AAS-6369-2020; Sadykov, Renat/AAN-9602-2020; Stoicea, Gabriel/B-6717-2011; Cervelli, Alberto/X-7416-2018; Legger, Federica/HNC-2568-2023; Kroll, Jiri/C-8465-2018; CAKIR, Orhan/L-1893-2015; Perez, Miguel Villaplana/B-2717-2015; Grinstein, Sebastian/ABE-1880-2020; Castillo, Victoria VC/B-5171-2015; Olszowska, Jolanta/P-2889-2017; Lazzaroni, Massimo/N-3675-2015; Betti, Alessandra/AAD-9964-2019; Tassi, Enrico/AAJ-9661-2020; govender, nicolin/CAH-3245-2022; Stoicea, Gabriel/L-5602-2019; Vaniachine, Alexandre/H-7796-2013; Ventura, Andrea/A-9544-2015; do Amaral Coutinho, Yara/AAU-7857-2021; zhang, jin/GXV-9154-2022; Grancagnolo, Sergio/J-3957-2015; Woźniak, Krzysztof/AAQ-6119-2021; Huseynov, Nazim/AAE-4663-2019; Francavilla, Paolo/HKE-1206-2023; Amoroso, Simone/AAW-4334-2021; Cavaliere, Viviana/CAG-2384-2022; Grabowska-Bołd, Iwona/ABI-7829-2020; Padilla, Cristobal/C-3218-2017; Ozcan, Veysi Erkcan/AAS-4508-2020; gaudio, gabriella/AAN-6039-2021; Masik, Jiri/AAE-1203-2021; O'Shea, Val/GQH-6388-2022; Pages, Andres Pacheco/C-5353-2011; Bogdanchikov, Alexander/AAB-9414-2022; Franchini, Matteo/AAC-9259-2021; Fuster, Juan/W-6189-2018; Kharlamova, Tatyana/AAQ-5430-2020; Doyle, Anthony T/C-5889-2009; Li, Hao/GPS-9834-2022; Kuze, Masahiro/V-4251-2018; Maevskiy, Artem/P-4101-2016; spagnolo, stefania/A-6359-2012; de Renstrom, Pawel Bruckman/AAG-7725-2021; Boyko, Igor R/J-3659-2013; Nechaeva, Polina Y/N-1148-2015; Lysak, Roman/H-2995-2014; Donadelli, Marisilvia/H-3710-2016; Alimonti, Gianluca/AAG-4603-2020; Petrucci, Fabrizio/P-3837-2019; Gravila, Paul/HLP-6245-2023; Livan, Michele M/D-7531-2012; Mindur, Bartosz/A-2253-2017; Marcisovsky, Michal/AAM-2404-2020; Giordani, Mario P/Q-6211-2018; Bruscino, Nello/ABA-8980-2021; Mikestikova, Marcela/H-1996-2014; Di Nardo, Roberto/J-4993-2012; Staszewski, Rafał/V-5240-2018; Carquin, Edson/GLU-9641-2022; Lacasta, Carlos/C-7254-2008; Vandelli, Wainer/ABI-8185-2020; Carquin López, Edson/GZG-4712-2022; Saraiva, Joao Gentil Mendes/D-1596-2017; Caprini, Irinel/AAP-3760-2020; Lee, Lawrence/AAU-6790-2021; Sessa, Marco/AAT-2850-2020; KHODINOV, ALEKSANDR/D-6269-2015; Zhukov, Konstantin I/M-6027-2015; Annovi, Alberto/AAA-8638-2020; Sultansoy, Saleh/AAA-8267-2019; Bates, Richard L/D-6596-2013; Casado, Pilar/H-1484-2015; Lee, Shih-Chang/AAV-7016-2021; Dyndal, Mateusz/AAB-1528-2020; Stabile, Alberto/F-2889-2013; AGHEORGHIESEI, Catalin/B-8596-2014; Zanzi, Daniele/AAY-4102-2020; Olszewski, Andrzej J/W-1825-2018; Balaji, S/J-1864-2019; Kuleshov, Sergey V/D-9940-2013; Ventura, Andrea/CAH-0226-2022; Berger, N/ABE-4064-2020; Alexa, Calin/F-6345-2010; beddall, ayda/AAG-6531-2020; Kostyukhin, Vadim/ABA-9033-2020; Valero, Alberto/G-9866-2015; Alberich, Leonor Cerdá/ABG-8534-2020; Smirnova, Oxana/A-4401-2013; Ahmadov, Faig N/B-3723-2018; Dziedzic, Bartosz/Q-4189-2017; Sapronov, Andrey/HGB-0392-2022; Trzupek, Adam/N-2448-2018; Kepka, Oldrich/ABF-5327-2021; Derendarz, Dominik/AAO-3512-2021; Fabbri, Laura/H-3442-2012; Norjoharuddeen, Nurfikri/S-3109-2018; b, m/HPE-0989-2023; Cheng, Hok-Chuen/GNP-8341-2022; perez, miguel/GXF-5077-2022; Czekierda, Sabina/N-9012-2018; Chizhov, Mihail/CAG-2076-2022; Coadou, Yann/G-2263-2010; Schernau, Michael/GRY-3808-2022; Bosman, Martine/J-9917-2014; Podberezko, Pavel/M-8985-2018; De, Kaushik/N-1953-2013; Buzykaev, Alexey/HIK-0117-2022; Buttar, Craig/AAG-5392-2021; Darbo, Giovanni/C-8175-2012; Gonçalo, Ricardo/M-3153-2016; da via, cinzia/AAS-3978-2021; Burdin, Sergey/AAZ-9062-2021; Tapia, Sebastian/ABB-6644-2021; Sanchez, Francisca J. Munoz/AAH-3909-2019; Artoni, Giacomo/ABA-2164-2020; García, Josu Cantero/AAS-9810-2020; Sopczak, Andre/I-4951-2015; Parodi, Fabrizio/AHE-5089-2022; Wolters, Helmut/M-4154-2013; Filipuzzi, Marco/AAQ-6793-2020; Gladilin, Leonid/B-5226-2011; Herten, Gregor/HNQ-9546-2023; Llorente Merino, Javier/AGP-3432-2022; Viaux, Nicolas/AAT-5715-2020; Jones, Roger WL/H-5578-2011; Dubinin, Filipp/M-9546-2015; de la Torre Perez, Hector/ABG-6942-2020; Torro Pastor, Emma/AAB-5979-2021; Bachas, Konstantinos/C-8101-2019; Sławińska, Magdalena/W-2551-2018; Glasman, Claudia/Y-8858-2019; Malek, Fairouz/E-4410-2018; Gurbuz, Saime/AAG-5583-2019; Wu, Xin/ABH-1729-2020; Berta, Peter/AAL-7109-2020; kucuk, hilal/AAM-5530-2020; Trzebinski, Maciej/AAO-6821-2021; Trzebinski, Maciej/W-1748-2018; Banaś, Elżbieta A/W-4583-2017; Castro, Nuno/AAB-3648-2019; Giuli, Francesco/HJI-6649-2023; arik, metin/T-4193-2019; Brooks, William K/C-8636-2013; Hejbal, Jiri/H-1358-2014; Sbarra, Carla/M-9669-2018; Kostyukhin, Vadim/F-3171-2019; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Franchini, Matteo/HNI-8314-2023; Kantserov, Vadim/M-9761-2015; Talyshev, Alexey/HGC-6910-2022; Gongadze, Alexi/AAY-5201-2021; Llácer, María Moreno/AAU-6582-2020; Cottin, Giovanna/AFR-3846-2022; Zemaityte, Gabija/AAI-7245-2021; Laycock, Paul/AAL-8261-2021; Chitan, Adrian/AAC-8946-2022; Lefebvre, Benoit/ABB-2272-2021; Staszewski, Rafał/AAO-6844-2021; Ferrer, Antonio/H-2942-2015; Barberis, Dario/ABF-1310-2021; Jiménez Peña, Javier/AFY-1817-2022; Bortfeldt, Jona/AAJ-5370-2021; Romaniouk, Anatoli/Q-6674-2017; Hunter, Robert/AAG-8342-2019; Gonzalez, Barbara Alvarez/ABG-7021-2020; Capua, Marcella/AAJ-8419-2020; Castillo, Luis Roberto Flores/W-3928-2018; Begalli, M/C-4121-2017; Chwastowski, Janusz J./I-4480-2012; Ciftci, Abbas Kenan/AAC-5487-2022; Pascual, Juan Antonio Garcia/B-3540-2019; Llácer, María Moreno/AAQ-7522-2020; Cavaliere, Viviana/CAE-8597-2022; Ibáñez, Carlos Escobar/B-3761-2017; Urbán, Susana Cabrera/H-1376-2015; Gustavino, Giuliano/AAK-6591-2020; Cheung, Kingman/A-8380-2008; Duarte-Campderrós, Jordi/F-5025-2018; Svatos, Michal/ABA-2041-2020; Bassalat, Ahmed Azzam/HHY-9901-2022; de Almeida, Fernando Marroquim Leao/D-6947-2019; Prokoshin, Fedor/E-2795-2012; Maneira, Jose/D-8486-2011; Bobrovnikov, Victor/AAB-8328-2022; Ippolito, Valerio/L-1435-2016; Gutierrez, Phillip/C-1161-2011; Testa, Marianna/L-9768-2019; Palka, Marek/GXW-2506-2022; YILDIZ, Hatice DURAN/AAN-3727-2021; Andreazza, Attilio/E-5642-2011; Tikhomirov, Vladimir/M-6194-2015; Capua, Marcella/A-8549-2015; Rosati, Stefano/AAF-6968-2019; Sýkora, Tomáš/Q-3174-2017; Sultansoy, Saleh/AAV-6853-2021; Villaplana Perez, Miguel/B-5772-2014; de la Hoz, Santiago Gonzalez/E-2494-2016; Wozniak, Krzysztof W/P-4475-2017; Price, Darren/AAB-5991-2021; Peleganchuk, Sergey V/J-6722-2014; Orlando, Nicola/AAL-1723-2021; Coccaro, Andrea/AAB-6990-2021; Godlewski, Jan/AAI-3300-2021; Kruchonak, Uladzimir/AAN-4371-2020; Negrini, Matteo/C-8906-2014; Donadelli, Marisilvia/ABD-5330-2021; Jiménez, Yesenia Hernández/ABH-1107-2020; 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Kukla, Romain/P-9760-2016; Vos, Marcel/G-8123-2015; Rodriguez Bosca, Sergi/HPC-6167-2023; , Alex/N-8715-2016; Davidek, Tomas/P-2697-2017 Suster, Carl J E/0000-0001-7021-9380; Barreiro, Fernando/0000-0002-3021-0258; Della Pietra, Massimo/0000-0003-4446-3368; Petrucci, Fabrizio/0000-0002-5278-2206; Introzzi, Gianluca/0000-0002-1314-2580; Maj, Klaudia/0000-0003-4819-9226; Kaczmarska, Anna/0000-0002-8880-4120; Reznicek, Pavel/0000-0003-4017-9829; Hamal, Petr/0000-0003-3139-7234; Giagu, Stefano/0000-0001-9192-3537; Benekos, Nektarios/0000-0001-7831-8762; Gabrielli, Alessandro/0000-0001-5346-7841; Kazanin, Vasily F./0000-0002-4906-5468; Zhemchugov, Alexey/0000-0002-3360-4965; Dabrowski, Wladyslaw/0000-0001-9061-9568; Sadykov, Renat/0000-0002-9157-6819; Stoicea, Gabriel/0000-0002-7511-4614; Cervelli, Alberto/0000-0002-0518-1459; Legger, Federica/0000-0003-1400-0709; Kroll, Jiri/0000-0001-6215-3326; CAKIR, Orhan/0000-0002-9016-138X; Perez, Miguel Villaplana/0000-0002-0048-4602; Grinstein, Sebastian/0000-0002-6460-8694; 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Gonski, Julia/0000-0003-2037-6315; Bethani, Agni/0000-0002-8150-7043; Majewski, Stephanie/0000-0002-6871-3395; Landon, Murrough/0000-0001-6828-9769; Charlton, David/0000-0003-0211-2041; Hill, John/0000-0002-8650-2807; KOULOURIS, AIMILIANOS/0000-0003-1012-4675; Lucotte, Arnaud/0000-0002-5992-0640; Formica, Andrea/0000-0001-8308-2643; Mkrtchyan, Tigran/0000-0002-5786-3136; Nobe, Takuya/0000-0002-5809-325X; franklin, melissa/0000-0002-6595-883X; Yorita, Kohei/0000-0003-1988-8401; , Alex/0000-0002-1051-3833; Nelson, Michael/0000-0002-0183-327X; Reeves, Kendall/0000-0003-3504-4882; /0000-0001-5765-1750; Walkowiak, Wolfgang/0000-0002-0385-3784; Beau, Tristan/0000-0002-2022-2140; Zakharchuk, Nataliia/0000-0002-4963-8836; Ponomarenko, Daniil/0000-0003-4213-1511; Lokajicek, Milos/0000-0001-8929-1243; Lloyd, Stephen/0000-0002-5073-2264; Kroninger, Kevin/0000-0001-9873-0228; Sato, Koji/0000-0001-8988-4065; Roos, Lydia/0000-0001-7151-9983; Ferrere, Didier/0000-0002-5687-9240; Besson, Nathalie/0000-0001-9248-6252; Pascuzzi, Vincent R./0000-0003-3167-8773; Onyisi, Peter/0000-0003-4201-7997; Nagel, Martin/0000-0002-2588-6691; Wardrope, David/0000-0002-8208-2964; Toth, Jozsef/0000-0001-9128-6080; Laycock, Paul/0000-0002-8572-5339; Antel, Claire/0000-0001-9683-0890; Moore, Roger/0000-0003-4160-4700; Lie, Ki/0000-0002-5779-5989; Bold, Tomasz/0000-0002-2432-411X; Mijovic, Liza/0000-0003-0162-2891; Ellert, Mattias/0000-0001-5265-3175; Davidek, Tomas/0000-0002-3770-8307; Mankinen, Katja/0000-0001-7357-9648; Marzin, Antoine/0000-0003-4364-4351 ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DSM/IRFU, France; SRNSF, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIA, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canada Council, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada; Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union; ERDF, European Union; FP7, European Union; Horizon, European Union; Investissements d'Avenir Labex, ANR, France; Investissements d'Avenir Idex, ANR,France; Region Auvergne, France; Fondation Partager le Savoir, France; DFG, Germany; AvH Foundation, Germany; EU-ESF; Greek NSRF; BSF, Israel; GIF, Israel; Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Spain; Generalitat Valenciana, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; Marie Sklodowska-Curie Actions, European Union; Canton of Geneva, Switzerland; Division Of Physics; Direct For Mathematical & Physical Scien [1624739] Funding Source: National Science Foundation; STFC [ST/M006425/1, ST/N000277/1, PP/E000452/1, ST/R002436/1, ST/J005509/1, ST/K001426/1, ST/I006064/1, ST/H001158/2, ST/L001217/1, ST/L001020/1, ST/N000447/1, ST/H001158/1, ATLAS Upgrades, ST/P00234X/1, ST/M00256X/1, ST/K000837/1, ST/R002576/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; SRNSF, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIA, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canada Council, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada(FQRNT); Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; FP7, European Union; Horizon, European Union; Investissements d'Avenir Labex, ANR, France(French National Research Agency (ANR)); Investissements d'Avenir Idex, ANR,France(French National Research Agency (ANR)); Region Auvergne, France(Region Auvergne-Rhone-Alpes); Fondation Partager le Savoir, France; DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); BSF, Israel(US-Israel Binational Science Foundation); GIF, Israel(German-Israeli Foundation for Scientific Research and Development); Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Spain; Generalitat Valenciana, Spain(Center for Forestry Research & Experimentation (CIEF)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); Marie Sklodowska-Curie Actions, European Union; Canton of Geneva, Switzerland; Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; SRNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIA, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, Canarie, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. 56 29 29 2 45 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B MAY 10 2018.0 780.0 557 577 10.1016/j.physletb.2018.03.023 0.0 21 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics GF7ZU Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000432187800078 0
J Yu, XT; Pan, XY; Zhang, SQ; Zhang, YH; Chen, L; Wan, SB; Huang, T; Cai, YD Yu, Xiangtian; Pan, XiaoYong; Zhang, ShiQi; Zhang, Yu-Hang; Chen, Lei; Wan, Sibao; Huang, Tao; Cai, Yu-Dong Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas FRONTIERS IN GENETICS English Article gene signature; expression pattern; epithelial-to-mesenchymal transition; single cell; classification Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients' deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT. [Yu, Xiangtian] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Clin Res Ctr, Shanghai, Peoples R China; [Pan, XiaoYong] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Key Lab Syst Control & Informat Proc, Minist Educ China, Shanghai, Peoples R China; [Zhang, ShiQi] Univ Copenhagen, Dept Biostat, Copenhagen, Denmark; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, CAS Key Lab Computat Biol, Biomed Big Data Ctr, CAS MPG Partner Inst Computat Biol,Shanghai Inst, Shanghai, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab PMMP, Shanghai, Peoples R China; [Wan, Sibao; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China Shanghai Jiao Tong University; Ministry of Education, China; Shanghai Jiao Tong University; University of Copenhagen; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Max Planck Society; Shanghai Maritime University; East China Normal University; Shanghai University Yu, XT (corresponding author), Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Clin Res Ctr, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, CAS Key Lab Computat Biol, Biomed Big Data Ctr, CAS MPG Partner Inst Computat Biol,Shanghai Inst, Shanghai, Peoples R China.;Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China. graceyu1985@163.com; tohuangtao@126.com; cai_yud@126.com Chen, Lei/A-2275-2011; Zhang, Yu-Hang/AAQ-9012-2021; Zhang, Yu-Hang/O-9774-2015 Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; Zhang, Yu-Hang/0000-0003-3825-0796 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Key R&D Program of China [2017YFC1201200, 2018YFC0910403]; National Natural Science Foundation of China [31701151, 31872418, 61803360]; Strategic Priority Research Program of Chinese Academy of Sciences [XDB38050200]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; fund of the Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] Shanghai Municipal Science and Technology Major Project; National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); fund of the Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This study was supported by Shanghai Municipal Science and TechnologyMajor Project (2017SHZDZX01), National Key R&D Program of China (2017YFC1201200 and 2018YFC0910403), National Natural Science Foundation of China (31701151, 31872418, and 61803360), Strategic Priority Research Program of Chinese Academy of Sciences (XDB38050200), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program (16YF1413800), Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), fund of the Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), and Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000). 87 4 4 1 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. JAN 28 2021.0 11.0 605012 10.3389/fgene.2020.605012 0.0 11 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity QF3KR 33584803.0 Green Accepted, gold 2023-03-23 WOS:000616797400001 0
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A., Jr.; Amato, S.; Amhis, Y.; Anderson, J.; Appleby, R. B.; Gutierrez, O. Aquines; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Bachmann, S.; Back, J. J.; Balagura, V.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Bates, A.; Bauer, C.; Bauer, Th.; Bay, A.; Beddow, J.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Benayoun, M.; Bencivenni, G.; Benson, S.; Benton, J.; Bernet, R.; Bettler, M. -O; van Beuzekom, M.; Bien, A.; Bifani, S.; Bird, T.; Bizzeti, A.; Bjornstad, P. M.; Blake, T.; Blanc, F.; Blanks, C.; Blouw, J.; Blusk, S.; Bobrov, A.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Bowcock, T. J. V.; Bozzi, C.; Brambach, T.; van den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brook, N. H.; Brown, H.; Buechler-Germann, A.; Burducea, I.; Bursche, A.; Buytaert, J.; Cadeddu, S.; Callot, O.; Calvi, M.; Calvo Gomez, M.; Camboni, A.; Campana, P.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cattaneo, M.; Cauet, Ch.; Charles, M.; Charpentier, Ph.; Chen, P.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Cid Vidal, X.; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coca, C.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, R.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Corti, G.; Couturier, B.; Cowan, G. A.; Craik, D.; Currie, R.; D'Ambrosio, C.; David, P.; David, P. N. Y.; De Bonis, I.; De Bruyn, K.; De Capua, S.; De Cian, M.; De Miranda, J. M.; De Paula, L.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Degaudenzi, H.; Del Buono, L.; Deplano, C.; Derkach, D.; Deschamps, O.; Dettori, F.; Dickens, J.; Dijkstra, H.; Diniz Batista, P.; Domingo Bonal, F.; Donleavy, S.; Dordei, F.; Dosil Suarez, A.; Dossett, D.; Dovbnya, A.; Dupertuis, F.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; van Eijk, D.; Eisele, F.; Eisenhardt, S.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Elsby, D.; Esperante Pereira, D.; Falabella, A.; Faerber, C.; Fardell, G.; Farinelli, C.; Farry, S.; Fave, V.; Fernandez Albor, V.; Ferro-Luzzi, M.; Filippov, S.; Fitzpatrick, C.; Fontana, M.; Fontanelli, F.; Forty, R.; Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Furcas, S.; Gallas Torreira, A.; Galli, D.; Gandelman, M.; Gandini, P.; Gao, Y.; Garnier, J. -C.; Garofoli, J.; Tico, J. Garra; Garrido, L.; Gascon, D.; Gaspar, C.; Gauld, R.; Gauvin, N.; Gersabeck, E.; Gersabeck, M.; Gershon, T.; Ghez, Ph.; Gibson, V.; Gligorov, V. V.; Goebel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.; Gordon, H.; Grabalosa Gandara, M.; Graciani Diaz, R.; Cardoso, L. A. Granado; Grauges, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Gruenberg, O.; Gui, B.; Gushchin, E.; Guz, Yu.; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hampson, T.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; Harrison, P. F.; Hartmann, T.; He, J.; Heijne, V.; Hennessy, K.; Henrard, P.; Hernando Morata, J. A.; van Herwijnen, E.; Hicks, E.; Hoballah, M.; Hopchev, P.; Hulsbergen, W.; Hunt, P.; Huse, T.; Huston, R. S.; Hutchcroft, D.; Hynds, D.; Iakovenko, V.; Ilten, P.; Imong, J.; Jacobsson, R.; Jaeger, A.; Hussein, M. Jahjah; Jans, E.; Jansen, F.; Jaton, P.; Jean-Marie, B.; Jing, F.; John, M.; Johnson, D.; Jones, C. R.; Jost, B.; Kaballo, M.; Kandybei, S.; Karacson, M.; Karbach, T. M.; Keaveney, J.; Kenyon, I. R.; Kerzel, U.; Ketel, T.; Keune, A.; Khanji, B.; Kim, Y. M.; Knecht, M.; Kochebina, O.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kruzelecki, K.; Kucharczyk, M.; Kudryavtsev, V.; Kvaratskheliya, T.; La Thi, V. N.; Lacarrere, D.; Lafferty, G.; Lai, A.; Lambert, D.; Lambert, R. W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Latham, T.; Lazzeroni, C.; Le Gac, R.; van Leerdam, J.; Lees, J. -P.; Lefevre, R.; Leflat, A.; Lefrancois, J.; Leroy, O.; Lesiak, T.; Li, L.; Li, Y.; Gioi, L. Li; Lieng, M.; Liles, M.; Lindner, R.; Linn, C.; Liu, B.; Liu, G.; von Loeben, J.; Lopes, J. H.; Lopez Asamar, E.; Lopez-March, N.; Lu, H.; Luisier, J.; Mac Raighne, A.; Machefert, F.; Machikhiliyan, I. V.; Maciuc, F.; Maev, O.; Magnin, J.; Malde, S.; Mamunur, R. M. D.; Manca, G.; Mancinelli, G.; Mangiafave, N.; Marconi, U.; Maerki, R.; Marks, J.; Martellotti, G.; Martens, A.; Martin, L.; Sanchez, A. Martin; Martinelli, M.; Santos, D. Martinez; Massafferri, A.; Mathe, Z.; Matteuzzi, C.; Matveev, M.; Maurice, E.; Maynard, B.; Mazurov, A.; McCarthy, J.; McGregor, G.; McNulty, R.; Meissner, M.; Merk, M.; Merkel, J.; Milanes, D. A.; Minard, M. -N.; Rodriguez, J. Molina; Monteil, S.; Moran, D.; Morawski, P.; Mountain, R.; Mous, I.; Muheim, F.; Mueller, K.; Muresan, R.; Muryn, B.; Muster, B.; Mylroie-Smith, J.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neufeld, N.; Nguyen, A. D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Nikitin, N.; Nikodem, T.; Nomerotski, A.; Novoselov, A.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Orlandea, M.; Otalora Goicochea, J. M.; Owen, P.; Pal, B. K.; Palacios, J.; Palano, A.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C. J.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrick, G. N.; Patrignani, C.; Pavel-Nicorescu, C.; Pazos Alvarez, A.; Pellegrino, A.; Penso, G.; Altarelli, M. Pepe; Perazzini, S.; Perego, D. L.; Perez Trigo, E.; Perez-Calero Yzquierdo, A.; Perret, P.; Perrin-Terrin, M.; Pessina, G.; Petrolini, A.; Phan, A.; Picatoste Olloqui, E.; Pie Valls, B.; Pietrzyk, B.; Pilar, T.; Pinci, D.; Plackett, R.; Playfer, S.; Plo Casasus, M.; Polci, F.; Polok, G.; Poluektov, A.; Polycarpo, E.; Popov, D.; Popovici, B.; Potterat, C.; Powell, A.; Prisciandaro, J.; Pugatch, V.; Puig Navarro, A.; Qian, W.; Rademacker, J. H.; Rakotomiaramanana, B.; Rangel, M. S.; Raniuk, I.; Raven, G.; Redford, S.; Reid, M. M.; dos Reis, A. C.; Ricciardi, S.; Richards, A.; Rinnert, K.; Romero, D. A. Roa; Robbe, P.; Rodrigues, E.; Rodrigues, F.; Rodriguez Perez, P.; Rogers, G. J.; Roiser, S.; Romanovsky, V.; Rosello, M.; Rouvinet, J.; Ruf, T.; Ruiz, H.; Sabatino, G.; Saborido Silva, J. J.; Sagidova, N.; Sail, P.; Saitta, B.; Salzmann, C.; Sanmartin Sedes, B.; Sannino, M.; Santacesaria, R.; Santamarina Rios, C.; Santinelli, R.; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Savrina, D.; Schaack, P.; Schiller, M.; Schindler, H.; Schleich, S.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M. -H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Senderowska, K.; Sepp, I.; Serra, N.; Serrano, J.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shatalov, P.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, O.; Shevchenko, V.; Shires, A.; Coutinho, R. Silva; Skwarnicki, T.; Smith, N. A.; Smith, E.; Smith, M.; Sobczak, K.; Soler, F. J. P.; Solomin, A.; Soomro, F.; Souza, D.; Souza De Paula, B.; Spaan, B.; Sparkes, A.; Spradlin, P.; Stagni, F.; Stahl, S.; Steinkamp, O.; Stoica, S.; Stone, S.; Storaci, B.; Straticiuc, M.; Straumann, U.; Subbiah, V. K.; Swientek, S.; Szczekowski, M.; Szczypka, P.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Teodorescu, E.; Teubert, F.; Thomas, C.; Thomas, E.; van Tilburg, J.; Tisserand, V.; Tobin, M.; Tolk, S.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, M. T.; Tsaregorodtsev, A.; Tuning, N.; Garcia, M. Ubeda; Ukleja, A.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vecchi, S.; Velthuis, J. J.; Veltri, M.; Vesterinen, M.; Viaud, B.; Videau, I.; Vieira, D.; Vilasis-Cardona, X.; Visniakov, J.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voss, C.; Voss, H.; Waldi, R.; Wallace, R.; Wandernoth, S.; Wang, J.; Ward, D. R.; Watson, N. K.; Webber, A. D.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wiggers, L.; Wilkinson, G.; Williams, M. P.; Williams, M.; Wilson, F. F.; Wishahi, J.; Witek, M.; Witzeling, W.; Wotton, S. A.; Wright, S.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, F.; Xing, Z.; Yang, Z.; Young, R.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, F.; Zhang, L.; Zhang, W. C.; Zhang, Y.; Zhelezov, A.; Zhong, L.; Zvyagin, A. LHCb Collaboration Measurement of the effective B-s(0) -> K+ K- lifetime PHYSICS LETTERS B English Article Flavour physics; Lifetime; B physics DECAY; TOOL A precise determination of the effective B-s(0) -> K+ K- lifetime can be used to constrain contributions from physics beyond the Standard Model in the B-s(0) meson system. Conventional approaches select B meson decay products that are significantly displaced from the B meson production vertex. As a consequence, B mesons with low decay times are suppressed, introducing a bias to the decay time spectrum which must be corrected. This analysis uses a technique that explicitly avoids a lifetime bias by using a neural network based trigger and event selection. Using 1.0 fb(-1) of data recorded by the LHCb experiment, the effective B-s(0) -> K+ K- lifetime is measured as 1.455 +/- 0.046 (stat.) +/- 0.006 (syst.) ps. (c) 2012 CERN. Published by Elsevier B.V. All rights reserved. [Barter, W.; Cliff, H. V.; Dickens, J.; Tico, J. Garra; Gibson, V.; Gregson, S.; Haines, S. C.; Jones, C. R.; Mangiafave, N.; Rogers, G. J.; Ward, D. R.; Wotton, S. A.; Wright, S.] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England; [Bediaga, I.; De Miranda, J. M.; Diniz Batista, P.; Magnin, J.; Massafferri, A.; Nasteva, I.; dos Reis, A. C.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Amato, S.; Carvalho Akiba, K.; De Paula, L.; Francisco, O.; Gandelman, M.; Gomes, A.; Lopes, J. H.; Otalora Goicochea, J. M.; Polycarpo, E.; Rangel, M. S.; Rodrigues, F.; Souza De Paula, B.; Vieira, D.] Univ Fed Rio de Janeiro, Rio De Janeiro, Brazil; [Chen, P.; Gao, Y.; Jing, F.; Li, L.; Li, Y.; Liu, B.; Lu, H.; Wu, S.; Yang, Z.; Yuan, X.; Zhang, F.; Zhang, Y.; Zhong, L.] Tsinghua Univ, Ctr High Energy Phys, Beijing 100084, Peoples R China; [De Bonis, I.; Decamp, D.; Ghez, Ph.; Hopchev, P.; Lees, J. -P.; Machikhiliyan, I. V.; Minard, M. -N.; Pietrzyk, B.; T'Jampens, S.; Tisserand, V.; Tournefier, E.] Univ Savoie, CNRS, IN2P3, LAPP, Annecy Le Vieux, France; [Ajaltouni, Z.; Cogneras, E.; Deschamps, O.; El Rifai, I.; Henrard, P.; Hoballah, M.; Hussein, M. Jahjah; Lefevre, R.; Gioi, L. Li; Monteil, S.; Niess, V.; Perret, P.; Romero, D. A. Roa; Sobczak, K.] Univ Blaise Pascal, CNRS, IN2P3, Clermont Univ,LPC, Clermont Ferrand, France; [Adrover, C.; Aslanides, E.; Cogan, J.; Khanji, B.; Le Gac, R.; Leroy, O.; Mancinelli, G.; Maurice, E.; Perrin-Terrin, M.; Sapunov, M.; Serrano, J.; Tsaregorodtsev, A.] Aix Marseille Univ, CNRS, IN2P3, CPPM, Marseille, France; [Barsuk, S.; Callot, O.; He, J.; Jean-Marie, B.; Kochebina, O.; Lefrancois, J.; Machefert, F.; Sanchez, A. Martin; Nicol, M.; Robbe, P.; Schune, M. -H.; Teklishyn, M.; Viaud, B.; Videau, I.] Univ Paris 11, CNRS, IN2P3, LAL, F-91405 Orsay, France; [Ben-Haim, E.; Benayoun, M.; David, P.; Del Buono, L.; Martens, A.; Polci, F.] Univ Paris Diderot, CNRS, IN2P3, LPNHE,Univ Pierre & Marie Curie, Paris, France; [Brambach, T.; Cauet, Ch.; Deckenhoff, M.; Ekelhof, R.; Kaballo, M.; Karbach, T. M.; Kruse, F.; Lieng, M.; Merkel, J.; Schleich, S.; Schlupp, M.; Spaan, B.; Swientek, S.; Wishahi, J.] Tech Univ Dortmund, Fak Phys, Dortmund, Germany; [Gutierrez, O. Aquines; Bauer, C.; Britsch, M.; Fontana, M.; Maciuc, F.; Popov, D.; Schmelling, M.; Volyanskyy, D.; Voss, H.; Zavertyaev, M.] Max Planck Inst Kernphys, D-69117 Heidelberg, Germany; [Adametz, A.; Bachmann, S.; Bien, A.; Blouw, J.; Dordei, F.; Eisele, F.; Faerber, C.; Gersabeck, E.; Hansmann-Menzemer, S.; Jaeger, A.; Kreplin, K.; Krocker, G.; Linn, C.; Marks, J.; Meissner, M.; Nikodem, T.; Seyfert, P.; Stahl, S.; van Tilburg, J.; Uwer, U.; Wandernoth, S.; Wiedner, D.; Zhelezov, A.] Heidelberg Univ, Inst Phys, D-6900 Heidelberg, Germany; [Bifani, S.; Farry, S.; Huston, R. S.; Ilten, P.; Keaveney, J.; Mathe, Z.; McNulty, R.; Moran, D.; Wallace, R.; Zhang, W. C.] Natl Univ Ireland Univ Coll Dublin, Sch Phys, Dublin 4, Ireland; [Milanes, D. A.; Palano, A.] Sez INFN Bari, Bari, Italy; [Carbone, A.; Derkach, D.; Falabella, A.; Galli, D.; Marconi, U.; Perazzini, S.; Vagnoni, V.; Valenti, G.; Zangoli, M.] Sez INFN Bologna, Bologna, Italy; [Bonivento, W.; Cadeddu, S.; Cardini, A.; Deplano, C.; Lai, A.; Manca, G.; Oldeman, R.; Saitta, B.] Sez INFN Cagliari, Cagliari, Italy; [Baldini, W.; Bozzi, C.; Falabella, A.; Mazurov, A.; Savrie, M.; Vecchi, S.] Sez INFN Ferrara, Ferrara, Italy; [Bettler, M. -O; Bizzeti, A.; Frosini, M.; Graziani, G.; Passaleva, G.; Veltri, M.] Sez INFN Firenze, Florence, Italy; [Archilli, F.; Bencivenni, G.; Campana, P.; De Simone, P.; Lanfranchi, G.; Palutan, M.; Sarti, A.; Sciascia, B.; Sciubba, A.; Soomro, F.] INFN Frascati, Lab Nazl, Frascati, Italy; [Cardinale, R.; Fontanelli, F.; Patrignani, C.; Petrolini, A.; Sannino, M.] Sez INFN Genova, Genoa, Italy; [Calvi, M.; Furcas, S.; Kucharczyk, M.; von Loeben, J.; Matteuzzi, C.; Perego, D. L.; Pessina, G.] Sez INFN Milano Bicocca, Milan, Italy; [Carboni, G.; De Capua, S.; Sabatino, G.; Santovetti, E.; Satta, A.] Sez INFN Roma Tor Vergata, Rome, Italy; [Alves, A. A., Jr.; Auriemma, G.; Bocci, V.; Martellotti, G.; Penso, G.; Pinci, D.; Santacesaria, R.; Satriano, C.] Sez INFN Roma La Sapienza, Rome, Italy; [Chrzaszcz, M.; Dziurda, A.; Kucharczyk, M.; Lesiak, T.; Morawski, P.; Polok, G.; Witek, M.] Polish Acad Sci, Henryk Niewodniczanski Inst Nucl Phys, Krakow, Poland; [Muryn, B.; Oblakowska-Mucha, A.; Senderowska, K.; Szumlak, T.] AGH Univ Sci & Technol, Krakow, Poland; [Szczekowski, M.; Ukleja, A.] Soltan Inst Nucl Studies, PL-00681 Warsaw, Poland; [Burducea, I.; Coca, C.; Grecu, A.; Muresan, R.; Orlandea, M.; Pavel-Nicorescu, C.; Popovici, B.; Stoica, S.; Straticiuc, M.; Teodorescu, E.] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Alkhazov, G.; Bondar, N.; Dzyuba, A.; Maev, O.; Matveev, M.; Sagidova, N.; Shcheglov, Y.; Vorobyev, A.] Petersburg Nucl Phys Inst, Gatchina, Russia; [Balagura, V.; Belogurov, S.; Belyaev, I.; Egorychev, V.; Golubkov, D.; Golutvin, A.; Kvaratskheliya, T.; Machikhiliyan, I. V.; Savrina, D.; Semennikov, A.; Shatalov, P.; Shevchenko, V.] Inst Theoret & Expt Phys, Moscow 117259, Russia; [Komarov, I.; Korolev, M.; Leflat, A.; Nikitin, N.] Moscow State Univ SINP MSU, Inst Nucl Phys, Moscow, Russia; [Filippov, S.; Gushchin, E.; Kravchuk, L.; Mazurov, A.] Russian Acad Sci INR RAN, Inst Nucl Res, Moscow, Russia; [Bobrov, A.; Bondar, A.; Eidelman, S.; Krokovny, P.; Kudryavtsev, V.; Poluektov, A.; Shekhtman, L.; Vorobyev, V.] Budker Inst Nucl Phys SB RAS, Novosibirsk, Russia; [Bobrov, A.; Bondar, A.; Eidelman, S.; Krokovny, P.; Kudryavtsev, V.; Poluektov, A.; Shekhtman, L.; Vorobyev, V.] Novosibirsk State Univ, Novosibirsk 630090, Russia; [Artamonov, A.; Belous, K.; Dzhelyadin, R.; Guz, Yu.; Novoselov, A.; Obraztsov, V.; Romanovsky, V.; Shapkin, M.; Yushchenko, O.] Inst High Energy Phys, Protvino, Russia; [Abellan Beteta, C.; Calvo Gomez, M.; Camboni, A.; Comerma-Montells, A.; Domingo Bonal, F.; Garrido, L.; Gascon, D.; Grabalosa Gandara, M.; Graciani Diaz, R.; Grauges, E.; Lopez Asamar, E.; Perez-Calero Yzquierdo, A.; Picatoste Olloqui, E.; Pie Valls, B.; Potterat, C.; Puig Navarro, A.; Rosello, M.; Ruiz, H.; Vazquez Gomez, R.; Vilasis-Cardona, X.] Univ Barcelona, Barcelona, Spain; [Adeva, B.; Alvarez Cartelle, P.; Cid Vidal, X.; Dosil Suarez, A.; Esperante Pereira, D.; Fernandez Albor, V.; Gallas Torreira, A.; Hernando Morata, J. A.; Pazos Alvarez, A.; Perez Trigo, E.; Plo Casasus, M.; Rodriguez Perez, P.; Saborido Silva, J. J.; Sanmartin Sedes, B.; Santamarina Rios, C.; Seco, M.; Vazquez Regueiro, P.; Visniakov, J.] Univ Santiago de Compostela, Santiago De Compostela, Spain; [Albrecht, J.; Alessio, F.; Archilli, F.; Artuso, M.; Balagura, V.; Barschel, C.; Blake, T.; Buytaert, J.; Campana, P.; Cardinale, R.; Cattaneo, M.; Charpentier, Ph.; Ciba, K.; Clemencic, M.; Closier, J.; Collins, R.; Corti, G.; Couturier, B.; D'Ambrosio, C.; Degaudenzi, H.; Derkach, D.; Dijkstra, H.; Ferro-Luzzi, M.; Forty, R.; Frank, M.; Frei, C.; Garnier, J. -C.; Gaspar, C.; Gersabeck, M.; Gershon, T.; Gligorov, V. V.; Golutvin, A.; Cardoso, L. A. Granado; Gys, T.; Haen, C.; van Herwijnen, E.; Jacobsson, R.; Jost, B.; Karacson, M.; Kerzel, U.; Kruzelecki, K.; Kucharczyk, M.; Kvaratskheliya, T.; Lacarrere, D.; Lanciotti, E.; Langenbruch, C.; Leflat, A.; Lindner, R.; Liu, G.; Maev, O.; Mamunur, R. M. D.; Santos, D. Martinez; Mazurov, A.; Neufeld, N.; Nomerotski, A.; Oldeman, R.; Panman, J.; Altarelli, M. Pepe; Roiser, S.; Ruf, T.; Santinelli, R.; Schindler, H.; Schmidt, B.; Schopper, A.; Schwemmer, R.; Shapoval, I.; Soomro, F.; Stagni, F.; Stone, S.; Subbiah, V. K.; Teubert, F.; Thomas, E.; Garcia, M. Ubeda; Vesterinen, M.; Wicht, J.; Witzeling, W.; Wyllie, K.; Zvyagin, A.] European Org Nucl Res CERN, Geneva, Switzerland; [Amhis, Y.; Bay, A.; Blanc, F.; Bressieux, J.; Chen, P.; Cowan, G. A.; Degaudenzi, H.; Dupertuis, F.; Fave, V.; Gauvin, N.; Haefeli, G.; Jaton, P.; Keune, A.; Knecht, M.; La Thi, V. N.; Lopez-March, N.; Luisier, J.; Maerki, R.; Muster, B.; Nakada, T.; Nguyen, A. D.; Nguyen-Mau, C.; Prisciandaro, J.; Rakotomiaramanana, B.; Rouvinet, J.; Schneider, O.; Szczypka, P.; Tourneur, S.; Tran, M. T.] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland; [Anderson, J.; Bernet, R.; Buechler-Germann, A.; Bursche, A.; Chiapolini, N.; De Cian, M.; Elsasser, Ch.; Mueller, K.; Palacios, J.; Salzmann, C.; Serra, N.; Steinkamp, O.; Straumann, U.; Tobin, M.; Vollhardt, A.] Univ Zurich, Inst Phys, Zurich, Switzerland; [Aaij, R.; Ali, S.; Bauer, Th.; van Beuzekom, M.; van den Brand, J.; Coco, V.; David, P. N. Y.; De Bruyn, K.; Dettori, F.; van Eijk, D.; Farinelli, C.; Heijne, V.; Hulsbergen, W.; Jans, E.; Jansen, F.; Ketel, T.; Koopman, R. F.; Koppenburg, P.; Kozlinskiy, A.; Lambert, R. W.; van Leerdam, J.; Martinelli, M.; Merk, M.; Mous, I.; Oggero, S.; Pellegrino, A.; Raven, G.; Schiller, M.; Storaci, B.; Tolk, S.; Tuning, N.; Wiggers, L.] Nikhef Natl Inst Subat Phys, Amsterdam, Netherlands; [van den Brand, J.; Dettori, F.; Ketel, T.; Koopman, R. F.; Lambert, R. W.; Raven, G.; Schiller, M.; Tolk, S.] Vrije Univ Amsterdam, Amsterdam, Netherlands; [Dovbnya, A.; Kandybei, S.; Raniuk, I.; Shapoval, I.; Shevchenko, O.] NSC KIPT, Kharkov, Ukraine; [Iakovenko, V.; Okhrimenko, O.; Pugatch, V.] Natl Acad Sci KINR, Inst Nucl Res, Kiev, Ukraine; [Elsby, D.; Kenyon, I. R.; Lazzeroni, C.; McCarthy, J.; Watson, N. K.] Univ Birmingham, Birmingham, W Midlands, England; [Adinolfi, M.; Benton, J.; Brook, N. H.; Cook, A.; Coombes, M.; Hampson, T.; Harnew, S. T.; Imong, J.; Naik, P.; Rademacker, J. H.; Solomin, A.; Souza, D.; Velthuis, J. J.; Voong, D.] Univ Bristol, HH Wills Phys Lab, Bristol BS8 1TL, Avon, England; [Back, J. J.; Craik, D.; Dossett, D.; Gershon, T.; Harrison, P. F.; Kreps, M.; Latham, T.; Pilar, T.; Poluektov, A.; Reid, M. M.; Coutinho, R. Silva; Whitehead, M.; Williams, M. P.] Univ Warwick, Dept Phys, Coventry CV4 7AL, W Midlands, England; [Easo, S.; Nandakumar, R.; Papanestis, A.; Patrick, G. N.; Ricciardi, S.; Smith, E.; Williams, M. P.; Wilson, F. F.] STFC Rutherford Appleton Lab, Didcot, Oxon, England; [Benson, S.; Clarke, P. E. L.; Currie, R.; Eisenhardt, S.; Fardell, G.; Fitzpatrick, C.; Kim, Y. M.; Lambert, D.; Muheim, F.; Needham, M.; Playfer, S.; Sparkes, A.; Xie, Y.; Young, R.] Univ Edinburgh, Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Alexander, M.; Bates, A.; Beddow, J.; Borghi, S.; Eklund, L.; Hynds, D.; Mac Raighne, A.; Ogilvy, S.; Pappagallo, M.; Plackett, R.; Rodrigues, E.; Sail, P.; Soler, F. J. P.; Spradlin, P.] Univ Glasgow, Sch Phys & Astron, Glasgow, Lanark, Scotland; [Affolder, A.; Bowcock, T. J. V.; Brown, H.; Casse, G.; Donleavy, S.; Hennessy, K.; Hicks, E.; Huse, T.; Hutchcroft, D.; Liles, M.; Mylroie-Smith, J.; Patel, G. D.; Rinnert, K.; Shears, T.; Smith, N. A.] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 3BX, Merseyside, England; [Blanks, C.; Carson, L.; Ciezarek, G.; Egede, U.; Golutvin, A.; Owen, P.; Parkinson, C. J.; Patel, M.; Richards, A.; Schaack, P.; Sepp, I.; Shires, A.; Tournefier, E.; Websdale, D.; Williams, M.] Univ London Imperial Coll Sci Technol & Med, London, England; [Appleby, R. B.; Barlow, R. J.; Bird, T.; Bjornstad, P. M.; Borghi, S.; Brett, D.; Harrison, J.; Lafferty, G.; McGregor, G.; Parkes, C.; Rodrigues, E.; Smith, M.; Webber, A. D.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Charles, M.; Contu, A.; Gandini, P.; Gauld, R.; Gordon, H.; Greening, E.; Harnew, N.; Hunt, P.; John, M.; Johnson, D.; Malde, S.; Martin, L.; Nomerotski, A.; Powell, A.; Redford, S.; Smith, E.; Thomas, C.; Topp-Joergensen, S.; Torr, N.; Wilkinson, G.; Xing, F.] Univ Oxford, Dept Phys, Oxford, England; [Artuso, M.; Blusk, S.; Borgia, A.; Britton, T.; Garofoli, J.; Gui, B.; Hadjivasiliou, C.; Maynard, B.; Mountain, R.; Pal, B. K.; Phan, A.; Qian, W.; Skwarnicki, T.; Stone, S.; Wang, J.; Xing, Z.; Zhang, L.] Syracuse Univ, Syracuse, NY USA; [Goebel, C.; Rodriguez, J. Molina] Pontificia Univ Catolica Rio de Janeiro PUC Rio, Rio De Janeiro, Brazil; [Gruenberg, O.; Hartmann, T.; Voss, C.; Waldi, R.] Univ Rostock, Inst Phys, Rostock, Germany; [Zavertyaev, M.] Russian Acad Sci LPI RAS, PN Lebedev Phys Inst, Moscow, Russia; [Palano, A.] Univ Bari, Bari, Italy; [Galli, D.; Perazzini, S.] Univ Bologna, Bologna, Italy; [Manca, G.; Oldeman, R.; Saitta, B.] Univ Cagliari, Cagliari, Italy; [Falabella, A.; Savrie, M.] Univ Ferrara, I-44100 Ferrara, Italy; [Frosini, M.] Univ Florence, Florence, Italy; [Veltri, M.] Univ Urbino, I-61029 Urbino, Italy; [Bizzeti, A.] Univ Modena & Reggio Emilia, Modena, Italy; [Cardinale, R.; Fontanelli, F.; Patrignani, C.; Petrolini, A.; Sannino, M.] Univ Genoa, Genoa, Italy; [Calvi, M.; Kucharczyk, M.; Perego, D. L.] Univ Milano Bicocca, Milan, Italy; [Carboni, G.; De Capua, S.; Sabatino, G.; Santovetti, E.] Univ Roma Tor Vergata, Rome, Italy; [Penso, G.; Sarti, A.; Sciubba, A.] Univ Roma La Sapienza, Rome, Italy; [Auriemma, G.; Satriano, C.] Univ Basilicata, I-85100 Potenza, Italy; [Abellan Beteta, C.; Calvo Gomez, M.; Domingo Bonal, F.; Rosello, M.; Vilasis-Cardona, X.] Univ Ramon Llull, LIFAELS, Barcelona, Spain; [Nguyen-Mau, C.] Hanoi Univ Sci, Hanoi, Vietnam University of Cambridge; Centro Brasileiro de Pesquisas Fisicas; Universidade Federal do Rio de Janeiro; Tsinghua University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Dortmund University of Technology; Max Planck Society; Ruprecht Karls University Heidelberg; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; AGH University of Science & Technology; Soltan Institute for Nuclear Studies; Horia Hulubei National Institute of Physics & Nuclear Engineering; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; University of Barcelona; Universidade de Santiago de Compostela; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Zurich; FOM National Institute for Subatomic Physics; Vrije Universiteit Amsterdam; National Academy of Sciences Ukraine; Institute for Nuclear Research of NASU; V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine; University of Birmingham; University of Bristol; University of Warwick; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Edinburgh; University of Glasgow; University of Liverpool; Imperial College London; University of Manchester; University of Oxford; Syracuse University; University of Rostock; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Universita degli Studi di Bari Aldo Moro; University of Bologna; University of Cagliari; University of Ferrara; University of Florence; University of Urbino; Universita di Modena e Reggio Emilia; University of Genoa; University of Milano-Bicocca; University of Rome Tor Vergata; Sapienza University Rome; University of Basilicata; Universitat Ramon Llull; Vietnam National University Hanoi Cliff, HV (corresponding author), Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England. cliff@hep.phy.cam.ac.uk Garrido, Lluis/D-6358-2017; Dosil, Alvaro/L-2530-2017; Naik, Paras/ABA-3875-2020; Bondar, Alexander E./O-9312-2017; Savrina, Daria/H-1456-2014; Graugés, Eugeni/M-6601-2017; Derkach, Denis/AAY-5330-2020; Ríos, Cibrán Santamarina/K-4686-2014; Puig, Albert/L-5196-2017; Schiller, Manuel Tobias/B-1229-2017; Solomin, Anatoly/C-3072-2016; Seco, Marcos/A-3886-2015; Morawski, Piotr/O-7125-2014; Vidal, Xabier Cid/L-2900-2014; Gersabeck, Evelina/ABI-6869-2020; Soler, Paul/E-8464-2011; Lesiak, Tadeusz/P-2206-2017; Ruiz, Hugo/H-4141-2013; Kravchuk, Leonid/L-4317-2017; Archilli, Flavio/AFP-2856-2022; Martinelli, Maurizio/AAC-8282-2021; Picatoste, Eduardo/K-2918-2017; Nasteva, Irina/AAU-1303-2020; Patrignani, Claudia/C-5223-2009; Kerzel, Ulrich/A-7275-2017; Martinelli, Maurizio/L-7337-2017; Lopez Asamar, Elias/B-3173-2017; Bifani, Simone/AAI-2336-2019; Orlandea, Marius Ciprian/J-7784-2013; stoica, sabin/J-3898-2014; Guz, Yury/AAV-1769-2021; Velthuis, Jaap/AAL-4020-2020; Filippov, Sergey/GXG-4925-2022; Zavertyaev, Mikhail/M-6844-2015; 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Parkinson, Christopher John/AAA-4740-2021; Gascon, David/AAD-2052-2022; Passaleva, Giovanni/ABF-5558-2021; Egorychev, Victor Yu./H-1076-2014; Watson, N/GLQ-5703-2022; Novoselov, Alexey/K-4244-2014; Popovici, Bogdan/B-6521-2012; Carbone, Angelo/C-8289-2012; Contu, Andrea/B-8054-2018; Dettori, Francesco/N-3963-2016; Dziurda, Agnieszka/V-4342-2018; Eklund, Lars/C-7709-2012 Garrido, Lluis/0000-0001-8883-6539; Dosil, Alvaro/0000-0003-4091-837X; Naik, Paras/0000-0001-6977-2971; Graugés, Eugeni/0000-0001-6571-4096; Derkach, Denis/0000-0001-5871-0628; Ríos, Cibrán Santamarina/0000-0002-9810-1816; Puig, Albert/0000-0001-8868-2947; Schiller, Manuel Tobias/0000-0001-8750-863X; Seco, Marcos/0000-0003-0354-8699; Vidal, Xabier Cid/0000-0002-0468-541X; Soler, Paul/0000-0002-4893-3729; Lesiak, Tadeusz/0000-0002-3966-2998; Martinelli, Maurizio/0000-0003-4792-9178; Picatoste, Eduardo/0000-0002-4958-644X; Nasteva, Irina/0000-0001-7115-7214; Patrignani, Claudia/0000-0002-5882-1747; Kerzel, Ulrich/0000-0002-4939-6726; Martinelli, Maurizio/0000-0003-4792-9178; Lopez Asamar, Elias/0000-0003-3046-0529; Bifani, Simone/0000-0001-7072-4854; Orlandea, Marius Ciprian/0000-0001-7851-5184; stoica, sabin/0000-0003-4632-7327; Guz, Yury/0000-0001-7552-400X; Velthuis, Jaap/0000-0002-4649-3221; Filippov, Sergey/0000-0003-3900-3914; Zavertyaev, Mikhail/0000-0002-4655-715X; Diaz, Ricardo Graciani/0000-0001-7166-5198; Witek, Mariusz/0000-0002-8317-385X; Diaz, Ricardo Graciani/0000-0001-7166-5198; Satriano, Celestina/0000-0002-4976-0460; Gomez, Ricardo Vazquez/0000-0001-5319-1128; Fitzpatrick, Conor/0000-0003-3674-0812; Trigo, Eliseo Perez/0000-0001-7650-6639; Golubkov, Dmitry/0000-0001-6216-1596; Grecu, Alexandru Tudor/0000-0002-7770-1839; Amato, Sandra/0000-0002-3277-0662; Lanfranchi, Gaia/0000-0002-9467-8001; Grecu, Alexandru Tudor/0000-0002-7770-1839; Adeva, Bernardo/0000-0001-9756-3712; Nasteva, Irina N/0000-0001-7115-7214; Cardini, Alessandro/0000-0002-6649-0298; Silva, Juan José Saborido/0000-0002-6270-130X; Filippov, Sergey/0000-0003-3900-3914; Rangel, Murilo Santana/0000-0002-8690-5198; Pappagallo, Marco/0000-0001-7601-5602; March, Neus Lopez/0000-0001-6586-0675; Szumlak, Tomasz/0000-0002-2562-7163; Gallas Torreira, Abraham/0000-0002-2745-7954; gushchin, evgeny/0000-0001-8857-1665; Krokovny, Pavel/0000-0002-1236-4667; Gandini, Paolo/0000-0001-7267-6008; Pereira, Daniel Esperante/0000-0003-0906-9023; Alexander, Semennikov/0000-0003-1130-2197; Gascon, David/0000-0001-9607-6154; Sierra, Carlos Vázquez/0000-0002-5865-0677; Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Vázquez Regueiro, Pablo/0000-0002-0767-9736; Hernando Morata, Jose Angel/0000-0002-8683-5142; de Mello, Carla Göbel Burlamaqui/0000-0003-0523-495X; Petrolini, Alessandro/0000-0003-0222-7594; Galli, Domenico/0000-0003-2375-6030; Vorobyev, Vitaly/0000-0002-6660-868X; Potterat, Cédric/0000-0002-0027-1316; Casasús, Máximo Pló/0000-0002-2289-918X; gandelman, miriam m/0000-0001-8192-8377; Schneider, Olivier P/0000-0002-6014-7552; Witek, Mariusz/0000-0002-8317-385X; Martinez Santos, Diego/0000-0002-6438-4483; Marconi, Umberto/0000-0002-5055-7224; de Paula, Leandro/0000-0002-4984-7734; Wiggers, Leo/0000-0003-1060-0520; Tsaregorodtsev, Andrei/0000-0003-4618-520X; Parkinson, Christopher John/0000-0003-0344-7361; Passaleva, Giovanni/0000-0002-8077-8378; Novoselov, Alexey/0000-0002-0019-1498; Popovici, Bogdan/0000-0001-6825-8954; Carbone, Angelo/0000-0002-7045-2243; Le Gac, Renaud/0000-0002-7551-6971; Sciascia, Barbara/0000-0003-0670-006X; Koppenburg, Patrick/0000-0001-8614-7203; Perrin-Terrin, Mathieu/0000-0002-3568-1956; Lafferty, George/0000-0003-0658-4919; Phan, Anna/0000-0002-6346-5628; Benayoun, Maurice/0000-0002-5902-7588; Frei, Christoph/0000-0001-5501-5611; Archilli, Flavio/0000-0002-1779-6813; serrano, justine/0000-0003-2489-7812; Coco, Victor/0000-0002-5310-6808; Rodrigues, Eduardo/0000-0003-2846-7625; Tisserand, Vincent/0000-0003-4916-0446; Gersabeck, Evelina/0000-0002-2860-6528; Watson, Nigel/0000-0002-8142-4678; De Cian, Michel/0000-0002-1268-9621; Sarti, Alessio/0000-0001-5419-7951; Comerma, Albert/0000-0002-8980-6048; Blanc, Frederic/0000-0001-5775-3132; Pugatch, Valery/0000-0002-5204-9821; Storaci, Barbara/0000-0002-0219-2750; Dordei, Francesca/0000-0002-2571-5067; Kandybei, Sergii/0000-0003-3598-0427; Mancinelli, Giampiero/0000-0003-1144-3678; MANCA, GIULIA/0000-0003-1960-4413; Pessina, Gianluigi Ezio/0000-0003-3700-9757; Cattaneo, Marco/0000-0001-7707-169X; Raven, Gerhard/0000-0002-2897-5323; Fernandez, Victor/0000-0001-5733-494X; Contu, Andrea/0000-0002-3545-2969; Perazzini, Stefano/0000-0002-1862-7122; Latham, Thomas/0000-0002-7195-8537; De Capua, Stefano/0000-0002-6285-9596; Lefevre, Regis/0000-0002-6917-6210; Carboni, Giovanni/0000-0003-1128-8276; Carvalho Akiba, Kazuyoshi/0000-0002-6736-471X; Calvo Gomez, Miriam/0000-0001-5588-1448; Egorychev, Victor/0000-0002-2539-673X; Dzyuba, Alexey/0000-0003-3612-3195; Veltri, Michele/0000-0001-7917-9661; Santovetti, Emanuele/0000-0002-5605-1662; Belous, Konstantin/0000-0003-0014-2589; Leroy, Olivier/0000-0002-2589-240X; Niess, Valentin/0000-0001-7148-6819; Wilson, Fergus/0000-0002-5552-0842; Steinkamp, Olaf/0000-0001-7055-6467; Gligorov, Vladimir/0000-0002-8189-8267; Keaveney, James/0000-0003-0766-5307; needham, matthew/0000-0002-8297-6714; Yang, Zhenwei/0000-0003-2937-9782; Alvarez Cartelle, Paula/0000-0003-1652-2834; Ukleja, Artur/0000-0003-0480-4850; Barlow, Roger/0000-0002-8295-8612; Cardinale, Roberta/0000-0002-7835-7638; David, Pieter/0000-0001-9260-9371; Moran, Dermot/0000-0002-1941-9333; Hutchcroft, David/0000-0002-4174-6509; Deschamps, Olivier/0000-0002-7047-6042; Dettori, Francesco/0000-0003-0256-8663; Shapoval, Illya/0000-0001-9350-8281; Bowcock, Themistocles/0000-0002-3505-6915; Patel, Mitesh/0000-0003-3871-5602; Souza Covacich, Daniel/0000-0003-2671-9227; Sciubba, Adalberto/0000-0002-3301-9176; Aquines, Osvaldo/0000-0002-4475-2808; Dziurda, Agnieszka/0000-0003-4338-7156; Molina Rodriguez, Josue Danilo/0000-0002-8025-0768; Schindler, Heinrich/0000-0002-1468-0479; Dossett, David/0000-0002-5670-5582; Matteuzzi, Clara/0000-0002-4047-4521; Eklund, Lars/0000-0002-2014-3864; Lazzeroni, Cristina/0000-0003-4074-4787; van Tilburg, Jeroen/0000-0003-2655-8742; Parkes, Chris/0000-0003-4174-1334; Alexander, Michael/0000-0002-8148-2392; Egede, Ulrik/0000-0001-5493-0762; Vagnoni, Vincenzo Maria/0000-0003-2206-311X; Rodrigues, Fernando/0000-0002-4274-5583; Seyfert, Paul/0000-0001-5419-2763; Solomin, Anatoly/0000-0003-0644-3227; van den Brand, Johannes/0000-0003-4434-5353; Craik, Daniel/0000-0002-3684-1560; Shears, Tara/0000-0002-2653-1366; Zhang, Yanxi/0000-0002-0157-188X; Poluektov, Anton/0000-0003-2222-9925; Alves Junior, Antonio Augusto/0000-0003-0073-3231; artuso, marina/0000-0002-5991-7273; Stone, Sheldon/0000-0002-2122-771X; Belyaev, Ivan/0000-0002-7458-7030; Bozzi, Concezio/0000-0001-6782-3982; Brook, Nicholas/0000-0002-1818-0113; Serra, Nicola/0000-0002-5033-0580; Eisenhardt, Stephan/0000-0002-4860-6779; Lai, Adriano/0000-0003-1633-0496; Bondar, Alexander/0000-0002-5089-5338 CAPES (Brazil); CNPq (Brazil); FAPERJ (Brazil); FINEP (Brazil); CERN; NSFC (China); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); MPG (Germany); SFI (Ireland); INFN (Italy); FOM (The Netherlands); NWO (The Netherlands); SCSR (Poland); ANCS (Romania); MinES of Russia and Rosatom (Russia); MICINN (Spain); XuntaGal (Spain); GENCAT (Spain); SNSF (Switzerland); SER (Switzerland); NAS Ukraine (Ukraine); STFC (United Kingdom); NSF (USA); ERC under FP7; Region Auvergne; STFC [ST/H00100X/1, ST/L001144/1, ST/H00100X/2, PP/E000347/1, ST/H00081X/2, ST/K001604/1, ST/G005974/1, ST/H001026/2, ST/K00073X/1, ST/H00081X/1, ST/H001093/2, ST/M001431/1, LHCb, ST/I505572/1, LHCb Upgrades, ST/K001361/1, PP/E002722/1, ST/G502412/1, ST/F007094/1, ST/I002839/1, ST/H001069/2, ST/K00140X/1, ST/K000705/1, ST/J004820/1, ST/K000616/1, ST/H001085/2, ST/H006737/1, ST/H001026/1, ST/H001093/1, ST/L001195/1, ST/F008015/1, ST/J005614/1, ST/L001225/1, PP/E000355/1] Funding Source: UKRI; Science and Technology Facilities Council [ST/H001085/2, ST/K00140X/1, ST/K001310/1 LHCb, ST/K001361/1 LHCb, ST/G502412/1, ST/H00100X/1, PP/E000355/1, LHCb, 1208164, ST/K001361/1 ATLAS Upgrades, ST/K001310/1 ATLAS Upgrades, ST/K00073X/1, GRIDPP, ST/K001310/1, ST/K001604/1 MICE/UKNF, ST/L001144/1, ST/H00100X/2, ST/J005614/1, ST/K001604/1 GRIDPP, ST/K001604/1 LHCb Upgrades, ST/K001604/1 DMUK, ST/K001310/1 LHCb Upgrades, ST/H006737/1, ST/J004820/1, ST/K000705/1, PP/E000347/1, ST/K001361/1 LHCb Upgrades, ST/H001093/1, ST/K001361/1, ST/K001604/1 CMS Upgrade, ST/K001256/1, PP/E002722/1, ST/K001604/1 SuperNEMO, ST/K001361/1 ATLAS, ST/H00081X/2, ST/K001604/1 LHCb, ST/L001195/1, LHCb Upgrades, ST/L001225/1, ST/H00081X/1, 1096173, ST/M001474/1, ST/I505572/1, ST/G005974/1, ST/H001026/2, ST/K001310/1 ATLAS, ST/K001361/1 MINOS/MINOS+, ST/H001069/2, ST/H001026/1, ST/K000616/1, ST/M001431/1, ST/I002839/1, ST/K001604/1 T2K, ST/H001093/2, ST/K001418/1, ST/F008015/1, ST/K001604/1, ST/F007094/1] Funding Source: researchfish CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FINEP (Brazil)(Financiadora de Inovacao e Pesquisa (Finep)); CERN; NSFC (China)(National Natural Science Foundation of China (NSFC)); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); MPG (Germany)(Max Planck Society); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); FOM (The Netherlands)(FOM (The Netherlands)Netherlands Government); NWO (The Netherlands)(Netherlands Organization for Scientific Research (NWO)Netherlands Government); SCSR (Poland); ANCS (Romania); MinES of Russia and Rosatom (Russia); MICINN (Spain)(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); XuntaGal (Spain); GENCAT (Spain); SNSF (Switzerland)(Swiss National Science Foundation (SNSF)); SER (Switzerland); NAS Ukraine (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); NSF (USA)(National Science Foundation (NSF)); ERC under FP7; Region Auvergne(Region Auvergne-Rhone-Alpes); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at CERN and at the LHCb institutes, and acknowledge support from the National Agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); CERN; NSFC (China); CNRS/IN2P3 (France); BMBF, DFG, HGF and MPG (Germany); SFI (Ireland); INFN (Italy); FOM and NWO (The Netherlands); SCSR (Poland); ANCS (Romania); MinES of Russia and Rosatom (Russia); MICINN, XuntaGal and GENCAT (Spain); SNSF and SER (Switzerland); NAS Ukraine (Ukraine); STFC (United Kingdom); NSF (USA). We also acknowledge the support received from the ERC under FP7 and the Region Auvergne. 22 17 17 0 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B OCT 2 2012.0 716.0 3-5 393 400 10.1016/j.physletb.2012.08.033 0.0 8 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 014KO Green Published, hybrid, Green Submitted, Green Accepted 2023-03-23 WOS:000309374800002 0
J Qin, ZD; Zhu, D; Zhu, XW; Chen, X; Shi, YH; Gao, Y; Lu, ZH; Shen, QH; Li, L; Pan, HB Qin, Zidi; Zhu, Di; Zhu, Xingwei; Chen, Xuan; Shi, Yinghuan; Gao, Yang; Lu, Zhonghai; Shen, Qinghong; Li, Li; Pan, Hongbing Accelerating Deep Neural Networks by Combining Block-Circulant Matrices and Low-Precision Weights ELECTRONICS English Article hardware acceleration; deep neural networks (DNNs); fully-connected layers; network compression; VLSI EXTREME LEARNING-MACHINE As a key ingredient of deep neural networks (DNNs), fully-connected (FC) layers are widely used in various artificial intelligence applications. However, there are many parameters in FC layers, so the efficient process of FC layers is restricted by memory bandwidth. In this paper, we propose a compression approach combining block-circulant matrix-based weight representation and power-of-two quantization. Applying block-circulant matrices in FC layers can reduce the storage complexity from O(k2) to O(k). By quantizing the weights into integer powers of two, the multiplications in the reference can be replaced by shift and add operations. The memory usages of models for MNIST, CIFAR-10 and ImageNet can be compressed by 171x, 2731x and 128x with minimal accuracy loss, respectively. A configurable parallel hardware architecture is then proposed for processing the compressed FC layers efficiently. Without multipliers, a block matrix-vector multiplication module (B-MV) is used as the computing kernel. The architecture is flexible to support FC layers of various compression ratios with small footprint. Simultaneously, the memory access can be significantly reduced by using the configurable architecture. Measurement results show that the accelerator has a processing power of 409.6 GOPS, and achieves 5.3 TOPS/W energy efficiency at 800 MHz. [Qin, Zidi; Zhu, Di; Zhu, Xingwei; Chen, Xuan; Shen, Qinghong; Li, Li; Pan, Hongbing] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China; [Shi, Yinghuan; Gao, Yang] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-11428 Stockholm, Sweden Nanjing University; Nanjing University; Royal Institute of Technology Pan, HB (corresponding author), Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China. qinzidi@smail.nju.edu.cn; zhudi@smail.nju.edu.cn; flzs@smail.nju.edu.cn; cx0705@smail.nju.edu.cn; syh@nju.edu.cn; gaoy@nju.edu.cn; zhonghai@kth.se; qhshen@nju.edu.cn; lili@nju.edu.cn; phb@nju.edu.cn Lu, Zhonghai/AAP-2154-2020; GAO, Yang/HMO-8142-2023 National Nature Science Foundation of China [61376075, 41412020201]; key Research and Development Program of Jiangsu Province [BE2015153]; Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) - National Nature Science Foundation of China [61376075] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); key Research and Development Program of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) - National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded in part by the National Nature Science Foundation of China under Grant No. 61376075 and 41412020201, in part by the key Research and Development Program of Jiangsu Province under Grant No. BE2015153, and in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD). The APC was funded by the National Nature Science Foundation of China under Grant No. 61376075. 31 1 2 0 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics JAN 2019.0 8.0 1 78 10.3390/electronics8010078 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics HJ4KL gold 2023-03-23 WOS:000457142800078 0
J Cheng, B; Chen, J; Konigsberg, A; Mayer, C; Rimmele, L; Patil, KR; Gerloff, C; Thomalla, G; Eickhoff, SB Cheng, Bastian; Chen, Ji; Koenigsberg, Alina; Mayer, Carola; Rimmele, Leander; Patil, Kaustubh R.; Gerloff, Christian; Thomalla, Gotz; Eickhoff, Simon B. Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale EBIOMEDICINE English Article Stroke; NIHSS; Dimensionality reduction; Lesion -de fi cit -mapping SUBCORTICAL ANATOMY; INFARCT LOCATION; NEGLECT; BRAIN; ATLAS; PATTERNS Background The National Institutes of Health Stroke Scale (NIHSS) is the most frequently applied clinical rating scale for standardized assessment of neurological deficits in acute stroke in both clinical and research settings. Notwithstanding this prominent role, important questions regarding its validity remain insufficiently addressed: Investigations of the underlying dimensional structure of the NIHSS yielded inconsistent results that are largely not generalizable across studies. Neurobiological validations by linking measured deficit dimensions to brain anatomy and function are missing.Methods We, therefore, employ advanced machine learning to identify an optimal representation of the dimensional structure of the NIHSS across two independent and heterogeneous stroke datasets (N = 503 and N = 690). Associated lesion locations are identified by multivariate lesion-deficit mapping (LDM) and their functional relevance is profiled based on a-priori task activation meta-data analysis, to provide an independent link to the behavioural level. Findings A five-factor structure of the NIHSS was identified as the most robust and generalizable representation of stroke deficit dimensions across study populations, settings, and clinical phenotypes. Specifically, the identified dimensions comprised NIHSS items for (F1) left motor deficits, (F2) right motor deficits, (F3) dysarthria and facial palsy, (F4) language, and (F5) deficits in spatial attention and gaze. LDM linked four of these factors to differentially localized, eloquent neuroanatomical areas. Functional characterization of LDM results aligned with detected deficit dimensions, revealing associations with motor functions, language processing, and various functions in the perception domain. Interpretation By cross-validating machine learning in heterogeneous multi-site stroke cohorts, we report evidence on the validity of the NIHSS: We identified an overarching structure of the NISHS containing a five-dimensional representation of stroke deficits. We provide an anatomical map of the NIHSS that is of value for future applications of individualized stroke treatment and rehabilitation.Copyright (c) 2022 The Author(s). Published by Elsevier B.V. [Cheng, Bastian; Koenigsberg, Alina; Mayer, Carola; Rimmele, Leander; Gerloff, Christian; Thomalla, Gotz] Univ Med Ctr Hamburg Eppendorf, Klin & Poliklin Neurol Kopf & Neurozentrum, Hamburg, Germany; [Chen, Ji] Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China; [Chen, Ji; Patil, Kaustubh R.; Eickhoff, Simon B.] Res Ctr Julich, Inst Neurosci & Med Brain & Behav INM 7, Julich, Germany; [Patil, Kaustubh R.; Eickhoff, Simon B.] Heinrich Heine Univ Dusseldorf, Med Fac, Inst Syst Neurosci, Dusseldorf, Germany University of Hamburg; University Medical Center Hamburg-Eppendorf; Zhejiang University; Helmholtz Association; Research Center Julich; Heinrich Heine University Dusseldorf Cheng, B (corresponding author), Univ Med Ctr Hamburg Eppendorf, Klin & Poliklin Neurol Kopf & Neurozentrum, Hamburg, Germany.;Chen, J (corresponding author), Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China.;Chen, J (corresponding author), Res Ctr Julich, Inst Neurosci & Med Brain & Behav INM 7, Julich, Germany. b.cheng@uke.de; jichen.allen@hotmail.com Thomalla, Götz/HPB-8558-2023 Thomalla, Götz/0000-0002-4785-1449; Chen, Ji/0000-0002-9476-1737 National Key R&D Program of China [2021YFC2502200]; National Human Brain Project of China [2022ZD0214000]; German Research Foundation (Deutsche Forschungsgemeinschaft) [178316478 (A1, C1, C2), 454012190, SPP 2041]; Helmholtz Portfolio Theme Supercomputing and Modelling for the Human Brain; Helmholtz Imaging Platform [ZT-I-PF-4-010] National Key R&D Program of China; National Human Brain Project of China; German Research Foundation (Deutsche Forschungsgemeinschaft)(German Research Foundation (DFG)); Helmholtz Portfolio Theme Supercomputing and Modelling for the Human Brain; Helmholtz Imaging Platform This research was supported by the National Key R&D Program of China (Grant No. 2021YFC2502200), the National Human Brain Project of China (Grant No. 2022ZD0214000), the German Research Foundation (Deutsche Forschungsgemeinschaft), Project 178316478 (A1, C1, C2), and Project 454012190 of the SPP 2041, the Helmholtz Portfolio Theme Supercomputing and Modelling for the Human Brain and Helmholtz Imaging Platform grant NimRLS (ZT-I-PF-4-010). 42 0 0 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-3964 EBIOMEDICINE EBioMedicine JAN 2023.0 87.0 104425 10.1016/j.ebiom.2022.104425 0.0 DEC 2022 16 Medicine, General & Internal; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine; Research & Experimental Medicine 7K2ZJ 36563488.0 gold, Green Accepted 2023-03-23 WOS:000905154300007 0
J Zhu, XZ; Tsang, DCW; Wang, L; Su, ZS; Hou, DY; Li, LC; Shang, J Zhu, Xinzhe; Tsang, Daniel C. W.; Wang, Lei; Su, Zhishan; Hou, Deyi; Li, Liangchun; Shang, Jin Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures JOURNAL OF CLEANER PRODUCTION English Article CO2 sequestration; Carbon adsorbents; Sustainable waste management; Low-carbon development; Biomass utilization SINGLE-STEP SYNTHESIS; SODIUM AMIDE ACTIVATION; DIOXIDE CAPTURE; LOW-TEMPERATURE; COMBINING AMMOXIDATION; KOH ACTIVATION; PORE-SIZE; ADSORBENTS; ENERGY; TEXTURE The growing environmental issues caused by CO2 emission accelerate the development of carbon capture and storage (CCS), especially bio-energy CCS as an environment-friendly and sustainable technique to capture CO2 using porous carbon materials (PCMs) produced from various biomass wastes. This study developed quantitative structure-property relationship models based on 6244 CO2 adsorption datasets of 155 PCMs to predict the CO2 adsorption capacity and analyze the relative significance of physicochemical properties. The results suggested that random forest (RF) models showed good accuracy and predictive performance based on physicochemical parameters of PCMs and adsorption conditions with the test dataset (R-2 > 0.9). In general, textural properties were more crucial than chemical compositions of porous carbons to the change of CO2 adsorption capacity. At a low pressure (0.1 bar), the volumes of mesopore and micropore played an important role according to the RF analysis, but had a negative correlation with CO2 adsorption capacity based on the Pearson correlation coefficient (PCC) analysis. The relative importance of ultra-micropore increased along with the increase of pressure. The PCC value between ultra-micropore volume and CO2 uptake amount was up to 0.715 (p < 0.01) at 1 bar and 0 degrees C. The influence of chemical compositions was complex. The N content was confirmed to positively correlate to the CO2 adsorption capacity but its contribution was much lower than that of ultra-micropores. This study provided a new approach for fostering the rational design of porous carbons for CO2 capture via statistical analysis and machine learning method, which facilitated adsorbents screening for the cleaner production. (C) 2020 Elsevier Ltd. All rights reserved. [Zhu, Xinzhe; Tsang, Daniel C. W.; Wang, Lei] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Kowloon, Hung Hom, Hong Kong, Peoples R China; [Wang, Lei] Tech Univ Dresden, Inst Construct Mat, D-01062 Dresden, Germany; [Su, Zhishan] Sichuan Univ, Coll Chem, Key Lab Green Chem & Technol, Minist Educ, 29 Wangjiang Rd, Chengdu 610064, Sichuan, Peoples R China; [Hou, Deyi] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China; [Li, Liangchun] Tongji Univ, Sch Chem Sci & Engn, Shanghai Key Lab Chem Assessment & Sustainabil, Shanghai 200092, Peoples R China; [Shang, Jin] City Univ Hong Kong, Kowloon, Sch Energy & Environm, Tat Chee Ave, Hong Kong, Peoples R China Hong Kong Polytechnic University; Technische Universitat Dresden; Sichuan University; Tsinghua University; Tongji University; City University of Hong Kong Tsang, DCW (corresponding author), Hong Kong Polytech Univ, Dept Civil & Environm Engn, Kowloon, Hung Hom, Hong Kong, Peoples R China.;Shang, J (corresponding author), City Univ Hong Kong, Kowloon, Sch Energy & Environm, Tat Chee Ave, Hong Kong, Peoples R China. dan.tsang@polyu.edu.hk; jinshang@cityu.edu.hk Zhu, Xinzhe/GNP-6013-2022; Tsang, Dan/E-5442-2012; WANG, Lei/C-8138-2018; SHANG, Jin/P-6532-2015 Tsang, Dan/0000-0002-6850-733X; WANG, Lei/0000-0002-0336-7241; SHANG, Jin/0000-0001-5165-0466 Hong Kong Research Grants Council [PolyU 15217818, CityU 21301817]; Hong Kong International Airport Environmental Fund (Phase 2) Hong Kong Research Grants Council(Hong Kong Research Grants Council); Hong Kong International Airport Environmental Fund (Phase 2) The authors appreciate the financial support from the Hong Kong Research Grants Council (PolyU 15217818; CityU 21301817) and Hong Kong International Airport Environmental Fund (Phase 2). 64 50 51 14 105 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. NOV 10 2020.0 273.0 122915 10.1016/j.jclepro.2020.122915 0.0 10 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology NR3DH 2023-03-23 WOS:000571442300002 0
J Wang, BH; Zhang, Q; Zhao, CY; Pepe, A; Niu, YF Wang, Baohang; Zhang, Qin; Zhao, Chaoying; Pepe, Antonio; Niu, Yufen Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Strain; Synthetic aperture radar; Time series analysis; Real-time systems; Satellites; Monitoring; Surface treatment; Big synthetic aperture radar interferometry (InSAR) data; deformation time series; Kalman filter (KF); sequential least squares (SLS) RADAR; INTERFEROMETRY; CHINA The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system. [Wang, Baohang; Zhang, Qin; Zhao, Chaoying] Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China; [Pepe, Antonio] Natl Res Council Italy, Inst Electromagnet Sensing Environm CNR IREA, I-80124 Naples, Italy; [Niu, Yufen] Hebei Univ Engn, Sch Min & Geomat Engn, Handan 056038, Peoples R China Chang'an University; Consiglio Nazionale delle Ricerche (CNR); Istituto Per Il Rilevamento Elettromagnetico Dell'Ambiente (IREA-CNR); Hebei University of Engineering Zhang, Q (corresponding author), Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China. wangbaohang@chd.edu.cn; dczhangq@chd.edu.cn; cyzhao@chd.edu.cn; pepe.a@irea.cnr.it; niuyufenpippa@163.com Pepe, Antonio/J-9454-2016 Pepe, Antonio/0000-0002-7843-3565 Natural Science Foundation of China [41874005, 41929001, 41731066]; Fundamental Research Funds for the Central Universities [300102269303, 300102269719] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the Natural Science Foundation of China under Grant 41874005, Grant 41929001, and Grant 41731066, and in part by the Fundamental Research Funds for the Central Universities under Grant 300102269303 and Grant 300102269719. 36 1 1 18 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 2437 2448 10.1109/JSTARS.2022.3159666 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 0D8IZ gold 2023-03-23 WOS:000776233600002 0
J Xiao, Z; Chen, YX; Jiang, HB; Hu, ZZ; Lui, JCS; Min, GY; Dustdar, S Xiao, Zhu; Chen, Yanxun; Jiang, Hongbo; Hu, Zhenzhen; Lui, John C. S.; Min, Geyong; Dustdar, Schahram Resource management in UAV-assisted MEC: state-of-the-art and open challenges WIRELESS NETWORKS English Article UAV; MEC; UAV-assisted MEC; Resources management JOINT TRAJECTORY DESIGN; CHANNEL ALLOCATION; RELAY ASSIGNMENT; OPTIMIZATION; COMMUNICATION; NETWORK; LOCATION; INTERNET; LOCALIZATION; MINIMIZATION Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of artificial intelligence, people's demand for computing is increasing, and the computing power of existing mobile computing devices cannot fully meet the users' needs for network quality. Therefore, people have proposed mobile edge computing technology (MEC), but MEC still has some shortcomings, such as poor dynamic performance. Therefore, combining the two will have better results. Due to the limited battery capacity of UAVs, the continuity of the UAV Communication Network is affected. Therefore, effectively using the limited spectrum resources, reducing the energy consumption of UAVs and meeting the users' quality of service needs have become urgent problems to be solved. Under the framework of UAV-assisted MEC, this paper focuses on the downlink communication energy efficiency of a single UAV Communication Network, arranges and analyzes the methods and technologies found in different research, reviews specific UAV practical applications, provides a relevant discussion and method analysis for the problems existing in the UAV Communication Network, and presents its existing problems and our future research direction. [Xiao, Zhu; Chen, Yanxun; Jiang, Hongbo; Hu, Zhenzhen] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China; [Lui, John C. S.] Chinese Univ Hong Kong CUHK, Comp Sci & Engn Dept, Hong Kong, Peoples R China; [Min, Geyong] Univ Exeter, Dept Math & Comp Sci, Exeter, Devon, England; [Dustdar, Schahram] TU Wien, A-1040 Vienna, Austria Hunan University; University of Exeter; Technische Universitat Wien Jiang, HB (corresponding author), Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China. zhxiao@hnu.edu.cn; chenyanxun@hnu.edu.cn; hongbojiang@hnu.edu.cn; hzz88@hnu.edu.cn; cslui@cse.cuhk.edu.hk; g.min@exeter.ac.uk; dustdar@infosys.tuwien.ac.at , zhxiao@hnu.edu.cn/AFT-5052-2022 Xiao, Zhu/0000-0001-5645-160X National Natural Science Foundation of China [U20A20181]; Key Research and Development Project of Hunan Province of China [2022GK2020]; Hunan Natural Science Foundation of China [2022JJ2059]; Funding Projects of Zhejiang Lab [2021LC0AB05] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Project of Hunan Province of China; Hunan Natural Science Foundation of China(Natural Science Foundation of Hunan Province); Funding Projects of Zhejiang Lab This work was supported in part by the National Natural Science Foundation of China under Grant U20A20181; in part by the Key Research and Development Project of Hunan Province of China under Grant 2022GK2020; in part by Hunan Natural Science Foundation of China under Grant 2022JJ2059, in part by the Funding Projects of Zhejiang Lab under Grant 2021LC0AB05. (The corresponding author of this paper is Hongbo Jiang) 115 0 0 13 15 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1022-0038 1572-8196 WIREL NETW Wirel. Netw. OCT 2022.0 28.0 7 3305 3322 10.1007/s11276-022-03051-4 0.0 JUL 2022 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 4P6DN 2023-03-23 WOS:000822299700002 0
J Lei, SB; Wang, L; Cui, W; Wozniak, M; Polap, D Lei, Songbo; Wang, Lei; Cui, Wei; Wozniak, Marcin; Polap, Dawid Research on a LSTM Based Method of Forecasting Primary Frequency Modulation of Grid JOURNAL OF INTERNET TECHNOLOGY English Article Grid; Frequency; LSTM; Data processing The increasing scale of power grid and the integration of a large number of new energy sources, the prediction accuracy of FM frequency is limited. In order to quickly and accurately predict the frequency change curve of power system after disturbance. In this paper, a prediction method of primary frequency modulation based on long short-term memory network (LSTM) is proposed. Firstly, this method uses correlation analysis method to analyze various factors affecting frequency fluctuation, and selects strong correlation quantity; then constructs the power grid frequency prediction model based on LSTM recurrent neural network; finally, uses the historical data of a power grid company from 2016 to 2019 as a simulation example to verify the validity of the model. This method can realize the real-time analysis of power grid frequency and provide decision support for power grid to make frequency control strategy after disturbance. [Lei, Songbo; Wang, Lei] Xian Univ Technol, Dept Comp Sci & Engn, Xian, Shaanxi, Peoples R China; [Cui, Wei] State Grid Corp, Northwest Control Ctr, Beijing, Peoples R China; [Wang, Lei] Shaanxi Univ Technol, Shaanxi Prov Key Lab Ind Automat, Hanzhong, Shaanxi, Peoples R China; [Wozniak, Marcin; Polap, Dawid] Silesian Tech Univ, Fac Appl Math, Gliwice, Poland Xi'an University of Technology; State Grid Corporation of China; Shaanxi University of Technology; Silesian University of Technology Wang, L (corresponding author), Xian Univ Technol, Dept Comp Sci & Engn, Xian, Shaanxi, Peoples R China.;Wang, L (corresponding author), Shaanxi Univ Technol, Shaanxi Prov Key Lab Ind Automat, Hanzhong, Shaanxi, Peoples R China. ray.songbo@foxmail.com; wangleeei@163.com; zuoyiyaodao@126.com; Marcin.Wozniak@polsl.pl; Dawid.Polap@polsl.pl Woźniak, Marcin/L-6640-2013; Połap, Dawid/D-1547-2017 Woźniak, Marcin/0000-0002-9073-5347; Połap, Dawid/0000-0003-1972-5979 National Natural Science Foundation of China [61773314]; Shaanxi Provincial Natural Science Basic Research Program [2019JZ-11]; Scientific Research Project of Education Department of Shaanxi Provincial Government [19JC011] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shaanxi Provincial Natural Science Basic Research Program; Scientific Research Project of Education Department of Shaanxi Provincial Government This work was partly supported by the National Natural Science Foundation of China under Grant No. 61773314, the Shaanxi Provincial Natural Science Basic Research Program under Grant No. 2019JZ-11, the Scientific Research Project of Education Department of Shaanxi Provincial Government under Grant No. 19JC011. 18 1 1 1 14 LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV HUALIEN LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN 1607-9264 2079-4029 J INTERNET TECHNOL J. Internet Technol. 2020.0 21.0 3 791 798 10.3966/160792642020052103016 0.0 8 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications LY1VX 2023-03-23 WOS:000540310600016 0
C Meng, XT; Liu, C; Zhang, ZY; Wang, D IEEE Meng, Xiangtao; Liu, Chao; Zhang, Zhiyong; Wang, Dong NOISY TRAINING FOR DEEP NEURAL NETWORKS 2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (CHINASIP) English Proceedings Paper 2nd IEEE China Summit / International Conference on Signal and Information Processing (IEEE ChinaSIP) JUL 09-13, 2014 Xian, PEOPLES R CHINA IEEE,Inst Elect & Elect Engineers Signal Proc Soc,NW Polytechn Univ, Shaanxi Key Lab Informat Acquisit & Proc,Natl Nat Sci Fdn China,KC Wong Educ Fdn,Texas Instruments,Xidian Univ, Natl Lab Radar Signal Proc deep neural network; noise injection; robust speech recognition INJECTION Deep neural networks (DNN) have gained remarkable success in speech recognition, partially attributed to its flexibility in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse environments such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting noises into the training speech intentionally and randomly, more generalizable DNN models can be learned. This 'noise injection' technique has been well-known to the neural computation community, however there is little knowledge if it would work for the DNN model which involves a highly complex objective function. The experiments presented in this paper confirm that the original assumptions of the noise injection approach largely holds when learning deep structures, and the noisy training may provide substantial performance improvement for DNN-based speech recognition. [Meng, Xiangtao; Liu, Chao; Zhang, Zhiyong; Wang, Dong] Tsinghua Univ, RIIT, CSLT, Beijing, Peoples R China; [Meng, Xiangtao] CUPT, Sch Compute Sci & Technol, Warsaw, Poland Tsinghua University Meng, XT (corresponding author), Tsinghua Univ, RIIT, CSLT, Beijing, Peoples R China. 16 8 8 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-5403-2 2014.0 16 20 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BE0QQ 2023-03-23 WOS:000366612600004 0
J Liu, L; Yang, JX; Meng, WZ Liu, Liang; Yang, Jingxiu; Meng, Weizhi Detecting malicious nodes via gradient descent and support vector machine in Internet of Things COMPUTERS & ELECTRICAL ENGINEERING English Article Internet of things; Malicious node detection; Support vector machine; Gradient descent; K-means; Trust management; Machine learning IoT devices have become much popular in our daily lives, while attackers often invade network nodes to launch various attacks. In this work, we focus on the detection of insider attacks in IoT networks. Most existing algorithms calculate the reputation of all nodes based on the routing path. However, they rely heavily on the assumption that different nodes in the same routing path have equal reputation, which may be not invalid in practice and cause inaccurate detection results. To solve this issue, we formulate it as a multivariate multiple linear regression problem and use the K-means classification algorithm to detect malicious nodes. Further, we optimize the routing path and design an enhanced detection scheme. Our results indicate that our proposed methods could achieve a detection accuracy rate of 90% or above in a common case, and the enhanced scheme could reach an even lower false detection rate, i.e., below 5%. (C) 2019 Elsevier Ltd. All rights reserved. [Liu, Liang; Yang, Jingxiu] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Jiangsu, Peoples R China; [Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark Nanjing University of Aeronautics & Astronautics; Technical University of Denmark Meng, WZ (corresponding author), Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark. liangliu@nuaa.edu.cn; yangjingxiu613zb@163.com; weme@dtu.dk National Natural Science Foundation of China [61402225]; China Postdoctoral Science Foundation [2013M540447]; Jiangsu Postdoctoral Science Foundation [1301020C]; Foundation of State Key Laboratory for smart grid protection and operation control; Science and Technology Funds from National State Grid Ltd. (The Research on Key Technologies of Distributed Parallel Database Storage and Processing based on Big Data) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Jiangsu Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Foundation of State Key Laboratory for smart grid protection and operation control; Science and Technology Funds from National State Grid Ltd. (The Research on Key Technologies of Distributed Parallel Database Storage and Processing based on Big Data) This work was supported by the National Natural Science Foundation of China under Grant No. 61402225; the China Postdoctoral Science Foundation under Grant No. 2013M540447; the Jiangsu Postdoctoral Science Foundation under Grant No. 1301020C. This work was also partially supported by the Foundation of State Key Laboratory for smart grid protection and operation control; the Science and Technology Funds from National State Grid Ltd. (The Research on Key Technologies of Distributed Parallel Database Storage and Processing based on Big Data). 25 12 13 0 12 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. JUL 2019.0 77.0 339 353 10.1016/j.compeleceng.2019.06.013 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IU5LT 2023-03-23 WOS:000483629600026 0
J Wang, YF; Deng, HW; Xin, S; Zhang, K; Shi, R; Bao, XW Wang, Yanfang; Deng, Haowen; Xin, Shan; Zhang, Kai; Shi, Run; Bao, Xuanwen Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma FRONTIERS IN GENETICS English Article LUAD; DNA methylation; regularized logistic regression; recursive feature elimination; LASSO Cox regression; metastasis GENE-EXPRESSION; CANCER; EGFR; EPIDEMIOLOGY; INACTIVATION; ASSOCIATION; MUTATIONS; DIAGNOSIS; SURVIVAL; HEAD Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TOGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods. [Wang, Yanfang; Xin, Shan; Shi, Run] Ludwig Maximilians Univ Munchen, Munich, Germany; [Deng, Haowen] Tech Univ Munich, Chair Comp Aided Med Procedures & Augmented Real, Munich, Germany; [Xin, Shan] German Res Ctr Environm Hlth, Helmholtz Ctr Munich, Inst Mol Toxicol & Pharmacol, Neuherberg, Germany; [Zhang, Kai] Zhejiang Univ, Sch Med, Dept Cardiol, Sir Run Run Shaw Hosp, Hangzhou, Zhejiang, Peoples R China; [Bao, Xuanwen] German Res Ctr Environm Hlth, Helmholtz Ctr Munich, Inst Radiat Biol, Neuherberg, Germany; [Bao, Xuanwen] Tech Univ Munich, Munich, Germany University of Munich; Technical University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Zhejiang University; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich Shi, R (corresponding author), Ludwig Maximilians Univ Munchen, Munich, Germany.;Bao, XW (corresponding author), German Res Ctr Environm Hlth, Helmholtz Ctr Munich, Inst Radiat Biol, Neuherberg, Germany.;Bao, XW (corresponding author), Tech Univ Munich, Munich, Germany. shirun@outlook.com; xuanwen.bao@helmhoitz-muenchen.de /0000-0002-6232-3783 Sino-German (CSC-DAAD) Postdoc Scholarship Program; China Scholarship Council (CSC) Sino-German (CSC-DAAD) Postdoc Scholarship Program; China Scholarship Council (CSC)(China Scholarship Council) We greatly thank Dr. Michael Rosemann and Prof. Dr. Michael J. Atkinson for helpful discussions and suggestions. We sincerely thank Sino-German (CSC-DAAD) Postdoc Scholarship Program for supporting the research of YW, and greatly thank the China Scholarship Council (CSC) for supporting the research and work of SX, RS, and XB. 41 33 33 0 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. APR 24 2019.0 10.0 349 10.3389/fgene.2019.00349 0.0 13 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity HV0LU 31105737.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000465684900001 0
J Ning, KJ; Kulvicius, T; Tamosiunaite, M; Worgotter, F Ning, KeJun; Kulvicius, Tomas; Tamosiunaite, Minija; Woergoetter, Florentin A Novel Trajectory Generation Method for Robot Control JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS English Article Trajectory generation; Dynamic trajectory joining; Control theory; Machine learning DYNAMIC MOVEMENT PRIMITIVES; MANIPULATORS; DESIGN This paper presents a novel trajectory generator based on Dynamic Movement Primitives (DMP). The key ideas from the original DMP formalism are extracted, reformulated and extended from a control theoretical viewpoint. This method can generate smooth trajectories, satisfy position- and velocity boundary conditions at start- and endpoint with high precision, and follow accurately geometrical paths as desired. Paths can be complex and processed as a whole, and smooth transitions can be generated automatically. Performance is analyzed for several cases and a comparison with a spline-based trajectory generation method is provided. Results are comparable and, thus, this novel trajectory generating technology appears to be a viable alternative to the existing solutions not only for service robotics but possibly also in industry. [Ning, KeJun; Kulvicius, Tomas; Tamosiunaite, Minija; Woergoetter, Florentin] Univ Gottingen, Inst Phys 3, Bernstein Ctr Computat Neurosci, D-37077 Gottingen, Germany; [Ning, KeJun] Lenovo, Res & Technol, Beijing 100085, Peoples R China University of Gottingen; Legend Holdings; Lenovo Ning, KJ (corresponding author), Univ Gottingen, Inst Phys 3, Bernstein Ctr Computat Neurosci, D-37077 Gottingen, Germany. nkj@sjtu.org European Community [270273 - Xperience]; German BMBF BFNT project 3a of the University Gottingen [01GQ0810] European Community(European Commission); German BMBF BFNT project 3a of the University Gottingen The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 - Challenge 2 - Cognitive Systems, Interaction, Robotics under grant agreement No 270273 - Xperience. It was also supported by the German BMBF BFNT 01GQ0810 project 3a of the University Gottingen. 42 6 6 0 20 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0921-0296 1573-0409 J INTELL ROBOT SYST J. Intell. Robot. Syst. NOV 2012.0 68.0 2 165 184 10.1007/s10846-012-9683-8 0.0 20 Computer Science, Artificial Intelligence; Robotics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics 028OR hybrid, Green Published 2023-03-23 WOS:000310432300006 0
C Wang, CY; Wang, YL; Zhang, KB; Muhammad, J; Lu, TH; Zhang, Q; Tian, QC; He, ZF; Sun, ZN; Zhang, YW; Liu, TB; Yang, W; Wu, DL; Liu, YF; Zhou, RY; Wu, HH; Zhang, H; Wang, JB; Wang, JY; Xiong, WT; Shi, XY; Zeng, S; Li, PH; Sun, HD; Wang, J; Zhang, JL; Wang, Q; Wu, HJ; Zhang, XH; Li, HQ; Chen, Y; Chen, L; Zhang, MH; Sun, Y; Zhou, ZY; Boutros, F; Damer, N; Kuijper, A; Tapia, J; Valenzuela, A; Busch, C; Gupta, G; Raja, K; Wu, X; Li, XJ; Yang, JF; Jing, HY; Wang, X; Kong, B; Yin, YB; Song, Q; Lyu, SW; Hu, S; Premk, L; Vitek, M; Struc, V; Peer, P; Khiarak, JN; Jaryani, F; Nasab, SS; Moafinejad, SN; Amini, Y; Noshad, M Yu, S; Yuen, PC; Fierrez, J; DeMarsico, M Wang, Caiyong; Wang, Yunlong; Zhang, Kunbo; Muhammad, Jawad; Lu, Tianhao; Zhang, Qi; Tian, Qichuan; He, Zhaofeng; Sun, Zhenan; Zhang, Yiwen; Liu, Tianbao; Yang, Wei; Wu, Dongliang; Liu, Yingfeng; Zhou, Ruiye; Wu, Huihai; Zhang, Hao; Wang, Junbao; Wang, Jiayi; Xiong, Wantong; Shi, Xueyu; Zeng, Shao; Li, Peihua; Sun, Haodong; Wang, Jing; Zhang, Jiale; Wang, Qi; Wu, Huijie; Zhang, Xinhui; Li, Haiqing; Chen, Yu; Chen, Liang; Zhang, Menghan; Sun, Ye; Zhou, Zhiyong; Boutros, Fadi; Damer, Naser; Kuijper, Arjan; Tapia, Juan; Valenzuela, Andres; Busch, Christoph; Gupta, Gourav; Raja, Kiran; Wu, Xi; Li, Xiaojie; Yang, Jingfu; Jing, Hongyan; Wang, Xin; Kong, Bin; Yin, Youbing; Song, Qi; Lyu, Siwei; Hu, Shu; Premk, Leon; Vitek, Matej; Struc, Vitomir; Peer, Peter; Khiarak, Jalil Nourmohammadi; Jaryani, Farhang; Nasab, Samaneh Salehi; Moafinejad, Seyed Naeim; Amini, Yasin; Noshad, Morteza NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization 2021 INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2021) English Proceedings Paper International Joint Conference on Biometrics (IJCB) AUG 04-07, 2021 ELECTR NETWORK Watrix AI,Iris King,IAPR,IEEE Biometr Council For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research. [Wang, Caiyong; Tian, Qichuan] Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing, Peoples R China; [Wang, Yunlong; Zhang, Kunbo; Muhammad, Jawad; Lu, Tianhao; Sun, Zhenan] CASIA, NLPR, CRIPAC, Beijing, Peoples R China; [Zhang, Qi] Peoples Publ Secur Univ China, Beijing, Peoples R China; [He, Zhaofeng] Beijing Univ Posts & Telecommun, Beijing, Peoples R China; [Zhang, Yiwen; Liu, Tianbao; Yang, Wei] Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China; [Wu, Dongliang; Liu, Yingfeng; Zhou, Ruiye; Wu, Huihai] Shanghai Univ Elect Power, Shanghai, Peoples R China; [Zhang, Hao; Wang, Junbao; Wang, Jiayi; Xiong, Wantong; Sun, Haodong; Wang, Jing; Zhang, Jiale; Wang, Qi] Northeastern Univ, Coll Sci, Shenyang, Peoples R China; [Shi, Xueyu; Zeng, Shao; Li, Peihua] Dalian Univ Technol, Dalian, Peoples R China; [Wu, Huijie; Zhang, Xinhui; Li, Haiqing] IriStar Technol Co Ltd, Beijing, Peoples R China; [Chen, Yu; Chen, Liang; Zhang, Menghan] Xian Quanxiu Technol Co Ltd, Xian, Peoples R China; [Sun, Ye; Zhou, Zhiyong] JiLin Univ, Changchun, Peoples R China; [Boutros, Fadi; Damer, Naser; Kuijper, Arjan] Fraunhofer Inst Comp Graph Res IGD, Darmstadt, Germany; [Tapia, Juan; Valenzuela, Andres; Busch, Christoph] Darmstadt Univ Appl Sci, Hsch Darmstadt, Darmstadt, Germany; [Tapia, Juan; Valenzuela, Andres; Busch, Christoph] TOC Biometr, Darmstadt, Germany; [Gupta, Gourav; Raja, Kiran] Norwegian Univ Sci & Technol, Trondheim, Norway; [Wu, Xi; Li, Xiaojie; Yang, Jingfu; Jing, Hongyan] Chengdu Univ Informat Technol, Chengdu, Peoples R China; [Wang, Xin; Kong, Bin; Yin, Youbing; Song, Qi] Keya Med, Seattle, WA USA; [Lyu, Siwei; Hu, Shu] Univ Buffalo, Buffalo, NY USA; [Premk, Leon; Vitek, Matej; Struc, Vitomir; Peer, Peter] Univ Ljubljana, Ljubljana, Slovenia; [Khiarak, Jalil Nourmohammadi] Warsaw Univ Technol, Warsaw, Poland; [Jaryani, Farhang] Arak Univ, Arak, Iran; [Nasab, Samaneh Salehi] Lorestan Univ, Khorramabad, Iran; [Moafinejad, Seyed Naeim] Shahid Beheshti Univ, Tehran, Iran; [Amini, Yasin] Univ Kharazmi, Tehran, Iran; [Noshad, Morteza] Stanford Univ, Stanford, CA 94305 USA Beijing University of Civil Engineering & Architecture; Chinese Academy of Sciences; Institute of Automation, CAS; People's Public Security University of China; Beijing University of Posts & Telecommunications; Southern Medical University - China; Shanghai University of Electric Power; Northeastern University - China; Dalian University of Technology; Jilin University; Fraunhofer Gesellschaft; Hochschule Darmstadt; Norwegian University of Science & Technology (NTNU); Chengdu University of Information Technology; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; University of Ljubljana; Warsaw University of Technology; Arak University; Lorestan University; Shahid Beheshti University; Kharazmi University; Stanford University Sun, ZN (corresponding author), CASIA, NLPR, CRIPAC, Beijing, Peoples R China. wangcaiyong@bucea.edu.cn; znsun@nlpr.ia.ac.cn 孙, 野/HGU-6891-2022; tian, qichuan/HHN-5466-2022 Valenzuela, Andres/0000-0002-8172-1541; Gupta, Gourav/0000-0003-3122-1976 Beijing University of Civil Engineering and Architecture Research Capacity Promotion Program for Young Scholars [X21079]; National Natural Science Foundation of China [U1836217, 62006225, 62071468, 61906199]; National Key Research and Development Program of China [2017YFC0821602] Beijing University of Civil Engineering and Architecture Research Capacity Promotion Program for Young Scholars; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work is supported by the Beijing University of Civil Engineering and Architecture Research Capacity Promotion Program for Young Scholars (Grant No. X21079), the National Natural Science Foundation of China (Grant No. U1836217, 62006225, 62071468, 61906199) and the National Key Research and Development Program of China (Grant No. 2017YFC0821602). 15 34 34 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-3780-6 2021.0 10.1109/IJCB52358.2021.9484336 0.0 10 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BT6RD 2023-03-23 WOS:000843558700008 0
C Wang, YW; Lyn, Y; Cao, YP; Yang, MY IEEE Wang, Yiwen; Lyn, Ye; Cao, Yanpeng; Yang, Michael Ying DEEP LEARNING FOR SEMANTIC SEGMENTATION OF UAV VIDEOS 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc FCN; Conv-LSTM; video semantic segmentation; UAV As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV. [Wang, Yiwen; Lyn, Ye; Yang, Michael Ying] Univ Twente, Scene Understanding Grp, Enschede, Netherlands; [Cao, Yanpeng] Zhejiang Univ, Sch Mech Engn, Hangzhou, Peoples R China University of Twente; Zhejiang University Wang, YW (corresponding author), Univ Twente, Scene Understanding Grp, Enschede, Netherlands. Wang, Yijun/GXW-1763-2022; Yang, Michael/AAC-6698-2019; Wang, Yixuan/GZK-6559-2022; wang, yixuan/GXW-2866-2022; wang, yi/GVT-8516-2022; Wang, Yin/HCI-9352-2022 Yang, Michael/0000-0002-0649-9987; 7 1 1 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 2459 2462 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH Green Published 2023-03-23 WOS:000519270602145 0
C Zhang, JL; Liu, JX; Luo, YL; Fu, Q; Bi, JJ; Qiu, SH; Cao, Y; Ding, XM IEEE Zhang, Jinlei; Liu, Junxiu; Luo, Yuling; Fu, Qiang; Bi, Jinjie; Qiu, Senhui; Cao, Yi; Ding, Xuemei Chemical Substance Classification Using Long Short-Term Memory Recurrent Neural Network 2017 17TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT 2017) English Proceedings Paper IEEE 17th International Conference on Communication Technology (ICCT) OCT 27-30, 2017 Chengdu, PEOPLES R CHINA IEEE recurrent neural networks; chemical substances; long short-term memory; feed forward neural networks This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy. [Zhang, Jinlei; Liu, Junxiu; Luo, Yuling; Fu, Qiang; Bi, Jinjie; Qiu, Senhui] Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China; [Qiu, Senhui] Guangxi Expt Ctr Informat Sci, Guilin, Peoples R China; [Cao, Yi] Univ Surrey, Surrey Business Sch, Dept Business Transformat & Sustainable Enterpris, Surrey, England; [Ding, Xuemei] Fujian Normal Univ, Fac Software, Fuzhou 350108, Fujian, Peoples R China; [Ding, Xuemei] Ulster Univ, Fac Comp & Engn, Sch Comp & Intelligent Syst, Coleraine BT48 7TL, Londonderry, North Ireland Guangxi Normal University; University of Surrey; Fujian Normal University; Ulster University Liu, JX (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China. liujunxiu@mailbox.gxnu.edu.cn National Natural Science Foundation of China [61661008, 61603104]; Guangxi Natural Science Foundation [2015GXNSFBA139256, 2016GXNSFCA 380017]; Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China [KY2016YB059]; Guangxi Key Lab of Multi-source Information Mining Security [MIMS15-07]; Doctoral Research Foundation of Guangxi Normal University; Guangxi Experiment Centre of Information Science; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry; Funds for Young Key Program of Education Department from Fujian Province, China [JZ160425]; Program of Education Department of Fujian Province, China [I201501005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China; Guangxi Key Lab of Multi-source Information Mining Security; Doctoral Research Foundation of Guangxi Normal University; Guangxi Experiment Centre of Information Science; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry(Scientific Research Foundation for the Returned Overseas Chinese Scholars); Funds for Young Key Program of Education Department from Fujian Province, China; Program of Education Department of Fujian Province, China This research was partially supported by the National Natural Science Foundation of China under Grants 61661008 and 61603104, the Guangxi Natural Science Foundation under Grants 2015GXNSFBA139256 and 2016GXNSFCA 380017, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining & Security under Grant MIMS15-07, the Doctoral Research Foundation of Guangxi Normal University, the grant from Guangxi Experiment Centre of Information Science, the Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry, the Funds for Young Key Program of Education Department from Fujian Province, China (Grant No. JZ160425), Program of Education Department of Fujian Province, China (Grant No. I201501005). 21 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-3944-9 2017.0 1994 1997 4 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BK3NU 2023-03-23 WOS:000435276600387 0
J Xiao, TQ; Wu, Z; Christofides, PD; Armaou, A; Ni, D Xiao, Tianqi; Wu, Zhe; Christofides, Panagiotis D.; Armaou, Antonios; Ni, Dong Recurrent Neural-Network-Based Model Predictive Control of a Plasma Etch Process INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH English Article FEATURE PROFILE EVOLUTION; DYNAMIC OPTIMIZATION; SYSTEMS; MULTISCALE; IDENTIFICATION; APPROXIMATION; POLYSILICON; SIMULATION; REDUCTION; ENERGY In this article, we propose the development of a recurrent neural network (RNN)-based model predictive controller (MPC) for a plasma etch process on a three-dimensional substrate using inductive coupled plasma (ICP) analysis. Specifically, the plasma etch process is simulated by a multiscale model: (1) A macroscopic fluid model is applied to simulate the gas flows and chemical reactions of plasma. (2) A kinetic Monte Carlo (kMC) model is applied to simulate the etching process on the substrate. Subsequently, proper orthogonal decomposition (POD) is used to derive the empirical eigenfunctions of the plasma model. Then the empirical eigenfunctions are utilized as basis functions within a Galerkin's model reduction framework to compute a low-order system Pre capturing dominant dynamics of the plasma model. Additionally, RNN is introduced to approximate dynamics of both the reduced-order plasma system and the microscopic etch process. The training data for the RNN models are generated from discrete sampling of open-loop simulations. A probability distribution function is also involved to present the stochastic characteristic of the kMC model. The trained RNN models are then implemented as the prediction model in the development of MPC to achieve desired control objectives. Closed-loop simulation results are presented to compare the performance of the model predictive controller and a proportional-integral (PI) controller, which show that the proposed MPC framework is effective and exhibits better performance than does a PI controller. [Xiao, Tianqi; Ni, Dong] Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China; [Wu, Zhe] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore; [Christofides, Panagiotis D.] Univ Calif Los Angeles, Dept Chem & Biomol Engn, Los Angeles, CA 90095 USA; [Christofides, Panagiotis D.] Univ Calif Los Angeles, Dept Elect & Comp Engn, Los Angeles, CA 90095 USA; [Armaou, Antonios] Penn State Univ, Dept Chem Engn, University Pk, PA 16802 USA; [Armaou, Antonios] Penn State Univ, Dept Mech Engn, University Pk, PA 16802 USA; [Armaou, Antonios] Univ Patras, Dept Chem Engn, Patras 26243, Greece Zhejiang University; National University of Singapore; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Patras Ni, D (corresponding author), Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China. dni@zju.edu.cn Ni, Dong/D-1177-2012 Ni, Dong/0000-0002-2227-2555; Xiao, Tianqi/0000-0002-8624-2689; Armaou, Antonios/0000-0002-8592-7934 National Natural Science Foundation of China [U1609213] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors are grateful for the financial support from National Natural Science Foundation of China (grant no. U1609213). 60 2 2 2 8 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0888-5885 IND ENG CHEM RES Ind. Eng. Chem. Res. JAN 12 2022.0 61.0 1 638 652 10.1021/acs.iecr.1c04251 0.0 DEC 2021 15 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering ZG6IE 2023-03-23 WOS:000737860700001 0
J Feng, LC; Cao, JD; Hu, J; Wu, ZH; Rutkowski, L Feng, Lichao; Cao, Jinde; Hu, Jun; Wu, Zhihui; Rutkowski, Leszek Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode NEURAL PROCESSING LETTERS English Article Hybrid recurrent neural networks; Random noises; Exponential stabilization; Discrete observations; Delay VARYING NONLINEAR-SYSTEMS; DIFFERENTIAL-EQUATIONS; MISSING MEASUREMENTS; BROWNIAN NOISE; STABILITY; SUPPRESSES; GROWTH; DESTABILIZATION; SYNCHRONIZATION; OPTIMIZATION Recently, the random noises derived from discrete state observations are creatively designed to realize the role of stabilization for deterministic systems in the existing result. However, for a hybrid neural network, except for the factor of discrete state observations, one always needs to consider the factors of delays and discrete mode identifications. Hence, taking delays and discrete mode identifications into account for random noises is more reasonable and practical than the original work. Motivated by the idea above, this brief is to design delayed random noises derived from discrete state observations and discrete mode identifications to almost surely exponentially stabilize an unstable hybrid recurrent neural networks, by virtue of M-matrix and stochastic analysis methods. [Feng, Lichao] North China Univ Sci & Technol, Coll Sci, Tangshan 063210, Peoples R China; [Feng, Lichao; Cao, Jinde] Southeast Univ, Sch Math, Jiangsu Prov Key Lab Networked Collect Intelligen, Nanjing 210096, Jiangsu, Peoples R China; [Hu, Jun; Wu, Zhihui] Harbin Univ Sci & Technol, Dept Math, Harbin 150080, Heilongjiang, Peoples R China; [Rutkowski, Leszek] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland North China University of Science & Technology; Southeast University - China; Harbin University of Science & Technology; Technical University Czestochowa Cao, JD (corresponding author), Southeast Univ, Sch Math, Jiangsu Prov Key Lab Networked Collect Intelligen, Nanjing 210096, Jiangsu, Peoples R China. fenglichao19820520@163.com; jdcao@seu.edu.cn Cao, Jinde/D-1482-2012; Rutkowski, Leszek/AAF-4693-2021; Hu, Jun/M-2635-2015 Cao, Jinde/0000-0003-3133-7119; Hu, Jun/0000-0002-7852-5064; Rutkowski, Leszek/0000-0001-6960-9525 National Natural Science Foundation of China [11571024, 61833005, 61573096, 61272530]; China Postdoctoral Science Foundation [2017M621588]; Science and Technology Research Foundation of Higher Education Institutions of Hebei Province of China [QN2017116]; Graduate Foundation of North China University of Science and Technology [J1905]; Polish National Science Centre [2017/27/B/ST6/02852] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Science and Technology Research Foundation of Higher Education Institutions of Hebei Province of China; Graduate Foundation of North China University of Science and Technology; Polish National Science Centre This project is partially supported by National Natural Science Foundation of China (Nos. 11571024, 61833005, 61573096 and 61272530), China Postdoctoral Science Foundation (No. 2017M621588), Science and Technology Research Foundation of Higher Education Institutions of Hebei Province of China (No. QN2017116), Graduate Foundation of North China University of Science and Technology (No. J1905), and by the Polish National Science Centre (No. 2017/27/B/ST6/02852). 57 2 2 1 9 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1370-4621 1573-773X NEURAL PROCESS LETT Neural Process. Lett. DEC 2019.0 50.0 3 2797 2819 10.1007/s11063-019-10059-z 0.0 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science JY3KP 2023-03-23 WOS:000504317600041 0
J Zhang, WM; Brandt, M; Prishchepov, AV; Li, ZF; Lyu, CG; Fensholt, R Zhang, Wenmin; Brandt, Martin; Prishchepov, Alexander V.; Li, Zhaofu; Lyu, Chunguang; Fensholt, Rasmus Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019) REMOTE SENSING English Article winter wheat; Landsat; time series; change detection; cloud computation; machine learning; North China Plain CLIMATE-CHANGE; HEAT-STRESS; YIELD; GROWTH; AREA; CLASSIFICATION; ALGORITHM; IMPACTS; COVER Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world's largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (similar to 28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999-2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth (TM) online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (beta = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999-2019 with overall accuracies ranging from 82% to 99% and the user's/producer's accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 x 10(6) ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China. [Zhang, Wenmin] Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China; [Zhang, Wenmin] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China; [Brandt, Martin; Prishchepov, Alexander V.; Fensholt, Rasmus] Univ Copenhagen, Dept Geosci & Nat Resource Management IGN, DK-1350 Copenhagen, Denmark; [Li, Zhaofu] Nanjing Agr Univ, Coll Resources & Environm Sci, Nanjing 210095, Peoples R China; [Lyu, Chunguang] Linyi Univ, Coll Resources & Environm, Shandong Prov Key Lab Water & Soil Conservat & En, Linyi 276005, Shandong, Peoples R China Nanjing Normal University; Nanjing Normal University; University of Copenhagen; Nanjing Agricultural University; Linyi University Lyu, CG (corresponding author), Linyi Univ, Coll Resources & Environm, Shandong Prov Key Lab Water & Soil Conservat & En, Linyi 276005, Shandong, Peoples R China. 90705@njnu.edu.cn; mabr@ign.ku.dk; alpr@ign.ku.dk; lizhaofu@njau.edu.cn; lvchunguang@lyu.edu.cn; rf@ign.ku.dk Fensholt, Rasmus/L-7951-2014; Prishchepov, Alexander/H-6708-2012; Brandt, Martin/E-4598-2015 Fensholt, Rasmus/0000-0003-3067-4527; Prishchepov, Alexander/0000-0003-2375-1651; Lyu, Chunguang/0000-0002-0245-9179; Li, Zhaofu/0000-0002-0287-8197; Brandt, Martin/0000-0001-9531-1239; Lyu, Chunguang/0000-0001-7508-6273 National Postdoctoral Program for Innovative Talents [BX20190154]; National Natural Science Foundation of China [42001349]; Shandong Province Natural Science Foundation [ZR2016DB06]; AXA postdoctoral research grant; DFF-Danish ERC Support Program [116491, 9127-00001B]; Villum Foundation through the project 'Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics' (DeReEco) National Postdoctoral Program for Innovative Talents; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Province Natural Science Foundation(Natural Science Foundation of Shandong Province); AXA postdoctoral research grant(AXA Research Fund); DFF-Danish ERC Support Program; Villum Foundation through the project 'Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics' (DeReEco) National Postdoctoral Program for Innovative Talents (BX20190154,) and the National Natural Science Foundation of China (grant number: 42001349), Shandong Province Natural Science Foundation (No. ZR2016DB06). M.B. is supported by the AXA postdoctoral research grant. A.V.P. is supported by DFF-Danish ERC Support Program (grant number: 116491, 9127-00001B). R.F. acknowledges support by the Villum Foundation through the project `Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics' (DeReEco). 50 4 4 11 37 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2021.0 13.0 6 1170 10.3390/rs13061170 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology SE3AS gold 2023-03-23 WOS:000651944300001 0
J Chen, ST; Qiu, XH; Tan, XY; Fang, ZJ; Jin, YC Chen, Shaotao; Qiu, Xihe; Tan, Xiaoyu; Fang, Zhijun; Jin, Yaochu A model-based hybrid soft actor-critic deep reinforcement learning algorithm for optimal ventilator settings INFORMATION SCIENCES English Article Optimal ventilator settings; Reinforcement learning; Hybrid action space; Optimal strategy; Machine learning SYSTEM A ventilator is a device that mechanically assists in pumping air into the lungs, which is a life-saving supportive therapy in an intensive care unit (ICU). In clinical scenarios, each patient has unique physiological circumstances and specific respiratory diseases, thus requiring individualized ventilator settings. Long-term supervision by experienced clini-cians is essential to perform the task of precisely adjusting ventilator parameters and mak-ing timely modifications. Moreover, a tiny clinical error can result in severe lung injury, induce multi-system organ dysfunction, and increase mortality. To reduce the workload of clinicians and prevent medical errors, machine learning (ML), or more specifically, rein-forcement learning (RL) methods, have been developed to automatically adjust the venti-lator's parameters and select optimal strategies. However, the ventilator settings contain both continuous (e.g., frequency) and discrete parameters (e.g., ventilation mode), making it challenging for conventional RL-based approaches to handle such problems. Meanwhile, it is necessary to develop models with high data efficiency to overcome medical data insuf-ficiency. In this paper, we propose a model-based hybrid soft actor-critic (MHSAC) algo-rithm that is developed based on the classic soft actor-critic (SAC) and model-based policy optimization (MBPO) framework. This algorithm can learn both continuous and dis-crete policies according to the current and predictive state of patient's physiological infor-mation with high data efficiency. Results reveal that our proposed model significantly outperforms the baseline models, achieving superior efficiency and high accuracy in the OpenAI Gym simulation environment. Our proposed model is capable of resolving mixed action space problems, enhancing data efficiency, and accelerating convergence, which can generate practical optimal ventilator settings, minimize possible medical errors, and provide clinical decision support.(c) 2022 Elsevier Inc. All rights reserved. [Chen, Shaotao; Qiu, Xihe; Fang, Zhijun] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai, Peoples R China; [Tan, Xiaoyu] Ant Grp, Hangzhou, Peoples R China; [Jin, Yaochu] Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany Shanghai University of Engineering Science; University of Bielefeld Qiu, XH (corresponding author), Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai, Peoples R China.;Jin, YC (corresponding author), Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany. qiuxihe1993@gmail.com; yaochu.jin@uni-bielefeld.de National Natural Science Foundation of China [62102241] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China (Grant No.62102241) . Yaochu Jin is sup- ported in part by an Alexander von Humboldt Professorship for Artificial Intelligence endowed by the German Federal Ministry of Education and Research. 50 1 1 13 13 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 2022.0 611.0 47 64 10.1016/j.ins.2022.08.028 0.0 AUG 2022 18 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5I2MM 2023-03-23 WOS:000868197500004 0
J Chen, ZL; Feng, RF; Kahlert, UD; Chen, ZT; Roche, LATD; Soliman, A; Miao, C; De Wilde, RL; Shi, WJ Chen, Zhilin; Feng, Ruifa; Kahlert, Ulf Dietrich; Chen, Zhitong; Torres-dela Roche, Luz Angela; Soliman, Amr; Miao, Chen; De Wilde, Rudy Leon; Shi, Wenjie Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer FRONTIERS IN ONCOLOGY English Article ceRNA; T cell; breast cancer; lncRNA; target PROLIFERATION; METASTASIS; PACKAGE; WOMEN BackgroundThe infiltration of CD8 T cells is usually linked to a favorable prognosis and may predict the therapeutic response of breast cancer patients to immunotherapy. The purpose of this research is to investigate the competing endogenous RNA (ceRNA) network correlated with the infiltration of CD8 T cells. MethodsBased on expression profiles, CD8 T cell abundances for each breast cancer (BC) patient were inferred using the bioinformatic method by immune markers and expression profiles. We were able to extract the differentially expressed RNAs (DEmRNAs, DEmiRNAs, and DElncRNAs) between low and high CD8 T-cell samples. The ceRNA network was constructed using Cytoscape. Machine learning models were built by lncRNAs to predict CD8 T-cell abundances. The lncRNAs were used to develop a prognostic model that could predict the survival rates of BC patients. The expression of selected lncRNA (XIST) was validated by quantitative real-time PCR (qRT-PCR). ResultsA total of 1,599 DElncRNAs, 89 DEmiRNAs, and 1,794 DEmRNAs between high and low CD8 T-cell groups were obtained. Two ceRNA networks that have positive or negative correlations with CD8 T cells were built. Among the two ceRNA networks, nine lncRNAs (MIR29B2CHG, NEAT1, MALAT1, LINC00943, LINC01146, AC092718.4, AC005332.4, NORAD, and XIST) were selected for model construction. Among six prevalent machine learning models, artificial neural networks performed best, with an area under the curve (AUC) of 0.855. Patients from the high-risk category with BC had a lower survival rate compared to those from the low-risk group. The qRT-PCR results revealed significantly reduced XIST expression in normal breast samples, which was consistent with our integrated analysis. ConclusionThese results potentially provide insights into the ceRNA networks linked with T-cell infiltration and provide accurate models for T-cell prediction. [Chen, Zhilin] Hainan Med Univ, Dept Breast & Thorac Oncol Surg, Affiliated Hosp 1, Haikou, Peoples R China; [Chen, Zhilin; Chen, Zhitong; Torres-dela Roche, Luz Angela; Soliman, Amr; De Wilde, Rudy Leon; Shi, Wenjie] Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany; [Feng, Ruifa] Guilin Med Univ, Affiliated Hosp 2, Breast Ctr, Guilin, Peoples R China; [Kahlert, Ulf Dietrich; Shi, Wenjie] Univ Med Magdeburg, Univ Clin Gen Visceral & Vasc Surg, Mol & Expt Surg, Magdeburg, Germany; [Miao, Chen] Nanjing Med Univ, Dept Pathol, Affiliated Hosp 1, Nanjing, Peoples R China; [Kahlert, Ulf Dietrich; Shi, Wenjie] Otto von Guericke Univ, Magdeburg, Germany Hainan Medical University; Pius-Hospital Oldenburg; Guilin Medical University; Nanjing Medical University; Otto von Guericke University De Wilde, RL; Shi, WJ (corresponding author), Univ Med Oldenburg, Univ Hosp Gynecol, Pius Hosp, Oldenburg, Germany.;Shi, WJ (corresponding author), Univ Med Magdeburg, Univ Clin Gen Visceral & Vasc Surg, Mol & Expt Surg, Magdeburg, Germany.;Miao, C (corresponding author), Nanjing Med Univ, Dept Pathol, Affiliated Hosp 1, Nanjing, Peoples R China.;Shi, WJ (corresponding author), Otto von Guericke Univ, Magdeburg, Germany. cm_njmu@163.com; rudy-leon.dewilde@pius-hospital.de; wenjie.shi@uni-oldenburg.de Kahlert, Ulf Dietrich/0000-0002-6021-1841 35 3 3 4 4 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. JUN 9 2022.0 12.0 883197 10.3389/fonc.2022.883197 0.0 9 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 2I5ZX 35756601.0 Green Accepted, gold 2023-03-23 WOS:000815057500001 0
J Chen, Z; Liu, XH; Li, FY; Li, C; Marquez-Lago, T; Leier, A; Akutsu, T; Webb, GI; Xu, DK; Smith, AI; Li, L; Chou, KC; Song, JN Chen, Zhen; Liu, Xuhan; Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana; Leier, Andre; Akutsu, Tatsuya; Webb, Geoffrey, I; Xu, Dakang; Smith, Alexander Ian; Li, Lei; Chou, Kuo-Chen; Song, Jiangning Large-scale comparative assessment of computational predictors for lysine post-translational modification sites BRIEFINGS IN BIOINFORMATICS English Article lysine post-translational modification; prediction model; sequence features; feature engineering; deep learning HUMAN UBIQUITINATION SITES; GLYCATION END-PRODUCTS; FLEXIBLE WEB SERVER; ACCURATE PREDICTION; SUMOYLATION SITES; SUCCINYLATION SITES; NONHISTONE PROTEINS; SECONDARY STRUCTURE; PSEUDO COMPONENTS; PSI-BLAST Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields. [Chen, Zhen] Qingdao Univ, Sch Basic Med Sci, Shandong, Peoples R China; [Liu, Xuhan] Beijing Oriental Yamei Gene Technol Inst Co Ltd, Dept Informat Technol, Beijing, Peoples R China; [Liu, Xuhan] Leiden Univ, Drug Discovery & Safety, Leiden Acad Ctr Drug Res, Leiden, Netherlands; [Li, Fuyi] Monash Univ, Biomed Discovery Inst, Melbourne, Vic, Australia; [Li, Fuyi; Smith, Alexander Ian; Song, Jiangning] Monash Univ, Dept Biochem & Mol Biol, Melbourne, Vic, Australia; [Li, Chen] Swiss Fed Inst Technol, Inst Mol Syst Biol, Zurich, Switzerland; [Li, Chen; Smith, Alexander Ian; Song, Jiangning] Monash Univ, Monash Biomed Discovery Inst, Melbourne, Vic, Australia; [Marquez-Lago, Tatiana; Leier, Andre] Univ Alabama Birmingham, Sch Med, Dept Genet, Birmingham, AL USA; [Marquez-Lago, Tatiana] Univ Alabama Birmingham, Sch Med, Dept Cell Dev & Integrat Biol, Birmingham, AL USA; [Leier, Andre] UAB, Comprehens Canc Ctr, Birmingham, AL USA; [Leier, Andre] Mem Univ Newfoundland, St John, NF, Canada; [Leier, Andre] Univ Queensland, Brisbane, Qld, Australia; [Leier, Andre] Swiss Fed Inst Technol, Zurich, Switzerland; [Akutsu, Tatsuya] Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Kyoto, Japan; [Webb, Geoffrey, I] Monash Univ, Monash Ctr Data Sci, Melbourne, Vic, Australia; [Webb, Geoffrey, I] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia; [Xu, Dakang] Monash Univ, Monash Inst Med Res, Melbourne, Vic, Australia; [Xu, Dakang] Shanghai Jiao Tong Univ, Sch Med, Shanghai, Peoples R China; [Smith, Alexander Ian] Monash Univ, Res & Res Infrastruct, Melbourne, Vic, Australia; [Li, Lei] Qingdao Univ, Sch Basic Med Sci, 38 Dengzhou Rd, Qingdao 266021, Shandong, Peoples R China; [Chou, Kuo-Chen] Gordon Life Sci Inst, Boston, MA 02478 USA; [Song, Jiangning] Monash Univ, Fac Informat Technol, Monash Ctr Data Sci, Melbourne, Vic, Australia; [Song, Jiangning] Monash Univ, ARC Ctr Excellence Adv Mol Imaging, Melbourne, Vic, Australia Qingdao University; Leiden University; Leiden University - Excl LUMC; Monash University; Monash University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Monash University; University of Alabama System; University of Alabama Birmingham; University of Alabama System; University of Alabama Birmingham; University of Alabama System; University of Alabama Birmingham; Memorial University Newfoundland; University of Queensland; Swiss Federal Institutes of Technology Domain; ETH Zurich; Kyoto University; Monash University; Monash University; Monash University; Shanghai Jiao Tong University; Monash University; Qingdao University; Gordon Life Science Institute; Monash University; Monash University Li, L (corresponding author), Qingdao Univ, Sch Basic Med Sci, 38 Dengzhou Rd, Qingdao 266021, Shandong, Peoples R China.;Chou, KC (corresponding author), Gordon Life Sci Inst, Boston, MA 02478 USA.;Song, JN (corresponding author), Monash Univ, Biomed Discovery Inst, Dept Biochem & Mol Biol, Melbourne, Vic 3800, Australia.;Song, JN (corresponding author), Monash Univ, Mol Biol & Monash Ctr Data Sci, Melbourne, Vic 3800, Australia. lileime@hotmail.com; kcchou@gordonlifescience.org; Jiangning.Song@monash.edu Webb, Geoff i/R-9967-2017; Song, Jiangning/F-9787-2010; Webb, Geoffrey/Q-7270-2019; Li, Fuyi/B-4609-2018 Webb, Geoff i/0000-0001-9963-5169; Song, Jiangning/0000-0001-8031-9086; Webb, Geoffrey/0000-0001-9963-5169; Li, Fuyi/0000-0001-5216-3213; Akutsu, Tatsuya/0000-0001-9763-797X; Chen, Zhen/0000-0002-9412-9774; Leier, Andre/0000-0002-2647-2693; Li, Chen/0000-0002-1847-754X Australian Research Council [LP110200333, DP120104460]; Young Scientists Fund of the National Natural Science Foundation of China [31701142]; National Natural Science Foundation of China [31770821]; National Health and Medical Research Council of Australia (NHMRC) [4909809]; National Institute of Allergy and Infectious Diseases of the National Institutes of Health [R01 AI111965]; Monash University; collaborative research program of the Institute for Chemical Research, Kyoto University; NHMRC CJ Martin Early Career Research Fellowship [1143366]; Informatics Institute of the School of Medicine at the University of Alabama at Birmingham; Australian Research Council [LP110200333] Funding Source: Australian Research Council; National Health and Medical Research Council of Australia [1143366] Funding Source: NHMRC Australian Research Council(Australian Research Council); Young Scientists Fund of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Health and Medical Research Council of Australia (NHMRC)(National Health and Medical Research Council (NHMRC) of Australia); National Institute of Allergy and Infectious Diseases of the National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Allergy & Infectious Diseases (NIAID)); Monash University(Monash University); collaborative research program of the Institute for Chemical Research, Kyoto University; NHMRC CJ Martin Early Career Research Fellowship(National Health and Medical Research Council (NHMRC) of Australia); Informatics Institute of the School of Medicine at the University of Alabama at Birmingham; Australian Research Council(Australian Research Council); National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia) This work was financially supported by grants from the Australian Research Council (LP110200333 and DP120104460), the Young Scientists Fund of the National Natural Science Foundation of China (31701142), the National Natural Science Foundation of China (31770821), the National Health and Medical Research Council of Australia (NHMRC) (4909809), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965), a major interdisciplinary research project awarded by Monash University and the collaborative research program of the Institute for Chemical Research, Kyoto University (2018-28). C.L. is currently supported by an NHMRC CJ Martin Early Career Research Fellowship (1143366). T.T.M.L. and A.L.'s work was supported in part by the Informatics Institute of the School of Medicine at the University of Alabama at Birmingham. 156 67 68 3 59 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. NOV 2019.0 20.0 6 2267 2290 10.1093/bib/bby089 0.0 24 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology KG1SC 30285084.0 Green Accepted, Green Published 2023-03-23 WOS:000509720200023 0
J Shu, X; Li, G; Zhang, YJ; Shen, SQ; Chen, Z; Liu, YG Shu, Xing; Li, Guang; Zhang, Yuanjian; Shen, Shiquan; Chen, Zheng; Liu, Yonggang Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION English Article State of charge; Estimation; Computational modeling; Mathematical model; Indium phosphide; III-V semiconductor materials; Lithium-ion batteries; Inconsistency; long short-term memory (LSTM) recurrent neural network (RNN); square root cubature Kalman filter (KF); state of charge (SOC) STATE; INCONSISTENCY Accurate estimation of the state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, first, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate the battery cell's SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation values from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and the cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited to 2% after convergence. [Shu, Xing; Shen, Shiquan; Chen, Zheng] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China; [Li, Guang; Chen, Zheng] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England; [Zhang, Yuanjian] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AG, Antrim, North Ireland; [Liu, Yonggang] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China Kunming University of Science & Technology; University of London; Queen Mary University London; Queens University Belfast; Chongqing University Chen, Z (corresponding author), Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China. shuxing92@kust.edu.cn; g.li@qmul.ac.uk; y.zhang@qub.ac.uk; shiquan@tju.edu.cn; chen@kust.edu.cn; andylyg@umich.edu Zhang, Yuanjian/HKN-4832-2023; Li, Guang/K-5119-2017 Zhang, Yuanjian/0000-0001-5563-8480; chen, zheng/0000-0002-1634-7231; Shu, Xing/0000-0003-1845-1988; Li, Guang/0000-0001-9323-5076 National Key R&D Program of China [2018YFB0104000]; National Natural Science Foundation of China [61763021, 51775063]; EU-funded Marie Sklodowska-Curie Individual Fellowships Project [845102-HOEMEV-H2020-MSCAIF-2018] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU-funded Marie Sklodowska-Curie Individual Fellowships Project This work was supported in part by the National Key R&D Program of China under Grant 2018YFB0104000; in part by the National Natural Science Foundation of China under Grant 61763021 and Grant 51775063; and in part by the EU-funded Marie Sklodowska-Curie Individual Fellowships Project under Grant 845102-HOEMEV-H2020-MSCAIF-2018. 33 25 26 23 111 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7782 IEEE T TRANSP ELECTR IEEE Trans. Transp. Electrif. SEP 2021.0 7.0 3 1271 1284 10.1109/TTE.2020.3041757 0.0 14 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation UD0GF Green Submitted 2023-03-23 WOS:000686895100030 0
J Qiu, H; Dong, T; Zhang, TW; Lu, JL; Memmi, G; Qiu, MK Qiu, Han; Dong, Tian; Zhang, Tianwei; Lu, Jialiang; Memmi, Gerard; Qiu, Meikang Adversarial Attacks Against Network Intrusion Detection in IoT Systems IEEE INTERNET OF THINGS JOURNAL English Article Feature extraction; Computational modeling; Internet of Things; Training; Perturbation methods; Mathematical model; Detectors; Adversarial examples (AEs); deep learning (DL); Internet of Things (IoT); network intrusion detection Deep learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples (AEs) is unknown. In this article, we design a novel adversarial attack against DL-based network intrusion detection systems (NIDSs) in the Internet-of-Things environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: 1) model extraction is adopted to replicate the black-box model with a small amount of training data and 2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate AEs using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate. [Qiu, Han; Memmi, Gerard] Telecom Paris, LTCI, F-91120 Palaiseau, France; [Dong, Tian; Lu, Jialiang] Shanghai Jiao Tong Univ, SPEIT, Shanghai 200240, Peoples R China; [Zhang, Tianwei] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Qiu, Meikang] Texas A&M Univ, Dept Comp Sci, Commerce, TX 75428 USA IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Shanghai Jiao Tong University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Texas A&M University System Qiu, MK (corresponding author), Texas A&M Univ, Dept Comp Sci, Commerce, TX 75428 USA. han.qiu@telecom-paris.fr; dongtian9702@gmail.com; tianwei.zhang@ntu.edu.sg; jialiang.lu@sjtu.edu.cn; gerard.memmi@telecom-paris.fr; meikang.qiu@tamuc.edu 30 39 40 2 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. JUL 1 2021.0 8.0 13 10327 10335 10.1109/JIOT.2020.3048038 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications SX4WM 2023-03-23 WOS:000665207100011 0
S Lin, L; Luo, YL Luo, Y Lin, Lin; Luo, Yonglun Functional Evaluation of CRISPR Activity by the Dual-Fluorescent Surrogate System: C-Check CRISPR GENE EDITING: Methods and Protocols Methods in Molecular Biology English Article; Book Chapter CRISPR; Cas9; Reporter vector; Surrogate vector; C-Check; Gene editing Rapid evaluation of the CRISPR gRNA activity is an essential step of employing the technology in editing genes. Through machine learning strategy, the rule sets for in silico designing gRNAs with high activity has greatly improved. However, there are still discrepancies between different prediction rule sets, and between the predicted and actual gRNA activities. Thus, experimentally validating gRNA activity is still the gold standard in defining the best gRNAs for gene editing experiments. One such approach for experimentally selecting gRNAs with high activity is fluorescent surrogate reporter vectors. We had previously developed a dual-fluorescent surrogate system, called C-Check, which based on single-strand annealing repair of the DNA double-strand breaks introduced by CRISPR-Cas9 to generate a functional EGFP. The system offers a tool for rapid functional evaluation of CRISPR gRNA activity, as well as for enrichment of gene edited cells. In this chapter, we will give a step-by-step instruction on the design, generation, and application of the C-Check system for quantifying gRNA activities. [Lin, Lin; Luo, Yonglun] Aarhus Univ, Dept Biomed, Aarhus, Denmark; [Luo, Yonglun] BGI Shenzhen, Shenzhen, Peoples R China; [Luo, Yonglun] Guangdong Prov Key Lab Genome Read & Write, Shenzhen, Peoples R China; [Luo, Yonglun] BGI Qingdao, Qingdao, Peoples R China Aarhus University; Beijing Genomics Institute (BGI); Beijing Genomics Institute (BGI) Lin, L (corresponding author), Aarhus Univ, Dept Biomed, Aarhus, Denmark. Luo, Yonglun/AAG-9253-2020; Luo, Yonglun/C-4708-2013 Luo, Yonglun/0000-0002-0007-7759 Lundbeck Foundation [R219-2016-1375, R173-2014-1105]; Danish Research Council for Independent Research [DFF-1337-00128]; Sapere Aude Young Research Talent Prize [DFF-1335-00763A]; Innovation Fund Denmark (BrainStem); Aarhus University Strategic Grant (AU-iCRISPR); Guangdong Provincial Key Laboratory of Genome Read and Write [2017B030301011] Lundbeck Foundation(Lundbeckfonden); Danish Research Council for Independent Research(Det Frie Forskningsrad (DFF)); Sapere Aude Young Research Talent Prize; Innovation Fund Denmark (BrainStem); Aarhus University Strategic Grant (AU-iCRISPR); Guangdong Provincial Key Laboratory of Genome Read and Write This work is partially supported by the Lundbeck Foundation (R219-2016-1375, R173-2014-1105), the Danish Research Council for Independent Research (DFF-1337-00128), the Sapere Aude Young Research Talent Prize (DFF-1335-00763A), the Innovation Fund Denmark (BrainStem), and Aarhus University Strategic Grant (AU-iCRISPR). Y.L is also supported by the Guangdong Provincial Key Laboratory of Genome Read and Write (No. 2017B030301011). 11 1 2 1 1 HUMANA PRESS INC TOTOWA 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA 1064-3745 1940-6029 978-1-4939-9170-9; 978-1-4939-9169-3 METHODS MOL BIOL Methods Mol. Biol. 2019.0 1961.0 67 77 10.1007/978-1-4939-9170-9_5 0.0 10.1007/978-1-4939-9170-9 11 Biochemical Research Methods; Biotechnology & Applied Microbiology; Medicine, Research & Experimental Book Citation Index – Science (BKCI-S) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Research & Experimental Medicine BU3SV 30912040.0 2023-03-23 WOS:000893312500006 0
J Govindan, K; Gholizadeh, H Govindan, Kannan; Gholizadeh, Hadi Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW English Article Resilient sustainable reverse logistics network; Robust optimization; Cross-entropy algorithm; Big data; ELVs SUPPLY CHAIN NETWORK; UTILIZING CONDITIONAL VALUE; OPTIMIZATION MODEL; DISRUPTION RISKS; DECISION-MAKING; CROSS-ENTROPY; UNCERTAINTY; WASTE; PERFORMANCE; MANAGEMENT With new global regulations on supply chains (SCs), sustainable regulation mechanisms have become subject to controversy. The intention is to create and expand green and sustainable supply chains (SSC) to meet environmental and economic standards and to boost one's position in competitive markets. This study examines the resilient sustainable reverse logistics network (RLN) process for end-of-life vehicles (ELVs) in Iran. We pursue both actual and uncertain situations that possess big data characteristics (3 V's) in information between facilities of the proposed reverse logistics (RL), and we consider recycling technology due to its societal impacts. Due to unpredictable environmental and social factors, the various proposed network facilities may not utilize their full capacity, so we also consider situations in which the network facility capacity is disrupted. Our primary objective is to minimize the total cost of the resilient sustainable RLN. For most parameters, finding the best solution through traditional methods is time-consuming and costly. Hence, to enhance decision-making power, the value of model parameters in each scenario is considered. A Cross-Entropy (CE) algorithm with basic scenario concepts is used in robust model optimization. The results demonstrate that changing the scenario situation significantly impacts optimal environmental and social costs. In particular, when the situation is pessimistic, environmental impact costs are at their highest levels. Hence, scenario-based modeling of the network is a good approach to implement under uncertainty conditions. On the other hand, results show that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition. [Govindan, Kannan] Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai 201306, Peoples R China; [Govindan, Kannan] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea; [Govindan, Kannan] Univ Southern Denmark, Danish Inst Adv Study, Dept Technol & Innovat, Ctr Sustainable Supply Chain Engn, Campusvej 55, Odense M, Denmark; [Gholizadeh, Hadi] Univ Laval, Dept Genie Mecan, Quebec City, PQ, Canada Shanghai Maritime University; Yonsei University; University of Southern Denmark; Laval University Govindan, K (corresponding author), Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai 201306, Peoples R China. kgov@iti.sdu.dk Govindan, Kannan/M-5996-2017 Govindan, Kannan/0000-0002-6204-1196 114 41 41 21 58 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1366-5545 1878-5794 TRANSPORT RES E-LOG Transp. Res. Pt. e-Logist. Transp. Rev. MAY 2021.0 149.0 102279 10.1016/j.tre.2021.102279 0.0 MAR 2021 30 Economics; Engineering, Civil; Operations Research & Management Science; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Engineering; Operations Research & Management Science; Transportation TE9DK 2023-03-23 WOS:000670306600010 0
J Wang, DY; Yue, CQ; ElAmraoui, A Wang, Deyun; Yue, Chenqiang; ElAmraoui, Adnen Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy CHAOS SOLITONS & FRACTALS English Article Electricity load; Multi-step-ahead forecasting; Error correction strategy; Time series decomposition; Hybrid forecasting model EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR REGRESSION; WAVELET NEURAL-NETWORK; SVR MODEL; DEMAND; OPTIMIZATION; CONSUMPTION; PREDICTION In this study, a novel architecture combining a hybrid learning paradigm and an error correction strategy is presented for multi-step-ahead electricity load forecasting. The detail of the proposed architecture is provided as follows: (1) a novel hybrid learning paradigm based on complementary ensemble empirical mode decomposition (CEEMD) and backpropagation (BP) neural network improved by particle swarm optimization (PSO-BP) is developed for preliminary prediction of the electricity load; (2) an error prediction approach based on variational mode decomposition (VMD) and PSO-BP is established for prediction of the subsequent error; (3) calibrate the preliminary prediction values using the forecast results of the error prediction model. Specifically, in the error correction process, the original data series is separated into three subsets to generate a reasonable historical error series used for establishing the error prediction model. Two case studies based on the data of PJM and Ontario electricity markets are presented and investigated to assess the effectiveness of the proposed architecture. The evaluation results demonstrate that the proposed architecture can yield results in higher accuracy than other benchmark models considered in this study. (c) 2021 Elsevier Ltd. All rights reserved. [Wang, Deyun] China Univ Geosci, Mineral Resource Strategy & Policy Res Ctr, Wuhan 430074, Peoples R China; [Wang, Deyun] China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China; [Yue, Chenqiang] Univ Liverpool, Sch Management, Liverpool L69 7ZH, Merseyside, England; [ElAmraoui, Adnen] Artois Univ, Lab Genie Informat & Automat Artois LGI2A, UR 3926, F-62400 Bethune, France China University of Geosciences; China University of Geosciences; University of Liverpool Wang, DY (corresponding author), China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China. wangdy@cug.edu.cn EL AMRAOUI, Adnen/HOH-5079-2023; Yue, Chenqiang/HLQ-1119-2023; Yue, Chenqiang/HLQ-1088-2023 EL AMRAOUI, Adnen/0000-0001-9233-8655; Hubei Provincial Natural Science Foundation of China [2020CFB822] Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank the editor and the anonymous reviewers for their constructive comments on improving an early version of this paper. This work was supported by Hubei Provincial Natural Science Foundation of China (Grant No. 2020CFB822) . 43 8 8 12 25 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-0779 1873-2887 CHAOS SOLITON FRACT Chaos Solitons Fractals NOV 2021.0 152.0 111453 10.1016/j.chaos.2021.111453 0.0 OCT 2021 15 Mathematics, Interdisciplinary Applications; Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Mathematics; Physics WI9VP 2023-03-23 WOS:000708700900005 0
C Giernacki, W; Rao, JJ; Sladic, S; Bondyra, A; Retinger, M; Espinoza-Fraire, T IEEE Giernacki, Wojciech; Rao, Jinjun; Sladic, Sasa; Bondyra, Adam; Retinger, Marek; Espinoza-Fraire, Tadeo DJI Tello Quadrotor as a Platform for Research and Education in Mobile Robotics and Control Engineering 2022 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS) International Conference on Unmanned Aircraft Systems English Proceedings Paper International Conference on Unmanned Aircraft Systems (ICUAS) JUN 21-24, 2022 Dubrovnik, CROATIA Multirotor unmanned aerial vehicles (UAVs) equipped with various sensors and cameras on board are commonly used as experimental flying platforms for research and education in mobile robotics and control engineering. In the academic community, especially low-cost, ready-to-use, compact-size flying robots attract a lot of researchers attention due to the high level of safety they offer during experiments conducted in GPS-denied indoor laboratory spaces. In recent years, the DJI Tello quadrotor has gained importance. This nano-class UAV may be found in laboratories around the world and is often the first drone used by students and young scientists in their projects. This paper presents the potential of the drone as a useful experimental platform. Its hardware and software architecture, mathematical model with parametrization, as well as proposed new state machine (node for Robot Operating System) are presented. Moreover, representative research results on UAV position control based on cascade fuzzy neural network are illustrated. Exemplary student projects conducted with the use of DJI Tello are also shown. These projects are as follows: (a) TelloBird (QR trajectory generator), (b) Sentry drone for detecting and tracking any humans entering a given area. The considered UAV is used there as a rapid prototyping platform supported by the tello_ros package in ROS. [Giernacki, Wojciech; Bondyra, Adam; Retinger, Marek] Poznan Univ Tech, Fac Control Robot & Elect Engn, Inst Robot & Machine Intelligence, Poznan, Poland; [Rao, Jinjun] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Intelligent Mfg & Robot, Shanghai, Peoples R China; [Sladic, Sasa] Univ Rijeka, Fac Engn, Rijeka, Croatia; [Espinoza-Fraire, Tadeo] Juarez Univ State Durango, Fac Engn Sci & Architecture, Durango, Mexico Poznan University of Technology; Shanghai University; University of Rijeka; Universidad Juarez del Estado de Durango Giernacki, W (corresponding author), Poznan Univ Tech, Fac Control Robot & Elect Engn, Inst Robot & Machine Intelligence, Poznan, Poland. wojciech.giernacki@put.poznan.pl Retinger, Marek/0000-0003-3592-0942 Poznan University of Technology [0214/SBAD/0229, 0214/SBAD/0237] Poznan University of Technology This research was financially supported as a statutory work of Poznan University of Technology (grant no. 0214/SBAD/0229 and grant no. 0214/SBAD/0237). 41 1 1 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2373-6720 978-1-6654-0593-5 INT CONF UNMAN AIRCR 2022.0 735 744 10.1109/ICUAS54217.2022.9836168 0.0 10 Engineering, Aerospace; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT8NF 2023-03-23 WOS:000854030400086 0
J El Hajjar, S; Dornaika, F; Abdallah, F; Barrena, N El Hajjar, S.; Dornaika, F.; Abdallah, F.; Barrena, N. Consensus graph and spectral representation for one-step multi-view kernel based clustering KNOWLEDGE-BASED SYSTEMS English Article Multi-view clustering; One-step clustering; Graph learning; Spectral representation; Nonnegative embedding; Automatic weighting; Clustering algorithms MATRIX Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multiview Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Dornaika, F.] Henan Univ, Sch Comp & Informat Engn, Kaifeng, Peoples R China; [El Hajjar, S.; Dornaika, F.; Barrena, N.] Univ Basque Country, UPV EHU, San Sebastian, Spain; [Dornaika, F.] Basque Fdn Sci, IKERBASQUE, Bilbao, Spain; [Abdallah, F.] Lebanese Univ, Beirut, Lebanon; [Abdallah, F.] Luxembourg Inst Socioecon Res LISER, Esch Sur Alzette, Luxembourg Henan University; University of Basque Country; Basque Foundation for Science; Lebanese University Dornaika, F (corresponding author), Univ Basque Country, UPV EHU, San Sebastian, Spain. fadi.dornaika@ehu.eus Abdallah, Fahed/0000-0002-9485-6787 51 7 7 3 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. APR 6 2022.0 241.0 108250 10.1016/j.knosys.2022.108250 0.0 FEB 2022 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0V2EK hybrid, Green Published 2023-03-23 WOS:000788156700002 0
J Gan, Y; Langendijk, JA; Oldehinkel, E; Scandurra, D; Sijtsema, NM; Lin, ZX; Both, S; Brouwer, CL Gan, Yong; Langendijk, Johannes A.; Oldehinkel, Edwin; Scandurra, Daniel; Sijtsema, Nanna M.; Lin, Zhixiong; Both, Stefan; Brouwer, Charlotte L. A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy RADIOTHERAPY AND ONCOLOGY English Article Head and neck cancer; Organs at risk; Auto-segmentation; Deep learning contouring; Deformable image registration; Dosimetric changes RISK; CANCER; ORGANS; DELINEATION Background and purpose: Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). Materials and methods: Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (D-mean) and con-sequent NTCP-predictions. The average Dmean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D-mean and NTCP-predictions (vertical bar AD(mean)vertical bar and vertical bar ANTCP vertical bar). Results: The average vertical bar AD(mean)vertical bar of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest vertical bar AD(mean)vertical bar in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest vertical bar AD(mean)vertical bar in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile vertical bar ANTCP vertical bar was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. Conclusions: Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases. (C) 2021 The Author(s). Published by Elsevier B.V. [Gan, Yong; Langendijk, Johannes A.; Oldehinkel, Edwin; Scandurra, Daniel; Sijtsema, Nanna M.; Both, Stefan; Brouwer, Charlotte L.] Univ Groningen, Univ Med Ctr Groningen, Dept Radiat Oncol, Groningen, Netherlands; [Lin, Zhixiong] Shantou Univ, Canc Hosp, Med Coll, Dept Radiotherapy, Shantou, Peoples R China University of Groningen; Shantou University Gan, Y (corresponding author), Univ Med Ctr Groningen, Dept Radiat Oncol, POB 30001, NL-9700 RB Groningen, Netherlands. y.gan@umcg.nl Langendijk, Johannes A./0000-0003-1083-372X; , Yong/0000-0002-4172-0915; Sijtsema, Nanna Maria/0000-0001-6644-274X 26 2 2 1 7 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0167-8140 1879-0887 RADIOTHER ONCOL Radiother. Oncol. NOV 2021.0 164.0 167 174 10.1016/j.radonc.2021.09.019 0.0 OCT 2021 8 Oncology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Oncology; Radiology, Nuclear Medicine & Medical Imaging WI6AO 34597740.0 Green Published, hybrid 2023-03-23 WOS:000708441100018 0
J Mantelero, A Mantelero, Alessandro Personal data for decisional purposes in the age of analytics: From an individual to a collective dimension of data protection COMPUTER LAW & SECURITY REVIEW English Article Big data; Right to privacy; Data protection; Group privacy; Collective interests; Data protection authorities; Risk assessment PRIVACY; BEHAVIOR; SELF; LAW In the big data era, new technologies and powerful analytics make it possible to collect and analyse large amounts of data in order to identify patterns in the behaviour of groups, communities and even entire countries. Existing case law and regulations are inadequate to address the potential risks and issues related to this change of paradigm in social investigation. This is due to the fact that both the right to privacy and the more recent right to data protection are protected as individual rights. The social dimension of these rights has been taken into account by courts and policymakers in various countries. Nevertheless, the rights holder has always been the data subject and the rights related to informational privacy have mainly been exercised by individuals. This atomistic approach shows its limits in the existing context of mass predictive analysis, where the larger scale of data processing and the deeper analysis of information make it necessary to consider another layer, which is different from individual rights. This new layer is represented by the collective dimension of data protection, which protects groups of persons from the potential harms of discriminatory and invasive forms of data processing. On the basis of the distinction between individual, group and collective dimensions of privacy and data protection, the author outlines the main elements that characterise the collective dimension of these rights and the representation of the underlying interests. (C) 2016 Alessandro Mantelero, Published by Elsevier Ltd. All rights reserved. [Mantelero, Alessandro] NUIST, Sch Publ Adm, Nanjing, Jiangsu, Peoples R China; [Mantelero, Alessandro] Polytech Univ Turin, Dept Management & Prod Engn, Turin, Italy; [Mantelero, Alessandro] Nexa Ctr Internet & Soc, Turin, Italy Nanjing University of Information Science & Technology; Polytechnic University of Turin Mantelero, A (corresponding author), Politecn Torino, Dept Management & Prod Engn, Cso Duca Abruzzi 24, I-10120 Turin, Italy. alessandro.mantelero@polito.it 182 49 49 3 49 ELSEVIER ADVANCED TECHNOLOGY OXFORD OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0267-3649 COMPUT LAW SECUR REV Comput. Law Secur. Rev. APR 2016.0 32.0 2 238 255 10.1016/j.clsr.2016.01.014 0.0 18 Law Social Science Citation Index (SSCI) Government & Law DJ6ZS 2023-03-23 WOS:000374362300006 0
J Muhammad, K; Obaidat, MS; Hussain, T; Del Ser, J; Kumar, N; Tanveer, M; Doctor, F Muhammad, Khan; Obaidat, Mohammad S.; Hussain, Tanveer; Del Ser, Javier; Kumar, Neeraj; Tanveer, Mohammad; Doctor, Faiyaz Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions ACM COMPUTING SURVEYS English Review Video surveillance; fuzzy logic; neural networks; soft computing techniques; big data; big video data; fuzzy logic survey; fuzzy tutorial; video summarization; video surveillance survey CONVOLUTIONAL NEURAL-NETWORKS; INTERVAL TYPE-2; FIRE DETECTION; OBJECT DETECTION; SYSTEM; RECOGNITION; IDENTIFICATION; ENVIRONMENTS; INTELLIGENCE; GENERATION CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognitionmethods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers' attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field. [Muhammad, Khan] Sejong Univ, Dept Software, Visual Analyt Knowledge Lab, Seoul 143747, South Korea; [Obaidat, Mohammad S.] Univ Sharjah, Coll Comp & Informat, Sharjah 27272, U Arab Emirates; [Obaidat, Mohammad S.] Univ Jordan, KASIT, Amman 11942, Jordan; [Obaidat, Mohammad S.] Univ Sci & Technol, Beijing 100083, Peoples R China; [Hussain, Tanveer] Sejong Univ, Dept Software, Intelligent Media Lab, Seoul 143747, South Korea; [Del Ser, Javier] Basque Res & Technol Alliance BRTA, TECNALIA, Derio 48160, Spain; [Del Ser, Javier] Univ Basque Country, UPV EHU, Bilbao 48013, Spain; [Kumar, Neeraj] Thapar Inst Engn & Technol Deemed Univ, Patiala 147004, Punjab, India; [Tanveer, Mohammad] Indian Inst Technol Indore, Dept Math, Indore 453552, India; [Doctor, Faiyaz] Univ Essex, Ctr Computat Intelligence, Sch Comp Sci & Elect Engn, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England; [Kumar, Neeraj] Univ Petr & Energy Studies, Sch Comp Sci, Dehra Dun, Uttarakhand, India; [Muhammad, Khan] Sejong Univ, Dept Software, Seoul 143747, South Korea; [Tanveer, Mohammad] Indian Inst Technol Indore, Discipline Math, Indore 453552, India Sejong University; University of Sharjah; University of Jordan; University of Science & Technology Beijing; Sejong University; University of Basque Country; Thapar Institute of Engineering & Technology; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Indore; University of Essex; University of Petroleum & Energy Studies (UPES); Sejong University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Indore Muhammad, K (corresponding author), Sejong Univ, Dept Software, Visual Analyt Knowledge Lab, Seoul 143747, South Korea.;Obaidat, MS (corresponding author), Univ Sharjah, Coll Comp & Informat, Sharjah 27272, U Arab Emirates.;Obaidat, MS (corresponding author), Univ Jordan, KASIT, Amman 11942, Jordan.;Obaidat, MS (corresponding author), Univ Sci & Technol, Beijing 100083, Peoples R China.;Muhammad, K (corresponding author), Sejong Univ, Dept Software, Seoul 143747, South Korea. khanmuhanunad@sju.ac.kr; msobaidat@gmail.com; mtanveer@iiti.ac.in; neeraj.kumar@thapar.edu; anveer445@ieee.org Kumar, Neeraj/L-3500-2016; Hussain, Tanveer/AAF-7138-2019; Muhammad, Khan/L-9059-2016; Hussain, Tanveer/GWQ-5172-2022 Kumar, Neeraj/0000-0002-3020-3947; Hussain, Tanveer/0000-0003-4861-8347; Muhammad, Khan/0000-0003-4055-7412; PR of China Ministry of Education Distinguished Possessor Grant [MS2017BJKJ003]; Basque Government for its funding support through the EMAITEK program; Consolidated Research Group MATHMODE [IT1294-19] PR of China Ministry of Education Distinguished Possessor Grant; Basque Government for its funding support through the EMAITEK program; Consolidated Research Group MATHMODE This work is supported in part by PR of China Ministry of Education Distinguished Possessor Grant given to Prof. Obaidat under Grant No. MS2017BJKJ003. Javier Del Ser thanks the Basque Government for its funding support through the EMAITEK program. He also receives funding support from the Consolidated Research Group MATHMODE (Grant No. IT1294-19) granted by the Department of Education of the same institution. 179 9 9 10 39 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. APR 2022.0 54.0 3 68 10.1145/3444693 0.0 33 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science SR6CQ Green Accepted 2023-03-23 WOS:000661130600022 0
J Guo, XP; Lu, K; Cheng, Y; Zhao, WL; Wu, HP; Li, DY; Li, JW; Yang, SZ; Zhang, Y Guo, Xinpeng; Lu, Kun; Cheng, Yong; Zhao, Wenlong; Wu, Huapeng; Li, Dongyi; Li, Junwei; Yang, Songzhu; Zhang, Yu Research on fault diagnosis method for hydraulic system of CFETR blanket transfer device based on CNN-LSTM FUSION ENGINEERING AND DESIGN English Article CFETR; Hydraulic system; Fault simulation; Fault diagnosis; CNN-LSTM CONCEPT DESIGN; RELIABILITY; MAINTENANCE; TURBINE Conducting fault diagnosis on the hydraulic system of the blanket transfer device Mover in the Chinese Fusion Engineering Test Reactor (CFETR) is a key technical issue that needs to be addressed urgently. In this article, a CNN (Convolutional Neural Networks)-LSTM (Long Short-Term Memory) deep learning model-based method is proposed for fault diagnosis, combining the advantages of feature extraction of the CNN model with the ad-vantages of the LSTM model for time series data processing. Therefore, this model shows a multi-perspective property, greatly improving its ability to extract features from data. In the fault diagnosis experiment under the condition of four typical faults, the proposed model has the highest accuracy of 98.56% on the test set and good efficiency in computation time compared to the other three models. This method provides some insights for future research on the Prognostics and Health Management (PHM) of the Mover's hydraulic system and the CFETR's remote handling intelligent operational decision system. [Guo, Xinpeng; Lu, Kun; Cheng, Yong; Zhao, Wenlong; Li, Dongyi; Li, Junwei; Yang, Songzhu; Zhang, Yu] Chinese Acad Sci, Inst Plasma Phys, Hefei Inst Phys Sci, Hefei 230031, Peoples R China; [Guo, Xinpeng; Li, Dongyi; Li, Junwei] Univ Sci & Technol China, Hefei 230026, Peoples R China; [Wu, Huapeng] Lappeenranta Univ Technol, Lappeenranta, Finland Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Lappeenranta University of Technology Cheng, Y (corresponding author), Chinese Acad Sci, Inst Plasma Phys, Hefei Inst Phys Sci, Hefei 230031, Peoples R China. chengyong@ipp.ac.cn Comprehensive Research Facility for Fusion Technology program of China; Anhui Extreme Environment Robot Engineering Laboratory; [2018-000052-73-01-001228] Comprehensive Research Facility for Fusion Technology program of China; Anhui Extreme Environment Robot Engineering Laboratory; Acknowledgments This work is supported by the Comprehensive Research Facility for Fusion Technology program of China under contract No. 2018-000052-73-01-001228 and Anhui Extreme Environment Robot Engineering Laboratory. The authors would like to express their sincere gratitude to all the members of CFETR remote handling engineering design team. 25 1 1 17 17 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0920-3796 1873-7196 FUSION ENG DES Fusion Eng. Des. DEC 2022.0 185.0 113321 10.1016/j.fusengdes.2022.113321 0.0 OCT 2022 9 Nuclear Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Nuclear Science & Technology 5V6LO 2023-03-23 WOS:000877339400001 0
J Li, M; Wang, X; Yao, H; Saxen, H; Yu, YW Li, Meng; Wang, Xu; Yao, Hao; Saxen, Henrik; Yu, Yaowei Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection PROCESSES English Article metallurgical coke; particle size distribution; object detection; YOLOv3 IMAGE SEGMENTATION; COAL; QUALITY Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant. [Li, Meng; Wang, Xu; Yao, Hao; Yu, Yaowei] Shanghai Univ, Sch Mat Sci & Engn, State Key Lab Adv Special Steel, Shanghai Key Lab Adv Ferromet, Shanghai 102100, Peoples R China; [Li, Meng; Saxen, Henrik] Abo Akad Univ, Fac Sci & Engn, Proc & Syst Engn Lab, Turku 20500, Finland Shanghai University; Abo Akademi University Yu, YW (corresponding author), Shanghai Univ, Sch Mat Sci & Engn, State Key Lab Adv Special Steel, Shanghai Key Lab Adv Ferromet, Shanghai 102100, Peoples R China. yaowei.yu@hotmail.com ; Saxen, Henrik/I-8352-2014 xu, Wang/0000-0002-9311-2559; Saxen, Henrik/0000-0002-5671-8367 China Scholarship Council [202106890009] China Scholarship Council(China Scholarship Council) This research was funded by China Scholarship Council, grant number 202106890009. 40 2 2 13 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-9717 PROCESSES Processes OCT 2022.0 10.0 10 1902 10.3390/pr10101902 0.0 15 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 5S2BG gold 2023-03-23 WOS:000875001600001 0
C Zhou, DD; Xu, Q; Wang, J; Zhang, JC; Hu, GQ; Kettunen, L; Chang, Z; Cong, FY IEEE Zhou, Dongdong; Xu, Qi; Wang, Jian; Zhang, Jiacheng; Hu, Guoqiang; Kettunen, Lauri; Chang, Zheng; Cong, Fengyu LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to existing ones. Furthermore, we convert the raw EEG data into spectrograms to speed up the training process. We evaluate the model performance on several public sleep datasets with different characteristics. Experimental results show that our lightweight model using spectrogram as input can achieve comparable overall accuracy and Cohen's kappa (SHHS100: 86.7%-81.3%, Sleep-EDF: 83.7% -77.5%, Sleep-EDF-v1: 883%-84.5%) compared to the state-of-the-art methods on experimental datasets. [Zhou, Dongdong; Wang, Jian; Hu, Guoqiang; Cong, Fengyu] Dalian Univ Technol, Fac Elect & Elect Engn, Sch Biomed Engn, Dalian 116024, Peoples R China; [Zhou, Dongdong; Wang, Jian; Kettunen, Lauri; Chang, Zheng; Cong, Fengyu] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland; [Xu, Qi; Cong, Fengyu] Dalian Univ Technol, Fac Elect & Elect Engn, Sch Artif Intelligence, Dalian 116024, Peoples R China; [Xu, Qi] Zhejiang Univ, 3Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China; [Zhang, Jiacheng] Dalian Univ Technol, Fac Elect & Elect Engn, Sch Informat & Commun Engn, Dalian 116024, Peoples R China; [Cong, Fengyu] Dalian Univ Technol, Key Lab Integrated Circuit & Biomed Elect Syst, Dalian 116024, Liaoning, Peoples R China Dalian University of Technology; University of Jyvaskyla; Dalian University of Technology; Zhejiang University; Dalian University of Technology; Dalian University of Technology Cong, FY (corresponding author), Dalian Univ Technol, Fac Elect & Elect Engn, Sch Biomed Engn, Dalian 116024, Peoples R China.;Cong, FY (corresponding author), Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland.;Xu, Q; Cong, FY (corresponding author), Dalian Univ Technol, Fac Elect & Elect Engn, Sch Artif Intelligence, Dalian 116024, Peoples R China.;Xu, Q (corresponding author), Zhejiang Univ, 3Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.;Cong, FY (corresponding author), Dalian Univ Technol, Key Lab Integrated Circuit & Biomed Elect Syst, Dalian 116024, Liaoning, Peoples R China. xuqi123@zju.edu.cn; cong@dlut.edu.cn Zhou, Dongdong/ABH-1028-2021 Zhou, Dongdong/0000-0002-8726-4855; Wang, Jian/0000-0003-4891-8382; Cong, Fengyu/0000-0003-0058-2429; Kettunen, Lauri/0000-0003-0432-2675 National Natural Science Foundation of China [91748105]; National Foundation in China [JCKY2019110B009, 2020-JCJQ-JJ-252]; Fundamental Research Funds for Central Universities [DUT2019] in Dalian University of Technology in China [DUT20LAB303]; China Scholarship Council [201806060164, 202006060226]; CAAI-Huawei MindSpore Open Fund [CAAIXSJLJJ-2020-024A] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Foundation in China; Fundamental Research Funds for Central Universities [DUT2019] in Dalian University of Technology in China; China Scholarship Council(China Scholarship Council); CAAI-Huawei MindSpore Open Fund This work was support by National Natural Science Foundation of China (Grant No.91748105), National Foundation in China (No. JCKY2019110B009, 2020-JCJQ-JJ-252), Fundamental Research Funds for Central Universities [DUT2019, DUT20LAB303] in Dalian University of Technology in China and the scholarships from China Scholarship Council (No.201806060164, No.202006060226), CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2020-024A). 18 3 3 3 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 43 46 10.1109/EMBC46164.2021.9629878 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34891235.0 Green Accepted 2023-03-23 WOS:000760910500010 0
J Decelle, A; Ricci-Tersenghi, F; Zhang, P Decelle, Aurelien; Ricci-Tersenghi, Federico; Zhang, Pan Data quality for the inverse Ising problem JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL English Article statistical inference; inverse Ising problem; big data; parallel dynamics; effective rank MODEL There are many methods proposed for inferring parameters of the Ising model from given data, that is a set of configurations generated according to the model itself. However little attention has been paid until now to the data, e.g. how the data is generated, whether the inference error using one set of data could be smaller than using another set of data, etc. In this paper we discuss the data quality problem in the inverse Ising problem, using as a benchmark the kinetic Ising model. We quantify the quality of data using effective rank of the correlation matrix, and show that data gathered in a out-of-equilibrium regime has a better quality than data gathered in equilibrium for coupling reconstruction. We also propose a matrix-perturbation based method for tuning the quality of given data and for removing bad-quality (i.e. redundant) configurations from data. [Decelle, Aurelien] Univ Paris Saclay, Univ Paris 11, CNRS, Lab Rech Informat,TAO INRIA, Bat 660, F-91190 Gif Sur Yvette, France; [Ricci-Tersenghi, Federico] Univ Roma La Sapienza, Dipartimento Fis, Ist Nazl Fis Nucl, Sez Roma1, Piazzale Aldo Moro 5, I-00185 Rome, Italy; [Ricci-Tersenghi, Federico] Univ Roma La Sapienza, CNR Nanotec, I-00185 Rome, Italy; [Zhang, Pan] Chinese Acad Sci, Inst Theoret Phys, Zhong Guan Cun Dong Lu 55, Beijing 100190, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Consiglio Nazionale delle Ricerche (CNR); Istituto di Nanotecnologia (NANOTEC-CNR); Sapienza University Rome; Chinese Academy of Sciences; Institute of Theoretical Physics, CAS Ricci-Tersenghi, F (corresponding author), Univ Roma La Sapienza, Dipartimento Fis, Ist Nazl Fis Nucl, Sez Roma1, Piazzale Aldo Moro 5, I-00185 Rome, Italy.;Ricci-Tersenghi, F (corresponding author), Univ Roma La Sapienza, CNR Nanotec, I-00185 Rome, Italy. federico.ricci@uniroma1.it Zhang, Pan/O-7877-2018; Decelle, Aurélien/T-6554-2019 Decelle, Aurélien/0000-0002-3017-0858; Ricci-Tersenghi, Federico/0000-0003-4970-7376 27 5 5 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1751-8113 1751-8121 J PHYS A-MATH THEOR J. Phys. A-Math. Theor. SEP 23 2016.0 49.0 38 384001 10.1088/1751-8113/49/38/384001 0.0 15 Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics DW3AZ Green Submitted 2023-03-23 WOS:000383515800002 0
C Liu, XN; Deng, YS; Han, C; Di Renzo, M IEEE Liu, Xiaonan; Deng, Yansha; Han, Chong; Di Renzo, Marco Learning-based Strategy for RIS-Assisted Terahertz Virtual Reality Networks 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) DEC 07-11, 2021 Madrid, SPAIN IEEE,Huawei,Vodafone,Intel,ZTE,Qualcomm,Assia,IMEC,Keysight Technologies,PRIME Alliance,Rohde & Schwarz,Vantage Towers,Guard,Usal Bisite Res Grp Terahertz transmission; reconfigurable intelligent surface; constrained deep reinforcement learning; convolutional neural network; virtual reality The quality of experience (QoE) requirement of wireless virtual reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via the abundant spectrum in the terahertz (THz) band. However, THz waves suffer from severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with adjustable phase-shift of each reflecting element. Motivated by these considerations, in this paper, we propose an RIS-assisted THz VR network in an indoor scenario, taking into account the viewpoint prediction and downlink transmission. We first propose a genie-aided online gated recurrent unit (GRU) and integration of online long-short term memory (LSTM) and convolutional neural network (CNN) algorithm to predict the viewpoint, location, and the line-of-sight (LoS) and non-line-of-sight (NLoS) statuses of the VR users over time, with the aim to optimize the long-term QoE of the VR users. We then develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS for the downlink transmission under latency constraints. Simulation results show that the proposed ensemble learning architecture achieves near-optimal QoE as that of an exhaustive algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme. [Liu, Xiaonan; Deng, Yansha] Kings Coll London, Dept Engn, London, England; [Han, Chong] Shanghai Jiao Tong Univ, Terahertz Wireless Commun Lab, Shanghai, Peoples R China; [Di Renzo, Marco] Univ Paris Saclay, Lab Signaux & Syst, Cent Supelec, CNRS, Gif Sur Yvette, France University of London; King's College London; Shanghai Jiao Tong University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Liu, XN (corresponding author), Kings Coll London, Dept Engn, London, England. Deng, Yansha/0000-0003-1001-7036 14 0 0 2 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-8104-2 IEEE GLOB COMM CONF 2021.0 10.1109/GLOBECOM46510.2021.9686000 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT0PJ 2023-03-23 WOS:000790747205049 0
C Ke, W; Chen, J; Jiao, JB; Zhao, GY; Ye, QX IEEE Ke, Wei; Chen, Jie; Jiao, Jianbin; Zhao, Guoying; Ye, Qixiang SRN: Side-output Residual Network for Object Symmetry Detection in the Wild 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUL 21-26, 2017 Honolulu, HI IEEE,IEEE Comp Soc,CVF EDGE-DETECTION In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry ground-truth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to real-world images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at https://github.com/KevinKecc/SRN. [Ke, Wei; Jiao, Jianbin; Ye, Qixiang] Univ Chinese Acad Sci, Beijing, Peoples R China; [Ke, Wei; Chen, Jie; Zhao, Guoying] Univ Oulu, CMVS, Oulu, Finland Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Oulu Ye, QX (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. kewei11@mails.ucas.ac.cn; jiechen@ee.oulu.fi; jiaojb@ucas.ac.cn; gyzhao@ee.oulu.fi; qxye@ucas.ac.cn Ke, Wei/O-5881-2018; Zhao, Guoying/ABE-7716-2020 Ke, Wei/0000-0002-2899-0371; Zhao, Guoying/0000-0003-3694-206X NSFC [61671427]; Beijing Municipal Science and Technology Commission [Z161100001616005]; Science and Technology Innovation Foundation of Chinese Academy of Sciences [CXJJ-16Q218] NSFC(National Natural Science Foundation of China (NSFC)); Beijing Municipal Science and Technology Commission(Beijing Municipal Science & Technology Commission); Science and Technology Innovation Foundation of Chinese Academy of Sciences This work is partially supported by NSFC under Grant 61671427, Beijing Municipal Science and Technology Commission under Grant Z161100001616005, and Science and Technology Innovation Foundation of Chinese Academy of Sciences under Grant CXJJ-16Q218. Tekes, Academy of Finland and Infotech Oulu are also gratefully acknowledged. 29 55 57 3 13 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-5386-0457-1 PROC CVPR IEEE 2017.0 302 310 10.1109/CVPR.2017.40 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ1XG Green Submitted, Green Accepted 2023-03-23 WOS:000418371400033 0
C Read, J; Bifet, A IEEE Read, Jesse; Bifet, Albert Data Stream Classification using Random Feature Functions and Novel Method Combinations 2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 2 IEEE Trustcom BigDataSE ISPA English Proceedings Paper Joint 14th IEEE Int Conf on Trust, Secur and Privacy in Comp and Commun / 13th IEEE Int Symposium on Parallel and Distributed Proc with Applications / 9th IEEE Int Conf on Big Data Science and Engineering (IEEE TrustCom-ISPA-BigDataSE) AUG 20-22, 2015 Aalto Univ, Helsinki, FINLAND IEEE,IEEE COMP SOC,IEEE Tech Comm Scalable Comp,Aalto Univ, Sch Elect Engn,Integrated Serv Networks, State Key Lab,NOKIA,SSH,ERICSSON,Tekes,Federat Finnish Learned Soc,Xidian Univ Aalto Univ Data streams are being generated in a faster, bigger, and more commonplace. In this scenario, Hoeffding Trees are an established method for classification. Several extensions exist, including high-performing ensemble setups such as online and leveraging bagging. Also, k-nearest neighbours is a popular choice, with most extensions dealing with the inherent performance limitations over a potentially-infinite stream. At the same time, gradient descent methods are becoming increasingly popular, owing to the proliferation of interest and successes in deep learning. Although deep neural networks can learn incrementally, they have so far proved too sensitive to hyperparameter options and initial conditions to be considered an effective `off-the-shelf' data streams solution. In this work, we look at combinations of Hoeffding trees, nearest neighbour, and gradient descent methods with a streaming preprocessing approach in the form of a random feature functions filter for additional predictive power. Our empirical evaluation yields positive results for the novel approaches that we experiment with, and also highlight important issues, and shed light on promising future directions in approaches to data stream classification. [Read, Jesse] Aalto Univ, Espoo, Finland; [Read, Jesse] Aalto Univ, Espoo, Finland; [Bifet, Albert] HUAWEI Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China Aalto University; Aalto University; Huawei Technologies Read, J (corresponding author), Aalto Univ, Espoo, Finland. jesse.read@aalto.fi; bifet.albert@huawei.com Bifet, Albert/E-4984-2017 Bifet, Albert/0000-0002-8339-7773 20 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2324-9013 978-1-4673-7952-6 IEEE TRUST BIG 2015.0 211 216 10.1109/Trustcom.2015.585 0.0 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG6YN Green Submitted 2023-03-23 WOS:000391000900032 0
J Wang, YT; Peng, HX; Su, Z; Luan, TH; Benslimane, A; Wu, Y Wang, Yuntao; Peng, Haixia; Su, Zhou; Luan, Tom H.; Benslimane, Abderrahim; Wu, Yuan A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Article Blockchain; AI-inspired consensus; federated learning; dynamic pool formation Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners' self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-HD degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL. [Wang, Yuntao; Su, Zhou; Luan, Tom H.] Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian 710049, Peoples R China; [Peng, Haixia] Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China; [Benslimane, Abderrahim] Avignon Univ, Lab Comp Sci, F-84029 Avignon, France; [Wu, Yuan] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China Xi'an Jiaotong University; Xi'an Jiaotong University; Avignon Universite; University of Macau Su, Z (corresponding author), Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian 710049, Peoples R China. zhousu@ieee.org Luan, Tom/HLG-0711-2023 NSFC [U20A20175, U1808207]; Fundamental Research Funds for the Central Universities [FDCT 0162/2019/A3, SKL-IOTSC(UM)-2021-2023] NSFC(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by NSFC under Grant U20A20175 and Grant U1808207 and in part by the Fundamental Research Funds for the Central Universities under Grant FDCT 0162/2019/A3 and Grant SKL-IOTSC(UM)-2021-2023. 41 2 2 8 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. DEC 2022.0 40.0 12 SI 3305 3324 10.1109/JSAC.2022.3213347 0.0 20 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 7A9LJ Green Submitted 2023-03-23 WOS:000898768000002 0
J Zhang, W; Lopez, H; Boselli, L; Bigini, P; Perez-Potti, A; Xie, ZC; Castagnola, V; Cai, Q; Silveira, CP; de Araujo, JM; Talamini, L; Panini, N; Ristagno, G; Violatto, MB; Devineau, S; Monopoli, MP; Salmona, M; Giannone, VA; Lara, S; Dawson, KA; Yan, Y Zhang, Wei; Lopez, Hender; Boselli, Luca; Bigini, Paolo; Perez-Potti, Andre; Xie, Zengchun; Castagnola, Valentina; Cai, Qi; Silveira, Camila P.; de Araujo, Joao M.; Talamini, Laura; Panini, Nicolo; Ristagno, Giuseppe; Violatto, Martina B.; Devineau, Stephanie; Monopoli, Marco P.; Salmona, Mario; Giannone, Valeria A.; Lara, Sandra; Dawson, Kenneth A.; Yan, Yan A Nanoscale Shape-Discovery Framework Supporting Systematic Investigations of Shape-Dependent Biological Effects and Immunomodulation ACS NANO English Article nanoscale shape; shape identification; microfluidic; tunable synthesis; biological effects; immunomodulation GOLD NANOPARTICLES; MICROFLUIDIC SYNTHESIS; BIOMOLECULAR CORONA; NUCLEATION; GROWTH; SIZE; MECHANISMS; BINDING Since it is now possible to make, in a controlled fashion, an almost unlimited variety of nanostructure shapes, it is of increasing interest to understand the forms of biological control that nanoscale shape allows. However, a priori rational investigation of such a vast universe of shapes appears to present intractable fundamental and practical challenges. This has limited the useful systematic investigation of their biological interactions and the development of innovative nanoscale shape-dependent therapies. Here, we introduce a concept of biologically relevant inductive nanoscale shape discovery and evaluation that is ideally suited to, and will ultimately become, a vehicle for machine learning discovery. Combining the reproducibility and tunability of microfluidic flow nanochemistry syntheses, quantitative computational shape analysis, and iterative feedback from biological responses in vitro and in vivo, we show that these challenges can be mastered, allowing shape biology to be explored within accepted scientific and biomedical research paradigms. Early applications identify significant forms of shape-induced biological and adjuvant-like immunological control. [Zhang, Wei; Dawson, Kenneth A.] Guangzhou Med Univ, Affiliated Hosp 2, Guangdong Prov Educ Dept, Key Lab Nanoimmunoregulat Tumor Microenvironm, Guangzhou 510260, Guangdong, Peoples R China; [Zhang, Wei; Lopez, Hender; Boselli, Luca; Perez-Potti, Andre; Xie, Zengchun; Castagnola, Valentina; Cai, Qi; Silveira, Camila P.; de Araujo, Joao M.; Devineau, Stephanie; Monopoli, Marco P.; Giannone, Valeria A.; Lara, Sandra; Dawson, Kenneth A.; Yan, Yan] Univ Coll Dublin, Ctr BioNano Interact, Sch Chem, Dublin 4, Ireland; [Lopez, Hender] Technol Univ Dublin, Sch Phys & Optometr & Clin Sci, Grangegorman D07XT95, Ireland; [Bigini, Paolo; Talamini, Laura; Panini, Nicolo; Violatto, Martina B.; Salmona, Mario] Ist Ric Farmacol Mario Negri IRCCS, Via Mario Negri 2, I-20156 Milan, Italy; [de Araujo, Joao M.] Univ Fed Rio Grande do Norte, Dept Fis Teor & Expt, BR-59078970 Natal, RN, Brazil; [Ristagno, Giuseppe] Univ Milan, Dept Pathophysiol & Transplantat, I-20122 Milan, Italy; [Yan, Yan] Univ Coll Dublin, Sch Biomol & Biomed Sci, UCD Conway Inst Biomol & Biomed Res, Dublin 4, Ireland Guangzhou Medical University; University College Dublin; Istituto di Ricerche Farmacologiche Mario Negri IRCCS; Universidade Federal do Rio Grande do Norte; University of Milan; University College Dublin Dawson, KA (corresponding author), Guangzhou Med Univ, Affiliated Hosp 2, Guangdong Prov Educ Dept, Key Lab Nanoimmunoregulat Tumor Microenvironm, Guangzhou 510260, Guangdong, Peoples R China.;Dawson, KA; Yan, Y (corresponding author), Univ Coll Dublin, Ctr BioNano Interact, Sch Chem, Dublin 4, Ireland.;Yan, Y (corresponding author), Univ Coll Dublin, Sch Biomol & Biomed Sci, UCD Conway Inst Biomol & Biomed Res, Dublin 4, Ireland. kenneth.a.dawson@cbni.ucd.ie; yan.yan@cbni.ucd.ie Boselli, Luca/ABD-9035-2020; Dawson, Kenneth A. A/L-2540-2016; Lopez, Hender/E-7903-2012; de Araujo, Joao M/J-1875-2016 Boselli, Luca/0000-0002-2732-5484; Dawson, Kenneth A. A/0000-0002-0568-6588; Yan, Yan/0000-0003-2938-4063; Zhang, Wei/0000-0002-2057-6563; xie, zengchun/0000-0001-5926-3321; Lopez, Hender/0000-0003-1083-6234; Cai, Qi/0000-0002-4820-805X; Devineau, Stephanie/0000-0002-1133-5223; de Araujo, Joao M/0000-0001-8462-4280 Science Foundation Ireland [15/SIRG/3423, 16/ENM-ERA/3457, 17/NSFC/4898]; Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumor Microenvironment [2019KSYS008]; Irish Research Council [EPSPD/2015/5, GOIPD/2016/128, GOIPG/2014/874]; Chinese Scholarship Council [201706220062, 201806220054, 201408300003] Science Foundation Ireland(Science Foundation Ireland); Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumor Microenvironment; Irish Research Council(Irish Research Council for Science, Engineering and Technology); Chinese Scholarship Council(China Scholarship Council) The authors acknowledge that this publication has emanated from research supported in part by grants from Science Foundation Ireland (15/SIRG/3423 and 16/ENM-ERA/3457, Y.Y. and 17/NSFC/4898, K.A.D.). K.A.D. acknowledges the funding of Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumor Microenvironment (2019KSYS008). H.L., V.C., and Q.C. acknowledge financial support from the Irish Research Council (EPSPD/2015/5, H.L.; GOIPD/2016/128, V.C.; GOIPG/2014/874, Q.C.). W.Z., Z.X., and Q.C. acknowledge the Chinese Scholarship Council (Agreement Nos. 201706220062, 201806220054, and 201408300003). The work described in this article, prior to reaching this level, evolved over some years with contributions from authors and other previous researchers at the Centre for BioNano Interactions (CBNI), University College Dublin. They were helpful in bringing the work to the present stage of development. Inge Nelissen performed the transcriptome array measurements.David Garry was also involved in the early phases during the isolation of B cells from the spleen and the creation of the cDNA library. Laurent Adumeau supported the development of the clean room syntheses protocols. Alirio Moura contributed to the writing of the original computational analysis code. 62 5 5 2 24 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1936-0851 1936-086X ACS NANO ACS Nano JAN 25 2022.0 16.0 1 1547 1559 10.1021/acsnano.1c10074 0.0 DEC 2021 13 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science ZQ6PA 34958549.0 Green Published, Green Accepted 2023-03-23 WOS:000738004400001 0
J Karyotakis, MA; Antonopoulos, N Karyotakis, Minos-Athanasios; Antonopoulos, Nikos Web Communication: A Content Analysis of Green Hosting Companies SUSTAINABILITY English Article big data; cultural heritage; data center; digital marketing; eco-friendly; environmental communication; green websites; green culture; green hosting; sustainability While many studies in the field of environmental communication have focused on exploring the environmental impact of social media, this research paper takes a different turn. It investigates, through a qualitative content analysis, 391 websites that support and provide green hosting services. This study is considered the first in the field that aims to examine in-depth how these green websites tend to communicate their green services. Therefore, its contribution is to enhance the relevant bibliography and present more insights regarding green websites and sustainability. The results showed that most of the websites were trying to highlight the positive impact their services will have on the environment. In addition, many websites tried to educate their consumers concerning sustainable development and make them part of a broader green cultural tradition. Nevertheless, on many websites, green hosting seemed a supplementary factor for choosing the company's services. [Karyotakis, Minos-Athanasios] Hong Kong Baptist Univ, Sch Commun, Hong Kong, Peoples R China; [Antonopoulos, Nikos] Ionian Univ, Dept Digital Media & Commun, New Media Commun & Usabil Lab, Kefalonia 28100, Greece; [Antonopoulos, Nikos] Hellen Open Univ, Dept Business Adm & Org, Patras 26335, Greece Hong Kong Baptist University; Hellenic Open University Karyotakis, MA (corresponding author), Hong Kong Baptist Univ, Sch Commun, Hong Kong, Peoples R China. minosathkar@yahoo.gr; nikos@antonopoulos.info Karyotakis, Minos-Athanasios/0000-0002-5197-9445 Hong Kong PhD Fellowship Scheme (HKPFS) Hong Kong PhD Fellowship Scheme (HKPFS) This article is based on research undertaken by the lead author while a doctoral student at Hong Kong Baptist University, supported by the Hong Kong PhD Fellowship Scheme (HKPFS). Apart from that scholarship, the authors received no other financial support for the research, authorship and/or publication of this article. 46 4 4 2 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JAN 2021.0 13.0 2 495 10.3390/su13020495 0.0 15 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology PY1AD gold, Green Published 2023-03-23 WOS:000611780200001 0
J Rossi, K; Juraskova, V; Wischert, R; Garel, L; Corminboeuf, C; Ceriotti, M Rossi, Kevin; Juraskova, Veronika; Wischert, Raphael; Garel, Laurent; Corminboeuf, Clemence; Ceriotti, Michele Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol JOURNAL OF CHEMICAL THEORY AND COMPUTATION English Article POTENTIAL-ENERGY SURFACES; MOLECULAR-DYNAMICS SIMULATIONS; CHEMISTRY; CONSTANTS; MECHANISM; DRIVEN; TOOL We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects. [Juraskova, Veronika; Corminboeuf, Clemence] Ecole Polytech Fed Lausanne EPFL, Inst Chem Sci & Engn, Lab Computat Mol Design LCMD, CH-1015 Lausanne, Switzerland; [Corminboeuf, Clemence; Ceriotti, Michele] Ecole Polytech Fed Lausanne, Natl Ctr Computat Design & Discovery Novel Mat MA, CH-1015 Lausanne, Switzerland; [Rossi, Kevin; Ceriotti, Michele] Ecole Polytech Fed Lausanne EPFL, Inst Mat, Lab Computat Sci & Modeling COSMO, CH-1015 Lausanne, Switzerland; [Wischert, Raphael] Solvay, RIC Shanghai, Ecoefficient Prod & Proc Lab, Shanghai 201108, Peoples R China; [Garel, Laurent] Solvay, RIC Lyon, Aroma Performance Lab, F-69190 St Fons, France Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Solvay SA; Solvay SA Corminboeuf, C (corresponding author), Ecole Polytech Fed Lausanne EPFL, Inst Chem Sci & Engn, Lab Computat Mol Design LCMD, CH-1015 Lausanne, Switzerland.;Corminboeuf, C; Ceriotti, M (corresponding author), Ecole Polytech Fed Lausanne, Natl Ctr Computat Design & Discovery Novel Mat MA, CH-1015 Lausanne, Switzerland.;Ceriotti, M (corresponding author), Ecole Polytech Fed Lausanne EPFL, Inst Mat, Lab Computat Sci & Modeling COSMO, CH-1015 Lausanne, Switzerland. clemence.corminboeuf@epfl.ch; michele.ceriotti@epfl.ch Wischert, Raphael/HIA-0664-2022; Ceriotti, Michele/C-2393-2009 Ceriotti, Michele/0000-0003-2571-2832; Juraskova, Veronika/0000-0002-6293-5616 Solvay; Swiss National Science Foundation (SNSF) Solvay(Solvay SA); Swiss National Science Foundation (SNSF)(Swiss National Science Foundation (SNSF)) The authors thank Venkat Kapil, Giulio Imbalzano, Federico Giberti, Piero Gasparotto, and Raimon Fabregat for their discussions. K.R. and V.J. were supported by an industrial grant from Solvay. This project was initiated within the framework of the NCCR MARVEL, funded by the Swiss National Science Foundation (SNSF). 81 21 21 0 11 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1549-9618 1549-9626 J CHEM THEORY COMPUT J. Chem. Theory Comput. AUG 11 2020.0 16.0 8 5139 5149 10.1021/acs.jctc.0c00362 0.0 11 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics ND8FT 32567854.0 hybrid, Green Submitted 2023-03-23 WOS:000562139200031 0
C Liu, YH; Guan, RC; Giunchiglia, F; Liang, YC; Feng, XY Assoc Computat Linguist Liu, Yonghao; Guan, Renchu; Giunchiglia, Fausto; Liang, Yanchun; Feng, Xiaoyue Deep Attention Diffusion Graph Neural Networks for Text Classification 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021) English Proceedings Paper Conference on Empirical Methods in Natural Language Processing (EMNLP) NOV 07-11, 2021 Punta Cana, DOMINICAN REP Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach. [Liu, Yonghao; Guan, Renchu; Feng, Xiaoyue] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China; [Liang, Yanchun] Zhuhai Coll Sci & Technol, Zhuhai Sub Lab, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Zhuhai, Peoples R China; [Giunchiglia, Fausto] Univ Trento, DISI, Trento, Italy Jilin University; University of Trento Feng, XY (corresponding author), Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China. yonghao20@mails.jlu.edu.cn; guanrenchu@jlu.edu.cn; fausto.giunchiglia@unitn.it; ycliang@jlu.edu.cn; fengxy@jlu.edu.cn Guan, Renchu/K-5223-2015 Guan, Renchu/0000-0002-7162-7826; Liu, Yonghao/0000-0001-8621-7144 National Natural Science Foundation of China [61972174, 62172187]; Science -Technology Development Plan Project of Jilin Province [20190303006SF, 20190302107GX, 20200801033GH, YDZJ202101ZYTS128]; Science and Technology Planning Project of Guangdong Province [2020A0505100018]; Guangdong Key -Project for Applied Fundamental Research [2018KZDXM076]; National Project PRIN Delphi National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science -Technology Development Plan Project of Jilin Province; Science and Technology Planning Project of Guangdong Province; Guangdong Key -Project for Applied Fundamental Research; National Project PRIN Delphi The authors are grateful for the support of the National Natural Science Foundation of China [Grants 61972174 and 62172187], the Science -Technology Development Plan Project of Jilin Province [Grants 20190303006SF, 20190302107GX, 20200801033GH, and YDZJ202101ZYTS128], the Science and Technology Planning Project of Guangdong Province [Grant 2020A0505100018], Guangdong Key -Project for Applied Fundamental Research [Grant 2018KZDXM076]. Fausto Giunchiglia's work is funded by National Project PRIN 2017 Delphi. 44 2 2 4 4 ASSOC COMPUTATIONAL LINGUISTICS-ACL STROUDSBURG 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA 978-1-955917-09-4 2021.0 8142 8152 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BT9HF 2023-03-23 WOS:000860727002020 0
J Qian, XY; Yang, JC; Brutti, A Qian, Xinyuan; Yang, Jichen; Brutti, Alessio Speaker front-back disambiguity using multi-channel speech signals ELECTRONICS LETTERS English Article LOCALIZATION; PHASE; CUES This paper tackles the front-back disambiguity problem in speaker localization when the audio signals are captured by a symmetric microphone array. To this end, a deep neural network is proposed with an attention-based mechanism designed to assign different weights to features obtained from individual microphones. For support, a real dataset with synchronized multichannel audio signals captured by a large linear microphone array is introduced, along with manual annotations. The experimental results demonstrate the effectiveness of the proposed method over the other approaches. In particular, more than 50% reduction in Equal Error Rate (EER) is achieved when comparing with the single-channel case. The designed multi-channel self-attention mechanism also brings further improvements. The dataset and source code will be released. [Qian, Xinyuan] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China; [Yang, Jichen] Guangdong Polytech Normal Univ, Sch Cyber Secur, Guangzhou, Peoples R China; [Brutti, Alessio] Ctr Informat Technol Fdn Bruno Kessler, Povo, Italy University of Science & Technology Beijing; Guangdong Polytechnic Normal University Yang, JC (corresponding author), Guangdong Polytech Normal Univ, Sch Cyber Secur, Guangzhou, Peoples R China. nisonyoung@163.com qian, xinyuan/ADN-5425-2022 Qian, Xinyuan/0000-0002-9511-6713 24 0 0 3 3 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0013-5194 1350-911X ELECTRON LETT Electron. Lett. DEC 2022.0 58.0 25 1012 1015 10.1049/ell2.12666 0.0 NOV 2022 4 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 7A6FE gold 2023-03-23 WOS:000891503300001 0
J Shazly, SA; Hortu, I; Shih, JC; Melekoglu, R; Fan, SR; Ahmed, FU; Karaman, E; Fatkullin, I; Pinto, PV; Irianti, S; Tochie, JN; Abdelbadie, AS; Ergenoglu, AM; Yeniel, AO; Sagol, S; Itil, IM; Kang, J; Huang, KY; Yilmaz, E; Liang, YH; Aziz, H; Akhter, T; Ambreen, A; Ates, C; Karaman, Y; Khasanov, A; Larisa, F; Akhmadeev, N; Vatanina, A; Machado, AP; Montenegro, N; Effendi, JS; Suardi, D; Pramatirta, AY; Aziz, MA; Siddiq, A; Ofakem, I; Dohbit, JS; Fahmy, MS; Anan, MA Shazly, Sherif A.; Hortu, Ismet; Shih, Jin-Chung; Melekoglu, Rauf; Fan, Shangrong; Ahmed, Farhat ul Ain; Karaman, Erbil; Fatkullin, Ildar; Pinto, Pedro, V; Irianti, Setyorini; Tochie, Joel Noutakdie; Abdelbadie, Amr S.; Ergenoglu, Ahmet M.; Yeniel, Ahmet O.; Sagol, Sermet; Itil, Ismail M.; Kang, Jessica; Huang, Kuan-Ying; Yilmaz, Ercan; Liang, Yiheng; Aziz, Hijab; Akhter, Tayyiba; Ambreen, Afshan; Ates, Cagri; Karaman, Yasemin; Khasanov, Albir; Larisa, Fatkullina; Akhmadeev, Nariman; Vatanina, Adelina; Machado, Ana Paula; Montenegro, Nuno; Effendi, Jusuf S.; Suardi, Dodi; Pramatirta, Ahmad Y.; Aziz, Muhamad A.; Siddiq, Amilia; Ofakem, Ingrid; Dohbit, Julius Sama; Fahmy, Mohamed S.; Anan, Mohamed A. Middle East Obstet Gynecology Grad Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study JOURNAL OF MATERNAL-FETAL & NEONATAL MEDICINE English Article Obstetric hemorrhage; placenta praevia; cesarean hysterectomy; morbidly adherent placenta; placenta accreta spectrum; machine learning Introduction Placenta accreta spectrum is a major obstetric disorder that is associated with significant morbidity and mortality. The objective of this study is to establish a prediction model of clinical outcomes in these women Materials and methods PAS-ID is an international multicenter study that comprises 11 centers from 9 countries. Women who were diagnosed with PAS and were managed in the recruiting centers between 1 January 2010 and 31 December 2019 were included. Data were reanalyzed using machine learning (ML) models, and 2 models were created to predict outcomes using antepartum and perioperative features. ML model was conducted using python(R) programing language. The primary outcome was massive PAS-associated perioperative blood loss (intraoperative blood loss >= 2500 ml, triggering massive transfusion protocol, or complicated by disseminated intravascular coagulopathy). Other outcomes include prolonged hospitalization >7 days and admission to the intensive care unit (ICU). Results 727 women with PAS were included. The area under curve (AUC) for ML antepartum prediction model was 0.84, 0.81, and 0.82 for massive blood loss, prolonged hospitalization, and admission to ICU, respectively. Significant contributors to this model were parity, placental site, method of diagnosis, and antepartum hemoglobin. Combining baseline and perioperative variables, the ML model performed at 0.86, 0.90, and 0.86 for study outcomes, respectively. Ethnicity, pelvic invasion, and uterine incision were the most predictive factors in this model. Discussion ML models can be used to calculate the individualized risk of morbidity in women with PAS. Model-based risk assessment facilitates a priori delineation of management. [Shazly, Sherif A.] Assiut Univ, Dept Obstet & Gynaecol, Assiut, Egypt; [Hortu, Ismet; Ergenoglu, Ahmet M.; Yeniel, Ahmet O.; Sagol, Sermet; Itil, Ismail M.] Ege Univ, Dept Obstet & Gynaecol, Sch Med, Izmir, Turkey; [Shih, Jin-Chung; Kang, Jessica; Huang, Kuan-Ying] Natl Taiwan Univ, Dept Obstet & Gynaecol, Coll Med, Taipei, Taiwan; [Melekoglu, Rauf; Yilmaz, Ercan] Inonu Univ, Dept Obstet & Gynaecol, Malatya, Turkey; [Fan, Shangrong; Liang, Yiheng] Peking Univ Shenzhen Hosp, Dept Obstet & Gynaecol, Shenzhen, Peoples R China; [Ahmed, Farhat ul Ain; Aziz, Hijab; Akhter, Tayyiba; Ambreen, Afshan] Fatima Mem Hosp, Dept Obstet & Gynaecol, Lahore, Pakistan; [Karaman, Erbil; Ates, Cagri] Yuzuncu Yil Univ, Dept Obstet & Gynaecol, Van, Turkey; [Fatkullin, Ildar; Khasanov, Albir; Larisa, Fatkullina; Akhmadeev, Nariman] Kazan State Med Univ, Dept Obstet & Gynaecol, Kazan, Russia; [Pinto, Pedro, V; Machado, Ana Paula; Montenegro, Nuno] Ctr Hosp Sao Joao, Serv Ginecol & Obstet, Porto, Portugal; [Irianti, Setyorini; Effendi, Jusuf S.; Suardi, Dodi; Pramatirta, Ahmad Y.; Aziz, Muhamad A.; Siddiq, Amilia] Univ Padjadjaran Bandung, Taskforce Placenta Accreta Spectrum, Bandung, Indonesia; [Tochie, Joel Noutakdie; Ofakem, Ingrid; Dohbit, Julius Sama] Univ Yaounde I, Fac Med & Biomed Sci, Dept Obstet & Gynaecol, Yaounde, Cameroon; [Abdelbadie, Amr S.; Fahmy, Mohamed S.; Anan, Mohamed A.] Aswan Univ Hosp, Dept Obstet & Gynaecol, Aswan, Egypt; [Karaman, Yasemin] Lokman Hekim Hayat Hosp, Dept Obstet & Gynaecol, Van, Turkey; [Vatanina, Adelina] Minist Healthcare Republ Tatarstan, Republ Clin Hosp, Kazan, Russia Egyptian Knowledge Bank (EKB); Assiut University; Ege University; National Taiwan University; Inonu University; Peking University; Yuzuncu Yil University; Kazan State Medical University; Sao Joao Hospital; Universitas Padjadjaran; University of Yaounde I; Egyptian Knowledge Bank (EKB); Aswan University; Lokman Hekim Hospital Shazly, SA (corresponding author), Assiut Univ, Fac Med, Dept Obstet & Gynaecol, Assiut 71515, Egypt. shazly.sherif2020@gmail.com MELEKOGLU, RAUF/AAF-1614-2019; karaman, erbil/AFU-7129-2022; Machado, Ana/HLV-9704-2023; Sagol, Sermet/GSN-9501-2022; Kang, Jessica/GZA-6384-2022; SHIH, JIN-CHUNG/AAQ-8651-2021 MELEKOGLU, RAUF/0000-0001-7113-6691; Sagol, Sermet/0000-0002-4693-4090; SHIH, JIN-CHUNG/0000-0002-0296-4327; Suardi, Dodi/0000-0003-3084-8101; Irianti, Setyorini/0000-0003-0865-2620; Shazly, Sherif/0000-0003-1062-9227; Akhmadeev, Nariman/0000-0003-0908-7256; Hortu, Ismet/0000-0003-3833-0999 11 7 7 0 3 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1476-7058 1476-4954 J MATERN-FETAL NEO M J. Matern.-Fetal Neonatal Med. 2022.0 35.0 25 6644 6653 10.1080/14767058.2021.1918670 0.0 JUL 2021 10 Obstetrics & Gynecology Science Citation Index Expanded (SCI-EXPANDED) Obstetrics & Gynecology 9B3TC 34233555.0 Green Submitted 2023-03-23 WOS:000670531000001 0
J Tu, ZG; Xie, W; Qin, QQ; Poppe, R; Veltkamp, RC; Li, BX; Yuan, JS Tu, Zhigang; Xie, Wei; Qin, Qianqing; Poppe, Ronald; Veltkamp, Remco C.; Li, Baoxin; Yuan, Junsong Multi-stream CNN: Learning representations based on human-related regions for action recognition PATTERN RECOGNITION English Article Convolutional Neural Network; Action recognition; Multi-Stream; Motion salient region SALIENT MOTION The most successful video-based human action recognition methods rely on feature representations extracted using Convolutional Neural Networks (CNNs). Inspired by the two-stream network (TS-Net), we propose a multi-stream Convolutional Neural Network (CNN) architecture to recognize human actions. We additionally consider human-related regions that contain the most informative features. First, by improving foreground detection, the region of interest corresponding to the appearance and the motion of an actor can be detected robustly under realistic circumstances. Based on the entire detected human body, we construct one appearance and one motion stream. In addition, we select a secondary region that contains the major moving part of an actor based on motion saliency. By combining the traditional streams with the novel human-related streams, we introduce a human-related multi-stream CNN (HR-MSCNN) architecture that encodes appearance, motion, and the captured tubes of the human-related regions. Comparative evaluation on the JHMDB, HMDB51, UCF Sports and UCF101 datasets demonstrates that the streams contain features that complement each other. The proposed multi-stream architecture achieves state-of-the-art results on these four datasets. (C) 2018 Elsevier Ltd. All rights reserved. [Tu, Zhigang] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Xie, Wei] Cent China Normal Univ, Sch Comp, LuoyuRd 152, Wuhan, Hubei, Peoples R China; [Qin, Qianqing] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, LuoyuRd 129, Wuhan, Hubei, Peoples R China; [Poppe, Ronald; Veltkamp, Remco C.] Univ Utrecht, Dept Informat & Comp Sci, Princetonpl 5, Utrecht, Netherlands; [Li, Baoxin] Arizona State Univ, Sch Comp Informat, Decis Syst Engn, Tempe, AZ 85287 USA; [Yuan, Junsong] SUNY Buffalo, Comp Sci & Engn Dept, Buffalo, NY USA Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Central China Normal University; Wuhan University; Utrecht University; Arizona State University; Arizona State University-Tempe; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo Qin, QQ (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, LuoyuRd 129, Wuhan, Hubei, Peoples R China. TUZG@ntu.edu.sg; XW@mail.ccnu.edu.cn; Qqqin@lmars.whu.edu.cn; R.W.Poppe@uu.nl; R.C.Veltkamp@uu.nl; Baoxin.Li@asu.edu; jsyuan@buffalo.edu Tu, Zhigang/AAG-2255-2020; Yuan, Junsong/R-4352-2019 Tu, Zhigang/0000-0001-5003-2260; Poppe, Ronald/0000-0002-0843-7878 Singapore Ministry of Education Academic Research Fund Tier 2 [MOE2015-T2-2-114]; Office of Naval Research, US; Natural Science Foundation of China [61501198]; Natural Science Foundation of Hubei Province [2014CFB461]; Wuhan Youth Science and Technology Chenguang program [2014072704011248]; Dutch national program COMMIT; Dutch NWO TOP grant ARBITER Singapore Ministry of Education Academic Research Fund Tier 2(Ministry of Education, Singapore); Office of Naval Research, US(Office of Naval Research); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province(Natural Science Foundation of Hubei Province); Wuhan Youth Science and Technology Chenguang program; Dutch national program COMMIT; Dutch NWO TOP grant ARBITER This work is supported by the Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-114, and by grants from Office of Naval Research, US. Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of ONR. It was also supported by the Natural Science Foundation of China (61501198), Natural Science Foundation of Hubei Province (2014CFB461), Wuhan Youth Science and Technology Chenguang program (2014072704011248), the Dutch national program COMMIT and Dutch NWO TOP grant ARBITER. 56 129 137 5 76 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUL 2018.0 79.0 32 43 10.1016/j.patcog.2018.01.020 0.0 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GE0KP 2023-03-23 WOS:000430903000003 0
J Xiao, HP; Sotelo, MA; Ma, YL; Cao, B; Zhou, YC; Xu, YC; Wang, RD; Li, ZX Xiao, Haipeng; Sotelo, Miguel Angel; Ma, Yulin; Cao, Bo; Zhou, Yuncheng; Xu, Youchun; Wang, Rendong; Li, Zhixiong An Improved LSTM Model for Behavior Recognition of Intelligent Vehicles IEEE ACCESS English Article Hidden Markov models; Logic gates; Training; Recurrent neural networks; Classification algorithms; Military computing; Transportation; Intelligent vehicles; LSTM RECURRENT NEURAL-NETWORK; NETS Long Short-Term Memory (LSTM) neural network has been widely used in many applications, but its application in classification of vehicle movement patterns is still limited. In this paper, LSTM is applied to the vehicle behavior recognition problem to identify the left turn, right turn and straight behavior of the vehicle at the intersection. On the basis of the traditional LSTM classification model, this paper transversely merges the input features and then inputs into a LSTM cell to get an improved model. The improved model can make full use of the input information and reduce unnecessary calculations, and the output of a single LSTM cell model can filter out interference information and retain important information, so it has better classification effect and faster training speed. The experimental results show that the proposed improved LSTM network classification model in this paper has a significant improvement in recognition accuracy and training speed compared with the improved model, the accuracy is increased by 1.6%, and the training time is reduced by 3.96 s. In addition, this paper also applies the improved model to regression problems, emotion classification and handwritten digit recognition and all of them have a good improvement effect, which improves the applicability and stability of LSTM in classification problems and provides a new way to deal with classification problems. [Xiao, Haipeng; Cao, Bo] Army Mil Transportat Univ, Dept Joint Delivery, Tianjin 300181, Peoples R China; [Sotelo, Miguel Angel] Univ Alcala, Dept Comp Engn, Alcala De Henares 28801, Spain; [Ma, Yulin] Tsinghua Univ, Suzhou Automot Res Inst, Suzhou 215134, Peoples R China; [Zhou, Yuncheng] China Design Grp Co Ltd, ITS Res Ctr, Nanjing 210014, Peoples R China; [Xu, Youchun; Wang, Rendong] Army Mil Transportat Univ, Inst Mil Transportat, Tianjin 300181, Peoples R China; [Li, Zhixiong] Univ Wollongong, Sch Mech Mat Mechatron & Biomed Engn, Wollongong, NSW 2522, Australia Universidad de Alcala; Tsinghua University; University of Wollongong Ma, YL (corresponding author), Tsinghua Univ, Suzhou Automot Res Inst, Suzhou 215134, Peoples R China.;Wang, RD (corresponding author), Army Mil Transportat Univ, Inst Mil Transportat, Tianjin 300181, Peoples R China. mayulin@tsari.tsinghua.edu.cn; wrd1992@163.com Ma, Yulin/K-4927-2013; Li, Zhixiong/G-8418-2018 Ma, Yulin/0000-0002-8637-418X; Li, Zhixiong/0000-0002-7265-0008 National Key Research and Development Program of China [2016YFB0100903]; NSFC [51979261]; Jiangsu Industrial Technology Research Institute (JITRI) Suzhou Automotive Research Institute Project [CEC20190404]; Taishan Scholar of Shandong Province of China [tsqn201812025]; Australia Research Council [DE190100931] National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Jiangsu Industrial Technology Research Institute (JITRI) Suzhou Automotive Research Institute Project; Taishan Scholar of Shandong Province of China; Australia Research Council(Australian Research Council) This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0100903, in part by NSFC under Grant 51979261, in part by the Jiangsu Industrial Technology Research Institute (JITRI) Suzhou Automotive Research Institute Project under Grant CEC20190404, in part by the Taishan Scholar of Shandong Province of China under Grant tsqn201812025, and in part by the Australia Research Council under Grant DE190100931. 40 10 10 8 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 101514 101527 10.1109/ACCESS.2020.2996203 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications MH0FL gold, Green Published 2023-03-23 WOS:000546406500036 0
C Maksymyuk, T; Gazda, J; Ruzicka, M; Slapak, E; Bugar, G; Han, LZ IEEE Maksymyuk, Taras; Gazda, Juraj; Ruzicka, Marek; Slapak, Eugen; Bugar, Gabriel; Han, Longzhe Deep Learning based Mobile Network Management for 5G and Beyond 15TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET - 2020) English Proceedings Paper 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) FEB 25-29, 2020 Lviv, UKRAINE Natl Univ Lviv Polytechn,IEEE,IEEE Ukraine Sect,Mil Univ Technol 5G; AI; GAN; coverage optimization Over the last decade the deployment paradigm of the mobile networks has shifted from complex hierarchical structures towards decoupling of the control and transmission planes. Such transformation enables software defined network configurations that allow improving the scalability and flexibility of the network. However, the process of network management is not trivial, considering the complexity of modern 5G infrastructure, and requires new advanced solutions. In this paper, we propose a new architecture with additional plane, which is responsible for intelligent knowledge generation and decision making. Proposed approach allows to train deep neural networks by using generative adversarial learning for the scenario with limited amount of real data. We study the performance of the proposed approach for the particular use case of small cells coverage optimization considering given spatial and temporal variation of the traffic demand. Simulation results show that proposed approach achieves quasi-optimal small cells coverage in terms of overall network throughput even in conditions when there is a lack of real world statistical data. [Maksymyuk, Taras] Lviv Polytech Natl Univ, Dept Telecommun, Lvov, Ukraine; [Gazda, Juraj; Ruzicka, Marek; Slapak, Eugen] Tech Univ Kosice, Dept Comp & Informat, Kosice, Slovakia; [Bugar, Gabriel] Tech Univ Kosice, Dept Elect & Multimedia Commun, Kosice, Slovakia; [Han, Longzhe] Nanchang Inst Technol, Jiangxi Prov Key Lab Water Informat Cooperat Sens, Nanchang, Jiangxi, Peoples R China Ministry of Education & Science of Ukraine; Lviv Polytech National University; Technical University Kosice; Technical University Kosice; Nanchang Institute Technology Maksymyuk, T (corresponding author), Lviv Polytech Natl Univ, Dept Telecommun, Lvov, Ukraine. taras.maksymyuk@gmail.com; juraj.gazda@tuke.sk; marek.ruzicka@tuke.sk; eugen.slapak@student.tuke.sk; gabriel.bugar@tuke.sk; longzhehan@gmail.com Gazda, Juraj/AAE-4542-2019; Ružička, Marek/ABA-9954-2022 Gazda, Juraj/0000-0002-7334-9540; Ruzicka, Marek/0000-0002-9606-2016; Bugar, Gabriel/0000-0002-1282-2904 Ukrainian government [0120U100674]; Slovak Research and Development Agency [APVV-18-0214]; Scientific Grant Agency of the Ministry of Education, science, research and sport of the Slovak Republic; National Natural Science Foundation of China [1/0268/19]; [61561035] Ukrainian government; Slovak Research and Development Agency(Slovak Research and Development Agency); Scientific Grant Agency of the Ministry of Education, science, research and sport of the Slovak Republic; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); This research is supported by the project No 0120U100674 Designing the novel decentralized mobile network based on blockchain architecture and artificial intelligence for 5G/6G development in Ukraine, funded by Ukrainian government, by the Slovak Research and Development Agency, project number APVV-18-0214, by the Scientific Grant Agency of the Ministry of Education, science, research and sport of the Slovak Republic under the contract: 1/0268/19, and by National Natural Science Foundation of China (No. 61561035). 20 1 1 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-5566-1 2020.0 890 893 10.1109/TCSET49122.2020.235565 0.0 4 Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BQ1XG 2023-03-23 WOS:000578041000186 0
J Barbero, F; Schulz, K; Velazquez-Quesada, FR; Xie, KB Barbero, Fausto; Schulz, Katrin; Velazquez-Quesada, Fernando R.; Xie, Kaibo Observing interventions: a logic for thinking about experiments JOURNAL OF LOGIC AND COMPUTATION English Article; Early Access Causal reasoning; epistemic reasoning; interventions; counterfactuals; dynamic epistemic operators; experiments CAUSAL; AXIOMATIZATIONS; KNOWLEDGE This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment (Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen, vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies, 160, 139-166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science, 3, 151-182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research, 12, 317-337; Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logics, we provide a sound and complete axiomatization. [Barbero, Fausto] Univ Helsinki, Dept Philosophy Hist & Art Studies, PL 24 Unioninkatu 40, Helsinki 00014, Finland; [Schulz, Katrin] Univ Amsterdam, Inst Log Language & Computat ILLC, Fac Sci, Sci Pk 107, NL-1098 XG Amsterdam, Netherlands; [Velazquez-Quesada, Fernando R.] Univ Bergen, Dept Informat Sci & Media Studies, N-5020 Bergen, Norway; [Xie, Kaibo] Tsinghua Univ, Dept Philosophy, Beijing 100084, Peoples R China University of Helsinki; University of Amsterdam; University of Bergen; Tsinghua University Barbero, F (corresponding author), Univ Helsinki, Dept Philosophy Hist & Art Studies, PL 24 Unioninkatu 40, Helsinki 00014, Finland. fausto.barbero@helsinki.fi; K.Schulz@uva.nl; Fernando.VelazquezQuesada@uib.no; xkb@mail.tsinghua.edu.cn 47 0 0 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0955-792X 1465-363X J LOGIC COMPUT J. Logic Comput. 10.1093/logcom/exac011 0.0 APR 2022 34 Computer Science, Theory & Methods; Logic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Science & Technology - Other Topics 0R6LI Green Submitted 2023-03-23 WOS:000785703400001 0
J Tao, F; Zhang, L; Liu, YK; Cheng, Y; Wang, LH; Xu, X Tao, Fei; Zhang, Lin; Liu, Yongkui; Cheng, Ying; Wang, Lihui; Xu, Xun Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME English Article manufacturing service; manufacturing service management (MSM); cloud manufacturing (CMfg); Internet of things (IoT); big data; service-oriented manufacturing WISE-SHOPFLOOR; RESOURCE; MODEL; OPTIMIZATION; INTERNET; TRANSACTION; ALGORITHM; FRAMEWORK; SELECTION; PROVIDER As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) has experienced rapid development in the past five years. The research on its theories, key technologies, developments, and applications still keeps attracting attentions from more and more researchers. One of the most important issues to its improvements and quality of service (QoS) is the manufacturing service management (MSM). CMfg aims to realize the full-scale sharing, free circulation and transaction, and on-demand use of various manufacturing resource and capabilities in the form of manufacturing service. Without the effective operation and technical support of MSM, the implementation of CMfg and its aim cannot be achieved. It is therefore necessary to summarize the existing works and technologies on MSM in CMfg. This paper first provides a brief overview of CMfg and then focuses on the problem of MSM in CMfg from the service lifecycle perspective. The advances on MSM technology from eleven aspects are investigated and summarized. Finally, future research directions are identified and discussed. It is evident that the future MSM in CMfg is closely related to Internet of things (IoT), big data, and cloud computing. [Tao, Fei; Zhang, Lin; Liu, Yongkui; Cheng, Ying] Beihang Univ BUAA, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Liu, Yongkui] Xidian Univ, Sch Mechanoelect Engn, Xian 710071, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, SE-10044 Stockholm, Sweden; [Xu, Xun] Univ Auckland, Deparment Mech Engn, Auckland 1142, New Zealand Beihang University; Xidian University; Royal Institute of Technology; University of Auckland Tao, F (corresponding author), Beihang Univ BUAA, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. ftao@buaa.edu.cn Liu, Yongkui/R-9307-2017; Tao, Fei/F-8944-2012; Wang, Lihui/O-3907-2014; Xu, Xun William/K-7899-2015; Cheng, Ying/F-3038-2012 Liu, Yongkui/0000-0003-2165-775X; Tao, Fei/0000-0002-9020-0633; Wang, Lihui/0000-0001-8679-8049; Xu, Xun William/0000-0001-6294-8153; Cheng, Ying/0000-0002-9103-6126 863 Program Project in China [2015AA042100]; National Natural Science Foundation of China (NSFC) [51475032, 61374199, 61203374]; Beijing Youth Talent Plan [29201411]; Beijing Natural Science Foundation [4152032]; Fundamental Research Funds for the Central Universities in China [JB140410] 863 Program Project in China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Beijing Youth Talent Plan; Beijing Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central Universities in China This work was supported in part by the 863 Program Project in China under Grant No. 2015AA042100 and by the National Natural Science Foundation of China (NSFC) under Grant Nos. 51475032, 61374199, and 61203374, the Beijing Youth Talent Plan under Grant No. 29201411, Beijing Natural Science Foundation (4152032), and the Fundamental Research Funds for the Central Universities in China under Grant No. JB140410. 102 138 145 5 255 ASME NEW YORK TWO PARK AVE, NEW YORK, NY 10016-5990 USA 1087-1357 1528-8935 J MANUF SCI E-T ASME J. Manuf. Sci. Eng.-Trans. ASME AUG 2015.0 137.0 4 40912 10.1115/1.4030510 0.0 11 Engineering, Manufacturing; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering CN2MM 2023-03-23 WOS:000358255600013 0
J Xu, P; Davoine, F; Zha, HB; Denoeux, T Xu, Philippe; Davoine, Franck; Zha, Hongbin; Denoeux, Thierry Evidential calibration of binary SVM classifiers INTERNATIONAL JOURNAL OF APPROXIMATE REASONING English Article; Proceedings Paper 3rd International Conference on Belief Functions (BELIEF) SEP 26-28, 2014 St Hughs Coll, Oxford, ENGLAND Belief Funct & Applicat Soc,Int Soc Informat Fus,Oxford Brookes Univ, Fac Technol Design & Environm,Onera French Aerosp Lab,Elsevier St Hughs Coll Classifier calibration; Theory of belief functions; Dempster-Shafer theory; Evidence theory; Support vector machine BELIEF FUNCTIONS; CLASSIFICATION; COMBINATION; CONFIDENCE; TREES In machine learning problems, the availability of several classifiers trained on different data or features makes the combination of pattern classifiers of great interest. To combine distinct sources of information, it is necessary to represent the outputs of classifiers in a common space via a transformation called calibration. The most classical way is to use class membership probabilities. However, using a single probability measure may be insufficient to model the uncertainty induced by the calibration step, especially in the case of few training data. In this paper, we extend classical probabilistic calibration methods to the evidential framework. Experimental results from the calibration of SVM classifiers show the interest of using belief functions in classification problems. (C) 2015 Elsevier Inc. All rights reserved. [Xu, Philippe; Davoine, Franck; Denoeux, Thierry] Univ Technol Compiegne, Univ Paris 04, CNRS, UMR Heudiasyc 7253, CS 60 319, F-60203 Compiegne, France; [Zha, Hongbin] Peking Univ, Key Lab Machine Percept MOE, Beijing 100871, Peoples R China Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; UDICE-French Research Universities; Sorbonne Universite; Peking University Xu, P (corresponding author), Univ Technol Compiegne, Univ Paris 04, CNRS, UMR Heudiasyc 7253, CS 60 319, F-60203 Compiegne, France. philippe.xu@hds.utc.fr Davoine, Franck/0000-0002-8587-6997; Denoeux, Thierry/0000-0002-0660-5436 38 48 49 1 11 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0888-613X 1873-4731 INT J APPROX REASON Int. J. Approx. Reasoning MAY 2016.0 72.0 SI 55 70 10.1016/j.ijar.2015.05.002 0.0 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science DK0PO Bronze, Green Submitted 2023-03-23 WOS:000374614900004 0
C Xu, KP; Nie, L; Zhang, ZQ; Yu, WX; Zhang, YY; Chen, W; Li, SY; Zhou, XX; Deng, SW; Yu, PF; Fan, YB; Zhang, H; Bouillon, V Gedeon, T; Wong, KW; Lee, M Xu, Kepeng; Nie, Li; Zhang, Zhiqiang; Yu, Wenxin; Zhang, Yunye; Chen, Wei; Li, Siyuan; Zhou, Xinxin; Deng, Shangwei; Yu, Pengfei; Fan, Yibo; Zhang, Hui; Bouillon, Valentin IRSNET: An Inception-Resnet Feature Reconstruction Model for Building Segmentation NEURAL INFORMATION PROCESSING, ICONIP 2019, PT V Communications in Computer and Information Science English Proceedings Paper 26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS) DEC 12-15, 2019 Sydney, AUSTRALIA Asia Pacific Neural Network Soc Aerial image; Building extraction; Convolution network; Semantic segmentation Effective analysis of remote sensing images remains a challenging topic in deep learning research field. This paper proposes a semantic segmentation approach based on the inception architecture. Specifically, the combination of residual network and Inception improves the feature extraction capability of encoder network. In addition, this paper discusses the problem of unbalanced information allocation caused by concatenated operation in the up-sampling process of network models such as Unet, and the RCSE module is proposed to complete feature reconstruction to solve this problem. The approach ensures accurate assignment of semantic labels to the buildings in the aerial images. Experiments based on the dataset proposed by CrowdAi certify the effectiveness of our approach with a 3.7% IoU improvement compared to Unet. [Xu, Kepeng; Nie, Li; Zhang, Zhiqiang; Yu, Wenxin; Zhang, Yunye; Chen, Wei; Li, Siyuan; Deng, Shangwei; Yu, Pengfei; Zhang, Hui] Southwest Univ Sci & Technol, Mianyang, Sichuan, Peoples R China; [Fan, Yibo] Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Bouillon, Valentin] Ecole Int Sci Traitement Informat, Cergy, France; [Zhou, Xinxin] Nanjing Normal Univ, Coll Geog Sci, Nanjing, Peoples R China; [Zhou, Xinxin] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing, Peoples R China Southwest University of Science & Technology - China; Fudan University; CY Cergy Paris Universite; Ecole Internationale des Sciences du Traitement de linformation; Nanjing Normal University; Nanjing Normal University Yu, WX (corresponding author), Southwest Univ Sci & Technol, Mianyang, Sichuan, Peoples R China. star_yuwenxin27@163.com Zhang, Zhiqiang/ABH-8540-2020; fan, yi/GYU-1036-2022; Zhang, Yunye/HPD-1804-2023 Zhang, Zhiqiang/0000-0002-2408-366X; Zhang, Yunye/0000-0001-8574-3089; Li, Siyuan/0000-0002-2354-4233 Sichuan Provincial Science and Technology Department [2018GZ0517, 2019YFS0146, 2019YFS0155]; State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province [2018KF003, 2017B010110007]; Sichuan's Training Program of Innovation and Entrepreneurship for Undergraduate [S201910619048] Sichuan Provincial Science and Technology Department; State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province; Sichuan's Training Program of Innovation and Entrepreneurship for Undergraduate This research was supported by [2018GZ0517] [2019YFS0146] [2019YFS0155] which supported by Sichuan Provincial Science and Technology Department, [2018KF003] Supported by State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province [2017B010110007]. Project S201910619048 supported by Sichuan's Training Program of Innovation and Entrepreneurship for Undergraduate. 11 0 0 3 6 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1865-0929 1865-0937 978-3-030-36802-9; 978-3-030-36801-2 COMM COM INF SC 2019.0 1143.0 41 48 10.1007/978-3-030-36802-9_5 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BR1LG 2023-03-23 WOS:000632759700005 0
C Sun, CX; Wang, LH; Wang, W; Cheng, XY; Li, Z; Zhang, J; Yang, F; Huang, JP; Zhu, YM Baozong, Y; Qiuqi, R; Yao, Z; Gaoyun, AN Sun, Caixia; Wang, Lihui; Wang, Wei; Cheng, Xinyu; Li, Zhi; Zhang, Jian; Yang, Feng; Huang, Jianping; Zhu, Yuemin Non-Small Cell Lung Cancer Staging Using Deep Restricted Boltzmann Machine PROCEEDINGS OF 2018 14TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) International Conference on Signal Processing English Proceedings Paper 14th IEEE International Conference on Signal Processing (ICSP) AUG 12-16, 2018 Beijing, PEOPLES R CHINA IEEE,Inst Engn & Technol,Union Radio Sci Int,Chinese Inst Elect,Beijing Jiaotong Univ,IEEE Beijing Sect,Inst Engn & Technol Beijing Branch,Natl Nat Sci Fdn China,CIE Signal Proc Soc,IEEE Signal Proc Soc Beijing Chapter,IEEE Comp Soc Beijing Chapter,Japan China Sci & Technol Exchange Assoc,Shenzhen Univ, Intelligent Informat Inst,CIC Commun & Signal Proc Soc,Beijing Inst Elect,Beijing Jiaotong Univ,Beijing Inst Elect & Signal Proc Soc radiomics; cancer staging; deep learning RADIOMICS; IMAGES Accurate stage prediction of Non-small cell lung cancer (NSCLC) is very essential for making the right treatment decisions. Radiomics is currently one promising means to capture intra-tumor heterogeneity and therefore to predict the cancer stage. In order to improve the prediction accuracy of radiomics for NSCLC stages, we proposed to use the deep restricted Boltzmann machine (DRBM) to replace the traditional classifiers used in the framework of radiomics. The experiments were performed with the datasets provided by MAASTRO Clinic in Maastricht. The experimental results show that the area under curve (AUC) of DRBM was increased by about 19% compared traditional logic regression and support vector machine methods, especially for stage I and stage IIIa, our proposed method is much better than the others. [Sun, Caixia; Wang, Lihui; Wang, Wei; Cheng, Xinyu; Li, Zhi; Zhang, Jian] Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Guizhou, Peoples R China; [Yang, Feng] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China; [Huang, Jianping] Northeast Forestry Univ, Coll Mech & Elect Engn, Harbin, Heilongjiang, Peoples R China; [Zhu, Yuemin] Univ Lyon, CNRS, INSERM, INSA Lyon,CREATIS,UMR 5220,U1206, F-69621 Lyon, France Guizhou University; Beijing Jiaotong University; Northeast Forestry University - China; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Wang, LH (corresponding author), Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Guizhou, Peoples R China. 1172454535@qq.com; wlh1984@gmail.com; hemanwang@foxmail.com; ai.cxy@qq.com; lizhigzu@163.com; zjgzu@qq.com; fengyang@bjtu.edu.cn; jianping829@gmail.com; zhu@creatis.insa-lyon.fr Zhu, Yuemin/K-7292-2014; Yang, Feng/HLQ-2152-2023 Zhu, Yuemin/0000-0001-6814-1449; Yang, Feng/0000-0002-8334-7450 National Nature Science Foundations of China [61661010, 61671049, 61701105, 61462013]; Funds for Talents of Guizhou University [2013(33)]; Nature Science Foundation of Guizhou province [20152044]; National Key RD Plan of China [2017YFB1400100]; French ANR [MOSIFAH ANR-13-MONU-0009-01]; Region Auvergne-Rhone-Alpes of France under the project CMIRA COOPERA/EXPLORA PRO 2016 National Nature Science Foundations of China(National Natural Science Foundation of China (NSFC)); Funds for Talents of Guizhou University; Nature Science Foundation of Guizhou province; National Key RD Plan of China; French ANR(French National Research Agency (ANR)); Region Auvergne-Rhone-Alpes of France under the project CMIRA COOPERA/EXPLORA PRO 2016 This work was supported by the National Nature Science Foundations of China (Grant No. 61661010, 61671049, 61701105, 61462013), the Funds for Talents of Guizhou University (No. 2013(33)), the Nature Science Foundation of Guizhou province (Qiankehe J No.20152044), the National Key R&D Plan of China under Grant No. 2017YFB1400100, the French ANR (under MOSIFAH ANR-13-MONU-0009-01) and the Region Auvergne-Rhone-Alpes of France under the project CMIRA COOPERA/EXPLORA PRO 2016. 16 2 2 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-5221 978-1-5386-4673-1 INT CONF SIGN PROCES 2018.0 1175 1178 4 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BM6LH 2023-03-23 WOS:000466949200235 0
C Huet, A; Saverimoutou, A; Ben Houidi, Z; Shi, H; Cai, SM; Xu, JC; Mathieu, B; Rossi, D IEEE Huet, Alexis; Saverimoutou, Antoine; Ben Houidi, Zied; Shi, Hao; Cai, Shengming; Xu, Jinchun; Mathieu, Bertrand; Rossi, Dario Revealing QoE of Web Users from Encrypted Network Traffic 2020 IFIP NETWORKING CONFERENCE AND WORKSHOPS (NETWORKING) English Proceedings Paper 19th IFIP Networking Conference (Networking) JUN 22-25, 2020 ELECTR NETWORK IFIP,IEEE,IEEE Commun Soc,Systematic,gandi.net,Orange,Nokia,Huawei,Sorbonne Univ,Univ Waterloo,IFIP TC 6 Internet Service Providers (ISPs) have a lot to gain from estimating the web browsing quality of their customers. However, unlike Content Providers who can easily access in-browser computed application-level metrics to estimate web browsing quality, ISPs come short mainly because of traffic encryption. In this paper, we use exact methods and machine learning to estimate well-known application-level web browsing QoS metrics (such as SpeedIndex and Page Load Time) from raw encrypted streams of network traffic. Particularly, we present and open-source a unique dataset targeting a large set of popular pages (Alexa top-500), from probes from several ISPs networks, browsers software (Chrome, Firefox) and viewport combinations, for over 200,000 experiments. Our results show our models to be accurate, and we particularly focus on their ability to generalize to previously unseen conditions, giving guidance concerning their retraining. [Huet, Alexis; Ben Houidi, Zied; Shi, Hao; Cai, Shengming; Xu, Jinchun; Rossi, Dario] Huawei Technol Co Ltd, Shenzhen, Peoples R China; [Saverimoutou, Antoine; Mathieu, Bertrand] Orange Labs, Paris, France Huawei Technologies; Orange SA Huet, A (corresponding author), Huawei Technol Co Ltd, Shenzhen, Peoples R China. alexis.huet@huawei.com; antoine.saverimoutou@orange.com; zied.ben.houidi@huawei.com; shihao19@huawei.com; caishengming@huawei.com; xujinchun@huawei.com; bertrand2.mathieu@orange.com; dario.rossi@huawei.com 20 5 5 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-3-903176-28-7 2020.0 28 36 9 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BQ8DM 2023-03-23 WOS:000619498400004 0
J Wu, Z; Ge, RJ; Shi, GL; Zhang, L; Chen, Y; Luo, LM; Cao, Y; Yu, HY Wu, Zhan; Ge, Rongjun; Shi, Gonglei; Zhang, Lu; Chen, Yang; Luo, Limin; Cao, Yu; Yu, Hengyong MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection PHYSICS IN MEDICINE AND BIOLOGY English Article pulmonary nodule; false-positive reduction; deep convolutional neural networks (DCNNs); multi-dimensional nodule correlation features CT IMAGES; COMPUTED-TOMOGRAPHY; CHEST RADIOGRAPHS; ENSEMBLE Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimensional nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimensional CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimensional CNNs (2D CNNs) with attention module. To incorporate different sizes and shapes of nodule candidates, a multi-scale ensemble strategy is employed for probability aggregation with weights. The proposed method is evaluated on the LUNA16 challenge dataset in ISBI 2016 with ten-fold cross-validation. Experiment results show that the proposed framework achieves classification performance with a CPM score of 0.9008. All of these indicate that our method enables an efficient, accurate and reliable pulmonary nodule detection for clinical diagnosis. [Wu, Zhan; Chen, Yang; Luo, Limin] Southeast Univ, Sch Cyberspace Secur, Nanjing, Jiangsu, Peoples R China; [Wu, Zhan; Yu, Hengyong] Univ Massachusetts Lowell, Dept Elect & Comp Engn, Lowell, MA USA; [Ge, Rongjun; Shi, Gonglei; Chen, Yang; Luo, Limin] Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing, Peoples R China; [Zhang, Lu] Univ Rennes, INSA Rennes, CNRS, IETR UMR 6164, F-35000 Rennes, France; [Chen, Yang; Luo, Limin] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China; [Chen, Yang; Luo, Limin] Ctr Rech Informat Biomed Sino Francais LIA CRIBs, Rennes, France; [Cao, Yu] Univ Massachusetts Lowell, Dept Comp Sci, Lowell, MA USA Southeast University - China; University of Massachusetts System; University of Massachusetts Lowell; Southeast University - China; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National des Sciences Appliquees de Rennes; Universite de Rennes; Southeast University - China; Universite de Rennes; University of Massachusetts System; University of Massachusetts Lowell Chen, Y (corresponding author), Southeast Univ, Sch Cyberspace Secur, Nanjing, Jiangsu, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.;Chen, Y (corresponding author), Ctr Rech Informat Biomed Sino Francais LIA CRIBs, Rennes, France. chenyang.list@seu.edu.cn; Hengyong-yu@ieee.org Wu, Zhan/ABX-1238-2022; Yu, Hengyong/AAH-5726-2019 Wu, Zhan/0000-0002-3914-0102; Yu, Hengyong/0000-0002-5852-0813 State's Key Project of Research and Development Plan [2017YFA0104302, 2017YFC0109202, 2017YFC0107900]; National Natural Science Foundation [61801003, 61871117, 81471752]; China Scholarship Council [201906090145] State's Key Project of Research and Development Plan; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This research was supported in part by the State's Key Project of Research and Development Plan under Grant Nos. 2017YFA0104302, 2017YFC0109202 and 2017YFC0107900, in part by the National Natural Science Foundation under Grant Nos. 61801003, 61871117 and 81471752, in part by the China Scholarship Council under No. 201906090145. 41 9 9 5 37 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0031-9155 1361-6560 PHYS MED BIOL Phys. Med. Biol. DEC 7 2020.0 65.0 23 235053 10.1088/1361-6560/aba87c 0.0 14 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging PA8AY 32698172.0 2023-03-23 WOS:000595852400001 0
J Chen, SC; Xu, DY; Li, S; Ji, WJ; Yang, MH; Zhou, Y; Hu, BF; Xu, HY; Shi, Z Chen, Songchao; Xu, Dongyun; Li, Shuo; Ji, Wenjun; Yang, Meihua; Zhou, Yin; Hu, Bifeng; Xu, Hanyi; Shi, Zhou Monitoring soil organic carbon in alpine soils using in situ vis-NIR spectroscopy and a multilayer perceptron LAND DEGRADATION & DEVELOPMENT English Article deep learning; hyperparameter optimization; proximal soil sensing; Qinghai-Tibet Plateau; soil monitoring NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES; BASE-LINE MAP; SPECTRAL LIBRARY; TIBETAN PLATEAU; LAND-USE; PREDICTION; MATTER; CLIMATE; SEQUESTRATION Soil quality in alpine ecosystems requires regular monitoring to assess its dynamics under changes in climate and land use. Visible near-infrared (vis-NIR) spectroscopy could offer an option, as sampling and transporting large numbers of soil samples in the Qinghai-Tibet Plateau is extremely difficult. However, the potential for in situ vis-NIR spectra and the optimal algorithms need to be defined in this region. We have therefore evaluated the performance of a deep learning method, multilayer perceptron (MLP), for in situ spectral measurement of soil organic carbon (SOC) with in situ vis-NIR spectroscopy in southeastern Tibet, China. A total of 39 soil cores (maximum depth 1 m), including 547 soil samples taken from each 5-cm depth interval, were collected. The spectra were also measured at each 5-cm depth interval accordingly. After spectral preprocessing, 4,096 MLP models were generated by taking all the combinations from six parameters defined in the MLP. The 10-fold-core cross-validation showed that MLP had a good performance for in situ SOC prediction, and the best MLP model had an R-2 of .92, which were much better than those of the partial least squares regression model (R-2 = .80). The results also suggested that the number of epochs, number of neurons, and dropout rate were the most important parameters in the MLP model. We concluded that in situ vis-NIR spectroscopy coupled with an MLP model has high potential for large-scale SOC monitoring in the Qinghai-Tibet Plateau. Our results also provide a reference for rapid hyperparameter optimization using MLP for future soil spectroscopic modeling. [Chen, Songchao; Xu, Dongyun; Yang, Meihua; Zhou, Yin; Xu, Hanyi; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China; [Chen, Songchao; Hu, Bifeng] INRA, Unite InfoSol, Orleans, France; [Chen, Songchao] INRA, SAS, Rennes, France; [Li, Shuo] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat, Wuhan, Peoples R China; [Ji, Wenjun] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China; [Yang, Meihua] Yuzhang Normal Univ, Dept Environm Engn, Nanchang, Jiangxi, Peoples R China; [Hu, Bifeng] INRA, Unite Sci Sol, Orleans, France; [Hu, Bifeng] Orleans Univ, Sci Terre & Univers, Orleans, France; [Shi, Zhou] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Peoples R China Zhejiang University; INRAE; INRAE; Universite de Rennes; Central China Normal University; China Agricultural University; INRAE; Chinese Academy of Sciences; Institute of Soil Science, CAS Shi, Z (corresponding author), Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China. shizhou@zju.edu.cn Hu, Bifeng/G-2190-2018; shi, Zhou/M-7845-2019; Chen, Songchao/S-7982-2019 shi, Zhou/0000-0003-3914-5402; Chen, Songchao/0000-0003-1245-0482; Hu, Bifeng/0000-0001-9353-0307 China Scholarship Council [201606320211]; National Natural Science Foundation of China [41571339, 41661061] China Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) China Scholarship Council, Grant/Award Number: 201606320211; National Natural Science Foundation of China, Grant/Award Numbers: No. 41571339, No. 41661061 94 27 28 15 86 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1085-3278 1099-145X LAND DEGRAD DEV Land Degrad. Dev. MAY 15 2020.0 31.0 8 1026 1038 10.1002/ldr.3497 0.0 JAN 2020 13 Environmental Sciences; Soil Science Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Agriculture LL3RS 2023-03-23 WOS:000506686200001 0
C Saha, S; Menzel, S; Minku, LL; Yao, X; Sendhoff, B; Wollstadt, P IEEE Saha, Sneha; Menzel, Stefan; Minku, Leandro L.; Yao, Xin; Sendhoff, Bernhard; Wollstadt, Patricia Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) English Proceedings Paper IEEE Symposium Series on Computational Intelligence (IEEE SSCI) DEC 01-04, 2020 ELECTR NETWORK IEEE,IEEE Computat Intelligence Soc Representation learning; geometric deep learning; point clouds; generative model; novelty During each cycle of automotive development, large amounts of geometric data are generated as results of design studies and simulation tasks. Discovering hidden knowledge from this data and making it available to the development team strengthens the design process by utilizing historic information when creating novel products. To this end, we propose to use powerful geometric deep learning models that learn low-dimensional representation of the design data in an unsupervised fashion. Trained models allow to efficiently explore the design space, as well as to generate novel designs. One popular class of generative models are variational autoencoders, which have however been rarely applied to geometric data. Hence, we use a variational autoencoder for 3D point clouds (PC-VAE) and explore the model's generative capabilities with a focus on the generation of realistic yet novel 3D shapes. We apply the PC-VAE to point clouds sampled from car shapes from a benchmark data set and employ quantitative measures to show that our PC-VAE generates realistic car shapes, wile returning a richer variety of unseen shapes compared to a baseline autoencoder. Finally, we demonstrate how the PC-VAE can be guided towards generating shapes with desired target properties by optimizing the parameters that maximize the output of a trained classifier for said target properties. We conclude that generative models are a powerful tool that may aid designers in automotive product development. [Saha, Sneha; Menzel, Stefan; Sendhoff, Bernhard; Wollstadt, Patricia] Honda Res Inst Europe GmbH, Carl Legien Str 30, D-63073 Offenbach, Germany; [Minku, Leandro L.; Yao, Xin] Univ Birmingham, Sch Comp Sci, CERCIA, Birmingham, W Midlands, England; [Yao, Xin] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China Honda Motor Company; University of Birmingham; Southern University of Science & Technology Saha, S (corresponding author), Honda Res Inst Europe GmbH, Carl Legien Str 30, D-63073 Offenbach, Germany. sneha.saha@honda-ri.de; stefan.menzel@honda-ri.de; l.l.minku@cs.bham.ac.uk; x.yao@cs.bham.ac.uk; bernhard.sendhoff@honda-ri.de; patricia.wollstadt@honda-ri.de Minku, Leandro/0000-0002-2639-0671 European Union's Horizon 2020 research and innovation programme [766186] European Union's Horizon 2020 research and innovation programme This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE). 35 7 7 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-2547-3 2020.0 1469 1477 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS0GU 2023-03-23 WOS:000682772901067 0
J Zhao, YX; Man, KL; Smith, J; Siddique, K; Guan, SU Zhao, Yuxuan; Man, Ka Lok; Smith, Jeremy; Siddique, Kamran; Guan, Sheng-Uei Improved two-stream model for human action recognition EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING English Article Action recognition; Two-stream CNN model; Spatial stream; LSTM-based model This paper addresses the recognitions of human actions in videos. Human action recognition can be seen as the automatic labeling of a video according to the actions occurring in it. It has become one of the most challenging and attractive problems in the pattern recognition and video classification fields. The problem itself is difficult to solve by traditional video processing methods because of several challenges such as the background noise, sizes of subjects in different videos, and the speed of actions. Derived from the progress of deep learning methods, several directions are developed to recognize a human action from a video, such as the long-short-term memory (LSTM)-based model, two-stream convolutional neural network (CNN) model, and the convolutional 3D model.In this paper, we focus on the two-stream structure. The traditional two-stream CNN network solves the problem that CNNs do not have satisfactory performance on temporal features. By training a temporal stream, which uses the optical flow as the input, a CNN can have the ability to extract temporal features. However, the optical flow only contains limited temporal information because it only records the movements of pixels on thex-axis and they-axis. Therefore, we attempt to design and implement a new two-stream model by using an LSTM-based model in its spatial stream to extract both spatial and temporal features in RGB frames. In addition, we implement a DenseNet in the temporal stream to improve the recognition accuracy. This is in-contrast to traditional approaches which typically utilize the spatial stream for extracting only spatial features. The quantitative evaluation and experiments are conducted on the UCF-101 dataset, which is a well-developed public video dataset. For the temporal stream, we choose the optical flow of UCF-101. Images in the optical flow are provided by the Graz University of Technology. The experimental result shows that the proposed method outperforms the traditional two-stream CNN method with an accuracy of at least 3%. For both spatial and temporal streams, the proposed model also achieves higher recognition accuracies. In addition, compared with the state of the art methods, the new model can still have the best recognition performance. [Zhao, Yuxuan; Man, Ka Lok; Guan, Sheng-Uei] Xian Jiaotong Liverpool Univ, Dept Comp Sci & Software Engn, Renai Rd, Suzhou, Peoples R China; [Man, Ka Lok] Katholieke Univ Leuven, Imec, DistriNet, Leuven, Belgium; [Man, Ka Lok] Swinburne Univ Technol, Sarawak, Malaysia; [Smith, Jeremy] Univ Liverpool, Dept Elect Engn & Elect, Liverpool, Merseyside, England; [Siddique, Kamran] Xiamen Univ Malaysia, Dept Informat & Commun Technol, Sepang, Malaysia Xi'an Jiaotong-Liverpool University; IMEC; KU Leuven; Swinburne University of Technology; Swinburne University of Technology Sarawak; University of Liverpool; Xiamen University Malaysia Campus Man, KL (corresponding author), Xian Jiaotong Liverpool Univ, Dept Comp Sci & Software Engn, Renai Rd, Suzhou, Peoples R China.;Man, KL (corresponding author), Katholieke Univ Leuven, Imec, DistriNet, Leuven, Belgium. ka.man@xjtlu.edu.cn Siddique, Kamran/AAI-9472-2020 Siddique, Kamran/0000-0003-2286-1728 Research Management Center, Xiamen University Malaysia under XMUM Research Program Cycle 3 [XMUMRF/2019-C3/IECE/0006]; Xi'an Jiaotong-Liverpool University, Suzhou, China; Research Development Fund [RDF-15-01-01]; AI University Research Centre (AI-URC), Xi'an Jiaotong-Liverpool University, Suzhou, China through the XJTLU Key Programme Special Fund [KSF-P-02] Research Management Center, Xiamen University Malaysia under XMUM Research Program Cycle 3; Xi'an Jiaotong-Liverpool University, Suzhou, China; Research Development Fund; AI University Research Centre (AI-URC), Xi'an Jiaotong-Liverpool University, Suzhou, China through the XJTLU Key Programme Special Fund The research presented in this paper is supported by the Research Management Center, Xiamen University Malaysia under XMUM Research Program Cycle 3 (Grant No: XMUMRF/2019-C3/IECE/0006). In addition, it is also supported by by Xi'an Jiaotong-Liverpool University, Suzhou, China, and the Research Development Fund (RDF-15-01-01). Ka Lok Man wishes to thank the AI University Research Centre (AI-URC), Xi'an Jiaotong-Liverpool University, Suzhou, China, for supporting his related research contributions to this article through the XJTLU Key Programme Special Fund (KSF-P-02). 23 25 25 2 18 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1687-5176 1687-5281 EURASIP J IMAGE VIDE EURASIP J. Image Video Process. JUN 17 2020.0 2020.0 1 24 10.1186/s13640-020-00501-x 0.0 9 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Imaging Science & Photographic Technology LY8GJ gold 2023-03-23 WOS:000540766400001 0
J Mokayed, H; Shivakumara, P; Woon, HH; Kankanhalli, M; Lu, T; Pal, U Mokayed, Hamam; Shivakumara, Palaiahnakote; Woon, Hon Hock; Kankanhalli, Mohan; Lu, Tong; Pal, Umapada A new DCT-PCM method for license plate number detection in drone images PATTERN RECOGNITION LETTERS English Article Discrete cosine transform; Phase congruency; License plate detection; Scene text detection; Deep learning; Drone images RECOGNITION License plate number detection in drone images is a complex problem because the images are generally captured at oblique angles and pose several challenges like perspective distortion, non-uniform illumination effect, degradations, blur, occlusion, loss of visibility etc. Unlike, most existing methods that focus on images captured by orthogonal direction (head-on), the proposed work focuses on drone text images. Inspired by the Phase Congruency Model (PCM), which is invariant to non-uniform illuminations, contrast variations, geometric transformation and to some extent to distortion, we explore the combination of DCT and PCM (DCT-PCM) for detecting license plate number text in drone images. Motivated by the strong discriminative power of deep learning models, the proposed method exploits fully connected neural networks for eliminating false positives to achieve better detection results. Furthermore, the proposed work constructs working model that fits for real environment. To evaluate the proposed method, we use our own dataset captured by drones and benchmark license plate datasets, namely, Medialab for experimentation. We also demonstrate the effectiveness of the proposed method on benchmark natural scene text detection datasets, namely, SVT, MSRA-TD-50 0, ICDAR 2017 MLT and Total-Text. (c) 2021 Elsevier B.V. All rights reserved. [Mokayed, Hamam] Lulea Univ Technol, Fac Comp Sci Elect & Space Engn, Lulea, Sweden; [Shivakumara, Palaiahnakote] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia; [Woon, Hon Hock] MIMOS Berhad, Adv Informat Lab, Kuala Lumpur, Malaysia; [Kankanhalli, Mohan] Natl Univ Singapore, Sch Comp, Singapore, Singapore; [Lu, Tong] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Peoples R China; [Pal, Umapada] Indian Stat Inst, Comp Vis & Pattern Recognit Unit, Kolkata, India Lulea University of Technology; Universiti Malaya; MIMOS Berhad; National University of Singapore; Nanjing University; Indian Statistical Institute; Indian Statistical Institute Kolkata Mokayed, H (corresponding author), Lulea Univ Technol, Fac Comp Sci Elect & Space Engn, Lulea, Sweden.;Shivakumara, P (corresponding author), Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. hamam.mokayed@ltu.se; shiva@um.edu.my; hockwoon.hon@mimos.my; mohan@comp.nus.edu.sg; lutong@nju.edu.cn; umapada@isical.ac.in Palaiahnakote, Shivakumara/B-6261-2013; Pal, Umapada/AAC-4930-2022 mokayed, hamam/0000-0001-6158-3543 FRGS, Ministry of Higher Education, Malaysia [FP104-2020]; Natural Science Foundation of China [61672273, 61832008]; Science Foundation for Distinguished Young Scholars of Jiangsu [BK20160021] FRGS, Ministry of Higher Education, Malaysia; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation for Distinguished Young Scholars of Jiangsu(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars) The authors acknowledge the support from the grant, FRGS, Ministry of Higher Education, Malaysia, FP104-2020 for this work. And also, the support from the Natural Science Foundation of China associated with Grants, 61672273, 61832008 and the Science Foundation for Distinguished Young Scholars of Jiangsu associated with the Grant BK20160021. 30 4 4 9 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. AUG 2021.0 148.0 45 53 10.1016/j.patrec.2021.05.002 0.0 MAY 2021 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science TL2JI 2023-03-23 WOS:000674680600008 0
J Ranjan, R; Kolodziej, J; Wang, LZ; Zomaya, AY Ranjan, Rajiv; Kolodziej, Joanna; Wang, Lizhe; Zomaya, Albert Y. Cross-Layer Cloud Resource Configuration Selection in the Big Data Era IEEE CLOUD COMPUTING English Article The emergence of cloud computing has facilitated resource sharing beyond organizational boundaries and among various applications. This cloud resource sharing is primarily driven by resource virtualization and utility computing (the pay-as-you-go pricing model). The generic multilayered cloud service model is appealing to many parties-from small businesses looking for a low upfront infrastructure investment, to enterprises wanting to cut the cost of managing infrastructures, to research communities requiring large-scale data processing and computing power. In a cloud environment, computing resources (processors, storage devices, network bandwidth, and so on) and applications are provided as services over the Internet. [Ranjan, Rajiv] CSIRO, Canberra, ACT, Australia; [Kolodziej, Joanna] Cracow Univ Technol, PL-30084 Krakow, Poland; [Wang, Lizhe] China Univ Geosci, Sch Comp Sci, Beijing, Peoples R China; [Zomaya, Albert Y.] Univ Sydney, Sch Informat Technol, High Performance Comp & Networking, Sydney, NSW 2006, Australia Commonwealth Scientific & Industrial Research Organisation (CSIRO); Cracow University of Technology; China University of Geosciences; University of Sydney Ranjan, R (corresponding author), CSIRO, Canberra, ACT, Australia. rajiv.ranjan@csiro.au; jokolodziej@pk.edu.pl; lizhewang@icloud.com; albert.zomaya@sydney.edu.au Ranjan, Rajiv/F-4700-2011; Zomaya, Albert Y./G-9697-2017; Wang, Lizhe/L-7453-2014 Ranjan, Rajiv/0000-0002-6610-1328; Zomaya, Albert Y./0000-0002-3090-1059; Wang, Lizhe/0000-0003-2766-0845 22 18 19 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2325-6095 IEEE CLOUD COMPUT IEEE Cloud Comput. MAY-JUN 2015.0 2.0 3 16 22 10.1109/MCC.2015.64 0.0 7 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science CZ1XN 2023-03-23 WOS:000366899300004 0
J Yu, H; Zhang, Z; Zhang, L; Dong, H; Yu, HB Yu, Han; Zhang, Zhuang; Zhang, Linus; Dong, Heng; Yu, Hongbing Improved Norfloxacin degradation by urea precipitation Ti/SnO2-Sb anode under photo-electro catalysis and kinetics investigation by BPneural-network-physical modeling JOURNAL OF CLEANER PRODUCTION English Article Photo-electro catalysis; Urea precipitation; Ti/SnO2-Sb; Norfloxacin; BP neural Network ELECTROCHEMICAL DEGRADATION; WASTE-WATER; RISK-ASSESSMENT; FENTON PROCESS; OXIDATION; OPTIMIZATION; NANOCOMPOSITES; CIPROFLOXACIN; ANTIBIOTICS; PERFORMANCE The photo-electro catalysis has emerged as efficient and sustainable degradation method for antibiotics, where metal oxide anode plays a critical role. Exploring novel preparation method for anodes catalysis to achieve larger active sites and diverse oxidants production can directly enhance the degradation performance. Therefore, in this work, a new urea precipitation method for anode formation was studied. Both novel urea precipitation Ti/SnO2-Sb anode (TSSA-U) and traditional electro-deposition Ti/SnO2-Sb anode (TSSA-E) were prepared for Norfloxacin degradation by photo-electro catalysis in saline water. A unique tubular Ti/SnO2-Sb units formatted by urea precipitation resulted in higher porosity. This led to dominating advantage for TSSA-U on photo/electrochemical activity and degradation performances by individual photo/electro catalysis, compared to TSSA-E. However, this gap of degradation performances shrank when synergistic effect of photo-electro catalysis involved. The highest TOC removal ratio of 91.1% was obtained from TSSA-U under photo-electro catalysis. Besides, a novel BP-neural-network-physical modeling (BP-ANN-P) was developed for analysis. According to this modeling, both direct (adsorptiondegradation, radiation, etc.) and indirect (mainly .Cl) routes contributed significantly in degradation work by TSSA-U, where indirect route shared 41.8-90.1% of total degradation ratio. An increasing of current density (from 5 to 25 mA cm(-2)) enhanced the kinetics for both routes. Indirect route preferred pH = 3-7 with direct route enhanced by neutral condition. Moreover, indirect route also showed better adaptability with higher initial NOR loadings. Both urea precipitation and BP-ANN-P have shown their value for high performance material formation and data analysis, respectively. Notably, higher catalysis performance and better analysis connect to low energy cost, shorter running time and more efficient judgement and selection, which meets the requirement of cleaner production and environmental sustainability. (c) 2020 Elsevier Ltd. All rights reserved. [Yu, Han; Zhang, Zhuang; Dong, Heng; Yu, Hongbing] Nankai Univ, Coll Environm Sci & Engn, Res Ctr Cleaner Prod, Tianjin 300350, Peoples R China; [Yu, Han; Zhang, Linus] Lund Univ, Div Water Resources Engn, S-22100 Lund, Sweden Nankai University; Lund University Yu, H; Dong, H (corresponding author), Nankai Univ, Coll Environm Sci & Engn, Res Ctr Cleaner Prod, Tianjin 300350, Peoples R China. han.yu@tvrl.lth.se; dongheng@nankai.edu.cn Zhang, Linus/ABH-1379-2021 Zhang, Linus/0000-0003-2676-3420; Yu, Han/0000-0002-0303-4877 National Key Research and Development Plan of China [2016YFC0209301]; National Natural Science Foundation of China [51708300]; Tianjin Development Program for Innovation and Entrepreneurship National Key Research and Development Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tianjin Development Program for Innovation and Entrepreneurship This research was supported by the National Key Research and Development Plan of China (2016YFC0209301), National Natural Science Foundation of China (51708300) and Tianjin Development Program for Innovation and Entrepreneurship. This research was partly carried out in Lund University, Sweden. 47 9 9 15 77 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. JAN 20 2021.0 280.0 1 124412 10.1016/j.jclepro.2020.124412 0.0 13 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology PM1LO 2023-03-23 WOS:000603570100004 0
J Niu, SJ; Luo, JT; Niemi, H; Li, XQ; Lu, Y Niu, Shuanghong Jenny; Luo, Jiutong; Niemi, Hannele; Li, Xiaoqing; Lu, Yu Teachers' and Students' Views of Using an AI-Aided Educational Platform for Supporting Teaching and Learning at Chinese Schools EDUCATION SCIENCES English Article artificial intelligence (AI); AI-aided educational platform; quality education; teaching and learning SYSTEM In Chinese schools in less advanced places, there is an urgent need to improve the quality of education and educational equity. This study aims to investigate how an AI-aided educational platform can be used to provide additional teaching and learning resources to serve this need. The AI-aided educational platform used in this study is called Smart-Learning Partner (SLP), which is based on AI technology to provide new opportunities for personalized learning and more educational resources. A qualitative research method was applied in this study. We interviewed and surveyed 98 students and 32 teachers at 9 Chinese schools located in less developed areas. We used content analysis to interpret the findings based on students' and teachers' experiences of using the SLP platform. The data demonstrated that this kind of AI-aided educational platform was viewed by students and teachers as a useful tool in students' learning and teachers' work. It provided additional possibilities to students and teachers with its rich assessment tools, personalized and overall student learning analysis reports, plentiful high-quality mini-lecture videos, and recommendations from the platform based on the students' needs for further enhancement study. However, challenges still exist. Adequate electronic devices for students are needed, especially in schools in less developed areas. Students and teachers called for user-friendly interfaces and features, social interaction aspects, and gamification mechanisms with recent online learning platforms. We conclude that based on the teachers' and students' views, AI-aided education platforms are useful tools for supporting teaching and learning in Chinese school. [Niu, Shuanghong Jenny; Niemi, Hannele] Univ Helsinki, Fac Educ Sci, Helsinki 00014, Finland; [Luo, Jiutong; Li, Xiaoqing; Lu, Yu] Beijing Normal Univ, Fac Educ, Adv Innovat Ctr Future Educ, Beijing 102206, Peoples R China; [Luo, Jiutong] Beijing Normal Univ, Ctr Educ Sci & Technol, Zhuhai 519087, Peoples R China University of Helsinki; Beijing Normal University; Beijing Normal University Niu, SJ (corresponding author), Univ Helsinki, Fac Educ Sci, Helsinki 00014, Finland.;Luo, JT (corresponding author), Beijing Normal Univ, Fac Educ, Adv Innovat Ctr Future Educ, Beijing 102206, Peoples R China.;Luo, JT (corresponding author), Beijing Normal Univ, Ctr Educ Sci & Technol, Zhuhai 519087, Peoples R China. jenny.niu@helsinki.fi; jtluo@bnu.edu.cn Lu, Yu/0000-0003-2378-4971; Niu, Shuanghong Jenny/0000-0002-4042-8736; Luo, Jiutong/0000-0003-4700-1927; Niemi, Hannele/0000-0003-0730-0674 19 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7102 EDUC SCI Educ. Sci. DEC 2022.0 12.0 12 858 10.3390/educsci12120858 0.0 16 Education & Educational Research Emerging Sources Citation Index (ESCI) Education & Educational Research 7E2TU gold, Green Published 2023-03-23 WOS:000901028000001 0
J Ma, J; Liu, X; Zou, XY; Yue, ML; Shang, PC; Kang, LY; Jemei, S; Lu, C; Ding, Y; Zerhouni, N; Cheng, YJ Ma, Jian; Liu, Xue; Zou, Xinyu; Yue, Meiling; Shang, Pengchao; Kang, Liyuan; Jemei, Samir; Lu, Chen; Ding, Yu; Zerhouni, Noureddine; Cheng, Yujie Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences ISA TRANSACTIONS English Article Proton exchange membrane fuel cell; Remaining useful life prediction; Transfer learning; Deep learning; Prognostic and health management USEFUL LIFE PREDICTION; WIND-SPEED PREDICTION; MODEL; NETWORK; ELM Proton exchange membrane fuel cell (PEMFC) has been widely used in diverse applications. However, degradation and durability problem is one of the biggest barriers to take PEMFCs into extensive commercial use. Prognostics and health management is an effective solution to this problem. In this study, we focus on its core technology prognostics and propose an individual difference conscious prediction method for PEMFC using a hybrid transfer learning approach to get higher accuracy. Firstly, a time-scale self-optimization local weighted regression method is designed to adaptively smooth the raw data to prominent the performance degradation trend. Then, to obtain a more similar curve to the predicted fuel cell as the training data of the prediction model, a transferability measurement method using cosine-distance selects the most similar historical test data. Furtherly, it is utilized to generate a more similar curve by a data transfer method combining a deep learning model named stacked autoencoder and a hybrid transfer learning strategy. Two types of transfer learning approaches are fused to maximally mine available information from historical data and previous models to help improve the similarity of the generated curve. In this process, the common degradation information of all cells and individual information of the predicted cells are considered to improve generation quality. Finally, a prediction model using stacked Long-short Term Memory(LSTM) having a significant advantage in modeling series relation is trained by the generated samples cut with variable width sliding windows and estimates remaining useful life(RUL) the target fuel cell. Experimental validation data are employed to verify the effectiveness of the proposed algorithm. Satisfying results are also obtained by accuracy comparison under different smoothing scales, numbers of transferable samples, and prediction methods. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved. [Ma, Jian; Liu, Xue; Zou, Xinyu; Shang, Pengchao; Kang, Liyuan; Lu, Chen; Cheng, Yujie] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Ma, Jian; Liu, Xue; Zou, Xinyu; Shang, Pengchao; Kang, Liyuan; Lu, Chen; Ding, Yu; Cheng, Yujie] Natl Key Lab Sci & Technol Reliabil & Environm En, Shenzhen, Peoples R China; [Ding, Yu] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China; [Yue, Meiling; Jemei, Samir; Zerhouni, Noureddine] UBFC, UFC, ENSMM, FEMTO ST Inst,CNRS,UMR 6174, 24 Rue Alain Savary, F-25000 Besancon, France; [Yue, Meiling; Jemei, Samir; Zerhouni, Noureddine] CNRS, FR 3539, FCLAB Res Federat, Rue Thierry Mieg, F-90010 Belfort, France Beihang University; Beihang University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Ding, Y (corresponding author), Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China. dingyu@buaa.edu.cn Jemeï, Samir/ACR-4931-2022 Ding, Yu/0000-0002-1053-8475; YUE, MEILING/0000-0002-4624-4743; JEMEI, Samir/0000-0003-0195-903X National Natural Science Foundation of China [51605014, 61973011, 61803013]; Fundamental Research Funds for the Central Universities [ZG140S1993]; French region of Bourgogne Franche Comte; R&D projects in key areas of Guangdong Province [2018B010108005]; National key Laboratory of Science and Technology on Reliability and Environmental Engineering [6142004180501]; Capital Science & Technology Leading Talent Program [Z191100006119029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); French region of Bourgogne Franche Comte; R&D projects in key areas of Guangdong Province; National key Laboratory of Science and Technology on Reliability and Environmental Engineering; Capital Science & Technology Leading Talent Program This research is supported by the National Natural Science Foundation of China [Grant Nos. 51605014, 61973011, 61803013], the Fundamental Research Funds for the Central Universities [Grant No. YWF-20-BJ-J-517]. The authors would like to thank the project PHyTie (2016Y04574) funded by the French region of Bourgogne Franche Comte for the support. It is also supported by R&D projects in key areas of Guangdong Province (Grant No.2018B010108005), the Fundamental Research Funds for the Central Universities (Grant No. ZG140S1993), National key Laboratory of Science and Technology on Reliability and Environmental Engineering (Grant Nos. 6142004180501), as well as the Capital Science & Technology Leading Talent Program (Grant No. Z191100006119029). 39 18 19 13 91 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-0578 1879-2022 ISA T ISA Trans. JUL 2021.0 113.0 149 165 10.1016/j.isatra.2020.06.005 0.0 MAY 2021 17 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation SO9NG 32591254.0 2023-03-23 WOS:000659299900012 0
C Liu, YY; Zhu, C; Mao, M; Song, FL; Dufaux, F; Zhang, X IEEE Liu, Yuyang; Zhu, Ce; Mao, Min; Song, Fangliang; Dufaux, Frederic; Zhang, Xiang Analytical Distortion Aware Video Coding for Computer Based Video Analysis 2017 IEEE 19TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP) IEEE International Workshop on Multimedia Signal Processing English Proceedings Paper 19th IEEE International Workshop on Multimedia Signal Processing (MMSP) OCT 16-18, 2017 Luton, ENGLAND IEEE,YouTube,Inst Engn & Technol,ELSEVIER,datafox Video coding; video analysis; rate-analytical distortion optimization; HEVC LAGRANGE MULTIPLIER; OPTIMIZATION With the development of artificial intelligence, more and more multimedia applications for various tasks have emerged in our daily life. Meanwhile, as one of the main information sources of the applications, a huge amount of video data has been being generated by portable or mounted cameras in daily basis for varying purposes including surveillance, in which case we may need computers to watch videos to save labor cost. However, most video coding standards are designed for the highest human perceptual quality given a bit rate by minimizing a fidelity cost function (e.g., mean squared error, MSE), assuming the content will be consumed by human beings. In view of the above considerations, this paper proposes a new rate-analytical-distortion optimization method (RADO) for video analysis. Specifically, we consider moving object detection as the analysis task. Accordingly, we develop a novel rate analytical distortion (RAD) model for video coding, where the analytical distortion is related to the object detection performance expressed in terms of F-measure. As shown in the experimental results, the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion) with a slight bit rate increase. [Liu, Yuyang; Zhu, Ce; Mao, Min; Song, Fangliang; Zhang, Xiang] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China; [Dufaux, Frederic] Lab Signaux & Syst, Paris, France University of Electronic Science & Technology of China; UDICE-French Research Universities; Universite Paris Saclay Liu, YY (corresponding author), Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China. yuyangliu@std.uestc.edu.cn; eczhu@uestc.edu.cn; minmao@std.uestc.edu.cn; songfangliang@std.uestc.edu.cn; frederic.dufaux@l2s.centralesupelec.fr; uestchero@uestc.edu.cn Zhu, Ce/AEN-1875-2022; Dufaux, Frederic/HJJ-1496-2023 Dufaux, Frederic/0000-0001-6388-4112 National Natural Science Foundation of China [61571102]; [2015AA015903]; [ZYGX2014Z003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; This work was suppoted in part by the National Natural Science Foundation of China under Grant 61571102 and in part by the Other Funds under Grant 2015AA015903 and Grant ZYGX2014Z003. 20 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2163-3517 978-1-5090-3649-3 IEEE INT WORKSH MULT 2017.0 6 Computer Science, Information Systems; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ7OT 2023-03-23 WOS:000427483700041 0
J Mukherjee, A; Jain, DK; Goswami, P; Xin, Q; Yang, LX; Rodrigues, JJPC Mukherjee, Amrit; Jain, Deepak Kumar; Goswami, Pratik; Xin, Qin; Yang, Lixia; Rodrigues, Joel J. P. C. Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems IEEE ACCESS English Article Back propagation neural networks; intelligent transportation systems; gradient descent; MIMO; wireless sensor networks; cooperative communication LOCALIZATION; DESIGN Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time. [Mukherjee, Amrit; Goswami, Pratik; Yang, Lixia] Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [Jain, Deepak Kumar] Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Cooperat Control U, Chongqing 400065, Peoples R China; [Xin, Qin] Univ Faroe Isl, Fac Sci & Technol, Torshavn 100, Faroe Islands; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Dept Elect Engn, BR-64000040 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-3810193 Aveiro, Portugal Jiangsu University; Chongqing University of Posts & Telecommunications; Universidade Federal do Piaui; Universidade de Aveiro Yang, LX (corresponding author), Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China.;Jain, DK (corresponding author), Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Cooperat Control U, Chongqing 400065, Peoples R China.;Rodrigues, JJPC (corresponding author), Univ Fed Piaui, Dept Elect Engn, BR-64000040 Teresina, Brazil.;Rodrigues, JJPC (corresponding author), Inst Telecomunicacoes, P-3810193 Aveiro, Portugal. deepak@cqupt.edu.cn; lixiayang@yeah.net; joeljr@ieee.org Mukherjee, Amrit/Q-3174-2016; Goswami, Pratik/AAB-4602-2019; Rodrigues, Joel J. P. C./A-8103-2013 Mukherjee, Amrit/0000-0002-6714-5568; Goswami, Pratik/0000-0002-4226-4536; Rodrigues, Joel J. P. C./0000-0001-8657-3800; Xin, Qin/0000-0002-6178-8538; Jain, Dr.Deepak Kumar/0000-0002-3400-1613 Foundation of Science and Technology/Ministry of Science, Technology and Higher Education (FCT/MCTES); EU Funds [UIDB/EEA/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5]; National Science Foundation of China [61601205]; Basic Research Program of Jiangsu Province [BK20160541]; Natural Science Foundation of the Jiangsu Higher Education Institutions of China [16KJB140003]; European Cooperation in Science and Technology (EU COST) Grants from the European Network for Cost Containment and Improved Quality of Health Care [CA15222] Foundation of Science and Technology/Ministry of Science, Technology and Higher Education (FCT/MCTES); EU Funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Basic Research Program of Jiangsu Province; Natural Science Foundation of the Jiangsu Higher Education Institutions of China(National Natural Science Foundation of China (NSFC)); European Cooperation in Science and Technology (EU COST) Grants from the European Network for Cost Containment and Improved Quality of Health Care This work was supported in part by the Foundation of Science and Technology/Ministry of Science, Technology and Higher Education (FCT/MCTES) through the National Funds and when applicable co-funded EU Funds under Project UIDB/EEA/50008/2020, in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 309335/2017-5, in part by the National Science Foundation of China under Grant 61601205, in part by the Basic Research Program of Jiangsu Province under Grant BK20160541, and in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 16KJB140003. The work of Qin Xin was supported by the European Cooperation in Science and Technology (EU COST) Grants from the European Network for Cost Containment and Improved Quality of Health Care under Grant CA15222. 50 27 29 1 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 28524 28532 10.1109/ACCESS.2020.2971969 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LC5WG gold 2023-03-23 WOS:000525401500018 0
B Zheng, Z; Zhou, SH Rasouli, S; Timmermans, H Zheng, Zhong; Zhou, Suhong Rasouli, S; Timmermans, H Characterizing Urban Structure Using Taxi GPS Data MOBILE TECHNOLOGIES FOR ACTIVITY-TRAVEL DATA COLLECTION AND ANALYSIS Advances in Data Mining and Database Management (ADMDM) Book Series English Article; Book Chapter SPATIAL STRUCTURE; PATTERNS; TRANSFORMATION; ECONOMIES Scholars' have explored urban structure from many perspectives. Developments in ICT have made it possible to discover spatial patterns in activities using big data. The identified patterns allow us to better understand urban structure. This chapter reports the collection of taxi GPS records for a single day in the inner city of Guangzhou, China. Taxi trips are connected to urban space by defining travel intensities. The spatial-temporal distribution of trips shows differences between three time periods (daytime, evening, before dawn). Different types of spatial facilities provide different activity places, the importance of which depends on their location and time of day. The study illustrates how descriptive analyses of taxi GPS data can enhance our understanding of urban space from the perspective of activities. [Zheng, Zhong] Eindhoven Univ Technol, Urban Planning Grp, NL-5600 MB Eindhoven, Netherlands; [Zhou, Suhong] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China Eindhoven University of Technology; Sun Yat Sen University Zheng, Z (corresponding author), Eindhoven Univ Technol, Urban Planning Grp, NL-5600 MB Eindhoven, Netherlands. 22 2 3 0 5 IGI GLOBAL HERSEY 701 E CHOCOLATE AVE, STE 200, HERSEY, PA 17033-1240 USA 978-1-4666-6171-4; 978-1-4666-6170-7 ADV DATA MIN DATABAS 2014.0 341 350 10.4018/978-1-4666-6170-7.ch021 0.0 10.4018/978-1-4666-6170-7 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems Book Citation Index – Science (BKCI-S) Computer Science BD7JK 2023-03-23 WOS:000363211700023 0
J Zhang, CS; Bi, JJ; Xu, SX; Ramentol, E; Fan, GJ; Qiao, BJ; Fujita, H Zhang, Chongsheng; Bi, Jingjun; Xu, Shixin; Ramentol, Enislay; Fan, Gaojuan; Qiao, Baojun; Fujita, Hamido Multi-Imbalance: An open-source software for multi-class imbalance learning KNOWLEDGE-BASED SYSTEMS English Article Multi-class imbalance leaning; Imbalanced data classification PARAMETERIZATION; CLASSIFICATION; DOMAIN Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. (C) 2019 Elsevier B.V. All rights reserved. [Zhang, Chongsheng; Bi, Jingjun; Xu, Shixin; Fan, Gaojuan; Qiao, Baojun] Henan Univ, Big Data Res Ctr, Kaifeng 475001, Peoples R China; [Ramentol, Enislay] SICS Swedish ICT, Isafjordsgatan 22,Box 1263, SE-16429 Kista, Sweden; [Fujita, Hamido] Ho Chi Minh City Univ Technol HUTECH, Fac Informat Technol, Ho Chi Minh City, Vietnam; [Fujita, Hamido] Iwate Prefectural Univ, Fac Software & Informat Sci, Takizawa, Iwate 0200693, Japan Henan University; RISE Research Institutes of Sweden; Ho Chi Minh City University of Technology (HUTECH); Vietnam National University Hochiminh City; Iwate Prefectural University Qiao, BJ (corresponding author), Henan Univ, Big Data Res Ctr, Kaifeng 475001, Peoples R China. chongsheng.zhang@yahoo.com; 2834335964@qq.com; xusxmail@qq.com; enislay@gmail.com; fangaojuan@126.com; qiaobaojun2009@163.com; HFujita-799@acm.org ramentol, enislay/T-1603-2017; Fujita, Hamido/D-6249-2012 ramentol, enislay/0000-0002-3460-2902; Fujita, Hamido/0000-0001-5256-210X European Research Consortium for Informatics and Mathematics (ERCIM), France Alain Bensoussan Fellowship Programme European Research Consortium for Informatics and Mathematics (ERCIM), France Alain Bensoussan Fellowship Programme The research of Enislay Ramentol is funded by the European Research Consortium for Informatics and Mathematics (ERCIM), France Alain Bensoussan Fellowship Programme. 38 115 116 23 127 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JUN 15 2019.0 174.0 137 143 10.1016/j.knosys.2019.03.001 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HZ3AB 2023-03-23 WOS:000468718800011 0
J Zhao, H; Li, LX; Peng, HP; Kurths, J; Xiao, JH; Yang, YX Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Juergen; Xiao, Jinghua; Yang, Yixian Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach EUROPEAN PHYSICAL JOURNAL B English Article EXPONENTIAL STABILITY; COMPLEX NETWORK In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall's inequality and adaptive control technique, an adaptive controller with update laws is developed to realize the exponential anti-synchronization. Adaptive controller can adjust itself behavior to get the best performance, according to the environment is changing or the environment has changed, which has the ability to adapt to environmental change. Furthermore, a numerical example is provided to validate the effectiveness of the proposed method. [Zhao, Hui; Xiao, Jinghua] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China; [Li, Lixiang; Peng, Haipeng; Yang, Yixian] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Potsdam Institut fur Klimafolgenforschung Zhao, H (corresponding author), Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China. li_lixiang2006@163.com jinghua, xiao/F-3710-2012; Li, lixiang/G-6222-2011 Li, lixiang/0000-0001-9949-8731; Li, Lixiang/0000-0001-8541-308X National Natural Science Foundation of China [61170269, 61472045]; Beijing Higher Education Young Elite Teacher Project [YETP0449]; Beijing Natural Science Foundation [4142016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Higher Education Young Elite Teacher Project; Beijing Natural Science Foundation(Beijing Natural Science Foundation) The authors thank all the editor and the anonymous referees for their constructive comments and valuable suggestions, which are helpful to improve the quality of this paper. The work is supported by the National Natural Science Foundation of China (Grant Nos. 61170269, 61472045), the Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0449), and the Beijing Natural Science Foundation (Grant No. 4142016). 30 13 13 3 43 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1434-6028 1434-6036 EUR PHYS J B Eur. Phys. J. B MAY 11 2015.0 88.0 5 109 10.1140/epjb/e2015-50798-9 0.0 10 Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Physics CH7EN 2023-03-23 WOS:000354198500008 0
J Xie, XF; Xie, XM; Martinez-Climent, C Xie, Xuefang; Xie, Xuemei; Martinez-Climent, Carla Identifying the factors determining the entrepreneurial ecosystem of internet cultural industries in emerging economies INTERNATIONAL ENTREPRENEURSHIP AND MANAGEMENT JOURNAL English Article Entrepreneurial ecosystem; Internet cultural industries; Netizens; Emerging economies; Government governance ARTIFICIAL-INTELLIGENCE; CREATIVE INDUSTRIES; INNOVATION; PERFORMANCE; POLICY; GROWTH; TECH; COMMUNITIES; TECHNOLOGY; INTENTIONS The integration of the Internet and the real economy has propelled the formation of a new economy. Internet cultural industries-defined as a collection of industries that engage in creative cultural content, production, circulation, and services based on Internet technology and core digitization-are a critical part of this new economy. This study argues that the development of Internet cultural industries has created a new entrepreneurial ecosystem, which is a dynamic and open ecosystem impacted by numerous factors. Using a structural equation model (SEM) method, we empirically examine the factors impacting the entrepreneurial ecosystem of Internet cultural industries. We find that both external ecosystem factors, including politics, the economy, sociocultural activities, and technological environments, and internal ecosystem factors, including netizens, governments, enterprises, and media and industry associations, are verified to significantly affect the entrepreneurial ecosystem performance of Internet culture industries. Most interestingly, we find that the political environment is the most crucial factor impacting the entrepreneurial ecosystem performance of Internet cultural industries. Thus, Internet cultural industries should prioritize the improvement of good government governance. Overall, our study deepens the research on the Internet entrepreneurial ecosystem by identifying its internal and external determining factors in the context of emerging economies. [Xie, Xuefang] Tongji Univ, Sch Humanities, Shanghai 200092, Peoples R China; [Xie, Xuemei] Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China; [Martinez-Climent, Carla] Univ Valencia, Valencia, Spain Tongji University; Shanghai University; University of Valencia Xie, XM (corresponding author), Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China. xxm1030@126.com Martínez-Climent, Carla/AAB-6099-2022; Xie, X.M./ABQ-6831-2022 Martínez-Climent, Carla/0000-0002-2834-8952; National Natural Science Foundation of China [71473176, 71772118, 71472118]; Shanghai Pujiang Program [18PJC056]; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Pujiang Program(Shanghai Pujiang Program); Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning This research was sponsored by the National Natural Science Foundation of China (Grant number: 71473176; 71772118; 71472118), Shanghai Pujiang Program (Grant number: 18PJC056), and Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning. 95 22 22 17 120 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1554-7191 1555-1938 INT ENTREP MANAG J Int. Entrep. Manag. J. JUN 2019.0 15.0 2 503 522 10.1007/s11365-019-00562-z 0.0 20 Business; Management Social Science Citation Index (SSCI) Business & Economics HY7SB 2023-03-23 WOS:000468335300009 0
J Chen, Y; Ban, Y; He, R; Cui, JM; Huang, YF; Li, CF; Guo, GC; Casanova, J Chen, Yan; Ban, Yue; He, Ran; Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can; Casanova, Jorge A neural network assisted Yb-171(+) quantum magnetometer NPJ QUANTUM INFORMATION English Article SPECTROSCOPY A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of(171)Yb(+) atomic sensors with adequately trained neural networks enables us to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses. [Chen, Yan; He, Ran; Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China; [Chen, Yan; He, Ran; Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phys, Hefei 230026, Peoples R China; [Ban, Yue; Casanova, Jorge] Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Ban, Yue; Casanova, Jorge] Univ Basque Country UPV EHU, EHU Quantum Ctr, Barrio Sarriena S-N, Leioa 48940, Biscay, Spain; [Ban, Yue] TECNALIA, Basque Res & Technol Alliance BRTA, Derio 48160, Spain; [Cui, Jin-Ming; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, Hefei Natl Lab, Hefei 230088, Peoples R China; [Casanova, Jorge] Basque Fdn Sci, IKERBASQUE, Plaza Euskadi 5, Bilbao 48009, Spain Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Basque Country; University of Basque Country; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Basque Foundation for Science He, R; Cui, JM; Huang, YF; Li, CF (corresponding author), Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China.;He, R; Cui, JM; Huang, YF; Li, CF (corresponding author), Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phys, Hefei 230026, Peoples R China.;Ban, Y (corresponding author), Univ Basque Country UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain.;Ban, Y (corresponding author), Univ Basque Country UPV EHU, EHU Quantum Ctr, Barrio Sarriena S-N, Leioa 48940, Biscay, Spain.;Ban, Y (corresponding author), TECNALIA, Basque Res & Technol Alliance BRTA, Derio 48160, Spain.;Cui, JM; Huang, YF; Li, CF (corresponding author), Univ Sci & Technol China, Hefei Natl Lab, Hefei 230088, Peoples R China. ybanxc@gmail.com; heran@mail.ustc.edu.cn; jmcui@ustc.edu.cn; hyf@ustc.edu.cn; cfli@ustc.edu.cn Ban, Yue/AAR-9240-2021; Casanova, Jorge/A-6293-2019 Ban, Yue/0000-0003-1764-4470; Chen, Yan/0000-0002-8445-1858; Casanova, Jorge/0000-0002-6110-671X; Li, Chuan-Feng/0000-0001-6815-8929 National Key Research and Development Program of China [2017YFA0304100]; National Natural Science Foundation of China [11774335, 11734015]; Key Research Program of Frontier Sciences, CAS [QYZDY-SSWSLH003]; Innovation Program for Quantum Science and Technology [2021ZD0301604, 2021ZD0301200]; Students' Innovation and Entrepreneurship Foundation of USTC; EU FET Open Grant Quromorphic [828826]; Basque Government [KK-2021/00070, IT1470-22]; Ramon y Cajal [RYC2018-025197-I]; Spanish Government [EUR2020-112117]; Nanoscale NMR and complex systems [PID2021-126694NB-C21]; ELKARTEK project Dispositivos en Tecnologias Cuanticas [KK-2022/00062] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences, CAS; Innovation Program for Quantum Science and Technology; Students' Innovation and Entrepreneurship Foundation of USTC; EU FET Open Grant Quromorphic; Basque Government(Basque Government); Ramon y Cajal(Spanish Government); Spanish Government(Spanish Government); Nanoscale NMR and complex systems; ELKARTEK project Dispositivos en Tecnologias Cuanticas This work was supported by the National Key Research and Development Program of China (No. 2017YFA0304100), the National Natural Science Foundation of China (Nos. 11774335 and 11734015), the Key Research Program of Frontier Sciences, CAS (No. QYZDY-SSWSLH003), Innovation Program for Quantum Science and Technology (Nos. 2021ZD0301604 and 2021ZD0301200). Y.C. acknowledges the support of the Students' Innovation and Entrepreneurship Foundation of USTC. This work was partially carried out at the USTC Center for Micro and Nanoscale Research and Fabrication. Y.B. acknowledges to the EU FET Open Grant Quromorphic (828826), the QUANTEK project (ELKARTEK program from the Basque Government, expedient no. KK-2021/00070), the project BRTA QUANTUM: Hacia una especializacion armonizada en tecnologias cuanticas en BRTA (expedient no. KK-2022/00041). J.C. acknowledges the Ramon y Cajal (RYC2018-025197-I) research fellowship, the financial support from Spanish Government via EUR2020-112117 and Nanoscale NMR and complex systems (PID2021-126694NB-C21) projects, the EU FET Open Grant Quromorphic (828826), the ELKARTEK project Dispositivos en Tecnologias Cuanticas (KK-2022/00062), and the Basque Government grant IT1470-22. 30 0 0 8 8 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2056-6387 NPJ QUANTUM INFORM npj Quantum Inform. DEC 29 2022.0 8.0 1 152 10.1038/s41534-022-00669-2 0.0 6 Quantum Science & Technology; Physics, Applied; Physics, Atomic, Molecular & Chemical; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Physics 7L4MK Green Submitted, gold, Green Published 2023-03-23 WOS:000905941800001 0
J Wang, KIK; Zhou, XK; Liang, W; Yan, Z; She, JH Wang, Kevin I-Kai; Zhou, Xiaokang; Liang, Wei; Yan, Zheng; She, Jinhua Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Transfer learning; Data privacy; Collaborative work; Data models; Smart manufacturing; Training data; Servers; Cross-domain prediction; federated learning; industry 4; 0; Internet of Things; transfer learning Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products, processes, and applications with limited training data while preserving data privacy. In this article, a new federated transfer learning framework, federated transfer learning for cross-domain prediction, is proposed to address the challenges of data scarcity and data privacy faced by most machine learning approaches in modern smart manufacturing with cross-domain applications. The framework architecture consists of a central server and several groups of smart devices, where each group handles a different application. The existing applications can share their knowledge through the central server as base models, while new applications can convert a base model to their target-domain models with limited application-specific data using a transfer learning technique. Meanwhile, the federated learning scheme is deployed within a group to further enhance the accuracy of the application-specific model. The integrated framework allows model sharing across the central server and different smart devices without exposing any raw data and, hence, protects the data privacy. Two public datasets, COCO and PETS2009, which represent the source and target applications, are employed for evaluations. The simulation results show that the proposed method outperforms two state-of-the-art machine learning approaches by achieving better learning efficiency and accuracy. [Wang, Kevin I-Kai] Univ Auckland, Dept Elect Comp & Software Engn, Auckland 1010, New Zealand; [Zhou, Xiaokang] Shiga Univ, Fac Data Sci, Hikone 5228522, Japan; [Zhou, Xiaokang] RIKEN, RIKEN Ctr Adv Intelligence Project, Tokyo 1030027, Japan; [Liang, Wei] Hunan Univ Technol & Business, Base Int Sci & Technol Innovat & Cooperat Big Dat, Changsha 410205, Peoples R China; [Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Ser Vices Networks, Xian 710071, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [She, Jinhua] Tokyo Univ Technol, Sch Engn, Tokyo 1920982, Japan University of Auckland; Shiga University; RIKEN; Hunan University of Technology & Business; Xidian University; Aalto University; Tokyo University of Technology Zhou, XK (corresponding author), Shiga Univ, Fac Data Sci, Hikone 5228522, Japan.;Zhou, XK (corresponding author), RIKEN, RIKEN Ctr Adv Intelligence Project, Tokyo 1030027, Japan.;Liang, W (corresponding author), Hunan Univ Technol & Business, Base Int Sci & Technol Innovat & Cooperat Big Dat, Changsha 410205, Peoples R China. kevin.wang@auckland.ac.nz; zhou@biwako.shiga-u.ac.jp; weiliang@csu.edu.cn; zyan@xidian.edu.cn; she@stf.teu.ac.jp yang, zheng/HGC-7753-2022; zheng, yan/GQY-6668-2022 Wang, Kevin/0000-0001-8450-2558; Liang, Wei/0000-0002-0689-256X National Natural Science Foundation of China [72088101, 72091313, 62072171]; National Key R&D Program of China [2017YFE0117500, 2019YFE0190500, 2019GK1010]; Natural Science Foundation of Hunan Province of China [2019JJ40150, TII-21-1859] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Natural Science Foundation of Hunan Province of China(Natural Science Foundation of Hunan Province) This work was supported in part by the National Natural Science Foundation of China under Grant 72088101, Grant 72091313, and Grant 62072171, in part by the National Key R&D Program of China under Grant 2017YFE0117500, Grant 2019YFE0190500, and Grant 2019GK1010, and in part by the Natural Science Foundation of Hunan Province of China under Grant 2019JJ40150 Paper no. TII-21-1859. 28 10 10 23 57 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. JUN 2022.0 18.0 6 4088 4096 10.1109/TII.2021.3088057 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering ZH8ZC Green Accepted 2023-03-23 WOS:000761218600052 0
J Zhang, ZY; Li, Y; Duan, J; Duan, YL; Guo, YX; Cao, YJ; Rehtanz, C Zhang, Zhenyu; Li, Yong; Duan, Jing; Duan, Yilong; Guo, Yixiu; Cao, Yijia; Rehtanz, Christian A non-intrusive load state identification method considering non-local spatiotemporal feature IET GENERATION TRANSMISSION & DISTRIBUTION English Article This paper presents a non-intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load on-off state switching points and the continuous features at on state, a deep convolutional method considering non-local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non-local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques. [Zhang, Zhenyu; Li, Yong; Duan, Jing; Duan, Yilong; Guo, Yixiu; Cao, Yijia] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China; [Rehtanz, Christian] TU Dortmund Univ, Inst Energy Syst Energy Efficiency & Energy Econ, Dortmund, Germany Hunan University; Dortmund University of Technology Li, Y (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China. yongli@hnu.edu.cn zhang, ZY/HJH-6535-2023 Zhang, Zhenyu/0000-0002-7410-4967 International Science and Technology Cooperation Program of China [2018YFE0125300]; national Nature Science Foundation of China [52061130217]; Innovative Construction Program of Hunan Province of China [2019RS1016]; 111 Project of China [B17016]; Excellent Innovation Youth Program of Changsha of China [KQ2009037] International Science and Technology Cooperation Program of China; national Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovative Construction Program of Hunan Province of China; 111 Project of China(Ministry of Education, China - 111 Project); Excellent Innovation Youth Program of Changsha of China This work was supported in part by the International Science and Technology Cooperation Program of China under Grant 2018YFE0125300, in part by the national Nature Science Foundation of China under Grant 52061130217, in part by the Innovative Construction Program of Hunan Province of China under Grant 2019RS1016, in part by the 111 Project of China under Grant B17016, and in part by the Excellent Innovation Youth Program of Changsha of China under Grant KQ2009037. 27 0 0 0 10 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-8687 1751-8695 IET GENER TRANSM DIS IET Gener. Transm. Distrib. FEB 2022.0 16.0 4 792 803 10.1049/gtd2.12330 0.0 OCT 2021 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering YQ4LG 2023-03-23 WOS:000710742500001 0
J Xu, LX; Wang, XF; Bai, L; Xiao, J; Liu, Q; Chen, EH; Jiang, XY; Luo, B Xu, Lixiang; Wang, Xiaofeng; Bai, Lu; Xiao, Jin; Liu, Qi; Chen, Enhong; Jiang, Xiaoyi; Luo, Bin Probabilistic SVM classifier ensemble selection based on GMDH-type neural network PATTERN RECOGNITION English Article Probabilistic SVM; Group method of data handling; Ensemble selection; Regularity criterion; Borda sorting SUPPORT VECTOR MACHINE; RANDOM SUBSPACE; PREDICTION Support vector machine (SVM) provides a good classification and regression ability, especially, for small sample learning. However, in practice, the learning ability of implemented SVM is occasionally far from the expected level. Group method of data handling neural network (GMDH-NN) has been applied in various fields for pattern recognition and data mining. It makes it possible to automatically find interrelations in data, to select an optimal structure of network or model and to improve the accuracy of existing algorithms. In this work we propose to take the advantages of GMDH-NN for further increasing the classification performance of SVM. One weakness of the symmetric regularity criterion of GMDH-NN is that if one of the input attributes has a relatively big range, then it may overcome the other attributes. Thus, we first define a standardized symmetric regularity criterion (SSRC) to evaluate and select the candidate models, and optimize a classifier ensemble selection approach. Secondly, we define a novel structure of initial model of GMDH-NN which is from the posterior probability outputs of SVMs. These probabilistic outputs are generated from the improved Platt's probabilistic outputs. Thirdly, in real classification tasks, different classifiers usually have different classification advantages. So we use probabilistic SVM as base learner and integrate the probabilistic SVMs with GMDH-NN, and then propose a special classifier ensemble selection approach for probabilistic SVM classifiers based on GMDH-NN called GMDH-PSVM. Moreover, we use the Borda sorting and Random weighted Borda sorting to discuss the results of our experiments. Experiments on standard UCI datasets demonstrate the effectiveness of our method. (C) 2020 Elsevier Ltd. All rights reserved. [Xu, Lixiang; Wang, Xiaofeng] Hefei Univ, Dept Math & Phys, Hefei 230601, Anhui, Peoples R China; [Bai, Lu] Cent Univ Finance & Econ, Sch Informat, Beijing 100081, Peoples R China; [Xiao, Jin] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China; [Xu, Lixiang; Liu, Qi; Chen, Enhong] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China; [Jiang, Xiaoyi] Univ Munster, Fac Math & Comp Sci, D-48149 Munster, Germany; [Luo, Bin] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China Hefei University; Central University of Finance & Economics; Sichuan University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Munster; Anhui University Bai, L (corresponding author), Cent Univ Finance & Econ, Sch Informat, Beijing 100081, Peoples R China.;Xiao, J (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China. xulixianghf@163.com; bailucs@cufe.edu.cn; xjxiaojin@126.com Jiang, Xiaoyi/AAA-3532-2022; LUO, BIN/Y-1233-2018 Jiang, Xiaoyi/0000-0001-7678-9528; LUO, BIN/0000-0001-5948-5055; Chen, Enhong/0000-0002-4835-4102 National Natural Science Foundation of China [61860206004, 61672204, 61727809, 61976235, 61602535, U1605251]; Nature Science Research Project of Anhui province [1908085MF185]; Major Scientific Research Projects of Universities of Anhui Province [KJ2019ZD61]; Key Projects of Excellent Young Talents Support Program [gxyq2019113]; National Social Science Foundation of China [18VZL006]; Outstanding Youth Foundation of Sichuan Province [20JCQN0225]; Program for Innovation Research in Central University of Finance and Economics; Tianfu Ten-thousand Talents Program of Sichuan Province; Sichuan University [sksyl201709]; EU [778062] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Nature Science Research Project of Anhui province; Major Scientific Research Projects of Universities of Anhui Province; Key Projects of Excellent Young Talents Support Program; National Social Science Foundation of China; Outstanding Youth Foundation of Sichuan Province; Program for Innovation Research in Central University of Finance and Economics; Tianfu Ten-thousand Talents Program of Sichuan Province; Sichuan University(Sichuan University); EU(European Commission) This work was financially supported by Key International Cooperation Projects of National Natural Science Foundation of China (61860206004), National Natural Science Foundation of China (61672204, 61727809, 61976235, 61602535), Key Projects of Joint Fund of National Natural Science Foundation of China (U1605251), Nature Science Research Project of Anhui province (1908085MF185), Major Scientific Research Projects of Universities of Anhui Province (KJ2019ZD61), Key Projects of Excellent Young Talents Support Program (gxyq2019113), Major Projects of National Social Science Foundation of China (18VZL006), Outstanding Youth Foundation of Sichuan Province (20JCQN0225), Program for Innovation Research in Central University of Finance and Economics, Tianfu Ten-thousand Talents Program of Sichuan Province, and Excellent Youth Fund of Sichuan University (sksyl201709), and the EU Horizon 2020 RISE Project ULTRACEPT > under Grant 778062. 37 16 16 3 38 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. OCT 2020.0 106.0 107373 10.1016/j.patcog.2020.107373 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering MA2VX 2023-03-23 WOS:000541777200001 0
J Xiao, BH; Zhu, MSY; Du, EZ; Liu, WH; Ma, JB; Huang, H; Gong, JS; Diacinti, D; Zhang, K; Gao, B; Liu, H; Jiang, RF; Ji, ZY; Xiong, XB; He, LC; Wu, L; Xu, CJ; Du, MM; Wang, XR; Chen, LM; Wu, KY; Yang, L; Xu, MS; Diacinti, D; Dou, Q; Kwok, TYC; Wang, YXJ Xiao, Ben-Heng; Zhu, Michael S. Y.; Du, Er-Zhu; Liu, Wei-Hong; Ma, Jian-Bing; Huang, Hua; Gong, Jing-Shan; Diacinti, Davide; Zhang, Kun; Gao, Bo; Liu, Heng; Jiang, Ri-Feng; Ji, Zhong-You; Xiong, Xiao-Bao; He, Lai-Chang; Wu, Lei; Xu, Chuan-Jun; Du, Mei-Mei; Wang, Xiao-Rong; Chen, Li-Mei; Wu, Kong-Yang; Yang, Liu; Xu, Mao-Sheng; Diacinti, Daniele; Dou, Qi; Kwok, Timothy Y. C.; Wang, Yi Xiang J. A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0 QUANTITATIVE IMAGING IN MEDICINE AND SURGERY English Article Osteoporosis; vertebral fracture; radiograph; chest; artificial intelligence; deep learning BONE-MINERAL DENSITY; HIP-FRACTURES; END-PLATE; OSTEOPOROSIS; PREVALENT; RISK; MANAGEMENT; CLASSIFICATION; PREVENTION; DIAGNOSIS Background: Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate. Methods: For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OW classification included: minimal and mild grades with <20% or >= 20-25% vertebral height lass respectively, moderate grade with >= 25-40% vertebral height loss, severe grade with >= 40%-2/3 vertebral height loss, and collapsed grade with >= 2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed 'base-model 1.0'. A user-friendly interface was also developed, with the synthesized software termed 'Ofeye 1.0'. Results: Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OW cases. Eighteen OVFs in 15 cases had OVFs of >= moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with >= moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows 'batch processing', for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer. Conclusions: A user-friendly software program was developed for CVF detection on elderly women's lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women. [Xiao, Ben-Heng; Chen, Li-Mei; Wu, Kong-Yang; Yang, Liu; Wang, Yi Xiang J.] Chinese Univ Hong Kong, Fac Med, Dept Imaging & Intervent Radiol, Hong Kong, Peoples R China; [Zhu, Michael S. Y.] Yingran Med Co Ltd, Hong Kong, Peoples R China; [Du, Er-Zhu] Dongguan Tradit Chinese Med Hosp, Dept Radiol, Dongguan, Peoples R China; [Liu, Wei-Hong] Gen Hosp China Resources, Dept Radiol, Wuhan, Peoples R China; [Liu, Wei-Hong] Wuhan Iron & Steel Corp, Wuhan, Peoples R China; [Ma, Jian-Bing] Jiaxing Univ, Affiliated Hosp, Hosp Jiaxing 1, Dept Radiol, Jiaxing, Peoples R China; [Huang, Hua] Southern Univ Sci & Technol, Affiliated Hosp 2, Peoples Hosp Shenzhen 3, Dept Radiol,Natl Clin Res Ctr Infect Dis, Shenzhen, Peoples R China; [Gong, Jing-Shan] Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Dept Radiol, Shenzhen, Peoples R China; [Gong, Jing-Shan] Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen, Peoples R China; [Diacinti, Davide; Diacinti, Daniele] Sapienza Univ Rome, Dept Radiol Sci Oncol & Pathol, Rome, Italy; [Diacinti, Davide] Univ Fdn Hosp Tor Vergata, Dept Diagnost & Mol Imaging Radiol & Radiotherapy, Rome, Italy; [Zhang, Kun] Hunan Univ Chinese Med, Affiliated Hosp 1, Dept Radiol, Changsha, Peoples R China; [Gao, Bo] Guizhou Med Univ, Affiliated Hosp, Dept Radiol, Guiyang, Peoples R China; [Liu, Heng] Zunyi Med Univ, Affiliated Hosp, Dept Radiol, Zunyi, Guizhou, Peoples R China; [Jiang, Ri-Feng] Fujian Med Univ, Union Hosp, Dept Radiol, Fuzhou, Peoples R China; [Ji, Zhong-You] Fujian Med Univ, Union Hosp, PET CT Ctr, Fuzhou, Peoples R China; [Xiong, Xiao-Bao] Zhejiang Prov Tongde Hosp, Dept Radiol, Hangzhou, Peoples R China; [He, Lai-Chang] Nanchang Univ, Affiliated Hosp 1, Dept Radiol, Nanchang, Jiangxi, Peoples R China; [Wu, Lei; Xu, Mao-Sheng] Zhejiang Chinese Med Univ, Affiliated Hosp 1, Dept Radiol, Hangzhou, Peoples R China; [Xu, Chuan-Jun] Nanjing Univ Chinese Med, Hosp Nanjing 2, Dept Radiol, Nanjing, Peoples R China; [Du, Mei-Mei] Wenzhou Med Univ, Affiliated Hosp 2, Dept Radiol, Wenzhou, Peoples R China; [Du, Mei-Mei] Wenzhou Med Univ, Yuying Childrens Hosp, Wenzhou, Peoples R China; [Wang, Xiao-Rong] Ningbo First Hosp, Dept Radiol, Ningbo, Peoples R China; [Wu, Kong-Yang] Jinan Univ, Coll Elect & Informat Engn, Guangzhou, Peoples R China; [Dou, Qi] Chinese Univ Hong Kong, Fac Engn, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Kwok, Timothy Y. C.] Chinese Univ Hong Kong, Fac Med, JC Ctr Osteoporosis Care & Control, Hong Kong, Peoples R China Chinese University of Hong Kong; China Baowu Steel Group; Jiaxing University; Southern University of Science & Technology; Jinan University; Southern University of Science & Technology; Sapienza University Rome; Hunan University of Chinese Medicine; Guizhou Medical University; Zunyi Medical University; Fujian Medical University; Fujian Medical University; Nanchang University; Zhejiang Chinese Medical University; Nanjing University of Chinese Medicine; Wenzhou Medical University; Wenzhou Medical University; Jinan University; Chinese University of Hong Kong; Chinese University of Hong Kong Wang, YXJ (corresponding author), Chinese Univ Hong Kong, Fac Med, Dept Imaging & Intervent Radiol, Shatin, Hong Kong, Peoples R China. yixiang_wang@cuhk.edu.hk wang, xiao/HGB-7081-2022 wang, xiao/0000-0002-4088-3341 Hong Kong ITSP project [ITS/334/18] Hong Kong ITSP project This study was supported by Hong Kong ITSP project (No. ITS/334/18). 72 2 2 1 4 AME PUBL CO SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2223-4292 2223-4306 QUANT IMAG MED SURG Quant. Imaging Med. Surg. AUG 2022.0 12.0 8 4259 + 10.21037/qims-22-433 0.0 MAY 2022 17 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 2T3KM 35919046.0 Green Accepted, gold 2023-03-23 WOS:000810608400001 0
J Jing, N; Shi, ZJ; Hu, Y; Yuan, J Jing, Nan; Shi, Zijing; Hu, Yi; Yuan, Ji Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases APPLIED INTELLIGENCE English Article Coronavirus disease 2019 (COVID-19); eXtreme gradient boosting (XGB); SHapley additive exPlanation (SHAP); Dual-stage attention-based recurrent neural network (DA-RNN) INFLUENZA; TRANSMISSION; EVOLUTION The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R-2. Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic. [Jing, Nan; Shi, Zijing; Hu, Yi] Shanghai Univ, SHU UTS SILC Business Sch, Shanghai 201800, Peoples R China; [Yuan, Ji] Onewo Space Tech Serv Co Ltd, Shenzhen 518049, Peoples R China; [Yuan, Ji] Tech Univ Munich, Engn Risk Anal Grp, D-80333 Munich, Germany Shanghai University; Technical University of Munich Yuan, J (corresponding author), Onewo Space Tech Serv Co Ltd, Shenzhen 518049, Peoples R China.;Yuan, J (corresponding author), Tech Univ Munich, Engn Risk Anal Grp, D-80333 Munich, Germany. yuanji1239@gmail.com Yuan, Ji/0000-0002-4369-647X 41 2 2 4 10 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-669X 1573-7497 APPL INTELL Appl. Intell. FEB 2022.0 52.0 3 3303 3318 10.1007/s10489-021-02616-8 0.0 JUL 2021 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science YY8RB 34764608.0 Bronze 2023-03-23 WOS:000669766500001 0
J Li, J; Song, KS; Behler, J Li, Jun; Song, Kaisheng; Behler, Joerg A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry PHYSICAL CHEMISTRY CHEMICAL PHYSICS English Article PRODUCT ENERGY DISPOSAL; MODE SPECIFICITY; BOND SELECTIVITY; SURFACES; REPRESENTATION The availability of accurate full-dimensional potential energy surfaces (PESs) is a mandatory condition for efficient computer simulations of molecular systems. Much effort has been devoted to developing reliable PESs with physically sound properties, such as the invariance of the energy with respect to the permutation of chemically identical atoms. In this work, we compare the performance of four neural network (NN)-based approaches with a rigorous permutation symmetry for fitting five typical reaction systems: OH + CO, H + H2S, H + NH3, H + CH4 and OH + CH4. The methods can be grouped into two categories, invariant polynomial based NNs and high-dimensional NN potentials (HD-NNPs). For the invariant polynomial based NNs, three types of polynomials, permutation invariant polynomials (PIPs), non-redundant PIPs (NRPIPs) and fundamental invariants (FIs), are used in the input layer of the NN. In HD-NNPs, the total energy is the sum of atomic contributions, each of which is given by an individual atomic NN with input vectors consisting of sets of atom-centered symmetry functions. Our results show that all methods exhibit a similar level of accuracy for the energies with respect to ab initio data obtained at the explicitly correlated coupled cluster level of theory (CCSD(T)-F12a). The HD-NNP method allows study of systems with larger numbers of atoms, making it more generally applicable than invariant polynomial based approaches, which in turn are computationally more efficient for smaller systems. To illustrate the applicability of the obtained potentials, quasi-classical trajectory calculations have been performed for the OH + CH4 -> H2O + CH3 reaction to reveal its complicated mode specificity. [Li, Jun; Song, Kaisheng] Chongqing Univ, Sch Chem & Chem Engn, Chongqing 401331, Peoples R China; [Li, Jun; Behler, Joerg] Univ Gottingen, Inst Phys Chem, Theoret Chem, Tammannstr 6, D-37077 Gottingen, Germany Chongqing University; University of Gottingen Li, J (corresponding author), Chongqing Univ, Sch Chem & Chem Engn, Chongqing 401331, Peoples R China.;Li, J; Behler, J (corresponding author), Univ Gottingen, Inst Phys Chem, Theoret Chem, Tammannstr 6, D-37077 Gottingen, Germany. jli15@cqu.edu.cn; joerg.behler@uni-goettingen.de Li, Jun/H-4980-2013; Behler, Jörg/C-8191-2014 Li, Jun/0000-0003-2392-8322; Behler, Jörg/0000-0002-1220-1542 National Natural Science Foundation of China [21573027]; Alexander von Humboldt Foundation; DFG [Be3264/11-2] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); DFG(German Research Foundation (DFG)) This work was financially supported by National Natural Science Foundation of China (Contract No. 21573027 to J. L.) and by the Alexander von Humboldt Foundation (Humboldt Fellowship for Experienced Researchers for JL). JB acknowledges the DFG for a Heisenberg Professorship (Be3264/11-2). 69 35 35 2 29 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1463-9076 1463-9084 PHYS CHEM CHEM PHYS Phys. Chem. Chem. Phys. MAY 21 2019.0 21.0 19 9672 9682 10.1039/c8cp06919k 0.0 11 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics IF4SH 30672927.0 hybrid 2023-03-23 WOS:000473071200057 0
J Shi, XH; Ke, ZX; Zhou, YL; Jin, H; Lu, L; Zhang, X; He, LG; Hu, ZY; Wang, F Shi, Xuanhua; Ke, Zhixiang; Zhou, Yongluan; Jin, Hai; Lu, Lu; Zhang, Xiong; He, Ligang; Hu, Zhenyu; Wang, Fei Deca: A Garbage Collection Optimizer for In-Memory Data Processing ACM TRANSACTIONS ON COMPUTER SYSTEMS English Article Data processing system; distributed system; garbage collection; in-memory; memory management BIG DATA; JAVA; PERFORMANCE; RUNTIME In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca, 1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2x to 22.7x speedup in terms of execution time in cases without data spilling and 16x to 41.6x speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems. [Shi, Xuanhua; Ke, Zhixiang; Jin, Hai] Huazhong Univ Sci & Technol, Serv Comp Technol & Syst Lab, Big Data Technol & Syst Lab, Sch Comp Sci & Technol, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China; [Zhou, Yongluan] Univ Copenhagen, Dept Comp Sci, DK-2100 Copenhagen, Denmark; [Lu, Lu] Alibaba Grp, Hangzhou, Zhejiang, Peoples R China; [Zhang, Xiong; Hu, Zhenyu; Wang, Fei] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China; [He, Ligang] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England Huazhong University of Science & Technology; University of Copenhagen; Alibaba Group; Huazhong University of Science & Technology; University of Warwick Shi, XH (corresponding author), Huazhong Univ Sci & Technol, Serv Comp Technol & Syst Lab, Big Data Technol & Syst Lab, Sch Comp Sci & Technol, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China. xhshi@hust.edu.cn; zhxke@hust.edu.cn; zhou@di.ku.dk; hjin@hust.edu.cn; shiyi.ll@alibaba-inc.com; wxzhang@hust.edu.cn; liganghe@dcs.warwick.ac.uk; cszhenyuhu@hust.edu.cn; feiwg@hust.edu.cn National Key R&D Program of China [2017YFC0803700]; NSFC [61772218, 61433019, U1435217]; Outstanding Youth Foundation of Hubei Province [2016CFA032] National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Outstanding Youth Foundation of Hubei Province This work is supported by the National Key R&D Program of China (No. 2017YFC0803700), the NSFC (No. 61772218, 61433019, U1435217), and the Outstanding Youth Foundation of Hubei Province (No. 2016CFA032). 54 5 5 1 14 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0734-2071 1557-7333 ACM T COMPUT SYST ACM Trans. Comput. Syst. MAR 2019.0 36.0 1 3 10.1145/3310361 0.0 47 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HQ6KS Bronze, Green Submitted, Green Accepted 2023-03-23 WOS:000462524200003 0
J Qarony, W; Khan, HA; Hossain, MI; Kozawa, M; Salleo, A; Hardeberg, JY; Fujiwara, H; Tsang, YH; Knipp, D Qarony, Wayesh; Khan, Haris Ahmad; Hossain, Mohammad Ismail; Kozawa, Masayuki; Salleo, Alberto; Hardeberg, Jon Yngve; Fujiwara, Hiroyuki; Tsang, Yuen Hong; Knipp, Dietmar Beyond Tristimulus Color Vision with Perovskite-Based Multispectral Sensors ACS APPLIED MATERIALS & INTERFACES English Article multispectral color sensor; perovskite; optical detectors; color sensor; colorimetry OPTICAL-PROPERTIES; FILTER ARRAYS; SOLAR-CELLS; STABILITY; EFFICIENT; CAMERA In this study, optical multispectral sensors based on perovskite semiconductors have been proposed, simulated, and characterized. The perovskite material system combined with the 3D vertical integration of the sensor channels allow for realizing sensors with high sensitivities and a high spectral resolution. The sensors can be applied in several emerging areas, including biomedical imaging, surveillance, complex motion planning of autonomous robots or vehicles, artificial intelligence, and agricultural applications. The sensor elements can be vertically integrated on a readout electronic to realize sensor arrays and multispectral digital cameras. In this study, three- and six-channel vertically stacked perovskite sensors are optically designed, electromagnetically simulated, and colorimetrically characterized to evaluate the color reproduction. The proposed sensors allow for the implementation of snapshot cameras with high sensitivity. The proposed sensor is compared to other sensor technologies in terms of sensitivity and selectivity. [Khan, Haris Ahmad] Wageningen Univ & Res, Farm Technol Grp, NL-6700 AA Wageningen, Netherlands; [Kozawa, Masayuki; Fujiwara, Hiroyuki] Gifu Univ, Dept Elect Elect & Comp Engn, Gifu 5011193, Japan; [Salleo, Alberto; Knipp, Dietmar] Stanford Univ, Geballe Lab Adv Mat, Dept Mat Sci & Engn, Stanford, CA 94305 USA; [Hardeberg, Jon Yngve] NTNU Norwegian Univ Sci & Technol, Norwegian Colour & Visual Comp Lab, N-2802 Gjovik, Norway; [Qarony, Wayesh; Hossain, Mohammad Ismail; Tsang, Yuen Hong] Hong Kong Polytech Univ, Dept Appl Phys, Kowloon, Hong Kong 999077, Peoples R China; [Qarony, Wayesh] Univ Calif Berkeley, Mat Sci Div, Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; [Qarony, Wayesh; Hossain, Mohammad Ismail] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA Wageningen University & Research; Gifu University; Stanford University; Norwegian University of Science & Technology (NTNU); Hong Kong Polytechnic University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; University of California System; University of California Berkeley Knipp, D (corresponding author), Stanford Univ, Geballe Lab Adv Mat, Dept Mat Sci & Engn, Stanford, CA 94305 USA.;Qarony, W; Hossain, MI; Tsang, YH (corresponding author), Hong Kong Polytech Univ, Dept Appl Phys, Kowloon, Hong Kong 999077, Peoples R China.;Qarony, W (corresponding author), Univ Calif Berkeley, Mat Sci Div, Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA.;Qarony, W; Hossain, MI (corresponding author), Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA. wayesh@berkeley.edu; m.hossain.jub@gmail.com; yuen.tsang@polyu.edu.hk; dknipp@stanford.edu Qarony, Dr. Wayesh/V-8427-2017; Tsang, Yuen Hong/K-8372-2012 Qarony, Dr. Wayesh/0000-0002-4860-9558; Hossain, Mohammad Ismail/0000-0003-1290-9329; Tsang, Yuen Hong/0000-0001-5632-5224 Research Grant Council of Hong Kong [GRF 152093/18E]; Innovation and Technology Commission of Hong Kong [GHP/040/19SZ]; Hong Kong Polytechnic University [B-Q65N, G-YBVG] Research Grant Council of Hong Kong(Hong Kong Research Grants Council); Innovation and Technology Commission of Hong Kong; Hong Kong Polytechnic University(Hong Kong Polytechnic University) This work was financially supported by the Research Grant Council of Hong Kong (project number: GRF 152093/18E), the Innovation and Technology Commission of Hong Kong (project number: GHP/040/19SZ), and the Hong Kong Polytechnic University (grant code: B-Q65N, G-YBVG). 64 2 2 19 46 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1944-8244 1944-8252 ACS APPL MATER INTER ACS Appl. Mater. Interfaces MAR 9 2022.0 14.0 9 11645 11653 10.1021/acsami.1c25095 0.0 9 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 0U3HI 35191665.0 2023-03-23 WOS:000787543300057 0
J Gu, S; Wang, LH; He, L; He, XD; Wang, J Gu, Song; Wang, Lihui; He, Long; He, Xianding; Wang, Jian Gaze Estimation via a Differential Eyes' Appearances Network with a Reference Grid ENGINEERING English Article Gaze estimation; Differential gaze; Siamese neural network; Cross-person evaluations; Human-robot collaboration A person's eye gaze can effectively express that person's intentions. Thus, gaze estimation is an important approach in intelligent manufacturing to analyze a person's intentions. Many gaze estimation methods regress the direction of the gaze by analyzing images of the eyes, also known as eye patches. However, it is very difficult to construct a person-independent model that can estimate an accurate gaze direction for every person due to individual differences. In this paper, we hypothesize that the difference in the appearance of each of a person's eyes is related to the difference in the corresponding gaze directions. Based on this hypothesis, a differential eyes' appearances network (DEANet) is trained on public datasets to predict the gaze differences of pairwise eye patches belonging to the same individual. Our proposed DEANet is based on a Siamese neural network (SNNet) framework which has two identical branches. A multi-stream architecture is fed into each branch of the SNNet. Both branches of the DEANet that share the same weights extract the features of the patches; then the features are concatenated to obtain the difference of the gaze directions. Once the differential gaze model is trained, a new person's gaze direction can be estimated when a few calibrated eye patches for that person are provided. Because person specific calibrated eye patches are involved in the testing stage, the estimation accuracy is improved. Furthermore, the problem of requiring a large amount of data when training a person-specific model is effectively avoided. A reference grid strategy is also proposed in order to select a few references as some of the DEANet's inputs directly based on the estimation values, further thereby improving the estimation accuracy. Experiments on public datasets show that our proposed approach outperforms the state-of-the-art methods. (C) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. [Gu, Song; He, Long; He, Xianding; Wang, Jian] Chengdu Aeronaut Polytech, Chengdu 610100, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden Royal Institute of Technology Wang, LH (corresponding author), KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden. lihuiw@kth.se Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049 Science and Technology Support Project of Sichuan Science and Technology Department [2018SZ0357]; China Scholarship Science and Technology Support Project of Sichuan Science and Technology Department; China Scholarship This work was supported by the Science and Technology Support Project of Sichuan Science and Technology Department (2018SZ0357) and China Scholarship. 52 3 3 2 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2095-8099 2096-0026 ENGINEERING-PRC Engineering JUN 2021.0 7.0 6 777 786 10.1016/j.eng.2020.08.027 0.0 JUL 2021 10 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering TQ6XV gold 2023-03-23 WOS:000678424600015 0
J Wang, JQ; Bedei, J; Deng, J; Andert, J; Zhu, DH; Li, LG Wang, Jinqiu; Bedei, Julian; Deng, Jun; Andert, Jakob; Zhu, Denghao; Li, Liguang Detection of transient low-temperature combustion characteristics by ion current-The missing link for homogeneous charge compression ignition control? APPLIED ENERGY English Article Homogeneous charge compression ignition; Ion current; Artificial neural network; In-cylinder pressure analysis; In-cycle control; Black-box modeling HCCI Homogeneous charge compression ignition is a promising low-temperature combustion mode because of its high efficiency and low emissions; however, its strong cycle-to-cycle coupling effect, which caused by the recirculation of exhaust gases, may entail problems with low combustion stability. In this study, a new concept that extracts more comprehensive combustion information in homogeneous charge compression ignition is proposed through the integration of ion current and in-cylinder pressure sensing. To analyze the correlations of combustion parameters and their relationships with the ion current parameters, steady-state measurements were conducted. Dynamic measurements were implemented to form a comprehensive database for artificial neural network training. To investigate the hypothesis that the ion current gives additional information beyond the pressure trace, black-box models based on experimental data are trained. The results show that the baseline model trained purely with the manipulated variables has the worst performance, while the model including both in-cylinder pressure and ion current derived parameters has the best predictability, with the overall root-mean square error reduced by 2.5% in predicting combustion phasing, compared with in-cylinder pressure based model. It demonstrates that a significant improvement in model quality can be achieved by the combination of ion current and in-cylinder pressure sensing, which indicates that the ion current signal contains information that goes beyond a sole analysis of the pressure trace. By complementing the in-cylinder pressure, the use of the ion current as a chemical sensor for low-temperature combustion thus appears very promising for the stable control of homogeneous charge compression ignition combustion. [Wang, Jinqiu; Deng, Jun; Zhu, Denghao; Li, Liguang] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China; [Bedei, Julian; Andert, Jakob] Rhein Westfal TH Aachen, Mechatron Syst Combust Engines, Aachen, Germany; [Li, Liguang] Tongji Univ, Chinesisch Deutsch Hsch Kolleg, Shanghai 201804, Peoples R China Tongji University; RWTH Aachen University; Tongji University Li, LG (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China. liguang@tongji.edu.cn Andert, Jakob Lukas/W-4495-2017 Andert, Jakob Lukas/0000-0002-6754-1907; Zhu, Denghao/0000-0002-5055-4496; Wang, Jinqiu/0000-0002-5754-6798; Bedei, Julian/0000-0001-8260-8754 German Research Association (Deutsche Forschungsgemeinschaft, DFG) [392430670]; China Natural Science Foundation [51761135105]; KSPG Professorship Chair of Chinesisch-Deutsche Hochschulkolleg (CDHK) in Tongji University; outstanding youth exchange program of the Chinese Association for Science and Technology German Research Association (Deutsche Forschungsgemeinschaft, DFG)(German Research Foundation (DFG)); China Natural Science Foundation(National Natural Science Foundation of China (NSFC)); KSPG Professorship Chair of Chinesisch-Deutsche Hochschulkolleg (CDHK) in Tongji University; outstanding youth exchange program of the Chinese Association for Science and Technology This research was performed as part of the joint Sino-German research project Ion-Current Sensor-based Closed-Loop Control of Lean Gasoline Combustion with High Compression Ratio, which is funded by the German Research Association (Deutsche Forschungsgemeinschaft, DFG grant number 392430670), China Natural Science Foundation (No.51761135105) and KSPG Professorship Chair of Chinesisch-Deutsche Hochschulkolleg (CDHK) in Tongji University. The first author would like to thank the outstanding youth exchange program of the Chinese Association for Science and Technology for their financial support during the author's overseas exchanges. 34 7 7 0 6 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy FEB 1 2021.0 283.0 116299 10.1016/j.apenergy.2020.116299 0.0 JAN 2021 16 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering QA2ND 2023-03-23 WOS:000613284200006 0
J Alizamir, A; Gholami, A; Bahrami, N; Ostadhassan, M Alizamir, Aida; Gholami, Amin; Bahrami, Nader; Ostadhassan, Mehdi Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models br ACS OMEGA English Article; Early Access SUPPORT VECTOR REGRESSION; ARTIFICIAL NEURAL-NETWORK; BUBBLE POINT PRESSURE; COMMITTEE MACHINE; ASPHALTENE PRECIPITATION; FUZZY-LOGIC; GENETIC ALGORITHM; DISSOLVED-GASES; LOAD TRANSPORT; BLOOD-GLUCOSE Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques-optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)-were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model's accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin's RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI. [Alizamir, Aida] Hamadan Univ Med Sci, Sch Med, Dept Pathol, Hamadan 6517838738, Iran; [Gholami, Amin] Iranian Offshore Oil Co, Reservoir Div, Tehran 1966653943, Iran; [Bahrami, Nader] Carsome Co, Financial Transact Dept, Petaling Jaya 47800, Selangor, Malaysia; [Ostadhassan, Mehdi] Ferdowsi Univ Mashhad, Dept Geol, Mashhad 9177948974, Iran; [Ostadhassan, Mehdi] Univ Kiel, Inst Geosci, Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany; [Ostadhassan, Mehdi] Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China Ferdowsi University Mashhad; University of Kiel; Northeast Petroleum University Ostadhassan, M (corresponding author), Ferdowsi Univ Mashhad, Dept Geol, Mashhad 9177948974, Iran.;Ostadhassan, M (corresponding author), Univ Kiel, Inst Geosci, Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany.;Ostadhassan, M (corresponding author), Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China. mehdi.ostadhassan@ifg.kiel.de Gholami, Amin/AAC-3218-2020 Gholami, Amin/0000-0003-0053-4566; Alizamir, Aida/0009-0002-0791-9063 86 0 0 2 2 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2470-1343 ACS OMEGA ACS Omega 10.1021/acsomega.2c00746 0.0 SEP 2022 14 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry 4P6VC 36188321.0 Green Accepted 2023-03-23 WOS:000855530400001 0
J Huang, ZX; Juarez, JM; Duan, HL; Li, HM Huang, Zhengxing; Juarez, Jose M.; Duan, Huilong; Li, Haomin Length of stay prediction for clinical treatment process using temporal similarity EXPERT SYSTEMS WITH APPLICATIONS English Article Length of stay; Temporal similarity; Case-based reasoning; Artificial intelligence in medicine INTENSIVE-CARE-UNIT; PATHWAYS; SUPPORT; DESIGN In clinical treatment processes, inpatient length of stay (LOS) is not only a readily available indicator of hospital activity, but also a reasonable proxy of resource consumption. Accurate inpatient LOS prediction has strong implications for health service delivery. Major techniques proposed (statistical approaches or artificial neuronal networks) consider a priori knowledge, such as demographics or patient physical factors, providing accurate methods to estimate LOS at early stages of the patient (admission). However, unexpected scenarios and variations are commonplaces of clinical treatment processes that have a dramatical impact on the LOS. Therefore, these predictors should deal with adaptability, considering the temporal evolution of the patient. In this paper, we propose an inpatient LOS prediction approach across various stages of clinical treatment processes. This proposal relies on a kind of regularity assumption demanding that patient traces of the specific treatment process with similar medical behaviors have similar LOS. Therefore, this approach follows a Case-based Reasoning methodology since it predicts an inpatient LOS of a partial patient trace by referring to the past traces of clinical treatment processes that have similar medical behaviors with the current one. The proposal is evaluated using 284 patient traces from the pulmonary infection CTPs, extracted from Zhejiang Huzhou Central Hospital of China. (c) 2013 Elsevier Ltd. All rights reserved. [Huang, Zhengxing; Duan, Huilong; Li, Haomin] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou 310003, Zhejiang, Peoples R China; [Juarez, Jose M.] Univ Murcia, Dept Informat & Commun Engn, E-30001 Murcia, Spain Zhejiang University; University of Murcia Li, HM (corresponding author), Zhejiang Univ, Coll Biomed Engn & Instrument Sci, 310008 Zhou Yiqing Bldg 510,Zheda Rd 38, Hangzhou 310003, Zhejiang, Peoples R China. lhm@vico-lab.com Juarez, Jose M./K-8042-2017; Li, Haomin/AAY-5044-2021 Juarez, Jose M./0000-0003-1776-1992; Li, Haomin/0000-0002-6420-7719 National Nature Science Foundation of China [81101126]; Seneca Research Foundation of the Region of Murcia [15277/PI/10]; Spanish Ministry of Science and Innovation + European FEDER + PlanE funds [TIN2009-14372-C03-01] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Seneca Research Foundation of the Region of Murcia(Fundacion Seneca); Spanish Ministry of Science and Innovation + European FEDER + PlanE funds(Ministry of Science and Innovation, Spain (MICINN)) This work was partially funded by the National Nature Science Foundation of China under Grant No. 81101126, by the Seneca Research Foundation of the Region of Murcia under project 15277/PI/10, and by the Spanish Ministry of Science and Innovation + European FEDER + PlanE funds under the project TIN2009-14372-C03-01. 36 19 20 0 31 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 15 2013.0 40.0 16 6330 6339 10.1016/j.eswa.2013.05.066 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 197NF 2023-03-23 WOS:000322857200012 0
J Liu, H; Tian, HQ; Li, YF Liu, Hui; Tian, Hong-qi; Li, Yan-fei Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions ENERGY CONVERSION AND MANAGEMENT English Article Wind energy; Wind speed forecasting; Wind speed predictions; Empirical mode decomposition; Fast ensemble empirical mode decomposition; Wavelet decomposition; Wavelet packet decomposition; Artificial neural networks EMPIRICAL MODE DECOMPOSITION; OPTIMIZATION; TRANSFORM The technology of wind speed prediction is important to guarantee the safety of wind power utilization. Compared to the single algorithms, the hybrid ones always have better performance in the wind speed predictions. In this paper, three most important decomposing algorithms [Wavelet Decomposition WD/Wavelet Packet Decomposition - WPD/Empirical Mode Decomposition - EMD] and a latest decomposing algorithm [Fast Ensemble Empirical Mode Decomposition - FEEMD] are all adopted to realize the wind speed high-precision predictions with two representative networks [MLP Neural Network/ANFIS Neural Network]. Based on the hybrid forecasting framework, two new wind speed forecasting methods [FEEMD-MLP and FEEMD-ANFIS] are proposed. Additionally, a series of performance comparison is provided, which includes EMD-MLP, FEEMD-MLP, EDM-ANFIS, FEEMD-ANFIS, WD-MLP, WD-ANFIS, WPD-MLP and WPD-ANFIS. The aim of the study is to investigate the decomposing and forecasting performance of the different hybrid models. Two experimental results show that: (1) Due to the inclusion of the decomposing algorithms, the hybrid ANN algorithms have better performance than their corresponding single ANN algorithms; (2) the proposed new FEEMD-MLP hybrid model has the best performance in the three-step predictions while the WPD-MLP hybrid model has the best performance in the one-step predictions; (3) among the decomposing algorithms, the FEEMD and WPD have better performance than the EMD and WD, respectively; (4) in the forecasting neural networks, the MLP has better performance than the ANFIS; and (5) all of the proposed hybrid algorithms are suitable for the wind speed predictions. (C) 2014 Elsevier Ltd. All rights reserved. [Liu, Hui; Tian, Hong-qi; Li, Yan-fei] Cent South Univ, Sch Traff & Transportat Engn, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Hunan, Peoples R China; [Liu, Hui] Univ Rostock, Fac Informat & Elect Engn, Inst Automat, D-18119 Rostock, Mecklenburg Vor, Germany Central South University; University of Rostock Liu, H (corresponding author), Cent South Univ, Sch Traff & Transportat Engn, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Hunan, Peoples R China. csuliuhui@csu.edu.cn Liu, Hui/0000-0001-6654-4965 National Natural Science Foundation of China [51308553, U1134203, U1334205]; Scientific Research Fund of Hunan Provincial Education Department; Shenghua Yu-ying Talents Program of the Central South University; Fundamental Research Funds for the Central Universities of China [2012QNZT029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific Research Fund of Hunan Provincial Education Department(Hunan Provincial Education Department); Shenghua Yu-ying Talents Program of the Central South University; Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) The authors would like to thank the referees for their precious reviewing. This study is fully supported by the National Natural Science Foundation of China (Grant No. 51308553), the Scientific Research Fund of Hunan Provincial Education Department (Principle Investigator: Dr. Hui Liu), the Shenghua Yu-ying Talents Program of the Central South University (Principle Investigator: Dr. Hui Liu) and the Fundamental Research Funds for the Central Universities of China (Project No. 2012QNZT029). This study is partly supported by the National Natural Science Foundation of China (Grant No. U1134203, U1334205). 37 102 108 5 143 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. JAN 1 2015.0 89.0 1 11 10.1016/j.enconman.2014.09.060 0.0 11 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics AW9BH 2023-03-23 WOS:000346552200001 0
J Liu, YJ; Kyne, M; Wang, S; Wang, S; Yu, XY; Wang, C Liu, Ya-Juan; Kyne, Michelle; Wang, Shuang; Wang, Sheng; Yu, Xi-Yong; Wang, Cheng A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY English Article Single-cell Raman spectroscopy; Graphical user-friendly interface; Matlab; Machine learning LEAST-SQUARES; COMPONENT ANALYSIS The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named CELL IMAGE for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow CELL IMAGE to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification. [Liu, Ya-Juan; Wang, Sheng; Yu, Xi-Yong] Guangzhou Med Univ, Sch Pharmaceut Sci, Key Lab Mol Target & Clin Pharmacol, Guangzhou 511436, Peoples R China; [Liu, Ya-Juan; Wang, Sheng; Yu, Xi-Yong] Guangzhou Med Univ, Sch Pharmaceut Sci, NMPA & State Key Lab Resp Dis, Guangzhou 511436, Peoples R China; [Liu, Ya-Juan; Wang, Sheng; Yu, Xi-Yong] Guangzhou Med Univ, Affiliated Hosp 5, Guangzhou 511436, Peoples R China; [Kyne, Michelle] Natl Univ Ireland, Sch Chem, Galway H91 CF50, Ireland; [Wang, Shuang] Northwest Univ, Inst Photon & Photon Technol, North Taibai Rd, Xian 710069, Shaanxi, Peoples R China; [Wang, Cheng] Trinity Coll Dublin, Smurfit Inst Genet, Dublin, Ireland Guangzhou Medical University; Guangzhou Medical University; Guangzhou Medical University; Northwest University Xi'an; Trinity College Dublin Yu, XY (corresponding author), Guangzhou Med Univ, Sch Pharmaceut Sci, Key Lab Mol Target & Clin Pharmacol, Guangzhou 511436, Peoples R China.;Yu, XY (corresponding author), Guangzhou Med Univ, Sch Pharmaceut Sci, NMPA & State Key Lab Resp Dis, Guangzhou 511436, Peoples R China.;Yu, XY (corresponding author), Guangzhou Med Univ, Affiliated Hosp 5, Guangzhou 511436, Peoples R China.;Wang, C (corresponding author), Trinity Coll Dublin, Smurfit Inst Genet, Dublin, Ireland. yuxycn@gzhmu.edu.cn; wangc4@tcd.ie National Natural Science Foundation of China [U1601227]; Science and Technology Programs of Guangdong Province [2015B020225006]; Bureau of Education of Guangzhou Municipality Foundation [201831836] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Programs of Guangdong Province; Bureau of Education of Guangzhou Municipality Foundation Acknowledgements We appreciate the support of National Natural Science Foundation of China (No. U1601227) , the Science and Technology Programs of Guangdong Province (2015B020225006) , and Bureau of Education of Guangzhou Municipality Foundation (No.201831836) . 26 0 0 7 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1386-1425 1873-3557 SPECTROCHIM ACTA A Spectroc. Acta Pt. A-Molec. Biomolec. Spectr. DEC 5 2022.0 282.0 121686 10.1016/j.saa.2022.121686 0.0 JUL 2022 9 Spectroscopy Science Citation Index Expanded (SCI-EXPANDED) Spectroscopy 3Q3YG 35921751.0 2023-03-23 WOS:000838169900004 0
J Elbeltagi, A; Raza, A; Hu, YG; Al-Ansari, N; Kushwaha, NL; Srivastava, A; Vishwakarma, DK; Zubair, M Elbeltagi, Ahmed; Raza, Ali; Hu, Yongguang; Al-Ansari, Nadhir; Kushwaha, N. L.; Srivastava, Aman; Vishwakarma, Dinesh Kumar; Zubair, Muhammad Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration APPLIED WATER SCIENCE English Article Reference evapotranspiration; Additive regression; Sensitivity and regression analysis; Machine learning; Hydrological modeling SUPPORT VECTOR MACHINE; RANDOM SUBSPACE METHOD; METEOROLOGICAL DATA; DIFFERENT CLIMATES; NEURAL-NETWORKS; MODELS; CROP; SVM For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (T-max, T-min), average relative humidity (RHavg), average wind speed (U-x), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that T-min, RHAvg, U-x, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms. [Elbeltagi, Ahmed] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt; [Raza, Ali; Hu, Yongguang] Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [Al-Ansari, Nadhir] Lulea Univ Technol, Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Kushwaha, N. L.] ICAR Indian Agr Res Inst, Div Agr Engn, New Delhi 110012, India; [Srivastava, Aman] Indian Inst Technol IIT Kharagpur, Dept Civil Engn, Kharagpur 721302, W Bengal, India; [Vishwakarma, Dinesh Kumar] GB Pant Univ Agr & Technol, Dept Irrigat & Drainage Engn, Pantnagar 263145, Uttarakhand, India; [Zubair, Muhammad] Southeast Univ, Sch Transportat, Nanjing 21009, Peoples R China Egyptian Knowledge Bank (EKB); Mansoura University; Jiangsu University; Lulea University of Technology; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; Govind Ballabh Pant University of Agriculture Technology; Southeast University - China Elbeltagi, A (corresponding author), Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt.;Raza, A (corresponding author), Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Jiangsu, Peoples R China.;Al-Ansari, N (corresponding author), Lulea Univ Technol, Environm & Nat Resources Engn, S-97187 Lulea, Sweden. ahmedelbeltagy81@mans.edu.eg; alir3201@gmail.com; deerhu@ujs.edu.cn; nadhir.alansari@ltu.se; nand.kushwaha@icar.gov.in; amansrivastava1397@kgpian.iitkgp.ac.in; dinesh.vishwakarma4820@gmail.com; 233217035@seu.edu.cn Kushwaha, N L/AAR-5739-2021; Raza, Ali/GLR-6548-2022; kumar, Pankaj/HPF-8395-2023; Kushwaha, Nand Lal/GSO-1668-2022 Raza, Ali/0000-0001-9207-5779; Kushwaha, Nand Lal/0000-0001-8171-1602; Srivastava, Aman/0000-0001-9253-3485 70 13 13 7 12 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2190-5487 2190-5495 APPL WATER SCI Appl. Water Sci. JUL 2022.0 12.0 7 152 10.1007/s13201-022-01667-7 0.0 18 Water Resources Science Citation Index Expanded (SCI-EXPANDED) Water Resources 1A5KU gold, Green Published 2023-03-23 WOS:000791796200001 0
C Ribeiro, B; Chen, N; Kovacec, A Onieva, E; Santos, I; Osaba, E; Quintian, H; Corchado, E Ribeiro, Bernardete; Chen, Ning; Kovacec, Alexander A Structural Pattern Mining Approach for Credit Risk Assessment HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2015) Lecture Notes in Artificial Intelligence English Proceedings Paper 10th International Conference on Hybrid Artificial Intelligence Systems (HAIS) JUN 22-24, 2015 Bilbao, SPAIN IEEE Spanish Sect,IEEE Syst Man & Cybernet Spanish Chapter,Univ Salamanca,Univ Deusto,DeustoTech,Int Federat Computat Log Graph mining; Classification; Financial applications In recent years graph mining took a valuable step towards harnessing the problem of efficient discovery of substructures in complex input data that do not fit into the usual data mining models. A graph is a general and powerful data representation formalism, which found widespread application in many scientific fields. Finding subgraphs capable of compressing data by abstracting instances of the substructures and identifying interesting patterns is thus crucial. When it comes to financial settings, data is very complex and in particular when risk factors relationships are not taken into account it seriously affects the goodness of predictions. In this paper, we posit that risk analysis can be leveraged if structure can be taken into account by discovering financial motifs in the input graphs. We use gBoost which learns from graph data using a mathematical linear programming procedure combined with a substructure mining algorithm. An algorithm is proposed which has shown to be efficient to extract graph structure from feature vector data. Furthermore, we empirically show that the graph-mining model is competitive with state-of-the-art machine learning approaches in terms of classification accuracy without increase in the computational cost. [Ribeiro, Bernardete] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal; [Chen, Ning] Henan Polytech Univ, Coll Comp Sci & Technol, Software Coll, Jiaozuo 454003, Henan, Peoples R China; [Kovacec, Alexander] Univ Coimbra, Dept Math, Coimbra, Portugal Universidade de Coimbra; Henan Polytechnic University; Universidade de Coimbra Ribeiro, B (corresponding author), Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal. bribeiro@dei.uc.pt; nchenyx@outlook.com; kovacec@mat.uc.pt Ribeiro, Bernardete/A-8010-2016 Ribeiro, Bernardete/0000-0002-9770-7672; Kovacec, Alexander/0000-0002-5242-1689 21 1 1 2 8 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 1611-3349 978-3-319-19644-2; 978-3-319-19643-5 LECT NOTES ARTIF INT 2015.0 9121.0 73 84 10.1007/978-3-319-19644-2_7 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BD7YD 2023-03-23 WOS:000363689900007 0
J Xu, Z; Escalera, S; Pavao, A; Richard, M; Tu, WW; Yao, QM; Zhao, H; Guyon, I Xu, Zhen; Escalera, Sergio; Pavao, Adrien; Richard, Magali; Tu, Wei-Wei; Yao, Quanming; Zhao, Huan; Guyon, Isabelle Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform PATTERNS English Article Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. [Xu, Zhen; Tu, Wei-Wei; Zhao, Huan] 4Paradigm, Beijing 100085, Peoples R China; [Escalera, Sergio] Univ Barcelona, Comp Vis Ctr, Barcelona 08007, Spain; [Pavao, Adrien; Guyon, Isabelle] Univ Paris Saclay, LISN, CNRS, INRIA, F-91190 Gif Sur Yvette, France; [Richard, Magali] Univ Grenoble Alpes, TIMC, Grenoble INP, VetAgro Sup,UMR 5525,CNRS, F-38000 Grenoble, France; [Yao, Quanming] Tsinghua Univ, Beijing 100084, Peoples R China; [Guyon, Isabelle] ChaLearn, Berkeley, CA 94708 USA Centre de Visio per Computador (CVC); University of Barcelona; Centre National de la Recherche Scientifique (CNRS); Inria; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); VetAgro Sup; Tsinghua University Xu, Z (corresponding author), 4Paradigm, Beijing 100085, Peoples R China.;Guyon, I (corresponding author), Univ Paris Saclay, LISN, CNRS, INRIA, F-91190 Gif Sur Yvette, France.;Guyon, I (corresponding author), ChaLearn, Berkeley, CA 94708 USA. xuzhen@4paradigm.com; guyon@chalearn.org ; Escalera, Sergio/L-2998-2015 Yao, Quanming/0000-0001-8944-8618; Guyon, Isabelle/0000-0002-9266-1783; Escalera, Sergio/0000-0003-0617-8873 Big Data Chair of Excellence FDS Paris-Saclay, Paris Region Ile-de-France; EU EIT project HADACA; EU EIT project COMETH; United Health Foundation INCITE project; ANR Chair of Artificial Intelligence HUMANIA [ANR-19-CHIA-0022]; ICREA under the ICREA Academia program; INSERM Cancer project ACACIA [232717]; MIAI @Grenoble Alpes [ANR-19P3IA-0003]; 4Paradigm; ChaLearn; Microsoft; Google; [PID2019-105093GB-I00] Big Data Chair of Excellence FDS Paris-Saclay, Paris Region Ile-de-France; EU EIT project HADACA; EU EIT project COMETH; United Health Foundation INCITE project; ANR Chair of Artificial Intelligence HUMANIA(French National Research Agency (ANR)); ICREA under the ICREA Academia program; INSERM Cancer project ACACIA; MIAI @Grenoble Alpes; 4Paradigm; ChaLearn; Microsoft(Microsoft); Google(Google Incorporated); The Codabench project shares the same community governance as CodaLab Competitions. The openness of Codabench is total: an Apache 2.0 license is used, the source code is on GitHub, and the development framework and all the used components are open sourced. Codabench has received important contributions from many people who did not co-author this paper, and we would like to thank their efforts in making Codabench what it is today, including early CodaLab Competitions developers and contributors (listed alphabetically): Pujun Bhatnagar, Justin Carden, Richard Caruana, Francis Cleary, Xiawei Guo, Ivan Judson, Lori Ada Kilty, Shaunak Kishore, Stephen Koo, Percy Liang, Zhengying Liu, Pragnya Maduskar, Simon Mercer, Arthur Pesah, Christophe Poulain, Lukasz Romaszko, Laurent Senta, Lisheng Sun, Sebastien Treguer, Cedric Vachaudez, Evelyne Viegas, Paul Viola, Erick Watson, Tony Yang, Flavio Zhingri, and Michael Zyskowski. We would like to particularly thank the people who contributed to the design, development, and testing of Codabench including (listed alphabetically) Alexis Arnaud, Xavier Baro ', Feng Bin, Yuna Blum, Eric Carmichael, Laurent Darre ', Hugo Jair Escalante, Sergio Escalera, Eric Frichot, Yuxuan He, James Keith, Anne-Catherine Letournel, Shouxiang Liu, Zhenwu Liu, Adrien Pavao, Magali Richard, Tyler Thomas, Nic Threfts, Bailey Trefts, Catherine Wallez, and Lanning Wei. The Universite ' Paris-Saclay is hosting the main instance of Codabench. Funding and support have been received via several research grants, including Big Data Chair of Excellence FDS Paris-Saclay, Paris Re ' gion Ile-de-France, EU EIT projects HADACA and COMETH, United Health Foundation INCITE project, ANR Chair of Artificial Intelligence HUMANIA ANR-19-CHIA-0022, the Spanish project PID2019-105093GB-I00, ICREA under the ICREA Academia program, INSERM Cancer project ACACIA 232717, MIAI @Grenoble Alpes (ANR-19P3IA-0003), 4Paradigm, ChaLearn, Microsoft, and Google. We also appreciate the following people and institutes for their open-source datasets that are used in our use cases: Andrew McCallum, C. Lee Giles, Ken Lang, Tom Mitchell, William L. Hamilton, Maximilian Mumme, Oleksandr Shchur, David D. Lewis, William Hersh, Just Research and Carnegie Mellon University, NEC Research Institute, Carnegie Mellon University, Stanford University, Technical University of Munich, AT&T Labs, and Oregon Health Sciences University. We are also very grateful to Joaquin Vanschoren for fruitful discussions. 21 0 0 0 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns JUL 8 2022.0 3.0 7 100543 10.1016/j.patter.2022.100543 0.0 JUL 2022 8 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science 3Z2DT 35845844.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000844230600012 0
J Cao, MX; Li, AJ; Fang, Q; Kaufmann, E; Kroger, BJ Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kroeger, Bernd J. Interconnected growing self-organizing maps for auditory and semantic acquisition modeling FRONTIERS IN PSYCHOLOGY English Article neural network; neurocomputational models; interconnected growing self-organizing map; auditory feature map; semantic feature map; auditory semantic association; language acquisition EARLY LEXICAL DEVELOPMENT; HUMAN SPEECH; NETWORK; ORGANIZATION; BIRDSONG Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model. [Cao, Mengxue; Li, Aijun; Fang, Qiang] Chinese Acad Social Sci, Inst Linguist, Lab Phonet & Speech Sci, Beijing 100732, Peoples R China; [Kaufmann, Emily] Univ Cologne, Fac Human Sci, Dept Special Educ, D-50931 Cologne, Germany; [Kroeger, Bernd J.] Rhein Westfal TH Aachen, Sch Med, Neurophonet Grp, Dept Phoniatr Pedaudiol & Commun Disorders, Aachen, Germany; [Kroeger, Bernd J.] Tianjin Univ, Sch Comp Sci & Technol, Cognit Computat & Applicat Lab, Tianjin 300072, Peoples R China Chinese Academy of Social Sciences; University of Cologne; RWTH Aachen University; Tianjin University Cao, MX (corresponding author), Chinese Acad Social Sci, Inst Linguist, Lab Phonet & Speech Sci, 5 Jian Guo Men Nei Da Jie, Beijing 100732, Peoples R China. mengxuecao@outlook.com Kröger, Bernd/AAW-5031-2021; Kröger, Bernd/A-4435-2009 Kröger, Bernd/0000-0002-4727-2957; Kröger, Bernd/0000-0002-4727-2957 NSFC [61233009]; CASS Innovation Program NSFC(National Natural Science Foundation of China (NSFC)); CASS Innovation Program The NSFC Key Project with No. 61233009, and the CASS Innovation Program. 66 5 6 0 14 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. MAR 20 2014.0 5.0 236 10.3389/fpsyg.2014.00236 0.0 20 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology CK0ZX 24688478.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000355935700001 0
J Wang, YB; Xu, CJ; Yao, SQ; Wang, L; Zhao, YZ; Ren, JC; Li, YC Wang, Yongbin; Xu, Chunjie; Yao, Sanqiao; Wang, Lei; Zhao, Yingzheng; Ren, Jingchao; Li, Yuchun Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition SCIENTIFIC REPORTS English Article IMPROVING FORECASTING ACCURACY; INTEGRATED MOVING AVERAGE; TIME-SERIES; NEURAL-NETWORK; TUBERCULOSIS; CHINA; OUTBREAK; ARIMA; TRANSMISSION; SEASONALITY In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions. [Wang, Yongbin; Yao, Sanqiao; Zhao, Yingzheng; Ren, Jingchao; Li, Yuchun] Xinxiang Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, 601 Jinsui Rd, Xinxiang 453003, Henan, Peoples R China; [Xu, Chunjie] Capital Med Univ, Sch Publ Hlth, Dept Occupat & Environm Hlth, Beijing, Peoples R China; [Wang, Lei] Humboldt Univ, Freie Univ Berlin, Charite Univ Med Berlin, Ctr Musculoskeletal Surg, Berlin, Germany; [Wang, Lei] Berlin Inst Hlth, Berlin, Germany Xinxiang Medical University; Capital Medical University; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Berlin Institute of Health Wang, YB (corresponding author), Xinxiang Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, 601 Jinsui Rd, Xinxiang 453003, Henan, Peoples R China. wybwho@163.com li, Yu/HCI-9086-2022; xu, cj/HJZ-3488-2023; li, yu/HGL-9518-2022; Li, yu/HHZ-5236-2022 Innovation Project for College Students of Xinxiang Medical University [XYXSKYZ201932] Innovation Project for College Students of Xinxiang Medical University We appreciated the WHO for sharing the prevalence and mortality time series. This project was supported by the Innovation Project for College Students of Xinxiang Medical University (Code: XYXSKYZ201932). 63 2 2 0 6 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep NOV 1 2021.0 11.0 1 21413 10.1038/s41598-021-00948-6 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics WQ3NW 34725416.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000713727200021 0
J Zeng, WZ; Xu, C; Gang, Z; Wu, JW; Huang, JS Zeng Wenzhi; Xu Chi; Gang Zhao; Wu Jingwei; Huang Jiesheng Estimation of Sunflower Seed Yield Using Partial Least Squares Regression and Artificial Neural Network Models PEDOSPHERE English Article leaf area index; microplot experiment; plant height; remote sensing; salinization; variable importance in projection score SPECTRAL INDEXES; TIME-SERIES; BIOMASS; WHEAT; PLS; EVAPOTRANSPIRATION; PREDICTION; NITROGEN; SALINITY; WOFOST Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression (PLSR) and artificial neural network (ANN) in predicting seed yields of sunflower (Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen (N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy (root mean square error (RMSE) = 0.93 t ha(-1), coefficient of determination (R-2) = 0.69) to that when using all measured indices (RMSE = 0.81 t ha(-1), R-2 = 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options (e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation. [Zeng Wenzhi; Xu Chi; Wu Jingwei; Huang Jiesheng] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China; [Gang Zhao] Univ Bonn, Inst Crop Sci & Resource Conservat, Crop Sci Grp, Katzenburgweg 5, D-53115 Bonn, Germany Wuhan University; University of Bonn Huang, JS (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China. sdjshuang@whu.edu.cn Huang, Jie/GWQ-5005-2022; Zeng, Wen/GPS-8058-2022 National Natural Science Foundation of China [51609175, 51790533, 51879196, 51439006] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Nos. 51609175, 51790533, 51879196, and 51439006). 60 15 16 7 53 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1002-0160 2210-5107 PEDOSPHERE Pedosphere OCT 2018.0 28.0 5 764 774 10.1016/S1002-0160(17)60336-9 0.0 11 Soil Science Science Citation Index Expanded (SCI-EXPANDED) Agriculture GV9HL 2023-03-23 WOS:000446466600006 0
J Leng, KJ; Jin, LB; Shi, W; Van Nieuwenhuyse, I Leng, Kaijun; Jin, Linbo; Shi, Wen; Van Nieuwenhuyse, Inneke Research on agricultural products supply chain inspection system based on internet of things CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS English Article RFID; Agricultural product; Supply chain; Internet of Things BIG-DATA In recent years, the problems of low degree of industrialization of agriculture, weak informatization ability and food safety have become increasingly serious. This article combines the detection of agricultural products supply chain and RFID technology and applies it to the testing of agricultural products supply chain. In this study, the agricultural product supply chain and agricultural product logistics information system were introduced. At the same time, the application of RFID technology in the production, processing and other aspects of the detection of agricultural products supply chain is elaborated, and the information system of RFID technology in the agricultural product supply chain is designed. Finally, the efficiency of RFID technology in the detection of agricultural products supply chain has been verified. Therefore, this technology is the future trend of agricultural logistics development, thereby promoting the development of agricultural products logistics supply chain testing. [Leng, Kaijun; Jin, Linbo] China Acad Social Sci, Natl Acad Econ Strategy, Beijing, Peoples R China; [Leng, Kaijun; Shi, Wen] Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China; [Van Nieuwenhuyse, Inneke] Katholieke Univ Leuven, Fac Econ & Business, Leuven, Belgium Chinese Academy of Social Sciences; Hubei University of Economics; KU Leuven Shi, W (corresponding author), Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China. lengkaijun@hbue.edu.cn; shi3wen@126.com; inneke.vannieuwenhuyse@kuleuven.be æ, æ/AAI-5741-2020; Leng, Kaijun/AAI-4417-2020; 穆, 儒/HGC-6534-2022; Van Nieuwenhuyse, Inneke/AAD-4356-2020 Leng, Kaijun/0000-0002-6962-2524; National Natural Science Foundation of China [71402048]; Hubei society of social sciences [2016101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hubei society of social sciences The authors acknowledge the National Natural Science Foundation of China (Grant: 71402048), Hubei society of social sciences (Grant: 2016101). 20 25 26 27 105 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1386-7857 1573-7543 CLUSTER COMPUT Cluster Comput. JUL 2019.0 22.0 4.0 S8919 S8927 10.1007/s10586-018-2021-6 0.0 9 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science JU9SH 2023-03-23 WOS:000502007000120 0
J Huang, ZX; Juarez, JM; Duan, HL; Li, HM Huang, Zhengxing; Juarez, Jose M.; Duan, Huilong; Li, Haomin Reprint of Length of stay prediction for clinical treatment process using temporal similarity EXPERT SYSTEMS WITH APPLICATIONS English Article Length of stay; Temporal similarity; Case-based reasoning; Artificial intelligence in medicine INTENSIVE-CARE-UNIT; PATHWAYS; SUPPORT; DESIGN In clinical treatment processes, inpatient length of stay (LOS) is not only a readily available indicator of hospital activity, but also a reasonable proxy of resource consumption. Accurate inpatient LOS prediction has strong implications for health service delivery. Major techniques proposed (statistical approaches or artificial neuronal networks) consider a priori knowledge, such as demographics or patient physical factors, providing accurate methods to estimate LOS at early stages of the patient (admission). However, unexpected scenarios and variations are common places of clinical treatment processes that have a dramatical impact on the LOS. Therefore, these predictors should deal with adaptability, considering the temporal evolution of the patient. In this paper, we propose an inpatient LOS prediction approach across various stages of clinical treatment processes. This proposal relies on a kind of regularity assumption demanding that patient traces of the specific treatment process with similar medical behaviors have similar LOS. Therefore, this approach follows a Case-based Reasoning methodology since it predicts an inpatient LOS of a partial patient trace by referring to the past traces of clinical treatment processes that have similar medical behaviors with the current one. The proposal is evaluated using 284 patient traces from the pulmonary infection CTPs, extracted from Zhejiang Huzhou Central Hospital of China. (C) 2013 Elsevier Ltd. All rights reserved. [Huang, Zhengxing; Duan, Huilong; Li, Haomin] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou 310003, Zhejiang, Peoples R China; [Juarez, Jose M.] Univ Murcia, Dept Informat & Commun Engn, E-30001 Murcia, Spain Zhejiang University; University of Murcia Li, HM (corresponding author), Zhejiang Univ, Coll Biomed Engn & Instrument Sci, 310008 Zhou Yiqing Bldg 510,Zheda Rd 38, Hangzhou 310003, Zhejiang, Peoples R China. lhm@vico-lab.com Juarez, Jose M./K-8042-2017; Li, Haomin/AAY-5044-2021 Juarez, Jose M./0000-0003-1776-1992; Li, Haomin/0000-0002-6420-7719 National Nature Science Foundation of China [81101126]; Seneca Research Foundation of the Region of Murcia [15277/PI/10]; Spanish Ministry of Science and Innovation [TIN2009-14372-C03-01] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Seneca Research Foundation of the Region of Murcia(Fundacion Seneca); Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government) This work was partially funded by the National Nature Science Foundation of China under Grant No. 81101126, by the Seneca Research Foundation of the Region of Murcia under project 15277/PI/10, and by the Spanish Ministry of Science and Innovation + European FEDER + PlanE funds under the project TIN2009-14372-C03-01. 36 1 1 0 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. FEB 1 2014.0 41.0 2 274 283 10.1016/j.eswa.2013.08.036 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 255ZS 2023-03-23 WOS:000327279900005 0
J van Ditmarsch, H; French, T; Velazquez-Quesada, FR; Wang, YN van Ditmarsch, Hans; French, Tim; Velazquez-Quesada, Fernando R.; Wang, Yi. N. Implicit, explicit and speculative knowledge ARTIFICIAL INTELLIGENCE English Article Modal logic; Awareness; Bisimulation; Expressivity; Multi-agent systems PARTITIONAL INFORMATION STRUCTURES; INTERACTIVE UNAWARENESS; AWARENESS; LOGIC We compare different epistemic notions in the presence of awareness of propositional variables: the logic of implicit knowledge (in which explicit knowledge is definable), the logic of explicit knowledge, and the logic of speculative knowledge. Speculative knowledge is a novel epistemic notion that permits reasoning about unawareness. These logics are interpreted on epistemic awareness models: these are multi-agent Kripke structures for propositional awareness (in each state an agent may only be aware of formulas containing occurrences of a subset of all propositional variables). Different notions of bisimulation are suitable for these logics. We provide correspondence between bisimulation and modal equivalence on image-finite models for these logics. Expressivity and axiomatizations are cinvestigated for models without restrictions, and for models with equivalence relations for all agents (modeling knowledge) and awareness introspection (agents know what they are aware of). We show that the logic of speculative knowledge is as expressive as the logic of explicit knowledge, and the logic of implicit knowledge is more expressive than the two other logics. We also present expressivity results for more restricted languages. We then provide and compare axiomatizations for the three logics; the axiomatizations for speculative knowledge are novel. We compare our results to those for awareness achieved in artificial intelligence, computer science, philosophy, and economics. (C) 2017 Elsevier B.V. All rights reserved. [van Ditmarsch, Hans] Univ Lorraine, CNRS, LORIA, Nancy, France; [French, Tim] Univ Western Australia, Nedlands, WA, Australia; [Velazquez-Quesada, Fernando R.] Univ Amsterdam, ILLC, Amsterdam, Netherlands; [Wang, Yi. N.] Zhejiang Univ, Philosophy Dept, Hangzhou, Zhejiang, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; University of Western Australia; University of Amsterdam; Zhejiang University Wang, YN (corresponding author), Zhejiang Univ, Philosophy Dept, Hangzhou, Zhejiang, Peoples R China. hans.van-ditmarsch@loria.fr; tim.french@uwa.edu.au; F.R.VelazquezQuesada@uva.nl; ynw@xixilogic.org Velázquez-Quesada, Fernando R./I-8511-2014 Velázquez-Quesada, Fernando R./0000-0003-4457-1414; French, Tim/0000-0002-0748-8040 European Research Council [EPS 313360]; National Social Science Foundation of China [16CZX048] European Research Council(European Research Council (ERC)European Commission); National Social Science Foundation of China Hans van Ditmarsch is also affiliated to IMSc, Chennai, as a research associate. He acknowledges support from European Research Council project EPS 313360. Yi N. Wang gratefully acknowledges funding support by the National Social Science Foundation of China (Grant No. 16CZX048). We thank the reviewers of Artificial Intelligence Journal for their thorough comments. 45 8 8 4 23 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0004-3702 1872-7921 ARTIF INTELL Artif. Intell. MAR 2018.0 256.0 35 67 10.1016/j.artint.2017.11.004 0.0 33 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science FW0CJ Bronze 2023-03-23 WOS:000424958700002 0
J Xie, J; He, NJ; Fang, LY; Ghamisi, P Xie, Jie; He, Nanjun; Fang, Leyuan; Ghamisi, Pedram Multiscale Densely-Connected Fusion Networks for Hyperspectral Images Classification IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Feature extraction; Training; Testing; Kernel; Hyperspectral imaging; Task analysis; Fuses; Hyperspectral images (HSIs) classification; multiscale; convolutional neural networks; densebolck SPECTRAL-SPATIAL CLASSIFICATION; NEURAL-NETWORK; FEATURE-EXTRACTION; SUPERPIXEL Convolutional neural network (CNN) has demonstrated to be a powerful tool for hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods only adopt the fixed-size patches to train the CNN model, and such single scale patches may not reflect the complex spatial structural information in the HSIs. In addition, although different layers of CNN can extract features of multiple scales, the traditional CNN model can only utilize features from the highest level for the classification task. These features, however, do not fully consider the strong complementary yet correlated information among different layers. To address these issues, in this paper, a multiscale densely-connected convolutional network (MS-DenseNet) framework is proposed to sufficiently exploit multiple scales information for the HSIs classification. Specifically, for each pixel, the MS-DenseNet, first, extracts its surrounding patches of multiple scales. These patches can separately constitute multiple scale training and testing samples. Within each specific scale sample, instead of using the forward convolutional layers, the MS-DenseNet adopts the dense blocks, which can connect each layer to other layers in a feed-forward fashion and thus can exploit the information among different layers for training and testing. Furthermore, since high correlations exist in patches of different scales, the MS-DenseNet introduces several dense blocks to fuse the multiscale information among different layers for the final HSI classification. Experimental results on several real HSIs demonstrate the superiority of the proposed MS-DenseNet over single scale-based CNN classification model and several well-known classification methods. [Xie, Jie; He, Nanjun; Fang, Leyuan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany Hunan University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Fang, LY (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. xj_xj@hnu.edu.cn; henanjun@hnu.edu.cn; fangleyuan@gmail.com; p.ghamisi@gmail.com Xie, Jie/AAX-8665-2020; Ghamisi, Pedram/ABD-5419-2021 Fang, Leyuan/0000-0003-2351-4461; He, Nanjun/0000-0003-3105-6499; Xie, Jie/0000-0002-4710-4275 National Natural Science Foundation of China [61922029]; National Natural Science Foundation [61771192]; Huxiang Young Talents Plan Project of Hunan Province [2019RS2016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Huxiang Young Talents Plan Project of Hunan Province This work was supported in part by the National Natural Science Foundation of China under Grant 61922029, in part by the National Natural Science Foundation under Grant 61771192, and in part by the Huxiang Young Talents Plan Project of Hunan Province under Grant 2019RS2016. 48 32 32 10 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. JAN 2021.0 31.0 1 246 259 10.1109/TCSVT.2020.2975566 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering PR7AJ 2023-03-23 WOS:000607384300020 0
J Zhao, XY; Jiang, N; Liu, JF; Yu, DR; Chang, JT Zhao, Xinyu; Jiang, Na; Liu, Jinfu; Yu, Daren; Chang, Juntao Short-term average wind speed and turbulent standard deviation forecasts based on one-dimensional convolutional neural network and the integrate method for probabilistic framework ENERGY CONVERSION AND MANAGEMENT English Article Turbulent standard deviation; Correlation analysis; 1-Dimensional CNN; Multi-model forecasts; Probabilistic wind speed; Quantile statistics SINGULAR SPECTRUM ANALYSIS; TIME-SERIES; MULTIOBJECTIVE OPTIMIZATION; PREDICTION; MULTISTEP; DECOMPOSITION; INTENSITY; COMBINATION; ALGORITHM; ENSEMBLE Accurate wind speed forecast can provide important information for power system dispatching. Many studies focus on this topic over the last decades, but it's consistently a tough issue due to the intense uncertainty of wind. In physical perspective, wind speed consists of average component and turbulent component. Therefore, oriented to actual demand and improve the forecast precision, this paper develops a novel data-driven method to realize short-term combination forecasts of average wind speed and wind turbulent standard deviation. According to atmospheric boundary layer theory and correlation analysis of the two prediction targets, their time-delay items are combined as the model input features. One-dimensional convolutional neural network is innovatively applied for this work to excavate the timing coupled information in data. Well-performed models can be established after adequate training and validation. Inspired by Pauta criterion, adaptive parameter named as turbulent standard deviation multiplicator is defined, which is the specific value of predicted average wind speed error and wind turbulent standard deviation. It is decided as the medium to extend the study to probabilistic framework. Based on its quantile analysis, the statistical significance of the parameter is verified and the prediction results can be integrated to achieve 4 h ahead probabilistic wind speed forecasts. Actual data from China wind farm is utilized to execute the case experiments. Superior performances indicate the feasibility and effectiveness of proposed method and the uncertainties of wind are better learned. [Zhao, Xinyu; Jiang, Na; Liu, Jinfu; Yu, Daren; Chang, Juntao] Harbin Inst Technol, Sch Energy Sci & Engn, Harbin 150001, Heilongjiang, Peoples R China; [Zhao, Xinyu] Konrad Zuse Ctr Informat Technol Berlin, Berlin, Germany Harbin Institute of Technology Liu, JF (corresponding author), Harbin Inst Technol, Sch Energy Sci & Engn, Harbin 150001, Heilongjiang, Peoples R China. liujinfu_hit@163.com; yudaren@hit.edu.cn Chang, Juntao/AAR-8427-2021 China Scholarship Council [201806120163] China Scholarship Council(China Scholarship Council) The authors would like to acknowledge the funding of China Scholarship Council under the grant No. 201806120163. And we appreciate Saihanba wind farm for the original wind speed data, the editors and reviewers for their valuable suggestions. 56 41 42 7 31 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. JAN 1 2020.0 203.0 112239 10.1016/j.enconman.2019.112239 0.0 11 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics JY6DU 2023-03-23 WOS:000504504000030 0
J Dong, M; Zeng, XY; Koehl, L; Zhang, JJ Dong, Min; Zeng, Xianyi; Koehl, Ludovic; Zhang, Junjie An interactive knowledge-based recommender system for fashion product design in the big data environment INFORMATION SCIENCES English Article Interactive knowledge-based design; recommender system; Sensory evaluation; Fuzzy techniques; Ontology; Design knowledge base FUZZY-LOGIC In this paper, we originally propose an interactive, knowledge-based design recommender system (IKDRS) for relevant personalised fashion product design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the designer's professional knowledge and perception in order to find the best existing design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and designer's perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire design knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and design factors by using fuzzy techniques. From these models, we set up an ontology-based design knowledge base, including key data and relevant relation models. This knowledge base can be updated in a big data environment by progressively learning from new design cases. On this basis, we propose an interactive, personalised design recommender system. This system works through a newly proposed design process: consumers' emotional requirement identification - design schemes generation - recommender - 3D virtual prototype display and evaluation - design factors adjustment. This process can be performed repeatedly until the designer is satisfied. The proposed system has been validated through a number of successful real design cases. (c) 2020 Elsevier Inc. All rights reserved. [Dong, Min; Zhang, Junjie] Wuhan Text Univ, Res Ctr Nonlinear Sci, Engn Res Ctr Hubei Prov Clothing Informat, Math & Comp Inst, Wuhan, Peoples R China; [Dong, Min; Zeng, Xianyi; Koehl, Ludovic] Cent Lille, Lab Genie & Mat Text GEMTEX, ENSAIT Text Inst, F-59000 Lille, France Wuhan Textile University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Centrale Lille Zeng, XY (corresponding author), Cent Lille, Lab Genie & Mat Text GEMTEX, ENSAIT Text Inst, F-59000 Lille, France. xianyi.zeng@ensait.fr; lodovic.koehl@ensait.fr Zeng, Xianyi/AAQ-1183-2021 Zeng, Xianyi/0000-0002-3236-6766 European H2020 Program [761122 - FBD_BModel]; China Textile Industry Association [2015081] European H2020 Program; China Textile Industry Association This work was partly supported by the European H2020 Program [Grant no. 761122 - FBD_BModel] and the China Textile Industry Association [Grant no.2015081, 2015]. 26 30 30 6 44 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. NOV 2020.0 540.0 469 488 10.1016/j.ins.2020.05.094 0.0 20 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science NR3KB Green Published, Bronze 2023-03-23 WOS:000571460300006 0
J Chen, C; Liu, Q; Wang, XC; Liao, CW; Zhang, DQ Chen, Chao; Liu, Qiang; Wang, Xingchen; Liao, Chengwu; Zhang, Daqing semi-Traj2Graph Identifying Fine-Grained Driving Style With GPS Trajectory Data via Multi-Task Learning IEEE TRANSACTIONS ON BIG DATA English Article Trajectory; Vehicles; Global Positioning System; Feature extraction; Semisupervised learning; Big Data; Task analysis; Trajectory data; driving style; fine-grained; graph-CNNs; semi-supervised learning BEHAVIOR; ACCELERATION; SYSTEM; SAFE Driving behaviour understanding is of vital importance in improving transportation safety and promoting the development of Intelligent Transportation Systems (ITS). As a long-standing research topic in driving behaviour analysis, driving style identification is non-trivial. Almost all previous studies emphasize the research on the granularity of an entire trip or a driver. Inspired by the fact that an aggressive driver may drive safely at some time. In this article, based on the widely available GPS trajectory big data that records the driving behaviours implicitly, we propose a multi-task learning (MTL) framework called semi-Traj2Graph to recognize the fine-grained driving styles in the temporal dimension accurately. The MTL framework can incorporate the learning capability of graph representation in extracting high-level and interpretable features regarding complex driving behaviours and semi-supervised in exploiting unlabelled data and reducing labelling effort. More specifically, in the graph representation learning, a multi-view graph is first built to capture a more complete view of driving behaviours from the raw GPS trajectory data, then graph convolutional neural networks (Graph-CNNs) are applied. In the semi-supervised learning, a pseudo-label labelling is adopted to make use of the unlabelled data. We evaluate the proposed framework extensively based on two taxi trajectory datasets collected from the city of Beijing and Chongqing, China, respectively. Experimental results show that semi-Traj2Graph outperforms compared to other baselines, achieving an overall accuracy of around 90 percent. We also implement the framework on users' smartphones via the collaborative cloud-edge computation manner to demonstrate the system usability in real cases. [Chen, Chao] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Chen, Chao; Liu, Qiang; Wang, Xingchen; Liao, Chengwu] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China; [Liu, Qiang; Wang, Xingchen; Liao, Chengwu] Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China; [Zhang, Daqing] Inst Polytech Paris, F-91011 Evry, France Chongqing University; Chongqing University; Chongqing University Chen, C (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China. cschaochen@cqu.edu.cn; csqiangliu@cqu.edu.cn; wangxingchen@cqu.edu.cn; lcw@cqu.edu.cn; daqing.zhang@telecom-sudparis.eu Liao, Chengwu/0000-0001-5098-4579 National Key Research and Development Project of China [2017YFB1002000]; National Natural Science Foundation of China [61872050]; Chongqing Basic and Frontier Research Program [cstc2018jcyjAX0551] National Key Research and Development Project of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chongqing Basic and Frontier Research Program This work was supported by the National Key Research and Development Project of China under Grant 2017YFB1002000, the National Natural Science Foundation of China under Grant 61872050, and the Chongqing Basic and Frontier Research Program under Grant cstc2018jcyjAX0551. The authors would like to thank Jie Zhao for his insightful discussions and kind help during the experiments. 54 9 9 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7790 IEEE T BIG DATA IEEE Trans. Big Data DEC 1 2022.0 8.0 6 1550 1565 10.1109/TBDATA.2021.3063048 0.0 16 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6E1TV 2023-03-23 WOS:000883167600008 0
C Li, M; Min, LQ; Litscher, G Xu, B Li, Min; Min, Lequan; Litscher, Gerhard An Automatical Algorithm based on Local Image Properties 2016 IEEE INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC) English Proceedings Paper IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) MAY 20-22, 2016 Chongqing, PEOPLES R CHINA IEEE edge detection; corner detection; automatical algoritham; local threshold; local gray scale difference EDGE-DETECTION METHOD; IDENTIFICATION; SEGMENTATION; RECOGNITION; LOCATION; VISION; MOTION; FILTER This study introduces the definition of local image properties. Using so-called local rules one can describe recognitions of interesting foreground object characteristics in images. We propose a novel algorithm to automatically detect interesting object characteristics in the images with local image properties, including the detections of edges or corners of objects in images. As applications, firstly, we compare the detection results of edges for objects in four images by our algorithm with the Roberts operator, Sobel operator, Prewitt operator, Log operator, Canny operator and cellular-neural-network-based algorithm (CNNBA). Secondly, we compare the detection results of corners for objects in three images by our algorithm with Harris operator, Shi-Tomasi operator and CNNBA. The experimental results have demonstrated that the proposed algorithm is able to detect correctly the all edges or corners of the objects in corresponding images, and has superior performance when compared with the other methods. [Li, Min; Min, Lequan] Univ Sci & Technol Beijing, Sch Math & Phys, Beijing 100083, Peoples R China; [Litscher, Gerhard] Med Univ Graz, Res Unit Biomed Engn Anesthesia & Intens Care Med, A-8036 Graz, Austria; [Litscher, Gerhard] Med Univ Graz, TCM Res Ctr Graz, A-8036 Graz, Austria University of Science & Technology Beijing; Medical University of Graz; Medical University of Graz Li, M (corresponding author), Univ Sci & Technol Beijing, Sch Math & Phys, Beijing 100083, Peoples R China. limin_mu@126.com; minlequan@sina.com; gerhard.litscher@medunigraz.at 33 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-9194-8 2016.0 438 443 6 Automation & Control Systems; Computer Science, Hardware & Architecture; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science BG5II 2023-03-23 WOS:000389505000092 0
C Xue, JR; Chen, XZ; Ding, HX; He, X ACM Xue, Jiarui; Chen, Xiangzhou; Ding, Huixia; He, Xiao Research on Real Time Processing and Intelligent Analysis Technology of Power Big Data INTERNATIONAL CONFERENCE ON BIG DATA AND INTERNET OF THINGS (BDIOT 2017) English Proceedings Paper International Conference on Big Data and Internet of Things (BDIOT) DEC 20-22, 2017 London, ENGLAND Univ Macau, Data Analyt & Collaborat Comp Lab,Nh China Univ Technol data information; intelligence; data flow; distributed This paper focuses on the research of all kinds of data in the power grid business system, and carries on the research of intelligent analysis technology. It focuses on breaking the technical difficulties of intelligent and efficient analysis and mining, distributed multi-stream real-time processing, developing large-scale stock data for power and high-frequency incremental data efficient analysis system prototype, for wide-area distributed multi-stream real-time computing, from the data quick access to valuable information to solve the problems in the grid business system to improve the overall business performance, to achieve support power Data real-time processing and other technology applications. [Xue, Jiarui] China Univ Min & Technol Beijing, 11st Coll Rd, Beijing 100089, Peoples R China; [Chen, Xiangzhou; Ding, Huixia] China Elect Power Res Inst, 15th Xiaoyingdong Rd, Beijing 100085, Peoples R China; [He, Xiao] ENSEA, 6 Ave Ponceau, F-95014 Cergy Pontoise, France China University of Mining & Technology Xue, JR (corresponding author), China Univ Min & Technol Beijing, 11st Coll Rd, Beijing 100089, Peoples R China. 1072584149@qq.com; Chenxiangzhou@epri.sgcc.com.cn; dhx@epri.sgcc.com.cn; xiao.he@ensea.fr State Grid Scientific Project State Grid Scientific Project This work is supported by the State Grid Scientific Project which names Research on Simulation Model and Simulation Method of Power Communication System (No. XX71-17-006) 12 0 0 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5430-1 2017.0 43 47 10.1145/3175684.3175717 0.0 5 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM3WR 2023-03-23 WOS:000462786800009 0
C Dolphin, R; Smyth, B; Xu, Y; Dong, RH SanchezRuiz, AA; Floyd, MW Dolphin, Rian; Smyth, Barry; Xu, Yang; Dong, Ruihai Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2021 Lecture Notes in Artificial Intelligence English Proceedings Paper 29th International Conference on Case-Based Reasoning (ICCBR) SEP 13-16, 2021 ELECTR NETWORK Case-based reasoning; Financial time series; Stock market; Similarity metric GENETIC ALGORITHMS; PREDICTION; MODELS Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction-traditional Euclidean and correlation based approaches are not effective for a variety of reasons-and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks. [Dolphin, Rian; Smyth, Barry; Dong, Ruihai] Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland; [Smyth, Barry; Dong, Ruihai] Univ Coll Dublin, Insight Ctr Data Analyt, Dublin, Ireland; [Xu, Yang] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China University College Dublin; University College Dublin; Beihang University Dolphin, R (corresponding author), Univ Coll Dublin, Sch Comp Sci, Dublin, Ireland. rian.dolphin@ucdconnect.ie; barry.smyth@ucd.ie; yang_xu@buaa.edu.cn; ruihai.dong@ucd.ie Dolphin, Rian/0000-0002-5607-9948; Dong, Ruihai/0000-0002-2509-1370; Smyth, Barry/0000-0003-0962-3362; xu, yang/0000-0001-6335-3602 Science Foundation Ireland [18/CRT/6183] Science Foundation Ireland(Science Foundation Ireland) This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6183. 38 0 0 2 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-86957-1; 978-3-030-86956-4 LECT NOTES ARTIF INT 2021.0 12877.0 64 78 10.1007/978-3-030-86957-1_5 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS4CJ Green Submitted 2023-03-23 WOS:000717226600005 0
J Marciano, A; Chen, D; Fabrocini, F; Fields, C; Greco, E; Gresnigt, N; Jinklub, K; Lulli, M; Terzidis, K; Zappala, E Marciano, Antonino; Chen, Deen; Fabrocini, Filippo; Fields, Chris; Greco, Enrico; Gresnigt, Niels; Jinklub, Krid; Lulli, Matteo; Terzidis, Kostas; Zappala, Emanuele Quantum Neural Networks and Topological Quantum Field Theories NEURAL NETWORKS English Article Topological quantum field theory; Topological quantum neural networks; Graph neural networks; Quantum amplitude classifiers; Quantum perceptron SHORT-TERM-MEMORY; STORAGE Our work intends to show that: (1) Quantum Neural Networks (QNNs) can be mapped onto spin-networks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theory (TQFT); (2) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNNs, relating it to the topological features of the graph structures involved. (c) 2022 Elsevier Ltd. All rights reserved. [Marciano, Antonino] Fudan Univ, Ctr Field Theory & Particle Phys, Jingwan Campus,Jingsan Rd, Shanghai 200433, Peoples R China; [Marciano, Antonino] Fudan Univ, Dept Phys, Jingwan Campus,Jingsan Rd, Shanghai 200433, Peoples R China; [Marciano, Antonino] Lab Nazl Frascati INFN, Via Enr Fermi 54, I-00044 Frascati, Rome, Italy; [Chen, Deen; Fabrocini, Filippo; Jinklub, Krid; Terzidis, Kostas] Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China; [Fabrocini, Filippo] Italy Natl Res Council, Inst Comp Applicat Mario Picone, Via Taurini 19, I-00185 Rome, Italy; [Fields, Chris] Tufts Univ, Allen Discovery Ctr, 200 Coll Ave, Medford, MA 02155 USA; [Greco, Enrico] Univ Trieste, Dept Chem & Pharmaceut Sci, Via Giorgieri 1, I-34127 Trieste, Italy; [Gresnigt, Niels] Xian Jiaotong Liverpool Univ, Dept Phys, 111 Renai Rd, Suzhou 215123, Peoples R China; [Lulli, Matteo] Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, 1088 Xueyuan Ave, Shenzhen 518055, Peoples R China; [Zappala, Emanuele] Yale Univ, Yale Sch Med, New Haven, CT 06510 USA Fudan University; Fudan University; Istituto Nazionale di Fisica Nucleare (INFN); Tongji University; Tufts University; University of Trieste; Xi'an Jiaotong-Liverpool University; Southern University of Science & Technology; Yale University Fabrocini, F (corresponding author), Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China.;Fabrocini, F (corresponding author), Italy Natl Res Council, Inst Comp Applicat Mario Picone, Via Taurini 19, I-00185 Rome, Italy.;Greco, E (corresponding author), Univ Trieste, Dept Chem & Pharmaceut Sci, Via Giorgieri 1, I-34127 Trieste, Italy. marciano@fudan.edu.cn; deenchen@tongji.edu.cn; fabrocini@tongji.edu.cn; fieldsres@gmail.com; enrico.greco@live.it; niels.gresnigt@xjtlu.edu.cn; jinklub_krid@tongji.edu.cn; lulli@sustech.edu.cn; kostas@tongji.edu.cn; zae@usf.edu Jinklub, Krid/0000-0003-3072-8919; Zappala, Emanuele/0000-0002-9684-9441; Greco, Enrico/0000-0003-1564-4661; Gresnigt, Niels/0000-0002-1993-3025 National Science Foundation of China [12050410244]; Fudan University [JJH1512105]; Foundation of Jiangsu Higher Education Institutions of China Programme [19KJB140018]; Xi'an Jiaotong-Liverpool University [REF-18-02-03]; Estonian Research Council [MOBJD679]; Shang-hai Municipality [KBH1512299] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fudan University; Foundation of Jiangsu Higher Education Institutions of China Programme; Xi'an Jiaotong-Liverpool University; Estonian Research Council(Estonian Research Council); Shang-hai Municipality A. Marciano acknowledges support by the National Science Foundation of China, through the Grant No. 11875113, the Shang-hai Municipality, through the Grant No. KBH1512299, and by Fudan University, through the Grant No. JJH1512105. N. Gresnigt acknowledges support through Foundation of the Jiangsu Higher Education Institutions of China Programme Grant No. 19KJB140018 and Xi'an Jiaotong-Liverpool University Grant No. REF-18-02-03. M. Lulli acknowledges the support from National Science Foundation of China Grant No. 12050410244. E. Zappala was supported by the Estonian Research Council through the grant MOBJD679. 61 2 2 7 8 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. SEP 2022.0 153.0 164 178 10.1016/j.neunet.2022.05.028 0.0 15 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology 2U2LT 35724478.0 2023-03-23 WOS:000822992800008 0
J Abramoff, RZ; Georgiou, K; Guenet, B; Torn, MS; Huang, YY; Zhang, HC; Feng, WT; Jagadamma, S; Kaiser, K; Kothawala, D; Mayes, MA; Ciais, P Abramoff, Rose Z.; Georgiou, Katerina; Guenet, Bertrand; Torn, Margaret S.; Huang, Yuanyuan; Zhang, Haicheng; Feng, Wenting; Jagadamma, Sindhu; Kaiser, Klaus; Kothawala, Dolly; Mayes, Melanie A.; Ciais, Philippe How much carbon can be added to soil by sorption? BIOGEOCHEMISTRY English Article Sorption; Additional sorption potential; Saturation; Soil organic carbon; Mineral association DISSOLVED ORGANIC-CARBON; PARTICLE-SIZE FRACTIONS; AGRICULTURAL SOILS; MATTER; FOREST; STABILIZATION; DYNAMICS; DECOMPOSITION; ASSOCIATIONS; UNCERTAINTY Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 +/- 13 Pg C to 1 m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock. [Abramoff, Rose Z.; Guenet, Bertrand; Ciais, Philippe] Univ Paris Saclay, CNRS, UVSQ, CEA,Lab Sci Climat & Environm,IPSL, F-91191 Gif Sur Yvette, France; [Georgiou, Katerina] Stanford Univ, Dept Earth Syst Sci, Yang & Yamazaki Bldg,473 Via Ortega, Stanford, CA 94305 USA; [Guenet, Bertrand] PSL Res Univ, Ecole Normale Super, CNRS, UMR 8538,Lab Geol, Paris, France; [Torn, Margaret S.] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA USA; [Huang, Yuanyuan] CSIRO Ocean & Atmosphere, Aspendale, Vic 3195, Australia; [Zhang, Haicheng] Univ Libre Bruxelles, Dept Geosci Environm & Soc, B-1050 Brussels, Belgium; [Feng, Wenting] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, 12 Zhongguancun South St, Beijing 10081, Peoples R China; [Jagadamma, Sindhu] Univ Tennessee, Dept Biosyst Engn & Soil Sci, 2506 EJ Chapman Dr, Knoxville, TN 37996 USA; [Kaiser, Klaus] Martin Luther Univ Halle Wittenberg, Soil Sci & Soil Protect, Von Seckendorff Pl 3, D-06120 Halle, Saale, Germany; [Mayes, Melanie A.] Oak Ridge Natl Lab, Environm Sci Div, Oak Ridge, TN 37831 USA; [Mayes, Melanie A.] Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN USA; [Kothawala, Dolly] Uppsala Univ, Dept Ecol & Genet, Uppsala 75236, Sweden UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Stanford University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Universite PSL; Ecole Normale Superieure (ENS); United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Universite Libre de Bruxelles; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; University of Tennessee System; University of Tennessee Knoxville; UT Institute of Agriculture; Martin Luther University Halle Wittenberg; United States Department of Energy (DOE); Oak Ridge National Laboratory; United States Department of Energy (DOE); Oak Ridge National Laboratory; Uppsala University Abramoff, RZ (corresponding author), Univ Paris Saclay, CNRS, UVSQ, CEA,Lab Sci Climat & Environm,IPSL, F-91191 Gif Sur Yvette, France. rose.abramoff@lsce.ipsl.fr; georgiou@stanford.edu; bertrand.guenet@lsce.ipsl.fr; mstorn@lbl.gov; Yuanyuan.Huang@csiro.au; haicheng.zhang@ulb.ac.be; fengwenting@caas.cn; sjagada1@utk.edu; klaus.kaiser@landw.uni-halle.de; dolly.kothawala@ebc.uu.se; mayesma@ornl.gov; philippe.ciais@lsce.ipsl.fr Zhang, Haicheng/HJY-0016-2023; Ciais, Philippe/A-6840-2011; Guenet, Bertrand/ABD-5907-2021; Torn, Margaret S/D-2305-2015; Kaiser, Klaus/J-1726-2014; Torn, Margaret/CAF-8960-2022; Jagadamma, Sindhu/F-7168-2012; Abramoff, Rose/I-6266-2016; Feng, Wenting/D-9822-2016 Zhang, Haicheng/0000-0002-9313-5953; Ciais, Philippe/0000-0001-8560-4943; Guenet, Bertrand/0000-0002-4311-8645; Torn, Margaret S/0000-0002-8174-0099; Kaiser, Klaus/0000-0001-7376-443X; Torn, Margaret/0000-0002-8174-0099; Jagadamma, Sindhu/0000-0003-4831-3308; Abramoff, Rose/0000-0002-3393-3064; Feng, Wenting/0000-0002-3189-3687; Georgiou, Katerina/0000-0002-2819-3292; Huang, Yuanyuan/0000-0003-4202-8071 French government grant Make Our Planet Great Again; Marie Sklodowska-Curie Individual Fellowship from the European Union's Horizon 2020 program [834-169]; US Department of Agriculture NIFA Postdoctoral Fellowship; French government under the ANR Investissements d'avenir program [CLAND ANR-16-CONV-0003]; United States Department of Energy, Office of Science, Office of Biological and Environmental Research; United States Department of Energy [DE-AC05-00OR22725] French government grant Make Our Planet Great Again; Marie Sklodowska-Curie Individual Fellowship from the European Union's Horizon 2020 program; US Department of Agriculture NIFA Postdoctoral Fellowship; French government under the ANR Investissements d'avenir program(French National Research Agency (ANR)); United States Department of Energy, Office of Science, Office of Biological and Environmental Research(United States Department of Energy (DOE)); United States Department of Energy(United States Department of Energy (DOE)) RZA was supported by the French government grant Make Our Planet Great Again and by a Marie Sklodowska-Curie Individual Fellowship (Grant No. 834-169) from the European Union's Horizon 2020 program. KG was supported by a US Department of Agriculture NIFA Postdoctoral Fellowship. BG was supported by French government under the ANR Investissements d'avenir program with the reference CLAND ANR-16-CONV-0003. MAM's participation in this research was supported through an Early Career Award from the United States Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the United States Department of Energy under contract DE-AC05-00OR22725. 93 15 15 14 47 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0168-2563 1573-515X BIOGEOCHEMISTRY Biogeochemistry FEB 2021.0 152.0 2-3 127 142 10.1007/s10533-021-00759-x 0.0 JAN 2021 16 Environmental Sciences; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology QJ8FY Green Published, hybrid 2023-03-23 WOS:000611915600001 0
J Luo, QW; Fang, XX; Liu, L; Yang, CH; Sun, YC Luo, Qiwu; Fang, Xiaoxin; Liu, Li; Yang, Chunhua; Sun, Yichuang Automated Visual Defect Detection for Flat Steel Surface: A Survey IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Automated optical inspection (AOI); automated visual inspection (AVI); flat steel; surface defect detection; survey LOCAL BINARY PATTERNS; ACTIVE CONTOUR MODEL; GABOR FILTER; INSPECTION ALGORITHM; FEATURE-EXTRACTION; CLASSIFICATION; STRIP; IMAGE; RECOGNITION; MORPHOLOGY Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level. [Luo, Qiwu; Yang, Chunhua] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Fang, Xiaoxin] Hefei Univ Technol, Sch Elect & Automat Engn, Hefei 230009, Peoples R China; [Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland; [Liu, Li] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China; [Sun, Yichuang] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, Herts, England Central South University; Hefei University of Technology; University of Oulu; National University of Defense Technology - China; University of Hertfordshire Yang, CH (corresponding author), Cent South Univ, Sch Automat, Changsha 410083, Peoples R China. ychh@csu.edu.cn National Natural Science Foundation of China [51704089, 61973323]; Anhui Provincial Natural Science Foundation of China [1808085QF190] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Anhui Provincial Natural Science Foundation of China(Natural Science Foundation of Anhui Province) This work was supported in part by the National Natural Science Foundation of China under Grant 51704089 and Grant 61973323 and in part by the Anhui Provincial Natural Science Foundation of China under Grant 1808085QF190. 117 135 137 70 231 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. MAR 2020.0 69.0 3 626 644 10.1109/TIM.2019.2963555 0.0 19 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation KO8ES Green Published, Green Accepted 2023-03-23 WOS:000515781200001 0
J Xu, P; Wang, K; Hassan, MM; Chen, CM; Lin, WG; Hassan, MR; Fortino, G Xu, Peng; Wang, Ke; Hassan, Mohammad Mehedi; Chen, Chien-Ming; Lin, Weiguo; Hassan, Md Rafiul; Fortino, Giancarlo Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article; Early Access Robustness; Computational modeling; Data models; Mathematical models; Analytical models; Deep learning; Computer architecture; Adversarial robustness; adversarial example; model compression and neural architecture search Edge AI technologies have been used for many Intelligent Transportation Systems, such as road traffic monitor systems. Neural Architecture Search (NAS) is a typcial way to search high-performance models for edge devices with limited computing resources. However, NAS is also vulnerable to adversarial attacks. In this paper, A One-Shot NAS is employed to realize derivative models with different scales. In order to study the relation between adversarial robustness and model scales, a graph-based method is designed to select best sub models generated from One-Shot NAS. Besides, an evaluation method is proposed to assess robustness of deep learning models under various scales of models. Experimental results shows an interesting phenomenon about the correlations between network sizes and model robustness, reducing model parameters will increase model robustness under maximum adversarial attacks, while, increasing model paremters will increase model robustness under minimum adversarial attacks. The phenomenon is analyzed, that is able to help understand the adversarial robustness of models with different scales for edge AI transportation systems. [Xu, Peng] Harbin Inst Technol Shenzhen, Dept Comp Sci, Shenzhen 518055, Peoples R China; [Wang, Ke] Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Peoples R China; [Hassan, Mohammad Mehedi] King Saud Univ, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11543, Saudi Arabia; [Chen, Chien-Ming] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Shandong, Peoples R China; [Lin, Weiguo] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China; [Hassan, Md Rafiul] Univ Maine Presque Isle, Coll Arts & Sci, Presque Isle, ME 04769 USA; [Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst, I-87036 Arcavacata Di Rende, Italy Harbin Institute of Technology; Jinan University; King Saud University; Shandong University of Science & Technology; Communication University of China; University of Maine System; University of Maine Presque Isle; University of Calabria Wang, K (corresponding author), Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Peoples R China.;Hassan, MM (corresponding author), King Saud Univ, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11543, Saudi Arabia. 19s051059@stu.hit.edu.cn; wangkehku@gmail.com; mmhassan@ksu.edu.sa; chienmingchen@ieee.org; linwei@cuc.edu.cn; md.hassan@maine.edu; giancarlo.fortino@unical.it Fortino, Giancarlo/J-2950-2017; Hassan, Mohammad Mehedi/D-4946-2016; Hassan, Mohammad/GZA-7507-2022; Chen, Chien-Ming/W-2133-2019 Fortino, Giancarlo/0000-0002-4039-891X; Hassan, Mohammad Mehedi/0000-0002-3479-3606; Hassan, Mohammad/0000-0002-1712-0004; Chen, Chien-Ming/0000-0002-6502-472X; Xu, Sean/0000-0001-5756-3494 Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China, China [SKLMCC2021KF008]; King Saud University, Riyadh, Saudi Arabia [RSP-2022/18] Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China, China; King Saud University, Riyadh, Saudi Arabia(King Saud University) This work was supported in part by the Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China, China, under Grant SKLMCC2021KF008; and in part by King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project, under Grant RSP-2022/18. 34 0 0 4 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. 10.1109/TITS.2022.3197713 0.0 AUG 2022 10 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 4G5UU 2023-03-23 WOS:000849262000001 0
J Ruan, GJ; Li, XY; Yuan, F; Cammarano, D; Ata-UI-Karim, ST; Liu, XJ; Tian, YC; Zhu, Y; Cao, WX; Cao, Q Ruan, Guojie; Li, Xinyu; Yuan, Fei; Cammarano, Davide; Ata-UI-Karim, Syed Tahir; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang Improving wheat yield prediction integrating proximal sensing and weather data with machine learning COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Yield prediction; Data fusion; Active canopy sensors; Feature selection FEATURE-SELECTION; NITROGEN STATUS; WINTER-WHEAT; CLIMATE DATA; SATELLITE; CORN; IDENTIFICATION; MODELS; REMOTE; INDEX Accurate and timely wheat yield prediction is of great importance to global food security. Early prediction of wheat yield at a field scale is essential for site-specific precision management. This study aimed to develop an in season wheat yield prediction model at field-scale by integrating proximal sensing and weather data. Nine multi N rates field experiments were conducted at five sites involving different wheat cultivars from 2010 to 2020. Proximal sensing data were collected from a Crop Circle sensor at the stem elongation stage and weather data were collected from 30 days before planting to the flowering date. Eleven statistical and machine learning (ML) regression algorithms were adopted, along with two aggregation intervals (disaggregated or aggregated data) and two feature selection methods (based on Pearson Correlation Coefficient or Recursive Feature Elimination). The results revealed that the ensemble learning models (Random Forest, eXtreme Gradient Boosting) achieved the best overall performance (R-2 = 0.74 0.78, RMSE = 0.78 similar to 0.85 t ha(-1)). Feature importance analysis showed that Normalized Difference Red Edge Index (NDRE), average temperature, minimum temperature, and relative humidity were the most contributory features, especially from the planting date to the stem elongation date (for weather features). The aggregation approach and feature selection method did not significantly affect the yield prediction performance for the seven ML methods. This study introduced a promising framework that complements county-scale models and provided insights into understanding yield responses to environmental conditions. The best prediction model can be applied for guiding real-time sensor-based precision fertilization. [Ruan, Guojie; Li, Xinyu; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang] Nanjing Agr Univ, MARA Key Lab Crop Syst Anal & Decis Making, Natl Engn & Technol Ctr Informat Agr,Jiangsu Coll, MOE Engn & Res Ctr Smart Agr,Jiangsu Key Lab Info, Nanjing 210095, Peoples R China; [Yuan, Fei] Minnesota State Univ, Dept Geog, Mankato, MN 56001 USA; [Cammarano, Davide] Aarhus Univ, Dept Agroecol, DK-8830 Tjele, Denmark; [Ata-UI-Karim, Syed Tahir] Univ Tokyo, Grad Sch Agr & Life Sci, Bunkyo Ku, Tokyo 1138657, Japan Nanjing Agricultural University; Minnesota State Colleges & Universities; Minnesota State University Mankato; Aarhus University; University of Tokyo Cao, Q (corresponding author), Nanjing Agr Univ, MARA Key Lab Crop Syst Anal & Decis Making, Natl Engn & Technol Ctr Informat Agr,Jiangsu Coll, MOE Engn & Res Ctr Smart Agr,Jiangsu Key Lab Info, Nanjing 210095, Peoples R China. qiangcao@njau.edu.cn Cammarano, Davide/CAF-3057-2022 Jiangsu Province Key Technologies Research and Development Program [BE2021308]; National Un-dergraduate Student Innovation Research and Entrepreneurship Training (SRT) of China [202110307003]; National Natural Science Foundation of China [31601222]; Academic Program Development of Jiangsu Higher Education Institutions (PAPD); 111 Project; [B16026] Jiangsu Province Key Technologies Research and Development Program; National Un-dergraduate Student Innovation Research and Entrepreneurship Training (SRT) of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academic Program Development of Jiangsu Higher Education Institutions (PAPD); 111 Project(Ministry of Education, China - 111 Project); This research was funded by the Jiangsu Province Key Technologies Research and Development Program (BE2021308) , the National Un-dergraduate Student Innovation Research and Entrepreneurship Training (SRT) of China (202110307003) , the National Natural Science Foundation of China (31601222) , the Academic Program Development of Jiangsu Higher Education Institutions (PAPD) , and the 111 Project (B16026) . We would like to thank F. Xiang and X. Li from Nanjing Agricultural University and S. Yue and D. Lu from China Agricultural University for their fieldwork and contributions in data collection. We would also like to thank J. Wang from the College of Foreign Studies, Nanjing Agricultural University, for her contributions to English corrections. 71 8 8 11 29 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. APR 2022.0 195.0 106852 10.1016/j.compag.2022.106852 0.0 MAR 2022 15 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science 0I8JA 2023-03-23 WOS:000779659100002 0
J Qi, YG; Tan, ZH Qi, Yonggang; Tan, Zheng-Hua SketchSegNet plus : An End-to-End Learning of RNN for Multi-Class Sketch Semantic Segmentation IEEE ACCESS English Article Stroke-level sketch segmentation; recurrent neural network We investigate the problem of stroke-level sketch segmentation, which is to automatically assign strokes of a given sketch with semantic labels. Solving the problem of sketch segmentation opens the door for fine-grained sketch interpretation, which can benefit many novel sketch-based applications, including sketch recognition and sketch-based image retrieval. In this paper, we propose an approach for multi-class sketch semantic segmentation by considering it as a sequence-to-sequence generation problem. Specifically, an end-to-end learned network SketchSegNet+, built on recurrent neural networks (RNN), is presented to translate a sequence of strokes into a sequence of semantic labels. In addition, a large-scale stroke-level sketch segmentation dataset is constructed for the first time, which is composed of 150K annotated free-hand human sketch selected from QuickDraw. The dataset will be released publicly. The experimental results of stroke-level sketch semantic segmentation on this novel dataset and the SPG dataset demonstrate the effectiveness of our approach. [Qi, Yonggang] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China; [Tan, Zheng-Hua] Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark Beijing University of Posts & Telecommunications; Aalborg University Qi, YG (corresponding author), Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China. qiyg@bupt.edu.cn Qi, Yonggang/0000-0001-8280-3541 23 8 10 4 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 102717 102726 10.1109/ACCESS.2019.2929804 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IR8JN gold, Green Submitted 2023-03-23 WOS:000481688500187 0
C Tang, H; Wei, H; Xiao, W; Wang, W; Xu, D; Yan, Y; Sebe, N IEEE Tang, Hao; Wei, Heng; Xiao, Wei; Wang, Wei; Xu, Dan; Yan, Yan; Sebe, Nicu Deep Micro-Dictionary Learning and Coding Network 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) IEEE Winter Conference on Applications of Computer Vision English Proceedings Paper 19th IEEE Winter Conference on Applications of Computer Vision (WACV) JAN 07-11, 2019 Waikoloa Village, HI IEEE,IEEE Comp Soc,IEEE Biometr Council,AF Res Lab,Amazon,Honeywell,Ancestry,Cognex,Google,Kitware,Percept Automata,SAP,Verisk Analyt,Voxel51,Qualcomm,Wolfram Language FACE RECOGNITION; IMAGE In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional layers are replaced by novel compound dictionary learning and coding layers. The dictionary learning layer learns an over-complete dictionary for the input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Next, the activated dictionary atoms are assembled together and passed to the next compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components which are shared among the input dictionary atoms. In this way, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare the proposed DDLCN with several dictionary learning methods and deep learning architectures. The experimental results on four popular benchmark datasets demonstrate that the proposed DDLCN achieves competitive results compared with state-of-the-art approaches. [Tang, Hao; Sebe, Nicu] Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy; [Wei, Heng] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China; [Xiao, Wei] Lingxi Artificial Interlligence Co Ltd, Shenzhen, Peoples R China; [Wang, Wei] Ecole Polytech Fed Lausanne, Comp Vis Lab, Lausanne, Switzerland; [Xu, Dan] Univ Oxford, Dept Engn Sci, Oxford, England; [Yan, Yan] Texas State Univ, Dept Comp Sci, San Marcos, TX USA University of Trento; Hong Kong Polytechnic University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Oxford; Texas State University System; Texas State University San Marcos Tang, H (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy. hao.tang@unitn.it; 15102924d@connect.polyu.hk; xiaoweithu@163.com; wei.wang@epfl.ch; danxu@robots.ox.ac.uk; y_y34@txstate.edu; niculae.sebe@unitn.it Wang, Wei/AAK-5521-2021 Wang, Wei/0000-0002-5477-1017; Sebe, Niculae/0000-0002-6597-7248 58 6 6 1 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2472-6737 978-1-7281-1975-5 IEEE WINT CONF APPL 2019.0 386 395 10.1109/WACV.2019.00047 0.0 10 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM8MK Green Submitted 2023-03-23 WOS:000469423400040 0
J Xiao, RW; Wu, ZW; Hamari, JH Xiao, Ruowei; Wu, Zhanwei; Hamari, Juho Internet-of-Gamification: A Review of Literature on IoT-enabled Gamification for User Engagement INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION English Review CUSTOMER ENGAGEMENT; BIG DATA; BEHAVIOR; MOTIVATION; MANAGEMENT; THINGS; GAMES; FLOW Engagement is a common goal pursued by most social and technical systems, because of its widely acknowledged effects on enhancing user acceptance and performance. Previous research has shown that a system's ability to engage users involves two known aspects: the technology foundation that determines the interactive paths for engaging users and the design methodology that determines the atop user experience to be conveyed through those paths. In recent years, an emerging and promising engagement approach that integrates both an advanced technology stack and novel design methodology, i.e. IoT-enabled Gamification (IeG), has attracted wide interest from both public and private sectors. This article aims to conduct a systematic review to answer some fundamental questions. 75 papers were reviewed under a 3-axis analysis framework of user engagement, the majority of which indicated that IeG is linked to increased engagement in a variety of application domains, stages, and population scales. [Xiao, Ruowei; Hamari, Juho] Tampere Univ, Fac Informat Technol & Commun Sci, Tampere, Finland; [Wu, Zhanwei] Shanghai Jiao Tong Univ, Sch Design, 800 Dongchuang Rd, Shanghai, Peoples R China Tampere University; Shanghai Jiao Tong University Wu, ZW (corresponding author), Shanghai Jiao Tong Univ, Sch Design, 800 Dongchuang Rd, Shanghai, Peoples R China. zhanwei_wu@sjtu.edu.cn Hamari, Juho/E-4989-2016 Hamari, Juho/0000-0002-6573-588X National Social Science Fund of China [16BGL191]; Academy of Finland [332168]; Business Finland [5654/31/2018] National Social Science Fund of China; Academy of Finland(Academy of Finland); Business Finland This work was supported by the National Social Science Fund of China [16BGL191]; Academy of Finland [332168]; Business Finland [5654/31/2018]. 134 3 3 11 37 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1044-7318 1532-7590 INT J HUM-COMPUT INT Int. J. Hum.-Comput. Interact. JUL 21 2022.0 38.0 12 1113 1137 10.1080/10447318.2021.1990517 0.0 DEC 2021 25 Computer Science, Cybernetics; Ergonomics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering 2B5GS hybrid 2023-03-23 WOS:000725988300001 0
J Shang, XQ; Shen, Z; Xiong, G; Wang, FY; Liu, S; Nyberg, TR; Wu, HY; Guo, C Shang, Xiuqin; Shen, Zhen; Xiong, Gang; Wang, Fei-Yue; Liu, Sheng; Nyberg, Timo R.; Wu, Huaiyu; Guo, Chao Moving from mass customization to social manufacturing: a footwear industry case study INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING English Article Collaborative manufacturing; mass customisation; optimisation SM resources SYSTEMS; DESIGN; SERVICE; PRODUCT A social manufacturing (SM) system for the footwear industry is proposed to improve customisation accuracy and efficiency through the use of big data and crowdsourcing. SM is a novel manufacturing paradigm for distributed, collaborative, service-oriented, crowdsourcing-based, intelligent, and customised footwear production. First, the definition and process of footwear SM are introduced. Next, key supporting technologies are discussed, including 3D scanning, an evaluation mechanism for supply-demand matching, and optimised allocation of socialised manufacturing resources. Finally, the system development and an application case are described. In SM, a prosumer is defined as someone who is both a producer and consumer at the same time, and can be involved in the full life cycle of a personalised product, including the design, manufacture, service, and consumption processes. This allows the experience of every prosumer to be enhanced through participation, potential demands for personalised product to be released, and the production efficiency of customisation to be improved. [Shang, Xiuqin; Shen, Zhen; Xiong, Gang; Wang, Fei-Yue; Liu, Sheng; Wu, Huaiyu; Guo, Chao] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China; [Shang, Xiuqin; Shen, Zhen] Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing, Peoples R China; [Xiong, Gang; Wang, Fei-Yue] Qingdao Acad Intelligent Ind, Qingdao, Peoples R China; [Nyberg, Timo R.] Aalto Univ, Sch Sci, Aalto, Finland Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Aalto University Liu, S (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China. sheng.liu@ia.ac.cn National Natural Science Foundation of China [61304201, 61533019, 61773381, 61773382, 71702182] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China [61304201, 61533019, 61773381, 61773382, 71702182]. 37 23 23 1 55 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0951-192X 1362-3052 INT J COMPUT INTEG M Int. J. Comput. Integr. Manuf. FEB 1 2019.0 32.0 2 194 205 10.1080/0951192X.2018.1550675 0.0 12 Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Operations Research & Management Science HF9SH 2023-03-23 WOS:000454582200008 0
C Hore, S; Chatterjee, S; Santhi, V; Dey, N; Ashour, AS; Balas, VE; Shi, FQ Balas, VE; Jain, LC; Zhao, X Hore, Sirshendu; Chatterjee, Sankhadeep; Santhi, V.; Dey, Nilanjan; Ashour, Amira S.; Balas, Valentina Emilia; Shi, Fuqian Indian Sign Language Recognition Using Optimized Neural Networks INFORMATION TECHNOLOGY AND INTELLIGENT TRANSPORTATION SYSTEMS, VOL 2 Advances in Intelligent Systems and Computing English Proceedings Paper International Conference on Information Technology and Intelligent Transportation Systems (ITITS) DEC 12-13, 2015 Xian, PEOPLES R CHINA Shaanxi Comp Soc,Changan Univ,Xian Univ Technol,NW Poly Tech Univ,Shaanxi Sirui Ind Co Ltd,CAS Indian sign language; Neural network; Genetic algorithm; Evolutionary algorithm; Particle swarm optimization Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic. [Hore, Sirshendu] Hooghly Engn & Technol Coll, Dept Comp Sci & Engn, Chinsura, India; [Chatterjee, Sankhadeep] Univ Calcutta, Dept Comp Sci & Engn, Kolkata, India; [Santhi, V.] VIT Univ, Vellore, Tamil Nadu, India; [Dey, Nilanjan] Techno India Coll Technol, Dept Informat Technol, Kolkata, India; [Ashour, Amira S.] Tanta Univ, Fac Engn, Dept Elect & Elect Commun Engn, Tanta, Egypt; [Ashour, Amira S.] Taif Univ, Coll CIT, At Taif, Saudi Arabia; [Balas, Valentina Emilia] Aurel Vlaicu Univ Arad, Fac Engn, Arad, Romania; [Shi, Fuqian] Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 325035, Peoples R China University of Calcutta; Vellore Institute of Technology (VIT); VIT Vellore; Egyptian Knowledge Bank (EKB); Tanta University; Taif University; Aurel Vlaicu University of Arad; Wenzhou Medical University Hore, S (corresponding author), Hooghly Engn & Technol Coll, Dept Comp Sci & Engn, Chinsura, India. sirshendu.hore@hetc.ac.in; sankha3521@gmail.com; vsanthinathan@gmail.com; neelanjandey@gmail.com; amirasashour@yahoo.com; valentina.balas@uav.ro; sfq@wmu.edu.cn Ashour, Amira S./T-5454-2019; Chatterjee, Sankhadeep/AAS-1054-2020; Balas, Valentina Emilia/F-4525-2010; Chatterjee, Sankhadeep/F-4672-2017 Ashour, Amira S./0000-0003-3217-6185; Chatterjee, Sankhadeep/0000-0002-3930-4699; Balas, Valentina Emilia/0000-0003-0885-1283; Chatterjee, Sankhadeep/0000-0002-3930-4699; Hore, Sirshendu/0000-0002-6045-5352; Santhi, V/0000-0002-4274-7474 26 32 32 1 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-5357 2194-5365 978-3-319-38771-0; 978-3-319-38769-7 ADV INTELL SYST 2017.0 455.0 2 553 563 10.1007/978-3-319-38771-0_54 0.0 11 Automation & Control Systems; Computer Science, Artificial Intelligence; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Transportation BL5XB 2023-03-23 WOS:000452856700054 0
J Zhou, SH; Zhou, YL; Xu, ZZ; Chang, WB; Cheng, Y Zhou, Shenghan; Zhou, Yuliang; Xu, Zhenzhong; Chang, Wenbing; Cheng, Yang The landing safety prediction model by integrating pattern recognition and Markov chain with flight data NEURAL COMPUTING & APPLICATIONS English Article Landing safety; Flight data; Pattern recognition; Markov chain QAR DATA; ALGORITHM This paper aims to predict the landing state during the landing phase to ensure landing safety and reduce the accidents loss. Some past researches have demonstrated the landing phase is the most dangerous phase in flight cycle and fatal accident. The landing safety problem has become a hot research problem in safety field. This study concentrates more on the prediction and advanced warning for landing safety. Firstly, four landing states are divided by three flight parameter variables including touchdown, vertical acceleration and distance to go; subsequently, pattern recognition based on BP neural network is used to establish the landing state prediction model; the genetic algorithm is used to initialize the model parameter; the Markov chain is proposed to revise and improve the model for higher prediction precision. Finally, in comparison with pattern recognition and the Markov chain revision results, the Markov chain revision method is demonstrated to be practical and effective. [Zhou, Shenghan; Zhou, Yuliang; Xu, Zhenzhong; Chang, Wenbing] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Cheng, Yang] Aalborg Univ, Ctr Ind Prod, Aalborg, Denmark Beihang University; Aalborg University Chang, WB (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China. 09450@buaa.edu.cn Cheng, Yang/AAJ-5739-2021; zhou, sheng/GYJ-6012-2022 Cheng, Yang/0000-0002-3082-4890; Zhou, Shenghan/0000-0001-7979-4912 National Natural Science Foundation of China [71271009, 71501007, 71672006]; Aviation Science Foundation of China [2017ZG51081]; Technical Research Foundation [JSZL2016601A004]; Graduate Student Education & Development Foundation of Beihang University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Aviation Science Foundation of China; Technical Research Foundation; Graduate Student Education & Development Foundation of Beihang University This work is supported by the National Natural Science Foundation of China (Grant Nos. 71271009 & 71501007 & 71672006). The study is also sponsored by the Aviation Science Foundation of China (2017ZG51081), the Technical Research Foundation (JSZL2016601A004) and the Graduate Student Education & Development Foundation of Beihang University. 25 6 7 3 20 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. JAN 2019.0 31.0 1.0 SI 147 159 10.1007/s00521-018-3669-9 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HU7HY Green Submitted 2023-03-23 WOS:000465453100014 0
J Shen, Y; Yuan, KQ; Chen, DY; Colloc, J; Yang, M; Li, YL; Lei, K Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Colloc, Joel; Yang, Min; Li, Yaliang; Lei, Kai An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription ARTIFICIAL INTELLIGENCE IN MEDICINE English Article Ontology; Clinical decision support; Decision support system; Diagnosis classification; Antibiotics prescription Background: The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. Methods and results: This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of SubClassOf' that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431 pairs of drug interaction relationships and 86 pairs of antibiotic-specific population contraindicated relationships. Compared with the existing infectious disease-relevant ontologies in the field of knowledge comprehension, this ontology is more complete. Analysis of IDDAP's performance in terms of classifiers based on receiver operating characteristic (ROC) curve results (89.91%) revealed IDDAP's advantages when combined with our ontology. Conclusions and significance: This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowledge base. (C) 2018 Elsevier B.V. All rights reserved. [Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Lei, Kai] Peking Univ, Shenzhen Grad Sch, Sch Elect & Comp Engn, Shenzhen, Peoples R China; [Colloc, Joel] Univ Havre, IDEES, Lab CIRTAI, Le Havre, France; [Yang, Min] Chinese Acad Sci, SIAT, Beijing, Peoples R China; [Li, Yaliang] Med Al Lab, Tencent, AZ USA Peking University; University Town of Shenzhen; Universite Le Havre Normandie; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS Lei, K (corresponding author), Peking Univ, Shenzhen Grad Sch, Sch Elect & Comp Engn, Shenzhen, Peoples R China. leik@pkusz.edu.cn Lei, Kai/W-5518-2019; SHEN, YING/HJH-6938-2023; shen, ying/HHS-5635-2022; Colloc, Joel/O-9518-2015 Lei, Kai/0000-0001-9197-895X; SHEN, YING/0000-0002-3220-904X; Colloc, Joel/0000-0001-9260-0166 National Natural Science Foundation of China [61602013]; Shenzhen Key Fundamental Research Projects [JCYJ20151030154330711, JCYJ20170412150946024] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Key Fundamental Research Projects This work was financially supported by the National Natural Science Foundation of China (No.61602013), and the Shenzhen Key Fundamental Research Projects (Grant No. JCYJ20151030154330711, and JCYJ20170412150946024). 34 30 31 2 28 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0933-3657 1873-2860 ARTIF INTELL MED Artif. Intell. Med. MAR 2018.0 86.0 20 32 10.1016/j.artmed.2018.01.003 0.0 13 Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Medical Informatics GD8QJ 29433958.0 2023-03-23 WOS:000430776900003 0
J Liu, Y; Wu, SJ; Chen, HB; Federici, M; Perricone, G; Li, Y; Lv, G; Munir, S; Luo, ZW; Mao, BH Liu, Ye; Wu, Sijin; Chen, Haibo; Federici, Matteo; Perricone, Guido; Li, Ying; Lv, Gang; Munir, Said; Luo, Zhiwen; Mao, Baohua Brake wear induced PM10 emissions during the world harmonised light-duty vehicle test procedure-brake cycle JOURNAL OF CLEANER PRODUCTION English Article Non-exhaust emissions; Brake wear PM10 emissions; WLTP-B cycle; FEA; Machine learning PARTICULATE MATTER; ULTRAFINE PARTICLES; FRICTION MATERIAL; DYNAMOMETER In this work, the particulate matter less than 10 mu m (PM10) emissions from a medium-sized passenger vehicle's front brake wear were studied using a finite element analysis (FEA) and experimental approaches. The world harmonised light-duty vehicle test procedure-brake (WLTP-B) cycle was chosen to simulate real-world driving. An electrical low-pressure impactor (ELPI+) was used to count the brake wear particles on a brake dynamometer sealed in a chamber. In addition, a machine learning method, namely, extreme gradient boosting (XGBoost), was employed to capture the feature importance rankings of braking conditions contributing to brake wear PM10 emissions. The simulated PM10 emissions were quite consistent with the measured ones, with an overall relative error of 9%, indicating that the proposed simulation approach is promising to predict brake wear PM10 during the WLTP-B cycle. The simulated and experimental PM10 emission factors during the WLTP-B cycle were 6.4 mg km(-1) veh(-1) and 7.0 mg km(-1) veh(-1), respectively. Among the 10 trips of the WLTP-B cycle, the measured PM10 of trip #10 was the largest contributor, accounting for 49% of total PM10 emissions. On the other hand, the XGBoost results revealed that the top five most important factors governing brake wear PM10 emissions were dissipation energy, initial braking speed, final rotor temperature, braking power, and deceleration rate. From the perspective of friendly driving behaviour and regulation, limiting severe braking and high-speed braking has the potential to reduce PM(10)emissions from brake wear. [Liu, Ye; Wu, Sijin; Chen, Haibo; Munir, Said] Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, England; [Federici, Matteo; Perricone, Guido] Brembo SpA, Via Europa 2, BG, I-24020 Stezzano, Italy; [Li, Ying] Dynnoteq, 61 Bridge St, Kington HR5 3DJ, England; [Lv, Gang] Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China; [Luo, Zhiwen] Univ Reading, Sch Built Environm, Reading RG6 6AW, England; [Mao, Baohua] Beijing Jiaotong Univ, Integrated Transport Res Ctr China, Beijing 100044, Peoples R China University of Leeds; Tianjin University; University of Reading; Beijing Jiaotong University Chen, HB (corresponding author), Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, England.;Lv, G (corresponding author), Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China. H.Chen@its.leeds.ac.uk; Lvg@tju.edu.cn Liu, Ye/0000-0002-9367-3532; Wu, Sijin/0000-0002-2120-3267 European Union [815189, 954377]; Natural Science Foundation of Tianjin [19JCZDJC40100] European Union(European Commission); Natural Science Foundation of Tianjin(Natural Science Foundation of Tianjin) This research was jointly supported by the European Union's Horizon 2020 research and innovation programme: MODALES (grant agreement No 815189, https://modales-project.eu/) aimed at modifying drivers' behaviour to reduce vehicle emissions from brake wear, and nPETS (grant agreement No 954377, https:// www.npets-project. eu/) aimed at studying the life of the sub 100 nm particles emitted from transport from their creation to their potential path to human beings and animals and the Natural Science Foundation of Tianjin (19JCZDJC40100) . 51 0 0 11 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. AUG 10 2022.0 361.0 132278 10.1016/j.jclepro.2022.132278 0.0 MAY 2022 9 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 1X9NO Green Published, Green Accepted, hybrid 2023-03-23 WOS:000807775600001 0
J Parkinson, PMS; Xu, H; Yu, PLH; Salvetti, D; Marelli, M; Falcone, AD Parkinson, P. M. Saz; Xu, H.; Yu, P. L. H.; Salvetti, D.; Marelli, M.; Falcone, A. D. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES ASTROPHYSICAL JOURNAL English Article gamma rays: stars; methods: statistical; pulsars: general LARGE-AREA TELESCOPE; MILLISECOND PULSAR BINARY; BLIND FREQUENCY SEARCHES; RADIO-QUIET; RADIATION; MULTIWAVELENGTH; REGRESSION; DISCOVERY; ASTRONOMY We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (similar to 90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies. [Parkinson, P. M. Saz] Univ Hong Kong, Dept Phys, Pokfulam Rd, Hong Kong, Hong Kong, Peoples R China; [Parkinson, P. M. Saz] Univ Hong Kong, Lab Space Res, Hong Kong, Hong Kong, Peoples R China; [Parkinson, P. M. Saz] Univ Calif Santa Cruz, Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA; [Xu, H.; Yu, P. L. H.] Univ Hong Kong, Dept Stat & Actuarial Sci, Pokfulam Rd, Hong Kong, Hong Kong, Peoples R China; [Salvetti, D.; Marelli, M.] INAF, Ist Astrofis Spaziale & Fis Cosm Milano, Via E Bassini 15, I-20133 Milan, Italy; [Falcone, A. D.] Penn State Univ, Dept Astron & Astrophys, University Pk, PA 16802 USA University of Hong Kong; University of Hong Kong; University of California System; University of California Santa Cruz; University of Hong Kong; Istituto Nazionale Astrofisica (INAF); Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park Parkinson, PMS (corresponding author), Univ Hong Kong, Dept Phys, Pokfulam Rd, Hong Kong, Hong Kong, Peoples R China.;Parkinson, PMS (corresponding author), Univ Hong Kong, Lab Space Res, Hong Kong, Hong Kong, Peoples R China.;Parkinson, PMS (corresponding author), Univ Calif Santa Cruz, Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA. Yu, Philip L.H./GXV-5370-2022 Yu, Philip L.H./0000-0002-9449-0420; Xu, Hang/0000-0003-3645-8972 National Aeronautics and Space Administration (NASA) [NNX10AP18G, NNX12AP41G, NNG12PP66P]; NASA [NNX13AB52G, G03-14070X, G03-14079X, NAS8-03060]; NASA [NNX13AB52G, 476615] Funding Source: Federal RePORTER National Aeronautics and Space Administration (NASA)(National Aeronautics & Space Administration (NASA)); NASA(National Aeronautics & Space Administration (NASA)); NASA(National Aeronautics & Space Administration (NASA)) We thank the anonymous referee for useful feedback on our original manuscript, helping us to significantly improve the final version. This work was supported by the National Aeronautics and Space Administration (NASA) through Fermi Guest Investigator grants NNX10AP18G, NNX12AP41G, and NNG12PP66P. Additional support was provided through NASA Grant and Cooperative Agreement NNX13AB52G and Chandra Awards G03-14070X and G03-14079X, issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. 66 66 66 0 10 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. MAR 20 2016.0 820.0 1 8 10.3847/0004-637X/820/1/8 0.0 20 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics DH4WT Green Submitted 2023-03-23 WOS:000372787000008 0
C Yang, C; Chen, W; Zheng, BL; He, TK; Zheng, K; Su, H Cai, Y; Ishikawa, Y; Xu, J Yang, Chen; Chen, Wei; Zheng, Bolong; He, Tieke; Zheng, Kai; Su, Han Predicting Passenger's Public Transportation Travel Route Using Smart Card Data WEB AND BIG DATA (APWEB-WAIM 2018), PT II Lecture Notes in Computer Science English Proceedings Paper 2nd International Joint Conference on Asia-Pacific Web (APWeb) / Web-Age Information Management (WAIM) JUL 23-25, 2018 PEOPLES R CHINA Transit prediction; Multimodal embedding; Smart card Transit prediction is a important task for public transport institutions and urban planners to provide better transit scheduling and urban planning. In recent years, there are a lot of research on traffic prediction, but the existing works focus predicting the monolithic traffic trend, and few works focus on passenger's public transportation travel route. In this paper, we study the passenger's travel route and duration prediction. We propose a prediction model based on LSTM neural network to predict passenger's travel route and duration. Specifically, we leverage multimodal embedding to extract passenger's features which are highly related to passenger's travel route and then use a LSTM-based model to improve the prediction accuracy. To verify the effectiveness of our model, we conduct extensive experiments using a real dataset which is collected from Brisbane in Australia for four months. The experimental results show that the accuracy of our model is better than baseline models. [Yang, Chen; Chen, Wei; Zheng, Kai; Su, Han] Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu, Sichuan, Peoples R China; [Zheng, Bolong] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Zheng, Bolong] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark; [He, Tieke] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China University of Electronic Science & Technology of China; Sun Yat Sen University; Aalborg University; Nanjing University Su, H (corresponding author), Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu, Sichuan, Peoples R China. chenyang@uestc.edu.cn; weichen@uestc.edu.cn; zblchris@gmail.com; hetieke@gmail.com; kaizheng@uestc.edu.cn; hansu@uestc.edu.cn 17 0 0 0 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-96893-3; 978-3-319-96892-6 LECT NOTES COMPUT SC 2018.0 10988.0 199 213 10.1007/978-3-319-96893-3_15 0.0 15 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN4OG 2023-03-23 WOS:000482623700015 0
J Marinazzo, D; Wu, GR; Pellicoro, M; Angelini, L; Stramaglia, S Marinazzo, Daniele; Wu, Guorong; Pellicoro, Mario; Angelini, Leonardo; Stramaglia, Sebastiano Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings PLOS ONE English Article NEURAL-NETWORK; CONNECTIVITY We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon. [Marinazzo, Daniele; Wu, Guorong] Univ Ghent, Dept Data Anal, Fac Psychol & Educ Sci, Ghent, Belgium; [Wu, Guorong] Univ Elect Sci & Technol China, Key Lab NeuroInformat, Minist Educ, Sch Life Sci & Technol, Chengdu 610054, Peoples R China; [Pellicoro, Mario; Angelini, Leonardo; Stramaglia, Sebastiano] Ist Nazl Fis Nucl, I-70126 Bari, Italy; [Pellicoro, Mario; Angelini, Leonardo; Stramaglia, Sebastiano] Univ Bari, Dipartmento Fis, Bari, Italy Ghent University; University of Electronic Science & Technology of China; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro Marinazzo, D (corresponding author), Univ Ghent, Dept Data Anal, Fac Psychol & Educ Sci, Ghent, Belgium. daniele.marinazzo@ugent.be Marinazzo, Daniele/H-4793-2019; stramaglia, sebastiano/H-7582-2015 Marinazzo, Daniele/0000-0002-9803-0122; stramaglia, sebastiano/0000-0002-5873-8564 China Scholarship Council [2011607033] China Scholarship Council(China Scholarship Council) GW is visiting Gent University with a grant of China Scholarship Council (no 2011607033). No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 36 22 22 0 13 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One SEP 18 2012.0 7.0 9 e45026 10.1371/journal.pone.0045026 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 040HS 23028745.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000311313900068 0
C Deng, L; Chen, XH; Zhao, Y; Zheng, K Jensen, CS; Lim, EP; Yang, DN; Lee, WC; Tseng, VS; Kalogeraki, V; Huang, JW; Shen, CY Deng, Liwei; Chen, Xuanhao; Zhao, Yan; Zheng, Kai HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2021), PT I Lecture Notes in Computer Science English Proceedings Paper 26th International Conference on Database Systems for Advanced Applications (DASFAA) APR 11-14, 2021 ELECTR NETWORK Multivariate time series; Anomaly detection; Graph neural networks Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems. Despite the recent emergence of a large number of anomaly detection algorithms for multivariate time series, most of them ignore the correlation modeling among multivariate, which can often lead to poor anomaly detection results. In this work, we propose a novel anomaly detection model for multivariate time series with HIgh-order Feature Interactions (HIFI). More specifically, HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions, in which the long-term temporal dependencies are modeled by attention mechanisms and a variational encoding technique is utilized to improve the model performance and robustness. Extensive experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches. [Deng, Liwei; Chen, Xuanhao; Zheng, Kai] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China; [Zhao, Yan] Aalborg Univ, Aalborg, Denmark University of Electronic Science & Technology of China; Aalborg University Zheng, K (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China. deng_liwei@std.uestc.edu.cn; xhc@std.uestc.edu.cn; yanz@cs.aau.dk; zhengkai@uestc.edu.cn NSFC [61972069, 61836007, 61832017]; Sichuan Science and Technology Program [2020JDTD0007] NSFC(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program This work is supported by NSFC (No. 61972069, 61836007, 61832017) and Sichuan Science and Technology Program under Grant 2020JDTD0007. 12 0 0 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-73193-9; 978-3-030-73194-6 LECT NOTES COMPUT SC 2021.0 12681.0 641 649 10.1007/978-3-030-73194-6_42 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU2UN Green Submitted 2023-03-23 WOS:000886771000042 0
J He, ZM; Yin, J; Wang, Y; Gui, G; Adebisi, B; Ohtsuki, T; Gacanin, H; Sari, H He, Zhimin; Yin, Jie; Wang, Yu; Gui, Guan; Adebisi, Bamidele; Ohtsuki, Tomoaki; Gacanin, Haris; Sari, Hikmet Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Internet of Things; edge device identification; network traffic; centralized learning; federated learning; deep learning IOT; INTERNET; CLASSIFICATION; SYSTEM; DDOS; 5G With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target's internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process. [He, Zhimin; Wang, Yu; Gui, Guan; Sari, Hikmet] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Yin, Jie] Jiangsu Police Inst, Dept Comp Informat & Cyber Secur, Nanjing 210031, Peoples R China; [Yin, Jie] Nanjing Univ, Jiangsu Prov Elect Data Forens & Anal Engn Res Ctr, Key Lab Digital Forens, Jiangsu Prov Publ Secur Dept, Nanjing 210023, Peoples R China; [Yin, Jie] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester M15 6BH, England; [Ohtsuki, Tomoaki] Keio Univ, Dept Informat & Comp Sci, Yokohama 2238521, Japan; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany Nanjing University of Posts & Telecommunications; Jiangsu Police Institute; Nanjing University; Nanjing University; Manchester Metropolitan University; Keio University; RWTH Aachen University Gui, G (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China.;Yin, J (corresponding author), Jiangsu Police Inst, Dept Comp Informat & Cyber Secur, Nanjing 210031, Peoples R China. 1019010407@njupt.edu.cn; yinjie@jspi.cn; 1018010407@njupt.edu.cn; guiguan@njupt.edu.cn; b.adebisi@mmu.ac.uk; ohtsuki@ics.keio.ac.jp; harisg@ice.rwth-aachen.de; hsari@ieee.org Adebisi, Bamidele/0000-0001-9071-9120; Ohtsuki, Tomoaki/0000-0003-3961-1426 JSPS KAKENHI [JP19H02142]; Major Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]; National Natural Science Foundation of China [61901228]; Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]; Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]; project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106]; Open Project Program of the State Key Laboratory of CAD&CG, Zhejiang University [A2102] JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Major Project of the Ministry of Industry and Information Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Summit of the Six Top Talents Program of Jiangsu; Program for High-Level Entrepreneurial and Innovative Team; project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China; Open Project Program of the State Key Laboratory of CAD&CG, Zhejiang University This work was supported in part by the JSPS KAKENHI under Grant JP19H02142, Major Project of the Ministry of Industry and Information Technology of China under Grant TC190A3WZ-2, National Natural Science Foundation of China under Grant 61901228, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106, the Open Project Program of the State Key Laboratory of CAD&CG under Grant A2102, Zhejiang University. 48 7 7 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. DEC 2022.0 8.0 4 1898 1909 10.1109/TCCN.2021.3101239 0.0 12 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications 7K7PB 2023-03-23 WOS:000905469100023 0
J Cichy, T; Prusek, S; Swiatek, J; Apel, DB; Pu, YY Cichy, Tomasz; Prusek, Stanislaw; Swiatek, Janina; Apel, Derek B.; Pu, Yuanyuan Use of Neural Networks to Forecast Seismic Hazard Expressed by Number of Tremors Per Unit of Surface PURE AND APPLIED GEOPHYSICS English Article Elastic strain energy; induced seismicity; neural networks The seismic and rock burst hazard should be considered as one of the most important hazards in Polish hard coal and copper ore mines. Over the last several years, Upper Silesia has witnessed a constant process of limiting the extraction of hard coal. Despite a significant decrease in production, no reduction in the number of registered high-energy tremors with energy higher than 10(5) J. is observed. Therefore, it is important to develop the existing methods for assessing the state of seismic hazard and seeking new ways to estimate the level of this threat. The article presents the results of research aimed at determining the possibility of using artificial neural networks to forecast the level of seismicity induced by mining operation. The data used in the conducted research came from the area of operation of a heavily seismic USCB mine. The presented research results showed that in the case of the discussed area, the use of the new tool, which is an artificial neural network, allowed to obtain good results of the forecast of the number of tremors. For the data set in question, the neural network with optimal architecture is composed of twelve neurons in the entrance layer, two neurons with bipolar characteristics in the hidden layer and one neuron with linear or bipolar characteristics in the initial layer. The results of calculations made for the highly seismic area of mining works carried out in a hard coal mine confirmed the possibility of using neural networks to estimate the changes in the size of induced seismicity associated with the deposit operation. [Cichy, Tomasz; Prusek, Stanislaw; Swiatek, Janina] Cent Min Inst, Katowice, Poland; [Apel, Derek B.] Univ Alberta, Edmonton, AB, Canada; [Pu, Yuanyuan] Chongqing Univ, Chongqing, Peoples R China Central Mining Institute (GIG); University of Alberta; Chongqing University Apel, DB (corresponding author), Univ Alberta, Edmonton, AB, Canada. derek.apel@ualberta.ca Apel, Derek B/AAK-1031-2020; Cichy, Tomasz/X-1315-2018 Apel, Derek B/0000-0002-5402-7036; Cichy, Tomasz/0000-0001-7455-9446; Prusek, Stanislaw/0000-0003-4113-397X 14 6 6 2 10 SPRINGER BASEL AG BASEL PICASSOPLATZ 4, BASEL, 4052, SWITZERLAND 0033-4553 1420-9136 PURE APPL GEOPHYS Pure Appl. Geophys. DEC 2020.0 177.0 12 5713 5722 10.1007/s00024-020-02602-0 0.0 OCT 2020 10 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics PB8XJ 2023-03-23 WOS:000583392000002 0
J Koutsos, V; Papadopoulos, D; Chatzopoulos, D; Tarkoma, S; Hui, P Koutsos, Vlasis; Papadopoulos, Dimitrios; Chatzopoulos, Dimitris; Tarkoma, Sasu; Hui, Pan Agora: A Privacy-Aware Data Marketplace IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING English Article Generators; Iron; Smart contracts; Data privacy; Encryption; Cryptography; Blockchains; Data marketplace; functional encryption; blockchain BIG DATA We propose Agora, the first blockchain-based data marketplace that enables multiple privacy-concerned parties to get compensated for contributing and exchanging data, without relying on a trusted third party during the exchange. Agora achieves data privacy, output verifiability, and atomicity of payments by leveraging cryptographic techniques, and is designed as a decentralized application via smart contracts. Particularly, data generators provide encrypted data to data brokers who use a functional secret key to learn nothing but the output of a specific, agreed upon, function over the raw data. Data consumers can purchase decrypted outputs from the brokers, accompanied by corresponding proofs of correctness. We implement a working prototype of Agora on Ethereum and experimentally evaluate its performance and deployment costs. As a core building block of Agora, we propose a new functional encryption scheme with additional public parameters that operate as a trust anchor for verifying decrypted results. [Koutsos, Vlasis; Papadopoulos, Dimitrios; Chatzopoulos, Dimitris; Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engeneering, Hong Kong, Peoples R China; [Tarkoma, Sasu; Hui, Pan] Univ Helsinki, Helsinki 00100, Finland Hong Kong University of Science & Technology; University of Helsinki Koutsos, V (corresponding author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engeneering, Hong Kong, Peoples R China. vkoutsos@cse.ust.hk; dipapado@cse.ust.hk; dcab@cse.ust.hk; sasu.tarkoma@helsinki.fi; panhui@cse.ust.hk Koutsos, Vlasios/0000-0002-8853-0625 Hong Kong RGC [ECS-26208318] Hong Kong RGC(Hong Kong Research Grants Council) We thank the anonymous reviewers for their valuable feedback. This work was supported in part by Hong Kong RGC under Grant ECS-26208318. 65 0 0 3 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5971 1941-0018 IEEE T DEPEND SECURE IEEE Trans. Dependable Secur. Comput. NOV 1 2022.0 19.0 6 3728 3740 10.1109/TDSC.2021.3105099 0.0 13 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6C4LL 2023-03-23 WOS:000881987900011 0
C Li, YH; Huang, YY; Su, X; Riekki, J; Flores, H; Sun, C; Wei, HY; Wang, H; Han, L IEEE Li, Yuhong; Huang, Yuanyuan; Su, Xiang; Riekki, Jukka; Flores, Huber; Sun, Chao; Wei, Hanyu; Wang, Hao; Han, Lei Gamma-modulated Wavelet Model for Internet of Things Traffic 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) MAY 21-25, 2017 Paris, FRANCE IEEE,IEEE France Sect,IEEE Commun Soc Internet of Things; traffic modelling; wavelet Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model. [Li, Yuhong; Huang, Yuanyuan] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China; [Su, Xiang; Riekki, Jukka; Flores, Huber] Univ Oulu, Ctr Ubiquitous Comp, Oulu, Finland; [Sun, Chao; Wei, Hanyu; Wang, Hao; Han, Lei] Huawei Technol Co Ltd, Network Technol Lab, Nanjing, Jiangsu, Peoples R China Beijing University of Posts & Telecommunications; University of Oulu; Huawei Technologies Li, YH (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China. Flores, Huber/AAP-5274-2021; li, Yu/HCI-9086-2022 Flores, Huber/0000-0003-4551-629X; Huawei HIRP program Huawei HIRP program(Huawei Technologies) The work is partly supported by Huawei HIRP program. 33 0 0 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-4673-8999-0 IEEE ICC 2017.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BJ4DG 2023-03-23 WOS:000424872101025 0
J Shang, XQ; Wang, FY; Xiong, G; Nyberg, TR; Yuan, Y; Liu, S; Guo, C; Bao, S Shang, Xiuqin; Wang, Fei-Yue; Xiong, Gang; Nyberg, Timo R.; Yuan, Yong; Liu, Sheng; Guo, Chao; Bao, Sen Social Manufacturing for High-end Apparel Customization IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Article Big data; collaborative management; social manufacturing cloud; 3D apparel customization ANALYTIC HIERARCHY PROCESS; SOCIETIES; SYSTEMS Social manufacturing (SM), a novel distributed, collaborative and intelligent manufacturing mode, is proposed and developed for high-end apparel customization. The main components of SM cloud are designed, and its research topics are summarized. Then, SM's key technologies are studied. 3D technologies for apparel customization, like 3D modeling, 3D fitting mirror and 3D customization, are developed to improve the customization precision and user experience. Information based collaborative management is realized to share, communicate, and handle the information efficiently among all groups and individuals of SM cloud. Suppliers' evaluation mechanism is designed to support the optimal decisions making. Next, SM cloud is constructed in five layers for high-end apparel customization. By using SM cloud based crowd-sourcing, social resources can be allocated rationally and utilized efficiently, consumer can customize the product in any processes like innovation, design, making, marketing and service, and traditional apparel enterprise can be upgraded into SM mode for keeping it competitive in the future customization markets. [Shang, Xiuqin; Wang, Fei-Yue; Xiong, Gang] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Nyberg, Timo R.; Bao, Sen] Aalto Univ, Sch Sci, FI-00076 Aalto, Finland; [Yuan, Yong; Liu, Sheng; Guo, Chao] Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China Chinese Academy of Sciences; Institute of Automation, CAS; Aalto University Xiong, G (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China. xiuqin.shang@ia.ac.cn; feiyue.wang@ia.ac.cn; gang.xiong@ia.ac.cn; timo.nyberg@aalto.fi; yong.yuan@ia.ac.cn; sheng.liu@ia.ac.cn; guochao2014@ia.ac.cn; sen.bao@aalto.fi Yuan, Yuan/GVS-5120-2022 National Natural Science Foundation of China [71232006, 61533019, 61233001, 61304201, 61773381, 61773382, 71472174, 71702182]; Finnish TEKESs project SoMa: Social Manufacturing [211560]; Chinese Guangdong's ST Project [2015B010103001, 2016B090910001, 2017B090912001]; Dongguan's Innovation Talents Project National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Finnish TEKESs project SoMa: Social Manufacturing; Chinese Guangdong's ST Project; Dongguan's Innovation Talents Project This work was supported in part by the National Natural Science Foundation of China (71232006, 61533019, 61233001, 61304201, 61773381, 61773382, 71472174, 71702182), Finnish TEKESs project SoMa 2020: Social Manufacturing (2015-2017, 211560), Chinese Guangdong's S&T Project (2015B010103001, 2016B090910001, 2017B090912001), and Dongguan's Innovation Talents Project (Gang Xiong). 33 22 23 0 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. MAR 2018.0 5.0 2 489 500 10.1109/JAS.2017.7510832 0.0 12 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems FV8KF 2023-03-23 WOS:000424835100010 0
C Lin, HQ; Yan, Z; Kantola, R IEEE Lin, Huaqing; Yan, Zheng; Kantola, Raimo CDController: A Cloud Data Access Control System based on Reputation 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) English Proceedings Paper 17th IEEE International Conference on Computer and Information Technology (CIT) / IEEE Int Workshop on Secure and Resource-Efficient Edge Computing (SecureEdge) / IEEE Int Symposium on Recent Advances of Computer and Information Technologies (RACIT) AUG 21-23, 2017 Aalto Univ, Helsinki, FINLAND IEEE,Aalto Univ, Sch Elect Engn,IEEE Comp Soc,IEEE Tech Comm Scalable Comp,iSN State Key Lab Integrated Serv Networks,Xidian Univ, Xidian Univ,Nokia,Tekes,Federat Finnish Learned Soc Aalto Univ Reputation; Access control system; Proxy re-encryption; Cloud computing; Data security The rapid development of cloud computing is changing the way of service provision, data usage and sharing over the Internet. Cloud data privacy and security protection is a crucial issue that impacts the success of cloud computing and big data. A number of cloud data access control schemes have been proposed in the literature. However, existing solutions suffer from high computation complexity and cost and therefore few of them have been really deployed in practice. In this paper, we introduce a cloud data access control system based on reputation named CDController. The system can securely control cloud data access and effectively reduce access risk according to the reputations of cloud computing entities by applying proxy re-encryption in the situation that the data owner is not available online or does not know how to control the access. System evaluation based on a prototype implementation shows the effectiveness of the CDController. [Lin, Huaqing; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China; [Kantola, Raimo] Aalto Univ, Dept Commun & Networking, Espoo, Finland Xidian University; Aalto University Lin, HQ (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China. 953959936@qq.com; zheng.yan@aalto.fi; raimo.kantola@aalto.fi yang, zheng/HGC-7753-2022; Kantola, Raimo A/G-2277-2013 Yan, Zheng/0000-0002-9697-2108 National Key Research and Development Program of China [2016YFB0800700]; NSFC [61672410, U1536202]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; 111 project [B08038, B16037]; Academy of Finland [308087] National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China; 111 project(Ministry of Education, China - 111 Project); Academy of Finland(Academy of Finland) This work is sponsored by the National Key Research and Development Program of China (grant 2016YFB0800700), the NSFC (grants 61672410 and U1536202), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), the 111 project (grants B08038 and B16037), and Academy of Finland (grant No. 308087). 17 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-0958-3 2017.0 223 230 10.1109/CIT.2017.20 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BJ5OI 2023-03-23 WOS:000426119400034 0
J Chen, XC; Zhou, B; Xie, HD; Shi, LY; Liu, H; Holler, W; Lin, MD; Liu, YW; Miller, EJ; Sinusas, AJ; Liu, C Chen, Xiongchao; Zhou, Bo; Xie, Huidong; Shi, Luyao; Liu, Hui; Holler, Wolfgang; Lin, MingDe; Liu, Yi-Hwa; Miller, Edward J.; Sinusas, Albert J.; Liu, Chi Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING English Article Deep learning; Attenuation correction; Dedicated SPECT; Myocardial perfusion imaging GATED SPECT; QUANTIFICATION; IMAGES; PET; CT; METHODOLOGY; ACCURACY Purpose Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (mu-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without R-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. Methods For dedicated SPECT, we developed strategies to predict truncated mu-maps from NAC images reconstructed with a small matrix, or full mu-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict mu-maps or AC images. Results For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full mu-maps was 1.20 +/- 0.72% as compared to 2.21 +/- 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 +/- 2.79% (R-2 = 0.9499) as compared to 4.77 +/- 3.96% (R-2 =0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 +/- 1.06% as compared to 1.37 +/- 1.16% using the indirect approaches. Conclusions We developed strategies of generating R-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches. [Chen, Xiongchao; Zhou, Bo; Xie, Huidong; Shi, Luyao; Sinusas, Albert J.; Liu, Chi] Yale Univ, Dept Biomed Engn, New Haven, CT 06520 USA; [Liu, Hui; Lin, MingDe; Miller, Edward J.; Sinusas, Albert J.; Liu, Chi] Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06520 USA; [Liu, Hui] Tsinghua Univ, Dept Engn Phys, Beijing, Peoples R China; [Holler, Wolfgang] Visage Imaging GmbH, Berlin, Germany; [Lin, MingDe] Visage Imaging Inc, San Diego, CA USA; [Liu, Yi-Hwa; Miller, Edward J.; Sinusas, Albert J.] Yale Univ, Dept Internal Med Cardiol, Sch Med, New Haven, CT USA; [Liu, Yi-Hwa] Natl Yang Ming Chiao Tung Univ, Sch Biomed Sci & Engn, Dept Biomed Imaging & Radiol Sci, Taipei, Taiwan Yale University; Yale University; Tsinghua University; Yale University; National Yang Ming Chiao Tung University Liu, C (corresponding author), Yale Univ, Dept Biomed Engn, New Haven, CT 06520 USA.;Liu, C (corresponding author), Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06520 USA. chi.liu@yale.edu Zhou, Bo/AAX-7787-2021; Xie, Huidong/AAV-5480-2020 Zhou, Bo/0000-0002-2906-0897; Xie, Huidong/0000-0002-1124-3548 Department of Radiology and Biomedical Imaging at Yale University; NIH [R01HL154345] Department of Radiology and Biomedical Imaging at Yale University; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) This work is supported by an internal funding from the Department of Radiology and Biomedical Imaging at Yale University, and the NIH grant R01HL154345. 49 8 8 5 7 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1619-7070 1619-7089 EUR J NUCL MED MOL I Eur. J. Nucl. Med. Mol. Imaging JUL 2022.0 49.0 9 3046 3060 10.1007/s00259-022-05718-8 0.0 FEB 2022 15 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 2Q2YF 35169887.0 2023-03-23 WOS:000755402300001 0
J Zhang, XX; Zhang, Y; Lu, XY; Bai, L; Chen, LF; Tao, JH; Wang, ZB; Zhu, LL Zhang, Xinxin; Zhang, Ying; Lu, Xiaoyan; Bai, Lu; Chen, Liangfu; Tao, Jinhua; Wang, Zhibao; Zhu, Lili Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM) REMOTE SENSING English Article lower-stratosphere-to-troposphere; ozone profile; ERA5; satellite data; LSTM VARIABLE IMPORTANCE; RELATIVE-HUMIDITY; SURFACE OZONE; RANDOM FOREST; O-3; POLLUTION; CHINA; VARIABILITY; ATTRIBUTION; RETRIEVALS Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model-measurement comparisons for the monitoring sites of WOUDC for the period 2019-2020 show that the mean correlation coefficients (R-2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R-2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018-2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815-0.889, root mean square errors (RMSEs) of 7.769-8.729 ppb, and mean absolute errors (MAEs) of 6.111-6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R-2 = 0.636 similar to 0.737, RMSE = 14.543-16.916 ppb, MAE = 11.130-12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas. [Zhang, Xinxin; Zhang, Ying; Chen, Liangfu; Tao, Jinhua; Zhu, Lili] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Chinese Acad Sci, Beijing 100101, Peoples R China; [Zhang, Xinxin; Chen, Liangfu; Zhu, Lili] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Lu, Xiaoyan] Guangxi Ecoenvironm Monitoring Ctr, Nanning 530028, Peoples R China; [Bai, Lu] Ulster Univ, Sch Comp, Belfast BT37 0QB, Antrim, North Ireland; [Wang, Zhibao] Northeast Petr Univ, Sch Comp & Informat Technol, Daqing 163318, Peoples R China Beijing Normal University; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ulster University; Northeast Petroleum University Zhang, Y (corresponding author), Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Chinese Acad Sci, Beijing 100101, Peoples R China. zhangxx2018@radi.ac.cn; zhangying01@radi.ac.cn; zhangying200907@aircas.ac.cn; l.bai@ulster.ac.uk; chenlf@radi.ac.cn; taojh@radi.ac.cn; wangzhibao@nepu.edu.cn; zhull@radi.ac.cn Bai, Lu/0000-0003-1242-5412 National Natural Science Foundation of China [91644216, 41771391]; National Key Research and Development Program of China [2018YFC0213903, 2018YFC0213904]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19010403, XDA19040201]; Guangxi Key Research and Development Project [Guike AB20238015]; TUOHAI special project 2020 [HBHZX202002]; project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University [KYCXTD201903] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Guangxi Key Research and Development Project; TUOHAI special project 2020; project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University This study is supported by the National Natural Science Foundation of China (Grant No. 91644216, 41771391), National Key Research and Development Program of China (2018YFC0213903, 2018YFC0213904), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19010403, XDA19040201), Guangxi Key Research and Development Project (Grant No. Guike AB20238015), TUOHAI special project (Grant No. HBHZX202002) 2020 and the project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University (Grant No. KYCXTD201903). 60 3 3 6 25 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. APR 2021.0 13.0 7 1374 10.3390/rs13071374 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology RL2IK gold, Green Submitted 2023-03-23 WOS:000638803100001 0
J Sotgiu, A; Demontis, A; Melis, M; Biggio, B; Fumera, G; Feng, XY; Roli, F Sotgiu, Angelo; Demontis, Ambra; Melis, Marco; Biggio, Battista; Fumera, Giorgio; Feng, Xiaoyi; Roli, Fabio Deep neural rejection against adversarial examples EURASIP JOURNAL ON INFORMATION SECURITY English Article Adversarial machine learning; Deep neural networks; Adversarial examples Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time. In this work, we propose a deep neural rejection mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. With respect to competing approaches, our method does not require generating adversarial examples at training time, and it is less computationally demanding. To properly evaluate our method, we define an adaptive white-box attack that is aware of the defense mechanism and aims to bypass it. Under this worst-case setting, we empirically show that our approach outperforms previously proposed methods that detect adversarial examples by only analyzing the feature representation provided by the output network layer. [Sotgiu, Angelo; Demontis, Ambra; Melis, Marco; Biggio, Battista; Fumera, Giorgio; Roli, Fabio] Univ Cagliari, DIEE, Piazza Armi, I-09123 Cagliari, Italy; [Biggio, Battista; Roli, Fabio] Pluribus One, Cagliari, Italy; [Feng, Xiaoyi] Northwestern Polytech Univ, Xian, Peoples R China University of Cagliari; Northwestern Polytechnical University Demontis, A (corresponding author), Univ Cagliari, DIEE, Piazza Armi, I-09123 Cagliari, Italy. ambra.demontis@unica.it Demontis, Ambra/ABW-7842-2022; Biggio, Battista/HCI-4766-2022 Biggio, Battista/0000-0001-7752-509X; Sotgiu, Angelo/0000-0003-2100-9517; ROLI, FABIO/0000-0003-4103-9190; Demontis, Ambra/0000-0001-9318-6913; Melis, Marco/0000-0003-3641-2093 PRIN 2017 project RexLearn - Italian Ministry of Education, University and Research; EU H2020 project ALOHA, under the European Union's Horizon 2020 research and innovation programme [780788] PRIN 2017 project RexLearn - Italian Ministry of Education, University and Research; EU H2020 project ALOHA, under the European Union's Horizon 2020 research and innovation programme This work was partly supported by the PRIN 2017 project RexLearn, funded by the Italian Ministry of Education, University and Research, and by the EU H2020 project ALOHA, under the European Union's Horizon 2020 research and innovation programme (grant no. 780788). 40 15 15 1 5 SPRINGER INT PUBL AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2510-523X EURASIP J INF SECUR EURASIP J. Inf. Secur. APR 7 2020.0 2020.0 1 5 10.1186/s13635-020-00105-y 0.0 10 Computer Science, Information Systems Emerging Sources Citation Index (ESCI) Computer Science LB0IX Green Published, gold, Green Submitted 2023-03-23 WOS:000524322900001 0
J Tan, QP; Zhang, TD; Wu, SC; Gao, JC; Song, B Tan, Qipeng; Zhang, Tiandong; Wu, Shaocheng; Gao, Jiachen; Song, Bin Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear SENSORS English Article partial discharge (PD); gas detection; fault classification; random forests; air-insulated switchgear FAULT-DIAGNOSIS Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O-3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear. [Tan, Qipeng; Zhang, Tiandong; Wu, Shaocheng; Gao, Jiachen; Song, Bin] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China; [Gao, Jiachen] Univ Bologna, Dept Elect Elect & Informat Engn Guglielmo Marcon, I-40136 Bologna, Italy Wuhan University; University of Bologna Song, B (corresponding author), Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China. qidipang1224@163.com; ztd1125296686@126.com; 2016302540157@whu.edu.cn; jiachen.gao2@unibo.it; binsong@whu.edu.cn Gao, Jiachen/AAY-2411-2021 Tan, Qipeng/0000-0002-6748-3153 27 0 0 7 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAR 2022.0 22.0 6 2395 10.3390/s22062395 0.0 11 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 0B6AL 35336566.0 Green Accepted, gold 2023-03-23 WOS:000774715200001 0
J Fang, LY; Liu, GY; Li, ST; Ghamisi, P; Benediktsson, JA Fang, Leyuan; Liu, Guangyun; Li, Shutao; Ghamisi, Pedram; Benediktsson, Jon Atli Hyperspectral Image Classification With Squeeze Multibias Network IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Convolutional neural network (CNN); hyperspectral image (HSI) classification; multibias module (MBM); squeeze convolution module (SCM); squeeze multibias network (SMBN) SPECTRAL-SPATIAL CLASSIFICATION; DEEP; FUSION A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. [Fang, Leyuan; Liu, Guangyun; Li, Shutao] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Fang, Leyuan; Liu, Guangyun; Li, Shutao] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany; [Benediktsson, Jon Atli] Univ Iceland, Fac Elect & Comp Engn, IS-101 Reykjavk, Iceland Hunan University; Hunan University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); University of Iceland Li, ST (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. fangleyuan@gmail.com; liuguangyun@hnu.edu.cn; shutao_li@hnu.edu.cn; p.ghamisi@gmail.com; benedikt@hi.is Li, Shutao/Y-3102-2019; Benediktsson, Jón Atli/F-2861-2010; Ghamisi, Pedram/ABD-5419-2021 Li, Shutao/0000-0002-0585-9848; Benediktsson, Jón Atli/0000-0003-0621-9647; Fang, Leyuan/0000-0003-2351-4461 National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]; National Natural Science Foundation [61771192]; Fund of Hunan Province for Science and Technology Plan Project [2017RS3024] National Natural Science Fund of China for International Cooperation and Exchanges; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Fund of Hunan Province for Science and Technology Plan Project This work was supported in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, in part by the National Natural Science Foundation under Grant 61771192, and in part by the Fund of Hunan Province for Science and Technology Plan Project under Grant 2017RS3024. 57 66 66 0 63 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing MAR 2019.0 57.0 3 1291 1301 10.1109/TGRS.2018.2865953 0.0 11 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology HN6UK 2023-03-23 WOS:000460321300005 0
J Yin, F; Pan, LS; Chen, TS; Theodoridis, S; Luo, ZQ; Zoubir, AM Yin, Feng; Pan, Lishuo; Chen, Tianshi; Theodoridis, Sergios; Luo, Zhi-Quan Tom; Zoubir, Abdelhak M. Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Kernel; GSM; Gaussian processes; Time series analysis; Computational modeling; Optimization; Bayes methods; ADMM; Gaussian processes; hyper-parameter optimization; linear multiple kernel; low-rank kernels; majorization-minimization; prediction; time series CONVEX Gaussian processes (GPs) for machine learning have been studied systematically over the past two decades. However, kernel design for GPs and the associated hyper-parameters optimization are still difficult, and to a large extent open problems. We consider GP regression for time series modeling and analysis. The underlying stationary kernel is approximated closely by a new grid spectral mixture (GSM) kernel, which is a linear combination of low-rank sub-kernels. In the case where a large number of the involved sub-kernels are used, either the Nystrom or the random Fourier feature approximations can be adopted to reduce the required computer storage. The unknown GP hyper-parameters consist of the nonnegative weights of all sub-kernels as well as the noise variance, and they are determined through the maximum-likelihood estimation method. Two optimization methods for solving the unknown hyper-parameters are introduced, including a sequential majorization-minimization (MM) method and a nonlinearly constrained alternating direction method of multipliers (ADMM). Experimental results, based on various time series datasets, corroborate that the proposed GSM kernel-based GP regression model outperforms several benchmarks in terms of prediction accuracy and numerical stability. [Yin, Feng; Pan, Lishuo; Chen, Tianshi; Theodoridis, Sergios; Luo, Zhi-Quan Tom] Chinese Univ Hong Kong, Shenzhen CUHKSZ, Shenzhen 518172, Peoples R China; [Yin, Feng; Pan, Lishuo; Chen, Tianshi; Luo, Zhi-Quan Tom] Shenzhen Res Inst Big Data SRIBD, Shenzhen 518172, Guangdong, Peoples R China; [Theodoridis, Sergios] Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 15784, Attica, Greece; [Zoubir, Abdelhak M.] Tech Univ Darmstadt, ETIT, Signal Proc Grp, Inst Telecommun, D-64283 Darmstadt, Hesse, Germany Chinese University of Hong Kong, Shenzhen; National & Kapodistrian University of Athens; Technical University of Darmstadt Yin, F (corresponding author), Chinese Univ Hong Kong, Shenzhen CUHKSZ, Shenzhen 518172, Peoples R China. yinfeng@cuhk.edu.cn; panls@seas.upenn.edu; tschen@cuhk.edu.cn; stheodor@di.uoa.gr; luozq@cuhk.edu.cn; zoubir@spg.tu-darmstadt.de Chen, Tianshi/C-2575-2014; Zoubir, Abdelhak M/AAW-6349-2021 Chen, Tianshi/0000-0001-8655-2655; ZOUBIR, Abdelhak M/0000-0002-4409-7743; Theodoridis, Sergios/0000-0001-5040-161X NSFC [61701426, 61731018]; Guangdong province Program [00201501, 2017ZT07X152]; Shenzhen Fundamental Research Fund [KQTD201503311441545, Ji-20170189] NSFC(National Natural Science Foundation of China (NSFC)); Guangdong province Program; Shenzhen Fundamental Research Fund This work was supported in part by the NSFC under Grants 61701426 and 61731018, in part by the Guangdong province Program under Grants 00201501 and 2017ZT07X152, and in part by Shenzhen Fundamental Research Fund under Grant KQTD201503311441545 and Ji-20170189. 42 11 11 4 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2020.0 68.0 5260 5275 10.1109/TSP.2020.3023008 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NY5EW Green Submitted 2023-03-23 WOS:000576413600001 0
J Pang, XY; Yu, ZQ; Tang, SY; Hong, J; Yuan, Z; Pasquato, M; Kouwenhoven, MBN Pang, Xiaoying; Yu, Zeqiu; Tang, Shih-Yun; Hong, Jongsuk; Yuan, Zhen; Pasquato, Mario; Kouwenhoven, M. B. N. Disruption of Hierarchical Clustering in the Vela OB2 Complex and the Cluster Pair Collinder 135 and UBC 7 with Gaia EDR3: Evidence of Supernova Quenching ASTROPHYSICAL JOURNAL English Article STAR-FORMATION; LOW-MASS; STELLAR POPULATION; GAS EXPULSION; BINARY; EVOLUTION; ASSOCIATION; SIMULATIONS; KINEMATICS; DISCOVERY We identify hierarchical structures in the Vela OB2 complex and the cluster pair Collinder 135 and UBC 7 with Gaia EDR3 using the neural network machine-learning algorithm StarGO. Five second-level substructures are disentangled in Vela OB2, which are referred to as Huluwa 1 (Gamma Velorum), Huluwa 2, Huluwa 3, Huluwa 4, and Huluwa 5. For the first time, Collinder 135 and UBC 7 are simultaneously identified as constituent clusters of the pair with minimal manual intervention. We propose an alternative scenario in which Huluwa 1-5 have originated from sequential star formation. The older clusters Huluwa 1-3, with an age of 10-22 Myr, generated stellar feedback to cause turbulence that fostered the formation of the younger-generation Huluwa 4-5 (7-20 Myr). A supernova explosion located inside the Vela IRAS shell quenched star formation in Huluwa 4-5 and rapidly expelled the remaining gas from the clusters. This resulted in global mass stratification across the shell, which is confirmed by the regression discontinuity method. The stellar mass in the lower rim of the shell is 0.32 +/- 0.14 M (circle dot) higher than in the upper rim. Local, cluster-scale mass segregation is observed in the lowest-mass cluster Huluwa 5. Huluwa 1-5 (in Vela OB2) are experiencing significant expansion, while the cluster pair suffers from moderate expansion. The velocity dispersions suggest that all five groups (including Huluwa 1A and Huluwa 1B) in Vela OB2 and the cluster pair are supervirial and are undergoing disruption, and also that Huluwa 1A and Huluwa 1B may be a coeval young cluster pair. N-body simulations predict that Huluwa 1-5 in Vela OB2 and the cluster pair will continue to expand in the future 100 Myr and eventually dissolve. [Pang, Xiaoying; Yu, Zeqiu; Kouwenhoven, M. B. N.] Xian Jiaotong Liverpool Univ, Dushu Lake Sci & Educ Innovat Dist, Dept Phys, 111 Renai Rd, Suzhou 215123, Jiangsu, Peoples R China; [Pang, Xiaoying] Shanghai Normal Univ, Shanghai Key Lab Astrophys, 100 Guilin Rd, Shanghai 200234, Peoples R China; [Tang, Shih-Yun] Lowell Observ, 1400 W Mars Hill Rd, Flagstaff, AZ 86001 USA; [Tang, Shih-Yun] No Arizona Univ, Dept Astron & Planetary Sci, Flagstaff, AZ 86011 USA; [Hong, Jongsuk] Korea Astron & Space Sci Inst, Daejeon 34055, South Korea; [Yuan, Zhen] Univ Strasbourg, CNRS, Observ Astron Strasbourg, UMR 7550, F-67000 Strasbourg, France; [Pasquato, Mario] New York Univ Abu Dhabi, Ctr Astro Particle & Planetary Phys CAP3, Abu Dhabi, U Arab Emirates; [Pasquato, Mario] INFN, Sez Padova, Via Marzolo 8, I-35131 Padua, Italy Xi'an Jiaotong-Liverpool University; Shanghai Normal University; Northern Arizona University; Korea Astronomy & Space Science Institute (KASI); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Istituto Nazionale di Fisica Nucleare (INFN) Pang, XY (corresponding author), Xian Jiaotong Liverpool Univ, Dushu Lake Sci & Educ Innovat Dist, Dept Phys, 111 Renai Rd, Suzhou 215123, Jiangsu, Peoples R China.;Pang, XY (corresponding author), Shanghai Normal Univ, Shanghai Key Lab Astrophys, 100 Guilin Rd, Shanghai 200234, Peoples R China. Xiaoying.Pang@xjtlu.edu.cn Yuan, zhen/HLW-3443-2023; Kouwenhoven, M.B.N./G-3854-2015 Pang, Xiaoying/0000-0003-3389-2263; Kouwenhoven, M.B.N./0000-0002-1805-0570; Tang, Shih-Yun/0000-0003-4247-1401; Pasquato, Mario/0000-0003-3784-5245; Yu, Zeqiu/0000-0001-6980-2309; Yuan, Zhen/0000-0002-8129-5415; Hong, Jongsuk/0000-0002-5097-8707 research development fund of Xi'an Jiaotong-Liverpool University [RDF-18-02-32]; XJTLU Summer Undergraduate Research Fund [SURF-2021119]; National Natural Science Foundation of China [12173029, 11573004]; Natural Science Foundation of Jiangsu Province [BK20200252]; Research Development Fund of Xi'an Jiaotong-Liverpool University (XJTLU) [RDF-SP-93]; Agence Nationale de la Recherche (ANR project) [ANR-18-CE31-0017]; European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme [834148]; Tamkeen under the NYU Abu Dhabi Research Institute grant CAP3 research development fund of Xi'an Jiaotong-Liverpool University; XJTLU Summer Undergraduate Research Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Research Development Fund of Xi'an Jiaotong-Liverpool University (XJTLU); Agence Nationale de la Recherche (ANR project)(French National Research Agency (ANR)); European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme(European Research Council (ERC)); Tamkeen under the NYU Abu Dhabi Research Institute grant CAP3 We wish to express our gratitude to an anonymous referee for providing comments and suggestions that helped to improve the quality of this paper. X.Y.P. is grateful for the financial support of the research development fund of Xi'an Jiaotong-Liverpool University (RDF-18-02-32). This study is supported by the XJTLU Summer Undergraduate Research Fund (SURF-2021119). X.Y.P. acknowledges grants of the National Natural Science Foundation of China (No. 12173029) and the Natural Science Foundation of Jiangsu Province (No. BK20200252). M.B.N.K. expresses gratitude to the National Natural Science Foundation of China (grant No. 11573004) and the Research Development Fund (grant RDF-SP-93) of Xi'an Jiaotong-Liverpool University (XJTLU). Z.Y. acknowledges funding from the Agence Nationale de la Recherche (ANR project ANR-18-CE31-0017) and the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 834148). M.P.'s contribution to this material is based on work supported by Tamkeen under the NYU Abu Dhabi Research Institute grant CAP3. We express gratitude to Prof. Dr. Pavel Kroupa (University of Bonn) for helpful discussion on the gas expulsion, and Prof. Dr. Simon Goodwin (University of Sheffield) for in-depth communication on the binary cluster evolution. We give thanks to Dr. Long Wang for discussion on the stellar evolution model and Dr. Chien-Cheng Lin for IRIS data download. We thank Prof. Dr. Youzhou Zhou (Xi'an Jiaotong-Liverpool University) for suggestions on 3D morphology interpretation. This work made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). This study also made use of the SIMBAD database and the VizieR catalog access tool, both operated at CDS, Strasbourg, France. 99 8 8 0 0 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. DEC 2021.0 923.0 1 20 10.3847/1538-4357/ac2838 0.0 22 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics XM0GS Green Submitted, Bronze 2023-03-23 WOS:000728516300001 0
J Kim, YJ; Jang, H; Lee, K; Park, S; Min, SG; Hong, C; Park, JH; Lee, K; Kim, J; Hong, W; Jung, H; Liu, YL; Rajkumar, H; Khened, M; Krishnamurthi, G; Yang, S; Wang, XY; Han, CH; Kwak, JT; Ma, JQ; Tang, Z; Marami, B; Zeineh, J; Zhao, ZX; Heng, PA; Schmitz, R; Madesta, F; Rosch, T; Werner, R; Tian, J; Puybareau, E; Bovio, M; Zhang, XF; Zhu, YF; Chun, SY; Jeong, WK; Park, P; Choi, J Kim, Yoo Jung; Jang, Hyungjoon; Lee, Kyoungbun; Park, Seongkeun; Min, Sung-Gyu; Hong, Choyeon; Park, Jeong Hwan; Lee, Kanggeun; Kim, Jisoo; Hong, Wonjae; Jung, Hyun; Liu, Yanling; Rajkumar, Haran; Khened, Mahendra; Krishnamurthi, Ganapathy; Yang, Sen; Wang, Xiyue; Han, Chang Hee; Kwak, Jin Tae; Ma, Jianqiang; Tang, Zhe; Marami, Bahram; Zeineh, Jack; Zhao, Zixu; Heng, Pheng-Ann; Schmitz, Ruediger; Madesta, Frederic; Roesch, Thomas; Werner, Rene; Tian, Jie; Puybareau, Elodie; Bovio, Matteo; Zhang, Xiufeng; Zhu, Yifeng; Chun, Se Young; Jeong, Won-Ki; Park, Peom; Choi, Jinwook PAIP 2019: Liver cancer segmentation challenge MEDICAL IMAGE ANALYSIS English Article Liver cancer; Tumor burden; Digital pathology; Challenge; Segmentation HEPATOCELLULAR-CARCINOMA Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation. (C) 2020 The Authors. Published by Elsevier B.V. [Kim, Yoo Jung; Park, Seongkeun; Choi, Jinwook] Seoul Natl Univ Hosp, Dept Biomed Engn, Seoul, South Korea; [Jang, Hyungjoon; Lee, Kanggeun; Kim, Jisoo; Hong, Wonjae; Chun, Se Young] Ulsan Natl Inst Sci & Technol, Sch Elect & Comp Engn, Ulsan, South Korea; [Lee, Kyoungbun; Min, Sung-Gyu; Hong, Choyeon] Seoul Natl Univ Hosp, Dept Pathol, Seoul, South Korea; [Park, Seongkeun; Park, Jeong Hwan] Seoul Metropolitan Govt Seoul Natl Univ, Boramae Med Ctr, Dept Pathol, Seoul, South Korea; [Jung, Hyun; Liu, Yanling] Frederick Natl Lab Canc Res, Frederick, MD USA; [Rajkumar, Haran; Khened, Mahendra; Krishnamurthi, Ganapathy] Indian Inst Technol Madras, Dept Engn Design, Chennai, Tamil Nadu, India; [Yang, Sen] Sichuan Univ, Chengdu, Sichuan, Peoples R China; [Yang, Sen] Tencent AI Lab, Chengdu, Sichuan, Peoples R China; [Wang, Xiyue] Sichuan Univ, Coll Comp Sci, Chengdu, Sichuan, Peoples R China; [Han, Chang Hee; Kwak, Jin Tae] Sejong Univ, Dept Comp Sci & Engn, Seoul, South Korea; [Ma, Jianqiang; Tang, Zhe] Alibaba Grp, Hangzhou, Peoples R China; [Marami, Bahram; Zeineh, Jack] Icahn Sch Med Mt Sinai, Ctr Computat & Syst Pathol, New York, NY 10029 USA; [Zhao, Zixu; Heng, Pheng-Ann] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Schmitz, Ruediger; Roesch, Thomas] Univ Med Ctr Hamburg Eppendorf, Dept Interdisciplinary Endoscopy, Hamburg, Germany; [Schmitz, Ruediger; Madesta, Frederic; Werner, Rene] Forschungszentrum Med Tech Hamburg, DAISYlabs, Hamburg, Germany; [Madesta, Frederic; Werner, Rene] Univ Med Ctr Hamburg Eppendorf, Inst Computat Neurosci, Hamburg, Germany; [Tian, Jie] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Puybareau, Elodie; Bovio, Matteo] LRDE EPITA, Paris, France; [Zhang, Xiufeng] Tianjin Chengjian Univ, Tianjin, Peoples R China; [Zhu, Yifeng] Univ Maine, Orono, ME USA; [Jeong, Won-Ki] Korea Univ, Dept Comp Sci & Engn, Coll Informat, Seoul 02841, South Korea; [Park, Peom] HuminTec, Suwon, South Korea Seoul National University (SNU); Seoul National University Hospital; Ulsan National Institute of Science & Technology (UNIST); Seoul National University (SNU); Seoul National University Hospital; Seoul National University (SNU); Seoul National University Hospital; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Sichuan University; Tencent; Sichuan University; Sejong University; Alibaba Group; Icahn School of Medicine at Mount Sinai; Chinese University of Hong Kong; University of Hamburg; University Medical Center Hamburg-Eppendorf; University of Hamburg; University Medical Center Hamburg-Eppendorf; Shanghai Jiao Tong University; Tianjin Chengjian University; University of Maine System; University of Maine Orono; Korea University Choi, J (corresponding author), Seoul Natl Univ Hosp, Dept Biomed Engn, Seoul, South Korea.;Chun, SY (corresponding author), Ulsan Natl Inst Sci & Technol, Sch Elect & Comp Engn, Ulsan, South Korea.;Jeong, WK (corresponding author), Korea Univ, Dept Comp Sci & Engn, Coll Informat, Seoul 02841, South Korea.;Park, P (corresponding author), HuminTec, Suwon, South Korea. yjkim191@gmail.com; jhj0110@unist.ac.kr; azi1003@snu.ac.kr; sychun@unist.ac.kr; wkjeong@korea.ac.kr; ppark@ajou.ac.kr; jinchoi@snu.ac.kr Wang, Xiyue/GXM-5641-2022; Lee, Kanggeun/GWC-4515-2022; Lee, Kyoungbun/J-2571-2016; Werner, Rene/B-4989-2017 Wang, Xiyue/0000-0002-3597-9090; Lee, Kanggeun/0000-0002-0800-2129; Lee, Kyoungbun/0000-0001-8427-3003; Kim, Yoo Jung/0000-0001-5755-5152; Werner, Rene/0000-0002-2296-3305; Park, Jeong Hwan/0000-0003-4522-9928; Chun, Se Young/0000-0001-8739-8960; Yang, Sen/0000-0002-0639-4122; Kwak, Jin Tae/0000-0003-0287-4097; Hong, Choyeon/0000-0002-9209-9719 Korea Health Technology RAMP;D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health AMP; Welfare, Republic of Korea [HI18C0316] Korea Health Technology RAMP;D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health AMP; Welfare, Republic of Korea This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI18C0316) Ethics approval: Approved by Seoul National University Hospital Institutional Review Board (IRB) (IRB No.H-1808-035-964). The authors would also like to thank all the other PAIP 2019 Challenge participants: Pingjun Chen, Jack R. Collins (Team newhyun00), Quoc Dang Vu (Team QuIIL), Huaxin Yao (Team Damo AIC), Marcel Prastawa, Brandon Veremis, Nina Shpalensky and Gerardo Fernandez (Team COSYPath), Xi Wang (Team CUHK-Med), Maximilian Nielsen (Team DAISYlab@UKE), Yuxiang Ye and Yinan Chen (Team Sig-IPath), Guillaume Tochon (Team LRDE), Zhisheng Li, Li Wang and Zhongwei Zhang (Team blackbear), Haruki Kokubo and Daisuke Komura (Team PRM-U), Woojoon Seok and Minsoo Yeo (Team U&I), Hatef Otroshi Shahreza, Ali Asghar Khani and Seyed Alireza Fatemi Jahromi (Team Phoenix), Tang-Kai Yin and Qi-Rui Fang (Team Yin&Fang), Gaoyuan Xu and Shuai Ye (Team Standing), Vo Thi Tuong Vi, ZhiWei Zhai, Adam Chuda ' s, Chao-Hui Huang, Haotian Cai, Jaehoon Jeong, Shen Yu-Jie, Hyeongsub Kim and Hongjun Yoon (Team DEEPNOID-POSTECH) 33 25 25 2 19 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JAN 2021.0 67.0 101854 10.1016/j.media.2020.101854 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging PF2LJ 33091742.0 hybrid, Green Published 2023-03-23 WOS:000598892100010 0
C Zhang, L; Sun, BB; Shu, F; Huang, Y Glanzel, W; Heeffer, S; Chi, PS; Rousseau, R Zhang, Lin; Sun, Beibei; Shu, Fei; Huang, Ying Comparing Paper Level Classifications across Different Methods and Systems: An Investigation on Nature Publications 18TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS (ISSI2021) Proceedings of the International Conference on Scientometrics and Informetrics English Proceedings Paper 18th International Conference on Scientometrics and Informetrics (ISSI) JUL 12-15, 2021 KU Leuven, ELECTR NETWORK Elsevier,Digital Sci,RISIS,JDIS,MIT Press,Clarivate ISI,Springer Nature,FRONTIERS,Flanders Res Fdn,Int Soc Scientometr & Informetr KU Leuven The classification of scientific literature into appropriate disciplines is one of the essential preconditions of valid scientometric analysis and is significant in the practice of research assessment. In this research-in-progress paper, we compared the disciplinary classifications of publications in Nature based on three different approaches across three different systems: WoS categories provided by Incites based on the disciplinary affiliation of the majority of its references; FOR major and minor fields provided by Dimensions based on the machine learning techniques; and Nature subjects constructed by Springer Nature based on the author-selected subject area tags in the journal's tagging system. The preliminary results show that, the single category assignment in Incites based on the disciplinary affiliation of cited references seems quite unreasonable for a large proportion of papers. The comparison results between Dimensions and Nature also show significant differences: only 1322 (16%) publications share the same classification results. Further investigations including a closer look at the inconsistent results, a deeper insight and methodological analysis is needed in the completion of the study. [Zhang, Lin; Sun, Beibei] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China; [Zhang, Lin; Sun, Beibei; Huang, Ying] Wuhan Univ, Ctr Sci Technol & Educ Assessment CSTEA, Wuhan 430072, Peoples R China; [Shu, Fei] Hangzhou Dianzi Univ, Chinese Acad Sci & Educ Evaluat, Hangzhou 310018, Peoples R China; [Huang, Ying] Katholieke Univ Leuven, Ctr R&D Monitoring ECOOM, B-3000 Leuven, Belgium; [Huang, Ying] Katholieke Univ Leuven, Dept MSI, B-3000 Leuven, Belgium Wuhan University; Wuhan University; Hangzhou Dianzi University; KU Leuven; KU Leuven Huang, Y (corresponding author), Wuhan Univ, Ctr Sci Technol & Educ Assessment CSTEA, Wuhan 430072, Peoples R China.;Huang, Y (corresponding author), Katholieke Univ Leuven, Ctr R&D Monitoring ECOOM, B-3000 Leuven, Belgium.;Huang, Y (corresponding author), Katholieke Univ Leuven, Dept MSI, B-3000 Leuven, Belgium. linzhang1117@whu.edu.cn; betty_sun@whu.edu.cn; fei.shu@hdu.edu.cn.at; ying.huang@kuleuven.be Sun, Beibei/Q-1493-2018; Shu, Fei/A-4056-2017; Zhang, Lin/M-3007-2017 Sun, Beibei/0000-0002-7965-8723; Shu, Fei/0000-0002-1946-5065; Zhang, Lin/0000-0003-0526-9677 National Natural Science Foundation of China [71573085, 71974150, 72004169]; National Laboratory Center for Library and Information Science in Wuhan University; Flemish Government National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Laboratory Center for Library and Information Science in Wuhan University; Flemish Government The authors would like to acknowledge support from the National Natural Science Foundation of China (Grant Nos. 71573085, 71974150, 72004169), the National Laboratory Center for Library and Information Science in Wuhan University, and the project Interdisciplinarity & Impact (2019-2023) funded by the Flemish Government. 14 1 1 1 37 INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI LEUVEN KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN, B-3000, BELGIUM 2175-1935 978-90-803282-2-8 PRO INT CONF SCI INF 2021.0 1319 1324 6 Computer Science, Interdisciplinary Applications; Information Science & Library Science Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Information Science & Library Science BS3CO 2023-03-23 WOS:000709638700145 0
J Chen, Q; Gong, YM; Lu, YB; Tang, J Chen, Qian; Gong, Yeming; Lu, Yaobin; Tang, Jing Classifying and measuring the service quality of AI chatbot in frontline service JOURNAL OF BUSINESS RESEARCH English Article AI chatbot; Service quality; Artificial intelligence; Scale development; Mixed-method approach INFORMATION-SYSTEMS SUCCESS; MULTIPLE-ITEM SCALE; BEHAVIORAL INTENTIONS; EMPIRICAL-TEST; MCLEAN MODEL; SATISFACTION; TECHNOLOGY; PERFORMANCE; LOYALTY; PERSPECTIVE AI chatbots have been widely applied in the frontline to serve customers. Yet, the existing dimensions and scales of service quality can hardly fit the new AI environment. To address this gap, we define the dimensions of AI chatbot service quality (AICSQ) and develop the associated scales with a mixed-method approach. In the qualitative analysis, with the coding of the interviews from 55 global organizations in 17 countries and 47 customers, we develop new multi-level dimensions of AICSQ, including seven second-order and 18 first-order constructs. Then we follow a 10-step scale development method to establish the valid scales. The nomological test result shows that AICSQ positively influences customers' satisfaction with, perceived value of, and intention of continuous use of AI chatbots. The innovative dimensions and scales of AI chatbot service quality provide conceptual classification and measurement instruments for the future study of chatbot service in the frontline. [Chen, Qian] Huazhong Agr Univ, Coll Econ & Management, Wuhan, Peoples R China; [Gong, Yeming] EMLYON Business Sch, F-69134 Ecully, France; [Lu, Yaobin] Huazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China; [Tang, Jing] Rochester Inst Technol, Saunders Coll Business, 105 Lomb Mem Dr,LOW A314, Rochester, NY 14623 USA Huazhong Agricultural University; EMLYON Business School; Huazhong University of Science & Technology; Rochester Institute of Technology Lu, YB (corresponding author), Huazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China. chen_qian@mail.hzau.edu.cn; gong@em-lyon.com; luyb@hust.edu.cn; jtang@saunders.rit.edu GONG, Yeming/I-7148-2012 GONG, Yeming/0000-0001-9270-5507 NSFC [71810107003, 72001085]; Fundamental Research Funds for the Central Universities [2662021JGQD006]; NSSFC [18ZDA109]; AIM institute; BIC center NSFC(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); NSSFC; AIM institute; BIC center We thank the constructive comments and suggestions from editors and reviewers. This work was supported by grant from the NSFC [number 72001085] , the Fundamental Research Funds for the Central Universities [number 2662021JGQD006] , NSFC [number 71810107003] , and NSSFC [number 18ZDA109] . Yeming GONG is supported by AIM institute and BIC center. 99 6 6 49 82 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0148-2963 1873-7978 J BUS RES J. Bus. Res. JUN 2022.0 145.0 552 568 10.1016/j.jbusres.2022.02.088 0.0 MAR 2022 17 Business Social Science Citation Index (SSCI) Business & Economics 1L3NN 2023-03-23 WOS:000799198500003 0
J Awais, M; Long, X; Yin, B; Abbasi, SF; Akbarzadeh, S; Lu, CM; Wang, XH; Wang, LS; Zhang, J; Dudink, J; Chen, W Awais, Muhammad; Long, Xi; Yin, Bin; Farooq Abbasi, Saadullah; Akbarzadeh, Saeed; Lu, Chunmei; Wang, Xinhua; Wang, Laishuan; Zhang, Jiong; Dudink, Jeroen; Chen, Wei A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Sleep; Pediatrics; Electroencephalography; Hospitals; Monitoring; Brain modeling; Support vector machines; Neonatal sleep monitoring; video and image analysis; facial expression; deep convolutional neural network; support vector machine CONVOLUTIONAL NEURAL-NETWORKS; PRETERM INFANTS; HEART-RATE; CLASSIFICATION; DEEP; IDENTIFICATION; ACTIGRAPHY; PATTERNS; CHILDREN; TERM Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 +/- 2.2% and an F1-score 0.93 +/- 0.3. [Awais, Muhammad; Farooq Abbasi, Saadullah; Akbarzadeh, Saeed; Chen, Wei] Fudan Univ, Sch Informat Sci & Technol, Dept Elect Engn, Ctr Intelligent Med Elect, Shanghai 200433, Peoples R China; [Long, Xi] Philips Res, NL-5656 AE Eindhoven, Netherlands; [Long, Xi] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 AZ Eindhoven, Netherlands; [Yin, Bin] Philips Res, Connected Care & Personal Hlth Dept, Shanghai 200032, Peoples R China; [Wang, Xinhua] Fudan Univ, Childrens Hosp, Dept Neurol, Shanghai 200032, Peoples R China; [Lu, Chunmei; Wang, Laishuan] Fudan Univ, Childrens Hosp, Dept Neonatol, Shanghai 200032, Peoples R China; [Zhang, Jiong] Univ Southern Calif, Keck Sch Med, Stevens Neuroimaging & Informat Inst, Los Angeles, CA 90033 USA; [Dudink, Jeroen] Univ Utrecht, Dept Neonatol, Univ Med Ctr Utrecht, Wilhelmina Childrens Hosp, NL-3584 EA Utrecht, Netherlands; [Chen, Wei] Fudan Univ, Human Phenome Inst, Shanghai 200433, Peoples R China Fudan University; Philips; Philips Research; Eindhoven University of Technology; Philips; Philips Research; Fudan University; Fudan University; University of Southern California; Utrecht University; Utrecht University Medical Center; Wilhelmina Kinderziekenhuis; Fudan University Chen, W (corresponding author), Fudan Univ, Sch Informat Sci & Technol, Dept Elect Engn, Ctr Intelligent Med Elect, Shanghai 200433, Peoples R China.;Long, X (corresponding author), Philips Res, NL-5656 AE Eindhoven, Netherlands.;Lu, CM (corresponding author), Fudan Univ, Childrens Hosp, Dept Neonatol, Shanghai 200032, Peoples R China.;Chen, W (corresponding author), Fudan Univ, Human Phenome Inst, Shanghai 200433, Peoples R China. 17110720061@fudan.edu.cn; x.long@tue.nl; bin.yin@philips.com; 18110720168@fudan.edu.cn; sd.akbarzadeh@gmail.com; luchunmei1975@163.com; xiner.1211@163.com; laishuanwang@yahoo.com; jiong.zhang@loni.usc.edu; j.dudink@umcutrecht.nl; w_chen@fudan.edu.cn Abbasi, Saadullah/HNB-9444-2023; Abbasi, Saadullah Farooq/AAJ-1154-2020; Long, Xi/AAU-4595-2020 Long, Xi/0000-0001-9505-1270; Akbarzadeh, Saeed/0000-0001-6750-7971; Abbasi, Saadullah Farooq/0000-0001-9814-3023; Dudink, Jeroen/0000-0003-0446-3646; Yin, Bin/0000-0001-9612-2376 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Key R&D Program of China [2017YFE0112000] Shanghai Municipal Science and Technology Major Project; National Key R&D Program of China This work was supported in part by the Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01, and in part by the National Key R&D Program of China under Grant 2017YFE0112000. 71 11 12 1 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. MAY 2021.0 25.0 5 1441 1449 10.1109/JBHI.2021.3073632 0.0 9 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics SA9LV 33857007.0 Green Published 2023-03-23 WOS:000649625200011 0
J Chen, L; Zeng, T; Pan, XY; Zhang, YH; Huang, T; Cai, YD Chen, Lei; Zeng, Tao; Pan, Xiaoyong; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES English Article breast cancer; subtype; methylation; pattern; multi-class classification EPIGENETIC MODIFICATIONS; EXPRESSION SIGNATURE; DNA METHYLATION; CELL-LINES; IDENTIFICATION; RISK; METASTASIS; WOMEN; PREDICTION; BIOMARKERS Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression-methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes. [Chen, Lei; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China; [Zeng, Tao] Chinese Acad Sci, Key Lab Syst Biol, Inst Biochem & Cell Biol, Shanghai 200031, Peoples R China; [Pan, Xiaoyong] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China; [Pan, Xiaoyong] Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China; [Pan, Xiaoyong] Univ Ghent, IDLab, Dept Elect & Informat Syst, B-9000 Ghent, Belgium; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China Shanghai University; Shanghai Maritime University; East China Normal University; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Shanghai Jiao Tong University; Ministry of Education, China; Ghent University; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; Shanghai Institutes for Biological Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China. tohuangtao@126.com; cai_yud@126.com Chen, Lei/A-2275-2011; Zhang, Yu-Hang/AAQ-9012-2021; Zeng, Tao/A-1711-2012; Zhang, Yu-Hang/O-9774-2015 Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; Zeng, Tao/0000-0002-0295-3994; Zhang, Yu-Hang/0000-0003-3825-0796; cai, yudong/0000-0001-5664-7979 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Key R&D Program of China [2018YFC0910403]; National Natural Science Foundation of China [31701151]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] Shanghai Municipal Science and Technology Major Project; National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This research was funded by the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), National Key R&D Program of China (2018YFC0910403), National Natural Science Foundation of China (31701151), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program (16YF1413800), the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000). 121 25 26 6 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1422-0067 INT J MOL SCI Int. J. Mol. Sci. SEP 1 2019.0 20.0 17 4269 10.3390/ijms20174269 0.0 20 Biochemistry & Molecular Biology; Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry IZ2AF 31480430.0 Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000486888400201 0
J Lu, FY; Zhou, GH; Zhang, C; Liu, Y; Chang, FT; Xiao, ZD Lu, Fengyi; Zhou, Guanghui; Zhang, Chao; Liu, Yang; Chang, Fengtian; Xiao, Zhongdong Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING English Article Energy efficiency; Parametric optimisation; Workpiece deformation; Deep reinforcement learning; Sustainable manufacturing TOOL PATH GENERATION; SURFACE-ROUGHNESS; PREDICTION; CONSUMPTION; WORKPIECES Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric opti-misation, significantly contributing to sustainable manufacturing. [Lu, Fengyi; Zhou, Guanghui; Zhang, Chao; Chang, Fengtian] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China; [Zhou, Guanghui; Zhang, Chao] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710054, Peoples R China; [Liu, Yang] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden; [Liu, Yang] Univ Oulu, Dept Ind Engn & Management, Oulu 90570, Finland; [Xiao, Zhongdong] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China Xi'an Jiaotong University; Xi'an Jiaotong University; Linkoping University; University of Oulu; Xi'an Jiaotong University Zhang, C (corresponding author), Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China.;Liu, Y (corresponding author), Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden. superzc@xjtu.edu.cn; yang.liu@liu.se Liu, Yang/C-8320-2013 Liu, Yang/0000-0001-8006-3236 National Natural Science Foundation of China; China Postdoctoral Science Foundation; Young Talent fund of University Associ-ation for Science and Technology in Shaanxi, China; [51975463]; [2021M692556]; [20210409] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Young Talent fund of University Associ-ation for Science and Technology in Shaanxi, China; ; ; Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 51975463) ; the China Postdoctoral Science Foundation (No. 2021M692556) ; and the Young Talent fund of University Associ-ation for Science and Technology in Shaanxi, China (No. 20210409) . 52 3 3 15 15 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0736-5845 1879-2537 ROBOT CIM-INT MANUF Robot. Comput.-Integr. Manuf. JUN 2023.0 81.0 102488 10.1016/j.rcim.2022.102488 0.0 12 Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Robotics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Robotics 7I2KB Green Published, hybrid 2023-03-23 WOS:000903720900002 0
J Zhou, C; Cule, B; Goethals, B Zhou, Cheng; Cule, Boris; Goethals, Bart Pattern Based Sequence Classification IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Sequence classification; interesting patterns; classification rules; feature vectors Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task. [Zhou, Cheng; Cule, Boris; Goethals, Bart] Univ Antwerp, Dept Math & Comp Sci, Antwerp, Belgium; [Zhou, Cheng] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China; [Cule, Boris] Univ Libre Bruxelles, Comp & Decis Engn Dept, Brussels, Belgium University of Antwerp; National University of Defense Technology - China; Universite Libre de Bruxelles Zhou, C; Cule, B; Goethals, B (corresponding author), Univ Antwerp, Dept Math & Comp Sci, Antwerp, Belgium.;Zhou, C (corresponding author), Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China.;Cule, B (corresponding author), Univ Libre Bruxelles, Comp & Decis Engn Dept, Brussels, Belgium. cheng.zhou@uantwerpen.be; boris.cule@uantwerpen.be; bart.goethals@uantwerpen.be Goethals, Bart/0000-0001-9327-9554 China Scholarship Council (CSC); SPICES project - Innoviris China Scholarship Council (CSC)(China Scholarship Council); SPICES project - Innoviris(Innoviris) Cheng Zhou is financially supported by the China Scholarship Council (CSC). Boris Cule is partially supported by the SPICES project funded by Innoviris. A preliminary version appeared as Itemset Based Sequence Classification, in the Proceedings of the 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases [1]. 38 34 37 2 20 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAY 1 2016.0 28.0 5 1285 1298 10.1109/TKDE.2015.2510010 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering DJ9GW 2023-03-23 WOS:000374523000015 0
J Liu, YC; Yan, C; Feng, ZM; Zheng, FX; Fan, XL; Zhang, YS; Li, C; Zhou, Y; Lin, ZH; Guo, YS; Zhang, Y; Ma, L; Zhou, WS; Liu, ZK; Dada, L; Dallenbach, K; Kontkanen, J; Cai, RL; Chan, T; Chu, BW; Du, W; Yao, L; Wang, YH; Cai, J; Kangasluoma, J; Kokkonen, T; Kujansuu, J; Rusanen, A; Deng, CJ; Fu, YY; Yin, RJ; Li, XX; Lu, YQ; Liu, YL; Lian, CF; Yang, DS; Wang, WG; Ge, MF; Wang, YS; Worsnop, DR; Junninen, H; He, H; Kerminen, VM; Zheng, J; Wang, L; Jiang, JK; Petaja, T; Bianchi, F; Kulmala, M Liu, Yongchun; Yan, Chao; Feng, Zemin; Zheng, Feixue; Fan, Xiaolong; Zhang, Yusheng; Li, Chang; Zhou, Ying; Lin, Zhuohui; Guo, Yishou; Zhang, Ying; Ma, Li; Zhou, Wenshuo; Liu, Zhikun; Dada, Lubna; Dallenbach, Kaspar; Kontkanen, Jenni; Cai, Runlong; Chan, Tommy; Chu, Biwu; Du, Wei; Yao, Lei; Wang, Yonghong; Cai, Jing; Kangasluoma, Juha; Kokkonen, Tom; Kujansuu, Joni; Rusanen, Anton; Deng, Chenjuan; Fu, Yueyun; Yin, Rujing; Li, Xiaoxiao; Lu, Yiqun; Liu, Yiliang; Lian, Chaofan; Yang, Dongsen; Wang, Weigang; Ge, Maofa; Wang, Yuesi; Worsnop, Douglas R.; Junninen, Heikki; He, Hong; Kerminen, Veli-Matti; Zheng, Jun; Wang, Lin; Jiang, Jingkun; Petaja, Tuukka; Bianchi, Federico; Kulmala, Markku Continuous and comprehensive atmospheric observations in Beijing: a station to understand the complex urban atmospheric environment BIG EARTH DATA English Article Continuous and comprehensive atmospheric observation; air pollution complex; photochemical smog; haze; new particle formation FINE PARTICULATE NITRATE; PEARL RIVER DELTA; NORTH CHINA PLAIN; HAZE POLLUTION; SULFURIC-ACID; METEOROLOGICAL INFLUENCES; OZONE CONCENTRATIONS; PARTICLE COMPOSITION; PHOTOCHEMICAL SMOG; NEUTRAL CLUSTER Due to profound impact on climate and human health, air quality has attracted attention from all levels of the civil society. The key step in the provision of required tools for the society to tackle the complex air quality problem is to characterize it in a comprehensive manner with a long-term perspective. Here, we describe a continuous and comprehensive observation station and its accompanying state-of-the-art instrumentation that was established to investigate the complex urban atmospheric environment in a rapidly developing Chinese Megacity. The station, located in downtown Beijing, aims to study air quality by identifying the major atmospheric pollutants and key processes determining their formation and loss mechanisms. A few hundreds of parameters are continuously measured with the state-of-the-art instruments, including trace gas concentrations, aerosol particle size distributions, and mass concentrations, covering aerosol particle chemical composition from molecules to micrometer-sized aerosol particles. This produced long-term, comprehensive big data with around 1 x 10 11 bytes per year. In this paper, we provide an overview on the facilities of the station, the instrumentation used, the workflow of continuous observations and examples of results from 2018 to 2019 and a basis for establishing a modern long-term, comprehensive atmospheric urban observation station in other megacities. [Liu, Yongchun; Yan, Chao; Feng, Zemin; Zheng, Feixue; Fan, Xiaolong; Zhang, Yusheng; Li, Chang; Zhou, Ying; Lin, Zhuohui; Guo, Yishou; Zhang, Ying; Ma, Li; Zhou, Wenshuo; Liu, Zhikun; Kangasluoma, Juha; Kujansuu, Joni; Petaja, Tuukka; Kulmala, Markku] Beijing Univ Chem Technol, Aerosol & Haze Lab, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing, Peoples R China; [Yan, Chao; Dada, Lubna; Dallenbach, Kaspar; Kontkanen, Jenni; Cai, Runlong; Chan, Tommy; Chu, Biwu; Du, Wei; Yao, Lei; Wang, Yonghong; Cai, Jing; Kangasluoma, Juha; Kokkonen, Tom; Kujansuu, Joni; Rusanen, Anton; Kerminen, Veli-Matti; Petaja, Tuukka; Bianchi, Federico; Kulmala, Markku] Univ Helsinki, Inst Atmospher & Earth Syst Res Phys, Fac Sci, Helsinki, Finland; [Cai, Runlong; Deng, Chenjuan; Fu, Yueyun; Yin, Rujing; Li, Xiaoxiao; Jiang, Jingkun] Tsinghua Univ, State Key Joint Lab Environm Simulat & Pollut Co, State Environm Protect Key Lab Sources & Control, Sch Environm, Beijing, Peoples R China; [Chu, Biwu; Lu, Yiqun; Liu, Yiliang; Wang, Lin] Chinese Acad Sci, State Key Joint Lab Environm Simulat & Pollut Con, Res Ctr Ecoenvironm Sci, Beijing, Peoples R China; [Lu, Yiqun; Liu, Yiliang] Fudan Univ, Dept Environm Sci & Engn, Shanghai, Peoples R China; [Lian, Chaofan; Wang, Weigang; Ge, Maofa] Chinese Acad Sci, State Key Lab Struct Chem Unstable & Stable Spec, Beijing Natl Lab Mol Sci, Inst Chem, Beijing, Peoples R China; [Yang, Dongsen; Zheng, Jun] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring &, Nanjing, Peoples R China; [Wang, Yuesi] Chinese Acad Sci, State Key Lab Atmospher Boundary Layer Phys & At, Inst Atmospher Phys, Beijing, Peoples R China; [Worsnop, Douglas R.] Aerodyne Res Inc, Billerica, MA USA; [Junninen, Heikki] Univ Tartu, Inst Phys, Tartu, Estonia; [Petaja, Tuukka; Kulmala, Markku] Nanjing Univ, Sch Atmospher Sci, Joint Int Res Lab Atmospher & Earth Syst Sci, Nanjing, Peoples R China Beijing University of Chemical Technology; University of Helsinki; Tsinghua University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Fudan University; Chinese Academy of Sciences; Institute of Chemistry, CAS; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Aerodyne Research; University of Tartu; University of Tartu Institute of Physics; Nanjing University Bianchi, F; Kulmala, M (corresponding author), Univ Helsinki, Inst Atmospher & Earth Syst Res Phys, Fac Sci, Helsinki, Finland.;Liu, YC (corresponding author), Beijing Univ Chem Technol, Aerosol & Haze Lab, Beijing, Peoples R China. liuyc@buct.edu.cn; federico.bianchi@helsinki.fi; markku.kulmala@-helsinki.fi Kangasluoma, Juha/AAY-7941-2021; Chu, Biwu/GQR-1691-2022; Bianchi, Federico/G-8428-2012; Zheng, Jun/AHB-5789-2022; Wang, Yuesi/AAF-3317-2019; Liu, Yongchun/F-3892-2011; Kokkonen, Tom Valtteri/AAY-4142-2021; Jiang, Jingkun/A-1076-2010; Wang, Yonghong/AAA-5402-2022; Daellenbach, Kaspar/AAY-4521-2020; Dada, Lubna/X-7559-2018; Cai, Jing/ABI-4308-2022; Wang, Weigang/L-1786-2013; Petäjä, Tuukka/A-8009-2008; Chu, Biwu/AAC-2615-2019; 刘, 永春/AAD-7929-2022; Du, Wei/AGX-6272-2022; ZEMIN, FENG/GZB-0684-2022; Yan, Chao/ABA-5642-2020 Kangasluoma, Juha/0000-0002-1639-1187; Chu, Biwu/0000-0002-7548-5669; Bianchi, Federico/0000-0003-2996-3604; Liu, Yongchun/0000-0002-6758-2151; Kokkonen, Tom Valtteri/0000-0002-4804-7516; Wang, Yonghong/0000-0003-2498-9143; Daellenbach, Kaspar/0000-0003-1246-6396; Dada, Lubna/0000-0003-1105-9043; Wang, Weigang/0000-0001-6911-1110; Petäjä, Tuukka/0000-0002-1881-9044; 刘, 永春/0000-0002-6758-2151; Du, Wei/0000-0001-7890-3099; ZEMIN, FENG/0000-0002-5964-5159; Yan, Chao/0000-0002-5735-9597; jiang, jingkun/0000-0001-6172-190X; Kontkanen, Jenni/0000-0002-5373-3537; Chan, Tommy/0000-0002-5700-7209; Yao, Lei/0000-0002-2680-1629 National Natural Science Foundation of China [41877306]; Ministry of Science and Technology of the People's Republic of China [2019YFC0214701]; Academy of Finland via Center of Excellence in Atmospheric Sciences [272041, 316114, 315203]; European Research Council [ATM-GTP 266, 742206]; Strategic Priority Research Program of Chinese Academy of Sciences; Beijing Advanced Innovation Center for Soft Matter Science and Engineering National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of the People's Republic of China(Ministry of Science and Technology, China); Academy of Finland via Center of Excellence in Atmospheric Sciences; European Research Council(European Research Council (ERC)European Commission); Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); Beijing Advanced Innovation Center for Soft Matter Science and Engineering This work was supported by the National Natural Science Foundation of China (41877306), the Ministry of Science and Technology of the People's Republic of China (2019YFC0214701), Academy of Finland via Center of Excellence in Atmospheric Sciences (272041, 316114, and 315203) and European Research Council via ATM-GTP 266 (742206), the Strategic Priority Research Program of Chinese Academy of Sciences and Beijing Advanced Innovation Center for Soft Matter Science and Engineering. 84 30 30 11 22 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 2096-4471 2574-5417 BIG EARTH DATA Big Earth Data JUL 2 2020.0 4.0 3 295 321 10.1080/20964471.2020.1798707 0.0 27 Computer Science, Information Systems; Geosciences, Multidisciplinary; Remote Sensing Emerging Sources Citation Index (ESCI) Computer Science; Geology; Remote Sensing TA5AC Green Published, gold 2023-03-23 WOS:000667260300004 0
J Sisodiya, SM; Whelan, CD; Hatton, SN; Huynh, K; Altmann, A; Ryten, M; Vezzani, A; Caligiuri, ME; Labate, A; Gambardella, A; Ives-Deliperi, V; Meletti, S; Munsell, BC; Bonilha, L; Tondelli, M; Rebsamen, M; Rummel, C; Vaudano, AE; Wiest, R; Balachandra, AR; Bargallo, N; Bartolini, E; Bernasconi, A; Bernasconi, N; Bernhardt, B; Caldairou, B; Carr, SJA; Cavalleri, GL; Cendes, F; Concha, L; Desmond, PM; Domin, M; Duncan, JS; Focke, NK; Guerrini, R; Hamandi, K; Jackson, GD; Jahanshad, N; Kalviainen, R; Keller, SS; Kochunov, P; Kowalczyk, MA; Kreilkamp, BAK; Kwan, P; Lariviere, S; Lenge, M; Lopez, SM; Martin, P; Mascalchi, M; Moreira, JCV; Morita-Sherman, ME; Pardoe, HR; Pariente, JC; Raviteja, K; Rocha, CS; Rodriguez-Cruces, R; Seeck, M; Semmelroch, MKHG; Sinclair, B; Soltanian-Zadeh, H; Stein, DJ; Striano, P; Taylor, PN; Thomas, RH; Thomopoulos, SI; Velakoulis, D; Vivash, L; Weber, B; Yasuda, CL; Zhang, JS; Thompson, PM; McDonald, CR Sisodiya, Sanjay M.; Whelan, Christopher D.; Hatton, Sean N.; Huynh, Khoa; Altmann, Andre; Ryten, Mina; Vezzani, Annamaria; Caligiuri, Maria Eugenia; Labate, Angelo; Gambardella, Antonio; Ives-Deliperi, Victoria; Meletti, Stefano; Munsell, Brent C.; Bonilha, Leonardo; Tondelli, Manuela; Rebsamen, Michael; Rummel, Christian; Vaudano, Anna Elisabetta; Wiest, Roland; Balachandra, Akshara R.; Bargallo, Nuria; Bartolini, Emanuele; Bernasconi, Andrea; Bernasconi, Neda; Bernhardt, Boris; Caldairou, Benoit; Carr, Sarah J. A.; Cavalleri, Gianpiero L.; Cendes, Fernando; Concha, Luis; Desmond, Patricia M.; Domin, Martin; Duncan, John S.; Focke, Niels K.; Guerrini, Renzo; Hamandi, Khalid; Jackson, Graeme D.; Jahanshad, Neda; Kalviainen, Reetta; Keller, Simon S.; Kochunov, Peter; Kowalczyk, Magdalena A.; Kreilkamp, Barbara A. K.; Kwan, Patrick; Lariviere, Sara; Lenge, Matteo; Lopez, Seymour M.; Martin, Pascal; Mascalchi, Mario; Moreira, Jose C. V.; Morita-Sherman, Marcia E.; Pardoe, Heath R.; Pariente, Jose C.; Raviteja, Kotikalapudi; Rocha, Cristiane S.; Rodriguez-Cruces, Raul; Seeck, Margitta; Semmelroch, Mira K. H. G.; Sinclair, Benjamin; Soltanian-Zadeh, Hamid; Stein, Dan J.; Striano, Pasquale; Taylor, Peter N.; Thomas, Rhys H.; Thomopoulos, Sophia I.; Velakoulis, Dennis; Vivash, Lucy; Weber, Bernd; Yasuda, Clarissa Lin; Zhang, Junsong; Thompson, Paul M.; McDonald, Carrie R. The ENIGMA-Epilepsy working group: Mapping disease from large data sets HUMAN BRAIN MAPPING English Review covariance; deep learning; DTI; event-based modeling; gene expression; genetics; imaging; MRI; quantitative; rsfMRI TEMPORAL-LOBE EPILEPSY; GRAPH-THEORETICAL ANALYSIS; NETWORK; CONNECTIVITY; ABNORMALITIES; CONNECTOMICS; ORGANIZATION; INSIGHTS; ANATOMY; MODELS Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy. [Sisodiya, Sanjay M.; Duncan, John S.] UCL Queen Sq Inst Neurol, Dept Clin & Expt Epilepsy, London WC1N 3BG, England; [Sisodiya, Sanjay M.; Duncan, John S.] Chalfont Ctr Epilepsy, Gerrards Cross, Bucks, England; [Whelan, Christopher D.] Royal Coll Surgeons Ireland, Dept Mol & Cellular Therapeut, Dublin, Ireland; [Hatton, Sean N.; Huynh, Khoa; Balachandra, Akshara R.] Univ Calif San Diego, Ctr Multimodal Imaging & Genet, La Jolla, CA 92093 USA; [Altmann, Andre; Lopez, Seymour M.] UCL, Dept Med Phys & Biomed Engn, Ctr Med Image Comp, London, England; [Ryten, Mina] UCL Queen Sq Inst Neurol, London, England; [Vezzani, Annamaria] IRCCS, Ist Ric Farmacol Mario Negri, Dept Neurosci, Milan, Italy; [Caligiuri, Maria Eugenia; Labate, Angelo; Gambardella, Antonio] Magna Graecia Univ Catanzaro, Dept Med & Surg Sci, Neurosci Res Ctr, Catanzaro, Italy; [Labate, Angelo; Gambardella, Antonio] Magna Graecia Univ Catanzaro, Inst Neurol, Catanzaro, Italy; [Ives-Deliperi, Victoria] Univ Cape Town, Inst Neurosci, Cape Town, South Africa; [Meletti, Stefano; Vaudano, Anna Elisabetta] Univ Modena & Reggio Emilia, Dept Biomed Metab & Neural Sci, Modena, Italy; [Meletti, Stefano; Tondelli, Manuela; Vaudano, Anna Elisabetta] AOU Modena, OCB Hosp, Neurol Unit, Modena, Italy; [Munsell, Brent C.] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA; [Munsell, Brent C.] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27515 USA; [Bonilha, Leonardo] Med Univ South Carolina, Dept Neurol, Charleston, SC 29425 USA; [Rebsamen, Michael; Rummel, Christian; Wiest, Roland] Univ Bern, Bern Univ Hosp, Univ Inst Diagnost & Intervent Neuroradiol, Support Ctr Adv Neuroimaging,Inselspital, Bern, Switzerland; [Balachandra, Akshara R.] Boston Univ, Sch Med, Boston, MA 02118 USA; [Bargallo, Nuria; Pariente, Jose C.] Univ Barcelona, Inst Invest Biomed August Pi & Sunyer IDIBAPS, Magnet Resonance Image Core Facil, Barcelona, Spain; [Bargallo, Nuria] Hosp Clin Barcelona, Ctr Image Diag, Dept Radiol, Barcelona, Spain; [Bartolini, Emanuele] Nuovo Osped Santo Stefano, USL Ctr Toscana, Neurol Unit, Prato, Italy; [Bernasconi, Andrea; Bernasconi, Neda; Caldairou, Benoit] McGill Univ, Montreal Neurol Inst, Neuroimaging Epilepsy Lab, Montreal, PQ, Canada; [Bernhardt, Boris; Lariviere, Sara] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada; [Carr, Sarah J. A.] Neurosci Inst Psychiat Psychol & Neurosci, London, England; [Cavalleri, Gianpiero L.] Royal Coll Surgeons Ireland, Sch Pharm & Biomol Sci, Dublin, Ireland; [Cavalleri, Gianpiero L.] FutureNeuro SFI Res Ctr, Dublin, Ireland; [Cendes, Fernando; Moreira, Jose C. V.; Morita-Sherman, Marcia E.; Rocha, Cristiane S.; Yasuda, Clarissa Lin] Univ Estadual Campinas, UNICAMP, Dept Neurol, Campinas, SP, Brazil; [Cendes, Fernando; Moreira, Jose C. V.; Morita-Sherman, Marcia E.; Rocha, Cristiane S.; Yasuda, Clarissa Lin] Univ Estadual Campinas, UNICAMP, Neuroimaging Lab, Campinas, SP, Brazil; [Concha, Luis; Rodriguez-Cruces, Raul] Univ Nacl Autonoma Mexico, Inst Neurobiol, Queretaro, Mexico; [Desmond, Patricia M.] Univ Melbourne, Royal Melbourne Hosp, Dept Radiol, Melbourne, Vic, Australia; [Domin, Martin] Univ Med Greifswald, Dept Diagnost Radiol & Neuroradiol, Funct Imaging Unit, Greifswald, Germany; [Focke, Niels K.] Univ Med Gottingen, Clin Neurophysiol, Gottingen, Germany; [Guerrini, Renzo; Lenge, Matteo] Univ Florence, Childrens Hosp A Meyer, Pediat Neurol Neurogenet & Neurobiol Unit & Labs, Florence, Italy; [Hamandi, Khalid] Univ Hosp Wales, Dept Neurol, Wales Epilepsy Unit, Cardiff, Wales; [Hamandi, Khalid] Cardiff Univ, Brain Res Imaging Ctr, Sch Psychol, Cardiff, Wales; [Jackson, Graeme D.] Austin Hlth, Dept Neurol, Heidelberg, Vic, Australia; [Jackson, Graeme D.; Kowalczyk, Magdalena A.; Semmelroch, Mira K. H. G.] Univ Melbourne, Florey Dept Neurosci & Mental Hlth, Melbourne, Vic, Australia; [Jahanshad, Neda; Thomopoulos, Sophia I.; Thompson, Paul M.] Univ Southern Calif, Mark & Mary Stevens Inst Neuroimaging & Informat, Imaging Genet Ctr, Keck Sch Med, Marina Del Rey, CA USA; [Kalviainen, Reetta] Kuopio Univ Hosp, EpiCARE ERN, Kuopio, Finland; [Kalviainen, Reetta] Univ Eastern Finland, Neurol, Inst Clin Med, Kuopio, Finland; [Keller, Simon S.; Kreilkamp, Barbara A. K.] Univ Liverpool, Inst Syst Mol & Integrat Biol, Liverpool, Merseyside, England; [Keller, Simon S.; Kreilkamp, Barbara A. K.] NHS Fdn Trust, Walton Ctr, Liverpool, Merseyside, England; [Kochunov, Peter] Univ Maryland, Sch Med, Dept Psychiat, Baltimore, MD 21201 USA; [Kwan, Patrick] Monash Univ, Cent Clin Sch, Dept Neurosci, Melbourne, Vic, Australia; [Lenge, Matteo] Univ Florence, Childrens Hosp A Meyer, Neurosurg Dept, Funct & Epilepsy Neurosurg Unit, Florence, Italy; [Martin, Pascal; Raviteja, Kotikalapudi] Univ Hosp Tubingen, Hertie Inst Clin Brain Res, Dept Neurol & Epileptol, Tubingen, Germany; [Mascalchi, Mario] Univ Florence, Mario Serio Dept Clin & Expt Med Sci, Florence, Italy; [Morita-Sherman, Marcia E.] Cleveland Clin, Neurol Inst, Cleveland, OH 44106 USA; [Pardoe, Heath R.] NYU, Dept Neurol, Sch Med, New York, NY 10016 USA; [Raviteja, Kotikalapudi] Univ Hosp Tubingen, Dept Diagnost & Intervent Neuroradiol, Tubingen, Germany; [Raviteja, Kotikalapudi] Univ Hosp Gottingen, Dept Clin Neurophysiol, Gottingen, Germany; [Rodriguez-Cruces, Raul] McGill Univ, Montreal Neurol Inst & Hosp, Montreal, PQ, Canada; [Seeck, Margitta] EEG & Epilepsy Unit, Geneva, Switzerland; [Semmelroch, Mira K. H. G.] Florey Inst Neurosci & Mental Hlth, Austin Campus, Heidelberg, Vic, Australia; [Sinclair, Benjamin; Vivash, Lucy] Monash Univ, Dept Neurosci, Melbourne, Vic, Australia; [Sinclair, Benjamin] Alfred Hlth, Melbourne, Vic, Australia; [Soltanian-Zadeh, Hamid] Henry Ford Hlth Syst, Radiol & Res Adm, Detroit, MI USA; [Soltanian-Zadeh, Hamid] Univ Tehran, Coll Engn, Sch Elect & Comp Engn, Tehran, Iran; [Stein, Dan J.] Univ Cape Town, Risk & Resilience Mental Disorders, Dept Psychiat, South African Med Res Council Unit Risk & Resilie, Cape Town, South Africa; [Stein, Dan J.] Univ Cape Town, Risk & Resilience Mental Disorders, Neurosci Inst, Cape Town, South Africa; [Striano, Pasquale] Ist Giannina Gaslini, IRCCS, Pediat Neurol & Musc Dis Unit, Genoa, Italy; [Striano, Pasquale] Univ Genoa, Dept Neurosci Rehabil Ophthalmol Genet Maternal &, Genoa, Italy; [Taylor, Peter N.] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England; [Thomas, Rhys H.] Newcastle Univ, Inst Translat & Clin Res, Newcastle Upon Tyne, Tyne & Wear, England; [Velakoulis, Dennis] Univ Melbourne, Royal Melbourne Hosp, Dept Med, Parkville, Vic, Australia; [Velakoulis, Dennis] Royal Melbourne Hosp, Dept Neuropsychiat, Parkville, Vic, Australia; [Vivash, Lucy] Royal Melbourne Hosp, Dept Neurol, Melbourne, Vic, Australia; [Weber, Bernd] Univ Bonn, Inst Expt Epileptol & Cognit Res, Bonn, Germany; [Zhang, Junsong] Xiamen Univ, Sch Informat, Cognit Sci Dept, Xiamen, Peoples R China; [McDonald, Carrie R.] Univ Calif San Diego, Dept Psychiat, La Jolla, CA 92093 USA University of London; University College London; Royal College of Surgeons - Ireland; University of California System; University of California San Diego; University of London; University College London; University of London; University College London; Istituto di Ricerche Farmacologiche Mario Negri IRCCS; Magna Graecia University of Catanzaro; Magna Graecia University of Catanzaro; University of Cape Town; Universita di Modena e Reggio Emilia; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Medical University of South Carolina; University of Bern; University Hospital of Bern; Boston University; University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS; University of Barcelona; Hospital Clinic de Barcelona; McGill University; McGill University; Royal College of Surgeons - Ireland; Universidade Estadual de Campinas; Universidade Estadual de Campinas; Universidad Nacional Autonoma de Mexico; Royal Melbourne Hospital; University of Melbourne; Greifswald Medical School; University of Gottingen; University of Florence; Azienda Ospedaliera Universitaria (AOU) MEYER; Cardiff University; Cardiff University; Austin Research Institute; Florey Institute of Neuroscience & Mental Health; University of Melbourne; University of Southern California; Kuopio University Hospital; University of Eastern Finland; University of Eastern Finland; University of Liverpool; Oxford University Hospitals NHS Foundation Trust; Walton Centre; University System of Maryland; University of Maryland Baltimore; Monash University; University of Florence; Azienda Ospedaliera Universitaria (AOU) MEYER; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; University of Florence; Cleveland Clinic Foundation; New York University; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; University of Gottingen; McGill University; Florey Institute of Neuroscience & Mental Health; Monash University; Henry Ford Health System; Henry Ford Hospital; University of Tehran; University of Cape Town; University of Cape Town; University of Genoa; IRCCS Istituto Giannina Gaslini; University of Genoa; Newcastle University - UK; Newcastle University - UK; Royal Melbourne Hospital; University of Melbourne; Royal Melbourne Hospital; Royal Melbourne Hospital; University of Bonn; Xiamen University; University of California System; University of California San Diego Sisodiya, SM (corresponding author), UCL Queen Sq Inst Neurol, Dept Clin & Expt Epilepsy, London WC1N 3BG, England. s.sisodiya@ucl.ac.uk Ives-Deliperi, Victoria/AAC-1865-2020; Bartolini, Emanuele/X-4137-2019; Altmann, Andre/ABF-1675-2020; Concha, Luis/M-5720-2019; Kowalczyk, Magdalena/AAR-1374-2020; Zhang, Junsong/HKW-6976-2023; Stein, Dan/A-1752-2008; Kreilkamp, Barbara/AAG-6433-2021; Gambardella, Antonio/F-5295-2012; Bartolini, Emanuele/K-8779-2016; yasuda, clarissa lin/D-4599-2018; Cendes, Fernando/C-1301-2012; Vezzani, Annamaria/AAB-1882-2020; Vivash, Lucy/HLG-7741-2023; Thompson, Paul M/C-4194-2018; Hatton, Sean/ABF-2549-2020; Striano, Pasquale/F-9117-2014; Thomas, Rhys/I-3840-2019; Rocha, Cristiane S/B-7671-2012; Weber, Bernd/H-5244-2012; Kochunov, Peter/GQH-9532-2022; Bernhardt, Boris/AAF-2896-2021; Jahanshad, Neda/ABD-5944-2021; Jackson, Graeme/AAD-9070-2019; Meletti, Stefano/O-3622-2015; Caligiuri, Maria Eugenia/K-4957-2018; Vaudano, Anna Elisabetta/O-2653-2015; Taylor, Peter/E-8927-2011; Cherubini, Andrea/D-1327-2009; Desmond, Patricia/D-1966-2014 Bartolini, Emanuele/0000-0002-5683-1941; Altmann, Andre/0000-0002-9265-2393; Concha, Luis/0000-0002-7842-3869; Kowalczyk, Magdalena/0000-0002-5410-0299; Stein, Dan/0000-0001-7218-7810; Gambardella, Antonio/0000-0001-7384-3074; Bartolini, Emanuele/0000-0002-5683-1941; yasuda, clarissa lin/0000-0001-9084-7173; Cendes, Fernando/0000-0001-9336-9568; Vezzani, Annamaria/0000-0003-0573-6175; Thompson, Paul M/0000-0002-4720-8867; Striano, Pasquale/0000-0002-6065-1476; Thomas, Rhys/0000-0003-2062-8623; Rocha, Cristiane S/0000-0001-5678-2070; Weber, Bernd/0000-0002-7811-9605; Bernhardt, Boris/0000-0001-9256-6041; Jackson, Graeme/0000-0002-7917-5326; Meletti, Stefano/0000-0003-0334-539X; Caligiuri, Maria Eugenia/0000-0002-2030-5552; Pardoe, Heath/0000-0002-0123-2167; Richardson, Mark Philip/0000-0001-8925-3140; Vivash, Lucy/0000-0002-1182-0907; Rebsamen, Michael/0000-0002-8441-1485; Vaudano, Anna Elisabetta/0000-0002-6280-7526; Duncan, John S/0000-0002-1373-0681; Taylor, Peter/0000-0003-2144-9838; Cherubini, Andrea/0000-0002-5946-4390; Whelan, Christopher/0000-0003-0308-5583; Guerrini, Renzo/0000-0002-7272-7079; Rummel, Christian/0000-0003-2345-7938; Kreilkamp, Barbara A.K./0000-0001-6881-5191; Desmond, Patricia/0000-0002-4803-6323; Tondelli, Manuela/0000-0001-6698-5437; Thomopoulos, Sophia I/0000-0002-0046-4070; Pariente Zorrilla, Jose Carlos/0000-0002-7358-7602; Balestrini, Simona/0000-0001-5639-1969; Keller, Simon/0000-0001-5247-9795; Lariviere, Sara/0000-0001-5701-1307; Cavalleri, Gianpiero/0000-0002-9802-0506; Abela, Eugenio/0000-0002-9356-1450 National Nature Science Foundation of China [61772440]; Health Research Board; South African Medical Research Council; Monash University; National Health and Medical Research Council [1091593, 1081151, R01 AG059874, R01 MH117601, S10OD023696, R01EB015611]; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung (SNF) [180365, 163398]; Tenure-track Clinician Scientist Fellowship [MR/N008324/1]; NIH; NINDS [1R21NS107739-01A1]; FAPESP-BRAINN [403726/2016-6, 2013/07599-3, 07559-3, 2013/07559-3]; Ministry of Health [NET-2013-02355313]; University of Tubingen [R01 NS065838, R21 NS107739, F1315030]; University of Melbourne [MRFF1136427]; Epilepsy Research UK [1085]; Medical Research Council [MR/S00355X/1, MR/K023152/1, L016311/1]; Deutsche Forschungsgemeinschaft (DFG) [FO750/5-1]; National Institute for Health Research (NIHR); NIH/NINDS [R01NS110347]; FRQS; SickKids Foundation [NI17-039]; NSERC [Discovery-1304413]; CIHR [FDN-154298, MOP-123520, MOP-130516, MOP-57840]; ENIGMA Sex Differences Initiative [R01 MH116147]; ENIGMAWorld Aging Center [R56 AG058854]; NIH Big Data to Knowledge (BD2K) program [U54EB020403]; UNAM-DGAPA [IB201712]; Mexican Council of Science and Technology [181508, 232676, 251216, 280283]; SwissNational Science Foundation [180365]; Sao Paulo Research Foundation [2013/07559-3]; FAPESP; European Regional Development Fund; Science Foundation Ireland [16/RC/3948, 08/RFP/ GEN1538]; Department of Health; European Union [602102]; National Health and Medical Research Council of Australia [1081151] Funding Source: NHMRC; Medical Research Council [MR/S00355X/1, MR/K023152/1] Funding Source: researchfish; MRC [MR/L016311/1, MR/K023152/1] Funding Source: UKRI National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Health Research Board; South African Medical Research Council(South African Medical Research CouncilUK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Monash University(Monash University); National Health and Medical Research Council(National Health and Medical Research Council (NHMRC) of Australia); Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung (SNF); Tenure-track Clinician Scientist Fellowship; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NINDS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); FAPESP-BRAINN; Ministry of Health; University of Tubingen; University of Melbourne(University of Melbourne); Epilepsy Research UK; Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); National Institute for Health Research (NIHR)(National Institute for Health Research (NIHR)); NIH/NINDS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); FRQS; SickKids Foundation; NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); CIHR(Canadian Institutes of Health Research (CIHR)); ENIGMA Sex Differences Initiative; ENIGMAWorld Aging Center; NIH Big Data to Knowledge (BD2K) program; UNAM-DGAPA(Universidad Nacional Autonoma de Mexico); Mexican Council of Science and Technology(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); SwissNational Science Foundation(Swiss National Science Foundation (SNSF)); Sao Paulo Research Foundation(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); European Regional Development Fund(European Commission); Science Foundation Ireland(Science Foundation Ireland); Department of Health; European Union(European Commission); National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) National Nature Science Foundation of China, Grant/Award Number: 61772440; Health Research Board; South African Medical Research Council; Monash University; National Health and Medical Research Council, Grant/ Award Numbers: 1091593, 1081151, R01 AG059874; R01 MH117601, S10OD023696; R01EB015611; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung (SNF), Grant/Award Numbers: 180365, 163398; Tenure-track Clinician Scientist Fellowship, Grant/Award Number: MR/N008324/1; NIH; NINDS, Grant/Award Number: 1R21NS107739-01A1; FAPESP-BRAINN, Grant/Award Numbers: 403726/2016-6, 2013/07599-3, 07559-3, 2013/07559-3; Ministry of Health, Grant/ Award Number: NET-2013-02355313; University of Tubingen, Grant/Award Numbers: R01 NS065838; R21 NS107739, F1315030; University of Melbourne, Grant/ Award Number: MRFF1136427; Epilepsy Research UK, Grant/Award Number: 1085; Medical Research Council, Grant/Award Numbers: MR/S00355X/1; MR/K023152/1, L016311/1; Deutsche Forschungsgemeinschaft (DFG), Grant/Award Number: FO750/5-1; National Institute for Health Research (NIHR); NIH/NINDS, Grant/Award Number: R01NS110347; FRQS; SickKids Foundation, Grant/AwardNumber: NI17-039; NSERC, Grant/ Award Number: Discovery-1304413; CIHR, Grant/Award Numbers: FDN-154298, MOP-123520; MOP-130516, MOP-57840; ENIGMA Sex Differences Initiative, Grant/Award Number: R01 MH116147; ENIGMAWorld Aging Center, Grant/Award Number: R56 AG058854; NIH Big Data to Knowledge (BD2K) program, Grant/ AwardNumber:U54EB020403; UNAM-DGAPA, Grant/Award Number: IB201712; Mexican Council of Science and Technology, Grant/Award Number: 181508; 232676; 251216; 280283; SwissNational Science Foundation, Grant/Award Number: 180365; Sao Paulo Research Foundation, Grant/Award Number: 2013/07559-3; FAPESP; European Regional Development Fund; Science Foundation Ireland, Grant/Award Number: 16/RC/3948; 08/RFP/ GEN1538; Department of Health; European Union, Grant/Award Number: 602102 62 29 29 5 23 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. JAN 2022.0 43.0 1 SI 113 128 10.1002/hbm.25037 0.0 MAY 2020 16 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging XP3FD 32468614.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000535952200001 0
C Zhang, MJ; Garcia, S; Terre, M IEEE Zhang Ming Jun; Garcia, Samuel; Terre, Michel Fast Learning Architecture for Neural Networks 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022) European Signal Processing Conference English Proceedings Paper 30th European Signal Processing Conference (EUSIPCO) AUG 29-SEP 02, 2022 Belgrade, SERBIA European Assoc Signa Proc Neural networks; Learning algorithms; Architecture; FPGA This paper proposes a solution to minimize the learning time of a fully connected neural network. The paper presents a processing architecture in which the treatments applied to the examples of the learning base are strongly parallelized and anticipated, even before the parameters adaptation of the previous examples are completed. This strategy finally leads to a delayed adaptation and the impact of this delay on the learning performances is analysed through a simple replicable school case study. It is shown that a reduction of the adaptation step size could be proposed to compensate errors due to the delayed adaptation. Finally, the gain in processing time for the learning phase is analysed as a function of the network parameters chosen in this study. [Zhang Ming Jun] DGUT CNAM, CEDRIC Laetitia, Dongguan, Peoples R China; [Garcia, Samuel; Terre, Michel] HESAM CNAM, CEDRIC Laetitia, Paris, France Zhang, MJ (corresponding author), DGUT CNAM, CEDRIC Laetitia, Dongguan, Peoples R China. ming-jun.zhang@lecnam.net; samuel.garcia@lecnam.net; michel.terre@lecnam.net 12 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2076-1465 978-90-827970-9-1 EUR SIGNAL PR CONF 2022.0 1611 1615 5 Acoustics; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU5UP 2023-03-23 WOS:000918827600316 0
C Andreas, A; Mavromoustakis, CX; Mastorakis, G; Batalla, JM; Mukherjee, M; Pallis, E IEEE Andreas, Andreou; Mavromoustakis, Constandinos X.; Mastorakis, George; Batalla, Jordi Mongay; Mukherjee, Mithun; Pallis, Evangelos Internal Virus Detection Framework Based on IoT Semantic Interoperability IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) MAY 16-20, 2022 Seoul, SOUTH KOREA IEEE,Samsung,LG,Huawei,Qualcomm,Technol Innovat Inst,ZTE,Elect & Telecommunicat Res Inst,KMW,SOLiD,ERICSSON LG,NI covid-19; teaching-learning; machine learning; air quality; epidemic; pandemic HYBRID Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses' variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher's phase, and the support vector machine method to compute the fitness function of optimisation. [Andreas, Andreou; Mavromoustakis, Constandinos X.] Univ Nicosia, Dept Comp Sci, Nicosia, Cyprus; [Andreas, Andreou; Mavromoustakis, Constandinos X.] Univ Nicosia Res Fdn, Nicosia, Cyprus; [Mastorakis, George] Hellen Mediterranean Univ Agios Nikolaos, Dept Management Sci & Technol, Iraklion, Greece; [Batalla, Jordi Mongay] Warsaw Univ Technol, Warsaw, Poland; [Batalla, Jordi Mongay] Natl Inst Telecommun, Warsaw, Poland; [Mukherjee, Mithun] Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing, Peoples R China; [Pallis, Evangelos] Univ West Attica, Sch Engn, Dept Ind Design & Prod Engn, Athens, Greece University of Nicosia; University of Nicosia; Warsaw University of Technology; National Institute of Telecommunications - Poland; Nanjing University of Information Science & Technology; University of West Attica Andreas, A (corresponding author), Univ Nicosia, Dept Comp Sci, Nicosia, Cyprus.;Andreas, A (corresponding author), Univ Nicosia Res Fdn, Nicosia, Cyprus. andreou.andreas@unic.ac.cy; mavromoustakis.c@unic.ac.cy; gmastorakis@hmu.gr; jordi.mongay.batalla@pw.edu.pl; m.mukherjee@ieee.org; epallis@uniwa.gr Andreou, Andreas/0000-0002-9432-916X Smart and Health Ageing through People Engaging in supporting Systems SHAPES project - European Union [857159]; National Science Centre in Poland; Sonata-bis Programme [2018/30/E/ST7/00413] Smart and Health Ageing through People Engaging in supporting Systems SHAPES project - European Union; National Science Centre in Poland(National Science Centre, Poland); Sonata-bis Programme This research work was funded by the Smart and Health Ageing through People Engaging in supporting Systems SHAPES project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857159. The work of the Polish investigators has been supported by the National Science Centre in Poland, Sonata-bis Programme, through the project framework Context-Aware Adaptation Framework for eMBB services in 5G networks, under Grant No 2018/30/E/ST7/00413. 19 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-5386-8347-7 IEEE ICC 2022.0 1444 1449 10.1109/ICC45855.2022.9838625 0.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BT9VG 2023-03-23 WOS:000864709901128 0
C Gao, YM; Zhang, XY; Xu, Y; Zhang, JS; Birkholz, P Int Speech Commun Assoc Gao, Yingming; Zhang, Xinyu; Xu, Yi; Zhang, Jinsong; Birkholz, Peter An Investigation of the Target Approximation Model for Tone Modeling and Recognition in Continuous Mandarin Speech INTERSPEECH 2020 Interspeech English Proceedings Paper Interspeech Conference OCT 25-29, 2020 Shanghai, PEOPLES R CHINA continuous Mandarin speech; tone modeling and recognition; target approximation model; LSTM neural network The complex f(0) variations in continuous speech make it rather difficult to perform automatic recognition of tones in a language like Mandarin Chinese. In this study, we tested the use of target approximation model (TAM) for continuous tone recognition on two datasets. TAM simulates f(0) production from the articulatory point of view and so allow to discover the underlying pitch targets from the surface f(0) contour. The f(0) contour of each tone represented by 30 equidistant points in the first dataset was simulated by the TAM model. Using a support vector machine (SVM) to classify tones showed that, compared to the representation by 30 f(0) values, the estimated three-dimensional TAM parameters had a comparable performance in characterizing tone patterns. The TAM model was further tested on the second dataset containing more complex tonal variations. With equal or a fewer number of features, the TAM parameters provided better performance than the coefficients of the cosine transform and a slightly worse performance than the statistical f(0) parameters for tone recognition. Furthermore, we investigated bidirectional LSTM neural network for modelling the sequential tonal variations, which proved to be more powerful than the SVM classifier. The BLSTM system incorporating TAM and statistical f(0) parameters achieved the best accuracy of 87.56%. [Gao, Yingming; Zhang, Xinyu; Birkholz, Peter] Tech Univ Dresden, Inst Acoust & Speech Commun, Dresden, Germany; [Xu, Yi] UCL, Dept Speech Hearing & Phonet Sci, London, England; [Zhang, Jinsong] Beijing Language & Culture Univ, Coll Informat Sci, Beijing, Peoples R China Technische Universitat Dresden; University of London; University College London; Beijing Language & Culture University Gao, YM (corresponding author), Tech Univ Dresden, Inst Acoust & Speech Commun, Dresden, Germany. yingming.gao@mailbox.tu-dresden.de; duanfengzxy@gmail.com; yi.xu@ucl.ac.uk; jinsong.zhang@blcu.edu.cn; peter.birkholz@tu-dresden.de China Scholarship Council; Science Foundation of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities) [20YJ040002] China Scholarship Council(China Scholarship Council); Science Foundation of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities) This study was partially supported by China Scholarship Council and the Science Foundation of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities, 20YJ040002). 21 0 0 1 1 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2020.0 1913 1917 10.21437/Interspeech.2020-2823 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4QT Green Published 2023-03-23 WOS:000833594102009 0
J Bo, YH; Zhang, P; Luo, ZQ; Li, S; Song, J; Liu, XJ Bo, Yeheng; Zhang, Peng; Luo, Ziqing; Li, Shuai; Song, Juan; Liu, Xinjun NbO2 Memristive Neurons for Burst-Based Perceptron ADVANCED INTELLIGENT SYSTEMS English Article bursting; dynamic behavior; memristors; neurons SYNAPSES Neuromorphic computing using spike-based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. Herein, the dynamic operating conditions of NbO2-based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed, and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks. [Bo, Yeheng; Zhang, Peng; Luo, Ziqing; Liu, Xinjun] Tianjin Univ, Fac Sci, Tianjin Key Lab Low Dimens Mat Phys & Preparat Te, Tianjin 300354, Peoples R China; [Li, Shuai] Univ Paris Saclay, CNRS Thales, Unite Mixte Phys, F-91767 Palaiseau, France; [Song, Juan] Shanghai Univ Tradit Chinese Med, Putuo Dist Cent Hosp, Dept Emergency Med, Shanghai 200062, Peoples R China Tianjin University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Shanghai University of Traditional Chinese Medicine Liu, XJ (corresponding author), Tianjin Univ, Fac Sci, Tianjin Key Lab Low Dimens Mat Phys & Preparat Te, Tianjin 300354, Peoples R China. xinjun.liu@tju.edu.cn Liu, Xinjun/A-5219-2012 Liu, Xinjun/0000-0001-9003-9299 National Natural Science Foundation of China [11674241, 11774253]; Natural Science Foundation of Tianjin City [18JCYBJC18000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Tianjin City(Natural Science Foundation of Tianjin) This work was partly funded by the National Natural Science Foundation of China (No. 11674241 and No. 11774253) and Natural Science Foundation of Tianjin City (No. 18JCYBJC18000). 17 9 9 3 14 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2640-4567 ADV INTELL SYST-GER Adv. Intell. Syst. AUG 2020.0 2.0 8 2000066 10.1002/aisy.202000066 0.0 7 Automation & Control Systems; Computer Science, Artificial Intelligence; Robotics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Robotics TE1ND gold 2023-03-23 WOS:000669782900011 0
J Zhang, YH; Guo, W; Zeng, T; Zhang, SQ; Chen, L; Gamarra, M; Mansour, RF; Escorcia-Gutierrez, J; Huang, T; Cai, YD Zhang, Yu-Hang; Guo, Wei; Zeng, Tao; Zhang, ShiQi; Chen, Lei; Gamarra, Margarita; Mansour, Romany F.; Escorcia-Gutierrez, Jose; Huang, Tao; Cai, Yu-Dong Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes FRONTIERS IN MICROBIOLOGY English Article type 2 diabetes; gut microbiome; machine learning; feature selection; support vector machine; microbiota biomarkers INSULIN-RESISTANCE; GUT MICROBIOME; DISORDERS; OBESITY; DEFECT Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes. [Zhang, Yu-Hang; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China; [Zhang, Yu-Hang] Harvard Med Sch, Channing Div Network Med, Brigham & Womens Hosp, Boston, MA 02115 USA; [Guo, Wei] Chinese Acad Sci, Key Lab Stem Cell Biol, Shanghai Inst Biol Sci, Shanghai, Peoples R China; [Guo, Wei] Shanghai Jiao Tong Univ, Sch Med, Shanghai, Peoples R China; [Zeng, Tao; Huang, Tao] Chinese Acad Sci, Biomed Big Data Ctr, CAS Key Lab Computat Biol, Shanghai Inst Nutr & Hlth,Univ Chinese Acad Sci, Shanghai, Peoples R China; [Zhang, ShiQi] Univ Copenhagen, Dept Biostat, Copenhagen, Denmark; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China; [Gamarra, Margarita] Univ Costa, Dept Computat Sci & Elect, CUC, Barranquilla, Colombia; [Mansour, Romany F.] New Valley Univ, Dept Math, Fac Sci, El Kharga, Egypt; [Escorcia-Gutierrez, Jose] Univ Autonoma Caribe, Elect & Telecommun Engn Program, Barranquilla, Colombia; [Huang, Tao] Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai, Peoples R China Shanghai University; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Shanghai Jiao Tong University; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS; University of Copenhagen; Shanghai Maritime University; Universidad de la Costa; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Biomed Big Data Ctr, CAS Key Lab Computat Biol, Shanghai Inst Nutr & Hlth,Univ Chinese Acad Sci, Shanghai, Peoples R China.;Escorcia-Gutierrez, J (corresponding author), Univ Autonoma Caribe, Elect & Telecommun Engn Program, Barranquilla, Colombia.;Huang, T (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai, Peoples R China. jose.escorcia47@uac.edu.co; tohuangtao@126.com; cai_yud@126.com Gamarra, Margarita/AAZ-8674-2021; Zhang, Yu-Hang/O-9774-2015; Zeng, Tao/A-1711-2012; Chen, Lei/A-2275-2011; Zhang, Yu-Hang/AAQ-9012-2021; Escorcia-Gutierrez, Jose/AAU-4212-2021; Mansour, Romany F./AAB-6085-2021; Gamarra, Margarita/AAW-3533-2021 Gamarra, Margarita/0000-0003-1834-2984; Zhang, Yu-Hang/0000-0003-3825-0796; Zeng, Tao/0000-0002-0295-3994; Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; Escorcia-Gutierrez, Jose/0000-0003-0518-3187; Gamarra, Margarita/0000-0003-1834-2984; , Romany F. Mansour/0000-0001-5857-8495 Strategic Priority Research Program of Chinese Academy of Sciences [XDB38050200]; National Key RAMP;D Program of China [2018YFC0910403, 2017YFC1201200]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Natural Science Foundation of China [31701151]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences [202002] Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); National Key RAMP;D Program of China; Shanghai Municipal Science and Technology Major Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB38050200), National Key R&D Program of China (2017YFC1201200), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), National Key R&D Program of China (2018YFC0910403), National Natural Science Foundation of China (31701151), Shanghai Sailing Program (16YF1413800), the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS; 2016245), and the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences (202002). 62 5 5 1 16 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-302X FRONT MICROBIOL Front. Microbiol. JUL 9 2021.0 12.0 711244 10.3389/fmicb.2021.711244 0.0 10 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology TM9GN 34305880.0 gold, Green Accepted 2023-03-23 WOS:000675855300001 0
J Balezentis, T; Li, TX; Streimikiene, D; Balezentis, A Balezentis, Tomas; Li, Tianxiang; Streimikiene, Dalia; Balezentis, Alvydas Is the Lithuanian economy approaching the goals of sustainable energy and climate change mitigation? Evidence from DEA-based environmental performance index JOURNAL OF CLEANER PRODUCTION English Article Environmental performance index; Data envelopment analysis; Economic sectors; Lithuania DATA ENVELOPMENT ANALYSIS; CARBON EMISSION PERFORMANCE; ECO-EFFICIENCY; CO2 EMISSIONS; NEURAL-NETWORK; CHINA; MODEL; INTENSITY; DRIVERS; GROWTH Even though Lithuania has seen extensive social and political transformations, there is still a gap in the literature on environmental performance of Lithuanian economy. Specifically, the environmental pressures stemming from economic activities have never been analysed in lines with the principles of production economics. This paper, therefore, aims to estimate the environmental performance index (EPI) for Lithuanian economic sectors. The paper presents the underlying trends in greenhouse gas emissions in Lithuania. The environmental performance index is estimated by employing the data envelopment analysis in the spirit of the Hicks-Moorsteen indices. The present analysis is based on the data from the World Input-Output Database. Specifically, the environmental technology is defined in terms of the value added, carbon and nitrous oxide emissions, labour, capital and energy. Results of the analysis imply that the analysed economic sectors can be grouped into the four clusters with respect to the mean EPI and its rate of growth. Pulp, paper and agricultural sectors fall into the best-performing group, featuring the highest mean EPI and the highest growth rate. Petroleum production and air transport sectors are those featuring the lowest environmental performance. Therefore, the latter sectors should be given an especial importance when developing strategies for increase in sustainability. (C) 2015 Elsevier Ltd. All rights reserved. [Balezentis, Tomas] Lithuanian Inst Agrarian Econ, V Kudirkos Str 18-2, LT-03105 Vilnius, Lithuania; [Li, Tianxiang] Nanjing Agr Univ, Coll Econ & Management, Weigang 1, Nanjing 210095, Jiangsu, Peoples R China; [Streimikiene, Dalia] Mykolas Romeris Univ, Ateities 20, LT-08303 Vilnius, Lithuania; [Balezentis, Alvydas] Mykolas Romeris Univ, Valakupiu Str 5, LT-10101 Vilnius, Lithuania Lithuanian Institute of Agrarian Economics; Nanjing Agricultural University; Mykolas Romeris University; Mykolas Romeris University Balezentis, T (corresponding author), Lithuanian Inst Agrarian Econ, V Kudirkos Str 18-2, LT-03105 Vilnius, Lithuania. tomas@laei.lt Streimikiene, Dalia/AAD-7762-2020; Balezentis, Tomas/D-8609-2015; Streimikiene, Dalia/AAF-2180-2020 Balezentis, Tomas/0000-0002-3906-1711; Streimikiene, Dalia/0000-0002-3247-9912 China Scholarship Council and China Institute for Rural Studies, Tsinghua University China Scholarship Council and China Institute for Rural Studies, Tsinghua University Tianxiang Li acknowledges the support of China Scholarship Council and China Institute for Rural Studies, Tsinghua University. 48 52 52 6 93 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. MAR 10 2016.0 116.0 23 31 10.1016/j.jclepro.2015.12.088 0.0 9 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology DE8KV 2023-03-23 WOS:000370885800003 0
J Keskinarkaus, A; Yang, RJ; Fylakis, A; Surat-E-Mostafa, M; Hautala, A; Hu, Y; Peng, JY; Zhao, GY; Seppanen, T; Karppinen, J Keskinarkaus, Anja; Yang, Ruijing; Fylakis, Angelos; Surat-E-Mostafa, Md; Hautala, Arto; Hu, Yong; Peng, Jinye; Zhao, Guoying; Seppanen, Tapio; Karppinen, Jaro Pain fingerprinting using multimodal sensing: pilot study MULTIMEDIA TOOLS AND APPLICATIONS English Article Low back pain; Machine learning; Facial expression; Audio analysis; Heart rate; Electroencephalography RECOGNITION; MECHANISMS Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain. [Keskinarkaus, Anja; Yang, Ruijing; Fylakis, Angelos; Surat-E-Mostafa, Md; Hautala, Arto; Zhao, Guoying; Seppanen, Tapio] Univ Oulu, Fac Informat Technol & Elect Engn, Ctr Machine Vis & Signal Anal CMVS, Oulu, Finland; [Yang, Ruijing; Peng, Jinye] Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China; [Hautala, Arto] Univ Jyvaskyla, Fac Sport & Hlth Sci, Jyvaskyla, Finland; [Hu, Yong] Univ Hong Kong, Li Ka Shing Fac Med, Dept Orthopaed & Traumatol, Neural Engn & Clin Electrophysiol Lab,Pok Fu Lam, Hong Kong, Peoples R China; [Karppinen, Jaro] Oulu Univ Hosp, Med Res Ctr Oulu, Oulu, Finland; [Karppinen, Jaro] Univ Oulu, Oulu, Finland University of Oulu; Northwest University Xi'an; University of Jyvaskyla; University of Hong Kong; University of Oulu; University of Oulu Keskinarkaus, A (corresponding author), Univ Oulu, Fac Informat Technol & Elect Engn, Ctr Machine Vis & Signal Anal CMVS, Oulu, Finland. anja.keskinarkaus@oulu.fi; ruijing.yang@oulu.fi; angelos.fylakis@oulu.fi; mmostafa@student.oulu.fi; arto.j.hautala@jyu.fi; yhud@hku.hk; pjy@nwu.edu.cn; guoying.zhao@oulu.fi; tapio.seppanen@oulu.fi; jaro.karppinen@oulu.fi Hautala, Arto/AAF-4669-2021 Hautala, Arto/0000-0002-7648-0130 University of Oulu; Oulu University Hospital; National Technology Agency of Finland (Business Finland) University of Oulu; Oulu University Hospital; National Technology Agency of Finland (Business Finland) Open Access funding provided by University of Oulu including Oulu University Hospital. University of Oulu, The National Technology Agency of Finland (Business Finland). 35 0 0 2 6 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. FEB 2022.0 81.0 4 5717 5742 10.1007/s11042-021-11761-8 0.0 DEC 2021 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering ZF7SH hybrid, Green Published 2023-03-23 WOS:000735722700002 0
J Li, FS; Zhou, ZZ; Xiao, JH; Chen, RZ; Lehtomaki, M; Elberink, SO; Vosselman, G; Hyyppa, J; Chen, YW; Kukko, A Li, Fashuai; Zhou, Zhize; Xiao, Jianhua; Chen, Ruizhi; Lehtomaki, Matti; Elberink, Sander Oude; Vosselman, George; Hyyppa, Juha; Chen, Yuwei; Kukko, Antero Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Roads; Point cloud compression; Three-dimensional displays; Semantics; Machine learning; Feature extraction; Shape; Densely connected conditional random fields; instance-aware semantic segmentation; mobile laser scanning point clouds; pole-like road furniture POINT CLOUDS; CONTEXTUAL CLASSIFICATION; OBJECTS; RECOGNITION; ALGORITHM In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture. [Li, Fashuai; Zhou, Zhize] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Peoples R China; [Li, Fashuai; Xiao, Jianhua] Wuhan Geomat Inst, Wuhan 430022, Peoples R China; [Li, Fashuai; Chen, Ruizhi] Wuhan Univ, LIESMARS, Wuhan 430022, Peoples R China; [Lehtomaki, Matti; Hyyppa, Juha; Chen, Yuwei; Kukko, Antero] Finnish Geospatial Res Inst, Masala 02430, Kirkkonummi, Finland; [Lehtomaki, Matti; Hyyppa, Juha; Chen, Yuwei; Kukko, Antero] Acad Finland, Ctr Excellence Laser Scanning Res, Helsinki 00521, Finland; [Lehtomaki, Matti; Hyyppa, Juha; Chen, Yuwei] Aalto Univ, Dept Built Environm, Espoo 02150, Finland; [Elberink, Sander Oude; Vosselman, George] Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7500 Enschede, Netherlands Zhejiang University; Wuhan University; The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); Academy of Finland; Aalto University Zhou, ZZ (corresponding author), Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Peoples R China. lifashuai@gmail.com; zhouzhize@zju.edu.cn; xjhwhkc@163.com; ruizhi.chen@whu.edu.cn; matti.lehtomaki@nls.fi; s.j.oudeelberink@utwente.nl; george.vosselman@utwente.nl; jutia.hyyppa@nls.fi; yuwei.chen@nls.fi; antero.kukko@nls.fi Elberink, Sander Oude/D-3829-2009; 陈, 雨薇/HKF-1175-2023; /D-3985-2009 Elberink, Sander Oude/0000-0002-4511-2095; Lehtomaki, Matti/0000-0003-3119-3493; Kukko, Antero/0000-0002-3841-6533; Zhou, Zhize/0000-0001-9576-1686; /0000-0001-8813-8028; Li, Fashuai/0000-0001-9443-777X National Key Research and Development Program of China [2020AAA0108901]; China Postdoctoral Science Foundation [2020M682505]; Open Fund of State Key Laboratory of CAD&CG, Zhejiang University [A2022]; Academy of Finland Flagship Project [337656]; Strategic Research Council at the Academy of Finland [293389, 314312]; Academy Finland Project [300066] National Key Research and Development Program of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Open Fund of State Key Laboratory of CAD&CG, Zhejiang University; Academy of Finland Flagship Project; Strategic Research Council at the Academy of Finland; Academy Finland Project This work was supported in part by the National Key Research and Development Program of China under Grant 2020AAA0108901; in part by the China Postdoctoral Science Foundation under Grant 2020M682505; in part by the Open Fund of State Key Laboratory of CAD&CG, Zhejiang University, under Grant A2022; in part by the Academy of Finland Flagship Project under Grant 337656; in part by the Strategic Research Council at the Academy of Finland Project under Grant 293389 and Grant 314312; and in part by the Academy Finland Project under Grant 300066. The Associate Editor for this article was D. F. Wolf. 62 0 0 1 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. OCT 2022.0 23.0 10 17516 17529 10.1109/TITS.2022.3157611 0.0 MAR 2022 14 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 5G4AU Green Published 2023-03-23 WOS:000773245000001 0
C Zhou, PY; Braud, T; Alhilal, A; Hui, P; Kangasharju, J IEEE Zhou, Pengyuan; Braud, Tristan; Alhilal, Ahmad; Hui, Pan; Kangasharju, Jussi ERL: Edge based Reinforcement Learning for optimized urban Traffic light control 2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) International Conference on Pervasive Computing and Communications English Proceedings Paper IEEE International Conference on Pervasive Computing and Communications (PerCom) MAR 11-15, 2019 Kyoto, JAPAN IEEE Traffic congestion is worsening in every major city and brings increasing costs to governments and drivers. Vehicular networks provide the ability to collect more data from vehicles and roadside units, and sense traffic in real time. They represent a promising solution to alleviate traffic jams in urban environments. However, while the collected information is valuable, an efficient solution for better and faster utilization to alleviate congestion has yet to be developed. Current solutions are either based on mathematical models, which do not account for complex traffic scenarios or small-scale machine learning algorithms. In this paper, we propose ERL, a solution based on Edge Computing nodes to collect traffic data. ERL alleviates congestion by providing intelligent optimized traffic light control in real time. Edge servers run fast reinforcement learning algorithms to tune the metrics of the traffic signal control algorithm ran for each intersection. ERL operates within the coverage area of the edge server, and uses aggregated data from neighboring edge servers to provide city-scale congestion control. The evaluation based on real map data shows that our system decreases 48.71 % average waiting time and 32.77% trip duration in normally congested areas, with very fast training in ordinary servers. [Zhou, Pengyuan; Hui, Pan; Kangasharju, Jussi] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Braud, Tristan; Alhilal, Ahmad; Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China University of Helsinki; Hong Kong University of Science & Technology Zhou, PY (corresponding author), Univ Helsinki, Dept Comp Sci, Helsinki, Finland. Hui, Pan/AAK-6660-2020; Zhou, Pengyuan/AAJ-2139-2021; Alhilal, Ahmad/AAK-9835-2021; ALHILAL, Ahmad/HMV-5885-2023; Kangasharju, Jussi/E-8922-2012 Hui, Pan/0000-0001-6026-1083; Zhou, Pengyuan/0000-0002-7909-4059; Alhilal, Ahmad/0000-0002-2575-1391; ALHILAL, Ahmad/0000-0002-2575-1391; Kangasharju, Jussi/0000-0001-6119-1638 20 13 15 0 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2474-2503 978-1-5386-9151-9 INT CONF PERVAS COMP 2019.0 849 854 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BN2MP 2023-03-23 WOS:000476951900174 0
J Jochems, F; Thijssen, B; De Conti, G; Jansen, R; Pogacar, Z; Groot, K; Wang, LQ; Schepers, A; Wang, C; Jin, HJ; Beijersbergen, RL; de Oliveira, RL; Wessels, LFA; Bernards, R Jochems, Fleur; Thijssen, Bram; De Conti, Giulia; Jansen, Robin; Pogacar, Ziva; Groot, Kelvin; Wang, Liqin; Schepers, Arnout; Wang, Cun; Jin, Haojie; Beijersbergen, Roderick L.; de Oliveira, Rodrigo Leite; Wessels, Lodewyk F. A.; Bernards, Rene The Cancer SENESCopedia: A delineation of cancer cell senescence CELL REPORTS English Article REDUCED CLINICAL BENEFIT; GENE-EXPRESSION; SECRETORY PHENOTYPE; BCL-2 FAMILY; ARREST; REGULARIZATION; MECHANISMS; INHIBITION; NAVITOCLAX; DELIVERY Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia. [Jochems, Fleur; Thijssen, Bram; De Conti, Giulia; Jansen, Robin; Pogacar, Ziva; Groot, Kelvin; Wang, Liqin; Schepers, Arnout; Wang, Cun; Jin, Haojie; de Oliveira, Rodrigo Leite; Wessels, Lodewyk F. A.; Bernards, Rene] Netherlands Canc Inst, Oncode Inst, Div Mol Carcinogenesis, Plesmanlaan 121, NL-1066 Amsterdam, Netherlands; [Beijersbergen, Roderick L.] Netherlands Canc Inst, NKI Robot & Screening Ctr, Div Mol Carcinogenesis, Plesmanlaan 121, NL-1066 Amsterdam, Netherlands; [Wessels, Lodewyk F. A.] Delft Univ Technol, Fac EEMCS, Delft, Netherlands; [Wang, Cun] Shanghai Jiao Tong Univ, Renji Hosp, Shanghai Canc Inst, State Key Lab Oncogenes & Related Genes,Sch Med, Shanghai, Peoples R China; [de Oliveira, Rodrigo Leite] Amsterdam UMC, Canc Ctr Amsterdam, CRISPR Expertise Ctr, Amsterdam, Netherlands Netherlands Cancer Institute; Netherlands Cancer Institute; Delft University of Technology; Shanghai Jiao Tong University; University of Amsterdam; Vrije Universiteit Amsterdam Wessels, LFA; Bernards, R (corresponding author), Netherlands Canc Inst, Oncode Inst, Div Mol Carcinogenesis, Plesmanlaan 121, NL-1066 Amsterdam, Netherlands.;Wessels, LFA (corresponding author), Delft Univ Technol, Fac EEMCS, Delft, Netherlands. l.wessels@nki.nl; r.bernards@nki.nl Groot, Kelvin/0000-0001-7177-938X; Pogacar, Ziva/0000-0003-3240-9233; Jochems, Fleur/0000-0001-7305-7321; DE CONTI, GIULIA/0000-0002-2647-228X; Leite de Oliveira, Rodrigo/0000-0002-9696-2014; Wang, Liqin/0000-0002-9881-6432 European Research Council [ERC 787925]; Dutch Cancer Society (KWF) through the Oncode Institute European Research Council(European Research Council (ERC)European Commission); Dutch Cancer Society (KWF) through the Oncode Institute We thank Kathy Jastrzebski for the kind gift of cell lines. We thank the NKI BioImaging facility and genomics core facility for technical support. This work was funded by a grant from the European Research Council (ERC 787925 to R.B.) and by The Dutch Cancer Society (KWF) through the Oncode Institute. 78 34 34 5 18 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 2211-1247 CELL REP Cell Reports JUL 27 2021.0 36.0 4 109441 10.1016/j.celrep.2021.109441 0.0 JUL 2021 22 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology TQ2DM 34320349.0 Green Accepted, gold 2023-03-23 WOS:000678096100017 0
J Chen, YM; Sun, X; Xu, L; Ma, SC; Li, J; Pang, YS; Cheng, G Chen, Yiming; Sun, Xu; Xu, Liang; Ma, Sencai; Li, Jun; Pang, Yusong; Cheng, Gang Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment SENSORS English Article belt conveyor; machine vision; KinD plus plus algorithm; YOLOv4 algorithm; low-light enhancement COAL; RECOGNITION; RETINEX The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the YOLOv4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective. [Chen, Yiming] Shandong Zhongheng Optoelect Technol Co Ltd, Zaozhuang 277000, Peoples R China; [Sun, Xu; Xu, Liang; Ma, Sencai; Li, Jun; Cheng, Gang] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China; [Pang, Yusong] Delft Univ Technol, Fac Mech Maritime & Mat Engn, NL-2628 Delft, Netherlands China University of Mining & Technology; Delft University of Technology Cheng, G (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China. chg@cumt.edu.cn MA, Sencai/GZL-4604-2022 MA, Sencai/0000-0001-9657-679X 23 0 0 31 31 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2022.0 22.0 18 6851 10.3390/s22186851 0.0 20 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 4V5XL 36146200.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000859549800001 0
J Gao, Y; Qiao, TZ; Zhang, HT; Yang, Y; Pang, YS; Wei, HY Gao, Yuan; Qiao, Tiezhu; Zhang, Haitao; Yang, Yi; Pang, Yusong; Wei, Hongyan A contactless measuring speed system of belt conveyor based on machine vision and machine learning MEASUREMENT English Article Belt conveyor; Contactless measuring speed; Image processing; Polynomial linear regression During the operation of the belt conveyor, measuring speed of the belt conveyor is vital to the safe and efficient operation. In the existing measuring speed system, the measurement instrument is required contacting with the surface of the belt. The contact measurement method cannot avoid the occurrence of measuring error caused by slipping on the contact surface and wear of the measurement instrument. In order to solve the problems mentioned above, a new contactless measuring speed system is proposed in this paper. The system uses the CCD camera to capture the side image of belt. The speed of belt conveyor can be obtained by measuring the regularity of image texture. The proposed measuring system can meet the requirement of measuring speed in long running process of belt conveyor. Experimental results show that the measuring accuracy indicators can reach RMSE of 0.018 m/s and MAE of 0.010 m/s. (C) 2019 Elsevier Ltd. All rights reserved. [Gao, Yuan; Qiao, Tiezhu; Zhang, Haitao; Yang, Yi; Wei, Hongyan] Taiyuan Univ Technol, Key Lab Adv Transducers & Intelligent Control Sys, Minist Educ, Taiyuan 030024, Shanxi, Peoples R China; [Pang, Yusong] Delft Univ Technol, Fac Mech Maritime & Mat Engn, Sect Transport Engn & Logist, NL-2628 CD Delft, Netherlands Taiyuan University of Technology; Delft University of Technology Qiao, TZ; Zhang, HT; Yang, Y (corresponding author), Taiyuan Univ Technol, Key Lab Adv Transducers & Intelligent Control Sys, Minist Educ, Taiyuan 030024, Shanxi, Peoples R China. Gao, Yuan/0000-0001-5351-6922 National Natural Science Foundation of China-Shanxi coal-based low-carbon joint fund [U1810121]; Natural Science Foundation of Shanxi [201801D121180] National Natural Science Foundation of China-Shanxi coal-based low-carbon joint fund; Natural Science Foundation of Shanxi(Natural Science Foundation of Shanxi Province) This work is supported by the National Natural Science Foundation of China-Shanxi coal-based low-carbon joint fund (Grant No. U1810121) and the Natural Science Foundation of Shanxi (Grant: No. 201801D121180). 24 17 17 6 68 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement JUN 2019.0 139.0 127 133 10.1016/j.measurement.2019.03.030 0.0 7 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation HT5XZ 2023-03-23 WOS:000464638000015 0
J Fathizad, H; Taghizadeh-Mehrjardi, R; Ardakani, MAH; Zeraatpisheh, M; Heung, B; Scholten, T Fathizad, Hassan; Taghizadeh-Mehrjardi, Ruhollah; Ardakani, Mohammad Ali Hakimzadeh; Zeraatpisheh, Mojtaba; Heung, Brandon; Scholten, Thomas Spatiotemporal Assessment of Soil Organic Carbon Change Using Machine-Learning in Arid Regions AGRONOMY-BASEL English Article random forest; machine learning; spatial distribution; variable importance analysis; vegetation index; temporal change MATTER; PREDICTION; FOREST; INDEX; OPTIMIZATION; SALINITY Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the spatiotemporal dynamics of SOC for the Yazd-Ardakan Plain, Iran, from 1986 to 2016. Here, a conditioned Latin hypercube sampling method was used to select 201 sampling sites. A set of 37 environmental predictors were obtained from Landsat imagery taken in 1986, 1999, 2010 and 2016. Here, SOC was modeled for 2016 using the Random Forest (RF), support vector regression (SVR), and artificial neural networks (ANN) machine-learners by correlating environmental predictors with soil data. The results showed that RF yielded the highest accuracy (R-2 = 0.53), compared to the other two learners. By performing a variable importance analysis of the RF model, normalized difference vegetation index, modified vegetation index, and ground-adjusted vegetation index were determined to be the most important environmental predictors. By applying the model calibrated from 2016 data to 1986, 1999 and 2010, the results showed a substantial decrease in SOC; these decreases in SOC were mainly attributed to land use changes and agricultural activities. [Fathizad, Hassan; Ardakani, Mohammad Ali Hakimzadeh] Yazd Univ, Sch Nat Resources & Desert Studies, Dept Arid & Desert Reg Management, Yazd 89195741, Iran; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, CRC 1070 Resource Cultures, Gartenstr 29, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, DFG Cluster Excellence Machine Learning, D-72074 Tubingen, Germany; [Zeraatpisheh, Mojtaba] Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China; [Zeraatpisheh, Mojtaba] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China; [Heung, Brandon] Dalhousie Univ, Fac Agr, Dept Plant Food & Environm Sci, Halifax, NS B3H 4R2, Canada University of Yazd; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Henan University; Henan University; Dalhousie University Fathizad, H (corresponding author), Yazd Univ, Sch Nat Resources & Desert Studies, Dept Arid & Desert Reg Management, Yazd 89195741, Iran.;Taghizadeh-Mehrjardi, R (corresponding author), Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, D-72070 Tubingen, Germany.;Taghizadeh-Mehrjardi, R (corresponding author), Univ Tubingen, CRC 1070 Resource Cultures, Gartenstr 29, D-72070 Tubingen, Germany.;Taghizadeh-Mehrjardi, R (corresponding author), Univ Tubingen, DFG Cluster Excellence Machine Learning, D-72074 Tubingen, Germany. h.fathizad@stu.yazd.ac.ir; ruhollah.taghizadeh-mehrjardi@mnf.uni-tuebingen.de; hakim@yazd.ac.ir; mojtaba.zeraatpisheh@henu.edu.cn; brandon.heung@dal.ca; thomas.scholten@uni-tuebingen.de Taghizadeh-Mehrjardi, Ruhollah/H-3682-2013; Zeraatpisheh, Mojtaba/V-1244-2018 Taghizadeh-Mehrjardi, Ruhollah/0000-0002-4620-6624; Zeraatpisheh, Mojtaba/0000-0001-7209-0744; Hakimzadeh, Mohammad Ali/0000-0003-1997-238X Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC [390727645, SFB 1070, 215859406]; National Key Research and Development Program of China [2017YFA0604302, 2018YFA0606500] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC(German Research Foundation (DFG)); National Key Research and Development Program of China Ruhollah Taghizadeh-Mehrjardi and Thomas Scholten have been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC number 2064/1-Project number 390727645, and collaborative research center SFB 1070 'ResourceCultures'-Project number 215859406. Mojtaba Zeraatpisheh's postdoctoral program at Henan University, China, has been supported by the National Key Research and Development Program of China, grant numbers 2017YFA0604302 and 2018YFA0606500. 57 3 3 9 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4395 AGRONOMY-BASEL Agronomy-Basel MAR 2022.0 12.0 3 628 10.3390/agronomy12030628 0.0 13 Agronomy; Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Plant Sciences 0C7TT gold 2023-03-23 WOS:000775512000001 0
J Huang, R; Xu, YS; Hong, DF; Yao, W; Ghamisi, P; Stilla, U Huang, Rong; Xu, Yusheng; Hong, Danfeng; Yao, Wei; Ghamisi, Pedram; Stilla, Uwe Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Semantic labeling; ALS point cloud; Deep learning; Feature embedding; Manifold learning; Graph optimization FORM LIDAR DATA; CONTEXTUAL CLASSIFICATION; DIMENSIONALITY REDUCTION; AUTOMATED EXTRACTION; ENERGY MINIMIZATION; SEGMENTATION; CONSTRUCTION; MODELS; FOREST Semantic interpretation of the 3D scene is one of the most challenging problems in point cloud processing, which also deems as an essential task in a wide variety of point cloud applications. The core task of semantic interpretation is semantic labeling, namely, obtaining a unique semantic label for each point in the point cloud. Despite several reported approaches, semantic labeling continues to be a challenge owing to the complexity of scenes, objects of various scales, and the non-homogeneity of unevenly distributed points. In this paper, we propose a novel method for obtaining semantic labels of airborne laser scanning (ALS) point clouds involving the embedding of local context information for each point with multi-scale deep learning, nonlinear manifold learning for feature dimension reduction, and global graph-based optimization for refining the classification results. Specifically, we address the tasks of learning discriminative features and global labeling smoothing. The key contribution of our study is threefold. First, a hierarchical data augmentation strategy is applied to enhance the learning of deep features based on the PointNet + + network and simultaneously eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. Subsequently, the learned hierarchical deep features are globally optimized and embedded into a low-dimensional space with a nonlinear manifold-based joint learning method with the removal of redundant and disturbing information. Finally, a graphstructured optimization based on the Markov random fields algorithm is performed to achieve global optimization of the initial classification results that are obtained using the embedded deep features by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. We conducted thorough experiments on ALS point cloud datasets to assess the performance of our framework. Results indicate that compared to other commonly used advanced classification methods, our method can achieve high classification accuracy. The overall accuracy (OA) of our approach on the ISPRS benchmark dataset can scale up to 83.2% for classifying nine semantic classes, thereby outperforming other compared point-based strategies. Additionally, we evaluated our framework on a selected portion of the AHN3 dataset, which provided OA up to 91.2%. [Huang, Rong; Xu, Yusheng; Stilla, Uwe] Tech Univ Munich TUM, Photogrammetry & Remote Sensing, Munich, Germany; [Hong, Danfeng] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, Grenoble, France; [Yao, Wei] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hung Hom, Hong Kong, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Chemnitzer Str 40, D-09599 Freiberg, Germany Technical University of Munich; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Hong Kong Polytechnic University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Xu, YS (corresponding author), Tech Univ Munich TUM, Photogrammetry & Remote Sensing, Munich, Germany. rong.huang@tum.de; yusheng.xu@tum.de; hongdanfeng1989@gmail.com; wei.hn.yao@polyu.edu.hk; p.ghamisi@gmail.com; stilla@tum.de Huang, Rong/AAW-3077-2020; Stilla, Uwe/H-1534-2011; Ghamisi, Pedram/ABD-5419-2021; Hong, Danfeng/U-6082-2019; Yao, Wei/E-8520-2017 Huang, Rong/0000-0002-4491-6059; Stilla, Uwe/0000-0002-1184-0924; Hong, Danfeng/0000-0002-3212-9584; Yao, Wei/0000-0001-7704-0615 China Scholarship Council China Scholarship Council(China Scholarship Council) This research is supported by the China Scholarship Council. This work was carried out within the frame of Leonhard Obermeyer Center (LOC) at Technische Universitat Munchen (TUM) [www.loc.tum.de]. 93 37 37 6 51 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. MAY 2020.0 163.0 62 81 10.1016/j.isprsjprs.2020.02.020 0.0 20 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology LF9DD 2023-03-23 WOS:000527712500005 0
J Foo, L; Lim, GYS; Lanca, C; Wong, CW; Hoang, QV; Zhang, XJ; Yam, JC; Schmetterer, L; Chia, AD; Wong, TY; Ting, DSW; Saw, SM; Ang, M Foo, Li Lian; Lim, Gilbert Yong San; Lanca, Carla; Wong, Chee Wai; Hoang, Quan V.; Zhang, Xiu Juan; Yam, Jason C.; Schmetterer, Leopold; Chia, Audrey; Wong, Tien Yin; Ting, Daniel S. W.; Saw, Seang-Mei; Ang, Marcus Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children NPJ DIGITAL MEDICINE English Article CHILDHOOD MYOPIA; FOLLOW-UP; ATROPINE; PROGRESSION; SINGAPORE; EFFICACY; SCHOOL; INTERVENTIONS; PREVALENCE; STRATEGIES Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE <= -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify at-risk children for early intervention. [Foo, Li Lian; Lim, Gilbert Yong San; Wong, Chee Wai; Hoang, Quan V.; Schmetterer, Leopold; Chia, Audrey; Wong, Tien Yin; Ting, Daniel S. W.; Saw, Seang-Mei; Ang, Marcus] Singapore Eye Res Inst, Singapore Natl Eye Ctr, Singapore, Singapore; [Foo, Li Lian; Wong, Chee Wai; Hoang, Quan V.; Schmetterer, Leopold; Chia, Audrey; Wong, Tien Yin; Ting, Daniel S. W.; Saw, Seang-Mei; Ang, Marcus] Natl Univ Singapore, Duke NUS Med Sch, Singapore, Singapore; [Lanca, Carla] Inst Politecn Lisboa, Escola Super Tecnol Saude Lisboa ESTeSL, Lisbon, Portugal; [Lanca, Carla] Univ Nova Lisboa, Comprehens Hlth Res Ctr CHRC, Escola Nacl Saude Publ, Lisbon, Portugal; [Wong, Chee Wai] Gleneagles Hosp, Asia Pacific Eye Ctr, Singapore, Singapore; [Hoang, Quan V.] Natl Univ Singapore, Yong Loo Lin Sch Med, Singapore, Singapore; [Hoang, Quan V.] Columbia Univ, Dept Ophthalmol, Columbia, SC USA; [Zhang, Xiu Juan; Yam, Jason C.] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China; [Yam, Jason C.] Hong Kong Eye Hosp, Hong Kong, Peoples R China; [Yam, Jason C.] Prince Wales Hosp, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China; [Yam, Jason C.] Chinese Univ Hong Kong, Hong Kong Hub Paediat Excellence, Hong Kong, Peoples R China; [Yam, Jason C.] Hong Kong Childrens Hosp, Dept Ophthalmol, Hong Kong, Peoples R China National University of Singapore; Singapore National Eye Center; National University of Singapore; Polytechnic Institute of Lisbon; Universidade Nova de Lisboa; National University of Singapore; Columbia University; Chinese University of Hong Kong; Chinese University of Hong Kong; Prince of Wales Hospital; Chinese University of Hong Kong Saw, SM; Ang, M (corresponding author), Singapore Eye Res Inst, Singapore Natl Eye Ctr, Singapore, Singapore.;Saw, SM; Ang, M (corresponding author), Natl Univ Singapore, Duke NUS Med Sch, Singapore, Singapore. ephssm@nus.edu.sg; marcus.ang@snec.com.sg National Medical Research Council Individual Research Grant [NMRC/0975/2005]; National Medical Research Council Center Grant [NMRC/CG/C010A/2017_SERI]; Nurturing Clinician Researcher Scheme Program Grant Award [05/FY2021/P2/11-A92] National Medical Research Council Individual Research Grant(National Medical Research Council, Singapore); National Medical Research Council Center Grant; Nurturing Clinician Researcher Scheme Program Grant Award This work is supported by National Medical Research Council Individual Research Grant (NMRC/0975/2005), National Medical Research Council Center Grant (NMRC/CG/C010A/2017_SERI) and Nurturing Clinician Researcher Scheme Program Grant Award (05/FY2021/P2/11-A92). 68 0 0 1 1 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2398-6352 NPJ DIGIT MED npj Digit. Med. JAN 26 2023.0 6.0 1 10 10.1038/s41746-023-00752-8 0.0 10 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics 8H2ME 36702878.0 Green Published, gold 2023-03-23 WOS:000920868800001 0
J Jiang, XH; Zhang, WY; Fernie, AR; Wen, WW Jiang, Xiaohui; Zhang, Weiyi; Fernie, Alisdair R.; Wen, Weiwei Combining novel technologies with interdisciplinary basic research to enhance horticultural crops PLANT JOURNAL English Article horticultural crops; sustainable development; interdisciplinary; bioengineering; multi-omics; artificial intelligence DRAFT GENOME; PROVIDE INSIGHTS; PLANT-SCIENCE; TOMATO; DOMESTICATION; FRUIT; EVOLUTION; QUALITY; TRANSFORMATION; MECHANISMS Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (Melaleuca alternifolia). They produce a variety of nutrients for the daily human diet in addition to the nutrition provided by staple crops, and some of them additionally possess ornamental and medicinal features. As such, horticultural crops make unique and important contributions to both food security and a colorful lifestyle. Under the current climate change scenario, the growing population and limited arable land means that agriculture, and especially horticulture, has been facing unprecedented challenges to meet the diverse demands of human daily life. Breeding horticultural crops with high quality and adaptability and establishing an effective system that combines cultivation, post-harvest handling, and sales becomes increasingly imperative for horticultural production. This review discusses characteristic and recent research highlights in horticultural crops, focusing on the breeding of quality traits and the mechanisms that underpin them. It additionally addresses challenges and potential solutions in horticultural production and post-harvest practices. Finally, we provide a prospective as to how emerging technologies can be implemented alongside interdisciplinary basic research to enhance our understanding and exploitation of horticultural crops. [Jiang, Xiaohui; Zhang, Weiyi; Wen, Weiwei] Huazhong Agr Univ, Coll Hort & Forestry Sci, Key Lab Hort Plant Biol MOE, Wuhan 430070, Peoples R China; [Jiang, Xiaohui] Guangdong Prov Acad Agr Sci, Res Inst, Guangdong Prov Key Lab Tea Plant Resources Innova, Guangzhou 510640, Guangdong, Peoples R China; [Jiang, Xiaohui; Wen, Weiwei] Huazhong Agr Univ, Shenzhen Inst Nutr & Hlth, Wuhan, Peoples R China; [Jiang, Xiaohui; Wen, Weiwei] Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Minist Agr, Genome Anal Lab,Shenzhen Branch,Guangdong Lab Lin, Shenzhen, Peoples R China; [Fernie, Alisdair R.] Max Planck Inst Mol Plant Physiol, Muehlenberg 1, D-14476 Potsdam, Germany Huazhong Agricultural University; Huazhong Agricultural University; China Construction Bank; Chinese Academy of Agricultural Sciences; Agriculture Genomes Institute at Shenzhen, CAAS; Ministry of Agriculture & Rural Affairs; Max Planck Society Wen, WW (corresponding author), Huazhong Agr Univ, Coll Hort & Forestry Sci, Key Lab Hort Plant Biol MOE, Wuhan 430070, Peoples R China.;Wen, WW (corresponding author), Huazhong Agr Univ, Shenzhen Inst Nutr & Hlth, Wuhan, Peoples R China.;Wen, WW (corresponding author), Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Minist Agr, Genome Anal Lab,Shenzhen Branch,Guangdong Lab Lin, Shenzhen, Peoples R China. wwwen@mail.hzau.edu.cn Wen, Weiwei/0000-0002-3413-4766 Huazhong Agricultural University Scientific AMP; Technological Self-Innovation Foundation [2017RC002, 2662018PY012, SZYJY2021004]; Science and Technology program of Guangdong Province [2019B030316026] Huazhong Agricultural University Scientific AMP; Technological Self-Innovation Foundation; Science and Technology program of Guangdong Province This work was supported by the Huazhong Agricultural University Scientific & Technological Self-Innovation Foundation (Program No. 2017RC002 and No. 2662018PY012) and the HZAU-AGIS Cooperation Fund (SZYJY2021004) to WW and the Science and Technology program of Guangdong Province (2019B030316026). 140 3 3 6 37 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0960-7412 1365-313X PLANT J Plant J. JAN 2022.0 109.0 1 35 46 10.1111/tpj.15553 0.0 NOV 2021 12 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences XW2NZ 34699639.0 2023-03-23 WOS:000716543700001 0
J Wang, DP; Forstmeier, W; Farine, DR; Maldonado-Chaparro, AA; Martin, K; Pei, YF; Alarcon-Nieto, G; Klarevas-Irby, JA; Ma, SW; Aplin, LM; Kempenaers, B Wang, Daiping; Forstmeier, Wolfgang; Farine, Damien R.; Maldonado-Chaparro, Adriana A.; Martin, Katrin; Pei, Yifan; Alarcon-Nieto, Gustavo; Klarevas-Irby, James A.; Ma, Shouwen; Aplin, Lucy M.; Kempenaers, Bart Machine learning reveals cryptic dialects that explain mate choice in a songbird NATURE COMMUNICATIONS English Article FEMALE ZEBRA FINCHES; BIRD-SONG; GEOGRAPHIC-VARIATION; SPECIES RECOGNITION; CULTURAL-EVOLUTION; EARLY EXPERIENCE; POPULATIONS; DISCRIMINATION; REPERTOIRES; PERCEPTION Culturally transmitted communication signals - such as human language or bird song - can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that 'cryptic song dialects' predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females' adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers. The authors show that captive populations of zebra finches, which have been kept in isolation for up to 100 generations, have diverged in song dialect. When individuals singing different dialects are mixed, mating is assortative for song dialect. [Wang, Daiping; Forstmeier, Wolfgang; Martin, Katrin; Pei, Yifan; Kempenaers, Bart] Max Planck Inst Ornithol, Dept Behav Ecol & Evolutionary Genet, D-82319 Seewiesen, Germany; [Farine, Damien R.; Maldonado-Chaparro, Adriana A.; Alarcon-Nieto, Gustavo] Max Planck Inst Anim Behav, Dept Collect Behav, D-78457 Constance, Germany; [Farine, Damien R.; Maldonado-Chaparro, Adriana A.; Klarevas-Irby, James A.; Aplin, Lucy M.] Univ Konstanz, Ctr Adv Study Collect Behav, Univ Str 10, D-78457 Constance, Germany; [Maldonado-Chaparro, Adriana A.; Klarevas-Irby, James A.] Univ Konstanz, Dept Biol, Univ Str 10, D-78457 Constance, Germany; [Alarcon-Nieto, Gustavo; Aplin, Lucy M.] Max Planck Inst Anim Behav, Cognit & Cultural Ecol Res Grp, Radolfzell am Bodensee, Germany; [Klarevas-Irby, James A.] Max Planck Inst Anim Behav, Dept Migrat, Radolfzell am Bodensee, Germany; [Ma, Shouwen] Max Planck Inst Ornithol, Dept Behav Neurobiol, Eberhard Gwinner Str, D-82319 Seewiesen, Germany; [Wang, Daiping] Chinese Acad Sci, Inst Zool, CAS Key Lab Anim Ecol & Conservat Biol, Beijing, Peoples R China; [Farine, Damien R.; Klarevas-Irby, James A.] Univ Zurich, Dept Evolutionary Biol & Environm Studies, CH-8047 Zurich, Switzerland; [Maldonado-Chaparro, Adriana A.] Univ Rosario, Fac Nat Sci, Dept Biol, Bogota, DC, Colombia Max Planck Society; Max Planck Society; University of Konstanz; University of Konstanz; Max Planck Society; Max Planck Society; Max Planck Society; Chinese Academy of Sciences; Institute of Zoology, CAS; University of Zurich; Universidad del Rosario Wang, DP; Forstmeier, W; Kempenaers, B (corresponding author), Max Planck Inst Ornithol, Dept Behav Ecol & Evolutionary Genet, D-82319 Seewiesen, Germany.;Farine, DR (corresponding author), Max Planck Inst Anim Behav, Dept Collect Behav, D-78457 Constance, Germany.;Farine, DR (corresponding author), Univ Konstanz, Ctr Adv Study Collect Behav, Univ Str 10, D-78457 Constance, Germany.;Wang, DP (corresponding author), Chinese Acad Sci, Inst Zool, CAS Key Lab Anim Ecol & Conservat Biol, Beijing, Peoples R China.;Farine, DR (corresponding author), Univ Zurich, Dept Evolutionary Biol & Environm Studies, CH-8047 Zurich, Switzerland. wangdaiping@ioz.ac.cn; forstmeier@orn.mpg.de; damien.farine@ieu.uzh.ch; b.kempenaers@orn.mpg.de Alarcon-Nieto, Gustavo/ACA-5214-2022 Alarcon-Nieto, Gustavo/0000-0003-4076-3750; Maldonado-Chaparro, Adriana A./0000-0003-0853-4966; Farine, Damien/0000-0003-2208-7613; Pei, Yifan/0000-0002-2411-4454 Max Planck Society; CAS pioneer hundred talents programme [E1516511]; Deutsche Forschungsgemeinschaft (DFG) Centre of Excellence 2117 'Centre for the Advanced Study of Collective Behaviour' under Germany's Excellence Strategy - EXC2117 [422037984]; DFG [FA 1420/4-1]; European Research Council (ERC) under the European Union [850859]; Swiss National Science Foundation [PCEFP3_187058] Max Planck Society(Max Planck SocietyFoundation CELLEX); CAS pioneer hundred talents programme; Deutsche Forschungsgemeinschaft (DFG) Centre of Excellence 2117 'Centre for the Advanced Study of Collective Behaviour' under Germany's Excellence Strategy - EXC2117(German Research Foundation (DFG)); DFG(German Research Foundation (DFG)); European Research Council (ERC) under the European Union(European Research Council (ERC)); Swiss National Science Foundation(Swiss National Science Foundation (SNSF)) We thank Henryk Milewski and Tomas Cordoba for carrying out the machine-learning analysis, Klaus Pichler and Felix Hartl for technical support, Nikolas Heinecke and Michael Harbich for IT support, Mihai Valcu for help with statistics and setting up the virtual machine, Sonja Bauer, Edith Bodendorfer, Jane Didsbury, Annemarie Grotsch, Andrea Kortner, Frank Lehmann, Petra Neubauer, Katharina Piehler, Frances Weigel and Barbara Worle for animal care, and Jochen Wolf and Henrik Brumm for comments on the manuscript. This study was funded by the Max Planck Society (to BK). DW was supported by the CAS pioneer hundred talents programme (E1516511). DRF and LMA received additional funding from the Deutsche Forschungsgemeinschaft (DFG) Centre of Excellence 2117 'Centre for the Advanced Study of Collective Behaviour' under Germany's Excellence Strategy - EXC2117 - 422037984. DRF received additional funding from a DFG grant (FA 1420/4-1 awarded to DRF) and the European Research Council (ERC awarded to DRF) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 850859) and an Eccellenza Professorship Grant of the Swiss National Science Foundation (Grant Number PCEFP3_187058 awarded to DRF). 67 2 2 7 20 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. MAR 28 2022.0 13.0 1 1630 10.1038/s41467-022-28881-w 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics 0A8QV 35347115.0 Green Published, gold 2023-03-23 WOS:000774213100001 0
C Chen, PP; Fuchs, C; Wilbik, A; Chan, TM; van Loon, S; Boer, AK; Lu, XD; Scharnhorst, V; Kaymak, U Medina, J; OjedaAciego, M; Verdegay, JL; Perfilieva, I; BouchonMeunier, B; Yager, RR Chen, Peipei; Fuchs, Caro; Wilbik, Anna; Chan, Tak-Ming; van Loon, Saskia; Boer, Arjen-Kars; Lu, Xudong; Scharnhorst, Volkher; Kaymak, Uzay On the Interaction Between Feature Selection and Parameter Determination in Fuzzy Modelling INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III Communications in Computer and Information Science English Proceedings Paper 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) JUN 11-15, 2018 Cadiz, SPAIN Feature selection; Model parameters; Number of clusters; Fuzzy models Nowadays the amount of data that is collected in various settings is growing rapidly. These elaborate data records enable the training of machine learning models that can be used to extract insights and for making better informed decisions. When doing the data mining task, on one hand, feature selection is often used to reduce the dimensionality of the data. On the other hand, we need to decide the structure (parameters) of the model when building the model. However, feature selection and the parameters of the model may interact and affect the performance of the model. Therefore, it is difficult to decide the optimal parameter and the optimal feature subset without an exhaustive search of all the combination of the parameters and the feature subsets which is time-consuming. In this paper, we study how the interaction between feature selection and the parameters of a model affect the performance of the model through experiments on four data sets. [Chen, Peipei; Lu, Xudong; Kaymak, Uzay] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou, Zhejiang, Peoples R China; [Chen, Peipei; Fuchs, Caro; Wilbik, Anna; Lu, Xudong; Kaymak, Uzay] Eindhoven Univ Technol, Informat Syst, Sch Ind Engn, Eindhoven, Netherlands; [Chan, Tak-Ming] Philips Res China, Hlth Syst, Shanghai, Peoples R China; [van Loon, Saskia; Boer, Arjen-Kars; Scharnhorst, Volkher] Catharina Hosp, Clin Chem, Michelangelolaan 2, NL-5623 EJ Eindhoven, Netherlands; [Scharnhorst, Volkher] Eindhoven Univ Technol, Fac Biomed Engn, Eindhoven, Netherlands Zhejiang University; Eindhoven University of Technology; Philips; Philips Research; Catharina Hospital; Eindhoven University of Technology Chen, PP (corresponding author), Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou, Zhejiang, Peoples R China.;Chen, PP (corresponding author), Eindhoven Univ Technol, Informat Syst, Sch Ind Engn, Eindhoven, Netherlands. p.chen@tue.nl; c.e.m.fuchs@tue.nl Kaymak, Uzay/A-3364-2008 Kaymak, Uzay/0000-0002-4500-9098; Wilbik, Anna/0000-0002-1989-0301; Scharnhorst, Volkher/0000-0002-1243-9251; Lu, Xudong/0000-0001-7658-5250 Philips Research within the scope of the BrainBridge Program Philips Research within the scope of the BrainBridge Program This work is partially supported by Philips Research within the scope of the BrainBridge Program. 19 1 1 0 0 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 1865-0929 1865-0937 978-3-319-91479-4; 978-3-319-91478-7 COMM COM INF SC 2018.0 855.0 III 150 161 10.1007/978-3-319-91479-4_13 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN4AS 2023-03-23 WOS:000481660700013 0
J Duran, C; Ciucci, S; Palladini, A; Ijaz, UZ; Zippo, AG; Sterbini, FP; Masucci, L; Cammarota, G; Ianiro, G; Spuul, P; Schroeder, M; Grill, SW; Parsons, BN; Pritchard, DM; Posteraro, B; Sanguinetti, M; Gasbarrini, G; Gasbarrini, A; Cannistraci, CV Duran, Claudio; Ciucci, Sara; Palladini, Alessandra; Ijaz, Umer Z.; Zippo, Antonio G.; Sterbini, Francesco Paroni; Masucci, Luca; Cammarota, Giovanni; Ianiro, Gianluca; Spuul, Pirjo; Schroeder, Michael; Grill, Stephan W.; Parsons, Bryony N.; Pritchard, D. Mark; Posteraro, Brunella; Sanguinetti, Maurizio; Gasbarrini, Giovanni; Gasbarrini, Antonio; Cannistraci, Carlo Vittorio Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome NATURE COMMUNICATIONS English Article PROTON-PUMP INHIBITORS; HELICOBACTER-PYLORI INFECTION; CATONELLA-MORBI; BETA-DIVERSITY; GEN-NOV; COMMUNITY; ACID; UNIFRAC; HEALTH; COEFFICIENTS The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities. Drug use or bacterial infection can cause significant alterations of gastric microbiome. Here, the authors show how advanced pattern recognition by nonlinear machine intelligence can help disclose a bacteria-metabolite network which enlightens mechanisms behind such perturbations. [Duran, Claudio; Ciucci, Sara; Palladini, Alessandra; Cannistraci, Carlo Vittorio] Tech Univ Dresden, Biomed Cybernet Grp,Dept Phys, Biotechnol Ctr BIOTEC,Cluster Excellence Phys Lif, Ctr Mol & Cellular Bioengn CMCB,Ctr Syst Biol Dre, Dresden, Germany; [Palladini, Alessandra] Tech Univ Dresden, Paul Langerhans Inst Dresden, Helmholtz Zentrum Munchen, Dresden, Germany; [Palladini, Alessandra] German Ctr Diabet Res DZD eV, Neuherberg, Germany; [Ijaz, Umer Z.] Univ Glasgow, Sch Engn, Dept Infrastruct & Environm, Glasgow, Lanark, Scotland; [Zippo, Antonio G.] CNR, Inst Neurosci, Milan, Italy; [Sterbini, Francesco Paroni; Masucci, Luca; Posteraro, Brunella; Sanguinetti, Maurizio] Univ Cattolica Sacro Cuore, Inst Microbiol, Rome, Italy; [Cammarota, Giovanni; Ianiro, Gianluca; Gasbarrini, Giovanni; Gasbarrini, Antonio] Univ Cattolica Sacro Cuore, Internal Med & Gastroenterol Unit, Rome, Italy; [Spuul, Pirjo] Tallinn Univ Technol, Dept Chem & Biotechnol, Div Gene Technol, EE-12618 Tallinn, Estonia; [Schroeder, Michael; Grill, Stephan W.] Tech Univ Dresden, Biotechnol Ctr BIOTEC, Ctr Mol & Cellular Bioengn CMCB, Dresden, Germany; [Grill, Stephan W.] Max Planck Inst Mol Cell Biol & Genet, Dresden, Germany; [Parsons, Bryony N.; Pritchard, D. Mark] Univ Liverpool, Dept Cellular & Mol Physiol, Inst Translat Med, Liverpool, Merseyside, England; [Pritchard, D. Mark] Royal Liverpool & Broadgreen Univ Hosp NHS Trust, Dept Gastroenterol, Liverpool, Merseyside, England; [Cannistraci, Carlo Vittorio] Tsinghua Univ, Ctr Complex Network Intelligence CCNI, Tsinghua Lab Brain & Intelligence THBI, Dept Biomed Engn, Beijing, Peoples R China Technische Universitat Dresden; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technische Universitat Dresden; University of Glasgow; Consiglio Nazionale delle Ricerche (CNR); Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli; Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli; Tallinn University of Technology; Technische Universitat Dresden; Max Planck Society; University of Liverpool; Royal Liverpool & Broadgreen University Hospitals NHS Trust; Royal Liverpool University Hospital; Tsinghua University Cannistraci, CV (corresponding author), Tech Univ Dresden, Biomed Cybernet Grp,Dept Phys, Biotechnol Ctr BIOTEC,Cluster Excellence Phys Lif, Ctr Mol & Cellular Bioengn CMCB,Ctr Syst Biol Dre, Dresden, Germany.;Cannistraci, CV (corresponding author), Tsinghua Univ, Ctr Complex Network Intelligence CCNI, Tsinghua Lab Brain & Intelligence THBI, Dept Biomed Engn, Beijing, Peoples R China. kalokagathos.agon@gmail.com Cammarota, Giovanni/AAD-1732-2022; Sterbini, Francesco Paroni/K-9290-2016; Posteraro, Brunella/K-8499-2018; Palladini, Alessandra/AAV-7838-2021; Sanguinetti, Maurizio/A-1453-2019; Ianiro, Gianluca/K-5578-2016; Zippo, Antonio Giuliano/O-7313-2017 Cammarota, Giovanni/0000-0002-3626-6148; Sterbini, Francesco Paroni/0000-0003-4708-1250; Posteraro, Brunella/0000-0002-1663-7546; Palladini, Alessandra/0000-0001-5307-8643; Sanguinetti, Maurizio/0000-0002-9780-7059; Ianiro, Gianluca/0000-0002-8318-0515; Zippo, Antonio Giuliano/0000-0001-8421-7554; Duran, Claudio/0000-0001-9447-7130; Parsons, Bryony/0000-0003-3599-1089; Ijaz, Umer Zeeshan/0000-0001-5780-8551; Masucci, Luca/0000-0002-8358-6726; Gasbarrini, Antonio/0000-0003-4863-6924 TUD Forschungspool [F-004243-552-694-3430000]; Dresden International Graduate School for Biomedicine and Bioengineering (DIGS-BB) - Deutsche Forschungsgemeinschaft (DFG); Estonian Research Council [PUT1130]; Research Grants-Doctoral Programs in Germany (DAAD) [57299294] TUD Forschungspool; Dresden International Graduate School for Biomedicine and Bioengineering (DIGS-BB) - Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Estonian Research Council(Estonian Research Council); Research Grants-Doctoral Programs in Germany (DAAD) This work was supported by the TUD Forschungspool grant number F-004243-552-694-3430000. S.C. wrap-up postdoc period was supported by the Dresden International Graduate School for Biomedicine and Bioengineering (DIGS-BB), granted by the Deutsche Forschungsgemeinschaft (DFG) in the context of the Excellence Initiative. Computing support was offered by Centre for Information Services and High-Performance Computing (ZIH) of the TUD. P.S. is supported by Estonian Research Council Starting Grant PUT1130. C.D. is funded by the Research Grants-Doctoral Programs in Germany (DAAD), Promotion program Nr: 57299294. We thank Alexander Mestiashvili and the BIOTEC System Administrators for their IT support and Claudia Matthes, Gloria Marchesi and Sabine Zeissig for their administrative assistance. 154 10 10 2 16 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. MAR 26 2021.0 12.0 1 1926 10.1038/s41467-021-22135-x 0.0 22 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics RG0MI 33771992.0 gold, Green Accepted 2023-03-23 WOS:000635230100030 0
J Huang, YK; Qiao, XQ; Ren, P; Dustdar, S; Chen, JL Huang, Yakun; Qiao, Xiuquan; Ren, Pei; Dustdar, Schahram; Chen, Junliang EdgeBooster: Edge-Assisted Real-Time Image Segmentation for the Mobile Web in WoT IEEE INTERNET OF THINGS JOURNAL English Article Image segmentation; Servers; Image edge detection; Real-time systems; Streaming media; Cameras; Media; Edge computing; image segmentation; mobile Web; Web-of-Things (WoT) applications AR Combining image segmentation with Web technology lays a good foundation for lightweight, cross-platform, and pervasive Web artificial intelligence applications, and further improves the capability of Web-of-Things (WoT) applications. However, no matter whether we use a Web real-time communication media server for advanced processing that views camera inputs as a video stream, or transfer continuous camera frames to the remote cloud for processing, we are unable to obtain a satisfactory real-time experience due to high resource consumption and unacceptable latency. In this article, we present EdgeBooster, a computational-efficient architecture that leverages a common edge server to minimize the communication costs, accelerates the camera frame segmentation, and guarantees an acceptable segmentation accuracy with the prior knowledge. EdgeBooster provides real-time segmentation by developing parallel technology that enables segmentation on slices of a camera frame and using presegmentation based on superpixels to accelerate the graph-based segmentation. It also introduces recent DNN-based segmentation results as the prior knowledge to improve the performance of the graph-based segmentation, especially in nonideal scenes, such as dark light and weak contrast. Finally, it creates a pure frontend segmentation that can provide continuous and stable services for mobile users in unstable networks, such as a weak network or with an unstable edge server. The experimental results show that EdgeBooster is able to achieve a considerable accuracy for the mobile Web, running at no less than 30 frames per second in real scenes. [Huang, Yakun; Qiao, Xiuquan; Ren, Pei; Chen, Junliang] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Dustdar, Schahram] Tech Univ Wien, Distributed Syst Grp, A-1040 Vienna, Austria Beijing University of Posts & Telecommunications; Technische Universitat Wien Qiao, XQ (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. ykhuang@bupt.edu.cn; qiaoxq@bupt.edu.cn; renpei@bupt.edu.cn; dustdar@dsg.tuwien.ac.at; chjl@bupt.edu.cn Dustdar, Schahram/G-9877-2015 Dustdar, Schahram/0000-0001-6872-8821 National Natural Science Foundation of China (NSFC) [61671081]; National Key Research and Development Program of China [2018YFE0205503]; Funds for International Cooperation and Exchange of NSFC [61720106007]; 111 Project [B18008]; Fundamental Research Funds for the Central Universities [2018XKJC01]; BUPT Excellent Ph.D.; Students Foundation [CX2019135] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Funds for International Cooperation and Exchange of NSFC(General Electric); 111 Project(Ministry of Education, China - 111 Project); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); BUPT Excellent Ph.D.; Students Foundation This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61671081; in part by the National Key Research and Development Program of China under Grant 2018YFE0205503; in part by the Funds for International Cooperation and Exchange of NSFC under Grant 61720106007; in part by the 111 Project under Grant B18008; in part by the Fundamental Research Funds for the Central Universities under Grant 2018XKJC01; and in part by the BUPT Excellent Ph.D. Students Foundation under Grant CX2019135. 50 2 2 3 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAY 1 2021.0 8.0 9 7288 7302 10.1109/JIOT.2020.3038689 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RQ9XY 2023-03-23 WOS:000642765500016 0
J Wu, B; Yang, ML; Li, KH; Huang, Z; Siniscalchi, SM; Wang, T; Lee, CH Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING English Article Deep neural networks (DNNs); Simultaneous speech dereverberation and beamforming; Auto-correlation function; Temporal and spatial contexts; Reverberation-time-aware (RTA) SPEECH DEREVERBERATION; PREDICTION; ALGORITHM A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error. [Wu, Bo; Yang, Minglei; Wang, Tong] Xidian Univ, Natl Lab Radar Signal Proc, Xian, Shaanxi, Peoples R China; [Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Siniscalchi, Sabato Marco] Univ Enna Kore, Dept Telecommun, Enna, Italy Xidian University; University System of Georgia; Georgia Institute of Technology; Universita Kore di ENNA Yang, ML (corresponding author), Xidian Univ, Natl Lab Radar Signal Proc, Xian, Shaanxi, Peoples R China. mlyang@xidian.edu.cn Siniscalchi, Sabato Marco Marco/I-3423-2012 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507 National Natural Science Foundation of China [61571344] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (61571344). The authors would like to thank Dr. Fengpei Ge at Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences for her valuable suggestions. 51 5 5 1 7 SPRINGEROPEN LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1687-6180 EURASIP J ADV SIG PR EURASIP J. Adv. Signal Process. DEC 6 2017.0 81 10.1186/s13634-017-0516-6 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering FO7XN gold 2023-03-23 WOS:000417092700001 0
C Wang, WW; Han, YC; Bromuri, S; Dumontier, M IEEE Wang, Weiwei; Han, Yuchen; Bromuri, Stefano; Dumontier, Michel Semantic Correlation Graph Embedding 2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) IEEE International Fuzzy Systems Conference Proceedings English Proceedings Paper IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC) JUL 18-23, 2022 Padua, ITALY IEEE,Int Neural Network Soc,IEEE Computat Intelligence Soc,Evolutionary Programming Soc,IET,Univ Padova, Dept Math Tullio Levi Civita,European Space Agcy,expert.ai,Elsevier,Springer Nature,Google,Baker & Hughes,NVIDIA Categorical data; Logistic regression; Knowledge graph; Graph embedding; TransE Many data sets include categorical features in the form of nominal and ordinal features. However, most machine learning algorithms cannot deal with categorical features directly because they require numerical input features. Categorical embeddings are an effective approach to converting categorical features into numerical vectors. This work proposes a novel embedding approach, called Semantic Correlation Graph Embedding, to create embeddings from knowledge graphs. The approach constructs a semantic correlation graph of triplets among the categorical features to learn numerical embeddings. Our approach aims to uncover relationships taking place in categorical data in terms of low-level knowledge and semantics that may help group the features of the data sets under semantic entities. Three distinct embedding models are proposed according to how the graph is constructed. The results are evaluated with two public data sets. They show that the learned embeddings produce a statistically significant improvement in the performance of the classification tasks in terms of AUC, F1 score, precision, and recall. [Wang, Weiwei] Maastricht Univ, Brightlands Inst Smart Soc BISS, Heerlen, Netherlands; [Han, Yuchen] Philips, Philips Res China, Shanghai, Peoples R China; [Bromuri, Stefano] Open Univ, Comp Sci Dept, Heerlen, Netherlands; [Dumontier, Michel] Maastricht Univ, Inst Data Sci, Maastricht, Netherlands Maastricht University; Philips; Philips Research; Open University Netherlands; Maastricht University Wang, WW (corresponding author), Maastricht Univ, Brightlands Inst Smart Soc BISS, Heerlen, Netherlands. w.wang@maastrichtuniversity.nl; yuchen.han@philips.com; stefano.bromuri@ou.nl; michel.dumontier@maastrichtuniversity.nl Province of Limburg, The Netherlands [SAS-2020-03117] Province of Limburg, The Netherlands(Netherlands Government) This research was supported by the Province of Limburg, The Netherlands, under grant number SAS-2020-03117. 27 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1544-5615 978-1-6654-6710-0 IEEE INT FUZZY SYST 2022.0 10.1109/FUZZ-IEEE55066.2022.9882620 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT9JF 2023-03-23 WOS:000861288500041 0
J Li, B; Peng, H; Luo, XH; Wang, J; Song, XX; Perez-Jimenez, MJ; Riscos-Nunez, A Li, Bo; Peng, Hong; Luo, Xiaohui; Wang, Jun; Song, Xiaoxiao; Perez-Jimenez, Mario J.; Riscos-Nunez, Agustin Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain INTERNATIONAL JOURNAL OF NEURAL SYSTEMS English Article Medical images; multi-modality; coupled neural P systems; nonsubsampled shearlet transform MULTI-FOCUS; FRAMEWORK Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance. [Li, Bo; Peng, Hong; Luo, Xiaohui; Wang, Jun; Song, Xiaoxiao] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China; [Perez-Jimenez, Mario J.; Riscos-Nunez, Agustin] Univ Seville, Dept Comp Sci & Artificial Intelligence, Res Grp Nat Comp, Seville 41012, Spain Xihua University; University of Sevilla Peng, H (corresponding author), Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China. ph.xhu@hotmil.com; wj.xhu@hotmail.com; marper@us.es; ariscosn@us.es Núñez, Agustín Riscos/A-7389-2013 Núñez, Agustín Riscos/0000-0002-5409-3578 57 42 43 20 80 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0129-0657 1793-6462 INT J NEURAL SYST Int. J. Neural Syst. JAN 2021.0 31.0 1 2050050 10.1142/S0129065720500501 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PQ4OQ 32808852.0 2023-03-23 WOS:000606525200005 0
J Krzywanski, J; Fan, HT; Feng, Y; Shaikh, AR; Fang, MX; Wang, QH Krzywanski, Jaroslaw; Fan, Hongtao; Feng, Yi; Shaikh, Abdul Rahim; Fang, Mengxiang; Wang, Qinhui Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers ENERGY CONVERSION AND MANAGEMENT English Article Biomass; Hydrogen; Calcium oxide; Fluidization; Genetic algorithms; Neural networks SITU CO2 CAPTURE; ENRICHED-GAS-PRODUCTION; BIOMASS STEAM GASIFICATION; PRESSURIZED FLUIDIZED-BED; HYDROGEN-PRODUCTION; NOX EMISSIONS; CAO; COAL; PREDICTION; MODEL The paper introduces artificial intelligence (AI) approach for the optimization study of a hydrogen concentrations in syngas via CaO Sorption. The performed model allows estimating the hydrogen content in the syngas produced from biomass in different types of facilities. Bubbling fluidized bed (FB) and circulating fluidized bed (CFB) are taken into account in the study. Comparing with the previous FB gasification results, CFB gasification was capable of producing syngas with higher hydrogen concentration. The model considers and covers a broad range of conditions, influencing the hydrogen rich-gas production. The presented non-iterative approach gives quick and accurate results as an answer to the input data sets. The H-2 concentration in the gas, estimated using the developed model, is in good agreement with the experimental data. Maximum relative error between measured and calculated data is lower than +/- 8%. The model allows also studying the influence of operating parameters on the hydrogen concentration in the gas. The method constitutes an easy to employ and useful complementary technique in relation to the other ways of data handling, including experimental procedures. The model can be used by scientists and engineers for optimizations purposes and can be applied as a submodel or a separate module in engineering calculations, capable to predict the H-2 concentration in the syngas from biomass via the CaO sorption both in FB and CFB gasifiers. [Krzywanski, Jaroslaw] Jan Dlugosz Univ Czestochowa, 13-15 Armii Krajowej Av, Czestochowa, Poland; [Fan, Hongtao; Feng, Yi; Shaikh, Abdul Rahim; Fang, Mengxiang; Wang, Qinhui] Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China Jan Dlugosz University; Zhejiang University Krzywanski, J (corresponding author), Jan Dlugosz Univ Czestochowa, 13-15 Armii Krajowej Av, Czestochowa, Poland. jkrzywanski@tlen.pl Krzywanski, Jaroslaw/AAL-7539-2021 Krzywanski, Jaroslaw/0000-0002-6364-7894 European Union [PIRSES GA-2013-612699] European Union(European Commission) This scientific work is funded from project: Long-term research activities in the area of advanced CO2 Capture Technologies for Clean Coal Energy Generation: the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement no PIRSES GA-2013-612699. The support is gratefully acknowledged. 55 45 45 2 33 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. SEP 1 2018.0 171.0 1651 1661 10.1016/j.enconman.2018.06.098 0.0 11 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics GZ2VW 2023-03-23 WOS:000449247500046 0
J Zhang, LQ; Liu, Y; Ren, LL; Teuling, AJ; Zhu, Y; Wei, LY; Zhang, LY; Jiang, SH; Yang, XL; Fang, XQ; Yin, H Zhang, Linqi; Liu, Yi; Ren, Liliang; Teuling, Adriaan J.; Zhu, Ye; Wei, Linyong; Zhang, Linyan; Jiang, Shanhu; Yang, Xiaoli; Fang, Xiuqin; Yin, Hang Analysis of flash droughts in China using machine learning HYDROLOGY AND EARTH SYSTEM SCIENCES English Article SOIL-MOISTURE; ONSET; INDICATORS; CHALLENGES; EVOLUTION; PRODUCTS The term flash drought describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural systems. Based on a global reanalysis dataset, we identify flash droughts across China during 1979-2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three machine learning (ML) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms in estimating RI and detecting droughts (flash droughts and traditional slowly evolving droughts) were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash drought identification among the three approaches. Spatially, the RF-based RI performed best in southeastern China, with an average CC of 0.90 and average RMSE of the 2.6 percentile per week, while poor performances were found in the Xinjiang region. For drought detection, all three ML technologies presented a better performance in monitoring flash droughts than in conventional slowly evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable in estimating the RI and monitoring flash droughts by consid- ering multiple meteorological variable anomalies in adjacent weeks to drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts compared to slowly developing droughts, along with asymmetrical compound influences in different regions of China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash droughts and highlight the potential of ML technologies in flash drought monitoring. [Zhang, Linqi; Liu, Yi; Ren, Liliang] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China; [Zhang, Linqi; Liu, Yi; Ren, Liliang; Wei, Linyong; Zhang, Linyan; Jiang, Shanhu; Yang, Xiaoli; Fang, Xiuqin] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Zhang, Linqi; Teuling, Adriaan J.] Wageningen Univ, Hydrol & Quantitat Water Management Grp, NL-6708PB Wageningen, Netherlands; [Zhu, Ye] Nanjing Univ Informat Sci Technol, Coll Hydrol & Water Resources, Nanjing 210044, Peoples R China; [Yin, Hang] Minist Water Resources, Inst Water Resources Pastoral Area, Inner Mongolia 010020, Peoples R China Hohai University; Hohai University; Wageningen University & Research; Nanjing University of Information Science & Technology Liu, Y; Ren, LL (corresponding author), Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China.;Liu, Y; Ren, LL (corresponding author), Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China. liuyihhdx@126.com; rll@hhu.edu.cn wei, linyong/GRS-2027-2022; Teuling, Ryan/D-2318-2014 Teuling, Ryan/0000-0003-4302-2835; Zhang, Linqi/0000-0001-5765-6199 National Natural Science Foundation of China [U2243203, 42171021, 41901037, 42071040]; Fundamental Research Funds for the Central Universities [B200204029, B200203054, 2019B05214]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_0468]; Central Guidance for Local Science and Technology Development fund projects [2021ZY0027] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Postgraduate Research & Practice Innovation Program of Jiangsu Province; Central Guidance for Local Science and Technology Development fund projects This research has been supported by the National Natural Science Foundation of China (grant no. U2243203), the Fundamental Research Funds for the Central Universities (grant nos. B200204029, B200203054, and 2019B05214), the National Natural Science Foundation of China (grant nos. 42171021, 41901037, and 42071040), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (grant no. KYCX20_0468), and the Central Guidance for Local Science and Technology Development fund projects (grant no. 2021ZY0027). 65 1 1 37 54 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1027-5606 1607-7938 HYDROL EARTH SYST SC Hydrol. Earth Syst. Sci. JUN 24 2022.0 26.0 12 3241 3261 10.5194/hess-26-3241-2022 0.0 21 Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Water Resources 2I7FE gold, Green Submitted, Green Accepted 2023-03-23 WOS:000815139100001 0
J Zhang, ZH; Paul, SA; Panda, BRC; Huang, YH; Garg, A; Zhang, Y; Garg, A; Zhang, WG Zhang, Zhenhao; Paul, Suvash C.; Panda, Biranchi; Huang, Yuhao; Garg, Ankit; Zhang, Yi; Garg, Akhil; Zhang, Wengang Assessment of flexural and splitting strength of steel fiber reinforced concrete using automated neural network search ADVANCES IN CONCRETE CONSTRUCTION English Article fiber aspect ratio; fiber content; compressive strength; flexural strength; splitting strength; FRC; ANS COMPRESSIVE STRENGTH; SHEAR BEHAVIOR; ASPECT RATIO; PREDICTION; PERFORMANCE; DURABILITY Flexural and splitting strength behavior of conventional concrete can significantly be improved by incorporating the fibers in it. A significant number of research studies have been conducted on various types of fibers and their influence on the tensile capacity of concrete. However, as an important property, tensile capacity of fiber reinforced concrete (FRC) is not modelled properly. Therefore, this paper intends to formulate a model based on experiments that show the relationship between the fiber properties such as the aspect ratio (length/diameter), fiber content, compressive strength, flexural strength and splitting strength of FRC. For the purpose of modeling, various FRC mixes only with steel fiber are adopted from the existing research papers. Automated neural network search (ANS) is then developed and used to investigate the effect of input parameters such as fiber content, aspect ratio and compressive strength to the output parameters of flexural and splitting strength of FRC. It is found that the ANS model can be used to predict the flexural and splitting strength of FRC in a sensible precision. [Zhang, Zhenhao] Changsha Univ Sci & Technol, Sch Civil Engn, 960 2nd Sect Wanjiali South Rd, Changsha, Hunan, Peoples R China; [Paul, Suvash C.] Monash Univ Malaysia, Sch Engn, Civil Engn, Jalan Lagoon Selatan, Bandar Sunway 47500, Selangor Darul, Malaysia; [Panda, Biranchi] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore Ctr 3D Printing, 50 Nanyang Ave, Singapore, Singapore; [Huang, Yuhao; Garg, Akhil] Shantou Univ, Minist Educ, Intelligent Mfg Key Lab, 243 Daxue Rd, Shantou City, Guangdong, Peoples R China; [Garg, Ankit] Shantou Univ, Dept Civil & Environm Engn, 243 Daxue Rd, Shantou City, Guangdong, Peoples R China; [Zhang, Yi] Leibniz Univ Hannover, 1 Welfengarten, Hannover, Germany; [Zhang, Wengang] Shandong Univ Technol, Sch Civil & Architectural Engn, 266 Xincun West Rd, Zibo, Shandong, Peoples R China Changsha University of Science & Technology; Monash University; Monash University Sunway; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Singapore Centre for 3D Printing; Shantou University; Shantou University; Leibniz University Hannover; Shandong University of Technology Zhang, WG (corresponding author), Shandong Univ Technol, Sch Civil & Architectural Engn, 266 Xincun West Rd, Zibo, Shandong, Peoples R China. ziwuzizwg@sdut.edu.cn huang, yuhao/AAY-8179-2020; PAUL, SUVASH C/L-5779-2018; Zhang, Yi/I-2244-2016; Zhang, Zhenhao/AEL-6974-2022; Paul, Suvash Chandra/I-9038-2019 PAUL, SUVASH C/0000-0001-9997-2077; Zhang, Yi/0000-0002-1113-8465; Paul, Suvash Chandra/0000-0001-9997-2077; Zhang, Zhenhao/0000-0002-3909-3625 47 2 2 4 14 TECHNO-PRESS DAEJEON PO BOX 33, YUSEONG, DAEJEON 305-600, SOUTH KOREA 2287-5301 2287-531X ADV CONCR CONSTR Adv. Concr. Constr. JUL 2020.0 10.0 1 81 92 10.12989/acc.2020.10.1.081 0.0 12 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science MR2GG 2023-03-23 WOS:000553407500008 0
J Tao, XW; Paoletti, ME; Han, LR; Wu, ZY; Ren, P; Plaza, J; Plaza, A; Haut, JM Tao, Xuanwen; Paoletti, Mercedes E.; Han, Lirong; Wu, Zhaoyue; Ren, Peng; Plaza, Javier; Plaza, Antonio; Haut, Juan M. A New Deep Convolutional Network for Effective Hyperspectral Unmixing IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Estimation; Three-dimensional displays; Hyperspectral imaging; Convolution; Convolutional neural networks; Task analysis; Feature extraction; Convolutional neural networks (CNNs); cross convolution; deep learning; hyperspectral unmixing ANOMALY DETECTION; LOW-RANK; COLLABORATIVE REPRESENTATION; FACTORIZATION; ALGORITHM Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms to complete the abundance estimation step. Due to the impressive learning and data fitting capabilities of convolutional neural networks (CNNs), deep learning (DL)-based hyperspectral unmixing technologies have rapidly developed in the literature. According to the procedure used to combine different layers (i.e., fully connected layers, convolution layers, and activation layers), these techniques are mainly divided into three main categories, i.e., those based on autoencoder networks, convolutional neural networks, and convolutional autoencoder networks. They usually extract the weight and output of a specific activation layer as endmember signatures and abundance maps, respectively. Moreover, most existing DL-based unmixing approaches usually use 2-D CNNs to learn the features contained in hyperspectral images, and very few approaches employ 3-D CNNs to extract spectral and spatial information. However, 2-D CNN-based techniques cannot capture good discriminative feature maps from the spectral viewpoint, and 3-D CNN-based techniques usually have high computational overload. In this work, to further exploit the feature extraction capability of CNNs, we combine 3- and 2-D convolutions to propose a cross-convolution unmixing network (CrossCUN) for hyperspectral unmixing. Simultaneously, to better illustrate the improvements of our proposed CrossCUN, we also build the corresponding 2-D convolution unmixing network (2-DCUN) and 3-D convolution unmixing network (3-DCUN). We evaluate the performance of our newly developed networks on two types of synthetic datasets and three real hyperspectral images. Experimental results show that the proposed networks not only obtain better results than other DL-based unmixing methods but also do not require any prior knowledge (e.g., the results of other endmember extraction algorithms) to estimate the abundance maps. [Tao, Xuanwen; Han, Lirong; Wu, Zhaoyue; Plaza, Javier; Plaza, Antonio; Haut, Juan M.] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain; [Paoletti, Mercedes E.] Univ Complutense Madrid, Dept Comp Architecture & Automat, Madrid 28040, Spain; [Ren, Peng] China Univ Petr East China, Coll Informat & Control Engn, Qingdao 266580, Peoples R China Universidad de Extremadura; Complutense University of Madrid; China University of Petroleum Plaza, A (corresponding author), Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain. taoxuanwenupc@gmail.com; mpaoletti@unex.es; lironghan_upc@163.com; zhaoyue_wu@163.com; pengren@upc.edu.cn; jplaza@unex.es; aplaza@unex.es; juanmariohaut@unex.es Plaza, Antonio/C-4455-2008; Plaza, Javier/C-4452-2008; Paoletti, Mercedes Eugenia/AAE-1946-2019 Plaza, Antonio/0000-0002-9613-1659; Plaza, Javier/0000-0002-2384-9141; Paoletti, Mercedes Eugenia/0000-0003-1030-3729; Haut, Juan Mario/0000-0001-6701-961X; han, lirong/0000-0002-8613-7037; Ren, Peng/0000-0003-3949-985X Consejeria de Economia, Ciencia y Agenda Digital of the Junta de Extremadura; European Regional Development Fund of the European Union [GR21040]; Spanish Ministerio de Ciencia e Innovacion (APRISA) [PID2019-110315RB-I00]; BBVA Foundation; Leonardo Grant for Researchers and Cultural Creators Consejeria de Economia, Ciencia y Agenda Digital of the Junta de Extremadura; European Regional Development Fund of the European Union; Spanish Ministerio de Ciencia e Innovacion (APRISA)(Ministry of Science and Innovation, Spain (MICINN)); BBVA Foundation(BBVA Foundation); Leonardo Grant for Researchers and Cultural Creators This work was supported in part by Consejeria de Economia, Ciencia y Agenda Digital of the Junta de Extremadura and by the European Regional Development Fund of the European Union under Reference GR21040, in part by the Spanish Ministerio de Ciencia e Innovacion (APRISA) under Project PID2019-110315RB-I00, and by in part by 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. 50 1 1 12 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 6999 7012 10.1109/JSTARS.2022.3200733 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 4G5VF gold 2023-03-23 WOS:000849263100004 0
J Long, ZG; Meng, H; Sioutis, M Long, Zhiguo; Meng, Hua; Sioutis, Michael Semi-Supervised Dimensionality Reduction by Linear Compression and Stretching IEEE ACCESS English Article Dimensionality reduction; pairwise constraints; PCA; FLD Dimensionality reduction is a fundamental and important research topic in the field of machine learning. This paper focuses on a dimensionality reduction technique that exploits semi-supervising information in the form of pairwise constraints; specifically, these constraints specify whether two instances belong to the same class or not. We propose two dual linear methods to accomplish dimensionality reduction under that setting. These two methods overcome the difficulty of maximizing between-class difference and minimizing within-class difference at the same time, by transforming the original data into a new space in such a way that the bi-objective problem is (almost) equivalently reduced to a single objective problem. Empirical evaluations on a broad range of public datasets show that the two proposed methods are superior to several existing methods for semi-supervised dimensionality reduction. [Long, Zhiguo] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Long, Zhiguo] Southwest Jiaotong Univ, Inst Artificial Intelligence, Chengdu 611756, Peoples R China; [Meng, Hua] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu 611756, Peoples R China; [Meng, Hua] Southwest Jiaotong Univ, Sch Math, Chengdu 611756, Peoples R China; [Sioutis, Michael] Univ Bamberg, Fac Informat Syst & Appl Comp Sci, D-96047 Bamberg, Germany Southwest Jiaotong University; Southwest Jiaotong University; Southwest Jiaotong University; Southwest Jiaotong University; Otto Friedrich University Bamberg Meng, H (corresponding author), Southwest Jiaotong Univ, Sch Civil Engn, Chengdu 611756, Peoples R China.;Meng, H (corresponding author), Southwest Jiaotong Univ, Sch Math, Chengdu 611756, Peoples R China. menghua@swjtu.edu.cn Long, Zhiguo/AAS-2638-2020 Long, Zhiguo/0000-0002-2714-3453; Sioutis, Michael/0000-0001-7562-2443 National Natural Science Foundation of China [61806170]; Humanities and Social Sciences Fund of Ministry of Education [18XJC72040001]; Fundamental Research Funds for the Central Universities [2682018CX25] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Sciences Fund of Ministry of Education; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61806170, in part by the Humanities and Social Sciences Fund of Ministry of Education under Grant 18XJC72040001, and in part by the Fundamental Research Funds for the Central Universities under Grant 2682018CX25. 32 2 2 0 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 27308 27317 10.1109/ACCESS.2020.2971562 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LC6TQ gold 2023-03-23 WOS:000525466900065 0
J Azizi, SP; Jafari, H; Faghan, Y; Neisy, A Azizi, S. Pourmohammad; Jafari, Hossein; Faghan, Yaser; Neisy, Abdolsadeh Inverse Problem Approach to Machine Learning with Application in the Option Price Correction OPTICAL MEMORY AND NEURAL NETWORKS English Article machine leaning; inverse problems; fractional differential equation; finance; volatility; option pricing EQUATION We investigate a new method in learning to fix the existence of an unsuitable subfunction of a general system. We assume this subfunction is dependent on the system input variables. In this process, we put a learner instead of the unsuitable subfunction and train it by a training model obtained from inverse problems and fractional derivatives, respectively. Finally, we implemented this method on a simple financial model and examined the results with simulated and real data. [Azizi, S. Pourmohammad] Zhengzhou Univ, Henan Acad Big Data, Zhengzhou Key Lab Big Data Anal & Applicat, Zhengzhou 450001, Henan, Peoples R China; [Azizi, S. Pourmohammad; Neisy, Abdolsadeh] Allameh Tabatabai Univ, Dept Math & Comp Sci, Tehran, Iran; [Jafari, Hossein] Univ South Africa, Dept Math Sci, ZA-0002 Pretoria, South Africa; [Jafari, Hossein] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 110122, Taiwan; [Faghan, Yaser] Univ Lisbon, Inst Super Econ & Gestao, P-1649004 Lisbon, Portugal; [Faghan, Yaser] Univ Lisbon, CEMAPRE, P-1649004 Lisbon, Portugal Zhengzhou University; Allameh Tabataba'i University; University of South Africa; China Medical University Taiwan; China Medical University Hospital - Taiwan; Universidade de Lisboa; Universidade de Lisboa Azizi, SP (corresponding author), Zhengzhou Univ, Henan Acad Big Data, Zhengzhou Key Lab Big Data Anal & Applicat, Zhengzhou 450001, Henan, Peoples R China.;Azizi, SP (corresponding author), Allameh Tabatabai Univ, Dept Math & Comp Sci, Tehran, Iran. eazizak@yahoo.com; Jafari.usern@gmail.com; yaser.kord@yahoo.com; a_neisy@atu.ac.ir Jafari, Hossein/E-9912-2016; Azizi, S Pourmohammad/HNP-4305-2023 Jafari, Hossein/0000-0001-6807-6675; 16 0 0 1 1 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1060-992X 1934-7898 OPT MEMORY NEURAL Opt. Mem. Neural Netw. MAR 2022.0 31.0 1 46 58 10.3103/S1060992X22010088 0.0 13 Optics Emerging Sources Citation Index (ESCI) Optics 0M8ZR 2023-03-23 WOS:000782437500005 0
J Sun, GL; Zhan, T; Owusu, BG; Daniel, AM; Liu, GS; Jiang, W Sun, Guolin; Zhan, Tong; Owusu, Boateng Gordon; Daniel, Ayepah-Mensah; Liu, Guisong; Jiang, Wei Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Reinforcement learning; Anchor graph hashing; K-means clustering; Autonomous cell activation; Cloud radio access networks RADIO ACCESS NETWORKS; ENERGY; DISCRETIZATION Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes to balance energy consumption and QoS satisfaction in wireless networks. We formulate the cell activation problem as a Markov decision process and set up a revised reinforcement learning model based on K-means clustering and anchor-graph hashing to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and user mobility. Extensive simulations are conducted to show the effectiveness of our proposed solution compared with existing schemes. (C) 2019 Elsevier B.V. All rights reserved. [Sun, Guolin; Zhan, Tong; Owusu, Boateng Gordon; Daniel, Ayepah-Mensah; Liu, Guisong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China; [Liu, Guisong] Univ Elect Sci & Technol China, Zhongshan Inst, Sch Comp Sci, Zhongshan 528400, Peoples R China; [Jiang, Wei] German Res Ctr Artificial Intelligence DFKI GmbH, Kaiserslautern, Germany University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; German Research Center for Artificial Intelligence (DFKI) Sun, GL (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China. guolin.sun@uestc.edu.cn Jiang, Wei/ADI-7167-2022; Boateng, Gordon Owusu/U-2969-2019 Boateng, Gordon Owusu/0000-0002-7923-367X; Ayepah-Mensah, Daniel/0000-0001-9159-0509; Liu, Guisong/0000-0003-2360-0466 National Natural Science Research Foundation of China [61771098]; Fundamental Research Funds for the Central Universities, China [ZYGX2018J049]; Department of Science and Technology of Sichuan Province, China [2017GFW0128, 8ZDYF2268, 2018JY0578, 2017JY0007]; ZTE Innovation Research Fund, China for Universities Program 2016 National Natural Science Research Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities); Department of Science and Technology of Sichuan Province, China; ZTE Innovation Research Fund, China for Universities Program 2016 This work is supported by National Natural Science Research Foundation of China, Grant no. 61771098, by the Fundamental Research Funds for the Central Universities, China under grant no. ZYGX2018J049, by the fund from the Department of Science and Technology of Sichuan Province, China, Grant No. 2017GFW0128, 8ZDYF2268, 2018JY0578 and 2017JY0007, and the ZTE Innovation Research Fund, China for Universities Program 2016. 38 5 5 0 33 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. MAR 2020.0 104.0 60 73 10.1016/j.future.2019.09.044 0.0 14 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science JZ0FV 2023-03-23 WOS:000504781200005 0
J Li, X; Yang, XF; Li, XT; Lu, SJ; Ye, YM; Ban, YF Li, Xian; Yang, Xiaofei; Li, Xutao; Lu, Shijian; Ye, Yunming; Ban, Yifang GCDB-UNet: A novel robust cloud detection approach for remote sensing images KNOWLEDGE-BASED SYSTEMS English Article Convolution neural network(CNN); Cloud detection; Attention PROBABILISTIC NEURAL-NETWORK; TEMPORAL UPDATING SCHEME; SHADOW DETECTION; DETECTION ALGORITHM; MODIS; CLASSIFICATION Cloud detection is a prerequisite in many remote sensing applications, and it has been tackled through different approaches from simple thresholding to complicated deep network training. On the other hand, existing approaches are susceptible to failures while handling thin clouds, largely because of their small sizes, sparse distributions, as well as high transparency and similarity to the non-cloud background regions. This paper presents global context dense block U-Net (GCDB-UNet), a robust cloud detection network that embeds global context dense block (GCDB) into the U-Net framework and is capable of detecting thin clouds effectively. GCDB consists of two feature extraction units for addressing the challenges in thin cloud detection, namely, a non-local self-attention unit that extracts sample correlation features by aggregating the sparsely distributed thin clouds and a squeeze excitation unit that extracts channel correlated features by differentiating their importance. In addition, a dense connection scheme is designed to exploit the multi-level fine-grained representations from the two types of extracted features and a recurrent refinement module is introduced for gradual enhancement of the predicted classification map. We also created a fully annotated cloud detection MODIS dataset that consists of 1192 training images, 80 validation images and 150 test images. Extensive experiments on Landsat8, SPARCS and MODIS datasets show that the proposed GCDB-UNet achieves superior cloud detection performance as compared with state-of-the-art methods. Our created MODIS cloud detection dataset is available at https://github.com/xiachangxue/MODIS-Dataset-for-Cloud-Detection. (c) 2021 Elsevier B.V. All rights reserved. [Li, Xian; Yang, Xiaofei; Li, Xutao; Ye, Yunming] Harbin Inst Technol Shenzhen, Shenzhen Key Lab Internet Informat Collaborat, Shenzhen 518055, Peoples R China; [Lu, Shijian] Nanyang Technol Univ, Sch Engn & Comp Sci, Singapore, Singapore; [Ban, Yifang] KTH Royal Inst Technol Stockholm, Dept Urban Planning & Environm, S-10044 Stockholm, Sweden Harbin Institute of Technology; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Royal Institute of Technology Li, XT (corresponding author), Harbin Inst Technol Shenzhen, Shenzhen Key Lab Internet Informat Collaborat, Shenzhen 518055, Peoples R China. lixutao@hit.edu; yifang@kth.se Yang, Xiaofei/0000-0003-2458-6774 Shenzhen Science and Technology Program, China [JCYJ20210324120208022, JCYJ20200109113014456, JCYJ20180507183823045] Shenzhen Science and Technology Program, China This work was partially supported by the Shenzhen Science and Technology Program, China under Grant JCYJ20210324120208022, and Grant JCYJ20200109113014456, Grant JCYJ20180507183823045. 58 9 9 11 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. FEB 28 2022.0 238.0 107890 10.1016/j.knosys.2021.107890 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0I1IU 2023-03-23 WOS:000779180700015 0
J Jabeen, G; Luo, P; Afzal, W Jabeen, Gul; Luo, Ping; Afzal, Wasif An improved software reliability prediction model by using high precision error iterative analysis method SOFTWARE TESTING VERIFICATION & RELIABILITY English Article artificial neural network; residual errors; sign estimator; software reliability growth models; software reliability prediction GROWTH-MODELS; FAULT-DETECTION; UNCERTAINTY; REMOVAL Software reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)-based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness-of-fit and predictive performance for every PSRGM in initial few iterations. [Jabeen, Gul; Luo, Ping] Tsinghua Univ, Sch Software, Key Lab Informat Syst Secur, Beijing 100084, Peoples R China; [Jabeen, Gul] Karakoram Int Univ, Dept Comp Sci, Gilgit 15100, Pakistan; [Afzal, Wasif] Malardalen Univ, Vasteras, Sweden Tsinghua University; Karakoram International University; Malardalen University Jabeen, G (corresponding author), Tsinghua Univ, Sch Software, Key Lab Informat Syst Secur, Beijing 100084, Peoples R China. jgl14@mails.tsinghua.edu.cn Beijing National Research Center for Information Science and Technology (BNRist); National Natural Science Foundation of China [90818021, 9071803]; Swedish Knowledge Foundation [20160139]; Swedish Innovation Agency (MegaM@Rt2); Electronic Component Systems for European Leadership [737494] Beijing National Research Center for Information Science and Technology (BNRist); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Swedish Knowledge Foundation(General Electric); Swedish Innovation Agency (MegaM@Rt2); Electronic Component Systems for European Leadership This research was supported by Beijing National Research Center for Information Science and Technology (BNRist) and National Natural Science Foundation of China under grant nos 90818021 and 9071803. This work was also sponsored in part by the Swedish Knowledge Foundation under grant 20160139 (Test-Mine) and in part by the Swedish Innovation Agency (MegaM@Rt2) and Electronic Component Systems for European Leadership (Joint Undertaking under grant 737494). 54 8 8 1 13 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0960-0833 1099-1689 SOFTW TEST VERIF REL Softw. Test. Verif. Reliab. SEP 2019.0 29.0 6-7 e1710 10.1002/stvr.1710 0.0 OCT 2019 22 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science JR2NY 2023-03-23 WOS:000492882800001 0
J Liu, GY; Agostinho, F; Duan, HB; Song, GH; Wang, XQ; Giannetti, BF; Santagata, R; Casazza, M; Lega, M Liu, Gengyuan; Agostinho, Feni; Duan, Huabo; Song, Guanghan; Wang, Xueqi; Giannetti, Biagio F.; Santagata, Remo; Casazza, Marco; Lega, Massimiliano Environmental impacts characterization of packaging waste generated by urban food delivery services. A big-data analysis in Jing-Jin-Ji region (China) WASTE MANAGEMENT English Article Food delivery services; Packaging waste; Big data; Environmental impact analysis LIFE-CYCLE ASSESSMENT; MANAGEMENT Controversies on food delivery services environmental impacts have been sparked due to the growth of this economic sector. This study focuses on the environmental impacts generated by packaging waste related to urban food delivery services. In particular, the Python based web-crawling and sample survey methods are used for big data mining, and LCA-based environmental impacts evaluation and Kernel density analysis methods are combined to determine the positioning trend of food delivery service providers and expansion direction of environmental pollution load in Jing-Jin-Ji region (North China). Results indicate that (1) food delivery service packages presently account for a very small proportion (<0.1%) of municipal solid waste (MSW). However, this study also evidences that food packaging accounts for 15.7% of the total MSW generated in this region. Even if this growing market sector might have a relatively low impact, households' lifestyle might affect the results. (2) In terms of consumption quantity, plastic bags are the most used packages, accounting for 35.08%; wooden chopsticks account for 32.21% and plastic boxes account for 27.43%. Among all environmental impact categories resulting from the process of production of packages, greenhouse effect is the most distinct one. Paper boxes generate the most serious environmental pollution. (3) The distribution of environmental pollution loads resulting from food delivery service packages positively correlates with the distribution of food delivery service providers in Jing-Jin-Ji. Shijiazhuang has the highest degree of pollution resulting from food delivery service packages with the interrupt value ratio of 80%, followed by Baoding City and Chengde City, which have the interrupt value ratios of 65.1% and 48.6%, respectively. Finally, as bridges between food delivery service providers and consumers, food delivery service platforms should improve their environmental protection mechanisms. Meanwhile, the government should define a standard concerning food delivery service packages to consolidate the concept of environmental protection in the society to change the ways people consume, in order to achieve a harmonious co-existence between resource utilization and environmental protection. (C) 2020 Elsevier Ltd. All rights reserved. [Liu, Gengyuan; Wang, Xueqi; Giannetti, Biagio F.] Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100875, Peoples R China; [Liu, Gengyuan] Beijing Engn Res Ctr Watershed Environm Restorat, Beijing 100875, Peoples R China; [Agostinho, Feni; Giannetti, Biagio F.] Univ Paulista UNIP, Lab Prod & Meio Ambiente, Programa Posgrad Engn Prod, Sao Paulo, Brazil; [Duan, Huabo; Song, Guanghan] Shenzhen Univ, Sch Civil Engn, Shenzhen 518060, Peoples R China; [Santagata, Remo] Univ Naples Parthenope, Ctr Direzionale, Dept Sci & Technol, Isola C4, I-80143 Naples, Italy; [Casazza, Marco; Lega, Massimiliano] Univ Naples Parthenope, Ctr Direzionale, Dept Engn, Isola C4, I-80143 Naples, Italy Beijing Normal University; Universidade Paulista; Shenzhen University; Parthenope University Naples; Parthenope University Naples Liu, GY (corresponding author), Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100875, Peoples R China. liugengyuan@bnu.edu.cn Santagata, Remo/M-4326-2018; Casazza, Marco/T-6783-2017; Agostinho, Feni/GQB-4411-2022 Santagata, Remo/0000-0002-2542-2520; Casazza, Marco/0000-0002-7579-3231; Liu, Gengyuan/0000-0002-3488-9536; Agostinho, Feni/0000-0002-6445-2175 Beijing Municipal Science & Technology Commission [Z181100009618030]; Sino-Italian Cooperation of China Natural Science Foundation [71861137001]; Italian Ministry of Foreign Affairs and International Cooperation [PGR00767]; National Key R&D Program of China [2016YFC0503005]; Fund for Innovative Research Group of the National Natural Science Foundation of China [51421065]; National Natural Science Foundation of China [71673029]; 111 Project [B17005] Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Sino-Italian Cooperation of China Natural Science Foundation; Italian Ministry of Foreign Affairs and International Cooperation(Ministry of Foreign Affairs and International Cooperation (Italy)); National Key R&D Program of China; Fund for Innovative Research Group of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project) This work is supported by the Beijing Municipal Science & Technology Commission (Z181100009618030), the Sino-Italian Cooperation of China Natural Science Foundation (71861137001) and the Italian Ministry of Foreign Affairs and International Cooperation (PGR00767), National Key R&D Program of China (2016YFC0503005), the Fund for Innovative Research Group of the National Natural Science Foundation of China (51421065), National Natural Science Foundation of China (71673029) and the 111 Project (B17005). 30 27 27 20 116 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0956-053X 1879-2456 WASTE MANAGE Waste Manage. NOV 1 2020.0 117.0 157 169 10.1016/j.wasman.2020.07.028 0.0 13 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology NU8PN 32828013.0 2023-03-23 WOS:000573900000015 0
J Zhuang, XL; Antonov, S; Li, LF; Feng, Q Zhuang, Xiaoli; Antonov, Stoichko; Li, Longfei; Feng, Qiang gamma '-Strengthened Multicomponent CoNi-Based Wrought Superalloys With Improved Comprehensive Properties METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE English Article; Early Access MICROSTRUCTURAL STABILITY; OXIDATION BEHAVIOR; AL-W; CREEP; SEGREGATION; ALLOYS In order to accelerate the adoption of gamma '-strengthened CoNi-based wrought superalloys in engineering applications, two alloys that were previously designed using a framework combining data from a multicomponent diffusion-multiple and machine learning. In this study, we evaluated the comprehensive properties of these alloys, one with spherical gamma ' morphology and the other with cuboidal, including the alloy density, phase transformation temperatures, microstructural stability, oxidation resistance and mechanical properties. The properties were discussed with respect to alloying effects and in regard to other CoNi-based and Ni-based wrought superalloys. The results show that the designed alloys have relatively low density, decent microstructural stability, good oxidation resistance and mechanical properties. This study will provide guidance for further design and optimization of gamma '-strengthened CoNi-based wrought superalloys. [Zhuang, Xiaoli; Li, Longfei; Feng, Qiang] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China; [Antonov, Stoichko] Max Planck Inst Eisenforschung GmbH, Max Planck Str 1, D-40237 Dusseldorf, Germany University of Science & Technology Beijing; Max Planck Society Li, LF; Feng, Q (corresponding author), Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China. lilf@skl.ustb.edu.cn; qfeng@skl.ustb.edu.cn National Natural Science Foundation of China [92060113, 52171095]; Science and Technology on Advanced High Temperature Structural Materials Laboratory [6142903210207]; Guangdong Province Key Area RD Program [2019B010943001]; National Key Research and Development Program of China [2017YFB0702902]; Fundamental Research Funds for the Central Universities [FRF-GF-20-30B] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology on Advanced High Temperature Structural Materials Laboratory; Guangdong Province Key Area RD Program; National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors would like to acknowledge the financial supports provided by the National Natural Science Foundation of China (Grant Nos.: 92060113 and 52171095), the Science and Technology on Advanced High Temperature Structural Materials Laboratory (Grant No.: 6142903210207), the Guangdong Province Key Area R & D Program (Grant No.: 2019B010943001), the National Key Research and Development Program of China (Grant No.: 2017YFB0702902), and the Fundamental Research Funds for the Central Universities (Grant No.: FRF-GF-20-30B). 46 0 0 3 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1073-5623 1543-1940 METALL MATER TRANS A Metall. Mater. Trans. A-Phys. Metall. Mater. Sci. 10.1007/s11661-023-06959-4 0.0 JAN 2023 12 Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Metallurgy & Metallurgical Engineering 8A1PY 2023-03-23 WOS:000916019300001 0
J Wang, F; Li, KP; Duic, N; Mi, ZQ; Hodge, BM; Shafie-khah, M; Catalao, JPS Wang, Fei; Li, Kangping; Duic, Neven; Mi, Zengqiang; Hodge, Bri-Mathias; Shafie-khah, Miadreza; Catalao, Joao P. S. Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns ENERGY CONVERSION AND MANAGEMENT English Article Electricity consumption pattern; Household characteristics; Association rule mining; Clustering; Apriori algorithm DEMAND RESPONSE; LOAD; MODEL; BUILDINGS The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions. [Wang, Fei; Li, Kangping; Mi, Zengqiang] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Baoding 071003, Peoples R China; [Wang, Fei] Univ Illinois, Dept Elect & Comp Engn, Urbana, IL 61801 USA; [Duic, Neven] Univ Zagreb, Fac Mech Engn & Navala Architecture, Ivana Lucica 5, Zagreb 10000, Croatia; [Hodge, Bri-Mathias] Natl Renewable Energy Lab, Golden, CO 80401 USA; [Shafie-khah, Miadreza; Catalao, Joao P. S.] Univ Beira Interior, C MAST, P-6201001 Covilha, Portugal; [Catalao, Joao P. S.] Univ Porto, INESC TEC, P-4200465 Porto, Portugal; [Catalao, Joao P. S.] Univ Porto, Fac Engn, P-4200465 Porto, Portugal; [Catalao, Joao P. S.] Univ Lisbon, Inst Super Tecn, INESC ID, P-1049001 Lisbon, Portugal North China Electric Power University; University of Illinois System; University of Illinois Urbana-Champaign; University of Zagreb; United States Department of Energy (DOE); National Renewable Energy Laboratory - USA; Universidade da Beira Interior; INESC TEC; Universidade do Porto; Universidade do Porto; INESC-ID; Universidade de Lisboa; Instituto Superior Tecnico Wang, F; Li, KP (corresponding author), North China Elect Power Univ, 18 Mail Box,619 North Yonghua St, Baoding 071003, Peoples R China. ncepu_wangfei@sina.com Shafie-khah, Miadreza/AAE-1539-2021; Catalão, João P. S./I-3927-2012; Duić, Neven/J-5242-2012; Hodge, Bri-Mathias S./H-4265-2019; Shafie-khah, Miadreza/AAS-6882-2020; Li, Kangping/AAE-5967-2020 Catalão, João P. S./0000-0002-2105-3051; Duić, Neven/0000-0003-2376-6466; Shafie-khah, Miadreza/0000-0003-1691-5355; National Natural Science Foundation of China [51577067]; Beijing Natural Science Foundation of China [3162033]; Hebei Natural Science Foundation of China [E2015502060]; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS18008]; Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) [5242001600FB]; Fundamental Research Funds for the Central Universities [2018QN077]; U.S. Department of Energy [DE-AC36-08-GO28308]; National Renewable Energy Laboratory; FEDER funds through COMPETE 2020; Portuguese funds through FCT [SAICT-PAC/0004/2015 - POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, 02/SAICT/2017 - POCI-01-0145-FEDER-029803]; EU 7th Framework Programme FP7/2007-2013 [309048] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation of China(Beijing Natural Science Foundation); Hebei Natural Science Foundation of China; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources; Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); U.S. Department of Energy(United States Department of Energy (DOE)); National Renewable Energy Laboratory(United States Department of Energy (DOE)); FEDER funds through COMPETE 2020; Portuguese funds through FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); EU 7th Framework Programme FP7/2007-2013 This work was supported in part by the National Natural Science Foundation of China (grant No. 51577067), the Beijing Natural Science Foundation of China (grant No. 3162033), the Hebei Natural Science Foundation of China (grant No. E2015502060), the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (grant No. LAPS18008), the Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) (grant No. 5242001600FB), the Fundamental Research Funds for the Central Universities (grant No. 2018QN077). This work was also supported by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory. J.P.S. Catalao acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 - POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, and 02/SAICT/2017 - POCI-01-0145-FEDER-029803, and also funding from the EU 7th Framework Programme FP7/2007-2013 under GA no. 309048. 45 92 95 8 80 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0196-8904 1879-2227 ENERG CONVERS MANAGE Energy Conv. Manag. SEP 1 2018.0 171.0 839 854 10.1016/j.enconman.2018.06.017 0.0 16 Thermodynamics; Energy & Fuels; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels; Mechanics GR8KJ 2023-03-23 WOS:000442972500067 0
J Larestani, A; Mousavi, SP; Hadavimoghaddam, F; Ostadhassan, M; Hemmati-Sarapardeh, A Larestani, Aydin; Mousavi, Seyed Pezhman; Hadavimoghaddam, Fahimeh; Ostadhassan, Mehdi; Hemmati-Sarapardeh, Abdolhossein Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree ALEXANDRIA ENGINEERING JOURNAL English Article Enhanced oil recovery; Artificial neural networks; Surfactant-polymer flooding; Intelligent model; Recovery factor; Net present value OPTIMIZATION; SIMULATION; MODEL Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR) techniques, which refers to the injection of surfactant slugs and polymer drives. Two crucial decision-making parameters in EOR operations are net present value (NPV) and oil recovery factor (RF). Herein, various intelligent models, based on multilayer perceptron (MLP), cascade neural network (CNN), radial basis function (RBF), neural networks as well as support vector regression (SVR), and decision tree (DT) algorithms are proposed toward estimating these two parameters with respect to polymer drive size, surfactant slug size, the salinity of polymer drive, Kv/Kh ratio, surfactant concentration, and polymer concentration in polymer drive and surfactant slug. The results exhibited the outperformance of the CNN model trained with the Levenberg Marquardt algorithm in forecasting the RF and NPV with average absolute errors of 0.66% and 1.95%, respectively. Moreover, the results of the sensitivity analysis reflected that the most effective inputs on the predicted value of RF were surfactant concentration and surfactant slug size, while surfactant [Ostadhassan, Mehdi] Northeast Petr Univ, State Key Lab Continental Shale Hydrocarbon Accum, Minist Educ, Daqing 163318, Peoples R China; [Larestani, Aydin; Mousavi, Seyed Pezhman; Hemmati-Sarapardeh, Abdolhossein] Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran; [Hadavimoghaddam, Fahimeh] Ufa State Petr Technol Univ, Ufa 450064, Russia; [Hadavimoghaddam, Fahimeh] Northeast Petr Univ, Inst Unconvent Oil & Gas, Daqing 163318, Heilongjiang, Peoples R China; [Hemmati-Sarapardeh, Abdolhossein] Jilin Univ, Coll Construct Engn, Changchun, Peoples R China; [Ostadhassan, Mehdi] Univ Kiel, Inst Geosci Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany; [Ostadhassan, Mehdi] Ferdowsi Univ Mashhad, Dept Geol, Mashhad, Razavi Khorasan, Iran Northeast Petroleum University; Shahid Bahonar University of Kerman (SBUK); Ufa State Petroleum Technological University; Northeast Petroleum University; Jilin University; University of Kiel; Ferdowsi University Mashhad Larestani, A; Ostadhassan, M; Hemmati-Sarapardeh, A (corresponding author), Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran. aidinlarestani@gmail.com; mehdi.ostadhassan@nepu.edu.ca; hemmati@uk.ac.ir Sarapardeh, Abdolhossein Hemmati/M-5047-2019; Larestani, Aydin/GNW-4882-2022 Sarapardeh, Abdolhossein Hemmati/0000-0002-5889-150X; Larestani, Aydin/0000-0002-8320-0176 Ministry of Science and Higher Education of the Russian Federation [075-15-2020-900] Ministry of Science and Higher Education of the Russian Federation Fahimeh Hadavimoghaddam would like to acknowledge the support of the Ministry of Science and Higher Education of the Russian Federation under agreement No. 075-15-2020-900 within the framework of the development program for a world-class Research Center. 45 4 4 4 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1110-0168 2090-2670 ALEX ENG J Alex. Eng. J. OCT 2022.0 61.0 10 7715 7731 10.1016/j.aej.2022.01.0231110-0168 0.0 17 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering YU6GJ 2023-03-23 WOS:000752138700015 0
J Zhang, XX; Zhao, HT; Zhu, HB; Adebisi, B; Gui, G; Gacanin, H; Adachi, F Zhang, Xixi; Zhao, Haitao; Zhu, Hongbo; Adebisi, Bamidele; Gui, Guan; Gacanin, Haris; Adachi, Fumiyuki NAS-AMR: Neural Architecture Search-Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Automatic modulation recognition (AMR); integrated sensing and communication (ISAC); deep neural network (DNN); neural architecture search (NAS) WAVE-FORM RECOGNITION; SIGNAL IDENTIFICATION; JOINT RADAR; CLASSIFICATION; NETWORK; INTELLIGENT; 6G Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will be very tasking. This paper proposes a neural architecture search (NAS) based AMR method to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints. The proposed NAS-AMR method will improve the flexibility of model search and overcome the difficulty of gradient propagation caused by the non-differentiable quantization function in the process of back propagation. Simulation results are provided to confirm that the proposed NAS-AMR method can identify the modulation types in various ISAC electromagnetic environments. Furthermore, compared with other fixed structure networks, our proposed method delivers the highest recognition accuracy, under the condition of low parameters and floating-point operations (FLOPs). [Zhang, Xixi; Zhao, Haitao; Zhu, Hongbo; Gui, Guan] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Dept Engn, Manchester M1 5GD, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, D-52062 Aachen, Germany; [Adachi, Fumiyuki] Tohoku Univ, Int Res Inst Disaster Sci, Sendai, Miyagi 9808572, Japan Nanjing University of Posts & Telecommunications; Manchester Metropolitan University; RWTH Aachen University; Tohoku University Zhao, HT; Gui, G (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China. 1220013926@njupt.edu.cn; zhaoht@njupt.edu.cn; zhuhb@njutp.edu.cn; b.adebisi@mmu.ac.uk; guiguan@njupt.edu.cn; harisg@ice.rwth-aachen.de; adachi@ecei.tohoku.ac.jp Adebisi, Bamidele/0000-0001-9071-9120 Jiangsu Provincial Key Research and Development Program [BE2020084-5]; Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]; Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106] Jiangsu Provincial Key Research and Development Program; Summit of the Six Top Talents Program of Jiangsu; Program for High-Level Entrepreneurial and Innovative Team; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China(Ministry of Education, China) This work was supported in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2020084-5, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. This work was presented in part [1] at the International Symposium on Antennas, Propagation and EM Theory, Zhuhai, China, December 1-4, 2021 [DOI: 10.1109/ISAPE54070.2021.9753650]. 59 5 5 15 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. SEP 2022.0 8.0 3 1374 1386 10.1109/TCCN.2022.3169740 0.0 13 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications 4K8TR 2023-03-23 WOS:000852215200008 0
J Hajri, SE; Assaad, M; Larranaga, M; Sari, H Hajri, Salah Eddine; Assaad, Mohamad; Larranaga, Maialen; Sari, Hikmet Heterogeneous Doppler Spread-Based CSI Estimation Planning for TDD Massive MIMO IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY English Article Massive MIMO; Doppler spread; CSI estimation planning; machine learning; POMDP WIRELESS Massive multi-input multi-output (MIMO) has been recognized as a key technology for fifth generation (5G) networks. Indeed, it enables to meet the challenging requirements of increased coverage, capacity and massive connectivity. Nevertheless, its performance is limited by channel estimation overhead which scales in time division duplexing (TDD) systems with the number of active users, consequently limiting the system's efficiency. In this paper, we address the bottleneck of channel estimation in TDD Massive MIMO through optimized lean carrier. We propose an adaptive uplink training scheme that exploits the heterogeneous Doppler spreads of the different users in order to reduce the periodicity of uplink sounding signal transmission. The idea is to enable the network to plan its uplink training decisions for long time periods while taking into consideration user mobility. To this end, we formulate a two time-scale control problem that takes into account the different rates of the wireless channel and location changes. In the fast time scale, an optimized uplink training policy is derived based on estimated user locations. In the slow time scale, positioning decisions are optimized. Simulation results show that the optimized training policies provide considerable improvement in the system efficiency even with partial location knowledge. [Hajri, Salah Eddine] Huawei Lund Res Ctr, S-22369 Lund, Sweden; [Assaad, Mohamad] Univ Paris Saclay, Cent Supelec, Lab Signaux & Syst, F-91190 Gif Sur Yvette, France; [Larranaga, Maialen] Capgemini Engn, Aeropk 3 Chemin Laporte, F-31300 Toulousen, France; [Sari, Hikmet] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China UDICE-French Research Universities; Universite Paris Saclay; Nanjing University of Posts & Telecommunications Assaad, M (corresponding author), Univ Paris Saclay, Cent Supelec, Lab Signaux & Syst, F-91190 Gif Sur Yvette, France. Mohamad.Assaad@centralesupelec.fr assaad, mohamad/0000-0003-3909-6241 38 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2644-125X IEEE OPEN J COMM SOC IEEE Open J. Commun. Soc. 2023.0 4.0 413 429 10.1109/OJCOMS.2023.3235274 0.0 17 Engineering, Electrical & Electronic; Telecommunications Emerging Sources Citation Index (ESCI) Engineering; Telecommunications 9A5JZ gold, Green Submitted 2023-03-23 WOS:000934095600002 0
J Chen, N; Ribeiro, B; Chen, A Chen, Ning; Ribeiro, Bernardete; Chen, An Financial credit risk assessment: a recent review ARTIFICIAL INTELLIGENCE REVIEW English Review Financial credit risk assessment; Business failure; Ensemble computing; Cost-sensitive learning; Dimensionality reduction; Subspace learning NEURAL-NETWORK APPLICATIONS; SUPPORT VECTOR MACHINES; BANKRUPTCY PREDICTION; DISCRIMINANT-ANALYSIS; IMBALANCED DATA; CLASSIFIER ENSEMBLES; REASONING ENSEMBLE; GENETIC ALGORITHM; FEATURE-SELECTION; BUSINESS The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area. [Chen, Ning] Henan Polytech Univ, Coll Comp Sci & Technol, Software Coll, Jiaozuo 454003, Henan, Peoples R China; [Chen, Ning] Inst Politecn Porto, Inst Super Engn Porto, GECAD, P-4200072 Oporto, Portugal; [Ribeiro, Bernardete] Univ Coimbra, Dept Informat Engn, CISUC, P-3030790 Coimbra, Portugal; [Chen, An] Henan Polytech Univ, Safety & Emergency Management Res Ctr, Jiaozuo 454003, Henan, Peoples R China; [Chen, An] Chinese Acad Sci, Inst Policy & Management, Beijing, Peoples R China Henan Polytechnic University; Polytechnic Institute of Porto; Universidade de Coimbra; Henan Polytechnic University; Chinese Academy of Sciences; CAS Institutes of Science & Development Chen, N (corresponding author), Henan Polytech Univ, Coll Comp Sci & Technol, Software Coll, 2001 Century Ave, Jiaozuo 454003, Henan, Peoples R China. nchenyx@outlook.com; bribeiro@dei.uc.pt; anchen@casipm.ac.cn Ribeiro, Bernardete/A-8010-2016 Ribeiro, Bernardete/0000-0002-9770-7672 Natural Science Foundation of China [91024004] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partly supported by Natural Science Foundation of China (Contract No. 91024004). 128 76 81 22 206 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. JAN 2016.0 45.0 1 1 23 10.1007/s10462-015-9434-x 0.0 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science DA3KC 2023-03-23 WOS:000367694600001 0
J Feng, XR; Li, HC; Li, J; Du, Q; Plaza, A; Emery, WJ Feng, Xin-Ru; Li, Heng-Chao; Li, Jun; Du, Qian; Plaza, Antonio; Emery, William J. Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep learning (DL); hyperspectral unmixing; nonnegative matrix factorization (NMF); sparsity constraint; total variation (TV) COMPONENT ANALYSIS; NMF; ALGORITHM; REGRESSION Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often imposed in the abundance estimation step. However, most of the existing NMF-based methods only consider the information in a single layer while neglecting the hierarchical features with hidden information. To alleviate such limitation, in this paper, we propose a new sparsity-constrained deep NMF with total variation (SDNMF-TV) technique for hyperspectral unmixing. First, by adopting the concept of deep learning, the NMF algorithm is extended to deep NMF model. The proposed model consists of pretraining stage and fine-tuning stage, where the former pretrains all factors layer by layer and the latter is used to reduce the total reconstruction error. Second, in order to exploit adequately the spectral and spatial information included in the original hyperspectral image, we enforce two constraints on the abundance matrix. Specifically, the L-1/2 constraint is adopted, since the distribution of each endmember is sparse in the 2-D space. The TV regularizer is further introduced to promote piecewise smoothness in abundance maps. For the optimization of the proposed model, multiplicative update rules are derived using the gradient descent method. The effectiveness and superiority of the SDNMF-TV algorithm are demonstrated by comparing with other unmixing methods on both synthetic and real data sets. [Feng, Xin-Ru; Li, Heng-Chao] Southwest Jiaotong Univ, Sichuan Prov Key Lab Informat Coding & Transmiss, Chengdu 610031, Sichuan, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10071, Spain; [Emery, William J.] Univ Colorado, Dept Aerosp Engn Sci, Boulder, CO 80309 USA Southwest Jiaotong University; Sun Yat Sen University; Mississippi State University; Universidad de Extremadura; University of Colorado System; University of Colorado Boulder Li, HC (corresponding author), Southwest Jiaotong Univ, Sichuan Prov Key Lab Informat Coding & Transmiss, Chengdu 610031, Sichuan, Peoples R China. lihengchao_78@163.com Li, Jun/AAD-7233-2020; Plaza, Antonio/C-4455-2008; Li, Jun/E-6840-2019; Du, Qian/AAB-8840-2022; du, qian/GYJ-7090-2022; Emery, William/L-6179-2017 Li, Jun/0000-0003-1613-9448; Plaza, Antonio/0000-0002-9613-1659; Li, Jun/0000-0003-1613-9448; Emery, William/0000-0002-7598-9082 National Natural Science Foundation of China [61371165, 61771496]; Frontier Intersection Basic Research Project for the Central Universities [A0920502051714-5] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Frontier Intersection Basic Research Project for the Central Universities This work was supported in part by the National Natural Science Foundation of China under Grant 61371165 and Grant 61771496 and in part by the Frontier Intersection Basic Research Project for the Central Universities under Grant A0920502051714-5. 55 82 85 7 69 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing OCT 2018.0 56.0 10 6245 6257 10.1109/TGRS.2018.2834567 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology GV7JP 2023-03-23 WOS:000446300700052 0
J Chu, MY; Liu, LJ; Zheng, Q; Franz, E; Seidel, HP; Theobalt, C; Zayer, R Chu, Mengyu; Liu, Lingjie; Zheng, Quan; Franz, Erik; Seidel, Hans-Peter; Theobalt, Christian; Zayer, Rhaleb Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data ACM TRANSACTIONS ON GRAPHICS English Article NeRF; Physics-Informed Deep Learning; Fluid Reconstruction NETWORKS High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a formidable challenge, due to the complexity of the underlying physics as well as the severe occlusion and complex lighting in the captured data. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting, and thus are unsuitable for real-world scenes with unknown lighting conditions or arbitrary obstacles. We present the first method to reconstruct dynamic fluid phenomena by leveraging the governing physics (ie, Navier-Stokes equations) in an end-to-end optimization from a mere set of sparse video frames without taking lighting conditions, geometry information, or boundary conditions as input. Our method provides a continuous spatio-temporal scene representation using neural networks as the ansatz of density and velocity solution functions for fluids as well as the radiance field for static objects. With a hybrid architecture that separates static and dynamic contents apart, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling. By augmenting time-varying neural radiance fields with physics-informed deep learning, our method benefits from the supervision of images and physical priors. Our progressively growing model with regularization further disentangles the density-color ambiguity in the radiance field, which allows for a more robust optimization from the given input of sparse views. A pretrained density-to-velocity fluid model is leveraged in addition as the data prior to avoid suboptimal velocity solutions which underestimate vorticity but trivially fulfill physical equations. Our method exhibits high-quality results with relaxed constraints and strong flexibility on a representative set of synthetic and real flow captures. Code and sample tests are at https://people.mpi- inf.mpg.de/similar to mchu/projects/PI-NeRF/. [Chu, Mengyu; Liu, Lingjie; Seidel, Hans-Peter; Theobalt, Christian; Zayer, Rhaleb] Max Planck Inst Informat, SIC, Saarbrucken, Germany; [Zheng, Quan] Chinese Acad Sci, Inst Software, Beijing, Peoples R China; [Franz, Erik] Tech Univ Munich, Munich, Germany Max Planck Society; Chinese Academy of Sciences; Institute of Software, CAS; Technical University of Munich Chu, MY (corresponding author), Max Planck Inst Informat, SIC, Saarbrucken, Germany. mchu@mpi-inf.mpg.de; lliu@mpi-inf.mpg.de; zhengquan@iscas.ac.cn; franzer@in.tum.de; hpseidel@mpi-sb.mpg.de; theobalt@mpi-inf.mpg.de; rzayer@mpi-inf.mpg.de Mengyu, Chu/AAP-5661-2021 Mengyu, Chu/0000-0002-7358-433X; Zheng, Quan/0000-0001-5053-5511 ERC Consolidator Grant 4DRepLy [770784]; Lise Meitner Postdoctoral Fellowships ERC Consolidator Grant 4DRepLy; Lise Meitner Postdoctoral Fellowships The project was supported by the ERC Consolidator Grant 4DRepLy (770784). Mengyu Chu and Lingjie Liu were supported by Lise Meitner Postdoctoral Fellowships. 78 0 0 7 8 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0730-0301 1557-7368 ACM T GRAPHIC ACM Trans. Graph. JUL 2022.0 41.0 4 119 10.1145/3528223.3530169 0.0 14 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 3F9OL Green Submitted 2023-03-23 WOS:000830989200116 0
J Tong, XY; Xia, GS; Zhu, XX Tong, Xin-Yi; Xia, Gui-Song; Zhu, Xiao Xiang Enabling country-scale land cover mapping with meter-resolution satellite imagery ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Land cover mapping; High-spatial resolution; Classification; Deep learning; Transfer learning; Domain adaptation; Dataset; Gaofen-2; Gaofen-1; PlanetScope; Sentinel-2; Megacity TIME-SERIES; SEMANTIC SEGMENTATION; DOMAIN ADAPTATION; SENTINEL-2; CLASSIFIER; ALIGNMENT; AREA; SET High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries caused by, e.g., geographical differences or acquisition conditions, few studies have applied high -resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high -resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 km(2), the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution. [Tong, Xin-Yi] German Aerosp Ctr, Remote Sensing Technol Inst, Munchener Str 20, D-82234 Wessling, Germany; [Xia, Gui-Song] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China; [Zhu, Xiao Xiang] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan 430072, Peoples R China; [Zhu, Xiao Xiang] Wuhan Univ, Inst Artificial Intelligence, Wuhan 430072, Peoples R China; [Zhu, Xiao Xiang] Tech Univ Munich, Chair Data Sci Earth Observat, Arcisstr 21, D-80333 Munich, Germany Helmholtz Association; German Aerospace Centre (DLR); Wuhan University; Wuhan University; Wuhan University; Technical University of Munich Zhu, XX (corresponding author), Tech Univ Munich, Chair Data Sci Earth Observat, Arcisstr 21, D-80333 Munich, Germany. xiaoxiang.zhu@tum.de Zhu, Xiao Xiang/0000-0001-5530-3613 European Research Council (ERC) [ERC-2016-StG-714087]; Helmholtz Association, Germany through the Framework of Helmholtz AI; Local Unit Munich Unit @Aeronautics, Space and Transport (MASTr), Germany; Helmholtz Excellent Professorship, Germany Data Science in Earth Observation -Big Data Fusion for Urban Research; German Federal Ministry of Education and Research (BMBF); German Federal Ministry of Economics and Technology; German Research Foundation (DFG); [ZT-I-PF-5-01]; [W2 -W3-100]; [01DD20001]; [50EE2201C]; [GZ: ZH 498/18-1]; [519016653] European Research Council (ERC)(European Research Council (ERC)European Commission); Helmholtz Association, Germany through the Framework of Helmholtz AI; Local Unit Munich Unit @Aeronautics, Space and Transport (MASTr), Germany; Helmholtz Excellent Professorship, Germany Data Science in Earth Observation -Big Data Fusion for Urban Research; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); German Federal Ministry of Economics and Technology(Federal Ministry for Economic Affairs and Energy (BMWi)); German Research Foundation (DFG)(German Research Foundation (DFG)); ; ; ; ; ; The work is jointly supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and in-novation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association, Germany through the Framework of Helmholtz AI (grant number: ZT-I-PF-5-01) -Local Unit Munich Unit @Aeronautics, Space and Transport (MASTr), Germanyand Helmholtz Excellent Professorship, Germany Data Science in Earth Observation -Big Data Fusion for Urban Research(grant number: W2 -W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab AI4EO - Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond(grant number: 01DD20001), by German Federal Ministry of Economics and Technology in the framework of the na-tional center of excellence ML4Earth(grant number: 50EE2201C) and by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653). 84 0 0 7 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. FEB 2023.0 196.0 178 196 10.1016/j.isprsjprs.2022.12.011 0.0 19 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology 8O5MZ 36824311.0 hybrid, Green Accepted, Green Submitted 2023-03-23 WOS:000925880300001 0
J Volk, W; Groche, P; Brosius, A; Ghiotti, A; Kinsey, BL; Liewald, M; Madej, L; Min, J; Yanagimoto, J Volk, Wolfram; Groche, Peter; Brosius, Alexander; Ghiotti, Andrea; Kinsey, Brad L.; Liewald, Mathias; Madej, Lukasz; Min, Junying; Yanagimoto, Jun Models and modelling for process limits in metal forming CIRP ANNALS-MANUFACTURING TECHNOLOGY English Article Modelling; Metal forming; Process limits CONTACT PRESSURE EVOLUTION; INDUCED RESIDUAL-STRESSES; THERMAL FATIGUE CRACKING; DISCRETE ELEMENT METHOD; DEEP-DRAWING PROCESS; CLOSED-LOOP CONTROL; DUCTILE FRACTURE; NEURAL-NETWORK; LOCALIZED NECKING; TOOL WEAR Modelling of metal forming processes is an essential task of production engineering. Due to the latest technological developments, a huge variety of models is already available and extending continuously. Thus, it is important to find a suitable model. This paper gives an overview on the common classification and characterization of modelling and models in metal forming, as well as introduces a model selection procedure. Based on this classification, various relevant process limits for metal forming are investigated regarding existing models. The conclusion of the paper shows open topics as well as upcoming challenges in modelling process limits in metal forming. (C) 2019 Published by Elsevier Ltd on behalf of CIRP. [Volk, Wolfram] Tech Univ Munich, Munich, Germany; [Groche, Peter] Tech Univ Darmstadt, Inst Prod Engn & Forming Machines, Darmstadt, Germany; [Brosius, Alexander] Tech Univ Dresden, Dresden, Germany; [Ghiotti, Andrea] Univ Padua, Dept Ind Engn, Padua, Italy; [Kinsey, Brad L.] Univ New Hampshire, Dept Mech Engn, Durham, NH 03824 USA; [Liewald, Mathias] Univ Stuttgart, Inst Met Forming Technol, Stuttgart, Germany; [Madej, Lukasz] AGH Univ Sci & Technol, Krakow, Poland; [Min, Junying] Tongji Univ, Sch Mech Engn, Shanghai, Peoples R China; [Yanagimoto, Jun] Univ Tokyo, Dept Mech Engn, Tokyo, Japan Technical University of Munich; Technical University of Darmstadt; Technische Universitat Dresden; University of Padua; University System Of New Hampshire; University of New Hampshire; University of Stuttgart; AGH University of Science & Technology; Tongji University; University of Tokyo Volk, W (corresponding author), Tech Univ Munich, Munich, Germany. wolfram.volk@utg.de Madej, Lukasz/A-7918-2012; Brosius, Alexander/GLR-1271-2022; Min, Junying/M-9634-2018; Ghiotti, Andrea/E-8306-2018 Madej, Lukasz/0000-0003-1032-6963; Min, Junying/0000-0002-1754-6259; Volk, Wolfram/0000-0002-5759-3551; Ghiotti, Andrea/0000-0003-1050-1741 269 41 45 6 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0007-8506 1726-0604 CIRP ANN-MANUF TECHN CIRP Ann-Manuf. Technol. 2019.0 68.0 2 775 798 10.1016/j.cirp.2019.05.007 0.0 24 Engineering, Industrial; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Engineering IR7YL 2023-03-23 WOS:000481657600008 0
C Saidi, L; Benbouzid, M IEEE Saidi, Lotfi; Benbouzid, Mohamed Renewable Energy Systems Prognostics and Health Management: A Review of Recent Advances IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY IEEE Industrial Electronics Society English Proceedings Paper 47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON) OCT 13-16, 2021 ELECTR NETWORK IEEE Ind Elect Soc Bearing; Data acquisition; Health indicator; Machine learning; Power converter; Prognostics and health management; Prognostics approaches; Remaining useful hie prediction; Wind turbine generators KALMAN FILTER; STATE; PREDICTION; MODEL This paper aims to highlight the methods of predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. Indeed, prognostic and health management techniques dedicated to renewable energy systems with special reference to wind turbine generators and papers published in the last ten years, are presented. Therefore some applications of prognostics in renewable energy systems, including power converter devices, batteries capacity degradation, and damage in wind turbines' high-speed shaft bearings, are highlighted. [Saidi, Lotfi] Univ Tunis, ENSIT Lab Signal Image & Energy Mastery SIME, LR 13ES03, Tunis 1008, Tunisia; [Saidi, Lotfi] Univ Sousse, Higher Sch Sci & Technol Hammam Sousse, Sousse, Tunisia; [Saidi, Lotfi; Benbouzid, Mohamed] Univ Brest, UMR CNRS 6027, IRDL, F-29238 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Shanghai 201306, Peoples R China Universite de Tunis; Universite de Sousse; Universite de Bretagne Occidentale; Shanghai Maritime University Saidi, L (corresponding author), Univ Tunis, ENSIT Lab Signal Image & Energy Mastery SIME, LR 13ES03, Tunis 1008, Tunisia.;Saidi, L (corresponding author), Univ Sousse, Higher Sch Sci & Technol Hammam Sousse, Sousse, Tunisia.;Saidi, L (corresponding author), Univ Brest, UMR CNRS 6027, IRDL, F-29238 Brest, France. lotfi_saidi@ieee.org 42 0 0 3 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1553-572X 978-1-6654-3554-3 IEEE IND ELEC 2021.0 10.1109/IECON48115.2021.9589308 0.0 6 Automation & Control Systems; Engineering, Industrial; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BS7UF 2023-03-23 WOS:000767230601077 0
J Lv, K; Tang, H; Bak-Jensen, B; Pillai, JR; Tan, Q; Zhang, QL Lv, Kai; Tang, Hao; Bak-Jensen, Birgitte; Radhakrishna Pillai, Jayakrishnan; Tan, Qi; Zhang, Qianli Hierarchical learning optimisation method for the coordination dispatch of the inter-regional power grid considering the quality of service index IET GENERATION TRANSMISSION & DISTRIBUTION English Article power grids; multi-agent systems; power generation dispatch; power generation economics; quality of service; optimisation; hierarchical systems; load dispatching; pricing; learning (artificial intelligence); hierarchical learning optimisation method; coordination dispatch; inter-regional power grid; service index; economic dispatch; multiple uncertain sources; regional grids; tie-line power schedule; regional power grids; focused dispatch problem; service method; service attribute; power dispatch process; model-free hierarchical optimisation method; improved reinforcement learning algorithm; optimal dispatch policy; dispatch policies WIND; GENERATION; COMMITMENT; IMPACT; CHINA The economic dispatch of the inter-regional power grid with multiple uncertain sources and loads is focused in this study. As the tie-line can transmit the power between regional grids, a coordination mechanism between the tie-line power schedule and the economic dispatch of regional power grids is built to solve the focused dispatch problem under the uncertain environment. Furthermore, the quality of service (QoS) method is introduced in this study to consider and study the service attribute in the power dispatch process, which is quantified by the responding behaviour of the price-sensitive consumers. Then, a model-free hierarchical optimisation method based on the learning technique is designed. The hierarchical structure consists of two levels and multiple agents, where the agents learn knowledge from the interaction between themselves and the environment. An improved reinforcement learning algorithm is adopted to find the optimal dispatch policy for each agent, which realises an online optimisation with the operation samples rather than the support of an accurate system model. Finally, the simulation results are shown to validate the effectiveness of the designed method. Specifically, the optimisation process and the obtained dispatch policies are analysed, and the impact of the QoS index on the optimisation is introduced. [Lv, Kai; Tang, Hao; Tan, Qi; Zhang, Qianli] Hefei Univ Technol, Sch Elect Engn & Automat, 193 Tunxi Rd, Hefei, Peoples R China; [Lv, Kai; Tang, Hao] Natl & Local Joint Engn Lab Renewable Energy Acce, 193 Tunxi Rd, Hefei, Peoples R China; [Bak-Jensen, Birgitte; Radhakrishna Pillai, Jayakrishnan] Aalborg Univ, Dept Energy Technol, Pontoppidansstrde 111, DK-9220 Aalborg, Denmark Hefei University of Technology; Aalborg University Tang, H (corresponding author), Hefei Univ Technol, Sch Elect Engn & Automat, 193 Tunxi Rd, Hefei, Peoples R China.;Tang, H (corresponding author), Natl & Local Joint Engn Lab Renewable Energy Acce, 193 Tunxi Rd, Hefei, Peoples R China. htang@hfut.edu.cn Bak-Jensen, Birgitte/0000-0001-8271-1356 National Key R&D Program of China [2017YFB0902600]; Science and Technology Project of SGCC [SGTYHT/18-JS-206, SGTYHT/19-JS-215] National Key R&D Program of China; Science and Technology Project of SGCC This work was supported by the National Key R&D Program of China (2017YFB0902600) and the Science and Technology Project of SGCC (SGTYHT/18-JS-206 and SGTYHT/19-JS-215). 34 2 3 8 35 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-8687 1751-8695 IET GENER TRANSM DIS IET Gener. Transm. Distrib. SEP 18 2020.0 14.0 18 3673 3684 10.1049/iet-gtd.2019.1869 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NK2IB Bronze 2023-03-23 WOS:000566559500006 0
J Liu, LY; He, J; Ren, KY; Lungu, J; Hou, YB; Dong, RH Liu, Leyuan; He, Jian; Ren, Keyan; Lungu, Jonathan; Hou, Yibin; Dong, Ruihai An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition ENTROPY English Article human activity recognition; information gain; attention mechanism; Attention-RNN Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively. [Liu, Leyuan; He, Jian; Ren, Keyan; Lungu, Jonathan; Hou, Yibin] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China; [He, Jian; Ren, Keyan; Hou, Yibin] Beijing Univ Technol, Beijing Engn Res Ctr IOT Software & Syst, Beijing 100124, Peoples R China; [Dong, Ruihai] Univ Coll Dublin, Sch Comp Sci, Dublin D04 V1W8 4, Ireland Beijing University of Technology; Beijing University of Technology; University College Dublin He, J; Ren, KY (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.;He, J; Ren, KY (corresponding author), Beijing Univ Technol, Beijing Engn Res Ctr IOT Software & Syst, Beijing 100124, Peoples R China. chinaleyuan@emails.bjut.edu.cn; jianhee@bjut.edu.cn; keyanren@bjut.edu.cn; JonathanLungu@emails.bjut.edu.cn; ybhou@bjut.edu.cn; ruihai.dong@ucd.ie 44 6 6 12 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy DEC 2021.0 23.0 12 1635 10.3390/e23121635 0.0 15 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics XY7QJ 34945941.0 gold, Green Accepted 2023-03-23 WOS:000737162200001 0
J Oliveri, G; Zardi, F; Rocca, P; Salucci, M; Massa, A Oliveri, Giacomo; Zardi, Francesco; Rocca, Paolo; Salucci, Marco; Massa, Andrea Building a Smart EM Environment-AI-Enhanced Aperiodic Micro-Scale Design of Passive EM Skins IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION English Article Layout; Optimized production technology; Surface waves; Surface impedance; Skin; Numerical models; Metasurfaces; Electromagnetic (EM) holography; iterative projection method; metasurfaces; metamaterials; next-generation communications; smart skins; system-by-design SYSTEM-BY-DESIGN; RECONFIGURABLE INTELLIGENT SURFACES; PARADIGM; ANTENNA An innovative process for the design of static passive smart skins (SPSSs) is proposed to take into account, within the synthesis, the electromagnetic (EM) interactions due to their finite (macrolevel) size and aperiodic (microscale) layouts. Such an approach leverages the combination of an inverse source (IS) formulation, to define the SPSS surface currents, and of an instance of the system-by-design paradigm, to synthesize the unit cell (UC) descriptors suitable for supporting these currents. As for this latter step, an enhanced artificial intelligence (IA)-based digital twin (DT) is built to efficiently and reliably predict the relationships among the UCs and the nonuniform coupling effects arising when the UCs are irregularly assembled to build the corresponding SPSS. Toward this end and unlike state-of-the-art approaches, an aperiodic finite small-scale model of the SPSS is derived to generate the training database for the DT implementation. A set of representative numerical experiments, dealing with different radiation objectives and smart skin apertures, is reported to assess the reliability of the conceived design process and illustrate the radiation features of the resulting layouts, validated with accurate full-wave simulations, as well. [Oliveri, Giacomo; Zardi, Francesco; Rocca, Paolo; Salucci, Marco; Massa, Andrea] Univ Trento, ELEDIA Res Ctr ELEDIA UniTN, Dept Civil Environm & Mech Engn DICAM, I-38123 Trento, Italy; [Rocca, Paolo] ELEDIA Res Ctr ELEDIA XIDIAN Xidian Univ, Xian 710071, Shaanxi, Peoples R China; [Massa, Andrea] ELEDIA Res Ctr ELEDIA UESTC UESTC, Sch Elect Engn, Chengdu 611731, Peoples R China; [Massa, Andrea] ELEDIA Res Ctr ELEDIA TSINGHUA Tsinghua Univ, Beijing 100084, Haidian, Peoples R China University of Trento Massa, A (corresponding author), ELEDIA Res Ctr ELEDIA UESTC UESTC, Sch Elect Engn, Chengdu 611731, Peoples R China. giacomo.oliveri@unitn.it; francesco.zardi@unitn.it; paolo.rocca@unitn.it; marco.salucci@unitn.it; andrea.massa@unitn.it Rocca, Paolo/F-1145-2011; Massa, Andrea/F-3276-2013; Salucci, Marco/S-8654-2016; Oliveri, Giacomo/F-1030-2011 Rocca, Paolo/0000-0003-4522-6903; Massa, Andrea/0000-0002-8429-8937; Salucci, Marco/0000-0002-6948-8636; Oliveri, Giacomo/0000-0002-5238-358X; Zardi, Francesco/0000-0003-4566-3687 Italian Ministry of Education, University, and Research [2017BHFZKH, CUP: E64I19000560001]; National Science Foundation of China [61721001]; Department of Science and Technology of Shaanxi Province [2021JZD-003] Italian Ministry of Education, University, and Research(Ministry of Education, Universities and Research (MIUR)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Department of Science and Technology of Shaanxi Province This work benefited from the networking activities carried out within the Project Cloaking Metasurfaces for a New Generation of Intelligent Antenna Systems (MANTLES) under Grant 2017BHFZKH through the Italian Ministry of Education, University, and Research under the PRIN2017 Program under Grant CUP: E64I19000560001, within the Project SPEED through the National Science Foundation of China under the Chang-Jiang Visiting Professorship Program under Grant 61721001, and within the Project Research on Uncertainty Factors and Propagation Mechanism of Conformal Load-Bearing Antenna Structure through the Department of Science and Technology of Shaanxi Province within the Program Natural Science Basic Research Plan in Shaanxi Province under Grant 2021JZD-003. 31 2 2 7 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-926X 1558-2221 IEEE T ANTENN PROPAG IEEE Trans. Antennas Propag. OCT 2022.0 70.0 10 8757 8770 10.1109/TAP.2022.3151354 0.0 14 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 6A5QJ Green Submitted 2023-03-23 WOS:000880709700013 0
J de Rezende, A; Malmali, M; Dral, PO; Lischka, H; Tunega, D; Aquino, AJA de Rezende, Armando; Malmali, Mahdi; Dral, Pavlo O.; Lischka, Hans; Tunega, Daniel; Aquino, Adelia J. A. Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage JOURNAL OF PHYSICAL CHEMISTRY C English Article INITIO MOLECULAR-DYNAMICS; TOTAL-ENERGY CALCULATIONS; CHEMISTRY; CHLORIDE Metal halide salts have been proposed as promising absorbents for high-temperature separation of ammonia in the intensified Haber-Bosch process. A challenge for the widespread application of metal halides in such applications is the regeneration of absorbents, which requires energy-intensive temperature and pressure swings. New mixed metal halides could be prepared from the pure metal halides, offering a huge number of combinations that could be used to optimize the operating costs. To achieve this goal, knowledge of the deammoniation reaction energies is needed to direct the swing cycles efficiently. Computational methods offer excellent opportunities to obtain these data in an economic way. This work presents a combined density functional theory (DFT) and machine learning (ML) approach to manage these large amounts of mixing possibilities by efficiently and accurately predicting the required deammoniation reaction energies. Mixed metal halides (MMHs) of Mg, Ca, Cl, and Br [Mg(x)Ca(1-x)ClyBr(2-y)(NH3)n, x = 0-1, y = 0- 2, and n = 6, 2, 1 and 0] were selected with deammoniation steps n following the coordination numbers 6 & RARR; 2 & RARR; 1 & RARR; 0. To construct a data-efficient and computationally inexpensive ML approach, we (i) developed an efficient interpolation scheme based on DFT calculations for the structures of the MMHs based on those of the pure compounds, (ii) performed fixed-structure DFT calculations for creating training and test sets, and (iii) analyzed the importance of the features to be used in the ML procedure. Remarkably, our ML approach required a very small training set of 45 cases from a total of 4096 cases per deammoniation reaction step to achieve satisfactory predictions with chemical accuracy. Deammoniation energies were calculated with a standard deviation of better than +/- 0.1 kcal/mol for the step 6 -> 2 and up to +/- 0.8 kcal/mol for the step 1 -> 0. This approach has a potential to be applicable to a broad range of metal halides composed of metals from alkaline earth elements and/or 3d and 4d metals. [de Rezende, Armando; Aquino, Adelia J. A.] Texas Tech Univ, Dept Mech Engn, Lubbock, TX 79409 USA; [Malmali, Mahdi] Texas Tech Univ, Dept Chem Engn, Lubbock, TX 79409 USA; [Dral, Pavlo O.] Xiamen Univ, Dept Chem, State Key Lab Phys Chem Solid Surfaces, Fujian Prov Key Lab Theoret & Computat Chem, Xiamen 361005, Peoples R China; [Dral, Pavlo O.] Xiamen Univ, Coll Chem & Chem Engn, Xiamen 361005, Peoples R China; [Tunega, Daniel] Univ Nat Resources & Life Sci, Inst Soil Res, A-1190 Vienna, Austria; [Lischka, Hans] Texas Tech Univ, Dept Chem & Biochem, Lubbock, TX 79409 USA Texas Tech University System; Texas Tech University; Texas Tech University System; Texas Tech University; Xiamen University; Xiamen University; University of Natural Resources & Life Sciences, Vienna; Texas Tech University System; Texas Tech University Aquino, AJA (corresponding author), Texas Tech Univ, Dept Mech Engn, Lubbock, TX 79409 USA.;Tunega, D (corresponding author), Univ Nat Resources & Life Sci, Inst Soil Res, A-1190 Vienna, Austria.;Lischka, H (corresponding author), Texas Tech Univ, Dept Chem & Biochem, Lubbock, TX 79409 USA. Hans.Lischka@univie.ac.at; Daniel.Tunega@boku.ac.at; Adelia.Aquino@ttu.edu ; Malmali, Mahdi/N-4441-2015 De Rezende, Armando/0000-0002-5474-7068; Tunega, Daniel/0000-0003-0822-1580; Malmali, Mahdi/0000-0001-5190-1261 National Natural Science Foundation of China [22003051]; Fundamental Research Funds for the Central Universities [20720210092]; HPCC facilities of Texas Tech University, at the Vienna Scientific Cluster (VSC) [70544]; Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); HPCC facilities of Texas Tech University, at the Vienna Scientific Cluster (VSC); Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces We are grateful for supply of computer time at the HPCC facilities of Texas Tech University, at the Vienna Scientific Cluster (VSC) , project No. 70544, and at the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http:// www.tacc.utexas.edu) . P.O.D. acknowledges funding by the National Natural Science Foundation of China (No. 22003051) , by the Fundamental Research Funds for the Central Universities (No. 20720210092) , and via the Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces. 53 1 1 1 4 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1932-7447 1932-7455 J PHYS CHEM C J. Phys. Chem. C JUL 28 2022.0 126.0 29 12184 12196 10.1021/acs.jpcc.2c02586 0.0 JUL 2022 13 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science 3O6HO 2023-03-23 WOS:000831509400001 0
J Bai, L; Le Bourdais, F; Miorelli, R; Calmon, P; Velichko, A; Drinkwater, BW Bai, Long; Le Bourdais, Florian; Miorelli, Roberto; Calmon, Pierre; Velichko, Alexander; Drinkwater, Bruce W. Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL English Article Scattering; Acoustics; Arrays; Imaging; Ultrasonic variables measurement; Training; Geometry; Deep neural networks; defect characterization; non-destructive testing (NDT); principal component analysis (PCA); ultrasonic arrays NEAR-SURFACE DEFECTS; RESOLUTION ENHANCEMENT; CRACK CHARACTERIZATION; FULL MATRIX; ARRAYS; RECONSTRUCTION; ALGORITHM Accurate defect characterization is desirable in the ultrasonic nondestructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterization using ultrasonic arrays, high-resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterization becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterization. Defect characterization can be performed based on a scattering matrix database that consists of the scattering matrices of idealized defects with varying parameters. In this article, the problem of characterizing small surface-breaking notches is studied using two different approaches. The first approach is based on the introduction of a general coherent noise model, and it performs characterization within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterization task. It is shown that convolutional neural networks (CNNs) can achieve the best characterization accuracy among the considered ML approaches, and they give similar characterization uncertainty to that of the Bayesian approach if a notch is favorably oriented. The performance of both approaches varied for unfavorably oriented notches, and the ML approach tends to give results with higher variance and lower biases. [Bai, Long] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China; [Le Bourdais, Florian; Miorelli, Roberto; Calmon, Pierre] Univ Paris Saclay, CEA, List, F-91120 Palaiseau, France; [Velichko, Alexander; Drinkwater, Bruce W.] Univ Bristol, Dept Mech Engn, Bristol BS8 1TR, Avon, England Huazhong University of Science & Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of Bristol Bai, L (corresponding author), Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China. bailong@hust.edu.cn Bai, Long/E-7864-2015 Bai, Long/0000-0002-0482-1514; Drinkwater, Bruce W/0000-0002-8307-1175; Velichko, Alexander/0000-0002-7304-4800; Miorelli, Roberto/0000-0003-3728-2227 National Natural Science Foundation of China [52005205]; European Union's Horizon 2020 Research and Innovation Programme [755500]; Engineering and Physical Sciences Research Council (EPSRC), U.K. [EP/L022125/1]; EPSRC [EP/L022125/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union's Horizon 2020 Research and Innovation Programme; Engineering and Physical Sciences Research Council (EPSRC), U.K.(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China under Grant 52005205, in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 755500, and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/L022125/1. 54 7 7 13 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-3010 1525-8955 IEEE T ULTRASON FERR IEEE Trans. Ultrason. Ferroelectr. Freq. Control OCT 2021.0 68.0 10 3143 3155 10.1109/TUFFC.2021.3084798 0.0 13 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering UY0VB 34048342.0 2023-03-23 WOS:000701250000012 0
J Xu, H; Zhang, SJ; Yi, XF; Plewczynski, D; Li, MLJ Xu, Hang; Zhang, Shijie; Yi, Xianfu; Plewczynski, Dariusz; Li, Mulin Jun Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL English Review Chromatin Conformation Capture; Enhancer-promoter interaction; cis-Regulatory element; Chromatin loop; Machine learning; Computational method HI-C DATA; GENOME ARCHITECTURE; HIGH-RESOLUTION; TRANSCRIPTIONAL ENHANCERS; CAP ANALYSIS; SINGLE; ELEMENTS; EXPRESSION; TOPOLOGY; LANDSCAPES Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. [Xu, Hang; Zhang, Shijie; Li, Mulin Jun] Tianjin Med Univ, Tianjin Key Lab Med Epigenet, Collaborat Innovat Ctr Tianjin Med Epigenet 2011, Tianjin, Peoples R China; [Zhang, Shijie; Li, Mulin Jun] Tianjin Med Univ, Sch Basic Med Sci, Dept Pharmacol, Tianjin Key Lab Inflammat Biol, Tianjin, Peoples R China; [Yi, Xianfu] Tianjin Med Univ, Sch Biomed Engn, Tianjin, Peoples R China; [Plewczynski, Dariusz] Univ Warsaw, Ctr New Technol, Banacha 2c, PL-02097 Warsaw, Poland; [Plewczynski, Dariusz] Warsaw Univ Technol, Fac Math & Informat Sci, Koszykowa 75, PL-00662 Warsaw, Poland Tianjin Medical University; Tianjin Medical University; Tianjin Medical University; University of Warsaw; Warsaw University of Technology Li, MLJ (corresponding author), Tianjin Med Univ, Sch Basic Med Sci, Dept Pharmacol, Tianjin Key Lab Inflammat Biol, Tianjin, Peoples R China. mulinli@connect.hku.hk zhang, shijie/HGF-0836-2022 Yi, Xianfu/0000-0002-8094-2676 National Natural Science Foundation of China [31871327]; Natural Science Foundation of Tianjin [19JCJQJC63600]; Polish National Science Centre [2014/15/B/ST6/05082]; Foundation for Polish Science - European Union under the European Regional Development Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Tianjin(Natural Science Foundation of Tianjin); Polish National Science Centre; Foundation for Polish Science - European Union under the European Regional Development Fund This work was supported by grants from the National Natural Science Foundation of China 31871327 (M.J.L), Natural Science Foundation of Tianjin 19JCJQJC63600 (M.J.L); and Polish National Science Centre (2014/15/B/ST6/05082) and Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund (D.P). 169 23 24 7 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2001-0370 COMPUT STRUCT BIOTEC Comp. Struct. Biotechnol. J.. 2020.0 18.0 558 570 10.1016/j.csbj.2020.02.013 0.0 13 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology PR5TM 32226593.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000607298800003 0
J Lv, ZH; Lloret, J; Song, HB Lv, Zhihan; Lloret, Jaime; Song, Houbing Guest Editorial Software Defined Internet of Vehicles IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Editorial Material Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by filtering and cleaning massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety. [Lv, Zhihan] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China; [Lloret, Jaime] Univ Politecn Valencia, Dept Commun, Valencia 46022, Spain; [Song, Houbing] Embry Riddle Aeronaut Univ, Dept Elect Engn & Comp Sci, Daytona Beach, FL 32114 USA Qingdao University; Universitat Politecnica de Valencia; Embry-Riddle Aeronautical University Lv, ZH (corresponding author), Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China. lvzhihan@gmail.com; jlloret@dcom.upv.es; h.song@ieee.org Song, Houbing/E-3628-2010; Lloret, Jaime/H-3994-2013; Lv, Zhihan/I-3187-2014; Lv, Zhihan/GLR-6000-2022 Song, Houbing/0000-0003-2631-9223; Lloret, Jaime/0000-0002-0862-0533; Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074 0 5 6 11 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. JUN 2021.0 22.0 6 3504 3510 10.1109/TITS.2021.3080875 0.0 7 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation SN5WW Bronze 2023-03-23 WOS:000658360600023 0
J Zhao, ZY; Cao, RJ; Un, KF; Yu, WH; Mak, PI; Martins, RP Zhao, Zhongyu; Cao, Rujian; Un, Ka-Fai; Yu, Wei-Han; Mak, Pui-In; Martins, Rui P. An FPGA-Based Transformer Accelerator Using Output Block Stationary Dataflow for Object Recognition Applications IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS English Article Transformers; Energy efficiency; Broadcasting; Convolutional neural networks; Integrated circuit modeling; Field programmable gate arrays; Random access memory; Dataflow; digital accelerator; energy-efficient; field-programmable gate array (FPGA); energy efficiency; image recognition; transformer CNN ACCELERATOR; EFFICIENT; HARDWARE The transformer-based model has great potential to deliver higher accuracy for object recognition applications when comparing it with the convolution neural network (CNN). Yet, the amount of weight sharing of a transformer-based model is significantly lower than that of the CNN, which should apply different dataflow to reduce the memory access. This brief proposes a transformer accelerator with an output block stationary (OBS) dataflow to minimize the repeated memory access by block-level and vector-level broadcasting while preserving a high digital signal processor (DSP) utilization rate, leading to higher energy efficiency. It also lowers the memory access bandwidth to the input and output. Verified through an FPGA, the proposed accelerator evaluates a transformer-in-transformer (TNT) model with a throughput of 728.3 GOPs, corresponding to energy efficiency of 58.31 GOPs/W. [Zhao, Zhongyu; Cao, Rujian; Un, Ka-Fai; Yu, Wei-Han; Mak, Pui-In; Martins, Rui P.] Univ Macau, State Key Lab Analog & Mixed Signal VLSI, Inst Microelect, Macau, Peoples R China; [Zhao, Zhongyu; Cao, Rujian; Un, Ka-Fai; Yu, Wei-Han; Mak, Pui-In; Martins, Rui P.] Univ Macau, Fac Sci & Technol ECE, Macau, Peoples R China; [Martins, Rui P.] Univ Lisbon, Inst Super Tecn, P-1649004 Lisbon, Portugal University of Macau; University of Macau; Universidade de Lisboa; Instituto Superior Tecnico Un, KF (corresponding author), Univ Macau, State Key Lab Analog & Mixed Signal VLSI, Inst Microelect, Macau, Peoples R China.;Un, KF (corresponding author), Univ Macau, Fac Sci & Technol ECE, Macau, Peoples R China. kafaiun@umac.mo; rmartins@umac.mo Mak, Pui-In/B-4626-2011; Martins, Rui/C-8270-2009 Mak, Pui-In/0000-0002-3579-8740; Martins, Rui/0000-0003-2821-648X; Zhao, Zhongyu/0000-0002-5221-6490 27 0 0 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-7747 1558-3791 IEEE T CIRCUITS-II IEEE Trans. Circuits Syst. II-Express Briefs JAN 2023.0 70.0 1 281 285 10.1109/TCSII.2022.3196055 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 7P4XZ 2023-03-23 WOS:000908711600057 0
J Denceux, T; Kanjanatarakul, O; Sriboonchitta, S Denceux, Thierry; Kanjanatarakul, Orakanya; Sriboonchitta, Songsak A new evidential K-nearest neighbor rule based on contextual discounting with partially supervised learning INTERNATIONAL JOURNAL OF APPROXIMATE REASONING English Article; Proceedings Paper 5th International Conference on Belief Functions (BELIEF) / 9th International Conference on Soft Methods in Probability and Statistics (SMPS) SEP 17-21, 2018 Compiegne, FRANCE Belief functions; Dempster-Shafer theory; Classification; Machine learning; Soft labels; Uncertain data BELIEF FUNCTIONS; CLASSIFICATION The evidential K nearest neighbor classifier is based on discounting evidence from learning instances in a neighborhood of the pattern to be classified. To adapt the method to partially supervised data, we propose to replace the classical discounting operation by contextual discounting, a more complex operation based on as many discount rates as classes. The parameters of the method are tuned by maximizing the evidential likelihood, an extension of the likelihood function based on uncertain data. The resulting classifier is shown to outperform alternative methods in partially supervised learning tasks. (C) 2019 Elsevier Inc. All rights reserved. [Denceux, Thierry] Univ Technol Compiegne, CNRS, UMR 7253, Heudiasyc, Compiegne, France; [Denceux, Thierry] Shanghai Univ, UTSEUS, Shanghai, Peoples R China; [Kanjanatarakul, Orakanya] Chiang Mai Rajabhat Univ, Fac Management Sci, Chiang Mai, Thailand; [Sriboonchitta, Songsak] Chiang Mai Univ, Fac Econ, Chiang Mai, Thailand Picardie Universites; Universite de Technologie de Compiegne; Centre National de la Recherche Scientifique (CNRS); Shanghai University; Chiang Mai Rajabhat University; Chiang Mai University Denceux, T (corresponding author), Univ Technol Compiegne, CNRS, UMR 7253, Heudiasyc, Compiegne, France. thierry.denoeux@utc.fr Denoeux, Thierry/0000-0002-0660-5436; yamaka, woraphon/0000-0002-0787-1437 Agence Nationale de la Recherche, Laboratory of Excellence MS2T [ANR-11-IDEX0004-02]; Centre of Excellence in Econometrics, Research Administration Centre at Chiang Mai University Agence Nationale de la Recherche, Laboratory of Excellence MS2T(French National Research Agency (ANR)); Centre of Excellence in Econometrics, Research Administration Centre at Chiang Mai University This research was supported by Agence Nationale de la Recherche, Laboratory of Excellence MS2T (reference ANR-11-IDEX0004-02) and by the Centre of Excellence in Econometrics, Research Administration Centre at Chiang Mai University. 39 26 28 0 14 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0888-613X 1873-4731 INT J APPROX REASON Int. J. Approx. Reasoning OCT 2019.0 113.0 287 302 10.1016/j.ijar.2019.07.009 0.0 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science IZ6BE Green Submitted, Bronze 2023-03-23 WOS:000487166000015 0
J Liu, LY; Hou, YB; He, J; Lungu, J; Dong, RH Liu, Leyuan; Hou, Yibin; He, Jian; Lungu, Jonathan; Dong, Ruihai An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People SENSORS English Article fall detection; ZigBee; FD-DNN; energy-efficient TECHNOLOGIES; SAFETY A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption. [Liu, Leyuan; Hou, Yibin; He, Jian; Lungu, Jonathan] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China; [Hou, Yibin; He, Jian] Beijing Univ Technol, Beijing Engn Res Ctr IOT Software & Syst, Beijing 100124, Peoples R China; [Dong, Ruihai] Univ Coll Dublin, Insight Ctr Data Analyt, Dublin 4, Ireland Beijing University of Technology; Beijing University of Technology; University College Dublin He, J (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.;He, J (corresponding author), Beijing Univ Technol, Beijing Engn Res Ctr IOT Software & Syst, Beijing 100124, Peoples R China. chinaleyuan@emails.bjut.edu.cn; ybhou@bjut.edu.cn; jianhee@bjut.edu.cn; Jtlungu77@hotmail.com; ruihai.dong@ucd.ie Liu, Leyuan/0000-0001-8401-1388 36 7 7 2 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors AUG 2020.0 20.0 15 4192 10.3390/s20154192 0.0 16 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation MZ3JO 32731465.0 Green Accepted, gold 2023-03-23 WOS:000559018700001 0
J He, SH; Lian, CF; Thorstad, W; Gay, H; Zhao, YJ; Ruan, S; Wang, XW; Li, H He, Shenghua; Lian, Chunfeng; Thorstad, Wade; Gay, Hiram; Zhao, Yujie; Ruan, Su; Wang, Xiaowei; Li, Hua A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers BIOINFORMATICS English Article FEATURE-SELECTION; MUTUAL INFORMATION; HEAD; PROBABILITIES; FEATURES; RISK; RULE Motivation: Predicting early in treatment whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing personalized cancer care. Most oropharyngeal squamous cell carcinoma (OPSCC) patients receive standard cancer therapy. However, the treatment outcomes vary significantly and are difficult to predict. Multiple studies indicate that microRNAs (miRNAs) are promising cancer biomarkers for the prognosis of oropharyngeal cancer. The reliable and efficient use of miRNAs for patient stratification and treatment outcome prognosis is still a very challenging task, mainly due to the relatively high dimensionality of miRNAs compared to the small number of observation sets; the redundancy, irrelevancy and uncertainty in the large amount of miRNAs; and the imbalanced observation patient samples. Results: In this study, a new machine learning-based prognosis model was proposed to stratify subsets of OPSCC patients with low and high risks for treatment failure. The model cascaded a two-stage prognostic biomarker selection method and an evidential K-nearest neighbors classifier to address the challenges and improve the accuracy of patient stratification. The model has been evaluated on miRNA expression profiling of 150 oropharyngeal tumors by use of overall survival and disease-specific survival as the end points of disease treatment outcomes, respectively. The proposed method showed superior performance compared to other advanced machine-learning methods in terms of common performance quantification metrics. The proposed prognosis model can be employed as a supporting tool to identify patients who are likely to fail standard therapy and potentially benefit from alternative targeted treatments. [He, Shenghua] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USA; [Lian, Chunfeng] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, Peoples R China; [Thorstad, Wade; Gay, Hiram; Wang, Xiaowei] Washington Univ, Dept Radiat Oncol, St Louis, MO 63130 USA; [Zhao, Yujie; Li, Hua] Carle Fdn Hosp, Carle Canc Ctr, Urbana, IL 61801 USA; [Ruan, Su] Univ Rouen, Lab LITIS EA 4108, Equipe Quantif, F-76183 Rouen, France; [Li, Hua] Univ Illinois, Dept Bioengn, Urbana, IL 61801 USA; [Li, Hua] Canc Ctr Illinois, Urbana, IL 61801 USA Washington University (WUSTL); Xi'an Jiaotong University; Washington University (WUSTL); Universite de Rouen Normandie; University of Illinois System; University of Illinois Urbana-Champaign Wang, XW (corresponding author), Washington Univ, Dept Radiat Oncol, St Louis, MO 63130 USA.;Li, H (corresponding author), Carle Fdn Hosp, Carle Canc Ctr, Urbana, IL 61801 USA.;Li, H (corresponding author), Univ Illinois, Dept Bioengn, Urbana, IL 61801 USA.;Li, H (corresponding author), Canc Ctr Illinois, Urbana, IL 61801 USA. xwang317@uic.edu; huali19@illinois.edu He, Shenghua/AAW-3544-2020 Wang, Xiaowei/0000-0001-9447-4685; Li, Hua/0000-0002-5629-2247 National Institutes of Health Award [R01DE026471, R01CA233873, R21CA223799] National Institutes of Health Award(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) This work was supported by National Institutes of Health Award [R01DE026471, R01CA233873 and R21CA223799]. 63 0 0 3 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics SEP 15 2021.0 37.0 19 3106 3114 10.1093/bioinformatics/btab242 0.0 APR 2021 9 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics XT8JM 34237137.0 Bronze 2023-03-23 WOS:000733827400003 0
J Tumasyan, A; Adam, W; Andrejkovic, JW; Bergauer, T; Chatterjee, S; Dragicevic, M; Del Valle, AE; Fruhwirth, R; Jeitler, M; Krammer, N; Lechner, L; Liko, D; Mikulec, I; Paulitsch, P; Pitters, FM; Schieck, J; Schofbeck, R; Spanring, M; Templ, S; Waltenberger, W; Wulz, CE; Chekhovsky, V; Litomin, A; Makarenko, V; Darwish, MR; De Wolf, EA; Janssen, X; Kello, T; Lelek, A; Sfar, HR; Van Mechelen, P; Van Putte, S; Van Remortel, N; Blekman, F; Bols, ES; D'Hondt, J; De Clercq, J; Delcourt, M; El Faham, H; Lowette, S; Moortgat, S; Morton, A; Muller, D; Sahasransu, AR; Tavernier, S; Van Doninck, W; Van Mulders, P; Stylianou, N; Beghin, D; Bilin, B; Clerbaux, B; De Lentdecker, G; Favart, L; Grebenyuk, A; Kalsi, AK; Lee, K; Mahdavikhorrami, M; Makarenko, I; Moureaux, L; Petre, L; Popov, A; Postiau, N; Starling, E; Thomas, L; Vanden Bemden, M; Vander Velde, C; Vanlaer, P; Vannerom, D; Wezenbeek, L; Fang, W; Gao, X; Cornelis, T; Dobur, D; Knolle, J; Lambrecht, L; Mestdach, G; Niedziela, M; Roskas, C; Samalan, A; Skovpen, K; Tran, TT; Tytgat, M; Verbeke, W; Vermassen, B; Vit, M; Bethani, A; Bruno, G; Bury, F; Caputo, C; David, P; Delaere, C; Donertas, IS; Giammanco, A; Jaffel, K; Lemaitre, V; Mondal, K; Prisciandaro, J; Taliercio, A; Teklishyn, M; Vischia, P; Wertz, S; Wuyckens, S; Alves, GA; Hensel, C; Moraes, A; Alda, WL; Pereira, MAG; Ferreira, MB; Malbouisson, HB; Carvalho, W; Chinellato, J; Da Costa, EM; Da Silveira, GG; Damiao, DD; De Souza, SF; Figueiredo, DM; Herrera, CM; Amarilo, KM; Mundim, L; Nogima, H; Teles, PR; Santoro, A; Do Amaral, SMS; Sznajder, A; Thiel, M; De Araujo, FTD; Pereira, AV; Bernardes, CA; Calligaris, L; Tomei, TRFP; Gregores, EM; Lemos, DS; Mercadante, PG; Novaes, SF; Padula, SS; Aleksandrov, A; Antchev, G; Hadjiiska, R; Iaydjiev, P; Misheva, M; Rodozov, M; Shopova, M; Sultanov, G; Dimitrov, A; Ivanov, T; Litov, L; Pavlov, B; Petkov, P; Petrov, A; Cheng, T; Guo, Q; Javaid, T; Mittal, M; Wang, H; Yuan, L; Ahmad, M; Bauer, G; Dozen, C; Hu, Z; Martins, J; Wang, Y; Yi, K; Chapon, E; Chen, GM; Chen, HS; Chen, M; Iemmi, F; Kapoor, A; Leggat, D; Liao, H; Liu, ZA; Monti, F; Sharma, R; Tao, J; Thomas-wilsker, J; Wang, J; Zhang, H; Zhang, S; Zhao, J; Agapitos, A; Ban, Y; Chen, C; Huang, Q; Levin, A; Li, Q; Lu, M; Lyu, X; Mao, Y; Qian, SJ; Wang, D; Wang, Q; Xiao, J; You, Z; Okawa, H; Xiao, M; Avila, C; Cabrera, A; Florez, C; Fraga, J; Sarkar, A; Delgado, MAS; Guisao, JM; Ramirez, F; Alvarez, JDR; Gonzalez, CAS; Giljanovic, D; Godinovic, N; Lelas, D; Puljak, I; Antunovic, Z; Kovac, M; Sculac, T; Brigljevic, V; Ferencek, D; Majumder, D; Roguljic, M; Starodumov, A; Susa, T; Attikis, A; Erodotou, E; Ioannou, A; Kole, G; Kolosova, M; Konstantinou, S; Mousa, J; Nicolaou, C; Ptochos, F; Razis, PA; Rykaczewski, H; Saka, H; Finger, M; Finger, M; Kveton, A; Ayala, E; Jarrin, EC; Abdelalim, AA; Khalil, S; Lotfy, A; Mahmoud, MA; Bhowmik, S; De Oliveira, ACA; Dewanjee, RK; Ehataht, K; Kadastik, M; Pata, J; Raidal, M; Veelken, C; Eerola, P; Forthomme, L; Kirschenmann, H; Osterberg, K; Voutilainen, M; Bharthuar, S; Brucken, E; Garcia, F; Havukainen, J; Kim, MS; Kinnunen, R; Lampen, T; Lassila-Perini, K; Lehti, S; Linden, T; Lotti, M; Martikainen, L; Ott, J; Siikonen, H; Tuominen, E; Tuominiemi, J; Luukka, P; Petrow, H; Tuuva, T; Amendola, C; Besancon, M; Couderc, F; Dejardin, M; Denegri, D; Faure, JL; Ferri, F; Ganjour, S; Givernaud, A; Gras, P; de Monchenault, GH; Jarry, P; Lenzi, B; Locci, E; Malcles, J; Rander, J; Rosowsky, A; Sahin, MO; Savoy-Navarro, A; Titov, M; Yu, GB; Mijuskovic, J; Ahuja, S; Beaudette, F; Bonanomi, M; Perraguin, AB; Busson, P; Cappati, A; Charlot, C; Davignon, O; Diab, B; Falmagne, G; Ghosh, S; de Cassagnac, RG; Hakimi, A; Kucher, I; Nguyen, M; Ochando, C; Paganini, P; Rembser, J; Salerno, R; Sauvan, JB; Sirois, Y; Zabi, A; Zghiche, A; Agram, JL; Andrea, J; Apparu, D; Bloch, D; Bourgatte, G; Brom, JM; Chabert, EC; Collard, C; Darej, D; Fontaine, JC; Goerlach, U; Grimault, C; Le Bihan, AC; Nibigira, E; Van Hove, P; Asilar, E; Beauceron, S; 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R.; Tabb, W.; Yan, F.; Agarwal, G.; Bandyopadhyay, H.; Hay, L.; Iashvili, I.; Kharchilava, A.; McLean, C.; Nguyen, D.; Pekkanen, J.; Rappoccio, S.; Williams, A.; Alverson, G.; Barberis, E.; Freer, C.; Haddad, Y.; Hortiangtham, A.; Li, J.; Madigan, G.; Marzocchi, B.; Morse, D. M.; Nguyen, V; Orimoto, T.; Parker, A.; Skinnari, L.; Tishelman-Charny, A.; Wamorkar, T.; Wang, B.; Wisecarver, A.; Wood, D.; Bueghly, J.; Chen, Z.; Gilbert, A.; Gunter, T.; Hahn, K. A.; Odell, N.; Schmitt, M. H.; Velasco, M.; Band, R.; Bucci, R.; Dev, N.; Goldouzian, R.; Hildreth, M.; Anampa, K. Hurtado; Jessop, C.; Lannon, K.; Loukas, N.; Marinelli, N.; Mcalister, I; McCauley, T.; Meng, F.; Mohrman, K.; Ruchti, R.; Siddireddy, P.; Wayne, M.; Wightman, A.; Wolf, M.; Zarucki, M.; Zygala, L.; Bylsma, B.; Cardwell, B.; Durkin, L. S.; Francis, B.; Hill, C.; Ornelas, M. Nunez; Wei, K.; Winer, B. L.; Yates, B. R.; Addesa, F. M.; Bonham, B.; Das, P.; Dezoort, G.; Elmer, P.; Frankenthal, A.; Greenberg, B.; Haubrich, N.; Higginbotham, S.; Kalogeropoulos, A.; Kopp, G.; Kwan, S.; Lange, D.; Lucchini, M. T.; Marlow, D.; Mei, K.; Ojalvo, I; Olsen, J.; Palmer, C.; Stickland, D.; Tully, C.; Norberg, S.; Bakshi, A. S.; Barnes, V. E.; Chawla, R.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Karmarkar, S.; Liu, M.; Negro, G.; Neumeister, N.; Paspalaki, G.; Peng, C. C.; Piperov, S.; Purohit, A.; Schulte, J. F.; Stojanovic, M.; Thieman, J.; Wang, F.; Xiao, R.; Xie, W.; Dolen, J.; Parashar, N.; Baty, A.; Decaro, M.; Dildick, S.; Ecklund, K. M.; Freed, S.; Gardner, P.; Geurts, F. J. M.; Li, W.; Padley, B. P.; Redjimi, R.; Shi, W.; Leiton, A. G. Stahl; Zhang, L.; Bodek, A.; de Barbaro, P.; Demina, R.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Hindrichs, O.; Khukhunaishvili, A.; Ranken, E.; Taus, R.; Chiarito, B.; Chou, J. P.; Gandrakota, A.; Gershtein, Y.; Halkiadakis, E.; Hart, A.; Heindl, M.; Hughes, E.; Kaplan, S.; Karacheban, O.; Laflotte, I; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thayil, S. A.; Thomas, S.; Acharya, H.; Delannoy, A. G.; Spanier, S.; Bouhali, O.; Dalchenko, M.; Delgado, A.; Eusebi, R.; Gilmore, J.; Huang, T.; Luo, S.; Malhotra, S.; Mueller, R.; Overton, D.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; Hegde, V; Kunori, S.; Lamichhane, K.; Mengke, T.; Muthumuni, S.; Peltola, T.; Volobouev, I; Whitbeck, A.; Appelt, E.; Greene, S.; Gurrola, A.; Johns, W.; Melo, A.; Ni, H.; Padeken, K.; Romeo, F.; Sheldon, P.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Cox, B.; Cummings, G.; Hakala, J.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, A.; Neu, C.; Tannenwald, B.; White, S.; Wolfe, E.; Poudyal, N.; Thapa, P.; Black, K.; Bose, T.; Buchanan, J.; Caillol, C.; Dasu, S.; De Bruyn, I; Everaerts, P.; Fienga, F.; Galloni, C.; He, H.; Herndon, M.; Herve, A.; Hussain, U.; Lanaro, A.; Loeliger, A.; Loveless, R.; Sreekala, J. Madhusudanan; Mallampalli, A.; Mohammadi, A.; Pinna, D.; Savin, A.; Shang, V; Smith, W. H.; Teague, D.; Trembath-reichert, S.; Vetens, W. CMS Collaboration Search for electroweak production of charginos and neutralinos in proton-proton collisions at root s=13 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron Scattering; Supersymmetry SUPERSYMMETRY; INVARIANT; MODEL A direct search for electroweak production of charginos and neutralinos is presented. Events with three or four leptons, with up to two hadronically decaying tau leptons, or two same-sign light leptons are analyzed. The data sample consists of 137 fb(-1) of proton-proton collisions with a center of mass energy of 13 TeV, recorded with the CMS detector at the LHC. The results are interpreted in terms of several simplified models. These represent a broad range of production and decay scenarios for charginos and neutralinos. A parametric neural network is used to target several of the models with large backgrounds. In addition, results using orthogonal search regions are provided for all the models, simplifying alternative theoretical interpretations of the results. Depending on the model hypotheses, charginos and neutralinos with masses up to values between 300 and 1450 GeV are excluded at 95% confidence level. [Tumasyan, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Del Valle, A. Escalante; Fruhwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I; Paulitsch, P.; Pitters, F. M.; Schieck, J.; Schofbeck, R.; Spanring, M.; Templ, S.; Waltenberger, W.; Wulz, C-E] Inst Hochenergiephys, Vienna, Austria; [Chekhovsky, V; Litomin, A.; Makarenko, V] Inst Nucl Problems, Minsk, BELARUS; [Darwish, M. R.; De Wolf, E. A.; Janssen, X.; Kello, T.; Lelek, A.; Sfar, H. Rejeb; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.] Univ Antwerp, Antwerp, Belgium; [Blekman, F.; Bols, E. S.; D'Hondt, J.; De Clercq, J.; Delcourt, M.; El Faham, H.; Lowette, S.; Moortgat, S.; Morton, A.; Muller, D.; Sahasransu, A. R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Stylianou, N.] Vrije Univ Brussel, Brussels, Belgium; [Kello, T.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I; Moureaux, L.; Petre, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wezenbeek, L.; Fang, W.; Gao, X.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Knolle, J.; Lambrecht, L.; Mestdach, G.; Niedziela, M.; Roskas, C.; Samalan, A.; Skovpen, K.; Tran, T. T.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.] Univ Ghent, Ghent, Belgium; [Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Jaffel, K.; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Vischia, P.; Wertz, S.; Wuyckens, S.] Catholic Univ Louvain, Louvain La Neuve, Belgium; [Alves, G. A.; Hensel, C.; Moraes, A.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Alda Junior, W. L.; Gallo Pereira, M. Alves; Ferreira Filho, M. Barroso; Malbouisson, H. Brandao; Carvalho, W.; Chinellato, J.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; De Souza, S. Fonseca; Figueiredo, D. Matos; Herrera, C. Mora; Amarilo, K. Mota; Mundim, L.; Nogima, H.; Teles, P. Rebello; Santoro, A.; Silva Do Amaral, S. M.; Sznajder, A.; Thiel, M.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.] Univ Estadual Paulista, Sao Paulo, Brazil; [Gregores, E. M.; Mercadante, P. G.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Antchev, G.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Dimitrov, A.; Ivanov, T.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.] Univ Sofia, Sofia, Bulgaria; [Fang, W.; Cheng, T.; Guo, Q.; Javaid, T.; Mittal, M.; Wang, H.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bauer, G.; Dozen, C.; Hu, Z.; Martins, J.; Wang, Y.; Yi, K.] Tsinghua Univ, Dept Phys, Beijing, Peoples R China; [Chapon, E.; Chen, G. M.; Chen, H. S.; Chen, M.; Iemmi, F.; Kapoor, A.; Leggat, D.; Liao, H.; Liu, Z-A; Monti, F.; Sharma, R.; Tao, J.; Thomas-wilsker, J.; Wang, J.; Zhang, H.; Zhang, S.; Zhao, J.] Inst High Energy Phys, Beijing, Peoples R China; [Agapitos, A.; Ban, Y.; Chen, C.; Huang, Q.; Levin, A.; Li, Q.; Lu, M.; Lyu, X.; Mao, Y.; Qian, S. 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A.] Fayoum Univ, Ctr High Energy Phys CHEP FU, Al Fayyum, Egypt; [Bhowmik, S.; De Oliveira, A. Carvalho Antunes; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Pata, J.; Raidal, M.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Bharthuar, S.; Brucken, E.; Garcia, F.; Havukainen, J.; Kim, M. S.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Lehti, S.; Linden, T.; Lotti, M.; Martikainen, L.; Ott, J.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Luukka, P.; Petrow, H.; Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Amendola, C.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Lenzi, B.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Savoy-Navarro, A.; Titov, M.; Yu, G. B.; Mijuskovic, J.] Univ Paris Saclay, IRFU, CEA, Gif Sur Yvette, France; [Ahuja, S.; Beaudette, F.; Bonanomi, M.; Perraguin, A. Buchot; Busson, P.; Cappati, A.; Charlot, C.; Davignon, O.; Diab, B.; Falmagne, G.; Ghosh, S.; de Cassagnac, R. Granier; Hakimi, A.; Kucher, I; Nguyen, M.; Ochando, C.; Paganini, P.; Rembser, J.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Zabi, A.; Zghiche, A.] Ecole Polytech, Lab Leprince Ringuet, Inst Polytech Paris, CNRS IN2P3, Palaiseau, France; [Agram, J-L; Andrea, J.; Apparu, D.; Bloch, D.; Bourgatte, G.; Brom, J-M; Chabert, E. C.; Collard, C.; Darej, D.; Fontaine, J-C; Goerlach, U.; Grimault, C.; Le Bihan, A-C; Nibigira, E.; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; [Asilar, E.; Beauceron, S.; Bernet, C.; Boudoul, G.; Camen, C.; Carle, A.; Chanon, N.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Jain, Sa; Laktineh, I. B.; Lattaud, H.; Lesauvage, A.; Lethuillier, M.; Mirabito, L.; Shchablo, K.; Torterotot, L.; Touquet, G.; Vander Donckt, M.; Viret, S.] Inst Phys 2 Infinis Lyon IP2I, Villeurbanne, France; [Khvedelidze, A.; Lomidze, I; Tsamalaidze, Z.; Mestvirishvili, A.] Georgian Tech Univ, Tbilisi, Georgia; [Feld, L.; Klein, K.; Lipinski, M.; Meuser, D.; Pauls, A.; Rauch, M. P.; Roewert, N.; Schulz, J.; Teroerde, M.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Ghosh, S.; Eliseev, D.; Erdmann, M.; Fackeldey, P.; Fischer, B.; Hebbeker, T.; Hoepfner, K.; Ivone, F.; Keller, H.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Mocellin, G.; Mondal, S.; Mukherjee, S.; Noll, D.; Novak, A.; Pook, T.; Pozdnyakov, A.; Rath, Y.; Reithler, H.; Roemer, J.; Schmidt, A.; Schuler, S. C.; Sharma, A.; Wiedenbeck, S.; Zaleski, S.; Borras, K.; Kasem, A.; Coubez, X.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Dziwok, C.; Fluegge, G.; Ahmad, W. Haj; Hlushchenko, O.; Kress, T.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.; Ziemons, T.] Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; [Borras, K.; Kasem, A.; Petersen, H. Aarup; Martin, M. Aldaya; Asmuss, P.; Babounikau, I; Baxter, S.; Behnke, O.; Martinez, A. Bermudez; Bin Anuar, A. A.; Botta, V; Brunner, D.; Campbell, A.; Cardini, A.; Cheng, C.; Rodriguez, S. Consuegra; Silva, G. Correia; Danilov, V; Didukh, L.; Eckerlin, G.; Eckstein, D.; Banos, L. I. Estevez; Filatov, O.; Gallo, E.; Geiser, A.; Giraldi, A.; Grohsjean, A.; Guthoff, M.; Jafari, A.; Jomhari, N. Z.; Jung, H.; Kasemann, M.; Kaveh, H.; Kleinwort, C.; Kruecker, D.; Lange, W.; Lidrych, J.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I-A; Metwally, J.; Meyer, A. B.; Meyer, M.; Mnich, J.; Mussgiller, A.; Otarid, Y.; Adan, D. Perez; Pitzl, D.; Raspereza, A.; Lopes, B. 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O.; Gottmann, A.; Hartmann, F.; Heidecker, C.; Husemann, U.; Katkov, I; Keicher, P.; Koppenhoefer, R.; Maier, S.; Metzler, M.; Mitra, S.; Mueller, Th; Neukum, M.; Nuernberg, A.; Quast, G.; Rabbertz, K.; Rauser, J.; Savoiu, D.; Schnepf, M.; Seith, D.; Shvetsov, I; Simonis, H. J.; Ulrich, R.; Van der Linden, J.; Von Cube, R. F.; Wassmer, M.; Weber, M.; Wieland, S.; Wolf, R.; Wozniewski, S.; Wunsch, S.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Asenov, P.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Stakia, A.] NCSR Demokritos, Inst Nucl & Particle Phys INPP, Aghia Paraskevi, Greece; [Diamantopoulou, M.; Karasavvas, D.; Karathanasis, G.; Kontaxakis, P.; Koraka, C. K.; Manousakis-katsikakis, A.; Panagiotou, A.; Papavergou, I; Saoulidou, N.; Theofilatos, K.; Tziaferi, E.; Vellidis, K.; Vourliotis, E.; Sphicas, P.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Bakas, G.; Kousouris, K.; Papakrivopoulos, I; Tsipolitis, G.; Zacharopoulou, A.] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Manthos, N.; Papadopoulos, I; Strologas, J.] Univ Ioannina, Ioannina, Greece; [Csanad, M.; Farkas, K.; Gadallah, M. M. A.; Lokos, S.; Major, P.; Mandal, K.; Mehta, A.; Pasztor, G.; Radl, A. J.; Suranyi, O.; Veres, G., I; Trocsanyi, Z. L.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Csorgo, T.; Nemes, F.] Wigner Res Ctr Phys, Budapest, Hungary; [Horvath, D.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Teyssier, D.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Trocsanyi, Z. L.; Bartok, M.; Karancsi, J.; Raics, P.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Lokos, S.; Csorgo, T.; Nemes, F.; Novak, T.] MATE Inst Technol, Karoly Robert Campus, Gyongyos, Hungary; [Komaragiri, J. R.; Kumar, D.; Panwar, L.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Dash, D.; Kar, C.; Mal, P.; Mishra, T.; Bindhu, V. K. Muraleedharan Nair; Nayak, A.; Saha, P.; Sur, N.; Swain, S. K.; Vats, D.] HBNI, Natl Inst Sci Educ & Res, Bhubaneswar, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V; Chaudhary, G.; Chauhan, S.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, M.; Kaur, S.; Kumari, P.; Meena, M.; Sandeep, K.; Singh, J. B.; Virdi, A. K.] Panjab Univ, Chandigarh, India; [Ahmed, A.; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Kumar, A.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, A.] Univ Delhi, Delhi, India; [Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhowmik, D.; Dutta, S.; Gomber, B.; Maity, M.; Nandan, S.; Palit, P.; Rout, P. K.; Saha, G.; Sahu, B.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] HBNI, Saha Inst Nucl Phys, Kolkata, India; [Sharma, A.; Behera, P. K.; Behera, S. C.; Kalbhor, P.; Muhammad, A.; Pradhan, R.; Pujahari, P. R.; Sikdar, A. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Dutta, D.; Jha, V; Kumar, V; Mishra, D. K.; Naskar, K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Dugad, S.; Kumar, M.; Sarkar, U.] Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; [Mukherjee, S.; Bhattacharya, S.; Banerjee, S.; Chudasama, R.; Guchait, M.; Karmakar, S.; Kumar, S.; Majumder, G.; Mazumdar, K.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Alpana, K.; Dube, S.; Kansal, B.; Pandey, S.; Rane, A.; Rastogi, A.; Sharma, S.] Indian Inst Sci Educ & Res IISER, Pune, Maharashtra, India; [Jafari, A.; Bakhshiansohi, H.; Zeinali, M.] Isfahan Univ Technol, Dept Phys, Esfahan, Iran; [Chenarani, S.; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi] Inst Res Fundamental Sci IPM, Tehran, Iran; [Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Aly, R.; Aruta, C.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Fiore, L.; Gelmi, A.; Gul, M.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Margjeka, I; Mastrapasqua, V; Merlin, J. A.; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Pugliese, G.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Simone, F. M.; Venditti, R.; Verwilligen, P.] Ist Nazl Fis Nucl, Sez Bari, Bari, Italy; [Abbrescia, M.; Aly, R.; Aruta, C.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Gelmi, A.; Ince, M.; Lezki, S.; Margjeka, I; Mastrapasqua, V; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Selvaggi, G.; Simone, F. M.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Cuffiani, M.; Dallavalle, G. M.; Diotalevi, T.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Giommi, L.; Grandi, C.; Guiducci, L.; Lo Meo, S.; Lunerti, L.; Marcellini, S.; Masetti, G.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Campanini, R.; Capiluppi, P.; Castro, A.; Cuffiani, M.; Diotalevi, T.; Fanfani, A.; Giommi, L.; Guiducci, L.; Lunerti, L.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Cassese, A.; Ceccarelli, R.; Ciulli, V; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.] Ist Nazl Fis Nucl, Sez Firenze, Florence, Italy; [Ceccarelli, R.; Ciulli, V; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Seidita, R.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Bozzo, M.; Mulargia, R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Brivio, F.; Cetorelli, F.; Ciriolo, V; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Ist Nazl Fis Nucl, Sez Milano Bicocca, Milan, Italy; [Brivio, F.; Cetorelli, F.; Ciriolo, V; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Carnevali, F.; Cavallo, N.; De Iorio, A.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Carnevali, F.; De Iorio, A.; Iorio, A. O. M.; Lista, L.; Sciacca, C.; Layer, L.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Layer, L.; Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Re, V; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.; Fallavollita, F.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Ressegotti, M.; Riccardi, C.; Vitulo, P.; Fallavollita, F.] Univ Pavia, Pavia, Italy; [Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Piccinelli, A.; Rossi, A.; Santocchia, A.; Tedeschi, T.; Magherini, M.] Univ Perugia, Perugia, Italy; [Azzurri, P.; Bagliesi, G.; Bertacchi, V; Bianchini, L.; Boccali, T.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Parolia, S.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Bossini, E.; Ciocci, M. A.; Messineo, A.; Parolia, S.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [Di Domenico, M. R.; Turini, N.] Univ Siena, Siena, Italy; [Campana, M.; Cavallari, F.; Cipriani, M.; Del Re, D.; Di Marco, E.; Diemoz, M.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Campana, M.; Cipriani, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ruspa, M.; Salvatico, R.; Shchelina, K.; Siviero, F.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Salvatico, R.; Shchelina, K.; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Trocino, D.; Rumerio, P.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Candelise, V; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Kim, J.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.; Kamon, T.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Francois, B.; Kim, T. J.; Park, J.] Hanyang Univ, Seoul, South Korea; [Lee, K.; Park, J.; Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K. S.; Lim, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Kim, J.; Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Lee, S.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I] Seoul Natl Univ, Seoul, South Korea; [Jang, W.; Jeon, D.; Kang, D. Y.; Kang, Y.; Kim, J. H.; Kim, S.; Ko, B.; Lee, J. S. H.; Lee, Y.; Park, I. C.; Roh, Y.; Song, D.; Watson, I. J.; Yang, S.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Lee, H.; Lee, Y.; Jeong, Y.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Beyrouthy, T.; Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Kuwait; [Torims, T.; Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.; Vaitkevicius, A.] Vilnius Univ, Vilnius, Lithuania; [Bin Norjoharuddeen, N.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Leon Coello, M.; Murillo Quijada, J. A.; Sehrawat, A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; Heredia-De la Cruz, I; Lopez-Fernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez-Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Ramirez-Garcia, M.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M., I; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Gorski, M.; Kazana, M.; Szleper, M.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Faccioli, P.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Niknejad, T.; Pisano, M.; Seixas, J.; Toldaiev, O.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Finger, M.; Finger, M., Jr.; Khvedelidze, A.; Tsamalaidze, Z.; Afanasiev, S.; Budkouski, D.; Golutvin, I; Gorbunov, I; Karjavine, V; Korenkov, V; Lanev, A.; Malakhov, A.; Matveev, V; Palichik, V; Perelygin, V; Savina, M.; Seitova, D.; Shalaev, V; Shmatov, S.; Shulha, S.; Smirnov, V; Teryaev, O.; Voytishin, N.; Yuldashev, B. S.; Zarubin, A.; Zhizhin, I] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V; Ivanov, Y.; Kim, V; Kuznetsova, E.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirpichnikov, D.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Tlisov, D.; Toropin, A.; Musienko, Y.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Nikitenko, A.; Popov, V; Spiridonov, A.; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.; Chadeeva, M.] Moscow Inst Phys & Technol, Moscow, Russia; [Chadeeva, M.; Oskin, A.; Parygin, P.; Popova, E.; Zhemchugov, E.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Zhemchugov, E.; Andreev, V; Azarkin, M.; Dremin, I; Kirakosyan, M.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Katkov, I; Belyaev, A.; Boos, E.; Bunichev, V; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V; Kodolova, O.; Lokhtin, I; Obraztsov, S.; Perfilov, M.; Petrushanko, S.; Savrin, V] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Dimova, T.; Kardapoltsev, L.; Kozyrev, A.; Ovtin, I; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Elumakhov, D.; Kachanov, V; Konstantinov, D.; Mandrik, P.; Petrov, V; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Natl Res Ctr, Inst High Energy Phys, Kurchatov Inst, Protvino, Russia; [Babaev, A.; Okhotnikov, V] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borchsh, V; Ivanchenko, V; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.; Milosevic, V] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.; Milosevic, V] Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I; Barrio Luna, M.; Bedoya, Cristina F.; Carrillo Montoya, C. A.; Cepeda, M.; Cerrada, M.; Colino, N.; De la Cruz, B.; Delgado Peris, A.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Leon Holgado, J.; Moran, D.; Navarro Tobar, A.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Sanchez Navas, S.; Urda Gomez, L.; Willmott, C.] Ctr Invest Energet Medioambientales & Tecnol, Madrid, Spain; [de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Palencia Cortezon, E.; Ramon Alvarez, C.; Ripoll Sau, J.; Rodriguez Bouza, V; Trapote, A.; Trevisani, N.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Matorras Cuevas, P.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I; Vizan Garcia, J. M.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Jayananda, Mk; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, Ddc] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Sharma, A.; Stahl, A.; Hartmann, F.; Sphicas, P.; Ciriolo, V; Valsecchi, D.; Meola, S.; Paolucci, P.; Fallavollita, F.; Aarrestad, T. K.; Abbaneo, D.; Alimena, J.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chhibra, S. S.; Cristella, L.; D'Enterria, D.; Dabrowski, A.; Daci, N.; David, A.; De Roeck, A.; Defranchis, M. M.; Deile, M.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fasanella, D.; Fiorendi, S.; Florent, A.; Franzoni, G.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Haranko, M.; Hegeman, J.; Iiyama, Y.; Innocente, V; James, T.; Janot, P.; Kaspar, J.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Long, K.; Lourenco, C.; Malgeri, L.; Mallios, S.; Mannelli, M.; Marini, A. C.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Pitt, M.; Qu, H.; Quast, T.; Rabady, D.; Racz, A.; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schafer, C.; Schwick, C.; Selvaggi, M.; Silva, P.; Snoeys, W.; Summers, S.; Tavolaro, V. R.; Treille, D.; Tsirou, A.; Van Onsem, G. P.; Verzetti, M.; Wanczyk, J.; Wozniak, K. A.; Zeuner, W. D.; Virdee, T.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Androsov, K.; Backhaus, M.; Berger, P.; Calandri, A.; Chernyavskaya, N.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Eble, F.; Espinosa, T. A. Gomez; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A-M; Manzoni, R. A.; Perez, C. Martin; Meinhard, M. T.; Micheli, F.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V; Perrin, G.; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Schonenberger, M.; Stampf, V; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Caminada, L.; Amsler, C.; Bartschi, P.; Botta, C.; Brzhechko, D.; Canelli, M. F.; Cormier, K.; De Wit, A.; Del Burgo, R.; Heikkila, J. K.; Huwiler, M.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I; Rauco, G.; Reimers, A.; Robmann, P.; Cruz, S. Sanchez; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Ceard, L.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W-S; Li, Y. Y.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Yazgan, E.; Yu, P. R.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dumanoglu, I; Eskut, E.; Guler, Y.; Guler, E. Gurpinar; Hos, I; Isik, C.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Cerci, S.; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; [Stylianou, N.; Anthony, D.; Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Krikler, B.; Paramesvaran, S.; El Nasr-Storey, S. Seif; Smith, V. J.; White, R.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Bell, K. W.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Ellis, K., V; Harder, K.; Harper, S.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Nikitenko, A.; Virdee, T.; Bainbridge, R.; Bloch, P.; Bonomally, S.; Borg, J.; Breeze, S.; Buchmuller, O.; Cepaitis, V; Chahal, G. S.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Egyptian Knowledge Bank (EKB); 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Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade Federal de Mato Grosso do Sul; Egyptian Knowledge Bank (EKB); Helwan University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Erzincan Binali Yildirim University; Brandenburg University of Technology Cottbus; Egyptian Knowledge Bank (EKB); Assiut University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Sharif University of Technology; University of Science & Technology of Mazandaran; Italian National Agency New Technical Energy & Sustainable Economics Development; Academy of Sciences of Uzbekistan; Peter the Great St. Petersburg Polytechnic University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Eastern University, Sri Lanka; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Sirnak University; Near East University; Konya Technical University; Istanbul University - Cerrahpasa; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Egyptian Knowledge Bank (EKB); Ain Shams University; Bingol University; Sinop University; Erciyes University; Qatar Foundation (QF); Texas A&M University Qatar Tumasyan, A (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Boimska, Bożena/Z-6088-2019; Lezki, Samet/AAG-8277-2019; Ligabue, Franco/ABI-1681-2020; Dudko, Lev/D-7127-2012; Ambrozas, Marijus/F-5551-2018; SEVINC KAYA, OZLEM/AAE-8069-2020; Lychkovskaya, Natalia/F-8341-2017; CAKIR, Altan/ABD-4450-2020; Gupta, Rajat/GVS-7964-2022; Lange, Torben/HOH-4787-2023; Konecki, Marcin/G-4164-2015; Vai, Ilaria/AAT-5836-2020; Androsov, Konstantin/HJA-2332-2022; Bortignon, Pierluigi/AAF-6561-2020; Lowette, Steven/HKV-3341-2023; Lannon, Kevin/HGU-5755-2022; Lokhtin, Igor/D-7004-2012; Pellecchia, Antonello/HNB-9377-2023; Calligaris, Luigi/K-2018-2017; Della Ricca, Giuseppe/B-6826-2013; Di Florio, Adriano/ABA-3631-2020; Ferro, Fabrizio/HMP-5516-2023; Donato, Silvio/GPK-2262-2022; De Iorio, Agostino/HJH-7155-2023; Fienga, Francesco/HKE-1204-2023; kaya, mithat/D-8062-2011; Simone, Federica Maria/HDM-3686-2022; Fouz, Maria Cruz/AAF-1105-2019; Saka, Halil/GPF-8892-2022; SIMSEK, Ali Eren/AFO-3050-2022; Savina, Maria/HDO-6544-2022; Strologas, John/GWR-2036-2022; Guler, Yalcin/AAG-7833-2019; Novaes, Sergio F./D-3532-2012; Shah, Aashaq/GYI-8983-2022; Bunichev, Viacheslav/E-2285-2012; Legger, Federica/HNC-2568-2023; Selvaggi, Michele/CAJ-2129-2022; Zorbilmez, Caglar/ABH-1905-2020; Tsirou, Andromachi/HHC-6869-2022; Petkov, Peicho/M-2080-2016; Matorras, Francisco/I-4983-2015; Rabady, Dinyar/GSN-9210-2022; Lokos, Sandor/A-4798-2019; M, F/HGF-3166-2022; Puerta Pelayo, Jesus/L-1632-2014; Schwarz, Dennis/GYA-4479-2022; Giommi, Luca/AGV-7154-2022; Sunar Cerci, Deniz/AHE-1706-2022; Yazgan, Efe/C-4521-2014; Aimè, Chiara/GXG-4131-2022; Demiroglu, Zuhal Seyma/A-5168-2016; Norjoharuddeen, Nurfikri/S-3109-2018; Goy Lopez, Silvia/K-9200-2017; Duarte, Javier Mauricio/AAA-5414-2020; Iorio, Alberto Orso Maria/GXA-0233-2022; Shmatov, Sergei/J-5621-2014; Aarrestad, Thea/GLV-3031-2022; Shopova, Mariana Filipova/HKW-0149-2023; Khvedelidze, Arsen/AAF-5025-2020; Iqbal, Muhammad/HJH-2369-2023; Blekman, Freya/ABD-7916-2020; Parygin, Pavel/C-8908-2016; Gozalez-Lopez, Oscar/AAH-3533-2019; Bermudez Martinez, Armando/HHN-4625-2022; Folgueras, Santiago/B-6549-2014; Casarsa, Massimo/L-3623-2018; Akgun, Bora/AAM-8311-2021; Ciangottini, Diego/HJH-5914-2023; Grzanka, Leszek/M-9052-2018; Sola, Valentina/E-7726-2015; Malawski, Maciej/H-9119-2012 Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Boimska, Bożena/0000-0002-4200-1541; Lezki, Samet/0000-0002-6909-774X; Ligabue, Franco/0000-0002-1549-7107; Dudko, Lev/0000-0002-4462-3192; Ambrozas, Marijus/0000-0003-2449-0158; SEVINC KAYA, OZLEM/0000-0002-8485-3822; CAKIR, Altan/0000-0002-8627-7689; Lange, Torben/0000-0001-6242-7331; Konecki, Marcin/0000-0001-9482-4841; Vai, Ilaria/0000-0003-0037-5032; Androsov, Konstantin/0000-0003-2694-6542; Bortignon, Pierluigi/0000-0002-5360-1454; Lowette, Steven/0000-0003-3984-9987; Lannon, Kevin/0000-0002-9706-0098; Pellecchia, Antonello/0000-0003-3279-6114; Calligaris, Luigi/0000-0002-9951-9448; Della Ricca, Giuseppe/0000-0003-2831-6982; Di Florio, Adriano/0000-0003-3719-8041; Donato, Silvio/0000-0001-7646-4977; De Iorio, Agostino/0000-0002-9258-1345; Fienga, Francesco/0000-0001-5978-4952; kaya, mithat/0000-0003-2890-4493; Simone, Federica Maria/0000-0002-1924-983X; Fouz, Maria Cruz/0000-0003-2950-976X; SIMSEK, Ali Eren/0000-0002-9074-2256; Savina, Maria/0000-0002-9020-7384; Guler, Yalcin/0000-0001-7598-5252; Novaes, Sergio F./0000-0003-0471-8549; Shah, Aashaq/0000-0002-6157-2016; Bunichev, Viacheslav/0000-0003-4418-2072; Legger, Federica/0000-0003-1400-0709; Zorbilmez, Caglar/0000-0002-5199-061X; Petkov, Peicho/0000-0002-0420-9480; Matorras, Francisco/0000-0003-4295-5668; Rabady, Dinyar/0000-0001-9239-0605; Lokos, Sandor/0000-0002-4447-4836; Puerta Pelayo, Jesus/0000-0001-7390-1457; Giommi, Luca/0000-0003-3539-4313; Sunar Cerci, Deniz/0000-0002-5412-4688; Yazgan, Efe/0000-0001-5732-7950; Demiroglu, Zuhal Seyma/0000-0001-7977-7127; Norjoharuddeen, Nurfikri/0000-0002-8818-7476; Duarte, Javier Mauricio/0000-0002-5076-7096; Iorio, Alberto Orso Maria/0000-0002-3798-1135; Shmatov, Sergei/0000-0001-5354-8350; Aarrestad, Thea/0000-0002-7671-243X; Shopova, Mariana Filipova/0000-0001-6664-2493; Khvedelidze, Arsen/0000-0002-5953-0140; Blekman, Freya/0000-0002-7366-7098; Parygin, Pavel/0000-0001-6743-3781; Gozalez-Lopez, Oscar/0000-0002-4532-6464; Bermudez Martinez, Armando/0000-0001-8822-4727; Folgueras, Santiago/0000-0001-7191-1125; Casarsa, Massimo/0000-0002-1353-8964; Akgun, Bora/0000-0001-8888-3562; Riccardi, Cristina/0000-0003-0165-3962; Heath, Helen/0000-0001-6576-9740; Pelliccioni, Mario/0000-0003-4728-6678; Aly, Reham/0000-0001-6808-1335; Lo Meo, Sergio/0000-0003-3249-9208; CECCARELLI, RUDY/0000-0003-3232-9380; gennai, simone/0000-0001-5269-8517; Tapper, Alexander/0000-0003-4543-864X; Dimova, Tatyana/0000-0002-9560-0660; Monteno, Marco/0000-0002-3521-6333; Grunewald, Martin/0000-0002-5754-0388; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Hall, Geoffrey/0000-0002-6299-8385; Grzanka, Leszek/0000-0002-3599-854X; Sola, Valentina/0000-0001-6288-951X; Covarelli, Roberto/0000-0003-1216-5235; Csanad, Mate/0000-0002-3154-6925; Fasanella, Daniele/0000-0002-2926-2691; Gavrilov, Vladimir/0000-0002-9617-2928; Mota Amarilo, Kevin/0000-0003-1707-3348; siviero, federico/0000-0002-4427-4076; Malawski, Maciej/0000-0001-6005-0243; Pradhan, Raghunath/0000-0001-7000-6510; Shi, Kun/0000-0001-9833-5674; Bainbridge, Robert/0000-0001-9157-4832; Primavera, Federica/0000-0001-6253-8656; Claes, Daniel/0000-0003-4198-8919; Manthos, Nikolaos/0000-0003-3247-8909 BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia); RFBR (Russia); NRC KI (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (U.S.A.); NSF (U.S.A.); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 765710, 824093]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe [390833306]; Deutsche Forschungsgemeinschaft (DFG) [400140256 GRK2497]; Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [Opus 2014/15/B/ST2/03998, 2015/19/B/ST2/02861]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [0723-2020-0041]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Thalis program; Aristeia program; EU-ESF; Greek NSRF; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.); Ministry of Science and Higher Education (Poland) BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MoST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (U.S.A.)(United States Department of Energy (DOE)); NSF (U.S.A.)(National Science Foundation (NSF)); Marie-Curie program (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); Horizon 2020 Grant (European Union); Leventis Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe(German Research Foundation (DFG)); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Hungarian Academy of Sciences (Hungary)(Hungarian Academy of Sciences); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); Latvian Council of Science(Latvian Ministry of Education and Science); National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Thalis program; Aristeia program; EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.); Ministry of Science and Higher Education (Poland)(Ministry of Science and Higher Education, Poland) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).; Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306, and under project number 400140256 GRK2497; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 86 1 1 14 28 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. APR 26 2022.0 4 147 10.1007/JHEP04(2022)147 0.0 73 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 0V8MV Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000788595100001 0
J Liang, ZZ; Chen, SC; Yang, YY; Zhao, RY; Shi, Z; Rossel, RAV Liang, Zongzheng; Chen, Songchao; Yang, Yuanyuan; Zhao, Ruiying; Shi, Zhou; Rossel, Raphael A. Viscarra National digital soil map of organic matter in topsoil and its associated uncertainty in 1980's China GEODERMA English Article Soil organic matter; Spatial modeling; Cubist machine learning algorithm; Soil map; Uncertainty assessment CARBON SEQUESTRATION; DEPTH FUNCTIONS; STORAGE; GLOBALSOILMAP; SCALE; AREA Accurate digital soil maps of soil organic matter (SOM) are needed to evaluate soil fertility, to estimate stocks, and for ecological and environment modeling. We used 5982 soil profiles collected during the second national soil survey of China, along with 19 environment predictors, to derive a spatial model of SOM concentration in the topsoil (0-20 cm layer). The environmental predictors relate to the soil forming factors, climate, vegetation, relief and parent material. We developed the model using the Cubist machine-learning algorithm combined with a non-parametric bootstrap to derive estimates of model uncertainty. We optimized the Cubist model using a 10-fold cross-validation and the best model used 17 rules. The correlation coefficient between the observed and predicted values was 0.65, and the root mean squared error was 0.28 g/kg. We then applied the model over China and mapped the SOM distribution at a resolution of 90 x 90 m. Our predictions show that there is more SOM in the eastern Tibetan Plateau, northern Heilongjiang province, northeast Mongolia, and a small area of Tianshan Mountain in Xinjiang. There is less SOM in the Loess Plateau and most of the desert areas in northwest China. The average topsoil SOM content is 24.82 g/kg. The study provides a map that can be used for decision-making and contribute towards a baseline assessment for inventory and monitoring. The map could also aid the design of future soil surveys and help with the development of a SOM monitoring network in China. [Liang, Zongzheng; Yang, Yuanyuan; Zhao, Ruiying; Shi, Zhou] Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Zhejiang, Peoples R China; [Liang, Zongzheng; Rossel, Raphael A. Viscarra] CSIRO Land & Water, Bruce E Butler Lab, POB 1700, Canberra, ACT 2601, Australia; [Chen, Songchao] INRA, Unite InfoSol, F-45075 Orleans, France; [Chen, Songchao] Agrocampus Ouest, INRA, UMR SAS, F-35000 Rennes, France Zhejiang University; Commonwealth Scientific & Industrial Research Organisation (CSIRO); INRAE; INRAE; Institut Agro; Agrocampus Ouest; Universite de Rennes Shi, Z (corresponding author), Zhejiang Univ, Environm & Resource Sci Coll, Hangzhou 310058, Zhejiang, Peoples R China. shizhou@zju.edu.cn yang, yuanyuan/HPG-2020-2023; Chen, Songchao/S-7982-2019; shi, Zhou/M-7845-2019; Viscarra Rossel, Raphael A./B-4061-2011; shi, Zhou/A-2283-2015 Chen, Songchao/0000-0003-1245-0482; shi, Zhou/0000-0003-3914-5402; Viscarra Rossel, Raphael A./0000-0003-1540-4748; shi, Zhou/0000-0003-3914-5402 National Key Research and Development Program [2017YFD0700501]; State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Institute of Soil Science, Chinese Academy of Sciences [Y412201430] National Key Research and Development Program; State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Institute of Soil Science, Chinese Academy of Sciences This study was supported by the National Key Research and Development Program (2017YFD0700501), and the Research Fund of State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Institute of Soil Science, Chinese Academy of Sciences (No. Y412201430). We appreciate the editor and anonymous reviewers for their valuable comments to improve this manuscript. 54 55 57 14 145 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0016-7061 1872-6259 GEODERMA Geoderma FEB 1 2019.0 335.0 47 56 10.1016/j.geoderma.2018.08.011 0.0 10 Soil Science Science Citation Index Expanded (SCI-EXPANDED) Agriculture GW6TR 2023-03-23 WOS:000447095700005 0
C Sun, B; Ma, LY; Cheng, W; Wen, W; Goswami, P; Bai, GH IEEE Sun, Bin; Ma, Liyao; Cheng, Wei; Wen, Wei; Goswami, Prashant; Bai, Guohua An Improved k-Nearest Neighbours Method for Traffic Time Series Imputation 2017 CHINESE AUTOMATION CONGRESS (CAC) Chinese Automation Congress English Proceedings Paper Chinese Automation Congress (CAC) OCT 20-22, 2017 Jinan, PEOPLES R CHINA IEEE,CAA,IEEE Syst Man & Cybernet Soc Traffic Time Series; Gap-Sensitive Windowed k-Nearest Neighbours (GSW-kNN); Missing Data Imputation FLOW PREDICTION; NEURAL-NETWORKS Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%. [Sun, Bin; Cheng, Wei; Wen, Wei; Goswami, Prashant; Bai, Guohua] Blekinge Inst Technol, S-37179 Karlskrona, Sweden; [Ma, Liyao] Univ Jinan, Sch Elect Engn, Jinan 250022, Shandong, Peoples R China; [Cheng, Wei] Kunming Univ Sci & Technol, Kunming 650093, Yunnan, Peoples R China Blekinge Institute Technology; University of Jinan; Kunming University of Science & Technology Sun, B (corresponding author), Blekinge Inst Technol, S-37179 Karlskrona, Sweden. bin.sun@bth.se; cse_maly@ujn.edu.cn; wei.cheng@bth.se; wei.wen@bth.se; prashant.goswami@bth.se; guohua.bai@bth.se Sun, Bin/A-2075-2015 Sun, Bin/0000-0001-5824-425X National Natural Science Foundation of China (NSFC) [61364019] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China (NSFC), agreement 61364019. 36 13 14 2 18 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2688-092X 2688-0938 978-1-5386-3524-7 CHIN AUTOM CONGR 2017.0 7346 7351 6 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BJ7XU 2023-03-23 WOS:000427816107081 0
J Hu, WH; Xu, ZM; Bian, XH; Tang, DH; Lu, W; Li, C; Teng, J; Cunha, A Hu, Wei-Hua; Xu, Zeng-Mao; Bian, Xiao-Han; Tang, De-Hui; Lu, Wei; Li, Chao; Teng, Jun; Cunha, Alvaro Operational modal analysis and continuous dynamic monitoring of high-rise building based on wireless distributed synchronized data acquisition system STRUCTURAL CONTROL & HEALTH MONITORING English Article continuous dynamic monitoring; high-rise building; operational modal analysis; whiplash effect; wireless distributed synchronized data acquisition (WDSA) NEURAL-NETWORK; IDENTIFICATION; SENSOR The paper mainly reports the structural dynamic behaviors of a skyscraper under operational conditions based on a wireless distributed synchronized data acquisition (WDSA) system. First, the WDSA system is developed to conveniently acquire the structural responses from a large and complex structure with the unified Global Navigation Satellite System (GNSS) time information. The phase synchronization accuracy of the WDSA method is validated by comparing the modal parameters estimated by both wired central data acquisition (WCA) and the WDSA systems. Subsequently, the dynamic properties of a skyscraper are presented by performing operational modal analysis (OMA) based on the proposed WDSA system. Finally, the WDSA-based continuous dynamic monitoring system is further developed to capture the long-term structural responses at different spatial positions. The structural dynamic behaviors of the skyscraper under normal wind, typhoon and earthquake conditions are reported. The structural whiplash effect under both normal wind and earthquake conditions are characterized by the acceleration-based accumulated contribution factor. [Hu, Wei-Hua; Xu, Zeng-Mao; Bian, Xiao-Han; Tang, De-Hui; Lu, Wei; Li, Chao; Teng, Jun] Harbin Inst Technol Shenzhen, Sch Civil & Environm Engn, Shenzhen, Peoples R China; [Cunha, Alvaro] Univ Porto, Fac Engn, Construct, Porto, Portugal Harbin Institute of Technology; Universidade do Porto Teng, J (corresponding author), Harbin Inst Technol Shenzhen, Sch Civil & Environm Engn, Shenzhen, Peoples R China. tengj@hit.edu.cn Cunha, Alvaro/0000-0001-7878-6224 National Natural Science Foundation of China [51878226, 52122804, 51978214]; Shenzhen Technology Innovation Programm [JCYJ20180508152238111] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Technology Innovation Programm National Natural Science Foundation of China, Grant/Award Number: 51878226;52122804;51978214; Shenzhen Technology Innovation Programm, Grant/Award Number: JCYJ20180508152238111 40 0 0 11 19 JOHN WILEY & SONS LTD CHICHESTER THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND 1545-2255 1545-2263 STRUCT CONTROL HLTH Struct. Control. Health Monit. NOV 2022.0 29.0 11 e3063 10.1002/stc.3063 0.0 AUG 2022 31 Construction & Building Technology; Engineering, Civil; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Instruments & Instrumentation 5E9SK 2023-03-23 WOS:000843015900001 0
C Li, ZQ; Liu, TJ; Wei, XY; Zhou, LT Srivastava, HM; Chen, CH Li, Zhengqiao; Liu, Tianjin; Wei, Xinyuan; Zhou, Letian Face Synthesis with Generative Adversarial Networks 2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELLING, AND INTELLIGENT COMPUTING (CAMMIC 2022) Proceedings of SPIE English Proceedings Paper 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC) MAR 25-27, 2022 ELECTR NETWORK Fed Univ Rio Grande,AEIC Acad Exchange Informat Ctr GAN; face synthesis; FakeCatcher Nowadays, machine learning has become a trend in computer programming techniques. It is a learning method that builds a model based on training data, in order to make predictions or decisions without being explicitly programmed to do so. Generative adversarial network (GAN) is the most advanced technology in unsupervised learning. The GAN is composed of a generator and a discriminator. Generator aims to generate realistic samples and discriminators to identify real data and generated fake data. GAN has been successfully implemented for solving a variety of tasks such as face synthesis, facial attribute manipulation, and image to image translation. Face synthesis gains great attention recently, especially after the emerge of Deepfakes. With this technology, realistic images or media can be easily created. In this paper, we will start with the introduction of GAN and then summarize and analyze five popular methods for face synthesis using GAN technology. Finally, we discuss some problems of existing methods and point out a future direction for face synthesis. [Li, Zhengqiao] Zsolna U 4, H-1125 Budapest, Hungary; [Liu, Tianjin] Beijing New Oriental Acad, Beijing 102206, Peoples R China; [Wei, Xinyuan] Centennial Collegiate, Saskatoon, SK S7S 1P5, Canada; [Zhou, Letian] Jiangsu Tianyi High Sch, Wuxi 21400, Jiangsu, Peoples R China Wei, XY (corresponding author), Centennial Collegiate, Saskatoon, SK S7S 1P5, Canada. zbggmm@gmail.com; 1932984821@qq.com; 5802275@spsd.sk.ca; 2975189134@qq.com 12 0 0 3 3 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-5520-1; 978-1-5106-5519-5 PROC SPIE 2022.0 12259.0 122595I 10.1117/12.2639259 0.0 7 Computer Science, Interdisciplinary Applications; Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BT5FQ 2023-03-23 WOS:000836381400197 0
C Xie, JY; Li, HL; Ma, ZY; Sun, Q; Wallin, F; Si, ZW; Guo, J Yan, J; Sun, F; Chou, SK; Desideri, U; Li, H; Campana, P; Xiong, R Xie, Jiyang; Li, Hailong; Ma, Zhanyu; Sun, Qie; Wallin, Fredrik; Si, Zhongwei; Guo, Jun Analysis of Key Factors in Heat Demand Prediction with Neural Networks 8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) Energy Procedia English Proceedings Paper 8th International Conference on Applied Energy (ICAE) OCT 08-11, 2016 Beijing Inst Technol, Beijing, PEOPLES R CHINA Appl Energy Innovat Inst,Malardalen UNiv,China Assoc Sci & Technologies,HOME Program,Sichuan Univ,Jiangsu Univ,China Univ Min & Technol,Tianjin Univ,Tongji Univ,SW Jiaotong Univ,Xian Jiaotong Univ,Collaborat Innovat Ctr Elect Vehicles Beijing,Technol Unnovat Local Scale Optimum Integrat Battery Energy Storage,BAIC,BJEV,YuTong,Shenwu Grp Beijing Inst Technol District heating; data processing; prediction; heat demand; CHP plant The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously. (C) 2017 The Authors. Published by Elsevier Ltd. [Xie, Jiyang; Ma, Zhanyu; Guo, Jun] Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China; [Li, Hailong; Wallin, Fredrik] Malardalen Univ, Sch Business Soc & Engn, Vasteras, Sweden; [Sun, Qie] Shandong Univ, Inst Thermal Sci & Technol, Jinan, Shandong, Peoples R China; [Si, Zhongwei] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing, Peoples R China Beijing University of Posts & Telecommunications; Malardalen University; Shandong University; Beijing University of Posts & Telecommunications Ma, ZY (corresponding author), Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China.;Li, HL (corresponding author), Malardalen Univ, Sch Business Soc & Engn, Vasteras, Sweden.;Sun, Q (corresponding author), Shandong Univ, Inst Thermal Sci & Technol, Jinan, Shandong, Peoples R China. hailong.li@mdh.se; mazhanyu@bupt.edu.cn; qie@sdu.edu.cn Sun, Qie/N-9520-2013 Sun, Qie/0000-0001-6539-845X National Natural Science Foundation of China [61402047, 61401037]; Beijing Natural Science Foundation [4162044]; Energimyndigheten and Energiforsk AB (Fjarrsynsprojekt: Dynamisk prismekanism) [5334]; Natural Science Foundation of Shandong Province [ZR2014EEM025] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Energimyndigheten and Energiforsk AB (Fjarrsynsprojekt: Dynamisk prismekanism); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work was supported in part by the National Natural Science Foundation of China under Grant 61402047 and 61401037, in part by the Beijing Natural Science Foundation under Grant 4162044, in part by the financial support from Energimyndigheten and Energiforsk AB (Fjarrsynsprojekt 5334: Dynamisk prismekanism), and in part by Natural Science Foundation of Shandong Province Grant ZR2014EEM025. 14 16 16 1 1 ELSEVIER SCIENCE BV AMSTERDAM SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1876-6102 ENRGY PROCED 2017.0 105.0 2965 2970 6 Energy & Fuels Conference Proceedings Citation Index - Science (CPCI-S) Energy & Fuels BI0MH gold, Green Published 2023-03-23 WOS:000404967903011 0
C Wang, ZZ; Huo, W; Yu, PP; Qi, L; Cao, N IEEE Wang, Zhenzhou; Huo, Wei; Yu, Pingping; Qi, Lin; Cao, Ning Research on Vehicle Taillight Detection and Semantic Recognition Based on Internet of Vehicle 2018 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC 2018) International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery English Proceedings Paper 10th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) OCT 18-20, 2018 Zhengzhou Univ, Zhengzhou, PEOPLES R CHINA IEEE Comp Soc,Teradata,Huawei,Tech Mahindra,Natl Instruments,Pivotal EMC,HP Enterprise,RainStor,Samsung,Microsoft,Comrise,InfoBeyond,Nanjing Univ Posts & Telecommunicat,IEEE Commun Soc,Univ Louisville Zhengzhou Univ intelligent transportation; internet of vehicle; taillamp detection; histogram distribution; lamp signal recognition Internet of Things(IOT) technology provides sufficient information technology and necessary decision-making basis for intelligent transportation. Internet of vehicles(IOV) is based on the architecture of the perception layer, network layer, and application layer. This article focuses on the tail light status in the perception layer of automotive active safety systems and proposes a method of vehicle taillamp detection and recognition based on active contour extraction and color feature analysis to solve the problem of information perception against the front vehicle. First, the front vehicles are detected and extracted by convolutional neural network and active contour models. Second, it analyzes the characteristics of taillamp pair and then segment the correct taillamp pair by color space conversion and location correlation principle. On this basis, the histograms distribution of taillamp are analyzed in different states and complete the recognition of taillamp. The experimental results show that the method can accurately identify the taillamp signal of the front vehicle in the daytime and effectively reduce the rate of false judgement caused by light interference. [Wang, Zhenzhou; Huo, Wei; Yu, Pingping; Qi, Lin] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang, Hebei, Peoples R China; [Cao, Ning] Univ Coll Dublin, Sch Comp Sci, Belfield, Ireland Hebei University of Science & Technology; University College Dublin Cao, N (corresponding author), Univ Coll Dublin, Sch Comp Sci, Belfield, Ireland. ning.cao2008@hotmail.com Cao, Ning/P-6652-2017 8 4 4 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2475-7020 978-1-7281-0974-9 INT CONF CYBER DIST 2018.0 147 150 10.1109/CyberC.2018.00038 0.0 4 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM4AN 2023-03-23 WOS:000462960100026 0
J Wang, T; Cui, XJ; Winther, KT; Abild-Pedersen, F; Bligaard, T; Norskov, JK Wang, Tao; Cui, Xinjiang; Winther, Kirsten T.; Abild-Pedersen, Frank; Bligaard, Thomas; Norskov, Jens K. Theory-Aided Discovery of Metallic Catalysts for Selective Propane Dehydrogenation to Propylene ACS CATALYSIS English Article propane dehydrogenation; DFT; kinetics; experiment; NiMo CLUSTERS; HYDROGEN; IDENTIFICATION; PERFORMANCE; PROMOTION; PLATINUM; ALLOY The design of selective and stable catalysts is a major challenge in heterogeneous catalysis, also true for propane dehydrogenation (PDH), an on-purpose route to produce propylene. Identifying nonprecious catalysts to replace Pt used today is another formidable task. Herein, we combine kinetic simulations, high-throughput computations, machine-learning concepts, and experiments to illustrate the key parameters controlling the performance of metallic catalysts in PDH. Our decision map successfully identifies NiMo as a very promising nonprecious metal catalyst, which is experimentally proven to outperform Pt in PDH. Our framework not only validates experimentally known catalysts but also provides ways for PDH catalyst engineering from theoretical high-throughput screening. We believe that the other 11 identified candidates will initiate further state-of-the-art experimental investigations and add important insights into the industrial development of catalysts for alkane dehydrogenation. [Wang, Tao; Winther, Kirsten T.] Stanford Univ, SUNCAT Ctr Interface Sci & Catalysis, Dept Chem Engn, Stanford, CA 94305 USA; [Wang, Tao] Westlake Univ, Ctr Artificial Photosynth Solar Fuels, Sch Sci, Hangzhou 310024, Zhejiang, Peoples R China; [Cui, Xinjiang] Chinese Acad Sci, Lanzhou Inst Chem Phys, State Key Lab Oxo Synth & Select Oxidat, Lanzhou 730000, Peoples R China; [Abild-Pedersen, Frank] Stanford Linear Accelerator Ctr, SUNCAT Ctr Interface Sci & Catalysis, Menlo Pk, CA 94025 USA; [Bligaard, Thomas] Tech Univ Denmark, Dept Energy Convers & Storage, DK-2800 Lyngby, Denmark; [Norskov, Jens K.] Tech Univ Denmark, Dept Phys, DK-2800 Lyngby, Denmark Stanford University; Westlake University; Chinese Academy of Sciences; Lanzhou Institute of Chemical Physics, CAS; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Technical University of Denmark; Technical University of Denmark Cui, XJ (corresponding author), Chinese Acad Sci, Lanzhou Inst Chem Phys, State Key Lab Oxo Synth & Select Oxidat, Lanzhou 730000, Peoples R China.;Abild-Pedersen, F (corresponding author), Stanford Linear Accelerator Ctr, SUNCAT Ctr Interface Sci & Catalysis, Menlo Pk, CA 94025 USA.;Bligaard, T (corresponding author), Tech Univ Denmark, Dept Energy Convers & Storage, DK-2800 Lyngby, Denmark. xinjiangcui@licp.cas.cn; abild@slac.stanford.edu; tbli@dtu.dk Norskov, Jens K/D-2539-2017; Abild-Pedersen, Frank/C-3248-2014; Winther, Kirsten/I-6434-2015 Norskov, Jens K/0000-0002-4427-7728; Abild-Pedersen, Frank/0000-0002-1911-074X; Winther, Kirsten/0000-0003-1254-1165 U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program; Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231] U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program(United States Department of Energy (DOE)); Office of Science of the U.S. Department of Energy(United States Department of Energy (DOE)) This research was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program to the SUNCAT Center for Interface Science and Catalysis. The authors would like to acknowledge the use of the computer time allocation at the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-05CH11231. 39 10 11 28 127 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2155-5435 ACS CATAL ACS Catal. MAY 21 2021.0 11.0 10 6290 6297 10.1021/acscatal.0c05711 0.0 MAY 2021 8 Chemistry, Physical Science Citation Index Expanded (SCI-EXPANDED) Chemistry SK2NH Green Submitted 2023-03-23 WOS:000656056200039 0
J Ammendola, R; Biagioni, A; Ciardiello, A; Cretaro, P; Frezza, O; Lamanna, G; Lo Cicero, F; Lonardo, A; Martinelli, M; Piandani, R; Pontisso, L; Raggi, M; Simula, F; Soldi, D; Turisini, M; Vicini, P Ammendola, R.; Biagioni, A.; Ciardiello, A.; Cretaro, P.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Piandani, R.; Pontisso, L.; Raggi, M.; Simula, F.; Soldi, D.; Turisini, M.; Vicini, P. Progress report on the online processing upgrade at the NA62 experiment JOURNAL OF INSTRUMENTATION English Article Pattern recognition; cluster finding; calibration and fitting methods; Trigger concepts and systems (hardware and software); Data processing methods; Trigger algorithms A new FPGA-based low-level trigger processor has been installed at the NA62 experiment. It is intended to extend the features of its predecessor due to a faster interconnection technology and additional logic resources available on the new platform. With the aim of improving trigger selectivity and exploring new architectures for complex trigger computation, a GPU system has been developed and a neural network on FPGA is in progress. They both process data streams from the ring imaging Cherenkov detector of the experiment to extract in real time high level features for the trigger logic. Description of the systems, latest developments and design flows are reported in this paper. [Biagioni, A.; Ciardiello, A.; Cretaro, P.; Frezza, O.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Pontisso, L.; Raggi, M.; Simula, F.; Turisini, M.; Vicini, P.] INFN, Sez Roma, Rome, Italy; [Ammendola, R.] INFN, Sez Roma Tor Vergata, Rome, Italy; [Soldi, D.] INFN, Sez Torino, Turin, Italy; [Raggi, M.] Univ Sapienza Roma, Rome, Italy; [Lamanna, G.] Univ Pisa, Pisa, Italy; [Piandani, R.] Univ Chinese Acad Sci, Beijing, Peoples R China; [Lamanna, G.] INFN, Sez Pisa, Pisa, Italy Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; University of Pisa; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Istituto Nazionale di Fisica Nucleare (INFN) Turisini, M (corresponding author), INFN, Sez Roma, Rome, Italy. matteo.turisini@roma1.infn.it Ammendola, Roberto/AAG-8353-2020; Ciardiello, Andrea/HJI-2500-2023; Lonardo, Alessandro/G-9758-2011; Vicini, Piero/A-5549-2015 Ammendola, Roberto/0000-0003-4501-3289; Ciardiello, Andrea/0000-0003-1903-4406; Lonardo, Alessandro/0000-0002-5909-6508; Vicini, Piero/0000-0002-4379-4563 EuroEXA project (G.A. EU H2020 FP) [754337]; TEXTAROSSA project (G.A. H2020-JTI-EuroHPC-2019-1) [956831] EuroEXA project (G.A. EU H2020 FP); TEXTAROSSA project (G.A. H2020-JTI-EuroHPC-2019-1) This work has been carried out within the EuroEXA project (G.A. EU H2020 FP No. 754337) and TEXTAROSSA project (G.A. H2020-JTI-EuroHPC-2019-1 No. 956831). 7 0 0 0 0 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-0221 J INSTRUM J. Instrum. APR 2022.0 17.0 4 C04002 10.1088/1748-0221/17/04/C04002 0.0 8 Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation 0Q0AH Green Submitted 2023-03-23 WOS:000784587900008 0
B Chen, JH; Ma, JH; Zhong, N; Yao, YY; Liu, JM; Huang, RH; Li, WB; Huang, ZS; Gao, Y Zhong, N; Ma, J; Liu, J; Huang, R; Tao, X Chen, Jianhui; Ma, Jianhua; Zhong, Ning; Yao, Yiyu; Liu, Jiming; Huang, Runhe; Li, Wenbin; Huang, Zhisheng; Gao, Yang WaaS-Wisdom as a Service WISDOM WEB OF THINGS Web Information Systems Engineering and Internet Technologies English Article; Book Chapter An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, from things to data, information, knowledge, wisdom, services, humans, and then back to things. The W2T cycle leads to a harmonious symbiosis among humans, computers and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet/ Web of Things, big data and other research fields have provided just such an open system architecture with resource sharing/services. The next step is therefore to develop an open and interoperable content architecture with intelligence sharing/services for the organization and transformation in the data, information, knowledge and wisdom (DIKW) hierarchy. This chapter introduces Wisdom as a Service (WaaS) as a content architecture based on the paying only for what you use IT business trend. The WaaS infrastructure, WaaS economics, and the main challenges in WaaS research and applications are discussed. A case study is described to demonstrate the usefulness and significance ofWaaS. Relying on the clouds (cloud computing), things (Internet of Things) and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications. [Chen, Jianhui] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Ma, Jianhua] Hosei Univ, Fac Comp & Informat Sci, Tokyo 1848584, Japan; [Zhong, Ning] Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma 3710816, Japan; [Zhong, Ning] Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Int WIC Inst, Beijing, Peoples R China; [Yao, Yiyu; Liu, Jiming; Huang, Runhe] Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China; [Yao, Yiyu] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada; [Liu, Jiming] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon Tong, Hong Kong, Peoples R China; [Huang, Runhe] Hosei Univ, Fac Comp & Informat Sci, Tokyo 1848584, Japan; [Li, Wenbin] Shijiazhuang Univ Econ, Dept Comp Sci, Shijiazhuang 050031, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands; [Gao, Yang] Nanjing Univ, Dept Comp Sci, Nanjing 210046, Jiangsu, Peoples R China Tsinghua University; Hosei University; Beijing University of Technology; Beijing University of Technology; University of Regina; Hong Kong Baptist University; Hosei University; Hebei GEO University; Vrije Universiteit Amsterdam; Nanjing University Zhong, N (corresponding author), Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma 3710816, Japan. chenjhnh@mail.tsinghua.edu.cn; jianhua@hosei.ac.jp; zhong@maebashi-it.ac.jp; yyao@cs.uregina.ca; jiming@comp.hkbu.edu.hk; rhuang@hosei.ac.jp; liwenbin@fireflymobile.cn; huang@cs.vu.nl; gaoy@nju.edu.cn GAO, Yang/HMO-8142-2023; Yao, Yiyu/B-2926-2008 Yao, Yiyu/0000-0001-6502-6226 31 6 6 0 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 978-3-319-44198-6; 978-3-319-44196-2 WEB INF SYST ENG INT 2016.0 27 46 10.1007/978-3-319-44198-6_2 0.0 10.1007/978-3-319-44198-6 20 Computer Science, Artificial Intelligence; Computer Science, Information Systems Book Citation Index – Science (BKCI-S) Computer Science BH4XD 2023-03-23 WOS:000400738800003 0
J Yang, Y; Zhu, DJ; Li, XF; Du, HW; Sun, YD; Huo, ZX; Wu, MR; Cao, N; Higgs, R Yang, Ye; Zhu, Dongjie; Li, Xiaofang; Du, Haiwen; Sun, Yundong; Huo, Zhixin; Wu, Mingrui; Cao, Ning; Higgs, Russell Research on Cross-domain Representation Learning Based on Multi-network Space Fusion INTELLIGENT AUTOMATION AND SOFT COMPUTING English Article Network embedding; cross-domain representation; graph neural network In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation learning research for cross-domain scenarios. First, we use different network representation learning methods to perform representation learning in a single network space. Second, we use the attention mechanism to fuse representations in different spaces to obtain a fusion representation of multiple network spaces; Finally, the model is verified through cross-domain experiments. The experimental results show that the fusion model proposed in this paper can improve the performance of cross-domain scenarios. [Yang, Ye; Cao, Ning] Wuxi Vocat Coll Sci & Technol, Sch Artificial Intelligence, Wuxi 214028, Jiangsu, Peoples R China; [Zhu, Dongjie; Huo, Zhixin; Wu, Mingrui] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China; [Li, Xiaofang] Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China; [Du, Haiwen; Sun, Yundong] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China; [Higgs, Russell] Univ Coll Dublin, Sch Math & Stat, Dublin, Ireland Harbin Institute of Technology; Harbin Institute of Technology; Harbin Institute of Technology; University College Dublin Zhu, DJ (corresponding author), Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China. zhudongjie@hit.edu.cn Du, Haiwen/AAU-3401-2020; Yundong, Sun/HLW-4281-2023 Higgs, Russell/0000-0002-0896-6788 State Grid Shandong Electric Power Company Science and Technology Project [520613200001, 520613180002, 62061318C002]; Weihai Scientific Research and Innovation Fund (2020) State Grid Shandong Electric Power Company Science and Technology Project; Weihai Scientific Research and Innovation Fund (2020) Funding Statement: This work is supported by State Grid Shandong Electric Power Company Science and Technology Project Funding under Grant no. 520613200001, 520613180002, 62061318C002, Weihai Scientific Research and Innovation Fund (2020) . 17 0 0 3 10 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1079-8587 2326-005X INTELL AUTOM SOFT CO Intell. Autom. Soft Comput. 2022.0 33.0 3 1379 1391 10.32604/iasc.2022.025181 0.0 13 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 0G6CM hybrid 2023-03-23 WOS:000778130900001 0
J Li, Y; Wang, Q; Zhang, L; Lafruit, G Li, Yan; Wang, Qiong; Zhang, Lu; Lafruit, Gauthier A Lightweight Depth Estimation Network for Wide-Baseline Light Fields IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Estimation; Feature extraction; Training; Cameras; Streaming media; Human computer interaction; Convolution; Light field; depth estimation; convolutional neural network; lightweight; wide-baseline; narrow-baseline; synthetic dataset Existing traditional and ConvNet-based methods for light field depth estimation mainly work on the narrow-baseline scenario. This paper explores the feasibility and capability of ConvNets to estimate depth in another promising scenario: wide-baseline light fields. Due to the deficiency of training samples, a large-scale and diverse synthetic wide-baseline dataset with labelled data is introduced for depth prediction tasks. Considering the practical goal for real-world applications, we design an end-to-end trained lightweight convolutional network to infer depths from light fields, called LLF-Net. The proposed LLF-Net is built by incorporating a cost volume which allows variable angular light field inputs and an attention module that enables to recover details at occlusion areas. Evaluations are made on the synthetic and real-world wide-baseline light fields, and experimental results show that the proposed network achieves the best performance when compared to recent state-of-the-art methods. We also evaluate our LLF-Net on narrow-baseline datasets, and it consequently improves the performance of previous methods. [Li, Yan; Lafruit, Gauthier] Univ Libre Bruxelles, Lab Image Synth & Anal LISA Dept, B-1050 Brussels, Belgium; [Wang, Qiong] Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China; [Wang, Qiong] Zhejiang Univ Technol, Key Lab Visual Media Intelligent Proc Technol Zhe, Hangzhou 310023, Peoples R China; [Zhang, Lu] INSA Rennes, Natl Inst Appl Sci Rennes, F-35700 Rennes, France; [Zhang, Lu] Inst Elect & Technol numeR IETR, UMR CNRS 6164, F-35700 Rennes, France Universite Libre de Bruxelles; Zhejiang University of Technology; Zhejiang University of Technology; Institut National des Sciences Appliquees de Rennes; Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Rennes Wang, Q (corresponding author), Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China. wangqiong819@gmail.com zhang, lu/0000-0002-8859-5453; Lafruit, Gauthier/0000-0002-2349-1936 China Scholarship Council; Van Buuren-Jaumotte-Demoulin doctoral funding; National Natural Science Foundation of China [62002326]; Fonds de la Recherche Scientifique-FNRS, Belgium [33679514]; EU [951989] China Scholarship Council(China Scholarship Council); Van Buuren-Jaumotte-Demoulin doctoral funding; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fonds de la Recherche Scientifique-FNRS, Belgium(Fonds de la Recherche Scientifique - FNRS); EU(European Commission) This work was supported in part by the China Scholarship Council funding, in part by the Van Buuren-Jaumotte-Demoulin doctoral funding, in part by the National Natural Science Foundation of China under Grant 62002326, Fonds de la Recherche Scientifique-FNRS, Belgium, under Grant 33679514, ColibriH, and the EU project no. 951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron. 47 7 7 0 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2021.0 30.0 2288 2300 10.1109/TIP.2021.3051761 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering QA4GN 33476269.0 Green Submitted 2023-03-23 WOS:000613403600015 0
C Li, TY; Wang, BP; Zio, E; Shi, J; Yang, Z IEEE Li, Tongyang; Wang, Sbaoping; Zio, Enrico; Shi, Jian; Yang, Zhe Aliasing Signal Separation for Superimposition of Inductive Debris Detection Using CNN-Based DUET PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019) IEEE Conference on Industrial Electronics and Applications English Proceedings Paper 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) JUN 19-21, 2019 Xian, PEOPLES R CHINA IEEE,IEEE Ind Elect Chapter Singapore,Xian Univ Technol,IEEE Singapore Sect,IEEE Ind Elect Soc aliasing signal separation; inductive debris detection; degenerate unmixing estimation technique; health management Wear debris which contain multiple degrading information are of great interest to the running machines' health management. Among the several kinds of debris detection methods, inductive sensors have shown great potential for the online monitoring applications, along with which the superimposed voltage caused by the debris with short distances becomes a major factor influencing the accuracy of the detection. An improved convolutional neural network (CNN) combined with degenerate unmixing estimation technique (DUET) is proposed in the paper which offers an online solution for the inductive aliasing signal separation. The experimental result shows that the proposed method is effective and provides an alternative online approach of the original two-dimensional weighted histogram method. [Li, Tongyang; Wang, Sbaoping; Shi, Jian] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Zio, Enrico; Yang, Zhe] Politecn Milan, Energy Dept, Via La Masa 34, I-20156 Milan, Italy Beihang University; Polytechnic University of Milan Li, TY (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. soredsun@163.com; sbaopingwang@vip.sina.com; enrico.zio@polimi.it; shijian@buaa.edu.cn; zhe.yang@polimi.it Li, Tongyang/0000-0002-8368-4764 National Natural Science Foundation of China [51575019, 51620105010]; Natural Science Foundation of Beijing Municipality [L171003]; Program 111 of China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Beijing Municipality(Beijing Natural Science Foundation); Program 111 of China(Ministry of Education, China - 111 Project) This study was co -supported by the National Natural Science Foundation of China (51575019, 51620105010), Natural Science Foundation of Beijing Municipality [6] (L171003) and Program 111 of China. 10 2 2 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-2318 978-1-5386-9490-9 C IND ELECT APPL 2019.0 211 215 5 Engineering, Industrial; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BP5KF 2023-03-23 WOS:000555952600039 0
J Liu, B; Nakhaei-Kohani, R; Bai, LH; Wen, ZG; Gao, YF; Tian, WC; Yang, L; Liu, KQ; Hemmati-Sarapardeh, A; Ostadhassan, M Liu, Bo; Nakhaei-Kohani, Reza; Bai, Longhui; Wen, Zhigang; Gao, Yifei; Tian, Weichao; Yang, Liang; Liu, Kouqi; Hemmati-Sarapardeh, Abdolhossein; Ostadhassan, Mehdi Integrating advanced soft computing techniques with experimental studies for pore structure analysis of Qingshankou shale in Southern Songliao Basin, NE China INTERNATIONAL JOURNAL OF COAL GEOLOGY English Article Qingshankou shale; Pore size distribution; Mercury intrusion capillary pressure; Artificial neural networks; N-2 adsorption NEURAL-NETWORK; GAS-ADSORPTION; SURFACE-AREA; ORGANIC PETROLOGY; BAKKEN SHALE; GULONG SAG; 1ST MEMBER; BIB-SEM; OIL; SYSTEMS Evaluating pore structure of unconventional shale reservoirs enables us to determine their productivity, allowing for better operational decisions. Despite extensive studies in this field, considering the complexity of shale plays, pore structure analysis of such formations still requires novelties and further research. In this study, 10 samples from the Qingshankou Formation (from 5 wells) were analyzed with X-ray diffraction (XRD), programmed pyrolysis, N-2 adsorption, and mercury intrusion capillary pressure (MICP). In the next step, several modern intelligent smart models including multilayer perceptron (MLP), radial basis function (RBF), generalized regression neural network (GRNN), cascaded forward neural network (CFNN), and least squares support vector machine (LSSVM), that were optimized by levenberg-marquardt (LM), Bayesian regularization (BR), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DE) algorithms were employed, to estimate the volumes of N-2 adsorbed and desorbed based on the mineralogy and geochemical properties of the samples. Results show that samples are mainly composed of clay (up to 42.3 wt%) and quartz (up to 34.6 wt%), low in total organic carbon (TOC) (up to 2.89%) and in the oil generation window. Complexity of smaller pores was found higher compared to medium and larger ones. In addition, deconvolution of N-2 adsorption pore size distribution (PSD) curve revealed that samples are composed of up to three families in the range of macropore size and different families in mesopore size. We found that LSSVM with applicability to the entire input dataset, outperformed all other models in predicting the amount of nitrogen adsorption and desorption with an average absolute percent relative error (AAPRE) value of 1.94%. Ultimately, clay minerals and potash feldspar had the greatest effect on increasing and decreasing the amount of nitrogen adsorbed and desorbed, respectively. The Leverage technique's findings demonstrate that more than 97% of total data points in the LSSVM model are in the valid domain. This study proves that smart methods if used properly, would enable us to study a large group of samples independent from exhaustive, time consuming and expensive experimental methods. [Liu, Bo; Wen, Zhigang; Tian, Weichao] Yangtze Univ, Hubei Key Lab Petr Geochem & Environm, Wuhan 430100, Peoples R China; [Nakhaei-Kohani, Reza] Shiraz Univ, Dept Chem & Petr Engn, Shiraz, Iran; [Liu, Bo; Bai, Longhui; Gao, Yifei; Ostadhassan, Mehdi] Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China; [Yang, Liang] PetroChina Jilin Oilfield Co, China Natl Petr Corp, Songyuan 138000, Peoples R China; [Liu, Kouqi] Peking Univ, Inst Energy, Beijing 100871, Peoples R China; [Hemmati-Sarapardeh, Abdolhossein] Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran; [Ostadhassan, Mehdi] Univ Kiel, Inst Geosci, Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany; [Ostadhassan, Mehdi] Ferdowsi Univ Mashhad, Dept Geol, Mashhad, Iran Yangtze University; Shiraz University; Northeast Petroleum University; China National Petroleum Corporation; Peking University; Shahid Bahonar University of Kerman (SBUK); University of Kiel; Ferdowsi University Mashhad Ostadhassan, M (corresponding author), Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China.;Hemmati-Sarapardeh, A (corresponding author), Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran.;Ostadhassan, M (corresponding author), Univ Kiel, Inst Geosci, Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany.;Ostadhassan, M (corresponding author), Ferdowsi Univ Mashhad, Dept Geol, Mashhad, Iran. hemmati@uk.ac.ir; mehdi.ostadhassan@nepu.edu.cn Liu, Bo/AAC-7509-2020; gao, yi/HCI-8298-2022; Sarapardeh, Abdolhossein Hemmati/M-5047-2019 Liu, Bo/0000-0003-0136-7237; Sarapardeh, Abdolhossein Hemmati/0000-0002-5889-150X; Gao, Yifei/0000-0002-5094-1349 National Natural Science Founda-tion of China [41972156]; Natural Science Foundation of Hei-longjiang Province [TD2021D001] National Natural Science Founda-tion of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hei-longjiang Province(Natural Science Foundation of Heilongjiang Province) Acknowledgements This study was supported by the National Natural Science Founda-tion of China (41972156) and the Natural Science Foundation of Hei-longjiang Province (TD2021D001) . 75 8 8 6 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0166-5162 1872-7840 INT J COAL GEOL Int. J. Coal Geol. MAY 15 2022.0 257.0 103998 10.1016/j.coal.2022.103998 0.0 MAY 2022 21 Energy & Fuels; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Geology 2M5TO 2023-03-23 WOS:000817762100001 0
J Shah, PM; Ullah, H; Ullah, R; Shah, D; Wang, YL; ul Islam, S; Gani, A; Rodrigues, JJPC Shah, Pir Masoom; Ullah, Hamid; Ullah, Rahim; Shah, Dilawar; Wang, Yulin; ul Islam, Saif; Gani, Abdullah; Rodrigues, Joel J. P. C. DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection EXPERT SYSTEMS English Article convolutional neural networks; COVID-19; deep-convolutional generative adversarial networks; synthetic images; X-rays PNEUMONIA; FEATURES Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision. [Shah, Pir Masoom; Wang, Yulin] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China; [Shah, Pir Masoom; Shah, Dilawar] Bacha Khan Univ, Dept Comp Sci, Charsadda, Pakistan; [Ullah, Hamid] Kohat Univ Sci & Technol, Dept Comp Sci, Kohat, Pakistan; [Ullah, Rahim] Univ Malakand, Dept Comp Sci, Malakand, Pakistan; [ul Islam, Saif] Inst Space Technol, Dept Comp Sci, KICSIT, Islamabad, Pakistan; [Gani, Abdullah] Univ Malaysia Sabah, Fac Comp & Informat, Labuan, Malaysia; [Rodrigues, Joel J. P. C.] Fed Univ Piaui UFPI, Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, Lisbon, Portugal Wuhan University; Kohat University of Science & Technology; University of Malakand; Universidade Federal do Piaui; Instituto de Telecomunicacoes Wang, YL (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;ul Islam, S (corresponding author), Inst Space Technol, Dept Comp Sci, Islamabad 44000, Pakistan. yulinwang@whu.edu.cn; saiflu2004@gmail.com Islam, Saif ul/AAX-4521-2020; Gani, Abdullah/C-2888-2009 Islam, Saif ul/0000-0002-9546-4195; shah, dilawar/0000-0003-2701-6646; Gani, Abdullah/0000-0002-4388-020X Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; Ministerio da Ciencia, Tecnologia e Ensino Superior Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Ministerio da Ciencia, Tecnologia e Ensino Superior Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; Ministerio da Ciencia, Tecnologia e Ensino Superior 33 8 8 0 3 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0266-4720 1468-0394 EXPERT SYST Expert Syst. MAR 2022.0 39.0 3 e12823 10.1111/exsy.12823 0.0 OCT 2021 13 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZG5YF 34898799.0 Green Accepted 2023-03-23 WOS:000708388400001 0
J Wu, LM Wu, Liming ENTROPICAL OPTIMAL TRANSPORT, SCHRODINGER'S SYSTEM AND ALGORITHMS ACTA MATHEMATICA SCIENTIA English Article entropical optimal transport; Schrodinger system; Sinkhorn's algorithm; gradient descent OPTIMAL MASS-TRANSPORT; ISOPERIMETRY In this exposition paper we present the optimal transport problem of Monge-Ampere-Kantorovitch (MAK in short) and its approximative entropical regularization. Contrary to the MAK optimal transport problem, the solution of the entropical optimal transport problem is always unique, and is characterized by the Schrodinger system. The relationship between the Schrodinger system, the associated Bernstein process and the optimal transport was developed by Leonard [32, 33] (and by Mikami [39] earlier via an h-process). We present Sinkhorn's algorithm for solving the Schrodinger system and the recent results on its convergence rate. We study the gradient descent algorithm based on the dual optimal question and prove its exponential convergence, whose rate might be independent of the regularization constant. This exposition is motivated by recent applications of optimal transport to different domains such as machine learning, image processing, econometrics, astrophysics etc.. [Wu, Liming] Harbins Inst Technol, Inst Adv Study Math, Harbin 150001, Peoples R China; [Wu, Liming] Univ Clermont Auvergne UCA, Lab Math Blaise Pascal, CNRS, UMR 6620, F-63000 Clermont Ferrand, France Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Clermont Auvergne (UCA) Wu, LM (corresponding author), Harbins Inst Technol, Inst Adv Study Math, Harbin 150001, Peoples R China.;Wu, LM (corresponding author), Univ Clermont Auvergne UCA, Lab Math Blaise Pascal, CNRS, UMR 6620, F-63000 Clermont Ferrand, France. Li-Ming.Wu@uca.fr 58 0 0 0 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0252-9602 1572-9087 ACTA MATH SCI Acta Math. Sci. NOV 2021.0 41.0 6 2183 2197 10.1007/s10473-021-0623-1 0.0 15 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics WS0ZS 2023-03-23 WOS:000714918800023 0
J Kholiavchenko, M; Sirazitdinov, I; Kubrak, K; Badrutdinova, R; Kuleev, R; Yuan, Y; Vrtovec, T; Ibragimov, B Kholiavchenko, M.; Sirazitdinov, I.; Kubrak, K.; Badrutdinova, R.; Kuleev, R.; Yuan, Y.; Vrtovec, T.; Ibragimov, B. Contour-aware multi-label chest X-ray organ segmentation INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY English Article Image segmentation; Convolutional neural networks; Deep learning architectures; Chest X-ray (CXR) images; JSRT database LUNG SEGMENTATION; RADIOGRAPHS; SHAPE; MR Purpose Segmentation of organs from chest X-ray images is an essential task for an accurate and reliable diagnosis of lung diseases and chest organ morphometry. In this study, we investigated the benefits of augmenting state-of-the-art deep convolutional neural networks (CNNs) for image segmentation with organ contour information and evaluated the performance of such augmentation on segmentation of lung fields, heart, and clavicles from chest X-ray images. Methods Three state-of-the-art CNNs were augmented, namely the UNet and LinkNet architecture with the ResNeXt feature extraction backbone, and the Tiramisu architecture with the DenseNet. All CNN architectures were trained on ground-truth segmentation masks and additionally on the corresponding contours. The contribution of such contour-based augmentation was evaluated against the contour-free architectures, and 20 existing algorithms for lung field segmentation. Results The proposed contour-aware segmentation improved the segmentation performance, and when compared against existing algorithms on the same publicly available database of 247 chest X-ray images, the UNet architecture with the ResNeXt50 encoder combined with the contour-aware approach resulted in the best overall segmentation performance, achieving a Jaccard overlap coefficient of 0.971, 0.933, and 0.903 for the lung fields, heart, and clavicles, respectively. Conclusion In this study, we proposed to augment CNN architectures for CXR segmentation with organ contour information and were able to significantly improve segmentation accuracy and outperform all existing solution using a public chest X-ray database. [Kholiavchenko, M.; Sirazitdinov, I.; Kubrak, K.; Kuleev, R.; Ibragimov, B.] Innopolis Univ, Innopolis, Russia; [Badrutdinova, R.] Kazan Fed Univ, Kazan, Russia; [Ibragimov, B.] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark; [Yuan, Y.] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Vrtovec, T.] Univ Ljubljana, Fac Elect Engn, Ljubljana, Slovenia Innopolis University; Kazan Federal University; University of Copenhagen; City University of Hong Kong; University of Ljubljana Ibragimov, B (corresponding author), Innopolis Univ, Innopolis, Russia.;Ibragimov, B (corresponding author), Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark. bulat@di.ku.dk Vrtovec, Tomaž/AAU-2952-2020; Kuleev, Ramil F/C-1408-2016 Kuleev, Ramil F/0000-0001-5323-9515; Vrtovec, Tomaz/0000-0002-6625-0307; Kholiavchenko, Maksim/0000-0001-6757-1957; Yuan, Yixuan/0000-0002-0853-6948; Ibragimov, Bulat/0000-0001-7739-7788 Russian Science Foundation [18-71-10072] Funding Source: Russian Science Foundation Russian Science Foundation(Russian Science Foundation (RSF)) 55 15 17 3 21 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1861-6410 1861-6429 INT J COMPUT ASS RAD Int. J. Comput. Assist. Radiol. Surg. MAR 2020.0 15.0 3 425 436 10.1007/s11548-019-02115-9 0.0 12 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging; Surgery Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging; Surgery KN9UD 32034633.0 2023-03-23 WOS:000515191900006 0
C Fu, Q; Luo, YL; Liu, JX; Bi, JJ; Qiu, SH; Cao, Y; Ding, XM IEEE Fu, Qiang; Luo, Yuling; Liu, Junxiu; Bi, Jinjie; Qiu, Senhui; Cao, Yi; Ding, Xuemei Improving Learning Algorithm Performance for Spiking Neural Networks 2017 17TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT 2017) English Proceedings Paper IEEE 17th International Conference on Communication Technology (ICCT) OCT 27-30, 2017 Chengdu, PEOPLES R CHINA IEEE spiking neural network; optimization method; learning performance NEURONS This paper proposes three methods to improve the learning algorithm for spiking neural networks (SNNs). The aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. The performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. The exclusive-or (XOR) and Wisconsin breast cancer (WBC) classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period. [Fu, Qiang; Luo, Yuling; Liu, Junxiu; Bi, Jinjie; Qiu, Senhui] Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China; [Qiu, Senhui] Guangxi Expt Ctr Informat Sci, Guilin, Peoples R China; [Cao, Yi] Univ Surrey, Surrey Business Sch, Dept Business Transformat & Sustainable Enterpris, Guildford, Surrey, England; [Ding, Xuemei] Fujian Normal Univ, Fac Software, Fuzhou 350108, Fujian, Peoples R China; [Ding, Xuemei] Ulster Univ, Fac Comp & Engn, Sch Comp & Intelligent Syst, Coleraine BT48 7JL, Londonderry, North Ireland Guangxi Normal University; University of Surrey; Fujian Normal University; Ulster University Luo, YL (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China. yuling0616@mailbox.gxnu.edu.cn National Natural Science Foundation of China [61661008, 61603104]; Guangxi Natural Science Foundation [2015GXNSFBA139256, 2016GXNSFCA 380017]; Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China [KY2016YB059]; Guangxi Key Lab of Multi-source Information Mining Security [MIMS15-07]; Doctoral Research Foundation of Guangxi Normal University; Guangxi Experiment Centre of Information Science; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry; Funds for Young Key Program of Education Department from Fujian Province, China [JZ160425]; Program of Education Department of Fujian Province, China [I201501005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Program of Guangxi Higher Education; Research Project of Guangxi University of China; Guangxi Key Lab of Multi-source Information Mining Security; Doctoral Research Foundation of Guangxi Normal University; Guangxi Experiment Centre of Information Science; Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry(Scientific Research Foundation for the Returned Overseas Chinese Scholars); Funds for Young Key Program of Education Department from Fujian Province, China; Program of Education Department of Fujian Province, China This research was partially supported by the National Natural Science Foundation of China under Grants 61661008 and 61603104, the Guangxi Natural Science Foundation under Grants 2015GXNSFBA139256 and 2016GXNSFCA 380017, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining & Security under Grant MIMS15-07, the Doctoral Research Foundation of Guangxi Normal University, the grant from Guangxi Experiment Centre of Information Science, the Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry, the Funds for Young Key Program of Education Department from Fujian Province, China (Grant No. JZ160425), Program of Education Department of Fujian Province, China (Grant No. I201501005). 14 7 7 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-3944-9 2017.0 1916 1919 4 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BK3NU 2023-03-23 WOS:000435276600372 0
J Wen, T; Tiewang, W; Arabameri, A; Nalivan, OA; Pal, SC; Saha, A; Costache, R Wen, Tang; Tiewang, Wang; Arabameri, Alireza; Nalivan, Omid Asadi; Pal, Subodh Chandra; Saha, Asish; Costache, Romulus Land-subsidence susceptibility mapping: assessment of an adaptive neuro-fuzzy inference system-genetic algorithm hybrid model GEOCARTO INTERNATIONAL English Article; Early Access Land-subsidence; geo-hazard; machine learning; hybrid model SUPPORT VECTOR MACHINE; LOGISTIC-REGRESSION; SPATIAL PREDICTION; LANDSLIDE; GROUNDWATER; AREA; DISTRIBUTIONS; NETWORKS; PROVINCE; RAVENNA Land-subsidence (LS) is a common geo-hazard in many regions of the world. The overexploitation of subsurface and ground water, followed by collapse of underground cavities, is one of the mechanisms that cause LS. Optimal prediction of LS is difficult due to limitations in monitoring, surveying and information about the mechanism and process of LS. Therefore, LS susceptibility mapping (LSSM) is crucial to prevent and reduce the economic damage of natural and manmade catastrophes. This study in the Isfahan Province, Iran, aimed to map LS susceptibility using a hybrid model of adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and three stand-alone machine-learning (ML) models: support vector machine, maximum entropy and ANFIS. In total, 155 LS historical points were collected, and 12 LS conditioning factors (LSCFs) were compiled for each site. These factors were elevation, slope, aspect, topographic wetness index, plan curvature, distance to stream, drainage density, groundwater withdrawal, lithology, distance to fault, normalized difference vegetation index and land use and land cover. These data were input into the models in five training-to-validation sample ratios, designated D1 (90:10), D2 (80:20), D3 (70:30), D4 (60:40) and D5 (50:50). The importance of these variables to the occurrence of LS was calculated using a random forest algorithm. Algorithm performance was evaluated through receiver operating characteristic-area under the curve analysis and the results show ANFIS-GA to be the optimal model, with average training and validation AUC values of 0.9644 and 0.9416, respectively. The results demonstrate that LSSMs can be effectively prepared to support decision makers in land use planning and allocation through the development of suitable management strategies. [Wen, Tang] Xian Shiyou Univ, Sch Earth Sci & Engn, Xian, Peoples R China; [Wen, Tang] Xian Shiyou Univ, Shaanxi Key Lab Petr Accumulat Geol, Xian, Peoples R China; [Tiewang, Wang] Geophys Res Inst, Changqing Branch, BGP, Xian, Peoples R China; [Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Tehran, Iran; [Nalivan, Omid Asadi] Gorgan Univ Agr Sci & Natu Ral Resources GUASNR, Dept Watershed Management, Gorgan, Golestan, Iran; [Pal, Subodh Chandra; Saha, Asish] Univ Burdwan, Dept Geog, Bardhaman, W Bengal, India; [Costache, Romulus] Danube Delta Natl Inst Res & Dev, Tulcea, Romania Xi'an Shiyou University; Xi'an Shiyou University; Tarbiat Modares University; University of Burdwan Wen, T (corresponding author), Xian Shiyou Univ, Sch Earth Sci & Engn, Xian, Peoples R China.;Wen, T (corresponding author), Xian Shiyou Univ, Shaanxi Key Lab Petr Accumulat Geol, Xian, Peoples R China.;Arabameri, A (corresponding author), Tarbiat Modares Univ, Dept Geomorphol, Tehran, Iran. tangw888888@sina.com; alireza.ameri91@yahoo.com Costache, Romulus/GVU-1762-2022 115 0 0 4 16 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1010-6049 1752-0762 GEOCARTO INT Geocarto Int. 10.1080/10106049.2022.2066198 0.0 APR 2022 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 1M7ZX 2023-03-23 WOS:000800186800001 0
J Li, Y; Miao, YX; Zhang, J; Cammarano, D; Li, SY; Liu, XJ; Tian, YC; Zhu, Y; Cao, WX; Cao, Q Li, Yue; Miao, Yuxin; Zhang, Jing; Cammarano, Davide; Li, Songyang; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating Multi-Source Data Across Different Agro-Ecological Zones FRONTIERS IN PLANT SCIENCE English Article precision nitrogen management; machine learning; environmental variables; management practices; variable selection; nitrogen nutrition index ACTIVE CANOPY SENSOR; USE EFFICIENCY; MANAGEMENT; CHINA; YIELD; PLANT; GROWTH; SOIL Timely and accurate estimation of plant nitrogen (N) status is crucial to the successful implementation of precision N management. It has been a great challenge to non-destructively estimate plant N status across different agro-ecological zones (AZs). The objective of this study was to use random forest regression (RFR) models together with multi-source data to improve the estimation of winter wheat (Triticum aestivum L.) N status across two AZs. Fifteen site-year plot and farmers' field experiments involving different N rates and 19 cultivars were conducted in two AZs from 2015 to 2020. The results indicated that RFR models integrating climatic and management factors with vegetation index (R-2 = 0.72-0.86) outperformed the models by only using the vegetation index (R-2 = 0.36-0.68) and performed well across AZs. The Pearson correlation coefficient-based variables selection strategy worked well to select 6-7 key variables for developing RFR models that could achieve similar performance as models using full variables. The contributions of climatic and management factors to N status estimation varied with AZs and N status indicators. In higher-latitude areas, climatic factors were more important to N status estimation, especially water-related factors. The addition of climatic factors significantly improved the performance of the RFR models for N nutrition index estimation. Climatic factors were important for the estimation of the aboveground biomass, while management variables were more important to N status estimation in lower-latitude areas. It is concluded that integrating multi-source data using RFR models can significantly improve the estimation of winter wheat N status indicators across AZs compared to models only using one vegetation index. However, more studies are needed to develop unmanned aerial vehicles and satellite remote sensing-based machine learning models incorporating multi-source data for more efficient monitoring of crop N status under more diverse soil, climatic, and management conditions across large regions. [Li, Yue; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang] Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, MOE Engn & Res Ctr Smart Agr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing, Peoples R China; [Miao, Yuxin] Univ Minnesota, Precis Agr Ctr, Dept Soil Water & Climate, St Paul, MN USA; [Zhang, Jing] Univ Georgia, Dept Crop & Soil Sci, Tifton, GA USA; [Cammarano, Davide] Aarhus Univ, Dept Agroecol, Tjele, Denmark; [Li, Songyang] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China Nanjing Agricultural University; University of Minnesota System; University of Minnesota Twin Cities; University System of Georgia; University of Georgia; Aarhus University; Hong Kong Polytechnic University Cao, WX; Cao, Q (corresponding author), Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, MOE Engn & Res Ctr Smart Agr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing, Peoples R China. caow@njau.edu.cn; qiangcao@njau.edu.cn Jiangsu Province Key Technologies Research and Development Program [BE2021308]; National Natural Science Foundation of China [31601222]; Norwegian Ministry of Foreign Affairs (SINOGRAIN II) [CHN-17/0019]; Academic Program Development of Jiangsu Higher Education Institutions (PAPD); 111 Project [B16026] Jiangsu Province Key Technologies Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Norwegian Ministry of Foreign Affairs (SINOGRAIN II); Academic Program Development of Jiangsu Higher Education Institutions (PAPD); 111 Project(Ministry of Education, China - 111 Project) This research was funded by the Jiangsu Province Key Technologies Research and Development Program (BE2021308), the National Natural Science Foundation of China (31601222), the Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019), the Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the 111 Project (B16026). 58 1 1 10 16 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-462X FRONT PLANT SCI Front. Plant Sci. JUN 10 2022.0 13.0 890892 10.3389/fpls.2022.890892 0.0 16 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences 2J6MR 35755650.0 gold, Green Accepted 2023-03-23 WOS:000815768900001 0
J Yao, YZ; Wang, ZH; Li, L; Lu, K; Liu, RY; Liu, ZY; Yan, J Yao, Yuanzhe; Wang, Zeheng; Li, Liang; Lu, Kun; Liu, Runyu; Liu, Zhiyuan; Yan, Jing An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE English Article PULSE DIAGNOSIS In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction. [Yao, Yuanzhe; Wang, Zeheng; Li, Liang; Liu, Runyu; Liu, Zhiyuan] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China; [Wang, Zeheng] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia; [Lu, Kun] Ludwig Maximilian Univ Munich, Fac Med, D-81377 Munich, Germany; [Yan, Jing] Zhejiang Chinese Med Univ, Clin Med Coll 1, Hangzhou 310053, Zhejiang, Peoples R China University of Electronic Science & Technology of China; University of New South Wales Sydney; University of Munich; Zhejiang Chinese Medical University Wang, ZH (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China.;Wang, ZH (corresponding author), Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia. zenwang@outlook.com li, zhiyuan/HGD-9581-2022; Wang, Zeheng/G-4109-2019 Wang, Zeheng/0000-0002-6994-1234 National Natural Science Foundation of China [61370202] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61370202. 34 11 13 0 18 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1748-670X 1748-6718 COMPUT MATH METHOD M Comput. Math. Method Med. OCT 1 2019.0 2019.0 8617503 10.1155/2019/8617503 0.0 7 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology JG2PQ 31662790.0 Green Submitted, gold, Green Published 2023-03-23 WOS:000491918100002 0
J Lu, Y; Luo, LK; Huang, D; Wang, YH; Chen, LM Lu, Ying; Luo, Lingkun; Huang, Di; Wang, Yunhong; Chen, Liming Knowledge Transfer in Vision Recognition: A Survey ACM COMPUTING SURVEYS English Article Knowledge transfer; transfer learning; vision recognition; computer vision; machine learning LEARN In this survey, we propose to explore and discuss the common rules behind knowledge transfer works for vision recognition tasks. To achieve this, we firstly discuss the different kinds of reusable knowledge existing in a vision recognition task, and then we categorize different knowledge transfer approaches depending on where the knowledge comes from and where the knowledge goes. Compared to previous surveys on knowledge transfer that are from the problem-oriented perspective or from the technique-oriented perspective, our viewpoint is closer to the nature of knowledge transfer and reveals common rules behind different transfer learning settings and applications. Besides different knowledge transfer categories, we also show some research works that study the transferability between different vision recognition tasks. We further give a discussion about the introduced research works and show some potential research directions in this field. [Lu, Ying; Chen, Liming] Ecole Cent Lyon, Ecully, France; [Luo, Lingkun] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Huang, Di; Wang, Yunhong] Beihang Univ, Beijing, Peoples R China Ecole Centrale de Lyon; Shanghai Jiao Tong University; Beihang University Lu, Y (corresponding author), Ecole Cent Lyon, Ecully, France. ying.lu@ec-lyon.fr; lolinkun1988@sjtu.edu.cn; dhuang@buaa.edu.cn; yhwang@buaa.edu.cn; liming.chen@ec-lyon.fr European Union through the FUI Pikaflex project within the FEDER program; French Research Agency (l'Agence Nationale de Recherche (ANR)) through the LabCom Ares project [ANR16-LCV2-0012-01]; Partner University Fund under the 4D Vision project; Beijing Advanced Innovation Center for Big Data and Brain Computing of Beihang University; French Research Agency (l'Agence Nationale de Recherche (ANR)) through the Alegoria project [ANR-17-CE38-0014-03]; French Research Agency (l'Agence Nationale de Recherche (ANR)) through the ERA-CHIST LEARNREAL project [ANR-18-CHR3-0002-01] European Union through the FUI Pikaflex project within the FEDER program; French Research Agency (l'Agence Nationale de Recherche (ANR)) through the LabCom Ares project(French National Research Agency (ANR)); Partner University Fund under the 4D Vision project; Beijing Advanced Innovation Center for Big Data and Brain Computing of Beihang University; French Research Agency (l'Agence Nationale de Recherche (ANR)) through the Alegoria project(French National Research Agency (ANR)); French Research Agency (l'Agence Nationale de Recherche (ANR)) through the ERA-CHIST LEARNREAL project(French National Research Agency (ANR)) This work is partially supported by the European Union through the FUI Pikaflex project within the FEDER program, and by the French Research Agency (l'Agence Nationale de Recherche (ANR)) through the LabCom Ares project under the grant ANR16-LCV2-0012-01, the Alegoria project under the grant ANR-17-CE38-0014-03, and the ERA-CHIST LEARNREAL project under the grant ANR-18-CHR3-0002-01. This work is also supported by the Partner University Fund under the 4D Vision project, and by the Beijing Advanced Innovation Center for Big Data and Brain Computing of Beihang University. 151 6 6 4 11 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. APR 2020.0 53.0 2 37 10.1145/3379344 0.0 35 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science OH4ZH 2023-03-23 WOS:000582585700016 0
J Johnson, H; Ali, A; Zhang, XH; Wang, TY; Simoulis, A; Wingren, AG; Persson, JL Johnson, Heather; Ali, Amjad; Zhang, Xuhui; Wang, Tianyan; Simoulis, Athanasios; Wingren, Anette Gjorloff; Persson, Jenny L. K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer CANCERS English Article machine learning algorithm; KRAS; breast cancer biomarkers; gene mutations; triple-negative breast cancer; luminal a breast cancer; progression-free survival; treatment response COMPREHENSIVE MOLECULAR PORTRAITS; LANDSCAPE Simple Summary Despite advances in treatment of subtypes of breast cancer, there still lacks reliable biomarkers with precision to predict treatment response at diagnosis. We used machine-learning tools and developed and validated a novel 12-Gene Algorithm as a biomarker for prediction of treatment response for breast cancer patients, especially those suffering triple-negative cancer. The 12-Gene Algorithm based on KRAS-associated gene-mutation profiles showed high accuracy at predicting the response of breast cancer patients including triple-negative subtype to first-line chemotherapy treatment in two independent patient cohorts. Our study suggests that the 12-Gene Algorithm has a potential to be used in clinical practice to improve breast cancer treatment decision-making, especially for triple-negative breast cancer patients. Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2-) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan-Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94-0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3-41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7-101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96-0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4-79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality. [Johnson, Heather] Olympia Diagnost Inc, Sunnyvale, CA 94086 USA; [Ali, Amjad; Wang, Tianyan; Persson, Jenny L.] Umea Univ, Dept Mol Biol, SE-90187 Umea, Sweden; [Zhang, Xuhui] Inst Basic Med Sci, Dept Biodiag, Beijing 100005, Peoples R China; [Simoulis, Athanasios] Skane Univ Hosp, Dept Clin Pathol & Cytol, SE-20502 Malmo, Sweden; [Wingren, Anette Gjorloff; Persson, Jenny L.] Malmo Univ, Dept Biomed Sci, SE-20606 Malmo, Sweden Umea University; Lund University; Skane University Hospital; Malmo University Persson, JL (corresponding author), Umea Univ, Dept Mol Biol, SE-90187 Umea, Sweden.;Persson, JL (corresponding author), Malmo Univ, Dept Biomed Sci, SE-20606 Malmo, Sweden. jenny.persson@umu.se Persson, Jenny/0000-0001-7682-7678 EU Targeted therapy for advanced colorectal cancer patients (REVERT) [848 098]; Swedish Cancer Society [CAN-2017-381]; Swedish Children Foundation [TJ2015-0097]; H2020-MSCA-ITN-2018-European Commission [721297]; Swedish National Research Council [2019-01318]; STINT Institutional Grant [IG20135595]; Malmo Cancer Foundation; Medical Faculty Instruments Grants, Norland fund for Cancer Forskning; Insamlingstiftelsen; Medical Faculty, Umea University, Kempestiftelserna; UCMR; Bio-Film Center, Malmo University; Kempestiftelserna; Norland fund for Cancer Forskning; Bioteknik medel; Kempe STF; Umea University EU Targeted therapy for advanced colorectal cancer patients (REVERT); Swedish Cancer Society(Swedish Cancer Society); Swedish Children Foundation; H2020-MSCA-ITN-2018-European Commission; Swedish National Research Council; STINT Institutional Grant; Malmo Cancer Foundation; Medical Faculty Instruments Grants, Norland fund for Cancer Forskning; Insamlingstiftelsen; Medical Faculty, Umea University, Kempestiftelserna; UCMR; Bio-Film Center, Malmo University; Kempestiftelserna; Norland fund for Cancer Forskning; Bioteknik medel; Kempe STF; Umea University This research was funded by EU Targeted therapy for advanced colorectal cancer patients (REVERT), grant number 848 098, grants from the Swedish Cancer Society (CAN-2017-381), The Swedish Children Foundation (TJ2015-0097), H2020-MSCA-ITN-2018-European Commission (721297), The Swedish National Research Council (2019-01318), STINT Institutional Grant (IG20135595), Malmo Cancer Foundation, Kempe STF, Umea University, Medical Faculty Instruments Grants, Norland fund for Cancer Forskning, Insamlingstiftelsen, Bioteknik medel, and Medical Faculty infrastructure Grants, Medical Faculty, Umea University, Kempestiftelserna to JLP. We acknowledge the Biochemical Imaging Center at Umea University (BICU) and the Grant from UCMR to JLP. Grant from the Bio-Film Center, Malmo University to A.G.W. and J.L.P. is acknowledged. 30 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6694 CANCERS Cancers NOV 2022.0 14.0 21 5322 10.3390/cancers14215322 0.0 16 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 6E6AH 36358741.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000883459900001 0
J Chen, SC; Noori, S; Villena, MA; Shi, YY; Han, TT; Zuo, Y; Pedeferri, M; Strukov, D; Lanza, M; Diamanti, MV Chen, Shaochuan; Noori, Seyedreza; Villena, Marco A.; Shi, Yuanyuan; Han, Tingting; Zuo, Ying; Pedeferri, MariaPia; Strukov, Dmitri; Lanza, Mario; Diamanti, Maria Vittoria Memristive Electronic Synapses Made by Anodic Oxidation CHEMISTRY OF MATERIALS English Article PHOSPHORUS INCORPORATION; TITANIUM; FILMS; NETWORK; DEVICE; TRANSITION; BEHAVIOR; MEMORY; ACID Memristors have recently gained growing interest due to their potential application as electronic synapses to build artificial neural networks for artificial intelligence systems. However, modulating the conductivity of memristors in a dynamic way to emulate biological synaptic behaviors is very challenging. Here we show the first fabrication of memristive electronic synapses using a dielectric film (TiO2-x) synthesized via an electrochemical anodization method. Pt/anodic TiO2-x/Ti memristive synapses show reliable and reproducible memristive behavior and fast switching times below 90 ns. By applying ramped voltage stresses, multilevel conductance states have been achieved (using different current compliances or reset voltages), and by applying pulsed voltage stresses, the potentiation and depression rates could be accurately controlled by tuning the pulse amplitudes. The switching is governed by homogeneous charge rearrangements at the TiO2-x/Ti interface and TiO2-x thickness modulation. It is concluded that the anodic oxidation method may be a cheap and effective route to fabricate competitive electronic synapses. [Chen, Shaochuan; Villena, Marco A.; Han, Tingting; Zuo, Ying; Lanza, Mario] Soochow Univ, Inst Funct Nano & Soft Mat, Collaborat Innovat Ctr Suzhou Nanosci & Technol, Suzhou 215123, Peoples R China; [Chen, Shaochuan; Strukov, Dmitri] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA; [Noori, Seyedreza; Pedeferri, MariaPia; Diamanti, Maria Vittoria] Politecn Milan, Dept Chem Mat & Chem Engn G Natta, Via Mancinelli 7, I-20131 Milan, Italy; [Villena, Marco A.] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Shi, Yuanyuan] Technion Israel Inst Technol, Andrew & Erna Viterbi Fac Elect Engn, IL-32000 Haifa, Israel Soochow University - China; University of California System; University of California Santa Barbara; Polytechnic University of Milan; Stanford University; Technion Israel Institute of Technology Lanza, M (corresponding author), Soochow Univ, Inst Funct Nano & Soft Mat, Collaborat Innovat Ctr Suzhou Nanosci & Technol, Suzhou 215123, Peoples R China. mlanza@suda.edu.cn Lanza, Mario/AAE-3986-2019; HAN, TINGTING/GQZ-8692-2022; Han, Tingting/AAL-3355-2021; Lanza, Mario/GLV-3473-2022; Chen, Shaochuan/AAG-3077-2021 Lanza, Mario/0000-0003-4756-8632; Lanza, Mario/0000-0003-4756-8632; Chen, Shaochuan/0000-0002-4304-7410; Han, Tingting/0000-0001-5688-8844; Villena Sanchez, Marco Antonio/0000-0001-5547-3380; , Dmitri/0000-0002-4526-4347 Young 1000 Global Talent Recruitment Program of the Ministry of Education of China; Ministry of Science and Technology of China [BRICS2018-211-2DNEURO]; National Natural Science Foundation of China [61502326, 41550110223, 11661131002, 61874075]; Ministry of Finance of China [SX21400213]; Young 973 National Program of the Chinese Ministry of Science and Technology [2015CB932700]; Fondazione Cariplo; Regione Lombardia, project ProACTIV-MOX-PROmoting research on AnodiC memris TIVe Metal OXide films for nano electronics Young 1000 Global Talent Recruitment Program of the Ministry of Education of China; Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Finance of China; Young 973 National Program of the Chinese Ministry of Science and Technology; Fondazione Cariplo(Fondazione Cariplo); Regione Lombardia, project ProACTIV-MOX-PROmoting research on AnodiC memris TIVe Metal OXide films for nano electronics This work has been supported by the Young 1000 Global Talent Recruitment Program of the Ministry of Education of China, the Ministry of Science and Technology of China (grant no. BRICS2018-211-2DNEURO), the National Natural Science Foundation of China (grants no. 61502326, 41550110223, 11661131002, 61874075), the Ministry of Finance of China (grant no. SX21400213), and the Young 973 National Program of the Chinese Ministry of Science and Technology (grant no. 2015CB932700). The Collaborative Innovation Center of Suzhou Nano Science & Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the 111 Project from the State Administration of Foreign Experts Affairs are also acknowledged. S.N., MP.P., and M.V.D. acknowledge the financial support of Fondazione Cariplo and Regione Lombardia, project ProACTIV-MOX-PROmoting research on AnodiC memris TIVe Metal OXide films for nano electronics. 54 16 16 3 37 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0897-4756 1520-5002 CHEM MATER Chem. Mat. OCT 22 2019.0 31.0 20 8394 8401 10.1021/acs.chemmater.9b02245 0.0 8 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science JH5HW 2023-03-23 WOS:000492800700016 0
J Simos, TE; Katsikis, VN; Mourtas, SD; Stanimirovic, PS Simos, Theodore E.; Katsikis, Vasilios N.; Mourtas, Spyridon D.; Stanimirovic, Predrag S. Unique non-negative definite solution of the time-varying algebraic Riccati equations with applications to stabilization of LTV systems MATHEMATICS AND COMPUTERS IN SIMULATION English Article Algebraic Riccati equations; Zeroing neural network; Eigendecomposition; Linear time-varying systems RECURRENT NEURAL-NETWORKS; ZNN MODELS; DESIGN; INVERSE In the context of infinite-horizon optimal control problems, the algebraic Riccati equations (ARE) arise when the stability of linear time-varying (LTV) systems is investigated. Using the zeroing neural network (ZNN) approach to solve the time-varying eigendecomposition-based ARE (TVE-ARE) problem, the ZNN model (ZNNTVE-ARE) for solving the TVE-ARE problem is introduced as a result of this research. Since the eigendecomposition approach is employed, the ZNNTVE-ARE model is designed to produce only the unique nonnegative definite solution of the time-varying ARE (TV-ARE) problem. It is worth mentioning that this model follows the principles of the ZNN method, which converges exponentially with time to a theoretical time-varying solution. The ZNNTVE-ARE model can also produce the eigenvector solution of the continuous-time Lyapunov equation (CLE) since the Lyapunov equation is a particular case of ARE. Moreover, this paper introduces a hybrid ZNN model for stabilizing LTV systems in which the ZNNTVE-ARE model is employed to solve the continuous-time ARE (CARE) related to the optimal control law. Experiments show that the ZNNTVE-ARE and HFTZNN-LTVSS models are both effective, and that the HFTZNN-LTVSS model always provides slightly better asymptotic stability than the models from which it is derived.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved. [Simos, Theodore E.] Hangzhou Dianzi Univ, Sch Mech Engn, Er Hao Da Jie 1158, Hangzhou 310018, Peoples R China; [Simos, Theodore E.] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan; [Simos, Theodore E.] Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Neijiang 641100, Peoples R China; [Simos, Theodore E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi 67100, Greece; [Katsikis, Vasilios N.; Mourtas, Spyridon D.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Sofokleous 1 St, Athens 10559, Greece; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia Hangzhou Dianzi University; China Medical University Taiwan; China Medical University Hospital - Taiwan; Neijiang Normal University; Democritus University of Thrace; National & Kapodistrian University of Athens; University of Nis Katsikis, VN (corresponding author), Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Sofokleous 1 St, Athens 10559, Greece. tsimos.conf@gmail.com; vaskatsikis@econ.uoa.gr; spirosmourtas@gmail.com; pecko@pmf.ni.ac.rs Mourtas, Spyridon D./AAB-3651-2022; Katsikis, Vasilios/AAD-7216-2019 Mourtas, Spyridon D./0000-0002-8299-9916; Katsikis, Vasilios/0000-0002-8208-9656; Stanimirovic, Predrag S./0000-0003-0655-3741 Ministry of Education, Science and Technological Development, Republic of Serbia [451-03-68/2022-14/200124]; Science Fund of the Republic of Serbia [7750185] Ministry of Education, Science and Technological Development, Republic of Serbia; Science Fund of the Republic of Serbia Predrag S. Stanimirovice is supported by the Ministry of Education, Science and Technological Development, Republic of Serbia (No. 451-03-68/2022-14/200124) . Predrag S. Stanimirovice is supported by the Science Fund of the Republic of Serbia, (No. 7750185, Quantitative Automata Models: Fundamental Problems and Applications-QUAM) . 44 5 5 2 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-4754 1872-7166 MATH COMPUT SIMULAT Math. Comput. Simul. DEC 2022.0 202.0 164 180 10.1016/j.matcom.2022.05.033 0.0 JUN 2022 17 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics 2U1ZO 2023-03-23 WOS:000822961000008 0
C Wu, HR; Su, H; Liu, YY; Gao, HB IEEE Wu, Haoran; Su, Hang; Liu, Yueyue; Gao, Hongbo Object Detection and Localization Using Stereo Cameras 2020 5TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2020) English Proceedings Paper 5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) DEC 18-21, 2020 Shenzhen, PEOPLES R CHINA IEEE,IEEE Syst, Man & Cybernet Soc,IEEE Robot & Automat Soc,IEEE SMC Tech Comm Bio Mechatron & Bio Robot Syst,IEEE RAS Tech Comm Neuro Robot Syst,Shenzhen Univ,Artificial Intelligence & Robot Assoc Guangdong Prov,Anhui Robot Soc,IEEE CAA Journal Automatica Sinica,Deltason Camera systems have become increasingly popular because cameras are cheap and easy to deploy. Compared with other depth cameras, the stereo camera is small, and it is easily carried by subjects. Through a fixed baseline, the stereo camera is able to compute depth information. However, the traditional stereo matching algorithm can not compute the depth information on the edge of the image. Meanwhile, due to the large amount in the 3D point cloud, there is no specific numerical relationship between semantic information and depth information. In order to solve this problem, estimating depth and semantic information in an accurate way is required. A deep neural network model is used to predict semantic information and depth at the same time. Further, we propose a robust method to deal with variation brightness and improve performance under actual conditions. [Wu, Haoran; Gao, Hongbo] Univ Sci & Technol China, Dept Automat, Hefei, Peoples R China; [Su, Hang] Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy; [Liu, Yueyue] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou, Peoples R China Chinese Academy of Sciences; University of Science & Technology of China, CAS; Polytechnic University of Milan; South China University of Technology Wu, HR (corresponding author), Univ Sci & Technol China, Dept Automat, Hefei, Peoples R China. haoran.wu@outlook.com; hang.su@polimi.it; lyy8313167@163.com; ghb48@126.com Fundamental Research Funds for the Central Universities; Science and Technology Innovation Planning Project of Ministry of Education of China; National Natural Science Foundation of China [U1804161]; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education (Anhui Polytechnic University, Wuhu, China) [GDSC202001, GDSC202007]; NVIDIA NVAIL program Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Science and Technology Innovation Planning Project of Ministry of Education of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education (Anhui Polytechnic University, Wuhu, China); NVIDIA NVAIL program This work was supported by the Fundamental Research Funds for the Central Universities, the Science and Technology Innovation Planning Project of Ministry of Education of China, NVIDIA NVAIL program, the National Natural Science Foundation of China under Grant No. U1804161, and Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education (Anhui Polytechnic University, Wuhu, China, 241000) under Grant Nos. GDSC202001 and GDSC202007. And experiments are conducted on NVIDIA DGX- 23 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-6479-3 2020.0 628 633 6 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BS5DA 2023-03-23 WOS:000728183800110 0
J Liao, WL; Wang, YS; Wang, YL; Powell, K; Liu, Q; Yang, Z Liao, Wenlong; Wang, Yusen; Wang, Yuelong; Powell, Kody; Liu, Qi; Yang, Zhe Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks CSEE JOURNAL OF POWER AND ENERGY SYSTEMS English Article Deep learning; generative moment matching networks; integrated energy systems; scenario generations OPTIMAL OPERATION; ELECTRICITY; PROFILES; TRACKING; SYSTEMS Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper, a novel deep generative network is proposed to model cooling, heating, and power load curves based on generative moment matching networks (GMMNs) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model, the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models, the proposed GMMN does not need to artificially assume the probability distribution of the load curves, which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves very well, but also accurately captures the shape (e.g., large peaks, fast ramps, and fluctuation), frequency-domain characteristics, and temporal-spatial correlations of cooling, heating, and power loads. Furthermore, the energy consumption of generated samples closely resembles that of real samples. [Liao, Wenlong; Yang, Zhe] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Wang, Yusen] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden; [Wang, Yuelong] State Grid Tianjin Chengxi Elect Power Supply Bran, Tianjin 300100, Peoples R China; [Powell, Kody] Univ Utah, Dept Chem Engn, Salt Lake City, UT 84112 USA; [Liu, Qi] Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China Aalborg University; Royal Institute of Technology; Utah System of Higher Education; University of Utah; Tianjin University Liu, Q (corresponding author), Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China. liuqi_tj@tju.edu.cn 35 0 0 2 2 CHINA ELECTRIC POWER RESEARCH INST BEIJING 15, QINGHE XIAOYING DONG LU, HAIDIAN-QU, BEIJING, 100192, PEOPLES R CHINA 2096-0042 CSEE J POWER ENERGY CSEE J. Power Energy Syst. NOV 2022.0 8.0 6 1730 1740 10.17775/CSEEJPES.2021.00680 0.0 11 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 7Z3TF gold, Green Submitted 2023-03-23 WOS:000915484800018 0
C Qi, CM; Zhou, ZB; Hu, LS; Wang, Q ElBaz, D; Bourgeois, J Qi, Chengming; Zhou, ZhangBing; Hu, Lishuan; Wang, Qun A Framework of Multiple Kernel Ensemble Learning for Hyperspectral Classification 2016 INT IEEE CONFERENCES ON UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING AND COMMUNICATIONS, CLOUD AND BIG DATA COMPUTING, INTERNET OF PEOPLE, AND SMART WORLD CONGRESS (UIC/ATC/SCALCOM/CBDCOM/IOP/SMARTWORLD) English Proceedings Paper Conference on UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld JUL 18-21, 2016 Toulouse, FRANCE IEEE,IEEE Comp Soc,IEEE Tech Comm Scalable Comp,NVIDIA Corp,Berger Levrault,Univ Toulouse, LAAS CNRS,CNRS,MARIE TOULOUSE Ensemble; Hyperspectral image; Stochastic Multiple Kernel Boosting IMAGES Hyperspectral image classification has been a very active area of research in recent years. Multiple kernel learning (MKL) and ensemble learning are promising family of machine learning algorithms and have been applied extensively in hyperspectral image classification. However, many MKL methods often formulate the problem as an optimization task. Due to the high computational cost of solving the complicated optimization problem and improve the efficiency of MKL, in this paper, an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost) and stochastic approach to learning multiple kernel-based classifier for multi-class classification problem, is presented. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results show that SMKB is more effective and efficient than traditional MKL techniques. [Qi, Chengming; Zhou, ZhangBing; Hu, Lishuan; Wang, Qun] China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China; [Qi, Chengming; Hu, Lishuan] Beijing Union Univ, Sch Automat, Beijing 100044, Peoples R China; [Zhou, ZhangBing] TELECOM Sud Paris, Dept Comp Sci, F-91001 Evry, France China University of Geosciences; Beijing Union University Zhou, ZB (corresponding author), China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China.;Zhou, ZB (corresponding author), TELECOM Sud Paris, Dept Comp Sci, F-91001 Evry, France. zhangbing.zhou@gmail.com National Natural Science Foundation of China [61379126]; Scientific Research Foundation for Returned Scholars, Ministry of Education of China; Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific Research Foundation for Returned Scholars, Ministry of Education of China(Scientific Research Foundation for the Returned Overseas Chinese ScholarsMinistry of Education, China); Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China) This work was supported partially by the National Natural Science Foundation of China (Grant No. 61379126), by the Scientific Research Foundation for Returned Scholars, Ministry of Education of China, and by the Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China). 19 2 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-2771-2 2016.0 456 460 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.72 0.0 5 Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG9JE 2023-03-23 WOS:000393306500059 0
C Zhao, B; Huang, H; Luo, JD; Wang, XG; Yao, XM; Yahyapour, R; Wang, ZX; Fu, XM IEEE Zhao, Bo; Huang, Hong; Luo, Jar-Der; Wang, Xinggang; Yao, Xiaoming; Yahyapour, Ramin; Wang, Zhenxuan; Fu, Xiaoming A Preliminary Study of E-commerce User Behavior Based on Mobile Big Data 2018 IEEE 87TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING) IEEE Vehicular Technology Conference VTC English Proceedings Paper 87th IEEE Vehicular Technology Conference (VTC-Spring) JUN 03-06, 2018 Porto, PORTUGAL IEEE The rapid popularity of mobile devices especially smart phones has changed human life style greatly. In this paper, we examine the consumer behaviors on several e-commerce platforms based on a large-scale dataset of mobile internet access records for about 3.5 months from a major telecom operator in China, which covers 126,388 users from Shanghai. We provide a preliminary study on users' daily and periodic online shopping behaviors, as well as the influence of special online shopping events and gender factors. These findings may be exploited by e-commerce providers e.g., for developing personalized recommendation systems to improve their service quality and profit. [Zhao, Bo; Yahyapour, Ramin] Gesell Wissenschaftliche Datenverarbeitung MbH Go, Gottingen, Germany; [Zhao, Bo; Huang, Hong; Yahyapour, Ramin] Univ Gottingen, Gottingen, Germany; [Huang, Hong] Huazhong Univ Sci & Technol, Wuhan, Peoples R China; [Luo, Jar-Der] Tsinghua Univ, Dept Sociol, Beijing, Peoples R China; [Wang, Xinggang; Yao, Xiaoming] China Telecom Co Ltd, Cloud Comp Branch, Hohhot, Peoples R China; [Wang, Zhenxuan; Fu, Xiaoming] Univ Gottingen, Inst Comp Sci, Gottingen, Germany University of Gottingen; Huazhong University of Science & Technology; Tsinghua University; China Telecom Corp. Ltd.; University of Gottingen Zhao, B (corresponding author), Gesell Wissenschaftliche Datenverarbeitung MbH Go, Gottingen, Germany.;Zhao, B (corresponding author), Univ Gottingen, Gottingen, Germany. Fu, Xiaoming/B-7208-2016; Yahyapour, Ramin/AAM-1875-2020; Fu, Xiaoming/AAD-2828-2022 Fu, Xiaoming/0000-0002-8012-4753; Yahyapour, Ramin/0000-0002-9057-4395; Fu, Xiaoming/0000-0002-8012-4753 19 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-6355-4 VEH TECHNOL CONFE 2018.0 5 Engineering, Electrical & Electronic; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Transportation BP2CP 2023-03-23 WOS:000541851800244 0
J Brandenburg, JD; Adam, J; Adamczyk, L; Adams, JR; Adkins, JK; Agakishiev, G; Aggarwal, MM; Ahammed, Z; Alekseev, I; Anderson, DM; Aoyama, R; Aparin, A; Arkhipkin, D; Aschenauer, EC; Ashraf, MU; Atetalla, F; Attri, A; Averichev, GS; Bai, X; Bairathi, V; Barish, K; Bassill, AJ; Behera, A; Beliwied, R; Bhasin, A; Bhati, AK; Bielcik, J; Bielcikova, J; Bland, LC; Bordyuzhin, IG; Brandenburg, JD; Brandin, AV; Brown, D; Bryslawskyj, J; Bunzarov, I; Butterworth, J; Caines, H; Sanchez, MCD; Cebra, D; Chakaberia, I; Chaloupka, P; Chan, BK; Chang, FH; Chang, Z; Chankova-Bunzarova, N; Chatterjee, A; Chattopadhyay, S; Chen, JH; Chen, X; Chen, X; Cheng, J; Cherney, M; Christie, W; Contin, G; Crawford, HJ; Csanad, M; Das, S; Dedovich, TG; Deppner, IM; Derevschikov, AA; Didenko, L; Dilks, C; Dong, X; Drachenberg, JL; Dunlop, JC; Efimov, LG; Elsey, N; Engelage, J; Eppley, G; Esha, R; Esumi, S; Evdokimov, O; Ewigleben, J; Eyser, O; Fatemi, R; Fazio, S; Federic, P; Fedorisin, J; Filip, P; Finch, E; Fisyak, Y; Flores, CE; Fulek, L; Gagliardi, CA; Galatyuk, T; Geurts, F; Gibson, A; Grosnick, D; Gunarathne, DS; Guo, Y; Gupta, A; Guryn, W; Hamad, AI; Hamed, A; Harlenderova, A; Harris, JW; He, L; Heppelmann, S; Heppelmann, S; Herrmann, N; Hirsch, A; Holub, L; Hong, Y; Horvat, S; Huang, B; Huang, HZ; Huang, SL; Huang, T; Huang, X; Humanic, TJ; Huo, P; Igo, G; Jacobs, WW; Jentsch, A; Jia, J; Jiang, K; Jowzaee, S; Ju, X; Judd, EG; Kabana, S; Kagamaster, S; Kalinkin, D; Kang, K; Kapukchyan, D; Kauder, K; Ke, HW; Keane, D; Kechechyan, A; Kikola, DP; Kim, C; Kinghorn, TA; Kisel, I; Kisiel, A; Kocan, M; Kochenda, L; Kosarzewski, LK; ICraishan, AF; Kramarik, L; Krauth, L; Kravtsov, P; Krueger, K; Kulathunga, N; Kumar, L; Elayavalli, RK; Kvapil, J; Kwasizur, JH; Lacey, R; Landgraf, JM; Lauret, J; Lebedev, A; Lednicky, R; Lee, JH; Li, C; Li, W; Li, X; Li, Y; Liang, Y; Licenik, R; Lidrych, J; Lin, T; Lipiec, A; Lisa, MA; Liu, F; Liu, H; Liu, P; Liu, P; Liu, Y; Liu, Z; Ljubicic, T; Llope, WJ; Lomnitz, M; Longacre, RS; Luo, S; Luo, X; Ma, GL; Ma, L; Ma, R; Ma, YG; Magdy, N; Majka, R; Mallick, D; Margetis, S; Markert, C; Matis, HS; Matonoha, O; Mazer, JA; Meehan, K; Mei, JC; Minaev, NG; Mioduszewski, S; Mishra, D; Mohanty, B; Mondal, MM; Mooney, I; Moravcova, Z; Morozov, DA; Nasim, M; Nayak, K; Negrete, JD; Nelson, JM; Nemes, DB; Nie, M; Nigmatkulov, G; Niida, T; Nogach, LV; Nonaka, T; Odyniec, G; Ogawa, A; Oh, K; Oh, S; Okorokov, VA; Olvitt, D; Page, BS; Pak, R; Panebratsev, Y; Pawlik, B; Pei, H; Perkins, C; Pinter, RL; Pluta, J; Porter, J; Posik, M; Pruthi, NK; Przybycien, M; Putschke, J; Quintero, A; Radhakrishnan, SK; Ramachandran, S; Ray, RL; Reed, R; Ritter, HG; Roberts, JB; Rogachevskiy, OV; Romero, JL; Ruan, L; Rusnak, J; Rusnakova, O; Sahoo, NR; Sahu, PK; Salur, S; Sandweiss, J; Schambach, J; Schmah, AM; Schmidke, WB; Schmitz, N; Schweid, BR; Seck, F; Seger, J; Sergeeva, M; Seto, R; Seyboth, P; Shah, N; Shahaliev, E; Shanmuganathan, PV; Shao, M; Shen, F; Shen, WQ; Shi, SS; Shou, QY; Sichtermann, EP; Siejka, S; Sikora, R; Simko, M; Singh, J; Singha, S; Smirnov, D; Smirnov, N; Solyst, W; Sorensen, P; Spinka, HM; Srivastava, B; Stanislaus, TDS; Stewart, DJ; Strikhanov, M; Stringfellow, B; Suaide, AAP; Sugiura, T; Sumbera, M; Summa, B; Sun, XM; Sun, Y; Surrow, B; Svirida, DN; Szymanski, P; Tang, AH; Tang, Z; Taranenko, A; Tarnowsky, T; Thomas, JH; Timmins, AR; Tlusty, D; Todoroki, T; Tokarev, M; Tomkiel, CA; Trentalange, S; Tribble, RE; Tribedy, P; Tripathy, SK; Tsai, OD; Tu, B; Ullrich, T; Underwood, DG; Upsal, I; Van Buren, G; Vanek, J; Vasiliev, AN; Vassiliev, I; Videbaek, F; Vokal, S; Voloshin, SA; Vossen, A; Wang, F; Wang, G; Wang, P; Wang, Y; Wang, Y; Webb, JC; Wen, L; Westfall, GD; Wieman, H; Wissink, SW; Witt, R; Wu, Y; Xiao, ZG; Xie, G; Xie, W; Xu, J; Xu, N; Xu, QH; Xu, YF; Xu, Z; Yang, C; Yang, Q; Yang, S; Yang, Y; Ye, Z; Yi, L; Yip, K; Yoo, IK; Zbroszczyk, H; Zha, W; Zhang, J; Zhang, J; Zhang, L; Zhang, S; Zhang, XP; Zhang, Y; Zhang, Z; Zhao, J; Zhong, C; Zhou, C; Zhu, X; Zhu, Z; Zyzak, M Brandenburg, James D.; Adam, J.; Adamczyk, L.; Adams, J. R.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Anderson, D. M.; Aoyama, R.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Ashraf, M. U.; Atetalla, F.; Attri, A.; Averichev, G. S.; Bai, X.; Bairathi, V.; Barish, K.; Bassill, A. J.; Behera, A.; Beliwied, R.; Bhasin, A.; Bhati, A. K.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Brandenburg, J. D.; Brandin, A. V.; Brown, D.; Bryslawskyj, J.; Bunzarov, I.; Butterworth, J.; Caines, H.; Sanchez, M. Calderon De la Barca; Cebra, D.; Chakaberia, I.; Chaloupka, P.; Chan, B. K.; Chang, F-H.; Chang, Z.; Chankova-Bunzarova, N.; Chatterjee, A.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Contin, G.; Crawford, H. J.; Csanad, M.; Das, S.; Dedovich, T. G.; Deppner, I. M.; Derevschikov, A. A.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Dunlop, J. C.; Efimov, L. G.; Elsey, N.; Engelage, J.; Eppley, G.; Esha, R.; Esumi, S.; Evdokimov, O.; Ewigleben, J.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Filip, P.; Finch, E.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Galatyuk, T.; Geurts, F.; Gibson, A.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, A.; Guryn, W.; Hamad, A. I.; Hamed, A.; Harlenderova, A.; Harris, J. W.; He, L.; Heppelmann, S.; Heppelmann, S.; Herrmann, N.; Hirsch, A.; Holub, L.; Hong, Y.; Horvat, S.; Huang, B.; Huang, H. Z.; Huang, S. L.; Huang, T.; Huang, X.; Humanic, T. J.; Huo, P.; Igo, G.; Jacobs, W. W.; Jentsch, A.; Jia, J.; Jiang, K.; Jowzaee, S.; Ju, X.; Judd, E. G.; Kabana, S.; Kagamaster, S.; Kalinkin, D.; Kang, K.; Kapukchyan, D.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Kikola, D. P.; Kim, C.; Kinghorn, T. A.; Kisel, I.; Kisiel, A.; Kocan, M.; Kochenda, L.; Kosarzewski, L. K.; ICraishan, A. F.; Kramarik, L.; Krauth, L.; Kravtsov, P.; Krueger, K.; Kulathunga, N.; Kumar, L.; Elayavalli, R. Kunnawalkam; Kvapil, J.; Kwasizur, J. H.; Lacey, R.; Landgraf, J. M.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, C.; Li, W.; Li, X.; Li, Y.; Liang, Y.; Licenik, R.; Lidrych, J.; Lin, T.; Lipiec, A.; Lisa, M. A.; Liu, F.; Liu, H.; Liu, P.; Liu, P.; Liu, Y.; Liu, Z.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, S.; Luo, X.; Ma, G. L.; Ma, L.; Ma, R.; Ma, Y. G.; Magdy, N.; Majka, R.; Mallick, D.; Margetis, S.; Markert, C.; Matis, H. S.; Matonoha, O.; Mazer, J. A.; Meehan, K.; Mei, J. C.; Minaev, N. G.; Mioduszewski, S.; Mishra, D.; Mohanty, B.; Mondal, M. M.; Mooney, I.; Moravcova, Z.; Morozov, D. A.; Nasim, Md.; Nayak, K.; Negrete, J. D.; Nelson, J. M.; Nemes, D. B.; Nie, M.; Nigmatkulov, G.; Niida, T.; Nogach, L. V.; Nonaka, T.; Odyniec, G.; Ogawa, A.; Oh, K.; Oh, S.; Okorokov, V. A.; Olvitt, D., Jr.; Page, B. S.; Pak, R.; Panebratsev, Y.; Pawlik, B.; Pei, H.; Perkins, C.; Pinter, R. L.; Pluta, J.; Porter, J.; Posik, M.; Pruthi, N. K.; Przybycien, M.; Putschke, J.; Quintero, A.; Radhakrishnan, S. K.; Ramachandran, S.; Ray, R. L.; Reed, R.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Salur, S.; Sandweiss, J.; Schambach, J.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Schweid, B. R.; Seck, F.; Seger, J.; Sergeeva, M.; Seto, R.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Shen, F.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Siejka, S.; Sikora, R.; Simko, M.; Singh, J.; Singha, S.; Smirnov, D.; Smirnov, N.; Solyst, W.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stewart, D. J.; Strikhanov, M.; Stringfellow, B.; Suaide, A. A. P.; Sugiura, T.; Sumbera, M.; Summa, B.; Sun, X. M.; Sun, Y.; Surrow, B.; Svirida, D. N.; Szymanski, P.; Tang, A. H.; Tang, Z.; Taranenko, A.; Tarnowsky, T.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Todoroki, T.; Tokarev, M.; Tomkiel, C. A.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Tsai, O. D.; Tu, B.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; Vanek, J.; Vasiliev, A. N.; Vassiliev, I.; Videbaek, F.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, F.; Wang, G.; Wang, P.; Wang, Y.; Wang, Y.; Webb, J. C.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y.; Xiao, Z. G.; Xie, G.; Xie, W.; Xu, J.; Xu, N.; Xu, Q. H.; Xu, Y. F.; Xu, Z.; Yang, C.; Yang, Q.; Yang, S.; Yang, Y.; Ye, Z.; Yi, L.; Yip, K.; Yoo, I. -K.; Zbroszczyk, H.; Zha, W.; Zhang, J.; Zhang, J.; Zhang, L.; Zhang, S.; Zhang, X. P.; Zhang, Y.; Zhang, Z.; Zhao, J.; Zhong, C.; Zhou, C.; Zhu, X.; Zhu, Z.; Zyzak, M. STAR Collaboration Muon Identification using Neural Networks With the Muon Telescope Detector at STAR NUCLEAR PHYSICS A English Article; Proceedings Paper 27th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter) MAY 13-19, 2018 Venice, ITALY Italian Inst Nucl Phys Muon; Neural Network; Muon Telescope Detector; STAR; Dimuon QUARK-GLUON PLASMA; PERSPECTIVE The installation of the Muon Telescope Detector (MTD) at STAR allows a measurement of the dimuon (mu(+)mu(-)) production in heavy-ion collisions over a large invariant mass range for the first time. Data has been collected with the MTD from Au+Au collisions at root S-NN = 200 GeV and from p+p collisions at root S = 200 GeV. These two datasets allow for new opportunities to measure the dimuon invariant mass spectra at STAR. Before any dimuon measurements can be made, muons must be identified. This contribution presents muon identification employing deep neural networks (DNN) and compares it with other multi-variate techniques. Applications of the DNN technique for data-driven purity measurements are discussed. [Brandenburg, James D.] Rice Univ, Houston, TX 77005 USA; [Drachenberg, J. L.] Abilene Christian Univ, Abilene, TX 79699 USA; [Adamczyk, L.; Fulek, L.; Przybycien, M.; Sikora, R.] AGH Univ Sci & Technol, FPACS, PL-30059 Krakow, Poland; [Alekseev, I.; Bordyuzhin, I. G.; Svirida, D. N.] Alikhanov Inst Theoret & Expt Phys, Moscow 117218, Russia; [Krueger, K.; Spinka, H. M.; Underwood, D. G.] Argonne Natl Lab, 9700 S Cass Ave, Argonne, IL 60439 USA; [Arkhipkin, D.; Bland, L. C.; Chang, Z.; Christie, W.; Didenko, L.; Dunlop, J. C.; Eyser, O.; Fazio, S.; Fisyak, Y.; Guryn, W.; Jia, J.; Kauder, K.; Ke, H. W.; Landgraf, J. M.; Lauret, J.; Lebedev, A.; Lee, J. H.; Ljubicic, T.; Longacre, R. S.; Ma, R.; Ogawa, A.; Page, B. S.; Pak, R.; Ruan, L.; Schmidke, W. B.; Smirnov, D.; Sorensen, P.; Tang, A. H.; Todoroki, T.; Tribedy, P.; Ullrich, T.; Upsal, I.; Van Buren, G.; Videbaek, F.; Webb, J. C.; Xu, Z.; Yang, S.; Yip, K.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Crawford, H. J.; Engelage, J.; Judd, E. G.; Nelson, J. M.; Perkins, C.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Sanchez, M. Calderon De la Barca; Cebra, D.; Flores, C. E.; Heppelmann, S.; Kinghorn, T. A.; Meehan, K.; Romero, J. L.] Univ Calif Davis, Davis, CA 95616 USA; [Chan, B. K.; Esha, R.; Huang, H. Z.; Igo, G.; Nasim, Md.; Sergeeva, M.; Trentalange, S.; Tsai, O. D.; Wang, G.; Wen, L.] Univ Calif Los Angeles, Los Angeles, CA 90095 USA; [Barish, K.; Bassill, A. J.; Bryslawskyj, J.; Kapukchyan, D.; Kim, C.; Krauth, L.; Negrete, J. D.; Seto, R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Bai, X.; Das, S.; Liu, F.; Luo, X.; Nayak, K.; Nonaka, T.; Pei, H.; Shi, S. S.; Sun, X. M.; Tu, B.; Wang, Y.; Xu, J.; Zhang, L.] Cent China Normal Univ, Wuhan 430079, Hubei, Peoples R China; [Evdokimov, O.; Huang, B.; Luo, S.; Xie, G.; Ye, Z.] Univ Illinois, Chicago, IL 60607 USA; [Adam, J.; Cherney, M.; Seger, J.] Creighton Univ, Omaha, NE 68178 USA; [Bielcik, J.; Chaloupka, P.; Harlenderova, A.; Holub, L.; Kocan, M.; Kosarzewski, L. K.; Kramarik, L.; Kvapil, J.; Licenik, R.; Lidrych, J.; Matonoha, O.; Moravcova, Z.; Rusnakova, O.] Czech Tech Univ, FNSPE, Prague 11519, Czech Republic; [Galatyuk, T.; Seck, F.] Tech Univ Darmstadt, D-64289 Darmstadt, Germany; [Csanad, M.; Pinter, R. L.] Eotvos Lorand Univ, H-1117 Budapest, Hungary; [Kisel, I.; Vassiliev, I.; Zyzak, M.] FIAS, D-60438 Frankfurt, Germany; [Ma, L.] Fudan Univ, Shanghai 200433, Peoples R China; [Deppner, I. M.; Herrmann, N.] Heidelberg Univ, D-69120 Heidelberg, Germany; [Beliwied, R.; Kulathunga, N.; Timmins, A. R.] Univ Houston, Houston, TX 77204 USA; [Jacobs, W. W.; Kalinkin, D.; Kwasizur, J. H.; Liu, H.; Solyst, W.; Vossen, A.; Wissink, S. W.] Indiana Univ, Bloomington, IN 47408 USA; [Chen, X.; Zhang, J.] Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Gansu, Peoples R China; [Mondal, M. M.; Sahu, P. K.; Tripathy, S. K.] Inst Phys, Bhubaneswar 751005, India; [Bhasin, A.; Gupta, A.] Univ Jammu, Jammu 180001, India; [Agakishiev, G.; Aparin, A.; Averichev, G. S.; Bunzarov, I.; Chankova-Bunzarova, N.; Dedovich, T. G.; Efimov, L. G.; Fedorisin, J.; Filip, P.; Kechechyan, A.; Lednicky, R.; Rogachevskiy, O. V.; Shahaliev, E.; Tokarev, M.; Vokal, S.] Joint Inst Nucl Res, Dubna 141980, Russia; [Atetalla, F.; Chakaberia, I.; Guo, Y.; Hamad, A. I.; Kabana, S.; Keane, D.; Liang, Y.; Margetis, S.; Singha, S.; Wu, Y.] Kent State Univ, Kent, OH 44242 USA; [Adkins, J. K.; Fatemi, R.; Ramachandran, S.] Univ Kentucky, Lexington, KY 40506 USA; [Contin, G.; Dong, X.; Hong, Y.; Lomnitz, M.; Matis, H. S.; Odyniec, G.; Porter, J.; Radhakrishnan, S. K.; Ritter, H. G.; Schmah, A. M.; Sichtermann, E. P.; Thomas, J. H.; Wieman, H.; Xu, N.; Zhang, J.] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; [Brown, D.; Ewigleben, J.; Kagamaster, S.; Reed, R.; Shanmuganathan, P. V.; Tomkiel, C. A.] Lehigh Univ, Bethlehem, PA 18015 USA; [Schmitz, N.; Seyboth, P.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [Tarnowsky, T.; Westfall, G. D.] Michigan State Univ, E Lansing, MI 48824 USA; [Alekseev, I.; Brandin, A. V.; Kochenda, L.; Kravtsov, P.; Nigmatkulov, G.; Okorokov, V. A.; Strikhanov, M.; Taranenko, A.] Natl Res Nucl Univ MEPhI, Moscow 115409, Russia; [Bairathi, V.; Mallick, D.; Mishra, D.; Mohanty, B.] HBNI, Natl Inst Sci Educ & Res, Jatni 752050, India; [Chang, F-H.; Huang, T.; Yang, Y.] Natl Cheng Kung Univ, Tainan 70101, Taiwan; [Bielcikova, J.; Federic, P.; Rusnak, J.; Simko, M.; Sumbera, M.; Vanek, J.] Nucl Phys Inst CAS, Rez 25068, Czech Republic; [Adams, J. R.; Humanic, T. J.; Lisa, M. A.] Ohio State Univ, Columbus, OH 43210 USA; [Pawlik, B.] Inst Nucl Phys PAN, PL-31342 Krakow, Poland; [Aggarwal, M. M.; Attri, A.; Bhati, A. K.; Kumar, L.; Pruthi, N. K.; Singh, J.] Panjab Univ, Chandigarh 160014, India; [Dilks, C.; Heppelmann, S.; Summa, B.] Penn State Univ, University Pk, PA 16802 USA; [Derevschikov, A. A.; Minaev, N. G.; Morozov, D. A.; Nogach, L. V.; Vasiliev, A. N.] Inst High Energy Phys, Protvino 142281, Russia; [He, L.; Hirsch, A.; Srivastava, B.; Stringfellow, B.; Wang, F.; Xie, W.; Zhao, J.] Purdue Univ, W Lafayette, IN 47907 USA; [Oh, K.; Yoo, I. -K.] Pusan Natl Univ, Pusan 46241, South Korea; [Brandenburg, J. D.; Butterworth, J.; Eppley, G.; Geurts, F.; Roberts, J. B.; Tlusty, D.] Rice Univ, Houston, TX 77251 USA; [Mazer, J. A.; Salur, S.] Rutgers State Univ, Piscataway, NJ 08854 USA; [Suaide, A. A. P.] Univ Sao Paulo, BR-05314970 Sao Paulo, Brazil; [Chen, X.; Jiang, K.; Ju, X.; Li, C.; Li, X.; Liu, Z.; Shao, M.; Sun, Y.; Tang, Z.; Wang, P.; Zha, W.; Zhang, S.; Zhang, Y.] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China; [Chakaberia, I.; Mei, J. C.; Shen, F.; Upsal, I.; Xu, Q. H.; Yang, C.; Yang, Q.; Yi, L.; Zhu, Z.] Shandong Univ, Qingdao 266237, Shandong, Peoples R China; [Chen, J. H.; Li, W.; Liu, P.; Ma, G. L.; Ma, Y. G.; Nie, M.; Shah, N.; Shen, W. Q.; Shou, Q. Y.; Xu, Y. F.; Zhang, S.; Zhang, Z.; Zhong, C.; Zhou, C.] Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China; [Finch, E.] Southern Connecticut State Univ, New Haven, CT 06515 USA; [Behera, A.; Huang, S. L.; Huo, P.; Jia, J.; Lacey, R.; Liu, P.; Magdy, N.; Schweid, B. R.] SUNY Stony Brook, Stony Brook, NY 11794 USA; [Gunarathne, D. S.; ICraishan, A. F.; Olvitt, D., Jr.; Posik, M.; Quintero, A.; Surrow, B.] Temple Univ, Philadelphia, PA 19122 USA; [Anderson, D. M.; Aschenauer, E. C.; Gagliardi, C. A.; Hamed, A.; Lin, T.; Liu, Y.; Mioduszewski, S.; Sahoo, N. R.; Tribble, R. E.] Texas A&M Univ, College Stn, TX 77843 USA; [Jentsch, A.; Markert, C.; Ray, R. L.; Schambach, J.] Univ Texas Austin, Austin, TX 78712 USA; [Ashraf, M. U.; Cheng, J.; Huang, X.; Kang, K.; Li, Y.; Wang, Y.; Xiao, Z. G.; Zhang, X. P.; Zhu, X.] Tsinghua Univ, Beijing 100084, Peoples R China; [Aoyama, R.; Esumi, S.; Panebratsev, Y.; Sugiura, T.] Univ Tsukuba, Tsukuba, Ibaraki 3058571, Japan; [Witt, R.] US Naval Acad, Annapolis, MD 21402 USA; [Gibson, A.; Grosnick, D.; Stanislaus, T. D. S.] Valparaiso Univ, Valparaiso, IN 46383 USA; [Ahammed, Z.; Chatterjee, A.; Chattopadhyay, S.] Variable Energy Cyclotron Ctr, Kolkata 700064, India; [Kikola, D. P.; Kisiel, A.; Lipiec, A.; Pluta, J.; Siejka, S.; Szymanski, P.; Zbroszczyk, H.] Warsaw Univ Technol, PL-00661 Warsaw, Poland; [Elsey, N.; Jowzaee, S.; Elayavalli, R. Kunnawalkam; Llope, W. J.; Mooney, I.; Niida, T.; Putschke, J.; Voloshin, S. A.] Wayne State Univ, Detroit, MI 48201 USA; [Caines, H.; Harris, J. W.; Horvat, S.; Majka, R.; Nemes, D. B.; Oh, S.; Sandweiss, J.; Smirnov, N.; Stewart, D. J.] Yale Univ, New Haven, CT 06520 USA Rice University; Abilene Christian University; AGH University of Science & Technology; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; United States Department of Energy (DOE); Argonne National Laboratory; United States Department of Energy (DOE); Brookhaven National Laboratory; University of California System; University of California Berkeley; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; Central China Normal University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Creighton University; Czech Technical University Prague; Technical University of Darmstadt; Eotvos Lorand University; Fudan University; Ruprecht Karls University Heidelberg; University of Houston System; University of Houston; Indiana University System; Indiana University Bloomington; Chinese Academy of Sciences; Institute of Modern Physics, CAS; Institute of Physics Bhubaneswar (IOPB); University of Jammu; Joint Institute for Nuclear Research - Russia; University System of Ohio; Kent State University; Kent State University Kent; Kent State University Salem; University of Kentucky; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Lehigh University; Max Planck Society; Michigan State University; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); National Cheng Kung University; Czech Academy of Sciences; Nuclear Physics Institute of the Czech Academy of Sciences; University System of Ohio; Ohio State University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Panjab University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Pusan National University; Rice University; Rutgers State University New Brunswick; Universidade de Sao Paulo; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Chinese Academy of Sciences; Shanghai Institute of Applied Physics, CAS; Connecticut State University System; Southern Connecticut State University; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin; Tsinghua University; University of Tsukuba; United States Department of Defense; United States Navy; United States Naval Academy; Valparaiso University; Variable Energy Cyclotron Centre; Warsaw University of Technology; Wayne State University; Yale University Brandenburg, JD (corresponding author), Rice Univ, Houston, TX 77005 USA. Taranenko, Arkadiy/AAH-9189-2019; Kabana, Sonia/ABF-5974-2020; Matis, Howard S/HMV-9747-2023; Sahoo, Nihar Ranjan/ABF-3516-2021; Nigmatkulov, Grigory/AAH-9428-2019; Bairathi, Vipul/AAR-3812-2021; Contin, Giacomo/HLW-2131-2023; Lednicky, Richard/K-4164-2013; Hamed, Ahmed Abdeen/AAF-4658-2020; YANG, CHI/AAF-6393-2021; Ashraf, Muhammad Usman/GNM-5453-2022; Zhao, Jie/AAD-3191-2022; Bordyuzhin, Igor/AGX-2537-2022; Bielcikova, Jana/AAB-4862-2020; Aparin, Alexey/AAE-1455-2022; Tokarev, Mikhail/GRE-9737-2022; Svirida, Dmitry N/R-4909-2016; Fazio, Salvatore/G-5156-2010; Strikhanov, Mikhail N/P-7393-2014; Matis, Howard/AAO-2082-2021; Alekseev, Igor G/J-8070-2014; Vasiliev, Alexander N/N-4742-2017; Kisel, Ivan/H-7077-2017; Bhasin, Anju/AAS-4745-2021; Suaide, Alexandre A P/L-6239-2016; Kumar, Lokesh/A-6154-2010; Ma, Yu-Gang/M-8122-2013 Kabana, Sonia/0000-0003-0568-5750; Matis, Howard S/0000-0003-0764-4389; Sahoo, Nihar Ranjan/0000-0003-4518-6630; Nigmatkulov, Grigory/0000-0003-2232-5124; Lednicky, Richard/0000-0001-6887-1804; YANG, CHI/0000-0001-9976-8631; Zhao, Jie/0000-0001-6408-2314; Svirida, Dmitry N/0000-0002-0334-7304; Fazio, Salvatore/0000-0002-4321-1946; Strikhanov, Mikhail N/0000-0003-2586-0405; Alekseev, Igor G/0000-0003-3358-9635; Kisel, Ivan/0000-0002-4808-419X; Suaide, Alexandre A P/0000-0003-2847-6556; Kumar, Lokesh/0000-0002-2746-9840; Ma, Yu-Gang/0000-0002-0233-9900 9 0 0 0 4 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0375-9474 1873-1554 NUCL PHYS A Nucl. Phys. A FEB 2019.0 982.0 192 194 10.1016/j.nuclphysa.2018.10.036 0.0 3 Physics, Nuclear Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Physics HJ9JU Green Submitted, hybrid, Green Published 2023-03-23 WOS:000457515500032 0
J Cai, ZC; Li, XP; Ruiz, R; Li, QM Cai, Zhicheng; Li, Xiaoping; Ruiz, Ruben; Li, Qianmu Price forecasting for spot instances in Cloud computing FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Cloud computing; Spot price; Forecast; Markov regime-switching; Scheduling MARKOV-SWITCHING MODEL Big data applications usually need to rent a large number of virtual machines from Cloud computing providers. As a result of the policies employed by Cloud providers, the prices of spot virtual machine instances behavior stochastically. Spot prices (prices of spot instances) fluctuate greatly or have multiple regimes. Choosing virtual machines according to trends in prices is helpful in decreasing the resource rental cost. Existing price prediction methods are unable to accurately predict prices in these environments. As a result, a dynamic-ARIMA and two markov regime-switching autoregressive model based forecasting methods have been developed in this paper. Experimental results show that the proposals are better than the existing MonthAR in most scenarios. (C) 2017 Elsevier B.V. All rights reserved. [Cai, Zhicheng; Li, Qianmu] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China; [Li, Xiaoping] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Ruiz, Ruben] Acc B Univ Politecn Valencia, Inst Tecnol Informat, Valencia, Spain Nanjing University of Science & Technology; Southeast University - China Cai, ZC (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China. caizhicheng@njust.edu.cn; xpli@seu.edu.cn; rruiz@eio.upv.es; qianmu@njust.edu.cn Ruiz, Rubén/G-3929-2015; Li, Xiaoping/I-8389-2012 Ruiz, Rubén/0000-0003-3295-3888; Li, Xiaoping/0000-0003-3201-0038; Cai, Zhicheng/0000-0002-8702-6216 National Natural Science Foundation of China [61602243, 61572127]; Natural Science Foundation of Jiangsu Province [BK20160846]; Jiangsu Key Laboratory of Image and Video Understanding for Social Safety [30916014107]; Spanish Ministry of Economy and Competitiveness - FEDER [DPI2015-65895-R] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Jiangsu Key Laboratory of Image and Video Understanding for Social Safety; Spanish Ministry of Economy and Competitiveness - FEDER The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and No. 61572127), the Natural Science Foundation of Jiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project SCHEYARD (No. DPI2015-65895-R) financed by FEDER funds. 46 13 13 4 35 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. FEB 2018.0 79.0 1 38 53 10.1016/j.future.2017.09.038 0.0 16 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science FR3LO Green Published 2023-03-23 WOS:000418968400004 0
J Homayounieh, F; Digumarthy, S; Ebrahimian, S; Rueckel, J; Hoppe, BF; Sabel, BO; Conjeti, S; Ridder, K; Sistermanns, M; Wang, L; Preuhs, A; Ghesu, F; Mansoor, A; Moghbel, M; Botwin, A; Singh, R; Cartmell, S; Patti, J; Huemmer, C; Fieselmann, A; Joerger, C; Mirshahzadeh, N; Muse, V; Kalra, M Homayounieh, Fatemeh; Digumarthy, Subba; Ebrahimian, Shadi; Rueckel, Johannes; Hoppe, Boj Friedrich; Sabel, Bastian Oliver; Conjeti, Sailesh; Ridder, Karsten; Sistermanns, Markus; Wang, Lei; Preuhs, Alexander; Ghesu, Florin; Mansoor, Awais; Moghbel, Mateen; Botwin, Ariel; Singh, Ramandeep; Cartmell, Samuel; Patti, John; Huemmer, Christian; Fieselmann, Andreas; Joerger, Clemens; Mirshahzadeh, Negar; Muse, Victorine; Kalra, Mannudeep An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study JAMA NETWORK OPEN English Article RADIOLOGY IMPORTANCE Most early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. OBJECTIVE To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included imageswere selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control. EXPOSURES All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. MAIN OUTCOMES AND MEASURES Each test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzedmetrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC). RESULTS Images from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4%(95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6%(95% CI, -1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, -2% to 9%) as compared with junior radiologists (4%; 95% CI, -3% to 5%). CONCLUSIONS AND RELEVANCE In this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience. [Homayounieh, Fatemeh; Digumarthy, Subba; Ebrahimian, Shadi; Moghbel, Mateen; Botwin, Ariel; Singh, Ramandeep; Cartmell, Samuel; Patti, John; Muse, Victorine; Kalra, Mannudeep] Massachusetts Gen Hosp, Dept Radiol, Boston, MA USA; [Homayounieh, Fatemeh; Digumarthy, Subba; Ebrahimian, Shadi; Moghbel, Mateen; Botwin, Ariel; Singh, Ramandeep; Cartmell, Samuel; Patti, John; Muse, Victorine; Kalra, Mannudeep] Harvard Med Sch, Boston, MA 02115 USA; [Rueckel, Johannes; Hoppe, Boj Friedrich; Sabel, Bastian Oliver] Ludwig Maximilian Univ Munich, Univ Hosp, Dept Radiol, Munich, Germany; [Conjeti, Sailesh; Preuhs, Alexander; Huemmer, Christian; Fieselmann, Andreas; Joerger, Clemens; Mirshahzadeh, Negar] Siemens Healthineers, Erlangen, Germany; [Ridder, Karsten; Sistermanns, Markus] Med Versorgungszentrum Prof Uhlenbrock & Partner, Dortmund, Germany; [Wang, Lei] Siemens Healthineers, Shanghai, Peoples R China; [Ghesu, Florin; Mansoor, Awais] Siemens Healthineers, Malvern, PA USA Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; University of Munich; Siemens AG; Siemens AG; Siemens AG Kalra, M (corresponding author), 75 Blossom Ct, Boston, MA 02114 USA. mkalra@mgh.harvard.edu GE Healthcare; Lunit Inc; Riverain Technologies Inc; Siemens Healthineers AG GE Healthcare(General ElectricGE Healthcare); Lunit Inc; Riverain Technologies Inc; Siemens Healthineers AG Massachusetts General Hospital received research grants from GE Healthcare, Lunit Inc, Riverain Technologies Inc, and Siemens Healthineers AG. 13 11 11 1 4 AMER MEDICAL ASSOC CHICAGO 330 N WABASH AVE, STE 39300, CHICAGO, IL 60611-5885 USA 2574-3805 JAMA NETW OPEN JAMA Netw. Open DEC 29 2021.0 4.0 12 e2141096 10.1001/jamanetworkopen.2021.41096 0.0 11 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine XZ9KF 34964851.0 gold 2023-03-23 WOS:000737963700007 0
J Zhang, YL; Wang, HB; Li, C; Chen, DS; Meriaudeau, F Zhang, Yonglin; Wang, Haibin; Li, Chao; Chen, Desheng; Meriaudeau, Fabrice Meta-learning-aided orthogonal frequency division multiplexing for underwater acoustic communicationsa) JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA English Article OFDM; CHANNELS In this paper, a meta-learning-based underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is proposed to deal with the environment mismatch in real-world UWA applications, which can effectively drive the model from the given UWA environment to the new UWA environment with a relatively small amount of data. With meta-learning, we consider multiple UWA environments as multi-UWA-tasks, wherein the meta-training strategy is utilized to learn a robust model from previously observed multi-UWA-tasks, and it can be quickly adapted to the unknown UWA environment with only a small number of updates. The experiments with the at-sea-measured WATERMARK dataset and the lake trial indicate that, compared with the traditional UWA-OFDM system and the conventional machine learning-based framework, the proposed method shows better bit error rate performance and stronger learning ability under various UWA scenarios. [Zhang, Yonglin; Wang, Haibin; Li, Chao; Chen, Desheng] Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China; [Meriaudeau, Fabrice] Univ Bourgogne Franche Comte, Lab ImViA, F-21078 Dijon, France; [Zhang, Yonglin; Wang, Haibin; Li, Chao; Chen, Desheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Chinese Academy of Sciences; Institute of Acoustics, CAS; Universite de Bourgogne; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Wang, HB (corresponding author), Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China.;Wang, HB (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. whb@mail.ioa.ac.cn Zhang, Yonglin/ABC-3973-2021 ZHANG, YONGLIN/0000-0001-5339-6502 China Scholarship Council; Chinese Academy of Sciences China Scholarship Council(China Scholarship Council); Chinese Academy of Sciences(Chinese Academy of Sciences) This research was supported by the China Scholarship Council and Chinese Academy of Sciences. 24 8 9 6 18 ACOUSTICAL SOC AMER AMER INST PHYSICS MELVILLE STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA 0001-4966 1520-8524 J ACOUST SOC AM J. Acoust. Soc. Am. JUN 2021.0 149.0 6 4596 4606 10.1121/10.0005474 0.0 11 Acoustics; Audiology & Speech-Language Pathology Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Audiology & Speech-Language Pathology TC4NI 34241419.0 2023-03-23 WOS:000668616800005 0
J Fu, ZY; Hu, LS; Chen, ZD; Zhang, F; Shi, Z; Hu, BF; Du, ZH; Liu, RY Fu, Zhiyi; Hu, Linshu; Chen, Zhende; Zhang, Feng; Shi, Zhou; Hu, Bifeng; Du, Zhenhong; Liu, Renyi Estimating spatial and temporal variation in ocean surface pCO(2) in the Gulf of Mexico using remote sensing and machine learning techniques SCIENCE OF THE TOTAL ENVIRONMENT English Article Surface pCO(2); Remote sensing; Gulf of Mexico; Data mining MISSISSIPPI RIVER PLUME; INTERANNUAL VARIABILITY; INORGANIC CARBON; ATLANTIC-OCEAN; NORTH-ATLANTIC; WIND STRESS; IN-SITU; CO2; FLUX; SALINITY Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO(2)) can reflect the seasonal and spatial heterogeneity of CO2 flux and is. therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO(2) and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO(2) in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO(2) in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO(2) in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 mu atm with a coefficient of determination (R-2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO(2). Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO(2), corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO(2) estimation and spatiotemporal heterogeneity analysis. (C) 2020 The Authors. Published by Elsevier B.V. [Fu, Zhiyi; Hu, Linshu; Chen, Zhende; Zhang, Feng; Du, Zhenhong; Liu, Renyi] Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Peoples R China; [Zhang, Feng; Du, Zhenhong; Liu, Renyi] Zhejiang Prov Key Lab Geog Informat Sci, Hangzhou 310028, Peoples R China; [Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China; [Hu, Bifeng] INRAE, Unite Rech Sci Sol, F-45075 Orleans, France; [Hu, Bifeng] Orleans Univ, Sci Terre & Univers, F-45067 Orleans, France; [Liu, Renyi] Zhejiang Univ, Ocean Acad, Zhoushan 316021, Peoples R China Zhejiang University; Zhejiang University; INRAE; Zhejiang University Zhang, F (corresponding author), Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Peoples R China. zfcarnation@zju.edu.cn Hu, Bifeng/G-2190-2018; shi, Zhou/M-7845-2019 shi, Zhou/0000-0003-3914-5402; Du, Zhenhong/0000-0001-9449-0415 National Key R&D Program of China [2018YFB0505000, 2017YFB0503604]; National Natural Science Foundation of China [41671391, 41922043, 41871287] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Key R&D Program of China (2018YFB0505000, 2017YFB0503604), National Natural Science Foundation of China (41671391, 41922043, 41871287). We also thank NOAA NCEI and LDEO for providing all the available cruise data and thankNASA for providingMODIS satellite data and processing software. 96 6 7 9 50 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. NOV 25 2020.0 745.0 140965 10.1016/j.scitotenv.2020.140965 0.0 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology OC7VL 32758741.0 hybrid, Green Published 2023-03-23 WOS:000579365600031 0
C Sadeghinia, M; Chincholi, C; Udyansky, A; Fischer, A IEEE Sadeghinia, Mahdi; Chincholi, Chalukya; Udyansky, Alexander; Fischer, Andreas Effect of material properties on PCB frequencies in electronic control unit 2019 20TH INTERNATIONAL CONFERENCE ON THERMAL, MECHANICAL AND MULTI-PHYSICS SIMULATION AND EXPERIMENTS IN MICROELECTRONICS AND MICROSYSTEMS (EUROSIME) English Proceedings Paper 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) MAR 24-27, 2019 Hannover, GERMANY With advancement in Artificial intelligence, Internet of things and next generation of mobility going towards autonomous driving, the electronic control units used for power steering need to be redundant, highly reliable and intelligent. The above challenge forces design engineers to smartly enhance the electronic components with use of multiple PCB stacked one above another. PCB is one of the main parts of the control unit on which SMD components are populated. Therefore, comprehensive knowledge of PCB dynamic behavior is important for fatigue evaluation of SMD and other electronic components. This paper discusses the mechanical dynamic responses of PCBs using different material properties, e.g. isotropic, orthotropic and PCB homogenizing properties. Simulations are carried out in Ansys to predict the PCB frequencies and mode shapes. In order to validate the simulation results, measurements are also performed to predict PCB frequencies, mode shapes and frequency response funsctions. Measurements are carried out with PSV-400 scanning laser vibrometer from Polytech and post processing of measurement data is done with help of Polytech for visualization of mode shapes. [Sadeghinia, Mahdi; Fischer, Andreas] Robert Bosch GmbH, Gerlingen, Germany; [Chincholi, Chalukya] Robert Bosch Engn & Business Solut Private Ltd, Bengaluru, Karnataka, India; [Udyansky, Alexander] Bosch Automot Prod Suzhou Co Ltd, Suzhou, Peoples R China Bosch Sadeghinia, M (corresponding author), Robert Bosch GmbH, Gerlingen, Germany. 2 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-8040-7 2019.0 4 Thermodynamics; Engineering, Electrical & Electronic; Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Thermodynamics; Engineering BN1XU 2023-03-23 WOS:000475685600063 0
J van Tonder, R; Cao, L; Sutcliffe, W; Welsch, M; Bernlochner, FU; Adachi, I; Aihara, H; Asner, DM; Aushev, T; Ayad, R; Babu, V; Behera, P; Belous, K; Bennett, J; Bessner, M; Bhardwaj, V; Bhuyan, B; Bilka, T; Biswal, J; Bobrov, A; Bodrov, D; Borah, J; Bozek, A; Branchini, P; Budano, A; Campajola, M; Chang, MC; Chang, P; Chekelian, V; Chen, A; Cheon, BG; Chilikin, K; Cho, HE; Cho, K; Cho, SJ; Choi, Y; Choudhury, S; Cinabro, D; Cunliffe, S; Das, S; De Nardo, G; De Pietro, G; Dhamija, R; Di Capua, F; Dingfelder, J; Dolezal, Z; Dong, TV; Dubey, S; Epifanov, D; Ferber, T; Fulsom, BG; Garg, R; Gaur, V; Gabyshev, N; Giri, A; Goldenzweig, P; Graziani, E; Gu, T; Guan, Y; Gudkova, K; Hadjivasiliou, C; Halder, S; Hartbrich, O; Hayasaka, K; Hayashii, H; Hou, WS; Hsu, CL; Iijima, T; Inami, K; Ishikawa, A; Itoh, R; Iwasaki, M; Jacobs, WW; Jia, S; Jin, Y; Joo, KK; Kaliyar, AB; Kang, KH; Karyan, G; Kichimi, H; Kiesling, C; Kim, CH; Kim, DY; Kim, KH; Kim, YK; Kinoshita, K; Kodys, P; Konno, T; Korpar, S; Kovalenko, E; Krizan, P; Kroeger, R; Krokovny, P; Kuhr, T; Kumar, M; Kumar, R; Kumara, K; Kuzmin, A; Kwon, YJ; Lange, JS; Laurenza, M; Lee, SC; Lewis, P; Li, CH; Li, J; Li, LK; Li, YB; Li Gioi, L; Libby, J; Lieret, K; Liventsev, D; MacQueen, C; Masuda, M; Matsuda, T; Matvienko, D; Merola, M; Metzner, F; Miyabayashi, K; Mizuk, R; Mohanty, GB; Mohanty, S; Mrvar, M; Nakao, M; Natkaniec, Z; Natochii, A; Nayak, L; Nisar, NK; Nishida, S; Nishimura, K; Ogawa, K; Ogawa, S; Ono, H; Oskin, P; Pakhlov, P; Pakhlova, G; Pang, T; Pardi, S; Park, H; Park, SH; Passeri, A; Patra, S; Paul, S; Pedlar, TK; Pestotnik, R; Piilonen, LE; Podobnik, T; Popov, V; Prencipe, E; Prim, MT; Rabusov, A; Rohrken, M; Rostomyan, A; Rout, N; Russo, G; Sahoo, D; Santelj, L; Savinov, V; Schnell, G; Schwanda, C; Seino, Y; Senyo, K; Sevior, ME; Shapkin, M; Sharma, C; Shen, CP; Shiu, JG; Sokolov, A; Solovieva, E; Stottler, ZS; Strube, JF; Sumihama, M; Sumiyoshi, T; Takizawa, M; Tamponi, U; Tanida, K; Tenchini, F; Trabelsi, K; Uchida, M; Uglov, T; Unno, Y; Uno, S; Urquijo, P; Vahsen, SE; Varner, G; Varvell, KE; Waheed, E; Wang, CH; Wang, E; Wang, MZ; Wang, P; Wang, XL; Watanabe, M; Watanuki, S; Yabsley, BD; Yan, W; Yang, SB; Ye, H; Yin, JH; Zhang, ZP; Zhilich, V; Zhukova, V van Tonder, R.; Cao, L.; Sutcliffe, W.; Welsch, M.; Bernlochner, F. U.; Adachi, I; Aihara, H.; Asner, D. M.; Aushev, T.; Ayad, R.; Babu, V; Behera, P.; Belous, K.; Bennett, J.; Bessner, M.; Bhardwaj, V; Bhuyan, B.; Bilka, T.; Biswal, J.; Bobrov, A.; Bodrov, D.; Borah, J.; Bozek, A.; Branchini, P.; Budano, A.; Campajola, M.; Chang, M-C; Chang, P.; Chekelian, V; Chen, A.; Cheon, B. G.; Chilikin, K.; Cho, H. E.; Cho, K.; Cho, S-J; Choi, Y.; Choudhury, S.; Cinabro, D.; Cunliffe, S.; Das, S.; De Nardo, G.; De Pietro, G.; Dhamija, R.; Di Capua, F.; Dingfelder, J.; Dolezal, Z.; Dong, T., V; Dubey, S.; Epifanov, D.; Ferber, T.; Fulsom, B. G.; Garg, R.; Gaur, V; Gabyshev, N.; Giri, A.; Goldenzweig, P.; Graziani, E.; Gu, T.; Guan, Y.; Gudkova, K.; Hadjivasiliou, C.; Halder, S.; Hartbrich, O.; Hayasaka, K.; Hayashii, H.; Hou, W-S; Hsu, C-L; Iijima, T.; Inami, K.; Ishikawa, A.; Itoh, R.; Iwasaki, M.; Jacobs, W. W.; Jia, S.; Jin, Y.; Joo, K. K.; Kaliyar, A. B.; Kang, K. H.; Karyan, G.; Kichimi, H.; Kiesling, C.; Kim, C. H.; Kim, D. Y.; Kim, K-H; Kim, Y-K; Kinoshita, K.; Kodys, P.; Konno, T.; Korpar, S.; Kovalenko, E.; Krizan, P.; Kroeger, R.; Krokovny, P.; Kuhr, T.; Kumar, M.; Kumar, R.; Kumara, K.; Kuzmin, A.; Kwon, Y-J; Lange, J. S.; Laurenza, M.; Lee, S. C.; Lewis, P.; Li, C. H.; Li, J.; Li, L. K.; Li, Y. B.; Li Gioi, L.; Libby, J.; Lieret, K.; Liventsev, D.; MacQueen, C.; Masuda, M.; Matsuda, T.; Matvienko, D.; Merola, M.; Metzner, F.; Miyabayashi, K.; Mizuk, R.; Mohanty, G. B.; Mohanty, S.; Mrvar, M.; Nakao, M.; Natkaniec, Z.; Natochii, A.; Nayak, L.; Nisar, N. K.; Nishida, S.; Nishimura, K.; Ogawa, K.; Ogawa, S.; Ono, H.; Oskin, P.; Pakhlov, P.; Pakhlova, G.; Pang, T.; Pardi, S.; Park, H.; Park, S-H; Passeri, A.; Patra, S.; Paul, S.; Pedlar, T. K.; Pestotnik, R.; Piilonen, L. E.; Podobnik, T.; Popov, V; Prencipe, E.; Prim, M. T.; Rabusov, A.; Rohrken, M.; Rostomyan, A.; Rout, N.; Russo, G.; Sahoo, D.; Santelj, L.; Savinov, V; Schnell, G.; Schwanda, C.; Seino, Y.; Senyo, K.; Sevior, M. E.; Shapkin, M.; Sharma, C.; Shen, C. P.; Shiu, J-G; Sokolov, A.; Solovieva, E.; Stottler, Z. S.; Strube, J. F.; Sumihama, M.; Sumiyoshi, T.; Takizawa, M.; Tamponi, U.; Tanida, K.; Tenchini, F.; Trabelsi, K.; Uchida, M.; Uglov, T.; Unno, Y.; Uno, S.; Urquijo, P.; Vahsen, S. E.; Varner, G.; Varvell, K. E.; Waheed, E.; Wang, C. H.; Wang, E.; Wang, M-Z; Wang, P.; Wang, X. L.; Watanabe, M.; Watanuki, S.; Yabsley, B. D.; Yan, W.; Yang, S. B.; Ye, H.; Yin, J. H.; Zhang, Z. P.; Zhilich, V; Zhukova, V Belle Collaboration Measurements of q(2) moments of inclusive B -> X(c)l(+)upsilon(l) decays with hadronic tagging PHYSICAL REVIEW D English Article CB-VERTICAL-BAR; HEAVY; IDENTIFICATION; (B)OVER-BAR; CONSTRAINTS; QCD We present the measurement of the first to fourth order moments of the four-momentum transfer squared, q(2), of inclusive B -> X(c)l(+)upsilon(l) decays using the full Belle dataset of 711 fb(-1) of integrated luminosity at the gamma(4S) resonance where l = e, mu. The determination of these moments and their systematic uncertainties open new pathways to determine the absolute value of the Cabibbo-Kobayashi-Maskawa matrix element V (cb) using a reduced set of matrix elements of the heavy quark expansion. In order to identify and reconstruct the X-c system, we reconstruct one of the two B-mesons using machine learning techniques in fully hadronic decay modes. The moments are measured with progressively increasing threshold selections on q(2) starting with a lower value of 3.0 GeV2 in steps of 0.5 GeV2 up to a value of 10.0 GeV2. The measured moments are further unfolded, correcting for reconstruction and selection effects as well as QED final state radiation. We report the moments separately for electron and muon final states and observe no lepton flavor universality violating effects. [van Tonder, R.; Cao, L.; Sutcliffe, W.; Welsch, M.; Bernlochner, F. U.; Dingfelder, J.; Lewis, P.; Prim, M. T.] Univ Bonn, D-53115 Bonn, Germany; [Schnell, G.] Univ Basque Country, Dept Phys, UPV EHU, Bilbao 48080, Spain; [Asner, D. M.; Nisar, N. K.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Bobrov, A.; Epifanov, D.; Gabyshev, N.; Gudkova, K.; Kovalenko, E.; Krokovny, P.; Kuzmin, A.; Matvienko, D.; Zhilich, V] Budker Inst Nucl Phys SB RAS, Novosibirsk 630090, Russia; [Bilka, T.; Dolezal, Z.; Kodys, P.] Charles Univ Prague, Fac Math & Phys, Prague 12116, Czech Republic; [Joo, K. K.] Chonnam Natl Univ, Gwangju 61186, South Korea; [Guan, Y.; Kinoshita, K.; Li, L. K.] Univ Cincinnati, Cincinnati, OH 45221 USA; [Babu, V; Cunliffe, S.; Ferber, T.; Karyan, G.; Rohrken, M.; Rostomyan, A.; Tenchini, F.; Ye, H.] DESY, D-22607 Hamburg, Germany; [Chang, M-C] Fu Jen Catholic Univ, Dept Phys, Taipei 24205, Taiwan; [Dong, T., V; Jia, S.; Shen, C. P.; Wang, X. L.] Fudan Univ, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai 200443, Peoples R China; [Dong, T., V; Jia, S.; Shen, C. P.; Wang, X. L.] Fudan Univ, Inst Modern Phys, Shanghai 200443, Peoples R China; [Lange, J. S.] Justus Liebig Univ Giessen, D-35392 Giessen, Germany; [Sumihama, M.] Gifu Univ, Gifu 5011193, Japan; [Adachi, I; Ishikawa, A.; Itoh, R.; Nakao, M.; Nishida, S.; Uno, S.] SOKENDAI Grad Univ Adv Studies, Hayama, Kanagawa 2400193, Japan; [Cheon, B. G.; Cho, H. E.; Kim, C. H.; Unno, Y.] Hanyang Univ, Dept Phys, Seoul 04763, South Korea; [Cheon, B. G.; Cho, H. E.; Kim, C. H.; Unno, Y.] Hanyang Univ, Inst Nat Sci, Seoul 04763, South Korea; [Bessner, M.; Dubey, S.; Hartbrich, O.; Natochii, A.; Nishimura, K.; Vahsen, S. E.; Varner, G.] Univ Hawaii, Honolulu, HI 96822 USA; [Adachi, I; Ishikawa, A.; Itoh, R.; Kichimi, H.; Liventsev, D.; Nakao, M.; Nishida, S.; Park, S-H; Uno, S.; Waheed, E.] High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki 3050801, Japan; [Takizawa, M.] High Energy Accelerator Res Org KEK, KEK Theory Ctr, J PARC Branch, Tsukuba, Ibaraki 3050801, Japan; [Aushev, T.; Bodrov, D.; Mizuk, R.; Pakhlova, G.; Popov, V; Uglov, T.] Natl Res Univ Higher Sch Econ, Moscow 101000, Russia; [Prencipe, E.] Forschungszentrum Julich, D-52425 Julich, Germany; [Schnell, G.] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain; [Bhardwaj, V; Patra, S.] Indian Inst Sci Educ & Res Mohali, Sas Nagar 140306, India; [Bhuyan, B.; Borah, J.] Indian Inst Technol Guwahati, Gauhati 781039, Assam, India; [Choudhury, S.; Dhamija, R.; Giri, A.; Nayak, L.] Indian Inst Technol Hyderabad, Hyderabad 502285, Telangana, India; [Behera, P.; Libby, J.; Rout, N.] Indian Inst Technol Madras, Chennai 600036, Tamil Nadu, India; [Jacobs, W. W.] Indiana Univ, Bloomington, IN 47408 USA; [Wang, P.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China; [Mrvar, M.; Schwanda, C.] Inst High Energy Phys, A-1050 Vienna, Austria; [Belous, K.; Shapkin, M.; Sokolov, A.] Inst High Energy Phys, Protvino 142281, Russia; [Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.; Pardi, S.] Ist Nazl Fis Nucl, Sez Napoli, I-80126 Naples, Italy; [Branchini, P.; Budano, A.; De Pietro, G.; Graziani, E.; Laurenza, M.; Passeri, A.] Ist Nazl Fis Nucl, Sez Roma Tre, I-00146 Rome, Italy; [Tamponi, U.] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; [Tanida, K.] Japan Atom Energy Agcy, Adv Sci Res Ctr, Naka, Ibaraki 3191195, Japan; [Biswal, J.; Korpar, S.; Krizan, P.; Pestotnik, R.; Podobnik, T.; Santelj, L.] J Stefan Inst, Ljubljana 1000, Slovenia; [Goldenzweig, P.; Metzner, F.] Karlsruher Inst Technol, Inst Expt Teilchenphys, D-76131 Karlsruhe, Germany; [Konno, T.] Kitasato Univ, Sagamihara, Kanagawa 2520373, Japan; [Cho, K.] Korea Inst Sci & Technol Informat, Daejeon 34141, South Korea; [Yang, S. B.; Yin, J. H.] Korea Univ, Seoul 02841, South Korea; [Kang, K. H.; Lee, S. C.; Li, J.; Park, H.] Kyungpook Natl Univ, Daegu 41566, South Korea; [Trabelsi, K.; Watanuki, S.] Univ Paris Saclay, IJCLab, CNRS IN2P3, F-91405 Orsay, France; [Bodrov, D.; Chilikin, K.; Matvienko, D.; Mizuk, R.; Oskin, P.; Pakhlov, P.; Pakhlova, G.; Solovieva, E.; Uglov, T.; Zhukova, V] Russian Acad Sci, PN Lebedev Phys Inst, Moscow 119991, Russia; [Li, C. H.] Liaoning Normal Univ, Dalian 116029, Peoples R China; [Krizan, P.; Podobnik, T.; Santelj, L.] Univ Ljubljana, Fac Math & Phys, Ljubljana 1000, Slovenia; [Kuhr, T.; Lieret, K.] Ludwig Maximilians Univ Munchen, D-80539 Munich, Germany; [Pedlar, T. K.] Luther Coll, Decorah, IA 52101 USA; [Das, S.; Kumar, M.; Sharma, C.] Malaviya Natl Inst Technol Jaipur, Jaipur 302017, Rajasthan, India; [Korpar, S.] Univ Maribor, Fac Chem & Chem Engn, Maribor 2000, Slovenia; [Chekelian, V; Kiesling, C.; Li Gioi, L.; Paul, S.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [MacQueen, C.; Sevior, M. E.; Urquijo, P.] Univ Melbourne, Sch Phys, Melbourne, Vic 3010, Australia; [Bennett, J.; Kroeger, R.] Univ Mississippi, University, MS 38677 USA; [Matsuda, T.] Univ Miyazaki, Miyazaki 8892192, Japan; [Pakhlov, P.] Moscow Phys Engn Inst, Moscow 115409, Russia; [Iijima, T.; Inami, K.] Nagoya Univ, Grad Sch Sci, Nagoya, Aichi 4648602, Japan; [Iijima, T.] Nagoya Univ, Kobayashi Maskawa Inst, Nagoya, Aichi 4648602, Japan; [Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.; Russo, G.] Univ Napoli Federico II, I-80126 Naples, Italy; [Hayashii, H.; Miyabayashi, K.] Nara Womens Univ, Nara 6308506, Japan; [Chen, A.] Natl Cent Univ, Chungli 32054, Taiwan; [Wang, C. H.] Natl United Univ, Miaoli 36003, Taiwan; [Chang, P.; Hou, W-S; Shiu, J-G; Wang, M-Z] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan; [Bozek, A.; Natkaniec, Z.] H Niewodniczanski Inst Nucl Phys, PL-31342 Krakow, Poland; [Ono, H.] Nippon Dent Univ, Niigata 9518580, Japan; [Hayasaka, K.; Ogawa, K.; Ono, H.; Seino, Y.; Watanabe, M.] Niigata Univ, Niigata 9502181, Japan; [Bobrov, A.; Epifanov, D.; Gabyshev, N.; Gudkova, K.; Kovalenko, E.; Krokovny, P.; Kuzmin, A.; Matvienko, D.; Zhilich, V] Novosibirsk State Univ, Novosibirsk 630090, Russia; [Iwasaki, M.] Osaka City Univ, Osaka 5588585, Japan; [Fulsom, B. G.; Hadjivasiliou, C.; Strube, J. F.] Pacific Northwest Natl Lab, Richland, WA 99352 USA; [Garg, R.] Panjab Univ, Chandigarh 160014, India; [Li, Y. B.] Peking Univ, Beijing 100871, Peoples R China; [Gu, T.; Pang, T.; Savinov, V; Wang, E.] Univ Pittsburgh, Pittsburgh, PA 15260 USA; [Kumar, R.] Punjab Agr Univ, Ludhiana 141004, Punjab, India; [Masuda, M.] Osaka Univ, Res Ctr Nucl Phys, Osaka 5670047, Japan; [Takizawa, M.] RIKEN, Meson Sci Lab, Cluster Pioneering Res, Saitama 3510198, Japan; [Laurenza, M.] Univ Roma Tre, Dipartimento Matemat & Fis, I-00146 Rome, Italy; [Yan, W.; Zhang, Z. P.] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China; [Yan, W.; Zhang, Z. P.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China; [Takizawa, M.] Showa Pharmaceut Univ, Tokyo 1948543, Japan; [Kim, D. Y.] Soongsil Univ, Seoul 06978, South Korea; [Choi, Y.] Sungkyunkwan Univ, Suwon 16419, South Korea; [Hsu, C-L; Varvell, K. E.; Yabsley, B. D.] Univ Sydney, Sch Phys, Sydney, NSW 2006, Australia; [Ayad, R.] Univ Tabuk, Fac Sci, Dept Phys, Tabuk 71451, Saudi Arabia; [Halder, S.; Kaliyar, A. B.; Mohanty, G. B.; Mohanty, S.; Sahoo, D.] Tata Inst Fundamental Res, Mumbai 400005, Maharashtra, India; [Paul, S.; Rabusov, A.] Tech Univ Munich, Dept Phys, D-85748 Garching, Germany; [Ogawa, S.] Toho Univ, Funabashi, Chiba 2748510, Japan; [Masuda, M.] Univ Tokyo, Earthquake Res Inst, Tokyo 1130032, Japan; [Aihara, H.; Jin, Y.] Univ Tokyo, Dept Phys, Tokyo 1130033, Japan; [Uchida, M.] Tokyo Inst Technol, Tokyo 1528550, Japan; [Sumiyoshi, T.] Tokyo Metropolitan Univ, Tokyo 1920397, Japan; [Mohanty, S.] Utkal Univ, Bhubaneswar 751004, Orissa, India; [Gaur, V; Piilonen, L. E.; Stottler, Z. S.] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA; [Cinabro, D.; Kumara, K.; Liventsev, D.] Wayne State Univ, Detroit, MI 48202 USA; [Senyo, K.] Yamagata Univ, Yamagata 9908560, Japan; [Cho, S-J; Kim, K-H; Kim, Y-K; Kwon, Y-J] Yonsei Univ, Seoul 03722, South Korea University of Bonn; University of Basque Country; United States Department of Energy (DOE); Brookhaven National Laboratory; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Charles University Prague; Chonnam National University; University System of Ohio; University of Cincinnati; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Fu Jen Catholic University; Fudan University; Chinese Academy of Sciences; Fudan University; Justus Liebig University Giessen; Gifu University; Graduate University for Advanced Studies - Japan; Hanyang University; Hanyang University; University of Hawaii System; High Energy Accelerator Research Organization (KEK); High Energy Accelerator Research Organization (KEK); HSE University (National Research University Higher School of Economics); Helmholtz Association; Research Center Julich; Basque Foundation for Science; Indian Institute of Science Education & Research (IISER) - Mohali; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Guwahati; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Hyderabad; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Indiana University System; Indiana University Bloomington; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Austrian Academy of Sciences; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Japan Atomic Energy Agency; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Helmholtz Association; Karlsruhe Institute of Technology; Kitasato University; Korea Institute of Science & Technology Information (KISTI); Korea University; Kyungpook National University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Liaoning Normal University; University of Ljubljana; University of Munich; National Institute of Technology (NIT System); Malaviya National Institute of Technology Jaipur; University of Maribor; Max Planck Society; University of Melbourne; University of Mississippi; University of Miyazaki; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Nagoya University; Nagoya University; University of Naples Federico II; Nara Womens University; National Central University; National United University; National Taiwan University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Nippon Dental University; Niigata University; Novosibirsk State University; Osaka Metropolitan University; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Panjab University; Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Punjab Agricultural University; Osaka University; RIKEN; Roma Tre University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Showa Pharmaceutical University; Soongsil University; Sungkyunkwan University (SKKU); University of Sydney; University of Tabuk; Tata Institute of Fundamental Research (TIFR); Technical University of Munich; Toho University; University of Tokyo; University of Tokyo; Tokyo Institute of Technology; Tokyo Metropolitan University; Utkal University; Virginia Polytechnic Institute & State University; Wayne State University; Yamagata University; Yonsei University van Tonder, R; Bernlochner, FU (corresponding author), Univ Bonn, D-53115 Bonn, Germany. vantonder@physik.uni-bonn.de; florian.bernlochner@uni-bonn.de Natkaniec, Zbigniew/AAP-2995-2021; Solovieva, Elena/B-2449-2014; Pakhlova, Galina V/C-5378-2014; Tamponi, Umberto/AGG-5576-2022; Chilikin, Kirill/B-4402-2014; Campajola, Marcello/V-1689-2019; Bodrov, Denis/AAH-3024-2021; Zhukova, Valentina/C-8878-2016; hou, wei/GWM-4800-2022; Pakhlov, Pavel N/K-2158-2013; Popov, Vitaliy/C-9925-2016; Nayak, Lopamudra/HPE-4040-2023; Strube, Jan/E-9310-2015 Natkaniec, Zbigniew/0000-0003-0486-9291; Solovieva, Elena/0000-0002-5735-4059; Pakhlova, Galina V/0000-0001-7518-3022; Chilikin, Kirill/0000-0001-7620-2053; Campajola, Marcello/0000-0003-2518-7134; Bodrov, Denis/0000-0001-5279-4787; Zhukova, Valentina/0000-0002-8253-641X; Pakhlov, Pavel N/0000-0001-7426-4824; Popov, Vitaliy/0000-0003-0208-2583; Zilic, Viktor/0000-0002-0907-5565; Metzner, Felix/0000-0002-0128-264X; Nayak, Lopamudra/0000-0002-7739-914X; De Pietro, Giacomo/0000-0001-8442-107X; Cunliffe, Sam/0000-0003-0167-8641; Strube, Jan/0000-0001-7470-9301; Vahsen, Sven/0000-0003-1685-9824; Yang, Seongbae/0000-0002-9543-7971; Hsu, Chia-Ling/0000-0002-1641-430X; Lange, Jens Soren/0000-0003-0234-0474; Ono, Hiroaki/0000-0003-4486-0064; van Tonder, Raynette/0000-0002-7448-4816; Dubey, Shawn/0000-0002-1345-0970; Belous, Konstantin/0000-0003-0014-2589; Sevior, Martin/0000-0002-4824-101X; Borah, Jyotirmoi/0000-0003-2990-1913; Natochii, Andrii/0000-0002-1076-814X; Yabsley, Bruce/0000-0002-2680-0474; BHUYAN, BIPUL/0000-0001-6254-3594; Kovalenko, Evgeniy/0000-0001-8084-1931; Dong, Thanh/0000-0003-3043-1939 DFG Emmy-Noether Grant [6075/1-1]; Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan; Japan Society for the Promotion of Science (JSPS); Tau-Lepton Physics Research Center of Nagoya University; Australian Research Council [DP180102629, DP170102389, DP170102204, DP150103061, FT130100303]; Austrian Federal Ministry of Education, Science and Research (FWF); FWF Austrian Science Fund [P 31361-N36]; National Natural Science Foundation of China [11435013, 11475187, 11521505, 11575017, 11675166, 11705209]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) [QYZDJ-SSW-SLH011]; CAS Center for Excellence in Particle Physics (CCEPP); Shanghai Pujiang Program [18PJ1401000]; Shanghai Science and Technology Committee (STCSM) [19ZR1403000]; Ministry of Education, Youth and Sports of the Czech Republic [LTT17020]; Horizon 2020 ERC [884719]; ERC InterLeptons (European Union) [947006]; Carl Zeiss Foundation; Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe; Volkswagen Stiftung; Department of Atomic Energy [RTI 4002]; Department of Science and Technology of India; Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea [2016R1D1A1B01010135, 2016R1D1A1B02012900, 2018R1A2B3003643, 2018R1A6A1A06024970, 2018R1D1A1B-07047294, 2019K1A3A7A09033840, 2019-R1I1A3A01058933]; Radiation Science Research Institute; Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Polish Ministry of Science and Higher Education; National Science Center; Ministry of Science and Higher Education of the Russian Federation [14.W03.31.0026]; HSE University Basic Research Program, Moscow; University of Tabuk Research (Saudi Arabia) [S-1440-0321, S-0256-1438, S-0280-1439]; Slovenian Research Agency Grants [J1-9124, P1-0135]; Ikerbasque, Basque Foundation for Science, Spain; Swiss National Science Foundation; Ministry of Education of Taiwan; Ministry of Science and Technology of Taiwan; United States Department of Energy; National Science Foundation DFG Emmy-Noether Grant(German Research Foundation (DFG)); Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Japan Society for the Promotion of Science (JSPS)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Tau-Lepton Physics Research Center of Nagoya University; Australian Research Council(Australian Research Council); Austrian Federal Ministry of Education, Science and Research (FWF); FWF Austrian Science Fund(Austrian Science Fund (FWF)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS); CAS Center for Excellence in Particle Physics (CCEPP); Shanghai Pujiang Program(Shanghai Pujiang Program); Shanghai Science and Technology Committee (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)Shanghai Science & Technology Committee); Ministry of Education, Youth and Sports of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Horizon 2020 ERC; ERC InterLeptons (European Union); Carl Zeiss Foundation; Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe(German Research Foundation (DFG)); Volkswagen Stiftung(Volkswagen); Department of Atomic Energy(Department of Atomic Energy (DAE)); Department of Science and Technology of India(Department of Science & Technology (India)); Istituto Nazionale di Fisica Nucleare of Italy(Istituto Nazionale di Fisica Nucleare (INFN)); National Research Foundation (NRF) of Korea(National Research Foundation of Korea); Radiation Science Research Institute; Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); National Science Center(National Science Centre, Poland); Ministry of Science and Higher Education of the Russian Federation; HSE University Basic Research Program, Moscow; University of Tabuk Research (Saudi Arabia); Slovenian Research Agency Grants(Slovenian Research Agency - Slovenia); Ikerbasque, Basque Foundation for Science, Spain; Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); Ministry of Education of Taiwan(Ministry of Education, Taiwan); Ministry of Science and Technology of Taiwan(Ministry of Science and Technology, Taiwan); United States Department of Energy(United States Department of Energy (DOE)); National Science Foundation(National Science Foundation (NSF)) We thank Keri Vos, Kevin Olschewsky, and Matteo Fael for useful discussions about the subject matter of this manuscript. R. v. T., L. C., W. S., and F. U. B. were supported by the DFG Emmy-Noether Grant No. BE 6075/1-1. We thank the KEKB group for the excellent operation of the accelerator; the KEK cryogenics group for the efficient operation of the solenoid; and the KEK computer group, and the Pacific Northwest National Laboratory (PNNL) Environmental Molecular Sciences Laboratory (EMSL) computing group for strong computing support; and the National Institute of Informatics, and Science Information NETwork 5 (SINET5) for valuable network support. We acknowledge support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, the Japan Society for the Promotion of Science (JSPS) , and the Tau-Lepton Physics Research Center of Nagoya University; the Australian Research Council including Grants No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, and No. FT130100303; Austrian Federal Ministry of Education, Science and Research (FWF) and FWF Austrian Science Fund No. P 31361-N36; the National Natural Science Foundation of China under Contracts No. 11435013, No. 11475187, No. 11521505, No. 11575017, No. 11675166, and No. 11705209; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) , Grant No. QYZDJ-SSW-SLH011; the CAS Center for Excellence in Particle Physics (CCEPP) ; the Shanghai Pujiang Program under Grant No. 18PJ1401000; the Shanghai Science and Technology Committee (STCSM) under Grant No. 19ZR1403000; the Ministry of Education, Youth and Sports of the Czech Republic under Con-tract No. LTT17020; Horizon 2020 ERC Advanced Grant No. 884719 and ERC Starting Grant No. 947006 InterLeptons (European Union) ; the Carl Zeiss Foundation, the Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe, and the Volkswagen Stiftung; the Department of Atomic Energy (Project Identification No. RTI 4002) and the Department of Science and Technology of India; the Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea Grants No. 2016R1D1A1B01010135, No. 2016R1D1A1B02012900, No. 2018R1A2B3003643, No. 2018R1A6A1A06024970, No. 2018R1D1A1B-07047294, No. 2019K1A3A7A09033840, and No. 2019-R1I1A3A01058933; Radiation Science Research Institute,Foreign Large-size Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Science and Higher Education of the Russian Federation, Agreement No. 14.W03.31.0026, and the HSE University Basic Research Program, Moscow; University of Tabuk Research Grants No. S-1440-0321, No. S-0256-1438, and No. S-0280-1439 (Saudi Arabia) ; the Slovenian Research Agency Grants No. J1-9124 and No. P1-0135; Ikerbasque, Basque Foundation for Science, Spain; the Swiss National Science Foundation; the Ministry of Education and the Ministry of Science and Technology of Taiwan; and the United States Department of Energy and the National Science Foundation. 67 4 4 1 4 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D DEC 30 2021.0 104.0 11 112011 10.1103/PhysRevD.104.112011 0.0 32 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics YM8WB Green Accepted, hybrid 2023-03-23 WOS:000746848700003 0
J Zhang, ZX; Qian, Z; Zhong, T; Chen, M; Zhang, K; Yang, Y; Zhu, R; Zhang, F; Zhang, HR; Zhou, FZ; Yu, JN; Zhang, BY; Lu, GN; Yan, JY Zhang, Zhixin; Qian, Zhen; Zhong, Teng; Chen, Min; Zhang, Kai; Yang, Yue; Zhu, Rui; Zhang, Fan; Zhang, Haoran; Zhou, Fangzhuo; Yu, Jianing; Zhang, Bingyue; Lu, Guonian; Yan, Jinyue Vectorized rooftop area data for 90 cities in China SCIENTIFIC DATA English Article; Data Paper CITY-SCALE; BUILDING EXTRACTION; GOOGLE EARTH; LIDAR DATA; GENERATION; IMAGES Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km(2) across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively. [Zhang, Zhixin; Qian, Zhen; Zhong, Teng; Chen, Min; Zhang, Kai; Yang, Yue; Zhou, Fangzhuo; Yu, Jianing; Zhang, Bingyue; Lu, Guonian] Nanjing Normal Univ, Minist Educ PRC, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China; [Zhang, Zhixin; Qian, Zhen; Zhong, Teng; Chen, Min; Zhang, Kai; Yang, Yue; Zhou, Fangzhuo; Yu, Jianing; Zhang, Bingyue; Lu, Guonian] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China; [Zhang, Zhixin; Qian, Zhen; Zhong, Teng; Chen, Min; Zhang, Kai; Yang, Yue] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China; [Chen, Min] Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China; [Zhu, Rui] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China; [Zhang, Fan] MIT, Senseable City Lab, Cambridge, MA 02139 USA; [Zhang, Haoran] Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan; [Zhang, Haoran] LocationMind Inc, Chiyoda Ku, 3-5-2 Iwamotocho, Tokyo 1010032, Japan; [Zhang, Haoran; Yan, Jinyue] Malardalen Univ, Future Energy Ctr, S-72123 Vasteras, Sweden; [Yan, Jinyue] KTH Royal Inst Technol, Dept Chem Engn, S-10044 Stockholm, Sweden Nanjing Normal University; Nanjing Normal University; Hong Kong Polytechnic University; Massachusetts Institute of Technology (MIT); University of Tokyo; Malardalen University; Royal Institute of Technology Chen, M (corresponding author), Nanjing Normal Univ, Minist Educ PRC, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.;Chen, M (corresponding author), Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China. chenmin0902@njnu.edu.cn Zhang, Fan/0000-0002-3643-018X; Qian, Zhen/0000-0002-0423-7430; Zhang, Zhixin/0000-0002-3898-0863 National Natural Science Foundation [41930648]; National Research Foundation Singapore; National Natural Science Foundation of China [U1811464] National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); National Research Foundation Singapore(National Research Foundation, Singapore); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We appreciate the detailed suggestions and comments from the anonymous reviewers. We express heartfelt thanks to the other members of the Smart City Sensing and Simulation lab, who undertook data collection and annotation work. This work was supported by the National Natural Science Foundation (Grant 41930648), Joint Fund Project of National Natural Science Foundation of China (Grant U1811464), and National Research Foundation Singapore. The data of this work is licensed and hosted by National Tibetan Plateau Data Center. 53 8 8 16 36 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2052-4463 SCI DATA Sci. Data MAR 2 2022.0 9.0 1 66 10.1038/s41597-022-01168-x 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics ZL1HH 35236863.0 gold, Green Published 2023-03-23 WOS:000763431100003 0
J Bao, Y; Zhang, DX; Zhao, C; Poppel, E; Zabotkina, V Bao, Yan; Zhang, Dongxue; Zhao, Chen; Poeppel, Ernst; Zabotkina, Vera An Aesthetic Frame for Three Modes of Knowing PSYCH JOURNAL English Article artificial intelligence; complementarity; aesthetics; knowledge; poetry TIME WINDOW; BRAIN; DISCRETE; FMRI This is the claim: Human knowledge expresses itself in three different modes: explicit, implicit, and sensory (e.g., visual) knowledge. The reference to knowledge as explicit knowledge only (as it is usually done) neglects the other modes of knowledge. Instead of using the noun knowledge, it is suggested that we use knowing as a term to stress the active mode in cognition. The three modes of knowing are reflected in different mechanisms of cognitive and neural processing; these can already be observed in classical documents of humankind. This unity of the different modes of knowing can also be extracted from cultural artifacts such as poetry. Although possessing different characteristics, the three modes of knowing are related to each other in a complementary way, thus providing an integrative network for cognition. We suggest that a unifying frame of the modes of knowing lies in aesthetic principles. These different modes of knowing with their unifying frame reflect some general features of cognition. We stress that basic concepts in artificial or machine intelligence do not match at all human modes of knowing. [Bao, Yan; Poeppel, Ernst] Peking Univ, Sch Psychol & Cognit Sci, Beijing, Peoples R China; [Bao, Yan] Peking Univ, Beijing Key Lab Behav & Mental Hlth, Beijing, Peoples R China; [Bao, Yan; Zhang, Dongxue; Zhao, Chen; Poeppel, Ernst] Ludwig Maximilian Univ Munich, Inst Med Psychol, Munich, Germany; [Zabotkina, Vera] Russian State Univ Humanities, Moscow, Russia Peking University; Peking University; University of Munich; Russian State University for the Humanities Bao, Y (corresponding author), Peking Univ, Sch Psychol & Cognit Sci, Beijing, Peoples R China. baoyan@pku.edu.cn Zhao, Chen/0000-0003-3844-3192 Parmenides Foundation, Pocking, Germany; National Natural Science Foundation of China [31771213, 31371018]; Chinese Scholarship Council; Russian Science Foundation [22-18-00594] Parmenides Foundation, Pocking, Germany; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Scholarship Council(China Scholarship Council); Russian Science Foundation(Russian Science Foundation (RSF)) Parmenides Foundation, Pocking, Germany; National Natural Science Foundation of China, Grants 31771213 and 31371018; Chinese Scholarship Council; Russian Science Foundation, Project 22-18-00594 71 1 1 5 5 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2046-0252 2046-0260 PSYCH J PsyCh J. OCT 2022.0 11.0 5 636 644 10.1002/pchj.588 0.0 SEP 2022 9 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology 5E0NH 36058886.0 2023-03-23 WOS:000849580200001 0
J Xiao, P; Liao, B; Deligiannis, N Xiao, Peng; Liao, Bin; Deligiannis, Nikos DeepFPC: A deep unfolded network for sparse signal recovery from 1-Bit measurements with application to DOA estimation SIGNAL PROCESSING English Article Deep unfolding; Interpretable deep residual learning; 1-Bit compressed sensing; DOA Estimation ARRIVAL In this paper, we introduce a novel deep neural network, coined DeepFPC, and investigate its application to tackling the problem of direction-of-arrival (DOA) estimation. DeepFPC is designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l(1)-norm (FPC-l(1)), which has been proposed for solving the 1-bit compressed sensing problem. The network architecture resembles that of deep residual learning and incorporates prior knowledge about the signal structure (i.e., sparsity), thereby offering interpretability by design. Once DeepFPC is properly trained, a sparse signal can be recovered fast and accurately from quantized measurements. The proposed model is then applied in DOA estimation and is shown to outperform state-of-the-art solutions; namely, the iterative FPC-l(1) algorithm and the deep convolution network (DCN) model. (C) 2020 Elsevier B.V. All rights reserved. [Xiao, Peng; Liao, Bin] Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518060, Peoples R China; [Xiao, Peng; Deligiannis, Nikos] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium; [Deligiannis, Nikos] IMEC, Kapeldreef 75, B-3001 Leuven, Belgium Shenzhen University; Vrije Universiteit Brussel; IMEC Liao, B (corresponding author), Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518060, Peoples R China. binliao@szu.edu.cn Deligiannis, Nikos/ABH-2381-2020 Deligiannis, Nikos/0000-0001-9300-5860; XIAO, PENG/0000-0001-5732-9112 Guangdong Basic and Applied Basic Research Foundation [2020A1515010410]; Natural Science Foundation of China [61771316, 61901273] Guangdong Basic and Applied Basic Research Foundation; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work was supported by Guangdong Basic and Applied Basic Research Foundation under grant 2020A1515010410, and the Natural Science Foundation of China under grants 61771316 and 61901273. 50 13 13 9 50 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0165-1684 1872-7557 SIGNAL PROCESS Signal Process. NOV 2020.0 176.0 107699 10.1016/j.sigpro.2020.107699 0.0 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NK6WC 2023-03-23 WOS:000566874300009 0
J He, S; Ecker, W; Peil, OE; Pippan, R; Razumovskiy, VI He, Shuang; Ecker, Werner; Peil, Oleg E.; Pippan, Reinhard; Razumovskiy, Vsevolod I. The effect of solute atoms on the bulk and grain boundary cohesion in Ni: Implications for hydrogen embrittlement MATERIALIA English Article First-principles calculation; Nickel-based superalloys; Grain boundary segregation; Solute strengthening; Hydrogen embrittlement SEGREGATION ENERGY; ALLOYING ELEMENTS; NICKEL; 1ST-PRINCIPLES; SULFUR; DECOHESION; ENERGETICS; DIFFUSION; KINETICS; FRACTURE Solute atoms dissolved in the Ni matrix can segregate to grain boundaries and have a pronounced effect on the performance of Ni-base alloys. Some elements, like, hydrogen lead to deterioration of interatomic bonding in the alloys resulting in a phenomenon known as hydrogen embrittlement. In this paper, we investigate the effect of 83 impurity atoms on the bulk and grain boundary decohesion resistance of Ni by calculating changes in the partial cohesive energy and in the grain boundary work of separation from density functional theory. The calculated results are used as training sets for two machine learning models. Both models are based on the use of the partial cohesive energies as one of the key features and posses very good predictive capability. The calculated data are analyzed in detail to identify the effect of all computed solutes on the grain boundary cohesive properties in Ni general and their effect on the overall resistance of Ni to hydrogen enhanced decohesion in particular. [He, Shuang; Ecker, Werner; Peil, Oleg E.; Razumovskiy, Vsevolod I.] Mat Ctr Leoben Forsch GmbH, Roseggerstr 12, A-8700 Leoben, Austria; [Pippan, Reinhard] Austrian Acad Sci, Erich Schmid Inst Mat Sci, Jahnstr 12, A-8700 Leoben, Austria; [He, Shuang] Xiangtan Univ, Sch Mat Sci & Engn, Xiangtan 411105, Peoples R China Materials Center Leoben Forschungs GmbH; Austrian Academy of Sciences; Xiangtan University He, S (corresponding author), Xiangtan Univ, Sch Mat Sci & Engn, Xiangtan 411105, Peoples R China. shuang.he@outlook.at Ecker, Werner/G-5468-2012 Ecker, Werner/0000-0002-1620-6121; He, Shuang/0000-0001-9354-7029 COMET program within the K2 Center Integrated Computational Material, Process and Product Engineering (IC-MPPE) [859480]; voestalpine BOHLER Edelstahl GmbH Co KG; Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT); Austrian Federal Ministry of Digital and Economic Affairs (BMDW); federal state of Styria; federal state of Upper Austria; federal state of Tyrol; Austrian Science Fund (FWF) [P 34179] COMET program within the K2 Center Integrated Computational Material, Process and Product Engineering (IC-MPPE); voestalpine BOHLER Edelstahl GmbH Co KG; Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT); Austrian Federal Ministry of Digital and Economic Affairs (BMDW); federal state of Styria; federal state of Upper Austria; federal state of Tyrol; Austrian Science Fund (FWF)(Austrian Science Fund (FWF)) The authors gratefully acknowledge the financial support under the scope of the COMET program within the K2 Center Integrated Computational Material, Process and Product Engineering (IC-MPPE)(Project No. 859480). This program is supported by voestalpine BOHLER Edelstahl GmbH & Co KG, the Austrian Federal Ministries for Transport, Innovation and Technology (BMVIT) and for Digital and Economic Affairs (BMDW), represented by the Austrian research funding association (FFG), and the federal states of Styria, Upper Austria and Tyrol. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). This research was funded in part by the Austrian Science Fund (FWF) [P 34179]. 98 3 3 3 7 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2589-1529 MATERIALIA Materialia MAR 2022.0 21.0 101293 10.1016/j.mtla.2021.101293 0.0 12 Materials Science, Multidisciplinary Emerging Sources Citation Index (ESCI) Materials Science 1G3GU 2023-03-23 WOS:000795740200008 0
C Bi, ZJ; Yang, Q; Zhang, MY; Goupil, A; Peng, LH IEEE Bi, Zejun; Yang, Qi; Zhang, Mengyi; Goupil, Alban; Peng, Lihang Accurate Football Detection and Localization for Nao Robot with the Improved HOG-SVM Approach 2018 CHINESE AUTOMATION CONGRESS (CAC) Chinese Automation Congress English Proceedings Paper Chinese Automation Congress (CAC) NOV 30-DEC 02, 2018 Xian, PEOPLES R CHINA CAA,IEEE,IEEE Syst Man & Cybernet Soc Nao Robot; Football Detection; Localization; HOG; SVM SUPPORT-VECTOR; BALL DETECTION Under the background of the RoboCup, this essay aims to solve the problem of finding and localizing the football in the limited computation hardware environment. Machine learning method has been used in the present study, for its much better robustness comparing with the traditional methods, such as template matching and contour detection. In this paper, we use hough circle transform for detection preliminarily. After this operation, only a few candidates will be left. Then we extract candidates' histogram of oriented gradient, using support vector machine (SVM) to predict if the candidate contains football. Relying on the successful detection, we construct a space model to retrieve the distance. In experiment section, the precision and recall were calculated on a test set, which indicates our method is accurate. Also, we compared the ground truth of distance with theoretical value, finding the error is negligible. [Bi, Zejun; Yang, Qi; Zhang, Mengyi; Peng, Lihang] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Jiangsu, Peoples R China; [Goupil, Alban] Univ Reims, CReSTIC, Reims, France Nanjing Tech University; Universite de Reims Champagne-Ardenne Zhang, MY; Peng, LH (corresponding author), Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Jiangsu, Peoples R China. bzj@njtech.edu.cn; yangqi929@outlook.com; myzhang@njtech.edu.cn; alban.goupil@univ-reims.fr; lfeng8@njtech.edu.cn Feng, Lihang/G-3411-2014 Feng, Lihang/0000-0002-5790-0478; Goupil, Alban/0000-0003-4308-9968 Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China [18KJB520021]; Natural Science Foundation of Jiangsu Province [BK20180701]; China Postdoctoral Science Foundation [2018M632292] Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) Research supported in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China (18KJB520021), in part by the Natural Science Foundation of Jiangsu Province (BK20180701), and in part by the China Postdoctoral Science Foundation (2018M632292). 10 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2688-092X 2688-0938 978-1-7281-1312-8 CHIN AUTOM CONGR 2018.0 567 571 5 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BM0WI 2023-03-23 WOS:000459239500106 0
C Ma, CY; Naser, K; Ricordel, V; Le Callet, P; Qing, CM IEEE Ma, Chengyue; Naser, Karam; Ricordel, Vincent; Le Callet, Patrick; Qing, Chunmei An Adaptive Lagrange Multiplier Determination Method for Dynamic Texture in HEVC 2016 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-CHINA (ICCE-CHINA) English Proceedings Paper IEEE International Conference on Consumer Electronics-China (ICCE-China) DEC 19-21, 2016 Guangzhou, PEOPLES R CHINA IEEE Lagrange multiplier; rate-distortion optimization; Dynamic textures This research proposed a method for adaptive Lagrange multiplier determination for rate-distortion optimization with dynamic texture in High Efficiency Video Coding (HEVC). Inspired by the experimental results of the Lagrange multiplier selection test, the presented approach adaptively predicts the optimum Lagrange multiplier for different dynamic texture sequences, based on the features of the dynamic texture sequences such as normal flow and spatial-temporal information. The Lagrange multiplier among the given values will be chosen based on the Bjontegaard delta measurements. After that, the data of training dynamic texture will be used for Support Vector Machine (SVM) in machine learning for getting the predicting results. The proposed algorithm has been fully integrated into HEVC reference codec. The result shows that the proposed method can improve 0.5 in Structural Similarity Metric (SSIM) and 2 in Peak Signal-to-Noise Ratio (PSNR). [Ma, Chengyue; Qing, Chunmei] South China Univ Technol, Guangzhou 510640, Guangdong, Peoples R China; [Ma, Chengyue; Naser, Karam; Ricordel, Vincent; Le Callet, Patrick] Univ Nantes, LUNAM Univ, Polytech Nantes, IRCCyN,UMR 6597,CNRS, Rue Christian Pauc BP 50609, F-44306 Nantes 3, France South China University of Technology; Centre National de la Recherche Scientifique (CNRS); Nantes Universite; Ecole Centrale de Nantes Ma, CY (corresponding author), South China Univ Technol, Guangzhou 510640, Guangdong, Peoples R China.;Ma, CY (corresponding author), Univ Nantes, LUNAM Univ, Polytech Nantes, IRCCyN,UMR 6597,CNRS, Rue Christian Pauc BP 50609, F-44306 Nantes 3, France. Le Callet, Patrick/F-5772-2010 Marie Sktodowska-Curie [608231]; Fundamental Research Funds for the Central Universities in China [2015ZZ032] Marie Sktodowska-Curie; Fundamental Research Funds for the Central Universities in China This work was supported by the Marie Sktodowska-Curie under the PROVISION (PeRceptually Optimised Video CompresSION) project bearing Grant Number 608231 and Call Identifier: FP7-PEOPLE-2013-ITN and the Fundamental Research Funds for the Central Universities in China under Grant 2015ZZ032. 12 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-6193-8 2016.0 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BH4NI 2023-03-23 WOS:000400497700024 0
J Li, JW; Liu, JY; Zhou, SH; Zhang, Q; Kasabov, NK Li, Jiawei; Liu, Jinyuan; Zhou, Shihua; Zhang, Qiang; Kasabov, Nikola K. Infrared and visible image fusion based on residual dense network and gradient loss INFRARED PHYSICS & TECHNOLOGY English Article Image fusion; Unsupervised learning; End-to-end model; Infrared image; Visible image MULTI-FOCUS; TRANSFORM Deep learning has made great progress in the field of image fusion. Compared with traditional methods, the image fusion approach based on deep learning requires no cumbersome matrix operations. In this paper, an end-to-end model for the infrared and visible image fusion is proposed. This unsupervised learning network architecture do not employ fusion strategy. In the stage of feature extraction, residual dense blocks are used to generate a fusion image, which preserves the information of source images to the greatest extent. In the model of feature reconstruction, shallow feature maps, residual dense information, and deep feature maps are merged in order to build a fused result. Gradient loss that we proposed for the network can cooperate well with special weight blocks extracted from input images to more clearly express texture details in fused images. In the training phase, we select 20 source image pairs with obvious characteristics from the TNO dataset, and expand them by random tailoring to serve as the training dataset of the network. Subjective qualitative and objective quantitative results show that the proposed model has advantages over state-of-the-art methods in the tasks of infrared and visible image fusion. We also use the RoadScene dataset to do ablation experiments to verify the effectiveness of the proposed network for infrared and visible image fusion. [Li, Jiawei; Zhou, Shihua; Zhang, Qiang] Dalian Univ, Sch Software Engn, Key Lab Adv Design & Intelligent Comp, Minist Educ, Dalian, Peoples R China; [Liu, Jinyuan] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China; [Zhang, Qiang] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China; [Kasabov, Nikola K.] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland 1010, New Zealand; [Kasabov, Nikola K.] Ulster Univ, Intelligent Syst Res Ctr, Londonderry BT52 1SA, North Ireland Dalian University; Dalian University of Technology; Dalian University of Technology; Auckland University of Technology; Ulster University Zhou, SH (corresponding author), Dalian Univ, Sch Software Engn, Key Lab Adv Design & Intelligent Comp, Minist Educ, Dalian, Peoples R China. zhoushihua@dlu.edu.cn li, jiawei/HOA-5023-2023 National Key Technology R&D Pro-gram of China; National Natural Science Foundation of China; LiaoNing Revitalization Talents Program; Innovation and Entrepreneurship Team of Dalian University; Natural Science Foundation of Liaoning Province; [2018YFC0910500]; [62272079]; [61751203]; [61972266]; [61802040]; [XLYC2008017]; [XQN202008]; [2021-MS-344]; [2021-KF-11-03]; [2022-KF-12-14] National Key Technology R&D Pro-gram of China(National Key Technology R&D Program); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); LiaoNing Revitalization Talents Program; Innovation and Entrepreneurship Team of Dalian University; Natural Science Foundation of Liaoning Province(Natural Science Foundation of Liaoning Province); ; ; ; ; ; ; ; ; ; Acknowledgments This work is supported by the National Key Technology R&D Pro-gram of China (No. 2018YFC0910500) , the National Natural Science Foundation of China (Nos. 62272079, 61751203, 61972266, 61802040) , LiaoNing Revitalization Talents Program (No. XLYC2008017) , the Innovation and Entrepreneurship Team of Dalian University (No. XQN202008) , Natural Science Foundation of Liaoning Province (Nos. 2021-MS-344, 2021-KF-11-03, 2022-KF-12-14) . 58 1 1 9 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1350-4495 1879-0275 INFRARED PHYS TECHN Infrared Phys. Technol. JAN 2023.0 128.0 104486 10.1016/j.infrared.2022.104486 0.0 DEC 2022 11 Instruments & Instrumentation; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation; Optics; Physics 7D9JZ 2023-03-23 WOS:000900799600002 0
J Liu, KL; Peng, Q; Sun, HB; Fei, MR; Ma, HM; Hu, TY Liu, Kailong; Peng, Qiao; Sun, Hongbin; Fei, Minrui; Ma, Huimin; Hu, Tianyu A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Batteries; Aging; Predictive models; Data models; State of charge; Degradation; Temperature measurement; Battery calendar capacity; data-science; energy and transportation informatics; health and life-cycle analysis; transfer learning PREDICTION Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 degrees C, 25 degrees C, and 45 degrees C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications. [Liu, Kailong] Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England; [Peng, Qiao] Queens Univ, Belfast BT7 1NN, Antrim, North Ireland; [Sun, Hongbin] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China; [Fei, Minrui] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China; [Ma, Huimin; Hu, Tianyu] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China University of Warwick; Queens University Belfast; Tsinghua University; Shanghai University; University of Science & Technology Beijing Ma, HM; Hu, TY (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China. kliu02@qub.ac.uk; qpeng01@qub.ac.uk; shb@tsinghua.edu.cn; mrfei@staff.shu.edu.cn; mumpub@ustb.edu.cn; tianyu@ustb.edu.cn Liu, Kailong/Y-1797-2019 Liu, Kailong/0000-0002-3564-6966 HVM Catapult [160080 CORE]; National Natural Science Foundation of China [U20B2062, 62172036]; Key Project of Science and Technology Commission of Shanghai Municipality [19500712300, 19510750300] HVM Catapult; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Project of Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) This work was supported in part by the HVM Catapult project under 160080 CORE, in part by the National Natural Science Foundation of China under Grant U20B2062 and Grant 62172036, and in part by the Key Project of Science and Technology Commission of Shanghai Municipality under Grant 19500712300 and Grant 19510750300. 30 22 22 19 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. NOV 2022.0 18.0 11 8172 8181 10.1109/TII.2022.3145573 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 4Q5TK Green Published 2023-03-23 WOS:000856145200081 0
J Jiang, GM; Wu, JP; Weidhaas, J; Li, X; Chen, Y; Mueller, J; Li, JY; Kumar, M; Zhou, X; Arora, S; Haramoto, E; Sherchan, S; Orive, G; Lertxundi, U; Honda, R; Kitajima, M; Jackson, G Jiang, Guangming; Wu, Jiangping; Weidhaas, Jennifer; Li, Xuan; Chen, Yan; Mueller, Jochen; Li, Jiaying; Kumar, Manish; Zhou, Xu; Arora, Sudipti; Haramoto, Eiji; Sherchan, Samendra; Orive, Gorka; Lertxundi, Unax; Honda, Ryo; Kitajima, Masaaki; Jackson, Greg Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology WATER RESEARCH English Article COVID-19; Wastewater-based epidemiology; SARS-CoV-2; Artificial neural network; Prevalence; Incidence TOBACCO CONSUMPTION; SARS-COV-2; SPECIMENS; HUMIDITY; ALCOHOL; TIME As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was devel-oped to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA. [Jiang, Guangming; Wu, Jiangping; Li, Xuan; Chen, Yan] Univ Wollongong, Sch Civil Min & Environm Engn, Wollongong, NSW, Australia; [Jiang, Guangming] Univ Wollongong, Illawarra Hlth & Med Res Inst IHMRI, Wollongong, NSW, Australia; [Weidhaas, Jennifer] Univ Utah, Civil & Environm Engn, 110 Cent Campus Dr,Suite 2000, Salt Lake City, UT USA; [Mueller, Jochen; Li, Jiaying] Univ Queensland, Queensland Alliance Environm Hlth Sci, Brisbane, Qld, Australia; [Kumar, Manish] Univ Petr & Energy Studies, Sch Engn, Sustainabil Cluster, Dehra Dun 248007, Uttarakhand, India; [Zhou, Xu] Harbin Inst Technol Shenzhen, Shenzhen Engn Lab Microalgal Bioenergy, Shenzhen 518055, Peoples R China; [Zhou, Xu] Harbin Inst Technol, Sch Environm, State Key Lab Urban Water Resource & Environm, Harbin 150090, Peoples R China; [Arora, Sudipti] Dr B Lal Inst Biotechnol, Jaipur, Rajasthan, India; [Haramoto, Eiji] Univ Yamanashi, Interdisciplinary Ctr River Basin Environm, Kofu, Yamanashi, Japan; [Sherchan, Samendra] Tulane Univ, Dept Environm Hlth Sci, New Orleans, LA USA; [Orive, Gorka; Lertxundi, Unax] Univ Basque Country, UPV EHU, Sch Pharm, Lab Pharmaceut,NanoBioCel Grp, Paseo Univ 7, Vitoria 01006, Spain; [Orive, Gorka; Lertxundi, Unax] Biomed Res Networking Ctr Bioengn Biomat & Nanome, Vitoria, Spain; [Honda, Ryo] Kanazawa Univ, Fac Geosci & Civil Engn, Kanazawa, Ishikawa 9201192, Japan; [Kitajima, Masaaki] Hokkaido Univ, Div Environm Engn, Sapporo, Hokkaido 0608628, Japan; [Jackson, Greg] Univ Queensland, Queensland Alliance Environm Hlth Sci QAEHS, Brisbane, Qld 4102, Australia University of Wollongong; University of Wollongong; Utah System of Higher Education; University of Utah; University of Queensland; University of Petroleum & Energy Studies (UPES); Harbin Institute of Technology; Harbin Institute of Technology; University of Yamanashi; Tulane University; University of Basque Country; CIBER - Centro de Investigacion Biomedica en Red; CIBERBBN; Kanazawa University; Hokkaido University; University of Queensland Jiang, GM (corresponding author), Univ Wollongong, Sch Civil Min & Environm Engn, Wollongong, NSW, Australia. gjiang@uow.edu.au Jiang, Guangming/F-8611-2011; Haramoto, Eiji/D-2301-2013; Li, Xuan/AAP-5160-2021; Li, Jiaying/AAH-8378-2021 Jiang, Guangming/0000-0001-5399-8239; Haramoto, Eiji/0000-0002-0126-0651; Li, Xuan/0000-0003-1768-9556; Li, Jiaying/0000-0002-2132-3330 Australian Research Council Discovery project [DP190100385]; COVID-19 Digital Grant - Australian Academy of Science; COVID-19 Digital Grant - Department of Industry, Science, Energy and Resources through the Regional Collaborations Programme; University of Wollongong PhD scholarship; China Scholarship Council; ARC Laureate Fellowship [FL200100028]; Australian Research Council [FL200100028] Funding Source: Australian Research Council Australian Research Council Discovery project(Australian Research Council); COVID-19 Digital Grant - Australian Academy of Science; COVID-19 Digital Grant - Department of Industry, Science, Energy and Resources through the Regional Collaborations Programme; University of Wollongong PhD scholarship; China Scholarship Council(China Scholarship Council); ARC Laureate Fellowship(Australian Research Council); Australian Research Council(Australian Research Council) This research was supported by the Australian Research Council Discovery project (DP190100385) and a COVID-19 Digital Grant funded by Australian Academy of Science and the Department of Industry, Science, Energy and Resources through the Regional Collaborations Programme. J. W. receives the support from a University of Wollongong PhD scholarship. Y. C. receives the support from China Scholarship Council. J. M. is the recipient of an ARC Laureate Fellowship (FL200100028). 87 6 6 14 36 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0043-1354 1879-2448 WATER RES Water Res. JUN 30 2022.0 218.0 118451 10.1016/j.watres.2022.118451 0.0 APR 2022 12 Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Water Resources 1D9EV 35447417.0 2023-03-23 WOS:000794100600001 0
J Aubreville, M; Stathonikos, N; Bertram, CA; Klopfleisch, R; ter Hoeve, N; Ciompi, F; Wilm, F; Marzahl, C; Donovan, TA; Maier, A; Breen, J; Ravikumar, N; Chung, YJ; Park, J; Nateghi, R; Pourakpour, F; Fick, RHJ; Ben Hadj, S; Jahanifar, M; Shephard, A; Dexl, J; Wittenberg, T; Kondo, S; Lafarge, MW; Koelzer, VH; Liang, JT; Wang, YB; Long, X; Liu, JX; Razavi, S; Khademi, A; Yang, S; Wang, XY; Erber, R; Klang, A; Lipnik, K; Bolfa, P; Dark, MJ; Wasinger, G; Veta, M; Breininger, K Aubreville, Marc; Stathonikos, Nikolas; Bertram, Christof A.; Klopfleisch, Robert; ter Hoeve, Natalie; Ciompi, Francesco; Wilm, Frauke; Marzahl, Christian; Donovan, Taryn A.; Maier, Andreas; Breen, Jack; Ravikumar, Nishant; Chung, Youjin; Park, Jinah; Nateghi, Ramin; Pourakpour, Fattaneh; Fick, Rutger H. J.; Ben Hadj, Saima; Jahanifar, Mostafa; Shephard, Adam; Dexl, Jakob; Wittenberg, Thomas; Kondo, Satoshi; Lafarge, Maxime W.; Koelzer, Viktor H.; Liang, Jingtang; Wang, Yubo; Long, Xi; Liu, Jingxin; Razavi, Salar; Khademi, April; Yang, Sen; Wang, Xiyue; Erber, Ramona; Klang, Andrea; Lipnik, Karoline; Bolfa, Pompei; Dark, Michael J.; Wasinger, Gabriel; Veta, Mitko; Breininger, Katharina Mitosis domain generalization in histopathology images - The MIDOG MEDICAL IMAGE ANALYSIS English Article Domain generalization; Histopathology; Challenge; Deep Learning; Mitosis BREAST-CANCER; SYSTEM; PROLIFERATION; INDEX; GRADE The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an 1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task. [Aubreville, Marc] TH Ingolstadt, Ingolstadt, Germany; [Stathonikos, Nikolas; ter Hoeve, Natalie] UMC Utrecht, Pathol Dept, Utrecht, Netherlands; [Bertram, Christof A.; Klang, Andrea; Lipnik, Karoline] Univ Vet Med, Inst Pathol, Vienna, Austria; [Klopfleisch, Robert] Free Univ Berlin, Inst Vet Pathol, Berlin, Germany; [Ciompi, Francesco] Radboud UMC, Computat Pathol Grp, Nijmegen, Netherlands; [Wilm, Frauke; Marzahl, Christian; Maier, Andreas] Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, Erlangen, Germany; [Donovan, Taryn A.] Schwarzman Anim Med Ctr, Dept Anat Pathol, New York, NY USA; [Breen, Jack; Ravikumar, Nishant] Univ Leeds, CISTIB Ctr Computat Imaging & Simulat Technol Bio, Sch Comp, Leeds, W Yorkshire, England; [Chung, Youjin; Park, Jinah] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Nateghi, Ramin] Shiraz Univ Technol, Elect & Elect Engn Dept, Shiraz, Iran; [Pourakpour, Fattaneh] Iranian Brain Mapping Biobank IBMB, Natl Brain Mapping Lab NBML, Tehran, Iran; [Fick, Rutger H. J.; Ben Hadj, Saima] Tribun Hlth, Paris, France; [Jahanifar, Mostafa; Shephard, Adam] Univ Warwick, Tissue Image Analyt Ctr, Dept Comp Sci, Warwick, England; [Dexl, Jakob; Wittenberg, Thomas] Fraunhofer Inst Integrated Circuits IIS, Erlangen, Germany; [Kondo, Satoshi] Muroran Inst Technol, Muroran, Hokkaido, Japan; [Lafarge, Maxime W.; Koelzer, Viktor H.] Univ Hosp, Dept Pathol & Mol Pathol, Zurich, Switzerland; [Lafarge, Maxime W.; Koelzer, Viktor H.] Univ Zurich, Zurich, Switzerland; [Liang, Jingtang; Wang, Yubo] Xidian Univ, Sch Life Sci & Technol, Xian, Shannxi, Peoples R China; [Long, Xi] Histopathol Diagnost Ctr, Shanghai, Peoples R China; [Liu, Jingxin] Xian Jiaotong Liverpool Univ, Suzhou, Peoples R China; [Razavi, Salar; Khademi, April] Ryerson Univ, Image Anal Med Lab IAMLAB, Elect Comp & Biomed Engn, Toronto, ON, Canada; [Yang, Sen] Tencent AI Lab, Shenzhen 518057, Peoples R China; [Wang, Xiyue] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China; [Erber, Ramona] Friedrich Alexander Univ Erlangen Nurnberg, Inst Pathol, Univ Hosp Erlangen, Erlangen, Germany; [Bolfa, Pompei] Ross Univ, Sch Vet Med, Basseterre, St Kitts & Nevi; [Dark, Michael J.] Univ Florida, Coll Vet Med, Gainesville, FL USA; [Wasinger, Gabriel] Med Univ Vienna, Gen Hosp Vienna, Dept Pathol, Vienna, Austria; [Veta, Mitko] TU Eindhoven, Med Image Anal Grp, Eindhoven, Netherlands; [Wilm, Frauke; Breininger, Katharina] Friedrich Alexander Univ Erlangen Nurnberg, Dept Artificial Intelligence Biomed Engn, Erlangen, Germany Utrecht University; Utrecht University Medical Center; University of Veterinary Medicine Vienna; Free University of Berlin; Radboud University Nijmegen; University of Erlangen Nuremberg; University of Leeds; Korea Advanced Institute of Science & Technology (KAIST); Shiraz University of Technology; University of Warwick; Fraunhofer Gesellschaft; Muroran Institute of Technology; University of Zurich; University Zurich Hospital; University of Zurich; Xidian University; Xi'an Jiaotong-Liverpool University; Toronto Metropolitan University; Tencent; Sichuan University; University of Erlangen Nuremberg; State University System of Florida; University of Florida; Medical University of Vienna; Eindhoven University of Technology; University of Erlangen Nuremberg Aubreville, M (corresponding author), TH Ingolstadt, Ingolstadt, Germany. marc.aubreville@thi.de Dexl, Jakob/0000-0002-0617-0460; Aubreville, Marc/0000-0002-5294-5247 64 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. FEB 2023.0 84.0 102699 10.1016/j.media.2022.102699 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging 8R9ZC 36463832.0 Green Published, Green Submitted 2023-03-23 WOS:000928244200001 0
C Huang, J; Cao, M; Zhou, N; Yang, QK; Bai, XS Huang, Jie; Cao, Ming; Zhou, Ning; Yang, Qingkai; Bai, Xiaoshan Distributed Behavioral Control for Second-Order Nonlinear Multi-Agent Systems IFAC PAPERSONLINE English Proceedings Paper 20th World Congress of the International-Federation-of-Automatic-Control (IFAC) JUL 09-14, 2017 Toulouse, FRANCE Int Federat Automat Control,Continental Automot,Occitanie Reg,Toulouse Metropole,CNES,Univ Toulouse III,Paul Sabatier,Inria,CNRS,OPTITRACK,MDPI,ISAE Supaero,iCODE,EECI,Int Journal Automat & Comp,IEEE CAA Journal Automatica Sinica,Moveo Behavioral control; distributed control; estimator; multi-behavior; multi-agent system; neural networks COORDINATION CONTROL; CONSENSUS In this paper, we investigate a novel distributed behavioral control scheme for second-order nonlinear multi-agent systems (MAS) directed network topologies. Unlike most existing behavioral control algorithms which require global information of the underlying network, we develop a distributed adaptive behavioral control using a local adaptive strategy via distributed state estimator, null-space-based behavioral projection, and neural-network based approximation. Some simple behaviors for each agent are defined and properly arranged according to their priority to achieve the assigned overall behavior. In particular, tracking is performed in a distributed manner, in which the behaviors of each agent only depend on local information concerning the agent's neighbors. Finally, we present a simulation example to verify and illustrate the theoretical results. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [Huang, Jie; Cao, Ming; Yang, Qingkai; Bai, Xiaoshan] Univ Groningen, Fac Sci & Engn, Engn & Technol Inst Groningen ENTEG, NL-9742 AG Groningen, Netherlands; [Zhou, Ning] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Fujian, Peoples R China University of Groningen; Fujian Agriculture & Forestry University Huang, J (corresponding author), Univ Groningen, Fac Sci & Engn, Engn & Technol Inst Groningen ENTEG, NL-9742 AG Groningen, Netherlands. autohuangjie@gmail.com; in.cao@rug.nl; zhouning2010@gmail.com; gingkyang@gmail.com; xiaoshan.bai@rug.nl Cao, Ming/B-6808-2013; Zhou, Ning/AAG-9151-2020 Cao, Ming/0000-0001-5472-562X; Zhou, Ning/0000-0002-7457-5048 European Research Council [ERC-StG-307207]; Netherlands Organization for Scientific Research [NWO-vidi-14134]; National Natural Science Foundation of China [61603094, 61603095, 61573062]; Natural Science Foundation of Fujian Province [2016J01286, 2016J01284]; Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University [XJQ201612] European Research Council(European Research Council (ERC)European Commission); Netherlands Organization for Scientific Research(Netherlands Organization for Scientific Research (NWO)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University This work is supported in part by the European Research Council (ERC-StG-307207), the Netherlands Organization for Scientific Research (NWO-vidi-14134), the National Natural Science Foundation of China (61603094, 61603095, and 61573062), the Natural Science Foundation of Fujian Province (2016J01286, and 2016J01284), the Research Foundation for Outstanding Young Scholars in the University of Fujian Province, and the Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University (XJQ201612). 21 3 3 0 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2017.0 50.0 1 2445 2450 10.1016/j.ifacol.2017.08.407 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems FU4TJ gold, Green Published 2023-03-23 WOS:000423845200394 0
J El Haddad, K; Rizk, Y; Heron, L; Hajj, N; Zhao, Y; Kim, J; Trung, NT; Lee, M; Doumit, M; Lin, P; Kim, Y; Cakmak, H El Haddad, Kevin; Rizk, Yara; Heron, Louise; Hajj, Nadine; Zhao, Yong; Kim, Jaebok; Ngo Trong Trung; Lee, Minha; Doumit, Marwan; Lin, Payton; Kim, Yelin; Cakmak, Huseyin End-to-End Listening Agent for Audiovisual Emotional and Naturalistic Interactions JOURNAL OF SCIENCE AND TECHNOLOGY OF THE ARTS English Article Listening Agent; Smile; Laughter; Head Movement; Eyebrow Movement; Speech Emotion Recognition; Nonverbal Expression Detection; Sequence-to-Sequence Prediction Systems; Multimodal Synthesis; Nonverbal Expression Synthesis; Emotion Database; Dyadic Conversation Database In this work, we established the foundations of a framework with the goal to build an end-to-end naturalistic expressive listening agent. The project was split into modules for recognition of the user's paralinguistic and nonverbal expressions, prediction of the agent's reactions, synthesis of the agent's expressions and data recordings of nonverbal conversation expressions. First, a multimodal multitask deep learning-based emotion classification system was built along with a rule-based visual expression detection system. Then several sequence prediction systems for nonverbal expressions were implemented and compared. Also, an audiovisual concatenation-based synthesis system was implemented. Finally, a naturalistic, dyadic emotional conversation database was collected. We report here the work made for each of these modules and our planned future improvements. [El Haddad, Kevin] Univ Mons, TCTS Lab, Polytech Fac, Mons, Belgium; [Rizk, Yara; Hajj, Nadine] Amer Univ Beirut, Dept Elect & Comp Engn, Beirut, Lebanon; [Heron, Louise] Univ Bath, Dept Psychol, Bath, Avon, England; [Zhao, Yong] Vrije Univ Brussel, VUB NPU Joint AVSP Res lab, Brussels, Belgium; [Zhao, Yong] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China; [Kim, Jaebok] Univ Twente, Human Media Interact Grp, Enschede, Netherlands; [Ngo Trong Trung] Univ Eastern Finland, Fac Sci & Forestry, Sch Comp, Kuopio, Finland; [Lee, Minha] Tech Univ Eindhoven, Human Technol Interact Grp, Eindhoven, Netherlands; [Doumit, Marwan] NPR, Washington, DC USA; [Lin, Payton] Acad Sinica, CITI, Taipei, Taiwan; [Kim, Yelin] SUNY Albany, Dept Elect & Comp Engn, Albany, NY 12222 USA University of Mons; American University of Beirut; University of Bath; Vrije Universiteit Brussel; Northwestern Polytechnical University; University of Twente; University of Eastern Finland; Eindhoven University of Technology; Academia Sinica - Taiwan; State University of New York (SUNY) System; State University of New York (SUNY) Albany El Haddad, K (corresponding author), Univ Mons, TCTS Lab, Polytech Fac, Mons, Belgium. kevin.elhaddad@umons.ac.be; yar01@mail.aub.edu; L.Heron@bath.ac.uk; njh05@aub.edu.lb; yzhaol@etrovub.be; j.kim@utwente.nl; trung.ngotrong@helsinki.fi; m.lee@tue.nl; yelinkim@albany.edu; huseyin.cakmak@umons.ac.be Haddad, Kevin El/AAT-8061-2020; rizk, yara/K-4740-2019 Haddad, Kevin El/0000-0003-1465-6273; 13 0 0 0 1 UNIV CATOLICA PORTUGUESA, CENTRO INVESTIGACAO CIENC & TECNOL ARTES OPORTO ESCOLA ARTES, RUA DIOGO BOTELHO 1327, OPORTO, 4169-005, PORTUGAL 1646-9798 2183-0088 J SCI TECHNOL ARTS J. Sci. Technol. Arts 2018.0 10.0 2 SI 49 61 10.7559/citarj.v10i2.424 0.0 13 Humanities, Multidisciplinary Emerging Sources Citation Index (ESCI) Arts & Humanities - Other Topics HE8CK gold, Green Submitted 2023-03-23 WOS:000453670700005 0
J Xu, ZQ; Aghaabbasi, M; Ali, M; Macioszek, E Xu, Zhiqiang; Aghaabbasi, Mahdi; Ali, Mujahid; Macioszek, Elzbieta Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership SUSTAINABILITY English Article household vehicle ownership; support vector machine; bayesian optimization algorithm; sustainable transport development CAR OWNERSHIP; CLASSIFICATION; FRAMEWORK; CHOICES Predicting household vehicle ownership (HVO) is a crucial component of travel demand forecasting. Furthermore, reliable HVO prediction is critical for achieving sustainable transportation development objectives in an era of rapid urbanization. This research predicted the HVO using a support vector machine (SVM) model optimized using the Bayesian Optimization (BO) algorithm. BO is used to determine the optimal SVM parameter values. This hybrid model was applied to two datasets derived from the US National Household Travel Survey dataset. Thus, two optimized SVM models were developed, namely SVMBO#1 and SVMBO#2. Using the confusion matrix, accuracy, receiver operating characteristic (ROC), and area under the ROC, the outcomes of these two hybrid models were examined. Additionally, the results of hybrid SVM models were compared with those of other machine learning models. The results demonstrated that the BO algorithm enhanced the performance of the standard SVM model for predicting the HVO. The BO method determined the Gaussian kernel to be the optimal kernel function for both datasets. The performance of the SVM#1 model was improved by 4.27% and 5.16% for the training and testing phases, respectively. For SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, respectively. Moreover, the BO method enhanced the AUC of the SVM models used to predict the HVO. The hybrid SVM models also outperformed other machine learning models developed in this study. The findings of this study showed that SVM models hybridized with the BO algorithm can effectively predict the HVO and can be employed in the process of travel demand forecasting. [Xu, Zhiqiang] Guangdong Univ Sci & Technol, Sch Mech & Elect Engn, Dongguan 523083, Guangdong, Peoples R China; [Aghaabbasi, Mahdi] Chulalongkorn Univ, Transportat Inst, Bangkok 10330, Thailand; [Ali, Mujahid] Univ Teknol Petronas, Dept Civil & Environm Engn, Seri Iskandar 32610, Perak, Malaysia; [Macioszek, Elzbieta] Silesian Tech Univ, Fac Transport & Avit Engn, Dept Transport Syst Traff Engn & Logist, Krasinskiego 8 St, PL-40019 Katowice, Poland Guangdong University of Science & Technology; Chulalongkorn University; Universiti Teknologi Petronas; Silesian University of Technology Xu, ZQ (corresponding author), Guangdong Univ Sci & Technol, Sch Mech & Elect Engn, Dongguan 523083, Guangdong, Peoples R China.;Macioszek, E (corresponding author), Silesian Tech Univ, Fac Transport & Avit Engn, Dept Transport Syst Traff Engn & Logist, Krasinskiego 8 St, PL-40019 Katowice, Poland. xuzhiqiang10121983@163.com; elzbieta.macioszek@polsl.pl Macioszek, Elżbieta/AAH-5949-2020; Ali, Mujahid/AEP-6014-2022 Macioszek, Elżbieta/0000-0002-1345-0022; Ali, Mujahid/0000-0003-4376-0459 67 1 1 8 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability SEP 2022.0 14.0 17 11094 10.3390/su141711094 0.0 17 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 4K4HJ gold 2023-03-23 WOS:000851912700001 0
C Looga, V; Ou, ZH; Deng, Y; Antti-Yla-Jaaski IEEE Looga, Vilen; Ou, Zhonghong; Deng, Yang; Antti-Yla-Jaaski PowerShark: IEEE 802.15.4 Mote Activity Analysis Using Power Traces and Neural Networks 2016 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper 59th Annual IEEE Global Communications Conference (IEEE GLOBECOM) DEC 04-08, 2016 Washington, DC IEEE,Natl Instruments,AT & T,Huawei,Intel,Qualcomm,Nokia,Samsung,Keysight Technologies,Rohde & Schwarz Analysing the power usage of an Internet of Things (IoT) device is essential in contexts such as smart building, testbed and forensics. For example, in the smart building case, schemes have been proposed to detect and analyse the activity of electric appliances based on the total power usage. In this work, we intend to take this concept a step further and devise a method to analyse the activity of a single IoT device (mote) based on its power usage. To that end, we develop a neural network (NN) based classifier PowerShark that is capable of classifying mote activity based solely on the power usage of the mote, both in online and offline mode. PowerShark can detect radio activity, hardware, operating system (OS), and the MAC layer used. To verify the accuracy of PowerShark, we use power traces from OpenMote and Zolertia Z1 motes, with ContikiOS configured either with ContikiMAC or X-MAC. Experimental results demonstrate the accuracy of our proposed classifier. Specifically, PowerShark can achieve over 95% accuracy in both training and testing phases. [Looga, Vilen; Deng, Yang; Antti-Yla-Jaaski] Aalto Univ, Sch Sci, Dept Comp Sci, Espoo, Finland; [Ou, Zhonghong] BUPT, Sch Comp Sci, Beijing, Peoples R China Aalto University; Beijing University of Posts & Telecommunications Looga, V (corresponding author), Aalto Univ, Sch Sci, Dept Comp Sci, Espoo, Finland. vilen.looga@aalto.fi; zhonghong.ou@bupt.edu.cn; yang.deng@aalto.fi; antti-yla-jaaski@aalto.fi Yla-Jaaski, Antti/G-2484-2013 Yla-Jaaski, Antti/0000-0002-2069-1721 Academy of Finland [278207]; Academy of Finland (AKA) [278207] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) The authors would like to thank Dr. Mark van Heeswijk for his insights regarding neural network optimization. This work was supported by the Academy of Finland, grant number 278207. 20 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 978-1-5090-1328-9 IEEE GLOB COMM CONF 2016.0 7 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BH6NX 2023-03-23 WOS:000401963304031 0
J Su, H; Zou, R; Zhang, XL; Liang, ZY; Ye, R; Liu, Y Su, Han; Zou, Rui; Zhang, Xiaoling; Liang, Zhongyao; Ye, Rui; Liu, Yong Exploring the type and strength of nonlinearity in water quality responses to nutrient loading reduction in shallow eutrophic water bodies: Insights from a large number of numerical simulations JOURNAL OF ENVIRONMENTAL MANAGEMENT English Article Nonlinearity; Water quality; Load reduction; EFDC; Machine learning CHLOROPHYLL RELATIONSHIPS; ECOSYSTEMS; SHIFTS; MODEL; LAKES Reducing the load of nutrients is essential to improve water quality while water quality may not respond to the load reduction in a linear way. Despite nonlinear water quality responses being widely mentioned by studies, there is a lack of comprehensive assessment on the extent and type of nonlinear responses considering the seasonal changes. This study aimed to measure the strength of nonlinearity of theoretically possible water quality responses and explore their potential types in shallow eutrophic water bodies. Hereto, we generated 14,710 numerical water body cases that describe the water quality processes using the Environmental Fluid Dynamics Code (EFDC) and applied eight load reduction scenarios on each water body case. Inflows are simplified from Lake Dianchi. The climate conditions consider three cases: Lake Dianchi, Wissahickon Creek, and Famosa Slough. We then developed a nonlinearity strength indicator to quantify the strength and frequency of nonlinear water quality responses. Based on the quantification of nonlinearity, we clustered all the samples of water quality responses using K-Means, an unsupervised Machine Learning algorithm, to find the potential types of nonlinear water quality responses for TN (total nitrogen), TP (total phosphorus), and Chla (chlorophyll a). Results show linear or near-linear response types account for 90%, 69%, and 20% of TN, TP, and Chla samples respectively. TP and Chla could perform more types of nonlinearity. Representative nonlinear water quality responses include disproportional improvement, peak change (disappear, move forwards or afterward), and seasonal deterioration of TN after load reduction. This study would contribute to the current understanding of nonlinear water quality responses to load reduction and provide a basis to study under which conditions the nonlinear responses may emerge. [Su, Han] Univ Twente, Fac Engn Technol, Multidisciplinary Water Management Grp, NL-7500 AE Enschede, Netherlands; [Su, Han; Zou, Rui; Zhang, Xiaoling] Beijing Inteliway Environm Ltd, Rays Computat Intelligence Lab, Huayuandonglu Rd 30 Bldg 5-5306, Beijing 100085, Peoples R China; [Liang, Zhongyao] Penn State Univ, Dept Ecosyst Sci & Management, State Coll, PA 16803 USA; [Ye, Rui] Nanjing Smart Water Co Ltd, Nanjing 210012, Peoples R China; [Liu, Yong] Peking Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab All Mat Flux River, Beijing 100871, Peoples R China University of Twente; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Peking University Zou, R (corresponding author), Beijing Inteliway Environm Ltd, Rays Computat Intelligence Lab, Huayuandonglu Rd 30 Bldg 5-5306, Beijing 100085, Peoples R China.;Liu, Y (corresponding author), Peking Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab All Mat Flux River, Beijing 100871, Peoples R China. rz5q2008@gmail.com; yongliu@pku.edu.cn Liu, Yong/B-1826-2008 Liu, Yong/0000-0001-8181-1080; Su, Han/0000-0001-9018-6846 National Natural Science Foundation of China [51721006, 42142047]; Open Fund Project of Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed [2016PL03] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund Project of Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed Acknowledgments This work was supported by the National Natural Science Foundation of China (51721006, 42142047) , Open Fund Project of Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed (grant number 2016PL03) and High-performance Computing Platform of Peking University. 45 1 1 13 26 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0301-4797 1095-8630 J ENVIRON MANAGE J. Environ. Manage. JUL 1 2022.0 313.0 115000 10.1016/j.jenvman.2022.115000 0.0 APR 2022 9 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 1V9JM 35390659.0 Green Published 2023-03-23 WOS:000806400100004 0
J Tao, ZY; Wei, T; Li, J Tao, Zhiyong; Wei, Tong; Li, Jie Wavelet Multi-Level Attention Capsule Network for Texture Classification IEEE SIGNAL PROCESSING LETTERS English Article Feature extraction; Tensors; Convolution; Discrete wavelet transforms; Data mining; Computer architecture; Frequency-domain analysis; Texture classification; capsule network; attention; wavelet decomposition Texture classification is one of the essential problems in computer vision. Due to the powerful feature extraction ability, convolutional neural network (CNN) based texture classification methods have attracted extensive attention in recent years. However, there are still some challenges, such as the extraction of multi-level texture features and their relationships. To address these problems, this letter proposes the wavelet multi-level attention capsule network (WMACapsNet), which integrates multi-scale wavelet decomposition and multi-level attention blocks into the capsule network. Specifically, multi-scale spectral features in frequency domain are extracted by multi-level wavelet transform; and then the self-attention block explores the dependencies of capsule features within each scale; finally, the cross-attention block refines capsule features and their relationships with attention mechanism across different scales. The proposed WMACapsNet provides an efficient way to explore spatial domain features, frequency domain features and their dependencies, useful for most texture classification tasks. Experimental results on several texture datasets show that the proposed WMACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness. [Tao, Zhiyong; Wei, Tong] Liaoning Tech Univ, Sch Elect & Informat Engn, Huludao 125105, Peoples R China; [Wei, Tong] Eotvos Lorand Univ, Fac Informat, H-1117 Budapest, Hungary; [Li, Jie] Chinese Acad Sci, Haixi Inst, Quanzhou Inst Equipment Mfg, Quanzhou 362000, Peoples R China Liaoning Technical University; Eotvos Lorand University; Chinese Academy of Sciences Tao, ZY (corresponding author), Liaoning Tech Univ, Sch Elect & Informat Engn, Huludao 125105, Peoples R China. xyzmail@126.com; weitong@student.elte.hu; leej95@163.com Li, Jie/0000-0002-5161-143X National Key R&&D Program of China [2018YFB1403303] National Key R&&D Program of China This work was supported by the National Key R&&D Program of China under Grant 2018YFB1403303. 25 4 5 8 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2021.0 28.0 1215 1219 10.1109/LSP.2021.3088052 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering SX1NX 2023-03-23 WOS:000664979700013 0
J Qu, S; Guan, Z; Verschuur, E; Chen, YK Qu, Shan; Guan, Zhe; Verschuur, Eric; Chen, Yangkang Automatic high-resolution microseismic event detection via supervised machine learning GEOPHYSICAL JOURNAL INTERNATIONAL English Article Event detection; High-resolution; Machine learning; Microseismic; SVM; 2-D texture features SEISMIC DATA; PHASE; SUPPRESSION; DEEP; TIME; IDENTIFICATION; SPECTRUM; FEATURES; PICKING Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic. [Qu, Shan; Verschuur, Eric] Delft Univ Technol, Dept Imaging Phys, Mekelweg 2, NL-2628 CD Delft, Netherlands; [Guan, Zhe] Rice Univ, Appl Phys Program, Houston, TX USA; [Chen, Yangkang] Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China Delft University of Technology; Rice University; Zhejiang University Chen, YK (corresponding author), Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China. chenyk2016@gmail.com Guan, Zhe/ABG-3737-2020 Guan, Zhe/0000-0001-6554-4242 Delphi consortium; Thousand Youth Talents Plan; Starting Funds from Zhejiang University Delphi consortium; Thousand Youth Talents Plan; Starting Funds from Zhejiang University Shan Qu and Eric Verschuur thank the sponsors of the Delphi consortium for their support. Yangkang Chen is financially supported by the 'Thousand Youth Talents Plan', and the Starting Funds from Zhejiang University. The authors appreciate Wei Chen for providing the field data sets from a shale play inside the Sichuan Basin. The authors thank Scikit-learn for providing free machine learning library in Python (scikit-learn.org). 57 21 21 2 38 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0956-540X 1365-246X GEOPHYS J INT Geophys. J. Int. SEP 2020.0 222.0 3 1881 1895 10.1093/gji/ggaa193 0.0 15 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics NJ2RR 2023-03-23 WOS:000565895200025 0
C Brunetta, C; Tsaloli, G; Liang, B; Banegas, G; Mitrokotsa, A Baek, J; Ruj, S Brunetta, Carlo; Tsaloli, Georgia; Liang, Bei; Banegas, Gustavo; Mitrokotsa, Aikaterini Non-interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning INFORMATION SECURITY AND PRIVACY, ACISP 2021 Lecture Notes in Computer Science English Proceedings Paper 26th Australasian Conference on Information Security and Privacy (ACISP) DEC 01-03, 2021 ELECTR NETWORK Secure aggregation; Privacy; Verifiability; Decentralization We propose a novel primitive called NIVA that allows the distributed aggregation of multiple users' secret inputs by multiple untrusted servers. The returned aggregation result can be publicly verified in a non-interactive way, i.e. the users are not required to participate in the aggregation except for providing their secret inputs. NIVA allows the secure computation of the sum of a large amount of users' data and can be employed, for example, in the federated learning setting in order to aggregate the model updates for a deep neural network. We implement N IVA and evaluate its communication and execution performance and compare it with the current state-of-the-art, i.e. Segal et al. protocol (CCS 2017) and Xu et al. VerifyNet protocol (IEEE TIES 2020), resulting in better user's communicated data and execution time. [Brunetta, Carlo; Tsaloli, Georgia; Mitrokotsa, Aikaterini] Chalmers Univ Technol, Gothenburg, Sweden; [Liang, Bei] Beijing Inst Math Sci & Applicat, Beijing, Peoples R China; [Banegas, Gustavo] Inst Polytech Paris, Ecole Polytech, INRIA, Palaiseau, France; [Banegas, Gustavo] Inst Polytech Paris, Lab Informat, Ecole Polytech, Palaiseau, France; [Mitrokotsa, Aikaterini] Univ St Gallen, Sch Comp Sci, St Gallen, Switzerland Chalmers University of Technology; Inria; Institut Polytechnique de Paris; Institut Polytechnique de Paris; University of St Gallen Brunetta, C (corresponding author), Chalmers Univ Technol, Gothenburg, Sweden. brunetta@chalmers.se; tsaloni@chalmers.se; lbei@bimsa.cn; gustava@cryptme.in; katerina.mitrokotsa@unisg.ch Brunetta, Carlo/HHR-8647-2022 Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; WASP expedition project Massive, Secure, and Low-Latency Connectivity for IoT Applications Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; WASP expedition project Massive, Secure, and Low-Latency Connectivity for IoT Applications Part of this work was carried out while the third author was a post-doc at Chalmers University of Technology. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation; and WASP expedition project Massive, Secure, and Low-Latency Connectivity for IoT Applications. 35 0 0 3 5 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-90567-5; 978-3-030-90566-8 LECT NOTES COMPUT SC 2021.0 13083.0 510 528 10.1007/978-3-030-90567-5_26 0.0 19 Computer Science, Information Systems; Computer Science, Theory & Methods; Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BS7SS 2023-03-23 WOS:000766433400026 0
C Wang, CY; Jin, H; Qin, YJ; Zheng, LR IEEE Wang, Chenyu; Jin, Han; Qin, Yajie; Zheng, Li-rong A Wearable ECG Monitoring Device with Flexible Embedded Denoising and Compression 2016 46TH EUROPEAN SOLID-STATE DEVICE RESEARCH CONFERENCE (ESSDERC) Proceedings of the European Solid-State Device Research Conference English Proceedings Paper 46th European Solid-State Device Research Conference (ESSDERC) / 42nd European Solid-State Circuits Conference (ESSCIRC) SEP 12-15, 2016 Lausanne, SWITZERLAND FRONT-END A wearable electrocardiogram (ECG) monitoring device with a customized SoC is reported. The SoC amplifies the ECG signal from passive electrodes and then digitizes and transforms it into wavelet coefficients. A low-power microcontroller (MCU) and a radio frequency (RF) module in the device resolve and send the wavelet coefficients to a mobile platform. The mobile platform uses machine learning algorithms to improve the performance of signal denoising and data compression by exploiting the characteristics of the sensed data, and consequently reduces power consumption in the wearable device. Measurement results show that the device can resolve ECG data from MIT-BIH arrhythmia database and actual ECG signals from human testers. After processing the ECG data with various noise models, the proposed device can improve the signal to noise ratio (SNR) and mean square error (MSE) by 23.8dB and 88.9%, respectively. When resolving actual ECG signals from testers, the typical compression ratio (CR) is 4.8:1 with 1.56% percentage root mean square difference (PRD). The SoC is fabricated in TSMC 0.18 mu m technology, and consumes 45 mu w for different applications. [Wang, Chenyu; Jin, Han; Qin, Yajie; Zheng, Li-rong] Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Qin, Yajie] Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China; [Zheng, Li-rong] KTH, Sch ICT, Stockholm, Sweden Fudan University; Royal Institute of Technology Qin, YJ (corresponding author), Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.;Qin, YJ (corresponding author), Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China. yajieqin@fudan.edu.cn ZHENG, Lirong/ABF-9274-2020 ZHENG, Lirong/0000-0001-9588-0239 9 3 3 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1930-8876 978-1-5090-2969-3 PROC EUR S-STATE DEV 2016.0 79 82 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BG1CL 2023-03-23 WOS:000386655900020 0
C Wang, ZH; Jiang, YX; Zheng, FC; Bennis, M; You, XH IEEE Wang, Zhiheng; Jiang, Yanxiang; Zheng, Fu-Chun; Bennis, Mehdi; You, Xiaohu Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) MAY 16-20, 2022 Seoul, SOUTH KOREA IEEE,Samsung,LG,Huawei,Qualcomm,Technol Innovat Inst,ZTE,Elect & Telecommunicat Res Inst,KMW,SOLiD,ERICSSON LG,NI F-RANs; popularity prediction; clustered federated learning; user preference; mobility-aware In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies. [Wang, Zhiheng; Jiang, Yanxiang; Zheng, Fu-Chun; You, Xiaohu] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China; [Jiang, Yanxiang; Zheng, Fu-Chun] Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen 518055, Peoples R China; [Bennis, Mehdi] Univ Oulu, Ctr Wireless Commun, Oulu 90014, Finland Southeast University - China; Harbin Institute of Technology; University of Oulu Jiang, YX (corresponding author), Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China.;Jiang, YX (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen 518055, Peoples R China. 220200860@seu.edu.cn; yxjiang@seu.edu.cn; fzheng@ieee.org; mehdi.bennis@oulu.fi; xhyu@seu.edu.cn You, Xiaohu/ABH-3174-2021 National Key Research and Development Program [2021YFB2900300]; National Natural Science Foundation of China [61971129]; Shenzhen Science and Technology Program [KQTD20190929172545139] National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Science and Technology Program This work was supported in part by the National Key Research and Development Program under Grant 2021YFB2900300, the National Natural Science Foundation of China under grant 61971129, and the Shenzhen Science and Technology Program under Grant KQTD20190929172545139. 23 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-5386-8347-7 IEEE ICC 2022.0 68 73 10.1109/ICC45855.2022.9839007 0.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BT9VG Green Submitted 2023-03-23 WOS:000864709900012 0
J Zhang, W; Li, TJ; Schutte, C Zhang, Wei; Li, Tiejun; Schuette, Christof Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks JOURNAL OF COMPUTATIONAL PHYSICS English Article Eigenvalue PDE; Metastable process; Molecular dynamics; Artificial neural network; Variational characterization MARKOV STATE MODELS; VARIATIONAL APPROACH; FREE-ENERGIES; ERGODICITY; REDUCTION In this paper, we consider the eigenvalue PDE problem of the infinitesimal generators of metastable diffusion processes. We propose a numerical algorithm based on training artificial neural networks for solving the leading eigenvalues and eigenfunctions of such high-dimensional eigenvalue problem. The algorithm is able to find multiple leading eigenpairs by solving a single training task. It is useful in understanding the dynamical behaviors of metastable processes on large timescales. We demonstrate the capability of our algorithm on a high-dimensional model problem, and on the simple molecular system alanine dipeptide. (c) 2022 Elsevier Inc. All rights reserved. [Zhang, Wei; Schuette, Christof] Zuse Inst Berlin, Takustr 7, D-14195 Berlin, Germany; [Li, Tiejun] Peking Univ, Lab Math & Appl Math LMAM, Beijing 100871, Peoples R China; [Li, Tiejun] Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China; [Schuette, Christof] Free Univ Berlin, Inst Math, D-14195 Berlin, Germany Zuse Institute Berlin; Peking University; Peking University; Free University of Berlin Zhang, W (corresponding author), Zuse Inst Berlin, Takustr 7, D-14195 Berlin, Germany. wei.zhang@fu-berlin.de; tieli@pku.edu.cn; christof.schuette@fu-berlin.de NSFC [11421101, 11825102]; DFG under Germanys Excellence Strategy-MATH+: The Berlin Mathematics Research Centre [EXC-2046/1, 390685689] NSFC(National Natural Science Foundation of China (NSFC)); DFG under Germanys Excellence Strategy-MATH+: The Berlin Mathematics Research Centre W. Zhang thanks Tony Lelievre for fruitful discussions on the numerical treatment of rotational and translational invariance in the alanine dipeptide example. T. Li is supported by the NSFC under grant Nos. 11421101 and 11825102. The work of C. Schuette and W. Zhang is supported by the DFG under Germanys Excellence Strategy-MATH+: The Berlin Mathematics Research Centre (EXC-2046/1)-project ID: 390685689. 71 0 0 1 2 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0021-9991 1090-2716 J COMPUT PHYS J. Comput. Phys. SEP 15 2022.0 465.0 111377 10.1016/j.jcp.2022.111377 0.0 JUN 2022 19 Computer Science, Interdisciplinary Applications; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physics 2I1LX Green Submitted 2023-03-23 WOS:000814746100004 0
C Wang, YX; Plataniotis, KN; Kwong, S; Leung, H; Yanushkevich, S; Karray, F; Hou, M; Howard, N; Fiorini, RA; Soda, P; Tunstel, E; Wang, JM; Patel, S Soda, P; Fiorini, RA; Wang, Y; Jacobs, G; Howard, N; Widrow, B; Feldman, J Wang, Yingxu; Plataniotis, Konstantinos N.; Kwong, Sam; Leung, Henry; Yanushkevich, Svetlana; Karray, Fakhri; Hou, Ming; Howard, Newton; Fiorini, Rodolfo A.; Soda, Paolo; Tunstel, Edward; Wang, Jianmin; Patel, Shushma On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence PROCEEDINGS OF THE 2019 IEEE 18TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC 2019) English Proceedings Paper 18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC) JUL 23-25, 2019 Polytechn Univ Milan, Milan, ITALY IEEE,IEEE Comp Soc,IEEE SMC Soc,Int Inst Cognit Informat & Cognit Comp Polytechn Univ Milan Autonomous systems; intelligence science; system science; framework of intelligence; cognitive systems; adaptive systems; unmanned systems; human-in-the-loop; human-robot taskforce Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering. [Wang, Yingxu; Leung, Henry; Yanushkevich, Svetlana] Univ Calgary, Schulich Sch Engn & Hotchkiss Brain Inst, Int Inst Cognit Informat & Cognit Comp ICIC, Dept Elect & Comp Engn, Calgary, AB, Canada; [Plataniotis, Konstantinos N.] Univ Toronto, Dept Elect & Comp Engg, Toronto, ON, Canada; [Kwong, Sam] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Karray, Fakhri] Univ Waterloo, Dept Elect & Comp Engg, Waterloo, ON, Canada; [Hou, Ming] DRDC, Toronto Res Ctr, Toronto, ON, Canada; [Howard, Newton] Univ Oxford, Computat Neurosci Lab, Oxford, England; [Fiorini, Rodolfo A.] Politecn Milano Univ, Dept Elect Informat & Bioengg, Milan, Italy; [Soda, Paolo] Univ Campus Biomed Rome, Dept Engn, Rome, Italy; [Tunstel, Edward] Dept UTRC, Autonomous & Intelligent Syst, E Hartford, CT USA; [Tunstel, Edward] IEEE SMC, E Hartford, CT USA; [Wang, Jianmin] Tsinghua Univ, Sch Software, Beijing, Peoples R China; [Patel, Shushma] London South Bank Univ, Sch Engn, London, England University of Calgary; University of Toronto; City University of Hong Kong; University of Waterloo; Defence Research & Development Canada; University of Oxford; Polytechnic University of Milan; University Campus Bio-Medico - Rome Italy; Tsinghua University; London South Bank University Wang, YX (corresponding author), Univ Calgary, Schulich Sch Engn & Hotchkiss Brain Inst, Int Inst Cognit Informat & Cognit Comp ICIC, Dept Elect & Comp Engn, Calgary, AB, Canada. yingxu@ucalgary.ca; kostas@ece.utoronto.ca; cssamk@cityu.edu.hk; leungh@ucalgary.ca; syanshk@ucalgary.ca; karray@uwaterloo.ca; ming.hou@drdc-rddc.gc.ca; newton.howard@nds.ox.ac; rodolfo.fiorini@polimi.it; p.soda@unicampus.it; tunstel@ieee.org; jimwang@mail.tsinghua.edu.cn; shushma@lsbu.ac.uk wang, jian/GVS-0711-2022; Yanushkevich, Svetlana/AAS-1052-2021; Kwong, Sam/C-9319-2012; Soda, Paolo/K-8126-2016 Yanushkevich, Svetlana/0000-0003-4794-9849; Kwong, Sam/0000-0001-7484-7261; Soda, Paolo/0000-0003-2621-072X IDEaS project - DRDC; NSERC, Canada IDEaS project - DRDC; NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)) This work is supported in part by an IDEaS project sponsored by DRDC and NSERC, Canada The authors would like to thank the anonymous reviewers for their valuable suggestions and comments on this paper. 1 5 5 4 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-1419-4 2019.0 7 12 6 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR3BQ Green Submitted 2023-03-23 WOS:000644299000007 0
J Talanov, M; Vallverdu, J; Hu, B; Moore, P; Toschev, A; Shatunova, D; Maganova, A; Sedlenko, D; Leukhin, A Talanov, Max; Vallverdu, Jordi; Hu, Bin; Moore, Philip; Toschev, Alexander; Shatunova, Diana; Maganova, Anzhela; Sedlenko, Denis; Leukhin, Alexey Emotional simulations and depression diagnostics BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES English Article Dopamine; Serotonin; Fear; Artificial intelligence; Simulation; Rat brain; Affective computing; Emotion modelling; Neuromodulation INTERVENTION; SEROTONIN; MORTALITY; NEURONS; DISEASE; ANXIETY; SYSTEMS; PEOPLE In this work we propose the following hypothesis: the neuromodulatory mechanisms that control the emotional states of mammals can be translated and re-implemented in a computer by controlling the computational performance of a hosted computational system. In our specific implementation, we represent the simulation of the 'fear-like' state based on the three dimensional neuromodulatory model of affects, in this paper 'affects' refer to the basic emotional inborn states, inherited from works of Hugo Ldvheim. Whilst dopamine controls attention, serotonin is the key for inhibition, and fear is a elicitator for inhibitory and protective processes. This inhibition can promote [in a cognitive system] to blocking behaviour which can be labelled as 'depression'. Therefore, our interest is how to reimplement biomimetically both action-regulators without the computational system to resulting in a 'failed' scenario. We have simulated 1000 ms of the dopamine system using NEST Neural Simulation Tool with the rat brain as the model. The results of the simulation experiments are reported with an evaluation to demonstrate the correctness of our hypothesis. (C) 2016 Elsevier B.V. All rights reserved. [Vallverdu, Jordi] Univ Autonoma Barcelona, Catalonia, Spain; [Talanov, Max; Toschev, Alexander; Shatunova, Diana; Maganova, Anzhela; Sedlenko, Denis; Leukhin, Alexey] Kazan Fed Univ, Kazan, Respublika Tata, Russia; [Hu, Bin; Moore, Philip] Lanzhou Univ, Lanzhou 730000, Peoples R China Autonomous University of Barcelona; Kazan Federal University; Lanzhou University Talanov, M (corresponding author), Kazan Fed Univ, Kazan, Respublika Tata, Russia. max.talanov@gmail.com; jordi.vallverdu@uab.cat; bh@lzu.edu.cn; ptmbcu@gmail.com; atoschev@kp-fu.ru; dianashatunova@gmail.com; anzhelamagano-va@gmail.com; denis.sedlenko@gmail.com; alexey.panzer@gmail.com Leukhin, Alexey/U-2064-2019; Moore, Philip/F-3981-2019; Talanov, Max/C-6254-2015; Toschev, Alexander/C-9688-2014; Vallverdu, Jordi/K-5536-2014 Leukhin, Alexey/0000-0001-5853-8768; Moore, Philip/0000-0003-3874-8981; Talanov, Max/0000-0002-6727-0983; Toschev, Alexander/0000-0003-4424-6822; Vallverdu, Jordi/0000-0001-9975-7780 DGICYT: Innovacion en la prctica cientfica: enfoques cognitivos y sus consecuencias filosficas [FFI 2011-23238] DGICYT: Innovacion en la prctica cientfica: enfoques cognitivos y sus consecuencias filosficas Part of the work was performed according to the Russian Government Program of Competitive Growth of Kazan Federal University. Prof. Vallverdu's research is supported by DGICYT: Innovacion en la prctica cientfica: enfoques cognitivos y sus consecuencias filosficas, (FFI 2011-23238). 74 6 6 1 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2212-683X 2212-6848 BIOL INSPIR COGN ARC Biol. Inspired Cogn. Archit. OCT 2016.0 18.0 41 50 10.1016/j.bica.2016.09.002 0.0 10 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology EF7ML 2023-03-23 WOS:000390513600005 0
C Wang, CY; Jin, H; Qin, YJ; Zheng, LR IEEE Wang, Chenyu; Jin, Han; Qin, Yajie; Zheng, Li-rong A Wearable ECG Monitoring Device with Flexible Embedded Denoising and Compression ESSCIRC CONFERENCE 2016 Proceedings of the European Solid-State Circuits Conference English Proceedings Paper 46th European Solid-State Device Research Conference (ESSDERC) / 42nd European Solid-State Circuits Conference (ESSCIRC) SEP 12-15, 2016 Lausanne, SWITZERLAND FRONT-END A wearable electrocardiogram (ECG) monitoring device with a customized SoC is reported. The SoC amplifies the ECG signal from passive electrodes and then digitizes and transforms it into wavelet coefficients. A low-power microcontroller (MCU) and a radio frequency (RF) module in the device resolve and send the wavelet coefficients to a mobile platform. The mobile platform uses machine learning algorithms to improve the performance of signal denoising and data compression by exploiting the characteristics of the sensed data, and consequently reduces power consumption in the wearable device. Measurement results show that the device can resolve ECG data from MIT-BIH arrhythmia database and actual ECG signals from human testers. After processing the ECG data with various noise models, the proposed device can improve the signal to noise ratio (SNR) and mean square error (MSE) by 23.8dB and 88.9%, respectively. When resolving actual ECG signals from testers, the typical compression ratio (CR) is 4.8:1 with 1.56% percentage root mean square difference (PRD). The SoC is fabricated in TSMC 0.18 mu m technology, and consumes 45 mu w for different applications. [Wang, Chenyu; Jin, Han; Qin, Yajie; Zheng, Li-rong] Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Qin, Yajie] Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China; [Zheng, Li-rong] KTH, Sch ICT, Stockholm, Sweden Fudan University; Royal Institute of Technology Qin, YJ (corresponding author), Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China. yajieqin@fudan.edu.cn ZHENG, Lirong/ABF-9274-2020 ZHENG, Lirong/0000-0001-9588-0239 9 1 1 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1930-8833 978-1-5090-2972-3 PROC EUR SOLID-STATE 2016.0 87 90 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BG1CM 2023-03-23 WOS:000386656300020 0
C Lv, ML; Wang, Y; Baldi, S; Liu, ZC; Shi, C; Fu, CQ; Meng, XF; Qi, Y Liu, D; Xie, S; Li, Y; Zhao, D; ElAlfy, ESM Lv, Maolong; Wang, Ying; Baldi, Simone; Liu, Zongcheng; Shi, Chao; Fu, Chaoqi; Meng, Xiangfei; Qi, Yao Adaptive Neural Control for Pure Feedback Nonlinear Systems with Uncertain Actuator Nonlinearity NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI Lecture Notes in Computer Science English Proceedings Paper 24th International Conference on Neural Information Processing (ICONIP) NOV 14-18, 2017 Guangzhou, PEOPLES R CHINA Chinese Acad Sci, Inst Automat,Guangdong Univ Technol,S China Univ Technol,Springers Lecture Notes Comp Sci,IEEE CAA Journal Automatica Sinica,Asia Pacific Neural Network Soc Adaptive neural control; Robust control; Actuator input nonlinearity; Non-affine function For the pure feedback systems with uncertain actuator nonlinearity and non-differentiable non-affine function, a novel adaptive neural control scheme is proposed. Firstly, the assumption that the non-affine function must be differentiable everywhere with respect to control input has been canceled; in addition, the proposed approach can not only be applicable to actuator input dead zone nonlinearity, but also to backlash nonlinearity without changing the controller. Secondly, the neural network (NN) is used to approximate unknown nonlinear functions of system generated in the process of control design and a nonlinear robust term is introduced to eliminate the actuator nonlinearity modeling error, the NN approximation error and the external disturbances. Semi-globally uniformly ultimately boundedness of all signals in the closed loop system is analytically proved by utilizing Lyapunov theory. Finally, the effectiveness of the designed method is demonstrated via two examples. [Lv, Maolong; Wang, Ying; Fu, Chaoqi; Meng, Xiangfei; Qi, Yao] Air Force Engn Univ, Coll Equipment Management & Safety Engn, Xian 710051, Shaanxi, Peoples R China; [Baldi, Simone] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands; [Liu, Zongcheng; Shi, Chao] Air Force Engn Univ, Coll Aeronaut & Astronaut Engn, Xian 710038, Shaanxi, Peoples R China Air Force Engineering University; Delft University of Technology; Air Force Engineering University Lv, ML (corresponding author), Air Force Engn Univ, Coll Equipment Management & Safety Engn, Xian 710051, Shaanxi, Peoples R China. 18037707161@163.com Baldi, Simone/C-6796-2015 Baldi, Simone/0000-0001-9752-8925 10 5 5 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-70136-3; 978-3-319-70135-6 LECT NOTES COMPUT SC 2017.0 10639.0 VI 201 211 10.1007/978-3-319-70136-3_22 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ1LL 2023-03-23 WOS:000576768500022 0
J Li, YY; Do, TV; Nguyen, HT Li, Yang Yuan; Tien Van Do; Nguyen, Hai T. A comparison of forecasting models for the resource usage of MapReduce applications NEUROCOMPUTING English Article MapReduce application; Resource usage parameters; LSTM model; Multiple linear regression model TRAFFIC FLOW PREDICTION; NEURAL-NETWORKS; LSTM In this paper, we construct forecasting models (multivariate long short-term memory recurrent neural networks and multiple linear regression) for the resource usage prediction of four MapReduce applica-tions and applications executed within the Spark framework. We have evaluated the impact of a sample size to prediction accuracy. Also, we propose a phase modelling approach for read/write-intensive applications. Our results show that models based on long short-term memory recurrent neural networks exhibit a higher accuracy than multiple linear regression models and the intensive characteristics of a resource are closely related to the prediction accuracy of forecasting models. We investigated the hyper parameter tuning of such models and showed that a randomly initialised, shallow, well-tuned network may outperform deeper models that use stacked autoencoder initialisation. Furthermore, multivariate long short-term memory recurrent neural network models are more sensitive to sample size than multiple linear regression models. We show that an LSTM model trained in a specific machine may be used to predict the resource usage in another machine. (C) 2020 Elsevier B.V. All rights reserved. [Li, Yang Yuan] Xian SiYuan Univ, Xian, Peoples R China; [Tien Van Do] Baoji Univ Arts & Sci, Baoji, Peoples R China; [Tien Van Do] Budapest Univ Technol & Econ, Dept Networked Syst & Serv, Magyar Tudosok Korutja 2, H-1117 Budapest, Hungary; [Nguyen, Hai T.] Budapest Univ Technol & Econ, Balatonfured Student Res Grp, Budapest, Hungary Baoji University of Arts & Sciences; Budapest University of Technology & Economics; Budapest University of Technology & Economics Do, TV (corresponding author), Baoji Univ Arts & Sci, Baoji, Peoples R China. do@hit.bme.hu European Union; European Social Fund [EFOP-3.6.2-162017-00013]; Higher Education Excellence Program of the Ministry of Human Capacities of Budapest University of Technology (BME FIKP-MI/SC) European Union(European Commission); European Social Fund(European Social Fund (ESF)); Higher Education Excellence Program of the Ministry of Human Capacities of Budapest University of Technology (BME FIKP-MI/SC) The research of H. T. Nguyen was supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.2-162017-00013, Thematic Fundamental Research Collaborations Grounding Innovation in Informatics and Infocommunications).; The research was partially supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology (BME FIKP-MI/SC). 56 4 4 1 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing DEC 22 2020.0 418.0 36 55 10.1016/j.neucom.2020.07.059 0.0 20 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science OS1FZ hybrid 2023-03-23 WOS:000589911100004 0
C Tang, Z; Li, CY; Ge, JD; Shen, XY; Zhu, ZL; Luo, B IEEE Tang, Ze; Li, Chuanyi; Ge, Jidong; Shen, Xiaoyu; Zhu, Zheling; Luo, Bin AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization 2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021 English Proceedings Paper 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) NOV 14-20, 2021 ELECTR NETWORK IEEE,Assoc Comp Machinery,Monash Univ,Facebook,NASA Ames Res Ctr,Amazon Web Servi,Huawei,IEEE,IEEE Comp Soc,IEEE Tech Council Software Engn,ACM SIGSOFT,ACM SIGAI,Deakin Univ tree-based neural network; source code summarization Code summarization aims to generate brief natural language descriptions for source code. As source code is highly structured and follows strict programming language grammars, its Abstract Syntax Tree (AST) is often leveraged to inform the encoder about the structural information. However, ASTs are usually much longer than the source code. Current approaches ignore the size limit and simply feed the whole linearized AST into the encoder. To address this problem, we propose AST-Transformer to efficiently encode tree-structured ASTs. Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce 90 similar to 95% of the computational complexity in the encoding process. [Tang, Ze; Li, Chuanyi; Ge, Jidong; Zhu, Zheling; Luo, Bin] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China; [Shen, Xiaoyu] Saarland Informat Campus, Saarbrucken, Germany Nanjing University Tang, Z (corresponding author), Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China. 15 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-0337-5 2021.0 1193 1195 10.1109/ASE51524.2021.9678882 0.0 3 Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS9CZ Green Submitted 2023-03-23 WOS:000779309000124 0
J Li, ZH; Liu, L; Zhu, QX Li, Zhihong; Liu, Lei; Zhu, Quanxin Mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching based on vector Lyapunov functions NEURAL NETWORKS English Article Exponential input-to-state stability; Cohen Grossberg neural network; Halanay inequality; M-matrix; Time delay; Markovian switching TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; ROBUST STABILITY; GENERAL-CLASS; SYSTEMS This paper studies the mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of M-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results. (C) 2016 Elsevier Ltd. All rights reserved. [Li, Zhihong; Liu, Lei] Hohai Univ, Coll Sci, Nanjing 210098, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany Hohai University; Nanjing Normal University; Nanjing Normal University; University of Bielefeld Liu, L (corresponding author), Hohai Univ, Coll Sci, Nanjing 210098, Jiangsu, Peoples R China. 1575388989@qq.com; liulei_hust@hhu.edu.cn; zqx22@126.com Zhu, Quanxin/X-3230-2018 Zhu, Quanxin/0000-0003-3130-4923 National Science Foundation of China [61304070, 11271146, 61374080]; Alexander von Humboldt Foundation of Germany [CHN/1163390]; Natural Science Foundation of Jiangsu Province [BK20161552]; Qing Lan Project of Jiangsu Province and the Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities of China [2015B19814] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation of Germany(Alexander von Humboldt Foundation); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project of Jiangsu Province and the Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) The work was jointly supported by the National Science Foundation of China (Grant Nos. 61304070, 11271146, 61374080), the Alexander von Humboldt Foundation of Germany (Fellowship CHN/1163390), the Natural Science Foundation of Jiangsu Province (BK20161552), Qing Lan Project of Jiangsu Province and the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Fundamental Research Funds for the Central Universities of China. (Grant No. 2015B19814). 30 24 25 4 59 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. DEC 2016.0 84.0 39 46 10.1016/j.neunet.2016.08.001 0.0 8 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology ED0RF 27639722.0 Bronze 2023-03-23 WOS:000388548900004 0
J Kang, ZF; Nielsen, M; Yang, B; Ghazi, MM Kang, Zhongfeng; Nielsen, Mads; Yang, Bo; Ghazi, Mostafa Mehdipour Partial feedback online transfer learning with multi-source domains INFORMATION FUSION English Article Online learning; Transfer learning; Partial feedback; Multi-class classification; Multi-source domains information fusion KERNEL Online machine learning is an effective way for observation-based learning when a static dataset is not available. However, it can be challenging in real-world applications, especially when there are missing labels in multi-class classification tasks. Although partial feedback can be applied to tackle the problem, it can make the learning process slow and limit the classification performance as the correct label information is missing when the instance is misclassified. To cope with the lack of the target domain knowledge in online learning, transfer learning can be applied to convey knowledge from one or multiple source domains to the target domain. To this end, we propose a partial feedback online transfer learning algorithm with multiple source domains (PFMSD) to transfer the knowledge learned from multi-source domains to the target domain and enhance the learning performance by exploring the correct label when there is an erroneous prediction. A mistake bound is derived for the proposed algorithm, and extensive experiments are conducted using several wildly-used benchmark datasets. The obtained results in all experiments show the superiority of the proposed algorithm over the state-of-the-art partial feedback algorithms. [Kang, Zhongfeng; Yang, Bo] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China; [Kang, Zhongfeng; Nielsen, Mads; Ghazi, Mostafa Mehdipour] Univ Copenhagen, Dept Comp Sci, Univ Pk 1, DK-2100 Copenhagen, Denmark University of Electronic Science & Technology of China; University of Copenhagen Kang, ZF; Yang, B (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China. kangzhf@gmail.com; madsn@di.ku.dk; yangbo@uestc.edu.cn; ghazi@di.ku.dk Yang, Bo/0000-0003-0805-7928; Kang, Zhongfeng/0000-0001-9025-0748; Nielsen, Mads/0000-0003-1535-068X China Scholarship Council; DNRF [P1]; National Natural Science Foundation of China [61977013]; Pioneer Centre for AI China Scholarship Council(China Scholarship Council); DNRF; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Pioneer Centre for AI This work is supported by the China Scholarship Council, the Pioneer Centre for AI, DNRF grant number P1, and the National Natural Science Foundation of China (Project No. 61977013). We also thank Maanik Arora, Naresh Manwani, Alina Beygelzimer, and Francesco Orabonay for providing us with their source codes. 35 0 0 23 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion JAN 2023.0 89.0 29 40 10.1016/j.inffus.2022.07.025 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5J2UR Bronze 2023-03-23 WOS:000868900700003 0
C Deng, T; Cheng, AJ; Han, W; Lin, HX Rocha, AP; Steels, L; VanDenHerik, J Deng, Tuo; Cheng, Aijie; Han, Wei; Lin, Hai-Xiang Visibility Forecast for Airport Operations by LSTM Neural Network PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2 English Proceedings Paper 11th International Conference on Agents and Artificial Intelligence (ICAART) FEB 19-21, 2019 Prague, CZECH REPUBLIC Atmospheric Visibility; Time Series Forecast Visibility forecast is a meteorological problems which has direct impact to daily lives. For instance, timely prediction of low visibility situations is very important for the safe operation in airports and highways. In this paper, we investigate the use of Long Short-Term Memory(LSTM) model to predict visibility. By adjusting the loss function and network structure, we optimize the original LSTM model to make it more suitable for practical applications, which is superior to previous models in short-term low visibility prediction. In addition, there is a time delay problem when the number of hours time ahead we try to forecast becomes larger, this problem is persistent given the limited amount of available training data. We report our attempt of applying re-sampling to deal with the time delay problem, and we find that this method can improve the accuracy of visibility prediction, especially for the low visibility case. [Deng, Tuo; Cheng, Aijie] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China; [Han, Wei] China Meteorol Adm, Numer Weather Predict Ctr, Beijing 100081, Peoples R China; [Lin, Hai-Xiang] Delft Univ Technol, Delft Inst Appl Math, Mekelweg 4, NL-2628 CD Delft, Netherlands Shandong University; China Meteorological Administration; Delft University of Technology Deng, T (corresponding author), Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China. 23 6 6 2 4 SCITEPRESS SETUBAL AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL 978-989-758-350-6 2019.0 466 473 10.5220/0007308204660473 0.0 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR8FT hybrid 2023-03-23 WOS:000671841000045 0
J Du, MA; Yi, J; Mazidi, P; Cheng, L; Guo, JB Du, Mian; Yi, Jun; Mazidi, Peyman; Cheng, Lin; Guo, Jianbo A Parameter Selection Method for Wind Turbine Health Management through SCADA Data ENERGIES English Article wind turbine; failure detection; SCADA data; feature extraction; mutual information; copula FEATURE-EXTRACTION; MUTUAL INFORMATION Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine's condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copula-based mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough. [Du, Mian; Cheng, Lin] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China; [Du, Mian; Yi, Jun; Guo, Jianbo] China Elect Power Res Inst, Beijing 100192, Peoples R China; [Mazidi, Peyman] KTH Royal Inst Technol, Dept Elect Power & Energy Syst EPE, S-10044 Stockholm, Sweden Tsinghua University; Royal Institute of Technology Du, MA (corresponding author), Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China.;Du, MA (corresponding author), China Elect Power Res Inst, Beijing 100192, Peoples R China. dm13@mails.tsinghua.edu.cn; yjun@epri.sgcc.com.cn; mazidi@kth.se; chenglin@mail.tsinghua.edu.cn; guojb@epri.sgcc.com.cn Mazidi, Peyman/G-6763-2014 Mazidi, Peyman/0000-0002-0396-3326 36 11 12 3 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies FEB 2017.0 10.0 2 253 10.3390/en10020253 0.0 14 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels EM7CN Green Published, gold 2023-03-23 WOS:000395469200107 0
J Tao, ZY; Zhou, XR; Xu, ZX; Lin, S; Hu, YL; Wei, T Tao, Zhiyong; Zhou, Xinru; Xu, Zhixue; Lin, Sen; Hu, Yalei; Wei, Tong Finger-Vein Recognition Using Bidirectional Feature Extraction and Transfer Learning MATHEMATICAL PROBLEMS IN ENGINEERING English Article Accuracy and efficiency are essential topics in the current biometric feature recognition and security research. This paper proposes a deep neural network using bidirectional feature extraction and transfer learning to improve finger-vein recognition performance. Above all, we make a new finger-vein database with the opposite position information of the original one and adopt transfer learning to make the network suitable for our overall recognition framework. Next, the feature extractor is constructed by adjusting the unidirectional database's parameters, capturing vein features from top to bottom and vice versa. Correspondingly, we concatenate the above two features to form the finger-veins' bidirectional features, which are trained and classified by Support Vector Machines (SVM) to realize recognition. Experiments are conducted on the Malaysian Polytechnic University's published database (FV-USM) and finger veins of Signal and Information Processing Laboratory (FV-SIPL). The accuracy of our proposed algorithm reaches 99.67% and 99.31%, which is significantly higher than the unidirectional recognition under each database. Compared with the algorithms cited in this paper, our proposed model based on bidirectional feature enjoys higher accuracy, faster recognition speed than the state-of-the-art frameworks, and excellent practical value. [Tao, Zhiyong; Zhou, Xinru; Xu, Zhixue; Hu, Yalei] Liaoning Tech Univ, Sch Elect & Informat Engn, Huludao 125105, Peoples R China; [Lin, Sen] Shenyang Ligong Univ, Sch Automat & Elect Engn, Shenyang 110159, Liaoning, Peoples R China; [Wei, Tong] Eotvos Lorand Univ, Fac Informat, H-1117 Budapest, Hungary Liaoning Technical University; Shenyang Ligong University; Eotvos Lorand University xyzmail@126.com; zxrxxn@163.com; 1263715304@qq.com; linsen@sylu.edu.cn; mrhu165981@163.com; weitong@student.elte.hu National Key R&D Program of China - Ministry of Science and Technology of China [2018YFB1403303] National Key R&D Program of China - Ministry of Science and Technology of China This work was supported by the National Key R&D Program of China funded by the Ministry of Science and Technology of China (no. 2018YFB1403303). 39 3 3 0 19 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. MAY 3 2021.0 2021.0 6664809 10.1155/2021/6664809 0.0 11 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics SX3AS gold 2023-03-23 WOS:000665082000015 0
J Ganjam, GK; Bolte, K; Matschke, LA; Neitemeier, S; Dolga, AM; Hollerhage, M; Hoglinger, GU; Adamczyk, A; Decher, N; Oertel, WH; Culmsee, C Ganjam, Goutham K.; Bolte, Kathrin; Matschke, Lina A.; Neitemeier, Sandra; Dolga, Amalia M.; Hoellerhage, Matthias; Hoeglinger, Guenter U.; Adamczyk, Agata; Decher, Niels; Oertel, Wolfgang H.; Culmsee, Carsten Mitochondrial damage by alpha-synuclein causes cell death in human dopaminergic neurons CELL DEATH & DISEASE English Article PARKINSONS-DISEASE; IN-VITRO; OXIDATIVE STRESS; APOPTOTIC DEATH; COMPLEX-I; DYSFUNCTION; FERROPTOSIS; MECHANISMS; INHIBITION; PROTEIN Evolving concepts on Parkinson's disease (PD) pathology suggest that alpha-synuclein (aSYN) promote dopaminergic neuron dysfunction and death through accumulating in the mitochondria. However, the consequence of mitochondrial aSYN localisation on mitochondrial structure and bioenergetic functions in neuronal cells are poorly understood. Therefore, we investigated deleterious effects of mitochondria-targeted aSYN in differentiated human dopaminergic neurons in comparison with wild-type (WT) aSYN overexpression and corresponding EGFP (enhanced green fluorescent protein)-expressing controls. Mitochondria-targeted aSYN enhanced mitochondrial reactive oxygen species (ROS) formation, reduced ATP levels and showed severely disrupted structure and function of the dendritic neural network, preceding neuronal death. Transmission electron microscopy illustrated distorted cristae and many fragmented mitochondria in response to WT-aSYN overexpression, and a complete loss of cristae structure and massively swollen mitochondria in neurons expressing mitochondria-targeted aSYN. Further, the analysis of mitochondrial bioenergetics in differentiated dopaminergic neurons, expressing WT or mitochondria-targeted aSYN, elicited a pronounced impairment of mitochondrial respiration. In a pharmacological compound screening, we found that the pan-caspase inhibitors QVD and zVAD-FMK, and a specific caspase-1 inhibitor significantly prevented aSYN-induced cell death. In addition, the caspase inhibitor QVD preserved mitochondrial function and neuronal network activity in the human dopaminergic neurons overexpressing aSYN. Overall, our findings indicated therapeutic effects by caspase-1 inhibition despite aSYN-mediated alterations in mitochondrial morphology and function. [Ganjam, Goutham K.; Neitemeier, Sandra; Dolga, Amalia M.; Culmsee, Carsten] Univ Marburg, Biochem Pharmacol Ctr, Inst Pharmacol & Clin Pharm, Marburg, Germany; [Ganjam, Goutham K.; Oertel, Wolfgang H.] Univ Marburg, Dept Neurol, Marburg, Germany; [Ganjam, Goutham K.; Oertel, Wolfgang H.; Culmsee, Carsten] CMBB, Marburg, Germany; [Bolte, Kathrin] Univ Marburg, Dept Biol, Lab Cell Biol 1, Marburg, Germany; [Matschke, Lina A.; Decher, Niels] Univ Marburg, Inst Physiol & Pathophysiol, Marburg, Germany; [Dolga, Amalia M.] Univ Groningen, Groningen Res Inst Pharm, Dept Mol Pharmacol, Groningen, Netherlands; [Hoellerhage, Matthias; Hoeglinger, Guenter U.] German Ctr Neurodegenerat Dis DZNE, Munich, Germany; [Adamczyk, Agata] Polish Acad Sci, Mossakowski Med Res Ctr, Dept Cellular Signaling, Warsaw, Poland; [Culmsee, Carsten] Zhengzhou Univ, Affiliated Hosp 3, Dept Pediat, Zhengzhou, Henan, Peoples R China Philipps University Marburg; Philipps University Marburg; Philipps University Marburg; Philipps University Marburg; University of Groningen; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE); Polish Academy of Sciences; Mossakowski Medical Research Institute of the Polish Academy of Sciences; Zhengzhou University Ganjam, GK (corresponding author), Univ Marburg, Biochem Pharmacol Ctr, Inst Pharmacol & Clin Pharm, Marburg, Germany.;Ganjam, GK (corresponding author), Univ Marburg, Dept Neurol, Marburg, Germany.;Ganjam, GK (corresponding author), CMBB, Marburg, Germany. ganjam@staff.uni-marburg.de Culmsee, Carsten/ABC-3120-2021; Decher, Niels/AAF-5002-2020; Höglinger, Günter U/F-2388-2015 Culmsee, Carsten/0000-0002-5121-5015; Höglinger, Günter U/0000-0001-7587-6187; Dolga, Amalia/0000-0001-5400-5614 ParkinsonFonds Deutschland (PFD); Charitable Hertie Foundation, Germany; Forschungscampus Mittelhessen Flexifunds; TransMIT Project Division for Mitochondrial Mechanisms of Neuronal Processes; Deutsche Forschungsgemeinschaft DFG [CU43/12-1] ParkinsonFonds Deutschland (PFD); Charitable Hertie Foundation, Germany; Forschungscampus Mittelhessen Flexifunds; TransMIT Project Division for Mitochondrial Mechanisms of Neuronal Processes; Deutsche Forschungsgemeinschaft DFG(German Research Foundation (DFG)) We thank Katharina Elsasser, Eliana Gaglio and Marion Debus for technical support and Emma Esser for editing of the paper. We also thank Prof. Uwe-G. Maier (Philipps University Marburg, Germany) for providing the electron microscopy facility. G.K.G. received a grant from the ParkinsonFonds Deutschland (PFD) and we thank Rene Kruijff of PFD. WHO is a Hertie-Senior-Research Professor supported by the Charitable Hertie Foundation, Germany. Work by C.C. is funded by the Forschungscampus Mittelhessen Flexifunds, the TransMIT Project Division for Mitochondrial Mechanisms of Neuronal Processes and the Deutsche Forschungsgemeinschaft DFG (CU43/12-1). 64 77 77 3 23 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2041-4889 CELL DEATH DIS Cell Death Dis. NOV 14 2019.0 10.0 865 10.1038/s41419-019-2091-2 0.0 16 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology JP0OS 31727879.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000497972100002 0
J Li, XS; Xie, Y; Hu, DP; Lan, ZG Li, Xusong; Xie, Yu; Hu, Deping; Lan, Zhenggang Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping JOURNAL OF CHEMICAL THEORY AND COMPUTATION English Article FREE-ENERGY LANDSCAPES; RELATE 2 SETS; MOLECULAR-DYNAMICS; IMPLEMENTATION; PHOTOISOMERIZATION; REPRESENTATION; PHOTODYNAMICS; PHYTOCHROME; DEFINITION; ROTATION On-the-fly trajectory-based nonadiabatic dynamics simulation has become an important approach to study ultrafast photochemical and photophysical processes in recent years. Because a large number of trajectories are generated from the dynamics simulation of polyatomic molecular systems with many degrees of freedom, the analysis of simulation results often suffers from the large amount of high-dimensional data. It is very challenging but meaningful to find dominating active coordinates from very complicated molecular motions. Dimensionality reduction techniques provide ideal tools to realize this purpose. We apply two dimensionality reduction approaches (classical multidimensional scaling and isometric feature mapping) to analyze the results of the on-the-fly surface-hopping nonadiabatic dynamics simulation. Two representative model systems, CH2NH2+ and the phytochromobilin chromophore model, are chosen to examine the performance of these dimensionality reduction approaches. The results show that these approaches are very promising, because they can extract the major molecular motion from complicated time-dependent molecular evolution without preknown knowledge. [Li, Xusong; Xie, Yu; Hu, Deping; Lan, Zhenggang] Chinese Acad Sci, CAS Key Lab Biobased Mat, Qingdao Inst Bioenergy & Bioproc Technol, Qingdao 266101, Peoples R China; [Li, Xusong; Lan, Zhenggang] Univ Chinese Acad Sci, Sinodanish Ctr Educ & Res, Sinodanish Coll, Beijing 100049, Peoples R China; [Li, Xusong; Hu, Deping; Lan, Zhenggang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Li, Xusong] Tech Univ Denmark, Dept Chem, Kemitorvet 207, DK-2800 Lyngby, Denmark Chinese Academy of Sciences; Qingdao Institute of Bioenergy & Bioprocess Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Technical University of Denmark Lan, ZG (corresponding author), Chinese Acad Sci, CAS Key Lab Biobased Mat, Qingdao Inst Bioenergy & Bioproc Technol, Qingdao 266101, Peoples R China.;Lan, ZG (corresponding author), Univ Chinese Acad Sci, Sinodanish Ctr Educ & Res, Sinodanish Coll, Beijing 100049, Peoples R China.;Lan, ZG (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. lanzg@qibebt.ac.cn Hu, Deping/D-8068-2015; Lan, Zhenggang/H-3676-2012; Hu, Deping/GSE-3893-2022 Hu, Deping/0000-0001-7161-1253; Lan, Zhenggang/0000-0002-8509-0388; Hu, Deping/0000-0001-7161-1253; xie, yu/0000-0001-8925-6958 NSFC [21673266, 21503248]; Natural Science Foundation of Shandong Province [JQ201504] NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work is supported by NSFC Project (Nos. 21673266 and 21503248) and the Natural Science Foundation of Shandong Province for Distinguished Young Scholars (JQ201504). 106 36 35 0 33 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1549-9618 1549-9626 J CHEM THEORY COMPUT J. Chem. Theory Comput. OCT 2017.0 13.0 10 4611 4623 10.1021/acs.jctc.7b00394 0.0 13 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics FJ7UR 28862858.0 2023-03-23 WOS:000412965700002 0
J Wang, J; Gao, XZ; Guo, P Wang, Jing; Gao, X. Z.; Guo, Ping Feature extraction based on sparse representation with application to epileptic EEG classification INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY English Article electroencephalogram signals; epilepsy seizures; seizure detection; overcomplete dictionary; sparse representation; Bayesian decision rule NEURAL-NETWORK; AUTOMATIC DETECTION; RECOGNITION Epilepsy seizure detection in electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy, and it can be considered as a classification problem. Considering the particular property of EEG, which is sparse in Garbor dictionary, a feature extraction method based on sparse representation has been applied to epilepsy detection. To improve classification accuracy, in this article, a novel feature vector is developed, which not only can reflect the main structure, but also can give expression to the relation between main structure and residual information. Classification accuracy, efficiency, and robustness to noise of the new feature are explored and analyzed with publicly available data set. It is demonstrated by experiments that the classification accuracy and the efficiency are simultaneously enhanced with this new feature extraction method, and that the novel classification feature proposed in this work greatly improves the classification performance of epilepsy detection. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 104113, 2013 [Wang, Jing; Guo, Ping] Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China; [Gao, X. Z.] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, Aalto, Finland; [Wang, Jing] Peking Univ, Hlth Sci Ctr, Sch Fdn Educ, Beijing 100871, Peoples R China Beijing Normal University; Aalto University; Peking University Guo, P (corresponding author), Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China. pguo@ieee.org guo, peng/AAG-4052-2019; GUO, Ping/A-3482-2015; GUO, Ping/AAG-2160-2019; Guo, Peng/GWC-0572-2022; Gao, Xiao-Zhi/Q-3038-2016 GUO, Ping/0000-0002-7122-1084; GUO, Ping/0000-0002-7122-1084; Gao, Xiao-Zhi/0000-0002-0078-5675 National Natural Science Foundation of China [60911130513] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to express their sincere thanks to Ms. X.L. Zhou for her many useful suggestions. The research work described in this paper was partly supported by the grants from the National Natural Science Foundation of China (Project No. 60911130513). 23 5 5 0 43 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0899-9457 1098-1098 INT J IMAG SYST TECH Int. J. Imaging Syst. Technol. JUN 2013.0 23.0 2 104 113 10.1002/ima.22045 0.0 10 Engineering, Electrical & Electronic; Optics; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Optics; Imaging Science & Photographic Technology 148ZM 2023-03-23 WOS:000319287800002 0
J Liu, N; Liu, J Liu, Nian; Liu, Jun Leading with hearts and minds: emotion contagion in China's online activism SOCIAL MOVEMENT STUDIES English Article; Early Access Emotion contagion; activism; social network analysis; anger; Weibo; China SOCIAL MEDIA; COLLECTIVE ACTION; OPINION LEADERS; PROTEST; DIFFUSION; NETWORKS; INFLUENTIALS; TWITTER; COMMUNICATION; MOVEMENTS Who are the prominent actors leading the diffusion of emotional messages in China's online activism? What roles do they play in this process in an emotion-discouraging context? In this exploratory study, we examine networked patterns of anger diffusion within the Red-Yellow-Blue kindergarten child abuse scandal on the Chinese social media Weibo. Using supervised machine learning for emotion labeling and a social network analysis approach, we identified three types of actors and profiled their distinctive roles in the process of anger contagion. Broadcasters (e.g., verified organization accounts) act as both an information source and a legitimate source to elicit other users' emotion through emotion-free information. Furthermore, emotion initiators like celebrities instigate and lead other users' emotions, while emotion brokers like micro-celebrities build bridges between different subgroups to form a massive-scale network of emotion contagion. These actors are indispensable and complement each other for emotion contagion in China. We conclude by discussing the implications of our findings on the understanding of emotion diffusion in online activism. [Liu, Nian] Capital Univ Econ & Business, Sch Culture & Commun, Dept Advertising, Beijing, Peoples R China; [Liu, Jun] Univ Copenhagen, Dept Commun, Karen Blixens Plads 8, DK-2300 Copenhagen, Denmark Capital University of Economics & Business; University of Copenhagen Liu, J (corresponding author), Univ Copenhagen, Dept Commun, Karen Blixens Plads 8, DK-2300 Copenhagen, Denmark. liujun@hum.ku.dk Capital University of Economics and Business' special fundamental research fund for Beijing municipal colleges and universities [XRZ2021001]; Independent Research Fund Denmark -Sapere Aude Starting Grant To Use or Not to Use? A Relational Approach to Information and Communication Technologies as Repertoire of Contention Capital University of Economics and Business' special fundamental research fund for Beijing municipal colleges and universities; Independent Research Fund Denmark -Sapere Aude Starting Grant To Use or Not to Use? A Relational Approach to Information and Communication Technologies as Repertoire of Contention This work was supported by the Capital University of Economics and Business' special fundamental research fund for Beijing municipal colleges and universities under Grant XRZ2021001; The Independent Research Fund Denmark -Sapere Aude Starting Grant To Use or Not to Use? A Relational Approach to Information and Communication Technologies as Repertoire of Contention 100 1 1 12 32 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1474-2837 1474-2829 SOC MOVEMENT STUD Soc. Mov. Stud. 10.1080/14742837.2021.2011716 0.0 DEC 2021 22 Political Science; Sociology Social Science Citation Index (SSCI) Government & Law; Sociology XL8ZJ 2023-03-23 WOS:000728429300001 0
C Zhou, N; Cheng, XD; Xia, YQ; Huang, J; Wang, QP Fu, M; Sun, J IEEE Zhou Ning; Cheng Xiaodong; Xia Yuanqing; Huang Jie; Wang Qiping Quaternion-Based Fault-Tolerant Control Design for Spacecraft Attitude Stabilization: An Anti-Saturation Method PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) Chinese Control Conference English Proceedings Paper 38th Chinese Control Conference (CCC) JUL 27-30, 2019 Guangzhou, PEOPLES R CHINA Chinese Assoc Automat, Tech Comm Control Theory,Syst Engn Soc China,Guangdong Univ Technol,Chinese Acad Sci, Acad Math & Syst Sci,China Soc Ind & Appl Math,S China Univ Technol,Automat Soc Guangdong Prov,Asian Control Assoc,IEEE Control Syst Soc,Inst Control, Robot & Syst,Soc Instrument & Control Engineers Fault-Tolerant Control; Attitude Stabilization; Input Saturation; Spacecraft System TRACKING CONTROL; TIME This paper investigates the problem of spacecraft attitude stabilization using an anti-saturation strategy. Taking into account the actuator faults or failures, input saturation, modeling uncertainties and external disturbances, we propose a novel adaptive neural network fault-tolerant scheme, in which a terminal sliding mode is embedded in a fault-tolerant controller (FTC) that is implemented based on radial basis function neural networks (RBFNNs). The proposed approach not only shows the robustness and adaptivity with respect to unknown mass properties and external disturbances but also is capable of accommodating actuator faults or failures. Moreover, as the designed adaptive parameters are scalars, it only requires light computational load and can avoid redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed method is illustrated via a numerical example. [Zhou Ning; Wang Qiping] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China; [Zhou Ning] Univ Groningen, Fac Sci & Engn, NL-9747 AG Groningen, Netherlands; [Cheng Xiaodong] Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands; [Xia Yuanqing] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China; [Huang Jie] Fuzhou Univ, Coll Elect Engn & Automat, Fuzhou 350108, Peoples R China Fujian Agriculture & Forestry University; University of Groningen; Eindhoven University of Technology; Beijing Institute of Technology; Fuzhou University Zhou, N (corresponding author), Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China.;Zhou, N (corresponding author), Univ Groningen, Fac Sci & Engn, NL-9747 AG Groningen, Netherlands. zhouning2010@gmail.com; x.cheng@tue.nl; xia_yuanqing@bit.edu.cn; jie.huang@fzu.edu.cn; wangqipings@gmail.com Cheng, Xiaoodong/AAU-9153-2020 Cheng, Xiaoodong/0000-0003-0702-8201 National Natural Science Foundation of China [61603095, 61603094]; China Scholarship Council [201707870005]; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University [xjq201612]; Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Foundation of Cloud Computing and Big Data for Agriculture and Forestry [117-612014063] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University; Research Foundation for Outstanding Young Scholars in the University of Fujian Province; Foundation of Cloud Computing and Big Data for Agriculture and Forestry The work was supported in part by the the National Natural Science Foundation of China (No.61603095, 61603094), the China Scholarship Council (No.201707870005), the Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University (No.xjq201612), the Research Foundation for Outstanding Young Scholars in the University of Fujian Province and the Foundation of Cloud Computing and Big Data for Agriculture and Forestry (No.117-612014063). 19 2 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-2927 978-9-8815-6397-2 CHIN CONTR CONF 2019.0 2558 2563 6 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science; Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering; Operations Research & Management Science; Mathematics BQ8TM 2023-03-23 WOS:000621599302091 0
C Feng, C; Cai, F; Chen, HH; de Rijke, M Cuzzocrea, A; Allan, J; Paton, N; Srivastava, D; Agrawal, R; Broder, A; Zaki, M; Candan, S; Labrinidis, A; Schuster, A; Wang, H Feng, Chong; Cai, Fei; Chen, Honghui; de Rijke, Maarten Attentive Encoder-based Extractive Text Summarization CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT English Proceedings Paper 27th ACM International Conference on Information and Knowledge Management (CIKM) OCT 22-26, 2018 Torino, ITALY Assoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Informat Retrieval,Assoc Comp Machinery SIGWEB,Univ Trieste Summarization; Attention mechanism; Encoder-decoder In previous work on text summarization, encoder-decoder architectures and attention mechanisms have both been widely used. Attention-based encoder-decoder approaches typically focus on taking the sentences preceding a given sentence in a document into account for document representation, failing to capture the relationships between a sentence and sentences that follow it in a document in the encoder. We propose an attentive encoder-based summarization (AES) model to generate article summaries. AES can generate a rich document representation by considering both the global information of a document and the relationships of sentences in the document. A unidirectional recurrent neural network (RNN) and a bidirectional RNN are considered to construct the encoders, giving rise to unidirectional attentive encoder-based summarization (Uni-AES) and bidirectional attentive encoder-based summarization (Bi-AES), respectively. Our experimental results show that Bi-AES outperforms Uni-AES. We obtain substantial improvements over a relevant start-of-the-art baseline. [Feng, Chong; Cai, Fei; Chen, Honghui] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China; [de Rijke, Maarten] Univ Amsterdam, Inst Informat, Amsterdam, Netherlands National University of Defense Technology - China; University of Amsterdam Cai, F (corresponding author), Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China. fengchong16@nudt.edu.cn; caifei@nudt.edu.cn; chenhonghui@nudt.edu.cn; derijke@uva.nl de Rijke, Maarten/0000-0002-1086-0202 National Natural Science Foundation of China [61702526]; Defense Industrial Technology Development Program [JCKY2017204B064]; National Advanced Research Project [6141B0801010b] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Defense Industrial Technology Development Program; National Advanced Research Project This research was supported by the National Natural Science Foundation of China under No. 61702526, the Defense Industrial Technology Development Program under No. JCKY2017204B064, and the National Advanced Research Project under No. 6141B0801010b. 10 5 5 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6014-2 2018.0 1499 1502 10.1145/3269206.3269251 0.0 4 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL7WP 2023-03-23 WOS:000455712300155 0
J Yin, S; Liu, C; Zhang, ZY; Lin, YY; Wang, D; Tejedor, J; Zheng, TF; Li, YG Yin, Shi; Liu, Chao; Zhang, Zhiyong; Lin, Yiye; Wang, Dong; Tejedor, Javier; Zheng, Thomas Fang; Li, Yinguo Noisy training for deep neural networks in speech recognition EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING English Article Speech recognition; Deep neural network; Noise injection INJECTION; INPUTS Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting moderate noises into the training data intentionally and randomly, more generalizable DNN models can be learned. This 'noise injection' technique, although known to the neural computation community already, has not been studied with DNNs which involve a highly complex objective function. The experiments presented in this paper confirm that the noisy training approach works well for the DNN model and can provide substantial performance improvement for DNN-based speech recognition. [Yin, Shi; Liu, Chao; Zhang, Zhiyong; Lin, Yiye; Wang, Dong; Zheng, Thomas Fang] Tsinghua Univ, Res Inst Informat Technol, Ctr Speech & Language Technol, Beijing 100084, Peoples R China; [Zhang, Zhiyong; Wang, Dong; Zheng, Thomas Fang] Tsinghua Natl Lab Informat Sci & Technol, Ctr Speech & Language Technol, Div Tech Innovat & Dev, Beijing 100084, Peoples R China; [Liu, Chao] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Yin, Shi; Li, Yinguo] Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China; [Lin, Yiye] Beijing Inst Technol, Beijing 100081, Peoples R China; [Tejedor, Javier] Univ Alcala, GEINTRA, Madrid, Spain Tsinghua University; Tsinghua University; Tsinghua University; Chongqing University of Posts & Telecommunications; Beijing Institute of Technology; Universidad de Alcala Wang, D (corresponding author), Tsinghua Univ, Res Inst Informat Technol, Ctr Speech & Language Technol, ROOM 1-303,BLDG FIT, Beijing 100084, Peoples R China. wangdong99@mails.tsinghua.edu.cn Tejedor, Javier/B-5203-2012; Tejedor, Javier/L-2075-2015 Tejedor, Javier/0000-0002-8441-0036; Tejedor, Javier/0000-0001-7699-5620 National Science Foundation of China (NSFC) [61371136, 61271389]; National Basic Research Program (973 Program) of China [2013CB329302]; MESTDC PhD Foundation [20130002120011]; Sinovoice and Huilan Ltd. National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Basic Research Program (973 Program) of China(National Basic Research Program of China); MESTDC PhD Foundation; Sinovoice and Huilan Ltd. This work was supported by the National Science Foundation of China (NSFC) under the Project No. 61371136 and No. 61271389. It was also supported by National Basic Research Program (973 Program) of China under Grant No. 2013CB329302 and the MESTDC PhD Foundation Project No. 20130002120011. Part of the research was supported by Sinovoice and Huilan Ltd. 37 57 59 2 24 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1687-4722 EURASIP J AUDIO SPEE EURASIP J. Audio Speech Music Process. JAN 20 2015.0 2 10.1186/s13636-014-0047-0 0.0 14 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering CQ8DU gold 2023-03-23 WOS:000360837300001 0
J Cheong, H; Devalla, SK; Pham, TH; Zhang, L; Tun, TA; Wang, XF; Perera, S; Schmetterer, L; Aung, T; Boote, C; Thiery, A; Girard, MJA Cheong, Haris; Devalla, Sripad Krishna; Tan Hung Pham; Zhang, Liang; Tin Aung Tun; Wang, Xiaofei; Perera, Shamira; Schmetterer, Leopold; Tin Aung; Boote, Craig; Thiery, Alexandre; Girard, Michael J. A. DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images TRANSLATIONAL VISION SCIENCE & TECHNOLOGY English Article glaucoma; generative adversarial network; deep learning; shadow removal LAMINA-CRIBROSA; GLAUCOMA; BIOMECHANICS; ENHANCEMENT Purpose: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ON H using a commercial OCT device for both eyes of 13 subjects. A custom generative adversarial network (named DeshadowGAN) was designed and trained with 2328 B-scans in order to remove blood vessel shadows in unseen B-scans. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast-a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow). This was computed in the retinal nerve fiber layer (RNFL), the inner plexiform layer (IPL), the photoreceptor (PR) layer, and the retinal pigment epithelium (RPE) layer. The performance of DeshadowGAN was also compared with that of compensation, the standard for shadow removal. Results: DeshadowGAN decreased the intralayer contrast in all tissue layers. On average, the intralayer contrast decreased by 33.7 +/- 6.81%, 28.8 +/- 10.4%, 35.9 +/- 13.0%, and 43.0 +/- 19.5% for the RNFL, IPL, PR layer, and RPE layer, respectively, indicating successful shadow removal across all depths. Output images were also free from artifacts commonly observed with compensation. Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a preprocessing step to improve the performance of a wide range of algorithms including those currently being used for OCT segmentation, denoising, and classification. Translational Relevance: DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies. [Cheong, Haris; Devalla, Sripad Krishna; Tan Hung Pham; Zhang, Liang; Tin Aung; Boote, Craig; Girard, Michael J. A.] Natl Univ Singapore, Fac Engn, Dept Biomed Engn, Ophthalm Engn & Innovat Lab, 4 Engn Dr 3,Block E4 04-08, Singapore 117583, Singapore; [Tin Aung Tun; Perera, Shamira; Schmetterer, Leopold; Tin Aung; Girard, Michael J. A.] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Wang, Xiaofei] Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, Sch Biol Sci & Med Engn, Beijing, Peoples R China; [Perera, Shamira; Schmetterer, Leopold] Duke NUS Med Sch, Ophthalmol Dept, Singapore, Singapore; [Schmetterer, Leopold; Thiery, Alexandre] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore, Singapore; [Schmetterer, Leopold] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore; [Schmetterer, Leopold] Med Univ Vienna, Dept Clin Pharmacol, Vienna, Austria; [Boote, Craig] Cardiff Univ, Sch Optometry & Vis Sci, Cardiff, Wales; [Boote, Craig] Newcastle Res & Innovat Inst, Singapore, Singapore National University of Singapore; National University of Singapore; Singapore National Eye Center; Beihang University; National University of Singapore; National University of Singapore; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Medical University of Vienna; Cardiff University Girard, MJA (corresponding author), Natl Univ Singapore, Fac Engn, Dept Biomed Engn, Ophthalm Engn & Innovat Lab, 4 Engn Dr 3,Block E4 04-08, Singapore 117583, Singapore. mgirard@nus.edu.sg Schmetterer, Leopold/0000-0002-7189-1707; Thiery, alexandre/0000-0002-9542-509X Singapore Ministry of Education Academic Research Funds Tier 1 [R-155-000-168-112, R-397-000-294-114]; National University of Singapore Young Investigator Award [NUSYIA FY16 P16, R-155-000-180-133, NUSYIA FY13 P03, R-397-000-174-133]; Singapore Ministry of Education Academic Research Funds Tier 2 [R-397-000-280-112, R-397-000-308-112]; National Medical Research Council [NMRC/STAR/0023/2014] Singapore Ministry of Education Academic Research Funds Tier 1(Ministry of Education, Singapore); National University of Singapore Young Investigator Award(National University of Singapore); Singapore Ministry of Education Academic Research Funds Tier 2(Ministry of Education, Singapore); National Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) Supported by Singapore Ministry of Education Academic Research Funds Tier 1 (R-155-000-168-112 to AT; R-397-000-294-114 to MJAG); National University of Singapore Young Investigator Award Grants (NUSYIA FY16 P16, R-155-000-180-133 to AT; NUSYIA FY13 P03, R-397-000-174-133 to MJAG); Singapore Ministry of Education Academic Research Funds Tier 2 (R-397-000-280-112, R-397-000-308-112 to MJAG); and National Medical Research Council Grant NMRC/STAR/0023/2014 (TA). 46 11 11 3 10 ASSOC RESEARCH VISION OPHTHALMOLOGY INC ROCKVILLE 12300 TWINBROOK PARKWAY, ROCKVILLE, MD 20852-1606 USA 2164-2591 TRANSL VIS SCI TECHN Transl. Vis. Sci. Technol. JAN 2020.0 9.0 2 23 10.1167/tvst.9.2.23 0.0 15 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology PG1FX 32818084.0 Green Submitted, gold, Green Accepted 2023-03-23 WOS:000599489500009 0
C Chen, WL; Zheng, ZB; Cui, JH; Ngai, E; Zheng, PL; Zhou, YR Assoc Comp Machinery Chen, Weili; Zheng, Zibin; Cui, Jiahui; Ngai, Edith; Zheng, Peilin; Zhou, Yuren Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018) English Proceedings Paper 27th World Wide Web (WWW) Conference APR 23-27, 2018 Lyon, FRANCE Assoc Comp Machinery,Int World Wide Web Conf Steering Comm,Univ Lyon,Digital League,Inria,Amazon,Baidu,Google,IDEXLYON,NAVER LINE,Yahoo! Res,Webcastor,Caisse Depots,Facebook,Inst Carnot, Telecom Soc Numerique,Mozilla,Spotify,MEDEF Lyon Rhone,MEDEF Auvergne Rhone Alpes,EFFEKTIV,Charli Charger Blockchain; Smart Contract; Ponzi Schemes; Ethereum SMART CONTRACTS Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams. [Chen, Weili; Zheng, Zibin; Cui, Jiahui; Zheng, Peilin; Zhou, Yuren] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Zheng, Zibin] Sun Yat Sen Univ, Key Lab Machine Intelligence & Adv Comp, Minist Educ, Guangzhou, Guangdong, Peoples R China; [Ngai, Edith] Uppsala Univ, Dept Informat Technol, Uppsala, Sweden Sun Yat Sen University; Sun Yat Sen University; Uppsala University Chen, WL (corresponding author), Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China. chenwli9@mail2.sysu.edu.cn; zhzibin@mail.sysu.edu.cn; cuijh6@mail2.sysu.edu.cn; edith.ngai@it.uu.se; zhengp13@mail2.sysu.edu.cn; zhouyuren@mail.sysu.edu.cn Zheng, Zibin/E-3024-2014; Zheng, Zibin/HCH-2408-2022 Zheng, Zibin/0000-0002-7878-4330; Zheng, Zibin/0000-0002-7878-4330 National Key Research and Development Program [2016YFB1000101]; National Natural Science Foundation of China [61722214, 61472338, 61773410]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2016ZT06D211]; Pearl River SAMP;T Nova Program of Guangzhou [201710010046]; STINT initiation grant for international collaboration [IB2017-6978] National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Pearl River SAMP;T Nova Program of Guangzhou; STINT initiation grant for international collaboration The work described in this paper was supported by the the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China (61722214, 61472338, 61773410), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (2016ZT06D211),the Pearl River S&T Nova Program of Guangzhou (201710010046). This collaboration between China and Sweden has also been supported by STINT initiation grant for international collaboration (grant number: IB2017-6978). Zibin Zheng is the corresponding author. 43 133 145 9 34 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5639-8 2018.0 1409 1418 10.1145/3178876.3186046 0.0 10 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM1PP Bronze, Green Published 2023-03-23 WOS:000460379000138 0
J Rice, TW; Ishwaran, H; Hofstetter, WL; Schipper, PH; Kesler, KA; Law, S; Lerut, T; Denlinger, CE; Salo, JA; Scott, WJ; Watson, TJ; Allen, MS; Chen, LQ; Rusch, VW; Cerfolio, RJ; Luketich, JD; Duranceau, A; Darling, GE; Pera, M; Apperson-Hansen, C; Blackstone, EH Rice, Thomas W.; Ishwaran, Hemant; Hofstetter, Wayne L.; Schipper, Paul H.; Kesler, Kenneth A.; Law, Simon; Lerut, Toni; Denlinger, Chadrick E.; Salo, Jarmo A.; Scott, Walter J.; Watson, Thomas J.; Allen, Mark S.; Chen, Long-Qi; Rusch, Valerie W.; Cerfolio, Robert J.; Luketich, James D.; Duranceau, Andre; Darling, Gail E.; Pera, Manuel; Apperson-Hansen, Carolyn; Blackstone, Eugene H. Esophageal Cancer: Associations With (pN plus ) Lymph Node Metastases ANNALS OF SURGERY English Article esophageal cancer; pN; Worldwide Esophageal Cancer Collaboration (WECC) CARCINOMA; TUMOR; NUMBER Objectives: To identify the associations of lymph node metastases (pN+), number of positive nodes, and pN subclassification with cancer, treatment, patient, geographic, and institutional variables, and to recommend extent of lymphadenectomy needed to accurately detect pN+ for esophageal cancer. Summary Background Data: Limited data and traditional analytic techniques have precluded identifying intricate associations of pN+ with other cancer, treatment, and patient characteristics. Methods: Data on 5806 esophagectomy patients from the Worldwide Esophageal Cancer Collaboration were analyzed by Random Forest machine learning techniques. Results: pN+, number of positive nodes, and pN subclassification were associated with increasing depth of cancer invasion (pT), increasing cancer length, decreasing cancer differentiation (G), and more regional lymph nodes resected. Lymphadenectomy necessary to accurately detect pN+ is 60 for shorter, well-differentiated cancers (<2.5 cm) and 20 for longer, poorly differentiated ones. Conclusions: In esophageal cancer, pN+, increasing number of positive nodes, and increasing pN classification are associated with deeper invading, longer, and poorly differentiated cancers. Consequently, if the goal of lymphadenectomy is to accurately define pN+ status of such cancers, few nodes need to be removed. Conversely, superficial, shorter, and well-differentiated cancers require a more extensive lymphadenectomy to accurately define pN+ status. [Rice, Thomas W.; Apperson-Hansen, Carolyn; Blackstone, Eugene H.] Cleveland Clin, Cleveland, OH 44106 USA; [Ishwaran, Hemant] Univ Miami, Miami, FL USA; [Hofstetter, Wayne L.] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA; [Schipper, Paul H.] Oregon Hlth & Sci Ctr, Portland, OR USA; [Kesler, Kenneth A.] Indiana Univ, Indianapolis, IN 46204 USA; [Law, Simon] Univ Hong Kong, Queen Mary Hosp, Hong Kong, Hong Kong, Peoples R China; [Lerut, Toni] Univ Hosp Leuven, Leuven, Belgium; [Denlinger, Chadrick E.] Med Univ South Carolina, Charleston, SC USA; [Salo, Jarmo A.] Helsinki Univ Hosp, Helsinki, Finland; [Scott, Walter J.] Fox Chase Canc Ctr, 7701 Burholme Ave, Philadelphia, PA 19111 USA; [Watson, Thomas J.] Univ Rochester, New York, NY USA; [Allen, Mark S.] Mayo Clin, Rochester, MN USA; [Chen, Long-Qi] Sichuan Univ, West China Hosp, Chengdu, Sichuan, Peoples R China; [Rusch, Valerie W.] Mem Sloan Kettering Canc Ctr, 1275 York Ave, New York, NY 10021 USA; [Cerfolio, Robert J.] Univ Alabama Birmingham, Birmingham, AL USA; [Luketich, James D.] Univ Pittsburgh, Sch Med, Pittsburgh, PA USA; [Duranceau, Andre] Univ Montreal, Montreal, PQ, Canada; [Darling, Gail E.] Toronto Gen Hosp, Toronto, ON, Canada; [Pera, Manuel] Univ Autonoma Barcelona, Hosp Univ Mar, Inst Hosp Mar Invest Med, Barcelona, Spain Cleveland Clinic Foundation; University of Miami; University of Texas System; UTMD Anderson Cancer Center; Oregon Health & Science University; Indiana University System; Indiana University-Purdue University Indianapolis; University of Hong Kong; KU Leuven; University Hospital Leuven; Medical University of South Carolina; University of Helsinki; Helsinki University Central Hospital; Fox Chase Cancer Center; University of Rochester; Mayo Clinic; Sichuan University; Memorial Sloan Kettering Cancer Center; University of Alabama System; University of Alabama Birmingham; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Universite de Montreal; University of Toronto; University Health Network Toronto; Toronto General Hospital; Autonomous University of Barcelona Rice, TW (corresponding author), Cleveland Clin, Dept Thorac & Cardiovasc Surg, 9500 Euclid Ave,Desk JJ-4, Cleveland, OH 44195 USA. ricet@ccf.org Law, Simon/C-4324-2009; luketich, james/AAY-6230-2020 Law, Simon/0000-0002-6518-5806; Chen, Longqi/0000-0002-5036-5345; Pera, Manuel/0000-0002-9449-1810; Rusch, Valerie/0000-0003-2345-6900; Cerfolio, Robert/0000-0002-5598-599X; Ishwaran, Hemant/0000-0003-2758-9647 Gus P. Karos Registry Fund; Daniel and Karen Lee Endowed Chair in Thoracic Surgery Gus P. Karos Registry Fund; Daniel and Karen Lee Endowed Chair in Thoracic Surgery This publication was made possible in part by the Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Other sources of funding included the Gus P. Karos Registry Fund, the Daniel and Karen Lee Endowed Chair in Thoracic Surgery, held by Rice, and the Kenneth Gee and Paula Shaw, PhD, Chair in Heart Research, held by Blackstone. The authors declare no conflicts of interest. 17 76 85 0 22 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA 0003-4932 1528-1140 ANN SURG Ann. Surg. JAN 2017.0 265.0 1 122 129 10.1097/SLA.0000000000001594 0.0 8 Surgery Science Citation Index Expanded (SCI-EXPANDED) Surgery EH9QS 28009736.0 Green Accepted 2023-03-23 WOS:000392106500026 0
J Chen, ZQ; Li, JB; Cheng, L; Liu, XF Chen, Zhiqiang; Li, Jianbin; Cheng, Long; Liu, Xiufeng Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation APPLIED ENERGY English Article Federated learning; Generative adversarial network; Energy consumption data; Data generation; Privacy-preserving ENERGY-CONSUMPTION; ELECTRICITY CONSUMPTION; INFORMATION; MODEL Energy consumption data are crucial for various smart energy management applications, such as demand forecasting, customer segmentation, and energy efficiency analysis. However, collecting sufficient training data can be a challenge due to data privacy concerns, technical barriers, and cost issues. This paper presents a novel data generation model called Federated-WDCGAN, which combines federated learning with an enhanced generative adversarial network to generate scalable and realistic energy consumption data while preserving privacy. The proposed model is trained in a federated manner, where the generator is trained to generate realistic energy consumption data, and the discriminators are trained to distinguish the generated data from real data. The proposed model is evaluated through analysis of the generated data and its use in a machine learning task for the classification of household characteristics, demonstrating its ability to produce high-quality data comparable to real data in terms of statistics, patterns, and classification performance. The privacy -preserving capabilities of the model are also assessed using non-independently identically distributed and independently identically distributed data, achieving satisfactory results. [Chen, Zhiqiang; Li, Jianbin; Cheng, Long] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China; [Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark North China Electric Power University; Technical University of Denmark Li, JB (corresponding author), North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China. lijb87@ncepu.edu.cn Liu, Xiufeng/0000-0001-5133-6688 Research of Distributed Energy Storage and Operations in Smart Grid [SGTYHT/21-JS-223]; Flexible Energy Denmark project - Innovationsfonden [8090-00069B] Research of Distributed Energy Storage and Operations in Smart Grid; Flexible Energy Denmark project - Innovationsfonden This research was supported by the Research of Distributed Energy Storage and Operations in Smart Grid (SGTYHT/21-JS-223) and by the Flexible Energy Denmark project (8090-00069B) funded by Innovationsfonden. 96 0 0 4 4 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy MAR 15 2023.0 334.0 120711 10.1016/j.apenergy.2023.120711 0.0 JAN 2023 21 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 8P6VN Green Published, hybrid 2023-03-23 WOS:000926660100001 0
C Bultel, X; Ciucanu, R; Matthieu, GB; Lafourcade, P; Ye, LH Zincir-Heywood, N; Bonfante, G; Debbabi, M; Garcia-Alfaro, J Bultel, Xavier; Ciucanu, Radu; Giraud, Matthieu; Lafourcade, Pascal; Ye, Lihua Secure Joins with MapReduce FOUNDATIONS AND PRACTICE OF SECURITY, FPS 2018 Lecture Notes in Computer Science English Proceedings Paper 11th International Symposium on Foundations and Practice of Security (FPS) NOV 13-15, 2018 Concordia Univ, Gina Cody Sch Engn & Comp Sci, Montreal, CANADA Concordia Univ, Gina Cody Sch Engn & Comp Sci Database query; MapReduce; Security; Natural joins MapReduce is one of the most popular programming paradigms that allows a user to process Big data sets. Our goal is to add privacy guarantees to the two standard algorithms of join computation for MapReduce: the cascade algorithm and the hypercube algorithm. We assume that the data is externalized in an honest-but-curious server and a user is allowed to query the join result. We design, implement, and prove the security of two approaches: (i) Secure-Private, assuming that the public cloud and the user do not collude, (ii) Collision-Resistant-Secure-Private, which resists to collusions between the public cloud and the user i.e., when the public cloud knows the secret key of the user. [Bultel, Xavier] Univ Rennes 1, IRISA, Rennes, France; [Ciucanu, Radu] Univ Orleans, INSA Ctr Val Loire, LIFO EA 4022, Bourges, France; [Giraud, Matthieu; Lafourcade, Pascal] Univ Clermont Auvergne, LIMOS, Clermont Ferrand, France; [Ye, Lihua] Harbin Inst Technol, Harbin, Peoples R China Universite de Rennes; Universite de Orleans; Universite Clermont Auvergne (UCA); Harbin Institute of Technology Matthieu, GB (corresponding author), Univ Clermont Auvergne, LIMOS, Clermont Ferrand, France. xavier.bultel@irisa.fr; radu.ciucanu@insa-cvl.fr; matthieu.giraud@uca.fr; pascal.lafourcade@uca.fr; 16s003041@stu.hit.edu.cn region council of Auvergne-Rhone-Alpes; University of Auvergne Foundation; Indo-French Centre for the Promotion of Advanced Research (IFCPAR); Center FrancoIndien Pour La Promotion De La Recherche Avancee (CEFIPRA) under DST-INRIA-CNRS Targeted Programme [DST/CNRS 2015-03]; FEDER program of 2014-2020 region council of Auvergne-Rhone-Alpes; University of Auvergne Foundation; Indo-French Centre for the Promotion of Advanced Research (IFCPAR)(Indo-French Centre for the Promotion of Advanced Research (IFCPAR/CEFIPRA)); Center FrancoIndien Pour La Promotion De La Recherche Avancee (CEFIPRA) under DST-INRIA-CNRS Targeted Programme; FEDER program of 2014-2020 This research was conducted with the support of the FEDER program of 2014-2020, the region council of Auvergne-Rh<^>one-Alpes, the support of the Digital Trust Chair from the University of Auvergne Foundation, the Indo-French Centre for the Promotion of Advanced Research (IFCPAR) and the Center FrancoIndien Pour La Promotion De La Recherche Avanc ' ee (CEFIPRA) through the project DST/CNRS 2015-03 under DST-INRIA-CNRS Targeted Programme. 24 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-18419-3; 978-3-030-18418-6 LECT NOTES COMPUT SC 2019.0 11358.0 78 94 10.1007/978-3-030-18419-3_6 0.0 17 Computer Science, Information Systems; Computer Science, Theory & Methods; Mathematics, Applied Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BU2PI 2023-03-23 WOS:000886226800006 0
J Kang, ZF; Nielsen, M; Yang, B; Deng, LH; Lorenzen, SS Kang, Zhongfeng; Nielsen, Mads; Yang, Bo; Deng, Lihui; Lorenzen, Stephan Sloth Online transfer learning with partial feedback EXPERT SYSTEMS WITH APPLICATIONS English Article Online learning; Transfer learning; Partial feedback; Multi-class classification; Online transfer learning Online learning for multi-class classification is a well-studied topic in machine learning. The standard multi -class classification online learning setting assumes continuous availability of the ground-truth class labels. However, in many real-life applications, only partial feedback of the predicted label can be obtained and only the correctness of the prediction is available. Hence, knowledge of the correct label is missing in the case of erroneous predictions. In this case, learning may be slower and classifiers less accurate than in the full feedback scenario. Although several online learning algorithms with partial feedback have been proposed, real-world applications would still benefit from further performance improvement. In this paper, we exploit transfer learning to improve learning in the case of erroneous predictions. We propose the Partial Feedback Online Transfer Learning (PFOTL) algorithm, which uses learned knowledge from the source domain in addition to received partial feedback, and present analysis and a mistake bound for the algorithm. In our experimental results on four benchmark datasets, the proposed algorithm achieves higher online cumulative accuracy than the comparable state-of-the-art algorithms. Two potential applications of our work would be online recommender systems and privacy protection. [Kang, Zhongfeng; Yang, Bo; Deng, Lihui] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China; [Kang, Zhongfeng; Nielsen, Mads; Lorenzen, Stephan Sloth] Univ Copenhagen, Dept Comp Sci, Univ Pk 1, DK-2100 Copenhagen, Denmark University of Electronic Science & Technology of China; University of Copenhagen Kang, ZF; Yang, B (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China. kangzhf@gmail.com; madsn@di.ku.dk; yangbo@uestc.edu.cn; denglh2019@126.com; lorenzen@di.ku.dk Kang, Zhongfeng/0000-0001-9025-0748; Nielsen, Mads/0000-0003-1535-068X; Yang, Bo/0000-0003-0805-7928 China Scholarship Council; Pioneer Centre for AI, PR China, DNRF, Denmark; National Natural Science Foundation of China [61977013] China Scholarship Council(China Scholarship Council); Pioneer Centre for AI, PR China, DNRF, Denmark; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the China Scholarship Council, the Pioneer Centre for AI, PR China, DNRF, Denmark grant number P1, and the National Natural Science Foundation of China (Project No. 61977013). We thank Maanik Arora and Naresh Manwani for providing us with the EPABF implementation, and Alina Beygelzimer and Francesco Orabonay for providing us with the SOBA implementation. We thank Jon Anthony Middleton for helping us improve the English language. 45 0 0 19 19 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. FEB 2023.0 212.0 118738 10.1016/j.eswa.2022.118738 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 5M1UH 2023-03-23 WOS:000870888900007 0
C Deng, T; Manders, A; Segers, A; Bai, YQ; Lin, HX Rocha, AP; Steels, L; VandenHerik, J Deng, Tuo; Manders, Astrid; Segers, Arjo; Bai, Yanqin; Lin, Hai Xiang Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2 English Proceedings Paper 13th International Conference on Agents and Artificial Intelligence (ICAART) FEB 04-06, 2021 ELECTR NETWORK Short-term Ozone Prediction; Transfer Learning Tropospheric ozone is a secondary pollutant which can affect human health and plant growth. In this paper, we investigated transferred convolutional neural network long short-term memory (TL-CNN-LSTM) model to predict ozone concentration. Hourly CNN-LSTM model is used to extract features and predict ozone for next hour, which is superior to commonly used models in previous studies. In the daily ozone prediction model, prediction over a large time-scale requires more data, however, only limited data are available, which causes the CNN-LSTM model to fail to accurately predict. Network-based transfer learning methods based on hourly models can obtain information from smaller temporal resolution. It can reduce prediction errors and shorten run time for model training. However, for extreme cases where the amount of data is severely insufficient, transfer learning based on smaller time scale cannot improve model prediction accuracy. [Deng, Tuo; Lin, Hai Xiang] Delft Univ Technol, Delft Inst Appl Math, Mekelweg 4, NL-2628 CD Delft, Netherlands; [Manders, Astrid; Segers, Arjo] TNO, Climate Air & Sustainabil, NL-3584 CB Utrecht, Netherlands; [Bai, Yanqin] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China Delft University of Technology; Netherlands Organization Applied Science Research; Shanghai University Deng, T (corresponding author), Delft Univ Technol, Delft Inst Appl Math, Mekelweg 4, NL-2628 CD Delft, Netherlands. 18 0 0 9 28 SCITEPRESS SETUBAL AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL 978-989-758-484-8 2021.0 1005 1012 10.5220/0010301710051012 0.0 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR6JN hybrid 2023-03-23 WOS:000661455800113 0
C Yu, ZT; Peng, W; Li, XB; Hong, XP; Zhao, GY IEEE Yu, Zitong; Peng, Wei; Li, Xiaobai; Hong, Xiaopeng; Zhao, Guoying Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) IEEE International Conference on Computer Vision English Proceedings Paper IEEE/CVF International Conference on Computer Vision (ICCV) OCT 27-NOV 02, 2019 Seoul, SOUTH KOREA IEEE,IEEE Comp Soc,CVF NONCONTACT Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing rPPG approaches rely on analyzing very fine details of facial videos, which are prone to be affected by video compression. Here we propose a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPPG signals from highly compressed videos. The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery. The rPPGNet can work on its own for robust rPPG measurement, and the STVEN network can be added and jointly trained to further boost the performance especially on highly compressed videos. Comprehensive experiments are performed on two benchmark datasets to show that, 1) the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, 2) it also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications. [Yu, Zitong; Peng, Wei; Li, Xiaobai; Hong, Xiaopeng; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Hong, Xiaopeng] Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian, Peoples R China; [Zhao, Guoying] Northwest Univ, Sch Informat & Technol, Xian, Peoples R China; [Hong, Xiaopeng] Peng Cheng Lab, Shenzhen, Peoples R China University of Oulu; Xi'an Jiaotong University; Northwest University Xi'an; Peng Cheng Laboratory Zhao, GY (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland.;Zhao, GY (corresponding author), Northwest Univ, Sch Informat & Technol, Xian, Peoples R China. zitong.yu@oulu.fi; wei.peng@oulu.fi; xiaobai.li@oulu.fi; xiaopeng.hong@oulu.fi; guoying.zhao@oulu.fi Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X National Natural Science Foundation of China [61772419]; Tekes Fidipro Program [1849/31/2015]; Business Finland Project [3116/31/2017]; Academy of Finland; Infotech Oulu National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tekes Fidipro Program(Finnish Funding Agency for Technology & Innovation (TEKES)); Business Finland Project; Academy of Finland(Academy of Finland); Infotech Oulu This work was supported by the National Natural Science Foundation of China (No. 61772419), Tekes Fidipro Program (No. 1849/31/2015), Business Finland Project (No. 3116/31/2017), Academy of Finland, and Infotech Oulu. 40 76 81 7 27 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1550-5499 978-1-7281-4803-8 IEEE I CONF COMP VIS 2019.0 151 160 10.1109/ICCV.2019.00024 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO9JW Green Submitted, Green Accepted 2023-03-23 WOS:000531438100016 0
J Pan, YF; Dikker, S; Goldstein, P; Zhu, Y; Yang, CR; Hu, Y Pan, Yafeng; Dikker, Suzanne; Goldstein, Pavel; Zhu, Yi; Yang, Cuirong; Hu, Yi Instructor-learner brain coupling discriminates between instructional approaches and predicts learning NEUROIMAGE English Article Instruction; Social interactive learning; Brain-to-brain coupling; fNIRS hyperscanning; Decoding NEURAL SYNCHRONIZATION; BEHAVIORAL SYNCHRONY; RELIABILITY; COOPERATION; PERFORMANCE; PERCEPTION; ENGAGEMENT; ATTENTION; FRAMEWORK; COHERENCE The neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we used functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. Results revealed that brain-to-brain coupling was correlated with learning outcomes, and, crucially, appeared to be driven by specific scaffolding behaviors on the part of the instructors (e.g., asking guiding questions or providing hints). Brain-to-brain coupling enhancement was absent when instructors used an explanation approach (e.g., providing definitions or clarifications). Finally, we found that machine-learning techniques were more successful when decoding instructional approaches (scaffolding vs. explanation) from brain-to-brain coupling data than when using a single-brain method. These findings suggest that brain-to-brain coupling as a pedagogically relevant measure tracks the naturalistic instructional process during instructor-learner interaction throughout constructive engagement, but not information clarification. [Pan, Yafeng; Zhu, Yi; Hu, Yi] East China Normal Univ, Shanghai Changning ECNU Mental Hlth Ctr, Sch Psychol & Cognit Sci, Shanghai, Peoples R China; [Pan, Yafeng] Univ Libre Bruxelles, UNI, Neuropsychol & Funct Neuroimaging Res Unit UR2NF, Brussels, Belgium; [Dikker, Suzanne] NYU, Dept Psychol, 6 Washington Pl, New York, NY 10003 USA; [Goldstein, Pavel] Univ Haifa, Sch Publ Hlth, Haifa, Israel; [Yang, Cuirong] Suzhou Univ Sci & Technol, Dept Psychol, Suzhou, Peoples R China; [Pan, Yafeng] Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden East China Normal University; Universite Libre de Bruxelles; New York University; University of Haifa; Suzhou University of Science & Technology; Karolinska Institutet Hu, Y (corresponding author), East China Normal Univ, Shanghai Changning ECNU Mental Hlth Ctr, Sch Psychol & Cognit Sci, Shanghai, Peoples R China.;Yang, CR (corresponding author), Suzhou Univ Sci & Technol, Dept Psychol, Suzhou, Peoples R China. yangsun2004@mail.usts.edu.cn; yhu@psy.ecnu.edu.cn Pan, Yafeng/AAG-3584-2020; Dikker, Suzanne/AGZ-1122-2022 Pan, Yafeng/0000-0002-5633-8313; Dikker, Suzanne/0000-0001-6576-4966; Zhu, Yi/0000-0001-6197-3904; Hu, Yi/0000-0003-0243-9060 National Natural Science Foundation of China [31872783]; Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning [ZK2015B01]; Programs Foundation of Shanghai Municipal Commission of Health and Family Planning [201540114]; NSF ECR Award [1661016]; General Project of Humanities and Social Sciences of the Ministry of Education [19YJA190010] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning; Programs Foundation of Shanghai Municipal Commission of Health and Family Planning; NSF ECR Award; General Project of Humanities and Social Sciences of the Ministry of Education This work was supported by the National Natural Science Foundation of China (31872783), the Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning (ZK2015B01), the Programs Foundation of Shanghai Municipal Commission of Health and Family Planning (201540114), NSF ECR Award (1661016), and the General Project of Humanities and Social Sciences of the Ministry of Education (19YJA190010). 89 60 61 25 78 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage MAY 1 2020.0 211.0 116657 10.1016/j.neuroimage.2020.116657 0.0 13 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging KU8SV 32068165.0 Green Submitted, gold 2023-03-23 WOS:000519983000019 0
C Boutellaa, E; Lopez, MB; Ait-Aoudia, S; Feng, XY; Hadid, A Fierrez, J; Li, SZ; Ross, A; Veldhuis, R; AlonsoFernandez, F; Bigun, J Boutellaa, Elhocine; Lopez, Miguel Bordallo; Ait-Aoudia, Samy; Feng, Xiaoyi; Hadid, Abdenour Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning 2016 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) International Conference on Biometrics English Proceedings Paper 9th International Conference on Biometrics (ICB) JUN 13-16, 2016 Halmstad Univ, Halmstad, SWEDEN IAPR Tech Comm 4 Biometr,IEEE Biometr Council,Fingerprint Cards AB,Safran Ident & Secur,EU Horizon 2020 Project IDENT,Speed Ident AB,Cognitec Halmstad Univ LOCAL BINARY PATTERNS Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role official dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods. [Boutellaa, Elhocine] Ctr Dev Technol Avancees, Baba Hassen, Algeria; [Boutellaa, Elhocine; Lopez, Miguel Bordallo; Hadid, Abdenour] Univ Oulu, SF-90100 Oulu, Finland; [Boutellaa, Elhocine; Ait-Aoudia, Samy] Ecole Natl Super Informat, Algiers, Algeria; [Feng, Xiaoyi; Hadid, Abdenour] Northwestern Polytech Univ, Xian, Peoples R China University of Oulu; Ecole Nationale Superieure d'Informatique; Northwestern Polytechnical University Boutellaa, E (corresponding author), Ctr Dev Technol Avancees, Baba Hassen, Algeria.;Boutellaa, E (corresponding author), Univ Oulu, SF-90100 Oulu, Finland.;Boutellaa, E (corresponding author), Ecole Natl Super Informat, Algiers, Algeria. 25 11 11 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-4201 978-1-5090-1869-7 INT CONF BIOMETR 2016.0 7 Engineering, Multidisciplinary; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Mathematical & Computational Biology BG6TE 2023-03-23 WOS:000390841200027 0
J Trebaul, L; Deman, P; Tuyisenge, V; Jedynak, M; Hugues, E; Rudrauf, D; Bhattacharjee, M; Tadel, F; Chanteloup-Foret, B; Saubat, C; Mejia, GCR; Adam, C; Nica, A; Pail, M; Dubeau, F; Rheims, S; Trebuchon, A; Wang, HX; Liu, S; Blauwblomme, T; Garces, M; De Palma, L; Valentin, A; Metsahonkala, EL; Petrescu, AM; Landre, E; Szurhaj, W; Hirsch, E; Valton, L; Rocamora, R; Schulze-Bonhage, A; Mindruta, I; Francione, S; Maillard, L; Taussig, D; Kahane, P; David, O Trebaul, Lena; Deman, Pierre; Tuyisenge, Viateur; Jedynak, Maciej; Hugues, Etienne; Rudrauf, David; Bhattacharjee, Manik; Tadel, Francois; Chanteloup-Foret, Blandine; Saubat, Carole; Mejia, Gina Catalina Reyes; Adam, Claude; Nica, Anca; Pail, Martin; Dubeau, Francois; Rheims, Sylvain; Trebuchon, Agnes; Wang, Haixiang; Liu, Sinclair; Blauwblomme, Thomas; Garces, Mercedes; De Palma, Luca; Valentin, Antonio; Metsahonkala, Eeva-Liisa; Petrescu, Ana Maria; Landre, Elizabeth; Szurhaj, William; Hirsch, Edouard; Valton, Luc; Rocamora, Rodrigo; Schulze-Bonhage, Andreas; Mindruta, Ioana; Francione, Stefano; Maillard, Louis; Taussig, Delphine; Kahane, Philippe; David, Olivier Probabilistic functional tractography of the human cortex revisited NEUROIMAGE English Article Brain atlas; Epilepsy; Intracranial electroencephalogram; Cortico-cortical evoked potentials; Connectivity mapping CORTICOCORTICAL EVOKED-POTENTIALS; TEMPORAL-LOBE EPILEPSY; PULSE ELECTRICAL-STIMULATION; HUMAN BRAIN; EFFECTIVE CONNECTIVITY; NEOCORTICAL EPILEPSY; LANGUAGE SYSTEM; MOTOR SYSTEM; ICTAL ONSET; IN-VIVO In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human. [Trebaul, Lena; Deman, Pierre; Tuyisenge, Viateur; Jedynak, Maciej; Hugues, Etienne; Rudrauf, David; Bhattacharjee, Manik; Tadel, Francois; Chanteloup-Foret, Blandine; Saubat, Carole; Mejia, Gina Catalina Reyes; Kahane, Philippe; David, Olivier] Inserm, U1216, F-38000 Grenoble, France; [Trebaul, Lena; Deman, Pierre; Tuyisenge, Viateur; Jedynak, Maciej; Hugues, Etienne; Rudrauf, David; Bhattacharjee, Manik; Tadel, Francois; Chanteloup-Foret, Blandine; Saubat, Carole; Mejia, Gina Catalina Reyes; Kahane, Philippe; David, Olivier] Univ Grenoble Alpes, GIN, F-38000 Grenoble, France; [Adam, Claude] Hop La Pitie Salpetriere, AP HP, Epilepsy Unit, Dept Neurol, Paris, France; [Nica, Anca] CHU Rennes, Neurol Dept, Rennes, France; [Pail, Martin] St Annes Univ Hosp, Brno Epilepsy Ctr, Dept Neurol, Brno, Czech Republic; [Pail, Martin] Masaryk Univ, Med Fac, Brno, Czech Republic; [Dubeau, Francois] Montreal Neurol Hosp & Inst, Montreal, PQ, Canada; [Rheims, Sylvain] Hosp Civils Lyon, Dept Funct Neurol & Epileptol, Lyon, France; [Rheims, Sylvain] Univ Lyon, Lyon, France; [Trebuchon, Agnes] Hop La Timone, AP HM, Serv Neurophysiol Clin, Marseille, France; [Wang, Haixiang] Tsinghua Univ, Yuquan Hosp Epilepsy Ctr, Beijing, Peoples R China; [Liu, Sinclair] Jinan Univ, Canton Sanjiu Brain Hosp Epilepsy Ctr, Guangzhou, Guangdong, Peoples R China; [Blauwblomme, Thomas] Univ Paris V Descartes, Dept Pediat Neurosurg, Sorbonne Paris Cite, Hop Necker Enfants Malad, Paris, France; [Garces, Mercedes] Hosp Univ & Politecn La Fe, Multidisciplinary Epilepsy Unit, Valencia, Spain; [De Palma, Luca] IRRCS, Dept Neurosci, Bambino Gesu Childrens Hosp, Rome, Italy; [Valentin, Antonio] Inst Psychiat Psychol & Neurosci IoPPN, Dept Basic & Clin Neurosci, London, England; [Metsahonkala, Eeva-Liisa] Hosp Children & Adolescents, Epilepsy Unit, Helsinki, Finland; [Petrescu, Ana Maria] Bicetre Hosp, Neurophysiol & Epilepsy Unit, Le Kremlin Bicetre, France; [Landre, Elizabeth] St Anne Hosp, Dept Neurosurg, Paris, France; [Szurhaj, William] Lille Univ Med Ctr, Epilepsy Unit, Dept Clin Neurophysiol, Lille, France; [Hirsch, Edouard] Univ Hosp, Dept Neurol, Strasbourg, France; [Valton, Luc] Univ Hosp, Dept Neurol, Toulouse, France; [Rocamora, Rodrigo] Hosp Mar IMIM, Dept Neurol, Epilepsy Monitoring Unit, Barcelona, Spain; [Schulze-Bonhage, Andreas] Univ Freiburg, Fac Med, Epilepsy Ctr, Med Ctr, Freiburg, Germany; [Mindruta, Ioana] Univ Emergency Hosp, Neurol Dept, Bucharest, Romania; [Francione, Stefano] Epilepsy Surg Ctr Niguarda Hosp, Milan, Italy; [Maillard, Louis] CHU Nancy, Nancy, France; [Taussig, Delphine] Fondat Rothschild, Serv Neurochirurg Pediat, Paris, France; [Kahane, Philippe] CHU Grenoble Alpes, Neurol Dept, Grenoble, France Institut National de la Sante et de la Recherche Medicale (Inserm); CEA; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Pitie-Salpetriere - APHP; UDICE-French Research Universities; Sorbonne Universite; CHU Rennes; Universite de Rennes; St Anne's University Hospital Brno (FNUSA-ICRC); Masaryk University Brno; McGill University; CHU Lyon; UDICE-French Research Universities; Aix-Marseille Universite; Assistance Publique-Hopitaux de Marseille; Tsinghua University; Jinan University; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Necker-Enfants Malades - APHP; Hospital Universitari i Politecnic La Fe; IRCCS Bambino Gesu; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Bicetre - APHP; UDICE-French Research Universities; Universite Paris Cite; GHU PARIS Psychiatrie Neurosciences; Universite de Lille - ISITE; CHU Lille; CHU Strasbourg; CHU de Toulouse; Institut Hospital del Mar d'Investigacions Mediques (IMIM); University of Freiburg; CHU de Nancy; CHU Grenoble Alpes David, O (corresponding author), Grenoble Inst Neurosci, Chemin Fortune Ferrini,Bat EJ Safra, F-38700 La Tronche, France. Olivier.David@inserm.fr Mindruta, Ioana Raluca/GLU-2187-2022; Rocamora, Rodrigo/M-3917-2016; David, Olivier/S-9463-2019; David, Olivier/A-6068-2009; De Palma, Luca/J-8849-2018; Nica, Anca/ABC-7635-2020; Rocamora, Rodrigo/AAE-8052-2021; KAHANE, PHILIPPE/P-5669-2016; Szurhaj, William/HMV-6413-2023; Francione, Stefano/AAC-7574-2022; Francione, Stefano/K-2615-2018; Schulze-Bonhage, Andreas/AAJ-3107-2020 Rocamora, Rodrigo/0000-0001-7345-4300; David, Olivier/0000-0003-0776-0216; David, Olivier/0000-0003-0776-0216; De Palma, Luca/0000-0002-0714-8230; Rocamora, Rodrigo/0000-0001-7345-4300; Szurhaj, William/0000-0003-4112-3691; Francione, Stefano/0000-0002-0577-5193; Valentin, Antonio/0000-0003-0449-5234; Schulze-Bonhage, Andreas/0000-0003-2382-0506; Rheims, Sylvain/0000-0002-4663-8515; Rudrauf, David/0000-0002-9621-1800; Bhattacharjee, Manik/0000-0002-8183-1521; Jedynak, Maciej/0000-0003-1876-5485 European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant [616268]; Rhone-Alpes region [CPER07_13] European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant; Rhone-Alpes region(Region Auvergne-Rhone-Alpes) The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 616268 F-TRACT. All the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment.ujf-grenoble.fr), which is supported by the Rhone-Alpes region (GRANT CPER07_13 CIRA: http://www.ci-ra.org). We thank Bruno Bzenik and Romain Cavagna for their help on the CIMENT facility, and Jean-Francois Mangin and Cyril Poupon from Neurospin, CEA Saclay, for providing us with the ARCHI DTI database. 54 55 55 2 31 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage NOV 1 2018.0 181.0 414 429 10.1016/j.neuroimage.2018.07.039 0.0 16 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging GU3GW 30025851.0 Green Published, hybrid, Green Accepted 2023-03-23 WOS:000445165600036 0
J Gao, L; Schulz, K; Bernhardt, M Gao, Lu; Schulz, Karsten; Bernhardt, Matthias Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm ADVANCES IN METEOROLOGY English Article REGIONAL CLIMATE-CHANGE; BIAS-CORRECTION; MESOSCALE PRECIPITATION; CHANGE IMPACTS; MODEL; TEMPERATURE; SELECTION; ENSEMBLE; STATES Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Therefore, downscaling local precipitation is necessary. To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25 degrees grid) and point-scale meteorological observations. LASSO was tested and validated against other three downscaling methods, local intensity scaling (LOCI), quantile-mapping (QM), and stepwise regression (Stepwise) at 50 meteorological stations, located in the high mountainous region of the central Alps. The downscaling procedure is implemented in two steps. Firstly, the dry or wet days are classified and the precipitation amounts conditional on the occurrence of wet days are modeled subsequently. Compared to other three downscaling methods, LASSO shows the best performances in precipitation occurrence and precipitation amount prediction on average. Furthermore, LASSO could reduce the error for certain sites, where no improvement could be seen when LOCI and QM were used. This study proves that LASSO is a reasonable alternative to other statistical methods with respect to the downscaling of precipitation data. [Gao, Lu] Fujian Normal Univ, Coll Geog Sci, Fuzhou 350007, Peoples R China; [Gao, Lu] Fujian Prov Engn Res Ctr Monitoring & Assessing T, Fuzhou 350007, Peoples R China; [Gao, Lu; Bernhardt, Matthias] Univ Munich, Dept Geog, D-80333 Munich, Germany; [Schulz, Karsten] Univ Nat Resources & Life Sci, Inst Water Management Hydrol & Hydraul Engn, A-1190 Vienna, Austria Fujian Normal University; University of Munich; University of Natural Resources & Life Sciences, Vienna Gao, L (corresponding author), Fujian Normal Univ, Coll Geog Sci, Fuzhou 350007, Peoples R China. l.gao@foxmail.com Bernhardt, Matthias/ABB-7909-2021; Bernhardt, Matthias/A-7168-2010; Gao, Lu/E-2867-2014 Bernhardt, Matthias/0000-0001-5503-7925; Bernhardt, Matthias/0000-0001-5503-7925; Schulz, Karsten/0000-0002-6616-2876; Gao, Lu/0000-0003-1934-5802 China Scholarship Council (CSC); ECMWF China Scholarship Council (CSC)(China Scholarship Council); ECMWF This research has been funded by China Scholarship Council (CSC). ERA-Interim data was supported by the ECMWF. The authors would like to thank MeteoSwiss (the Swiss Federal Office of Meteorology and Climatology) for providing the needed meteorological data. The authors appreciate OURA-NOS and Josef Schmid (Ludwig-Maximilians-Universitat Munchen) for providing a tool for the data processing. 70 29 29 3 34 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-9309 1687-9317 ADV METEOROL Adv. Meteorol. 2014.0 2014.0 472741 10.1155/2014/472741 0.0 16 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences AL5OL Green Published, gold, Green Submitted 2023-03-23 WOS:000339183000001 0
C Song, DP; Qiao, Y; Corbetta, A IEEE Song, Diping; Qiao, Yu; Corbetta, Alessandro Depth Driven People Counting Using Deep Region Proposal Network 2017 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (IEEE ICIA 2017) English Proceedings Paper IEEE International Conference on Information and Automation (ICIA) JUL 18-20, 2017 Macau, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,Chinese Univ Hong Kong,State Key Lab Robot & Syst,Univ Macau,Shenzhen Inst Adv Technol,Harbin Inst Technol,Shanghai Gaitech Sci Instruments Co Ltd,Apes Robot Co Ltd,Chinese Assoc Automat,Amer Soc Mech Engineers,Robot Soc Japan,Japan Soc Mech Engineers,Soc Instrument & Control Engineers People counting; Head detection; Convolutional Neural Network; Depth image People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU. [Song, Diping] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China; [Song, Diping; Qiao, Yu] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Comp Vis & Virtual Real Te, Shenzhen, Peoples R China; [Qiao, Yu] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China; [Corbetta, Alessandro] Eindhoven Univ Technol, Dept Appl Phys, Eindhoven, Netherlands Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese University of Hong Kong; Eindhoven University of Technology Song, DP (corresponding author), Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China.;Song, DP (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Comp Vis & Virtual Real Te, Shenzhen, Peoples R China. dp.song@siat.ac.cn; yu.qiao@siat.ac.cn; a.corbetta@tue.nl Qiao, Yu/ABD-5787-2021; Corbetta, Alessandro/N-9954-2015 Corbetta, Alessandro/0000-0001-6979-3414 External Cooperation Program of BIC Chinese Academy of Sciences Grant [172644KYSB20150019]; Shenzhen Research Program [JSGG20150925164740726, KQCX2015033117354153]; JSTP research programme Vision driven visitor behaviour analysis and crowd management [341-10-001]; Netherlands Organisation for Scientific Research (NWO) External Cooperation Program of BIC Chinese Academy of Sciences Grant; Shenzhen Research Program; JSTP research programme Vision driven visitor behaviour analysis and crowd management; Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)) This work is partly supported by External Cooperation Program of BIC Chinese Academy of Sciences Grant #172644KYSB20150019, and Shenzhen Research Program Grant #JSGG20150925164740726, Grant #KQCX2015033117354153. This work is part of the JSTP research programme Vision driven visitor behaviour analysis and crowd management with project number 341-10-001, which is financed by the Netherlands Organisation for Scientific Research (NWO). 28 14 14 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-3154-6 2017.0 416 421 6 Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Robotics BJ5FM 2023-03-23 WOS:000425855100072 0
C Ciucanu, R; Giraud, M; Lafourcade, P; Ye, LH Obaidat, M; Samarati, P Ciucanu, Radu; Giraud, Matthieu; Lafourcade, Pascal; Ye, Lihua Secure Strassen-Winograd Matrix Multiplication with MapReduce PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS, VOL 2: SECRYPT English Proceedings Paper 16th International Joint Conference on E-Business and Telecommunications (ICETE) JUL 26-28, 2019 Prague, CZECH REPUBLIC Matrix Multiplication; Strassen-Winograd Algorithm; MapReduce; Security Matrix multiplication is a mathematical brick for solving many real life problems. We consider the Strassen-Winograd algorithm (SW), one of the most efficient matrix multiplication algorithm. Our first contribution is to redesign SW algorithm MapReduce programming model that allows to process big data sets in parallel on a cluster. Moreover, our main contribution is to address the inherent security and privacy concerns that occur when outsourcing data to a public cloud. We propose a secure approach of SW with MapReduce called S2M3, for Secure Strassen-Winograd Matrix Multiplication with Mapreduce. We prove the security of our protocol in a standard security model and provide a proof-of-concept empirical evaluation suggesting its efficiency. [Ciucanu, Radu] Univ Orleans, INSA Ctr Val Loire, LIFO EA 4022, Bourges, France; [Giraud, Matthieu; Lafourcade, Pascal] Univ Clermont Auvergne, LIMOS, UMR 6158, Aubiere, France; [Ye, Lihua] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China Universite de Orleans; Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA); Harbin Institute of Technology Ciucanu, R (corresponding author), Univ Orleans, INSA Ctr Val Loire, LIFO EA 4022, Bourges, France. lafourcade, pascal/0000-0002-4459-511X 30 1 1 0 0 SCITEPRESS SETUBAL AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL 978-989-758-378-0 2019.0 220 227 10.5220/0007916302200227 0.0 8 Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BQ0AX Green Submitted 2023-03-23 WOS:000571438700018 0
J Liang, YL; Kortoci, P; Zhou, PY; Lee, LH; Mehrabi, A; Hui, P; Tarkoma, S; Crowcroft, J Liang, Yilei; Kortoci, Pranvera; Zhou, Pengyuan; Lee, Lik-Hang; Mehrabi, Abbas; Hui, Pan; Tarkoma, Sasu; Crowcroft, Jon Federated split GANs for collaborative training with heterogeneous devices SOFTWARE IMPACTS English Article Federated learning; Split learning; GAN; Hardware heterogeneous; Privacy preservation Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device heterogeneity, greatly hindering their applicability in practice. In order to address such limitations, we developed a framework based on both FL and SL to share the training load of the discriminative part of a GAN to different client devices. We make our framework available as open-source software1. [Liang, Yilei; Crowcroft, Jon] Univ Cambridge, Cambridge, England; [Kortoci, Pranvera; Tarkoma, Sasu] Univ Helsinki, Helsinki, Finland; [Zhou, Pengyuan] USTC, Hefei, Peoples R China; [Lee, Lik-Hang] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Mehrabi, Abbas] Northumbria Univ, Newcastle Upon Tyne, Tyne & Wear, England; [Hui, Pan] HKUST, Hong Kong, Peoples R China University of Cambridge; University of Helsinki; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Korea Advanced Institute of Science & Technology (KAIST); Northumbria University; Hong Kong University of Science & Technology Kortoci, P (corresponding author), Univ Helsinki, Helsinki, Finland. yl841@cst.cam.ac.uk; pranvera.kortoci@helsinki.fi; pyzhou@ustc.edu.cn; likhang.lee@kaist.ac.kr; abbas.mehrabidavoodabadi@northumbria.ac.uk; panhui@cse.ust.hk; sasu.tarkoma@helsinki.fi; jon.crowcroft@cl.cam.ac.uk Zhou, Pengyuan/0000-0002-7909-4059; Kortoci, Pranvera/0000-0003-4006-8969; Lee, Lik Hang/0000-0003-1361-1612; Mehrabi, Abbas/0000-0002-8758-0882 12 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2665-9638 SOFTW IMPACTS Software Impacts NOV 2022.0 14.0 100436 10.1016/j.simpa.2022.100436 0.0 NOV 2022 3 Computer Science, Software Engineering Emerging Sources Citation Index (ESCI) Computer Science 7O9YO Green Accepted, Green Published, gold 2023-03-23 WOS:000908370700006 0
J Tirindelli, M; Victorova, M; Esteban, J; Kim, ST; Navarro-Alarcon, D; Zheng, YP; Navab, N Tirindelli, Maria; Victorova, Maria; Esteban, Javier; Kim, Seong Tae; Navarro-Alarcon, David; Zheng, Yong Ping; Navab, Nassir Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next Level IEEE ROBOTICS AND AUTOMATION LETTERS English Article Medical robots and systems; computer vision for medical robotics IDENTIFICATION Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically done by back palpation or under X-ray guidance, yielding either higher chances of procedure failure or exposure to ionizing radiation. Preliminary studies have been conducted in the literature, suggesting that ultrasound imaging may be a precise and safe alternative to X-ray for spine level detection. However, ultrasound data are noisy and complicated to interpret. In this study, a robotic-ultrasound approach for automatic vertebral level detection is introduced. The method relies on the fusion of ultrasound and force data, thus providing both tactile and visual feedback during the procedure, which results in higher performances in presence of data corruption. A robotic arm automatically scans the volunteer's back along the spine by using force-ultrasound data to locate vertebral levels. The occurrences of vertebral levels are visible on the force trace as peaks, which are enhanced by properly controlling the force applied by the robot on the patient back. Ultrasound data are processed with a Deep Learning method to extract a 1D signal modelling the probabilities of having a vertebra at each location along the spine. Processed force and ultrasound data are fused using both a non deep learning method and a Temporal Convolutional Network to compute the locations of the vertebral levels. The benefits of fusing force and image signals for the identification of vertebrae locations are showcased through extensive evaluation. [Tirindelli, Maria; Esteban, Javier; Kim, Seong Tae; Navab, Nassir] Tech Univ Munich, Comp Aided Med Procedures, D-80333 Munich, Germany; [Victorova, Maria; Navarro-Alarcon, David; Zheng, Yong Ping] Hong Kong Polytech Univ, Hung Hom, Hong Kong, Peoples R China; [Navab, Nassir] Johns Hopkins Univ, Comp Aided Med Procedures, Baltimore, MD 21218 USA Technical University of Munich; Hong Kong Polytechnic University; Johns Hopkins University Tirindelli, M (corresponding author), Tech Univ Munich, Comp Aided Med Procedures, D-80333 Munich, Germany. maria.tirindelli@mail.polimi.it; maryviktory@gmail.com; javier.esteban@tum.de; seongtae.kim@tum.de; davidnavarroalarcon@gmail.com; yongping.zheng@polyu.edu.hk; navab@cs.tum.edu Kim, Seong Tae/N-8137-2019; Navarro-Alarcon, David/V-6917-2017; Nuñez, David/HLW-7333-2023; Zheng, Yong-Ping/M-1197-2015 Kim, Seong Tae/0000-0002-2132-6021; Navarro-Alarcon, David/0000-0002-3426-6638; Zheng, Yong-Ping/0000-0002-3407-9226 Bayerische Forschungsstiftung [DOK-180-19] Bayerische Forschungsstiftung This work was supported by the Bayerische Forschungsstiftung, under Grant DOK-180-19. 20 11 11 5 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. OCT 2020.0 5.0 4 5661 5668 10.1109/LRA.2020.3009069 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics MT3TP Green Submitted 2023-03-23 WOS:000554891100006 0
J Pignatti, S; Casa, R; Laneve, G; Li, ZH; Liu, LY; Marzialetti, P; Mzid, N; Pascucci, S; Silvestro, PC; Tolomio, M; Upreti, D; Yang, H; Yang, GJ; Huang, WJ Pignatti, Stefano; Casa, Raffaele; Laneve, Giovanni; Li, Zhenhai; Liu, Linyi; Marzialetti, Pablo; Mzid, Nada; Pascucci, Simone; Silvestro, Paolo Cosmo; Tolomio, Massimo; Upreti, Deepak; Yang, Hao; Yang, Guijun; Huang, Wenjiang Sino-EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources REMOTE SENSING English Article multispectral data analysis; satellite data assimilation; crop variables estimation; modeling; crop pest and disease DATA ASSIMILATION; WHEAT; PRODUCTIVITY; SYSTEM; TIME Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t center dot ha(-1) vs 4.42 t center dot ha(-1) RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%. [Pignatti, Stefano; Pascucci, Simone] Natl Council Res CNR, Inst Methodol Environm Anal IMAA, I-85050 Tito, Italy; [Casa, Raffaele; Mzid, Nada; Tolomio, Massimo] Univ Tuscia, Dept Agr Forests Nat & Energy DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy; [Laneve, Giovanni; Marzialetti, Pablo] Univ Roma La Sapienza, Sch Aerosp Engn SIA, SIA, Via Salaria,851, I-00138 Rome, Italy; [Li, Zhenhai; Yang, Hao; Yang, Guijun] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China; [Liu, Linyi; Huang, Wenjiang] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Silvestro, Paolo Cosmo] ESRIN, European Space Agcy, Via Galileo Galilei 1, I-00044 Frascati, Italy; [Upreti, Deepak] Maynooth Univ, Dept Comp Sci, Dublin W23 F2H6, Ireland Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Tuscia University; Sapienza University Rome; Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Chinese Academy of Sciences; European Space Agency Pignatti, S (corresponding author), Natl Council Res CNR, Inst Methodol Environm Anal IMAA, I-85050 Tito, Italy. stefano.pignatti@imaa.cnr.it; rcasa@unitus.it; giovanni.laneve@uniroma1.it; liz@nercita.org.cn; liuly01@aircas.ac.cn; pablo.marzialetti@uniroma1.it; nada.mzid@unitus.it; simone.pascucci@imaa.cnr.it; paolo.cosmo.silvestro@esa.int; massimo.tolomio@unitus.it; deepak.upreti@mu.ie; yangh@nercita.org.cn; yanggj@nercita.org.cn; huangwj@aircas.ac.cn Tolomio, Massimo/AAE-3441-2022; Tolomio, Massimo/AAA-1930-2020; Pignatti Morano, Stefano/A-4640-2008 Tolomio, Massimo/0000-0001-9377-6307; Mzid, Nada/0000-0002-3773-4265; CASA, Raffaele/0000-0003-3091-7680; Yang, Guijun/0000-0002-6425-8321; Yang, Hao/0000-0001-8506-7295; Pascucci, Simone/0000-0002-8311-8615; Silvestro, Paolo Cosmo/0000-0001-7375-2906; Pignatti Morano, Stefano/0000-0002-0587-8926; Li, Zhenhai/0000-0001-9878-3274; Laneve, Giovanni/0000-0001-6108-9764 ASI; ESA project Hyperspectral Imaging Mission Concept ASI(Agenzia Spaziale Italiana (ASI)); ESA project Hyperspectral Imaging Mission Concept This research was also supported by ASI with project PRISCAV, and ESA project Hyperspectral Imaging Mission Concept. 54 3 3 2 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13.0 15 2889 10.3390/rs13152889 0.0 26 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology TW3NX gold 2023-03-23 WOS:000682312400001 0
J Lan, K; Fong, S; Song, W; Vasilakos, AV; Millham, RC Lan, Kun; Fong, Simon; Song, Wei; Vasilakos, Athanasios V.; Millham, Richard C. Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring SYMMETRY-BASEL English Article data stream pre-processing; self-adaptive segmentation; clustering-based particle swarm optimization (CPSO); Internet of Things (IoT) datasets SELECTION Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others. [Lan, Kun; Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa 999078, Macau, Peoples R China; [Song, Wei] North China Univ Technol, Sch Comp Sci, Beijing 100144, Peoples R China; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, SE-97187 Lulea, Sweden; [Millham, Richard C.] Durban Univ Technol, Dept Informat Technol, Ritson Campus,POB 1334, Durban, South Africa University of Macau; North China University of Technology; Lulea University of Technology; Durban University of Technology Fong, S (corresponding author), Univ Macau, Dept Comp & Informat Sci, Taipa 999078, Macau, Peoples R China. yb67405@umac.mo; ccfong@umac.mo; sw@ncut.edu.cn; athanasios.vasilakos@ltu.se; Richardm1@dut.ac.za ; Fong, Simon/C-9388-2009 Vasilakos, Athanasios/0000-0003-1902-9877; Song, Wei/0000-0002-5909-9661; Millham, Richard/0000-0002-7970-9615; Fong, Simon/0000-0002-1848-7246 Research & Development Administration Office (RDAO) / Faculty of Science and Technology (FST) [MYRG2016-00069]; University of Macau; Macau Special Administration Region (SAR) government; Science and Technology Development Fund (FDCT) of Macau SAR government [FDCT/126/2014/A3] Research & Development Administration Office (RDAO) / Faculty of Science and Technology (FST); University of Macau; Macau Special Administration Region (SAR) government; Science and Technology Development Fund (FDCT) of Macau SAR government The authors are thankful to the financial support from the following research grants: (1) MYRG2016-00069, titled 'Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance' offered by Research & Development Administration Office (RDAO) / Faculty of Science and Technology (FST), the University of Macau, and the Macau Special Administration Region (SAR) government; (2) FDCT/126/2014/A3, titled 'A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel' offered by Science and Technology Development Fund (FDCT) of Macau SAR government. 33 8 8 0 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-8994 SYMMETRY-BASEL Symmetry-Basel OCT 2017.0 9.0 10 244 10.3390/sym9100244 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics FM3MT Green Submitted, gold 2023-03-23 WOS:000414911000047 0
C Qingqing, L; Queralta, JP; Gia, TN; Zou, Z; Tenhunen, H; Westerlund, T Quocbao, VN; Trung, NL Qingqing, L.; Queralta, J. Pena; Gia, T. Nguyen; Zou, Z.; Tenhunen, H.; Westerlund, T. Detecting Water Reflection Symmetries in Point Clouds for Camera Position Calibration in Unmanned Surface Vehicles ISCIT 2019: PROCEEDINGS OF 2019 19TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT) English Proceedings Paper 19th International Symposium on Communications and Information Technologies (ISCIT) SEP 25-27, 2019 Ho Chi Minh City, VIETNAM DAI HOC CONG NGHE,Post & Telecommunicat Inst Technol,Journal Informat & Commun Technol,IEEE Commun Soc, Vietnam Chapter,REV,IEEE SSCS Vietnam Chapter,IEEE Computat Inetllgence Soc, Vietnam Chapter,EiC,ECTI Assoc,Vietnam Natl Univ Hanoi, VNU Univ Engn &Technol,Vietnam Minist Informat & Commun Camera Calibration; Reflection Symmetry; Symmetries; Stereo Vision; Intel RealSense; Point Cloud; PCL; ROS; Autonomous Vessels; USV; Surface Vehicles; Autonomous Surface Vehicles; Autonomous Vehicles NAVIGATION; IMAGES The development of autonomous vehicles has seen considerable advances over the past decade. However, specific challenges remain in the area of autonomous waterborne navigation. Two key aspects in autonomous surface vehicles are sensor calibration and segmentation of water surface. Cameras and other sensors in a car or drone can be installed accurately in a specific position and orientation. In a large vessel, this is not always possible, as sensors might be installed around the vessel or on masts. Taking advantage of the medium in which these vehicles operate, the water plane can be used as a reference for different sensors to calibrate their orientation. This allows more accurate localization of obstacles of objects. State-of-the-art deep learning techniques have been successfully applied for water surface segmentation in open sea. However, in other environments such as small rivers or lakes with still waters, a different approach might enable more accurate water surface estimation. We propose a method to estimate the water plane based on the detection of local symmetry planes that naturally occur when objects are reflected in the water. By using a point cloud generated with a stereo camera, we are able to accurately estimate the water level and, at the same time, calibrate the camera position and improve the localization of obstacles. We assume that an approximate position and orientation of the camera is known with respect to the sea level. We demonstrate the efficiency of our method with data obtained in the Aura river, Finland, with our prototype vessel facing both the riverside and the center of the river. [Qingqing, L.; Queralta, J. Pena; Gia, T. Nguyen; Westerlund, T.] Univ Turku, Dept Future Technol, Turku, Finland; [Qingqing, L.; Zou, Z.] Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China; [Tenhunen, H.] KTH Royal Inst Technol, Dept Elect, Stockholm, Sweden University of Turku; Fudan University; Royal Institute of Technology Qingqing, L (corresponding author), Univ Turku, Dept Future Technol, Turku, Finland.;Qingqing, L (corresponding author), Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China. qingqingli16@fudan.edu.cn; jopequ@utu.fi; tunggi@utu.fi; zhuo@fudan.edu.cn; hannu@kth.se; tovewe@utu.fi Nguyen Gia, Tuan/HNS-5110-2023; liu, qingqing/HHD-0360-2022; Gia, Tuan Nguyen/R-5012-2016; Li, Qingqing/AAM-8348-2021; Liu, Qingqing/HMV-4816-2023 Nguyen Gia, Tuan/0000-0002-9851-9868; Gia, Tuan Nguyen/0000-0002-9851-9868; Li, Qingqing/0000-0001-6556-2213; Pena Queralta, Jorge/0000-0003-3091-3217 27 1 1 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-5009-3 2019.0 507 512 6 Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BT6XE Green Submitted 2023-03-23 WOS:000844625700095 0
J Dai, CX; Fullgrabe, A; Pfeuffer, J; Solovyeva, EM; Deng, JW; Moreno, P; Kamatchinathan, S; Kundu, DJ; George, N; Fexova, S; Gruning, B; Foll, MC; Griss, J; Vaudel, M; Audain, E; Locard-Paulet, M; Turewicz, M; Eisenacher, M; Uszkoreit, J; Van den Bossche, T; Schwammle, V; Webel, H; Schulze, S; Bouyssie, D; Jayaram, S; Duggineni, VK; Samaras, P; Wilhelm, M; Choi, M; Wang, MX; Kohlbacher, O; Brazma, A; Papatheodorou, I; Bandeira, N; Deutsch, EW; Vizcaino, JA; Bai, MZ; Sachsenberg, T; Levitsky, LI; Perez-Riverol, Y Dai, Chengxin; Fullgrabe, Anja; Pfeuffer, Julianus; Solovyeva, Elizaveta M.; Deng, Jingwen; Moreno, Pablo; Kamatchinathan, Selvakumar; Kundu, Deepti Jaiswal; George, Nancy; Fexova, Silvie; Gruening, Bjoern; Foell, Melanie Christine; Griss, Johannes; Vaudel, Marc; Audain, Enrique; Locard-Paulet, Marie; Turewicz, Michael; Eisenacher, Martin; Uszkoreit, Julian; Van den Bossche, Tim; Schwammle, Veit; Webel, Henry; Schulze, Stefan; Bouyssie, David; Jayaram, Savita; Duggineni, Vinay Kumar; Samaras, Patroklos; Wilhelm, Mathias; Choi, Meena; Wang, Mingxun; Kohlbacher, Oliver; Brazma, Alvis; Papatheodorou, Irene; Bandeira, Nuno; Deutsch, Eric W.; Vizcaino, Juan Antonio; Bai, Mingze; Sachsenberg, Timo; Levitsky, Lev I.; Perez-Riverol, Yasset A proteomics sample metadata representation for multiomics integration and big data analysis NATURE COMMUNICATIONS English Review STANDARD The number of publicly available proteomics datasets is growing rapidly, but a standardized approach for describing the associated metadata is lacking. Here, the authors propose a format and a software pipeline to present and validate metadata, and integrate them into ProteomeXchange repositories. The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets. [Dai, Chengxin; Deng, Jingwen; Bai, Mingze] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Big Data Bio Intelligence, Chongqing, Peoples R China; [Fullgrabe, Anja; Moreno, Pablo; Kamatchinathan, Selvakumar; Kundu, Deepti Jaiswal; George, Nancy; Fexova, Silvie; Brazma, Alvis; Papatheodorou, Irene; Vizcaino, Juan Antonio; Perez-Riverol, Yasset] European Bioinformat Inst, European Mol Biol Lab, Wellcome Genome Campus, Hinxton, England; [Pfeuffer, Julianus] Free Univ Berlin, Algorithm Bioinformat, Berlin, Germany; [Pfeuffer, Julianus] Zuse Inst Berlin, Visualizat & Data Anal, Berlin, Germany; [Solovyeva, Elizaveta M.] Moscow Inst Phys & Technol, Dolgoprudnyi, Moscow Region, Russia; [Solovyeva, Elizaveta M.; Levitsky, Lev I.] Russian Acad Sci, NN Semenov Fed Res Ctr Chem Phys, VL Talrose Inst Energy Problems Chem Phys, Moscow, Russia; [Gruening, Bjoern] Albert Ludwigs Univ Freiburg, Dept Comp Sci, Bioinformat Grp, Freiburg, Germany; [Foell, Melanie Christine] Univ Freiburg, Fac Med, Med Ctr, Inst Surg Pathol, Freiburg, Germany; [Foell, Melanie Christine] Northeastern Univ, Khoury Coll Comp Sci, Boston, MA 02115 USA; [Griss, Johannes] Med Univ Vienna, Dept Dermatol, Vienna, Austria; [Vaudel, Marc] Univ Bergen, Dept Clin Sci, Bergen, Norway; [Audain, Enrique] Univ Klinikum Schleswig Holstein Kiel, Dept Congenital Heart Dis & Pediat Cardiol, Kiel, Germany; [Locard-Paulet, Marie; Webel, Henry] Univ Copenhagen, Novo Nordisk Fdn, Ctr Prot Res, Copenhagen, Denmark; [Turewicz, Michael; Eisenacher, Martin; Uszkoreit, Julian] Ruhr Univ Bochum, Med Fac, Med Proteom Ctr, D-44801 Bochum, Germany; [Turewicz, Michael; Eisenacher, Martin; Uszkoreit, Julian] Ruhr Univ Bochum, Ctr Prot Diagnost PRODI, Med Proteome Anal, D-44801 Bochum, Germany; [Van den Bossche, Tim] Univ Ghent, VIB, Ctr Med Biotechnol, Ghent, Belgium; [Van den Bossche, Tim] Univ Ghent, Dept Biomol Med, Fac Med & Hlth Sci, Ghent, Belgium; [Schwammle, Veit] Univ Southern Denmark, Dept Biochem & Mol Biol, Campusvej 55, DK-5230 Odense, Denmark; [Schulze, Stefan] Univ Penn, Dept Biol, Philadelphia, PA 19104 USA; [Bouyssie, David] Univ Toulouse, CNRS, UPS, Inst Pharmacol & Struct Biol, Toulouse, France; [Jayaram, Savita; Duggineni, Vinay Kumar] Nference Labs, Bengaluru 560017, KA, India; [Samaras, Patroklos; Wilhelm, Mathias] Tech Univ Munich, Chair Prote & Bioanalyt, Munich, Germany; [Choi, Meena] Genentech Inc, Dept Microchem Prote & Lipid, San Francisco, CA USA; [Wang, Mingxun; Bandeira, Nuno] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, La Jolla, CA 92093 USA; [Kohlbacher, Oliver; Sachsenberg, Timo] Univ Tubingen, Dept Comp Sci, Appl Bioinformat, D-72076 Tubingen, Germany; [Kohlbacher, Oliver] Univ Tubingen, Inst Biol & Med Informat, D-72076 Tubingen, Germany; [Kohlbacher, Oliver] Univ Hosp Tubingen, Inst Translat Bioinformat, D-72076 Tubingen, Germany; [Bandeira, Nuno] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, Dept Comp Sci & Engn, Ctr Computat Mass Spectrometry, San Diego, CA 92093 USA; [Deutsch, Eric W.] Inst Syst Biol, 401 Terry Ave N, Seattle, WA 98109 USA; [Bai, Mingze] Beijing Inst Life Omics, Beijing Proteome Res Ctr, Natl Ctr Prot Sci Beijing, State Key Lab Prote, Beijing 102206, Peoples R China Chongqing University of Posts & Telecommunications; European Molecular Biology Laboratory (EMBL); Free University of Berlin; Zuse Institute Berlin; Moscow Institute of Physics & Technology; Russian Academy of Sciences; N.N. Semenov Federal Research Centre for Chemical Physics, Russian Academy of Sciences; University of Freiburg; University of Freiburg; Northeastern University; Medical University of Vienna; University of Bergen; University of Kiel; Schleswig Holstein University Hospital; Novo Nordisk Foundation; University of Copenhagen; Ruhr University Bochum; Ruhr University Bochum; Flanders Institute for Biotechnology (VIB); Ghent University; Ghent University; University of Southern Denmark; University of Pennsylvania; Centre National de la Recherche Scientifique (CNRS); Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Technical University of Munich; Roche Holding; Genentech; University of California System; University of California San Diego; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; University of California System; University of California San Diego; Institute for Systems Biology (ISB) Bai, MZ (corresponding author), Chongqing Univ Posts & Telecommun, Chongqing Key Lab Big Data Bio Intelligence, Chongqing, Peoples R China.;Perez-Riverol, Y (corresponding author), European Bioinformat Inst, European Mol Biol Lab, Wellcome Genome Campus, Hinxton, England.;Levitsky, LI (corresponding author), Russian Acad Sci, NN Semenov Fed Res Ctr Chem Phys, VL Talrose Inst Energy Problems Chem Phys, Moscow, Russia.;Sachsenberg, T (corresponding author), Univ Tubingen, Dept Comp Sci, Appl Bioinformat, D-72076 Tubingen, Germany.;Bai, MZ (corresponding author), Beijing Inst Life Omics, Beijing Proteome Res Ctr, Natl Ctr Prot Sci Beijing, State Key Lab Prote, Beijing 102206, Peoples R China. baimz@cqupt.edu.cn; timo.sachsenberg@uni-tuebingen.de; lev.levitsky@phystech.edu; yperez@ebi.ac.uk Levitsky, Lev I./G-9702-2017; Perez-Riverol, Yasset/H-2873-2019; Uszkoreit, Julian/AAD-1957-2021; Schulze, Stefan/T-7360-2019; Kohlbacher, Oliver/B-7310-2008; Wang, Mingxun/HGV-1545-2022; Wilhelm, Mathias/HKP-1594-2023; Vizcaino, Juan Antonio/C-3691-2009; Uszkoreit, Julian/GQA-8317-2022 Levitsky, Lev I./0000-0002-4048-0876; Perez-Riverol, Yasset/0000-0001-6579-6941; Uszkoreit, Julian/0000-0001-7522-4007; Schulze, Stefan/0000-0002-4771-7987; Kohlbacher, Oliver/0000-0003-1739-4598; Wang, Mingxun/0000-0001-7647-6097; Wilhelm, Mathias/0000-0002-9224-3258; Vizcaino, Juan Antonio/0000-0002-3905-4335; Uszkoreit, Julian/0000-0001-7522-4007 Wellcome Trust [208391/Z/17/Z]; Novo Nordisk Foundation [NNF14CC0001]; de.NBI, a project of the German Federal Ministry of Education and Research (BMBF) [FKZ 031 A 534 A, FKZ 031 A 535 A]; Ministry of Innovation, Science, and Research of North-Rhine Westphalia, Germany; Research Foundation-Flanders [1S90918N]; NIGMS [R01GM087221, R24GM127667]; NSF [1933311, 1922871, 1817518]; National Key Research and Development Program of China [2017YFC0908404, 2017YFC0908405]; Natural Science Foundation of Chongqing, China [cstc2018jcyjAX0225]; ELIXIR Implementation Study; Russian Basic Science Foundation [18-29-13015] Wellcome Trust(Wellcome TrustEuropean Commission); Novo Nordisk Foundation(Novo Nordisk FoundationNovocure Limited); de.NBI, a project of the German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); Ministry of Innovation, Science, and Research of North-Rhine Westphalia, Germany; Research Foundation-Flanders(FWO); NIGMS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS)); NSF(National Science Foundation (NSF)); National Key Research and Development Program of China; Natural Science Foundation of Chongqing, China(Natural Science Foundation of Chongqing); ELIXIR Implementation Study; Russian Basic Science Foundation YPR, SK, DJK, and JAV would like to acknowledge funding from the Wellcome Trust grant number 208391/Z/17/Z and EMBL core funding. MLP is supported financially by the Novo Nordisk Foundation (Grant agreement NNF14CC0001). MT and TS are supported by de.NBI, a project of the German Federal Ministry of Education and Research (BMBF) [grant number FKZ 031 A 534 A and FKZ 031 A 535 A]. ME and JU are members of the Center for Protein Diagnostics (PRODI), a grant from the Ministry of Innovation, Science, and Research of North-Rhine Westphalia, Germany. TVDB is supported by the Research Foundation-Flanders (SB grant 1S90918N). EWD acknowledges NIGMS grants R01GM087221, R24GM127667, and NSF grants 1933311 and 1922871. SS was supported by the NSF grant 1817518. CD and MB are supported by the National Key Research and Development Program of China (2017YFC0908404, 2017YFC0908405) and the Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0225). Funds for the overall project were also made available by an ELIXIR Implementation Study. LIL and EMS are supported by the Russian Basic Science Foundation (grant #18-29-13015). 36 15 15 4 22 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. OCT 6 2021.0 12.0 1 5854 10.1038/s41467-021-26111-3 0.0 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics WF3WK Green Accepted, gold, Green Published, Green Submitted 2023-03-23 WOS:000706238600007 0
J Niu, YK; Tondi, B; Zhao, Y; Barni, M Niu, Yakun; Tondi, Benedetta; Zhao, Yao; Barni, Mauro Primary Quantization Matrix Estimation of Double Compressed JPEG Images via CNN IEEE SIGNAL PROCESSING LETTERS English Article Digital image forensics; deep learning for forensics; double JPEG compression; quantization matrix estimation Available model-based techniques for the estimation of the primary quantization matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a wide range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and Accuracy, especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches. [Niu, Yakun; Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Niu, Yakun; Zhao, Yao] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math Sci, I-53100 Siena, SI, Italy Beijing Jiaotong University; Beijing Jiaotong University; University of Siena Zhao, Y (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. niuyakun@bjtu.edu.cn; benedettatondi@gmail.com; yzhao@bjtu.edu.cn; barni@dii.unisi.it BARNI, Mauro/0000-0002-7368-0866; Niu, Yakun/0000-0002-2793-2823; Zhao, Yao/0000-0002-8581-9554 DARPA; Air Force Research Laboratory (AFRL) [FA8750-16-2-0173]; National Key Research and Development of China [2016YFB0800404]; National Science Foundation of China [U1936212, 61532005]; China Scholarship Council DARPA(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Air Force Research Laboratory (AFRL)(United States Department of DefenseUS Air Force Research Laboratory); National Key Research and Development of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported in part by DARPA and the Air Force Research Laboratory (AFRL) under Agreement Number FA8750-16-2-0173, in part by the National Key Research and Development of China under Grant 2016YFB0800404, in part by the National Science Foundation of China under Grant U1936212 and Grant 61532005, and in part by the China Scholarship Council. The U.S. Government is authorized to reproduce and distribute reprints forGovernmental purposes notwithstanding any copyright notation thereon. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Roberto Caldelli. 21 14 15 1 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2020.0 27.0 191 195 10.1109/LSP.2019.2962997 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering KI5TE Green Submitted 2023-03-23 WOS:000511411900009 0
J Wang, F; Tong, S; Sun, YQ; Xie, YS; Zhen, Z; Li, GQ; Cao, CM; Duic, N; Liu, DG Wang, Fei; Tong, Shuang; Sun, Yiqian; Xie, Yongsheng; Zhen, Zhao; Li, Guoqing; Cao, Chunmei; Duic, Neven; Liu, Dagui Wind process pattern forecasting based ultra-short-term wind speed hybrid prediction ENERGY English Article Wind process division; Ultra-short-term wind speed prediction; Wind process pattern; Hybrid prediction NEURAL-NETWORK; POWER; CLASSIFICATION; GENERATION; MODEL Wind power has received extensive attention due to its superiorities of clean and pollution-free. However, because of the randomness and volatility of wind power, accurate power prediction is needed to help its consumption. Wind speed is the key to wind power prediction, but traditional prediction method cannot accurately grasp the wind speed variation trend and the traditional wind process partition method has some defects, an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting is proposed in this paper. Firstly, a wind process (WP) division method considering the influence of wind speed on the operation state, mode switch and output power of wind turbine is proposed. Secondly, according to the operating characteristics of the wind turbine, all the WPs is classified into different wind process patterns (WPP), and the effectiveness of the classification is verified. Then, the Adaboost algorithm is used to forecast the WPP of the next 4 h. Finally, the wind speed hybrid prediction model of each pattern is established, the corresponding model is automatically selected based on WPP to predict the wind speed. Simulation results show that the proposed model can reliably forecast future WPP and the prediction accuracy is better than conventional models. (C) 2022 Elsevier Ltd. All rights reserved. [Wang, Fei; Tong, Shuang; Zhen, Zhao] North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China; [Wang, Fei] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China; [Wang, Fei] North China Elect Power Univ, Hebei Key Lab Distributed Energy Storage & Microg, Baoding 071003, Peoples R China; [Sun, Yiqian; Xie, Yongsheng; Li, Guoqing; Liu, Dagui] State Grid Xinjiang Elect Power Co Ltd, Urumqi 830018, Peoples R China; [Cao, Chunmei] North China Elect Power Univ, Dept Math & Phys, Baoding 071003, Peoples R China; [Duic, Neven] Univ Zagreb, Fac Mech Engn & Naval Architecture, Dept Energy Power & Environm Engn, Ivana Lucica 5, HR-10000 Zagreb, Croatia North China Electric Power University; North China Electric Power University; North China Electric Power University; State Grid Corporation of China; North China Electric Power University; University of Zagreb; University of Zagreb Faculty of Mechanical Engineering & Naval Architecture Zhen, Z (corresponding author), North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China. zhenzhao@ncepu.edu.cn National Natural Science Foundation of China [52007092]; Science & Technology Project of State Grid Xinjiang Electric Power Co., Ltd, China [SGXJ0000TKJS2100234] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science & Technology Project of State Grid Xinjiang Electric Power Co., Ltd, China This work is supported by National Natural Science Foundation of China (52007092) and the Science & Technology Project of State Grid Xinjiang Electric Power Co., Ltd, China (SGXJ0000TKJS2100234). 41 2 2 27 39 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy SEP 15 2022.0 255.0 124509 10.1016/j.energy.2022.124509 0.0 12 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 3I8FY 2023-03-23 WOS:000832946500005 0
J Sheng, CC; Zhu, XZ; Xu, HY; Pietikainen, M; Liu, L Sheng, Changchong; Zhu, Xinzhong; Xu, Huiying; Pietikainen, Matti; Liu, Li Adaptive Semantic-Spatio-Temporal Graph Convolutional Network for Lip Reading IEEE TRANSACTIONS ON MULTIMEDIA English Article Lips; Visualization; Feature extraction; Mouth; Task analysis; Convolution; Hidden Markov models; Lip reading; semantic-spatio-temporal; adaptive graph convolution network; two-stream FEATURES The goal of this work is to recognize words, phrases, and sentences being spoken by a talking face without given the audio. Current deep learning approaches for lip reading focus on exploring the appearance and optical flow information of videos. However, these methods do not fully exploit the characteristics of lip motion. In addition to appearance and optical flow, the mouth contour deformation usually conveys significant information that is complementary to others. However, the modeling of dynamic mouth contour has received little attention than that of appearance and optical flow. In this work, we propose a novel model of dynamic mouth contours called Adaptive Semantic-Spatio-Temporal Graph Convolution Network (ASST-GCN), to go beyond previous methods by automatically learning both the spatial and temporal information from videos. To combine the complementary information from appearance and mouth contour, a two-stream visual front-end network is proposed. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art lip reading methods on several large-scale lip reading benchmarks. [Sheng, Changchong] Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Peoples R China; [Sheng, Changchong; Pietikainen, Matti; Liu, Li] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland; [Zhu, Xinzhong; Xu, Huiying] Res Inst Ningbo Cixing Co Ltd, Ningbo 315336, Peoples R China; [Zhu, Xinzhong; Xu, Huiying] Zhejiang Normal Univ, Coll Math Phys & Informat Engn, Jinhua 321004, Zhejiang, Peoples R China National University of Defense Technology - China; University of Oulu; Zhejiang Normal University Zhu, XZ (corresponding author), Res Inst Ningbo Cixing Co Ltd, Ningbo 315336, Peoples R China. shengcc.nudt@gmail.com; zxz@zjnu.edu.cn; xhy@zjnu.edu.cn; matti.pietikainen@oulu.fi; li.liu@oulu.fi Pietikainen, Matti/0000-0003-2263-6731; Liu, li/0000-0002-2011-2873 Academy of Finland [331883]; Outstanding Talents of Ten Thousand Talents Plan in Zhejiang Province project [2018R51001]; Natural Science Foundation of China [61976196] Academy of Finland(Academy of Finland); Outstanding Talents of Ten Thousand Talents Plan in Zhejiang Province project; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Academy of Finland under Grant 331883, in part by the Outstanding Talents of Ten Thousand Talents Plan in Zhejiang Province project under Grant 2018R51001, and in part by the Natural Science Foundation of China project under Grant 61976196. 73 1 1 3 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia AUG 16 2021.0 24.0 3545 3557 10.1109/TMM.2021.3102433 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 2W7NH Green Accepted 2023-03-23 WOS:000824706600001 0
J Kashyap, R Kashyap, Ravi Behavioural Bias Benefits: Beating Benchmarks By Bundling Bouncy Baskets ACCOUNTING AND FINANCE English Article Pandemic; Investment Hypothesis; Market Rebound; Risk Management; Volatility; Big Data ACTIVE INVESTMENT STRATEGIES; STOCK-MARKET; GREAT-DEPRESSION; ORIGIN; RECIPROCITY; VOLATILITY; IMPACT; CRASH; TRUST; GAME We consider in detail an investment strategy, titled 'the bounce basket', designed for someone to express a bullish view on the market by allowing them to take long positions on securities that would benefit the most from a rally in the markets. We demonstrate the use of quantitative metrics and large amounts of historical data towards decision-making goals. This investment concept combines macroeconomic views with characteristics of individual securities to beat the market returns. The central idea of this theme is to identity securities from a regional perspective that are heavily shorted and yet are fundamentally sound with at least a minimum buy rating from a consensus of stock analysts covering the securities. We discuss the components of creating such a strategy including the mechanics of constructing the portfolio. Using simulations, in which securities lending data is modelled as geometric Brownian motions, we provide a few flavors of creating a ranking of securities to identity the ones that are heavily shorted. An investment strategy of this kind will be ideal in market scenarios when a downturn happens due to unexpected extreme events and the markets are anticipated to bounce back thereafter. This situation is especially applicable to incidents being observed, and relevant proceedings, during the Coronavirus pandemic in 2020-2021. This strategy is one particular way to overcome a potential behavioural bias related to investing, which we term the 'rebound effect'. [Kashyap, Ravi] Estonian Business Sch, Tallinn, Estonia; [Kashyap, Ravi] Format Fi, Hong Kong, Peoples R China; [Kashyap, Ravi] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China Estonian Business School; City University of Hong Kong Kashyap, R (corresponding author), Estonian Business Sch, Tallinn, Estonia.;Kashyap, R (corresponding author), Format Fi, Hong Kong, Peoples R China.;Kashyap, R (corresponding author), City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China. Kashyap, Ravi/0000-0002-9315-2390 90 0 0 4 9 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0810-5391 1467-629X ACCOUNT FINANC Account . Financ. SEP 2021.0 61.0 3 4885 4921 10.1111/acfi.12826 0.0 AUG 2021 37 Business, Finance Social Science Citation Index (SSCI) Business & Economics UK5GY Green Submitted 2023-03-23 WOS:000683166000001 0
J Gao, YG; Yao, XY; Zhai, YM; Li, L; Li, HN; Sun, XQ; Yu, P; Xue, TK; Li, YZ; Hu, YZ Gao, Yuge; Yao, Xinyu; Zhai, Yumeng; Li, Li; Li, Huini; Sun, Xianqi; Yu, Pei; Xue, Tiankuo; Li, Yuzhen; Hu, Yizhou Single cell transcriptional zonation of human psoriasis skin identifies an alternative immunoregulatory axis conducted by skin resident cells CELL DEATH & DISEASE English Article ANTIGEN PRESENTATION; DENDRITIC CELLS; CLASS-I; DIFFERENTIATION; KERATINOCYTES; EXPRESSION; RECEPTOR; IMMUNOPATHOGENESIS; SUSCEPTIBILITY; HETEROGENEITY Psoriasis is the most common skin disease in adults. Current experimental and clinical evidences suggested the infiltrating immune cells could target local skin cells and thus induce psoriatic phenotype. However, recent studies indicated the existence of a potential feedback signaling loop from local resident skin cells to infiltrating immune cells. Here, we deconstructed the full-thickness human skins of both healthy donors and patients with psoriasis vulgaris at single cell transcriptional level, and further built a neural-network classifier to evaluate the evolutional conservation of skin cell types between mouse and human. Last, we systematically evaluated the intrinsic and intercellular molecular alterations of each cell type between healthy and psoriatic skin. Cross-checking with psoriasis susceptibility gene loci, cell-type based differential expression, and ligand-receptor communication revealed that the resident psoriatic skin cells including mesenchymal and epidermis cell types, which specifically harbored the target genes of psoriasis susceptibility loci, intensively evoked the expression of major histocompatibility complex (MHC) genes, upregulated interferon (INF), tumor necrosis factor (TNF) signalling and increased cytokine gene expression for primarily aiming the neighboring dendritic cells in psoriasis. The comprehensive exploration and pathological observation of psoriasis patient biopsies proposed an uncovered immunoregulatory axis from skin local resident cells to immune cells, thus provided a novel insight for psoriasis treatment. In addition, we published a user-friendly website to exhibit the transcriptional change of each cell type between healthy and psoriatic human skin. [Gao, Yuge; Zhai, Yumeng; Sun, Xianqi; Yu, Pei; Xue, Tiankuo; Li, Yuzhen] Harbin Med Univ, Dept Dermatol, Affiliated Hosp 2, 246 Xuefu Rd, Harbin 150001, Peoples R China; [Gao, Yuge; Li, Huini] Univ Helsinki, HILIFE Unit, Inst Biotechnol, Helsinki 00014, Finland; [Yao, Xinyu] Peking Univ, Dept Dermatol, Hosp 1, 8 XiShiKu St, Beijing 100034, Peoples R China; [Li, Li] Harbin Med Univ, Dept Oncol, Affiliated Hosp 2, 246 Xuefu Rd, Harbin 150001, Peoples R China; [Hu, Yizhou] Karolinska Inst, Dept Med Biochem & Biophys, Div Mol Neurobiol, Solnavagen 9, S-17165 Stockholm, Sweden Harbin Medical University; University of Helsinki; Peking University; Harbin Medical University; Karolinska Institutet Li, YZ (corresponding author), Harbin Med Univ, Dept Dermatol, Affiliated Hosp 2, 246 Xuefu Rd, Harbin 150001, Peoples R China.;Hu, YZ (corresponding author), Karolinska Inst, Dept Med Biochem & Biophys, Div Mol Neurobiol, Solnavagen 9, S-17165 Stockholm, Sweden. lyzdermatology@163.com; yizhou.hu@ki.se Hu, Yizhou/AAP-4047-2020 Hu, Yizhou/0000-0002-2635-0258; Li, Huini/0000-0002-7884-9624 National Natural Science Foundation of China [81702464]; Swedish Society for Medical Research Scholarship National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Swedish Society for Medical Research Scholarship This work is supported by grants from the National Natural Science Foundation of China (81972925), the National Natural Science Foundation of China (81702464) and the Swedish Society for Medical Research Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 72 15 15 1 6 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2041-4889 CELL DEATH DIS Cell Death Dis. MAY 6 2021.0 12.0 5 450 10.1038/s41419-021-03724-6 0.0 13 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology SK5LP 33958582.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000656257000002 0
J Workman, RL; Burkert, VD; Crede, V; Klempt, E; Thoma, U; Tiator, L; Agashe, K; Aielli, G; Allanach, BC; Amsler, C; Antonelli, M; Aschenauer, EC; Asner, DM; Baer, H; Banerjee, S; Barnett, RM; Baudis, L; Bauer, CW; Beatty, JJ; Belousov, VI; Beringer, J; Bettini, A; Biebel, O; Black, KM; Blucher, E; Bonventre, R; Bryzgalov, VV; Buchmuller, O; Bychkov, MA; Cahn, RN; Carena, M; Ceccucci, A; Cerri, A; Chivukula, RS; Cowan, G; Cranmer, K; Cremonesi, O; D'Ambrosio, G; Damour, T; de Florian, D; de Gouvea, A; DeGrand, T; de Jong, P; Demers, S; Dobrescu, BA; D'Onofrio, M; Doser, M; Dreiner, HK; Eerola, P; Egede, U; Eidelman, S; El-Khadra, AX; Ellis, J; Eno, SC; Erler, J; Ezhela, VV; Fetscher, W; Fields, BD; Freitas, A; Gallagher, H; Gershtein, Y; Gherghetta, T; Gonzalez-Garcia, MC; Goodman, M; Grab, C; Gritsan, AV; Grojean, C; Groom, DE; Grunewald, M; Gurtu, A; Gutsche, T; Haber, HE; Hamel, M; Hanhart, C; Hashimoto, S; Hayato, Y; Hebecker, A; Heinemeyer, S; Hernandez-Rey, JJ; Hikasa, K; Hisano, J; Hocker, A; Holder, J; Hsu, L; Huston, J; Hyodo, T; Ianni, A; Kado, M; Karliner, M; Katz, UF; Kenzie, M; Khoze, VA; Klein, SR; Krauss, F; Kreps, M; Krizan, P; Krusche, B; Kwon, Y; Lahav, O; Laiho, J; Lellouch, LP; Lesgourgues, J; Liddle, AR; Ligeti, Z; Lin, CJ; Lippmann, C; Liss, TM; Littenberg, L; Lourenco, C; Lugovsky, KS; Lugovsky, SB; Lusiani, A; Makida, Y; Maltoni, F; Mannel, T; Manohar, AV; Marciano, WJ; Masoni, A; Matthews, J; Meissner, UG; Melzer-Pellmann, IA; Mikhasenko, M; Miller, DJ; Milstead, D; Mitchell, RE; Monig, K; Molaro, P; Moortgat, F; Moskovic, M; Nakamura, K; Narain, M; Nason, P; Navas, S; Nelles, A; Neubert, M; Nevski, P; Nir, Y; Olive, KA; Patrignani, C; Peacock, JA; Petrov, VA; Pianori, E; Pich, A; Piepke, A; Pietropaolo, F; Pomarol, A; Pordes, S; Profumo, S; Quadt, A; Rabbertz, K; Rademacker, J; Raffelt, G; Ramsey-Musolf, M; Ratcliff, BN; Richardson, P; Ringwald, A; Robinson, DJ; Roesler, S; Rolli, S; Romaniouk, A; Rosenberg, LJ; Rosner, JL; Rybka, G; Ryskin, MG; Ryutin, RA; Sakai, Y; Sarkar, S; Sauli, F; Schneider, O; Schonert, S; Scholberg, K; Schwartz, AJ; Schwiening, J; Scott, D; Sefkow, F; Seljak, U; Sharma, V; Sharpe, SR; Shiltsev, V; Signorelli, G; Silari, M; Simon, F; Sjostrand, T; Skands, P; Skwarnicki, T; Smoot, GF; Soffer, A; Sozzi, MS; Spanier, S; Spiering, C; Stahl, A; Stone, SL; Sumino, Y; Syphers, MJ; Takahashi, F; Tanabashi, M; Tanaka, J; Tasevsky, M; Terao, K; Terashi, K; Terning, J; Thorne, RS; Titov, M; Tkachenko, NP; Tovey, DR; Trabelsi, K; Urquijo, P; Valencia, G; Van de Water, R; Varelas, N; Venanzoni, G; Verde, L; Vivarelli, I; Vogel, P; Vogelsang, W; Vorobyev, V; Wakely, SP; Walkowiak, W; Walter, CW; Wands, D; Weinberg, DH; Weinberg, EJ; Wermes, N; White, M; Wiencke, LR; Willocq, S; Wohl, CG; Woody, CL; Yao, WM; Yokoyama, M; Yoshida, R; Zanderighi, G; Zeller, GP; Zenin, OV; Zhu, RY; Zhu, SL; Zimmermann, F; Zyla, PA; Anderson, J; Basaglia, T; Schaffner, P; Zheng, W Workman, R. L.; Burkert, V. D.; Crede, V; Klempt, E.; Thoma, U.; Tiator, L.; Agashe, K.; Aielli, G.; Allanach, B. C.; Amsler, C.; Antonelli, M.; Aschenauer, E. C.; Asner, D. M.; Baer, H.; Banerjee, Sw; Barnett, R. M.; Baudis, L.; Bauer, C. W.; Beatty, J. J.; Belousov, V., I; Beringer, J.; Bettini, A.; Biebel, O.; Black, K. M.; Blucher, E.; Bonventre, R.; Bryzgalov, V. V.; Buchmuller, O.; Bychkov, M. A.; Cahn, R. N.; Carena, M.; Ceccucci, A.; Cerri, A.; Chivukula, R. Sekhar; Cowan, G.; Cranmer, K.; Cremonesi, O.; D'Ambrosio, G.; Damour, T.; de Florian, D.; de Gouvea, A.; DeGrand, T.; de Jong, P.; Demers, S.; Dobrescu, B. A.; D'Onofrio, M.; Doser, M.; Dreiner, H. K.; Eerola, P.; Egede, U.; Eidelman, S.; El-Khadra, A. X.; Ellis, J.; Eno, S. C.; Erler, J.; Ezhela, V. V.; Fetscher, W.; Fields, B. D.; Freitas, A.; Gallagher, H.; Gershtein, Y.; Gherghetta, T.; Gonzalez-Garcia, M. C.; Goodman, M.; Grab, C.; Gritsan, A., V; Grojean, C.; Groom, D. E.; Grunewald, M.; Gurtu, A.; Gutsche, T.; Haber, H. E.; Hamel, Matthieu; Hanhart, C.; Hashimoto, S.; Hayato, Y.; Hebecker, A.; Heinemeyer, S.; Hernandez-Rey, J. J.; Hikasa, K.; Hisano, J.; Hocker, A.; Holder, J.; Hsu, L.; Huston, J.; Hyodo, T.; Ianni, Al; Kado, M.; Karliner, M.; Katz, U. F.; Kenzie, M.; Khoze, V. A.; Klein, S. R.; Krauss, F.; Kreps, M.; Krizan, P.; Krusche, B.; Kwon, Y.; Lahav, O.; Laiho, J.; Lellouch, L. P.; Lesgourgues, J.; Liddle, A. R.; Ligeti, Z.; Lin, C-J; Lippmann, C.; Liss, T. M.; Littenberg, L.; Lourenco, C.; Lugovsky, K. S.; Lugovsky, S. B.; Lusiani, A.; Makida, Y.; Maltoni, F.; Mannel, T.; Manohar, A., V; Marciano, W. J.; Masoni, A.; Matthews, J.; Meissner, U-G; Melzer-Pellmann, I-A; Mikhasenko, M.; Miller, D. J.; Milstead, D.; Mitchell, R. E.; Moenig, K.; Molaro, P.; Moortgat, F.; Moskovic, M.; Nakamura, K.; Narain, M.; Nason, P.; Navas, S.; Nelles, A.; Neubert, M.; Nevski, P.; Nir, Y.; Olive, K. A.; Patrignani, C.; Peacock, J. A.; Petrov, V. A.; Pianori, E.; Pich, A.; Piepke, A.; Pietropaolo, F.; Pomarol, A.; Pordes, S.; Profumo, S.; Quadt, A.; Rabbertz, K.; Rademacker, J.; Raffelt, G.; Ramsey-Musolf, M.; Ratcliff, B. N.; Richardson, P.; Ringwald, A.; Robinson, D. J.; Roesler, S.; Rolli, S.; Romaniouk, A.; Rosenberg, L. J.; Rosner, J. L.; Rybka, G.; Ryskin, M. G.; Ryutin, R. A.; Sakai, Y.; Sarkar, S.; Sauli, F.; Schneider, O.; Schoenert, S.; Scholberg, K.; Schwartz, A. J.; Schwiening, J.; Scott, D.; Sefkow, F.; Seljak, U.; Sharma, V; Sharpe, S. R.; Shiltsev, V; Signorelli, G.; Silari, M.; Simon, F.; Sjostrand, T.; Skands, P.; Skwarnicki, T.; Smoot, G. F.; Soffer, A.; Sozzi, M. S.; Spanier, S.; Spiering, C.; Stahl, A.; Stone, S. L.; Sumino, Y.; Syphers, M. J.; Takahashi, F.; Tanabashi, M.; Tanaka, J.; Tasevsky, M.; Terao, K.; Terashi, K.; Terning, J.; Thorne, R. S.; Titov, M.; Tkachenko, N. P.; Tovey, D. R.; Trabelsi, K.; Urquijo, P.; Valencia, G.; Van de Water, R.; Varelas, N.; Venanzoni, G.; Verde, L.; Vivarelli, I; Vogel, P.; Vogelsang, W.; Vorobyev, V.; Wakely, S. P.; Walkowiak, W.; Walter, C. W.; Wands, D.; Weinberg, D. H.; Weinberg, E. J.; Wermes, N.; White, M.; Wiencke, L. R.; Willocq, S.; Wohl, C. G.; Woody, C. L.; Yao, W-M; Yokoyama, M.; Yoshida, R.; Zanderighi, G.; Zeller, G. P.; Zenin, O., V; Zhu, R-Y; Zhu, Shi-Lin; Zimmermann, F.; Zyla, P. A.; Anderson, J.; Basaglia, T.; Schaffner, P.; Zheng, W. REVIEW OF PARTICLE PHYSICS PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS English Review PROTON-PROTON COLLISIONS; DEEP-INELASTIC SCATTERING; HIGGS-BOSON PRODUCTION; TO-LEADING-ORDER; ANOMALOUS MAGNETIC-MOMENT; SQUARE-ROOT-S; GRAND UNIFIED THEORIES; COLD DARK-MATTER; ELECTROWEAK RADIATIVE-CORRECTIONS; FERMION-PAIR PRODUCTION The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app. [Workman, R. L.] George Washington Univ, Dept Phys, Washington, DC 20052 USA; [Burkert, V. D.] Jefferson Lab, Newport News, VA USA; [Crede, V] Florida State Univ, Dept Phys, Tallahassee, FL 32306 USA; [Klempt, E.; Thoma, U.; Meissner, U-G] Univ Bonn, Helmholtz Inst Strahlen & Kernphys, Bonn, Germany; [Tiator, L.; Erler, J.] Johannes Gutenberg Univ Mainz, Inst Kernphys, Mainz, Germany; [Agashe, K.; Eno, S. C.] Univ Maryland, Dept Phys, College Pk, MD 20742 USA; [Aielli, G.] Univ Roma Tor Vergata, Rome, Italy; [Allanach, B. C.] Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England; [Amsler, C.] Austrian Acad Sci, Stefan Meyer Inst Subatom Phys, Vienna, Austria; [Antonelli, M.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Aschenauer, E. C.; Asner, D. M.; Littenberg, L.; Marciano, W. J.; Nevski, P.; Woody, C. L.] Brookhaven Natl Lab, Nucl & Particle Phys Directorate, Upton, NY 11973 USA; [Baer, H.] Univ Oklahoma, Dept Phys & Astron, Norman, OK 73019 USA; [Banerjee, Sw] Univ Louisville, Louisville, KY 40292 USA; [Barnett, R. M.; Bauer, C. W.; Beringer, J.; Bonventre, R.; Cahn, R. N.; Groom, D. E.; Ligeti, Z.; Lin, C-J; Lugovsky, K. S.; Pianori, E.; Robinson, D. J.; Seljak, U.; Smoot, G. F.; White, M.; Wohl, C. G.; Yao, W-M; Zyla, P. A.; Anderson, J.; Schaffner, P.] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA USA; [Baudis, L.] Univ Zurich, Phys Inst, Zurich, Switzerland; [Beatty, J. J.] Ohio State Univ, Dept Phys, 174 W 18th Ave, Columbus, OH 43210 USA; [Belousov, V., I; Bryzgalov, V. V.; Ezhela, V. V.; Lugovsky, K. S.; Lugovsky, S. B.; Petrov, V. A.; Ryutin, R. A.; Tkachenko, N. P.; Zenin, O., V] Natl Res Ctr, Inst High Energy Phys, COMPAS Grp, Kurchatov Inst, Protvino, Russia; [Bettini, A.] Univ Padua, Ist Nazl Fis Nucl, Padua, Italy; [Bettini, A.] Univ Padua, Dipartimento Fis Astron, Padua, Italy; [Biebel, O.; Mikhasenko, M.] Ludwig Maximilians Univ Munchen, Fak Phys, Munich, Germany; [Black, K. M.] Univ Wisconsin, Dept Phys, 1150 Univ Ave, Madison, WI 53706 USA; [Blucher, E.; Carena, M.; Rosner, J. L.; Wakely, S. P.] Univ Chicago, Enrico Fermi Inst, Chicago, IL 60637 USA; [Blucher, E.; Carena, M.; Rosner, J. L.; Wakely, S. P.] Univ Chicago, Dept Phys, Chicago, IL 60637 USA; [Buchmuller, O.] Imperial Coll, Blackett Lab, High Energy Phys Grp, London, England; [Bychkov, M. A.] Univ Virginia, Dept Phys, Charlottesville, VA 22901 USA; [Carena, M.; Dobrescu, B. A.; Hsu, L.; Pordes, S.; Shiltsev, V; Syphers, M. J.; Van de Water, R.; Zeller, G. P.] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA; [Carena, M.; Wakely, S. P.] Univ Chicago, Kavli Inst Cosmol Phys, Chicago, IL 60637 USA; [Ceccucci, A.; Doser, M.; Ellis, J.; Gurtu, A.; Hocker, A.; Lourenco, C.; Moortgat, F.; Moskovic, M.; Pietropaolo, F.; Roesler, S.; Sauli, F.; Silari, M.; Zimmermann, F.; Basaglia, T.] CERN, European Org Nucl Res, Geneva, Switzerland; [Cerri, A.; Vivarelli, I] Univ Sussex, Dept Phys & Astron, Brighton, E Sussex, England; [Chivukula, R. Sekhar; Manohar, A., V; Sharma, V] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA; [Cowan, G.] Royal Holloway Univ London, Dept Phys, London, England; [Cranmer, K.] SUNY Stony Brook, Inst Theoret Phys, Stony Brook, NY 11794 USA; [Cremonesi, O.; Nason, P.] Ist Nazl Fis Nucl, Sez Milano Bicocca, Piazza Sci, Milan, Italy; [D'Ambrosio, G.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Damour, T.] Inst Hautes Etud Sci, Bures Sur Yvette, France; [de Florian, D.] UNSAM Univ Nacl San Martin, Int Ctr Adv Studies ICAS, Buenos Aires, DF, Argentina; [de Florian, D.] Inst Ciencias Fis ICIFI, Buenos Aires, DF, Argentina; [de Gouvea, A.] Northwestern Univ, Dept Phys & Astron, Evanston, IL USA; [DeGrand, T.] Univ Colorado, Dept Phys, Boulder, CO 80309 USA; [de Jong, P.] Nikhef, Amsterdam, Netherlands; [de Jong, P.] Univ Amsterdam, Amsterdam, Netherlands; [Demers, S.] Yale Univ, New Haven, CT USA; [D'Onofrio, M.] Univ Liverpool, Dept Phys, Liverpool, Merseyside, England; [Dreiner, H. K.; Wermes, N.] Univ Bonn, Phys Inst, Bonn, Germany; [Eerola, P.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Egede, U.; Skands, P.; Valencia, G.] Monash Univ, Sch Phys & Astron, Melbourne, Vic, Australia; [Eidelman, S.; Vorobyev, V.] Budker Inst Nucl Phys SB RAS, Novosibirsk, Russia; [Eidelman, S.; Vorobyev, V.] Novosibirsk State Univ, Novosibirsk, Russia; [El-Khadra, A. X.; Fields, B. D.] Univ Illinois, Dept Phys, 1110 W Green St, Urbana, IL 61801 USA; [Ellis, J.] Kings Coll London, Dept Phys, London, England; [Fetscher, W.; Grab, C.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys, Zurich, Switzerland; [Fields, B. D.] Univ Illinois, Dept Astron, 1002 W Green St, Urbana, IL 61801 USA; [Freitas, A.] Univ Pittsburgh, Dept Phys & Astron, Pittsburgh, PA 15260 USA; [Gallagher, H.] Tufts Univ, Dept Phys & Astron, Medford, MA 02155 USA; [Gershtein, Y.] Rutgers State Univ, Dept Phys & Astron, Piscataway, NJ USA; [Gherghetta, T.; Olive, K. A.] Univ Minnesota, Sch Phys & Astron, Minneapolis, MN 55455 USA; [Gonzalez-Garcia, M. C.] SUNY Stony Brook, CN Yang Inst Theoret Phys, Stony Brook, NY 11794 USA; [Gonzalez-Garcia, M. C.; Verde, L.] Inst Catalana Recerca & Estudis Avancats, Barcelona, Spain; [Gonzalez-Garcia, M. C.; Verde, L.] Univ Barcelona, Inst Ciencias Cosmos ICC, Barcelona, Spain; [Goodman, M.; Yoshida, R.] Argonne Natl Lab, 9700 S Cass Ave, Argonne, IL 60439 USA; [Gritsan, A., V] Johns Hopkins Univ, Baltimore, MD USA; [Grojean, C.; Melzer-Pellmann, I-A; Ringwald, A.; Sefkow, F.] Deutsch Elektronen Synchrotron DESY, Hamburg, Germany; [Grojean, C.] Humboldt Univ, Inst Phys, Berlin, Germany; [Grunewald, M.] Univ Coll Dublin, Sch Phys, Dublin, Ireland; [Gurtu, A.] TIFR, Mumbai, Maharashtra, India; [Gutsche, T.; Vogelsang, W.] Univ Tubingen, Inst Theoret Phys, Tubingen, Germany; [Haber, H. E.; Profumo, S.] Univ Calif Santa Cruz, Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA; [Hamel, Matthieu] Univ Paris Saclay, LIST, CEA, F-91120 Palaiseau, France; [Hanhart, C.; Meissner, U-G] Forschungszentrum Julich, Inst Kernphys, Julich, Germany; [Hanhart, C.; Meissner, U-G] Forschungszentrum Julich, Inst Adv Simulat, Julich, Germany; [Hashimoto, S.; Makida, Y.; Nakamura, K.; Sakai, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Hayato, Y.] Univ Tokyo, Kamioka Observ, ICRR, Tokyo, Japan; [Hayato, Y.; Nakamura, K.; Yokoyama, M.] Univ Tokyo, Univ Tokyo Inst Adv Study, Kavli IPMU WPI, Kashiwa, Chiba, Japan; [Hebecker, A.] Heidelberg Univ, Inst Theoret Phys, Heidelberg, Germany; [Heinemeyer, S.] Univ Autonoma Madrid, Inst Fis Teor UAM CSIC, Madrid, Spain; [Hernandez-Rey, J. J.; Pich, A.] Univ Valencia, IFIC Inst Fis Corpuscular, CSIC, Valencia, Spain; [Hikasa, K.] Tohoku Univ, Div Interdisciplinary Adv Res & Educ, Sendai, Miyagi, Japan; [Hikasa, K.] Tohoku Univ, Inst Liberal Arts & Sci, Sendai, Miyagi, Japan; [Hikasa, K.; Sumino, Y.; Takahashi, F.] Tohoku Univ, Dept Phys, Sendai, Miyagi, Japan; [Hisano, J.; Tanabashi, M.] Nagoya Univ, Kobayashi Maskawa Inst, Nagoya, Aichi, Japan; [Holder, J.] Univ Delaware, Dept Phys & Astron, Newark, DE 19716 USA; [Holder, J.] Univ Delaware, Bartol Res Inst, Newark, DE 19716 USA; [Huston, J.] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA; [Hyodo, T.] Tokyo Metropolitan Univ, Dept Phys, Tokyo, Japan; [Ianni, Al] Ist Nazl Fis Nucl, Lab Nazl Gran Sasso, Assergi, Italy; [Kado, M.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Kado, M.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Kado, M.; Trabelsi, K.] Univ Paris Saclay, IJCLab, CNRS IN2P3, Orsay, France; [Karliner, M.; Soffer, A.] Tel Aviv Univ, Dept Particle Phys, Tel Aviv, Israel; [Katz, U. F.; Nelles, A.] Friedrich Alexander Univ Erlangen Nurnberg, Erlangen Ctr Astroparticle Phys, Erlangen, Germany; [Kenzie, M.; Kreps, M.] Univ Warwick, Dept Phys, Coventry, W Midlands, England; [Khoze, V. A.; Krauss, F.; Richardson, P.] Univ Durham, Inst Particle Phys Phenomenol, Dept Phys, Durham, England; [Klein, S. R.] Lawrence Berkeley Natl Lab, Nucl Sci Div, Berkeley, CA USA; [Klein, S. R.; Seljak, U.; Smoot, G. F.; White, M.] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA; [Krizan, P.] Univ Ljubljana, Fac Math & Phys, Jadranska, Slovenia; [Krizan, P.] Jozef Stefan Inst, Ljubljana, Slovenia; [Krusche, B.] Univ Basel, Inst Phys, Basel, Switzerland; [Kwon, Y.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Lahav, O.; Thorne, R. S.] UCL, Dept Phys & Astron, London, England; [Laiho, J.; Skwarnicki, T.; Stone, S. L.] Syracuse Univ, Dept Phys, Syracuse, NY USA; [Lellouch, L. P.] Aix Marseille Univ, Univ Toulon, CPT, CNRS, Marseille, France; [Lesgourgues, J.] Rhein Westfal TH Aachen, Inst Theoret Particle Phys & Cosmol TTK, Aachen, Germany; [Liddle, A. R.] Univ Lisbon, Inst Astrofis & Ciencias Espaco, Lisbon, Portugal; [Lippmann, C.; Schwiening, J.] Helmholtzzentrum Schwerionenforsch, GSI, Darmstadt, Germany; [Liss, T. M.] CUNY City Coll, New York, NY 10031 USA; [Lusiani, A.] Scuola Normale Super Pisa, Pisa, Italy; [Lusiani, A.; Signorelli, G.; Venanzoni, G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Maltoni, F.] Catholic Univ Louvain, Ctr Cosmol Particle Phys & Phenomenol CP3, Louvain La Neuve, Belgium; [Maltoni, F.] Univ Bologna, Bologna, Italy; [Maltoni, F.] Ist Nazl Fis Nucl, Dipartimento Fis & Astron, Bologna, Italy; [Mannel, T.; Walkowiak, W.] Univ Siegen, Dept Phys, Siegen, Germany; [Masoni, A.] Ist Nazl Fis Nucl, Sez Cagliari, Monserrato, Italy; [Matthews, J.] Louisiana State Univ, Dept Phys & Astron, Baton Rouge, LA 70803 USA; [Miller, D. 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E.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Moenig, K.; Nelles, A.; Spiering, C.] Deutsch Elektronen Synchrotron DESY, Zeuthen, Germany; [Molaro, P.] INAF OATS, Trieste, Italy; [Molaro, P.] Inst Fundamental Phys Universe, Trieste, Italy; [Moortgat, F.] Univ Ghent, Dept Phys & Astron, Ghent, Belgium; [Narain, M.] Brown Univ, Dept Phys, Providence, RI 02912 USA; [Nason, P.] Univ Milano Bicocca, Dip Fis G Occhialini, Milan, Italy; [Navas, S.] Univ Granada, Dept Fis Teor & Cosmos, Granada, Spain; [Navas, S.] CAFPE, Granada, Spain; [Neubert, M.] Johannes Gutenberg Univ Mainz, PRISMA Cluster Excellence, Mainz, Germany; [Neubert, M.] Johannes Gutenberg Univ Mainz, Mainz Inst Theoret Phys, Mainz, Germany; [Nir, Y.] Weizmann Inst Sci, Dept Particle Phys & Astrophys, Rehovot, Israel; [Patrignani, C.] Univ Bologna, Rimini, Italy; [Patrignani, C.] Ist Nazl Fis Nucl, Dip Sci Qualita Vita, Rimini, Italy; [Peacock, J. A.] Univ Edinburgh, Inst Astron, Royal Observ, Edinburgh, Midlothian, Scotland; [Piepke, A.] Univ Alabama, Dept Phys & Astron, Tuscaloosa, AL 35487 USA; [Pietropaolo, F.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Pomarol, A.] Univ Autonoma Barcelona, Dept Fis, Barcelona, Spain; [Pomarol, A.] Univ Autonoma Barcelona, IFAE, Barcelona, Spain; [Quadt, A.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Rabbertz, K.] Karlsruhe Inst Technol, Karlsruhe, Germany; [Rademacker, J.] Univ Bristol, HH Wills Phys Lab, Bristol, Avon, England; [Raffelt, G.; Simon, F.] Max Planck Inst Phys & Astrophys, Werner Heisenberg Inst, Munich, Germany; [Ramsey-Musolf, M.] Shanghai Jiao Tong Univ, Tsung Dao Lee Inst, Shanghai, Peoples R China; [Ramsey-Musolf, M.] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Ramsey-Musolf, M.; Willocq, S.] Univ Massachusetts, Dept Phys, Amherst, MA USA; [Ratcliff, B. N.; Terao, K.] SLAC Natl Accelerator Lab, Menlo Pk, CA USA; [Rolli, S.] US DOE, Washington, DC 20585 USA; [Romaniouk, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Romaniouk, A.] Natl Res Nucl Univ MEPhI, Moscow, Russia; [Rosenberg, L. J.; Rybka, G.; Sharpe, S. R.] Univ Washington, Dept Phys, Seattle, WA 98195 USA; [Ryskin, M. G.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Sarkar, S.] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Oxford, England; [Schneider, O.] Ecole Polytech Fed Lausanne EPFL, Inst Phys, Lausanne, Switzerland; [Schoenert, S.] Tech Univ Munich, Dept Phys, Munich, Germany; [Scholberg, K.; Walter, C. W.] Duke Univ, Phys Dept, Durham, NC USA; [Schwartz, A. J.] Univ Cincinnati, Dept Phys, Cincinnati, OH USA; [Scott, D.] Univ British Columbia, Dept Phys & Astron, Vancouver, BC, Canada; [Sjostrand, T.] Lund Univ, Dept Astron & Theoret Phys, Lund, Sweden; [Smoot, G. F.] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China; [Smoot, G. F.] Donostia Int Phys Ctr DIPC, Donostia San Sebastian, Spain; [Smoot, G. F.] Univ Paris, Paris Ctr Cosmol Phys, APC CNRS, Paris, France; [Sozzi, M. S.] Pisa Univ, Pisa, Italy; [Spanier, S.] Univ Tennessee, Dept Phys & Astron, Knoxville, TN 37996 USA; [Stahl, A.] Rhein Westfal TH Aachen, Phys Inst 3, Phys Zentrum, Aachen, Germany; [Syphers, M. J.] Northern Illinois Univ, Dept Phys, De Kalb, IL 60115 USA; [Tanabashi, M.] Nagoya Univ, Dept Phys, Nagoya, Aichi, Japan; [Tanaka, J.; Terashi, K.] Univ Tokyo, Int Ctr Elementary Particle Phys ICEPP, Tokyo, Japan; [Tasevsky, M.] Czech Acad Sci, Inst Phys, Prague, Czech Republic; [Terao, K.] Stanford Univ, Stanford, CA 94305 USA; [Terning, J.] Univ Calif Davis, Dept Phys, Davis, CA 95616 USA; [Titov, M.] Univ Paris Saclay, CEA, IRFU, F-91191 Gif Sur Yvette, France; [Tovey, D. 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Columbia University; Colorado School of Mines; University of Tokyo; Max Planck Society; Moscow Institute of Physics & Technology; California Institute of Technology; Peking University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS Workman, RL (corresponding author), George Washington Univ, Dept Phys, Washington, DC 20052 USA. de Florian, Daniel/B-6902-2011; Sarkar, Subir/G-5978-2011; Kenzie, Matthew/AAK-5674-2021; Beatty, James/D-9310-2011; Patrignani, Claudia/C-5223-2009; Hernández-Rey, Juan José/N-5955-2014; Navas, Sergio/N-4649-2014; Sarkar, Subir/GPT-4902-2022; Pich, Antonio/A-9907-2018; Chivukula, R. Sekhar/C-3367-2012; Yokoyama, Masashi/A-4458-2011; Baudis, Laura/ABE-1796-2021; Katz, Uli F/E-1925-2013; Vorobyev, Vitaly/H-9799-2018; White, Martin/I-3880-2015; Schneider, Olivier/R-9566-2018; Verde, Licia/L-9241-2014 de Florian, Daniel/0000-0002-3724-0695; Sarkar, Subir/0000-0002-3542-858X; Kenzie, Matthew/0000-0001-7910-4109; Beatty, James/0000-0003-0481-4952; Patrignani, Claudia/0000-0002-5882-1747; Hernández-Rey, Juan José/0000-0002-1527-7200; Navas, Sergio/0000-0003-1688-5758; Pich, Antonio/0000-0002-8019-5463; Chivukula, R. Sekhar/0000-0002-4142-1077; Yokoyama, Masashi/0000-0003-2742-0251; Baudis, Laura/0000-0003-4710-1768; Katz, Uli F/0000-0002-7063-4418; Meissner, Ulf-G./0000-0003-1254-442X; Buchmueller, Oliver/0000-0002-3293-3759; Trabelsi, Karim/0000-0001-6567-3036; Terao, Kazuhiro/0000-0003-1767-8929; Walter, Christopher/0000-0003-2035-2380; Haber, Howard/0000-0001-7338-8104; Moskovic, Micha/0000-0002-7638-5686; Vorobyev, Vitaly/0000-0002-6660-868X; Grunewald, Martin/0000-0002-5754-0388; White, Martin/0000-0001-9912-5070; Wands, David/0000-0001-9509-8386; Schneider, Olivier/0000-0002-6014-7552; Hikasa, Ken-ichi/0000-0002-9183-0207; Willocq, Stephane/0000-0002-4120-1453; El-Khadra, Aida X./0000-0001-9105-8213; Verde, Licia/0000-0003-2601-8770; Romaniouk, Anatoli/0000-0001-9241-1189; ZHU, Shi-Lin/0000-0002-4055-6906; Skands, Peter/0000-0003-0024-3822; Gonzalez Garcia, Maria Concepcion/0000-0003-4681-8521; Scholberg, Kate/0000-0002-7007-2021; Salam, Gavin/0000-0002-2655-4373 Office of Science, Office of High Energy Physics of the U.S. Department of Energy [DE-AC02-05CH11231]; government of Japan (Ministry of Education, Culture, Sports, Science and Technology); Italian National Institute of Nuclear Physics (INFN); Physical Society of Japan (JPS); European Laboratory for Particle Physics (CERN); government of United States (DOE) Office of Science, Office of High Energy Physics of the U.S. Department of Energy(United States Department of Energy (DOE)); government of Japan (Ministry of Education, Culture, Sports, Science and Technology); Italian National Institute of Nuclear Physics (INFN)(Istituto Nazionale di Fisica Nucleare (INFN)); Physical Society of Japan (JPS); European Laboratory for Particle Physics (CERN); government of United States (DOE)(United States Department of Energy (DOE)) The publication of the Review of Particle Physics is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; by an implementing arrangement between the governments of Japan (MEXT: Ministry of Education, Culture, Sports, Science and Technology) and the United States (DOE) on cooperative research and development; by the Italian National Institute of Nuclear Physics (INFN); by the Physical Society of Japan (JPS); and by the European Laboratory for Particle Physics (CERN). Individual collaborators receive support for their PDG activities from their respective institutes or funding agencies. 11645 3402 3430 1 1 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 2050-3911 PROG THEOR EXP PHYS Prog. Theor. Exp. Phys. AUG 8 2022.0 2022.0 8 083C01 10.1093/ptep/ptac097 0.0 2270 Physics, Multidisciplinary; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 3V1KG Green Published, Green Submitted, gold 2023-03-23 WOS:000841419600001 0
J Lourenco, MP; Anastacio, AD; Rosa, AL; Frauenheim, T; da Silva, MC Lourenco, Maicon Pierre; dos Santos Anastacio, Alexandre; Rosa, Andreia L.; Frauenheim, Thomas; da Silva, Mauricio Chagas An adaptive design approach for defects distribution modeling in materials from first-principle calculations JOURNAL OF MOLECULAR MODELING English Article Machine Learning; Adaptive Design; Efficient Global Optimization; Material defect modeling; DFT EFFICIENT GLOBAL OPTIMIZATION; INITIO MOLECULAR-DYNAMICS; IN-SITU; GOETHITE; STABILITY; DISCOVERY Designing and understanding the mechanism of non-stoichiometric materials with enhanced properties is challenging, both experimentally and even computationally, due to the large number of chemical spaces and their distributions through the material. In the current work, it is proposed a Machine Learning approach coupled with the Efficient Global Optimization (EGO) method-an Adaptive Design (AD)-to model local defects in materials from first-principle calculations. Our method takes into account the smallest sample set as possible, envisioning the material defect structure relationship with target properties for new insights. As an example, the AD framework allows us to study the stability and the structure of the modified goethite (Fe0.875Al0.125OOH) by considering a proper defect distribution, from first-principle calculations. The chemical space search for the modified goethite was evaluated by starting from different sizes and configurations of the samples as well as different surrogate models (ANN and Gaussian Process; GP), acquisition functions, and descriptors. Our results show that the same local solution of several defect arrangements in Fe0.875Al0.125OOH is found regardless of the initial sample and regression model. This indicates the efficiency of our search method. We also discuss the role of the descriptors in the accelerated global search for defects in material modeling. We conclude that the AD method applied in material defects is a successful approach in automating the search within huge chemical spaces from first-principle calculations by considering small samples. This method can be applied to mechanistic elucidation of non-stoichiometric materials, solid solutions, alloys, and Schottky and Frenkel defects, essential for material design and discovery. [Lourenco, Maicon Pierre; dos Santos Anastacio, Alexandre] Univ Fed Espirito Santo, Dept Quim & Fis, Ctr Ciencias Exatas Nat & Saude CCENS, BR-29500000 Alegre, ES, Brazil; [Rosa, Andreia L.] Univ Fed Goias, Inst Fis, Av Esperanca S-N,Campus Samambaia, BR-74690900 Goiania, Go, Brazil; [Rosa, Andreia L.; Frauenheim, Thomas; da Silva, Mauricio Chagas] Univ Bremen, Bremen Ctr Computat Mat Sci, POB 330440, D-28334 Bremen, Germany; [Frauenheim, Thomas] Computat Sci & Appl Res Inst Shenzhen, Computat Sci Res Ctr, 10 East Xibeiwang Rd, Beijing 100193, Peoples R China; [da Silva, Mauricio Chagas] Max Planck Inst Struct & Dynam Matter, Luruper Chaussee 149,Geb 99 CFEL, D-22761 Hamburg, Germany Universidade Federal do Espirito Santo; Universidade Federal de Goias; University of Bremen; Max Planck Society Lourenco, MP (corresponding author), Univ Fed Espirito Santo, Dept Quim & Fis, Ctr Ciencias Exatas Nat & Saude CCENS, BR-29500000 Alegre, ES, Brazil. maiconpl01@gmail.com da Silva, Maurício C/D-2921-2017; da Rosa, Andreia Luisa/A-2629-2019 da Silva, Maurício C/0000-0002-6890-0182; da Rosa, Andreia Luisa/0000-0002-2780-6448; Lourenco, Maicon/0000-0002-0110-8318 Brazilian agency: Fundacao de Amparo a Pesquisa do Espirito Santo (FAPES) [CNPq/FAPES PPP 22/2018]; Brazilian agency: Conselho Nacional para o Desenvolvimento Cientifico e Tecnologico (CNPq); Brazilian agency: Coordenacao de Aperfeicoamento de Pessoal de Ensino Superior (CAPES); DFG [DE1158/8-1]; Research Training Group grant [DFG-RTG2247]; Max-Planck-Institute for Structure and Dynamics of Matter (MPSD) Brazilian agency: Fundacao de Amparo a Pesquisa do Espirito Santo (FAPES); Brazilian agency: Conselho Nacional para o Desenvolvimento Cientifico e Tecnologico (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)); Brazilian agency: Coordenacao de Aperfeicoamento de Pessoal de Ensino Superior (CAPES)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); DFG(German Research Foundation (DFG)); Research Training Group grant; Max-Planck-Institute for Structure and Dynamics of Matter (MPSD) The support of the Brazilian agencies: Fundacao de Amparo a Pesquisa do Espirito Santo (FAPES)-project CNPq/FAPES PPP 22/2018, Conselho Nacional para o Desenvolvimento Cientifico e Tecnologico (CNPq), and Coordenacao de Aperfeicoamento de Pessoal de Ensino Superior (CAPES) are gratefully acknowledged. Also, we acknowledge the Computational resources provided by the Centro Nacional de Processamento de Alto Desempenho em SAo Paulo (CENAPAD-SP) and by the Bremen Center for Computational Material Science (BCCMS). Financial support from the DFG grant Nr. DE1158/8-1, the Research Training Group grant DFG-RTG2247, and the Max-Planck-Institute for Structure and Dynamics of Matter (MPSD) are gratefully acknowledged by the author M. C. da Silva. 58 9 9 0 13 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1610-2940 0948-5023 J MOL MODEL J. Mol. Model. JUL 1 2020.0 26.0 7 187 10.1007/s00894-020-04438-w 0.0 12 Biochemistry & Molecular Biology; Biophysics; Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biophysics; Chemistry; Computer Science MH7RA 32613379.0 2023-03-23 WOS:000546920200001 0
J Chen, CS; Li, ML; Ferreira, A; Huang, JW; Cai, RZ Chen, Changsheng; Li, Mulin; Ferreira, Anselmo; Huang, Jiwu; Cai, Rizhao A Copy-Proof Scheme Based on the Spectral and Spatial Barcoding Channel Models IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Copy-proof; 2D barcode; Fourier domain; local binary pattern TEXTURE CLASSIFICATION; SECURITY; AUTHENTICATION; CODES The traditional two-dimensional (2D) barcode has been employed in anti-counterfeiting systems as a storage media for serial numbers. However, an attack can be initiated by simply copying the 2D barcode and attaching it to a counterfeit product. In this paper, we aim at proposing an authentication scheme with a mobile imaging device for a 2D barcode. This work presents a competitive solution among the 2D barcode authentication schemes that have been verified under mobile imaging conditions. The proposed copy-proof scheme is composed of two sets of features which are extracted by exploiting the characteristics of barcoding channel models. The proposed features identify the intrinsic differences between genuine and counterfeit barcode images in the frequency and spatial domains. An efficient two-stage barcode authentication framework is then proposed by combining the two sets of features in a cascading manner. To evaluate the practicality of the proposed authentication scheme, four databases with different devices (printers, scanners, mobile cameras), barcode sizes, and barcode designs are considered in the experiments. By comparing with the existing texture descriptors and some deep learning-based approaches, it is shown that the proposed scheme has a higher authentication accuracy under various conditions, such as cross-database, cross-size and cross-pattern experiments which study the generalities of a pre-trained model towards challenging conditions commonly found in real-world scenarios. Last but not least, the proposed scheme has been evaluated under some state-of-the-art attack scenarios where the attacker employs several realizations of genuine patterns or the deep learning-based technique to produce a counterfeit copy. The source code and data for producing the results in our experiments are available at https://bit.ly/2FOlJH7. [Chen, Changsheng; Huang, Jiwu] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China; [Chen, Changsheng; Huang, Jiwu] Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen 518060, Peoples R China; [Chen, Changsheng; Huang, Jiwu] Shenzhen Univ, Coll Elect & Informat Engn, Natl Engn Lab Big Data Syst Comp Technol, Shenzhen 518060, Peoples R China; [Chen, Changsheng; Huang, Jiwu] Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen 518172, Peoples R China; [Li, Mulin] Tencent Holdings Ltd, Shenzhen 518057, Peoples R China; [Ferreira, Anselmo] Univ Cagliari, Dept Math & Informat, I-09100 Cagliari, Italy; [Cai, Rizhao] Nanyang Technol Univ, Rapid Rich Object Search Lab, Singapore 639798, Singapore Shenzhen University; Shenzhen University; Shenzhen University; Tencent; University of Cagliari; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Chen, CS (corresponding author), Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China.;Chen, CS (corresponding author), Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen 518060, Peoples R China. cschen@szu.edu.cn; jwhuang@szu.edu.cn Ferreira, Anselmo/J-5210-2014 Ferreira, Anselmo/0000-0002-2196-7232 NSFC [61702340, U1636202]; Natural Science Foundation of Guangdong Province, China [2017A030310382]; Guangdong Province Research and Development Plan in Key Areas [2019B010139003]; Science and Technology Innovation Commission of Shenzhen, China [JCYJ20180305124550725, 827/000213] NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); Guangdong Province Research and Development Plan in Key Areas; Science and Technology Innovation Commission of Shenzhen, China This work was supported in part by the NSFC Project under Grant 61702340 and Grant U1636202, in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2017A030310382, in part by the Guangdong Province Research and Development Plan in Key Areas under Grant 2019B010139003, and in part by the Science and Technology Innovation Commission of Shenzhen, China, under Project JCYJ20180305124550725 and Project 827/000213. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Pedro Comesana. 50 9 9 2 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2020.0 15.0 1056 1071 10.1109/TIFS.2019.2934861 0.0 16 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering JI6GT 2023-03-23 WOS:000493566500039 0
J Wang, C; Peng, GH; De Baets, B Wang, Chen; Peng, Guohua; De Baets, Bernard Deep feature fusion through adaptive discriminative metric learning for scene recognition INFORMATION FUSION English Article Deep feature fusion; Adaptive discriminative metric learning; Scene recognition IMAGE CLASSIFICATION; SCALE With the development of deep learning techniques, fusion of deep features has demonstrated the powerful capability to improve recognition performance. However, most researchers directly fuse different deep feature vectors without considering the complementary and consistent information among them. In this paper, from the view-point of metric learning, we propose a novel deep feature fusion method, called deep feature fusion through adaptive discriminative metric learning (DFF-ADML), to explore the complementary and consistent information for scene recognition. Concretely, we formulate an adaptive discriminative metric learning problem, which not only fully exploits discriminative information from each deep feature vector, but also adaptively fuses complementary information from different deep feature vectors. Besides, we map different deep feature vectors of the same image into a common space by different linear transformations, such that the consistent information can be preserved as much as possible. Moreover, DFF-ADML is extended to a kernelized version. Extensive experiments on both natural scene and remote sensing scene datasets demonstrate the superiorly and robustness of the proposed deep feature fusion method. [Wang, Chen; Peng, Guohua] Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Shaanxi, Peoples R China; [Wang, Chen; De Baets, Bernard] Univ Ghent, KERMIT, Dept Data Anal & Math Modelling, Coupure Links 653, B-9000 Ghent, Belgium Northwestern Polytechnical University; Ghent University Wang, C (corresponding author), Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Shaanxi, Peoples R China.;Wang, C (corresponding author), Univ Ghent, KERMIT, Dept Data Anal & Math Modelling, Coupure Links 653, B-9000 Ghent, Belgium. chenchenwang@mail.nwpu.edu.cn; penggh@nwpu.edu.cn; bernard.debaets@ugent.be Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme This research received funding from the Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme. 54 27 27 12 52 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion NOV 2020.0 63.0 1 12 10.1016/j.inffus.2020.05.005 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science NS3KD 2023-03-23 WOS:000572163500001 0
J Di, X; Woelfer, M; Kuhn, S; Zhang, ZG; Biswal, BB Di, Xin; Woelfer, Marie; Kuehn, Simone; Zhang, Zhiguo; Biswal, Bharat B. Estimations of the weather effects on brain functions using functional MRI: A cautionary note HUMAN BRAIN MAPPING English Article daylight length; environmental effects on the brain; machine learning regression; resting-state; scanner stability; temperature; weather REGIONAL HOMOGENEITY; HIPPOCAMPAL VOLUME; SEASONAL-VARIATION; MOOD; HEAD The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross-validation using the resting-state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long-term effects using MRI. [Di, Xin; Woelfer, Marie; Biswal, Bharat B.] New Jersey Inst Technol, Dept Biomed Engn, 607 Fenster Hall, Newark, NJ 07102 USA; [Di, Xin; Biswal, Bharat B.] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Chengdu, Peoples R China; [Woelfer, Marie] Otto von Guericke Univ, Clin Affect Neuroimaging Lab CANLAB, Magdeburg, Germany; [Woelfer, Marie] Leibniz Inst Neurobiol, Dept Behav Neurol, Magdeburg, Germany; [Kuehn, Simone] Max Planck Inst Human Dev, Ctr Lifespan Psychol, Berlin, Germany; [Kuehn, Simone] Univ Clin Hamburg Eppendorf, Clin & Polyclin Psychiat & Psychotherapy, Hamburg, Germany; [Zhang, Zhiguo] Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Shenzhen, Peoples R China; [Zhang, Zhiguo] Guangdong Prov Key Lab Biomed Measurements & Ult, Shenzhen, Peoples R China New Jersey Institute of Technology; University of Electronic Science & Technology of China; Otto von Guericke University; Leibniz Institut fur Neurobiologie (LIN); Max Planck Society; University of Hamburg; University Medical Center Hamburg-Eppendorf; Shenzhen University Biswal, BB (corresponding author), New Jersey Inst Technol, Dept Biomed Engn, 607 Fenster Hall, Newark, NJ 07102 USA. bbiswal@yahoo.com Di, Xin/B-2009-2009 Di, Xin/0000-0002-2422-9016; Biswal, Bharat/0000-0002-3710-3500; Kuhn, Simone/0000-0001-6823-7969 (US) National Institute of Health [R01AT009829, R01DA038895] (US) National Institute of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) We thank Dr. James Hyde for his insightful comments on an earlier version of this manuscript. This analysis was funded by (US) National Institute of Health grants: R01AT009829 and R01DA038895. 44 1 1 1 1 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. AUG 1 2022.0 43.0 11 3346 3356 10.1002/hbm.25576 0.0 MAY 2022 11 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging 2Q0MB 35586932.0 Green Accepted, Green Submitted 2023-03-23 WOS:000797288000001 0
C Liu, W; Park, EK; Krieger, U; Zhu, SS IEEE Liu, W.; Park, E. K.; Krieger, U.; Zhu, S. S. Smart and Connected e-Health Lab for Standards Validation and Conformance 2016 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC) English Proceedings Paper International Conference on Computing, Networking and Communications (ICNC) FEB 15-18, 2016 Kauai, HI IEEE,IEEE Comp Soc Smart-and-Connected e-Health; e-Health Lab Platform; e-Health Services; Flow Validation; e-Health Standards Conformance PRIVACY This paper reports our innovation in a Smart and Connected e-Health Service (SCeHS) lab approach for e-Health standards validation. The lab platform has been designed for SCeHS which is a next generation solution following the initial Big Data e-Health Service (BDeHS) initiative. After a number of e-Health standards are summarized, we present our validation designs for conformance to those e-Health standards. [Liu, W.] Georgia Gwinnett Coll, Sch Sci & Technol, Lawrenceville, GA 30043 USA; [Park, E. K.] CSU, Res, Chico, CA USA; [Park, E. K.] CSU, Grad Studies, Chico, CA USA; [Krieger, U.] Univ Bamberg, Comp Sci Commun & Networks, Bamberg, Germany; [Zhu, S. S.] Shantou Univ, Dept Comp Sci, Shantou, Guangdong, Peoples R China University System of Georgia; Georgia Gwinnett College; California State University System; California State University Chico; California State University System; California State University Chico; Otto Friedrich University Bamberg; Shantou University Liu, W (corresponding author), Georgia Gwinnett Coll, Sch Sci & Technol, Lawrenceville, GA 30043 USA. 20 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-8579-4 2016.0 5 Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BF5KF 2023-03-23 WOS:000382143300079 0
J Du, A; Guo, H; Lu, J; Su, Y; Ma, Q; Ruchay, A; Marinello, F; Pezzuolo, A Du, Ao; Guo, Hao; Lu, Jie; Su, Yang; Ma, Qin; Ruchay, Alexey; Marinello, Francesco; Pezzuolo, Andrea Automatic livestock body measurement based on keypoint detection with multiple depth cameras COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Precision livestock farming; 2D-3D fusion; Deep learning; Point cloud PIGS The body measurement of livestock is an important task in precision livestock farming. To reduce the cost of manual measurement, an increasing number of studies have proposed non-contact body measurement methods using depth cameras. However, these methods only use 3D data to construct geometric features for body measurements, which is prone to error on incomplete and noisy point clouds. This paper introduces a 2D-3D fusion body measurement method, developed in order to exploit the potential of raw scanned data including highresolution RGB images and 3D spatial information. The keypoints for body measurement are detected on RGB images with a deep learning model. Then these keypoints are projected onto the surface of livestock point clouds by utilizing the intrinsic parameters of the camera. Combining the process of interpolation and the pose normalization method, 9 body measurements of cattle and 5 body measurements of pig (including body lengths, body widths, body heights, and heart girth) are measured. To verify the feasibility of this method, the experiments are performed on 103 cattle data and 13 pig data. Compared with manual measurements, the MAPEs (mean absolute percentage errors) of 5 cattle body measurements and 1 pig body measurement are reduced to less than 10%. Body widths are more susceptible to non-standard posture. The MAPEs of 2 cattle body widths are larger than 20% and the MAPE of 1 pig body width reaches 30%. In comparison with a previous girth measurement method, the presented method is more accurate and robust for the cattle dataset. The same approach can be adapted and implemented for non-contact body measurement for different livestock species. [Du, Ao; Guo, Hao; Lu, Jie; Su, Yang] China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China; [Du, Ao; Guo, Hao; Ma, Qin] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China; [Ruchay, Alexey] Russian Acad Sci, Fed Res Ctr Biol Syst & Agrotechnol, 9 Yanvarya 29, Orenburg 460000, Russia; [Marinello, Francesco; Pezzuolo, Andrea] Univ Padua, Dept Land Environm Agr & Forestry, Viale Univ 16, I-35020 Legnaro, Italy China Agricultural University; China Agricultural University; Russian Academy of Sciences; Federal Research Centre of Biological Systems & Agro-technologies of the Russian Academy of Sciences; University of Padua Guo, H (corresponding author), China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China.;Guo, H (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. guohaolys@cau.edu.cn Ruchay, Alexey/N-7404-2016; Marinello, Francesco/H-5619-2019 Marinello, Francesco/0000-0002-3283-5665 National Natural Sci-ence Foundation of China [42071449, 41601491]; RFBR [20-416-740003] National Natural Sci-ence Foundation of China(National Natural Science Foundation of China (NSFC)); RFBR(Russian Foundation for Basic Research (RFBR)) Acknowledgments The authors wish to thank everyone who participated in the method assessment. This research was supported by the National Natural Sci-ence Foundation of China [Grant Nos. 42071449, 41601491] and RFBR research project No 20-416-740003. 28 5 5 29 41 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. JUL 2022.0 198.0 107059 10.1016/j.compag.2022.107059 0.0 MAY 2022 11 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science 1W4FS 2023-03-23 WOS:000806731500001 0
J Gayen, A; Pourghasemi, HR; Saha, S; Keesstra, S; Bai, SB Gayen, Amiya; Pourghasemi, Hamid Reza; Saha, Sunil; Keesstra, Saskia; Bai, Shibiao Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms SCIENCE OF THE TOTAL ENVIRONMENT English Article Gully erosion; Flexible discriminant analysis; Multivariate additive regression splines; Support vector machine; Random forest; Geospatial modelling ADAPTIVE REGRESSION SPLINES; EVIDENTIAL BELIEF FUNCTION; SUPPORT VECTOR MACHINE; DATA-MINING TECHNIQUES; LANDSLIDE SUSCEPTIBILITY; LOGISTIC-REGRESSION; SOIL-EROSION; CONDITIONAL-PROBABILITY; GOLESTAN PROVINCE; FREQUENCY RATIO Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off-site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard-prone areas in the Pathro River Basin of India using four well-known machine learning models, namely, multivariate additive regression splines (MARS), flexible discriminant analysis (FDA), random forest ( RF), and support vector machine (SVM). To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS), topographical wetness index (TWI), drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2%, whereas for those for the FDA, MARS, and SVM models were 842%, 91.4%, and 88.3%, respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas. (C) 2019 Elsevier B.V. All rights reserved. [Gayen, Amiya; Saha, Sunil] Univ Gour Banga, Dept Geog, Malda, W Bengal, India; [Pourghasemi, Hamid Reza; Bai, Shibiao] Nanjing Normal Univ, Coll Marine Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China; [Pourghasemi, Hamid Reza] Shiraz Univ, Dept Nat Resources & Environm Engn, Coll Agr, Shiraz, Iran; [Keesstra, Saskia] Wageningen Environm Res, Team Soil Water & Land Use, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands; [Keesstra, Saskia] Univ Newcastle, Civil Surveying & Environm Engn, Callaghan, NSW 2308, Australia University of Gour Banga; Nanjing Normal University; Shiraz University; Wageningen University & Research; University of Newcastle Pourghasemi, HR (corresponding author), Nanjing Normal Univ, Coll Marine Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China. hamidreza.pourghasemi@yahoo.com SAHA, SUNIL/AAJ-6035-2020; Pourghasemi, Hamid Reza/G-9926-2014; keesstra, saskia/Z-5477-2019; Gayen, Amiya/AAF-8404-2019 SAHA, SUNIL/0000-0003-2739-3716; Pourghasemi, Hamid Reza/0000-0003-2328-2998; Gayen, Amiya/0000-0001-8019-0745; bai, shibiao/0000-0002-1699-0825 Jiangsu provincial key R&D Program (Social Development) [BE2015704]; National Natural Science Foundation of China [41877522] Jiangsu provincial key R&D Program (Social Development); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Dr. Hamid Reza Pourghasemi would like to thank the 2015 Jiangsu provincial key R&D Program (Social Development) (BE2015704), the National Natural Science Foundation of China (No. 41877522), for providing fellowship which is thankfully acknowledged. 98 134 134 20 156 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. JUN 10 2019.0 668.0 124 138 10.1016/j.scitotenv.2019.02.436 0.0 15 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology HQ9VV 30851678.0 2023-03-23 WOS:000462776800014 0
J Liu, JX; Sun, TN; Luo, YL; Yang, S; Cao, Y; Zhai, J Liu, Junxiu; Sun, Tiening; Luo, Yuling; Yang, Su; Cao, Yi; Zhai, Jia An echo state network architecture based on quantum logic gate and its optimization NEUROCOMPUTING English Article Quantum computation; Echo state network; Particle swarm optimization; Time series; Financial applications PARTICLE SWARM OPTIMIZATION; QUBIT NEURAL-NETWORK; SYSTEM Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance. (C) 2019 Elsevier B.V. All rights reserved. [Liu, Junxiu; Sun, Tiening; Luo, Yuling] Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China; [Yang, Su] Ulster Univ, Sch Comp Engn & Intelligent Syst, Londonderry BT48 7JL, North Ireland; [Cao, Yi] Univ Edinburgh, Business Sch, Management Sci & Business Econ Grp, 29 Buccleuch Pl, Edinburgh EH8 9JS, Midlothian, Scotland; [Zhai, Jia] Univ Salford, Salford Business Sch, Salford M5 4WT, Lancs, England Guangxi Normal University; Ulster University; University of Edinburgh; University of Salford Luo, YL (corresponding author), Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China. yuling0616@gxnu.edu.cn Luo, Yuling/0000-0002-0117-4614; Cao, Yi/0000-0002-5087-8861 National Natural Science Foundation of China [61976063, 61603104]; Guangxi Natural Science Foundation [2017GXNSFAA198180]; Overseas 100 Talents Program of Guangxi Higher Education; 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program. National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Program of Guangxi Higher Education; 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program. This research is supported by the National Natural Science Foundation of China under Grants 61976063 and 61603104, the Guangxi Natural Science Foundation under Grant 2017GXNSFAA198180, the funding of Overseas 100 Talents Program of Guangxi Higher Education, and 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program. 34 11 12 2 31 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 2 2020.0 371.0 100 107 10.1016/j.neucom.2019.09.002 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science JJ1VT Green Accepted, Green Submitted 2023-03-23 WOS:000493950600009 0
J Arabameri, A; Saha, S; Roy, J; Chen, W; Blaschke, T; Bui, DT Arabameri, Alireza; Saha, Sunil; Roy, Jagabandhu; Chen, Wei; Blaschke, Thomas; Dieu Tien Bui Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran REMOTE SENSING English Article landslide susceptibility (LSM); alternative decision tree (ADTree); Fisher's linear discriminant function (FLDA); machine learning approach; R program LOGISTIC-REGRESSION; NEURAL-NETWORKS; GIS; MODELS; BIVARIATE; MULTIVARIATE; ENSEMBLE; ZONATION; HAZARD; AREA This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher's Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem. [Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran; [Saha, Sunil; Roy, Jagabandhu] Univ Gour Banga, Dept Geog, Malda 732101, W Bengal, India; [Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Chen, Wei] Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Peoples R China; [Blaschke, Thomas] Univ Salzburg, Dept Geoinformat ZGIS, A-5020 Salzburg, Austria; [Dieu Tien Bui] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Tarbiat Modares University; University of Gour Banga; Xi'an University of Science & Technology; Ministry of Natural Resources of the People's Republic of China; Salzburg University; Duy Tan University Arabameri, A (corresponding author), Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran.;Bui, DT (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. a.arabameri@modares.ac.ir; sunilgeo.88@gmail.com; jagabandhuroy1991@gmail.com; chenwei0930@xust.edu.cn; thomas.blaschke@sbg.ac.at; buitiendieu@duytan.edu.vn Blaschke, Thomas/AAR-9037-2021; Chen, Wei/ABB-8669-2020; Blaschke, Thomas/F-3342-2011; SAHA, SUNIL/AAJ-6035-2020 Blaschke, Thomas/0000-0002-1860-8458; Chen, Wei/0000-0002-5825-1422; Blaschke, Thomas/0000-0002-1860-8458; SAHA, SUNIL/0000-0003-2739-3716; ARABAMERI, ALIREZA/0000-0002-1142-1666 Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg [DK W 1237-N23] Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg(Austrian Science Fund (FWF)) This research was partly funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W 1237-N23) at the University of Salzburg. 116 92 93 19 83 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2020.0 12.0 3 475 10.3390/rs12030475 0.0 29 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology KO2QH gold 2023-03-23 WOS:000515393800134 0
J Mortazavi, B; Shojaei, F; Zhuang, X Mortazavi, B.; Shojaei, F.; Zhuang, X. A novel two-dimensional C36 fullerene network; an isotropic, auxetic semiconductor with low thermal conductivity and remarkable stiffness MATERIALS TODAY NANO English Article 2D fullerene; Auxetic; Semiconductor; Machine learning; First -principles C-36; CARBON; LAYER; 1ST-PRINCIPLES; MORPHOLOGY; GRAPHENE; CRYSTAL The synthesis of two-dimensional (2D) C60 fullerene network (Nature (2022), 606, 507) with an aniso-tropic lattice is among the most exciting advances in the field of carbon-based materials, which has the potential to expand and establish a new class of 2D materials. In this work, a novel C36 fullerene 2D network with an isotropic structure is designed by screening of extensive and diverse fullerene lattices. Density functional theory calculations confirm that the herein predicted C36 fullerene network can exhibit an outstanding thermal stability up to 1500 K, an elastic modulus of 266 GPa, a negative Poisson's ratio of-0.05, and an indirect semiconducting electronic nature, with a HSE06(PBE) bad gap of 1.63 (0.97) eV. The phonon dispersion relation, mechanical and failure responses, and lattice thermal con-ductivity are explored with the aid of machine learning interatomic potentials (MLIPs). MLIP-based calculations close to the ground state confirm the dynamical stability, a negative Poisson's ratio of-0.06, an elastic modulus of 269 GPa, and a high tensile strength of around 26.8 GPa for the predicted 2D network. Room temperature phononic thermal conductivity and tensile strength are also predicted to be 9.8 +/- 1 W/m.K and 15.9 GPa, respectively. This study introduces a novel isotropic and auxetic sem-iconducting full-carbon nanoporous nanosheet, with low thermal conductivity and appealing electronic, optical, and mechanical features, highlighting a bright prospect for the design and synthesis of novel fullerene-based 2D networks.(c) 2022 Elsevier Ltd. All rights reserved. [Mortazavi, B.; Zhuang, X.] Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, B.] Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Optics & Engn I, Hannover, Germany; [Shojaei, F.] Persian Gulf Univ, Fac Nano & Biosci & Technol, Dept Chem, Bushehr, Iran; [Zhuang, X.] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Persian Gulf University; Tongji University Mortazavi, B; Zhuang, X (corresponding author), Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany.;Mortazavi, B (corresponding author), Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Optics & Engn I, Hannover, Germany.;Zhuang, X (corresponding author), Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai, Peoples R China. bohayra.mortazavi@gmail.com; zhuang@iopuni-hannover.de Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Persian Gulf University Research Council, Iran Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Persian Gulf University Research Council, Iran M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). F.S. thanks the Persian Gulf University Research Council, Iran, for the support of this study. B.M. is greatly thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources. The authors would like to thank Prof. Alexander Shapeev for very valuable discussions and support to this work. 50 1 1 39 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2588-8420 MATER TODAY NANO Mater. Today Nano MAR 2023.0 21.0 100280 10.1016/j.mtnano.2022.100280 0.0 8 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 6Q9KD 2023-03-23 WOS:000891927400002 0
J Camilleri, JA; Eickhoff, SB; Weis, S; Chen, J; Amunts, J; Sotiras, A; Genon, S Camilleri, J. A.; Eickhoff, S. B.; Weis, S.; Chen, J.; Amunts, J.; Sotiras, A.; Genon, S. A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System SCIENTIFIC REPORTS English Article EXPLORATORY FACTOR-ANALYSIS; INDIVIDUAL-DIFFERENCES; DYSEXECUTIVE SYMPTOMS; OLDER-ADULTS; TESTS; REPLICATION; PSYCHOLOGY; SAMPLE; INTELLIGENCE; FLEXIBILITY While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools. [Camilleri, J. A.; Eickhoff, S. B.; Weis, S.; Chen, J.; Amunts, J.; Genon, S.] Forschungszentrum Julich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany; [Camilleri, J. A.; Eickhoff, S. B.; Weis, S.; Chen, J.; Amunts, J.; Genon, S.] Heinrich Heine Univ, Inst Syst Neurosci, Dusseldorf, Germany; [Chen, J.] Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China; [Sotiras, A.] Washington Univ, Inst Informat, Mallinckrodt Inst Radiol, St Louis, MO 63110 USA Helmholtz Association; Research Center Julich; Heinrich Heine University Dusseldorf; Zhejiang University; Washington University (WUSTL) Camilleri, JA (corresponding author), Forschungszentrum Julich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany.;Camilleri, JA (corresponding author), Heinrich Heine Univ, Inst Syst Neurosci, Dusseldorf, Germany. j.camilleri@fz-juelich.de Chen, Ji/0000-0002-9476-1737 Deutsche Forschungsgemeinschaft (DFG) [GE 2835/2-1, EI 816/16-1, EI 816/21-1]; National Institute of Mental Health [R01-MH074457]; Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union's Horizon 2020 Research and Innovation Programme [785907, 945539]; Virtual Brain Cloud (EU H2020) [826421]; National Institute on Aging [R01AG067103] Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); National Institute of Mental Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain; European Union's Horizon 2020 Research and Innovation Programme; Virtual Brain Cloud (EU H2020); National Institute on Aging(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)) This study was supported by the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, EI 816/16-1 and EI 816/21-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain, the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 785907 (HBP SGA2), 945539 (HBP SGA3), The Virtual Brain Cloud (EU H2020, no. 826421), and the National Institute on Aging (R01AG067103). 85 1 1 1 5 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep AUG 19 2021.0 11.0 1 16896 10.1038/s41598-021-96342-3 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics UC8JQ 34413412.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000686768700076 0
J Tan, HL; Debarros, J; He, SH; Pogosyan, A; Aziz, TZ; Huang, YZ; Wang, SY; Timmermann, L; Visser-Vandewalle, V; Pedrosa, DJ; Green, AL; Brown, P Tan, Huiling; Debarros, Jean; He, Shenghong; Pogosyan, Alek; Aziz, Tipu Z.; Huang, Yongzhi; Wang, Shouyan; Timmermann, Lars; Visser-Vandewalle, Veerle; Pedrosa, David J.; Green, Alexander L.; Brown, Peter Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor BRAIN STIMULATION English Article Local field potential; Essential tremor; Ventral intermediate thalamus; Deep brain stimulation DEEP BRAIN-STIMULATION; LOCAL-FIELD POTENTIALS; SUBTHALAMIC NUCLEUS; OSCILLATIONS; INVOLVEMENT; RECORDINGS; DISORDER; EFFICACY; TIME; DBS Background: High frequency Deep brain stimulation (DBS) targeting motor thalamus is an effective therapy for essential tremor (ET). However, conventional continuous stimulation may deliver unnecessary current to the brain since tremor mainly affects voluntary movements and sustained postures in ET. Objective: We aim to decode both voluntary movements and the presence of postural tremor from the Local field potentials (LFPs) recorded from the electrode implanted in motor thalamus for stimulation, in order to close the loop for DBS so that stimulation could be delivered on demand, without the need for peripheral sensors or additional invasive electrodes. Methods: LFPs from the motor thalamus, surface electromyographic (EMG) signals and/or behavioural measurements were simultaneously recorded in seven ET patients during temporary lead externalisation 3-5 days after the first surgery for DBS when they performed different voluntary upper limb movements. Nine different patients were recorded during the surgery, when they were asked to lift their arms to trigger postural tremor. A machine learning based binary classifier was used to detect voluntary movements and postural tremor based on features extracted from thalamic LFPs. Results: Cross-validation demonstrated that both voluntary movements and postural tremor can be decoded with an average sensitivity of 0.8 and false detection rate of 0.2. Oscillatory activities in the beta frequency bands (13-23 Hz) and the theta frequency bands (4-7 Hz) contributed most to the decoding of movements and postural tremor, respectively, though incorporating features in different frequency bands using a machine learning approach increased the accuracy of decoding. (C) 2019 The Author(s). Published by Elsevier Inc. [Tan, Huiling; Debarros, Jean; He, Shenghong; Pogosyan, Alek; Brown, Peter] Univ Oxford, Med Res Council Brain Network Dynam Unit, Oxford OX1 3TH, England; [Tan, Huiling; Debarros, Jean; He, Shenghong; Pogosyan, Alek; Brown, Peter] Univ Oxford, Nuffield Dept Clin Neurosci, John Radcliffe Hosp, Level 6,West Wing, Oxford OX3 9DU, England; [Aziz, Tipu Z.; Huang, Yongzhi; Green, Alexander L.] Univ Oxford, Nuffield Dept Surg Sci, Oxford, England; [Wang, Shouyan] Fudan Univ, Neural & Intelligence Engn Ctr, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China; [Timmermann, Lars; Pedrosa, David J.] Univ Hosp Giessen & Marburg, Dept Neurol, Marburg, Germany; [Visser-Vandewalle, Veerle] Univ Hosp Cologne, Dept Stereotact & Funct Neurosurg, D-50924 Cologne, Germany UK Research & Innovation (UKRI); Medical Research Council UK (MRC); University of Oxford; University of Oxford; University of Oxford; Fudan University; University Hospital of Giessen & Marburg; University of Cologne Tan, HL; Brown, P (corresponding author), Univ Oxford, Nuffield Dept Clin Neurosci, John Radcliffe Hosp, Level 6,West Wing, Oxford OX3 9DU, England. huiling.tan@ndcn.ox.ac.uk; peter.brown@ndcn.ox.ac.uk Huang, Yongzhi/ABE-3960-2020; Huang, Yongzhi/AAB-5758-2020; Pedrosa, David J./HJP-5325-2023 Huang, Yongzhi/0000-0002-2503-1589; Pedrosa, David J./0000-0002-5403-1959; Timmermann, Lars/0000-0002-5301-3580; Tan, Huiling/0000-0001-8038-3029 MRC [MR/P012272/1, MC_UU_12024/1]; Rosetrees Trust; National Institute of Health Research Oxford Biomedical Research Centre; MRC [MC_UU_12024/1, MR/P012272/1] Funding Source: UKRI MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Rosetrees Trust(Rosetrees Trust); National Institute of Health Research Oxford Biomedical Research Centre(National Institute for Health Research (NIHR)); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) This work was supported by the MRC (MR/P012272/1 and MC_UU_12024/1), the Rosetrees Trust, and the National Institute of Health Research Oxford Biomedical Research Centre. 42 32 33 1 8 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1935-861X 1876-4754 BRAIN STIMUL Brain Stimul. JUL-AUG 2019.0 12.0 4 858 867 10.1016/j.brs.2019.02.011 0.0 10 Clinical Neurology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology IE6JM 30827864.0 Green Accepted, hybrid, Green Published, Green Submitted 2023-03-23 WOS:000472482500005 0
J Bosch, D; Wang, J; Blancafort, L Bosch, Daniel; Wang, Jun; Blancafort, Lluis Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers CHEMICAL SCIENCE English Article MELANIN Eumelanin is the biopolymer responsible for photoprotection in living beings and holds great promise as a smart biomaterial, but its detailed structure has not been characterized experimentally. Theoretical models are urgently needed to improve our knowledge of eumelanin's function and exploit its properties, but the enormous amount of possible oligomer components has made modelling not possible until now. Here we show that the stability and lowest vertical optical absorption of 5,6-dihydroxyindole (DHI) eumelanin dimer components can be modeled with deep neural networks, using fingerprint-like molecular representations as input. In spite of the modest data set size, average errors of only 6 and 9% for stability and S-1 absorption energy are obtained. Our fingerprints code the connectivity and oxidation patterns of the dimers in a straightforward, unambiguous way and can be extended to larger oligomers. This proof-of-principle work shows that machine learning can be applied to help solve the structural challenge of melanin. [Bosch, Daniel; Blancafort, Lluis] Univ Girona, Fac Ciencies, Inst Quim Computat & Catalisi, Dept Quim, C-M A Capmany 69, Girona 17003, Spain; [Wang, Jun] Huaiyin Normal Univ, Jiangsu Engn Lab Environm Funct Mat, Jiangsu Key Lab Chem Low Dimens Mat, 111 West Changjiang Rd, Huaian 223300, Jiangsu, Peoples R China Universitat de Girona; Huaiyin Normal University Blancafort, L (corresponding author), Univ Girona, Fac Ciencies, Inst Quim Computat & Catalisi, Dept Quim, C-M A Capmany 69, Girona 17003, Spain. lluis.blancafort@udg.edu Blancafort, Lluis/B-5679-2009; Wang, Jun/R-9310-2018 Blancafort, Lluis/0000-0002-0003-5540; Wang, Jun/0000-0003-0222-6380; Bosch, Daniel/0000-0003-1791-1129 European Commission [844230]; Ministerio de Ciencia, Innovacion y Universidades (Spain) [PID-2019104654GB-I00]; [QSB-2019-3-0006]; [QSB-2020-1-006]; [QSB-2020-2-0011]; [QSB-2020-3-0015] European Commission(European CommissionEuropean Commission Joint Research Centre); Ministerio de Ciencia, Innovacion y Universidades (Spain)(Spanish Government); ; ; ; Financial support from the European Commission, project 844230 (MSCA fellowship for JW), and Ministerio de Ciencia, Innovaci ' on y Universidades (Spain), project PID-2019104654GB-I00, and computational time at Red Espa~nola de Supercomputaci ' on, projects QSB-2019-3-0006, QSB-2020-1-006, QSB-2020-2-0011, and QSB-2020-3-0015, are gratefully acknowledged. 21 0 0 5 9 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2041-6520 2041-6539 CHEM SCI Chem. Sci. AUG 10 2022.0 13.0 31 8942 8946 10.1039/d2sc02461f 0.0 JUL 2022 6 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry 3R2KC 36091209.0 gold 2023-03-23 WOS:000823761100001 0
J Kan, ZH; Tang, LL; Kwan, MP; Ren, C; Liu, D; Pei, T; Liu, Y; Deng, M; Li, QQ Kan, Zihan; Tang, Luliang; Kwan, Mei-Po; Ren, Chang; Liu, Dong; Pei, Tao; Liu, Yu; Deng, Min; Li, Qingquan Fine-grained analysis on fuel-consumption and emission from vehicles trace JOURNAL OF CLEANER PRODUCTION English Article Fuel consumption; Emissions; Big data; Activity analysis; GPS trace; CMEM GEOGRAPHICAL INFORMATION-SYSTEMS; SPACE-TIME PRISM; TRAVEL PATTERNS; BIG DATA; CHINA; SHANGHAI; MODELS Traffic-related fuel consumption and emissions pose a severe problem with adverse impact on human health and urban sustainability. GPS trajectory data can provide useful insights into the quantities and distributions of fuel consumption and emissions. Previous research has primarily focused on understanding the spatiotemporal distributions of fuel consumption and emissions with GPS trajectory data, but has not paid adequate attention to estimation accuracy. Thus, this study proposes a method that estimates vehicular fuel consumption and emissions at a fine-grained level based on analysis of vehicles' mobile activities, stationary activities with engine-on, and stationary activities with engine-off. Using the analytical framework of space-time paths in time geography, this study first builds space-time paths of individual vehicles, extracts moving parameters and analyzes the activities from each space-time path segment (SIPS). Based on the activity analysis, we then estimate fuel consumption and emissions using a microscopic model (CMEM), and distinguish between the cold-start phases and the hot phases in the space-time paths. In the case study, the fuel consumption and emissions for individual trajectories and a road network were estimated and analyzed. The distribution of activity-related fuel consumption was also explored. The effectiveness of the proposed methodology is illustrated using three datasets that were collected from vehicles with various types of engines, with estimation accuracy of over 90%. (C) 2018 Elsevier Ltd. All rights reserved. [Kan, Zihan; Tang, Luliang; Ren, Chang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China; [Kwan, Mei-Po; Liu, Dong] Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3508 TC Utrecht, Netherlands; [Pei, Tao] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Liu, Yu] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China; [Deng, Min] Cent S Univ, Dept Surveying & Geoinformat, Changsha 410084, Hunan, Peoples R China; [Li, Qingquan] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Guangdong, Peoples R China Wuhan University; University of Illinois System; University of Illinois Urbana-Champaign; Utrecht University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Peking University; Central South University; Shenzhen University Tang, LL (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China. tll@whu.edu.cn Liu, Yu/D-4022-2012; Ren, Chang/AAH-3167-2019; Liu, Dong/ADM-2704-2022; Kwan, Mei-Po/S-4162-2016 Liu, Yu/0000-0002-0016-2902; Ren, Chang/0000-0003-1760-0865; Liu, Dong/0000-0001-9090-5732; Kwan, Mei-Po/0000-0001-8602-9258; Kan, Zihan/0000-0002-6364-0537; Li, Qingquan/0000-0002-2438-6046 National Key Research and Development Plan of China [2017YFB0503604, 2016YFE0200400]; National Natural Science Foundation of China [41671442, 41571430, 41271442, 41529101]; Joint Foundation of Ministry of Education of China [6141A02022341]; John Simon Guggenheim Memorial Foundation Fellowship National Key Research and Development Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Joint Foundation of Ministry of Education of China; John Simon Guggenheim Memorial Foundation Fellowship This work was supported by grants from the National Key Research and Development Plan of China (2017YFB0503604, 2016YFE0200400), the National Natural Science Foundation of China (41671442, 41571430, 41271442, 41529101), and the Joint Foundation of Ministry of Education of China (6141A02022341). In addition, Mei-Po Kwan was supported by a John Simon Guggenheim Memorial Foundation Fellowship. 43 26 28 5 62 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. DEC 1 2018.0 203.0 340 352 10.1016/j.jclepro.2018.08.222 0.0 13 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology GX2RV 2023-03-23 WOS:000447568700026 0
J Weng, LB; Dornaika, F; Jin, Z Weng, Libo; Dornaika, Fadi; Jin, Zhong Graph construction based on data self-representativeness and Laplacian smoothness NEUROCOMPUTING English Article Graph construction; Data self-representativeness; Laplacian smoothness; Semi-supervised learning; Label propagation; Image classification ROBUST FACE RECOGNITION; DIMENSIONALITY REDUCTION Recently, graph-based semi-supervised learning (SSL) becomes a hot topic in machine learning and pattern recognition. It has been shown that constructing an informative graph is one of the most important steps in SST. since a good graph can significantly affect the final performance of learning algorithms. This paper has the following main contributions. First, we introduce a new graph construction method based on data self-representativeness and Laplacian smoothness (SRLS). Second, this method is refined by incorporating an adaptive coding scheme aiming at getting a sparse graph. Third, we propose two kernelized versions of the SRLS method. A series of experiments on several public image data sets show that the proposed methods can out-perform many state-of-the-art methods. It is shown that Laplacian smoothness criterion is indeed a powerful tool to get informative graphs. (C) 2016 Elsevier B.V. All rights reserved. [Weng, Libo; Jin, Zhong] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Weng, Libo; Dornaika, Fadi] Univ Basque Country UPVEHU, Dept Comp Sci & Artificial Intelligence, San Sebastian, Spain; [Dornaika, Fadi] Basque Fdn Sci, Ikerbasque, Bilbao, Spain Nanjing University of Science & Technology; University of Basque Country; Basque Foundation for Science Dornaika, F (corresponding author), Univ Basque Country UPVEHU, Dept Comp Sci & Artificial Intelligence, San Sebastian, Spain.;Dornaika, F (corresponding author), Basque Fdn Sci, Ikerbasque, Bilbao, Spain. National Natural Science Foundation of China [61420201, 61472187, 61233011, 61125305, 61375007, 61220301, 61373063]; National Basic Research Program of China [2014CB349303]; [EHU13/40] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Basic Research Program of China(National Basic Research Program of China); This work is partially supported by National Natural Science Foundation of China under Grant Nos. 61420201; 61472187, 61233011, 61125305, 61375007, 61220301, 61373063, and by National Basic Research Program of China under Grant No. 2014CB349303. This work is partially supported by project EHU13/40. 45 8 8 0 22 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 26 2016.0 207.0 476 487 10.1016/j.neucom.2016.05.021 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DV3AX 2023-03-23 WOS:000382794500044 0
J Zhao, ZP; Bao, ZT; Zhao, YQ; Zhang, ZX; Cummins, N; Ren, Z; Schuller, B Zhao, Ziping; Bao, Zhongtian; Zhao, Yiqin; Zhang, Zixing; Cummins, Nicholas; Ren, Zhao; Schuller, Bjorn Exploring Deep Spectrum Representations via Attention-Based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition IEEE ACCESS English Article Speech emotion recognition; bidirectional long short-term memory; fully convolutional networks; attention mechanism; spectrogram representation The automatic detection of an emotional state from human speech, which plays a crucial role in the area of human-machine interaction, has consistently been shown to be a difficult task for machine learning algorithms. Previous work on emotion recognition has mostly focused on the extraction of carefully hand-crafted and highly engineered features. Results from these works have demonstrated the importance of discriminative spatio-temporal features to model the continual evolutions of different emotions. Recently, spectrogram representations of emotional speech have achieved competitive performance for automatic speech emotion recognition (SER). How machine learning algorithms learn the effective compositional spatio-temporal dynamics for SER has been a fundamental problem of deep representations, herein denoted as deep spectrum representations. In this paper, we develop a model to alleviate this limitation by leveraging a parallel combination of attention-based bidirectional long short-term memory recurrent neural networks with attention-based fully convolutional networks (FCN). The extensive experiments were undertaken on the interactive emotional dyadic motion capture (IEMOCAP) and FAU aibo emotion corpus (FAU-AEC) to highlight the effectiveness of our approach. The experimental results indicate that deep spectrum representations extracted from the proposed model are well-suited to the task of SER, achieving a WA of 68.1 % and a UA of 67.0 % on IEMOCAP, and 45.4% for UA on FAU-AEC dataset. Key results indicate that the extracted deep representations combined with a linear support vector classifier are comparable in performance with eGeMAPS and COMPARE, two standard acoustic feature representations. [Zhao, Ziping; Bao, Zhongtian; Zhao, Yiqin] Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin 300387, Peoples R China; [Zhang, Zixing; Schuller, Bjorn] Imperial Coll London, GLAM, London SW7 2AZ, England; [Zhao, Ziping; Cummins, Nicholas; Ren, Zhao; Schuller, Bjorn] Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, D-86159 Augsburg, Germany; [Schuller, Bjorn] Tianjin Normal Univ, Int Res Ctr Affect Intelligence, Tianjin 300387, Peoples R China Tianjin Normal University; Imperial College London; University of Augsburg; Tianjin Normal University Schuller, B (corresponding author), Imperial Coll London, GLAM, London SW7 2AZ, England.;Schuller, B (corresponding author), Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, D-86159 Augsburg, Germany.;Schuller, B (corresponding author), Tianjin Normal Univ, Int Res Ctr Affect Intelligence, Tianjin 300387, Peoples R China. schuller@ieee.org Cummins, Nicholas/AAC-6431-2019 Cummins, Nicholas/0000-0002-1178-917X; Schuller, Bjorn/0000-0002-6478-8699; Zhao, Ziping/0000-0002-8719-6389 National Natural Science Foundation of China [61702370]; Key Program of the Natural Science Foundation of Tianjin [18JCZDJC36300]; Open Projects Program of the National Laboratory of Pattern Recognition; Senior Visiting Scholar Program of Tianjin Normal University; European Union's Horizon 2020 Research And Innovation Programme [826506] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Program of the Natural Science Foundation of Tianjin; Open Projects Program of the National Laboratory of Pattern Recognition; Senior Visiting Scholar Program of Tianjin Normal University; European Union's Horizon 2020 Research And Innovation Programme This work was supported in part by the National Natural Science Foundation of China under Grant 61702370, in part by the Key Program of the Natural Science Foundation of Tianjin under Grant 18JCZDJC36300, in part by the Open Projects Program of the National Laboratory of Pattern Recognition, in part by the Senior Visiting Scholar Program of Tianjin Normal University, and in part by the European Union's Horizon 2020 Research And Innovation Programme under Grant 826506 (sustAGE). 58 53 55 0 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 97515 97525 10.1109/ACCESS.2019.2928625 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IN8YP Green Submitted, gold 2023-03-23 WOS:000478966000001 0
J Mkrtchyan, GV; Veviorskiy, A; Izumchenko, E; Shneyderman, A; Pun, FW; Ozerov, IV; Aliper, A; Zhavoronkov, A; Scheibye-Knudsen, M Mkrtchyan, Garik, V; Veviorskiy, Alexander; Izumchenko, Evgeny; Shneyderman, Anastasia; Pun, Frank W.; Ozerov, Ivan, V; Aliper, Alex; Zhavoronkov, Alex; Scheibye-Knudsen, Morten High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders CELL DEATH & DISEASE English Article ATAXIA-TELANGIECTASIA; BREAST-CANCER; GENE-EXPRESSION; STEM-CELLS; DYSREGULATION; ORGANIZATION; PATHWAYS; DISEASES; REVEALS; PROTEIN Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform (https://pandaomics.com/) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization. [Mkrtchyan, Garik, V; Scheibye-Knudsen, Morten] Univ Copenhagen, Ctr Hlth Aging, Dept Cellular & Mol Med, Copenhagen, Denmark; [Veviorskiy, Alexander; Shneyderman, Anastasia; Pun, Frank W.; Ozerov, Ivan, V; Aliper, Alex; Zhavoronkov, Alex] Insil Med, Hong Kong, Peoples R China; [Izumchenko, Evgeny] Univ Chicago, Dept Med, Sect Hematol & Oncol, 5841 S Maryland Ave, Chicago, IL 60637 USA University of Copenhagen; University of Chicago Scheibye-Knudsen, M (corresponding author), Univ Copenhagen, Ctr Hlth Aging, Dept Cellular & Mol Med, Copenhagen, Denmark. mscheibye@sund.ku.dk ; Zhavoronkov, Alex/E-7141-2014 Scheibye-Knudsen, Morten/0000-0002-6637-1280; Zhavoronkov, Alex/0000-0001-7067-8966 Novo Nordisk Foundation Challenge Programme [NNF17OC0027812]; Nordea Foundation [02-2017-1749]; Neye Foundation; Lundbeck Foundation [R324-2019-1492]; Ministry of Higher Education and Science [0238-00003B]; VitaDAO; Insilico Medicine Novo Nordisk Foundation Challenge Programme; Nordea Foundation; Neye Foundation; Lundbeck Foundation(Lundbeckfonden); Ministry of Higher Education and Science(Ministry of Science and Higher Education, Poland); VitaDAO; Insilico Medicine This work is funded by Novo Nordisk Foundation Challenge Programme (#NNF17OC0027812), Nordea Foundation (#02-2017-1749), Neye Foundation, Lundbeck Foundation (#R324-2019-1492), Ministry of Higher Education and Science (#0238-00003B), VitaDAO, and Insilico Medicine. 69 0 0 1 1 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2041-4889 CELL DEATH DIS Cell Death Dis. NOV 26 2022.0 13.0 11 999 10.1038/s41419-022-05437-w 0.0 9 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology 6M4BI 36435816.0 gold, Green Accepted 2023-03-23 WOS:000888814300002 0
J Wu, WG; Zhou, ZC; Adhikary, AR; Dutta, B Wu, Wenguo; Zhou, Zhengchun; Adhikary, Avik Ranjan; Dutta, Bapi Discrete space reinforcement learning algorithm based on twin support vector machine classification PATTERN RECOGNITION LETTERS English Article Twin support vector machines; Actor-Critic; Reinforcement learning; Small-scale discrete space environment Reinforcement learning (RL) has become one of the key component of various machine learning algorithms in recent years. However, traditional RL algorithms lack convergence speed and accuracy in small-scale discrete space environment. Recently An et al. proposed RL algorithm based on support vector machines (SVMs) (Pattern Recognit. Lett. 111 (2018) 30-35) which adopts the Advantage Actor-Critic (A2C) framework and improves the speed and accuracy of convergence in discrete space. Owing to the advantages of twin support vector machines (TWSVMs) over SVMs, in this paper, we propose a RL algorithm based on TWSVM classification. The proposed algorithm adopts a modified A2C framework, where there are multiple Actors and a single Critic. Finally, we compare the performance of the proposed algorithm with some existing algorithms in traditional RL environment. Interestingly, the proposed algorithm outperforms the existing algorithms in terms of convergence speed and accuracy. (c) 2022 Elsevier B.V. All rights reserved. [Wu, Wenguo; Zhou, Zhengchun; Adhikary, Avik Ranjan] Southwest Jiaotong Univ, Sch Math, Chengdu 611756, Peoples R China; [Dutta, Bapi] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain Southwest Jiaotong University; Universidad de Jaen Zhou, ZC (corresponding author), Southwest Jiaotong Univ, Sch Math, Chengdu 611756, Peoples R China. wwg994@my.swjtu.edu.cn; zzc@swjtu.edu.cn; a.adhikary@swjtu.edu.cn; bdutta@ujaen.es National Natural Science Foundation of China (NSFC) [12001450, 20212023, UJA13MZ] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) The authors are very greatful to the Editor and the anonymous reviewers for their very detailed comments and suggestions, which greatly improved the quality and presentation of the paper. The work of Avik Ranjan Adhikary was supported in part by National Natural Science Foundation of China (NSFC) under Grants 12001450. The work of Bapi Dutta is supported by the Grants for the Requalification of the Spanish University System for 20212023 in the Maria Zambrano modality (UJA13MZ). 43 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. DEC 2022.0 164.0 254 260 10.1016/j.patrec.2022.11.017 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8F3AA 2023-03-23 WOS:000919537100019 0
J Wang, HL; Chen, YS; Ni, ZJ; Samori, P Wang, Hanlin; Chen, Yusheng; Ni, Zhenjie; Samori, Paolo An Electrochemical-Electret Coupled Organic Synapse with Single-Polarity Driven Reversible Facilitation-to-Depression Switching ADVANCED MATERIALS English Article electrochemical doping; facilitation-to-depression transition; neuromorphic devices; organic electret memory; organic synapses; polyelectrolyte-gated organic transistors TRANSISTOR; PLASTICITY Neuromorphic engineering and artificial intelligence demands hardware elements that emulates synapse algorithms. During the last decade electrolyte-gated organic conjugated materials have been explored as a platform for artificial synapses for neuromorphic computing. Unlike biological synapses, in current devices the synaptic facilitation and depression are triggered by voltages with opposite polarity. To enhance the reliability and simplify the operation of the synapse without lowering its sophisticated functionality, here, an electrochemical-electret coupled organic synapse (EECS) possessing a reversible facilitation-to-depression switch, is devised. Electret charging counterbalances channel conductance changes due to electrochemical doping, inducing depression without inverting the gate polarity. Overall, EECS functions as a threshold-controlled synaptic switch ruled by its amplitude-dependent, dual-modal operation, which can well emulate information storage and erase as in real synapses. By varying the energy level offset between the channel material and the electret, the EECS's transition threshold can be adjusted for specific applications, e.g., imparting additional light responsiveness to the device operation. The novel device architecture represents a major step forward in the development of artificial organic synapses with increased functional complexity and it opens new perspectives toward the fabrication of abiotic neural networks with higher reliability, efficiency, and endurance. [Wang, Hanlin; Chen, Yusheng; Samori, Paolo] Univ Strasbourg, ISIS, CNRS, 8 Allee Gaspard Monge, F-67000 Strasbourg, France; [Ni, Zhenjie] Univ Chinese Acad Sci, Sch Chem Sci, Beijing 101408, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Samori, P (corresponding author), Univ Strasbourg, ISIS, CNRS, 8 Allee Gaspard Monge, F-67000 Strasbourg, France. samori@unistra.fr Samori, Paolo/A-6381-2010 Samori, Paolo/0000-0001-6256-8281 European Commission through the Marie Sklodowska Curie ETN project BORGES [GA-813863]; Interdisciplinary Thematic Institute SysChem via the IdEx Unistra within the program Investissement d'Avenir program [ANR-10-IDEX-0002]; International Center for Frontier Research in Chemistry (icFRC); Institut Universitaire de France (IUF); Chinese Scholarship Council European Commission through the Marie Sklodowska Curie ETN project BORGES; Interdisciplinary Thematic Institute SysChem via the IdEx Unistra within the program Investissement d'Avenir program(French National Research Agency (ANR)); International Center for Frontier Research in Chemistry (icFRC); Institut Universitaire de France (IUF); Chinese Scholarship Council(China Scholarship Council) The authors acknowledge funding by European Commission through the Marie Sklodowska Curie ETN project BORGES (GA-813863), the Interdisciplinary Thematic Institute SysChem via the IdEx Unistra (ANR-10-IDEX-0002) within the program Investissement d'Avenir program, the International Center for Frontier Research in Chemistry (icFRC), the Institut Universitaire de France (IUF) and the Chinese Scholarship Council. 41 0 0 76 76 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0935-9648 1521-4095 ADV MATER Adv. Mater. DEC 2022.0 34.0 50 10.1002/adma.202205945 0.0 NOV 2022 9 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics 7Q4SP 36201378.0 hybrid, Green Published 2023-03-23 WOS:000877923700001 0
J Yin, GF; Verger, A; Descals, A; Filella, I; Penuelas, J Yin, Gaofei; Verger, Aleixandre; Descals, Adria; Filella, Iolanda; Penuelas, Josep Nonlinear Thermal Responses Outweigh Water Limitation in the Attenuated Effect of Climatic Warming on Photosynthesis in Northern Ecosystems GEOPHYSICAL RESEARCH LETTERS English Article TEMPERATURE; FLUORESCENCE; MECHANISMS Plant photosynthesis is strongly limited by temperature at high northern latitudes. The temperature sensitivity of plant photosynthesis in scenarios of future climatic warming, however, is highly uncertain. We used sun-induced fluorescence-a satellite proxy for plant photosynthesis-to analyze the spatiotemporal response of photosynthesis to temperature at high northern latitudes. We detected a widespread decline in the sensitivity of photosynthesis to temperature during the last two decades: 3.25 versus 2.19%/degrees C, in the 2000s and 2010s, respectively. We used methods of machine learning and temporal composition to characterize the contribution of nonlinear thermal responses and water limitation. Both methods consistently identified the nonlinear thermal responses as the main driver of the attenuated positive warming effect. We highlighted the traditionally temperature-limited ecosystems at high northern latitudes may be approaching the temperature tipping point, beyond which the warming effect on plant photosynthesis would transition from positive to negative. [Yin, Gaofei] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Peoples R China; [Verger, Aleixandre] CIDE CSIC, Valencia, Spain; [Verger, Aleixandre; Descals, Adria; Filella, Iolanda; Penuelas, Josep] CREAF, Barcelona, Spain; [Descals, Adria; Filella, Iolanda; Penuelas, Josep] CSIC, Global Ecol Unit CREAF CSIC UAB, Barcelona, Spain Southwest Jiaotong University; Consejo Superior de Investigaciones Cientificas (CSIC); University of Valencia; CSIC-GV-UV - Centro de Investigaciones sobre Desertificacion (CIDE); Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); University of Barcelona; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Consejo Superior de Investigaciones Cientificas (CSIC) Yin, GF (corresponding author), Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Peoples R China. yingf@swjtu.edu.cn Penuelas, Josep/D-9704-2011; OCO, OCO/GVS-4314-2022 Penuelas, Josep/0000-0002-7215-0150; Verger, Aleixandre/0000-0001-9374-1745; Filella, Iolanda/0000-0001-6262-5733; Yin, Gaofei/0000-0002-9828-7139 Sichuan Science and Technology Program [2021JDJQ0007, 2020JDTD0003]; National Natural Science Foundation of China [41971282]; Spanish Government [PID2019-110521GB-I00]; Catalan government [SGR2017-1005, 2020PANDE00117]; Fundacion Ramon Areces project ELEMENTAL-CLIMATE Sichuan Science and Technology Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Government(Spanish Government); Catalan government; Fundacion Ramon Areces project ELEMENTAL-CLIMATE This research was financially supported by the Sichuan Science and Technology Program 2021JDJQ0007 and 2020JDTD0003, the National Natural Science Foundation of China 41971282, the Spanish Government project PID2019-110521GB-I00, the Catalan government projects SGR2017-1005 and 2020PANDE00117, and the Fundacion Ramon Areces project ELEMENTAL-CLIMATE. This work represents a contribution to the CSIC-PTI TELEDETECT. 44 0 0 19 20 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. AUG 28 2022.0 49.0 16 e2022GL100096 10.1029/2022GL100096 0.0 8 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology 3X2EY 2023-03-23 WOS:000842856200001 0
J Zhang, X; Onieva, E; Perallos, A; Osaba, E Zhang, Xiao; Onieva, Enrique; Perallos, Asier; Osaba, Eneko Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION English Article genetic algorithm; fuzzy logic; classification system; breast cancer diagnosis; variable selection INTELLIGENT SYSTEM; ALGORITHM; RULES Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently. [Zhang, Xiao] South Cent Univ Nationalities, Coll Comp Sci, Hubei Prov Engn Res Ctr Intelligent Management Mf, Wuhan, Peoples R China; [Onieva, Enrique; Perallos, Asier] Univ Deusto, Deusto Inst Technol DeustoTech, Bilbao, Spain; [Osaba, Eneko] ETSI Bilbao UPV EHU, TECNALIA Res & Innovat, Calle Luis Brinas 5, Bilbao 48013, Spain South Central Minzu University; University of Deusto Zhang, X (corresponding author), South Cent Univ Nationalities, Coll Comp Sci, Hubei Prov Engn Res Ctr Intelligent Management Mf, Wuhan, Peoples R China. xiao.zhang@my.cityu.edu.hk; enrique.onieva@deusto.es; perallos@deusto.es; eneko.osaba@tecnalia.com Onieva, Enrique/J-6098-2013; Perallos, Asier/U-7348-2019 Onieva, Enrique/0000-0001-9581-1823; Perallos, Asier/0000-0001-9661-3861 National Natural Science Foundation of China [61902437]; Fundamental Research Funds for the Central Universities, South-Central University for Nationalities [CZT19010]; Research Start-up Funds of South-Central University for Nationalities [YZZ18006] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, South-Central University for Nationalities; Research Start-up Funds of South-Central University for Nationalities This work was supported in part by the National Natural Science Foundation of China under Grant 61902437, the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities under Grant CZT19010, and the Research Start-up Funds of South-Central University for Nationalities under Grant YZZ18006. 37 2 2 0 9 INDERSCIENCE ENTERPRISES LTD GENEVA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND 1758-0366 1758-0374 INT J BIO-INSPIR COM Int. J. Bio-Inspired Comput.. 2020.0 15.0 3 194 205 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science NA4EA 2023-03-23 WOS:000559767200006 0
C Chersoni, E; Pannitto, L; Santus, E; Lenci, A; Huang, CR Calzolari, N; Bechet, F; Blache, P; Choukri, K; Cieri, C; Declerck, T; Goggi, S; Isahara, H; Maegaard, B; Mariani, J; Mazo, H; Moreno, A; Odijk, J; Piperidis, S Chersoni, Emmanuele; Pannitto, Ludovica; Santus, Enrico; Lenci, Alessandro; Huang, Chu-Ren Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020) English Proceedings Paper 12th International Conference on Language Resources and Evaluation (LREC) MAY 11-16, 2020 Marseille, FRANCE Semantics; Cognitive Methods; Statistical and Machine Learning Methods MODELS; VERBS While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles. [Chersoni, Emmanuele; Huang, Chu-Ren] Hong Kong Polytech Univ, Chinese & Bilingual Studies, Hung Hom, 11 Yuk Choi Rd, Hong Kong, Peoples R China; [Pannitto, Ludovica] Univ Trento, Ctr Mind Brain Sci, Corso Bettini 31, Rovereto, Italy; [Santus, Enrico] MIT, MIT CSAIL, 32 Vassar St, Cambridge, MA USA; [Lenci, Alessandro] Univ Pisa, Via Santa Maria 36, I-56126 Pisa, Italy Hong Kong Polytechnic University; University of Trento; Massachusetts Institute of Technology (MIT); University of Pisa Chersoni, E (corresponding author), Hong Kong Polytech Univ, Chinese & Bilingual Studies, Hung Hom, 11 Yuk Choi Rd, Hong Kong, Peoples R China. emmanuele.chersoni@polyu.edu.hk; ludovica.pannitto@unitn.it; esantus@mit.edu; alessandro.lenci@unipi.it; churen.huang@polyu.edu.hk 49 2 2 0 0 EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PARIS 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE 979-10-95546-34-4 2020.0 5708 5713 6 Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BS4ZZ 2023-03-23 WOS:000724697206085 0
J Wang, N; Li, ZL; Tang, BH; Zeng, FNA; Li, CR Wang, Ning; Li, Zhao-Liang; Tang, Bo-Hui; Zeng, Funian; Li, Chuanrong Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using a neural network technique INTERNATIONAL JOURNAL OF REMOTE SENSING English Article TEMPERATURE; EMISSIVITY; ALGORITHM; PROFILES; DESIGN; SENSOR Land surface temperature (LST), land surface emissivity (LSE), and atmospheric profiles are of great importance in many applications. Radiances observed by satellites depend not only on land surface parameters (LST and LSE) but also on atmospheric conditions, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. This work aims to establish a neural network (NN) to retrieve atmospheric profiles, LST, and LSE simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The distributions of surface materials, LST, and atmospheric profiles were elaborated carefully to generate the simulated data. The simulated at-sensor radiances were divided into two sub-ranges in the spectral domain: one in the atmospheric window and the other in the water absorption band. Subsequently, the radiances were transformed in the eigen-domain in each sub-range, and then the transformed coefficients were used as the inputs for the network. Similarly, the atmospheric profiles, LST, and LSE were used as outputs after the eigen-domain transformation. The validation of the NN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 1.6 K, and the RMSE of the temperature profiles is approximately 2 K in the troposphere. Meanwhile, the RMSE of total water content is approximately 0.3 g cm(-2), and that of LSE is less than 0.01 in the spectral interval where the wave number is less than 1000 cm(-1). Two experiments using actual thermal hyperspectral satellite data were carried out to further validate the proposed NN. All of these studies showed that the proposed NN is capable of retrieving atmospheric and land surface parameters with compromised accuracies. Because of its simplicity, the proposed NN can be used to yield preliminary results employed as first estimates for physics-based retrieval models. [Wang, Ning; Li, Zhao-Liang; Tang, Bo-Hui] Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Wang, Ning] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China; [Li, Zhao-Liang] Univ Strasbourg UDS, LSIIT, CNRS, F-67412 Illkirch Graffenstaden, France; [Zeng, Funian] China Aero Geophys Survey, Beijing 100083, Peoples R China; [Zeng, Funian] Remote Sensing Ctr Land & Resources, Beijing 100083, Peoples R China; [Wang, Ning; Li, Chuanrong] Chinese Acad Sci, Acad Optoelect, Beijing 100094, Peoples R China Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; Academy of Opto-Electronics, CAS Li, ZL (corresponding author), Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. lizl@igsnrr.ac.cn Hi-Tech Research and Development Programme of China (863 Plan Programme) [2006AA12Z121, 2008AA121805]; National Natural Science Foundation of China [41071231] Hi-Tech Research and Development Programme of China (863 Plan Programme); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Hi-Tech Research and Development Programme of China (863 Plan Programme) under Grant 2006AA12Z121, Grant 2008AA121805, and it was also supported by the National Natural Science Foundation of China under Grant 41071231. The authors sincerely thank NOVELTIS Inc. for providing the 4A/OP model, JPL for providing the JHU spectral library, and the Laboratoire de Meteorologie Dynamique for providing the TIGR database. 35 37 40 3 32 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. MAY 1 2013.0 34.0 9-10 SI 3485 3502 10.1080/01431161.2012.716536 0.0 18 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology 298CO 2023-03-23 WOS:000330301700011 0
J Wang, LN; Mao, MX; Xie, JL; Liao, Z; Zhang, H; Li, HX Wang, Lining; Mao, Mingxuan; Xie, Jili; Liao, Zheng; Zhang, Hao; Li, Huanxin Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model ENERGY English Article Solar PV power Forecasting; Frequency-domain decomposition; Improved long-short-term-memory network; Support vector regression; Deep learning NEURAL-NETWORKS; RADIATION; IRRADIANCE; ENERGY; OUTPUT; LOAD; SVM; EMD The stability operation and real-time control of the integrated energy system with distributed energy resources determines the higher and higher requirements for the accuracy of solar photovoltaic (PV) output power pre-diction. This paper proposes an accurate PV power prediction interval approach based on frequency-domain decomposition and hybrid deep learning (DL) model. In the proposed approach, ensemble empirical mode decomposition (EEMD) is firstly used to decompose and reconstruct the original PV energy time-series data into high and low-frequency sub-series followed by the statistical feature extraction process. Furthermore, an improved long-short-term-memory network (LSTM) model with the designed hyperparameters based on Bayesian optimization (BO) is proposed to predict the sub-series with the different minute-hour-day intervals. Moreover, support vector regression (SVR) is utilized to analyze the initial time node and reduce the fluctuation error of the prediction value near zero. Finally, a comparative study with SVR, KNN, BPNN, GRU, Stacked-LSTM, LSTM, LSTM-SVR, and LSTM-SVR-BO models is constructed by using an actual dataset collected from Arizona, US. The simulation results on the datasets show the proposed prediction model outperforms the other 7 models for PV power forecasting in 1 day, 7 days, and 14 days ahead prediction with the different minute-hour-day intervals. Especially, in the seven days ahead prediction case, the proposed model's average RMSE and Abs-DEV values are as low as 4.157 and 0.116, where the prediction accuracy and prediction stability are improved by about 15% on average compared to the other prediction models. [Wang, Lining; Mao, Mingxuan] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England; [Mao, Mingxuan] Chongqing Univ, Sch Elect Engn, Chongqing 400044, Peoples R China; [Xie, Jili] Univ Bologna, Dept Elect Elect & Informat Engn Guglielmo Marconi, I-40136 Bologna, Italy; [Liao, Zheng] Elect Power Res Inst Chongqing Power Grid Co Ltd, Chongqing 400041, Peoples R China; [Zhang, Hao] Imperial Coll London, Dept Mat, London SW7 2AZ, England; [Zhang, Hao] Imperial Coll London, London Ctr Nanotechnol, London SW7 2AZ, England; [Li, Huanxin] Engn Dept, Nanomat & Spect Grp, Cambridge, England Imperial College London; Chongqing University; University of Bologna; Imperial College London; Imperial College London; University of London; University College London; University of Cambridge Mao, MX (corresponding author), Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England. m.mao@imperial.ac.uk Li, Huanxin/HNP-2596-2023 Li, Huanxin/0000-0002-6169-3942; Mao, Mingxuan/0000-0003-0455-1507 National Natural Science Foundation of China [52107177]; Natural Science Foundation of Chongqing, China [cstc2019jcyj-bshX0047]; International Post-doctoral Exchange Fellowship Program [2020045] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Chongqing, China(Natural Science Foundation of Chongqing); International Post-doctoral Exchange Fellowship Program This work was jointly supported by the National Natural Science Foundation of China (52107177) , the Natural Science Foundation of Chongqing, China (cstc2019jcyj-bshX0047) and the International Post-doctoral Exchange Fellowship Program under Grant 2020045. The authors acknowledge Yu Wang and Jianli Gao (Department of Electrical and Electronic Engineering, Imperial College London) , Siyu Chen and Zhonghao Li (School of Electrical Engineering, Chongqing University) for writing-review & editing in the revision process. 53 0 0 61 61 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy JAN 1 2023.0 262.0 B 125592 10.1016/j.energy.2022.125592 0.0 18 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 5J3AA 2023-03-23 WOS:000868915300003 0
J Chen, Q; Lu, YB; Gong, YM; Xiong, J Chen, Qian; Lu, Yaobin; Gong, Yeming; Xiong, Jie Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty INTERNET RESEARCH English Article; Early Access AI chatbots; Service quality; Satisfaction; Cognitive trust; Affective trust; Perceived value; Customer loyalty INFORMATION-SYSTEMS SUCCESS; MULTIPLE-ITEM SCALE; E-COMMERCE; CONSUMER PERCEPTIONS; PERCEIVED VALUE; USABILITY CONCEPTUALIZATION; MODERATING ROLE; MCLEAN MODEL; SATISFACTION; TRUST PurposeThis study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization.Design/methodology/approachBased on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty.FindingsThe results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction.Originality/valueThis study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots. [Chen, Qian] Huazhong Agr Univ, Wuhan, Peoples R China; [Lu, Yaobin] Huazhong Univ Sci & Technol, Wuhan, Peoples R China; [Gong, Yeming] Emlyon Business Sch, Ecully, France; [Xiong, Jie] ESSCA Sch Management, Angers, France Huazhong Agricultural University; Huazhong University of Science & Technology; EMLYON Business School; ESSCA Ecole de Management Lu, YB (corresponding author), Huazhong Univ Sci & Technol, Wuhan, Peoples R China. chen_qian@mail.hzau.edu.cn; luyb@hust.edu.cn; gong@em-lyon.com; jie.xiong@essca.fr GONG, Yeming/I-7148-2012 GONG, Yeming/0000-0001-9270-5507 NSFC [72001085, 71810107003, 72271107]; NSSFC [18ZDA109]; Fundamental Research Funds for the Central Universities [2662021JGQD006, 2662021JGPYH01]; AIM Institute; BIC Center NSFC(National Natural Science Foundation of China (NSFC)); NSSFC; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); AIM Institute; BIC Center The authors thank the constructive comments and suggestions from editors and reviewers. This work was supported by a grant from the NSFC (number 72001085, 71810107003 and 72271107), NSSFC (number 18ZDA109), the Fundamental Research Funds for the Central Universities (number 2662021JGQD006 and 2662021JGPYH01). Yeming GONG is supported by the AIM Institute and BIC Center. 137 0 0 39 39 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 1066-2243 INTERNET RES Internet Res. 10.1108/INTR-09-2021-0686 0.0 JAN 2023 39 Business; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Computer Science; Telecommunications 7X2GG 2023-03-23 WOS:000914020800001 0
J Bai, ZW; Li, Y; Wozniak, M; Zhou, ML; Li, D Bai, Zongwen; Li, Ying; Wozniak, Marcin; Zhou, Meili; Li, Di DecomVQANet: Decomposing visual question answering deep network via tensor decomposition and regression PATTERN RECOGNITION English Article Tensor decomposition; Tensor regression layer; Tensor contraction layer; Visual question answering The model we developed is a novel comprehensive solution to compress and accelerate the Visual Question Answering systems. In our algorithm Convolutional Neural Network is compressed with Long Short Term Memory to accelerate processing simultaneously. We propose to conduct various decomposition methods and regression strategies on different layers, including Canonical Polyadic, Tucker, and Tensor Train to decompose Fully Connected layers in CNN and LSTM. The Flattening Layer and Fully Connected layer at the end of the model are replaced with Tensor Regression layers. In order to compress the parameter further, the feature flow between the layers is compressed by Tensor Contraction layer. The proposed tensor decomposition model was evaluated on VQA 2.0 dataset with Pythia as baseline model. Our proposed model achieved from 77% to 91% of compression ratio, and only from 1% to 5% accuracy drop. (c) 2020 Elsevier Ltd. All rights reserved. [Bai, Zongwen; Li, Ying; Li, Di] Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Prov Key Lab Speech & Image Informat Proc, Sch Comp Sci, Xian 710129, Peoples R China; [Bai, Zongwen; Zhou, Meili] Yanan Univ, Shaanxi Key Lab Intelligent Proc Big Energy Data, Sch Phys & Elect Informat, Yanan 716000, Peoples R China; [Wozniak, Marcin] Silesian Tech Univ, Fac Appl Math, Kaszubska 23, PL-44100 Gliwice, Poland Northwestern Polytechnical University; Yanan University; Silesian University of Technology Bai, ZW; Li, Y (corresponding author), Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Prov Key Lab Speech & Image Informat Proc, Sch Comp Sci, Xian 710129, Peoples R China. ydbzw@yau.edu.cn; lybyp@nwpu.edu.cn Woźniak, Marcin/L-6640-2013 Woźniak, Marcin/0000-0002-9073-5347; bai, zongwen/0000-0001-8795-4373 National Natural Science Foundation of China [61761042, 61871460, 61941112, 61702091]; Foundation Project for Advanced Research Field of China [614023804016HK03002]; Key Research and Development Program of Yanan [2017KG-01, 2017WZZ-04-01]; Natural Science Foundation of Shaanxi Province [2020JM-556] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation Project for Advanced Research Field of China; Key Research and Development Program of Yanan; Natural Science Foundation of Shaanxi Province(Natural Science Foundation of Shaanxi Province) This work was supported by the National Natural Science Foundation of China (Grant No. 61761042, 61871460, 61941112, 61702091), the Foundation Project for Advanced Research Field of China (Grant No. 614023804016HK03002), Key Research and Development Program of Yanan (Grant No. 2017KG-01, 2017WZZ-04-01), and the Natural Science Foundation of Shaanxi Province (Grant No. 2020JM-556). 26 35 36 0 30 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. FEB 2021.0 110.0 107538 10.1016/j.patcog.2020.107538 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OL4HW 2023-03-23 WOS:000585304300013 0
C Xu, P; Davoine, F; Denoeux, T Cuzzolin, F Xu, Philippe; Davoine, Franck; Denoeux, Thierry Evidential Logistic Regression for Binary SVM Classifier Calibration BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2014) Lecture Notes in Artificial Intelligence English Proceedings Paper 3rd International Conference on Belief Functions (BELIEF) SEP 26-28, 2014 St Hughs Coll, Oxford, ENGLAND Belief Funct & Applicat Soc,Int Soc Informat Fus,Oxford Brookes Univ, Fac Technol Design & Environm,Onera French Aerosp Lab,Elsevier St Hughs Coll Classifier calibration; theory of belief functions; Dempster-Shafer theory; support vector machines; logistic regression The theory of belief functions has been successfully used in many classification tasks. It is especially useful when combining multiple classifiers and when dealing with high uncertainty. Many classification approaches such as k-nearest neighbors, neural network or decision trees have been formulated with belief functions. In this paper, we propose an evidential calibration method that transforms the output of a classifier into a belief function. The calibration, which is based on logistic regression, is computed from a likelihood-based belief function. The uncertainty of the calibration step depends on the number of training samples and is encoded within a belief function. We apply our method to the calibration and combination of several SVM classifiers trained with different amounts of data. [Xu, Philippe; Davoine, Franck; Denoeux, Thierry] Univ Technol Compiegne, Heudiasyc, UMR CNRS 7253, Compiegne, France; [Davoine, Franck] CNRS, LIAMA, Beijing, Peoples R China Picardie Universites; Universite de Technologie de Compiegne; Centre National de la Recherche Scientifique (CNRS) Xu, P (corresponding author), Univ Technol Compiegne, Heudiasyc, UMR CNRS 7253, Compiegne, France. philippe.xu@hds.utc.fr; franck.davoine@hds.utc.fr; thierry.denoeux@hds.utc.fr Denoeux, Thierry/0000-0002-0660-5436; Davoine, Franck/0000-0002-8587-6997 French Government [ANR-11-IDEX-0004-02]; ANR-NSFC SinoFrench PRETIV project [ANR-11-IS03-0001] French Government; ANR-NSFC SinoFrench PRETIV project(French National Research Agency (ANR)) This research was carried out in the framework of the LabexMS2T, which was funded by the French Government, through the program Investments for the future managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02). It was supported by the ANR-NSFC SinoFrench PRETIV project (Reference ANR-11-IS03-0001). 14 5 5 1 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-11191-9; 978-3-319-11190-2 LECT NOTES ARTIF INT 2014.0 8764.0 49 57 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematics, Applied; Logic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics; Science & Technology - Other Topics BS4HQ 2023-03-23 WOS:000719371600006 0
J Ajani, CK; Zhu, ZW; Sun, DW Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da-Wen Microstructural Classification and Reconstruction of the Computational Geometry of Steamed Bread Using Descriptor-Based Approach TRANSPORT IN POROUS MEDIA English Article Bread microstructures; Automatic classification; Reconstruction; Geometrical descriptors; Machine learning; Microscale modelling VACUUM COOLING PROCESS; COOKED BEEF PRODUCT; FOOD MICROSTRUCTURE; HEAT-TRANSFER; MASS-TRANSFER; WATER; QUALITY; MEAT; ELLIPSE; JOINTS Microstructures affect the properties of food products; accurate and relatively less complex microstructural representations are thus needed for modelling of transport phenomena during food processing. Hence, the present study aimed at developing computational microstructures of steamed bread using descriptor-based approach. Relevant information was extracted from the scanning electron microscope (SEM) images of the steamed bread and evaluated using seven classifiers. For the automatic classification and using all descriptors, bagged trees ensembles (BTE) had the highest accuracy of 98.40%, while Gaussian Naive Bayes was the least with 92.10% accuracy. In the step forward analysis, five descriptors had higher classification accuracy (98.80%) than all descriptors, implying that increase in descriptors might or might not increase classification accuracy. Microstructural validation revealed that the ellipse fitting method with a p value of 0.7984 for the area was found to be superior to the Voronoi method with a corresponding p value of 1.4554 x 10(-5), confirming that the ellipse developed microstructure was more suitable for microscale modelling of transport phenomena in steamed bread. [Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da-Wen] South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Peoples R China; [Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da-Wen] South China Univ Technol, Acad Contemporary Food Engn, Guangzhou Higher Educ Mega Ctr, Guangzhou 510006, Peoples R China; [Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da-Wen] Guangzhou Higher Educ Mega Ctr, Engn & Technol Res Ctr Guangdong Prov Intelligent, Guangzhou 510006, Peoples R China; [Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da-Wen] Guangzhou Higher Educ Mega Ctr, Guangdong Prov Engn Lab Intelligent Cold Chain Lo, Guangzhou 510006, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol FRCFT, Univ Coll Dublin, Dublin 4, Ireland South China University of Technology; South China University of Technology; University College Dublin Sun, DW (corresponding author), South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Peoples R China.;Sun, DW (corresponding author), South China Univ Technol, Acad Contemporary Food Engn, Guangzhou Higher Educ Mega Ctr, Guangzhou 510006, Peoples R China.;Sun, DW (corresponding author), Guangzhou Higher Educ Mega Ctr, Engn & Technol Res Ctr Guangdong Prov Intelligent, Guangzhou 510006, Peoples R China.;Sun, DW (corresponding author), Guangzhou Higher Educ Mega Ctr, Guangdong Prov Engn Lab Intelligent Cold Chain Lo, Guangzhou 510006, Peoples R China.;Sun, DW (corresponding author), Natl Univ Ireland, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol FRCFT, Univ Coll Dublin, Dublin 4, Ireland. dawen.sun@ucd.ie Guangzhou Key Laboratory for Intelligent Sensing and Quality Control of Agricultural Products [202102100009]; Contemporary International Collaborative Research Centre of Guangdong Province on Food Innovative Processing and Intelligent Control [2019A050519001]; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products [2021KJ145, 2018GXZ013421]; China Scholarship Council Guangzhou Key Laboratory for Intelligent Sensing and Quality Control of Agricultural Products; Contemporary International Collaborative Research Centre of Guangdong Province on Food Innovative Processing and Intelligent Control; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products; China Scholarship Council(China Scholarship Council) The authors are grateful to the Guangzhou Key Laboratory for Intelligent Sensing and Quality Control of Agricultural Products (202102100009) for its support. This research was also supported by the Contemporary International Collaborative Research Centre of Guangdong Province on Food Innovative Processing and Intelligent Control (2019A050519001) and the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2021KJ145). Clement Kehinde Ajani is in receipt of a PhD scholarship (2018GXZ013421) from the China Scholarship Council. 59 2 2 4 10 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0169-3913 1573-1634 TRANSPORT POROUS MED Transp. Porous Media SEP 2022.0 144.0 2 317 336 10.1007/s11242-022-01796-2 0.0 JUN 2022 20 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 4D0MQ hybrid 2023-03-23 WOS:000807293000001 0
J Khoder, A; Dornaika, F Khoder, A.; Dornaika, F. Ensemble learning via feature selection and multiple transformed subsets: Application to image classification APPLIED SOFT COMPUTING English Article Ensemble learning; Feature subsets; Multi-models; Machine learning; Feature selection; Image classification; Class sparsity least square regression INDEPENDENT ESTIMATORS; LINEAR-REGRESSION; PREDICTION; ALGORITHM In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient Inter-class sparsity discriminative least square regression method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods. (C) 2021 The Author(s). Published by Elsevier B.V. [Khoder, A.; Dornaika, F.] Henan Univ, Kaifeng, Peoples R China; [Dornaika, F.] Univ Basque Country UPV EHU, San Sebastian, Spain; [Dornaika, F.] Ikerbasque, Basque Fdn Sci, Bilbao, Spain Henan University; University of Basque Country; Basque Foundation for Science Dornaika, F (corresponding author), Univ Basque Country UPV EHU, San Sebastian, Spain. fadi.dornaika@ehu.eus 73 1 1 2 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. DEC 2021.0 113.0 B 108006 10.1016/j.asoc.2021.108006 0.0 NOV 2021 14 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science XE8MA Green Published, hybrid 2023-03-23 WOS:000723635100003 0
C Chen, Q; Song, YB; Jennings, B; Zhang, F; Xiao, B; Gao, S IEEE Chen, Qi; Song, Yubo; Jennings, Brendan; Zhang, Fan; Xiao, Bin; Gao, Shang IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) DEC 07-11, 2021 Madrid, SPAIN IEEE,Huawei,Vodafone,Intel,ZTE,Qualcomm,Assia,IMEC,Keysight Technologies,PRIME Alliance,Rohde & Schwarz,Vantage Towers,Guard,Usal Bisite Res Grp IoT device identification; device fingerprinting; feature selection; genetic algorithm; machine learning Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics fingerprinting have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift- relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved. [Chen, Qi; Song, Yubo] Southeast Univ, Sch Cyber Sci & Engn, Key Lab Comp Network Technol Jiangsu Prov, Nanjing, Peoples R China; [Chen, Qi; Song, Yubo] Purple Mt Labs, Nanjing, Peoples R China; [Xiao, Bin; Gao, Shang] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Jennings, Brendan; Zhang, Fan] Waterford Inst Technol, Telecommun Software & Syst Grp, Waterford, Ireland Southeast University - China; Hong Kong Polytechnic University; South East Technological University (SETU) Chen, Q (corresponding author), Southeast Univ, Sch Cyber Sci & Engn, Key Lab Comp Network Technol Jiangsu Prov, Nanjing, Peoples R China.;Chen, Q (corresponding author), Purple Mt Labs, Nanjing, Peoples R China. chenqi@seu.edu.cn; songyubo@seu.edu.cn; bjennings@tssg.org; 20062507@mail.wit.ie; csbxiao@polyu.edu.hk; goldensaintgao@qq.com Xiao, Bin/AAZ-4848-2020 Xiao, Bin/0000-0003-4223-8220; Zhang, Fan/0000-0002-4460-9091 National Key R&D Program of China and Frontiers Science Center for Mobile Information Communication and Security [2018YFB2202200, 2018YFB2100403]; Science Foundation Ireland (SFI) [13/RC/2077] National Key R&D Program of China and Frontiers Science Center for Mobile Information Communication and Security; Science Foundation Ireland (SFI)(Science Foundation Ireland) This work is supported in part by National Key R&D Program of China and Frontiers Science Center for Mobile Information Communication and Security, under Grant Nos. 2018YFB2202200 and 2018YFB2100403, and in part by a research grant from Science Foundation Ireland (SFI) that is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. The corresponding author is Yubo Song. 9 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-8104-2 IEEE GLOB COMM CONF 2021.0 10.1109/GLOBECOM46510.2021.9685693 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT0PJ 2023-03-23 WOS:000790747203109 0
C Jin, S; Yang, H; Chen, L; Gao, Y; Sun, RC; McLoone, S Jin, Sheng; Yang, Hui; Chen, Liang; Gao, Yu; Sun, Rongchuan; McLoone, Sean Binary GAN based Approach for Unsupervised Loop Closure Detection in Autonomous Unmanned Systems IFAC PAPERSONLINE English Proceedings Paper 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges JUL 11-17, 2020 ELECTR NETWORK Int Federat Automat Control,Siemens,Bayer,ABB,MathWorks,Phoenix Contact,Ifak Technol,Berlin Heart,Elsevier,De Gruyter,Tele Medi GmbH loop closure detection; bag of visual word; vSLAM; binary GAN; feature extraction Inspired by generative adversarial network (GAN), we propose a novel unsupervised approach for loop closure detection in autonomous unmanned systems. A binary GAN model dedicated to mobile application scenarios is designed to obtain binary feature descriptors, which are further incorporated into the most commonly used Bag of Visual Words (BoVW) model for loop closure detection. Compared with those hand-crafted features like SIFT and ORB, the performance of loop closure detection in complex environments with strong viewpoint and condition changes can be greatly improved. Compared with existing supervised approach based on convolutional neural network like AlexNet and AMOSNet, the cost-expensive task of supervised data annotation is totally avoided, which make the proposed approach more practical. Copyright (C) 2020 The Authors. [Jin, Sheng; Yang, Hui; Chen, Liang; Gao, Yu; Sun, Rongchuan] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China; [McLoone, Sean] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Soochow University - China; Queens University Belfast Chen, L (corresponding author), Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China. chenl@suda.edu.cn 18 0 0 0 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53.0 2 9716 9721 10.1016/j.ifacol.2020.12.2626 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SF2LQ gold 2023-03-23 WOS:000652593100150 0
C Drossos, K; Mimilakis, SI; Gharib, S; Li, YX; Virtanen, T IEEE Drossos, Konstantinos; Mimilakis, Stylianos, I; Gharib, Shayan; Li, Yanxiong; Virtanen, Tuomas Sound Event Detection with Depthwise Separable and Dilated Convolutions 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) JUL 19-24, 2020 ELECTR NETWORK IEEE,IEEE Computat Intelligence Soc,Int Neural Network Soc sound event detection; depthwise separable convolution; dilated convolution NETWORKS State-of-the-art sound event detection (SED) methods usually employ a series of convolutional neural networks (CNNs) to extract useful features from the input audio signal, and then recurrent neural networks (RNNs) to model longer temporal context in the extracted features. The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions. Additionally, the usually long sequences that are used as an input to an SED method along with the employment of an RNN, introduce implications like increased training time, difficulty at gradient flow, and impeding the parallelization of the SED method. To tackle all these problems, we propose the replacement of the CNNs with depthwise separable convolutions and the replacement of the RNNs with dilated convolutions. We compare the proposed method to a baseline convolutional neural network on a SED task, and achieve a reduction of the amount of parameters by 85% and average training time per epoch by 78%, and an increase the average frame-wise F-1 score and reduction of the average error rate by 4.6% and 3.8%, respectively. [Drossos, Konstantinos; Gharib, Shayan; Virtanen, Tuomas] Tampere Univ, Audio Res Grp, Tampere, Finland; [Mimilakis, Stylianos, I] Fraunhofer IDMT, Semant Mus Technol Grp, Ilmenau, Germany; [Li, Yanxiong] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China Tampere University; South China University of Technology Drossos, K (corresponding author), Tampere Univ, Audio Res Grp, Tampere, Finland. konstantinos.drossos@tuni.fi; mis@idmt.fraunhofer.de; shayan.gharib@tuni.fi; eeyxli@scut.edu.cn; tuomas.virtanen@tuni.fi Drosos, Konstantinos/0000-0002-3605-7127 European Research Council under the European Union's H2020 Framework Programme through ERC Grant EVERYSOUND [637422]; German Research Foundation [AB 675/2-1, MU 2686/11-1] European Research Council under the European Union's H2020 Framework Programme through ERC Grant EVERYSOUND; German Research Foundation(German Research Foundation (DFG)) Part of the computations leading to these results was performed on a TITAN-X GPU donated by NVIDIA. Part of the research leading to these results has received funding from the European Research Council under the European Union's H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND. Stylianos I. Mimilakis is supported in part by the German Research Foundation (AB 675/2-1, MU 2686/11-1). The authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. 31 9 9 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-7281-6926-2 IEEE IJCNN 2020.0 7 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ9MM 2023-03-23 WOS:000626021407044 0
J Zhang, HW; Avrithis, Y; Furon, T; Amsaleg, L Zhang, Hanwei; Avrithis, Yannis; Furon, Teddy; Amsaleg, Laurent Smooth adversarial examples EURASIP JOURNAL ON INFORMATION SECURITY English Article Adversarial example; Image classification; Deep neural network This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may be noisy on its textured areas and sharp across its edges.This operation relies on Laplacian smoothing, well-known in graph signal processing, which we integrate in the attack pipeline. We benchmark several attacks with and without smoothing under a white box scenario and evaluate their transferability. Despite the additional constraint of smoothness, our attack has the same probability of success at lower distortion. [Zhang, Hanwei; Avrithis, Yannis; Furon, Teddy; Amsaleg, Laurent] Univ Rennes, CNRS, INRIA, IRISA, Campus Beaulieu, Rennes, France; [Zhang, Hanwei] East China Normal Univ, North Zhongshan Rd Campus, Shanghai, Peoples R China Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Rennes; East China Normal University Zhang, HW (corresponding author), Univ Rennes, CNRS, INRIA, IRISA, Campus Beaulieu, Rennes, France.;Zhang, HW (corresponding author), East China Normal Univ, North Zhongshan Rd Campus, Shanghai, Peoples R China. hanwei.zhang@irisa.fr Furon, Teddy/ABG-2584-2020 Furon, Teddy/0000-0002-1565-765X Chinese Scholarship Council; chaire on artificial intelligence SAIDA Chinese Scholarship Council(China Scholarship Council); chaire on artificial intelligence SAIDA The Ph.D. thesis of Hanwei Zhang is funded by the Chinese Scholarship Council. Teddy Furon is funded by the chaire on artificial intelligence SAIDA. 44 1 1 0 0 SPRINGER INT PUBL AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2510-523X EURASIP J INF SECUR EURASIP J. Inf. Secur. NOV 17 2020.0 2020.0 1 15 10.1186/s13635-020-00112-z 0.0 12 Computer Science, Information Systems Emerging Sources Citation Index (ESCI) Computer Science OS5IV Green Submitted, Green Published, gold 2023-03-23 WOS:000590198400001 0
J Wang, C; Zheng, JQ; Du, J; Wang, GT; Klemes, JJ; Wang, BH; Liao, Q; Liang, YT Wang, Chang; Zheng, Jianqin; Du, Jian; Wang, Guotao; Klemes, Jiri Jaromir; Wang, Bohong; Liao, Qi; Liang, Yongtu Weather condition-based hybrid models for multiple air pollutants forecasting and minimisation JOURNAL OF CLEANER PRODUCTION English Article Air pollutants forecasting; Hybrid model; Model fusion; Spatiotemporal characteristics WRF-CMAQ; SYSTEM; MACHINE; CNN With the deterioration of air quality in recent years, the establishment of accurate and efficient forecasting models for pollutants has become the top priority. Due to the imperfect internal mechanism, the traditional numerical model, Weather Research and Forecast - Community Multiscale Air Quality (WRF-CMAQ), whose performance is limited in predicting the concentration of pollutants. To solve that issue presented, this study proposed two hybrid models for pollutant concentration forecasting based on weather conditions of various monitoring points. The hybrid model I applies long and short-term memory neural networks (LSTM) to extract the temporal characteristics and random forest (RF) to extract the non-line characteristics. Then, a fusion layer is built to combine them, which is optimised by the particle swarm optimisation (PSO) algorithm. Based on hybrid model I, hybrid model II also considers the regional synergy of different monitoring points to capture the spatial correlation of weather conditions. Taking a certain region of China as an example, the performance of these two hybrid models is proved. The results and discussions indicate that not only do the hybrid models achieve higher accuracy than other comparable models such as LSTM, convolutional neural network (CNN), and WRF-CMAQ, but they also prove that the regional synergy can significantly improve the effectiveness of air pollutants forecasting. The root mean squared error (RMSE) of the hybrid model II for predicted six pollutants concentration dropped to 1.781, 6.630, 5.556, 4.154, 49.558, 4.074 compared with the RMSE values of the hybrid model I and WRF-CMAQ, which are 1.972, 6.734, 6.731, 4.937, 63.487, 5.422 and 7.98, 38.175, 29.511, 21.077, 78.479, 22.810. This work provides the high-precision prediction and comprehensive evaluation of primary pollutants, which provides a targeting option to deal with the highest predicted pollutants. [Wang, Chang; Zheng, Jianqin; Du, Jian; Wang, Guotao; Liao, Qi; Liang, Yongtu] China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China; [Klemes, Jiri Jaromir] Brno Univ Technol, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab SPIL,VUT BRNO, Tech 2896-2, Brno 61669, Czech Republic; [Wang, Bohong] Zhejiang Ocean Univ, Natl Local Joint Engn Lab Harbour Oil & Gas Stora, Zhejiang Key Lab Petrochem Environm Pollut Contro, 1 Haida South Rd, Zhoushan 316022, Peoples R China China University of Petroleum; Brno University of Technology; Zhejiang Ocean University Zheng, JQ; Liang, YT (corresponding author), China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China. 2018214074@student.cup.edu.cn; liangyt21st@cup.edu.cn Klemes, Jiri Jaromir/B-7291-2009; wang, Logan/HHS-5627-2022; Wang, Bohong/M-2379-2019 Klemes, Jiri Jaromir/0000-0002-7450-7029; Wang, Bohong/0000-0003-1206-475X National Natural Science Foundation of China [51874325]; EU [CZ.02.1.01/0.0/0.0/15_003/0000456] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU(European Commission) This work was part of the Program of Study on Optimisation and Supply-side Reliability of Oil Product Supply Chain Logistics System funded under the National Natural Science Foundation of China, grant number 51874325. Additional funding comes from EU project Sustainable Process Integration Laboratory - SPIL, project No. CZ.02.1.01/0.0/0.0/15_003/0000456 funded by EU CZ Operational Programme Research, Development and Education, Priority 1: Strengthening capacity for quality research. The authors are grateful to all study participants. 46 2 2 15 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. JUN 10 2022.0 352.0 131610 10.1016/j.jclepro.2022.131610 0.0 APR 2022 15 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 1C9VS 2023-03-23 WOS:000793460700002 0
J Zhang, B; Wu, YF; Zhao, BY; Chanussot, J; Hong, DF; Yao, J; Gao, LR Zhang, Bing; Wu, Yuanfeng; Zhao, Boya; Chanussot, Jocelyn; Hong, Danfeng; Yao, Jing; Gao, Lianru Progress and Challenges in Intelligent Remote Sensing Satellite Systems IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Satellites; Remote sensing; Spatial resolution; Imaging; Sensors; Real-time systems; Satellite broadcasting; Artificial intelligence; hyperspectral remote sensing; intelligent remote sensing satellite; onboard real-time processing Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts and key technologies in the intelligent hyperspectral remote sensing satellite system have been proposed since 2011. The aim of these intelligent remote sensing satellites is to provide real-time, accurate, and personalized remote sensing information services. This article reviews the current developments in new-generation intelligent remote sensing satellite systems, with a focus on intelligent remote sensing satellite platforms, imaging payloads, onboard processing systems, and other key technological chains. The technological breakthroughs and current defects of intelligence-oriented designs are also analyzed. Intelligent remote sensing satellites collect personalized remote sensing data and information, with real-time data features and information interaction between remote sensing satellites or between satellites and the ground. Such developments will expand the use of remote sensing applications beyond government departments and industrial users to a massive number of individual users. However, this extension faces challenges regarding privacy protection, societal values, and laws regarding the sharing and distribution of data and information. [Zhang, Bing; Wu, Yuanfeng; Zhao, Boya; Hong, Danfeng; Yao, Jing; Gao, Lianru] Chinese Acad Sci, Key Lab Digital Earth Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, INRIA, CNRS, Grenoble INP LJK, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Chinese Academy of Sciences Zhang, B (corresponding author), Chinese Acad Sci, Key Lab Digital Earth Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China. zb@radi.ac.cn; wuyf@radi.ac.cn; zhaoby@aircas.ac.cn; jocelyn.chanussot@grenoble-inp.fr; hongdf@aircas.ac.cn; jasonyao92@gmail.com; gaolr@aircas.ac.cn wu, yuanfeng/GVU-6762-2022 Yao, Jing/0000-0003-1301-9758; wu, yuan feng/0000-0001-8427-9851; Chanussot, Jocelyn/0000-0003-4817-2875 National Key R&D Program of China [2021YFA0715203]; National Natural Science Foundation of China [41871245, 62001455] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China under Grant 2021YFA0715203 and in part by the National Natural Science Foundation of China under Grant 41871245 and Grant 62001455. 41 28 29 187 265 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 1814 1822 10.1109/JSTARS.2022.3148139 0.0 9 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology ZP2PC gold 2023-03-23 WOS:000766265800001 0
J Xue, J; Shao, JF; Burlion, N Xue, J.; Shao, J. F.; Burlion, N. Estimation of constituent properties of concrete materials with an artificial neural network based method CEMENT AND CONCRETE RESEARCH English Article Multi-scale modeling; Homogenization; Parameter identification; Artificial neural network; Concrete; Heterogeneous materials COMPRESSIVE STRENGTH; SENSITIVITY-ANALYSIS; CEMENT PASTE; TENSILE-STRENGTH; MODEL; PREDICTION; PORES; PERFORMANCE; PLASTICITY; FRAMEWORK Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important microstructural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy. [Xue, J.; Shao, J. F.] Hohai Univ, Coll Civil Engn & Transportat, Nanjing 210098, Jiangsu, Peoples R China; [Xue, J.; Shao, J. F.; Burlion, N.] Univ Lille, CNRS, Cent Lille, LaMcube,UMR9013, F-59000 Lille, France Hohai University; Centre National de la Recherche Scientifique (CNRS); Universite de Lille - ISITE; Centrale Lille; Universite de Lille Shao, JF (corresponding author), Hohai Univ, Coll Civil Engn & Transportat, Nanjing 210098, Jiangsu, Peoples R China. jian-fu.shao@polytech-lille.fr shao, jianfu/N-2447-2018 shao, jianfu/0000-0002-6632-8207 Work Package MAGIC; China Scholarship Council (CSC) [201908230319] Work Package MAGIC; China Scholarship Council (CSC)(China Scholarship Council) The present study was jointly supported by the Work Package MAGIC in the framework of the European Joint Programm on radioactive waste management EURAD. The scholarship provided by the China Scholar-ship Council (CSC, grant No. 201908230319) to the first author is also gratefully acknowledged. 66 9 9 7 35 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0008-8846 1873-3948 CEMENT CONCRETE RES Cem. Concr. Res. DEC 2021.0 150.0 106614 10.1016/j.cemconres.2021.106614 0.0 SEP 2021 12 Construction & Building Technology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Materials Science WJ2AH Bronze 2023-03-23 WOS:000708849900005 0
J Liu, Y; Ma, XY; Shu, L; Yang, Q; Zhang, Y; Huo, ZQ; Zhou, ZB Liu, Ye; Ma, Xiaoyuan; Shu, Lei; Yang, Qing; Zhang, Yu; Huo, Zhiqiang; Zhou, Zhangbing Internet of Things for Noise Mapping in Smart Cities: State of the Art and Future Directions IEEE NETWORK English Article Computational modeling; Smart phones; Crowdsensing; Noise measurement; Atmospheric modeling; Working environment noise; Monitoring Noise pollution has been an issue since ancient times. Recently, this problem has been exacerbated due to rapid population growth and urbanization. Noise mapping is a strategic action plan that visualizes the long-term and real-time noise pollution of our cities, industrial sites, and other regions of interest. This article first discusses the working principle of general model-based noise mapping and the lessons learned. Then, in-depth descriptions of the technical challenges and design issues of noise mapping using mobile crowdsensing and acoustic sensor networks are presented. Finally, we provide our insights for future research directions regarding artificial intelligence assisted noise prediction, constructive interference for multimedia transmission, and simultaneous noise sensing and sound energy harvesting as well as inaudible sound attacks and defense. [Liu, Ye; Shu, Lei] Nanjing Agr Univ, Nanjing, Peoples R China; [Ma, Xiaoyuan] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai, Peoples R China; [Shu, Lei] Osaka Univ, Dept Multimedia Engn, Osaka, Japan; [Shu, Lei; Zhang, Yu; Huo, Zhiqiang] Univ Lincoln, Lincoln, England; [Yang, Qing] Univ North Texas, Denton, TX 76203 USA; [Zhang, Yu] Loughborough Univ, Digital Engn, Dept Aeronaut & Automot Engn, Loughborough, Leics, England; [Zhou, Zhangbing] China Univ Geosci Beijing, Beijing, Peoples R China; [Zhou, Zhangbing] TELECOM SudParis, Paris, France Nanjing Agricultural University; Chinese Academy of Sciences; Shanghai Advanced Research Institute, CAS; Osaka University; University of Lincoln; University of North Texas System; University of North Texas Denton; Loughborough University; China University of Geosciences; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Shu, L (corresponding author), Nanjing Agr Univ, Nanjing, Peoples R China.;Shu, L (corresponding author), Univ Lincoln, Lincoln, England. yeliu@njau.edu.cn; maxy@sari.ac.cn; lei.shu@ieee.org; Qing.Yang@unt.edu; Y.Zhang@lboro.ac.uk; zhuo@lincoln.ac.uk; zbzhou@cugb.edu.cn Ma, Xiaoyuan/0000-0001-6655-3001; Liu, Ye/0000-0001-9156-9515 National Natural Science Foundation of China [61902188]; Fundamental Research Funds for the Central Universities [KJQN202047]; China Postdoctoral Science Foundation [2019M651713]; Science and Technology Planning Project of Guangdong Province [2017A050506057] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Science and Technology Planning Project of Guangdong Province This work was partially supported by the National Natural Science Foundation of China (No. 61902188); the Fundamental Research Funds for the Central Universities (No. KJQN202047); the China Postdoctoral Science Foundation (No. 2019M651713); and the Science and Technology Planning Project of Guangdong Province (No. 2017A050506057). 15 17 18 3 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. JUL-AUG 2020.0 34.0 4 112 118 10.1109/MNET.011.1900634 0.0 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications MP5WF Green Published 2023-03-23 WOS:000552273900017 0
J Li, JH; Wang, JZ; Peng, H; Zhang, LB; Hu, YB; Su, H Li, Jiehao; Wang, Junzheng; Peng, Hui; Zhang, Longbin; Hu, Yingbai; Su, Hang Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction NEUROCOMPUTING English Article Wheel-legged robot; Autonomous tracking control; Model predictive control; Neural fuzzy approximation 4-WHEEL-INDEPENDENT-DRIVE ELECTRIC VEHICLES; LATERAL MOTION CONTROL; WALK-MAN; NETWORK The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal-robot friction and external-robot and environment interaction forces, should be considered in the robot's dynamical system. In this article, a neural fuzzy-based model predictive tracking scheme (NFMPC) for reliable tracking control is proposed to the developed four wheel-legged robot, and the fuzzy neural network approximation is applied to estimate the unknown physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the four wheel-legged robot (BIT-NAZA) is introduced. Finally, co-simulation and experiment results using the BIT-NAZA robot derived from the proposed hybrid control strategy indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability. This research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities, and facilitate the control performance of the wheel-legged robot in a practical system. (C) 2020 Elsevier B.V. All rights reserved. [Li, Jiehao; Wang, Junzheng; Peng, Hui] Beijing Inst Technol, State Key Lab Intelligent Control & Decis Complex, Beijing 100081, Peoples R China; [Zhang, Longbin] KTH Royal Inst Technol, BioMEx Ctr, SE-10044 Stockholm, Sweden; [Zhang, Longbin] KTH Royal Inst Technol, KTH Mech, SE-10044 Stockholm, Sweden; [Hu, Yingbai] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Li, Jiehao; Su, Hang] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy Beijing Institute of Technology; Royal Institute of Technology; Royal Institute of Technology; Technical University of Munich; Polytechnic University of Milan Su, H (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy. jiehao.li@bit.edu.cn; longbin@kth.se; yingbai.hu@tum.de; hang.su@polimi.it Li, Jiehao/AAH-9089-2021 National Natural Science Foundation of China [61773060]; China Scholarship Council [201906030066] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This study was supported by the National Natural Science Foundation of China under Grant 61773060, and China Scholarship Council under Grant 201906030066. 38 71 71 13 59 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 14 2020.0 410.0 342 353 10.1016/j.neucom.2020.05.091 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science OD4CB 2023-03-23 WOS:000579799300029 0
J Gheorghe, LL; Mobasseri, S; Agricola, E; Wang, DD; Milla, F; Swaans, M; Pandis, D; Adams, DH; Yadav, P; Sievert, H; Ailawadi, G; Sorajja, P Gheorghe, Livia L.; Mobasseri, Sara; Agricola, Eustachio; Wang, Dee Dee; Milla, Federico; Swaans, Martin; Pandis, Dimosthenis; Adams, David H.; Yadav, Pradeep; Sievert, Horst; Ailawadi, Gorav; Sorajja, Paul Imaging for Native Mitral Valve Surgical and Transcatheter Interventions JACC-CARDIOVASCULAR IMAGING English Review Barlow mitral valve; beating heart chordal implant; MitraClip; mitral regurgitation; mitral valve anatomy for interventions; mitral valve; transcatheter mitral valve interventions; transcatheter mitral valve repair; transcatheter mitral valve replacement OUTFLOW TRACT OBSTRUCTION; TRANSESOPHAGEAL ECHOCARDIOGRAPHY; CORONARY-SINUS; REPAIR; REGURGITATION; ANNULOPLASTY; REPLACEMENT; ANNULUS; PREVALENCE; COMPLEXITY There has been rapid progress in transcatheter therapies for mitral regurgitation. These developments have elevated the need for the imager to have a core understanding of the functional mitral valve anatomy. Pre- and intraoperative echocardiography for surgical mitral valve repair for mitrat regurgitation has defined contemporary interventional imaging in many ways. The central tenets of these principles apply to interventional imaging of transcatheter mitral valve interventions. However, the heightened emphasis on procedural planning and procedural imaging is one of the new challenges posed by transcatheter interventions. This need for accurate and reliable information has required the imager to be agnostic to the imaging modality. Cardiac computed tomography has become critical in procedural planning in this new paradigm. The expanded use of pre-procedural cardiac magnetic resonance to quantify mitral regurgitation and characterize the left ventricle is another illustration of this newer approach. Other illustrations of the new world of interventional imaging include the expanded use of 3-dimensional (3D) transesophageal echocardiography and real-time fusion of echocardiography and fluoroscopy images. Imaging data are also the basis for computational modeling, 3D printing, and artificial intelligence. These technologies are being increasingly explored to improve therapy selection and prediction of procedural outcomes. This review provides an update of the essentials in present interventional imaging for surgical and transcatheter interventions for mitral regurgitation. (C) 2021 by the American College of Cardiology Foundation. [Gheorghe, Livia L.; Swaans, Martin] St Antonius Hosp, Dept Cardiol, Nieuwegein, Netherlands; [Gheorghe, Livia L.] Puerta del Mar Hosp, Dept Cardiol, Cadiz, Spain; [Mobasseri, Sara; Milla, Federico; Yadav, Pradeep] Marcus Heart Valve Ctr, Piedmont Heart Inst, Atlanta, GA USA; [Agricola, Eustachio] Univ Vita Salute San Raffaele, Sch Med, Cardiothorac Vasc Dept, Milan, Italy; [Agricola, Eustachio] IRCCS San Raffaele Sci Inst, Cardiothorac Vasc Dept, Cardiovasc Imaging Unit, Milan, Italy; [Wang, Dee Dee] Henry Ford Hlth Syst, Ctr Struct Heart Dis, Div Cardiol, Detroit, MI USA; [Pandis, Dimosthenis; Adams, David H.] Icahn Sch Med Mt Sinai, Dept Cardiovasc Surg, New York, NY 10029 USA; [Sievert, Horst] CardioVasc Ctr Frankfurt, Frankfurt, Germany; [Sievert, Horst] Anglia Ruskin Univ, Chelmsford, England; [Sievert, Horst] Univ Calif San Francisco, San Francisco, CA 94143 USA; [Sievert, Horst] Yunnan Hosp Fuwai, Kunming, Yunnan, Peoples R China; [Ailawadi, Gorav] Univ Virginia, Dept Surg, Div Thorac & Cardiovasc Surg, Charlottesville, VA USA; [Sorajja, Paul] Abbott NW Hosp, Minneapolis Heart Inst Fdn, Valve Sci Ctr, Minneapolis, MN USA St. Antonius Hospital Utrecht; Universidad de Cadiz; Hospital Universitario Puerta del Mar; Piedmont Heart Institute; Vita-Salute San Raffaele University; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele; Henry Ford Health System; Henry Ford Hospital; Icahn School of Medicine at Mount Sinai; Cardiovascular Center (CVC) Frankfurt; Anglia Ruskin University; University of California System; University of California San Francisco; University of Virginia; Minneapolis Heart Institute Foundation Gheorghe, LL (corresponding author), St Antonius Hosp, Dept Cardiol, Nieuwegein, Netherlands. livia_gheorghe_ro@yahoo.es Pandis, Dimosthenis/AAD-8615-2022 Pandis, Dimosthenis/0000-0001-7124-7009; Agricola, Eustachio/0000-0002-4834-2187 66 8 8 2 8 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1936-878X 1876-7591 JACC-CARDIOVASC IMAG JACC-Cardiovasc. Imag. JAN 2021.0 14.0 1 112 127 10.1016/j.jcmg.2020.11.021 0.0 JAN 2021 16 Cardiac & Cardiovascular Systems; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology; Radiology, Nuclear Medicine & Medical Imaging PW4AJ 33413881.0 2023-03-23 WOS:000610613700006 0
C Rivero, J; Ribeiro, B; Chen, N; Leite, FS Liu, D; Xie, S; Li, Y; Zhao, D; ElAlfy, ESM Rivero, Jorge; Ribeiro, Bernardete; Chen, Ning; Leite, Fatima Silva A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection NEURAL INFORMATION PROCESSING, ICONIP 2017, PT I Lecture Notes in Computer Science English Proceedings Paper 24th International Conference on Neural Information Processing (ICONIP) NOV 14-18, 2017 Guangzhou, PEOPLES R CHINA Chinese Acad Sci, Inst Automat,Guangdong Univ Technol,S China Univ Technol,Springers Lecture Notes Comp Sci,IEEE CAA Journal Automatica Sinica,Asia Pacific Neural Network Soc Zero-Shot Learning; Grassmannian; Intrusion detection One of the main problems in Network Intrusion Detection comes from constant rise of new attacks, so that not enough labeled examples are available for the new classes of attacks. Traditional Machine Learning approaches hardly address such problem. This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Learning and the Inference Stage. The goal of this paper is to propose a new Inference Stage algorithm for Network Intrusion Detection. In order to attain this objective, we firstly put forward an experimental setup for the evaluation of the Zero-Shot Learning in Network Intrusion Detection related tasks. Secondly, a decision tree based algorithm is applied to extract rules for generating the attributes in the AL stage. Finally, using a representation of a Zero-Shot Class as a point in the Grassmann manifold, an explicit formula for the shortest distance between points in that manifold can be used to compute the geodesic distance between the Zero-Shot Classes which represent the new attacks and the Known Classes corresponding to the attack categories. The experimental results in the datasets KDD Cup 99 and NSL-KDD show that our approach with Zero-Shot Learning successfully addresses the Network Intrusion Detection problem. [Rivero, Jorge; Ribeiro, Bernardete] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal; [Chen, Ning] Henan Polytech Univ, Coll Comp Sci & Technol, Jiaozuo, Henan, Peoples R China; [Leite, Fatima Silva] Univ Coimbra, Inst Syst & Robot ISR, Dept Math, Coimbra, Portugal Universidade de Coimbra; Henan Polytechnic University; Universidade de Coimbra Ribeiro, B (corresponding author), Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal. rivero@dei.uc.pt; bribeiro@dei.uc.pt; nchenyx@outlook.com; fleite@mat.uc.pt Pérez, Jorge Luis Rivero/AAY-8908-2020; Ribeiro, Bernardete/A-8010-2016; Leite, Fatima Silva/B-6042-2013 Pérez, Jorge Luis Rivero/0000-0001-5070-1719; Ribeiro, Bernardete/0000-0002-9770-7672; Leite, Fatima Silva/0000-0003-2227-4259 Erasmus Mundus Action 2; SASSI Project [33/SI/2015DT] Erasmus Mundus Action 2; SASSI Project Erasmus Mundus Action 2 is acknowledged for partial funding of the first author.; SASSI Project (33/SI/2015&DT) is gratefully acknowledged for partial financial support. 10 3 4 3 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-70087-8; 978-3-319-70086-1 LECT NOTES COMPUT SC 2017.0 10634.0 I 565 575 10.1007/978-3-319-70087-8_59 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ1LK Green Submitted 2023-03-23 WOS:000576768000059 0
J Wan, L; Liu, JX; Harkin, J; McDaid, L; Luo, YL Wan, Lei; Liu, Junxiu; Harkin, Jim; McDaid, Liam; Luo, Yuling Layered tile architecture for efficient hardware spiking neural networks MICROPROCESSORS AND MICROSYSTEMS English Article; Proceedings Paper 19th Euromicro Conference on Digital System Design (DSD) AUG 31-SEP 02, 2016 Limassol, CYPRUS Univ Cyprus,Technolog Educ Inst Western Greece Spiking neural networks; Layer-level tile architecture (LTA); FPGAs; Sharing mechanism ADAPTIVE NETWORK; NEURONS; IMPLEMENTATIONS; SIMULATION; PLATFORM; ANALOG; MODEL Spiking Neural Network (SNN) is the most recent computational model that can emulate the behaviour of biological neuron system. However, its main drawback is that it is computationally intensive, which limits the system scalability. This paper highlights and discusses the importance and significance of emulating SNNs in hardware devices. A layer-level tile architecture (LTA) is proposed for hardware-based SNNs. The LTA employs a two-level sharing mechanism of computing components at the synapse and neuron levels, and achieves a trade-off between computational complexity and hardware resource costs. The LTA is implemented on a Xilinx FPGA device. Experimental results demonstrate that this approach is capable of scaling to large hardware-based SNNs. (C) 2017 Elsevier B.V. All rights reserved. [Wan, Lei; Liu, Junxiu; Luo, Yuling] Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China; [Harkin, Jim; McDaid, Liam] Ulster Univ, Sch Comp & Intelligent Syst, Derry BT48 7JL, North Ireland Guangxi Normal University; Ulster University Liu, JX (corresponding author), Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China. liujunxiu@mailbox.gxnu.edu.cn Harkin, Jim/F-8958-2013 Harkin, Jim/0000-0001-7484-8205 National Natural Science Foundation of China [61603104, 61661008]; Guangxi Natural Science Foundation [2015GXNSFBA139256, 2016GXNSFCA380017]; funding of Overseas 100 Talents Programme of Guangxi Higher Education; Research Project of Guangxi University of China [KY2016YB059]; Guangxi Key Lab of Multi-source Information Mining Security [MIMS15-07]; Doctoral Research Foundation of Guangxi Normal University; Research Project of Guangxi Centre of Humanities & Social Sciences - Ecological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone [ZX2016030]; Innovation Project of Guangxi Graduate Education [YCSZ2016034] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); funding of Overseas 100 Talents Programme of Guangxi Higher Education; Research Project of Guangxi University of China; Guangxi Key Lab of Multi-source Information Mining Security; Doctoral Research Foundation of Guangxi Normal University; Research Project of Guangxi Centre of Humanities & Social Sciences - Ecological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone; Innovation Project of Guangxi Graduate Education This research was supported by the National Natural Science Foundation of China under grants 61603104 and 61661008, the Guangxi Natural Science Foundation under grants 2015GXNSFBA139256 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Programme of Guangxi Higher Education, the Research Project of Guangxi University of China under grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining & Security under grant MIMS15-07, the Doctoral Research Foundation of Guangxi Normal University, the Research Project of Guangxi Centre of Humanities & Social Sciences - Ecological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone (ZX2016030), and the Innovation Project of Guangxi Graduate Education (YCSZ2016034). 60 4 4 1 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0141-9331 1872-9436 MICROPROCESS MICROSY Microprocess. Microsyst. AUG 2017.0 53.0 21 32 10.1016/j.micpro.2017.07.005 0.0 12 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering FH9PQ 2023-03-23 WOS:000411544600002 0
J Barbano, R; Leuschner, J; Schmidt, M; Denker, A; Hauptmann, A; Maass, P; Jin, BT Barbano, Riccardo; Leuschner, Johannes; Schmidt, Maximilian; Denker, Alexander; Hauptmann, Andreas; Maass, Peter; Jin, Bangti An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING English Article Electronics packaging; Image reconstruction; Task analysis; Imaging; Computed tomography; Training; Neural networks; deep image prior; pretraining INVERSE PROBLEMS; ADAPTATION Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the model output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network's parameters to adapt to the target reconstruction task. We provide a thorough empirical analysis to shed insights into the impacts of pretraining in the context of image reconstruction. We showcase that pretraining considerably speeds up and stabilizes the subsequent reconstruction task from real-measured 2D and 3D micro computed tomography data of biological specimens. [Barbano, Riccardo; Hauptmann, Andreas] UCL, Dept Comp Sci, London WC1E 6EA, England; [Leuschner, Johannes; Schmidt, Maximilian; Denker, Alexander; Maass, Peter] Univ Bremen, Ctr Ind Math, D-28359 Bremen, Germany; [Hauptmann, Andreas] Univ Oulu, Res Unit Math Sci, Oulu 90570, Finland; [Jin, Bangti] Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong 999077, Peoples R China University of London; University College London; University of Bremen; University of Oulu; Chinese University of Hong Kong Barbano, R (corresponding author), UCL, Dept Comp Sci, London WC1E 6EA, England. riccardo.barbano.19@ucl.ac.uk; jleuschn@uni-bremen.de; schmidt4@uni-bremen.de; adenker@uni-bremen.de; andreas.hauptmann@oulu.fi; pmaass@uni-bremen.de; b.jin@ucl.ac.uk Jin, Bangti/0000-0002-3775-9155; barbano, riccardo/0000-0003-1863-2092 Riccardo Barbano; U.K. EPSRC [EP/S021930/1]; Alan Turing Institute [EP/N510129/1]; Alexander Denker [GRK 2224/1]; Federal Ministry of Education and Research, (BMBF) [05M20LBB]; Klaus Tschira Stiftung [00.010.2019]; Academy of Finland [338408, 336796, 353093, 334817]; Sino-German Center for Research Promotion (CDZ) -; U.K. EPSRC [EP/T000864/1, EP/V026259/1] Riccardo Barbano; U.K. EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Alan Turing Institute; Alexander Denker; Federal Ministry of Education and Research, (BMBF)(Federal Ministry of Education & Research (BMBF)); Klaus Tschira Stiftung; Academy of Finland(Academy of Finland); Sino-German Center for Research Promotion (CDZ) -; U.K. EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) The work of Riccardo Barbano was supported in part by thei4health Ph.D. studentship, U.K. EPSRC under Grant EP/S021930/1, and in partby The Alan Turing Institute through U.K. EPSRC under Grant EP/N510129/1.The work of Johannes Leuschner, Maximilian Schmidt, and Alexander Denkerwas supported in part by German Research Foundation, DFG, under GrantGRK 2224/1. The work of Johannes Leuschner and Maximilian Schmidt wassupported by the Federal Ministry of Education and Research, (BMBF) throughDELETO Project under Grant 05M20LBB. The work of Alexander Denkerwas supported by the Klaus Tschira Stiftung through the Project MALDISTARunder Grant 00.010.2019. The work of Andreas Hauptmann was supported bythe Academy of Finland under Grants 338408, 336796, 353093, and 334817.The work of Peter Maass was supported by the Sino-German Center for Research Promotion (CDZ) through the Mobility Programme 2021: Inverse Problems -Theories, Methods and Implementations (IP-TMI). The work of Bangti Jin was supported by U.K. EPSRC under Grants EP/T000864/1 and EP/V026259/1.The associate editor coordinating the review of this manuscript and approvingit for publication was Dr. Singanallur Venkatakrishnan.(Riccardo Barbano andJohannes Leuschner contributed equally to this work.) 64 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2573-0436 2333-9403 IEEE T COMPUT IMAG IEEE Trans. Comput. Imaging 2022.0 8.0 1210 1222 10.1109/TCI.2022.3233188 0.0 13 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Imaging Science & Photographic Technology 8B9XW Green Submitted, Green Accepted 2023-03-23 WOS:000917273400001 0
J Chen, P; Liu, HY; Xin, RY; Carval, T; Zhao, JL; Xia, YN; Zhao, ZM Chen, Peng; Liu, Hongyun; Xin, Ruyue; Carval, Thierry; Zhao, Jiale; Xia, Yunni; Zhao, Zhiming Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model COMPUTER JOURNAL English Article Data Infrastructure; Anomaly Detection; Generative Adversarial Networks (GAN); Unsupervised Learning OUTLIER DETECTION; ALGORITHMS Quality of data services is crucial for operational large-scale internet-of-things (IoT) research data infrastructure, in particular when serving large amounts of distributed users. Effectively detecting runtime anomalies and diagnosing their root cause helps to defend against adversarial attacks, thereby essentially boosting system security and robustness of the IoT infrastructure services. However, conventional anomaly detection methods are inadequate when facing the dynamic complexities of these systems. In contrast, supervised machine learning methods are unable to exploit large amounts of data due to the unavailability of labeled data. This paper leverages popular GAN-based generative models and end-to-end one-class classification to improve unsupervised anomaly detection. A novel heterogeneous BiGAN-based anomaly detection model Heterogeneous Temporal Anomaly-reconstruction GAN (HTA-GAN) is proposed to make better use of a one-class classifier and a novel anomaly scoring function. The Generator-Encoder-Discriminator BiGAN structure can lead to practical anomaly score computation and temporal feature capturing. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. The results show that HTA-GAN outperforms its competitors and demonstrates better robustness. [Chen, Peng] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China; [Chen, Peng; Liu, Hongyun; Xin, Ruyue; Zhao, Zhiming] Univ Amsterdam, Multiscale Networked Syst Res Grp, NL-1098 XH Amsterdam, Netherlands; [Carval, Thierry] Euro Argo Eric, F-29280 Plouzane, France; [Zhao, Jiale; Xia, Yunni] Chongqing Univ, Sch Comp Sci, Chongqing 400044, Peoples R China Xihua University; University of Amsterdam; Chongqing University Zhao, ZM (corresponding author), Univ Amsterdam, Multiscale Networked Syst Res Grp, NL-1098 XH Amsterdam, Netherlands.;Xia, YN (corresponding author), Chongqing Univ, Sch Comp Sci, Chongqing 400044, Peoples R China. xiayunni@hotmail.com; z.zhao@uva.nl zhao, jiale/AGC-8467-2022 Chen, Peng/0000-0001-5221-3655 European Union [825134, 824068, 862409]; Talent Program of Xihua University [Z202047]; China Scholarship Council, Science and Technology Program of Sichuan Province [2020JDRC0067, 2020YFG0326]; LifeWatch ERIC European Union(European Commission); Talent Program of Xihua University; China Scholarship Council, Science and Technology Program of Sichuan Province(China Scholarship Council); LifeWatch ERIC The authors wish to thank Erwan Bodere, Misha Mesarcik, Yu Wang, Tingyan Long, Lei Liu and Zhihao Gan for their informative suggestions and help. This work has been partially supported by the European Union's Horizon 2020 research and innovation programme by the ARTICONF project grant agreement No.825134, by the ENVRI-FAIR project grant agreement No.824068, by the BLUECLOUD project grant agreement No.862409, by the LifeWatch ERIC, by China Scholarship Council, Science and Technology Program of Sichuan Province under Grant No.2020JDRC0067 and No.2020YFG0326 and Talent Program of Xihua University under Grant No.Z202047. 55 1 1 8 8 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0010-4620 1460-2067 COMPUT J Comput. J. NOV 11 2022.0 65.0 11 2909 2925 10.1093/comjnl/bxac085 0.0 OCT 2022 17 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6J1TR Green Submitted 2023-03-23 WOS:000863000600001 0
J Sun, YW; Yin, H; Lu, X; Notholt, J; Palm, M; Liu, C; Tian, Y; Zheng, B Sun, Youwen; Yin, Hao; Lu, Xiao; Notholt, Justus; Palm, Mathias; Liu, Cheng; Tian, Yuan; Zheng, Bo The drivers and health risks of unexpected surface ozone enhancements over the Sichuan Basin, China, in 2020 ATMOSPHERIC CHEMISTRY AND PHYSICS English Article STRATOSPHERE-TROPOSPHERE EXCHANGE; ANTHROPOGENIC EMISSION INVENTORY; GROUND-LEVEL OZONE; EASTERN CHINA; GLOBAL BURDEN; AIR-QUALITY; DRY DEPOSITION; MODEL; POLLUTION; TRANSPORT Following a continuous increase in the surface ozone (O-3) level from 2013 to 2019, the overall summertime O-3 concentrations across China showed a significant reduction in 2020. In contrast to this overall reduction in surface O3 across China, unexpected surface O-3 enhancements of 10.2 +/- 0.8 ppbv (23.4 %) were observed in May-June 2020 (relative to 2019) over the Sichuan Basin (SCB), China. In this study, we use high-resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method, and the exposure-response relationship to determine the drivers and evaluate the health risks due to the unexpected surface O-3 enhancements. We first use the XGBoost machine learning method to correct the GEOS-Chem model-measurement O-3 discrepancy over the SCB. The relative contributions of meteorology and anthropogenic emission changes to the unexpected surface O-3 enhancements are then quantified with a combination of GEOS-Chem and XGBoost models. In order to assess the health risks caused by the unexpected O-3 enhancements over the SCB, total premature mortalities are estimated. The results show that changes in anthropogenic emissions caused a 0.9 +/- 0.1 ppbv O-3 reduction, whereas changes in meteorology caused an 11.1 +/- 0.7 ppbv O-3 increase in May-June 2020 relative to 2019. The meteorology-induced surface O-3 increase is mainly attributed to an increase in temperature and decreases in precipitation, specific humidity, and cloud fractions over the SCB and surrounding regions in May-June 2020 relative to 2019. These changes in meteorology combined with the complex basin effect enhance biogenic emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), speed up O-3 chemical production, and inhibit the ventilation of O-3 and its precursors; therefore, they account for the surface O-3 enhancements over the SCB. The total premature mortality due to the unexpected surface O-3 enhancements over the SCB has increased by 89.8 % in May-June 2020 relative to 2019. [Sun, Youwen; Yin, Hao] Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, HFIPS, Hefei 230031, Peoples R China; [Sun, Youwen; Yin, Hao; Liu, Cheng] Univ Sci & Technol China, Key Lab Precis Sci Instrumentat Anhui Higher Educ, Hefei 230026, Peoples R China; [Lu, Xiao] Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai 519082, Peoples R China; [Notholt, Justus; Palm, Mathias] Univ Bremen, Inst Environm Phys, D-28334 Bremen, Germany; [Tian, Yuan] Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China; [Zheng, Bo] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Inst Environm & Ecol, Shenzhen 518055, Peoples R China Chinese Academy of Sciences; Anhui Institute of Optics & Fine Mechanics (AIOFM), CAS; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Sun Yat Sen University; University of Bremen; Anhui University; Tsinghua University Yin, H (corresponding author), Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, HFIPS, Hefei 230031, Peoples R China.;Yin, H (corresponding author), Univ Sci & Technol China, Key Lab Precis Sci Instrumentat Anhui Higher Educ, Hefei 230026, Peoples R China.;Lu, X (corresponding author), Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai 519082, Peoples R China. ywsun@aiofm.ac.cn; yhyh95@mail.ustc.edu.cn; luxiao25@mail.sysu.edu.cn Lu, Xiao/O-2380-2019; Zheng, Bo/X-1616-2018; Palm, Mathias/D-9173-2017 Lu, Xiao/0000-0002-5989-0912; Zheng, Bo/0000-0001-8344-3445; Palm, Mathias/0000-0001-7191-6911 National Key Research and Development Program of China [2019YFC0214802]; Youth Innovation Promotion Association of the Chinese Academy of Sciences [2019434]; Sino-German Mobility programme [M-0036] National Key Research and Development Program of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Sino-German Mobility programme This work has been jointly supported by the National Key Research and Development Program of China (grant no. 2019YFC0214802), the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2019434), and the Sino-German Mobility programme (grant no. M-0036). 92 7 8 21 74 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1680-7316 1680-7324 ATMOS CHEM PHYS Atmos. Chem. Phys. DEC 22 2021.0 21.0 24 18589 18608 10.5194/acp-21-18589-2021 0.0 20 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences XS7SX Green Submitted, gold 2023-03-23 WOS:000733104800001 0
J Arabameri, A; Chen, W; Loche, M; Zhao, X; Li, Y; Lombardo, L; Cerda, A; Pradhan, B; Bui, DT Arabameri, Alireza; Chen, Wei; Loche, Marco; Zhao, Xia; Li, Yang; Lombardo, Luigi; Cerda, Artemi; Pradhan, Biswajeet; Dieu Tien Bui Comparison of machine learning models for gully erosion susceptibility mapping GEOSCIENCE FRONTIERS English Article Oil erosion; GIS; Alternating decision tree model; Logistic model tree model MULTICRITERIA DECISION-MAKING; LANDSLIDE SUSCEPTIBILITY; LOGISTIC-REGRESSION; CHANNEL INITIATION; EVENTS APPLICATION; FREQUENCY RATIO; WATER EROSION; DEBRIS FLOW; SOIL LOSS; BIVARIATE Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory, especially in the Northern provinces. A number of studies have been recently undertaken to study this process and to predict it over space and ultimately, in a broader national effort, to limit its negative effects on local communities. We focused on the Bastam watershed where 9.3% of its surface is currently affected by gullying. Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability. However, unlike the bivariate statistical models, their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors. To cope with such weakness, we interpret preconditioning causes on the basis of a bivariate approach namely, Index of Entropy. And, we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely, Alternating Decision Tree (ADTree), Naive-Bayes tree (NBTree), and Logistic Model Tree (LMT). We dichotomized the gully information over space into gully presence/absence conditions, which we further explored in their calibration and validation stages. Being the presence/absence information and associated factors identical, the resulting differences are only due to the algorithmic structures of the three models we chose. Such differences are not significant in terms of performances; in fact, the three models produce outstanding predictive AUC measures (ADTree = 0.922; NBTree = 0.939; LMT = 0.944). However, the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns. This is a strong indication of what model combines best performance and mapping for any natural hazard - oriented application. [Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Tehran, Iran; [Chen, Wei; Zhao, Xia; Li, Yang] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Chen, Wei] Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Peoples R China; [Loche, Marco] Univ Cagliari, Dipartimento Sci Chim & Geol, I-09042 Cagliari, Italy; [Lombardo, Luigi] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, POB 217, NL-7500 AE Enschede, Netherlands; [Cerda, Artemi] Univ Valencia, Dept Geog, Soil Eros & Degradat Res Grp, Blasco Ibanez 28, Valencia 46010, Spain; [Pradhan, Biswajeet] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Ultimo, NSW, Australia; [Pradhan, Biswajeet] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; [Dieu Tien Bui] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Tarbiat Modares University; Xi'an University of Science & Technology; Ministry of Natural Resources of the People's Republic of China; University of Cagliari; University of Twente; University of Valencia; University of Technology Sydney; Sejong University; Duy Tan University Pradhan, B (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Ultimo, NSW, Australia.;Bui, DT (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. Biswajeet.Pradhan@uts.edu.au; buitiendieu@gmail.com Loche, Marco/AFS-7016-2022; Cerda, Artemi/I-4670-2013; Chen, Wei/ABB-8669-2020; Lombardo, Luigi/AAY-6370-2021; Pradhan, Biswajeet/E-8226-2010 Loche, Marco/0000-0002-0756-2175; Cerda, Artemi/0000-0001-5326-4489; Chen, Wei/0000-0002-5825-1422; Lombardo, Luigi/0000-0003-4348-7288; Pradhan, Biswajeet/0000-0001-9863-2054; Zhao, Xia/0000-0002-9403-9330 99 70 71 11 62 CHINA UNIV GEOSCIENCES, BEIJING HAIDIAN DISTRICT 29 XUEYUAN RD, HAIDIAN DISTRICT, 100083, PEOPLES R CHINA 1674-9871 GEOSCI FRONT Geosci. Front. SEP 2020.0 11.0 5 1609 1620 10.1016/j.gsf.2019.11.009 0.0 12 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology NA4XO Green Published, gold 2023-03-23 WOS:000559821700010 0
J Li, LL; Ding, SX Li, Linlin; Ding, Steven X. Performance Supervised Fault Detection Schemes for Industrial Feedback Control Systems and their Data-Driven Implementation IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Fault detection; System performance; Degradation; Feedback control; Real-time systems; Mathematical model; Process control; Bellman equation; data-driven; performance supervised fault detection (PSFD); performance prediction IDENTIFICATION; DESIGN This article addresses performance supervised fault detection (PSFD) issues for industrial feedback control systems based on performance degradation prediction. To be specific, three performance indicators are first introduced based on Bellman equation to predict system performance degradations for industrial processes with the aid of machine learning techniques. Based on them, three PSFD schemes are proposed by embedding the performance indicators as supervising information. In this context, the data-driven implementation of PSFD schemes are investigated for linear systems with unmeasurable state variables. A case study on rolling mill process, a typical benchmark in the steel manufacturing processes, is given at the end of this article to illustrate the applications of the proposed fault detection schemes. [Li, Linlin] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China; [Ding, Steven X.] Univ Duisburg Essen, Inst Automat Control & Complex Syst AKS, D-47057 Duisburg, Germany University of Science & Technology Beijing; University of Duisburg Essen Li, LL (corresponding author), Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China. linlin.li@ustb.edu.cn; steven.ding@uni-due.de Ding, Steven X./ABF-2356-2020 Ding, Steven/0000-0002-5149-5918 National Natural Science Foundation of China [61603033, TII-19-2815] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61603033. Paper no. TII-19-2815. 30 14 14 5 46 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. APR 2020.0 16.0 4 2849 2858 10.1109/TII.2019.2940099 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering KH8LB 2023-03-23 WOS:000510901000068 0
J Mojahedin, A; Hashemitaheri, M; Salavati, M; Fu, XL; Rabczuk, T Mojahedin, Arvin; Hashemitaheri, Maryam; Salavati, Mohammad; Fu, Xiaolong; Rabczuk, Timon A Deep Energy Method for the Analysis of Thermoporoelastic Functionally Graded Beams INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS English Article Thermoporoelastic analysis; functionally graded material; Euler-Bernoulli beam; Timoshenko beam; deep energy method ARTIFICIAL NEURAL-NETWORKS; FGM TIMOSHENKO BEAMS; STABILITY; PLATE In this paper, we present a deep energy method for functionally graded beams based on both Euler-Bernoulli and Timoshenko beam theory to study their mechanical and thermal properties. We consider the effect of temperature as well as porosity under saturated and unsaturated conditions. The objective function is related to the total potential energy (and boundary conditions) and minimized through neural network training. The results are validated by comparison with benchmark problems available in the literature. [Mojahedin, Arvin; Rabczuk, Timon] Bauhaus Univ Weimar, Inst Struct Mech, Marienstr 7, D-99423 Weimar, Germany; [Hashemitaheri, Maryam] UNC Charlotte, Dept Mech Engn & Engn Sci, Charlotte, NC USA; [Salavati, Mohammad] Tech Univ Berlin, Inst Mat Sci & Technol, Dept Mat Engn, D-10623 Berlin, Germany; [Fu, Xiaolong] Xian Modern Chem Res Inst, Xian 710065, Peoples R China Bauhaus-Universitat Weimar; University of North Carolina; University of North Carolina Charlotte; Technical University of Berlin; Xi'an Modern Chemistry Research Institute Mojahedin, A (corresponding author), Bauhaus Univ Weimar, Inst Struct Mech, Marienstr 7, D-99423 Weimar, Germany. mojahedin.arvin@gmail.com 28 0 0 8 8 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0219-8762 1793-6969 INT J COMP METH-SING Int. J. Comput. Methods OCT 2022.0 19.0 8 10.1142/S0219876221430209 0.0 SEP 2022 18 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics 7J8OQ 2023-03-23 WOS:000855854500001 0
J Xu, DT; Song, Y; Meng, Y; Istvan, B; Gu, YD Xu, Datao; Song, Yang; Meng, Yao; Istvan, Biro; Gu, Yaodong Relationship between Firefighter Physical Fitness and Special Ability Performance: Predictive Research Based on Machine Learning Algorithms INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article tactical population; partial least-squares regression (PLSR); linear regression predictive analysis BODY-MASS INDEX; CARDIORESPIRATORY FITNESS; FIRE; MORTALITY; DEMANDS; TESTS; SUPPRESSION; REGRESSION; CAPACITY; ERROR Firefighters require a high level of physical fitness to meet the demands of their job. The correlations and contributions of individual physical health parameters to the tasks of firefighting would enable firefighters to focus on the effects of specific physical conditions during their physical training programs. Therefore, the purpose of the present study was to identify the relationships between various physical health parameters (weight, maximum oxygen uptake, body fat percentage, upper body muscular power and lower body muscular power) and performance on simulated firefighting ability tasks, which included a set of seven tasks (rope climb, run 200 m round trip with load, 60 m carrying a ladder, climb stairs with load, evacuation of 400 m with supplies, run 5 km with an air respirator, run 100 m with the water hose). Through use of a partial least-squares regression (PLSR) algorithm to analyze the linear correlation, we revealed the change in various training performances of specific ability tests with physical fitness parameters. The present study demonstrated significant relationships among physical health parameters and performance on simulated firefighting ability tasks, which also represent that those parameters contributed significantly to the model's predictive power and were suitable predictors of the simulated firefighting tasks score. [Xu, Datao; Song, Yang; Meng, Yao; Gu, Yaodong] Ningbo Univ, Fac Sports Sci, Ningbo 315211, Peoples R China; [Song, Yang; Meng, Yao; Istvan, Biro] Obuda Univ, Doctoral Sch Safety & Secur Sci, H-1034 Budapest, Hungary; [Song, Yang; Meng, Yao; Istvan, Biro] Univ Szeged, Fac Engn, H-6724 Szeged, Hungary Ningbo University; Obuda University; Szeged University Gu, YD (corresponding author), Ningbo Univ, Fac Sports Sci, Ningbo 315211, Peoples R China. xudatao3@gmail.com; nbusongyang@hotmail.com; jymengyao@gmail.com; biro-i@mk.u-szeged.hu; guyaodong@nbu.edu.cn Song, Yang/0000-0001-7438-6290; gu, yaodong/0000-0003-2187-9440; Xu, Datao/0000-0002-1918-0756 Major Program of the National Social Science Foundation of China [19ZDA352]; Zhejiang Provincial Philosophy and Social Science Planning Project [21NDJC005Z]; K. C. Wong Magna Fund in Ningbo University Major Program of the National Social Science Foundation of China; Zhejiang Provincial Philosophy and Social Science Planning Project; K. C. Wong Magna Fund in Ningbo University This work was supported by the Major Program of the National Social Science Foundation of China (19ZDA352), Zhejiang Provincial Philosophy and Social Science Planning Project (21NDJC005Z) and K. C. Wong Magna Fund in Ningbo University. 45 5 5 5 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health OCT 2020.0 17.0 20 7689 10.3390/ijerph17207689 0.0 10 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health OM0DQ 33096792.0 Green Accepted, gold 2023-03-23 WOS:000585699900001 0
J Gu, CY; Chang, CH; Liu, WQ; Yu, SC; Wang, YL; O'Neill, M Gu, Chongyan; Chang, Chip-Hong; Liu, Weiqiang; Yu, Shichao; Wang, Yale; O'Neill, Maire A Modeling Attack Resistant Deception Technique for Securing Lightweight-PUF-Based Authentication IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS English Article Protocols; Authentication; Hardware; Integrated circuit modeling; Resistance; Physical unclonable function; Authentication protocol; deception protocol; machine learning attacks; physical unclonable function (PUF); Poison attack ARBITER Silicon physical unclonable function (PUF) has emerged as a promising spoof-proof solution for low-cost device authentication. Due to practical constraints in preventing phishing through a public network or insecure communication channels, simple PUF-based authentication protocol with unrestricted queries and transparent responses is vulnerable to modeling and replay attacks. In this article, we present a modeling attack resistant PUF-based mutual authentication scheme to mitigate the practical limitations in applications where a resource-rich server authenticates a device with no strong restriction imposed on the type of PUF design or any additional protection on the binary channel used for the authentication. Our scheme uses an active deception protocol to prevent machine learning (ML) attacks on a device with a monolithic integration of a genuine strong PUF (SPUF), a fake PUF, a pseudorandom number generator (PRNG), a register, a binary counter, a comparator, and a simple controller. The hardware encapsulation makes the collection of challenge-response pairs (CRPs) easy for model building during enrollment but prohibitively time consuming upon device deployment through the same interface. A genuine server can perform a mutual authentication with the device using a combined fresh challenge contributed by both the server and the device. The message exchanged in clear cannot be manipulated by the adversary to derive unused authentic CRPs. The adversary will have to either wait for an impractically long time to collect enough real CRPs by directly querying the device or the ML model derived from the collected CRPs will be poisoned. The false PUF multiplexing is fortified against the prediction of waiting time by doubling the time penalty for every unsuccessful guess. Our implementation results on field-programmable gate array (FPGA) device and security analysis have corroborated the low hardware overheads and attack resistance of the proposed deception protocol. [Gu, Chongyan; Yu, Shichao; O'Neill, Maire] Queens Univ Belfast, Inst Elect Commun & Informat Technol, Ctr Secure Informat Technol, Belfast BT3 9DT, Antrim, North Ireland; [Chang, Chip-Hong] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Liu, Weiqiang; Wang, Yale] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China Queens University Belfast; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanjing University of Aeronautics & Astronautics Liu, WQ (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China. cgu01@qub.ac.uk; echchang@ntu.edu.sg; liuweiqiang@nuaa.edu.cn; syu08@qub.ac.uk; yalewang@nuaa.edu.cn; m.oneill@ecit.qub.ac.uk Chang, Chip Hong/A-5128-2011; GU, CHONGYAN/ABD-5437-2021 Chang, Chip Hong/0000-0002-8897-6176; GU, CHONGYAN/0000-0002-3028-8004; Liu, Weiqiang/0000-0001-8398-8648; Wang, Yale/0000-0002-2183-4886; Yu, Shichao/0000-0001-8861-1671 Singapore MOE Tier 1 [MOE2018-T1-001-131 RG87/18]; Engineering and Physical Sciences Research Council (EPSRC) [EP/N508664/-CSIT2]; National Natural Science Foundation of China [62022041, 61771239] Singapore MOE Tier 1(Ministry of Education, Singapore); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China This work was supported in part by the Singapore MOE Tier 1 under Grant MOE2018-T1-001-131 RG87/18(S); in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N508664/-CSIT2; and in part by the National Natural Science Foundation of China under Grant 62022041 and Grant 61771239. 49 22 23 2 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0070 1937-4151 IEEE T COMPUT AID D IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. JUN 2021.0 40.0 6 1183 1196 10.1109/TCAD.2020.3036807 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SF5JS Green Accepted 2023-03-23 WOS:000652792400014 0
J Shen, JM; Li, W; Deng, SF; Xu, D; Chen, SY; Liu, FY Shen, Jianmin; Li, Wei; Deng, Shengfeng; Xu, Dian; Chen, Shiyang; Liu, Feiyi Machine learning of pair-contact process with diffusion SCIENTIFIC REPORTS English Article PHASE-TRANSITIONS; CRITICAL-BEHAVIOR The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised learning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent nu(perpendicular to), which, to a large degree, avoids the uncertainty of data collapses through naked eyes. [Shen, Jianmin; Li, Wei; Xu, Dian; Chen, Shiyang; Liu, Feiyi] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China; [Shen, Jianmin; Li, Wei; Xu, Dian; Chen, Shiyang; Liu, Feiyi] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China; [Deng, Shengfeng] Ctr Energy Res, Inst Tech Phys & Mat Sci, H-1121 Budapest, Hungary; [Liu, Feiyi] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary Central China Normal University; Central China Normal University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Centre for Energy Research; Hungarian Research Centre for Natural Sciences; Eotvos Lorand University Li, W (corresponding author), Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Li, W (corresponding author), Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China. liw@mail.ccnu.edu.cn Liu, Feiyi/0000-0002-2074-669X Fundamental Research Funds for the Central Universities, China [CCNU19QN029]; National Natural Science Foundation of China [11505071, 61702207, 61873104]; Key Laboratory of Quark and Lepton Physics (MOE); Central China Normal University [QLPL2022P01]; Programme of Introducing Talents of Discipline to Universities (State Administration of Foreign Experts Affairs) under the 111 Project 2.0 [BP0820038]; Programme of Introducing Talents of Discipline to Universities (Ministry of Education, PRC) under the 111 Project 2.0 [BP0820038] Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Laboratory of Quark and Lepton Physics (MOE); Central China Normal University; Programme of Introducing Talents of Discipline to Universities (State Administration of Foreign Experts Affairs) under the 111 Project 2.0; Programme of Introducing Talents of Discipline to Universities (Ministry of Education, PRC) under the 111 Project 2.0 This work was supported in part by the Fundamental Research Funds for the Central Universities, China (Grant No. CCNU19QN029), the National Natural Science Foundation of China (Grants Nos. 11505071, 61702207, and 61873104), Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University (Grant No. QLPL2022P01), and the Programme of Introducing Talents of Discipline to Universities (sponsored by the State Administration of Foreign Experts Affairs and the Ministry of Education, PRC) under the 111 Project 2.0 with Grant No. BP0820038. 58 0 0 1 1 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep NOV 17 2022.0 12.0 1 19728 10.1038/s41598-022-23350-2 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 6H0WS 36396692.0 Green Accepted, gold, Green Submitted 2023-03-23 WOS:000885172100094 0
J Xu, C; Lemaitre, RP; Soto, J; Markl, V Xu, Chen; Lemaitre, Rudi Poepsel; Soto, Juan; Markl, Volker Fault-Tolerance for Distributed Iterative Dataflows in Action PROCEEDINGS OF THE VLDB ENDOWMENT English Article Distributed dataflow systems (DDS) are widely employed in graph processing and machine learning (ML), where many of these algorithms are iterative in nature. Typically, DDS achieve fault-tolerance using checkpointing mechanisms or they exploit algorithmic properties to enable fault-tolerance without the need for checkpoints. Recently, for graph processing, we proposed utilizing unblocking checkpointing, to parallelize the execution pipeline and checkpoint writing, as well as confined recovery, to enable fast recovery upon partial node failures. Furthermore, for ML algorithms implemented using broadcast variables, we proposed utilizing replica recovery, to leverage broadcast variable replicas and facilitate failure recovery checkpointing-free. In this demonstration, we showcase these fault-tolerance techniques using Apache Flink. Attendees will be able to: (i) run representative iterative algorithms including PageRank, Connected Components, and K-Means, (ii) explore the internal behavior of DDS under the influence of unblocking checkpointing, and (iii) trigger failures, to observe the effects of confined recovery and replica recovery. [Xu, Chen] East China Normal Univ, Shanghai 200062, Peoples R China; [Lemaitre, Rudi Poepsel; Soto, Juan; Markl, Volker] Tech Univ Berlin, D-10587 Berlin, Germany East China Normal University; Technical University of Berlin Xu, C (corresponding author), East China Normal Univ, Shanghai 200062, Peoples R China. cxu@dase.ecnu.edu.cn; r.poepsellemaitre@campus.tu-berlin.de; juan.soto@tu-berlin.de; volker.markl@tu-berlin.de Markl, Volker/AAX-1862-2020 Poepsel-Lemaitre, Rudi/0000-0002-1022-9015 German Ministry for Education and Research [01IS14013A] German Ministry for Education and Research(Federal Ministry of Education & Research (BMBF)) This work has been supported through grants by the German Ministry for Education and Research as Berlin Big Data Center BBDC (funding mark 01IS14013A). 6 0 0 1 1 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 2150-8097 PROC VLDB ENDOW Proc. VLDB Endow. AUG 2018.0 11.0 12 1990 1993 10.14778/3229863.3236242 0.0 4 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HD5CA 2023-03-23 WOS:000452544100035 0
J Liu, H; He, BY; Zhou, YD; Kutser, T; Toming, K; Feng, Q; Yang, XQ; Fu, CJ; Yang, F; Li, W; Peng, F Liu, Hui; He, Baoyin; Zhou, Yadong; Kutser, Tiit; Toming, Kaire; Feng, Qi; Yang, Xiaoqin; Fu, Congju; Yang, Fan; Li, Wen; Peng, Feng Trophic state assessment of optically diverse lakes using Sentinel-3-derived trophic level index INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Sentinel-3 OLCI; Inland waters; Optical Water Type (OWT); Trophic Level Index (TLI); Wuhan CHLOROPHYLL-A CONCENTRATION; TURBID PRODUCTIVE WATERS; COMPLEX WATERS; SECCHI DEPTH; RETRIEVAL; INLAND; ALGORITHMS; COASTAL; DEEP; EUTROPHICATION An accurate estimation of trophic state of lakes with satellite remote sensing is a challenge due to the optical complexity and variability associated with inland waters. Match-up data from 393 sampling stations that has concurrent Sentinel-3 OLCI images were acquired across Wuhan lakes. Trophic Level Index (TLI) algorithms were developed within a global Optical Water Type (OWT) classification system. The performance of algorithms with limited training data gathered by using spectral similarity of highest Sowt was not improved compared with that on basis of no classification. In contrast, using spectral similarity of Sowt > 0.9 rather than the highest Sowt to group more training data with similar traits for each OWT can help build more robust algorithms, which performance is better than that on basis of no classification. Algorithm performance statistics of the test dataset for the stepwise multiple linear regression (SMLR) method were the following: Mean Absolute Error (MAE) = 5.56; Mean Absolute Percentage Error (MAPE) = 11.02 %; Root Mean Square Error (RMSE) = 7.24 and for the back propagation neural network on the basis of the Levenberg-Marquardt-Bayesian regularization algorithm (LMBRBPNN) method MAE = 4.56; MAPE = 8.33 %; RMSE = 5.98. We detected 8 different OWTs (2,3,4,5,9,10,11,12) in Wuhan lakes and clear spatio-temporal patterns of the trophic state between 2018 and 2020.Our results revealed that the trophic state of Wuhan lakes did not decrease as expected during the COVID-19 lockdown period. [Liu, Hui; He, Baoyin; Zhou, Yadong; Feng, Qi; Fu, Congju; Yang, Fan; Li, Wen] Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Environm & Disaster Monitoring & Evaluat H, Wuhan 430071, Peoples R China; [Liu, Hui; Zhou, Yadong; Yang, Fan; Li, Wen] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Kutser, Tiit] Univ Tartu, Estonian Marine Inst, Maealuse 14, EE-12618 Tallinn, Estonia; [Toming, Kaire] Estonian Univ Life Sci, Inst Agr & Environm Sci, Chair Hydrobiol & Fishery, Kreutzwaldi 5, EE-51006 Tartu, Estonia; [Yang, Xiaoqin] Hubei Prov Hydrol & Water Resources Bur, Wuhan 430071, Peoples R China; [Peng, Feng] Hubei Prov Water Resources Dept, Wuhan 430071, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Tartu; Estonian Marine Institute; Estonian University of Life Sciences He, BY (corresponding author), Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Environm & Disaster Monitoring & Evaluat H, Wuhan 430071, Peoples R China. heby@apm.ac.cn Water Resources Department of Hubei Province [HBSLKY202127]; Science and Technology Department of Hubei Province [2019CFA019]; Wuhan Environmental Protection Bureau [10020200916WHSHBJ]; Key Research and Development Program of Hubei Province [2020BCA074]; Innovation Academy for Precision Measurement Science and Technology, CAS [S21S7102]; Estonian Research Council [PRG709, PRG302]; European Regional Developing Fund; program Mobilitas Pluss [MOBTP106] Water Resources Department of Hubei Province; Science and Technology Department of Hubei Province; Wuhan Environmental Protection Bureau; Key Research and Development Program of Hubei Province; Innovation Academy for Precision Measurement Science and Technology, CAS; Estonian Research Council(Estonian Research Council); European Regional Developing Fund; program Mobilitas Pluss This work was supported by the Water Resources Department of Hubei Province [grant number HBSLKY202127]; Science and Technology Department of Hubei Province [grant number 2019CFA019]; Wuhan Environmental Protection Bureau [grant number 10020200916WHSHBJ]; Key Research and Development Program of Hubei Province [grant number 2020BCA074]; Innovation Academy for Precision Measurement Science and Technology, CAS [grant number S21S7102]; the Estonian Research Council [grants number PRG709 and PRG302]; and the European Regional Developing Fund and the program Mobilitas Pluss [grant number MOBTP106]. 59 3 3 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-8432 1872-826X INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. NOV 2022.0 114.0 103026 10.1016/j.jag.2022.103026 0.0 15 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing 5U9PE gold 2023-03-23 WOS:000876871000002 0
J Liang, MH; Tao, JC; Ma, N; Kuang, Y; Zhang, YY; Wu, S; Jiang, XJ; He, Y; Chen, CR; Yang, WD; Zhou, YQ; Cheng, P; Xu, WY; Hong, J; Wang, QQ; Zhao, CS; Zhou, GS; Sun, YL; Zhang, Q; Su, H; Cheng, YF Liang, Minghua; Tao, Jiangchuan; Ma, Nan; Kuang, Ye; Zhang, Yanyan; Wu, Sen; Jiang, Xuejuan; He, Yao; Chen, Chunrong; Yang, Wenda; Zhou, Yaqing; Cheng, Peng; Xu, Wanyun; Hong, Juan; Wang, Qiaoqiao; Zhao, Chunsheng; Zhou, Guangsheng; Sun, Yele; Zhang, Qiang; Su, Hang; Cheng, Yafang Prediction of CCN spectra parameters in the North China Plain using a random forest model ATMOSPHERIC ENVIRONMENT English Article Cloud condensation nuclei; Aerosol; Machine learning CLOUD CONDENSATION NUCLEI; AEROSOL OPTICAL-PROPERTIES; LONG-TERM OBSERVATIONS; AMAZON RAIN-FOREST; HYGROSCOPIC GROWTH; PART 1; SIZE DISTRIBUTION; ORGANIC AEROSOL; MIXING STATE; PRECIPITATION Cloud condensation nuclei (CCN) spectra, that indicate the dependence of CCN number concentrations (N-CCN) on supersaturation, are important factors in aerosol-cloud interactions. Owing to the lack of direct measurements of N-CCN, calculating the CCN spectra based on conventional observational data of aerosols is important to obtain N-CCN; however, this is challenging owing to the complex relationship between aerosol properties and CCN activity. Machine learning techniques have recently been applied to estimate N-CCN and found to be a promising method for CCN prediction. In this study, the random forest (RF) model was applied to predict CCN spectral parameters using the observation data measured in four campaigns in the North China Plain, and the effects of chemical and optical properties of aerosol on the estimation of CCN spectra were investigated. The results show that the RF model trained with the data of one campaign can be used to estimate CCN spectral parameters in another campaign, with the coefficient of determination between the estimated and measured CCN spectral parameters being approximately 0.5. The deviations of the estimation by the RF model may result from the difference in both the aerosol properties and measurement uncertainties among different campaigns, whose influence on the deviations can be further magnified by overfitting the RF model. Further analysis revealed that the major aerosol properties among the input variables of the RF model were the mass concentration of black carbon and aerosol hemispheric backscattering fraction at a wavelength of 450 nm. In addition, the roles of chemical compositions of aerosol in estimating CCN spectra parameters are different among different campaigns because the poor correlation between aerosol chemical compositions and CCN spectra parameter can affect the performance of the RF model. For estimating CCN spectra, using aerosol optical properties, including the hemispheric backscattering fraction and absorption coefficient, as model inputs is more recommended than using aerosol chemical properties. [Liang, Minghua; Tao, Jiangchuan; Ma, Nan; Kuang, Ye; Zhang, Yanyan; Wu, Sen; Jiang, Xuejuan; He, Yao; Zhou, Yaqing; Hong, Juan; Wang, Qiaoqiao] Jinan Univ, Inst Environm & Climate Res, Guangzhou 511443, Peoples R China; [Liang, Minghua; Tao, Jiangchuan; Ma, Nan; Kuang, Ye; Zhang, Yanyan; Wu, Sen; Jiang, Xuejuan; He, Yao; Zhou, Yaqing; Hong, Juan; Wang, Qiaoqiao] Guangdong Hongkong Macau Joint Lab Collaborat Inno, Guangzhou 511443, Guangdong, Peoples R China; [Chen, Chunrong; Zhang, Qiang] Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China; [Yang, Wenda; Cheng, Peng] Jinan Univ, Inst Mass Spectrometry & Atmospher Environm, Guangzhou 510632, Peoples R China; [Zhao, Chunsheng] Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing, Peoples R China; [Xu, Wanyun; Zhou, Guangsheng] Chinese Acad Meteorol Sci, Inst Atmospher Composit, State Key Lab Severe Weather, Key Lab Atmospher Chem, Beijing 100081, Peoples R China; [Sun, Yele] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atmo, Beijing, Peoples R China; [Su, Hang] Max Planck Inst Chem, Multiphase Chem Dept, D-55128 Mainz, Germany; [Cheng, Yafang] Max Planck Inst Chem, Minerva Res Grp, D-55128 Mainz, Germany Jinan University; Tsinghua University; Jinan University; Peking University; Chinese Academy of Meteorological Sciences (CAMS); Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Max Planck Society; Max Planck Society Tao, JC; Ma, N (corresponding author), Jinan Univ, Inst Environm & Climate Res, Guangzhou 511443, Peoples R China. taojch@jnu.edu.cn; nan.ma@jnu.edu.cn Ma, Nan/HNP-2146-2023; Cheng, Yafang/F-9362-2010; Sun, Yele/F-1314-2010; Wang, Qiaoqiao/M-3884-2016; Kuang, Ye/U-8498-2017 Ma, Nan/0000-0003-4494-3337; Cheng, Yafang/0000-0003-4912-9879; Sun, Yele/0000-0003-2354-0221; Kuang, Ye/0000-0003-4813-9784 Fundamental Research Funds for the Central Universities [21620420]; National Natural Science Foundation of China [41805110, 91644218]; Ministry of Science and Technology of the People?s Republic of China [2017YFC0210104]; Guangdong Inno- vative and Entrepreneurial Research Team Program [2016ZT06N263]; Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province [2019B121205004]; Basic Research Fund of CAMS [2020Z002] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of the People?s Republic of China(Ministry of Science and Technology, China); Guangdong Inno- vative and Entrepreneurial Research Team Program; Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province; Basic Research Fund of CAMS This research has been supported by the Fundamental Research Funds for the Central Universities (grant no. 21620420) , the National Natural Science Foundation of China (grant nos. 41805110 and 91644218) , the Ministry of Science and Technology of the People?s Republic of China (grant no. 2017YFC0210104) , the Guangdong Inno- vative and Entrepreneurial Research Team Program (Research team on atmospheric environmental roles and effects of carbonaceous species, grant no. 2016ZT06N263) , the Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (grant no. 2019B121205004) , and the Basic Research Fund of CAMS (grant no. 2020Z002) . 70 0 0 30 33 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1352-2310 1873-2844 ATMOS ENVIRON Atmos. Environ. NOV 15 2022.0 289.0 119323 10.1016/j.atmosenv.2022.119323 0.0 AUG 2022 12 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences 4F5IN 2023-03-23 WOS:000848545600003 0
J Peters, R; Lin, Y; Berger, U Peters, Ronny; Lin, Yue; Berger, Uta Machine learning meets individual-based modelling: Self-organising feature maps for the analysis of below-ground competition among plants ECOLOGICAL MODELLING English Article Pattern recognition; Pi model; Plant-plant interactions; Pattern-oriented modelling; Inverse modelling; Artificial neural networks ARTIFICIAL NEURAL-NETWORKS; PATTERN; INFORMATION; ABUNDANCE; PREDICT Individual-based models (IBM) simulate populations and communities whose dynamics are shaped by the properties, interactions and behaviour of the constituent organisms as well as the corresponding abiotic boundary conditions. Structurally realistic IBM can provide insights into the functioning of such systems and predict the effects of variable scenarios. We suggest complementing IBM with machine learning (ML) methods in order (i) to visualise correlation patterns between model inputs and model outputs, (ii) to provide simulation-based decision tools for non-modellers, and (iii) to derive information about factors difficult to obtain in the field on the basis of data that are more readily measurable. On top of this, ML methods can complement the established pattern-oriented modelling approach used to analyse the behaviour of IBM and to detect model uncertainties. As an example to demonstrate the strength of an IBM-ML connection, we combined the individual-based Plant Interaction Model (Pi model) with self organising feature maps (SOM) - a special type of ML. Based on simulation experiments with complete knowledge of the simulated system, the SOM was trained and used to visualise the nonlinear relationship between two IBM inputs (namely the mode of below-ground competition and below-ground resource limitation) and two model outputs (the mortality rate and the Clark Evans Index of the spatial distribution of plants). Our study also highlights an application of the SOM to infer the modes of below-ground competition (either symmetric or asymmetric) from the remaining measurable variables (resource limitation, mortality rate and Clark Evans Index). This procedure was successful in 92% of cases, revealing its great potential as a means to assess parameters difficult to measure in nature. This example shows that SOM are powerful tools to revert the hierarchy of variables and to generalise dependencies of parameters in individual based modelling. (C) 2015 Elsevier B.V. All rights reserved. [Peters, Ronny; Berger, Uta] Tech Univ Dresden, Inst Forest Growth & Comp Sci, PO 1117, D-01735 Tharandt, Germany; [Lin, Yue] NW Univ Xian, Sch Life Sci, Xian 710069, Peoples R China Technische Universitat Dresden; Northwest University Xi'an Peters, R (corresponding author), Tech Univ Dresden, Inst Forest Growth & Comp Sci, PO 1117, D-01735 Tharandt, Germany. ronny.peters@tu-dresden.de Lin, Yue/0000-0002-0346-6493; Peters, Ronny/0000-0003-0473-2973 46 6 6 0 31 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0304-3800 1872-7026 ECOL MODEL Ecol. Model. APR 24 2016.0 326.0 SI 142 151 10.1016/j.ecolmodel.2015.10.014 0.0 10 Ecology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology DH4RG 2023-03-23 WOS:000372772500014 0
J Dehbi, Y; Henn, A; Groger, G; Stroh, V; Plumer, L Dehbi, Youness; Henn, Andre; Groeger, Gerhard; Stroh, Viktor; Pluemer, Lutz Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds TRANSACTIONS IN GIS English Article BUILDING FOOTPRINTS; HYBRID APPROACH This article proposes a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data. We propose an active sampling strategy which applies a cascade of filters focusing on promising samples at an early stage, thus avoiding the pitfalls of RANSAC-based approaches. Filters are based on prior knowledge represented by (nonparametric) density distributions. In our approach samples are pairs of surflets-3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs provide parameters for model candidates such as azimuth, inclination and ridge height, as well as parameters estimating internal precision and consistency. This provides a ranking of roof model candidates and leads to a small number of promising hypotheses. Building footprints are derived in a preprocessing step using machine learning methods, in particular support vector machines. [Dehbi, Youness; Stroh, Viktor] Univ Bonn, Inst Geodesy & Geoinformat, Meckenheimer Allee 172, D-53115 Bonn, Germany; [Henn, Andre] Terrestris GmbH & Co KG, Bonn, Germany; [Groeger, Gerhard] CPA ReDev GmbH, Siegburg, Germany; [Pluemer, Lutz] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Peoples R China University of Bonn; Southwest Jiaotong University Dehbi, Y (corresponding author), Univ Bonn, Inst Geodesy & Geoinformat, Meckenheimer Allee 172, D-53115 Bonn, Germany. dehbi@igg.uni-bonn.de Dehbi, Youness/0000-0003-0133-4099 53 7 7 1 11 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1361-1682 1467-9671 T GIS Trans. GIS FEB 2021.0 25.0 1 112 133 10.1111/tgis.12659 0.0 22 Geography Social Science Citation Index (SSCI) Geography QH5YH hybrid, Green Published 2023-03-23 WOS:000618351200006 0
C Lei, Y; Yang, P; Tang, K; Zhou, DX Wallach, H; Larochelle, H; Beygelzimer, A; d'Alche-Buc, F; Fox, E; Garnett, R Lei, Yunwen; Yang, Peng; Tang, Ke; Zhou, Ding-Xuan Optimal Stochastic and Online Learning with Individual Iterates ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) Advances in Neural Information Processing Systems English Proceedings Paper 33rd Conference on Neural Information Processing Systems (NeurIPS) DEC 08-14, 2019 Vancouver, CANADA SUBGRADIENT METHODS; ALGORITHMS Stochastic composite mirror descent (SCMD) is a simple and efficient method able to capture both geometric and composite structures of optimization problems in machine learning. Existing strategies require to take either an average or a random selection of iterates to achieve optimal convergence rates, which, however, can either destroy the sparsity of solutions or slow down the practical training speed. In this paper, we propose a theoretically sound strategy to select an individual iterate of the vanilla SCMD, which is able to achieve optimal rates for both convex and strongly convex problems in a non-smooth learning setting. This strategy of outputting an individual iterate can preserve the sparsity of solutions which is crucial for a proper interpretation in sparse learning problems. We report experimental comparisons with several baseline methods to show the effectiveness of our method in achieving a fast training speed as well as in outputting sparse solutions. [Lei, Yunwen; Yang, Peng; Tang, Ke] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen 518055, Peoples R China; [Lei, Yunwen] Tech Univ Kaiserslautern, Dept Comp Sci, D-67653 Kaiserslautern, Germany; [Zhou, Ding-Xuan] City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China; [Zhou, Ding-Xuan] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China Southern University of Science & Technology; University of Kaiserslautern; City University of Hong Kong; City University of Hong Kong Tang, K (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen 518055, Peoples R China. leiyw@sustech.edu.cn; yangp@sustech.edu.cn; tangk3@sustech.edu.cn; mazhou@cityu.edu.hk Zhou, Ding-Xuan/B-3160-2013; Lei, Yunwen/V-2782-2018 Zhou, Ding-Xuan/0000-0003-0224-9216; Lei, Yunwen/0000-0002-5383-467X National Key Research and Development Program of China [2017YFB1003102]; National Natural Science Foundation of China [61806091, 61806090, 61672478]; Program for University Key Laboratory of Guangdong Province [2017KSYS008]; Shenzhen Peacock Plan [KQTD2016112514355531]; Research Grants Council of Hong Kong [CityU 11338616]; National Nature Science Foundation of China [11671307]; Alexander von Humboldt Foundation National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for University Key Laboratory of Guangdong Province; Shenzhen Peacock Plan; Research Grants Council of Hong Kong(Hong Kong Research Grants Council); National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation) The work of Y. Lei, P. Yang and K. Tang is supported partially by the National Key Research and Development Program of China (Grant No. 2017YFB1003102), the National Natural Science Foundation of China (Grant Nos. 61806091, 61806090 and 61672478), the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008) and Shenzhen Peacock Plan (Grant No. KQTD2016112514355531). The work of D.-X. Zhou is supported partially by the Research Grants Council of Hong Kong [Project No. CityU 11338616] and by National Nature Science Foundation of China [Grant No. 11671307]. Y. Lei also acknowledges a Humboldt Research Fellowship from the Alexander von Humboldt Foundation. 46 1 1 0 2 NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) LA JOLLA 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA 1049-5258 ADV NEUR IN 2019.0 32.0 11 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP0ID 2023-03-23 WOS:000534424305041 0
J Zhang, WP; Mao, YH; Kumar, M; Li, JL; Liu, JQ Zhang, Weiping; Mao, Yihua; Kumar, Mohit; Li, Jalin; Liu, Jingqing A new method for on-line demand calibration of WDS hydraulic model DESALINATION AND WATER TREATMENT English Article; Proceedings Paper 3rd International Conference on Recent Advancements in Chemical, Environmental and Energy Engineering (RACEEE) FEB 15-16, 2018 SSN Coll Engn, Chennai, INDIA SSN Coll Engn Water distribution system; Fuzzy mapping; Deep learning; Demand calibration WATER DISTRIBUTION-SYSTEMS; DISTRIBUTION NETWORKS; DATA ASSIMILATION; STATE ESTIMATION The current approaches to the demand calibration of hydraulic models in water distribution system (WDS) are typically slow and not sufficiently reliable. This study introduces deep fuzzy mapping (DFM) as the fast and reliable tool for an on-line demand calibration of WDS hydraulic models based on supervisory control and data acquisition. Also, an on-line WDS hydraulic simulation platform is built which consists of a data resource layer, a model processing layer, and an access display layer. The on-line simulation platform is applied to the demo network, and it is verified that a DFM learning algorithm serves as an effective method for the on-line demand calibration of WDS hydraulic model in WDS. [Zhang, Weiping] Nanchang Univ, Dept Elect Informat Engn, Nanchang, Jiangxi, Peoples R China; [Zhang, Weiping; Kumar, Mohit; Li, Jalin] Zhejiang Univ, Binhai Ind Technol Res Inst, Tianjin 300301, Peoples R China; [Mao, Yihua; Liu, Jingqing] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310027, Zhejiang, Peoples R China; [Kumar, Mohit] Univ Rostock, Fac Comp Sci & Elect Engn, D-18055 Rostock, Germany Nanchang University; Zhejiang University; Zhejiang University; University of Rostock Liu, JQ (corresponding author), Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310027, Zhejiang, Peoples R China. zhangweiping@zjubh.com; maoyihua@zjubh.com; mohitkumar@zjubh.com; zjubh@zjubh.com; zjubh@zjubh.com , Mohit/0000-0002-7368-5157; Zhang, Weiping/0000-0001-7206-5901 National Natural Science Foundation of China [61662045] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors acknowledge the National Natural Science Foundation of China (Grant no. 61662045) and the Special Program of talents Development for Excellent Youth Scholars in Tianjin. 21 1 1 0 15 DESALINATION PUBL HOPKINTON 36 WALCOTT VALLEY DRIVE,, HOPKINTON, MA 01748 USA 1944-3994 1944-3986 DESALIN WATER TREAT Desalin. Water Treat. JUL 2018.0 121.0 111 117 10.5004/dwt.2018.22374 0.0 7 Engineering, Chemical; Water Resources Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Water Resources GW0VC 2023-03-23 WOS:000446586600015 0
C Li, J; Klein, R; Yao, A IEEE Li, Jun; Klein, Reinhard; Yao, Angela A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) IEEE International Conference on Computer Vision English Proceedings Paper 16th IEEE International Conference on Computer Vision (ICCV) OCT 22-29, 2017 Venice, ITALY IEEE,IEEE Comp Soc Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to over-fitting and achieves better accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows that our depth predictions are competitive with state-of-the-art and lead to faithful 3D projections. [Li, Jun; Klein, Reinhard; Yao, Angela] Univ Bonn, Bonn, Germany; [Li, Jun] Natl Univ Def Technol, Changsha, Hunan, Peoples R China University of Bonn; National University of Defense Technology - China Li, J (corresponding author), Univ Bonn, Bonn, Germany.;Li, J (corresponding author), Natl Univ Def Technol, Changsha, Hunan, Peoples R China. lij@cs.uni-bonn.de; rk@cs.uni-bonn.de; yao@cs.uni-bonn.de 30 115 123 2 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-5499 978-1-5386-1032-9 IEEE I CONF COMP VIS 2017.0 3392 3400 10.1109/ICCV.2017.365 0.0 9 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ4TW Green Submitted 2023-03-23 WOS:000425498403048 0
J He, ZY; Shao, HD; Wang, P; Lin, J; Cheng, JS; Yang, Y He Zhiyi; Shao Haidong; Wang Ping; Lin, Janet (Jing); Cheng Junsheng; Yang Yu Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples KNOWLEDGE-BASED SYSTEMS English Article Deep transfer multi-wavelet auto-encode; Gearbox fault; Transfer diagnosis; Variable working conditions; Few target training samples CONVOLUTIONAL NEURAL-NETWORK; CLASSIFICATION; APPROXIMATION; BEARINGS; ENTROPY; MODEL Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes. (C) 2019 Elsevier B.V. All rights reserved. [He Zhiyi; Shao Haidong; Cheng Junsheng; Yang Yu] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China; [Shao Haidong; Lin, Janet (Jing)] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Wang Ping] AECC Hunan Aviat Powerplant Res Inst, Zhuzhou 412002, Peoples R China; [Wang Ping] AECC Key Lab Aeroengine Vibrat Technol, Zhuzhou 412002, Peoples R China Hunan University; Lulea University of Technology Shao, HD (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China. hdshao@hnu.edu.cn Cheng, Junsheng/HKE-6532-2023; Lin, Jing/B-3076-2015; Yang, Yu/HKE-6499-2023; Shao, Haidong/AAE-5814-2022; Lin, Jing/AAF-3344-2021 Lin, Jing/0000-0002-7458-6820; Yang, Yu/0000-0001-9113-6023; Lin, Jing/0000-0002-7458-6820; He, Zhiyi/0000-0002-1334-8677; Shao, Haidong/0000-0002-8018-1774 National Natural Science Foundation of China [51875183, 51905160]; Fundamental Research Funds for the Central Universities, China [53111 8010335] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities) This research is supported by the National Natural Science Foundation of China (51875183 and 51905160) and the Fundamental Research Funds for the Central Universities, China (53111 8010335). 52 121 122 37 186 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAR 5 2020.0 191.0 105313 10.1016/j.knosys.2019.105313 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KR5MT 2023-03-23 WOS:000517663200035 0
J Yu, GQ; Li, F; Chin, T; Fiano, F; Usai, A Yu, Ganqian; Li, Fa; Chin, Tachia; Fiano, Fabio; Usai, Antonio Preventing food waste in the Chinese catering supply chain:from a tacit knowledge viewpoint JOURNAL OF KNOWLEDGE MANAGEMENT English Article Food waste; Catering supply chain; Tacit knowledge; Knowledge management system; China Purpose In China food waste during the catering service process namely the back end of the entire food supply chain has neither received sufficient attention nor been evaluated precisely even though it has been the cause of food security concerns. In response considering the complex tacit knowledge embedded at the front end of the Chinese catering supply chain this article aimed at addressing relevant issues from the viewpoint of tacit knowledge. Design/Methodology/Approach This article is conceptual in nature. From the perspective of knowledge management (KM), we used a deductive method to explore intricate tacit knowledge embedded at the front end of the catering supply chain in China. Findings The key challenges in preventing food waste in the Chinese catering supply chains are: (1) a variety of knowledge icebergs in Chinese catering culture, (2) the complex tacit dimension of knowledge in Chinese cookery, and (3) difficulties in bringing standardization to the Chinese catering business. The three KM-based step-by-step solutions are: (1) standardizing the essential cookery techniques of Chinese cuisine, (2) encouraging catering enterprises to create online knowledge-sharing platforms to reduce food waste for all stakeholders, and (3) using big data to build a nationwide KM system. Practical Implications Our research indicates that the of a comprehensive KM system specific to the Chinese catering supply chain is crucial for standardizing the essential cookery techniques of Chinese cuisine and analyzing big data, which could help improve Chinese catering processes and gastronomic habits in reality, so as to more effectively prevent food waste. Originality/Value Our research contributes to the literature by connecting the reduction of food waste issues with the KM perspective. A key to more efficiently reducing food waste in China lies in the lack of a deeper, more systematic understanding of the tacit dimension of knowledge in Chinese cookery and impressive dietary culture, namely the front end of the catering supply chain. [Yu, Ganqian; Li, Fa; Chin, Tachia] Honghe Univ, Coll Business, Mengzi, Peoples R China; [Fiano, Fabio] Link Campus Univ, Res Dept, Rome, Italy; [Usai, Antonio] Univ Sassari, Dept Econ & Business, Sassari, Italy Honghe University; University of Sassari Li, F (corresponding author), Honghe Univ, Coll Business, Mengzi, Peoples R China. fl8868@126.com National Social Science Foundation [19BJY181] National Social Science Foundation Research on Policies of Industrial Transformation of High-quality Development of China's Catering Industry Projects Supported By National Social Science Foundation (19BJY181). 26 1 1 14 41 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 1367-3270 1758-7484 J KNOWL MANAG J. Knowl. Manag. NOV 2 2022.0 26.0 10 2805 2814 10.1108/JKM-11-2021-0864 0.0 FEB 2022 10 Information Science & Library Science; Management Social Science Citation Index (SSCI) Information Science & Library Science; Business & Economics 5W2PD 2023-03-23 WOS:000752312300001 0
J Wang, CS; Wang, SY; Cui, HX; Sebela, MB; Zhang, C; Gu, XW; Fang, X; Hu, ZW; Tang, QD; Wang, YQ Wang, Chisheng; Wang, Shuying; Cui, Hongxing; Sebela, Monja B.; Zhang, Ce; Gu, Xiaowei; Fang, Xu; Hu, Zhongwen; Tang, Qiandi; Wang, Yongquan Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Clouds; Aircraft; Remote sensing; Satellites; Cameras; Optical sensors; Optical imaging; Cloud removal; deep learning; haze removal; multiple viewing angles; passenger aircraft; photogrammetry IMAGE QUALITY ASSESSMENT; DETECTION ALGORITHM; OPTICAL-DATA; REMOVAL; NETWORK; SAR Cloud removal in optical remote sensing imagery is essential for many Earth observation applications. To recover the cloud obscured information, some preconditions must be satisfied. For example, the cloud must be semitransparent or relationships between contaminated and cloud-free pixels must be assumed. Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Recently, the use of passenger aircraft as a platform for remote sensing has been proposed by some researchers and institutes, including Airbus and the Japan Aerospace Exploration Agency. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multiangle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: first, multiangle image acquisition from passenger aircraft, second, cloud detection based on deep learning semantic segmentation models, third, cloud removal by image stacking, and fourth, image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and predetermination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate. [Wang, Chisheng; Wang, Shuying; Cui, Hongxing; Fang, Xu; Hu, Zhongwen; Tang, Qiandi; Wang, Yongquan] Minist Nat Resources MNR, Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518000, Peoples R China; [Wang, Chisheng; Wang, Shuying; Cui, Hongxing; Fang, Xu; Hu, Zhongwen; Tang, Qiandi; Wang, Yongquan] Sch Architecture & Urban Planning, Guangdong Key Lab Urban Informat, Shenzhen 518000, Peoples R China; [Wang, Chisheng; Wang, Shuying; Cui, Hongxing; Fang, Xu; Hu, Zhongwen; Tang, Qiandi; Wang, Yongquan] Sch Architecture & Urban Planning, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518000, Peoples R China; [Cui, Hongxing] South Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China; [Cui, Hongxing] Hong Kong Univ Sci & Technol, Dept Ocean Sci, Hong Kong, Peoples R China; [Sebela, Monja B.] Sinergise Lab Geog Informat Syst Ltd, Ljubljana 1000, Slovenia; [Zhang, Ce] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Gu, Xiaowei] Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3DB, Dyfed, Wales Ministry of Natural Resources of the People's Republic of China; Hong Kong University of Science & Technology; Lancaster University; Aberystwyth University Cui, HX (corresponding author), Minist Nat Resources MNR, Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518000, Peoples R China.;Cui, HX (corresponding author), Sch Architecture & Urban Planning, Guangdong Key Lab Urban Informat, Shenzhen 518000, Peoples R China.;Cui, HX (corresponding author), Sch Architecture & Urban Planning, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518000, Peoples R China.;Cui, HX (corresponding author), South Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China. wangchisheng@163.com; wangshuying2019@email.szu.edu.cn; hcuiaf@connect.ust.hk; monja.sebela@gmail.com; c.zhang9@lancaster.ac.uk; xig4@aber.ac.uk; fangxu622@126.com; zwhoo@szu.edu.cn; tangqiandi5105@163.com; wongyq1994@gmail.com Hu, Zhongwen/AAX-7567-2021; wang, shuying/HHZ-4516-2022 Cui, Hongxing/0000-0002-1110-672X; Wang, Chisheng/0000-0003-3489-173X; Zhang, Ce/0000-0001-5100-3584 Chang'an University (Xi'an, China) through the National Key Research and Development Program of China [2020YFC1512001]; National Natural Science Foundation of China [41974006]; Shenzhen Scientific Research and Development Funding Program [KQJSCX20180328093453763, JCYJ20180305125101282, 20200812164904001]; Department of Education of Guangdong Province [2018KTSCX196]; Guangdong Special Support Program [2019BT02H594] Chang'an University (Xi'an, China) through the National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Scientific Research and Development Funding Program; Department of Education of Guangdong Province(National Natural Science Foundation of Guangdong Province); Guangdong Special Support Program This work was supported in part by Chang'an University (Xi'an, China) through the National Key Research and Development Program of China under Grant 2020YFC1512001, in part by the National Natural Science Foundation of China under Grant 41974006, in part by the Shenzhen Scientific Research and Development Funding Program under Grant KQJSCX20180328093453763, Grant JCYJ20180305125101282, and Grant 20200812164904001, in part by the Department of Education of Guangdong Province under Grant 2018KTSCX196, and in part by the Guangdong Special Support Program under Grant 2019BT02H594. 59 1 1 6 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 6923 6936 10.1109/JSTARS.2021.3094586 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology TQ5RV Green Published, gold, Green Accepted 2023-03-23 WOS:000678338200002 0
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Gomez; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Elayavalli, R. Kunnawalkam; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Hernandez, A. Castaneda; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Pernie, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Alvarez, J. D. Ruiz; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Comber, B.; Grothe, M.; Herndon, M.; Herve, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Rekovic, V; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Woods, N. CMS Collaboration Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying tau leptons at root s=13 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron scattering (experiments); Higgs physics; Top physics LIKELIHOOD METHOD; MISSING MOMENTUM; CROSS-SECTION; PP COLLISIONS; SEARCH; RECONSTRUCTION; EVENTS Results of a search for the standard model Higgs boson produced in association with a top quark pair (t (t) over barH) in final states with electrons, muons, and hadronically decaying tau leptons are presented. The analyzed data set corresponds to an integrated luminosity of 35.9 fb(-1) recorded in proton-proton collisions at root s = 13 TeV by the CMS experiment in 2016. The sensitivity of the search is improved by using matrix element and machine learning methods to separate the signal from backgrounds. The measured signal rate amounts to 1.23(-0.43)(+0.45) times the production rate expected in the standard model, with an observed (expected) significance of 3.2 sigma (2.8 sigma), which represents evidence for t (t) over barH production in those final states. An upper limit on the signal rate of 2.1 times the standard model production rate is set at 95% confidence level. [Sirunyan, A. M.; Tumasyan, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Eroe, J.; Del Valle, A. Escalante; Flechl, M.; Friedl, M.; Fruehwirth, R.; Ghete, V. M.; Grossmann, J.; Hrubec, J.; Jeitler, M.; Koenig, A.; Krammer, N.; Kraetschmer, I; Liko, D.; Madlener, T.; Mikulec, I; Pree, E.; Rad, N.; Rohringer, H.; Schieck, J.; Schoefbeck, R.; Spanring, M.; Spitzbart, D.; Taurok, A.; Waltenberger, W.; Wittmann, J.; Wulz, C-E; Zarucki, M.] Inst Hochenergiephys, Vienna, Austria; [Chekhovsky, V; Mossolov, V; Gonzalez, J. Suarez] Inst Nucl Problems, Minsk, BELARUS; [De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.] Univ Antwerp, Antwerp, Belgium; [Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I] Vrije Univ Brussel, Brussels, Belgium; [Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. K.; Lenzi, T.; Luetic, J.; Seva, T.; Starling, E.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Fang, W.; Gao, X.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I; Poyraz, D.; Roskas, C.; Trocino, D.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Zaganidis, N.] Univ Ghent, Ghent, Belgium; [Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caputo, C.; Caudron, A.; David, P.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Krintiras, G.; Lemaitre, V; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Saggio, A.; Marono, M. Vidal; Wertz, S.; Zobec, J.] Catholic Univ Louvain, Louvain La Neuve, Belgium; [Alda Junior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Correia Silva, G.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; Fonseca De Souza, S.; Malbouisson, H.; Medina Jaime, M.; Melo De Almeida, M.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Sanchez Rosas, L. J.; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Torres Da Silva De Araujo, F.; Vilela Pereira, A.] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Ahuja, S.; Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Novaes, S. F.; Padula, Sandra S.; Ruiz Vargas, J. C.] Univ Estadual Paulista, Sao Paulo, Brazil; [Gregores, E. M.; Mercadante, P. G.; Romero Abad, D.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.] Univ Sofia, Sofia, Bulgaria; [Fang, W.; Gao, X.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Li, B.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhao, J.] Inst High Energy Phys, Beijing, Peoples R China; [Ban, Y.; Chen, G.; Li, J.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing, Peoples R China; [Wang, Y.] Tsinghua Univ, Beijing, Peoples R China; [Avila, C.; Cabrera, A.; Carrillo Montoya, C. A.; Chaparro Sierra, L. F.; Florez, C.; Gonzalez Hernandez, C. F.; Segura Delgado, M. A.] Univ Los Andes, Bogota, Colombia; [Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I; Cipriano, P. M. Ribeiro; Sculac, T.] Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; [Antunovic, Z.; Kovac, M.] Univ Split, Fac Sci, Split, Croatia; [Brigljevic, V; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.] Inst Rudjer Boskovic, Zagreb, Croatia; [Ather, M. W.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.] Univ Cyprus, Nicosia, Cyprus; [Finger, M.; Finger, M., Jr.] Charles Univ Prague, Prague, Czech Republic; [Carrera Jarrin, E.] Univ San Francisco Quito, Quito, Ecuador; [Abdalla, H.; Assran, Y.] Acad Sci Res & Technol Arab Republ Egypt, Egyptian Network High Energy Phys, Cairo, Egypt; [Bhowmik, S.; Dewanjee, R. K.; Kadastik, M.; Perrini, L.; Raidal, M.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Kirschenmann, H.; Pekkanen, J.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Havukainen, J.; Heikkila, J. K.; Jarvinen, T.; Karimaki, V; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Linden, T.; Luukka, P.; Maenpaa, T.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Khvastunov, I; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Ghosh, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Leloup, C.; Locci, E.; Machet, M.; Malcles, J.; Negro, G.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Titov, M.] Univ Paris Saclay, CEA, IRFU, Gif Sur Yvette, France; [Abdulsalam, A.; Amendola, C.; Antropov, I; Baffoni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Charlot, C.; de Cassagnac, R. Granier; Jo, M.; Kucher, I; Lisniak, S.; Lobanov, A.; Blanco, J. Martin; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Leiton, A. G. Stahl; Yilmaz, Y.; Zabi, A.; Zghiche, A.] Univ Paris Saclay, Lab Leprince Ringuet, CNRS, IN2P3,Ecole Polytech, Palaiseau, France; [Agram, J-L; Andrea, J.; Bloch, D.; Brom, J-M; Buttignol, M.; Chabert, E. C.; Collard, C.; Conte, E.; Coubez, X.; Drouhin, F.; Fontaine, J-C; Gele, D.; Goerlach, U.; Jansova, M.; Juillot, P.; Le Bihan, A-C; Tonon, N.; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France; [Gadrat, S.] IN2P3, Ctr Calcul, CNRS, Villeurbanne, France; [Beauceron, S.; Bernet, C.; Boudoul, G.; Chanon, N.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Finco, L.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lattaud, H.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sordini, V; Vander Donckt, M.; Viret, S.; Zhang, S.] Univ Claude Bernard Lyon 1, Univ Lyon, CNRS, Inst Phys Nucl Lyon,IN2P3, Villeurbanne, France; [Khvedelidze, A.] Georgian Tech Univ, Tbilisi, Georgia; [Tsamalaidze, Z.] Tbilisi State Univ, Tbilisi, Georgia; [Autermann, C.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Rauch, M. P.; Schomakers, C.; Schulz, J.; Teroerde, M.; Wittmer, B.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Albert, A.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Gueth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Teyssier, D.; Thueer, S.; Borras, K.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Fluegge, G.; Kargoll, B.; Kress, T.; Kuensken, A.; Mueller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.] Rhein Westfal TH Aachen, Physikal Inst B 3, Aachen, Germany; [Martin, M. Aldaya; Arndt, T.; Asawatangtrakuldee, C.; Beernaert, K.; Behnke, O.; Behrens, U.; Martinez, A. Bermudez; Bin Anuar, A. A.; Borras, K.; Botta, V; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Danilov, V; De Wit, A.; Pardos, C. Diez; Damiani, D. Dominguez; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Eren, E.; Gallo, E.; Garcia, J. Garay; Geiser, A.; Luyando, J. M. Grados; Grohsjean, A.; Gunnellini, P.; Guthoff, M.; Harb, A.; Hauk, J.; Hempel, M.; Jung, H.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Knolle, J.; Korol, I; Kruecker, D.; Lange, W.; Lelek, A.; Lenz, T.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I-A; Meyer, A. B.; Meyer, M.; Missiroli, M.; Mittag, G.; Mnich, J.; Mussgiller, A.; Pitzl, D.; Raspereza, A.; Savitskyi, M.; Saxena, P.; Shevchenko, R.; Stefaniuk, N.; Tholen, H.; Van Onsem, G. P.; Walsh, R.; Wen, Y.; Wichmann, K.; Wissing, C.; Zenaiev, O.] DESY, Hamburg, Germany; [Gallo, E.; Aggleton, R.; Bein, S.; Blobel, V; Vignali, M. Centis; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hinzmann, A.; Hoffmann, M.; Karavdina, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Kurz, S.; Marconi, D.; Multhaup, J.; Niedziela, M.; Nowatschin, D.; Peiffer, T.; Perieanu, A.; Reimers, A.; Scharf, C.; Schleper, P.; Schmidt, A.; Schumann, S.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Stober, F. M.; Stoever, M.; Troendle, D.; Usai, E.; Vanhoefer, A.; Vormwald, B.] Univ Hamburg, Hamburg, Germany; [Akbiyik, M.; Barth, C.; Baselga, M.; Baur, S.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; Faltermann, N.; Freund, B.; Friese, R.; Giffels, M.; Harrendorf, M. A.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Kassel, F.; Kudella, S.; Mildner, H.; Mozer, M. U.; Mueller, Th; Plagge, M.; Quast, G.; Rabbertz, K.; Schroeder, M.; Shvetsov, I; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Woehrmann, C.; Wolf, R.] Inst Expt Teilchenphys, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Topsis-Giotis, I] NCSR Demokritos, INPP, Aghia Paraskevi, Greece; [Karathanasis, G.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Sphicas, P.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Kousouris, K.; Papakrivopoulos, I] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Mallios, S.; Manthos, N.; Papadopoulos, I; Paradas, E.; Strologas, J.; Triantis, F. A.; Tsitsonis, D.] Univ Ioannina, Ioannina, Greece; [Csanad, M.; Filipovic, N.; Pasztor, G.; Suranyi, O.; Veres, G., I; Vesztergombi, G.; Bartok, M.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bencze, G.; Hajdu, C.; Horvath, D.; Hunyadi, A.; Sikler, F.; Vami, T. A.; Veszpremi, V.; Vesztergombi, G.] Wigner Res Ctr Phys, Budapest, Hungary; [Horvath, D.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Karancsi, J.; Bartok, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Choudhury, S.; Komaragiri, J. R.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Swain, S. K.] Natl Inst Sci Educ & Res, Bhubaneswar, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V; Chauhan, S.; Chawla, R.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, M.; Kaur, S.; Kumar, R.; Kumari, P.; Lohan, M.; Mehta, A.; Sharma, S.; Singh, J. B.; Walia, G.] Panjab Univ, Chandigarh, India; [Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Keshri, S.; Kumar, A.; Kumar, Ashok; Malhotra, S.; Naimuddin, M.; Ranjan, K.; Shah, Aashaq; Sharma, R.] Univ Delhi, Delhi, India; [Ghosh, S.; Bhardwaj, R.; Bhattacharya, R.; Bhattacharya, S.; Bhawandeep, U.; Bhowmik, D.; Dey, S.; Dutt, S.; Dutta, S.; Majumdar, N.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Rout, P. K.; Roy, A.; Chowdhury, S. Roy; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] HBNI, Saha Inst Nucl Phys, Kolkata, India; [Behera, P. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Chudasama, R.; Dutta, D.; Jha, V; Kumar, V; Mohanty, A. K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.; Topkar, A.] Bhabha Atom Res Ctr, Bombay, Maharashtra, India; [Aziz, T.; Dugad, S.; Mahakud, B.; Mitra, S.; Mohanty, G. B.; Sur, N.; Sutar, B.] Tata Inst Fundamental Res A, Bombay, Maharashtra, India; [Bhattacharya, S.; Banerjee, S.; Chatterjee, S.; Das, P.; Guchait, M.; Jain, Sa; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Sahoo, N.; Sarkar, T.; Wickramage, N.] Tata Inst Fundamental Res B, Bombay, Maharashtra, India; [Chauhan, S.; Sharma, S.; Dube, S.; Hegde, V; Kapoor, A.; Kothekar, K.; Pandey, S.; Rane, A.] IISER, Pune, Maharashtra, India; [Chenarani, S.; Tadavani, E. Eskandari; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi; Naseri, M.; Mehdiabadi, S. Paktinat; Hosseinabadi, F. Rezaei; Safarzadeh, B.; Zeinali, M.] Inst Res Fundamental Sci IPM, Tehran, Iran; [Felcini, M.; Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Calabria, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Di Florio, A.; Errico, F.; Fiore, L.; Gelmi, A.; Iaselli, G.; Lezki, S.; Maggi, G.; Maggi, M.; Marangelli, B.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Zito, G.] INFN, Sez Bari, Bari, Italy; [Abbrescia, M.; Calabria, C.; Cristella, L.; De Palma, M.; Di Florio, A.; Errico, F.; Gelmi, A.; Lezki, S.; Marangelli, B.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Selvaggi, G.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Odorici, F.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] INFN, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Fanfani, A.; Fasanella, D.; Guiducci, L.; Navarria, F. L.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.] INFN, Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Chatterjee, K.; Ciulli, V; Civinini, C.; D'Alessandro, R.; Focardi, E.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.] INFN, Sez Firenze, Florence, Italy; [Chatterjee, K.; Ciulli, V; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.] Univ Firenze, Florence, Italy; [Fabbri, F.; Benussi, L.; Bianco, S.; Piccolo, D.] INFN, Lab Nazl Frascati, Frascati, Italy; [Calvelli, V; Ferro, F.; Ravera, F.; Robutti, E.; Tosi, S.] INFN, Sez Genova, Genoa, Italy; [Calvelli, V; Ravera, F.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Brianza, L.; Brivio, F.; Ciriolo, V; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Malvezzi, S.; Manzoni, R. A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pauwels, K.; Pedrini, D.; Pigazzini, S.; Ragazzi, S.; de Fatis, T. Tabarelli] INFN, Sez Milano Bicocca, Milan, Italy; [Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V; Dinardo, M. E.; Fiorendi, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Manzoni, R. A.; Paganoni, M.; Pauwels, K.; Ragazzi, S.; de Fatis, T. Tabarelli] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Cavallo, N.; Di Guida, S.; Fabozzi, F.; Fienga, F.; Galati, G.; Iorio, A. O. M.; Khan, W. A.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Voevodina, E.] INFN, Sez Napoli, Naples, Italy; [Fienga, F.; Galati, G.; Iorio, A. O. M.; Sciacca, C.; Voevodina, E.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Di Guida, S.; Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Benato, L.; Boletti, A.; Carlin, R.; De Oliveira, A. Carvalho Antunes; Checchia, P.; Dall'Osso, M.; Manzano, P. De Castro; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Meneguzzo, A. T.; Pegoraro, M.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zumerle, G.] INFN, Sez Padova, Padua, Italy; [Benato, L.; Boletti, A.; Carlin, R.; De Oliveira, A. Carvalho Antunes; Dall'Osso, M.; Gasparini, F.; Gasparini, U.; Meneguzzo, A. T.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Zanetti, M.; Zotto, P.; Zumerle, G.] Univ Padua, Padua, Italy; [Lujan, P.; Tiko, A.] Univ Trento, Trento, Italy; [Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.; Fallavollita, F.] INFN, Sez Pavia, Pavia, Italy; [Montagna, P.; Ratti, S. P.; Ressegotti, M.; Riccardi, C.; Vai, I; Vitulo, P.; Fallavollita, F.] Univ Pavia, Pavia, Italy; [Solestizi, L. Alunni; Biasini, M.; Bilei, G. M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Manoni, E.; Mantovani, G.; Mariani, V; Menichelli, M.; Rossi, A.; Santocchia, A.; Spiga, D.] INFN, Sez Perugia, Perugia, Italy; [Solestizi, L. Alunni; Biasini, M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Mariani, V; Rossi, A.; Santocchia, A.] Univ Perugia, Perugia, Italy; [Androsov, K.; Azzurri, P.; Bagliesi, G.; Bianchini, L.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fedi, G.; Giannini, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Rizzi, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.] INFN, Sez Pisa, Pisa, Italy; [Ciocci, M. A.; Messineo, A.; Rizzi, A.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Borrello, L.; Giannini, L.; Ligabue, F.; Manca, E.; Mandorli, G.] Scuola Normale Super Pisa, Pisa, Italy; [Barone, L.; Cavallari, F.; Cipriani, M.; Daci, N.; Del Re, D.; Di Marco, E.; Diemoz, M.; Gelli, S.; Longo, E.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.] INFN, Sez Roma, Rome, Italy; [Barone, L.; Cipriani, M.; Del Re, D.; Di Marco, E.; Gelli, S.; Longo, E.; Marzocchi, B.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Santanastasio, F.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Castello, R.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V; Monteil, E.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V; Solano, A.; Staiano, A.] INFN, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Castello, R.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Kiani, B.; Migliore, E.; Monaco, V; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Romero, A.; Sacchi, R.; Shchelina, K.; Solano, A.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Zanetti, A.] INFN, Sez Trieste, Trieste, Italy; [Della Ricca, G.] Univ Trieste, Trieste, Italy; [Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Kamon, T.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.; Oh, G.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Cifuentes, J. A. Brochero; Goh, J.; Kim, T. J.] Hanyang Univ, Seoul, South Korea; [Lee, S.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lim, J.; Park, S. K.; Roh, Y.] Korea Univ, Seoul, South Korea; [Lee, K.; Almond, J.; Kim, J.; Kim, J. S.; Lee, H.; Nam, K.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. H.; Yang, U. K.; Yoo, H. D.; Yu, G. B.] Seoul Natl Univ, Seoul, South Korea; [Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.] Univ Seoul, Seoul, South Korea; [Lee, J.; Choi, Y.; Hwang, C.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Dudenas, V; Juodagalvis, A.; Vaitkus, J.] Vilnius Univ, Vilnius, Lithuania; [Ahmed, I; Ibrahim, Z. A.; Ali, M. A. B. Md; Idris, F. Mohamad; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Duran-Osuna, M. C.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Ramirez-Sanchez, G.; Heredia-De La Cruz, I; Rabadan-Trejo, R., I; Lopez-Fernandez, R.; Mejia Guisao, J.; Reyes-Almanza, R.; Sanchez-Hernandez, A.] Ctr Invest Estudios Avanzados IPN, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Eysermans, J.; Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Morelos Pineda, A.] Univ Autonoma San Luis Potosi, San Luis Potosi, Mexico; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, M.; Ahmad, A.; Hassan, Q.; Hoorani, H. R.; Saddique, A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Nawrocki, K.; Szleper, M.; Traczyk, P.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Pyskir, A.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Bargassa, P.; Beirao Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Galinhas, B.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M., V; Seixas, J.; Strong, G.; Toldaiev, O.; Vadruccio, D.; Varela, J.] Lab Instrumentacao & Fisica Expt Partculas, Lisbon, Portugal; [Finger, M.; Finger, M., Jr.; Khvedelidze, A.; Tsamalaidze, Z.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I; Gorbunov, I; Kamenev, A.; Karjavin, V; Lanev, A.; Malakhov, A.; Matveev, V; Moisenz, P.; Palichik, V; Perelygin, V; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V; Voytishin, N.; Zarubin, A.] Joint Inst Nucl Res, Dubna, Russia; [Ivanov, Y.; Kim, V; Kuznetsova, E.; Levchenko, P.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Vavilov, S.; Vorobyev, A.] St Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Musienko, Y.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Popov, V; Pozdnyakov, I; Safronov, G.; Spiridonov, A.; Stepennov, A.; Stolin, V; Toms, M.; Vlasov, E.; Zhokin, A.; Nikitenko, A.] Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys & Technol, Moscow, Russia; [Matveev, V; Bylinkin, A.; Chadeeva, M.; Parygin, P.; Philippov, D.; Polikarpov, S.; Popova, E.; Rusinov, V; Azarkin, M.; Dremin, I; Kirakosyan, M.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Chadeeva, M.; Andreev, V; Azarkin, M.; Dremin, I; Kirakosyan, M.; Rusakov, S., V; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Popov, A.; Zhukov, V; Baskakov, A.; Belyaev, A.; Boos, E.; Bunichev, V; Dubinin, M.; Dudko, L.; Klyukhin, V; Kodolova, O.; Korneeva, N.; Lokhtin, I; Miagkov, I; Obraztsov, S.; Perfilov, M.; Petrushanko, S.; Savrin, V] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Shtol, D.] NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Bitioukov, S.; Elumakhov, D.; Godizov, A.; Kachanov, V; Kalinin, A.; Konstantinov, D.; Mandrik, P.; Petrov, V; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] State Res Ctr Russian Federat, Inst High Energy Phys NRC & Quot, Kurchatov Inst & Quot, Protvino, Russia; [Babaev, A.] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.; Milenovic, P.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.; Milenovic, P.] Vinca Inst Nucl Sci, Belgrade, Serbia; [Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I; Barrio Luna, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Bedoya, C.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Moran, D.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Soares, M. S.; Triossi, A.] Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid, Spain; [Albajar, C.; de Troconiz, J. F.] Univ Autonoma Madrid, Madrid, Spain; [Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Vischia, P.; Vizan Garcia, J. M.] Univ Oviedo, Oviedo, Spain; [Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Ruiz del Arbol, P. Martinez; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I; Vilar Cortabitarte, R.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Chen, Y.; Stahl, A.; Hartmann, F.; Kassel, F.; Mohanty, A. K.; Sharma, A.; Silvestris, L.; Primavera, F.; Ciriolo, V; Meola, S.; Paolucci, P.; Azzurri, P.; Abbaneo, D.; Akgun, B.; Auffray, E.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Botta, C.; Camporesi, T.; Cepeda, M.; Cerminara, G.; Chapon, E.; d'Enterria, D.; Dabrowski, A.; Daponte, V; David, A.; De Gruttola, M.; De Roeck, A.; Deelen, N.; Dobson, M.; du Pree, T.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fallavollita, F.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gilbert, A.; Gill, K.; Glege, F.; Gulhan, D.; Hegeman, J.; Innocente, V; Jafari, A.; Janot, P.; Karacheban, O.; Kieseler, J.; Knunz, V; Kornmayer, A.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenco, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Neugebauer, H.; Ngadiuba, J.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pitters, F. M.; Rabady, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schafer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Silva, P.; Sphicas, P.; Stakia, A.; Steggemann, J.; Stoye, M.; Tosi, M.; Treille, D.; Tsirou, A.; Veckalns, V.; Verweij, M.; Zeuner, W. D.; Virdee, T.] CERN, European Org Nucl Res, Geneva, Switzerland; [Bertl, W.; Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.] Paul Scherrer Inst, Villigen, Switzerland; [Backhaus, M.; Bani, L.; Berger, P.; Casal, B.; Chernyavskaya, N.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Klijnsma, T.; Lustermann, W.; Marionneau, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessitedaldi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Reichmann, M.; Ruini, D.; Becerra, D. A. Sanz; Schoenenberger, M.; Shchutska, L.; Tavolaro, Vr; Theofilatos, K.; Olsson, M. L. Vesterbacka; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Caminada, L.; Aarrestad, T. K.; Amsler, C.; Brzhechko, D.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Neutelings, I; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Schweiger, K.; Seitz, C.; Takahashi, Y.; Zucchetta, A.] Univ Zurich, Zurich, Switzerland; [Candelise, V; Chang, Y. H.; Cheng, K. Y.; Doan, T. H.; Jain, Sh; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W-S; Hsiung, Y.; Kumar, Arun; Liu, Y. F.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Tsai, J. F.] NTU, Taipei, Taiwan; [Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Bakirci, M. N.; Bat, A.; Boran, F.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Dumanoglu, I; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Karapinar, G.; Ocalan, K.; Yalvac, M.; Zeyrek, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Gulmez, E.; Kaya, M.; Kaya, O.; Tekten, S.] Bogazici Univ, Istanbul, Turkey; [Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Komurcu, Y.] Istanbul Tech Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; [Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Newbold, D. M.; Paramesvaran, S.; Sakuma, T.; El Nasr-storey, S. Seif; Smith, D.; Smith, V. J.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Newbold, D. M.; Bell, K. W.; Brew, C.; Brown, R. M.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Linacre, J.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, W. J.] Rutherford Appleton Lab, Didcot, Oxon, England; [Auzinger, G.; Bainbridge, R.; Bloch, P.; Borg, J.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; Della Negra, M.; Di Maria, R.; Elwood, A.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Komm, M.; Lane, R.; Laner, C.; Lyons, L.; Magnan, A-M; Malik, S.; Mastrolorenzo, L.; Matsushita, T.; Nash, J.; Nikitenko, A.; Palladino, V; Pesaresi, M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Strebler, T.; Summers, S.; Tapper, A.; Uchida, K.; Acosta, M. Vazquez; Virdee, T.; Wardle, N.; Winterbottom, D.; Wright, J.; Zenz, S. C.] Imperial Coll, London, England; [Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Morton, A.; Reid, I. D.; Teodorescu, L.; Zahid, S.] Brunel Univ, Uxbridge, Middx, England; [Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Smith, C.] Baylor Univ, Waco, TX 76798 USA; [Bartek, R.; Dominguez, A.] Catholic Univ Amer, Washington, DC 20064 USA; [Buccilli, A.; Cooper, S., I; Henderson, C.; Rumerio, P.; West, C.] Univ Alabama, Tuscaloosa, AL USA; [Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.] Boston Univ, Boston, MA 02215 USA; [Lee, J.; Benelli, G.; Cutts, D.; Hadley, M.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Pazzini, J.; Piperov, S.; Sagir, S.; Syarif, R.; Yu, D.] Brown Univ, Providence, RI 02912 USA; [Wang, Z.; Burns, D.; Band, R.; Brainerd, C.; Breedon, R.; Sanchez, M. Calderon De La Barca; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Stolp, D.; Taylor, D.; Tos, K.; Tripathi, M.; Zhang, F.] Univ Calif Davis, Davis, CA 95616 USA; [Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Regnard, S.; Saltzberg, D.; Schnaible, C.; Valuev, V.] Univ Calif Los Angeles, Los Angeles, CA USA; [Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Shirazi, S. M. A. Ghiasi; Hanson, G.; Karapostoli, G.; Kennedy, E.; Lacroix, F.; Long, O. R.; Negrete, M. Olmedo; Paneva, M., I; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Branson, J. G.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Gilbert, D.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V; Letts, J.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Wood, J.; Wurthwein, F.; Yagil, A.; Della Porta, G. Zevi] Univ Calif San Diego, La Jolla, CA 92093 USA; [Amin, N.; Bhandari, R.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Tsinghua University; Universidad de los Andes (Colombia); University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Georgian Technical University; Ivane Javakhishvili Tbilisi State University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); University of Hamburg; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Indian Institute of Science (IISC) - Bangalore; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Seoul National University (SNU); University of Seoul; Sungkyunkwan University (SKKU); Vilnius University; Universiti Malaya; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; Universidad Autonoma de San Luis Potosi; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; National Centre for Nuclear Research; University of Warsaw; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; Novosibirsk State University; Tomsk Polytechnic University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida International University; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rockefeller University; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Universidade Federal de Pelotas; Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Suez University; Egyptian Knowledge Bank (EKB); British University in Egypt; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; King Abdulaziz University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Brandenburg University of Technology Cottbus; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; Visva Bharati University; University Ruhuna; Isfahan University of Technology; University of Yazd; Islamic Azad University; University of Siena; International Islamic University Malaysia; Warsaw University of Technology; Peter the Great St. Petersburg Polytechnic University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Istituto Nazionale di Fisica Nucleare (INFN); Riga Technical University; Gaziosmanpasa University; Adiyaman University; Istanbul Aydin University; Mersin University; Piri Reis University; Izmir Institute of Technology; Necmettin Erbakan University; Marmara University; Kafkas University; Istanbul Bilgi University; University of Southampton; Monash University; Instituto de Astrofisica de Canarias; Utah System of Higher Education; Utah Valley University; Beykent University; Bingol University; Erzincan Binali Yildirim University; Sinop University; Qatar Foundation (QF); Texas A&M University Qatar Sirunyan, AM (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. 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Aslı/AAQ-8732-2020; Bernardes, Cesar Augusto/D-2408-2015; Matorras, Francisco/I-4983-2015; Chernyavskaya, Nadezda/F-2161-2015; Bedoya, Cristina F/K-8066-2014; CAKIR, Altan/ABD-4450-2020; Leonardo, Nuno T/M-6940-2016; Chaparro Sierra, Luisa Fernanda/AHD-3083-2022; Finco, Linda/AAH-3618-2019; Calderon, Alicia/K-3658-2014; Amapane, Nicola/J-3683-2012; Trocino, Daniele/AGI-2155-2022; Ramos, Juan Pablo Fernández/AAF-3114-2019; Majumder, Devdatta/X-2542-2019; Cadamuro, Luca/AAO-8637-2020; de Jesus Damiao, Dilson/G-6218-2012; Seo, Seon Hee/K-9250-2014; Fasanella, Daniele/ABD-9759-2020; Pásztor, Gabriella/D-9716-2016; Hernández, José M./H-9127-2015; Acosta, Monica Vazquez/AAB-2841-2019; Loukas, Dimitris/ABF-1478-2020; Faccioli, Pietro/AAF-1499-2019; Tiras, Emrah/ABG-2354-2020; Goh, Junghwan/ABC-9283-2020; Duarte, Javier Mauricio/AAA-5414-2020; Missiroli, Marino/AAA-9072-2021; Casarsa, Massimo/L-3623-2018; Rovelli, Tiziano/K-4432-2015; Roy, Ashim/HLV-9248-2023; Padula, Sandra S./AAE-4304-2020; Chenarani, Shirin/AAO-8918-2021; Shah, Aashaq/ABI-4173-2020; Fabozzi, Francesco/T-6468-2019; Mussgiller, Andreas/AAF-6933-2019; Kim, Victor T/L-1648-2013; Theofilatos, Konstantinos/AAL-9162-2021; celik, Ali/K-6506-2019; Saoulidou, Niki/AAA-2891-2020; Dudko, Lev/D-7127-2012; Tsirou, Andromachi/HHC-6869-2022; Comber, Barbara M/ABA-9709-2020; Ferencek, Dinko/G-6411-2015; Vargas, Julio Celso Borello/AAR-4671-2021; Safronov, Grigory/F-8494-2017; Vischia, Pietro/AAB-7811-2021; Hos, ilknur/T-1165-2019; Sguazzoni, Giacomo/J-4620-2015; polatoz, ayse/C-3463-2018; Ulrich, Ralf/Q-3882-2019; Pauwels, Kristof/AAE-1951-2019; Fanò, Livio/K-2460-2015; Malbouisson, H./N-6733-2015; Giammanco, Andrea/O-5017-2019; Scodellaro, Luca/K-9091-2014; Civinini, Carlo/AAL-6403-2020; Cepeda, Maria/HNO-9314-2023; Blekman, Freya/ABD-7916-2020; Biino, Cristina/AAF-1585-2020; De La Cruz Burelo, Eduard/AAR-9103-2021; Cuevas-Maestro, Francisco Javier/ABE-2705-2020; Parygin, Pavel/C-8908-2016; Gokbulut, Gul/G-2141-2018; Daskalakis, Georgios/M-4480-2013; KUMAR, ASHOK/AAF-4055-2021; Menichelli, Mauro/AAQ-4026-2020; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Gozalez-Lopez, Oscar/AAH-3533-2019; Benaglia, Andrea/AGF-5495-2022; de Souza Sandro, Fonseca/ABB-8505-2020; Zolkapli, Zukhaimira/AAA-4039-2019; MANJHVAR, AMIT KUMAR/HNB-6459-2023; Ocalan, Kadir/ABA-7004-2021; Heredia De La Cruz, Ivan/L-2610-2018; Sanchez-Hernandez, Alberto/AAH-3473-2020; D'Hondt, Jorgen/ABD-7930-2020; Flix, Josep/G-5414-2012; Al-bataineh, Ayman Ahmad/AAK-1197-2021; Sharma, Sandeep/AAE-2538-2021; Pinna Angioni, Gian Luca/AAZ-6834-2021; de Paula Carvalho, Wagner/V-3343-2019; Doroba, krzysztof/AAA-3419-2021; Agaras, Merve Nazlim/AAB-5221-2021; Lezki, Samet/AAG-8277-2019; Rossi, Alessandro/I-8230-2017; Galati, Giuliana/AAV-8088-2020; Herrera, Maria Clemencia Mora/AAD-6466-2021; Dallavalle, Gaetano Marco/AFW-4654-2022; Pompili, Alexis/ABG-3556-2021; Kumar, Dr Ashok/HNR-6570-2023; Folgueras, Santiago/B-6549-2014; Ferro, Fabrizio/HMP-5516-2023; Battilana, Carlo/AAA-7345-2020; Tinoco Mendes, Andre David/D-4314-2011; Ligabue, Franco/ABI-1681-2020; Verwilligen, Piet Omer J/AAA-8819-2019; Aushev, Tagir/AAN-9735-2020; Bartosik, Nazar/ABF-2326-2020; Viliani, Lorenzo/AAU-3462-2021; Khvedelidze, Arsen/AAF-5025-2020; Piperov, Stefan/Q-1980-2017; Wimpenny, Stephen J/K-8848-2013; Gil Da Silveira, Gustavo/N-7279-2014; de la Cruz, Begona/K-7552-2014; Abdulsalam, Abdulla/B-3386-2019; Ogul, Hasan/S-7951-2016; Polikarpov, Sergey/C-8939-2016; Terkulov, Adel/M-8581-2015; Mousa, Jehad/I-8019-2019; Dremin, Igor/K-8053-2015; Cepeda, Maria/AAF-4992-2019; Duarte-Campderrós, Jordi/F-5025-2018; De Guio, Federico/ABI-2018-2020; Chadeeva, Marina/M-9644-2019; Calligaris, Luigi/K-2018-2017; Benussi, Luigi/O-9684-2014; Kumar, Ramandeep/AAY-6388-2020; Fallavollita, Francesco/AAL-8850-2020; Titov, Maxim/AAM-2140-2020; Krücker, Dirk/X-1844-2019; BAT, Ayse/GOK-2363-2022; Tytgat, Michael G./F-3732-2018; Canelli, Florencia/AAI-1616-2021; Rossin, Roberto/C-8376-2019; Comber, Barbara M/K-7498-2016; Anampa, Kenyi Hurtado/ABA-2626-2020; Behera, Prafulla Kumar/AAK-8686-2020; ozok, ferhat/A-6803-2018; Errico, Filippo/AAD-7755-2019; Lychkovskaya, Natalia/F-8341-2017; Tuominen, Eija M/A-5288-2017; ASILAR, Ece/ABC-4577-2020; Wulz, Claudia-Elisabeth E/H-5657-2011; Seo, Seon-Hee/M-1189-2019; Asavapibhop, Burin/X-1477-2019; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Pedraza Morales, Maria Isabel/ABC-7289-2021; kaya, mithat/D-8062-2011; Ocalan, Kadir/AAX-5235-2021; Manganote, Edmilson J.T./K-8251-2013; Gerosa, Raffaele/AGL-5921-2022; Csanad, Mate/D-5960-2012; Bortignon, Pierluigi/AAF-6561-2020; LECOQ, Paul/S-7246-2019; Iorio, Alberto Orso Maria/GXA-0233-2022; Sculac, Toni/AAE-4141-2019; Klyukhin, Vyacheslav I/D-6850-2012; Bodek, Arie/AAD-2811-2019; Venkateswararao, Gollakaram/B-5490-2015; Smirnov, Vitaly A/B-5001-2017; Demiroglu, Zuhal Seyma/A-5168-2016; Chadeeva, Marina/C-8789-2016; Strologas, John/GWR-2036-2022; Caballero, Isidro González/K-3062-2019; Voevodina, Elena/G-7314-2016; Oglakci, Mehmet/AAG-3754-2021; GULER, E GURPINAR/AAH-4748-2019; Trejo, Raul Iraq Rabadan/F-7924-2019; celik, Ali/A-3563-2019; AbuZeid, Shimaa/AAC-9872-2021; Tuo, Shengquan/ABA-9032-2021; Santra, Arka/AEE-4946-2022; Lokhtin, Igor/D-7004-2012; Bunichev, Viacheslav/E-2285-2012; Cortabitarte, Rocio Vilar/P-8480-2014; Kumar, Amit/HJP-6588-2023; Arcidiacono, Roberta/ABF-3918-2020; Della Ricca, Giuseppe/B-6826-2013; Selvaggi, Michele/CAJ-2129-2022; Meridiani, Paolo/ABH-1655-2020; Singh, J B/ABF-5574-2021; Stahl, Achim/E-8846-2011; Luukka, Panja-Riina/S-8956-2018; Fienga, Francesco/HKE-1204-2023; de gruttola, michele/AAN-7271-2020; Konecki, Marcin/G-4164-2015; Lannon, Kevin/HGU-5755-2022; Bourilkov, Dimitri/AAO-4908-2020; Novaes, Sergio F./D-3532-2012; Guler, Yalcin/AAG-7833-2019; Torassa, Ezio/I-1788-2012; Abad, David Romero/AAV-5247-2020; Cankocak, Kerem/A-1507-2018; Shopova, Mariana Filipova/HKW-0149-2023; Veckalns, Viesturs/AAZ-3105-2020; d'Enterria, David/W-7763-2019; Felcini, Marta A./S-2684-2019; De La Cruz Burelo, Eduard/Q-6021-2019; Andreev, Vladimir F/M-8665-2015; Vai, Ilaria/AAT-5836-2020; Paktinat Mehdiabadi, Saeid/AAY-8501-2020; Akgun, Bora/AAM-8311-2021; Montanari, Alessandro/J-2420-2012; Özdemir, Kadri/P-8058-2014; ., MOHD. ADLI BIN MD. ALI/AAD-2818-2019; Fouz, Maria Cruz/AAF-1105-2019; Fiorendi, Sara/ABA-3311-2021; Bermudez Martinez, Armando/HHN-4625-2022; Saka, Halil/GPF-8892-2022; Shchutska, Lesya/M-7052-2017; González Caballero, Isidro/E-7350-2010; Dorigo, Tommaso/AAY-6673-2020; Lista, Luca/M-2912-2019; Donato, Silvio/GPK-2262-2022; Kumar, Amit/HMP-4853-2023; Grandi, Claudio/B-5654-2015; de la Hoz, Santiago Gonzalez/E-2494-2016; Arneodo, Michele/ABF-7197-2020; Krammer, Manfred/A-6508-2010; Palladino, Vito/AAA-5328-2020; Teles, Patricia/ABF-9723-2021; Fernandez Menendez, Javier/B-6550-2014; Philippov, Dmitry/H-6800-2016; Bornheim, Adi/L-2750-2019; Peris, Antonio Delgado/AAA-5165-2019; Aarrestad, Thea/GLV-3031-2022; Di Florio, Adriano/ABA-3631-2020; TOPAKSU, Aysel KAYİS/B-8910-2018; Zeyrek, Mehmet/ABA-7235-2020; Sola, Valentina/E-7726-2015; Navarria, Francesco Luigi/ACP-7823-2022; Sznajder, Andre/L-1621-2016; Lee, Jason/B-9701-2014; Josa Mutuberria, Isabel/K-5184-2014; Paganoni, Marco/A-4235-2016; Rolandi, Luigi (Gigi)/E-8563-2013; Haddad, Yacine/GRX-5022-2022; D'Alessandro, Raffaello/F-5897-2015; Spagnolo, Paolo/G-3401-2017; Paulini, Manfred/N-7794-2014; Gulmez, Erhan/P-9518-2015; da Cruz e Silva, Cristovao/K-7229-2013; Hobson, Peter/C-8919-2016; Redondo, Ignacio/K-8639-2014; Belyaev, Alexander/F-6637-2015; Vilela Pereira, Antonio/L-4142-2016; Malik, Sudhir/D-6621-2018; Marco de Lucas, Jesus/B-8735-2008; My, Salvatore/I-5160-2015; Gonzalez Suarez, Rebeca/L-6128-2014; Kim, Tae Jeong/P-7848-2015; BARRILLON, Stephanie/ABA-6873-2021; Boimska, Bozena/Z-6088-2019; Popov, Andrey/E-1052-2012; Mundim, Luiz/A-1291-2012; Govoni, Pietro/K-9619-2016; Azzi, Patrizia/H-5404-2012; Paganis, Stathes/J-8413-2017; Petkov, Peicho/M-2080-2016; Reyes-Almanza, Rogelio/GQJ-8236-2022; Fiori, Francesco/H-1454-2018; Gomez, Gervasio/K-2860-2014 Voevodina, Elena/0000-0003-2987-3772; Khakzad, Mohsen/0000-0002-2212-5715; Yazgan, Efe/0000-0001-5732-7950; Zorbilmez, Caglar/0000-0002-5199-061X; Steggemann, Jan/0000-0003-4420-5510; Sagir, Sinan/0000-0002-2614-5860; TUVE', Cristina/0000-0003-0739-3153; Najafabadi, Mojtaba Mohammadi/0000-0001-6131-5987; Puerta Pelayo, Jesus/0000-0001-7390-1457; Amapane, Nicola/0000-0001-9449-2509; Rabady, Dinyar/0000-0001-9239-0605; Zhu, Ren-Yuan/0000-0003-3091-7461; Moon, Chang-Seong/0000-0001-8229-7829; Barria, Patrizia/0000-0002-3924-7380; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Shah, Aashaq/0000-0002-6157-2016; Buchanan, James/0000-0001-8207-5556; Shmatov, Sergei/0000-0001-5354-8350; Androsov, Konstantin/0000-0003-2694-6542; MAESTRE, JUAN ALCARAZ/0000-0003-0914-7474; Giacomelli, Paolo/0000-0002-6368-7220; Dubinin, Mikhail N/0000-0002-7766-7175; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; Lowette, Steven/0000-0003-3984-9987; Traczyk, Piotr/0000-0001-5422-4913; Waltenberger, Wolfgang/0000-0002-6215-7228; Ruiz, Alberto/0000-0002-3639-0368; Lange, Clemens/0000-0002-3632-3157; Baskakov, Alexey V./0000-0002-3970-8407; Bagliesi, Giuseppe/0000-0003-4298-1620; Palencia Cortezon, Jose Enrique/0000-0001-8264-0287; Yetkin, E. Aslı/0000-0002-9007-8260; Bernardes, Cesar Augusto/0000-0001-5790-9563; Matorras, Francisco/0000-0003-4295-5668; Chernyavskaya, Nadezda/0000-0002-2264-2229; Bedoya, Cristina F/0000-0001-8057-9152; CAKIR, Altan/0000-0002-8627-7689; Leonardo, Nuno T/0000-0002-9746-4594; Chaparro Sierra, Luisa Fernanda/0000-0002-6570-9075; Calderon, Alicia/0000-0002-7205-2040; Amapane, Nicola/0000-0001-9449-2509; Trocino, Daniele/0000-0002-2830-5872; Ramos, Juan Pablo Fernández/0000-0002-0122-313X; Majumder, Devdatta/0000-0002-7578-0027; Cadamuro, Luca/0000-0001-8789-610X; de Jesus Damiao, Dilson/0000-0002-3769-1680; Seo, Seon Hee/0000-0002-1496-624X; Pásztor, Gabriella/0000-0003-0707-9762; Hernández, José M./0000-0001-6436-7547; Acosta, Monica Vazquez/0000-0002-2409-9792; Loukas, Dimitris/0000-0002-7431-3857; Faccioli, Pietro/0000-0003-1849-6692; Tiras, Emrah/0000-0002-5628-7464; Goh, Junghwan/0000-0002-1129-2083; Duarte, Javier Mauricio/0000-0002-5076-7096; Missiroli, Marino/0000-0002-1780-1344; Casarsa, Massimo/0000-0002-1353-8964; 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Folgueras, Santiago/0000-0001-7191-1125; Battilana, Carlo/0000-0002-3753-3068; Tinoco Mendes, Andre David/0000-0001-5854-7699; Ligabue, Franco/0000-0002-1549-7107; Verwilligen, Piet Omer J/0000-0002-9285-8631; Aushev, Tagir/0000-0002-6347-7055; Bartosik, Nazar/0000-0002-7196-2237; Viliani, Lorenzo/0000-0002-1909-6343; Khvedelidze, Arsen/0000-0002-5953-0140; Piperov, Stefan/0000-0002-9266-7819; Wimpenny, Stephen J/0000-0003-0505-4908; Gil Da Silveira, Gustavo/0000-0003-3514-7056; de la Cruz, Begona/0000-0001-9057-5614; Abdulsalam, Abdulla/0000-0002-5424-7418; Ogul, Hasan/0000-0002-5121-2893; Polikarpov, Sergey/0000-0001-6839-928X; Terkulov, Adel/0000-0003-4985-3226; Mousa, Jehad/0000-0002-2978-2718; Cepeda, Maria/0000-0002-6076-4083; Duarte-Campderrós, Jordi/0000-0003-0687-5214; De Guio, Federico/0000-0001-5927-8865; Chadeeva, Marina/0000-0003-1814-1218; Calligaris, Luigi/0000-0002-9951-9448; Benussi, Luigi/0000-0002-2363-8889; Kumar, Ramandeep/0000-0002-5754-5410; Fallavollita, Francesco/0000-0003-2315-2499; 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d'Enterria, David/0000-0002-5754-4303; Felcini, Marta A./0000-0002-2051-9331; Andreev, Vladimir F/0000-0002-5492-6920; Vai, Ilaria/0000-0003-0037-5032; Paktinat Mehdiabadi, Saeid/0000-0001-7303-0217; Akgun, Bora/0000-0001-8888-3562; Montanari, Alessandro/0000-0003-2748-6373; Özdemir, Kadri/0000-0002-0103-1488; ., MOHD. ADLI BIN MD. ALI/0000-0001-7203-0610; Fouz, Maria Cruz/0000-0003-2950-976X; Fiorendi, Sara/0000-0003-3273-9419; Bermudez Martinez, Armando/0000-0001-8822-4727; Shchutska, Lesya/0000-0003-0700-5448; González Caballero, Isidro/0000-0002-8087-3199; Lista, Luca/0000-0001-6471-5492; Donato, Silvio/0000-0001-7646-4977; Grandi, Claudio/0000-0001-5998-3070; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Arneodo, Michele/0000-0002-7790-7132; Krammer, Manfred/0000-0003-2257-7751; Palladino, Vito/0000-0002-9786-9620; Teles, Patricia/0000-0001-9029-8506; Fernandez Menendez, Javier/0000-0002-5213-3708; Philippov, Dmitry/0000-0003-4577-6630; Peris, Antonio Delgado/0000-0002-8511-7958; Aarrestad, Thea/0000-0002-7671-243X; Di Florio, Adriano/0000-0003-3719-8041; TOPAKSU, Aysel KAYİS/0000-0001-5819-6913; Zeyrek, Mehmet/0000-0002-9270-7403; Bluj, Michal/0000-0003-1229-1442; Azzurri, Paolo/0000-0002-1717-5654; Tosi, Silvano/0000-0002-7275-9193; Schweiger, Korbinian/0000-0002-5846-3919; Stepennov, Anton/0000-0001-7747-6582; 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Wittich, Peter/0000-0002-7401-2181; Alves, Gilvan/0000-0002-8369-1446; Tully, Christopher/0000-0001-6771-2174; Gritsan, Andrei/0000-0002-3545-7970; Bravo, Cameron/0000-0003-1102-8247; Pastrone, Nadia/0000-0001-7291-1979; Klanner, Robert/0000-0002-7004-9227; Prosper, Harrison Bertrand/0000-0002-4077-2713; Ruiz, Jose/0000-0002-3306-0363; Riccardi, Cristina/0000-0003-0165-3962; Litov, Leandar/0000-0002-8511-6883; Goldstein, Joel/0000-0003-1591-6014; CONTE, Eric/0000-0003-3154-3123; Hurtado Anampa, Kenyi/0000-0002-9779-3566; Ngadiuba, Jennifer/0000-0002-0055-2935; Gozzelino, Andrea/0000-0002-6284-1126; Monaco, Vincenzo/0000-0002-3617-2432; Vami, Tamas Almos/0000-0002-0959-9211; Gavrilov, Vladimir/0000-0002-9617-2928; Fedi, Giacomo/0000-0001-9101-2573; Starling, Elizabeth/0000-0002-4399-7213; Cecchi, Claudia/0000-0002-2192-8233; Dragicevic, Marko/0000-0003-1967-6783; Paganis, Stathes/0000-0002-1950-8993; Reis, Thomas/0000-0003-3703-6624; Malgeri, Luca/0000-0002-0113-7389; CUFFIANI, Marco/0000-0003-2510-5039; 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Martelli, Arabella/0000-0003-3530-2255; Zorbakir, Ibrahim Soner/0000-0002-5962-2221; Lychkovskaya, Natalia/0000-0001-5084-9019; Kasemann, Matthias/0000-0002-0429-2448; Sanz-Becerra, Diego Alejandro/0000-0002-6610-4019; Faltermann, Nils/0000-0001-6506-3107; Poudyal, Nabin/0000-0003-4278-3464 BMWFW (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia); RFBR (Russia); RAEP (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); SFFR (Ukraine); STFC (United Kingdom); DOE (U.S.A.); NSF (U.S.A.); Marie-Curie program; European Research Council; Horizon 2020 Grant (European Union) [675440]; Leventis Foundation; A.P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium) under the Excellence of Science - EOS - be.h project [30820817]; FWO (Belgium) under the Excellence of Science - EOS - be.h project [30820817]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Lendulet (Momentum) Program; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105]; Council of Science and Industrial Research, India; HOMING PLUS program of the Foundation for Polish Science; European Union (Poland); Regional Development Fund (Poland); Mobility Plus program of the Ministry of Science and Higher Education (Poland); National Science Center (Poland) [Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406]; National Priorities Research Program by Qatar National Research Fund; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Thalis program - EU-ESF; Aristeia program - EU-ESF; Greek NSRF; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.); STFC [ST/N000242/1] Funding Source: UKRI BMWFW (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MoST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); COLCIENCIAS (Colombia)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSES (Croatia); CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia)(Estonian Research Council); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); RAEP (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); SFFR (Ukraine)(State Fund for Fundamental Research (SFFR)); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (U.S.A.)(United States Department of Energy (DOE)); NSF (U.S.A.)(National Science Foundation (NSF)); Marie-Curie program(European Commission); European Research Council(European Research Council (ERC)European Commission); Horizon 2020 Grant (European Union); Leventis Foundation; A.P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium) under the Excellence of Science - EOS - be.h project(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium) under the Excellence of Science - EOS - be.h project(FWO); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Lendulet (Momentum) Program; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); HOMING PLUS program of the Foundation for Polish Science; European Union (Poland); Regional Development Fund (Poland); Mobility Plus program of the Ministry of Science and Higher Education (Poland); National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Thalis program - EU-ESF; Aristeia program - EU-ESF; Greek NSRF(Greek Ministry of Development-GSRT); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS - be.h project n. 30820817; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 76 42 42 4 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. AUG 13 2018.0 8 66 10.1007/JHEP08(2018)066 0.0 55 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics GR8TO Green Submitted, Green Accepted, Green Published, gold 2023-03-23 WOS:000443003700001 0
J Zhou, TH; Qiu, DJ; Wu, ZH; Alberti, SAN; Bag, S; Schneider, J; Meyer, J; Gamez, JA; Gieler, M; Reithmeier, M; Seidel, A; Muller-Plathe, F Zhou, Tianhang; Qiu, Dejian; Wu, Zhenghao; Alberti, Simon A. N.; Bag, Saientan; Schneider, Jurek; Meyer, Jan; Gamez, Jose A.; Gieler, Mandy; Reithmeier, Marina; Seidel, Andreas; Mueller-Plathe, Florian Compatibilization Efficiency of Graft Copolymers in Incompatible Polymer Blends: Dissipative Particle Dynamics Simulations Combined with Machine Learning MACROMOLECULES English Article INTERFACIAL PROPERTIES; THERMAL-CONDUCTIVITY; MONTE-CARLO; POLYPROPYLENE; OPPORTUNITIES; BEHAVIOR; IMPACT Graft copolymers are widely used as compatibilizers in homopolymer blends. Computational modeling techniques for predicting the compatibilization efficiency of such polymeric materials have substantially accelerated their development. We employ an efficient particle-based simulation method, namely dissipative particle dynamics (DPD), to systematically investigate the compatibilization efficiency of graft copolymers for a wide range of design parameters such as polymer chemistry, backbone and side chain lengths, and the number of side chains. We find that regular graft copolymers (with regular side chain distribution) exhibit different compatibilization efficiencies at the same areal concentrations. This indicates that the molecular architecture plays a critical role in their compatibilization efficiency. To understand these observations, detailed analysis has been performed. Specifically, the relative shape anisometry of the graft copolymers, which is defined as the ratio of their gyration tensor elements in directions normal and parallel to the surface, is found to be strongly correlated to their compatibilization efficiency. Furthermore, we have investigated three specific graft copolymer types, namely, double-end-grafted (side chains concentrated near both chain ends of the backbone), mid-grafted (side chains concentrated on the center of the backbone), and single-end-grafted (side chains only concentrated near one end of the backbone), to understand the influence of varying side chain distributions. Compared to all other series, the mid-grafted copolymers exhibit the best compatibilization efficiency. Combining the obtained DPD results with five models of machine learning (ML), including linear regression (LR), elastic net (EN), random forest (RF), extra tree (ET), and gradient boosting (GB), provides effective predictions for the compatibilization efficiency. The GB model, which yields the best accuracy, has been further used to acquire the feature importance rank (FIR). Starting from these ML models and the FIR analysis, we have developed a framework for fast predictions of the compatibilization efficiency of graft copolymers. This novel framework utilizes physical insights into effects of material properties, such as chemistries and molecular architectures, on the compatibilization efficiency of graft copolymers and paves the way for advanced design of polymer compatibilizers. [Zhou, Tianhang; Qiu, Dejian; Wu, Zhenghao; Alberti, Simon A. N.; Bag, Saientan; Schneider, Jurek; Mueller-Plathe, Florian] Tech Univ Darmstadt, Eduard Zintl Inst Anorgan & Phys Chem, D-64287 Darmstadt, Germany; [Meyer, Jan; Gamez, Jose A.; Gieler, Mandy; Reithmeier, Marina; Seidel, Andreas] Covestro Deutschland AG, D-51373 Leverkusen, Germany; [Zhou, Tianhang] China Univ Petr, Coll Carbon Neutral Future Technol, Fuxue Rd 18, Beijing 102249, Peoples R China Technical University of Darmstadt; China University of Petroleum Zhou, TH; Muller-Plathe, F (corresponding author), Tech Univ Darmstadt, Eduard Zintl Inst Anorgan & Phys Chem, D-64287 Darmstadt, Germany.;Zhou, TH (corresponding author), China Univ Petr, Coll Carbon Neutral Future Technol, Fuxue Rd 18, Beijing 102249, Peoples R China. zhouth@cup.edu.cn; f.mueller-plathe@theo.chemie.tu-darmstadt.de Müller-Plathe, Florian/A-1922-2010; Bag, Saientan/HPD-2269-2023; wu, zhenghao/AAL-8964-2021 wu, zhenghao/0000-0003-2862-4432; Schneider, Jurek/0000-0002-0286-1678; Alberti, Simon/0000-0002-5361-4124; Bag, Saientan/0000-0003-1000-7719 Covestro AG Covestro AG We thank Covestro AG for funding this work. 52 0 0 23 25 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0024-9297 1520-5835 MACROMOLECULES Macromolecules SEP 13 2022.0 55.0 17 7893 7907 10.1021/acs.macromol.2c00821 0.0 SEP 2022 15 Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Polymer Science 4R6SL 2023-03-23 WOS:000851411900001 0
J Zhou, YD; Liu, H; He, BY; Yang, XQ; Feng, Q; Kutser, T; Chen, F; Zhou, XM; Xiao, F; Kou, JF Zhou, Yadong; Liu, Hui; He, Baoyin; Yang, Xiaoqing; Feng, Qi; Kutser, Tiit; Chen, Feng; Zhou, Xinmeng; Xiao, Fei; Kou, Jiefeng Secchi Depth estimation for optically-complex waters based on spectral angle mapping-derived water classification using Sentinel-2 data INTERNATIONAL JOURNAL OF REMOTE SENSING English Article Classification-based methods for estimating water quality parameter (WQP) using remote sensing have shown great application potential in inland waters. Water classification algorithms have seen progress in water remote sensing. In this paper, we conducted the Secchi Depth value (Z (SD)) estimation based on a global water typology for the Wuhan area. Firstly, we classified the water into seven types using the Sentinel-2 data. The procedure was based on spectral angle mapping (SAM) of the 13 inland water optical types (IWOTs). Afterwards, seven IWOTs were summarized into four categories for Wuhan area. We then developed empirical models for each water category by stepwise multiple linear regression, generalized regression neural network (GRNN), and sparse spectrum Gaussian process regression (SSGPR), and applied the better approaches (GRNN and SSGPR) to three full satellite images (27 October 2018, 10 May 2019, and 29 July 2019). Finally, the retrieved results were validated using in situ-satellite match-ups and compared with the results based on unclassified imagery. With root-mean-square error (RMSE) of three satellite-derived results reduced from 0.32 m (without classification) to 0.16 m (with classification), and mean absolute percentage error (MAPE) reduced from 52% to 18%, from 0.51 m (MAPE = 57%) to 0.19 m (MAPE = 21%), and from 0.18 m (MAPE = 35%) to 0.09 m (MAPE = 17%), Z (SD) estimations over optically complex waters were improved based on this water classification. Due to its low cost and ease of operation, the SAM - derived classification applied in this paper provides a possibility for dynamic and high-precision monitoring for water management. [Zhou, Yadong; Liu, Hui; He, Baoyin; Feng, Qi; Xiao, Fei] Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan, Peoples R China; [Zhou, Yadong; Liu, Hui] Univ Chinese Acad Sci, Beijing, Peoples R China; [Yang, Xiaoqing] Hubei Prov Dept Water Resources, Wuhan, Peoples R China; [Kutser, Tiit] Univ Tartu, Estonian Marine Inst, Tallinn, Estonia; [Chen, Feng] Xiamen Univ Technol, Coll Comp & Informat Engn, Xiamen, Peoples R China; [Chen, Feng] Xiamen Univ Technol, Big Data Inst Digital Nat Disaster Monitoring Fuj, Xiamen, Peoples R China; [Zhou, Xinmeng] Wuhan Environm Monitoring Ctr, Wuhan, Peoples R China; [Kou, Jiefeng] South China Inst Environm Sci, Guangzhou, Peoples R China Chinese Academy of Sciences; Institute of Geodesy & Geophysics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Tartu; Estonian Marine Institute; Xiamen University of Technology; Xiamen University of Technology He, BY (corresponding author), Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan, Peoples R China. heby@whigg.ac.cn Kutser, Tiit/H-1304-2015 Zhou, Yadong/0000-0001-5119-2485 key research project of Water Conservancy in Hubei Province [HBSLKY201910]; Hubei Provincial Natural Science Foundation for Innovation Groups [2019CFA019]; Wuhan Environmental Protection Bureau [10020200916WHSHBJ]; Technological Innovation Special Major Project of Hubei Province [2016ACA168]; Hubei Provincial Department of Water Resources [HBSLKY201910]; Technological Innovation Special Major Project of Hubei Province, Science and Technology Department of Hubei Province [2016ACA168] key research project of Water Conservancy in Hubei Province; Hubei Provincial Natural Science Foundation for Innovation Groups; Wuhan Environmental Protection Bureau; Technological Innovation Special Major Project of Hubei Province; Hubei Provincial Department of Water Resources; Technological Innovation Special Major Project of Hubei Province, Science and Technology Department of Hubei Province This work was funded by the key research project of Water Conservancy in Hubei Province under Grant No. HBSLKY201910; this work was supported in part by Hubei Provincial Natural Science Foundation for Innovation Groups (No. 2019CFA019); this work was funded by the and the government procurement project of Wuhan Environmental Protection Bureau under Grant No.10020200916WHSHBJ and the Technological Innovation Special Major Project of Hubei Province under Grant No. 2016ACA168; Hubei Provincial Department of Water Resources [HBSLKY201910]; Wuhan Environmental Protection Bureau [10020200916WHSHBJ]; Technological Innovation Special Major Project of Hubei Province, Science and Technology Department of Hubei Province [2016ACA168]. 72 3 3 3 37 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. APR 18 2021.0 42.0 8 3123 3145 10.1080/01431161.2020.1868606 0.0 23 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology PZ0FM 2023-03-23 WOS:000612416700001 0
J Fang, HZ; Tan, HW; Kosonen, R; Yuan, XL; Jiang, K; Ding, RR Fang, Haizhou; Tan, Hongwei; Kosonen, Risto; Yuan, Xiaolei; Jiang, Kai; Ding, Renrong Study of the Data Augmentation Approach for Building Energy Prediction beyond Historical Scenarios BUILDINGS English Article energy consumption prediction; data augmentation; data-driven; physical simulation; beyond historical scenarios CONSUMPTION PREDICTION Building energy consumption predictive modeling using data-driven machine learning is currently highly prevalent. However, the model typically performs poorly when the predicted day's energy consumption exceeds the upper bound of the historical data. In this study, energy consumption projections are examined outside of historical boundary scenarios, including three occupancy behavior data (HVAC system, lighting, and equipment) and three operating future scenarios (Scenario 1: utilization rate is highest simultaneously; Scenario 2: energy-saving lighting renovation; Scenario 3: the number of people working is decreased). We propose using data augmentation based on the occupancy behavior (DAOB) method, which expands the building's three occupancy behaviors. The case study showed that, among the three future operating scenario prediction tasks, scenario 1's performance was the least accurate, with an average relative error of 50.21% compared to the DAOB method's average relative error of 7.07%. The average relative error in Scenario 2 decreased from 15.83% to 10.10%. The average relative error in Scenario 3 decreased from 20.97% to 6.5%. This provided an efficient method of combining physical models with data-driven models, which significantly increased robustness and reliability of the model. [Fang, Haizhou; Tan, Hongwei; Yuan, Xiaolei; Jiang, Kai; Ding, Renrong] Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China; [Fang, Haizhou; Kosonen, Risto; Yuan, Xiaolei] Aalto Univ, Sch Mech Engn, Espoo 02150, Finland; [Tan, Hongwei] Tongji Univ, Res Ctr Green Bldg & New Energy, Shanghai 200092, Peoples R China; [Tan, Hongwei] Tongji Univ, UNEP Tongji Inst Environm Sustainable Dev, Shanghai 200092, Peoples R China Tongji University; Aalto University; Tongji University; Tongji University Tan, HW (corresponding author), Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China.;Tan, HW (corresponding author), Tongji Univ, Res Ctr Green Bldg & New Energy, Shanghai 200092, Peoples R China.;Tan, HW (corresponding author), Tongji Univ, UNEP Tongji Inst Environm Sustainable Dev, Shanghai 200092, Peoples R China. hw_tan@tongji.edu.cn 46 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-5309 BUILDINGS-BASEL BUILDINGS-BASEL FEB 2023.0 13.0 2 326 10.3390/buildings13020326 0.0 20 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 9G9EK Green Published, gold 2023-03-23 WOS:000938447200001 0
J Ding, Z; Zhao, LS; Zhou, WH; Bezuijen, A Ding, Zhi; Zhao, Lin-Shuang; Zhou, Wan-Huan; Bezuijen, Adam Intelligent Prediction of Multi-Factor-Oriented Ground Settlement During TBM Tunneling in Soft Soil FRONTIERS IN BUILT ENVIRONMENT English Article tunneling-induced settlement; feature selection; non-parametric; pitching angle; operational factor MAXIMUM SURFACE SETTLEMENT; ARTIFICIAL NEURAL-NETWORKS; SHIELD TUNNELS; SHEARING DISLOCATION; SHALLOW TUNNELS; FACE STABILITY; FINITE-ELEMENT; MODEL; PRESSURE; BEHAVIOR Tunneling-induced ground surface settlement is associated with many complex influencing factors. Beyond factors related to tunnel geometry and surrounding geological conditions, operational factors related to the shield machine are highly significant because of the complexity of shield-soil interactions. Distinguishing the most relevant factors can be very difficult, for all factors seem to affect tunneling-induced settlement to some degree, with none clearly the most influential. In this research, a machine learning method is adopted to intelligently select features related to tunneling-induced ground settlement based on measured data and form a robust non-parametric model with which to make a prediction. The recorded data from a real construction site were compiled and 12 features related to the operational factors were summarized. Using the intelligent method, two other features in addition to cover depth-pitching angle and rolling angle-were distinguished from among the 12 feature candidates as those most influencing the settlement trough. Another new finding is that advance rate does not emerge in the top 10 selected models from the observational data used. The generated non-parametric model was validated by comparing the measured data from the testing dataset and performance on a new dataset. Sensitivity analysis was conducted to evaluate the contribution of each factor. According to the results, engineers in general practice should attend closely to pitching angle during tunnel excavation in soft soil conditions. [Ding, Zhi] Zhejiang Univ City Coll, Sch Engn, Dept Civil & Environm Engn, Hangzhou, Peoples R China; [Ding, Zhi; Zhou, Wan-Huan] Univ Macau, Dept Civil & Environm Engn, State Key Lab Internet Things Smart City, Macau Sar, Peoples R China; [Zhao, Lin-Shuang] Shantou Univ, Coll Engn, Dept Civil & Environm Engn, MOE Key Lab Intelligence Mfg Technol, Shantou, Peoples R China; [Bezuijen, Adam] Univ Ghent, Fac Engn & Architecture, Dept Civil Engn, Lab Geotech, Ghent, Belgium; [Bezuijen, Adam] Deltares, Delft, Netherlands Zhejiang University City College; University of Macau; Shantou University; Ghent University; Deltares Zhao, LS (corresponding author), Shantou Univ, Coll Engn, Dept Civil & Environm Engn, MOE Key Lab Intelligence Mfg Technol, Shantou, Peoples R China. lshzhao@stu.edu.cn Chinese National Natural Science Foundation [42102308, 51508506]; Research Funding of Shantou University for New Faculty Member [STKJ2021168]; Special Fund for Science and Technology of Guangdong Province [SKL-IOTSC-2018-2020]; Science and Technology Development Fund, Macau SAR [FDCT/0035/2019/A1, MYRG 2018-00173-FST]; University of Macau Research Fund [LHZ20E080001]; Joint Fund of Zhejiang Provincial Natural Science Foundation [NTF21008-2021] Chinese National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Research Funding of Shantou University for New Faculty Member; Special Fund for Science and Technology of Guangdong Province; Science and Technology Development Fund, Macau SAR; University of Macau Research Fund; Joint Fund of Zhejiang Provincial Natural Science Foundation The authors wish to thank the support funded by the Chinese National Natural Science Foundation (No. 42102308 and 51508506), the Research Funding of Shantou University for New Faculty Member (Grant No. NTF21008-2021), the Special Fund for Science and Technology of Guangdong Province in 2021 (STKJ2021168), the Science and Technology Development Fund, Macau SAR (File no. SKL-IOTSC-2018-2020 and FDCT/0035/2019/A1), the University of Macau Research Fund (MYRG 2018-00173-FST), and the Joint Fund of Zhejiang Provincial Natural Science Foundation (LHZ20E080001). 76 1 1 4 11 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2297-3362 FRONT BUILT ENVIRON Front. Built Environ. APR 5 2022.0 8.0 848158 10.3389/fbuil.2022.848158 0.0 16 Construction & Building Technology; Engineering, Civil Emerging Sources Citation Index (ESCI) Construction & Building Technology; Engineering 1B8OK Green Published, gold 2023-03-23 WOS:000792691600001 0
J Li, ZL; Tang, BH; Wu, H; Ren, HZ; Yan, GJ; Wan, ZM; Trigo, IF; Sobrino, JA Li, Zhao-Liang; Tang, Bo-Hui; Wu, Hua; Ren, Huazhong; Yan, Guangjian; Wan, Zhengming; Trigo, Isabel F.; Sobrino, Jose A. Satellite-derived land surface temperature: Current status and perspectives REMOTE SENSING OF ENVIRONMENT English Review Land surface temperature; Land surface emissivity; Retrieval; Thermal infrared THERMAL-INFRARED DATA; SPLIT-WINDOW ALGORITHM; TRACK SCANNING RADIOMETER; HIGH-RESOLUTION RADIOMETER; NEURAL-NETWORK TECHNIQUE; IN-FLIGHT VALIDATION; EMISSIVITY RETRIEVAL; ATMOSPHERIC CORRECTION; SINGLE-CHANNEL; WATER-VAPOR Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested. (c) 2012 Elsevier Inc. All rights reserved. [Li, Zhao-Liang; Tang, Bo-Hui; Wu, Hua] Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Li, Zhao-Liang] CNRS, UdS, LSIIT, Blvd Sebastien Brant,BP10413, F-67412 Illkirch Graffenstaden, France; [Ren, Huazhong; Yan, Guangjian] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Wan, Zhengming] Univ Calif Santa Barbara, ERI, Santa Barbara, CA 93106 USA; [Trigo, Isabel F.] Inst Portugues Mar & Atmosfera, Lisbon, Portugal; [Trigo, Isabel F.] Univ Lisbon, Inst Dom Luiz, P-1699 Lisbon, Portugal; [Sobrino, Jose A.] Univ Valencia, Image Proc Lab, Valencia 46071, Spain Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Beijing Normal University; University of California System; University of California Santa Barbara; Instituto Portugues do Mar e da Atmosfera; Universidade de Lisboa; University of Valencia Li, ZL (corresponding author), CNRS, UdS, LSIIT, Blvd Sebastien Brant,BP10413, F-67412 Illkirch Graffenstaden, France. lizl@igsnrr.ac.cn Wu, Hua/G-9271-2012; Trigo, Isabel/H-1630-2011; Sobrino, José A./M-1585-2014 Wu, Hua/0000-0002-5982-8422; Trigo, Isabel/0000-0001-8640-9170; Sobrino, José A./0000-0003-3787-9373; Yan, Guangjian/0000-0001-5030-748X Hi-Tech Research and Development Program of China (863 Plan Program) [2012AA12A304]; National Natural Science Foundation of China [41231170, 41171287]; State Key Laboratory of Resources and Environmental Information System [088RA800KA] Hi-Tech Research and Development Program of China (863 Plan Program)(National High Technology Research and Development Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Resources and Environmental Information System The authors would like to thank Professor A.R. Gillespie and other reviewers for their valuable and stimulating comments that have greatly improved the paper. The authors are greatly indebted to Dr. S.J. Hook for reading carefully the draft of this paper and for giving many useful suggestions for improving its presentation. This work was supported by the Hi-Tech Research and Development Program of China (863 Plan Program) under grant no. 2012AA12A304, by the National Natural Science Foundation of China under grant no. 41231170 and 41171287, and by the State Key Laboratory of Resources and Environmental Information System under grant 088RA800KA. 218 1202 1287 54 590 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. APR 15 2013.0 131.0 14 37 10.1016/j.rse.2012.12.008 0.0 24 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology 098JZ hybrid 2023-03-23 WOS:000315546900002 0
J Lin, D; Vasilakos, AV; Tang, Y; Yao, YZ Lin, Di; Vasilakos, Athanasios V.; Tang, Yu; Yao, Yuanzhe Neural networks for computer-aided diagnosis in medicine: A review NEUROCOMPUTING English Review Algorithms; Artificial neural network (ANN); Computer-aided medical diagnosis (CAMD) INTERSTITIAL LUNG-DISEASE; DIFFERENTIAL-DIAGNOSIS; FORMAL METHODS; CARE; MODEL; MANAGEMENT; KNOWLEDGE; SYSTEMS; INTELLIGENCE; TECHNOLOGIES This survey makes an overview of the most recent applications on the neural networks for the computer aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate the automation of decision making, extraction and visualization of complex characteristics for clinical diagnosis purposes. Over the past decade, neural networks have attained considerable research interest and are widely employed to complex CAMD systems in diverse clinical application domains, such as detecting diseases, classification of diseases, testing the compatibility of new drugs, etc. Overall, this paper reviews the state-of-the-art of neural networks for CAMD. It helps the readers understand the topic of neural networks for CAMD by summarizing the findings addressed in recent academic papers as well as presenting a few open issues of developing the research on this topic. (C) 2016 Elsevier B.V. All rights reserved. [Lin, Di; Tang, Yu; Yao, Yuanzhe] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Peoples R China; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Skelleftea, Sweden University of Electronic Science & Technology of China; Lulea University of Technology Lin, D (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Peoples R China. abu3163abu@yahoo.co.uk Vasilakos, Athanasios/J-2824-2017 Vasilakos, Athanasios/0000-0003-1902-9877 National Natural Science Foundation of China [61370202]; Fundamental Research Funds for the Central Universities [ZYGX2015J071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was partially supported by the National Natural Science Foundation of China (No. 61370202), and partially supported by the Fundamental Research Funds for the Central Universities (No. ZYGX2015J071). 144 49 52 2 55 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing DEC 5 2016.0 216.0 700 708 10.1016/j.neucom.2016.08.039 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science ED3VU 2023-03-23 WOS:000388777400066 0
J Alves, A; Freitas, FF Alves, Alexandre; Freitas, Felipe F. Towards recognizing the light facet of the Higgs boson MACHINE LEARNING-SCIENCE AND TECHNOLOGY English Article Higgs boson; light jets; convolutional neural networks; ensemble learning ENERGY The Higgs boson couplings to bottom and top quarks have been measured and agree well with the Standard Model predictions. Decays to lighter quarks and gluons, however, remain elusive. Observing these decays is essential to complete the picture of the Higgs boson interactions. In this work, we present the perspectives for the 14 TeV LHC to observe the Higgs boson decay to gluon jets assembling convolutional neural networks, trained to recognize abstract jet images constructed embodying particle flow information, and boosted decision trees with kinetic information from Higgs-strahlung ZH -> l(+)l(-) + gg events. We show that this approach might be able to observe Higgs to gluon decays with a significance of around 2.4 sigma improving significantly previous prospects based on cut-and-count analysis. An upper bound of BR(H -> gg)<= 1.74 x BRSM (H -> gg) at 95% confidence level after 3000 fb(-1) of data is obtained using these machine learning techniques. [Alves, Alexandre] Univ Fed Sao Paulo UNIFESP, Dept Fis, Diadema, Brazil; [Freitas, Felipe F.] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China; [Freitas, Felipe F.] Univ Aveiro, Dept Fis, Campus Santiago, P-3810183 Aveiro, Portugal; [Freitas, Felipe F.] CIDMA, Campus Santiago, P-3810183 Aveiro, Portugal Universidade Federal de Sao Paulo (UNIFESP); Chinese Academy of Sciences; Institute of Theoretical Physics, CAS; Universidade de Aveiro; Universidade de Aveiro Alves, A (corresponding author), Univ Fed Sao Paulo UNIFESP, Dept Fis, Diadema, Brazil. aalves@unifesp.br; felipefreitas@ua.pt Alves, Alexandre/K-1469-2013 Alves, Alexandre/0000-0002-2454-7874; Ferreira de Freitas, Felipe/0000-0003-3270-3395 Conselho Nacional de Desenvolvimento Cientifico (CNPq) [307265/2017-0]; China Postdoctoral Science Foundation [Y8Y2411B11]; Higgs Phenomenology to the Unification of Fundamental Interactions [PTDC/FIS-PAR/31000/2017, BPD-32 (19661/2019)]; Fundação para a Ciência e a Tecnologia [PTDC/FIS-PAR/31000/2017] Funding Source: FCT Conselho Nacional de Desenvolvimento Cientifico (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Higgs Phenomenology to the Unification of Fundamental Interactions; Fundação para a Ciência e a Tecnologia AA thanks Conselho Nacional de Desenvolvimento Cientifico (CNPq) for its financial support, grant 307265/2017-0. FFF is partially supported by the China Postdoctoral Science Foundation project Y8Y2411B11 and the project From Higgs Phenomenology to the Unification of Fundamental Interactions PTDC/FIS-PAR/31000/2017 grant BPD-32 (19661/2019). FFF thanks Prof. C. Herdeiro and Prof. A. P. Morais for the hospitality during his stay at Aveiro university. AA would like to thank Tilman Plehn, Gregor Kasieczka, Juan Gonzales-Fraile and Anja Butter and for helpful discussions in the early stages of this project. 73 4 4 1 1 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 2632-2153 MACH LEARN-SCI TECHN Mach. Learn.-Sci. Technol. DEC 2020.0 1.0 4 45025 10.1088/2632-2153/aba8e6 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Science & Technology - Other Topics VK7YC Green Published, Green Accepted, gold 2023-03-23 WOS:000754876300028 0
J Shao, HD; Ding, ZY; Cheng, JS; Jiang, HK Shao Haidong; Ding Ziyang; Cheng Junsheng; Jiang Hongkai Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO ISA TRANSACTIONS English Article Intelligent fault diagnosis; Different rotating machines; Novel stacked transfer auto-encoder; Parameter transfer strategy; Particle swarm optimization PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORK; AUTOENCODER; ALGORITHM; ENTROPY; VOLTAGE; SYSTEM Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved. [Shao Haidong; Ding Ziyang; Cheng Junsheng] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China; [Shao Haidong] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Jiang Hongkai] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China Hunan University; Lulea University of Technology; Northwestern Polytechnical University Shao, HD (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China. ndshao@hnu.eau.cn Cheng, Junsheng/HKE-6532-2023; Shao, Haidong/AAE-5814-2022 Shao, Haidong/0000-0002-8018-1774 National Natural Science Foundation of China [51905160] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The study of this paper was funded by the National Natural Science Foundation of China (No. 51905160). 55 63 65 13 63 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-0578 1879-2022 ISA T ISA Trans. OCT 2020.0 105.0 308 319 10.1016/j.isatra.2020.05.041 0.0 12 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation OC9QI 32473735.0 2023-03-23 WOS:000579489500025 0
J Monachesi, P; Witteborn, S Monachesi, Paola; Witteborn, Saskia Building the sustainable city through Twitter: Creative skilled migrants and innovative technology use TELEMATICS AND INFORMATICS English Article Creative migrants; Smart city; Social media; Sustainability; Technology; Twitter SMART CITIES; BIG DATA; KNOWLEDGE-WORKERS; MIGRATION; MOBILITY; REGIME; MEDIA We investigate the role of creative skilled migrants in broadcasting an alternative use of technology in support of a sustainable smart city. We do so by analyzing the themes they produced on Twitter. We focus on Amsterdam as a case, and urban planners and designers as examples of creative migrants. Computational methodology allowed for a selection of naturally occurring data in social media. We show that the creative migrants actively contribute to shaping the smart-sustainable city through the themes of top-down technological solutions and bottom-up participation by highlighting innovative uses of technology in support of the environment and citizens' needs. However, the migrants do not question received historical and geopolitical power constellations. Moreover, they propose the Western city as a role model for solving pressing urban problems. [Monachesi, Paola] Univ Utrecht, Dept Languages Literature & Commun, Utrecht, Netherlands; [Witteborn, Saskia] CUHK, Sch Journalism & Commun, Hong Kong, Peoples R China Utrecht University; Chinese University of Hong Kong Monachesi, P (corresponding author), Univ Utrecht, Dept Languages Literature & Commun, Utrecht, Netherlands. P.Monachesi@uu.nl; sawit@cuhk.edu.hk Witteborn, Saskia/ABQ-6594-2022 Utrecht University Utrecht University The study received financial support from Utrecht University. The authors wish to thank Marcia Goncalves and Laurens Wes for their assistance with the data collection as well as Marina Turco, Rolien Hoyng, Andrea Morrison, and Rense Corten for discussions on aspects of the paper. 92 6 6 7 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0736-5853 TELEMAT INFORM Telemat. Inform. MAY 2021.0 58.0 101531 10.1016/j.tele.2020.101531 0.0 10 Information Science & Library Science Social Science Citation Index (SSCI) Information Science & Library Science SH3RK hybrid, Green Published 2023-03-23 WOS:000654053900010 0
J Wang, J; Li, H; Zhou, XL; Gao, XZ; Wang, M Wang, Jing; Li, Hui; Zhou, Xiuling; Gao, Xiao-Zhi; Wang, Ming A study of hepatic fibrosis staging methods using diffraction enhanced imaging EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING English Article Hepatic fibrosis staging; Diffraction-enhanced imaging; Feature fusion; Machine learning LIVER-BIOPSY; CT; CLASSIFICATION The early hepatic fibrosis staging is very important for timely diagnosis, prognosis, and treatment of all chronic liver diseases. Diffraction-enhanced imaging, which can provide much more information on soft tissue morphology than conventional absorption radiography, might be a potential noninvasive technique to diagnose and stage hepatic fibrosis. This paper presents different feature extraction strategies and classification methods to automatically classify hepatic fibrosis using diffraction-enhanced imaging images. Texture features are obtained using a total of three methods including first order feature, gray level co-occurrence matrix, and grayscale gradient co-occurrence matrix. The fusion of these texture features is also studied. The principal component analysis is used to reduce the dimension of the features and redundant information among data. The features are classified using two popular classification techniques, namely, K-nearest neighbors and support vector machines. On the basis of the comparison of different feature strategies and classification methods, we can identify the suitable methods for grading hepatic fibrosis. The proposed approach efficiently classifies the hepatic fibrosis DEI images into four classes with the highest classification accuracy of 99.99%. We further demonstrate the potential of the DEI images in staging hepatic fibrosis. [Wang, Jing; Wang, Ming] Peking Univ, Dept Hlth Informat Management, Hlth Sci Ctr, Beijing 100191, Peoples R China; [Li, Hui] Peking Univ, Dept Med Technol, Hlth Sci Ctr, Beijing 100191, Peoples R China; [Zhou, Xiuling] Beijing City Univ, Dept Technol & Ind Dev, Beijing 100083, Peoples R China; [Gao, Xiao-Zhi] Univ Eastern Finland, Sch Comp, Kuopio, Finland Peking University; Peking University; University of Eastern Finland Wang, J (corresponding author), Peking Univ, Dept Hlth Informat Management, Hlth Sci Ctr, Beijing 100191, Peoples R China. wang_jing@bjmu.edu.cn interdisciplinary medicine Seed Fund of Peking University [BMU2018MX007]; Beijing Natural Science Foundation of China [4162027, 7142084]; National Natural Science Foundation of China (NSFC) [51875113] interdisciplinary medicine Seed Fund of Peking University; Beijing Natural Science Foundation of China(Beijing Natural Science Foundation); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is supported by the grants from the interdisciplinary medicine Seed Fund of Peking University (Project No. BMU2018MX007), Beijing Natural Science Foundation of China (Project No. 4162027 and 7142084), and Xiao-Zhi Gao's research work was also partially supported by the National Natural Science Foundation of China (NSFC) under Grant 51875113. 30 0 0 1 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1687-5176 1687-5281 EURASIP J IMAGE VIDE EURASIP J. Image Video Process. AUG 6 2020.0 2020.0 1 32 10.1186/s13640-020-00520-8 0.0 17 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Imaging Science & Photographic Technology MW5LH gold 2023-03-23 WOS:000557077600001 0
J Wu, MH; Gao, YB; Jung, A; Zhang, Q; Du, ST Wu, Menghao; Gao, Yanbin; Jung, Alexander; Zhang, Qiang; Du, Shitong The Actor-Dueling-Critic Method for Reinforcement Learning SENSORS English Article reinforcement learning; continuous control; DDPG; dueling network; advantage NAVIGATION Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments. [Wu, Menghao; Gao, Yanbin; Zhang, Qiang; Du, Shitong] Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China; [Wu, Menghao; Jung, Alexander] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland Harbin Engineering University; Aalto University Gao, YB (corresponding author), Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China. wumenghao@hrbeu.edu.cn; gaoyanbin@hrbeu.edu.cn; alexander.jung@aalto.fi; 18846425693@hrbeu.edu.cn; dushitong@hrbeu.edu.cn Jung, Alexander/M-4407-2016 Jung, Alexander/0000-0001-7538-0990 China Scholarship Council (CSC) [201706680063]; National Natural Science Foundation of China (NSFC) [61803118] China Scholarship Council (CSC)(China Scholarship Council); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) Menghao Wu is sponsored by the China Scholarship Council (CSC) grant number 201706680063 for his joint Ph.D. research program at Aalto University, Finland. This work was partially supported by the National Natural Science Foundation of China (NSFC) grant number 61803118. 57 5 7 0 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 1 2019.0 19.0 7 1547 10.3390/s19071547 0.0 20 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HU8WA 30935035.0 Green Submitted, gold, Green Published, Green Accepted 2023-03-23 WOS:000465570700066 0
J Sawadwuthikul, G; Tothong, T; Lodkaew, T; Soisudarat, P; Nutanong, S; Manoonpong, P; Dilokthanakul, N Sawadwuthikul, Guntitat; Tothong, Tanyatep; Lodkaew, Thanawat; Soisudarat, Puchong; Nutanong, Sarana; Manoonpong, Poramate; Dilokthanakul, Nat Visual Goal Human-Robot Communication Framework With Few-Shot Learning: A Case Study in Robot Waiter System IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Robots; Visualization; Feature extraction; Robot kinematics; Target recognition; Entropy; Uncertainty; Active learning; human-robot communications; human-robot interaction; imbalanced dataset; transfer learning; visual goal A conventional adopted method for operating a waiter robot is based on the static position control, where predefined goal positions are marked on a map. However, this solution is not optimal in a dynamic setting, such as in a coffee shop or an outdoor catering event, because the customers often change their positions. This article explores an alternative human-robot interface design where a human operator communicates the identity of the customer to the robot instead. Inspired by how human communicates, we propose a framework for communicating a visual goal to the robot, through interactive two-way communications. The framework exploits concepts from two machine learning domains: human-in-the-loop machine learning, where active learning is used to acquire informative data, and deep metric learning, where a suitable embedding can improve the learning ability of a classifier. We also propose novel class imbalance handling techniques, which aim to actively alleviate the class imbalance problem found to be important in this mode of communication. The framework is evaluated using publicly available pedestrian datasets. We demonstrate that the proposed framework can help reduce the number of required two-way interactions and increases the robustness of the predictive model. We successfully implement the framework on a mobile robot for a delivery service in a cafe-like environment. Through the online visual goal human-robot communication, the robot can detect, recognize, and autonomously navigate to the target customer. [Sawadwuthikul, Guntitat] Vidyasirimedhi Inst Sci & Technol, Kamnoetvidya Sci Acad, Rayong 21210, Thailand; [Sawadwuthikul, Guntitat] Korea Adv Inst Sci & Technol, Daejeon 34141, South Korea; [Lodkaew, Thanawat] KASIKORN Business Technol Grp, Bangkok 11120, Thailand; [Tothong, Tanyatep; Soisudarat, Puchong; Nutanong, Sarana; Manoonpong, Poramate; Dilokthanakul, Nat] Vidyasirimedhi Inst Sci & Technol, Sch Informat Sci & Technol, Rayong 21210, Thailand; [Manoonpong, Poramate] Nanjing Univ Aeronaut & Astronaut, Nanjing 210016, Peoples R China; [Manoonpong, Poramate] Univ Southern Denmark, DK-5230 Odense, Denmark Vidyasirimedhi Institute of Science & Technology; Korea Advanced Institute of Science & Technology (KAIST); Vidyasirimedhi Institute of Science & Technology; Nanjing University of Aeronautics & Astronautics; University of Southern Denmark Dilokthanakul, N (corresponding author), Vidyasirimedhi Inst Sci & Technol, Sch Informat Sci & Technol, Rayong 21210, Thailand. guntitats@gmail.com; tanyatep.tot@gmail.com; lodkaew.thanawat@gmail.com; puchong.s_s18@vistec.ac.th; snutanon@vistec.ac.th; poramate.m@vistec.ac.th; natd_pro@vistec.ac.th Manoonpong, Poramate/Y-8479-2018 Manoonpong, Poramate/0000-0002-4806-7576; Dilokthanakul, Nat/0000-0001-7721-7564; Sawadwuthikul, Guntitat/0000-0002-8200-5475 PTT Robotics AI, and Intelligent Solutions via the VIS-RA project; VISTEC PTT Robotics AI, and Intelligent Solutions via the VIS-RA project; VISTEC This work was supported in part by the PTT Robotics AI, and Intelligent Solutions via the VIS-RA project (P.M., the project coordinator) and in part by the startup grant on Bioinspired Robotics of VISTEC (P.M., the project PI). Paper no. TII-20-5287. 28 6 6 9 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. MAR 2022.0 18.0 3 1883 1891 10.1109/TII.2021.3049831 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering XL5OS 2023-03-23 WOS:000728195600046 0
J Hu, R; Hu, WH; Gokmen, N; Li, PF; Huang, Q; Chen, Z Hu, Rui; Hu, Weihao; Gokmen, Nuri; Li, Pengfei; Huang, Qi; Chen, Zhe High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map RENEWABLE ENERGY English Article Wind speed forecasting; Short-term; High-resolution; Phase space reconstruction; Paralleling computing; Self-organizing map EMBEDDING DIMENSION Wind power has already brought lots of benefits for energy market and environment. To promote the utilization of wind energy further and achieve the ambitious goals of applying sustainable energy, the improvement of the performance of wind speed forecasting will play a significant role. The whole system will benefit from an accurate wind forecasting system with high resolution. This paper focused on short-term wind speed forecasting and proposed a method providing the wind speed forecasting with short-term horizon and high resolution. In this paper, a novel method based on the wavelet decomposition, the phase space reconstruction and the self-organizing map (SOM) artificial neural network was proposed. Study case was provided to demonstrate the performance of the proposed forecasting method. Comparative results from several classic forecasting methods were provided as well. Based on the outcomes of study cases and comparison results, it can be concluded that the proposed method performed well in short term forecasting horizon. (C) 2019 Elsevier Ltd. All rights reserved. [Hu, Rui; Gokmen, Nuri; Li, Pengfei; Chen, Zhe] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark; [Hu, Weihao; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Sichuan, Peoples R China Aalborg University; University of Electronic Science & Technology of China Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Sichuan, Peoples R China. rhu@et.aau.dk; whu@uestc.edu.cn; ngo@et.aau.dk; pli@et.aau.dk; hwong@uestc.edu.cn; zch@et.aau.dk Gökmen, Nuri/G-4287-2013; Hu, Weihao/AAE-7945-2019 Gökmen, Nuri/0000-0001-5495-4465; Hu, Weihao/0000-0002-7019-7289; Chen, Zhe/0000-0002-2919-4481; Huang, Qi/0000-0002-8637-0269 National Natural Science Foundation of China [51707029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (51707029). 53 32 34 3 56 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-1481 RENEW ENERG Renew. Energy SEP 2019.0 140.0 17 31 10.1016/j.renene.2019.03.041 0.0 15 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels HV8RJ 2023-03-23 WOS:000466250700003 0
J Pawlak, M; Pejcha, O; Jakubcik, P; Jayasinghe, T; Kochanek, CS; Stanek, KZ; Shappee, BJ; Holoien, TWS; Thompson, TA; Prieto, JL; Dong, S; Shields, JV; Pojmanski, G; Britt, CA; Will, D Pawlak, Michal; Pejcha, O.; Jakubcik, P.; Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Shappee, B. J.; Holoien, T. W-S; Thompson, Todd A.; Prieto, J. L.; Dong, S.; Shields, J. V.; Pojmanski, G.; Britt, C. A.; Will, D. The ASAS-SN catalogue of variable stars - IV. Periodic variables in the APOGEE survey MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article catalogues; surveys; stars: variables: general RR LYRAE STARS; DIGITAL SKY SURVEY; MACHO PROJECT LMC; OGLE COLLECTION; ECLIPSING BINARIES; CLASSIFICATION; CEPHEIDS; INVENTORY; ALGORITHM; DISTANCE We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RRLyrae, and delta Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects. [Pawlak, Michal; Pejcha, O.; Jakubcik, P.] Charles Univ Prague, Inst Theoret Phys, Fac Math & Phys, Prague 18000, Czech Republic; [Jakubcik, P.] Univ Oxford, New Coll, Holywell St, Oxford OX1 3BN, England; [Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Thompson, Todd A.; Shields, J. V.; Will, D.] Ohio State Univ, Dept Astron, 174 West 18Th Ave, Columbus, OH 43210 USA; [Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Thompson, Todd A.] Ohio State Univ, Ctr Cosmol & Astroparticle Phys, 191 W Woodruff Ave, Columbus, OH 43210 USA; [Shappee, B. J.] Univ Hawaii, Inst Astron, 2680 Woodlawn Dr, Honolulu, HI 96822 USA; [Holoien, T. W-S] Carnegie Observ, 813 Santa Barbara St, Pasadena, CA 91101 USA; [Thompson, Todd A.] Inst Adv Study, Princeton, NJ 08540 USA; [Prieto, J. L.] Univ Diego Port, Fac Ingn & Ciencias, Nucleo Astron, Av Ejercito 441, Santiago, Chile; [Prieto, J. L.] Millennium Inst Astrophys, Santiago, Chile; [Dong, S.] Peking Univ, Kavli Inst Astron & Astrophys, Yi He Yuan Rd 5, Hai Dian Dist, Peoples R China; [Pojmanski, G.] Univ Warsaw Observ, Al Ujazdowskie 4, PL-00478 Warsaw, Poland; [Britt, C. A.; Will, D.] ASC Technol Serv, Mendenhall Lab 433, 125 South Oval Mall, Columbus, OH 43210 USA Charles University Prague; University of Oxford; University System of Ohio; Ohio State University; University System of Ohio; Ohio State University; University of Hawaii System; Carnegie Institution for Science; Institute for Advanced Study - USA; University Diego Portales; Peking University; University of Warsaw; Warsaw University Observatory Pawlak, M; Pejcha, O (corresponding author), Charles Univ Prague, Inst Theoret Phys, Fac Math & Phys, Prague 18000, Czech Republic. michal.pawlak@utf.mff.cuni.cz Pawlak, Michał/AGD-5351-2022; Dong, Subo/AIB-2671-2022 Pawlak, Michał/0000-0002-5632-9433; Dong, Subo/0000-0002-1027-0990; Shappee, Benjamin/0000-0003-4631-1149; Jakubcik, Petr/0000-0002-2977-1189; Shields, Joshua/0000-0002-1560-5286; Holoien, Thomas/0000-0001-9206-3460; Grzegorz, Pojmanski/0000-0002-6495-0676 Charles University in Prague [PRIMUS/SCI/17]; INTER-EXCELLENCE grant from the Czech Ministry of Education, Youth, and Sports [LTAUSA18093]; Horizon 2020ERCStartingGrant 'Cat-In-hAT' [803158]; Gordon and Betty Moore Foundation [GBMF5490]; NSF [AST-1515927, AST-0908816, AST-1515876, AST-181440]; Mt. Cuba Astronomical Foundation; Center for Cosmology and AstroParticle Physics at the Ohio State University; Chinese Academy of Sciences South America Center for Astronomy (CAS-SACA); Villum Foundation; George Skestos; Research Corporation [24216]; Simons Foundation Fellowship from the Institute for Advanced Study, Princeton; IBM Einstein Fellowship from the Institute for Advanced Study, Princeton; FONDECYT [1151445]; Ministry of Economy, Development, and Tourism's Millennium Science Initiative [IC120009]; NSFC [11573003]; Robert Martin Ayers Sciences Fund; National Aeronautics and Space Administration; National Science Foundation; Alfred P. Sloan Foundation; US Department of Energy; Japanese Monbukagakusho; Max Planck Society; University of Chicago; Fermilab; Institute for Advanced Study; Japan Participation Group; Johns Hopkins University; Los Alamos National Laboratory; MaxPlanck-Institute for Astronomy (MPIA); Max-Planck-Institute for Astrophysics (MPA); New Mexico State University; University of Pittsburgh; Princeton University; United States Naval Observatory; University of Washington Charles University in Prague; INTER-EXCELLENCE grant from the Czech Ministry of Education, Youth, and Sports; Horizon 2020ERCStartingGrant 'Cat-In-hAT'; Gordon and Betty Moore Foundation(Gordon and Betty Moore Foundation); NSF(National Science Foundation (NSF)); Mt. Cuba Astronomical Foundation; Center for Cosmology and AstroParticle Physics at the Ohio State University(Ohio State University); Chinese Academy of Sciences South America Center for Astronomy (CAS-SACA); Villum Foundation(Villum Fonden); George Skestos; Research Corporation(Research Corporation for Science Advancement); Simons Foundation Fellowship from the Institute for Advanced Study, Princeton; IBM Einstein Fellowship from the Institute for Advanced Study, Princeton(International Business Machines (IBM)); FONDECYT(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT); Ministry of Economy, Development, and Tourism's Millennium Science Initiative; NSFC(National Natural Science Foundation of China (NSFC)); Robert Martin Ayers Sciences Fund; National Aeronautics and Space Administration(National Aeronautics & Space Administration (NASA)); National Science Foundation(National Science Foundation (NSF)); Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); US Department of Energy(United States Department of Energy (DOE)); Japanese Monbukagakusho(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Max Planck Society(Max Planck SocietyFoundation CELLEX); University of Chicago(University of Chicago); Fermilab; Institute for Advanced Study(Institute for Advanced Study); Japan Participation Group(Sloan Digital Sky Survey (SDSS)); Johns Hopkins University(Johns Hopkins University); Los Alamos National Laboratory(United States Department of Energy (DOE)Los Alamos National Laboratory); MaxPlanck-Institute for Astronomy (MPIA); Max-Planck-Institute for Astrophysics (MPA); New Mexico State University; University of Pittsburgh(University of Pittsburgh); Princeton University(Princeton University); United States Naval Observatory; University of Washington(University of Washington) The work of MP, OP, and PJ has been supported by the PRIMUS/SCI/17 award fromCharles University in Prague, INTER-EXCELLENCE grant LTAUSA18093 from the Czech Ministry of Education, Youth, and Sports, and Horizon 2020ERCStartingGrant 'Cat-In-hAT' (grant agreement #803158).; ASAS-SN is supported by the Gordon and Betty Moore Foundation through grant GBMF5490 to the Ohio State University and NSF grant AST-1515927. Development of ASAS-SN has been supported by NSF grant AST-0908816, theMt. Cuba Astronomical Foundation, the Center for Cosmology and AstroParticle Physics at the Ohio State University, the Chinese Academy of Sciences South America Center for Astronomy (CAS-SACA), the Villum Foundation, and George Skestos.; CSK is supported by NSF grants AST-1515876, AST-1515927, and AST-181440. TAT is supported in part by Scialog Scholar grant 24216 from the Research Corporation. TAT acknowledges support from a Simons Foundation Fellowship and from an IBM Einstein Fellowship from the Institute for Advanced Study, Princeton. Support for JLP is provided in part by FONDECYT through the grant 1151445 and by the Ministry of Economy, Development, and Tourism's Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS. SD acknowledges Project 11573003 supported by NSFC.; This research was made possible through the use of the AAVSO Photometric All-Sky Survey (APASS), funded by the Robert Martin Ayers Sciences Fund. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.; Funding for the Sloan Digital Sky Survey (SDSS) has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the US Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society. The SDSS Web site is http://www.sdss.org/.; The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are The University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, The Johns Hopkins University, Los Alamos National Laboratory, the MaxPlanck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, University of Pittsburgh, Princeton University, the United States Naval Observatory, and the University of Washington.; This work has made use of data from the European Space Agency (ESA) mission Gaia, processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. 81 21 21 0 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. AUG 2019.0 487.0 4 5932 5945 10.1093/mnras/stz1681 0.0 14 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics IJ4QK Green Submitted, Bronze 2023-03-23 WOS:000475888500111 0
J Sun, HJ; Zhang, JX; Karimi, HR Sun, Hui-Jie; Zhang, Jinxiu; Karimi, Hamid Reza Optimal control of Markovian jump systems via a neural network-based ADP iterative algorithm q NEUROCOMPUTING English Article Optimal control; Markovian jump systems; ADP; Value iteration TIME Adaptive dynamic programming (ADP) technique is adopted in this work to investigate the optimal control problem of Markovian jump systems. By utilizing Bellman's optimality principle, a discrete Hamilton Jacobi Bellman (HJB) equation is established to design the optimal controller for the system under consideration. Then, based on value iteration, a new ADP algorithm is proposed for finding the solution of the established HJB equation. It is proven that the iterative solution sequence generated by the developed ADP iterative approach under zero initial values is monotonically convergent. Neural networks are constructed to accomplish the presented value iteration ADP algorithm. At last, simulation researches for two Markovian jump systems demonstrate the effectiveness of the proposed optimal control method. (c) 2021 Elsevier B.V. All rights reserved. [Sun, Hui-Jie; Zhang, Jinxiu] Sun Yat Sen Univ, Guangzhou 510000, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, Milan, Italy Sun Yat Sen University; Polytechnic University of Milan Zhang, JX (corresponding author), Sun Yat Sen Univ, Guangzhou 510000, Peoples R China. National Natural Science Foundation of China [61903103] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China under Grant No. 61903103. 23 2 2 7 20 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 11 2022.0 468.0 441 451 10.1016/j.neucom.2021.09.059 0.0 NOV 2021 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WY5RU 2023-03-23 WOS:000719336800007 0
J Liu, JL; Lughofer, E; Zeng, XY; Li, ZX Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi; Li, Zhengxin The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article MODEL; APPRECIATION How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models arc also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features. [Liu, Jianli] Jiangnan Univ, Coll Text & Clothing, Wuxi 214122, Peoples R China; [Lughofer, Edwin] Johannes Kepler Univ Linz, Dept Knowledge Based Math Syst, A-4040 Linz, Austria; [Zeng, Xianyi] Univ Lille Nord France, F-59000 Lille, France; [Zeng, Xianyi] ENSAIT, GEMTEX, F-59056 Roubaix, France; [Li, Zhengxin] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China Jiangnan University; Johannes Kepler University Linz; Universite de Lille - ISITE; Universite de Lille; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); ShanghaiTech University Liu, JL (corresponding author), Jiangnan Univ, Coll Text & Clothing, Wuxi 214122, Peoples R China. jian-li.liu@hotmail.com Zeng, Xianyi/0000-0002-3236-6766 National Natural Science Foundation of China [61203364]; Austrian COMET-K2 program of the Linz Center of Mechatronics - Austrian Federal Government; Federal State of Upper Austria National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Austrian COMET-K2 program of the Linz Center of Mechatronics - Austrian Federal Government; Federal State of Upper Austria This project was financially supported by a grant from the National Natural Science Foundation of China (Grant number 61203364). The authors also acknowledge the support of the Austrian COMET-K2 program of the Linz Center of Mechatronics, which is funded by the Austrian Federal Government and the Federal State of Upper Austria. 31 2 2 0 20 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. 2018.0 2018.0 1812980 10.1155/2018/1812980 0.0 8 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology GU8AC 30271431.0 gold, Green Published 2023-03-23 WOS:000445553900001 0
C Li, XC; Zhao, KQ; Cong, G; Jensen, CS; Wei, W IEEE Li, Xiucheng; Zhao, Kaiqi; Cong, Gao; Jensen, Christian S.; Wei, Wei Deep Representation Learning for Trajectory Similarity Computation 2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) IEEE International Conference on Data Engineering English Proceedings Paper 34th IEEE International Conference on Data Engineering Workshops (ICDEW) APR 16-19, 2018 Paris, FRANCE IEEE,IEEE Comp Soc,U S Natl Sci Fdn Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search. [Li, Xiucheng; Zhao, Kaiqi; Cong, Gao] Nanyang Technol Univ, Singapore, Singapore; [Jensen, Christian S.] Aalborg Univ, Aalborg, Denmark; [Wei, Wei] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Aalborg University; Huazhong University of Science & Technology Li, XC (corresponding author), Nanyang Technol Univ, Singapore, Singapore. xli055@e.ntu.edu.sg; kzhao002@e.ntu.edu.sg; gaocong@ntu.edu.sg; csj@cs.aau.dk; weiw@hust.edu.cn Zhao, Kaiqi/U-2398-2019 Zhao, Kaiqi/0000-0002-0984-1629; Wei, Wei/0000-0003-4488-0102 Rapid-Rich Object Search (ROSE) Lab at Nanyang Technological University; National Research Foundation, Prime Minister's Office, Singapore, under its IDM Futures Funding Initiative; MOE [MOE2016-T2-1-137, RG31/17]; NSFC [61602197, 61772537]; Microsoft; DiCyPS project; Obel Family Foundation; NSF [2016CFB192] Rapid-Rich Object Search (ROSE) Lab at Nanyang Technological University(Nanyang Technological University); National Research Foundation, Prime Minister's Office, Singapore, under its IDM Futures Funding Initiative(National Research Foundation, Singapore); MOE(Ministry of Higher Education & Scientific Research (MHESR)); NSFC(National Natural Science Foundation of China (NSFC)); Microsoft(Microsoft); DiCyPS project; Obel Family Foundation; NSF(National Science Foundation (NSF)) This work is supported in part by the Rapid-Rich Object Search (ROSE) Lab at Nanyang Technological University. The ROSE Lab is supported by the National Research Foundation, Prime Minister's Office, Singapore, under its IDM Futures Funding Initiative, administered by the Interactive and Digital Media Programme Office. This work was also supported by the MOE Tier-2 grant MOE2016-T2-1-137, MOE Tier-1 grant RG31/17, NSFC under the grant 61772537, and a grant from Microsoft. C. S. Jensen was supported by the DiCyPS project and by a grant from the Obel Family Foundation. W. Wei was supported by NSFC under the grant 61602197, NSF under the grant 2016CFB192. 41 80 87 3 16 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1084-4627 978-1-5386-5520-7 PROC INT CONF DATA 2018.0 617 628 10.1109/ICDE.2018.00062 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO0OZ 2023-03-23 WOS:000492836500054 0
J Wang, XR; Yan, Z; Zhang, R; Zhang, P Wang, Xuerui; Yan, Zheng; Zhang, Rui; Zhang, Peng Attacks and defenses in user authentication systems: A survey JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Authentication system; Attack detection; Defense mechanisms; Spoofing attack; Liveness detection; CAPTCHA; Biometric authentication; Machine learning; Deep neural networks FACE RECOGNITION; IRIS; FINGERPRINT; VULNERABILITIES; VERIFICATION; NETWORKS; SECURITY User authentication systems (in short authentication systems) have wide utilization in our daily life. Unfortunately, existing authentication systems are prone to various attacks while both system security and usability are expected to be satisfied. But the current research still lacks a thorough survey on various types of attacks and corresponding countermeasures regarding user authentication, including traditional password-based and emerging biometric-based systems. In this paper, we make a comprehensive review on attacks and defenses of the authentication systems. We firstly introduce a number of common attacks by classifying them into different categories based on attacker knowledge, attack target, attack form and attack strength. Then, we propose a set of evaluation criteria for evaluating different kinds of attack defense mechanisms. Furthermore, we review and evaluate the existing methods of detecting and resisting attacks in the authentication systems by employing the proposed evaluation criteria as a common measure. Specifically, we focus on comparing and analyzing the performance of different defense mechanisms in different types of authentication systems. Through serious review and analysis, we put forward a number of open issues and propose some promising future research directions, hoping to inspire further research in this field. [Wang, Xuerui; Yan, Zheng; Zhang, Rui] Xidian Univ, Sch Cyber Engn, State Key Lab ISN, Xian 710071, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Zhang, Peng] Zalando, Helsinki, Finland Xidian University; Aalto University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab ISN, Xian 710071, Peoples R China. zyab@xidian.edu.cn zheng, yan/GQY-6668-2022; yang, zheng/HGC-7753-2022 Yan, Zheng/0000-0002-9697-2108; Zhang, Rui/0000-0001-5174-5713 National Natural Science Foundation of China [62072351]; Academy of Finland [308087, 335262]; Shaanxi Innovation Team Project [2018TD007]; 111 Project [B16037]; Academy of Finland (AKA) [308087] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Shaanxi Innovation Team Project; 111 Project(Ministry of Education, China - 111 Project); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported in part by the National Natural Science Foundation of China under Grant 62072351; in part by the Academy of Finland under Grant 308087 and Grant 335262; in part by the Shaanxi Innovation Team Project under Grant 2018TD007; and in part by the 111 Project under Grant B16037. 206 11 11 3 19 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 1095-8592 J NETW COMPUT APPL J. Netw. Comput. Appl. AUG 15 2021.0 188.0 103080 10.1016/j.jnca.2021.103080 0.0 JUN 2021 21 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science SS0GE 2023-03-23 WOS:000661420100003 0
J Zhou, B; Poppel, E; Wang, LY; Yang, TX; Zaytseva, Y; Bao, Y Zhou, Bin; Poeppel, Ernst; Wang, Lingyan; Yang, Taoxi; Zaytseva, Yuliya; Bao, Yan Seeing without knowing: Operational principles along the early visual pathway PSYCH JOURNAL English Article attentional control; binocular rivalry; blindsight; brightness perception; color perception; consciousness; eccentricity effect; habituation; neuroplasticity; restitution of function COLOR-GENERATING INTERACTIONS; STRIATE CORTEX; APPERCEPTIVE AGNOSIA; SUPERIOR COLLICULUS; RETINAL LESIONS; RESIDUAL VISION; TRAVELING-WAVES; CEREBRAL-CORTEX; FIELD DEFECTS; INHIBITION Single cases may lead to unexpected hypotheses in psychology. We retrospectively analyzed single case studies that suggested organizational principles along the early visual pathway, which have remained unanswered until now. First case: In spite of the inhomogeneity of sensitivity, paradoxically the visual field on the subjective level appears to be homogeneous; constancy of brightness of supra-threshold stimuli throughout the visual field is claimed to be responsible for homogeneity; specific summation properties of retinal ganglion cells are hypothesized to guarantee this effect. Second case: With a brain-injured patient having suffered a partial visual field loss it can be shown that color induction is a retinal phenomenon; lateral inhibitory processes at the level of amacrine cells are hypothesized as neural network. Third case: In a patient having suffered a bilateral occipital lobe infarction, some functional recovery has been demonstrated; divergence and convergence of projection in the ascending neural pathway are suggested as a structural basis for recovery. Slowed down binocular rivalry discloses a sequential mechanism in the construction of a visual percept. Fourth case: The pre-wired projection of the retina to the visual cortex in spite of a severe squint of one eye is confirmed, but paradoxically some local neuroplasticity is also suggested. Fifth case: Using habituation of local sensitivity in the visual field and its resetting by interhemispheric interactions as an experimental paradigm, it is suggested that spatial attention is controlled at the midbrain level. Sixth case: Observations on residual vision or blindsight support the hypothesis that the visual cortex is the one and only structure responsible for visual perception on a conscious level. The unifying principle of these retrospective analyses is that subjective visual phenomena can lead to unexpected but testable hypotheses of neural processing on the structural and functional level in the early visual pathway. [Zhou, Bin] Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China; [Poeppel, Ernst; Wang, Lingyan; Bao, Yan] Peking Univ, Key Lab Machine Percept, Sch Psychol & Cognit Sci, Minist Educ, Beijing, Peoples R China; [Poeppel, Ernst; Wang, Lingyan; Bao, Yan] Peking Univ, Beijing Key Lab Behav & Mental Hlth, Beijing, Peoples R China; [Poeppel, Ernst; Yang, Taoxi; Zaytseva, Yuliya; Bao, Yan] Univ Munich, Inst Med Psychol, Munich, Germany; [Poeppel, Ernst; Yang, Taoxi; Zaytseva, Yuliya; Bao, Yan] Univ Munich, Human Sci Ctr, Munich, Germany; [Zaytseva, Yuliya] Natl Inst Mental Hlth, Dept Natl IT Syst Mental Hlth & Brain Monitoring, Klecany, Czech Republic; [Zaytseva, Yuliya] Charles Univ Prague, Dept Psychiat & Med Psychol, Fac Med 3, Prague, Czech Republic Chinese Academy of Sciences; Institute of Psychology, CAS; Peking University; Peking University; University of Munich; University of Munich; National Institute of Mental Health - Czech Republic; Charles University Prague Bao, Y (corresponding author), Peking Univ, Sch Psychol & Cognit Sci, 5 Yiheyuan Rd, Beijing 100871, Peoples R China. baoyan@pku.edu.cn Zaytseva, Yuliya/0000-0002-6560-8127 National Natural Science Foundation of China [31371018, 31100735, 91120004]; Chinese Academy of Sciences (CAS Visiting Professorships for Senior International Scientists) [2013T1S0029]; German Academic Exchange Service (DAAD); Chinese Scholarship Council [[2014]3026] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS Visiting Professorships for Senior International Scientists); German Academic Exchange Service (DAAD)(Deutscher Akademischer Austausch Dienst (DAAD)); Chinese Scholarship Council(China Scholarship Council) The authors were supported by the National Natural Science Foundation of China (31371018, 31100735, and 91120004), the Chinese Academy of Sciences (CAS Visiting Professorships for Senior International Scientists, 2013T1S0029), the German Academic Exchange Service (DAAD), and the Chinese Scholarship Council ([2014]3026). 79 17 17 1 25 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2046-0252 2046-0260 PSYCH J PsyCh J. SEP 2016.0 5.0 3 145 160 10.1002/pchj.141 0.0 16 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology DY0XN 27678480.0 2023-03-23 WOS:000384819600001 0
C Zahra, O; Navarro-Alarcon, D; Tolu, S IEEE Zahra, Omar; Navarro-Alarcon, David; Tolu, Silvia A Fully Spiking Neural Control System Based on Cerebellar Predictive Learning for Sensor-Guided Robots 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 30-JUN 05, 2021 Xian, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,So Univ Sci & Technol,Harbin Inst Technol,Xian Jiaotong Univ,Univ Toronto,Nat Sci & Engn Res Council Canada,Chinese Univ Hong Kong,DJI,Biomimet Intelligence & Robot,Toyota Res Inst,Baidu,Mech Mind Robot Technologies,Facebook AI,Nokov,Manycore,Franka Emika,Zhejiang Univ, Huzhou Inst,Jing Tian,ATI Ind Automat,Amber,Direct Drive Tech,Inceptio,Flexiv,Deeproute AI,Unitree,Waymo,Kuka,Pudu Robot,Deep Robot,Nvidia,MathWorks MODELS; NEURONS The cerebellum plays a distinctive role within our motor control system to achieve fine and coordinated motions. While cerebellar lesions do not lead to a complete loss of motor functions, both action and perception are severally impacted. Hence, it is assumed that the cerebellum uses an internal forward model to provide anticipatory signals by learning from the error in sensory states. In some studies, it was demonstrated that the learning process relies on the jointspace error. However, this may not exist. This work proposes a novel fully spiking neural system that relies on a forward predictive learning by means of a cellular cerebellar model. The forward model is learnt thanks to the sensory feedback in task-space and it acts as a Smith predictor. The latter predicts sensory corrections in input to a differential mapping spiking neural network during a visual servoing task of a robot arm manipulator. In this paper, we promote the developed control system to achieve more accurate target reaching actions and reduce the motion execution time for the robotic reaching tasks thanks to the cerebellar predictive capabilities. [Zahra, Omar; Navarro-Alarcon, David] Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong, Peoples R China; [Tolu, Silvia] Univ Denmark, Dept Elect Engn, Copenhagen, Denmark Hong Kong Polytechnic University Navarro-Alarcon, D (corresponding author), Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong, Peoples R China. dna@ieee.org ; Tolu, Silvia/G-3818-2016; Navarro-Alarcon, David/V-6917-2017 Zahra, Omar/0000-0003-1644-6480; Tolu, Silvia/0000-0003-1825-8440; Navarro-Alarcon, David/0000-0002-3426-6638 Research Grants Council [14203917]; Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme [ZG9V]; Key-Area Research and Development Program of Guangdong Province 2020 [76]; PolyU [YBYT, ZZHJ] Research Grants Council(Hong Kong Research Grants Council); Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme; Key-Area Research and Development Program of Guangdong Province 2020; PolyU This research work was supported in part by the Research Grants Council (grant 14203917), in part by the Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme (grant ZG9V), in part by the Key-Area Research and Development Program of Guangdong Province 2020 (project 76) and in part by PolyU (grants YBYT and ZZHJ). 29 0 0 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-9077-8 IEEE INT CONF ROBOT 2021.0 4423 4429 10.1109/ICRA48506.2021.9561127 0.0 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BS7PC Green Submitted 2023-03-23 WOS:000765738803066 0
J Cai, JL; Fang, B; Zhang, LK; Lv, WX; Zhang, BS; Zhou, TJ; Finocchio, G; Zeng, ZM Cai, Jialin; Fang, Bin; Zhang, Like; Lv, Wenxing; Zhang, Baoshun; Zhou, Tiejun; Finocchio, Giovanni; Zeng, Zhongming Voltage-Controlled Spintronic Stochastic Neuron Based on a Magnetic Tunnel Junction PHYSICAL REVIEW APPLIED English Article Stochastic units based on magnetic tunnel junctions have shown a high energy-efficient pathway to perform neuromorphic computing. We propose a voltage-controlled spintronic stochastic device based on a magnetic tunnel junction by introducing perpendicular anisotropy into the free layer. We observe a stochastic behavior at low bias and demonstrate that this behavior can be used to mimic the artificial neurons of an artificial neural network to recognize the handwritten digits in the Mixed National Institute of Standards and Technology (MNIST) database. Furthermore, the stochastic behavior can be modulated by a bias voltage owing to the voltage-controlled magnetic anisotropy effect. The voltage-controlled stochastic behavior is theoretically and experimentally studied, which indicates that it can be used to perform as an adaptive neuron. These results provide a way for building energy-efficient spintronic neuromorphic computing systems. [Cai, Jialin; Fang, Bin; Zhang, Like; Lv, Wenxing; Zhang, Baoshun; Zeng, Zhongming] Chinese Acad Sci, Key Lab Multifunct Nanomat & Smart Syst, Suzhou Inst Nanotech & Nanobion, Suzhou 215123, Jiangsu, Peoples R China; [Cai, Jialin; Zhang, Like; Lv, Wenxing; Zhang, Baoshun; Zeng, Zhongming] Univ Sci & Technol China, Sch Nano Technol & Nano Bion, Hefei 230026, Anhui, Peoples R China; [Zhou, Tiejun] Hangzhou Dianzi Univ, Coll Elect & Informat, Hangzhou 310018, Zhejiang, Peoples R China; [Finocchio, Giovanni] Univ Messina, Dept Math & Comp Sci Phys Sci & Earth Sci, I-98166 Messina, Italy Chinese Academy of Sciences; Suzhou Institute of Nano-Tech & Nano-Bionics, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Hangzhou Dianzi University; University of Messina Zeng, ZM (corresponding author), Chinese Acad Sci, Key Lab Multifunct Nanomat & Smart Syst, Suzhou Inst Nanotech & Nanobion, Suzhou 215123, Jiangsu, Peoples R China.;Zeng, ZM (corresponding author), Univ Sci & Technol China, Sch Nano Technol & Nano Bion, Hefei 230026, Anhui, Peoples R China. jlcai2015@sinano.ac.cn Zeng, Zhongming/AAL-7444-2021; JiaLin, Cai/W-1262-2019 JiaLin, Cai/0000-0001-9462-5586; Zhang, Like/0000-0002-0869-3417; Finocchio, Giovanni/0000-0002-1043-3876 Ministero degli Affari Esteri e della Cooperazione Internazionale [CN16GR09, 2016YFE0104100]; National Science Foundation of China [51761145025, 11474311, 11804370]; National Postdoctoral Program for Innovative Talents [BX201700275]; China Postdoctoral Science Foundation [2017M621858] Ministero degli Affari Esteri e della Cooperazione Internazionale(Ministry of Foreign Affairs and International Cooperation (Italy)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by the Executive Programme of Scientific and Technological Cooperation between Italy and China for the years 2016-2018 (code CN16GR09, Grant No. 2016YFE0104100) titled Nanoscale broadband spintransfer-torque microwave detector funded by the Ministero degli Affari Esteri e della Cooperazione Internazionale. This work was supported in part by the National Science Foundation of China (Grants No. 51761145025, No. 11474311, and No. 11804370) and the National Postdoctoral Program for Innovative Talents (Grant No. BX201700275) and China Postdoctoral Science Foundation Funded Project (Grant No. 2017M621858). 43 42 43 1 60 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2331-7019 PHYS REV APPL Phys. Rev. Appl. MAR 6 2019.0 11.0 3 34015 10.1103/PhysRevApplied.11.034015 0.0 7 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics HO1OB 2023-03-23 WOS:000460676500004 0
J Liu, YL; Zhou, GM; Du, HQ; Berninger, F; Mao, FJ; Li, XJ; Chen, L; Cui, L; Li, YG; Zhu, DE; Xu, L Liu, Yuli; Zhou, Guomo; Du, Huaqiang; Berninger, Frank; Mao, Fangjie; Li, Xuejian; Chen, Liang; Cui, Lu; Li, Yangguang; Zhu, Di'en; Xu, Lin Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest JOURNAL OF ENVIRONMENTAL MANAGEMENT English Article Phyllostachys praecox; Net ecosystem exchange; Ecosystem respiration; Gross ecosystem productivity; Seasonal variation; Random forest CANOPY CONDUCTANCE; FLUX MEASUREMENTS; CLIMATE CONTROLS; ENERGY FLUX; CO2 FLUX; ECOSYSTEM; TEMPERATE; MODEL; BOREAL; WATER Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011-2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called random forest (RF) was used to identify factors that affected carbon fluxes. The RF model predicted well the gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE), and displayed variations in the drivers between different seasons. Mean annual NEE, RE, and GEP were -105.2 +/- 23.1, 1264.5 +/- 45.2, and 1369.6 +/- 52.5 g C m(-2), respectively. Climate warming increased RE more than GEP when water inputs were not limiting. Summer drought played little role in suppressing GEP, but low soil moisture contents suppressed RE and increased the carbon sink during drought in the summer. The most important drivers of NEE were soil temperature in spring, summer, and winter, and photosynthetically active radiation in autumn. Air and soil temperature were important drivers of GEP in all seasons. [Liu, Yuli; Zhou, Guomo; Du, Huaqiang; Mao, Fangjie; Li, Xuejian; Chen, Liang; Cui, Lu; Li, Yangguang; Zhu, Di'en; Xu, Lin] Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Linan 311300, Zhejiang, Peoples R China; [Liu, Yuli; Zhou, Guomo; Du, Huaqiang; Berninger, Frank; Mao, Fangjie; Li, Xuejian; Chen, Liang; Cui, Lu; Li, Yangguang; Zhu, Di'en; Xu, Lin] Zhejiang A&F Univ, Key Lab Carbon Cycling Forest Ecosyst & Carbon Se, Linan 311300, Zhejiang, Peoples R China; [Liu, Yuli; Zhou, Guomo; Du, Huaqiang; Mao, Fangjie; Li, Xuejian; Chen, Liang; Cui, Lu; Li, Yangguang; Zhu, Di'en; Xu, Lin] Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Zhejiang, Peoples R China; [Berninger, Frank] Univ Helsinki, Dept Forest Ecol, POB 27, FI-00014 Helsinki, Finland; [Berninger, Frank] Univ Helsinki, Inst Atmospher & Earth Syst Res Forest Sci, Fac Agr & Forestry, Helsinki, Finland Zhejiang A&F University; Zhejiang A&F University; Zhejiang A&F University; University of Helsinki; University of Helsinki Zhou, GM; Du, HQ (corresponding author), Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Linan 311300, Zhejiang, Peoples R China. zhougm@zafu.edu.cn; dhqrs@126.com Mao, Fangjie/GXZ-7139-2022; Chen, Liang/AAM-1075-2021 Mao, Fangjie/0000-0003-2005-3452; Chen, Liang/0000-0002-1811-5717 National Natural Science Foundation of China [31670644, 31370637]; Natural Science Foundation of Zhejiang Province [LR14C160001]; Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization; Key Science and Technology Projects of Zhejiang Province [2015C03008]; Key Discipline of Forestry of Creative Technology Project of Zhejiang Province [201510]; Department of Forestry of Zhejiang Province [2017SY04]; Chinese Academy of Forestry [2017SY04] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization; Key Science and Technology Projects of Zhejiang Province; Key Discipline of Forestry of Creative Technology Project of Zhejiang Province; Department of Forestry of Zhejiang Province; Chinese Academy of Forestry This study was supported by the National Natural Science Foundation of China (31670644, 31370637), Natural Science Foundation of Zhejiang Province (LR14C160001), Joint Research fund of Department of Forestry of Zhejiang Province and Chinese Academy of Forestry (No. 2017SY04), Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization, the Key Science and Technology Projects of Zhejiang Province (2015C03008), and the Key Discipline of Forestry of Creative Technology Project of Zhejiang Province (201510). The authors wish to acknowledge Prof. Petri Penttinen, Zhejiang A&F University, for his help in revising the manuscript. The authors also would like to thank the editor and three reviewers for their contribution to the peer reviews of this study. 67 25 26 6 69 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0301-4797 1095-8630 J ENVIRON MANAGE J. Environ. Manage. OCT 1 2018.0 223.0 713 722 10.1016/j.jenvman.2018.06.046 0.0 10 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology GQ9AE 29975899.0 2023-03-23 WOS:000442057500071 0
J Gao, W; Zhang, YQ; Guirao, JLG; Chen, YJ Gao, Wei; Zhang, Yunqing; Guirao, Juan L. G.; Chen, Yaojun A discrete dynamics approach to sparse calculation and applied in ontology science JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS English Article Ontology; similarity measuring; ontology mapping; sparse computing INTERIOR-POINT METHOD; GRADIENT ESTIMATOR; LEARNING ALGORITHM; OPTIMIZATION; MANAGEMENT; SYSTEM; MODEL In the era of big data, with the increase of data processing information and the increase of data complexity, higher requirements are put on the tools and algorithms of data processing. As a tool for structured information representation, ontology has been used in engineering fields such as chemistry, biology, pharmacy, and materials. As a dynamic structure, the increasing concepts contributes to a gradual increase of a single ontology. In order to solve the problem of computational complexity decreasing in the procedure of similarity calculating, the techniques of dimensionality reduction and sparse computing are applied to ontology learning. This article presents discrete dynamics approach showing several tricks on applying the sparse computing method to ontology learning, and verify its efficiency through experiments. [Gao, Wei; Zhang, Yunqing; Chen, Yaojun] Nanjing Univ, Dept Math, Nanjing 210093, Jiangsu, Peoples R China; [Gao, Wei] Yunnan Normal Univ, Sch Informat Sci & Technol, Kunming, Yunnan, Peoples R China; [Guirao, Juan L. G.] Univ Politecn Cartagena, Hosp Marina, Dept Matemat Aplicada & Estadist, Cartagena, Spain Nanjing University; Yunnan Normal University; Universidad Politecnica de Cartagena Chen, YJ (corresponding author), Nanjing Univ, Dept Math, Nanjing 210093, Jiangsu, Peoples R China. yaojunc@nju.edu.cn GUIRAO, JUAN L.G./C-5189-2014 GUIRAO, JUAN L.G./0000-0003-2788-809X 61 4 5 0 7 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1023-6198 1563-5120 J DIFFER EQU APPL J. Differ. Equ. Appl. OCT 3 2019.0 25.0 9-10 SI 1239 1254 10.1080/10236198.2018.1551383 0.0 16 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics JQ5VC 2023-03-23 WOS:000499012000003 0
J Wen, PF; Zhao, S; Chen, SW; Li, Y Wen, Pengfei; Zhao, Shuai; Chen, Shaowei; Li, Yong A generalized remaining useful life prediction method for complex systems based on composite health indicator RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Multiple sensors; Data fusion; Degradation modeling; Remaining useful life; Prognostics PERFORMANCE DEGRADATION ASSESSMENT; RECURRENT NEURAL-NETWORK; DATA FUSION; PROGNOSTICS; MODEL; INDEX; STATE; ENSEMBLE; FEATURES; PROBABILITY As one of the key techniques in Prognostics and Health Management (PHM), accurate Remaining Useful Life (RUL) prediction can effectively reduce the number of downtime maintenance and significantly improve economic benefits. In this paper, a generalized RUL prediction method is proposed for complex systems with multiple Condition Monitoring (CM) signals. A stochastic degradation model is proposed to characterize the system degradation behavior, based on which the respective reliability characteristics such as the RUL and its Confidence Interval (CI) are explicitly derived. Considering the degradation model, two desirable properties of the Health Indicator (HI) are put forward and their respective quantitative evaluation methods are developed. With the desirable properties, a nonlinear data fusion method based on Genetic Programming (GP) is proposed to construct a superior composite HI. In this way, the multiple CM signals are fused to provide a better prediction capability. Finally, the proposed integrated methodology is validated on the C-MAPSS data set of aircraft turbine engines. [Wen, Pengfei; Chen, Shaowei; Li, Yong] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China; [Zhao, Shuai] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Northwestern Polytechnical University; Aalborg University Zhao, S (corresponding author), Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark. szh@et.aau.dk Zhao, Shuai/AAP-2135-2020; Wen, Pengfei/ABB-1421-2021 Zhao, Shuai/0000-0001-7441-5434; Wen, Pengfei/0000-0002-9384-3845 55 44 44 17 102 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. JAN 2021.0 205.0 107241 10.1016/j.ress.2020.107241 0.0 15 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science OQ9JE 2023-03-23 WOS:000589091300026 0
J Mortazavi, B; Shojaei, F; Shapeev, AV; Zhuang, XY Mortazavi, Bohayra; Shojaei, Fazel; Shapeev, Alexander V.; Zhuang, Xiaoying A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides CARBON English Article 2D Carbon nitride; Semiconductors; Mechanical; Nanoporous; Machine-learning THERMAL-CONDUCTIVITY; EFFICIENT; MORPHOLOGY Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of the recent advances, novel C-C bridged heptazine of C6N7 with a nanoporous structure has been fabricated. Based on the experimentally realized C6N7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C6N7-C2, C6N7-BN and C6N7-C2H2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C6N7-based monolayers. The dynamical stability and mechanical properties are explored using machinelearning interatomic potentials (MLIPs). The presented results confirm that C6N7-based monolayers are stable and strong semiconductors with notable absorption of the ultraviolet range of light. Remarkable accuracy of the developed computationally-efficient classical models is confirmed by comparing the predictions with those by DFT. Findings by the combined DFT and MLIP methods confirm the stability of novel C6N7-based nanosheets and provide a comprehensive vision on their highly appealing physical properties. More importantly, this study confirms the outstanding robustness and efficiency of MLIPs in substituting the computationally expensive DFT methods in the exploration of complex phononic and mechanical/failure responses of low-symmetry and highly-porous conductive frameworks. 0 2022 Elsevier Ltd. All rights reserved. [Mortazavi, Bohayra; Shojaei, Fazel] Leibniz Univ Hannover, Chair Computat Sci & Simulat Technol, Dept Math & Phys, Appelstr 11, D-30167 Hannover, Germany; [Mortazavi, Bohayra] Leibniz Univ Hannover, Cluster Excellence PhoenixD Photon Opt & Engn Inn, Hannover, Germany; [Shapeev, Alexander V.] Persian Gulf Univ, Fac Nano & Biosci & Technol, Dept Chem, Bushehr 75169, Iran; [Zhuang, Xiaoying] Skolkovo Inst Sci & Technol, Skolkovo Innovat Ctr, Bolshoy Bulvar 30, Moscow 143026, Russia; [Shojaei, Fazel] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, 1239 Siping Rd, Shanghai, Peoples R China Leibniz University Hannover; Leibniz University Hannover; Persian Gulf University; Skolkovo Institute of Science & Technology; Tongji University Mortazavi, B; Zhuang, XY (corresponding author), Leibniz Univ Hannover, Dept Math & Phys, Computat Sci & Simulat Technol, Appelstr 11, D-30167 Hannover, Germany. bohayra.mortazavi@gmail.com; zhuang@iop.uni-hannover.de Shapeev, Alexander/0000-0002-7497-5594 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [EXC 2122, 390833453]; Russian Science Foundation [18-13-00479]; Persian Gulf University Research Council; VEGAS cluster at the Bauhaus University of Weimar Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(German Research Foundation (DFG)); Russian Science Foundation(Russian Science Foundation (RSF)); Persian Gulf University Research Council; VEGAS cluster at the Bauhaus University of Weimar B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453) . A.V.S. is supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/) . F.S. thanks the Persian Gulf University Research Council for support of this study. The authors are greatly thankful to the VEGAS cluster at the Bauhaus University of Weimar for providing the computational resources. 43 8 8 9 14 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0008-6223 1873-3891 CARBON Carbon JUL 2022.0 194.0 230 239 10.1016/j.carbon.2022.03.068 0.0 APR 2022 10 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science 1P8AL 2023-03-23 WOS:000802226300007 0
J Zhao, YC; Zhang, JY; Xu, ZW; Sun, SJ; Langner, S; Hartono, NTP; Heumueller, T; Hou, Y; Elia, J; Li, N; Matt, GJ; Du, XY; Meng, W; Osvet, A; Zhang, KC; Stubhan, T; Feng, YX; Hauch, J; Sargent, EH; Buonassisi, T; Brabec, CJ Zhao, Yicheng; Zhang, Jiyun; Xu, Zhengwei; Sun, Shijing; Langner, Stefan; Hartono, Noor Titan Putri; Heumueller, Thomas; Hou, Yi; Elia, Jack; Li, Ning; Matt, Gebhard J.; Du, Xiaoyan; Meng, Wei; Osvet, Andres; Zhang, Kaicheng; Stubhan, Tobias; Feng, Yexin; Hauch, Jens; Sargent, Edward H.; Buonassisi, Tonio; Brabec, Christoph J. Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning NATURE COMMUNICATIONS English Article METAL-HALIDE PEROVSKITES; SOLAR-CELLS; THERMAL-STABILITY; PHOTOVOLTAICS Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140 degrees C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100 degrees C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30 degrees C. Current view of the impact of A-site cation on the stability of perovskite materials and devices is derived from accelerated ageing tests at high temperature, which is beyond normal operation range. Here, the authors reveal the great impact of ageing condition on assessing the photothermal stability of mixed-cation perovskites using high-throughput robot system coupled with machine learning. [Zhao, Yicheng; Zhang, Jiyun; Langner, Stefan; Heumueller, Thomas; Li, Ning; Du, Xiaoyan; Stubhan, Tobias; Hauch, Jens; Brabec, Christoph J.] Helmholtz Inst Erlangen Nurnberg HI ERN, Erlangen, Germany; [Zhao, Yicheng; Zhang, Jiyun; Langner, Stefan; Heumueller, Thomas; Elia, Jack; Li, Ning; Matt, Gebhard J.; Meng, Wei; Osvet, Andres; Zhang, Kaicheng; Brabec, Christoph J.] Friedrich Alexander Univ Erlangen Nurnberg, Inst Mat Elect & Energy Technol i MEET, Dept Mat Sci & Engn, Erlangen, Germany; [Xu, Zhengwei; Feng, Yexin] Hunan Univ, Sch Phys & Elect, Hunan Prov Key Lab Low Dimens Struct Phys & Devic, Changsha, Peoples R China; [Sun, Shijing; Hartono, Noor Titan Putri; Buonassisi, Tonio] MIT, Photovalta Res Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Hou, Yi; Sargent, Edward H.] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON, Canada University of Erlangen Nuremberg; Hunan University; Massachusetts Institute of Technology (MIT); University of Toronto Zhao, YC; Brabec, CJ (corresponding author), Helmholtz Inst Erlangen Nurnberg HI ERN, Erlangen, Germany.;Zhao, YC; Brabec, CJ (corresponding author), Friedrich Alexander Univ Erlangen Nurnberg, Inst Mat Elect & Energy Technol i MEET, Dept Mat Sci & Engn, Erlangen, Germany.;Feng, YX (corresponding author), Hunan Univ, Sch Phys & Elect, Hunan Prov Key Lab Low Dimens Struct Phys & Devic, Changsha, Peoples R China. yicheng.zhao@fau.de; yexinfeng@pku.edu.cn; christoph.brabec@fau.de Li, Ning/AAF-2901-2019; Zhang, Jiyun/HNS-5562-2023; Brabec, Christoph J./N-1897-2013; Hou, Yi/AAE-9581-2019 Li, Ning/0000-0003-1208-4638; Zhang, Jiyun/0000-0001-6939-4771; Brabec, Christoph J./0000-0002-9440-0253; Hou, Yi/0000-0002-1532-816X; zhao, yicheng/0000-0002-8248-7021; Meng, Wei/0000-0002-4887-1732; Hartono, Noor Titan Putri/0000-0002-0748-0620; Zhang, Kaicheng/0000-0003-3468-3543; Heumueller, Thomas/0000-0002-6974-410X Alexander von Humboldt Foundation [1199604]; grant ELF-PV - Design and Development of Solution-Processed Functional Materials for the Next Generations of PV Technologies by the Bavarian State Government [FKZ 20.2-3410.5-4-5]; grant Solar Factory of the Future by the Bavarian State Government [FKZ 20.2-3410.5-4-5]; Aufbruch Bayern initiative of the state of Bavaria (EnCN); Aufbruch Bayern initiative of the state of Bavaria (Solar Factory of the Future); Bavarian Initiative Solar Technologies go Hybrid (SolTech); DFG [ITRG 2495, SFB 953]; German Research Foundation [DFG INST 90/917-1 FUGG]; National Basic Research Programs of China [2016YFA0300900]; NSFC [DFG INST 90/917-1 FUGG, 11974105]; China Scholarship Council Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); grant ELF-PV - Design and Development of Solution-Processed Functional Materials for the Next Generations of PV Technologies by the Bavarian State Government; grant Solar Factory of the Future by the Bavarian State Government; Aufbruch Bayern initiative of the state of Bavaria (EnCN); Aufbruch Bayern initiative of the state of Bavaria (Solar Factory of the Future); Bavarian Initiative Solar Technologies go Hybrid (SolTech); DFG(German Research Foundation (DFG)); German Research Foundation(German Research Foundation (DFG)); National Basic Research Programs of China(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) Y.Z. acknowledges the Alexander von Humboldt Foundation for supporting his scientific research during the postdoctoral period (Grant number 1199604). Y.Z. thanks Yao Lu and Haifeng Yuan for the great support provided in editing the whole manuscript. The authors gratefully acknowledge the grants ELF-PV - Design and Development of Solution-Processed Functional Materials for the Next Generations of PV Technologies (no. 44-6521a/20/4) and Solar Factory of the Future (FKZ 20.2-3410.5-4-5) by the Bavarian State Government. C.J.B. gratefully acknowledges the financial support through the Aufbruch Bayern initiative of the state of Bavaria (EnCN and Solar Factory of the Future), the Bavarian Initiative Solar Technologies go Hybrid (SolTech), ITRG 2495 (DFG) and the SFB 953 (DFG). The authors acknowledge the financial support from the German Research Foundation with grant DFG INST 90/917-1 FUGG; Y.F. acknowledges the National Basic Research Programs of China (Grant No. 2016YFA0300900), and the NSFC with Grant No. 11974105, DFG INST 90/917-1 FUGG. J.Z., W.M., and K.Z. gratefully acknowledge support from the China Scholarship Council. 38 37 37 17 94 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. APR 13 2021.0 12.0 1 2191 10.1038/s41467-021-22472-x 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics RN8YC 33850155.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000640638000003 0
J Li, PF; Lu, H; Kanhabua, N; Zhao, S; Pan, G Li, Pengfei; Lu, Hua; Kanhabua, Nattiya; Zhao, Sha; Pan, Gang Location Inference for Non-Geotagged Tweets in User Timelines IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Twitter; location inference; bayes; LSTM EFFICIENT; TWITTER Social media like Twitter have become globally popular in the past decade. Thanks to the high penetration of smartphones, social media users are increasingly going mobile. This trend has contributed to foster various location based services deployed on social media, the success of which heavily depends on the availability and accuracy of users' location information. However, only a very small fraction of tweets in Twitter are geo-tagged. Therefore, it is necessary to infer locations for tweets in order to attain the purpose of those location based services. In this paper, we tackle this problem by scrutinizing Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt two machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. The Bayes based model focuses on the information gain of words with location implications in the user-generated contents. The convolutional LSTM model treats user-generated contents and their associated locations as sequences and employs bidirectional LSTM and convolution operation to make location inferences. The two models are evaluated on a large set of real Twitter data. The experimental results suggest that our models are effective at inferring locations for non-geotagged tweets and the models outperform the state-of-the-art and alternative approaches significantly in terms of inference accuracy. [Li, Pengfei; Zhao, Sha; Pan, Gang] Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China; [Lu, Hua] Aalborg Univ, Dept Comp Sci, DK-9220 Aalborg, Denmark; [Kanhabua, Nattiya] NTENT, Barcelona 08018, Spain; [Pan, Gang] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310027, Zhejiang, Peoples R China Zhejiang University; Aalborg University; Zhejiang University Pan, G (corresponding author), Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China.;Lu, H (corresponding author), Aalborg Univ, Dept Comp Sci, DK-9220 Aalborg, Denmark.;Pan, G (corresponding author), Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310027, Zhejiang, Peoples R China. pfl@zju.edu.cn; luhua@cs.aau.dk; nattiya@gmail.com; szhao@zju.edu.cn; gpan@zju.edu.cn Pan, Gang/B-5978-2013 Pan, Gang/0000-0002-4049-6181; Lu, Hua/0000-0003-1199-6678 Natural Science Foundation of China [61772460]; National Key Research and Development Plan [2016YFB1001200]; Zhejiang Provincial Natural Science foundation of China [LR15F020001] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Plan; Zhejiang Provincial Natural Science foundation of China(Natural Science Foundation of Zhejiang Province) This research is supported by Natural Science Foundation of China (No. 61772460), National Key Research and Development Plan (2016YFB1001200) and Zhejiang Provincial Natural Science foundation of China (LR15F020001). 68 16 16 0 16 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. JUN 1 2019.0 31.0 6 1150 1165 10.1109/TKDE.2018.2852764 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering HW8IT Green Published 2023-03-23 WOS:000466933700010 0
C Zhu, XL; Yan, Z; Ruan, JF; Zheng, QH; Dong, B IEEE Zhu, Xulyu; Yan, Zheng; Ruan, Jianfei; Zheng, Qinghua; Dong, Bo IRTED-TL: An Inter-Region Tax Evasion Detection Method based on Transfer Learning 2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE) IEEE Trustcom BigDataSE ISPA English Proceedings Paper 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE) JUL 31-AUG 03, 2018 New York, NY IEEE,IEEE Comp Soc,IEEE TCSC,IEEE STC Smart Comp,Columbia Univ,N Amer Chinese Talents Assoc tax evasion; transfer learning; interpretability; inter-region detection Tax evasion detection plays a crucial role in addressing tax revenue loss. Many efforts have been made to develop tax evasion detection models by leveraging machine learning techniques, but they have not constructed a uniform model for different geographical regions because an ample supply of training examples is a fundamental prerequisite for an effective detection model. When sufficient tax data are not readily available, the development of a representative detection model is more difficult due to unequal feature distributions in different regions. Existing methods face a challenge in explaining and tracing derived results. To overcome these challenges, we propose an Inter-Region Tax Evasion Detection method based on Transfer Learning (IRTED-TL), which is optimized to simultaneously augment training data and induce interpretability into the detection model. We exploit evasion-related knowledge in one region and leverage transfer learning techniques to reinforce the tax evasion detection tasks of other regions in which training examples are lacking. We provide a unified framework that takes advantage of auxiliary data using a transfer learning mechanism and builds an interpretable classifier for inter-region tax evasion detection. Experimental tests based on real-world tax data demonstrate that the IRTED-TL can detect tax evaders with higher accuracy and better interpretability than existing methods. [Zhu, Xulyu; Ruan, Jianfei; Zheng, Qinghua] Xi An Jiao Tong Univ, Shaanxi Prov Key Lab STN Tech R&D, Xian 710049, Shaanxi, Peoples R China; [Zhu, Xulyu; Ruan, Jianfei; Zheng, Qinghua] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China; [Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Dong, Bo] Xi An Jiao Tong Univ, Sch Continuing Educ, Xian 710049, Shaanxi, Peoples R China; [Dong, Bo] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China Xi'an Jiaotong University; Xi'an Jiaotong University; Xidian University; Aalto University; Xi'an Jiaotong University; Xi'an Jiaotong University Zhu, XL (corresponding author), Xi An Jiao Tong Univ, Shaanxi Prov Key Lab STN Tech R&D, Xian 710049, Shaanxi, Peoples R China.;Zhu, XL (corresponding author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China. zhuxvlv@163.com; zyan@xidian.edu.cn; xjtu_jfruan@163.com; qhzheng@xjtu.edu.cn; dong.bo@xjtu.edu.cn RUAN, JIANFEI/HKO-0993-2023; yang, zheng/HGC-7753-2022 Yan, Zheng/0000-0002-9697-2108 The Fundamental Theory and Applications of Big Data with Knowledge Engineering of the National Key Research and Development Program of China [2016YFB1000903]; Innovative Research Group of the National Natural Science Foundation of China [61721002]; Innovation Research Team of Ministry of Education [IRT_17R86]; National Science Foundation of China [61502379, 61532015, 61672410]; Project of China Knowledge Centre for Engineering Science and Technology; Academy of Finland [308087] The Fundamental Theory and Applications of Big Data with Knowledge Engineering of the National Key Research and Development Program of China; Innovative Research Group of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Research Team of Ministry of Education; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of China Knowledge Centre for Engineering Science and Technology; Academy of Finland(Academy of Finland) This work was sponsored by The Fundamental Theory and Applications of Big Data with Knowledge Engineering of the National Key Research and Development Program of China under Grant No. 2016YFB1000903, Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), the National Science Foundation of China under Grant Nos. 61502379, 61532015 and 61672410, Project of China Knowledge Centre for Engineering Science and Technology, and the Academy of Finland (Grant No. 308087). 40 8 8 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2324-9013 978-1-5386-4387-7 IEEE TRUST BIG 2018.0 1224 1235 10.1109/TrustCom/BigDataSE.2018.00169 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO1DM Green Accepted 2023-03-23 WOS:000495072100160 0
C He, YL; Wang, LH; Yang, F; Clarysse, P; Zhu, YM Yuan, BZ; Ruan, QQ; Zhao, Y; An, GY He, Yunlong; Wang, Lihui; Yang, Feng; Clarysse, Patrick; Zhu, Yuemin Deep Group-Wise Angular Translation of Cardiac Diffusion MRI in q-space via Manifold Regularized GAN PROCEEDINGS OF 2020 IEEE 15TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP 2020) International Conference on Signal Processing English Proceedings Paper 15th IEEE International Conference on Signal Processing (ICSP) DEC 06-09, 2020 Beijing, PEOPLES R CHINA IEEE,Inst Engn & Technol,Union Radio Sci Int,Chinese Inst Elect,Inst Engn & Technol Beijing Local Network,IEEE Beijing Sect,Chinese Inst Elect, Signal Proc Soc,Natl Nat Sci Fdn China,IEEE Signal Proc Soc Beijing Chapter,IEEE Comp Soc Beijing Chapter,Japan China Sci & Technol Exchange Assoc,CIC Commun & Signal Proc Soc,Beijing Inst Elect,Shenzhen Univ, Intelligent Informat Inst,Beijing Jiaotong Univ diffusion MRI; super-resolution; cardiac DTI; image synthesis; deep learning; spatial-angular information RESOLUTION Diffusion magnetic resonance imaging (dMRI) has become an indispensable tool for non-invasive characterization of fiber structures of tissues. Clinical applicability of dMRI is often shackled by trade-off between image quality and long acquisition time. We propose a novel group-wise image translation method to improve the angular resolution of cardiac dMRI data. It consists in using a generative adversarial network (GAN) model to estimate a sequence of images from given DW images acquired in a limited number of diffusion gradient directions. We embed a supervised manifold regularized term in the GAN loss function to exploit the correlation between multiple DW images acquired in different gradient directions. Experimental results on cardiac dMRI data demonstrated that our method can significantly improve the quality of diffusion tensor imaging (DTI) reconstruction. [He, Yunlong; Clarysse, Patrick; Zhu, Yuemin] Univ Claude Bernard Lyon 1, Univ Lyon, INSA Lyon, UJM St Etienne,CNRS,Inserm,CREATIS,UMR 5220,U1206, F-69621 Lyon, France; [Wang, Lihui] Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China; [Yang, Feng] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon; UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Guizhou University; Beijing Jiaotong University Zhu, YM (corresponding author), Univ Claude Bernard Lyon 1, Univ Lyon, INSA Lyon, UJM St Etienne,CNRS,Inserm,CREATIS,UMR 5220,U1206, F-69621 Lyon, France.;Wang, LH (corresponding author), Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China. Yunlong.He@creatis.insa-lyon.fr; wlh1984@gmail.com; fengyang@bjtu.edu.cn; patrick.clarysse@creatis.insa-lyon.fr; Yue-Min.Zhu@creatis.insa-lyon.fr Yang, Feng/HLQ-2152-2023; Clarysse, Patrick/G-6371-2014 Yang, Feng/0000-0002-8334-7450; Clarysse, Patrick/0000-0002-5495-7655 Program PHC-Cai Yuanpei [41400TC]; CNRS International Research Project METISLAB Program PHC-Cai Yuanpei; CNRS International Research Project METISLAB This work was funded in part by the Program PHC-Cai Yuanpei 2018 (No 41400TC), and the CNRS International Research Project METISLAB. 24 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-5221 978-1-7281-4480-1 INT CONF SIGN PROCES 2020.0 510 514 10.1109/ICSP48669.2020.9320925 0.0 5 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BR4RA Green Submitted 2023-03-23 WOS:000652335700098 0
C Manni, F; Cai, CC; van der Sommen, F; Zinger, S; Shan, CF; Edstrom, E; Elmi-Terander, A; Fabelo, H; Ortega, S; Callico, GM; de With, PHN Linte, CA; Siewerdsen, JH Manni, Francesca; Cai, Chuchen; van der Sommen, Fons; Zinger, Svitlana; Shan, Caifeng; Edstrom, Erik; Elmi-Terander, Adrian; Fabelo, Himar; Ortega, Samuel; Callico, Gustavo M.; de With, Peter H. N. Hyperspectral imaging for tissue classification in glioblastoma tumor patients: a deep spectral-spatial approach MEDICAL IMAGING 2021: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING Proceedings of SPIE English Proceedings Paper Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions, and Modeling FEB 15-19, 2021 ELECTR NETWORK SPIE Hyperspectral imaging; malignant brain tumor; ant colony optimization; 3D-2D convolutional neural network (CNN) Surgery is a crucial treatment for malignant brain tumors where gross total resection improves the prognosis. Tissue samples taken during surgery are either subject to a preliminary intraoperative histological analysis, or sent for a full pathological evaluation which can take days or weeks. Whereas a lengthy complete pathological analysis includes an array of techniques to be executed, a preliminary tissue analysis on frozen tissue is performed as quickly as possible (30-45 minutes on average) to provide fast feedback to the surgeon during the surgery. The surgeon uses the information to confirm that the resected tissue is indeed tumor and may, at least in theory, initiate repeated biopsies to help achieve gross total resection. However, due to the total turn-around time of the tissue inspection for repeated analyses, this approach may not be feasible during a single surgery. In this context, intraoperative image-guided techniques can improve the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the potential to extract combined spectral-spatial information. By exploiting HSI for human brain-tissue classification in 13 in-vivo hyperspectral images from 9 patients, a brain-tissue classifier is developed. The framework consists of a hybrid 3D-2D CNN-based approach and a band-selection step to enhance the capability of extracting both spectral and spatial information from the hyperspectral images. An overall accuracy of 77% was found when tumor, normal and hyper-vascularized tissue are classified, which clearly outperforms the state-of-the-art approaches (SVM, 2D-CNN). These results may open an attractive future perspective for intraoperative brain-tumor classification using HSI. [Manni, Francesca; Cai, Chuchen; van der Sommen, Fons; Zinger, Svitlana; de With, Peter H. N.] Eindhoven Univ Technol, Eindhoven, Netherlands; [Shan, Caifeng] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao, Peoples R China; [Edstrom, Erik; Elmi-Terander, Adrian] Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden; [Edstrom, Erik; Elmi-Terander, Adrian] Karolinska Univ Hosp, Dept Neurosurg, Stockholm, Sweden; [Fabelo, Himar; Ortega, Samuel; Callico, Gustavo M.] Univ Las Palmas Gran Canaria, Inst Appl Microelect, Las Palmas Gran Canaria, Spain Eindhoven University of Technology; Shandong University of Science & Technology; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; Universidad de Las Palmas de Gran Canaria Manni, F (corresponding author), Eindhoven Univ Technol, Eindhoven, Netherlands. F.manni@tue.nl Shan, Caifeng/W-6178-2019; Elmi-Terander, Adrian/AAC-7997-2019; Callico, Gustavo/L-6036-2014 Shan, Caifeng/0000-0002-2131-1671; Elmi-Terander, Adrian/0000-0002-3776-6136; Callico, Gustavo/0000-0002-3784-5504; van der Sommen, Fons/0000-0002-3593-2356 Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain We acknowledge the Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain, for the dataset and the support on conducting this study. 15 0 0 0 0 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-4026-9 PROC SPIE 2021.0 11598.0 1159810 10.1117/12.2580158 0.0 9 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging BT7SH 2023-03-23 WOS:000850698500033 0
C Sun, RY; Hu, SC; Zhao, HJ; Moze, M; Aioun, F; Guillemard, F IEEE Sun, Ruoyu; Hu, Shaochi; Zhao, Huijing; Moze, Mathieu; Aioun, Francois; Guillemard, Franck Human-like Highway Trajectory Modeling based on Inverse Reinforcement Learning 2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper IEEE Intelligent Transportation Systems Conference (IEEE-ITSC) OCT 27-30, 2019 Auckland, NEW ZEALAND IEEE,Intel,Griffith Univ,Auckland Tourism Events & Econ Dev Autonomous driving is one of the current cutting edge technologies. For autonomous cars, their driving actions and trajectories should not only achieve autonomy and safety, but also obey human drivers' behavior patterns, when sharing the roads with other human drivers on the highway. Traditional methods, though robust and interpretable, demands much human labor in engineering the complex mapping from current driving situation to vehicle's future control. For newly developed deep-learning methods, though they can automatically learn such complex mapping from data and demands fewer humans' engineering, they mostly act like black-box, and are less interpretable. We proposed a new combined method based on inverse reinforcement learning to harness the advantages of both. Experimental validations on lane-change prediction and human-like trajectory planning show that the proposed method approximates the state-of-the-art performance in modeling human trajectories, and is both interpretable and data-driven. [Sun, Ruoyu; Hu, Shaochi; Zhao, Huijing] Peking Univ, Key Lab Machine Percept, Beijing, Peoples R China; [Moze, Mathieu; Aioun, Francois; Guillemard, Franck] Groupe PSA, Velizy Villacoublay, France Peking University Zhao, HJ (corresponding author), Peking Univ, Key Lab Machine Percept, Beijing, Peoples R China. zhaohj@cis.pku.edu.cn MOZE, Mathieu/0000-0003-0825-7014 Groupe PSA's OpenLab program (Multimodal Perception and Reasoning for Intelligent Vehicles); NSFC [61573027] Groupe PSA's OpenLab program (Multimodal Perception and Reasoning for Intelligent Vehicles); NSFC(National Natural Science Foundation of China (NSFC)) This work is partially supported by Groupe PSA's OpenLab program (Multimodal Perception and Reasoning for Intelligent Vehicles) and the NSFC Grants 61573027. R.Sun, S.Hu, H.Zhao are with the Key Lab of Machine Perception, Peking University, Beijing, China. M.Moze, F.Aioun and F.Guillemard are with Groupe PSA, Velizy, France. 28 3 3 2 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-5386-7024-8 IEEE INT C INTELL TR 2019.0 1482 1489 8 Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Transportation BO6LS 2023-03-23 WOS:000521238101084 0
J Pan, WA; Wang, TB; Wang, ZB; Yang, YH; Wu, H; Verdoolaege, G; Yang, ZC; Liu, CH; Guo, WP; Li, BL; Liu, ZJ; Wu, WB; Xu, M Pan, Wenan; Wang, Tianbo; Wang, Zhibin; Yang, Yonghao; Wu, Hao; Verdoolaege, Geert; Yang, Zengchen; Liu, Chunhua; Guo, Wenping; Li, Bingli; Liu, Zijie; Wu, Wenbin; Xu, Min Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method PLASMA SCIENCE & TECHNOLOGY English Article electron cyclotron emission; Thomson scattering; electron temperature; Bayesian inference FUSION; DIAGNOSTICS; SYSTEM Data analysis on tokamak plasmas is mainly based on various diagnostic systems, which are usually modularized and independent of each other. This leads to a large amount of data not being fully and effectively exploited so that it is not conducive to revealing the deep physical mechanism. In this work, Bayesian probability inference with machine learning methods have been applied to the electron cyclotron emission and Thomson scattering diagnostic systems on HL-2A/2M, and the effects of integrated data analysis (IDA) on the electron temperature of HL-2A with Bayesian probability inference are demonstrated. A program is developed to infer the whole electron temperature profile with a confidence interval, and the program can be applied in online analysis. The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA. The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool for HL-2A/2M in the near future. [Pan, Wenan; Wang, Zhibin; Yang, Yonghao; Wu, Wenbin] Sun Yat Sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Peoples R China; [Pan, Wenan; Wang, Tianbo; Wu, Hao; Yang, Zengchen; Liu, Chunhua; Guo, Wenping; Li, Bingli; Xu, Min] Southwestern Inst Phys, Chengdu 610041, Peoples R China; [Wu, Hao; Verdoolaege, Geert] Univ Ghent, B-9000 Ghent, Belgium; [Liu, Zijie] Univ Sci & Technol China, Hefei 230000, Peoples R China Sun Yat Sen University; Southwestern Institute of Physics - China; Ghent University; Chinese Academy of Sciences; University of Science & Technology of China, CAS Wang, ZB (corresponding author), Sun Yat Sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Peoples R China.;Wang, TB (corresponding author), Southwestern Inst Phys, Chengdu 610041, Peoples R China. wangtianbo@swip.ac.cn; wangzhb8@sysu.edu.cn National Magnetic Confinement Fusion Energy Research and Development Program of China [2019YFE03090100, 2019YFE03040004]; National Science Foundation for Young Scientists of China [12005052] National Magnetic Confinement Fusion Energy Research and Development Program of China; National Science Foundation for Young Scientists of China This work was supported by the National Magnetic Confinement Fusion Energy Research and Development Program of China (Nos. 2019YFE03090100, 2019YFE03040004), and the National Science Foundation for Young Scientists of China (No. 12005052). 25 0 0 5 5 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1009-0630 2058-6272 PLASMA SCI TECHNOL Plasma Sci. Technol. MAY 1 2022.0 24.0 5 55601 10.1088/2058-6272/ac5c25 0.0 16 Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Physics 1B5HH 2023-03-23 WOS:000792467300001 0
J Xiao, JH; Zhang, X; Zhang, D; Xue, LH; Sun, SQ; Stransky, J; Wang, YH Xiao, Junhua; Zhang, Xiao; Zhang, De; Xue, Lihua; Sun, Siqi; Stransky, Jan; Wang, Yanhai Morphological reconstruction method of irregular shaped ballast particles and application in numerical simulation of ballasted track TRANSPORTATION GEOTECHNICS English Article Ballast; Curvature index; Morphological reconstruction; Numerical simulation RAILWAY BALLAST; GENERATION; BEHAVIOR To quantitatively investigate the morphological features of railway ballast, a new statistical index, Curvature Index (CI), was presented. With a set of 584 digitized railway ballast particles obtained through 3D scanning, the statistical distribution functions of CI and existing global shape indices (i.e., long axis, middle axis, short axis and sphericity index) were obtained. Then, based on Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) Neural Network, a new shape reconstruction method was proposed to generate an arbitrary number of ballast particles that met the desired probability density distribution of morphological indices. And based on local curvature distribution on particle surface, a new simplification algorithm was implemented to reduce the number of contour points of each particle to no more than 31 on the premise of preserving global and local morphological features, which improved the efficiency of numerical simulations. These methods were used to reconstruct and simplify the arbitrary shapes of gravel ballast particles. Then, a DEM-FEM coupling model of ballasted track-subgrade was established by applying the regenerated ballast particles to analyze the contact stress at the ballast-soil interface. The numerical simulations can effectively reflect the contact stress distributions resulted from laboratory tests, which further confirmed that the reconstruction of the ballast particles reflected the morphological characteristics of real ballast. [Xiao, Junhua] Tongji Univ, Shanghai Key Lab Rail Infrastruct Durabil & Syst, Shanghai 201804, Peoples R China; [Xiao, Junhua; Zhang, Xiao; Zhang, De; Xue, Lihua; Sun, Siqi] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China; [Stransky, Jan] Czech Tech Univ, Dept Mech, Fac Civil Engn, Prague, Czech Republic; [Wang, Yanhai] Univ Tennessee, Dept Civil & Environm Engn, Knoxville, TN 37996 USA Tongji University; Tongji University; Czech Technical University Prague; University of Tennessee System; University of Tennessee Knoxville Zhang, D (corresponding author), Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China. dz2015@tongji.edu.cn Zhang, De/ABG-2295-2021 De, Zhang/0000-0003-1477-2557 National Natural Science Foundation of China [51678447] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors gratefully acknowledge the project supported by the National Natural Science Foundation of China (Grant no. 51678447). 35 9 9 13 32 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-3912 TRANSP GEOTECH Transp. Geotech. SEP 2020.0 24.0 100374 10.1016/j.trgeo.2020.100374 0.0 12 Engineering, Civil; Engineering, Geological Science Citation Index Expanded (SCI-EXPANDED) Engineering MZ5PY 2023-03-23 WOS:000559177200006 0
J Zhang, HX; Sanin, C; Szczerbicki, E Zhang, Haoxi; Sanin, Cesar; Szczerbicki, Edward Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study CYBERNETICS AND SYSTEMS English Article knowledge representation; Internet of Things; Knowledge Structure; intelligent systems; Decisional DNA; Set of Experience EXPERIENCE In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work-SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks. [Zhang, Haoxi] Chengdu Univ Informat Technol, Chengdu, Peoples R China; [Sanin, Cesar] Univ Newcastle, Newcastle, NSW 2300, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, 24 Block 1,Xuefu Rd, Chengdu, Peoples R China. haoxi@cuit.edu.cn Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912; Szczerbicki, Edward/0000-0001-7794-2862 Scientific Research Foundation of Chengdu University of Information Technology [KYTZ201422] Scientific Research Foundation of Chengdu University of Information Technology This work was supported as part of the Project KYTZ201422 by the Scientific Research Foundation of Chengdu University of Information Technology. 24 4 4 3 13 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. FEB 17 2015.0 46.0 1-2 SI 84 93 10.1080/01969722.2015.1007738 0.0 10 Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science CE3ZA 2023-03-23 WOS:000351767400007 0
C Duan, QY; Kim, T; Ghauch, H; Wong, EWM IEEE Duan, Qiyou; Kim, Taejoon; Ghauch, Hadi; Wong, Eric W. M. Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus DEC 07-11, 2020 ELECTR NETWORK IEEE,IEEE Commun Soc,Chunghwa Telecom,Foxconn,Huawei,Mediatek,6G Off,Askey,Ericsson,FET Qualcomm,Taiwan Mobile,Auden Techno Corp,Keysight Technologies,Telecom Technol Ctr,NI CHANNEL ESTIMATION We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based approach, called Kolmogorov model (KM). Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave MIMO beam alignment are presented. [Duan, Qiyou; Wong, Eric W. M.] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China; [Kim, Taejoon] Univ Kansas, Dept Elect Engn & Comp Sci, Lawrence, KS 66045 USA; [Ghauch, Hadi] Telecom ParisTech, Dept COMELEC, Paris, France City University of Hong Kong; University of Kansas; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Duan, QY (corresponding author), City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China. qyduan.ee@my.cityu.edu.hk; taejoonkim@ku.edu; hadi.ghauch@telecom-paristech.fr; eeewong@cityu.edu.hk DUAN, Qiyou/0000-0003-4451-687X; WONG, Wing Ming Eric/0000-0002-1641-6903 Innovation and Technology Funding (ITF), Hong Kong SAR, China [ITS/359/17]; National Science Foundation (NSF) [CNS 1955561, AST 2037864] Innovation and Technology Funding (ITF), Hong Kong SAR, China; National Science Foundation (NSF)(National Science Foundation (NSF)) The work was supported by a grant from the Innovation and Technology Funding (ITF), Hong Kong SAR, China [ITS/359/17].; T. Kim was supported in part by the National Science Foundation (NSF) under CNS 1955561 and AST 2037864. 24 2 2 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-8298-8 IEEE GLOB COMM CONF 2020.0 10.1109/GLOBECOM42002.2020.9322149 0.0 6 Computer Science, Artificial Intelligence; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BR7QI Green Submitted 2023-03-23 WOS:000668970500092 0
C Lin, H; Marquez, A; Wu, F; Liu, JX; Luo, H; Franquelo, LG; Wu, LG IEEE Lin, Hao; Marquez, Abraham; Wu, Feng; Liu, Jianxing; Luo, Hao; Franquelo, Leopoldo G.; Wu, Ligang MRAS-Based Sensorless Control of PMSM with BPN in Prediction Mode 2019 IEEE 28TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) Proceedings of the IEEE International Symposium on Industrial Electronics English Proceedings Paper 28th IEEE International Symposium on Industrial Electronics (IEEE-ISIE) JUN 12-14, 2019 Vancouver, CANADA IEEE,IEEE Ind Elect Soc Permanent magnet synchronous motor (PMSM); model reference adaptive system (MRAS); back propagation network (BPN); sensorless control; prediction mode INDUCTION-MOTOR; SPEED CONTROL; OBSERVER; POSITION; OPERATION In this paper, a novel model reference adaptive system (MRAS) observer based sensorless control of permanent magnet synchronous motor (PMSM) is proposed. This new speed observer uses the system model as the reference model, and the discrete system model with estimated rotor speed as the adaptive model to estimate the stator current, then, uses the gradient descent with an optimized proportional coefficient as the adaptive law to estimate the rotor speed. The adaptive model is a linear neural network which can be trained on line by means of back propagation. Moreover, the adaptive model is in prediction mode, which has a better performance compared with the simulation mode. The performance of the proposed method has been verified by Matlab/Simulink. [Lin, Hao; Liu, Jianxing; Luo, Hao; Franquelo, Leopoldo G.; Wu, Ligang] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Heilongjiang, Peoples R China; [Marquez, Abraham; Franquelo, Leopoldo G.] Univ Seville, Sch Engn, Elect Engn Dept, Seville 41092, Spain; [Wu, Feng] Shanghai Aerosp Control Technol Inst, Shanghai, Peoples R China Harbin Institute of Technology; University of Sevilla Lin, H (corresponding author), Harbin Inst Technol, Sch Astronaut, Harbin 150001, Heilongjiang, Peoples R China. haolin@hit.edu.cn Marquez Alcaide, Abraham/H-1781-2018; Luo, Hao/GQO-9490-2022; Franquelo, Leopoldo Garcia/D-5450-2009 Marquez Alcaide, Abraham/0000-0003-1647-7527; Luo, Hao/0000-0003-2143-2438; Franquelo, Leopoldo Garcia/0000-0002-1976-9747 26 3 3 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2163-5137 978-1-7281-3666-0 PROC IEEE INT SYMP 2019.0 1755 1760 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BO1LA 2023-03-23 WOS:000500998700265 0
J Wu, JS; Qiu, SJ; Zeng, R; Kong, YY; Senhadji, L; Shu, HZ Wu, Jiasong; Qiu, Shijie; Zeng, Rui; Kong, Youyong; Senhadji, Lotfi; Shu, Huazhong Multilinear Principal Component Analysis Network for Tensor Object Classification IEEE ACCESS English Article Deep learning; MPCANet; PCANet; tensor object classification; medical image classification REPRESENTATION The recently proposed principal component analysis network (PCANet) has performed well with respect to the classification of 2-D images. However, feature extraction may perform less well when dealing with multi-dimensional images, since the spatial relationships within the structures of the images are not fully utilized. In this paper, we develop a multilinear principal component analysis network (MPCANet), which is a tensor extension of PCANet, to extract the high-level semantic features from multi-dimensional images. The extracted features largely minimize the intraclass invariance of tensor objects by making efficient use of spatial relationships within multi-dimensional images. The proposed MPCANet outperforms traditional methods on a benchmark composed of three data sets, including the UCF sports action database, the UCF11 database, and a medical image database. It is shown that even a simple one-layer MPCANet may outperform a two-layer PCANet. [Wu, Jiasong; Qiu, Shijie; Zeng, Rui; Kong, Youyong; Shu, Huazhong] Southeast Univ, Key Lab Comp Network & Informat Integrat, LIST, Minist Educ, Nanjing 210096, Jiangsu, Peoples R China; [Wu, Jiasong; Qiu, Shijie; Zeng, Rui; Kong, Youyong; Senhadji, Lotfi; Shu, Huazhong] Ctr Rech Informat Biomed Sino Francais, Nanjing 210096, Jiangsu, Peoples R China; [Senhadji, Lotfi] INSERM, F-35000 Rennes, France; [Senhadji, Lotfi] Univ Rennes 1, Lab Traitement Signal & Image, F-35000 Rennes, France Southeast University - China; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes; Universite de Rennes Wu, JS (corresponding author), Southeast Univ, Key Lab Comp Network & Informat Integrat, LIST, Minist Educ, Nanjing 210096, Jiangsu, Peoples R China.;Wu, JS (corresponding author), Ctr Rech Informat Biomed Sino Francais, Nanjing 210096, Jiangsu, Peoples R China. jswu@seu.edu.cn Senhadji, Lotfi/E-5903-2013; wu, jiasong/A-3999-2009 Senhadji, Lotfi/0000-0001-9434-6341; wu, jiasong/0000-0001-7171-1318 36 23 25 1 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2017.0 5.0 3322 3331 10.1109/ACCESS.2017.2675478 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications EQ1DP gold, Green Submitted 2023-03-23 WOS:000397809900053 0
J Xiang, CQ; Zhang, L; Tang, Y; Zou, WB; Xu, C Xiang, Canqun; Zhang, Lu; Tang, Yi; Zou, Wenbin; Xu, Chen MS-CapsNet: A Novel Multi-Scale Capsule Network IEEE SIGNAL PROCESSING LETTERS English Article Capsule networks; multi-scale; capsule dropout; deep learning NEURAL-NETWORKS Capsule network is a novel architecture to encode the properties and spatial relationships of the feature in an image, which shows encouraging results on image classification. However, the original capsule network is not suitable for some classification tasks, where the target objects are complex internal representations. Hence, we propose a multi-scale capsule network that is more robust and efficient for feature representation in image classification. The proposed multi-scale capsule network consists of two stages. In the first stage, structural and semantic information are obtained by multi-scale feature extraction. In the second stage, the hierarchy of features is encoded to multi-dimensional primary capsules. Moreover, we propose an improved dropout to enhance the robustness of the capsule network. Experimental results show that our method has a competitive performance on FashionMNIST and CI FA RIO datasets. [Xiang, Canqun; Tang, Yi; Zou, Wenbin] Shenzhen Univ, Shenzhen Key Lab Adv Machine Learning & Applicat, Coll Informat Engn, Shenzhen 518060, Peoples R China; [Zhang, Lu] UMR CNRS 6164, INSA Rennes, IETR, F-35708 Rennes, France; [Xu, Chen] Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China Shenzhen University; Institut National des Sciences Appliquees de Rennes; Shenzhen University Zou, WB (corresponding author), Shenzhen Univ, Shenzhen Key Lab Adv Machine Learning & Applicat, Coll Informat Engn, Shenzhen 518060, Peoples R China. cq.xiang@foxmail.com; piggy@bupt.edu.cn; wzou@szu.edu.cn; xuchen@szu.edu.cn zhang, lu/AAP-5753-2020 zhang, lu/0000-0002-8859-5453; xiang, canqun/0000-0003-0396-2427; , SZU/0000-0001-9551-1054; Tang, Yi/0000-0002-4882-5234 NSFC Project [61771321, 61872429, 61871273]; Natural Science Foundation of Shenzhen [KQJSCX20170327151357330, JCYJ20170818091621856, JSGG20170822153717702]; Interdisciplinary Innovation Team of Shenzhen University NSFC Project(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shenzhen; Interdisciplinary Innovation Team of Shenzhen University This work was supported in part by the NSFC Project under Grants 61771321, 61872429, and 61871273, in part by the Natural Science Foundation of Shenzhen under Grants KQJSCX20170327151357330, JCYJ20170818091621856, and JSGG20170822153717702, and in part by the Interdisciplinary Innovation Team of Shenzhen University. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Wei Li. 23 106 116 9 104 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. DEC 2018.0 25.0 12 1850 1854 10.1109/LSP.2018.2873892 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering GZ4WO Green Submitted 2023-03-23 WOS:000449409600006 0
C Liu, YH; Liu, C; Bierig, R ACM Liu, Ying-Hsang; Liu, Chang; Bierig, Ralf User Interactions with Search Systems PROCEEDINGS OF THE 2018 ACM SIGIR INTERNATIONAL CONFERENCE ON THEORY OF INFORMATION RETRIEVAL (ICTIR'18) English Proceedings Paper 8th ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) SEP 14-17, 2018 Tianjin, PEOPLES R CHINA Assoc Comp Machinery,ACM SIGIR Interactive information retrieval; Information retrieval evaluation; Methods and methodologies; User studies; Search interfaces TASK The proposed course on User Interactions with Search Systems provides students an overview on interactive information retrieval (IIR) evaluation studies that is specifically focused on the modelling of users, tasks and contexts, and methods for IIR studies. This thread of research has attracted more information retrieval (IR) researchers in recent years, as evidenced by the establishment of the ACM SIGIR Conference on Human Information Interaction and Retrieval series (CHIIR). Given recent interests in machine learning, social media data analysis and emerging system-oriented interactive search models, this course provides students with a more complete picture for understanding user search behaviour and user-oriented approaches for the evaluation of IR systems. It emphasises how user aspects should be addressed from theoretical, conceptual, design and evaluation perspectives for IR theory and system development. This important research area has been rarely taught at the International Conference on the Theory of Information Retrieval (ICTIR). [Liu, Ying-Hsang] Charles Sturt Univ, Sch Informat Studies, Bathurst, NSW, Australia; [Liu, Ying-Hsang] Charles Sturt Univ, Inst Land Water & Soc, Bathurst, NSW, Australia; [Liu, Chang] Peking Univ, Beijing, Peoples R China; [Bierig, Ralf] Maynooth Univ, Maynooth, Kildare, Ireland; [Liu, Ying-Hsang] Australian Natl Univ, Res Sch Comp Sci, Canberra, ACT, Australia Charles Sturt University; Charles Sturt University; Peking University; Maynooth University; Australian National University Liu, YH (corresponding author), Charles Sturt Univ, Sch Informat Studies, Bathurst, NSW, Australia.;Liu, YH (corresponding author), Charles Sturt Univ, Inst Land Water & Soc, Bathurst, NSW, Australia. yingliu@csu.edu.au; imliuc@pku.edu.cn; ralf.bierig@mu.ie LIU, Ying-Hsang/0000-0001-6504-4598 26 2 2 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-5656-5 2018.0 3 4 10.1145/3234944.3234981 0.0 2 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS4RB 2023-03-23 WOS:000722236000002 0
J Wang, W; Samat, A; Abuduwaili, J; Ge, YX; De Maeyer, P; van de Voorde, T Wang, Wei; Samat, Alim; Abuduwaili, Jilili; Ge, Yongxiao; De Maeyer, Philippe; van de Voorde, Tim A novel hybrid sand and dust storm detection method using MODIS data on GEE platform EUROPEAN JOURNAL OF REMOTE SENSING English Article word; sand and dust storm detection; support vector machine (SVM); Multi-Band Snow Cloud Index (MBSCI); Google Earth Engine (GEE); MODIS INDEX; WATER Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection. [Wang, Wei; Samat, Alim; Abuduwaili, Jilili; Ge, Yongxiao] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Xinjiang, Peoples R China; [Wang, Wei; Samat, Alim; Abuduwaili, Jilili; Ge, Yongxiao] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Res Ctr Ecol & Environm Cent Asia, Urumqi, Xinjiang, Peoples R China; [Wang, Wei; Samat, Alim; Abuduwaili, Jilili] Univ Chinese Acad Sci, Beijing, Peoples R China; [van de Voorde, Tim] Univ Ghent, Dept Geog, Ghent, Belgium; [De Maeyer, Philippe; van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, Ghent, Belgium; [Wang, Wei; De Maeyer, Philippe; van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, Urumqi, Xinjiang, Peoples R China Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ghent University; Ghent University Samat, A (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Jilili Abuduwaili State Key Lab Desert & Oasis Ec, Urumqi, Peoples R China. alim_smt@ms.xjb.ac.cn ge, yongxiao/K-1251-2012; De Maeyer, Philippe/F-2985-2011 ge, yongxiao/0000-0001-8111-7259; Van de Voorde, Tim/0000-0002-9324-5087; ji, li li/0000-0001-8483-1554; Samat, Alim/0000-0002-9091-6033; Wang, Wei/0000-0003-1813-0551; De Maeyer, Philippe/0000-0001-8902-3855 National Natural Science Foundation of China [42171014, 42071424]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA20060301]; China Scholarship Council [202104910412]; UNEP-NSFC Internat ional Cooperation Project [42161144004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); China Scholarship Council(China Scholarship Council); UNEP-NSFC Internat ional Cooperation Project This work was supported by the National Natural Science Foundation of China 42071424,42171014 (grant nos. 42171014, 42071424), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20060301);China Scholarship Council [202104910412];UNEP-NSFC Internat ional Cooperation Project [42161144004]; 30 2 2 14 18 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 2279-7254 EUR J REMOTE SENS Eur. J. Remote Sens. DEC 31 2022.0 55.0 1 420 428 10.1080/22797254.2022.2093278 0.0 9 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing 2S3EE Green Published, gold 2023-03-23 WOS:000821677900001 0
J Mia, MU; Rahman, M; Elbeltagi, A; Abdullah-Al-Mahbub, M; Sharma, G; Islam, HMT; Pal, SC; Costache, R; Islam, AMT; Islam, MM; Chen, NS; Alam, E; Washakh, RMA Mia, Md Uzzal; Rahman, Mahfuzur; Elbeltagi, Ahmed; Abdullah-Al-Mahbub, Md; Sharma, Gitika; Islam, H. M. Touhidul; Pal, Subodh Chandra; Costache, Romulus; Islam, Abu Reza Md Towfiqul; Islam, Md Monirul; Chen, Ningsheng; Alam, Edris; Washakh, Rana Muhammad Ali Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology GEOCARTO INTERNATIONAL English Article; Early Access Blockchain; relief; sustainable development; flash floods; deep learning algorithm SUPPORT VECTOR MACHINE; BIVARIATE STATISTICAL-MODELS; FUZZY INFERENCE SYSTEM; WEIGHTS-OF-EVIDENCE; SUSCEPTIBILITY ASSESSMENT; HAZARD ASSESSMENT; RIVER-BASINS; GIS; VULNERABILITY; CHINA The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events. [Mia, Md Uzzal; Abdullah-Al-Mahbub, Md; Islam, H. M. Touhidul; Islam, Abu Reza Md Towfiqul] Begum Rokeya Univ, Dept Disaster Management, Rangpur, Bangladesh; [Rahman, Mahfuzur; Islam, Md Monirul] Int Univ Business Agr & Technol IUBAT, Dept Civil Engn, Dhaka, Bangladesh; [Elbeltagi, Ahmed] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura, Egypt; [Sharma, Gitika] Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Patiala, Punjab, India; [Pal, Subodh Chandra] Univ Burdwan, Dept Geog, Bardhaman, W Bengal, India; [Costache, Romulus] Transilvania Univ Brasov, Dept Civil Engn, Brasov, Romania; [Costache, Romulus] Danube Delta Natl Inst Res & Dev, Tulcea, Romania; [Chen, Ningsheng] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu, Peoples R China; [Chen, Ningsheng] Univ Chinese Acad Sci, Beijing, Peoples R China; [Chen, Ningsheng] Acad Plateau Sci & Sustainabil, Xining, Peoples R China; [Alam, Edris] Fac Resilience, Abu Dhabi, U Arab Emirates; [Alam, Edris] Univ Chittagong, Dept Geog & Environm Studies, Chittagong, Bangladesh; [Washakh, Rana Muhammad Ali] Neijiang Normal Univ, Sch Architecture, Neijiang, Sichuan, Peoples R China International University of Business Agriculture & Technology (IUBAT); Egyptian Knowledge Bank (EKB); Mansoura University; Thapar Institute of Engineering & Technology; University of Burdwan; Transylvania University of Brasov; Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Chittagong; Neijiang Normal University Islam, AMT (corresponding author), Begum Rokeya Univ, Dept Disaster Management, Rangpur, Bangladesh.;Rahman, M (corresponding author), Int Univ Business Agr & Technol IUBAT, Dept Civil Engn, Dhaka, Bangladesh. mfz.rahman@iubat.edu; towfiq_dm@brur.ac.bd Sharma, Gitika/GPX-5577-2022; kumar, Pankaj/HPF-8395-2023; Islam, H. M. Touhidul/ABC-2522-2020; Islam, Abu Reza Md. Towfiqul/O-8554-2019; Costache, Romulus/GVU-1762-2022; Abdullah-Al-Mahbub, Md./ACJ-3437-2022 Islam, H. M. Touhidul/0000-0003-2146-2864; Islam, Abu Reza Md. Towfiqul/0000-0001-5779-1382; Abdullah-Al-Mahbub, Md./0000-0002-7417-1473; Rahman, Mahfuzur/0000-0001-8402-156X; Washakh, Rana Muhammad Ali/0000-0003-3234-7458; Pal, Subodh Chandra/0000-0003-0805-8007; sharma, Gitika/0000-0002-6390-327X; Costache, Romulus/0000-0002-6876-8572 123 1 1 19 22 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1010-6049 1752-0762 GEOCARTO INT Geocarto Int. 10.1080/10106049.2022.2112982 0.0 AUG 2022 29 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 3Y3EK hybrid 2023-03-23 WOS:000843609600001 0
J Heremans, S; Dong, QH; Zhang, BE; Bydekerke, L; Van Orshoven, J Heremans, Stien; Dong, Qinghan; Zhang, Beier; Bydekerke, Lieven; Van Orshoven, Jos Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data JOURNAL OF APPLIED REMOTE SENSING English Article wheat yield estimation; empirical models; ensemble trees RANDOM FORESTS; NDVI DATA; CLIMATIC INFORMATION; FEATURE-SELECTION; EARTH OBSERVATION; REGRESSION TREES; AVHRR DATA; MODEL; CLASSIFICATION; IMAGERY We aimed at analyzing the potential of two ensemble tree machine learning methods-boosted regression trees and random forests-for (early) prediction of winter wheat yield from short time series of remotely sensed vegetation indices at low spatial resolution and of in situ meteorological data in combination with annual fertilization levels. The study area was the Huaibei Plain in eastern China, and all models were calibrated and validated for five separate prefectures. To this end, a cross-validation process was developed that integrates model meta-parameterization and simple forward feature selection. We found that the resulting models deliver early estimates that are accurate enough to support decision making in the agricultural sector and to allow their operational use for yield forecasting. To attain maximum prediction accuracy, incorporating predictors from the end of the growing season is, however, recommended. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) [Heremans, Stien; Van Orshoven, Jos] KU Leuven Univ Leuven, Dept Earth & Environm Sci, BE-3001 Leuven, Belgium; [Dong, Qinghan; Bydekerke, Lieven] Flemish Inst Technol Res VITO, Dept Remote Sensing, BE-2400 Mol, Belgium; [Zhang, Beier] Anhui Inst Econ Res, Hefei 230001, Peoples R China KU Leuven; VITO Heremans, S (corresponding author), KU Leuven Univ Leuven, Dept Earth & Environm Sci, Celestijnenlaan 200E, BE-3001 Leuven, Belgium. stien.heremans@ees.kuleuven.be Heremans, Stien/AAO-2471-2020 Heremans, Stien/0000-0002-5356-1093; Van Orshoven, Jos/0000-0001-5756-7188 Chinese Ministry of Agriculture; Flemish Institute for Science and Innovation (IWT) Chinese Ministry of Agriculture(Ministry of Agriculture & Rural Affairs); Flemish Institute for Science and Innovation (IWT) We would like to acknowledge the E-AGRI project, conducted in the EU's Seventh Framework Programme for Research (FP7) for providing the input data. We also thank the Dragon 3 program of the Chinese Ministry of Agriculture and the Flemish Institute for Science and Innovation (IWT) for providing the specific grant and the PhD scholarship that made this research possible. 75 22 22 0 34 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. MAR 12 2015.0 9.0 97095 10.1117/1.JRS.9.097095 0.0 20 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology CF8TD 2023-03-23 WOS:000352833900001 0
C Yang, H; Yuan, YL Lee, CJ Sehiemy, RE; Reaz, MBI Yang, Hong; Yuan, Yali Lee, CJ A General Gender Inference Method Based on Web PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016) Advances in Intelligent Systems Research English Proceedings Paper 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE) NOV 20-21, 2016 Beijing, PEOPLES R CHINA component; data mining; gender prediction; demographic; big data Gender information, as a crucial part of human demographics, is valuable for its abundant connotations and potential applications. Though much effort has been made on the problem of gender inference, most existing methods are highly dependent on data from specific sources, like Twitter, and are difficult to be generalized to other tasks. In this work, we propose a general Web-based method for gender inference. We show that our model significantly outperforms state-of-the-art without much human workload or any limits on specific scenarios. Based on that, we also present a voting framework to efficiently incorporate several methods to further improve performance. Experiments show that our voting framework can achieve 96.9% accuracy. [Yang, Hong] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Yuan, Yali] Inst Comp Sci, Gottingen, Germany Tsinghua University Yang, H (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China. 19 0 0 0 0 ATLANTIS PRESS PARIS 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE 1951-6851 978-94-6252-271-8 ADV INTEL SYS RES 2016.0 133.0 210 213 4 Computer Science, Artificial Intelligence; Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BG8DH 2023-03-23 WOS:000392232200049 0
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Shaked; ; ATLAS Collaboration Study of the hard double-parton scattering contribution to inclusive four-lepton production in pp collisions at root s=8 TeV with the ATLAS detector PHYSICS LETTERS B English Article Four-lepton production; Double Drell-Yan; Double parton-scattering; Higgs golden decay channel EVENTS; SEARCH The inclusive production of four isolated charged leptons in pp collisions is analysed for the presence of hard double-parton scattering, using 20.2 fb(-1) of data recorded in the ATLAS detector at the LHC at centre-of-mass energy root s = 8 TeV. In the four-lepton invariant-mass range of 80 < m(4l) < 1000 GeV, an artificial neural network is used to enhance the separation between single- and double-parton scattering based on the kinematics of the four leptons in the final state. An upper limit on the fraction of events originating from double-parton scattering is determined at 95% confidence level to be f(DPS) = 0.042, which results in an estimated lower limit on the effective cross section at 95% confidence level of 1.0 mb. (C) 2019 The Author(s). Published by Elsevier B.V. [Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Cairo, V. M. M.; Jain, V; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Dehghanian, N.; Gingrich, D. M.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Costanza, F.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. 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M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Bartos, P.; Blazek, T.; Dado, T.; Dubovsky, M.; Majersky, O.; Melo, M.; Smiesko, J.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Kladiva, E.; Murin, P.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Dept Subnucl Phys, Inst Expt Phys, Kosice, Slovakia; [Assamagan, K.; Barone, G.; Begel, M.; Cavaliere, V; Chen, H.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Hu, Q.; Iakovidis, G.; Klimentov, A.; Kouskoura, V; Lancon, E.; Lanni, F.; Lee, C. A.; Liu, H. B.; Lynn, D.; Ma, H.; Maeno, T.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Bossio Sola, J. D.; Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Batley, J. R.; Brunt, B. H.; Chapman, J. D.; Cottin, G.; Fawcett, W. J.; Hill, J. C.; Hommels, B. H.; Lester, C. G.; Lin, C. Y.; Malone, C.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Williams, S.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Hamilton, A.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; [Boye, D.; Casadei, D.; Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; [Jimenez, Y. Hernandez; Hlaluku, D. R.; Jivan, H.; Kar, D.; Lagouri, T.; Mdhluli, J. E.; Garcia, B. R. Mellado; Reed, R. G.; Roy, D.; Ruan, X.; Haddad, E. Sideras; Tomiwa, K. G.; Von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Cree, G.; Di Valentino, D.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Oakham, F. G.; Park, T. H.; Sherafati, N.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chafaq, A.; Hoummada, A.] Univ Hassan 2, Fac Sci Ain Chock, Reseau Univ Phys Hautes Energies, Casablanca, Morocco; Ctr Natl Energie Sci Tech Nucl CNESTEN, Rabat, Morocco; [El Kacimi, M.; Goujdami, D.] Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA, Marrakech, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; Dahbi, S.; Ezzi, M.; Fassi, F.; Haddad, N.; Idrissi, Z.; Tayalati, Y.] Univ Mohammed 5, Fac sci, Rabat, Morocco; [Aleksa, M.; Gonzalez, B. Alvarez; Aperiobella, L.; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Bielski, R.; Bortfeldt, J.; Boyd, J.; Buat, Q.; Butti, P.; Buttinger, W.; Camarda, S.; Camincher, C.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Cattai, A.; Chelstowska, M. A.; Chisholm, A. S.; Cortes-Gonzalez, A.; Czodrowski, P.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Dittus, F.; Dudarev, A.; Duehrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Froidevaux, D.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Gumpert, C.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Javurek, T.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Kuehn, S.; Kuwertz, E. S.; Langenberg, R. J.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Malyukov, S.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Berlingen, J. Montejo; Llacer, M. Moreno; Morley, A. K.; Mornacchi, G.; Mountricha, E.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pandini, C. E.; Pani, P.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Polifka, R.; Pontecorvo, L.; Poveda, J.; Astigarraga, M. E. Pozo; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Salzburger, A.; Samarati, J.; Schaepe, S.; Schioppa, E. J.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sidiropoulou, O.; Sanchez, C. A. Solans; Spigo, G.; Staerz, S.; Stelzer, H. J.; Stewart, G. A.; Stockton, M. C.; Tenkate, H.; Unal, G.; Vandelli, W.; Schroeder, T. Vazquez; Venturi, N.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Young, C. J. S.; Zanzi, D.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Gardner, R. W.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Merritt, F. S.; Miller, D. W.; Olsson, M. J. R.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Stark, G. H.; Swiatlowski, M.; Vukotic, I; Wu, M.; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Angelidakis, S.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Gris, Ph; Morales, F. A. Jimenez; Madar, R.; Marjanovic, M.; Nibigira, E.; Pallin, D.; Santoni, C.; Senkin, S.; Vazeille, F.] Univ Clermont Auvergne, LPC, CNRS IN2P3, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Carbone, R. M.; Chen, J.; Clark, M. R.; Cole, B.; Emerman, A.; Havener, L. B.; Iordanidou, K.; Mahon, D. J.; Mohapatra, S.; Ochoa, I; Parsons, J. A.; Rybar, M.; Tian, Y.; Tuts, P. M.] Columbia Univ, Nevis Lab, Irvington, NY USA; [AbouZeid, O. S.; Alonso, A.; Bajic, M.; Besjes, G. J.; Camplani, A.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Monk, J.; Nielsen, D. S.; Petersen, T. C.; Hstark, S.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Coll Cosenza, Frascati, Italy; [Feickert, M.; Kama, S.; Kehoe, R.; Lo Sterzo, F.; McCarn, A.; McKay, M. A.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Wang, P.; Ye, J.; Zhou, L.] Southern Methodist Univ, Dept Phys, Dallas, TX USA; [Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Bertoli, G.; Bohm, C.; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Bertoli, G.; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Amoroso, S.; Arling, J-H; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I; Braren, F.; Brendlinger, K.; Brenner, L.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Madsen, A.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] DESY, Hamburg, Germany; [Amoroso, S.; Arling, J-H; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I; Braren, F.; Brendlinger, K.; Brenner, L.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Madsen, A.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] DESY, Zeuthen, Germany; [Burmeister, I; Cinca, D.; Erdmann, J.; Freundlich, E. M.; Gessner, G.; Goessling, C.; Homann, M.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Ucchielli, G.; Weingarten, J.; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys IV, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Herrmann, T.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Maerker, M.; Ramos, J. Manjarres; Morgenstern, S.; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Palazzo, S.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becker, K.; Buehrer, F.; Coniavitis, E.; Gargiulo, S.; Gonella, G.; Gugel, R.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hohn, D.; Jakobs, K.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V; Megy, T. M.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schleicher, K. E.; Schnoor, U.; Schumacher, M.; Tornambe, P.; Weiser, C.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Beck, H. C.; Bindi, M.; Bisanz, T.; Buschmann, E.; Dado, T.; Drechsler, E.; Dreyer, T.; Geisen, J.; Gama, R. Goncalves; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lange, J. C.; Mchedlidze, G.; Mellenthin, J. D.; Nellist, C.; Oerdek, S.; Pokharel, I; Quadt, A.; Rosien, N-A; Rzehorz, G. F.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Stolte, P.; Torres, R. E. Ticse; Veatch, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Akilli, E.; Amrouche, C. S.; Ancu, L. S.; Benoit, M.; Calace, N.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; March, L.; Mermod, P.; Nessi, M.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Pinto, M. Vicente Barreto; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Coccaro, A.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Dueren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Callea, G.; Crawley, S. J.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; O'Shea, V; Owen, M.; Robson, A.; Spiteri, D. P.; Thompson, A. S.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Grenoble INP, LPSC, CNRS IN2P3, Grenoble, France; [Asbah, N.; Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kato, C.; Kondrashova, N.; Li, L.; Li, S.; Li, X.; Mittal, M.; Nishu, N.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, KLPPAC MoE, SKLPPC, Shanghai, Peoples R China; [Kato, C.; Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J., I; Dunford, M.; Franchino, S.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kugel, A.; Zu Theenhausen, H. Meyer; Napolitano, F.; Villar, D. I. Narrias; Sahinsoy, M.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; de Lima, D. E. Ferreira; Giulini, M.; Kolb, M.; Lisovyi, M.; Schoening, A.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Cheng, H. C.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Lu, H.; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lo, C. Y.; Orlando, N.; Hernandez, D. Paredes; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys & Inst Adv Study, Kowloon, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Palacino, G.; Roland, C. P. A.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] Ist Nazl Fis Nucl, Grp Collegato Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Chim Fis & Ambiente, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, Lecce, Italy; [Bachas, K.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Carra, S.; Cavalli, D.; Citterio, M.; D'Auria, S.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Manzoni, S.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.; Perez, M. Villaplana] Ist Nazl Fis Nucl, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; Carra, S.; D'Auria, S.; Fanti, M.; Lazzaroni, M.; Manzoni, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.; Sekhniaidze, G.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Fraternali, M.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.; Vercesi, V.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Farina, E. M.; Fraternali, M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; De Maria, A.; Di Gregorio, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; De Maria, A.; Di Gregorio, G.; Francavilla, P.; Roda, C.; Sotiropoulou, C. L.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Safaitehrani, F.; Sebastiani, C. D.; Vari, R.; Veneziano, S.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Liberti, B.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Vanadia, M.] Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Baroncelli, A.; Biglietti, M.; Ceradini, F.; Di Micco, B.; Farilla, A.; Iodice, M.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Stanescu, C.; Vecchio, V.; Verducci, M.] Ist Nazl Fis Nucl, Sez Roma Tre, Rome, Italy; [Ceradini, F.; Di Micco, B.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Vecchio, V.; Verducci, M.] Univ Roma Tre, Dipartimento Matemat Fis, Rome, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Ist Nazl Fis Nucl, TIFPA, Trento, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Beermann, T. A.; Jakobsen, S.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [An, F.; Chen, C. H.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Liu, B.; Pluth, D.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M., V; Dedovich, D., V; Demichev, M.; Gongadze, A.; Gostkin, M., I; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V; Kulchitsky, Y.; Ladygin, E.; Lyubushkin, V; Lyubushkina, T.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Tsiareshka, P., V; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I; Zhemchugov, A.; Zimine, N., I] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.; Peralva, B. S.; Oliveira, M. V. Silva] Univ Fed Juiz de Fora, Dept Engn Eletr, Juiz de Fora, Brazil; [Amaral Coutinho, Y.; Araujo Ferraz, V; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Freund, W. S.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio De Janeiro, COPPE EE IF, Rio De Janeiro, Brazil; [Do Vale, M. A. B.] Univ Fed Sao Joao del Rei, Sao Joao del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Aoki, M.; Hanagaki, K.; Higashino, S.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Mizukami, A.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Odaka, S.; Okuyama, T.; Sasaki, O.; Shimizu, S.; Suzuki, S.; Takubo, Y.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I; Unno, Y.; Usui, J.; Yamamoto, A.; Yasu, Y.] KEK, High Energy Accelerator Res Org, Tsukuba, Ibaraki, Japan; [Chen, Y.; Kawade, K.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Takeda, K.; Wakamiya, K.; Yamane, F.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I; Guzik, M. P.; Janus, P. A.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Kremer, J. A.; Mindur, B.; Przybycien, M.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Bahmani, M.; Banas, E.; de Renstrom, P. A. Bruckman; Burka, K.; Chwastowski, J. J.; Czekierda, S.; Derendarz, D.; Dziedzic, B. S.; Godlewski, J.; Kaczmarska, A.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa; Olszewski, A.; Olszowska, J.; Slawinska, M.; Smykiewicz, A.; Stanecka, E.; Staszewski, R.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; 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Iowa State University; Joint Institute for Nuclear Research - Russia; Universidade Federal de Juiz de Fora; Universidade Federal do Rio de Janeiro; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; High Energy Accelerator Research Organization (KEK); Kobe University; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Kyoto University; Kyoto University of Education; Kyushu University; Kyushu University; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; Lund University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Autonomous University of Madrid; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; City University of New York (CUNY) System; Council for Scientific & Industrial Research (CSIR) - South Africa; Autonomous University of Barcelona; Universidade de Lisboa; Instituto Superior Tecnico; University of Sharjah; University of Aegean; University of Louisville; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; Stanford University; University of Fribourg; University of Michigan System; University of Michigan; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Ilia State University; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Istanbul University; Manhattan College; Moscow Institute of Physics & Technology; Sun Yat Sen University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. 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Huseynov, Nazim/AAE-4663-2019; Panizzo, Giancarlo/AAS-2911-2020; Leisos, Antonios/AAJ-2351-2021; della Volpe, Domenico/B-4482-2012; Annovi, Alberto/AAA-8638-2020; Garcia, Carmen/W-2465-2018; Sopkova, Filomena/HKV-6270-2023; Orlando, Nicola/AAL-1723-2021; Benekos, Nektarios/I-9928-2017; zhang, jin/GXV-9154-2022; Capua, Marcella/AAJ-8419-2020; Artoni, Giacomo/ABA-2164-2020; Napolitano, Fabrizio/CAI-2163-2022; Dubinin, Filipp/M-9546-2015; Cavaliere, Viviana/CAE-8597-2022; Wozniak, Krzysztof W/P-4475-2017; Cetin, Serkant Ali/AGF-0147-2022; Llorente Merino, Javier/AGP-3432-2022; Gutierrez, Phillip/C-1161-2011; Adiguzel, Aytul/AAC-9049-2020; Billoud, Thomas/AAY-1569-2020; banerjee, swagato/AAN-3165-2021; Chitan, Adrian/AAC-8946-2022; Wolters, Helmut/M-4154-2013; Santra, Arka/AEE-4946-2022; KHODINOV, ALEKSANDR/D-6269-2015; Kantserov, Vadim/M-9761-2015; de Renstrom, Pawel Bruckman/AAG-7725-2021; Kostyukhin, Vadim/ABA-9033-2020; Jiménez, Yesenia Hernández/ABH-1107-2020; Trzebinski, Maciej/W-1748-2018; Tikhomirov, Vladimir/M-6194-2015; Wu, Xin/ABH-1729-2020; Pascual, Juan Antonio Garcia/B-3540-2019; Reznicek, Pavel/C-1989-2017; Carquin, Edson/GLU-9641-2022; Song, Weimin/AAJ-5415-2020; da via, cinzia/AAS-3978-2021; Sapronov, Andrey/HGB-0392-2022; Parodi, Fabrizio/AHE-5089-2022; Norjoharuddeen, Nurfikri/S-3109-2018; Fabbri, Laura/H-3442-2012; YILDIZ, Hatice DURAN/AAN-3727-2021; Buttar, Craig/AAG-5392-2021; Coadou, Yann/G-2263-2010; Caprini, Irinel/AAP-3760-2020; Bachas, Konstantinos/C-8101-2019; Kroll, Jiri/C-8465-2018; Franchini, Matteo/AAC-9259-2021; Ippolito, Valerio/L-1435-2016; Lagouri, Theodota/AAC-7358-2021; Giuli, Francesco/HJI-6649-2023; Glasman, Claudia/Y-8858-2019; Fassouliotis, Dimitrios/AAL-7081-2021; Ahmadov, Faig N/B-3723-2018; Balaji, S/J-1864-2019; Ferrer, Antonio/H-2942-2015; Lazzaroni, Massimo/N-3675-2015; Godlewski, Jan/AAI-3300-2021; Kartvelishvili, Vakhtang G/K-2312-2013; Bailey, Adam J/T-9828-2017; Beddall, Andrew John/AAE-5820-2022; Malek, Fairouz/E-4410-2018; Svatos, Michal/ABA-2041-2020; 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MESTD, Serbia; MSSR, Slovakia; ARRS Slovenia; MIZS, Slovenia; MINECO, Spain; Wallenberg Foundation, Sweden; SNSF, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE United States of America; NSF, United States of America; BCKDF; COST, ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society; Leverhulme Trust, United Kingdom; NRC Canada; CAS, China; MOST, China; IN2P3-CNRS, France; SRNSFG, Georgia; HGF, Germany; CNRST, Morocco; MES of Russia; DST/NRF, South Africa; SRC, Sweden; SERI, Switzerland; Canton of Bern, Switzerland; Canton Geneva, Switzerland; STFC [ST/N000463/1, ST/S00095X/1, ST/N000420/1, ST/H001158/2, ST/P002439/1, ST/S000860/1, ST/N000277/1, ST/N000234/1, ST/L003449/1, ST/N000447/1, ST/S000747/1, ST/N000307/1, ST/J005533/1, ST/J004804/1, ST/L006162/1, ST/M000753/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); CFI, Canada(Canada Foundation for Innovation); CONICYT Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); CEA-DRF/IRFU, France; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; NRC KI, Russian Federation; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; MINECO, Spain(Spanish Government); Wallenberg Foundation, Sweden; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF; COST, ERC; ERDF(European Commission); Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); DFG(German Research Foundation (DFG)); AvH Foundation, Germany - EU-ESF(Alexander von Humboldt Foundation); Greek NSRF, Greece; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; Royal Society(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); NRC Canada; CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); SRNSFG, Georgia; HGF, Germany; CNRST, Morocco; MES of Russia; DST/NRF, South Africa; SRC, Sweden; SERI, Switzerland; Canton of Bern, Switzerland; Canton Geneva, Switzerland; STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSFNSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. 69 19 19 1 26 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B MAR 10 2019.0 790.0 595 614 10.1016/j.physletb.2019.01.062 0.0 20 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics HN3XJ Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000460118200077 0
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S.; Sharma, A.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; ; ATLAS Collaboration Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC EUROPEAN PHYSICAL JOURNAL C English Article The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies. [Banerjee, S.; Dang, N. P.; Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Cairo, V. M. M.; Jain, V.; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Dehghanian, N.; Gingrich, D. M.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Costanza, F.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. 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F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Oakham, F. G.; Sherafati, N.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chafaq, A.; Fassi, F.; Hoummada, A.] Univ Hassan 2, Reseau Univ Phys Hautes Energies, Fac Sci Ain Chock, Casablanca, Morocco; Ctr Natl Energie Sci Tech Nucl CNESTEN, Rabat, Morocco; [ElKacimi, M.; Goujdami, D.] Univ Cadi Ayyad, LPHEA Marrakech, Fac Sci Semlalia, Marrakech, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; Cherkaoui El Moursli, R.; Dahbi, S.; Ezzi, M.; Haddad, N.; Idrissi, Z.; Tayalati, Y.] Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Aleksa, M.; Gonzalez, B. Alvarez; Aperio Bella, L.; Armbruster, A. J.; Avolio, G.; Barisits, M. -S.; Beermann, T. 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H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V.; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Madsen, A.; Meloni, F.; Moenig, K.; Naranjo Garcia, R. F.; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Hamburg, Germany; [Amoroso, S.; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I.; Braren, F.; Brendlinger, K.; Brenner, L.; Bryngemark, L.; Chen, Y. -H.; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Grivaz, J. -F.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V.; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Madsen, A.; Meloni, F.; Moenig, K.; Naranjo Garcia, R. F.; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Zeuthen, Germany; [Burmeister, I.; Cinca, D.; Erdmann, J.; Gessner, G.; Goessling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I.; Weingarten, J.; Zeissner, S. V.] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Ramos, J. Manjarres; Morgenstern, S.; Novgorodova, O.; Siegert, F.; Straessner, A.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.; Yang, H. T.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becker, K.; Buehrer, F.; Coniavitis, E.; Gonella, G.; Gugel, R.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Jakobs, K.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V.; Megy, T. M.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schleicher, K. E.; Schnoor, U.; Schumacher, M.; Tornambe, P.; Weiser, C.; Wiik-Fuchs, L. A. M.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Beck, H. C.; Bindi, M.; Bisanz, T.; Buschmann, E.; Dado, T.; De Maria, A.; Drechsler, E.; Dreyer, T.; Geisen, J.; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lemmer, B.; Mantoani, M.; Mchedlidze, G.; Mellenthin, J. D.; Nellist, C.; Oerdek, S.; Pokharel, I.; Quadt, A.; Rosien, N-A.; Rzehorz, G. F.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Stolte, P.; Ticse Torres, R. E.; Veatch, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Akilli, E.; Amrouche, C. S.; Ancu, L. S.; Benoit, M.; Calace, N.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; March, L.; Mermod, P.; Nessi, M.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Vicente Barreto Pinto, M.; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Coccaro, A.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] INFN, Sez Genova, Genoa, Italy; [Dueren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Breaden Madden, W. D.; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; O'Shea, V.; Owen, M.; Robson, A.; Spiteri, D. P.; Thompson, A. S.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J. -Y.; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, CNRS, Grenoble INP, LPSC,IN2P3, Grenoble, France; [Asbah, N.; Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C. -Q.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C. -Q.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Peoples R China; [Barnes, S. L.; Cano Bret, M.; Guo, J.; Hadef, A.; Hu, S.; Kato, C.; Kondrashova, N.; Li, L.; Li, S.; Li, X.; Nishu, N.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, KLPPAC MoE, Shanghai, Peoples R China; [Kato, C.; Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V.; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J. I.; Dunford, M.; Franchino, S.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kugel, A.; Meyer Zu Theenhausen, H.; Napolitano, F.; Narrias Villar, D. I.; Sahinsoy, M.; Schoening, A.; Schultz-Coulon, H. -C.; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; Britzger, D.; Ferreira de Lima, D. E.; Giulini, M.; Kolb, M.; Lisovyi, M.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Marceca, G.; Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Chan, Y. L.; Chu, M. C.; Flores Castillo, L. R.; Iturbe Ponce, J. M.; Lau, T. S.; Lu, H.; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Lo, C. Y.; Orlando, N.; Paredes Hernandez, D.; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys & Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Palacino, G.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] INFN, Grp Collegato Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Chim Fis & Ambiente, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] INFN, Sez Lecce, Lecce, Italy; [Bachas, K.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Carra, S.; Cavalli, D.; Citterio, M.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Manzoni, S.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.; Perez, M. Villaplana] INFN, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; Carra, S.; Fanti, M.; Lazzaroni, M.; Manzoni, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V.; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] INFN, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V.; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.; Vercesi, V.] INFN, Sez Pavia, Pavia, Italy; [Farina, E. M.; Fraternali, M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N. V.; Calvetti, M.; Cavasinni, V.; Chiarelli, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.] INFN, Sez Pisa, Pisa, Italy; [Biesuz, N. V.; Calvetti, M.; Cavasinni, V.; De Maria, A.; Francavilla, P.; Roda, C.; Sotiropoulou, C. L.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Gentile, S.; Giagu, S.; Ippolito, V.; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Veneziano, S.] INFN, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; Gentile, S.; Giagu, S.; Ippolito, V.; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bortolotto, V.; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Liberti, B.; Massa, L.; Pinamonti, M.; Vanadia, M.; Vari, R.] INFN, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bortolotto, V.; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Massa, L.; Pinamonti, M.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Baroncelli, A.; Biglietti, M.; Ceradini, F.; Di Micco, B.; Farilla, A.; Iodice, M.; Orestano, D.; Petrucci, F.; Salamanna, G.; Stanescu, C.; Vecchio, V.; Verducci, M.] INFN, Sez Roma Tre, Rome, Italy; [Ceradini, F.; Di Micco, B.; Orestano, D.; Petrucci, F.; Salamanna, G.; Vecchio, V.; Verducci, M.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Follega, F. M.; Iuppa, R.; Ricci, E.] INFN TIFPA, Povo, Italy; [Follega, F. M.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Jakobsen, S.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [Chen, C. H.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Liu, B.; Pluth, D.; Prell, S.; Infante, C. M. Vergel; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gongadze, A.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Lyubushkin, V.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Simic, L.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Goncalves Gama, R.; Peralva, B. S.; Silva Oliveira, M. V.] Univ Fed Juiz De Fora UFJF, Dept Engn Eletr, Juiz De Fora, Brazil; [Amaral Coutinho, Y.; Araujo Ferraz, V.; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Freund, W. S.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio De Janeiro, COPPE EE IF, Rio De Janeiro, Brazil; [Do Vale, M. A. B.] Univ Fed Sao Joao Del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Aoki, M.; Hanagaki, K.; Higashino, S.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Jakobi, K. B.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Mizukami, A.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Odaka, S.; Okuyama, T.; Sasaki, O.; Shimizu, S.; Suzuki, S.; Takubo, Y.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I.; Unno, Y.; Usui, J.; Werner, M. D.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Kawade, K.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Takeda, K.; Wakamiya, K.; Yamane, F.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I.; Guzik, M. P.; Janus, P. A.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Kremer, J. A.; Mindur, B.; Przybycien, M.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Bahmani, M.; Banas, E.; Bruckman de Renstrom, P. A.; Burka, K.; Chwastowski, J. J.; Czekierda, S.; Derendarz, D.; Dziedzic, B. S.; Godlewski, J.; Kaczmarska, A.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Slawinska, M.; Smykiewicz, A.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Akatsuka, S.; Kunigo, T.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Tsinghua University; Nanjing University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; Humboldt University of Berlin; University of Bern; University of Bern; University of Birmingham; Universidad Antonio Narino; University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Bonn; Boston University; Brandeis University; Transylvania University of Brasov; Horia Hulubei National Institute of Physics & Nuclear Engineering; Alexandru Ioan Cuza University; National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; Polytechnic University of Bucharest; West University of Timisoara; Comenius University Bratislava; Slovak Academy of Sciences; United States Department of Energy (DOE); Brookhaven National Laboratory; University of Buenos Aires; University of Cambridge; University of Cape Town; University of Johannesburg; University of Witwatersrand; Carleton University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; European Organization for Nuclear Research (CERN); University of Chicago; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Columbia University; University of Copenhagen; Niels Bohr Institute; University of Calabria; Istituto Nazionale di Fisica Nucleare (INFN); Southern Methodist University; University of Texas System; University of Texas Dallas; Stockholm University; Oskar Klein Centre; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Shandong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; National Tsing Hua University; Indiana University System; Indiana University Bloomington; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; Istituto Nazionale di Fisica Nucleare (INFN); University of Salento; Istituto Nazionale di Fisica Nucleare (INFN); University of Milan; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; Universidade Federal de Juiz de Fora; Universidade Federal do Rio de Janeiro; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; High Energy Accelerator Research Organization (KEK); Kobe University; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Kyoto University; Kyoto University of Education; Kyushu University; Kyushu University; National University of La Plata; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; Lund University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Autonomous University of Madrid; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; City University of New York (CUNY) System; California State University System; California State University East Bay; Council for Scientific & Industrial Research (CSIR) - South Africa; Autonomous University of Barcelona; Universidade de Lisboa; Instituto Superior Tecnico; University of Sharjah; University of Aegean; University of Louisville; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; Stanford University; University of Fribourg; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Ilia State University; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Istanbul University; Manhattan College; Moscow Institute of Physics & Technology; Sun Yat Sen University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. Oh, Alexander/HHZ-4386-2022; Gurbuz, Saime/AAG-5583-2019; Lee, Lawrence/AAU-6790-2021; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Santra, Arka/AEE-4946-2022; Saraiva, Joao Gentil Mendes/D-1596-2017; Fassi, Farida/F-3571-2016; Kurashige, Hisaya/H-4916-2012; Introzzi, Gianluca/K-2497-2015; b, m/HPE-0989-2023; spagnolo, stefania/A-6359-2012; Torro Pastor, Emma/AAB-5979-2021; de la Hoz, Santiago Gonzalez/E-2494-2016; Vandelli, Wainer/ABI-8185-2020; Lagouri, Theodota/AAC-7358-2021; Kepka, Oldrich/ABF-5327-2021; Berta, Peter/AAL-7109-2020; Parodi, Fabrizio/AHE-5089-2022; Grabowska-Bołd, Iwona/ABI-7829-2020; Kazanin, Vasily F./HHM-2056-2022; Wu, Xin/ABH-1729-2020; de la Torre Perez, Hector/ABG-6942-2020; Norjoharuddeen, Nurfikri/S-3109-2018; Sapronov, Andrey/HGB-0392-2022; Chwastowski, Janusz J./I-4480-2012; Ippolito, Valerio/L-1435-2016; de Almeida, Fernando Marroquim Leao/D-6947-2019; Hejbal, Jiri/H-1358-2014; Palka, Marek/GXW-2506-2022; Burdin, Sergey/AAZ-9062-2021; zhang, jin/GXV-9154-2022; Cavaliere, Viviana/CAG-2384-2022; Gustavino, Giuliano/AAK-6591-2020; do Amaral Coutinho, Yara/AAU-7857-2021; Cetin, Serkant Ali/AGF-0147-2022; Talyshev, Alexey/HGC-6910-2022; Giordani, Mario P/Q-6211-2018; Brahimi, Nihal/HNJ-5325-2023; Maneira, Jose/D-8486-2011; Jones, Roger WL/H-5578-2011; Luci, Claudio/HKW-0143-2023; Franchini, Matteo/AAC-9259-2021; Gutierrez, Phillip/C-1161-2011; Bortfeldt, Jona/AAJ-5370-2021; Olszewski, Andrzej J/W-1825-2018; Cheng, Hok-Chuen/GNP-8341-2022; perez, miguel/GXF-5077-2022; Ventura, Andrea/CAH-0226-2022; Stabile, Alberto/F-2889-2013; AGHEORGHIESEI, Catalin/B-8596-2014; Doyle, Anthony T/C-5889-2009; Gabrielli, Alessandro/H-4931-2012; Mitsou, Vasiliki A./D-1967-2009; Sadykov, Renat/AAN-9602-2020; Betti, Alessandra/AAD-9964-2019; Ahmadov, Faig N/B-3723-2018; Castillo, Luis Roberto Flores/W-3928-2018; Mikestikova, Marcela/H-1996-2014; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Kartvelishvili, Vakhtang G/K-2312-2013; Di Nardo, Roberto/J-4993-2012; Ricci, Ester/AAJ-1523-2020; Chizhov, Mihail/CAG-2076-2022; da via, cinzia/AAS-3978-2021; Smirnova, Oxana/A-4401-2013; Masik, Jiri/AAE-1203-2021; Darbo, Giovanni/C-8175-2012; Trzebinski, Maciej/AAO-6821-2021; Balaji, S/J-1864-2019; Barreiro, Fernando/D-9808-2012; Negrini, Matteo/C-8906-2014; Peleganchuk, Sergey V/J-6722-2014; Dubinin, Filipp/M-9546-2015; Zanzi, Daniele/AAY-4102-2020; Smirnov, Sergei Y/F-1014-2011; Capua, Marcella/A-8549-2015; Gladilin, Leonid/B-5226-2011; Gonella, Laura/GLR-3838-2022; Dabrowski, Wladyslaw/AAS-6369-2020; Bassalat, Ahmed Azzam/HHY-9901-2022; Reznicek, Pavel/C-1989-2017; Chitan, Adrian/AAC-8946-2022; Garcia, Carmen/W-2465-2018; Jiménez Peña, Javier/AFY-1817-2022; Napolitano, Fabrizio/CAI-2163-2022; Ibáñez, Carlos Escobar/B-3761-2017; Camarri, Paolo/AAF-9629-2020; Olszowska, Jolanta/P-2889-2017; Fullana Torregrosa, Esteban/AAC-9079-2022; Koperny, Stefan/ABB-4747-2020; Torregrosa, Esteban Fullana/A-7305-2016; Song, Weimin/AAJ-5415-2020; Pascual, Juan Antonio Garcia/B-3540-2019; Camarero, Daniel/Z-1924-2019; Banerjee, Swagato/AAN-3326-2021; Beddall, Andrew John/AAE-5820-2022; Costa, Maria Jose/H-5962-2015; Tikhomirov, Vladimir/M-6194-2015; Dyndal, Mateusz/AAB-1528-2020; Bobrovnikov, Victor/AAB-8328-2022; Caprini, Irinel/AAP-3760-2020; Cottin, Giovanna/AFR-3846-2022; banerjee, swagato/AAN-3165-2021; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Herten, Gregor/HNQ-9546-2023; Marcisovsky, Michal/AAM-2404-2020; Giagu, Stefano/H-6455-2013; MEONI, Evelin/ABD-9498-2021; CAKIR, Orhan/L-1893-2015; ezzi, mohammed/AAD-7543-2020; Wolters, Helmut/M-4154-2013; Sopczak, Andre/I-4951-2015; Smykiewicz, Andrzej/W-6236-2018; Tassi, Enrico/AAJ-9661-2020; Gonzalez, Barbara Alvarez/ABG-7021-2020; Fiorini, Luca/W-6250-2018; Panizzo, Giancarlo/AAS-2911-2020; Lacasta, Carlos/C-7254-2008; Maevskiy, Artem/P-4101-2016; Hunter, Robert/AAG-8342-2019; Kroll, Jiri/C-8465-2018; Coccaro, Andrea/AAB-6990-2021; Carquin, Edson/GLU-9641-2022; Berger, N/ABE-4064-2020; govender, nicolin/CAH-3245-2022; Artoni, Giacomo/ABA-2164-2020; Casado, Pilar/H-1484-2015; Chelkov, Georgy A./G-9934-2019; Tapia, Sebastian/ABB-6644-2021; Annovi, Alberto/AAA-8638-2020; Alimonti, Gianluca/AAG-4603-2020; Romano, Marino/ACW-0715-2022; Giuli, Francesco/HJI-6649-2023; Godlewski, Jan/AAI-3300-2021; Pages, Andres Pacheco/C-5353-2011; Sessa, Marco/AAT-2850-2020; Derendarz, Dominik/AAO-3512-2021; YILDIZ, Hatice DURAN/AAN-3727-2021; Fabbri, Laura/H-3442-2012; Suster, Carl J E/L-3714-2016; Bogdanchikov, Alexander/AAB-9414-2022; Gravila, Paul/HLP-6245-2023; Ozcan, Veysi Erkcan/AAS-4508-2020; Woźniak, Krzysztof/AAQ-6119-2021; Llácer, María Moreno/AAU-6582-2020; Kruchonak, Uladzimir/AAN-4371-2020; Hamal, Petr/G-5540-2014; Franchini, Matteo/HNI-8314-2023; Barberis, Dario/ABF-1310-2021; Adiguzel, Aytul/AAC-9049-2020; Villaplana Perez, Miguel/B-5772-2014; Valero, Alberto/G-9866-2015; Llácer, María Moreno/AAQ-7522-2020; Huseynov, Nazim/AAE-4663-2019; Snesarev, Andrei/H-5090-2013; de Renstrom, Pawel Bruckman/AAG-7725-2021; Kuze, Masahiro/V-4251-2018; Panizzo, Giancarlo/AAS-2986-2020; Ohm, Christian/AAU-6572-2020; Carquin López, Edson/GZG-4712-2022; Sopkova, Filomena/HKV-6270-2023; Brooks, William K/C-8636-2013; Ferrer, Antonio/H-2942-2015; Zemaityte, Gabija/AAI-7245-2021; Wang, Song-Ming/AAP-9832-2021; García, Josu Cantero/AAS-9810-2020; Leisos, Antonios/AAJ-2351-2021; Glasman, Claudia/Y-8858-2019; Orlando, Nicola/AAL-1723-2021; Banaś, Elżbieta A/W-4583-2017; Amoroso, Simone/AAW-4334-2021; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Padilla, Cristobal/C-3218-2017; Maj, Klaudia/Q-2724-2019; Kuleshov, Sergey V/D-9940-2013; Calvetti, Milene/AAQ-1337-2020; Malek, Fairouz/E-4410-2018; Fassouliotis, Dimitrios/AAL-7081-2021; Bruscino, Nello/ABA-8980-2021; Schernau, Michael/GRY-3808-2022; Viaux, Nicolas/AAT-5715-2020; Capua, Marcella/AAJ-8419-2020; Schioppa, Enrico Junior/F-4731-2019; D'Onofrio, Adelina/AAT-3903-2020; Gao, Y./GWZ-3665-2022; Buzykaev, Alexey/HIK-0117-2022; Legger, Federica/HNC-2568-2023; Trzupek, Adam/N-2448-2018; Castro, Nuno/AAB-3648-2019; Lefebvre, Benoit/ABB-2272-2021; Billoud, Thomas/AAY-1569-2020; gaudio, gabriella/AAN-6039-2021; Bailey, Adam J/T-9828-2017; Sanchez, Francisca J. Munoz/AAH-3909-2019; Petrucci, Fabrizio/G-8348-2012; Zhemchugov, Alexey/N-1717-2017; Perez, Miguel Villaplana/B-2717-2015; Svatos, Michal/ABA-2041-2020; Jiménez, Yesenia Hernández/ABH-1107-2020; Rosati, Stefano/AAF-6968-2019; Trzebinski, Maciej/W-1748-2018; Lee, Shih-Chang/AAV-7016-2021; Cavaliere, Viviana/CAE-8597-2022; Staszewski, Rafał/AAO-6844-2021; Grinstein, Sebastian/ABE-1880-2020; Nechaeva, Polina Y/N-1148-2015; Kharlamova, Tatyana/AAQ-5430-2020; Wozniak, Krzysztof W/P-4475-2017; Alberich, Leonor Cerdá/ABG-8534-2020; Buttar, Craig/AAG-5392-2021; Francavilla, Paolo/HKE-1206-2023; Llorente Merino, Javier/AGP-3432-2022; Buckley, Andy/B-8362-2014; Worm, Steven/I-3575-2012; Mamuzic, Judita/U-3509-2017; Albert, Justin/J-4152-2017; Dziedzic, Bartosz/Q-4189-2017; Zenis, Tibor/T-5270-2018; Bachas, Konstantinos/C-8101-2019; Aguilar Saavedra, Juan Antonio/F-1256-2016; Sykora, Ivan/T-5252-2018; Blue, Andrew/C-9882-2016; Wolter, Marcin/A-7412-2012; Penc, Ondrej/H-3032-2014; Carli, Ina/C-2189-2017; xella, stefania/E-6752-2015; Rebuzzi, Daniela Marcella/D-9727-2018; Chu, Ming-chung/M-2655-2018; Bosman, Martine/J-9917-2014; Vos, Marcel/G-8123-2015; D'Eramo, Louis/Q-5816-2017; La Rosa, Alessandro/I-1856-2013; Maeda, Junpei/B-8131-2018; Britton, David/F-2602-2010; Kaczmarska, Anna/B-2753-2019; LIVAN, Michele/D-7531-2012; Faltova, Jana/P-6842-2017; Garcia Navarro, Jose Enrique/H-6339-2015; Lysak, Roman/H-2995-2014; Ungaro, Francesca/G-4554-2018; McKee, Shawn/B-6435-2012; Li, Liang/O-1107-2015; Carquin, Edson/G-5221-2015; Monzani, Simone/D-6328-2017; , Alex/N-8715-2016; Kodys, Peter/P-2636-2017; Benekos, Nektarios/I-9928-2017; Stanecka, Ewa/V-6242-2018; Petersen, Troels/P-5538-2015; Juste, Aurelio/I-2531-2015; Lazzaroni, Massimo/N-3675-2015; Della Pietra, Massimo/J-5008-2012; Grancagnolo, Sergio/J-3957-2015; Schultz-Coulon, Hans-Christian/X-5006-2018; Kukla, Romain/P-9760-2016; Korcyl, Krzysztof/W-2111-2018; Konig, Adriaan/B-6439-2013; Monk, James/A-9021-2017; Vranjes Milosavljevic, Marija/F-9847-2016; Davidek, Tomas/P-2697-2017; Vanadia, Marco/K-5870-2016; Tartarelli, Giuseppe Francesco/A-5629-2016; Hansen, Peter Henrik/C-2098-2015; Baldin, Evgenii/A-6186-2014; Rodriguez Bosca, Sergi/HPC-6167-2023; cerri, alessandro/Q-6884-2016; Goncalo, Jose/M-3153-2016; Sioli, Maximiliano/Q-1597-2016 Lee, Lawrence/0000-0002-5590-335X; Santra, Arka/0000-0003-4644-2579; Saraiva, Joao Gentil Mendes/0000-0002-7006-0864; Fassi, Farida/0000-0002-6423-7213; Kurashige, Hisaya/0000-0003-3932-016X; Introzzi, Gianluca/0000-0002-1314-2580; spagnolo, stefania/0000-0001-7482-6348; Torro Pastor, Emma/0000-0002-5507-7924; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Vandelli, Wainer/0000-0001-6581-9410; Berta, Peter/0000-0003-0780-0345; Grabowska-Bołd, Iwona/0000-0001-9159-1210; Kazanin, Vasily F./0000-0002-4906-5468; Wu, Xin/0000-0001-7655-389X; de la Torre Perez, Hector/0000-0002-4516-5269; Norjoharuddeen, Nurfikri/0000-0002-8818-7476; Chwastowski, Janusz J./0000-0002-6190-8376; Ippolito, Valerio/0000-0001-5126-1620; de Almeida, Fernando Marroquim Leao/0000-0003-2265-784X; Hejbal, Jiri/0000-0002-4600-3659; Palka, Marek/0000-0002-7185-3540; Burdin, Sergey/0000-0003-4831-4132; Cavaliere, Viviana/0000-0002-4297-8539; Gustavino, Giuliano/0000-0002-5938-4921; Cetin, Serkant Ali/0000-0001-5050-8441; Talyshev, Alexey/0000-0001-8560-3756; Giordani, Mario P/0000-0002-0792-6039; Brahimi, Nihal/0000-0003-0992-3509; Maneira, Jose/0000-0002-3222-2738; Jones, Roger WL/0000-0002-6427-3513; Franchini, Matteo/0000-0002-4554-252X; Gutierrez, Phillip/0000-0003-2326-3877; Bortfeldt, Jona/0000-0002-0777-985X; Olszewski, Andrzej J/0000-0003-3368-5475; Cheng, Hok-Chuen/0000-0002-8912-4389; Ventura, Andrea/0000-0002-3368-3413; Stabile, Alberto/0000-0002-6868-8329; AGHEORGHIESEI, Catalin/0000-0003-3695-1847; Doyle, Anthony T/0000-0001-6322-6195; Gabrielli, Alessandro/0000-0001-5346-7841; Mitsou, Vasiliki A./0000-0002-1533-8886; Sadykov, Renat/0000-0002-9157-6819; Betti, Alessandra/0000-0003-0839-9311; Ahmadov, Faig N/0000-0003-3644-540X; 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Vadla, Knut Oddvar Hoie/0000-0001-6729-1584; Croft, Vincent Alexander/0000-0002-8731-4525; Wu, Sau Lan/0000-0001-5866-1504; Heim, Sarah/0000-0002-2639-6571; Besson, Nathalie/0000-0001-9248-6252; CARRA, SONIA/0000-0001-8650-942X; Corriveau, Francois/0000-0002-4970-7600; Grefe, Christian/0000-0001-7050-5301; Vanadia, Marco/0000-0003-2684-276X; Yabsley, Bruce/0000-0002-2680-0474; David, Claire/0000-0002-1794-1443; Orellana, Gonzalo Enrique/0000-0002-4753-4048; Ruehr, Frederik/0000-0003-4452-620X; Cardillo, Fabio/0000-0002-4478-3524; Panduro Vazquez, Jose Guillermo/0000-0003-2605-8940; Spolidoro Freund, Werner/0000-0003-4473-1027; TAYLOR, GEOFFREY/0000-0002-1831-4871; Kouskoura, Vasiliki/0000-0002-8987-3208; Stark, Giordon/0000-0001-6616-3433; Genest, Marie-Helene/0000-0002-4098-2024; Callea, Giuseppe/0000-0001-5969-3786; Nikiforou, Nikiforos/0000-0003-1267-7740; Feligioni, Lorenzo/0000-0002-1403-0951; Benitez, J.F./0000-0002-2633-6712; /0000-0001-5493-6486; Mohapatra, Soumya/0000-0003-3006-6337; van Daalen, Tal/0000-0002-2254-125X; Zhang, Yu/0000-0002-4554-2554; Kurchaninov, Leonid/0000-0001-9392-3936; Tartarelli, Giuseppe Francesco/0000-0002-4244-502X; Hansen, Peter Henrik/0000-0002-6764-4789; Vari, Riccardo/0000-0002-2814-1337; Colasurdo, Luca/0000-0003-3901-8884; franklin, melissa/0000-0002-6595-883X; Weber, Michele/0000-0002-2770-9031; Guzik, Marcin/0000-0001-7840-0889; Broughton, James/0000-0002-0797-5578; Proklova, Nadezda/0000-0002-5237-0201; Baines, John/0000-0003-0770-2702; Sato, Koji/0000-0001-8988-4065; Sabatini, Paolo/0000-0003-0159-697X; Hoya, Joaquin/0000-0002-7562-0234; URQUIJO, PHILLIP/0000-0002-0887-7953; Shimojima, Makoto/0000-0002-8738-1664; Baldin, Evgenii/0000-0002-9854-975X; Rodriguez Bosca, Sergi/0000-0002-4571-2509; Roland, Christophe/0000-0003-2084-369X; Leonidopoulos, Christos/0000-0002-7241-2114; Vincter, Manuella/0000-0002-5338-8972; ; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, France; ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos program; Thales program; Aristeia program - EU-ESF; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC [ST/N000277/1, ST/S000860/1, ST/N000463/1, ST/P002439/1, ST/S00095X/1, ST/S000747/1, ST/N000234/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, France(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos program; Thales program(Thales Group); Aristeia program - EU-ESF; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Skodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.Major contributors of computing resources are listed in Ref. [113]. 110 20 20 2 29 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C APR 30 2019.0 79.0 5 375 10.1140/epjc/s10052-019-6847-8 0.0 54 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics HW0YC Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000466407600007 0
C Ge, CJ; Qu, QX; Gu, IYH; Jakola, AS IEEE Ge, Chenjie; Qu, Qixun; Gu, Irene Yu-Hua; Jakola, Asgeir Store MULTISCALE DEEP CONVOLUTIONAL NETWORKS FOR CHARACTERIZATION AND DETECTION OF ALZHEIMER'S DISEASE USING MR IMAGES 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) IEEE International Conference on Image Processing ICIP English Proceedings Paper 26th IEEE International Conference on Image Processing (ICIP) SEP 22-25, 2019 Taipei, TAIWAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Alzheimer's disease detection; MR images; multiscale features; multiscale CNN; feature fusion and enhancement This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject-separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method. [Ge, Chenjie; Qu, Qixun; Gu, Irene Yu-Hua] Chalmers Univ Technol, Dept Elect Engn, Stockholm, Sweden; [Jakola, Asgeir Store] Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Gothenburg, Sweden; [Ge, Chenjie] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China Chalmers University of Technology; University of Gothenburg; Shanghai Jiao Tong University Ge, CJ (corresponding author), Chalmers Univ Technol, Dept Elect Engn, Stockholm, Sweden.;Ge, CJ (corresponding author), Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China. chenjie@chalmers.se; quqixun@gmail.com; irenegu@chalmers.se; as-geir.jakola@vgregion.se Jakola, Asgeir/J-1738-2018 Jakola, Asgeir/0000-0002-2860-9331 STINT in Sweden; CSC scholarship in China STINT in Sweden; CSC scholarship in China The project is partially financed by STINT in Sweden and CSC scholarship in China 15 9 9 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1522-4880 978-1-5386-6249-6 IEEE IMAGE PROC 2019.0 789 793 5 Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Imaging Science & Photographic Technology BO6RM 2023-03-23 WOS:000521828600157 0
J Zhang, L; Sun, BB; Shu, F; Huang, Y Zhang, Lin; Sun, Beibei; Shu, Fei; Huang, Ying Comparing paper level classifications across different methods and systems: an investigation of Nature publications SCIENTOMETRICS English Article Paper level classifications; WoS subject categories; Fields of research (FoR); Nature subjects; InCites; Dimensions; Springer Nature COUNTRY RANK CLASSIFICATION; DISCIPLINARY DIVERSITY; SCIENCE; CITATIONS The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper's references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher's tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. [Zhang, Lin; Sun, Beibei; Huang, Ying] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China; [Zhang, Lin; Sun, Beibei; Huang, Ying] Wuhan Univ, Ctr Sci Technol & Educ Assessment, Wuhan, Peoples R China; [Zhang, Lin; Sun, Beibei; Huang, Ying] Katholieke Univ Leuven, Dept MSI, Leuven, Belgium; [Zhang, Lin; Sun, Beibei; Huang, Ying] Katholieke Univ Leuven, ECOOM, Leuven, Belgium; [Shu, Fei] Hangzhou Dianzi Univ, Chinese Acad Sci & Educ Evaluat, Hangzhou, Peoples R China Wuhan University; Wuhan University; KU Leuven; KU Leuven; Hangzhou Dianzi University Zhang, L (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.;Zhang, L (corresponding author), Wuhan Univ, Ctr Sci Technol & Educ Assessment, Wuhan, Peoples R China.;Zhang, L (corresponding author), Katholieke Univ Leuven, Dept MSI, Leuven, Belgium.;Zhang, L (corresponding author), Katholieke Univ Leuven, ECOOM, Leuven, Belgium. zhanglin_1117@126.com Sun, Beibei/Q-1493-2018; Zhang, Lin/M-3007-2017 Sun, Beibei/0000-0002-7965-8723; Zhang, Lin/0000-0003-0526-9677; HUANG, Ying/0000-0003-0115-4581 National Natural Science Foundation of China [71974150, 72004169, 72074029]; National Laboratory Centre for Library and Information Science at Wuhan University; Flemish Government National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Laboratory Centre for Library and Information Science at Wuhan University; Flemish Government The present study is an extended version of an article presented at the 18th International Conference on Scientometrics and Informetrics, Leuven (Belgium), 12-15 July 2021 (Zhang et al. 2021b). The authors would like to acknowledge support from the National Natural Science Foundation of China (Grant Nos. 71974150, 72004169, 72074029), the National Laboratory Centre for Library and Information Science at Wuhan University, and the project Interdisciplinarity & Impact (2019-2023) funded by the Flemish Government. 48 0 0 24 48 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0138-9130 1588-2861 SCIENTOMETRICS Scientometrics DEC 2022.0 127.0 12 7633 7651 10.1007/s11192-022-04352-3 0.0 MAR 2022 19 Computer Science, Interdisciplinary Applications; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science 6U3BO 2023-03-23 WOS:000773181300001 0
C Giachanou, A; Zhang, GB; Rosso, P Sojka, P; Kopecek, I; Pala, K; Horak, A Giachanou, Anastasia; Zhang, Guobiao; Rosso, Paolo Multimodal Fake News Detection with Textual, Visual and Semantic Information TEXT, SPEECH, AND DIALOGUE (TSD 2020) Lecture Notes in Artificial Intelligence English Proceedings Paper 23rd Annual International Conference on Text, Speech, and Dialogue (TSD) SEP 08-11, 2020 ELECTR NETWORK Masaryk Univ, Fac Informat,Univ W Bohemia Plzen, Fac Appl Sci,Lexical Comp Ltd,IBM Ceska Republika spol s r o,Amazon Alexa Multimodal fake news detection; Visual features; Textual features; Image-text similarity Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to differentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news. [Giachanou, Anastasia; Zhang, Guobiao; Rosso, Paolo] Univ Politecn Valencia, Valencia, Spain; [Zhang, Guobiao] Wuhan Univ, Wuhan, Peoples R China Universitat Politecnica de Valencia; Wuhan University Giachanou, A (corresponding author), Univ Politecn Valencia, Valencia, Spain. angia9@upv.es; zgb0537@whu.edu.cn; prosso@dsic.upv.es Giachanou, Anastasia/0000-0002-7601-8667 SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland [P2TIP2 181441]; China Scholarship Council (CSC) from the Ministry of Education of P.R. China; Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech [PGC2018-096212-B-C31] SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland; China Scholarship Council (CSC) from the Ministry of Education of P.R. China; Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). 24 20 20 13 32 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-58323-1; 978-3-030-58322-4 LECT NOTES ARTIF INT 2020.0 12284.0 30 38 10.1007/978-3-030-58323-1_3 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Linguistics BQ6GA Green Published 2023-03-23 WOS:000611543200003 0
J Hong, HY; Tsangaratos, P; Ilia, I; Liu, JZ; Zhu, AX; Chen, W Hong, Haoyuan; Tsangaratos, Paraskevas; Ilia, Ioanna; Liu, Junzhi; Zhu, A-Xing; Chen, Wei Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China SCIENCE OF THE TOTAL ENVIRONMENT English Article Flood susceptibility; Fuzzy WofE; Data mining methods; China SUPPORT VECTOR MACHINE; ARTIFICIAL-INTELLIGENCE APPROACH; LANDSLIDE SUSCEPTIBILITY; SPATIAL PREDICTION; LOGISTIC-REGRESSION; DECISION-MAKING; FREQUENCY RATIO; RANDOM-FOREST; STATISTICAL-MODELS; NEURAL-NETWORKS In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofERF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies. (C) 2017 Elsevier B.V. All rights reserved. [Hong, Haoyuan; Liu, Junzhi; Zhu, A-Xing] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan; Liu, Junzhi; Zhu, A-Xing] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan; Liu, Junzhi; Zhu, A-Xing] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China; [Tsangaratos, Paraskevas; Ilia, Ioanna] Natl Tech Univ Athens, Sch Min & Met Engn, Dept Geol Sci, Lab Engn Geol & Hydrogeol, Zografou Campus Heroon Polytech 9, Zografos 15780, Greece; [Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China Nanjing Normal University; National Technical University of Athens; Xi'an University of Science & Technology Liu, JZ (corresponding author), Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China.;Tsangaratos, P (corresponding author), Natl Tech Univ Athens, Sch Min & Met Engn, Dept Geol Sci, Lab Engn Geol & Hydrogeol, Zografou Campus Heroon Polytech 9, Zografos 15780, Greece. ptsag@metal.ntua.gr; liujunzhi@njnu.edu Tsangaratos, Paris/AAS-5876-2020; Hong, Haoyuan/C-8455-2014; Liu, Junzhi/AAG-5846-2021; Tsangaratos, Paraskevas/D-4966-2019; Chen, Wei/ABB-8669-2020; Liu, Junzhi/AGE-4547-2022 Hong, Haoyuan/0000-0001-6224-069X; Tsangaratos, Paraskevas/0000-0002-7396-4754; Chen, Wei/0000-0002-5825-1422; Liu, Junzhi/0000-0002-7354-4207 National Natural Science Foundation of China [41431177, 41601413]; Natural Science Research Program of Jiangsu [BK20150975, 14KJA170001]; National Basic Research Program of China [2015CB954102]; Program of Innovative Research Team of Jiangsu Higher Education Institutions of China; International Partnership Program of Chinese Academy of Sciences [115242KYSB20170022]; University of Wisconsin-Madison; One-Thousand Talents Program of China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Research Program of Jiangsu; National Basic Research Program of China(National Basic Research Program of China); Program of Innovative Research Team of Jiangsu Higher Education Institutions of China; International Partnership Program of Chinese Academy of Sciences; University of Wisconsin-Madison; One-Thousand Talents Program of China We express our great thankful to Damia Barcelo and Wei Ouyang, Editor of the journal Science of the Total Environment and three reviewers, with their comments and suggestions, it improved the quality of our paper. National Natural Science Foundation of China (Project No.: 41431177, 41601413), Natural Science Research Program of Jiangsu (Project No.: BK20150975), Natural Science Research Program of Jiangsu (Project No.: 14KJA170001), National Basic Research Program of China (Project No.: 2015CB954102), Program of Innovative Research Team of Jiangsu Higher Education Institutions of China, the International Partnership Program of Chinese Academy of Sciences (Grant No. 115242KYSB20170022), Supports to A-Xing Zhu through the Vilas Associate Award, the Hammel Faculty Fellow Award, the Manasse Chair Professorship from the University of Wisconsin-Madison, and the One-Thousand Talents Program of China are greatly appreciated. 111 201 206 24 321 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. JUN 1 2018.0 625.0 575 588 10.1016/j.scitotenv.2017.12.256 0.0 14 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology FX8PH 29291572.0 2023-03-23 WOS:000426356600058 0
J Tong, XH; Xu, X; Plaza, A; Xie, H; Pan, HY; Cao, W; Lv, D Tong, Xiaohua; Xu, Xiong; Plaza, Antonio; Xie, Huan; Pan, Haiyan; Cao, Wen; Lv, Dong A New Genetic Method for Subpixel Mapping Using Hyperspectral Images IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Genetic algorithms; hyperspectral imaging; spectral unmixing; subpixel mapping SPATIAL-RESOLUTION; NEURAL-NETWORK; MAP MODEL; PIXEL; ALGORITHM Subpixel mapping techniques aim to obtain the spatial location and distribution of subpixels by transforming the information coming from a set of input abundance maps into a classification result with higher spatial resolution. However, traditional subpixel mapping algorithms generally ignore the possible errors that are due to abundance estimation inaccuracies by spectral unmixing techniques. In this paper, we propose a new genetic algorithm-based subpixel mapping technique that solves the subpixel mapping problem by correcting the potential errors in the estimated abundance fractions used as input to the subpixel mapping process. The proposed algorithm has been compared with other two genetic subpixel mapping methods, using both synthetic and real hyperspectral images. Our experimental results demonstrate that the proposed approach outperforms traditional subpixel mapping algorithms, thus providing an effective option to improve the accuracy of subpixel mapping for remotely sensed hyperspectral images. [Tong, Xiaohua; Xu, Xiong; Xie, Huan; Pan, Haiyan; Cao, Wen; Lv, Dong] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Xu, Xiong; Plaza, Antonio] Univ Exremadura, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Escuela Politecn, Caceres 10003, Spain Tongji University Tong, XH (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. xhtong@tongji.edu.cn; xvxiong@tongji.edu.cn; aplaza@unex.es; huanxie@tongji.edu.cn; hypan@tongji.edu.cn; caowenrs@126.com; 1134601@tongji.edu.cn Plaza, Antonio/C-4455-2008; xie, huan/GQQ-4398-2022 Plaza, Antonio/0000-0002-9613-1659; China Postdoctoral Science Foundation [2014M560353, 2015T80450]; National Natural Science Foundation of China [41401398, 41325005, 41201426, 41171352, 41171327]; Fund of Shanghai Outstanding Academic Leaders Program [12XD1404900]; Kwang-Hua Foundation of College of Civil Engineering, Tongji University China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fund of Shanghai Outstanding Academic Leaders Program; Kwang-Hua Foundation of College of Civil Engineering, Tongji University This work was supported in part by China Postdoctoral Science Foundation (Project nos. 2014M560353, 2015T80450), in part by the National Natural Science Foundation of China (Project no. 41401398, 41325005, 41201426, 41171352, and 41171327), in part by the Fund of Shanghai Outstanding Academic Leaders Program (Project No. 12XD1404900), and in part by Kwang-Hua Foundation of College of Civil Engineering, Tongji University. (Corresponding author: Xiaohua Tong.) 45 26 26 5 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. SEP 2016.0 9.0 9 4480 4491 10.1109/JSTARS.2015.2496660 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology DY6NW 2023-03-23 WOS:000385245000014 0
C Chen, JJ; Hu, WH; Cao, D; Zhang, ZY; Chen, Z IEEE Chen, Jianjun; Hu, Weihao; Cao, Di; Zhang, Zhenyuan; Chen, Zhe A Novel Multi-task Learning Method with Attention Mechanism for Wind Turbine Blades Imbalance Fault Diagnosis 2022 4TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM (AEEES 2022) English Proceedings Paper 4th Asia Energy and Electrical Engineering Symposium (AEEES) MAR 25-28, 2022 Chengdu, PEOPLES R CHINA IEEE,UESTC, Sch Mech & Elect Engn,IEEE Power & Energy Soc,Chengdu Univ Technol multi-task learning; deep learning; attention mechanism; wind turbine (WT); blade ice accretion; imbalance fault diagnosis NETWORK With the rapid development of wind power generation in recent years, wind power plays a more and more important role in the modern power system. Accurate and efficient condition monitoring and fault diagnosis of wind turbines (WTs) is essential to ensure the safe and stable operation of wind farm and the stability of power system. Because of the characteristic of wind power, the imbalance fault caused by ice accretion on WT blades poses a threat to the reliable operation of WT. To this end, a multi-task learning method with attention mechanism has been proposed in this paper to address the problem of imbalance fault diagnosis of WT. The proposed model can complete the diagnosis of fault type and ice accretion level at the same time. The performance of the proposed model is evaluated on a simulation dataset. The diagnosis results show that the proposed framework can reach a state-of-the-art performance compared with other approaches, which can strongly illustrate that the proposed multi-task framework with attention mechanism can improve the effectiveness of each task. [Chen, Jianjun; Hu, Weihao; Cao, Di; Zhang, Zhenyuan] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China; [Chen, Zhe] Aalborg Univ, Aalborg, Denmark University of Electronic Science & Technology of China; Aalborg University Chen, JJ (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China. chjj@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; zhangzhenyuan@uestc.edu.cn; zch@et.aau.dk Zhang, Zhenyuan/ABR-9488-2022 27 0 0 4 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-7914-1 2022.0 857 862 10.1109/AEEES54426.2022.9759816 0.0 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT3XB 2023-03-23 WOS:000824213000146 0
J Scholz, RW; Bartelsman, EJ; Diefenbach, S; Franke, L; Grunwald, A; Helbing, D; Hill, R; Hilty, L; Hojer, M; Klauser, S; Montag, C; Parycek, P; Prote, JP; Renn, O; Reichel, A; Schuh, G; Steiner, G; Pereira, GV Scholz, Roland W.; Bartelsman, Eric J.; Diefenbach, Sarah; Franke, Lude; Grunwald, Arnim; Helbing, Dirk; Hill, Richard; Hilty, Lorenz; Hojer, Mattias; Klauser, Stefan; Montag, Christian; Parycek, Peter; Prote, Jan Philipp; Renn, Ortwin; Reichel, Andre; Schuh, Guenther; Steiner, Gerald; Pereira, Gabriela Viale Unintended Side Effects of the Digital Transition: European Scientists' Messages from a Proposition-Based Expert Round Table SUSTAINABILITY English Article digital transformation; digital curtain; digital vaulting; unintended side effects (unseens); proposition-based expert round tables PRODUCTIVITY PARADOX; ECONOMIC-GROWTH; INFORMATION; SYSTEMS; TECHNOLOGY; INTERNET; FUTURE; ICT; SUSTAINABILITY; MULTILEVEL We present the main messages of a European Expert Round Table (ERT) on the unintended side effects (unseens) of the digital transition. Seventeen experts provided 42 propositions from ten different perspectives as input for the ERT. A full-day ERT deliberated communalities and relationships among these unseens and provided suggestions on (i) what the major unseens are; (ii) how rebound effects of digital transitioning may become the subject of overarching research; and (iii) what unseens should become subjects of transdisciplinary theory and practice processes for developing socially robust orientations. With respect to the latter, the experts suggested that the ownership, economic value, use and access of data and, related to this, algorithmic decision-making call for transdisciplinary processes that may provide guidelines for key stakeholder groups on how the responsible use of digital data can be developed. A cluster-based content analysis of the propositions, the discussion and inputs of the ERT, and a theoretical analysis of major changes to levels of human systems and the human-environment relationship resulted in the following greater picture: The digital transition calls for redefining economy, labor, democracy, and humanity. Artificial Intelligence (AI)-based machines may take over major domains of human labor, reorganize supply chains, induce platform economics, and reshape the participation of economic actors in the value chain. (Digital) Knowledge and data supplement capital, labor, and natural resources as major economic variables. Digital data and technologies lead to a post-fuel industry (post-) capitalism. Traditional democratic processes can be (intentionally or unintentionally) altered by digital technologies. The unseens in this field call for special attention, research and management. Related to the conditions of ontogenetic and phylogenetic development (humanity), the ubiquitous, global, increasingly AI-shaped interlinkage of almost every human personal, social, and economic activity and the exposure to indirect, digital, artificial, fragmented, electronically mediated data affect behavioral, cognitive, psycho-neuro-endocrinological processes on the level of the individual and thus social relations (of groups and families) and culture, and thereby, the essential quality and character of the human being (i.e., humanity). The findings suggest a need for a new field of research, i.e., focusing on sustainable digital societies and environments, in which the identification, analysis, and management of vulnerabilities and unseens emerging in the sociotechnical digital transition play an important role. [Scholz, Roland W.; Prote, Jan Philipp] Danube Univ Krems, Dept Knowledge & Informat Management, A-3500 Krems An Der Donau, Austria; [Scholz, Roland W.] Swiss Fed Inst Technol, Dept Environm Syst Sci, CH-8092 Zurich, Switzerland; [Bartelsman, Eric J.] Vrije Univ Amsterdam, Dept Econ, NL-1081 HV Amsterdam, Netherlands; [Bartelsman, Eric J.] Vrije Univ Amsterdam, Tinbergen Inst, NL-1081 HV Amsterdam, Netherlands; [Diefenbach, Sarah] Ludwig Maximilians Univ Munchen, Dept Psychol, D-80539 Munich, Germany; [Franke, Lude] Univ Groningen, Univ Med Ctr Groningen, Dept Genet, NL-9700 CC Groningen, Netherlands; [Grunwald, Arnim] KIT, Inst Technol Assessment & Syst Anal ITAS, D-76131 Karlsruhe, Germany; [Grunwald, Arnim] Off Technol Assessment German Bundestag TAB, D-10178 Berlin, Germany; [Helbing, Dirk; Klauser, Stefan] Swiss Fed Inst Technol, Dept Humanities Social & Polit Sci, CH-9092 Zurich, Switzerland; [Hill, Richard] Hill & Associates, CH-1207 Geneva, Switzerland; [Hilty, Lorenz] Univ Zurich, Dept Informat, CH-8050 Zurich, Switzerland; [Hilty, Lorenz] Swiss Fed Labs Mat Sci & Technol EMPA, CH-9014 St Gallen, Switzerland; [Hojer, Mattias] KTH Royal Inst Technol, Dept Sustainable Dev Environm Sci & Engn, Div Strateg Sustainable Studies, SE-10044 Stockholm, Sweden; [Montag, Christian] Ulm Univ, Inst Psychol & Educ, Dept Mol Psychol, D-89069 Ulm, Germany; [Montag, Christian] Univ Elect Sci & Technol China, Chengdu Brain Sci Inst, Clin Hosp, SCAN Lab, Chengdu 611731, Sichuan, Peoples R China; [Montag, Christian] Univ Elect Sci & Technol China, Key Lab Neuroinformat, Chengdu 611731, Sichuan, Peoples R China; [Parycek, Peter] Ctr Competence Publ IT Fraunhofer FOKUS, D-10589 Berlin, Germany; [Parycek, Peter; Pereira, Gabriela Viale] Danube Univ Krems, Dept E Governance & Adm, A-3500 Krems An Der Donau, Austria; [Prote, Jan Philipp; Schuh, Guenther] Rhein Westfal TH Aachen, Lab Machine Tools & Prod Engn WZL, Prod Management Dept, D-52056 Aachen, Germany; [Renn, Ortwin] IASS, D-14467 Potsdam, Germany; [Reichel, Andre] ISM, D-70180 Stuttgart, Germany Danube University Krems; Swiss Federal Institutes of Technology Domain; ETH Zurich; Vrije Universiteit Amsterdam; Tinbergen Institute; Vrije Universiteit Amsterdam; University of Munich; University of Groningen; Helmholtz Association; Karlsruhe Institute of Technology; Helmholtz Association; Karlsruhe Institute of Technology; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; Swiss Federal Institutes of Technology Domain; Swiss Federal Laboratories for Materials Science & Technology (EMPA); Royal Institute of Technology; Ulm University; University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; Danube University Krems; RWTH Aachen University Scholz, RW (corresponding author), Danube Univ Krems, Dept Knowledge & Informat Management, A-3500 Krems An Der Donau, Austria.;Scholz, RW (corresponding author), Swiss Fed Inst Technol, Dept Environm Syst Sci, CH-8092 Zurich, Switzerland. roland.scholzr@emeritus.ethz.ch; e.j.bartelsman@vu.nl; sarah.diefenbach@psy.lmu.de; l.h.franke@umcg.nl; armin.grunwald@kit.edu; dirk.helbing@gess.ethz.ch; rhill@hill-a.ch; hilty@ifi.uzh.ch; hojer@kth.se; stefan.klauser@gess.ethz.ch; christian.montag@uni-ulm.de; peter.parycek@donau-uni.ac.at; j.prote@wzl.rwth-aachen.de; ortwin.renn@iass-potsdam.de; andre.reichel@ism.de; G.Schuh@wzl.rwth-aachen.de; Gerald.Steiner@donau-uni.ac.at; gabriela.viale-pereira@donau-uni.ac.at Pereira, Gabriela Monteiro/HIK-3109-2022; HÃjer, Mattias/AAG-8778-2020; Scholz, Roland/I-6534-2019; Montag, Christian/H-6536-2019; Scholz, Roland W./AAH-8385-2020; Bartelsman, Eric/ABE-2895-2021; Renn, Ortwin/AAF-4881-2020; Scholz, Roland W./B-2076-2008 Montag, Christian/0000-0001-8112-0837; Scholz, Roland W./0000-0003-2506-3254; Grunwald, Armin/0000-0003-3683-275X; Diefenbach, Sarah/0000-0002-4347-5028; Hilty, Lorenz Manuel/0000-0001-5020-0586; Renn, Ortwin/0000-0002-2283-4247; Reichel, Andre/0000-0001-8645-9098 German Federal Ministry of Education and Research (BMBF), Berlin German Federal Ministry of Education and Research (BMBF), Berlin(Federal Ministry of Education & Research (BMBF)) The European Round Table on Structuring Research on Sustainable Digital Environments received funding from the German Federal Ministry of Education and Research (BMBF), Berlin. 153 28 28 11 59 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JUN 2018.0 10.0 6 2001 10.3390/su10062001 0.0 48 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology GK9LE Green Submitted, Green Accepted, Green Published, gold 2023-03-23 WOS:000436570100313 0
J Yang, SG; Tian, JX; Jiang, H; Mulero, A; Cachadina, I Yang, Shangguo; Tian, Jianxiang; Jiang, Hua; Mulero, A.; Cachadina, I Corresponding-States Model for the Correlation and Prediction of the Surface Tension of Silanes INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH English Article ARTIFICIAL NEURAL-NETWORK; LARGE-DATA-SET; VAPOR PRESSURE; EQUATION; BIODIESEL; PARACHOR A new corresponding-states model for the calculation of the surface tension of silanes is proposed. It requires, as input properties, the critical point temperature, the minimum temperature at which one data for the surface tension is available, and the corresponding maximum value of this property. It includes two adjustable coefficients, which are calculated by considering the data available at the DIPPR database for 5 silanes. Then the model is applied to predict the surface tension values for other 31 silanes. Averaged absolute deviations below 12% are found for the 36 silanes, being below 5% for 33 of them. Results obtained by using the other five corresponding-states models available in the literature, one of them specifically designed for silanes, are also obtained. That proposed here gives clearly the best overall results. [Yang, Shangguo; Tian, Jianxiang] Qufu Normal Univ, Dept Phys, Qufu 273165, Shandong, Peoples R China; [Tian, Jianxiang] Dalian Univ Technol, Dept Phys, Dalian 116024, Peoples R China; [Mulero, A.; Cachadina, I] Univ Extremadura, Dept Appl Phys, Badajoz 06006, Spain; [Jiang, Hua] Linyi Univ, Sch Phys & Elect Engn, Linyi 276005, Shandong, Peoples R China Qufu Normal University; Dalian University of Technology; Universidad de Extremadura; Linyi University Tian, JX (corresponding author), Qufu Normal Univ, Dept Phys, Qufu 273165, Shandong, Peoples R China.;Tian, JX (corresponding author), Dalian Univ Technol, Dept Phys, Dalian 116024, Peoples R China.;Mulero, A (corresponding author), Univ Extremadura, Dept Appl Phys, Badajoz 06006, Spain. jxtian@dlut.edu.cn; mulero@unex.es Cachadiña, Isidro/AAG-6898-2022; Cachadiña, Isidro/AFL-6752-2022 Cachadiña, Isidro/0000-0002-1139-1284; Cachadiña, Isidro/0000-0002-1139-1284; Yang, Shangguo/0000-0002-5211-4603; Mulero, Angel/0000-0003-2148-0915; Tian, Jianxiang/0000-0001-6373-6953 National Natural Science Foundation of China [11274200]; Natural Science Foundation of Shandong Province [ZR2011AM017]; foundation of QFNU; foundation of DUT; Junta de Extremadura [GR18081]; European Regional Development Fund (FEDER) [GR18081]; FEDER [PGC2018-098418-B-I00]; Ministerio de Ciencia, Innovacion y Universidades [PGC2018-098418-B-I00] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); foundation of QFNU; foundation of DUT; Junta de Extremadura; European Regional Development Fund (FEDER)(European Commission); FEDER(European Commission); Ministerio de Ciencia, Innovacion y Universidades The National Natural Science Foundation of China under grant no. 11274200, the Natural Science Foundation of Shandong Province under Grant No. ZR2011AM017 and the foundations of QFNU and DUT have supported this work. A.M. thanks the Junta de Extremadura and European Regional Development Fund (FEDER) for financial support through project GR18081, and the Ministerio de Ciencia, Innovacio ' n y Universidades and FEDER for financial support through project PGC2018-098418-B-I00. 59 6 6 0 3 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0888-5885 IND ENG CHEM RES Ind. Eng. Chem. Res. APR 1 2020.0 59.0 13 6336 6344 10.1021/acs.iecr.0c00123 0.0 9 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering LE0NO 2023-03-23 WOS:000526421900086 0
J Rizwan, M; Wan, WG; Cervantes, O; Gwiazdzinski, L Rizwan, Muhammad; Wan, Wanggen; Cervantes, Ofelia; Gwiazdzinski, Luc Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article big data; social network; lbsn; check-in; gender difference BIG DATA; HUMAN MOBILITY; SELF-DISCLOSURE; NETWORKS; SHANGHAI; TWITTER; INFORMATION; DEFINITION; GOVERNMENT; AWARENESS Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as Weibo) in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference. [Rizwan, Muhammad; Wan, Wanggen] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China; [Rizwan, Muhammad; Wan, Wanggen] Shanghai Univ, Inst Smart City, Shanghai 200444, Peoples R China; [Cervantes, Ofelia] Univ Americas Puebla, Comp Elect & Mechatron Dept, Cholula 72810, Mexico; [Gwiazdzinski, Luc] Univ Grenoble Alpes, IGA, F-38100 Grenoble, France Shanghai University; Shanghai University; Universidad Americas Puebla (UDLAP); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA) Rizwan, M (corresponding author), Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China.;Rizwan, M (corresponding author), Shanghai Univ, Inst Smart City, Shanghai 200444, Peoples R China. rizwan@shu.edu.cn; wanwg@staff.shu.edu.cn; ofelia.cervantes@udlap.mx; lucmarcg@gmail.com Muhammad, Dr. Rizwan/R-5074-2016; Cervantes, Ofelia/AAN-6767-2020 Muhammad, Dr. Rizwan/0000-0001-8903-5426; GWIAZDZINSKI, Luc/0000-0003-2128-9088 National Natural Science Foundation of China [61711530245]; key project of Shanghai Science and Technology Commission [17511106802] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); key project of Shanghai Science and Technology Commission(Science & Technology Commission of Shanghai Municipality (STCSM)) This work is supported by the National Natural Science Foundation of China (61711530245) and the key project of Shanghai Science and Technology Commission (17511106802). 87 29 31 2 47 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. MAY 2018.0 7.0 5 196 10.3390/ijgi7050196 0.0 17 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Physical Geography; Remote Sensing GJ3KW Green Submitted, Green Published, gold 2023-03-23 WOS:000435194700036 0
J Ye, Y; Zhang, CY; He, CL; Wang, X; Huang, JJ; Deng, JH Ye, Yong; Zhang, Chiya; He, Chunlong; Wang, Xi; Huang, Jianjun; Deng, Jiahao A Review on Applications of Capacitive Displacement Sensing for Capacitive Proximity Sensor IEEE ACCESS English Review Electrodes; Displacement measurement; Capacitive sensors; Strain; Dielectric measurement; Dielectric constant; Capacitive proximity sensor (CPS); capacitive displacement sensing; distance measurement; indirect measurement; smart environment CLEARANCE MEASUREMENT; RECOGNITION; SYSTEM; IMPLEMENTATION; LOCALIZATION; PLATFORM; TACTILE; DESIGN; GAS; PERFORMANCE Capacitive proximity sensors (CPSs) are ubiquitous because of their simple design, low cost and low consumption. Capacitive displacement sensing, as one of the three sensing modalities, works for long distance and can be unitized to measure more physical quantities compared with capacitive volume and deformation sensing. In this paper, we firstly introduce the concept of capacitive displacement sensing. After that, we present applications of capacitive displacement sensing under three broad categories: distance measurements, indirect measurements, and the applications applied in smart environments. Finally, we discuss the challenges and possible solutions for CPSs development. We show that both the detection range and accuracy of CPS can be improved by multi-sensor fusion, and the application scenarios can be extensive through machine/deep learning approaches. We aim to provide a comprehensive, and state-of-the-art review of the capacitive displacement sensing, and inspire more researchers and developers to find wide application perspectives. [Ye, Yong; He, Chunlong; Huang, Jianjun] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China; [Ye, Yong; Wang, Xi] KTH Royal Inst Technol, Dept Prod Engn, S-11428 Stockholm, Sweden; [Zhang, Chiya] Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen 518055, Guangdong, Peoples R China; [Deng, Jiahao] Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China Shenzhen University; Royal Institute of Technology; Harbin Institute of Technology; Beijing Institute of Technology Zhang, CY (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen 518055, Guangdong, Peoples R China. chiya.zhang@foxmail.com Huang, Jianjun/AAB-3051-2019; Wang, Vincent/AAP-4776-2020; Wang, Xi Vincent V/O-4662-2014 Huang, Jianjun/0000-0001-7040-3591; Wang, Vincent/0000-0001-9694-0483; Wang, Xi Vincent V/0000-0001-9694-0483 Natural Science Foundation of China [61902252]; Shenzhen Overseas High-level Talents Innovation and Entrepreneurship [KQJSCX20180328093835762]; Natural Science Funding of Guangdong Province [2018A030310509]; Science and Technology Innovation Commission of Shenzhen [JCYJ20190808122409660] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Overseas High-level Talents Innovation and Entrepreneurship; Natural Science Funding of Guangdong Province; Science and Technology Innovation Commission of Shenzhen This work was supported in part by the Natural Science Foundation of China under Grant 61902252, in part by the Shenzhen Overseas High-level Talents Innovation and Entrepreneurship under Grant KQJSCX20180328093835762, in part by the Natural Science Funding of Guangdong Province under Grant 2018A030310509, and in part by the Science and Technology Innovation Commission of Shenzhen under Grant JCYJ20190808122409660. 137 44 46 46 150 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 45325 45342 10.1109/ACCESS.2020.2977716 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LB5ZK gold 2023-03-23 WOS:000524713900016 0
J Qin, Y; Huttlin, EL; Winsnes, CF; Gosztyla, ML; Wacheul, L; Kelly, MR; Blue, SM; Zheng, F; Chen, M; Schaffer, LV; Licon, K; Backstrom, A; Vaites, LP; Lee, JJ; Ouyang, W; Liu, SN; Zhang, T; Silva, E; Park, J; Pitea, A; Kreisberg, JF; Gygi, SP; Ma, JZ; Harper, JW; Yeo, GW; Lafontaine, DLJ; Lundberg, E; Ideker, T Qin, Yue; Huttlin, Edward L.; Winsnes, Casper F.; Gosztyla, Maya L.; Wacheul, Ludivine; Kelly, Marcus R.; Blue, Steven M.; Zheng, Fan; Chen, Michael; Schaffer, Leah, V; Licon, Katherine; Backstrom, Anna; Vaites, Laura Pontano; Lee, John J.; Ouyang, Wei; Liu, Sophie N.; Zhang, Tian; Silva, Erica; Park, Jisoo; Pitea, Adriana; Kreisberg, Jason F.; Gygi, Steven P.; Ma, Jianzhu; Harper, J. Wade; Yeo, Gene W.; Lafontaine, Denis L. J.; Lundberg, Emma; Ideker, Trey A multi-scale map of cell structure fusing protein images and interactions NATURE English Article PRE-RIBOSOMAL-RNA; BINDING; NETWORKS; SNRNP The cell is a multi-scale structure with modular organization across at least four orders of magnitude(1). Two central approaches for mapping this structure-protein fluorescent imaging and protein biophysical association-each generate extensive datasets, but of distinct qualities and resolutions that are typically treated separately(2,3). Here we integrate immunofluorescence images in the Human Protein Atlas(4) with affinity purifications in BioPlex(5) to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps. [Qin, Yue; Kelly, Marcus R.; Zheng, Fan; Chen, Michael; Schaffer, Leah, V; Licon, Katherine; Lee, John J.; Liu, Sophie N.; Silva, Erica; Park, Jisoo; Pitea, Adriana; Kreisberg, Jason F.; Ideker, Trey] Univ Calif San Diego, Dept Med, La Jolla, CA 92093 USA; [Qin, Yue; Yeo, Gene W.; Ideker, Trey] Univ Calif San Diego, Bioinformat & Syst Biol Program, La Jolla, CA 92093 USA; [Huttlin, Edward L.; Vaites, Laura Pontano; Zhang, Tian; Gygi, Steven P.; Harper, J. Wade] Harvard Med Sch, Dept Cell Biol, Boston, MA 02115 USA; [Winsnes, Casper F.; Backstrom, Anna; Ouyang, Wei; Lundberg, Emma] KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, Stockholm, Sweden; [Gosztyla, Maya L.; Blue, Steven M.; Yeo, Gene W.] Univ Calif San Diego, Dept Cellular & Mol Med, La Jolla, CA 92093 USA; [Gosztyla, Maya L.; Blue, Steven M.; Yeo, Gene W.] Univ Calif San Diego, Stem Cell Program, La Jolla, CA 92093 USA; [Gosztyla, Maya L.; Blue, Steven M.; Yeo, Gene W.; Ideker, Trey] Univ Calif San Diego, Inst Genom Med, La Jolla, CA 92093 USA; [Wacheul, Ludivine; Lafontaine, Denis L. J.] Univ Libre Bruxelles ULB, RNA Mol Biol, Fonds Rech Sci FRS FNRS, Gosselies, Belgium; [Ma, Jianzhu] Peking Univ, Inst Artificial Intelligence, Beijing, Peoples R China; [Lundberg, Emma] Stanford Univ, Dept Genet, Stanford, CA 94305 USA; [Lundberg, Emma] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA; [Ideker, Trey] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA; [Ideker, Trey] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA University of California System; University of California San Diego; University of California System; University of California San Diego; Harvard University; Harvard Medical School; Royal Institute of Technology; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego; Fonds de la Recherche Scientifique - FNRS; Peking University; Stanford University; University of California System; University of California San Diego; University of California System; University of California San Diego Ideker, T (corresponding author), Univ Calif San Diego, Dept Med, La Jolla, CA 92093 USA.;Ideker, T (corresponding author), Univ Calif San Diego, Bioinformat & Syst Biol Program, La Jolla, CA 92093 USA.;Lundberg, E (corresponding author), KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, Stockholm, Sweden.;Ideker, T (corresponding author), Univ Calif San Diego, Inst Genom Med, La Jolla, CA 92093 USA.;Lundberg, E (corresponding author), Stanford Univ, Dept Genet, Stanford, CA 94305 USA.;Lundberg, E (corresponding author), Chan Zuckerberg Biohub, San Francisco, CA 94158 USA.;Ideker, T (corresponding author), Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA.;Ideker, T (corresponding author), Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA. emma.lundberg@scilifelab.se; tideker@ucsd.edu Gosztyla, Maya/GLV-4865-2022; Qin, Yue/GWV-0264-2022; Zheng, Fan/ABF-6176-2020 Gosztyla, Maya/0000-0003-4758-4800; Zheng, Fan/0000-0002-4403-112X; Kaller Lundberg, Emma/0000-0001-7034-0850; Yeo, Eugene/0000-0002-0799-6037; Ideker, Trey/0000-0002-1708-8454; Liu, Sophie/0000-0001-7934-8605; Huttlin, Edward/0000-0002-1822-1173; Kreisberg, Jason/0000-0002-3616-079X; Ludivine, Wacheul/0000-0002-0443-8865; Qin, Yue/0000-0002-0496-7134; Winsnes, Casper/0000-0002-0028-5865; Blue, Steven/0000-0003-0279-6690; Lee, John/0000-0002-7967-3384 National Institutes of Health (NIH) [U54 CA209891, U01 MH115747, P41 GM103504, R01 HG009979, F99 CA264422, U24 HG006673, U41 HG009889, R01s HL137223, HG004659, R50 CA243885]; Erling-Persson family foundation; Knut and Alice Wallenberg Foundation [2016.0204]; Swedish Research Council [2017-05327]; Belgian Fonds de la Recherche Scientifique (F.R.S./FNRS); Universite Libre de Bruxelles (ULB); European Joint Programme on Rare Diseases ('RiboEurope'); European Joint Programme on Rare Diseases ('DBAcure'); Region Wallonne (SPW EER) ('RIBOcancer'); Internationale Brachet Stiftung; Epitran COST action [CA16120] National Institutes of Health (NIH)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Erling-Persson family foundation; Knut and Alice Wallenberg Foundation(Knut & Alice Wallenberg Foundation); Swedish Research Council(Swedish Research Council); Belgian Fonds de la Recherche Scientifique (F.R.S./FNRS)(Fonds de la Recherche Scientifique - FNRS); Universite Libre de Bruxelles (ULB); European Joint Programme on Rare Diseases ('RiboEurope'); European Joint Programme on Rare Diseases ('DBAcure'); Region Wallonne (SPW EER) ('RIBOcancer'); Internationale Brachet Stiftung; Epitran COST action(European Cooperation in Science and Technology (COST)) We thank C. Ng, A. Palmer, Q. Zhang, Y. Quan, members of the laboratories of T.I. and E.L., the Human Protein Atlas and J. Swedlow for discussion and comments; M. Dow for helping us to improve the MuSIC GitHub repository and test the MuSIC pipeline; and the Cell Profiling facility and C. Stadler at the Science for Life Laboratory for help with in situ fractionation. This work was supported by the National Institutes of Health (NIH) under grants U54 CA209891, U01 MH115747, P41 GM103504 and R01 HG009979 to T.I., F99 CA264422 to Y.Q., U24 HG006673 to E.L.H., S.P.G. and J.W.H., U41 HG009889 and R01s HL137223 and HG004659 to G.W.Y. and R50 CA243885 to J.F.K.; by a gift from Google Ventures to J.W.H. and S.P.G.; by the Erling-Persson family foundation, Knut and Alice Wallenberg Foundation (2016.0204) and the Swedish Research Council (2017-05327) to E.L.; and by the Belgian Fonds de la Recherche Scientifique (F.R.S./FNRS), the Universite Libre de Bruxelles (ULB), the European Joint Programme on Rare Diseases (`RiboEurope' and `DBAcure'), the Region Wallonne (SPW EER) (`RIBOcancer'), the Internationale Brachet Stiftung and the Epitran COST action (CA16120) to D.L.J.L. 32 16 16 12 45 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 0028-0836 1476-4687 NATURE Nature DEC 16 2021.0 600.0 7889 536 + 10.1038/s41586-021-04115-9 0.0 NOV 2021 18 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics XP3FN 34819669.0 Green Published, Green Submitted, Green Accepted 2023-03-23 WOS:000722128900003 0
C Mu, YW; Gomez, H; Montes, AC; Martinez, CA; Wang, XT; Gao, HM Zhu, X; Fang, ZG; Davis, H Mu, Yawei; Gomez, Hernandez; Cano Montes, Antonio; Alcaraz Martinez, Carlos; Wang, Xuetian; Gao, Hongmin Speech Emotion Recognition Using Convolutional-Recurrent Neural Networks with Attention Model 2ND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE AND INTERNET TECHNOLOGY, CII 2017 DEStech Transactions on Computer Science and Engineering English Proceedings Paper 2nd International Conference on Computer Engineering, Information Science and Internet Technology (CII) NOV 11-12, 2017 Sanya, PEOPLES R CHINA Speech emotion recognition; Distributed convolution neural networks; Bidirectional recurrent neural networks; Attention mechanism Speech Emotion Recognition (SER) plays an important role in human-computer interface and assistant technologies. In this paper, a new method is proposed using distributed Convolution Neural Networks (CNN) to automatically learn affect-salient features from raw spectral information, and then applying Bidirectional Recurrent Neural Network (BRNN) to obtain the temporal information from the output of CNN. In the end, an Attention Mechanism is implemented on the output sequence of the BRNN to focus on target emotion-pertinent parts of an utterance. This attention mechanism not only improves the classification accuracy, but also provides model's interpretability. Experimental results show that this approach can gain 64.08% weighted accuracy and 56.41% unweighted accuracy for four-emotion classification in IEMOCAP dataset, which outperform previous results reported for this dataset. [Mu, Yawei; Gao, Hongmin] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China; [Gomez, Hernandez; Cano Montes, Antonio; Alcaraz Martinez, Carlos] Univ Politecn Madrid, Grp Aplicac Proc Senal, Ave Complutense 30, Madrid, Spain Beijing Institute of Technology; Universidad Politecnica de Madrid Mu, YW (corresponding author), Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China. muyawei822@gmail.com Cano Montes, Antonio/0000-0002-0435-4987 Estudios y Soluciones Avanzadas de Ingenieria (ESAI) Estudios y Soluciones Avanzadas de Ingenieria (ESAI) This research was financially supported by Estudios y Soluciones Avanzadas de Ingenieria (ESAI). 21 5 6 0 1 DESTECH PUBLICATIONS, INC LANCASTER 439 DUKE STREET, LANCASTER, PA 17602-4967 USA 2475-8841 978-1-60595-504-9 DESTECH TRANS COMP 2017.0 341 350 10 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Multidisciplinary Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ6ZX 2023-03-23 WOS:000426985800048 0
J Saad, B; Bradley, A Saad, Bradford; Bradley, Adam Digital suffering: why it's a problem and how to prevent it INQUIRY-AN INTERDISCIPLINARY JOURNAL OF PHILOSOPHY English Article; Early Access Artificial intelligence; machine ethics; consciousness; dancing qualia argument; functionalism; alignment problem INTEGRATED INFORMATION; META-PROBLEM As ever more advanced digital systems are created, it becomes increasingly likely that some of these systems will be digital minds, i.e. digital subjects of experience. With digital minds comes the risk of digital suffering. The problem of digital suffering is that of mitigating this risk. We argue that the problem of digital suffering is a high stakes moral problem and that formidable epistemic obstacles stand in the way of solving it. We then propose a strategy for solving it: Access Monitor Prevent (AMP). AMP uses a 'dancing qualia' argument to link the functional states of certain digital systems to their experiences-this yields epistemic access to digital minds. With that access, we can prevent digital suffering by only creating advanced digital systems that we have such access to, monitoring their functional profiles, and preventing them from entering states with functional markers of suffering. After introducing and motivating AMP, we confront limitations it faces and identify some options for overcoming them. We argue that AMP fits especially well with-and so provides a moral reason to prioritize-one approach to creating such systems: whole brain emulation. We also contend that taking other paths to digital minds would be morally risky. [Saad, Bradford] Univ Utrecht, Dept Philosophy, Religious Studies, Utrecht, Netherlands; [Saad, Bradford] Sentience Inst, New York, NY 10006 USA; [Bradley, Adam] Lingnan Univ, Dept Philosophy, Hong Kong, Peoples R China; [Bradley, Adam] Hong Kong Catastroph Risk Ctr, Hong Kong, Peoples R China Utrecht University; Lingnan University Saad, B (corresponding author), Univ Utrecht, Dept Philosophy, Religious Studies, Utrecht, Netherlands.;Saad, B (corresponding author), Sentience Inst, New York, NY 10006 USA. t.b.saad@uu.nl Saad, Thomas/0000-0003-2492-6214 62 0 0 4 4 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0020-174X 1502-3923 INQUIRY Inquiry-Interdiscip. J. Philos. 10.1080/0020174X.2022.2144442 0.0 NOV 2022 36 Ethics; Philosophy Social Science Citation Index (SSCI); Arts & Humanities Citation Index (A&HCI) Social Sciences - Other Topics; Philosophy 6N0AM hybrid, Green Published 2023-03-23 WOS:000889224300001 0
J Hu, HL; Li, Z; Elofsson, A; Xie, SX Hu, Hailong; Li, Zhong; Elofsson, Arne; Xie, Shangxin A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure APPLIED SCIENCES-BASEL English Article protein secondary structure; sequence analysis; Bi-LSTM; ensemble algorithm; deep learning NEURAL-NETWORKS The prediction of protein secondary structure continues to be an active area of research in bioinformatics. In this paper, a Bi-LSTM based ensemble model is developed for the prediction of protein secondary structure. The ensemble model with dual loss function consists of five sub-models, which are finally joined by a Bi-LSTM layer. In contrast to existing ensemble methods, which generally train each sub-model and then join them as a whole, this ensemble model and sub-models can be trained simultaneously and the performance of each model can be observed and compared during the training process. Three independent test sets (e.g., data1199, 513 protein Cuff & Barton set (CB513) and 203 proteins from Critical Appraisals Skills Programme (CASP203)) are employed to test the method. On average, the ensemble model achieved 84.3% in Q(3) accuracy and 81.9% in segment overlap measure (SOV) score by using 10-fold cross validation. There is an improvement of up to 1% over some state-of-the-art prediction methods of protein secondary structure. [Hu, Hailong; Li, Zhong] Zhejiang Sci Tech Univ, Fac Mech Engn & Automat, Hangzhou 310018, Zhejiang, Peoples R China; [Hu, Hailong] Zhejiang A&F Univ, Sch Sci, Hangzhou 311300, Zhejiang, Peoples R China; [Li, Zhong; Xie, Shangxin] Zhejiang Sci Tech Univ, Sch Sci, Hangzhou 310018, Zhejiang, Peoples R China; [Elofsson, Arne] Stockholm Univ, Sci Life Lab, Dept Biochem & Biophys, S-17121 Solna, Sweden Zhejiang Sci-Tech University; Zhejiang A&F University; Zhejiang Sci-Tech University; Stockholm University Li, Z (corresponding author), Zhejiang Sci Tech Univ, Fac Mech Engn & Automat, Hangzhou 310018, Zhejiang, Peoples R China.;Li, Z (corresponding author), Zhejiang Sci Tech Univ, Sch Sci, Hangzhou 310018, Zhejiang, Peoples R China. lizhong@zstu.edu.cn National Natural Science Foundation of China [11671009]; Zhejiang Provincial Natural Science Foundation of China [LZ19A010002, LY19F010014] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work was supported by National Natural Science Foundation of China under Grant No. 11671009, Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ19A010002 and LY19F010014. 34 7 7 1 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel SEP 1 2019.0 9.0 17 3538 10.3390/app9173538 0.0 11 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics JB5LE gold 2023-03-23 WOS:000488603600100 0
C Herodotou, H; Odysseos, L; Chen, YX; Lu, JH IEEE Comp Soc Herodotou, Herodotos; Odysseos, Lambros; Chen, Yuxing; Lu, Jiaheng Automatic Performance Tuning for Distributed Data Stream Processing Systems 2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022) IEEE International Conference on Data Engineering English Proceedings Paper 38th IEEE International Conference on Data Engineering (ICDE) MAY 09-11, 2022 ELECTR NETWORK IEEE,IEEE Comp Soc Performance tuning; data stream processing; parameter tuning; Storm; Flink; Spark Streaming Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark Streaming are now routinely used to process continuous data streams in (near) real-time. However, achieving the low latency and high throughput demanded by today's streaming applications can be a daunting task, especially since the performance of DSPSs highly depends on a large number of system parameters that control load balancing, degree of parallelism, buffer sizes, and various other aspects of system execution. This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. The approaches are organized into five main categories based on their methodologies and features: cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. The categories of approaches will be analyzed in depth and compared to each other, exposing their various strengths and weaknesses. Finally, we will identify several open research problems and challenges related to automatic performance tuning for DSPSs. [Herodotou, Herodotos; Odysseos, Lambros] Cyprus Univ Technol, Limassol, Cyprus; [Chen, Yuxing] Tencent Inc, Shenzhen, Peoples R China; [Lu, Jiaheng] Univ Helsinki, Helsinki, Finland Cyprus University of Technology; Tencent; University of Helsinki Herodotou, H (corresponding author), Cyprus Univ Technol, Limassol, Cyprus. herodotos.herodotou@cut.ac.cy; lambros.odysseos@cut.ac.cy; axingguchen@tencent.com; jiaheng.lu@helsinki.fi Lu, Jiaheng/0000-0003-2067-454X 26 0 0 2 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1084-4627 978-1-6654-0883-7 PROC INT CONF DATA 2022.0 3194 3197 10.1109/ICDE53745.2022.00296 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT8PW 2023-03-23 WOS:000855078403031 0
J Zeng, ZZ; Zhang, CZ; Xia, Y; Fan, ZY; Wolverton, C; Chen, Y Zeng, Zezhu; Zhang, Cunzhi; Xia, Yi; Fan, Zheyong; Wolverton, Chris; Chen, Yue Nonperturbative phonon scatterings and the two-channel thermal transport in Tl3VSe4 PHYSICAL REVIEW B English Article CONDUCTIVITY We study the role of nonperturbative phonon scattering in strongly anharmonic materials having ultralow lattice thermal conductivity with unusual temperature dependence. We take Tl3VSe4 as an example and investigate its lattice dynamics using perturbation theory (PT) up to the fourth order and molecular dynamics (MD) with a machine-learning potential. We find distinct differences of phonon linewidth between PT and MD in the whole Brillouin zone. The comparison between the theoretical phonon linewidths and experiments suggests that PT severely underestimates the phonon scatterings, even when the fourth-order anharmonicity is included. Moreover, we extend our calculations to higher temperatures and evaluate the two-channel thermal conductivity based on the unified theory developed by Simoncelli et al. [Nat. Phys. 15, 809 (2019)]. We find a crucial coherence contribution to the total thermal conductivity at high temperatures. Our results pave the path for future studies of phonon properties and lattice thermal conductivities of strongly anharmonic crystals beyond the conventional PT realm. [Zeng, Zezhu; Zhang, Cunzhi; Chen, Yue] Univ Hong Kong, Dept Mech Engn, Pokfulam Rd, Hong Kong, Peoples R China; [Xia, Yi; Wolverton, Chris] Northwestern Univ, Dept Mat Sci & Engn, Evanston, IL 60208 USA; [Fan, Zheyong] Aalto Univ, QTF Ctr Excellence, Dept Appl Phys, FI-00076 Aalto, Finland; [Chen, Yue] HKU Zhejiang Inst Res & Innovat, 1623 Dayuan Rd, Lin An 311305, Peoples R China University of Hong Kong; Northwestern University; Aalto University Chen, Y (corresponding author), Univ Hong Kong, Dept Mech Engn, Pokfulam Rd, Hong Kong, Peoples R China.;Chen, Y (corresponding author), HKU Zhejiang Inst Res & Innovat, 1623 Dayuan Rd, Lin An 311305, Peoples R China. yuechen@hku.hk Fan, Zheyong/G-8235-2012; Chen, Y/HLQ-7159-2023 Fan, Zheyong/0000-0002-2253-8210; Zeng, Zezhu/0000-0001-5126-4928 National Natural Science Foundation of China [11874313]; Zhejiang Provincial Natural Science Foundation [LR19A040001]; Research Grants Council of Hong Kong [17201019, 17300018]; National Key Research and Development Program of China [2019YFA0209904]; Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0014520] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation(Natural Science Foundation of Zhejiang Province); Research Grants Council of Hong Kong(Hong Kong Research Grants Council); National Key Research and Development Program of China; Department of Energy, Office of Science, Basic Energy Sciences(United States Department of Energy (DOE)) This work is supported by the National Natural Science Foundation of China (11874313), the Zhejiang Provincial Natural Science Foundation (LR19A040001), the Research Grants Council of Hong Kong (17201019 and 17300018), and the National Key Research and Development Program of China (2019YFA0209904). Y.X. and C.W. acknowledge financial supports from the Department of Energy, Office of Science, Basic Energy Sciences under Grant DE-SC0014520 (theory of anharmonic phonons). The authors are grateful for the research computing facilities offered by ITS, HKU. 57 18 18 5 25 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B JUN 21 2021.0 103.0 22 224307 10.1103/PhysRevB.103.224307 0.0 7 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics SW3RJ Green Submitted, Green Published 2023-03-23 WOS:000664434600005 0
C Ding, WX; Yan, Z; Qian, XR; Deng, RH IEEE Ding, Wenxiu; Yan, Zheng; Qian, Xinren; Deng, Robert H. Computing Maximum and Minimum with Privacy Preservation and Flexible Access Control 2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) DEC 09-13, 2019 Waikoloa, HI IEEE,Huawei,Intel,ZTE,Google,Qualcomm,Natl Instruments,IEEE Commun Soc maximum & minimum; privacy preservation; access control; homomorphic encryption; attribute-based encryption FULLY HOMOMORPHIC ENCRYPTION; EFFICIENT With the fast development of Internet of Things, huge volume of data is being collected from various sensors and devices, aggregated at gateways, and processed in the cloud. Due to privacy concern, data are usually encrypted before being outsourced to the cloud. However, encryption seriously impedes both computation over the data and sharing of the computation results. Computing maximum and minimum among a data set are two of the most basic operations in machine learning and data mining algorithms. In this paper, we study how to compute maximum and minimum over encrypted data and control the access to the computation result in a privacy-preserving manner. We present four schemes to realize privacy-preserving maximum and minimum computations with flexible access control that can adapt to various application scenarios. We further analyze their security and show their efficiency through extensive evaluations and comparisons with existing work. [Ding, Wenxiu; Yan, Zheng; Qian, Xinren] Xidian Univ, Sch Cyber Engn, Xian, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Deng, Robert H.] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore Xidian University; Aalto University; Singapore Management University Ding, WX (corresponding author), Xidian Univ, Sch Cyber Engn, Xian, Shaanxi, Peoples R China. wxding@xidian.edu.cn; zyan@xidian.edu.cn; xinrenqian@gmail.com; robertdeng@smu.edu.sg yang, zheng/HGC-7753-2022; Ding, Wenxiu/AAP-5812-2021 Ding, Wenxiu/0000-0002-8531-9226 National Natural Science Foundation of China [61802293, 61672410, U1536202]; National Postdoctoral Program for Innovative Talents [BX20180238]; National Key Research and Development Program of China [2016YFB0800700]; Academy of Finland [308087, 314203]; China Postdoctoral Science Foundation [2018M633461]; Fundamental Research Funds for the Central Universities [JB191504]; Key Lab of Information Network Security, Ministry of Public Security [C18614]; 111 project [B16037]; open grant of the Tactical Data Link Lab of the 20th Research Insti-tute of China Electronics Technology Group Corporation, P.R. China [CLDL-20182119] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; National Key Research and Development Program of China; Academy of Finland(Academy of Finland); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Key Lab of Information Network Security, Ministry of Public Security; 111 project(Ministry of Education, China - 111 Project); open grant of the Tactical Data Link Lab of the 20th Research Insti-tute of China Electronics Technology Group Corporation, P.R. China This work is sponsored by the National Natural Science Foundation of China (Grants 61802293, 61672410 and U1536202), National Postdoctoral Program for Innovative Talents (grant BX20180238), the National Key Research and Development Program of China (Grant 2016YFB0800700), the Academy of Finland (Grant 308087 and 314203), the Project funded by China Postdoctoral Science Foundation (grant 2018M633461), the Fundamental Research Funds for the Central Universities (grant JB191504), the open grant of the Tactical Data Link Lab of the 20th Research Insti-tute of China Electronics Technology Group Corporation, P.R. China (grant No. CLDL-20182119), the Key Lab of Information Network Security, Ministry of Public Security (Grant C18614), and the 111 project (Grants B16037). 21 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 978-1-7281-0962-6 IEEE GLOB COMM CONF 2019.0 7 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BP4GG 2023-03-23 WOS:000552238604057 0
J Zhou, YM; Liu, YG; Gao, XZ; Qiu, GP Zhou, Yangming; Liu, Yangguang; Gao, Xiao-Zhi; Qiu, Guoping A label ranking method based on Gaussian mixture model KNOWLEDGE-BASED SYSTEMS English Article Machine learning; Label ranking; Multi-label learning; Gaussian mixture model; Clustering Label ranking studies the issue of learning a model that maps instances to rankings over a finite set of predefined labels. In order to relieve the cost of memory and time during training and prediction, we propose a novel approach for label ranking problem based on Gaussian mixture model in this paper. The key idea of the approach is to divide the label ranking training data into multiple clusters using clustering algorithm, and each cluster is described by a Gaussian prototype. Then, a Gaussian mixture model is introduced to model the mapping from instances to rankings. Finally, a predicted ranking is obtained with maximum posterior probability. In the experiments, we compare our method with two state-of-the-art label ranking approaches. Experimental results show that our method is fully competitive in terms of predictive accuracy. Moreover, the proposed method also provides a measure of the reliability of the corresponding predicted ranking. (C) 2014 Elsevier B.V. All rights reserved. [Zhou, Yangming; Liu, Yangguang] Zhejiang Univ, Ningbo Inst Technol, Ningbo, Zhejiang, Peoples R China; [Zhou, Yangming] Zhejiang Univ, Dept Control Sci & Engn, Hangzhou 310003, Zhejiang, Peoples R China; [Gao, Xiao-Zhi] Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland; [Qiu, Guoping] Univ Nottingham, Sch Comp Sci, Nottingham NG7 2RD, England Zhejiang University; Zhejiang University; Aalto University; University of Nottingham Liu, YG (corresponding author), Zhejiang Univ, Ningbo Inst Technol, Ningbo, Zhejiang, Peoples R China. zhou.yangming@yahoo.com; ygliu@acm.org; xiao-zhi.gao@aalto.fi; guoping.qiu@nottingham.ac.uk Zhou, Yangming/AGL-5372-2022 Zhou, Yangming/0000-0002-4254-6517; Liu, Yangguang/0000-0001-9468-9651 Zhejiang Provincial Natural Science Foundation of China [Y1101202]; Academy of Finland [135225]; Finnish Funding Agency for Technology and Innovation (TEKES); Academy of Finland (AKA) [135225] Funding Source: Academy of Finland (AKA) Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); Academy of Finland(Academy of Finland); Finnish Funding Agency for Technology and Innovation (TEKES)(Finnish Funding Agency for Technology & Innovation (TEKES)); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) We would like to thank the anonymous reviewers for their insightful comments that helped to considerably improve this paper. This work was funded by the Zhejiang Provincial Natural Science Foundation of China under Grant Y1101202, the Academy of Finland under Grant 135225 and Finnish Funding Agency for Technology and Innovation (TEKES). 23 8 10 2 17 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. DEC 2014.0 72.0 108 113 10.1016/j.knosys.2014.08.029 0.0 6 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science AU3CT 2023-03-23 WOS:000345491100010 0
J Wang, MY; Tichelaar, J; Pasquale, LR; Shen, LQ; Boland, MV; Wellik, SR; De Moraes, CG; Myers, JS; Ramulu, P; Kwon, M; Saeedi, OJ; Wang, H; Baniasadi, N; Li, D; Bex, PJ; Elze, T Wang, Mengyu; Tichelaar, Jorryt; Pasquale, Louis R.; Shen, Lucy Q.; Boland, Michael V.; Wellik, Sarah R.; De Moraes, Carlos Gustavo; Myers, Jonathan S.; Ramulu, Pradeep; Kwon, MiYoung; Saeedi, Osamah J.; Wang, Hui; Baniasadi, Neda; Li, Dian; Bex, Peter J.; Elze, Tobias Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence JAMA OPHTHALMOLOGY English Article QUALITY-OF-LIFE; AUTOMATED PERIMETRY; RISK; CLASSIFICATION; RELIABILITY; PROGRESSION; DEFECTS; IMPACT Questions What are the typical patterns of central visual field loss in end-stage glaucoma, and do deteriorations follow trajectories specific to their baseline patterns? Findings In this cohort study of 1103 patients with glaucoma, 14 central visual field patterns were determined by archetypal analysis, most of which preserve the inferotemporal regions entirely or partially. Initial encroachments on an intact central visual field at follow-up in end-stage glaucoma were more likely to be nasal loss. Meaning These results suggest that central visual field loss in end-stage glaucoma exhibits characteristic patterns that might be related to different subtypes, and initial central visual field loss is likely to be nasal loss. This cohort study provides a quantitative characterization and classification of central visual field loss among patients with end-stage glaucoma. Importance Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss. Objective To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma. Design, Setting, and Participants This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019. Main Outcomes and Measures Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones. Results Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not. Conclusions and Relevance In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss. [Wang, Mengyu; Tichelaar, Jorryt; Wang, Hui; Baniasadi, Neda; Li, Dian; Elze, Tobias] Harvard Med Sch, Schepens Eye Res Inst, Massachusetts Eye & Ear, 20 Staniford St, Boston, MA 02114 USA; [Tichelaar, Jorryt] Radboud Univ Nijmegen, Donders Ctr Med Neurosci, Med Ctr, Nijmegen, Netherlands; [Tichelaar, Jorryt] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands; [Pasquale, Louis R.] Icahn Sch Med Mt Sinai, Eye & Vis Res Inst, New York, NY 10029 USA; [Pasquale, Louis R.] Harvard Med Sch, Channing Div Network Med, Brigham & Womens Hosp, Boston, MA 02115 USA; [Shen, Lucy Q.] Harvard Med Sch, Massachusetts Eye & Ear, Boston, MA 02115 USA; [Boland, Michael V.; Ramulu, Pradeep] Johns Hopkins Univ, Sch Med, Wilmer Eye Inst, Baltimore, MD 21205 USA; [Wellik, Sarah R.] Univ Miami, Sch Med, Bascom Palmer Eye Inst, Miami, FL USA; [De Moraes, Carlos Gustavo] Columbia Univ, Med Ctr, Edward S Harkness Eye Inst, New York, NY USA; [Myers, Jonathan S.] Thomas Jefferson Univ, Wills Eye Hosp, Philadelphia, PA 19107 USA; [Kwon, MiYoung] Univ Alabama Birmingham, Dept Ophthalmol & Visual Sci, Birmingham, AL USA; [Saeedi, Osamah J.] Univ Maryland, Sch Med, Dept Ophthalmol & Visual Sci, Baltimore, MD 21201 USA; [Wang, Hui] Jilin Univ Finance & Econ, Inst Psychol & Behav, Changchun, Peoples R China; [Bex, Peter J.] Northeastern Univ, Dept Psychol, Boston, MA 02115 USA; [Elze, Tobias] Max Planck Inst Math Sci, Leipzig, Germany Harvard University; Harvard Medical School; Massachusetts Eye & Ear Infirmary; Schepens Eye Research Institute; Radboud University Nijmegen; Radboud University Nijmegen; Icahn School of Medicine at Mount Sinai; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Harvard University; Harvard Medical School; Massachusetts Eye & Ear Infirmary; Johns Hopkins University; Johns Hopkins Medicine; Bascom Palmer Eye Institute; University of Miami; Columbia University; Jefferson University; University of Alabama System; University of Alabama Birmingham; University System of Maryland; University of Maryland Baltimore; Jilin University of Finance & Economics; Northeastern University; Max Planck Society Elze, T (corresponding author), Harvard Med Sch, Schepens Eye Res Inst, Massachusetts Eye & Ear, 20 Staniford St, Boston, MA 02114 USA. tobias-elze@tobias-elze.de Tichelaar, Jorryt/ABD-4383-2021; Boland, Michael V/HJI-9286-2023; Li, Dian/AAQ-3482-2020 Boland, Michael V/0000-0003-2506-7095; Li, Dian/0000-0002-5374-7314 NEI/NIH [R21 EY030142, R21 EY030631, R01 EY030575, R01 EY015473, R01 EY027857, R01 EY025253, P30 EY003790]; BrightFocus Foundation; Lions Foundation; Research to Prevent Blindness; NIH, Harvard Glaucoma Center of Excellence [K99 EY028631, K23 EY025014]; Eleanor and Miles Shore Fellowship; Alice Adler Fellowship; Grimshaw-Gudewicz Foundation NEI/NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Eye Institute (NEI)); BrightFocus Foundation(BrightFocus Foundation); Lions Foundation; Research to Prevent Blindness(Research to Prevent Blindness (RPB)); NIH, Harvard Glaucoma Center of Excellence(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Eleanor and Miles Shore Fellowship; Alice Adler Fellowship; Grimshaw-Gudewicz Foundation This study was supported by grants R21 EY030142 (Dr Elze), R21 EY030631 (Dr Elze), R01 EY030575 (Dr Elze), R01 EY015473 (Dr Pasquale), R01 EY027857 (Dr Kwon), and R01 EY025253 (Dr De Moraes) and core grant P30 EY003790 (Drs M. Wang and Elze, Ms Li, and Drs H. Wang and Baniasadi) from the NEI/NIH, BrightFocus Foundation (Drs M. Wang and Elze), Lions Foundation (Drs M. Wang, Baniasadi, and Elze), Grimshaw-Gudewicz Foundation (Drs M. Wang, Baniasadi, and Elze), Research to Prevent Blindness (Drs M. Wang, Baniasadi, and Elze), grants K99 EY028631 (Dr M. Wang) and K23 EY025014 (Dr Saeedi) from the NIH, Harvard Glaucoma Center of Excellence (Drs M. Wang, Elze, and Shen), the Eleanor and Miles Shore Fellowship (Dr Shen), a departmental grant from Research to Prevent Blindness (Dr De Moraes), and the Alice Adler Fellowship (Dr Elze). 34 21 21 0 7 AMER MEDICAL ASSOC CHICAGO 330 N WABASH AVE, STE 39300, CHICAGO, IL 60611-5885 USA 2168-6165 2168-6173 JAMA OPHTHALMOL JAMA Ophthalmol. FEB 2020.0 138.0 2 190 198 10.1001/jamaophthalmol.2019.5413 0.0 9 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology KO4IZ 31895454.0 Green Accepted, Green Published, Bronze 2023-03-23 WOS:000515514700015 0
J Guo, Y; Tao, J; Yang, FQ; Chen, C; Reniers, G Guo, Yong; Tao, Jing; Yang, Fuqiang; Chen, Chao; Reniers, Genserik An evaluation of the information literacy of safety professionals SAFETY SCIENCE English Article Safety professional; information literacy (IL); analytic hierarchy process (AHP); Fuzzy comprehensive evaluation FUZZY COMPREHENSIVE EVALUATION; ANALYTIC HIERARCHY PROCESS; BIG-DATA; ORGANIZATIONAL SAFETY; DECISION-MAKING; PROCESS AHP; WORKPLACE; MODEL; PERFORMANCE; SKILLS Information literacy has gradually become one of the necessary qualities in current and future safety practices. The calculation and assessment of information literacy of safety professionals is an effective way to understand their information literacy level. This paper, therefore, aims to evaluate the information literacy level of safety management personnel, for improving their ability to comprehend safety language/technology/information. Based on the theory of safety information systems and the characteristics of safety professionals, this study develops an index system to assess the information literacy level of safety professionals. The index system consists of five indexes: safety information demand consciousness, safety information acquisition ability, safety information evaluation ability, safety information utilization ability, and information ethics. According to the analytic hierarchy process method, the weight of the index can be determined. This developed method was implemented to evaluate the safety information literacy level of 40 safety professionals from four different corporations. The quantitative results of the fuzzy evaluation are in good agreement with the qualitative analysis results, indicating that the index system has excellent applicability and can be applied to the evaluation of the information literacy level of safety professionals. Besides, recommendations are put forward to improve the information literacy of safety professionals. [Guo, Yong; Yang, Fuqiang] Fuzhou Univ, Coll Environm & Safety Engn, Fuzhou 350116, Fujian, Peoples R China; [Tao, Jing] Fuzhou Univ, Sch Law, Fuzhou 350116, Fujian, Peoples R China; [Chen, Chao] Southwest Petr Univ, Sch Petr Engn, Chengdu 610500, Peoples R China; [Chen, Chao; Reniers, Genserik] Delft Univ Technol, Fac Technol Policy & Management, Safety & Secur Sci Grp, NL-2628 BX Delft, Netherlands; [Reniers, Genserik] Univ Antwerp, Fac Appl Econ, Antwerp Res Grp Safety & Secur ARGoSS, B-2000 Antwerp, Belgium; [Reniers, Genserik] KULeuven, CEDON, Campus Brussels, B-1000 Brussels, Belgium Fuzhou University; Fuzhou University; Southwest Petroleum University; Delft University of Technology; University of Antwerp; KU Leuven Yang, FQ; Chen, C (corresponding author), 2 Xueyuan Rd, Fuzhou, Fujian, Peoples R China. fqouyang82@163.com; c.chen-1@tudelft.nl Chen, Chao/AAB-6879-2020 Chen, Chao/0000-0002-1443-8017 National Natural Science Foundation of China [51874100]; China Law Society [201806655019]; China Scholarship Council [CLS (2019) C16] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Law Society; China Scholarship Council(China Scholarship Council) This work was supported by the National Natural Science Foundation of China (51874100) ; the Project of China Law Society (CLS (2019) C16) , the China Scholarship Council (201806655019) . 55 0 0 28 51 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-7535 1879-1042 SAFETY SCI Saf. Sci. JUL 2022.0 151.0 105734 10.1016/j.ssci.2022.105734 0.0 MAR 2022 11 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Operations Research & Management Science 1B9RZ hybrid, Green Published 2023-03-23 WOS:000792768900009 0
C Zhou, XK; Deng, YS; Xia, HY; Wu, SC; Bennis, M IEEE Zhou, Xiaokang; Deng, Yansha; Xia, Huiyun; Wu, Shaochuan; Bennis, Mehdi Resource Allocation for Time-triggered Federated Learning over Wireless Networks IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) MAY 16-20, 2022 Seoul, SOUTH KOREA IEEE,Samsung,LG,Huawei,Qualcomm,Technol Innovat Inst,ZTE,Elect & Telecommunicat Res Inst,KMW,SOLiD,ERICSSON LG,NI The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead. [Zhou, Xiaokang; Xia, Huiyun; Wu, Shaochuan] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin, Peoples R China; [Zhou, Xiaokang; Deng, Yansha] Kings Coll London, Dept Engn, London, England; [Bennis, Mehdi] Univ Oulu, Ctr Wireless Commun CWC, Oulu 90570, Finland Harbin Institute of Technology; University of London; King's College London; University of Oulu Zhou, XK (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin, Peoples R China. kangsenneo@sina.com; yansha.deng@kcl.ac.uk; summerxiahy@163.com; scwu@hit.edu.cn; mehdi.bennis@oulu.fi Deng, Yansha/0000-0003-1001-7036 Natural Science Foundation of China [61671173, 6217011221, 61831002]; China Scholarship Council Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported by the Natural Science Foundation of China under Grants 61671173, 6217011221, and 61831002. The work of Xiaokang Zhou was supported by the China Scholarship Council. 14 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-5386-8347-7 IEEE ICC 2022.0 2810 2815 10.1109/ICC45855.2022.9838329 0.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BT9VG 2023-03-23 WOS:000864709903015 0
C Wang, ZH; Jiang, DD; Lv, ZH; Song, HB IEEE Wang, Zhihao; Jiang, Dingde; Lv, Zhihan; Song, Houbing A Deep Reinforcement Learning based Intrusion Detection Strategy for Smart Vehicular Networks IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) IEEE Conference on Computer Communications Workshops English Proceedings Paper IEEE Conference on Computer Communications (IEEE INFOCOM) MAY 02-05, 2022 ELECTR NETWORK IEEE deep reinforcement learning; intrusion detection; deep q network; network security SECURITY; INTERNET Smart vehicular network (SVN) intellectualizes traditional transportation network, significantly enhancing traffic convenience and safety. However, high connectivity and massive devices bring more vulnerabilities, which severely compromise the security, privacy, and trust of the facilities and data. To address the ever-increasing security threats in SVN, we introduce an intrusion detection system to distinguish the abnormal traffic or behavior. A Deep Reinforcement Learning (DRL)-based intrusion detection strategy is proposed in this paper to optimize the detection performance. We exploit a modified Dueling DQN (Deep Q Learning) model, in which interaction between agent and environment is transformed into a supervised machine learning task. Through action taking and reward feedback, the Dueling DQN model can be trained to learn the intrinsic features of traffic data. Finally, simulation result on benchmark intrusion detection dataset also verifies the feasibility and effectiveness of the proposed strategy. [Wang, Zhihao; Jiang, Dingde] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden; [Song, Houbing] Embry Riddle Aeronaut Univ, Dept Elect Comp Software & Syst Engn, Daytona Beach, FL 32114 USA University of Electronic Science & Technology of China; Uppsala University; Embry-Riddle Aeronautical University Jiang, DD (corresponding author), Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China. jiangdd@uestc.edu.cn Lv, Zhihan/I-3187-2014; Song, Houbing/E-3628-2010 Lv, Zhihan/0000-0003-2525-3074; Song, Houbing/0000-0003-2631-9223 [2020-JCJQ-ZD-016-11] This work was supported in part by the Fund Projects (No. 2020-JCJQ-ZD-016-11). Dr. Dingde Jiang is corresponding author of this paper (email: jiangdd@uestc.edu.cn). 17 0 0 8 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-4228 978-1-6654-0926-1 IEEE CONF COMPUT 2022.0 10.1109/INFOCOMWKSHPS54753.2022.9798344 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT7ZD 2023-03-23 WOS:000851573100168 0
J Zhang, ZH; Chen, X; Wang, BZ; Hu, GS; Zuo, WM; Hancock, ER Zhang, Zhihong; Chen, Xu; Wang, Beizhan; Hu, Guosheng; Zuo, Wangmeng; Hancock, Edwin R. Face Frontalization Using an Appearance-Flow-Based Convolutional Neural Network IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Face frontalization; face synthesis; optical flow; face recognition RECOGNITION Facial pose variation is one of the major factors making face recognition (FR) a challenging task. One popular solution is to convert non-frontal faces to frontal ones on which FR is performed. Rotating faces causes facial pixel value changes. Therefore, existing CNN-based methods learn to synthesize frontal faces in color space. However, this learning problem in a color space is highly non-linear, causing the synthetic frontal faces to lose fine facial textures. In this paper, we take the view that the nonfrontal-frontal pixel changes are essentially caused by geometric transformations (rotation, translation, and so on) in space. Therefore, we aim to learn the nonfrontal-frontal facial conversion in the spatial domain rather than the color domain to ease the learning task. To this end, we propose an appearance-flow-based face frontalization convolutional neural network (A3F-CNN). Specifically, A3F-CNN learns to establish the dense correspondence between the non-frontal and frontal faces. Once the correspondence is built, frontal faces are synthesized by explicitly moving pixels from the non-frontal one. In this way, the synthetic frontal faces can preserve fine facial textures. To improve the convergence of training, an appearance-flow-guided learning strategy is proposed. In addition, generative adversarial network loss is applied to achieve a more photorealistic face, and a face mirroring method is introduced to handle the self-occlusion problem. Extensive experiments are conducted on face synthesis and pose invariant FR. Results show that our method can synthesize more photorealistic faces than the existing methods in both the controlled and uncontrolled lighting environments. Moreover, we achieve a very competitive FR performance on the Multi-PIE, LFW and IJB-A databases. [Zhang, Zhihong; Chen, Xu; Wang, Beizhan] Xiamen Univ, Xiamen 361005, Peoples R China; [Hu, Guosheng] Anyvis Grp, Belfast BT3 9AD, Antrim, North Ireland; [Zuo, Wangmeng] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China; [Hancock, Edwin R.] Univ York, Dept Comp Sci, York YO10 5GH, N Yorkshire, England Xiamen University; Harbin Institute of Technology; University of York - UK Wang, BZ (corresponding author), Xiamen Univ, Xiamen 361005, Peoples R China. zhihong@xmu.edu.cn; wangbz@xmu.edu.cn Zuo, Wangmeng/B-3701-2008; Hancock, Edwin/N-7548-2019; Hancock, Edwin R/C-6071-2008 Hancock, Edwin/0000-0003-4496-2028; Hancock, Edwin R/0000-0003-4496-2028; Chen, Xu/0000-0002-0367-3003; Zuo, Wangmeng/0000-0002-3330-783X National Natural Science Foundation of China [61402389]; Fundamental Research Funds for the Central Universities in China [20720160073] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities in China This work was supported in part by the National Natural Science Foundation of China under Grant 61402389 and in part by the Fundamental Research Funds for the Central Universities in China under Grant 20720160073. 77 21 22 2 94 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. MAY 2019.0 28.0 5 2187 2199 10.1109/TIP.2018.2883554 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering HI6CY 30507505.0 2023-03-23 WOS:000456542000008 0
J Zeng, J; Sun, JL; Gui, G; Adebisi, B; Ohtsuki, T; Gacanin, H; Sari, H Zeng, Jun; Sun, Jinlong; Gui, Guan; Adebisi, Bamidele; Ohtsuki, Tomoaki; Gacanin, Haris; Sari, Hikmet Downlink CSI Feedback Algorithm With Deep Transfer Learning for FDD Massive MIMO Systems IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Deep transfer learning (DTL); downlink CSI; limited feedback; FDD; massive MIMO; model-agnostic meta-learning (MAML) CHANNEL ESTIMATION; NEURAL-NETWORK; PREDICTION; DELAY In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE). [Zeng, Jun; Sun, Jinlong; Gui, Guan; Sari, Hikmet] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Manchester M1 5GD, Lancs, England; [Ohtsuki, Tomoaki] Keio Univ, Dept Informat & Comp Sci, Yokohama, Kanagawa 2238521, Japan; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany Nanjing University of Posts & Telecommunications; Manchester Metropolitan University; Keio University; RWTH Aachen University Gui, G; Sari, H (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China. 1019010408@njupt.edu.cn; sunjinlong@njupt.edu.cn; guiguan@njupt.edu.cn; b.adebisi@mmu.ac.uk; ohtsuki@ics.keio.ac.jp; harisg@ice.rwth-aachen.de; hikmet@njupt.edu.cn SUN, JIN/GPX-9641-2022; Gui, Guan/AAG-3593-2019 Gui, Guan/0000-0001-7428-4980; Ohtsuki, Tomoaki/0000-0003-3961-1426; Adebisi, Bamidele/0000-0001-9071-9120; zeng, jun/0000-0001-7441-0406 Major Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]; JSPS KAKENHI [JP19H02142]; National Natural Science Foundation of China [61901228]; Six Top Talents Program of Jiangsu [XYDXX-010]; Program for HighLevel Entrepreneurial and Innovative Team [CZ002SC19001]; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106] Major Project of the Ministry of Industry and Information Technology of China; JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Six Top Talents Program of Jiangsu; Program for HighLevel Entrepreneurial and Innovative Team; Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China(Ministry of Education, China) This work was supported by the Major Project of the Ministry of Industry and Information Technology of China under Grant TC190A3WZ-2, the JSPS KAKENHI under grant JP19H02142, the National Natural Science Foundation of China under Grant 61901228, the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for HighLevel Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. The associate editor coordinating the review of this article and approving it for publication was T. Chen. 41 14 14 4 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. DEC 2021.0 7.0 4 1253 1265 10.1109/TCCN.2021.3084409 0.0 13 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications XL4WA 2023-03-23 WOS:000728144400022 0
J Jin, JY; Yuan, Y; Goncalves, J Jin, Junyang; Yuan, Ye; Goncalves, Jorge A Full Bayesian Approach to Sparse Network Inference Using Heterogeneous Datasets IEEE TRANSACTIONS ON AUTOMATIC CONTROL English Article Kernel; Network topology; Topology; Transfer functions; Bayes methods; Monte Carlo methods; Linear systems; Dynamical structure function (DSF); network inference; reversible jump Markov chain Monte Carlo (RJMCMC); sparse networks; system identification SYSTEM-IDENTIFICATION; MODEL SELECTION Network inference has been attracting increasing attention in several fields, notably systems biology and biomedicine. Indeed, one of our biggest challenges is to uncover and understand complex molecular networks behind cells and organisms. A network is mainly characterized by its topology and internal dynamics. In particular, sparse topologies with stable dynamics are properties present in most real-world networks. Moreover, experiments typically measure a partial set of nodes. Linear systems have been used as approximations of complex nonlinear systems in a wide range of applications. Although they exhibit simpler dynamics, they can easily model unmeasured nodes, e.g., via transfer functions, which is not possible for nonlinear systems. This article explores these properties and considers general linear network models. It develops a method, based on reversible jump Markov chain Monte Carlo, to estimate the confidence of all links and produce the most likely topology. Monte Carlo simulations indicate that our approach consistently produces more accurate networks compared with other state-of-the-art methods, including kernel-based methods, nonlinear ordinary-differential-equation-based methods (iCheMA), and machine-learning-based methods (dynGINIE3). We show that this is also true on a well-known biological nonlinear model. The proposed method can be used in a wide range of applications, such as systems biology and biomedicine, and fault detection and diagnosis. [Jin, Junyang] HUST Wuxi Res Inst, Wuxi 214100, Jiangsu, Peoples R China; [Jin, Junyang; Goncalves, Jorge] Luxembourg Ctr Syst Biomed, L-4362 Esch Sur Alzette, Luxembourg; [Yuan, Ye] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China; [Goncalves, Jorge] Univ Cambridge, Dept Engn, Cambridge CB2 1TN, England University of Luxembourg; Huazhong University of Science & Technology; University of Cambridge Yuan, Y (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China. jj415@cantab.ac.uk; yye@hust.edu.cn; jmg77@cam.ac.uk Goncalves, Jorge M/GRN-8101-2022; Goncalves, Jorge/GYD-3756-2022 Goncalves, Jorge M/0000-0002-5228-6165; 26 0 0 2 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9286 1558-2523 IEEE T AUTOMAT CONTR IEEE Trans. Autom. Control JUL 2021.0 66.0 7 3282 3288 10.1109/TAC.2020.3016964 0.0 7 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering TC7ZP Green Submitted 2023-03-23 WOS:000668858300030 0
J Chen, ZY; Zhang, R; Xie, JW; Liu, PW; Zhang, CY; Zhao, J; Laplante, JP; Feng, TY Chen, Zhiyi; Zhang, Rong; Xie, Jiawei; Liu, Peiwei; Zhang, Chenyan; Zhao, Jia; Laplante, Justin Paul; Feng, Tingyong Hybrid brain model accurately predict human procrastination behavior COGNITIVE NEURODYNAMICS English Article Procrastinators; Fusion data; Machine learning; Multiple canonical correlation analysis; Diagnostic biomarkers MCCA PLUS JICA; LOW-FREQUENCY FLUCTUATION; SELF-REGULATION FAILURE; FUNCTIONAL CONNECTIVITY; INDIVIDUAL-DIFFERENCES; STATISTICAL-INFERENCE; MULTIMODAL FUSION; ICA-AROMA; FMRI; CLASSIFICATION Procrastination behavior is quite ubiquitous, and should warrant cautions to us owing to its significant influences in poor mental health, low subjective well-beings and bad academic performance. However, how to identify this behavioral problem have not yet to be fully elucidated. 1132 participants were recruited as distribution of benchmark. 81 high trait procrastinators (HP) and matched low trait procrastinators (LP) were screened. To address this issue, we have built upon the hybrid brain model by using hierarchical machine learning techniques to classify HP and LP with multi-modalities neuroimaging data (i.e., grey matter volume, fractional anisotropy, static/dynamic amplitude of low frequency fluctuation and static/dynamic degree centrality). Further, we capitalized on the multiple Canonical Correlation Analysis (mCCA) and joint Independent Component Analysis algorithm (mCCA + jICA) to clarify its fusion neural components as well. The hybrid brain model showed high accuracy to discriminate HP and LP (accuracy rate = 87.04%, sensitivity rate = 86.42%, specificity rate = 85.19%). Moreover, results of mCCA + jICA model revealed several joint-discriminative neural independent components (ICs) of this classification, showing wider co-variants of frontoparietal cortex and hippocampus networks. In addition, this study demonstrated three modal-specific discriminative ICs for classification, highlighting the temporal variants of brain local and global natures in ventromedial prefrontal cortex (vmPFC) and PHC in HP. To sum-up, this research developed a hybrid brain model to identify trait procrastination with high accuracy, and further revealed the neural hallmarks of this trait by integrating neuroimaging fusion data. [Chen, Zhiyi; Zhang, Rong; Zhao, Jia; Feng, Tingyong] Southwest Univ, Fac Psychol, Sch Psychol, Tian Sheng RD,2 Beibei, Chongqing 400715, Peoples R China; [Chen, Zhiyi; Zhang, Rong; Zhao, Jia; Feng, Tingyong] Minist Educ, Key Lab Cognit & Personal, Chongqing, Peoples R China; [Xie, Jiawei] Univ Sheffield, Dept Psychol, Sheffield, S Yorkshire, England; [Liu, Peiwei] Univ Florida, Dept Psychol, Gainesville, FL 32611 USA; [Zhang, Chenyan] Leiden Univ, Fac Social & Behav Sci, Inst Psychol, Cognit Psychol Unit, Leiden, Netherlands; [Laplante, Justin Paul] Clark Univ, Francis L Hyatt Sch Psychol, Worcester, MA 01610 USA Southwest University - China; University of Sheffield; State University System of Florida; University of Florida; Leiden University; Leiden University - Excl LUMC; Clark University Feng, TY (corresponding author), Southwest Univ, Fac Psychol, Sch Psychol, Tian Sheng RD,2 Beibei, Chongqing 400715, Peoples R China.;Feng, TY (corresponding author), Minist Educ, Key Lab Cognit & Personal, Chongqing, Peoples R China. fengty0@swu.edu.cn Zhang, Chenyan/0000-0002-2945-6584; Chen, Zhiyi/0000-0003-1744-4647 National Natural Science Foundation of China [31971026, 31571128]; Fundamental Research Funds for the Central Universities [SWU1809357]; ChongQing Innovative Research Funds for Graduates [CYB21082] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); ChongQing Innovative Research Funds for Graduates This work was supported by the National Natural Science Foundation of China (31971026, 31571128), the Fundamental Research Funds for the Central Universities (SWU1809357) and the ChongQing Innovative Research Funds for Graduates (CYB21082). 75 0 0 8 19 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1871-4080 1871-4099 COGN NEURODYNAMICS Cogn. Neurodynamics OCT 2022.0 16.0 5 1107 1121 10.1007/s11571-021-09765-z 0.0 JAN 2022 15 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 4S2ZQ 36237406.0 2023-03-23 WOS:000745821200001 0
J Hu, J; Zhang, H; Sfarra, S; Pivarciova, E; Yao, Y; Duan, YX; Ibarra-Castanedo, C; Tian, GY; Maldague, X Hu, Jue; Zhang, Hai; Sfarra, Stefano; Pivarciova, Elena; Yao, Yuan; Duan, Yuxia; Ibarra-Castanedo, Clemente; Tian, Guiyun; Maldague, Xavier Autonomous dynamic line-scan continuous-wave terahertz non-destructive inspection system combined with unsupervised exposure fusion NDT & E INTERNATIONAL English Article NDT; Line-scan; Continuous-wave terahertz; Infrared thermography; Unsupervised fusion THERMOGRAPHY The investigation of cultural heritage objects is commonly carried out by using non-destructive inspection techniques. In fact, due to the artistic peculiarities of cultural heritage objects, conventional contact-type in-spection techniques such as ultrasonic scan cannot always be applied. The objective of this study is to develop a new autonomous dynamic line-scan continuous-wave terahertz (CW THz) non-destructive inspection system combined with long-wave infrared (LWIR) thermography. The newly developed system is aimed at producing clear external and internal maps for wooden objects inherent to the cultural patrimony. Additionally, a new unsupervised fusion algorithm is also proposed for multi-energy density data fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. The algorithm is designed in an encoder -decoder deep learning structure with dense blocks. Finally, it is worth mentioning that the newly developed system has the capability of speedy detection (up to 49.2 mm/s) and fast in-line processing (of the order of seconds). [Hu, Jue; Tian, Guiyun] Univ Elect Sci & Technol China, Sch Automat, Chengdu 611731, Peoples R China; [Zhang, Hai] Harbin Inst Technol, Ctr Composite Mat & Struct CCMS, Harbin 150001, Peoples R China; [Hu, Jue; Zhang, Hai; Ibarra-Castanedo, Clemente; Maldague, Xavier] Laval Univ, Dept Elect & Comp Engn, Comp Vis & Syst Lab CVSL, 1065 Av Med, Quebec City, PQ G1V 0A6, Canada; [Sfarra, Stefano] Univ Aquila, Dept Ind & Informat Engn & Econ, Piazzale E Pontieri 1, I-67100 Laquila, AQ, Italy; [Pivarciova, Elena] Tech Univ Zvolen, Fac Environm & Technol Mfg, Ul TG Masaryka 2117-24, Zvolen 96053, Slovakia; [Yao, Yuan] Natl Tsing Hua Univ, Dept Chem Engn, 101,Sect 2,Kuang Fu Rd, Hsinchu 30013, Taiwan; [Duan, Yuxia] Cent South Univ, Sch Phys & Elect, 932 Lushan South Rd, Changsha 410083, Hunan, Peoples R China University of Electronic Science & Technology of China; Harbin Institute of Technology; Laval University; University of L'Aquila; Technical University Zvolen; National Tsing Hua University; Central South University Zhang, H (corresponding author), Harbin Inst Technol, Ctr Composite Mat & Struct CCMS, Harbin 150001, Peoples R China. hai.zhang.1@ulaval.ca Pivarčiová, Elena/GRY-2699-2022; Ibarra-Castanedo, Clemente/R-6731-2017; Duan, Yuxia/D-7334-2013; Zhang, Hai/L-4767-2016 Pivarčiová, Elena/0000-0002-6676-8245; Hu, Jue/0000-0002-2000-8267; Ibarra-Castanedo, Clemente/0000-0002-0198-7439; Duan, Yuxia/0000-0002-4581-0686; Zhang, Hai/0000-0003-3025-9999 Natural Sciences and Engineering Research Council (NSERC) Canada; National Key Laboratory of Science and Technology on Advanced Composites in Special Environments [6142905213301] Natural Sciences and Engineering Research Council (NSERC) Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); National Key Laboratory of Science and Technology on Advanced Composites in Special Environments This research is supported by the Natural Sciences and Engineering Research Council (NSERC) Canada through Discovery and CREATE 'oN DuTy!' programs, as well as Canada Research Chair in Multipolar Infrared Vision (MiViM) . This research is also supported by the National Key Laboratory of Science and Technology on Advanced Composites in Special Environments through Open-end Research Fund program (no. 6142905213301) . The authors would like to thank Dr. Mario Pelliccione and Dr. Simone Ciarcelluti (Italian restorers) for the preparation of the oil painting. 34 2 2 18 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0963-8695 1879-1174 NDT&E INT NDT E Int. DEC 2022.0 132.0 102705 10.1016/j.ndteint.2022.102705 0.0 AUG 2022 11 Materials Science, Characterization & Testing Science Citation Index Expanded (SCI-EXPANDED) Materials Science 3Z1DT 2023-03-23 WOS:000844160700005 0
J Cai, YD; Zhang, SQ; Zhang, YH; Pan, XY; Feng, KY; Chen, L; Huang, T; Kong, XY Cai, Yu-Dong; Zhang, Shiqi; Zhang, Yu-Hang; Pan, Xiaoyong; Feng, KaiYan; Chen, Lei; Huang, Tao; Kong, Xiangyin Identification of the Gene Expression Rules That Define the Subtypes in Glioma JOURNAL OF CLINICAL MEDICINE English Article glioma; gene expression; Monte Carlo feature selection; Johnson reducer algorithm; support vector machine CARLO FEATURE-SELECTION; CELL-PROLIFERATION; METABOLIC PATHWAY; HOX PROTEINS; SELF-RENEWAL; STEM-CELLS; GLIOBLASTOMA; INVASION; DIFFERENTIATION; GROWTH As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as IGFBP2, IGF2BP3, PRDX1, NOV, NEFL, HOXA10, GNG12, SPRY4, and BCL11A, were identified. In addition, the underlying rules of classifying the three subtypes were produced by Johnson reducer algorithm. We found that in diffuse astrocytoma, PRDX1 is highly expressed, and in glioblastoma, the expression level of PRDX1 is low. These rules revealed the difference among the three subtypes, and how they are formed and transformed. These genes are not only biomarkers for glioma subtypes, but also drug targets that may switch the clinical features or even reverse the tumor progression. [Cai, Yu-Dong; Zhang, Shiqi] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; [Zhang, Shiqi] Univ Copenhagen, Dept Biostat, DK-2099 Copenhagen, Denmark; [Zhang, Yu-Hang; Huang, Tao; Kong, Xiangyin] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; [Pan, Xiaoyong] Erasmus MC, Dept Med Informat, NL-3014 ZK Rotterdam, Netherlands; [Feng, KaiYan] Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab Pure Math & Math Practice PMMP, Shanghai 200241, Peoples R China Shanghai University; University of Copenhagen; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Erasmus University Rotterdam; Erasmus MC; Shanghai Maritime University; East China Normal University Kong, XY (corresponding author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China. caiyudong@staff.shu.edu.cn; shiqi.zhang@sund.ku.dk; yhzhang@sibs.ac.cn; x.pan@erasmusmc.nl; kyfeng@gdaib.edu.cn; lchen@shmtu.edu.cn; huangtao@sibs.ac.cn; xykong@sibs.ac.cn Zhang, Yu-Hang/O-9774-2015; Chen, Lei/A-2275-2011; Zhang, Yu-Hang/AAQ-9012-2021 Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3068-1583; Zhang, Yu-Hang/0000-0003-3825-0796; cai, yudong/0000-0001-5664-7979 National Natural Science Foundation of China [31701151]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This research was funded by the National Natural Science Foundation of China (31701151), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program, the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000). 83 29 30 1 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-0383 J CLIN MED J. Clin. Med. OCT 2018.0 7.0 10 350 10.3390/jcm7100350 0.0 15 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine GY7TW 30322114.0 Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000448819000058 0
J Song, C; Chen, ZX; Qi, XZ; Zhao, BL; Hu, Y; Liu, SB; Zhang, JW Song, Chao; Chen, Zhixian; Qi, Xiaozhi; Zhao, Baoliang; Hu, Ying; Liu, Shoubin; Zhang, Jianwei Human trajectory prediction for automatic guided vehicle with recurrent neural network JOURNAL OF ENGINEERING-JOE English Article The accurate prediction of the pedestrian trajectory is necessary to endow automatic guided vehicle with the capabilities to adjust velocity and path dynamically for the navigation in real pedestrian scenes. For this purpose, this study presents a social conscious prediction model considering two main factors that affect the pedestrians' walking in the crowd - relative distance and moving direction. To form an effective model, the authors' conscious pooling layer is added to the Long Shot Term Memory network (LTSM) model to build the relationship between pedestrians, learning the current position m and movement trend. The experiments are conducted to compare the proposed model with the previous state-of-the-art model on several public datasets. The experimental results show that the proposed model predicts pedestrian trajectories more accurately. [Song, Chao; Chen, Zhixian; Qi, Xiaozhi; Zhao, Baoliang; Hu, Ying] Chinese Acad Sci, Shenzhen Key Lab Minimally Invas Surg Robot & Sys, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China; [Song, Chao; Liu, Shoubin] Harbin Inst Technol Shenzhen, Shenzhen, Peoples R China; [Chen, Zhixian; Qi, Xiaozhi; Zhao, Baoliang; Hu, Ying] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Hamburg, Germany Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Harbin Institute of Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Hamburg Hu, Y (corresponding author), Chinese Acad Sci, Shenzhen Key Lab Minimally Invas Surg Robot & Sys, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.;Hu, Y (corresponding author), Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China. ying.hu@siat.ac.cn Hu, Ying/GQZ-8100-2022; zhang, jian/HPD-1712-2023 National Natural Science Foundation of China [U1613224, U1713221]; Shenzhen Fundamental Research Funds [JCYJ20170413104438332, JCYJ20160608153218487, JCYJ20160429190300857]; Shenzhen Peacock Plan [KQTD2016113010571019]; Shenzhen Key Laboratory Project [ZDSYS201707271637577] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Fundamental Research Funds; Shenzhen Peacock Plan; Shenzhen Key Laboratory Project This work was financially supported by the National Natural Science Foundation of China (Nos. U1613224 and U1713221), in part by the Shenzhen Fundamental Research Funds (Nos. JCYJ20170413104438332, JCYJ20160608153218487 and JCYJ20160429190300857), in part by the Shenzhen Peacock Plan (No. KQTD2016113010571019) and the Shenzhen Key Laboratory Project (No. ZDSYS201707271637577). 24 7 7 0 15 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2051-3305 J ENG-JOE J. Eng.-JOE NOV 2018.0 16 1574 1578 10.1049/joe.2018.8264 0.0 5 Engineering, Multidisciplinary Emerging Sources Citation Index (ESCI) Engineering HB5GV gold 2023-03-23 WOS:000451088400030 0
J Huang, XY; Zhou, N; Huang, J; Zhang, HD; Pedrycz, W; Choi, KS Huang, Xiuyu; Zhou, Nan; Huang, Jian; Zhang, Huaidong; Pedrycz, Witold; Choi, Kup-Sze Center transfer for supervised domain adaptation APPLIED INTELLIGENCE English Article; Early Access Supervised domain adaptation; Deep learning; Center transfer loss; Transfer learning CLASSIFICATION Domain adaptation (DA) is a popular strategy for pattern recognition and classification tasks. It leverages a large amount of data from the source domain to help train the model applied in the target domain. Supervised domain adaptation (SDA) approaches are desirable when only few labeled samples from the target domain are available. They can be easily adopted in many real-world applications where data collection is expensive. In this study, we propose a new supervision signal, namely center transfer loss (CTL), to efficiently align features under the SDA setting in the deep learning (DL) field. Unlike most previous SDA methods that rely on pairing up training samples, the proposed loss is trainable only using one-stream input based on the mini-batch strategy. The CTL exhibits two main functionalities in training to increase the performance of DL models, i.e., domain alignment and increasing the feature's discriminative power. The hyper-parameter to balance these two functionalities is waived in CTL, which is the second improvement from the previous approaches. Extensive experiments completed on well-known public datasets show that the proposed method performs better than recent state-of-the-art approaches. [Huang, Xiuyu; Choi, Kup-Sze] Hong Kong Polytech Univ, Ctr Smart Hlth, Hong Kong 999077, Peoples R China; [Huang, Xiuyu; Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2R3, Canada; [Zhou, Nan] Chengdu Univ, Sch Elect Informat & Elect Engn, Chengdu 610000, Peoples R China; [Huang, Jian] Chengdu Univ Informat Technol, Coll Control Engn, Chengdu 610101, Peoples R China; [Zhang, Huaidong] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510000, Peoples R China; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Istinye Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Southern Ethiop Hong Kong Polytechnic University; University of Alberta; Chengdu University; Chengdu University of Information Technology; South China University of Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; King Abdulaziz University Huang, XY; Choi, KS (corresponding author), Hong Kong Polytech Univ, Ctr Smart Hlth, Hong Kong 999077, Peoples R China.;Huang, XY; Pedrycz, W (corresponding author), Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2R3, Canada.;Pedrycz, W (corresponding author), Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland.;Pedrycz, W (corresponding author), King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia.;Pedrycz, W (corresponding author), Istinye Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Southern Ethiop. xiuyu.huang@connect.polyu.hk; wpedrycz@ualberta.ca; thomasks.choi@polyu.edu.hk HUANG, Xiuyu/0000-0003-1600-9109 Hong Kong Research Grants Council [PolyU 152006/19E] Hong Kong Research Grants Council(Hong Kong Research Grants Council) AcknowledgementsThe work in this paper was supported in part by the Hong Kong Research Grants Council (PolyU 152006/19E). 46 0 0 7 7 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-669X 1573-7497 APPL INTELL Appl. Intell. 10.1007/s10489-022-04414-2 0.0 JAN 2023 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8D7ZJ 36718382.0 Bronze 2023-03-23 WOS:000918506600001 0
J Jia, Y; Jin, SG; Savi, P; Yan, QY; Li, WM Jia, Yan; Jin, Shuanggen; Savi, Patrizia; Yan, Qingyun; Li, Wenmei Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach REMOTE SENSING English Article Global Navigation Satellite System-Reflectometry (GNSS-R); soil moisture retrieval; signal-to-noise ratio (SNR); random forest (RF); support vector machine (SVM) GPS SIGNALS; SEA-ICE; LAND; REFLECTOMETRY; REGRESSION; SYSTEM; REFLECTIVITY; TEMPERATURE; RECEIVER; BIOMASS Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth's surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data. [Jia, Yan; Li, Wenmei] Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210046, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Jin, Shuanggen] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Savi, Patrizia] Politecn Torino, Corso Duca Abruzzi 24, I-10129 Turin, Italy Nanjing University of Posts & Telecommunications; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Polytechnic University of Turin Jin, SG (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China.;Jin, SG (corresponding author), Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China. jiayan@njupt.edu.cn; sgjin@nuist.edu.cn; patrizia.savi@polito.it; qy2543@mun.ca; liwm@njupt.edu.cn Jin, Shuanggen/B-8094-2008 Jin, Shuanggen/0000-0002-5108-4828; SAVI, Patrizia/0000-0001-9585-310X; Yan, Qingyun/0000-0001-6693-957X National Natural Science Foundation of China [42001375, 42001362]; Natural Science Foundation of Jiangsu Province [BK20180765, BK20191384]; Nanjing Technology Innovation Foundation for Selected Overseas Scientists [RK032YZZ18003]; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) [217152, 219066]; Shanghai Leading Talent Project [E056061]; Strategic Priority Research Program Project of the Chinese Academy of Sciences [XDA23040100] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Nanjing Technology Innovation Foundation for Selected Overseas Scientists; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF); Shanghai Leading Talent Project; Strategic Priority Research Program Project of the Chinese Academy of Sciences This work was supported in part by the National Natural Science Foundation of China under Grant 42001375, 42001362, by the Natural Science Foundation of Jiangsu Province under Grant BK20180765, BK20191384, in part by the Nanjing Technology Innovation Foundation for Selected Overseas Scientists under Grant RK032YZZ18003, 'Research on Advanced Land Surface Detection System using GNSS-R', in part by the Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) under Grant 217152, 219066, Shanghai Leading Talent Project (Grant No. E056061) and Strategic Priority Research Program Project of the Chinese Academy of Sciences (Grant No. XDA23040100). 69 16 18 7 60 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2020.0 12.0 22 3679 10.3390/rs12223679 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology OZ6SX gold 2023-03-23 WOS:000595054500001 0
C Carpi, F; Davoli, L; Martalo, M; Cilfone, A; Yu, YJ; Wang, Y; Ferrari, G IEEE Carpi, Fabrizio; Davoli, Luca; Martalo, Marco; Cilfone, Antonio; Yu, Yingjie; Wang, Yi; Ferrari, Gianluigi RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios 2019 16TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATION SYSTEMS (ISWCS) International Symposium on Wireless Communication Systems English Proceedings Paper 16th International Symposium on Wireless Communication Systems (ISWCS) AUG 27-30, 2019 Oulu, FINLAND Line-Of-Sight (LOS); Non-LOS (NLOS); channel state identification; IEEE 802.11 MITIGATION; LOCALIZATION In this paper, we investigate classification methods aiming at identifying the Line-Of-Sight (LOS) or Non-LOS (NLOS) condition of a wireless channel. Our approach is based on the computation of statistical features over N consecutive channel measurements at the receiver (namely, N Received Signal Strength Indicator, RSSI, values). First, threshold classification criteria, on the considered features, are derived in order to perform LOS/NLOS identification. The thresholds' values are tuned according to the behaviour of the statistical features in the considered environment. This method is compared to a sample-based (whose aim is to detect the data distribution) and a machine learning-based approaches. Although our approach is general, we present experimental results for IEEE 802.11 indoor channels. Our results show that simple threshold-based classification criteria on the considered statistical features may yield approximately 85 divided by 90% LOS/NLOS classification accuracy, making them an attractive strategy for future 5G systems. [Carpi, Fabrizio; Davoli, Luca; Martalo, Marco; Cilfone, Antonio; Ferrari, Gianluigi] Univ Parma, Internet Things IoT Lab, Dept Engn & Architecture, Parma, Italy; [Yu, Yingjie; Wang, Yi] Huawei Technol Co Ltd, Shanghai, Peoples R China University of Parma; Huawei Technologies Carpi, F (corresponding author), Univ Parma, Internet Things IoT Lab, Dept Engn & Architecture, Parma, Italy. fabrizio.carpi1@studenti.unipr.it; luca.davoli@unipr.it; marco.martalo@unipr.it; antonio.cilfone@unipr.it; yuyingjie1@huawei.com; yi.wang@huawei.com; gianluigi.ferrari@unipr.it Davoli, Luca/ABH-1099-2021 Davoli, Luca/0000-0002-4396-8885; Carpi, Fabrizio/0000-0003-0406-9016 Huawei Innovation Research Program (HIRP) [0892] Huawei Innovation Research Program (HIRP) This work was supported by the Huawei Innovation Research Program (HIRP) under Grant No. 0892. 10 5 6 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2154-0217 978-1-7281-2527-5 INT SYM WIRELESS COM 2019.0 171 175 5 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BQ4OR 2023-03-23 WOS:000591678700033 0
J He, L; Tiwari, P; Su, R; Shi, XY; Marttinen, P; Kumar, N He, Lang; Tiwari, Prayag; Su, Rui; Shi, Xiuying; Marttinen, Pekka; Kumar, Neeraj COVIDNet: An Automatic Architecture for COVID-19 Detection With Deep Learning From Chest X-Ray Images IEEE INTERNET OF THINGS JOURNAL English Article COVID-19; Feature extraction; Computed tomography; Databases; Solid modeling; Internet of Things; Training; Coronavirus disease 2019 (COVID-19); deep learning (DL); NetVLAD; spatial pyramid pooling (SPP) SOCIETY Up to now, the coronavirus disease 2019 (COVID-19) has been sweeping across all over the world, which has affected individual's lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This article presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. The context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97% and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99% and specificity of 99.4% of the ResNet-50. [He, Lang] Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Shaanxi Key Lab Network Data Anal & Intelligent P, Xian 710121, Shaanxi, Peoples R China; [He, Lang] Xian Univ Posts & Telecommun, Xian Key Lab Big Data & Intelligent Comp, Xian 710121, Shaanxi, Peoples R China; [Tiwari, Prayag; Marttinen, Pekka] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland; [Su, Rui] Northwest Univ, Sch Foreign Languages, Xian 710127, Shaanxi, Peoples R China; [Shi, Xiuying] Yanan Univ, Affiliated Hosp, Yanan 716000, Shaanxi, Peoples R China; [Kumar, Neeraj] Thapar Inst Engn & Technol Deemed Univ, Dept Comp Sci & Engn, Patiala 147004, Punjab, India; [Kumar, Neeraj] Univ Petr & Energy Studies, Sch Comp Sci, Dehra Dun 248007, Uttarakhand, India; [Kumar, Neeraj] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 413, Taiwan; [Kumar, Neeraj] King Abdulaziz Univ, Jeddah, Saudi Arabia Xi'an University of Posts & Telecommunications; Xi'an University of Posts & Telecommunications; Aalto University; Northwest University Xi'an; Yanan University; Thapar Institute of Engineering & Technology; University of Petroleum & Energy Studies (UPES); Asia University Taiwan; King Abdulaziz University Tiwari, P (corresponding author), Aalto Univ, Dept Comp Sci, Espoo 02150, Finland.;Kumar, N (corresponding author), Thapar Inst Engn & Technol Deemed Univ, Dept Comp Sci & Engn, Patiala 147004, Punjab, India. langhe@xupt.edu.cn; prayag.tiwari@aalto.fi; pekka.marttinen@aalto.fi; neeraj.kumar@thapar.edu Tiwari, Prayag/N-6261-2017; Kumar, Neeraj/L-3500-2016; Marttinen, Pekka/N-6234-2015 Tiwari, Prayag/0000-0002-2851-4260; Kumar, Neeraj/0000-0002-3020-3947; Marttinen, Pekka/0000-0001-7078-7927 Shaanxi Provincial Social Science Foundation [2021K015]; Shaanxi Provincial Natural Science Foundation [2021JQ-824]; Shaanxi Provincial Education Department [19JS028]; Academy of Finland [336033, 315896]; Business Finland [884/31/2018]; EU H2020 [101016775] Shaanxi Provincial Social Science Foundation; Shaanxi Provincial Natural Science Foundation; Shaanxi Provincial Education Department; Academy of Finland(Academy of Finland); Business Finland; EU H2020 This work was supported in part by the Shaanxi Provincial Social Science Foundation under Grant 2021K015; in part by the Shaanxi Provincial Natural Science Foundation under Grant 2021JQ-824; in part by the Scientific Research Program Funded by Shaanxi Provincial Education Department under Program 19JS028; in part by the the Academy of Finland under Grant 336033 and Grant 315896; in part by the Business Finland under Grant 884/31/2018; and in part by EU H2020 under Grant 101016775. 37 1 1 2 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. JUL 1 2022.0 9.0 13 11376 11384 10.1109/JIOT.2021.3126471 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 2E9JE Green Accepted 2023-03-23 WOS:000812536000081 0
J Sansone, E; De Natale, FGB; Zhou, ZH Sansone, Emanuele; De Natale, Francesco G. B.; Zhou, Zhi-Hua Efficient Training for Positive Unlabeled Learning IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Machine learning; one-class classification; positive unlabeled learning; open set recognition; kernel methods ONE-CLASS CLASSIFICATION; SUPPORT VECTOR; NOVELTY DETECTION; ANOMALY DETECTION Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning. [Sansone, Emanuele; De Natale, Francesco G. B.] Univ Trento, Dept Informat Engn & Comp Sci DISI, I-38123 Trento, Italy; [Zhou, Zhi-Hua] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China University of Trento; Nanjing University Sansone, E (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci DISI, I-38123 Trento, Italy. e.sonsone@unitn.it; francesco.denatale@unitn.it; zhouzh@nju.edu.cn Giuli, Francesco/HJI-6649-2023 De Natale, Francesco/0000-0003-2566-6995 National Key R&D Program of China [2018YFB1004300]; NSFC [61333014]; Collaborative Innovation Center of Novel Software Technology and Industrialization; NVIDIA Corporation National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Collaborative Innovation Center of Novel Software Technology and Industrialization; NVIDIA Corporation This research was partially supported by the National Key R&D Program of China (2018YFB1004300), NSFC (61333014) and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Part of this research was conducted when E. Sansonewas visiting the LAMDA Group, Nanjing University. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal machine to support this research. 72 22 22 0 33 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. NOV 2019.0 41.0 11 2584 2598 10.1109/TPAMI.2018.2860995 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering JD2XM 30059293.0 Green Submitted 2023-03-23 WOS:000489838200004 0
J Mei, G; Tu, JZ; Xiao, L; Piccialli, F Mei, Gang; Tu, Jingzhi; Xiao, Lei; Piccialli, Francesco An efficient graph clustering algorithmby exploiting k-core decomposition and motifs COMPUTERS & ELECTRICAL ENGINEERING English Article Machine Learning; Clustering; Spectral clustering; Graph; k-core decomposition; Network structure Clustering analysis has been widely used in trust evaluation for various complex networks such as wireless sensor networks and online social networks. Spectral clustering is one of the most commonly used algorithms for graph-structured data (networks). However, conventional spectral clustering is inherently difficult to perform in large networks. In this paper, we proposed an efficient graph clustering algorithm, KCoreMotif, specifically for large networks by exploiting k-core decomposition and motifs. We first conducted the k-core decomposition of the large input network, then performed the motif-based spectral clustering for the top k-core subgraphs, and finally grouped the remaining vertices in the rest (k-1)-core subgraphs into previously found clusters to obtain the final clusters. Comparative results of 18 groups of real-world datasets demonstrated that KCoreMotif was accurate yet efficient for large networks, which also means it can be further used to evaluate the intra-cluster and inter-cluster trusts for large networks. [Mei, Gang; Tu, Jingzhi; Xiao, Lei] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China; [Piccialli, Francesco] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy China University of Geosciences; University of Naples Federico II Piccialli, F (corresponding author), Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy. francesco.piccialli@unina.it Mei, Gang/C-9124-2016 Mei, Gang/0000-0003-0026-5423; xiao, lei/0000-0003-4985-8633 National Natural Science Foundation of China [11602235]; Fundamental Research Funds for China Central Universities [2652018091]; Major Program of Science and Technology of Xinjiang Production and Construction Corps, China [2020AA002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for China Central Universities; Major Program of Science and Technology of Xinjiang Production and Construction Corps, China First of all, the authors would like to thank Austin Benson, David Gleich, Jure Leskovec, and Chengbin Peng for providing the source code of their work. This research was jointly supported by the National Natural Science Foundation of China (11602235), the Fundamental Research Funds for China Central Universities (2652018091), and the Major Program of Science and Technology of Xinjiang Production and Construction Corps, China (2020AA002). The authors would like to thank the editors and the reviewers for their valuable comments. 24 0 0 4 8 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. DEC 2021.0 96.0 B 107564 10.1016/j.compeleceng.2021.107564 0.0 16 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YX9QN Green Submitted 2023-03-23 WOS:000754429700001 0
J Jia, Y; Jin, SG; Chen, HL; Yan, QY; Savi, P; Jin, Y; Yuan, Y Jia, Yan; Jin, Shuanggen; Chen, Haolin; Yan, Qingyun; Savi, Patrizia; Jin, Yan; Yuan, Yuan Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Spatial resolution; Soil moisture; Maximum likelihood estimation; Spaceborne radar; Reflectivity; Moisture; Vegetation mapping; CYGNSS; GNSS-Reflectometry; preclassifica; tion; SMAP; soil moisture; XGBoost RETRIEVAL; GPS; REFLECTOMETRY; REFLECTIVITY; SENSITIVITY; LAND Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm(3)/cm(3) is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm(3)/cm(3) are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm(3)/cm(3). The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data. [Jia, Yan; Chen, Haolin; Jin, Yan; Yuan, Yuan] Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Savi, Patrizia] Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy Nanjing University of Posts & Telecommunications; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Polytechnic University of Turin Jin, SG (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China. jiayan@njupt.edu.cn; sgjin@shao.ac.cn; 787284069@qq.com; jayyqy@qq.com; patrizia.savi@polito.it; jinyan@njupt.edu.cn; yuanyuan@njupt.edu.cn Jin, Shuanggen/B-8094-2008; Yuan, Yuan/GXW-1549-2022 Jin, Shuanggen/0000-0002-5108-4828; Yuan, Yuan/0000-0003-1860-3275; Yan, Qingyun/0000-0001-6693-957X; SAVI, Patrizia/0000-0001-9585-310X; Jin, Yan/0000-0002-6978-163X National Natural Science Foundation of China [42001375, 42001362, 41901356, 42001332]; Natural Science Foundation of Jiangsu Province [BK20180765]; Nanjing Technology Innovation Foundation [RK032YZZ18003]; NUPTSF [219066]; Shanghai Leading Talent Project [E056061]; Strategic Priority Research Program Project of theChinese Academy of Sciences [XDA23040100] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Nanjing Technology Innovation Foundation; NUPTSF; Shanghai Leading Talent Project; Strategic Priority Research Program Project of theChinese Academy of Sciences This work was supported in part by the National Natural Science Foundation of China under Grant 42001375, Grant 42001362, Grant 41901356, and Grant 42001332, in part by the Natural Science Foundation of Jiangsu Province underGrant BK20180765, in part by theNanjing Technology Innovation Foundation for Selected Overseas Scientists underGrant RK032YZZ18003, in part by the NUPTSF under Grant 219066, and in part by the Shanghai Leading Talent Project under Grant E056061, and in part by the Strategic Priority Research Program Project of theChinese Academy of Sciences under Grant XDA23040100. 50 11 11 6 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 4879 4893 10.1109/JSTARS.2021.3076470 0.0 15 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology SJ9JX gold 2023-03-23 WOS:000655843100010 0
J Xia, LQ; Chen, G; Wu, T; Gao, Y; Mohammadzadeh, A; Ghaderpour, E Xia, Lingqin; Chen, Guang; Wu, Tao; Gao, Yu; Mohammadzadeh, Ardashir; Ghaderpour, Ebrahim Optimal Intelligent Control for Doubly Fed Induction Generators MATHEMATICS English Article intelligent control; machine learning; type-2 fuzzy logic; fuzzy systems; stability analysis; adaptive control PREDICTIVE CONTROL; MODEL; TYPE-2 For the first time, a novel concept of merging computational intelligence (the type-2 fuzzy system) and control theory (optimal control) for regulator and reference tracking in doubly fed induction generators (DFIGs) is proposed in this study. The goal of the control system is the reference tracking of torque and stator reactive power. In this case, the type-2 fuzzy controller is activated to enhance the performance of the optimum control. For instance, in abrupt changes of the reference signal or uncertainty in the parameters, the type-2 fuzzy system performs a complementary function. Both parametric uncertainty and a perturbation signal are used to challenge the control system in the simulation. The findings demonstrate that the presence of a type-2 fuzzy system as an additional controller or compensator significantly enhances the control system. The root mean square error of the suggested method's threshold was 0.012, quite acceptable for a control system. [Xia, Lingqin; Chen, Guang; Wu, Tao; Gao, Yu] Zhejiang Inst Mech & Elect Engn, Dept Addit Mfg, Hangzhou 310053, Peoples R China; [Mohammadzadeh, Ardashir] Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China; [Ghaderpour, Ebrahim] Sapienza Univ Rome, Dept Earth Sci, Piazzale Aldo Moro 5, I-00185 Rome, Italy Zhejiang Institute of Mechanical & Electrical Engineering; Shenyang University of Technology; Sapienza University Rome Xia, LQ (corresponding author), Zhejiang Inst Mech & Elect Engn, Dept Addit Mfg, Hangzhou 310053, Peoples R China.;Mohammadzadeh, A (corresponding author), Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China.;Ghaderpour, E (corresponding author), Sapienza Univ Rome, Dept Earth Sci, Piazzale Aldo Moro 5, I-00185 Rome, Italy. xialingqin@zime.edu.cn; a.mzadeh@sut.edu.cn; ebrahim.ghaderpour@uniroma1.it Ghaderpour, Ebrahim/ABF-8335-2020 Ghaderpour, Ebrahim/0000-0002-5165-1773 Zhejiang Key Laboratory of Parts Rolling Technology [PR-22002]; general scientific research projects of the Zhejiang Provincial Department of Education [A-0275-21-059]; Key Research and Development Program of Zhejiang Province [2022C01147] Zhejiang Key Laboratory of Parts Rolling Technology; general scientific research projects of the Zhejiang Provincial Department of Education; Key Research and Development Program of Zhejiang Province The paper is supported by Zhejiang Key Laboratory of Parts Rolling Technology (no. PR-22002), the general scientific research projects of the Zhejiang Provincial Department of Education (no. A-0275-21-059) and Key Research and Development Program of Zhejiang Province (no. 2022C01147). 36 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics JAN 2023.0 11.0 1 20 10.3390/math11010020 0.0 16 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 7R7TM gold, Green Published 2023-03-23 WOS:000910272000001 0
J Xiong, P; Long, C; Zhou, HY; Battiston, R; Zhang, XM; Shen, XH Xiong, Pan; Long, Cheng; Zhou, Huiyu; Battiston, Roberto; Zhang, Xuemin; Shen, Xuhui Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning REMOTE SENSING English Article earthquake; seismic precursors; DEMETER satellites; electromagnetic field; machine learning SEISMO-IONOSPHERIC ANOMALIES; ELECTRON-DENSITY; STATISTICAL-ANALYSIS; SATELLITE DEMETER; FIELD EXPERIMENT; HISS; PUER The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky's formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields' frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations. [Xiong, Pan; Zhang, Xuemin] China Earthquake Adm, Inst Earthquake Forecasting, Beijing 100036, Peoples R China; [Long, Cheng] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Zhou, Huiyu] Univ Leicester, Sch Informat, Leicester LE1 7RH, Leics, England; [Battiston, Roberto] Univ Trento, Dept Phys, I-38123 Trento, Italy; [Battiston, Roberto] Trento Inst Fundamental Phys & Applicat, Natl Inst Nucl Phys, I-38123 Trento, Italy; [Shen, Xuhui] Minist Emergency Management China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China China Earthquake Administration; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; University of Leicester; University of Trento Shen, XH (corresponding author), Minist Emergency Management China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China. xiongpan@ief.ac.cn; c.long@ntu.edu.sg; hz143@leicester.ac.uk; roberto.battiston@unitn.it; zxm@ief.ac.cn; shenxh@seis.ac.cn Xiong, Pan/ACK-9984-2022; Battiston, Roberto/AAV-2632-2021; Battiston, Roberto/AAW-5182-2021; Long, Cheng/AAK-7724-2021 Battiston, Roberto/0000-0002-5808-7239; National Key R&D Program of China [2018YFC1503505]; Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration [2020IEF0510, 2020IEF0705] National Key R&D Program of China; Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration This work is supported in part by the National Key R&D Program of China under Grant No. 2018YFC1503505, and in part by the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration under Grant 2020IEF0510 and Grant 2020IEF0705. 89 10 11 6 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2020.0 12.0 21 3643 10.3390/rs12213643 0.0 27 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology OX5RC Green Submitted, gold 2023-03-23 WOS:000593620600001 0
C You, CW; Montes-de-Oca, M; Bao, TJ; Lane, ND; Lu, H; Cardone, G; Torresani, L; Campbell, AT Assoc Comp Machinery You, Chuang-Wen; Montes-de-Oca, Martha; Bao, Thomas J.; Lane, Nicholas D.; Lu, Hong; Cardone, Giuseppe; Torresani, Lorenzo; Campbell, Andrew T. CarSafe: A Driver Safety App that Detects Dangerous Driving Behavior using Dual-Cameras on Smartphones UBICOMP'12: PROCEEDINGS OF THE 2012 ACM INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING English Proceedings Paper 14th ACM International Conference on Ubiquitous Computing (UbiComp) SEP 05-08, 2012 Carnegie Mellon Univ, Pittsburgh, PA Assoc Comp Machinery,ACM SIGCHI,ACM SIGMOBILE,Natl Sci Fdn,Intel Sci & Technol Ctr Pervas Comp,Intel Sci & Technol Ctr Social Comp,Microsoft Res,Qualcomm,Google,Motorola,Telefonica Investigac Desarrollo,Dejan Res Carnegie Mellon Univ Dangerous driving behavior; dual cameras; smartphones Driving while being tired or distracted is dangerous. We are developing the CafeSafe app for Android phones, which fuses information from both front and back cameras and others embedded sensors on the phone to detect and alert drivers to dangerous driving conditions in and outside of the car. CarSafe uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front camera while at the same time tracking road conditions using the back camera. CarSafe is the first dual-camera application for smartphones. [You, Chuang-Wen; Bao, Thomas J.; Lu, Hong; Torresani, Lorenzo; Campbell, Andrew T.] Dartmouth Coll, Hanover, NH 03755 USA; [Montes-de-Oca, Martha] Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico; [Lane, Nicholas D.] Microsoft Res Asia, Beijing, Peoples R China; [Cardone, Giuseppe] Univ Bologna, Bologna, Italy Dartmouth College; Universidad Nacional Autonoma de Mexico; Microsoft; Microsoft Research Asia; University of Bologna You, CW (corresponding author), Dartmouth Coll, Hanover, NH 03755 USA. you.chuang-wen@dartmouth.edu; martham@unam.mx; thomas358@gmail.com; niclane@microsoft.com; hong@cs.dartmouth.edu; giuseppe.cardone@unibo.it; lorenzo@cs.dartmouth.edu; campbell@cs.dartmouth.edu 1 33 34 0 4 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-1224-0 2012.0 671 672 2 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BO1DD 2023-03-23 WOS:000494987600120 0
C Tsaloli, G; Liang, B; Brunetta, C; Banegas, G; Mitrokotsa, A Liu, JK; Katsikas, S; Meng, W; Susilo, W; Intan, R Tsaloli, Georgia; Liang, Bei; Brunetta, Carlo; Banegas, Gustavo; Mitrokotsa, Aikaterini DEVA: Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning INFORMATION SECURITY (ISC 2021) Lecture Notes in Computer Science English Proceedings Paper 24rd International Conference on Information Security (ISC) NOV 10-12, 2021 ELECTR NETWORK Petra Christian Univ Secure aggregation; Privacy; Verifiability; Decentralization Aggregating data from multiple sources is often required in multiple applications. In this paper, we introduce DEVA, a protocol that allows a distributed set of servers to perform secure and verifiable aggregation of multiple users' secret data, while no communication between the users occurs. DEVA computes the sum of the users' input and provides public verifiability, i.e., anyone can be convinced about the correctness of the aggregated sum computed from a threshold amount of servers. A direct application of the DEVA protocol is its employment in the machine learning setting, where the aggregation of multiple users' parameters (used in the learning model), can be orchestrated by multiple servers, contrary to centralized solutions that rely on a single server. We prove the security and verifiability of the proposed protocol and evaluate its performance for the execution time and bandwidth, the verification execution, the communication cost, and the total bandwidth usage of the protocol. We compare our findings to the prior work, concluding that DEVA requires less communication cost for a big amount of users. [Tsaloli, Georgia; Brunetta, Carlo; Mitrokotsa, Aikaterini] Chalmers Univ Technol, Gothenburg, Sweden; [Liang, Bei] Beijing Inst Math Sci & Applicat, Beijing, Peoples R China; [Banegas, Gustavo] INRIA, Palaiseau, France; [Banegas, Gustavo] Inst Polytech Paris, Lab Informat, Ecole Polytech, Palaiseau, France; [Mitrokotsa, Aikaterini] Univ St Gallen, Sch Comp Sci, St Gallen, Switzerland Chalmers University of Technology; Inria; Institut Polytechnique de Paris; University of St Gallen Tsaloli, G (corresponding author), Chalmers Univ Technol, Gothenburg, Sweden. tsaloli@chalmers.se; lbei@abimsa.cn; brunetta@chalmers.se; gustavo@cryptme.in; kaerina.mitrokotsa@unisg.ch Brunetta, Carlo/HHR-8647-2022 Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. 23 0 0 2 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-91356-4; 978-3-030-91355-7 LECT NOTES COMPUT SC 2021.0 13118.0 296 319 10.1007/978-3-030-91356-4_16 0.0 24 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS7UK 2023-03-23 WOS:000767940700016 0
J Ye, L; Wang, L; Ferdinando, H; Seppanen, T; Alasaarela, E Ye, Liang; Wang, Le; Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko A Video-Based DT-SVM School Violence Detecting Algorithm SENSORS English Article activity recognition; image processing; pattern recognition; school violence detecting School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT-SVM (Decision Tree-SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT-SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement. [Ye, Liang; Wang, Le] Harbin Inst Technol, Dept Informat & Commun Engn, Harbin 150080, Heilongjiang, Peoples R China; [Ye, Liang; Ferdinando, Hany; Alasaarela, Esko] Univ Oulu, OPEM Res Unit, Oulu 90014, Finland; [Wang, Le] Huawei Beijing Inst, Beijing 100085, Peoples R China; [Ferdinando, Hany] Petra Christian Univ, Dept Elect Engn, Surabaya 60236, Indonesia; [Seppanen, Tapio] Univ Oulu, Physiol Signal Anal Team, Oulu 90014, Finland Harbin Institute of Technology; University of Oulu; Universitas Kristen Petra; University of Oulu Ye, L (corresponding author), Harbin Inst Technol, Dept Informat & Commun Engn, Harbin 150080, Heilongjiang, Peoples R China.;Ye, L (corresponding author), Univ Oulu, OPEM Res Unit, Oulu 90014, Finland. yeliang@hit.edu.cn; wangle68@huawei.com; Hany.Ferdinando@oulu.fi; tapio.seppanen@oulu.fi; Esko.Alasaarela@oulu.fi ; Seppanen, Tapio/O-1300-2015 Ferdinando, Hany/0000-0003-0857-2946; Ye, Liang/0000-0001-6076-0261; Seppanen, Tapio/0000-0002-3963-0750 National Natural Science Foundation of China [41861134010]; Basic scientific research project of Heilongjiang Province [KJCXZD201704]; Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security [2018JYWXTX01]; Finnish Cultural Foundation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Basic scientific research project of Heilongjiang Province; Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security; Finnish Cultural Foundation(Finnish Cultural FoundationFinnish IT center for science) This research was funded by National Natural Science Foundation of China, grant number 41861134010; Basic scientific research project of Heilongjiang Province, grant number KJCXZD201704; Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security, grant number 2018JYWXTX01, and partly by Finnish Cultural Foundation, North-Ostrobothnia Regional Fund 2017. 23 2 2 1 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2020.0 20.0 7 2018 10.3390/s20072018 0.0 18 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation LT5KW 32260274.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000537110500214 0
J Liang, RL; Bai, YQ; Lin, HX Liang, Renli; Bai, Yanqin; Lin, Hai Xiang A splitting method for the locality regularized semi-supervised subspace clustering OPTIMIZATION English Article Subspace segmentation; low-rank representation; graph regularization; image clustering THRESHOLDING ALGORITHM; SPARSE GRAPH Graph-based semi-supervised learning (G-SSL) methods play an increasingly important role in machine learning systems. Recently, latent low-rank representation (LatLRR) graph has gained great success in subspace clustering. However, LatLRR only considers the global structure, while the local geometric information, which is often important to many real applications, is ignored. In this paper, we propose a locality regularized LatLRR model (LR-LatLRR) for semi-supervised subspace clustering problems. This model incorporates two regularization terms into LatLRR by taking the local structure of data into account. Then, we develop an efficient splitting algorithm for solving LR-LatLRR. In addition, we also prove the global convergence of the proposed algorithm. Furthermore, we extend the LR-LatLRR model to a case of including the non-negative constraint. Finally, we conduct experiments on a synthetic data and several real data sets for the semi-supervised clustering problems. Experimental results show that our method can obtain high classification accuracy and outperforms several state-of-the-art G-SSL methods. [Liang, Renli; Bai, Yanqin] Shanghai Univ, Dept Math, Shanghai, Peoples R China; [Liang, Renli] Chongqing Jiaotong Univ, Coll Math & Stat, Chongqing, Peoples R China; [Lin, Hai Xiang] Delft Univ Technol, Dept Appl Math, Delft, Netherlands Shanghai University; Chongqing Jiaotong University; Delft University of Technology Bai, YQ (corresponding author), Shanghai Univ, Dept Math, Shanghai, Peoples R China. yqbai@t.shu.edu.cn Liang, Renli/AAS-8162-2021 Lin, Hai Xiang/0000-0002-1653-4854 National Natural Science Foundation of China [11771275] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported by a grant from the National Natural Science Foundation of China (no. 11771275). 40 6 6 2 15 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0233-1934 1029-4945 OPTIMIZATION Optimization MAY 3 2020.0 69.0 5 1069 1096 10.1080/02331934.2019.1671841 0.0 OCT 2019 28 Operations Research & Management Science; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science; Mathematics LK7TA 2023-03-23 WOS:000489436500001 0
C Dai, YJ; Zhang, TJ; Lin, ZD; Yin, F; Theodoridis, S; Cui, SG Peters, J; Sontag, D Dai, Yijue; Zhang, Tianjian; Lin, Zhidi; Yin, Feng; Theodoridis, Sergios; Cui, Shuguang An Interpretable and Sample Efficient Deep Kernel for Gaussian Process CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020) Proceedings of Machine Learning Research English Proceedings Paper Conference on Uncertainty in Artificial Intelligence (UAI) AUG 03-06, 2020 ELECTR NETWORK We propose a novel Gaussian process kernel that takes advantage of a deep neural network (DNN) structure but retains good interpretability. The resulting kernel is capable of addressing four major issues of the previous works of similar art, i.e., the optimality, explainability, model complexity, and sample efficiency. Our kernel design procedure comprises three steps: (1) Derivation of an optimal kernel with a non-stationary dot product structure that minimizes the prediction/test mean-squared-error (MSE); (2) Decomposition of this optimal kernel as a linear combination of shallow DNN subnetworks with the aid of multi-way feature interaction detection; (3) Updating the hyperparameters of the subnetworks via an alternating rationale until convergence. The designed kernel does not sacrifice interpretability for optimality. On the contrary, each subnetwork explicitly demonstrates the interaction of a set of features in a transformation function, leading to a solid path toward explainable kernel learning. We test the proposed kernel with both synthesized and real-world data sets, and the proposed kernel is superior to its competitors in terms of prediction performance in most cases. Moreover, it tends to maintain the prediction performance and be robust to data over-fitting issue, when reducing the number of samples. [Dai, Yijue; Zhang, Tianjian; Lin, Zhidi; Yin, Feng; Theodoridis, Sergios; Cui, Shuguang] Chinese Univ Hong Kong Shenzhen, Shenzhen 518172, Peoples R China; [Dai, Yijue; Zhang, Tianjian; Lin, Zhidi; Yin, Feng; Theodoridis, Sergios; Cui, Shuguang] SRIBD, Shenzhen 518172, Peoples R China; [Theodoridis, Sergios] Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 15772, Greece Chinese University of Hong Kong, Shenzhen; National & Kapodistrian University of Athens Yin, F (corresponding author), Chinese Univ Hong Kong Shenzhen, Shenzhen 518172, Peoples R China.;Yin, F (corresponding author), SRIBD, Shenzhen 518172, Peoples R China. yinfeng@cuhk.edu.cn National Key R&D Program of China [2018YFB1800800]; Natural Science Foundation of China [61701426]; Guangdong Zhujiang Project [2017ZT07X152]; Shenzhen Peacock Plan [KQTD2015033114415450] National Key R&D Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Zhujiang Project; Shenzhen Peacock Plan This work was mainly supported by the National Key R&D Program of China with grant No. 2018YFB1800800, and partly by Natural Science Foundation of China No. 61701426, Guangdong Zhujiang Project No. 2017ZT07X152, and Shenzhen Peacock Plan No. KQTD2015033114415450. 31 2 2 0 0 JMLR-JOURNAL MACHINE LEARNING RESEARCH SAN DIEGO 1269 LAW ST, SAN DIEGO, CA, UNITED STATES 2640-3498 PR MACH LEARN RES 2020.0 124.0 759 768 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematics, Applied; Statistics & Probability Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics BS4VS 2023-03-23 WOS:000723388600077 0
C Lindfors, M; Chen, TS; Naesseth, CA Lindfors, Martin; Chen, Tianshi; Naesseth, Christian A. Robust Gaussian process regression with G-confluent likelihood IFAC PAPERSONLINE English Proceedings Paper 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges JUL 11-17, 2020 ELECTR NETWORK Int Federat Automat Control,Siemens,Bayer,ABB,MathWorks,Phoenix Contact,Ifak Technol,Berlin Heart,Elsevier,De Gruyter,Tele Medi GmbH Bayesian methods; Machine learning; Nonparametric methods; Gaussian process regression; Outliers; Variational inference INFERENCE For robust Gaussian process regression problems where the measurements are contaminated by outliers, a likelihood/measurement noise model with heavy-tailed distributions should be used to improve the prediction performance. In this paper, we propose to use a G-confluent distribution as the measurement noise model and a coordinate ascent variational inference method to infer the overall statistical model. In contrast with the commonly used Student's t distribution, the G-confluent distribution can also be written as a Gaussian scale mixture, but its inverse scale follows a Beta distribution rather than a Gamma distribution, and its main advantage is that it is more flexible for modeling outliers while being equally suitable for variational inference. Numerical simulations based on benchmark data show that the G-confluent distribution performs better than or as well as the Student's t distribution. Copyright (C) 2020 The Authors. [Lindfors, Martin] Linkoping Univ, Dept Elect Engn, S-58183 Linkoping, Sweden; [Lindfors, Martin; Chen, Tianshi] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China; [Lindfors, Martin; Chen, Tianshi] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China; [Naesseth, Christian A.] Columbia Univ, Data Sci Inst, New York, NY 10027 USA Linkoping University; Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen; Columbia University Lindfors, M (corresponding author), Linkoping Univ, Dept Elect Engn, S-58183 Linkoping, Sweden.;Lindfors, M (corresponding author), Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China.;Lindfors, M (corresponding author), Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China. martin.lindfors@liu.se; tschen@cuhk.edu.cn Wallenberg AI, Autonomous Systems and Software Program (wasp) - Knut and Alice Wallenberg Foundation; National Natural Science Foundation of China [61773329]; Thousand Youth Talents Plan - central government of China; Shenzhen Science and Technology Innovation Council [Ji-20170189 (JCY20170411102101881)]; Chinese University of Hong Kong, Shenzhen [2014.0003.23]; [PF. 01.000249] Wallenberg AI, Autonomous Systems and Software Program (wasp) - Knut and Alice Wallenberg Foundation; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Thousand Youth Talents Plan - central government of China; Shenzhen Science and Technology Innovation Council; Chinese University of Hong Kong, Shenzhen; Tianshi Chen is the corresponding author. This work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (wasp) funded by the Knut and Alice Wallenberg Foundation. This work is also supported in part by the National Natural Science Foundation of China under contract No. 61773329, the Thousand Youth Talents Plan funded by the central government of China, the Shenzhen Projects Ji-20170189 (JCY20170411102101881) funded by the Shenzhen Science and Technology Innovation Council, the President's grant under contract No. PF. 01.000249 and the Startup grant under contract No. 2014.0003.23 funded by the Chinese University of Hong Kong, Shenzhen. 17 0 0 0 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53.0 2 401 406 10.1016/j.ifacol.2020.12.197 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SF2LK gold 2023-03-23 WOS:000652592500065 0
J Rao, LL; Xu, J; Efremenko, DS; Loyola, DG; Doicu, A Rao, Lanlan; Xu, Jian; Efremenko, Dmitry S.; Loyola, Diego G.; Doicu, Adrian Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Aerosol information retrieval; neural networks; TROPOspheric Monitoring Instrument/Sentinel-5 Precursor (TROPOMI/S5P) RADIATIVE-TRANSFER MODEL; ICE WATER PATH; CLOUD RETRIEVAL; OPTICAL DEPTH; BAND; ATMOSPHERE; O-2; ALGORITHMS; OXYGEN; OCEAN In this article, we present three algorithms for aerosol parameters retrieval from TROPOspheric Monitoring Instrument measurements in the O-2 A-band. These algorithms use neural networks 1) to emulate the radiative transfer model and a Bayesian approach to solve the inverse problem, 2) to learn the inverse model from the synthetic radiances, and 3) to learn the inverse model from the principal-component transform of synthetic radiances. The training process is based on full-physics radiative transfer simulations. The accuracy and efficiency of the neural network based retrieval algorithms are analyzed with synthetic and real data. [Rao, Lanlan; Efremenko, Dmitry S.; Loyola, Diego G.; Doicu, Adrian] German Aerosp Ctr DLR, Remote Sensing Technol Inst, D-82234 Oberpfaffenhofen, Germany; [Xu, Jian] Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China Helmholtz Association; German Aerospace Centre (DLR); Chinese Academy of Sciences; National Space Science Center, CAS Xu, J (corresponding author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China. Lanlan.rao@dlr.de; xujian@nssc.ac.cn; Dmitry.Efremenko@dlr.de; diego.loyola@dlr.de; Adrian.Doicu@dlr.de Xu, Jian/AAP-5179-2021; Efremenko, Dmitry S/G-9954-2015; Loyola, Diego/GYO-3213-2022 Xu, Jian/0000-0003-2348-125X; Efremenko, Dmitry S/0000-0002-7449-5072; Loyola R., Diego G./0000-0002-8547-9350; Rao, Lanlan/0000-0003-4439-0496 German Aerospace Center (DLR) [2 472 469]; Chinese Academy of Sciences (CAS) [E1RC2WB2]; Chinese Scholarship Council German Aerospace Center (DLR)(Helmholtz AssociationGerman Aerospace Centre (DLR)); Chinese Academy of Sciences (CAS)(Chinese Academy of Sciences); Chinese Scholarship Council(China Scholarship Council) This work was supported in part by the German Aerospace Center (DLR) programmatic [Nachwuchsgruppe Retrieval der nachsten Generation, 2 472 469] and in part by the Chinese Academy of Sciences (CAS) Pioneering Initiative Talents Program under Grant E1RC2WB2. The work of Lanlan Rao was supported by the Chinese Scholarship Council. (Corresponding author: Jian Xu.) 62 0 0 6 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 6473 6484 10.1109/JSTARS.2022.3196843 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 3X8FM Green Accepted, gold 2023-03-23 WOS:000843270500014 0
J Zhuo, ZZ; Qu, LY; Zhang, P; Duan, YY; Cheng, D; Xu, XL; Sun, T; Ding, JL; Xie, C; Liu, X; Haller, S; Barkhof, F; Zhang, LW; Liu, Y Zhuo, Zhizheng; Qu, Liying; Zhang, Peng; Duan, Yunyun; Cheng, Dan; Xu, Xiaolu; Sun, Ting; Ding, Jinli; Xie, Cong; Liu, Xing; Haller, Sven; Barkhof, Frederik; Zhang, Liwei; Liu, Yaou Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING English Article Brainstem glioma; Amide proton transfer-weighted imaging; Radiomics; Machine learning DIFFUSE INTRINSIC PONTINE; CLASSIFICATION; MUTATIONS; PROGNOSIS; TUMORS; MRI Purpose H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer-weighted (APTw) imaging and radiomic features. Methods Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student's T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. Results BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status. Conclusion BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients. [Zhuo, Zhizheng; Qu, Liying; Duan, Yunyun; Cheng, Dan; Xu, Xiaolu; Sun, Ting; Ding, Jinli; Xie, Cong; Liu, Yaou] Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing 100070, Peoples R China; [Zhang, Peng; Zhang, Liwei] Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 10070, Peoples R China; [Liu, Xing] Capital Med Univ, Beijing Tiantan Hosp, Dept Pathol, Beijing 10070, Peoples R China; [Haller, Sven] Univ Geneva, Dept Imaging & Med Informat, Univ Hosp Geneva, Geneva, Switzerland; [Haller, Sven] Univ Geneva, Fac Med, Geneva, Switzerland; [Barkhof, Frederik] UCL Inst Neurol & Healthcare Engn, London, England; [Barkhof, Frederik] Amsterdam Univ Med Ctr, Dept Radiol & Nucl Med, Amsterdam, Netherlands Capital Medical University; Capital Medical University; Capital Medical University; University of Geneva; University of Geneva; University of London; University College London Liu, Y (corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing 100070, Peoples R China.;Zhang, LW (corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 10070, Peoples R China. zlwtt@aliyun.com; liuyaou@bjtth.org Barkhof, Frederik/0000-0003-3543-3706; Haller, Sven/0000-0001-7433-0203 National Science Foundation of China [81870958, 81571631]; Beijing Municipal Natural Science Foundation for Distinguished Young Scholars [JQ20035]; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority [XTYB201831]; NIHR biomedical research center at UCLH National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation for Distinguished Young Scholars; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority; NIHR biomedical research center at UCLH This study was funded by the National Science Foundation of China (Nos. 81870958 and 81571631), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (No. XTYB201831). FB was supported by the NIHR biomedical research center at UCLH. 32 14 15 4 15 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1619-7070 1619-7089 EUR J NUCL MED MOL I Eur. J. Nucl. Med. Mol. Imaging DEC 2021.0 48.0 13 4426 4436 10.1007/s00259-021-05455-4 0.0 JUN 2021 11 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging WR3GD 34131804.0 Green Submitted 2023-03-23 WOS:000661768400002 0
J Yan, J; Peng, ZF; Yin, HL; Wang, J; Wang, X; Shen, YS; Stechele, W; Cremers, D Yan, Jun; Peng, Zifeng; Yin, Huilin; Wang, Jie; Wang, Xiao; Shen, Yuesong; Stechele, Walter; Cremers, Daniel Trajectory prediction for intelligent vehicles using spatial-attention mechanism IET INTELLIGENT TRANSPORT SYSTEMS English Article road vehicles; learning (artificial intelligence); traffic engineering computing; mean square error methods; decoding; trajectory prediction; intelligent vehicles; spatial-attention mechanism; autonomous vehicles; safe trajectory; interaction aware prediction methods; previous trajectories; sequence-to-sequence prediction problem; LSTM encoder-decoder framework; context attention; lane attention; Spatial-attention mechanisms; selective-attention mechanism; prediction accuracy It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence-to-sequence prediction problem. They tackle this problem with an LSTM encoder-decoder framework. Moreover, they propose two spatial-attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial-attention mechanisms adopt the selective-attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors' methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state-of-the-art methods. Code is available at https://github.com/momo1986/Spatial-attention. [Yan, Jun; Peng, Zifeng; Yin, Huilin] Tongji Univ, Dept Elect & Informat Engn, Shanghai 201804, Peoples R China; [Wang, Jie; Stechele, Walter] Tech Univ Munich, Dept Elect & Comp Engn, D-80333 Munich, Germany; [Wang, Xiao; Shen, Yuesong; Cremers, Daniel] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany Tongji University; Technical University of Munich; Technical University of Munich Yin, HL (corresponding author), Tongji Univ, Dept Elect & Informat Engn, Shanghai 201804, Peoples R China. yinhuilin@tongji.edu.cn National Natural Science Foundation of China [61701348]; Ministry of Science and Technology [2016YFB0100901, 2018YFB0105101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology(Ministry of Science, ICT & Future Planning, Republic of Korea) This work was supported by the National Natural Science Foundation of China under grant no. 61701348. It was also supported by the Ministry of Science and Technology under grant nos. 2016YFB0100901 and 2018YFB0105101. The authors thank TUEV SUED for the kind support. The authors thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript. 36 7 7 34 123 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-956X 1751-9578 IET INTELL TRANSP SY IET Intell. Transp. Syst. DEC 15 2020.0 14.0 13 1855 1863 10.1049/iet-its.2020.0274 0.0 9 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation QI7MP 2023-03-23 WOS:000619167800013 0
J Jia, Y; Jin, SG; Yan, QY; Savi, P; Zhang, RC; Li, WM Jia, Yan; Jin, Shuanggen; Yan, Qingyun; Savi, Patrizia; Zhang, Rongchun; Li, Wenmei An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Spatial resolution; Reflectivity; Data models; Vegetation mapping; Remote sensing; Microwave radiometry; Maximum likelihood estimation; Cyclone-GNSS (CYGNSS); global navigation satellite system-reflectometry (GNSS-R); machine learning (ML); soil moisture (SM); soil moisture active passive (SMAP) RETRIEVAL; REFLECTIVITY Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 x 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm(3)/cm(3) and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm(3)/cm(3) and 0.057 cm(3)/cm(3), respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source. [Jia, Yan; Zhang, Rongchun; Li, Wenmei] Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Savi, Patrizia] Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy Nanjing University of Posts & Telecommunications; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Polytechnic University of Turin Jin, SG (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China. jiayan@njupt.edu.cn; sgjin@shao.ac.cn; jayyqy@qq.com; patrizia.savi@polito.it; rongchun-zhang@njupt.edu.cn; liwm@njupt.edu.cn Jin, Shuanggen/B-8094-2008 Jin, Shuanggen/0000-0002-5108-4828; SAVI, Patrizia/0000-0001-9585-310X; Yan, Qingyun/0000-0001-6693-957X; Li, Wenmei/0000-0002-1108-0507 National Natural Science Foundation of China [42001375, 42001362]; Natural Science Foundation of Jiangsu Province [BK20180765, BK20191384]; Nanjing Technology Innovation Foundation for Selected Overseas Scientists [RK032YZZ18003]; Shanghai Leading Talent Project [E056061]; Strategic Priority Research Program Project of the Chinese Academy of Sciences [XDA23040100] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Nanjing Technology Innovation Foundation for Selected Overseas Scientists; Shanghai Leading Talent Project; Strategic Priority Research Program Project of the Chinese Academy of Sciences This work was supported in part by the National Natural Science Foundation of China under Grant 42001375 and Grant 42001362, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180765 and Grant BK20191384, in part by the Nanjing Technology Innovation Foundation for Selected Overseas Scientists underGrant RK032YZZ18003, in part by the Shanghai Leading Talent Project under Grant E056061, and in part by the Strategic Priority Research Program Project of the Chinese Academy of Sciences under Grant XDA23040100. 31 0 0 3 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 4234 4247 10.1109/JSTARS.2022.3176031 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 1V0PC gold 2023-03-23 WOS:000805801800009 0
J Chen, N; Ribeiro, B; Chen, A; Tang, CS Chen, Ning; Ribeiro, Bernardete; Chen, An; Tang, Chaosheng Optimization of selective ensemble for cost-sensitive classification: An empirical study INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS English Article Cost-sensitive classification; selective ensemble learning; evolutionary computing; pairwise comparison matrix; learning vector quantization neural network Cost-sensitive classification is broadly investigated in many real-life decision-making applications where the different misclassification errors may cause asymmetric costs. However, the cost values are usually hardly specified in practice for decision makers especially when they are faced with the complex multi-class decision problems. In this paper we attempt to take advantage of the pairwise comparisons originally proposed in Analytic Hierarchy Process (AHP) and offer decision makers a flexible, qualitative manner for cost specification rather than the definite, quantitative cost assignment. The cost ratios associated with the classes are then derived from the comparison matrix and used as the parameter of the subsequent cost-sensitive classifiers. To promote the performance of single classifier we construct the ensembles of multiple cost-sensitive Learning Vector Quantization Neural Networks (LVQ-NNs) trained independently and then combined together by various weighted voting approaches. Empirical study on some real-world databases reveal that the well optimized cost-sensitive ensembles based on evolutionary computing approaches perform significantly better than the best single classifier within the ensemble in terms of generalization ability and stability. [Chen, Ning; Tang, Chaosheng] Henan Polytech Univ, Coll Comp Sci & Technol, 2001 Century Ave, Jiaozuo 454003, Henan, Peoples R China; [Ribeiro, Bernardete] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal; [Chen, An] Henan Polytech Univ, Safety & Emergency Management Res Ctr, Jiaozuo, Henan, Peoples R China; [Chen, An] Chinese Acad Sci, Inst Sci & Dev, Beijing, Peoples R China Henan Polytechnic University; Universidade de Coimbra; Henan Polytechnic University; Chinese Academy of Sciences Chen, N (corresponding author), Henan Polytech Univ, Coll Comp Sci & Technol, 2001 Century Ave, Jiaozuo 454003, Henan, Peoples R China. nchenyx@outlook.com Ribeiro, Bernardete/A-8010-2016; Tang, Chaosheng/AGX-8816-2022 Ribeiro, Bernardete/0000-0002-9770-7672; Tang, Chaosheng/0000-0001-6923-855X Beijing National Science Foundation [9182017]; President Youth Fund of Institutes of Sciences and Development, CAS [Y7X1151Q01]; National Natural Science Foundation of China [11601129]; Introduction of Talent Research Fund of Henan Polytechnic University [Y2017-1] Beijing National Science Foundation; President Youth Fund of Institutes of Sciences and Development, CAS; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Introduction of Talent Research Fund of Henan Polytechnic University This work was supported by national funds through the Beijing National Science Foundation (9182017), President Youth Fund of Institutes of Sciences and Development, CAS (Y7X1151Q01), National Natural Science Foundation of China (11601129), and Introduction of Talent Research Fund of Henan Polytechnic University (Y2017-1). 32 0 0 0 1 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1872-4981 1875-8843 INTELL DECIS TECHNOL Intell. Decis. Technol.-Neth. 2018.0 12.0 4 399 410 10.3233/IDT-180344 0.0 12 Computer Science, Artificial Intelligence Emerging Sources Citation Index (ESCI) Computer Science HI3ZS 2023-03-23 WOS:000456391100002 0
J Wang, CH; Xiao, JZ; Zhang, CZ; Xiao, XW Wang, Chunhui; Xiao, Jianzhuang; Zhang, Chuanzeng; Xiao, Xuwen Structural health monitoring and performance analysis of a 12-story recycled aggregate concrete structure ENGINEERING STRUCTURES English Article Recycled aggregate concrete; Natural aggregate concrete; Full-scale structure; Structural health monitoring; Amplitude-dependent characteristic; Artificial neural network DAMAGE DETECTION; FLEXURAL PERFORMANCE; ELASTIC-MODULUS; NEURAL-NETWORKS; IDENTIFICATION; BUILDINGS; TEMPERATURE; STRENGTH; BEAMS; CONSTRUCTION To encourage the application of recycled coarse aggregates (RCA) produced from construction and demolition (C&D) waste in newly-built structures, this study investigates the dynamic behaviors of a 12-story recycled aggregate concrete (RAC) in the operational environment through structural health monitoring (SHM), and compares with an adjacent 12-story natural aggregate concrete (NAC) structure with similar arrangements of components and structural construction. The SHM has undergone 12 months, and has provided a wealth of data detailing the dynamic behaviors with the variation in the operational environment as well as significant structural responses excited by typhoons and earthquakes. Firstly, the first 5 vibration modes of both structures are identified, and the effects of the ambient temperature, hourly mean wind speed and structural response amplitude are analyzed. A lower natural frequency and a larger damping ratio of the fundamental vibration mode of the RAC structure are found than those of the NAC structure. For both the RAC and NAC structures, the fundamental frequency increases with the increasing ambient temperature, the decreasing hourly mean wind speed, and the decreasing structural response amplitude. The opposite trend of the damping ratios is identified. Secondly, the stick-slip model is used to explain the differences between the modal parameters of the RAC and the NAC structures, and to discover the mechanism of the effects of the ambient temperature, the hourly mean wind speed, and the structural response amplitude. Thirdly, the serviceability of the RAC structure is evaluated based on the full-scale measurements during typhoons. Finally, the artificial neural network (ANN) is validated to have the capacity for predicting the modal parameters precisely, and the model error of the ANN has the potential as an indicator for detecting abnormal structural performances. [Wang, Chunhui; Xiao, Jianzhuang; Xiao, Xuwen] Tongji Univ, Dept Struct Engn, Shanghai 200092, Peoples R China; [Xiao, Jianzhuang] Tongji Univ, State Key Lab Disaster Reduct Civil Eng, Shanghai 200092, Peoples R China; [Zhang, Chuanzeng] Univ Siegen, Dept Civil Engn, D-57076 Siegen, Germany Tongji University; Tongji University; Universitat Siegen Xiao, JZ (corresponding author), Tongji Univ, Dept Struct Engn, Shanghai 200092, Peoples R China. jzx@tongji.edu.cn Xiao, Jian/GYU-4351-2022 Wang, Chunhui/0000-0002-8731-3450 National Natural Science Foundation of China (NSFC) [51438007, 51325802, 51661145023]; Shanghai Science and Technology Committee [14231201300]; China Scholarship Council (CSC) [201806260172] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Shanghai Science and Technology Committee(Shanghai Science & Technology Committee); China Scholarship Council (CSC)(China Scholarship Council) The authors would like to gratefully acknowledge the research grants from the National Natural Science Foundation of China (NSFC) (51438007, 51325802 and the joint research project between NSFC and Pakistan Science Foundation (PSF), No. 51661145023) and the Shanghai Science and Technology Committee (No. 14231201300). The authors are grateful for the valuable support supplied by the Shanghai Urban Construction Materials Co. Ltd during the monitoring processes. Chunhui Wang would like to gratefully acknowledge the financial support by the China Scholarship Council (CSC, Project-No. 201806260172) for his scientific visit at the Chair of Structural Mechanics, University of Siegen, Germany. 74 25 26 10 58 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0141-0296 1873-7323 ENG STRUCT Eng. Struct. FEB 15 2020.0 205.0 110102 10.1016/j.engstruct.2019.110102 0.0 18 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering KH4JN 2023-03-23 WOS:000510612600048 0
J Allen, GI; Amoroso, N; Anghel, C; Balagurusamy, V; Bare, CJ; Beaton, D; Bellotti, R; Bennett, DA; Boehme, KL; Boutros, PC; Caberlotto, L; Caloian, C; Campbell, F; Neto, EC; Chang, YC; Chen, BB; Chen, CY; Chien, TY; Clark, T; Das, S; Davatzikos, C; Deng, JY; Dillenberger, D; Dobson, RJB; Dong, QL; Doshi, J; Duma, D; Errico, R; Erus, G; Everett, E; Fardo, DW; Friend, SH; Frohlich, H; Gan, J; St George-Hyslop, P; Ghosh, SS; Glaab, E; Green, RC; Guan, YF; Hong, MY; Huang, C; Hwang, J; Ibrahim, J; Inglese, P; Iyappan, A; Jiang, QJ; Katsumata, Y; Kauwe, JSK; Klein, A; Kong, D; Krause, R; Lalonde, E; Lauria, M; Lee, E; Lin, XH; Liu, ZD; Livingstone, J; Logsdon, BA; Lovestone, S; Ma, TW; Malhotra, A; Mangravite, LM; Maxwell, TJ; Merrill, E; Nagorski, J; Namasivayam, A; Narayan, M; Naz, M; Newhouse, SJ; Norman, TC; Nurtdinov, RN; Oyang, YJ; Pawitan, Y; Peng, SW; Peters, MA; Piccolo, SR; Praveen, P; Priami, C; Sabelnykova, VY; Senger, P; Shen, X; Simmons, A; Sotiras, A; Stolovitzky, G; Tangaro, S; Tateo, A; Tung, YA; Tustison, NJ; Varol, E; Vradenburg, G; Weiner, MW; Xiao, GH; Xie, L; Xie, Y; Xu, J; Yang, HJ; Zhan, XW; Zhou, YY; Zhu, F; Zhu, HT; Zhu, SF Allen, Genevera I.; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J.; Beaton, Derek; Bellotti, Roberto; Bennett, David A.; Boehme, Kevin L.; Boutros, Paul C.; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Chaibub Neto, Elias; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard J. B.; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W.; Friend, Stephen H.; Froehlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S.; Glaab, Enrico; Green, Robert C.; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John S. K.; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A.; Lovestone, Simon; Ma, Tsung-wei; Malhotra, Ashutosh; Mangravite, Lara M.; Maxwell, Taylor J.; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J.; Norman, Thea C.; Nurtdinov, Ramil N.; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Peters, Mette A.; Piccolo, Stephen R.; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y.; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J.; Varol, Erdem; Vradenburg, George; Weiner, Michael W.; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng Alzheimer's Dis Neuroimaging Initi Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease ALZHEIMERS & DEMENTIA English Article Azheimer's disease; Biomarkers; Crowdsource; Big data; Bioinformatics; Cognitive decline; Imaging; Genetics GENOME-WIDE ASSOCIATION; RISK PREDICTION; IMPAIRMENT; BIOMARKERS; DEMENTIA; PATTERNS; ATROPHY; PROJECT; HEALTH; MEMORY Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance. (C) 2016 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association. This is an open access article under the CC BY-NC-ND license. [Allen, Genevera I.; Campbell, Frederick; Everett, Evan; Gan, Jessica; Jiang, Qijia; Liu, Zhandong; Nagorski, John; Narayan, Manjari] Rice Univ, Dept Stat & Elect & Comp Engn, Houston, TX USA; [Amoroso, Nicola; Bellotti, Roberto; Tateo, Andrea] Univ Bari A Moro, Dipartimento Fis M Merlin, Bari, Italy; [Amoroso, Nicola; Bellotti, Roberto; Tangaro, Sabina] Ist Nazl Fis Nucl, Sez Bari, I-70126 Bari, Italy; [Anghel, Catalina; Boutros, Paul C.; Caloian, Cristian; Lalonde, Emilie; Lin, Xihui; Livingstone, Julie; Sabelnykova, Veronica Y.] Ontario Inst Canc Res, Informat & Biocomp Program, MaRS Ctr, Toronto, ON, Canada; [Balagurusamy, Venkat; Dillenberger, Donna; Stolovitzky, Gustavo] IBM Res, IBM Computat Biol Ctr, Yorktown Hts, NY USA; [Bare, Christopher J.; Chaibub Neto, Elias; Friend, Stephen H.; Klein, Arno; Logsdon, Benjamin A.; Mangravite, Lara M.; Norman, Thea C.; Peters, Mette A.] Sage Bionetworks, Seattle, WA USA; [Beaton, Derek] Univ Texas Dallas, Sch Behav & Brain Sci, Richardson, TX 75083 USA; [Bennett, David A.] Rush Univ, Rush Alzheimers Dis Ctr, Med Ctr, Chicago, IL 60612 USA; [Boehme, Kevin L.; Kauwe, John S. K.; Piccolo, Stephen R.] Brigham Young Univ, Dept Biol, Provo, UT 84602 USA; [Boutros, Paul C.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada; [Boutros, Paul C.] Univ Toronto, Dept Pharmacol & Toxicol, Toronto, ON, Canada; [Caberlotto, Laura; Lauria, Mario; Praveen, Paurush; Priami, Corrado] Microsoft Res, Univ Trento Ctr Computat & Syst Biol COSBI, Rovereto, Italy; [Chang, Yu-Chuan; Oyang, Yen-Jen] Natl Taiwan Univ, Grad Inst Biomed Elect & Bioinformat, Taipei 10764, Taiwan; [Chen, Beibei; Ma, Tsung-wei; Xiao, Guanghua; Xie, Yang; Xu, Jia; Zhan, Xiaowei; Zhou, Yunyun] Univ Texas SW Med Ctr Dallas, Quantitat Biomed Res Ctr, Dallas, TX 75390 USA; [Chen, Chien-Yu; Hong, Ming-Yi] Natl Taiwan Univ, Dept Bioind Mechatron Engn, Taipei 10764, Taiwan; [Chien, Ting-Ying] Yuan Ze Univ, Innovat Ctr Big Data & Digital Convergence, Taoyuan, Taiwan; [Clark, Tim; Das, Sudeshna; Merrill, Emily] Massachusetts Gen Hosp, Dept Neurol, Cambridge, MA USA; [Clark, Tim; Das, Sudeshna] Harvard Univ, Sch Med, Dept Neurol, Boston, MA 02115 USA; [Davatzikos, Christos; Doshi, Jimit; Erus, Guray; Sotiras, Aristeidis; Varol, Erdem] Univ Penn, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; [Deng, Jieyao; Dong, Qilin; Peng, Shengwen; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Deng, Jieyao; Dong, Qilin; Peng, Shengwen; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [Dobson, Richard J. B.; Newhouse, Stephen J.; Simmons, Andrew] Kings Coll London, NIHR Biomed Res Ctr Mental Hlth, London WC2R 2LS, England; [Dobson, Richard J. B.] Kings Coll London, MRC Social Genet & Dev Psychiat Ctr, Inst Psychiat Psychol & Neurosci, London WC2R 2LS, England; [Duma, Denise] Baylor Coll Med, Dept Pediat Neurol, Houston, TX 77030 USA; [Errico, Rosangela] Univ Genoa, Genoa, Italy; [Fardo, David W.] Univ Kentucky, Sanders Brown Ctr Aging, Lexington, KY 40536 USA; [Fardo, David W.; Katsumata, Yuriko; Shen, Xia] Univ Kentucky, Dept Biostat, Lexington, KY USA; [Froehlich, Holger; Iyappan, Anandhi; Malhotra, Ashutosh; Naz, Mufassra; Praveen, Paurush] Univ Bonn, Bonn Aachen Int Ctr IT, Bonn, Germany; [St George-Hyslop, Peter] Univ Cambridge, Cambridge Inst Med Res, Cambridge CB2, England; [St George-Hyslop, Peter] Univ Toronto, Cambridge CB2, England; [Ghosh, Satrajit S.] MIT, McGovern Inst Brain Res, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Ghosh, Satrajit S.] Harvard Univ, Sch Med, Dept Otol & Laryngol, Boston, MA 02115 USA; [Glaab, Enrico; Krause, Roland; Namasivayam, Aishwarya] Univ Luxembourg, Luxembourg Ctr Syst Biomed, Esch Sur Alzette, Luxembourg; [Green, Robert C.] Brigham & Womens Hosp, Dept Med, Div Genet, Broad Inst, 75 Francis St, Boston, MA 02115 USA; [Green, Robert C.] Harvard Univ, Sch Med, Boston, MA USA; [Guan, Yuanfang; Zhu, Fan] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA; [Guan, Yuanfang] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA; [Guan, Yuanfang] Univ Michigan, Dept Internal Med, Ann Arbor, MI 48109 USA; [Huang, Chao; Ibrahim, Joseph; Kong, Dehan; Lee, Eunjee; Yang, Hojin; Zhu, Hongtu] Univ N Carolina, Dept Biostat, Chapel Hill, NC USA; [Hwang, Jinseub] Daegu Univ, Dept Comp Sci & Stat, Gyongsan, Gyeongsangbuk D, South Korea; [Inglese, Paolo] Univ London Imperial Coll Sci Technol & Med, Dept Surg & Canc, Fac Med, London, England; [Lovestone, Simon] Univ Oxford, Warneford Hosp, Dept Psychiat, Oxford, England; [Iyappan, Anandhi; Malhotra, Ashutosh; Naz, Mufassra; Senger, Philipp] Schloss Birlinghoven, Dept Bioinformat, Fraunhofer Inst Algorithms & Sci Comp SCAI, St Augustin, Germany; [Maxwell, Taylor J.] George Washington Univ, Computat Biol Inst, Ashburn, VA USA; [Newhouse, Stephen J.] Kings Coll London, Dept Biostat, London WC2R 2LS, England; [Nurtdinov, Ramil N.] Hosp Univ Vall dHebron, Dept Neuroimmunol, Fdn Inst Recerca, Barcelona, Spain; [Pawitan, Yudi; Shen, Xia] Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden; [Stolovitzky, Gustavo] Icahn Sch Med Mt Sinai, Genet & Genom Sci Dept, New York, NY 10029 USA; [Tung, Yi-An] Natl Taiwan Univ, Genome & Syst Biol Degree Program, Taipei 10764, Taiwan; [Tustison, Nicholas J.] Univ Virginia, Dept Radiol & Med Imaging, Charlottesville, VA USA; [Vradenburg, George] Global CEO Initiat Alzheimers Dis, Washington, DC USA; [Weiner, Michael W.] UCSF, SFVAMC, Radiol Med Psychiat & Neurol, San Francisco, CA USA; [Xie, Lei] CUNY Hunter Coll, Dept Comp Sci, New York, NY 10021 USA; [Zhu, Shanfeng] Fudan Univ, Ctr Computat Syst Biol, Shanghai 200433, Peoples R China; [Shen, Xia] Univ Edinburgh, Usher Inst Populat Hlth Sci & Informat, Edinburgh, Midlothian, Scotland; [Shen, Xia] Univ Edinburgh, MRC Inst Genet & Mol Med, Edinburgh, Midlothian, Scotland; [Dobson, Richard J. B.] UCL, Farr Inst Hlth Informat Res, UCL Inst Hlth Informat, London WC1E 6BT, England Rice University; Universita degli Studi di Bari Aldo Moro; Istituto Nazionale di Fisica Nucleare (INFN); Ontario Institute for Cancer Research; University of Toronto; International Business Machines (IBM); University of Texas System; University of Texas Dallas; Rush University; Brigham Young University; University of Toronto; University of Toronto; National Taiwan University; University of Texas System; University of Texas Southwestern Medical Center Dallas; National Taiwan University; Yuan Ze University; Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; University of Pennsylvania; Fudan University; Fudan University; University of London; King's College London; University of London; King's College London; Baylor College of Medicine; University of Genoa; University of Kentucky; University of Kentucky; University of Bonn; University of Cambridge; University of Toronto; Massachusetts Institute of Technology (MIT); Harvard University; Harvard Medical School; University of Luxembourg; Harvard University; Brigham & Women's Hospital; Massachusetts Institute of Technology (MIT); Broad Institute; Harvard University; Harvard Medical School; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; Daegu University; Imperial College London; University of Oxford; Fraunhofer Gesellschaft; Fraunhofer Institute Center Schloss Birlinghoven; George Washington University; University of London; King's College London; Hospital Universitari Vall d'Hebron; Karolinska Institutet; Icahn School of Medicine at Mount Sinai; National Taiwan University; University of Virginia; University of California System; University of California San Francisco; City University of New York (CUNY) System; Hunter College (CUNY); Fudan University; University of Edinburgh; University of Edinburgh; University of London; University College London Kauwe, JSK (corresponding author), Brigham Young Univ, Dept Biol, Provo, UT 84602 USA. Inglese, Paolo/HDN-0003-2022; Narayan, Manjari/H-2796-2019; Inglese, Paolo/D-8931-2014; Shen, Xia/AAO-4233-2020; Tangaro, Sabina/AAN-6617-2020; Davatzikos, Christos/ABE-2057-2021; Katsumata, Yuriko/H-9222-2019; Kauwe, John/AAC-6646-2021; Simmons, Andrew/B-8848-2008; Glaab, Enrico/A-9136-2012; Huang, Chao/L-1445-2019; Huang, Chao/AAK-3228-2020; Hong, Mingyi/H-6274-2013; Klein, Arno/Z-4901-2019; Erus, Guray/L-6712-2018; Nurtdinov, Ramil N/H-4713-2015; Fröhlich, Holger/H-1976-2017; dobson, richard/C-9269-2011; Lauria, Mario/G-6796-2012 Inglese, Paolo/0000-0001-6179-9643; Narayan, Manjari/0000-0001-5348-270X; Inglese, Paolo/0000-0001-6179-9643; Shen, Xia/0000-0003-4390-1979; Tangaro, Sabina/0000-0002-1372-3916; Kauwe, John/0000-0001-8641-2468; Simmons, Andrew/0000-0003-2306-5811; Glaab, Enrico/0000-0003-3977-7469; Huang, Chao/0000-0002-1157-4291; Klein, Arno/0000-0002-0707-2889; Erus, Guray/0000-0001-6633-4861; Nurtdinov, Ramil N/0000-0002-9753-6287; Fröhlich, Holger/0000-0002-5328-1243; Amoroso, Nicola/0000-0003-0211-0783; Katsumata, Yuriko/0000-0002-0188-8094; Davatzikos, Christos/0000-0002-1025-8561; Krause, Roland/0000-0001-9938-7126; Tateo, Andrea/0000-0001-9570-0277; Mangravite, Lara/0000-0001-7841-3612; TUSTISON, NICHOLAS/0000-0001-9418-5103; Newhouse, Stephen/0000-0002-1843-9842; Das, Sudeshna/0000-0002-9486-6811; lee, eunjee/0000-0001-5268-1707; Clark, Timothy/0000-0003-4060-7360; Lalonde, Emilie/0000-0002-8573-1966; dobson, richard/0000-0003-4224-9245; Lovestone, Simon/0000-0003-0473-4565; Guan, Yuanfang/0000-0001-8275-2852; St George-Hyslop, Peter/0000-0003-0796-7209; Boutros, Paul/0000-0003-0553-7520; Yang, Hojin/0000-0001-7645-6774; Bare, J. Christopher/0000-0003-1006-1491; CHEN, CHIEN-YU/0000-0002-6940-6389; Lauria, Mario/0000-0001-5983-7292; Liu, Zhandong/0000-0002-7608-0831; Fardo, David/0000-0002-7207-4696; Bellotti, Roberto/0000-0003-3198-2708; Livingstone, Julie/0000-0002-8424-3768; Anghel, Catalina/0000-0001-7569-5319; Sotiras, Aristeidis/0000-0003-0795-8820 National Institutes of Health [U01 AG024904]; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; NIH grants [P30 AG010129, K01 AG030514, P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152]; Rush Alzheimer's Disease Center; Rush University Medical Center; Illinois Department of Public Health; Translational Genomics Research Institute; EFPIA companies; EU under the FP6 programme; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd; Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; Takeda Pharmaceutical Company; Canadian Institutes of Health Research; SMEs; McGill University; MSSM; UCSF; Goteborg University; EMA; University of Washington; UCLA; BrightFocus Foundation; NIA; Alzheimer's Research UK; European Medicines Agency; Global CEO Initiative on Alzheimer's Disease; Pfizer, Inc; Ray and Dagmar Dolby Family Fund; Rosenberg Alzheimer's Project; Sanofi S.A; Takeda Pharmaceutical Company Ltd; USA gainst Alzheimer's National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Institute on Aging(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); National Institute of Biomedical Imaging and Bioengineering(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Biomedical Imaging & Bioengineering (NIBIB)); NIH grants(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Rush Alzheimer's Disease Center; Rush University Medical Center; Illinois Department of Public Health; Translational Genomics Research Institute; EFPIA companies; EU under the FP6 programme; Alzheimer's Association(Alzheimer's Association); Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.(Biogen); Bristol-Myers Squibb Company(Bristol-Myers Squibb); Eisai Inc.(Eisai Co Ltd); Elan Pharmaceuticals, Inc.; Eli Lilly and Company(Eli Lilly); F. Hoffmann-La Roche Ltd(Hoffmann-La Roche); Genentech, Inc.(Roche HoldingGenentech); GE Healthcare(General ElectricGE Healthcare); Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.(Johnson & JohnsonJohnson & Johnson USA); Medpace, Inc.; Merck Co., Inc.(Merck & Company); Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation(Novartis); Pfizer Inc.(Pfizer); Piramal Imaging; Servier(Servier); Synarc Inc.; Takeda Pharmaceutical Company(Takeda Pharmaceutical Company Ltd); Canadian Institutes of Health Research(Canadian Institutes of Health Research (CIHR)); SMEs; McGill University; MSSM; UCSF; Goteborg University; EMA; University of Washington(University of Washington); UCLA(University of California System); BrightFocus Foundation(BrightFocus Foundation); NIA(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); Alzheimer's Research UK(Alzheimer's Research UK (ARUK)); European Medicines Agency; Global CEO Initiative on Alzheimer's Disease; Pfizer, Inc(Pfizer); Ray and Dagmar Dolby Family Fund; Rosenberg Alzheimer's Project; Sanofi S.A; Takeda Pharmaceutical Company Ltd(Takeda Pharmaceutical Company Ltd); USA gainst Alzheimer's This study was supported by the following individuals and organizations: Alan Evans (McGill University), Gaurav Pandey (MSSM), Gil Rabinovici (UCSF), Kaj Blennow (Goteborg University), Kristine Yaffe (UCSF), Maria Isaac (EMA), Nolan Nichols (University of Washington), Paul Thompson (UCLA), Reisa Sperling (Harvard), Scott Small (Columbia), Guy Eakin (BrightFocus Foundation), Maria Carillo (Alzheimer's Association), Neil Buckholz (NIA), Alzheimer's Research UK, European Medicines Agency, Global CEO Initiative on Alzheimer's Disease, Pfizer, Inc, Ray and Dagmar Dolby Family Fund, Rosenberg Alzheimer's Project, Sanofi S.A, and Takeda Pharmaceutical Company Ltd, USA gainst Alzheimer's.; Study data were provided by the following groups: (1) The Alzheimer's Disease Neuroimaging Initiative (ADNI)-ADNI is funded by the National Institutes of Health (U01 AG024904), the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research was also supported by NIH grants P30 AG010129 and K01 AG030514. (2) The Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago-Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, and U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute. (3) Euro-pean AddNeuroMed study-The AddNeuroMed data are from a public-private partnership supported by EFPIA companies, SMEs and the EU under the FP6 programme. Clinical leads responsible for data collection are Iwona Kloszewska (Lodz), Simon Lovestone (London), Patrizia Mecocci (Perugia), Hilkka Soininen (Kuopio), Magda Tsolaki (Thessaloniki), and Bruno Vellas (Toulouse). 33 53 54 2 21 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 1552-5260 1552-5279 ALZHEIMERS DEMENT Alzheimers. Dement. JUN 2016.0 12 6 645 653 10.1016/j.jalz.2016.02.006 0.0 9 Clinical Neurology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology DO3TW 27079753.0 Green Submitted, Green Published, hybrid, Green Accepted 2023-03-23 WOS:000377705600002 0
J Yin, CT; Xiong, Z; Chen, H; Wang, JY; Cooper, D; David, B Yin ChuanTao; Xiong Zhang; Chen Hui; Wang JingYuan; Cooper Daven; David Bertrand A literature survey on smart cities SCIENCE CHINA-INFORMATION SCIENCES English Review smart city; architecture; data-centric; data vitalization; urban data BIG DATA; SPATIAL-RESOLUTION; REFERENCE MODEL; DIGITAL CITIES; HUMAN MOBILITY; CITY; TEXTURE; METRICS; CLASSIFICATION; TECHNOLOGIES Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation. [Yin ChuanTao] Beihang Univ, Sino French Engn Sch, Beijing 100191, Peoples R China; [Xiong Zhang; Chen Hui; Wang JingYuan; Cooper Daven] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China; [Yin ChuanTao] Beihang Univ, Res Inst, Shenzhen 518057, Peoples R China; [David Bertrand] Ecole Cent Lyon, LIRIS Lab, F-69130 Ecully, France Beihang University; Beihang University; Beihang University; Ecole Centrale de Lyon; Institut National des Sciences Appliquees de Lyon - INSA Lyon Xiong, Z (corresponding author), Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China. xiongz@buaa.edu.cn National Natural Science Foundation of China [61402028, 61204426]; China's National Key Technology RD Program [2014BAF07B03] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China's National Key Technology RD Program The work was supported by National Natural Science Foundation of China (Grants Nos. 61402028, 61204426) and China's National Key Technology R&D Program (Grant No. 2014BAF07B03). 117 137 137 14 248 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-733X 1869-1919 SCI CHINA INFORM SCI Sci. China-Inf. Sci. OCT 2015.0 58 10 100102 10.1007/s11432-015-5397-4 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering CT3HX Green Submitted 2023-03-23 WOS:000362698900002 0
C Shan, CH; Mamoulis, N; Cheng, R; Li, GL; Li, X; Qian, YQ IEEE Shan, Caihua; Mamoulis, Nikos; Cheng, Reynold; Li, Guoliang; Li, Xiang; Qian, Yuqiu An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms 2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) IEEE International Conference on Data Engineering English Proceedings Paper IEEE 36th International Conference on Data Engineering (ICDE) APR 20-24, 2020 Dallas, TX IEEE,IEEE Comp Soc crowdsourcing platform; task arrangement; reinforcement learning; deep Q-Network In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they do not consider the real dynamic environments (e.g., dynamic tasks, dynamic workers), so they may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and the future rewards and can be updated quickly to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework. [Shan, Caihua; Cheng, Reynold; Li, Xiang] Univ Hong Kong, Hong Kong, Peoples R China; [Mamoulis, Nikos] Univ Ioannina, Ioannina, Greece; [Li, Guoliang] Tsinghua Univ, Beijing, Peoples R China; [Qian, Yuqiu] Tencent Inc, Shenzhen, Peoples R China University of Hong Kong; University of Ioannina; Tsinghua University; Tencent Shan, CH (corresponding author), Univ Hong Kong, Hong Kong, Peoples R China. chshan@cs.hku.hk; nikos@cs.uoi.gr; ckcheng@cs.hku.hk; liguoliang@tsinghua.edu.cn; xli2@cs.hku.hk; yuqiuqian@tencent.com Li, Xiang/0000-0003-0945-145X Research Grants Council of HK (RGC Projects) [HKU 17229116, 106150091, 17205115]; HKU [104004572, 102009508, 104004129]; Innovation and Technology Commission of HK (ITF project) [MRP/029/18]; European Regional Development Fund, Research-Create-Innovate project Proximiot [T1EDK-04810]; NSF of China [61925205, 61632016]; Huawei; TAL education Research Grants Council of HK (RGC Projects); HKU(University of Hong Kong); Innovation and Technology Commission of HK (ITF project); European Regional Development Fund, Research-Create-Innovate project Proximiot; NSF of China(National Natural Science Foundation of China (NSFC)); Huawei(Huawei Technologies); TAL education We would like to thank the reviewers for the insightful comments. Caihua Shan and Reynold Cheng were supported by the Research Grants Council of HK (RGC Projects HKU 17229116, 106150091, and 17205115), HKU (Projects 104004572, 102009508, and 104004129), and the Innovation and Technology Commission of HK (ITF project MRP/029/18). Nikos Mamoulis has been cofinanced by the European Regional Development Fund, Research-Create-Innovate project Proximiot (T1EDK-04810). Guoliang Li was supported by NSF of China (61925205, 61632016), Huawei, and TAL education. Xiang Li is the corresponding author. 36 8 10 0 5 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1084-4627 978-1-7281-2903-7 PROC INT CONF DATA 2020.0 49 60 10.1109/1CDE48307.2020.00012 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ3CR 2023-03-23 WOS:000584252700005 0
J Karrer, TM; Bassett, DS; Derntl, B; Gruber, O; Aleman, A; Jardri, R; Laird, AR; Fox, PT; Eickhoff, SB; Grisel, O; Varoquaux, G; Thirion, B; Bzdok, D Karrer, Teresa M.; Bassett, Danielle S.; Derntl, Birgit; Gruber, Oliver; Aleman, Andre; Jardri, Renaud; Laird, Angela R.; Fox, Peter T.; Eickhoff, Simon B.; Grisel, Olivier; Varoquaux, Gael; Thirion, Bertrand; Bzdok, Danilo Brain-based ranking of cognitive domains to predict schizophrenia HUMAN BRAIN MAPPING English Article BrainMap database; coordinate-based meta-analysis; ontology of the mind; pattern recognition; predictive analytics; statistical learning AUDITORY VERBAL HALLUCINATIONS; LIKELIHOOD ESTIMATION; SOCIAL COGNITION; WORKING-MEMORY; GUSTATORY HALLUCINATIONS; COMPUTATIONAL PSYCHIATRY; FUNCTIONAL ARCHITECTURE; MISMATCH NEGATIVITY; METAANALYSIS; PERCEPTION Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data-driven machine-learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research. [Karrer, Teresa M.; Bzdok, Danilo] Rhein Westfal TH Aachen, Dept Psychiat Psychotherapy & Psychosomat, Fac Med, Aachen, Germany; [Bassett, Danielle S.] Univ Penn, Dept Bioengn, Philadelphia, PA 19104 USA; [Bassett, Danielle S.] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA; [Bassett, Danielle S.] Hosp Univ Penn, Dept Neurol, 3400 Spruce St, Philadelphia, PA 19104 USA; [Bassett, Danielle S.] Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA; [Derntl, Birgit; Bzdok, Danilo] JARA, Translat Brain Med, Aachen, Germany; [Derntl, Birgit] Univ Tubingen, Dept Psychiat & Psychotherapy, Tubingen, Germany; [Gruber, Oliver] Heidelberg Univ, Dept Psychiat, Heidelberg, Germany; [Aleman, Andre] Univ Groningen, Univ Med Ctr Groningen, BCN Neuroimaging Ctr, Groningen, Netherlands; [Jardri, Renaud] Univ Lille, CNRS, UMR 9193, Div Psychiat,SCALab, Lille, France; [Jardri, Renaud] Fontan Hosp, CHU Lille, Lille, France; [Laird, Angela R.] Florida Int Univ, Dept Phys, Miami, FL 33199 USA; [Fox, Peter T.] Univ Texas Hlth Sci Ctr San Antonio, Res Imaging Inst, San Antonio, TX 78229 USA; [Fox, Peter T.] South Texas Vet Hlth Care Syst, San Antonio, TX USA; [Fox, Peter T.] Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China; [Eickhoff, Simon B.] Heinrich Heine Univ, Inst Syst Neurosci, Dusseldorf, Germany; [Eickhoff, Simon B.] Res Ctr Julich, Inst Neurosci & Med INM 7, Julich, Germany; [Grisel, Olivier; Varoquaux, Gael; Thirion, Bertrand; Bzdok, Danilo] INRIA Saclay, NeuroSpin, Parietal Team, Palaiseau, France RWTH Aachen University; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania; Pennsylvania Medicine; University of Pennsylvania; Eberhard Karls University of Tubingen; Ruprecht Karls University Heidelberg; University of Groningen; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Humanities & Social Sciences (INSHS); Universite de Lille - ISITE; Universite de Lille; Universite de Lille - ISITE; CHU Lille; State University System of Florida; Florida International University; University of Texas System; University of Texas Health San Antonio; US Department of Veterans Affairs; Veterans Health Administration (VHA); Audie L. Murphy Memorial Veterans Hospital; University of Hong Kong; Heinrich Heine University Dusseldorf; Helmholtz Association; Research Center Julich Bzdok, D (corresponding author), Univ Klinikum Aachen, Fac Med, Dept Psychiat Psychotherapy & Psychosomat, Pauwelsstr 30, D-52074 Aachen, Germany. danilo.bzdok@rwth-aachen.de Gruber, Oliver/AAY-5563-2020; Jardri, Renaud/J-9202-2012; Laird, Angela/B-5800-2010 Jardri, Renaud/0000-0003-4596-1502; Laird, Angela/0000-0003-3379-8744 Deutsche Forschungsgemeinschaft [BZ2/2-1, BZ2/3-1, BZ2/4-1]; Paul Allen Foundation; John D. and Catherine T. MacArthur Foundation; Alfred P. Sloan Foundation; ISI Foundation; Exploratory Research Space [OPSF449]; START-Program of the Faculty of Medicine [126/16]; International Research Training Group [IRTG2150]; Amazon AWS Research Grant Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); Paul Allen Foundation; John D. and Catherine T. MacArthur Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); ISI Foundation; Exploratory Research Space; START-Program of the Faculty of Medicine(Austrian Science Fund (FWF)); International Research Training Group; Amazon AWS Research Grant Deutsche Forschungsgemeinschaft, Grant/Award Numbers: BZ2/2-1, BZ2/3-1, BZ2/4-1; Paul Allen Foundation; John D. and Catherine T. MacArthur Foundation; Alfred P. Sloan Foundation; ISI Foundation; Exploratory Research Space, Grant/Award Number: OPSF449; START-Program of the Faculty of Medicine, Grant/Award Number: 126/16; Amazon AWS Research Grant; International Research Training Group, Grant/Award Number: IRTG2150 144 18 18 1 11 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. OCT 15 2019.0 40 15 4487 4507 10.1002/hbm.24716 0.0 JUL 2019 21 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging RB6YY 31313451.0 Bronze, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000476081600001 0
C Eduardo, D; Fuentes, D; Toro, FG; Becker, U; Manz, H; Lu, DB; Cai, BG IEEE Eduardo, Damian; Fuentes, Diaz; Toro, Federico Grasso; Becker, Uwe; Manz, Hansjoerg; Lu, Debiao; Cai, Baigen Simulink based prototype for real-time Intelligent GNSS-based localisation system 2015 INTERNATIONAL ASSOCIATION OF INSTITUTES OF NAVIGATION WORLD CONGRESS (IAIN) English Proceedings Paper 15th World Congress of the International-Association-of-Institutes-of-Navigation (lAIN) OCT 20-23, 2015 Prague, CZECH REPUBLIC Int Assoc Inst Navigat,IEEE,Cesky Inst Navigace Real-time; Simulink; GNSS; Particle Filter; Artificial Neural Network In the frame of the development of artificial intelligent (AI) based validation tool for Global Navigation Satellite Systems (GNSS) localisation systems MATLAB Simulink models for all the developed AI-based methodologies have been created. The combination of Artificial Neural Network (ANN) based validation tools with the Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation for quantitative and qualitative analysis of trueness and precision, as well as Particle Filter (PF) techniques for position estimation to aid the reference system result in significant improvements for GNSS-aided localisation system, allowing the construction of a GNSS-dependent reference system for when no independent reference is available. These additional elements are the bases for future intelligent GNSS-based localisation systems with both quantitative and qualitative validation tools. The proposed prototype constructed within System functions (S-Functions) for extension of MATLAB Simulink capabilities presents total interconnectivity in real-time, implemented using commodity microcomputers and providing an interface between the MATLAB Simulink implemented algorithms and the real world. In the presented prototype a Raspberry Pi Model B microcomputer running a modified Debian Linux distribution provides General Purpose and UART ports that enable the implementation of the low-level communication driver for the GNSS receiver. The prototype enables real-time application of the algorithms. The prototype includes a GNSS receiver, with a sampling position rate and constellation data at 1Hz. NMEA format provides the necessary data to be processed with the MATLAB Simulink model of the ANN-based validation tools and the PF-based estimator. All algorithms are executed in a Windows PC and the communication between the microcomputer and the MATLAB Simulink model is achieved via TCP Sockets, with interface S-Functions to handle the communication. These methodologies will enable future safety-relevant applications, such as on-board uncertainty evaluation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective. [Eduardo, Damian; Toro, Federico Grasso] Inst Qual Safety & Transportat, Braunschweig, Germany; [Becker, Uwe; Manz, Hansjoerg] Tech Univ Carolo Wilhelmina Braunschweig, Inst Traff Safety & Automat Engn, Braunschweig, Germany; [Lu, Debiao] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China; [Cai, Baigen] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran, Beijing Shi, Peoples R China Braunschweig University of Technology; Beijing Jiaotong University; Beijing Jiaotong University Eduardo, D (corresponding author), Inst Qual Safety & Transportat, Braunschweig, Germany. damian.diaz@iqst.de; u.becker@tu-bs.de 9 0 0 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-7634-1 2015.0 7 Engineering, Electrical & Electronic; Remote Sensing; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Remote Sensing; Telecommunications BG0DJ 2023-03-23 WOS:000386184800008 0
J Zhang, HY; Jin, YC; Hao, KR Zhang, Haoyu; Jin, Yaochu; Hao, Kuangrong Evolutionary Search for Complete Neural Network Architectures With Partial Weight Sharing IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION English Article Computer architecture; Training; Task analysis; Computational modeling; Neural networks; Network architecture; Switches; Convolutional neural networks (CNNs); evolutionary optimization; image classification; one-shot neural architecture search (OSNAS); pyramidal convolution (PyConv) operations EFFICIENT Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious since training and evaluating complete neural architectures over a large search space are computationally prohibitive. Recently, one-shot NAS (OSNAS) has attracted great attention in the NAS community because it significantly speeds up the candidate architecture evaluation procedure through weight sharing. However, the full weight sharing training paradigm in OSNAS may result in strong interference across candidate architectures and mislead the architecture search. To alleviate the problem, we propose a partial weight sharing OSNAS framework that directly evolves complete neural network architectures. In particular, we suggest a novel node representation scheme that randomly activates a subset of nodes of the one-shot model in each generation to reduce the weight coupling in the one-shot model. During the evolutionary search, a tailored crossover operator randomly samples the nodes from two parent individuals or a single parent to construct new candidate architectures, thus effectively constraining the degree of weight sharing. Furthermore, we introduce a new mutation operator that replaces the chosen nodes of the one-shot model with randomly generated nodes to enhance the exploratory capability. Finally, we encode a set of pyramidal convolution operations in the search space, enabling the evolved neural networks to capture different levels of details in the images. The proposed method is examined and compared with 26 state-of-the-art algorithms on ten image classification tasks, including CIFAR series, CINIC10, ImageNet, and MedMNIST series. The experimental results demonstrate that the proposed method can computationally much more efficiently find neural architectures that achieve comparable classification accuracy to the state-of-the-art designs. [Zhang, Haoyu; Jin, Yaochu; Hao, Kuangrong] Donghua Univ, Coll Informat Sci & Technol, Engn Res Ctr Digitized Text & Apparel Technol, Minist Educ, Shanghai 201620, Peoples R China; [Jin, Yaochu] Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany Donghua University; University of Bielefeld Jin, YC (corresponding author), Donghua Univ, Coll Informat Sci & Technol, Engn Res Ctr Digitized Text & Apparel Technol, Minist Educ, Shanghai 201620, Peoples R China. zhy920816@sina.cn; yaochu.jin@uni-bielefeld.de; krhao@dhu.edu.cn 郝, 矿荣/HHN-4859-2022; Jin, Yaochu/B-3776-2012 Jin, Yaochu/0000-0003-1100-0631 Fundamental Research Funds for the Central Universities of Donghua University [CUSF-DH-D-2021047]; Graduate Student Innovation Fund of Donghua University [CUSF-DH-D-2021047]; Alexander von Humboldt Professorship for Artificial Intelligence by the German Federal Ministry of Education and Research Fundamental Research Funds for the Central Universities of Donghua University; Graduate Student Innovation Fund of Donghua University; Alexander von Humboldt Professorship for Artificial Intelligence by the German Federal Ministry of Education and Research This work was supported in part by the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University under Grant CUSF-DH-D-2021047. The work of Yaochu Jin was supported by the Alexander von Humboldt Professorship for Artificial Intelligence endowed by the German Federal Ministry of Education and Research. 73 1 1 10 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1089-778X 1941-0026 IEEE T EVOLUT COMPUT IEEE Trans. Evol. Comput. OCT 2022.0 26 5 1072 1086 10.1109/TEVC.2022.3140855 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4Z7LL 2023-03-23 WOS:000862385200023 0
J Yang, T; Zhou, XD; Yu, ZB; Krysanova, V; Wang, B Yang, Tao; Zhou, Xudong; Yu, Zhongbo; Krysanova, Valentina; Wang, Bo Drought projection based on a hybrid drought index using Artificial Neural Networks HYDROLOGICAL PROCESSES English Article hybrid drought index; drought projection; climate change; Artificial Neural Network; endorheic arid basin PEARL RIVER-BASIN; CLIMATE-CHANGE; TIME-SERIES; RUNOFF; IMPACTS; XINJIANG; MIDDLE; MODEL; FLOW The Tarim River Basin is a special endorheic arid drainage basin in Central Asia, characterized by limited rainfall and high evaporation as common in deserts, while water is supplied mainly by glacier and snow melt from the surrounding mountains. The existing drought indices can hardly capture the drought features in this region as droughts are caused by two dominant factors (meteorological and hydrological conditions). To overcome the problem, a new hybrid drought index (HDI), integrating the meteorological and hydrological drought regimes, was developed and tested in the basin in the work. The index succeeded in revealing the drought characteristics and the ensemble influence better than the single standardized precipitation index or the hydrological index. The Artificial Neural Network approach based on temperature and precipitation observations was set up to simulate the HDI change. The method enabled constructing scenarios of future droughts in the region using climate simulation of the GCMs under four RCP scenarios from the latest CMIP5 project. The simulations in the study have shown that the water budget patterns in the Tarim River Basin are more sensitive to temperature than to precipitation. Dominated by temperature rise causing an accelerating snow/glacier melt, the frequency of drought months is projected to decrease by about 14% in the next decades (until 2035). The drought duration is expected to be shortened to 3months on average, with the severity alleviated. However, the region would still suffer more severe droughts with a high intensity in some years. The general decrease in drought frequency and intensity over the region in the future would be beneficial for water resources management and agriculture development in the oases. Copyright (c) 2014 John Wiley & Sons, Ltd. [Yang, Tao; Zhou, Xudong; Yu, Zhongbo; Wang, Bo] Hohai Univ, Ctr Global Change & Water Cycle, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China; [Yang, Tao] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; [Krysanova, Valentina] Potsdam Inst Climate Impact Res, Res Domain Climate Impacts & Vulnerabil 2, D-14412 Potsdam, Germany Hohai University; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Potsdam Institut fur Klimafolgenforschung Yang, T (corresponding author), Hohai Univ, Ctr Global Change & Water Cycle, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China. yang.tao@ms.xjb.ac.cn Krysanova, Valentina/AAR-2324-2020; xudong, zhou/AAK-7004-2021 xudong, zhou/0000-0001-7180-8187 National Natural Science Foundation of China [41371051]; Ministry of Science and Technology of China [2013BAC10B01]; Chinese Academy of Sciences [KZZD-EW-12-1] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of China(Ministry of Science and Technology, China); Chinese Academy of Sciences(Chinese Academy of Sciences) The work was supported by a key grant of the National Natural Science Foundation of China (41371051) and the grants from Ministry of Science and Technology of China (2013BAC10B01) and Chinese Academy of Sciences (KZZD-EW-12-1). 63 16 16 2 76 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0885-6087 1099-1085 HYDROL PROCESS Hydrol. Process. MAY 30 2015.0 29 11 2635 2648 10.1002/hyp.10394 0.0 14 Water Resources Science Citation Index Expanded (SCI-EXPANDED) Water Resources CI5OH 2023-03-23 WOS:000354806200013 0
C Bishnoi, R; Wu, LZ; Fieback, M; Munch, C; Nair, SM; Tahoori, M; Wang, Y; Li, HW; Hamdioui, S IEEE Comp Soc Bishnoi, Rajendra; Wu, Lizhou; Fieback, Moritz; Muench, Christopher; Nair, Sarath Mohanachandran; Tahoori, Mehdi; Wang, Ying; Li, Huawei; Hamdioui, Said Special Session - Emerging Memristor Based Memory and CIM Architecture: Test, Repair and Yield Analysis 2020 IEEE 38TH VLSI TEST SYMPOSIUM (VTS 2020) IEEE VLSI Test Symposium English Proceedings Paper IEEE 38th VLSI Test Symposium (VTS) APR 06-08, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,IEEE Philadelphia Sect,IEEE Comp Soc Test Technol Tech Council STT-MRAM; DEFECTS; FAILURE; MODEL Emerging memristor-based architectures are promising for data-intensive applications as these can enhance the computation efficiency, solve the data transfer bottleneck and at the same time deliver high energy efficiency using their normally-off/instant-on attributes. However, their storing devices are more susceptible to manufacturing defects compared to the traditional memory technologies because they are fabricated with new materials and require different manufacturing processes. Hence, in order to ensure correct functionalities for these technologies, it is necessary to have accurate fault modeling as well as proper test methodologies with high test coverage. In this paper, we propose technology specific cell-level defect modeling, accurate fault analysis and yield improvement solutions for memristor-based memory as well as Computation-In-Memory (CIM) architectures. Our overall contributions cover three abstraction levels, namely, device, architecture and system. First, we propose a device-aware test methodology in which we have introduced a key device-level characteristic to develop accurate defect model. Second, we demonstrate a yield analysis framework for memristor arrays considering reliability and permanent faults due to parametric variations and explore fault-tolerant solutions. Third, a lightweight on-line test and repair schemes is proposed for emerging CIM devices in machine learning applications. [Bishnoi, Rajendra; Wu, Lizhou; Fieback, Moritz; Hamdioui, Said] Delft Univ Technol, Comp Engn Lab, Delft, Netherlands; [Muench, Christopher; Nair, Sarath Mohanachandran; Tahoori, Mehdi] Karlsruhe Inst Technol, Karlsruhe, Germany; [Wang, Ying; Li, Huawei] Chinese Acad Sci, Inst Comp Technol, SKLCA, Beijing, Peoples R China Delft University of Technology; Helmholtz Association; Karlsruhe Institute of Technology; Chinese Academy of Sciences; Institute of Computing Technology, CAS Bishnoi, R (corresponding author), Delft Univ Technol, Comp Engn Lab, Delft, Netherlands. 58 0 0 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1093-0167 978-1-7281-5359-9 IEEE VLSI TEST SYMP 2020.0 10 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BQ4LT 2023-03-23 WOS:000590395200018 0
J Zhang, YZ; Wang, JL; Liu, YC; Rong, L; Zheng, Q; Song, DW; Tiwari, P; Qin, J Zhang, Yazhou; Wang, Jinglin; Liu, Yaochen; Rong, Lu; Zheng, Qian; Song, Dawei; Tiwari, Prayag; Qin, Jing A Multitask learning model for multimodal sarcasm, sentiment and emotion recognition in conversations INFORMATION FUSION English Article Multimodal sarcasm recognition; Sentiment analysis; Emotion recognition; Multitask learning; Affective computing INTERACTION DYNAMICS Sarcasm, sentiment and emotion are tightly coupled with each other in that one helps the understanding of another, which makes the joint recognition of sarcasm, sentiment and emotion in conversation a focus in the research in artificial intelligence (AI) and affective computing. Three main challenges exist: Context dependency, multimodal fusion and multitask interaction. However, most of the existing works fail to explicitly leverage and model the relationships among related tasks. In this paper, we aim to generically address the three problems with a multimodal joint framework. We thus propose a multimodal multitask learning model based on the encoder-decoder architecture, termed M2Seq2Seq. At the heart of the encoder module are two attention mechanisms, i.e., intramodal (Ia) attention and intermodal (Ie) attention. Ia attention is designed to capture the contextual dependency between adjacent utterances, while Ie attention is designed to model multimodal interactions. In contrast, we design two kinds of multitask learning (MTL) decoders, i.e., single -level and multilevel decoders, to explore their potential. More specifically, the core of a single-level decoder is a masked outer-modal (Or) self-attention mechanism. The main motivation of Or attention is to explicitly model the interdependence among the tasks of sarcasm, sentiment and emotion recognition. The core of the multilevel decoder contains the shared gating and task-specific gating networks. Comprehensive experiments on four bench datasets, MUStARD, Memotion, CMU-MOSEI and MELD, prove the effectiveness of M2Seq2Seq over state-of-the-art baselines (e.g., CM-GCN, A-MTL) with significant improvements of 1.9%, 2.0%, 5.0%, 0.8%, 4.3%, 3.1%, 2.8%, 1.0%, 1.7% and 2.8% in terms of Micro F1. [Zhang, Yazhou; Rong, Lu; Zheng, Qian] Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450002, Peoples R China; [Wang, Jinglin; Liu, Yaochen; Song, Dawei] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China; [Tiwari, Prayag] Halmstad Univ, Sch Informat Technol, Halmstad, Sweden; [Qin, Jing] Hong Kong Polytech Univ, Ctr Smart Hlth, Sch Nursing, Hongkong, Peoples R China Zhengzhou University of Light Industry; Beijing Institute of Technology; Halmstad University; Hong Kong Polytechnic University Zheng, Q (corresponding author), Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450002, Peoples R China. yzzhang@zzuli.edu.cn; zhengqian@zzuli.edu.cn; dwsong@bit.edu.cn; prayag.tiwari@ieee.org; harry.qin@polyu.edu.hk Tiwari, Prayag/N-6261-2017 Tiwari, Prayag/0000-0002-2851-4260 Project of Strategic Importance grant of The Hong Kong Polytechnic University, PR China [1-ZE2Q]; National Science Foundation of China [62006212]; State Key Lab; Novel Software Technology in Nanjing University, PR China [KFKT2021B41]; Doctoral Scientific Research Foundation of Henan Province, PR China [0215-13501050028, 2020BSJJ031]; Foundation and Cutting-Edge Technologies Research Program of Henan Province, PR China [222102210031, 212102210418, 212102310088]; Foundation of Key Laboratory of Dependable Service Computing in Cyber-Physical-Society (Ministry of Education) , Chongqing University, PR China [CPSDSC202103] Project of Strategic Importance grant of The Hong Kong Polytechnic University, PR China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Lab; Novel Software Technology in Nanjing University, PR China; Doctoral Scientific Research Foundation of Henan Province, PR China; Foundation and Cutting-Edge Technologies Research Program of Henan Province, PR China; Foundation of Key Laboratory of Dependable Service Computing in Cyber-Physical-Society (Ministry of Education) , Chongqing University, PR China This work is partly supported by a Project of Strategic Importance grant of The Hong Kong Polytechnic University, PR China (project no. 1-ZE2Q) . This work is supported by National Science Foundation of China under grant No. 62006212, the fund of State Key Lab. for Novel Software Technology in Nanjing University, PR China under grant No. KFKT2021B41, the Doctoral Scientific Research Foundation of Henan Province, PR China (grant No. 0215-13501050028, 2020BSJJ031) , the Foundation and Cutting-Edge Technologies Research Program of Henan Province, PR China (grant No. 222102210031, 212102210418, 212102310088) , Foundation of Key Laboratory of Dependable Service Computing in Cyber-Physical-Society (Ministry of Education) , Chongqing University, PR China (PJ.No:CPSDSC202103) . 72 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion MAY 2023.0 93 282 301 10.1016/j.inffus.2023.01.005 0.0 JAN 2023 20 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8L6QV 2023-03-23 WOS:000923909900001 0
J Pop, F; Zhu, XM; Yang, LT Pop, Florin; Zhu, Xiaomin; Yang, Laurence T. MidHDC: Advanced topics on middleware services for heterogeneous distributed computing. Part 2 FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Middleware services; Resource management; Mobile computing; Cloud computing; HPC; Heterogeneous distributed systems Currently distributes systems support different computing paradigms like Cluster Computing, Grid Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These computing distributed systems are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use. The second part of this special issue presents advances in allocation algorithms, service selection, VM consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows, optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big Data processing systems, distributed transaction processing middleware, data storage, trust evaluation, information diffusion, mobile systems, integration of robots in Cloud systems. (C) 2017 Published by Elsevier B.V. [Pop, Florin] Univ Politehn Bucuresti, Comp Sci Dept, Bucharest, Romania; [Pop, Florin] Natl Inst Res & Dev Informat, Bucharest, Romania; [Zhu, Xiaomin] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China; [Yang, Laurence T.] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS, Canada Polytechnic University of Bucharest; National Institute R&D Informatics Bucharest; National University of Defense Technology - China; Saint Francis Xavier University - Canada Pop, F (corresponding author), Univ Politehn Bucuresti, Comp Sci Dept, Bucharest, Romania.;Pop, F (corresponding author), Natl Inst Res & Dev Informat, Bucharest, Romania. florin.pop@cs.pub.ro; xmzhu@nudt.edu.cn; ltyang@ieee.org Laurence T. Yang, FCAE/AAA-1898-2019; Pop, Florin/B-5687-2011 Laurence T. Yang, FCAE/0000-0002-7986-4244; Pop, Florin/0000-0002-4566-1545 25 3 4 0 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. SEP 2017.0 74 86 89 10.1016/j.future.2017.05.010 0.0 4 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science EY0CD 2023-03-23 WOS:000403624900009 0
J Gao, DJ; Jiang, Y; Zhao, N Gao, Diju; Jiang, Yao; Zhao, Nan A novel load prediction method for hybrid electric ship based on working condition classification TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL English Article Hybrid power ship; condition classification; prediction; load demand ENERGY MANAGEMENT; MODEL; SVM; VEHICLE In order to effectively optimize the load distribution between power sources during the navigation of hybrid ships, a method for predicting ship load demand based on real-time classification according to different working conditions is proposed. The k-means clustering algorithm is used to quantify the voyage history data to classify the ship's navigation conditions into fast-changing conditions and slow-changing conditions. Some characteristic parameters related to working conditions are selected as input. Then, input and the category of working conditions are put into least squares support vector machine to learn and train to get an online working condition classifier. The genetic algorithm is used to optimize the radial-based neural network to predict the load demand under fast-changing conditions, use the Markov chain model to predict the load demand under slow-changing conditions, so as to obtain the most accurate future load demand of the ship. The simulation results show that the proposed prediction models under different conditions have higher precision, which is an effective means of predicting the load demand for hybrid power ships. [Gao, Diju; Jiang, Yao] Shanghai Maritime Univ, Minist Transport, Key Lab Marine Technol & Control Engn, 1550 Haigang Ave, Shanghai 201306, Peoples R China; [Zhao, Nan] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin, Ireland Shanghai Maritime University; University College Dublin Jiang, Y (corresponding author), Shanghai Maritime Univ, Minist Transport, Key Lab Marine Technol & Control Engn, 1550 Haigang Ave, Shanghai 201306, Peoples R China. wil1jiang@163.com Jiang, Yao/0000-0002-3694-8528; Zhao, Nan/0000-0002-2502-2324 National Natural Science Foundation of China [61673260, 61304186]; NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1709215] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the National Natural Science Foundation of China under Grant 61673260 and Grant 61304186, and in part by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant U1709215. 23 4 4 10 34 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0142-3312 1477-0369 T I MEAS CONTROL Trans. Inst. Meas. Control JAN 2022.0 44 1 SI 5 14 142331220923767 10.1177/0142331220923767 0.0 JUN 2020 10 Automation & Control Systems; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Instruments & Instrumentation XR1VK 2023-03-23 WOS:000539089000001 0
J Xiao, ZW; Xu, X; Zhang, HX; Szczerbicki, E Xiao, Zhiwen; Xu, Xin; Zhang, Haoxi; Szczerbicki, Edward A new multi-process collaborative architecture for time series classification KNOWLEDGE-BASED SYSTEMS English Article Time series classification; Learning systems; Capsule networks; Data mining; Multi-head convolutional neural networks; Signal processing Time series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery of the temporal relationship within time series data, our approach derives better feature extraction with different scaled capsule routings and enhances representation learning. Unlike the original CapsNet, our proposed approach does not need to reconstruct to increase the accuracy of the model. We examine our proposed method through a set of experiments running on the domain-agnostic TSC benchmark datasets from the UCR Time Series Archive. The results show that, compared to a number of recently developed and currently used algorithms, we achieve 36 best accuracies out of 128 datasets. The accuracy analysis of the proposed approach demonstrates its significance in TSC by offering very high classification confidence with the potential of making inroads into plentiful future applications. (c) 2021 Elsevier B.V. All rights reserved. [Xiao, Zhiwen; Zhang, Haoxi] Chengdu Univ Informat Technol, Chengdu, Peoples R China; [Xu, Xin] China Univ Min & Technol, Xuzhou, Jiangsu, Peoples R China; [Szczerbicki, Edward] Gdansk Univ Technol, Gdansk, Poland; [Xiao, Zhiwen] Northwest A&F Univ, Yangling, Shaanxi, Peoples R China Chengdu University of Information Technology; China University of Mining & Technology; Fahrenheit Universities; Gdansk University of Technology; Northwest A&F University - China Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Chengdu, Peoples R China. haoxi@cuit.edu.cn Xiao, Zhiwen/AAR-3321-2021 Xiao, Zhiwen/0000-0001-9651-111X; Zhang, Haoxi/0000-0002-1341-1912 33 9 9 3 13 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAY 23 2021.0 220 10.1016/j.knosys.2021.106934 0.0 MAR 2021 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RJ5ZU 2023-03-23 WOS:000637681300016 0
J Owolabi, C; Ruan, HB; Yamazaki, Y; Li, JF; Zhong, JH; Eyelade, AV; Priyadarshi, S; Yoshikawa, A Owolabi, Charles; Ruan, Haibing; Yamazaki, Yosuke; Li, Jinfeng; Zhong, Jiahao; Eyelade, A. V.; Priyadarshi, Shishir; Yoshikawa, Akimasa Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS English Article spherical harmonic analysis; empirical orthogonal function analysis; artificial neural network; Sq current EQUATORIAL ELECTROJET; CURRENT SYSTEM; FIELD; QUIET; SQ; DYNAMO; VARIABILITY; PREDICTION; COMPONENTS Given the potential importance of solar quiet (Sq) ionospheric current in geomagnetic field modeling, it is vital to obtain accurate parameters characterizing its variations, particularly the spatial and temporal variations. In this paper, we derived the Sq current function based on the spherical harmonic analysis (SHA) technique using a 14-year (2006-2019) quiet geomagnetic field record over the American sector. The empirical orthogonal function (EOF) analysis was then applied to deduce temporal and spatial variations of the Sq current. It is observed that the first EOF mode of the Sq current function is dominated by solar activity, while the second and third EOF modes exhibit annual and semiannual variations, respectively. Also, the artificial neural network (ANN) model of Sq current function was constructed to validate the EOF model predictions. While the Sq current intensity predicted by the ANN model is underestimated by 2.83%, the EOF model underpredicted the Sq current intensity by 1.92% relative to the observation. The root mean square error (RMSE) of the EOF model is 0.64 kA. This RMSE is about 79% smaller than that of the ANN model. In addition, both the EOF and ANN models capture the variation of the total Sq current (J(total)) intensity with respect to solar activity. In principle, the EOF model had an optimal performance at nearly 98% accuracy, with the ANN model exhibiting almost the same degree of accuracy, which appears to be a reference point for ionospheric conditions when looking for space weather applications. [Owolabi, Charles; Li, Jinfeng] Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China; [Owolabi, Charles] Fed Univ Technol Akure, Dept Phys, Akure, Nigeria; [Ruan, Haibing] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing, Peoples R China; [Yamazaki, Yosuke] GFZ German Res Ctr Geosci, Potsdam, Germany; [Zhong, Jiahao] Sun Yat Sen Univ, Sch Atmospher Sci, Planetary Environm & Astrobiol Res Lab PEARL, Zhuhai, Peoples R China; [Eyelade, A. V.] Univ Santiago Chile, Dept Phys, Santiago, Chile; [Priyadarshi, Shishir] Univ Bath, Dept Elect & Elect Engn, Bath, Avon, England; [Yoshikawa, Akimasa] Kyushu Univ, Int Ctr Space Weather Sci & Educ, Fukuoka, Japan Southern University of Science & Technology; Nanjing University of Information Science & Technology; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences; Sun Yat Sen University; Universidad de Santiago de Chile; University of Bath; Kyushu University Ruan, HB (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing, Peoples R China.;Yoshikawa, A (corresponding author), Kyushu Univ, Int Ctr Space Weather Sci & Educ, Fukuoka, Japan. rhb@nuist.edu.cn; yoshi@geo.kyushu-u.ac.jp Eyelade, Adetayo Victor/AAD-9716-2020; li, jinfeng/GVS-5425-2022; Owolabi, Charles/K-9841-2015; Zhong, Jiahao/C-5362-2015; Yamazaki, Yosuke/J-9484-2015 Eyelade, Adetayo Victor/0000-0002-2301-307X; Owolabi, Charles/0000-0001-6080-7402; Zhong, Jiahao/0000-0001-5130-8185; Yamazaki, Yosuke/0000-0002-7624-4752; Ruan, Haibing/0000-0002-5410-7909; Priyadarshi, Shishir/0000-0002-1775-8964 National Natural Science Foundation (NSF) of China [41804151, 42074186, 41804150]; Natural Science Foundation of Jiangsu Province [BK20211036]; Guangdong Basic and Applied Basic Research Foundation [2021A1515011216]; Japan Society for the Promotion of Science (JSPS); Canadian Space Agency; Canadian Space Agency/Geospace Observatories (GO) Canada Program; NASA; AFOSR; NSF National Natural Science Foundation (NSF) of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Guangdong Basic and Applied Basic Research Foundation; Japan Society for the Promotion of Science (JSPS)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Canadian Space Agency(Canadian Space Agency); Canadian Space Agency/Geospace Observatories (GO) Canada Program; NASA(National Aeronautics & Space Administration (NASA)); AFOSR(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)); NSF(National Science Foundation (NSF)) The first author is indebted to Prof. Jiuhou Lei for his guidance over the years in the pursuit of understanding the ionospheric electrodynamics and to Prof. Yufeng Lin for his support and encouragement during this work. This work was in part supported by the National Natural Science Foundation (NSF) of China (Grant Numbers: 41804151, 42074186 and 41804150), Natural Science Foundation of Jiangsu Province (Grant Number: BK20211036), and the Guangdong Basic and Applied Basic Research Foundation (Grant Number: 2021A1515011216). The authors are thankful to the INTERMAGNET, LISN, MAGDAS/CPMN, AUTUMNX, CARISMA, EMBRACE, WDC and SAMBA members for providing the geomagnetic data used in this study. The LISN is a project led by Boston College in collaboration with the Geophysical Institute of Peru, and other institutions that provide information for the benefit of the scientific community. The MAGDAS/CPMN network is operated by the International Center for Space Weather Science and Education, Kyushu University, Fukuoka, Japan and funded by the Japan Society for the Promotion of Science (JSPS). The CARISMA network is operated by the University of Alberta, funded by the Canadian Space Agency. The AUTUMNX is funded through the Canadian Space Agency/Geospace Observatories (GO) Canada Program. The AMBER is operated by Boston College and funded by NASA and AFOSR. The SAMBA is also operated by UCLA and funded by NSF. We are appreciative to IAGA for providing a quasi-dipole geomagnetic coordinate calculator. We are grateful to SPDF/GSFC for providing records of F10.7 flux indices used in the present study. We thank the Helmholtz Center Potsdam, German Research Center for Geosciences GFZ for providing records of the IQDs. We thank the two anonymous reviewers for their helpful criticism that helped us improve the paper. 55 2 2 3 11 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 1542-7390 SPACE WEATHER Space Weather NOV 2021.0 19 11 e2021SW002831 10.1029/2021SW002831 0.0 17 Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology & Atmospheric Sciences XD1JH Green Published 2023-03-23 WOS:000722467500012 0
J Ling, HF; Fang, Y; Wu, L; Li, P; Chen, JZ; Zou, FH; Shen, JL Ling, Hefei; Fang, Yang; Wu, Lei; Li, Ping; Chen, Jiazhong; Zou, Fuhao; Shen, Jialie Balanced Deep Supervised Hashing CMC-COMPUTERS MATERIALS & CONTINUA English Article Deep supervised hashing; equilibrium point; posterior probability Recently, Convolutional Neural Network (CNN) based hashing method has achieved its promising performance for image retrieval task. However, tackling the discrepancy between quantization error minimization and discriminability maximization of network outputs simultaneously still remains unsolved. Motivated by the concern, we propose a novel Balanced Deep Supervised Hashing (BDSH) based on variant posterior probability to learn compact discriminability-preserving binary code for large scale image data. Distinguished from the previous works, BDSH can search an equilibrium point within the discrepancy. Towards the goal, a delicate objective function is utilized to maximize the discriminability of the output space with the variant posterior probability of the pair-wise label. A quantization regularizer is utilized as a relaxation from real-value outputs to the desired discrete values (e.g., -1/+1). Extensive experiments on the benchmark datasets show that our method can yield state-of-the-art image retrieval performance from various perspectives. [Ling, Hefei; Fang, Yang; Wu, Lei; Li, Ping; Chen, Jiazhong; Zou, Fuhao] Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China; [Shen, Jialie] Queens Univ, Belfast, Antrim, North Ireland Huazhong University of Science & Technology; Queens University Belfast Li, P (corresponding author), Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China. lpshome@hust.edu.cn Shen, Jialie/AAX-6851-2020 Natural Science Foundation of China [U1536203, 61672254]; National key research and development program of China [2016QY01W0200]; Major Scientific and Technological Project of Hubei Province [2018AAA068] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National key research and development program of China; Major Scientific and Technological Project of Hubei Province This work was supported in part by the Natural Science Foundation of China under Grant U1536203 and 61672254, in part by the National key research and development program of China (2016QY01W0200), in part by the Major Scientific and Technological Project of Hubei Province (2018AAA068). 32 5 5 0 3 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2019.0 60 1 85 100 10.32604/cmc.2019.05588 0.0 16 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science KH2BP gold 2023-03-23 WOS:000510451400006 0
J Ciotti, M; Ciccozzi, M; Terrinoni, A; Jiang, WC; Wang, CB; Bernardini, S Ciotti, Marco; Ciccozzi, Massimo; Terrinoni, Alessandro; Jiang, Wen-Can; Wang, Cheng-Bin; Bernardini, Sergio The COVID-19 pandemic CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES English Review SARS-CoV-2; rRT-PCR; COVID-19; cytokine storm; pneumonia; ACE2; serology RESPIRATORY SYNDROME CORONAVIRUS; SARS-CORONAVIRUS; PROTEIN; APOPTOSIS; PNEUMONIA; REPLICATION; INFECTION; RESPONSES; OUTBREAK; THERAPY In December 2019, an outbreak of pneumonia of unknown origin was reported in Wuhan, Hubei Province, China. Pneumonia cases were epidemiologically linked to the Huanan Seafood Wholesale Market. Inoculation of respiratory samples into human airway epithelial cells, Vero E6 and Huh7 cell lines, led to the isolation of a novel respiratory virus whose genome analysis showed it to be a novel coronavirus related to SARS-CoV, and therefore named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is a betacoronavirus belonging to the subgenusSarbecovirus. The global spread of SARS-CoV-2 and the thousands of deaths caused by coronavirus disease (COVID-19) led the World Health Organization to declare a pandemic on 12 March 2020. To date, the world has paid a high toll in this pandemic in terms of human lives lost, economic repercussions and increased poverty. In this review, we provide information regarding the epidemiology, serological and molecular diagnosis, origin of SARS-CoV-2 and its ability to infect human cells, and safety issues. Then we focus on the available therapies to fight COVID-19, the development of vaccines, the role of artificial intelligence in the management of the pandemic and limiting the spread of the virus, the impact of the COVID-19 epidemic on our lifestyle, and preparation for a possible second wave. [Ciotti, Marco] Tor Vergata Univ Covid Hosp, Virol Unit, Rome, Italy; [Ciccozzi, Massimo] Univ Campus Biomed Rome, Unit Med Stat & Mol Epidemiol, Rome, Italy; [Terrinoni, Alessandro; Bernardini, Sergio] Univ Tor Vergata, Dept Expt Med, Via Montpellier 1, I-00133 Rome, Italy; [Jiang, Wen-Can; Wang, Cheng-Bin] Chinese Peoples Liberat Army Gen Hosp, Dept Lab Med, Beijing, Peoples R China; [Bernardini, Sergio] Int Federat Clin Chem & Lab Med IFCC, Emerging Technol Div, Milan, Italy University Campus Bio-Medico - Rome Italy; University of Rome Tor Vergata; Chinese People's Liberation Army General Hospital Bernardini, S (corresponding author), Univ Tor Vergata, Dept Expt Med, Via Montpellier 1, I-00133 Rome, Italy. bernardini@med.uniroma2.it Ciccozzi, Massimo/AAC-3271-2022 Ciccozzi, Massimo/0000-0003-3866-9239 146 227 229 8 16 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1040-8363 1549-781X CRIT REV CL LAB SCI Crit. Rev. Clin. Lab. Sci. AUG 17 2020.0 57 6 SI 365 388 10.1080/10408363.2020.1783198 0.0 JUL 2020 24 Medical Laboratory Technology Science Citation Index Expanded (SCI-EXPANDED) Medical Laboratory Technology ND4LK 32645276.0 2023-03-23 WOS:000547158200001 0
J Amani, M; Mehravar, S; Asiyabi, RM; Moghimi, A; Ghorbanian, A; Ahmadi, SA; Ebrahimy, H; Moghaddam, SHA; Naboureh, A; Ranjgar, B; Mohseni, F; Nazari, ME; Mahdavi, S; Mirmazloumi, SM; Ojaghi, S; Jin, SG Amani, Meisam; Mehravar, Soroosh; Asiyabi, Reza Mohammadi; Moghimi, Armin; Ghorbanian, Arsalan; Ahmadi, Seyed Ali; Ebrahimy, Hamid; Moghaddam, Sayyed Hamed Alizadeh; Naboureh, Amin; Ranjgar, Babak; Mohseni, Farzane; Nazari, Mohsen Eslami; Mahdavi, Sahel; Mirmazloumi, S. Mohammad; Ojaghi, Saeid; Jin, Shuanggen Ocean Remote Sensing Techniques and Applications: A Review (Part II) WATER English Review remote sensing; sea surface temperature; ocean salinity; ocean color; ocean chlorophyll; ocean oil spill; bathymetry; aquatic vegetation; coral reef; fishery SEA-SURFACE TEMPERATURE; OIL-SPILL DETECTION; SUBMERGED AQUATIC VEGETATION; SHALLOW-WATER BATHYMETRY; SPECTRAL-RESOLUTION RADIOMETER; AUTOMATIC ICEBERG DETECTION; INHERENT OPTICAL-PROPERTIES; SIDE-SCAN SONAR; C ICE SHELF; NEARSHORE BATHYMETRY As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. [Amani, Meisam; Mahdavi, Sahel] WSP Environm & Infrastruct Canada Ltd, Ottawa, ON K2E 7L5, Canada; [Mehravar, Soroosh] Univ Tehran, Univ Coll Engn, Dept Geomat, Tehran 1439957131, Iran; [Asiyabi, Reza Mohammadi] Univ POLITEHN Bucharest UPB, Res Ctr Spatial Informat CEOSpaceTech, Bucharest 011061, Romania; [Moghimi, Armin; Ghorbanian, Arsalan; Ahmadi, Seyed Ali; Ranjgar, Babak; Mohseni, Farzane] KN Toosi Univ Technol, Dept Photogrammetry & Remote Sensing, Fac Geodesy & Geomat Engn, Tehran 1969715433, Iran; [Ebrahimy, Hamid] Shahid Beheshti Univ, Fac Earth Sci, Ctr Remote Sensing & GIS Res, Tehran 1983969411, Iran; [Moghaddam, Sayyed Hamed Alizadeh] Queens Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada; [Naboureh, Amin] Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China; [Nazari, Mohsen Eslami] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1C 5S7, Canada; [Mirmazloumi, S. Mohammad] Ctr Tecnol Telecomunicac Catalunya CTTC CERCA, Geomat Res Unit, Av Gauss 7, E-08860 Barcelona, Spain; [Ojaghi, Saeid] Univ Sherbrooke, Fac Letters & Human Sci, Dept Appl Geomat, Sherbrooke, PQ J1K 2R1, Canada; [Jin, Shuanggen] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China; [Jin, Shuanggen] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China University of Tehran; K. N. Toosi University of Technology; Shahid Beheshti University; Queens University - Canada; Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS; Memorial University Newfoundland; University of Sherbrooke; Henan Polytechnic University; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS Amani, M (corresponding author), WSP Environm & Infrastruct Canada Ltd, Ottawa, ON K2E 7L5, Canada. meisam.amani@wsp.com Ghorbanian, Arsalan/AAH-5707-2019; Ahmadi, Seyed Ali/J-7885-2019; Jin, Shuanggen/B-8094-2008; Moghimi, Armin/AAF-8581-2021 Ghorbanian, Arsalan/0000-0001-8406-683X; Ahmadi, Seyed Ali/0000-0003-3920-2390; Jin, Shuanggen/0000-0002-5108-4828; Moghimi, Armin/0000-0002-0455-4882; Ranjgar, Babak/0000-0002-8324-4339; Mohammadi Asiyabi, Reza/0000-0002-0162-2376; Naboureh, Amin/0000-0003-1214-2359 379 2 2 46 46 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water NOV 2022.0 14 21 3401 10.3390/w14213401 0.0 58 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources 6B7PC Green Published, gold 2023-03-23 WOS:000881520000001 0
J Guo, ZG; Zhang, YF; Lv, JX; Liu, Y; Liu, Y Guo, Zhengang; Zhang, Yingfeng; Lv, Jingxiang; Liu, Yang; Liu, Ying An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Roads; Real-time systems; Forecasting; Routing; Optimization; Predictive models; Collaboration; Online learning; collaborative optimization; traffic forecasting; routing optimization; cyber-physical systems CYBER-PHYSICAL SYSTEMS; BIG DATA; KALMAN FILTER; ARCHITECTURE; CHALLENGES; PREDICTION; NETWORKS; MODELS; ART Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems in urban traffic. With the help of IoT technologies, online data from road segments are captured by monitoring devices, while real-time data from vehicles are collected through preinstalled sensors. Based on these data, a CPS model is constructed to depict real-time status and dynamic behavior of road segments and vehicles. An online learning data-driven model is developed to extract prior knowledge and enhance collaboration between road segments and vehicles by combining short-term traffic forecasting and real-time routing optimization. A case study based on Xi'an city is presented to demonstrate the feasibility and efficiency of the proposed method, showing a reduction in the travel time with reasonable computation time, without much compromising the travel distance and fuel consumption. This work potentially strengthens the transparency and intelligence of urban traffic systems. [Guo, Zhengang; Zhang, Yingfeng] Northwestern Polytech Univ, Sch Mech Engn, Minist Ind & Informat Technol, Key Lab Ind Engn & Intelligent Mfg, Xian 710072, Peoples R China; [Zhang, Yingfeng] Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China; [Lv, Jingxiang] Changan Univ, Sch Construct Machinery, Minist Educ, Key Lab Rd Construct Technol & Equipment, Xian 710064, Peoples R China; [Liu, Yang] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden; [Liu, Yang] Univ Vaasa, Dept Prod, Vaasa 65200, Finland; [Liu, Ying] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland Northwestern Polytechnical University; Southern University of Science & Technology; Chang'an University; Linkoping University; University of Vaasa; University of Glasgow Zhang, YF (corresponding author), Northwestern Polytech Univ, Sch Mech Engn, Minist Ind & Informat Technol, Key Lab Ind Engn & Intelligent Mfg, Xian 710072, Peoples R China.;Liu, Y (corresponding author), Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland. guozg@mail.nwpu.edu.cn; zhangyf@nwpu.edu.cn; lvjx@chd.edu.cn; yang.liu@liu.se; ying.liu@glasgow.ac.uk yang, liu/GVU-8760-2022; Liu, Yang/C-8320-2013; z, y/HPC-0477-2023; Guo, Zhengang/S-9197-2018 Liu, Yang/0000-0001-8006-3236; Zhang, Yingfeng/0000-0001-9547-381X; LIU, YING/0000-0001-6216-5538; Guo, Zhengang/0000-0002-3783-408X National Science Foundation of China [51675441]; NPU through the 111 Project [B13044]; Fundamental Research Funds for the Central Universities [31020190505001]; State Scholarship Fund [201806290042] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); NPU through the 111 Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Scholarship Fund This work was supported in part by the National Science Foundation of China under Grant 51675441, in part by the NPU through the 111 Project under Grant B13044, in part by the Fundamental Research Funds for the Central Universities under Grant 31020190505001, and in part by the State Scholarship Fund under Grant 201806290042. 61 6 6 17 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. OCT 2021.0 22 10 6634 6645 10.1109/TITS.2020.2986158 0.0 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation WC2UO Green Submitted 2023-03-23 WOS:000704117000047 0
J Shen, XM; Fantacci, R; Chen, SZ Shen, Xuemin; Fantacci, Romano; Chen, Shanzhi Internet of Vehicles PROCEEDINGS OF THE IEEE English Editorial Material Vehicular communication networks have emerged to enable numerous vehicular data services and applications. Conventional vehicular ad hoc networks (VANETs) are often operated in the ad hoc mode and mainly focus on road safety applications based on the connection between vehicles and roadside units (RSUs). To support vehicular communications, dedicated shortrange communication (DSRC) and car-to-car communication consortium (C2C-CC) have been initiated in the United States and Europe, respectively. With the new era of the Internet of Things (IoT), the conventional VANETs have evolved to the Internet of Vehicles (IoV). In IoV, each vehicle is envisioned as an intelligent object, equipped with sensing platforms, computing facilities, control units, and storages and is connected to any entity (other vehicles, RSUs, charging/gas stations, cloud, and so on) via vehicle-to-everything (V2X) communications. Intelligent vehicles can take different roles, i.e., being both a client and a server, taking and providing big data services, leading to numerous new IoV applications, from assisted/autonomous driving and platooning, secure information sharing and learning to traffic control and optimization. [Shen, Xuemin] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada; [Shen, Xuemin] Engn Inst Canada, Ottawa, ON, Canada; [Shen, Xuemin] Canadian Acad Engn, Ottawa, ON, Canada; [Shen, Xuemin] Royal Soc Canada, Ottawa, ON, Canada; [Shen, Xuemin] Chinese Acad Engn Foreign, Beijing, Peoples R China; [Shen, Xuemin] IEEE Globecom 16, Washington, DC USA; [Shen, Xuemin] IEEE Infocom 14, Toronto, ON, Canada; [Shen, Xuemin] IEEE VTC 10 Fall, Ottawa, ON, Canada; [Shen, Xuemin] IEEE Globecom 07, Washington, DC USA; [Shen, Xuemin] IEEE ICC 10, Vancouver, BC, Canada; [Shen, Xuemin] IEEE VTC 11 Spring, Budapest, Hungary; [Shen, Xuemin] IEEE Commun Soc Tech Comm Wireless Commun, New York, NY USA; [Shen, Xuemin; Fantacci, Romano] IEEE Commun Soc, New York, NY USA; [Shen, Xuemin] IEEE Vehicular Technol Soc, Antwerp, Belgium; [Fantacci, Romano] Univ Florence, Dept Informat Engn, Florence, Italy; [Fantacci, Romano] IEICE, Tokyo, Japan; [Fantacci, Romano] IEEE COMSOC Power Line Commun Comm, Tokyo, Japan; [Chen, Shanzhi] Datang Telecom Technol & Ind Grp, Beijing, Peoples R China; [Chen, Shanzhi] CATT, Beijing, Peoples R China; [Chen, Shanzhi] EVP R&D, Beijing, Peoples R China; [Chen, Shanzhi] State Key Lab Wireless Mobile Commun, Beijing, Peoples R China University of Waterloo; University of Florence; Datang Telecom Technology & Industry Shen, XM (corresponding author), Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada.;Shen, XM (corresponding author), Engn Inst Canada, Ottawa, ON, Canada.;Shen, XM (corresponding author), Canadian Acad Engn, Ottawa, ON, Canada.;Shen, XM (corresponding author), Royal Soc Canada, Ottawa, ON, Canada.;Shen, XM (corresponding author), Chinese Acad Engn Foreign, Beijing, Peoples R China. Shen, Xuemin/AAH-2564-2020; Fantacci, Romano/M-4708-2015 Shen, Xuemin/0000-0002-4140-287X; Fantacci, Romano/0000-0001-5934-3321; chen, shanzhi/0000-0002-5409-8168 0 28 29 3 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9219 1558-2256 P IEEE Proc. IEEE FEB 2020.0 108 2 242 245 10.1109/JPROC.2020.2964107 0.0 4 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering KH5GK Bronze 2023-03-23 WOS:000510677500003 0
S Escalante, HJ; Tu, WW; Guyon, I; Silver, DL; Viegas, E; Chen, YQ; Dai, WY; Yang, Q Escalera, S; Herbrich, R Escalante, Hugo Jair; Tu, Wei-Wei; Guyon, Isabelle; Silver, Daniel L.; Viegas, Evelyne; Chen, Yuqiang; Dai, Wenyuan; Yang, Qiang AutoML @ NeurIPS 2018 Challenge: Design and Results NEURIPS'18 COMPETITION: FROM MACHINE LEARNING TO INTELLIGENT CONVERSATIONS Springer Series on Challenges in Machine Learning English Article; Book Chapter We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its 2 months duration. This chapter describes the design of the challenge and summarizes its main results. [Escalante, Hugo Jair] Inst Nacl Astrofis Opt & Electr, Tonantzintla, Mexico; [Escalante, Hugo Jair] Ctr Invest & Estudios Avanzados IPN, San Pedro Zacatenco, Mexico; [Escalante, Hugo Jair; Guyon, Isabelle; Silver, Daniel L.] ChaLearn, Berkeley, CA 94708 USA; [Tu, Wei-Wei; Chen, Yuqiang; Dai, Wenyuan] 4Paradigm, Beijing, Peoples R China; [Guyon, Isabelle] Univ Paris Saclay, UPSud INRIA, Orsay, France; [Silver, Daniel L.] Acad Univ, Wolfville, NS, Canada; [Viegas, Evelyne] Microsoft Res, Redmond, WA USA; [Yang, Qiang] Hong Kong Univ Sci & Technol, Kowloon, Peoples R China Instituto Nacional de Astrofisica, Optica y Electronica; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; UDICE-French Research Universities; Universite Paris Saclay; Acadia University; Microsoft; Hong Kong University of Science & Technology Escalante, HJ (corresponding author), Inst Nacl Astrofis Opt & Electr, Tonantzintla, Mexico.;Escalante, HJ (corresponding author), Ctr Invest & Estudios Avanzados IPN, San Pedro Zacatenco, Mexico.;Escalante, HJ (corresponding author), ChaLearn, Berkeley, CA 94708 USA. hugojair@inaoep.mx; tuweiwei@4paradigm.com; guyon@chalearn.org; danny.silver@acadiau.ca; evelynev@microsoft.com; chenyuqiang@4paradigm.com 涂, 威威/GRF-5615-2022 涂, 威威/0000-0002-2407-0252 4Paradigm; Microsoft Research; ChaLearn; NeurIPS2018 competition chairs 4Paradigm; Microsoft Research(Microsoft); ChaLearn; NeurIPS2018 competition chairs The authors are grateful with the challenge sponsors 4Paradigm, Microsoft Research and ChaLearn, as well as the NeurIPS2018 competition chairs. 17 2 2 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2520-131X 978-3-030-29135-8; 978-3-030-29134-1 SPRING SER CHALLENGE 2020.0 209 229 10.1007/978-3-030-29135-8_8 0.0 10.1007/978-3-030-29135-8 21 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Book Citation Index – Science (BKCI-S) Computer Science BT2JZ Green Submitted 2023-03-23 WOS:000809968400008 0
J Gao, DW; Zhu, YS; Yan, K; Fu, H; Ren, ZJ; Kang, W; Soares, CG Gao, Da-wei; Zhu, Yong-sheng; Yan, Ke; Fu, Hong; Ren, Zhi-jun; Kang, Wei; Soares, C. Guedes Joint learning system based on semi-pseudo-label reliability assessment for weak-fault diagnosis with few labels MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Pseudo-label; Semi-supervised learning; Weak-fault diagnosis; Sample reliability; Label noise ROLLING ELEMENT BEARING; NEURAL-NETWORKS; MODEL; MACHINE; TOOL Deep neural networks exhibit excellent performance in fault feature extraction for considerable amounts of data. However, data labeling is a difficult task in practical engineering, which may lead to problems in fault diagnosis particularly when faults are weak. To resolve the foregoing, a semi-pseudo-labeling diagnosis system is proposed in this paper. The proposed system considers the confidence and reliability of samples to cope with situations where labels are insufficient and faults are weak. By adding pseudo-labels, unlabeled data whose fault information is swamped by a large amount of noise can achieve low-density separation and entropy regularization in the sample space. Consequently, the training of deep learning models for weak-fault diagnosis is supported. Regarding the traditional pseudo-labeling problems in weak-fault-related feature extraction, a series of solutions has been proposed to solve the problems in the field of fault diagnosis. The designed model reduces pseudo-label noise and enhances the capability of weak-fault-related feature extraction. The effectiveness of this method was validated on the datasets collected by simulating faulty bearings and those sustaining actual failure. [Gao, Da-wei; Zhu, Yong-sheng; Yan, Ke; Ren, Zhi-jun; Kang, Wei] Xi An Jiao Tong Univ, Key Lab Educ Minist Modern Design & Rotor Bearing, Xian 710049, Shaanxi, Peoples R China; [Gao, Da-wei; Soares, C. Guedes] Univ Lisbon, Ctr Marine Technol & Ocean Engn CENTEC, Inst Super Tecn, P-1049001 Lisbon, Portugal; [Fu, Hong] Educ Univ Hong Kong, Dept Math & Informat Technol, 10 Lo Ping Rd, Hong Kong, Peoples R China Xi'an Jiaotong University; Universidade de Lisboa; Instituto Superior Tecnico; Education University of Hong Kong (EdUHK) Zhu, YS (corresponding author), Xi An Jiao Tong Univ, Key Lab Educ Minist Modern Design & Rotor Bearing, Xian 710049, Shaanxi, Peoples R China.;Fu, H (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, 10 Lo Ping Rd, Hong Kong, Peoples R China. hfu@eduhk.hk ren, zhi jun/0000-0001-8976-0396; fu, hong/0000-0003-2246-7552 National Key R & D Program of China [2020YFB2007905]; China Scholarship Council [202106280192] National Key R & D Program of China; China Scholarship Council(China Scholarship Council) This research was supported by the National Key R & D Program of China (No. 2020YFB2007905) , and the China Scholarship Council (No. 202106280192) . 62 1 1 16 16 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. APR 15 2023.0 189 110089 10.1016/j.ymssp.2022.110089 0.0 JAN 2023 23 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 8E5HZ 2023-03-23 WOS:000919005500001 0
J Clausen, AN; Fercho, KA; Monsour, M; Disner, S; Salminen, L; Haswell, CC; Rubright, EC; Watts, AA; Buckley, MN; Maron-Katz, A; Sierk, A; Manthey, A; Suarez-Jimenez, B; Olatunji, BO; Averill, CL; Hofmann, D; Veltman, DJ; Olson, EA; Li, G; Forster, GL; Walter, H; Fitzgerald, J; Theberge, J; Simons, JS; Bomyea, JA; Frijling, JL; Krystal, JH; Baker, JT; Phan, KL; Ressler, K; Han, LKM; Nawijn, L; Lebois, LAM; Schmaall, L; Densmore, M; Shenton, ME; van Zuiden, M; Stein, M; Fani, N; Simons, RM; Neufeld, RWJ; Lanius, R; van Rooij, S; Koch, SBJ; Bonomo, S; Jovanovic, T; DeRoon-Cassini, T; Ely, TD; Magnotta, VA; He, XF; Abdallah, CG; Etkin, A; Schmahl, C; Larson, C; Rosso, IM; Blackford, JU; Stevens, JS; Daniels, JK; Herzog, J; Kaufman, ML; Olff, M; Davidson, RJ; Sponheim, SR; Mueller, SC; Straube, T; Zhu, X; Neria, Y; Baugh, LA; Cole, JH; Thompson, PM; Morey, RA Clausen, Ashley N.; Fercho, Kelene A.; Monsour, Molly; Disner, Seth; Salminen, Lauren; Haswell, Courtney C.; Rubright, Emily Clarke; Watts, Amanda A.; Buckley, M. Nicole; Maron-Katz, Adi; Sierk, Anika; Manthey, Antje; Suarez-Jimenez, Benjamin; Olatunji, Bunmi O.; Averill, Christopher L.; Hofmann, David; Veltman, Dick J.; Olson, Elizabeth A.; Li, Gen; Forster, Gina L.; Walter, Henrik; Fitzgerald, Jacklynn; Theberge, Jean; Simons, Jeffrey S.; Bomyea, Jessica A.; Frijling, Jessie L.; Krystal, John H.; Baker, Justin T.; Phan, K. Luan; Ressler, Kerry; Han, Laura K. M.; Nawijn, Laura; Lebois, Lauren A. M.; Schmaall, Lianne; Densmore, Maria; Shenton, Martha E.; van Zuiden, Mirjam; Stein, Murray; Fani, Negar; Simons, Raluca M.; Neufeld, Richard W. J.; Lanius, Ruth; van Rooij, Sanne; Koch, Saskia B. J.; Bonomo, Serena; Jovanovic, Tanja; DeRoon-Cassini, Terri; Ely, Timothy D.; Magnotta, Vincent A.; He, Xiaofu; Abdallah, Chadi G.; Etkin, Amit; Schmahl, Christian; Larson, Christine; Rosso, Isabelle M.; Blackford, Jennifer Urbano; Stevens, Jennifer S.; Daniels, Judith K.; Herzog, Julia; Kaufman, Milissa L.; Olff, Miranda; Davidson, Richard J.; Sponheim, Scott R.; Mueller, Sven C.; Straube, Thomas; Zhu, Xi; Neria, Yuval; Baugh, Lee A.; Cole, James H.; Thompson, Paul M.; Morey, Rajendra A. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups BRAIN AND BEHAVIOR English Article aging; machine learning; mega-analysis; neuroimaging; posttraumatic stress disorder; trauma CHILDHOOD TRAUMA; DEPRESSION; ANXIETY; SCHIZOPHRENIA; PREDICTION; MATURITY; VALIDITY; VOLUME Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan. [Clausen, Ashley N.; Haswell, Courtney C.; Rubright, Emily Clarke; Watts, Amanda A.; Buckley, M. Nicole; Morey, Rajendra A.] VA Mid Atlantic Mental Illness Res Educ & Clin Ct, Durham, NC 27705 USA; [Clausen, Ashley N.; Monsour, Molly; Haswell, Courtney C.; Rubright, Emily Clarke; Watts, Amanda A.; Buckley, M. Nicole; Morey, Rajendra A.] Duke Univ, Brain Imaging & Anal Ctr, Durham, NC USA; [Clausen, Ashley N.; Morey, Rajendra A.] Kansas City VA Med Ctr, Kansas City, MO USA; [Olff, Miranda; Morey, Rajendra A.] ARQ Natl Psychotrauma Ctr, Diemen, Netherlands; [Forster, Gina L.] Univ Otago, Brain Hlth Res Ctr, Dept Anat, Dunedin, New Zealand; [Fercho, Kelene A.; Disner, Seth; Simons, Jeffrey S.; Simons, Raluca M.; Baugh, Lee A.] Univ South Dakota, Ctr Brain & Behav Res, Vermillion, SD USA; [Davidson, Richard J.] Univ Wisconsin, Dept Psychol, Ctr Hlth Minds, 1202 W Johnson St, Madison, WI 53706 USA; [Cole, James H.] UCL, Ctr Med Image Comp Comp Sci, London, England; [Schmaall, Lianne] Univ Melbourne, Ctr Youth Mental Hlth, Melbourne, Vic, Australia; [Fercho, Kelene A.] US Fed Aviat Adm, Civil Aerosp Med Inst, Oklahoma City, OK USA; [Averill, Christopher L.; Krystal, John H.; Abdallah, Chadi G.] Natl Ctr PTSD, Clin Neurosci Div, West Haven, CT USA; [Suarez-Jimenez, Benjamin; He, Xiaofu; Zhu, Xi; Neria, Yuval] Columbia Univ, Med Ctr, Manhattan, KS USA; [Cole, James H.] UCL, Inst Neurol, Dementia Res Ctr, London, England; [Mueller, Sven C.] Univ Ghent, Dept Expt Clin & Hlth Psychol, Ghent, Belgium; [Theberge, Jean] Univ Western Ontario, Dept Med Biophys, London, ON, Canada; [Neufeld, Richard W. J.; Lanius, Ruth] Western Univ, Dept Neurosci, London, ON, Canada; [Mueller, Sven C.] Univ Deusto, Dept Personal Psychol Assessment & Treatment, Bilbao, Spain; [Phan, K. Luan] Ohio State Univ, Dept Psychiat & Behav Hlth, Columbus, OH 43210 USA; [Jovanovic, Tanja] Wayne State Univ, Dept Psychiat & Behav Neurosci, Detroit, MI USA; [Fani, Negar; van Rooij, Sanne; Ely, Timothy D.; Stevens, Jennifer S.] Emory Univ, Sch Med, Dept Psychiat & Behav Sci, Atlanta, GA USA; [Maron-Katz, Adi; Etkin, Amit] Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA USA; [Blackford, Jennifer Urbano] Vanderbilt Univ Sch Med, Dept Psychiat & Behav Sci, Nashville, TN USA; [Frijling, Jessie L.; Nawijn, Laura; van Zuiden, Mirjam; Koch, Saskia B. J.; Olff, Miranda] Univ Amsterdam, Dept Psychiat, Amsterdam Univ Med Ctr Locat, Acad Med Ctr,Amsterdam Neurosci, Amsterdam, Netherlands; [Veltman, Dick J.; Nawijn, Laura] Vrije Univ Amsterdam, VU Univ Med Ctr, Amsterdam Univ Med Ctr Locat, Dept Psychiat, Amsterdam, Netherlands; [Han, Laura K. M.; Morey, Rajendra A.] Vrije Univ Amsterdam Med Ctr, GGZ Geest Amsterdam Neurosci, Amsterdam Univ Med Ctr, Dept Psychiat, Amsterdam, Netherlands; [Ressler, Kerry; Lebois, Lauren A. M.; Shenton, Martha E.] Harvard Med Sch, Dept Psychiat, Boston, MA 02115 USA; [Disner, Seth; Sponheim, Scott R.] Univ Minnesota, Sch Med, Dept Psychiat, Minneapolis, MN 55455 USA; [Shenton, Martha E.] VA Boston Healthcare Syst, Dept Psychiat, Brockton, MA USA; [Theberge, Jean; Densmore, Maria; Neufeld, Richard W. J.; Lanius, Ruth] Western Univ, Dept Psychiat, London, ON, Canada; [Averill, Christopher L.; Krystal, John H.; Abdallah, Chadi G.] Yale Univ, Sch Med, Dept Psychiat, New Haven, CT USA; [Neufeld, Richard W. J.] Univ British Columbia, Dept Psychol, Okanagan, BC, Canada; [Li, Gen] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China; [Simons, Raluca M.] Univ South Dakota, Dept Psychol, Vermillion, SD USA; [Olatunji, Bunmi O.] Vanderbilt Univ, Dept Psychol, Nashville, TN 37240 USA; [Schmahl, Christian; Herzog, Julia] Heidelberg Univ, Med Fac Mannheim, Cent Inst Mental Hlth, Dept Psychosomat Med & Psychotherapy, Mannheim, Germany; [Blackford, Jennifer Urbano] Tennessee Valley Healthcare Syst, Dept Vet Affairs, Nashville, TN USA; [Shenton, Martha E.] Harvard Med Sch, Dept Radiol, Boston, MA 02115 USA; [Magnotta, Vincent A.] Univ Iowa, Dept Radiol, Iowa City, IA 52242 USA; [Magnotta, Vincent A.] Univ Iowa, Dept Psychiat, Iowa City, IA 52242 USA; [Magnotta, Vincent A.] Univ Iowa, Dept Biomed Engn, Iowa City, IA 52242 USA; [Fercho, Kelene A.; Forster, Gina L.; Baugh, Lee A.] Univ South Dakota, Sanford Sch Med, Div Basic Biomed Sci, Vermillion, SD USA; [Ressler, Kerry; Lebois, Lauren A. M.] McLean Hosp, Div Depress & Anxiety Disorders, 115 Mill St, Belmont, MA 02178 USA; [Kaufman, Milissa L.] McLean Hosp, Div Womens Mental Hlth, 115 Mill St, Belmont, MA 02178 USA; [Koch, Saskia B. J.] Radboud Univ Nijmegen, Ctr Cognit Neuroimaging, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands; [Olson, Elizabeth A.; Rosso, Isabelle M.] Harvard Med Sch, Boston, MA 02115 USA; [Theberge, Jean] Lawson Hlth Res Inst, Imaging Div, London, ON, Canada; [Baker, Justin T.] Harvard Univ, McLean Hosp, Inst Technol Psychiat, Belmont, MA 02178 USA; [Hofmann, David; Straube, Thomas] Univ Munster, Inst Med Psychol & Syst Neurosci, Munster, Germany; [Li, Gen] Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Lab Traumat Stress Studies, Beijing, Peoples R China; [Fitzgerald, Jacklynn] Marquette Univ, Milwaukee, WI 53233 USA; [Olson, Elizabeth A.; Rosso, Isabelle M.] McLean Hosp, 115 Mill St, Belmont, MA 02178 USA; [DeRoon-Cassini, Terri] Med Coll Wisconsin, Milwaukee, WI 53226 USA; [Disner, Seth; Sponheim, Scott R.] Minneapolis VA Hlth Care Syst, Minneapolis, MN USA; [Suarez-Jimenez, Benjamin; Bonomo, Serena; He, Xiaofu; Zhu, Xi; Neria, Yuval] New York State Psychiat Inst & Hosp, New York, NY 10032 USA; [Schmaall, Lianne] Orygen, Parkville, Vic, Australia; [Shenton, Martha E.] Brigham & Womens Hosp, Psychiat Neuroimaging Lab, 75 Francis St, Boston, MA 02115 USA; [Fercho, Kelene A.; Simons, Jeffrey S.; Baugh, Lee A.] Sioux Falls VA Hlth Care Syst, Sioux Falls, SD USA; [Stein, Murray] Univ Calif San Diego, Dept Family Med & Publ Hlth, San Deigo, CA USA; [Bomyea, Jessica A.; Stein, Murray] Univ Calif San Diego, Dept Psychiat, San Deigo, CA USA; [Sierk, Anika; Manthey, Antje; Walter, Henrik] Univ Med Ctr Charite, Berlin, Germany; [Daniels, Judith K.] Univ Groningen, Groningen, Netherlands; [Larson, Christine] Univ Wisconsin, Milwaukee, WI 53201 USA; [Bomyea, Jessica A.] VA San Diego Healthcare Syst, Ctr Excellence Stress & Mental Hlth, San Deigo, CA USA; [Etkin, Amit] Stanford Univ, Wu Tsai Neurosci Inst, Stanford, CA 94305 USA; [Neufeld, Richard W. J.] Western Univ, Dept Psychol, London, ON, Canada; [Salminen, Lauren; Thompson, Paul M.] Univ Southern Calif, Keck Sch Med, Imaging Genet Ctr, Mark & Mary Stevens Neuroimaging & Informat Inst, Marina Del Rey, CA USA; [Abdallah, Chadi G.] Michael E DeBakey VA Med Ctr, Houston, TX USA; [Abdallah, Chadi G.] Baylor Coll Med, Menninger Dept Psychiat, Houston, TX 77030 USA Duke University; University of Otago; University of South Dakota; University of Wisconsin System; University of Wisconsin Madison; University of London; University College London; Orygen, The National Centre of Excellence in Youth Mental Health; University of Melbourne; Columbia University; University of London; University College London; Ghent University; Western University (University of Western Ontario); Western University (University of Western Ontario); University of Deusto; University System of Ohio; Ohio State University; Wayne State University; Emory University; Stanford University; Vanderbilt University; University of Amsterdam; Academic Medical Center Amsterdam; Vrije Universiteit Amsterdam; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; Harvard University; Harvard Medical School; University of Minnesota System; University of Minnesota Twin Cities; US Department of Veterans Affairs; Veterans Health Administration (VHA); Harvard University; VA Boston Healthcare System; Western University (University of Western Ontario); Yale University; University of British Columbia; University of British Columbia Okanagan; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of South Dakota; Vanderbilt University; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; US Department of Veterans Affairs; Veterans Health Administration (VHA); VA Tennessee Valley Healthcare System; Harvard University; Harvard Medical School; University of Iowa; University of Iowa; University of Iowa; University of South Dakota; Harvard University; McLean Hospital; Harvard University; McLean Hospital; Radboud University Nijmegen; Harvard University; Harvard Medical School; Western University (University of Western Ontario); Harvard University; McLean Hospital; University of Munster; Chinese Academy of Sciences; Institute of Psychology, CAS; Marquette University; Harvard University; McLean Hospital; Medical College of Wisconsin; US Department of Veterans Affairs; Veterans Health Administration (VHA); Minneapolis VA Health Care System; New York State Psychiatry Institute; Orygen, The National Centre of Excellence in Youth Mental Health; Harvard University; Brigham & Women's Hospital; University of California System; University of California San Diego; University of California System; University of California San Diego; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; University of Groningen; University of Wisconsin System; University of Wisconsin Milwaukee; US Department of Veterans Affairs; Veterans Health Administration (VHA); VA San Diego Healthcare System; Stanford University; Western University (University of Western Ontario); University of Southern California; Baylor College of Medicine; Baylor College of Medicine Clausen, AN (corresponding author), VA Mid Atlantic Mental Illness Res Educ & Clin Ct, Durham, NC 27705 USA. ashley.n.clausen@gmail.com Salminen, Lauren/HOF-9443-2023; Stevens, Jennifer Strafford/T-4290-2018; Theberge, Jean/A-3087-2008; Lebois, Lauren/AAB-3224-2021; Han, Laura/AGV-3472-2022; Rosso, Isabelle M/AAA-6166-2019 Stevens, Jennifer Strafford/0000-0003-4674-0314; Theberge, Jean/0000-0001-7578-4469; Lebois, Lauren/0000-0001-5816-9181; Han, Laura/0000-0001-9647-3723; Walter, Henrik/0000-0002-9403-6121; Li, Gen/0000-0002-9625-208X; deRoon-Cassini, Terri/0000-0002-9485-0625; Magnotta, Vincent/0000-0001-8639-5354; Baugh, Lee/0000-0001-6227-497X; Clarke-Rubright, Emily/0000-0003-3553-2990; Daniels, Judith/0000-0001-6304-2310; Davidson, Richard/0000-0002-8506-4964 Academic Medical Center Research Council; Center for Brain and Behavior Research Pilot Grant; National Center for Advancing Translational Sciences; Department of Veterans Affairs RRD; Canadian Institute for Military and Veteran Health Research; National Center for Research Resources [CX001600]; National Institutes of Health [K01MH122774, R01 MH106574]; Brain and Behavior Research Foundation [07040]; VA Career Development Award; National Institute on Alcohol Abuse and Alcoholism; South Dakota Governor's Research Center Grant; National Institute of Mental Health [F31MH113271, K23MH112873, R01-MH043454, T32MH018931, T32MH13043]; Trauma Scholars Fund; NIH from the Big Data to Knowledge (BD2K) program [U54 EB020403]; German Research Society; U.S. Department of Defense [W81XWH-10-1-0925, W81XWH082-0159]; NHMRC Career Development Fellowship; Deutsche Forschungsgemeinschaft [DA1222/4-1, SFB/TRR 58, WA1539/8-2]; U.S. Department of Veterans Affairs; Barlow Family Fund; Veterans Affairs Merit Review Program; Institute of Neurosciences, Mental Health and Addiction; German Federal Ministry of Education and Research; Congressionally Directed Medical Research Programs [W81XWH08-2-0038]; Kasparian Fund; Netherlands organization for Health Research and Development; MRC/UKRI Innovation Fellowship; VISN6 MIRECC; Bijzonder Onderzoeksfonds UGent; Anonymous Women's Health Fund Academic Medical Center Research Council; Center for Brain and Behavior Research Pilot Grant; National Center for Advancing Translational Sciences(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Center for Advancing Translational Sciences (NCATS)); Department of Veterans Affairs RRD(US Department of Veterans Affairs); Canadian Institute for Military and Veteran Health Research; National Center for Research Resources(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Center for Research Resources (NCRR)); National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Brain and Behavior Research Foundation; VA Career Development Award; National Institute on Alcohol Abuse and Alcoholism(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse & Alcoholism (NIAAA)); South Dakota Governor's Research Center Grant; National Institute of Mental Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); Trauma Scholars Fund; NIH from the Big Data to Knowledge (BD2K) program(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); German Research Society(German Research Foundation (DFG)); U.S. Department of Defense(United States Department of Defense); NHMRC Career Development Fellowship(National Health and Medical Research Council (NHMRC) of Australia); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); U.S. Department of Veterans Affairs(US Department of Veterans Affairs); Barlow Family Fund; Veterans Affairs Merit Review Program(US Department of Veterans Affairs); Institute of Neurosciences, Mental Health and Addiction; German Federal Ministry of Education and Research(Federal Ministry of Education & Research (BMBF)); Congressionally Directed Medical Research Programs(United States Department of Defense); Kasparian Fund; Netherlands organization for Health Research and Development(Netherlands Organization for Health Research and Development); MRC/UKRI Innovation Fellowship(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); VISN6 MIRECC; Bijzonder Onderzoeksfonds UGent; Anonymous Women's Health Fund Academic Medical Center Research Council; Center for Brain and Behavior Research Pilot Grant; National Center for Advancing Translational Sciences; Department of Veterans Affairs RR & D; Canadian Institute for Military and Veteran Health Research; National Center for Research Resources, Grant/Award Number: CX001600; National Institutes of Health, Grant/Award Numbers: K01MH122774, R01 MH106574; Brain and Behavior Research Foundation, Grant/Award Number: 07040; VA Career Development Award; National Institute on Alcohol Abuse and Alcoholism; South Dakota Governor's Research Center Grant; National Institute of Mental Health, Grant/Award Numbers: F31MH113271, K23MH112873, R01-MH043454, T32MH018931, T32MH13043; Kasparian Fund; Trauma Scholars Fund; NIH U54 EB020403 from the Big Data to Knowledge (BD2K) program; German Research Society; U.S. Department of Defense, Grant/Award Numbers: W81XWH-10-1-0925, W81XWH082-0159; NHMRC Career Development Fellowship; Deutsche Forschungsgemeinschaft, Grant/Award Numbers: DA1222/4-1, SFB/TRR 58, C07, WA1539/8-2, C06; The Netherlands organization for Health Research and Development; U.S. Department of Veterans Affairs; Barlow Family Fund; Veterans Affairs Merit Review Program; Institute of Neurosciences, Mental Health and Addiction; German FederalMinistry of Education and Research; MRC/UKRI Innovation Fellowship; Congressionally Directed Medical Research Programs, Grant/Award Number: W81XWH08-2-0038; VISN6 MIRECC; Bijzonder Onderzoeksfonds UGent; AnonymousWomen's Health Fund; Narsad Young Investigator Academic Medical Center Research Council; Center for Brain and Behavior Research Pilot Grant; National Center for Advancing Translational Sciences; Department of Veterans Affairs RR & D; Canadian Institute for Military and Veteran Health Research; National Center for Research Resources, Grant/Award Number: CX001600; National Institutes of Health, Grant/Award Numbers: K01MH122774, R01 MH106574; Brain and Behavior Research Foundation, Grant/Award Number: 07040; VA Career Development Award; National Institute on Alcohol Abuse and Alcoholism; South Dakota Governor's Research Center Grant; National Institute of Mental Health, Grant/Award Numbers: F31MH113271, K23MH112873, R01-MH043454, T32MH018931, T32MH13043; Kasparian Fund; Trauma Scholars Fund; NIH U54 EB020403 from the Big Data to Knowledge (BD2K) program; German Research Society; U.S. Department of Defense, Grant/Award Numbers: W81XWH-10-1-0925, W81XWH082-0159; NHMRC Career Development Fellowship; Deutsche Forschungsgemeinschaft, Grant/Award Numbers: DA1222/4-1, SFB/TRR 58, C07, WA1539/8-2, C06; The Netherlands organization for Health Research and Development; U.S. Department of Veterans Affairs; Barlow Family Fund; Veterans Affairs Merit Review Program; Institute of Neurosciences, Mental Health and Addiction; German FederalMinistry of Education and Research; MRC/UKRI Innovation Fellowship; Congressionally Directed Medical Research Programs, Grant/Award Number: W81XWH08-2-0038; VISN6 MIRECC; Bijzonder Onderzoeksfonds UGent; AnonymousWomen's Health Fund; Narsad Young Investigator 75 10 10 2 10 WILEY HOBOKEN 111 RIVER ST, HOBOKEN, NJ 07030 USA 2162-3279 BRAIN BEHAV Brain Behav. JAN 2022.0 12 1 e2413 10.1002/brb3.2413 0.0 DEC 2021 14 Behavioral Sciences; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Behavioral Sciences; Neurosciences & Neurology YL7CM 34907666.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000730010700001 0
J Wu, DX; Fang, FZ Wu, Dongxu; Fang, Fengzhou Development of surface reconstruction algorithms for optical interferometric measurement FRONTIERS OF MECHANICAL ENGINEERING English Review surface topography; measurement; optical interferometry; coherence envelope; phase-shifting algorithm PHASE-SHIFTING INTERFEROMETRY; WINDOWED FOURIER-TRANSFORM; WHITE-LIGHT INTERFEROMETRY; FRINGE-PATTERN-ANALYSIS; WAVELENGTH SCANNING INTERFEROMETRY; ADVANCED ITERATIVE ALGORITHM; ON-MACHINE MEASUREMENT; PROFILE MEASUREMENT; SHAPE MEASUREMENT; HIGH-SPEED Optical interferometry is a powerful tool for measuring and characterizing areal surface topography in precision manufacturing. A variety of instruments based on optical interferometry have been developed to meet the measurement needs in various applications, but the existing techniques are simply not enough to meet the ever-increasing requirements in terms of accuracy, speed, robustness, and dynamic range, especially in on-line or on-machine conditions. This paper provides an in-depth perspective of surface topography reconstruction for optical interferometric measurements. Principles, configurations, and applications of typical optical interferometers with different capabilities and limitations are presented. Theoretical background and recent advances of fringe analysis algorithms, including coherence peak sensing and phase-shifting algorithm, are summarized. The new developments in measurement accuracy and repeatability, noise resistance, self-calibration ability, and computational efficiency are discussed. This paper also presents the new challenges that optical interferometry techniques are facing in surface topography measurement. To address these challenges, advanced techniques in image stitching, on-machine measurement, intelligent sampling, parallel computing, and deep learning are explored to improve the functional performance of optical interferometry in future manufacturing metrology. [Wu, Dongxu; Fang, Fengzhou] Univ Coll Dublin, Ctr Micro Nano Mfg Technol MNMT Dublin, Dublin 4, Ireland; [Wu, Dongxu] Northeastern Univ Qinhuangdao, Sch Control Engn, Qinhuangdao 066004, Hebei, Peoples R China; [Fang, Fengzhou] Tianjin Univ, Ctr Micro Nano Mfg Technol MNMT, State Key Lab Precis Measuring Technol & Instrume, Tianjin 300072, Peoples R China University College Dublin; Northeastern University - China; Tianjin University Fang, FZ (corresponding author), Univ Coll Dublin, Ctr Micro Nano Mfg Technol MNMT Dublin, Dublin 4, Ireland. fengzhou.fang@ucd.ie Fang, Fengzhou/0000-0002-8716-5988 Enterprise Ireland; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement [713654]; National Natural Science Foundation of China [51705070]; Science Foundation Ireland (SFI) [15/RP/B3208] Enterprise Ireland; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation Ireland (SFI)(Science Foundation Ireland) This work received funding from the Enterprise Ireland and from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement (Grant No. 713654), the National Natural Science Foundation of China (Grant No. 51705070), and the Science Foundation Ireland (SFI) (Grant No. 15/RP/B3208). The authors appreciate the fruitful discussions and suggestions from Szymon Baron of DePuy Synthes. The authors would also like to thank Chengwei Kang of University College Dublin for his comments on the paper. 246 7 7 25 78 HIGHER EDUCATION PRESS BEIJING CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R CHINA 2095-0233 2095-0241 FRONT MECH ENG-PRC Front. Mech. Eng. MAR 2021.0 16 1 1 31 10.1007/s11465-020-0602-6 0.0 DEC 2020 31 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering QW7GG hybrid 2023-03-23 WOS:000599034900002 0
J Ding, WM; Wu, SL Ding, Weimin; Wu, Shengli ABC-based stacking method for multilabel classification TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES English Article Ensemble learning; stacking; artificial bee colony; cross-entropy; multilabel learning PARTICLE SWARM OPTIMIZATION; BEE COLONY ALGORITHM; FEATURE-SELECTION; ENSEMBLE Multilabel classification is a supervised learning problem wherein each individual instance is associated with multiple labels. Ensemble methods are effective in managing multilabel classification problems by creating a set of accurate, diverse classifiers and then combining their outputs to produce classifications. This paper presents a novel stacking-based ensemble algorithm, ABC-based stacking, for multilabel classification. The artificial bee colony algorithm, along with a single-layer artificial neural network, is used to find suitable meta-level classifier configurations. The optimization goal of the meta-level classifier is to maximize the average accuracy of classification of all the instances involved. We run an experiment on 10 benchmark datasets of varying domains and compare the proposed approach to five other ensemble algorithms to demonstrate the feasibility and effectiveness of ABC-based stacking. [Ding, Weimin; Wu, Shengli] Jiangsu Univ, Sch Comp Sci, Zhenjiang, Jiangsu, Peoples R China; [Ding, Weimin] Weifang Univ, Sch Math & Informat Sci, Weifang, Peoples R China; [Wu, Shengli] Ulster Univ, Sch Comp, Newtownabbey, North Ireland Jiangsu University; Weifang University; Ulster University Ding, WM (corresponding author), Jiangsu Univ, Sch Comp Sci, Zhenjiang, Jiangsu, Peoples R China.;Ding, WM (corresponding author), Weifang Univ, Sch Math & Informat Sci, Weifang, Peoples R China. dwm007dwm007@126.com wu, shengli/0000-0003-2008-1736 36 1 1 2 12 TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY ANKARA ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY 1300-0632 1303-6203 TURK J ELECTR ENG CO Turk. J. Electr. Eng. Comput. Sci. 2019.0 27 6 4231 4245 10.3906/elk-1902-188 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering KB0BY Green Submitted 2023-03-23 WOS:000506165400014 0
J Imamoglu, N; Gomez-Tames, J; Gonzalez, J; Gu, DY; Yu, WW Imamoglu, Nevrez; Gomez-Tames, Jose; Gonzalez, Jose; Gu, Dongyun; Yu, Wenwei Pulse-Coupled Neural Network Segmentation and Bottom-Up Saliency-On Feature Extraction for Thigh Magnetic Resonance Imaging Based 3D Model Construction JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS English Article Muscle Segmentation; Saliency; PCNN; 3D Muscle Reconstruction FRAMEWORK; MUSCLE Recently, the number of studies focusing on 3D anatomically-based models have been increasing rapidly. The purpose of the models is to understand the underlying principles behind the biomechanical dynamics of the musculoskeletal system that can support many research fields to increase the quality of life. 3D models are constructed using 2D medical image (i.e., MRI, CT, etc.) segmentation where region of interest (RoI) in sequential slices are extracted. Manual segmentation is one common way to segment RoI that can achieve high accuracy with the expertise of operators and the reliability of software employed; however, it is time consuming to process a sequence of MR images only by manual operation. Therefore, in order to reduce experimenter interaction and speed up the 3D construction process, in our previous work, we proposed an unsupervised and bio-inspired automatic segmentation model that takes advantage of saliency-on feature maps and pulse-coupled neural network (PCNN) to enhance the basic conventional approaches such as k-means clustering and likelihood image extraction. In this study, we investigated further the potential of the proposed method, and explore the parameters of the PCNN module, to acquire the best results considering both uniformity and accuracy on detected muscle regions. A new metric, acceptable segmented image accuracy (ASIA), was also defined to show the automatic algorithm success ratio to be used in 3D model construction. Then, overall accuracy can be obtained by combining F-measure (harmonic average of precision and recall) and ASIA by referencing both objective and subjective results, in which computational experiment demonstrated promising results over 90% matching compared to the manual segmentation. [Imamoglu, Nevrez; Gomez-Tames, Jose; Yu, Wenwei] Chiba Univ, Grad Sch Engn, Dept Med Syst Engn, Chiba 2638522, Japan; [Gonzalez, Jose] Spanish Res Council, Bioengn Grp, Madrid 28500, Spain; [Gu, Dongyun] Shanghai Ninth Peoples Hosp, Shanghai 200011, Peoples R China Chiba University; Shanghai Jiao Tong University Yu, WW (corresponding author), Chiba Univ, Grad Sch Engn, Dept Med Syst Engn, Chiba 2638522, Japan. Gonzalez, Jose/M-2629-2014; Yu, Wenwei/AAI-1296-2019; Gomez-Tames, Jose/T-6862-2019; İmamoğlu, Nevrez/AAC-4082-2019; İmamoğlu, Nevrez/B-1746-2017 Gonzalez, Jose/0000-0002-3448-7393; Gomez-Tames, Jose/0000-0003-1917-8979; İmamoğlu, Nevrez/0000-0002-2661-599X; İmamoğlu, Nevrez/0000-0002-2661-599X Japan Society for the Promotion of Science [23300206]; Grants-in-Aid for Scientific Research [23300206, 14J06745] Funding Source: KAKEN Japan Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported by the Japan Society for the Promotion of Science's Grant-in-Aid for scientific research, B, 23300206. 48 3 4 0 27 AMER SCIENTIFIC PUBLISHERS VALENCIA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA 2156-7018 2156-7026 J MED IMAG HEALTH IN J. Med. Imaging Health Inform. APR 2014.0 4 2 220 229 10.1166/jmihi.2014.1245 0.0 10 Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging AD0ML 2023-03-23 WOS:000332928200008 0
J Jung, M; Schwalm, C; Migliavacca, M; Walther, S; Camps-Valls, G; Koirala, S; Anthoni, P; Besnard, S; Bodesheim, P; Carvalhais, N; Chevallier, F; Gans, F; Goll, DS; Haverd, V; Kohler, P; Ichii, K; Jain, AK; Liu, JZ; Lombardozzi, D; Nabel, JEMS; Nelson, JA; O'Sullivan, M; Pallandt, M; Papale, D; Peters, W; Pongratz, J; Rodenbeck, C; Sitch, S; Tramontana, G; Walker, A; Weber, U; Reichstein, M Jung, Martin; Schwalm, Christopher; Migliavacca, Mirco; Walther, Sophia; Camps-Valls, Gustau; Koirala, Sujan; Anthoni, Peter; Besnard, Simon; Bodesheim, Paul; Carvalhais, Nuno; Chevallier, Frederic; Gans, Fabian; Goll, Daniel S.; Haverd, Vanessa; Kohler, Philipp; Ichii, Kazuhito; Jain, Atul K.; Liu, Junzhi; Lombardozzi, Danica; Nabel, Julia E. M. S.; Nelson, Jacob A.; O'Sullivan, Michael; Pallandt, Martijn; Papale, Dario; Peters, Wouter; Pongratz, Julia; Roedenbeck, Christian; Sitch, Stephen; Tramontana, Gianluca; Walker, Anthony; Weber, Ulrich; Reichstein, Markus Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach BIOGEOSCIENCES English Article NET ECOSYSTEM EXCHANGE; CHLOROPHYLL FLUORESCENCE; INTERANNUAL VARIABILITY; SATELLITE-OBSERVATIONS; TURBULENT FLUX; WATER FLUXES; CO2; DIOXIDE; RESPIRATION; ASSIMILATION FLUXNET comprises globally distributed eddycovariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere- atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R-2 > 0.94 at 1 degrees spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008-2010 from FLUXCOM members vary within 106 and 130 PgC yr(-1) with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R-2 similar to 0.75) than with GPP from DGVMs (mean global pixel-wise R-2 similar to 0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R-2 > 0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R-2 = 0.87 for 2001-2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall. [Jung, Martin; Migliavacca, Mirco; Walther, Sophia; Koirala, Sujan; Besnard, Simon; Bodesheim, Paul; Carvalhais, Nuno; Gans, Fabian; Liu, Junzhi; Nelson, Jacob A.; Weber, Ulrich; Reichstein, Markus] Max Planck Inst Biogeochem, Dept Biogeochem Integrat, D-07745 Jena, Germany; [Schwalm, Christopher] Woods Hole Res Ctr, Falmouth, MA 02540 USA; [Camps-Valls, Gustau] Univ Valencia, IPL, Paterna 46980, Spain; [Anthoni, Peter] Karlsruhe Inst Technol, Inst Meteorol & Climate Res Atmospher Environm Re, D-82467 Garmisch Partenkirchen, Germany; [Besnard, Simon] Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, NL-6708 PB Wageningen, Netherlands; [Bodesheim, Paul] Friedrich Schiller Univ Jena, Comp Vis Grp Math & Comp Sci, D-07743 Jena, Germany; [Carvalhais, Nuno] Univ Nova Lisboa, FCT, DCEA, P-2829516 Caparica, Portugal; [Chevallier, Frederic] Univ Paris Saclay, IPSL, LSCE, F-91198 Gif Sur Yvette, France; [Goll, Daniel S.] Univ Augsburg, Dept Geog, D-86159 Augsburg, Germany; [Haverd, Vanessa] CSIRO Oceans & Atmosphere, Dept Continental Biogeochem Cycles, Canberra, ACT 2601, Australia; [Kohler, Philipp] CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA; [Ichii, Kazuhito] Chiba Univ, Ctr Environm Remote Sensing CEReS, Chiba 2638522, Japan; [Ichii, Kazuhito] Natl Inst Environm Studies, Ctr Global Environm Res, Tsukuba, Ibaraki 3058506, Japan; [Jain, Atul K.] Univ Illinois, Dept Atmospher Sci, 105 S Gregory Ave, Urbana, IL 61801 USA; [Liu, Junzhi] Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China; [Lombardozzi, Danica] Natl Ctr Atmospher Res, Climate & Global Dynam Lab, POB 3000, Boulder, CO 80307 USA; [Nabel, Julia E. M. S.; Pongratz, Julia] Max Planck Inst Meteorol, Dept Land Earth Syst LES, D-20146 Hamburg, Germany; [O'Sullivan, Michael; Sitch, Stephen] Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4QE, Devon, England; [Pallandt, Martijn; Roedenbeck, Christian] Max Planck Inst Biogeochem, Dept Biogeochem Signals, D-07745 Jena, Germany; [Papale, Dario; Tramontana, Gianluca] Tuscia Univ, Dept Innovat Biol Agrifood & Forest Syst DIBAF, I-01100 Viterbo, Italy; [Papale, Dario] Euromediterranean Ctr Climate Change CMCC, IAFES, I-01100 Viterbo, Italy; [Peters, Wouter] Wageningen Univ & Res, Dept Meteorol & Air Qual, NL-6700 AA Wageningen, Netherlands; [Pongratz, Julia] Ludwig Maximilians Univ Munchen, Dept Geog, D-80333 Munich, Germany; [Walker, Anthony] Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN USA Max Planck Society; Woods Hole Research Center; University of Valencia; Helmholtz Association; Karlsruhe Institute of Technology; Wageningen University & Research; Friedrich Schiller University of Jena; Universidade Nova de Lisboa; UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); University of Augsburg; Commonwealth Scientific & Industrial Research Organisation (CSIRO); California Institute of Technology; Chiba University; National Institute for Environmental Studies - Japan; University of Illinois System; University of Illinois Urbana-Champaign; Nanjing Normal University; National Center Atmospheric Research (NCAR) - USA; Max Planck Society; University of Exeter; Max Planck Society; Tuscia University; Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC); Wageningen University & Research; University of Munich; United States Department of Energy (DOE); Oak Ridge National Laboratory Jung, M (corresponding author), Max Planck Inst Biogeochem, Dept Biogeochem Integrat, D-07745 Jena, Germany. mjung@bgc-jena.mpg.de Jain, Atul/D-2851-2016; OSullivan, Mike/HJY-6367-2023; Papale, Dario/W-7302-2019; Peters, Wouter/B-8305-2008; Liu, Junzhi/AGE-4547-2022; Pongratz, Julia/AAG-3274-2019; Reichstein, Markus/A-7494-2011; Camps-Valls, Gustavo/A-2532-2011; Chevallier, Frederic/E-9608-2016; Migliavacca, Mirco/J-9986-2019; Walker, Anthony P/G-2931-2016; , Gustavo/ABC-1706-2022; Ichii, Kazuhito/D-2392-2010; Bodesheim, Paul/AAX-3821-2021; Sitch, Stephen A/F-8034-2015; Goll, Daniel/K-6620-2017; Liu, Junzhi/AAG-5846-2021; Köhler, Philipp/AAV-1936-2020 Jain, Atul/0000-0002-4051-3228; Papale, Dario/0000-0001-5170-8648; Peters, Wouter/0000-0001-8166-2070; Liu, Junzhi/0000-0002-7354-4207; Reichstein, Markus/0000-0001-5736-1112; Camps-Valls, Gustavo/0000-0003-1683-2138; Chevallier, Frederic/0000-0002-4327-3813; Migliavacca, Mirco/0000-0003-3546-8407; Walker, Anthony P/0000-0003-0557-5594; Ichii, Kazuhito/0000-0002-8696-8084; Bodesheim, Paul/0000-0002-3564-6528; Sitch, Stephen A/0000-0003-1821-8561; Goll, Daniel/0000-0001-9246-9671; Köhler, Philipp/0000-0002-7820-1318; Besnard, Simon/0000-0002-1137-103X; Pongratz, Julia/0000-0003-0372-3960; Pallandt, Martijn/0000-0003-0858-8227; Tramontana, Gianluca/0000-0003-4014-7958; Carvalhais, Nuno/0000-0003-0465-1436; Walther, Sophia/0000-0003-1681-9304 European Space Agency [ESRIN/4000123002/18/I-NB]; H2020 European Institute of Innovation and Technology [CHE (776186), VERIFY (776810), BACI (640176)]; E-SHAPE [GA 820852]; SEDAL [647423]; National Science Foundation [1852977]; U.S. Department of Energy [DE-SC0016323] European Space Agency(European Space AgencyEuropean Commission); H2020 European Institute of Innovation and Technology; E-SHAPE; SEDAL; National Science Foundation(National Science Foundation (NSF)); U.S. Department of Energy(United States Department of Energy (DOE)) This research has been supported by the European Space Agency (grant no. ESRIN/4000123002/18/I-NB), the H2020 European Institute of Innovation and Technology (grant nos. CHE (776186), VERIFY (776810) and BACI (640176)), E-SHAPE (GA 820852), SEDAL (grant agreement 647423), the National Science Foundation under cooperative agreement no. 1852977 and the U.S. Department of Energy (no. DE-SC0016323) 92 184 188 43 165 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1726-4170 1726-4189 BIOGEOSCIENCES Biogeosciences MAR 16 2020.0 17 5 1343 1365 10.5194/bg-17-1343-2020 0.0 23 Ecology; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology KW4KK Green Published, gold, Green Submitted 2023-03-23 WOS:000521134600001 0
J Li, CF; Wu, H; Yang, ZL; Wang, YJ; Sun, ZY Li, Chuanfeng; Wu, Hao; Yang, Zhile; Wang, Yongji; Sun, Zeyu A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty COMPLEXITY English Article GUARANTEED COST CONTROL; ADAPTIVE-CONTROL; NONLINEAR-SYSTEMS; CONTROL LAW; MODEL; PERFORMANCE; STATE Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC) theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI) method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system. [Li, Chuanfeng; Sun, Zeyu] Luoyang Inst Sci & Technol, Sch Comp & Informat Engn, Luoyang 471023, Peoples R China; [Li, Chuanfeng] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland; [Wu, Hao] Beijing Aerosp Automat Control Inst, Beijing 100854, Peoples R China; [Yang, Zhile] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China; [Wang, Yongji] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China Luoyang Institute of Science & Technology; Queens University Belfast; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Huazhong University of Science & Technology Yang, ZL (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China. zyang07@qub.ac.uk Yang, Zhile/0000-0001-8580-534X; Sun, Zeyu/0000-0002-0470-4203 National Natural Science Foundation of China [61473124, 61503173]; Science and Technology Research Project of Education Department of Henan Province [15A413016]; Science and Technology Key Project of Henan Province [162102410051]; National Scholarship Fund; EPSRC [EP/L001063/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Research Project of Education Department of Henan Province; Science and Technology Key Project of Henan Province; National Scholarship Fund; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China (61473124, 61503173), the Science and Technology Research Project of Education Department of Henan Province (15A413016), and the Science and Technology Key Project of Henan Province (162102410051). This paper was also supported by the National Scholarship Fund. 38 3 3 0 9 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1076-2787 1099-0526 COMPLEXITY Complexity 2017.0 6034786 10.1155/2017/6034786 0.0 14 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Science & Technology - Other Topics FL2ZK Green Submitted, gold 2023-03-23 WOS:000414089800001 0
J Hussain, S; Xi, XM; Ullah, I; Inam, SA; Naz, F; Shaheed, K; Ali, SA; Tian, CH Hussain, Sumaira; Xi, Xiamong; Ullah, Inam; Inam, Syed Azeem; Naz, Farah; Shaheed, Kashif; Ali, Syed Asif; Tian, Cuihuan A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation COMPUTERS IN BIOLOGY AND MEDICINE English Article Deep learning; Medical image segmentation; Discriminative information; Level set IMAGE SEGMENTATION; EVOLUTION Background: Breast tumor segmentation in B-mode ultrasound imaging is important for analyzing, identifying, and diagnosing tumors. The level set is an approach most widely used in breast segmentation, and the refinement is still in progress. However, its effectiveness is harmed by a dearth of semantic information. On the other hand, deep networks contain rich semantic information but loss much influential low-level details.Method: This paper proposes a novel deep-feature embedded level set group to exploit semantically enriched features for breast tumor segmentation. First, a UNet-based network is trained to extract different features at different stages. Each stage has unique features depiction. Then, a novel level-set method is integrated at the end of each stage to approach more accurate and precise features maps. A new feature-discriminator is devised in the energy function of the level set method to refine the low confidence pixels at the boundaries. Lastly, the outputs of the level set method at different stages are incorporated into final feature maps to further empower the segmentation process. Two datasets comprising 349 breast ultrasound images from various hospitals have been utilized to assess the proposed approach's performance. The model's effectiveness is estimated on different metrics, including Accuracy, Sensitivity or True Positive rate, Specificity or True Negative rate, False Positive rate Dice, and IoU values for both datasets. Furthermore, the efficiency of the model is investigated by performing a comparison with several state-of-the-art classic segmentation methods and deep learning methods.Result: The proposed method outperformed segmenting breast ultrasound tumors in terms of Dice and IoU for datasets A and B (with p-value < 0.005 against compared methods). Additionally, the performance of the proposed approach is evaluated using the Area Under Receiver Operating Characteristics curve (AUC) and Mean Absolute Error (MAE). Our findings indicate that the proposed method seems to gain superiority over other methods by obtaining a lower MAE rate with the highest value of the AUC.Conclusion: Experiments determine that our method has obtained the best cut-off to deal with the noticeable glitches present in other approaches and generates more accurate segmentation results for tumors in complex images. Hence, the results confirm the proposed method's effectiveness compared to classic segmentation methods over ultrasound images with blurry boundaries, noise, and intensity inhomogeneity. Moreover, our approach gives unprecedented prediction accuracy and similarity for malignant tumors. [Hussain, Sumaira; Xi, Xiamong; Ullah, Inam] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China; [Hussain, Sumaira] Shandong Univ, Sch Software Engn, Jinan 250101, Peoples R China; [Inam, Syed Azeem; Ali, Syed Asif] Sindh Madressatul Islam Univ, Dept Artificial Intelligence & Math Sci, Karachi 74000, Pakistan; [Naz, Farah] Shandong Univ, Sch Math, Jinan 250101, Peoples R China; [Naz, Farah] Univ Pavia, Dept Math, I-27100 Pavia, Italy; [Shaheed, Kashif] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Dept Multimedia Syst, PL-80233 Gdansk, Poland; [Tian, Cuihuan] Shandong Univ, Sch Med, Jinan 250100, Peoples R China; [Tian, Cuihuan] Shandong Univ, Hlth Management Ctr, Qilu Hosp, Jinan 250000, Peoples R China; [Tian, Cuihuan] Shandong Univ, 27 Shanda Nanlu, Jinan, Peoples R China Shandong Jianzhu University; Shandong University; Shandong University; University of Pavia; Fahrenheit Universities; Gdansk University of Technology; Shandong University; Shandong University; Shandong University Xi, XM (corresponding author), Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.;Tian, CH (corresponding author), Shandong Univ, 27 Shanda Nanlu, Jinan, Peoples R China. hussain-s@outlook.com; fyzq10@126.com; inamullah538@gmail.com; syed.azeem@smiu.edu.pk; farah.naz01@universitadipavia.it; kashifshaheed1@gmail.com; asifkhi@hotmail.com; shandamla@163.com Inam, Syed Azeem/HHZ-2381-2022 Inam, Syed Azeem/0000-0002-8876-0834; ULLAH, INAM/0000-0002-2624-8093; NAZ, FARAH/0000-0001-5066-0355; Hussain, Sumiara/0000-0002-7084-9670 54 2 2 14 14 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0010-4825 1879-0534 COMPUT BIOL MED Comput. Biol. Med. OCT 2022.0 149 105995 10.1016/j.compbiomed.2022.105995 0.0 AUG 2022 15 Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology 5R8MS 36055157.0 2023-03-23 WOS:000874760000001 0
J Monshizadeh, M; Khatri, V; Atli, BG; Kantola, R; Yan, Z Monshizadeh, Mehrnoosh; Khatri, Vikramajeet; Atli, Buse Gul; Kantola, Raimo; Yan, Zheng Performance Evaluation of a Combined Anomaly Detection Platform IEEE ACCESS English Article Anomaly detection; data mining; feature selection; machine learning; security Hybrid Anomaly Detection Model (HADM) is a platform that filters network traffic and identifies malicious activities on the network. The platform applies data mining techniques to tackle effectively the security issues in high load communication networks. The platform uses a combination of linear and learning algorithms combined with protocol analyzer. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks while the learning algorithms use these attributes and features to identify new types of cyber-attacks. The protocol analyzer in this platform classifies and filters vulnerable protocols to avoid unnecessary computation load. The use of linear algorithms in conjunction with learning algorithms and protocol analyzer allows the HADM to achieve improved efficiency in terms of accuracy and computation time to detect cyber-attacks over existing solutions. While authors' previous paper evaluated HADM efficiency (accuracy and computation time) against related studies, this paper, concentrates on HADM robustness and scalability. For this purpose, five datasets, including ISCX-2012, UNSW-NB15 Jan, UNSW-NB15 Feb, ISCX-2017, and MAWILab-2018, with various size and diverse attacks have been used. Different feature selection methods are applied to find the best features. The feature selection methods are selected based on the algorithms' computation time and detection rate. The best algorithms are then selected through a benchmark on applied datasets and based on the metrics such as cross-entropy loss, precision, recall, and computation time. The result of HADM platform shows robustness and scalability against datasets with different size and diverse attacks. [Monshizadeh, Mehrnoosh] Nokia Bell Labs, F-91620 Nozay, France; [Monshizadeh, Mehrnoosh; Kantola, Raimo; Yan, Zheng] Aalto Univ, Dept Comnet, Espoo 02150, Finland; [Khatri, Vikramajeet] Nokia Bell Labs, Espoo 02610, Finland; [Atli, Buse Gul] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland; [Yan, Zheng] Xidian Univ, State Key Lab ISN, Xian 710071, Shaanxi, Peoples R China Nokia Corporation; Aalto University; Nokia Corporation; Nokia Finland; Aalto University; Xidian University Monshizadeh, M (corresponding author), Nokia Bell Labs, F-91620 Nozay, France.;Monshizadeh, M (corresponding author), Aalto Univ, Dept Comnet, Espoo 02150, Finland. mehmoosh.monshizadeh@nokia-bell-labs.com yang, zheng/HGC-7753-2022; Khatri, Vikramajeet/AAB-4403-2020; Kantola, Raimo A/G-2277-2013 Khatri, Vikramajeet/0000-0002-3386-8952; Yan, Zheng/0000-0002-9697-2108 21 15 16 0 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 100964 100978 10.1109/ACCESS.2019.2930832 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IR8JN gold, Green Published 2023-03-23 WOS:000481688500037 0
J Huang, YC; He, ZX; Liu, YX; Yang, RY; Zhang, XF; Cheng, G; Yi, JG; Ferreira, JP; Liu, T Huang, Yongchuang; He, Zexia; Liu, Yuxuan; Yang, Ruiyuan; Zhang, Xiufeng; Cheng, Guang; Yi, Jingang; Ferreira, Joao Paulo; Liu, Tao Real-Time Intended Knee Joint Motion Prediction by Deep-Recurrent Neural Networks IEEE SENSORS JOURNAL English Article Intention prediction; RNN; knee joint; electromyographic signals; IMU LOWER-LIMB; EMG Human-assisting intelligent systems demand certain methods to precisely predict motorized limb joint angles. This paper presents the application of deep-recurrent neural networks (RNNs), which is a type of neural network for processing sequential data, for predicting the knee joint angle in real-time. This model is created based on a combination of electromyographic (EMG) signals, (with electrodes being placed on three leg muscles), and inertial measurements of the upper and lower legs. The data collected from different subjects when they performed different gaits were used to construct the model, which was evaluated in a real-time setting. The proposed RNN model based on fusion information contains a balance between computational complexity and prediction accuracy. Results on a microcontroller show that, within a predicted horizon of 50 ms, the model has a low prediction error of +/- 2.93 degrees. [Huang, Yongchuang; He, Zexia; Liu, Yuxuan; Yang, Ruiyuan; Liu, Tao] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Zhejiang, Peoples R China; [Zhang, Xiufeng] Minist Civil Affairs, Natl Res Ctr Rehabil Tech Aids, Key Lab Rehabil Tech Aids Technol & Syst, Beijing 100000, Peoples R China; [Cheng, Guang] Beijing Union Univ, Urban Rail Transit & Logist Coll, Beijing 100000, Peoples R China; [Yi, Jingang] Rutgers State Univ, Dept Mech & Aerosp Engn, Piscataway, NJ 08854 USA; [Ferreira, Joao Paulo] Inst Super Engn Coimbra, P-3030199 Coimbra, Portugal Zhejiang University; Beijing Union University; Rutgers State University New Brunswick; Universidade de Coimbra Liu, T (corresponding author), Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Zhejiang, Peoples R China. zhangxiufeng@hit.edu.cn; chengguang@buu.edu.cn; jgyi@rutgers.edu; ferreira@mail.isec.pt; liutao@zju.edu.cn Ferreira, João Paulo/L-5034-2019; Ferreira, João P/E-3816-2016; Yi, Jingang/B-4069-2011 Ferreira, João Paulo/0000-0003-0143-9421; Ferreira, João P/0000-0003-0143-9421; LIU, YUXuan/0000-0002-9607-0518; Yi, Jingang/0000-0003-0628-9098 NSFC [51775485, U1613203]; Beijing Municipal Education Commission Technology Plan [KZ201811417048]; Fundamental Research Funds for the Central Universities [2018QNA4004]; Fundacao para a Ciencia e a Tecnologia (FCT); COMPETE 2020 Program Automatic Adaptation of a Humanoid Robot Gait to Different Floor-Robot Friction Coefficients [PTDC/EEI-AUT/5141/2014]; Fundação para a Ciência e a Tecnologia [PTDC/EEI-AUT/5141/2014] Funding Source: FCT NSFC(National Natural Science Foundation of China (NSFC)); Beijing Municipal Education Commission Technology Plan; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Fundacao para a Ciencia e a Tecnologia (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); COMPETE 2020 Program Automatic Adaptation of a Humanoid Robot Gait to Different Floor-Robot Friction Coefficients; Fundação para a Ciência e a Tecnologia This work was supported in part by the NSFC under Grant 51775485 and Grant U1613203, in part by the Beijing Municipal Education Commission Technology Plan under Grant KZ201811417048, in part by the Fundamental Research Funds for the Central Universities under Grant 2018QNA4004, in part by the Fundacao para a Ciencia e a Tecnologia (FCT), and in part by the COMPETE 2020 Program Automatic Adaptation of a Humanoid Robot Gait to Different Floor-Robot Friction Coefficients under Grant PTDC/EEI-AUT/5141/2014. The associate editor coordinating the review of this article and approving it for publication was Dr. Ying Zhang. 29 30 30 9 56 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. DEC 1 2019.0 19 23 11503 11509 10.1109/JSEN.2019.2933603 0.0 7 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics JW9SG 2023-03-23 WOS:000503385100060 0
C Pan, YP; Guo, K; Sun, TR; Darouach, M Pan, Yongping; Guo, Kai; Sun, Tairen; Darouach, Mohamed Bioinspired Composite Learning Control Under Discontinuous Friction for Industrial Robots IFAC PAPERSONLINE English Proceedings Paper 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS) JUN 29-JUL 01, 2022 Casablanca, MOROCCO Int Federat Automat Control, Tech Comm 1 2 Adapt & Learning Syst,Int Federat Automat Control, Tech Comm 2 2 Linear Control Syst,Int Federat Automat Control, Tech Comm 2 3 Non Linear Control Syst,Int Federat Automat Control, Tech Comm 2 6 Distributed Parameter Syst,Int Federat Automat Control, Tech Comm 3 2 Computat Intelligence Control,Int Federat Automat Control, Tech Comm 4 2 Mechatron Syst,Int Federat Automat Control, Tech Comm 4 3 Robot,Int Federat Automat Control, Tech Comm 6 1 Chem Proc Control,Int Federat Automat Control, Tech Comm 6 3 Power & Energy Syst,Int Federat Automat Control, Tech Comm 7 3 Aerosp,Int Federat Automat Control, Tech Comm 7 5 Intelligent Autonomous Vehicles,Int Federat Automat Control, Tech Comm 8 2 Biol & Med Syst,Int Federat Automat Control, Tech Comm 8 3 Modelling & Control Environm Syst,Univ Hassan II Casablanca,Univ Caen Normandie,Ecole Normale Superieure Enseignement Tech Mohammedia,Ecole Nationale Superieure Electricite Mecanique,IEEE Control Syst Soc,Soc Automatique Genie Ind Productique,Moroccan Assoc Automat Control,SIDOQ,SERDILAB,Int Federat Automat Control, Tech Comm 1 1 Modelling, Identificat & Signal Proc,Ctr Natl Rech Sci Tech,Groupe Rech Modelisat, Analyse Conduite Systemes Dynamiques Robot control; Adaptive control; discontinuous friction; data memory; feedback error learning; parameter learning NEURAL-NETWORK; MANIPULATORS; DESIGN; JOINT Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot. Copyright (C) 2022 The Authors. [Pan, Yongping] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China; [Guo, Kai] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China; [Sun, Tairen] Univ Shanghai Sci & Technol, Sch Med Instrument & Food Engn, Shanghai 200093, Peoples R China; [Darouach, Mohamed] Univ Lorraine, CRAN UMR CNRS 7035, F-54400 Cosnes Et Romain, France Sun Yat Sen University; Shandong University; University of Shanghai for Science & Technology; Universite de Lorraine Pan, YP (corresponding author), Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China. panyongp@mail.sysu.edu.cn; kaiguo@sdu.edu.cn; suntren@gmail.com; mohamed.darouach@univ-lorraine.fr Guo, Kai/HNR-2076-2023; Pan, Yongping/B-3511-2012 Pan, Yongping/0000-0002-8587-6065 Guangdong Pearl River Talent Program of China [2019QN01X154] Guangdong Pearl River Talent Program of China This work was supported in part by the Guangdong Pearl River Talent Program of China under Grant No. 2019QN01X154 36 0 0 3 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2022.0 55 12 85 90 10.1016/j.ifacol.2022.07.292 0.0 AUG 2022 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems 5E9RV Green Submitted, gold 2023-03-23 WOS:000865959700014 0
C Chen, YF; Shu, L; Crespi, N; Lee, GM; Guizani, M IEEE Chen, Yuanfang; Shu, Lei; Crespi, Noel; Lee, Gyu Myoung; Guizani, Mohsen Understanding the Impact of Network Structure on Propagation Dynamics based on Mobile Big Data 2016 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) International Wireless Communications and Mobile Computing Conference English Proceedings Paper 12th International Wireless Communications & Mobile Computing Conference (IWCMC) SEP 05-09, 2016 Paphos, CYPRUS IEEE,IEEE Cyprus Sect,Frederick Univ Network structure; propagation dynamics; mobile big data; Internet of Things Understanding the propagation dynamics of information/an epidemic on complex networks is very important for discovering and controlling a terrorist attack, and even for predicting a disease outbreak. As an effective method, with analyzing the structure of a propagation network, a large number of previous studies have analyzed the propagation dynamics. Most of these studies are based on a special network structure to make such analysis. However, a propagation network has dynamically changed structure during the propagation. How to track, recognize and model such dynamic change is a big challenge. Along with the popularity of smart devices and the rapid development of the Internet of Things (IoT), massive mobile data is automatically collected. In this article, as a typical use case, we investigate the impact of network structure on epidemic propagation dynamics by analyzing the massive mobile data collected from smart devices carried by the volunteers of Ebola outbreak areas. From this investigation, we obtain two observations. Based on these observations and the analytical ability of Apache Spark on streaming data and graphs, we propose a simple model to track and recognize the dynamic structure of a network. Moreover, we introduce and discuss open issues and future work for developing this proposed recognition model. [Chen, Yuanfang; Crespi, Noel] Telecom SudParis, Inst Mines Telecom, Paris, France; [Chen, Yuanfang; Shu, Lei] Guangdong Univ Petrochem Technol, Maoming Shi, Guangdong, Peoples R China; [Lee, Gyu Myoung] Liverpool John Moores Univ, Liverpool, Merseyside, England; [Guizani, Mohsen] Qatar Univ, Doha, Qatar IMT - Institut Mines-Telecom; IMT Atlantique; Institut Polytechnique de Paris; Guangdong University of Petrochemical Technology; Liverpool John Moores University; Qatar University Chen, YF (corresponding author), Telecom SudParis, Inst Mines Telecom, Paris, France.;Chen, YF (corresponding author), Guangdong Univ Petrochem Technol, Maoming Shi, Guangdong, Peoples R China. yuanfang.chen.2009@ieee.org; lei.shu@ieee.org; noel.crespi@mines-telecom.fr; G.M.Lee@ljmu.ac.uk; mguizani@ieee.org Crespi, Noel/ABE-7052-2020; Guizani, Mohsen/AAX-4534-2021; Chen, Yuanfang/AAJ-5465-2020; Shu, Lei/HHT-0694-2022 Crespi, Noel/0000-0003-2962-192X; Guizani, Mohsen/0000-0002-8972-8094; Chen, Yuanfang/0000-0002-1557-087X; Shu, Lei/0000-0002-6700-9347 14 4 4 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-6492 978-1-5090-0304-4 INT WIREL COMMUN 2016.0 1018 1023 6 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BG1ZO Green Accepted 2023-03-23 WOS:000387181100175 0
C Seufert, M; Casas, P; Wehner, N; Gang, L; Li, K IEEE Seufert, Michael; Casas, Pedro; Wehner, Nikolas; Gang, Li; Li, Kuang Features that Matter: Feature Selection for On-line Stalling Prediction in Encrypted Video Streaming IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM 2019 WKSHPS) IEEE Conference on Computer Communications Workshops English Proceedings Paper IEEE Conference on Computer Communications (IEEE INFOCOM) APR 29-MAY 02, 2019 Paris, FRANCE IEEE Despite the vast literature and major developments in HTTP adaptive video streaming (HAS) technology, stalling events due to re-buffering still are by far the worst Quality of Experience (QoE) degradation, and thus, represent a major issue for ISPs. In this paper, we address the problem of realtime QoE monitoring of HAS, focusing on the detection of rebuffering events and QoE-relevant metrics, for the particular case of YouTube. Given the wide adoption of end-to-end encryption, we resort to machine learning models to predict these metrics directly from the analysis of the encrypted traffic. The salient feature of our approach ViCrypt is its ability to perform QoE predictions in real-time, during the course of an ongoing YouTube streaming session, relying on constant memory, stream-like inputs continuously extracted from the encrypted stream of packets. We show through empirical evaluations that ViCrypt can predict the occurrence of re-buffering events with a time granularity as small as one second with very high accuracy. By aggregating independent predictions, ViCrypt is able to accurately estimate per-video-session QoE-relevant metrics such as initial playback delay, number of re-buffering events and stalling ratio. In this situation, we investigate if a decent prediction performance can also be reached by selecting reduced feature sets based on the relevance of the features. Moreover, we explore the potential of including recurrent features, namely, stalling predictions from past time slots, to improve the prediction performance. [Seufert, Michael; Casas, Pedro; Wehner, Nikolas] AIT Austrian Inst Technol GmbH, Vienna, Austria; [Seufert, Michael] Univ Wurzburg, Wurzburg, Germany; [Gang, Li; Li, Kuang] Huawei Technol, Dept R&D, Shenzhen, Peoples R China Austrian Institute of Technology (AIT); University of Wurzburg; Huawei Technologies Seufert, M (corresponding author), AIT Austrian Inst Technol GmbH, Vienna, Austria.;Seufert, M (corresponding author), Univ Wurzburg, Wurzburg, Germany. michael.seufert@uni-wuerzburg.de; pedro.casas@ait.ac.at; nikolas.wehner@ait.ac.at; lig619@huawei.com; kuangli@huawei.com Seufert, Michael/AAR-7846-2021 Seufert, Michael/0000-0002-5036-5206 27 11 11 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-4228 978-1-7281-1878-9 IEEE CONF COMPUT 2019.0 688 695 8 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BO7XY 2023-03-23 WOS:000526051100114 0
C Zhang, JR; Hua, Y; Xue, ZG; Song, T; Zheng, CY; Ma, RH; Guan, HB IEEE COMP SOC Zhang, Jiaru; Hua, Yang; Xue, Zhengui; Song, Tao; Zheng, Chengyu; Ma, Ruhui; Guan, Haibing Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework. Even though Bayesian neural networks have been found more robust to adversarial attacks compared with vanilla neural networks, their ability to deal with adversarial noises in practice is still limited. In this paper, we propose Spectral Expectation Bound Regularization (SEBR) to enhance the robustness of Bayesian neural networks. Our theoretical analysis reveals that training with SEBR improves the robustness to adversarial noises. We also prove that training with SEBR can reduce the epistemic uncertainty of the model and hence it can make the model more confident with the predictions, which verifies the robustness of the model from another point of view Experiments on multiple Bayesian neural network structures and different adversarial attacks validate the correctness of the theoretical findings and the effectiveness of the proposed approach. [Zhang, Jiaru; Xue, Zhengui; Song, Tao; Zheng, Chengyu; Ma, Ruhui; Guan, Haibing] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Hua, Yang] Queens Univ Belfast, Belfast, Antrim, North Ireland Shanghai Jiao Tong University; Queens University Belfast Song, T (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. jiaruzhang@sjtu.edu.cn; Y.Hua@qub.ac.uk; zhenguixue@sjtu.edu.cn; songt333@sjtu.edu.cn; zhengcy@sjtu.edu.cn; ruhuima@sjtu.edu.cn; hbguan@sjtu.edu.cn guan, haibing/G-8142-2011; hua, yang/GSE-0594-2022 guan, haibing/0000-0002-4714-7400; Hua, Yang/0000-0001-5536-503X National NSF of China [61872234, 61732010, 61525204]; Shanghai Key Laboratory of Scalable Computing and Systems National NSF of China(National Natural Science Foundation of China (NSFC)); Shanghai Key Laboratory of Scalable Computing and Systems This work was supported in part by National NSF of China (NO. 61872234, 61732010, 61525204) and Shanghai Key Laboratory of Scalable Computing and Systems. 41 2 2 2 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 3814 3823 10.1109/CVPR46437.2021.00381 0.0 10 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5QS Green Accepted 2023-03-23 WOS:000739917304002 0
C Yang, XL; Denis, L; Tupin, F; Yang, W IEEE Yang, Xiangli; Denis, Loic; Tupin, Florence; Yang, Wen SAR Image Despeckling Using Pre-trained Convolutional Neural Network Models 2019 JOINT URBAN REMOTE SENSING EVENT (JURSE) Joint Urban Remote Sensing Event English Proceedings Paper Joint Urban Remote Sensing Event (JURSE) MAY 22-24, 2019 Vannes, FRANCE CNRS,Univ Bertagne Sud,UMR IRISA,UMR 6554 CNRS LETG SAR; image despeckling; convolutional neural networks; pre-trained models Despeckling is a longstanding topic in synthetic aperture radar (SAR) imaging. Many different schemes have been proposed for the restoration of SAR images. Among the different possible strategies, the methods based on convolutional neural networks (CNNs) have shown to produce state-of-the-art results on SAR image restoration. However, to learn an effective model it is necessary to collect a large number of speckle-free SAR images for training. To bypass this problem, we propose to directly use pre-trained CNN models on additive white Gaussian noise (AWGN) and transfer them to process SAR speckle. To include such CNNs Gaussian denoisers, we use the multi-channel logarithm approach with Gaussian denoising (MuLoG). Experimental results, both on synthetic and real SAR data, show the method achieves good performance. [Yang, Xiangli; Tupin, Florence] Univ Paris Saclay, Telecom ParisTech, LTCI, Paris, France; [Yang, Xiangli; Yang, Wen] Wuhan Univ, Elect Informat Sch, Wuhan, Peoples R China; [Denis, Loic] Univ Lyon, UJM St Etienne, CNRS, Grad Sch,Inst Opt,Lab Hubert Curien,UMR 5516, F-42023 St Etienne, France IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Wuhan University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite Jean Monnet Yang, XL (corresponding author), Univ Paris Saclay, Telecom ParisTech, LTCI, Paris, France.;Yang, XL (corresponding author), Wuhan Univ, Elect Informat Sch, Wuhan, Peoples R China. xiangli.yang@telecom-paristech.fr; loic.denis@univ-st-etienne.fr; florence.tupin@telecom-paristech.fr; yangwen@whu.edu.cn LTCI -Telecom Paristech; National Natural Science Foundation of China [61771351]; Funds for Creative Research Groups of Natural Science Foundation of Hubei Province [2018CFA006] LTCI -Telecom Paristech; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Funds for Creative Research Groups of Natural Science Foundation of Hubei Province This work has been carried out with financial support from LTCI -Telecom Paristech, and also financial support from China, the National Natural Science Foundation of China under Grant 61771351 and the Funds for Creative Research Groups of Natural Science Foundation of Hubei Province under Grant 2018CFA006. 16 1 1 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0932 978-1-7281-0009-8 JOINT URB REMOTE SEN 2019.0 4 Engineering, Electrical & Electronic; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Remote Sensing BP6SF 2023-03-23 WOS:000560604400056 0
J Dubey, R; Luo, ZW; Gunasekaran, A; Akter, S; Hazen, BT; Douglas, MA Dubey, Rameshwar; Luo, Zongwei; Gunasekaran, Angappa; Akter, Shahriar; Hazen, Benjamin T.; Douglas, Matthew A. Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT English Article Big data; Information exchange; Coordination; Supply chain management; Predictive analytics; Confirmatory factor analysis; Resource-based view; Humanitarian supply chain; Humanitarian operations; Swift trust; Contingent resource-based view; Regression analysis RESOURCE-BASED THEORY; FIRM PERFORMANCE; COMPETITIVE ADVANTAGE; DYNAMIC CAPABILITIES; INFORMATION-TECHNOLOGY; BUSINESS STRATEGY; CONCEPTUAL-MODEL; DATA SCIENCE; MANAGEMENT; LOGISTICS Purpose The purpose of this paper is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains. Design/methodology/approach The authors conceptualize a research model grounded in contingent resource-based view where the authors propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using ordinary least squares regression, the authors test the hypotheses using survey data collected from informants at 205 international non-government organizations. Findings The results indicate that BDPA has a significant influence on visibility and coordination. Further, the results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust acts as a mediating construct. Hence, the authors argue that swift trust is not the condition for improving coordination among the actors in humanitarian supply chains. Research limitations/implications The major limitation of the study is that the authors have used cross-sectional survey data to test the research hypotheses. Following Guide and Ketokivi (2015), the authors present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias or endogeneity-related problems. Practical implications Managers can use this framework to understand: first, how organizational resources can be used to create BDPA, and second, how BDPA can help build swift trust and be used to improve visibility and coordination in the humanitarian supply chain. Originality/value This is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster-relief-related activities. [Dubey, Rameshwar] Montpellier Business Sch, Montpellier, France; [Luo, Zongwei] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China; [Gunasekaran, Angappa] Univ Massachusetts Dartmouth, Charlton Coll Business, Dept Decis & Informat Sci, N Dartmouth, MA USA; [Akter, Shahriar] Univ Wollongong, Sch Management Operat & Mkt, Wollongong, NSW, Australia; [Hazen, Benjamin T.; Douglas, Matthew A.] US Air Force, Inst Technol, Dept Operat Sci, Wright Patterson AFB, OH 45433 USA; [Hazen, Benjamin T.] Univ Tennessee, Dept Mkt & Supply Chain Management, Knoxville, TN USA Montpellier Business School; Southern University of Science & Technology; University of Massachusetts System; University Massachusetts Dartmouth; University of Wollongong; Air Force Institute of Technology (AFIT); United States Department of Defense; United States Air Force; University of Tennessee System; University of Tennessee Knoxville Dubey, R (corresponding author), Montpellier Business Sch, Montpellier, France. rameshwardubey@gmail.com Luo, Zongwei/N-1985-2015; Dubey, Rameshwar/AAT-1348-2020; Dubey, Rameshwar/U-7022-2018; Akter, Shahriar/S-2888-2019 Luo, Zongwei/0000-0001-9322-959X; Dubey, Rameshwar/0000-0002-3913-030X; Akter, Shahriar/0000-0002-2050-9985 123 102 104 12 97 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0957-4093 1758-6550 INT J LOGIST MANAG Int. J. Logist. Manag. 2018.0 29 2 SI 485 512 10.1108/IJLM-02-2017-0039 0.0 28 Management Social Science Citation Index (SSCI) Business & Economics GH8EC 2023-03-23 WOS:000433898900002 0
J Qi, XB; Zhao, GY; Shen, LL; Li, QQ; Pietikainen, M Qi, Xianbiao; Zhao, Guoying; Shen, Linlin; Li, Qingquan; Pietikainen, Matti LOAD: Local orientation adaptive descriptor for texture and material classification NEUROCOMPUTING English Article Local orientation adaptive descriptor; Texture classification; Material recognition; Improved Fisher vector; Convolutional neural network REPRESENTATION; SCALE In this paper, we propose a novel local feature, called Local Orientation Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD, we proposed to define point description on an Adaptive Coordinate System (ACS), adopt a binary sequence descriptor to capture relationships between one point and its neighbors and use multi-scale strategy to enhance the discriminative power of the descriptor. The proposed LOAD enjoys not only discriminative power to capture the texture information, but also has strong robustness to illumination variation and image rotation. Extensive experiments on benchmark data sets of texture classification and real-world material recognition show that the LOAD yields the state-of-the-art performance. It is worth to mention that we achieve a superior classification accuracy on Flickr Material Database by using a single feature. Moreover, by combining LOAD with Convolutional Neural Networks (CNN), we obtain significantly better performance than both the LOAD and CNN. This result confirms that the LOAD is complementary to the learning-based features. (C) 2015 Elsevier B.V. All rights reserved. [Qi, Xianbiao; Zhao, Guoying; Pietikainen, Matti] Univ Oulu, Ctr Machine Vis & Signal Anal, POB 4500, FIN-90014 Oulu, Finland; [Qi, Xianbiao; Shen, Linlin; Li, Qingquan] Shenzhen Univ, Shenzhen Key Lab Spatial Informat Smart Sensing &, Shenzhen 518000, Peoples R China University of Oulu; Shenzhen University Qi, XB (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, POB 4500, FIN-90014 Oulu, Finland.;Qi, XB (corresponding author), Shenzhen Univ, Shenzhen Key Lab Spatial Informat Smart Sensing &, Shenzhen 518000, Peoples R China. qixianbiao@gmail.com; gyzhao@ee.oulu.fi; llshen@szu.edu.cn; liqq@szu.edu.cn; mkp@ee.oulu.fi Shen, Linlin/AEX-9392-2022; Zhao, Guoying/ABE-7716-2020 Shen, Linlin/0000-0003-1420-0815; Zhao, Guoying/0000-0003-3694-206X; Qi, Xianbiao/0000-0002-8493-1966 Academy of Finland; Infotech Oulu; Shenzhen Scientific Research and Development Funding Program [ZDSY20121019111146499, JSGG20121026111056204]; Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program [JCYJ20121019111128765] Academy of Finland(Academy of Finland); Infotech Oulu; Shenzhen Scientific Research and Development Funding Program; Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program This work was supported by the Academy of Finland and Infotech Oulu. Q. Li and L. Shen are supported by grants from Shenzhen Scientific Research and Development Funding Program (Nos. ZDSY20121019111146499, JSGG20121026111056204), and Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (No. JCYJ20121019111128765). 40 12 12 0 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing APR 5 2016.0 184 SI 28 35 10.1016/j.neucom.2015.07.142 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DJ7AM Green Submitted 2023-03-23 WOS:000374364300004 0
J Liu, B; Mohammadi, MR; Ma, ZL; Bai, LH; Wang, L; Xu, YH; Ostadhassan, M; Hemmati-Sarapardeh, A Liu, Bo; Mohammadi, Mohammad-Reza; Ma, Zhongliang; Bai, Longhui; Wang, Liu; Xu, Yaohui; Ostadhassan, Mehdi; Hemmati-Sarapardeh, Abdolhossein Evolution of porosity in kerogen type I during hydrous and anhydrous pyrolysis: Experimental study, mechanistic understanding, and model development FUEL English Article Pore structure characterization; N-2 adsorption; Anhydrous and hydrous pyrolysis; Fractal dimension analysis; Machine learning PORE-STRUCTURE CHARACTERIZATION; ORGANIC-RICH SHALES; CRETACEOUS QINGSHANKOU FORMATION; SCANNING-ELECTRON-MICROSCOPY; SILURIAN LONGMAXI SHALES; FRACTAL CHARACTERISTICS; SONGLIAO BASIN; SICHUAN BASIN; GAS-ADSORPTION; SURFACE-AREA In this study, to clarify the role of water during thermal maturation on organic porosity evolution, pore structure characteristics of the Qingshankou shale (type I kerogen) were studied during anhydrous and hydrous pyrolysis (AHP and HP) over wide a range of temperature (300-450 degrees C). Following each step of thermal maturation, geochemical properties, and pore structure were analyzed with Rock Eval and low-pressure N2 adsorption, respectively. Furthermore, the deconvolution technique was applied to the pore size distribution curves to investigate different pore families and their complexity was assessed using fractal dimension analysis. Next, the volume of N2 adsorbed/desorbed was modeled using several intelligent models. It was found that organic matter displayed Type IV isotherm with hysteresis loops and mesopores stood out as the prevailing type of pores within the samples. Moreover, total surface area and pore volume were found less for AHP samples than the HP samples at all temperatures, which revealed the role of water in the evolution of organic porosity. Pore diameter in AHP samples was larger compared to HP ones, while the diameter of pores increased with the progress of thermal advance in both scenarios. The original sample exhibited five mesopore and two macropore families, while with rising temperature pore families with smaller average pore widths were decreased, and pore families with larger average pore widths were created and expanded. Furthermore, heterogeneity of the pores with maturation was more evident in AHP samples due to the decrease in the number of pores with smaller pore widths. The role of water in the creation of fractures within pyrobitumen was remarkable since it caused a substantial increase in the total pore volume of the organic matter during the gas window. Finally, modeling outcomes showed that cascade forward neural network (CFNN) outperformed other models in predictions of N2 volume adsorbed/desorbed with an average absolute percent relative error (AAPRE) of 2.48 %. It was understood that relative pressure followed by pyrolysis method (AHP or HP) were the most important variables in the estimation of N2 adsorbed/desorbed volumes based on sensitivity analysis. Collectively, the role of water on organic porosity evolution was confirmed based on distinct discrepancies in the two groups of samples pore structure details. [Liu, Bo; Xu, Yaohui] Yangtze Univ, Hubei Key Lab Petr Geochem & Environm, Wuhan 430100, Peoples R China; [Liu, Bo; Bai, Longhui; Wang, Liu; Ostadhassan, Mehdi] Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China; [Mohammadi, Mohammad-Reza; Hemmati-Sarapardeh, Abdolhossein] Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran; [Ma, Zhongliang] Sinopec Petr Explorat & Prod Res Inst, Wuxi Res Inst Petr Geol, Wuxi 214126, Peoples R China; [Ostadhassan, Mehdi] Univ Kiel, Inst Geosci Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany Yangtze University; Northeast Petroleum University; Shahid Bahonar University of Kerman (SBUK); Sinopec; University of Kiel Ostadhassan, M (corresponding author), Northeast Petr Univ, Key Lab Continental Shale Hydrocarbon Accumulat &, Minist Educ, Daqing 163318, Peoples R China.;Hemmati-Sarapardeh, A (corresponding author), Shahid Bahonar Univ Kerman, Dept Petr Engn, Kerman, Iran.;Ostadhassan, M (corresponding author), Univ Kiel, Inst Geosci Marine & Land Geomech & Geotecton, D-24118 Kiel, Germany. mehdi.ostadhassan@ifg.uni-kiel.de; hemmati@uk.ac.ir Liu, Bo/AAC-7509-2020; Mohammadi, Mohammad Reza/AAZ-5153-2021 Liu, Bo/0000-0003-0136-7237; Mohammadi, Mohammad Reza/0000-0002-6303-4246 National Science Foundation of China [41972156]; Nature Science Foundation of Heilongjiang Province [TD2021D001] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Nature Science Foundation of Heilongjiang Province(Natural Science Foundation of Heilongjiang Province) This study was supported by the National Science Foundation of China (41972156) and the Nature Science Foundation of Heilongjiang Province (TD2021D001) . 106 0 0 2 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0016-2361 1873-7153 FUEL Fuel APR 15 2023.0 338 127149 10.1016/j.fuel.2022.127149 0.0 JAN 2023 21 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 8K8XQ 2023-03-23 WOS:000923378200001 0
J Akusok, A; Miche, Y; Karhunen, J; Bjork, KM; Nian, R; Lendasse, A Akusok, Anton; Miche, Yoan; Karhunen, Juha; Bjork, Kaj-Mikael; Nian, Rui; Lendasse, Amaury Arbitrary Category Classification of Websites Based on Image Content IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE English Article PERFORMANCE EVALUATION; SCALE; NETWORKS; RECOGNITION; MACHINES; FEATURES; OBJECT; ELM This paper presents a comprehensive methodology for general large-scale image-based classification tasks. It addresses the Big Data challenge in arbitrary image classification and more specifically, filtering of millions of websites with abstract target classes and high levels of label noise. Our approach uses local image features and their color descriptors to build image representations with the help of a modified k-NN algorithm. Image representations are refined into image and website class predictions by a two-stage classifier method suitable for a very large-scale real dataset. A modification of an Extreme Learning Machine is found to be a suitable classifier technique. The methodology is robust to noise and can learn abstract target categories; website classification accuracy surpasses 97% for the most important categories considered in this study. [Akusok, Anton; Lendasse, Amaury] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA; [Akusok, Anton; Lendasse, Amaury] Univ Iowa, Iowa Informat Initiat, Iowa City, IA 52242 USA; [Miche, Yoan; Karhunen, Juha] Aalto Univ Sch Sci, Dept Informat & Comp Sci, Espoo, Finland; [Miche, Yoan] Nokia Solut & Networks Grp, Espoo, Finland; [Bjork, Kaj-Mikael] Arcada Univ Appl Sci, Helsinki, Finland; [Nian, Rui] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China University of Iowa; University of Iowa; Aalto University; Nokia Corporation; Nokia Finland; Arcada University of Applied Sciences; Ocean University of China Akusok, A (corresponding author), Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA. 59 17 19 0 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-603X 1556-6048 IEEE COMPUT INTELL M IEEE Comput. Intell. Mag. MAY 2015.0 10 2 30 41 10.1109/MCI.2015.2405317 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CF9RG 2023-03-23 WOS:000352902900003 0
J Lu, C; Wang, Y; Ragulskis, M; Cheng, YJ Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie Fault Diagnosis for Rotating Machinery: A Method based on Image Processing PLOS ONE English Article EMD ENERGY ENTROPY; BISPECTRUM; DECOMPOSITION; SIGNALS Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. [Lu, Chen; Wang, Yang; Cheng, Yujie] Beihang Univ, Sch Reliabil & Syst Engn, Xueyuan Rd 37, Beijing, Peoples R China; [Lu, Chen; Wang, Yang; Cheng, Yujie] Sci & Technol Reliabil & Environm Engn Lab, Xueyuan Rd 37, Beijing, Peoples R China; [Ragulskis, Minvydas] Kaunas Univ Technol, Res Grp Math & Numer Anal Dynam Syst, Studentu 50-146, LT-51368 Kaunas, Lithuania Beihang University; Kaunas University of Technology Cheng, YJ (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Xueyuan Rd 37, Beijing, Peoples R China.;Cheng, YJ (corresponding author), Sci & Technol Reliabil & Environm Engn Lab, Xueyuan Rd 37, Beijing, Peoples R China. chengyujie@buaa.edu.cn Lu, Chen/B-6310-2016; Ragulskis, Minvydas/A-1546-2008 Ragulskis, Minvydas/0000-0002-3348-9717 Fundamental Research Funds for the Central Universities [YWF-16-BJ-J-18]; National Natural Science Foundation of China [51575021, 51605014]; Technology Foundation Program of National Defense [Z132013B002] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Technology Foundation Program of National Defense This study was supported by the Fundamental Research Funds for the Central Universities (Grant No. YWF-16-BJ-J-18) and the National Natural Science Foundation of China (Grant Nos. 51575021 and 51605014), as well as the Technology Foundation Program of National Defense (Grant No. Z13201B002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 41 65 70 8 95 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One OCT 6 2016.0 11 10 e0164111 10.1371/journal.pone.0164111 0.0 22 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics DZ2UR 27711246.0 gold, Green Submitted, Green Published 2023-03-23 WOS:000385697600078 0
J Hu, ZY; Chen, DL; Chen, X; Zhou, QM; Peng, YZ; Li, JF; Sang, YF Hu, Zengyun; Chen, Deliang; Chen, Xi; Zhou, Qiming; Peng, Yuzhou; Li, Jianfeng; Sang, Yanfang CCHZ-DISO: A Timely New Assessment System for Data Quality or Model Performance From Da Dao Zhi Jian GEOPHYSICAL RESEARCH LETTERS English Article CCHZ-DISO assessment system; model performance; data quality; statistical metric QUARANTINE STRATEGIES With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question. However, most existing lots of metrics focus on specific aspects of the assessment, and comprehensive assessment is rare. Therefore, it is very necessary to develop new assessment system. To address this problem, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)-distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ-DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Due to its simplicity and flexibility, CCHZ-DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple. [Hu, Zengyun; Chen, Xi] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; [Hu, Zengyun; Chen, Xi] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China; [Hu, Zengyun; Chen, Xi; Peng, Yuzhou] Univ Chinese Acad Sci, Beijing, Peoples R China; [Chen, Deliang] Univ Gothenburg, Dept Earth Sci, Gothenburg, Sweden; [Zhou, Qiming; Li, Jianfeng] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China; [Peng, Yuzhou] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geoph, Beijing, Peoples R China; [Sang, Yanfang] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Gothenburg; Hong Kong Baptist University; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Chen, X (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China.;Chen, X (corresponding author), Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China.;Chen, X (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. chenxi@ms.xjb.ac.cn Zhou, Qiming/N-8735-2016; Chen, Deliang/A-5107-2013 Zhou, Qiming/0000-0003-0934-0602; Chen, Deliang/0000-0003-0288-5618; Hu, Zengyun/0000-0002-4259-9141 National Natural Science Foundation of P.R. China [42230708, 41971386]; Alliance of International Science Organizations [ANSO-CR-KP-2021-02]; Western Scholars of the Chinese Academy of Sciences [2020-XBQNXZ-010]; National Youth Talent Project [E1190301]; Third Xinjiang Scientific Expedition Program [2021xjkk1300]; Shenzhen science and technology innovations Committee [JCYJ20210324101406019] National Natural Science Foundation of P.R. China(National Natural Science Foundation of China (NSFC)); Alliance of International Science Organizations; Western Scholars of the Chinese Academy of Sciences; National Youth Talent Project; Third Xinjiang Scientific Expedition Program; Shenzhen science and technology innovations Committee This study was supported by the National Natural Science Foundation of P.R. China (Grant 42230708, 41971386), the Alliance of International Science Organizations (Grant ANSO-CR-KP-2021-02), Western Scholars of the Chinese Academy of Sciences (2020-XBQNXZ-010), the National Youth Talent Project (E1190301), the Third Xinjiang Scientific Expedition Program (Grant 2021xjkk1300), and the Shenzhen science and technology innovations Committee (JCYJ20210324101406019). 16 0 0 4 4 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. DEC 16 2022.0 49 23 10.1029/2022GL100681 0.0 9 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology 8R7DT 2023-03-23 WOS:000928052000023 0
J Zhou, L; Xu, Y; Wang, TY; Feng, K; Shi, JG Zhou, Lin; Xu, Yue; Wang, Tianyi; Feng, Kun; Shi, Jingang Microphone Array Speech Separation Algorithm Based on TC-ResNet CMC-COMPUTERS MATERIALS & CONTINUA English Article Residual networks; temporal convolution; neural networks; speech separation SOURCE LOCALIZATION; TIME Traditional separation methods have limited ability to handle the speech separation problem in high reverberant and low signal-to-noise ratio (SNR) environments, and thus achieve unsatisfactory results. In this study, a convolutional neural network with temporal convolution and residual network (TC-ResNet) is proposed to realize speech separation in a complex acoustic environment. A simplified steered-response power phase transform, denoted as GSRP-PHAT, is employed to reduce the computational cost. The extracted features are reshaped to a special tensor as the system inputs and implements temporal convolution, which not only enlarges the receptive field of the convolution layer but also significantly reduces the network computational cost. Residual blocks are used to combine multiresolution features and accelerate the training procedure. A modified ideal ratio mask is applied as the training target. Simulation results demonstrate that the proposed microphone array speech separation algorithm based on TC-ResNet achieves a better performance in terms of distortion ratio, source-to-interference ratio, and short-time objective intelligibility in low SNR and high reverberant environments, particularly in untrained situations. This indicates that the proposed method has generalization to untrained conditions. [Zhou, Lin; Xu, Yue; Wang, Tianyi; Feng, Kun] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China; [Shi, Jingang] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland Southeast University - China; University of Oulu Zhou, L (corresponding author), Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China. Linzhou@seu.edu.cn National Key Research and Development Program of China [2020YFC2004003, 2020YFC2004002]; National Nature Science Foundation of China (NSFC) [61571106] National Key Research and Development Program of China; National Nature Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is supported by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002, and the National Nature Science Foundation of China (NSFC) under Grant No. 61571106. 23 0 0 4 11 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2021.0 69 2 2705 2716 10.32604/cmc.2021.017080 0.0 12 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science TP6AR Green Submitted, gold 2023-03-23 WOS:000677680600035 0
J Zhou, L; Ying, SG; Yu, NK; Ying, MS Zhou, Li; Ying, Shenggang; Yu, Nengkun; Ying, Mingsheng Strassen's theorem for quantum couplings THEORETICAL COMPUTER SCIENCE English Article Quantum coupling; Strassen's theorem; Quantum lifting Strassen's theorem for probabilistic couplings is a fundamental theorem in probability theory that can be used to bound the probability of an event in a distribution by the probability of an event in another distribution coupled with the first. It has been widely applied in computer science for analysis of random algorithms, machine learning and verification of security and privacy protocols. We extend the coupling techniques in probability theory to quantum systems. A quantum generalisation of the notion of lifting, a coupling under certain constraints, is introduced. Several interesting examples and basic properties of quantum couplings and liftings are presented. Finally, a quantum extension of Strassen's theorem is established. (C) 2019 Elsevier B.V. All rights reserved. [Zhou, Li] Max Planck Inst Secur & Privacy, Bochum, Germany; [Zhou, Li; Ying, Mingsheng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Ying, Shenggang; Ying, Mingsheng] Chinese Acad Sci, Inst Software, Beijing, Peoples R China; [Ying, Shenggang; Yu, Nengkun; Ying, Mingsheng] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Software & Informat, Sydney, NSW, Australia Tsinghua University; Chinese Academy of Sciences; Institute of Software, CAS; University of Technology Sydney Zhou, L (corresponding author), Max Planck Inst Secur & Privacy, Bochum, Germany.;Zhou, L (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China. zhou31416@gmail.com; yingshenggang@gmail.com; nengkunyu@gmail.com; mingshengying@gmail.com Ying, Mingsheng/A-8379-2008 Ying, Mingsheng/0000-0003-4847-702X; Zhou, Li/0000-0002-9868-8477; Yu, Nengkun/0000-0003-1188-3032 National Key R&D Program of China [2018YFA0306701]; National Natural Science Foundation of China [61832015]; Australian Research Council [DP160101652, DE180100156]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDJ-SSW-SYS003]; Australian Research Council [DE180100156] Funding Source: Australian Research Council National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Australian Research Council(Australian Research Council); Key Research Program of Frontier Sciences, Chinese Academy of Sciences; Australian Research Council(Australian Research Council) This work was partly supported by the National Key R&D Program of China (Grant No: 2018YFA0306701), the National Natural Science Foundation of China (Grant No: 61832015), Australian Research Council (Grant No: DP160101652, DE180100156) and the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No: QYZDJ-SSW-SYS003). 26 5 5 0 2 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0304-3975 1879-2294 THEOR COMPUT SCI Theor. Comput. Sci. JAN 8 2020.0 802 67 76 10.1016/j.tcs.2619.08.026 0.0 10 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science JU4BI 2023-03-23 WOS:000501622000004 0
J Wu, Y; Wu, YP; Guerrero, JM; Vasquez, JC Wu, Ying; Wu, Yanpeng; Guerrero, Josep M.; Vasquez, Juan C. Digitalization and decentralization driving transactive energy Internet: Key technologies and infrastructures INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Digitalization; Decentralization; Energy Internet; IoT; Blockchain; Microgrids; Energy router; Energy transaction BIG DATA ANALYTICS; CHALLENGES; MANAGEMENT; THINGS; GRIDS; IOT With the increasing access of renewable energy resources and fast ubiquitous connection of everything, the traditional one-way power flow from centralized generation to end consumers will give way to bidirectional-way power flow with multidirectional energy network among central grids and distributed prosumers. To empower the prosumer-centric Energy Internet (EI) and enhance the integration of energy-aware services, digitalization and decentralization are the key enablers to achieve transactive EI. This article presents a systematic overview on how Internet of Things (IoT) drives the digitalization of transactive EI and how blockchain empowers the decentralization of transactive EI. A comprehensive discussion on the key infrastructures is provided for presenting how to implement digitalization and decentralization of transactive EI, including the last mile Advanced metering infrastructure (AMI), renewables integrator smart inverter, energy flow adjuster energy router, and coordinator Microgrid. Challenges and future trends are discussed from an extensive point of view, including energy physical space, data cyber space and human social space. [Wu, Ying] Xian Shiyou Univ, Sch Comp Sci, Xian 710065, Shaanxi, Peoples R China; [Wu, Ying; Wu, Yanpeng; Guerrero, Josep M.; Vasquez, Juan C.] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Xi'an Shiyou University; Aalborg University Wu, Y (corresponding author), Xian Shiyou Univ, Sch Comp Sci, Xian 710065, Shaanxi, Peoples R China. wuyg1226@hotmail.com Guerrero, Josep M./D-5519-2014; Vasquez, Juan C./J-2247-2014 Guerrero, Josep M./0000-0001-5236-4592; Vasquez, Juan C./0000-0001-6332-385X; Wu, Ying/0000-0001-5602-3015 VILLUM FONDEN under the VILLUM Investigator Grant: Center for Research on Microgrids (CROM) [25920]; National Natural Science Foundation of China [51707158, 61802301]; AAU Talent Programme: The Energy Internet - Integrating IoT into the Smart Grid VILLUM FONDEN under the VILLUM Investigator Grant: Center for Research on Microgrids (CROM); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); AAU Talent Programme: The Energy Internet - Integrating IoT into the Smart Grid This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920): Center for Research on Microgrids (CROM), AAU Talent Programme: The Energy Internet - Integrating IoT into the Smart Grid, National Natural Science Foundation of China under Grant 51707158 and 61802301. 60 42 42 6 62 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. MAR 2021.0 126 A 106593 10.1016/j.ijepes.2020.106593 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering PH4EB 2023-03-23 WOS:000600367000009 0
J von Marttens, R; Casarini, L; Napolitano, NR; Wu, SR; Amaro, V; Li, R; Tortora, C; Canabarro, A; Wang, Y von Marttens, Rodrigo; Casarini, Luciano; Napolitano, Nicola R.; Wu, Sirui; Amaro, Valeria; Li, Rui; Tortora, Crescenzo; Canabarro, Askery; Wang, Yang Inferring galaxy dark halo properties from visible matter with machine learning MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article methods: data analysis; galaxies: general; dark matter KILO-DEGREE SURVEY; COSMOLOGICAL PARAMETER-ESTIMATION; STELLAR POPULATION SYNTHESIS; MASS ASSEMBLY GAMA; DIGITAL SKY SURVEY; TO-LIGHT RATIOS; SCALING RELATIONS; ILLUSTRISTNG SIMULATIONS; VELOCITY DISPERSIONS; FUNDAMENTAL PLANE Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved nature of dark matter (DM). At galaxy scales, the density distribution of DM is strongly affected by feedback processes, which are difficult to fully account for in classical techniques to derive galaxy masses. We explore the capability of supervised machine learning (ML) algorithms to predict the DM content of galaxies from 'luminous' observational-like parameters, using the TNG100 simulation. In particular, we use photometric (magnitudes in different bands), structural (the stellar half-mass radius and three different baryonic masses), and kinematic (1D velocity dispersion and the maximum rotation velocity) parameters to predict the total DM mass, DM half-mass radius, and DM mass inside one and two stellar half-mass radii. We adopt the coefficient of determination, R-2, as a metric to evaluate the accuracy of these predictions. We find that using all observational quantities together (photometry, structural, and kinematics), we reach high accuracy for all DM quantities (up to R-2 similar to 0.98). This first test shows that ML tools are promising to predict the DM in real galaxies. The next steps will be to implement the observational realism of the training sets, by closely selecting samples that accurately reproduce the typical observed 'luminous' scaling relations. The so-trained pipelines will be suitable for real galaxy data collected from Rubin/Large Synoptic Survey Telescope (LSST), Euclid, Chinese Survey Space Telescope (CSST), 4-metre Multi-Object Spectrograph Telescope (4MOST), Dark Energy Spectroscopic Instrument (DESI), to derive e.g. the properties of their central DM fractions. [von Marttens, Rodrigo] Observ Nacl, BR-20921400 Rio De Janeiro, RJ, Brazil; [Casarini, Luciano] Univ Fed Sergipe, Dept Phys, Ave Marechal Rondon S-N, BR-49100000 Sao Cristovao, SE, Brazil; [Napolitano, Nicola R.; Wu, Sirui; Amaro, Valeria; Li, Rui] Sun Yat Sen Univ, Sch Phys & Astron, Zhuhai Campus,2 Daum Rd, Xiangzhou Dist 519082, Zhuhai, Peoples R China; [Napolitano, Nicola R.; Wu, Sirui; Wang, Yang] CSST Sci Ctr Guangdong Hong Kong Macau Great Bay, Zhuhai 519082, Peoples R China; [Tortora, Crescenzo] INAF Osservatorio Astron Capodimonte, Salita Moiariello 16, I-80131 Naples, Italy; [Canabarro, Askery] Univ Fed Alagoas, Grp Fis Mat Condensada, Nucleo Ciencias Exatas NCEx, BR-57309005 Arapiraca, AL, Brazil; [Wang, Yang] Peng Cheng Lab, Dept Math & Theories, 2 Xingke 1st St, Shenzhen 518000, Peoples R China Universidade Federal de Sergipe; Sun Yat Sen University; Istituto Nazionale Astrofisica (INAF); Universidade Federal de Alagoas; Peng Cheng Laboratory Casarini, L (corresponding author), Univ Fed Sergipe, Dept Phys, Ave Marechal Rondon S-N, BR-49100000 Sao Cristovao, SE, Brazil.;Napolitano, NR (corresponding author), Sun Yat Sen Univ, Sch Phys & Astron, Zhuhai Campus,2 Daum Rd, Xiangzhou Dist 519082, Zhuhai, Peoples R China.;Napolitano, NR (corresponding author), CSST Sci Ctr Guangdong Hong Kong Macau Great Bay, Zhuhai 519082, Peoples R China. lcasarini@academico.ufs.br; napolitano@mail.sysu.edu.cn Crescenzo, Tortora/H-4394-2019; vom Marttens, Rodrigo/E-2493-2017; Canabarro, Askery/F-3213-2012 Crescenzo, Tortora/0000-0001-7958-6531; Casarini, Luciano/0000-0002-0869-9405; vom Marttens, Rodrigo/0000-0003-3954-5756; Canabarro, Askery/0000-0001-5048-2317; Napolitano, Nicola Rosario/0000-0003-0911-8884; Wang, Yang/0000-0002-1512-5653 Programa de Capacitacao Institucional PCI/ON/MCTI; CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico do Brazil); One hundred top talent program of Sun Yat-sen University [71000-18841229]; European Union [721463]; Simons Foundation [884966]; Brazilian National Council for Scientific and Technological Development (CNPq) [311375/2020-0] Programa de Capacitacao Institucional PCI/ON/MCTI; CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico do Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)); One hundred top talent program of Sun Yat-sen University; European Union(European Commission); Simons Foundation; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) The authors thank the anonymous referee whose valuable suggestions helped improving the clarity of the paper. RvM acknowledges support from the Programa de Capacitacao Institucional PCI/ON/MCTI. LC acknowledges financial support from CNPq (Conselho Nacional de Desenvolvimento Cient ' ifico e Tecnol ' ogico do Brazil), and P. O. Baqui for discussions. NRN acknowledges financial support from the 'One hundred top talent program of Sun Yat-sen University' grant no. 71000-18841229 and the European Union Horizon 2020 Framework Programme under the Marie Skodowska-Curie grant agreement no. 721463 to the SUNDIAL ITN network. AC acknowledges the Simons Foundation (grant no. 884966, AF), the Brazilian National Council for Scientific and Technological Development (CNPq) via grant no. 311375/2020-0 133 1 1 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. SEP 20 2022.0 516 3 3924 3943 10.1093/mnras/stac2449 0.0 20 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 4S1BJ Green Submitted 2023-03-23 WOS:000857185000017 0
J Fiorini, P; Goldberg, KY; Liu, YH; Taylor, RH Fiorini, Paolo; Goldberg, Ken Y.; Liu, Yunhui; Taylor, Russell H. Concepts and Trends in Autonomy for Robot-Assisted Surgery PROCEEDINGS OF THE IEEE English Article Ethics; Pathology; Medical robotics; Law; Surgery; Market research; Knowledge representation; Autonomous systems; Autonomy; knowledge representation; machine learning (ML); machine perception; surgical actions; surgical robotics DATA-DRIVEN APPROACH; SOFT-TISSUE; NEEDLE INSERTION; DEFORMATION; SYSTEM; MANIPULATION; BRACHYTHERAPY; OBJECTS Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing surgeon-assist-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lane-keeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends. [Fiorini, Paolo] Univ Verona, Dept Comp Sci, I-37134 Verona, Italy; [Goldberg, Ken Y.] Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA; [Goldberg, Ken Y.] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA; [Liu, Yunhui] Chinese Univ Hong Kong, T Stone Robot Inst, Dept Mech & Automat Engn, Hong Kong, Peoples R China; [Taylor, Russell H.] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA; [Taylor, Russell H.] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA; [Taylor, Russell H.] Johns Hopkins Univ, Dept Radiol, Baltimore, MD 21218 USA; [Taylor, Russell H.] Johns Hopkins Univ, Dept Surg, Baltimore, MD 21218 USA; [Taylor, Russell H.] Johns Hopkins Univ, Dept Otolaryngol Head & Neck Surg, Baltimore, MD 21218 USA; [Taylor, Russell H.] Johns Hopkins Univ, Lab Computat Sensing & Robot, Baltimore, MD 21218 USA University of Verona; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Chinese University of Hong Kong; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University Fiorini, P (corresponding author), Univ Verona, Dept Comp Sci, I-37134 Verona, Italy. paolo.fiorini@univr.it; goldberg@berkeley.edu; yhliu@cuhk.edu.hk; rht@jhu.edu Taylor, Russell/0000-0001-6272-1100; Goldberg, Ken/0000-0002-2661-4524; Fiorini, Paolo/0000-0002-0711-8605 Intuitive Surgical; European Union [ERC-ADG 742671, ERC-PoC 875523]; Technology & Advanced Telemedicine Research Center (TATRC) through a medical Telerobotic Operative Network (TRON) project [W81XWH-19-C-0096]; Hong Kong RGC [TR42-409/18-R]; NIH [5R44AI134500, 5R01EB025883, R01EB030511, R01 AR080315, 2R01 EB023943, R01EB031009] Intuitive Surgical; European Union(European Commission); Technology & Advanced Telemedicine Research Center (TATRC) through a medical Telerobotic Operative Network (TRON) project; Hong Kong RGC(Hong Kong Research Grants Council); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA) The work of Paolo Fiorini was supported in part by an equipment grant from Intuitive Surgical and in part by the European Union under Grant ERC-ADG 742671 and Grant ERC-PoC 875523. The work of Ken Y. Goldberg was supported in part by an equipment grant from Intuitive Surgical and in part by the Technology & Advanced Telemedicine Research Center (TATRC) under Project W81XWH-19-C-0096 through a medical Telerobotic Operative Network (TRON) project led by SRI International. The work of Yunhui Liu was supported in part by Hong Kong RGC under Grant TR42-409/18-R. The work of Russell H. Taylor was supported by NIH under Grant 5R44AI134500, Grant 5R01EB025883, Grant R01EB030511, Grant R01 AR080315, Grant 2R01 EB023943, and Grant R01EB031009. 179 2 2 16 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9219 1558-2256 P IEEE Proc. IEEE JUL 2022.0 110 7 993 1011 10.1109/JPROC.2022.3176828 0.0 19 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 2J4XZ 35911127.0 Green Accepted 2023-03-23 WOS:000815663100015 0
J Pang, SC; Ding, T; Rodriguez-Paton, A; Song, T; Zheng, P Pang, Shanchen; Ding, Tong; Rodriguez-Paton, Alfonso; Song, Tao; Zheng, Pan A Parallel Bioinspired Framework for Numerical Calculations Using Enzymatic P System With an Enzymatic Environment IEEE ACCESS English Article Artificial intelligence; Parallel architectures; Membrane computing SYNAPSES WORKING; RULES; UNIVERSALITY Enzymatic numerical P systems are inspired by the biological structure of cells and the processing of information regulated by enzymes on chemical objects, where natural numbers are basic entities to work with. Enzymatic numerical P systems can perform arithmetic operations but not complex numerical calculations, such as obtaining suitable values or finding the maximum or minimum element from the data sets. In this paper, a variant of enzymatic numerical P systems named an enzymatic numerical P system with an enzymatic environment is proposed, which can make numerical operations more flexible with regulations of external enzymes. We design several parallel computational frameworks for complex numerical calculations by the enzymatic numerical P system with an enzymatic environment. A parallel framework is achieved for performing support vector machine (SVM) calculations and sequential minimal optimization by the enzymatic numerical P system with an enzymatic environment. The experimental results obtained using a GPU prove that our method is 5.34 times more efficient than the traditional serial SVM calculation, without any loss in accuracy. [Pang, Shanchen; Ding, Tong; Song, Tao] China Univ Petr, Coll Comp & Commun Engn, Qingdao 266580, Peoples R China; [Rodriguez-Paton, Alfonso; Song, Tao] Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain; [Zheng, Pan] Univ Canterbury, Dept Accounting & Informat Syst, Christchurch 8041, New Zealand China University of Petroleum; Universidad Politecnica de Madrid; University of Canterbury Song, T (corresponding author), China Univ Petr, Coll Comp & Commun Engn, Qingdao 266580, Peoples R China.;Song, T (corresponding author), Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain. tsong@upc.edu.cn Zheng, PAN/A-5435-2009; Rodríguez-Patón, Alfonso/H-1003-2011; Song, Tao/T-7360-2018 Zheng, PAN/0000-0002-6067-626X; Rodríguez-Patón, Alfonso/0000-0001-7289-2114; Song, Tao/0000-0002-0130-3340 National Natural Science Foundation of China [61502535, 61572522, 61572523, 61672033, 61672248, 61873280]; PetroChina Innovation Foundation [2016D-5007-0305]; Fundamental Research Funds for the Central Universities [16CX02006A]; AEI/FEDER, Spain-EU [TIN2016-81079-R]; Talento-Comunidad de Madrid [2016-T2/TIC-2024]; MINECO AEI/FEDER, Spain-EU [TIN2016-81079-R]; InGEMICS-CM project (FSE/FEDER, Comunidad de Madrid-EU) [B2017/BMD-3691] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); PetroChina Innovation Foundation; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); AEI/FEDER, Spain-EU; Talento-Comunidad de Madrid; MINECO AEI/FEDER, Spain-EU; InGEMICS-CM project (FSE/FEDER, Comunidad de Madrid-EU) This work was supported in part by the National Natural Science Foundation of China under Grant 61502535, Grant 61572522, Grant 61572523, Grant 61672033, Grant 61672248, and Grand 61873280, in part by the PetroChina Innovation Foundation under Grant 2016D-5007-0305, in part by the Fundamental Research Funds for the Central Universities under Grant 16CX02006A, in part by AEI/FEDER, Spain-EU, under Project TIN2016-81079-R, in part by Talento-Comunidad de Madrid under Grant 2016-T2/TIC-2024, in part by MINECO AEI/FEDER, Spain-EU under Project TIN2016-81079-R, and in part by the InGEMICS-CM project (FSE/FEDER, Comunidad de Madrid-EU) under Grant B2017/BMD-3691. 27 12 12 0 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 65548 65556 10.1109/ACCESS.2018.2876364 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HB8WS gold 2023-03-23 WOS:000451372700001 0
J Yao, ZX; Wu, HP; Yang, Y; Cheng, Y; Pan, HT; Zhang, T; Yin, RC Yao, Zhixin; Wu, Huapeng; Yang, Yang; Cheng, Yong; Pan, Hongtao; Zhang, Tao; Yin, Ruochen On-line precision control of CFETR multipurpose overload robot using deformation model FUSION ENGINEERING AND DESIGN English Article CMOR; Online precision control; Deformation model; BPNN CONCEPT DESIGN; MAINTENANCE The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is an important part of the CFETR remote handling (RH) system. It is a type of serial robot with a redundant degree of freedom and heavy loads. Due to its long cantilever and heavy load, the deformation is large during operation. However, the deformation of the CMOR in the RH application cannot be ignored. In this paper, an online precision control algorithm using a deformation model is presented. Using this algorithm, the deformations of the CMOR at a limited number of positions are calculated by finite element analysis and used as training datasets. The joint parameters and loads are used as inputs, and the actual position of the CMOR is the output of the Back Prop-agation Neural Network (BPNN). The deformation model is trained by the BPNN offline, which can calculate the actual position in the workspace. The deformation of the CMOR can be accurately obtained and rapidly compensated to realize online precision control. During the RH operation in the CFETR, the deformation was updated to 2 Hz and the precision control accuracy was up to 5 mm. [Yao, Zhixin; Wu, Huapeng; Yang, Yang; Cheng, Yong; Pan, Hongtao; Yin, Ruochen] Chinese Acad Sci, Hefei Inst Phys Sci, Inst Plasma Phys, Hefei 230031, Peoples R China; [Yao, Zhixin; Cheng, Yong; Yin, Ruochen] Univ Sci & Technol China, Hefei 230026, Peoples R China; [Yao, Zhixin; Wu, Huapeng; Yin, Ruochen] Lappeenranta Univ Technol, Lappeenranta, Finland; [Zhang, Tao] Hefei Univ Technol, Sch Mech Engn, Hefei 230031, Peoples R China Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Lappeenranta University of Technology; Hefei University of Technology Cheng, Y (corresponding author), Chinese Acad Sci, Hefei Inst Phys Sci, Inst Plasma Phys, Hefei 230031, Peoples R China.;Cheng, Y (corresponding author), Univ Sci & Technol China, Hefei 230026, Peoples R China. chengyong@ipp.ac.cn Zhang, Tao/0000-0003-0750-0123 Comprehensive Research Facility for Fusion Technology Program of China [2018-000052-73-01-001228] Comprehensive Research Facility for Fusion Technology Program of China Acknowledgments This work was supported by Comprehensive Research Facility for Fusion Technology Program of China under Contract No. 2018-000052-73-01-001228. We also thank all the members of the CFETR remote handling team for their hard work. 14 2 2 8 11 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0920-3796 1873-7196 FUSION ENG DES Fusion Eng. Des. JAN 2022.0 174 112967 10.1016/j.fusengdes.2021.112967 0.0 JAN 2022 9 Nuclear Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Nuclear Science & Technology 0P6GB 2023-03-23 WOS:000784324000002 0
J Lin, T; Liu, XF; Song, JC; Zhang, GQ; Jia, YQ; Tu, ZZ; Zheng, ZH; Liu, CL Lin, Tao; Liu, Xiaofang; Song, Jinchao; Zhang, Guoqin; Jia, Yuqiu; Tu, Zhezhi; Zheng, Zehua; Liu, Chaolun Urban waterlogging risk assessment based on internet open data: A case study in China HABITAT INTERNATIONAL English Article Waterlogging; Risk; Open data; Field investigation; Management efficiency; Spatial-temporal distribution MANAGEMENT; EFFICIENCY Urban waterlogging caused by rainstorm occurred frequently in many cities of China these years and seriously influenced city safety and residents' daily life. Meanwhile, many remarkable achievements with open data and big data have been applied in urban research, but rare to urban waterlogging research. Taking Xiamen city in China as a case, this study aims to analyze spatial-temporal distribution of waterlogging and assess waterlogging risk of each district in the city. Risk assessment model was built by considering both population and urban public facilities, which acquired from coupling urban intemet open data and field investigation. Then we analyzed the government management efficiency through comparison of urban government management plan and waterlogging information from internet. The result indicates that internet open data can be utilized as an effective tool to identify urban waterlogging risk in China, to verify urban waterlogging management efficiency, and to support for urban waterlogging risk prevention and management combining with field investigation. [Lin, Tao; Liu, Xiaofang; Zhang, Guoqin; Jia, Yuqiu] Chinese Acad Sci, Key Lab Urban Environm & Hlth, Inst Urban Environm, Xiamen 361021, Peoples R China; [Liu, Xiaofang; Jia, Yuqiu] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Liu, Xiaofang; Jia, Yuqiu] Tianjin Univ, Sch Architecture, Tianjin 300072, Peoples R China; [Liu, Xiaofang; Tu, Zhezhi; Zheng, Zehua; Liu, Chaolun] Huaqiao Univ, Sch Architecture, Xiamen 361021, Peoples R China; [Song, Jinchao] Univ Copenhagen, Dept Geosci & Nat Resource Management, DK-1958 Copenhagen, Denmark Chinese Academy of Sciences; Institute of Urban Environment, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Tianjin University; Huaqiao University; University of Copenhagen Zhang, GQ (corresponding author), Chinese Acad Sci, Key Lab Urban Environm & Hlth, Inst Urban Environm, Xiamen 361021, Peoples R China. tlin@iue.ac.cn; xfliu@iue.ac.cn; songjinchao08@163.com; gozhang@iue.ac.cn; yojia@iue.ac.cn; 314497096@qq.com; tvxo5_1015@163.com; example1995.exarnle@qq.com CAS, KLUEH/T-5743-2019; Lin, Tao/B-7256-2009 Lin, Tao/0000-0002-7479-2333 State's Key Project of Research and Development Plan [2016YFC0502702]; China High-Resolution Earth Observation System; National Science Foundation of China [41371540, 41671444, 41771570, 41771573]; Natural Science Foundation of Fujian Province [2015J05086] State's Key Project of Research and Development Plan; China High-Resolution Earth Observation System; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province) The authors are grateful for State's Key Project of Research and Development Plan (2016YFC0502702), the support of the China High-Resolution Earth Observation System, the National Science Foundation of China (41371540, 41671444, 41771570, 41771573) and the Natural Science Foundation of Fujian Province (2015J05086). 46 37 40 15 142 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0197-3975 1873-5428 HABITAT INT Habitat Int. JAN 2018.0 71 88 96 10.1016/j.habitatint.2017.11.013 0.0 9 Development Studies; Environmental Studies; Regional & Urban Planning; Urban Studies Social Science Citation Index (SSCI) Development Studies; Environmental Sciences & Ecology; Public Administration; Urban Studies FX4FK 2023-03-23 WOS:000426027900010 0
J Gao, XQ; Erhard, M; Zeilinger, A; Krenn, M Gao, Xiaoqin; Erhard, Manuel; Zeilinger, Anton; Krenn, Mario Computer-Inspired Concept for High-Dimensional Multipartite Quantum Gates PHYSICAL REVIEW LETTERS English Article ON-CHIP GENERATION; ENTANGLEMENT; COMPUTATION; TELEPORTATION; STATES An open question in quantum optics is how to manipulate and control complex quantum states in an experimentally feasible way. Here we present concepts for transformations of high-dimensional multiphotonic quantum systems. The proposals rely on two new ideas: (i) a novel high-dimensional quantum nondemolition measurement, (ii) the encoding and decoding of the entire quantum transformation in an ancillary state for sharing the necessary quantum information between the involved parties. Many solutions can readily be performed in laboratories around the world and thereby we identify important pathways for experimental research in the near future. The concepts have been found using the computer algorithm MELVIN for designing computer-inspired quantum experiments. As opposed to the field of machine learning, here the human learns new scientific concepts by interpreting and analyzing the results presented by the machine. This demonstrates that computer algorithms can inspire new ideas in science, which has a widely unexplored potential that goes far beyond experimental quantum information science. [Gao, Xiaoqin; Erhard, Manuel; Zeilinger, Anton; Krenn, Mario] Univ Vienna, Fac Phys, A-1190 Vienna, Austria; [Gao, Xiaoqin; Erhard, Manuel; Zeilinger, Anton; Krenn, Mario] Austrian Acad Sci, Inst Quantum Opt & Quantum Informat IQOQI Vienna, A-1190 Vienna, Austria; [Gao, Xiaoqin] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China; [Gao, Xiaoqin] Southeast Univ, Quantum Informat Res Ctr, Nanjing 210096, Peoples R China; [Krenn, Mario] Univ Toronto, Dept Chem & Comp Sci, Toronto, ON MSS 3G4, Canada; [Krenn, Mario] Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada University of Vienna; Austrian Academy of Sciences; Southeast University - China; Southeast University - China; University of Toronto Krenn, M (corresponding author), Univ Vienna, Fac Phys, A-1190 Vienna, Austria.;Krenn, M (corresponding author), Austrian Acad Sci, Inst Quantum Opt & Quantum Informat IQOQI Vienna, A-1190 Vienna, Austria.;Krenn, M (corresponding author), Univ Toronto, Dept Chem & Comp Sci, Toronto, ON MSS 3G4, Canada.;Krenn, M (corresponding author), Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada. mario.krenn@univie.ac.at Zeilinger, Anton/A-1170-2011; Krenn, Mario/A-2799-2013 Gao, Xiaoqin/0000-0001-5043-1122; Krenn, Mario/0000-0003-1620-9207 Austrian Academy of Sciences; Austrian Science Fund [SFB F40]; National Natural Science Foundation of China [61571105]; China Scholarship Council; Austrian Science Fund via the Erwin Schrodinger Fellowship [J4309] Austrian Academy of Sciences; Austrian Science Fund(Austrian Science Fund (FWF)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Austrian Science Fund via the Erwin Schrodinger Fellowship The authors thank Borivoje Dakic, Giuseppe Vitagliano, Robert Fickler, Paul Appel, Marcus Huber, Nicolai Friis, Fabien Clivaz, Jaroslav Kysela, and Yelena Guryanova for helpful discussions. X. G. thanks Bin Sheng and Zaichen Zhang for support. This work was supported by the Austrian Academy of Sciences and the Austrian Science Fund with SFB F40 (FoQuS). X. G. acknowledges support from the National Natural Science Foundation of China (Grant No. 61571105) and the China Scholarship Council. M. K. acknowledges support from the Austrian Science Fund via the Erwin Schrodinger Fellowship No. J4309. 57 17 17 10 33 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. JUL 27 2020.0 125 5 50501 10.1103/PhysRevLett.125.050501 0.0 7 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics MQ0TS 32794870.0 Green Submitted 2023-03-23 WOS:000552610300002 0
J Qiu, Y; Liu, Y; Li, SJ; Xu, J Qiu, Yu; Liu, Yun; Li, Shijie; Xu, Jing MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Computer-aided coronavirus disease 2019 (COVID-19) screening; COVID-19 segmentation; lesion segmentation; lightweight networks CHEST CT; RESNET; MODEL The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. [Qiu, Yu; Xu, Jing] Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China; [Liu, Yun] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Comp Vis Lab, CH-8092 Zurich, Switzerland; [Li, Shijie] Univ Bonn, Dept Informat Syst & Artificial Intelligence, D-53115 Bonn, Germany Nankai University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Bonn Xu, J (corresponding author), Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China.;Liu, Y (corresponding author), Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Comp Vis Lab, CH-8092 Zurich, Switzerland. yun.liu@vision.ee.ethz.ch; xujing@nankai.edu.cn Liu, Yun/0000-0001-6143-0264 95 0 0 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3230821 0.0 DEC 2022 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 7S1GN 2023-03-23 WOS:000910509700001 0
J Spreco, A; Eriksson, O; Dahlstrom, O; Cowling, BJ; Timpka, T Spreco, Armin; Eriksson, Olle; Dahlstrom, Orjan; Cowling, Benjamin John; Timpka, Toomas Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009-2014 EMERGING INFECTIOUS DISEASES English Article SURVEILLANCE The growing availability of big data in healthcare and public health opens possibilities for infectious disease control in local settings. We prospectively evaluated a method for integrated local detection and prediction (nowcasting) of influenza epidemics over 5 years, using the total population in Ostergotland County, Sweden. We used routine health information system data on influenza-diagnosis cases and syndromic telenursing data for July 2009-June 2014 to evaluate epidemic detection, peak-timing prediction, and peak-intensity prediction. Detection performance was satisfactory throughout the period, except for the 2011-12 influenza A(H3N2) season, which followed a season with influenza B and pandemic influenza A(H1N1) pdm09 virus activity. Peak-timing prediction performance was satisfactory for the 4 influenza seasons but not the pandemic. Peak-intensity levels were correctly categorized for the pandemic and 2 of 4 influenza seasons. We recommend using versions of this method modified with regard to local use context for further evaluations using standard methods. [Spreco, Armin] Linkoping Univ, Field Syndr Surveillance, Linkoping, Sweden; [Eriksson, Olle; Dahlstrom, Orjan; Timpka, Toomas] Linkoping Univ, Linkoping, Sweden; [Spreco, Armin; Timpka, Toomas] Ctr Hlth Serv Dev, Linkoping, Sweden; [Cowling, Benjamin John] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China Linkoping University; Linkoping University; University of Hong Kong Spreco, A (corresponding author), Linkoping Univ, Div Social Med, Dept Med & Hlth Sci, Fac Hlth Sci, SE-58183 Linkoping, Sweden. armin.spreco@liu.se Timpka, Toomas/E-8065-2018; Cowling, Benjamin/C-4263-2009 Cowling, Benjamin/0000-0002-6297-7154 Swedish Civil Contingencies Agency [2010-2788]; Swedish Research Council [2008-5252] Swedish Civil Contingencies Agency; Swedish Research Council(Swedish Research Council) This study was supported by grants from the Swedish Civil Contingencies Agency (2010-2788) and the Swedish Research Council (2008-5252). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. 22 6 7 0 13 CENTERS DISEASE CONTROL ATLANTA 1600 CLIFTON RD, ATLANTA, GA 30333 USA 1080-6040 1080-6059 EMERG INFECT DIS Emerg. Infect. Dis OCT 2018.0 24 10 1868 1873 10.3201/eid2410.171940 0.0 6 Immunology; Infectious Diseases Science Citation Index Expanded (SCI-EXPANDED) Immunology; Infectious Diseases GT8PR 30226160.0 Green Published, gold, Green Submitted 2023-03-23 WOS:000444801900010 0
C Lin, QJ; Yin, RQ; Li, M; Bredin, H; Barras, C Int Speech Commun Assoc Lin, Qingjian; Yin, Ruiqing; Li, Ming; Bredin, Herve; Barras, Claude LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization INTERSPEECH 2019 Interspeech English Proceedings Paper Interspeech Conference SEP 15-19, 2019 Graz, AUSTRIA Speaker diarization; segments similarity measurement; Bi-LSTM; PLDA; spectral clustering More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALL-HOME database. [Lin, Qingjian; Li, Ming] Duke Kunshan Univ, Data Sci Res Ctr, Kunshan, Peoples R China; [Lin, Qingjian] Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou, Peoples R China; [Yin, Ruiqing; Bredin, Herve; Barras, Claude] Univ Paris Saclay, Univ Paris Sud, LIMSI, CNRS, Orsay, France Duke Kunshan University; Sun Yat Sen University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Li, M (corresponding author), Duke Kunshan Univ, Data Sci Res Ctr, Kunshan, Peoples R China. ming.11369@dukekunshan.edu.cn National Natural Science Foundation of China [61773413]; Natural Science Foundation of Guangzhou City [201707010363]; Six talent peaks project in Jiangsu Province [JY-074]; Science and Technology Program of Guangzhou City [201903010040]; ANR through the ODESSA [ANR-15-CE39-0010]; Lenovo; ANR through PLUMCOT [ANR-16CE92-0025] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangzhou City; Six talent peaks project in Jiangsu Province; Science and Technology Program of Guangzhou City; ANR through the ODESSA(French National Research Agency (ANR)); Lenovo; ANR through PLUMCOT(French National Research Agency (ANR)) This research was funded in part by the National Natural Science Foundation of China (61773413), Natural Science Foundation of Guangzhou City (201707010363), Six talent peaks project in Jiangsu Province (JY-074), Science and Technology Program of Guangzhou City (201903010040), ANR through the ODESSA (ANR-15-CE39-0010), PLUMCOT (ANR-16CE92-0025) and Lenovo. We thank Lin Yang, Xuyang Wang, Junjie Wang, Yulong Liang and yingjie Li from AI lab of Lenovo research who provided insight and expertise that greatly assisted this research. 23 9 9 1 1 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2019.0 366 370 10.21437/Interspeech.2019-1388 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4LP Green Submitted 2023-03-23 WOS:000831796400074 0
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Gomez; Goncharov, M.; Harris, P.; Hu, M.; Klute, M.; Kovalskyi, D.; Krupa, J.; Lee, Y-J; Maier, B.; Marini, A. C.; Mironov, C.; Paus, C.; Rankin, D.; Roland, C.; Roland, G.; Shi, Z.; Stephans, G. S. F.; Tatar, K.; Wang, Z.; Wyslouch, B.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Hiltbrand, J.; Jain, Sh; Krohn, M.; Kubota, Y.; Lesko, Z.; Mans, J.; Revering, M.; Rusack, R.; Saradhy, R.; Schroeder, N.; Strobbe, N.; Wadud, M. A.; Acosta, J. G.; Oliveros, S.; Bloom, K.; Bryson, M.; Claes, D. R.; Fangmeier, C.; Finco, L.; Golf, F.; Fernandez, J. R. Gonzalez; Joo, C.; Kravchenko, I; Siado, J. E.; Snow, G. R.; Tabb, W.; Yan, F.; Agarwal, G.; Bandyopadhyay, H.; Hay, L.; Iashvili, I.; Kharchilava, A.; McLean, C.; Nguyen, D.; Pekkanen, J.; Rappoccio, S.; Williams, A.; Alverson, G.; Barberis, E.; Freer, C.; Haddad, Y.; Hortiangtham, A.; Li, J.; Madigan, G.; Marzocchi, B.; Morse, D. M.; Nguyen, V; Orimoto, T.; Parker, A.; Skinnari, L.; Tishelman-Charny, A.; Wamorkar, T.; Wang, B.; Wisecarver, A.; Wood, D.; Bueghly, J.; Chen, Z.; Gilbert, A.; Gunter, T.; Hahn, K. A.; Odell, N.; Schmitt, M. H.; Sung, K.; Velasco, M.; Band, R.; Bucci, R.; Dev, N.; Goldouzian, R.; Hildreth, M.; Anampa, K. Hurtado; Jessop, C.; Lannon, K.; Loukas, N.; Marinelli, N.; Mcalister, I; Meng, F.; Mohrman, K.; Musienko, Y.; Ruchti, R.; Siddireddy, P.; Wayne, M.; Wightman, A.; Wolf, M.; Zarucki, M.; Zygala, L.; Bylsma, B.; Cardwell, B.; Durkin, L. S.; Francis, B.; Hill, C.; Lefeld, A.; Winer, B. L.; Yates, B. R.; Addesa, F. M.; Bonham, B.; Das, P.; Dezoort, G.; Elmer, P.; Frankenthal, A.; Greenberg, B.; Haubrich, N.; Higginbotham, S.; Kalogeropoulos, A.; Kopp, G.; Kwan, S.; Lange, D.; Lucchini, M. T.; Marlow, D.; Mei, K.; Ojalvo, I; Olsen, J.; Palmer, C.; Stickland, D.; Tully, C.; Norberg, S.; Bakshi, A. S.; Barnes, V. E.; Chawla, R.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Karmarkar, S.; Liu, M.; Negro, G.; Neumeister, N.; Paspalaki, G.; Peng, C. C.; Piperov, S.; Purohit, A.; Schulte, J. F.; Stojanovic, M.; Thieman, J.; Wang, F.; Xiao, R.; Xie, W.; Dolen, J.; Parashar, N.; Baty, A.; Dildick, S.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Li, W.; Padley, B. P.; Redjimi, R.; Roberts, J.; Shi, W.; Leiton, A. G. Stahl; Bodek, A.; de Barbaro, P.; Demina, R.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Hindrichs, O.; Khukhunaishvili, A.; Ranken, E.; Taus, R.; Chiarito, B.; Chou, J. P.; Gandrakota, A.; Gershtein, Y.; Halkiadakis, E.; Hart, A.; Heindl, M.; Hughes, E.; Kaplan, S.; Karacheban, O.; Laflotte, I; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thayil, S. A.; Thomas, S.; Acharya, H.; Delannoy, A. G.; Spanier, S.; Bouhali, O.; Dalchenko, M.; Delgado, A.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Luo, S.; Malhotra, S.; Mueller, R.; Overton, D.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; Hegde, V; Kunori, S.; Lamichhane, K.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I; Whitbeck, A.; Appelt, E.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Romeo, F.; Sheldon, P.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Cox, B.; Cummings, G.; Hakala, J.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, A.; Neu, C.; Tannenwald, B.; Wolfe, E.; Karchin, P. E.; Poudyal, N.; Thapa, P.; Black, K.; Bose, T.; Buchanan, J.; Caillol, C.; Dasu, S.; De Bruyn, I; Everaerts, P.; Fienga, F.; Galloni, C.; He, H.; Herndon, M.; Herve, A.; Hussain, U.; Lanaro, A.; Loeliger, A.; Loveless, R.; Sreekala, J. Madhusudanan; Mallampalli, A.; Mohammadi, A.; Pinna, D.; Savin, A.; Shang, V; Smith, W. H.; Teague, D.; Trembath-reichert, S.; Vetens, W. CMS Collaboration Search for W ' bosons decaying to a top and a bottom quark at root s=13 TeV in the hadronic final state PHYSICS LETTERS B English Article CMS; W ' search; Top resonances; Boosted top A search is performed for W ' bosons decaying to a top and a bottom quark in the all-hadronic final state, in proton-proton collisions at a center-of-mass energy of 13TeV. The analyzed data were collected by the CMS experiment between 2016 and 2018 and correspond to an integrated luminosity of 137fb(-1). Deep neural network algorithms are used to identify the jet initiated by the bottom quark and the jet containing the decay products of the top quark when the W ' boson from the top quark decays hadronically. No excess above the estimated standard model background is observed. Upper limits on the production cross sections of W ' bosons decaying to a top and a bottom quark are set. Both left- and right-handed W ' bosons with masses below 3.4TeV are excluded at 95% confidence level, and the most stringent limits to date on W ' bosons decaying to a top and a bottom quark in the all-hadronic final state are obtained. (C) 2021 The Author(s). Published by Elsevier B.V. [Sirunyan, A. M.; Tumasyan, A.; Belyaev, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Del Valle, A. Escalante; Fruhwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I; Pitters, F. M.; Schieck, J.; Schofbeck, R.; Spanring, M.; Templ, S.; Waltenberger, W.; Wulz, C-E; Chahal, G. S.] Inst Hochenergiephys, Vienna, Austria; [Chekhovsky, V; Litomin, A.; Makarenko, V; Nash, J.] Inst Nucl Problems, Minsk, BELARUS; [Darwish, M. R.; De Wolf, E. A.; Janssen, X.; Kello, T.; Lelek, A.; Sfar, H. Rejeb; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.; Rumerio, P.] Univ Antwerp, Antwerp, Belgium; [Blekman, F.; Bols, E. S.; De Clercq, J.; Delcourt, M.; Lowette, S.; Moortgat, S.; Morton, A.; Muller, D.; Sahasransu, A. R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Stylianou, N.; Hogan, J. M.] Vrije Univ Brussel, Brussels, Belgium; [Kello, T.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I; Moureaux, L.; Petre, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wezenbeek, L.; Fang, W.; Gao, X.; Sagir, S.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Gruchala, M.; Mestdach, G.; Niedziela, M.; Roskas, C.; Skovpen, K.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Dilsiz, K.] Univ Ghent, Ghent, Belgium; [Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Vischia, P.; Wertz, S.; Wuyckens, S.; Mestvirishvili, A.] Catholic Univ Louvain, Louvain La Neuve, Belgium; [Alves, G. A.; Hensel, C.; Moraes, A.; Ogul, H.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Alda Junior, W. L.; Ferreira Filho, M. Barroso; Malbouisson, H. Brandao; Carvalho, W.; Chinellato, J.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; De Souza, S. Fonseca; Figueiredo, D. Matos; Herrera, C. Mora; Amarilo, K. Mota; Mundim, L.; Nogima, H.; Teles, P. Rebello; Rosas, L. J. Sanchez; Santoro, A.; Silva Do Amaral, S. M.; Sznajder, A.; Thiel, M.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela; Ozok, F.] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. 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Buchot; Busson, P.; Charlot, C.; Davignon, O.; Diab, B.; Falmagne, G.; Ghosh, S.; de Cassagnac, R. Granier; Hakimi, A.; Kucher, I; Lobanov, A.; Nguyen, M.; Ochando, C.; Paganini, P.; Rembser, J.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Zabi, A.; Zghiche, A.] Ecole Polytech, Lab Leprince Ringuet, Inst Polytech Paris, CNRS IN2P3, Palaiseau, France; [Agram, J-L; Andrea, J.; Apparu, D.; Bloch, D.; Bourgatte, G.; Brom, J-M; Chabert, E. C.; Collard, C.; Darej, D.; Fontaine, J-C; Goerlach, U.; Grimault, C.; Le Bihan, A-C; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; [Asilar, E.; Beauceron, S.; Bernet, C.; Boudoul, G.; Camen, C.; Carle, A.; Chanon, N.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Jain, Sa; Laktineh, I. 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M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cuffiani, M.; Diotalevi, T.; Fanfani, A.; Fontanesi, E.; Giommi, L.; Guiducci, L.; Iemmi, F.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Monti, F.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Ist Nazl Fis Nucl, Sez Milano Bicocca, Milan, Italy; [Brivio, F.; Cetorelli, F.; Ciriolo, V; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Monti, F.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Carnevali, F.; Cavallo, N.; De Iorio, A.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Carnevali, F.; De Iorio, A.; Iorio, A. O. M.; Lista, L.; Sciacca, C.; Layer, L.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Layer, L.; Margoni, M.; Meneguzzo, A. T.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Re, V; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.; Fallavollita, F.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Ressegotti, M.; Riccardi, C.; Vitulo, P.; Fallavollita, F.] Univ Pavia, Pavia, Italy; [Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Piccinelli, A.; Rossi, A.; Santocchia, A.; Tedeschi, T.] Univ Perugia, Perugia, Italy; [Azzurri, P.; Bagliesi, G.; Bertacchi, V; Bianchini, L.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Ciocci, M. A.; Messineo, A.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [Bossini, E.; Di Domenico, M. R.; Turini, N.] Univv Siena, Siena, Italy; [Cavallari, F.; Cipriani, M.; Del Re, D.; Di Marco, E.; Diemoz, M.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Cipriani, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cappati, A.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ruspa, M.; Salvatico, R.; Shchelina, K.; Siviero, F.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Cappati, A.; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monaco, V; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Salvatico, R.; Shchelina, K.; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Trocino, D.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Candelise, V; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Kim, T. J.; Park, J.] Hanyang Univ, Seoul, South Korea; [Lee, K.; Park, J.; Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K. S.; Lim, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Lee, S.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I] Seoul Natl Univ, Seoul, South Korea; [Jeon, D.; Kim, J. H.; Ko, B.; Lee, J. S. H.; Park, I. C.; Roh, Y.; Song, D.; Watson, I. J.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Lee, H.; Choi, Y.; Jeong, Y.; Lee, Y.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Beyrouthy, T.; Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Kuwait; [Mukherjee, S.; Roy, D.; Bhattacharya, S.; Banerjee, S.; Chudasama, R.; Guchait, M.; Karmakar, S.; Kumar, S.; Majumder, G.; Mazumdar, K.; Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.; Vaitkevicius, A.] Vilnius Univ, Vilnius, Lithuania; [Bin Norjoharuddeen, N.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Murillo Quijada, J. A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; De la Cruz-Burelo, E.; Heredia-De la Cruz, I; Lopez-Fernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez-Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Ramirez-Garcia, M.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M., I; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Szleper, M.; Traczyk, P.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Faccioli, P.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Niknejad, T.; Seixas, J.; Toldaiev, O.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Finger, M.; Khvedelidze, A.; Tsamalaidze, Z.; Afanasiev, S.; Budkouski, D.; Bunin, P.; Gavrilenko, M.; Golutvin, I; Gorbunov, I; Kamenev, A.; Karjavine, V; Lanev, A.; Malakhov, A.; Matveev, V; Palichik, V; Perelygin, V; Savina, M.; Seitova, D.; Shalaev, V; Shmatov, S.; Shulha, S.; Smirnov, V; Teryaev, O.; Voytishin, N.; Zarubin, A.; Zhizhin, I] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V; Ivanov, Y.; Kim, V; Kuznetsova, E.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Tlisov, D.; Toropin, A.; Musienko, Y.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Nikitenko, A.; Popov, V; Safronov, G.; Spiridonov, A.; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys & Technol, Moscow, Russia; [Matveev, V; Chadeeva, M.; Oskin, A.; Parygin, P.; Popova, E.; Rusinov, V] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Chadeeva, M.; Andreev, V; Azarkin, M.; Dremin, I; Kirakosyan, M.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Katkov, I; Belyaev, A.; Boos, E.; Bunichev, V; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V; Korneeva, N.; Lokhtin, I; Obraztsov, S.; Perfilov, M.; Savrin, V; Volkov, P.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Dimova, T.; Kardapoltsev, L.; Ovtin, I; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Kachanov, V; Kalinin, A.; Konstantinov, D.; Petrov, V; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Natl Res Ctr, Inst High Energy Phys, Kurchatov Inst, Protvino, Russia; [Babaev, A.; Okhotnikov, V; Sukhikh, L.] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borchsh, V; Ivanchenko, V; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.; Milosevic, V] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.; Milosevic, V] Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I; Barrio Luna, M.; Bedoya, Cristina F.; Carrillo Montoya, C. A.; Cepeda, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Leon Holgado, J.; Moran, D.; Navarro Tobar, A.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Sanchez Navas, S.; Soares, M. S.; Urda Gomez, L.; Willmott, C.] Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid, Spain; [de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Palencia Cortezon, E.; Ramon Alvarez, C.; Ripoll Sau, J.; Rodriguez Bouza, V; Trapote, A.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Ricci-Tam, F.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I; Vizan Garcia, J. M.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Jayananda, M. K.; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, D. D. C.] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Sharma, A.; Stahl, A.; Hartmann, F.; Ciriolo, V; Valsecchi, D.; Meola, S.; Paolucci, P.; Aarrestad, T. K.; Abbaneo, D.; Alimena, J.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Beni, N.; Bianco, M.; Bocci, A.; Brondolin, E.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chhibra, S. S.; Cristella, L.; Dabrowski, A.; Daci, N.; David, A.; De Roeck, A.; Deile, M.; Di Maria, R.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fallavollita, F.; Fasanella, D.; Fiorendi, S.; Florent, A.; Franzoni, G.; Fulcher, J.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Haranko, M.; Hegeman, J.; Iiyama, Y.; Innocente, V; James, T.; Janot, P.; Kaspar, J.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Long, K.; Lourenco, C.; Malgeri, L.; Mallios, S.; Mannelli, M.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pitt, M.; Qu, H.; Quast, T.; Rabady, D.; Racz, A.; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schafer, C.; Schwick, C.; Selvaggi, M.; Silva, P.; Snoeys, W.; Sphicas, P.; Summers, S.; Tavolaro, V. R.; Treille, D.; Tsirou, A.; Van Onsem, G. P.; Verzetti, M.; Wozniak, K. A.; Zeuner, W. D.; Virdee, T.] European Org Nucl Res, CERN, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Androsov, K.; Backhaus, M.; Berger, P.; Calandri, A.; Chernyavskaya, N.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Gadek, T.; Espinosa, T. A. Gomez; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A-M; Manzoni, R. A.; Perez, C. Martin; Meinhard, M. T.; Micheli, F.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V; Perrin, G.; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Schonenberger, M.; Stampf, V; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Caminada, L.; Amsler, C.; Bartschi, P.; Botta, C.; Brzhechko, D.; Canelli, M. F.; De Wit, A.; Del Burgo, R.; Heikkila, J. K.; Huwiler, M.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I; Rauco, G.; Reimers, A.; Robmann, P.; Cruz, S. Sanchez; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Ceard, L.; Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W-S; Li, Y. Y.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Yazgan, E.; Yu, P. R.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dumanoglu, I; Eskut, E.; Gokbulut, G.; Guler, Y.; Guler, E. Gurpinar; Hos, I; Isik, C.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Ukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Sen, F. Aydogmus; Cerci, S.; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; [Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Krikler, B.; Paramesvaran, S.; Sakuma, T.; El Nasr-Storey, S. Seif; Smith, V. J.; Stylianou, N.; Taylor, J.; Titterton, A.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Bell, K. W.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Ellis, K., V; Harder, K.; Harper, S.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Egyptian Knowledge Bank (EKB); 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Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Kyung Hee University; Sejong University; Seoul National University (SNU); University of Seoul; Yonsei University; Sungkyunkwan University (SKKU); American University of Middle East; Riga Technical University; Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; University of Montenegro; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; AGH University of Science & Technology; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade Federal de Mato Grosso do Sul; Nanjing Normal University; Egyptian Knowledge Bank (EKB); Ain Shams University; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; Egyptian Knowledge Bank (EKB); British University in Egypt; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Erzincan Binali Yildirim University; Brandenburg University of Technology Cottbus; Egyptian Knowledge Bank (EKB); Assiut University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Sharif University of Technology; University of Science & Technology of Mazandaran; Italian National Agency New Technical Energy & Sustainable Economics Development; Peter the Great St. Petersburg Polytechnic University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Eastern University, Sri Lanka; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Sirnak University; Near East University; Konya Technical University; Istanbul University - Cerrahpasa; Mersin University; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University Sirunyan, AM (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. SIMSEK, Ali Eren/AFO-3050-2022; Lange, Torben/HOH-4787-2023; Hos, ilknur/T-1165-2019; Goy Lopez, Silvia/K-9200-2017; Calligaris, Luigi/K-2018-2017; Lezki, Samet/AAG-8277-2019; Lychkovskaya, Natalia/F-8341-2017; Bortignon, Pierluigi/AAF-6561-2020; Simone, Federica Maria/HDM-3686-2022; Shmatov, Sergei/J-5621-2014; Bermudez Martinez, Armando/HHN-4625-2022; Joo, Changwoo/ABI-4034-2020; Kirakosyan, Martin R./N-2701-2015; Snoeys, Walter J M/K-8259-2015; Tsirou, Andromachi/HHC-6869-2022; Parygin, Pavel/C-8908-2016; Ambrozas, Marijus/F-5551-2018; Lezki, Merve Ince/AAG-5283-2021; Dallavalle, Gaetano Marco/AFW-4654-2022; Shopova, Mariana Filipova/HKW-0149-2023; M, F/HGF-3166-2022; Novaes, Sergio F./D-3532-2012; Aimè, Chiara/GXG-4131-2022; Moscatelli, Francesco/AEL-1878-2022; Akgun, Bora/AAM-8311-2021; Peris, Antonio Delgado/AAA-5165-2019; Özdemir, Kadri/P-8058-2014; Chenarani, Shirin/AAO-8918-2021; Singh, J B/ABF-5574-2021; Ivanchenko, Vladimir N/L-5254-2017; Gozalez-Lopez, Oscar/AAH-3533-2019; Benaglia, Andrea/AGF-5495-2022; Blekman, Freya/ABD-7916-2020; Selvaggi, Michele/CAJ-2129-2022; Kim, Victor T/L-1648-2013; Saka, Halil/GPF-8892-2022; Savina, Maria/HDO-6544-2022; Cepeda, Maria/AAF-4992-2019; Fouz, Maria Cruz/AAF-1105-2019; Sunar Cerci, Deniz/AHE-1706-2022; Zorbilmez, Caglar/ABH-1905-2020; Petkov, Peicho/M-2080-2016; Soares, Mara Senghi/GZM-6676-2022; Shah, Aashaq/GYI-8983-2022; Rabady, Dinyar/GSN-9210-2022; Boimska, Bożena/Z-6088-2019; Cassese, Antonio/R-1713-2016; Ciangottini, Diego/HJH-5914-2023; Strologas, John/GWR-2036-2022; Demiroglu, Zuhal Seyma/A-5168-2016; Fiorendi, Sara/ABA-3311-2021; Chadeeva, Marina/C-8789-2016; Moraes, Arthur/F-6478-2010; Pásztor, Gabriella/D-9716-2016; Vai, Ilaria/AAT-5836-2020; CAKIR, Altan/ABD-4450-2020; Viliani, Lorenzo/AAU-3462-2021; Iqbal, Muhammad/HJH-2369-2023; Gupta, Rajat/GVS-7964-2022; Bunichev, Viacheslav/E-2285-2012; Matorras, Francisco/I-4983-2015; Lowette, Steven/HKV-3341-2023; SEVINC KAYA, OZLEM/AAE-8069-2020; Ligabue, Franco/ABI-1681-2020; Legger, Federica/HNC-2568-2023; Ferro, Fabrizio/HMP-5516-2023; Duarte, Javier Mauricio/AAA-5414-2020; Norjoharuddeen, Nurfikri/S-3109-2018; Andreev, Vladimir F/M-8665-2015; Puerta Pelayo, Jesus/L-1632-2014; TUVE', Cristina/P-3933-2015; Terkulov, Adel/M-8581-2015; Fienga, Francesco/HKE-1204-2023; Lokos, Sandor/A-4798-2019; Benussi, Luigi/O-9684-2014; kaya, mithat/D-8062-2011; Gerosa, Raffaele/AGL-5921-2022; Cepeda, Maria/HNO-9314-2023; Tinoco Mendes, Andre David/D-4314-2011; Tcherniaev, Evgueni/G-3453-2016; Iorio, Alberto Orso Maria/GXA-0233-2022; Trocino, Daniele/AGI-2155-2022; Felcini, Marta A./S-2684-2019; Giommi, Luca/AGV-7154-2022; Watson, Ian James/AFL-9360-2022; Khakzad, Mohsen/ABB-1945-2021; Pellecchia, Antonello/HNB-9377-2023; de guio, federico/B-8553-2009; Sert, H/ABA-5645-2020; Dudko, Lev/D-7127-2012; Schwarz, Dennis/GYA-4479-2022; Aydogmus, Fatma/I-4879-2014; Teles, Patricia/ABF-9723-2021; Hernández Calama, José María/AAW-6394-2021; Melo da Costa, Eliza Melo/AAX-2394-2021; Casarsa, Massimo/L-3623-2018; Lokhtin, Igor/D-7004-2012; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Guler, Yalcin/AAG-7833-2019; De Iorio, Agostino/HJH-7155-2023; Sguazzoni, Giacomo/J-4620-2015; Della Ricca, Giuseppe/B-6826-2013; Lannon, Kevin/HGU-5755-2022; Quijada, Javier Alberto Murillo/AAS-4234-2021; Azarkin, Maxim/N-2578-2015; Khvedelidze, Arsen/AAF-5025-2020; Donato, Silvio/GPK-2262-2022; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Gil Da Silveira, Gustavo/N-7279-2014; Watson, Ian James/AAH-1423-2022; Mejia Guisao, Jhovanny Andres/ABG-3627-2021; Aarrestad, Thea/GLV-3031-2022; Yazgan, Efe/C-4521-2014; Androsov, Konstantin/HJA-2332-2022; Di Florio, Adriano/ABA-3631-2020; de Souza Sandro, Fonseca/ABB-8505-2020; Elkafrawy, Tamer/Q-8786-2016; Palencia Cortezon, Jose Enrique/F-1059-2017; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Sola, Valentina/E-7726-2015; Paulini, Manfred/N-7794-2014; Azzi, Patrizia/H-5404-2012; Godinovic, Nikola/G-6124-2017; Fernandez Menendez, Javier/B-6550-2014; Govoni, Pietro/K-9619-2016; Goh, Junghwan/Q-3720-2016; Grandi, Claudio/B-5654-2015; D'Alessandro, Raffaello/F-5897-2015; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; HAJ AHMAD (Vael HAJAHMAD), Wael/E-6738-2016; Kim, Tae Jeong/P-7848-2015; Giacomelli, Paolo/B-8076-2009; Malik, Sudhir/D-6621-2018; Ivanov, Andrew/A-7982-2013; Navarria, Francesco Luigi/ACP-7823-2022; Gulmez, Erhan/P-9518-2015; Moon, Chang-Seong/J-3619-2014; Leonardo, Nuno/M-6940-2016; Gonzalez Caballero, Isidro/E-7350-2010; Dubinin, Mikhail/I-3942-2016; ALCARAZ MAESTRE, JUAN/I-5763-2015; Duarte-Campderros, Jordi/F-5025-2018; Vilela Pereira, Antonio/L-4142-2016; Konecki, Marcin/G-4164-2015; Mora Herrera, Maria Clemencia/L-3893-2016; Heredia de la Cruz, Ivan/L-2610-2018; Redondo, Ignacio/K-8639-2014; Sagir, Sinan/A-5219-2018; Rolandi, Luigi (Gigi)/E-8563-2013; Malawski, Maciej/H-9119-2012; Josa Mutuberria, Isabel/K-5184-2014; Sanchez-Hernandez, Alberto/L-4955-2017; Belyaev, Alexander/F-6637-2015; Bernardes, Cesar Augusto/D-2408-2015; Kyberd, Paul/P-1092-2014; Lee, Jason/B-9701-2014; Grzanka, Leszek/M-9052-2018; Fernandez Bedoya, Cristina/K-8066-2014; Padula, Sandra S./G-3560-2012; Popov, Andrey/E-1052-2012; Ragazzi, Stefano/D-2463-2009; Haddad, Yacine/GRX-5022-2022; Sznajder, Andre/L-1621-2016; Raidal, Martti/F-4436-2012; Spagnolo, Paolo/G-3401-2017; Bhatnagar, Vipin/Z-1079-2018; Rossi, Biagio/F-4137-2018; Dharmaratna, Welathantri/F-6745-2018; Mundim, Luiz/A-1291-2012; Ruiz Jimeno, Alberto/E-4473-2011 SIMSEK, Ali Eren/0000-0002-9074-2256; Lange, Torben/0000-0001-6242-7331; Hos, ilknur/0000-0002-7678-1101; Calligaris, Luigi/0000-0002-9951-9448; Lezki, Samet/0000-0002-6909-774X; Bortignon, Pierluigi/0000-0002-5360-1454; Simone, Federica Maria/0000-0002-1924-983X; Shmatov, Sergei/0000-0001-5354-8350; Bermudez Martinez, Armando/0000-0001-8822-4727; Snoeys, Walter J M/0000-0003-3541-9066; Parygin, Pavel/0000-0001-6743-3781; Ambrozas, Marijus/0000-0003-2449-0158; Lezki, Merve Ince/0000-0001-6907-0195; Dallavalle, Gaetano Marco/0000-0002-8614-0420; Shopova, Mariana Filipova/0000-0001-6664-2493; Novaes, Sergio F./0000-0003-0471-8549; Moscatelli, Francesco/0000-0002-7676-3106; Akgun, Bora/0000-0001-8888-3562; Peris, Antonio Delgado/0000-0002-8511-7958; Özdemir, Kadri/0000-0002-0103-1488; Chenarani, Shirin/0000-0002-1425-076X; Singh, J B/0000-0001-9029-2462; Ivanchenko, Vladimir N/0000-0002-1844-5433; Gozalez-Lopez, Oscar/0000-0002-4532-6464; Benaglia, Andrea/0000-0003-1124-8450; Blekman, Freya/0000-0002-7366-7098; Kim, Victor T/0000-0001-7161-2133; Savina, Maria/0000-0002-9020-7384; Cepeda, Maria/0000-0002-6076-4083; Fouz, Maria Cruz/0000-0003-2950-976X; Sunar Cerci, Deniz/0000-0002-5412-4688; Zorbilmez, Caglar/0000-0002-5199-061X; Petkov, Peicho/0000-0002-0420-9480; Soares, Mara Senghi/0000-0001-9676-6059; Shah, Aashaq/0000-0002-6157-2016; Rabady, Dinyar/0000-0001-9239-0605; Boimska, Bożena/0000-0002-4200-1541; Cassese, Antonio/0000-0003-3010-4516; Demiroglu, Zuhal Seyma/0000-0001-7977-7127; Fiorendi, Sara/0000-0003-3273-9419; Chadeeva, Marina/0000-0003-1814-1218; Moraes, Arthur/0000-0002-5157-5686; Pásztor, Gabriella/0000-0003-0707-9762; Vai, Ilaria/0000-0003-0037-5032; CAKIR, Altan/0000-0002-8627-7689; Viliani, Lorenzo/0000-0002-1909-6343; Bunichev, Viacheslav/0000-0003-4418-2072; Matorras, Francisco/0000-0003-4295-5668; Lowette, Steven/0000-0003-3984-9987; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Ligabue, Franco/0000-0002-1549-7107; Legger, Federica/0000-0003-1400-0709; Duarte, Javier Mauricio/0000-0002-5076-7096; Norjoharuddeen, Nurfikri/0000-0002-8818-7476; Andreev, Vladimir F/0000-0002-5492-6920; Puerta Pelayo, Jesus/0000-0001-7390-1457; TUVE', Cristina/0000-0003-0739-3153; Terkulov, Adel/0000-0003-4985-3226; Fienga, Francesco/0000-0001-5978-4952; Lokos, Sandor/0000-0002-4447-4836; Benussi, Luigi/0000-0002-2363-8889; kaya, mithat/0000-0003-2890-4493; Cepeda, Maria/0000-0002-6076-4083; Tinoco Mendes, Andre David/0000-0001-5854-7699; Tcherniaev, Evgueni/0000-0002-3685-0635; Iorio, Alberto Orso Maria/0000-0002-3798-1135; Trocino, Daniele/0000-0002-2830-5872; Felcini, Marta A./0000-0002-2051-9331; Giommi, Luca/0000-0003-3539-4313; Watson, Ian James/0000-0003-2141-3413; Khakzad, Mohsen/0000-0002-2212-5715; Pellecchia, Antonello/0000-0003-3279-6114; Dudko, Lev/0000-0002-4462-3192; Aydogmus, Fatma/0000-0003-1434-2143; Teles, Patricia/0000-0001-9029-8506; Hernández Calama, José María/0000-0001-6436-7547; Melo da Costa, Eliza Melo/0000-0002-5016-6434; Casarsa, Massimo/0000-0002-1353-8964; Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Guler, Yalcin/0000-0001-7598-5252; De Iorio, Agostino/0000-0002-9258-1345; Sguazzoni, Giacomo/0000-0002-0791-3350; Della Ricca, Giuseppe/0000-0003-2831-6982; Lannon, Kevin/0000-0002-9706-0098; Quijada, Javier Alberto Murillo/0000-0003-4933-2092; Azarkin, Maxim/0000-0002-7448-1447; Khvedelidze, Arsen/0000-0002-5953-0140; Donato, Silvio/0000-0001-7646-4977; Gil Da Silveira, Gustavo/0000-0003-3514-7056; Watson, Ian James/0000-0003-2141-3413; Aarrestad, Thea/0000-0002-7671-243X; Yazgan, Efe/0000-0001-5732-7950; Androsov, Konstantin/0000-0003-2694-6542; Di Florio, Adriano/0000-0003-3719-8041; de Souza Sandro, Fonseca/0000-0001-7830-0837; Sharma, Varun/0000-0003-1287-1471; Backhaus, Malte/0000-0002-5888-2304; Flix Molina, Jose/0000-0003-2688-8047; Azzurri, Paolo/0000-0002-1717-5654; Tricomi, Alessia Rita/0000-0002-5071-5501; Lucchini, Marco Toliman/0000-0002-7497-7450; Meridiani, Paolo/0000-0002-8480-2259; Vami, Tamas Almos/0000-0002-0959-9211; Golf, Frank/0000-0003-3567-9351; Elkafrawy, Tamer/0000-0001-9930-6445; Pfeiffer, Andreas/0000-0001-5328-448X; Migliore, Ernesto/0000-0002-2271-5192; Sosa Ricardo, Rafael Eduardo/0000-0002-2240-6699; Bossini, Edoardo/0000-0002-2303-2588; Venturi, Andrea/0000-0002-0249-4142; De Jesus Damiao, Dilson/0000-0002-3769-1680; Palencia Cortezon, Jose Enrique/0000-0001-8264-0287; Goldstein, Joel/0000-0003-1591-6014; Zorbakir, Ibrahim Soner/0000-0002-5962-2221; Cristella, Leonardo/0000-0002-4279-1221; CECCARELLI, RUDY/0000-0003-3232-9380; Seidel, Markus/0000-0003-3550-6151; Gomez-Ceballos, Guillelmo/0000-0003-1683-9460; Grohsjean, Alexander/0000-0003-0748-8494; Andrea, Jeremy/0000-0002-8298-7560; Flaecher, Henning/0000-0002-5371-941X; Mohammadi Najafabadi, Mojtaba/0000-0001-6131-5987; Lee, Sung-Won/0000-0002-3388-8339; Pugliese, Gabriella/0000-0001-5460-2638; Aime, Chiara/0000-0003-0449-4717; Waltenberger, Wolfgang/0000-0002-6215-7228; Sola, Valentina/0000-0001-6288-951X; Dominguez, Aaron/0000-0002-7420-5493; Garcia, Francisco/0000-0002-4023-7964; Trevisani, Nicolo/0000-0002-5223-9342; Schweiger, Korbinian/0000-0002-5846-3919; Meyer, Arnd/0000-0001-9598-6623; Tao, Junquan/0000-0003-2006-3490; Paulini, Manfred/0000-0002-6714-5787; Chapon, Emilien/0000-0001-6968-9828; Saygin, Kadir/0000-0002-8419-1400; Brigljevic, Vuko/0000-0001-5847-0062; Landsberg, Greg/0000-0002-4184-9380; Blumenfeld, Barry/0000-0003-1150-1735; Wallny, Rainer/0000-0001-8038-1613; Krikler, Benjamin/0000-0001-9712-0030; Presilla, Matteo/0000-0003-2808-7315; Haller, Johannes/0000-0001-9347-7657; Rabbertz, Klaus/0000-0001-7040-9846; Tully, Christopher/0000-0001-6771-2174; Cuevas Maestro, Francisco Javier/0000-0001-5080-0821; Di Croce, Davide/0000-0002-1122-7919; Galli Mercadante, Pedro/0000-0001-8333-4302; Ptochos, Fotios/0000-0002-3432-3452; Azzi, Patrizia/0000-0002-3129-828X; Canelli, Florencia/0000-0001-6361-2117; Godinovic, Nikola/0000-0002-4674-9450; Sciacca, Crisostomo/0000-0002-8412-4072; Fernandez Menendez, Javier/0000-0002-5213-3708; Pastrone, Nadia/0000-0001-7291-1979; siviero, federico/0000-0002-4427-4076; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Mrenna, Stephen/0000-0001-8731-160X; Govoni, Pietro/0000-0002-0227-1301; Erdmann, Martin/0000-0002-1653-1303; Attia Mahmoud, Mohammed/0000-0001-8692-5458; Naimuddin, Md/0000-0003-4542-386X; Hall, Geoffrey/0000-0002-6299-8385; Meuser, Danilo/0000-0002-2722-7526; Lo Meo, Sergio/0000-0003-3249-9208; Elmer, Peter/0000-0001-6830-3356; Lotti, Mikko/0000-0002-6799-3065; Goh, Junghwan/0000-0002-1129-2083; Das, Pallabi/0000-0002-9770-1377; Grandi, Claudio/0000-0001-5998-3070; Escalante del Valle, Alberto/0000-0002-9702-6359; Dragicevic, Marko/0000-0003-1967-6783; Forthomme, Laurent/0000-0002-3302-336X; Barroso Ferreira, Mapse/0000-0003-3904-0571; Vischia, Pietro/0000-0002-7088-8557; D'Hondt, Jorgen/0000-0002-9598-6241; Pedro, Kevin/0000-0003-2260-9151; Faltermann, Nils/0000-0001-6506-3107; Malgeri, Luca/0000-0002-0113-7389; Perez Prada, Maximilian/0000-0002-2831-463X; D'Alessandro, Raffaello/0000-0001-7997-0306; Zuolo, Davide/0000-0003-3072-1020; Missiroli, Marino/0000-0002-1780-1344; Schulte, Jan-Frederik/0000-0003-4421-680X; Gutsche, Oliver/0000-0002-8015-9622; Botta, Cristina/0000-0002-8072-795X; Zghiche, Amina/0000-0002-1178-1450; Chauhan, Shubhanshu/0000-0002-6544-5794; Stepennov, Anton/0000-0001-7747-6582; Kasemann, Matthias/0000-0002-0429-2448; Titov, Maxim/0000-0002-1119-6614; Wilson, Graham/0000-0003-0917-4763; Smith, Wesley/0000-0003-3195-0909; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226; Hinzmann, Andreas/0000-0002-2633-4696; Klanner, Robert/0000-0002-7004-9227; Cepaitis, Vilius/0000-0002-4809-4056; Benitez, J.F./0000-0002-2633-6712; Mousa, Jehad/0000-0002-2978-2718; Primavera, Federica/0000-0001-6253-8656; Sarica, Ulascan/0000-0002-1557-4424; Antchev, Gueorgui/0000-0003-3210-5037; Schroder, Matthias/0000-0001-8058-9828; Buontempo, Salvatore/0000-0001-9526-556X; Collard, Caroline/0000-0002-5230-8387; HAJ AHMAD (Vael HAJAHMAD), Wael/0000-0003-1491-0446; Harb, Ali/0000-0001-5750-3889; Kanuganti, Ankush Reddy/0000-0002-0789-1200; Ruiz, Jose/0000-0002-3306-0363; Gomez Espinosa, Tirso Alejandro/0000-0002-9443-7769; Zanetti, Marco/0000-0003-4281-4582; De La Cruz Burelo, Eduard/0000-0002-7469-6974; Li, Qiang/0000-0002-8290-0517; Habibullah, Redwan/0000-0002-3161-8300; Lange, Johannes/0000-0001-7513-6330; Kolberg, Ted/0000-0002-0211-6109; Kim, Tae Jeong/0000-0001-8336-2434; Donega, Mauro/0000-0001-9830-0412; Fernandez Ramos, Juan Pablo/0000-0002-0122-313X; Rizzi, Andrea/0000-0002-4543-2718; Cousins, Robert/0000-0002-5963-0467; Elmetenawee, Walaa/0000-0001-7069-0252; Giacomelli, Paolo/0000-0002-6368-7220; 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CHANG, PAO-TI/0000-0003-4064-388X; Borg, Johan/0000-0002-7716-7621; Rossi, Biagio/0000-0002-0807-8772; Martelli, Arabella/0000-0003-3530-2255; Lotfy Abdelfattah Ahmad, Ahmad/0000-0003-4681-0079; CUFFIANI, Marco/0000-0003-2510-5039; Wardle, Nicholas/0000-0003-1344-3356; Dharmaratna, Welathantri/0000-0002-6366-837X; Mariani, Valentina/0000-0001-7108-8116; Marzocchi, Badder/0000-0001-6687-6214; Gershtein, Yuri/0000-0002-4871-5449; Ecklund, Karl/0000-0002-6976-4637; Belloni, Alberto/0000-0002-1727-656X; Gul, Muhammad/0000-0002-5704-1896; gennai, simone/0000-0001-5269-8517; Frankenthal, Andre/0000-0002-2583-5982; Papadopoulos, Ioannis/0000-0002-9937-3063; Bodek, Arie/0000-0003-0409-0341; Mundim, Luiz/0000-0001-9964-7805; Usai, Emanuele/0000-0001-9323-2107; Santanastasio, Francesco/0000-0003-2505-8359; Cappati, Alessandra/0000-0003-4386-0564; Ghosh, Saranya Samik/0000-0001-6717-0803; You, Zhengyun/0000-0001-8324-3291; Dini, Paolo/0000-0001-7375-4899; Bethani, Agni/0000-0002-8150-7043; Ruiz Jimeno, Alberto/0000-0002-3639-0368; FORD, WILLIAM/0000-0001-8703-6943; Kreczko, Luke/0000-0003-2341-8330; Martinez Ruiz del Arbol, Pablo/0000-0002-7737-5121; Jaramillo Gallego, Johny/0000-0003-3885-6608; Pujahari, Prabhat Ranjan/0000-0002-0994-7212; Bainbridge, Robert/0000-0001-9157-4832 BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MOST (China); NSFC (China); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); LNS (Mexico); 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Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) under the Excellence of Science -EOS -be.h project [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports(MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe [390833306, 400140256 -GRK2497]; Lendulet (Momentum) Program (Hungary); Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; Ministry of Science and Higher Education (Poland); National Science Centre (Poland) [Opus 2014/15/B/ST2/03998, 2015/19/B/ST2/02861]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [0723-2020-0041]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C1845]; Weston Havens Foundation (USA) BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MOST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); LNS (Mexico); SEP (Mexico); UASLPFAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); ROSATOM (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MoSTR(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (USA)(United States Department of Energy (DOE)); NSF (USA)(National Science Foundation (NSF)); Marie-Curie program(European Commission); European Research Council and Horizon 2020 Grant; Leventis Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium) under the Excellence of Science -EOS -be.h project(FWO); Beijing Municipal Science AMP; Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports(MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe(German Research Foundation (DFG)); Lendulet (Momentum) Program (Hungary); Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); Ministry of Science and Higher Education (Poland)(Ministry of Science and Higher Education, Poland); National Science Centre (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (USA) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MOST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLPFAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, ROSATOM, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MoSTR(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAKand TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306, and under project number 400140256 -GRK2497; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Ministry of Science and Higher Education and the National Science Centre, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no.; 0723-2020-0041 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C1845; and the Weston Havens Foundation (USA). 75 3 3 8 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B SEP 10 2021.0 820 136535 10.1016/j.physletb.2021.136535 0.0 JUL 2021 26 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics WP4SU Green Published, gold, Green Accepted, Green Submitted 2023-03-23 WOS:000713124400035 0
J Huang, K; Li, BW; Chen, S; Claesen, L; Xi, W; Chen, JJ; Jiang, XW; Liu, ZL; Xiong, DL; Yan, XL Huang, Kai; Li, Bowen; Chen, Siang; Claesen, Luc; Xi, Wei; Chen, Junjian; Jiang, Xiaowen; Liu, Zhili; Xiong, Dongliang; Yan, Xiaolang Structured Term Pruning for Computational Efficient Neural Networks Inference IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS English Article Algorithm-architecture codesign; compression and acceleration; neural networks; quantization; systolic array (SA) The state-of-the-art convolutional neural network accelerators are showing a growing interest in exploiting the bit-level sparsity and eliminating the ineffectual computations of zero bits. However, the excessive redundancy and the irregular distribution of nonzero bits limit the real speedup in the accelerators. To address this, we propose an algorithm-architecture codesign, named structured term pruning (STP), to boost the computation efficiency of neural networks inference. Specifically, we enhance the bit sparsity by guiding the weights toward the value with fewer power-of-two terms. Then, we structure the terms with layer-wise group budgets. Retraining is adopted to recover the accuracy drop. We also design the hardware of the group processing element and the fast signed-digital encoder for efficient implementation of STP networks. The system design of STP is realized with some easy alterations on an input stationary systolic array design. Extensive evaluation results demonstrate that STP can reduce significant inference computation costs, and achieve $2.35\times $ computational energy saving for the ResNet18 network on the ImageNet dataset. [Huang, Kai; Li, Bowen; Chen, Siang; Jiang, Xiaowen; Xiong, Dongliang; Yan, Xiaolang] Zhejiang Univ, Inst VLSI Design, Hangzhou 310030, Peoples R China; [Claesen, Luc] Univ Hasselt, Engn Technol Elect ICT Dept, B-3590 Diepenbeek, Belgium; [Xi, Wei; Chen, Junjian] CSG, Digital Grid Res Inst, Guangzhou 510670, Peoples R China; [Liu, Zhili] Prod Ctr Dept, Sec Chip, Hangzhou 310030, Peoples R China Zhejiang University; Hasselt University Xiong, DL (corresponding author), Zhejiang Univ, Inst VLSI Design, Hangzhou 310030, Peoples R China. huangk@zju.edu.cn; libw@zju.edu.cn; chensiang@zju.edu.cn; luc.claesen@uhasselt.be; xiwei@csg.cn; chenjj4@csg.cn; xiaowen_jiang@zju.edu.cn; liuzhili@sec-chip.com; xiongdl@zju.edu.cn; yan@vlsi.zju.edu.cn National Key Research and Development Program of China [2020YFB0906000, 2020YFB0906001] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2020YFB0906000 and Grant 2020YFB0906001. This article was recommended by Associate Editor D. Marculescu 0 1 1 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0070 1937-4151 IEEE T COMPUT AID D IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. JAN 2023.0 42 1 190 203 10.1109/TCAD.2022.3168506 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 8H1MG 2023-03-23 WOS:000920800400015 0
J Jing, W; Yaseen, ZM; Shahid, S; Saggi, MK; Tao, H; Kisi, O; Salih, SQ; Al-Ansari, N; Chau, KW Jing, Wang; Yaseen, Zaher Mundher; Shahid, Shannsuddin; Saggi, Mandeep Kaur; Tao, Hai; Kisi, Ozgur; Salih, Sinan Q.; Al-Ansari, Nadhir; Chau, Kwok-Wing Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS English Review evapotranspiration prediction; state of the art; evolutionary computing models; input variability; future research directions GENETIC PROGRAMMING APPROACH; PARTICLE SWARM OPTIMIZATION; DATA-DRIVEN; NEURAL-NETWORKS; FIREFLY ALGORITHM; PREDICTION; WATER; EVAPORATION; SVM; REGRESSION Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC. [Jing, Wang; Tao, Hai] Baoji Univ Arts & Sci, Dept Comp Sci, Baoji, Shaanxi, Peoples R China; [Jing, Wang] Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Pahang, Malaysia; [Yaseen, Zaher Mundher] Ton Duc Thang Univ, Fac Civil Engn, Sustainable Dev Civil Engn Res Grp, Ho Chi Minh City, Vietnam; [Shahid, Shannsuddin] Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Johor Baharu, Malaysia; [Saggi, Mandeep Kaur] Thapar Inst Engn & Technol, Dept Comp Sci, Patiala, Punjab, India; [Kisi, Ozgur] Illa State Univ, Fac Nat Sci & Engn, Tbilisi, Georgia; [Salih, Sinan Q.] Duy Tan Univ, Inst Res & Dev, Da Nang, Vietnam; [Al-Ansari, Nadhir] Lulea Univ Technol, Civil Environm & Nat Resources Engn, Lulea, Sweden; [Chau, Kwok-Wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Salih, Sinan Q.] Univ Anbar, Coll Comp Sci & Informat Technol, Comp Sci Dept, Ramadi, Iraq Baoji University of Arts & Sciences; Universiti Malaysia Pahang; Ton Duc Thang University; Universiti Teknologi Malaysia; Thapar Institute of Engineering & Technology; Duy Tan University; Lulea University of Technology; Hong Kong Polytechnic University; University of Anbar Yaseen, ZM (corresponding author), Ton Duc Thang Univ, Fac Civil Engn, Sustainable Dev Civil Engn Res Grp, Ho Chi Minh City, Vietnam. yaseen@tdtu.edu.vn Saggi, Mandeep/ABI-4341-2020; Hai, Tao/AFU-5851-2022; HAI, TAO/D-9580-2011; SHAHID, SHAMSUDDIN/B-5185-2010; Kisi, Ozgur/AAD-8932-2019; Salih, Sinan/F-6284-2019; Yaseen, Zaher Mundher/G-7029-2018; Chau, Kwok-wing/E-5235-2011; Kaur, Mandeep/AAS-7505-2020 SHAHID, SHAMSUDDIN/0000-0001-9621-6452; Salih, Sinan/0000-0003-0717-7506; Yaseen, Zaher Mundher/0000-0003-3647-7137; Chau, Kwok-wing/0000-0001-6457-161X; 79 51 52 12 44 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1994-2060 1997-003X ENG APPL COMP FLUID Eng. Appl. Comp. Fluid Mech. JAN 1 2019.0 13 1 811 823 10.1080/19942060.2019.1645045 0.0 13 Engineering, Multidisciplinary; Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics IP7PT gold, Green Published 2023-03-23 WOS:000480241200001 0
C Shi, SS; Cheng, Y; Pan, HT; Zhao, WL; Wu, HP Kosecka, J Maciejewski, AA; Okamura, A; Bicchi, A; Stachniss, C; Song, DZ; Lee, DH; Chaumette, F; Ding, H; Li, JS; Wen, J; Roberts, J; Masamune, K; Chong, NY; Amato, N; Tsagwarakis, N; Rocco, P; Asfour, T; Chung, WK; Yasuyoshi, Y; Sun, Y; Maciekeski, T; Althoefer, K; AndradeCetto, J; Chung, WK; Demircan, E; Dias, J; Fraisse, P; Gross, R; Harada, H; Hasegawa, Y; Hayashibe, M; Kiguchi, K; Kim, K; Kroeger, T; Li, Y; Ma, S; Mochiyama, H; Monje, CA; Rekleitis, I; Roberts, R; Stulp, F; Tsai, CHD; Zollo, L Shi, Shanshuang; Cheng, Yong; Pan, Hongtao; Zhao, Wenlong; Wu, Huapeng Kosecka, J Development and error compensation of a flexible multi-joint manipulator applied in nuclear fusion environment 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper 25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) OCT 01-05, 2018 Madrid, SPAIN IEEE Robot & Automat Soc,IEEE Ind Elect Soc,Robot Soc Japan,Soc Instrument & Control Engineers,New Technol Fdn,IEEE,Adept MobileRobots,Willow Garage,Aldebaran Robot,Natl Instruments,Reflexxes GmbH,Schunk Intec S L U,Univ Carlos III Madrid,BOSCH,JD COM,Pal Robot,KUKA,Santander,Squirrel AI Learning,Baidu,Generat Robots,KINOVA Robot,Ouster,Univ Pablo Olavide Sevilla,Rapyuta Robot,SICK,TOYOTA,UP,Amazon,ARGO,Built Robot,Disney Res,Easy Mile,Hitachi,Robot,Khalifa Univ,Magazino,MathWorks,New Dexterity,Schunk,nuTonomy,PILZ,Prophesee,Rootnik,Saga Robot,Shadow,Soft Bank Robot,Anyverse,GalTech,Generat Robot,IEEE CAA Journal Automatica Sinica,Sci Robot, AAAS,TERAS CONCEPTUAL DESIGN Experimental Advanced Superconducting Tokamak (EAST) is the world's first fully superconducting tokamak fusion device with non-circular cross-section which was built in China The EAST articulated maintenance arm (EAMA) system is developed for real-time detection and rapid repair operations to damaged internal components during plasma discharges without breaking the EAST ultra-high vacuum (UHV) condition. To achieve the desired objectives, the EAMA system design should guarantee that the robot can stably run in the harsh environments of high temperature (80-120 degrees C) and high vacuum (similar to 10(-5) Pa). Meanwhile, the errors caused by the deformation of long flexible robot arms should also be predicted and compensated in real-time to obtain high accuracy for maintenance operations. In this paper, the vacuum-available design scheme of the manipulator system was firstly introduced. Secondly, inverse kinematics and obstacle avoidance strategy of the highly redundant EAMA robot was built. Then, flexible errors were predicted utilizing a back-propagation neural network (BPNN) model which was established on the basis of real experimental data. Finally, an integrated control strategy for error prediction and compensation was developed. [Shi, Shanshuang; Cheng, Yong; Pan, Hongtao; Zhao, Wenlong] Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Anhui, Peoples R China; [Wu, Huapeng] Lappeenranta Univ Technol, Lab Intelligent Machines, Lappeenranta, Finland Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; Lappeenranta University of Technology Shi, SS (corresponding author), Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Anhui, Peoples R China. National 973 Program of China (Chinese ITER Special Support Project [2017YFE0300503]; ASIPP China; LUT Finland National 973 Program of China (Chinese ITER Special Support Project; ASIPP China; LUT Finland This work was supported by the National 973 Program of China (Chinese ITER Special Support Project, No. 2017YFE0300503) and a collaboration framework between ASIPP China and LUT Finland. 9 1 1 0 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-5386-8094-0 IEEE INT C INT ROBOT 2018.0 3587 3592 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics BM0LT 2023-03-23 WOS:000458872703053 0
J Mignan, A; Ouillon, G; Sornette, D; Freund, F Mignan, A.; Ouillon, G.; Sornette, D.; Freund, F. Global Earthquake Forecasting System (GEFS): The challenges ahead EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS English Article We conclude this special issue on the Global Earthquake Forecasting System (GEFS) by briefly reviewing and analyzing the claims of non-seismic precursors made in the present volume, and by reflecting on the current limitations and future directions to take. We find that most studies presented in this special volume, taken individually, do not provide strong enough evidence of non-seismic precursors to large earthquakes. The majority of the presented results are hampered by the fact that the task at hand is susceptible to potential biases in data selection and possible overfitting. The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. The only systematic search on satellite data available so far, those of the DEMETER mission, did not find a robust precursory pattern. The conclusion that we can draw is that the overall absence of convincing evidence is likely due to a deficit in systematically applying robust statistical methods and in integrating scientific knowledge of different fields. Most authors are specialists of their field while the study of earthquake precursors requires a system approach combined with the knowledge of many specific characteristics of seismicity. Relating non-seismic precursors to earthquakes remains a challenging multidisciplinary field of investigation. The plausibility of these precursors predicted by models of lithosphere-atmosphere-ionosphere coupling, together with the suggestive evidence collected here, call for further investigations. The primary goal of the GEFS is thus to build a global database of candidate signals, which could potentially improve earthquake predictability (if the weak signals observed are real and false positives sufficiently uncorrelated between different data sources). Such a stacking of disparate and voluminous data will require big data storage and machine learning pipelines, which has become feasible only recently. This special issue compiled an eclectic list of non-seismic precursor candidates, which is in itself a valuable source of information for seismologists, geophysicists and other scientists who may not be familiar with such types of investigations. It also forms the foundation for a coherent, multi-disciplinary collaboration on earthquake prediction. [Mignan, A.; Sornette, D.] Southern Univ Sci & Technol SUSTech, Acad Adv Interdisciplinary Studies, Inst Risk Anal Predict & Management, Shenzhen, Peoples R China; [Mignan, A.] Southern Univ Sci & Technol SUSTech, Dept Earth & Space Sci, Shenzhen, Peoples R China; [Ouillon, G.] Lithophyse, Nice, France; [Ouillon, G.; Sornette, D.] Swiss Fed Inst Technol, Dept Management Technol & Econ D MTEC, Zurich, Switzerland; [Sornette, D.] Tokyo Inst Technol, Inst Innovat Res, Tokyo, Japan; [Freund, F.] NASA, GeoCosmo Sci Ctr, Ames Res Pk, Moffett Field, CA USA Southern University of Science & Technology; Southern University of Science & Technology; Swiss Federal Institutes of Technology Domain; ETH Zurich; Tokyo Institute of Technology; National Aeronautics & Space Administration (NASA) Mignan, A (corresponding author), Southern Univ Sci & Technol SUSTech, Acad Adv Interdisciplinary Studies, Inst Risk Anal Predict & Management, Shenzhen, Peoples R China.;Mignan, A (corresponding author), Southern Univ Sci & Technol SUSTech, Dept Earth & Space Sci, Shenzhen, Peoples R China. mignana@sustech.edu.cn 116 5 5 10 23 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1951-6355 1951-6401 EUR PHYS J-SPEC TOP Eur. Phys. J.-Spec. Top. JAN 2021.0 230 1 SI 473 490 10.1140/epjst/e2020-000261-8 0.0 18 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics PU7LS hybrid, Green Published 2023-03-23 WOS:000609483400023 0
J Yin, F; Gunnarsson, F Yin, Feng; Gunnarsson, Fredrik Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Article Distributed algorithm; Gaussian processes; particle filtering; recursive algorithm; RSS fingerprinting POSITIONING SYSTEM; REGRESSION We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution of the RSS on a fixed set of grids as training data gradually become available. The training input positions can be either precise or subject to errors of known distribution. All the local RSS maps are then fused to give a global map in the second step. The proposed framework is of significantly reduced computational complexity and scalable to big data generated from large-scale sensor networks. We further demonstrate its use in both static fingerprinting and mobile target tracking. The experimental results show that with our distributed framework satisfactory positioning accuracy can be achieved with much less complexity and storage than the standard framework. [Yin, Feng] Chinese Univ Hong Kong, Shenzhen & Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China; [Gunnarsson, Fredrik] Ericsson Res, SE-58330 Linkoping, Sweden Chinese University of Hong Kong, Shenzhen; Ericsson Yin, F (corresponding author), Chinese Univ Hong Kong, Shenzhen & Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China. yinfeng@cuhk.edu.cn; fredrik.gunnarsson@ericsson.com Zhang, Huaan/E-4836-2018 European Union FP7 Marie Curie training programme on Tracking in Complex Sensor Systems (TRAX) [607400] European Union FP7 Marie Curie training programme on Tracking in Complex Sensor Systems (TRAX) This work was supported by the European Union FP7 Marie Curie training programme on Tracking in Complex Sensor Systems (TRAX) under Grant 607400. 27 27 28 1 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. APR 2017.0 11 3 492 503 10.1109/JSTSP.2017.2678105 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering ES6QQ 2023-03-23 WOS:000399674500005 0
J Leung, KH; Rowe, SP; Leal, JP; Ashrafinia, S; Sadaghiani, MS; Chung, HW; Dalaie, P; Tulbah, R; Yin, YF; VanDenBerg, R; Werner, RA; Pienta, KJ; Gorin, MA; Du, Y; Pomper, MG Leung, Kevin H.; Rowe, Steven P.; Leal, Jeffrey P.; Ashrafinia, Saeed; Sadaghiani, Mohammad S.; Chung, Hyun Woo; Dalaie, Pejman; Tulbah, Rima; Yin, Yafu; VanDenBerg, Ryan; Werner, Rudolf A.; Pienta, Kenneth J.; Gorin, Michael A.; Du, Yong; Pomper, Martin G. Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET EJNMMI RESEARCH English Article PSMA-RADS; PSMA PET; Deep learning; Classification; t-SNE; Prostate cancer LESIONS; IMAGES Background: Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa. Methods: This was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [F-18]DCFPyL PET/CT scans were assigned to PSMA reporting and data system (PSMA-RADS) categories and randomly partitioned into training, validation, and test sets. The framework extracted image features, radiomic features, and tissue type information from a cropped PET image slice containing a lesion and performed PSMA-RADS and PCa classification. Performance was evaluated by assessing the area under the receiver operating characteristic curve (AUROC). A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed. Confidence and probability scores were measured. Statistical significance was determined using a two-tailed t test. Results: PSMA PET scans from 267 men with PCa had 3794 lesions assigned to PSMA-RADS categories. The framework yielded AUROC values of 0.87 and 0.90 for lesion-level and patient-level PSMA-RADS classification, respectively, on the test set. The framework yielded AUROC values of 0.92 and 0.85 for lesion-level and patient-level PCa classification, respectively, on the test set. A t-SNE analysis revealed learned relationships between the PSMA-RADS categories and disease findings. Mean confidence scores reflected the expected accuracy and were significantly higher for correct predictions than for incorrect predictions (P < 0.05). Measured probability scores reflected the likelihood of PCa consistent with the PSMA-RADS framework. Conclusion: The framework provided lesion-level and patient-level PSMA-RADS and PCa classification on PSMA PET images. The framework was interpretable and provided confidence and probability scores that may assist physicians in making more informed clinical decisions. [Leung, Kevin H.; Pomper, Martin G.] Johns Hopkins Univ, Sch Med, Dept Biomed Engn, 601 N Caroline St,JHOC 4263, Baltimore, MD 21287 USA; [Leung, Kevin H.; Rowe, Steven P.; Leal, Jeffrey P.; Ashrafinia, Saeed; Sadaghiani, Mohammad S.; Dalaie, Pejman; Tulbah, Rima; VanDenBerg, Ryan; Du, Yong; Pomper, Martin G.] Johns Hopkins Univ, Sch Med, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD USA; [Rowe, Steven P.; Pienta, Kenneth J.; Pomper, Martin G.] Johns Hopkins Univ, Sch Med, James Buchanan Brady Urol Inst, Baltimore, MD USA; [Rowe, Steven P.; Pienta, Kenneth J.; Pomper, Martin G.] Johns Hopkins Univ, Sch Med, Dept Urol, Baltimore, MD USA; [Chung, Hyun Woo] Konkuk Univ, Sch Med, Med Ctr, Dept Nucl Med, Seoul, South Korea; [Yin, Yafu] Shanghai Jiao Tong Univ, Xinhua Hosp, Sch Med, Dept Nucl Med, Shanghai, Peoples R China; [Werner, Rudolf A.] Univ Hosp Wurzburg, Dept Nucl Med, Wurzburg, Germany; [Gorin, Michael A.] Icahn Sch Med Mt Sinai, Milton & Carroll Petrie Dept Urol, New York, NY USA Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Konkuk University; Konkuk University Medical Center; Shanghai Jiao Tong University; University of Wurzburg; Icahn School of Medicine at Mount Sinai Leung, KH (corresponding author), Johns Hopkins Univ, Sch Med, Dept Biomed Engn, 601 N Caroline St,JHOC 4263, Baltimore, MD 21287 USA. kleung8@jhmi.edu VanDenBerg, Ryan/0000-0002-3331-6007 National Institutes of Health; [R01CA134675]; [P41EB024495]; [R01NS094227]; [U01CA140204]; [T32EB006351] National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); ; ; ; ; Financial support for this project was provided by the National Institutes of Health under grant numbers R01CA134675, P41EB024495, R01CA184228, R01NS094227, U01CA140204, and T32EB006351. 44 0 0 1 1 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2191-219X EJNMMI RES EJNMMI Res. DEC 29 2022.0 12 1 76 10.1186/s13550-022-00948-1 0.0 15 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 7L4HQ 36580220.0 gold 2023-03-23 WOS:000905929400001 0
C Zhou, KY; Wang, KW; Yang, KL IEEE Zhou, Keyang; Wang, Kaiwei; Yang, Kailun PADENet: An Efficient and Robust Panoramic Monocular Depth Estimation Network for Outdoor Scenes 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) SEP 20-23, 2020 ELECTR NETWORK IEEE Depth estimation is a basic problem in computer vision, which provides three-dimensional information by assigning depth values to pixels. With the development of deep learning, researchers have focused on estimating depth based on a single image, which is known as the monocular depth estimation problem. Moreover, panoramic images have been introduced to obtain a greater view angle recently, but the corresponding model for monocular depth estimation is scarce in the state of the art. In this paper, we propose PADENet for panoramic monocular depth estimation and re-design the loss function adapted for panoramic images. We also perform model transferring to panoramic scenes after training. A series of experiments show that our PADENet and loss function can effectively improve the accuracy of panoramic depth prediction while maintaining a high level of robustness and reaching the state of the art on the CARLA Dataset. [Zhou, Keyang; Wang, Kaiwei] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Peoples R China; [Yang, Kailun] Karlsruhe Inst Technol, Inst Anthropomat & Robot, Karlsruhe, Germany Zhejiang University; Helmholtz Association; Karlsruhe Institute of Technology Zhou, KY (corresponding author), Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Peoples R China. zhoukeyang@zju.edu.cn; wangkaiwei@zju.edu.cn; kailun.yang@kit.edu Yang, Kailun/U-2491-2019 Yang, Kailun/0000-0002-1090-667X project Research on Vision Sensor Technology Fusing Multidimensional Parameters by Hangzhou SurImage Technology Co., Ltd [111303I21805)]; Hangzhou KrVision Technology Co., Ltd; AccessibleMaps project by Federal Ministry of Labor and Social Affairs project Research on Vision Sensor Technology Fusing Multidimensional Parameters by Hangzhou SurImage Technology Co., Ltd; Hangzhou KrVision Technology Co., Ltd; AccessibleMaps project by Federal Ministry of Labor and Social Affairs This work has been partially funded through the project Research on Vision Sensor Technology Fusing Multidimensional Parameters (111303I21805) by Hangzhou SurImage Technology Co., Ltd and supported by Hangzhou KrVision Technology Co., Ltd (krvision.cn). This work has also been funded in part through the AccessibleMaps project by Federal Ministry of Labor and Social Affairs. 22 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-7281-4149-7 IEEE INT C INTELL TR 2020.0 6 Engineering, Electrical & Electronic; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Transportation BS0GS 2023-03-23 WOS:000682770700035 0
J Wang, XX; Xu, ZS; Ge, ZJ; Zavadskas, EK; Skackauskas, P Wang, Xinxin; Xu, Zeshui; Ge, Zijing; Zavadskas, Edmundas Kazimieras; Skackauskas, Paulius AN OVERVIEW OF A LEADER JOURNAL IN THE FIELD OF TRANSPORT: A BIBLIOMETRIC ANALYSIS OF COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING FROM 2000 TO 2019 TRANSPORT English Article Computer-Aided Civil and Infrastructure Engineering; journal; article; bibliometric analysis; collaboration networks; development trends; hot topics WAVELET NEURAL-NETWORK; STRUCTURAL DAMAGE DETECTION; GENETIC ALGORITHM; RENEWABLE ENERGY; KNOWLEDGE DOMAIN; DECISION-MAKING; CRACK DETECTION; RESEARCH TRENDS; MODEL; IDENTIFICATION Computer-Aided Civil And Infrastructure Engineering (CACAIE) is an international journal, and the first documents was published from 1980. This article is to make an overview based on bibliometric analysis to celebrate the 35th anniversary of CACAIE till 2019. At present, 1045 publications can be indexed in the Clarivate Analytics Web of Science (WoS) from 2000 to 2019, and we explore the characteristics of these publications by bibliometric methods and tools (VOSviewer and CiteSpace). First, the fundamental information of publications is given with the help of some bibliometric indicators, such as the number of citations and h-index. According to high-citing and high-cited publications, we analyse that who pays closer attention to the journal and what the journal most focuses on considering sources, countries/regions, institutions and authors. After that, the influential countries/regions and references are presented, and collaboration networks are given to show the relationship among countries/regions, institutions and authors. In order to understand the development trends and hot topics, co-occurrence analysis and timeline view of keywords are made to be visual. In addition, publications in four fields - Construction & Building Technology; Engineering, Civil; Transportation Science & Technology; Computer Science, Interdisciplinary Applications - that CACAIE refers are summarized, and further discussions are made for the journal and scholars. Finally, some main findings are concluded according to all analysis. This article provides a certain reference for scholars and journals to further research and promote the scientific-technological progress. [Wang, Xinxin; Xu, Zeshui; Ge, Zijing] Sichuan Univ, Business Sch, Chengdu, Peoples R China; [Zavadskas, Edmundas Kazimieras] Vilnius Gediminas Tech Univ, Inst Sustainable Construct, Vilnius, Lithuania; [Skackauskas, Paulius] Vilnius Gediminas Tech Univ, Dept Mobile Machinery & Railway Transport, Vilnius, Lithuania Sichuan University; Vilnius Gediminas Technical University; Vilnius Gediminas Technical University Xu, ZS (corresponding author), Sichuan Univ, Business Sch, Chengdu, Peoples R China. xuzeshui@263.net Skackauskas, Paulius/0000-0002-2082-9372 National Natural Science Foundation of China [71571123, 71771155]; Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing [2019QYJ03]; Fundamental Research Funds for the Central Universities [YJ202063] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The work was supported by:; the National Natural Science Foundation of China (Grants No 71571123, 71771155);; the Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing (Grant No 2019QYJ03);; the Fundamental Research Funds for the Central Universities (Grant No YJ202063). 58 6 6 7 33 VILNIUS GEDIMINAS TECH UNIV VILNIUS SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA 1648-4142 1648-3480 TRANSPORT-VILNIUS Transport 2020.0 35 6 557 575 10.3846/transport.2020.14140 0.0 19 Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Transportation PU2BZ gold 2023-03-23 WOS:000609112800002 0
J Zhang, SJ; Du, HT; Borucki, S; Jin, SF; Hou, TT; Li, ZX Zhang, Shoujing; Du, Haotian; Borucki, Sebastian; Jin, Shoufeng; Hou, Tiantian; Li, Zhixiong Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm MACHINES English Article dual resource constrained; flexible job shop scheduling; improved quantum genetic algorithm; niching technique; adaptive rotation angle OPTIMIZATION; TIME Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application. [Zhang, Shoujing; Du, Haotian; Jin, Shoufeng; Hou, Tiantian] Xian Polytech Univ, Dept Ind Engn, Xian Key Lab Modern Intelligent Text Equipment, Xian 710600, Peoples R China; [Borucki, Sebastian] Opole Univ Technol, Inst Elect Power Engn & Renewable Energy, PL-45758 Opole, Poland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, 50 Yonsei Ro, Seoul 03722, South Korea Xi'an Polytechnic University; Opole University of Technology; Yonsei University Li, ZX (corresponding author), Yonsei Univ, Yonsei Frontier Lab, 50 Yonsei Ro, Seoul 03722, South Korea. zhangshoujing@xpu.edu.cn; 2018022067@stu.xpu.edu.cn; s.borucki@po.edu.pl; jinshoufeng@xpu.edu.cn; 200211006@stu.xpu.edu.cn; zhixiong.li@yonsei.ac.kr Borucki, Sebastian/0000-0003-0748-5488 Shaanxi Provincial Education Department [17JK0321]; Xi'an Key Laboratory of Modern Intelligent Textile Equipment [2019220614SYS021CG043]; National Key Research and Development Program of China [2019YFB1707205] Shaanxi Provincial Education Department; Xi'an Key Laboratory of Modern Intelligent Textile Equipment; National Key Research and Development Program of China The authors would like to acknowledge the support of the Scientific Research Program funded by the Shaanxi Provincial Education Department (no.17JK0321), Xi'an Key Laboratory of Modern Intelligent Textile Equipment (no.2019220614SYS021CG043). and National Key Research and Development Program of China (no. 2019YFB1707205). Thanks to Xi'an Typical Industries Co., Ltd. for providing data support. 36 7 7 16 57 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-1702 MACHINES Machines JUN 2021.0 9 6 108 10.3390/machines9060108 0.0 15 Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering SZ8BQ gold 2023-03-23 WOS:000666783900001 0
C Chen, S; Huang, K; Xiong, DL; Li, BW; Claesen, L Farkas, I; Masulli, P; Wermter, S Chen, Siang; Huang, Kai; Xiong, Dongliang; Li, Bowen; Claesen, Luc Fine-Grained Channel Pruning for Deep Residual Neural Networks ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II Lecture Notes in Computer Science English Proceedings Paper 29th International Conference on Artificial Neural Networks (ICANN) SEP 15-18, 2020 Bratislava, SLOVAKIA Channel pruning; Residual neural network; Efficient network structure Pruning residual neural networks is a challenging task due to the constraints induced by cross layer connections. Many existing approaches assign channels connected by skip-connections to the same group and prune them simultaneously, limiting the pruning ratio on those troublesome filters. Instead, we propose a Fine-grained Channel Pruning (FCP) method that allows any channels to be pruned independently. To avoid the misalignment problem between convolution and skip connection, we always keep the residual addition operations alive. Thus we can obtain a novel efficient residual architecture by removing any unimportant channels without the alignment constraint. Besides classification, We further apply FCP on residual models for image super-resolution, which is a low-level vision task. Extensive experimental results show that FCP can achieve better performance than other state-of-the-art methods in terms of parameter and computation cost. Notably, on CIFAR-10, FCP reduces more than 78% FLOPs on ResNet-56 with no accuracy drop. Moreover, it achieves more than 48% FLOPs reduction on MSR-ResNet with negligible performance degradation. [Chen, Siang; Huang, Kai; Xiong, Dongliang; Li, Bowen] Zhejiang Univ, Inst VLSI Design, Hangzhou, Peoples R China; [Claesen, Luc] Hasselt Univ, Engn Technol Elect ICT Dept, B-3590 Diepenbeek, Belgium Zhejiang University; Hasselt University Huang, K (corresponding author), Zhejiang Univ, Inst VLSI Design, Hangzhou, Peoples R China. 11631032@zju.edu.cn; huangk@zju.edu.cn; xiongdl@zju.edu.cn; 11631033@zju.edu.cn; luc.claesen@uhasselt.be National Key R&D Program of China [2018YFB0904900, 2018YFB0904902] National Key R&D Program of China Supported by the National Key R&D Program of China (2018YFB0904900, 2018YFB0904902). 22 1 1 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-61616-8; 978-3-030-61615-1 LECT NOTES COMPUT SC 2020.0 12397 3 14 10.1007/978-3-030-61616-8_1 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS3OJ Green Submitted 2023-03-23 WOS:000713797800001 0
J Cao, Y; Raise, A; Mohammadzadeh, A; Rathinasamy, S; Band, SS; Mosavi, A Cao, Yan; Raise, Amir; Mohammadzadeh, Ardashir; Rathinasamy, Sakthivel; Band, Shahab S.; Mosavi, Amirhosein Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction ENERGY REPORTS English Article Fuzzy logic; Renewable energy; Learning algorithm; Deep learning; Solar energy; Wind turbines MANAGEMENT A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme. (C) 2021 Published by Elsevier Ltd. [Cao, Yan] Xian Technol Univ, Sch Mech Engn, Xian 710021, Peoples R China; [Raise, Amir] Xian Technol Univ, Dept Mech Engn, Xian, Shaanxi, Peoples R China; [Mohammadzadeh, Ardashir] Univ Bonab, Dept Elect Engn, Bonab, Iran; [Rathinasamy, Sakthivel] Bharathiar Univ, Dept Appl Math, Coimbatore 641046, Tamil Nadu, India; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu, Yunlin 64002, Taiwan; [Mosavi, Amirhosein] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary Xi'an Technological University; Xi'an Technological University; University of Bonab; Bharathiar University; National Yunlin University Science & Technology; Obuda University Mohammadzadeh, A (corresponding author), Univ Bonab, Dept Elect Engn, Bonab, Iran.;Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu, Yunlin 64002, Taiwan.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary. a.mzadeh@ubonab.ac.ir; shamshirbands@yuntech.edu.tw; amir.mosavi@kvk.uni-obuda.hu Mohammadzadeh, Ardashir/AEN-2013-2022; Rathinasamy, Sakthivel/AAD-6066-2019; Mosavi, Amir/I-7440-2018; S. Band, Shahab/ABB-2469-2020 Mohammadzadeh, Ardashir/0000-0001-5173-4563; Rathinasamy, Sakthivel/0000-0002-5528-2709; Mosavi, Amir/0000-0003-4842-0613; S. Band, Shahab/0000-0001-6109-1311 41 32 32 4 6 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 2352-4847 ENERGY REP Energy Rep. NOV 2021.0 7 8115 8127 10.1016/j.egyr.2021.07.004 0.0 NOV 2021 13 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels XK9JR gold 2023-03-23 WOS:000727772800007 0
J Ouyang, W; Winsnes, CF; Hjelmare, M; Cesnik, AJ; Akesson, L; Xu, H; Sullivan, DP; Dai, SB; Lan, J; Jinmo, P; Galib, SM; Henkel, C; Hwang, K; Poplavskiy, D; Tunguz, B; Wolfinger, RD; Gu, YZ; Li, CP; Xie, JB; Buslov, D; Fironov, S; Kiselev, A; Panchenko, D; Cao, X; Wei, RM; Wu, YH; Zhu, X; Tseng, KL; Gao, ZF; Ju, C; Yi, XH; Zheng, HD; Kappel, C; Lundberg, E Ouyang, Wei; Winsnes, Casper F.; Hjelmare, Martin; Cesnik, Anthony J.; Akesson, Lovisa; Xu, Hao; Sullivan, Devin P.; Dai, Shubin; Lan, Jun; Jinmo, Park; Galib, Shaikat M.; Henkel, Christof; Hwang, Kevin; Poplavskiy, Dmytro; Tunguz, Bojan; Wolfinger, Russel D.; Gu, Yinzheng; Li, Chuanpeng; Xie, Jinbin; Buslov, Dmitry; Fironov, Sergei; Kiselev, Alexander; Panchenko, Dmytro; Cao, Xuan; Wei, Runmin; Wu, Yuanhao; Zhu, Xun; Tseng, Kuan-Lun; Gao, Zhifeng; Ju, Cheng; Yi, Xiaohan; Zheng, Hongdong; Kappel, Constantin; Lundberg, Emma Analysis of the Human Protein Atlas Image Classification competition NATURE METHODS English Article Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by similar to 20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications. [Ouyang, Wei; Winsnes, Casper F.; Hjelmare, Martin; Akesson, Lovisa; Xu, Hao; Sullivan, Devin P.; Lundberg, Emma] KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, Stockholm, Sweden; [Cesnik, Anthony J.; Lundberg, Emma] Stanford Univ, Dept Genet, Stanford, CA 94305 USA; [Cesnik, Anthony J.; Lundberg, Emma] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA; [Lan, Jun] Winning Hlth Technol Grp Co Ltd, Shanghai, Peoples R China; [Galib, Shaikat M.] Missouri Univ Sci & Technol, Rolla, MO 65409 USA; [Henkel, Christof] Khumbu Ai, Munich, Germany; [Hwang, Kevin] Qualcomm Inc, Cupertino, CA USA; [Tunguz, Bojan] H2O Ai, Greencastle, IN USA; [Wolfinger, Russel D.] SAS Inst Inc, Cary, NC USA; [Gu, Yinzheng; Li, Chuanpeng; Xie, Jinbin] Jilian Technol Grp Video, Shanghai, Peoples R China; [Buslov, Dmitry] SAP, Moscow, Russia; [Fironov, Sergei] BDO Unicon, St Petersburg, Russia; [Wei, Runmin] UT MD Anderson Canc Ctr, Houston, TX USA; [Zhu, Xun] Univ Hawaii, Canc Ctr, Honolulu, HI 96822 USA; [Gao, Zhifeng] Microsoft Res, Beijing, Peoples R China; [Ju, Cheng] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Zheng, Hongdong] Peking Univ, Beijing, Peoples R China; [Kappel, Constantin] Leica Microsyst, Mannheim, Germany Royal Institute of Technology; Stanford University; University of Missouri System; Missouri University of Science & Technology; Qualcomm; SAS Institute Inc; University of Texas System; UTMD Anderson Cancer Center; Cancer Research Center of Hawaii; University of Hawaii System; Microsoft; University of California System; University of California Berkeley; Peking University Lundberg, E (corresponding author), KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, Stockholm, Sweden.;Lundberg, E (corresponding author), Stanford Univ, Dept Genet, Stanford, CA 94305 USA.;Lundberg, E (corresponding author), Chan Zuckerberg Biohub, San Francisco, CA 94158 USA. emma.lundberg@scilifelab.se Wei, Runmin/J-6128-2019; Cao, Xuan/AAI-6307-2020; Lundberg, Emma S K/F-3891-2010; Galib, Shaikat/ABA-7320-2021; Cesnik, Anthony/J-9238-2014 Wei, Runmin/0000-0001-7707-0160; Galib, Shaikat/0000-0002-8192-578X; Sullivan, Devin/0000-0001-6176-108X; Henkel, Christof/0000-0002-2913-3662; Ouyang, Wei/0000-0002-0291-926X; Cesnik, Anthony/0000-0002-5326-7134; Kaller Lundberg, Emma/0000-0001-7034-0850; Hjelmare, Martin/0000-0001-7375-9681; Winsnes, Casper/0000-0002-0028-5865; Panchenko, Dmytro/0000-0001-5454-5661 Knut and Alice Wallenberg Foundation [2016.0204]; Swedish Research Council [2017-05327] Knut and Alice Wallenberg Foundation(Knut & Alice Wallenberg Foundation); Swedish Research Council(Swedish Research Council) We thank all the participants of the Human Protein Atlas Image Classification competition. We also acknowledge the staff at Kaggle for providing a competition platform that enabled this study and the competition prize sponsors Leica Microsystems and NVIDIA. The staff of the HPA program provided valuable contributions, such as data storage and management, and J. Fall helped with project administrative tasks. Funding was provided by the Knut and Alice Wallenberg Foundation (grant no. 2016.0204) and the Swedish Research Council (grant no. 2017-05327) to E.L. 50 50 50 1 15 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 1548-7091 1548-7105 NAT METHODS Nat. Methods DEC 2019.0 16 12 1254 + 10.1038/s41592-019-0658-6 0.0 10 Biochemical Research Methods Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology JR5GJ 31780840.0 Green Published, hybrid 2023-03-23 WOS:000499653100025 0
J Chen, Y; Tsionas, MG; Zelenyuk, V Chen, Ya; Tsionas, Mike G.; Zelenyuk, Valentin LASSO plus DEA for small and big wide data OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE English Article DEA; Data enabled analytics; Sign-constrained convex nonparametric; least squares (SCNLS); Machine learning; LASSO; Elastic net; Big wide data DATA ENVELOPMENT ANALYSIS; REGRESSION SHRINKAGE; SELECTION; MODELS In data envelopment analysis (DEA), the curse of dimensionality problem may jeopardize the accuracy or even the relevance of results when there is a relatively large dimension of inputs and outputs, even for relatively large samples. Recently, a machine learning approach based on the least absolute shrink-age and selection operator (LASSO) for variable selection was combined with sign-constrained convex nonparametric least squares (SCNLS, a special case of DEA), and dubbed as LASSO-SCNLS, as a way to circumvent the curse of dimensionality problem. In this paper, we revisit this interesting approach, by considering various data generating processes. We also explore a more advanced version of LASSO, the so-called elastic net (EN) approach, adapt it to DEA and propose the EN-DEA. Our Monte Carlo simula-tions provide additional and to some extent, new evidence and conclusions. In particular, we find that none of the considered approaches clearly dominate the others. To circumvent the curse of dimension-ality of DEA in the context of big wide data, we also propose a simplified two-step approach which we call LASSO + DEA. We find that the proposed simplified approach could be more useful than the existing more sophisticated approaches for reducing very large dimensions into sparser, more parsimonious DEA models that attain greater discriminatory power and suffer less from the curse of dimensionality. (c) 2021 Elsevier Ltd. All rights reserved. [Chen, Ya] Hefei Univ Technol, Sch Econ, Hefei 230601, Anhui, Peoples R China; [Chen, Ya] Hefei Univ Technol, Ctr Ind Informat & Econ, Hefei 230601, Anhui, Peoples R China; [Tsionas, Mike G.] Montpellier Business Sch, 2300 Ave Moulins, F-34080 Montpellier, France; [Tsionas, Mike G.] Univ Lancaster, Management Sch, Lancaster LA1 4YX, England; [Chen, Ya; Zelenyuk, Valentin] Univ Queensland, Sch Econ, Brisbane, Qld, Australia; [Chen, Ya; Zelenyuk, Valentin] Univ Queensland, Ctr Efficiency & Prod Anal, Brisbane, Qld, Australia Hefei University of Technology; Hefei University of Technology; Montpellier Business School; Lancaster University; University of Queensland; University of Queensland Chen, Y (corresponding author), Hefei Univ Technol, Sch Econ, Hefei 230601, Anhui, Peoples R China.;Chen, Y (corresponding author), Hefei Univ Technol, Ctr Ind Informat & Econ, Hefei 230601, Anhui, Peoples R China.;Chen, Y (corresponding author), Univ Queensland, Sch Econ, Brisbane, Qld, Australia.;Chen, Y (corresponding author), Univ Queensland, Ctr Efficiency & Prod Anal, Brisbane, Qld, Australia. ychen@hfut.edu.cn; m.tsionas@lancaster.ac.uk; v.zelenyuk@uq.edu.au National Natural Science Foundation of China [71601064, 72071067, 71801067, 71871081]; Major Project of the National Social Science Foundation of China [18ZDA064]; Australian Research Council [FT170100401]; University of Queensland; Australian Research Council [FT170100401] Funding Source: Australian Research Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Project of the National Social Science Foundation of China; Australian Research Council(Australian Research Council); University of Queensland(University of Queensland); Australian Research Council(Australian Research Council) Ya Chen thanks and acknowledges the financial support from National Natural Science Foundation of China (Nos. 71601064, 72071067, 71801067 and 71871081), and the Major Project of the National Social Science Foundation of China (No. 18ZDA064). Ya Chen also thanks Jianhui Xie, and seminar and conference participants at The University of Queensland, the 2019 International Conference on Data Envelopment Analysis and Data Analytics, and the First Lecture Series of Wenlan Economic Measurement and Applied Econometrics in Wenlan School of Business at Zhongnan University of Economics and Law for valuable discussions and comments. Valentin Zelenyuk thanks and acknowledges the financial support from the Australian Research Council (from the ARC Future Fellowship grant FT170100401) and from The University of Queensland. We also thank Bao Hoang Nguyen, Ruby Nguyen, Mengyuan Wang and Evelyn Smart for proofreading and their feedback and Zhichao Wang for the checks of the coding and replications of the computations. These individuals and organizations are not responsible for the views expressed in this paper. 61 9 9 4 28 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0305-0483 1873-5274 OMEGA-INT J MANAGE S Omega-Int. J. Manage. Sci. JUL 2021.0 102 102419 10.1016/j.omega.2021.102419 0.0 APR 2021 21 Management; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Operations Research & Management Science RO3DX Green Accepted 2023-03-23 WOS:000640927800006 0
J Zhou, XY; Pan, YF; Zhang, RQ; Bei, LT; Li, XC Zhou, Xiaoyu; Pan, Yafeng; Zhang, Ruqian; Bei, Litian; Li, Xianchun Mortality threat mitigates interpersonal competition: an EEG-based hyperscanning study SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE English Article mortality threat; competition; cooperation; hyperscanning; EEG TERROR MANAGEMENT THEORY; INTER-BRAIN SYNCHRONY; SELF-ESTEEM; NEURAL RESPONSES; DEATH ANXIETY; SALIENCE; COOPERATION; MECHANISMS; DYNAMICS; DEFENSE Awareness of death has been shown to influence human cognition and behavior. Yet, how mortality threat (MT) impacts our daily social behavior remains elusive. To address this issue, we developed a dyadic experimental model and recruited 86 adults (43 dyads) to complete two computer-based tasks (i.e. competitive and cooperative button-pressing). We manipulated dyads' awareness of death [MT vs neutral control (NC)] and simultaneously measured their neurophysiological activity using electroencephalography during the task. Several fundamental observations were made. First, the MT group showed significantly attenuated competition and slightly promoted cooperation. Second, compared to NC, MT significantly decreased gamma-band inter-brain synchronization (IBS) in the competitive context, which was associated with increased subjective fear of death within dyads. Notably, those effects were context-specific: we did not observe comparable results in the cooperative context. Finally, a machine-learning approach was successfully used to discriminate between the MT and NC groups based on accumulated IBS. Together, these findings indicate that MT to some extent mitigates interpersonal competition, and such mitigation might be associated with changes in gamma-band IBS. [Zhou, Xiaoyu; Zhang, Ruqian; Bei, Litian; Li, Xianchun] East China Normal Univ, Affiliated Mental Hlth Ctr ECNU, Sch Psychol & Cognit Sci, Shanghai Key Lab Mental Hlth & Psychol Crisis Int, Room 514,Junxiu Bldg, Shanghai 200062, Peoples R China; [Pan, Yafeng] Karolinska Inst, Dept Clin Neurosci, S-17165 Stockholm, Sweden East China Normal University; Karolinska Institutet Li, XC (corresponding author), East China Normal Univ, Affiliated Mental Hlth Ctr ECNU, Sch Psychol & Cognit Sci, Shanghai Key Lab Mental Hlth & Psychol Crisis Int, Room 514,Junxiu Bldg, Shanghai 200062, Peoples R China. xcli@psy.ecnu.edu.cn Pan, Yafeng/AAG-3584-2020 Pan, Yafeng/0000-0002-5633-8313 Shanghai Key Base of Humanities and Social Sciences; National Natural Science Foundation of China [32071082, 71942001]; Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning [ZK2015B01]; Programs Foundation of Shanghai Municipal Commission of Health and Family Planning [201540114] Shanghai Key Base of Humanities and Social Sciences; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning; Programs Foundation of Shanghai Municipal Commission of Health and Family Planning This work was supported by the Shanghai Key Base of Humanities and Social Sciences (Psychology, 2018), the National Natural Science Foundation of China (32071082 and 71942001), Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning (ZK2015B01), and the Programs Foundation of Shanghai Municipal Commission of Health and Family Planning (201540114). 80 3 3 10 49 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1749-5016 1749-5024 SOC COGN AFFECT NEUR Soc. Cogn. Affect. Neurosci. JUN 2021.0 16 6 621 631 10.1093/scan/nsab033 0.0 MAR 2021 11 Neurosciences; Psychology; Psychology, Experimental Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Psychology SN3XQ 33755182.0 gold, Green Published 2023-03-23 WOS:000658226900007 0
C Zhao, XX; Qiao, YY; Si, ZW; Yang, J; Lindgren, A ACM Zhao, Xiaoxing; Qiao, Yuanyuan; Si, Zhongwei; Yang, Jie; Lindgren, Anders Prediction of User App Usage Behavior from Geo-Spatial Data THIRD INTERNATIONAL ACM WORKSHOP ON MANAGING AND MINING ENRICHED GEO-SPATIAL DATA English Proceedings Paper 3rd ACM International Workshop on Managing and Mining Enriched Geo-Spatial Data JUN 26, 2016 San Francisco, CA TomTom,ESRI,ACM app usage behavior; human mobility; geo-spatial data; prediction accuracy; mobile Internet In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet. [Zhao, Xiaoxing; Qiao, Yuanyuan; Si, Zhongwei; Yang, Jie] Beijing Univ Posts & Telecommun, Ctr Data Sci, Beijing, Peoples R China; [Lindgren, Anders] Lulea Univ Technol, SICS Swedish ICT, Lulea, Sweden Beijing University of Posts & Telecommunications; Lulea University of Technology; RISE Research Institutes of Sweden Zhao, XX (corresponding author), Beijing Univ Posts & Telecommun, Ctr Data Sci, Beijing, Peoples R China. shanyni@buptedu.cn; yydiao@buptedu.cn; sizhongwei@buptedu.cn; janeyang@buptedu.cn; andersl@sics.se Qiao, Yuanyuan/AAB-9230-2020 Qiao, Yuanyuan/0000-0002-3573-9847 20 0 0 3 11 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-4309-1 2016.0 37 42 10.1145/2948649.2948656 0.0 6 Computer Science, Information Systems; Geography, Physical; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Physical Geography; Remote Sensing BG6LF 2023-03-23 WOS:000390470000007 0
J Zhou, J; Urhahne, D Zhou, Ji; Urhahne, Detlef Self-regulated learning in the museum: understanding the relationship of visitor's goals, learning strategies, and appraisals SCANDINAVIAN JOURNAL OF EDUCATIONAL RESEARCH English Article Self-regulated learning; museum learning; goal; learning strategy; appraisal ACHIEVEMENT GOALS; IMPLEMENTATION INTENTIONS; MOTIVATION; STUDENTS; ORIENTATIONS; PERFORMANCE; CLASSROOM; KNOWLEDGE; EFFICACY; METAANALYSIS Self-regulated learning (SRL) in the museum was explored by 2 investigations. The first one investigated 233 visitors on their goals and intended learning strategies by questionnaire before they visited the science museum. Results indicated visitors' learning goals can predict their intended deep-learning strategy. Moreover, visitors can be clustered into 4 groups and their cluster identity can also predict the intended learning strategies. The second investigation asked 244 visitors about their actual learning strategies and motivational appraisals (self-efficacy and control beliefs) after visiting. In all, 5 kinds of learning strategies were found: elaborating, help-seeking, effort-making, reorganizing, and surface-learning. These strategies can further predict their motivational appraisals. The characteristics of SRL in the informal learning context were discussed. [Zhou, Ji] Beijing Normal Univ, Sch Psychol, Beijing, Peoples R China; [Zhou, Ji] Ludwig Maximilians Univ Munchen, Dept Psychol, Munich, Germany; [Urhahne, Detlef] Univ Passau, Dept Philosophy, Passau, Germany Beijing Normal University; University of Munich; University of Passau Zhou, J (corresponding author), Beijing Normal Univ, Sch Psychol, Beijing, Peoples R China.;Zhou, J (corresponding author), Ludwig Maximilians Univ Munchen, Dept Psychol, Munich, Germany. jizhou@bnu.edu.cn Zhou, Ji/0000-0001-8318-1457 73 2 2 3 34 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0031-3831 1470-1170 SCAND J EDUC RES Scand. J. Educ. Res. 2017.0 61 4 394 410 10.1080/00313831.2016.1147071 0.0 17 Education & Educational Research Social Science Citation Index (SSCI) Education & Educational Research EW9KU 2023-03-23 WOS:000402839500002 0
J Mohammed, S; Arabnia, HR; Qu, XB; Zhang, DL; Kim, TH; Zhao, JD Mohammed, Sabah; Arabnia, Hamid R.; Qu, Xiaobo; Zhang, Dalin; Kim, Tai-Hoon; Zhao, Jiandong IEEE ACCESS SPECIAL SECTION EDITORIAL: BIG DATA TECHNOLOGY AND APPLICATIONS IN INTELLIGENT TRANSPORTATION IEEE ACCESS English Editorial Material During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture. [Mohammed, Sabah] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada; [Arabnia, Hamid R.] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA; [Qu, Xiaobo] Chalmers Univ Technol, Dept Architecture & Civil Engn, S-41296 Gothenburg, Sweden; [Zhang, Dalin] Beijing Jiaotong Univ, Natl Res Ctr Railway Safety Assessment, Beijing 100044, Peoples R China; [Kim, Tai-Hoon] Univ Tasmania, Dept Comp Sci, Hobart, Tas 7005, Australia; [Zhao, Jiandong] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China Lakehead University; University System of Georgia; University of Georgia; Chalmers University of Technology; Beijing Jiaotong University; University of Tasmania; Beijing Jiaotong University Mohammed, S (corresponding author), Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada. Qu, Xiaobo/AAG-4777-2021 Zhang, Dalin/0000-0003-0346-7020 3 7 7 2 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 201331 201344 10.1109/ACCESS.2020.3035440 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications OS8XW gold, Green Published 2023-03-23 WOS:000590442600001 0
C Zhao, XM; Gao, LR; Chen, ZC; Zhang, B; Liao, WZ IEEE Zhao, Xuemei; Gao, Lianru; Chen, Zhengchao; Zhang, Bing; Liao, Wenzhi CNN-based Large Scale Landsat Image Classification 2018 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) Asia-Pacific Signal and Information Processing Association Annual Summit and Conference English Proceedings Paper 10th Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC) NOV 12-15, 2018 Honolulu, HI Asia Pacific Signal & Informat Proc Assoc,IEEE Signal Proc Soc COVER Large scale Landsat image classification is the key to acquire national even global land cover map. Traditional methods typically use only a small set of samples to train the classifier and result in unsatisfied classification results. To improve the performance of large scale Landsat image classification, we apply a convolutional neural network (CNN)-based method named PSPNet in this paper to learn spectral-spatial features from a large training set. By considering the complexities and the various sizes of objects captured in large scale Landsat images, PSPNet can utilize the global information as well as consider the targets with different sizes. In addition, the research area is oversampled with a small offset which can increase the amount of training samples in order to improve the performance of PSPNet on Landsat images. Moreover, PSPNet is finely tuned on the pretrained Resnet50. Experimental results show the efficiency of the CNN based methods for the large-scale land cover mapping. In particular, PSPNet can produce better results even than the provided reference land cover map, with overall accuracy reaching 83%. [Zhao, Xuemei; Gao, Lianru; Chen, Zhengchao; Zhang, Bing] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Zhang, Bing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Liao, Wenzhi] Univ Ghent, Dept Telecommun & Informat Proc, B-9000 Ghent, Belgium Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ghent University Gao, LR (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080302]; China Postdoctoral Science Foundation [2017M620947] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA19080302, and by the 62-class General Financial Grant from the China Postdoctoral Science Foundation under Grant No. 2017M620947. 26 7 7 5 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2309-9402 978-9-8814-7685-2 ASIAPAC SIGN INFO PR 2018.0 611 617 7 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM7TA 2023-03-23 WOS:000468383400094 0
J Zhang, WJ; Jung, MR; Migliavacca, M; Poyatos, R; Miralles, DG; El-Madany, TS; Galvagno, M; Carrara, A; Arriga, N; Ibrom, A; Mammarella, I; Papale, D; Cleverly, JR; Liddell, M; Wohlfahrt, G; Markwitz, C; Mauder, M; -Limoges, EP; Schmidt, M; Wolf, S; Bruemmer, C; Arain, MA; Fares, S; Kato, T; Ardo, J; Oechel, W; Hanson, C; Korkiakoski, M; Biraud, S; Steinbrecher, R; Billesbach, D; Montagnani, L; Woodgate, W; Shao, CL; Carvalhais, N; Reichstein, M; Nelson, JA Zhang, Weijie; Jung, Martin; Migliavacca, Mirco; Poyatos, Rafael; Miralles, Diego G.; El-Madany, Tarek S.; Galvagno, Marta; Carrara, Arnaud; Arriga, Nicola; Ibrom, Andreas; Mammarella, Ivan; Papale, Dario; Cleverly, Jamie R.; Liddell, Michael; Wohlfahrt, Georg; Markwitz, Christian; Mauder, Matthias; -Limoges, Eugenie Paul; Schmidt, Marius; Wolf, Sebastian; Bruemmer, Christian; Arain, M. Altaf; Fares, Silvano; Kato, Tomomichi; Ardo, Jonas; Oechel, Walter; Hanson, Chad; Korkiakoski, Mika; Biraud, Sebastien; Steinbrecher, Rainer; Billesbach, Dave; Montagnani, Leonardo; Woodgate, William; Shao, Changliang; Carvalhais, Nuno; Reichstein, Markus; Nelson, Jacob A. The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation AGRICULTURAL AND FOREST METEOROLOGY English Article Evapotranspiration; Energy balance closure; Latent energy; FLUXNET; Eddy covariance ENERGY-BALANCE CLOSURE; WATER-VAPOR; EVAPOTRANSPIRATION ALGORITHM; AGRICULTURAL MANAGEMENT; FOREST; CARBON; ATTENUATION; EXCHANGE; MODIS; SAPFLUXNET While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evap-oration or 'evapotranspiration', ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underesti-mation is highly site-specific. We then propose a machine learning based method to correct for this underesti-mation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE. [Zhang, Weijie; Jung, Martin; Migliavacca, Mirco; El-Madany, Tarek S.; Carvalhais, Nuno; Reichstein, Markus; Nelson, Jacob A.] Max Planck Inst Biogeochem, Dept Biogeochem Integrat, Jena, Germany; [Zhang, Weijie; Miralles, Diego G.] Univ Ghent, Fac Biosci Engn, Hydroclimate Extremes Lab H CEL, Ghent, Belgium; [Migliavacca, Mirco; Arriga, Nicola] European Commiss, Joint Res Ctr, , (VA), Ispra, VA, Italy; [Poyatos, Rafael] CREAF, Cerdanyola Del Valles, Spain; [Poyatos, Rafael] Univ Autonoma Barcelona, Cerdanyola Del Valles, Spain; [Galvagno, Marta] Environm Protect Agcy Aosta Valley, Climate Change Unit, Aosta, Italy; [Carrara, Arnaud] Fdn Ctr Estudios Ambientales Mediterraneo, Paterna 46980, Valencia, Spain; [Ibrom, Andreas] Tech Univ Denmark, Dept Environm & Resource Management, Lyngby, Denmark; [Mammarella, Ivan; Korkiakoski, Mika] Univ Helsinki, Inst Atmospher & Earth Syst Res Phys INAR, Fac Sci, POB 68, Helsinki 00014, Finland; [Papale, Dario] Univ Tuscia, DIBAF, I-01100 Viterbo, Italy; [Cleverly, Jamie R.; Liddell, Michael] James Cook Univ, Coll Sci & Engn, Cairns, Qld 4878, Australia; [Wohlfahrt, Georg] Univ Innsbruck, Dept Ecol, Innsbruck, Austria; [Markwitz, Christian] Univ Gottingen, Bioclimatol, Gottingen, Germany; [Mauder, Matthias] Tech Univ Dresden, Fac Environm Sci, Dresden, Germany; [-Limoges, Eugenie Paul] Univ Zurich, Dept Geog, CH-8057 Zurich, Switzerland; [Schmidt, Marius] Forschungszentrum Julich, Agrosphere Inst, IBG 3, D-52425 Julich, Germany; [Wolf, Sebastian] Swiss Fed Inst Technol, Dept Environm Syst Sci, CH-8092 Zurich, Switzerland; [Bruemmer, Christian] Thunen Inst Climate Smart Agr, Braunschweig, Germany; [Arain, M. Altaf; Kato, Tomomichi] McMaster Univ, Sch Earth Environm & Soc, Hamilton, ON, Canada; [Fares, Silvano] Natl Res Council Italy, Inst Bioecon, Rome, Italy; [Kato, Tomomichi; Oechel, Walter] Hokkaido Univ, Res Fac Agr, Sapporo, Japan; [Ardo, Jonas; Oechel, Walter] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; [Oechel, Walter] San Diego State Univ, Global Change Res Grp, San Diego, CA USA; [Oechel, Walter] Univ Exeter, Dept Geog, Exeter, England; [Hanson, Chad] Oregon State Univ, Coll Forestry, Dept Forests Ecosyst & Soc, Corvallis, OR USA; [Biraud, Sebastien] Lawrence Berkeley Natl Lab, Climate Sci Dept, Berkeley, CA USA; [Steinbrecher, Rainer] Karlsruhe Inst Technol KIT, Inst Meteorol & Climate Res IMK, Dept Atmospher Environm Res IFU, Garmisch Partenkirchen, Germany; [Billesbach, Dave] Univ Nebraska, Lincoln, NE USA; [Montagnani, Leonardo] Free Univ Bozen Bolzano, Fac Sci & Technol, I-39100 Bolzano, Italy; [Woodgate, William] Univ Queensland, Sch Earth & Environm Sci, St Lucia, Qld 4072, Australia; [Woodgate, William] CSIRO, Space & Astron, Kensington 6151, Australia; [Shao, Changliang] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing, Peoples R China; [Carvalhais, Nuno] Univ Nova Lisboa, Fac Ciencias & Tecnol FCT, Dept Ciencias & Engn Ambiente DCEA, Lisbon, Portugal; [Reichstein, Markus] Michael Stifel Ctr Jena Data Driven & Simulat Sci, Jena, Germany Max Planck Society; Ghent University; European Commission Joint Research Centre; EC JRC ISPRA Site; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Autonomous University of Barcelona; Regional Environmental Protection Agency - Italy; Technical University of Denmark; University of Helsinki; Tuscia University; James Cook University; University of Innsbruck; University of Gottingen; Technische Universitat Dresden; University of Zurich; Helmholtz Association; Research Center Julich; Swiss Federal Institutes of Technology Domain; ETH Zurich; Johann Heinrich von Thunen Institute; McMaster University; Consiglio Nazionale delle Ricerche (CNR); Istituto per la BioEconomia (IBE-CNR); Hokkaido University; Lund University; California State University System; San Diego State University; University of Exeter; Oregon State University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Helmholtz Association; Karlsruhe Institute of Technology; University of Nebraska System; University of Nebraska Lincoln; Free University of Bozen-Bolzano; University of Queensland; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Universidade Nova de Lisboa Zhang, WJ (corresponding author), Max Planck Inst Biogeochem, Dept Biogeochem Integrat, Jena, Germany.;Zhang, WJ (corresponding author), Univ Ghent, Fac Biosci Engn, Hydroclimate Extremes Lab H CEL, Ghent, Belgium. wzhang@bgc-jena.mpg.de Carrara, Arnaud/ABH-5319-2020; Montagnani, Leonardo/F-1837-2016; Poyatos, Rafael/F-8305-2010; Wohlfahrt, Georg/D-2409-2009; Mammarella, Ivan/E-7782-2016; Korkiakoski, Mika/B-2846-2018 Carrara, Arnaud/0000-0002-9095-8807; Montagnani, Leonardo/0000-0003-2957-9071; Poyatos, Rafael/0000-0003-0521-2523; Wohlfahrt, Georg/0000-0003-3080-6702; Mammarella, Ivan/0000-0002-8516-3356; Zhang, Wenzhong/0000-0001-9184-0723; Ibrom, Andreas/0000-0002-1341-921X; Korkiakoski, Mika/0000-0001-6875-9978; Markwitz, Christian/0000-0001-8215-7420 Spanish State Research Agency (DATAFORUSE) [RTI2018-095297-J-I00]; Alexander von Humboldt Foundation (Germany) [CGL 2017-83538-C3-3-R]; project ELEMENTAL, MINECO-FEDER; Australian Research Council DECRA Fellowship [CGL 2017-83538-C3-3-R]; ENVRI-FAIR H2020 project [DE190101182]; U.S. Department of Energy's Office of Science [GA 824068]; CDIAC [DE-AC02-05CH11231]; ICOS Ecosystem Thematic Center Spanish State Research Agency (DATAFORUSE); Alexander von Humboldt Foundation (Germany)(Alexander von Humboldt Foundation); project ELEMENTAL, MINECO-FEDER; Australian Research Council DECRA Fellowship(Australian Research Council); ENVRI-FAIR H2020 project; U.S. Department of Energy's Office of Science(United States Department of Energy (DOE)); CDIAC; ICOS Ecosystem Thematic Center We thank associated PIs for confirming the sensor types and spectral corrections. RP acknowledges support from the Spanish State Research Agency (DATAFORUSE, RTI2018-095297-J-I00) and the Alexander von Humboldt Foundation (Germany) . AC thanks project ELEMENTAL (CGL 2017-83538-C3-3-R, MINECO-FEDER) . WW is supported by an Australian Research Council DECRA Fellowship (DE190101182) . DP thanks for the support of the ENVRI-FAIR H2020 project (GA 824068) . This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, China-Flux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, TERENO, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. TheFLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, the AmeriFlux Management Project (supported by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02-05CH11231) , and Flux-data project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the TERN OzFlux, ChinaFlux, and AsiaFlux offices. 74 0 0 8 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0168-1923 1873-2240 AGR FOREST METEOROL Agric. For. Meteorol. MAR 1 2023.0 330 109305 10.1016/j.agrformet.2022.109305 0.0 JAN 2023 11 Agronomy; Forestry; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Forestry; Meteorology & Atmospheric Sciences 8J8WP Green Published, hybrid 2023-03-23 WOS:000922693400001 0
J Ding, CW; Hong, H; Zou, Y; Chu, H; Zhu, XH; Fioranelli, F; Le Kernec, J; Li, CZ Ding, Chuanwei; Hong, Hong; Zou, Yu; Chu, Hui; Zhu, Xiaohua; Fioranelli, Francesco; Le Kernec, Julien; Li, Changzhi Continuous Human Motion Recognition With a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Continuous human motion recognition; dynamic range-Doppler trajectory (DRDT) method; frequency-modulated continuous-wave (FMCW) radar; fusion of multidomain features; machine learning HUMAN ACTIVITY CLASSIFICATION; MICRO-DOPPLER; FALL DETECTION; SIGNATURES; SENSORS; SYSTEM Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification. [Ding, Chuanwei; Hong, Hong; Zou, Yu; Chu, Hui; Zhu, Xiaohua] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Jiangsu, Peoples R China; [Ding, Chuanwei; Li, Changzhi] Texas Tech Univ, Dept Elect & Comp Engn, Lubbock, TX 79409 USA; [Fioranelli, Francesco; Le Kernec, Julien] Univ Glasgow, Sch Engn, Commun Sensing & Imaging Grp, Glasgow G12 8QQ, Lanark, Scotland; [Le Kernec, Julien] Univ Elect Sci & Technol China, Sch Informat & Commun, Chengdu 610000, Sichuan, Peoples R China; [Le Kernec, Julien] Univ Cergy Pontoise, Image & Signal Proc Lab, F-95011 Cergy, France Nanjing University of Science & Technology; Texas Tech University System; Texas Tech University; University of Glasgow; University of Electronic Science & Technology of China; CY Cergy Paris Universite Hong, H (corresponding author), Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Jiangsu, Peoples R China. hongnju@njust.edu.cn; francesco.fioranelli@glasgow.ac.uk; julien.lekernec@glasgow.ac.uk; changzhi.li@ttu.edu Fioranelli, Francesco/Y-8612-2019; Li, Changzhi/P-3098-2016 Ding, Chuanwei/0000-0002-5139-5036; le kernec, julien/0000-0003-2124-6803; Fioranelli, Francesco/0000-0001-8254-8093; Li, Changzhi/0000-0003-2188-4506 National Natural Science Foundation of China [61871224, 81601568]; Key Research and Development Plan of Jiangsu Province [BE2018729]; Fundamental Research Funds for the Central Universities [30917011316]; State Scholarship Fund of the China Scholarship Council [201806840055]; National Science Foundation (NSF) [ECCS-1808613, CNS-1718483]; U. K. Engineering and Physical Sciences Research Council [INSHEP EP/R041679/1]; EPSRC [EP/R041679/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Plan of Jiangsu Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Scholarship Fund of the China Scholarship Council(China Scholarship Council); National Science Foundation (NSF)(National Science Foundation (NSF)); U. K. Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61871224 and Grant 81601568, in part by the Key Research and Development Plan of Jiangsu Province under Grant BE2018729, in part by the Fundamental Research Funds for the Central Universities under Grant 30917011316, the State Scholarship Fund of the China Scholarship Council under Grant 201806840055, and in part by the National Science Foundation (NSF) under Grant ECCS-1808613 and Grant CNS-1718483. The work of F. Fioranelli was supported by U. K. Engineering and Physical Sciences Research Council under Grant INSHEP EP/R041679/1. 34 66 70 16 74 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2019.0 57 9 6821 6831 10.1109/TGRS.2019.2908758 0.0 11 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology IV3YB hybrid, Green Accepted 2023-03-23 WOS:000484209000041 0
C Yang, LF; Gao, YM; Xie, YL; Zhang, JS Tong, R; Zhang, Y; Lu, Y; Dong, M Yang, Longfei; Gao, Yingming; Xie, Yanlu; Zhang, Jinsong Improving Pronunciation Erroneous Tendency Detection with Convolutional Long Short-Term Memory 2017 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP) International Conference on Asian Language Processing English Proceedings Paper International Conference on Asian Language Processing (IALP) DEC 05-07, 2017 Natl Univ Singapore, Singapore, SINGAPORE Chinese & Oriental Languages Informat Proc Soc,IEEE,APSIPA,Singapore tourist board Natl Univ Singapore component:CAPT; mispronunciation detection; deep learning Corrective feedbacks are much more desirable than pure scores since they provide more information to guide learners to correct their erroneous pronunciations in the area of computer assisted pronunciation teaching (CAPT). For this purpose, we previously proposed pronunciation erroneous tendency (PET), which represents the errors from the aspects of articulation manner and constriction place. And we implemented PET detection system with Gaussian Mixture Model (GMM) and Deep Neural Networks (DNN) in previous work [1-2]. However, it is still challenging to achieve a high-performance system because of context dependency of PETs and data sparseness problem. In this paper, we first introduced data augmentation scheme to mitigate data sparseness problem. To further improve the performance, we proposed taking advantage of the LSTM and CNN by combining them into a unified system. Experimental results suggested that the proposed CNN-LSTM outperformed other models in our previous work. [Yang, Longfei; Xie, Yanlu; Zhang, Jinsong] Beijing Language & Culture Univ, Coll Informat Sci, Beijing 100083, Peoples R China; [Gao, Yingming] Tech Univ Dresden, Inst Acoust & Speech Commun, D-01062 Dresden, Germany Beijing Language & Culture University; Technische Universitat Dresden Zhang, JS (corresponding author), Beijing Language & Culture Univ, Coll Informat Sci, Beijing 100083, Peoples R China. yanglongfei908@gmail.com; gaoyingming1@sina.com; xyl@blcu.edu.cn; jinsong.zhang@blcu.edu.cn Advanced Innovation Center for Language Resources and Intelligence; Special Program for Key Basic Research fund of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities) [16ZDJ03]; Research Funds of Beijing Language and Culture University [16YCX220] Advanced Innovation Center for Language Resources and Intelligence; Special Program for Key Basic Research fund of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities); Research Funds of Beijing Language and Culture University This work is supported by funds of Advanced Innovation Center for Language Resources and Intelligence, the Special Program for Key Basic Research fund of Beijing Language and Culture University (the Fundamental Research Funds for the Central Universities) (16ZDJ03) and the Research Funds of Beijing Language and Culture University (16YCX220). The asterisked author is the corresponding author 19 3 3 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-1962 2159-1970 978-1-5386-1981-0 INT CONF ASIAN LANG 2017.0 52 56 5 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ8KS 2023-03-23 WOS:000428370700013 0
C Cao, YP; Liu, ZN; Kuang, ZF; Kobbelt, L; Hu, SM Ferrari, V; Hebert, M; Sminchisescu, C; Weiss, Y Cao, Yan-Pei; Liu, Zheng-Ning; Kuang, Zheng-Fei; Kobbelt, Leif; Hu, Shi-Min Learning to Reconstruct High-Quality 3D Shapes with Cascaded Fully Convolutional Networks COMPUTER VISION - ECCV 2018, PT IX Lecture Notes in Computer Science English Proceedings Paper 15th European Conference on Computer Vision (ECCV) SEP 08-14, 2018 Munich, GERMANY High-fidelity 3D reconstruction; Cascaded architecture We present a data-driven approach to reconstructing high-resolution and detailed volumetric representations of 3D shapes. Although well studied, algorithms for volumetric fusion from multi-view depth scans are still prone to scanning noise and occlusions, making it hard to obtain high-fidelity 3D reconstructions. In this paper, inspired by recent advances in efficient 3D deep learning techniques, we introduce a novel cascaded 3D convolutional network architecture, which learns to reconstruct implicit surface representations from noisy and incomplete depth maps in a progressive, coarse-to-fine manner. To this end, we also develop an algorithm for end-to-end training of the proposed cascaded structure. Qualitative and quantitative experimental results on both simulated and real-world datasets demonstrate that the presented approach outperforms existing state-of-the-art work in terms of quality and fidelity of reconstructed models. [Cao, Yan-Pei; Liu, Zheng-Ning; Kuang, Zheng-Fei; Hu, Shi-Min] Tsinghua Univ, Beijing, Peoples R China; [Cao, Yan-Pei] Owlii Inc, Beijing, Peoples R China; [Kobbelt, Leif] Rhein Westfal TH Aachen, Aachen, Germany Tsinghua University; RWTH Aachen University Cao, YP (corresponding author), Tsinghua Univ, Beijing, Peoples R China.;Cao, YP (corresponding author), Owlii Inc, Beijing, Peoples R China. caoyanpei@gmail.com; lzhengning@gmail.com; kzf15@mails.tsinghua.edu.cn; kobbelt@cs.rwth-aachen.de; shimin@tsinghua.edu.cn Kobbelt, Leif/0000-0002-7880-9470 Joint NSFC-DFG Research Program [61761136018]; Natural Science Foundation of China [61521002] Joint NSFC-DFG Research Program; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Joint NSFC-DFG Research Program (project number 61761136018), and the Natural Science Foundation of China (Project Number 61521002). 63 16 17 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-01240-3; 978-3-030-01239-7 LECT NOTES COMPUT SC 2018.0 11213 626 643 10.1007/978-3-030-01240-3_38 0.0 18 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BQ4SD 2023-03-23 WOS:000594233000038 0
C Xiong, ZY; Weber, C; Hu, XL IEEE Xiong, Zheyang; Weber, Cornelius; Hu, Xiaolin Frame Difference-Based Real-Time Video Stylization in Video Calls 2018 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP) English Proceedings Paper 9th International Conference on Intelligent Control and Information Processing (ICICIP) NOV 09-11, 2018 Wanzhou, PEOPLES R CHINA Chongqing Three Gorges Univ,Huazhong Univ Sci & Technol,S China Univ Technol,SW Univ,IEEE Computat Intelligence Soc,IEEE Syst Man, & Cybernet Soc Deep learning; real-lime video processing; image stylization; morphology transformation; computer vision The naive video stylization method is to perform neural-style transfer on individual frames, but this method would result in a flickering effect, which is particularly visible in static regions. Previous remedies extract optical flow from a video and use this information to stabilize the stylized videos. However, computing optical slow is complex and time-consuming. We consider stylizing videos in which the background is fixed and only the foreground object moves, which is the case in video calls. We propose a simple method to stylize such videos in real time based on frame difference. The main idea is to use the frame difference to detect foreground and rebuild it in the next frame while maintaining the stylized background from the previous frame. This method is easy to implement and can stylize videos in real time with stabilized frames. [Xiong, Zheyang] Nansha Coll Preparatory Acad, Guangzhou 511458, Guangdong, Peoples R China; [Weber, Cornelius] Univ Hamburg, Dept Informat, D-22527 Hamburg, Germany; [Hu, Xiaolin] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China University of Hamburg; Tsinghua University Hu, XL (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. xlhu@mail.tsinghua.edu.cn Research Science Initiative program at Tsinghua University; National Natural Science Foundation of China [61332007, 61621136008, 61273023]; German Research Foundation (DFG) [TRR 169] Research Science Initiative program at Tsinghua University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG)(German Research Foundation (DFG)) This work was done in the Research Science Initiative program at Tsinghua University. And this work was supported in part by the National Natural Science Foundation of China under grants nos. 61332007, 61621136008, and 61273023 and by the German Research Foundation (DFG) under project Transregio Crossmodal Learning (TRR 169). 13 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-5860-4 2018.0 333 339 7 Automation & Control Systems; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science BL9TN 2023-03-23 WOS:000458317800059 0
J Deng, YY; Tang, F; Dong, WM; Wu, FZ; Deussen, O; Xu, CS Deng, Yingying; Tang, Fan; Dong, Weiming; Wu, Fuzhang; Deussen, Oliver; Xu, Changsheng Selective clustering for representative paintings selection MULTIMEDIA TOOLS AND APPLICATIONS English Article Digital arts analysis; Pattern mining; Rejection mechanism; Deep feature representation REJECT OPTION; CLASSIFICATION; STYLE Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods. [Deng, Yingying; Tang, Fan; Dong, Weiming; Xu, Changsheng] Chinese Acad Sci, Inst Automat, NLPR LIAMA, Beijing, Peoples R China; [Deng, Yingying; Tang, Fan] Univ Chinese Acad Sci, Beijing, Peoples R China; [Wu, Fuzhang] Chinese Acad Sci, Inst Software, Beijing, Peoples R China; [Deng, Yingying; Tang, Fan; Deussen, Oliver] Univ Konstanz, Constance, Germany Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Software, CAS; University of Konstanz Dong, WM (corresponding author), Chinese Acad Sci, Inst Automat, NLPR LIAMA, Beijing, Peoples R China. weiming.dong@ia.ac.cn; oliver.deussen@uni-konstanz.de; changsheng.xu@ia.ac.cn Xu, Chang/GQP-7280-2022; xu, cj/HJZ-3488-2023; Deussen, Oliver/HKF-2004-2023; Tang, Fan/O-3923-2018; DONG, Weiming/AAG-7678-2020 Tang, Fan/0000-0002-3975-2483; DONG, Weiming/0000-0001-6502-145X National Natural Science Foundation of China [61832016, 61672520, 61702488]; National Laboratory of Pattern Recognition National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Laboratory of Pattern Recognition This work was supported by National Natural Science Foundation of China under nos. 61832016, 61672520 and 61702488, as well as the independent research project of National Laboratory of Pattern Recognition. 43 7 7 2 13 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUL 2019.0 78 14 19305 19323 10.1007/s11042-019-7271-7 0.0 19 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IJ2BS Green Submitted 2023-03-23 WOS:000475703800017 0
C Wei, JM; Pei, EC; Jiang, DM; Sahli, H; Xie, L; Fu, ZH IEEE Wei, Jiamei; Pei, Ercheng; Jiang, Dongmei; Sahli, Hichem; Xie, Lei; Fu, Zhonghua Multimodal Continuous Affect Recognition Based on LSTM and Multiple Kernel Learning 2014 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA) Asia-Pacific Signal and Information Processing Association Annual Summit and Conference English Proceedings Paper Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA) DEC 09-12, 2014 Angkor, CAMBODIA Asia Pacif Signal & Informat Proc Assoc In this paper, we propose a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and multiple kernel learning (MKL) based multi-modal affect recognition scheme (LSTM-MKL). It takes the LSTM-RNN advantage to model the long range dependencies between successive observations, and uses the MKL power to model the non-linear correlations between the inputs and outputs. For each of the affect dimensions (arousal, valence, expectancy, and power), two LSTM-RNN models are trained, one for each modality. In the recognition phase, the audio and visual features are input to the corresponding learned LSTM models, which in turn produce initial estimates of the affect dimensions. The LSTM outputs are further input into a multi-kernel support vector regression (MK-SVR) for the final recognition. Experimental results carried out on the AVEC2012 database, show that compared to the traditional SVR-LLR (Support Vector Machine - local linear regression) or MK-SVR fusion scheme, the proposed LSTM-MKL fusion scheme obtains higher recognition results, with an correlation coefficient (COR) of 0.354, compared to a COR of 0.124 for SVR-LLR, and 0.168 for MK-SVR, respectively. [Wei, Jiamei; Pei, Ercheng; Jiang, Dongmei; Xie, Lei; Fu, Zhonghua] Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, VUB NPU Joint AVSP Res Lab, Xian, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel, Interuniv Microelect Ctr, IMEC, Dept ETRO,VUB NPU Joint AVSP Res Lab, Brussels, Belgium Northwestern Polytechnical University; IMEC; Interuniversity Microelectronics Centre; Vrije Universiteit Brussel Wei, JM (corresponding author), Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, VUB NPU Joint AVSP Res Lab, Xian, Peoples R China. jiangdm@nwpu.edu.cn; hsahli@vub.ac.be National Natural Science Foundation of China [61273265]; EU FP7 project ALIZ-E [248116]; VUB-IRP EmoApp project [VUB-IRP5] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU FP7 project ALIZ-E; VUB-IRP EmoApp project This work is supported within the framework of the National Natural Science Foundation of China (grant 61273265), the EU FP7 project ALIZ-E (grant 248116), and the VUB-IRP EmoApp project (grant VUB-IRP5). 15 0 0 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2309-9402 978-616-361-823-8 ASIAPAC SIGN INFO PR 2014.0 4 Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BG8ZG 2023-03-23 WOS:000392861900231 0
J Feng, MZ; Sun, X; Dong, JY; Zhao, HR Feng, Mingzhe; Sun, Xin; Dong, Junyu; Zhao, Haoran Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images REMOTE SENSING English Article remote sensing; semantic segmentation; deep learning; convolutional neural networks Different scales of the objects pose a great challenge for the segmentation of remote sensing images of special scenes. This paper focuses on the problem of large-scale variations of the target objects via a dynamical receptive field of the deep network. We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding. Moreover, we propose a new Gaussian pyramid pooling (GPP) for multi-scale object segmentation. The proposed network can expand the size of the receptive field and improve its efficiency in aggregating contextual information. Experiments verify that our method outperforms the popular semantic segmentation methods on large remote sensing image datasets, including iSAID and LoveDA. Moreover, we conduct experiments to demonstrate that the Gaussian dynamic convolution works more effectively on remote sensing images than other convolutional layers. [Feng, Mingzhe; Sun, Xin; Dong, Junyu] Ocean Univ China, Coll Informat Sci & Engn, Haide Coll, Qingdao 266100, Peoples R China; [Feng, Mingzhe; Sun, Xin; Dong, Junyu] Ocean Univ China, Inst Adv Ocean Study, Qingdao 266100, Peoples R China; [Sun, Xin] Tech Univ Munich, Dept Aerosp & Geodesy, D-80333 Munich, Germany; [Zhao, Haoran] Qingdao Univ Technol, Sch Informat & Control Engn, Qingdao 266520, Peoples R China Ocean University of China; Ocean University of China; Technical University of Munich; Qingdao University of Technology Sun, X (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Haide Coll, Qingdao 266100, Peoples R China.;Sun, X (corresponding author), Ocean Univ China, Inst Adv Ocean Study, Qingdao 266100, Peoples R China.;Sun, X (corresponding author), Tech Univ Munich, Dept Aerosp & Geodesy, D-80333 Munich, Germany. sunxin1984@ieee.org National Natural Science Foundation of China [61971388]; Alexander von Humboldt Foundation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation) This work was supported in part by National Natural Science Foundation of China under Project Nos. 61971388 and Alexander von Humboldt Foundation. 28 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2022.0 14 22 5736 10.3390/rs14225736 0.0 11 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 6K7EE gold 2023-03-23 WOS:000887659600001 0
J Liu, MX; Mu, J; Yu, ZT; Ruan, K; Shu, BY; Yang, J Liu, Mingxin; Mu, Jiong; Yu, Zitong; Ruan, Kun; Shu, Baiyi; Yang, Jie Adversarial learning and decomposition-based domain generalization for face anti-spoofing PATTERN RECOGNITION LETTERS English Article Deep learning; Face anti-spoofing; Domain generalization; Low-rank decomposition Face anti-spoofing (FAS) plays a critical role in the face recognition community for securing the face presentation attacks. Many works have been proposed to regard FAS as a domain generalization problem for robust deployment in real-world scenarios. However, existing methods focus on extracting intrinsic spoofing cues to improve the generalization ability, yet neglect to train a robust classifier. In this paper, we propose a framework to improve the generalization ability of face anti-spoofing in two folds:) a generalized feature space is obtained via aggregation of all live faces while dispersing each domain's spoof faces; and) a domain agnostic classifier is trained through low-rank decomposition. Specifically, a Common Specific Decomposition for Specific (CSD-S) layer is deployed in the last layer of the network to select common features while discarding domain-specific ones among multiple source domains. The abovementioned two components are integrated into an end-to-end framework, ensuring the generalization ability to unseen scenarios. The extensive experiments demonstrate that the proposed method achieves state-of-the-art results on four public datasets, including CASIA-MFSD, MSU-MFSD, Replay-Attack, and OULU-NPU. (C) 2021 Published by Elsevier B.V. [Liu, Mingxin; Mu, Jiong; Ruan, Kun; Shu, Baiyi; Yang, Jie] Sichuan Agr Univ, Yaan 625000, Sichuan, Peoples R China; [Yu, Zitong] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland Sichuan Agricultural University; University of Oulu Mu, J (corresponding author), Sichuan Agr Univ, Yaan 625000, Sichuan, Peoples R China.;Yu, ZT (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland. lmx@stu.sicau.edu.cn; jmu@sicau.edu.cn; zitong.yu@oulu.fi; qkmc@outlook.com; shubaiyi100@stu.sicau.edu.cn; yangjie1@stu.sicau.edu.cn Key Laboratory of Agricultural Information Engineering of Sichuan Province, Sichuan Agricultural University; Sichuan Science and Technology Innovation Seedling Project Funding Project [2019025]; Sichuan Agricultural University Innovation and Entrepreneurship Training Program [201910626026] Key Laboratory of Agricultural Information Engineering of Sichuan Province, Sichuan Agricultural University; Sichuan Science and Technology Innovation Seedling Project Funding Project; Sichuan Agricultural University Innovation and Entrepreneurship Training Program This paper was supported by the Key Laboratory of Agricultural Information Engineering of Sichuan Province, Sichuan Agricultural University. This study was financially supported by Sichuan Science and Technology Innovation Seedling Project Funding Project under Grant no. 2019025, Sichuan Agricultural University Innovation and Entrepreneurship Training Program (Grant no. 201910626026). 45 3 3 4 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. MAR 2022.0 155 171 177 10.1016/j.patrec.2021.10.014 0.0 MAR 2022 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1N0OC 2023-03-23 WOS:000800362500024 0
J Mao, YQ; Chen, KQ; Diao, WH; Sun, X; Lu, XN; Fu, K; Weinmann, M Mao, Yongqiang; Chen, Kaiqiang; Diao, Wenhui; Sun, Xian; Lu, Xiaonan; Fu, Kun; Weinmann, Martin Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Airborne laser scanning; Point cloud; Classification; Deep learning; Dilated graph convolution; Multi-scale receptive fields CONTEXTUAL CLASSIFICATION; ATTENTIONAL NETWORK; LIDAR DATA The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging for the distinguishment of the areas with complex structures and extreme scale variations. In this article, for the objective of configuring multi-receptive field features, we propose a novel receptive field fusion-and stratification network (RFFS-Net). With a novel dilated graph convolution (DGConv) and its extension annular dilated convolution (ADConv) as basic building blocks, the receptive field fusion process is implemented with the dilated and annular graph fusion (DAGFusion) module, which obtains multi-receptive field feature representation through capturing dilated and annular graphs with various receptive regions. The stratification of the receptive fields with point sets of different resolutions as the calculation bases is performed with Multi-level Decoders nested in RFFS-Net and driven by the multi-level receptive field aggregation loss (MRFALoss) to drive the network to learn in the direction of the supervision labels with different resolutions. With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net significantly outperforms the baseline (i.e. PointConv) approach by 5.3% on mF1 and 5.4% on mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU of 58.2%. The experiments show that our RFFS-Net achieves a new state-of-the-art classification performance on powerline, car, and fence classes. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art classification performance. The code is available at github.com/ WingkeungM/RFFS-Net. [Mao, Yongqiang; Chen, Kaiqiang; Diao, Wenhui; Sun, Xian; Lu, Xiaonan; Fu, Kun] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China; [Mao, Yongqiang; Chen, Kaiqiang; Diao, Wenhui; Sun, Xian; Lu, Xiaonan; Fu, Kun] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China; [Mao, Yongqiang; Diao, Wenhui; Sun, Xian; Lu, Xiaonan; Fu, Kun] Univ Chinese Acad Sci, Beijing 100190, Peoples R China; [Mao, Yongqiang; Diao, Wenhui; Sun, Xian; Lu, Xiaonan; Fu, Kun] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China; [Weinmann, Martin] Karlsruhe Inst Technol, Inst Photogrammetry & Remote Sensing, Karlsruhe, Germany Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Helmholtz Association; Karlsruhe Institute of Technology Chen, KQ (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China. maoyongqiang19@mails.ucas.ac.cn; chenkaiqiang14@mails.ucas.ac.cn; diaowh@aircas.ac.cn; sunxian@aircas.ac.cn; luxiaonan19@mails.ucas.ac.cn; martin.weinmann@kit.edu Mao, Yongqiang/0000-0001-9256-3668 National Natural Science Foundation of China (NSFC) [62171436] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) Acknowledgement This work for airborne laser scanning point cloud classification was supported by the National Natural Science Foundation of China (NSFC) under Grant 62171436. 69 2 3 6 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. JUN 2022.0 188 45 61 10.1016/j.isprsjprs.2022.03.019 0.0 APR 2022 17 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology 1V8TV Green Submitted 2023-03-23 WOS:000806358700001 0
J Deng, SG; Xiang, ZZ; Zhao, P; Taheri, J; Gao, HH; Yin, JW; Zomaya, AY Deng, Shuiguang; Xiang, Zhengzhe; Zhao, Peng; Taheri, Javid; Gao, Honghao; Yin, Jianwei; Zomaya, Albert Y. Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Servers; Resource management; Task analysis; Cloud computing; Edge computing; Learning (artificial intelligence); Edge computing; Internet-of-Things (IoT); trust management; resource allocation MOBILE; STRATEGY Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines. [Deng, Shuiguang; Zhao, Peng] Zhejiang Univ, Sch Med, Affiliated Hosp 1, Hangzhou 310003, Peoples R China; [Deng, Shuiguang] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China; [Xiang, Zhengzhe; Yin, Jianwei] Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China; [Taheri, Javid] Karlstad Univ, Dept Comp Sci, S-65188 Karlstad, Sweden; [Gao, Honghao] Shanghai Univ, Comp Ctr, Shanghai 200444, Peoples R China; [Zomaya, Albert Y.] Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia Zhejiang University; Zhejiang University; Zhejiang University; Karlstad University; Shanghai University; University of Sydney Zhao, P (corresponding author), Zhejiang Univ, Sch Med, Affiliated Hosp 1, Hangzhou 310003, Peoples R China.;Gao, HH (corresponding author), Shanghai Univ, Comp Ctr, Shanghai 200444, Peoples R China. dengsg@zju.edu.cn; xiangzhengzhe@zju.edu.cn; zhaop@zju.edu.cn; javid.taheri@kau.se; gaohonghao@shu.edu.cn; zjuyjw@cs.zju.edu.cn; albert.zomaya@sydney.edu.au Taheri, Javid/I-2045-2013; Zomaya, Albert Y./G-9697-2017; Gao, Honghao/AAX-4529-2020 Taheri, Javid/0000-0001-9194-010X; Zomaya, Albert Y./0000-0002-3090-1059; Gao, Honghao/0000-0001-6861-9684; Deng, Shuiguang/0000-0001-5015-6095 National Key Research and Development Program of China [2017YFB1400600]; National Science Foundation of China [61772461, 61825205]; Natural Science Foundation of Zhejiang Province [LR18F020003]; Key Research Innovation Project of Hangzhou [20182011A06, TII-20-0416] National Key Research and Development Program of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Key Research Innovation Project of Hangzhou This research was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1400600, in part by the National Science Foundation of China under Grant 61772461 and Grant 61825205, in part by the Natural Science Foundation of Zhejiang Province under Grant LR18F020003, and in part by the Key Research Innovation Project of Hangzhou under Grant 20182011A06. Paper no. TII-20-0416. 31 86 87 12 83 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. SEP 2020.0 16 9 6103 6113 10.1109/TII.2020.2974875 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering MC0CY 2023-03-23 WOS:000542966300048 0
J Wu, CY; Lin, BT; Shi, K; Zhang, QJ; Gao, R; Yu, ZG; De Marinis, Y; Zhang, YS; Liu, ZP Wu, Chuanyan; Lin, Bentao; Shi, Kai; Zhang, Qingju; Gao, Rui; Yu, Zhiguo; De Marinis, Yang; Zhang, Yusen; Liu, Zhi-Ping PEPRF: Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-Based Features via Random Forest CURRENT BIOINFORMATICS English Article Essential protein prediction; graph energy; feature extraction; ReliefF-based feature selection; random forest classifier; PEPRF IDENTIFYING ESSENTIAL PROTEINS; SUBCELLULAR-LOCALIZATION; COMPUTATIONAL PREDICTION; CENTRALITY Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, information-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, ReliefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential proteins. [Wu, Chuanyan; Lin, Bentao; Yu, Zhiguo] Shandong Management Univ, Sch Intelligent Engn, Jinan 250357, Peoples R China; [Shi, Kai] Shandong Transport Vocat Coll, Dept Traff Engn, Weifang 261000, Peoples R China; [Zhang, Qingju] Shandong Univ Sci & Technol, Sch Intelligent Equipment, Tai An 271019, Shandong, Peoples R China; [Gao, Rui; Liu, Zhi-Ping] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China; [De Marinis, Yang] Lund Univ, Diabet & Endocrinol, S-20502 Malmo, Sweden; [Zhang, Yusen] Shandong Univ, Sch Math & Stat, Weihai 264209, Peoples R China Shandong Management University; Shandong University of Science & Technology; Shandong University; Lund University; Shandong University Yu, ZG (corresponding author), Shandong Management Univ, Sch Intelligent Engn, Jinan 250357, Peoples R China.;Gao, R (corresponding author), Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.;De Marinis, Y (corresponding author), Lund Univ, Diabet & Endocrinol, S-20502 Malmo, Sweden. gaorui@sdu.edu.cn; yzg@sdmu.edu.cn; yang.de_marinis@med.lu.se De Marinis, Yang/D-4992-2016; Zhang, Yusen/R-8508-2017; Liu, Zhi-Ping/C-9846-2010 De Marinis, Yang/0000-0002-8081-2142; Zhang, Yusen/0000-0003-3842-1153; Wu, Chuanyan/0000-0003-2705-866X; Liu, Zhi-Ping/0000-0001-7742-9161 Natural Science Foundation of China [U1806202, 61533011, 61877064]; Shandong Province Social Science Planning Project [18CHLJ21, 20CSDJ37]; Key Laboratory of public safety management technology of scientific research and innovation platform in Universities during the 13th Five Year Plan Period; Collaborative innovation center of Internet plus intelligent manufacturing of Shandong Universities; intelligent manufacturing and data application engineering laboratory of Shandong Province, China Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Province Social Science Planning Project; Key Laboratory of public safety management technology of scientific research and innovation platform in Universities during the 13th Five Year Plan Period; Collaborative innovation center of Internet plus intelligent manufacturing of Shandong Universities; intelligent manufacturing and data application engineering laboratory of Shandong Province, China This work was supported by the Natural Science Foundation of China [Grant Numbers U1806202, 61533011, 61877064], Shandong Province Social Science Planning Project [Grant Numbers 18CHLJ21, 20CSDJ37] and the Key Laboratory of public safety management technology of scientific research and innovation platform in Universities during the 13th Five Year Plan Period, Collaborative innovation center of Internet plus intelligent manufacturing of Shandong Universities, and intelligent manufacturing and data application engineering laboratory of Shandong Province, China. 49 7 7 2 15 BENTHAM SCIENCE PUBL LTD SHARJAH EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES 1574-8936 2212-392X CURR BIOINFORM Curr. Bioinform. 2021.0 16 9 1161 1168 10.2174/1574893616666210617162258 0.0 8 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology WN3EV 2023-03-23 WOS:000711656200006 0
J Jiang, W; Zhou, KQ; Sarkheyli-Hagele, A; Zain, AM Jiang, Wei; Zhou, Kai-Qing; Sarkheyli-Hagele, Arezoo; Zain, Azlan Mohd Modeling, reasoning, and application of fuzzy Petri net model: a survey ARTIFICIAL INTELLIGENCE REVIEW English Article Fuzzy Petri net; Knowledge representation; Modeling; Reasoning; Industrial application STATE-EXPLOSION PROBLEM; KNOWLEDGE-BASED SYSTEM; FAULT-DIAGNOSIS; INDUSTRIAL APPLICATIONS; MANUFACTURING SYSTEMS; TRACKING CONTROL; RISK-ASSESSMENT; NEURAL-NETWORK; POWER-SYSTEMS; REPRESENTATION A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions. [Jiang, Wei; Zhou, Kai-Qing] Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Hunan, Peoples R China; [Sarkheyli-Hagele, Arezoo] Malmo Univ, Internet Things & People Res Ctr, Dept Comp Sci & Media Technol, S-20506 Malmo, Sweden; [Zain, Azlan Mohd] Univ Teknol Malaysia, UTM Big Data Ctr, Skudai 80310, Johor, Malaysia Jishou University; Malmo University; Universiti Teknologi Malaysia Zhou, KQ (corresponding author), Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Hunan, Peoples R China. jsu_computer@163.com Zhou, Kai-Qing/U-6327-2019; Zain, Azlan Mohd/K-6829-2012; Zain, Azlan Mohd/AAY-3710-2021 Zhou, Kai-Qing/0000-0001-5779-7135; Zain, Azlan Mohd/0000-0003-2004-3289; Zain, Azlan Mohd/0000-0003-2004-3289 National Natural Science Foundation of China [62066016]; Natural Science Foundation of Hunan Province, China [2020JJ5458, 2019JJ40234]; Research Foundation of Education Bureau of Hunan Province, China [18B317, 19C1535]; Jishou University Graduate Research and Innovation Project [JDY19046]; open project of the collaborative innovation center of industrial technology of manganese, zinc, vanadium [MXF201926] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province, China(Natural Science Foundation of Hunan Province); Research Foundation of Education Bureau of Hunan Province, China; Jishou University Graduate Research and Innovation Project; open project of the collaborative innovation center of industrial technology of manganese, zinc, vanadium This work was supported by the National Natural Science Foundation of China (No. 62066016), the Natural Science Foundation of Hunan Province, China (Nos. 2020JJ5458 and 2019JJ40234), the Research Foundation of Education Bureau of Hunan Province, China (Nos. 18B317 and 19C1535), the Jishou University Graduate Research and Innovation Project (No. JDY19046), and the open project of the collaborative innovation center of industrial technology of manganese, zinc, vanadium (No. MXF201926). 207 2 2 13 27 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. DEC 2022.0 55 8 6567 6605 10.1007/s10462-022-10161-0 0.0 MAR 2022 39 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5V1ZW 2023-03-23 WOS:000767725000001 0
J Fu, HM; Liu, J; Wu, GF; Xu, Y; Sutcliffe, G Fu, Huimin; Liu, Jun; Wu, Guanfeng; Xu, Yang; Sutcliffe, Geoff Improving probability selection based weights for satisfiability problems KNOWLEDGE-BASED SYSTEMS English Article Boolean Satisfiability problem; Stochastic local search; Weights LOCAL SEARCH; SAT; ALGORITHM Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great break-throughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively.(C) 2022 Elsevier B.V. All rights reserved. [Fu, Huimin] Southwestern Univ Finance & Econ, Sch Comp & Artificial Intelligence, Chengdu 611130, Peoples R China; [Liu, Jun] Ulster Univ, Sch Comp, Coleraine, North Ireland; [Wu, Guanfeng; Xu, Yang] Southwest Jiaotong Univ, Sch Math, Chengdu 610031, Peoples R China; [Sutcliffe, Geoff] Univ Miami, Dept Comp Sci, Coral Gables, FL USA Southwestern University of Finance & Economics - China; Ulster University; Southwest Jiaotong University; University of Miami Fu, HM (corresponding author), Southwestern Univ Finance & Econ, Sch Comp & Artificial Intelligence, Chengdu 611130, Peoples R China.;Wu, GF (corresponding author), Southwest Jiaotong Univ, Sch Math, Chengdu 610031, Peoples R China. fuhuimin@swufe.edu.cn; wgf1024@swjtu.edu.cn National Natural Science Foundation of China [62106206]; Sichuan Science and Technology Program, China [2020YJ0270]; Fundamental Research Funds for the Central Universities, China [2682017ZT12, 2682016CX119, 2682019ZT16, 2682020CX59] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program, China; Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities) Acknowledgments This work is partially supported by National Natural Science Foundation of China (Grant No: 62106206) , and Sichuan Science and Technology Program, China (Grant No. 2020YJ0270) , and the Fundamental Research Funds for the Central Universities, China (Grant No. 2682017ZT12, 2682016CX119, 2682019ZT16, 2682020CX59) . 57 2 2 3 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JUN 7 2022.0 245 108572 10.1016/j.knosys.2022.108572 0.0 APR 2022 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1Q9CM 2023-03-23 WOS:000802978200003 0
J Farzamnik, E; Ianiro, A; Discetti, S; Deng, N; Oberleithner, K; Noack, BR; Guerrero, V Farzamnik, E.; Ianiro, A.; Discetti, S.; Deng, N.; Oberleithner, K.; Noack, B. R.; Guerrero, V. From snapshots to manifolds - a tale of shear flows JOURNAL OF FLUID MECHANICS English Article wakes; low-dimensional models; machine learning PRECESSING VORTEX CORE; MECHANISMS; SELECTION We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and, K-nearest neighbours (KNN) algorithm as decoder. The proposed technique is applied to numerical and experimental datasets including the fluidic pinball, a swirling jet and the wake behind a couple of tandem cylinders. Analysing the fluidic pinball, the manifold is able to describe the pitchfork bifurcation and the chaotic regime with only three feature coordinates. These coordinates are linked to the vortex-shedding phases and the force coefficients. The manifold coordinates of the swirling jet are comparable to the POD mode amplitudes, yet allow for a more distinct and less noise-sensitive manifold identification. A similar observation is made for the wake of two tandem cylinders. The tandem cylinders are aligned and located at a streamwise distance which corresponds to the transition between the single bluff body and the reattachment regimes of vortex shedding. Isomap unveils these two shedding regimes while the Lissajous plot of the first two POD mode amplitudes features a single circle. The reconstruction error of the manifold model is small compared with the fluctuation level, indicating that the low embedding dimensions contain the coherent structure dynamics. The proposed Isomap-KNN manifold learner is expected to be of great importance in estimation, dynamic modelling and control for a large range of configurations with dominant coherent structures. [Farzamnik, E.; Guerrero, V.] Univ Carlos III Madrid, Dept Stat, Getafe 28903, Spain; [Ianiro, A.; Discetti, S.] Univ Carlos III Madrid, Dept Aerosp Engn, Leganes 28911, Spain; [Deng, N.; Noack, B. R.] Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China; [Deng, N.] Inst Polytech Paris, Inst Mech Sci & Ind Applicat, ENSTA Paris, F-91120 Palaiseau, France; [Oberleithner, K.] Tech Univ Berlin, Lab Flow Instabil & Dynam, D-10623 Berlin, Germany; [Noack, B. R.] Tech Univ Berlin, Hermann Fottinger Inst, D-10623 Berlin, Germany Universidad Carlos III de Madrid; Universidad Carlos III de Madrid; Harbin Institute of Technology; Institut Polytechnique de Paris; Technical University of Berlin; Technical University of Berlin Farzamnik, E (corresponding author), Univ Carlos III Madrid, Dept Stat, Getafe 28903, Spain.;Ianiro, A (corresponding author), Univ Carlos III Madrid, Dept Aerosp Engn, Leganes 28911, Spain. ehsan.farzamnik@gmail.com; aianiro@ing.uc3m.es Ianiro, Andrea/B-3130-2012; deng, nan/AAG-9957-2021; Noack, Bernd/B-1242-2012; Discetti, Stefano/F-1731-2016 Ianiro, Andrea/0000-0001-7342-4814; deng, nan/0000-0001-6847-2352; Noack, Bernd/0000-0001-5935-1962; Discetti, Stefano/0000-0001-9025-1505 Madrid Government (Comunidad de Madrid) under the Multiannual Agreement; Universidad Carlos III de Madrid [PID2019-109717RBI00/AEI]; Spanish State Research Agency; National Science Foundation of China (NSFC) [12172109, 12172111, 12202121]; Guandgong province, China; Natural Science and Engineering [2022A1515011492]; Shenzhen Research Foundation for Basic Research, China [JCYJ20220531095605012]; HangHua company (Dalian, China); APC: Universidad Carlos III de Madrid (Read and Publish Carlos III University of Madrid) Madrid Government (Comunidad de Madrid) under the Multiannual Agreement; Universidad Carlos III de Madrid; Spanish State Research Agency(Spanish Government); National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Guandgong province, China; Natural Science and Engineering; Shenzhen Research Foundation for Basic Research, China; HangHua company (Dalian, China); APC: Universidad Carlos III de Madrid (Read and Publish Carlos III University of Madrid) This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with Universidad Carlos III de Madrid in the line of 'Fostering Young Doctors Research' (PITUFLOW-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). This work has also been partially supported by the project ARTURO, ref. PID2019-109717RBI00/AEI/10.13039/501100011033, funded by the Spanish State Research Agency. B.R.N. and N.D. acknowledge funding by the National Science Foundation of China (NSFC) through grants 12172109 and 12172111 and 12202121, by the Guandgong province, China, via the Natural Science and Engineering grant 2022A1515011492, by the Shenzhen Research Foundation for Basic Research, China, via grant JCYJ20220531095605012, and HangHua company (Dalian, China) for their scientific support. The authors warmly thank Dr F. Luckoff and Dr M. Raiola for providing the swirling jet and the tandem cylinder data sets. Funding for APC: Universidad Carlos III de Madrid (Read and Publish Carlos III University of Madrid). 55 0 0 1 1 CAMBRIDGE UNIV PRESS NEW YORK 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA 0022-1120 1469-7645 J FLUID MECH J. Fluid Mech. JAN 19 2023.0 955 A34 10.1017/jfm.2022.1039 0.0 24 Mechanics; Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Physics 8B9GL Green Submitted, hybrid 2023-03-23 WOS:000917227800001 0
J Garg, L; McClean, SI; Barton, M; Meenan, BJ; Fullerton, K; Kontonatsios, G; Trovati, M; Konkontzelos, I; Xu, XL; Farid, M Garg, Lalit; McClean, Sally, I; Barton, Maria; Meenan, Brian J.; Fullerton, Ken; Kontonatsios, Georgios; Trovati, Marcello; Konkontzelos, Ioannis; Xu, Xiaolong; Farid, Mohsen Evaluating Different Selection Criteria for Phase Type Survival Tree Construction BIG DATA RESEARCH English Article Phase type survival tree; Model selection information criteria; Text mining MODEL SELECTION; TIME-SERIES; INFORMATION; REGRESSION; ORDER Due to its interpretability and intuitiveness, survival tree based analysis is a powerful Artificial Intelligence method for modelling longitudinal survival data, its relationship with covariates and the interrelationship between covariates. Furthermore, it is being increasingly used for a range of applications including clustering, prognostication and classification. Phase type survival tree methods have been demonstrated to have important applications, including clustering patients into clinically meaningful groups, patient pathway prognostication and forecasting bed requirements. In this article, we critically investigate and assess several selection information criteria with regards to their suitability and limitations when used as splitting criteria in phase type survival tree construction. As shown in Table 12, the results of this analysis are compared and discussed. Furthermore, a text mining approach is utilised to further assess correlations, which have been extracted from hospital data, between the three underlying diseases and the two different types of population groups, namely age and gender groups. Its aim is to provide further investigative tools. In fact, due to its ability to analyse large volumes of textual data, text mining can provide a useful approach to this research area. (C) 2021 Elsevier Inc. All rights reserved. [Garg, Lalit; McClean, Sally, I; Barton, Maria] Univ Ulster, Sch Comp & Informat Engn, Coleraine, Londonderry, North Ireland; [Meenan, Brian J.] Univ Ulster, Sch Engn, Jordanstown, North Ireland; [Fullerton, Ken] Queens Univ, Sch Med Dent & Biomed Sci, Belfast, Antrim, North Ireland; [Kontonatsios, Georgios; Trovati, Marcello; Konkontzelos, Ioannis] Edge Hill Univ, Dept Comp Sci, Ormskirk, England; [Xu, Xiaolong] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China; [Farid, Mohsen] Univ Derby, Sch Elect Comp & Math, Derby, England Ulster University; Ulster University; Queens University Belfast; Edge Hill University; Nanjing University of Posts & Telecommunications; University of Derby Trovati, M (corresponding author), Edge Hill Univ, Dept Comp Sci, Ormskirk, England. marcello.trovati@edgehill.ac.uk Garg, Lalit/AAE-6453-2019; Trovati, Marcello/H-5075-2015 Garg, Lalit/0000-0002-3868-0481; McClean, Sally/0000-0002-6871-3504; Trovati, Marcello/0000-0001-6607-422X; Korkontzelos, Ioannis/0000-0001-8052-2471 34 1 1 1 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-5796 BIG DATA RES Big Data Res. JUL 15 2021.0 25 100250 10.1016/j.bdr.2021.100250 0.0 AUG 2021 19 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science UH0CI 2023-03-23 WOS:000689608700011 0
C Li, J; Ji, RR; Liu, H; Hong, XP; Gao, Y; Tian, Q IEEE Li, Jie; Ji, Rongrong; Liu, Hong; Hong, Xiaopeng; Gao, Yue; Tian, Qi Universal Perturbation Attack Against Image Retrieval 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) IEEE International Conference on Computer Vision English Proceedings Paper IEEE/CVF International Conference on Computer Vision (ICCV) OCT 27-NOV 02, 2019 Seoul, SOUTH KOREA IEEE,IEEE Comp Soc,CVF Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods. [Li, Jie; Ji, Rongrong; Liu, Hong] Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Xiamen, Peoples R China; [Ji, Rongrong; Hong, Xiaopeng] Peng Cheng Lab, Shenzhen, Peoples R China; [Hong, Xiaopeng] Xi An Jiao Tong Univ, Fac Elect & Informat Engn, MOE Key Lab Intelligent Networks & Network Secur, Xian, Peoples R China; [Hong, Xiaopeng] Univ Oulu, Oulu, Finland; [Gao, Yue] Tsinghua Univ, Beijing, Peoples R China; [Tian, Qi] Huawei Noahs Ark Lab, Beijing, Peoples R China Xiamen University; Peng Cheng Laboratory; Xi'an Jiaotong University; University of Oulu; Tsinghua University; Huawei Technologies Ji, RR (corresponding author), Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Xiamen, Peoples R China.;Ji, RR (corresponding author), Peng Cheng Lab, Shenzhen, Peoples R China. lijie32@stu.xmu.edu.cn; rrji@xmu.edu.cn; lynnliu.xmu@gmail.com; hongxiaopeng@mail.xjtu.edu.cn; kevin.gaoy@gmail.com; tian.qi1@huawei.com liu, feng/HPC-8076-2023 Li, Jie/0000-0003-3102-6425 National Key RD Program [2017YFC0113000, 2016YFB1001503]; Nature Science Foundation of China [61772443, 61572410]; Scientific Research Project of National Language Committee of China; Nature Science Foundation of Fujian Province, China [2017J01125, 2018J01106] National Key RD Program; Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific Research Project of National Language Committee of China; Nature Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province) This work is supported by the National Key R&D Program (No.2017YFC0113000 and No.2016YFB1001503), Nature Science Foundation of China (No.U1705262, No.61772443, and No.61572410), Scientific Research Project of National Language Committee of China (No.YB135-49), and Nature Science Foundation of Fujian Province, China (No.2017J01125 and No.2018J01106). 45 35 39 0 4 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1550-5499 978-1-7281-4803-8 IEEE I CONF COMP VIS 2019.0 4898 4907 10.1109/ICCV.2019.00500 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO9JW Green Submitted 2023-03-23 WOS:000531438105007 0
C Zhai, YK; Cao, H; Cao, L; Ma, H; Gan, JY; Zeng, JY; Piuri, V; Scotti, F; Deng, WB; Zhi, YH; Wang, JX Zhou, J; Wang, Y; Sun, Z; Jia, Z; Feng, J; Shan, S; Ubul, K; Guo, Z Zhai, Yikui; Cao, He; Cao, Lu; Ma, Hui; Gan, Junyin; Zeng, Junying; Piuri, Vincenzo; Scotti, Fabio; Deng, Wenbo; Zhi, Yihang; Wang, Jinxin A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN BIOMETRIC RECOGNITION, CCBR 2018 Lecture Notes in Computer Science English Proceedings Paper 13th Chinese Conference on Biometric Recognition (CCBR) AUG 11-12, 2018 Urumqi, PEOPLES R CHINA Chinese Assoc Artificial Intelligence,Chinese Acad Sci, Inst Automat,Springer,Xinjiang Univ Finger-knuckle-print; Batch-normalized; Data augmentation PALMPRINT; SURFACE Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition. [Zhai, Yikui; Cao, He; Cao, Lu; Ma, Hui; Gan, Junyin; Zeng, Junying; Deng, Wenbo; Wang, Jinxin] Wuyi Univ, Sch Informat Engn, Dongchengstr 22, Jiangmen 529020, Peoples R China; [Zhi, Yihang] Wuyi Univ, Sch Comp, Dongchengstr 22, Jiangmen 529020, Peoples R China; [Piuri, Vincenzo; Scotti, Fabio] Univ Milan, Dipartimento Informat, I-26013 Milan, CR, Italy Wuyi University; Wuyi University; University of Milan Cao, L (corresponding author), Wuyi Univ, Sch Informat Engn, Dongchengstr 22, Jiangmen 529020, Peoples R China. yikuizhai@163.com; caohe115@163.com; caolu20001742@163.com; mahuiwuyi@163.com; junyinggan@163.com; zengjunying@163.com; vincenzo.piuri@unimi.it; fabio.scotti@unimi.it; wenbodeng92@163.com; yihangzhi0@163.com; 18326410857@163.com Scotti, Fabio/0000-0002-4277-3701; Deng, Wenbo/0000-0003-2038-5844 National of Nature Science Foundation [61372193, 61771347]; Guangdong Higher Education Outstanding Young Teachers Training Program Grant [SYQ2014001]; Characteristic Innovation Project of Guangdong Province [2015KTSCX 143, 2015KTSCX145, 2015KTSCX148]; Youth Innovation Talent Project of Guangdong Province [2015KQNCX172, 2016KQNCX171]; Science and Technology Project of Jiangmen City [201501003001556, 201601003002191]; China National Oversea Study Scholarship Fund National of Nature Science Foundation; Guangdong Higher Education Outstanding Young Teachers Training Program Grant; Characteristic Innovation Project of Guangdong Province; Youth Innovation Talent Project of Guangdong Province; Science and Technology Project of Jiangmen City; China National Oversea Study Scholarship Fund This work is supported by National of Nature Science Foundation Grant (No. 61372193, No. 61771347), Guangdong Higher Education Outstanding Young Teachers Training Program Grant (No. SYQ2014001), Characteristic Innovation Project of Guangdong Province (No. 2015KTSCX 143, 2015KTSCX145, 2015KTSCX148), Youth Innovation Talent Project of Guangdong Province (No. 2015KQNCX172, No. 2016KQNCX171), Science and Technology Project of Jiangmen City (No. 201501003001556, No. 201601003002191), and China National Oversea Study Scholarship Fund. 33 15 15 0 6 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-97909-0; 978-3-319-97908-3 LECT NOTES COMPUT SC 2018.0 10996 11 21 10.1007/978-3-319-97909-0_2 0.0 11 Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Mathematical & Computational Biology BL7NH 2023-03-23 WOS:000455228100002 0
J Pham, TD; Yan, H Pham, Tuan D.; Yan, Hong Tensor Decomposition of Gait Dynamics in Parkinson's Disease IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING English Article Parkinson's disease; gait dynamics; time series; multi-sensors; tensor decomposition; pattern classification GROUND REACTION FORCE; COMPONENTS; RHYTHM Objective: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multichannel or multisensor data analysis of gait dynamics in Parkinson's disease. Method: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multisensor time series of gait force between Parkinson's disease and healthy control cohorts. Results: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross validations. Conclusion: Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease. Significance: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression. [Pham, Tuan D.] Linkoping Univ, Dept Biomed Engn, S-58183 Linkoping, Sweden; [Yan, Hong] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China Linkoping University; City University of Hong Kong Pham, TD (corresponding author), Linkoping Univ, Dept Biomed Engn, S-58183 Linkoping, Sweden. tuan.pham@liu.se YAN, Hong/0000-0001-9661-3095 Hong Kong Research Grants Council [C1007-15G] Hong Kong Research Grants Council(Hong Kong Research Grants Council) This work was partially supported by the Hong Kong Research Grants Council (Project C1007-15G). 39 29 30 2 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9294 1558-2531 IEEE T BIO-MED ENG IEEE Trans. Biomed. Eng. AUG 2018.0 65 8 1820 1827 10.1109/TBME.2017.2779884 0.0 8 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering GN8CJ 29989951.0 2023-03-23 WOS:000439382300016 0
J Zhang, Z; Zhu, YG; Zhu, WT; Chen, HY; Hong, XZ; Chen, JJ Zhang, Zhuo; Zhu, Yaguang; Zhu, Wentao; Chen, Huayang; Hong, Xuezhi; Chen, Jiajia Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning OPTICS EXPRESS English Article Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion., the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement [Zhang, Zhuo; Zhu, Yaguang; Zhu, Wentao; Chen, Huayang; Hong, Xuezhi; Chen, Jiajia] South China Normal Univ, South China Acad Adv Optoelect, Guangzhou 510006, Guangdong, Peoples R China; [Chen, Jiajia] KTH Royal Inst Technol, Isafjordsgatan 22,Electrum 229, S-16440 Kista, Sweden; [Chen, Jiajia] Chalmers Univ Technol, Horsalsvagen 9, S-41296 Gothenburg, Sweden South China Normal University; Royal Institute of Technology; Chalmers University of Technology Hong, XZ; Chen, JJ (corresponding author), South China Normal Univ, South China Acad Adv Optoelect, Guangzhou 510006, Guangdong, Peoples R China.;Chen, JJ (corresponding author), KTH Royal Inst Technol, Isafjordsgatan 22,Electrum 229, S-16440 Kista, Sweden.;Chen, JJ (corresponding author), Chalmers Univ Technol, Horsalsvagen 9, S-41296 Gothenburg, Sweden. xuezhi.hong@coer-scnu.org; jiajiac@chalmers.se hong, xuezhi/AAW-1701-2020 Swedish Foundation for Strategic Research; Goran Gustafsson Stiftelse; Swedish Research Council; Swedish ICT-TNG; National Natural Science Foundation of China (NSFC) [61605047, 61671212, 61550110240]; Natural Science Foundation of Guangdong Province [2016A030313438] Swedish Foundation for Strategic Research(Swedish Foundation for Strategic Research); Goran Gustafsson Stiftelse; Swedish Research Council(Swedish Research Council); Swedish ICT-TNG; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province) The Swedish Foundation for Strategic Research, the Goran Gustafsson Stiftelse, the Swedish Research Council, the Swedish ICT-TNG, National Natural Science Foundation of China (NSFC) (61605047, 61671212, 61550110240), and the Natural Science Foundation of Guangdong Province (2016A030313438). 22 12 12 3 14 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express AUG 5 2019.0 27 16 22161 22172 10.1364/OE.27.022161 0.0 12 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics IN6LL 31510511.0 gold, Green Published 2023-03-23 WOS:000478790400036 0
C Xu, D; Wang, W; Tang, H; Liu, H; Sebe, N; Ricci, E IEEE Xu, Dan; Wang, Wei; Tang, Hao; Liu, Hong; Sebe, Nicu; Ricci, Elisa Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-23, 2018 Salt Lake City, UT IEEE,CVF,IEEE Comp Soc Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce a novel approach for monocular depth estimation. Similarly to previous works, our method employs a continuous CRF to fuse multi-scale information derived from different layers of a front-end Convolutional Neural Network (CNN). Differently from past works, our approach benefits from a structured attention model which automatically regulates the amount of information transferred between corresponding features at different scales. Importantly, the proposed attention model is seamlessly integrated into the CRF allowing end-to-end training of the entire architecture. Our extensive experimental evaluation demonstrates the effectiveness of the proposed method which is competitive with previous methods on the KITH benchmark and outperforms the state of the art on the NYU Depth V2 dataset. [Xu, Dan; Wang, Wei; Tang, Hao; Sebe, Nicu; Ricci, Elisa] Univ Trento, Multimedia & Human Understanding Grp, Trento, Italy; [Liu, Hong] Peking Univ, Shenzhen Grad Sch, Key Lab Machine Percept, Beijing, Peoples R China; [Ricci, Elisa] Fdn Bruno Kessler, Technol Vis Grp, Trento, Italy University of Trento; Peking University; University Town of Shenzhen; Fondazione Bruno Kessler Ricci, E (corresponding author), Univ Trento, Multimedia & Human Understanding Grp, Trento, Italy.;Liu, H (corresponding author), Peking Univ, Shenzhen Grad Sch, Key Lab Machine Percept, Beijing, Peoples R China.;Ricci, E (corresponding author), Fdn Bruno Kessler, Technol Vis Grp, Trento, Italy. dan.xu@unitn.it; wei.wang@unitn.it; hao.tang@unitn.it; hongliu@pku.edu.cn; niculae.sebe@unitn.it; e.ricci@unitn.it Wang, Wei/AAK-5521-2021 Wang, Wei/0000-0002-5477-1017; Sebe, Niculae/0000-0002-6597-7248; Ricci, Elisa/0000-0002-0228-1147 National Natural Science Foundation of China (NSFC) [U1613209] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is partially supported by National Natural Science Foundation of China (NSFC, No.U1613209). The authors would like to thank NVIDIA for GPU donation. 38 167 178 1 19 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-5386-6420-9 PROC CVPR IEEE 2018.0 3917 3925 10.1109/CVPR.2018.00412 0.0 9 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL9NZ Green Submitted 2023-03-23 WOS:000457843604007 0
C Zhou, T; Liu, K; Li, L; Liu, Z; Klein, J; Bissyande, TF IEEE Comp Soc Zhou, Teng; Liu, Kui; Li, Li; Liu, Zhe; Klein, Jacques; Bissyande, Tegawende F. SMARTGIFT: Learning to Generate Practical Inputs for Testing Smart Contracts 2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2021) Proceedings-IEEE International Conference on Software Maintenance English Proceedings Paper 37th IEEE International Conference on Software Maintenance and Evolution (ICSME) SEP 27-OCT 01, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,IEEE Tech Council Software Engn Test Input Generation; Smart Contract; Deep Learning With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, SMARTGIFT, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, SMARTGIFT is able to generate relevant test inputs for similar to 77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by SMARTGIFT, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark. [Zhou, Teng; Liu, Kui; Liu, Zhe] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China; [Liu, Kui] Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Nanjing, Peoples R China; [Liu, Kui] State Key Lab Math Engn & Adv Comp, Wuxi, Jiangsu, Peoples R China; [Li, Li] Monash Univ, Melbourne, Vic, Australia; [Klein, Jacques; Bissyande, Tegawende F.] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Luxembourg, Luxembourg Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics; Monash University; University of Luxembourg Liu, K; Liu, Z (corresponding author), Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China.;Liu, K (corresponding author), Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Nanjing, Peoples R China.;Liu, K (corresponding author), State Key Lab Math Engn & Adv Comp, Wuxi, Jiangsu, Peoples R China. tengzhou@nuaa.edu.cn; kiu.liu@nuaa.edu.cn; li.li@monash.edu; zhe.liu@nuaa.edu.cn; jacques.klein@uni.lu; tegawende.bissyande@uni.lu Klein, Jacques/0000-0003-4052-475X; LIU, Kui/0000-0003-0145-615X National Key R&D Program of China [2020YFB1005500, 2020AAA010 7704]; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing [2020A06]; Open Project Program of the Key Laboratory of Safety-Critical Software (NUAA), Ministry of Industry and Information Technology [XCA20026]; European Research Council (ERC) under the European Union [949014] National Key R&D Program of China; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing; Open Project Program of the Key Laboratory of Safety-Critical Software (NUAA), Ministry of Industry and Information Technology; European Research Council (ERC) under the European Union(European Research Council (ERC)) This research was supported by the National Key R&D Program of China (No. 2020YFB1005500 and 2020AAA010 7704), the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (No. 2020A06), and the Open Project Program of the Key Laboratory of Safety-Critical Software (NUAA), Ministry of Industry and Information Technology (No. XCA20026), as well as the funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 949014). 52 3 4 2 4 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6773 978-1-6654-2882-8 PROC IEEE INT CONF S 2021.0 23 34 10.1109/ICSME52107.2021.00009 0.0 12 Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT0PP Bronze, Green Submitted 2023-03-23 WOS:000790782500003 0
C Tao, RJ; Lee, KA; Das, RK; Hautamaki, V; Li, HZ IEEE Tao, Ruijie; Lee, Kong Aik; Das, Rohan Kumar; Hautamaki, Ville; Li, Haizhou SELF-SUPERVISED SPEAKER RECOGNITION WITH LOSS-GATED LEARNING 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 22-27, 2022 Singapore, SINGAPORE Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc self-supervised speaker recognition; pseudo label selection; loss-gated learning In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals. It is a known fact that a speaker recognition model doesn't always benefit from pseudo labels due to their unreliability. In this work, we observe that a speaker recognition network tends to model the data with reliable labels faster than those with unreliable labels. This motivates us to study a loss-gated learning (LGL) strategy, which extracts the reliable labels through the fitting ability of the neural network during training With the proposed LGL, our speaker recognition model obtains a 46.3% performance gain over the system without it. Further, the proposed self-supervised speaker recognition with LGL trained on the VoxCeleb2 dataset without any labels achieves an equal error rate of 1.66% on the VoxCelebl original test set. [Tao, Ruijie; Hautamaki, Ville; Li, Haizhou] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore; [Lee, Kong Aik] ASTAR, Inst Infocomm Res, Singapore, Singapore; [Das, Rohan Kumar] Fortemedia Singapore, Singapore, Singapore; [Hautamaki, Ville] Univ Eastern Finland, Kuopio, Finland; [Li, Haizhou] Chinese Univ Hong Kong, Shenzhen, Peoples R China National University of Singapore; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); University of Eastern Finland; Chinese University of Hong Kong, Shenzhen Tao, RJ (corresponding author), Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore. Human-Robot Interaction Phase 1, National Research Foundation (NRF) Singapore under the National Robotics Programme [1922500054]; Neuromorphic Computing, the Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain) [A1687b0033]; Human Robot Collaborative AI for AME, NRF Singapore [A18A2b0046] Human-Robot Interaction Phase 1, National Research Foundation (NRF) Singapore under the National Robotics Programme; Neuromorphic Computing, the Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain); Human Robot Collaborative AI for AME, NRF Singapore This work is supported by 1) Human-Robot Interaction Phase 1 (Grant No. 1922500054), National Research Foundation (NRF) Singapore under the National Robotics Programme 2) Neuromorphic Computing (Grant No. A1687b0033), the Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain) 3) Human Robot Collaborative AI for AME (Grant No. A18A2b0046), NRF Singapore 35 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-6654-0540-9 INT CONF ACOUST SPEE 2022.0 6142 6146 10.1109/ICASSP43922.2022.9747162 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering BT9UB Green Submitted 2023-03-23 WOS:000864187906086 0
J Chang, Z; Chen, T Chang, Zheng; Chen, Tao Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network With Hybrid Energy Supply IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS English Article Resource management; Wireless networks; Virtualization; Optimization; Indium phosphide; III-V semiconductor materials; Hybrid power systems; Energy harvesting; ADMM; reinforcement learning; deep learning; wireless network virtualization; resource allocation USER ASSOCIATION; SYSTEMS In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes. [Chang, Zheng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China; [Chang, Zheng] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland; [Chen, Tao] VTT Tech Res Ctr Finland, Espoo 02044, Finland University of Electronic Science & Technology of China; University of Jyvaskyla; VTT Technical Research Center Finland Chang, Z (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China. zheng.chang@jyu.fi ; Chang, Zheng/G-2873-2018 Chen, Tao/0000-0003-1382-6242; Chang, Zheng/0000-0003-3766-820X NSFC [62071105] NSFC(National Natural Science Foundation of China (NSFC)) This work was supported in part by the NSFC under Grant 62071105. This article was presented in part at the WCNC 2018 [DOI: 10.1109/WCNC.2018.8377258]. 40 3 3 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1276 1558-2248 IEEE T WIREL COMMUN IEEE Trans. Wirel. Commun. MAR 2022.0 21 3 1886 1896 10.1109/TWC.2021.3107867 0.0 11 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications ZP8GS Green Accepted 2023-03-23 WOS:000766657100035 0
C Zhang, D; Gregoire, E IEEE Zhang, Du; Gregoire, Eric Learning through Overcoming Inconsistencies 2016 27TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS (DEXA) International Workshop on Database and Expert Systems Applications-DEXA English Proceedings Paper 1st International Workshop on Consistency and Inconsistency (COIN) at 27th International Conference on Database and Expert Systems Applications (DEXA) SEP 05-08, 2016 Porto, PORTUGAL perpetual learning; STEP elements; inconsistencies; learning stimuli; inconsistency-induced learning KNOWLEDGE-BASE Of all the perspectives about what inconsistencies entail and how we can handle them, one that escapes our attention is that inconsistencies can serve as effective stimuli to learning because they often help reveal the inadequacies, gaps, deficiencies, or boundary conditions in an agent's problem-solving knowledge. In this paper, we describe a new machine learning approach: inconsistency-induced learning where a learning episode is triggered by some inconsistent phenomenon and learning is essentially embodied in the process of finding ways to overcome such inconsistent phenomenon. Each learning episode causes an agent's task-performing knowledge to be revised, refined, or augmented, which in turn incrementally improves its problem-solving performance. If such a learning agent can be engaged in an open-ended and alternating sequence of task-performing episodes and learning episodes, we can refer to it as a perpetual learning agent. We also provide an overview of related work in the paper. [Zhang, Du] Macau Univ Sci & Technol, Fac Informat Technol, Ave Wai Long, Taipa, Macau, Peoples R China; [Gregoire, Eric] Univ Artois, CRIL, F-62300 Lens, France; [Gregoire, Eric] CNRS, F-62300 Lens, France Macau University of Science & Technology; Universite d'Artois; Centre National de la Recherche Scientifique (CNRS) Zhang, D (corresponding author), Macau Univ Sci & Technol, Fac Informat Technol, Ave Wai Long, Taipa, Macau, Peoples R China. duzhang@must.edu.mo; gregoire@cril.fr 58 1 1 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1529-4188 978-1-5090-3635-6 INT WORKSHOP DATABAS 2016.0 121 128 10.1109/DEXA.2016.27 0.0 8 Computer Science, Information Systems; Information Science & Library Science Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Computer Science; Information Science & Library Science BI1BS 2023-03-23 WOS:000405495300021 0
J Wang, B; Chen, ZQ; Dewulf, W; Pauwels, R; Yao, ZY; Hou, QH; Xiao, YS Wang, Bo; Chen, Zhiqiang; Dewulf, Wim; Pauwels, Ruben; Yao, Zhiyang; Hou, Qinhan; Xiao, Yongshun U-net-based blocked artifacts removal method for dynamic computed tomography APPLIED OPTICS English Article Airplane engines are vital aircraft components, so regular inspections of the engines are required to ensure their stable operation. A dynamic computed tomography (CT) system has been proposed by our group for in situ nondestructive testing of airplane engines, which takes advantage of the rotor's self-rotation. However, static parts of the engines cause blocked artifacts in the reconstructed image, leading to misinterpretations of the condition of engines. In this paper, in order to remove the artifacts produced by the projection of the static parts in CT reconstruction, two deep-learning-based methods are proposed, which use U-Net to perform correction in the projection domain. The projection of static parts can be estimated by a well-trained U-Net and subsequently can be subtracted from the projections of the engine. Finally, the rotor can be reconstructed from the corrected projections. The results shown in this paper indicate that the proposed methods are practical and effective for removing those blocked artifacts and recovering the details of rotating parts, which will, in turn, maximize the utilization of the dynamic CT system for in situ engine tests. (C) 2019 Optical Society of America [Wang, Bo; Chen, Zhiqiang; Yao, Zhiyang; Hou, Qinhan; Xiao, Yongshun] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China; [Wang, Bo; Chen, Zhiqiang; Yao, Zhiyang; Hou, Qinhan; Xiao, Yongshun] Tsinghua Univ, Key Lab Particle & Radiat Imaging, Minist Educ, Beijing, Peoples R China; [Dewulf, Wim; Pauwels, Ruben] Katholieke Univ Leuven, Mech Engn Dept, Leuven, Belgium Tsinghua University; Tsinghua University; KU Leuven Xiao, YS (corresponding author), Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.;Xiao, YS (corresponding author), Tsinghua Univ, Key Lab Particle & Radiat Imaging, Minist Educ, Beijing, Peoples R China. xiaoysh@mail.tsinghua.edu.cn Dewulf, Wim/D-6981-2018; Wang, Bo/AAU-1329-2021; Pauwels, Ruben/G-9041-2017 Dewulf, Wim/0000-0002-9184-051X; Wang, Bo/0000-0001-8947-8526; Pauwels, Ruben/0000-0002-9462-7546; Wang, Bo/0000-0001-7317-9222 National Natural Science Foundation of China (NSFC) [51727813] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China (NSFC) (51727813). 22 5 5 2 6 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics MAY 10 2019.0 58 14 3748 3753 10.1364/AO.58.003748 0.0 6 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics HX6VE 31158188.0 Green Accepted 2023-03-23 WOS:000467540700010 0
J Perez-Garcia, M; Wang, L; Svriz-Wucherer, O; Fernandez-de-Pinedo, N; Diaz-Ordonez, M Perez-Garcia, Manuel; Wang, Li; Svriz-Wucherer, Omar; Fernandez-de-Pinedo, Nadia; Diaz-Ordonez, Manuel Big Data and New Global History: Global Goods and Trade Networks in Early Modern China and Europe ITINERARIO-INTERNATIONAL JOURNAL ON THE HISTORY OF EUROPEAN EXPANSION AND GLOBAL INTERACTION English Article global history; digital humanities; consumption; trade networks; China EMPIRES This paper introduces an innovative method applied to global (economic) history using the tools of digital humanities through the design and development of the GECEM Project Database (www.gecem.eu; www.gecemdatabase.eu). This novel database goes beyond the static Excel files frequently used by conventional scholarship in early modern history studies to mine new historical data through a bottom-up process and analyse the global circulation of goods, consumer behaviour, and trade networks in early modern China and Europe. Macau and Marseille, as strategic entrepots for the redistribution of goods, serve as the main case study. This research is framed within a polycentric approach to analyse the connectivity of south Chinese and European markets with trade zones of Spain, France, South America, and the Pacific. [Perez-Garcia, Manuel] Shanghai Jiao Tong Univ, Sch Humanities, Dept Hist, Shanghai, Peoples R China; [Perez-Garcia, Manuel] Pablo de Olavide Univ, Seville, Spain; [Wang, Li; Svriz-Wucherer, Omar] Pablo de Olavide Univ, Dept Hist, GECEM Project, Seville, Spain; [Svriz-Wucherer, Omar] Univ Seville, Juan de la Cierva Formac, Seville, Spain; [Fernandez-de-Pinedo, Nadia; Diaz-Ordonez, Manuel] Pablo de Olavide Univ, GECEM Project, Seville, Spain; [Fernandez-de-Pinedo, Nadia] Univ Autonoma Madrid, Fac Econ, Madrid, Spain; [Diaz-Ordonez, Manuel] Univ Seville, Fac Econ, Seville, Spain Shanghai Jiao Tong University; Universidad Pablo de Olavide; Universidad Pablo de Olavide; University of Sevilla; Universidad Pablo de Olavide; Autonomous University of Madrid; University of Sevilla Perez-Garcia, M (corresponding author), Shanghai Jiao Tong Univ, Sch Humanities, Dept Hist, Shanghai, Peoples R China.;Perez-Garcia, M (corresponding author), Pablo de Olavide Univ, Seville, Spain. Manuel.Perez@alumni.eui.eu Fernandez-de-Pinedo, Nadia/E-2111-2012; Diaz-Ordonez, Manuel/A-5215-2015 Fernandez-de-Pinedo, Nadia/0000-0001-6708-1480; Diaz-Ordonez, Manuel/0000-0002-3492-6210 GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille, 1680-1840); ERC under the European Union [679371] GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille, 1680-1840); ERC under the European Union This research has been sponsored and financially supported by GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille, 1680-1840), a project hosted by the Pablo de Olavide University (UPO) of Seville (Spain) and funded by ERC Starting-Grant, ref. 679371, under the European Union's Horizon 2020 Research and Innovation Programme, www.gecem.eu.The Principal Investigator is Manuel Perez-Garcia. This research has also been part of the academic activities of the Global History Network in China (GHN), www.globalhistorynetwork.com. 66 2 2 5 15 CAMBRIDGE UNIV PRESS CAMBRIDGE EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND 0165-1153 2041-2827 ITINERARIO Itinerario APR 2022.0 46 1 14 39 PII S0165115321000310 10.1017/S0165115321000310 0.0 DEC 2021 26 History Social Science Citation Index (SSCI); Arts & Humanities Citation Index (A&HCI) History 0I9SA 2023-03-23 WOS:000732482000001 0
J Wang, JY; Liao, JX; Li, TH; Wang, J Wang, Jingyu; Liao, Jianxin; Li, Tonghong; Wang, Jing Asymmetrical Multi-path Selection Game for Wireless Overlay Networks WIRELESS PERSONAL COMMUNICATIONS English Article Multipath transfer; Multipath selection; Nash equilibrium; Overlay; Fairness In order to transfer the increasing big data we need to make use of multiple wireless paths. Overlay networks provide the possibility of taking advantage of multiple available routing paths to realize the bandwidth aggregating. We present a game-theoretic study of the selfish strategic collaboration of multiple heterogeneous overlays when they are allowed to use massively-multipath transfer. Overlays are modeled as players in this multipath selection game model, we discuss the asymmetric case where all overlays have the different round trip times (RTT) and different wastefulness level, and demonstrate the existence and uniqueness of Nash equilibrium (NE). Then we find overlays differing only in their RTTs still receive equal throughput shares and utilities at the NE. However, if overlays differ only in their wastefulness levels, a more wasteful overlay has a larger utility and a larger throughput (bandwidth) share than a less wasteful overlay. [Wang, Jingyu; Liao, Jianxin; Wang, Jing] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Li, Tonghong] Tech Univ Madrid, Madrid 28660, Spain Beijing University of Posts & Telecommunications; Universidad Politecnica de Madrid Wang, JY (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. tony.wangjy@gmail.com; liaojx@bupt.edu.cn; tonghong@fi.upm.es; wangjing@bupt.edu.cn li, tonghong/0000-0003-1533-4954 National Basic Research Program of China [2013CB329102]; National Natural Science Foundation of China [61471063, 61421061, 61372120, 61271019, 61101119, 61121001]; Chinese Ministry of Education [MCM20130310]; Beijing Municipal Natural Science Foundation [4152039]; Beijing Higher Education Young Elite Teacher Project [YETP0473]; MICINN [TIN2010-19077]; CAM [S2009TIC-1692] National Basic Research Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Ministry of Education(Ministry of Education, China); Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Beijing Higher Education Young Elite Teacher Project; MICINN(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); CAM This work was jointly supported by: (1) the National Basic Research Program of China (No. 2013CB329102); (2) National Natural Science Foundation of China (No. 61471063, 61421061, 61372120,61271019, 61101119, 61121001); (3) the Key (Keygrant) Project of Chinese Ministry of Education.( No. MCM20130310); (4) Beijing Municipal Natural Science Foundation (No. 4152039); (5) Beijing Higher Education Young Elite Teacher Project (No. YETP0473); (6) MICINN (No.TIN2010-19077); (7) CAM (No.S2009TIC-1692). 23 0 0 1 5 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0929-6212 1572-834X WIRELESS PERS COMMUN Wirel. Pers. Commun. DEC 2015.0 85 4 2407 2429 10.1007/s11277-015-2912-2 0.0 23 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications CX5GL 2023-03-23 WOS:000365730000043 0
J Tian, R; Zhang, YZ; Feng, YH; Yang, LH; Cao, ZZ; Coleman, S; Kerr, D Tian, Rui; Zhang, Yunzhou; Feng, Yonghui; Yang, Linghao; Cao, Zhenzhong; Coleman, Sonya; Kerr, Dermot Accurate and Robust Object SLAM With 3D Quadric Landmark Reconstruction in Outdoors IEEE ROBOTICS AND AUTOMATION LETTERS English Article Localization; SLAM; vision-based navigation Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this letter, we propose a stereo visual SLAM with a robust quadric landmark representation method.The system consists of four components, including deep learning detection, quadric landmark initialization, object data association and object pose optimization. State-of-the-art quadric-based SLAM algorithms always face observation-related problems and are sensitive to observation noise, which limits their application in outdoor scenes. To solve this problem, we propose a quadric initialization method based on the separation of the quadric parameters method, which improves the robustness to observation noise. The sufficient object data association algorithm and object-oriented optimization with multiple cues enable a highly accurate object pose estimation that is robust to local observations. Experimental results show that the proposed system is more robust to observation noise and significantly outperforms current state-of-the-art methods in outdoor environments. In addition, the proposed system demonstrates real-time performance. [Tian, Rui; Zhang, Yunzhou; Feng, Yonghui; Yang, Linghao; Cao, Zhenzhong] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China; [Coleman, Sonya; Kerr, Dermot] Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine BT52 1SA, Londonderry, North Ireland Northeastern University - China; Ulster University Zhang, YZ (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China. 364368795@qq.com; zhangyunzhou@ise.neu.edu.cn; yhfeng1995@gmail.com; yanglinghaoneu@163.com; 332212807@qq.com; sa.coleman@ulster.ac.uk; d.kerr@ulster.ac.uk Tian, Rui/0000-0002-8944-1966 National Natural Science Foundation of China [61973066, 61471110]; Major Science and Technology Projects of Liaoning Province [2021JH1/10400049]; Fundation of Key Laboratory of Aerospace System Simulation [6142002200301]; Fundation ofKey Laboratory of Equipment Reliability [WD2C20205500306]; Major Science and Technology Innovation Engineering Projects of Shandong Province [2019JZZY010128] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Science and Technology Projects of Liaoning Province; Fundation of Key Laboratory of Aerospace System Simulation; Fundation ofKey Laboratory of Equipment Reliability; Major Science and Technology Innovation Engineering Projects of Shandong Province This work was supported in part by the National Natural Science Foundation of China under Grants 61973066 and 61471110, in part by the Major Science and Technology Projects of Liaoning Province under Grant 2021JH1/10400049, in part by the Fundation of Key Laboratory of Aerospace System Simulation under Grant 6142002200301, in part by the Fundation ofKey Laboratory of Equipment Reliability under Grant WD2C20205500306, and in part by the Major Science and Technology Innovation Engineering Projects of Shandong Province under Grant 2019JZZY010128. 23 4 4 7 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. APR 2022.0 7 2 1534 1541 10.1109/LRA.2021.3137896 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics YG8HC Green Submitted 2023-03-23 WOS:000742721400006 0
C Chen, LF; Guo, GD; Wang, SR; Kong, XZ Neagu, D; Kiran, M; Trundle, P Chen, Lifei; Guo, Gongde; Wang, Shengrui; Kong, Xiangzeng Kernel Learning Method for Distance-Based Classification of Categorical Data 2014 14TH UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE (UKCI) English Proceedings Paper 14th UK Annual Workshop on Computational Intelligence (UKCI) SEP 08-10, 2014 University of Bradford, Bradford, ENGLAND IEEE Computat Intelligence Soc University of Bradford Kernel-based methods have become popular in machine learning; however, they are typically designed for numeric data. These methods are established in vector spaces, which are undefined for categorical data. In this paper, we propose a new kind of kernel trick, showing that mapping of categorical samples into kernel spaces can be alternatively described as assigning a kernel-based weight to each categorical attribute of the input space, so that common distance measures can be employed. A data-driven approach is then proposed to kernel bandwidth selection by optimizing feature weights. We also make use of the kernel-based distance measure to effectively extend nearest-neighbor classification to classify categorical data. Experimental results on real-world data sets show the outstanding performance of this approach compared to that obtained in the original input space. [Chen, Lifei; Guo, Gongde] Fujian Normal Univ, Sch Math & Comp Sci, Fujian 350117, Peoples R China; [Wang, Shengrui] Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ J1K 2R1, Canada; [Kong, Xiangzeng] Univ Ulster, Sch Comp & Math, Jordanstown BT37 0QB, North Ireland Fujian Normal University; University of Sherbrooke; Ulster University Chen, LF (corresponding author), Fujian Normal Univ, Sch Math & Comp Sci, Fujian 350117, Peoples R China. clfei@fjnu.edu.cn; ggd@fjnu.edu.cn; shengrui.wang@usherbrooke.ca; kong-x@email.ulster.ac.uk Chen, Lifei/W-5995-2019 25 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-5538-1 2014.0 58 64 7 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BC5YF 2023-03-23 WOS:000353643200009 0
J Chen, QJ; Wang, LH; Wang, L; Deng, ZY; Zhang, J; Zhu, YM Chen, Qijian; Wang, Lihui; Wang, Li; Deng, Zeyu; Zhang, Jian; Zhu, Yuemin Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics IEEE ACCESS English Article Wavelet scattering; radiomics; machine learning; glioma grading; peritumoral BRAIN; CLASSIFICATION; INFORMATION; REGRESSION; TEXTURE; IMAGES Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making. We present a novel wavelet scattering-based radiomic method to predict noninvasively and accurately the glioma grades. The method consists of wavelet scattering feature extraction, dimensionality reduction, and glioma grade prediction. The dimensionality reduction was achieved using partial least squares (PLS) regression and the glioma grade prediction using support vector machine (SVM), logistic regression (LR) and random forest (RF). The prediction obtained on multimodal magnetic resonance images of 285 patients with well-labeled intratumoral and peritumoral regions showed that the area under the receiver operating characteristic curve (AUC) of glioma grade prediction was increased up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which represents an increase of about 13& x0025; compared to traditional radiomics. In addition, the features extracted from peritumoral regions further increase the accuracy of glioma grading. [Chen, Qijian; Wang, Lihui; Wang, Li; Deng, Zeyu; Zhang, Jian] Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang 550025, Peoples R China; [Zhu, Yuemin] Univ Lyon, INSA Lyon, CNRS, CREATIS,Inserm,UMR 5220,U1044, F-69621 Lyon, France Guizhou University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Wang, LH (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang 550025, Peoples R China. lhwang2@gzu.edu.cn National Natural Science Foundation of China [61661010]; Natural Science Foundation of Guizhou Province [[2020]1Y255]; Program PHC-Cai Yuanpei 2018 [41400TC]; Guizhou Science and Technology Plan Project [Qiankehe [2018]5301] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guizhou Province; Program PHC-Cai Yuanpei 2018; Guizhou Science and Technology Plan Project This work was supported in part by the National Natural Science Foundation of China under Grant 61661010, in part by the Natural Science Foundation of Guizhou Province under Grant Qiankehe J No. [2020]1Y255, in part by the Program PHC-Cai Yuanpei 2018 under Grant 41400TC, and in part by the Guizhou Science and Technology Plan Project under Grant Qiankehe [2018]5301. 45 4 4 6 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 106564 106575 10.1109/ACCESS.2020.3000895 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications MD5VP gold, Green Submitted 2023-03-23 WOS:000544040800009 0
J Huang, YF; Cheng, L; Xue, LT; Liu, C; Li, YC; Li, JB; Ward, T Huang, Yifeng; Cheng, Long; Xue, Lianting; Liu, Cong; Li, Yuancheng; Li, Jianbin; Ward, Tomas Deep Adversarial Imitation Reinforcement Learning for QoS-Aware Cloud Job Scheduling IEEE SYSTEMS JOURNAL English Article Quality of service; Cloud computing; Scheduling; Processor scheduling; Real-time systems; Training; Trajectory; Adversarial imitation learning; cloud computing; deep reinforcement learning (DRL); job scheduling; quality of service (QoS); real-time jobs TASKS Although cloud computing is one of the promising technologies for online business services, how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging current techniques. With the advancing of machine learning, deep reinforcement learning (DRL) has demonstrated its outstanding capability in dispatching time-sensitive tasks. However, the reinforced rewards in DRL are typically unavailable until the completion of the scheduling for all the jobs. Considering the fact that the trajectory of jobs in cloud is always long, current DRL-based solutions will meet challenges in finding the trajectories with high rewards, and thus would have issues such that the finally trained scheduling policy is suboptimal. To improve the problem, in this article, we propose a more advanced approach called a deep adversarial imitation reinforcement learning (AIRL) framework for scheduling time-sensitive cloud jobs. Specifically, we focus on scheduling user requests in a way to maximize job successful rate along with a significant reduction on job response time. We present the detailed design of our method, and our experimental results demonstrate that AIRL can generally outperform the existing cloud job scheduling approaches, including the DRL-based method, in the presence of different real-time workload and computing resource configurations. [Huang, Yifeng; Cheng, Long; Xue, Lianting; Li, Yuancheng; Li, Jianbin] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China; [Liu, Cong] Shandong Univ Technol, Sch Comp Sci & Technol, zibo 255049, Shandong, Peoples R China; [Ward, Tomas] Dublin City Univ, Insight SFI Res Ctr Data Analyt, Sch Comp, Dublin D09NA55, Ireland North China Electric Power University; Shandong University of Technology; Dublin City University Cheng, L (corresponding author), North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China. huangyifeng1997214@outlook.com; lcheng@ncepu.edu.cn; l.Xue@ncepu.edu.cn; liucongchina@sdust.edu.cn; ycli@ncepu.edu.cn; lijb87@ncepu.edu.cn; tomas.ward@dcu.ie Cheng, Long/ADJ-1122-2022; Ward, Tomas/AEN-2410-2022 Cheng, Long/0000-0003-1638-059X; Ward, Tomas/0000-0002-6173-6607; Xue, Lianting/0000-0002-7453-6575; Li, Yuancheng/0000-0001-8074-7259 Fundamental Research Funds for the Central Universities [2021MS017]; National Science Foundation of China [61902222]; Taishan Scholar Youth Program of Shandong Province [tsqn201909109]; Science Foundation Ireland [SFI/12/RC/2289P2] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Taishan Scholar Youth Program of Shandong Province; Science Foundation Ireland(Science Foundation Ireland) This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2021MS017, in part by the National Science Foundation of China under 61902222, in part the Taishan Scholar Youth Program of Shandong Province under Grant tsqn201909109, and in part by the Science Foundation Ireland under Grant SFI/12/RC/2289P2. (Corresponding author: Long Cheng.) 48 19 19 13 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-8184 1937-9234 IEEE SYST J IEEE Syst. J. SEP 2022.0 16 3 4232 4242 10.1109/JSYST.2021.3122126 0.0 NOV 2021 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Operations Research & Management Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science; Telecommunications 4A5PC 2023-03-23 WOS:000732891900001 0
J Clark, WT; Kasak, L; Bakolitsa, C; Hu, ZQ; Andreoletti, G; Babbi, G; Bromberg, Y; Casadio, R; Dunbrack, R; Folkman, L; Ford, CT; Jones, D; Katsonis, P; Kundu, K; Lichtarge, O; Martelli, PL; Mooney, SD; Nodzak, C; Pal, LR; Ivojac, PRR; Savojardo, C; Shi, XH; Zhou, YQ; Uppal, A; Xu, QF; Yin, YZ; Pejaver, V; Wang, M; Wei, LP; Moult, J; Yu, GK; Brenner, SE; LeBowitz, JH Clark, Wyatt T.; Kasak, Laura; Bakolitsa, Constantina; Hu, Zhiqiang; Andreoletti, Gaia; Babbi, Giulia; Bromberg, Yana; Casadio, Rita; Dunbrack, Roland; Folkman, Lukas; Ford, Colby T.; Jones, David; Katsonis, Panagiotis; Kundu, Kunal; Lichtarge, Olivier; Martelli, Pier L.; Mooney, Sean D.; Nodzak, Conor; Pal, Lipika R.; Ivojac, Pred Rag Rad; Savojardo, Castrense; Shi, Xinghua; Zhou, Yaoqi; Uppal, Aneeta; Xu, Qifang; Yin, Yizhou; Pejaver, Vikas; Wang, Meng; Wei, Liping; Moult, John; Yu, Guoying Karen; Brenner, Steven E.; LeBowitz, Jonathan H. Assessment of predicted enzymatic activity of alpha-N-acetylglucosaminidase variants of unknown significance for CAGI 2016 HUMAN MUTATION English Article CAGI; critical assessment; enzymatic activity; machine learning; Sanfilippo syndrome; variants of unknown significance; alpha-N-acetylglucosaminidase; NAGLU WEB SERVER; PROTEIN; SEQUENCE; GLUCOSAMINIDASE; PATHOGENICITY; STABILITY; PHENOTYPE; GENOTYPE; IMPACT The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase alpha-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis. [Clark, Wyatt T.; Yu, Guoying Karen; LeBowitz, Jonathan H.] BioMarin Pharmaceut, Human Genet, San Rafael, CA USA; [Kasak, Laura; Bakolitsa, Constantina; Hu, Zhiqiang; Andreoletti, Gaia; Brenner, Steven E.] Univ Calif Berkeley, Dept Plant & Microbial Biol, Berkeley, CA 94720 USA; [Kasak, Laura; Martelli, Pier L.] Univ Tartu, Dept Biomed, Inst Biomed & Translat Med, Tartu, Estonia; [Babbi, Giulia; Casadio, Rita; Savojardo, Castrense] Univ Bologna, Dept Pharm & Biotechnol, Biocomp Grp, Bologna, Italy; [Bromberg, Yana] Rutgers State Univ, Dept Biochem & Microbiol, New Brunswick, NJ USA; [Dunbrack, Roland; Xu, Qifang] Fox Chase Canc Ctr, Inst Canc Res, 7701 Burholme Ave, Philadelphia, PA 19111 USA; [Folkman, Lukas] ETH, Dept Biosyst Sci & Engn, Machine Learning & Computat Biol Lab, Zurich, Switzerland; [Ford, Colby T.; Nodzak, Conor; Shi, Xinghua; Uppal, Aneeta] Univ North Carolina Charlotte, Dept Bioinformat & Genom, Charlotte, NC USA; [Jones, David] UCL, Bioinformat Grp, Dept Comp Sci, London, England; [Katsonis, Panagiotis; Lichtarge, Olivier] Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA; [Kundu, Kunal; Pal, Lipika R.; Yin, Yizhou; Moult, John] Univ Maryland, Inst Biosci & Biotechnol Res, Rockville, MD USA; [Lichtarge, Olivier] Baylor Coll Med, Dept Biochem & Mol Biol, Houston, TX 77030 USA; [Lichtarge, Olivier] Baylor Coll Med, Dept Pharmacol, Houston, TX 77030 USA; [Lichtarge, Olivier] Baylor Coll Med, Computat & Integrat Biomed Res Ctr, Houston, TX 77030 USA; [Mooney, Sean D.] Buck Inst Res Aging, Novato, CA USA; [Ivojac, Pred Rag Rad; Pejaver, Vikas] Indiana Univ, Dept Comp Sci, Bloomington, IN 47405 USA; [Zhou, Yaoqi] Indiana Univ Sch Med, Ctr Computat Biol & Bioinformat, Indianapolis, IN 46202 USA; [Pejaver, Vikas] Indiana Univ, Dept Informat, Bloomington, IN USA; [Wang, Meng; Wei, Liping] Peking Univ, Ctr Bioinformat, State Key Lab Prot & Plant Gene Res, Sch Life Sci, Beijing, Peoples R China; [Kundu, Kunal; Yin, Yizhou] Univ Maryland, Computat Biol Bioinformat & Genom, Biol Sci Grad Program, College Pk, MD 20742 USA; [Moult, John] Univ Maryland, Dept Cell Biol & Mol Genet, College Pk, MD 20742 USA BioMarin Pharmaceutical Inc.; University of California System; University of California Berkeley; University of Tartu; University of Bologna; Rutgers State University New Brunswick; Fox Chase Cancer Center; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of North Carolina; University of North Carolina Charlotte; University of London; University College London; Baylor College of Medicine; Baylor College of Medicine; Baylor College of Medicine; Baylor College of Medicine; Buck Institute for Research on Aging; Indiana University System; Indiana University Bloomington; Indiana University System; Indiana University Bloomington; Indiana University System; Indiana University Bloomington; Peking University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park Clark, WT (corresponding author), BioMarin Pharmaceut, 105 Digital Dr, Novato, CA 94949 USA. wyatt.clark@bmrn.com Hu, Zhiqiang/P-5083-2019; Bromberg, Yana/AAA-7801-2020; Brenner, Steven E/A-8729-2008; Kasak, Laura/AAD-1742-2020; Dunbrack, Roland/ABG-7141-2020; Andreoletti, Gaia/AAB-5724-2022; Babbi, Giulia/AAO-4603-2020; Zhou, Yaoqi/B-3284-2009; Folkman, Lukas/AAP-1908-2020; Martelli, Pier Luigi/I-8824-2012; Ford, Colby T./AAB-9304-2021; Savojardo, Castrense/J-3441-2012; Savojardo, Castrense/ADK-4436-2022; Zhou, Yaoqi/ABD-1125-2020 Hu, Zhiqiang/0000-0001-8854-3410; Bromberg, Yana/0000-0002-8351-0844; Brenner, Steven E/0000-0001-7559-6185; Kasak, Laura/0000-0003-4182-2396; Babbi, Giulia/0000-0002-9816-4737; Zhou, Yaoqi/0000-0002-9958-5699; Folkman, Lukas/0000-0002-5811-8875; Martelli, Pier Luigi/0000-0002-0274-5669; Ford, Colby T./0000-0002-7859-3622; Savojardo, Castrense/0000-0002-7359-0633; MOONEY, SEAN D/0000-0003-2654-0833; Dunbrack, Roland/0000-0001-7674-6667; Bakolitsa, Constantina/0000-0002-6980-9831 Eesti Teadusagentuur [IUT34-12] Funding Source: Medline; National Health and Medical Research Council of Australia [1059775, 1121629, 1083450] Funding Source: Medline; NHGRI NIH HHS [R13 HG006650, U41 HG007346] Funding Source: Medline; NIA NIH HHS [R01 AG061105] Funding Source: Medline; NIGMS NIH HHS [R01 GM066099, R01 GM115486, U01 GM115486, R01 GM079656] Funding Source: Medline; NIMH NIH HHS [R01 MH105524] Funding Source: Medline; NLM NIH HHS [K99 LM012992, R01 LM009722] Funding Source: Medline Eesti Teadusagentuur(Estonian Research Council); National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia); NHGRI NIH HHS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Human Genome Research Institute (NHGRI)); NIA NIH HHS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA)); NIGMS NIH HHS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS)); NIMH NIH HHS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); NLM NIH HHS(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Library of Medicine (NLM)) 36 4 5 1 5 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1059-7794 1098-1004 HUM MUTAT Hum. Mutat. SEP 2019.0 40 9 SI 1519 1529 10.1002/humu.23875 0.0 11 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity IX9FW 31342580.0 Green Accepted, Green Submitted, gold 2023-03-23 WOS:000485994200030 0
J Wei, L; Li, HB; Tamagnone, L; You, H Wei, Li; Li, Hongbo; Tamagnone, Luca; You, Hua Semaphorins and Their Receptors in Hematological Malignancies FRONTIERS IN ONCOLOGY English Review semaphorins; Neuropilin; Plexin; leukemia; lymphoma; multiple myeloma B-CELL; LEUKEMIA; NEUROPILIN-1; ANTIBODY; EXPRESSION; SURVIVAL; TARGET; GROWTH; CD100 While semaphorins were initially identified as axonal guidance cues for wiring the neural network, it was then recognized their wide relevance in tissue development and homeostasis. Notably, semaphorin activities were also extensively studied in many types of solid tumors; however, their relevance in hematological malignancies is far from understood. In this mini-review, we surveyed the current knowledge about semaphorins and their receptors in leukemias, lymphomas, and multiple myeloma. Noteworthy, current data support a promoting role for Semaphorin 4D and Neuropilin-1 in these tumors, while Semaphorin 3A seems to consistently act as oncosuppressor in leukemias and multiple myeloma. The expression levels and functional activities of SEMA3B, SEMA3F, and Neuropilin-2 have furthermore been investigated in leukemias and lymphoma cells. Herein, we reviewed the state of the art and highlighted some of the open questions to be addressed in the field. [Wei, Li; You, Hua] Guangzhou Med Univ, Affiliated Canc Hosp & Inst, Guangzhou, Guangdong, Peoples R China; [Li, Hongbo] Hubei Univ Med, Taihe Hosp, Dept Pulm & Crit Care Med, Shiyan, Peoples R China; [Tamagnone, Luca] Univ Cattolica Sacro Cuore, Ist Istol & Embriol, Rome, Italy; [Tamagnone, Luca] IRCCS, Fdn Policlin Univ Agostino Gemelli, Rome, Italy; [You, Hua] YouJiang Med Univ Nationalities, Baise, Peoples R China; [You, Hua] Acad Mil Med Sci, Affiliated Hosp, Beijing, Peoples R China Guangzhou Medical University; Hubei University of Medicine; Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli; Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli; Youjiang Medical University for Nationalities; Academy of Military Medical Sciences - China You, H (corresponding author), Guangzhou Med Univ, Affiliated Canc Hosp & Inst, Guangzhou, Guangdong, Peoples R China.;Tamagnone, L (corresponding author), Univ Cattolica Sacro Cuore, Ist Istol & Embriol, Rome, Italy.;Tamagnone, L (corresponding author), IRCCS, Fdn Policlin Univ Agostino Gemelli, Rome, Italy.;You, H (corresponding author), YouJiang Med Univ Nationalities, Baise, Peoples R China.;You, H (corresponding author), Acad Mil Med Sci, Affiliated Hosp, Beijing, Peoples R China. luca.tamagnone@unicatt.it; youhua307@163.com Guangzhou Medical University; National Natural Science Foundation of China [81670180, 81711540047, 81850410547, 81370077, 81001220]; National Natural Science Foundation of China (NSFC); Swedish Foundation for International Cooperation in Research and Higher Education (STINT); Beijing Nova Program of the Beijing Municipal Science and Technology Commission [Z171100001117091]; Guangzhou key medical discipline construction project; Italian Association for Cancer Research (AIRC-IG) [19923]; Italian Association for Cancer Research (AIRC-Multiunit-5perMille grant) [21052] Guangzhou Medical University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Swedish Foundation for International Cooperation in Research and Higher Education (STINT); Beijing Nova Program of the Beijing Municipal Science and Technology Commission; Guangzhou key medical discipline construction project; Italian Association for Cancer Research (AIRC-IG)(Fondazione AIRC per la ricerca sul cancro); Italian Association for Cancer Research (AIRC-Multiunit-5perMille grant) The work in the laboratory of HY is mainly supported by grants from Guangzhou Medical University (the start-up funds). HY is supported by the National Natural Science Foundation of China (81670180, 81711540047, 81850410547, 81370077, and 81001220), the National Natural Science Foundation of China (NSFC) and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) bilateral cooperation agreement (Joint China-Sweden Mobility), and the Beijing Nova Program of the Beijing Municipal Science and Technology Commission (Z171100001117091), and Guangzhou key medical discipline construction project. The research activity in LT lab is supported by the Italian Association for Cancer Research (AIRC-IG #19923 and AIRC-Multiunit-5perMille grant #21052). 42 9 9 1 8 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. MAY 9 2019.0 9 382 10.3389/fonc.2019.00382 0.0 5 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology HX5CL 31143707.0 Green Published, gold 2023-03-23 WOS:000467418100001 0
J Kong, FT; Picetti, F; Lipari, V; Bestagini, P; Tang, XM; Tubaro, S Kong, Fantong; Picetti, Francesco; Lipari, Vincenzo; Bestagini, Paolo; Tang, Xiaoming; Tubaro, Stefano Deep Prior-Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Interpolation; Convolution; Inverse problems; Task analysis; Image reconstruction; Training; Standards; Convolutional neural networks (CNNs); interpolation; inverse problems; seismic data processing DATA INTERPOLATION Irregularity and coarse spatial sampling of seismic data strongly affect the performances of processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in most of the processing workflows. In this work, we propose a seismic data interpolation method based on the deep prior paradigm: an ad hoc convolutional neural network is used as a prior to solve the interpolation inverse problem, avoiding any costly and prone-to-overfitting training stage. In particular, the proposed method leverages a multiresolution U-Net with 3-D convolution kernels exploiting correlations in cubes of seismic data, at different scales in all directions. Numerical examples on different corrupted synthetic and field data sets show the effectiveness and promising features of the proposed approach. [Kong, Fantong; Tang, Xiaoming] China Univ Petr East, Sch Geosci, Qingdao 266580, Peoples R China; [Picetti, Francesco; Lipari, Vincenzo; Bestagini, Paolo; Tubaro, Stefano] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy China University of Petroleum; Polytechnic University of Milan Picetti, F (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy. kft_upc@hotmail.com; francesco.picetti@polimi.it; vincenzo.lipari@polimi.it; paolo.bestagini@polimi.it; stefano.tubaro@polimi.it Picetti, Francesco/AAB-3055-2020 Picetti, Francesco/0000-0002-2162-9671; Lipari, Vincenzo/0000-0002-6514-8052; Kong, Fantong/0000-0003-3675-4819 China Scholarship Council; Fundamental Research Funds for the Central Universities [18CX06021A] China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the China Scholarship Council and Fundamental Research Funds for the Central Universities under Grant 18CX06021A. 27 8 8 2 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19 7501305 10.1109/LGRS.2020.3044455 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XY1VP Green Submitted 2023-03-23 WOS:000736769300055 0
J Qian, LX; Yang, P; Guan, YL; Liu, ZL; Xiao, Y; Jiang, K; Xiao, M Qian, Liangxin; Yang, Ping; Guan, Yong Liang; Liu, Zilong; Xiao, Yue; Jiang, Ke; Xiao, Ming Multi-Dimensional Polarized Modulation for Land Mobile Satellite Communications IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Article Deep neural network; land mobile satellite; polarized modulation; power allocation; reconfigurable intelligent surfaces SPATIAL MODULATION; MIMO In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems. [Qian, Liangxin; Yang, Ping; Xiao, Yue; Jiang, Ke] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Peoples R China; [Qian, Liangxin; Yang, Ping] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China; [Guan, Yong Liang] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore; [Liu, Zilong] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England; [Xiao, Ming] Royal Inst Technol KTH, Sch Elect Engn, Dept Informat Sci & Engn, S-10044 Stockholm, Sweden University of Electronic Science & Technology of China; Xidian University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; University of Essex; Royal Institute of Technology Yang, P (corresponding author), Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Peoples R China. 201921220237@std.uestc.edu.cn; yang.ping@uestc.edu.cn; eylguan@ntu.edu.sg; zilong.liu@essex.ac.uk; xiaoyue@uestc.edu.cn; jiangke@std.uestc.edu.cn; mingx@kth.se Qian, Liangxin/ACH-3966-2022; Guan, Yong Liang/A-5090-2011; Xiao, Ming/I-5517-2018 Qian, Liangxin/0000-0002-7686-4580; Guan, Yong Liang/0000-0002-9757-630X; Xiao, Ming/0000-0002-5407-0835 National Science Foundation of China [U19B2014]; Open Project of State Key Laboratory of Integrated Services Networks of Xidian University [ISN21-14] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Project of State Key Laboratory of Integrated Services Networks of Xidian University This work is supported by the National Science Foundation of China under Grant number 61876033, the Open Project of State Key Laboratory of Integrated Services Networks of Xidian University under Grant number ISN21-14, and the National Science Foundation of China under Grant number U19B2014. 36 4 4 3 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. JUN 2021.0 7 2 383 397 10.1109/TCCN.2021.3072593 0.0 15 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications SP3BW Green Accepted 2023-03-23 WOS:000659548800005 0
C Biondi, G; Franzoni, V; Mancinelli, A; Milani, A Gervasi, O; Murgante, B; Misra, S; Rocha, AMAC; Garau, C Biondi, Giulio; Franzoni, Valentina; Mancinelli, Alessio; Milani, Alfredo Student Behaviour Models for a University LMS COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2022 WORKSHOPS, PART III Lecture Notes in Computer Science English Proceedings Paper 22nd International Conference on Computational Science and its Applications (ICCSA) JUL 04-07, 2022 Malaga, SPAIN Springer Int Publishing AG,Comp Open Access Journal,Computat Open Access Journal,Univ Malaga,Univ Perugia,Univ Basilicata,Monash Univ,Kyushu Sangyo Univ,Univ Minho,Univ Cagliari e-learning; Behavioural models; Academic learning Learning Management Systems (LMSs) enable teachers and educational institutions to manage the organization of the courses offered and deliver courses in blended form, with LMSs offering support to in-person teaching, or fully online. LMSs, despite having been used for a long time, saw a dramatic increase in usage due to the Covid-19 pandemic; the purpose of this study is the analysis of student behaviour within the Moodle platform, by exploiting the user interaction logs as recorded by the platform itself. Two models are proposed to predict the final outcome of students' exams based on their behaviour within the platform. The first model consists of a support vector machine, while the second model consists of an artificial neural network; both models were tested on two real-world data sets, delivering outstanding results in terms of accuracy, above 90% for some of the tested configurations. [Biondi, Giulio; Franzoni, Valentina; Mancinelli, Alessio; Milani, Alfredo] Univ Perugia, Dept Math & Comp Sci, Perugia, Italy; [Franzoni, Valentina] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China University of Perugia; Hong Kong Baptist University Franzoni, V (corresponding author), Univ Perugia, Dept Math & Comp Sci, Perugia, Italy.;Franzoni, V (corresponding author), Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. giulio.biondi@unipg.it; valentina.franzoni@dmi.unipg.it; alessio.mancinelli@studenti.unipg.it; alfredo.milani@unipg.it Biondi, Giulio/0000-0002-1854-2196 MIUR PRIN PHRAME Project MIUR PRIN PHRAME Project(Ministry of Education, Universities and Research (MIUR)) This work has been supported by MIUR PRIN PHRAME Project, and EmoRe. 0 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-10545-6; 978-3-031-10544-9 LECT NOTES COMPUT SC 2022.0 13379 33 43 10.1007/978-3-031-10545-6_3 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU6IA 2023-03-23 WOS:000925182900003 0
J Sun, T; Liu, HB; McLoone, S; Ji, SX; Wu, XD Sun, Tao; Liu, Hongbo; McLoone, Sean; Ji, Shaoxiong; Wu, Xindong Time series indexing by dynamic covering with cross-range constraints VLDB JOURNAL English Article Time series; Dynamic time warping; Indexing; R-tree; Dynamic covering; Cross-range constraints LOWER-BOUND FUNCTIONS; RETRIEVAL; SELECTION Time series indexing plays an important role in querying and pattern mining of big data. This paper proposes a novel structure for tightly covering a given set of time series under the dynamic time warping similarity measurement. The structure, referred to as dynamic covering with cross-range constraints (DCRC), enables more efficient and scalable indexing to be developed than current hypercube-based partitioning approaches. In particular, a lower bound of the DTW distance from a given query time series to a DCRC-based cover set is introduced. By virtue of its tightness, which is proven theoretically, the lower bound can be used for pruning when querying on an indexing tree. If the DCRC-based lower bound (LB_DCRC) of an upper node in an index tree is larger than a given threshold, all child nodes can be pruned yielding a significant reduction in computational time. A hierarchical DCRC (HDCRC) structure is proposed to generate the DCRC-tree-based indexing and used to develop time series indexing and insertion algorithms. Experimental results for a selection of benchmark time series datasets are presented to illustrate the tightness of LB_DCRC, as well as the pruning efficiency on the DCRC-tree, especially when the time series have large deformations. [Sun, Tao] Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116023, Peoples R China; [Liu, Hongbo] Dalian Maritime Univ, Inst Cognit Informat Technol, Dalian 116026, Peoples R China; [McLoone, Sean] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland; [Ji, Shaoxiong] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland; [Wu, Xindong] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230009, Peoples R China; [Wu, Xindong] Mininglamp Acad Sci, Mininglamp Technol, Beijing 100084, Peoples R China Dalian University of Technology; Dalian Maritime University; Queens University Belfast; Aalto University; Hefei University of Technology Liu, HB (corresponding author), Dalian Maritime Univ, Inst Cognit Informat Technol, Dalian 116026, Peoples R China. dlutst@dlut.edu.cn; lhb@dlmu.edu.cn; s.mcloone@qub.ac.uk; shaoxiong.ji@aalto.fi; xwu@hfut.edu.cn Ji, Shaoxiong/AAI-9628-2020; Liu, Hongbo/H-2790-2013; Wu, Xindong/AAB-6713-2022 Ji, Shaoxiong/0000-0003-3281-8002; Liu, Hongbo/0000-0001-9296-9975; Wu, Xindong/0000-0003-2396-1704 National Natural Science Foundation of China [61751205, 91746209, 61772102] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors sincerely thank the editors and the anonymous reviewers for the very helpful and kind comments that have enhanced the presentation of our paper. The authors would also like to thank the UCR time series classification archive and Prof. Keogh for providing the datasets used in the study. This work is supported in part by the National Natural Science Foundation of China (Grant Nos. 61751205, 91746209, 61772102) 40 0 0 1 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1066-8888 0949-877X VLDB J VLDB J. NOV 2020.0 29 6 SI 1365 1384 10.1007/s00778-020-00614-9 0.0 MAY 2020 20 Computer Science, Hardware & Architecture; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science PD4OV Green Submitted, Green Accepted 2023-03-23 WOS:000559591000001 0
C Boucetta, C; Nour, B; Cusin, A; Moungla, H IEEE Boucetta, Cherifa; Nour, Boubakr; Cusin, Alberic; Moungla, Hassine QoS in IoT Networks based on Link Quality Prediction IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (ICC) JUN 14-23, 2021 ELECTR NETWORK IEEE,Telus,Huawei,Ciena,Nokia,Samsung,Qualcomm,Cisco,Google Cloud Internet of Things (IoT); Link quality; QoS; TSCH The success of the Internet of Things (IoT) depends on the ability to provide reliable communication to the billions of devices that are used in many applications. In essence, estimating the quality of wireless links ensures the optimization of several protocols, reduces the end-to-end latency, and increases the reliability and the network lifetime. In this paper, we study the link quality in the Time Slotted Channel Hopping (TSCH) network by analyzing the received signal strength (RSSI) and error rates. The objective is to understand the temporal properties of these parameters which is important to select the appropriate channels for the critical applications and to enhance the Quality of Service (QoS) of the network. We apply machine learning techniques to a real dataset collected from a testbed IoT network deployed at Grenoble, France. We define five classes and present a classification of the 16 channels by comparing the performances of KNN (k-Nearest Neighbor) and LSTM (Long Short-Term Memory) algorithms. [Boucetta, Cherifa] Univ Reims, CReSTIC EA 3804, F-51097 Reims, France; [Cusin, Alberic; Moungla, Hassine] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China; [Nour, Boubakr] Univ Paris, LIPADE, F-75006 Paris, France; [Moungla, Hassine] Inst Polytech Paris, Telecom SudParis, UMR 5157, CNRS, Palaiseau, France Universite de Reims Champagne-Ardenne; Beijing Institute of Technology; UDICE-French Research Universities; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS) Boucetta, C (corresponding author), Univ Reims, CReSTIC EA 3804, F-51097 Reims, France. cherifa.boucetta@univ-reims.fr; n.boubakr@bit.edu.cn; alberic.cusin@u-paris.fr; hassine.moungla@u-paris.fr 19 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-7281-7122-7 IEEE ICC 2021.0 10.1109/ICC42927.2021.9500396 0.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BS4HX Green Submitted 2023-03-23 WOS:000719386000152 0
J Ren, S; Shi, LC; Liu, Y; Cai, WH; Zhang, YF Ren, Shan; Shi, Lichun; Liu, Yang; Cai, Weihua; Zhang, Yingfeng A personalised operation and maintenance approach for complex products based on equipment portrait of product-service system ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING English Article Equipment portrait; Product-service system; Operation and maintenance; Personalised service INVERSE DOCUMENT FREQUENCY; BIG DATA ANALYTICS; PERFORMANCE; OPTIMIZATION Based on the holistic data of product-service system (PSS) delivery processes, equipment portrait can be used to describe personalised user requirements and conduct targeted analysis on the performance of complex products. Therefore, a promising application combining PSS and equipment portrait is to establish a more refined portrait model to improve the accuracy and applicability of operation and maintenance (OM) schemes for industrial products. However, studies in the above field are facing many challenges. For example, the research on equip-ment portrait in the industrial field is still in its infancy. PSS and equipment portrait are studied separately, and the overall solution that integrates PSS and equipment portrait for complex products OM service is almost vacant. A personalised OM approach for complex products (POMA-CP) is proposed to address these challenges. First, a framework of POMA-CP is developed to show how the processes of refined OM can be implemented. Then, a solution of POMA-CP based on the framework is designed. A multi-level case library, dynamic equipment portrait model, and case-pushing mechanism are established and developed. Active pushing of the best similar cases and automatic generation of service schemes are realised. Finally, an application scenario for a high-speed electric multiple units (EMU) bogie is presented to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy and applicability for service schemes are achieved, resulting in the efficient reusing of OM knowledge, proactive implementation of refined maintenance, and reducing maintenance cost and resource consumption. [Ren, Shan] Xian Univ Posts & Telecommun, Sch Modern Post, Xian 710061, Peoples R China; [Shi, Lichun; Zhang, Yingfeng] Northwestern Polychn Univ, Sch Mech Engn, Xian 710072, Peoples R China; [Liu, Yang] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden; [Liu, Yang] Univ Oulu, Ind Engn & Management, Oulu 90570, Finland; [Cai, Weihua] CRRC Tangshan Corp Ltd, Operat & Control Ctr, Tangshan 064000, Peoples R China Xi'an University of Posts & Telecommunications; Linkoping University; University of Oulu; CRRC Corporation Zhang, YF (corresponding author), Northwestern Polychn Univ, Sch Mech Engn, Xian 710072, Peoples R China.;Liu, Y (corresponding author), Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden.;Liu, Y (corresponding author), Univ Oulu, Ind Engn & Management, Oulu 90570, Finland. yang.liu@liu.se; zhangyf@nwpu.edu.cn Liu, Yang/C-8320-2013; z, y/HPC-0477-2023 Liu, Yang/0000-0001-8006-3236; National Natural Science Foundation of China; Key Research and Development Program of Shaanxi; [52005408]; [U2001201]; [2021GY-069] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Shaanxi; ; ; Acknowledgements The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 52005408 and U2001201) and the Key Research and Development Program of Shaanxi (2021GY-069) . 70 1 1 10 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0736-5845 1879-2537 ROBOT CIM-INT MANUF Robot. Comput.-Integr. Manuf. APR 2023.0 80 102485 10.1016/j.rcim.2022.102485 0.0 19 Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Robotics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Robotics 7H8JS hybrid, Green Published 2023-03-23 WOS:000903444400001 0
J Quost, B; Denoeux, T; Li, SM Quost, Benjamin; Denoeux, Thierry; Li, Shoumei Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression ADVANCES IN DATA ANALYSIS AND CLASSIFICATION English Article Partially supervised learning; Belief functions; Dempster-Shafer theory; Machine learning; Uncertain data; Discriminant analysis; Logistic regression MAXIMUM-LIKELIHOOD; BELIEF; EXTENSIONS; FUZZY Partially supervised learning extends both supervised and unsupervised learning, by considering situations in which only partial information about the response variable is available. In this paper, we consider partially supervised classification and we assume the learning instances to be labeled by Dempster-Shafer mass functions, called soft labels. Linear discriminant analysis and logistic regression are considered as special cases of generative and discriminative parametric models. We show that the evidential EM algorithm can be particularized to fit the parameters in each of these models. We describe experimental results with simulated data sets as well as with two real applications: K-complex detection in sleep EEGs signals and facial expression recognition. These results confirm the interest of using soft labels for classification as compared to potentially erroneous crisp labels, when the true class membership is partially unknown or ill-defined. [Quost, Benjamin; Denoeux, Thierry] Univ Technol Compiegne, Sorbonne Univ, Heudiasyc UMR 7253, CNRS, Compiegne, France; [Denoeux, Thierry; Li, Shoumei] Beijing Univ Technol, Coll Appl Sci, Beijing, Peoples R China Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; UDICE-French Research Universities; Sorbonne Universite; Beijing University of Technology Denoeux, T (corresponding author), Univ Technol Compiegne, Sorbonne Univ, Heudiasyc UMR 7253, CNRS, Compiegne, France.;Denoeux, T (corresponding author), Beijing Univ Technol, Coll Appl Sci, Beijing, Peoples R China. benjamin.quost@utc.fr; thierry.denoeux@utc.fr; lisma@bjut.edu.cn Denoeux, Thierry/0000-0002-0660-5436 Beijing Government Beijing Government The author thanks Cedric Richard and Regis Lengelle for providing the EEG data, as well as the Foundation for Applied Neuroscience Research in Psychiatry (CHS de Rouffach, 68250 Rouffach, France) for authorizing their use. They also thank Franck Davoine for providing the facial expression data. This research was supported by a grant from the Beijing Government as part of the Overseas Talents program. 48 18 18 2 20 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1862-5347 1862-5355 ADV DATA ANAL CLASSI Adv. Data Anal. Classif. DEC 2017.0 11 4 SI 659 690 10.1007/s11634-017-0301-2 0.0 32 Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Mathematics FN8CV Green Submitted 2023-03-23 WOS:000416247700002 0
J De Oliveira, H; Augusto, V; Jouaneton, B; Lamarsalle, L; Prodel, M; Xie, XL De Oliveira, Hugo; Augusto, Vincent; Jouaneton, Baptiste; Lamarsalle, Ludovic; Prodel, Martin; Xie, Xiaolan Automatic and Explainable Labeling of Medical Event Logs With Autoencoding IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Medical services; Data mining; Complexity theory; Databases; Medical diagnostic imaging; Machine learning; Hidden Markov models; Process mining; event log; healthcare data; patient pathways; autoencoding PROCESS MODELS Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways. [De Oliveira, Hugo; Augusto, Vincent; Xie, Xiaolan] Univ Clermont Auvergne, Ctr CIS, CNRS, Mines St Etienne,UMR 6158,LIMOS, F-42023 St Etienne, France; [De Oliveira, Hugo; Jouaneton, Baptiste; Lamarsalle, Ludovic; Prodel, Martin] HEVA, 186 Ave Thiers, F-69465 Lyon, France; [Xie, Xiaolan] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200030, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA); IMT - Institut Mines-Telecom; Mines Saint-Etienne; Shanghai Jiao Tong University De Oliveira, H (corresponding author), Univ Clermont Auvergne, Ctr CIS, CNRS, Mines St Etienne,UMR 6158,LIMOS, F-42023 St Etienne, France. hdeoliveira@hevaweb.com; augusto@emse.fr; bjouaneton@hevaweb.com; llamarsalle@hevaweb.com; mprodel@hevaweb.com; xie@emse.fr Xie, Xiaolan/0000-0002-6579-1523; Augusto, Vincent/0000-0002-5063-0831; De Oliveira, Hugo/0000-0001-8409-8292 28 6 6 1 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. NOV 2020.0 24 11 3076 3084 10.1109/JBHI.2020.3021790 0.0 9 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics OP2KL 32886615.0 Green Submitted 2023-03-23 WOS:000587913300005 0
J Wan, J; Li, MS; OGrady, MJ; Gu, X; Alawlaqi, MAAH; OHare, GMP Wan, Jie; Li, MingSong; OGrady, Michael J.; Gu, Xiang; Alawlaqi, Munassar A. A. H.; OHare, Gregory M. P. Time-Bounded Activity Recognition for Ambient Assisted Living IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING English Article Activity recognition; Real-time systems; Microsoft Windows; Smart homes; Hidden Markov models; Ambient assisted living; Data models; Activity recognition; ambient assisted living; ambient intelligence; smart homes; stream processing IMPLEMENTATION; ACCELEROMETER; SEGMENTATION Robust activity recognition in near real-time is a prerequisite for delivering the smartness intrinsic to the pragmatic realisation of smart homes, environments and so forth. Many of the physical devices necessary for equipping a smart home are already available as consumer electronic devices and certified for use by the public. Yet activity recognition remains the preserve of the research community, despite the array of machine learning and other AI techniques currently available. To-date, research has been dominated by the use of pre-segmented data, resulting in the recognition of an arbitrary activity subsequent to its completion. For assistive paradigms dependent on smart technologies, for example Ambient Assisted Living, such approaches are insufficient. The overall objective must be the identification of an activity within an appropriative confidence level as soon as possible after activity commencement. This paper presents a novel approach, Cumulatively Overlapping windowing approach for AmBient Recognition of Activities (COBRA), for near real-time activity recognition, specifically within 10, 30, 60 or 120 seconds of the commencement of an activity. COBRA utilizes an innovative combination of sliding windows augmented with a logistic regression model. The approach is evaluated using the well-established, open, CASAS dataset. [Wan, Jie; Gu, Xiang; Alawlaqi, Munassar A. A. H.] NanTong Univ, Sch Comp Sci & Technol, Nantong Shi 226000, Jiangsu, Peoples R China; [Li, MingSong] TeraData Ltd, Beijing, Peoples R China; [OGrady, Michael J.; OHare, Gregory M. P.] Univ Coll Dublin, Sch Comp Sci Informat, Dublin 0004, Ireland Nantong University; University College Dublin Wan, J (corresponding author), NanTong Univ, Sch Comp Sci & Technol, Nantong Shi 226000, Jiangsu, Peoples R China. jiewan@ntu.edu.cn; Ms_li_2006@hotmail.com; michael.j.ogrady@ucd.ie; gu.x@ntu.edu.cn; eng.muntasser@gmail.com; gregory.ohare@ucd.ie O'Hare, Gregory M.P./AAA-7756-2022 O'Hare, Gregory M.P./0000-0002-5124-1686; O'Grady, Michael/0000-0002-3024-3666 Jiangsu Provincial Education Department [16KJB520038]; Nantong University Jiangsu China Jiangsu Provincial Education Department; Nantong University Jiangsu China This work is supported by Jiangsu Provincial Education Department under grant 16KJB520038, within Nantong University Jiangsu China. 47 19 19 0 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-6750 IEEE T EMERG TOP COM IEEE Trans. Emerg. Top. Comput. JAN 1 2021.0 9 1 471 483 10.1109/TETC.2018.2870047 0.0 13 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications QW8QH 2023-03-23 WOS:000628912400041 0
J Liang, HM; Dong, YC; Ding, ZG; Urena, R; Chiclana, F; Herrera-Viedma, E Liang, Haiming; Dong, Yucheng; Ding, Zhaogang; Urena, Raquel; Chiclana, Francisco; Herrera-Viedma, Enrique Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Numerical models; Time factors; Artificial intelligence; Computer science; Analytical models; Decision making; Informatics; Bounded confidence; consensus; group decision making (GDM); minimum adjustments; opinion dynamics (OD); time constraints GROUP DECISION-MAKING; OPINION DYNAMICS; SOCIAL NETWORK; COST; MODEL; FRAMEWORK; CONTEXT In the bounded confidence model, it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this article develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility. [Liang, Haiming; Dong, Yucheng] Sichuan Univ, Business Sch, Chengdu 610065, Peoples R China; [Dong, Yucheng] Hubei Univ Econ, Sch Informat Management & Stat, Wuhan 430205, Peoples R China; [Ding, Zhaogang] Northwest Univ, Sch Publ Management, Xian 710069, Peoples R China; [Urena, Raquel; Chiclana, Francisco] De Montfort Univ, Sch Comp Sci & Informat, Inst Artificial Intelligence, Leicester LE1 9BH, Leics, England; [Chiclana, Francisco; Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18, Spain; [Herrera-Viedma, Enrique] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia Sichuan University; Hubei University of Economics; Northwest University Xi'an; De Montfort University; University of Granada; King Abdulaziz University Dong, YC (corresponding author), Sichuan Univ, Business Sch, Chengdu 610065, Peoples R China. hmliang@scu.edu.cn; ycdong@scu.edu.cn; zgding@nwu.edu.cn; raquel.urena@dmu.ac.uk; inv.chiclana@ugr.es; viedma@decsai.ugr.es Chiclana, Francisco/B-9031-2008; Dong, Yucheng/J-1668-2017; Herrera-Viedma, Enrique/C-2704-2008; Urena, Raquel/F-9894-2016 Chiclana, Francisco/0000-0002-3952-4210; Dong, Yucheng/0000-0002-0028-6796; Herrera-Viedma, Enrique/0000-0002-7922-4984; Urena, Raquel/0000-0002-4099-7437 National Science Foundation of China [71871149, 71971149, 71601133]; Sichuan University [sksyl201705, 2018hhs-58, YJ201906]; FEDER funds provided in the National Spanish [TIN2016-75850-R, H2020-MSCA-IF-2016-DeciTrustNET-746398] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan University(Sichuan University); FEDER funds provided in the National Spanish This work was supported in part by the National Science Foundation of China under Grant 71871149, Grant 71971149, and Grant 71601133, in part by Sichuan University under Grant sksyl201705, Grant 2018hhs-58, and Grant YJ201906, and in part by FEDER funds provided in the National Spanish under Project TIN2016-75850-R and under EUProject H2020-MSCA-IF-2016-DeciTrustNET-746398. 39 29 29 21 91 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. OCT 2020.0 28 10 2466 2479 10.1109/TFUZZ.2019.2939970 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OA8AT Green Submitted 2023-03-23 WOS:000578002800015 0
J Cui, HZ; Jiang, LM; Paloscia, S; Santi, E; Pettinato, S; Wang, J; Fang, XY; Liao, WJ Cui, Huizhen; Jiang, Lingmei; Paloscia, Simonetta; Santi, Emanuele; Pettinato, Simone; Wang, Jian; Fang, Xiyao; Liao, Wanjin The Potential of ALOS-2 and Sentinel-1 Radar Data for Soil Moisture Retrieval With High Spatial Resolution Over Agroforestry Areas, China IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Advanced Land Observing Satellite-2 (ALOS-2); agroforestry area; artificial neural network (ANN); Sentinel-1; soil moisture content (SMC) REGRESSION NEURAL-NETWORKS; L-BAND; SURFACE-ROUGHNESS; MICROWAVE BACKSCATTERING; EMPIRICAL-MODEL; C-BAND; SCATTERING; SENSITIVITY; PARAMETERS; SERIES Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10 similar to 30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (ILIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm(3)/cm(3), respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm(3)/cm(3)) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-hand and L-band SMC retrievals have higher RMSE (up to 0.047 cm(3)/cm(3)) at low incidence angle (<20 degrees) and high incidence angle (>50 degrees). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-I and AIRS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing. [Cui, Huizhen; Jiang, Lingmei; Wang, Jian; Fang, Xiyao] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Cui, Huizhen; Jiang, Lingmei; Wang, Jian; Fang, Xiyao] Beijing Normal Univ, Aerosp Informat Res Inst, Fac Geog Sci, Chinese Acad Sci, Beijing 100875, Peoples R China; [Paloscia, Simonetta; Santi, Emanuele; Pettinato, Simone] Natl Res Council IFAC CNR, Inst Appl Phys, I-1050019 Florence, Italy; [Liao, Wanjin] Beijing Normal Univ, Coll Life Sci, State Key Lab Earth Surface Proc & Resource Ecol, Minist Educ,Key Lab Biodivers Sci & Ecol Engn, Beijing 100875, Peoples R China Beijing Normal University; Beijing Normal University; Chinese Academy of Sciences; Consiglio Nazionale delle Ricerche (CNR); Istituto di Fisica Applicata Nello Carrara (IFAC-CNR); Istituto di Fisiologia Clinica (IFC-CNR); Beijing Normal University Jiang, LM (corresponding author), Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.;Jiang, LM (corresponding author), Beijing Normal Univ, Aerosp Informat Res Inst, Fac Geog Sci, Chinese Acad Sci, Beijing 100875, Peoples R China. cuihz@mail.bnu.edu.cn; jiang@bnu.edu.cn; s.paloscia@ifac.cnr.it; e.santi@ifac.cnr.it; s.pettinato@ifac.cnr.it; wjian@mail.bnu.edu.cn; fang_xiyao@163.com; liaowj@bnu.edu.cn Jiang, Lingmei/L-6099-2016 Jiang, Lingmei/0000-0002-9847-9034; Liao, Wan-Jin/0000-0001-7928-9043; Cui, Huizhen/0000-0002-1165-1998; santi, emanuele/0000-0003-1882-6321 Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [2019QZKK0206]; National Natural Science Foundation of China [42090014]; Beijing Advanced Innovation Program for Land Surface Science Second Tibetan Plateau Scientific Expedition and Research Program (STEP); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Advanced Innovation Program for Land Surface Science This work was supported in part by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) under Grant 2019QZKK0206, in part by the National Natural Science Foundation of China under Grant 42090014, and in part by the Beijing Advanced Innovation Program for Land Surface Science. 82 5 5 11 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 10.1109/TGRS.2021.3082805 0.0 JUN 2021 17 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG3HV 2023-03-23 WOS:000732913900001 0
J Allouche, D; Bessiere, C; Boizumault, P; de Givry, S; Gutierrez, P; Lee, JHM; Leung, KL; Loudni, S; Metivier, JP; Schiex, T; Wu, Y Allouche, David; Bessiere, Christian; Boizumault, Patrice; de Givry, Simon; Gutierrez, Patricia; Lee, Jimmy H. M.; Leung, Ka Lun; Loudni, Samir; Metivier, Jean-Philippe; Schiex, Thomas; Wu, Yi Tractability-preserving transformations of global cost functions ARTIFICIAL INTELLIGENCE English Article Graphical model; Weighted constraint satisfaction problem; Cost function network; Constraint programming; Constraint satisfaction problem; Global cost functions; Decomposition; Computational complexity DEAD-END ELIMINATION; ARC CONSISTENCY; OPTIMIZATION; CONSTRAINTS Graphical model processing is a central problem in artificial intelligence. The optimization of the combined cost of a network of local cost functions federates a variety of famous problems including CSP, SAT and Max-SAT but also optimization in stochastic variants such as Markov Random Fields and Bayesian networks. Exact solving methods for these problems typically include branch and bound and local inference-based bounds. In this paper we are interested in understanding when and how dynamic programming based optimization can be used to efficiently enforce soft local consistencies on Global Cost Functions, defined as parameterized families of cost functions of unbounded arity. Enforcing local consistencies in cost function networks is performed by applying so-called Equivalence Preserving Transformations (EPTs) to the cost functions. These EPTs may transform global cost functions and make them intractable to optimize. We identify as tractable projection-safe those global cost functions whose optimization is and remains tractable after applying the EPTs used for enforcing arc consistency. We also provide new classes of cost functions that are tractable projection-safe thanks to dynamic programming. We show that dynamic programming can either be directly used inside filtering algorithms, defining polynomially DAG-filterable cost functions, or emulated by arc consistency filtering on a Berge-acyclic network of bounded-arity cost functions, defining Berge-acyclic network decomposable cost functions. We give examples of such cost functions and we provide a systematic way to define decompositions from existing decomposable global constraints. These two approaches to enforcing consistency in global cost functions are then embedded in a solver for extensive experiments that confirm the feasibility and efficiency of our proposal. (C) 2016 Elsevier B.V. All rights reserved. [Allouche, David; de Givry, Simon; Schiex, Thomas] INRA, MIAT, UR 875, F-31320 Castanet Tolosan, France; [Bessiere, Christian] Univ Montpellier, CNRS, Montpellier, France; [Gutierrez, Patricia] Univ Autonoma Barcelona, IIIA CSIC, Bellaterra 08193, Spain; [Lee, Jimmy H. M.; Leung, Ka Lun] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China; [Loudni, Samir; Metivier, Jean-Philippe] Univ Caen Basse Normandie, GREYC, 6 Blvd Marechal Juin, F-14032 Caen 5, France INRAE; Centre National de la Recherche Scientifique (CNRS); Universite de Montpellier; Autonomous University of Barcelona; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Investigacion en Inteligencia Artificial (IIIA); Chinese University of Hong Kong; Universite de Caen Normandie Schiex, T (corresponding author), INRA, MIAT, UR 875, F-31320 Castanet Tolosan, France.;Lee, JHM (corresponding author), Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China. thomas.schiex@toulouse.inra.fr Gutierrez, Patricia/GVT-7143-2022; Gutierrez, Patricia/AAK-7386-2021; Schiex, Thomas/F-3077-2011 Schiex, Thomas/0000-0001-6049-3415; Lee, Jimmy/0000-0001-9526-5850 Agence Nationale de la Recherche [ANR-10-BLA-0214]; Partenariat Hubert Curien PROCORE [28680VH]; Research Grants Council of Hong Kong SAR grant [CUHK413710, F-HK019/12T]; Consulat General de France de Hong Kong Agence Nationale de la Recherche(French National Research Agency (ANR)); Partenariat Hubert Curien PROCORE; Research Grants Council of Hong Kong SAR grant; Consulat General de France de Hong Kong This work has been partly funded by the Agence Nationale de la Recherche (ANR-10-BLA-0214), a Partenariat Hubert Curien PROCORE grant number 28680VH, the Research Grants Council of Hong Kong SAR grant CUHK413710, and the Consulat General de France de Hong Kong with Research Grants Council of Hong Kong SAR joint grant F-HK019/12T. 64 7 7 0 14 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0004-3702 1872-7921 ARTIF INTELL Artif. Intell. SEP 2016.0 238 166 189 10.1016/j.artint.2016.06.005 0.0 24 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DS2KX Green Submitted 2023-03-23 WOS:000380599500009 0
J Tian, HY; Li, YH; Pian, WG; Kabore, AK; Liu, K; Habib, A; Klein, J; Bissyande, TF Tian, Haoye; Li, Yinghua; Pian, Weiguo; Kabore, Abdoul Kader; Liu, Kui; Habib, Andrew; Klein, Jacques; Bissyande, Tegawende F. Predicting Patch Correctness Based on the Similarity of Failing Test Cases ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY English Article Program repair; patch correctness; test behavior; patch semantics AUTOMATED PROGRAM REPAIR; CODE How do we know a generated patch is correct? This is a key challenging question that automated program repair (APR) systems struggle to address given the incompleteness of available test suites. Our intuition is that we can triage correct patches by checking whether each generated patch implements code changes (i.e., behavior) that are relevant to the bug it addresses. Such a bug is commonly specified by a failing test case. Towards predicting patch correctness in APR, we propose a novel yet simple hypothesis on how the link between the patch behavior and failing test specifications can be drawn: similar failing test cases should require similar patches. We then propose BATS, an unsupervised learning-based approach to predict patch correctness by checking patch Behavior Against failing Test Specification. BATS exploits deep representation learning models for code and patches: For a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases to identify the associated applied patches, which are then used as a proxy for assessing the correctness of the APR-generated patches. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1,278 plausible patches (written by developers or generated by 32 APR tools), we use BATS to predict correct patches: BATS achieves AUC between 0.557 to 0.718 and recall between 0.562 and 0.854 in identifying correct patches. Our approach outperforms state-of-the-art techniques for identifying correct patches without the need for large labeled patch datasets-as is the case with machine learning-based approaches. While BATS is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: When combined with a recent approach that relies on supervised learning, BATS improves the overall recall in detecting correct patches. We finally show that BATS is complementary to the state-of-the-art PATCH-SIM dynamic approach for identifying correct patches generated by APR tools. [Tian, Haoye; Li, Yinghua; Pian, Weiguo; Kabore, Abdoul Kader; Habib, Andrew; Klein, Jacques; Bissyande, Tegawende F.] Univ Luxembourg, Luxembourg, Luxembourg; [Liu, Kui] Huawei, Software Engn Applicat Technol Lab, Shenzhen, Peoples R China University of Luxembourg; Huawei Technologies Liu, K (corresponding author), Huawei, Software Engn Applicat Technol Lab, Shenzhen, Peoples R China. haoye.tian@uni.lu; yinghua.li@uni.lu; weiguo.pian@uni.lu; abdoulkader.kabore@uni.lu; liukui0811@163.com; andrew.a.habib@gmail.com; jacques.klein@uni.lu; tegawende.bissyande@uni.lu TIAN, Haoye/0000-0002-8049-3997; KABORE, Abdoul Kader/0000-0002-3151-9433; LIU, Kui/0000-0003-0145-615X; Habib, Andrew/0000-0002-5857-1864; Bissyande, Tegawende F./0000-0001-7270-9869 European Research Council (ERC) under the European Union [949014]; National Natural Science Foundation of China [62172214]; Natural Science Foundation of Jiangsu Province [BK20210279]; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing [2020A06] European Research Council (ERC) under the European Union(European Research Council (ERC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing This work was supported by the funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 949014), the National Natural Science Foundation of China (Grant No. 62172214), the Natural Science Foundation of Jiangsu Province (Grant No. BK20210279), and the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (No. 2020A06). 93 1 1 2 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1049-331X 1557-7392 ACM T SOFTW ENG METH ACM Trans. Softw. Eng. Methodol. OCT 2022.0 31 4 77 10.1145/3511096 0.0 30 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4V3NS Green Published, Bronze, Green Submitted 2023-03-23 WOS:000859387700021 0
J Shu, BR; Zhu, SH; Qu, Y; Zhang, HH; Li, X; Carsjens, GJ Shu, Bangrong; Zhu, Shouhong; Qu, Yi; Zhang, Honghui; Li, Xin; Carsjens, Gerrit J. Modelling multi-regional urban growth with multilevel logistic cellular automata COMPUTERS ENVIRONMENT AND URBAN SYSTEMS English Article Urban land expansion; Multilevel modelling; Logistic cellular automata; Nonstationary; Tongshan County LAND-USE; NEURAL-NETWORK; SIMULATION; EXPANSION; UNCERTAINTY; SCENARIOS; RULES; LEVEL Simulation models based on cellular automata (CA) are useful for revealing the complex mechanisms and processes involved in urban growth and have become supplementary tools for urban land use planning and management. Although the urban growth mechanism is characterized by multilevel and spatiotemporal heterogeneity, most existing studies focus only on simulating the urban growth of singular regions without considering the heterogeneity of the urban growth process and the multilevel factors driving urban growth within regions that consist of multiple subregions. Thus, urban growth models have limited performance when simulating the urban growth of multi-regional areas. To address this issue, we propose a multilevel logistic CA model (MLCA) by incorporating a multilevel logistic regression model into the traditional logistic CA model (LCA). In the MLCA, multilevel driving factors are considered, and the multilevel logistic model allows the transition rules to not only vary in space, but also change when the subregional level factors change. To verify the MLCA's validity, it was applied to simulate the urban growth of Tongshan County, located in China's Xuzhou Prefecture. The results were compared with three comparative models, LCA1, which only considered grid cell-level factors; LCA2, which considered both grid cell- and subregional-level factors; and artificial neural network CA. Urban growth data for the periods 2000-2009 and 2009-2017 were used. The results show that the MLCA performs better on both visual comparison and indicators for accuracy verification. The Kappa of the results increased by < 5%, but the improvement was significant, while increases for the accuracy of urban land and figure of merit were much higher than 5%. In addition, the results of MLCA had the smallest mean absolute percentage error when allocating new urban land areas to the various subregions. The results reveal that higher-level (e.g., town level) factors either strengthened or weakened the effects of grid cell-level factors on urban growth, which indirectly affected the spatial allocation of new urban land. The MLCA model is an effective step towards simulating nonstationary urban growth of multi-regional areas, using the comprehensive effects of multilevel driving factors. [Shu, Bangrong; Zhu, Shouhong; Qu, Yi; Li, Xin] Jiangsu Normal Univ, Sch Geog Geomat & Planning, 101 Shanghai Rd, Xuzhou 221116, Jiangsu, Peoples R China; [Shu, Bangrong; Li, Xin] Jiangsu Normal Univ, Characterist Township Construct & Land Management, Xuzhou 221116, Jiangsu, Peoples R China; [Zhang, Honghui] Hunan Normal Univ, Coll Resources & Environm Sci, Changsha 410081, Peoples R China; [Zhang, Honghui] Guangzhou Lantu Geog Informat Sci Technol Co Ltd, Guangzhou 510075, Peoples R China; [Carsjens, Gerrit J.] Wageningen Univ, Land Use Planning Grp, POB 47, NL-6700 AA Wageningen, Netherlands Jiangsu Normal University; Jiangsu Normal University; Hunan Normal University; Wageningen University & Research Shu, BR (corresponding author), Jiangsu Normal Univ, Sch Geog Geomat & Planning, 101 Shanghai Rd, Xuzhou 221116, Jiangsu, Peoples R China. brshu@jsnu.edu.cn Carsjens, Gerrit J/B-8048-2015 Carsjens, Gerrit J/0000-0001-8001-4645 Natural Science Foundation of Jiangsu Province [BK20191004]; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China [41871318]; Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship This study was supported by the Natural Science Foundation of Jiangsu Province [BK20191004]; A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; the National Natural Science Foundation of China [41871318]; Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship. We sincerely thank the three anonymous reviewers for their constructive comments and suggestions. 56 15 18 5 36 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0198-9715 1873-7587 COMPUT ENVIRON URBAN Comput. Environ. Urban Syst. MAR 2020.0 80 101457 10.1016/j.compenvurbsys.2019.101457 0.0 12 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science; Regional & Urban Planning Social Science Citation Index (SSCI) Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science; Public Administration KO0AX 2023-03-23 WOS:000515209500018 0
C Van Houtte, J; Bazrafkan, S; Vandenberghe, F; Zheng, GY; Sijbers, J IEEE Van Houtte, Jeroen; Bazrafkan, Shabab; Vandenberghe, Filip; Zheng, Guoyan; Sijbers, Jan A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs 2019 NINTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) International Conference on Image Processing Theory Tools and Applications English Proceedings Paper 9th International Conference on Image Processing Theory, Tools and Applications (IPTA) NOV 06-09, 2019 Istanbul, TURKEY EURASIP,IEEE,Yeditepe Univ,IEEE Turkey Sect,Univ Paris Saclay,IEEE France Sect,IEEE Yeditepe, KEKAM Digitally reconstructed radiographs; Convolutional neural networks; Articulating shape model LIMITS Convolutional neural networks (CNN) are popular for segmentation and classification of bones in radiology. However, their training typically requires a database of thousands of manually segmented experimental images. In many cases, such a large dataset is not readily available in the community. In addition, manual segmentation is often too time intensive and prone to human perception, especially in cases of low image quality. In this paper, we show that a CNN can be accurately trained on the digitally reconstructed radiographs (DRR) of a 3D articulating shape model of the object of interest, bypassing the need for a manually-segmented database. The articulating model ensures a realistic appearance of the bones of interest in the DRR, thereby providing suitable training data for segmentation. As a proof-of-concept, we train a CNN on DRRs with the purpose of segmenting the phalanges of a horse leg from radiographs and show that it outperforms a geodesic active contour segmentation method in this particular case. Our proposed training procedure is effective for articulating objects and the resulting CNN can then be applied to real-data segmentation tasks, if preceded by appropriate augmentation. [Van Houtte, Jeroen; Bazrafkan, Shabab; Sijbers, Jan] Univ Antwerp, Vis Lab, IMEC, Antwerp 2610, Belgium; [Vandenberghe, Filip] Equine Hosp Bosdreef, Moerbeke 9180, Belgium; [Zheng, Guoyan] Shanghai Jiao Tong Univ, Ctr Image Guided Therapy & Intervent, Shanghai 200240, Peoples R China IMEC; University of Antwerp; Shanghai Jiao Tong University Van Houtte, J (corresponding author), Univ Antwerp, Vis Lab, IMEC, Antwerp 2610, Belgium. jeroen.vanhoutte@uantwerpen.be Sijbers, Jan/C-4214-2011; Zheng, Guoyan/C-8079-2014 Sijbers, Jan/0000-0003-4225-2487; Van Houtte, Jeroen/0000-0001-7520-7143 Research Foundation in Flanders [FWO-SB 1563918N]; NVIDIA Corporation Research Foundation in Flanders; NVIDIA Corporation This work was supported by the Research Foundation in Flanders (FWO-SB 1563918N).; The authors would like to thank John R. Hutchinson from Royal Veterinary College for sharing the horse CT-data.; We also gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for this research. 38 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2154-512X 978-1-7281-3975-3 INT CONF IMAG PROC 2019.0 6 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BO8SZ 2023-03-23 WOS:000529320000012 0
J Zang, YF; Meng, FC; Lindenbergh, R; Truong-Hong, L; Li, BJ Zang, Yufu; Meng, Fancong; Lindenbergh, Roderik; Truong-Hong, Linh; Li, Bijun Deep Localization of Static Scans in Mobile Mapping Point Clouds REMOTE SENSING English Article point cloud localization; mobile laser scanning; terrestrial laser scanning; place recognition; pose refinement TERRESTRIAL; REGISTRATION; AIRBORNE Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method). [Zang, Yufu] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Zang, Yufu; Meng, Fancong; Lindenbergh, Roderik; Truong-Hong, Linh] Delft Univ Technol, Dept Geosci & Remote Sensing, Stevinweg 1, NL-2628 CN Delft, Netherlands; [Li, Bijun] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China Nanjing University of Information Science & Technology; Delft University of Technology; Wuhan University Meng, FC (corresponding author), Delft Univ Technol, Dept Geosci & Remote Sensing, Stevinweg 1, NL-2628 CN Delft, Netherlands. 3dmapzangyufu@nuist.edu.cn; Meng@student.tudelft.nl; R.C.Lindenbergh@tudelft.nl; L.Truong@tudelft.nl; lee@whu.edu.cn Zang, Yufu/D-5450-2019; Truong-Hong, Linh/E-7085-2011 Zang, Yufu/0000-0002-3524-9629; Truong-Hong, Linh/0000-0003-2126-6409; Lindenbergh, Roderik/0000-0001-8655-5266; li, bijun/0000-0001-7180-7627 National Science Foundation of China project [41701529]; OpenFund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [18S02] National Science Foundation of China project; OpenFund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University This research was funded by [National Science Foundation of China project under] Grant No. 41701529, and [OpenFund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University] Grant No. 18S02. 42 1 1 1 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2021.0 13 2 219 10.3390/rs13020219 0.0 26 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology PX7QX gold, Green Published 2023-03-23 WOS:000611550100001 0
J Huang, M; Zhou, XY; Zhang, YL; Zhou, LS; Alducin, M; Jiang, B; Guo, H Huang, Meng; Zhou, Xueyao; Zhang, Yaolong; Zhou, Linsen; Alducin, Maite; Jiang, Bin; Guo, Hua Adiabatic and nonadiabatic energy dissipation during scattering of vibrationally excited CO from Au(111) PHYSICAL REVIEW B English Article ELECTRON-GAS; DYNAMICS; SURFACES; SPECTROSCOPY; MOLECULES A high-dimensional potential energy surface (PES) for CO interaction with the Au(111) surface is developed using a machine-learning algorithm. Including both molecular and surface coordinates, this PES enables the simulations of the recent experiment on scattering of vibrationally excited CO from Au(111). Trapping in a physisorption well is observed to increase with decreasing incidence energy. While the energy dissipation of physisorbed CO is slow, due to weak coupling with both the phonons and electron-hole pairs, the access of the impinging CO to the chemisorption well facilitates its fast vibrational relaxation through nonadiabatic coupling with surface electron-hole pairs. The latter is proposed as a mechanism for the experimentally observed fast as well as slow components of the CO(nu = 1) product. [Huang, Meng; Zhou, Xueyao; Zhou, Linsen; Guo, Hua] Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA; [Zhou, Xueyao; Zhang, Yaolong; Jiang, Bin] Univ Sci & Technol China, Dept Chem Phys, Hefei 230026, Anhui, Peoples R China; [Alducin, Maite] CSIC UPV EHU, Ctr Fis Mat, P Manuel de Lardizabal 5, San Sebastian 20018, Spain; [Alducin, Maite] Donostia Int Phys Ctr DIPC, P Manuel de Lardizabal 5, San Sebastian 20018, Spain University of New Mexico; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Consejo Superior de Investigaciones Cientificas (CSIC); University of Basque Country; CSIC - UPV EHU - Centro de Fisica de Materiales (CFM) Guo, H (corresponding author), Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA. hguo@unm.edu Alducin, Maite/F-6993-2016; Guo, Hua/J-2685-2014 Alducin, Maite/0000-0002-1264-7034; Huang, Meng/0000-0003-4318-5466; Guo, Hua/0000-0001-9901-053X US National Science Foundation [CHE-1462109]; National Key R&D Program of China [2017YFA0303500]; National Natural Science Foundation of China [21573203, 91645202, 21722306]; Anhui Initiative in Quantum Information Technologies [AHY090200]; Spanish Ministerio de Economia, Industria y Competitividad [FIS2016-76471-P]; Alexander von Humboldt Foundation US National Science Foundation(National Science Foundation (NSF)); National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Anhui Initiative in Quantum Information Technologies; Spanish Ministerio de Economia, Industria y Competitividad; Alexander von Humboldt Foundation(Alexander von Humboldt Foundation) We acknowledge support by the US National Science Foundation (CHE-1462109 to H.G.), National Key R&D Program of China (2017YFA0303500 to B.J.), National Natural Science Foundation of China (21573203, 91645202, and 21722306 to B.J.), Anhui Initiative in Quantum Information Technologies (AHY090200 to B.J.), and the Spanish Ministerio de Economia, Industria y Competitividad (FIS2016-76471-P to M.A.). H. G. also acknowledges Alec Wodtke, Dan Auerbach, Sascha Kandratsenka, and Igor Rahinov for several stimulating discussions and the Alexander von Humboldt Foundation for support from a Humboldt Research Award. 46 20 20 2 39 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B NOV 25 2019.0 100 20 201407 10.1103/PhysRevB.100.201407 0.0 5 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics JQ3PI Green Submitted 2023-03-23 WOS:000498860700003 0
J Liu, YW; Wu, HP; Rezaee, K; Khosravi, MR; Khalaf, OI; Khan, AA; Ramesh, D; Qi, LY Liu, Yuwen; Wu, Huiping; Rezaee, Khosro; Khosravi, Mohammad R.; Khalaf, Osamah Ibrahim; Khan, Arif Ali; Ramesh, Dharavath; Qi, Lianyong Interaction-Enhanced and Time-Aware Graph Convolutional Network for Successive Point-of-Interest Recommendation in Traveling Enterprises IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Hidden Markov models; Convolution; Informatics; Smart cities; Recurrent neural networks; Recommender systems; Computer science; Aggregator; augmented Intelligence of Things; graph convolution network; self-attention; successive point-of-interest recommendation Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an Interaction-enhanced and Time-aware Graph Convolution Network (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods. [Liu, Yuwen; Qi, Lianyong] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China; [Wu, Huiping; Khosravi, Mohammad R.] Weifang Univ Sci & Technol, Shandong Prov Univ Lab Protected Hort, Weifang 262799, Peoples R China; [Rezaee, Khosro] Meybod Univ, 6X3Q VH8, Meybod, Iran; [Khosravi, Mohammad R.] Persian Gulf Univ, Dept Comp Engn, Bushehr 75169, Iran; [Khalaf, Osamah Ibrahim] Al Nahrain Univ, Al Nahrain Nanorenewable Energy Res Ctr, Baghdad 64074, Iraq; [Khan, Arif Ali] Univ Oulu, M3S Empir Software Engn Res Unit, Oulu 90570, Finland; [Ramesh, Dharavath] Indian Inst Technol ISM, Dept Comp Sci & Engn, Dhanbad 826004, Bihar, India China University of Petroleum; Persian Gulf University; Al-Nahrain University; University of Oulu; Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad Qi, LY (corresponding author), China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China. yuwenliu97@gmail.com; wuhp760731@wfust.edu.cn; kh.rezaee@meybod.ac.ir; m.khosravi@wfust.edu.cn; usama81818@nahrainuniv.edu.iq; arif.khan@oulu.fi; drramesh@iitism.ac.in; lianyongqi@gmail.com khan, Arif/HMV-3165-2023; Dharavath, Ramesh/P-5311-2019 Dharavath, Ramesh/0000-0003-3338-6520; Rezaee, Khosro/0000-0001-6763-6626 National Key Research and Development Program of China [2020YFB1707600]; National Natural Science Foundation of China [61872219, 62177014] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1707600 and in part by the National Natural Science Foundation of China under Grant 61872219 and Grant 62177014. 34 3 3 11 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. JAN 2023.0 19 1 635 643 10.1109/TII.2022.3200067 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 6A4VJ Green Accepted 2023-03-23 WOS:000880654600064 0
J Gu, J; Meng, GF; Redi, JA; Xiang, SM; Pan, CH Gu, Jie; Meng, Gaofeng; Redi, Judith A.; Xiang, Shiming; Pan, Chunhong Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling IEEE TRANSACTIONS ON MULTIMEDIA English Article Convolutional neural network; image quality assessment; perceptual image quality; object oriented pooling; vector regression PERCEPTUAL IMAGE; NEURAL-NETWORKS; FRAMEWORK; DATABASE; SCORES This paper presents an effective method based on vector regression and object oriented pooling for blind image quality assessment. Unlike previous models that map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability. [Gu, Jie; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China; [Gu, Jie; Xiang, Shiming] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Redi, Judith A.] Delft Univ Technol, Dept Intelligent Syst, NL-2628 CD Delft, Netherlands Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Delft University of Technology Gu, J (corresponding author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. jie.gu@nlpr.ia.ac.cn; gfmeng@nlpr.ia.ac.cn; J.A.Redi@tudelft.nl; smxiang@nlpr.ia.ac.cn; chpan@nlpr.ia.ac.cn National Natural Science Foundation of China [61370039, 91646207, 91338202]; Beijing Nature Science Foundation [4162064] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Nature Science Foundation(Beijing Natural Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 61370039, Grant 91646207, and Grant 91338202, and in part by the Beijing Nature Science Foundation under Grant 4162064. 66 30 31 0 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia MAY 2018.0 20 5 1140 1153 10.1109/TMM.2017.2761993 0.0 14 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications GD7YD Green Published 2023-03-23 WOS:000430728400010 0
J Huang, N; Liu, DF; Sun, ZY; Duan, ZS; Lu, Q; Chen, ZP Huang, Na; Liu, Danfu; Sun, Zhiyong; Duan, Zhisheng; Lu, Qiang; Chen, Zhangping Distributed Consensus Seeking With Different Convergence Performance Requirements: A Unified Control Framework IEEE TRANSACTIONS ON CYBERNETICS English Article; Early Access Convergence; Laplace equations; Task analysis; Scalability; Observers; Graph theory; Eigenvalues and eigenfunctions; Consensus; convergence time; gradient neural network (GNN) FIXED-TIME CONSENSUS; MULTIAGENT SYSTEMS; COOPERATIVE CONTROL; NETWORKS; TRACKING; DESIGN; SYNCHRONIZATION In this article, we investigate distributed consensus seeking with multiple convergence performance requirements for single-integrator multiagent systems under undirected graphs. A unified distributed control framework is proposed to ensure consensus or practical consensus as well as performance time requirements, which contains most existing complex protocol schemes as special cases. In the proposed framework, three functions with specific properties in the controller play different roles and can be freely designed to achieve desired convergence performances, which guarantee a high-level scalability for multiple control requirements in addition to convergence time. For highlighting the compatibility and flexibility of the proposed method, four typical scenarios are discussed to reach exponential, finite-time, fixed-time, and appointed-time consensus seeking, respectively. Finally, numerical simulations are carried out to verify the effectiveness of the theoretical analysis. [Huang, Na; Liu, Danfu; Lu, Qiang; Chen, Zhangping] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China; [Huang, Na; Liu, Danfu; Lu, Qiang; Chen, Zhangping] Hangzhou Dianzi Univ, HDU ITMO Joint Inst, Hangzhou 310018, Peoples R China; [Sun, Zhiyong] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 AZ Eindhoven, Netherlands; [Duan, Zhisheng] Peking Univ, Coll Engn, Dept Mech & Engn Sci, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China Hangzhou Dianzi University; Hangzhou Dianzi University; Eindhoven University of Technology; Peking University Chen, ZP (corresponding author), Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China.;Chen, ZP (corresponding author), Hangzhou Dianzi Univ, HDU ITMO Joint Inst, Hangzhou 310018, Peoples R China. huangna@hdu.edu.cn; 2757086769@qq.com; sun.zhiyong.cn@gmail.com; duanzs@pku.edu.cn; lvqiang@hdu.edu.cn; czp.apple@hdu.edu.cn Sun, Zhiyong/0000-0002-4700-1479; Lu, Qiang/0000-0002-1586-5598 Natural Science Foundation of Zhejiang Province [LZ22F030005]; National Natural Science Foundation of China [61703130, 62073108, T2121002, 62173006, 51877058]; Key Projects of Science and Technology Plan of Zhejiang Province [2021C01144, 2019C04018] Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Projects of Science and Technology Plan of Zhejiang Province This work was supported in part by the Natural Science Foundation of Zhejiang Province under Grant LZ22F030005; in part by the National Natural Science Foundation of China under Grant 61703130, Grant 62073108, Grant T2121002, Grant 62173006, and Grant 51877058; and in part by the Key Projects of Science and Technology Plan of Zhejiang Province under Grant 2021C01144 and Grant 2019C04018. 46 0 0 8 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. 10.1109/TCYB.2022.3155734 0.0 MAR 2022 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 0F3WG 35349465.0 2023-03-23 WOS:000777292700001 0
J Sass, HM Sass, Hans-Martin The Coronavirus also Attacks Political and Corporate Bodies INTERNATIONAL JOURNAL OF CHINESE & COMPARATIVE PHILOSOPHY OF MEDICINE English Article Bioethics; Cultures; Corporations; Disease; Domestic Circulation; Environment; Gross Happiness Product; Health; Leviathan; Localism; Pandemic; Politics; Risk; Tools; Virus La Peste (1947) by Albert Camus relates the story of a cholera plague that not only killed people in a town but also devastated the town's political and business bodies. How is the COVID-19 pandemic harming not only us, our friends, and our families, but also the integrated interactions of our bodies with technologies, corporations, cultures, and politics? How will we get water, food, and information if we have no electricity, due to either the COVID-19 disaster or immoral people? How will we find new solid forms of living together in health, peace, and liberty? I pose the following five questions. (1) Will better domestic circulation and gross happiness products offer new opportunities for stable and healthy political bodies? (2) Will the Belt-and-Road Initiative have healthy solutions for all bodies involved? (3) Will smaller political and corporate bodies be more robust and healthier, and how can we grow them? (4) Will artificial intelligence build strong organs in future political bodies, or will they disintegrate and destroy them? (5) Will integrated bioethics and biopolitics find other solutions? [Sass, Hans-Martin] Ruhr Univ, Philosophy, Bochum, Germany; [Sass, Hans-Martin] Georgetown Univ, Washington, DC 20057 USA; [Sass, Hans-Martin] Renmin Univ, Beijing, Peoples R China; [Sass, Hans-Martin] Peking Union Coll, Beijing, Peoples R China Ruhr University Bochum; Georgetown University; Renmin University of China Sass, HM (corresponding author), Ruhr Univ, Philosophy, Bochum, Germany.;Sass, HM (corresponding author), Georgetown Univ, Washington, DC 20057 USA.;Sass, HM (corresponding author), Renmin Univ, Beijing, Peoples R China.;Sass, HM (corresponding author), Peking Union Coll, Beijing, Peoples R China. 36 8 8 0 8 GLOBAL SCHOLARLY PUBLICATIONS NEW YORK 220 MADISON AVE, STE 11-G, NEW YORK, NY 10016 USA 1386-6354 INT J CHIN COMP PHIL Int. J. Chin. Comp. Phil. Med. 2020.0 18 2 7 37 2 31 History & Philosophy Of Science Emerging Sources Citation Index (ESCI) History & Philosophy of Science PI5JG 2023-03-23 WOS:000601126400002 0
C Lyu, Y; Vosselman, G; Xia, GS; Yang, MY IEEE Lyu, Ye; Vosselman, George; Xia, Gui-Song; Yang, Michael Ying LIP: Learning Instance Propagation for Video Object Segmentation 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) IEEE International Conference on Computer Vision Workshops English Proceedings Paper IEEE/CVF International Conference on Computer Vision (ICCV) OCT 27-NOV 02, 2019 Seoul, SOUTH KOREA IEEE,IEEE Comp Soc,CVF In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we propose a single end-to-end trainable deep neural network, convolutional gated recurrent Mask-RCNN, for tackling the semi-supervised VOS task. We take advantage of both the instance segmentation network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS task. The instance segmentation network predicts masks for instances, while the visual memory module learns to selectively propagate information for multiple instances simultaneously, which handles the appearance change, the variation of scale and pose and the occlusions between objects. After offline and online training under purely instance segmentation losses, our approach is able to achieve satisfactory results without any post-processing or synthetic video data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017 dataset have demonstrated the effectiveness of our method for video object segmentation task. [Lyu, Ye; Vosselman, George; Yang, Michael Ying] Univ Twente, Enschede, Netherlands; [Xia, Gui-Song] Wuhan Univ, Wuhan, Peoples R China University of Twente; Wuhan University Lyu, Y (corresponding author), Univ Twente, Enschede, Netherlands. y.lyu@utwente.nl; george.vosselman@utwente.nl; guisong.xia@whu.edu.cn; michael.yang@utwente.nl Xia, Gui-Song/HII-9232-2022; Vosselman, George/D-3985-2009; Yang, Michael/AAC-6698-2019 Xia, Gui-Song/0000-0001-7660-6090; Vosselman, George/0000-0001-8813-8028; Yang, Michael/0000-0002-0649-9987 56 0 0 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2473-9936 978-1-7281-5023-9 IEEE INT CONF COMP V 2019.0 2739 2748 10.1109/ICCVW.2019.00335 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BP4UH Green Published, Green Submitted 2023-03-23 WOS:000554591602097 0
C Jia, SD; Xu, XY; Pang, YS; Yan, GW IEEE Jia, Songda; Xu, Xinying; Pang, Yusong; Yan, Gaowei Similarity Measurement Based on Cloud Models for Time Series Prediction PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) Chinese Control and Decision Conference English Proceedings Paper 28th Chinese Control and Decision Conference MAY 28-30, 2016 Yinchuan, PEOPLES R CHINA NE Univ, State Key Lab Synthet Automat Proc Ind,IEEE Ind Elect Chapter,Beifang Univ Nationalities,IEEE Control Syst Soc,Syst Engn Soc China,Chinese Assoc Artificial Intelligence,Chinese Assoc Artificial Intelligence, Tech Comm Control Theory time series prediction; similarity measure; cloud model; neural network Time series prediction has been extensively used for decision-making in many areas such as economics, engineering and medicine. And the useful data can be excavated by similarity measure of time series from a mass of historical data for predicting. The collected data from the real world is often uncertain, and the cloud model can be a good solution for the problem of uncertainty. This paper proposes a method based on the similarity degree of cloud model and combines it with back propagation network for prediction. In addition to the sequence itself, the trend of the sequence is used as another index of similarity. The neighbour set of query sequence from the training set is selected by similarity measure. Based on the neighbour set, a back propagation network is trained and used for prediction. Experimental results from the six time series show that the proposed method obtains better prediction accuracies than the comparative methods, which reveal its effectiveness. [Jia, Songda; Xu, Xinying; Yan, Gaowei] Taiyuan Univ Technol, Coll Informat Engn, Taiyuan 030024, Peoples R China; [Pang, Yusong] Delft Univ Technol, Sect Transport Engn & Logist, NL-2628 CD Delft, Netherlands Taiyuan University of Technology; Delft University of Technology Jia, SD (corresponding author), Taiyuan Univ Technol, Coll Informat Engn, Taiyuan 030024, Peoples R China. jiasongda0226@link.tyut.edu.cn; xuxinying@tyut.edu.cn; Y.Pang@tudelft.nl; yangaowei@tyut.edu.cn 17 0 1 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1948-9439 978-1-4673-9714-8 CHIN CONT DECIS CONF 2016.0 5138 5142 5 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BF6JW 2023-03-23 WOS:000383222305073 0
C Liu, LH; Lin, WY; Wu, LH; Yu, Y; Yang, MY Hua, G; Jegou, H Liu, Lihang; Lin, Weiyao; Wu, Lisheng; Yu, Yong; Yang, Michael Ying Unsupervised Deep Domain Adaptation for Pedestrian Detection COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II Lecture Notes in Computer Science English Proceedings Paper 14th European Conference on Computer Vision (ECCV) OCT 08-16, 2016 Amsterdam, NETHERLANDS Unsupervised domain adaptation; Unsupervised regularizer; Deep neural network; People detection This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes. First, we utilize an iterative algorithm to iteratively select and auto-annotate positive pedestrian samples with high confidence as the training samples for the target domain. Meanwhile, we also reuse negative samples from the source domain to compensate for the imbalance between the amount of positive samples and negative samples. Second, based on the deep network we also design an unsupervised regularizer to mitigate influence from data noise. More specifically, we transform the last fully connected layer into two sub-layers - an element-wise multiply layer and a sum layer, and add the unsupervised regularizer to further improve the domain adaptation accuracy. In experiments for pedestrian detection, the proposed method boosts the recall value by nearly 30% while the precision stays almost the same. Furthermore, we perform our method on standard domain adaptation benchmarks on both supervised and unsupervised settings and also achieve state-of-the-art results. [Liu, Lihang; Lin, Weiyao; Wu, Lisheng; Yu, Yong] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Yang, Michael Ying] Univ Twente, ITC EOS, Enschede, Netherlands Shanghai Jiao Tong University; University of Twente Lin, WY (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. wylin@sjtu.edu.cn Yang, Michael/AAC-6698-2019 Yang, Michael/0000-0002-0649-9987; Lin, Weiyao/0000-0001-8307-7107 23 16 16 0 5 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-48881-3; 978-3-319-48880-6 LECT NOTES COMPUT SC 2016.0 9914 676 691 10.1007/978-3-319-48881-3_48 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG5HT Green Submitted 2023-03-23 WOS:000389501700048 0
J Xu, W; Murphy, F; Xu, X; Xing, WP Xu, Wei; Murphy, Finbarr; Xu, Xian; Xing, Wenpeng Dynamic communication and perception of cyber risk: Evidence from big data in media COMPUTERS IN HUMAN BEHAVIOR English Article Cyber risk; Risk communication; Text mining; Risk perception INFORMATION ACCESSIBILITY; SOCIAL AMPLIFICATION; NEWS; EMOTION; SENTIMENT; SECURITY; COVERAGE; SEARCH; AIDS; FEAR Cyber risk is consistently viewed as a threat to the proper function of commercial and societal activity. Regardless of whether this risk is real or perceived, understanding how societal communication and perception of it change over time has important implications for both regulatory authorities and insurers. This contribution analyzes Chinese media news over the years 2009-2018 to identify the dynamics of cyber risk sources and associated societal assessment. Taking the psychometric paradigm as its point of departure and applying combination of computational and statistical methods, we identify 34 sources of cyber risk. The actions of government turn out to have a significant impact on public attention to the different sources of cyber risk, an influence that has been neglected in past research. The dynamics of societal aversion against most cyber risk sources are found to present inverted-U shapes. Adaptation and learning effects are found to explain this dynamic. Another finding is that news sentiment has a strong correlation with cyber risk perception, an insight of importance for regulators and insurers. [Xu, Wei; Murphy, Finbarr] Univ Limerick, Kemmy Business Sch, Dept Accounting & Finance, Limerick, Ireland; [Xu, Wei; Xu, Xian] Fudan Univ, Sch Econ, Dept Insurance, Shanghai, Peoples R China; [Xing, Wenpeng] Hong Kong Baptist Univ, Fac Sci, Dept Comp Sci, Hong Kong, Peoples R China University of Limerick; Fudan University; Hong Kong Baptist University Xu, W (corresponding author), Univ Limerick, Kemmy Business Sch, Dept Accounting & Finance, Limerick, Ireland. Wei.Xu@ul.ie xing, wenpeng/0000-0001-5848-9417; , Wei/0000-0002-2321-6424 87 2 2 13 31 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0747-5632 1873-7692 COMPUT HUM BEHAV Comput. Hum. Behav. SEP 2021.0 122 106851 10.1016/j.chb.2021.106851 0.0 MAY 2021 14 Psychology, Multidisciplinary; Psychology, Experimental Social Science Citation Index (SSCI) Psychology SV3LB 2023-03-23 WOS:000663722900011 0
J Zhang, W; Qiao, TZ; Pang, YS; Yang, Y; Chen, H; Hao, GR Zhang, Wei; Qiao, Tiezhu; Pang, Yusong; Yang, Yi; Chen, Hong; Hao, Guirong A Novel Defect Diagnosis Method for Kyropoulos Process-Based Sapphire Growth IEEE SENSORS JOURNAL English Article CCD sensor; defect-diagnosing Siamese network; necking-down process; sapphire growth defects CONVOLUTIONAL NEURAL-NETWORK; CRYSTAL-GROWTH; RECOGNITION When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way. [Zhang, Wei; Qiao, Tiezhu; Yang, Yi] Taiyuan Univ Technol, Minist Educ, Key Lab Adv Transducers & Intelligent Control Sys, Taiyuan 030024, Peoples R China; [Zhang, Wei; Qiao, Tiezhu; Yang, Yi] Taiyuan Univ Technol, Coll Phys & Optoelect, Taiyuan 030024, Peoples R China; [Pang, Yusong] Delft Univ Technol, Sect Transport Engn & Logist, NL-2628 Delft, Netherlands; [Chen, Hong; Hao, Guirong] Shanxi Zhongjujingke Semicond Co Ltd, Jinzhong 030600, Peoples R China Taiyuan University of Technology; Taiyuan University of Technology; Delft University of Technology Zhang, W (corresponding author), Taiyuan Univ Technol, Minist Educ, Key Lab Adv Transducers & Intelligent Control Sys, Taiyuan 030024, Peoples R China. zw19930606@sina.com; qiaotiezhu@tyut.edu.cn; y.pang@tudelft.nl; yangyi01@tyut.edu.cn; 353881118@qq.com; 981946425@qq.com National Natural Science Foundation of China-Shanxi Coal-based LowCarbon Joint Fund [U1810121]; Key Research and Development Plan for Industrial Science and Technology, Jinzhong, Shanxi, China [201803D03111003]; High-End Foreign Expert Introduction Program [G20190204013] National Natural Science Foundation of China-Shanxi Coal-based LowCarbon Joint Fund; Key Research and Development Plan for Industrial Science and Technology, Jinzhong, Shanxi, China; High-End Foreign Expert Introduction Program This work was supported in part by the National Natural Science Foundation of China-Shanxi Coal-based LowCarbon Joint Fund under Grant U1810121, in part by the Key Research and Development Plan for Industrial Science and Technology, Jinzhong, Shanxi, China, under Grant 201803D03111003, and in part by the High-End Foreign Expert Introduction Program under Grant G20190204013. The associate editor coordinating the review of this article and approving it for publication was Dr. Amitava Chatterjee. 24 1 1 2 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. MAY 15 2020.0 20 10 5435 5441 10.1109/JSEN.2020.2969963 0.0 7 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics LH5SR Green Published 2023-03-23 WOS:000528845200039 0
J Wu, ZJ; Trigo, V Wu, Zijian; Trigo, Virginia Impact of information system integration on the healthcare management and medical services INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT English Review Information technology integration; data sharing; big data; healthcare services; clinic decision Medical care combined with information technology is a new trend and the completeness and consistency of information directly affect the efficiency and quality of healthcare services as well as the safety of diagnosis and treatment. The level of internet information technology is reflected in its realization degree of integration between information systems and processes. This concept is defined as Information Systems (IS) integration, the degree to which data and applications are shared and accessed over different communication networks. In order to fully realize IS integration, ensure security of the massive medical data as well as efficient, smooth and real-time sharing, and to realize scientific analysis and processing, all the technologies including internet technology, cloud computing, Internet of Things, Data Analytics, and Blockchain should be utilized. This paper reviews the previous studies in this field and puts forward the related concepts. With the analysis of the literature on IS Integration, we find that the development of IS Integration of healthcare management and medical services is worthy of advocacy and promotion although there are still different obstacles to be overcome. Opportunities and challenges coexist in the development of IS integration. [Wu, Zijian] Guangzhou Med Univ, Clin Res Management Dept, Canc Ctr, Yuexiu Dist, Peoples R China; [Trigo, Virginia] Inst Univ Lisboa ISCTEIUL, Business Sch, Lisbon, Portugal Guangzhou Medical University Wu, ZJ (corresponding author), Guangzhou Med Univ, Canc Ctr, 78 Hengzhigang Rd, Guangzhou 510095, Peoples R China. ngken@126.com Wu, Zijian/GQO-9782-2022 Wu, Zijian/0000-0001-5304-1189 42 0 0 1 17 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 2047-9700 2047-9719 INT J HEALTHCARE MAN Int. J. Healthcare Manag. OCT 2 2021.0 14 4 1348 1356 10.1080/20479700.2020.1760015 0.0 MAY 2020 9 Health Policy & Services Emerging Sources Citation Index (ESCI) Health Care Sciences & Services WN9ND 2023-03-23 WOS:000533867200001 0
J Stephansen, JB; Olesen, AN; Olsen, M; Ambati, A; Leary, EB; Moore, HE; Carrillo, O; Lin, L; Han, F; Yan, H; Sun, YL; Dauvilliers, Y; Scholz, S; Barateau, L; Hogl, B; Stefani, A; Hong, SC; Kim, TW; Pizza, F; Plazzi, G; Vandi, S; Antelmi, E; Perrin, D; Kuna, ST; Schweitzer, PK; Kushida, C; Peppard, PE; Sorensen, HBD; Jennum, P; Mignot, E Stephansen, Jens B.; Olesen, Alexander N.; Olsen, Mads; Ambati, Aditya; Leary, Eileen B.; Moore, Hyatt E.; Carrillo, Oscar; Lin, Ling; Han, Fang; Yan, Han; Sun, Yun L.; Dauvilliers, Yves; Scholz, Sabine; Barateau, Lucie; Hogl, Birgit; Stefani, Ambra; Hong, Seung Chul; Kim, Tae Won; Pizza, Fabio; Plazzi, Giuseppe; Vandi, Stefano; Antelmi, Elena; Perrin, Dimitri; Kuna, Samuel T.; Schweitzer, Paula K.; Kushida, Clete; Peppard, Paul E.; Sorensen, Helge B. D.; Jennum, Poul; Mignot, Emmanuel Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy NATURE COMMUNICATIONS English Article DIABETIC-RETINOPATHY; AMERICAN ACADEMY; SLOW-WAVE; CLASSIFICATION; RELIABILITY; VALIDATION; CATAPLEXY; RECHTSCHAFFEN; TRANSITIONS; PREDICTORS Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies. [Stephansen, Jens B.; Olesen, Alexander N.; Olsen, Mads; Ambati, Aditya; Leary, Eileen B.; Moore, Hyatt E.; Carrillo, Oscar; Lin, Ling; Peppard, Paul E.; Mignot, Emmanuel] Stanford Univ, Ctr Sleep Sci & Med, Stanford, CA 94304 USA; [Stephansen, Jens B.; Olesen, Alexander N.; Olsen, Mads; Sorensen, Helge B. D.] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark; [Olesen, Alexander N.; Olsen, Mads; Jennum, Poul] Rigshosp, Danish Ctr Sleep Med, DK-2600 Glostrup, Denmark; [Han, Fang; Yan, Han; Sun, Yun L.] Peking Univ Peoples Hosp, Dept Pulm Med, Beijing 100044, Peoples R China; [Dauvilliers, Yves; Scholz, Sabine; Barateau, Lucie] CHU Montpellier, Sleep Wake Disorders Ctr, Dept Neurol, Gui de Chauliac Hosp, F-34295 Montpellier, France; [Dauvilliers, Yves; Scholz, Sabine; Barateau, Lucie] Univ Montpellier I, INSERM, U1061, F-34090 Montpellier, France; [Hogl, Birgit; Stefani, Ambra] Innsbruck Med Univ, Dept Neurol, A-6020 Innsbruck, Austria; [Hong, Seung Chul; Kim, Tae Won] Catholic Univ Korea, Dept Psychiat, St Vincents Hosp, Seoul 16247, South Korea; [Pizza, Fabio; Plazzi, Giuseppe; Vandi, Stefano; Antelmi, Elena] Univ Bologna, Dept Biomed & Neuromotor Sci, I-40123 Bologna, Italy; [Pizza, Fabio; Plazzi, Giuseppe; Vandi, Stefano; Antelmi, Elena] IRCCS Ist Sci Neurol Bologna, I-40139 Bologna, Italy; [Perrin, Dimitri] Queensland Univ Technol, Sch Elect Engn & Comp Sci, Brisbane, Qld 4001, Australia; [Kuna, Samuel T.] Univ Penn, Dept Med, Philadelphia, PA 19104 USA; [Kuna, Samuel T.] Univ Penn, Ctr Sleep & Circadian Neurobiol, Philadelphia, PA 19104 USA; [Schweitzer, Paula K.] St Lukes Hosp, Sleep Med & Res Ctr, Chesterfield, MO 63017 USA; [Peppard, Paul E.] Univ Wisconsin, Dept Populat Hlth Sci, Madison, WI 53726 USA Stanford University; Technical University of Denmark; Rigshospitalet; Peking University; Universite de Montpellier; CHU de Montpellier; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Montpellier; Medical University of Innsbruck; Catholic University of Korea; University of Bologna; IRCCS Istituto delle Scienze Neurologiche di Bologna (ISNB); Queensland University of Technology (QUT); University of Pennsylvania; University of Pennsylvania; Pennsylvania Medicine; Saint Luke's Hospital - Missouri; University of Wisconsin System; University of Wisconsin Madison Mignot, E (corresponding author), Stanford Univ, Ctr Sleep Sci & Med, Stanford, CA 94304 USA. mignot@stanford.edu Stefani, Ambra/AAK-2172-2020; Olsen, Mads/HJG-6556-2022; Plazzi, Giuseppe/AAB-9627-2020; Pizza, Fabio/AAC-1794-2020; Perrin, Dimitri/H-8630-2013; Antelmi, Elena/K-3252-2018; Olesen, Alexander Neergaard/P-4929-2019; Ambati, Aditya/K-4095-2019; Sorensen, Helge/P-8223-2019; Leary, Eileen B/AAX-2519-2021; Vandi, Stefano/AAC-1709-2020 Stefani, Ambra/0000-0003-4259-8824; Olsen, Mads/0000-0002-9071-6760; Perrin, Dimitri/0000-0002-4007-5256; Olesen, Alexander Neergaard/0000-0001-6881-9766; Ambati, Aditya/0000-0001-8462-3552; Sorensen, Helge/0000-0001-5716-7600; Hogl, Birgit/0000-0003-1894-7641; Barateau, Lucie/0000-0002-6620-5127; Leary, Eileen/0000-0002-7389-6384; Dauvilliers, yves/0000-0003-0683-6506; Vandi, Stefano/0000-0003-4217-3471; Antelmi, Elena/0000-0003-1739-6139 Jazz Pharmaceuticals; NIH [R01HL62252]; Ministry of Science and Technology [2015CB856405]; National Foundation of Science of China [81420108002, 81670087]; Lundbeck Foundation; Center for Healthy Aging, University of Copenhagen; Klarman Family Foundation; Augustinus Foundation; Otto Monsted Foundation; Stibo Foundation; Vera & Carl Johan Michaelsens Foundation; Knud Hojgaards Foundation; Reinholdt W. Jorck and Hustrus Foundation; Technical University of Denmark; H. Lundbeck A/S Jazz Pharmaceuticals(Jazz Pharmaceuticals); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Ministry of Science and Technology(Ministry of Science, ICT & Future Planning, Republic of Korea); National Foundation of Science of China; Lundbeck Foundation(Lundbeckfonden); Center for Healthy Aging, University of Copenhagen; Klarman Family Foundation; Augustinus Foundation; Otto Monsted Foundation; Stibo Foundation; Vera & Carl Johan Michaelsens Foundation; Knud Hojgaards Foundation; Reinholdt W. Jorck and Hustrus Foundation; Technical University of Denmark; H. Lundbeck A/S(Lundbeck Corporation) This research was mostly supported by a grant from Jazz Pharmaceuticals to E.M. Additional funding came from: NIH grant R01HL62252 (to P.E.P.); Ministry of Science and Technology 2015CB856405 and National Foundation of Science of China 81420108002,81670087 (to F.H.); H. Lundbeck A/S, Lundbeck Foundation, Technical University of Denmark and Center for Healthy Aging, University of Copenhagen (to P.J. and H.B.D.S). Additional support was provided by the Klarman Family, Otto Monsted, Stibo, Vera & Carl Johan Michaelsens, Knud Hojgaards, Reinholdt W. Jorck and Hustrus and Augustinus Foundations (to A.N.O.). We thank Briana Gomez for helping on performing the last corrections on this manuscript. 75 122 124 2 24 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. DEC 6 2018.0 9 5229 10.1038/s41467-018-07229-3 0.0 15 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics HD1PL 30523329.0 Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000452283400001 0
C Shi, LN; Zhang, X; Wang, WX; Zhang, Y; Wang, Z; Vladimirescu, A; Wang, JT IEEE Shi, Lina; Zhang, Xun; Wang, Wenxiao; Zhang, Yue; Wang, Zhan; Vladimirescu, Andrei; Wang, Jintao PAPR reduction based on deep autoencoder for VLC DCO-OFDM system 2019 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB) IEEE International Symposium on Broadband Multimedia Systems and Broadcasting English Proceedings Paper IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) JUN 05-07, 2019 SOUTH KOREA IEEE,IEEE Broadcast Technol Soc,Korean Inst Broadcast & Media Engineers,Elect & Telecommunicat Res Inst,Sungkyunkwan Univ, Inst Informat & Commun Technol VLC; DCO-OFDM; autoencoder; deep learning; PAPR NETWORKS; SCHEME DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is widely used in visible light communication (VLC) systems to reduce the effects of intersymbol interference (ISI) and to achieve high speed data transmission. However, DCO-OFDM VLC systems suffer from high peak-to-average power ratio (PAPR) issue which causes serious degradation of system performance. In this paper, a novel deep autoencoder-based PAPR reduction scheme for VLC DCO-OFDM is proposed. The autoencoder could adaptively adjust the constellation symbols in order to minimize the PAPR while maintaining an acceptable bit error rate (BER) for VLC transmission. The simulation results show that PAPR is reduced to 6.5dB by comparing with conventional methods. [Shi, Lina; Zhang, Xun; Wang, Wenxiao; Wang, Zhan; Vladimirescu, Andrei] Inst Super Elect Paris, Paris, France; [Zhang, Yue] Univ Leicester, Dept Engn, Leicester, Leics, England; [Zhang, Yue; Wang, Jintao] Tsinghua Univ, Deparlment Elect Engn, Beijing, Peoples R China University of Leicester; Tsinghua University Zhang, X (corresponding author), Inst Super Elect Paris, Paris, France. lina.shi@isep.fr zhang, xun/AAI-8012-2020 zhang, xun/0000-0002-8501-1969 National Key R&D Program of China [2017YFE011230]; EU Horizon 2020 program towards the Internet of Radio-Light project [H2020-ICT 761992] National Key R&D Program of China; EU Horizon 2020 program towards the Internet of Radio-Light project The authors gratefully acknowledge the financial support of the National Key R&D Program of China (Grant No.2017YFE011230) and the EU Horizon 2020 program towards the Internet of Radio-Light project H2020-ICT 761992. 28 0 0 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2155-5044 978-1-7281-2150-5 IEEE INT SYM BROADB 2019.0 4 Computer Science, Information Systems; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BS4XX 2023-03-23 WOS:000723995900036 0
J Tao, Y; Xiong, ST; Conway, SJ; Muller, JP; Guimpier, A; Fawdon, P; Thomas, N; Cremonese, G Tao, Yu; Xiong, Siting; Conway, Susan J.; Muller, Jan-Peter; Guimpier, Anthony; Fawdon, Peter; Thomas, Nicolas; Cremonese, Gabriele Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets REMOTE SENSING English Article DTM; digital terrain model; deep learning; 3D mapping; 3D reconstruction; real-time 3D; TGO; CaSSIS; Oxia Planum; ExoMars; high-resolution 3D; Mars MARTIAN SURFACE; MARS The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express's High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency's Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest. [Tao, Yu; Muller, Jan-Peter] Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Holmbury St Mary, Surrey RH5 6NT, England; [Xiong, Siting] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China; [Conway, Susan J.; Guimpier, Anthony] Univ Nantes, Lab Planetol & Geodynam, CNRS, UMR 6112, F-44300 Nantes, France; [Fawdon, Peter] Open Univ, Sch Phys Sci, Walton Hall, Milton Keynes MK7 6AA, Bucks, England; [Thomas, Nicolas] Univ Bern, Phys Inst, Sidlerstr 5, CH-3012 Bern, Switzerland; [Cremonese, Gabriele] Osserv Astron Padova, INAF, I-35122 Padua, Italy University of London; University College London; Shenzhen University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); Nantes Universite; Open University - UK; University of Bern; Istituto Nazionale Astrofisica (INAF); University of Padua Tao, Y (corresponding author), Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Holmbury St Mary, Surrey RH5 6NT, England. yu.tao@ucl.ac.uk; xiongsiting@szu.edu.cn; susan.conway@univ-nantes.fr; j.muller@ucl.ac.uk; anthony.guimpier@univ-nantes.fr; peter.fawdon@open.ac.uk; nicolas.thomas@space.unibe.ch; gabriele.cremonese@inaf.it Thomas, Nicolas/G-4693-2015 Thomas, Nicolas/0000-0002-0146-0071; Muller, Jan-Peter/0000-0002-5077-3736; Tao, Yu/0000-0001-9170-6655 UKSA Aurora programme (2018-2021) [ST/S001891/1]; STFC MSSL Consolidated Grant [ST/K000977/1]; Shenzhen Scientific Research and Development Funding Programme [JCYJ20190808120005713]; China Postdoctoral Science Foundation [2019M663073]; French Space Agency CNES; UK Space Agency [ST/L006456/1, ST/R002355/1, ST/V002678/1]; UKSA [ST/R001413/1] UKSA Aurora programme (2018-2021); STFC MSSL Consolidated Grant(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Shenzhen Scientific Research and Development Funding Programme; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); French Space Agency CNES(Centre National D'etudes Spatiales); UK Space Agency; UKSA The research leading to these results is receiving funding from the UKSA Aurora programme (2018-2021) under grant no. ST/S001891/1 as well as partial funding from the STFC MSSL Consolidated Grant ST/K000977/1. S.X. has received funding from the Shenzhen Scientific Research and Development Funding Programme (grant No. JCYJ20190808120005713) and China Postdoctoral Science Foundation (grant No. 2019M663073). S.J.C. is grateful to the French Space Agency CNES for supporting her CaSSIS and HiRISE related work. The CTX stereo single-strip DTM processing was carried out at the Natural History Museum, London, supported by UK Space Agency grants ST/L006456/1, ST/R002355/1, ST/V002678/1. P.F. is grateful to the UKSA grant ST/R001413/1 for supporting his CTX related work. 54 8 8 3 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13 15 2877 10.3390/rs13152877 0.0 27 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology TW1GS Green Published, gold, Green Accepted 2023-03-23 WOS:000682158500001 0
J Zhang, JH; Li, C; Kosov, S; Grzegorzek, M; Shirahama, K; Jiang, T; Sun, CH; Li, ZH; Li, H Zhang, Jinghua; Li, Chen; Kosov, Sergey; Grzegorzek, Marcin; Shirahama, Kimiaki; Jiang, Tao; Sun, Changhao; Li, Zihan; Li, Hong LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation * PATTERN RECOGNITION English Article Environmental miroorganisms; Image segmentation; Deep convolutional neural networks; Low-cost In this paper, we propose a novel Low-cost U-Net (LCU-Net) for the Environmental Microorganism (EM) image segmentation task to assist microbiologists in detecting and identifying EMs more effectively. The LCU-Net is an improved Convolutional Neural Network (CNN) based on U-Net, Inception, and concatenate operations. It addresses the limitation of single receptive field setting and the relatively high memory cost of U-Net. Experimental results show the effectiveness and potential of the proposed LCU-Net in the practical EM image segmentation field. (c) 2021 Elsevier Ltd. All rights reserved. [Zhang, Jinghua; Li, Chen; Sun, Changhao; Li, Zihan; Li, Hong] Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang, Peoples R China; [Kosov, Sergey] Jacobs Univ Bremen, Fac Data Engn, Bremen, Germany; [Grzegorzek, Marcin] Univ Lubeck, Inst Med Informat, Lubeck, Germany; [Shirahama, Kimiaki] Kindai Univ, Fac Sci & Engn, Higashiosaka, Osaka, Japan; [Jiang, Tao] Chengdu Univ Informat Technol, Control Engn Coll, Chengdu, Peoples R China; [Sun, Changhao] Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Peoples R China Northeastern University - China; Jacobs University; University of Lubeck; Kindai University (Kinki University); Chengdu University of Information Technology; Chinese Academy of Sciences; Shenyang Institute of Automation, CAS Li, C (corresponding author), Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang, Peoples R China. lichen201096@hotmail.com SUN, CHANG/GXM-3680-2022 Zhang, Jinghua/0000-0003-3303-4752; Sun, Changhao/0000-0001-9087-0349 National Natural Science Foundation of China [61806047] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China (No. 61806047). 58 48 49 7 38 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUL 2021.0 115 107885 10.1016/j.patcog.2021.107885 0.0 MAR 2021 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering RM6CK 2023-03-23 WOS:000639745600011 0
C Martinez, CM; Zhang, FH; Clarke, D; Hinz, G; Cao, DP IEEE Martinez, Clara Marina; Zhang, Feihu; Clarke, Daniel; Hinz, Gereon; Cao, Dongpu Feature Uncertainty Estimation in Sensor Fusion Applied to Autonomous Vehicle Location 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) English Proceedings Paper 20th International Conference on Information Fusion (Fusion) JUL 10-13, 2017 Xian, PEOPLES R CHINA Xian Jiaotong Univ,Int Soc Informat Fus,SATPRO,CEEC,China Gezhouba Grp No 3 Engn Co Ltd,Minist Educ, Key Lab Informat Fus Technol,Hangzhou Dianzi Univ,China Elect Technol Grp Corp, Sci & Technol Informat Syst Engn Lab, 28 Res Inst,China Elect Technol Grp Corp, 10 Res Inst,Wuhan Digital Engn Inst,Jiangsu Automat Res Inst,CNGC N Automat Control Technol Inst ALGORITHM Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly from data. This technique is particularly applied to vehicle location through odometry measurements, vehicle speed and orientation, to estimate the location uncertainty at any point along the trajectory. Neural networks are shown to be a suitable modeling technique, presenting good generalization capability and robust results. [Martinez, Clara Marina; Cao, Dongpu] Cranfield Univ, Adv Vehicle Engn Ctr, Cranfield MK43 0AL, Beds, England; [Zhang, Feihu] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian, Shaanxi, Peoples R China; [Clarke, Daniel] Cogsense Technol Ltd, Stanmore, Greater London, England; [Hinz, Gereon] Institut Tech Univ Munchen, Fortiss GmbH, Guerickestr 25, D-80805 Munich, Germany Cranfield University; Northwestern Polytechnical University; fortiss; Technical University of Munich Martinez, CM (corresponding author), Cranfield Univ, Adv Vehicle Engn Ctr, Cranfield MK43 0AL, Beds, England. c.m.marina@cranfield.ac.uk; feihn.zhang@nwpu.pu.edu.cn; Daniel.clarke@cogsense.co.uk; hintz@fortiss.org; d.cao@cranfield.ac.uk Cao, Dongpu/0000-0003-2541-5272 23 2 3 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-0-9964-5270-0 2017.0 256 262 7 Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BI3FN 2023-03-23 WOS:000410938300038 0
J Zheng, YJ; Liu, SC; Du, Q; Zhao, H; Tong, XH; Dalponte, M Zheng, Yongjie; Liu, Sicong; Du, Qian; Zhao, Hui; Tong, Xiaohua; Dalponte, Michele A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Image classification; Feature extraction; Logic gates; Convolutional neural networks; Satellites; Convolution; Spatial resolution; Convolutional neural network (CNN); deep feature fusion; land use; land cover (LULC) classification; long short term memory (LSTM); multitemporal images LAND-COVER CLASSIFICATION High-resolution (HR) satellite images, due to the technical constraints on spectral and spatial resolutions, usually contain only several broad spectral bands but with a very high spatial resolution. This provides rich spatial details of the objects on the Earth surface, while their spectral discrimination is relatively low. Recently, the increase of the satellite revisit times made it possible to acquire more frequent data coverage for finer classification. In this article, we proposed a novel multitemporal deep fusion network (MDFN) for short-term multitemporal HR images classification. Specifically, a two-branch structure of MDFN is designed, which includes a long short-term memory (LSTM) and a convolutional neural network (CNN). The LSTM branch is mainly used to learn the joint expression of different temporal-spectral features. For the CNN branch, the three-dimensional (3-D) convolution is firstly applied along the temporal and spectral dimensions to jointly learn the temporal-spatial and spectral-spatial information, respectively, and then the 2-D convolution is performed along the spatial dimension to further extract the spatial context information. Finally, features generated from the two different branches are fused to obtain the discriminative high-level semantic information for classification. Experimental results carried on two real multitemporal HR remote sensing datasets demonstrate that the proposed MDFN provides better classification performance over the state-of-the-art methods, and it also shows the potentiality to use short-term multitemporal HR images for more accurate land use/land cover mapping. [Zheng, Yongjie; Liu, Sicong; Zhao, Hui; Tong, Xiaohua] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Dalponte, Michele] Res & Innovat Ctr Fdn E Mach, Dept Sustainable Agroecosyst & Bioresources, I-38010 San Michele All Adige, Italy Tongji University; Mississippi State University Liu, SC (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. yongjie@tongji.edu.cn; sicong.liu@tongji.edu.cn; du@ece.msstate.edu; zhaohui@tongji.edu.cn; hui@tongji.edu.cn; michele.dalponte@fmach.it Liu, Sicong/J-7094-2013; Du, Qian/AAB-8840-2022; Zheng, yongjie/HKO-1755-2023; du, qian/GYJ-7090-2022; Dalponte, Michele/E-5117-2011 Liu, Sicong/0000-0003-1612-4844; Zheng, yongjie/0000-0002-7030-6713; Dalponte, Michele/0000-0001-9850-8985 National Key R&D Program of China [2018YFB0505000]; Natural Science Foundation of China [42071324]; Shanghai RisingStar Program [21QA1409100] National Key R&D Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai RisingStar Program This work was supported in part by the National Key R&D Program of China underGrant 2018YFB0505000, in part by theNatural Science Foundation of China under Grant 42071324, and in part by the Shanghai RisingStar Program under Grant 21QA1409100. 41 9 10 3 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14 10691 10704 10.1109/JSTARS.2021.3119942 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology WR0MR gold 2023-03-23 WOS:000714204000008 0
J Akbar, S; Li, RX; Waqas, M; Jan, AV Akbar, Saeed; Li, Ruixuan; Waqas, Muhammad; Jan, Avais Server temperature prediction using deep neural networks to assist thermal-aware scheduling SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS English Article Data center; Resource allocation; Thermal-aware; Temperature prediction; Deep neural network ENERGY-EFFICIENT; RESOURCE-MANAGEMENT; DATA CENTERS; WORKLOAD MANAGEMENT; INFRASTRUCTURE; SIMULATION; TOOL Thermal-Aware (TA) scheduling is a primitive thermal-management tool to avoid hotspots and attain a better thermal profile inside data centers. However, TA scheduling depends on the accuracy of server temperature calculation for making reliable scheduling decisions. Existing TA simulation frameworks rely on the CRAC, RC, and thermodynamics model to calculate the temperature of a server. However, these models are not very efficient computationally while calculating the temperature of computing nodes. Hence, there is a need for an efficient and lightweight temperature prediction model to fill this gap. Moreover, existing TA allocation and migration schemes neglect the workload heterogeneity regarding execution time (length) of user tasks in batch workloads. Ignoring task heterogeneity may lead to higher ambient temperature on its neighboring servers- assigning a hotter job with a longer duration to a server with higher ambient effect results in significantly higher cooling expenses.Our contribution in this paper is twofold: (1) firstly, we design and train a deep neural network to predict the temperature of servers; our proposed DNN model achieves 96.11% prediction accuracy, and (2) secondly, we propose a TA algorithm for job allocation and migration. Specifically, we consider the length and thermal profile of user tasks and servers during allocation and migration. We compare the proposed strategy against existing TA scheduling designs such as TA Scheduling Algorithm (TASA) and TA Control Strategy (TACS) using simulations. Results demonstrate that the proposed TA approach significantly reduces the overall energy consumption by up to 12.03% and 8.28% compared to the TASA and TACS, respectively. [Akbar, Saeed] Zhejiang Normal Univ, Sch Comp Sci & Math, Jinhua 321004, Peoples R China; [Akbar, Saeed; Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China; [Waqas, Muhammad] Univ Ca Foscari Venezia, DAIS, Venice, Italy; [Jan, Avais] Georgia State Univ, Coll Arts & Sci, 38 Peachtree Ctr Ave, Atlanta, GA USA Zhejiang Normal University; Huazhong University of Science & Technology; Universita Ca Foscari Venezia; University System of Georgia; Georgia State University Akbar, S (corresponding author), Zhejiang Normal Univ, Sch Comp Sci & Math, Jinhua 321004, Peoples R China.;Akbar, S (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China. saedakbr@gmail.com Akbar, Saeed/0000-0002-7093-9318 46 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-5379 2210-5387 SUSTAIN COMPUT-INFOR Sust. Comput. DEC 2022.0 36 100809 10.1016/j.suscom.2022.100809 0.0 NOV 2022 12 Computer Science, Hardware & Architecture; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7B8KF 2023-03-23 WOS:000899375500003 0
C Tsai, CW; Pelov, A; Chiang, MC; Yang, CS; Hong, TP IEEE Tsai, Chun-Wei; Pelov, Alexander; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei A Brief Introduction to Classification for Smart Grid 2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 13-16, 2013 Manchester, ENGLAND IEEE,IEEE Comp Soc Smart grid; data mining; and machine learning METERS There is no doubt about the potentials of smart grid, for the traditional power grid has been kind of out of date, in terms of not only its infrastructure but also its restrictions on the way information is communicated. To provide better and smarter services via smart grid, the first thing we have to do is to make it more intelligent. Among the technologies that can be applied to smart grid to make it more intelligent, data mining will certainly play a vital role. This paper begins with a brief introduction to smart grid, followed by a discussion on the supervised learning (classification) and the unsupervised learning (clustering). Several open and possible research issues are then given to depict the future trends of smart grid. [Tsai, Chun-Wei] Chia Nan Univ Pharm & Sci, Dept Appl Informat & Multimedia, Tainan 71710, Taiwan; [Pelov, Alexander] TELECOM Bretagne, Networks Security & Multimedia, Plouzane, France; [Chiang, Ming-Chao] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan; [Yang, Chu-Sing] Natl Cheng Kung Univ, Dept Elect Engn, Tai An 70101, Shandong, Peoples R China; [Hong, Tzung-Pei] Natl Kaohsiung Univ, Dept Comp Sci & Informat Engn, Kaohsiung 81148, Taiwan Chia Nan University of Pharmacy & Science; IMT - Institut Mines-Telecom; IMT Atlantique; National Sun Yat Sen University; National Cheng Kung University; National University Kaohsiung Tsai, CW (corresponding author), Chia Nan Univ Pharm & Sci, Dept Appl Informat & Multimedia, Tainan 71710, Taiwan. cwtsai0807@gmail.com; alexander.pelov@telecom-bretagne.eu; mcchiang@cse.nsysu.edu.tw; csyang@ee.ncku.edu.tw; tphong@nuk.edu.tw Hong, Tzung-Pei/AEB-0613-2022; Tsai, Chun-Wei/R-6389-2019 Hong, Tzung-Pei/0000-0001-7305-6492 National Science Council of Taiwan, R.O.C [NSC102-2221-E-041-006, NSC99-2221-E-110-052, NSC102-2221-E-110-054, NSC102-2219-E-006-001]; Ministry of Education of Taiwan, R.O.C [D102-23015] National Science Council of Taiwan, R.O.C(Ministry of Science and Technology, Taiwan); Ministry of Education of Taiwan, R.O.C The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the National Science Council of Taiwan, R.O.C., under Contracts NSC102-2221-E-041-006, NSC99-2221-E-110-052, NSC102-2221-E-110-054, and NSC102-2219-E-006-001 and in part by the Ministry of Education of Taiwan, R.O.C., under Contract D102-23015. 34 3 3 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-4799-0652-9 IEEE SYS MAN CYBERN 2013.0 2905 2909 10.1109/SMC.2013.495 0.0 5 Computer Science, Cybernetics; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BA0SU 2023-03-23 WOS:000332201903006 0
J Islam, F; Ahmad, MN; Janjuhah, HT; Ullah, M; Islam, IU; Kontakiotis, G; Skilodimou, HD; Bathrellos, GD Islam, Fakhrul; Ahmad, Muhammad Nasar; Janjuhah, Hammad Tariq; Ullah, Matee; Islam, Ijaz Ul; Kontakiotis, George; Skilodimou, Hariklia D.; Bathrellos, George D. Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS-Based Models APPLIED SCIENCES-BASEL English Article soil erosion; GIS and RS-based models; Murree; Pakistan LANDSLIDE HAZARD; LOSS EQUATION; LAND-COVER; RISK; USLE; TOPOGRAPHY; CATCHMENT; HILLSLOPE; DISTRICT; WEIGHT Soil erosion is one of Pakistan's most serious environmental threats. This study used geospatial modelling to identify the distinct zones susceptible to soil erosion in Murree, Pakistan. Using a machine learning technique in the Google Earth engine (GEE) and Google Earth, we identified 1250 soil erosion events. The inventory (dependent variable) was separated into two datasets, one for training (70%) and one for testing (30%). Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were prepared in ArcGIS and considered as independent variables in the current research. GIS and RS-based models such as WOE, FR, and IV were used to assess the relationship between both variables and produce soil erosion susceptibility maps. Finally, the Area Under Curve (AUC) approach was used to confirm the research results. According to the validation data, the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. The present study's validation results show that the PRC for WOE, FR, and IV are 92%, 94%, and 90%, respectively. Based on the AUC validation approach, we determined that the FR model had the highest accuracy when compared to the other two techniques, the WOE and IV models. The current analysis and final susceptibility maps of soil erosion could be useful for decision-makers in the future to prevent soil erosion and its negative repercussions. [Islam, Fakhrul] Khushal Khan Khattak Univ, Dept Geol, Karak 27200, Pakistan; [Ahmad, Muhammad Nasar] Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430072, Peoples R China; [Janjuhah, Hammad Tariq] Shaheed Benazir Bhutto Univ, Dept Geol, Sheringal 18050, Pakistan; [Ullah, Matee] Fac Earth Sci Geog & Astron, A-1090 Vienna, Austria; [Islam, Ijaz Ul] Abdul Wali Khan Univ, Dept Comp Sci, Mardan 23200, Pakistan; [Kontakiotis, George] Natl & Kapodistrian Univ Athens, Fac Geol & Geoenvironm, Sch Earth Sci, Dept Hist Geol Paleontol, Athens 15784, Greece; [Skilodimou, Hariklia D.; Bathrellos, George D.] Univ Patras, Dept Geol, Patras 26504, Greece Wuhan University; Abdul Wali Khan University; National & Kapodistrian University of Athens; University of Patras Ahmad, MN (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430072, Peoples R China.;Janjuhah, HT (corresponding author), Shaheed Benazir Bhutto Univ, Dept Geol, Sheringal 18050, Pakistan. mnasarahmad@whu.edu.cn; hammad@sbbu.edu.pk Ahmad, Muhammad Nasar/CAG-5867-2022; Bathrellos, George/H-2717-2018 Ahmad, Muhammad Nasar/0000-0002-4468-8105; Kontakiotis, George/0000-0001-9371-6726; janjuhah, hammad Tariq/0000-0002-1522-5988; Bathrellos, George/0000-0001-6922-6639 67 0 0 4 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel DEC 2022.0 12 23 12211 10.3390/app122312211 0.0 18 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 6V7WB gold 2023-03-23 WOS:000895251900001 0
J Li, DW; Zhang, HQ; Li, TR; Bouras, A; Yu, X; Wang, T Li, Daiwei; Zhang, Haiqing; Li, Tianrui; Bouras, Abdelaziz; Yu, Xi; Wang, Tao Hybrid Missing Value Imputation Algorithms Using Fuzzy C-Means and Vaguely Quantified Rough Set IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Clustering algorithms; Machine learning algorithms; Approximation algorithms; Task analysis; Prediction algorithms; Vegetation; Classification algorithms; Fuzzy C-Means (FCM) clustering imputation; fuzzy membership relations; missing value imputation (MVI); nearest neighbor imputation; rough set REGRESSION; SELECTION; MACHINE; CLASSIFICATION; UNCERTAINTY; INTERVAL In real cases, missing values tend to contain meaningful information that should be acquired or should be analyzed before the incomplete dataset is used for machine learning tasks. In this work, two algorithms named jointly fuzzy C-Means and vaguely quantified nearest neighbor (VQNN) imputation (JFCM-VQNNI) and jointly fuzzy C-Means and fitted VQNN imputation (JFCM-FVQNNI) have been proposed by considering clustering conception and sufficient extraction of uncertain information. In the proposed JFCM-VQNNI and JFCM-FVQNNI algorithm, the missing value is regarded as a decision feature, and then, the prediction is generated for the objects that contain at least one missing value. Specially, as for JFCM-VQNNI algorithm, indistinguishable matrixes, tolerance relations, and fuzzy membership relations are adopted to identify the potential closest filled values based on corresponding similar objects and related clusters. On the basis of JFCM-VQNNI algorithm, JFCM-FVQNNI algorithm synthetic analyzes the fuzzy membership of the dependent features for instances with each cluster. In order to fill the missing values more accurately, JFCM-FVQNNI algorithm performs fuzzy decision membership adjustment in each object with respect to the related clusters by considering highly relevant decision attributes. The experiments have been carried out on five datasets. Based on the analysis of root-mean-square error, mean absolute error, comparison of imputation values with actual values, and classification accuracy results analysis, we can draw the conclusion that the proposed JFCM-FVQNNI and JFCM-VQNNI algorithms yields sufficient and reasonable imputation performance results by comparing with fuzzy C-Means parameter-based imputation algorithm and fuzzy C-Means rough parameter-based imputation algorithm. [Li, Daiwei; Li, Tianrui] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Li, Daiwei; Zhang, Haiqing] Chengdu Univ Informat Technol, Coll Software Engn, Chengdu 610225, Peoples R China; [Bouras, Abdelaziz] Qatar Univ, Comp Sci Dept, Doha 2731, Qatar; [Yu, Xi] Chengdu Univ, Coll Informat Sci & Technol, Chengdu 610106, Peoples R China; [Yu, Xi] Chengdu Univ, Key Lab Pattern Recognit & Intelligent Informat P, Inst Higher Educ Sichuan Prov, Chengdu 610106, Peoples R China; [Wang, Tao] Univ Jean Monnet St Etienne, Natl Inst Appl Sci Lyon, Decis & Informat Sci Prod Syst Lab, F-42300 Roanne, France Southwest Jiaotong University; Chengdu University of Information Technology; Qatar University; Chengdu University; Chengdu University; Institut National des Sciences Appliquees de Lyon - INSA Lyon Zhang, HQ (corresponding author), Chengdu Univ Informat Technol, Coll Software Engn, Chengdu 610225, Peoples R China. ldwcuit@cuit.edu.cn; haiqing_zhang_zhq@163.com; trli@swjtu.edu.cn; abdelaziz.bouras@qu.edu.qa; yuxi@cdu.edu.cn; tao.wang@univ-st-etienne.fr ; Li, Tianrui/F-4974-2019 zhang, haiqing/0000-0001-8881-361X; Wang, Tao/0000-0001-8100-6743; Daiwei, Li/0000-0001-8868-4440; Li, Tianrui/0000-0001-7780-104X National Key R&D Program of China [2019YFB2101802]; Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, through the High-End Talent Introduction Project From Abroad in 2020 [G20200236016]; Sichuan Science and Technology Program [2021YFH0107]; Building Skills 4.0 through University and Enterprise Collaboration (Erasmus+ Shyfte 4.0) Project (Erasmus+Programme) [598649-EPP-1-2018-1FR-EPPKA2-CBHE-JP] National Key R&D Program of China; Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, through the High-End Talent Introduction Project From Abroad in 2020; Sichuan Science and Technology Program; Building Skills 4.0 through University and Enterprise Collaboration (Erasmus+ Shyfte 4.0) Project (Erasmus+Programme) This work was supported in part by the National Key R&D Program of China under Grant 2019YFB2101802, in part by the Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, through the High-End Talent Introduction Project From Abroad in 2020 under Grant G20200236016, in part by Sichuan Science and Technology Program under Grant 2021YFH0107, and in part by Building Skills 4.0 through University and Enterprise Collaboration (Erasmus+ Shyfte 4.0) Project (Erasmus+Programme: 598649-EPP-1-2018-1FR-EPPKA2-CBHE-JP, http://shyfte.eu). 47 7 7 9 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. MAY 2022.0 30 5 1396 1408 10.1109/TFUZZ.2021.3058643 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 0Z1BV 2023-03-23 WOS:000790815800019 0
J Liu, CZ; Cichon, A; Krolczyk, G; Li, ZX Liu, Chengze; Cichon, Andrzej; Krolczyk, Grzegorz; Li, Zhixiong Technology development and commercial applications of industrial fault diagnosis system: a review INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Review Condition-based monitoring; Fault diagnosis system; Signal processing; Diagnostics and prognostics; Commercialization REMAINING USEFUL LIFE; EMPIRICAL MODE DECOMPOSITION; MISSION RELIABILITY EVALUATION; GAUSSIAN PROCESS REGRESSION; CONDITION-BASED MAINTENANCE; ARTIFICIAL NEURAL-NETWORK; LOCAL MEAN DECOMPOSITION; TIME FOURIER-TRANSFORM; OF-THE-ART; ROTATING MACHINERY Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed. [Liu, Chengze] Ocean Univ China, Sch Engn, Qingdao 266100, Peoples R China; [Cichon, Andrzej] Opole Univ Technol, Fac Elect Engn, PL-45758 Opole, Poland; [Krolczyk, Grzegorz; Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland Ocean University of China; Opole University of Technology; Opole University of Technology Li, ZX (corresponding author), Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland. zhixiong.li@ieee.org Krolczyk, Grzegorz/D-6709-2013 Krolczyk, Grzegorz/0000-0002-2967-1719 National Science Foundation of China (NSFC) [51979261]; Taishan Scholar of Shandong of China [tsqn201812025]; Narodowego Centrum Nauki of Poland [2020/37/K/ST8/02748] National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Taishan Scholar of Shandong of China; Narodowego Centrum Nauki of Poland This research is supported by the National Science Foundation of China (NSFC) under the grant no. 51979261, the Taishan Scholar of Shandong of China under the grant no. tsqn201812025), and Narodowego Centrum Nauki of Poland under the grant no. 2020/37/K/ST8/02748. 200 10 10 23 78 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. FEB 2022.0 118 11-12 3497 3529 10.1007/s00170-021-08047-6 0.0 OCT 2021 33 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering YP2VR hybrid 2023-03-23 WOS:000708378100005 0
J Dogan, RI; Kim, S; Chatr-aryamontri, A; Wei, CH; Comeau, DC; Antunes, R; Matos, S; Chen, QY; Elangovan, A; Panyam, NC; Verspoor, K; Liu, HF; Wang, YS; Liu, Z; Altinel, B; Husunbeyi, ZM; Ozgur, A; Fergadis, A; Wang, CK; Dai, HJ; Tran, T; Kavuluru, R; Luo, L; Steppi, A; Zhang, JF; Qu, JC; Lu, ZY Dogan, Rezarta Islamaj; Kim, Sun; Chatr-aryamontri, Andrew; Wei, Chih-Hsuan; Comeau, Donald C.; Antunes, Rui; Matos, Sergio; Chen, Qingyu; Elangovan, Aparna; Panyam, Nagesh C.; Verspoor, Karin; Liu, Hongfang; Wang, Yanshan; Liu, Zhuang; Altinel, Berna; Husunbeyi, Zehra Melce; Ozgur, Arzucan; Fergadis, Aris; Wang, Chen-Kai; Dai, Hong-Jie; Tran, Tung; Kavuluru, Ramakanth; Luo, Ling; Steppi, Albert; Zhang, Jinfeng; Qu, Jinchan; Lu, Zhiyong Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION English Article TEXT; INFORMATION; EXTRACTION; TMVAR The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct textmining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the textmining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-4/ [Dogan, Rezarta Islamaj; Kim, Sun; Wei, Chih-Hsuan; Comeau, Donald C.; Lu, Zhiyong] Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bethesda, MD 20894 USA; [Chatr-aryamontri, Andrew] Univ Montreal, Inst Res Immunol & Canc, Montreal, PQ H3T 1J4, Canada; [Antunes, Rui; Matos, Sergio] Univ Aveiro, Inst Elect & Informat Engn Aveiro IEETA, DETI, P-3810193 Aveiro, Portugal; [Chen, Qingyu; Elangovan, Aparna; Panyam, Nagesh C.; Verspoor, Karin] Univ Melbourne, Sch Comp & Informat Syst, Melbourne, Vic 3010, Australia; [Liu, Hongfang; Wang, Yanshan] Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA; [Liu, Zhuang] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China; [Altinel, Berna] Marmara Univ, Dept Comp Engn, TR-34722 Istanbul, Turkey; [Husunbeyi, Zehra Melce] Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey; [Fergadis, Aris] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15780, Greece; [Wang, Chen-Kai] Taipei Med Univ, Grad Inst Biomed Informat, Taipei 11031, Taiwan; [Dai, Hong-Jie] Natl Kaousiung Univ Sci & Technol, Dept Elect Engn, Kaohsiung 80778, Taiwan; [Tran, Tung] Univ Kentucky, Dept Comp Sci, Lexington, KY 40508 USA; [Kavuluru, Ramakanth] Univ Kentucky, Dept Internal Med, Div Biomed Informat, Lexington, KY 40536 USA; [Luo, Ling] Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Peoples R China; [Steppi, Albert; Zhang, Jinfeng; Qu, Jinchan] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM); Universite de Montreal; Universidade de Aveiro; University of Melbourne; Mayo Clinic; Dalian University of Technology; Marmara University; Bogazici University; National Technical University of Athens; Taipei Medical University; University of Kentucky; University of Kentucky; Dalian University of Technology; State University System of Florida; Florida State University Lu, ZY (corresponding author), Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bethesda, MD 20894 USA. zhiyong.lu@nih.gov Chen, Qingyu/Q-8343-2019; Luo, Ling/AAA-3476-2019; Matos, Sérgio/G-4941-2010; Ozgur, Arzucan/I-3147-2012; Hüsünbeyi, Zehra Melce/ABA-2284-2020; Altınel, Berna/AAG-7928-2019; Elangovan, Aparna/CAF-2609-2022; Verspoor, Karin/G-6034-2016; Antunes, Rui/E-9272-2017; Wang, Yanshan/H-4686-2018 Chen, Qingyu/0000-0002-6036-1516; Luo, Ling/0000-0002-5141-0259; Matos, Sérgio/0000-0003-1941-3983; Ozgur, Arzucan/0000-0001-8376-1056; Altınel, Berna/0000-0001-5544-0925; Elangovan, Aparna/0000-0003-3559-9917; Verspoor, Karin/0000-0002-8661-1544; Antunes, Rui/0000-0003-3533-8872; Wang, Yanshan/0000-0003-4433-7839; Islamaj, Rezarta/0000-0001-5651-1860; Wei, Chih-Hsuan/0000-0001-5094-7321 National Institutes of Health Intramural Research Program National Library of Medicine; National Institutes of Health Office of Research Infrastructure Programs [R01OD010929, R24OD011194]; NATIONAL LIBRARY OF MEDICINE [ZIALM091813] Funding Source: NIH RePORTER National Institutes of Health Intramural Research Program National Library of Medicine; National Institutes of Health Office of Research Infrastructure Programs; NATIONAL LIBRARY OF MEDICINE(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Library of Medicine (NLM)) National Institutes of Health Intramural Research Program National Library of Medicine; National Institutes of Health Office of Research Infrastructure Programs (R01OD010929 and R24OD011194 to A.C.-a.). 58 22 22 0 5 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1758-0463 DATABASE-OXFORD Database JAN 28 2019.0 bay147 10.1093/database/bay147 0.0 17 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology HL8FY 30689846.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000458978800001 0
J Gao, Y; Wang, SH; Guan, KY; Wolanin, A; You, LZ; Ju, WM; Zhang, YG Gao, Yun; Wang, Songhan; Guan, Kaiyu; Wolanin, Aleksandra; You, Liangzhi; Ju, Weimin; Zhang, Yongguang The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA REMOTE SENSING English Article sun-induced chlorophyll fluorescence; crop yield; soybean; corn; deep neural network SOLAR-INDUCED FLUORESCENCE; SENSED VEGETATION INDEXES; LAND-SURFACE PHENOLOGY; DEEP NEURAL-NETWORKS; CROP YIELD; STATISTICAL-MODELS; CLIMATE DATA; WHEAT YIELD; NDVI DATA; PHOTOSYNTHESIS Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data. [Gao, Yun; Wang, Songhan; Ju, Weimin; Zhang, Yongguang] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China; [Gao, Yun; Wang, Songhan; Ju, Weimin; Zhang, Yongguang] Nanjing Univ, Key Lab Land Satellite Remote Sensing Applicat, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Minist Nat Resources,Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China; [Gao, Yun; Wang, Songhan; Ju, Weimin; Zhang, Yongguang] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210023, Peoples R China; [Guan, Kaiyu] Univ Illinois, Coll Agr Consumer & Environm Sci, Urbana, IL 61801 USA; [Guan, Kaiyu] Univ Illinois, Natl Ctr Supercomp Applicat, Urbana, IL 61801 USA; [Wolanin, Aleksandra] Helmholtz Ctr, Sect Remote Sensing 1 4, GFZ German Res Ctr Geosci, D-14473 Potsdam, Germany; [You, Liangzhi] Int Food Policy Res Inst, Environm & Prod Technol Div EPTD, Washington, DC 20005 USA; [You, Liangzhi] Huazhong Agr Univ, Macro Agr Res Inst, Coll Econ & Management, Wuhan 430070, Peoples R China Nanjing University; Ministry of Natural Resources of the People's Republic of China; Nanjing University; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences; CGIAR; International Food Policy Research Institute (IFPRI); Huazhong Agricultural University Zhang, YG (corresponding author), Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China.;Zhang, YG (corresponding author), Nanjing Univ, Key Lab Land Satellite Remote Sensing Applicat, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Minist Nat Resources,Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China.;Zhang, YG (corresponding author), Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210023, Peoples R China. gaoyun@smail.nju.edu.cn; wangsonghan2@gmail.com; juweimin@nju.edu.cn; yongguang_zhang@nju.edu.cn OCO, OCO/GVS-4314-2022; Guan, Kaiyu/N-5772-2015 Guan, Kaiyu/0000-0002-3499-6382; Zhang, Yongguang/0000-0001-8286-300X DFG [41761134082]; Jiangsu Provincial Natural Science Fund for Distinguished Young Scholars of China [BK20170018]; NSFC [41761134082] DFG(German Research Foundation (DFG)); Jiangsu Provincial Natural Science Fund for Distinguished Young Scholars of China; NSFC(National Natural Science Foundation of China (NSFC)) This research was funded by International Cooperation and Exchange Programs between NSFC and DFG, grant number 41761134082, and Jiangsu Provincial Natural Science Fund for Distinguished Young Scholars of China, grant number BK20170018. 101 10 10 10 44 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. APR 2020.0 12 7 1111 10.3390/rs12071111 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology LU4EF Green Published, gold 2023-03-23 WOS:000537709600061 0
J Firouzkouhi, N; Amini, A; Nazari, M; Alkhatib, F; Bordbar, H; Cheng, C; Davvaz, B; Rashidi, M Firouzkouhi, Narjes; Amini, Abbas; Nazari, Marziyeh; Alkhatib, Fadi; Bordbar, Hashem; Cheng, Chun; Davvaz, Bijan; Rashidi, Maria Advanced artificial intelligence system by intuitionistic fuzzy (sic)-subring for automotive robotic manufacturing ARTIFICIAL INTELLIGENCE REVIEW English Article; Early Access Intelligent systems; Robotic manufacturing; Intuitionistic fuzzy set; Image and inverse image; Upper and lower bound level GAMMA; SETS In recent years, robotic engineering has been enriched with Artificial Intelligence (AI) technology, preparing the industries to enter the Industry 4.0 era. The powerful neoteric paradigm of AI can serve automotive industries (as one of the largest sectors in the world), to inevitably change their outdated manufacturing strategies. These industrial sectors are increasingly encountering mega data that inevitably carry uncertainty, for which the available methodologies are not capable to deal with that efficiently. To theoretically resolve this gap, a generalized intuitionistic fuzzy set (IFS) theory is proposed here as an efficient, fast, and flexible method. Based on the membership and non-membership degrees, multi-aspect gamma-systems is developed to model the complex real systems. Inspired by multi-attribute gamma-systems and IFS approach, a novel mathematical concept namely intuitionistic fuzzy gamma-subring (IF gamma R) method, is developed to establish an AI platform for robotic automotive manufacturing. Significant characteristics of IF gamma R are developed, including the overlapping of elements with IF gamma R property is IF gamma R, also image and inverse image of elements with IF gamma R property are IF gamma R under gamma-ring homomorphism. Additionally, the connection between upper and lower bound level cuts and image/inverse image property are parametrically discussed. With the effect of surjective homomorphism on upper and lower level cuts, there would be equivalent upper and lower level cuts of image/inverse image in IF gamma R environment. The developed notion of IF gamma I is obtained as the generalization of gamma-ideal under gamma-ring R along with the resultant fundamental properties of IF gamma I, where the overlapping/intersection family of IF gamma Is is proved to be IF gamma I. Also, the upper and lower bound level cuts of elements with IF gamma I property are gamma-ideals. Finally, the proposed IF gamma gamma R method is utilized for automotive AI systems (AAIS) by means of mathematical algebraic notions of gamma-ring, IFS, gamma-ring isomorphism, and upper and lower bound levels. The developed methodology is validated using real dataset of industrial robots in supply chain and then, the elements are characterized in terms of metric overall factory effectiveness. With a systematic pattern of gamma-ring structure, the IF gamma R model is accomplished on elements, and the intercomponent correspondence of AAIS is established with the gamma-ring isomorphism. Based on QC (quality criteria) and non-QC indexes, as the derivation of upper and lower bound level cuts, the analysis of parameters (robots) is simplified for the identification of effective and compatible components in AAIS. The generalized IFS-based method for complex systems has a potential to be used in different AI platforms. [Firouzkouhi, Narjes; Davvaz, Bijan] Yazd Univ, Dept Math, Yazd, Iran; [Amini, Abbas; Alkhatib, Fadi] Australian Univ Kuwait, Coll Engn, Dept Mech Engn, Safat 13015, Kuwait; [Amini, Abbas; Rashidi, Maria] Western Sydney Univ, Ctr Infrastructure Engn, Penrith, NSW 2751, Australia; [Nazari, Marziyeh] Australian Univ Kuwait, Coll Engn, Dept Math & Phys, Safat 13015, Kuwait; [Bordbar, Hashem] Univ Nova Gor, Ctr Informat Technol & Appl Math, Nova Gorica, Slovenia; [Cheng, Chun] Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China University of Yazd; Western Sydney University; University of Nova Gorica; Southern University of Science & Technology Amini, A (corresponding author), Australian Univ Kuwait, Coll Engn, Dept Mech Engn, Safat 13015, Kuwait. narjes.firoozkoohi@yahoo.com; a.amini@au.edu.kw; m.nazari@au.edu.kw; f.alkhatib@au.edu.kw; hashem.bordbar@ung.si; chengc@sustech.edu.cn; davvaz@yazd.ac.ir; m.rashidi@westernsydney.edu.au 47 0 0 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. 10.1007/s10462-023-10396-5 0.0 FEB 2023 26 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 9A7CD 2023-03-23 WOS:000934210800001 0
J Ma, Q; Dai, CL; Jin, HJ; Liang, SH; Bense, VF; Lan, YC; Marchenko, SS; Wang, C Ma, Qiang; Dai, Changlei; Jin, Huijun; Liang, Sihai; Bense, Victor F.; Lan, Yongchao; Marchenko, Sergey S.; Wang, Chuang Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955-2040 and Their Implications WATER English Article warming climate; streamflow data correction for damming; streamflow forecasting; artificial neural network time-series method; Headwater Area of Yellow River ACTIVE-LAYER THICKNESS; CLIMATE-CHANGE; SOURCE REGION; PERMAFROST; VARIABILITY; BASIN; IMPACTS; TEMPERATURE; DISCHARGE; DYNAMICS Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins. [Ma, Qiang; Dai, Changlei; Wang, Chuang] Heilongjiang Univ, Inst Groundwater Cold Reg, Harbin 150040, Peoples R China; [Ma, Qiang; Dai, Changlei; Wang, Chuang] Heilongjiang Univ, Sch Hydraul & Elect Power, Harbin 150040, Peoples R China; [Ma, Qiang; Jin, Huijun; Bense, Victor F.; Lan, Yongchao] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Frozen Soils Engn, Lanzhou 730000, Peoples R China; [Jin, Huijun] Northeast Forestry Univ, Inst Cold Reg Sci & Engn, Minist Educ, Northeast China Observ & Res Stn Permafrost GeoEn, Harbin 150040, Peoples R China; [Jin, Huijun] Northeast Forestry Univ, Sch Civil Engn, Harbin 150040, Peoples R China; [Liang, Sihai] China Univ Geosci, Sch Water Resources & Environm, Beijing 100083, Peoples R China; [Bense, Victor F.] Wageningen Univ, Dept Environm Sci, Hydrol & Quantitat Water Management Grp, NL-6708 PB Wageningen, Netherlands; [Marchenko, Sergey S.] Univ Alaska Fairbanks, Geophys Inst, Permafrost Lab, Fairbanks, AK 99775 USA Heilongjiang University; Heilongjiang University; Chinese Academy of Sciences; Northeast Forestry University - China; Northeast Forestry University - China; China University of Geosciences; Wageningen University & Research; University of Alaska System; University of Alaska Fairbanks Jin, HJ (corresponding author), Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Frozen Soils Engn, Lanzhou 730000, Peoples R China.;Jin, HJ (corresponding author), Northeast Forestry Univ, Inst Cold Reg Sci & Engn, Minist Educ, Northeast China Observ & Res Stn Permafrost GeoEn, Harbin 150040, Peoples R China.;Jin, HJ (corresponding author), Northeast Forestry Univ, Sch Civil Engn, Harbin 150040, Peoples R China. maqiang8002@lzb.ac.cn; daichanglei@126.com; hjjin@nefu.edu.cn; liangsh@cugb.edu.cn; victor.bense@wul.nl; lyc@lzb.ac.cn; ssmarchenko@alaska.edu; wangchuang1202@163.com ma, qiang/0000-0002-7477-1743; Marchenko, Sergey/0000-0003-3084-176X Ministry of Science and Technology of China Key RD Program [2017YFC0405704]; Chinese Academy of Sciences (CAS) Strategic Priority Research Program [XDA20100103]; CAS Overseas Professorship of Victor F. Bense at the Northwest Institute of Eco-Environment and Resources; CAS; CAS Strategic Priority Research Program [XDA20100103] Ministry of Science and Technology of China Key RD Program; Chinese Academy of Sciences (CAS) Strategic Priority Research Program; CAS Overseas Professorship of Victor F. Bense at the Northwest Institute of Eco-Environment and Resources; CAS(Chinese Academy of Sciences); CAS Strategic Priority Research Program This work was funded by Ministry of Science and Technology of China Key R&D Program (Grant No. 2017YFC0405704), the Chinese Academy of Sciences (CAS) Strategic Priority Research Program (Grant No. XDA20100103), and CAS Overseas Professorship of Victor F. Bense at the former Cold and Arid Regions Environmental and Engineering Research Institute (now renamed to the Northwest Institute of Eco-Environment and Resources), CAS. The article processing charges (APC) are funded by the CAS Strategic Priority Research Program (Grant No. XDA20100103). 84 3 3 17 54 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water MAY 2021.0 13 10 1360 10.3390/w13101360 0.0 17 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources SJ0JW gold 2023-03-23 WOS:000655214400001 0
J Keasling, J; Martin, HG; Lee, TS; Mukhopadhyay, A; Singer, SW; Sundstrom, E Keasling, Jay; Martin, Hector Garcia; Lee, Taek Soon; Mukhopadhyay, Aindrila; Singer, Steven W.; Sundstrom, Eric Microbial production of advanced biofuels NATURE REVIEWS MICROBIOLOGY English Review ENGINEERING ESCHERICHIA-COLI; HIGH-LEVEL PRODUCTION; SACCHAROMYCES-CEREVISIAE; SYNTHETIC BIOLOGY; CORYNEBACTERIUM-GLUTAMICUM; BIOSYNTHETIC PATHWAYS; POLYKETIDE SYNTHASES; YARROWIA-LIPOLYTICA; METABOLIC NETWORK; ALPHA-HUMULENE Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs. Biofuels produced by conversion of biomass by engineered microorganisms have the potential to replace fossil fuels and reduce carbon emissions. In this Review, Keasling and colleagues discuss engineering of metabolic pathways to produce advanced biofuels and approaches to reduce metabolite toxicity and cost and increase titre, rate and yield. [Keasling, Jay; Martin, Hector Garcia; Lee, Taek Soon; Mukhopadhyay, Aindrila; Singer, Steven W.] Joint BioEnergy Inst, Emeryville, CA 94608 USA; [Keasling, Jay] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA; [Keasling, Jay] Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA; [Keasling, Jay; Martin, Hector Garcia; Lee, Taek Soon; Mukhopadhyay, Aindrila; Singer, Steven W.; Sundstrom, Eric] Lawrence Berkeley Natl Lab, Biol Syst & Engn Div, Berkeley, CA 94720 USA; [Keasling, Jay] Danish Tech Univ, Ctr Biosustainabil, Lyngby, Denmark; [Keasling, Jay] Shenzhen Inst Adv Technol, Inst Synthet Biol, Ctr Synthet Biochem, Shenzhen, Peoples R China; [Martin, Hector Garcia] DOE Agile BioFoundry, Emeryville, CA USA; [Martin, Hector Garcia] Basque Ctr Appl Math, Bilbao, Spain; [Martin, Hector Garcia; Mukhopadhyay, Aindrila] Lawrence Berkeley Natl Lab, Environm Genom & Syst Biol Div, Berkeley, CA USA; [Sundstrom, Eric] Adv Biofuels & Bioprod Proc Dev Unit, Emeryville, CA USA United States Department of Energy (DOE); Joint BioEnergy Institute - JBEI; University of California System; University of California Berkeley; University of California System; University of California Berkeley; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Technical University of Denmark; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Basque Center for Applied Mathematics (BCAM); United States Department of Energy (DOE); Lawrence Berkeley National Laboratory Keasling, J (corresponding author), Joint BioEnergy Inst, Emeryville, CA 94608 USA.;Keasling, J (corresponding author), Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA.;Keasling, J (corresponding author), Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA.;Keasling, J (corresponding author), Lawrence Berkeley Natl Lab, Biol Syst & Engn Div, Berkeley, CA 94720 USA.;Keasling, J (corresponding author), Danish Tech Univ, Ctr Biosustainabil, Lyngby, Denmark.;Keasling, J (corresponding author), Shenzhen Inst Adv Technol, Inst Synthet Biol, Ctr Synthet Biochem, Shenzhen, Peoples R China. Keasling@berkeley.edu Mukhopadhyay, Aindrila/AAW-7257-2021; Keasling, Jay/HPG-6073-2023; Garcia Martin, Hector/B-5357-2009 Mukhopadhyay, Aindrila/0000-0002-6513-7425; Garcia Martin, Hector/0000-0002-4556-9685 DOE, Office of Science, Office of Biological and Environmental Research; DOE, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office; Co-Optimization of Fuels & Engines project - DOE, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office and Vehicle Technologies Office [DEAC02-05CH11231] DOE, Office of Science, Office of Biological and Environmental Research(United States Department of Energy (DOE)); DOE, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office(United States Department of Energy (DOE)); Co-Optimization of Fuels & Engines project - DOE, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office and Vehicle Technologies Office(United States Department of Energy (DOE)) The authors thank C. Scown (Lawrence Berkeley National Laboratory) for helpful discussions on life cycle and technoeconomic analyses of biofuel production. This work was performed as part of the US Department of Energy (DOE) Joint BioEnergy Institute (https://www.jbei.org) supported by the DOE, Office of Science, Office of Biological and Environmental Research, and by the DOE, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and as part of the Co-Optimization of Fuels & Engines project sponsored by the DOE, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office and Vehicle Technologies Office, under contract DEAC02-05CH11231 between the DOE and Lawrence Berkeley National Laboratory. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the US Government or any agency thereof. Neither the US Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product or process disclosed, or represents that its use would not infringe privately owned rights. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for US Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). 189 49 49 14 98 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 1740-1526 1740-1534 NAT REV MICROBIOL Nat. Rev. Microbiol. NOV 2021.0 19 11 701 715 10.1038/s41579-021-00577-w 0.0 JUN 2021 15 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology WF2LG 34172951.0 Green Submitted 2023-03-23 WOS:000666936300001 0
J Chen, LH; Lai, ZB; Vivone, G; Jeon, G; Chanussot, J; Yang, XM Chen, Lihui; Lai, Zhibing; Vivone, Gemine; Jeon, Gwanggil; Chanussot, Jocelyn; Yang, Xiaomin ArbRPN: A Bidirectional Recurrent Pansharpening Network for Multispectral Images With Arbitrary Numbers of Bands IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Pansharpening; Training; Image reconstruction; Convolutional neural networks; Sensors; Recurrent neural networks; Distortion; Deep learning (DL); image fusion; multispectral (MS) images; pansharpening; recurrent neural networks (RNNs); remote sensing PAN-SHARPENING METHOD; DATA-FUSION; SPARSE REPRESENTATION; VARIATIONAL APPROACH; INJECTION; MULTIRESOLUTION; REGRESSION; CONTRAST; QUALITY; MODEL Although the performance of pansharpening has been significantly improved by advanced deep-learning (DL) technologies in recent years, most DL-based methods fail to process multispectral (MS) images with arbitrary numbers of bands by a single model. Consequently, it is inevitable to train separate models for MS images with different numbers of bands, which is time- and storage-consuming as well as inefficient in practice. To tackle the above problem, we propose a bidirectional recurrent pansharpening network (named ArbRPN) for MS images with arbitrary numbers of bands. Our ArbRPN can dynamically reconstruct high-resolution (HR) MS images with different numbers of bands by adaptively changing the number of recurrence to the number of bands of the low-resolution (LR) MS images. Leveraging on the ability of the ArbRPN to process MS images with any number of bands, one can even customize the bands to be pansharpened. Moreover, to achieve superior performance, spectral discrepancy and dependence are considered in the ArbRPN. Details from the panchromatic (PAN) image are adaptively injected into the fused product according to the captured spectral dependence. Furthermore, training strategies of existing DL-based pansharpening methods can only group MS images with a constant number of bands into mini-batches. Therefore, we present a mask-based training method (called mask-training) to solve this problem. Benefiting from the mask-training, our ArbRPN can achieve superior performance and robustness during pansharpening. Extensive experiments show the superior performance of our ArbRPN with respect to the state-of-the-art (SOTA) methods applied to MS images with different numbers of bands. The code of our ArbRPN is available on https://github.com/Lihui-Chen/ArbRPN.git. [Chen, Lihui; Lai, Zhibing; Yang, Xiaomin] Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610065, Peoples R China; [Vivone, Gemine] CNR, Inst Methodol Environm Anal CNR IMAA, I-85050 Tito, Italy; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea; [Chanussot, Jocelyn] Univ Grenoble Alpes, INRIA, CNRS, Grenoble INP,Lab Jean Kuntzmann LJK, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Sichuan University; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Incheon National University; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Chinese Academy of Sciences Yang, XM (corresponding author), Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610065, Peoples R China. lihuichen@stu.scu.edu.cn; laizhibing@stu.scu.edu.cn; gemine.vivone@imaa.cnr.it; ggjeon@gmail.com; jocelyn.chanussot@grenoble-inp.fr; arielyang@scu.edu.cn VIVONE, Gemine/ABE-5121-2020; yang, xiao/HJI-7815-2023 VIVONE, Gemine/0000-0001-9542-0638; Chanussot, Jocelyn/0000-0003-4817-2875; Chen, Lihui/0000-0002-0948-1600 National Key Research and Development Program of China [2020AAA0104500]; Sichuan University [2020SCUNG205]; China Scholarship Council (CSC) [202006240191] National Key Research and Development Program of China; Sichuan University(Sichuan University); China Scholarship Council (CSC)(China Scholarship Council) This work was supported in part by the National Key Research and Development Program of China under Grant 2020AAA0104500, in part by Sichuan University under Grant 2020SCUNG205, and in part by the China Scholarship Council (CSC) under Grant 202006240191. 73 3 3 2 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5406418 10.1109/TGRS.2021.3131228 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZH9FN 2023-03-23 WOS:000761235300017 0
J Orlando, JI; Fu, HZ; Breda, JB; van Keer, K; Bathula, DR; Diaz-Pinto, A; Fang, R; Heng, PA; Kim, J; Lee, J; Lee, J; Li, XX; Liu, P; Lu, S; Murugesan, B; Naranjo, V; Phaye, SSR; Shankaranarayana, SM; Sikka, A; Son, J; van den Hengel, A; Wang, SJ; Wu, JY; Wu, ZF; Xu, GH; Xu, YL; Yin, PS; Li, F; Zhang, XL; Xu, YW; Bogunovic, H Orlando, Jose Ignacio; Fu, Huazhu; Breda, Joao Barbossa; van Keer, Karel; Bathula, Deepti R.; Diaz-Pinto, Andres.; Fang, Ruogu; Heng, Pheng-Ann; Kim, Jeyoung; Lee, JoonHo; Lee, Joonseok; Li, Xiaoxiao; Liu, Peng; Lu, Shuai; Murugesan, Balamurali; Naranjo, Valery; Phaye, Sai Samarth R.; Shankaranarayana, Sharath M.; Sikka, Apoorva; Son, Jaemin; van den Hengel, Anton; Wang, Shujun; Wu, Junyan; Wu, Zifeng; Xu, Guanghui; Xu, Yongli; Yin, Pengshuai; Li, Fei; Zhang, Xiulan; Xu, Yanwu; Bogunovic, Hrvoje REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs MEDICAL IMAGE ANALYSIS English Article Glaucoma; Fundus photography; Deep learning; Image segmentation; Image classification COLOR RETINAL IMAGES; OPTIC-NERVE HEAD; MACULAR DEGENERATION; VESSEL SEGMENTATION; CUP SEGMENTATION; DISC; DIAGNOSIS; ALGORITHM; NETWORK; MODEL Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results. (C) 2019 Elsevier B.V. All rights reserved. [Orlando, Jose Ignacio; Bogunovic, Hrvoje] Med Univ Vienna, Dept Ophthalmol & Optometry, VRC, Christian Doppler Lab Ophthalm Image Anal OPTIMA, Spitalgasse 23, A-1090 Vienna, Austria; [Fu, Huazhu] Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates; [Breda, Joao Barbossa] Univ Porto, Fac Medicine, Ophthalmol Unit, Surg & Physiol Dept, Porto, Portugal; [Breda, Joao Barbossa; van Keer, Karel] Katholieke Univ Leuven, Res Grp Ophthalmol, Leuven, Belgium; [Bathula, Deepti R.; Phaye, Sai Samarth R.; Sikka, Apoorva] Indian Inst Technol IIT Ropar, Dept Comp Sci & Engn, Rupnagar 140001, Punjab, India; [Diaz-Pinto, Andres.; Naranjo, Valery] Univ Politecn Valencia, I3B, E-46022 Valencia, Spain; [Fang, Ruogu; Liu, Peng] Univ Florida, J Crayton Pruitt Family Dept Biomed Engn, Gainesville, FL 32611 USA; [Heng, Pheng-Ann; Wang, Shujun] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong 999077, Peoples R China; [Kim, Jeyoung] Gachon Univ, Seongnam Si 461701, Gyeonggi Do, South Korea; [Lee, JoonHo; Lee, Joonseok] Samsung SDS AI Res Ctr, Seoul 06765, South Korea; [Li, Xiaoxiao] Yale Univ, New Haven, CT 06510 USA; [Lu, Shuai; Xu, Yongli] Beijing Univ Chem Technol, Fac Sci, Beijing 100029, Peoples R China; [Murugesan, Balamurali] IIT Madras, Healthcare Technol Innovat Ctr, Chennai, Tamil Nadu, India; [Shankaranarayana, Sharath M.] IIT Madras, Dept Elect Engn, Chennai, Tamil Nadu, India; [Son, Jaemin] VUNO Inc, Seoul 137810, South Korea; [van den Hengel, Anton; Wu, Zifeng] Australian Inst Machine Learning, Adelaide, SA, Australia; [Wu, Junyan] Cleerly Inc, New York, NY 10022 USA; [Xu, Guanghui; Yin, Pengshuai] South China Univ Technol, Guangzhou 510006, Guangdong, Peoples R China; [Li, Fei; Zhang, Xiulan] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, Guangzhou, Guangdong, Peoples R China; [Xu, Yanwu] Baidu Inc, Artificial Intelligence Innovat Business, Beijing, Peoples R China; [Xu, Yanwu] Chinese Acad Sci, Cixi Inst BioMed Engn, Beijing, Peoples R China Medical University of Vienna; Universidade do Porto; KU Leuven; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Ropar; Universitat Politecnica de Valencia; State University System of Florida; University of Florida; Chinese University of Hong Kong; Gachon University; Yale University; Beijing University of Chemical Technology; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; University of Adelaide; South China University of Technology; Sun Yat Sen University; Baidu; Chinese Academy of Sciences Xu, YW (corresponding author), Baidu Inc, Artificial Intelligence Innovat Business, Beijing, Peoples R China.;Xu, YW (corresponding author), Chinese Acad Sci, Cixi Inst BioMed Engn, Beijing, Peoples R China. zhangxl2@mail.sysu.edu.cn; ywxu@ieee.org Xu, Yanwu/AAK-8156-2020; Wu, Jun/HJP-1242-2023; Son, Jaemin/GQQ-8857-2022; Wang, Shujun/AAD-7001-2020; Diaz-Pinto, Andres/W-3759-2019; Li, Fei/ABB-7789-2021; Fu, Huazhu/A-1411-2014; Fang, Ruogu/AAY-6687-2020; Fang, Ruogu/AAY-8923-2020; Orlando, José Ignacio/AAB-3411-2020; Bogunovic, Hrvoje/J-3445-2014 Xu, Yanwu/0000-0002-1779-931X; Wang, Shujun/0000-0003-1495-3278; Diaz-Pinto, Andres/0000-0002-4865-8296; Fu, Huazhu/0000-0002-9702-5524; Fang, Ruogu/0000-0003-3980-3532; Fang, Ruogu/0000-0003-3980-3532; Orlando, José Ignacio/0000-0001-9734-5571; Bogunovic, Hrvoje/0000-0002-9168-0894; Naranjo, Valery/0000-0002-0181-3412; Barbosa-Breda, Joao/0000-0001-7816-816X; Yin, Pengshuai/0000-0003-4224-9915; van den Hengel, Anton/0000-0003-3027-8364; Murugesan, Balamurali/0000-0002-3002-5845; Lu, Shuai/0000-0002-3532-7498; Lee, JoonHo/0000-0002-6577-298X 94 177 181 12 59 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. JAN 2020.0 59 101570 10.1016/j.media.2019.101570 0.0 21 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging JU0YT 31630011.0 Green Published, Green Submitted 2023-03-23 WOS:000501405800009 0
J Chen, J; Patel, VM; Liu, L; Kellokumpu, V; Zhao, GY; Pietikainen, M; Chellappa, R Chen, Jie; Patel, Vishal M.; Liu, Li; Kellokumpu, Vili; Zhao, Guoying; Pietikainen, Matti; Chellappa, Rama Robust local features for remote face recognition IMAGE AND VISION COMPUTING English Article Robust local feature; Remote face recognition BINARY PATTERNS; DIMENSIONALITY; SCALE In this paper, we propose a robust local descriptor for face recognition. It consists of two components, one based on a shearlet-decomposition and the other on local binary pattern (LBP). Shearlets can completely analyze the singular structures of piecewise smooth images, which is useful since singularities and irregular structures carry useful information in an underlying image. Furthermore, LBP is effective for describing the edges extracted by shearlets even when the images contain high level of noise. Experimental results using the Face Recognition Grand Challenge dataset show that the proposed local descriptor significantly outperforms many widely used features (e.g., Gabor and deep learning-based features) when the images are corrupted by random noise, demonstrating robustness to noise. In addition, experimental results show promising results for two challenging datasets which have poor image quality, i.e., a remote face dataset and the Point and Shoot Face Recognition Challenge dataset. (C) 2017 Elsevier B.V. All rights reserved. [Chen, Jie; Liu, Li; Kellokumpu, Vili; Zhao, Guoying; Pietikainen, Matti] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Patel, Vishal M.] Rutgers State Univ, Dept Elect & Comp Engn, Piscataway, NJ USA; [Chellappa, Rama] Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20742 USA; [Liu, Li] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China University of Oulu; Rutgers State University New Brunswick; University System of Maryland; University of Maryland College Park; National University of Defense Technology - China Liu, L (corresponding author), Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China. jiechen@ee.oulu.fi; vishal.m.patel@rutgers.edu; lili@ee.oulu.fi; kello@ee.oulu.fi; gyzhao@ee.oulu.fi; mkp@ee.oulu.fi; rama@umiacs.umd.edu Zhao, Guoying/ABE-7716-2020; Chellappa, Rama/AAJ-1504-2020; Chellappa, Rama/B-6573-2012; Chellappa, Rama/AAV-8690-2020 Zhao, Guoying/0000-0003-3694-206X; Kellokumpu, Vili/0000-0002-5247-9311 Academy of Finland [277395]; Tekes Fidipro Program [1849/31/2015]; Infotech Oulu; US Office of Naval Research (ONR) [YIP N00014-16-1-3134]; Academy of Finland (AKA) [277395] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); Tekes Fidipro Program(Finnish Funding Agency for Technology & Innovation (TEKES)); Infotech Oulu; US Office of Naval Research (ONR)(Office of Naval Research); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported by Academy of Finland 277395, Tekes Fidipro Program 1849/31/2015 and Infotech Oulu. VMP was supported by US Office of Naval Research (ONR) Grant YIP N00014-16-1-3134. 60 31 31 1 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0262-8856 1872-8138 IMAGE VISION COMPUT Image Vis. Comput. AUG 2017.0 64 34 46 10.1016/j.imavis.2017.05.006 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Optics FH0ZW Green Accepted 2023-03-23 WOS:000410870200004 0
S Sariyska, R; Montag, C Baumeister, H; Montag, C Sariyska, Rayna; Montag, Christian An Overview on Doing Psychodiagnostics in Personality Psychology and Tracking Physical Activity via Smartphones DIGITAL PHENOTYPING AND MOBILE SENSING: NEW DEVELOPMENTS IN PSYCHOINFORMATICS Studies in Neuroscience Psychology and Behavioral Economics English Article; Book Chapter BIG DATA; EXERCISE; INTERNET; SCIENCE; TRAITS; USAGE; DEFINITIONS; DEPRESSION; STABILITY; RATINGS The aim of this chapter is to introduce and describe how digital technologies, in particular smartphones, can be used in research in two areas, namely (i) to conduct personality assessment and (ii) to assess and promote physical activity. This area of research is very timely, because it demonstrates how the ubiquitously available smartphone technology-next to its known advantages in day-to-day life-can provide insights into many variables, relevant for psycho-social research, beyond what is possible within the classic spectrum of self-report inventories and laboratory experiments. The present chapter gives a brief overview on first empirical studies and discusses both opportunities and challenges in this rapidly developing research area. [Sariyska, Rayna; Montag, Christian] Ulm Univ, Inst Psychol & Educ, Ulm, Germany; [Montag, Christian] Univ Elect Sci & Technol China, Clin Hosp, MOE Key Lab Neuroinformat, Chengdu Brain Sci Inst, Chengdu, Peoples R China Ulm University; University of Electronic Science & Technology of China Sariyska, R (corresponding author), Ulm Univ, Inst Psychol & Educ, Ulm, Germany. rayna.sariyska@uni-ulm.de Montag, Christian/H-6536-2019 Montag, Christian/0000-0001-8112-0837 83 7 7 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2196-6605 2196-6613 978-3-030-31620-4; 978-3-030-31619-8 STUD NEUROSCI Stud. Neurosci. Psychol. Behav. Econ. 2019.0 45 63 10.1007/978-3-030-31620-4_4 0.0 10.1007/978-3-030-31620-4 19 Computer Science, Artificial Intelligence; Psychology, Applied; Medical Informatics; Psychology Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Book Citation Index – Science (BKCI-S) Computer Science; Psychology; Medical Informatics BP9ZS 2023-03-23 WOS:000571350300006 0
C Kustner, T; Pan, JZ; Gilliam, C; Qi, HK; Cruz, G; Hammernik, K; Yang, B; Blu, T; Rueckert, D; Botnar, R; Prieto, C; Gatidis, S IEEE Kuestner, Thomas; Pan, Jiazhen; Gilliam, Christopher; Qi, Haikun; Cruz, Gastao; Hammernik, Kerstin; Yang, Bin; Blu, Thierry; Rueckert, Daniel; Botnar, Rene; Prieto, Claudia; Gatidis, Sergios Deep-learning based motion-corrected image reconstruction in 4D magnetic resonance imaging of the body trunk 2020 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) Asia-Pacific Signal and Information Processing Association Annual Summit and Conference English Proceedings Paper Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC) DEC 07-10, 2020 Auckland, NEW ZEALAND Asia Pacific Signal & Informat Proc Assoc,IEEE Signal Proc Soc ACCELERATED DYNAMIC MRI; ANGIOGRAPHY; SPARSITY; ARTIFACTS; NETWORKS; SENSE Respiratory and cardiac motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with motion by fast imaging sequences with integrated motion tracking under free-movement conditions. These acquisitions perform sub-Nyquist sampling and retrospectively bin the data into the respective motion states, yielding subsampled and motion-resolved k-space data. The motion-resolved k-spaces are linked to each other by non-rigid deformation fields. The accurate estimation of such motion is thus an important task in the successful correction of respiratory and cardiac motion. Usually this problem is formulated in image space via diffusion, parametric-spline or optical flow methods. Image-based registration can be however impaired by aliasing artifacts or by estimation from low-resolution images. Subsequently, any motion-corrected reconstruction can be biased by errors in the deformation fields. In this work, we propose a novel deep-learning based motion-corrected 41) (31) spatial + time) image reconstruction which combines a non-rigid registration network and a (3+1)D reconstruction network. Non-rigid motion is estimated directly in k-space based on an optical flow idea and incorporated into the reconstruction network. The proposed method is evaluated on in-vivo 4D motion-resolved magnetic resonance images of patients with suspected liver or lung metastases and healthy subjects. [Kuestner, Thomas; Gatidis, Sergios] Med Image & Data Anal MIDAS, Dept Diagnost & Intervent Radiol, Tubingen, Germany; [Kuestner, Thomas; Qi, Haikun; Cruz, Gastao; Botnar, Rene; Prieto, Claudia] Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England; [Pan, Jiazhen; Yang, Bin] Univ Stuttgart, Inst Signal Proc & Syst Theory, Stuttgart, Germany; [Gilliam, Christopher] RMIT Univ, Melbourne, Vic, Australia; [Hammernik, Kerstin; Rueckert, Daniel] Imperial Coll London, Dept Comp, London, England; [Blu, Thierry] Chinese Univ Hong Kong, Hong Kong, Peoples R China Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Guy's & St Thomas' NHS Foundation Trust; University of London; King's College London; University of Stuttgart; Royal Melbourne Institute of Technology (RMIT); Imperial College London; Chinese University of Hong Kong Kustner, T (corresponding author), Med Image & Data Anal MIDAS, Dept Diagnost & Intervent Radiol, Tubingen, Germany.;Kustner, T (corresponding author), Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England. Gilliam, Christopher/B-6606-2018; Kuestner, Thomas/ABE-7866-2020; Botnar, Rene/E-6875-2012; Gatidis, Sergios/AAF-4858-2020 Gilliam, Christopher/0000-0002-6730-8419; Kuestner, Thomas/0000-0002-0353-4898; Botnar, Rene/0000-0003-2811-2509; Cruz, Gastao/0000-0002-7397-9104; Prieto, Claudia/0000-0003-4602-2523; Gatidis, Sergios/0000-0002-6928-4967 76 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2309-9402 978-988-14768-8-3 ASIAPAC SIGN INFO PR 2020.0 976 985 10 Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BR9MX 2023-03-23 WOS:000678729400168 0
J Yu, S; Chen, X; Yang, L; Wu, D; Bennis, M; Zhang, JS Yu, Shuai; Chen, Xu; Yang, Lei; Wu, Di; Bennis, Mehdi; Zhang, Junshan Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading IEEE WIRELESS COMMUNICATIONS English Article Mobile handsets; Computational modeling; Task analysis; Delays; Electronics packaging; Training data; Decision making In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation offloading framework for MEC networks. A key objective for the framework is to minimize the offloading cost in time-varying network environments through optimal behavioral cloning. Specifically, we first introduce our computation offloading model for MEC in detail. Then we make fine-grained offloading decisions for a mobile device, and the problem is formulated as a multi-label classification problem, with local execution cost and remote network resource usage consideration. To minimize the offloading cost, we train our decision making engine by leveraging the deep imitation learning method, and further evaluate its performance through an extensive numerical study. Simulation results show that our proposal outperforms other benchmark policies in offloading accuracy and offloading cost reduction. At last, we discuss the directions and advantages of applying deep learning methods to multiple MEC research areas, including edge data analytics, dynamic resource allocation, security, and privacy, respectively. [Yu, Shuai; Wu, Di] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China; [Chen, Xu] Sun Yat Sen Univ, Guangzhou, Peoples R China; [Yang, Lei] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA; [Bennis, Mehdi] Univ Oulu, Ctr Wireless Commun, Oulu, Finland; [Zhang, Junshan] Arizona State Univ, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA Sun Yat Sen University; Sun Yat Sen University; Nevada System of Higher Education (NSHE); University of Nevada Reno; University of Oulu; Arizona State University; Arizona State University-Tempe Chen, X (corresponding author), Sun Yat Sen Univ, Guangzhou, Peoples R China. Bennis, Mehdi/ABE-5838-2020; Wu, Di/HNP-3772-2023; Chen, Xu/GXW-3072-2022 Bennis, Mehdi/0000-0003-0261-0171; National Key Research and Development Program of China [2017YFB1001703]; National Science Foundation of China [U1711265, 61972432]; Fundamental Research Funds for the Central Universities [17lgjc40, 19LGZD37]; Program for Basic and Applied Basic Research Fund of Guangdong [2019A1515010030]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X355]; Pearl River Talent Recruitment Program [2017GC010465]; U.S. National Science Foundation [IIS-1838024]; Guangdong Special Support Program [2017TX04X148] National Key Research and Development Program of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Program for Basic and Applied Basic Research Fund of Guangdong; Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Pearl River Talent Recruitment Program; U.S. National Science Foundation(National Science Foundation (NSF)); Guangdong Special Support Program This work was supported in part by the National Key Research and Development Program of China (No. 2017YFB1001703); the National Science Foundation of China (No. U1711265, No. 61972432); the Fundamental Research Funds for the Central Universities (No. 17lgjc40, 19LGZD37); the Program for Basic and Applied Basic Research Fund of Guangdong (No. 2019A1515010030); the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No. 2017ZT07X355); the Pearl River Talent Recruitment Program (No. 2017GC010465); the U.S. National Science Foundation (IIS-1838024); and the Guangdong Special Support Program (2017TX04X148). 15 45 48 5 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. FEB 2020.0 27 1 92 99 10.1109/MWC.001.1900232 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications KU3BT Green Accepted 2023-03-23 WOS:000519583500014 0
J Khoder, A; Dornaika, F Khoder, A.; Dornaika, F. An enhanced approach to the robust discriminant analysis and class sparsity based embedding NEURAL NETWORKS English Article Supervised learning; Discriminant model; Feature extraction; Linear embedding; Inter-class sparsity; Image categorization SELECTION In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems. Inspired by recent frameworks for robust sparse LDA and Inter-class sparsity, we propose a unifying criterion able to retain the advantages of these two powerful linear discriminant methods. We introduce an iterative alternating minimization scheme in order to estimate the linear transformation and the orthogonal matrix. The linear transformation is efficiently updated via the steepest descent gradient technique. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. We used our proposed method to fine tune the linear solutions delivered by two recent linear methods: RSLDA and RDA_FSIS. Experiments have been conducted on public image datasets of different types including objects, faces, and digits. The proposed framework compared favorably with several competing methods. (C) 2020 Published by Elsevier Ltd. [Dornaika, F.] Henan Univ, Kaifeng, Peoples R China; [Khoder, A.; Dornaika, F.] Univ Basque Country, UPV EHU, San Sebastian, Spain; [Dornaika, F.] Ikerbasque, Basque Fdn Sci, Bilbao, Spain Henan University; University of Basque Country; Basque Foundation for Science Dornaika, F (corresponding author), Univ Basque Country, UPV EHU, San Sebastian, Spain. fadi.dornaika@ehu.eus Khoder, Ahmad/0000-0001-5565-363X; Dornaika, Fadi/0000-0001-6581-9680 28 3 3 0 7 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. APR 2021.0 136 11 16 10.1016/j.neunet.2020.12.025 0.0 JAN 2021 6 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology RC4VL 33422928.0 2023-03-23 WOS:000632800900002 0
J Zhang, L; Fan, SW; Vera, J; Lai, X Zhang, Le; Fan, Shiwei; Vera, Julio; Lai, Xin A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL English Article Systems medicine; Drug repositioning; Network oncology; Gene prioritization; Machine learning; Pan-cancer diagnosis and prognosis LUNG-CANCER; DYSREGULATION; EXPRESSION; SURVIVAL; CLASSIFICATION; MECHANISMS; HALLMARKS; DISCOVERY; SELECTION; PATHWAYS Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer net-works. We developed an integrative network medicine approach to identify novel biomarkers and inves-tigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proxim-ity approach to systematically screen drugs and identified dozens of candidates with repurposable poten-tial in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed com-putational workflow.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/). [Zhang, Le; Fan, Shiwei] Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China; [Vera, Julio; Lai, Xin] Univ Klinikum Erlangen, Dept Dermatol, Lab Syst Tumor Immunol, Erlangen, Germany; [Vera, Julio; Lai, Xin] Friedrich Alexander Univ Erlangen Nurnberg, Erlangen, Germany; [Vera, Julio; Lai, Xin] Deutsch Zent Immuntherapie, Erlangen, Germany; [Vera, Julio; Lai, Xin] Comprehens Canc Ctr Erlangen, Erlangen, Germany; [Lai, Xin] Tampere Univ, Fac Med & Hlth Technol, BioMediTech, Tampere, Finland; [Lai, Xin] Univ Klinikum Erlangen, Erlangen, Germany; [Lai, Xin] Tampere Univ, Tampere, Finland Sichuan University; University of Erlangen Nuremberg; University of Erlangen Nuremberg; University of Erlangen Nuremberg; Tampere University; University of Erlangen Nuremberg; Tampere University Lai, X (corresponding author), Univ Klinikum Erlangen, Erlangen, Germany.;Lai, X (corresponding author), Tampere Univ, Tampere, Finland. lai.xin@proton.me Lai, Xin/H-7719-2018; Zhang, Le/AAD-9104-2019 Lai, Xin/0000-0003-4913-5822; Zhang, Le/0000-0002-3708-1727 National Science and Technology Major Project [2021YFF1201200]; Sichuan Science and Technology Program [2022YFS0048]; German Federal Ministry of Education and Research (BMBF) [e:Med MelAutim 01ZX1905A, KI-VesD 031L0244A] National Science and Technology Major Project; Sichuan Science and Technology Program; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) This work was supported by the National Science and Technology Major Project (2021YFF1201200 to L.Z.) and Sichuan Science and Technology Program (2022YFS0048 to L.Z.). We thank the support from German Federal Ministry of Education and Research (BMBF) [e:Med MelAutim 01ZX1905A to X.L. and J.V. and KI-VesD 031L0244A to J.V.]. 91 1 1 15 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2001-0370 COMPUT STRUCT BIOTEC Comp. Struct. Biotechnol. J.. 2023.0 21 34 45 10.1016/j.csbj.2022.11.037 0.0 12 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology 7J2TL 36514340.0 Green Accepted 2023-03-23 WOS:000904436800004 0
J Stefani, D; Guo, CL; Ornago, L; Cabosart, D; El Abbassi, M; Sheves, M; Cahen, D; van der Zant, HSJ Stefani, Davide; Guo, Cunlan; Ornago, Luca; Cabosart, Damien; El Abbassi, Maria; Sheves, Mordechai; Cahen, David; van der Zant, Herre S. J. Conformation-dependent charge transport through short peptides NANOSCALE English Article We report on charge transport across single short peptides using the Mechanically Controlled Break Junction (MCBJ) method. We record thousands of electron transport events across single-molecule junctions and with an unsupervised machine learning algorithm, we identify several classes of traces with multifarious conductance values that may correspond to different peptide conformations. Data analysis shows that very short peptides, which are more rigid, show conductance plateaus at low conductance values of about 10(-3)G(0) and below, with G(0) being the conductance quantum, whereas slightly longer, more flexible peptides also show plateaus at higher values. Fully stretched peptide chains exhibit conductance values that are of the same order as that of alkane chains of similar length. The measurements show that in the case of short peptides, different compositions and molecular lengths offer a wide range of junction conformations. Such information is crucial to understand mechanism(s) of charge transport in and across peptide-based biomolecules. [Stefani, Davide; Ornago, Luca; Cabosart, Damien; El Abbassi, Maria; van der Zant, Herre S. J.] Delft Univ Technol, Kavli Inst Nanosci, Delft, Netherlands; [Guo, Cunlan; Sheves, Mordechai; Cahen, David] Weizmann Inst Sci, Mat & Interfaces, Rehovot, Israel; [Guo, Cunlan] Wuhan Univ, Coll Chem & Mol Sci, 299 Bayi Rd, Wuhan 430072, Hubei, Peoples R China Delft University of Technology; Weizmann Institute of Science; Wuhan University van der Zant, HSJ (corresponding author), Delft Univ Technol, Kavli Inst Nanosci, Delft, Netherlands.;Guo, CL (corresponding author), Weizmann Inst Sci, Mat & Interfaces, Rehovot, Israel. cunlanguo@whu.edu.cn; h.s.j.vanderzant@tudelft.nl Guo, Cunlan/E-8077-2011 Guo, Cunlan/0000-0001-7706-5230; Ornago, Luca/0000-0001-5293-2887 EU [767187]; NSFC [21974102]; Israel Science Foundation; Weizmann Inst. EU(European Commission); NSFC(National Natural Science Foundation of China (NSFC)); Israel Science Foundation(Israel Science Foundation); Weizmann Inst. DS, M. El Abbassi and HSJZ acknowledge financial support by the EU through the H2020 FET QuIET project (no. 767187). Device fabrication was done at the Kavli Nanolab at Delft. CG thanks the Weizmann Inst. for partial support as a sr. PD fellow and the NSFC (21974102) for financial support. DC, MS and CG thank the Israel Science Foundation for partial support. 47 6 6 8 38 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2040-3364 2040-3372 NANOSCALE Nanoscale FEB 7 2021.0 13 5 3002 3009 10.1039/d0nr08556a 0.0 8 Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics QG7MR 33508063.0 Green Published, hybrid 2023-03-23 WOS:000617768200026 0
J Tisi, D; Zhang, LF; Bertossa, R; Wang, H; Car, R; Baroni, S Tisi, Davide; Zhang, Linfeng; Bertossa, Riccardo; Wang, Han; Car, Roberto; Baroni, Stefano Heat transport in liquid water from first-principles and deep neural network simulations PHYSICAL REVIEW B English Article 1ST PRINCIPLES SIMULATIONS; DENSITY-FUNCTIONAL THEORY; MOLECULAR-DYNAMICS; IRREVERSIBLE-PROCESSES; THERMAL-CONDUCTIVITY; GAUGE-INVARIANCE; ACCURACY; QUANTIZATION; COEFFICIENTS We compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two. [Tisi, Davide; Bertossa, Riccardo; Baroni, Stefano] SISSA Scuola Int Super Studi Avanzati, I-34136 Trieste, Italy; [Zhang, Linfeng; Car, Roberto] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA; [Wang, Han] Inst Appl Phys & Computat Math, Lab Computat Phys, Huayuan Rd 6, Beijing 100088, Peoples R China; [Car, Roberto] Princeton Univ, Dept Chem, Dept Phys, Princeton, NJ 08544 USA; [Car, Roberto] Princeton Univ, Princeton Inst Sci & Technol Mat, Princeton, NJ 08544 USA; [Baroni, Stefano] CNR, SISSA Unit, Ist Officina Mat, I-34136 Trieste, Italy International School for Advanced Studies (SISSA); Princeton University; Chinese Academy of Sciences; Institute of Applied Physics & Computational Mathematics - China; Princeton University; Princeton University; Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); International School for Advanced Studies (SISSA) Tisi, D (corresponding author), SISSA Scuola Int Super Studi Avanzati, I-34136 Trieste, Italy. ; Baroni, Stefano/F-2982-2011 Tisi, Davide/0000-0001-7229-6101; Bertossa, Riccardo/0000-0002-8551-1939; Baroni, Stefano/0000-0002-3508-6663 EU through the MAX Centre of Excellence for supercomputing applications [824143]; DOE [DE-SC0019394]; National Science Foundation of China [11871110]; Center Chemistry in Solution EU through the MAX Centre of Excellence for supercomputing applications; DOE(United States Department of Energy (DOE)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Center Chemistry in Solution D.T., R.B., and S.B. are grateful to Federico Grasselli for enlightening discussions throughout the completion of this work. This work was partially funded by the EU through the MAX Centre of Excellence for supercomputing appli-cations (Project No. 824143). L.Z. and R.C. acknowledge support from the Center Chemistry in Solution and at Interfaces funded by the DOE Award No. DE-SC0019394. H.W. is supported by the National Science Foundation of China under Grant No. 11871110. 69 8 8 16 40 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B DEC 13 2021.0 104 22 224202 10.1103/PhysRevB.104.224202 0.0 12 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics XU1XY Green Submitted 2023-03-23 WOS:000734068100002 0
J Yang, C; Yang, ZY; Ke, Y; Chen, T; Grzegorzek, M; See, J Yang, Cong; Yang, Zhenyu; Ke, Yan; Chen, Tao; Grzegorzek, Marcin; See, John Doing More With Moire Pattern Detection in Digital Photos IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Moire pattern; moire pattern detection; moire removal; moire image restoration; adaptive kernel EXTRACTION Detecting moire patterns in digital photographs is meaningful as it provides priors towards image quality evaluation and demoireing tasks. In this paper, we present a simple yet efficient framework to extract moire edge maps from images with moire patterns. The framework includes a strategy for training triplet (natural image, moire layer, and their synthetic mixture) generation, and a Moire Pattern Detection Neural Network (MoireDet) for moire edge map estimation. This strategy ensures consistent pixel-level alignments during training, accommodating characteristics of a diverse set of camera-captured screen images and real-world moire patterns from natural images. The design of three encoders in MoireDet exploits both high-level contextual and low-level structural features of various moire patterns. Through comprehensive experiments, we demonstrate the advantages of MoireDet: better identification precision of moire images on two datasets, and a marked improvement over state-of-the-art demoireing methods. [Yang, Cong; Chen, Tao] Soochow Univ, Sch Future Sci & Engn, Suzhou, Peoples R China; [Yang, Zhenyu] Southeast Univ, Chien Shiung Wu Coll, Nanjing, Peoples R China; [Ke, Yan] Clobotics, Shanghai, Peoples R China; [Grzegorzek, Marcin] Univ Lubeck, Inst Med Informat, Lubeck, Germany; [Grzegorzek, Marcin] Univ Econ Katowice, Dept Knowledge Engn, PL-40287 Katowice, Poland; [See, John] Heriot Watt Univ Malaysia, Sch Math & Comp Sci, Putrajaya, Malaysia Soochow University - China; Southeast University - China; University of Lubeck; University of Economics in Katowice; Heriot Watt University Yang, C (corresponding author), Soochow Univ, Sch Future Sci & Engn, Suzhou, Peoples R China. yangcong955@126.com See, John/C-8633-2013 See, John/0000-0003-3005-4109 Research Fund of Clobotics [KB1801ZW201609-02]; Natural Science Foundation of the Jiangsu Higher Education Institutions of China [22KJB520008]; National Natural Science Foundation of China [62073229]; Jiangsu Policy Guidance Program, International Science and Technology Cooperation; Belt and Road Initiative Innovative Cooperation Projects [BZ2021016] Research Fund of Clobotics; Natural Science Foundation of the Jiangsu Higher Education Institutions of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Policy Guidance Program, International Science and Technology Cooperation; Belt and Road Initiative Innovative Cooperation Projects This work was supported in part by the Research Fund of Clobotics under Grant KB1801ZW201609-02; in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 22KJB520008; in part by the National Natural Science Foundation of China under Grant 62073229; and in part by the Jiangsu Policy Guidance Program, International Science and Technology Cooperation, The Belt and Road Initiative Innovative Cooperation Projects, under Grant BZ2021016. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Marta Mrak. (Cong Yang and Zhenyu Yang contributed equally to this work.) 74 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2023.0 32 694 708 10.1109/TIP.2022.3232232 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 7U9FW hybrid 2023-03-23 WOS:000912431500001 0
C Dragicevic, N; Ullrich, A; Tsui, E; Gronau, N Marimon, F; MasMachuca, M; BerbegalMirabent, J; Bastida, R Dragicevic, Nikolina; Ullrich, Andre; Tsui, Eric; Gronau, Norbert Modelling Knowledge Dynamics in Industry 4.0: A Smart Grid Scenario PROCEEDINGS OF THE 18TH EUROPEAN CONFERENCE ON KNOWLEDGE MANAGEMENT (ECKM 2017), VOLS 1 AND 2 Proceedings of the European Conference on Knowledge Management English Proceedings Paper 18th European Conference on Knowledge Management (ECKM) SEP 07-08, 2017 Int Univ Catalonia, Barcelona, SPAIN Int Univ Catalonia knowledge dynamics; big data; tacit knowledge; Industry 4.0; internet of things; internet of services; smart grid; knowledge management systems New digital technological advancements are giving rise to the fourth industrial revolution, commonly termed as Industry 4.0, in which the physical and the digital world merge and the boundaries between products and services are blurring. This transformation is powered by smart, autonomous objects that communicate and interact among themselves or with actors through the connected, multi-loop, and multi-layer network of Internet of Things and Services. These interconnected smart objects create, carry, and share large volumes of data, leading to many potential opportunities for creating value from such data. Albeit particularly Big Data has been a much-discussed term in research concerning the Industry 4.0 paradigm, the role of knowledge, especially tacit knowledge has been neglected thus far. Furthermore, a conceptual model that would provide an aggregated framework to understand knowledge-based activities and flows does not exist yet. In this paper, we attempt to bridge this gap by firstly critically examining and clarifying the terms data, information, and tacit knowledge by drawing upon relevant knowledge management theories, foremost on Polanyi's, a common (albeit often misinterpreted) reference point for his successors. Then, by reviewing and analyzing related literature, we develop a conceptual model of knowledge dynamics in the Smart Grid ecosystem, which is one of the potential application fields of Industry 4.0. Specifically, we conceptualize main components and their relations, and describe how knowledge based activities are embedded in the multi-feedback loop and multi-layer network of Internet of Things and Services. We exemplarily outline a use case a Smart Grid program of CLP Power Hong Kong Limited, the largest supplier of electricity in Hong Kong as an example of how value is co-created through knowledge dynamics within such ecosystems. Furthermore, we discuss the theoretical and practical implications of the emerging knowledge dynamics model for the design of knowledge management systems. [Dragicevic, Nikolina; Tsui, Eric] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China; [Ullrich, Andre; Gronau, Norbert] Univ Potsdam, Chair Business Informat Esp Proc & Syst, Potsdam, Germany Hong Kong Polytechnic University; University of Potsdam Dragicevic, N (corresponding author), Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China. nikolina.dragicevic@connect.polyu.hk; aullrich@lswi.de; eric.tsui@polyu.edu.hk; ngronau@lswi.de Dragicevic, Nikolina/HHM-5333-2022; Ullrich, André/HKN-3211-2023 Dragicevic, Nikolina/0000-0003-4046-8487; Ullrich, André/0000-0002-6953-1550; TSUI, Eric/0000-0002-6711-1042 20 3 3 0 8 ACAD CONFERENCES LTD NR READING CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND 2048-8963 978-1-911218-48-7; 978-1-911218-49-4 PROC EUR CONF KNOWL 2017.0 267 274 8 Business; Information Science & Library Science; Management Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Business & Economics; Information Science & Library Science BL6HA 2023-03-23 WOS:000453882900033 0
J Liu, ZC; Alcantud, JCR; Qin, KY; Pei, Z Liu, Zhicai; Alcantud, Jose Carlos R.; Qin, Keyun; Pei, Zheng The relationship between soft sets and fuzzy sets and its application JOURNAL OF INTELLIGENT & FUZZY SYSTEMS English Article fuzzy sets; soft sets; binary-coded; IOWA operator; equivalent transformation DECISION-MAKING; OPERATORS; COMBINATION This paper reviews and compares theories of fuzzy sets and soft sets from the perspective of transformation, and we prove that every fuzzy set on a universe U can be considered as a soft set, and show that any soft set can be regarded as even a fuzzy set. This paper presents two mapping methods to implement the transformation, namely, the methods of the binary-coded genetic algorithm (BCGA) and the ordered weighted averaging (OWA) operators. In practical applications, it can be used to establish the membership function of fuzzy sets, and it can also be applied to pattern recognition, decision-making, etc. In general, it provides a new perspective to observe the relationship between soft sets and fuzzy sets, and it can be regarded as a general strategy to establish the membership function of fuzzy sets. Further, it reveals that the transformation method is similar to the process of building neurons, which opens a door to machine learning for soft set theory. [Liu, Zhicai; Pei, Zheng] Xihua Univ, Ctr Radio Adm & Technol Dev, Chengdu, Sichuan, Peoples R China; [Liu, Zhicai; Qin, Keyun] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Sichuan, Peoples R China; [Alcantud, Jose Carlos R.] Univ Salamanca, BORDA Res Unit, Salamanca, Spain; [Alcantud, Jose Carlos R.] Univ Salamanca, Multidisciplinary Inst Enterprise IME, Salamanca, Spain Xihua University; Southwest Jiaotong University; University of Salamanca; University of Salamanca Liu, ZC (corresponding author), Xihua Univ, Ctr Radio Adm & Technol Dev, Chengdu, Sichuan, Peoples R China.;Liu, ZC (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Sichuan, Peoples R China. liuzc@mail.xhu.edu.cn Alcantud, José Carlos/E-3258-2010 Alcantud, José Carlos/0000-0002-4533-9281 National Natural Science Foundation of China [61175044, 61372187, 61473239]; Fundamental Research Funds for the Central Universities of China [SWJTU11ZT29]; open research fund of key laboratory of intelligent network information processing, Xihua University [SZJJ2012-032, SZJJ2014-052, 16224221] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); open research fund of key laboratory of intelligent network information processing, Xihua University This work has been partially supported by the National Natural Science Foundation of China (Grant No. 61175044, 61372187, 61473239) and the Fundamental Research Funds for the Central Universities of China (Grant No. SWJTU11ZT29), and the open research fund of key laboratory of intelligent network information processing, Xihua University (SZJJ2012-032, SZJJ2014-052, 16224221). 53 8 8 2 9 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1064-1246 1875-8967 J INTELL FUZZY SYST J. Intell. Fuzzy Syst. 2019.0 36 4 SI 3751 3764 10.3233/JIFS-18559 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science HT3EW 2023-03-23 WOS:000464448100061 0
J Chen, XQ; Wei, CX; Zhou, GL; Wu, HF; Wang, ZY; Biancardo, SA Chen, Xinqiang; Wei, Chenxin; Zhou, Guiliang; Wu, Huafeng; Wang, Zhongyu; Biancardo, Salvatore Antonio Automatic Identification System (AIS) Data Supported Ship Trajectory Prediction and Analysis via a Deep Learning Model JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article maritime traffic flow; Bi-LSTM model; prediction; maritime traffic management; traffic safety TRAFFIC FLOW PREDICTION Automatic Identification System (AIS) data-supported ship trajectory analysis consistently helps maritime regulations and practitioners make reasonable traffic controlling and management decisions. Significant attentions are paid to obtain an accurate ship trajectory by learning data feature patterns in a feedforward manner. A ship may change her moving status to avoid potential traffic accident in inland waterways, and thus, the ship trajectory variation pattern may differ from previous data samples. The study proposes a novel ship trajectory exploitation and prediction framework with the help of the bidirectional long short-term memory (LSTM) (Bi-LSTM) model, which extracts intrinsic ship trajectory features with feedforward and backward manners. We have evaluated the proposed ship trajectory performance with single and multiple ship scenarios. The indicators of mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) suggest that the proposed Bi-LSTM model can obtained satisfied ship trajectory prediction performance. [Chen, Xinqiang; Zhou, Guiliang] Huaiyin Inst Technol, Jiangsu Key Lab Traff & Transportat Secur, Huaian 223003, Peoples R China; [Chen, Xinqiang] Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China; [Chen, Xinqiang; Wei, Chenxin] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China; [Wu, Huafeng] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China; [Wang, Zhongyu] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai 201306, Peoples R China; [Biancardo, Salvatore Antonio] Univ Naples Federico II, Dept Civil Architectural & Environm Engn DICEA, I-80125 Naples, Italy Huaiyin Institute of Technology; Fudan University; Shanghai Maritime University; Shanghai Maritime University; Shanghai Maritime University; University of Naples Federico II Zhou, GL (corresponding author), Huaiyin Inst Technol, Jiangsu Key Lab Traff & Transportat Secur, Huaian 223003, Peoples R China. ZGL@hyit.edu.cn Vukša, Srđan/T-4521-2017 Vukša, Srđan/0000-0003-3185-3308; Biancardo, Salvatore Antonio/0000-0003-2567-7977; Wu, Huafeng/0000-0002-3150-3407 National Key R&D Program of China [2021YFC2801002]; National Natural Science Foundation of China [52102397, 52071200]; Foundation for Jiangsu key Laboratory of Traffic and Transportation Security [TTS2021-05]; fellowship of China Postdoctoral Science Foundation [2021M700790, 2022M712027] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Jiangsu key Laboratory of Traffic and Transportation Security; fellowship of China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was jointly supported by the National Key R&D Program of China (2021YFC2801002), the National Natural Science Foundation of China (52102397, 52071200), the Foundation for Jiangsu key Laboratory of Traffic and Transportation Security (No. TTS2021-05), and the fellowship of China Postdoctoral Science Foundation (2021M700790, 2022M712027). 35 1 1 54 54 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. SEP 2022.0 10 9 1314 10.3390/jmse10091314 0.0 12 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 4T6QF gold 2023-03-23 WOS:000858238400001 0
J Zou, HQ; Li, S; Huang, YH; Liu, Y; Bauer, R; Peng, L; Tao, O; Yan, SR; Yan, YH Zou, Hui-Qin; Li, Shuo; Huang, Ying-Hua; Liu, Yong; Bauer, Rudolf; Peng, Lian; Tao, Ou; Yan, Su-Rong; Yan, Yong-Hong Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE English Article ELECTRONIC NOSE; DISCRIMINATION; FINGERPRINT Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers' safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model. [Zou, Hui-Qin; Huang, Ying-Hua] Beijing Univ Chinese Med, Beijing 100029, Peoples R China; [Li, Shuo] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China; [Liu, Yong; Peng, Lian; Tao, Ou; Yan, Su-Rong; Yan, Yong-Hong] Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing 100102, Peoples R China; [Bauer, Rudolf] Graz Univ, Inst Pharmaceut Sci, A-8010 Graz, Austria Beijing University of Chinese Medicine; Beijing University of Posts & Telecommunications; Beijing University of Chinese Medicine; University of Graz Zou, HQ (corresponding author), Beijing Univ Chinese Med, 11 Bei San Huan Dong Lu, Beijing 100029, Peoples R China. zouhuiqin_bucm@sina.cn; lxdyyh@yeah.net Bauer, Rudolf/F-8468-2010; Zou, Huiqin/N-4215-2014 Bauer, Rudolf/0000-0002-0057-5547; Zou, Huiqin/0000-0003-3066-6180 Independent Project from Beijing University of Chinese Medicine [JYB22-XS041]; China Scholarship Council Independent Project from Beijing University of Chinese Medicine; China Scholarship Council(China Scholarship Council) This study was supported by grants from Independent Project from Beijing University of Chinese Medicine (no. JYB22-XS041). In addition, the authors would like to send their gratitude to China Scholarship Council for providing scholarship to one of their team members and sponsoring the abroad research in University of Graz in Austria. 17 12 12 2 40 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1741-427X 1741-4288 EVID-BASED COMPL ALT Evid.-based Complement Altern. Med. 2014.0 2014 425341 10.1155/2014/425341 0.0 6 Integrative & Complementary Medicine Science Citation Index Expanded (SCI-EXPANDED) Integrative & Complementary Medicine AP3XU 25214873.0 Green Submitted, gold, Green Published 2023-03-23 WOS:000342012100001 0
J Chen, XQ; Zhou, F; Zhang, FL; Bonsangue, M Chen, Xueqin; Zhou, Fan; Zhang, Fengli; Bonsangue, Marcello Modeling microscopic and macroscopic information diffusion for rumor detection INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS English Article knowledge distillation; macroscopic diffusion; microscopic diffusion; rumor detection PREDICTION; NEWS; CLASSIFICATION; PROPAGATION Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection. [Chen, Xueqin; Zhou, Fan; Zhang, Fengli] Univ Elect Sci & Technol China, Sch Informat & Software Engn, 4,Sect 2,North Jianshe Rd, Chengdu 610054, Peoples R China; [Chen, Xueqin; Bonsangue, Marcello] Leiden Univ, Leiden Inst Adv Comp Sci, Leiden, Netherlands University of Electronic Science & Technology of China; Leiden University; Leiden University - Excl LUMC Zhou, F (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Software Engn, 4,Sect 2,North Jianshe Rd, Chengdu 610054, Peoples R China. fan.zhou@uestc.edu.cn Bonsangue, Marcello/0000-0003-3746-3618; Zhou, Fan/0000-0002-8038-8150; chen, xue qin/0000-0003-1538-3713 National Natural Science Foundation of China [62072077]; Sichuan Regional Innovation Cooperation Project [2020YFQ0018]; National Key RAMP;D Program of China [2019YFB1406202] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Regional Innovation Cooperation Project; National Key RAMP;D Program of China National Natural Science Foundation of China, Grant/Award Number: 62072077; Sichuan Regional Innovation Cooperation Project, Grant/Award Number: 2020YFQ0018; National Key R&D Program of China, Grant/Award Number: 2019YFB1406202 51 7 7 10 26 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0884-8173 1098-111X INT J INTELL SYST Int. J. Intell. Syst. OCT 2021.0 36 10 5449 5471 10.1002/int.22518 0.0 JUN 2021 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UI7JP Green Published, gold 2023-03-23 WOS:000659301900001 0
J Guo, JM; Yang, DY; Siegmund, N; Apel, S; Sarkar, A; Valov, P; Czarnecki, K; Wasowski, A; Yu, HQ Guo, Jianmei; Yang, Dingyu; Siegmund, Norbert; Apel, Sven; Sarkar, Atrisha; Valov, Pavel; Czarnecki, Krzysztof; Wasowski, Andrzej; Yu, Huiqun Data-efficient performance learning for configurable systems EMPIRICAL SOFTWARE ENGINEERING English Article Performance prediction; Configurable systems; Regression; Model selection; Parameter tuning PREDICTION Many software systems today are configurable, offering customization of functionality by feature selection. Understanding how performance varies in terms of feature selection is key for selecting appropriate configurations that meet a set of given requirements. Due to a huge configuration space and the possibly high cost of performance measurement, it is usually not feasible to explore the entire configuration space of a configurable system exhaustively. It is thus a major challenge to accurately predict performance based on a small sample of measured system variants. To address this challenge, we propose a data-efficient learning approach, called DECART, that combines several techniques of machine learning and statistics for performance prediction of configurable systems. DECART builds, validates, and determines a prediction model based on an available sample of measured system variants. Empirical results on 10 real-world configurable systems demonstrate the effectiveness and practicality of DECART. In particular, DECART achieves a prediction accuracy of 90% or higher based on a small sample, whose size is linear in the number of features. In addition, we propose a sample quality metric and introduce a quantitative analysis of the quality of a sample for performance prediction. [Guo, Jianmei] Alibaba Grp, Hangzhou, Zhejiang, Peoples R China; [Yang, Dingyu] Shanghai Dianji Univ, Shanghai, Peoples R China; [Siegmund, Norbert] Bauhaus Univ Weimar, Weimar, Germany; [Apel, Sven] Univ Passau, Software Engn, Passau, Germany; [Sarkar, Atrisha] Univ Waterloo, David R Cheriton Sch Comp, Waterloo, ON, Canada; [Sarkar, Atrisha] Univ Waterloo, Autonomoose Self Driving Car Project, Waterloo, ON, Canada; [Valov, Pavel] Univ Waterloo, Waterloo, ON, Canada; [Czarnecki, Krzysztof] Univ Waterloo, Elect & Comp Engn, Waterloo, ON, Canada; [Wasowski, Andrzej] IT Univ Copenhagen, Copenhagen, Denmark; [Yu, Huiqun] East China Univ Sci & Technol, Shanghai, Peoples R China Alibaba Group; Shanghai Dianji University; Bauhaus-Universitat Weimar; University of Passau; University of Waterloo; University of Waterloo; University of Waterloo; University of Waterloo; IT University Copenhagen; East China University of Science & Technology Guo, JM (corresponding author), Alibaba Grp, Hangzhou, Zhejiang, Peoples R China.;Yang, DY (corresponding author), Shanghai Dianji Univ, Shanghai, Peoples R China. jianmei.gjm@alibaba-inc.com; yangdy@sdju.edu.cn Siegmund, Norbert/U-1325-2019 Guo, Jianmei/0000-0001-5787-6781 National Natural Science Foundation of China [61772200, 61702320]; Shanghai Pujiang Talent Program [17PJ1401900]; Shanghai Municipal Natural Science Foundation [17ZR1406900]; Shanghai Municipal Education Commission Funds of Young Teacher Training Program [ZZSDJ17021]; Specialized Fund of Shanghai Municipal Commission of Economy and Informatization [201602008]; Specialized Research Fund for Doctoral Program of Higher Education [20130074110015]; DFG [AP 206/4, AP 206/5, AP 206/7, SI 2171/2, SI 2171/3]; Natural Sciences and Engineering Research Council of Canada; Pratt Whitney Canada National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Pujiang Talent Program; Shanghai Municipal Natural Science Foundation; Shanghai Municipal Education Commission Funds of Young Teacher Training Program; Specialized Fund of Shanghai Municipal Commission of Economy and Informatization; Specialized Research Fund for Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); DFG(German Research Foundation (DFG)); Natural Sciences and Engineering Research Council of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)CGIAR); Pratt Whitney Canada We would like to thank anonymous reviewers for their insightful comments. This research was partially supported by National Natural Science Foundation of China (No. 61772200, 61702320), Shanghai Pujiang Talent Program (No. 17PJ1401900), Shanghai Municipal Natural Science Foundation (No. 17ZR1406900), Shanghai Municipal Education Commission Funds of Young Teacher Training Program (No. ZZSDJ17021), Specialized Fund of Shanghai Municipal Commission of Economy and Informatization (No. 201602008), Specialized Research Fund for Doctoral Program of Higher Education (No. 20130074110015), the DFG grants (AP 206/4, AP 206/5, AP 206/7, SI 2171/2, and SI 2171/3), Natural Sciences and Engineering Research Council of Canada, and Pratt & Whitney Canada. 49 39 39 1 6 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1382-3256 1573-7616 EMPIR SOFTW ENG Empir. Softw. Eng. JUN 2018.0 23 3 1826 1867 10.1007/s10664-017-9573-6 0.0 42 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science GH6DH 2023-03-23 WOS:000433532300018 0
J El Yousfi, Y; Himi, M; El Ouarghi, H; Aqnouy, M; Benyoussef, S; Gueddari, H; Hmeid, HA; Alitane, A; Chaibi, M; Zahid, M; Essahlaoui, N; Hitouri, S; Essahlaoui, A; Elaaraj, A El Yousfi, Yassine; Himi, Mahjoub; El Ouarghi, Hossain; Aqnouy, Mourad; Benyoussef, Said; Gueddari, Hicham; Hmeid, Hanane Ait; Alitane, Abdennabi; Chaibi, Mohamed; Zahid, Muhammad; Essahlaoui, Narjisse; Hitouri, Sliman; Essahlaoui, Ali; Elaaraj, Abdallah Assessment and Prediction of the Water Quality Index for the Groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco) SUSTAINABILITY English Article groundwater quality; prediction; water quality index; Ghiss-Nekkor; WHO; artificial neural network ARTIFICIAL NEURAL-NETWORK; IRRIGATION PURPOSES; SEAWATER INTRUSION; COASTAL AQUIFER; ANDHRA-PRADESH; RIVER-BASIN; DRINKING; DISTRICT; SALINIZATION; VULNERABILITY Water quality index (WQI) is the primary method applied to characterize water quality in the world. The current study employed the statistical analysis and multilayer perceptron (MLP) approaches for predicting groundwater quality in the Ghiss-Nekkor aquifer, northeast of Al Hoceima, Morocco. Fifty sampled groundwater were identified and analyzed for major anions and cations throughout May 2019. Several physicochemical parameters of all the samples were identified in this investigation, such as TDS, pH, EC, Na, K, Ca, Mg, HCO3, NO3, Br, SO4, and Cl. The entropy-weighted groundwater quality index (EWQI) was calculated from these parameters. The WQI procedure determined the suitability of groundwater for consumption. The WQI value varied from 90.98 to 337.28. The EC, TDS, WQI, and Cl- spatial distribution showed that EC and Cl- are associated with poor groundwater quality. A single sample (W16) represented unsuitable water for drinking purposes and offered a WQI value of 337.28, indicating poor drinking quality due to seawater intrusion, overexploitation, and harsh weather conditions. The majority of the values obtained for the parameters exceeded the recommended limit of the World Health Organization (WHO)'s guidelines for consumption. The findings show that using parameters is a straightforward method for predicting water quality indexes with sufficient and suitable precision. The MLP model shows good predictive performances in terms of the coefficient of determination R-2, mean absolute error (MAE), and root-mean-square error (RMSE) with values of 0.9885, 5.8031, and 4.7211, respectively. The ANN approach was applied to develop a model that can accurately predict WQI utilizing mineralization, TH, NO3, and NO2 as inputs. The MAE for the model's performance was calculated to be 4.72. A Bland-Altman test was used to validate that the model is suitable. Following the test, it was determined that the model is appropriate for predicting WQI, with an error of just 0.1%. [El Yousfi, Yassine; Himi, Mahjoub; El Ouarghi, Hossain; Benyoussef, Said] Abdelmalek Essaadi Univ, Environm Management & Civil Engn Team, ENSAH, Tetouan 93030, Morocco; [Himi, Mahjoub] Univ Barcelona, Fac Geol, Marti I Franques S N, Barcelona 08028, Spain; [Aqnouy, Mourad] Moulay Ismail Univ Meknes, Fac Sci & Tech, Appl Geol Res Lab, Appl Geol & Remote Sensing Res Team, Errachidia 52000, Morocco; [Benyoussef, Said] Univ Moulay Ismail, Errachidia Fac Sci & Technol, Dept Biol, Res Team Biol Environm & Hlth, Meknes 50000, Morocco; [Gueddari, Hicham; Hmeid, Hanane Ait] Mohamed First Univ, Multidisciplinary Fac Nador, OLMAN BPGE Lab, Oujda 60000, Morocco; [Alitane, Abdennabi; Essahlaoui, Narjisse; Essahlaoui, Ali] Moulay Ismail Univ, Fac Sci, Geol Dept, Geoengn & Environm Lab,Res Grp Water Sci & Environ, Marjane 2,POB 298, Meknes 50050, Morocco; [Alitane, Abdennabi] Vrije Univ Brussels VUB, Hydrol & Hydraul Engn Dept, B-1050 Brussels, Belgium; [Chaibi, Mohamed] Univ Moulay Ismail, Fac Sci, Dept Phys, Team Renewable Energy & Energy Efficiency, POB 11201, Meknes 50050, Morocco; [Zahid, Muhammad] Heilongjiang Univ, Key Lab Funct Inorgan Mat Chem, Minist Educ Peoples Republ China, Harbin 150080, Peoples R China; [Zahid, Muhammad] Natl Univ Sci & Technol, Sch Nat Sci, Islamabad 44000, Pakistan; [Hitouri, Sliman] Univ Ibn Tofail, Fac Sci, Dept Geol, Geosci Lab, Box 133, Kenitra 14000, Morocco; [Elaaraj, Abdallah] Polydisciplinary Fac Taza, Geol Dept, Nat Resources & Environm Lab, Route Oujda,POB 1223, Taza 35000, Morocco; [Elaaraj, Abdallah] Sidi Mohamed Ben Abdellah Univ FEZ, Fac Sci & Technol, Engn Sci & Tech Ctr, Environm Dept, POB 2202, Fes 30000, Morocco Abdelmalek Essaadi University of Tetouan; University of Barcelona; Moulay Ismail University of Meknes; Moulay Ismail University of Meknes; Mohammed First University of Oujda; Moulay Ismail University of Meknes; Vrije Universiteit Brussel; Moulay Ismail University of Meknes; Heilongjiang University; National University of Sciences & Technology - Pakistan; Ibn Tofail University of Kenitra; Sidi Mohamed Ben Abdellah University of Fez; Sidi Mohamed Ben Abdellah University of Fez Alitane, A (corresponding author), Moulay Ismail Univ, Fac Sci, Geol Dept, Geoengn & Environm Lab,Res Grp Water Sci & Environ, Marjane 2,POB 298, Meknes 50050, Morocco.;Alitane, A (corresponding author), Vrije Univ Brussels VUB, Hydrol & Hydraul Engn Dept, B-1050 Brussels, Belgium. abdennabi.alitane@vub.be Aqnouy, Mourad/CAI-4060-2022; Zahid, Muhammad/AAC-6372-2022; Ouarghi, Hossain El/ABC-1921-2020 Zahid, Muhammad/0000-0002-9618-4814; Ouarghi, Hossain El/0000-0001-6866-6579; ELAARAJ, ABDALLAH/0000-0001-6190-0641; GUEDDARI, Hicham/0000-0001-9439-6867; mourad, aqnouy/0000-0003-1633-2870; EL YOUSFI, Yassine/0000-0002-5003-2604; ALITANE, Abdennabi/0000-0003-4695-0338 77 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JAN 2023.0 15 1 402 10.3390/su15010402 0.0 19 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 7R3CE gold 2023-03-23 WOS:000909952700001 0
C Zenglin, S; Mettes, P; Zheng, G; Snoek, C Engelhardt, S; Oksuz, I; Zhu, D; Yuan, Y; Mukhopadhyay, A; Heller, N; Huang, SX; Nguyen, H; Sznitman, R; Xue, Y Shi, Zenglin; Mettes, Pascal; Zheng, Guoyan; Snoek, Cees Frequency-Supervised MR-to-CT Image Synthesis DEEP GENERATIVE MODELS, AND DATA AUGMENTATION, LABELLING, AND IMPERFECTIONS Lecture Notes in Computer Science English Proceedings Paper 1st Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) / 1st MICCAI Workshop on Data Augmentation, Labelling, and Imperfections (DALI) SEP 27-OCT 01, 2021 ELECTR NETWORK MICCAI,Univ Strasbourg,iCUBE Deep learning; CT synthesis; Frequency supervision ATTENUATION CORRECTION; SEGMENTATION This paper strives to generate a synthetic computed tomography (CT) image from a magnetic resonance (MR) image. The synthetic CT image is valuable for radiotherapy planning when only an MR image is available. Recent approaches have made large strides in solving this challenging synthesis problem with convolutional neural networks that learn a mapping from MR inputs to CT outputs. In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images. To address this common limitation, we introduce frequency-supervised deep networks to explicitly enhance high-frequency MR-to-CT image reconstruction. We propose a frequency decomposition layer that learns to decompose predicted CT outputs into low- and high-frequency components, and we introduce a refinement module to improve high-frequency reconstruction through high-frequency adversarial learning. Experimental results on a new dataset with 45 pairs of 3D MR-CT brain images show the effectiveness and potential of the proposed approach. Code is available at https://github.com/shizenglin/Frequency-Supervised-MR-to-CT-Image-Synthesis. [Shi, Zenglin; Mettes, Pascal; Snoek, Cees] Univ Amsterdam, Amsterdam, Netherlands; [Zheng, Guoyan] Shanghai Jiao Tong Univ, Shanghai, Peoples R China University of Amsterdam; Shanghai Jiao Tong University Zenglin, S (corresponding author), Univ Amsterdam, Amsterdam, Netherlands. z.shi@uva.nl 30 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-88210-5; 978-3-030-88209-9 LECT NOTES COMPUT SC 2021.0 13003 3 13 10.1007/978-3-030-88210-5_1 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Radiology, Nuclear Medicine & Medical Imaging BU0EB Green Submitted 2023-03-23 WOS:000867639100001 0
J Qu, S; Guan, Z; Verschuur, E; Chen, YK Qu, Shan; Guan, Zhe; Verschuur, Eric; Chen, Yangkang RETRACTED: Automatic high-resolution microseismic event detection via supervised machine learning (Publication with Expression of Concern. See vol. 221, pg. 2056, 2020) (Retracted article. See vol. 222, pg. 1896, 2020) GEOPHYSICAL JOURNAL INTERNATIONAL English Article; Retracted Publication Event detection; High-resolution; Machine learning; Microseismic; SVM; 2-D texture features SEISMIC DATA; NOISE; TIME; PHASE; DEEP; SUPPRESSION; SPECTRUM; PICKING; SPARSE; DOMAIN Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML event detection microseismic. [Qu, Shan; Verschuur, Eric] Delft Univ Technol, Dept Imaging Phys, Mekelweg 2, NL-2628 CD Delft, Netherlands; [Guan, Zhe] Rice Univ, Appl Phys, Houston, TX USA; [Chen, Yangkang] Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China Delft University of Technology; Rice University; Zhejiang University Chen, YK (corresponding author), Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China. chenyk2016@gmail.com Guan, Zhe/ABG-3737-2020 Guan, Zhe/0000-0001-6554-4242 Delphi consortium; Thousand Youth Talents Plan; Zhejiang University Delphi consortium; Thousand Youth Talents Plan; Zhejiang University Shan Qu and Eric Verschuur thank the sponsors of the Delphi consortium for their support. Yangkang Chen is financially supported by the 'Thousand Youth Talents Plan', and the Starting Funds from Zhejiang University. The authors appreciate Wei Chen for providing the field data sets from a shale play inside the Sichuan Basin. The authors thank Scikit-learn for providing free machine learning library in Python (scikit-learn.org). 90 25 25 1 38 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0956-540X 1365-246X GEOPHYS J INT Geophys. J. Int. SEP 2019.0 218 3 2106 2121 10.1093/gji/ggz273 0.0 16 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics IS7AK Bronze 2023-03-23 WOS:000482302200039 0
J Lamiche, I; Bin, G; Jing, Y; Yu, ZW; Hadid, A Lamiche, Imane; Bin, Guo; Jing, Yao; Yu, Zhiwen; Hadid, Abdenour A continuous smartphone authentication method based on gait patterns and keystroke dynamics JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING English Article Continuous authentication; Behavioral biometrics; Gait analysis; Keystroke dynamics; Smartphone sensing RECOGNITION Behavioral biometrics, such as gait patterns and keystroke dynamics, have been becoming increasingly used in human identity recognition research for enhancing the smartphone security. A new multimodal authentication method able to strengthen the smartphone authentication system is proposed in this paper. The proposed mechanism acquires gait patterns from the accelerometer, as well as keystroke dynamics, continuously without user intervention through simultaneous walk and text input. More specifically, features are extracted from both modalities. Afterward, a feature level fusion method is applied to build a multimodal biometrics profile for the user. Fused feature vectors are subjected to the sequential floating forward selection algorithm to reduce their dimensions as well as the computational complexity. The effectiveness of the proposed method is examined through a real multimodal dataset collected from 20 subjects under various scenarios, using different machine learning classifiers. The experimental results achieved a promising accuracy of 99.11% when using multilayer perceptron classifier with the average false acceptance rate, false rejection rate and equal error rate values of 0.684%, 7%, and 1%, respectively. Furthermore, the security strength of the proposed method was evaluated against two types of attacks, the zero-effort attack and minimal-effort mimicking attack. Results demonstrate that our approach represents a robust and secure authentication solution. [Lamiche, Imane; Bin, Guo; Jing, Yao; Yu, Zhiwen] Northwestern Polytech Univ, 127 YouYi XiLu, Xian 710072, Shaanxi, Peoples R China; [Hadid, Abdenour] Univ Oulu, Pentti Kaiteran Katu 1, FIN-90570 Oulu, Finland Northwestern Polytechnical University; University of Oulu Bin, G (corresponding author), Northwestern Polytech Univ, 127 YouYi XiLu, Xian 710072, Shaanxi, Peoples R China. stic0303@gmail.com; guob@nwpu.edu.cn; 1191479758@qq.com; zhiwenyu@nwpu.edu.cn; hadidab@hotmail.com Lamiche, Imane/AAP-9072-2020; ARSLAN, Okan/AAA-3232-2020 Yu, Zhiwen/0000-0002-9905-3238 National Key R&D Program of China [2017YFB1001803]; National Natural Science Foundation of China [61772428, 61725205] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partially supported by the National Key R&D Program of China (2017YFB1001803), and the National Natural Science Foundation of China (nos. 61772428, 61725205). 54 27 28 0 15 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-5137 1868-5145 J AMB INTEL HUM COMP J. Ambient Intell. Humaniz. Comput. NOV 2019.0 10 11 SI 4417 4430 10.1007/s12652-018-1123-6 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications JI4SJ 2023-03-23 WOS:000493457400015 0
C Ferreira, A; Chen, CS; Barni, M IEEE Ferreira, Anselmo; Chen, Changsheng; Barni, Mauro Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection 2021 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS) IEEE International Workshop on Information Forensics and Security English Proceedings Paper IEEE International Workshop on Information Forensics and Security (WIFS) DEC 07-10, 2021 Montpellier, FRANCE IEEE 2D Barcode; Copy Detection; Anti-Counterfeiting; Texture Descriptors Nowadays, 2D barcodes have been widely used for advertisement, mobile payment, and product authentication. However, in applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. In this paper, we employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (re-broadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process. The proposed features are composed of global and local descriptors, which characterize the multi-scale texture appearance and the points of interest distribution, respectively. The proposed descriptors are compared against some existing texture descriptors and deep learning-based approaches under various scenarios, such as cross-datasets and cross-size. Experimental results highlight the practicality of the proposed method in real-world settings. [Ferreira, Anselmo; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, Siena, Italy; [Chen, Changsheng] Shenzhen Univ, Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen Key Lab Media Secur, Shenzhen, Guangdong, Peoples R China University of Siena; Shenzhen University Chen, CS (corresponding author), Shenzhen Univ, Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen Key Lab Media Secur, Shenzhen, Guangdong, Peoples R China. anselmo.castelo@unisi.it; cschen@szu.edu.cn; barni@dii.unisi.it Key-Area Research and Development Program of Guangdong Province, China [2019B010139003, 892757]; National National Science Foundation of China [62072313] Key-Area Research and Development Program of Guangdong Province, China; National National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partially funded by the Key-Area Research and Development Program of Guangdong Province, China (2019B010139003), by the MSCAEU project PrintOut (Grant #892757) and also by the National National Science Foundation of China (Grant #62072313). Corresponding author: Changsheng Chen (cschen@szu.edu.cn) 14 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2157-4766 978-1-6654-1717-4 IEEE INT WORKS INFOR 2021.0 104 109 10.1109/WIFS53200.2021.9648391 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS9KY Green Submitted 2023-03-23 WOS:000782381000018 0
C Zhao, ZD; Chen, XJ; Xuan, YX; Dong, Y; Wang, DK; Liang, KT IEEE COMP SOC Zhao, Zhendong; Chen, Xiaojun; Xuan, Yuexin; Dong, Ye; Wang, Dakui; Liang, Kaitai DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-24, 2022 New Orleans, LA IEEE,CVF,IEEE Comp Soc Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model. These attacks are susceptible to backdoor detection algorithms and even visual inspection. In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms. We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can I) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data. [Zhao, Zhendong; Chen, Xiaojun; Xuan, Yuexin; Dong, Ye; Wang, Dakui] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China; [Zhao, Zhendong; Chen, Xiaojun; Xuan, Yuexin; Dong, Ye; Wang, Dakui] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China; [Liang, Kaitai] Delft Univ Technol, Delft, Netherlands Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Information Engineering, CAS; Delft University of Technology Chen, XJ (corresponding author), Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.;Chen, XJ (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China. zhaozhendong@iie.ac.cn; chenxiaojun@iie.ac.cn; xuanyuexin@iie.ac.cn; dongye@iie.ac.cn; wangdakui@iie.ac.cn; kaitai.liang@tudelft.nl National Key Research and Development Program of China [2021YFB3101400]; Strategic Priority Research Program of Chinese Academy of Sciences [XDC02040400]; European Union's Horizon 2020 research and innovation programme [952697, 101021727] National Key Research and Development Program of China; Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); European Union's Horizon 2020 research and innovation programme This work is supported by The National Key Research and Development Program of China No. 2021YFB3101400, the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040400, and European Union's Horizon 2020 research and innovation programme under grant agreement No. 952697 (ASSURED) and No. 101021727 (IRIS). 44 0 0 3 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-6946-3 PROC CVPR IEEE 2022.0 15192 15201 10.1109/CVPR52688.2022.01478 0.0 10 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU0OQ 2023-03-23 WOS:000870783001001 0
J Geng, XX; Ji, SP; Lu, M; Zhao, LL Geng, Xiaoxiao; Ji, Shunping; Lu, Meng; Zhao, Lingli Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation REMOTE SENSING English Article LiDAR point cloud segmentation; attentive skip connection; channel attentive enhancement; multi-scale aggregation; deep learning Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud feature representation and super segmentation performance. First, upon a baseline encoder-decoder architecture for point cloud segmentation, namely, RandLA-Net, an attentive skip connection was proposed to replace the commonly used concatenation to balance the encoder and decoder features of the same scales. Second, a channel attentive enhancement module was introduced to the local attention enhancement module to boost the local feature discriminability and aggregate the local channel structure information. Third, we developed a multi-scale feature aggregation method to capture the global structure of a point cloud from both the encoder and the decoder. The experimental results reported that our MSAAN significantly outperformed state-of-the-art methods, i.e., at least 15.3% mIoU improvement for scene-2 of CSPC dataset, 5.2% for scene-5 of CSPC dataset, and 6.6% for Toronto3D dataset. [Geng, Xiaoxiao; Ji, Shunping; Zhao, Lingli] Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China; [Lu, Meng] Univ Utrecht, Fac Geosci, Dept Phys Geog, Princetonlaan 8, NL-3584 CB Utrecht, Netherlands Wuhan University; Utrecht University Ji, SP (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China. gengxiaoxiao@whu.edu.cn; jishunping@whu.edu.cn; m.lu@uu.nl; zhaolingli@whu.edu.cn Lu, Meng/AAF-1711-2019 Lu, Meng/0000-0002-6850-581X; Ji, Shunping/0000-0002-3088-1481 National Key Research and Development Program of China [2018YFB0505003] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB0505003). 44 11 11 8 29 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2021.0 13 4 691 10.3390/rs13040691 0.0 12 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology QQ3QC gold 2023-03-23 WOS:000624437800001 0
J Chen, TS; Andersen, MS Chen, Tianshi; Andersen, Martin S. On semiseparable kernels and efficient implementation for regularized system identification and function estimation AUTOMATICA English Article Kernel-based regularization; Semiseparable kernels; Output kernels; Kernel design; Efficient implementation GAUSSIAN PROCESSES; INPUT-DESIGN A long-standing problem for kernel-based regularization methods is their high computational complexity O(N-3), where N is the number of data points. In this paper, we make a breakthrough for this problem. In particular, we show that it is possible to design general semiseparable kernels through either the system theory perspective or the machine learning perspective, leading to semiseparable simulation-induced kernels or amplitude modulated locally stationary kernels, respectively. Moreover, for many frequently used test input signals in automatic control, and by exploring the semiseparable structure of a kernel and the corresponding output kernel, their computational complexity, without any approximations, can be lowered to O(Nq(2)) or O(Nq(3)), where q is the semiseparability rank of the output kernel that only depends on the chosen kernel and the input signal. Numerical simulation shows that the proposed implementation can be 10(4) times faster than a state of art implementation. (C) 2021 Elsevier Ltd. All rights reserved. [Chen, Tianshi] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China; [Chen, Tianshi] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China; [Andersen, Martin S.] Tech Univ Denmark, Dept Appl Math & Comp Sci, Barcelona, Spain Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen Chen, TS (corresponding author), Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China.;Chen, TS (corresponding author), Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China. tschen@cuhk.edu.cn; mskan@dtu.dk Chen, Tianshi/C-2575-2014 Chen, Tianshi/0000-0001-8655-2655; Andersen, Martin Skovgaard/0000-0002-4654-3946 36 0 0 1 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0005-1098 1873-2836 AUTOMATICA Automatica OCT 2021.0 132 109682 10.1016/j.automatica.2021.109682 0.0 JUL 2021 14 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering UG8DM 2023-03-23 WOS:000689475700002 0
C Ferriol-Galmes, M; Cheng, XL; Shi, X; Xiao, SH; Barlet-Ros, P; Cabellos-Aparicio, A IEEE Ferriol-Galmes, Miquel; Cheng, Xiangle; Shi, Xiang; Xiao, Shihan; Barlet-Ros, Pere; Cabellos-Aparicio, Albert FLOWDT: A FLOW-AWARE DIGITAL TWIN FOR COMPUTER NETWORKS 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 22-27, 2022 Singapore, SINGAPORE Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Network Modeling; Machine Learning; Graph Neural Networks Network modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing them in real production networks. Recently, Graph Neural Networks (GNNs) have emerged as a practical solution to produce network models that can learn and extract complex patterns from real data without making any assumptions. However, state-of-the-art GNN-based network models only work with traffic matrices, this is a very coarse and simplified representation of network traffic. Although this assumption has shown to work well in certain use-cases, it is a limiting factor because, in practice, networks operate with flows. In this paper, we present FlowDT a new DL-based solution designed to model computer networks at the fine-grained flow level. In our evaluation, we show how FlowDT can accurately predict relevant per-flow performance metrics with an error of 3.5%, FlowDT's performance is also benchmarked against vanilla DL models as well as with Queuing Theory. [Ferriol-Galmes, Miquel; Barlet-Ros, Pere; Cabellos-Aparicio, Albert] Univ Politecn Cataluna, Barcelona, Spain; [Cheng, Xiangle; Shi, Xiang; Xiao, Shihan] Huawei Technol, Shenzhen, Peoples R China Universitat Politecnica de Catalunya; Huawei Technologies Ferriol-Galmes, M (corresponding author), Univ Politecn Cataluna, Barcelona, Spain. Spanish Government [PID2020-118011GB-C21]; Catalan Institution for Research and Advanced Studies (ICREA); FEDER Spanish Government(Spanish Government); Catalan Institution for Research and Advanced Studies (ICREA)(ICREA); FEDER(European Commission) This work has been supported by the Spanish Government through project TRAINER-A (PID2020-118011GB-C21) with FEDER contribution and the Catalan Institution for Research and Advanced Studies (ICREA). 26 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-6654-0540-9 INT CONF ACOUST SPEE 2022.0 8907 8911 10.1109/ICASSP43922.2022.9746953 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering BT9UB Green Published 2023-03-23 WOS:000864187909044 0
J Duranthon, O; Marsili, M; Xie, R Duranthon, O.; Marsili, M.; Xie, R. Maximal relevance and optimal learning machines JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT English Article learning theory; machine learning; statistical inference NEURAL-NETWORKS We explore the hypothesis that learning machines extract representations of maximal relevance, where the relevance is defined as the entropy of the energy distribution of the internal representation. We show that the mutual information between the internal representation of a learning machine and the features that it extracts from the data is bounded from below by the relevance. This motivates our study of models with maximal relevance-that we call optimal learning machines-as candidates of maximally informative representations. We analyse how the maximisation of the relevance is constrained both by the architecture of the model used and by the available data, in practical cases. We find that sub-extensive features that do not affect the thermodynamics of the model, may affect significantly learning performance, and that criticality enhances learning performance, but the existence of a critical point is not a necessary condition. On specific learning tasks, we find that (i) the maximal values of the likelihood are achieved by models with maximal relevance, (ii) internal representations approach the maximal relevance that can be achieved in a finite dataset and (iii) learning is associated with a broadening of the spectrum of energy levels of the internal representation, in agreement with the maximum relevance hypothesis. [Duranthon, O.] Ecole Normale Super, Dept Phys, 24 Rue Lhomond, F-75005 Paris, France; [Marsili, M.] Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy; [Xie, R.] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China; [Xie, R.] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China UDICE-French Research Universities; Universite PSL; Ecole Normale Superieure (ENS); Abdus Salam International Centre for Theoretical Physics (ICTP); Central China Normal University; Central China Normal University Marsili, M (corresponding author), Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy. marsili@ictp.it Marsili, Matteo/AAX-2124-2021 Marsili, Matteo/0000-0001-9437-6989 Chinal Scholarship Council (CSC) [202006770018] Chinal Scholarship Council (CSC) We benefitted from discussions with Jean Barbier, Riccardo Zecchina, Federico Ricci-Tersenghi and Yasser Roudi. We are grateful to the authors of [25] for sharing their data with us. We thank an anonymous referee of MSML2020 for pointing out the example in section 3.1. Rongrong Xie acknowledges a fellowship from the Chinal Scholarship Council (CSC) under Grant CSC No. 202006770018. 34 3 3 1 5 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1742-5468 J STAT MECH-THEORY E J. Stat. Mech.-Theory Exp. MAR 2021.0 2021 3 33409 10.1088/1742-5468/abe6ff 0.0 30 Mechanics; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Physics QZ4RH Green Submitted 2023-03-23 WOS:000630715000001 0
J Yang, PP; Baracchi, D; Ni, RR; Zhao, Y; Argenti, F; Piva, A Yang, Pengpeng; Baracchi, Daniele; Ni, Rongrong; Zhao, Yao; Argenti, Fabrizio; Piva, Alessandro A Survey of Deep Learning-Based Source Image Forensics JOURNAL OF IMAGING English Review image forensics; multimedia forensics; source identification; data driven methods CONVOLUTIONAL NEURAL-NETWORKS; COMPUTER-GENERATED IMAGES; ADVERSARIAL ATTACKS; SOURCE CAMERA; IDENTIFICATION; MODEL; CLASSIFIER Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field. [Yang, Pengpeng; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Yang, Pengpeng; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Baracchi, Daniele; Argenti, Fabrizio; Piva, Alessandro] Univ Florence, Dept Informat Engn, Via S Marta 3, I-50139 Florence, Italy Beijing Jiaotong University; Beijing Jiaotong University; University of Florence Ni, RR (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China.;Ni, RR (corresponding author), Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China.;Piva, A (corresponding author), Univ Florence, Dept Informat Engn, Via S Marta 3, I-50139 Florence, Italy. 14120339@bjtu.edu.cn; daniele.baracchi@unifi.it; rrni@bjtu.edu.cn; yzhao@bjtu.edu.cn; fabrizio.argenti@unifi.it; alessandro.piva@unifi.it Baracchi, Daniele/AHD-5162-2022 Argenti, Fabrizio/0000-0001-7776-4015 National Key Research and Development of China [2016YFB0800404]; National NSF of China [61672090, 61532005, 61332012]; Fundamental Research Funds for the Central Universities [2018JBZ001, 2017YJS054] National Key Research and Development of China; National NSF of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development of China (No. 2016YFB0800404), the National NSF of China (Nos. 61672090, 61532005, 61332012) and the Fundamental Research Funds for the Central Universities (Nos. 2018JBZ001, 2017YJS054). 118 24 24 2 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2313-433X J IMAGING J. Imaging MAR 2020.0 6 3 9 10.3390/jimaging6030009 0.0 24 Imaging Science & Photographic Technology Emerging Sources Citation Index (ESCI) Imaging Science & Photographic Technology LA0PR 34460606.0 gold, Green Accepted 2023-03-23 WOS:000523659600001 0
C Han, ZY; Xu, FL; Shi, JH; Shang, Y; Ma, HR; Hui, P; Li, Y ASSOC COMP MACHINERY Han, Zhenyu; Xu, Fengli; Shi, Jinghan; Shang, Yu; Ma, Haorui; Hui, Pan; Li, Yong Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT English Proceedings Paper 29th ACM International Conference on Information and Knowledge Management (CIKM) OCT 19-23, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGIR,ACM SIGWEB Recommender System; Heterogeneous Information Network; Automated Machine Learning; Graph Convolutional Network In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits bruteforce search. To address these challenges, we propose Genetic MetaStructure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over 6% performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design. [Han, Zhenyu; Xu, Fengli; Shang, Yu; Ma, Haorui; Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Beijing 100084, Peoples R China; [Shi, Jinghan] Beijing Univ Posts & Telecommun, BUPT, Beijing, Peoples R China; [Hui, Pan] Univ Helsinki, Helsinki, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China Tsinghua University; Beijing University of Posts & Telecommunications; University of Helsinki; Hong Kong University of Science & Technology Han, ZY (corresponding author), Tsinghua Univ, Dept Elect Engn, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Beijing 100084, Peoples R China. hanzy19@mails.tsinghua.edu.cn; fenglixu2020@hotmail.com; superali@bupt.edu.cn; shang-y17@mails.tsinghua.edu.cn; mhr17@mails.tsinghua.edu.cn; pan.hui@helsinki.fi; liyong07@tsinghua.edu.cn li, yong/HDN-3885-2022 National Key Research and Development Program of China [2018YFB1800804]; National Nature Science Foundation of China [U1936217, 61971267, 61972223, 61941117, 61861136003]; Beijing Natural Science Foundation [L182038]; Beijing National Research Center for Information Science and Technology [20031887521]; Tsinghua University -Tencent Joint Laboratory for Internet Innovation Technology National Key Research and Development Program of China; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Beijing National Research Center for Information Science and Technology; Tsinghua University -Tencent Joint Laboratory for Internet Innovation Technology This work was supported in part by The National Key Research and Development Program of China under grant 2018YFB1800804, the National Nature Science Foundation of China under U1936217, 61971267, 61972223, 61941117, 61861136003, Beijing Natural Science Foundation under L182038, Beijing National Research Center for Information Science and Technology under 20031887521, and research fund of Tsinghua University -Tencent Joint Laboratory for Internet Innovation Technology. 21 3 3 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6859-9 2020.0 455 464 10.1145/3340531.3412015 0.0 10 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LW Green Submitted 2023-03-23 WOS:000749561300048 0
J Li, XY; Lin, CL; Simos, TE; Mourtas, SD; Katsikis, VN Li, Xingyuan; Lin, Chia-Liang; Simos, Theodore E.; Mourtas, Spyridon D.; Katsikis, Vasilios N. Computation of Time-Varying {2,3}- and {2,4}-Inverses through Zeroing Neural Networks MATHEMATICS English Article {2; 3}-inverse; 4}-inverse; zeroing neural networks; dynamical system MOORE-PENROSE INVERSE; ZNN MODELS; (2,3)-INVERSES; ALGORITHM; ZFS This paper investigates the problem of computing the time-varying {2,3}- and {2,4}-inverses through the zeroing neural network (ZNN) method, which is presently regarded as a state-of-the-art method for computing the time-varying matrix Moore-Penrose inverse. As a result, two new ZNN models, dubbed ZNN23I and ZNN24I, for the computation of the time-varying {2,3}- and {2,4}-inverses, respectively, are introduced, and the effectiveness of these models is evaluated. Numerical experiments investigate and confirm the efficiency of the proposed ZNN models for computing the time-varying {2,3}- and {2,4}-inverses. [Li, Xingyuan] Zhejiang Fash Inst Technol, 495 Fenghua Rd, Ningbo 315211, Peoples R China; [Lin, Chia-Liang] Natl & Kapodistrian Univ Athens, Gen Dept, Euripus Campus, Evia 34400, Greece; [Lin, Chia-Liang] Huzhou Univ, Dept Visual Commun Design, Huzhou 313000, Peoples R China; [Simos, Theodore E.] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan; [Simos, Theodore E.] Ulyanovsk State Tech Univ, Lab Interdisciplinary Problems Energy Prod, 32 Severny Venetz St, Ulyanovsk 432027, Russia; [Simos, Theodore E.] Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China; [Simos, Theodore E.] Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi 67100, Greece; [Mourtas, Spyridon D.; Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Dept Econ Math Informat & Stat Economet, Sofokleous 1 St, Athens 10559, Greece; [Mourtas, Spyridon D.] Siberian Fed Univ, Lab Hybrid Methods Modelling & Optimizat Complex S, Prosp Svobodny 79, Krasnoyarsk 660041, Russia National & Kapodistrian University of Athens; Huzhou University; China Medical University Taiwan; China Medical University Hospital - Taiwan; Ulyanovsk State Technical University; Democritus University of Thrace; National & Kapodistrian University of Athens; Siberian Federal University Simos, TE (corresponding author), China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan.;Simos, TE (corresponding author), Ulyanovsk State Tech Univ, Lab Interdisciplinary Problems Energy Prod, 32 Severny Venetz St, Ulyanovsk 432027, Russia.;Simos, TE (corresponding author), Neijing Normal Univ, Data Recovery Key Lab Sichun Prov, Neijiang 641100, Peoples R China.;Simos, TE (corresponding author), Democritus Univ Thrace, Dept Civil Engn, Sect Math, Xanthi 67100, Greece. simost@susu.ru Mourtas, Spyridon D./AAB-3651-2022; Katsikis, Vasilios/AAD-7216-2019 Mourtas, Spyridon D./0000-0002-8299-9916; Katsikis, Vasilios/0000-0002-8208-9656 Zhejiang Provincial Philosophy and Social Sciences Planning Project; Humanities and Social Sciences Research Project of the Ministry of Education; [20NDJC28Z]; [21YJAZH044] Zhejiang Provincial Philosophy and Social Sciences Planning Project; Humanities and Social Sciences Research Project of the Ministry of Education; ; This paper is supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project (20NDJC28Z) and is funded by the Humanities and Social Sciences Research Project of the Ministry of Education (21YJAZH044). 36 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics DEC 2022.0 10 24 4759 10.3390/math10244759 0.0 13 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 7H2TK gold, Green Published 2023-03-23 WOS:000903059400001 0
J Hong, HY; Panahi, M; Shirzadi, A; Ma, TW; Liu, JZ; Zhu, AX; Chen, W; Kougias, I; Kazakis, N Hong, Haoyuan; Panahi, Mahdi; Shirzadi, Ataollah; Ma, Tianwu; Liu, Junzhi; Zhu, A-Xing; Chen, Wei; Kougias, Ioannis; Kazakis, Nerantzis Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution SCIENCE OF THE TOTAL ENVIRONMENT English Article Climate change; Flood susceptibility; ANFIS; Genetic algorithm; Differential evolution; Hengfeng County SUPPORT VECTOR MACHINE; ARTIFICIAL-INTELLIGENCE APPROACH; LAND-USE CHANGES; FLASH-FLOOD; HAZARD ASSESSMENT; SEDIMENT TRANSPORT; SPATIAL PREDICTION; CLIMATE-CHANGE; RIVER-BASIN; RISK-ASSESSMENT Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithologic parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for allmodelswas > 0.80. The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the RMSE and MSE methods, the ANFIS-DE hybrid model is more suitable for flood susceptibility mapping in the study area. The proposed method is adaptable and can easily be applied in other sites for flood management and prevention. (c) 2017 Elsevier B.V. All rights reserved. [Hong, Haoyuan; Ma, Tianwu; Liu, Junzhi; Zhu, A-Xing] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan; Ma, Tianwu; Liu, Junzhi; Zhu, A-Xing] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China; [Hong, Haoyuan; Ma, Tianwu; Liu, Junzhi; Zhu, A-Xing] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China; [Panahi, Mahdi] Islamic Azad Univ, North Tehran Branch, Young Researchers & Elites Club, Tehran, Iran; [Shirzadi, Ataollah] Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj, Iran; [Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; [Kougias, Ioannis] JRC, European Commiss, Directorate Energy Transport & Climate, Ispra, Italy; [Kazakis, Nerantzis] Aristotle Univ Thessaloniki, Sch Geol, Thessaloniki, Greece Nanjing Normal University; Islamic Azad University; University of Kurdistan; Xi'an University of Science & Technology; European Commission Joint Research Centre; EC JRC ISPRA Site; Aristotle University of Thessaloniki Zhu, AX (corresponding author), Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China. hong_haoyuan@outlook.com; azhu@wisc.edu Hong, Haoyuan/C-8455-2014; Shirzadi, Ataollah/AAX-9800-2020; Ma, Tianwu/AAM-8641-2021; Liu, Junzhi/AGE-4547-2022; Kazakis, Nerantzis/Q-5205-2016; Kougias, Ioannis/AAJ-4053-2020; Panahi/M-4175-2017; Chen, Wei/ABB-8669-2020; Liu, Junzhi/AAG-5846-2021 Hong, Haoyuan/0000-0001-6224-069X; Ma, Tianwu/0000-0002-6199-9298; Liu, Junzhi/0000-0002-7354-4207; Kazakis, Nerantzis/0000-0002-0406-9561; Kougias, Ioannis/0000-0002-5655-4462; Panahi/0000-0001-7601-9208; Chen, Wei/0000-0002-5825-1422; Shirzadi, Ataollah/0000-0003-1666-1180 National Natural Science Foundation of China [41431177, 41601413]; Natural Science Research Program of Jiangsu [14KJA170001, BK20150975]; National Basic Research Program of China [2015CB954102]; CAS-VPST Silk Road Science Fund [GJHZ1855]; Vilas Associate Award; Hammel Faculty Fellow Award; University of Wisconsin-Madison; One-Thousand Talents Program of China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Research Program of Jiangsu; National Basic Research Program of China(National Basic Research Program of China); CAS-VPST Silk Road Science Fund; Vilas Associate Award; Hammel Faculty Fellow Award; University of Wisconsin-Madison; One-Thousand Talents Program of China We express our great thanks to Wei Ouyang, Associate Editor of the journal Science of the Total Environment and all reviewers, with their comments and suggestions, which improved the quality of our paper. National Natural Science Foundation of China (Project No.: 41431177, 41601413), Natural Science Research Program of Jiangsu (Project No.: BK20150975), Natural Science Research Program of Jiangsu (Project No.: 14KJA170001), National Basic Research Program of China (Project No.: 2015CB954102), and CAS-VPST Silk Road Science Fund 2018 (Grant No. GJHZ1855). Support to A-Xing Zhu through the Vilas Associate Award, the Hammel Faculty Fellow Award, the Manasse Chair Professorship from the University of Wisconsin-Madison, and the One-Thousand Talents Program of China is greatly appreciated. We thank the Baidu Company (www.baidu.com) for providing the flood event photo. 132 215 218 14 181 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. APR 15 2018.0 621 1124 1141 10.1016/j.scitotenv.2017.10.114 0.0 18 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology FU9SJ 29074239.0 2023-03-23 WOS:000424196800112 0
J He, L; Rao, YZ; Li, J; Chanussot, J; Plaza, A; Zhu, JW; Li, B He, Lin; Rao, Yizhou; Li, Jun; Chanussot, Jocelyn; Plaza, Antonio; Zhu, Jiawei; Li, Bo Pansharpening via Detail Injection Based Convolutional Neural Networks IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Convolutional neural networks (CNNs); detail injection; pansharpening SPECTRAL RESOLUTION IMAGES; MULTISPECTRAL IMAGES; FUSION; ENHANCEMENT; CONTRAST; QUALITY; MS Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains. In this paper, we design a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injection based CNN (DiCNN1) mines MS details through the PAN image and the MS image, and the second one (DiCNN2) utilizes only the PAN image. The main advantage of the proposed DiCNNs is that they provide explicit physical interpretations and can achieve fast convergence while achieving high pansharpening quality. Furthermore, the effectiveness of the proposed approaches is also analyzed from a relatively theoretical point of view. Our methods are evaluated via experiments on real MS image datasets, achieving excellent performance when compared to other state-of-the-art methods. [He, Lin; Rao, Yizhou; Zhu, Jiawei] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China; [Li, Jun] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, CNRS, GIPSA Lab, F-38000 Grenoble, France; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, E-10071 Caceres, Spain; [Li, Bo] Beihang Univ, Beijing Key Lab Digital Media, Sch Comp Sci & Engn, Beijing 100191, Peoples R China; [Li, Bo] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China South China University of Technology; Hunan University; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Universidad de Extremadura; Beihang University; Beihang University Li, J (corresponding author), Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. helin@scut.edu.cn; raoyizhou@139.com; lijun48@mail.sysu.edu.cn; joecelyn.chanussot@gipsa-lab.grenoble-inp.fr; aplaza@unex.es; zjw7342@163.com; boli@buaa.edu.cn Plaza, Antonio/C-4455-2008; Li, Jun/E-6840-2019; Chanussot, Jocelyn/Y-2395-2019; Li, Bo/AAA-8968-2020; Li, Jun/AAD-7233-2020 Plaza, Antonio/0000-0002-9613-1659; Li, Jun/0000-0003-1613-9448; Li, Bo/0000-0002-7294-6888; Li, Jun/0000-0003-1613-9448; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [61571195, 61771496, 61633010, 61836003]; Guangdong Provincial Natural Science Foundation [2016A030313254, 2016A030313516, 2017A030313382]; National Key Research and Development Program of China [2017YFB0502900]; China Scholarship Council [201706155080] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); National Key Research and Development Program of China; China Scholarship Council(China Scholarship Council) This work was supported in part by the National Natural Science Foundation of China under Grants 61571195, 61771496, 61633010, and 61836003, in part by the Guangdong Provincial Natural Science Foundation under Grants 2016A030313254, 2016A030313516, and 2017A030313382, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by China Scholarship Council under Grant 201706155080. 52 93 95 5 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. APR 2019.0 12 4 1188 1204 10.1109/JSTARS.2019.2898574 0.0 17 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology HT7PJ Green Submitted 2023-03-23 WOS:000464756600012 0
J Chen, XQ; Zhou, F; Zhang, FL; Bonsangue, M Chen, Xueqin; Zhou, Fan; Zhang, Fengli; Bonsangue, Marcello Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning INFORMATION PROCESSING & MANAGEMENT English Article Rumor detection; Participant-level; User influence; Susceptibility; Temporal SOCIAL MEDIA; FAKE NEWS; CLASSIFICATION; PROPAGATION; INFORMATION; INTERESTS Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms. [Chen, Xueqin; Zhou, Fan; Zhang, Fengli] Univ Elect Sci & Technol China, Chengdu, Peoples R China; [Chen, Xueqin; Bonsangue, Marcello] Leiden Univ, Leiden, Netherlands University of Electronic Science & Technology of China; Leiden University; Leiden University - Excl LUMC Zhou, F (corresponding author), Univ Elect Sci & Technol China, Chengdu, Peoples R China. nedchen0728@gmail.com; fan.zhou@uestc.edu.cn; fzhang@uestc.edu.cn; m.m.bonsangue@liacs.leidenuniv.nl Zhou, Fan/0000-0002-8038-8150; chen, xue qin/0000-0003-1538-3713; Bonsangue, Marcello/0000-0003-3746-3618 National Natural Science Foundation of China [62072077, 61602097]; Sichuan Regional Innovation Cooperation Project [2020YFQ0018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Regional Innovation Cooperation Project This work was supported by National Natural Science Foundation of China (Grant No. 62072077 and No. 61602097) , Sichuan Regional Innovation Cooperation Project (Grant No. 2020YFQ0018) . 64 12 12 6 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. SEP 2021.0 58 5 102678 10.1016/j.ipm.2021.102678 0.0 JUL 2021 19 Computer Science, Information Systems; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science TP5ZC Green Published 2023-03-23 WOS:000677676500006 0
J Fang, LY; Zhou, P; Liu, XX; Ghamisi, P; Chen, SW Fang, Leyuan; Zhou, Peng; Liu, Xinxin; Ghamisi, Pedram; Chen, Siwei Context Enhancing Representation for Semantic Segmentation in Remote Sensing Images IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Semantics; Context modeling; Image segmentation; Convolution; Decoding; Remote sensing; Predictive models; Context modeling; deep learning; feature alignment and enhancement; remote sensing images (RSIs); semantic segmentation As the foundation of image interpretation, semantic segmentation is an active topic in the field of remote sensing. Facing the complex combination of multiscale objects existing in remote sensing images (RSIs), the exploration and modeling of contextual information have become the key to accurately identifying the objects at different scales. Although several methods have been proposed in the past decade, insufficient context modeling of global or local information, which easily results in the fragmentation of large-scale objects, the ignorance of small-scale objects, and blurred boundaries. To address the above issues, we propose a contextual representation enhancement network (CRENet) to strengthen the global context (GC) and local context (LC) modeling in high-level features. The core components of the CRENet are the local feature alignment enhancement module (LFAEM) and the superpixel affinity loss (SAL). The LFAEM aligns and enhances the LC in low-level features by constructing contextual contrast through multilayer cascaded deformable convolution and is then supplemented with high-level features to refine the segmentation map. The SAL assists the network to accurately capture the GC by supervising semantic information and relationship learned from superpixels. The proposed method is plug-and-play and can be embedded in any FCN-based network. Experiments on two popular RSI datasets demonstrate the effectiveness of our proposed network with competitive performance in qualitative and quantitative aspects. [Fang, Leyuan; Zhou, Peng; Liu, Xinxin] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Fang, Leyuan] Peng Cheng Lab, Shenzhen 518000, Peoples R China; [Zhou, Peng; Liu, Xinxin] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf HZDR, D-09599 Freiberg, Germany; [Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, A-1030 Vienna, Austria; [Chen, Siwei] Natl Univ Def Technol, State Key Lab Complex Electromagnet Environm Effe, Changsha 410073, Peoples R China Hunan University; Peng Cheng Laboratory; Hunan University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); National University of Defense Technology - China Liu, XX (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China.;Liu, XX (corresponding author), Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China. fangleyuan@gmail.com; pengzhou926@hnu.edu.cn; liuxinxin@hnu.edu.cn; p.ghamisi@gmail.com; chenswnudt@163.com Chen, Si-Wei/0000-0001-8713-7664; Fang, Leyuan/0000-0003-2351-4461 National Natural Science Foundation of China [61922029, 61901166]; Key Research and Development Project of Science and Technology Plan of Hunan Province [2021SK2039]; Natural Science Foundation of Hunan Province of China [2022JJ40054]; Fundamental Research Funds for the Central Universities [531118010209] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Project of Science and Technology Plan of Hunan Province; Natural Science Foundation of Hunan Province of China(Natural Science Foundation of Hunan Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61922029 and Grant 61901166, in part by the Key Research and Development Project of Science and Technology Plan of Hunan Province under Grant 2021SK2039, in part by the Natural Science Foundation of Hunan Province of China under Grant 2022JJ40054, and in part by the Fundamental Research Funds for the Central Universities under Grant 531118010209. 57 1 1 33 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3201820 0.0 SEP 2022 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4O2SI 36083964.0 2023-03-23 WOS:000854554100001 0
J Zhang, HX; Xiao, ZW; Wang, J; Li, F; Szczerbicki, E Zhang, Haoxi; Xiao, Zhiwen; Wang, Juan; Li, Fei; Szczerbicki, Edward A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention IEEE INTERNET OF THINGS JOURNAL English Article Feature extraction; Activity recognition; Internet of Things; Convolutional neural networks; Wireless sensor networks; Computer architecture; Attention mechanism; deep learning; human activity recognition (HAR); Internet of Things (IoT) INTERNET; THINGS Together with the fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing HAR methods depend on exploratory case-based shallow feature learning architectures, which struggle with correct activity recognition when put into real-life practice. To tackle this problem, we propose a novel approach that utilizes the convolutional neural networks (CNNs) and the attention mechanism for HAR. In the presented method, the activity recognition accuracy is improved by incorporating attention into multihead CNNs for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available data set from wireless sensor data mining (WISDM) lab. The results demonstrate a higher accuracy of our proposed approach in comparison with the current methods. [Zhang, Haoxi; Xiao, Zhiwen; Wang, Juan; Li, Fei] Chengdu Univ Informat Technol, Sch Cybersecur, Chengdu 610225, Peoples R China; [Szczerbicki, Edward] Gdansk Univ Technol, Fac Management & Econ, Dept Management, PL-80233 Gdansk, Poland Chengdu University of Information Technology; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, Sch Cybersecur, Chengdu 610225, Peoples R China. haoxi@cuit.edu.cn; xiao1994zw@163.com; wangjuan@cuit.edu.cn; lifei@cuit.edu.cn; edward.szczerbicki@zie.pg.gda.pl Xiao, Zhiwen/AAR-3321-2021 Xiao, Zhiwen/0000-0001-9651-111X; Szczerbicki, Edward/0000-0001-7794-2862; Zhang, Haoxi/0000-0002-1341-1912 Sichuan Science and Technology Program [2019YFH0185] Sichuan Science and Technology Program This work was supported by the Sichuan Science and Technology Program under Grant 2019YFH0185. 34 69 70 6 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. FEB 2020.0 7 2 1072 1080 10.1109/JIOT.2019.2949715 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications KX6IB 2023-03-23 WOS:000521981800022 0
J Jiang, YT; Deng, SL; Li, HX; Liu, Y Jiang, Yuting; Deng, Shengli; Li, Hongxiu; Liu, Yong Predicting user personality with social interactions in Weibo ASLIB JOURNAL OF INFORMATION MANAGEMENT English Article Social interaction; Personality; DISC; Social media; Weibo BEHAVIORAL STYLES; 5-FACTOR MODEL; FACEBOOK; MEDIA; CONCEPTUALIZATION; INTENTION; TRAITS; IMPACT; SITES Purpose The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality. Design/methodology/approach Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data. Findings The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. Reposting, being reposted, commenting and being commented on were found to be the key interaction features that reflected Weibo users' personalities, whereas liking was not found to be a key feature. Originality/value The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China. [Jiang, Yuting; Liu, Yong] Aalto Univ, Helsinki, Finland; [Deng, Shengli] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China; [Li, Hongxiu] Tampere Univ, Tampere, Finland Aalto University; Wuhan University; Tampere University Deng, SL (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China. yuting.jiang@aalto.fi; victorydc@sina.com; hongxiu.li@tuni.fi; yong.liu@aalto.fi Li, Hongxiu/0000-0002-7447-1222; deng, sheng li/0000-0001-7489-4439; Liu, Yong/0000-0002-0228-8971 National Natural Science Foundation, PR China [71974149]; Wuhan University artificial intelligence project [2020AI021] National Natural Science Foundation, PR China(National Natural Science Foundation of China (NSFC)); Wuhan University artificial intelligence project This research is supported in part by National Natural Science Foundation, PR China (Grant No. 71974149) and Wuhan University artificial intelligence project (Grant No. 2020AI021). 61 6 6 10 42 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 2050-3806 1758-3748 ASLIB J INFORM MANAG Aslib J. Inf. Manag. OCT 13 2021.0 73 6 839 864 10.1108/AJIM-02-2021-0048 0.0 SEP 2021 26 Computer Science, Information Systems; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science WF8IA 2023-03-23 WOS:000691677500001 0
J Farahani, B; Firouzi, F; Chang, V; Badaroglu, M; Constant, N; Mankodiya, K Farahani, Bahar; Firouzi, Farshad; Chang, Victor; Badaroglu, Mustafa; Constant, Nicholas; Mankodiya, Kunal Towards fog-driven IoT eHealth: Promises and challenges of loT in medicine and healthcare FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Internet of Things; eHealth; Fog Computing; Big data DEEP BRAIN-STIMULATION; BIG DATA; MEDICATION ADHERENCE; ATRIAL-FIBRILLATION; INTERNET; SYSTEM; SENSOR; SECURITY; THINGS; MANAGEMENT Internet of Things (loT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of loT in healthcare and medicine by presenting a holistic architecture of loT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven loT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy. (C) 2017 Elsevier B.V. All rights reserved. [Farahani, Bahar] Univ Tehran, Sch Elect & Comp Engn, Tehran, Iran; [Firouzi, Farshad] Katholieke Univ Leuven, Dept Elect & Comp Engn, Leuven, Belgium; [Chang, Victor] Xian Jiaotong Liverpool Univ, Int Business Sch Suzhou, Informat Management & Informat Syst, Suzhou, Peoples R China; [Badaroglu, Mustafa] Qualcomm Technol, San Diego, CA USA; [Constant, Nicholas; Mankodiya, Kunal] Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA University of Tehran; KU Leuven; Xi'an Jiaotong-Liverpool University; University of Rhode Island Farahani, B (corresponding author), Univ Tehran, Sch Elect & Comp Engn, Tehran, Iran. bahar.farahani@ut.ac.ir Chang, Victor/AAC-7582-2019; Mankodiya, Kunal/Z-4179-2019 Chang, Victor/0000-0002-8012-5852; Farahani, Bahar/0000-0002-7016-6853 National Science Foundation (NSF) [1565962, 1652538] National Science Foundation (NSF)(National Science Foundation (NSF)) The case study on the smart glove was supported by National Science Foundation (NSF) research grants (#1565962 & #1652538). 134 397 401 19 349 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JAN 2018.0 78 2 659 676 10.1016/j.future.2017.04.036 0.0 18 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science FJ9AS Green Accepted, Green Submitted 2023-03-23 WOS:000413060400015 0
J Zschaeck, S; Weingaertner, J; Lombardo, E; Marschner, S; Hajiyianni, M; Beck, M; Zips, D; Li, YM; Lin, Q; Amthauer, H; Troost, EGC; van den Hoff, J; Budach, V; Kotzerke, J; Ferentinos, K; Karagiannis, E; Kaul, D; Gregoire, V; Holzgreve, A; Albert, NL; Nikulin, P; Bachmann, M; Kopka, K; Krause, M; Baumann, M; Kazmierska, J; Cegla, P; Cholewinski, W; Strouthos, I; Zoephel, K; Majchrzak, E; Landry, G; Belka, C; Stromberger, C; Hofheinz, F Zschaeck, Sebastian; Weingaertner, Julian; Lombardo, Elia; Marschner, Sebastian; Hajiyianni, Marina; Beck, Marcus; Zips, Daniel; Li, Yimin; Lin, Qin; Amthauer, Holger; Troost, Esther G. C.; van den Hoff, Joerg; Budach, Volker; Kotzerke, Joerg; Ferentinos, Konstantinos; Karagiannis, Efstratios; Kaul, David; Gregoire, Vincent; Holzgreve, Adrien; Albert, Nathalie L.; Nikulin, Pavel; Bachmann, Michael; Kopka, Klaus; Krause, Mechthild; Baumann, Michael; Kazmierska, Joanna; Cegla, Paulina; Cholewinski, Witold; Strouthos, Iosif; Zoephel, Klaus; Majchrzak, Ewa; Landry, Guillaume; Belka, Claus; Stromberger, Carmen; Hofheinz, Frank 18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization FRONTIERS IN ONCOLOGY English Article head and neck squamous cell carcinoma (HNSCC); fluorodeoxyglucose positron emission tomography (FDG PET); radiotherapy; metabolic tumor volume (MTV); standardized uptake value (SUV) RADIATION-THERAPY; PROGNOSTIC VALUE; UPTAKE RATIO; TRIAL Purpose18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis. MethodsOriginal analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). ResultsFDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUVmax was well suited to identify patients with excellent LRC for organ preservation. Patients with SUVmax of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer. ConclusionFDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging approaches including radiomics or artificial intelligence methods should implement the identified cut-off values as benchmark routine imaging parameters. [Zschaeck, Sebastian; Weingaertner, Julian; Hajiyianni, Marina; Beck, Marcus; Zips, Daniel; Budach, Volker; Kaul, David; Stromberger, Carmen] Humboldt Univ, Corp Member Freie Univ Berlin, Berlin Inst Hlth, Dept Radiat Oncol, Berlin, Germany; [Zschaeck, Sebastian; Weingaertner, Julian] Berlin Inst Hlth BIH, Berlin, Germany; [Zschaeck, Sebastian; Troost, Esther G. C.; Krause, Mechthild; Baumann, Michael] Univ Hosp Carl Gustav Carus, Tech Univ Dresden, Fac Med, Dept Radiotherapy & Radiat Oncol, Dresden, Germany; [Zschaeck, Sebastian; Troost, Esther G. C.; Kotzerke, Joerg; Krause, Mechthild; Baumann, Michael; Zoephel, Klaus] German Canc Res Ctr DKFZ Heidelberg, German Canc Consortium DKTK, Partner Site Dresden, Heidelberg, Germany; [Zschaeck, Sebastian; Troost, Esther G. C.; Kotzerke, Joerg; Krause, Mechthild; Baumann, Michael; Zoephel, Klaus] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Fac Med, OncoRay Natl Ctr Radiat Res Oncol, Dresden, Germany; [Lombardo, Elia; Marschner, Sebastian; Landry, Guillaume; Belka, Claus] Ludwig Maximilians Univ LMU Munich, Univ Hosp, Dept Radiat Oncol, Munich, Germany; [Marschner, Sebastian; Belka, Claus] German Canc Consortium DKTK, Partner Site Munich, Munich, Germany; [Zips, Daniel] German Canc Res Ctr DKFZ Heidelberg, German Canc Consortium DKTK, Partner Site Tubingen, Heidelberg, Germany; [Zips, Daniel] Eberhard Karls Univ Tubingen, Univ Hosp, Med Fac, Dept Radiat Oncol, Tubingen, Germany; [Li, Yimin; Lin, Qin] Xiamen Univ, Affiliated Hosp 1, Xiamen Canc Ctr, Dept Radiat Oncol, Xiamen, Peoples R China; [Amthauer, Holger] Humboldt Univ, Corp Member Freie Univ Berlin, Berlin Inst Hlth, Dept Nucl Med, Berlin, Germany; [Troost, Esther G. C.; Krause, Mechthild; Baumann, Michael] Helmholtz Zent Dresden Rossendorf, Inst Radiooncol OncoRay, Dresden, Germany; [Troost, Esther G. C.; Krause, Mechthild] Natl Ctr Tumor Dis NCT, German Canc Res Ctr DKFZ, Partner Site Dresden, Heidelberg, Germany; [Troost, Esther G. C.; Krause, Mechthild] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Fac Med, Dresden, Germany; [Troost, Esther G. C.; Krause, Mechthild] Helmholtz Zent Dresden Rossendorf HZDR, Helmholtz Assoc, Dresden, Germany; [van den Hoff, Joerg; Nikulin, Pavel; Bachmann, Michael; Kopka, Klaus; Hofheinz, Frank] Helmholtz Zent Dresden Rossendorf, Inst Radiopharmaceut Canc Res, Dresden, Germany; [Kotzerke, Joerg; Zoephel, Klaus] Univ Hosp Carl Gustav Carus, Fac Med, Dept Nucl Med, Dresden, Germany; [Ferentinos, Konstantinos; Karagiannis, Efstratios; Strouthos, Iosif] European Univ Cyprus, German Oncol Ctr, Dept Radiat Oncol, Limassol, Cyprus; [Gregoire, Vincent] Leon Berard Canc Ctr, Radiat Oncol Dept, Lyon, France; [Holzgreve, Adrien; Albert, Nathalie L.] Ludwig Maximilians Univ LMU Munich, Univ Hosp, Dept Nucl Med, Munich, Germany; [Baumann, Michael] German Canc Res Ctr, Heidelberg, Germany; [Kazmierska, Joanna; Cholewinski, Witold] Univ Med Sci, Electroradiol Dept, Poznan, Poland; [Kazmierska, Joanna] Greater Poland Canc Ctr, Radiotherapy Dept 2, Poznan, Poland; [Cegla, Paulina; Cholewinski, Witold] Greater Poland Canc Ctr, Dept Nucl Med, Poznan, Poland; [Zoephel, Klaus] Klinikum Chemnitz, Dept Nucl Med, Chemnitz, Germany; [Majchrzak, Ewa] Poznan Univ Med Sci, Greater Poland Canc Ctr, Dept Head & Neck Surg, Poznan, Poland Berlin Institute of Health; Humboldt University of Berlin; Berlin Institute of Health; Technische Universitat Dresden; Carl Gustav Carus University Hospital; Helmholtz Association; German Cancer Research Center (DKFZ); Technische Universitat Dresden; Carl Gustav Carus University Hospital; University of Munich; Helmholtz Association; German Cancer Research Center (DKFZ); Helmholtz Association; German Cancer Research Center (DKFZ); Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Xiamen University; Berlin Institute of Health; Humboldt University of Berlin; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Technische Universitat Dresden; Carl Gustav Carus University Hospital; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Technische Universitat Dresden; Carl Gustav Carus University Hospital; European University Cyprus; UNICANCER; Centre Leon Berard; University of Munich; Helmholtz Association; German Cancer Research Center (DKFZ); Wielkopolskie Centrum Onkologii; Wielkopolskie Centrum Onkologii; Chemnitz Clinic; Poznan University of Medical Sciences; Wielkopolskie Centrum Onkologii Hofheinz, F (corresponding author), Helmholtz Zent Dresden Rossendorf, Inst Radiopharmaceut Canc Res, Dresden, Germany. f.hofheinz@hzdr.de Landry, Guillaume/AFL-3214-2022; Cholewinski, Witold/HKO-3894-2023; Cegla, Paulina/GPP-3976-2022; Marschner, Sebastian Norbert/HMD-2437-2023 Landry, Guillaume/0000-0003-1707-4068; Cegla, Paulina/0000-0002-6676-3436; Budach, Volker/0000-0002-2191-9871; Zips, Daniel/0000-0001-5779-9675; Zschaeck, Sebastian/0000-0003-3109-0662; Amthauer, Holger/0000-0003-4414-0657; Baumann, Michael/0000-0002-9340-974X Berliner Krebsgesellschaft [ZSF201720]; German Federal Ministry of Education and Research (BMBF) [03ZIK42/OncoRay] Berliner Krebsgesellschaft; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) This work was partly supported by the Berliner Krebsgesellschaft (ZSF201720) and by the German Federal Ministry of Education and Research (BMBF contract 03ZIK42/OncoRay). The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication. 35 2 2 2 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. JUN 8 2022.0 12 870319 10.3389/fonc.2022.870319 0.0 11 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 2H3RW 35756665.0 gold 2023-03-23 WOS:000814216700001 0
J Sarkar, T; Mukherjee, A; Chatterjee, K; Shariati, MA; Rebezov, M; Rodionova, S; Smirnov, D; Dominguez, R; Lorenzo, JM Sarkar, Tanmay; Mukherjee, Alok; Chatterjee, Kingshuk; Shariati, Mohammad Ali; Rebezov, Maksim; Rodionova, Svetlana; Smirnov, Denis; Dominguez, Ruben; Lorenzo, Jose M. Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of Oyster Mushrooms FOOD ANALYTICAL METHODS English Article Oyster mushroom; Major and minor feature; Freshness class; Artificial neural network (ANN); Food safety FOOD-PRODUCTS; QUALITY; NETWORK; CLASSIFICATION; PREDICTION; VISION; FRUITS Automatic assessment of the quality of fruits and vegetables is a growing field of research in this modern era in order to enable faster processing of good quality foods. In this work, we have analyzed ten major colour variant features of two sets of oyster mushrooms in terms of histograms of each layer of the red-green-blue colourmap, hue-saturation-vital component colourmap, luminance-chrominance colourmap and the greyscale image. Besides, texture analysis has been carried out using entropy window filtering. Apart from that, five other minor features, such as mean, standard deviation, entropy, kurtosis and skewness of each of these layers, and four other greyscale features, such as contrast, correlation, energy and homogeneity are analyzed in this work. Two different freshness assessment models employing statistical methods like principal component analysis (PCA) and supervised learning algorithms such as artificial neural network (ANN) have been used here to investigate the different features of the mushroom images and classify the same into fresh and deteriorated classes. Analysis revealed that the ANN classifier outperforms the PCA threshold classifier with almost all the features. The highest classifier accuracy is obtained as 94.4% using the ANN model and 93.3% using the PCA threshold freshness detector. Most importantly, the use of smartphones ensures portability, as well as the possibility of widespread application of the proposed models. [Sarkar, Tanmay] Govt West Bengal, West Bengal State Council Tech Educ, Malda Polytech, Malda 732102, India; [Mukherjee, Alok; Chatterjee, Kingshuk] Govt Coll Engn & Ceram Technol, Kolkata, India; [Shariati, Mohammad Ali; Rebezov, Maksim; Rodionova, Svetlana; Smirnov, Denis] First Cossack Univ, KG Razumovsky Moscow State Univ Technol & Managem, 73 Zemlyanoy Val St, Moscow 109004, Russia; [Shariati, Mohammad Ali; Rebezov, Maksim] Liaocheng Univ, 34 Wenhua Rd, Liaocheng, Shandong, Peoples R China; [Rebezov, Maksim] Russian Acad Sci, VM Gorbatov Fed Res Ctr Food Syst, 26 Talalikhina St, Moscow 109316, Russia; [Dominguez, Ruben; Lorenzo, Jose M.] Ctr Tecnol La Carne Galicia, Parque Tecnol Galicia,Rua Galicia 4, San Cibrao Das Vinas 32900, Ourense, Spain; [Lorenzo, Jose M.] Univ Vigo, Fac Ciencias Ourense, Area Tecnol Alimentos, Orense 32004, Spain K.G. Razumovsky Moscow State University of Technologies & Management the First Cossack University; Liaocheng University; Russian Academy of Sciences; Universidade de Vigo Sarkar, T (corresponding author), Govt West Bengal, West Bengal State Council Tech Educ, Malda Polytech, Malda 732102, India.;Mukherjee, A (corresponding author), Govt Coll Engn & Ceram Technol, Kolkata, India.;Lorenzo, JM (corresponding author), Ctr Tecnol La Carne Galicia, Parque Tecnol Galicia,Rua Galicia 4, San Cibrao Das Vinas 32900, Ourense, Spain.;Lorenzo, JM (corresponding author), Univ Vigo, Fac Ciencias Ourense, Area Tecnol Alimentos, Orense 32004, Spain. tanmays468@gmail.com; alokmukherjee.ju@gmail.com; jmlorenzo@ceteca.net Shariati, Mohammad Ali/HCI-4375-2022; MUKHERJEE, ALOK/AAD-2387-2022; Rebezov, Maksim/E-5487-2016; Domínguez, Ruben/K-5793-2017; Lorenzo, Jose Manuel/K-6375-2014 Shariati, Mohammad Ali/0000-0001-9376-5771; Rebezov, Maksim/0000-0003-0857-5143; Domínguez, Ruben/0000-0002-2764-504X; Lorenzo, Jose Manuel/0000-0002-7725-9294; MUKHERJEE, ALOK/0000-0003-4404-1042; Sarkar, Tanmay/0000-0003-3869-1604 GAIN (Axencia Galega de Innovacion) [IN607A2019/01] GAIN (Axencia Galega de Innovacion) Thanks to GAIN (Axencia Galega de Innovacion) for supporting this review (grant number IN607A2019/01). 53 6 6 3 12 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1936-9751 1936-976X FOOD ANAL METHOD Food Anal. Meth. APR 2022.0 15 4 917 939 10.1007/s12161-021-02161-7 0.0 NOV 2021 23 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology 0B9VD 2023-03-23 WOS:000720186700001 0
C Pickup, LC; Pan, Z; Wei, DL; Shih, YC; Zhang, CS; Zisserman, A; Scholkopf, B; Freeman, WT IEEE Pickup, Lyndsey C.; Pan, Zheng; Wei, Donglai; Shih, YiChang; Zhang, Changshui; Zisserman, Andrew; Schoelkopf, Bernhard; Freeman, William T. Seeing the Arrow of Time 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR) JUN 23-28, 2014 Columbus, OH Comp Vis Fdn,IEEE,IEEE Comp Soc We explore whether we can observe Time's Arrow in a temporal sequence-is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time. [Pickup, Lyndsey C.; Zisserman, Andrew] Univ Oxford, Dept Engn Sci, Visual Geometry Grp, Oxford OX1 2JD, England; [Pan, Zheng; Zhang, Changshui] Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing, Peoples R China; [Wei, Donglai; Shih, YiChang; Freeman, William T.] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA; [Schoelkopf, Bernhard] Max Planck Inst Intelligent Syst, Tubingen, Germany University of Oxford; Tsinghua University; Massachusetts Institute of Technology (MIT); Max Planck Society Pickup, LC (corresponding author), Univ Oxford, Dept Engn Sci, Visual Geometry Grp, Oxford OX1 2JD, England. Schölkopf, Bernhard/A-7570-2013 Schölkopf, Bernhard/0000-0002-8177-0925; Wei, Donglai/0000-0002-2329-5484 25 29 30 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-4799-5117-8 PROC CVPR IEEE 2014.0 2043 2050 10.1109/CVPR.2014.262 0.0 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BD5LI 2023-03-23 WOS:000361555602012 0
J Dornaika, F; Weng, L; Jin, Z Dornaika, F.; Weng, L.; Jin, Z. Structured sparse graphs using manifold constraints for visual data analysis NEUROCOMPUTING English Article Graph construction; Sparse representation; Manifold constraints; Graph-based semi-supervised learning; Label propagation; Image classification ROBUST FACE RECOGNITION; DIMENSIONALITY REDUCTION; REPRESENTATION; CONSTRUCTION Data-driven graphs constitute the cornerstone of many machine learning approaches. Recently, it was shown that sparse graphs (sparse representation based graphs) provide a powerful approach to graph-based semi-supervised classification. In this paper, we introduce a new structured sparse graph that is derived by integrating manifold-type constraints on the sparse coefficients without any a priori graph or similarity matrix. Furthermore, we introduce a direct and efficient solution to the proposed optimization problem. Unlike recent sparse graph construction methods that are based on the use of hand-crafted constraints or a predefined reference similarity matrix, our constraints are directly defined on the graph weights themselves, and can provide additional information to both local and global structures of the sparse graph. Experiments conducted on several image databases show that the proposed graph can give better results than many state-of-the-art sparse graphs when applied to the problem of graph-based label propagation. (c) 2018 Elsevier B.V. All rights reserved. [Dornaika, F.; Weng, L.] Univ Basque Country, UPV EHU, San Sebastian, Spain; [Dornaika, F.] Ikerbasque, Basque Fdn Sci, Bilbao, Spain; [Jin, Z.] Nanjing Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China University of Basque Country; Basque Foundation for Science; Nanjing University of Science & Technology Dornaika, F (corresponding author), Univ Basque Country, UPV EHU, San Sebastian, Spain. fadi.dornaika@ehu.es 49 4 4 0 14 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 13 2018.0 315 107 114 10.1016/j.neucom.2018.06.067 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GV2PW 2023-03-23 WOS:000445934400011 0
C Yuan, J; Lu, G; Friedrich, P; Xu, YX IEEE Yuan, Jenny; Lu, Gary; Friedrich, Philipp; Xu, Yixin Real-time long-range road estimation in unknown environments 2020 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) IEEE Intelligent Vehicles Symposium English Proceedings Paper 31st IEEE Intelligent Vehicles Symposium (IV) JUN 23-26, 2020 ELECTR NETWORK IEEE Real-time road estimation is important for dynamic path planning of autonomous driving, especially when construction sites or accidents occur along the road. Among the existing practices, main pain points are strongly dependent on lane markings and brightness. In addition, roads with a variable number of lanes are difficult to predict. To address these issues, we fuse the data sampled from camera, lidar, radar and predict the lanes 90m ahead of the ego vehicle in the top-view perspective by using a deep neural network. Our method is evaluated on real urban and highway scenes under good and bad weather conditions at different daytime, the results showed that our approach can handle roads with a variable number of lanes or roads with small T-intersections. Furthermore, the method can also deal with incomplete or missing lane markings at 166.66 fps. [Yuan, Jenny; Lu, Gary] BMW Grp, Shanghai 200092, Peoples R China; [Friedrich, Philipp] Tech Univ Munich, Fac Comp Sci, D-80333 Munich, Germany; [Xu, Yixin] Karlsruhe Inst Technol, Fac Elect Engn, D-76131 Karlsruhe, Germany Technical University of Munich; Helmholtz Association; Karlsruhe Institute of Technology Yuan, J (corresponding author), BMW Grp, Shanghai 200092, Peoples R China. jenny.yuan@bmw.com; gary.lu@bmw.com; Philipp.friedrich@tum.de; urffr@student.kit.edu Friedrich, Philipp/0000-0003-3250-3289 19 1 1 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-0587 978-1-7281-6673-5 IEEE INT VEH SYM 2020.0 83 88 6 Computer Science, Artificial Intelligence; Robotics; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics; Transportation BR4SU 2023-03-23 WOS:000653124200014 0
C Shi, DP; Zhou, JH; Xu, JR; Yang, J; Li, XK; Zhao, ZM; Chen, JC; Rong, YM Wang, G; Han, Q; Bhuiyan, MZA; Ma, X; Loulergue, F; Li, P; Roveri, M; Chen, L Shi, Depeng; Zhou, Jiehan; Xu, Jirui; Yang, Jun; Li, Xuekun; Zhao, Zeming; Chen, Junchuang; Rong, Yiming Machine Vision-based Segmentation and Classification Method for Intelligent Roller Surface Monitoring 2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) English Proceedings Paper IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) NOV 07-11, 2018 Guangzhou, PEOPLES R CHINA IEEE Comp Soc,Guangzhou Univ optical defect detection; machine vision; big data; roller surface monitoring EXTREME LEARNING-MACHINE; ACTIVE CONTOURS The surface quality of steel rollers is a key factor determining the quality of final products such as metal sheets and foils in the rolling industry. It is important to examine the surface quality of rollers since rollers with optical defects will always duplicate the defects onto the metal sheet or foil during rolling. The typical optical defects of rollers after finish grinding include speckles, chatter marks, swirl marks and combination of all of the above. They can hardly be modeled or shaped by the approach of micro topography or SEM (scanning electrical microscope). In this paper, an on-site machine vision system is firstly applied for stable inspection for the optical defects on roller surfaces. Then, an improved optical defect segmentation algorithm is developed based on the active contour model and the images including chatter marks and swirl marks. The normal surface state is classified by the combination of methods of Gabor filters, KPCA method and ELM neural networks. Finally, experiment are carried out to verify the efficiency of the improved segmentation method and the recognition rate of the combined classification algorithm. [Shi, Depeng] Southern Univ Sci & Technol, Dept MEE, Shenzhen, Peoples R China; [Zhou, Jiehan] Oulu Univ, Fac Informat Technol & Elect Engn, Oulu, Finland; [Xu, Jirui; Yang, Jun; Li, Xuekun] Tsinghua Univ, Dept Mech Engn, Beijing, Peoples R China; [Li, Xuekun] Tsinghua Univ, State Key Lab Tribol, Beijing, Peoples R China; [Zhao, Zeming; Chen, Junchuang] HIECISE Precis Equipment Co Ltd, Kunshan, Peoples R China; [Rong, Yiming] Southern Univ Sci & Technol, Mech & Energy Engn, Shenzhen, Peoples R China Southern University of Science & Technology; University of Oulu; Tsinghua University; Tsinghua University; Southern University of Science & Technology Li, XK (corresponding author), Tsinghua Univ, Dept Mech Engn, Beijing, Peoples R China.;Li, XK (corresponding author), Tsinghua Univ, State Key Lab Tribol, Beijing, Peoples R China. 11649096@mail.sustc.edu.cn; jiehan.zhou@oulu.fi; xjr15@mails.tsinghua.edu.cn; j-yang16@mails.tsinghua.edu.cn; xli@tsinghua.edu.cn; zeming.zhao@hiecise.com; junchuang.chen@hiecise.com; rongym@sustc.edu.cn Rong, Yiming/V-1397-2018 Rong, Yiming/0000-0002-4506-7976 Tsinghua University Initiative Scientific Research Program; Tsinghua-RWTH Aachen Collaborative Innovation; Southern University of Science and Technology Fund; [NSAF-U1430116]; [2017ZX04007001] Tsinghua University Initiative Scientific Research Program; Tsinghua-RWTH Aachen Collaborative Innovation; Southern University of Science and Technology Fund; ; This research is supported by Project NSAF-U1430116 and Project 2017ZX04007001. The authors would also like to thank the support from Tsinghua University Initiative Scientific Research Program and Tsinghua-RWTH Aachen Collaborative Innovation and Southern University of Science and Technology Funding. 27 0 0 2 14 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-9380-3 2018.0 1811 1817 10.1109/SmartWorld.2018.00305 0.0 7 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM0GQ Green Accepted 2023-03-23 WOS:000458742900269 0
J Li, ZR; Shi, K; Dey, N; Ashour, AS; Wang, D; Balas, VE; McCauley, P; Shi, FQ Li, Zairan; Shi, Kai; Dey, Nilanjan; Ashour, Amira S.; Wang, Dan; Balas, Valentina E.; McCauley, Pamela; Shi, Fuqian Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction NEURAL COMPUTING & APPLICATIONS English Article KANSEI engineering; Variable precision rough set; Fuzzy set; Back propagation neural networks; Bayesian regularization; Shoe product form design DIFFERENTIAL EVOLUTION ALGORITHM; CONSUMER-ORIENTED TECHNOLOGY; GENETIC ALGORITHM; ASSOCIATION RULES; AFFECTIVE DESIGN; CLASSIFICATION; SYSTEM; MODELS; OPTIMIZATION Nonlinear operators for KANSEI evaluation dataset were significantly developed such as uncertainty reason techniques including rough set, fuzzy set and neural networks. In order to extract more accurate KANSEI knowledge, rule-based presentation was concluded a promising way in KANSEI engineering research. In the present work, variable precision rough set was applied in rule-based system to reduce the complexity of the knowledge database from normal item dataset to high frequent rule set. In addition, evidence theory's reliability indices, namely the support and confidence for rule-based knowledge presentation, were proposed by using back propagation neural network with Bayesian regularization algorithm. The proposed method was applied in shoes KANSEI evaluation system; for a certain KANSEI adjective, the key form features of products were predicted. Some similar algorithms such as Levenberg-Marquardt and scaled conjugate gradient were also discussed and compared to establish the effectiveness of the proposed approach. The experimental results established the effectiveness and feasibility of the proposed algorithms customized for shoe industry, where the proposed back propagation neural network/Bayesian regularization approach achieved superior performance compared to the other algorithms in terms of the performance, gradient, Mu, Effective number of parameter, and the sum square parameter in KANSEI support and confidence time series prediction. [Li, Zairan; Wang, Dan] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China; [Li, Zairan; Shi, Kai] Wenzhou Vocat & Tech Coll, Wenzhou 325035, Peoples R China; [Dey, Nilanjan] Techno India Coll Technol, Dept IT, Kolkata 740000, W Bengal, India; [Ashour, Amira S.] Tanta Univ, Fac Engn, Dept Elect & Elect Commun Engn, Tanta 31111, Egypt; [Balas, Valentina E.] Aurel Vlaicu Univ Arad, Dept Automat & Appl Informat, Arad 310130, Romania; [McCauley, Pamela] Univ Cent Florida, Dept Ind Engn & Management Syst, Orlando, FL 32825 USA; [Shi, Fuqian] Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 325035, Peoples R China Tianjin University; Egyptian Knowledge Bank (EKB); Tanta University; Aurel Vlaicu University of Arad; State University System of Florida; University of Central Florida; Wenzhou Medical University Shi, FQ (corresponding author), Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 325035, Peoples R China. sfq@wmu.edu.cn Ashour, Amira S./T-5454-2019; Balas, Valentina Emilia/F-4525-2010 Ashour, Amira S./0000-0003-3217-6185; Balas, Valentina Emilia/0000-0003-0885-1283 Zhejiang Provincial Natural Science Fund of China [LY17F030014] Zhejiang Provincial Natural Science Fund of China This work was supported by Zhejiang Provincial Natural Science Fund of China under No. LY17F030014. 66 37 41 2 105 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. MAR 2017.0 28 3 613 630 10.1007/s00521-016-2707-8 0.0 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science EL1MO 2023-03-23 WOS:000394384700015 0
J Gao, S; Pang, HC; Gallinari, P; Guo, J; Kato, N Gao, Sheng; Pang, Huacan; Gallinari, Patrick; Guo, Jun; Kato, Nei A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Embedding models; energy-based function; information diffusion; margin-based optimization With the increase of social networking web-sites and the interaction frequency among users, the prediction of information diffusion is required to support effective generalization and efficient inference in the context of social big data era. However, the existing models either rely on expensive probabilistic modeling of information diffusion based on partially known network structures, or discover the implicit structures of diffusion from users' behaviors without considering the impacts of different diffused contents. To address the issues, in this paper, we propose a novel information-dependent embedding-based diffusion prediction (IEDP) model to map the users in observed diffusion process into a latent embedding space, then the temporal order of users with the timestamps in the cascade can be preserved by the embedding distance of users. Our proposed model further learns the propagation probability of information in the cascade as a function of the relative positions of information-specific user embeddings in the information-dependent subspace. Then, the problem of temporal propagation prediction can be converted into the task of spatial probability learning in the embedding space. Moreover, we present an efficient margin-based optimization algorithm with a fast computation to make the inference of the information diffusion in the latent embedding space. When applying our proposed method to several social network datasets, the experimental results show the effectiveness of our proposed approach for the information diffusion prediction and the efficiency with respect to the inference speed compared with the state-of-the-art methods. [Gao, Sheng; Guo, Jun] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China; [Pang, Huacan] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing, Peoples R China; [Pang, Huacan] Beijing Univ Posts & Telecommun, Informat Engn, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China; [Gallinari, Patrick] Univ Paris 06, F-75005 Paris, France; [Kato, Nei] Tohoku Univ, Res Org Elect Commun, Sendai, Miyagi, Japan Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; UDICE-French Research Universities; Sorbonne Universite; Tohoku University Gao, S (corresponding author), Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China. gaosheng@bupt.edu.cn; 13120144593@163.com; patrick.gallinari@lip6.fr; guojun@bupt.edu.cn; kato@it.is.tohoku.ac.jp KATO, NEI/T-5892-2019 KATO, NEI/0000-0001-8769-302X 35 37 41 2 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. AUG 2017.0 13 4 2097 2105 10.1109/TII.2017.2684160 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering FC6EO 2023-03-23 WOS:000406933400061 0
J Zhang, GZ; Hu, WH; Cao, D; Yi, JB; Huang, Q; Liu, Z; Chen, Z; Blaabjerg, F Zhang, Guozhou; Hu, Weihao; Cao, Di; Yi, Jianbo; Huang, Qi; Liu, Zhou; Chen, Zhe; Blaabjerg, Frede A data-driven approach for designing STATCOM additional damping controller for wind farms INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Wind turbine; Adaptive robust control agent; STATCOM-ADC; DDPG; Estimator POWER-SYSTEM; FACTS DEVICES; TCSC; OSCILLATIONS; STABILIZER; PSS Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds. [Zhang, Guozhou; Hu, Weihao; Cao, Di; Yi, Jianbo; Huang, Qi] Univ Elect Sci & Technol China, Sichuan Prov Key Lab Power Syst Wide Area Measure, Chengdu 611731, Sichuan, Peoples R China; [Liu, Zhou; Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark University of Electronic Science & Technology of China; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sichuan Prov Key Lab Power Syst Wide Area Measure, Chengdu 611731, Sichuan, Peoples R China. zgz@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; Jimbo_yi@uestc.edu.cn; hwong@uestc.edu.cn; zli@et.aau.dk; zch@et.aau.dk; fbl@et.aau.dk Blaabjerg, Frede/A-5008-2008; Hu, Weihao/AAE-7945-2019 Blaabjerg, Frede/0000-0001-8311-7412; Hu, Weihao/0000-0002-7019-7289; Huang, Qi/0000-0002-8637-0269; Chen, Zhe/0000-0002-2919-4481 National Natural Science Foundation of China [51707029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (51707029). 35 27 30 3 33 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. MAY 2020.0 117 105620 10.1016/j.ijepes.2019.105620 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering KH3DU 2023-03-23 WOS:000510527500107 0
C Li, GQ; Shi, JF; Zhu, QY; Lan, J; Mitrouchev, P Wang, K; Wang, Y; Strandhagen, JO; Yu, T Li, Guiqin; Shi, Jinfeng; Zhu, Qiuyu; Lan, Jian; Mitrouchev, Peter Design of Integrated Information Platform for Smart Ship ADVANCED MANUFACTURING AND AUTOMATION VIII Lecture Notes in Electrical Engineering English Proceedings Paper 8th International Workshop of Advanced Manufacturing and Automation (IWAMA) SEP 25-26, 2018 Changzhou Univ, Changzhou, PEOPLES R CHINA Univ Plymouth,Norwegian Univ Sci & Technol,Fdn Ind & Tech Res,Shanghai Second Polytechn Univ,Changshu Inst Technol,Tongji Univ,Shandong Agr Univ,Univ Stavanger,Shanghai Univ,Hong Kong Polytechn Univ,Univ Malaga,Xiamen Univ Technol,Arct Univ Norway,China Univ Mining & Technol,Dr B R Ambedkar Natl Inst Technol,China Instrument & Control Soc,Tongji Univ,Dalian Univ,Peter The Great St Petersbur Polytechn Univ,Univ Firenze Changzhou Univ Smart ship; Integrated information platform; Cloud computation platform; OPC UA; Data visualization Ship Intelligence has become an inevitable trend in the development of shipbuilding and sailing. With the development of ship technology, the level of informatization and integration of ship has been improved a lot in recent years. Though some functionally independent and information-closed systems for single purpose are already existed, there are still some problems on interacting with other sub-system waiting to be resolved. This paper proposes a comprehensive information platform design scheme based on cloud computation, big data and internet of things technology, which is established on the basis of the cloud computation sub-system and supported by the OPC UA data transmission protocol. OPC UA protocol is used to achieve a unified and safe data interaction, which will help visualize data presentation of each sub-system. [Li, Guiqin; Shi, Jinfeng] Shanghai Univ, Shanghai Key Lab Intelligent Mfg & Robot, 99 Shangda Rd, Shanghai, Peoples R China; [Zhu, Qiuyu; Lan, Jian] Shanghai Univ, 99 Shangda Rd, Shanghai, Peoples R China; [Mitrouchev, Peter] Univ Grenoble Alpes, G SCOP, F-38031 Grenoble, France Shanghai University; Shanghai University; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Li, GQ (corresponding author), Shanghai Univ, Shanghai Key Lab Intelligent Mfg & Robot, 99 Shangda Rd, Shanghai, Peoples R China.;Mitrouchev, P (corresponding author), Univ Grenoble Alpes, G SCOP, F-38031 Grenoble, France. leeching@shu.edu.cn; peter.mitrouchev@g-scop.inpg.fr ZHU, Qiuyu/GRX-5167-2022 6 0 0 2 5 SPRINGER-VERLAG SINGAPORE PTE LTD SINGAPORE 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE 1876-1100 1876-1119 978-981-13-2375-1; 978-981-13-2374-4 LECT NOTES ELECTR EN 2019.0 484 53 58 10.1007/978-981-13-2375-1_8 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BT5VP 2023-03-23 WOS:000838684300008 0
J Zhang, YJ; Duan, YY; Wang, XY; Zhuo, ZZ; Haller, S; Barkhof, F; Liu, YO Zhang, Yajing; Duan, Yunyun; Wang, Xiaoyang; Zhuo, Zhizheng; Haller, Sven; Barkhof, Frederik; Liu, Yaou A deep learning algorithm for white matter hyperintensity lesion detection and segmentation NEURORADIOLOGY English Article White matter hyperintensity; Automated detection and segmentation; Multiple sclerosis; Multicentre; FLAIR SMALL-VESSEL DISEASE; MULTIPLE-SCLEROSIS; MRI; DIAGNOSIS; SPECTRUM; BURDEN; TOOL Purpose White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. Methods We developed and evaluated DeepWML, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). Results The overall median Dice similarity coefficient was 0.78 (range 0.64 similar to 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 similar to 0.94) and 0.81 (range 0.64 similar to 0.92), respectively. The tool's performance increased with larger lesion volumes. Conclusion DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation. [Zhang, Yajing; Wang, Xiaoyang] Philips Healthcare, MR Clin Sci, 258 Zhongyuan Rd, Suzhou, Peoples R China; [Duan, Yunyun; Zhuo, Zhizheng; Liu, Yaou] Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, 119 West Southern 4th Ring Rd, Beijing 100070, Peoples R China; [Haller, Sven] Univ Geneva, Dept Imaging & Med Informat, Geneva, Switzerland; [Haller, Sven] Univ Geneva, Fac Med, Geneva, Switzerland; [Barkhof, Frederik] Univ Amsterdam, Med Ctr, Dept Radiol & Nucl Med, Amsterdam, Netherlands; [Barkhof, Frederik] UCL, Queen Sq Inst Neurol, London, England; [Barkhof, Frederik] UCL, Ctr Med Image Comp, London, England Philips; Philips Healthcare; Capital Medical University; University of Geneva; University of Geneva; University of Amsterdam; Vrije Universiteit Amsterdam; University of London; University College London; University of London; University College London Liu, YO (corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, 119 West Southern 4th Ring Rd, Beijing 100070, Peoples R China. liuyaou@bjtth.org Barkhof, Frederik/0000-0003-3543-3706; Zhang, Yajing/0000-0003-4034-0686; Liu, Yaou/0000-0002-9930-0331; Haller, Sven/0000-0001-7433-0203; Wang, Xiaoyang/0000-0003-4213-0762 National Natural Science Foundation of China [81571631, 81870958]; Beijing Nova Program [xx2013045]; Natural Science Foundation of Beijing Municipality [7133244] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Nova Program(Beijing Municipal Science & Technology Commission); Natural Science Foundation of Beijing Municipality(Beijing Natural Science Foundation) This work was supported by National Natural Science Foundation of China (grant numbers: 81571631, 81870958), Beijing Nova Program (grant number: xx2013045) and Natural Science Foundation of Beijing Municipality (grant number: 7133244). 32 2 2 2 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0028-3940 1432-1920 NEURORADIOLOGY Neuroradiology APR 2022.0 64 4 727 734 10.1007/s00234-021-02820-w 0.0 OCT 2021 8 Clinical Neurology; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging ZP7MM 34599377.0 Green Submitted 2023-03-23 WOS:000702594100001 0
J Niu, Q; Li, K; Irwin, GW Niu, Q.; Li, K.; Irwin, G. W. Differential evolution combined with clonal selection for dynamic economic dispatch JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE English Article clonal selection algorithm; differential evolution; dynamic economic load dispatch; valve-point effects LOAD DISPATCH; HYBRID EP; ALGORITHM; OPTIMIZATION; SECURITY; GENERATORS; UNITS; SQP Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed; however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics. [Niu, Q.] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China; [Niu, Q.; Li, K.; Irwin, G. W.] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Intelligent Syst & Control, Belfast BT9 5AH, Antrim, North Ireland Shanghai University; Queens University Belfast Niu, Q (corresponding author), Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China. comelycc@hotmail.com RCUK [EP/G042594/1]; National Natural Science Foundation of China [61273040]; Shanghai Rising-Star Program [12QA1401100]; Shanghai Municipal Education Commission [12YZ020]; EPSRC [EP/L001063/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/L001063/1] Funding Source: researchfish RCUK(UK Research & Innovation (UKRI)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Rising-Star Program; Shanghai Municipal Education Commission(Shanghai Municipal Education Commission (SHMEC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research is supported by the RCUK funded Science Bridge Project [grant number EP/G042594/1], the National Natural Science Foundation of China [grant number 61273040], the Shanghai Rising-Star Program [grant number 12QA1401100] and the Project of Shanghai Municipal Education Commission [grant number 12YZ020]. 43 6 6 0 18 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0952-813X 1362-3079 J EXP THEOR ARTIF IN J. Exp. Theor. Artif. Intell. MAY 4 2015.0 27 3 325 350 10.1080/0952813X.2014.954277 0.0 26 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CH6XO 2023-03-23 WOS:000354180000001 0
J Li, SC; Hu, WH; Cao, D; Dragicevic, T; Huang, Q; Chen, Z; Blaabjerg, F Li, Sichen; Hu, Weihao; Cao, Di; Dragicevic, Tomislav; Huang, Qi; Chen, Zhe; Blaabjerg, Frede Electric Vehicle Charging Management Based on Deep Reinforcement Learning JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY English Article Artificial neural networks; Electric vehicle charging; Schedules; Reinforcement learning; Feature extraction; Optimization; Batteries; Deep reinforcement learning; data-driven control; uncertainty; electric vehicles (EVs) ENERGY-MANAGEMENT; INFORMATION; STRATEGY; SYSTEM A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner's commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods. [Li, Sichen; Hu, Weihao; Cao, Di; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China; [Dragicevic, Tomislav] Tech Univ Denmark, Dept Elect Engn, Ctr Elect Power & Energy Smart Elect Components, Copenhagen, Denmark; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark University of Electronic Science & Technology of China; Technical University of Denmark; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China. sichenli@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; tomdr@elektro.dtu.dk; hwong@uestc.edu.cn; zch@et.aau.dk; fbl@et.aau.dk Chen, Zhe/0000-0002-2919-4481 Sichuan Science and Technology Program [2020JDJQ0037] Sichuan Science and Technology Program This work was supported by the Sichuan Science and Technology Program (No. 2020JDJQ0037). 49 24 25 49 65 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2196-5625 2196-5420 J MOD POWER SYST CLE J. Mod. Power Syst. Clean Energy MAY 2022.0 10 3 719 730 10.35833/MPCE.2020.000460 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 1I8HL gold, Green Published 2023-03-23 WOS:000797467700018 0
J Li, S; Wu, L; Meng, WZ; Xu, ZH; Qin, CY; Wang, H Li, Su; Wu, Lei; Meng, Weizhi; Xu, Zihui; Qin, Chengyi; Wang, Hao DVPPIR: privacy-preserving image retrieval based on DCNN and VHE NEURAL COMPUTING & APPLICATIONS English Article Privacy-preserving EIIs; DCNN; VHE; K-means outsourcing; Access control With 5G and Internet technologies developing rapidly, outsourcing images to cloud servers has attracted growing attention. In existing technologies, images are often outsourced to cloud servers to reduce storage and computing burdens. However, outsourcing images to cloud servers without any processing may reveal the users' privacy, because the images may contain sensitive information about users, such as faces and locations, especially in electronic investigation. To overcome the security problems in image retrieval, we propose a privacy-preserving image retrieval scheme based on deep convolutional neural network (DCNN) and vector homomorphic encryption (VHE). We adopt DCNN and hash algorithms to extract image feature vectors, which improves retrieval accuracy. By combining VHE and K-means outsourcing clustering algorithms, the cloud server can build encrypted index trees, which speeds up the search and reduces the computational cost. In addition, a lightweight access control technique is used to allow image owners to set access policies for datasets flexibly. We prove the security of the proposed scheme and show the effectiveness of the scheme through experiments. Our scheme is suitable for application in electronic image investigation systems (EIIs) to optimize the storage and search of police data. [Li, Su; Wu, Lei; Xu, Zihui; Qin, Chengyi; Wang, Hao] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China; [Wu, Lei] Henan Key Lab Network Cryptog Technol, Zhengzhou, Peoples R China; [Wu, Lei; Wang, Hao] Shandong Prov Key Lab Novel Distributed Comp Soft, Jinan, Peoples R China; [Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark Shandong Normal University; Technical University of Denmark Wu, L (corresponding author), Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.;Wu, L (corresponding author), Henan Key Lab Network Cryptog Technol, Zhengzhou, Peoples R China.;Wu, L (corresponding author), Shandong Prov Key Lab Novel Distributed Comp Soft, Jinan, Peoples R China. 13864106238@163.com; wulei@sdnu.edu.cn; weme@dtu.dk; xuzihui994@163.com; QCY521111@163.com; wanghao@sdnu.edu.cn Natural Science Foundation of Shandong Province [ZR2020MF056]; Henan Key Laboratory of Network Cryptography Technology [LNCT2021-A12]; National Natural Science Foundation of China [62071280]; Major Scientific and Technological Innovation Project of Shandong Province [2020CXGC010115] Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Henan Key Laboratory of Network Cryptography Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Scientific and Technological Innovation Project of Shandong Province This work was supported in part by the Natural Science Foundation of Shandong Province under Grant ZR2020MF056, in part by Henan Key Laboratory of Network Cryptography Technology (LNCT2021-A12), in part by the National Natural Science Foundation of China under Grant 62071280, and in part by the Major Scientific and Technological Innovation Project of Shandong Province under Grant 2020CXGC010115. 40 1 1 9 16 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. SEP 2022.0 34 17 SI 14355 14371 10.1007/s00521-022-07286-2 0.0 MAY 2022 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 3T5RN 2023-03-23 WOS:000790648300001 0
J Wang, C; Peng, GH; De Baets, B Wang, Chen; Peng, Guohua; De Baets, Bernard Class-specific discriminative metric learning for scene recognition PATTERN RECOGNITION English Article Class-specific distance metrics; Discriminative metric learning; Scene recognition FEATURE FUSION; CLASSIFICATION; FEATURES A B S T R A C T Metric learning aims to learn an appropriate distance metric for a given machine learning task. Despite its impressive performance in the field of image recognition, it may still not be discriminative enough for scene recognition because of the high within-class diversity and high between-class similarity of scene images. In this paper, we propose a novel class-specific discriminative metric learning method (CSDML) to alleviate these problems. More specifically, we learn a distinctive linear transformation for each class (or, equivalently, a Mahalanobis distance metric for each class), which allows to project the samples of that class into a corresponding low-dimensional discriminative space. The overall aim is to simultaneously minimize the Euclidean distances between the projections of samples of the same class (or, equivalently, the Mahalanobis distances between these samples) and maximize the Euclidean distances between the projections of samples of different classes. Additionally, we incorporate least squares regression into the optimization problem, rendering class-specific metric learning more flexible and better suited to tackle scene recognition. Experimental results on four benchmark scene datasets demonstrate that the proposed method outperforms most of the state-of-the-art approaches. (c) 2022 Elsevier Ltd. All rights reserved. [Wang, Chen; Peng, Guohua] Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Shaanxi, Peoples R China; [Wang, Chen; De Baets, Bernard] Univ Ghent, Dept Data Anal & Math Modelling, KERMIT, Coupure Links 653, B-9000 Ghent, Belgium Northwestern Polytechnical University; Ghent University Wang, C (corresponding author), Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Shaanxi, Peoples R China. chenchenwang@mail.nwpu.edu.cn; penggh@nwpu.edu.cn; bernard.debaets@ugent.be China Scholarship Council (CSC) [202006290147]; Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme China Scholarship Council (CSC)(China Scholarship Council); Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme Chen Wang is supported by the China Scholarship Council (CSC.) 202006290147 . This research received funding from the Flemish Government under the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen programme. 44 2 2 2 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUN 2022.0 126 108589 10.1016/j.patcog.2022.108589 0.0 FEB 2022 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering ZH6ZV 2023-03-23 WOS:000761085300013 0
C Xie, HN; Wang, T; Qiao, MN; Zhang, MY; Shan, GC; Snoussi, H IEEE Xie, Haining; Wang, Tian; Qiao, Meina; Zhang, Mengyi; Shan, Guangcun; Snoussi, Hichem Robust Object detection for tiny and dense targets in VHR Aerial Images 2017 CHINESE AUTOMATION CONGRESS (CAC) Chinese Automation Congress English Proceedings Paper Chinese Automation Congress (CAC) OCT 20-22, 2017 Jinan, PEOPLES R CHINA IEEE,CAA,IEEE Syst Man & Cybernet Soc Object Detection; Convolutional Neural Network; NWPU VHR-10 dataset; Pri-SDL dataset Object detection in Very High Resolution (VEIR) optical remote sensing images is a challenged work for objects are usually dense and tiny. With random orientation, various backgrounds as well as unpredictable noise make traditional image processing methods perform badly. In this paper, we propose using state-of-art Region-based fully convolutional networks to solve object detection tasks in aerial images. To make the whole system efficient we choose to utilize position-sensitive score maps which not only fully take advantage of the convolutional feature maps but also achieve a balance between translation-variance in object detection and translation-invariance in classification. In addition, with 101-layer Residual networks as feature extractors, we achieve a satisfying result which is low time consuming and shows 99.45 percent and 94.41 percent precision respectively on two datasets. [Xie, Haining; Wang, Tian; Qiao, Meina; Shan, Guangcun] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; [Zhang, Mengyi] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Jiangsu, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, UMR STMR CNRS 6279, LM2S, Inst Charles Delaunay, Troyes, France Beihang University; Nanjing Tech University; Centre National de la Recherche Scientifique (CNRS); Universite de Technologie de Troyes Wang, T (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China. xiehaining@buaa.edu.cn; wangtian@buaa.edu.cn; meinaqiao@buaa.edu.cn; myzhang@njtech.edu.cn; gcshan@buaa.edu.cn; hichem.snoussi@utt.fr Shan, Guangcun/D-8247-2011 Shan, Guangcun/0000-0002-3314-1614 21 6 6 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2688-092X 2688-0938 978-1-5386-3524-7 CHIN AUTOM CONGR 2017.0 6397 6401 5 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BJ7XU 2023-03-23 WOS:000427816106087 0
J Shao, HD; Cheng, JS; Jiang, HK; Yang, Y; Wu, ZT Shao Haidong; Cheng Junsheng; Jiang Hongkai; Yang Yu; Wu Zhantao Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing KNOWLEDGE-BASED SYSTEMS English Article Enhanced deep gated recurrent unit; Bearing; Early fault prognosis; Energy moment entropy; Modified training algorithm CONVOLUTIONAL NEURAL-NETWORK; BELIEF NETWORK; DIAGNOSIS; MANIFOLD; ENCODER Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods. (C) 2019 Elsevier B.V. All rights reserved. [Shao Haidong; Cheng Junsheng; Yang Yu; Wu Zhantao] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China; [Shao Haidong; Cheng Junsheng; Yang Yu; Wu Zhantao] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China; [Shao Haidong] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Jiang Hongkai] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China Hunan University; Hunan University; Lulea University of Technology; Northwestern Polytechnical University Shao, HD (corresponding author), Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China.;Shao, HD (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China.;Shao, HD (corresponding author), Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden. hdshao@hnu.edu.cn Yang, Yu/HKE-6499-2023; Cheng, Junsheng/HKE-6532-2023; Shao, Haidong/AAE-5814-2022 Yang, Yu/0000-0001-9113-6023; Shao, Haidong/0000-0002-8018-1774 National Natural Science Foundation of China [51575168, 51875183]; Fundamental Research Funds for the Central Universities [531118010335] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research is supported by the National Natural Science Foundation of China (51575168 and 51875183) and the Fundamental Research Funds for the Central Universities (531118010335). 38 133 135 27 132 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JAN 5 2020.0 188 105022 10.1016/j.knosys.2019.105022 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KL2YR 2023-03-23 WOS:000513295000018 0
J Vo-Thanh, T; Zaman, M; Thai, TDH; Hasan, R; Senbeto, DL Vo-Thanh, Tan; Zaman, Mustafeed; Thai, Trung Dam-Huy; Hasan, Rajibul; Senbeto, Dagnachew Leta Perceived customer journey innovativeness and customer satisfaction: a mixed-method approach ANNALS OF OPERATIONS RESEARCH English Article; Early Access Perceived innovativeness; Customer journey innovation; Customer satisfaction; Fine-dining restaurants; Customer-generated data; AHP; Fuzzy-TOPSIS BIG DATA ANALYTICS; SERVICE; PERFORMANCE; MODEL; ATTRIBUTES; EXPERIENCE; BEHAVIOR; QUALITY; IMPACT; AGENDA This research aims to understand the link between perceived innovativeness and customer satisfaction in the fine-dining catering segment. By employing a mixed-method approach, this paper proposes a multidimensional framework for measuring the perceived innovativeness of restaurants throughout the entire customer journey. Customer satisfaction was measured by considering online customer-generated data from TripAdvisor. The study not only finds a strong correlation between perceived innovativeness and customer satisfaction but also presents how fine-dining restaurants can employ user-generated data to co-innovate entire customer journeys and restaurant experiences. The results highlight menu-, service-, and customer experience-related innovativeness as the three most important criteria for fine-dining restaurant customers. Additionally, the results of the qualitative study indicate that in the context of fine-dining catering, the quality of the dishes, the service, and the customers' experience with the service staff and chefs are the main elements of satisfaction that restaurants should consider in promoting innovation. [Vo-Thanh, Tan] CEREGE UR 13564, Excelia Business Sch, Dept Mkt, 102 Rue Coureilles, F-17024 La Rochelle, France; [Zaman, Mustafeed] Normandie Business Sch, Metis Lab, Dept Mkt, 20 Quai Frissard, F-76600 Le Havre, France; [Thai, Trung Dam-Huy] Univ Econ Ho Chi Minh City, Int Sch Business, Ho Chi Minh City, Vietnam; [Hasan, Rajibul] Maynooth Univ, Sch Business, Maynooth, Ireland; [Senbeto, Dagnachew Leta] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Tsim Sha Tsui, Kowloon, 17 Sci Museum Rd, Hong Kong, Peoples R China Ho Chi Minh City University Economics; Hong Kong Polytechnic University Zaman, M (corresponding author), Normandie Business Sch, Metis Lab, Dept Mkt, 20 Quai Frissard, F-76600 Le Havre, France. vothanht@excelia-group.com; mzaman@em-normandie.fr; trungd.h.thai@isb.edu.vn; rajibul.hasan@mu.ie; dag.senbeto@polyu.edu.hk Vo-Thanh, Tan/O-5735-2019 Vo-Thanh, Tan/0000-0001-9964-3724; Thai, Trung Dam-Huy/0000-0001-9287-1150; Zaman, Mustafeed/0000-0002-8582-9445; Senbeto, Dagnachew/0000-0002-8743-3348 100 0 0 5 5 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0254-5330 1572-9338 ANN OPER RES Ann. Oper. Res. 10.1007/s10479-022-05079-3 0.0 NOV 2022 26 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science 7Y0LZ 36471800.0 Bronze 2023-03-23 WOS:000914583400001 0
J Xu, JW; Huan, YX; Huang, BM; Chu, HM; Jin, Y; Zheng, LR; Zou, Z Xu, Jiawei; Huan, Yuxiang; Huang, Boming; Chu, Haoming; Jin, Yi; Zheng, Li-Rong; Zou, Zhuo A Memory-Efficient CNN Accelerator Using Segmented Logarithmic Quantization and Multi-Cluster Architecture IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS English Article Convolutional neural network (CNN); dataflow; quantization; memory-efficient accelerator CONVOLUTION This brief presents a memory-efficient CNN accelerator design for resource-constrained devices in Internet of Things (IoT) and autonomous systems. A segmented logarithmic (SegLog) quantization method is exploited to mitigate the on-chip memory and bandwidth requirements, thus accommodating more processing elements (PEs) in a given chip area to organize a reconfigurable multi-cluster architecture. The evaluation results show that SegLog quantization can achieve 6.4 x model compression with less than 2.5% accuracy loss on various CNNs. An ASIC implementation with 168 PEs configuration is validated in a 40-nm CMOS process, with 2.54 TOPs/W energy efficiency and 0.8 mm(2) chip area reported. The accelerator has also been implemented on FPGA with 1512 PEs configured and 468 kB on-chip memory, achieving a 1.29 GOPs/kB memory efficiency. Compared with the state-of-the-art accelerators, our ASIC implementation enhances area efficiency and arithmetic intensity by 1.94x and 5.62 x , while the FPGA implementation achieves the memory efficiency improvement by a factor of 2.34x. [Xu, Jiawei; Huan, Yuxiang; Huang, Boming; Chu, Haoming; Jin, Yi; Zheng, Li-Rong; Zou, Zhuo] Fudan Univ, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China; [Huan, Yuxiang] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-11428 Stockholm, Sweden Fudan University; Royal Institute of Technology Zheng, LR; Zou, Z (corresponding author), Fudan Univ, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China. yxhuan@kth.se; lrzheng@fudan.edu.cn; zhuo@fudan.edu.cn ZHENG, Lirong/ABF-9274-2020; xu, jiawei/GWM-9710-2022 ZHENG, Lirong/0000-0001-9588-0239; zou, zhuo/0000-0002-8546-1329; JIN, YI/0000-0003-4335-6691 16 6 6 2 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-7747 1558-3791 IEEE T CIRCUITS-II IEEE Trans. Circuits Syst. II-Express Briefs JUN 2021.0 68 6 2142 2146 10.1109/TCSII.2020.3038897 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering SJ9KK 2023-03-23 WOS:000655844400081 0
C Rudol, P; Doherty, P Nayak, AC; Sharma, A Rudol, Piotr; Doherty, Patrick Evaluation of Human Body Detection Using Deep Neural Networks with Highly Compressed Videos for UAV Search and Rescue Missions PRICAI 2019: TRENDS IN ARTIFICIAL INTELLIGENCE, PT III Lecture Notes in Artificial Intelligence English Proceedings Paper 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI) AUG 26-30, 2019 Cuvu, FIJI Univ S Pacific,Griffith Univ,Macquarie Univ,Fiji Natl Univ,RIKEN Ctr Integrat Med Sci,Univ Western Australia,Australian Comp Soc,Springer Nat CNN; Compression; UAV; Search & rescue; Cloud robotics Dealing with compressed video streams in mobile robotics is an unavoidable fact of life. Transferring images between mobile robots or to the Cloud using wireless links can practically only be achieved using lossy video compression. This introduces artifacts that often make image processing challenging. Recent algorithms based on deep neural networks, as advanced as they are, are commonly trained and evaluated on datasets of high-fidelity images which are typically not captured from aerial views. In this work we evaluate a number of deep neural network based object detection algorithms in the context of aerial search and rescue scenarios where real-time and robust detection of human bodies is a priority. We provide an evaluation using a number of video sequences collected in-flight using Unmanned Aerial Vehicle (UAV) platforms in different environmental conditions. We also describe the detection performance degradation under limited bitrate compression using H.264, H.265 and VP9 video codecs, in addition to analyzing the timing effects of moving image processing tasks to off-board entities. [Rudol, Piotr; Doherty, Patrick] Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden; [Doherty, Patrick] Jinan Univ Zhuhai Campus, Sch Intelligent Syst & Engn, Zhuhai, Peoples R China Linkoping University; Jinan University Rudol, P (corresponding author), Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden. piotr.rudol@liu.se; patrick.doherty@liu.se Rudol, Piotr/0000-0003-3392-6742; Doherty, Patrick/0000-0003-2308-7412 ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic Research (SymbiKBot Project); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Research Arena Public Safety (WARA-PS) ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic Research (SymbiKBot Project); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Research Arena Public Safety (WARA-PS) Supported by the ELLIIT network organization for Information and Communication Technology, the Swedish Foundation for Strategic Research (SymbiKBot Project), and the Wallenberg AI, Autonomous Systems and Software Program (WASP) - Research Arena Public Safety (WARA-PS). 15 1 1 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-29894-4; 978-3-030-29893-7 LECT NOTES ARTIF INT 2019.0 11672 402 417 10.1007/978-3-030-29894-4_33 0.0 16 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP5SB 2023-03-23 WOS:000558137800033 0
J Wang, YS; Liao, WL; Chang, YQ Wang, Yusen; Liao, Wenlong; Chang, Yuqing Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting ENERGIES English Article photovoltaic power forecasting; GRU network; Pearson coefficient; K-means NEURAL-NETWORK; PREDICTION Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods. [Wang, Yusen] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden; [Liao, Wenlong; Chang, Yuqing] Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin 300072, Peoples R China Royal Institute of Technology; Tianjin University Liao, WL (corresponding author), Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin 300072, Peoples R China. yusenw@kth.se; wenlongliao@cau.edu.cn; cieezyb2014@163.com Liao, Wenlong/0000-0003-1175-0280 25 88 91 10 42 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies AUG 2018.0 11 8 2163 10.3390/en11082163 0.0 14 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels GW1BJ gold, Green Submitted 2023-03-23 WOS:000446604100242 0
J He, ZY; Shao, HD; Jing, L; Cheng, JS; Yang, Y He Zhiyi; Shao Haidong; Jing Lin; Cheng Junsheng; Yang Yu Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder MEASUREMENT English Article Enhanced deep auto-encoder model; Transfer diagnosis; Limited labeled information; Bearing fault; Different machines NEURAL-NETWORK; LEARNING-METHOD; FEATURES; GEARBOX; MOTORS The collected vibration data with labeled information from bearing is far insufficient in engineering practice, which is challenging for training an intelligent diagnosis model. For this purpose, enhanced deep transfer auto-encoder is proposed for fault diagnosis of bearing installed in different machines. First, scaled exponential linear unit is used to improve the quality of the mapped vibration data collected from bearing. Second, nonnegative constraint is adopted for modifying the loss function to improve reconstruction effect. Then, the parameter knowledge of the well-trained source model is transferred to the target model. Finally, target training samples with limited labeled information are adopted for fine-tuning the target model to match the characteristics of the target testing samples. The proposed approach is applied for analyzing the measured vibration signals of bearings installed in different machines. The analysis results show that the proposed approach holds better transfer diagnosis performance compared with the existing approaches. (C) 2019 Elsevier Ltd. All rights reserved. [He Zhiyi; Shao Haidong; Cheng Junsheng; Yang Yu] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China; [Shao Haidong; Jing Lin] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden Hunan University; Lulea University of Technology Shao, HD (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China. hdshao@hnu.edu.cn Lin, Jing/B-3076-2015; Lin, Jing/AAF-3344-2021; Shao, Haidong/AAE-5814-2022; Cheng, Junsheng/HKE-6532-2023; Yang, Yu/HKE-6499-2023 Lin, Jing/0000-0002-7458-6820; Lin, Jing/0000-0002-7458-6820; Yang, Yu/0000-0001-9113-6023; Shao, Haidong/0000-0002-8018-1774; He, Zhiyi/0000-0002-1334-8677 National Natural Science Foundation of China [51905160, 51875183]; Fundamental Research Funds for the Central Universities [531118010335] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research is supported by the National Natural Science Foundation of China (51905160 and 51875183) and the Fundamental Research Funds for the Central Universities (531118010335). 37 125 125 30 154 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement FEB 2020.0 152 107393 10.1016/j.measurement.2019.107393 0.0 13 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation KE9ZH 2023-03-23 WOS:000508908600107 0
J Tang, CG; Zhu, CS; Wei, XL; Wu, HM; Li, Q; Rodrigues, JJPC Tang, Chaogang; Zhu, Chunsheng; Wei, Xianglin; Wu, Huaming; Li, Qing; Rodrigues, Joel J. P. C. Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network IEEE ACCESS English Article Resource management; Task analysis; Edge computing; Cloud computing; Delays; Optimization; Wireless communication; Intelligent transportation system; intelligent; vehicular; RSU; service provisioning Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency). [Tang, Chaogang] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China; [Zhu, Chunsheng] Southern Univ Sci & Technol, SUSTech Inst Future Networks, Shenzhen 518055, Peoples R China; [Zhu, Chunsheng] Peng Cheng Lab, PCL Res Ctr Networks & Commun, Shenzhen 518055, Peoples R China; [Wei, Xianglin] Natl Univ Def Technol, Res Inst 63, Changsha 410073, Peoples R China; [Wu, Huaming] Tianjin Univ, Ctr Appl Math, Tianjin 300072, Peoples R China; [Li, Qing] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, BR-64049550 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal China University of Mining & Technology; Southern University of Science & Technology; Peng Cheng Laboratory; National University of Defense Technology - China; Tianjin University; Hong Kong Polytechnic University; Universidade Federal do Piaui; Instituto de Telecomunicacoes Zhu, CS (corresponding author), Southern Univ Sci & Technol, SUSTech Inst Future Networks, Shenzhen 518055, Peoples R China.;Zhu, CS (corresponding author), Peng Cheng Lab, PCL Res Ctr Networks & Commun, Shenzhen 518055, Peoples R China. chunsheng.tom.zhu@gmail.com Rodrigues, Joel J. P. C./A-8103-2013; Wu, Huaming/F-1049-2019 Rodrigues, Joel J. P. C./0000-0001-8657-3800; Wu, Huaming/0000-0002-4761-9973 Fundamental Research Funds for the Central Universities [2019XKQYMS46]; PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications Project [LZC0019]; FCT/MCTES; EU Funds [UIDB/EEA/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications Project; FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU Funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2019XKQYMS46, in part by the PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications Project under Grant LZC0019, in part by the FCT/MCTES through national funds, in part by the EU Funds under Projfx8fx8ect UIDB/EEA/50008/2020, and in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 309335/2017-5. 21 14 14 0 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 94453 94462 10.1109/ACCESS.2020.2995797 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LZ3JT gold 2023-03-23 WOS:000541125000028 0
J Li, CX; Li, JJ; Tan, T; Chen, K; Xu, Y; Wu, R Li, Chunxiao; Li, Jiajun; Tan, Tao; Chen, Kun; Xu, Yi; Wu, Rong Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY English Article COMPUTER-AIDED DIAGNOSIS; ELASTOGRAPHY; PERFORMANCE; LESIONS; CANCER PURPOSE We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE). METHODS Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's. statistics were calculated. RESULTS In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the USmode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001). CONCLUSION AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability. [Li, Chunxiao; Wu, Rong] Shanghai Jiao Tong Univ, Shanghai Gen Hosp Shanghai, Dept Ultrasound, Sch Med, Shanghai, Peoples R China; [Li, Jiajun; Chen, Kun; Xu, Yi] Shanghai Jiao Tong Univ, Shanghai Key Lab Digital Media Proc & Transmiss, Shanghai, Peoples R China; [Tan, Tao] Eindhoven Univ Technol, Ctr Anal Sci Comp & Applicat W&I, Dept Math & Comp Sci, Eindhoven, Netherlands Shanghai Jiao Tong University; Shanghai Jiao Tong University; Eindhoven University of Technology Wu, R (corresponding author), Shanghai Jiao Tong Univ, Shanghai Gen Hosp Shanghai, Dept Ultrasound, Sch Med, Shanghai, Peoples R China.;Xu, Y (corresponding author), Shanghai Jiao Tong Univ, Shanghai Key Lab Digital Media Proc & Transmiss, Shanghai, Peoples R China. xuyi@sjtu.edu.cn; wurong7111@163.com 28 3 3 4 13 TURKISH SOC RADIOLOGY ANKARA HOSDERE CAD, GUZELKENT SOK, CANKAYA EVLERI, F-2, ANKARA, 06540, TURKEY 1305-3612 DIAGN INTERV RADIOL Diagn. Interv. Radiol. MAY-JUN 2021.0 27 3 315 322 10.5152/dir.2021.20018 0.0 8 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging SD0XD 34003119.0 Green Accepted 2023-03-23 WOS:000651088900002 0
J Hua, JQ; Li, YG; Liu, CQ; Wang, LH Hua, Jiaqi; Li, Yingguang; Liu, Changqing; Wang, Lihui A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING English Article Intelligent manufacturing; Feature extraction; Causal inference; State prediction TOOL WEAR The state prediction of key components in manufacturing processes plays an important role in intelligent manufacturing, as it could improve the production quality, efficiency and reduce costs. Data-driven methods could learn well-performed prediction models from large volume of data. However, in complex manufacturing systems, the lack of prior knowledge limits the performance of prediction models, where the manufacturing environments changes continuously. In order to address this issue, this paper proposed a zero-shot prediction method for complex manufacturing systems based on causal inference. A deep convolutional neural network and a causal representation model are jointly optimized to extract invariant causal signal features, which can be generalized to non-stationary manufacturing environments without any new data. The experiment of tool wear prediction under non-stationary working conditions is carried out as a research example. The proposed method is verified with the open dataset on tool wear prediction, and experimental results show that the prediction accuracy could be obviously improved over existing methods. [Hua, Jiaqi; Li, Yingguang; Liu, Changqing] Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden Nanjing University of Aeronautics & Astronautics; Royal Institute of Technology Li, YG (corresponding author), Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China. liyingguang@nuaa.edu.cn Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049 National Science Foundation of China for Distinguished Young Scholars [51925505]; National Nat-ural Science Foundation of China [52175467, 51921003] National Science Foundation of China for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Nat-ural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Science Foundation of China for Distinguished Young Scholars (grant No. 51925505) , National Nat-ural Science Foundation of China (grant No. 52175467, 51921003) . 43 3 3 25 29 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0736-5845 1879-2537 ROBOT CIM-INT MANUF Robot. Comput.-Integr. Manuf. OCT 2022.0 77 102356 10.1016/j.rcim.2022.102356 0.0 APR 2022 9 Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Robotics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Robotics 2S3IJ 2023-03-23 WOS:000821688800002 0
J Li, RR; Cao, PX; Zhang, SB; Xu, W; Ekevad, M; Guo, XL Li, Rongrong; Cao, Pingxiang; Zhang, Shuangbao; Xu, Wei; Ekevad, Mats; Guo, Xiaolei PREDICTION OF CUTTING FORCE DURING GYPSUM FIBER COMPOSITE MILLING PROCESS USING RESPONSE SURFACE METHODOLOGY WOOD AND FIBER SCIENCE English Article Gypsum fiber composite; milling; Cutting force; RSM ARTIFICIAL NEURAL-NETWORK; DENSITY FIBERBOARD; TOOL WEAR; CBN TOOL; ROUGHNESS; OPTIMIZATION; PARAMETERS; REGRESSION; MODEL; RSM Gypsum fiber composite (GFC) is a kind of building material widely used in interior decoration. Milling is the most commonly used machining process for GFC. Cutting force as an important cutting characteristic parameter has significant influence on the quality of machined surface, power consumption, and tools wear. The tangential force (Fx) and normal force (Fy) were measured and analyzed to find out the effects of milling parameters on these cutting forces. Milling parameters considered were spindle speed, feed rate, and depth of cut. The response surface methodology (RSM) was selected to develop mathematical models and optimize milling parameters. The results showed that with the increase of feed rate and depth of cut, the Fx and Fy increased. But the cutting forces decreased with the increase of spindle speed. The optimization results indicated that high spindle speed, low feed rate, and small depth of cut are preferable for milling of GFC to obtain the best result. [Li, Rongrong; Xu, Wei] Nanjing Forestry Univ, Coll Furnishings & Ind Design, Nanjing 210037, Jiangsu, Peoples R China; [Cao, Pingxiang; Guo, Xiaolei] Nanjing Forestry Univ, Fac Mat Sci & Engn, Nanjing 210037, Jiangsu, Peoples R China; [Zhang, Shuangbao] Beijing Forestry Univ, Dept Mat Sci & Technol, Beijing 100083, Peoples R China; [Ekevad, Mats] Lulea Univ Technol, Div Wood Sci & Engn, SE-93187 Skelleftea, Sweden Nanjing Forestry University; Nanjing Forestry University; Beijing Forestry University; Lulea University of Technology Li, RR (corresponding author), Nanjing Forestry Univ, Coll Furnishings & Ind Design, Nanjing 210037, Jiangsu, Peoples R China. lirr722@hotmail.com; caopx@njfu.com.cn; shuangbaozhang@163.com; xuwei@njfu.edu.cn; mats.ekevad@ltu.se; youngleiguo@hotmail.com Li, Rongrong/B-8365-2019; Ekevad, Mats/J-1620-2012 Li, Rongrong/0000-0002-0485-3188; Ekevad, Mats/0000-0002-0145-080X National Key R&D Program of China [2017YFD0601104]; Nanjing Forestry University Starting Foundation [GXL2016031]; Nanjing Forestry University; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) National Key R&D Program of China; Nanjing Forestry University Starting Foundation; Nanjing Forestry University; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) The authors are grateful for the support from National Key R&D Program of China (2017YFD0601104), Nanjing Forestry University Starting Foundation (No. GXL2016031), Nanjing Forestry University Youth Science and Technology Innovation Fund and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). 21 12 13 0 9 SOC WOOD SCI TECHNOL MADISON ONE GIFFORD PINCHOT DR, MADISON, WI 53705 USA 0735-6161 WOOD FIBER SCI Wood Fiber Sci. OCT 2017.0 49 4 453 460 8 Forestry; Materials Science, Paper & Wood; Materials Science, Textiles Science Citation Index Expanded (SCI-EXPANDED) Forestry; Materials Science FK7QN 2023-03-23 WOS:000413702400010 0
C Chen, SG; Lu, ZH IEEE Chen, Shenggang; Lu, Zhonghai Hardware Acceleration of Multilayer Perceptron Based on Inter-Layer Optimization 2019 IEEE 37TH INTERNATIONAL CONFERENCE ON COMPUTER DESIGN (ICCD 2019) Proceedings IEEE International Conference on Computer Design English Proceedings Paper 37th IEEE International Conference on Computer Design (ICCD) NOV 17-20, 2019 New York Univ Abu Dhabi, Abu Dhabi, U ARAB EMIRATES IEEE,IEEE Comp Soc,IEEE Circuits & Syst Soc,New York Univ Abu Dhabi, Ctr Cyber Secur,New York Univ Abu Dhabi Inst New York Univ Abu Dhabi Multilayer Perceptron; Hardware Accelerator; Inter-Layer Optimization RECONFIGURABLE ARCHITECTURE; NEURAL-NETWORK; PERFORMANCE Multilayer Perceptron (MLP) is used in a broad range of applications. Hardware acceleration of MLP is one most promising way to provide better performance-energy efficiency. Previous works focused on the intra-layer optimization and layer-after-layer processing, while leaving the inter-layer optimization never studied. In this paper, we propose hardware acceleration of MLPs based on inter-layer optimization which allows us to overlap the execution of MLP layers. First we describe the inter-layer optimization from software and mathematical perspectives. Then, a reference Two-Neuron architecture which is efficient to support the inter-layer optimization is proposed and implemented. Discussions about area cost, performance and energy consumption are carried out to explore the scalability of the Two-Neuron architecture. Results show that the proposed MLP design optimized across layers achieves better performance and energy efficiency than the conventional intra-layer optimized designs. As such, the inter-layer optimization provides another possible direction other than the intra-layer optimization to gain further performance and energy improvements for the hardware acceleration of MLPs. [Chen, Shenggang] Natl Univ Def Technol, Sch Comp, Changsha, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Dept Elect & Embedded Syst, Stockholm, Sweden National University of Defense Technology - China; Royal Institute of Technology Chen, SG (corresponding author), Natl Univ Def Technol, Sch Comp, Changsha, Peoples R China. shgchen@nudt.edu.cn; zhonghai@kth.se 23 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6404 978-1-5386-6648-7 PR IEEE COMP DESIGN 2019.0 164 172 10.1109/ICCD46524.2019.00028 0.0 9 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP2DJ 2023-03-23 WOS:000542037800019 0
J Liu, BH; Verbraeck, A Liu, Baohong; Verbraeck, Alexander Multi-resolution modeling based on quotient space and DEVS SIMULATION MODELLING PRACTICE AND THEORY English Article Resolution; Equivalence relation; Quotient set; Multi-resolution modeling (MRM); Discrete-event system specification (DEVS) SPECIFICATION; SIMULATION As simulation systems get more and more complex, the study of multi-resolution modeling (MRM) remains an exciting and fertile area of research. Contrasting its abundant successful use cases, a rigorous mathematical foundation is still lacking in MRM. In this paper, we propose a quotient space based multi-resolution modeling (QMRM) theory based on granular computing in artificial intelligence and on discrete-event system specification (DEVS) in modeling and simulation. Based on quotient sets, resolution, multi-resolution modeling and other related concepts are defined and a general concept framework is constructed. Based on the concepts of quotient set and natural projection, several MRM principles are derived. The internal consistency principle guarantees consistency among different perspectives of an atomic model, whereas the external consistency principle guarantees that different components in a coupled model are consistent. The false-preserving principle indicates that if a construction relation or state transformation relation of a component does not exist in a low resolution model, then the corresponding relations should not exist in its high resolution model. The true-preserving principle tells us that a high resolution model can be simplified by choosing the proper low resolution model. QMRM is not only a formal specification, but also a fundamental framework to understand MRM concepts, a guiding ideology to design specific MRM methods, and a modeling methodology to develop MRM systems. QMRM is created from a general simulation perspective, not limited by any specific application or problem domain aspects. The results of this paper can serve as a starting point for further study of multi-resolution problems in different domains. (C) 2016 Elsevier B.V. All rights reserved. [Liu, Baohong] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China; [Verbraeck, Alexander] Delft Univ Technol, Fac Technol Policy & Management, Jaffalaan 5, NL-2628 BX Delft, Netherlands National University of Defense Technology - China; Delft University of Technology Liu, BH (corresponding author), Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China. liubh@nudt.edu.cn; a.verbraeck@tudelft.nl Verbraeck, Alexander/L-6775-2016 Verbraeck, Alexander/0000-0002-1572-0997 National Natural Science Foundation of China [61374185] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The research work was supported by National Natural Science Foundation of China under Grant No. 61374185. 69 6 7 1 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-190X 1878-1462 SIMUL MODEL PRACT TH Simul. Model. Pract. Theory JAN 2017.0 70 36 51 10.1016/j.simpat.2016.10.004 0.0 16 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science EF7IV Green Published 2023-03-23 WOS:000390504200003 0
J Nazir, MS; Alturise, F; Alshmrany, S; Nazir, HMJ; Bilal, M; Abdalla, AN; Sanjeevikumar, P; Ali, ZM Nazir, Muhammad Shahzad; Alturise, Fahad; Alshmrany, Sami; Nazir, Hafiz M. J.; Bilal, Muhammad; Abdalla, Ahmad N.; Sanjeevikumar, P.; Ali, Ziad M. Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend SUSTAINABILITY English Review environment; sustainability; quality of life; forecasting; instrument; ANN; wind energy POWER CURVE; ENERGY-PRODUCTION; REGRESSION-MODEL; SPEED; ANEMOMETER; PREDICTION; PERFORMANCE; SYSTEM; FARM; PARAMETERS To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method. [Nazir, Muhammad Shahzad] Huaiyin Inst Technol, Fac Automat, Huaian 223003, Peoples R China; [Alturise, Fahad] Qassim Univ, Coll Sci & Arts Ar Rass, Comp Dept, Ar Rass 51921, Saudi Arabia; [Alshmrany, Sami] Islamic Univ Madinah, Fac Comp & Informat Syst, Madinah 42351, Saudi Arabia; [Nazir, Hafiz M. J.] Guangzhou Univ, Sch Networking, Inst Adv Space Res Technol, Guangzhou 510006, Peoples R China; [Bilal, Muhammad] Huaiyin Inst Technol, Sch Life Sci & Food Engn, Huaian 223003, Peoples R China; [Abdalla, Ahmad N.] Huaiyin Inst Technol, Fac Informat & Commun Engn, Huaian 223003, Peoples R China; [Sanjeevikumar, P.] Aalborg Univ, Dept Energy Technol, DK-6700 Esbjerg, Denmark; [Ali, Ziad M.] Prince Sattam Bin Abdulaziz Univ, Coll Engn Wadi Addawaser, Wadi Addawaser 11991, Saudi Arabia; [Ali, Ziad M.] Aswan Univ, Fac Engn, Elect Engn Dept, Aswan 81542, Egypt Huaiyin Institute of Technology; Qassim University; Islamic University of Al Madinah; Guangzhou University; Huaiyin Institute of Technology; Huaiyin Institute of Technology; Aalborg University; Prince Sattam Bin Abdulaziz University; Egyptian Knowledge Bank (EKB); Aswan University Nazir, MS (corresponding author), Huaiyin Inst Technol, Fac Automat, Huaian 223003, Peoples R China. msn_bhutta88@yahoo.com; falturise@qu.edu.sa; 5948@iu.edu.sa; hmj.nazir@yahoo.com; bilaluaf@hyit.edu.cn; dramaidecn@gmail.com; san@et.aau.dk; dr.ziad.elhalwany@aswu.edu.eg Bilal, Muhammad/AAH-6461-2020; Ali, Ziad M./B-5794-2019; Javed, Muhammd Sufyan/K-1592-2019; Padmanaban, Sanjeevikumar/K-5187-2019; Ali, Ziad/HKO-1368-2023; Nazir, Muhammad Shahzad/Y-6466-2019; Abdalla, Ahmed/ABG-5090-2020; Padmanaban, Sanjeevikumar/N-2245-2014 Bilal, Muhammad/0000-0001-5388-3183; Ali, Ziad M./0000-0002-6959-9686; Padmanaban, Sanjeevikumar/0000-0003-3212-2750; Nazir, Muhammad Shahzad/0000-0001-9877-9590; Abdalla, Ahmed/0000-0002-8633-1833; Padmanaban, Sanjeevikumar/0000-0003-3212-2750; Alturise, Fahad/0000-0001-9176-7984; Malik, Jamsheed/0000-0002-8859-5656 122 34 35 6 31 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability MAY 2020.0 12 9 3778 10.3390/su12093778 0.0 27 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology LU0TK gold, Green Submitted 2023-03-23 WOS:000537476200276 0
J Chen, SW; Wang, MN; Huang, DS; Wen, PF; Wang, SY; Zhao, S Chen, Shaowei; Wang, Meinan; Huang, Dengshan; Wen, Pengfei; Wang, Shengyue; Zhao, Shuai Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation IEEE ACCESS English Article Degradation; Predictive models; Stochastic processes; Correlation; Trajectory; Uncertainty; Training; RUL prediction; particle filter; multiple degradation indicators; parameter correlation; kernel smoothing NEURAL-NETWORK; RELIABILITY; PROGNOSTICS; MODEL In practical applications, the failure of large-scale complex equipment is often caused by the simultaneous degradation of multiple components. It is necessary to predict the remaining useful life (RUL) of the equipment with multiple degradation indicators. This article proposes a new joint-RUL-prediction method in the presence of multiple degradation indicators based on parameter correlation. The stochastic process model is established for each degradation indicator, and the model parameters are estimated by kernel smoothing particle filter (KS-PF) and maximum likelihood estimation (MLE). Meanwhile, to facilitate the dependencies between multiple degradation indicators, correlations of the degradation model parameter between multiple degradation indicators are established in KS-PF. In addition, optimal tuning (OT) is introduced to choose the best kernel parameter. A case study on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is applied to verify the proposed method, the experiment shows that the proposed joint-RUL-prediction method based on parameter correlation possesses a superior prediction performance compared with that by using a single degradation indicator. [Chen, Shaowei; Wang, Meinan; Huang, Dengshan; Wen, Pengfei; Wang, Shengyue] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China; [Zhao, Shuai] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Northwestern Polytechnical University; Aalborg University Zhao, S (corresponding author), Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark. szh@et.aau.dk Wen, Pengfei/ABB-1421-2021; Zhao, Shuai/AAP-2135-2020 Wen, Pengfei/0000-0002-9384-3845; Zhao, Shuai/0000-0001-7441-5434 28 3 4 4 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 215145 215156 10.1109/ACCESS.2020.3041682 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PC7QU Green Submitted, gold 2023-03-23 WOS:000597192000001 0
J van der Heide, T; Kullgren, J; Broqvist, P; Bacic, V; Frauenheim, T; Aradi, B van der Heide, T.; Kullgren, J.; Broqvist, P.; Bacic, V.; Frauenheim, T.; Aradi, B. Fortnet, a software package for training Behler-Parrinello neural networks COMPUTER PHYSICS COMMUNICATIONS English Article Fortnet; Fortran; Neural networks; BPNN; DFTB; Many -body repulsive TOTAL-ENERGY CALCULATIONS; MOLECULAR-DYNAMICS; NUMBER GENERATOR; SIMULATIONS; MODELS; PARAMETERIZATION; IMPLEMENTATION; APPROXIMATION; POTENTIALS; SELECTION A new, open source, parallel, stand-alone software package (Fortnet) has been developed, which implements Behler-Parrinello neural networks. It covers the entire workflow from feature generation to the evaluation of generated potentials, coupled with higher-level analysis such as the analytic calculation of atomic forces. The functionality of the software package is demonstrated by driving the training for the fitted correction functions of the density functional tight binding (DFTB) method, which are commonly used to compensate the inaccuracies resulting from the DFTB approximations to the Kohn -Sham Hamiltonian. The usual two-body form of those correction functions limits the transferability of the parametrizations between very different structural environments. The recently introduced DFTB+ANN approach strives to lift these limitations by combining DFTB with a near-sighted artificial neural network (ANN). After investigating various approaches, we have found the combination of DFTB with an ANN acting on-top of some baseline correction functions (delta learning) the most accurate one. It allowed to introduce many-body corrections on top of two-body parametrizations, while excellent transferability to chemical environments with deviating energetics could be demonstrated. [van der Heide, T.; Frauenheim, T.; Aradi, B.] Univ Bremen, Bremen Ctr Computat Mat Sci, Bremen, Germany; [Kullgren, J.; Broqvist, P.] Uppsala Univ, Dept Chem, Angstrom Lab, Box 538, S-75121 Uppsala, Sweden; [Bacic, V.] Jacobs Univ Bremen, Dept Phys & Earth Sci, Campus Ring 1, D-28759 Bremen, Germany; [Frauenheim, T.] Beijing Computat Sci Res Ctr, Beijing 100193, Peoples R China; [Frauenheim, T.] Shenzhen JL Computat Sci & Appl Res Inst CSAR, Shenzhen 518110, Peoples R China University of Bremen; Uppsala University; Jacobs University; Chinese Academy of Engineering Physics; Beijing Computational Science Research Center (CSRC) Broqvist, P (corresponding author), Uppsala Univ, Dept Chem, Angstrom Lab, Box 538, S-75121 Uppsala, Sweden. peter.broqvist@kemi.uu.se DFG DFG(German Research Foundation (DFG)) We would like to acknowledge the Swedish e-science initiative eSSENCE, the Swedish national infrastructure for computing (SNIC) , and the Swedish research council (VR) . The simulations were per-formed on the HPC cluster Aether at the University of Bremen, financed by DFG within the scope of the Excellence Initiative. 79 0 0 3 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0010-4655 1879-2944 COMPUT PHYS COMMUN Comput. Phys. Commun. MAR 2023.0 284 108580 10.1016/j.cpc.2022.108580 0.0 13 Computer Science, Interdisciplinary Applications; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physics 7Y0NS hybrid, Green Submitted 2023-03-23 WOS:000914587900001 0
J Wu, XY; Fauconnier, ML; Bi, JF Wu, Xinye; Fauconnier, Marie-Laure; Bi, Jinfeng Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China FOODS English Article apple; flash GC E-nose; volatile; multivariate analysis; discrimination; decision tree CHROMATOGRAPHY ELECTRONIC NOSE; CULTIVAR CLASSIFICATION; AROMA; MS; QUALITY; IDENTIFICATION; ADULTERATION; LIQUORS; TONGUE; SENSOR Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory. [Wu, Xinye; Bi, Jinfeng] Chinese Acad Agr Sci, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storag, Minist Agr & Rural Affairs, POB 5109, Beijing 100193, Peoples R China; [Wu, Xinye; Fauconnier, Marie-Laure] Univ Liege, Gembloux Agro Biotech, Lab Chem Nat Mol, Passage Deportes 2, B-5030 Gembloux, Belgium Chinese Academy of Agricultural Sciences; Institute of Food Science & Technology, CAAS; Ministry of Agriculture & Rural Affairs; University of Liege Bi, JF (corresponding author), Chinese Acad Agr Sci, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storag, Minist Agr & Rural Affairs, POB 5109, Beijing 100193, Peoples R China. wuxinye@caas.cn; marie-laure.fauconnier@uliege.be; bijinfeng@caas.cn Fauconnier, Marie-Laure/0000-0002-2027-3896 Agricultural Science and Technology Innovation Program, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences [CAAS-ASTIP-2022-IFST] Agricultural Science and Technology Innovation Program, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences This work was supported by the Agricultural Science and Technology Innovation Program, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2022-IFST). 45 2 3 14 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2304-8158 FOODS Foods JUN 2022.0 11 11 1631 10.3390/foods11111631 0.0 14 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology 1Z5TH 35681382.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000808886200001 0
J Xin, T; Yang, Y; Zheng, XL; Lin, J; Wang, S; Wang, PS Xin, Tao; Yang, Yi; Zheng, Xiaoli; Lin, Jing; Wang, Sen; Wang, Pengsong Time Series Recovery Using Adjacent Channel Data Based on LSTM: A Case Study of Subway Vibrations APPLIED SCIENCES-BASEL English Article multi-channel data; time-series recovery; neural network; regression analysis; data recovery; time domain; frequency domain PREDICTION Multi-sensor technology has been widely applied in the condition monitoring of rail transit. In practice, the data of some channels in the high channel counts are often abnormal or lost due to the abnormality and damage of the sensors, thus resulting in a large amount of data waste. A method for the data recovery of lost channels by using adjacent channel data is proposed to solve this problem. Based on the LSTM network algorithm, a data recovery model is established based on the sequence-to-sequence regression analysis of adjacent channel data. Taking the measured vibration data of a subway as an example, the network is trained with multi-channel measured data to recover the lost channel data of time-series characteristics. The results show that this multi-channel data recovery model is feasible, and the accuracy is up to 98%. This method can also further reduce the number of channels that need to be collected. [Xin, Tao; Yang, Yi; Wang, Sen; Wang, Pengsong] Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China; [Xin, Tao] Beijing Jiaotong Univ, Beijing Key Lab Track Engn, Beijing 100044, Peoples R China; [Zheng, Xiaoli] Beijing Municipal Inst City Planning & Design, Beijing 100045, Peoples R China; [Lin, Jing] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Lin, Jing] Malardalen Univ, Sch Innovat Design & Engn, S-63220 Eskilstuna, Sweden; [Wang, Sen] Frontiers Sci Ctr Smart High Speed Railway Syst, Beijing 100044, Peoples R China; [Wang, Pengsong] Collaborat Innovat Ctr Railway Traff Safety, Beijing 100044, Peoples R China Beijing Jiaotong University; Beijing Jiaotong University; Lulea University of Technology; Malardalen University Xin, T (corresponding author), Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China.;Xin, T (corresponding author), Beijing Jiaotong Univ, Beijing Key Lab Track Engn, Beijing 100044, Peoples R China. xint@bjtu.edu.cn Lin, Jing (Janet)/B-3076-2015; XIN, Tao/G-7989-2011 Lin, Jing (Janet)/0000-0002-7458-6820; XIN, Tao/0000-0002-4283-0913 Beijing Nova Program [Z191100001119126]; Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety [R202101]; Fundamental Research Funds for the Central Universities [2020JBM049]; 111 Project [B20040] Beijing Nova Program(Beijing Municipal Science & Technology Commission); Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); 111 Project(Ministry of Education, China - 111 Project) This study is sponsored by the Beijing Nova Program (Z191100001119126), the Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety (R202101), and the Fundamental Research Funds for the Central Universities (2020JBM049). The support from the 111 Project (B20040) is also greatly appreciated. 25 0 0 6 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel NOV 2022.0 12 22 11497 10.3390/app122211497 0.0 18 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 6J8IG gold 2023-03-23 WOS:000887061600001 0
J Zhao, ZW; Li, YG; Liu, CQ; Chen, ZB; Chen, JS; Wang, LH Zhao, Zhiwei; Li, Yingguang; Liu, Changqing; Chen, Zhibin; Chen, Junsong; Wang, Lihui A subsequent-machining-deformation prediction method based on the latent field estimation using deformation force JOURNAL OF MANUFACTURING SYSTEMS English Article Machining deformation; Deformation force; Deformation prediction; Latent variables INITIAL RESIDUAL-STRESS Machining deformation control for large structural parts is an intractable problem, which is highly important for the dimensional accuracy and fatigue life of parts, and deformation prediction is the basis for deformation control. Existing prediction methods rely on the measurement of residual stress, which is limited by the measurement accuracy of residual stress distributed within thick blanks, and it is still a worldwide challenge. To address the above issue, this paper proposes a machining deformation prediction method based on estimation of latent filed for residual stress field using deformation force. The residual stress field is represented by latent field, which is estimated by deformation force monitoring data during the machining process based on the proposed physical-field estimation neural network. The estimated latent field is used to predict the subsequent deformation force and deformation via an inference network by combining the machining process information. The proposed method is verified by both simulation and actual environment, and it can provide a helpful reference for other machining related difficult-to-measure field. [Zhao, Zhiwei; Li, Yingguang; Liu, Changqing; Chen, Zhibin; Chen, Junsong] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Natl Key Lab Sci & Technol Helicopter Transmiss, Nanjing 210016, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden Nanjing University of Aeronautics & Astronautics; Royal Institute of Technology Li, YG (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Natl Key Lab Sci & Technol Helicopter Transmiss, Nanjing 210016, Peoples R China. liyingguang@nuaa.edu.cn Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049 National Natural Science Foundation of China [51775278]; National Science Fund of China for Distinguished Young Scholars [51925505] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Fund of China for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars) Acknowledgement The reported research was funded by National Natural Science Foundation of China (grant No. 52175467) , National Science Fund of China for Distinguished Young Scholars (grant No. 51925505) , and National Natural Science Foundation of China (grant No. 51775278) . 31 1 1 19 33 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-6125 1878-6642 J MANUF SYST J. Manuf. Syst. APR 2022.0 63 224 237 10.1016/j.jmsy.2022.03.012 0.0 APR 2022 14 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 1A2CW 2023-03-23 WOS:000791572300001 0
J Wu, Y; Chen, M; Li, HY; Chadli, M Wu, Ying; Chen, Mou; Li, Hongyi; Chadli, Mohammed Event-Triggered-Based Adaptive NN Cooperative Control of Six-Rotor UAVs With Finite-Time Prescribed Performance IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article; Early Access Six-rotor unmanned aerial vehicles; switching threshold event-triggered mechanism; finite-time prescribed performance; neural networks; disturbance observer FUZZY NEURAL CONTROLLER; UNMANNED AERIAL VEHICLE; ROBUST TRACKING CONTROL; MULTIAGENT SYSTEMS; NONLINEAR-SYSTEMS This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. Note to Practitioners-The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand. [Wu, Ying; Chen, Mou] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Peoples R China; [Wu, Ying; Chadli, Mohammed] Univ Paris Saclay, Univ Evry, Lab IBISC, F-91020 Evry, France; [Li, Hongyi] Guangdong Univ Technol, Sch Automat, Guangdong Prov Key Lab Intelligent Decis & Coopera, Guangzhou 510006, Peoples R China Nanjing University of Aeronautics & Astronautics; UDICE-French Research Universities; Universite Paris Saclay; Guangdong University of Technology Chen, M (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Peoples R China. wuying_2018@163.com; chenmou@nuaa.edu.cn; ihongyi2009@gmail.com; mchadli20@gmail.com National Science Fund for Distinguished Young Scholars [61825302]; National Natural Science Foundation of China [U2013201, 62033003, 62121004]; Local Innovative and Research Teams Project of Guangdong Special Support Program [2019BT02X353]; Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX22_0365] National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Local Innovative and Research Teams Project of Guangdong Special Support Program; Postgraduate Research and Practice Innovation Program of Jiangsu Province This work was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61825302; in part by the National Natural Science Foundation of China under Grant U2013201, Grant 62033003, and Grant 62121004; in part by the Local Innovative and Research Teams Project of Guangdong Special Support Program under Grant 2019BT02X353; and in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX22_0365. 40 0 0 14 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. 10.1109/TASE.2023.3241182 0.0 FEB 2023 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems 9C8QG 2023-03-23 WOS:000935674600001 0
C Gong, XL; Wang, J; Wang, YJ; Zurada, JM Hirose, A; Ozawa, S; Doya, K; Ikeda, K; Lee, M; Liu, D Gong, Xiaoling; Wang, Jian; Wang, Yanjiang; Zurada, Jacek M. A Conjugate Gradient-Based Efficient Algorithm for Training Single-Hidden-Layer Neural Networks NEURAL INFORMATION PROCESSING, ICONIP 2016, PT IV Lecture Notes in Computer Science English Proceedings Paper 23rd International Conference on Neural Information Processing (ICONIP) OCT 16-21, 2016 Kyoto, JAPAN Asia Pacific Neural Network Soc,Japanese Neural Network Soc Neural network; Extreme learning machine; Conjugate gradient; MNIST MULTILAYER FEEDFORWARD NETWORKS; EXTREME LEARNING-MACHINE A single hidden layer neural networks (SHLNNs) learning algorithm has been proposed which is called Extreme Learning Machine (ELM). It shows extremely faster than typical back propagation (BP) neural networks based on gradient descent method. However, it requires many more hidden neurons than BP neural networks to achieve assortive classification accuracy. This then leads more test time which plays an important role in practice. A novel learning algorithm (USA) for SHLNNs has been presented which updates the weights by using gradient method in the ELM framework. In this paper, we employ the conjugate gradient method to train the SHLNNs on the MNIST digit recognition problem. The simulated experiment demonstrates the better generalization and less required hidden neurons than the common ELM and USA. [Gong, Xiaoling; Wang, Jian; Wang, Yanjiang; Zurada, Jacek M.] China Univ Petr, Coll Informat & Control Engn, Qingdao 266580, Peoples R China; [Gong, Xiaoling; Wang, Jian; Wang, Yanjiang; Zurada, Jacek M.] China Univ Petr, Coll Sci, Qingdao 266580, Peoples R China; [Gong, Xiaoling; Wang, Jian; Wang, Yanjiang; Zurada, Jacek M.] Univ Louisville, Dept Elect & Comp Engn, Louisville, KY 40292 USA; [Gong, Xiaoling; Wang, Jian; Wang, Yanjiang; Zurada, Jacek M.] Univ Social Sci, Inst Informat Technol, PL-90113 Lodz, Poland China University of Petroleum; China University of Petroleum; University of Louisville; University of Social Sciences Gong, XL (corresponding author), China Univ Petr, Coll Informat & Control Engn, Qingdao 266580, Peoples R China.;Gong, XL (corresponding author), China Univ Petr, Coll Sci, Qingdao 266580, Peoples R China.;Gong, XL (corresponding author), Univ Louisville, Dept Elect & Comp Engn, Louisville, KY 40292 USA.;Gong, XL (corresponding author), Univ Social Sci, Inst Informat Technol, PL-90113 Lodz, Poland. s14050610@s.upc.edu.cn; wangjiannl@upc.edu.cn; yjwang@upc.edu.cn; jacek.zurada@louisville.edu WANG, JIAN/F-4224-2010 WANG, JIAN/0000-0002-4316-932X 14 0 0 0 2 SPRINGER INT PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 978-3-319-46681-1; 978-3-319-46680-4 LECT NOTES COMPUT SC 2016.0 9950 470 478 10.1007/978-3-319-46681-1_56 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG5XO 2023-03-23 WOS:000389804700056 0
J Zou, ZQ; Farnoosh, A; McNamara, T Zou, Zhiqin; Farnoosh, Arash; McNamara, Tom Risk analysis in the management of a green supply chain STRATEGIC CHANGE-BRIEFINGS IN ENTREPRENEURIAL FINANCE English Article In order to implement or maintain a green supply chain (GSC) that produces goods and services responsibly and sustainably, supply chain managers should use tools that allow for the efficient identification, quantification, and mitigation of the ever-present risks. The objective of the present research is to identify the risk factors associated with the processes involved in GSC management. Based on an analysis of the characteristics of GSC risk, the authors put forward a list of risk design principles and a risk criteria evaluation system for a GSC. Gray relation analysis method was then used to clarify the degree of connection between certain supply chain risk factors and select key risk factors. Finally, Back Propagation Artificial Neural Network (BP-ANN) method was used to determine the risk level associated with a GSC. The determination of risk level will help companies to develop effective strategic management initiatives in a GSC environment. [Zou, Zhiqin] China Univ Petr, Sch Econ & Management, Beijing, Peoples R China; [Farnoosh, Arash] IFP Energies Nouvelles, IFP Sch, 232 Ave Napoleon Bonaparte, F-92852 Rueil Malmaison, France; [McNamara, Tom] Rennes Sch Business, Rennes, France China University of Petroleum; IFP Energies Nouvelles; Universite de Rennes Farnoosh, A (corresponding author), IFP Energies Nouvelles, IFP Sch, 232 Ave Napoleon Bonaparte, F-92852 Rueil Malmaison, France. arash.farnoosh@ifpen.fr FARNOOSH, Arash/0000-0003-3162-5575 China scholarship committee [201906440185] China scholarship committee China scholarship committee, Grant/Award Number: 201906440185 53 5 5 2 15 JOHN WILEY & SONS LTD CHICHESTER THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND 1086-1718 1099-1697 STRATEG CHANG Strateg. Chang. JAN 2021.0 30 1 SI 5 17 10.1002/jsc.2383 0.0 13 Business, Finance Emerging Sources Citation Index (ESCI) Business & Economics PV7WH Green Submitted 2023-03-23 WOS:000610193400001 0
J Gao, DJ; Zhou, Y; Wang, TN; Wang, YD Gao, Diju; Zhou, Yong; Wang, Tianzhen; Wang, Yide A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient ENERGIES English Article lithium-ion battery; particle filter (PF); remaining useful life (RUL); NARX neural network STATE; PROGNOSTICS With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value. [Gao, Diju; Zhou, Yong; Wang, Tianzhen; Wang, Yide] Shanghai Maritime Univ, Minist Transport, Key Lab Marine Technol & Control Engn, Shanghai 201306, Peoples R China; [Wang, Yide] Univ Nantes, UMR CNRS 6164, Inst Elect & Technol NumeR, F-44000 Nantes, France Shanghai Maritime University; Nantes Universite Zhou, Y (corresponding author), Shanghai Maritime Univ, Minist Transport, Key Lab Marine Technol & Control Engn, Shanghai 201306, Peoples R China. djgao@shmtu.edu.cn; zhouyong24@stu.shmtu.edu.cn; tzwang@shmtu.edu.cn; yide.wang@univ-nantes.fr wang, tianzhen/0000-0002-7525-8466; WANG, Yide/0000-0002-1461-2003 National Natural Science Foundation of China [61673260] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61673260. 16 12 12 9 37 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies AUG 2020.0 13 16 4183 10.3390/en13164183 0.0 13 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels NK9HX Green Submitted, gold 2023-03-23 WOS:000567040800001 0
C Wang, ZT; Li, JF; Yan, YH; Vincent, E IEEE Wang, Ziteng; Li, Junfeng; Yan, Yonghong; Vincent, Emmanuel SEMI-SUPERVISED LEARNING WITH DEEP NEURAL NETWORKS FOR RELATIVE TRANSFER FUNCTION INVERSE REGRESSION 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) English Proceedings Paper IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) APR 15-20, 2018 Calgary, CANADA Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc relative transfer function; semi-supervised learning; deep neural network; manifold regularization ENHANCEMENT Prior knowledge of the relative transfer function (RTF) is useful in many applications but remains little studied. In this paper, we propose a semi-supervised learning algorithm based on deep neural networks (DNNs) for RTF inverse regression, that is to generate the full-band RTF vector directly from the source-receiver pose (position and orientation). Two typical scenarios are discussed: training on labeled RTFs only, or on additional unlabeled RTFs. Both setups utilize the low-dimensional manifold property of RTF in stationary environments. With this property as an additional regularization term, a smooth mapping solution with respect to the manifold is obtained. Experimental simulations show that the proposed method achieves a lower mean prediction error than the free field model with few labeled RTFs, and the unlabeled RTFs are essential in improving the inverse regression performance. [Wang, Ziteng; Li, Junfeng; Yan, Yonghong] Univ Chinese Acad Sci, Inst Acoust, Beijing, Peoples R China; [Vincent, Emmanuel] INRIA, F-54600 Villers Les Nancy, France Chinese Academy of Sciences; Institute of Acoustics, CAS; University of Chinese Academy of Sciences, CAS; Inria Wang, ZT (corresponding author), Univ Chinese Acad Sci, Inst Acoust, Beijing, Peoples R China. CSC [201604910623] CSC We thank CSC (201604910623) for funding. This work was performed while the author was at Inria. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). 28 4 4 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-4658-8 2018.0 191 195 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BL0QY 2023-03-23 WOS:000446384600039 0
J Wang, T; Chen, Y; Qiao, MN; Snoussi, H Wang, Tian; Chen, Yang; Qiao, Meina; Snoussi, Hichem A fast and robust convolutional neural network-based defect detection model in product quality control INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article; Proceedings Paper 12th International Conference on Frontiers of Design and Manufacturing (ICFDM) AUG 10-12, 2016 Natl Nat Sci Fdn China, Shenyang, PEOPLES R CHINA Shien Ming Wu Fdn USA Natl Nat Sci Fdn China Product quality control; Defect detection; Convolutional neural networks VISION SYSTEM; INSPECTION; EXTRACTION The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods. [Wang, Tian; Chen, Yang; Qiao, Meina] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, Inst Charles Delaunay, LM2S, UMR STMR CNRS 6281, Troyes, France Beihang University; Universite de Technologie de Troyes Wang, T (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China. wangtian@buaa.edu.cn ANR AutoFerm project; Platform CAPSEC - Region Champagne-Ardenne; Platform CAPSEC - FEDER; Fundamental Research Funds for the Central Universities [YWF-14-RSC-102]; National Natural Science Foundation of China [U1435220, 61503017]; Aeronautical Science Foundation of China [2016ZC51022] ANR AutoFerm project(French National Research Agency (ANR)); Platform CAPSEC - Region Champagne-Ardenne; Platform CAPSEC - FEDER; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Aeronautical Science Foundation of China This work is partially supported by the ANR AutoFerm project and the Platform CAPSEC funded by Region Champagne-Ardenne and FEDER, the Fundamental Research Funds for the Central Universities (YWF-14-RSC-102), the National Natural Science Foundation of China (U1435220, 61503017), the Aeronautical Science Foundation of China (2016ZC51022). 25 218 235 33 222 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. FEB 2018.0 94 9-12 3465 3471 10.1007/s00170-017-0882-0 0.0 7 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering FW8NM 2023-03-23 WOS:000425592900036 0
J Liu, MM; Zhang, YC; Dong, WJ; Yu, ZX; Liu, SF; Gomes, S; Liao, HL; Deng, SH Liu, Meimei; Zhang, Yicha; Dong, Wenjie; Yu, Zexin; Liu, Sifeng; Gomes, Samuel; Liao, Hanlin; Deng, Sihao Grey modeling for thermal spray processing parameter analysis GREY SYSTEMS-THEORY AND APPLICATION English Article Less data environment; Grey modelling; Thermal spray technology; Manufacturing knowledge FLIGHT PARTICLE CHARACTERISTICS; ARTIFICIAL NEURAL-NETWORK; HVOF; OPTIMIZATION; WEAR; COATINGS; PREDICTION; WC-12CO; BEHAVIOR Purpose This paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment. Design/methodology/approach Based on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters. Findings Model A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature. Originality/value It is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge. [Liu, Meimei; Zhang, Yicha; Yu, Zexin; Gomes, Samuel; Liao, Hanlin; Deng, Sihao] UTBM, CNRS, ICB PMDM, Sevenans, France; [Dong, Wenjie] NUAA, Nanjing, Peoples R China; [Liu, Sifeng] Nanjing Univ Aeronaut & Astronaut, Coll Aerosp Engn, Inst Grey Syst Studies, Nanjing, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite de Bourgogne; Universite de Technologie de Belfort-Montbeliard (UTBM); Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics Zhang, YC (corresponding author), UTBM, CNRS, ICB PMDM, Sevenans, France. yicha.zhang@utbm.fr ; DENG, Sihao/C-8175-2018 Dong, Wenjie/0000-0002-8591-1756; DENG, Sihao/0000-0003-0307-4550 39 5 5 4 19 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 2043-9377 2043-9385 GREY SYST Grey Syst. JUL 6 2020.0 10 3 SI 265 279 10.1108/GS-12-2019-0063 0.0 MAR 2020 15 Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Mathematics MD6GQ 2023-03-23 WOS:000524836600001 0
J Kalvodova, L; Simons, K Kalvodova, Lucie; Simons, Kai Lipidomics is going great guns: Interview with Kai Simons about the power of shotgun lipidomics EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY English Editorial Material There are thousands of lipid species in living cells but we do not fully understand this diversity as it is not possible to pin down a specific function to each lipid species. Lipids have structural, metabolic and signaling functions. While signaling lipids can be understood and functionally characterized in a way similar to proteins, membrane lipids act collectively. This may have been one of the reasons for lipid neglect in the past. In order to understand the complexity of lipids in membranes, lipoproteins, body fluids, and organisms in general, we need to know the big picture which can only be delivered by modern lipidomic analyses. This has been increasingly appreciated mainly in the biomedical area. It is likely that interpretation of blood cell lipidomes will lead to the discovery of new biomarkers and lipid signatures of pathological states and cardiovascular disease risk, and so the era of HDL/LDL, TAG and cholesterol quantification may soon be over. But that is just one example. The motto of this year's Euro Fed Lipid Congress reads From lipidomics to industrial innovation. Indeed with the advancement of mass-spectrometry and computational approaches to analyze and understand big data, lipidomics is becoming more interesting for clinical applications and industry. [Simons, Kai] Max Planck Inst Mol Cell Biol & Genet, Dresden, Germany; [Simons, Kai] Lipotype GmbH, Dresden, Germany; [Simons, Kai] European Life Scientist Org, Brussels, Belgium; [Simons, Kai] Chinese Acad Sci, Shanghai Inst Adv Studies, Beijing 100864, Peoples R China Max Planck Society; Chinese Academy of Sciences Kalvodova, Lucie/A-1546-2013 , Lucie/0000-0003-3270-9463 0 0 0 0 7 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1438-7697 1438-9312 EUR J LIPID SCI TECH Eur. J. Lipid Sci. Technol. OCT 2014.0 116 10 SI 1344 1346 10.1002/ejlt.201400405 0.0 3 Food Science & Technology; Nutrition & Dietetics Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology; Nutrition & Dietetics AQ4MX 2023-03-23 WOS:000342773300010 0
C Khaled, B; Yue, ML; Samir, J; Noureddine, Z IEEE Khaled, Benaggoune; Yue, Meiling; Samir, Jemei; Noureddine, Zerhouni A Knowledge Transfer Approach for Online PEMFC Degradation prediction with Uncertainty Quantification PROCEEDINGS OF 2022 12TH INTERNATIONAL CONFERENCE ON POWER, ENERGY AND ELECTRICAL ENGINEERING (CPEEE 2022) English Proceedings Paper 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) FEB 25-27, 2022 Ritsumeikan Univ, Shiga, JAPAN IEEE,IEEE Power & Energy Soc,Kogakuin Univ,Tokyo Univ Technol,Texas Tech Univ,Kyushu Sangyo Univ,Pusan Natl Univ,Kocaeli Univ Ritsumeikan Univ PEMFC; Prognostics; RUL; Transfer Learning FUEL-CELL; PROGNOSTICS Proton Exchange Membrane Fuel Cells (PEMFCs) are a key challenger for the world's future clean and renewable energy solution. Yet, fuel cells are susceptible to operating conditions and hydrogen impurities, leading to performance loss over time in service. Hence, performance degradation prediction is gaining attention recently for fuel cell system reliability. In this work, we present a knowledge transfer approach for online voltage drop prediction. A dual-path convolution neural network is proposed to extract linearity and non-linearity from historical data and performs multi-steps ahead prediction with uncertainty quantification. Online voltage prediction is then evaluated with and without knowledge transfer using two different PEMFC datasets. Results indicate that our proposed approach with transfer knowledge can predict the voltage drop accurately with a small uncertainty range compared to the conventional approach. [Khaled, Benaggoune; Samir, Jemei; Noureddine, Zerhouni] Univ Bourgogne Franche Comte, FEMTO ST Inst, FCLAB, Besancon, France; [Yue, Meiling] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Beijing Jiaotong University Khaled, B (corresponding author), Univ Bourgogne Franche Comte, FEMTO ST Inst, FCLAB, Besancon, France. khaled.benaggoune@femto-st.fr; yueml@bjtu.edu.cn; samirjemei@univ-fcomte.fr; noureddine.zerhouni@ens2m.fr EIPHI Graduate School [ANR-17-EURE-0002]; EIPHI ULYSSE; Region Bourgogne Franche-Comte, France; Fundamental Research Funds for the Central Universities, China [2021RC279] EIPHI Graduate School; EIPHI ULYSSE; Region Bourgogne Franche-Comte, France; Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities) This work has been supported by the EIPHI Graduate School [contract ANR-17-EURE-0002], EIPHI ULYSSE, and the Region Bourgogne Franche-Comte, France. It has also been supported by the Fundamental Research Funds for the Central Universities [grant number 2021RC279], China. 17 0 0 3 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2049-5 2022.0 65 70 10.1109/CPEEE54404.2022.9738717 0.0 6 Construction & Building Technology; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Construction & Building Technology; Engineering BT2WD 2023-03-23 WOS:000814732300012 0
C Liu, TY; Yang, YK; Wang, Y; Sun, M; Fan, WH; Wu, C; Bunger, C Assoc Comp Machinery Liu, Tianyu; Yang, Yukang; Wang, Yu; Sun, Ming; Fan, Wenhui; Wu, Cheng; Bunger, Cody Spinal Curve Assessment of Idiopathic Scoliosis with a Small Dataset via a Multi-scale Keypoint Estimation Approach UBICOMP/ISWC '20 ADJUNCT: PROCEEDINGS OF THE 2020 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2020 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS English Proceedings Paper ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM International Symposium on Wearable Computers (ISWC) SEP 12-17, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGCHI,ACM SIGMOBILE,Intel,Microsoft,Stabilo Spinal curve assessment; Keypoint estimation; Small-size dataset Idiopathic scoliosis (IS) is the most common type of spinal deformity, which leads to severe pain and potential heart and lung damage. The clinical diagnosis and treatment strategies for IS highly depend on the radiographic assessment of spinal curve. With improvements in image recognition via deep learning, learning-based methods can be applied to facilitate clinical decision-making. However, these methods usually require sufficiently large training datasets with precise annotation, which are very laborious and time-consuming especially for medical images. Moreover, the medical images of serious IS always contain the blurry and occlusive parts, which would make the strict annotation of the spinal curve more difficult. To address these challenges, we utilize the dot annotations approach to simply annotate the medical images instead of precise annotation. Then, we design a multi-scale keypoint estimation approach that incorporates Squeeze-and-Excitation(SE) blocks to improve the representational capacity of the model, achieving the assessment of spinal curve without large-size dataset. The proposed approach uses pose estimation framework to detect keypoints of spine with simple annotation and small-size dataset for the first time. Finally, we conduct experiments on a collected clinical dataset, and results illustrate that our approach outperforms the mainstream approaches. [Liu, Tianyu; Yang, Yukang; Fan, Wenhui; Wu, Cheng] Tsinghua Univ, Beijing, Peoples R China; [Wang, Yu] Peking Univ First Hosp, Beijing, Peoples R China; [Sun, Ming; Bunger, Cody] Aarhus Univ Hosp, Aarhus, Denmark Tsinghua University; Peking University; Aarhus University Liu, TY (corresponding author), Tsinghua Univ, Beijing, Peoples R China. ty-liu17@mails.tsinghua.edu.cn; yyk19@mails.tsinghua.edu.cn; wangyuspine@pkufh.com; ming.sun@clin.au.dk; fanwenhui@tsinghua.edu.cn; wuc@tsinghua.edu.cn; codybung@rm.dk Sun, Ming/0000-0002-7763-447X; Yang, Yukang/0000-0001-6807-3052 21 2 2 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8076-8 2020.0 665 670 10.1145/3410530.3414317 0.0 6 Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT6KA 2023-03-23 WOS:000842375700127 0
J Wang, CJ; Yang, G; Papanastasiou, G; Zhang, HY; Rodrigues, JJPC; de Albuquerque, VHC Wang, Chengjia; Yang, Guang; Papanastasiou, Giorgos; Zhang, Heye; Rodrigues, Joel J. P. C.; de Albuquerque, Victor Hugo C. Industrial Cyber-Physical Systems-Based Cloud IoT Edge for Federated Heterogeneous Distillation IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Clouds; Training; Task analysis; Servers; Computational modeling; Knowledge engineering; Training data; Deep learning; heterogeneous classifiers; Internet of Things (IoT); knowledge distillation (KD); online learning Deep convoloutional networks have been widely deployed in modern cyber-physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this article, we propose the heterogeneous brain storming (HBS) method for object recognition tasks in real-world Internet of Things (IoT) scenarios. Our method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new brain storming mechanism and optimizable temperature parameters. In our comparison experiments, this HBS method outperformed multiple state-of-the-art single-model compression methods, as well as the newest multinetwork knowledge distillation methods with both homogeneous and heterogeneous classifiers. The ablation experiment results proved that the trainable temperature parameter into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of authors' knowledge, this is the first IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks. [Wang, Chengjia] Univ Edinburgh, BHF Ctr Cardiovasc Sci, Edinburgh EH16 4TJ, Midlothian, Scotland; [Yang, Guang] Royal Brompton Hosp, Cardiovasc Res Ctr, London SW7 2AZ, England; [Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London SW7 2AZ, England; [Papanastasiou, Giorgos] Univ Edinburgh, Edinburgh Imaging Facil QMRI, Edinburgh EH16 4TJ, Midlothian, Scotland; [Zhang, Heye] Sun Yat Sen Univ, Sch Biomed Engn, Guangzhou 510275, Peoples R China; [Rodrigues, Joel J. P. C.] Fed Univ Piaui UFPI, BR-64049550 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal; [de Albuquerque, Victor Hugo C.] Univ Fortaleza, BR-60811905 Fortaleza, Ceara, Brazil University of Edinburgh; Royal Brompton Hospital; Imperial College London; University of Edinburgh; Sun Yat Sen University; Universidade Federal do Piaui; Instituto de Telecomunicacoes; Universidade Fortaleza Zhang, HY (corresponding author), Sun Yat Sen Univ, Sch Biomed Engn, Guangzhou 510275, Peoples R China. chengjia.wang@ed.ac.uk; g.yang@imperial.ac.uk; g.papanas@ed.ac.uk; heye.zhang@gmail.com; joeljr@ieee.org; victor.albuquerque@unifor.br de Albuquerque, Victor Hugo C./C-3677-2016; Rodrigues, Joel J. P. C./A-8103-2013; Yang, Guang/S-5032-2016 de Albuquerque, Victor Hugo C./0000-0003-3886-4309; Rodrigues, Joel J. P. C./0000-0001-8657-3800; Yang, Guang/0000-0001-7344-7733; Papanastasiou, Giorgos/0000-0002-1939-296X FCT/MCTES; EU funds [UIDB/EEA/50008/2020]; Brazilian National Council for Scientific and Technological Development (CNPq) [309335/2017-5, 304315/2017-6, 430274/2018-1]; Key Program for International Cooperation Projects of Guangdong Province [2018A050506031]; KeyArea Research and Development Program of Guangdong Province [2019B010110001]; Guangdong Natural Science Funds for Distinguished Young Scholar [2019B151502031]; Fundamental Research Funds for the Central Universities [19LGZD36] FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU funds; Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Key Program for International Cooperation Projects of Guangdong Province; KeyArea Research and Development Program of Guangdong Province; Guangdong Natural Science Funds for Distinguished Young Scholar; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/EEA/50008/2020, in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 309335/2017-5, Grant 304315/2017-6, and Grant 430274/2018-1, in part by the Key Program for International Cooperation Projects of Guangdong Province under Grant 2018A050506031, in part by the KeyArea Research and Development Program of Guangdong Province under Grant 2019B010110001, in part by the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant 2019B151502031, and in part by the Fundamental Research Funds for the Central Universities under Grant 19LGZD36. 35 21 21 8 51 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. AUG 2021.0 17 8 5511 5521 10.1109/TII.2020.3007407 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering RX7NM Green Accepted 2023-03-23 WOS:000647406400036 0
J Wu, FZ; Wu, JS; Kong, YY; Yang, CF; Yang, GY; Shu, HZ; Carrault, G; Senhadji, L Wu, Fuzhi; Wu, Jiasong; Kong, Youyong; Yang, Chunfeng; Yang, Guanyu; Shu, Huazhong; Carrault, Guy; Senhadji, Lotfi Convolutional modulation theory: A bridge between convolutional neural networks and signal modulation theory NEUROCOMPUTING English Article Convolutional neural network; Signal modulation theory; Energy spectrum distribution; Classification; Segmentation Although there have been a lot of researches on convolutional neural networks (CNNs), still what hap-pens in this black box remains a mystery. In this paper, we establish the connection between CNNs and signal modulation. From a signal modulation point of view, the forward-propagation process of CNNs can be explained as a process of modulating the input signals to the vicinity of a special energy spectrum distribution, and the back-propagation process is searching for the appropriate distribution which is better for classification or other tasks. Several experiments have been carried out to verify the modulated explanation of CNNs. Furthermore, we verify that modulating the signal to the appropriate energy spectrum distribution in advance can effectively improve the classification and segmentation accuracy.(c) 2022 Elsevier B.V. All rights reserved. [Wu, Fuzhi; Wu, Jiasong; Kong, Youyong; Yang, Chunfeng; Yang, Guanyu; Shu, Huazhong] Southeast Univ, Lab Image Sci & Technol LIST, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China; [Wu, Fuzhi; Wu, Jiasong; Kong, Youyong; Yang, Chunfeng; Yang, Guanyu; Shu, Huazhong; Carrault, Guy; Senhadji, Lotfi] Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China; [Wu, Fuzhi; Carrault, Guy; Senhadji, Lotfi] Univ Rennes, INSERM, LTSI UMR 1099, F-35042 Rennes, France; [Wu, Fuzhi; Wu, Jiasong; Kong, Youyong; Yang, Chunfeng; Yang, Guanyu; Shu, Huazhong; Carrault, Guy; Senhadji, Lotfi] Univ Rennes, Ctr Rech Informat Biomed Sino francais CRIBs, INSERM, F-35042 Rennes, France Southeast University - China; Southeast University - China; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes Shu, HZ (corresponding author), Southeast Univ, Lab Image Sci & Technol LIST, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China. shu.list@seu.edu.cn Senhadji, Lotfi/E-5903-2013; wu, fuzhi/HGB-1213-2022; wu, jiasong/A-3999-2009 Senhadji, Lotfi/0000-0001-9434-6341; wu, jiasong/0000-0001-7171-1318 National Key Research and Development Program of China [2021ZD0113202]; National Natural Science Foundation of China [61876037, 62171125, 31800825, 61871117, 61871124, 61773117, 61872079]; INSERM; [50912040302] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); INSERM(Institut National de la Sante et de la Recherche Medicale (Inserm)); This work was supported in part by the National Key Research and Development Program of China (No. 2021ZD0113202), and in part by the National Natural Science Foundation of China under Grants 61876037, 62171125, 31800825, 61871117, 61871124, 61773117, 61872079, and in part by the Pre-Research Foundation of 50912040302, and in part by INSERM under the Grant the calls IAL and IRP. The authors would like to thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations in this paper. 61 0 0 6 6 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing DEC 1 2022.0 514 195 215 10.1016/j.neucom.2022.09.088 0.0 OCT 2022 21 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5K2AV 2023-03-23 WOS:000869534600002 0
J Zhao, ZH; Wang, Y; Ding, XM; Li, HY; Fu, JH; Zhang, K; Burokur, SN; Wu, Q Zhao, Zihan; Wang, Yue; Ding, Xumin; Li, Haoyu; Fu, Jiahui; Zhang, Kuang; Burokur, Shah Nawaz; Wu, Qun Compact logic operator utilizing a single-layer metasurface PHOTONICS RESEARCH English Article DEEP NEURAL-NETWORKS; PHASE DISCONTINUITIES; HUYGENS METASURFACE; DESIGN In this paper, we design and demonstrate a compact logic operator based on a single-layer metasurface at microwave frequency. By mapping the nodes in the trained fully connected neural network (FCNN) to the specific unit cells with phase control function of the metasurface, a logic operator with only one hidden layer is physically realized. When the incident wave illuminates specific operating regions of the metasurface, corresponding unit cells are activated and can scatter the incident wave to two designated zones containing logical information in the output layer. The proposed metasurface logic operator is experimentally verified to achieve three basic logic operations (NOT, OR, and AND) under different input signals. Our design shows great application potential in compact optical systems, low-power consumption information transmission, and ultrafast wave-based full signal processing. (C) 2022 Chinese Laser Press [Zhao, Zihan; Wang, Yue; Ding, Xumin; Li, Haoyu] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Adv Microscopy & Instrumentat Res Ctr, Harbin 150080, Peoples R China; [Zhao, Zihan; Fu, Jiahui; Zhang, Kuang; Wu, Qun] Harbin Inst Technol, Dept Microwave Engn, Harbin 150001, Peoples R China; [Burokur, Shah Nawaz] Univ Paris Nanterre, UPL, LEME, F-92410 Ville Davray, France Harbin Institute of Technology; Harbin Institute of Technology Ding, XM (corresponding author), Harbin Inst Technol, Sch Instrumentat Sci & Engn, Adv Microscopy & Instrumentat Res Ctr, Harbin 150080, Peoples R China. xuminding@hit.edu.cn; lihaoyu@hit.edu.cn; zhangkuang@hit.edu.cn; sburokur@parisnanterre.fr Zhang, Kuang/AFC-5821-2022; Burokur, Shah Nawaz/A-4601-2014 Zhang, Kuang/0000-0002-5642-967X; Ding, Xumin/0000-0002-7725-790X; Burokur, Shah Nawaz/0000-0002-1848-4862 Natural Science Foundation of Heilongjiang Province [YQ2021F004]; National Natural Science Foundation of China [61871152, 62171153] Natural Science Foundation of Heilongjiang Province(Natural Science Foundation of Heilongjiang Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Natural Science Foundation of Heilongjiang Province (YQ2021F004); National Natural Science Foundation of China (61871152, 62171153). 68 4 4 25 75 CHINESE LASER PRESS SHANGHAI PO BOX 800-211, SHANGHAI, 201800, PEOPLES R CHINA 2327-9125 PHOTONICS RES Photonics Res. FEB 1 2022.0 10 2 316 322 10.1364/PRJ.439036 0.0 7 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics YS3VY 2023-03-23 WOS:000750609100008 0
J Liu, Y; Shen, WH; Man, Y; Liu, Z; Seferlis, P Liu, Yin; Shen, Wenhao; Man, Yi; Liu, Zhang; Seferlis, Panos Optimal scheduling ratio of recycling waste paper with NSGAII based on deinked-pulp properties prediction COMPUTERS & INDUSTRIAL ENGINEERING English Article Waste paper; Deinked pulp; Scheduling; Ratio; BP-NN; NSGAII SUPPORT VECTOR MACHINE; GENETIC ALGORITHM; NEURAL-NETWORK; MODEL The recycling of waste paper has been an effective way to achieve the environmental-friendly growth of papermaking industry. Focusing on the mixed-pulping process which has been generally employed, to ensure the required properties of the deinking pulp (DIP) and minimize the purchase cost of waste paper, an intelligent model scheduling the mixing ratio of waste paper was developed in the study. Primarily, driven by the field data of mixing ratio of waste paper and DIP properties, the prediction models of DIP properties were developed by SVM, GA-SVM, and BP-NN algorithms. Subsequently, based on the best prediction model, the scheduling model for the mixing ratio of waste paper was set up by NSGAII algorithm. The experimental results showed that, based on the obtained best BP-NN prediction model of DIP properties, the developed NSGAII scheduling model for the ratio of waste paper could achieve excellent scheduling results under the rigorous constraints and multi-objective. Compared with the previous study, the developed prediction and scheduling models in this study reduced the purchase cost of waste paper by 2.16%, and improved the acceptable proportion of DIP property from 56.07% to 100%. [Liu, Yin; Shen, Wenhao; Man, Yi; Liu, Zhang] South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510641, Guangdong, Peoples R China; [Seferlis, Panos] Aristotle Univ Thessaloniki, Dept Mech Engn, Thessaloniki 54124, Greece South China University of Technology; Aristotle University of Thessaloniki Shen, WH (corresponding author), South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510641, Guangdong, Peoples R China. 201610104557@mail.scut.edu.cn; ppwhshen@scut.edu.cn; manyi@scut.edu.cn; seferlis@auth.gr Man, Yi/B-4243-2019 Man, Yi/0000-0002-3745-9128; SHEN, Wenhao/0000-0002-9828-1052; Seferlis, Panos/0000-0002-4077-4284 Research Funds of National Science Foundation of Guangdong, China [2016A030313478]; State Key Laboratory of Pulp and Paper Engineering [2017ZD03] Research Funds of National Science Foundation of Guangdong, China; State Key Laboratory of Pulp and Paper Engineering The research was supported by the Research Funds of National Science Foundation of Guangdong, China (No. 2016A030313478), State Key Laboratory of Pulp and Paper Engineering (No. 2017ZD03). 32 8 9 4 25 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-8352 1879-0550 COMPUT IND ENG Comput. Ind. Eng. JUN 2019.0 132 74 83 10.1016/j.cie.2019.04.021 0.0 10 Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IE9KL 2023-03-23 WOS:000472693200006 0
C Wang, MY; Gourbesville, P; Liong, SY; Kim, D; Liu, JD Gourbesville, P; Caignaert, G Wang, Mingyan; Gourbesville, Philippe; Liong, Shie-Yui; Kim, Dongeon; Liu, Jiandong Flood Analysis and Simulation Attempts of the Newly Proposed Capital City of Indonesia ADVANCES IN HYDROINFORMATICS: MODELS FOR COMPLEX AND GLOBAL WATER ISSUES-PRACTICES AND EXPECTATIONS Springer Water English Proceedings Paper SimHydro Conference - Models for Complex and Global Water Issues - Practices and Expectations (SymHydro) JUN 16-18, 2021 Polytech Nice Sophia, Sophia Antipolis, FRANCE French Hydrotechn Soc,Assoc Francaise Mecanique,Univ Cote Azur, Polytech Nice Sophia,CNR,IRSN,EDF,DHI France,SETEC HYDRATEC Polytech Nice Sophia DEM; Remote sensing; Artificial neural network; Flood simulation; 2D hydraulic model; MIKE 21 FM Many cities in Southeast Asia suffer from severe flooding due to the increasing rainfall intensity caused by climate change. Jakarta, the capital city of Indonesia, is an example. Jakarta experienced four devastating floods during the past 20 years and is also facing a serious challenge with a land subsidence problem due to overexploitation of groundwater combined with the rising sea level. In view of these phenomena, a capital relocation project is proposed by the Indonesian government to move the capital city to Kalimantan island. This study provides flood assessment of the new capital city of Indonesia at its present condition. Due to the scarcity of good quality digital elevation model (DEM) data, which is a key component for the accuracy of flood simulations, a recently introduced DEM improvement scheme using remote sensing technology and artificial neural network (ANN) was applied. The scheme improved two publicly accessible satellite DEM data, SRTM DEM and AW3D30 DEM. These data, together with a multispectral imagery of Sentinel-2 satellite data, are used to derive a high-accuracy DEM using ANN. The DEM improvement scheme was tested at different countries (France and New Zealand) prior to its application on the study area. MIKE 21 Flow Model Flexible Mesh (MIKE 21 FM) was selected and applied to Kalimantan to simulate flood and identify the flood prone areas. Input data to MIKE 21 FM were DEM (from SRTM and AW3D30) and rainfall proxy data from WRF driven by ERAI. This study showed the flood map of 100-year return period of the study area. [Wang, Mingyan; Gourbesville, Philippe] Univ Cote dAzur, Polytech Lab, 930 Route Colles, F-06903 Sophia Antipolis, France; [Liong, Shie-Yui; Kim, Dongeon; Liu, Jiandong] Natl Univ Singapore, Trop Marine Sci Inst, 18 Kent Ridge Rd, Singapore, Singapore; [Gourbesville, Philippe] China Inst Water Resources & Hydropower Res, Beijing, Peoples R China UDICE-French Research Universities; Universite Cote d'Azur; National University of Singapore; China Institute of Water Resources & Hydropower Research Wang, MY (corresponding author), Univ Cote dAzur, Polytech Lab, 930 Route Colles, F-06903 Sophia Antipolis, France. mingyan.wang@etu.unice.fr; philippe.gourbesville@univ-cotedazur.fr; yui.liong@h2oclimate.org; tmsliuj@nus.edu.sg wang, ming/HPC-6329-2023 China Scholarship Council; National Research Foundation, Singapore under its AI Singapore Programme (AISG Award) [AISG-100E/GC/RP/RPKS-2019-045] China Scholarship Council(China Scholarship Council); National Research Foundation, Singapore under its AI Singapore Programme (AISG Award) This study is funded by China Scholarship Council and is partially also supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-100E/GC/RP/RPKS-2019-045). 14 0 0 4 4 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2364-6934 2364-8198 978-981-19-1600-7; 978-981-19-1599-4 SPRINGER WATER 2022.0 1145 1163 10.1007/978-981-19-1600-7_73 0.0 19 Computer Science, Interdisciplinary Applications; Mathematics, Applied; Water Resources Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematics; Water Resources BT9XF 2023-03-23 WOS:000865798000073 0
J He, JJ; Sandstrom, R He, Jun-Jing; Sandstrom, Rolf Creep rupture prediction using constrained neural networks with error estimates MATERIALS AT HIGH TEMPERATURES English Article Creep rupture extrapolation; constrained neural networks; constrained derivatives; austenitic stainless steels; error estimates SANICRO 25; EXTRAPOLATION; MECHANISMS; STRENGTH; BEHAVIOR; FRACTURE; CURVES; STEELS; ALLOY; LIFE Creep rupture prediction for materials serving in fossil-fired power plants has been a critical issue for many decades. There are many empirical methods with several fitting or adjustable parameters that have been proposed for creep rupture strength prediction. Fundamental models based on the microstructure mechanism have been developed but are not widely used due to their complicated nature. Neural Network (NN) is a powerful tool for handling complicated mechanical behaviour; However, unexpected results and excessive errors can be generated. To avoid this, constraints on the first and second derivatives of the creep rupture curves have been introduced and combined with the NN. With the constrained NN models, extrapolated results with controlled errors can be obtained, which is verified by methods for error analysis. Furthermore, ECCC Post Assessment Tests (PATs) 1.1 to 2.2 are satisfied. The methods are illustrated for two creep-resistant austenitic steels Sanicro 25 and Super304H. [He, Jun-Jing] Hangzhou Dianzi Univ, New Energy Mat Res Ctr, Theory & Computat Mat, Mat & Environm Engn, Hangzhou 310018, Zhejiang, Peoples R China; [Sandstrom, Rolf] KTH Royal Inst Technol, Mat Sci & Engn, S-10044 Stockholm, Sweden Hangzhou Dianzi University; Royal Institute of Technology He, JJ (corresponding author), Hangzhou Dianzi Univ, New Energy Mat Res Ctr, Theory & Computat Mat, Mat & Environm Engn, Hangzhou 310018, Zhejiang, Peoples R China.;Sandstrom, R (corresponding author), KTH Royal Inst Technol, Mat Sci & Engn, S-10044 Stockholm, Sweden. junjing@kth.se; rsand@kth.se He, Jun Jing/F-8615-2017; Sandstrom, Rolf/J-7904-2013 He, Jun Jing/0000-0002-8348-1633; Sandstrom, Rolf/0000-0002-8494-3983 National Natural Science Foundation of China [51901061]; Natural Science Foundation of Zhejiang Province [LY22E010003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province) This work was supported by National Natural Science Foundation of China [51901061]; Natural Science Foundation of Zhejiang Province [LY22E010003]. 52 1 1 5 6 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0960-3409 1878-6413 MATER HIGH TEMP Mater. High Temp. MAY 4 2022.0 39 3 239 251 10.1080/09603409.2022.2078147 0.0 JUN 2022 13 Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Metallurgy & Metallurgical Engineering 1X3ME 2023-03-23 WOS:000805681600001 0
J Zhao, ZQ; Wu, XD; Lu, CY; Glotin, H; Gao, J Zhao ZhongQiu; Wu XinDong; Lu CanYi; Glotin, Herve; Gao Jun Optimizing widths with PSO for center selection of Gaussian radial basis function networks SCIENCE CHINA-INFORMATION SCIENCES English Article GRBFNN; RBF widths; objective function; generalization capability; particle swarm optimization NEURAL-NETWORK; OPTIMIZATION; ALGORITHM The radial basis function (RBF) centers play different roles in determining the classification capability of a Gaussian radial basis function neural network (GRBFNN) and should hold different width values. However, it is very hard and time-consuming to optimize the centers and widths at the same time. In this paper, we introduce a new insight into this problem. We explore the impact of the definition of widths on the selection of the centers, propose an optimization algorithm of the RBF widths in order to select proper centers from the center candidate pool, and improve the classification performance of the GRBFNN. The design of the objective function of the optimization algorithm is based on the local mapping capability of each Gaussian RBF. Further, in the design of the objective function, we also handle the imbalanced problem which may occur even when different local regions have the same number of examples. Finally, the recursive orthogonal least square (ROLS) and genetic algorithm (GA), which are usually adopted to optimize the RBF centers, are separately used to select the centers from the center candidates with the initialized widths, in order to testify the validity of our proposed width initialization strategy on the selection of centers. Our experimental results show that, compared with the heuristic width setting method, the width optimization strategy makes the selected centers more appropriate, and improves the classification performance of the GRBFNN. Moreover, the GRBFNN constructed by our method can attain better classification performance than the RBF LS-SVM, which is a state-of-the-art classifier. [Zhao ZhongQiu; Wu XinDong; Gao Jun] Hefei Univ Technol, Coll Comp Sci & Informat Engn, Hefei 230009, Peoples R China; [Wu XinDong] Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA; [Lu CanYi] Chinese Acad Sci, Hefei Inst Intelligent Machines, Hefei 230031, Peoples R China; [Lu CanYi] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China; [Glotin, Herve] Univ Sud Toulon & Var, Lab Sci Informat & Syst, F-83957 La Garde, France Hefei University of Technology; University of Vermont; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Science & Technology of China, CAS; UDICE-French Research Universities; Aix-Marseille Universite Wu, XD (corresponding author), Hefei Univ Technol, Coll Comp Sci & Informat Engn, Hefei 230009, Peoples R China. xwu@cems.uvm.edu Wu, Xindong/AAB-6713-2022 Wu, Xindong/0000-0003-2396-1704 National Natural Science Foundation of China [61005007, 60875012, 61100161]; National High-tech R&D Program (863 Program) [2012AA011005]; US National Science Foundation [CCF-0905337]; French ANR Foundation [AVEIR ANR-06-MDCA-002]; China Postdoctoral Science Foundation [20100470825, 201104324] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National High-tech R&D Program (863 Program)(National High Technology Research and Development Program of China); US National Science Foundation(National Science Foundation (NSF)); French ANR Foundation(French National Research Agency (ANR)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by National Natural Science Foundation of China (Grant Nos. 61005007, 60875012, 61100161), National High-tech R&D Program (863 Program) (Grant No. 2012AA011005), US National Science Foundation (Grant No. CCF-0905337), French ANR Foundation (Grant No. AVEIR ANR-06-MDCA-002), and China Postdoctoral Science Foundation (Grant Nos. 20100470825, 201104324). The authors would like to thank Brian Birge, who provided the PSO MATLAB codes on the Internet for sharing (http://www.mathworks.com/matlabcentral/fileexchange/authors/18133). 33 7 7 1 18 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-733X 1869-1919 SCI CHINA INFORM SCI Sci. China-Inf. Sci. MAY 2014.0 57 5 52105 10.1007/s11432-013-4850-5 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering AF6YN 2023-03-23 WOS:000334860600007 0
J Meng, Y; Chen, YZ; Lu, LH; Ding, YM; Cusano, A; Fan, JA; Hu, QM; Wang, KY; Xie, ZW; Liu, ZT; Yang, YM; Liu, Q; Gong, ML; Xiao, QR; Sun, SL; Zhang, MM; Yuan, XC; Ni, XJ Meng, Yuan; Chen, Yizhen; Lu, Longhui; Ding, Yimin; Cusano, Andrea; Fan, Jonathan A.; Hu, Qiaomu; Wang, Kaiyuan; Xie, Zhenwei; Liu, Zhoutian; Yang, Yuanmu; Liu, Qiang; Gong, Mali; Xiao, Qirong; Sun, Shulin; Zhang, Minming; Yuan, Xiaocong; Ni, Xingjie Optical meta-waveguides for integrated photonics and beyond LIGHT-SCIENCE & APPLICATIONS English Review SURFACE-PLASMON POLARITONS; POLARIZATION BEAM SPLITTER; ORBITAL ANGULAR-MOMENTUM; HIGH-HARMONIC GENERATION; ON-FIBER TECHNOLOGY; BROAD-BAND; INVERSE DESIGN; ULTRA-COMPACT; TOPOLOGY OPTIMIZATION; HIGH-EFFICIENCY The growing maturity of nanofabrication has ushered massive sophisticated optical structures available on a photonic chip. The integration of subwavelength-structured metasurfaces and metamaterials on the canonical building block of optical waveguides is gradually reshaping the landscape of photonic integrated circuits, giving rise to numerous meta-waveguides with unprecedented strength in controlling guided electromagnetic waves. Here, we review recent advances in meta-structured waveguides that synergize various functional subwavelength photonic architectures with diverse waveguide platforms, such as dielectric or plasmonic waveguides and optical fibers. Foundational results and representative applications are comprehensively summarized. Brief physical models with explicit design tutorials, either physical intuition-based design methods or computer algorithms-based inverse designs, are cataloged as well. We highlight how meta-optics can infuse new degrees of freedom to waveguide-based devices and systems, by enhancing light-matter interaction strength to drastically boost device performance, or offering a versatile designer media for manipulating light in nanoscale to enable novel functionalities. We further discuss current challenges and outline emerging opportunities of this vibrant field for various applications in photonic integrated circuits, biomedical sensing, artificial intelligence and beyond. [Meng, Yuan; Liu, Zhoutian; Yang, Yuanmu; Liu, Qiang; Gong, Mali; Xiao, Qirong] Tsinghua Univ, Dept Precis Instrument, State Key Lab Precis Measurement Technol & Instru, Beijing 100084, Peoples R China; [Chen, Yizhen; Sun, Shulin] Fudan Univ, Shanghai Engn Res Ctr Ultraprecis Opt Mfg, Shanghai 200433, Peoples R China; [Chen, Yizhen; Sun, Shulin] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; [Lu, Longhui; Hu, Qiaomu; Wang, Kaiyuan; Zhang, Minming] Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan 430074, Peoples R China; [Ding, Yimin; Ni, Xingjie] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA; [Cusano, Andrea] Univ Sannio, Dept Engn, Optoelect Div, I-82100 Benevento, Italy; [Fan, Jonathan A.] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Xie, Zhenwei; Yuan, Xiaocong] Shenzhen Univ, Nanophoton Res Ctr, Shenzhen Key Lab Microscale Opt Informat Technol, Shenzhen 518060, Peoples R China; [Liu, Qiang; Gong, Mali; Xiao, Qirong] Tsinghua Univ, Key Lab Photon Ic Control Technol, Minist Educ, Beijing 100084, Peoples R China; [Sun, Shulin] Fudan Univ, Yiwu Res Inst, Chengbei Rd, Yiwu City 322000, Zhejiang, Peoples R China; [Zhang, Minming] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China Tsinghua University; Fudan University; Fudan University; Huazhong University of Science & Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Sannio; Stanford University; Shenzhen University; Tsinghua University; Fudan University; Huazhong University of Science & Technology Xiao, QR (corresponding author), Tsinghua Univ, Dept Precis Instrument, State Key Lab Precis Measurement Technol & Instru, Beijing 100084, Peoples R China.;Sun, SL (corresponding author), Fudan Univ, Shanghai Engn Res Ctr Ultraprecis Opt Mfg, Shanghai 200433, Peoples R China.;Sun, SL (corresponding author), Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.;Zhang, MM (corresponding author), Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan 430074, Peoples R China.;Xiao, QR (corresponding author), Tsinghua Univ, Key Lab Photon Ic Control Technol, Minist Educ, Beijing 100084, Peoples R China.;Sun, SL (corresponding author), Fudan Univ, Yiwu Res Inst, Chengbei Rd, Yiwu City 322000, Zhejiang, Peoples R China.;Zhang, MM (corresponding author), Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China. xiaoqirong@nail.tsinghua.edu.cn; sls@fudan.edu.cn; mmz@hust.edu.cn Meng, Yuan/O-5772-2018; Cusano, Andrea/AHE-5832-2022; Ding, Yimin/AFT-4091-2022; Sun, Shulin/K-7940-2012; Xie, Zhenwei/HNI-0169-2023; 肖, 起榕/H-3405-2018 Meng, Yuan/0000-0002-5409-5610; Sun, Shulin/0000-0003-3046-1142; Xie, Zhenwei/0000-0002-4526-9746; 肖, 起榕/0000-0001-9419-3939; CUSANO, Andrea/0000-0002-9297-6339; Ding, Yimin/0000-0002-2159-7825 National Natural Science Foundation of China [62075113, 61675114, 11874118, 61775069, 61635004, 61935013, U1701661, 61975133]; National Key Research and Development Program of China [2020YFA0710101, 2017YFA0303504]; Natural Science Foundation of Shanghai [18ZR1403400, 20JC1414601]; Fudan University-CIOMP Joint Fund [FC2018-008]; Office of Naval Research [N00014-20-1-2105]; ARPA-E [DE-AR0001212]; Natural Science Foundation of Guangdong Province [2020A1515011185]; Science and Technology Innovation Commission of Shenzhen [JCYJ20180507182035270, JCYJ20200109114018750] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Fudan University-CIOMP Joint Fund; Office of Naval Research(Office of Naval Research); ARPA-E; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Science and Technology Innovation Commission of Shenzhen The authors would like to thank Prof. Nanfang Yu from Columbia University and Prof. Hong-Bo Sun from Tsinghua University for constructive discussions. Q.X. acknowledges support from National Natural Science Foundation of China (Grants Nos. 62075113, 61675114). S.S. is supported by National Key Research and Development Program of China (Nos. 2020YFA0710101, 2017YFA0303504), National Natural Science Foundation of China (11874118), Natural Science Foundation of Shanghai (18ZR1403400, 20JC1414601), and Fudan University-CIOMP Joint Fund (No. FC2018-008). M.Z. is supported by National Natural Science Foundation of China (61775069, 61635004). J.A.F. is supported by Office of Naval Research (under Award No. N00014-20-1-2105) and ARPA-E (under Award No. DE-AR0001212). Z.X. and X.Y. acknowledge support from National Natural Science Foundation of China (61935013, U1701661, 61975133), the Natural Science Foundation of Guangdong Province (2020A1515011185), and the Science and Technology Innovation Commission of Shenzhen (JCYJ20180507182035270, JCYJ20200109114018750). 542 66 67 152 361 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2095-5545 2047-7538 LIGHT-SCI APPL Light-Sci. Appl. NOV 22 2021.0 10 1 235 10.1038/s41377-021-00655-x 0.0 44 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics XC4RU 34811345.0 gold, Green Accepted 2023-03-23 WOS:000722002600002 0
J Yau, HM; Su, N Yau, Hon Man; Su, Nan On the generalizability of artificial neural networks in spin models SCIPOST PHYSICS CORE English Article ANTIFERROMAGNETIC POTTS MODELS; SQUARE-LATTICE The applicability of artificial neural networks (ANNs) is typically limited to the models they are trained with and little is known about their generalizability, which is a pressing issue in the practical application of trained ANNs to unseen problems. Here, by using the task of identifying phase transitions in spin models, we establish a systematic generalizability such that simple ANNs trained with the two-dimensional ferromagnetic Ising model can be applied to the ferromagnetic q-state Potts model in different dimensions for q >= 2. The same scheme can be applied to the highly nontrivial antiferromagnetic q-state Potts model. We demonstrate that similar results can be obtained by reducing the exponentially large state space spanned by the training data to one that comprises only three representative configurations artificially constructed through symmetry considerations. We expect our findings to simplify and accelerate the development of machine learning-assisted tasks in spin-model related disciplines in physics and materials science. [Yau, Hon Man] Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China; [Su, Nan] Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany Chinese Academy of Sciences; Institute of Geology & Geophysics, CAS Su, N (corresponding author), Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany. nansu@fias.uni-frankfurt.de National Natural Science Foundation of China [41806083, 91858212]; FIAS of SAMSON AG National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); FIAS of SAMSON AG We are thankful to Yi-Lun Du for stimulating discussions and a detailed reading of the manuscript. We are grateful to Tian-Yao Hao, Ya Xu, and Qing-Yu You for their encouragement and providing computational resources. We acknowledge Fang-Zhou Nan and Yuan Wang for technical support. This research was supported in part by the National Natural Science Foundation of China through grants 41806083 and 91858212, and the AI grant at FIAS of SAMSON AG. 28 0 0 1 1 SCIPOST FOUNDATION AMSTERDAM C/O J S CAUX, INST PHYSICS, UNIV AMSTERDAM, AMSTERDAM, 1098 XH, NETHERLANDS 2666-9366 SCIPOST PHYSICS CORE SciPost Phys. Core APR-JUN 2022.0 5 2 32 10.21468/SciPostPhysCore.5.2.032 0.0 27 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics 4M3YQ Green Submitted, gold 2023-03-23 WOS:000853261600014 0
C Li, J; Cui, YJ; Gu, CY; Wang, CH; Liu, WQ IEEE Li, Jiang; Cui, Yijun; Gu, Chongyan; Wang, Chenghua; Liu, Weiqiang Dynamically Configurable Physical Unclonable Function based on RRAM Crossbar 2021 IEEE/ACM INTERNATIONAL SYMPOSIUM ON NANOSCALE ARCHITECTURES (NANOARCH) IEEE International Symposium on Nanoscale Architectures English Proceedings Paper IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) NOV 08-10, 2021 ELECTR NETWORK IEEE,Assoc Comp Machinery Physical unclonable function (PUF); resistive random access memory (RRAM); RRAM crossbar; dynamically configuration Physical unclonable function (PUF) has been an effective solution for hardware security with the popularity of the internet of things (IoT). Due to low power consumption and high area efficiency, an emerging nonvolatile memory, resistive random access memory (RRAM) based PUF designs have attracted many attentions. Due to the bottleneck in the existing RRAM PUFs that it can not be fully compatible with the memory architecture, a dynamically configurable PUF based on the mainstream RRAM crossbar is proposed in this paper. Utilizing the device-to-device variation of the RRAM resistance, abundant challenge-response pairs (CRPs) are generated with a flexible configuration of an RRAM crossbar. Furthermore, different from the existing RRAM-based PUF designs, the proposed RRAM PUF can be dynamically configured between a memory cell and a PUF cell, without requiring additional sense circuits, leading to a minimal design overhead. The simulation results show that the proposed PUF exhibits good performance with a high uniqueness and reliability. Moreover, it achieves a great resistance against machine learning (ML) attack. [Li, Jiang; Cui, Yijun; Wang, Chenghua; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Coll Integrated Circuits, Nanjing, Peoples R China; [Gu, Chongyan] Queens Univ Belfast, Ctr Secure Informat Technol, Belfast, Antrim, North Ireland Nanjing University of Aeronautics & Astronautics; Queens University Belfast Li, J (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Coll Integrated Circuits, Nanjing, Peoples R China. lijiang@nuaa.edu.cn; yijun.cui@nuaa.edu.cn; c.gu@qub.ac.uk; chwang@nuaa.edu.cn; liuweiqiang@nuaa.edu.cn Li, Jiang/0000-0002-2792-3951 National Natural Science Foundation of China [62104107]; Natural Science Foundation of Jiangsu Province [BK20210287]; Fundamental Research Funds for the Central Universities [NS2021031] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This project is supported by the National Natural Science Foundation of China (Grants No. 62104107), Natural Science Foundation of Jiangsu Province (Grants No. BK20210287) and the Fundamental Research Funds for the Central Universities (Grants No. NS2021031). 25 0 0 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2327-8218 978-1-6654-0959-9 IEEE INT SYMP NANO 2021.0 10.1109/NANOARCH53687.2021.9642245 0.0 6 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Nanoscience & Nanotechnology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Science & Technology - Other Topics BS7BP Green Accepted 2023-03-23 WOS:000758415000013 0
C Wei, ZW; Cui, YJ; Chen, YP; Wang, CH; Gu, CY; Liu, WQ IEEE Wei, Ziwei; Cui, Yijun; Chen, Yunpeng; Wang, Chenghua; Gu, Chongyan; Liu, Weiqiang Transformer PUF : A Highly Flexible Configurable RO PUF Based on FPGA 2020 IEEE WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS) IEEE Workshop on Signal Processing Systems English Proceedings Paper 34th IEEE Workshop on Signal Processing Systems (SiPS) OCT 20-22, 2020 ELECTR NETWORK IEEE,Univ Coimbra,Univ Lisbon,Inst Telecomunicacoes,INESC ID,Springer,IEEE Signal Proc Soc, Design & Implementat Signal Proc Syst Tech Comm,IEEE Circuits & Syst Soc, Tech Comm Circuits & Syst Commun Physical Unclonable Function; Configurable Ring Oscillator; FPGA Implementation; Hardware Efficiency AUTHENTICATION; LIGHTWEIGHT Physical unclonable function (PUF) is a promising security primitive for IP protection and user authentication. As there are plenty of reconfigurable resources in a field programmable gate array (FPGA), configurable ring oscillator (CRO) PUF is one of the most hardware efficient PUF designs. Previous CRO PUF designs have relatively improved the yield of challenge and response pairs (CRPs). In this paper, a highly flexible CRO PUF based on FPGA, defined as Transformer PUF, is proposed. The proposed PUF design, which has multiple reconfigurability from XOR gates and multiplexers, can be deformable between different CRO PUFs. Compared with the traditional CRO PUFs, it is more resistant to two common machine learning attack techniques, logistic regression (LR) and covariance matrix adaptation evolutionary strategies (CMA-ES), with a small sample set size. Moreover, the Transformer PUF achieves the highest hardware efficiency among CRO PUFs. The results of the experiment carried out on Xilinx Artix-7 development board demonstrate that Transformer PUF has a good uniqueness of 49.44% and a high reliability of 98.12%. [Wei, Ziwei; Cui, Yijun; Chen, Yunpeng; Wang, Chenghua; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China; [Gu, Chongyan] Queens Univ Belfast, Ctr Secure Informat Technol CSIT, Belfast BT3 9DT, Antrim, North Ireland Nanjing University of Aeronautics & Astronautics; Queens University Belfast Liu, WQ (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China. liuweiqiang@nuaa.edu.cn 21 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6130 978-1-7281-8099-1 IEEE WRK SIG PRO SYS 2020.0 181 186 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BS9RR 2023-03-23 WOS:000783760500032 0
J Wang, DS; Tiwari, P; Garg, S; Zhu, HY; Bruza, P Wang, Dongsheng; Tiwari, Prayag; Garg, Sahil; Zhu, Hongyin; Bruza, Peter Structural block driven enhanced convolutional neural representation for relation extraction APPLIED SOFT COMPUTING English Article Relation extraction; Deep learning; CNNs; Dependency parsing ANOMALY DETECTION In this paper, we propose a novel lightweight relation extraction approach of structural block driven convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale Convolutional Neural Networks (CNNs). This is to (1) eliminate the noisy from irrelevant part of a sentence; meanwhile (2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset. (C) 2019 Elsevier B.V. All rights reserved. [Wang, Dongsheng] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark; [Tiwari, Prayag] Univ Padua, Dept Informat Engn, Padua, Italy; [Garg, Sahil] Ecole Technol Super, Montreal, PQ H3C 1K3, Canada; [Zhu, Hongyin] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China; [Bruza, Peter] Queensland Univ Technol, Sch Informat Syst, 2 George St, Brisbane, Qld 4000, Australia University of Copenhagen; University of Padua; University of Quebec; Ecole de Technologie Superieure - Canada; Chinese Academy of Sciences; Institute of Automation, CAS; Queensland University of Technology (QUT) Tiwari, P (corresponding author), Univ Padua, Dept Informat Engn, Padua, Italy. wang@di.ku.dk; prayag.tiwari@dei.unipd.it; sahil.garg@ieee.org; zhuhongyin2014@ia.ac.cn; p.bruza@qut.edu.au Garg, Sahil/AAN-2480-2020; Pandey, Hari Mohan/M-9658-2015; Tiwari, Prayag/N-6261-2017; Zhu, Hongyin/HJA-5160-2022 Pandey, Hari Mohan/0000-0002-9128-068X; Tiwari, Prayag/0000-0002-2851-4260; Zhu, Hongyin/0000-0001-5786-7594; Bruza, Peter/0000-0001-7999-0534; Garg, Sahil/0000-0003-0229-608X European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [721321] European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant(European CommissionEuropean Commission Joint Research Centre) This project receive funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721321. 56 12 13 3 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. JAN 2020.0 86 105913 10.1016/j.asoc.2019.105913 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science JW9TJ Green Submitted 2023-03-23 WOS:000503388200064 0
J Shang, MJ; Li, HJ; Ahmad, A; Ahmad, W; Ostrowski, KA; Aslam, F; Joyklad, P; Majka, TM Shang, Meijun; Li, Hejun; Ahmad, Ayaz; Ahmad, Waqas; Ostrowski, Krzysztof Adam; Aslam, Fahid; Joyklad, Panuwat; Majka, Tomasz M. Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms MATERIALS English Article mechanical properties; aggregate; concrete; compressive strength; split tensile strength; fiber SELF-COMPACTING CONCRETE; RECYCLED AGGREGATE; EMISSIONS; STRENGTH; WASTE; MODEL; CEMENT; STEEL Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R-2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model's performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties. [Shang, Meijun] Changchun Sci Tech Unversity, Sch Architetrue & Civil Engn, Changchun 130600, Peoples R China; [Li, Hejun] Jilin Northeast Architectural & Municipal Engn De, Changchun 130062, Peoples R China; [Ahmad, Ayaz; Ahmad, Waqas] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan; [Ahmad, Ayaz; Ostrowski, Krzysztof Adam] Cracow Univ Technol, Fac Civil Engn, 24 Warszawska Str, PL-31155 Krakow, Poland; [Aslam, Fahid] Prince Sattam Bin Abdulaziz Univ, Coll Engn Al Kharj, Dept Civil Engn, Al Kharj 11942, Saudi Arabia; [Joyklad, Panuwat] Srinakharinwirot Univ, Fac Engn, Dept Civil & Environm Engn, Nakhonnayok 26120, Thailand; [Majka, Tomasz M.] Cracow Univ Technol, Fac Chem Engn & Technol, Dept Chem & Technol Polymers, Warszawska 24, PL-31155 Krakow, Poland COMSATS University Islamabad (CUI); Cracow University of Technology; Prince Sattam Bin Abdulaziz University; Srinakharinwirot University; Cracow University of Technology Shang, MJ (corresponding author), Changchun Sci Tech Unversity, Sch Architetrue & Civil Engn, Changchun 130600, Peoples R China.;Ahmad, A (corresponding author), COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan.;Ahmad, A (corresponding author), Cracow Univ Technol, Fac Civil Engn, 24 Warszawska Str, PL-31155 Krakow, Poland. shangmeijun0525@sina.com; lihejun0720@sina.com; ayazahmad@cuiatd.edu.pk; waqasahmad@cuiatd.edu.pk; krzysztof.ostrowski.1@pk.edu.pl; f.aslam@psau.edu.sa; panuwatj@g.swu.ac.th; tomasz.majka@pk.edu.pl Aslam, Fahid/AAG-4938-2020 Aslam, Fahid/0000-0003-2863-3283; Majka, Tomasz/0000-0002-7913-0750; Ahmad, Ayaz/0000-0002-0312-2965; Ahmad, Waqas/0000-0002-1668-7607; Ostrowski, Krzysztof/0000-0001-5047-5862; Joyklad, Panuwat/0000-0003-1069-1653 57 26 26 22 32 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials JAN 2022.0 15 2 647 10.3390/ma15020647 0.0 27 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics ZB6DF 35057364.0 Green Accepted, gold 2023-03-23 WOS:000756929700001 0
J Rao, N; Liu, K; Machaczek, M; Pollet, L Rao, Nihal; Liu, Ke; Machaczek, Marc; Pollet, Lode Machine-learned phase diagrams of generalized Kitaev honeycomb magnets PHYSICAL REVIEW RESEARCH English Article We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine, to investigate the low-temperature classical phase diagram of a generalized HeisenbergKitaev-Gamma (J-K-F) model on a honeycomb lattice. Aside from reproducing phases reported by previous quantum and classical studies, our machine finds a hitherto missed nested zigzag-stripy order and establishes the robustness of a recently identified modulated S-3 x Z(3) phase, which emerges through the competition between the Kitaev and Gamma spin liquids, against Heisenberg interactions. The results imply that, in the restricted parameter space spanned by the three primary exchange interactions-J, K, and Gamma, the representative Kitaev material alpha-RuCl3 lies close to the boundaries of several phases, including a simple ferromagnet, the unconventional S-3 x Z(3), and nested zigzag-stripy magnets. A zigzag order is stabilized by a finite Gamma' and/or J(3) term, whereas the four magnetic orders may compete in particular if Gamma' is antiferromagnetic. [Rao, Nihal; Liu, Ke; Machaczek, Marc; Pollet, Lode] Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany; [Rao, Nihal; Liu, Ke; Machaczek, Marc; Pollet, Lode] Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany; [Pollet, Lode] Shanghai Jiao Tong Univ, Wilczek Quantum Ctr, Sch Phys & Astron, Shanghai 200240, Peoples R China University of Munich; Shanghai Jiao Tong University Liu, K (corresponding author), Univ Munich, Arnold Sommerfeld Ctr Theoret Phys, Theresienstr 37, D-80333 Munich, Germany.;Liu, K (corresponding author), Munich Ctr Quantum Sci & Technol MCQST, Schellingstr 4, D-80799 Munich, Germany. ke.liu@lmu.de Pollet, Lode C/F-6845-2010; Liu, Ke/A-3401-2017 Pollet, Lode C/0000-0002-7274-2842; Liu, Ke/0000-0002-5768-1631; Rao, Nihal/0000-0003-4824-4381 FP7/ERC Consolidator Grant QSIMCORR [771891]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC-2111 - 390814868] FP7/ERC Consolidator Grant QSIMCORR; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy(German Research Foundation (DFG)) N.R., K.L., M.M., and L.P. acknowledge support from FP7/ERC Consolidator Grant QSIMCORR, No. 771891, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2111 - 390814868. Our simulations make use of the.SVM formulation [72], the LIBSVM library [73,74], and the ALPSCore library [75]. The TK-SVM library has been made openly available with documentation and examples [71]. The data used in this work are available upon request. 86 6 6 1 1 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2643-1564 PHYS REV RES Phys. Rev. Res. SEP 8 2021.0 3 3 33223 10.1103/PhysRevResearch.3.033223 0.0 12 Physics, Multidisciplinary Emerging Sources Citation Index (ESCI) Physics UN5IH Green Submitted, gold 2023-03-23 WOS:000694048300003 0
J Chen, BJ; Tan, WJ; Coatrieux, G; Zheng, YH; Shi, YQ Chen, Beijing; Tan, Weijin; Coatrieux, Gouenou; Zheng, Yuhui; Shi, Yun-Qing A Serial Image Copy-Move Forgery Localization Scheme With Source/Target Distinguishment IEEE TRANSACTIONS ON MULTIMEDIA English Article Convolution; Feature extraction; Forgery; Correlation; Standards; Task analysis; Decoding; Copy-move; image forgery; deep neural network; atrous convolution; attention mechanism In this paper, we improve the parallel deep neural network (DNN) scheme BusterNet for image copy-move forgery localization with source/target region distinguishment. BusterNet is based on two branches, i.e., Simi-Det and Mani-Det, and suffers from two main drawbacks: (a) it should ensure that both branches correctly locate regions; (b) the Simi-Det branch only extracts single-level and low-resolution features using VGG16 with four pooling layers. To ensure the identification of the source and target regions, we introduce two subnetworks that are constructed serially: the copy-move similarity detection network (CMSDNet) and the source/target region distinguishment network (STRDNet). Regarding the second drawback, the CMSDNet subnetwork improves Simi-Det by removing the last pooling layer in VGG16 and by introducing atrous convolution into VGG16 to preserve field-of-views of filters after the removal of the fourth pooling layer; double-level self-correlation is also considered for matching hierarchical features. Moreover, atrous spatial pyramid pooling and attention mechanism allow the capture of multiscale features and provide evidence for important information. Finally, STRDNet is designed to determine the similar regions obtained from CMSDNet directly as tampered regions and untampered regions. It determines regions at the image-level rather than at the pixel-level as made by Mani-Det of BusterNet. Experimental results on four publicly available datasets (new synthetic dataset, CASIA, CoMoFoD, and COVERAGE) demonstrate that the proposed algorithm is superior to the state-of-the-art algorithms in terms of similarity detection ability and source/target distinguishment ability. [Chen, Beijing; Tan, Weijin; Zheng, Yuhui] Nanjing Univ Informat Sci & Technol, Minist Educ, Engn Res Ctr Digital Forens, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China; [Chen, Beijing; Tan, Weijin; Zheng, Yuhui] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Jiangsu, Peoples R China; [Chen, Beijing; Tan, Weijin; Zheng, Yuhui] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China; [Coatrieux, Gouenou] IMT Atlantique Bretagne Pays Loire, INSERM, UMR1101, LaTIM, F-29000 Brest, France; [Shi, Yun-Qing] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; IMT - Institut Mines-Telecom; IMT Atlantique; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; New Jersey Institute of Technology Zheng, YH (corresponding author), Nanjing Univ Informat Sci & Technol, Minist Educ, Engn Res Ctr Digital Forens, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China.;Zheng, YH (corresponding author), Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Jiangsu, Peoples R China. nbutimage@126.com; 13913301359@163.com; gouenou.coatrieux@imt-atlantique.fr; zheng_yuhui@nuist.edu.cn; shi@njit.edu National Natural Science Foundation of China [62072251, 61972206]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund This work was supported in part by the National Natural Science Foundation of China under Grants 62072251 and 61972206; in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yongdong Zhang. 37 30 31 11 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia 2021.0 23 3506 3517 10.1109/TMM.2020.3026868 0.0 12 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications WJ5OB 2023-03-23 WOS:000709093100006 0
J Wang, LY; Hu, P; Zheng, HW; Liu, Y; Cao, XW; Hellwich, O; Liu, T; Luo, GP; Bao, AM; Chen, X Wang, Lingyue; Hu, Ping; Zheng, Hongwei; Liu, Ying; Cao, Xingwen; Hellwich, Olaf; Liu, Tie; Luo, Geping; Bao, Anming; Chen, Xi Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images GEODERMA English Article Soil salinity; Sparse samples; Mixup; Light gradient boosting machine (LightGBM); Bayesian optimization; Shapley additive explanations (SHAP) MACHINE; CHINA; OPTIMIZATION; PREDICTION; PARAMETERS; MOISTURE Soil salinization is a major environmental risk caused by natural or human activities especially in arid and semi -arid regions. Machine learning for rapidly monitoring large-scale spatial soil salinization becomes possible. However, machine learning often needs large training samples and obtaining extensive soil salinization infor-mation by field investigation is laborious and difficult. In practice, the field soil sampling datasets are often sparse and non-normally distributed. The intricacy of features extracted from remote sensing images increases the model complexity and often leads to degradation in the prediction performance. To solve this problem, an integrative framework is proposed to predict soil salt content (SSC) based on light gradient boosting machine (LGBM). In this model, we first introduce the data augmentation method (Mixup) to improve sample diversity and alleviate model overfitting by the sparsity of samples. To improve the generalization and robustness of the model in different spatial heterogeneity of soil salinization, the Mixup-LGBM model is adaptively and jointly optimized by combining hyperparameters and feature selection in a Bayesian optimization framework. Furthermore, model interpretability is improved using shapley additive explanations (SHAP) value based on the combination of the confidence of the synthetic data through model visualization and feature importance assessment. In addition, different cases are simulated to test the model performance. In Case I, the raw sample-sparsity model using the data augmentation algorithm has higher prediction accuracy than other unused models. In Case II, the extreme sample-sparsity model still achieves satisfactory results while the other models can't learn any effective information after multiple iterations. The experimental results reveal that the proposed model can automatically find representative features in heterogeneous environments and has strong adaptability in different study areas. This finding indicates that digital elevation model (DEM) has a high influence on SSC in both study areas. Besides the DEM, soil salinization in the Manasi River Basin is more sensitive to human ac-tivities, while that in the Werigan-Kuqa River Delta Oasis is more sensitive to natural factors. The Mixup-LGBM model is suitable for predicting SSC in different sample sparsity scenarios while ensuring the high accuracy. The model has considerable potential for dealing with other complex sample sparsity regression tasks. [Wang, Lingyue; Zheng, Hongwei; Liu, Ying; Cao, Xingwen; Liu, Tie; Luo, Geping; Bao, Anming; Chen, Xi] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; [Wang, Lingyue; Zheng, Hongwei; Liu, Ying; Cao, Xingwen; Liu, Tie; Luo, Geping; Bao, Anming; Chen, Xi] Univ Chinese Acad Sci, Beijing, Peoples R China; [Wang, Lingyue; Zheng, Hongwei; Liu, Ying; Cao, Xingwen; Liu, Tie; Luo, Geping; Bao, Anming; Chen, Xi] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China; [Hu, Ping] Xinjiang Univ, Sch Informat Sci & Engn, Urumqi 830017, Peoples R China; [Hellwich, Olaf] Tech Univ Berlin, Dept Comp Vis & Remote Sensing, D-10587 Berlin, Germany Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Xinjiang University; Technical University of Berlin Zheng, HW (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China. hzheng@ms.xjb.ac.cn Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region [2018D04027]; National Natural Science Foundation of China [41877012]; High -End Foreign Experts Project; Strategic Priority Research Program of the Chinese Academy of Sciences; Pan-Third Pole Environment Study for a Green Silk Road; [XDA20060303] Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High -End Foreign Experts Project; Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Pan-Third Pole Environment Study for a Green Silk Road; This work was supported by the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region under Grant [2018D04027] , the National Natural Science Foundation of China under Grant [41877012] , the High -End Foreign Experts Project [2020-2023] , the Strategic Priority Research Program of the Chinese Academy of Sciences, and Pan-Third Pole Environment Study for a Green Silk Road (Grant No. XDA20060303) . 73 0 0 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0016-7061 1872-6259 GEODERMA Geoderma FEB 2023.0 430 116321 10.1016/j.geoderma.2022.116321 0.0 JAN 2023 16 Soil Science Science Citation Index Expanded (SCI-EXPANDED) Agriculture 8Q2QG 2023-03-23 WOS:000927056700001 0
J Rosen, J; de Aguiar, HB; Anand, V; Baek, Y; Gigan, S; Horisaki, R; Hugonnet, H; Juodkazis, S; Lee, K; Liang, HW; Liu, YK; Ludwig, S; Osten, W; Park, Y; Pedrini, G; Sarkar, T; Schindler, J; Singh, AK; Singh, RK; Situ, G; Takeda, M; Xie, XS; Yang, WQ; Zhou, JY Rosen, Joseph; de Aguiar, Hilton B.; Anand, Vijayakumar; Baek, YoonSeok; Gigan, Sylvain; Horisaki, Ryoichi; Hugonnet, Herve; Juodkazis, Saulius; Lee, KyeoReh; Liang, Haowen; Liu, Yikun; Ludwig, Stephan; Osten, Wolfgang; Park, YongKeun; Pedrini, Giancarlo; Sarkar, Tushar; Schindler, Johannes; Singh, Alok Kumar; Singh, Rakesh Kumar; Situ, Guohai; Takeda, Mitsuo; Xie, Xiangsheng; Yang, Wanqin; Zhou, Jianying Roadmap on chaos-inspired imaging technologies (CI2-Tech) APPLIED PHYSICS B-LASERS AND OPTICS English Article APERTURE CORRELATION HOLOGRAPHY; DEEP-LEARNING APPROACH; STRUCTURED ILLUMINATION MICROSCOPY; SINGLE-SHOT; SPECKLE-CORRELATION; SCATTERING MEDIA; RESOLUTION ENHANCEMENT; OBJECT RECOGNITION; NOISE SUPPRESSION; DIGITAL HOLOGRAMS In recent years, rapid developments in imaging concepts and computational methods have given rise to a new generation of imaging technologies based on chaos. These chaos-inspired imaging technologies (CI2-Tech) consist of two directions: non-invasive and invasive. Non-invasive imaging, a much older research direction with a goal of imaging through scattering layers, has reached faster, smarter, and sharper imaging capabilities in recent years. The invasive imaging direction is based on exploiting the chaos to achieve imaging characteristics and increase dimensionalities beyond the limits of conventional imagers. In this roadmap, the current and future challenges in invasive and non-invasive imaging technologies are presented. [Rosen, Joseph] Ben Gurion Univ Negev, Sch Elect & Comp Engn, POB 653, IL-8410501 Beer Sheva, Israel; [de Aguiar, Hilton B.; Gigan, Sylvain] Sorbonne Univ, ENS Univ PSL, Coll France, Lab Kastler Brossel,CNRS, 24 Rue Lhomond, F-75005 Paris, France; [Anand, Vijayakumar; Juodkazis, Saulius] Swinburne Univ Technol, Sch Sci, Opt Sci Ctr, Hawthorn, Vic 3122, Australia; [Anand, Vijayakumar; Juodkazis, Saulius] Swinburne Univ Technol, Sch Sci, ARC Training Ctr Surface Engn Adv Mat SEAM, Hawthorn, Vic 3122, Australia; [Baek, YoonSeok; Hugonnet, Herve; Lee, KyeoReh; Park, YongKeun] Korea Adv Inst Sci & Technol, Dept Phys, Daejeon 34141, South Korea; [Horisaki, Ryoichi] Univ Tokyo, Grad Sch Informat Sci & Technol, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan; [Juodkazis, Saulius] Tokyo Inst Technol, Sch Mat & Chem Technol, Tokyo Tech World Res Hub Initiat WRHI, Meguro Ku, 2-12-1 Ookayama, Tokyo 1528550, Japan; [Liang, Haowen; Liu, Yikun; Xie, Xiangsheng; Zhou, Jianying] Sun Yat Sen Univ, Sch Phys, State Key Lab Optoelect Mat & Technol, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Guangzhou 510275, Peoples R China; [Ludwig, Stephan; Osten, Wolfgang; Pedrini, Giancarlo; Schindler, Johannes; Singh, Alok Kumar] Univ Stuttgart, Inst Fuer Tech Opt, Pfaffenwaldring 9, D-70569 Stuttgart, Germany; [Park, YongKeun; Singh, Rakesh Kumar] Tomocube Inc, Daejeon 34051, South Korea; [Sarkar, Tushar] Indian Inst Technol BHU, Dept Phys, Varanasi 221005, Uttar Pradesh, India; [Situ, Guohai; Yang, Wanqin] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China; [Situ, Guohai; Yang, Wanqin] Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China; [Situ, Guohai] Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Hangzhou 310024, Peoples R China; [Takeda, Mitsuo] Utsunomiya Univ, Ctr Opt Res & Educ, Utsunomiya, Tochigi 3218585, Japan; [Xie, Xiangsheng] Shantou Univ, Coll Sci, Dept Phys, Shantou 515063, Guangdong, Peoples R China; [Anand, Vijayakumar] Univ Tartu, Inst Phys, EE-50411 Tartu, Estonia Ben Gurion University; UDICE-French Research Universities; Universite PSL; College de France; Ecole Normale Superieure (ENS); Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Swinburne University of Technology; Swinburne University of Technology; Korea Advanced Institute of Science & Technology (KAIST); University of Tokyo; Tokyo Institute of Technology; Southern Marine Science & Engineering Guangdong Laboratory; Sun Yat Sen University; University of Stuttgart; Indian Institute of Technology System (IIT System); Indian Institute of Technology BHU Varanasi (IIT BHU Varanasi); Chinese Academy of Sciences; Shanghai Institute of Optics & Fine Mechanics; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Utsunomiya University; Shantou University; University of Tartu; University of Tartu Institute of Physics Anand, V (corresponding author), Swinburne Univ Technol, Sch Sci, Opt Sci Ctr, Hawthorn, Vic 3122, Australia.;Anand, V (corresponding author), Swinburne Univ Technol, Sch Sci, ARC Training Ctr Surface Engn Adv Mat SEAM, Hawthorn, Vic 3122, Australia.;Anand, V (corresponding author), Univ Tartu, Inst Phys, EE-50411 Tartu, Estonia. vijayakumar.anand@ut.ee Anand, Vijayakumar/L-1283-2013; Juodkazis, Saulius/Q-6943-2019; Rosen, Joseph/F-1889-2012; Gigan, Sylvain/AAD-6347-2020 Anand, Vijayakumar/0000-0001-8867-1949; Juodkazis, Saulius/0000-0003-3542-3874; Rosen, Joseph/0000-0002-9739-2180; Gigan, Sylvain/0000-0002-9914-6231; Baek, YoonSeok/0000-0003-1132-6009; Singh, Rakesh Kumar/0000-0002-3117-2695 Israel Science Foundation [1669/16]; NATO [SPS-985048]; European Union's Horizon 2020 research and innovation programme [857627]; National Natural Science Foundation of China [11534017, 61991450, 61991452, 11704421, 12074444, 62061136005]; Department of Education of Guangdong Province [2018KCXTD011]; Key R&D Program of Guangdong Province [2019B010152001]; Guangdong Basic and Applied Basic Research Foundation [2020A1515011184]; Guangzhou Science and Technology Project [201805010004]; German Research Association DFG [Os 111/49-1]; Baden-Wuerttemberg Stiftung; University Grant Commission, India; Science and Engineering Research Board (SERB) [CORE/2019/000026]; Council of Scientific and Industrial Research (CSIR), India [80 (0092)/20/EMR-II]; KAIST UP program; BK21+ program; Science-X; National Research Foundation of Korea [2015R1A3A2066550, 2021R1C1C2009220]; Institute of Information and communications Technology Planning and Evaluation (IITP) Grant [2021-000745]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDB-SSW-JSC002]; FET-Open [Dynamic-863203]; European Research Council ERC Consolidator Grant [SMARTIES-724473] Israel Science Foundation(Israel Science Foundation); NATO(NATO (North Atlantic Treaty Organisation)); European Union's Horizon 2020 research and innovation programme; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Department of Education of Guangdong Province(National Natural Science Foundation of Guangdong Province); Key R&D Program of Guangdong Province; Guangdong Basic and Applied Basic Research Foundation; Guangzhou Science and Technology Project; German Research Association DFG(German Research Foundation (DFG)); Baden-Wuerttemberg Stiftung; University Grant Commission, India(University Grants Commission, India); Science and Engineering Research Board (SERB); Council of Scientific and Industrial Research (CSIR), India(Council of Scientific & Industrial Research (CSIR) - India); KAIST UP program; BK21+ program(Ministry of Education & Human Resources Development (MOEHRD), Republic of Korea); Science-X; National Research Foundation of Korea(National Research Foundation of Korea); Institute of Information and communications Technology Planning and Evaluation (IITP) Grant(Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea); Key Research Program of Frontier Sciences, Chinese Academy of Sciences; FET-Open; European Research Council ERC Consolidator Grant(European Research Council (ERC)) Section 2: Israel Science Foundation (1669/16). Section 3: NATO Grant no. SPS-985048 and European Union's Horizon 2020 research and innovation programme under grant agreement No. 857627 (CIPHR). Section 4: this work was supported by the National Natural Science Foundation of China (11534017, 61991450, 61991452, 11704421, 12074444); Department of Education of Guangdong Province (2018KCXTD011); The Key R&D Program of Guangdong Province (2019B010152001); Guangdong Basic and Applied Basic Research Foundation (2020A1515011184); Guangzhou Science and Technology Project (201805010004). Section 5: this work has been funded by the The German Research Association DFG with Grant no. Os 111/49-1 and the Baden-Wuerttemberg Stiftung within the project Tiefenaufgeloste Fluoreszenzdetektion in streuenden Medien (FluoTis). We thank our partners at ILM for the production of the phantoms and the collaboration during the project. Mitsuo Takeda is thankful to Alexander von Humboldt Foundation for the opportunity of his research stay at ITO, University of Stuttgart. Section 6: T. Sarkar would like to acknowledge the University Grant Commission, India for financial support as Senior Research Fellowship. Supports from the Science and Engineering Research Board (SERB): CORE/2019/000026 and the Council of Scientific and Industrial Research (CSIR), IndiaGrant No 80 (0092)/20/EMR-II, are acknowledged. Section 7: this work was supported by KAIST UP program, BK21+ program, Science-X, National Research Foundation of Korea (2015R1A3A2066550, 2021R1C1C2009220), and Institute of Information and communications Technology Planning and Evaluation (IITP) Grant (2021-000745). Section 8: this work was supported by the National Natural Science Foundation of China (62061136005, 61991452) and the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant no. QYZDB-SSW-JSC002). Section 10: this research has been funded by the FET-Open (Dynamic-863203), European Research Council ERC Consolidator Grant (SMARTIES-724473). 171 9 9 12 27 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0946-2171 1432-0649 APPL PHYS B-LASERS O Appl. Phys. B-Lasers Opt. MAR 2022.0 128 3 49 10.1007/s00340-021-07729-z 0.0 26 Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics YY9ZR 2023-03-23 WOS:000755143900003 0
J Toju, H; Peay, KG; Yamamichi, M; Narisawa, K; Hiruma, K; Naito, K; Fukuda, S; Ushio, M; Nakaoka, S; Onoda, Y; Yoshida, K; Schlaeppi, K; Bai, Y; Sugiura, R; Ichihashi, Y; Minamisawa, K; Kiers, ET Toju, Hirokazu; Peay, Kabir G.; Yamamichi, Masato; Narisawa, Kazuhiko; Hiruma, Kei; Naito, Ken; Fukuda, Shinji; Ushio, Masayuki; Nakaoka, Shinji; Onoda, Yusuke; Yoshida, Kentaro; Schlaeppi, Klaus; Bai, Yang; Sugiura, Ryo; Ichihashi, Yasunori; Minamisawa, Kiwamu; Kiers, E. Toby Core microbiomes for sustainable agroecosystems NATURE PLANTS English Review HETEROCONIUM-CHAETOSPIRA; RHIZOSPHERE MICROBIOME; BACTERIAL COMMUNITIES; PLANT BIODIVERSITY; BIOLOGICAL-CONTROL; ROOT MICROBIOTA; GUT MICROBIOTA; DIVERSITY; ECOSYSTEM; NETWORKS In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of 'core microbiomes', which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems. [Toju, Hirokazu; Ushio, Masayuki] Kyoto Univ, Ctr Ecol Res, Otsu, Shiga, Japan; [Toju, Hirokazu; Hiruma, Kei; Fukuda, Shinji; Ushio, Masayuki; Nakaoka, Shinji; Yoshida, Kentaro; Sugiura, Ryo; Ichihashi, Yasunori] Japan Sci & Technol Agcy, PRESTO, Kawaguchi, Saitama, Japan; [Peay, Kabir G.] Stanford Univ, Dept Biol, Stanford, CA 94305 USA; [Yamamichi, Masato] Univ Tokyo, Dept Gen Syst Studies, Meguro Ku, Tokyo, Japan; [Narisawa, Kazuhiko] Ibaraki Univ, Coll Agr, Dept Bioresource Sci, Ami, Ibaraki, Japan; [Hiruma, Kei] Nara Inst Sci & Technol, Dept Biol Sci, Nara, Japan; [Hiruma, Kei] Natl Agr & Food Res Org NARO, Genet Resource Ctr, Tsukuba, Ibaraki, Japan; [Fukuda, Shinji] Keio Univ, Inst Adv Biosci, Tsuruoka, Yamagata, Japan; [Fukuda, Shinji] Kanagawa Inst Ind Sci & Technol, Intestinal Microbiota Project, Kawasaki, Kanagawa, Japan; [Fukuda, Shinji] Univ Tsukuba, Transborder Med Res Ctr, Tsukuba, Ibaraki, Japan; [Nakaoka, Shinji] Univ Tokyo, Inst Ind Sci, Tokyo, Japan; [Onoda, Yusuke] Kyoto Univ, Grad Sch Agr, Kyoto, Japan; [Yoshida, Kentaro] Kobe Univ, Grad Sch Agr Sci, Nada Ku, Kobe, Hyogo, Japan; [Schlaeppi, Klaus] Univ Bern, Inst Plant Sci, Bern, Switzerland; [Schlaeppi, Klaus] Agroscope, Dept Agroecol & Environm, Zurich, Switzerland; [Bai, Yang] Chinese Acad Sci, Inst Genet & Dev Biol, State Key Lab Plant Genom, Beijing, Peoples R China; [Bai, Yang] Chinese Acad Sci, Inst Genet & Dev Biol, Ctr Excellence Plant & Microbial Sci CEPAMS, Beijing, Peoples R China; [Bai, Yang] John Innes Ctr, Beijing, Peoples R China; [Sugiura, Ryo] NARO Natl Agr & Food Res Org, Hokkaido Agr Res Ctr, Memuro, Hokkaido, Japan; [Ichihashi, Yasunori] RIKEN Ctr Sustainable Resource Sci, Yokohama, Kanagawa, Japan; [Minamisawa, Kiwamu] Tohoku Univ, Grad Sch Life Sci, Sendai, Miyagi, Japan; [Kiers, E. Toby] Vrije Univ Amsterdam, Dept Ecol Sci, Amsterdam, Netherlands; [Ichihashi, Yasunori] RIKEN BioResource Res Ctr, Tsukuba, Ibaraki, Japan Kyoto University; Japan Science & Technology Agency (JST); Stanford University; University of Tokyo; Ibaraki University; Nara Institute of Science & Technology; National Agriculture & Food Research Organization - Japan; Keio University; Kanagawa Academy Science & Technology; University of Tsukuba; University of Tokyo; Kyoto University; Kobe University; University of Bern; Swiss Federal Research Station Agroscope; Chinese Academy of Sciences; Institute of Genetics & Developmental Biology, CAS; Chinese Academy of Sciences; Institute of Genetics & Developmental Biology, CAS; RIKEN; Tohoku University; Vrije Universiteit Amsterdam; RIKEN Toju, H (corresponding author), Kyoto Univ, Ctr Ecol Res, Otsu, Shiga, Japan.;Toju, H (corresponding author), Japan Sci & Technol Agcy, PRESTO, Kawaguchi, Saitama, Japan. toju.hirokazu.4c@kyoto-u.ac.jp Bai, Yang/AAH-1518-2019; Yamamichi, Masato/F-8836-2019; Ichihashi, Yasunori/D-1503-2017; Ushio, Masayuki/F-8667-2010; Schlaeppi, Klaus/ABE-6731-2020; Toju, Hirokazu/S-7307-2017; Hiruma, Kei/U-1626-2019; Toju, Hirokazu/T-7355-2019; Schlaeppi, Klaus/HCI-8011-2022 Yamamichi, Masato/0000-0003-2136-3399; Ichihashi, Yasunori/0000-0003-1935-4397; Ushio, Masayuki/0000-0003-4831-7181; Schlaeppi, Klaus/0000-0003-3620-0875; Toju, Hirokazu/0000-0002-3362-3285; Schlaeppi, Klaus/0000-0003-3620-0875; Peay, Kabir/0000-0002-7998-7412; Naito, Ken/0000-0002-0273-7837; Minamisawa, Kiwamu/0000-0001-7302-4079; Bai, Yang/0000-0003-2652-7022; Hiruma, Kei/0000-0002-5487-5414 JSPS KAKENHI Grant [26711026]; JST PRESTO [JPMJPR16Q6]; Funding Program for Next Generation World-Leading Researchers of Cabinet Office, the Government of Japan [GS014]; DOE Award [DE-SC0016097]; European Research Council Grant [335542]; Grants-in-Aid for Scientific Research [16K18618, 26711026] Funding Source: KAKEN JSPS KAKENHI Grant(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); JST PRESTO(Japan Science & Technology Agency (JST)); Funding Program for Next Generation World-Leading Researchers of Cabinet Office, the Government of Japan; DOE Award(United States Department of Energy (DOE)); European Research Council Grant(European Research Council (ERC)); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) We thank Takashi Akagi and three anonymous reviewers for their insightful comments on the manuscript. This work was financially supported by JSPS KAKENHI Grant (26711026), JST PRESTO (JPMJPR16Q6), and the Funding Program for Next Generation World-Leading Researchers of Cabinet Office, the Government of Japan (GS014) to H.T, DOE Award DE-SC0016097 to KGP, and by a European Research Council Grant (335542) to E.T.K. 118 359 380 76 726 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2055-026X 2055-0278 NAT PLANTS Nat. Plants MAY 2018.0 4 5 247 257 10.1038/s41477-018-0139-4 0.0 11 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences GI7OD 29725101.0 2023-03-23 WOS:000434690900009 0
C Wang, JF; Chen, JJ; Wang, H Zheng, H; Callejas, Z; Griol, D; Wang, H; Hu, X; Schmidt, H; Baumbach, J; Dickerson, J; Zhang, L Wang, Jinfeng; Chen, Jiajie; Wang, Hui A New Model based on Fuzzy integral for Cancer Prediction PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) IEEE International Conference on Bioinformatics and Biomedicine-BIBM English Proceedings Paper IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics DEC 03-06, 2018 Madrid, SPAIN Ulster Univ,Univ Granada,Univ Carlos III Madrid,TCCLS,IEEE,IEEE Comp Soc,Natl Sci Fdn,Syst Med,Mary Ann Liebert Inc publishers Cancer prediction; Gene selection; Fuzzy measure HIGH-DIMENSIONAL QSAR; GENE SELECTION; LOGISTIC-REGRESSION; ADAPTIVE LASSO; CLASSIFICATION Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods. [Wang, Jinfeng; Chen, Jiajie] South China Agr Univ, Dept Math & Informat, Guangzhou, Guangdong, Peoples R China; [Wang, Hui] Univ Ulster, Sch Comp & Math, Belfast, Antrim, North Ireland South China Agricultural University; Ulster University Wang, H (corresponding author), Univ Ulster, Sch Comp & Math, Belfast, Antrim, North Ireland. wangjinfeng@scau.edu.cn; chen_jia_jie@sina.cn; h.wang@ulster.co.uk EU [690238]; Technology Planning Project of Guangdong Province [2017A040406023]; Technology Planning Project of Guangzho City [201804010353] EU(European Commission); Technology Planning Project of Guangdong Province; Technology Planning Project of Guangzho City This work is supported by the EU Horizon 2020 (No.: 690238), the Technology Planning Project of Guangdong Province (No.: 2017A040406023) and the Technology Planning Project of Guangzho City (No.: 201804010353). 45 2 2 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-1125 2156-1133 978-1-5386-5488-0 IEEE INT C BIOINFORM 2018.0 2309 2315 7 Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematical & Computational Biology BM0DB 2023-03-23 WOS:000458654000391 0
J Zhang, W; Zhu, B; Xu, H; Wang, Y Zhang, Wei; Zhu, Bing; Xu, Hui; Wang, Yong Applying extended intrinsic mean spin tensor in evolution algorithm for RANS modelling of turbulent rotating channel flow JOURNAL OF HYDRODYNAMICS English Article Extended intrinsic mean spin tensor; RANS modeling; rotating frame of reference; gene expression programming REYNOLDS; SIMULATIONS We present a machine learning based method for RANS modeling in the rotating frame of reference (RFR). The extended intrinsic mean spin tensor (EIMST) is adopted in a novel expansion of the evolution algorithm, named multi-dimensional gene expression programming (MGEP). Based on DNS data, a constrain free model for Reynolds stress is created by considering system rotating. The anisotropy behavior of Reynolds stress is considered in the model, which is then for the first time applied for modeling turbulent flow inside a rotating channel. Compared with the traditional RANS model, the new model can predict the non-symmetric profile of Reynolds stress. Meanwhile, the Taylor-Gortler vortex is captured in our simulations with the new model. It is demonstrated that the application of EIMST in MGEP can be successfully adopted for RANS modeling in the RFR. [Zhang, Wei] Marine Design & Res Inst China, Sci & Technol Water Jet Prop Lab, Shanghai 200011, Peoples R China; [Zhu, Bing] Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 210098, Peoples R China; [Xu, Hui] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200011, Peoples R China; [Wang, Yong] Max Planck Inst Dynam & Self Org, D-37077 Gottingen, Germany Marine Design & Research Institute of China (MARIC); University of Shanghai for Science & Technology; Shanghai Jiao Tong University; Max Planck Society Wang, Y (corresponding author), Max Planck Inst Dynam & Self Org, D-37077 Gottingen, Germany. waynezw0618@163.com; yong.wang@ds.mpg.de Wang, Yong/E-2391-2013 Wang, Yong/0000-0001-8518-6745 National Natural Science Foundation of China [91852117, 91852106]; MOE Key Laboratory of Hydrodynamics, Shanghai Jiao Tong University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MOE Key Laboratory of Hydrodynamics, Shanghai Jiao Tong University Project supported by the National Natural Science Foundation of China (Grant Nos. 91852117, 91852106), the MOE Key Laboratory of Hydrodynamics, Shanghai Jiao Tong University. 14 5 5 0 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1001-6058 1878-0342 J HYDRODYN J. Hydrodyn. DEC 2019.0 31 6 1255 1258 10.1007/s42241-019-0093-2 0.0 4 Mechanics Science Citation Index Expanded (SCI-EXPANDED) Mechanics KF2SS 2023-03-23 WOS:000509098300019 0
J Hou, ZR; Guo, JH; Xing, JM; Guo, C; Zhang, YJ Hou, Zhuoran; Guo, Jianhua; Xing, Jiaming; Guo, Chong; Zhang, Yuanjian Machine learning and whale optimization algorithm based design of energy management strategy for plug-in hybrid electric vehicle IET INTELLIGENT TRANSPORT SYSTEMS English Article PREDICTIVE CONTROL In this paper, a novel energy management strategy with the improved adaptability to various conditions for plug-in hybrid electric vehicle (PHEV) is proposed. The control parameters, derived from the benchmark test, are optimized offline for different driving conditions. The optimized parameters are implemented according to different driving behaviours identified online. The offline and online cooperation improves performance of energy management strategy in different driving conditions. Three main efforts have been made: Firstly, the valuable features that describe different driving conditions are extracted by random forest (RF) and the features are used for determining driving condition categories, utilized for online driving condition identification by support vector machine (SVM). Secondly, the control thresholds in the developed control strategy are optimized by whale optimization algorithm (WOA) under different driving conditions. The optimal control thresholds for different driving conditions will be called online after certain traffic condition is categorized. At last, simulation-based evaluation is performed, validating the enhanced performance of the proposed methods in energy-saving in different driving conditions. [Hou, Zhuoran; Guo, Jianhua; Xing, Jiaming; Guo, Chong] Jilin Univ, State Key Lab Automot Simulat & Control, 5988 Renmin St, Changchun 130022, Jilin, Peoples R China; [Zhang, Yuanjian] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AG, Antrim, North Ireland Jilin University; Queens University Belfast Guo, C (corresponding author), Jilin Univ, State Key Lab Automot Simulat & Control, 5988 Renmin St, Changchun 130022, Jilin, Peoples R China.;Zhang, YJ (corresponding author), Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AG, Antrim, North Ireland. guochong@jlu.edu.cn; Y.Zhang@qub.ac.uk Zhang, Yuanjian/HKN-4832-2023 Zhang, Yuanjian/0000-0001-5563-8480 Jilin ProvinceKey Scientific andTechnologicalResearch Projects of China [20170204085GX]; Tianjin Science andTechnology Plan [20YFZCGX00770]; OutstandingYoungTalents Foundation of Jilin Province of China [20190103160JH] Jilin ProvinceKey Scientific andTechnologicalResearch Projects of China; Tianjin Science andTechnology Plan; OutstandingYoungTalents Foundation of Jilin Province of China Thisworkwas supported by Jilin ProvinceKey Scientific andTechnologicalResearch Projects of China (GrantNo. 20170204085GX), Tianjin Science andTechnology Plan (GrantNo. 20YFZCGX00770) and the OutstandingYoungTalents Foundation of Jilin Province of China (GrantNo. 20190103160JH). 41 5 5 7 35 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-956X 1751-9578 IET INTELL TRANSP SY IET Intell. Transp. Syst. AUG 2021.0 15 8 1076 1091 10.1049/itr2.12084 0.0 JUN 2021 16 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation TD5UK Green Published 2023-03-23 WOS:000658038500001 0
J Chen, DJ; Fu, LY; Hu, DH; Klukas, C; Chen, M; Kaufmann, K Chen, Dijun; Fu, Liang-Yu; Hu, Dahui; Klukas, Christian; Chen, Ming; Kaufmann, Kerstin The HTPmod Shiny application enables modeling and visualization of large-scale biological data COMMUNICATIONS BIOLOGY English Article GENE-EXPRESSION; INTEGRATIVE ANALYSIS; TRANSCRIPTOME; DNA; PHENOMICS; RESPONSES; DYNAMICS; PLATFORM; TOMATO; SYSTEM The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod. [Chen, Dijun; Fu, Liang-Yu; Kaufmann, Kerstin] Humboldt Univ, Inst Biol, Dept Plant Cell & Mol Biol, D-10115 Berlin, Germany; [Chen, Dijun; Klukas, Christian] Leibniz Inst Plant Genet & Crop Plant Res IPK, Corrensstr 3, D-06466 Gatersleben, Germany; [Hu, Dahui; Chen, Ming] Zhejiang Univ, Coll Life Sci, Dept Bioinformat, Hangzhou 310058, Zhejiang, Peoples R China; [Klukas, Christian] BASF SE, Digitalizat Res & Dev ROM, D-67056 Ludwigshafen, Germany Humboldt University of Berlin; Leibniz Institut fur Pflanzengenetik und Kulturpflanzenforschung; Zhejiang University; BASF Chen, DJ; Kaufmann, K (corresponding author), Humboldt Univ, Inst Biol, Dept Plant Cell & Mol Biol, D-10115 Berlin, Germany.;Chen, DJ (corresponding author), Leibniz Inst Plant Genet & Crop Plant Res IPK, Corrensstr 3, D-06466 Gatersleben, Germany.;Chen, M (corresponding author), Zhejiang Univ, Coll Life Sci, Dept Bioinformat, Hangzhou 310058, Zhejiang, Peoples R China. chendijun2012@gmail.com; mchen@zju.edu.cn; kerstin.kaufmann@hu-berlin.de Kaufmann, Kerstin/GXF-3402-2022; Chen, Ming/AAV-3682-2020; Chen, Dijun/P-3877-2014 Kaufmann, Kerstin/0000-0001-7960-6256; Chen, Ming/0000-0002-9677-1699; Chen, Dijun/0000-0002-7456-2511; Fu, Liangyu/0000-0002-0017-118X Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); National Natural Sciences Foundation of China [31571366, 31771477]; National Key Research and Development Program of China [2016YFA0501704]; 111 Project; Fundamental Research Funds for the Central Universities; Alexander-von-Humboldt foundation; Federal Ministry of Education and Research Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); National Natural Sciences Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; 111 Project(Ministry of Education, China - 111 Project); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Alexander-von-Humboldt foundation(Alexander von Humboldt Foundation); Federal Ministry of Education and Research(Federal Ministry of Education & Research (BMBF)) This work was partially supported by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), the National Natural Sciences Foundation of China (No. 31571366, 31771477), and the National Key Research and Development Program of China (2016YFA0501704). D.H. and M.C. are grateful to the support by the 111 Project and the Fundamental Research Funds for the Central Universities. K.K. wishes to thank the Alexander-von-Humboldt foundation and the Federal Ministry of Education and Research for support. 49 7 7 0 4 NATURE PUBLISHING GROUP NEW YORK 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA 2399-3642 COMMUN BIOL Commun. Biol. 2018.0 1 89 10.1038/s42003-018-0091-x 0.0 8 Biology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics HO7LC 30271970.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000461126500089 0
C Gao, YP; Gao, HC; Luo, SN; Zi, Y; Zhang, SD; Mao, WJ; Wang, P; Shen, YL; Yan, E USENIX Assoc Gao, Yipeng; Gao, Haichang; Luo, Sainan; Zi, Yang; Zhang, Shudong; Mao, Wenjie; Wang, Ping; Shen, Yulong; Yan, Jeff Research on the Security of Visual Reasoning CAPTCHA PROCEEDINGS OF THE 30TH USENIX SECURITY SYMPOSIUM English Proceedings Paper 30th USENIX Security Symposium AUG 11-13, 2021 ELECTR NETWORK USENIX Assoc CAPTCHA is an effective mechanism for protecting computers from malicious bots. With the development of deep learning techniques, current mainstream text-based CAPTCHAs have been proven to be insecure. Therefore, a major effort has been directed toward developing image-based CAPTCHAs, and image-based visual reasoning is emerging as a new direction of such development. Recently, Tencent deployed the Visual Turing Test (VTT) CAPTCHA. This appears to have been the first application of a visual reasoning scheme. Subsequently, other CAPTCHA service providers (Geetest, NetEase, Dingxiang, etc.) have proposed their own visual reasoning schemes to defend against bots. It is, therefore, natural to ask a fundamental question: are visual reasoning CAPTCHAs as secure as their designers expect? This paper presents the first attempt to solve visual reasoning CAPTCHAs. We implemented a holistic attack and a modular attack, which achieved overall success rates of 67.3% and 88.0% on VTT CAPTCHA, respectively. The results show that visual reasoning CAPTCHAs are not as secure as anticipated; this latest effort to use novel, hard AI problems for CAPTCHAs has not yet succeeded. Based on the lessons we learned from our attacks, we also offer some guidelines for designing visual CAPTCHAs with better security. [Gao, Yipeng; Gao, Haichang; Luo, Sainan; Zi, Yang; Zhang, Shudong; Mao, Wenjie; Wang, Ping; Shen, Yulong] Xidian Univ, Sch Comp Sci & Technol, Xian, Peoples R China; [Yan, Jeff] Linkoping Univ, Dept Comp & Informat Sci, Linkoping, Sweden Xidian University; Linkoping University Gao, HC (corresponding author), Xidian Univ, Sch Comp Sci & Technol, Xian, Peoples R China. hchgao@xidian.edu.cn Mao, Wenjie/0000-0003-2442-1195 Natural Science Foundation of China [61972306]; Zhejiang Lab [2021KD0AB03] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Lab We would like to thank our shepherd David Freeman and the anonymous reviewers for their valuable suggestions for improving this paper. This paper was supported by the Natural Science Foundation of China under Grant 61972306 and sponsored by Zhejiang Lab (No. 2021KD0AB03). 57 0 0 2 4 USENIX ASSOC BERKELEY SUITE 215, 2560 NINTH ST, BERKELEY, CA 94710 USA 978-1-939133-24-3 2021.0 3291 3308 18 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS4QP 2023-03-23 WOS:000722006803032 0
J Guo, H; Hu, S; Wang, F; Zhang, LJ Guo, Hui; Hu, Shan; Wang, Fei; Zhang, Lijun A novel method for quantitative fault diagnosis of photovoltaic systems based on data-driven ELECTRIC POWER SYSTEMS RESEARCH English Article Photovoltaic (PV) system; Quantitative fault diagnosis; Data-driven; Clustering algorithm; Long short-term memory (LSTM) neural; network; Transfer learning PROTECTION Photovoltaic (PV) systems are usually exposed outdoors for a long time, easily lead to various faults. Apart from fault location and classification widely studied, accurate and quantitative fault severity diagnosis is vital for the decision making, given the cost of eliminating faults. However, there are scarce investigations into this problem. Therefore, a novel quantitative fault diagnosis method is proposed based on the deviation between the predicted power of fault states and the actual power of normal states. (1) Since the main characteristic of PV faults is the decline of power generation, the prediction model of PV power generation under the normal state is established via clustering algorithm and long short-term memory (LSTM) neural network, which has better processing ability for different weather conditions and long time-series data. (2) In the actual condition of limited fault data, the prediction model is innovatively used to quantitative fault diagnosis through transfer learning, which will provide reasonable and effective guidance for the intelligent operation and maintenance of PV system when the severity of predicted fault reaches the set threshold. Finally, the effectiveness and accuracy of the proposed quantitative fault diagnosis method are verified by experimental results. [Guo, Hui; Hu, Shan; Wang, Fei] Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China; [Zhang, Lijun] Univ Lisbon, Inst Super Tecn, Lisbon, Portugal Shanghai University; Universidade de Lisboa; Instituto Superior Tecnico Wang, F (corresponding author), Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China. f.wang@shu.edu.cn National Natural Science Foundation of China [51977126]; China Postdoctoral Science Foundation [2020M681262] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) Acknowledgments This work was supported in part by the National Natural Science Foundation of China under Grant 51977126 and in part by the China Postdoctoral Science Foundation under Grant 2020M681262. 30 1 1 17 37 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7796 1873-2046 ELECTR POW SYST RES Electr. Power Syst. Res. SEP 2022.0 210 108121 10.1016/j.epsr.2022.108121 0.0 MAY 2022 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 1Z0QW 2023-03-23 WOS:000808539800008 0
J Gong, SM; Hoang, DT; Niyato, D; El Shafie, A; De Domenico, A; Strinati, EC; Hoydis, J Gong, Shimin; Hoang, Dinh Thai; Niyato, Dusit; El Shafie, Ahmed; De Domenico, Antonio; Strinati, Emilio Calvanese; Hoydis, Jakob Introduction to the special section on deep reinforcement learning for future wireless communication networks IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING English Editorial Material Special issues and sections; Deep learning; Reinforcement learning; Wireless networks; Communication networks We are delighted to introduce the readers to this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at exploring recent advances and addressing practical challenges in the applications of deep reinforcement learning (DRL) in modern wireless networks. We have received a total of 29 submissions, and after a rigorous review process, 12 articles have been selected for publication, which are briefly discussed as follows. [Gong, Shimin] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Guangdong, Peoples R China; [Hoang, Dinh Thai] Univ Technol Sydney, Sch Elect & Data Engn, Sydney, NSW 2007, Australia; [Niyato, Dusit] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Niyato, Dusit] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore 639798, Singapore; [El Shafie, Ahmed] Qualcomm Technol, San Diego, CA 92121 USA; [De Domenico, Antonio] CEA LETICMINATEC, F-38054 Grenoble, France; [Strinati, Emilio Calvanese] CEA LETICMINATEC, Smart Devices & Telecommun European Collaborat St, F-38054 Grenoble, France; [Hoydis, Jakob] Nokia Bell Labs, Res Dept, F-91620 Paris, France Sun Yat Sen University; University of Technology Sydney; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nokia Corporation Gong, SM (corresponding author), Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Guangdong, Peoples R China. Strinati, Emilio Calvanese/AAS-3806-2021; Dinh, Hoang Thai/I-1910-2018; Hoydis, Jakob/AAG-6646-2020; Hoang, Dinh Thai/K-5135-2012 Strinati, Emilio Calvanese/0000-0001-9346-8478; Dinh, Hoang Thai/0000-0002-9528-0863; Hoydis, Jakob/0000-0002-0438-967X; Hoang, Dinh Thai/0000-0002-9528-0863; Niyato, Dusit/0000-0002-7442-7416 0 3 3 0 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7731 IEEE T COGN COMMUN IEEE Trans. Cogn. Commun. Netw. DEC 2019.0 5 4 1019 1023 10.1109/TCCN.2019.2954144 0.0 5 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications JW0ZY Bronze, Green Published 2023-03-23 WOS:000502789700014 0
J Wang, WX; Tu, A; Bergholm, F Wang, Weixing; Tu, Angyan; Bergholm, Fredrik Improved Minimum Spanning Tree based Image Segmentation with Guided Matting KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS English Article Graph Theory; Guided Feathering; Image Segmentation; Minimum Spanning Tree EXTRACTION; TRACKING; FUZZY; CUTS In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority. [Wang, Weixing] Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China; [Tu, Angyan] Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China; [Bergholm, Fredrik] Royal Inst Technol, S-10044 Stockholm, Sweden Chang'an University; Shaoxing University; Royal Institute of Technology Wang, WX (corresponding author), Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China.;Tu, A (corresponding author), Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China. wxwang@chd.edu.cn; lucytu@shu.edu.cn; fredrikb@kth.se Natural Science Foundation of Zhejiang Province [TY22F025548]; Zhejiang Provincial Natural Science Foundation of China [LY20F020011] Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This research is financially supported by Natural Science Foundation of Zhejiang Province (Grant No.TY22F025548, and Zhejiang Provincial Natural Science Foundation of China (Grant No.LY20F020011). 38 6 6 3 6 KSII-KOR SOC INTERNET INFORMATION GANGNAM-GU KOR SCI & TECHNOL CTR, 409 ON 4TH FLR, MAIN BLDG, 635-4 YEOKSAM 1-DONG, GANGNAM-GU, SEOUL 00000, SOUTH KOREA 1976-7277 KSII T INTERNET INF KSII Trans. Internet Inf. Syst. JAN 31 2022.0 16 1 211 230 10.3837/tiis.2022.01.012 0.0 20 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications YU8KO gold 2023-03-23 WOS:000752285800011 0
J Tonkin, M; Lemeire, J; Santtila, P; Winter, JM Tonkin, Matthew; Lemeire, Jan; Santtila, Pekka; Winter, Jan M. Linking property crime using offender crime scene behaviour: A comparison of methods JOURNAL OF INVESTIGATIVE PSYCHOLOGY AND OFFENDER PROFILING English Article classification tree analysis; crime linkage; decision-support tools; logistic regression; offender crime scene behaviour; probabilistic models NEURAL-NETWORK MODELS; LOGISTIC-REGRESSION; LINKAGE ANALYSIS; CLASSIFICATION; TREE; ACCURACY; VIOLENCE; UTILITY This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression-based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree-based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research. [Tonkin, Matthew] Univ Leicester, Dept Criminol, 156 Upper New Walk, Leicester LE1 7QA, Leics, England; [Lemeire, Jan] Vrije Univ Brussel, Dept Elect & Informat ETRO, Brussels, Belgium; [Santtila, Pekka] New York Univ, Shanghai, Peoples R China; [Winter, Jan M.] Dutch Natl Police, The Hague, Netherlands; [Winter, Jan M.] Vrije Univ Brussel, Dept Clin & Life Span Psychol KLEP, Brussels, Belgium University of Leicester; Vrije Universiteit Brussel; Vrije Universiteit Brussel Tonkin, M (corresponding author), Univ Leicester, Dept Criminol, 156 Upper New Walk, Leicester LE1 7QA, Leics, England. mjt46@le.ac.uk Lemeire, Jan/AAJ-6474-2020; Winter, Jan-Martin/AAC-1014-2020 Lemeire, Jan/0000-0002-2106-448X; Winter, Jan-Martin/0000-0001-5867-8971 British Academy British Academy British Academy, Grant/Award Number: SQ120046; British Academy 49 3 3 3 18 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1544-4759 1544-4767 J INVEST PSYCHOL OFF J. Investig. Psychol. Offender Profiling JUN 2019.0 16 2 75 90 10.1002/jip.1525 0.0 16 Criminology & Penology; Psychology, Applied Social Science Citation Index (SSCI) Criminology & Penology; Psychology IB1IA Green Submitted 2023-03-23 WOS:000470017700001 0
J Qammar, A; Ding, JG; Ning, HS Qammar, Attia; Ding, Jianguo; Ning, Huansheng Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions ARTIFICIAL INTELLIGENCE REVIEW English Article Federated learning; Security; Privacy; Attack surface; Cyber defence DATA PRIVACY; FRAMEWORK; SECURE; SYSTEM Federated learning (FL) has received a great deal of research attention in the context of privacy protection restrictions. By jointly training deep learning models, a variety of training tasks can be competently performed with the help of invited participants. However, FL is concerned with a large number of attacks involving privacy and security aspects. This paper shows a federated learning workflow process and how a malicious client can exploit vulnerabilities in the FL system to attack the system. A systematic survey of existing research on the taxonomy of federated learning attack surface and the classification is presented. As with the FL attack surface, attackers compromise security, privacy, gain free incentives and abuse the Confidentiality, Integrity, and Availability (CIA) security triad. In addition, state-of-the-art defensive approaches against FL attacks are elaborated which help to protect and minimize the likelihood of attacks. FL models and tools for privacy attacks are explained, along with their best aspects and drawbacks. Finally, technical challenges and possible research guidelines are discussed as future work to build robust FL systems. [Qammar, Attia; Ning, Huansheng] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China; [Ding, Jianguo] Blekinge Inst Technol, Dept Comp Sci, Karlskrona, Sweden University of Science & Technology Beijing; Blekinge Institute Technology Ning, HS (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China. jianguo.ding@bth.se; ninghuansheng@ustb.edu.cn 139 3 3 13 42 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. JUN 2022.0 55 5 3569 3606 10.1007/s10462-021-10098-w 0.0 NOV 2021 38 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1D6JS 2023-03-23 WOS:000718221200001 0
J Fu, KR; Zhao, QJ; Gu, IYH Fu, Keren; Zhao, Qijun; Gu, Irene Yu-Hua Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection IEEE TRANSACTIONS ON MULTIMEDIA English Article Salient object detection; refinement; convolutional network; image segmentation REGION DETECTION; IMAGE; ATTENTION; CONTRAST; MODEL; COLOR Compared to conventional saliency detection by handcrafted features, deep convolutional neural networks (CNNs) recently have been successfully applied to saliency detection field with superior performance on locating salient objects. However, due to repeated sub-sampling operations inside CNNs such as pooling and convolution, many CNN-based saliency models fail to maintain fine-grained spatial details and boundary structures of objects. To remedy this issue, this paper proposes a novel end-to-end deep learning-based refinement model named Refinet, which is based on fully convolutional network augmented with segmentation hypotheses. Intermediate saliency maps that are edge-aware are computed from segmentation-based pooling and then feed to a two-tier fully convolutional network for effective fusion and refinement, leading to more precise object details and boundaries. In addition, the resolution of feature maps in the proposed Refinet is carefully designed to guarantee sufficient boundary clarity of the refined saliency output. Compared to widely employed dense conditional random field, Refinet is able to enhance coarse saliency maps generated by existing models with more accurate spatial details, and its effectiveness is demonstrated by experimental results on seven benchmark datasets. [Fu, Keren; Zhao, Qijun] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Sichuan, Peoples R China; [Gu, Irene Yu-Hua] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden Sichuan University; Chalmers University of Technology Zhao, QJ (corresponding author), Sichuan Univ, Coll Comp Sci, Chengdu 610065, Sichuan, Peoples R China. fkrsuper@scu.edu.cn; qjzhao@scu.edu.cn; irenegu@chalmers.se Fu, Keren/HPG-4742-2023; Gu, Irene Yu-Hua/D-4044-2018 Fu, Keren/0000-0002-3195-2077; Gu, Irene Yu-Hua/0000-0003-4759-7038 National Science Foundation, China [61703077, 61773270]; Fundamental Research Funds for the Central Universities [YJ201755]; National Key Research and Development Program of China [2017YFB0802300, 2016YFC0801100]; National Key Scientific Instrument and Equipment Development Projects of China [2013YQ49087904] National Science Foundation, China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Key Research and Development Program of China; National Key Scientific Instrument and Equipment Development Projects of China This research is supported in part by the National Science Foundation, China, under Grants 61703077 and 61773270, in part by the Fundamental Research Funds for the Central Universities under Grant YJ201755, in part by the National Key Research and Development Program of China under Grants 2017YFB0802300 and 2016YFC0801100, and in part by the National Key Scientific Instrument and Equipment Development Projects of China under Grant 2013YQ49087904. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Chang-Su Kim. 75 38 40 3 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia FEB 2019.0 21 2 457 469 10.1109/TMM.2018.2859746 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications HJ7GT hybrid 2023-03-23 WOS:000457364400015 0
C Li, F; Lu, FF; Chen, XR Destech Publicat Inc Li, Fang; Lu, Fangfang; Chen, Xinrong A Learning Method based on DBN for Establishing the Correspondence between Abnormal Point Sets INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE) DEStech Transactions on Computer Science and Engineering English Proceedings Paper INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE) OCT 21-23, 2017 Shanghai, PEOPLES R CHINA REGISTRATION; ALGORITHM The establishment of the right correspondence is one of the most basic and the most critical technology in point set registration. For the damaged data of point set in the existence of outliers, noise or missing points, it is difficult to distinguish the abnormal points from the normal and the correspondence between the point sets are affected by these abnormal points. Based on the prior that there is some relation or distinction between the normal points and the abnormal points, we formulate the estimation of correspondence problem by the machine learning procedure. In this paper, considering the feature of error between two point sets, a Deep Belief Networks (DBN) based learning method is proposed to train the networks with the normal point sets. Using the trained DBN, the outlier and unmatched points can be identified. Experiments confirm that our method can well detect noise in the point sets. Even in the case of missing data, our method can identify almost all matched points. [Li, Fang; Lu, Fangfang] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai 200090, Peoples R China; [Chen, Xinrong] Fudan Univ, Sch Basic Med Sci, Digital Med Res Ctr, Shanghai 200032, Peoples R China; [Chen, Xinrong] Shanghai Key Lab Med Image Comp & Comp Assisted I, Shanghai, Peoples R China; [Chen, Xinrong] Forschungszentrum Julich, Inst Neurosci & Med, D-52425 Julich, Germany Shanghai University of Electric Power; Fudan University; Helmholtz Association; Research Center Julich Chen, XR (corresponding author), Fudan Univ, Sch Basic Med Sci, Digital Med Res Ctr, Shanghai 200032, Peoples R China.;Chen, XR (corresponding author), Shanghai Key Lab Med Image Comp & Comp Assisted I, Shanghai, Peoples R China.;Chen, XR (corresponding author), Forschungszentrum Julich, Inst Neurosci & Med, D-52425 Julich, Germany. 12 0 0 1 4 DESTECH PUBLICATIONS, INC LANCASTER 439 DUKE STREET, LANCASTER, PA 17602-4967 USA 2475-8841 978-1-60595-505-6 DESTECH TRANS COMP 2017.0 190 173 180 8 Computer Science, Theory & Methods; Engineering, Multidisciplinary Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ6XH 2023-03-23 WOS:000426973900021 0
J Yang, C; Oh, SK; Pedrycz, W; Fu, ZW; Yang, B Yang, Cheng; Oh, Sung-Kwun; Pedrycz, Witold; Fu, Zunwei; Yang, Bo Design of Reinforced Fuzzy Radial Basis Function Neural Network Classifier Driven With the Aid of Iterative Learning Techniques and Support Vector-Based Clustering IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Robustness; Support vector machines; Computer architecture; Radial basis function networks; Clustering algorithms; Quadratic programming; Outliers and noises; reinforced fuzzy radial basis function neural network (R-FRBFNN); Softmax-based Iterative Quadratic Programming (IQP); Softmax-based iterative reweighted least square (IRLS); support vector (SV)-based FCM SYSTEM; IDENTIFICATION; ALGORITHM In this article, a reinforced fuzzy radial basis function neural network (R-FRBFNN) classifier is proposed. It focuses on the development of methodologies of reinforced architecture to improve classification accuracy and enhance the robust capability based on two learning strategies. The two learning strategies are summarized: 1) R-FRBFNN designed via support vector (SV)-based fuzzy C-means (FCM) clustering and softmax-based iterative reweighted least square (IRLS), which concentrate on improving the classification performance of R-FRBFNN; and 2) R-FRBFNN designed via SV-based FCM and softmax-based iterative quadratic programming (IQP), which focus on improving the robust abilities of the R-FRBFNN and reducing the effects of noise and outliers. The essential points of the proposed R-FRBFNN classifier are summarized as follows. a) The proposed R-FRBFNN consists of three phases: condition, conclusion, and inference. b) An SV-based FCM is considered for prioritizing the classification boundary and improving the classification performance of the proposed classifier. c) Three types of polynomials construct the conclusion phase. Two learning techniques are designed to update the coefficients of the polynomials. Softmax-based IRLS is a type of iterative learning technique based on Newton's method. Softmax-based IQP is more robust and avoids the degradation of generalization capabilities caused by outliers and noisy data. d) In the concept of reinforced architecture, SV-based FCM imposes compensation (membership degrees) on learning techniques according to the data characteristics encountered in the inference phase. Experimental results reported for benchmark data and outliers/noisy datasets demonstrate that the proposed classifier shows improved classification performance compared with other previously studied methods. [Yang, Cheng] Univ Suwon, Dept Comp, Hwaseong Si 18323, South Korea; [Oh, Sung-Kwun; Fu, Zunwei] Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Shandong, Peoples R China; [Oh, Sung-Kwun] Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, South Korea; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland; [Pedrycz, Witold] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia; [Yang, Bo] Linyi Univ, Sch Informat Sci & Engn, Linyi 276005, Shandong, Peoples R China; [Yang, Bo] Jinan Univ, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China Suwon University; Linyi University; Suwon University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; King Abdulaziz University; Linyi University; University of Jinan Oh, SK (corresponding author), Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Shandong, Peoples R China.;Yang, B (corresponding author), Linyi Univ, Sch Informat Sci & Engn, Linyi 276005, Shandong, Peoples R China. lyyc58@suwon.ac.kr; ohsk@suwon.ac.kr; wpedrycz@ualberta.ca; fuzunwei@lyu.edu.cn; yangbo@ujn.edu.cn Fu, Zunwei/0000-0001-9109-4142 Korea Electric Power Corporation [R19XO01-18]; Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education [NRF-2017R1D1A1B03032333] Korea Electric Power Corporation; Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education This work was supported in part by Korea Electric Power Corporation under Grant R19XO01-18, and in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education under Grant NRF-2017R1D1A1B03032333. 49 3 3 3 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. SEP 2021.0 29 9 2506 2520 10.1109/TFUZZ.2020.3001740 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering UL3KJ 2023-03-23 WOS:000692553900008 0
J Li, XY; Zhang, C; Behrens, H; Holtz, F Li, Xiaoyan; Zhang, Chao; Behrens, Harald; Holtz, Francois Calculating biotite formula from electron microprobe analysis data using a machine learning method based on principal components regression LITHOS English Article Biotite; Mineral formula; Principal component regression; Oxidation state; Ti-oxy substitution TI-RICH PHLOGOPITE; F-CL-OH; FERRIC IRON; CRYSTAL-CHEMISTRY; VOLCANIC-ROCKS; MT. VULTURE; MOSSBAUER-SPECTROSCOPY; HYDROGEN DEFICIENCY; HYDROXYL EXCHANGE; OXIDATION-STATE We present a new machine learning method for calculating biotite (sensu lato) structural formula from electron microprobe analysis (EMPA) data, which is based on principal components regression (PCR) of a dataset consisting of 155 fully analyzed biotite references that have chemistry and crystal structure refinement. The dataset is randomly grouped into a training set (75% in amount) and a test set (25% in amount). The training set is used to implement the structural formula and the test set is used to evaluate the performance of the model. The resulting linear regression coefficient matrix is then applied to calculate mole proportions of cations and anions of biotite samples using their compositional data from EMPA. Through this method, the distribution of the different cations and anions in the different sites can be calculated, including the tetrahedral Fe3+, octahedral Fe2+, octahedral Fe3+, OH and O-w(2-) at the O(4) site. The O(4) site is assumed to be occupied by anions with a relation of 2 = F + Cl + OH + O-w(2-). Octahedral and interlayer vacancies could also be estimated in this model. The prediction quality for major elements is perfect with R-2 > 0.95. The absolute errors in the estimated octahedral Fe2+, octahedral Al and OH at O(4) site are determined to be +/- 0.2 apfu (atom per formula unit based on 110 + 2(F, Cl, OH, O)), while those in total Fe3+ and O-w(2-) at O(4) site are approximately +/- 0.3 apfu. A funnel-shaped relationship between absolute error in Fe3+/Sigma Fe ratio and FeOT wt% is observed, with the majority falling in the range of +/- 20%. Compared to previous normalization schemes, our model shows significant improvements in estimating Fe3+/Sigma Fe and O-w(2-) at O(4) site. Our model is capable for calculating mineral formulae of common igneous and hydrothermal biotites, but not suitable for those that have been modified in a post-formation oxidation or reduction process. A supplementary Excel spreadsheet is provided that can be easily used for performing calculation from EMPA data. (C) 2020 Published by Elsevier B.V. [Li, Xiaoyan; Zhang, Chao; Behrens, Harald; Holtz, Francois] Leibniz Univ Hannover, Inst Mineral, Callinstr 3, D-30167 Hannover, Germany; [Li, Xiaoyan; Zhang, Chao] Northwest Univ, Dept Geol, State Key Lab Continental Dynam, Xian 710069, Peoples R China Leibniz University Hannover; Northwest University Xi'an Zhang, C (corresponding author), Northwest Univ, Dept Geol, State Key Lab Continental Dynam, Xian 710069, Peoples R China. zhangchao@nwu.edu.cn Zhang, Chao/P-4689-2019 Holtz, Francois/0000-0002-2043-8603; Li, Xiaoyan/0000-0001-7802-6479; Zhang, Chao/0000-0001-7019-5075 DFG (German Research Foundation) [BE 1720/40] DFG (German Research Foundation)(German Research Foundation (DFG)) This study was supported by DFG (German Research Foundation) project BE 1720/40. We thank two anonymous reviewers and their comments have greatly improved this paper. The Microsoft Excel spreadsheet for calculating biotite formula from EMPA data are provided as a supplementary file online. 80 33 37 11 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0024-4937 1872-6143 LITHOS Lithos MAR 2020.0 356 105371 10.1016/j.lithos.2020.105371 0.0 10 Geochemistry & Geophysics; Mineralogy Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Mineralogy KW1OT 2023-03-23 WOS:000520941500007 0
J Sun, JL; Gui, G; Sari, H; Gacanin, H; Adachi, F Sun, Jinlong; Gui, Guan; Sari, Hikmet; Gacanin, Haris; Adachi, Fumiyuki Aviation Data Lake: Using Side Information to Enhance Future Air-Ground Vehicle Networks IEEE VEHICULAR TECHNOLOGY MAGAZINE English Article Surveillance; Drones; Computer architecture; Aircraft; Collision avoidance; Handover Future denser air-ground vehicle networks (AGVNs) face challenges such as resource allocation, mobility management, secure transmission, and so on. At the same time, surveillance is a must for modern air traffic management. This motivates us to find opportunities in the aerial vertical by forming a conceptual surveillance plane for aerial vehicles. In this article, we propose an enhanced software-defined network architecture where the surveillance plane can provide local and global surveillance information to macro stations, acting as a side system for the communication links. We review air- ground communications and, by summarizing challenges and opportunities, propose the enhanced architecture of side-information-assisted networks in detail. We then present how we obtain, organize, manage, and utilize the local and global side information by a so-called aviation data lake (ADL). The data lake can be easily connected with advanced machine learning schemes and, thus, provide timely, context-aware metrics and predictions. [Sun, Jinlong; Gui, Guan; Sari, Hikmet] Nanjing Univ Posts & Telecommun, Nanjing, Peoples R China; [Sari, Hikmet] Sequans Commun, Paris, France; [Gacanin, Haris] Rhein Westfal TH Aachen, Aachen, Germany; [Gacanin, Haris] Inst Elect Informat & Commun Engineers, Tokyo, Japan; [Adachi, Fumiyuki] Res Org Elect Commun, Sendai, Miyagi, Japan Nanjing University of Posts & Telecommunications; RWTH Aachen University Sun, JL (corresponding author), Nanjing Univ Posts & Telecommun, Nanjing, Peoples R China. sunjinlong@njupt.edu.cn; guiguan@njupt.edu.cn; hsari@ieee.org; harisg@ieee.org; adachi@ecei.tohoku.ac.jp Adachi, Fumiyuki/ABD-7025-2021; Gui, Guan/AAG-3593-2019; SUN, JIN/GPX-9641-2022 Gui, Guan/0000-0001-7428-4980; Sari, Hikmet/0000-0001-8114-6164 Industrial Internet Innovation and Development Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]; National Natural Science Foundation of China [61901228]; Six Top Talents Program of Jiangsu [XYDXX010]; 1311 Talent Plan of Nanjing University of Posts and Telecommunications Industrial Internet Innovation and Development Project of the Ministry of Industry and Information Technology of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Six Top Talents Program of Jiangsu; 1311 Talent Plan of Nanjing University of Posts and Telecommunications This work was supported by the Project Funded by the Industrial Internet Innovation and Development Project of the Ministry of Industry and Information Technology of China under grant TC190A3WZ-2, the National Natural Science Foundation of China under grant 61901228, the Six Top Talents Program of Jiangsu under grant XYDXX010, and the 1311 Talent Plan of Nanjing University of Posts and Telecommunications. The corresponding author of this article is Guan Gui. 15 9 9 1 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6072 1556-6080 IEEE VEH TECHNOL MAG IEEE Veh. Technol. Mag. MAR 2021.0 16 1 40 48 10.1109/MVT.2020.3014598 0.0 9 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation QG0ZI 2023-03-23 WOS:000617315900007 0
J Wang, Y; Abliz, A; Ma, HB; Liu, L; Kurban, A; Halik, U; Pietikainen, M; Wang, WJ Wang, Yuan; Abliz, Abdugheni; Ma, Hongbing; Liu, Li; Kurban, Alishir; Halik, Umut; Pietikainen, Matti; Wang, Wenjuan Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Data enhancement (DA); few-shot learning (FSL); improved attentive interpretable tabular learning (TabNet); improved convolutional neural network (CNN); optimal band combination algorithm; partial least square regression (PLSR); soil auxiliary information; soil Cu; soil heavy metal pollution; soil hyperspectrum DIFFUSE-REFLECTANCE SPECTROSCOPY; HEAVY-METAL CONCENTRATIONS; ORGANIC-MATTER; AGRICULTURAL SOILS; MINING AREA; ADSORPTION; PREDICTION; SPECTRA; CU2+; DIFFERENTIATION China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research not only formulated a new preprocessing method enlightened by few-shot learning for soil hyperspectral data but also combined it with other soil-related auxiliary information to extract effective information from the soil hyperspectrum, at the end of which different regression methods were adopted to predict soil heavy metal contamination. This test used 168 actual soil samples from the Eastern Junggar coalfield in Xinjiang for verification. Since copper in the soil is a trace element and the corresponding spectral characteristics are affected by other impurities, improper use of hyperspectral preprocessing methods may introduce interference information or may delete useful information, which makes the model effect unsatisfied. To effectively address the above-mentioned problems, the preprocessing method of this experiment combined the second-order diffrrential derivation, and the data enhancement (DA) method together with the addition of auxiliary information to allow more effective features to be entered into the model. Next, the attentive interpretable tabular learning (TabNet) model was improved in three different ways using the original TabNet model and three improved TabNet models to create regression models. One of the improved TabNet models had the best effect, with a list of the top 30 features according to the degree of importance. Meanwhile, the regression prediction of Cu content using four different convolutional neural networks (CNNs) revealed that the model with the residual block was the strongest and slightly outperformed the improved TabNet model, but lacked interpretation of the input data. Besides, this experiment also employed different preprocessing methods for regression prediction on various models and found that the traditional preprocessing methods performed hest in traditional regression models [e.g., partial least square regression (PLSR)] and underperformed in deep learning models. The selected optimal model was compared with PLSR and CNN models. The results indicated that both the improved TabNet model and the improved CNN model had better performance using the new preprocessing approach proposed in this article, with improved TabNet yielding a coefficient of determination (R-2), root-mean-square error (RMSE), and the ratio of performance to interquartile range (RPIQ) of 0.94, 1341, and 4.474, respectively. The improved CNN model had a coefficient of determination of 0.942, an RMSE of 1324, and an interquartile range of 4.531 in the test dataset. [Wang, Yuan] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China; [Wang, Yuan; Ma, Hongbing] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China; [Wang, Yuan] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China; [Abliz, Abdugheni] Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830046, Peoples R China; [Liu, Li] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China; [Liu, Li; Pietikainen, Matti] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland; [Kurban, Alishir] Chinese Acad Sci, Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China; [Kurban, Alishir] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China; [Halik, Umut] Xinjiang Univ, Sch Resources & Environm Sci, Urumqi 830046, Peoples R China; [Wang, Wenjuan] St Petersburg State Univ, Dept Appl Ecol, St Petersburg 199178, Russia Xinjiang University; Tsinghua University; Tsinghua University; Xinjiang University; National University of Defense Technology - China; University of Oulu; Chinese Academy of Sciences; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Xinjiang University; Saint Petersburg State University Wang, Y (corresponding author), Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China. a123penny@163.com; abduhini0997@126.com; hbma@mail.tsinghua.edu.cn; li.liu@oulu.fi; alishir@ms.xjb.ac.cn; umut.halik@qq.com; matti.pietikainen@oulu.fi; st082527@student.spbu.ru Wang, wenjuan/HIR-3631-2022; Kurban, Alishir/AGK-9193-2022 Wang, wenjuan/0000-0002-9173-3028; Kurban, Alishir/0000-0001-9387-8127; Liu, li/0000-0002-2011-2873; Wang, Yuan/0000-0002-1742-0744; Pietikainen, Matti/0000-0003-2263-6731 Autonomous Region Postgraduate Research Innovation [XJ2021G064]; National Natural Science Foundation of China [51704259]; Shanghai Aerospace Science and Technology Innovation Fund [SAST2019-048] Autonomous Region Postgraduate Research Innovation; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Aerospace Science and Technology Innovation Fund This work was supported in part by the Autonomous Region Postgraduate Research Innovation under Project XJ2021G064, in part by the National Natural Science Foundation of China under Grant 51704259, and in part by the Shanghai Aerospace Science and Technology Innovation Fund under Grant SAST2019-048. (Yuan Wang and Abdugheni Abliz are co-first authors.) 76 0 0 26 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5534020 10.1109/TGRS.2022.3190310 0.0 20 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3D1HW 2023-03-23 WOS:000829062600019 0
J Sun, Z; Li, BH; Duan, F; Jia, H; Wang, S; Liu, Y; Cichocki, A; Caiafa, CF; Sole-Casals, J Sun, Zhe; Li, Binghua; Duan, Feng; Jia, Hao; Wang, Shan; Liu, Yu; Cichocki, Andrzej; Caiafa, Cesar F.; Sole-Casals, Jordi WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks IEEE ACCESS English Article Shallow neural network; electroencephalogram; human computer interface; workload estimation; EEG BRAIN-COMPUTER-INTERFACE; FEATURE-EXTRACTION; EEG; CLASSIFICATION; SIGNALS; SEIZURE; SYSTEM Electroencephalography (EEG) is a non-invasive technology used for the human brain-computer interface. One of its important applications is the evaluation of the mental state of an individual, such as workload estimation. In previous works, common spatial pattern feature extraction methods have been proposed for the EEG-based workload detection. Recently, several novel methods were introduced to detect EEG pattern workloads. However, it is still unknown which one of these methods is the one that offers the best performance for the workload EEG pattern feature detection. In this article, four methods were used to extract workload EEG features: (a) common spatial pattern feature extraction; (b) temporally constrained sparse group spatial pattern feature extraction; (c) EEGnet; and (d) the new proposed shallow convolutional neural network for workload estimation (WLnet). The classification accuracy of these four methods was compared. Experimental results demonstrate that the proposed WLnet achieved the best detection accuracy in both stress and non-stress conditions. We believe that the proposed methods may be relevant to real-life applications of mental workload estimation. [Sun, Zhe] RIKEN, Head Off Informat Syst & Cybersecur, Computat Engn Applicat Unit, Wako, Saitama 3510198, Japan; [Li, Binghua; Duan, Feng; Jia, Hao; Wang, Shan; Caiafa, Cesar F.; Sole-Casals, Jordi] Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China; [Liu, Yu] Shanghai Univ Sport, Key Lab Exercise & Hlth Sci, Minist Educ, Shanghai 200438, Peoples R China; [Cichocki, Andrzej] Skolkowo Inst Sci & Technol, Moscow 121205, Russia; [Caiafa, Cesar F.] UNLP, CCT La Plata, Inst Argentino Radioastron, CIC,PBA,CONICET, V Elisa 1894, Buenos Aires, DF, Argentina; [Caiafa, Cesar F.] RIKEN, Ctr Adv Intelligence Project, Tensor Learning Team, Tokyo 1030027, Japan; [Sole-Casals, Jordi] Univ Cambridge, Dept Psychiat, Cambridge CB2 1TN, England; [Sole-Casals, Jordi] Cent Univ Catalonia, Univ Vic, Data & Signal Proc Res Grp, Vic 08500, Spain RIKEN; Nankai University; Shanghai University of Sport; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Instituto Argentino de Radioastronomia; National University of La Plata; RIKEN; University of Cambridge; Universitat de Vic - Universitat Central de Catalunya (UVic-UCC) Duan, F (corresponding author), Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China. duanf@nankai.edu.cn Solé-Casals, Jordi/B-7754-2008; Solé-Casals, Jordi/GRX-7991-2022; Cichocki, Andrzej/A-1545-2015 Solé-Casals, Jordi/0000-0002-6534-1979; zhe, sun/0000-0002-6531-0769; Caiafa, Cesar F./0000-0001-5437-6095; Duan, Feng/0000-0002-2179-2460; Jia, Hao/0000-0003-1356-7463; Cichocki, Andrzej/0000-0002-8364-7226; Wang, Shan/0000-0003-1491-9133; Li, Binghua/0000-0002-2595-4762 National Key Research and Development Program of China [2017YFE0129700]; National Natural Science Foundation of China [11932013, 61673224]; Tianjin Natural Science Foundation for Distinguished Young Scholars [18JCJQJC46100]; Ministerio de Economia y Competitividad of Spain [TEC2016-77791-C4-2-R]; Ministry of Education and Science, Russian Federation [14.756.31.0001]; European Cooperation in Science and Technology (COST) under Grant COST Action [CA18106] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tianjin Natural Science Foundation for Distinguished Young Scholars; Ministerio de Economia y Competitividad of Spain(Spanish Government); Ministry of Education and Science, Russian Federation(Ministry of Education and Science, Russian FederationRussian Federation); European Cooperation in Science and Technology (COST) under Grant COST Action This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFE0129700, in part by the National Natural Science Foundation of China (Key Program) under Grant 11932013, in part by the National Natural Science Foundation of China under Grant 61673224, in part by the Tianjin Natural Science Foundation for Distinguished Young Scholars under Grant 18JCJQJC46100, in part by the Ministerio de Economia y Competitividad of Spain under Grant TEC2016-77791-C4-2-R, and in part by the Ministry of Education and Science, Russian Federation under Grant 14.756.31.0001. The work of Jordi Sole-Casals was supported by the European Cooperation in Science and Technology (COST) under Grant COST Action CA18106. 54 2 2 7 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 3165 3173 10.1109/ACCESS.2020.3044732 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PS2AJ gold 2023-03-23 WOS:000607730600236 0
J Wang, H; Mumtaz, S; Li, HJ; Liu, JX; Yang, F Wang, Huan; Mumtaz, Shahid; Li, Houjun; Liu, JingXian; Yang, Fan An identification strategy for unknown attack through the joint learning of space-time features FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Spatiotemporal fusion; Industrial Internet of Things; Double-layer LSTM; Attack recognition NETWORKS Deep learning (DL) can effectively extract the features of attack behaviours and identify unknown attack behaviours. However, the current DL-based methods separately learn spatial feature and temporal features and fail to consider the spatiotemporal correlation of cyber events. To make up for the gap, this paper proposes an identification strategy for unknown attack behaviours through the joint learning of spatiotemporal features. First, a double-layer long short-term memory (LSTM) was adopted to learn the spatial features of data packet and the temporal feature of the network flow, which makes the attack behaviour recognition less dependent on prior knowledge. Next, the temporal attention was constructed to suppress the noises in the spatial features of the data packet; the spatial attention was designed to reduce the temporal features of low-density information; the spatial attention was fused with the temporal attention to establish the spatiotemporal dependence of cyber-attack behaviours and distinguish the importance of spatiotemporal features. Finally, our identification strategy was experimentally compared with the identification models solely based on spatial features or temporal features. The comparison shows that our strategy outperformed the contrastive models by 2% in recognition accuracy. Thus, the fusion between spatial and temporal features can effectively promote the identification accuracy of unknown attack behaviours. (C) 2020 Elsevier B.V. All rights reserved. [Wang, Huan; Li, Houjun; Liu, JingXian; Yang, Fan] Guangxi Univ Sci & Technol, Liuzhou 545006, Peoples R China; [Mumtaz, Shahid] Inst Telecomunicaces, P-3750011 Aveiro, Portugal Guangxi University of Science & Technology Liu, JX (corresponding author), Guangxi Univ Sci & Technol, Liuzhou 545006, Peoples R China. wanghuan@gxust.edu.cn; Dr.shahid.mumtaz@ieee.org; lihoujun@gxust.edu.cn; ljx43031@gxust.edu.cn; 489486943@qq.com Mumtaz, Dr shahid/V-3603-2019 Mumtaz, Dr shahid/0000-0001-6364-6149 Guangxi Natural Science Foundation, China [2020GXNSFBA159042]; National Natural Science Foundation of China [62061003]; Guangxi Education Department Program, China [2020KY08020]; Doctoral Fund of Guangxi University of Science and Technology, China [XiaoKe Bo19Z33] Guangxi Natural Science Foundation, China(National Natural Science Foundation of Guangxi Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Education Department Program, China; Doctoral Fund of Guangxi University of Science and Technology, China This work was supported in part by the Guangxi Natural Science Foundation, China under Grant 2020GXNSFBA159042, in part by the National Natural Science Foundation of China under Grant 62061003, in part by the Guangxi Education Department Program, China under Grant 2020KY08020, and in part by the Doctoral Fund of Guangxi University of Science and Technology, China under Grant XiaoKe Bo19Z33. 19 1 1 0 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. APR 2021.0 117 145 154 10.1016/j.future.2020.11.023 0.0 10 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science PY5TN 2023-03-23 WOS:000612106900012 0
J Lin, H; Tse, R; Tang, SK; Qiang, ZP; Pau, G Lin, Hong; Tse, Rita; Tang, Su-Kit; Qiang, Zhen-ping; Pau, Giovanni Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition FRONTIERS IN PLANT SCIENCE English Article few-shot learning; meta-learning; multi-scale feature fusion; attention; plant disease recognition; cross-domain; training strategy; sub-class classification Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application. [Lin, Hong; Tse, Rita; Tang, Su-Kit; Pau, Giovanni] Macao Polytech Univ, Fac Appl Sci, Macau, Peoples R China; [Tse, Rita; Tang, Su-Kit] Macao Polytech Univ, Engn Res Ctr Appl Technol Machine Translat & Artif, Minist Educ, Macau, Peoples R China; [Qiang, Zhen-ping] Southwest Forestry Univ, Coll Big Data & Intelligent Engn, Kunming, Peoples R China; [Pau, Giovanni] Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy; [Pau, Giovanni] Univ Calif Los Angeles, Samueli Comp Sci Dept, Los Angeles, CA 90095 USA Southwest Forestry University - China; University of Bologna; University of California System; University of California Los Angeles Pau, G (corresponding author), Macao Polytech Univ, Fac Appl Sci, Macau, Peoples R China.;Qiang, ZP (corresponding author), Southwest Forestry Univ, Coll Big Data & Intelligent Engn, Kunming, Peoples R China.;Pau, G (corresponding author), Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy.;Pau, G (corresponding author), Univ Calif Los Angeles, Samueli Comp Sci Dept, Los Angeles, CA 90095 USA. qzp@swfu.edu.cn; giovanni.pau@unibo.it Tang, Su-Kit/0000-0001-8104-7887; Pau, Giovanni/0000-0003-2216-7170 Natural Science Foundation of China [12163004]; Yunnan Fundamental Research Projects [202101BD070001-053]; Fundamental Research Projects of Yunnan Provincial Department of Education [2022J0496]; Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities [RP/ESCA-01/2020] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Yunnan Fundamental Research Projects; Fundamental Research Projects of Yunnan Provincial Department of Education; Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities This research was funded by the projects of Natural Science Foundation of China (Grant No. 12163004), the Yunnan Fundamental Research Projects (Grant No. 202101BD070001-053), and the Fundamental Research Projects of Yunnan Provincial Department of Education (Grant No. 2022J0496). This work was also supported in part by the Macao Polytechnic University-Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities (RP/ESCA-01/2020). 60 1 1 9 9 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-462X FRONT PLANT SCI Front. Plant Sci. SEP 16 2022.0 13 907916 10.3389/fpls.2022.907916 0.0 17 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences 5A0NZ 36186021.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000862594700001 0
J Jia, PP; Zhang, JH; He, W; Yuan, D; Hu, Y; Zamanian, K; Jia, KL; Zhao, XN Jia, Pingping; Zhang, Junhua; He, Wei; Yuan, Ding; Hu, Yi; Zamanian, Kazem; Jia, Keli; Zhao, Xiaoning Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning REMOTE SENSING English Article soil electrical conductivity; variable projection importance; Hetao Plain; salinization; soil degradation; soil quality INFRARED SPECTROSCOPY; SOUTHERN XINJIANG; REGRESSION; PLS Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson's correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R-2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing. [Jia, Pingping; He, Wei; Yuan, Ding; Hu, Yi; Zamanian, Kazem; Zhao, Xiaoning] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China; [Jia, Pingping; Jia, Keli] Ningxia Univ, Sch Geog & Planning, Yinchuan 750021, Ningxia, Peoples R China; [Zhang, Junhua] Ningxia Univ, Sch Ecol & Environm, Yinchuan 750021, Ningxia, Peoples R China; [Zhang, Junhua] Ningxia Univ, Breeding Base State Key Lab Land Degradat & Ecol, Yinchuan 750021, Ningxia, Peoples R China; [Zamanian, Kazem] Leibniz Univ Hannover, Inst Soil Sci, D-30419 Hannover, Germany Nanjing University of Information Science & Technology; Ningxia University; Ningxia University; Ningxia University; Leibniz University Hannover Zhao, XN (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China. zhaoxiaoning@nuist.edu.cn Zamanian, Kazem/E-4961-2016 Zamanian, Kazem/0000-0002-9536-9251; Zhang, Junhua/0000-0002-3585-5338 National Natural Science Foundation of China [41877109, 42050410320, 42067003, 42061047]; Jiangsu Specially-Appointed Professor Project, China [R2020T29]; Key R&D Project of Ningxia, China [2021BEG03002]; National Key R&D Program of China [2021YFD1900602]; Open Fund of Tsinghua University-Ningxia Yinchuan Joint Research Institute of Water Networking and DigitalWater Control [SKLHSE-2022-IOW11] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Specially-Appointed Professor Project, China; Key R&D Project of Ningxia, China; National Key R&D Program of China; Open Fund of Tsinghua University-Ningxia Yinchuan Joint Research Institute of Water Networking and DigitalWater Control This work was supported by the National Natural Science Foundation of China (Grant numbers 41877109; 42050410320; 42067003; 42061047); the Jiangsu Specially-Appointed Professor Project, China (Grant number R2020T29); the Key R&D Project of Ningxia, China (Grant number 2021BEG03002); the National Key R&D Program of China (Grant number 2021YFD1900602); and the Open Fund of Tsinghua University-Ningxia Yinchuan Joint Research Institute of Water Networking and DigitalWater Control (SKLHSE-2022-IOW11). 65 0 0 15 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2022.0 14 22 5639 10.3390/rs14225639 0.0 17 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 6K9FX gold 2023-03-23 WOS:000887799400001 0
J Wang, WX; Lv, ZH; Li, XM; Xu, WP; Zhang, BY; Zhu, YY; Yan, Y Wang, Weixi; Lv, Zhihan; Li, Xiaoming; Xu, Weiping; Zhang, Baoyun; Zhu, Yuanyuan; Yan, Yan Spatial query based virtual reality GIS analysis platform NEUROCOMPUTING English Article WebVRGIS; WebVR; Big Data; 3D globe; Spatial query GENERATION; INTERNET; NETWORK; IMAGE A virtual reality GIS analysis platform based on spatial query is proposed in this paper. The proposed platform serves 3D digital city and supports integrated VRGIS functions including 3D spatial analysis functions and 3D visualization functions for spatial process. The 3D analysis and visualization of the concerned city massive information are conducted on the platform. The amount of information that can be visualized through this platform is overwhelming, and the GIS-based navigational scheme allows great flexibility in accessing different available data sources. A multi-level mixed three-dimensional space index is built for the virtual scene. Multi-task parallel scheduling and pre-scheduling algorithms are designed to support the distribution and query of spatial data. Experiment result indicates that the designed query algorithm improves the performance of the proposed system. (C) 2017 Elsevier B.V. All rights reserved. [Wang, Weixi; Li, Xiaoming; Zhu, Yuanyuan] Minist Land & Resources, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen, Peoples R China; [Wang, Weixi; Li, Xiaoming; Zhu, Yuanyuan] Shenzhen Res Ctr Digital City Engn, Shenzhen, Peoples R China; [Lv, Zhihan] Qingdao Univ, Qingdao, Peoples R China; [Xu, Weiping] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China; [Zhang, Baoyun] JIAT, Jining, Peoples R China; [Yan, Yan] Univ Trento, MHUG Grp, Trento, Italy; [Li, Xiaoming] Shenzhen Univ, Shenzhen, Peoples R China Ministry of Natural Resources of the People's Republic of China; Qingdao University; Wuhan University; University of Trento; Shenzhen University Lv, ZH (corresponding author), Qingdao Univ, Qingdao, Peoples R China. Ivzhihan@gmail.com Lv, Zhihan/I-3187-2014; Lv, Zhihan/GLR-6000-2022; Li, Xiaoming/GSD-8174-2022 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Li, Xiaoming/0000-0002-7804-368X National Natural Science Fund for the Youth of China [41301439]; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources [KF-2015-01-027]; Science and technology project of Guangdong Province [2015A010103010]; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing [15I03] National Natural Science Fund for the Youth of China; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources; Science and technology project of Guangdong Province; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing The authors are thankful to the National Natural Science Fund for the Youth of China (41301439), the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources (KF-2015-01-027), Science and technology project of Guangdong Province (2015A010103010), the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (15I03). 54 11 11 1 46 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 24 2018.0 274 SI 88 98 10.1016/j.neucom.2016.06.099 0.0 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FQ4SB 2023-03-23 WOS:000418346900011 0
J Bian, LM; Zhang, HC; Ge, YF; Cepl, J; Stejskal, J; EL-Kassaby, YA Bian, Liming; Zhang, Huichun; Ge, Yufeng; Cepl, Jaroslav; Stejskal, Jan; EL-Kassaby, Yousry A. Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry ANNALS OF FOREST SCIENCE English Review Forest tree; Phenotyping; Platforms; Sensor; Stress tolerance; Deep learning; Phenological measurement CHLOROPHYLL CONTENT; POINT CLOUDS; TREE; LIDAR; PHENOLOGY; CLASSIFICATION; BIOMASS; SPECTROSCOPY; TRAITS; HEIGHT Key message The lack of efficient phenotyping capacities has been recognized as a bottleneck in forestry phenotyping and breeding. Modern phenotyping technologies use systems equipped with various imaging sensors to automatically collect high volume phenotypic data that can be used to assess trees' various attributes. Context Efficient phenotyping has the potential to spark a new Green Revolution, and it would provide an opportunity to acquire growth parameters and dissect the genetic bases of quantitative traits. Phenotyping platforms aim to link information from several sources to derive knowledge about trees' attributes. Aims Various tree phenotyping techniques were reviewed and analyzed along with their different applications. Methods This article presents the definition and characteristics of forest tree phenotyping and reviews newly developed imaging-based practices in forest tree phenotyping. Results This review addressed a wide range of forest trees phenotyping applications, including a survey of actual inter- and intra-specific variability, evaluating genotypes and species response to biotic and abiotic stresses, and phenological measurements. Conclusion With the support of advanced phenotyping platforms, the efficiency of traits phenotyping in forest tree breeding programs is accelerated. [Bian, Liming] Nanjing Forestry Univ, Coll Forestry, Nanjing 210037, Peoples R China; [Bian, Liming] Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Minist Educ, Nanjing 210037, Peoples R China; [Bian, Liming] Nanjing Forestry Univ, Key Lab Forest Genet & Biotechnol, Minist Educ, Nanjing 210037, Peoples R China; [Zhang, Huichun] Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China; [Zhang, Huichun] Nanjing Forestry Univ, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat F, Nanjing 210037, Peoples R China; [Ge, Yufeng] Univ Nebraska, Dept Biol Syst Engn, Lincoln, NE 68583 USA; [Cepl, Jaroslav; Stejskal, Jan] Czech Univ Life Sci, Fac Forestry & Wood Sci, Dept Genet & Physiol Forest Trees, Kamycka 1176, Prague 16521 6, Suchdol, Czech Republic; [EL-Kassaby, Yousry A.] Univ British Columbia, Fac Forestry, Dept Forest & Conservat Sci, Vancouver, BC V6T 1Z4, Canada Nanjing Forestry University; Nanjing Forestry University; Nanjing Forestry University; Nanjing Forestry University; Nanjing Forestry University; University of Nebraska System; University of Nebraska Lincoln; Czech University of Life Sciences Prague; University of British Columbia Zhang, HC (corresponding author), Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China.;Zhang, HC (corresponding author), Nanjing Forestry Univ, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat F, Nanjing 210037, Peoples R China. njzhanghc@hotmail.com Stejskal, Jan/0000-0001-9965-5477 National Natural Science Foundation of China [NSFC 32171790, NSFC 32171818]; Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration Promotion Project [NJ2020-18]; Key Research and Development Program of Jiangsu Province [BE2021307]; Six Talent Peaks Project in Jiangsu Province; 333 Project of Jiangsu Province; Qinglan Project Foundation of Jiangsu Province National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration Promotion Project; Key Research and Development Program of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; 333 Project of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qinglan Project Foundation of Jiangsu Province This work is supported by National Natural Science Foundation of China (NSFC 32171790 and NSFC 32171818), Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration Promotion Project (NJ2020-18), Key Research and Development Program of Jiangsu Province (BE2021307), Six Talent Peaks Project in Jiangsu Province, Qinglan Project Foundation of Jiangsu Province and 333 Project of Jiangsu Province. 159 6 6 29 48 SPRINGER FRANCE PARIS 22 RUE DE PALESTRO, PARIS, 75002, FRANCE 1286-4560 1297-966X ANN FOREST SCI Ann. For. Sci. DEC 2022.0 79 1 22 10.1186/s13595-022-01143-x 0.0 21 Forestry Science Citation Index Expanded (SCI-EXPANDED) Forestry 1B7OU gold 2023-03-23 WOS:000792623500001 0
J Angora, G; Brescia, M; Cavuoti, S; Paolillo, M; Longo, G; Cantiello, M; Capaccioli, M; D'Abrusco, R; D'Ago, G; Hilker, M; Iodice, E; Mieske, S; Napolitano, N; Peletier, R; Pota, V; Puzia, T; Riccio, G; Spavone, M Angora, G.; Brescia, M.; Cavuoti, S.; Paolillo, M.; Longo, G.; Cantiello, M.; Capaccioli, M.; D'Abrusco, R.; D'Ago, G.; Hilker, M.; Iodice, E.; Mieske, S.; Napolitano, N.; Peletier, R.; Pota, V.; Puzia, T.; Riccio, G.; Spavone, M. Astroinformatics-based search for globular clusters in the Fornax Deep Survey MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article methods: data analysis; globular clusters: general; galaxies: elliptical and lenticular, cD FEATURE-SELECTION; CLASSIFICATION; NETWORK; SYSTEM; CORE In the last years, Astroinformatics has become a well-defined paradigm for many fields of Astronomy. In this work, we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multiband photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analysed in thiswork consist of deep, multiband, partially overlapping images centred on the core of the Fornax cluster. In this work, we use a Neural Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method (Phi LAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics-based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single-band HST data and two approaches based, respectively, on a morpho-photometric and a Principal Component Analysis using the same data discussed in this work. [Angora, G.] Univ Ferrara, Dept Phys & Earth Sci, Via Saragat 1, I-44122 Ferrara, Italy; [Angora, G.; Brescia, M.; Cavuoti, S.; Paolillo, M.; Capaccioli, M.; Iodice, E.; Pota, V.; Riccio, G.; Spavone, M.] INAF, Astron Observ Capodimonte, Via Moiariello 16, I-80131 Naples, Italy; [Cavuoti, S.; Paolillo, M.; Longo, G.; Capaccioli, M.] Univ Naples Federico II, Dept Phys E Pancini, Via Cinthia 21, I-80126 Naples, Italy; [Cavuoti, S.; Paolillo, M.; Longo, G.] INFN, Napoli Unit, Via Cinthia 21, I-80126 Naples, Italy; [Longo, G.] CALTECH, Dept Astron, Pasadena, CA 90125 USA; [Cantiello, M.] INAF, Astron Observ Abruzzo, Via Mentore Maggini Snc, I-64100 Teramo, Italy; [D'Abrusco, R.] Harvard & Smithsonian, Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA; [D'Ago, G.; Puzia, T.] Pontificia Univ Catolica Chile, Inst Astrophys, Av Vicuna Mackenna 4860, Santiago 7820436, Chile; [Hilker, M.] European Southern Observ, Karl Schwarzschild Str 2, D-85748 Garching, Germany; [Mieske, S.] European Southern Observ, Alonso Cordova 3107, Santiago 7630355, Chile; [Napolitano, N.] Sun Yat Sen Univ, Sch Phys & Astron, Zhuhai Campus, Guangzhou 519082, Guangdong, Peoples R China; [Peletier, R.] Univ Groningen, Kapteyn Astron Inst, POB 800, NL-9700 AV Groningen, Netherlands University of Ferrara; Istituto Nazionale Astrofisica (INAF); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); California Institute of Technology; Istituto Nazionale Astrofisica (INAF); Smithsonian Institution; Pontificia Universidad Catolica de Chile; European Southern Observatory; European Southern Observatory; Sun Yat Sen University; University of Groningen Angora, G (corresponding author), Univ Ferrara, Dept Phys & Earth Sci, Via Saragat 1, I-44122 Ferrara, Italy.;Angora, G; Brescia, M; Cavuoti, S (corresponding author), INAF, Astron Observ Capodimonte, Via Moiariello 16, I-80131 Naples, Italy.;Cavuoti, S (corresponding author), Univ Naples Federico II, Dept Phys E Pancini, Via Cinthia 21, I-80126 Naples, Italy.;Cavuoti, S (corresponding author), INFN, Napoli Unit, Via Cinthia 21, I-80126 Naples, Italy. gius.angora@gmail.com; massimo.brescia@inaf.it; stefano.cavuoti@gmail.com Riccio, Giuseppe/AFE-8548-2022; Iodice, Enrichetta/AAY-1704-2020; Brescia, Massimo/J-2774-2019; Peletier, Reynier F/B-9633-2012; D'Ago, Giuseppe/N-8318-2016; Cantiello, Michele/ABB-2950-2020; Cavuoti, Stefano/B-4650-2017; Paolillo, Maurizio/ABE-8393-2020; Angora, Giuseppe/GYV-2776-2022 Riccio, Giuseppe/0000-0001-7020-1172; Iodice, Enrichetta/0000-0003-4291-0005; Brescia, Massimo/0000-0001-9506-5680; D'Ago, Giuseppe/0000-0001-9697-7331; Cantiello, Michele/0000-0003-2072-384X; Cavuoti, Stefano/0000-0002-3787-4196; Paolillo, Maurizio/0000-0003-4210-7693; Angora, Giuseppe/0000-0002-0316-6562; Spavone, Marilena/0000-0002-6427-7039; Peletier, Reynier/0000-0001-7621-947X; Puzia, Thomas H./0000-0003-0350-7061 INAF Progetto di Ricerca di Interesse Nazionale -Square Kilometer Array (PRIN-SKA) 2017 program [1.05.01.88.04]; MIUR Premiale 2016: Mining the Cosmos -Big Data and Innovative italian technology for Frontier Astrofhysics and Cosmology (MITIC); Progetto di Ricerca di Interesse Nazionale (PRIN) INAF 2014 'Fornax Cluster Imaging and Spectroscopic Deep Survey'; project 'Quasars at high redshift: physics and cosmology' - ASI/INAF [2017-14-H.0]; European Union [721463]; 100 Top Talent Program of the Sun Yat-sen University, Guandong Province; VST project; NASA [NAS8-03060]; Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) [Basal AFB-170002] INAF Progetto di Ricerca di Interesse Nazionale -Square Kilometer Array (PRIN-SKA) 2017 program; MIUR Premiale 2016: Mining the Cosmos -Big Data and Innovative italian technology for Frontier Astrofhysics and Cosmology (MITIC); Progetto di Ricerca di Interesse Nazionale (PRIN) INAF 2014 'Fornax Cluster Imaging and Spectroscopic Deep Survey'(Ministry of Education, Universities and Research (MIUR)); project 'Quasars at high redshift: physics and cosmology' - ASI/INAF; European Union(European Commission); 100 Top Talent Program of the Sun Yat-sen University, Guandong Province; VST project; NASA(National Aeronautics & Space Administration (NASA)); Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)) The authors thank the anonymous referee for all very helpful comments and suggestions that improved the scientific quality of the presented work. MB acknowledges the INAF Progetto di Ricerca di Interesse Nazionale -Square Kilometer Array (PRIN-SKA) 2017 program1.05.01.88.04 and the funding from MIUR Premiale 2016: Mining the Cosmos -Big Data and Innovative italian technology for Frontier Astrofhysics and Cosmology (MITIC). MP acknowledges support from Progetto di Ricerca di Interesse Nazionale (PRIN) INAF 2014 'Fornax Cluster Imaging and Spectroscopic Deep Survey'. MPand SCacknowledge support from the project 'Quasars at high redshift: physics and cosmology' financed by the ASI/INAF agreement 2017-14-H.0. GL, RP, and NN acknowledge support from the European Union's Horizon 2020 Sundial Innovative Training Network, grant no. 721463. NN acknowledges support from the 100 Top Talent Program of the Sun Yat-sen University, Guandong Province. MS and EI acknowledge financial support from the VST project. RD'A is supported by NASA contract NAS8-03060 (Chandra X-ray Center). GD acknowledges support from Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) project Basal AFB-170002. DAMEWARE has been used for ML experiments (Brescia et al. 2014). Topcat has been used for this work (Taylor 2005). C3 has been used for efficient catalogue cross-matching (Riccio et al. 2017). 89 4 4 0 1 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. DEC 2019.0 490 3 4080 4106 10.1093/mnras/stz2801 0.0 27 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics JX8QI Green Submitted, Bronze, Green Accepted 2023-03-23 WOS:000503993100072 0
J Liu, D; Sun, DW; Zeng, XA Liu, Dan; Sun, Da-Wen; Zeng, Xin-An Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry FOOD AND BIOPROCESS TECHNOLOGY English Review Wavelength selection; Hyperspectral imaging; Multispectral; Food; Chemometrics SUCCESSIVE PROJECTIONS ALGORITHM; LEAST-SQUARES REGRESSION; UNINFORMATIVE VARIABLE ELIMINATION; ESCHERICHIA-COLI CONTAMINATION; SUPPORT VECTOR MACHINES; NEAR-INFRARED SPECTRA; QUALITY EVALUATION; COMPUTER VISION; NONDESTRUCTIVE DETERMINATION; MULTIVARIATE CALIBRATION During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems. [Liu, Dan; Sun, Da-Wen; Zeng, Xin-An] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland Univ Coll Dublin, Agr & Food Sci Ctr, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Sun, Da-Wen/B-1899-2012 Sun, Da-Wen/0000-0002-3634-9963 Guangdong Province Government (China); China Postdoctoral Science Foundation [2013M530366] Guangdong Province Government (China); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors are grateful to the Guangdong Province Government (China) for support through the program of Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by China Postdoctoral Science Foundation (2013M530366). Prof Don and Karen Barnes and Dr Rana Muhammad Aadil are gratefully acknowledged for kind assistance and revisions of English quality and styling of this paper. 117 261 271 15 251 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1935-5130 1935-5149 FOOD BIOPROCESS TECH Food Bioprocess Technol. FEB 2014.0 7 2 307 323 10.1007/s11947-013-1193-6 0.0 17 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology 288WF 2023-03-23 WOS:000329642000001 0
J Zhou, BF; Svetashova, Y; Gusmao, A; Soylu, A; Cheng, G; Mikut, R; Waaler, A; Kharlamov, E Zhou, Baifan; Svetashova, Yulia; Gusmao, Andre; Soylu, Ahmet; Cheng, Gong; Mikut, Ralf; Waaler, Arild; Kharlamov, Evgeny SemML: Facilitating development of ML models for condition monitoring with semantics JOURNAL OF WEB SEMANTICS English Article Condition monitoring; Ontologies; Templates; Data integration; Machine learning; Software architecture; Industry 4.0 REMOVING TRANSIENT CHANGES; KNOWLEDGE DISCOVERY; ONTOLOGY; CLASSIFICATION; DIAGNOSTICS; CHALLENGES; ACCESS Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results. (C) 2021 The Author( s ). Published by Elsevier B.V. [Zhou, Baifan; Svetashova, Yulia] Bosch Corp, Res Ctr, D-71272 Renningen, Germany; [Zhou, Baifan; Mikut, Ralf] Karlsruhe Inst Technol, Inst Automat & Appl Informat, D-76344 Eggenstein Leopoldshafen, Germany; [Zhou, Baifan; Soylu, Ahmet; Waaler, Arild; Kharlamov, Evgeny] Univ Oslo, Dept Informat, N-0316 Oslo, Norway; [Svetashova, Yulia] Karlsruhe Inst Technol, Inst Appl Informat & Formal Descript Methods, D-76133 Karlsruhe, Germany; [Gusmao, Andre; Soylu, Ahmet] Norwegian Univ Sci & Technol NTNU, Dept Comp Sci, Teknol Vegen 22, N-2815 Gjovik, Norway; [Soylu, Ahmet] OsloMet Oslo Metropolitan Univ, Dept Comp Sci, Pilestredet 46, N-0167 Oslo, Norway; [Cheng, Gong] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China; [Kharlamov, Evgeny] Bosch Ctr Artificial Intelligence, D-71272 Renningen, Germany Helmholtz Association; Karlsruhe Institute of Technology; University of Oslo; Helmholtz Association; Karlsruhe Institute of Technology; Norwegian University of Science & Technology (NTNU); Oslo Metropolitan University (OsloMet); Nanjing University Zhou, BF; Svetashova, Y (corresponding author), Bosch Corp, Res Ctr, D-71272 Renningen, Germany.;Zhou, BF (corresponding author), Karlsruhe Inst Technol, Inst Automat & Appl Informat, D-76344 Eggenstein Leopoldshafen, Germany.;Zhou, BF (corresponding author), Univ Oslo, Dept Informat, N-0316 Oslo, Norway.;Svetashova, Y (corresponding author), Karlsruhe Inst Technol, Inst Appl Informat & Formal Descript Methods, D-76133 Karlsruhe, Germany. baifanz@ifi.uio.no; yulia.svetashova@de.bosch.com; andrgusm@stud.ntnu.no; ahmet.soylu@oslomet.no; gcheng@nju.edu.cn; ralf.mikut@kit.edu; arild@ifi.uio.no; evgeny.kharlamov@de.bosch.com Mikut, Ralf/A-5949-2013 Mikut, Ralf/0000-0001-9100-5496; Soylu, Ahmet/0000-0001-6034-4137; Zhou, Baifan/0000-0003-3698-0541 SIRIUS Centre, Norwegian Research Council [237898] SIRIUS Centre, Norwegian Research Council This work was partially supported by SIRIUS Centre, Norwegian Research Council project number 237898. We are thankful to Tim Pychynski for insightful discussions. 98 10 10 1 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1570-8268 1873-7749 J WEB SEMANT J. Web Semant. NOV 2021.0 71 100664 10.1016/j.websem.2021.100664 0.0 OCT 2021 21 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science XK3OO Green Published, hybrid 2023-03-23 WOS:000727379400006 0
J Loos, D; Zhang, L; Beemelmanns, C; Kurzai, O; Panagiotou, G Loos, Daniel; Zhang, Lu; Beemelmanns, Christine; Kurzai, Oliver; Panagiotou, Gianni DAnIEL: A User-Friendly Web Server for Fungal ITS Amplicon Sequencing Data FRONTIERS IN MICROBIOLOGY English Article metagenomics; fungi; mycobiome; web server; ITS INTERNAL TRANSCRIBED SPACER; MARKER; TOOLS Trillions of microbes representing all kingdoms of life are resident in, and on, humans holding essential roles for the host development and physiology. The last decade over a dozen online tools and servers, accessible via public domain, have been developed for the analysis of bacterial sequences; however, the analysis of fungi is still in its infancy. Here, we present a web server dedicated to the comprehensive analysis of the human mycobiome for (i) translating raw sequencing reads to data tables and high-standard figures, (ii) integrating statistical analysis and machine learning with a manually curated relational database and (iii) comparing the user's uploaded datasets with publicly available from the Sequence Read Archive. Using 1,266 publicly available Internal transcribed spacers (ITS) samples, we demonstrated the utility of DAnIEL web server on large scale datasets and show the differences in fungal communities between human skin and soil sites. [Loos, Daniel; Zhang, Lu; Panagiotou, Gianni] Leibniz Inst Nat Prod Res & Infect Biol, Syst Biol & Bioinformat Grp, Jena, Germany; [Beemelmanns, Christine] Leibniz Inst Nat Prod Res & Infect Biol, Chem Biol Microbe Host Interact Grp, Jena, Germany; [Kurzai, Oliver] Univ Wurzburg, Inst Hyg & Microbiol, Wurzburg, Germany; [Kurzai, Oliver] Leibniz Inst Nat Prod Res & Infect Biol, Natl Reference Ctr Invas Fungal Infect NRZMyk, Jena, Germany; [Panagiotou, Gianni] Univ Hong Kong, Sch Biol Sci, Syst Biol & Bioinformat Grp, Fac Sci, Hong Kong, Peoples R China Hans Knoll Institute (HKI); Hans Knoll Institute (HKI); University of Wurzburg; Hans Knoll Institute (HKI); University of Hong Kong Panagiotou, G (corresponding author), Leibniz Inst Nat Prod Res & Infect Biol, Syst Biol & Bioinformat Grp, Jena, Germany.;Panagiotou, G (corresponding author), Univ Hong Kong, Sch Biol Sci, Syst Biol & Bioinformat Grp, Fac Sci, Hong Kong, Peoples R China. gianni.panagiotou@leibniz-hki.de Panagiotou, Ioannis/B-7083-2013 Panagiotou, Ioannis/0000-0001-9393-124X; Kurzai, Oliver/0000-0002-7277-2646; Loos, Daniel/0000-0002-4024-4443 Deutsche Forschungsgemeinschaft (DFG) CRC/Transregio 124 [B5]; INF (FungiNet) [210879364]; ERC funding (ERC Starting Grant Project) [802736] Deutsche Forschungsgemeinschaft (DFG) CRC/Transregio 124(German Research Foundation (DFG)); INF (FungiNet); ERC funding (ERC Starting Grant Project) Funding. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) CRC/Transregio 124 Pathogenic Fungi and Their Human Host: Networks of Interaction, subprojects B5 and INF (FungiNet; number 210879364). CB and LZ greatly acknowledge the ERC funding (ERC Starting Grant Project 802736 MORPHEUS). 39 2 2 0 4 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-302X FRONT MICROBIOL Front. Microbiol. AUG 17 2021.0 12 720513 10.3389/fmicb.2021.720513 0.0 8 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology UM0DY 34484161.0 gold, Green Published, Green Submitted 2023-03-23 WOS:000693012500001 0
J Fernex, D; Noack, BR; Semaan, R Fernex, Daniel; Noack, Bernd R.; Semaan, Richard Cluster-based network modeling-From snapshots to complex dynamical systems SCIENCE ADVANCES English Article REDUCTION; EQUATION We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields. [Fernex, Daniel; Semaan, Richard] Tech Univ Carolo Wilhelmina Braunschweig, Inst Stromungsmech, Hermann Blenk Str 37, D-38108 Braunschweig, Germany; [Noack, Bernd R.] Harbin Inst Technol, Ctr Turbulence Control, Shenzhen 518058, Peoples R China; [Noack, Bernd R.] Tech Univ Berlin, Hermann Fottinger Inst, Muller Breslau Str 8, D-10623 Berlin, Germany Braunschweig University of Technology; Harbin Institute of Technology; Technical University of Berlin Semaan, R (corresponding author), Tech Univ Carolo Wilhelmina Braunschweig, Inst Stromungsmech, Hermann Blenk Str 37, D-38108 Braunschweig, Germany.;Noack, BR (corresponding author), Harbin Inst Technol, Ctr Turbulence Control, Shenzhen 518058, Peoples R China.;Noack, BR (corresponding author), Tech Univ Berlin, Hermann Fottinger Inst, Muller Breslau Str 8, D-10623 Berlin, Germany. bernd.noack@hit.edu.cn; r.semaan@tu-braunschweig.de Noack, Bernd/B-1242-2012 Noack, Bernd/0000-0001-5935-1962 Deutsche Forschungsgemeinschaft (DFG) [SE 2504/2-1] Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)) We are grateful to T. Sapsis, S. Brunton, W. Schroder, and M. Albers for the stimulating discussions and for providing some of the data used. Funding: The research was funded by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the research projects SE 2504/2-1. 49 13 13 2 36 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 2375-2548 SCI ADV Sci. Adv. JUN 2021.0 7 25 eabf5006 10.1126/sciadv.abf5006 0.0 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics SX1FX 34134987.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000664958400002 0
J Wang, HW; Li, YJ; Xi, SJ; Wang, SF; Pathan, MS; Dev, S Wang, Hewei; Li, Yijie; Xi, Shijia; Wang, Shaofan; Pathan, Muhammad Salman; Dev, Soumyabrata AMDCNet: An attentional multi-directional convolutional network for stereo matching? DISPLAYS English Article Stereomatching; Convolutionaggregationnetwork; Visualsensitive; Costaggregation CAMERA; CALIBRATION; SUPPORT Stereo matching refers to finding the correspondence of a point in the real world between two different storage mediums (e.g., intensity images, depth images, three-dimensional points). There are existing stereo matching methods in the literature, but they exhibit two shortcomings. Firstly, during the feature region extraction of stereo matching, these methods require measuring the distance of regions, but measuring the texture distribution of the region is difficult and might lead to the failure of matching. Secondly, the templates used in these methods are rectangles with a fixed size, while most of the natural images exhibit rich information and are more suitable for flexible templates. In this paper, we propose an attentional multi-directional convolutional network (AMDCNet) for circumventing these issues. Our AMDCNet approach consists of three stages: extract the visual sensitivity factor, construct the multi-directional aggregation template and utilize left-right consistency detection to optimize. We evaluate our approach using standard images in the Middlebury test dataset, Scene Flow and KITTI 2015. Experimental results show that AMDCNet can reduce the mismatch rate, and also show significant improvement in accuracy compared with some classical method. In some scenarios, it surpasses some advanced methods based on deep learning. The model code, dataset, and results of the experiments in this paper are available at: https://github.com/WangHewei16/Attentional-Multi-Directional-Convolution-Network. [Wang, Hewei; Li, Yijie; Xi, Shijia] Beijing Univ Technol, Beijing Dublin Int Coll, Beijing 100124, Peoples R China; [Wang, Shaofan] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China; [Pathan, Muhammad Salman; Dev, Soumyabrata] ADAPT SFI Res Ctr, Dublin D04V1W8, Ireland; [Pathan, Muhammad Salman; Dev, Soumyabrata] Univ Coll Dublin, Sch Comp Sci, Dublin D04V1W8, Ireland Beijing University of Technology; Beijing University of Technology; University College Dublin Dev, S (corresponding author), Univ Coll Dublin, Sch Comp Sci, Dublin D04V1W8, Ireland. hewei.wang@ucdconnect.ie; yijie.li@ucdconnect.ie; shijia.xi@ucdconnect.ie; wangshaofan@bjut.edu.cn; salman.pathan@adaptcentre.ie; soumyabrata.dev@ucd.ie LI, yi/HKO-0480-2023; Pathan, Muhammad Salman/AAJ-1458-2020 Pathan, Muhammad Salman/0000-0002-0210-3121; Wang, Hewei/0000-0002-6952-0886 Science Foundation Ireland [13/RC/2106_P2]; Science Foundation Ireland at the ADAPT SFI Research Centre at University College Dublin [13/RC/2106_P2]; Science Foundation Ireland; European Union [801522]; Science Foundation Ireland; European Regional Development Fund through the ADAPT Centre for Digital Content Technology Science Foundation Ireland(Science Foundation Ireland); Science Foundation Ireland at the ADAPT SFI Research Centre at University College Dublin(Science Foundation Ireland); Science Foundation Ireland(Science Foundation Ireland); European Union(European Commission); Science Foundation Ireland(Science Foundation Ireland); European Regional Development Fund through the ADAPT Centre for Digital Content Technology This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at University College Dublin. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Cen-tres Programme. This research is supported by the European Union?s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology grant number 13/RC/2106_P2. 53 0 0 8 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0141-9382 1872-7387 DISPLAYS Displays SEP 2022.0 74 102243 10.1016/j.displa.2022.102243 0.0 JUL 2022 12 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Instruments & Instrumentation; Optics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Instruments & Instrumentation; Optics 3B3SV hybrid 2023-03-23 WOS:000827865100001 0
J Shi, B; Yuan, YF; Zhuang, TX; Xu, X; Schmidhalter, U; Ata-UI-Karim, ST; Zhao, B; Liu, XJ; Tian, YC; Zhu, Y; Cao, WX; Cao, Q Shi, Bo; Yuan, Yifan; Zhuang, Tingxuan; Xu, Xuan; Schmidhalter, Urs; Ata-UI-Karim, Syed Tahir; Zhao, Ben; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang Improving water status prediction of winter wheat using multi-source data with machine learning EUROPEAN JOURNAL OF AGRONOMY English Article Canopy water content; Plant water content; Canopy equivalent water thickness; Hyperspectral; Data fusion; Precision water management DROUGHT STRESS; DRY-MATTER; NITROGEN; MASS; TRANSLOCATION; REFLECTANCE; TEMPERATURE; QUALITY; REGION; YIELD Water and nitrogen (N) are the most important factors limiting crop productivity. Effective monitoring of the water status of winter wheat under different N treatments is imperative for precision irrigation in improved crop management. Hyperspectral remote sensing is widely used for monitoring the crop water status. However, changes in canopy architecture during ontogeny lead to poor inversion of crop properties and limit the use of spectral indices. This study aimed to improve the water status prediction of winter wheat using multi-source data with machine learning (ML). Two multi-irrigation levels (0, 120, 240, 360 mm) and N rates (75, 225 kg N ha-1) experiments were conducted during the 2019-2021 wheat growing seasons under field conditions using a rainout shelter. Hyperspectral, soil, plant, and climate data were evaluated with two feature selection methods. Selected results were chosen as input variables for prediction models by using three ML algorithms. By constructing the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI), the DSI(2015, 2375), NDSI(2175, 2245), and RSI(720, 1200) showed the strongest correlation with canopy water content (CWC), plant water content (PWC), and canopy equivalent water thickness (CEWT), respectively. The best feature selection method and data were delivered by the Pearson correlation coefficient together with the soil, plant, and climate data. The best performing ML algorithm for CWC and PWC prediction was RF, while SVM was the best ML algorithm for CEWT prediction. The R2 of the optimal models ranged from 0.86 to 0.97. These models with multi-source data significantly improved the prediction accuracy of the water status and can thus assist in precision irrigation management of winter wheat. [Shi, Bo; Yuan, Yifan; Zhuang, Tingxuan; Xu, Xuan; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang] Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, MOE Engn & Res Ctr Smart Agr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing 210095, Peoples R China; [Schmidhalter, Urs] Tech Univ Munich, Dept Plant Sci, Freising Weihenstephan, Germany; [Ata-UI-Karim, Syed Tahir] Univ Tokyo, Grad Sch Agr & Life Sci, Bunkyo, Tokyo 1138657, Japan; [Zhao, Ben] Chinese Acad Agr Sci, Farmland Irrigat Res Inst, Key Lab Crop Water Use & Regulat, Minist Agr, Xinxiang 453099, Peoples R China Nanjing Agricultural University; Technical University of Munich; University of Tokyo; Chinese Academy of Agricultural Sciences; Farmland Irrigation Research Institute, CAAS; Ministry of Agriculture & Rural Affairs Cao, Q (corresponding author), Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, MOE Engn & Res Ctr Smart Agr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing 210095, Peoples R China. qiangcao@njau.edu.cn Schmidhalter, Urs/C-5477-2012 Schmidhalter, Urs/0000-0003-4106-7124 Jiangsu Province Key Technologies Research and Development Program [BE2021308]; National Natural Science Foundation of China [31601222]; Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (PAPD) [B16026]; 111 Project Jiangsu Province Key Technologies Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (PAPD); 111 Project(Ministry of Education, China - 111 Project) This research was funded by the Jiangsu Province Key Technologies Research and Development Program (BE2021308) , the National Natural Science Foundation of China (31601222) , the Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (PAPD) , and the 111 Project (B16026) . 59 0 0 19 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1161-0301 1873-7331 EUR J AGRON Eur. J. Agron. SEP 2022.0 139 126548 10.1016/j.eja.2022.126548 0.0 MAY 2022 12 Agronomy Science Citation Index Expanded (SCI-EXPANDED) Agriculture 2M6QQ 2023-03-23 WOS:000817822400005 0
J Liu, W; Wei, DT; Chen, QL; Yang, WJ; Meng, J; Wu, GR; Bi, TY; Zhang, QL; Zuo, XN; Qiu, J Liu, Wei; Wei, Dongtao; Chen, Qunlin; Yang, Wenjing; Meng, Jie; Wu, Guorong; Bi, Taiyong; Zhang, Qinglin; Zuo, Xi-Nian; Qiu, Jiang Longitudinal test-retest neuroimaging data from healthy young adults in southwest China SCIENTIFIC DATA English Article; Data Paper FUNCTIONAL CONNECTIVITY MRI; BRAIN CONNECTIVITY; HEAD MOTION; CREATIVITY; BEHAVIOR Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed-demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches. [Liu, Wei; Wei, Dongtao; Chen, Qunlin; Yang, Wenjing; Meng, Jie; Wu, Guorong; Bi, Taiyong; Zhang, Qinglin; Qiu, Jiang] Minist Educ, Key Lab Cognit & Personal SWU, Chongqing 400715, Peoples R China; [Liu, Wei; Wei, Dongtao; Chen, Qunlin; Yang, Wenjing; Meng, Jie; Wu, Guorong; Bi, Taiyong; Zhang, Qinglin; Zuo, Xi-Nian; Qiu, Jiang] Southwest Univ, Fac Psychol, Chongqing 400715, Peoples R China; [Liu, Wei] Radboud Univ Nijmegen, Med Ctr, Donders Inst Brain Cognit & Behav, NL-6525 EZ Nijmegen, Netherlands; [Zuo, Xi-Nian] Chinese Acad Sci, Inst Psychol, Lab Funct Connectome & Dev, Beijing 100101, Peoples R China; [Zuo, Xi-Nian] Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China; [Zuo, Xi-Nian] Guangxi Teachers Educ Univ, Sch Educ Sci, Dept Psychol, Nanning 530000, Peoples R China Southwest University - China; Radboud University Nijmegen; Chinese Academy of Sciences; Institute of Psychology, CAS; Chinese Academy of Sciences; Institute of Psychology, CAS; Nanning Normal University Qiu, J (corresponding author), Minist Educ, Key Lab Cognit & Personal SWU, Chongqing 400715, Peoples R China.;Zuo, XN; Qiu, J (corresponding author), Southwest Univ, Fac Psychol, Chongqing 400715, Peoples R China.;Zuo, XN (corresponding author), Chinese Acad Sci, Inst Psychol, Lab Funct Connectome & Dev, Beijing 100101, Peoples R China.;Zuo, XN (corresponding author), Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China.;Zuo, XN (corresponding author), Guangxi Teachers Educ Univ, Sch Educ Sci, Dept Psychol, Nanning 530000, Peoples R China. zuoxn@psych.ac.cn; qiuj318@swu.edu.cn Zuo, Xi-Nian/A-7273-2009 Zuo, Xi-Nian/0000-0001-9110-585X; Bi, Taiyong/0000-0002-3757-1937; Chen, Qunlin/0000-0002-7396-1297 National Natural Science Foundation of China [31271087, 31470981, 31571137, 31500885]; National Outstanding young people plan; Program for the Top Young Talents by Chongqing; Fundamental Research Funds for the Central Universities [SWU1509383, SWU1509451]; Natural Science Foundation of Chongqing [cstc2015jcyjA10106]; Fok Ying Tung Education Foundation [151023]; China Postdoctoral Science Foundation [2015M572423, 2015M580767]; Chongqing Postdoctoral Science Foundation [Xm2015037]; Key research for Humanities and social sciences of Ministry of Education [14JJD880009]; National Basic Research Program (973 Program) [2015CB351702]; Natural Science Foundation of China [81471740, 81220108014] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Outstanding young people plan; Program for the Top Young Talents by Chongqing; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Natural Science Foundation of Chongqing(Natural Science Foundation of Chongqing); Fok Ying Tung Education Foundation(Fok Ying Tung Education Foundation); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Chongqing Postdoctoral Science Foundation; Key research for Humanities and social sciences of Ministry of Education; National Basic Research Program (973 Program)(National Basic Research Program of China); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This dataset was supported by: The National Natural Science Foundation of China (31271087; 31470981; 31571137; 31500885); National Outstanding young people plan; the Program for the Top Young Talents by Chongqing; the Fundamental Research Funds for the Central Universities (SWU1509383, SWU1509451); Natural Science Foundation of Chongqing (cstc2015jcyjA10106); Fok Ying Tung Education Foundation (151023); General Financial Grant from the China Postdoctoral Science Foundation (2015M572423, 2015M580767); Special Funds from the Chongqing Postdoctoral Science Foundation (Xm2015037); Key research for Humanities and social sciences of Ministry of Education (14JJD880009) to J.Q, D.T.W and W.J.Y. National Basic Research Program (973 Program: 2015CB351702) Natural Science Foundation of China (81471740 and 81220108014) to X.N.Z. We are grateful to all of graduate students who contributed their time and wisdom to this data repository, including but not limited to Xue Du, Kang cheng Wang, Xin Hou and Junyi Yang. We are also thank Michael Milham and David O'Connor (INDI and Child Mind Institute) for the data repository. 29 72 74 3 25 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2052-4463 SCI DATA Sci. Data FEB 14 2017.0 4 170017 10.1038/sdata.2017.17 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics EK3VI 28195583.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000393856100003 0
C Wang, Q; Huang, HL; Huang, JC; Feng, YH; Liu, Z; Cheng, GQ; Shi, W; Huang, KH; Wu, JM IEEE Wang, Qi; Huang, Honglan; Huang, Jincai; Feng, Yanghe; Liu, Zhong; Cheng, Guangquan; Shi, Wei; Huang, Kuihua; Wu, Jinming On the Application of Energy Inspired Model to Label Denoising 2018 4TH INTERNATIONAL CONFERENCE ON BIG DATA AND INFORMATION ANALYTICS (BIGDIA) English Proceedings Paper 4th International Conference on Big Data and Information Analytics (BigDIA) DEC 17-19, 2018 HOUSTON, TX IEEE,Gulf Coast Consortia,Univ Texas Hlth Sci Ctr Houston, Dept biostatistics & Data Sci, Sch Public Hlth Label Noise; K-Nearest Neighbors (KNN); Energy Minimization; Random Coordinate Descent Learning in the presence of label noise is a promising research issue, which can well accommodate to the reality of weakly supervised annotations in the era of big data. The existing methodologies to address such issue mainly focus on either roughly preprocessing on the collected dataset or performing modifications on original model, but the latter relies on strict assumptions about noise generation process, and parameters are tough to tune or estimate. In this paper, we report an energy inspired model in label denoising application, which can well characterize relationships between collected data and latent labels and act as a noisy label filter. Meanwhile, the local proximities in patterns are believed to be beneficial in repairing wrong labels, and the experiments on some typical synthetic dataset demonstrate its great power in label denoising. Our proposed model has better explanations and can be flexibly extended to multi-class cases. [Wang, Qi] Univ Amsterdam, Inst Informat, Amsterdam, Netherlands; [Huang, Honglan; Huang, Jincai; Feng, Yanghe; Liu, Zhong; Cheng, Guangquan; Huang, Kuihua; Wu, Jinming] Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China; [Shi, Wei] Ctr Assessment & Demonstrat Res, Beijing, Peoples R China University of Amsterdam; National University of Defense Technology - China Feng, YH (corresponding author), Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China. q.wang3@uva.nl; huanghonglan17@nudt.edu.cn; huangjincai@nudt.edu.cn; fengyanghe@nudt.edu.cn; phillipliu@263.net; cgq299@163.com; shanwshanke@163.com; kuihh@163.com; 1099624430@qq.com Huang, Honglan/GRR-2513-2022 National Science Foundation (NSF) [71701205, 71701206]; China Scholarship Council (CSC) National Science Foundation (NSF)(National Science Foundation (NSF)); China Scholarship Council (CSC)(China Scholarship Council) Thanks for the support from the National Science Foundation (NSF) sponsorship No.71701205 and No.71701206. Dr. Qi Wang gratefully acknowledges financial support from China Scholarship Council (CSC). 25 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-6888-7 2018.0 5 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM0CY 2023-03-23 WOS:000458645000006 0
J Gkoumas, D; Li, QC; Lioma, C; Yu, YJ; Song, DW Gkoumas, Dimitris; Li, Qiuchi; Lioma, Christina; Yu, Yijun; Song, Dawei What makes the difference? An empirical comparison of fusion strategies for multimodal language analysis INFORMATION FUSION English Article Multimodal human language understanding; Video sentiment analysis; Emotion recognition; Reproducibility in multimodal machine learning NETWORK; REPRESENTATIONS; TRANSLATION Multimodal video sentiment analysis is a rapidly growing area. It combines verbal (i.e., linguistic) and non-verbal modalities (i.e., visual, acoustic) to predict the sentiment of utterances. A recent trend has been geared towards different modality fusion models utilizing various attention, memory and recurrent components. However, there lacks a systematic investigation on how these different components contribute to solving the problem as well as their limitations. This paper aims to fill the gap, marking the following key innovations. We present the first large-scale and comprehensive empirical comparison of eleven state-of-the-art (SOTA) modality fusion approaches in two video sentiment analysis tasks, with three SOTA benchmark corpora. An in-depth analysis of the results shows that the attention mechanisms are the most effective for modelling crossmodal interactions, yet they are computationally expensive. Second, additional levels of crossmodal interaction decrease performance. Third, positive sentiment utterances are the most challenging cases for all approaches. Finally, integrating context and utilizing the linguistic modality as a pivot for non-verbal modalities improve performance. We expect that the findings would provide helpful insights and guidance to the development of more effective modality fusion models. [Gkoumas, Dimitris; Yu, Yijun; Song, Dawei] Open Univ, Milton Keynes, Bucks, England; [Li, Qiuchi] Univ Padua, Padua, Italy; [Lioma, Christina] Univ Copenhagen, Copenhagen, Denmark; [Song, Dawei] Beijing Inst Technol, Beijing, Peoples R China Open University - UK; University of Padua; University of Copenhagen; Beijing Institute of Technology Gkoumas, D; Song, DW (corresponding author), Open Univ, Milton Keynes, Bucks, England.;Song, DW (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. dimitris.gkoumas@open.ac.uk; qiuchili@dei.unipd.it; c.lioma@di.ku.dk; yijun.yu@open.ac.uk; dawei.song@open.ac.uk , 莺瑰之殇/ABG-2761-2020; Lioma, Christina/N-2203-2016 Gkoumas, Dimitris/0000-0002-0524-0332; LI, QIUCHI/0000-0002-8219-0869; Lioma, Christina/0000-0003-2600-2701; Yu, Yijun/0000-0002-7154-8570 Quantum Information Access and Retrieval Theory (QUARTZ) - European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [721321]; Natural Science Foundation of China [U1636203] Quantum Information Access and Retrieval Theory (QUARTZ) - European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This study is supported by the Quantum Information Access and Retrieval Theory (QUARTZ) project, which has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721321, and Natural Science Foundation of China (grant No.: U1636203). 76 16 16 5 50 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion FEB 2021.0 66 184 197 10.1016/j.inffus.2020.09.005 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science OO8AK Green Accepted 2023-03-23 WOS:000587596900012 0
J Xu, WT; Xue, WL; Lin, Q; Lan, GH; Feng, XY; Wei, B; Luo, CW; Li, W; Zomaya, AY Xu, Weitao; Xue, Wanli; Lin, Qi; Lan, Guohao; Feng, Xingyu; Wei, Bo; Luo, Chengwen; Li, Wei; Zomaya, Albert Y. PrivGait: An Energy-Harvesting-Based Privacy-Preserving User-Identification System by Gait Analysis IEEE INTERNET OF THINGS JOURNAL English Article Gait recognition; Privacy; Sensors; Feature extraction; Energy harvesting; Smart spaces; Wearable computers; Gait recognition; IoT security; privacy preserving; smart space DIFFERENTIAL PRIVACY; RECOGNITION; CLASSIFICATION Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customized services. A fundamental requirement of these services is person identification. Although a variety of person-identification approaches has been proposed, they suffer from several limitations in practical applications, such as low energy efficiency, accuracy degradation, and privacy issue. This article proposes an energy-harvesting-based privacy-preserving gait recognition scheme for smart space, which is named PrivGait. In PrivGait, we extract discriminative features from 1-D gait signal and design an attention-based long short-term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of PrivGait, we design a proof-of-concept prototype using off-the-shelf energy-harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state of the art by 6%-10% and incurs low system cost while preserving user's privacy. [Xu, Weitao] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Xue, Wanli] Cyber Secur Cooperat Res Ctr, Joondalup, WA 6027, Australia; [Lin, Qi] Univ New South Wales, Sch Comp Sci, Sydney, NSW 2052, Australia; [Lan, Guohao] Delft Univ Technol, Dept Software Technol, NL-2628 CD Delft, Netherlands; [Feng, Xingyu; Luo, Chengwen] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [Wei, Bo] Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England; [Li, Wei; Zomaya, Albert Y.] Univ Sydney, Sch Comp Sci, Sydney, NSW 2006, Australia City University of Hong Kong; University of New South Wales Sydney; Delft University of Technology; Shenzhen University; Northumbria University; University of Sydney Xu, WT (corresponding author), City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. weitaoxu@cityu.edu.hk; wanli.xue@cybersecuritycrc.org.au; qi.lin@unsw.edu.au; g.lan@tudelft.nl; bo.wei@northumbria.ac.uk; chengwen@szu.edu.cn; weiwilson.li@sydney.edu.au; albert.zomaya@sydney.edu.au Xu, Weitao/W-7023-2019 Xu, Weitao/0000-0001-9741-5912; Wei, Bo/0000-0002-0781-9655; Li, Wei/0000-0003-4731-3226 Early Career Scheme (ECS) Grant from Hong Kong Research Grants Council [21201420]; CityU New Research Initiatives/Infrastructure Support from Central (APRC) [9610485]; City University of Hong Kong [7200642]; Chow Sang Sang Group Research Fund - Chow Sang Sang Holdings International Ltd. [9229062] Early Career Scheme (ECS) Grant from Hong Kong Research Grants Council; CityU New Research Initiatives/Infrastructure Support from Central (APRC); City University of Hong Kong(City University of Hong Kong); Chow Sang Sang Group Research Fund - Chow Sang Sang Holdings International Ltd. This work was supported in part by the Early Career Scheme (ECS) Grant from Hong Kong Research Grants Council under Project 21201420; in part by the CityU New Research Initiatives/Infrastructure Support from Central (APRC) Grant under Project 9610485 and the Start-Up Grant under Project 7200642 from the City University of Hong Kong; and in part by the Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Ltd. under Project 9229062. 43 1 1 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. NOV 15 2022.0 9 22 22048 22060 10.1109/JIOT.2021.3089618 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5Y1KT 2023-03-23 WOS:000879049400007 0
C Baldi, S; Rosa, MR; Wang, YZ IEEE Baldi, Simone; Rosa, Muhammad Ridho; Wang, Yuzhang Model plus Learning-based Optimal Control: an Inverted Pendulum Study 2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA) IEEE International Conference on Control and Automation ICCA English Proceedings Paper 16th IEEE International Conference on Control and Automation (ICCA) OCT 09-11, 2020 ELECTR NETWORK IEEE This work extends and compares some recent model+learning-based methodologies for optimal control with input saturation. We focus on two methodologies: a model-based actor-critic (MBAC) strategy, and a nonlinear policy iteration strategy. To evaluate the performance of the algorithms, these strategies are applied to the swinging up an inverted pendulum. Numerical simulations show that the neural network approximation in the MBAC strategy can be poor, and the algorithm may converge far from the optimum. In the MBAC approach neither stabilization nor monotonic convergence can be guaranteed, and it is observed that the best value function is not always corresponding to the last one. On the other side the nonlinear policy iteration approach guarantees that every new control policy is stabilizing and generally leads to a monotonically decreasing cost. [Baldi, Simone] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China; [Baldi, Simone; Rosa, Muhammad Ridho; Wang, Yuzhang] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 Delft, Netherlands; [Rosa, Muhammad Ridho] Telkom Univ, Sch Elect Engn, Bandung 40257, Indonesia; [Wang, Yuzhang] Harbin Inst Technol, Ctr Precis Engn, Harbin, Peoples R China Southeast University - China; Delft University of Technology; Telkom University; Harbin Institute of Technology Baldi, S (corresponding author), Southeast Univ, Sch Math, Nanjing 210096, Peoples R China.;Baldi, S (corresponding author), Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 Delft, Netherlands. s.baldi@tudelft.nl; mridhorosa@telkomuniversity.ac.id; wangyuzhang67@163.com Baldi, Simone/C-6796-2015 Baldi, Simone/0000-0001-9752-8925 Fundamental Research Funds for the Central Universities [3207012004A2, 3307012001A, 6207011901] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) All authors equally contributed as first authors. This work was partially supported by the Fundamental Research Funds for the Central Universities under Grant 3207012004A2, and by the special guiding fund for double first-class under Grants 3307012001A, 6207011901. (Corresponding author: Simone Baldi). 22 0 0 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1948-3449 978-1-7281-9093-8 IEEE INT CONF CON AU 2020.0 773 778 6 Automation & Control Systems; Engineering, Electrical & Electronic; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Operations Research & Management Science BR3HM Green Published 2023-03-23 WOS:000646357300132 0
J Zhong, QG; Li, XC; Zhang, HM; Chi, LF Zhong, Qigang; Li, Xuechao; Zhang, Haiming; Chi, Lifeng Noncontact atomic force microscopy: Bond imaging and beyond SURFACE SCIENCE REPORTS English Review Noncontact atomic force microscopy; QPlus sensor; Chemical bond resolution; Atomic force spectroscopy; Molecular manipulation; Tip modification; On-surface synthesis ON-SURFACE SYNTHESIS; SINGLE-MOLECULE; TUNNELING-MICROSCOPY; SUBMOLECULAR RESOLUTION; GRAPHENE NANORIBBONS; CHEMICAL-STRUCTURE; CARBON-MONOXIDE; CHARGE-STATE; CO; CONTRAST It was a long-cherished dream for chemists to take a direct look at chemical bonding, a fundamental component of chemistry. This dream was finally accomplished by the state-of-the-art noncontact atomic force microscopy (NC-AFM) equipped with qPlus force sensors and carbon monoxide (CO) functionalized tips. The resolved interconnectivity between atoms and molecules in NC-AFM frequency shift images is interpreted as chemical bonding, providing essential knowledge of the bond length, bond angle and even bond order. The featured contrast of different chemical bonds can serve as fingerprints for further interpretation of chemical structures toward unknown species synthesized on surfaces. This breakthrough enriches characterization tools for surface science and brings our understanding of on-surface reactions to a new level. Beyond bond imaging, the application of NC-AFM has been extended to quantifying interatomic interactions, identifying three-dimensional nanostructures, manipulating molecules and reactions, as well as determining molecular electronic characteristics. Moreover, some recent efforts address the improvement of the usability and versatility of the bond-resolved NC-AFM technique, including high-resolution molecular investigation on bulk insulators, application-specific tip modification, stable bond imaging above liquid helium temperature and autonomous experimentation implemented by artificial intelligence. (C) 2020 Elsevier B.V. All rights reserved. [Zhong, Qigang; Li, Xuechao; Zhang, Haiming; Chi, Lifeng] Soochow Univ, Inst Funct Nano & Soft Mat FUNSOM, Jiangsu Key Lab Carbon Based Funct Mat & Devices, Suzhou 215123, Peoples R China; [Zhong, Qigang] Justus Liebig Univ, Inst Appl Phys, Heinrich Buff Ring 16, D-35392 Giessen, Germany Soochow University - China; Justus Liebig University Giessen Chi, LF (corresponding author), Soochow Univ, Inst Funct Nano & Soft Mat FUNSOM, Jiangsu Key Lab Carbon Based Funct Mat & Devices, Suzhou 215123, Peoples R China. chilf@suda.edu.cn , Lxchaos/HGD-5519-2022; Zhong, Qigang/ABH-5343-2020 Zhong, Qigang/0000-0001-7100-7363 National Natural Science Foundation of China (NSFC) [21790053, 51821002]; Ministry of Science and Technology of People's Republic of China [2017YFA0205002]; Collaborative Innovation Center of Suzhou Nano Science Technology; 111 Project National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of People's Republic of China(Ministry of Science and Technology, China); Collaborative Innovation Center of Suzhou Nano Science Technology; 111 Project(Ministry of Education, China - 111 Project) We acknowledge the financial support by the National Natural Science Foundation of China (NSFC, Grant Nos. 21790053 and 51821002), the Ministry of Science and Technology of People's Republic of China (2017YFA0205002), the Collaborative Innovation Center of Suzhou Nano Science & Technology and the 111 Project. 255 13 13 20 133 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0167-5729 1879-274X SURF SCI REP Surf. Sci. Rep. NOV 2020.0 75 4 100509 10.1016/j.surfrep.2020.100509 0.0 34 Chemistry, Physical; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics PG5ZV 2023-03-23 WOS:000599814400001 0
J Kong, FC; Zhu, QX; Wang, K; Nieto, JJ Kong, Fanchao; Zhu, Quanxin; Wang, Kai; Nieto, Juan J. Stability analysis of almost periodic solutions of discontinuous BAM neural networks with hybrid time-varying delays and D operator JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS English Article GLOBAL EXPONENTIAL STABILITY; NEUTRAL-TYPE; ASYMPTOTIC STABILITY; ANTIPERIODIC SOLUTIONS; EXISTENCE; LEAKAGE; BEHAVIOR; CONVERGENCE; ACTIVATIONS; DYNAMICS In the paper, we are concerned with a class of discontinuous BAM neural networks with hybrid time-varying delays and D operator. Based on the concept of Filippov solution, by means of the differential inclusions theory and the non-smooth analysis theory with Lyapunov-like approach, some new and novel sufficient conditions are derived to guarantee the existence, uniqueness and global exponential stability of almost-periodic solution of our proposed neural network model. To the authors' knowledge, the results established in the paper are the only available results on the BAM neural networks, connecting the three main characteristics, i.e., discontinuous activation functions, hybrid time-varying delays and D operator. Some previous works in the literature are significantly extend and complement. Finally, two topical simulation examples are given to show the effectiveness of the established main results. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. [Kong, Fanchao] Anhui Normal Univ, Sch Math & Stat, Wuhu 241000, Anhui, Peoples R China; [Kong, Fanchao] Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Shandong, Peoples R China; [Kong, Fanchao; Zhu, Quanxin] Hunan Normal Univ, Coll Math & Stat, Key Lab HPC SIP MOE, Changsha 410081, Hunan, Peoples R China; [Wang, Kai] Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu, Peoples R China; [Nieto, Juan J.] Univ Santiago de Compostela, Inst Matemat, Santiago De Compostela 15782, Spain Anhui Normal University; Qingdao University of Science & Technology; Hunan Normal University; Anhui University of Finance & Economics; Universidade de Santiago de Compostela Kong, FC (corresponding author), Anhui Normal Univ, Sch Math & Stat, Wuhu 241000, Anhui, Peoples R China. fanchaokong88@yahoo.com; zqx22@126.com Kong, Fanchao/AAW-9078-2021; Kong, Fanchao/AFD-8517-2022; Kong, Fanchao/AAE-9432-2022 Kong, Fanchao/0000-0001-8860-4877; Nieto, Juan J./0000-0001-8202-6578 National Natural Science Foundation of China [61773217, 61374080, 71803001]; Key Program of University Natural Science Research Fund of Anhui Province [KJ2018A0437]; Natural Science Foundation of Anhui Province [1708085MA17]; Hunan Provincial Science and Technology Project Foundation [2019RS1033]; Hunan Provincial Education Department [18A013]; Hunan Normal University National Outstanding Youth Cultivation Project [XP1180101]; Construct Program of the Key Discipline in Hunan Province National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Program of University Natural Science Research Fund of Anhui Province; Natural Science Foundation of Anhui Province(Natural Science Foundation of Anhui Province); Hunan Provincial Science and Technology Project Foundation; Hunan Provincial Education Department(Hunan Provincial Education Department); Hunan Normal University National Outstanding Youth Cultivation Project; Construct Program of the Key Discipline in Hunan Province The authors are deeply grateful to the anonymous referees and the editor for their careful reading and correction of some errors, which have greatly improved the quality of the paper. The work was supported by National Natural Science Foundation of China (Nos. 61773217, 61374080, 71803001), Key Program of University Natural Science Research Fund of Anhui Province (KJ2018A0437), Natural Science Foundation of Anhui Province (1708085MA17), Hunan Provincial Science and Technology Project Foundation (2019RS1033), the Scientific Research Fund of Hunan Provincial Education Department (18A013), Hunan Normal University National Outstanding Youth Cultivation Project (XP1180101) and the Construct Program of the Key Discipline in Hunan Province. 67 37 37 2 27 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-0032 1879-2693 J FRANKLIN I J. Frankl. Inst.-Eng. Appl. Math. DEC 2019.0 356 18 SI 11605 11637 10.1016/j.jfranklin.2019.09.030 0.0 33 Automation & Control Systems; Engineering, Multidisciplinary; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics JU0EZ 2023-03-23 WOS:000501354400034 0
J Li, B; de Groot, M; Steketee, RME; Meijboom, R; Smits, M; Vernooij, MW; Ikram, MA; Liu, JR; Niessen, WJ; Bron, EE Li, Bo; de Groot, Marius; Steketee, Rebecca M. E.; Meijboom, Rozanna; Smits, Marion; Vernooij, Meike W.; Ikram, M. Arfan; Liu, Jiren; Niessen, Wiro J.; Bron, Esther E. Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging NEUROIMAGE English Article CNN; Diffusion MRI; White matter tract; Segmentation; Neurodegeneration WHITE-MATTER TRACTS; ALZHEIMERS-DISEASE; FRONTOTEMPORAL DEMENTIA; SPATIAL STATISTICS; FIBER TRACTS; MRI; TRACTOGRAPHY; ATLAS; OPTIMIZATION; ROBUST Subtle changes in white matter (WM) microstructure have been associated with normal aging and neuro-degeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N = 9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, kappa = 0.72-0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: epsilon = 1%-5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N = 58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure ob-tained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice. [Li, Bo; Liu, Jiren] Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Shenyang, Peoples R China; [Li, Bo; de Groot, Marius; Steketee, Rebecca M. E.; Meijboom, Rozanna; Smits, Marion; Vernooij, Meike W.; Ikram, M. Arfan; Niessen, Wiro J.; Bron, Esther E.] Erasmus MC, Dept Radiol & Nucl Med, Rotterdam, Netherlands; [de Groot, Marius; Vernooij, Meike W.; Ikram, M. Arfan] Erasmus MC, Dept Epidemiol, Rotterdam, Netherlands; [Ikram, M. Arfan] Erasmus MC, Dept Neurol, Rotterdam, Netherlands; [Niessen, Wiro J.] Delft Univ Technol, Appl Sci, Imaging Phys, Delft, Netherlands; [Meijboom, Rozanna] Univ Edinburgh, Ctr Clin Brain Sci, Edinburgh, Midlothian, Scotland Northeastern University - China; Erasmus University Rotterdam; Erasmus MC; Erasmus University Rotterdam; Erasmus MC; Erasmus University Rotterdam; Erasmus MC; Delft University of Technology; University of Edinburgh Li, B (corresponding author), Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Shenyang, Peoples R China. b.li@erasmusmc.nl Smits, Marion/E-4795-2011; Ikram, M. Arfan/AAM-5225-2021; Smits, Marion/AAB-3985-2019; Vernooij, Meike Willemijn/E-4061-2016 Smits, Marion/0000-0001-5563-2871; Steketee, Rebecca/0000-0003-2689-5490; Vernooij, Meike/0000-0003-4658-2176 Medical Delta Diagnostics 3.0: Dementia and Stroke; EU Horizon 2020 project EuroPOND [666992]; Netherlands CardioVascular Research Initiative [CVON2012-06, CVON2018-28]; Dutch Heart Foundation [2018B011] Medical Delta Diagnostics 3.0: Dementia and Stroke; EU Horizon 2020 project EuroPOND; Netherlands CardioVascular Research Initiative; Dutch Heart Foundation(Netherlands Heart Foundation) B. Li and W.J. Niessen acknowledge funding from Medical Delta Diagnostics 3.0: Dementia and Stroke. M. de Groot acknowledges funding from the EU Horizon 2020 project EuroPOND (666992). E.E. Bron acknowledges support from the Netherlands CardioVascular Research Initiative (Heart-Brain Connection: CVON2012-06, CVON2018-28) and the Dutch Heart Foundation (PPP Allowance, 2018B011). In addition, M. de Groot has a financial interest in the GSK company. The GSK had no role in this study. 69 14 14 0 6 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage SEP 2020.0 218 116993 10.1016/j.neuroimage.2020.116993 0.0 13 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging MU1UW 32492510.0 Green Submitted, gold, Green Published 2023-03-23 WOS:000555459400008 0
C Hao, QH; Feng, QH; Wei, Y; Sbert, M; Lu, WH; Xu, Q Hong, R; Cheng, WH; Yamasaki, T; Wang, M; Ngo, CW Hao, Qiaohong; Feng, Qinghe; Wei, Ying; Sbert, Mateu; Lu, Wenhuan; Xu, Qing Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval ADVANCES IN MULTIMEDIA INFORMATION PROCESSING, PT I Lecture Notes in Computer Science English Proceedings Paper 19th Pacific-Rim Conference on Multimedia (PCM) SEP 21-22, 2018 Hefei Univ Technol, Hefei, PEOPLES R CHINA Springer,Anhui Assoc Artificial Intelligence,Shandong Artificial Intelligence Inst,Kuaishou Co Ltd,Zhongke Leinao Co Ltd Hefei Univ Technol Local binary pattern; Multi-level color quantizer; Color distribution prior; Pairwise cross pattern; Content-based image retrieval The local binary pattern (LBP) has been widely considered an excellent and extensive feature descriptor, but it is limited to grayscale image processing. Inspired by human visual system, we develop a novel yet simple rotation-invariant color-LBP descriptor-pairwise cross pattern (PCP) to extend LBP to color image processing. In the proposed descriptor, the color information map is firstly extracted using a multilevel color quantizer which is designed based on a color distribution prior in the L* a* b* color space. Then, the color information and LBP maps are paired in parallel to construct a pairwise cross pattern, which is easily extended to the uniform pairwise cross pattern (UPCP) and the rotation-invariant pairwise cross pattern (RIPCP). Finally, compared to numerous state-of-the-art schemes and convolutional neural network (CNN)-based models, the experimental results illustrate that the proposed method is efficient, effective and robust in content-based image retrieval task. [Hao, Qiaohong; Sbert, Mateu; Xu, Qing] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China; [Feng, Qinghe; Wei, Ying] Northeastern Univ, Sch Informat Sci & Engn, Shenyang, Liaoning, Peoples R China; [Lu, Wenhuan] Tianjin Univ, Sch Software, Tianjin, Peoples R China; [Sbert, Mateu] Univ Girona, Inst Informat & Applicat, Girona, Spain Tianjin University; Northeastern University - China; Tianjin University; Universitat de Girona Sbert, M; Xu, Q (corresponding author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China.;Sbert, M (corresponding author), Univ Girona, Inst Informat & Applicat, Girona, Spain. qiaohonghao@gmail.com; fqh136880889@126.com; weiying@ise.neu.edu.cn; mateusbert@mac.com; wenhuan@tju.edu.cn; qingxu@tju.edu.cn National Natural Science Foundation of China [61471261, 61771335, TIN2016-75866-C3-3-R]; Spanish Government National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Government(Spanish Government) This work has been funded by National Natural Science Foundation of China under grants No. 61471261 and No. 61771335, and by grants TIN2016-75866-C3-3-R from Spanish Government. 22 3 3 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-00776-8; 978-3-030-00775-1 LECT NOTES COMPUT SC 2018.0 11164 I 290 300 10.1007/978-3-030-00776-8_27 0.0 11 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN2TA 2023-03-23 WOS:000477700900027 0
J Huang, YJ; Fei, T; Kwan, MP; Kang, YH; Li, J; Li, YZ; Li, X; Bian, M Huang, Yingjing; Fei, Teng; Kwan, Mei-Po; Kang, Yuhao; Li, Jun; Li, Yizhuo; Li, Xiang; Bian, Meng GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human-Environment Relationships ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Review human-environment relationship; collective emotion; GIS-based emotional computing LIFE SATISFACTION; FACIAL EXPRESSIONS; NEGATIVE AFFECT; DEPRESSED MOOD; AIR-POLLUTION; PERCEPTIONS; TWITTER; SCALE; SENSE; CONNECTEDNESS In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human-environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible. [Huang, Yingjing; Fei, Teng; Li, Jun; Li, Yizhuo] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430000, Peoples R China; [Kwan, Mei-Po] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3584 CB Utrecht, Netherlands; [Kang, Yuhao] Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA; [Li, Xiang] Informat Engn Univ, Inst Surveying & Mapping, Zhengzhou 450000, Peoples R China; [Bian, Meng] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430000, Peoples R China Wuhan University; Chinese University of Hong Kong; Chinese University of Hong Kong; Utrecht University; University of Wisconsin System; University of Wisconsin Madison; PLA Information Engineering University; Wuhan University Fei, T (corresponding author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430000, Peoples R China. huangyingjing@whu.edu.cn; feiteng@whu.edu.cn; mpkwan@cuhk.edu.hk; yuhao.kang@wisc.edu; leejun@whu.edu.cn; liyizhuo@whu.edu.cn; lixiangzzchxy@163.com; bian@whu.edu.cn Huang, Yingjing/AAX-1361-2021; Kang, Yuhao/U-2821-2019; Kwan, Mei-Po/S-4162-2016; fei, teng/AAF-5448-2021 Huang, Yingjing/0000-0002-8772-1403; Kang, Yuhao/0000-0003-3810-9450; Kwan, Mei-Po/0000-0001-8602-9258; fei, teng/0000-0002-3415-1654 State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [19E02] State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University This work is supported by Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (Grant No. 19E02). 123 11 14 21 91 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. Geo-Inf. SEP 2020.0 9 9 551 10.3390/ijgi9090551 0.0 15 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Physical Geography; Remote Sensing OD6XN gold 2023-03-23 WOS:000579994300001 0
C Huang, J; Zhou, N; Chen, RQ; Zhang, WD IEEE Huang, Jie; Zhou, Ning; Chen, Riqing; Zhang, Weida H-infinity Formation Control Design for Multiple Euler-Lagrange Agents Subjected to Switching Topologies 2017 11TH ASIAN CONTROL CONFERENCE (ASCC) Asian Control Conference ASCC English Proceedings Paper 11th Asian Control Conference (ASCC) DEC 17-20, 2017 Gold Coast, AUSTRALIA CONTAINMENT CONTROL; TRACKING CONTROL; SYSTEMS; CONSENSUS; LEADER; SYNCHRONIZATION; COORDINATION; NETWORK In this paper, the H-infinity formation control of platooning agents in unknown environments under switching communication condition is studied. Each agent is an Euler-Lagrange system with unknown external disturbance. To achieve the control goal, a compound control strategy is developed. To be specific, neural network-based approximators are developed to estimate the unknown nonlinear term of the transformed agents; In terms of the exogenous disturbances, H-infinity control strategy is employed to guarantee the system states to be robust; meanwhile, artificial potential method produces proper attractive or repulsive powers to aid the agents in maintaining desired distance with obstacles and themselves, respectively. All the tracking error signals are uniformly ultimately bounded (UUB) in the paper. [Huang, Jie; Zhou, Ning] Univ Groningen, Fac Sci & Engn, Groningen, Netherlands; [Zhou, Ning; Chen, Riqing] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou, Fujian, Peoples R China; [Zhang, Weida] Univ Oxford, Dept Engn Sci, Oxford, England University of Groningen; Fujian Agriculture & Forestry University; University of Oxford Huang, J (corresponding author), Univ Groningen, Fac Sci & Engn, Groningen, Netherlands. autohuangjie@gmail.com; zhouning2010@gmail.com; riqing.chen@fafu.edu.cn; weida.zhang@eng.ox.ac.uk China Scholarship Council [201707870005]; National Natural Science Foundation of China [61573062, 61603095, 61603094]; Natural Science Foundation of Fujian Province [2016J01286, 2016J01284]; Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University [xjq201612]; Foundation of Cloud Computing and Big Data for Agriculture and Forestry [117-612014063]; Research Foundation for Outstanding Young Scholars in the University of Fujian Province China Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University; Foundation of Cloud Computing and Big Data for Agriculture and Forestry; Research Foundation for Outstanding Young Scholars in the University of Fujian Province This work is supported in part by the National Natural Science Foundation of China (No. 61603094, 61603095, 61573062), the Foundation of Cloud Computing and Big Data for Agriculture and Forestry (No. 117-612014063), the China Scholarship Council (No. 201707870005), the Natural Science Foundation of Fujian Province (No. 2016J01284, 2016J01286), the Research Foundation for Outstanding Young Scholars in Fujian Agriculture and Forestry University (No. xjq201612), and the Research Foundation for Outstanding Young Scholars in the University of Fujian Province. 23 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2072-5639 978-1-5090-1573-3 ASIA CONTROL CONF AS 2017.0 2382 2386 5 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BJ6UG 2023-03-23 WOS:000426957300415 0
J Guo, SC; Wang, LH; Chen, QJ; Wang, L; Zhang, J; Zhu, YM Guo, Shunchao; Wang, Lihui; Chen, Qijian; Wang, Li; Zhang, Jian; Zhu, Yuemin Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification FRONTIERS IN ONCOLOGY English Article glioma classification; multimodal MRI images; decision fusion; tumor segmentation; deep learning SEGMENTATION; PREDICTION; RADIOMICS; SYSTEM; SELECTION; TUMORS PurposeGlioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images. MethodMRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max-min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc. ResultsThe proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen's Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods. ConclusionCompared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice. [Guo, Shunchao; Wang, Lihui; Chen, Qijian; Wang, Li; Zhang, Jian] Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China; [Guo, Shunchao] Qiannan Normal Univ Natl, Coll Comp & Informat, Duyun, Peoples R China; [Zhu, Yuemin] Univ Lyon, CNRS, UMR 5220, INSERM,U1044,CREATIS,INSA Lyon, Lyon, France Guizhou University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Wang, LH (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China.;Zhu, YM (corresponding author), Univ Lyon, CNRS, UMR 5220, INSERM,U1044,CREATIS,INSA Lyon, Lyon, France. lhwang2@gzu.edu.cn; yue-min.zhu@creatis.insa-lyon.fr National Natural Science Foundation of China [62161004]; Natural Science Foundation of Guizhou Province [[2020]1Y255]; Program PHC-Cai Yuanpei [41400TC]; Guizhou Province Education Department Project [KY[2016]321]; Guizhou Science and Technology Plan Project [ZK[2021]002]; [1Y255]; [Qianjiaohe KY[2016]321]; [Qiankehe ZK[2021]002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guizhou Province; Program PHC-Cai Yuanpei; Guizhou Province Education Department Project; Guizhou Science and Technology Plan Project; ; ; This work was supported in part by the National Natural Science Foundation of China under Grant 62161004, in part by the Natural Science Foundation of Guizhou Province under Grant Qiankehe J No. [2020]1Y255, in part by the Program PHC-Cai Yuanpei 2018 under Grant 41400TC, in part by the Guizhou Province Education Department Project under Grant Qianjiaohe KY[2016]321, and in part by the Guizhou Science and Technology Plan Project under Grant Qiankehe ZK[2021]002. 45 5 5 10 37 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. FEB 24 2022.0 12 819673 10.3389/fonc.2022.819673 0.0 13 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology ZR8IQ 35280828.0 Green Accepted, gold 2023-03-23 WOS:000768021900001 0
J Khellat-Kihel, S; Muhammad, J; Sun, ZA; Tistarelli, M Khellat-Kihel, Souad; Muhammad, Jawad; Sun, Zhenan; Tistarelli, Massimo Gender and ethnicity recognition based on visual attention-driven deep architectures JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Visual-attention; Periocular regions; Gender prediction; Deep learning; Local description FACE RECOGNITION; CLASSIFICATION; FEATURES Most of the time, when people observe, interact or speak to each other, they focus the attention on the ocular parts of the face. This daily life experience has a strong impact on the analysis of periocular facial regions. These facial regions may be exploited in order to identify individuals for several applications, including access control and services such as telebanking and electronic transactions. In this paper we suggest studying the efficiency of the periocular regions on gender and race prediction. Most researchers propose a local texture description based on LBP (Local Binary Pattern) and HoG (Histogram of Oriented Gradients) for the purpose of predicting gender. On the other hand, Deep learning techniques were proposed to predict the gender. However, this requires a huge labeled periocular data for gender which is not available. Also, the expressivity of gender and race can be decreased on the final representation of the Deep architectures comparing to the earlier stages. To overcome these points and for the aim of predicting gender and race, considering also the high impact of DCNNs (Deep Convolutional Neural Networks) techniques to solve several aspects in biometrics, we suggest a Deep architecture based on visual attention on the periocular part. The visual saliency extraction is based on primary layers' activation by analyzing the feature-maps. We study how the visual attention-based features coupled to Deep Neural Networks can be used to discriminate between gender and race, hence extract a significant feature from periocular regions. Different pretrained architectures such as Alexnet and ResNet-50 were considered to extract visual saliency points or interest points. Several experiments were performed on periocular regions and a comparative study was conducted. The present results not only demonstrate the feasibility but also the robustness of the extracted interest points. [Khellat-Kihel, Souad] Univ Djillali Liabes Sidi Bel Abbes, Comp Sci Dept, EEDIS Lab, Sidi Bel Abbes, Algeria; [Tistarelli, Massimo] Univ Sassari, Comp Vis Lab, Viale Italia 39, I-07100 Sassari, Italy; [Muhammad, Jawad; Sun, Zhenan] Chinese Acad Sci, Inst Automat, N95 ZhongGuanCun East St, Beijing 100190, Peoples R China University Djillali Liabes Sidi Bel Abbes; University of Sassari; Chinese Academy of Sciences; Institute of Automation, CAS Khellat-Kihel, S (corresponding author), Univ Djillali Liabes Sidi Bel Abbes, Comp Sci Dept, EEDIS Lab, Sidi Bel Abbes, Algeria. souad.khellat-kihel@univ-sba.dz European Commission; Italian Ministry of Research [690907] European Commission(European CommissionEuropean Commission Joint Research Centre); Italian Ministry of Research(Ministry of Education, Universities and Research (MIUR)) This research work has been partially supported by a grant from the European Commission (H2020 MSCA RISE 690907 IDENTITY) and by a grant of the Italian Ministry of Research (PRIN 2015) . 37 1 1 2 2 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. OCT 2022.0 88 103627 10.1016/j.jvcir.2022.103627 0.0 8 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4W9VR 2023-03-23 WOS:000860502800008 0
J Zhang, HW; Avrithis, Y; Furon, T; Amsaleg, L Zhang, Hanwei; Avrithis, Yannis; Furon, Teddy; Amsaleg, Laurent Walking on the Edge: Fast, Low-Distortion Adversarial Examples IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Distortion; Manifolds; Training; Perturbation methods; Distortion measurement; Quantization (signal); Optimization; Adversarial attack; deep learning Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evaluation. In this work, we argue that speed is important as well, especially when considering that fast attacks are required by adversarial training. Given more time, iterative methods can always find better solutions. We investigate this speed-distortion trade-off in some depth and introduce a new attack called boundary projection (BP) that improves upon existing methods by a large margin. Our key idea is that the classification boundary is a manifold in the image space: we therefore quickly reach the boundary and then optimize distortion on this manifold. [Zhang, Hanwei; Avrithis, Yannis; Furon, Teddy; Amsaleg, Laurent] Univ Rennes 1, IRISA, CNRS, INRIA, F-35042 Rennes, France; [Zhang, Hanwei] East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200062, Peoples R China Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Rennes; East China Normal University Furon, T (corresponding author), Univ Rennes 1, IRISA, CNRS, INRIA, F-35042 Rennes, France. teddy.furon@inria.fr Furon, Teddy/ABG-2584-2020 Furon, Teddy/0000-0002-1565-765X; Avrithis, Yannis/0000-0001-7476-4482 ANR-AID Chaire SAIDA ANR-AID Chaire SAIDA The work of Teddy Furon was supported by the ANR-AID Chaire SAIDA. 46 8 9 3 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2021.0 16 701 713 10.1109/TIFS.2020.3021899 0.0 13 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering NY2ZO Green Submitted 2023-03-23 WOS:000576264500001 0
J Qiao, HY; Tang, CX; Schoepf, UJ; Bayer, RR; Tesche, C; Jiang, MD; Yin, CQ; Zhou, CS; Zhou, F; Lu, MJ; Jiang, JW; Lu, GM; Ni, QQ; Zhang, LJ Qiao, Hong Yan; Tang, Chun Xiang; Schoepf, U. Joseph; Bayer, Richard R.; Tesche, Christian; Jiang, Meng Di; Yin, Chang Qing; Zhou, Chang Sheng; Zhou, Fan; Lu, Meng Jie; Jiang, Jian Wei; Lu, Guang Ming; Ni, Qian Qian; Zhang, Long Jiang One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study EUROPEAN RADIOLOGY English Article Coronary artery disease; Computed tomography angiography; Invasive coronary angiography; Fractional flow reserve FRACTIONAL FLOW RESERVE; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; NORTH-AMERICAN SOCIETY; CT ANGIOGRAPHY; GUIDED PCI; MULTICENTER; PERFORMANCE; MANAGEMENT; COMMITTEE; COLLEGE Objectives To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. Methods In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. Results In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). Conclusion In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. [Qiao, Hong Yan; Tang, Chun Xiang; Jiang, Meng Di; Yin, Chang Qing; Zhou, Chang Sheng; Zhou, Fan; Lu, Meng Jie; Lu, Guang Ming; Ni, Qian Qian; Zhang, Long Jiang] Nanjing Univ, Jinling Hosp, Dept Diagnost Radiol, Med Sch, Nanjing 210002, Jiangsu, Peoples R China; [Qiao, Hong Yan; Jiang, Jian Wei] Jiangnan Univ, Dept Med Imaging, Affiliated Hosp, Wuxi 214041, Jiangsu, Peoples R China; [Schoepf, U. Joseph; Bayer, Richard R.; Tesche, Christian] Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Ashley River Tower,MSC 226,25 Courtenay Dr, Charleston, SC 29425 USA; [Tesche, Christian] Ludwig Maximilians Univ Munchen, Munich Univ Clin, Dept Cardiol, Munich, Germany; [Tesche, Christian] St Johannes Hosp, Dept Internal Med, Dortmund, Germany Nanjing University; Jiangnan University; Medical University of South Carolina; University of Munich; St. Johannes Hospital Lu, GM; Zhang, LJ (corresponding author), Nanjing Univ, Jinling Hosp, Dept Diagnost Radiol, Med Sch, Nanjing 210002, Jiangsu, Peoples R China. cjr.luguangming@vip.163.com; kevinzhlj@163.com Lu, Meng/GQZ-7036-2022 National Key Research and Development Program of China [2017YFC0113400] National Key Research and Development Program of China This study was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.). 36 2 3 4 6 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. AUG 2022.0 32 8 5179 5188 10.1007/s00330-022-08604-x 0.0 FEB 2022 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 2W6CK 35175380.0 2023-03-23 WOS:000757369300001 0
J Tong, Z; Yuan, DD; Gao, J; Wang, ZJ Tong, Zheng; Yuan, Dongdong; Gao, Jie; Wang, Zhenjun Pavement defect detection with fully convolutional network and an uncertainty framework COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING English Article CRACK DETECTION; REJECTION DECISIONS; NEURAL-NETWORKS; RECOGNITION; CLASSIFICATION; IDENTIFICATION; MODEL; CNN Image segmentation has been implemented for pavement defect detection, from which types, locations, and geometric information can be obtained. In this study, an integration of a fully convolutional network with a Gaussian-conditional random field (G-CRF), an uncertainty framework, and probability-based rejection is proposed for detecting pavement defects. First, a fully convolutional network is designed to generate preliminary segmentation results, and a G-CRF is used to refine the segmentation. Second, epistemic and aleatory uncertainties in the model and database are considered to overcome the disadvantages of traditional deep-learning methods. Last, probability-based rejection is conducted to remove unreasonable segmentations. The proposed method is evaluated on a data set of images that were obtained from 16 highways. The proposed integration segments pavement distresses from digital images with desirable performance. It also provides a satisfactory means to improve the accuracy and generalization performance of pavement defect detection without introducing a delay into the segmentation process. [Tong, Zheng] Univ Technol Compiegne, Sorbonne Univ, CNRS, Compiegne, France; [Yuan, Dongdong] Changan Univ, Sch Highway, Xian, Peoples R China; [Gao, Jie] East China Jiaotong Univ, Sch Transportat & Logist, Nanchang, Jiangxi, Peoples R China; [Wang, Zhenjun] Changan Univ, Sch Mat Sci & Engn, Xian 710061, Peoples R China Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; UDICE-French Research Universities; Sorbonne Universite; Chang'an University; East China Jiaotong University; Chang'an University Wang, ZJ (corresponding author), Changan Univ, Sch Mat Sci & Engn, Xian 710061, Peoples R China. zjwang@chd.edu.cn Gao, Jie/AAM-9632-2020 Tong, Zheng/0000-0001-6894-3521; Gao, Jie/0000-0002-2296-907X National Natural Science Foundation of China [51978067]; Science andTechnology Development Project of Xinjiang Production and Construction Corps [2019AB013]; KeyResearch andDevelopment Programof Shaanxi Province of China [2019GY-174]; China Scholarship Council [CSC201801810108] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science andTechnology Development Project of Xinjiang Production and Construction Corps; KeyResearch andDevelopment Programof Shaanxi Province of China; China Scholarship Council(China Scholarship Council) National Natural Science Foundation of China, Grant/AwardNumber: 51978067; Science andTechnology Development Project of Xinjiang Production and Construction Corps, Grant/AwardNumber: 2019AB013; KeyResearch andDevelopment Programof Shaanxi Province of China, Grant/AwardNumber: 2019GY-174; China Scholarship Council, Grant/Award Number: CSC201801810108 67 34 34 13 107 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1093-9687 1467-8667 COMPUT-AIDED CIV INF Comput.-Aided Civil Infrastruct. Eng. AUG 2020.0 35 8 832 849 10.1111/mice.12533 0.0 JAN 2020 18 Computer Science, Interdisciplinary Applications; Construction & Building Technology; Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Construction & Building Technology; Engineering; Transportation MH4SB 2023-03-23 WOS:000509101700001 0
J Quan, D; Wang, S; Li, Y; Yang, BW; Huyan, N; Chanussot, J; Hou, BA; Jiao, LC Quan, Dou; Wang, Shuang; Li, Yi; Yang, Bowu; Huyan, Ning; Chanussot, Jocelyn; Hou, Biao; Jiao, Licheng Multi-Relation Attention Network for Image Patch Matching IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Feature extraction; Measurement; Training; Deep learning; Correlation; Task analysis; Lighting; Image matching; feature relations; attention learning Deep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features. Their performances will degenerate due to the complex relation between matching patches caused by various imagery changes. To tackle this challenge, we propose a multi-relation attention learning network (MRAN) for image patch matching. Specifically, we propose to fuse multiple feature relations (MR) for matching, which can benefit from the complementary advantages between different feature relations and achieve significant improvements on matching tasks. Furthermore, we propose a relation attention learning module to learn the fused relation adaptively. With this module, meaningful feature relations are emphasized and the others are suppressed. Extensive experiments show that our MRAN achieves best matching performances, and has good generalization on multi-modal image patch matching, multi-modal remote sensing image patch matching and image retrieval tasks. [Quan, Dou; Wang, Shuang; Yang, Bowu; Huyan, Ning; Hou, Biao; Jiao, Licheng] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China; [Li, Yi] Xidian Univ, Sch Telecommun, Xian 710071, Peoples R China; [Chanussot, Jocelyn] Grenoble Inst Technol Grenoble INP, Grenoble Images Speech Signals & Automat Lab, F-38031 Grenoble, France; [Chanussot, Jocelyn] Res Ctr Inria Grenoble Rhone Alpes, F-38330 Montbonnot St Martin, France Xidian University; Xidian University; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble Wang, S (corresponding author), Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China. dquan@stu.xidian.edu.cn; shwang@mail.xidian.edu.cn Huyan, Ning/AAM-1882-2020 Huyan, Ning/0000-0002-6123-8659; Quan, Dou/0000-0001-6943-4657; Chanussot, Jocelyn/0000-0003-4817-2875; Li, Yi/0000-0003-4226-6635 National Natural Science Foundation of China [61771379, 61671350]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61771379 and Grant 61671350 and in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005. 57 4 4 2 39 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2021.0 30 7127 7142 10.1109/TIP.2021.3101414 0.0 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering TZ7ZM 34351861.0 2023-03-23 WOS:000684690500002 0
J Li, YJ; Steinicke, F; Wang, M Li, Yi-Jun; Steinicke, Frank; Wang, Miao A Comprehensive Review of Redirected Walking Techniques: Taxonomy, Methods, and Future Directions JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY English Article virtual reality; locomotion; redirected walking VIRTUAL ENVIRONMENTS; NAVIGATION; ROTATION; SCALE; GAINS Virtual reality (VR) allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual objects. However, the limited physical tracking space usually restricts the exploration of large virtual spaces, and VR users have to use special locomotion techniques to move from one location to another. Among these techniques, redirected walking (RDW) is one of the most natural locomotion techniques to solve the problem based on near-natural walking experiences. The core idea of the RDW technique is to imperceptibly guide users on virtual paths, which might vary from the paths they physically walk in the real world. In a similar way, some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking space. In this survey, we first present a taxonomy of existing RDW work. Based on this taxonomy, we compare and analyze both contributions and shortcomings of the existing methods in detail, and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries, while virtual environment manipulation methods can provide users with consistent movement but have limited application scenarios. Finally, we discuss possible future research directions, indicating combining artificial intelligence with this area will be effective and intriguing. [Li, Yi-Jun; Wang, Miao] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China; [Steinicke, Frank] Univ Hamburg, Dept Informat, D-20146 Hamburg, Germany; [Wang, Miao] Peng Cheng Lab, Shenzhen 518055, Peoples R China Beihang University; University of Hamburg; Peng Cheng Laboratory Wang, M (corresponding author), Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China.;Wang, M (corresponding author), Peng Cheng Lab, Shenzhen 518055, Peoples R China. yaoling@buaa.edu.cn; frank.steinicke@uni-hamburg.de; miaow@buaa.edu.cn Steinicke, Frank/AAC-2976-2020 Steinicke, Frank/0000-0001-9879-7414 National Natural Science Foundation of China [61902012, 61932003]; BMBF; BMWi; DFG; EU National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); BMBF(Federal Ministry of Education & Research (BMBF)); BMWi(Federal Ministry for Economic Affairs and Energy (BMWi)); DFG(German Research Foundation (DFG)); EU(European Commission) This paper was supported by the National Natural Science Foundation of China under Grant Nos. 61902012 and 61932003. Frank Steinicke was supported by funds from the BMBF, BMWi, DFG, and EU. 124 0 0 11 12 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1000-9000 1860-4749 J COMPUT SCI TECH-CH J. Comput. Sci. Technol. JUN 2022.0 37 3 SI 561 583 10.1007/s11390-022-2266-7 0.0 23 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2E9DE 2023-03-23 WOS:000812520400006 0
J Jonas, RA; Barkovich, E; Choi, AD; Griffin, WF; Riess, J; Marques, H; Chang, HJ; Choi, JH; Doh, JH; Her, AY; Koo, BK; Nam, CW; Park, HB; Shin, SH; Cole, J; Gimelli, A; Khan, MA; Lu, B; Gao, Y; Nabi, F; Nakazato, R; Schoepf, UJ; Driessen, RS; Bom, MJ; Thompson, RC; Jang, JJ; Ridner, M; Rowan, C; Avelar, E; Genereux, P; Knaapen, PH; de Waard, GA; Pontone, G; Andreini, D; Guglielmo, M; Al-Mallah, MH; Jennings, RS; Crabtree, TR; Earls, JP Jonas, Rebecca A.; Barkovich, Emil; Choi, Andrew D.; Griffin, William F.; Riess, Joanna; Marques, Hugo; Chang, Hyuk-Jae; Choi, Jung Hyun; Doh, Joon-Hyung; Her, Ae-Young; Koo, Bon-Kwon; Nam, Chang-Wook; Park, Hyung-Bok; Shin, Sang-Hoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lu, Bin; Gao, Yang; Nabi, Faisal; Nakazato, Ryo; Schoepf, U. Joseph; Driessen, Roel S.; Bom, Michiel J.; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Genereux, Philippe; Knaapen, Paul H.; de Waard, Guus A.; Pontone, Gianluca; Andreini, Daniele; Guglielmo, Marco; Al-Mallah, Mouaz H.; Jennings, Robert S.; Crabtree, Tami R.; Earls, James P. The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography CLINICAL IMAGING English Article Coronary computed tomography angiography; CCTA; Coronary artery disease; Atherosclerosis; Artificial intelligence; Image quality COMPUTED TOMOGRAPHIC ANGIOGRAPHY; IMAGE QUALITY; HEART-RATE; ITERATIVE RECONSTRUCTION; CONTRAST MATERIAL; PROGNOSTIC VALUE; PREDICTION; PHASE Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with > 50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 +/- 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of > 50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated > 50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had < 50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 & PLUSMN; 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for > 50% stenosis based on contrast type (isoosmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (< 350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, > 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables.Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters. [Jonas, Rebecca A.] Thomas Jefferson Univ, Med Ctr, Dept Internal Med, Philadelphia, PA 19107 USA; [Barkovich, Emil; Choi, Andrew D.; Griffin, William F.; Riess, Joanna; Andreini, Daniele; Earls, James P.] George Washington Univ, Sch Med, Dept Radiol, Washington, DC 20052 USA; [Barkovich, Emil; Choi, Andrew D.; Griffin, William F.; Riess, Joanna; Andreini, Daniele; Earls, James P.] George Washington Univ, Sch Med, Div Cardiol, Washington, DC 20052 USA; [Marques, Hugo] Nova Med Sch, Fac Ciencias Med, Lisbon, Portugal; [Chang, Hyuk-Jae] Yonsei Univ Hlth Syst, Yonsei Univ Coll Med, Div Cardiol, Severance Cardiovasc Hosp, Seoul, South Korea; [Chang, Hyuk-Jae] Yonsei Univ Hlth Syst, Yonsei Univ Coll Med, Severance Biomed Sci Inst, Seoul, South Korea; [Choi, Jung Hyun] Ontact Hlth Inc, Seoul, South Korea; [Doh, Joon-Hyung] Inje Univ, Div Cardiol, Ilsan Paik Hosp, Gimhae, South Korea; [Her, Ae-Young] Kang Won Natl Univ Hosp, Chunchon, South Korea; [Koo, Bon-Kwon] Seoul Natl Univ Hosp, Dept Internal Med, Seoul, South Korea; [Nam, Chang-Wook] Keimyung Univ Dongsan Hosp, Cardiovasc Ctr, Daegu, South Korea; [Park, Hyung-Bok] Catholic Kwandong Univ, Int St Marys Hosp, Dept Internal Med, Div Cardiol,Coll Med, Incheon, South Korea; [Shin, Sang-Hoon] Ilsan Hosp, Natl Hlth Insurance Serv, Goyang, South Korea; [Cole, Jason] Mobile Cardiol Associates, Mobile, AL USA; [Cole, Jason; Gimelli, Alessia] Fdn Toscana Gabriele Monasterio, Dept Imaging, Pisa, Italy; [Gimelli, Alessia; Khan, Muhammad Akram] Cardiac Ctr Texas, Mckinney, TX USA; [Gimelli, Alessia; Lu, Bin] Fuwai Hosp, State Key Lab Cardiovasc Dis, Beijing, Peoples R China; [Lu, Bin; Knaapen, Paul H.] Houston Methodist Hosp, Houston, TX 77030 USA; [Gao, Yang; Nakazato, Ryo] St Lukes Int Hosp, Cardiovasc Ctr, Tokyo, Japan; [Schoepf, U. Joseph] Med Univ South Carolina, Charleston, SC 29425 USA; [Bom, Michiel J.; Ridner, Michael; Knaapen, Paul H.; de Waard, Guus A.] Vrije Univ Amsterdam Med Ctr, Amsterdam Univ Med Ctr, Amsterdam, Netherlands; [Nakazato, Ryo; Thompson, Randall C.] St Lukes Mid Amer Heart Inst, Kansas City, MO USA; [Schoepf, U. Joseph; Jang, James J.] Kaiser Permanente San Jose Med Ctr, San Jose, CA USA; [Driessen, Roel S.; Ridner, Michael] Heart Ctr Res LLC, Huntsville, AL USA; [Bom, Michiel J.; Rowan, Chris] Renown Heart & Vasc Inst, Reno, NV USA; [Thompson, Randall C.; Avelar, Erick] St Marys Hosp, Oconee Heart & Vasc Ctr, Athens, GA USA; [Jang, James J.; Genereux, Philippe] Morristown Med Ctr, Gagnon Cardiovasc Inst, Morristown, NJ USA; [Rowan, Chris; Avelar, Erick; Genereux, Philippe; Pontone, Gianluca; Andreini, Daniele; Guglielmo, Marco] IRCCS, Ctr Cardiol Monzino, Milan, Italy; [de Waard, Guus A.; Pontone, Gianluca; Andreini, Daniele] Cleerly Inc, New York, NY 10006 USA Jefferson University; George Washington University; George Washington University; Universidade Nova de Lisboa; Yonsei University; Yonsei University Health System; Yonsei University; Yonsei University Health System; Inje University; Seoul National University (SNU); Seoul National University Hospital; Keimyung University; Catholic Kwandong University; National Health Insurance Service; NHIS Ilsan Hospital; Chinese Academy of Medical Sciences - Peking Union Medical College; Fu Wai Hospital - CAMS; The Methodist Hospital System; The Methodist Hospital - Houston; St. Luke's International Hospital; Medical University of South Carolina; Vrije Universiteit Amsterdam; VU UNIVERSITY MEDICAL CENTER; Saint Luke's Mid America Heart Institute; Kaiser Permanente; Atlantic Health System; IRCCS Centro Cardiologico Monzino Earls, JP (corresponding author), George Washington Univ, Sch Med, Washington, DC 20052 USA. jearls@mfa.gwu.edu lu, bin/HPE-4790-2023 Pinto Marques, Hugo/0000-0003-3540-0488; Chang, Hyuk-Jae/0000-0002-6139-7545; Al-Mallah, Mouaz/0000-0003-2348-0484; Guglielmo, Marco/0000-0003-1718-9949; Marques, Hugo/0000-0001-8210-2075 34 1 1 2 5 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0899-7071 1873-4499 CLIN IMAG Clin. Imaging APR 2022.0 84 149 158 10.1016/j.clinimag.2022.01.016 0.0 FEB 2022 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 0P5XL 35217284.0 Green Published 2023-03-23 WOS:000784301000008 0
J Chen, H; Wang, YG; Lucas, RM Chen, Hao; Wang, Yugang; Martinez Lucas, Raquel Optimizing model and algorithm for railway freight loading problem OPEN PHYSICS English Article Railway transportation; freight loading problem; genetic annealing; layout optimal degree; Optimizing model PACKING This paper constructed an optimizing model on multiple vehicles and multiple goods, which is used in a study on the loading problem for railway freight. We take the maximum utilization coefficient of car loading capacity, volume capacity and layout optimal degree as objective functions. Several major factors in railway transportation have been cited as constraints: center of gravity, non-overlapping freight, transportation circumscription, car marked loading capacity and volume capacity. In order to increase the car utilization coefficient and obtain flat layers in the loading scheme, an improved genetic annealing algorithm is proposed to solve this optimizing model. The proposed algorithm can efficiently obtain a satisfactory loading scheme in railway transportation. At the end, a numerical example shows that the proposed model and algorithm are better than First Fit Algorithm and Neural Network Algorithm in goods loading capacity and volume capacity, the new loading schemes are more flat on each layer. [Wang, Yugang] Henan Polytech Univ, Sch Energy Sci & Engn, Jiaozuo, Peoples R China; [Chen, Hao] Xihua Univ, Sch Transportat & Automot Engn, Chengdu, Sichuan, Peoples R China; [Martinez Lucas, Raquel] Univ Castilla La Mancha, Dept Math, Campus Cuenca, Cuenca 16071, Castilla La Man, Spain Henan Polytechnic University; Xihua University; Universidad de Castilla-La Mancha Wang, YG (corresponding author), Henan Polytech Univ, Sch Energy Sci & Engn, Jiaozuo, Peoples R China. gvlkgpo@163.com; 1220170055@mail.xhu.edu.cn; raquel.martinez@uclm.es LUCAS, RAQUEL MARTINEZ/K-1826-2017 LUCAS, RAQUEL MARTINEZ/0000-0003-1339-8276 Science and Technology Project of China Ministry of Railways [2013X014-E, 2012S14071, 2009X012-I]; Fundamental Research Funds for the Central Universities [SWJTU09BR135] Science and Technology Project of China Ministry of Railways; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The research described in this paper was jointly supported by the Science and Technology Project of China Ministry of Railways (grant 2013X014-E, 2012S14071 and 2009X012-I) and the Fundamental Research Funds for the Central Universities (grant SWJTU09BR135). These supports are gratefully acknowledged. 18 0 0 1 8 SCIENDO WARSAW DE GRUYTER POLAND SP Z O O, BOGUMILA ZUGA 32A STR, 01-811 WARSAW, POLAND 2391-5471 OPEN PHYS Open Phys. JAN 2019.0 17 1 135 143 10.1515/phys-2019-0014 0.0 9 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics HY7RV gold 2023-03-23 WOS:000468334700006 0
C Zou, WB; Komodakis, N IEEE Zou, Wenbin; Komodakis, Nikos HARF: Hierarchy-associated Rich Features for Salient Object Detection 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) IEEE International Conference on Computer Vision English Proceedings Paper IEEE International Conference on Computer Vision DEC 11-18, 2015 Santiago, CHILE Amazon,Microsoft,Sansatime,Baidu,Intel,Facebook,Adobe,Panasonic,360,Google,Omron,Blippar,iRobot,Hiscene,nVidia,Mvrec,Viscovery,AiCure ATTENTION The state-of-the-art salient object detection models are able to perform well for relatively simple scenes, yet for more complex ones, they still have difficulties in highlighting salient objects completely from background, largely due to the lack of sufficiently robust features for saliency prediction. To address such an issue, this paper proposes a novel hierarchy-associated feature construction framework for salient object detection, which is based on integrating elementary features from multi-level regions in a hierarchy. Furthermore, multi-layered deep learning features are introduced and incorporated as elementary features into this framework through a compact integration scheme. This leads to a rich feature representation, which is able to represent the context of the whole object/background and is much more discriminative as well as robust for salient object detection. Extensive experiments on the most widely used and challenging benchmark datasets demonstrate that the proposed approach substantially outperforms the state-of-the-art on salient object detection. [Zou, Wenbin] Shenzhen Univ, Coll Informat Engn, Shenzhen Key Lab Adv Telecommun & Informat Proc, Shenzhen, Peoples R China; [Komodakis, Nikos] Univ Paris Est, Ecole Ponts ParisTech, Champs Sur Marne, France Shenzhen University; Ecole des Ponts ParisTech; Universite Gustave-Eiffel Zou, WB (corresponding author), Shenzhen Univ, Coll Informat Engn, Shenzhen Key Lab Adv Telecommun & Informat Proc, Shenzhen, Peoples R China. zouszu@sina.com; nikos.komodakis@enpc.fr 43 33 35 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-5499 978-1-4673-8390-5 IEEE I CONF COMP VIS 2015.0 406 414 10.1109/ICCV.2015.54 0.0 9 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BF1NZ 2023-03-23 WOS:000380414100046 0
J Jin, YC; Wang, HD; Chugh, T; Guo, D; Miettinen, K Jin, Yaochu; Wang, Handing; Chugh, Tinkle; Guo, Dan; Miettinen, Kaisa Data-Driven Evolutionary Optimization: An Overview and Case Studies IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION English Article Data science; data-driven optimization; evolutionary algorithms (EAs); machine learning; model management; surrogate MULTIOBJECTIVE OPTIMIZATION; SURROGATE-MODEL; FITNESS APPROXIMATION; EXPECTED IMPROVEMENT; GENETIC ALGORITHM; NEURAL-NETWORKS; TRAUMA SYSTEM; DESIGN; CONVERGENCE; FEASIBILITY Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems. [Jin, Yaochu; Wang, Handing] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England; [Jin, Yaochu] Taiyuan Univ Sci & Technol, Dept Comp Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China; [Jin, Yaochu; Guo, Dan] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China; [Wang, Handing] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Shaanxi, Peoples R China; [Chugh, Tinkle] Univ Exeter, Dept Comp Sci, Exeter EX4 4QD, Devon, England; [Miettinen, Kaisa] Univ Jyvaskyla, Fac Informat Technol, FI-40014 Jyvaskyla, Finland University of Surrey; Taiyuan University of Science & Technology; Northeastern University - China; Xidian University; University of Exeter; University of Jyvaskyla Jin, YC (corresponding author), Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England.;Jin, YC (corresponding author), Taiyuan Univ Sci & Technol, Dept Comp Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China.;Jin, YC (corresponding author), Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China. yaochu.jin@surrey.ac.uk; hdwang@xidian.edu.cn; t.chugh@exeter.ac.uk; guodan717@163.com; kaisa.miettinen@jyu.fi Jin, Yaochu/GRY-7004-2022; Chugh, Tinkle/P-3788-2017; Jin, Yaochu/B-3776-2012 Jin, Yaochu/0000-0003-1100-0631; Chugh, Tinkle/0000-0001-5123-8148; Jin, Yaochu/0000-0003-1100-0631; Wang, Handing/0000-0002-4805-3780; Miettinen, Kaisa/0000-0003-1013-4689 EPSRC [EP/M017869/1]; National Natural Science Foundation of China [61590922, 61876123]; Finland Distinguished Professor Project DeCoMo at the University of Jyvaskyla - Finnish Funding Agency for Innovation (Tekes); Natural Environment Research Council [NE/P017436/1]; EPSRC [EP/M017869/1] Funding Source: UKRI; NERC [NE/P017436/1] Funding Source: UKRI EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Finland Distinguished Professor Project DeCoMo at the University of Jyvaskyla - Finnish Funding Agency for Innovation (Tekes); Natural Environment Research Council(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); NERC(UK Research & Innovation (UKRI)Natural Environment Research Council (NERC)) This work was supported in part by the EPSRC under Grant EP/M017869/1, in part by the National Natural Science Foundation of China under Grant 61590922 and Grant 61876123, in part by a Finland Distinguished Professor Project DeCoMo at the University of Jyvaskyla funded by Finnish Funding Agency for Innovation (Tekes), and in part by the Natural Environment Research Council under Grant NE/P017436/1. The work is related to the thematic research area on Decision Analytics (DEMO) at the University of Jyvaskyla. 119 209 221 43 205 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1089-778X 1941-0026 IEEE T EVOLUT COMPUT IEEE Trans. Evol. Comput. JUN 2019.0 23 3 442 458 10.1109/TEVC.2018.2869001 0.0 17 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science IB1IJ Green Accepted, Green Submitted 2023-03-23 WOS:000470018600006 0
J Wang, CS; Grau, A; Guerra, E; Shen, ZG; Hu, JX; Fan, HY Wang, Chuansheng; Grau, Antoni; Guerra, Edmundo; Shen, Zhiguo; Hu, Jinxing; Fan, Haoyi Semi-supervised wildfire smoke detection based on smoke-aware consistency FRONTIERS IN PLANT SCIENCE English Article wildfire smoke detection; semi-supervised learning; smoke-aware consistency; triple classification assistance; smoke detection network CONVOLUTIONAL NEURAL-NETWORK; FIRE-DETECTION; INTERNET The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions. [Wang, Chuansheng; Grau, Antoni; Guerra, Edmundo] Univ Politecn Cataluna, Dept Automatic Control Tech, Barcelona, Spain; [Shen, Zhiguo] Henan Acad Forestry, Zhengzhou, Henan, Peoples R China; [Hu, Jinxing] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China; [Fan, Haoyi] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou, Peoples R China Universitat Politecnica de Catalunya; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Zhengzhou University Grau, A (corresponding author), Univ Politecn Cataluna, Dept Automatic Control Tech, Barcelona, Spain.;Fan, HY (corresponding author), Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou, Peoples R China. antoni.grau@upc.edu; fanhaoyi@zzu.edu.cn Wang, Chuansheng/0000-0003-4340-7831 MINECO national project; [PID2019-106702RB-C21/AEI/10.13039/501100011033] MINECO national project; Funding This research has been funded by the MINECO national project with reference (PID2019-106702RB-C21/AEI/10.13039/501100011033). 63 0 0 8 8 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-462X FRONT PLANT SCI Front. Plant Sci. NOV 8 2022.0 13 980425 10.3389/fpls.2022.980425 0.0 17 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences 6N8GZ 36426142.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000889790600001 0
J Ahmad, M; Kaminski, P; Olczak, P; Alam, M; Iqbal, MJ; Ahmad, F; Sasui, S; Khan, BJ Ahmad, Mahmood; Kaminski, Pawel; Olczak, Piotr; Alam, Muhammad; Iqbal, Muhammad Junaid; Ahmad, Feezan; Sasui, Sasui; Khan, Beenish Jehan Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques APPLIED SCIENCES-BASEL English Article AdaBoost; support vector machine; k-nearest neighbor; random forest; rockfill materials; shear strength CLASSIFIER; ENSEMBLE; TREE Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms-support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R-2), Nash-Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R-2 together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R-2 = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R-2 = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R-2 = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R-2 = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM. [Ahmad, Mahmood; Iqbal, Muhammad Junaid] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Bannu 28100, Pakistan; [Kaminski, Pawel] AGH Univ Sci & Technol, Fac Min & Geoengn, Mickiewicza 30 Av, PL-30059 Krakow, Poland; [Olczak, Piotr] Polish Acad Sci, Mineral & Energy Econ Res Inst, 7A Wybickiego St, PL-31261 Krakow, Poland; [Alam, Muhammad] Univ Engn & Technol, Dept Civil Engn, Mardan 23200, Pakistan; [Ahmad, Feezan] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116024, Peoples R China; [Sasui, Sasui] Chungnam Natl Univ, Dept Architectural Engn, Daejeon 34134, South Korea; [Khan, Beenish Jehan] CECOS Univ IT & Emerging Sci, Dept Civil Engn, Peshawar 25000, Pakistan AGH University of Science & Technology; Polish Academy of Sciences; Mineral & Energy Economy Research Institute of the Polish Academy of Sciences; Dalian University of Technology; Chungnam National University Kaminski, P (corresponding author), AGH Univ Sci & Technol, Fac Min & Geoengn, Mickiewicza 30 Av, PL-30059 Krakow, Poland. ahmadm@uetpeshawar.edu.pk; pkamin@agh.edu.pl; olczak@min-pan.krakow.pl; emalam82@gmail.com; engrjunaidiqbal@uetpeshawar.edu.pk; ahmadf@mail.dlut.edu.cn; sassuikhuwaja126@gmail.com; beenish@cecos.edu.pk Olczak, Piotr/AAF-4891-2020; Kamiński, Paweł/T-7756-2017; Ahmad, Mahmood/HHD-1034-2022 Olczak, Piotr/0000-0002-4926-0845; Kamiński, Paweł/0000-0002-1450-5881; Ahmad, Mahmood/0000-0002-1139-1831; , Sasui/0000-0002-0479-0551; Iqbal, Muhammad Junaid/0000-0001-6939-0180 63 18 18 6 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JUL 2021.0 11 13 6167 10.3390/app11136167 0.0 22 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics TF4MN gold 2023-03-23 WOS:000670693100001 0
J Liu, MX; Hu, S; Ge, Y; Heuvelink, GBM; Ren, ZP; Huang, XR Liu, Mengxiao; Hu, Shan; Ge, Yong; Heuvelink, Gerard B. M.; Ren, Zhoupeng; Huang, Xiaoran Using multiple linear regression and random forests to identify spatial poverty determinants in rural China SPATIAL STATISTICS English Article Poverty; Spatial; Determinants; LMG; Random forest; Variable importance RELATIVE IMPORTANCE; VARIABLE IMPORTANCE; ALLEVIATION; IDENTIFICATION; PREDICTORS; INDICATORS; EROSION; ACCESS; SLOPE Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random forest (RF) machine learning. A case study was conducted in Yunyang, a poverty-stricken county in China, to evaluate the performances of the two methods for identifying village-level poverty determinants. The results indicated that: (1) MLR and RF had similar explanation accuracy; (2) LMG and RF were consistent in the three main determinants of poverty; (3) LMG better identified the importance of variables that were highly related to poverty but correlated with other variables, while RF better identified the non-linear relationships between poverty and explanatory variables; (4) accessibility metrics are the most important variables influencing poverty in Yunyang and have a linear relationship with poverty. (C) 2020 Elsevier B.V. All rights reserved. [Liu, Mengxiao; Hu, Shan; Ge, Yong; Ren, Zhoupeng; Huang, Xiaoran] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Liu, Mengxiao; Ge, Yong] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Heuvelink, Gerard B. M.] Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands; [Heuvelink, Gerard B. M.] ISRIC World Soil Informat, POB 353, NL-6700 AJ Wageningen, Netherlands Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Wageningen University & Research Ge, Y (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. liumx@lreis.ac.cn; hus@lreis.ac.cn; gey@lreis.ac.cn; renzp@lreis.ac.cn; huangxr@lreis.ac.cn Ren, Zhoupeng/AAV-2806-2020; Heuvelink, Gerard/AAD-5916-2022 Ren, Zhoupeng/0000-0003-1766-2820; Heuvelink, Gerard/0000-0003-0959-9358; Liu, Mengxiao/0000-0002-8596-636X Key Technologies Research and Development Program of China [2012BAH33B01] Key Technologies Research and Development Program of China(National Key Technology R&D Program) This work was supported by the Key Technologies Research and Development Program of China under Grant (2012BAH33B01). 60 27 27 44 86 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2211-6753 SPAT STAT-NETH Spat. Stat. APR 2021.0 42 100461 10.1016/j.spasta.2020.100461 0.0 MAR 2021 19 Geosciences, Multidisciplinary; Mathematics, Interdisciplinary Applications; Remote Sensing; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Geology; Mathematics; Remote Sensing RG1EX 2023-03-23 WOS:000635278600002 0
J Chen, L; Pan, XY; Hu, XH; Zhang, YH; Wang, SP; Huang, T; Cai, YD Chen, Lei; Pan, Xiaoyong; Hu, XiaoHua; Zhang, Yu-Hang; Wang, ShaoPeng; Huang, Tao; Cai, Yu-Dong Gene expression differences among different MSI statuses in colorectal cancer INTERNATIONAL JOURNAL OF CANCER English Article microsatellite instability; colorectal cancer; mRMR; support vector machine MICROSATELLITE INSTABILITY; MOLECULAR CHARACTERIZATION; PROMOTER HYPERMETHYLATION; MOLYBDENUM COFACTOR; MUTUAL INFORMATION; PROTEIN; IDENTIFICATION; MUTATIONS; COLON; RISK Colorectal cancer is the third most common cancer in males and second in females. This disease can be caused by genetic and acquired/environmental factors. Microsatellite instability (MSI) is one of the major mechanisms in colorectal cancer. This mechanism is a specific condition of genetic hyper mutability that results from incompetent DNA mismatch repair. MSI has been applied to classify different colorectal cancer subtypes. However, the effects of MSI status on gene expression are largely unknown. In our study, we integrated the gene expression profile and MSI status of all CRC samples from the TCGA database, and then categorized the CRC samples into three subgroups, namely, MSI-stable, MSI-low, and MSI-high, according to the MSI status. We applied a novel computational method based on machine learning and screened the genes specifically expressed for the different colorectal cancer subtypes. The results showed the distinct mechanisms of the different colorectal cancer subtypes with MSI status and provided the genes that may be the optimal standards to further classify the various molecular subtypes of colorectal cancer with distinct MSI status. [Chen, Lei; Wang, ShaoPeng; Cai, Yu-Dong] Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Pan, Xiaoyong] Erasmus MC, Dept Med Informat, Rotterdam, Netherlands; [Hu, XiaoHua] Fudan Univ, Sch Life Sci, Dept Biostat & Computat Biol, Shanghai 200438, Peoples R China; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China Shanghai University; Shanghai Maritime University; Erasmus University Rotterdam; Erasmus MC; Fudan University; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China.;Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China. tohuangtao@126.com; cai_yud@126.com Zhang, Yu-Hang/O-9774-2015; Zhang, Yu-Hang/AAQ-9012-2021; Chen, Lei/A-2275-2011 Zhang, Yu-Hang/0000-0003-3825-0796; Zhang, Yu-Hang/0000-0003-3825-0796; Chen, Lei/0000-0003-3068-1583 National Natural Science Foundation of China [31371335, 31701151, 31571343, 61462018]; Natural Science Foundation of Shanghai [17ZR1412500, 16ZR1403100]; Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences Grant sponsor: National Natural Science Foundation of China; Grant numbers: 31371335, 31701151, 31571343 and 61462018; Grant sponsor: Natural Science Foundation of Shanghai; Grant number: 17ZR1412500 and 16ZR1403100; Grant sponsor: Shanghai Sailing Program; Grant sponsor: Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); Grant number: 2016245; Grant sponsor: Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Grant number: 201703 73 68 68 4 32 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0020-7136 1097-0215 INT J CANCER Int. J. Cancer OCT 1 2018.0 143 7 1731 1740 10.1002/ijc.31554 0.0 10 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology GS2PB 29696646.0 2023-03-23 WOS:000443392100019 0
C Ren, J; Yuan, L; Nurmi, P; Wang, XM; Ma, M; Gao, L; Tang, ZY; Zheng, J; Wang, Z IEEE Ren, Jie; Yuan, Lu; Nurmi, Petteri; Wang, Xiaoming; Ma, Miao; Gao, Ling; Tang, Zhanyong; Zheng, Jie; Wang, Zheng Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS IEEE INFOCOM English Proceedings Paper 39th IEEE International Conference on Computer Communications (IEEE INFOCOM) JUL 06-09, 2020 ELECTR NETWORK IEEE Web technology underpins many interactive mobile applications. However, energy-efficient mobile web interactions is an outstanding challenge. Given the increasing diversity and complexity of mobile hardware, any practical optimization scheme must work for a wide range of users, mobile platforms and web workloads. This paper presents CAMEL, a novel energy optimization system for mobile web interactions. CAMEL leverages machine learning techniques to develop a smart, adaptive scheme to judiciously trade performance for reduced power consumption. Unlike prior work, CAMEL directly models how a given web content affects the user expectation and uses this to guide energy optimization. It goes further by employing transfer learning and conformal predictions to tune a previously learned model in the end-user environment and improve it over time. We apply CAMEL to Chromium and evaluate it on four distinct mobile systems involving 1,000 testing webpages and 30 users. Compared to four state-of-the-art web-event optimizers, CAMEL delivers 22% more energy savings, but with 49% fewer violations on the quality of user experience, and exhibits orders of magnitudes less overhead when targeting a new computing environment. [Ren, Jie; Wang, Xiaoming; Ma, Miao] Shannxi Normal Univ, Xian, Peoples R China; [Nurmi, Petteri] Univ Helsinki, Helsinki, Finland; [Yuan, Lu; Gao, Ling; Tang, Zhanyong; Zheng, Jie] Northwest Univ, Xian, Peoples R China; [Wang, Zheng] Univ Leeds, Leeds, W Yorkshire, England Shaanxi Normal University; University of Helsinki; Northwest University Xi'an; University of Leeds Ren, J (corresponding author), Shannxi Normal Univ, Xian, Peoples R China. renjie@snnu.edu.cn; z.wang5@leeds.ac.uk Wang, Zheng/0000-0001-6157-0662 NSF China [61902229, 61872294, 61877038, 61602290, 61877037]; Natural Science Basic Research Program of Shaanxi Province [2019JQ-271] NSF China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Program of Shaanxi Province This work was supported in part by the NSF China under grant agreements 61902229, 61872294, 61877038, 61602290 and 61877037; and the Natural Science Basic Research Program of Shaanxi Province under Grant No. 2019JQ-271. For any correspondence, please contact Zheng Wang (E-mail: z.wang5@leeds.ac.uk). 71 5 6 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0743-166X 978-1-7281-6412-0 IEEE INFOCOM SER 2020.0 119 128 10 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BQ8NB Green Accepted 2023-03-23 WOS:000620945800013 0
J Zhang, XY; Wang, JY; Li, Y; Jantti, R; Pan, M; Han, Z Zhang, Xinyue; Wang, Jingyi; Li, Yong; Jantti, Riku; Pan, Miao; Han, Zhu Catching All Pokemon: Virtual Reward Optimization With Tensor Voting Based Trajectory Privacy IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Virtual reward maximization; tensor voting; trajectory privacy; obfuscation; inference attacks PROTECTING LOCATION PRIVACY; ALGORITHMS The widespread adoption of mobile devices with global positioning system enables location-based games (LBGs) to use real world maps, while locations and objectives in LBGs can make the progression, achievements, and virtual rewards feel more palpable and entertaining. However, allowing location sharing in LBGs gives dishonest parties opportunities to learn users' trajectories, which compromises the users' privacy. In this paper, we propose a novel scheme jointly maximizing LBG players' virtual rewards while preserving their trajectory privacy. Briefly, we first introduce a quantitative machine learning-based approach to model trajectory inference attacks via tensor voting. Then, to thwart this attack, we propose a tensor voting-based k-anonymous obfuscation strategy. Considering the trajectory privacy concerns and power constraint of hand-held mobile devices, we mathematically formulate the LBG players' virtual reward maximization optimization into the mixed integer problem and develop the feasible solutions. Simulation results and analysis show that the proposed scheme can effectively preserve LBG players' trajectory privacy against tensor voting based inference attacks while maximizing LBG players' virtual rewards. [Zhang, Xinyue; Wang, Jingyi; Pan, Miao; Han, Zhu] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77204 USA; [Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China; [Jantti, Riku] Aalto Univ, Dept Commun & Networking, FI-00076 Aalto, Finland University of Houston System; University of Houston; Tsinghua University; Aalto University Pan, M (corresponding author), Univ Houston, Dept Elect & Comp Engn, Houston, TX 77204 USA. xzhang67@uh.edu; jwang86@uh.edu; liyong07@tsinghua.edu.cn; riku.jantti@aalto.fi; mpan2@uh.edu; zhan2@uh.edu li, yong/HDN-3885-2022; WANG, JINGYI/GSJ-1241-2022; Jäntti, Riku/G-2270-2013; Han, Zhu/ABG-1222-2021; wang, jing/HJA-5384-2022; wang, jing/GRS-7509-2022 Jäntti, Riku/0000-0002-5398-2381; U.S. National Science Foundation [CNS-1343361, CNS-1350230, CNS-1646607, CNS-1702850, CNS-1801925]; Academy of Finland project AIMHIS [311760] U.S. National Science Foundation(National Science Foundation (NSF)); Academy of Finland project AIMHIS This work was supported in part by the U.S. National Science Foundation under Grants CNS-1343361, CNS-1350230 (CAREER), CNS-1646607, CNS-1702850, and CNS-1801925 and in part by the Academy of Finland project AIMHIS (decision no. 311760). The review of this paper was coordinated by Dr. A. Chatterjee. 27 3 3 5 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. JAN 2019.0 68 1 883 892 10.1109/TVT.2018.2882733 0.0 10 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation HJ6MC 2023-03-23 WOS:000457302900070 0
C Zhang, SX; Liao, HJ; Zhou, ZY; Wang, Y; Zhang, H; Wang, XY; Mumtaz, S; Guizani, M IEEE Zhang, Sunxuan; Liao, Haijun; Zhou, Zhenyu; Wang, Yang; Zhang, Hui; Wang, Xiaoyan; Mumtaz, Shahid; Guizani, Mohsen Federated Deep Actor-Critic-Based Task Offloading in Air-Ground Electricity IoT 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) DEC 07-11, 2021 Madrid, SPAIN IEEE,Huawei,Vodafone,Intel,ZTE,Qualcomm,Assia,IMEC,Keysight Technologies,PRIME Alliance,Rohde & Schwarz,Vantage Towers,Guard,Usal Bisite Res Grp OPTIMIZATION; MOBILITY The integration of air-ground electricity internet of things (AGE-IoT) and machine learning, enables flexible network coverage and intelligent task offloading. However, dynamics of AGE-IoT networks, incomplete information, and resource allocation coupling are still major challenges in achieving intelligent AGE-IoT. In this paper, we investigate a joint multi-timescale task offloading and power control optimization problem to minimize the queuing delay of all the EIoT devices under the long-term constraint of energy consumption. We firstly decompose the joint optimization problem and transform it to large-timescale task offloading optimization and small-timescale power control optimization. Then, we propose a federated deep actor-critic-based task offloading algorithm (FDAC) with two actor-critic networks for multi-timescale optimization. Numerical results show that FDAC has excellent performances in queuing delay and energy consumption compared with existing algorithms. [Zhang, Sunxuan; Liao, Haijun; Zhou, Zhenyu] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China; [Wang, Yang; Zhang, Hui] State Grid Corp China, China Elect Power Res Inst Co Ltd, Inst Informat & Commun, Beijing 100192, Peoples R China; [Wang, Xiaoyan] Ibaraki Univ, Grad Sch Sci & Engn, Ibaraki 3108512, Japan; [Mumtaz, Shahid] Univ Aveiro, Inst Telecomunicacoes, Campus Univ Santiago, P-3810193 Aveiro, Portugal; [Guizani, Mohsen] Qatar Univ, Comp Sci & Engn Dept, Doha, Qatar North China Electric Power University; State Grid Corporation of China; Ibaraki University; Universidade de Aveiro; Qatar University Zhang, SX (corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China. sunxuan_zhang@ncepu.edu.cn; haijun_liao@ncepu.edu.cn; zhenyu_zhou@ncepu.edu.cn; yangw@epri.sgcc.com.cn; zhanghui@epri.sgcc.com.cn; xiaoyan.wang.shawn@vc.ibaraki.ac.jp; smumtaz@av.it.pt; mguizani@ieee.org Science and Technology Project of State Grid Corporation of China [SGSDDK00KJJS1900405] Science and Technology Project of State Grid Corporation of China This work was supported by the Science and Technology Project of State Grid Corporation of China under Grant Number SGSDDK00KJJS1900405. 11 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-8104-2 IEEE GLOB COMM CONF 2021.0 10.1109/GLOBECOM46510.2021.9686001 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT0PJ 2023-03-23 WOS:000790747205050 0
J Greaves, RF; Bernardini, S; Ferraris, M; Fortina, P; Gouget, B; Gruson, D; Lang, T; Loh, TP; Morris, HA; Park, JY; Roessler, M; Yin, P; Kricka, LJ Greaves, Ronda F.; Bernardini, Sergio; Ferraris, Maurizio; Fortina, Paolo; Gouget, Bernard; Gruson, Damien; Lang, Tim; Loh, Tze Ping; Morris, Howard A.; Park, Jason Y.; Roessler, Markus; Yin, Peng; Kricka, Larry J. Key questions about the future of laboratory medicine in the next decade of the 21st century: A report from the IFCC-Emerging Technologies Division CLINICA CHIMICA ACTA English Article Emerging technologies; Disruptive technologies; Laboratory medicine ELECTRONIC HEALTH RECORDS; TANDEM MASS-SPECTROMETRY; QUALITY-ASSURANCE; LIBERTARIAN RIGHT; VALUE PROPOSITION; INBORN-ERRORS; MOVING SUM; OF-CARE; MANAGEMENT; BLOOD This review advances the discussion about the future of laboratory medicine in the 2020s. In five major topic areas: 1. the big picture of healthcare; 2. pre-analytical factors; 3. Analytical factors; 4. post-analytical factors; and 5. relationships, which explores a next decade perspective on laboratory medicine and the likely impact of the predicted changes by means of a number of carefully focused questions that draw upon predictions made since 2013. The big picture of healthcare explores the effects of changing patient populations, the brain-to-brain loop, direct access testing, robots and total laboratory automation, and green technologies and sustain ability. The pre-analytical section considers the role of different sample types, drones, and biobanks. The analytical section examines advances in point-of-care testing, mass spectrometry, genomics, gene and immunotherapy, 3D-printing, and total laboratory quality. The post-analytical section discusses the value of laboratory medicine, the emerging role of artificial intelligence, the management and interpretation of omics data, and common reference intervals and decision limits. Finally, the relationships section explores the role of laboratory medicine scientific societies, the educational needs of laboratory professionals, communication, the relationship between laboratory professionals and clinicians, laboratory medicine financing, and the anticipated economic opportunities and outcomes in the 2020's. [Greaves, Ronda F.] Murdoch Childrens Res Inst, Victorian Clin Genet Serv, Parkville, Vic, Australia; [Greaves, Ronda F.] RMIT Univ, Sch Hlth & Biomed Sci, Melbourne, Vic, Australia; [Bernardini, Sergio] Univ Tor Vergata, Dept Expt Med, Rome, Italy; [Ferraris, Maurizio] Univ Vita Salute San Raffaele, Milan, Italy; [Ferraris, Maurizio] IRCCS San Raffaele Hosp, Milan, Italy; [Fortina, Paolo] Thomas Jefferson Univ, Dept Canc Biol, Kimmel Canc Ctr, Philadelphia, PA 19107 USA; [Gouget, Bernard] Comm Com Francais Accreditat Cofrac, President Healthcare Div, Paris, France; [Gruson, Damien] Clinl Univ St Luc, Dept Lab Clin, Biochim Med, Brussels, Belgium; [Lang, Tim] Univ Hosp North Durham, Durham, England; [Loh, Tze Ping] Sengkang Gen Hosp, Dept Pathol, Singapore, Singapore; [Loh, Tze Ping] Natl Univ Singapore Hosp, Dept Lab Med, Singapore, Singapore; [Morris, Howard A.] Univ South Australia, Sch Pharm & Med Sci, Adelaide, SA, Australia; [Park, Jason Y.] Childrens Med Ctr, Dept Pathol, 1935 Med Dist Dr, Dallas, TX 75235 USA; [Park, Jason Y.] Childrens Med Ctr, Eugene McDermott Ctr Human Growth & Dev, 1935 Med Dist Dr, Dallas, TX 75235 USA; [Park, Jason Y.] Univ Texas Southwestern Med Sch, 1935 Med Dist Dr, Dallas, TX 75235 USA; [Roessler, Markus] Roche Diagnost GmbH, Centralised & Point Of Care Solut Early Dev & Rea, Penzberg, Germany; [Yin, Peng] Abbott Diagnost Div, Shanghai, Peoples R China; [Kricka, Larry J.] Univ Penn, Med Ctr, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA Murdoch Children's Research Institute; Royal Melbourne Institute of Technology (RMIT); University of Rome Tor Vergata; Vita-Salute San Raffaele University; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele; Jefferson University; National University of Singapore; University of South Australia; Roche Holding; University of Pennsylvania Bernardini, S (corresponding author), Univ Tor Vergata, Chair Emerging Technol, Div Int Federat Clin Chem & Lab Med, Dept Expt Med, I-00174 Rome, Italy. bernardini@med.uniroma2.it Lang, Timothy/K-9741-2019; Fortina, Paolo/ABE-8350-2020; Greaves, Ronda/J-7121-2014 Greaves, Ronda/0000-0001-7823-8797; Loh, Tze Ping/0000-0002-4272-0001; Fortina, Paolo/0000-0002-5898-2081 201 33 35 3 26 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0009-8981 1873-3492 CLIN CHIM ACTA Clin. Chim. Acta AUG 2019.0 495 570 589 10.1016/j.cca.2019.05.021 0.0 20 Medical Laboratory Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Medical Laboratory Technology IL0CR 31145895.0 2023-03-23 WOS:000476964200085 0
J Chen, HY; Wozniak, M Chen, Haiying; Wozniak, Marcin Mathematical Model Simulation of Detailed Classification of Telemedicine Sensing Data MOBILE NETWORKS & APPLICATIONS English Article; Early Access Telemedicine; Sensing data; Data refinement and classification; Mathematical model; Simulation test COVID-19 CLASSIFICATION; FUSION Medical and health field is a hot application field of wireless sensor networks. How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4 similar to 12 s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance. [Chen, Haiying] Tianmen Vocat Coll, Dept Publ Courses, Tianmen 431700, Peoples R China; [Wozniak, Marcin] Silesian Tech Univ, Fac Appl Math, Gliwice, Poland Silesian University of Technology Wozniak, M (corresponding author), Silesian Tech Univ, Fac Appl Math, Gliwice, Poland. marcin.wozniak@polsl.pl Scientific research project of Hubei Vocational and technical education society [ZJGB2021061]; Silesian University of Technology, Gliwice,Poland [09/010/RGJ22/0068] Scientific research project of Hubei Vocational and technical education society; Silesian University of Technology, Gliwice,Poland This work was supported by the Scientific research project of Hubei Vocational and technical education society (No.ZJGB2021061). Authors would like to acknowledge contribution to this research from the Rector of the Silesian University of Technology, Gliwice,Poland under pro-quality grant no. 09/010/RGJ22/0068. 22 0 0 5 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1383-469X 1572-8153 MOBILE NETW APPL Mobile Netw. Appl. 10.1007/s11036-022-02025-2 0.0 AUG 2022 10 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 3X2QV hybrid 2023-03-23 WOS:000842887500001 0
J Liu, PT; Verdi, C; Karsai, F; Kresse, G Liu, Peitao; Verdi, Carla; Karsai, Ferenc; Kresse, Georg Phase transitions of zirconia: Machine-learned force fields beyond density functional theory PHYSICAL REVIEW B English Article APPROXIMATION; CHEMISTRY Machine-learned force fields (MLFFs) are increasingly used to accelerate first-principles simulations of many materials properties. However, MLFFs are generally trained from density functional theory (DFT) data and thus suffer from the same limitations as DFT. To achieve more predictive accuracy, MLFFs based on higher levels of theory are required, but the training becomes exceptionally arduous. Here, we present an approach to generate MLFFs with beyond DFT accuracy which combines an efficient on-the-fly active learning method and Delta-machine learning. Using this approach, we generate an MLFF for zirconia based on the random phase approximation (RPA). Specifically, an MLFF trained on the fly during DFT-based molecular dynamics simulations is corrected by another MLFF that is trained on the differences between RPA and DFT calculated energies, forces, and stress tensors. We show that owing to the relatively smooth nature of these differences, the expensive RPA calculations can be performed only on a small number of representative structures of small unit cells selected by rank compression of the kernel matrix. This dramatically reduces the computational cost and allows one to generate an MLFF fully capable of reproducing high-level quantum-mechanical calculations beyond DFT. We carefully validate our approach and demonstrate its success in studying the phase transitions of zirconia. These results open the way to many-body calculations of finite-temperature properties of materials. [Liu, Peitao; Karsai, Ferenc; Kresse, Georg] VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria; [Liu, Peitao; Verdi, Carla; Kresse, Georg] Univ Vienna, Fac Phys, Kolingasse 14-16, A-1090 Vienna, Austria; [Liu, Peitao; Verdi, Carla; Kresse, Georg] Univ Vienna, Ctr Computat Mat Sci, Kolingasse 14-16, A-1090 Vienna, Austria; [Liu, Peitao] Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang, Peoples R China University of Vienna; University of Vienna; Chinese Academy of Sciences; Institute of Metal Research, CAS Liu, PT (corresponding author), VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria.;Liu, PT (corresponding author), Univ Vienna, Fac Phys, Kolingasse 14-16, A-1090 Vienna, Austria.;Liu, PT (corresponding author), Univ Vienna, Ctr Computat Mat Sci, Kolingasse 14-16, A-1090 Vienna, Austria.;Liu, PT (corresponding author), Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang, Peoples R China. peitao.liu@univie.ac.at Verdi, Carla/GVS-9136-2022; Kresse, Georg/X-2531-2018 Kresse, Georg/0000-0001-9102-4259; Verdi, Carla/0000-0001-8337-1066 Advanced Materials Simulation Engineering Tool (AMSET) project - US Naval Nuclear Laboratory (NNL); Austrian Science Fund (FWF) within the SFB TACO [F 81-N] Advanced Materials Simulation Engineering Tool (AMSET) project - US Naval Nuclear Laboratory (NNL); Austrian Science Fund (FWF) within the SFB TACO(Austrian Science Fund (FWF)) P.L. thanks M. Kaltak for useful discussions. This work was funded by the Advanced Materials Simulation Engi-neering Tool (AMSET) project, sponsored by the US Naval Nuclear Laboratory (NNL) and directed by Materials Design, Inc. The funding from the Austrian Science Fund (FWF) within the SFB TACO (Project No. F 81-N) is gratefully acknowledged. 61 5 5 21 43 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B FEB 16 2022.0 105 6 L060102 10.1103/PhysRevB.105.L060102 0.0 6 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics ZH8FD Green Submitted 2023-03-23 WOS:000761166700002 0
J Jafari, S; Sprott, JC; Pham, VT; Volos, C; Li, CB Jafari, Sajad; Sprott, J. C.; Viet-Thanh Pham; Volos, Christos; Li, Chunbiao Simple chaotic 3D flows with surfaces of equilibria NONLINEAR DYNAMICS English Article Chaotic flows; Surface equilibrium; Hidden attractors MEMRISTIVE NEURAL-NETWORK; CONVECTIVE FLUID MOTION; HIDDEN ATTRACTORS; CIRCULAR EQUILIBRIUM; HYPERCHAOTIC SYSTEM; NONLINEAR TERM; NO EQUILIBRIA; IMPLEMENTATION; DESIGN Using a systematic computer search, twelve simple three-dimensional chaotic flows were found that have surfaces of equilibria. Although there are some four-dimensional systems with surfaces of equilibria, there is no such system in three-dimensional state space reported in the literature. Such systems are not difficult to design, but they can have some practical benefits. Study of chaotic flows with surfaces of equilibria provides a good reference for building systems with attractors that are protected from external influences, which can increase the safety of engineering. [Jafari, Sajad] Amirkabir Univ Technol, Dept Biomed Engn, Tehran 158754413, Iran; [Sprott, J. C.] Univ Wisconsin, Dept Phys, 1150 Univ Ave, Madison, WI 53706 USA; [Viet-Thanh Pham] Hanoi Univ Sci & Technol, Sch Elect & Telecommun, 01 Dai Co Viet, Hanoi, Vietnam; [Volos, Christos] Aristotle Univ Thessaloniki, Dept Phys, Thessaloniki 54124, Greece; [Li, Chunbiao] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Meteorol Observat & Informat Proc, Nanjing 210044, Jiangsu, Peoples R China; [Li, Chunbiao] Nanjing Univ Informat Sci & Technol, Sch Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China Amirkabir University of Technology; University of Wisconsin System; University of Wisconsin Madison; Hanoi University of Science & Technology; Aristotle University of Thessaloniki; Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology Jafari, S (corresponding author), Amirkabir Univ Technol, Dept Biomed Engn, Tehran 158754413, Iran. sajadjafari@aut.ac.ir Jafari, Sajad/P-7778-2017; Pham, Viet-Thanh/J-3644-2016; Volos, Christos/AAF-7096-2019 Jafari, Sajad/0000-0002-6845-7539; VOLOS, CHRISTOS/0000-0001-8763-7255; Li, Chunbiao/0000-0002-9932-0914 46 114 114 1 22 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. OCT 2016.0 86 2 1349 1358 10.1007/s11071-016-2968-x 0.0 10 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics DW5FE 2023-03-23 WOS:000383667400048 0
C Gao, YB; Jia, MH; Li, SA; Cai, X; Ye, M; Dufaux, F IEEE Gao, Yanbo; Jia, Menghu; Li, Shuai; Cai, Xun; Ye, Mao; Dufaux, Frederic A MULTISCALE GRADIENT-BACKPROPAGATION OPTIMIZATION FRAMEWORK FOR DEFORMABLE CONVOLUTION BASED COMPRESSED VIDEO ENHANCEMENT 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 22-27, 2022 Singapore, SINGAPORE Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Video coding; Quality enhancement; Deformable convolution Deep learning based compressed video quality enhancement has raised lots of interest recently. To explore the information over multiple frames, deformable convolution has been used for temporal alignment. However, in the existing methods, the deformable convolution is used in a relatively naive way, without differing the characteristics of offset and features, and their behavior in gradient backpropagation. In this paper, a multiscale gradient-backpropagation optimization framework is proposed for the deformable convolution based compressed video quality enhancement. By analyzing the gradient backpropagation mechanism of deformable convolution, a multi-scale deformable convolution alignment structure is developed to facilitate the gradient backpropagation at all scales. Moreover, a progressive offset prediction module is developed, which decouples the offset prediction from the feature up-sampling, thus reducing the noise flow over scales. Experimental results show that the proposed method achieves the state-of-the-art performance, with 25.6% BD-rate saving compared to the HEVC reference software (HM). [Gao, Yanbo; Cai, Xun] Shandong Univ, Sch Software, Jinan, Peoples R China; [Jia, Menghu; Ye, Mao] Univ Elect Sci & Technol China, Sch Info & Comm Engn, Chengdu, Peoples R China; [Li, Shuai] Shandong Univ, Sch Control Sci & Engn, Jinan, Peoples R China; [Dufaux, Frederic] Univ Paris Saclay, Lab Signaux & Syst, Cent Supelec, CNRS, Paris, France Shandong University; University of Electronic Science & Technology of China; Shandong University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Gao, YB (corresponding author), Shandong Univ, Sch Software, Jinan, Peoples R China. National Key R&D Program of China [2018YFE0203900]; National Natural Science Foundation of China [61901083, 62001092]; SDU QILU Young Scholars program National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); SDU QILU Young Scholars program This work was partly supported by the National Key R&D Program of China (2018YFE0203900), the National Natural Science Foundation of China (No. 61901083, 62001092) and SDU QILU Young Scholars program. 25 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-6654-0540-9 INT CONF ACOUST SPEE 2022.0 2110 2114 10.1109/ICASSP43922.2022.9746932 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering BT9UB Green Submitted 2023-03-23 WOS:000864187902077 0
J Rozycki, M; Satterthwaite, TD; Koutsouleris, N; Erus, G; Doshi, J; Wolf, DH; Fan, Y; Gur, RE; Gur, RC; Meisenzahl, EM; Zhuo, CJ; Ying, H; Yan, H; Yue, WH; Zhang, D; Davatzikos, C Rozycki, Martin; Satterthwaite, Theodore D.; Koutsouleris, Nikolaos; Erus, Guray; Doshi, Jimit; Wolf, Daniel H.; Fan, Yong; Gur, Raquel E.; Gur, Ruben C.; Meisenzahl, Eva M.; Zhuo, Chuanjun; Ying, Hong; Yan, Hao; Yue, Weihua; Zhang, Dai; Davatzikos, Christos Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals SCHIZOPHRENIA BULLETIN English Article psychosis; neuroimaging; MRI; brain; biomarker RISK MENTAL STATES; GRAY-MATTER VOLUME; REGISTRATION ALGORITHMS; LIKELIHOOD ESTIMATION; BRAIN CHANGES; METAANALYSIS; PATTERN; CLASSIFICATION; PSYCHOSIS; 1ST-EPISODE Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case-control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification. Analyses were conducted at multiple scales, including regional volumes, voxelwise measures, and complex distributed patterns. Single-subject classification was tested for single-site, pooled-site, and leave-site-out generalizability. Regional and voxelwise analyses revealed a pattern of widespread reduced regional gray matter volume, particularly in the medial prefrontal, temporolimbic and peri-Sylvian cortex, along with ventricular and pallidum enlargement. Multivariate classification using pooled data achieved a cross-validated prediction accuracy of 76% (AUC = 0.84). Critically, the leave-site-out validation of the detected schizophrenia signature showed accuracy/AUC range of 72-77%/0.73-0.91, suggesting a robust generalizability across sites and patient cohorts. Finally, individualized patient classifications displayed significant correlations with clinical measures of negative, but not positive, symptoms. Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia. A web-accessible portal is offered to allow the community to obtain individualized classifications of magnetic resonance imaging scans using the methods described herein. [Rozycki, Martin; Satterthwaite, Theodore D.; Erus, Guray; Doshi, Jimit; Wolf, Daniel H.; Fan, Yong; Davatzikos, Christos] Univ Penn, Dept Radiol, Perelman Sch Med, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; [Satterthwaite, Theodore D.; Wolf, Daniel H.; Gur, Raquel E.; Gur, Ruben C.] Univ Penn, Dept Psychiat, Brain Behav Lab, Perelman Sch Med, Philadelphia, PA 19104 USA; [Koutsouleris, Nikolaos] Ludwig Maximilians Univ Munchen, Dept Psychiat & Psychotherapy, Munich, Germany; [Meisenzahl, Eva M.] Heinrich Heine Univ Dusseldorf, Dept Psychiat & Psychotherapy, Dusseldorf, Germany; [Zhuo, Chuanjun] Tianjin Anning Hosp, Tianjin, Peoples R China; [Ying, Hong] Fourth Mil Med Univ, Xijing Hosp, Dept Radiol, Xian, Shaanxi, Peoples R China; [Yan, Hao; Yue, Weihua; Zhang, Dai] Peking Univ, Hosp 6, Minist Hlth, Inst Mental Hlth,Key Lab, Beijing, Peoples R China; [Yan, Hao; Yue, Weihua; Zhang, Dai] Peking Univ, Hosp 6, Natl Clin Res Ctr Mental Disorders, Beijing, Peoples R China University of Pennsylvania; Pennsylvania Medicine; University of Pennsylvania; Pennsylvania Medicine; University of Munich; Heinrich Heine University Dusseldorf; Air Force Military Medical University; Peking University; Peking University Davatzikos, C (corresponding author), Univ Penn, Richards Bldg,7th Floor,3700 Hamilton Walk, Philadelphia, PA 19104 USA. christos@rad.upenn.edu Koutsouleris, Nikolaos/AAJ-7428-2020; Erus, Guray/L-6712-2018; Yan, Hao/U-1330-2017; Gur, Ruben/K-1999-2019; Fan, Yong/O-4412-2014; Davatzikos, Christos/ABE-2057-2021; Wolf, Daniel/H-2581-2013 Erus, Guray/0000-0001-6633-4861; Yan, Hao/0000-0003-0376-9037; Gur, Ruben/0000-0002-9657-1996; Fan, Yong/0000-0001-9869-4685; Wolf, Daniel/0000-0002-9731-8781 National Institute of Health [P50MH096891, R01MH107703, R01MH101111]; Dowshen Program for Neuroscience National Institute of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Dowshen Program for Neuroscience The research at the University of Pennsylvania site was supported in part by the National Institute of Health (P50MH096891 to R.E.G., R01MH107703 to T.D.S., R01MH101111 to D.H.W.) and the Dowshen Program for Neuroscience. 67 74 75 2 29 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0586-7614 1745-1701 SCHIZOPHRENIA BULL Schizophr. Bull. SEP 2018.0 44 5 1035 1044 10.1093/schbul/sbx137 0.0 10 Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Psychiatry GR8TN 29186619.0 Bronze, Green Published, Green Accepted 2023-03-23 WOS:000443003600016 0
C He, LX; Beer, M; Broggi, M; Wei, PF; Gomes, AT IEEE He, Longxue; Beer, Michael; Broggi, Matteo; Wei, Pengfei; Gomes, Antonio Topa Sensitivity analysis of prior beliefs in advanced Bayesian networks 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) English Proceedings Paper IEEE Symposium Series on Computational Intelligence (SSCI) DEC 06-09, 2019 Xiamen, PEOPLES R CHINA IEEE,IEEE Computat Intelligence Soc,Xiamen Univ,Xi AN Univ Posts & Telecommunicat,Fujian Assoc Artificial Intelligence Global sensitivity analysis; Dependence measure; Advanced Bayesian networks; Imprecise stochastic simulation; Second-order probability model Bayesian Network (BN) is an efficient model tool for approximate reasoning based on machine learning. It has been widely used for supporting the decision in many engineering applications such as geotechnical engineering. However, the current studies on BN are mostly on uncertainty quantification and decision-making, while the sensitivity analysis on BN, which may provide much more insights for decision-making, has not received much attention. The current research on sensitivity analysis of BN mainly focuses on local method, and there is a need to develop global sensitivity analysis (GSA) for both forward and backward inferences of BN. We present in this paper GSA analysis for BN within two different settings. For the first setting, it is assumed that the BN nodes, as well as their connection are characterized by precise (conditional) probabilities, and we introduce GSA for both forward and backward analysis. It is shown that, by forward analysis, the GSA indices can effectively identify the nodes which make the most contribution to the end nodes directly related to the reliability; by backward analysis, the GSA indices can inform the most important information needs to be collected for BN model updating. The second setting concerns the incomplete knowledge of nodes and their connections, and it is assumed these quantities are characterized by imprecise probability models. In this setting, the GSA is then introduced, and implemented with the newly developed non-intrusive imprecise stochastic simulation (NISS) method, for learning the most important epistemic uncertainty sources, by reducing which the robustness of the BN inference can be enhanced the most. The above theoretical developments are then applied to an infinite slope reliability analysis problem. [He, Longxue; Beer, Michael; Broggi, Matteo] Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany; [Wei, Pengfei] Northwestern Polytech Univ, Sch Mech Civil Engn & Architecture, Xian, Peoples R China; [Gomes, Antonio Topa] Univ Porto, CONSTRUCT, Fac Engn, Porto, Portugal Leibniz University Hannover; Northwestern Polytechnical University; Universidade do Porto He, LX (corresponding author), Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany. longxue@irz.uni-hannover.de; beer@irz.uni-hannover.de; broggi@irz.uni-hannover.de; pengfeiwei@nwpu.edu.cn; atgomes@fe.up.pt Topa Gomes, Antonio/0000-0002-5149-8748; Broggi, Matteo/0000-0002-3683-3907; Beer, Michael/0000-0002-0611-0345 Chinese Scholarship Council Chinese Scholarship Council(China Scholarship Council) This work is supported by Chinese Scholarship Council. 24 0 0 1 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-2485-8 2019.0 776 783 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP5GE 2023-03-23 WOS:000555467200110 0
C Wang, ZW; She, Q; Chalasani, T; Smolic, A IEEE COMP SOC Wang, Zhengwei; She, Qi; Chalasani, Tejo; Smolic, Aljosa CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 14-19, 2020 ELECTR NETWORK IEEE,CVF,IEEE Comp Soc Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://github.com/villawang/CatNet. [Wang, Zhengwei; Chalasani, Tejo; Smolic, Aljosa] Trinity Coll Dublin, V SENSE, Dublin, Ireland; [She, Qi] Intel Labs China, Beijing, Peoples R China Trinity College Dublin; Intel Corporation She, Q (corresponding author), Intel Labs China, Beijing, Peoples R China. zhengwei.wang@tcd.ie; qi.she@intel.com; CHALASAT@tcd.ie; SMOLICA@tcd.ie Science Foundation Ireland (SFI) [15/RP/2776] Science Foundation Ireland (SFI)(Science Foundation Ireland) This work is finacially supported by Science Foundation Ireland (SFI) under the Grant Number 15/RP/2776. 46 0 0 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2160-7508 978-1-7281-9360-1 IEEE COMPUT SOC CONF 2020.0 935 944 10.1109/CVPRW50498.2020.00123 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT0JZ Green Submitted 2023-03-23 WOS:000788279001001 0
J Tadei, A; Haajanen, J; Pensar, J; Santtila, P; Antfolk, J Tadei, Alessandro; Haajanen, Juulia; Pensar, Johan; Santtila, Pekka; Antfolk, Jan Counteracting deceptive responding in the Finnish Investigative Instrument of Child Sexual Abuse (FICSA) JOURNAL OF SEXUAL AGGRESSION English Article Child sexual abuse; FICSA; deception; machine learning; Naï ve Bayes classifier CREDIBILITY; PREVALENCE; TESTIMONY; MEMORY; LIES; TELL; AGE Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child's background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional trap questions were constructed. The trap questions were designed to seem statistically related to CSA although they were not. Combining the answers of 278 real victims and 275 16-year-old students, instructed to simulate being CSA victims, we built a Naive Bayes classifier that successfully separated the two groups (AUC = 0.91 for boys and 0.92 for girls). By identifying false allegations early in the investigation, authorities' resources can be directed towards allegations that are more likely true, effectively helping actual CSA victims. [Tadei, Alessandro; Antfolk, Jan] Abo Akad Univ, Dept Psychol, Turku, Finland; [Haajanen, Juulia] Univ Turku, Fac Social Sci, Turku, Finland; [Pensar, Johan] Univ Oslo, Dept Math, Oslo, Norway; [Santtila, Pekka] New York Univ, Fac Arts & Sci, Shanghai, Peoples R China Abo Akademi University; University of Turku; University of Oslo Tadei, A (corresponding author), Abo Akad Univ, Dept Psychol, Turku, Finland. atadei@abo.fi Antfolk, Jan/D-2926-2018 Antfolk, Jan/0000-0003-0334-4987 49 0 0 1 4 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1355-2600 1742-6545 J SEX AGGRESS J. Sex. Aggress. SEP 2 2021.0 27 3 401 412 10.1080/13552600.2020.1846802 0.0 NOV 2020 12 Criminology & Penology Social Science Citation Index (SSCI) Criminology & Penology WP9EU Green Submitted, hybrid, Green Published 2023-03-23 WOS:000592751800001 0
J Dong, MT; Wang, C; Li, HY; Yan, Y; Ma, XT; Li, HR; Li, XS; Wang, HH; Zhang, YX; Qi, WH; Meng, K; Tian, WY; Wang, YM; Fan, AP; Han, C; Donders, GGG; Xue, FX Dong, Mengting; Wang, Chen; Li, Huiyang; Yan, Ye; Ma, Xiaotong; Li, Huanrong; Li, Xingshuo; Wang, Huihui; Zhang, Yixuan; Qi, Wenhui; Meng, Ke; Tian, Wenyan; Wang, Yingmei; Fan, Aiping; Han, Cha; Donders, Gilbert G. G.; Xue, Fengxia Aerobic Vaginitis Diagnosis Criteria Combining Gram Stain with Clinical Features: An Establishment and Prospective Validation Study DIAGNOSTICS English Article aerobic vaginitis; diagnostic criteria; Gram stain; clinical features; 16S rRNA gene sequencing BACTERIAL VAGINOSIS; VAGINAL PH; INFECTIONS; FLORA; WOMEN Wet-mount microscopy aerobic vaginitis (AV) diagnostic criteria need phase-contrast microscopy and keen microscopists, and the preservation of saline smears is less common in clinical practice. This research work developed new AV diagnostic criteria that combine Gram stain with clinical features. We enrolled 325 AV patients and 325 controls as a study population to develop new AV diagnostic criteria. Then, an independent group, which included 500 women, was used as a validation population. AV-related microscopic findings on Gram-stained and wet-mount smears from the same participants were compared. The accuracy of bacterial indicators from the two methods was verified by bacterial 16S rRNA V4 sequencing (n = 240). Logistic regression was used to analyse AV-related clinical features. The screened clinical features were combined with Gram-stain microscopic indicators to establish new AV diagnostic criteria. There were no significant differences in the leukocyte counts or the parabasal epitheliocytes (PBC) proportion between the Gram-stain and wet-mount methods (400x). Gram stain (1000x) satisfied the ability to identify bacteria as verified by 16S rRNA sequencing but failed to identify toxic leukocytes. The new criteria included: Lactobacillary grades (LBG) and background flora (Gram stain, 1000x), leukocytes count and PBC proportion (Gram stain, 400x), and clinical features (vaginal pH > 4.5, vagina hyperemia, and yellow discharge). These criteria satisfied the accuracy and reliability for AV diagnosis (Se = 86.79%, Sp = 95.97%, and Kendall's W value = 0.899) in perspective validation. In summary, we proposed an alternative and valuable AV diagnostic criteria based on the Gram stain, which can make it possible to diagnose common vaginitis like AV, BV, VVC, and mixed infections on the same smear and can be available for artificial intelligence diagnosis in the future. [Dong, Mengting; Wang, Chen; Li, Huiyang; Yan, Ye; Ma, Xiaotong; Li, Huanrong; Li, Xingshuo; Wang, Huihui; Zhang, Yixuan; Qi, Wenhui; Meng, Ke; Tian, Wenyan; Wang, Yingmei; Fan, Aiping; Han, Cha; Xue, Fengxia] Tianjin Med Univ, Dept Obstet & Gynecol, Gen Hosp, 154 Anshan Rd, Tianjin 300052, Peoples R China; [Dong, Mengting; Wang, Chen; Li, Huiyang; Yan, Ye; Ma, Xiaotong; Li, Huanrong; Li, Xingshuo; Wang, Huihui; Zhang, Yixuan; Qi, Wenhui; Meng, Ke; Tian, Wenyan; Wang, Yingmei; Fan, Aiping; Han, Cha; Xue, Fengxia] Tianjin Key Lab Female Reprod Hlth & Eugen, 154 Anshan Rd, Tianjin 300052, Peoples R China; [Donders, Gilbert G. G.] Univ Antwerp, Dept Obstet & Gynecol, B-2550 Antwerp, Belgium; [Donders, Gilbert G. G.] Femicare Clin Res Women, B-3300 Tienen, Belgium; [Donders, Gilbert G. G.] Reg Hosp, Dept Obstet & Gynecol, B-3300 Tienen, Belgium Tianjin Medical University; University of Antwerp Xue, FX (corresponding author), Tianjin Med Univ, Dept Obstet & Gynecol, Gen Hosp, 154 Anshan Rd, Tianjin 300052, Peoples R China.;Xue, FX (corresponding author), Tianjin Key Lab Female Reprod Hlth & Eugen, 154 Anshan Rd, Tianjin 300052, Peoples R China.;Donders, GGG (corresponding author), Univ Antwerp, Dept Obstet & Gynecol, B-2550 Antwerp, Belgium.;Donders, GGG (corresponding author), Femicare Clin Res Women, B-3300 Tienen, Belgium.;Donders, GGG (corresponding author), Reg Hosp, Dept Obstet & Gynecol, B-3300 Tienen, Belgium. dongmengting1993@126.com; wangchener32@163.com; Lihuiyang1992@tmu.edu.cn; yanye@tmu.edu.cn; xiaotongma1992@163.com; Lihuiyang1992@tmu.edu.cn; xingchoco@163.com; erwanghuihui@163.com; karliezhang9@126.com; qiwenhui1987@163.com; mengke1104@163.com; tianwenyan1108@163.com; wangyingmei1978@126.com; fanaipinger@163.com; tjhancha@163.com; gilbert.donders@femicare.net; xuefengxia@tmu.edu.cn Wang, Chen/0000-0003-3995-4242; Donders, Gilbert/0000-0001-9890-6254; Ma, Xiaotong/0000-0002-8402-109X; Qi, Wenhui/0000-0002-4872-6957; Yan, Ye/0000-0002-7921-0101 Science and Technology Major Projects in the Control and Prevention of Major Diseases from the Tianjin Municipal Science and Technology Commission Special Foundation [18ZXDBSY00200]; Chen Wang e National Natural Science Fund of China [82101705]; Tianjin Health Science and Technology Project [KJ20003, KJ20176]; Scientific Research Project of Tianjin Education Commission [2020KJ158] Science and Technology Major Projects in the Control and Prevention of Major Diseases from the Tianjin Municipal Science and Technology Commission Special Foundation; Chen Wang e National Natural Science Fund of China; Tianjin Health Science and Technology Project; Scientific Research Project of Tianjin Education Commission Fengxia Xue is funded by Science and Technology Major Projects in the Control and Prevention of Major Diseases from the Tianjin Municipal Science and Technology Commission Special Foundation (Grant No. 18ZXDBSY00200). Chen Wang e National Natural Science Fund of China (Grant No. 82101705), Tianjin Health Science and Technology Project (KJ20003), and Scientific Research Project of Tianjin Education Commission (2020KJ158). Huiyang Li is founded by Tianjin Health Science and Technology Project (KJ20176). 38 3 3 7 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-4418 DIAGNOSTICS Diagnostics JAN 2022.0 12 1 185 10.3390/diagnostics12010185 0.0 14 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine ZD9HL 35054351.0 gold, Green Accepted 2023-03-23 WOS:000758505100001 0
J Lin, J; Li, W; Gao, YM; Xie, YL; Chen, NF; Siniscalchi, SM; Zhang, JS; Lee, CH Lin, Ju; Li, Wei; Gao, Yingming; Xie, Yanlu; Chen, Nancy F.; Siniscalchi, Sabato Marco; Zhang, Jinsong; Lee, Chin-Hui Improving Mandarin Tone Recognition Based on DNN by Combining Acoustic and Articulatory Features Using Extended Recognition Networks JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY English Article Articulatory features; MFCC; Posterior probabilities; Deep neural network; Mandarin tone recognition; Tone-based extended recognition network SPEECH; MODEL In this paper, we investigate the effectiveness of articulatory information for Mandarin tone modeling and recognition in a deep neural network - hidden Markov model (DNN-HMM) framework. In conventional approaches, prosodic evidence (e.g., F0, duration and energy) is used to build tone classifiers, we here propose performance enhancement techniques in three areas: (i) adding articulatory features (AFs) and acoustic features, such as MFCCs (Mel frequency cepstrum coefficients), for tone modeling; (ii) adopting phone-dependent tone modeling; and (iii) using tone-based extended recognition network (ERN) to reduce the tone search space. The first approach is feature-related, it explicitly employs the AFs as a form of tonal features and is implemented through a multi-stage procedure. The second approach is model-related and directly extends to phone-dependent tone modeling so that each modeling unit (e.g., tonal phone) not only contains tone information, but also integrates the phone/articulatory information. Finally, the third technique is search-related with a phone-dependent tone-based expanding searching network. A series of comprehensive experiments is conducted using different input feature sets. It is demonstrated that (i) tone recognition accuracy is boosted by incorporating articulatory information, and (ii) ERN, attains the lowest tone error rate of 7.17%, with a 56% relative error reduction from the prosody-only baseline system error of 16.36%. [Lin, Ju; Gao, Yingming; Xie, Yanlu; Zhang, Jinsong] Beijing Language & Culture Univ, Coll Informat Sci, Beijing, Peoples R China; [Li, Wei; Siniscalchi, Sabato Marco; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Chen, Nancy F.] Inst Infocomm Res, Singapore, Singapore; [Siniscalchi, Sabato Marco] Kore Univ Enna, Dept Telemat, Enna, Italy Beijing Language & Culture University; University System of Georgia; Georgia Institute of Technology; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); Universita Kore di ENNA Zhang, JS (corresponding author), Beijing Language & Culture Univ, Coll Informat Sci, Beijing, Peoples R China. linjucs@163.com; lee.wei@gatech.edu; gaoyingming1@sina.com; xieyanlu@blcu.edu.cn; nfychen@i2r.a-star.edu.sg; marco.siniscalchi@unikore.it; jinsong.zhang@blcu.edu.cn; chl@ece.gatech.edu Siniscalchi, Sabato Marco Marco/I-3423-2012; Chen, Nancy/GSD-8813-2022 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507; Zhang, Jinsong/0000-0002-1603-3136 Beijing Wutong Innovation Platform of Beijing Language and Culture University [16PT05]; BLCU support project for young researchers program [16YCX163]; China Scholarship Council Beijing Wutong Innovation Platform of Beijing Language and Culture University; BLCU support project for young researchers program; China Scholarship Council(China Scholarship Council) This work is supported by Beijing Wutong Innovation Platform of Beijing Language and Culture University (16PT05) and BLCU support project for young researchers program (16YCX163) (Special Funds of Basic Research Costs for the National University). The second author was partially supported by a grant from the China Scholarship Council. 49 10 11 0 6 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1939-8018 1939-8115 J SIGNAL PROCESS SYS J. Signal Process. Syst. Signal Image Video Technol. JUL 2018.0 90 7 SI 1077 1087 10.1007/s11265-018-1334-2 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GH6LK 2023-03-23 WOS:000433555600011 0
J Cong, L; Shi, JC; Wang, TJ; Yang, F; Zhu, TT Cong, Lin; Shi, Jiachen; Wang, Tongjing; Yang, Fan; Zhu, Tiantong A method to evaluate the segregation of compacted asphalt pavement by processing the images of paved asphalt mixture CONSTRUCTION AND BUILDING MATERIALS English Article Asphalt pavement; Segregation; Image processing; Gray level co-occurrence matrix; Principal component analysis; Naive Bayesian classifier AGGREGATE GRADATION; INTELLIGENT; QUALITY Segregation in hot-mix asphalt pavement is a common failure during the construction process. The prevailing segregation detection methods can be used to detect and evaluate segregation only after segregation occurs. This study proposes a real time segregation detection method by using machine learning classifier to categorize the images of the paved mixture (IPM) during construction. The study first manually labeled 224 various levels of hot mix asphalt segregation images. Then, 14 texture features such as contrast, correlation of the IPM were calculated by the gray level co-occurrence matrix (GLCM). Next, the principal component analysis (PCA) was done to reduce the 14 features to 6 main components. Later on, the 6 main components were fed to a Naive Bayesian classifier to categorize the segregation level. Finally, the classification results indicate that the Naive Bayesian classifier has 80% accuracy when compared with the manually labelled results. Results of this study can potentially be adapted for real-time and large-scale hot mix asphalt segregation evaluation. (C) 2019 Elsevier Ltd. All rights reserved. [Cong, Lin; Shi, Jiachen; Yang, Fan; Zhu, Tiantong] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China; [Wang, Tongjing] Delft Univ Technol, Fac Architecture & Built Environm, Julianalaan 134, NL-2628 BL Delft, Netherlands; [Zhu, Tiantong] Shanghai Urban Construct Nichireki Special Asphal, Shanghai 200433, Peoples R China Tongji University; Delft University of Technology Cong, L (corresponding author), Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China. conglin@tongji.edu.cn Shi, Jiachen/GQQ-0473-2022 Shi, Jiachen/0000-0001-7493-3389 National Natural Science Foundation of China [51878499]; Special Fund of Shanghai Municipal Commission of Economy and Informatization [HU J-2018-29] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Special Fund of Shanghai Municipal Commission of Economy and Informatization This study was sponsored by National Natural Science Foundation of China (No. 51878499), Special Fund of Shanghai Municipal Commission of Economy and Informatization (No. HU J-2018-29). The authors are grateful to these financial supports. At the same time, the workers helped us are gratefully acknowledged too. 39 25 28 6 66 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. NOV 10 2019.0 224 622 629 10.1016/j.conbuildmat.2019.07.041 0.0 8 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science JA1JH 2023-03-23 WOS:000487572800053 0
C Tallon-Ballesteros, AJ; Cavique, L; Fong, S Alvarez, FM; Lora, AT; Munoz, JAS; Quintian, H; Corchado, E Tallon-Ballesteros, Antonio J.; Cavique, Luis; Fong, Simon Addressing Low Dimensionality Feature Subset Selection: ReliefF(-k) or Extended Correlation-Based Feature Selection(eCFS)? 14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019) Advances in Intelligent Systems and Computing English Proceedings Paper 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO) MAY 13-15, 2019 Seville, SPAIN Startup Ole,IEEE SMC Spanish Chapter Machine learning; Correlation; Feature subset selection; Feature ranking; Extended feature subset selection; CFS This paper tackles problems where attribute selection is not only able to choose a few features but also to achieve a low performance classification in terms of accuracy compared to the full attribute set. Correlation-based feature selection (CFS) has been set as the baseline attribute subset selector due to its popularity and high performance. Around hundred data sets have been collected and submitted to CFS; then the problems fulfilling simultaneously the conditions: (a) a number of selected attributes lower than six and (b) a percentage of selected attributes lower than a forty per cent, have been tested onto two directions. Firstly, in the scope of data selection at the feature level, an advanced contemporary approach have been conducted as well as some options proposed in a prior work. Secondly, the pre-processed and initial problems have been tested with some sturdy classifiers. Moreover, this work introduces a new taxonomy of feature selection according to the solution type and the followed way to compute it. The test bed comprises seven problems featured by a low dimensionality after the CFS application, three out of them report a single selected attribute, another one with two extracted features and the three remaining data sets with four or five retained attributes; additionally, the initial feature set is between six and twenty-nine and the complexity of the problems, in terms of classes, fluctuates between two and twenty-one, throwing averages of sixteen and around five for both aforementioned properties. The contribution concluded that the advanced procedure (extended CFS) is suitable for problems where only one or two attributes are selected by CFS; for data sets with more than two selected features the baseline method is preferable to the advanced one, although the considered feature ranking method achieved intermediate results. [Tallon-Ballesteros, Antonio J.] Univ Huelva, Dept Elect Comp Syst & Automat Engn, Huelva, Spain; [Cavique, Luis] Univ Aberta, Lisbon, Portugal; [Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa, Macao, Peoples R China Universidad de Huelva; Universidade Aberta; University of Macau Tallon-Ballesteros, AJ (corresponding author), Univ Huelva, Dept Elect Comp Syst & Automat Engn, Huelva, Spain. antonio.tallon@diesia.uhu.es Cavique, Luis/A-7249-2010; Tallón-Ballesteros, Antonio J/HNB-6529-2023; Fong, Simon/C-9388-2009 Cavique, Luis/0000-0002-5590-1493; Fong, Simon/0000-0002-1848-7246 Spanish Inter-Ministerial Commission of Science and Technology (MICYT) [TIN2014-55894-C2-R, TIN2017-88209-C2-2-R]; FEDER funds; Junta de Andalucia (Spain) [P11-TIC-7528] Spanish Inter-Ministerial Commission of Science and Technology (MICYT); FEDER funds(European Commission); Junta de Andalucia (Spain)(Junta de AndaluciaEuropean Commission) This work has been partially subsidised by TIN2014-55894-C2-R and TIN2017-88209-C2-2-R projects of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7528 project of the Junta de Andalucia (Spain). 17 1 1 0 6 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-5357 2194-5365 978-3-030-20055-8; 978-3-030-20054-1 ADV INTELL SYST 2020.0 950 251 260 10.1007/978-3-030-20055-8_24 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO0FX Green Submitted 2023-03-23 WOS:000490706700024 0
J Wang, B; Spessa, AC; Feng, PY; Hou, X; Yue, C; Luo, JJ; Ciais, P; Waters, C; Cowie, A; Nolan, RH; Nikonovas, T; Jin, HD; Walshaw, H; Wei, JH; Guo, XW; Liu, DL; Yu, Q Wang, Bin; Spessa, Allan C.; Feng, Puyu; Hou, Xin; Yue, Chao; Luo, Jing-Jia; Ciais, Philippe; Waters, Cathy; Cowie, Annette; Nolan, Rachael H.; Nikonovas, Tadas; Jin, Huidong; Walshaw, Henry; Wei, Jinghua; Guo, Xiaowei; Liu, De Li; Yu, Qiang Extreme fire weather is the major driver of severe bushfires in southeast Australia SCIENCE BULLETIN English Article Remote sensing; Forest fires; Climate drivers; Burnt area modelling; Machine learning; Southeast Australia ORGANIC-CARBON STOCKS; EL-NINO; RANDOM FORESTS; CLIMATE; VARIABILITY; RISK; REGRESSION; RANGELANDS; PATTERNS; RAINFALL In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25 degrees grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Nino Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires. (C) 2021 Science China Press. Published by Elsevier B.V. and Science China Press. [Wang, Bin; Yue, Chao; Yu, Qiang] Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China; [Wang, Bin; Liu, De Li] Wagga Wagga Agr Inst, New South Wales Dept Primary Ind, Wagga Wagga, NSW 2650, Australia; [Spessa, Allan C.; Nikonovas, Tadas] Swansea Univ, Coll Sci, Dept Geog, Singleton Pk, Swansea SA2 8PP, W Glam, Wales; [Feng, Puyu] China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China; [Hou, Xin] Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China; [Luo, Jing-Jia; Wei, Jinghua] Nanjing Univ Informat Sci & Technol, Inst Climate & Applicat Res ICAR, Key Lab Meteorol Disaster, Minist Educ KLME, Nanjing 210044, Peoples R China; [Ciais, Philippe] CEA CNRS UVSQ, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Waters, Cathy] New South Wales Dept Primary Ind, Dubbo 2830, Australia; [Cowie, Annette] New South Wales Dept Primary Ind, Armidale, NSW 2351, Australia; [Cowie, Annette] Univ New England, Sch Environm & Rural Sci, Armidale, NSW 2351, Australia; [Nolan, Rachael H.] Western Sydney Univ, Hawkesbury Inst Environm, Penrith, NSW 2751, Australia; [Jin, Huidong] CSIRO Data61, Canberra, ACT 2601, Australia; [Walshaw, Henry] Python Charmers Pty Ltd, Hawthorn, Vic 3122, Australia; [Guo, Xiaowei] Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810008, Peoples R China; [Liu, De Li] Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia; [Yu, Qiang] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China Northwest A&F University - China; NSW Department of Primary Industries; Swansea University; China Agricultural University; Northwest A&F University - China; Nanjing University of Information Science & Technology; UDICE-French Research Universities; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); NSW Department of Primary Industries; NSW Department of Primary Industries; University of New England; Western Sydney University; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Chinese Academy of Sciences; University of New South Wales Sydney; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Wang, B; Yu, Q (corresponding author), Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China.;Wang, B (corresponding author), Wagga Wagga Agr Inst, New South Wales Dept Primary Ind, Wagga Wagga, NSW 2650, Australia.;Luo, JJ (corresponding author), Nanjing Univ Informat Sci & Technol, Inst Climate & Applicat Res ICAR, Key Lab Meteorol Disaster, Minist Educ KLME, Nanjing 210044, Peoples R China.;Yu, Q (corresponding author), Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China. bin.a.wang@dpi.nsw.gov.au; jjluo@nuist.edu.cn; yuq@nwsuaf.edu.cn Wang, Bin/AFI-6568-2022; Wei, Jinghua/HOH-7306-2023; Cowie, Annette/E-1485-2016; jin, Huidong/B-3187-2009 Wang, Bin/0000-0002-6422-5802; Cowie, Annette/0000-0002-3858-959X; Liu, De Li/0000-0003-2574-1908; jin, Huidong/0000-0002-3925-0256; Nolan, Rachael/0000-0001-9277-5142; guo, xiao wei/0000-0002-2519-7087 National Natural Science Foundation of China [42088101, 42030605]; research project: Towards an Operational Fire Early Warning System for Indonesia (TOFEWSI); UK's National Environment Research Council/Newton Fund on behalf of the UK Research Innovation [NE/P014801/1]; Natural Science Foundation of Qinghai [2021-HZ-811] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); research project: Towards an Operational Fire Early Warning System for Indonesia (TOFEWSI); UK's National Environment Research Council/Newton Fund on behalf of the UK Research Innovation; Natural Science Foundation of Qinghai This work was supported by the National Natural Science Foundation of China (42088101 and 42030605) . We are grateful to the ECMWF (European Centre for Medium-Range Weather Forecasts) for providing freely available historical fire weather indices via the Copernicus Data server as part of the Copernicus Emergency Management Service. Allan Spessa and Tadas Nikonovas appreciate support from the research project: Towards an Operational Fire Early Warning System for Indonesia (TOFEWSI) . The TOFEWSI project was funded from October 2017-October 2021 through the UK's National Environment Research Council/Newton Fund on behalf of the UK Research & Innovation (NE/P014801/1) (UK Prin-cipal Investigator: Allan Spessa) (https://tofewsi.github.io/) . Xiao-wei Guo receives financial support from the Natural Science Foundation of Qinghai (2021-HZ-811) . We thank Dr. Sarah Harris from Bushfire Management Country Fire Authority and two anony-mous reviewers for their helpful comments to improve the manuscript. 76 4 4 26 32 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2095-9273 2095-9281 SCI BULL Sci. Bull. MAR 30 2022.0 67 6 655 664 10.1016/j.scib.2021.10.001 0.0 MAR 2022 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 2G3OX 36546127.0 Green Published, hybrid 2023-03-23 WOS:000813508200017 0
J Kumari, S; Venkatesh, VG; Shi, YY Kumari, Sneha; Venkatesh, V. G.; Shi, Yangyan Virtual technologies adoption to sustain quality in agricultural sector: An emerging economy perspective JOURNAL OF CLEANER PRODUCTION English Article Agriculture value chain; Collective action; Farmers; Quality management; Sustainability; Virtualtechnology SUPPLY CHAIN; BIG DATA; COMPLEXITY THEORY; FOOD-PRODUCTS; TRACEABILITY; MANAGEMENT; UNCERTAINTY; PERFORMANCE; GOVERNANCE; ANALYTICS The study aims to identify the antecedents responsible for sustaining quality control in the context of under-studied agriculture value chains. There is a pressing need to understand the dynamics of technology imple-mentation in an emerging economy context. The conceptual model is designed from complexity, and collective action theory perspectives. An empirical study validates the model and considers the role of traceability, col-lective action, big data predictive capability, and behavioral uncertainties in sustaining quality in agriculture value chains (AVCs). The paper addresses the call for in-depth deliberations on exploring the factors of AVC quality. The empirical validation of the theoretical model shows a significant positive relationship among the antecedents. The path coefficients and effect size have been derived from understanding the regression equations of cause and effect groups. It also identifies the avenues for improving quality in value chains. This is a pio-neering study to design a new operation model for sustaining the quality of AVCs. It also adds a new theoretical debate to AVC technology deployment in the context of an emerging economy. The study is limited to recog-nizing a few antecedents from the literature in the Indian context. The deliberations contribute significantly to minimizing agricultural waste in developing countries through quality control. [Kumari, Sneha] Symbiosis Sch Econ, Symbiosis Int, Pune, India; [Venkatesh, V. G.] Normandie Business Sch, Metis Lab, Le Havre, France; [Venkatesh, V. G.] Indian Inst Management, Sambalpur, India; [Shi, Yangyan] Jiangsu Univ Technol, Sch Econ, Changzhou, Peoples R China; [Shi, Yangyan] Macquarie Univ, Macquarie Business Sch, Sydney, Australia Symbiosis International University; Symbiosis School of Economics (SSE); Indian Institute of Management (IIM System); Indian Institute of Management Sambalpur; Jiangsu University of Technology; Macquarie University Shi, YY (corresponding author), Macquarie Univ, Macquarie Business Sch, Sydney, Australia. snehakumari1201@gmail.com; vgv1976@gmail.com; ys102@hotmail.com Venkatesh, V G/0000-0002-5340-4637; Shi, Yangyan/0000-0002-5551-0652 90 0 0 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. FEB 15 2023.0 388 135988 10.1016/j.jclepro.2023.135988 0.0 JAN 2023 11 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 8L3LM 2023-03-23 WOS:000923686500001 0
J Zhang, A; Zhong, RY; Farooque, M; Kang, K; Venkatesh, VG Zhang, Abraham; Zhong, Ray Y.; Farooque, Muhammad; Kang, Kai; Venkatesh, V. G. Blockchain-based life cycle assessment: An implementation framework and system architecture RESOURCES CONSERVATION AND RECYCLING English Article Blockchain; Life cycle assessment; Supply chain sustainability; Environmental sustainability; Operational excellence SUPPLY CHAIN MANAGEMENT; CIRCULAR ECONOMY; IMPACT ASSESSMENT; WASTE MANAGEMENT; ASSESSMENT LCA; TECHNOLOGY; ENERGY; SUSTAINABILITY; INTERNET; THINGS Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with intemet-of-things (loT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, loT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications. [Zhang, Abraham; Farooque, Muhammad] Auckland Univ Technol, AUT Univ, Business Sch, Private Bag 92006, Auckland 1142, New Zealand; [Zhang, Abraham] Excelsia Coll, Lumen Res Inst, 69-71 Waterloo Rd, Macquarie Pk, NSW 2113, Australia; [Zhang, Abraham] Indiana Wesleyan Univ, 69-71 Waterloo Rd, Macquarie Pk, NSW 2113, Australia; [Zhong, Ray Y.; Kang, Kai] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Hong Kong, Peoples R China; [Farooque, Muhammad] Sukkur IBA Univ, Sukkur, Pakistan; [Venkatesh, V. G.] Ecole Management Normandie, 30 Rue Richelieu, F-76087 Le Havre, France Auckland University of Technology; University of Hong Kong; Sukkur IBA University Zhang, A (corresponding author), Auckland Univ Technol, AUT Univ, Business Sch, Private Bag 92006, Auckland 1142, New Zealand. abraham.zhang@aut.ac.nz Farooque, Muhammad/AAY-2680-2020; Zhang, Abraham/AAA-7214-2019 Zhang, Abraham/0000-0002-3804-6392; Kang, Kai/0000-0002-3142-5883; Farooque, Muhammad/0000-0001-6875-2784; Venkatesh, V G/0000-0002-5340-4637 116 104 106 20 210 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0921-3449 1879-0658 RESOUR CONSERV RECY Resour. Conserv. Recycl. JAN 2020.0 152 104512 10.1016/j.resconrec.2019.104512 0.0 11 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Environmental Sciences & Ecology JN4KI Green Accepted 2023-03-23 WOS:000496867700010 0
J Setio, AAA; Traverso, A; de Bel, T; Berens, MSN; van den Bogaard, C; Cerello, P; Chen, H; Dou, Q; Fantacci, ME; Geurts, B; van der Gugten, R; Heng, PA; Jansen, B; de Kaste, MMJ; Kotov, V; Lin, JYH; Manders, JTMC; Sonora-Mengana, A; Garcia-Naranjo, JC; Papavasileiou, E; Prokop, M; Saletta, M; Schaefer-Prokop, CM; Scholten, ET; Scholten, L; Snoeren, MM; Torres, EL; Vandemeulebroucke, J; Walasek, N; Zuidhof, GCA; van Ginneken, B; Jacobs, C Setio, Arnaud Arindra Adiyoso; Traverso, Alberto; de Bel, Thomas; Berens, Moira S. N.; van den Bogaard, Cas; Cerello, Piergiorgio; Chen, Hao; Dou, Qi; Evelina Fantacci, Maria; Geurts, Bram; van der Gugten, Robbert; Heng, Pheng Ann; Jansen, Bart; de Kaste, Michael M. J.; Kotov, Valentin; Lin, Jack Yu-Hung; Manders, Jeroen T. M. C.; Sonora-Mengana, Alexander; Carlos Garcia-Naranjo, Juan; Papavasileiou, Evgenia; Prokop, Mathias; Saletta, Marco; Schaefer-Prokop, Cornelia M.; Scholten, Ernst T.; Scholten, Luuk; Snoeren, Miranda M.; Lopez Torres, Ernesto; Vandemeulebroucke, Jef; Walasek, Nicole; Zuidhof, Guido C. A.; van Ginneken, Bram; Jacobs, Colin Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge MEDICAL IMAGE ANALYSIS English Article Pulmonary nodules; Computed tomography; Computer-aided detection; Medical image challenges; Deep learning; Convolutional networks LUNG NODULES; AIDED DETECTION; SELECTIVE ENHANCEMENT; DETECTION SYSTEM; CANCER; SEGMENTATION; PERFORMANCE; SCANS Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems. (C) 2017 Elsevier B.V. All rights reserved. [Setio, Arnaud Arindra Adiyoso; Traverso, Alberto; Prokop, Mathias; Schaefer-Prokop, Cornelia M.; Scholten, Ernst T.; van Ginneken, Bram; Jacobs, Colin] Radboud Univ Nijmegen, Med Ctr, Diagnost Image Anal Grp, Nijmegen, Netherlands; [Geurts, Bram; Snoeren, Miranda M.] Radboud Univ Nijmegen, Med Ctr, Dept Radiol & Nucl Med, Nijmegen, Netherlands; [Schaefer-Prokop, Cornelia M.] Meander Medisch Ctr, Dept Radiol, Amersfoort, Netherlands; [Sonora-Mengana, Alexander; Carlos Garcia-Naranjo, Juan] Univ Oriente, Ctr Biofis Med, Santiago De Cuba, Cuba; [Jansen, Bart; Sonora-Mengana, Alexander; Papavasileiou, Evgenia; Vandemeulebroucke, Jef] Vrije Univ Brussel, Dept Elect & Informat, Brussels, Belgium; [Jansen, Bart; Vandemeulebroucke, Jef] IMEC, Leuven, Belgium; [Traverso, Alberto] Polytechn Univ Turin, Dept Appl Sci & Technol, Turin, Italy; [Traverso, Alberto; Cerello, Piergiorgio; Saletta, Marco] Turin Sect Ist Nazl Fis Nucl, Turin, Italy; [Evelina Fantacci, Maria] Pisa Sect Ist Nazl Fis Nucl, Pisa, Italy; [Lin, Jack Yu-Hung] Yanan Xi Lu 129,9th floor, Shanghai, Peoples R China; [Evelina Fantacci, Maria] Univ Pisa, Dept Phys, Pisa, Italy; [Sonora-Mengana, Alexander; Lopez Torres, Ernesto] Ctr Appl Technol & Nucl Dev, Havana, Cuba; [Chen, Hao; Dou, Qi; Heng, Pheng Ann] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China; [Berens, Moira S. N.; van der Gugten, Robbert; de Kaste, Michael M. J.; Manders, Jeroen T. M. C.; Zuidhof, Guido C. A.] Radboud Univ Nijmegen, Nijmegen, Netherlands; [de Bel, Thomas; van den Bogaard, Cas; Kotov, Valentin; Scholten, Luuk; Walasek, Nicole] Radboud Univ Nijmegen, Inst Comp & Informat Sci, Nijmegen, Netherlands; [van Ginneken, Bram] Fraunhofer MEVIS, Bremen, Germany Radboud University Nijmegen; Radboud University Nijmegen; Meander Medisch Centrum; Universidad de Oriente Santiago de Cuba; Vrije Universiteit Brussel; IMEC; Polytechnic University of Turin; University of Pisa; Chinese University of Hong Kong; Radboud University Nijmegen; Radboud University Nijmegen Setio, AAA (corresponding author), Radboud Univ Nijmegen, Med Ctr, Diagnost Image Anal Grp, Nijmegen, Netherlands. arnaud.setio@gmail.com Snoeren, Miranda/Q-7623-2019; Dou, Qi/I-8175-2019; Jansen, Bart/GLR-1063-2022; van Ginneken, Bram/A-3728-2012; Prokop, W. Mathias/H-8081-2014; Geurts, Bram HJ/G-4493-2016; Jacobs, Colin/P-6938-2015; Chen, Hao/V-4299-2019; Walasek, Nicole/GRS-5262-2022; Traverso, Alberto/AAS-2766-2020; Vandemeulebroucke, Jef/AAK-2722-2020; Fantacci, Maria Evelina/ABD-4227-2020; Lin, Jack/HLG-6010-2023; Vandemeulebroucke, Jef/F-2596-2016; Papavasileiou, Evgenia/AAN-3066-2021 Snoeren, Miranda/0000-0002-7613-5433; Dou, Qi/0000-0002-3416-9950; van Ginneken, Bram/0000-0003-2028-8972; Prokop, W. Mathias/0000-0001-8157-8055; Geurts, Bram HJ/0000-0002-1960-4491; Jacobs, Colin/0000-0003-1180-3805; Chen, Hao/0000-0002-8400-3780; Vandemeulebroucke, Jef/0000-0001-5714-3254; Fantacci, Maria Evelina/0000-0003-2130-4372; Vandemeulebroucke, Jef/0000-0001-5714-3254; Papavasileiou, Evgenia/0000-0002-8687-2808; Heng, Pheng Ann/0000-0003-3055-5034; Cerello, Piergiorgio/0000-0002-2443-5178; Walasek, Nicole/0000-0003-4411-319X; Jansen, Bart/0000-0001-8042-6834; Traverso, Alberto/0000-0001-6183-4429 Netherlands Organization for Scientific Research [639.023.207]; Belgian Development Cooperation through the VLIR-UOS program; Universidad de Oriente in Santiago de Cuba; Research Foundation Flanders (FWO) Netherlands Organization for Scientific Research(Netherlands Organization for Scientific Research (NWO)); Belgian Development Cooperation through the VLIR-UOS program; Universidad de Oriente in Santiago de Cuba; Research Foundation Flanders (FWO)(FWO) The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC-IDRI Database used in this study. This work was supported by a research grant from the Netherlands Organization for Scientific Research [project number 639.023.207]. A. Sonora was supported by the Belgian Development Cooperation through the VLIR-UOS program with Universidad de Oriente in Santiago de Cuba. E. Papavasileiou was supported by a PhD grant of the Research Foundation Flanders (FWO). 61 426 462 22 157 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. DEC 2017.0 42 1 13 10.1016/j.media.2017.06.015 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging FN1VA 28732268.0 Green Submitted, Green Published 2023-03-23 WOS:000415778100001 0
J Zhang, SY; Zanchettin, AM; Villa, R; Dai, SL Zhang, Shiyu; Zanchettin, Andrea Maria; Villa, Renzo; Dai, Shuling Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction MECHANISM AND MACHINE THEORY English Article Real-time trajectory planning; Human-robot interaction; Non-linear optimization; Machine learning; Serial robot PREDICTION; SYSTEMS; SAFETY In order to perform safe and natural interactions with humans, robots are required to adjust their motions quickly according to human behaviors. Performing the complex calculation and updating the trajectories in real-time is a particular challenge. In this paper, we present a real-time optimization-based trajectory planning method for serial robots. We encode the trajectory planning problem into a series of optimization problems. To solve the high-dimensional complex non-linear optimization problems in real-time, we provide a joint-decoupling method that transforms the original joint-coupled optimization problem into multiple joint-independent optimization problems, with much lower computational complexity. We implement and validate our method in a specific human-robot interaction case. Experimental results show that the computational feasibility and efficiency of optimization solution were greatly improved by the joint-decoupling transformation. Smooth, safe, and rapid motion of the robot was generated in real-time, establishing a basis for safe and reactive human-robot interactions. (C) 2019 Elsevier Ltd. All rights reserved. [Zhang, Shiyu; Dai, Shuling] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China; [Zanchettin, Andrea Maria; Villa, Renzo] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy Beihang University; Polytechnic University of Milan Zhang, SY (corresponding author), Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China. zhangshiyu@buaa.edu.cn Zhang, Shiyu/0000-0003-2474-7451 38 13 13 1 36 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0094-114X 1873-3999 MECH MACH THEORY Mech. Mach. Theory FEB 2020.0 144 103664 10.1016/j.mechmachtheory.2019.103664 0.0 22 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering JT3WZ 2023-03-23 WOS:000500924800012 0
J Jiang, S; Zhang, X; Cheng, YH; Xu, DM; De Pablos, PO; Wang, XY Jiang, Shan; Zhang, Xi; Cheng, Yihang; Xu, Dongming; Ordonez De Pablos, Patricia; Wang, Xuyan Dynamic impact of social network on knowledge contribution loafing in mobile collaboration: a hidden Markov model JOURNAL OF KNOWLEDGE MANAGEMENT English Article Hidden markov model; Network centrality; Knowledge contribution loafing; Mobile collaboration MOTIVATION; PERFORMANCE; EXCHANGE; SHARE Purpose Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state. Design/methodology/approach This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model. Findings First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state. Research limitations/implications - The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TFIDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network. Practical implications - This study helps companies infer tendency of different KCL and dynamically re-organize amobile collaborative team for better knowledge contribution. Originality/value First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states. [Jiang, Shan; Zhang, Xi; Cheng, Yihang; Wang, Xuyan] Tianjin Univ, Coll Management & Econ, Tianjin, Peoples R China; [Xu, Dongming] Univ Queensland, Brisbane, Qld, Australia; [Ordonez De Pablos, Patricia] Univ Oviedo, Dept Business Adm & Accountabil, Oviedo, Spain Tianjin University; University of Queensland Zhang, X (corresponding author), Tianjin Univ, Coll Management & Econ, Tianjin, Peoples R China. jackyzhang@tju.edu.cn Ordóñez de Pablos, Patricia/AAC-9329-2022; Zhang, Jacky/HHS-9302-2022 Ordonez de Pablos, Patricia/0000-0002-8388-6382; Zhang, Xi/0000-0002-1105-9417 National Natural Science Foundation of China [71722005, 71571133, 71790594, 71790590]; Tianjin Natural Science Foundation [18JCJQJC45900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tianjin Natural Science Foundation(Natural Science Foundation of Tianjin) The work was supported by National Natural Science Foundation of China (No. 71722005, 71571133, 71790594, 71790590), and Tianjin Natural Science Foundation (No. 18JCJQJC45900). The early version of this paper was presented at the CONF-IRM2018 (Zhang et al., 2017), which, as the AIS affiliated conference, encourages authors to submit extended papers to journals. The authors appreciate the reviewers' and audience's comments. 69 7 7 11 68 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 1367-3270 1758-7484 J KNOWL MANAG J. Knowl. Manag. NOV 11 2019.0 23 9 SI 1901 1920 10.1108/JKM-10-2018-0641 0.0 20 Information Science & Library Science; Management Social Science Citation Index (SSCI) Information Science & Library Science; Business & Economics JR6ZJ 2023-03-23 WOS:000499770700012 0
J Liu, TW; Bai, JB; Fantuzzi, N; Bu, GY; Li, D Liu, Tian-Wei; Bai, Jiang-Bo; Fantuzzi, Nicholas; Bu, Guang-Yu; Li, Dong Multi-objective optimisation designs for thin-walled deployable composite hinges using surrogate models and Genetic Algorithms COMPOSITE STRUCTURES English Article Deployable composite hinge (DCH); Multi-objective optimisation; Surrogate models; Genetic Algorithms; Benchmarking NONDOMINATED SORTING APPROACH; RESPONSE-SURFACE METHODOLOGY; HANDLING CONSTRAINTS; DEPLOYMENT; TUBE Thin-walled deployable composite hinges (DCHs) can achieve foldable and deployable functions by storing and releasing strain energy, which have great application potential in deployable structures, such as satellite antennas and solar wings. This paper presented multi-objective optimisation designs for DCHs. Firstly, an optimisation problem was established to obtain three conflicting objectives, minimising the peak folding moment, maximising the peak torsional moment and minimising the mass. Three design variables and one constraint had been considered. Moreover, four surrogate models were employed, including response surface methodology (RSM) and machine learning models. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R-2) were used to determine the surrogate model with the highest accuracy. Furthermore, four state-of-the-art Genetic Algorithms were benchmarked to obtain the optimal designs. The mimicked inverted generational distance (mIGD) was applied to determine the best optimiser. The research results have significance to practical engineering application of DCHs. [Liu, Tian-Wei; Bai, Jiang-Bo; Bu, Guang-Yu; Li, Dong] Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China; [Bai, Jiang-Bo] Beihang Univ, Jingdezhen Res Inst, Jingdezhen 333000, Jiangxi, Peoples R China; [Fantuzzi, Nicholas] Univ Bologna, DICAM Dept, I-40136 Bologna, Italy Beihang University; Beihang University; University of Bologna Bai, JB (corresponding author), Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China. baijiangbo@buaa.edu.cn Liu, Tian-Wei/0000-0003-4367-1528 National Natural Science Foundation of China [51875026]; National Defense Basic Research Program of China [JCKY2019205C002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Defense Basic Research Program of China(National Basic Research Program of China) This project was supported by the National Natural Science Foundation of China (Grant No. 51875026) and the National Defense Basic Research Program of China (Grant No. JCKY2019205C002). 55 6 6 3 35 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-8223 1879-1085 COMPOS STRUCT Compos. Struct. JAN 15 2022.0 280 114757 10.1016/j.compstruct.2021.114757 0.0 NOV 2021 17 Mechanics; Materials Science, Composites Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Materials Science WX7TF 2023-03-23 WOS:000718793200001 0
J Xia, T; Lin, JJ; Li, Y; Feng, J; Hui, P; Sun, FN; Guo, DS; Jin, DP Xia, Tong; Lin, Junjie; Li, Yong; Feng, Jie; Hui, Pan; Sun, Funing; Guo, Diansheng; Jin, Depeng 3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA English Article Graph neural network; traffic flow prediction; urban computing Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3-Dimensional Graph Convolution Network (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin-destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%similar to 19.5% for the next-time-interval prediction. [Xia, Tong; Lin, Junjie; Li, Yong; Feng, Jie; Jin, Depeng] Tsinghua Univ, Dept Elect Engn, Rohm Bldg 10-202, Beijing 100084, Peoples R China; [Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Sun, Funing; Guo, Diansheng] Tencent Inc, Xibeiwang10, Beijing 100084, Peoples R China Tsinghua University; University of Helsinki; Hong Kong University of Science & Technology; Tencent Xia, T (corresponding author), Tsinghua Univ, Dept Elect Engn, Rohm Bldg 10-202, Beijing 100084, Peoples R China. xia-t17@tsinghua.org.cn; lin-jj15@mails.lsinghua.edu.cn; liyong07@tsinghua.edu.cn; feng-j16@mails.tsinghua.edu.cn; jindp@tsinghua.edu.cn Xia, Tong/HDL-9669-2022; Alidadi, Mehdi/HJZ-0235-2023; li, yong/HDN-3885-2022; lin, junjie/HDL-7170-2022 Alidadi, Mehdi/0000-0001-5183-7829; Guo, Diansheng/0000-0003-3483-4153 National Key Research and Development Program of China [SQ2018YFB180012]; National Nature Science Foundation of China [61971267, 61972223, 61861136003, 61621091]; Beijing Natural Science Foundation [L182038]; Beijing National Research Center for Information Science and Technology [20031887521]; Tsinghua UniversityTencent Joint Laboratory for Internet Innovation Technology National Key Research and Development Program of China; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Beijing National Research Center for Information Science and Technology; Tsinghua UniversityTencent Joint Laboratory for Internet Innovation Technology This work was supported in part by The National Key Research and Development Program of China under grant SQ2018YFB180012, in part by the National Nature Science Foundation of China under 61971267, 61972223, 61861136003, and 61621091, in part by the Beijing Natural Science Foundation under L182038, in part by the Beijing National Research Center for Information Science and Technology under 20031887521, and in part by the research fund of Tsinghua UniversityTencent Joint Laboratory for Internet Innovation Technology. 53 5 5 30 36 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1556-4681 1556-472X ACM T KNOWL DISCOV D ACM Trans. Knowl. Discov. Data DEC 2021.0 15 6 110 10.1145/3451394 0.0 21 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZP1SE 2023-03-23 WOS:000766204500017 0
J Chen, TS; Pillonetto, G Chen, Tianshi; Pillonetto, Gianluigi On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses AUTOMATICA English Article Linear system identification; Reproducing kernel Hilbert spaces; BIBO stability; Kernel-based regularization; Inverse problems SYSTEM-IDENTIFICATION; STOCHASTIC-PROCESSES; BAYESIAN-ESTIMATION; NETWORKS Reproducing kernel Hilbert spaces (RKHSs) have proved themselves to be key tools for the development of powerful machine learning algorithms, the so-called regularized kernel-based approaches. Recently, they have also inspired the design of new linear system identification techniques able to challenge classical parametric prediction error methods. These facts motivate the study of the RKHS theory within the control community. In this note, we focus on the characterization of stable RKHSs, i.e. RKHSs of functions representing stable impulse responses. Related to this, working in an abstract functional analysis framework, Carmeli et al. (2006) has provided conditions for an RKHS to be contained in the classical Lebesgue spaces L-p. In particular, we specialize this analysis to the discrete-time case with p=1. The necessary and sufficient conditions for the stability of an RKHS are worked out by a quite simple proof, more easily accessible to the control community. (C) 2018 Elsevier Ltd. All rights reserved. [Chen, Tianshi] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen, Peoples R China; [Chen, Tianshi] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China; [Pillonetto, Gianluigi] Univ Padua, Dept Informat Engn, Padua, Italy Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen; University of Padua Pillonetto, G (corresponding author), Univ Padua, Dept Informat Engn, Padua, Italy. tschen@cuhk.edu.cn; giapi@dei.unipd.it Chen, Tianshi/C-2575-2014 Chen, Tianshi/0000-0001-8655-2655 Thousand Youth Talents Plan - Central Government of China; NSFC [61773329]; Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2014-5894] Thousand Youth Talents Plan - Central Government of China; NSFC(National Natural Science Foundation of China (NSFC)); Shenzhen Science and Technology Innovation Council; Chinese University of Hong Kong, Shenzhen; Swedish Research Council(Swedish Research Council) This work was supported by the Thousand Youth Talents Plan funded by the Central Government of China, the general project funded by NSFC under contract No. 61773329, the Shenzhen Research Projects Ji-20170189 and Ji-20160207 funded by Shenzhen Science and Technology Innovation Council, the President's grant under contract No. PF. 01.000249 and the start-up grant under contract No. 2014.0003.23 funded by the Chinese University of Hong Kong, Shenzhen, as well as by a research grant for junior researchers under contract No. 2014-5894, funded by Swedish Research Council. 28 9 9 0 7 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0005-1098 1873-2836 AUTOMATICA Automatica SEP 2018.0 95 529 533 10.1016/j.automatica.2018.05.017 0.0 5 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering GQ6RX 2023-03-23 WOS:000441853900057 0
J Almasan, P; Ferriol-Galmes, M; Paillisse, J; Suarez-Varela, J; Perino, D; Lopez, D; Perales, AAP; Harvey, P; Ciavaglia, L; Wong, L; Ram, V; Xiao, SH; Shi, X; Cheng, XL; Cabellos-Aparicio, A; Barlet-Ros, P Almasan, Paul; Ferriol-Galmes, Miquel; Paillisse, Jordi; Suarez-Varela, Jose; Perino, Diego; Lopez, Diego; Perales, Antonio Agustin Pastor; Harvey, Paul; Ciavaglia, Laurent; Wong, Leon; Ram, Vishnu; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Cabellos-Aparicio, Albert; Barlet-Ros, Pere Network Digital Twin: Context, Enabling Technologies, and Opportunities IEEE COMMUNICATIONS MAGAZINE English Article The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators in managing their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then we present a case study that leverages an ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks. [Almasan, Paul; Ferriol-Galmes, Miquel; Paillisse, Jordi; Suarez-Varela, Jose; Cabellos-Aparicio, Albert; Barlet-Ros, Pere] Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Barcelona, Spain; [Perino, Diego; Lopez, Diego; Perales, Antonio Agustin Pastor] Tel Res, Madrid, Spain; [Harvey, Paul; Ciavaglia, Laurent] Rakuten Mobile, Tokyo, Japan; [Wong, Leon] Rakuten Mobile, Res & Innovat Lab, Tokyo, Japan; [Xiao, Shihan; Shi, Xiang; Cheng, Xiangle] Huawei Technol Co Ltd, Shenzhen, Peoples R China Universitat Politecnica de Catalunya; Rakuten Group, Inc; Rakuten Group, Inc; Huawei Technologies Almasan, P (corresponding author), Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Barcelona, Spain. felician.paul.almasan@upc.edu Suárez-Varela, José/AAA-5418-2019 Suárez-Varela, José/0000-0002-7141-3414; Almasan, Paul/0000-0003-3903-6759 MCIN/AEI [PID2020-118011GB-C21]; Catalan Institution for Research and Advanced Studies (ICREA) of the Government of Catalonia; Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia; European Social Fund MCIN/AEI; Catalan Institution for Research and Advanced Studies (ICREA) of the Government of Catalonia; Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia; European Social Fund(European Social Fund (ESF)) This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. 15 1 1 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. NOV 2022.0 60 11 22 27 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 6C4IS 2023-03-23 WOS:000881980700005 0
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P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; ; ATLAS collaboration Measurement of the inclusive cross-sections of single top-quark and top-antiquark t-channel production in pp collisions at root s=13 TeV with the ATLAS detector JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron scattering (experiments) PARTON DISTRIBUTIONS; HADRON COLLIDERS; PLUS PLUS; QCD A measurement of the t-channel single-top-quark and single-top-antiquark production cross-sections in the lepton+jets channel is presented, using 3.2 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV, recorded with the ATLAS detector at the LHC in 2015. Events are selected by requiring one charged lepton (electron or muon), missing transverse momentum, and two jets with high transverse momentum, exactly one of which is required to be b-tagged. Using a binned maximum-likelihood fit to the discriminant distribution of a neural network, the cross-sections are determined to be sigma(tq) = 156 +/- 5 (stat.) +/- 27 (syst.) +/- 3 (lumi.) pb for single top-quark production and sigma((t) over barq) = 91 +/- 4 (stat.) +/- 18 (syst.) +/- 2 (lumi.) pb for single top-antiquark production, assuming a top-quark mass of 172.5 GeV. The cross-section ratio is measured to be R-t = sigma(tq) / sigma((t) over barq) = 1.72 +/- 0.09 (stat.) +/- 0.18 (syst.). All results are in agreement with Standard Model predictions. [Jackson, P.; Lee, L.; Petridis, A.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Bouffard, J.; Ernst, J.; Fischer, A.; Guindon, S.; Jain, V.] SUNY Albany, Dept Phys, Albany, NY USA; [Czodrowski, P.; Dassoulas, J.; Dehghanian, N.; Gingrich, D. M.; Jabbar, S.; Karamaoun, A.; Moore, R. W.; Pinfold, J. L.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Ciftci, A. K.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Barnovska-Blenessy, Z.; Berger, N.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Grevtsov, K.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Koletsou, I.; Lafaye, R.; Leveque, J.; Mastrandrea, P.; Sauvan, E.; Smart, B. H.; Todorov, T.; Wingerter-Seez, I.; Yatsenko, E.] CNRS, IN2P3, LAPP, Annecy Le Vieux, France; [Barnovska-Blenessy, Z.; Berger, N.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Grevtsov, K.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Koletsou, I.; Lafaye, R.; Leveque, J.; Mastrandrea, P.; Sauvan, E.; Smart, B. H.; Todorov, T.; Wingerter-Seez, I.; Yatsenko, E.] Univ Savoie Mont Blanc, Annecy Le Vieux, France; [Blair, R. E.; Chekanov, S.; LeCompte, T.; Love, J.; Malon, D.; Metcalfe, J.; Nguyen, D. H.; Paramonov, A.; Price, L. E.; Proudfoot, J.; Ryu, S.; Stanek, R. W.; van Gemmeren, P.; Wang, R.; Webster, J. S.; Yoshida, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Cheu, E.; Johns, K. A.; Jones, S.; Lampl, W.; Lei, X.; Leone, R.; Loch, P.; Nayyar, R.; O'grady, F.; Rutherfoord, J. P.; Shupe, M. A.; Varnes, E. W.; Veeraraghavan, V.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Abdallah, J.; Brandt, A.; Bullock, D.; Darmora, S.; De, K.; Farbin, A.; Feremenga, L.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Kim, H. Y.; Ozturk, N.; Schovancova, J.; Stradling, A. R.; Usai, G.; Vartapetian, A.; White, A.] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA; [Angelidakis, S.; Chouridou, S.; Fassouliotis, D.; Giokaris, N.; Ioannou, P.; Kourkoumelis, C.; Tsirintanis, N.] Univ Athens, Phys Dept, Athens, Greece; [Alexopoulos, T.; Benekos, N.; Dris, M.; Gazis, E. N.; Karakostas, K.; Karastathis, N.; Karentzos, E.; Leontsinis, S.; Maltezos, S.; Panagiotopoulou, E. St.; Papadopoulou, Th. D.; Tsipolitis, G.; Vlachos, S.] Natl Tech Univ Athens, Phys Dept, Zografos, Greece; [Andeen, T.; Ilchenko, Y.; Narayan, R.; Onyisi, P. U. E.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Abdinov, O.; Ahmadov, F.; Huseynov, N.; Javadov, N.; Khalil-Zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan; [Anjos, N.; Bosman, M.; Casado, M. P.; Casolino, M.; Cavallaro, E.; Cavalli-Sforza, M.; Farooque, T.; Fernandez Perez, S.; Fischer, C.; Fracchia, S.; Gerbaudo, D.; Grinstein, S.; Juste Rozas, A.; Korolkov, I.; Lange, J. C.; Lopez Paz, I.; Martinez, M.; Mir, L. M.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I.; Rizzi, C.; Rodina, Y.; Rodriguez Perez, A.; Sorin, V.; Terzo, S.; Tripiana, M. F.; Tsiskaridze, S.; Valery, L.] Barcelona Inst Sci & Technol, Inst Fis Altes Energies, Barcelona, Spain; [Agatonovic-Jovin, T.; Bogavac, D.; Bokan, P.; Dimitrievska, A.; Krstic, J.; Marjanovic, M.; Sijacki, Dj.; Simic, Lj.; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Dale, O.; Eigen, G.; Kastanas, A.; Liebig, W.; Lipniacka, A.; Maeland, S.; Stugu, B.; Yang, Z.; Zalieckas, J.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Heim, T.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA 94720 USA; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Heim, T.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Biedermann, D.; Dietrich, J.; Giorgi, F. M.; Grancagnolo, S.; Herbert, G. H.; Hristova, I.; Kind, O. M.; Kolanoski, H.; Lacker, H.; Lohse, T.; Mergelmeyer, S.; Nikiforov, A.; Rehnisch, L.; Rieck, P.; Schulz, H.; Sperlich, D.; Stamm, S.; zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Meloni, F.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Meloni, F.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.] Univ Bern, Lab High Energy Phys, Bern, Switzerland; [Allport, P. P.; Andari, N.; Bella, L. Aperio; Baca, M. J.; Bracinik, J.; Broughton, J. H.; Casadei, D.; Charlton, D. G.; Daniells, A. C.; Foster, A. G.; Gonella, L.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Levy, M.; Mudd, R. D.; Quijada, J. A. Murillo; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Arik, M.; Istin, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Phys Engn, Gaziantep, Turkey; [Cetin, S. A.] Istanbul Bilgi Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Beddall, A. J.] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Ctr Invest, Bogota, Colombia; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Ciocca, C.; D'amen, G.; De Castro, S.; Fabbri, F.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giorgi, F. M.; Grafstrom, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Negrini, M.; Piccinini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Spighi, R.; Tupputi, S. A.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Alberghi, G. L.; Biondi, S.; Ciocca, C.; D'amen, G.; De Castro, S.; Fabbri, F.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Grafstrom, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Piccinini, M.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Tupputi, S. A.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Arslan, O.; Bechtle, P.; Bernlochner, F. U.; Brock, I.; Bruscino, N.; Caudron, J.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Gaycken, G.; Geich-Gimbel, Ch.; Ghneimat, M.; Grefe, C.; Hageboeck, S.; Hansen, M. C.; Hohn, D.; Huegging, F.; Janssen, J.; Kostyukhin, V. V.; Kroseberg, J.; Krueger, H.; Lantzsch, K.; Lenz, T.; Leyko, A. M.; Liebal, J.; Moles-Valls, R.; Obermann, T.; Pohl, D.; Ricken, O.; Sarrazin, B.; Schaepe, S.; Schopf, E.; Schultens, M. J.; Schwindt, T.; Seema, P.; Stillings, J. A.; von Toerne, E.; Wagner, P.; Wang, T.; Wermes, N.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wong, K. H. Yau; Yuen, S. P. Y.] Univ Bonn, Phys Inst, Bonn, Germany; [Ahlen, S. P.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Kruskal, M.; Long, B. A.; Shank, J. T.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Amundsen, G.; Barone, G.; Bensinger, J. R.; Bianchini, L.; Blocker, C.; Dhaliwal, S.; Goblirsch-Kolb, M.; Herde, H.; Loew, K. M.; Sciolla, G.; Venturini, A.; Zengel, K.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Amaral Coutinho, Y.; Caloba, L. P.; Maidantchik, C.; Marroquim, F.; Nepomuceno, A. A.; Seixas, J. M.] Univ Fed Rio De Janeiro, COPPE, EE, IF, Rio De Janeiro, Brazil; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.; Peralva, B. S.] Fed Univ Juiz de Fora UFJF, Elect Circuits Dept, Juiz De Fora, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Begel, M.; Buttinger, W.; Chen, H.; Chernyatin, V.; Debbe, R.; Elmsheuser, J.; Ernst, M.; Gibbard, B.; Gordon, H. A.; Iakovidis, G.; Klimentov, A.; Kouskoura, V.; Kravchenko, A.; Lanni, F.; Lee, C. A.; Liu, H.; Lynn, D.; Ma, H.; Maeno, T.; Mountricha, E.; Nevski, P.; Nilsson, P.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Perepelitsa, D. V.; Pleier, M. -A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Radeka, V.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Caprini, I.; Caprini, M.; Chitan, A.; Ciubancan, M.; Constantinescu, S.; Dita, P.; Dita, S.; Dobre, M.; Ducu, O. A.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Pantea, D.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Phys Dept, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ, Timisoara, Romania; [Bossio Sola, J. D.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.; Sacerdoti, S.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Brunt, B. H.; Carter, J. R.; Chapman, J. D.; Cottin, G.; Gillam, T. P. S.; Hill, J. C.; Kaneti, S.; Lester, C. G.; Malone, C.; Mueller, T.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Yusuff, I.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Bellerive, A.; Cree, G.; Di Valentino, D.; Gillberg, D.; Koffas, T.; Lacey, J.; Leight, W. A.; Nomidis, I.; Oakham, F. G.; Pasztor, G.; Ruiz-Martinez, A.; Vincter, M. G.; Weber, S. A.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Aleksa, M.; Gonzalez, B. Alvarez; Amoroso, S.; Anders, G.; Anghinolfi, F.; Arnaez, O.; Avolio, G.; Baak, M. A.; Backhaus, M.; Barak, L.; Barisits, M-S; Beermann, T. A.; Beltramello, O.; Bianco, M.; Bogaerts, J. A.; Bortfeldt, J.; Boveia, A.; Boyd, J.; Burckhart, H.; Camarda, S.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Carrillo-Montoya, G. D.; Catinaccio, A.; Cattai, A.; Cerv, M.; Chisholm, A. S.; Chromek-Burckhart, D.; Conti, G.; Cortes-Gonzalez, A.; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Girolamo, B.; Di Nardo, R.; Dittus, F.; Dobos, D.; Dudarev, A.; Duehrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Faltova, J.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Fressard-Batraneanu, S. M.; Froidevaux, D.; Gadatsch, S.; Goossens, L.; Gorini, B.; Gray, H. M.; Gumpert, C.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Jakobsen, S.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Lapoire, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Lichard, P.; Malyukov, S.; Manousos, A.; Mapelli, L.; Marzin, A.; Berlingen, J. Montejo; Morgenstern, S.; Mornacchi, G.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Oide, H.; Palestini, S.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Pommes, K.; Poppleton, A.; Poulard, G.; Poveda, J.; Astigarraga, M. E. Pozo; Rammensee, M.; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Ruthmann, N.; Salzburger, A.; Schaefer, D.; Schlenker, S.; Schmieden, K.; Sforza, F.; Sanchez, C. A. Solans; Spigo, G.; Staerz, S.; Stelzer, H. J.; Teischinger, F. A.; Ten Kate, H.; Unal, G.; van Woerden, M. C.; Vandelli, W.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Wotschack, J.; Young, C. J. S.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Toro, R. Camacho; Cheng, Y.; Dandoy, J. R.; Facini, G.; Gardner, R. W.; Kapliy, A.; Kim, Y. K.; Krizka, K.; Li, H. L.; Merritt, F. S.; Miller, D. W.; Oreglia, M. J.; Pilcher, J. E.; Saxon, J.; Shochet, M. J.; Stark, G. H.; Swiatlowski, M.; Vukotic, I.; Wu, M.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Blunier, S.; Diaz, M. A.; Ochoa-Ricoux, J. P.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Carquin, E.; Kuleshov, S.; Lopez, J. A.; Pezoa, R.; Prokoshin, F.; Salazar Loyola, J. E.; Tapia Araya, S.; Vasquez, G. A.; White, R.] Univ Tecn Fed Santa Mar, Dept Fis, Valparaiso, Chile; [Bai, Y.; da Costa, J. Barreiro Guimaraes; Cheng, H. J.; Fang, Y.; Jin, S.; Li, Q.; Liang, Z.; Merino, J. Llorente; Lou, X.; Mansour, J. D.; Ouyang, Q.; Peng, C.; Ren, H.; Shan, L. Y.; Sun, X.; Xu, D.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Chen, S.; Wang, C.; Zhang, H.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Chen, X.; Zhou, N.] Tsinghua Univ, Phys Dept, Beijing 100084, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] Clermont Univ, Lab Phys Corpusculaire, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] Univ Blaise Pascal, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] CNRS, IN2P3, Clermont Ferrand, France; [Alkire, S. P.; Angerami, A.; Brooijmans, G.; Carbone, R. M.; Clark, M. R.; Cole, B.; Hughes, E. W.; Iordanidou, K.; Klein, M. H.; Mohapatra, S.; Ochoa, I.; Parsons, J. A.; Smith, M. N. K.; Smith, R. W.; Thompson, E. N.; Tuts, P. M.; Wang, T.; Zhou, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Besjes, G. J.; Dam, M.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Monk, J.; Mortensen, S. S.; Pedersen, L. E.; Petersen, T. C.; Pingel, A.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Cairo, V. M.; Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; Mastroberardino, A.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Collegato Cosenza, Arcavacata Di Rende, Italy; [Cairo, V. M.; Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; Mastroberardino, A.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Mindur, B.; Przybycien, M.; Zemla, A.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Banas, E.; de Renstrom, P. A. Bruckman; Burka, K.; Chwastowski, J. J.; Derendarz, D.; Godlewski, J.; Gornicki, E.; Hajduk, Z.; Kaczmarska, A.; Knapik, J.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Cao, T.; Firan, A.; Gupta, R.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Wang, H.; Ye, J.; Zhao, X.; Zhou, L.] Southern Methodist Univ, Dept Phys, Dallas, TX USA; [Izen, J. M.; Leyton, M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Britzger, D.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E. M.; Lohwasser, K.; Madsen, A.; Medinnis, M.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Queitsch-Maitland, M.; Robinson, J. E. M.; Schaefer, R.; Schmitt, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Wang, J.; Zakharchuk, N.] DESY, Hamburg, Germany; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Britzger, D.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E. M.; Lohwasser, K.; Madsen, A.; Medinnis, M.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Queitsch-Maitland, M.; Robinson, J. E. M.; Schaefer, R.; Schmitt, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Wang, J.; Zakharchuk, N.] DESY, Zeuthen, Germany; [Burmeister, I.; Cinca, D.; Dette, K.; Erdmann, J.; Esch, H.; Goessling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.] Tech Univ Dortmund, Lehrstuhl Exp Phys 4, Dortmund, Germany; [Duschinger, D.; Friedrich, F.; Gutschow, C.; Hauswald, L.; Kobel, M.; Mader, W. F.; Novgorodova, O.; Siegert, F.; Socher, F.; Straessner, A.; Vest, A.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Goshaw, A. T.; Kajomovitz, E.; Kotwal, A.; Kruse, M. C.; Li, L.; Li, S.; Liu, M.; Oh, S. H.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bristow, T. M.; Clark, P. J.; Dias, F. A.; Edwards, N. C.; Gao, Y.; Walls, F. M. Garay; Glaysher, P. C. F.; Harrington, R. D.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Mills, C.; Pino, S. A. Olivares; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, Sch Phys & Astron, SUPA, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Esposito, B.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Arnold, H.; Betancourt, C.; Boehler, M.; Bruneliere, R.; Buehrer, F.; Burgard, C. D.; Buescher, D.; Cardillo, F.; Coniavitis, E.; Consorti, V.; Dang, N. P.; Dao, V.; Di Simone, A.; Glatzer, J.; Gonella, G.; Herten, G.; Hirose, M.; Jakobs, K.; Javurek, T.; Jenni, P.; Kiss, F.; Koeneke, K.; Kopp, A. K.; Kuehn, S.; Landgraf, U.; Luedtke, C.; Nagel, M.; Pagacova, M.; Parzefall, U.; Ronzani, M.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schillo, C.; Schnoor, U.; Schumacher, M.; Sommer, P.; Sundermann, J. E.; Ta, D.; Temming, K. K.; Tornambe, P.; Tsiskaridze, V.; Weiser, C.; Werner, M.; Zhang, L.; Zimmermann, S.] Albert Ludwigs Univ, Fak Math & Phys, Freiburg, Germany; [Ancu, L. S.; De Mendizabal, J. Bilbao; Calace, N.; Chatterjee, A.; Clark, A.; Coccaro, A.; Delitzsch, C. M.; Della Volpe, D.; Ferrere, D.; Golling, T.; Gonzalez-Sevilla, S.; Gramling, J.; Guescini, F.; Iacobucci, G.; Katre, A.; Khoo, T. J.; Lanfermann, M. C.; Lionti, A. E.; March, L.; Mermod, P.; Nackenhorst, O.; Paolozzi, L.; Ristic, B.; Schramm, S.; Sfyrla, A.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Darbo, G.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Guido, E.; Miglioranzi, S.; Morettini, P.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Barberis, D.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Guido, E.; Miglioranzi, S.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Tskhadadze, E. G.] Iv Javakhishvili Tbilisi State Univ, E Andronikashvili Inst Phys, Tbilisi, Georgia; [Djobava, T.; Durglishvili, A.; Khubua, J.; Mosidze, M.] Tbilisi State Univ, High Energy Phys Inst, Tbilisi, Georgia; [Dueren, M.; Kreutzfeldt, K.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Buzatu, A.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Gul, U.; Knue, A.; Mullen, P.; O'Shea, V.; Owen, M.; Pollard, C. S.; Qin, G.; Quilty, D.; Ravenscroft, T.; Robson, A.; St Denis, R. D.; Stewart, G. A.; Thompson, A. S.] Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow, Lanark, Scotland; [Bindi, M.; Bisanz, T.; Blumenschein, U.; Brandt, G.; De Maria, A.; Drechsler, E.; Graber, L.; Grosse-Knetter, J.; Janus, M.; Kareem, M. J.; Kawamura, G.; Lai, S.; Lemmer, B.; Magradze, E.; Mantoani, M.; Mchedlidze, G.; Llacer, M. Moreno; Musheghyan, H.; Quadt, A.; Rieger, J.; Rosien, N. -A.; Rzehorz, G. F.; Shabalina, E.; Stolte, P.; Veatch, J.; Weingarten, J.; Zinonos, Z.] Georg August Univ, Phys Inst 2, Gottingen, Germany; [Albrand, S.; Berlendis, S.; Bethani, A.; Camincher, C.; Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Gabaldon, C.; Genest, M. H.; Gradin, P. O. J.; Hostachy, J-Y.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Petit, E.; Stark, J.; Trocme, B.; Wu, M.] Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subatom & Cosmol, Grenoble, France; [Chan, S. K.; Clark, B. L.; Franklin, M.; Giromini, P.; Huth, J.; Ippolito, V.; Lazovich, T.; Mateos, D. Lopez; Morii, M.; Rogan, C. S.; Skottowe, H. P.; Sun, S.; Tolley, E.; Tong, B.; Tuna, A. N.; Yen, A. L.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA USA; [Gao, J.; Geng, C.; Guo, Y.; Han, L.; Hu, Q.; Jiang, Y.; Li, B.; Li, C.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y.; Peng, H.; Song, H. Y.; Wang, W.; Zhang, G.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Andrei, V.; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J. I.; Dunford, M.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kluge, E. -E.; Lang, V. S.; Meier, K.; Theenhausen, H. Meyer Zu; Villar, D. I. Narrias; Sahinsoy, M.; Scharf, V.; Schultz-Coulon, H. -C.; Stamen, R.; Starovoitov, P.; Suchek, S.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; de Lima, D. E. Ferreira; Giulini, M.; Kolb, M.; Lisovyi, M.; Schaetzel, S.; Schoening, A.; Sosa, D.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Kretz, M.; Kugel, A.] Heidelberg Univ, ZITI Inst Tech Informat, Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Bortolotto, V.; Chan, Y. L.; Castillo, L. R. Flores; Lu, H.; Salvucci, A.; Tsui, K. M.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Bortolotto, V.; Orlando, N.; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Hong Kong, Peoples R China; [Bortolotto, V.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys, Clear Water Bay, Kowloon, Hong Kong, Peoples R China; [Bortolotto, V.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Clear Water Bay, Kowloon, Hong Kong, Peoples R China; [Choi, K.; Evans, H.; Gagnon, P.; Kopeliansky, R.; Lammers, S.; Martinez, N. Lorenzo; Luehring, F.; Ogren, H.; Penwell, J.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Guenther, J.; Iwanski, W.; Jansky, R.; Kneringer, E.; Lukas, W.; Milic, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Pluth, D.; Prell, S.; Werner, M. D.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gongadze, A.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Lyubushkin, V.; Minashvili, I. A.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Pozdnyakov, V.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] JINR Dubna, Joint Inst Nucl Res, Dubna, Russia; [Amako, K.; Aoki, M.; Arai, Y.; Hanagaki, K.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Nagai, R.; Nagano, K.; Nakamura, K.; Nozaki, M.; Odaka, S.; Okuyama, T.; Sasaki, O.; Suzuki, S.; Takubo, Y.; Tanaka, S.; Terada, S.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Hasegawa, M.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Shimizu, S.; Tanioka, R.; Yamazaki, Y.; Yuan, L.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Kunigo, T.; Monden, R.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto, Japan; [Kawagoe, K.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka, Japan; [Alconada Verzini, M. J.; Alonso, A.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Alconada Verzini, M. J.; Alonso, A.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Bertram, I. A.; Borissov, G.; Bouhova-Thacker, E. V.; Dearnaley, W. J.; Fox, H.; Grimm, K.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Muenstermann, D.; Parker, A. J.; Skinner, M. B.; Smizanska, M.; Walder, J.; Wharton, A. M.] Univ Lancaster, Phys Dept, Lancaster, England; [Aliev, M.; Bachas, K.; Chiodini, G.; Gorini, E.; Longo, L.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, Lecce, Italy; [Aliev, M.; Bachas, K.; Gorini, E.; Longo, L.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Affolder, A. A.; Anders, J. K.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Gwilliam, C. B.; Hayward, H. S.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Lehan, A.; Maxfield, S. J.; Mehta, A.; Readioff, N. P.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool, Merseyside, England; [Cindro, V.; Filipcic, A.; Gorisek, A.; Kanjir, L.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Muskinja, M.; Sfiligoj, T.; Sokhrannyi, G.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Cindro, V.; Filipcic, A.; Gorisek, A.; Kanjir, L.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Muskinja, M.; Sfiligoj, T.; Sokhrannyi, G.] Univ Ljubljana, Ljubljana, Slovenia; [Armitage, L. J.; Bevan, A. J.; Bona, M.; Hays, J. M.; Hickling, R.; Landon, M. P. J.; Lewis, D.; Lloyd, S. L.; Morris, J. D.; Nooney, T.; Piccaro, E.; Rizvi, E.; Sandbach, R. L.] Queen Mary Univ London, Sch Phys & Astron, London, England; [Berry, T.; Boisvert, V.; Brooks, T.; Connelly, I. A.; Cowan, G.; Giannelli, M. Faucci; Gadomski, S.; George, S.; Gibson, S. M.; Kempster, J. J.; Kilby, C. R.; Vazquez, J. G. Panduro; Pastore, Fr.; Savage, G.; Sowden, B. C.; Spano, F.; Teixeira-Dias, P.; Thomas-Wilsker, J.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Bell, A. S.; Butterworth, J. M.; Campanelli, M.; Christodoulou, V.; Cooper, B. D.; Davison, P.; Falla, R. J.; Freeborn, D.; Gregersen, K.; Grout, Z. J.; Ortiz, N. G. Gutierrez; Hesketh, G. G.; Jiggins, S.; Konstantinidis, N.; Korn, A.; Kucuk, H.; Leney, K. J. C.; Martyniuk, A. C.; McClymont, L. I.; Mcfayden, J. A.; Nurse, E.; Richter, S.; Scanlon, T.; Sherwood, P.; Simmons, B.; Wardrope, D. R.; Waugh, B. M.] UCL, Dept Phys & Astron, London, England; [Greenwood, Z. D.; Grossi, G. C.; Jana, D. K.; Sawyer, L.] Louisiana Tech Univ, Ruston, LA 71270 USA; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchior, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchior, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] Univ Paris Diderot, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchior, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] CNRS, IN2P3, Paris, France; [Akesson, T. P.; Bocchetta, S. S.; Bryngemark, L.; Doglioni, C.; Hedberg, V.; Jarlskog, G.; Lytken, E.; Mjornmark, J. U.; Smirnova, O.; Viazlo, O.] Lund Univ, Fys Inst, Lund, Sweden; [Barreiro, F.; Calvente Lopez, S.; Cueto, A.; Del Peso, J.; Glasman, C.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor C15, Madrid, Spain; [Artz, S.; Becker, M.; Bertella, C.; Blum, W.; Buescher, V.; Cuth, J.; Dudder, A. Chr.; Endner, O. C.; Ertel, E.; Fiedler, F.; Torregrosa, E. Fullana; Geisen, M.; Groh, S.; Heck, T.; Jakobi, K. B.; Kaluza, A.; Karnevskiy, M.; Kleinknecht, K.; Koepke, L.; Lin, T. H.; Masetti, L.; Mattmann, J.; Meyer, C.; Moritz, S.; Pleskot, V.; Rave, S.; Reiss, A.; Schaeffer, J.; Schaefer, U.; Schmitt, C.; Schmitz, S.; Schott, M.; Schuh, N.; Schulte, A.; Simioni, E.; Simon, M.; Tapprogge, S.; Urrejola, P.; Webb, S.; Yildirim, E.; Zimmermann, C.; Zinser, M.] Johannes Gutenberg Univ Mainz, Inst Phys, Mainz, Germany; [Barnes, S. L.; Bielski, R.; Cox, B. E.; Da Via, C.; Dann, N. S.; Forcolin, G. T.; Forti, A.; Ponce, J. M. Iturbe; Li, X.; Loebinger, F. K.; Marsden, S. P.; Masik, J.; Sanchez, F. J. Munoz; Neep, T. J.; Oh, A.; Ospanov, R.; Pater, J. R.; Peters, R. F. Y.; Pilkington, A. D.; Pin, A. W. J.; Price, D.; Qin, Y.; Raine, J. A.; Schweiger, H.; Shaw, K.; Tomlinson, L.; Watts, S.; Wilk, F.; Woudstra, M. J.; Wyatt, T. R.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Aad, G.; Alstaty, M.; Barbero, M.; Calandri, A.; Calvet, T. P.; Coadou, Y.; Diaconu, C.; Djama, F.; Ellajosyula, V.; Feligioni, L.; Hadef, A.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagy, E.; Pralavorio, P.; Rodina, Y.; Rozanov, A.; Talby, M.; Theveneaux-Pelzer, T.; Torres, R. E. Ticse; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.] Aix Marseille Univ, CPPM, Marseille, France; [Aad, G.; Alstaty, M.; Barbero, M.; Calandri, A.; Calvet, T. P.; Coadou, Y.; Diaconu, C.; Djama, F.; Ellajosyula, V.; Feligioni, L.; Hadef, A.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagy, E.; Pralavorio, P.; Rodina, Y.; Rozanov, A.; Talby, M.; Theveneaux-Pelzer, T.; Torres, R. E. Ticse; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.] CNRS, IN2P3, Marseille, France; [Bellomo, M.; Bernard, N. R.; Brau, B.; Dallapiccola, C.; Moyse, E. J. W.; Pais, P.; Pettersson, N. E.; Picazio, A.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; 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Universiti Malaya Aaboud, M (corresponding author), Barcelona Inst Sci & Technol, Inst Fis Altes Energies, Barcelona, Spain.;Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. della Volpe, Domenico/B-4482-2012; kucuk, hilal/AAM-5530-2020; Amorim, Antonio/C-8460-2013; Capua, Marcella/A-8549-2015; Maneira, Jose/D-8486-2011; Cheremushkina, Evgeniya/E-9881-2018; Billoud, Thomas/AAY-1569-2020; Kaczmarska, Anna/B-2753-2019; Grancagnolo, Sergio/J-3957-2015; Fuster, Juan/W-6189-2018; Hamal, Petr/G-5540-2014; MEONI, Evelin/ABD-9498-2021; Sanchez, Francisca J. Munoz/AAH-3909-2019; O'Shea, Val/G-1279-2010; Ohm, Christian/AAU-6572-2020; Camarri, Paolo/M-7979-2015; Sapronov, Andrey/HGB-0392-2022; Jones, Roger WL/H-5578-2011; Trzupek, Adam/N-2448-2018; Sultansoy, Saleh/AAV-6853-2021; Oh, Alexander/HHZ-4386-2022; Bassalat, Ahmed Azzam/HHY-9901-2022; Giordani, Mario P/Q-6211-2018; Kurashige, Hisaya/H-4916-2012; Carquin López, Edson/GZG-4712-2022; Giagu, Stefano/H-6455-2013; Berge, David/P-2179-2017; Sultansoy, Saleh/AAA-8267-2019; Mikestikova, Marcela/H-1996-2014; Peleganchuk, Sergey V/J-6722-2014; Busato, Emmanuel/AAZ-4423-2020; Llácer, María Moreno/AAQ-7522-2020; Malek, Fairouz/E-4410-2018; Alexa, Calin/F-6345-2010; Schernau, Michael/GRY-3808-2022; Staszewski, Rafał/AAO-6844-2021; Petrucci, Fabrizio/G-8348-2012; Ciftci, Abbas Kenan/AAC-5487-2022; Stoicea, Gabriel/B-6717-2011; Fassi, Farida/F-3571-2016; Barreiro, Fernando/D-9808-2012; Talyshev, Alexey/HGC-6910-2022; Maevskiy, Artem/P-4101-2016; Klimentov, Alexei A/I-4270-2013; Derendarz, Dominik/AAO-3512-2021; Dyndal, Mateusz/AAB-1528-2020; Alberich, Leonor Cerdá/ABG-8534-2020; Zenin, Oleg/E-6116-2018; Carvalho, Joao/M-4060-2013; Ibáñez, Carlos Escobar/B-3761-2017; Dolejší, Jiří/P-1509-2017; Smirnova, Oxana/A-4401-2013; Cavaliere, Viviana/CAG-2384-2022; Petrucci, Fabrizio/P-3837-2019; Mamuzic, Judita/U-3509-2017; Olszewski, Andrzej J/W-1825-2018; Introzzi, Gianluca/K-2497-2015; Jiménez Peña, Javier/AFY-1817-2022; Wozniak, Krzysztof W/P-4475-2017; Hrabovsky, Milan/H-4413-2014; Kepka, Oldrich/ABF-5327-2021; Bin Yusuff, Imran/ABA-5986-2020; Davidek, Tomas/P-2697-2017; Grabowska-Bołd, Iwona/ABI-7829-2020; Norjoharuddeen, Nurfikri/S-3109-2018; Coadou, Yann/G-2263-2010; Vandelli, Wainer/ABI-8185-2020; Sadykov, Renat/AAN-9602-2020; Doležal, Zdeněk/K-6861-2017; Lazzaroni, Massimo/N-3675-2015; Busato, Emmanuel/AAS-5459-2020; Donadelli, Marisilvia/ABD-5330-2021; Quijada, Javier Alberto Murillo/AAS-4234-2021; Price, Darren/AAB-5991-2021; Mindur, Bartosz/A-2253-2017; Huseynov, Nazim/AAE-4663-2019; Amoroso, Simone/AAW-4334-2021; Cavaliere, Viviana/CAE-8597-2022; Bruscino, Nello/ABA-8980-2021; Orlando, Nicola/AAL-1723-2021; Artoni, Giacomo/ABA-2164-2020; Kostyukhin, Vadim/F-3171-2019; Lagouri, Theodota/AAC-7358-2021; Banaś, Elżbieta A/W-4583-2017; Gutierrez, Phillip/C-1161-2011; Zhemchugov, Alexey/N-1717-2017; Rotaru, Marina/A-3097-2011; Kartvelishvili, Vakhtang G/K-2312-2013; Dabrowski, Wladyslaw/AAS-6369-2020; Tapia, Sebastian/ABB-6644-2021; Li, Hao/GPS-9834-2022; Giuli, Francesco/HJI-6649-2023; Llácer, María Moreno/AAU-6582-2020; Luci, Claudio/HKW-0143-2023; Testa, Marianna/L-9768-2019; Kamenshchikov, Andrey/R-1112-2017; Smirnov, Sergei Y/F-1014-2011; Saraiva, Joao Gentil Mendes/D-1596-2017; Ventura, Andrea/CAH-0226-2022; Maj, Klaudia/Q-2724-2019; Staroba, Pavel/G-8850-2014; spagnolo, stefania/A-6359-2012; Pásztor, Gabriella/D-9716-2016; do Amaral Coutinho, Yara/AAU-7857-2021; Casado, Pilar/H-1484-2015; Górnicki, Edward/C-4075-2019; Laycock, Paul/AAL-8261-2021; Coccaro, Andrea/AAB-6990-2021; Caprini, Irinel/AAP-3760-2020; govender, nicolin/CAH-3245-2022; Burdin, Sergey/AAZ-9062-2021; de Groot, Nicolo/A-2675-2009; Ungaro, Francesca C/G-4554-2018; Herten, Gregor/HNQ-9546-2023; Camarri, Paolo/AAF-9629-2020; Franchini, Matteo/HNI-8314-2023; Hejbal, Jiri/H-1358-2014; Nordberg, Markus/L-4255-2017; Tassi, Enrico/AAJ-9661-2020; Buzykaev, Alexey/HIK-0117-2022; CAKIR, Orhan/L-1893-2015; Glasman, Claudia/Y-8858-2019; Evangelos, Gazis/L-3966-2017; Negrini, Matteo/C-8906-2014; Albert, Justin/J-4152-2017; Stoicea, Gabriel/L-5602-2019; Lefebvre, Benoit/ABB-2272-2021; Parodi, Fabrizio/AHE-5089-2022; Lysak, Roman/H-2995-2014; Bosman, Martine/J-9917-2014; Sýkora, Tomáš/Q-3174-2017; Woźniak, Krzysztof/AAQ-6119-2021; Romaniouk, Anatoli/Q-6674-2017; Francavilla, Paolo/HKE-1206-2023; Berta, Peter/AAL-7109-2020; Godlewski, Jan/AAI-3300-2021; Krasnopevtsev, Dimitrii/R-2157-2017; Iengo, Paolo/AAR-7518-2020; Balaji, S/J-1864-2019; Kharlamova, Tatyana/AAQ-5430-2020; Beddall, Andrew John/AAE-5820-2022; Rosati, Stefano/AAF-6968-2019; de Almeida, Fernando Marroquim Leao/D-6947-2019; Zanzi, Daniele/AAY-4102-2020; Pages, Andres Pacheco/C-5353-2011; Sbarra, Carla/M-9669-2018; Snesarev, Andrei/H-5090-2013; Padilla, Cristobal/C-3218-2017; Kostyukhin, Vadim/ABA-9033-2020; Cetin, Serkant Ali/AGF-0147-2022; Warburton, Andreas/N-8028-2013; Salamon, Andrea/AGN-6159-2022; gaudio, gabriella/AAN-6039-2021; Belyaev, Nikita/N-3676-2016; Nevski, Pavel/AAH-1839-2019; Masik, Jiri/AAE-1203-2021; Kazanin, Vasily F./HHM-2056-2022; Buttar, Craig/AAG-5392-2021; Gladilin, Leonid/B-5226-2011; Tsybychev, Dmitri/J-3733-2017; Gonella, Laura/GLR-3838-2022; de la Torre Perez, Hector/ABG-6942-2020; Rozen, Yoram/H-1880-2017; Donadelli, Marisilvia/H-3710-2016; de Renstrom, Pawel Bruckman/AAG-7725-2021; Kosek, Tomáš/Q-1906-2017; Kodys, Peter/P-2636-2017; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Kuze, Masahiro/V-4251-2018; Valero, Alberto/G-9866-2015; Lee, Lawrence/AAU-6790-2021; Nechaeva, Polina Y/N-1148-2015; Bates, Richard L/D-6596-2013; Darbo, Giovanni/C-8175-2012; Mashinistov, Ruslan/M-8356-2015; Kantserov, Vadim/M-9761-2015; Burka, Klaudia/Q-4624-2017; Boyko, Igor R/J-3659-2013; Bobrovnikov, Victor/AAB-8328-2022; Grinstein, Sebastian/ABE-1880-2020; Tikhomirov, Vladimir/M-6194-2015; Doyle, Anthony T/C-5889-2009; Fassouliotis, Dimitrios/AAL-7081-2021; YILDIZ, Hatice DURAN/AAN-3727-2021; Mitsou, Vasiliki A./D-1967-2009; Svatos, Michal/ABA-2041-2020; Della Pietra, Massimo/J-5008-2012; García, Josu Cantero/AAS-9810-2020; arik, metin/T-4193-2019; Sessa, Marco/AAT-2850-2020; Wu, Xin/ABH-1729-2020; Wosiek, Barbara/K-5811-2017; Gongadze, Alexi/AAY-5201-2021; Wolters, Helmut/M-4154-2013; Ippolito, Valerio/L-1435-2016; Fabbri, Laura/H-3442-2012; Potamianos, Karolos/V-2525-2019; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Sopczak, Andre/I-4951-2015; Chwastowski, Janusz J./I-4480-2012; Fiorini, Luca/W-6250-2018; Gongadze, Alexi/T-9162-2017; Gao, Y./GWZ-3665-2022; Price, Darren D/E-6162-2012; Torro Pastor, Emma/AAB-5979-2021; Marcisovsky, Michal/AAM-2404-2020; Nevski, Pavel/M-6292-2015; Palka, Marek/GXW-2506-2022; Zhukov, Konstantin I/M-6027-2015; Lacasta, Carlos/C-7254-2008; Ahmadov, Faig N/B-3723-2018; Muino, Patricia Conde/F-7696-2011; Urbán, Susana Cabrera/H-1376-2015; Crispin-Ortuzar, Mireia/AAN-7108-2020; Annovi, Alberto/AAA-8638-2020; b, m/HPE-0989-2023; Stabile, Alberto/F-2889-2013; de la Hoz, Santiago Gonzalez/E-2494-2016; do Vale, Maria Aline Barros/D-2357-2019; Ferrer, Antonio/H-2942-2015; Andreazza, Attilio/E-5642-2011; Berger, N/ABE-4064-2020; Benekos, Nektarios/I-9928-2017; Torregrosa, Esteban Fullana/A-7305-2016; Chitan, Adrian/AAC-8946-2022; Ventura, Andrea/A-9544-2015; Legger, Federica/HNC-2568-2023; Alimonti, Gianluca/AAG-4603-2020; Reznicek, Pavel/C-1989-2017; Prokoshin, Fedor/E-2795-2012; zhang, jin/GXV-9154-2022; Chizhov, Mihail/CAG-2076-2022; Castillo, Luis Roberto Flores/W-3928-2018; Duarte-Campderrós, Jordi/F-5025-2018; Trzebinski, Maciej/W-1748-2018; Livan, Michele M/D-7531-2012; Brooks, William K/C-8636-2013; Bogdanchikov, Alexander/AAB-9414-2022; Kuleshov, Sergey V/D-9940-2013; Perez, Miguel Villaplana/B-2717-2015; Lee, Shih-Chang/AAV-7016-2021; Bachas, Konstantinos/C-8101-2019; Garcia, Carmen/W-2465-2018; Malecki, Paweł/W-1710-2018; Carquin, Edson/GLU-9641-2022; Castro, Nuno/AAB-3648-2019; Ros, Eduardo/P-2625-2018; Trzebinski, Maciej/AAO-6821-2021; Vaniachine, Alexandre/H-7796-2013; Gravila, Paul/HLP-6245-2023; Benekos, Nektarios/J-8629-2017; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Olszowska, Jolanta/P-2889-2017; Koperny, Stefan/ABB-4747-2020; Chelkov, Georgy A./G-9934-2019; Llorente Merino, Javier/AGP-3432-2022; SULIN, Vladimir V/J-6966-2014; perez, miguel/GXF-5077-2022; Jiménez, Yesenia Hernández/ABH-1107-2020; Franchini, Matteo/AAC-9259-2021; Villaplana Perez, Miguel/B-5772-2014; Gonçalo, Ricardo/M-3153-2016; Di Nardo, Roberto/J-4993-2012; Gonzalez, Barbara Alvarez/ABG-7021-2020; Gustavino, Giuliano/AAK-6591-2020; Nessi, Marzio/L-5194-2017; Fullana Torregrosa, Esteban/AAC-9079-2022; Ozcan, Veysi Erkcan/AAS-4508-2020; da via, cinzia/AAS-3978-2021; 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Andreazza, Attilio/0000-0001-5161-5759; Berger, N/0000-0002-7963-9725; Benekos, Nektarios/0000-0001-7831-8762; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Ventura, Andrea/0000-0002-3368-3413; Legger, Federica/0000-0003-1400-0709; Alimonti, Gianluca/0000-0002-7128-9046; Reznicek, Pavel/0000-0003-4017-9829; Prokoshin, Fedor/0000-0001-6389-5399; Chizhov, Mihail/0000-0001-5841-3316; Castillo, Luis Roberto Flores/0000-0003-1551-5974; Duarte-Campderrós, Jordi/0000-0003-0687-5214; Trzebinski, Maciej/0000-0002-5151-7101; Livan, Michele M/0000-0002-5877-0062; Brooks, William K/0000-0001-6161-3570; Kuleshov, Sergey V/0000-0002-3065-326X; Perez, Miguel Villaplana/0000-0002-0048-4602; Lee, Shih-Chang/0000-0002-3353-2658; Bachas, Konstantinos/0000-0002-9047-6517; Garcia, Carmen/0000-0003-1625-7452; Malecki, Paweł/0000-0001-8183-0468; Castro, Nuno/0000-0001-8491-4376; Ros, Eduardo/0000-0003-2812-9554; Trzebinski, Maciej/0000-0002-5151-7101; Vaniachine, Alexandre/0000-0002-0367-5666; Gravila, Paul/0000-0002-0154-577X; Olszowska, Jolanta/0000-0003-0520-9500; SULIN, Vladimir V/0000-0003-3943-2495; Jiménez, Yesenia Hernández/0000-0001-9844-6200; Franchini, Matteo/0000-0002-4554-252X; Villaplana Perez, Miguel/0000-0002-0048-4602; Gonçalo, Ricardo/0000-0002-3826-3442; Di Nardo, Roberto/0000-0003-1111-3783; Gonzalez, Barbara Alvarez/0000-0001-7767-4810; Gustavino, Giuliano/0000-0002-5938-4921; Fullana Torregrosa, Esteban/0000-0003-3082-621X; Ozcan, Veysi Erkcan/0000-0003-4643-6347; da via, cinzia/0000-0001-6154-7323; De, Kaushik/0000-0002-5647-4489; Gabrielli, Alessandro/0000-0001-5346-7841; Cottin, Giovanna/0000-0002-5308-5808; Volpi, Matteo/0000-0002-6608-7626; McKee, Shawn/0000-0002-4551-4502; Bold, Tomasz/0000-0002-2432-411X; Lokajicek, Milos/0000-0001-8929-1243; Galhardo, Bruno/0000-0003-0641-301X; Casolino, Mirkoantonio/0000-0003-2350-7528; Ragusa, Francesco/0000-0002-4064-0489; Konstantinidis, Nikolaos/0000-0002-4140-6360; Hill, John/0000-0002-8650-2807; Calafiura, Paolo/0000-0002-1692-1678; Butterworth, Jonathan/0000-0002-5905-5394 ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; I-CORE, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia, Russian Federation; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada; Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union; ERDF, European Union; FP7, European Union; Horizon and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, France; ANR, France; Region Auvergne and Fondation Partager le Savoir, France; DFG, Germany; AvH Foundation, Germany; EUESF; Greek NSRF; BSF, Israel; GIF, Israel; Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Spain; Generalitat Valenciana, Spain; Royal Society and Leverhulme Trust, United Kingdom; MOST, China; Benoziyo Center, Israel; STFC [ST/J00488X/1, ST/M001431/1, ST/H001093/2, ST/L005662/1, ST/N000277/1, ST/K001361/1, ST/N000420/1, ST/N000463/1, ST/I005846/1, ST/M006417/1, ST/K003666/1, ST/L001144/1] Funding Source: UKRI; Science and Technology Facilities Council [ST/K003666/1, ST/H001093/2, ST/M001431/1, ST/M006417/1, ST/K001361/1, 1366825, ST/I005846/1, ST/N000420/1, ST/J00488X/1, ST/L005662/1, GRIDPP, ST/L001144/1] Funding Source: researchfish; ICREA Funding Source: Custom; Fundação para a Ciência e a Tecnologia [CERN/FIS-NUC/0005/2015] Funding Source: FCT; Direct For Mathematical & Physical Scien; Division Of Physics [1624739] Funding Source: National Science Foundation ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); I-CORE, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia, Russian Federation(Russian Federation); NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada(FQRNT); Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; FP7, European Union; Horizon and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, France(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); Region Auvergne and Fondation Partager le Savoir, France(Region Auvergne-Rhone-Alpes); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); EUESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); BSF, Israel(US-Israel Binational Science Foundation); GIF, Israel(German-Israeli Foundation for Scientific Research and Development); Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Spain; Generalitat Valenciana, Spain(Center for Forestry Research & Experimentation (CIEF)); Royal Society and Leverhulme Trust, United Kingdom; MOST, China(Ministry of Science and Technology, China); Benoziyo Center, Israel; STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ICREA(ICREA); Fundação para a Ciência e a Tecnologia; Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EUESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier- 2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [83]. 83 14 14 1 55 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. APR 14 2017.0 4 86 10.1007/JHEP04(2017)086 0.0 41 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics ES8SC Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000399826200001 0
J Zha, FS; Liu, YZ; Wang, X; Chen, F; Li, JX; Guo, W Zha, Fusheng; Liu, Yizhou; Wang, Xin; Chen, Fei; Li, Jingxuan; Guo, Wei Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model COMPLEXITY English Article The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples' rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminatemodel fitting errors due to environmental changes. Finally, themodel is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency. [Zha, Fusheng; Liu, Yizhou; Li, Jingxuan; Guo, Wei] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin, Heilongjiang, Peoples R China; [Wang, Xin] Shenzhen Acad Aerosp Technol, Shenzhen, Peoples R China; [Chen, Fei] Ist Italiano Tecnol, Via Morego 30, Genoa, Italy Harbin Institute of Technology; Istituto Italiano di Tecnologia - IIT Wang, X (corresponding author), Shenzhen Acad Aerosp Technol, Shenzhen, Peoples R China.;Chen, F (corresponding author), Ist Italiano Tecnol, Via Morego 30, Genoa, Italy. xinwanghit07s@gmail.com; fei.chen@iit.it Chen, Fei/0000-0003-4397-0931; Li, Jingxuan/0000-0002-9526-3043; Liu, Yizhou/0000-0001-5327-9496 National Natural Science Foundation of China [U1713222, 61773139, 61473015]; Natural Science Foundation of Heilongjiang Province, China [F2015008]; Shenzhen Peacock Plan [KQTD2016112515134654] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Heilongjiang Province, China(Natural Science Foundation of Heilongjiang Province); Shenzhen Peacock Plan This work is supported by National Natural Science Foundation of China (No. U1713222, No. 61773139, and No. 61473015), Natural Science Foundation of Heilongjiang Province, China (Grant No. F2015008), and Shenzhen Peacock Plan (No. KQTD2016112515134654). 31 3 3 2 14 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1076-2787 1099-0526 COMPLEXITY Complexity 2018.0 4358747 10.1155/2018/4358747 0.0 14 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Science & Technology - Other Topics HA3IG gold, Green Submitted, Green Published 2023-03-23 WOS:000450142700001 0
J Lio, GE; Ferraro, A; Ritacco, T; Aceti, DM; De Luca, A; Giocondo, M; Caputo, R Lio, Giuseppe Emanuele; Ferraro, Antonio; Ritacco, Tiziana; Aceti, Dante Maria; De Luca, Antonio; Giocondo, Michele; Caputo, Roberto Leveraging on ENZ Metamaterials to Achieve 2D and 3D Hyper-Resolution in Two-Photon Direct Laser Writing ADVANCED MATERIALS English Article apochromatic broadband metalenses; hyper‐ resolution; metamaterials; two‐ photon direct laser writing; ultraflat metalenses A novel technique is developed to improve the resolution of two-photon direct laser writing lithography. Thanks to the high collimation enabled by extraordinary epsilon(NZ) (near-zero) metamaterial features, ultrathin dielectric hyper-resolute nanostructures are within reach. With respect to the standard direct laser writing approach, a size reduction of 89% and 50%, in height and width respectively, is achieved with the height of the structures adjustable between 5 and 50 nm. The retrieved 2D fabrication parameters are exploited for realizing extremely thin all-dielectric metalenses tailored through deep machine learning codes. The hyper-resolution achieved in the writing process enables the fabrication of a highly detailed dielectric 3D bas-relief (with full height of 500 nm) of Da Vinci's Lady with an Ermine. The proof-of-concept results show intriguing cues for the current and trendsetting research scenario in anti-counterfeiting applications and ultracompact photonics, paving the way for the realization of all-dielectric and apochromatic ultrathin imaging systems. [Lio, Giuseppe Emanuele; Ferraro, Antonio; Ritacco, Tiziana; De Luca, Antonio; Giocondo, Michele; Caputo, Roberto] CNR, Inst Nanotechnol Nanotec, Ponte P Bucci Cubo 33C, I-87036 Arcavacata Di Rende, Italy; [Lio, Giuseppe Emanuele; Ferraro, Antonio; Ritacco, Tiziana; Aceti, Dante Maria; De Luca, Antonio; Caputo, Roberto] Univ Calabria, Phys Dept, I-87036 Arcavacata Di Rende, CS, Italy; [Lio, Giuseppe Emanuele; Caputo, Roberto] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Nanotecnologia (NANOTEC-CNR); University of Calabria; University of Electronic Science & Technology of China Lio, GE; Ferraro, A; Caputo, R (corresponding author), CNR, Inst Nanotechnol Nanotec, Ponte P Bucci Cubo 33C, I-87036 Arcavacata Di Rende, Italy.;Lio, GE; Ferraro, A; Caputo, R (corresponding author), Univ Calabria, Phys Dept, I-87036 Arcavacata Di Rende, CS, Italy.;Lio, GE; Caputo, R (corresponding author), Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China. giuseppe.lio@unical.it; antonio.ferraro@unical.it; roberto.caputo@unical.it Giocondo, MIchele/HDO-4091-2022; Lio, Giuseppe Emanuele/S-8130-2019; Ferraro, Antonio/Q-1181-2018; Caputo, Roberto/A-6193-2012; De Luca, Antonio/P-6976-2018 Lio, Giuseppe Emanuele/0000-0002-8925-7202; Ferraro, Antonio/0000-0003-0189-6729; Caputo, Roberto/0000-0002-0065-8422; Ritacco, Tiziana/0000-0001-5036-2518; De Luca, Antonio/0000-0003-2428-9075; Aceti, Dante Maria/0000-0003-0208-532X 52 39 40 9 71 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 0935-9648 1521-4095 ADV MATER Adv. Mater. MAY 2021.0 33 18 2008644 10.1002/adma.202008644 0.0 MAR 2021 9 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics RX3KL 33783047.0 Green Submitted 2023-03-23 WOS:000634658700001 0
J Zhang, SY; Dai, SL; Zanchettin, AM; Villa, R Zhang, Shiyu; Dai, Shuling; Zanchettin, Andrea Maria; Villa, Renzo Trajectory planning based on non-convex global optimization for serial manipulators APPLIED MATHEMATICAL MODELLING English Article Real-time trajectory planning; Non-convex optimization; Global optimization; Machine learning; Robotics To perform specific tasks in dynamic environments, robots are required to rapidly update trajectories according to changing factors. A continuous trajectory planning methodology for serial manipulators based on non-convex global optimization is presented in this paper. First, a kinematic trajectory planning model based on non-convex optimization is constructed to balance motion rapidity and safety. Then, a model transformation method for the non-convex optimization model is presented. In this way, the accurate global solution can be obtained with an iterative solver starting from arbitrary initializations, which can greatly improve the computational accuracy and efficiency. Furthermore, an efficient initialization method for the iterative solver based on multivariable-multiple regression is presented, which further speeds up the solution process. The results show that trajectory planning efficiency is significantly enhanced by model transformation and initialization improvement for the iterative solver. Consequently, real-time continuous trajectory planning for serial manipulators with many degrees of freedom can be achieved, which lays a basis for performing dynamic tasks in complex environments. (C) 2020 Elsevier Inc. All rights reserved. [Zhang, Shiyu; Dai, Shuling] Beihang Univ Beijing, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Zanchettin, Andrea Maria; Villa, Renzo] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy Polytechnic University of Milan Zhang, SY (corresponding author), Beihang Univ Beijing, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. zhangshiyu@buaa.edu.cn Zhang, Shiyu/0000-0003-2474-7451 32 10 10 4 46 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0307-904X 1872-8480 APPL MATH MODEL Appl. Math. Model. AUG 2020.0 84 89 105 10.1016/j.apm.2020.03.004 0.0 17 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics; Mechanics LS1IF 2023-03-23 WOS:000536144700006 0
J Xu, XO; Stoffelen, A Xu, Xingou; Stoffelen, Ad Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry ATMOSPHERIC MEASUREMENT TECHNIQUES English Article Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications. [Xu, Xingou] Chinese Acad Sci, Natl Space Sci Ctr, CAS Key Lab Microwave Remote Sensing, Beijing 100190, Peoples R China; [Stoffelen, Ad] Royal Netherlands Meteorol Inst, Satellite Observat, NL-3730 AE De Bilt, Netherlands Chinese Academy of Sciences; National Space Science Center, CAS; Royal Netherlands Meteorological Institute Stoffelen, A (corresponding author), Royal Netherlands Meteorol Inst, Satellite Observat, NL-3730 AE De Bilt, Netherlands. ad.stoffelen@knmi.nl Stoffelen, Ad/0000-0002-4018-4073 45 1 1 0 6 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1867-1381 1867-8548 ATMOS MEAS TECH Atmos. Meas. Tech. NOV 30 2021.0 14 12 7435 7451 10.5194/amt-14-7435-2021 0.0 17 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences XF2QD gold, Green Submitted 2023-03-23 WOS:000723919500001 0
J Ke, J; Alieva, T; Oktem, FS; Silveira, PEX; Wetzstein, G; Willomitzer, F Ke, Jun; Alieva, Tatiana; Oktem, Figen S.; Silveira, Paulo E. X.; Wetzstein, Gordon; Willomitzer, Florian Computational Optical Sensing and Imaging 2021: introduction to the feature issue APPLIED OPTICS English Editorial Material This feature issue includes two reviews and 34 research papers that highlight recent works in the field of computational optical sensing and imaging. Many of the works were presented at the 2021 Optica (formerly OSA) Topical Meeting on Computational Optical Sensing and Imaging, held virtually from 19 July to 23 July 2021. Papers in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue. (C) 2022 Optica Publishing Group [Ke, Jun] Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China; [Alieva, Tatiana] Univ Complutense Madrid, Dept Opt, Madrid 28040, Spain; [Oktem, Figen S.] Middle East Tech Univ METU, Dept Elect Engn, TR-06800 Ankara, Turkey; [Silveira, Paulo E. X.] Occipital Inc, 1155 Canyon Blvd, Boulder, CO 80302 USA; [Wetzstein, Gordon] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Willomitzer, Florian] Northwestern Univ, Dept Elect & Comp Engn, Evanston, IL 60208 USA Beijing Institute of Technology; Complutense University of Madrid; Middle East Technical University; Stanford University; Northwestern University Ke, J (corresponding author), Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China. jke@bit.edu.cn Oktem, Figen S./A-9569-2018; Alieva, Tatiana/K-1319-2014 Oktem, Figen S./0000-0002-7882-5120; Ke, Jun/0000-0001-6027-7659; Alieva, Tatiana/0000-0002-7638-9150 36 1 1 5 12 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics MAR 20 2022.0 61 9 COSI1 COSI4 10.1364/AO.456133 0.0 4 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics ZW0RU 35333228.0 2023-03-23 WOS:000770929600001 0
J Yao, QJ; Jahanshahi, H; Bekiros, S; Mihalache, SF; Alotaibi, ND Yao, Qijia; Jahanshahi, Hadi; Bekiros, Stelios; Mihalache, Sanda Florentina; Alotaibi, Naif D. Indirect Neural-Enhanced Integral Sliding Mode Control for Finite-Time Fault-Tolerant Attitude Tracking of Spacecraft MATHEMATICS English Article attitude tracking control; finite-time control; fault-tolerant control; integral sliding mode control; indirect neural approximation RIGID SPACECRAFT; CONTROL DESIGN; ASYMPTOTIC STABILIZATION; TRAJECTORY TRACKING; MANIPULATOR; SATELLITE In this article, a neural integral sliding mode control strategy is presented for the finite-time fault-tolerant attitude tracking of rigid spacecraft subject to unknown inertia and disturbances. First, an integral sliding mode controller was developed by originally constructing a novel integral sliding mode surface to avoid the singularity problem. Then, the neural network (NN) was embedded into the integral sliding mode controller to compensate the lumped uncertainty and replace the robust switching term. In this way, the chattering phenomenon was significantly suppressed. Particularly, the mechanism of indirect neural approximation was introduced through inequality relaxation. Benefiting from this design, only a single learning parameter was required to be adjusted online, and the computation burden of the proposed controller was extremely reduced. The stability argument showed that the proposed controller could guarantee that the attitude and angular velocity tracking errors were regulated to the minor residual sets around zero in a finite time. It was noteworthy that the proposed controller was not only strongly robust against unknown inertia and disturbances, but also highly insensitive to actuator faults. Finally, the effectiveness and advantages of the proposed control strategy were validated using simulations and comparisons. [Yao, Qijia] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China; [Jahanshahi, Hadi] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada; [Bekiros, Stelios] Univ Malta, FEMA, MSD-2080 Msida, Malta; [Bekiros, Stelios] London Sch Econ & Polit Sci, Dept Hlth Policy, LSE Hlth, London WC2A 2AE, England; [Bekiros, Stelios] IPAG Business Sch IPAG, 184 Blvd St Germain, F-75006 Paris, France; [Mihalache, Sanda Florentina] Petr Gas Univ Ploiesti, Automat Control Comp & Elect Dept, Ploiesti 100680, Romania; [Alotaibi, Naif D.] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia Beijing Institute of Technology; University of Manitoba; University of Malta; University of London; London School Economics & Political Science; Oil & Gas University of Ploiesti; King Abdulaziz University Jahanshahi, H (corresponding author), Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada. qijia_yao@126.com; jahanshahi.hadi90@gmail.com; stelios.bekiros@um.edu.mt; sfrancu@upg-ploiesti.ro; ndalotabi@kau.edu.sa Mihalache, Sanda Florentina/P-4220-2016; Jahanshahi, Hadi/L-5815-2019; Bekiros, Stelios/K-4041-2014 Bekiros, Stelios/0000-0002-9376-8726; YAO, Qijia/0000-0001-7902-407X; Mihalache, Sanda Florentina/0000-0002-8738-5091; Jahanshahi, Hadi/0000-0001-7810-6479 Institutional Fund Projects [IFPDP-23122]; King Abdulaziz University (KAU), Jeddah, Saudi Arabia; Ministry of Education Institutional Fund Projects; King Abdulaziz University (KAU), Jeddah, Saudi Arabia; Ministry of Education This research work was funded by Institutional Fund Projects under Grant no. (IFPDP-23122). Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University (KAU), Jeddah, Saudi Arabia. 52 5 5 5 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics JUL 2022.0 10 14 2467 10.3390/math10142467 0.0 18 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 3J2OV Green Accepted, gold 2023-03-23 WOS:000833240700001 0
J Chen, J; Zhang, Y; Kuzyakov, Y; Wang, D; Olesen, JE Chen, Ji; Zhang, Yong; Kuzyakov, Yakov; Wang, Dong; Olesen, Jorgen Eivind Challenges in upscaling laboratory studies to ecosystems in soil microbiology research GLOBAL CHANGE BIOLOGY English Article; Early Access field in situ observation; global change factors; laboratory incubation; microbial-based models; soil biogeochemistry; soil microbiology CARBON; FEEDBACK; SYSTEM Soil microbiology has entered into the big data era, but the challenges in bridging laboratory-, field-, and model-based studies of ecosystem functions still remain. Indeed, the limitation of factors in laboratory experiments disregards interactions of a broad range of in situ environmental drivers leading to frequent contradictions between laboratory- and field-based studies, which may consequently mislead model development and projections. Upscaling soil microbiology research from laboratory to ecosystems represents one of the grand challenges facing environmental scientists, but with great potential to inform policymakers toward climate-smart and resource-efficient ecosystems. The upscaling is not only a scale problem, but also requires disentangling functional relationships and processes on each level. We point to three potential reasons for the gaps between laboratory- and field-based studies (i.e., spatiotemporal dynamics, sampling disturbances, and plant-soil-microbial feedbacks), and three key issues of caution when bridging observations and model predictions (i.e., across-scale effect, complex-process coupling, and multi-factor regulation). Field-based studies only cover a limited range of environmental variation that must be supplemented by laboratory and mesocosm manipulative studies when revealing the underlying mechanisms. The knowledge gaps in upscaling soil microbiology from laboratory to ecosystems should motivate interdisciplinary collaboration across experimental, observational, theoretic, and modeling research. [Chen, Ji] Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian, Peoples R China; [Chen, Ji; Olesen, Jorgen Eivind] Aarhus Univ, Dept Agroecol, Tjele, Denmark; [Chen, Ji; Olesen, Jorgen Eivind] Aarhus Univ, iCLIMATE Interdisciplinary Ctr Climate Change, Roskilde, Denmark; [Zhang, Yong] Yunnan Univ, Sch Ecol & Environm Sci, Key Lab Soil Ecol & Hlth Univ Yunnan Prov, Kunming, Peoples R China; [Kuzyakov, Yakov] Univ Gottingen, Dept Soil Sci Temperate Ecosyst, Gottingen, Germany; [Wang, Dong] Henan Univ, Sch Life Sci, Int Joint Res Lab Global Change Ecol, Kaifeng, Peoples R China; [Olesen, Jorgen Eivind] Aarhus Univ, Ctr Circular Bioecon, Tjele, Denmark; [Wang, Dong] Henan Univ, Sch Life Sci, Int Joint Res Lab Global Change Ecol, Kaifeng 475004, Henan, Peoples R China Chinese Academy of Sciences; Institute of Earth Environment, CAS; Aarhus University; Aarhus University; Yunnan University; University of Gottingen; Henan University; Aarhus University; Henan University Wang, D (corresponding author), Henan Univ, Sch Life Sci, Int Joint Res Lab Global Change Ecol, Kaifeng 475004, Henan, Peoples R China. wangdong19882005@163.com Chen, Ji/A-6299-2018; Zhang, Yong/GZL-5040-2022; Kuzyakov, Yakov/D-1605-2010; Wang, Dong/R-1144-2018; Olesen, Jorgen E./C-2905-2016 Chen, Ji/0000-0001-7026-6312; Zhang, Yong/0000-0002-1181-032X; Kuzyakov, Yakov/0000-0002-9863-8461; Wang, Dong/0000-0003-4533-1615; Olesen, Jorgen E./0000-0002-6639-1273 Aarhus Universitets Forskningsfond [AUFF-E- 2019- 7- 1]; Det Frie Forskningsrad [1127-00015B]; EU H2020 Marie Sklodowska-Curie Actions [839806]; Nordic Committee of Agriculture and Food Research; RUDN University Strategic Academic Leadership Program; National Natural Science Foundation of China [32130066] Aarhus Universitets Forskningsfond; Det Frie Forskningsrad(Det Frie Forskningsrad (DFF)); EU H2020 Marie Sklodowska-Curie Actions; Nordic Committee of Agriculture and Food Research; RUDN University Strategic Academic Leadership Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Aarhus Universitets Forskningsfond, Grant/Award Number: AUFF-E- 2019- 7- 1; Det Frie Forskningsrad, Grant/Award Number: 1127-00015B; EU H2020 Marie Sklodowska-Curie Actions, Grant/Award Number: 839806; Nordic Committee of Agriculture and Food Research; RUDN University Strategic Academic Leadership Program; National Natural Science Foundation of China, Grant/Award Number: 32130066 25 5 5 17 17 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1354-1013 1365-2486 GLOBAL CHANGE BIOL Glob. Change Biol. 10.1111/gcb.16537 0.0 NOV 2022 6 Biodiversity Conservation; Ecology; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology 6R2XA 36443278.0 hybrid 2023-03-23 WOS:000892170100001 0
C Zhang, XQ; Liu, YW; Liu, JX; Argyriou, A; Han, YN IEEE Zhang, Xueqing; Liu, Yanwei; Liu, Jinxia; Argyriou, Antonios; Han, Yanni D2D-Assisted Federated Learning in Mobile Edge Computing Networks 2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) IEEE Wireless Communications and Networking Conference English Proceedings Paper IEEE Wireless Communications and Networking Conference (WCNC) MAR 29-APR 01, 2021 Nanjing, PEOPLES R CHINA IEEE Edge Intelligence; Federated Learning; D2D; MEC With the proliferation of edge intelligence and the breakthroughs in machine learning, Federated Learning (FL) is capable of learning a shared model across several edge devices by preserving their private data from being exposed to external adversaries. However, the distributed architecture of FL naturally introduces communication between the central parameter server and the distributed learning nodes. The huge communication cost poses a challenge to practical FL, especially for FL in mobile edge computing (MEC) networks. Existing communication-efficient FL systems predominantly optimize their intrinsic learning process and are not concerned with the implications on the network. In this paper we propose a FL scheme that leverages Device-to-Device (D2D) communication (hence called D2D-FedAvg) and is suitable for mobile edge networks. D2D-FedAvg creates a two-tier learning model where D2D learning groups communicate their results as a single entity to the MEC server leading to traffic reduction. We propose the schemes for D2D grouping, master UE selection, and also D2D exit in the learning process and then form a complete D2D-assisted federated averaging algorithm. Via extensive simulations on the Federated Extended MNIST dataset, the feasibility and convergence of D2D-FedAvg scheme are evaluated. Our results show that D2D-FedAvg lowers the communication cost relative to the typical Federated Averaging (FedAvg) in cellular networks as the number of users is increased (for 100 cellular users 37% traffic reduction), while keeping the same learning accuracy with FedAvg across the board. [Zhang, Xueqing; Liu, Yanwei; Han, Yanni] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China; [Zhang, Xueqing; Liu, Yanwei] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China; [Liu, Jinxia] Zhejiang Wanli Univ, Ningbo, Peoples R China; [Argyriou, Antonios] Univ Thessaly, Volos, Greece Chinese Academy of Sciences; Institute of Information Engineering, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Zhejiang Wanli University; University of Thessaly Liu, YW (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.;Liu, YW (corresponding author), Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China. National Natural Science Foundation of China [61771469]; Ningbo Natural Science Foundation [2019A610109] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ningbo Natural Science Foundation This work was supported in part by National Natural Science Foundation of China under Grant 61771469 and Ningbo Natural Science Foundation under Grant 2019A610109. 27 3 3 2 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1525-3511 978-1-7281-9505-6 IEEE WCNC 2021.0 10.1109/WCNC49053.2021.9417459 0.0 7 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS2OB 2023-03-23 WOS:000704226500214 0
J Elola, A; Aramendi, E; Irusta, U; Alonso, E; Lu, YZ; Chang, MP; Owens, P; Idris, AH Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Alonso, Erik; Lu, Yuanzheng; Chang, Mary P.; Owens, Pamela; Idris, Ahamed H. Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest RESUSCITATION English Article Return of spontaneous circulation (ROSC); ROSC detection; Capnography; End-tidal CO2 (EtCO2); Electrocardiogram (ECG); Thoracic impedance TIDAL CARBON-DIOXIDE; PULSELESS ELECTRICAL-ACTIVITY; CARDIOPULMONARY-RESUSCITATION; EXTERNAL DEFIBRILLATORS; CHEST COMPRESSIONS; CPR-QUALITY; MONITOR; ACCURACY; CHECKING; VALUES Background: Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO2 for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC. Materials and methods: A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed, First, EtCO2 values were compared for ROSC and no ROSC cases, Second, 5098 artefactfree 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard, Machine learning classifiers were designed using features obtained from the ECG, 1-1 and the EtCO2 value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC. Results: EtCO2 values increased significantlyfrom 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO2 was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p < 0.05). Adding EtCO2 to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO2 had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively. Conclusion: Adding EtCO2 improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC. [Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Alonso, Erik] Univ Basque Country, UPV EHU, Commun Engn Dept, Bilbao 48013, Spain; [Lu, Yuanzheng] Sun Yat Sen Univ, Affiliated Hosp 7, Emergency & Disaster Med Ctr, Shenzhen, Peoples R China; [Chang, Mary P.; Owens, Pamela; Idris, Ahamed H.] Univ Texas Southwestern Med Ctr Dallas, Dept Emergency Med, UTSW, Dallas, TX 75390 USA University of Basque Country; Sun Yat Sen University; University of Texas System; University of Texas Southwestern Medical Center Dallas Elola, A (corresponding author), Univ Basque Country, UPV EHU, Commun Engn Dept, Bilbao 48013, Spain. andoni.elola@ehu.es Alonso, Erik/S-3672-2018; Irusta, Unai/T-6327-2019; Aramendi, Elisabete/H-8092-2015 Alonso, Erik/0000-0002-1796-6704; Irusta, Unai/0000-0001-9521-1852; Aramendi, Elisabete/0000-0002-6409-1429; Elola, Andoni/0000-0003-4401-7376; Idris, Ahamed/0000-0001-7113-6499 Spanish Ministerio de Economia y Competitividad; Fondo Europeo de Desarrollo Regional (FEDER) [TEC2015-64678-R]; University of the Basque Country via the Ayudas a Grupos de Investigacion [GIU17/031]; Basque Government [PRE_2017_1_0112] Spanish Ministerio de Economia y Competitividad(Spanish Government); Fondo Europeo de Desarrollo Regional (FEDER)(European CommissionSpanish Government); University of the Basque Country via the Ayudas a Grupos de Investigacion; Basque Government(Basque Government) This work has been partially supported by the Spanish Ministerio de Economia y Competitividad, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R, by the University of the Basque Country via the Ayudas a Grupos de InvestigacionGIU17/031, and by the Basque Government through the grant PRE_2017_1_0112. 41 12 12 1 17 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0300-9572 1873-1570 RESUSCITATION Resuscitation SEP 2019.0 142 153 161 10.1016/j.resuscitation.2019.03.048 0.0 9 Critical Care Medicine; Emergency Medicine Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine; Emergency Medicine IT1RN 31005583.0 2023-03-23 WOS:000482624100026 0
J Antonelli, D; Zeng, QF; Aliev, K; Liu, XM Antonelli, Dario; Zeng, Qingfei; Aliev, Khurshid; Liu, Xuemei Robust Assembly Sequence Generation in a Human-Robot Collaborative Workcell by Reinforcement Learning FME TRANSACTIONS English Article Reinforcement Learning; Machine Learning; Task-based Robot Programming; Human-Robot Collaboration; Markov Decision Process; Assembly TASKS Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the human co-workers. The method is presented with the help of a benchmark case study. [Antonelli, Dario; Aliev, Khurshid] Politecn Torino, DIGEP, Turin, Italy; [Zeng, Qingfei; Liu, Xuemei] Tongji Univ, Sch Mech & Engn China, Shanghai, Peoples R China Polytechnic University of Turin; Tongji University Antonelli, D (corresponding author), Politecn Torino, Dept Management & Prod Engn, Corso Duca Abruzzi 24, I-10129 Turin, Italy. dario.antonelli@polito.it International Exchange Program for Graduate Students, Tongji University [201902042] International Exchange Program for Graduate Students, Tongji University This work is supported by International Exchange Program for Graduate Students, Tongji University (No. 201902042). 21 0 0 9 16 UNIV BELGRADE, FAC MECHANICAL ENGINEERING BELGRADE KRALJICE MARIJE 16, 35, BELGRADE, 11120, SERBIA 1451-2092 2406-128X FME TRANS FME Trans. 2021.0 49 4 851 858 10.5937/fme2104851A 0.0 8 Engineering, Mechanical Emerging Sources Citation Index (ESCI) Engineering XU2FW gold 2023-03-23 WOS:000734088700010 0
J Qian, LX; Yang, P; Xiao, M; Dobre, OA; Di Renzo, M; Li, J; Han, Z; Yi, Q; Zhao, JR Qian, Liangxin; Yang, Ping; Xiao, Ming; Dobre, Octavia A.; Di Renzo, Marco; Li, Jun; Han, Zhu; Yi, Qin; Zhao, Jiarong Distributed Learning for Wireless Communications: Methods, Applications and Challenges IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Article Servers; Distance learning; Computer aided instruction; Wireless communication; Machine learning; Signal processing algorithms; Computer architecture; Distributed learning; federated learning; wireless communications VEHICULAR NETWORKS; D2D COMMUNICATION; POWER ALLOCATION; MASSIVE MIMO; MACHINE; INTERNET; ADMM; INTELLIGENT; FRAMEWORK; EFFICIENT With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, research on distributed learning for wireless communications is still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed. [Qian, Liangxin; Yang, Ping; Yi, Qin; Zhao, Jiarong] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China; [Dobre, Octavia A.] Mem Univ Newfoundland, St John, NL A1B 3X9, Canada; [Xiao, Ming] Royal Inst Technol, S-11428 Stockholm, Sweden; [Di Renzo, Marco] Univ Paris Saclay, Cent Supelec, CNRS, Lab Signaux & Syst, F-91192 Gif Sur Yvette, France; [Li, Jun] Nanjing Univ Sci & Technol, Nanjing 210094, Peoples R China; [Han, Zhu] Univ Houston, Houston, TX 77004 USA University of Electronic Science & Technology of China; Memorial University Newfoundland; Royal Institute of Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Nanjing University of Science & Technology; University of Houston System; University of Houston Yang, P (corresponding author), Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China. 201921220237@std.uestc.edu.cn; yang.ping@uestc.edu.cn; mingx@kth.se; odobre@mun.ca; marco.di-renzo@universite-paris-saclay.fr; jun.li@njust.edu.cn; zhan2@uh.edu; yq1835940812@163.com; zhaojiarong21@163.com Qian, Liangxin/ACH-3966-2022; Dobre, Octavia/B-1435-2008; Xiao, Ming/I-5517-2018 Qian, Liangxin/0000-0002-7686-4580; Dobre, Octavia/0000-0001-8528-0512; Xiao, Ming/0000-0002-5407-0835 National Key R&D Program of China [2020YFB1807203]; National Science Foundation of China [61876033, U19B2014] National Key R&D Program of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Key R&D Program of China underGrant 2020YFB1807203, the National Science Foundation of China under Grant 61876033 and the National Science Foundation of China under Grant U19B2014. The guest editor coordinating the review of this manuscript and approving it for publicationwas Prof. T. Q. S. Quek. 147 4 4 10 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. APR 2022.0 16 3 326 342 10.1109/JSTSP.2022.3156756 0.0 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 1I7QC Green Submitted 2023-03-23 WOS:000797421100006 0
C Shardt, YAW; Yang, X; Brooks, K; Torgashov, A Shardt, Yuri A. W.; Yang, Xu; Brooks, Kevin; Torgashov, Andrei Data Quality Assessment for System Identification in the Age of Big Data and Industry 4.0 IFAC PAPERSONLINE English Proceedings Paper 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges JUL 11-17, 2020 ELECTR NETWORK Int Federat Automat Control,Siemens,Bayer,ABB,MathWorks,Phoenix Contact,Ifak Technol,Berlin Heart,Elsevier,De Gruyter,Tele Medi GmbH data quality assessment; system identification; big data; Industry 4.0; soft sensors DETECTING CHANGES; FEEDBACK As the amount of data stored from industrial processes increases with the demands of Industry 4.0, there is an increasing interest in finding uses for the stored data. However, before the data can be used its quality must be determined and appropriate regions extracted. Initially, such testing was done manually using graphs or basic rules, such as the value of a variable. With large data sets, such an approach will not work, since the amount of data to tested and the number of potential rules is too large. Therefore, there is a need for automated segmentation of the data set into different components. Such an approach has recently been proposed and tested using various types of industrial data. Although the industrial results are promising, there still remain many unanswered questions including how to handle a priori knowledge, over-or undersegmentation of the data set, and setting the appropriate thresholds for a given application. Solving these problems will provide a robust and reliable method for determining the data quality of a given data set. Copyright (C) 2020 The Authors. [Shardt, Yuri A. W.] Tech Univ Ilmenau, Ilmenau, Thuringia, Germany; [Yang, Xu] Univ Sci & Technol Beijing, Beijing, Peoples R China; [Brooks, Kevin] BlueSP, Sandton, South Africa; [Brooks, Kevin] Univ Witwatersrand, Johannesburg, South Africa; [Torgashov, Andrei] Inst Automat & Control Proc FEB RAS, Vladivostok, Russia Technische Universitat Ilmenau; University of Science & Technology Beijing; University of Witwatersrand; Russian Academy of Sciences; Institute of Automation & Control Processes, Far East Branch of the Russian Academy of Sciences Shardt, YAW (corresponding author), Tech Univ Ilmenau, Ilmenau, Thuringia, Germany. yuri.shardt@uni-due.de; yangxu@ustb.edu.cn; kevin.brooks@bluesp.co.za; torgashov@iacp.dvo.ru Shardt, Yuri/H-5056-2015; Brooks, Kevin/AFU-8135-2022 Shardt, Yuri/0000-0002-9311-7739; Brooks, Kevin/0000-0003-1939-142X National Natural Science Foundation of China [61673053]; Fundamental Research Funds for the Central Universities [FRF-BD-19-002A]; National Key R&D Programme of China [2017YFB0306403] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Key R&D Programme of China Yuri A.W. Shardt and Xu Yang would like to thank the National Natural Science Foundation of China under grant #61673053, the Fundamental Research Funds for the Central Universities under Grant #FRF-BD-19-002A, and the National Key R&D Programme of China under grant #2017YFB0306403 for funding. 32 4 4 1 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53 2 111 120 10.1016/j.ifacol.2020.12.103 0.0 10 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SF2LK Green Published, gold 2023-03-23 WOS:000652592500020 0
J Xu, FY; Huang, BF; Cun, X; Wang, FH; Yuan, HL; Lai, LL; Vaccaro, A Xu, Fangyuan; Huang, Baifu; Cun, Xin; Wang, Fenghua; Yuan, Haoliang; Lai, Loi Lei; Vaccaro, Alfredo Classifier economics of Semi-Intrusive Load Monitoring INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Load monitoring; Machine learning; Classifier evaluation; Smart grid; Smart meter DISAGGREGATION; IDENTIFICATION Non-Intrusive Load Monitoring (NILM) and Semi-Intrusive Load Monitoring (SILM) are fast developing techniques for devices operation recognition in system monitoring. Many traditional researches focus on feature space improvements for better recognition accuracy and classifier/meter quantity reduction. But practically, cost of each classifier/meter will influence the optimal NILM/SILM solution. A feature space with better accuracy in NILM may require more cost than a SILM solution with multiple classifiers with simpler feature spaces. Facing this issue, this paper initiates a new classifier network construction method for NILM/SILM. Instead of creating a classifier for NILM or SILM, this method helps decision maker to select different types of classifiers and optimally allocates the classifiers' positions. In this method, economics of each type of classifier is considered to ensure decision maker's cost reduction. A combinatorial optimization problem is established on a tree-type model to the optimized classifier network. Numerical studies on a public data set REDD and an industrial operational data are implemented to support the feasibility of the method. [Xu, Fangyuan; Huang, Baifu; Cun, Xin; Yuan, Haoliang; Lai, Loi Lei] Guangdong Univ Technol, Guangzhou, Guangdong, Peoples R China; [Wang, Fenghua] State Grid Zhejiang Elect Power Co Ltd, Hangzhou, Zhejiang, Peoples R China; [Vaccaro, Alfredo] Univ Sannio, Benevento, Italy Guangdong University of Technology; State Grid Corporation of China; University of Sannio Yuan, HL; Lai, LL (corresponding author), Guangdong Univ Technol, Guangzhou, Guangdong, Peoples R China. Hunteryuan@126.com Vaccaro, Alfredo/AAJ-3427-2020; Lai, Loi Lei/AAQ-1715-2021 Vaccaro, Alfredo/0000-0001-7256-2705; Lai, Loi Lei/0000-0003-4786-7931 Guangdong University of Technology, Guangzhou, China under Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; National Science Foundation of China: A Time-Based-Demand-Response Program of Compensated Multiple-Shape Pricing Scheme [51707041]; State Grid Technology Project: the Smart Monitoring Techniques Research in Self-Correlated Framework for Power Utility [5211011600RJ] Guangdong University of Technology, Guangzhou, China under Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; National Science Foundation of China: A Time-Based-Demand-Response Program of Compensated Multiple-Shape Pricing Scheme; State Grid Technology Project: the Smart Monitoring Techniques Research in Self-Correlated Framework for Power Utility This research work was supported in part by the Guangdong University of Technology, Guangzhou, China under Grant from the Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; in part by the National Science Foundation of China: A Time-Based-Demand-Response Program of Compensated Multiple-Shape Pricing Scheme, Grant No. 51707041; in part by State Grid Technology Project: the Smart Monitoring Techniques Research in Self-Correlated Framework for Power Utility (Grant No. 5211011600RJ). 32 9 9 2 42 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. DEC 2018.0 103 224 232 10.1016/j.ijepes.2018.05.010 0.0 9 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering GO1VD 2023-03-23 WOS:000439746200022 0
J Wu, ZC; Li, J; Wang, YS; Hu, ZW; Molinier, M Wu, Zhaocong; Li, Jun; Wang, Yisong; Hu, Zhongwen; Molinier, Matthieu Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Image restoration; Remote sensing; Gallium nitride; Generative adversarial networks; Clouds; Training; Optimization; Cloud detection; deep learning (DL); generative adversarial network (GAN); remote sensing; self-attention Cloud detection is an important step in the processing of remote sensing images. Most methods based on convolutional neural networks (CNNs) for cloud detection require pixel-level labels, which are time-consuming and expensive to annotate. To overcome this challenge, this letter proposes a novel semisupervised algorithm for cloud detection by training a self-attentive generative adversarial network (SAGAN) to extract the feature difference between cloud images and cloud-free images. Our main idea is to introduce visual attention into the process of generating real cloud-free images. The training of SAGAN is based on three guiding principles: expansion of attention maps of cloud regions which will be replaced with translated cloud-free images, reduction of attention maps to coincide with cloud boundaries, and optimization of a self-attentive network to handle the extreme cases. The inputs for SAGAN training are the images and image-level labels, which are easier, cheaper, and more time-saving than the existing methods based on CNN. To test the performance of SAGAN, experiments are conducted on the Sentinel-2A Level 1C image data. The results show that the proposed method achieves very promising results with only the image-level labels of training samples. [Wu, Zhaocong; Li, Jun; Wang, Yisong] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Hu, Zhongwen] Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518061, Peoples R China; [Molinier, Matthieu] VTT Tech Res Ctr Finland Ltd, Espoo 02044, Finland Wuhan University; Shenzhen University; VTT Technical Research Center Finland Li, J (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China. jun__li@whu.edu.cn Hu, Zhongwen/AAX-7567-2021; Molinier, Matthieu/O-4638-2016; Jun, Li/HGB-6870-2022 Molinier, Matthieu/0000-0002-2656-001X; Jun, Li/0000-0003-0941-8713 National Key Research and Development Program of China [2017YFC0506200]; National Natural Science Foundation of China [41501369, 41871227] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0506200 and in part by the National Natural Science Foundation of China under Grant 41501369 and Grant 41871227. 19 18 18 10 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. OCT 2020.0 17 10 1792 1796 10.1109/LGRS.2019.2955071 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology NU7GL 2023-03-23 WOS:000573808500027 0
J Sahu, A; Wang, XM; Munson, P; Klomp, JPG; Wang, XQ; Gu, SS; Han, Y; Qian, GG; Nicol, P; Zeng, ZX; Wang, CF; Tokheim, C; Zhang, WB; Fu, JX; Wang, J; Nair, NU; Rens, JAP; Bourajjaj, M; Jansen, B; Leenders, I; Lemmers, J; Musters, M; van Zanten, S; van Zelst, L; Worthington, J; Lu, JS; Juric, D; Meyer, CA; Oubrie, A; Liu, XS; Fisher, DE; Flaherty, KT Sahu, Avinash; Wang, Xiaoman; Munson, Phillip; Klomp, Jan P. G.; Wang, Xiaoqing; Gu, Shengqing Stan; Han, Ya; Qian, Gege; Nicol, Phillip; Zeng, Zexian; Wang, Chenfei; Tokheim, Collin; Zhang, Wubing; Fu, Jingxin; Wang, Jin; Nair, Nishanth Ulhas; Rens, Joost A. P.; Bourajjaj, Meriem; Jansen, Bas; Leenders, Inge; Lemmers, Jaap; Musters, Mark; van Zanten, Sanne; van Zelst, Laura; Worthington, Jenny; Lu, Jun S.; Juric, Dejan; Meyer, Clifford A.; Oubrie, Arthur; Liu, X. Shirley; Fisher, David E.; Flaherty, Keith T. Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifi es Estrogen-Related Receptor Alpha CANCER DISCOVERY English Article TRANSCRIPTION FACTOR; CANCER; CELLS; TUMOR; EXPRESSION; RESISTANCE; AUTOPHAGY; BLOCKADE; PROTEIN; SEQ Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefi ts. Here, we fi rst developed an in silico multiomics approach (BipotentR) to fi nd cancer cell-specifi c regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identifi ed regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinfl ammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8 + T cells into tumors. We also demonstrate a wide util-ity of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR ( http://bipotentr.dfci.harvard.edu/ ) provides a resource for evaluating patient response and discover-ing drug targets that act simultaneously through multiple mechanisms. [Sahu, Avinash; Wang, Xiaoman; Wang, Xiaoqing; Gu, Shengqing Stan; Han, Ya; Qian, Gege; Nicol, Phillip; Zeng, Zexian; Wang, Chenfei; Tokheim, Collin; Zhang, Wubing; Fu, Jingxin; Wang, Jin; Meyer, Clifford A.; Liu, X. Shirley] Dana Farber Canc Inst, Dept Data Sci, 44 Binney St, Boston, MA 02115 USA; [Sahu, Avinash] Univ Colorado, Dept Obstet & Gynecol, Sch Med, Aurora, CO USA; [Wang, Xiaoman] Chinese Acad Med Sci & Peking Union Med Coll, Inst Basic Med Sci, State Key Lab Med Mol Biol, Dept Biochem & Mol Biol, Beijing, Peoples R China; [Munson, Phillip; Juric, Dejan; Fisher, David E.; Flaherty, Keith T.] Dept Med, Boston, MA USA; [Munson, Phillip; Juric, Dejan; Fisher, David E.; Flaherty, Keith T.] Harvard Med Sch, Massachusetts Gen Hosp, Ctr Canc, Boston, MA 02115 USA; [Klomp, Jan P. G.; Rens, Joost A. P.; Bourajjaj, Meriem; Jansen, Bas; Leenders, Inge; Lemmers, Jaap; Musters, Mark; van Zanten, Sanne; van Zelst, Laura; Oubrie, Arthur] Lead Pharm, Kloosterstr, Oss, Netherlands; [Wang, Xiaoqing] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Affiliated Peoples Hosp 6, Dept Cardiol, Shanghai, Peoples R China; [Han, Ya] Tongji Univ, Sch Life Sci & Technol, Shanghai, Peoples R China; [Nair, Nishanth Ulhas] NCI, Canc Data Sci Lab, NIH, Bethesda, MD 20892 USA; [Worthington, Jenny] Axis Bioserv, Coleraine, Londonderry, North Ireland; [Lu, Jun S.] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA; [Fisher, David E.] Massachusetts Gen Hosp, Dept Dermatol, Boston, MA 02114 USA Harvard University; Dana-Farber Cancer Institute; University of Colorado System; University of Colorado Anschutz Medical Campus; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; Harvard University; Harvard Medical School; Massachusetts General Hospital; Shanghai Jiao Tong University; Tongji University; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Harvard University; Harvard University; Massachusetts General Hospital Sahu, A (corresponding author), Dana Farber Canc Inst, Dept Data Sci, 44 Binney St, Boston, MA 02115 USA.;Flaherty, KT (corresponding author), Massachusetts Gen Hosp, Ctr Canc, Dev Therapeut, 55 Fruit St, Boston, MA 02114 USA.;Fisher, DE (corresponding author), Charlestown Navy Yard Bldg 149,149 13th St, Charlestown, MA 02129 USA. asahu@ds.dfci.harvard.edu; dfisher3@mgh.harvard.edu; kflaherty@mgh.harvard.edu ; Jansen, Bastiaan/C-1091-2009 Han, Ya/0000-0003-1892-4239; Munson, Phillip/0000-0003-1635-3313; wang, Xiaoman/0000-0002-7833-6108; Sahu, Avinash/0000-0002-2193-6276; Jansen, Bastiaan/0000-0001-5985-7656; Gu, Shengqing/0000-0002-4200-0864 NIH/NCI [K99CA248953, R01AR043369, P01CA163222, R01CA222871, MP19-02-190]; Human Vaccines Project [R01AR072304]; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; China Scholarship Council [201806210422]; Damon Runyon Cancer Research Foundation [DRQ-04-20]; Intramural Research Program of the NIH; NCI; Center for Cancer Research NIH/NCI(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Human Vaccines Project; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; China Scholarship Council(China Scholarship Council); Damon Runyon Cancer Research Foundation; Intramural Research Program of the NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NCI(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); Center for Cancer Research(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)) A. Sahu was supported by the NIH/NCI (K99CA248953) and the Human Vaccines Project (MP19-02-190). D.E. Fisher was supported by the NIH/NCI (R01AR072304, R01AR043369, P01CA163222, and R01CA222871). K.T. Flaherty and D.E. Fisher were supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foun-dation. X. Wang was supported by the China Scholarship Council (201806210422). C. Tokheim was supported by the Damon Runyon Cancer Research Foundation (DRQ-04-20). N.U. Nair was supported in part by the Intramural Research Program of the NIH, NCI, and the Center for Cancer Research. The authors from Lead Pharma, includ-ing A. Oubrie, J.P.G. Klomp, J.A.P. Rens, M. Bourajjaj, B. Jansen, I. Leenders, J. Lemmers, M. Musters, S. van Zanten, and L. van Zelst, were supported by Eurostars (Eureka, PREVAIL 11590). We thank Rafael A. Irizarry and Myles A. Brown for their valuable suggestions.The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked advertisement in accordance with 18 USC section 1734. 148 0 0 3 3 AMER ASSOC CANCER RESEARCH PHILADELPHIA 615 CHESTNUT ST, 17TH FLOOR, PHILADELPHIA, PA 19106-4404 USA 2159-8274 2159-8290 CANCER DISCOV Cancer Discov. MAR 2023.0 13 3 672 701 10.1158/2159-8290.CD-22-0244 0.0 30 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 9M9GV 36745048.0 hybrid, Green Accepted 2023-03-23 WOS:000942530200001 0
C Su, Z; Liu, WZ; Yu, ZT; Hu, DW; Liao, Q; Tian, Q; Pietikainen, M; Liu, L IEEE Su, Zhuo; Liu, Wenzhe; Yu, Zitong; Hu, Dewen; Liao, Qing; Tian, Qi; Pietikainen, Matti; Liu, Li Pixel Difference Networks for Efficient Edge Detection 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) English Proceedings Paper 18th IEEE/CVF International Conference on Computer Vision (ICCV) OCT 11-17, 2021 ELECTR NETWORK IEEE,CVF,IEEE Comp Soc Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the high performance of CNN based edge detection is achieved with a large pretrained CNN backbone, which is memory and energy consuming. In addition, it is surprising that the previous wisdom from the traditional edge detectors, such as Canny, Sobel, and LBP are rarely investigated in the rapid-developing deep learning era. To address these issues, we propose a simple, lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection. PiDiNet adopts novel pixel difference convolutions that integrate the traditional edge detection operators into the popular convolutional operations in modern CNNs for enhanced performance on the task, which enjoys the best of both worlds. Extensive experiments on BSDS500, NYUD, and Multicue are provided to demonstrate its effectiveness, and its high training and inference efficiency. Surprisingly, when training from scratch with only the BSDS500 and VOC datasets, PiDiNet can surpass the recorded result of human perception (0.807 vs. 0.803 in ODS F-measure) on the BSDS500 dataset with 100 FPS and less than 1M parameters. A faster version of PiDiNet with less than 0.1M parameters can still achieve comparable performance among state of the arts with 200 FPS. Results on the NYUD and Multicue datasets show similar observations. The codes are available at https://github.com/zhuoinoulu/pidinet. [Su, Zhuo; Yu, Zitong; Pietikainen, Matti; Liu, Li] Univ Oulu, Ctr Machine Vision & Signal Anal, Oulu, Finland; [Liu, Wenzhe; Hu, Dewen; Liu, Li] Natl Univ Def Technol, Hefei, Peoples R China; [Liao, Qing] Harbin Inst Technol Shenzhen, Shenzhen, Peoples R China; [Tian, Qi] Xidian Univ, Xian, Peoples R China University of Oulu; National University of Defense Technology - China; Harbin Institute of Technology; Xidian University Liu, L (corresponding author), Univ Oulu, Ctr Machine Vision & Signal Anal, Oulu, Finland.;Liu, L (corresponding author), Natl Univ Def Technol, Hefei, Peoples R China. zhuo.su@oulu.fi; liuwenzhe15@nudt.edu.cn; zitong.yu@oulu.fi; dwhu@nudt.edu.cn; liaoqing@hit.edu.cn; wywqtian@gmail.com; matti.pietikainen@oulu.fi; li.liu@oulu.fi Academy of Finland [331883]; National Natural Science Foundation of China [61872379, 62022091, 71701205] Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partially supported by the Academy of Finland under grant 331883 and the National Natural Science Foundation of China under Grant 61872379, 62022091, and 71701205. The authors also wish to acknowledge CSC IT Center for Science, Finland, for computational resources. 67 16 17 6 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2812-5 2021.0 5097 5107 10.1109/ICCV48922.2021.00507 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT1IF Green Submitted 2023-03-23 WOS:000797698905032 0
J Zheng, SN; Hu, YQ; Chong, AYL; Tan, CW Zheng, Shuning; Hu, Yueqiu; Chong, Alain Yee Loong; Tan, Chee-Wee Leveraging blockchain technology to control contextualized business risks: Evidence from China INFORMATION & MANAGEMENT English Article Blockchain; Risk management; Opportunistic risk; Trust-evoking technology BEHAVIORAL INTEGRITY; ECONOMIC-GROWTH; FIRM RISK; BIG DATA; SYSTEMS; MANAGEMENT; TRUST; INFORMATION; GUANXI; PERFORMANCE The complexity and diversity of socio-economic environments call for a more nuanced consideration of contextualized risks confronting enterprises operating in these environments. By dissecting six cases that successfully adopted blockchain technology in China, we present findings of a grounded theory study into the deployment of blockchain for managing contextualized risks and opportunistic risks. Findings reveal that applying blockchain can augment risk management by controlling opportunistic risk, the latter of which denotes the variability arising from opportunistic practices of internal agents or external parties afforded by their immediate socio-economic environment. Particularly, we not only identify credibility, predatory, and compliance risk as three distinct types of opportunistic risk stemming from the unique socio-economic environment of China, but we also illustrate how blockchain could be leveraged to deal with such opportunistic risks through truste-voking mechanisms. We discovered that blockchain could evoke trust between individuals-to-organization, organization-to-organization, and organization-to-individuals by bolstering competency, fostering benevolence, and gauging integrity in individual, inter-organizational and intra-organizational contexts, respectively. [Zheng, Shuning; Hu, Yueqiu; Chong, Alain Yee Loong] Univ Nottingham Ningbo China, Nottingham Univ Business Sch China, Dept Entrepreneurship Mkt & Management Syst, Ningbo, Peoples R China; [Tan, Chee-Wee] Copenhagen Business Sch, Dept Digitizat, Howitzvej 60,5-24, DK-2000 Frederiksberg, Denmark University of Nottingham Ningbo China; Copenhagen Business School Tan, CW (corresponding author), Copenhagen Business Sch, Dept Digitizat, Howitzvej 60,5-24, DK-2000 Frederiksberg, Denmark. Shuning.Zheng@nottingham.edu.cn; Yueqiu.Hu@nottingham.edu.cn; Alain.Chong@nottingham.edu.cn; ct.digi@cbs.dk zheng, shuning/0000-0001-6227-1197 102 2 2 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-7206 1872-7530 INFORM MANAGE-AMSTER Inf. Manage. NOV 2022.0 59 7 103628 10.1016/j.im.2022.103628 0.0 13 Computer Science, Information Systems; Information Science & Library Science; Management Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science; Business & Economics 8X3XN 2023-03-23 WOS:000931949400009 0
J Guo, J; Song, B; Zhang, P; Ma, MD; Luo, WW; Iv, JM Guo, Jie; Song, Bin; Zhang, Peng; Ma, Mengdi; Luo, Wenwen; Iv, Junmei Affective video content analysis based on multimodal data fusion in heterogeneous networks INFORMATION FUSION English Article Multimodal data; Affective video content; Data fusion; Deep learning; Common space learning In heterogeneous networks, different modalities are coexisting. For example, video sources with certain lengths usually have abundant time-varying audiovisual data. From the users' perspective, different video segments will trigger different kinds of emotions. In order to better interact with users in heterogeneous networks and improve their user experiences, affective video content analysis to predict users' emotions is essential. Academically, users' emotions can be evaluated by arousal and valence values, and fear degree, which provides an approach to quantize the prediction accuracy of the reaction of the audience and users towards videos. In this paper, we propose the multimodal data fusion method for integrating the visual and audio data in order to perform the affective video content analysis. Specifically, to align the visual and audio data, the temporal attention filters are proposed to obtain the time-span features of the entire video segments. Then, by using the two-branch network structure, matched visual and audio features are integrated in the common space. At last, the fused audiovisual feature is employed for the regression and classification subtasks in order to measure the emotional responses of users. Simulation results show that the proposed method can accurately predict the subjective feelings of users towards the video contents, which provides a way to predict users' preferences and recommend videos according to their own demand. [Guo, Jie; Song, Bin; Ma, Mengdi; Luo, Wenwen; Iv, Junmei] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Zhang, Peng] Zalando Co, Helsinki, Finland Xidian University Song, B (corresponding author), Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China. bsong@mail.xidian.edu.cn ARSLAN, Okan/AAA-3232-2020 National Natural Science Foundation of China [61772387, 61802296]; China Postdoctoral Science Foundation [2017M620438]; Fundamental Research Funds for the Central Universities [JB180101]; Fundamental Research Funds of Ministry of Education and China Mobile [MCM20170202]; ISN State Key Laboratory National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Fundamental Research Funds of Ministry of Education and China Mobile; ISN State Key Laboratory This work has been supported by the National Natural Science Foundation of China (Nos. 61772387, 61802296), China Postdoctoral Science Foundation Grant (No. 2017M620438), the Fundamental Research Funds for the Central Universities (No. JB180101), Fundamental Research Funds of Ministry of Education and China Mobile (No. MCM20170202), and also supported by the ISN State Key Laboratory. 30 13 14 0 61 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion NOV 2019.0 51 224 232 10.1016/j.inffus.2019.02.007 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HZ9DI 2023-03-23 WOS:000469155600017 0
J Qin, YX; Yu, ZT; Yan, LB; Wang, ZZ; Zhao, CX; Lei, Z Qin, Yunxiao; Yu, Zitong; Yan, Longbin; Wang, Zezheng; Zhao, Chenxu; Lei, Zhen Meta-Teacher For Face Anti-Spoofing IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Detectors; Face recognition; Training; Faces; Feature extraction; Training data; Optimization; Face anti-spoofing; meta-teacher; pixel-wise supervision; deep-learning Face anti-spoofing (FAS) secures face recognition from presentation attacks (PAs). Existing FAS methods usually supervise PA detectors with handcrafted binary or pixel-wise labels. However, handcrafted labels may are not the most adequate way to supervise PA detectors learning sufficient and intrinsic spoofing cues. Instead of using the handcrafted labels, we propose a novel Meta-Teacher FAS (MT-FAS) method to train a meta-teacher for supervising PA detectors more effectively. The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues. The bi-level optimization contains two key components: 1) a lower-level training in which the meta-teacher supervises the detector's learning process on the training set; and 2) a higher-level training in which the meta-teacher's teaching performance is optimized by minimizing the detector's validation loss. Our meta-teacher differs significantly from existing teacher-student models because the meta-teacher is explicitly trained for better teaching the detector (student), whereas existing teachers are trained for outstanding accuracy neglecting teaching ability. Extensive experiments on five FAS benchmarks show that with the proposed MT-FAS, the trained meta-teacher 1) provides better-suited supervision than both handcrafted labels and existing teacher-student models; and 2) significantly improves the performances of PA detectors. [Qin, Yunxiao] Commun Univ China, Neurosci & Intelligent Media Inst NIMI, Beijing 100024, Peoples R China; [Qin, Yunxiao; Yan, Longbin] Northwestern Polytech Univ, Xian 710072, Peoples R China; [Yu, Zitong] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland; [Wang, Zezheng] Beijing Kwai Technol Co Ltd, Beijing 102600, Peoples R China; [Zhao, Chenxu] MiningLamp Technol, Beijing 100000, Peoples R China; [Lei, Zhen] Chinese Acad Sci CASIA, Ctr Biometr & Secur Res CBSR, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China; [Lei, Zhen] Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Lei, Zhen] Chinese Acad Sci, Ctr Artificial Intelligence & Robot, Hong Kong Inst Sci & Innovat, Hong Kong, Peoples R China Communication University of China; Northwestern Polytechnical University; University of Oulu; Chinese Academy of Sciences Lei, Z (corresponding author), Chinese Acad Sci CASIA, Ctr Biometr & Secur Res CBSR, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China. qyxqyx@mail.nwpu.edu.cn; zitong.yu@oulu.fi; yanlongbin@mail.nwpu.edu.cn; wangzezheng@kuaishou.com; zhaochenxu@mininglamp.com; zlei@nlpr.ia.ac.cn National Key Research and Development Program of China [2020AAA0140002]; National Natural Science Foundation of China [61876178, 61976229] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2020AAA0140002 and in part by the National Natural Science Foundation of China under Grants 61876178 and 61976229. 74 10 10 9 13 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. OCT 1 2022.0 44 10 6311 6326 10.1109/TPAMI.2021.3091167 0.0 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4N2UL 34156938.0 Green Submitted, Green Accepted 2023-03-23 WOS:000853875300035 0
J Sun, H; Xiao, K; Zeng, ZZ; Yang, B; Duan, HB; Zhao, HZ; Zhang, YF Sun, Hao; Xiao, Ke; Zeng, Zizhou; Yang, Bo; Duan, Huabo; Zhao, Huazhang; Zhang, Yifeng Electroactive biofilm-based sensor for volatile fatty acids monitoring: A review CHEMICAL ENGINEERING JOURNAL English Review Anaerobic digestion; Biosensor; Electroactive biofilm; Microbial electrochemical system; Online monitoring; Volatile fatty acids MICROBIAL FUEL-CELL; ANAEROBIC-DIGESTION PROCESSES; WASTE-WATER TREATMENT; AMPEROMETRIC DETERMINATION; ACETIC-ACID; BIOSENSOR; ACETATE; SYSTEM; SPECTROSCOPY; PERFORMANCE Quantitative analysis of volatile fatty acids (VFAs) is critical for the monitoring, control and optimization of biological processes. However, commonly used detection techniques such as titration and chromatography are too complex, time-consuming or costly, making them not optimal for industrial applications. Biosensors, which can directly convert chemical energy stored in organics into electrical signals, have emerged as an attractive alternative for VFA measurements. They are simple, sensitive and low-cost, and have shown great potential for online monitoring. Among them, the electroactive biofilm (EAB)-based sensor is the most adopted. Based on a summary of the frontiers in EAB sensor development, direct/indirect contact of biofilm with VFA in tests can largely determine their performance. Various applications can illustrate the feasibility of such biosensors for off- or on-line VFA monitoring in different biological processes. These remarkable progresses have been made in recent years, but they are still facing several challenges that need to be addressed for field applications. Microbial analysis of EAB, introduction of machine learning, and optimization of biosensor design and operation will be interesting for future studies. This review can provide guidelines and references for the research, development and application of EAB sensors for online VFA monitoring. [Sun, Hao; Duan, Huabo] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China; [Sun, Hao; Xiao, Ke; Zeng, Zizhou; Yang, Bo] Shenzhen Univ, Coll Chem & Environm Engn, Water Sci & Environm Engn Res Ctr, Shenzhen 518060, Peoples R China; [Sun, Hao; Zhao, Huazhang] Peking Univ, Coll Environm Sci & Engn, Key Lab Water & Sediment Sci, Minist Educ, Beijing 100871, Peoples R China; [Zhao, Huazhang] Shanxi Univ, Shanxi Lab Yellow River, Taiyuan 030006, Peoples R China; [Zhang, Yifeng] Tech Univ Denmark, Dept Environm Engn, DK-2800 Lyngby, Denmark Shenzhen University; Shenzhen University; Peking University; Shanxi University; Technical University of Denmark Xiao, K (corresponding author), Shenzhen Univ, Coll Chem & Environm Engn, Water Sci & Environm Engn Res Ctr, Shenzhen 518060, Peoples R China.;Zhao, HZ (corresponding author), Peking Univ, Coll Environm Sci & Engn, Key Lab Water & Sediment Sci, Minist Educ, Beijing 100871, Peoples R China.;Zhao, HZ (corresponding author), Shanxi Univ, Shanxi Lab Yellow River, Taiyuan 030006, Peoples R China. xiaoke@szu.edu.cn; zhaohuazhang@pku.edu.cn Zhang, Yifeng/D-9604-2011 Zhang, Yifeng/0000-0002-2832-2277; Sun, Hao/0000-0001-8499-6375 National Science Fund for Distinguished Young Scholars [21925801]; China Postdoctoral Science Foundation [2021M702258]; National Natural Science Foundation of China [22178222, 22108173]; Guangdong Basic and Applied Basic Research Foundation [2021A1515010540] National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation The work was supported by National Science Fund for Distinguished Young Scholars [Grant No. 21925801], China Postdoctoral Science Foundation [Grant No. 2021M702258], National Natural Science Foundation of China [Grant Nos. 22178222 and 22108173], and Guangdong Basic and Applied Basic Research Foundation [Grant No. 2021A1515010540]. We likewise thank Dr. Weiwei Xu for grammar checking and language polishing. 135 1 1 44 55 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1385-8947 1873-3212 CHEM ENG J Chem. Eng. J. DEC 1 2022.0 449 137833 10.1016/j.cej.2022.137833 0.0 JUL 2022 17 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 3Z4PP 2023-03-23 WOS:000844399400003 0
J Fang, LM; Zhao, B; Li, Y; Liu, Z; Ge, CP; Meng, WZ Fang, Liming; Zhao, Bo; Li, Yang; Liu, Zhe; Ge, Chunpeng; Meng, Weizhi Countermeasure Based on Smart Contracts and AI against DoS/DDoS Attack in 5G Circumstances IEEE NETWORK English Article The development of 5G has substantially increased the destructiveness of DoS/DDoS attacks because the data processing capability of computers has not been accordingly enhanced, and this contradiction creates a vulnerability for attackers to compromise a server by sending a massive data flow. in practical 5G circumstances, it is difficult to extract distinct features between malicious and benign massive data flows. This amplifies the difficulties of DoS/DDoS detection. Thus, precautions against DoS/DDoS attack in 5G are of great importance. in this article, we present a solution based on smart contracts and machine learning as a countermeasure against DoS/DDoS attacks in the 5G background by hiding a protected server in a blockchain network and flexibly restricting the scale of DoS/DDoS via transaction fees. We also leverage non-repudiation of smart contracts, analyzing users' malicious behavior of communication and executing punishment via smart contracts. Our scheme could effectively mitigate massive DoS/DDoS attacks in advance and dynamically punitively charge DoS/DDoS attacks. Compared to existing DoS/DDoS defense in 4G, our scheme offers numerous benefits, including making benign communication always dominate rational users and countering DoS/DDoS attacks before they are launched. Moreover, compared to common DoS/DDoS detection based on Ai, we consider the source trustworthiness of training samples and take measures to avoid backdoors where model trainers may compromise Ai models to launch DoS/DDoS attacks. [Zhao, Bo; Li, Yang; Liu, Zhe] Nanjing Univ Aeronaut & Astronaut, Cyberspace Secur, Nanjing, Peoples R China; [Liu, Zhe] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Peoples R China; [Ge, Chunpeng] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China; [Fang, Liming] Nanjing Univ Aeronaut, Nanjing, Peoples R China; [Fang, Liming] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing, Peoples R China; [Liu, Zhe] State Key Lab Cryptol, Nanjing, Peoples R China; [Meng, Weizhi] Tech Univ Denmark, Cyber Secur Sect, Dept Appl Math & Comp Sci, Lyngby, Denmark Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics; Technical University of Denmark Fang, LM (corresponding author), Nanjing Univ Aeronaut, Nanjing, Peoples R China.;Fang, LM (corresponding author), Collaborat Innovat Ctr Novel Software Technol & I, Nanjing, Peoples R China. fangliming@nuaa.edu.cn; bozhao@nuaa.edu.cn; lyang0314@nuaa.edu.cn; Liuzhenuaa@163.com; gecp@nuaa.edu.cn; yuxin.meng@my.cityu.edu.hk National Key R&D Program of China [2017YFC1201200, 2019YFA0803000]; National Natural Science Foundation of China [61872181, 61702236, 61802180]; National Science Foundation for Post-Doctoral Scientists of China [2019M651826]; Natural Science Foundation of Jiangsu Province [BK20180421]; National Cryptography Development Fund [MMJ J20180105]; Fundamental Research Funds for the Central Universities [NE2018106]; JSPS [JP19J15225, 18H03240, 18K11298]; H 2020-SU :1-03-2018: CyberSec4Europe [830929]; Grants-in-Aid for Scientific Research [18K11298, 18H03240] Funding Source: KAKEN National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Foundation for Post-Doctoral Scientists of China(China Postdoctoral Science FoundationNational Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); National Cryptography Development Fund; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); H 2020-SU :1-03-2018: CyberSec4Europe; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work is supported by the National Key R&D Program of China under Grant Nos. 2017YFC1201200 and 2019YFA0803000, the National Natural Science Foundation of China under Grant Nos. 61872181, 61702236, and61802180, tire National Science Foundation for Post-Doctoral Scientists of China under Grant 2019M651826, the Natural Science Foundation of Jiangsu Province under Grant No. BK20180421, the National Cryptography Development Fund under Grant No. MMJ J20180105, the Fundamental Research Funds for the Central Universities under Grant No. NE2018106, JSPS Grant -in -Aid for Scientific Research (DC2) under Grant JP19J15225, JSPS Kiban(B) under Grant 18H03240, JSPS Kiban(C) under Grant 18K11298, and H 2020-SU :1-03-2018: CyberSec4Europe under Grant No. 830929. 14 7 7 3 48 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. NOV-DEC 2020.0 34 6 54 61 10.1109/MNET.021.1900614 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PE8XD 2023-03-23 WOS:000598643800009 0
J Brito-Zeron, P; Acar-Denizli, N; Zeher, M; Rasmussen, A; Seror, R; Theander, E; Li, XM; Baldini, C; Gottenberg, JE; Danda, D; Quartuccio, L; Priori, R; Hernandez-Molina, G; Kruize, AA; Valim, V; Kvarnstrom, M; Sene, D; Gerli, R; Praprotnik, S; Isenberg, D; Solans, R; Rischmueller, M; Kwok, SK; Nordmark, G; Suzuki, Y; Giacomelli, R; Devauchelle-Pensec, V; Bombardieri, M; Hofauer, B; Bootsma, H; Brun, JG; Fraile, G; Carsons, SE; Gheita, TA; Morel, J; Vollenveider, C; Atzeni, F; Retamozo, S; Horvath, IF; Sivils, K; Mandl, T; Sandhya, P; De Vita, S; Sanchez-Guerrero, J; van der Heijden, E; Trevisani, VFM; Wahren-Herlenius, M; Mariette, X; Ramos-Casals, M Brito-Zeron, Pilar; Acar-Denizli, Nihan; Zeher, Margit; Rasmussen, Astrid; Seror, Raphaele; Theander, Elke; Li, Xiaomei; Baldini, Chiara; Gottenberg, Jacques-Eric; Danda, Debashish; Quartuccio, Luca; Priori, Roberta; Hernandez-Molina, Gabriela; Kruize, Aike A.; Valim, Valeria; Kvarnstrom, Marika; Sene, Damien; Gerli, Roberto; Praprotnik, Sonja; Isenberg, David; Solans, Roser; Rischmueller, Maureen; Kwok, Seung-Ki; Nordmark, Gunnel; Suzuki, Yasunori; Giacomelli, Roberto; Devauchelle-Pensec, Valerie; Bombardieri, Michele; Hofauer, Benedikt; Bootsma, Hendrika; Brun, Johan G.; Fraile, Guadalupe; Carsons, Steven E.; Gheita, Tamer A.; Morel, Jacques; Vollenveider, Cristina; Atzeni, Fabiola; Retamozo, Soledad; Horvath, Ildiko Fanny; Sivils, Kathy; Mandl, Thomas; Sandhya, Pulukool; De Vita, Salvatore; Sanchez-Guerrero, Jorge; van der Heijden, Eefje; Moca Trevisani, Virginia Fernandes; Wahren-Herlenius, Marie; Mariette, Xavier; Ramos-Casals, Manuel EULAR-SS Task Force Big Data Conso Influence of geolocation and ethnicity on the phenotypic expression of primary Sjogren's syndrome at diagnosis in 8310 patients: a cross-sectional study from the Big Data Sjogren Project Consortium ANNALS OF THE RHEUMATIC DISEASES English Article CLASS-II GENES; AUTOIMMUNE; LUPUS; GEOEPIDEMIOLOGY; SUSCEPTIBILITY; CLASSIFICATION; CRITERIA; DISEASES; CHINESE Objectives To analyse the influence of geolocation and ethnicity on the clinical presentation of primary Sjgren's syndrome (SjS) at diagnosis. Methods The Big Data Sjogren Project Consortium is an international, multicentre registry designed in 2014. By January 2016, 20 centres from five continents were participating. Multivariable logistic regression analyses were performed. Results We included 7748 women (93%) and 562 men (7%), with a mean age at diagnosis of primary SjS of 53 years. Ethnicity data were available for 7884 patients (95%): 6174 patients (78%) were white, 1066 patients (14%) were Asian, 393 patients (5%) were Hispanic, 104 patients (1%) were black/African-American and 147 patients (2%) were of other ethnicities. SjS was diagnosed a mean of 7 years earlier in black/African-American compared with white patients; the female-to-male ratio was highest in Asian patients (27: 1) and lowest in black/African-American patients (7: 1); the prevalence of sicca symptoms was lowest in Asian patients; a higher frequency of positive salivary biopsy was found in Hispanic and white patients. A north-south gradient was found with respect to a lower frequency of ocular involvement in northern countries for dry eyes and abnormal ocular tests in Europe (OR 0.46 and 0.44, respectively) and Asia (OR 0.18 and 0.49, respectively) compared with southern countries. Higher frequencies of antinuclear antibodies (ANAs) were reported in northern countries in America (OR=1.48) and Asia (OR=3.80) while, in Europe, northern countries had lowest frequencies of ANAs (OR=0.67) and Ro/La (OR=0.69). Conclusions This study provides the first evidence of a strong influence of geolocation and ethnicity on the phenotype of primary SjS at diagnosis. [Brito-Zeron, Pilar] Hosp CIMA, Dept Med, Autoimmune Dis Unit, Barcelona, Spain; [Brito-Zeron, Pilar; Ramos-Casals, Manuel] Univ Barcelona, Hosp Clin, IDIBAPS CELLEX,Sjogren Syndrome Res Grp AGAUR, ICMiD,Dept Autoimmune Dis,Lab Autoimmune Dis Jose, Barcelona, Spain; [Acar-Denizli, Nihan] Mimar Sinan Fine Arts Univ, Fac Sci & Letters, Dept Stat, Istanbul, Turkey; [Zeher, Margit] Univ Debrecen, Fac Med, Div Clin Immunol, Debrecen, Hungary; [Rasmussen, Astrid] Oklahoma Med Res Fdn, Arthritis & Clin Immunol Res Program, 825 NE 13th St, Oklahoma City, OK 73104 USA; [Seror, Raphaele; Mariette, Xavier] Le Kremlin Bicetre Univ Paris Sud, Hop Univ Paris Sud, Assistance Publ Hop Paris,U1184, Ctr Immunol Viral Infect & Autoimmune Dis,INSERM, Paris, France; [Theander, Elke] Lund Univ, Malmo Univ Hosp, Dept Rheumatol, Lund, Sweden; [Li, Xiaomei] Anhui Prov Hosp, Dept Rheumatol & Immunol, Hefei, Peoples R China; [Baldini, Chiara] Univ Pisa, Rheumatol Unit, Pisa, Italy; [Gottenberg, Jacques-Eric] Univ Strasbourg, Strasbourg Univ Hosp, Dept Rheumatol, Strasbourg, France; [Danda, Debashish; Sandhya, Pulukool] Christian Med Coll & Hosp, Dept Clin Immunol, Vellore, Tamil Nadu, India; [Quartuccio, Luca; De Vita, Salvatore] Univ Hosp Santa Maria Misericordia, Dept Med & Biol Sci, Clin Rheumatol, Udine, Italy; [Priori, Roberta] Sapienza Univ Rome, Dept Internal Med & Med Specialties, Rheumatol Clin, Rome, Italy; [Hernandez-Molina, Gabriela; Sanchez-Guerrero, Jorge] Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Immunol & Rheumatol Dept, Mexico City, DF, Mexico; [Kruize, Aike A.; van der Heijden, Eefje] Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands; [Valim, Valeria] Univ Fed Espirito Santo, Dept Med, Vitoria, Brazil; [Kvarnstrom, Marika; Wahren-Herlenius, Marie] Karolinska Inst, Dept Med Solna, Unit Expt Rheumatol, Stockholm, Sweden; [Kvarnstrom, Marika; Wahren-Herlenius, Marie] Karolinska Univ Hosp, Stockholm, Sweden; [Sene, Damien] Univ Paris VII, Assistance Publ Hop Paris, Hop Lariboisiere, Dept Med Interne, Paris, France; [Gerli, Roberto] Univ Perugia, Dept Med, Rheumatol Unit, Perugia, Italy; [Praprotnik, Sonja] Univ Med Ctr, Dept Rheumatol, Ljubljana, Slovenia; [Isenberg, David] UCL, Ctr Rheumatol, Div Med, London, England; [Solans, Roser] Hosp Hebron, Dept Internal Med, Barcelona, Spain; [Rischmueller, Maureen] Discipline Med Univ Adelaide, Queen Elizabeth Hosp, Dept Rheumatol, Adelaide, SA, Australia; [Kwok, Seung-Ki] Cathol Univ Korea, Seoul St Marys Hosp, Coll Med, Div Rheumatol, Seoul, South Korea; [Nordmark, Gunnel] Uppsala Univ, Dept Med Sci, Rheumatol, Uppsala, Sweden; [Suzuki, Yasunori] Kanazawa Univ Hosp, Div Rheumatol, Kanazawa, Ishikawa, Japan; [Giacomelli, Roberto] Univ Laquila, Sch Med, Clin Unit Rheumatol, Laquila, Italy; [Devauchelle-Pensec, Valerie] Brest Univ Hosp, Rheumatol Dept, Brest, France; [Bombardieri, Michele] Queen Mary Univ London, Ctr Expt Med & Rheumatol, London, England; [Hofauer, Benedikt] Tech Univ Munich, Otorhinolaryngol Head & Neck Surg, Munich, Germany; [Bootsma, Hendrika] Univ Groningen, Univ Med Ctr Groningen, Dept Rheumatol & Clin Immunol, Groningen, Netherlands; [Brun, Johan G.] Univ Bergen, Dept Clin Sci, Bergen, Norway; [Brun, Johan G.] Haukeland Hosp, Dept Rheumatol, Bergen, Norway; [Fraile, Guadalupe] Hosp Ramon & Cajal, Dept Internal Med, Madrid, Spain; [Carsons, Steven E.] SUNY Stony Brook, Allergy & Immunol Winthrop Univ Hosp, Div Rheumatol, Mineola, NY 11501 USA; [Gheita, Tamer A.] Cairo Univ, Kasr Al Ainy Sch Med, Rheumatol Dept, Cairo, Egypt; [Morel, Jacques] Teaching Hosp, Dept Rheumatol, Montpellier, France; [Morel, Jacques] Univ Montpellier, Montpellier, France; [Vollenveider, Cristina] German Hosp, Buenos Aires, DF, Argentina; [Atzeni, Fabiola] IRCCS Galeazzi Orthoped Inst, Milan, Italy; [Retamozo, Soledad] Inst Univ Biomed Sci Univ Cordoba IUCBC, Hosp Privado Univ Cordoba, Cordoba, Argentina; [Moca Trevisani, Virginia Fernandes] Univ Fed Sao Paulo, Sao Paulo, Brazil; [Ramos-Casals, Manuel] Univ Barcelona, Dept Med, Barcelona, Spain University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS; Mimar Sinan Guzel Sanatlar University; University of Debrecen; Oklahoma Medical Research Foundation; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Paul-Brousse - APHP; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Aix-Marseille Universite; Assistance Publique-Hopitaux de Marseille; Universite Paris Saclay; Lund University; Skane University Hospital; University of Pisa; CHU Strasbourg; UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Christian Medical College & Hospital (CMCH) Vellore; Hospital Santa Maria della Misericordia; Sapienza University Rome; Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran - Mexico; Utrecht University; Utrecht University Medical Center; Universidade Federal do Espirito Santo; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; UDICE-French Research Universities; Aix-Marseille Universite; Assistance Publique-Hopitaux de Marseille; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Lariboisiere-Fernand-Widal - APHP; University of Perugia; University Medical Centre Ljubljana; University of London; University College London; Hospital Universitari Vall d'Hebron; University of Adelaide; Catholic University of Korea; Seoul St. Mary's Hospital; Uppsala University; Kanazawa University; University of L'Aquila; CHU Brest; University of London; Queen Mary University London; Technical University of Munich; University of Groningen; University of Bergen; University of Bergen; Haukeland University Hospital; Hospital Universitario Ramon y Cajal; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Winthrop University Hospital; Egyptian Knowledge Bank (EKB); Cairo University; Universite de Montpellier; CHU de Montpellier; Universite de Montpellier; Hospital Aleman; University of Buenos Aires; IRCCS Istituto Ortopedico Galeazzi; Hospital Privado - Universitario de Cordoba; Universidade Federal de Sao Paulo (UNIFESP); University of Barcelona Ramos-Casals, M (corresponding author), Hosp Clin Barcelona, Serv Malalties Autoimmunes Sistemi, C Villarroel,170, Barcelona 08036, Spain. mramos@clinic.ub.es sene, damien/R-3970-2017; Hernandez-Molina, Gabriela/ABC-9915-2020; Baldini, Chiara/K-9829-2016; Scofield, Robert Hal/AEJ-8314-2022; Hofauer, Benedikt/HDN-5761-2022; Giacomelli, Roberto/AAB-9962-2022; danda, debashish/K-2650-2013; Rasmussen, Astrid/A-4878-2013; Nordmark, Gunnel/A-9652-2016; SEROR, Raphaele/E-2283-2017; danda, debashish/AAH-1775-2020; Trevisani, Virginia/AAI-5225-2020; Gheita, Tamer/HHC-8095-2022; Puéchal, Xavier/P-1555-2017; priori, roberta/Z-6259-2019; Acar-Denizli, Nihan/D-5954-2019; Mandl, Thomas/AAS-1548-2020; Guisado Vasco, Pablo/I-1366-2019; HACHULLA, ERIC/R-8488-2018; trevisani, Virginia Fernandes Moça/I-1137-2013; Akasbi Montalvo, Miriam/GNW-3111-2022; Chamorro, Antonio-J/A-6257-2013; Quartuccio, Luca/AAC-4803-2022; Wahren-Herlenius, Marie/AAN-6942-2021; Retamozo, Soledad/AFT-7300-2022; Ramos-Casals, Manuel/HJH-4887-2023 sene, damien/0000-0001-8640-4580; Baldini, Chiara/0000-0002-4454-1824; danda, debashish/0000-0002-2121-0942; Rasmussen, Astrid/0000-0001-7744-2948; Nordmark, Gunnel/0000-0002-3829-7431; SEROR, Raphaele/0000-0002-5523-1856; danda, debashish/0000-0002-2121-0942; Trevisani, Virginia/0000-0002-7180-6285; Gheita, Tamer/0000-0002-1155-9729; Puéchal, Xavier/0000-0003-3573-9203; priori, roberta/0000-0002-6695-1445; Acar-Denizli, Nihan/0000-0002-0012-8632; Mandl, Thomas/0000-0002-7143-7088; Guisado Vasco, Pablo/0000-0003-3019-4398; HACHULLA, ERIC/0000-0001-7432-847X; trevisani, Virginia Fernandes Moça/0000-0002-7180-6285; Akasbi Montalvo, Miriam/0000-0001-5888-4409; Chamorro, Antonio-J/0000-0002-2345-2873; Wahren-Herlenius, Marie/0000-0002-0915-7245; Ramos-Casals, Manuel/0000-0001-5709-6734; CANORA, JESUS/0000-0002-5231-9721; Quartuccio, Luca/0000-0002-0134-6439; , Salvatore/0000-0002-2757-3373; Siso Almirall, Antoni/0000-0001-9832-2689; Sandhya, Pulukool/0000-0001-9513-4535; Goeb, Vincent/0000-0002-3807-545X; Ravaud, Philippe/0000-0001-8264-9206; Kvarnstrom, Marika/0000-0002-4948-8380; Scofield, Robert Hal/0000-0003-1015-5850; Sibilia, Jean/0000-0002-1601-4795; Dieude, Philippe/0000-0002-4814-0307 Grants Fondo de Investigaciones Sanitarias (MRC) [INT15/00085]; 'Ajut per a la Recerca Josep Font' (PBZ, Hospital Clinic-Barcelona); MRC [MR/N003063/1] Funding Source: UKRI; Medical Research Council [MR/N003063/1] Funding Source: researchfish; Versus Arthritis [21268] Funding Source: researchfish Grants Fondo de Investigaciones Sanitarias (MRC)(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); 'Ajut per a la Recerca Josep Font' (PBZ, Hospital Clinic-Barcelona); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Versus Arthritis(Versus Arthritis) Supported by Grants Fondo de Investigaciones Sanitarias (MRC, INT15/00085) and 'Ajut per a la Recerca Josep Font' (PBZ, Hospital Clinic-Barcelona 2012). 27 95 99 0 19 BMJ PUBLISHING GROUP LONDON BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND 0003-4967 1468-2060 ANN RHEUM DIS Ann. Rheum. Dis. JUN 2017.0 76 6 1042 1050 10.1136/annrheumdis-2016-209952 0.0 9 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology EU6IX 27899373.0 Green Submitted 2023-03-23 WOS:000401138800017 0
J Band, SS; Bateni, SM; Almazroui, M; Sajjadi, S; Chau, KW; Mosavi, A Band, Shahab S.; Bateni, Sayed M.; Almazroui, Mansour; Sajjadi, Shahin; Chau, Kwok-wing; Mosavi, Amir Evaluating the potential of offshore wind energy in the Gulf of Oman using the MENA-CORDEX wind speed data simulations ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS English Article Artificial intelligence; wind energy; wind speed; MENA-CORDEX; ERA5 renewable energies; climate change CLIMATE-CHANGE; RESOURCE ASSESSMENT; WAVE ENERGY; ENSEMBLE; IMPACTS; SUSTAINABILITY; PRECIPITATION; TEMPERATURE; REANALYSIS; SYSTEMS The MENA-CORDEX (the Middle East and North Africa-COordinated Regional Downscaling Experiment) wind speed data at the height of 10 m is converted to the turbine hub-height for estimating the mean wind power in the Gulf of Oman. The results show high spatial variability of the historical and future mean wind power projection. Furthermore, the impact of climate change on the mean wind power (under the representative concentration pathway 8.5, RCP8.5, scenario) is found to be less than 5%, which indicates the sustainability of wind renewable energy. Based on the wind power distribution, bathymetry, and distance from the coast, five sites are selected in the Gulf of Oman as tentative energy hotspots to provide a detailed assessment of the directional variability of wind power for the historical and future periods. Significant variability of the directional distribution of wind power is found in the five study sites, which make it imperative to optimally design wind turbines and farms to achieve the best efficiency. The findings also indicate that the main direction and magnitude of wind power in the five sites under the RCP8.5 climate change scenario will remain almost the same as those of the historical period. Overall, the sustainability of power production under climate change impacts is promising to plan for wind energy extraction in the Gulf of Oman. [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Taiwan; [Bateni, Sayed M.] Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI 96822 USA; [Bateni, Sayed M.] Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI 96822 USA; [Almazroui, Mansour] King Abdulaziz Univ, Dept Meteorol, Ctr Excellence Climate Change Res, Jeddah, Saudi Arabia; [Sajjadi, Shahin] Lamar Univ, Dept Civil & Environm Engn, Beaumont, TX 77710 USA; [Chau, Kwok-wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Mosavi, Amir] Tech Univ Dresden, Fac Civil Engn, Dresden, Germany; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary; [Mosavi, Amir] Norwegian Univ Life Sci, Sch Econ & Business, As, Norway; [Mosavi, Amir] J Selye Univ, Fac Informat, Komarno, Slovakia National Yunlin University Science & Technology; University of Hawaii System; University of Hawaii Manoa; University of Hawaii System; University of Hawaii Manoa; King Abdulaziz University; Texas State University System; Lamar University; Hong Kong Polytechnic University; Technische Universitat Dresden; Obuda University; Norwegian University of Life Sciences; J. Selye University Mosavi, A (corresponding author), Tech Univ Dresden, Fac Civil Engn, Dresden, Germany.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary.;Mosavi, A (corresponding author), Norwegian Univ Life Sci, Sch Econ & Business, As, Norway.;Mosavi, A (corresponding author), J Selye Univ, Fac Informat, Komarno, Slovakia. amir.mosavi@mailbox.tu-dresden.de Mosavi, Amir/I-7440-2018; S. Band, Shahab/ABB-2469-2020; S.Band, Shahab/AAD-3311-2021; Chau, Kwok-wing/E-5235-2011 Mosavi, Amir/0000-0003-4842-0613; S. Band, Shahab/0000-0001-6109-1311; Chau, Kwok-wing/0000-0001-6457-161X; Bateni, Sayed/0000-0002-7134-0067; Almazroui, Mansour/0000-0002-3962-4588 Alexander von Humboldt Foundation; TU Dresden Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); TU Dresden This work was supported in part by the Alexander von Humboldt Foundation, and by the Open Access Funding by the Publication Fund of the TU Dresden. 60 9 9 1 18 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1994-2060 1997-003X ENG APPL COMP FLUID Eng. Appl. Comp. Fluid Mech. JAN 1 2021.0 15 1 613 626 10.1080/19942060.2021.1893225 0.0 14 Engineering, Multidisciplinary; Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics RH9VB gold 2023-03-23 WOS:000636558900001 0
J Luo, QW; Su, JJ; Yang, CH; Silven, O; Liu, L Luo, Qiwu; Su, Jiaojiao; Yang, Chunhua; Silven, Olli; Liu, Li Scale-selective and noise-robust extended local binary pattern for texture classification PATTERN RECOGNITION English Article Local binary pattern (LBP); Texture descriptor; Feature extraction; Texture classification DESCRIPTOR; FACE As one of the most successful local feature descriptors, the local binary pattern (LBP) estimates the texture distribution rule of an image based on the signs of differences between neighboring pixels to obtain intensity- and rotation- invariance. In this paper, we propose a novel image descriptor to address scale transformation and noise interference simultaneously. We name it scale-selective and noise-robust extended LBP (SNELBP). First, each image in training sets is transformed into different scale spaces by a Gaussian filter. Second, noise-robust pattern histograms are obtained from each scale space by using our previously proposed median robust extended LBP (MRELBP). Then, scale-invariant histograms are determined by selecting the maximum among all scale levels for a certain image. Finally, the most informative patterns are selected from the dictionary pretrained by the two-stage compact dominant feature selection method (CDFS), maintaining the descriptor more lightweight with sufficiently low time cost. Extensive experiments on five public databases (Outex_TC_0 0 011, TC_0 0 012, KTH-TIPS, UMD and NEU) and one fresh texture database (JoJo) under two kinds of interferences (Gaussian and salt pepper) indicate that our SNELBP yields more competitive results than thirty classical LPB variants as well as eight typical deep learning methods.(c) 2021 Elsevier Ltd. All rights reserved. [Luo, Qiwu; Su, Jiaojiao; Yang, Chunhua] Cent South Univ, Changsha 410083, Peoples R China; [Silven, Olli] Univ Oulu, Oulu 90014, Finland; [Liu, Li] Natl Univ Def Technol, Changsha 410073, Peoples R China Central South University; University of Oulu; National University of Defense Technology - China Luo, QW (corresponding author), Cent South Univ, Changsha 410083, Peoples R China. luoqiwu@csu.edu.cn National Natural Science Foundation of China [61973323, 62111530071]; Hunan Provincial Natural Science Foundation of China [2021JJ20078]; Science and Technology Innovation Program of Hunan Province [2021RC3019, 2021RC1001]; Innovation and Development Project of Ministry of Industry and Information Technology of the People's Republic of China [TC19084DY] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hunan Provincial Natural Science Foundation of China(Natural Science Foundation of Hunan Province); Science and Technology Innovation Program of Hunan Province; Innovation and Development Project of Ministry of Industry and Information Technology of the People's Republic of China This work was supported in part by the National Natural Science Foundation of China under Grant 61973323, and Grant 62111530071, and by the Hunan Provincial Natural Science Foundation of China under Grant 2021JJ20078, by the Science and Technology Innovation Program of Hunan Province under Grant 2021RC3019 and Grant 2021RC1001, and in part by the Innovation and Development Project of Ministry of Industry and Information Technology of the People's Republic of China under Grant TC19084DY. 55 1 1 13 14 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. DEC 2022.0 132 108901 10.1016/j.patcog.2022.108901 0.0 AUG 2022 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4F6HC 2023-03-23 WOS:000848609900008 0
J Li, MX; Wu, Y; Bao, YS; Liu, BF; Petropoulos, GP Li, Meixin; Wu, Ying; Bao, Yansong; Liu, Bofan; Petropoulos, George P. Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data REMOTE SENSING English Article Random Forest; NO2; TropOMI; near-surface pollution; remote sensing NITROGEN-DIOXIDE; SATELLITE; EMISSIONS; POLLUTION; CHINA; TROPOSPHERE; RETRIEVALS; OBJECTIVES; SCIAMACHY; MORTALITY In the present study, a daily model is proposed for estimating the near-surface NO2 concentration in China, combining for the first time the Random Forest (RF) machine learning algorithm with the tropospheric NO2 columns from the TROPOspheric Monitoring Instrument (TropOMI) satellite and meteorological and NO2 data of surface sites in China for the year 2019. Furthermore, near-surface NO2 concentration data of ground sites during the COVID-19 outbreak from 1-5 February 2020 were used to verify the developed model. The daily model was verified by the ten-fold cross-validation method, revealing a coefficient of determination (R-2) of 0.78 and root-mean-square error (RMSE) of 7.04 mu g/m(3), which are reasonable and also comparable to other published studies. In addition, our model showed that near-surface NO2 in China during the COVID-19 pandemic was significantly reduced compared with 2019, and these predictions were in good agreement with reference ground data. Our proposed model can also provide NO2 estimates for areas in western China where there are few ground monitoring sites. Therefore, all in all, our study findings suggest that the model established herein is suitable for estimating the daily NO2 concentration near the surface in China and, as such, can be used if there is a lack of surface sites and/or missing observations in some areas. [Li, Meixin; Wu, Ying; Bao, Yansong; Liu, Bofan] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Key Lab Aerosol Cloud Precipitat, China Meteorol Adm, Nanjing 210044, Peoples R China; [Li, Meixin] Focused Photon Hangzhou Inc, Hangzhou 310052, Peoples R China; [Wu, Ying; Bao, Yansong] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China; [Liu, Bofan] Nanjing Univ Informat Sci & Technol, Reading Acad, Nanjing 210044, Peoples R China; [Petropoulos, George P.] Harokopio Univ Athens, Dept Geog, Athens 17671, Greece China Meteorological Administration; Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Harokopio University Athens Wu, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Key Lab Aerosol Cloud Precipitat, China Meteorol Adm, Nanjing 210044, Peoples R China.;Wu, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China. 20181204011@nuist.edu.cn; yingwu@nuist.edu.cn; ysbao@nuist.edu.cn; bofan.liu@nuist.edu.cn; gpetropoulos@hua.gr Petropoulos, George P./F-2384-2013 Petropoulos, George P./0000-0003-1442-1423 National Key Research and Development Program of China [2017YFC1501704]; National Natural Science Foundation of China [41975046]; NUIST Students' Platform for Innovation and Entrepreneurship Training Program [202210300113Y, Reading2022003] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NUIST Students' Platform for Innovation and Entrepreneurship Training Program This study was supported by the National Key Research and Development Program of China (2017YFC1501704) and National Natural Science Foundation of China (41975046) and NUIST Students' Platform for Innovation and Entrepreneurship Training Program (202210300113Y, Reading2022003). 57 0 0 9 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2022.0 14 15 3612 10.3390/rs14153612 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 3S7AQ gold 2023-03-23 WOS:000839745300001 0
J Zhang, J; Zhang, Z; Wang, CZ; Wang, XH; Zhang, LL; Ma, XL; Tao, FL Zhang, J.; Zhang, Z.; Wang, C. Z.; Wang, X. H.; Zhang, L. L.; Ma, X. L.; Tao, F. L. Weather Index Insurance Can Offset Heat-Induced Rice Losses Under Global Warming EARTHS FUTURE English Article HIGH-TEMPERATURE STRESS; CROP INSURANCE; CLIMATE TRENDS; ELEVATED CO2; CHINA; AGRICULTURE; YIELD; RISK; PRODUCTIVITY; DISASTERS Extreme heat is one major warming-related threat to food security and farmers' livelihood, especially in Asia. As an economic adaptation, weather index insurance (WII) can transfer such weather-related risks, secure farms' incomes, and recover agricultural systems soon after yield losses. However, few relevant studies evaluate its potential in the warming future. Here, we investigated the efficiency of WII to compensate for future heat-induced yield losses (YHloss ${Y}_{Hloss}$) under SSP1-2.6, SSP3-7.0, and SSP5-8.5 (Shared Socioecomoic Pathways, SSP) in southern China, a typical smallholder-dominated rice production area in the world. Combining a crop model with machine learning, we found that heat-induced rice vulnerability showed large spatial variability. Rice vulnerability and warming scenarios co-determined future YHloss ${Y}_{Hloss}$ and economic losses at the county scale, with more significant losses in the mid-far future (2051-2100) than in the near future (2021-2050). Compared with the historical period (1961-2010), YHloss ${Y}_{Hloss}$ will increase approximately by up to 5% under SSP1-2.6, 18% under SSP3-7.0%, and 26% under SSP5-8.5. As an ex-ante strategy, county-specific WII will effectively improve (by up to 13%) and stabilize (by up to 36%) farmers' incomes, even though the pure premium rate will increase by 10% at 2050 and by 30% at 2100. Overall, our study demonstrates that WII is an effective economic adaption in the future for assisting smallholder livelihood in a developing country. WII has the potential to be applied in other countries for other crop failures to mitigate climate-related risk for global food systems in the future. [Zhang, J.; Zhang, Z.; Zhang, L. L.] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China; [Zhang, J.; Zhang, Z.; Zhang, L. L.] Minist Emergency Management, Acad Disaster Reduct & Emergency Management, Beijing, Peoples R China; [Zhang, J.; Zhang, Z.; Zhang, L. L.] Minist Educ, Beijing, Peoples R China; [Zhang, J.] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Zhang, Z.; Zhang, L. L.] Beijing Normal Univ, Sch Natl Safety & Emergency Management, Beijing, Peoples R China; [Wang, C. Z.; Wang, X. H.] Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing, Peoples R China; [Wang, C. Z.] Leibniz Ctr Agr Landscape Res ZALF, Muncheberg, Germany; [Ma, X. L.] Tsinghua Univ, Sch Publ Policy & Management, Beijing, Peoples R China; [Tao, F. L.] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; [Tao, F. L.] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; [Tao, F. L.] Nat Resources Inst Finland Luke, Helsinki, Finland Beijing Normal University; Beijing Normal University; Beijing Normal University; Peking University; Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF); Tsinghua University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Natural Resources Institute Finland (Luke) Zhang, Z (corresponding author), Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China.;Zhang, Z (corresponding author), Minist Emergency Management, Acad Disaster Reduct & Emergency Management, Beijing, Peoples R China.;Zhang, Z (corresponding author), Minist Educ, Beijing, Peoples R China.;Zhang, Z (corresponding author), Beijing Normal Univ, Sch Natl Safety & Emergency Management, Beijing, Peoples R China. zhangzhao@bnu.edu.cn Wang, Xuhui/0000-0003-0818-9816; Tao, F/0000-0001-8574-0080 61 0 0 11 11 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2328-4277 EARTHS FUTURE Earth Future NOV 2022.0 10 11 e2021EF002534 10.1029/2021EF002534 0.0 15 Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences 6N3RP 2023-03-23 WOS:000889477000001 0
C Yang, JH; Abrahamsson, T Wang, K; Wang, Y; Strandhagen, JO; Yu, T Yang, Jinghui; Abrahamsson, Timmie A DEMO of Smart Manufacturing for Mass Customization in a Lab ADVANCED MANUFACTURING AND AUTOMATION VIII Lecture Notes in Electrical Engineering English Proceedings Paper 8th International Workshop of Advanced Manufacturing and Automation (IWAMA) SEP 25-26, 2018 Changzhou Univ, Changzhou, PEOPLES R CHINA Univ Plymouth,Norwegian Univ Sci & Technol,Fdn Ind & Tech Res,Shanghai Second Polytechn Univ,Changshu Inst Technol,Tongji Univ,Shandong Agr Univ,Univ Stavanger,Shanghai Univ,Hong Kong Polytechn Univ,Univ Malaga,Xiamen Univ Technol,Arct Univ Norway,China Univ Mining & Technol,Dr B R Ambedkar Natl Inst Technol,China Instrument & Control Soc,Tongji Univ,Dalian Univ,Peter The Great St Petersbur Polytechn Univ,Univ Firenze Changzhou Univ Industry 4.0; Mass customization Intelligent manufacturing systems; Smart factory SYSTEM; TIME The demand from customer is becoming more and more personalized and with more requirements like shorter production time and lower price. To be able to solve these demands, the production would need to become extremely complex with difficulties in planning and operation. This could be solves by using smart manufacturing for mass customization. Mass customization involves using smart production to produce customized personal products or services at a high rate to a low cost. Internet of Things, Big Data and Cyber-Physical Systems are three important concepts of Industry 4.0. In SSPU, Industry 4.0 and smart manufacturing is currently being demonstrated in a laboratory where the user can customize their own toy house. The house consist of different panels (modules) that can be chosen from a variety of different designs and colors. The windows on the panels may also be customized by design and color. The production line consists of three assembly robots and one smaller robot for transporting the product to storage. [Yang, Jinghui] Shanghai Polytech Univ, Coll Engn, Jinhai Rd 2360, Shanghai, Peoples R China; [Abrahamsson, Timmie] Blekinge Inst Technol, Ind Management & Engn, S-37179 Karlskrona, Sweden Shanghai Polytechnic University; Blekinge Institute Technology Yang, JH (corresponding author), Shanghai Polytech Univ, Coll Engn, Jinhai Rd 2360, Shanghai, Peoples R China. Jhyang@sspu.eud.cn discipline construction of mechanical engineering of Shanghai Polytechnic University [XXKZD1603] discipline construction of mechanical engineering of Shanghai Polytechnic University This project is supported by discipline construction of mechanical engineering of Shanghai Polytechnic University (XXKZD1603). 11 0 0 0 2 SPRINGER-VERLAG SINGAPORE PTE LTD SINGAPORE 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE 1876-1100 1876-1119 978-981-13-2375-1; 978-981-13-2374-4 LECT NOTES ELECTR EN 2019.0 484 12 18 10.1007/978-981-13-2375-1_2 0.0 7 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems BT5VP 2023-03-23 WOS:000838684300002 0
J Zhang, YP; Cheng, Y; Wang, XV; Zhong, RY; Zhang, YF; Tao, F Zhang, Yongping; Cheng, Ying; Wang, Xi Vincent; Zhong, Ray Y.; Zhang, Yingfeng; Tao, Fei Data-driven smart production line and its common factors INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Smart production line (SPL); Common factors; Data-driven; Integration; Energy consumption DECISION-SUPPORT-SYSTEM; QUALITY-CONTROL; BIG DATA; PREVENTIVE MAINTENANCE; WIRELESS SENSOR; SHOP-FLOOR; OPTIMIZATION; SERVICE; PARAMETERS; KNOWLEDGE Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example. [Zhang, Yongping; Cheng, Ying; Tao, Fei] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Wang, Xi Vincent] KTH Royal Inst Technol, Sch Ind Engn & Management, Stockholm, Sweden; [Zhong, Ray Y.] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Hong Kong, Peoples R China; [Zhang, Yingfeng] Northwestern Polytech Univ, Key Lab Contemporary Design & Integrated Mfg Tech, Minist Educ, Xian 710072, Shaanxi, Peoples R China Beihang University; Royal Institute of Technology; University of Hong Kong; Northwestern Polytechnical University Zhang, YP (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China. zyping@buaa.edu.cn Cheng, Ying/F-3038-2012; z, y/HPC-0477-2023; Tao, Fei/F-8944-2012 Cheng, Ying/0000-0002-9103-6126; Tao, Fei/0000-0002-9020-0633 National Key Research and Development Program of China [2016YFB1101700]; National Natural Science Foundation of China (NSFC) [51705014, 51875030] National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is financially supported in part by National Key Research and Development Program of China (No. 2016YFB1101700), National Natural Science Foundation of China (NSFC) under Grant 51705014 and National Natural Science Foundation of China (NSFC) under Grant 51875030. This paper is an expanded version of a paper entitled smart production line: common factors and data-driven implementation method presented at The 12th ASME International Manufacturing Science and Engineering Conference (MSEC2017), June 4-8, 2017, Los Angeles, CA, USA. 69 16 16 9 79 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. JUL 2019.0 103 1-4 1211 1223 10.1007/s00170-019-03469-9 0.0 13 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering IJ5CV 2023-03-23 WOS:000475921300089 0
J Liu, YJ; Kyne, M; Wang, C; Yu, XY Liu, Ya-Juan; Kyne, Michelle; Wang, Cheng; Yu, Xi-Yong Data mining in Raman imaging in a cellular biological system COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL English Review Data mining; Machine learning; Pattern recognition; Multivariate analysis; Raman imaging; Cell LEAST-SQUARES; COMPONENT ANALYSIS; CANCER CELLS; DIFFERENTIATION; CLASSIFICATION; SPECTROSCOPY; MICROSCOPY; ALGORITHM; LIVER The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. [Liu, Ya-Juan; Yu, Xi-Yong] Guangzhou Med Univ, Sch Pharmaceut Sci, Key Lab Mol Target & Clin Pharmacol, Guangzhou 511436, Peoples R China; [Liu, Ya-Juan; Yu, Xi-Yong] Guangzhou Med Univ, Sch Pharmaceut Sci, State Key Lab Resp Dis, Guangzhou 511436, Peoples R China; [Liu, Ya-Juan; Yu, Xi-Yong] Guangzhou Med Univ, Affiliated Hosp 5, Guangzhou 511436, Peoples R China; [Kyne, Michelle] Natl Univ Ireland, Sch Chem, Galway H91 CF50, Ireland; [Wang, Cheng] Trinity Coll Dublin, Smurfit Inst Genet, Dublin 2, Ireland Guangzhou Medical University; Guangzhou Medical University; State Key Laboratory of Respiratory Disease; Guangzhou Medical University; Trinity College Dublin Yu, XY (corresponding author), Guangzhou Med Univ, Sch Pharmaceut Sci, Key Lab Mol Target & Clin Pharmacol, Guangzhou 511436, Peoples R China.;Yu, XY (corresponding author), Guangzhou Med Univ, Sch Pharmaceut Sci, State Key Lab Resp Dis, Guangzhou 511436, Peoples R China.;Yu, XY (corresponding author), Guangzhou Med Univ, Affiliated Hosp 5, Guangzhou 511436, Peoples R China.;Wang, C (corresponding author), Trinity Coll Dublin, Smurfit Inst Genet, Dublin 2, Ireland. wangc4@tcd.ie; yuxycn@gzhmu.edu.cn Kyne, Michelle/0000-0003-4483-4245; Wang, Cheng/0000-0001-7886-2143 National Natural Science Foundation of China [U1601227]; Science and Technology Programs of Guangdong Province [2015B020225006]; Bureau of Education of Guangzhou Municipality Foundation [201831836] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Programs of Guangdong Province; Bureau of Education of Guangzhou Municipality Foundation Dr. Peter Mojzes is thanked for providing the pure spectral data of protein, lipid, and nucleus. We appreciate the support of National Natural Science Foundation of China (No. U1601227), the Science and Technology Programs of Guangdong Province (2015B020225006), and Bureau of Education of Guangzhou Municipality Foundation (No.201831836). 64 11 12 2 24 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2001-0370 COMPUT STRUCT BIOTEC Comp. Struct. Biotechnol. J.. 2020.0 18 2920 2930 10.1016/j.csbj.2020.10.006 0.0 11 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology PR5TV 33163152.0 gold 2023-03-23 WOS:000607299700006 0
J Zhou, DM; Gao, F; Breaz, E; Ravey, A; Miraoui, A Zhou, Daming; Gao, Fei; Breaz, Elena; Ravey, Alexandre; Miraoui, Abdellatif Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach ENERGY English Article Degradation prediction; Proton exchange membrane fuel cell (PEMFC); Moving window method; Hybrid prognostic approach AUTOREGRESSIVE NEURAL-NETWORK; USEFUL LIFE PREDICTION; ION BATTERY; MODEL; PERFORMANCE; VEHICLES; SYSTEMS; MANAGEMENT; FRAMEWORK; STRATEGY In this paper, an innovative robust prediction algorithm for performance degradation of proton exchange membrane fuel cell (PEMFC) is proposed based on a combination of model-based and data-driven prognostic method. A novel approach using the moving window method is applied, in order to 1) train the developed models; 2) update the weight factors of each method and 3) further fuse the predicted results iteratively. In the proposed approach, both model-based and data-driven methods are simultaneously used to achieve a better accuracy. During the prediction process, each dataset in the proposed moving window are divided into three sections respectively: training, evaluation and prediction. The training data are used first to identify the models parameters. The evaluation data are then used to measure the weight of each method, which represents the degree of confidence of each method in the actual state. Based on these dynamically adjusting weight factors, the prediction results from different methods are then fused using weighted average methodology to calculate the final prediction results. In order to verify the proposed method, three experimental validations with different aging testing profiles have been performed. The results demonstrate that the proposed hybrid prognostic approach can achieve a higher accuracy than conventional prediction methods. In addition, in order to find the satisfactory trade-off between the prediction accuracy and forecast time for optimizing on-line prognostic, the performance variation of proposed approach with different moving window length is further showed and discussed. (C) 2017 Elsevier Ltd. All rights reserved. [Zhou, Daming; Gao, Fei; Breaz, Elena; Ravey, Alexandre; Miraoui, Abdellatif] Univ Bourgogne Franche Comte, UTBM, Energy Dept, FEMTO ST,UMR CNRS 6174, Rue Thierry Mieg, F-90010 Belfort, France; [Zhou, Daming] Northwestern Polytech Univ, Sch Astronaut, Xian 710072, Shaanxi, Peoples R China; [Zhou, Daming; Gao, Fei; Breaz, Elena; Ravey, Alexandre] Univ Bourgogne Franche Comte, UTBM, FCLAB, FR CNRS 3539, Rue Thierry Mieg, F-90010 Belfort, France; [Breaz, Elena] Tech Univ Cluj Napoca, Dept Elect Engn, Cluj Napoca, Romania Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Northwestern Polytechnical University; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Technical University of Cluj Napoca Zhou, DM (corresponding author), Univ Bourgogne Franche Comte, UTBM, Energy Dept, FEMTO ST,UMR CNRS 6174, Rue Thierry Mieg, F-90010 Belfort, France. daming.zhou@utbm.fr; fei.gao@utbm.fr; elena.breaz@utbm.fr; alexandre.ravey@utbm.fr; abdellatif.miraoui@utbm.fr Zhou, Daming/GYD-4796-2022; Gao, Fei/E-7932-2012; Ravey, Alexandre/E-6690-2017; Breaz, Elena V/W-1399-2017 Gao, Fei/0000-0001-9076-9718; Breaz, Elena V/0000-0002-2769-7472; Ravey, Alexandre/0000-0002-3377-2408; Zhou, Daming/0000-0002-4304-6709 European Commission H grant ESPESA (H-TWINN) EU [692224]; Franche-Comte regional council grant PEMFC aging Regional Grant [2013C-5588/92] European Commission H grant ESPESA (H-TWINN) EU; Franche-Comte regional council grant PEMFC aging Regional Grant This work is supported by: 1) European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU Grant agreement No: 692224; 2) Franche-Comte regional council grant PEMFC aging Regional Grant agreement No: 2013C-5588/92. 31 63 67 11 105 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy NOV 1 2017.0 138 1175 1186 10.1016/j.energy.2017.07.096 0.0 12 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels FN1RQ 2023-03-23 WOS:000415769300095 0
J Li, SP; Li, LL; Meng, XQ; Sun, PG; Liu, Y; Song, YT; Zhang, SJ; Jiang, CL; Cai, JQ; Zhao, Z Li, Shupeng; Li, Lulu; Meng, Xiangqi; Sun, Penggang; Liu, Yi; Song, Yuntang; Zhang, Sijia; Jiang, Chuanlu; Cai, Jinquan; Zhao, Zheng DREAM: a database of experimentally supported protein-coding RNAs and drug associations in human cancer MOLECULAR CANCER English Article Drugs; Human cancers; Database; Data visualization The Drug Response Gene Expression Associated Map, also referred as DREAM (http://bio-big-data.cn:8080/DREAM), is a manually curated database of experimentally supported protein-coding RNAs and drugs associations in human cancers. The current version of the DREAM documents 3048 entries about scientific literatures supported drug sensitivity or drug intervention related protein-coding RNAs from PubMed database and 195 high-throughput microarray data about drug sensitivity or drug intervention related protein-coding RNAs data from GEO database. Each entry in DREAM database contains detailed information on protein-coding RNA, drug, cancer, and other information including title, PubMed ID, journal, publish time. The DREAM database also provides some data visualization and online analysis services such as volcano plot, GO/KEGG enrichment function analysis, and novel drug discovery analysis. We hope the DREAM database should serve as a valuable resource for clinical practice and basic research, which could help researchers better understand the effects of protein-coding RNAs on drug response in human cancers. [Li, Shupeng; Li, Lulu; Meng, Xiangqi; Sun, Penggang; Liu, Yi; Song, Yuntang; Jiang, Chuanlu; Cai, Jinquan] Harbin Med Univ, Heilongjiang Acad Med Sci, Neurosci Inst, Dept Neurosurg,Affiliated Hosp 2, Harbin 150086, Peoples R China; [Zhang, Sijia] Harbin Med Univ, Dept Educ Adm, Affiliated Hosp 2, Harbin 150086, Peoples R China; [Cai, Jinquan] Karolinska Inst, Dept Microbiol Tumor & Cell Biol MTC, Biomed, S-17165 Stockholm, Sweden; [Zhao, Zheng] Capital Med Univ, Beijing Neurosurg Inst, Beijing 100070, Peoples R China Harbin Medical University; Harbin Medical University; Karolinska Institutet; Capital Medical University Jiang, CL; Cai, JQ (corresponding author), Harbin Med Univ, Heilongjiang Acad Med Sci, Neurosci Inst, Dept Neurosurg,Affiliated Hosp 2, Harbin 150086, Peoples R China.;Cai, JQ (corresponding author), Karolinska Inst, Dept Microbiol Tumor & Cell Biol MTC, Biomed, S-17165 Stockholm, Sweden.;Zhao, Z (corresponding author), Capital Med Univ, Beijing Neurosurg Inst, Beijing 100070, Peoples R China. jcl6688@163.com; caijinquan666777@126.com; zhaozheng0503@ccmu.edu.com National College Students Innovation and Entrepreneurship Training Program [202110226013]; National Natural Science Foundation of China [81874204, 81972817, 82073298, 82002647]; Excellent Young Talents Project of Central Government Supporting Local University Reform and Development Fund [0202-300011190006]; Karolinska Institutet Research Foundation [FS-2020:0007]; China Postdoctoral Science Foundation [2019M660074, 2020T130157]; Heilongjiang Postdoctoral Science Foundation [LBH-Z19029]; Research Project of the Health and Family Planning Commission of Heilongjiang Province [2019-102] National College Students Innovation and Entrepreneurship Training Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Excellent Young Talents Project of Central Government Supporting Local University Reform and Development Fund; Karolinska Institutet Research Foundation(Karolinska Institutet); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Heilongjiang Postdoctoral Science Foundation; Research Project of the Health and Family Planning Commission of Heilongjiang Province This study was supported by 1. National College Students Innovation and Entrepreneurship Training Program (202110226013); 2. National Natural Science Foundation of China (81874204, 81972817, 82073298, and 82002647); 3. Excellent Young Talents Project of Central Government Supporting Local University Reform and Development Fund (0202-300011190006); 4. Karolinska Institutet Research Foundation Grants 2020-2021 (FS-2020:0007); 5. China Postdoctoral Science Foundation (2019M660074, 2020T130157); 6. The Heilongjiang Postdoctoral Science Foundation (LBH-Z19029); 7. The Research Project of the Health and Family Planning Commission of Heilongjiang Province (2019-102). 11 3 3 1 6 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1476-4598 MOL CANCER Mol. Cancer NOV 13 2021.0 20 1 148 10.1186/s12943-021-01436-1 0.0 6 Biochemistry & Molecular Biology; Oncology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Oncology WW7KB 34774049.0 Green Accepted, gold 2023-03-23 WOS:000718089500001 0
C Wang, GL; Zuo, XJ; Hesenius, M; Xu, Y; Han, YB; Gruhn, V Cai, Y; Ishikawa, Y; Xu, J Wang, Guiling; Zuo, Xiaojiang; Hesenius, Marc; Xu, Yao; Han, Yanbo; Gruhn, Volker A Data Services Composition Approach for Continuous Query on Data Streams WEB AND BIG DATA (APWEB-WAIM 2018), PT II Lecture Notes in Computer Science English Proceedings Paper 2nd International Joint Conference on Asia-Pacific Web (APWeb) / Web-Age Information Management (WAIM) JUL 23-25, 2018 PEOPLES R CHINA Data streams; Query rewriting; Data services; Service composition; Continuous query We witness a rapid increase in the number of data streams due to Cloud Computing, Big Data and IoT development. We would like to access and share data streams using a data service approach. In this paper, we propose a flexible continuous data service model and a continuous data service composition algorithm for answering queries across data streams. Service operation instance is modeled as a view defined on data streams composed of two parts: a data part and a time synchronization part. The composition algorithm extends the traditional Bucket algorithm to find the contained rewriting of user query on views satisfying the containment relationship of both data part and time synchronization part. We also present use case and experimental studies indicating that the approach is effective and efficient. [Wang, Guiling; Zuo, Xiaojiang; Han, Yanbo] North China Univ Technol, Beijing Key Lab Integrat & Anal Large Scale Strea, 5 Jinyuanzhuang Rd, Beijing 100144, Peoples R China; [Wang, Guiling; Xu, Yao] Ocean Informat Technol Co, CETC Ocean Corp, China Elect Technol Grp Corp, 11 Shuangyuan Rd,Badachu Hitech Pk, Beijing 100041, Peoples R China; [Hesenius, Marc; Gruhn, Volker] Univ Duisburg Essen, Paluno Ruhr Inst Software Technol, Schutzenbahn 70, D-45127 Essen, Germany North China University of Technology; China Electronics Technology Group; University of Duisburg Essen Wang, GL (corresponding author), North China Univ Technol, Beijing Key Lab Integrat & Anal Large Scale Strea, 5 Jinyuanzhuang Rd, Beijing 100144, Peoples R China.;Wang, GL (corresponding author), Ocean Informat Technol Co, CETC Ocean Corp, China Elect Technol Grp Corp, 11 Shuangyuan Rd,Badachu Hitech Pk, Beijing 100041, Peoples R China. wangguiling@ict.ac.cn Hesenius, Marc/0000-0002-6826-9212 Beijing Natural Science Foundation [4172018]; National Natural Science Foundation of China [61672042]; University Cooperation Projects Foundation of CETC Ocean Corp. Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); University Cooperation Projects Foundation of CETC Ocean Corp. This work is supported by Beijing Natural Science Foundation No. 4172018, National Natural Science Foundation of China No. 61672042, and University Cooperation Projects Foundation of CETC Ocean Corp. 18 0 0 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-96893-3; 978-3-319-96892-6 LECT NOTES COMPUT SC 2018.0 10988 106 120 10.1007/978-3-319-96893-3_9 0.0 15 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN4OG 2023-03-23 WOS:000482623700009 0
J Li, SH; Ngai, E; Voigt, T Li, Shenghui; Ngai, Edith; Voigt, Thiemo Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Byzantine robust; federated learning (FL); geometric median (GM); Industrial Internet of Things (IIoTs); security and privacy Federated learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things (IIoT) due to its capability of training machine learning models across multiple IIoT devices while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this article, we propose a flexible and robust aggregation rule, called auto-weighted geometric median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value of AutoGM, we design an algorithm based on the alternating optimization strategy. Using AutoGM as aggregation rule, we propose two robust FL solutions AutoGM_FL and AutoGM_PFL. AutoGM_FL learns a shared global model using the standard FL paradigm, and AutoGM_PFL learns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks. [Li, Shenghui; Voigt, Thiemo] Uppsala Univ, Dept Informat Technol, S-75236 Uppsala, Sweden; [Ngai, Edith] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China; [Voigt, Thiemo] Res Inst Sweden RISE, S-41756 Gothenburg, Sweden Uppsala University; University of Hong Kong; RISE Research Institutes of Sweden Ngai, E (corresponding author), Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China. shenghui.li@it.uu.se; chngai@eee.hku.hk; thiemo.voigt@it.uu.se RGC Grant Research Fund from Hong Kong; [17203320]; Swedish Research Council [2017-04543]; European Union's Horizon 2020 research, and innovation programme [101015922] RGC Grant Research Fund from Hong Kong;; Swedish Research Council(Swedish Research Council); European Union's Horizon 2020 research, and innovation programme This work was supported in part by the RGC Grant Research Fund under Grant 17203320 from Hong Kong; in part by the Swedish Research Council project under Grant 2017-04543 and in part by the European Union's Horizon 2020 research, and innovation programme under Grant 101015922. 43 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2023.0 19 2 1165 1175 10.1109/TII.2021.3128164 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 8Q1HM Green Published 2023-03-23 WOS:000926964700005 0
J Wang, ZG; Zhou, Y; Zhao, RY; Wang, N; Biswas, A; Shi, Z Wang, Zhige; Zhou, Yue; Zhao, Ruiying; Wang, Nan; Biswas, Asim; Shi, Zhou High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model JOURNAL OF CLEANER PRODUCTION English Article PM2.5 prediction; AOD; LSTM; Time series data modeling FINE PARTICULATE MATTER; AEROSOL OPTICAL DEPTH; GROUND-LEVEL PM2.5; NEURAL-NETWORK; AIR-POLLUTION; SATELLITE-OBSERVATIONS; HAZE; TRENDS; MODIS; TERRAIN The concentration of fine particulate matter (PM2.5) has a significant impact on the environment and human health. However, strong spatial heterogeneity and spatiotemporal dependence increases the difficulty of prediction. Moreover, due to the lag of the update of auxiliary variables at national scale in the prediction application, it is still difficult to achieve the timely nationwide PM2.5 prediction at present. To better model and predict real time concentrations and spatial distributions of PM2.5, this study developed a workflow of future PM2.5 concentrations prediction based on long short-term memory (LSTM) model. Using ground-based station PM2.5 data in 2014-2018, the 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product and other auxiliary data to predict PM2.5 concentrations in the next year and generate a high-resolution national PM2.5 concentration spatial distribution map. The LSTM model outperformed random forest (RF) and Cubist approaches for prediction PM2.5 because of its recurrent neural network structure that can capture time dependence and nonlinear relationships among PM2.5 concentrations and other independent variables, and exhibited a stable accuracy with an R-2 of 0.83, by applying the annual time series, with an improvement of 0.04-0.09, compared to daily and monthly data. The results indicated that PM2.5 pollution had gradually decreased in 2019 after application of pollution controls, with annual mean PM2.5 concentrations of 27.33 +/- 15.56 mu g m(-3), although there were still some areas with severe pollution, including the North China Plain, parts of the Loess Plateau, and the Taklimakan Desert. The LSTM model makes it possible to predict fine-scale PM2.5 spatial distributions nationwide in the future and may thus be useful for sustainable management and control of air pollution at a national scale. (C) 2021 Elsevier Ltd. All rights reserved. [Wang, Zhige; Zhao, Ruiying; Wang, Nan; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China; [Wang, Zhige; Zhao, Ruiying; Wang, Nan; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Key Lab Environm Remediat & Ecol Hlth, Minist Educ, Hangzhou 310058, Peoples R China; [Zhou, Yue] Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, B-1348 Louvain La Neuve, Belgium; [Biswas, Asim] Univ Guelph, Sch Environm Sci, Alexander Hall 135,50 Stone Rd East, Guelph, ON N1G 2W1, Canada Zhejiang University; Zhejiang University; Universite Catholique Louvain; University of Guelph Shi, Z (corresponding author), Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China. shizhou@zju.edu.cn Biswas, Asim/D-1859-2015; Wang, Zhige/AAE-5576-2022; shi, Zhou/M-7845-2019 Biswas, Asim/0000-0003-0801-3546; shi, Zhou/0000-0003-3914-5402 Science and Technology Innovation Leading Talent Program of Zhejiang Province [2019R52004]; National Key Research and Development Program [2017YFD0700501] Science and Technology Innovation Leading Talent Program of Zhejiang Province; National Key Research and Development Program This work was supported by the Science and Technology Innovation Leading Talent Program of Zhejiang Province (2019R52004), and the National Key Research and Development Program (2017YFD0700501). 59 10 11 13 86 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. MAY 15 2021.0 297 126493 10.1016/j.jclepro.2021.126493 0.0 MAR 2021 11 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology SO6VS 2023-03-23 WOS:000659113300001 0
C Qureshi, UM; Hancke, GP; Gebremichael, T; Jennehag, U; Forsstrom, S; Gidlund, M IEEE Qureshi, Umair Muitaba; Hancke, Gerhard Petrus; Gebremichael, Teklay; Jennehag, Ulf; Forsstrom, Stefan; Gidlund, Mikael Survey of Proximity Based Authentication Mechanisms for the Industrial Internet of Things IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY IEEE Industrial Electronics Society English Proceedings Paper 44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON) OCT 20-23, 2018 Washington, DC IEEE Ind Elect Soc,Inst Elect & Elect Engineers Authentication; Industrial Internet of Things; Proximity; Survey USER AUTHENTICATION; BIG DATA; SECURITY In this paper we present an overview of the various proximity based authentication mechanisms that can be used in the Industrial Internet of Things (IIoT). We seek to identify and highlight from a holistic point of view which mechanisms can enable proximity based authentication for the Industrial Internet of Things. In addition, we identify which upcoming proximity authentication mechanisms are most important for the proliferation of the Industrial Internet of Things, and highlight major obstacles that remain unsolved with regard to authentication. In answering this, we present seven mechanisms for proximity based authentication (i.e. wire, radio, acoustics, light, image, gesture and biometrics) and discuss each mechanism in perspective of their vulnerability to different kind of attacks (such as eavesdropping, impersonation and denial of service attacks etc.) and their usability (such as proximity range, hardware requirement and ease of use) in terms of the practicality in IIoT environment in the light of which we present two typical IIoT use cases that require proximity based authentication. [Qureshi, Umair Muitaba; Hancke, Gerhard Petrus] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Gebremichael, Teklay; Jennehag, Ulf; Forsstrom, Stefan; Gidlund, Mikael] Mid Sweden Univ, Dept Informat Syst & Technol, Sundsvall, Sweden City University of Hong Kong; Mid-Sweden University Qureshi, UM (corresponding author), City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. umqureshi2-c@my.cityu.edu.hk; gp.hancke@cityu.edu.hk; teklay.gebremichael@miun.se; ulf.jennehag@miun.se; stefan.forsstrom@miun.se; mikael.gidlund@miun.se Qureshi, Umair Mujtaba/AAL-6511-2020; Gidlund, Mikael/L-7643-2018; Hancke, Gerhard/J-7187-2013 QURESHI, Umair Mujtaba/0000-0001-8422-0788; Gidlund, Mikael/0000-0003-0873-7827; Hancke, Gerhard/0000-0002-2388-3542 STINT foundation [IB2017-7022]; Aforsk foundation [17-336]; ERUF [20201010] STINT foundation; Aforsk foundation; ERUF This work is supported by grant IB2017-7022 of the STINT foundation, grant 17-336 of the Aforsk foundation and ERUF ID20201010 SMART - Smarta system och tjanster for ett effektivt och innovativt samhalle. 47 11 11 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1553-572X 978-1-5090-6684-1 IEEE IND ELEC 2018.0 5246 5251 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BO2HR 2023-03-23 WOS:000505811105029 0
J Zhou, C; Liu, TH; Zhu, HQ; Li, FB; Huang, KK; Sun, B; Todorov, Y Zhou, Can; Liu, Tianhao; Zhu, Hongqiu; Li, Fanbiao; Huang, Keke; Sun, Bei; Todorov, Yancho A Multiple Light Scenes Suited Turbidity Analysis Method Based on Image Recognition and Information Fusion IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Cameras; Water resources; Light sources; Image recognition; Particle measurements; Atmospheric measurements; Optical variables measurement; Decision-level fusion; deep learning; image recognition; turbidity DESIGN; SYSTEM; INSTRUMENT; ALGORITHM; SENSOR Turbidity has been used as a significant indicator of water quality, so turbidity measurement is widely applied in sewage treatment and other fields. In the traditional measurement method of turbidity, a dark, closed measuring environment is required to reduce the interference of ambient light, which limits the application of turbidity measurement. To improve the adaptability of turbidity measurement to different light scenes, a multiple light scenes suited turbidity analysis method based on image recognition and information fusion is proposed. First, a turbidity image acquisition system is designed. After image preprocessing, prediction network groups for multiple light scenes are established, and two optimal prediction networks are adaptively selected according to different ambient light scenes, improving adaptability to multiple measuring environments. Second, to improve prediction accuracy, Dempster-Shafer (D-S) evidence theory is adopted to realize the information fusion of network prediction results. Three different light scenes of 0, 50, and 100 lx are built through experiments, and the results show that the accuracy of the proposed method in the three light scenes is above 95%, which demonstrates the adaptability to multiple light scenes and provides a new way of industrial online measurement. [Zhou, Can; Liu, Tianhao; Zhu, Hongqiu; Li, Fanbiao; Huang, Keke; Sun, Bei] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Zhou, Can; Huang, Keke] Pengcheng Lab, Shenzhen 518000, Peoples R China; [Todorov, Yancho] VTT Tech Res Ctr, Grp Electoral Powertrains & Battery Technol, Espoo 02100, Finland Central South University; VTT Technical Research Center Finland Huang, KK (corresponding author), Cent South Univ, Sch Automat, Changsha 410083, Peoples R China. zhoucan@csu.edu.cn; tianhaoliu@csu.edu.cn; hqcsu@csu.edu.cn; fanbiaoli@csu.edu.cn; huangkeke@csu.edu.cn; sunbei@csu.edu.cn; yancho.todorov@ieee.org Li, Fan/GRX-7461-2022; Todorov, Yancho/C-3238-2012 Zhou, Can/0000-0002-2778-0022; Todorov, Yancho/0000-0003-2870-5806; Huang, Keke/0000-0003-3553-3424; Liu, Tianhao/0000-0002-7662-7745 National Key Research and Development Program of China [2019YFB1704703]; International Cooperation and Exchange, National Natural Science Foundation of China [61860206014]; Natural Science Foundation of Hunan Province [2019JJ50823, 2021JJ30880]; Excellent Youth Natural Science Foundation of Hunan Province [2019JJ30032] National Key Research and Development Program of China; International Cooperation and Exchange, National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Excellent Youth Natural Science Foundation of Hunan Province This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1704703, in part by the International Cooperation and Exchange, National Natural Science Foundation of China under Grant 61860206014, in part by the Natural Science Foundation of Hunan Province under Grant 2019JJ50823 and Grant 2021JJ30880, and in part by the Excellent Youth Natural Science Foundation of Hunan Province under Grant 2019JJ30032. The Associate Editor coordinating the review process was Dr. Chi-Hung Hwang. 33 0 0 9 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 5002813 10.1109/TIM.2022.3146521 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation ZH9KI 2023-03-23 WOS:000761247800003 0
J Kostyszyn, D; Fechter, T; Bartl, N; Grosu, AL; Gratzke, C; Sigle, A; Mix, M; Ruf, J; Fassbender, TF; Kiefer, S; Bettermann, AS; Nicolay, NH; Spohn, S; Kramer, MU; Bronsert, P; Guo, HQ; Qiu, XF; Wang, F; Henkenberens, C; Werner, RA; Baltas, D; Meyer, PT; Derlin, T; Chen, MX; Zamboglou, C Kostyszyn, Dejan; Fechter, Tobias; Bartl, Nico; Grosu, Anca L.; Gratzke, Christian; Sigle, August; Mix, Michael; Ruf, Juri; Fassbender, Thomas F.; Kiefer, Selina; Bettermann, Alisa S.; Nicolay, Nils H.; Spohn, Simon; Kramer, Maria U.; Bronsert, Peter; Guo, Hongqian; Qiu, Xuefeng; Wang, Feng; Henkenberens, Christoph; Werner, Rudolf A.; Baltas, Dimos; Meyer, Philipp T.; Derlin, Thorsten; Chen, Mengxia; Zamboglou, Constantinos Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network JOURNAL OF NUCLEAR MEDICINE English Article PSMA-PET; convolutional neuronal networks; prostate cancer; histopathology; segmentation GA-68-PSMA PET/CT; VOLUME DELINEATION; RADIATION-THERAPY; MRI; HISTOPATHOLOGY; CT Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumor (GTV-CNN) in PSMA PET. Methods: The CNN (3D U-Net) was trained on the Ga-68-PSMA PET images of 152 patients from 2 different institutions, and the training labels were generated manually using a validated technique. The CNN was tested on 2 independent internal (cohort 1: Ga-68-PSMA PET, n = 18 and cohort 2: F-18-PSMA PET, n = 19) and 1 external (cohort 3: Ga-68-PSMA PET, n 5 20) test datasets. Accordance between manual contours and GTV-CNN was assessed with the Dice-Sorensen coefficient (DSC). Sensitivity and specificity were calculated for the 2 internal test datasets (cohort 1: n = 18, cohort 2: n = 11) using whole-mount histology. Results: The median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93), and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for the GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 s for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in Ga-68-PSMA and F-18-PSMA PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology as a reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary prostate cancer. The trained model and the study's source code are available in an open source repository. [Kostyszyn, Dejan; Fechter, Tobias; Baltas, Dimos] Univ Freiburg, Dept Radiat Oncol, Fac Med, Div Med Phys,Med Ctr, Freiburg, Germany; [Kostyszyn, Dejan] Univ Freiburg, Fac Engn, Freiburg, Germany; [Fechter, Tobias; Bartl, Nico; Grosu, Anca L.; Nicolay, Nils H.; Spohn, Simon; Bronsert, Peter; Baltas, Dimos; Zamboglou, Constantinos] German Canc Consortium DKTK, Partner Site Freiburg, Freiburg, Germany; [Fechter, Tobias; Bartl, Nico; Grosu, Anca L.; Bettermann, Alisa S.; Nicolay, Nils H.; Spohn, Simon; Kramer, Maria U.; Zamboglou, Constantinos] Univ Freiburg, Med Ctr, Fac Med, Dept Radiat Oncol, Freiburg, Germany; [Gratzke, Christian; Sigle, August] Univ Freiburg, Med Ctr, Fac Med, Dept Urol, Freiburg, Germany; [Mix, Michael; Ruf, Juri; Fassbender, Thomas F.; Meyer, Philipp T.] Univ Freiburg, Med Ctr, Fac Med, Dept Nucl Med, Freiburg, Germany; [Kiefer, Selina; Bronsert, Peter] Univ Freiburg, Med Ctr, Fac Med, Inst Surg Pathol, Freiburg, Germany; [Guo, Hongqian; Qiu, Xuefeng; Chen, Mengxia] Nanjing Univ, Med Sch, Affiliated Drum Tower Hosp, Dept Urol, Nanjing, Peoples R China; [Wang, Feng] Nanjing Univ, Med Sch, Affiliated Drum Tower Hosp, Dept Nucl Med, Nanjing, Peoples R China; [Henkenberens, Christoph] Hannover Med Sch, Dept Radiat Oncol, Hannover, Germany; [Werner, Rudolf A.; Derlin, Thorsten] Hannover Med Sch, Dept Nucl Med, Hannover, Germany University of Freiburg; University of Freiburg; Helmholtz Association; German Cancer Research Center (DKFZ); University of Freiburg; University of Freiburg; University of Freiburg; University of Freiburg; Nanjing University; Nanjing University; Hannover Medical School; Hannover Medical School Zamboglou, C (corresponding author), German Canc Consortium DKTK, Partner Site Freiburg, Freiburg, Germany.;Zamboglou, C (corresponding author), Univ Freiburg, Med Ctr, Fac Med, Dept Radiat Oncol, Freiburg, Germany. constantinos.zamboglou@uniklinik-freiburg.de Sigle, August/GPJ-9679-2022; Werner, Rudolf/AGY-2610-2022 Werner, Rudolf/0000-0003-3372-6046; Qiu, Xuefeng/0000-0002-3554-5145; Spohn, Simon Konrad Benedict/0000-0003-2727-8005 ERA PerMed call 2018 (BMBF) ERA PerMed call 2018 (BMBF)(Federal Ministry of Education & Research (BMBF)) This study was funded from the ERA PerMed call 2018 (BMBF). No other potential conflict of interest relevant to this article was reported. 28 15 16 1 11 SOC NUCLEAR MEDICINE INC RESTON 1850 SAMUEL MORSE DR, RESTON, VA 20190-5316 USA 0161-5505 1535-5667 J NUCL MED J. Nucl. Med. JUN 1 2021.0 62 6 823 828 10.2967/jnumed.120.254623 0.0 6 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging SN2UV 33127624.0 Bronze 2023-03-23 WOS:000658151100018 0
J Bota, P; Wang, C; Fred, A; Silva, H Bota, Patricia; Wang, Chen; Fred, Ana; Silva, Hugo Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet? SENSORS English Article emotion recognition; physiological signals; machine learning; signal processing RECOGNITION Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets. [Bota, Patricia; Fred, Ana; Silva, Hugo] Inst Super Tecn IST, Dept Bioengn DBE, Av Rovisco Pais 1,Torre Norte Piso 10, P-1049001 Lisbon, Portugal; [Bota, Patricia; Fred, Ana; Silva, Hugo] Inst Telecomunicacoes IT, Av Rovisco Pais 1,Torre Norte Piso 10, P-1049001 Lisbon, Portugal; [Wang, Chen] Xinhua News Agcy & Future Media Convergence Inst, State Key Lab Media Convergence Prod Technol & Sy, Jinxuan Bldg,129 Xuanwumen West St, Beijing 100031, Peoples R China Universidade de Lisboa; Instituto Superior Tecnico Bota, P (corresponding author), Inst Super Tecn IST, Dept Bioengn DBE, Av Rovisco Pais 1,Torre Norte Piso 10, P-1049001 Lisbon, Portugal.;Bota, P (corresponding author), Inst Telecomunicacoes IT, Av Rovisco Pais 1,Torre Norte Piso 10, P-1049001 Lisbon, Portugal. patricia.bota@tecnico.ulisboa.pt; wangchen@news.cn; afred@lx.it.pt; hsilva@lx.it.pt Plácido da Silva, Hugo/AAA-4845-2022; Fred, Ana/A-7464-2016 Plácido da Silva, Hugo/0000-0001-6764-8432; Fred, Ana/0000-0003-1320-5024; Bota, Patricia/0000-0002-0514-7517 Xinhua Net Future Media Convergence Institute [S-0003-LX-18]; Ministry of Economy and Competitiveness of the Spanish Government; ERDF (PhysComp project) [TIN2017-85409-P]; FCT/MCTES; EU [UIDB/EEA/50008/2020] Xinhua Net Future Media Convergence Institute; Ministry of Economy and Competitiveness of the Spanish Government(Spanish Government); ERDF (PhysComp project); FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU(European Commission) This work has been partially funded by the Xinhua Net Future Media Convergence Institute under project S-0003-LX-18, by the Ministry of Economy and Competitiveness of the Spanish Government co-founded by the ERDF (PhysComp project) under Grant TIN2017-85409-P, and by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020. 53 18 18 4 24 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2020.0 20 17 4723 10.3390/s20174723 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Chemistry; Engineering; Instruments & Instrumentation NR5LY 32825624.0 gold, Green Accepted 2023-03-23 WOS:000571605500001 0
J Yang, GL; Fini, E; Xu, D; Rota, P; Ding, ML; Nabi, M; Alameda-Pineda, X; Ricci, E Yang, Guanglei; Fini, Enrico; Xu, Dan; Rota, Paolo; Ding, Mingli; Nabi, Moin; Alameda-Pineda, Xavier; Ricci, Elisa Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Knowledge distillation; contrastive learning; incremental learning; semantic segmentation A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e., the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (UCD). In a nutshell, UCDis operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. In order to mitigate catastrophic forgetting, we contrast features of the new model with features extracted by a frozen model learned at the previous incremental step. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks for incremental semantic segmentation. [Yang, Guanglei; Ding, Mingli] Harbin Inst Technol HIT, Sch Instrument Sci & Engn, Harbin 150001, Peoples R China; [Fini, Enrico; Rota, Paolo] Univ Trento, Dept Informat Engn & Comp Sci, I-38122 Trento, TN, Italy; [Xu, Dan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Nabi, Moin] SAP AI Res, D-10115 Berlin, Germany; [Alameda-Pineda, Xavier] INRIA, Percept Grp, F-78150 Le Chesnay Rocquencourt, France; [Ricci, Elisa] Univ Trento, Dept Informat Engn & Comp Sci, I-38122 Trento, TN, Italy; [Ricci, Elisa] Fdn Bruno Kessler, Deep Visual Learning Grp, I-38123 Povo, TN, Italy Harbin Institute of Technology; University of Trento; Hong Kong University of Science & Technology; Inria; University of Trento; Fondazione Bruno Kessler Ding, ML (corresponding author), Harbin Inst Technol HIT, Sch Instrument Sci & Engn, Harbin 150001, Peoples R China. yangguanglei@hit.edu.cn; enrico.fini@unitn.it; danxu@cse.ust.hk; paolo.rota@unitn.it; dingml@hit.edu.cn; m.nabi@sap.com; xavier.alameda-pineda@inria.fr; elisa.ricci@unitn.it yang, guanglei/0000-0002-5324-3642; Ding, Mingli/0000-0002-7510-7043; Rota, Paolo/0000-0003-0663-5659; Alameda-Pineda, Xavier/0000-0002-5354-1084; Fini, Enrico/0000-0002-1671-875X China Scholarship Council (CSC); H2020 EU Projects SPRING and AI4Media; Caritro Foundation; Deep Learning Lab of the ProM Facility; European Commission [GA 871245]; European Institute of Innovation & Technology (EIT); ARN grant [ML3RI, ANR-19-CE33-0008-01] China Scholarship Council (CSC)(China Scholarship Council); H2020 EU Projects SPRING and AI4Media; Caritro Foundation(Fondazione Caritro); Deep Learning Lab of the ProM Facility; European Commission(European CommissionEuropean Commission Joint Research Centre); European Institute of Innovation & Technology (EIT); ARN grant This work was supported in part by the China Scholarship Council (CSC) during the visit of Guanglei Yang to the University of Trento, in part by the H2020 EU Projects SPRING and AI4Media, in part by the Caritro Foundation, in part by the Deep Learning Lab of the ProM Facility, and in part by the European Commission under Grant GA 871245. It was carried out under the Vision and Learning joint Laboratory between FBK and UNITN. The work of Enrico Fini was supported by the European Institute of Innovation & Technology (EIT). The work of Xavier Alameda-Pineda was supported by the ARN grant ML3RI Project under Grant ANR-19-CE33-0008-01. 67 1 1 5 7 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. FEB 1 2023.0 45 2 2567 2581 10.1109/TPAMI.2022.3163806 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 7U8OK 35358042.0 Green Submitted 2023-03-23 WOS:000912386000079 0
C Jan, A; Ding, HX; Meng, HY; Chen, LM; Li, HB IEEE Jan, Asim; Ding, Huaxiong; Meng, Hongying; Chen, Liming; Li, Huibin Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition PROCEEDINGS 2018 13TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2018) IEEE International Conference on Automatic Face and Gesture Recognition and Workshops English Proceedings Paper 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG) MAY 15-19, 2018 Xi an, PEOPLES R CHINA IEEE Comp Soc,IEEE Biometr Council Affective Computing; Facial expression recognition; Human-Computer Interaction Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep feature fusion of facial parts. Experiments are conducted on the BU-3DFE database, demonstrating the effectiveness of combing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting. [Jan, Asim; Meng, Hongying] Brunel Univ London, Dept Elect & Comp Engn, London, England; [Ding, Huaxiong; Chen, Liming] Ecole Cent Lyon, CNRS, LIRIS Lab, UMR 5205, Ecully, France; [Li, Huibin] Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Shaanxi, Peoples R China Brunel University; Centre National de la Recherche Scientifique (CNRS); Ecole Centrale de Lyon; Institut National des Sciences Appliquees de Lyon - INSA Lyon; Xi'an Jiaotong University Jan, A (corresponding author), Brunel Univ London, Dept Elect & Comp Engn, London, England. Meng, Hongying/O-5192-2014 Meng, Hongying/0000-0002-8836-1382 Royal Society; National Natural Science Foundation of China (NSFC) [61711530242]; French Research Agency, Agence Nationale de Recherche (ANR) [ANR-13-CORD-0004-02]; Partner University Fund (PUF) through the 4D Vision project Royal Society(Royal Society of London); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); French Research Agency, Agence Nationale de Recherche (ANR)(French National Research Agency (ANR)); Partner University Fund (PUF) through the 4D Vision project This work is partially supported by Royal Society and National Natural Science Foundation of China (NSFC) under cost share research exchange project No. 61711530242. It was also partially supported by the French Research Agency, Agence Nationale de Recherche (ANR) through the Jemime Project under Grant ANR-13-CORD-0004-02 and the Partner University Fund (PUF) through the 4D Vision project. 33 17 18 0 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2326-5396 978-1-5386-2335-0 IEEE INT CONF AUTOMA 2018.0 466 472 10.1109/FG.2018.00075 0.0 7 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL7GJ Green Published, Green Submitted 2023-03-23 WOS:000454996700065 0
C Li, WB; Li, Y; Chen, D; Chan, JCW IEEE Li, Wenbo; Li, Ying; Chen, Di; Chan, Jonathan Cheung-Wai THIN CLOUD REMOVAL WITH RESIDUAL SYMMETRICAL CONCATENATION NETWORK 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Convolutional neural network; residual blocks; symmetrical concatenation; thin cloud removal Thin cloud removal is very important for optical remote sensing imagery. Different from thick cloud removal, the pixels contaminated by thin cloud still preserve some surface information. Therefore, thin cloud removal methods usually focus on suppressing the cloud influence instead of replacing the cloudy pixels. In this paper, we proposed a deep residual symmetrical concatenation network (RSC-Net) to make end-to-end cloud removal. The RSC-Net is based on an encoding-decoding framework consisting of multiple residual convolutional layers and residual deconvolutional layers. The feature maps of each convolutional layer are copied and concatenated to their symmetrical deconvolutional layers. Using supervised training with real Landsat-8 data, input samples include one cloudy image and one cloud-free reference image, and the cloud-free reference image also serves as the target. When the RSC-Net is fully trained, it is able to take cloudy image as input and produce cloud-free image as output. Experimental results show that our method has significant advantages in removing thin cloud contaminations in different bands when compared with other traditional and state-of-the-art methods. [Li, Wenbo; Li, Ying; Chen, Di] Northwest Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China; [Li, Wenbo; Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel Li, Y (corresponding author), Northwest Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China. National Natural Science Foundation of China [61871460, 61876152]; Foundation Project for Advanced Research Field of China [614023804016HK03002]; Natural Science Basic Research Plan in Shaanxi Province of China [2018JM6066]; China Scholarship Council [201706290107] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation Project for Advanced Research Field of China; Natural Science Basic Research Plan in Shaanxi Province of China; China Scholarship Council(China Scholarship Council) This work was supported by the National Natural Science Foundation of China (61871460, 61876152), the Foundation Project for Advanced Research Field of China (614023804016HK03002), and the Natural Science Basic Research Plan in Shaanxi Province of China (2018JM6066), in part by the China Scholarship Council for joint Ph.D. students (201706290107). 9 0 0 1 18 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 1974 1977 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH 2023-03-23 WOS:000519270602039 0
J Fu, RH; Xu, H; Wang, ZJ; Shen, L; Cao, MS; Liu, TW; Novaak, D Fu, Ronghua; Xu, Hao; Wang, Zijian; Shen, Lei; Cao, Maosen; Liu, Tongwei; Novak, Drahomir Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks SENSORS English Article concrete crack identification; convolutional neural network; homomorphic filtering; structural health monitoring; signal processing CNN Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated. [Fu, Ronghua; Shen, Lei; Cao, Maosen; Liu, Tongwei] Hohai Univ, Dept Engn Mech, Nanjing 210098, Jiangsu, Peoples R China; [Xu, Hao] Dalian Univ Technol, Sch Aeronaut & Astronaut, Fac Vehicle Engn & Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China; [Xu, Hao] Northwestern Univ, Dept Civil & Environm Engn, Chicago, IL 60626 USA; [Wang, Zijian] Southeast Univ, Minist Educ, Key Lab C&PC Struct, Nanjing 211189, Jiangsu, Peoples R China; [Cao, Maosen] Jiangxi Univ Sci & Technol, Jiangxi Prov Key Lab Environm Geotech Engn & Dis, Ganzhou 341000, Peoples R China; [Novak, Drahomir] Brno Univ Technol, Fac Civil Engn, Inst Struct Mech, Brno 60200, Czech Republic Hohai University; Dalian University of Technology; Northwestern University; Southeast University - China; Jiangxi University of Science & Technology; Brno University of Technology Cao, MS (corresponding author), Hohai Univ, Dept Engn Mech, Nanjing 210098, Jiangsu, Peoples R China.;Cao, MS (corresponding author), Jiangxi Univ Sci & Technol, Jiangxi Prov Key Lab Environm Geotech Engn & Dis, Ganzhou 341000, Peoples R China. ronghua@hhu.edu.cn; xuhao@dlut.edu.cn; wzj@seu.edu.cn; leishen@hhu.edu.cn; cmszhy@hhu.edu.cn; twliu@hhu.edu.cn; novak.d@fce.vutbr.cz Cao, Maosen/HNI-6007-2023 Cao, Maosen/0000-0001-6640-1905; Fu, Ronghua/0000-0002-1133-1409; Liu, Tongwei/0000-0001-5120-815X; Novak, Drahomir/0000-0003-0744-8265 Transportation Science Research Project of Jiangsu Province [2019Z02]; Key R&D Project of Jiangxi Science and Technology Department [20192BBH80021]; Chinese (Jiangsu)-Czech Bilateral Co-funding RD Project [BZ2018022] Transportation Science Research Project of Jiangsu Province; Key R&D Project of Jiangxi Science and Technology Department; Chinese (Jiangsu)-Czech Bilateral Co-funding RD Project This work is supported by the Transportation Science Research Project of Jiangsu Province (No.: 2019Z02), the Key R&D Project of Jiangxi Science and Technology Department (No.: 20192BBH80021), and the Chinese (Jiangsu)-Czech Bilateral Co-funding R&D Project (No.: BZ2018022). 49 5 5 10 44 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors APR 2020.0 20 7 2021 10.3390/s20072021 0.0 24 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation LT5KW 32260302.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000537110500217 0
C Li, Y; Yang, L; Chen, HF; Jiang, DM; Sahli, H IEEE Li, Yan; Yang, Le; Chen, Haifeng; Jiang, Dongmei; Sahli, Hichem Audio Visual Multimodal Classification of Bipolar Disorder Episodes 2019 8TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION WORKSHOPS AND DEMOS (ACIIW) English Proceedings Paper 8th International Conference on Affective Computing and Intelligent Interaction (ACII) SEP 03-06, 2019 Cambridge, ENGLAND Bipolar Disorder; Motion History Histogram; Histogram of Displacement Range; Multimodal classification framework Bipolar disorder is a highly prevalent and complex medical syndrome of multifactorial origin. In this paper, we propose an audio visual multi-modal framework for classifying the different episodes (Remission, Hypomania or Mania) of bipolar disorder. To represent the temporal dynamics of face and body poses, we propose to compute the Motion History Histogram (MHH) of facial landmarks as well as Histogram of Displacement Range (HDR) of body keypoints as the visual features. For audio features, functionals of the low level descriptors (LLDs) of speech are computed as global features. Each feature stream is input into a Convolutional Neural Network (CNN) to get the initial classification result of the patient's episode, which are then concatenated into a vector and fed into a random forest for the final classification. Experimental results on the development set of Audio Visual Emotion Challenge (AVEC2018) Bipolar Disorder Sub-Challenge demonstrate that the proposed visual features and bipolar disorder classification framework achieve promising results with the unweighted average recall (UAR) reaching 0.749, which is better or comparable with the state of the art results. [Li, Yan; Yang, Le; Chen, Haifeng; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R China; [Li, Yan; Yang, Le; Chen, Haifeng; Jiang, Dongmei] Peng Cheng Lab, Shenzhen, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel VUB, Dept Ecectron & Informat ETRO, NPU VUB Joint AVSP Res Lab, Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, Heverlee, Belgium Northwestern Polytechnical University; Peng Cheng Laboratory; Vrije Universiteit Brussel; IMEC Li, Y (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R China.;Li, Y (corresponding author), Peng Cheng Lab, Shenzhen, Peoples R China. liyan1998@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn; hsahli@etrovub.be Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14]; VUB Interdisciplinary Research Program through the EMO-App project Shaanxi Provincial International Science and Technology Collaboration Project; VUB Interdisciplinary Research Program through the EMO-App project This work was supported by the Shaanxi Provincial International Science and Technology Collaboration Project (grant 2017KW-ZD-14), and the VUB Interdisciplinary Research Program through the EMO-App project. 28 2 2 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-3891-6 2019.0 115 120 6 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT0ZF 2023-03-23 WOS:000794297900028 0
J Huang, LJ; Shen, SH; Zhu, LN; Shi, QX; Zhang, JW Huang, Liangjun; Shen, Shihui; Zhu, Luning; Shi, Qingxuan; Zhang, Jianwei Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting SENSORS English Article dense crowd counting; multi-scale feature learning; convolutional neural network In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting. [Huang, Liangjun; Shen, Shihui; Zhu, Luning] Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai 201418, Peoples R China; [Shi, Qingxuan] Hebei Univ, Sch Cyber Secur & Comp, Baoding 071000, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Dept Informat, D-20146 Hamburg, Germany Shanghai Institute of Technology; Hebei University; University of Hamburg Huang, LJ (corresponding author), Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai 201418, Peoples R China. gene_huang@sit.edu.cn; 206142102@mail.sit.edu.cn; 196141112@mail.sit.edu.cn; shiqingxuan@hbu.edu.cn; zhang@informatik.uni-hamburg.de zhang, jian/HPD-1712-2023 Natural Science Foundation of Hebei Province [F2019201451] Natural Science Foundation of Hebei Province(Natural Science Foundation of Hebei Province) This work is supported by the Natural Science Foundation of Hebei Province under Grant No. F2019201451. 58 0 0 9 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2022.0 22 9 3233 10.3390/s22093233 0.0 16 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 1E5ZS 35590922.0 gold 2023-03-23 WOS:000794566800001 0
C Cao, H; Chen, G; Li, ZJ; Lin, JJ; Knoll, A IEEE Cao, Hu; Chen, Guang; Li, Zhijun; Lin, Jianjie; Knoll, Alois Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 30-JUN 05, 2021 Xian, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,So Univ Sci & Technol,Harbin Inst Technol,Xian Jiaotong Univ,Univ Toronto,Nat Sci & Engn Res Council Canada,Chinese Univ Hong Kong,DJI,Biomimet Intelligence & Robot,Toyota Res Inst,Baidu,Mech Mind Robot Technologies,Facebook AI,Nokov,Manycore,Franka Emika,Zhejiang Univ, Huzhou Inst,Jing Tian,ATI Ind Automat,Amber,Direct Drive Tech,Inceptio,Flexiv,Deeproute AI,Unitree,Waymo,Kuka,Pudu Robot,Deep Robot,Nvidia,MathWorks This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300 x 300 depth images as input. Specifically, a residual squeezeand-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm. [Cao, Hu; Lin, Jianjie; Knoll, Alois] Tech Univ Munich, Robot Artificial Intelligence & Realtime Syst, Munich, Germany; [Chen, Guang] Tongji Univ, Shanghai, Peoples R China; [Cao, Hu; Chen, Guang] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China; [Li, Zhijun] Univ Sci & Technol China, Hefei, Peoples R China Technical University of Munich; Tongji University; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS Chen, G (corresponding author), Tongji Univ, Shanghai, Peoples R China.;Chen, G (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China. Cao, Hu/HJP-8442-2023 Cao, Hu/0000-0001-8225-858X State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences [20190109] State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences This research was funded by the open project of State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences under Grant No.20190109. 39 5 5 3 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-9077-8 IEEE INT CONF ROBOT 2021.0 13445 13451 10.1109/ICRA48506.2021.9561836 0.0 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BS8DT 2023-03-23 WOS:000771405405012 0
J Li, JZ; Li, YH; Ye, M; Yao, SQ; Yu, CC; Wang, L; Wu, WD; Wang, YB Li, Jizhen; Li, Yuhong; Ye, Ming; Yao, Sanqiao; Yu, Chongchong; Wang, Lei; Wu, Weidong; Wang, Yongbin Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China INFECTION AND DRUG RESISTANCE English Article tuberculosis; incidence rate; time series analysis; statistical models; forecasting TIME-SERIES ANALYSIS; NEURAL-NETWORK; QINGHAI PROVINCE; STATISTICAL-ANALYSIS; SEASONALITY; TRENDS; SARIMA; SCHISTOSOMIASIS; PREVALENCE; ELMAN Objective: The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet. Methods: The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model. Results: By comparing the accuracy level of the forecasting measurements including rootmean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5-8.1). Conclusion: This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet. [Li, Jizhen; Yao, Sanqiao; Yu, Chongchong; Wu, Weidong; Wang, Yongbin] Xinxiang Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Xinxiang 453000, Henan, Peoples R China; [Li, Yuhong] China Ctr Dis Control & Prevent, Natl Ctr TB Control & Prevent, Beijing, Peoples R China; [Ye, Ming] Xinxiang Ctr Dis Control & Prevent, Prevent Med Clin, Xinxiang, Henan, Peoples R China; [Wang, Lei] Charite Univ Med Berlin, Ctr Musculoskeletal Surg, Berlin, Germany; [Wang, Lei] Free Univ Berlin, Berlin, Germany; [Wang, Lei] Humboldt Univ, Berlin, Germany; [Wang, Lei] Berlin Inst Hlth, Berlin, Germany Xinxiang Medical University; Chinese Center for Disease Control & Prevention; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health Wang, YB (corresponding author), Xinxiang Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Xinxiang 453000, Henan, Peoples R China. wybwho@163.com Innovation and Entrepreneurship Training Project for University Students of Henan Province [S202010472007]; Xinxiang Medical University [XYXSKYZ201932]; Key Scientific Research Project of Universities in Henan [21A330004] Innovation and Entrepreneurship Training Project for University Students of Henan Province; Xinxiang Medical University; Key Scientific Research Project of Universities in Henan We appreciate the Chinese CDC for providing the monthly number of TB cases. This project was supported by the Innovation and Entrepreneurship Training Project for University Students of Henan Province (S202010472007) and Xinxiang Medical University (XYXSKYZ201932) and the Key Scientific Research Project of Universities in Henan (21A330004). 66 3 4 1 7 DOVE MEDICAL PRESS LTD ALBANY PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND 1178-6973 INFECT DRUG RESIST Infect. Drug Resistance 2021.0 14 1941 1955 10.2147/IDR.S299704 0.0 15 Infectious Diseases; Pharmacology & Pharmacy Science Citation Index Expanded (SCI-EXPANDED) Infectious Diseases; Pharmacology & Pharmacy SN6CV 34079304.0 gold 2023-03-23 WOS:000658376300001 0
J Zeng, C; Li, S; Chen, ZP; Yang, CG; Sun, FC; Zhang, JW Zeng, Chao; Li, Shuang; Chen, Zhaopeng; Yang, Chenguang; Sun, Fuchun; Zhang, Jianwei Multifingered Robot Hand Compliant Manipulation Based on Vision-Based Demonstration and Adaptive Force Control IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Robots; Force; Grasping; Task analysis; Impedance; Force control; Behavioral sciences; Adaptive impedance; force control; neural network model; robot-compliant manipulation; vision-based teleoperation LEVEL IMPEDANCE CONTROL; TELEOPERATION; MOTION; POSE Multifingered hand dexterous manipulation is quite challenging in the domain of robotics. One remaining issue is how to achieve compliant behaviors. In this work, we propose a human-in-the-loop learning-control approach for acquiring compliant grasping and manipulation skills of a multifinger robot hand. This approach takes the depth image of the human hand as input and generates the desired force commands for the robot. The markerless vision-based teleoperation system is used for the task demonstration, and an end-to-end neural network model (i.e., TeachNet) is trained to map the pose of the human hand to the joint angles of the robot hand in real-time. To endow the robot hand with compliant human-like behaviors, an adaptive force control strategy is designed to predict the desired force control commands based on the pose difference between the robot hand and the human hand during the demonstration. The force controller is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angles. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment compliantly. Our approach has been verified in both simulation and real-world task scenarios based on a multifingered robot hand, that is, the Shadow Hand, and has shown more reliable performances than the current widely used position control mode for obtaining compliant grasping and manipulation behaviors. [Zeng, Chao; Li, Shuang; Zhang, Jianwei] Univ Hamburg, Dept Informat, Tech Aspects Multimodal Syst TAMS Grp, D-22527 Hamburg, Germany; [Chen, Zhaopeng] Agile Robots AG, D-81477 Munich, Germany; [Yang, Chenguang] Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England; [Sun, Fuchun] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100190, Peoples R China University of Hamburg; University of Bristol; University of West England; Tsinghua University Zeng, C (corresponding author), Univ Hamburg, Dept Informat, Tech Aspects Multimodal Syst TAMS Grp, D-22527 Hamburg, Germany. chaozeng@ieee.org zhang, jian/HPD-1712-2023 German Research Foundation (DFG); National Science Foundation of China (NSFC) [410916101]; Project in Cross Modal Learning [TRR-169] German Research Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Project in Cross Modal Learning This work was supported in part by the German Research Foundation (DFG), in part by the National Science Foundation of China (NSFC) through the Project DEXMAN under Grant 410916101, and in part by the Project in Cross Modal Learning TRR-169. (Chao Zeng and Shuang Li contributed equally to this work.) (Corresponding author: Chao Zeng.) 47 2 2 13 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3184258 0.0 JUN 2022 12 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 2W8HM 35767493.0 2023-03-23 WOS:000824759200001 0
C Yang, L; Li, Y; Chen, HF; Jiang, DM; Oveneke, MC; Sahli, H ACM Yang, Le; Li, Yan; Chen, Haifeng; Jiang, Dongmei; Oveneke, Meshia Cedric; Sahli, Hichem Bipolar Disorder Recognition with Histogram Features of Arousal and Body Gestures PROCEEDINGS OF THE 2018 AUDIO/VISUAL EMOTION CHALLENGE AND WORKSHOP (AVEC'18) English Proceedings Paper 8th Workshop on Audio-Visual Emotion Challenge (AVEC) OCT 22, 2018 Seoul, SOUTH KOREA Assoc Comp Machinery,ACM SIGMM bipolar disorder; arousal; histogram of displacement range; model fusion; ensemble learning PSYCHOMOTOR RETARDATION This paper targets the Bipolar Disorder Challenge (BDC) task of Audio Visual Emotion Challenge (AVEC) 2018. Firstly, two novel features are proposed: 1) a histogram based arousal feature, in which the continuous arousal values are estimated from the audio cues by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model; 2) a Histogram of Displacement (HDR) based upper body posture feature, which characterizes the displacement and velocity of the key body points in the video segment. In addition, we propose a multi-stream bipolar disorder classification framework with Deep Neural Networks (DNNs) and a Random Forest, and adopt the ensemble learning strategy to alleviate the possible over-fitting problem due to the limited training data. Experimental results show that the proposed arousal feature and upper body posture feature are discriminative for different bipolar episodes, and our proposed framework achieves promising classification results on the development set, with the unweighted average recall (UAR) of 0.714, which is higher than the baseline result 0.635. On test set evaluation, our system obtains the same UAR (0.574) as the challenge baseline. [Yang, Le; Li, Yan; Chen, Haifeng; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint AVSP Res lab, Shaanxi Key Lab Speech & Image Informat Proc, Xian, Shaanxi, Peoples R China; [Oveneke, Meshia Cedric; Sahli, Hichem] Vrije Univ Brussel, Dept Elect & Informat ETRO, NPU VUB Joint AVSP Res lab, Pl Laan 2, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, Kepeldreef 75, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel; IMEC Yang, L (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, NPU VUB Joint AVSP Res lab, Shaanxi Key Lab Speech & Image Informat Proc, Xian, Shaanxi, Peoples R China. yangle.cst@gmail.com; liyan1998@mail.nwpu.edu.cn; chenhaifeng2018@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn; mcovenek@etro.vub.ac.be; hsahli@etrovub.be Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14]; National Natural Science Foundation of China [61273265]; VUB Interdisciplinary Research Program through the EMO-App project Shaanxi Provincial International Science and Technology Collaboration Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); VUB Interdisciplinary Research Program through the EMO-App project This work is supported by the Shaanxi Provincial International Science and Technology Collaboration Project (grant 2017KW-ZD-14), the National Natural Science Foundation of China (grant 61273265), and the VUB Interdisciplinary Research Program through the EMO-App project. 31 14 14 1 7 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5983-2 2018.0 15 21 10.1145/3266302.3266308 0.0 7 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM0AZ 2023-03-23 WOS:000458567200003 0
J Lu, JL; Pan, BF; Liu, Q; Sun, MH; Liu, PF; Oeser, M Lu, Jiale; Pan, Baofeng; Liu, Quan; Sun, Minghao; Liu, Pengfei; Oeser, Markus A novel noncontact method for the pavement skid resistance evaluation based on surface texture TRIBOLOGY INTERNATIONAL English Article Pavement skid resistance; Pavement surface texture; British Pendulum Test; Convolutional neural networ CRACK DETECTION; FRICTION; PARAMETERS A convolutional neural network is proposed to feature the primary relationship between pavement surface texture and in-situ skid resistance measurement namely British Pendulum Test. The influence of texture collection interval was firstly analyzed. Then, effective contact textures were extracted. Finally, the sample size required for model training, which could serve as a reference for further texture-related research was addressed. Results indicated that textures with wavelengths above 2.40 mm is key for the wet friction. Textures below the cross section whose area is 0.6 times the nominal area do not contact the rubber. The sample size should be more than 100. The newly developed non-contact method shows high feasibility in predicting the skid resistance and effectively control the relative error within 14%. [Lu, Jiale] Dalian Univ Technol, Fac Infrastruct Engn, Dalian 116024, Peoples R China; [Pan, Baofeng; Sun, Minghao] Dalian Univ Technol, Sch Transportat & Logist, Dalian 116024, Peoples R China; [Lu, Jiale; Liu, Quan; Liu, Pengfei; Oeser, Markus] Rhein Westfal TH Aachen, Inst Highway Engn, D-52074 Aachen, Germany Dalian University of Technology; Dalian University of Technology; RWTH Aachen University Pan, BF (corresponding author), Dalian Univ Technol, Sch Transportat & Logist, Dalian 116024, Peoples R China. panbf@dlut.edu.cn Liu, Quan/0000-0002-6730-539X 36 4 4 7 24 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0301-679X 1879-2464 TRIBOL INT Tribol. Int. JAN 2022.0 165 107311 10.1016/j.triboint.2021.107311 0.0 OCT 2021 9 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering WR9ND 2023-03-23 WOS:000714818500004 0
J Zhao, Y; Ren, SS; Kurths, J Zhao, Yong; Ren, Shanshan; Kurths, Juergen Synchronization of coupled memristive competitive BAM neural networks with different time scales NEUROCOMPUTING English Article Memristor; Competitive neural network; Lyapunov-Krasovskii functional; Synchronization control STATE STABILITY ANALYSIS; COMMUNICATION; DISCRETE; SYSTEMS; MEMORY In this paper, synchronization of coupled memristive competitive bidirectional associative memory (BAM) neural networks with different time scales is discussed. Two kinds of feedback controllers are designed such that the response system and the drive system can reach synchronization. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve asymptotical synchronization of competitive BAM neural networks. The proposed synchronization can be easily realized. An illustrative example is given to show the feasibility of our theoretical results. CO 2020 Elsevier B.V. All rights reserved. [Zhao, Yong] Guangdong Polytech Normal Univ, Sch Math & Syst Sci, Guangzhou 510665, Peoples R China; [Ren, Shanshan] Henan Polytech Univ, Sch Math & Informat Sci, Jiaozuo 454000, Henan, Peoples R China; [Kurths, Juergen] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Kurths, Juergen] Saratov NG Chernyshevskii State Univ, Saratov 410012, Russia Guangdong Polytechnic Normal University; Henan Polytechnic University; Humboldt University of Berlin; Potsdam Institut fur Klimafolgenforschung; Saratov State University Zhao, Y (corresponding author), Guangdong Polytech Normal Univ, Sch Math & Syst Sci, Guangzhou 510665, Peoples R China. zhaoyong_54@163.com National Natural Science Foundation of China [11972115, 11502073]; project RF Government [075-15-2019-1885] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); project RF Government This work was supported by the National Natural Science Foundation of China (No. 11972115, No. 11502073). J. Kurths was supported by the project RF Government Grant (No. 075-15-2019-1885). 36 11 11 4 29 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing FEB 28 2021.0 427 110 117 10.1016/j.neucom.2020.11.023 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PX0PQ 2023-03-23 WOS:000611067800010 0
C Christakis, M; Eniser, HF; Hermanns, H; Hoffmann, J; Kothari, Y; Li, JL; Navas, JA; Wustholz, V Silva, A; Leino, KRM Christakis, Maria; Eniser, Hasan Ferit; Hermanns, Holger; Hoffmann, Joerg; Kothari, Yugesh; Li, Jianlin; Navas, Jorge A.; Wuestholz, Valentin Automated Safety Verification of Programs Invoking Neural Networks COMPUTER AIDED VERIFICATION (CAV 2021), PT I Lecture Notes in Computer Science English Proceedings Paper 33rd International Conference on Computer-Aided Verification (CAV) JUL 20-23, 2021 ELECTR NETWORK ABSTRACT DOMAIN; MODEL CHECKING; CRASH State-of-the-art program-analysis techniques are not yet able to effectively verify safety properties of heterogeneous systems, that is, systems with components implemented using diverse technologies. This shortcoming is pinpointed by programs invoking neural networks despite their acclaimed role as innovation drivers across many application areas. In this paper, we embark on the verification of system-level properties for systems characterized by interaction between programs and neural networks. Our technique provides a tight two-way integration of a program and a neural-network analysis and is formalized in a general framework based on abstract interpretation. We evaluate its effectiveness on 26 variants of a widely used, restricted autonomous-driving benchmark. [Christakis, Maria; Eniser, Hasan Ferit; Kothari, Yugesh] MPI SWS, Kaiserslautern, Germany; [Christakis, Maria; Eniser, Hasan Ferit; Kothari, Yugesh] MPI SWS, Saarbrucken, Germany; [Hermanns, Holger; Hoffmann, Joerg; Li, Jianlin] Saarland Univ, Saarland Informat Campus, Saarbrucken, Germany; [Li, Jianlin] Chinese Acad Sci, Inst Software, SKLCS, Beijing, Peoples R China; [Navas, Jorge A.] SRI Int, 333 Ravenswood Ave, Menlo Pk, CA 94025 USA; [Wuestholz, Valentin] ConsenSys, Kaiserslautern, Germany; [Hermanns, Holger] Inst Intelligent Software, Guangzhou, Peoples R China; [Li, Jianlin] Univ Chinese Acad Sci, Beijing, Peoples R China Saarland University; Chinese Academy of Sciences; Institute of Software, CAS; SRI International; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Eniser, HF (corresponding author), MPI SWS, Kaiserslautern, Germany.;Eniser, HF (corresponding author), MPI SWS, Saarbrucken, Germany. maria@mpi-sws.org; hfeniser@mpi-sws.org; hermanns@cs.uni-saarland.de; hoffmann@cs.uni-saarland.de; ykothari@mpi-sws.org; ljlin@ios.ac.cn; jorge.navas@sri.com; valentin.wustholz@consensys.net Li, Jianlin/0000-0001-7371-3034; Christakis, Maria/0000-0002-2649-1958 DFG [389792660, TRR 248]; NSF [1816936]; Guangdong Province Science Grant [2018B010107004] DFG(German Research Foundation (DFG)); NSF(National Science Foundation (NSF)); Guangdong Province Science Grant We are grateful to the reviewers for their constructive feedback. This work has been supported by DFG Grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science).Jorge Navas has been supported by NSF Grant 1816936. Holger Hermanns and Jianlin Li have been supported by Guangdong Province Science Grant 2018B010107004. 70 2 2 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-81685-8; 978-3-030-81684-1 LECT NOTES COMPUT SC 2021.0 12759 201 224 10.1007/978-3-030-81685-8_9 0.0 24 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS2BH hybrid 2023-03-23 WOS:000698732400009 0
J Wang, QL; Psillakis, HE; Sun, CY; Lewis, FL Wang, Qingling; Psillakis, Haris E.; Sun, Changyin; Lewis, Frank L. Adaptive NN Distributed Control for Time-Varying Networks of Nonlinear Agents With Antagonistic Interactions IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Antagonistic time-varying interactions; bipartite consensus; Nussbaum-type functions; unknown control directions (UCDs) UNKNOWN CONTROL DIRECTIONS; FREQUENCY GAIN SIGNS; MULTIAGENT SYSTEMS; CONSENSUS CONTROL; BIPARTITE CONSENSUS; OUTPUT REGULATION; DELAY SYSTEMS; VEHICLE; HARDWARE; DESIGN This article proposes an adaptive neural network (NN) distributed control algorithm for a group of high-order nonlinear agents with nonidentical unknown control directions (UCDs) under signed time-varying topologies. An important lemma on the convergence property is first established for agents with antagonistic time-varying interactions, and then by using Nussbaum-type functions, a new class of NN distributed control algorithms is proposed. If the signed time-varying topologies are cut-balanced and uniformly in time structurally balanced, then convergence is achieved for a group of nonlinear agents. Moreover, the proposed algorithms are adopted to achieve the bipartite consensus of high-order nonlinear agents with nonidentical UCDs under signed graphs, which are uniformly quasi-strongly delta-connected. Finally, simulation examples are given to illustrate the effectiveness of the NN distributed control algorithms. [Wang, Qingling; Sun, Changyin] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China; [Wang, Qingling; Sun, Changyin] Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Peoples R China; [Psillakis, Haris E.] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15780, Greece; [Lewis, Frank L.] Univ Texas Arlington, Univ Texas Arlington Res Inst, Ft Worth, TX 76118 USA Southeast University - China; National Technical University of Athens; University of Texas System; University of Texas Arlington Sun, CY (corresponding author), Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China. csuwql@gmail.com; hpsilakis@central.ntua.gr; cysun@seu.edu.cn; lewis@uta.edu SUN, CHANG/GXM-3680-2022 Wang, Qingling/0000-0003-2045-2920; Psillakis, Haris/0000-0003-2019-6955 National Natural Science Foundation of China [61921004, 61973074, U1713209, 61520106009]; Science and Technology on Information System Engineering Laboratory [05201902]; Fundamental Research Funds for the Central Universities of China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology on Information System Engineering Laboratory; Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61921004, Grant 61973074, Grant U1713209, and Grant 61520106009, in part by the Science and Technology on Information System Engineering Laboratory under Grant 05201902, and in part by the Fundamental Research Funds for the Central Universities of China. 53 11 11 14 40 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JUN 2021.0 32 6 2573 2583 10.1109/TNNLS.2020.3006840 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SN5SQ 32692681.0 2023-03-23 WOS:000658349600021 0
J Kalinin, SV; Ophus, C; Voyles, PM; Erni, R; Kepaptsoglou, D; Grillo, V; Lupini, AR; Oxley, MP; Schwenker, E; Chan, MKY; Etheridge, J; Li, X; Han, GGD; Ziatdinov, M; Shibata, N; Pennycook, SJ Kalinin, Sergei V.; Ophus, Colin; Voyles, Paul M.; Erni, Rolf; Kepaptsoglou, Demie; Grillo, Vincenzo; Lupini, Andrew R.; Oxley, Mark P.; Schwenker, Eric; Chan, Maria K. Y.; Etheridge, Joanne; Li, Xiang; Han, Grace G. D.; Ziatdinov, Maxim; Shibata, Naoya; Pennycook, Stephen J. Machine learning in scanning transmission electron microscopy NATURE REVIEWS METHODS PRIMERS English Review DIFFERENTIAL PHASE-CONTRAST; SURFACE-PLASMON RESONANCES; MODEL-BASED QUANTIFICATION; ENERGY-LOSS SPECTROSCOPY; HIGH-RESOLUTION; ATOMIC-SCALE; ABERRATION MEASUREMENT; SINGLE ATOMS; VIBRATIONAL SPECTROSCOPY; UNCONVENTIONAL METHODS Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful tool for structural and functional imaging of materials on the atomic level. Driven by advances in aberration correction, STEM now allows the routine imaging of structures with single-digit picometre-level precision for localization of atomic units. This Primer focuses on the opportunities emerging at the interface between STEM and machine learning (ML) methods. We review the primary STEM imaging methods, including structural imaging, electron energy loss spectroscopy and its momentum-resolved modalities and 4D-STEM. We discuss the quantification of STEM structural data as a necessary step towards meaningful ML applications and its analysis in terms of the relevant physics and chemistry. We show examples of the opportunities offered by structural STEM imaging in elucidating the chemistry and physics of complex materials and how the latter connect to first-principles and phase-field models to yield consistent interpretation of generative physics. We present the critical infrastructural needs for the broad adoption of ML methods in the STEM community, including the storage of data and metadata to allow the reproduction of experiments. Finally, we discuss the application of ML to automating experiments and novel scanning modes. [Kalinin, Sergei V.; Lupini, Andrew R.; Oxley, Mark P.; Ziatdinov, Maxim] Oak Ridge Natl Lab, Ctr Nanophase Mat Sci, Oak Ridge, TN USA; [Ophus, Colin] Lawrence Berkeley Natl Lab, NCEM, Mol Foundry, Berkeley, CA USA; [Voyles, Paul M.] Univ Wisconsin, Dept Mat Sci & Engn, Madison, WI USA; [Erni, Rolf] EMPA Swiss Fed Labs Mat Sci & Technol, Electron Microscopy Ctr, Dubendorf, Switzerland; [Kepaptsoglou, Demie] SuperSTEM Lab, Sci Tech Daresbury Campus, Daresbury, England; [Kepaptsoglou, Demie] Univ York, Dept Phys, York, N Yorkshire, England; [Grillo, Vincenzo] CNR NANO, Modena, Italy; [Schwenker, Eric; Chan, Maria K. Y.] Argonne Natl Lab, Ctr Nanoscale Mat, Lemont, IL USA; [Schwenker, Eric] Northwestern Univ, Mat Sci & Engn Dept, Evanston, IL USA; [Etheridge, Joanne] Monash Univ, Monash Ctr Electron Microscopy, Clayton, Vic, Australia; [Etheridge, Joanne] Monash Univ, Dept Mat Sci & Engn, Clayton, Vic, Australia; [Li, Xiang; Han, Grace G. D.] Brandeis Univ, Dept Chem, Waltham, MA USA; [Ziatdinov, Maxim] Oak Ridge Natl Lab, Computat Sci & Engn Div, Oak Ridge, TN USA; [Shibata, Naoya] Univ Tokyo, Sch Engn, Inst Engn Innovat, Tokyo, Japan; [Pennycook, Stephen J.] Univ Tennessee, Dept Mat Sci & Engn, Knoxville, TN USA; [Pennycook, Stephen J.] Univ Chinese Acad Sci, Sch Phys Sci, Beijing, Peoples R China; [Pennycook, Stephen J.] Univ Chinese Acad Sci, CAS Key Lab Vacuum Sci, Beijing, Peoples R China United States Department of Energy (DOE); Oak Ridge National Laboratory; Center for Nanophase Materials Sciences; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of Wisconsin System; University of Wisconsin Madison; Swiss Federal Institutes of Technology Domain; Swiss Federal Laboratories for Materials Science & Technology (EMPA); University of York - UK; Consiglio Nazionale delle Ricerche (CNR); Istituto Nanoscienze (NANO-CNR); United States Department of Energy (DOE); Argonne National Laboratory; Northwestern University; Monash University; Monash University; Brandeis University; United States Department of Energy (DOE); Oak Ridge National Laboratory; University of Tokyo; University of Tennessee System; University of Tennessee Knoxville; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Kalinin, SV (corresponding author), Oak Ridge Natl Lab, Ctr Nanophase Mat Sci, Oak Ridge, TN USA. sergei2@ornl.gov Ziatdinov, Maxim/L-1991-2016; Erni, Rolf/P-7435-2014 Ziatdinov, Maxim/0000-0003-2570-4592; Erni, Rolf/0000-0003-2391-5943; Kepaptsoglou, Demie/0000-0003-0499-0470; Voyles, Paul/0000-0001-9438-4284; Chan, Maria K. Y./0000-0003-0922-1363; Ophus, Colin/0000-0003-2348-8558 US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division; Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility; European Union [766970, 964591, 101035013]; Engineering and Physical Sciences Research Council (EPSRC); DOE BES [DE-FG02-08ER46547]; Brandeis NSF MRSEC, Bioinspired Soft Materials [DMR-2011846]; Office of Science, Office of Basic Energy Sciences, of the US Department of Energy [DE-AC02-05CH11231]; Center for Nanoscale Materials (DOE SUF) [DE-AC02-06CH11357]; DOE Early Career Research Awards; JSPS KAKENHI [20H05659, 19H05788]; Australian Research Council Discovery Project [DP150104483, DP160104679] US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division(United States Department of Energy (DOE)); Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility; European Union(European Commission); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); DOE BES(United States Department of Energy (DOE)); Brandeis NSF MRSEC, Bioinspired Soft Materials; Office of Science, Office of Basic Energy Sciences, of the US Department of Energy(United States Department of Energy (DOE)); Center for Nanoscale Materials (DOE SUF)(United States Department of Energy (DOE)); DOE Early Career Research Awards(United States Department of Energy (DOE)); JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Australian Research Council Discovery Project(Australian Research Council) This work is based upon work supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (S.V.K., M.P.O. and A.R.L.) and was performed and partially supported (M.Z.) at Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility. V.G. acknowledges the support of the European Union Horizon 2020 Research and Innovation Programme under grant agreement no. 766970 Q-SORT (H2020-FETOPEN-1-2016-2017), no. 964591 Smart-electrons (H2020-FETOPEN-1-2016-2017) and no. 101035013 MINEON (H2020-FETOPEN-03-2018-2019-2020 - FET Innovation Launchpad). SuperSTEM (D.K.) is the UK National Facility for Advanced Electron Microscopy funded by the Engineering and Physical Sciences Research Council (EPSRC). P.M.V. acknowledges support from DOE BES (DE-FG02-08ER46547). G.G.D.H. and X.L. acknowledge support from Brandeis NSF MRSEC, Bioinspired Soft Materials, DMR-2011846. Work at the Molecular Foundry (C.O.) was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under contract no. DE-AC02-05CH11231. E.S. and M.K.Y.C. acknowledges support from the Center for Nanoscale Materials (DOE SUF) under contract no. DE-AC02-06CH11357. C.O. and M.K.Y.C. acknowledge support from DOE Early Career Research Awards. N.S. acknowledges support from the JSPS KAKENHI (grants 20H05659 and 19H05788). J.E. acknowledges the support of the Australian Research Council Discovery Project (grants DP150104483 and DP160104679). The authors are extremely grateful to K. More (from CNMS) for careful reading and editing of the manuscript. 379 10 10 21 21 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-8449 NAT REV METHOD PRIME Nat. Rev. Method. Prim. MAR 3 2022.0 2 1 11 10.1038/s43586-022-00095-w 0.0 28 Multidisciplinary Sciences Emerging Sources Citation Index (ESCI) Science & Technology - Other Topics 6M0JF Green Accepted 2023-03-23 WOS:000888563300001 0
J Martini, ML; Neifert, SN; Shuman, WH; Chapman, EK; Schupper, AJ; Oermann, EK; Mocco, J; Todd, M; Torner, JC; Molyneux, A; Mayer, S; Le Roux, P; Vergouwen, MDI; Rinkel, GJE; Wong, GKC; Kirkpatrick, P; Quinn, A; Hanggi, D; Etminan, N; van den Bergh, WM; Jaja, BNR; Cusimano, M; Schweizer, TA; Suarez, JI; Fukuda, H; Yamagata, S; Lo, B; Manoel, ALD; Boogaarts, HD; Macdonald, RL Martini, Michael L.; Neifert, Sean N.; Shuman, William H.; Chapman, Emily K.; Schupper, Alexander J.; Oermann, Eric K.; Mocco, J.; Todd, Michael; Torner, James C.; Molyneux, Andrew; Mayer, Stephan; Le Roux, Peter; Vergouwen, Mervyn D., I; Rinkel, Gabriel J. E.; Wong, George K. C.; Kirkpatrick, Peter; Quinn, Audrey; Hanggi, Daniel; Etminan, Nima; van den Bergh, Walter M.; Jaja, Blessing N. R.; Cusimano, Michael; Schweizer, Tom A.; Suarez, Jose, I; Fukuda, Hitoshi; Yamagata, Sen; Lo, Benjamin; de Oliveira Manoel, Airton Leonardo; Boogaarts, Hieronymus D.; Macdonald, R. Loch SAHIT Collaboration Rescue therapy for vasospasm following aneurysmal subarachnoid hemorrhage: a propensity score-matched analysis with machine learning JOURNAL OF NEUROSURGERY English Article subarachnoid hemorrhage; vasospasm; delayed cerebral ischemia; rescue therapy; machine learning; feature importance; propensity score matching; vascular disorders DELAYED CEREBRAL-ISCHEMIA; VEHICLE-CONTROLLED TRIAL; DOUBLE-BLIND; TIRILAZAD MESYLATE; OPEN-LABEL OBJECTIVE Rescue therapies have been recommended for patients with angiographic vasospasm (aVSP) and delayed cerebral ischemia (DCI) following subarachnoid hemorrhage (SAH). However, there is little evidence from randomized clinical trials that these therapies are safe and effective. The primary aim of this study was to apply game theory-based methods in explainable machine learning (ML) and propensity score matching to determine if rescue therapy was associated with better 3-month outcomes following post-SAH aVSP and DCI. The authors also sought to use these explainable ML methods to identify patient populations that were more likely to receive rescue therapy and factors associated with better outcomes after rescue therapy. METHODS Data for patients with aVSP or DCI after SAH were obtained from 8 clinical trials and 1 observational study in the Subarachnoid Hemorrhage International Trialists repository. Gradient boosting ML models were constructed for each patient to predict the probability of receiving rescue therapy and the 3-month Glasgow Outcome Scale (GOS) score. Favorable outcome was defined as a 3-month GOS score of 4 or 5. Shapley Additive Explanation (SNAP) values were calculated for each patient-derived model to quantify feature importance and interaction effects. Variables with high S HAP importance in predicting rescue therapy administration were used in a propensity score-matched analysis of rescue therapy and 3-month GOS scores. RESULTS The authors identified 1532 patients with aVSP or DCI. Predictive, explainable ML models revealed that aneurysm characteristics and neurological complications, but not admission neurological scores, carried the highest relative importance rankings in predicting whether rescue therapy was administered. Younger age and absence of cerebral ischemia/ infarction were invariably linked to better rescue outcomes, whereas the other important predictors of outcome varied by rescue type (interventional or noninterventional). In a propensity score-matched analysis guided by SHAP-based variable selection, rescue therapy was associated with higher odds of 3-month GOS scores of 4-5 (OR 1.63, 95% CI 1.22-2.17). CONCLUSIONS Rescue therapy may increase the odds of good outcome in patients with aVSP or DCI after SAH. Given the strong association between cerebral ischemia/infarction and poor outcome, trials focusing on preventative or therapeutic interventions in these patients may be most able to demonstrate improvements in clinical outcomes. Insights developed from these models may be helpful for improving patient selection and trial design. [Martini, Michael L.; Neifert, Sean N.; Shuman, William H.; Chapman, Emily K.; Schupper, Alexander J.; Oermann, Eric K.; Mocco, J.] Mt Sinai Hlth Syst, Dept Neurosurg, New York, NY USA; [Todd, Michael] Univ Minnesota, Dept Anesthesiol, Minneapolis, MN 55455 USA; [Torner, James C.] Univ Iowa, Coll Publ Hlth, Dept Epidemiol, Iowa City, IA USA; [Torner, James C.] Univ Iowa, Coll Publ Hlth, Dept Surg, Iowa City, IA USA; [Torner, James C.] Univ Iowa, Coll Publ Hlth, Dept Neurosurg, Iowa City, IA USA; [Molyneux, Andrew] Univ Oxford, Nuffield Dept Surg Sci, Oxford, England; [Mayer, Stephan] Wayne State Univ, Sch Med, Detroit, MI USA; [Le Roux, Peter] Bassett HealthCare, Cooperstown, NY USA; [Vergouwen, Mervyn D., I; Rinkel, Gabriel J. E.] Univ Utrecht, Univ Med Ctr Utrecht, Brain Ctr, Dept Neurol & Neurosurg, Utrecht, Netherlands; [Wong, George K. C.] Prince Wales Hosp, Div Neurosurg, Hong Kong, Peoples R China; [Wong, George K. C.] Chinese Univ Hong Kong, Hong Kong, Peoples R China; [Kirkpatrick, Peter] Univ Cambridge, Nuffield Hlth Cambridge Hosp, Cambridge, England; [Quinn, Audrey] James Cook Univ Hosp, Dept Anaesthesia, Cheriton House, Middlesbrough, Cleveland, England; [Hanggi, Daniel] Heinrich Heine Univ, Dusseldorf Univ Hosp, Dept Neurosurg, Dusseldorf, Germany; [Etminan, Nima] Heidelberg Univ, Med Fac Mannheim, Dept Neurosurg, Mannheim, Germany; [van den Bergh, Walter M.] Univ Groningen, Univ Med Ctr Groningen, Dept Crit Care, Groningen, Netherlands; [Jaja, Blessing N. R.] St Michaels Hosp, Div Neurosurg, Toronto, ON, Canada; [Jaja, Blessing N. R.] St Michaels Hosp, Div Neurol, Toronto, ON, Canada; [Jaja, Blessing N. R.; Schweizer, Tom A.] Univ Toronto, Neurosci Res Program, Keenan Res Ctr Biomed Sci, Toronto, ON, Canada; [Jaja, Blessing N. R.; Schweizer, Tom A.] Univ Toronto, Li Ka Shing Knowledge Inst, St Michaels Hosp, Dept Surg, Toronto, ON, Canada; [Cusimano, Michael] Univ Toronto, Educ & Publ Hlth, St Michaels Hosp, Keenan Res Ctr, Toronto, ON, Canada; [Cusimano, Michael] Li Ka Shing Knowledge Inst, Toronto, ON, Canada; [Suarez, Jose, I] Johns Hopkins Univ, Sch Med, Dept Anesthesiol & Crit Care Med, Baltimore, MD 21205 USA; [Suarez, Jose, I] Johns Hopkins Univ, Sch Med, Dept Neurol, Baltimore, MD 21205 USA; [Suarez, Jose, I] Johns Hopkins Univ, Sch Med, Dept Neurosurg, Baltimore, MD 21205 USA; [Fukuda, Hitoshi; Yamagata, Sen] Kurashiki Cent Hosp, Dept Neurosurg, Kurashiki, Okayama, Japan; [Lo, Benjamin] Lenox Hill Hosp, Dept Neurosurg, New York, NY 10021 USA; [de Oliveira Manoel, Airton Leonardo] Hosp Israelita Albert Einstein, Dept Crit Care, Sao Paulo, Brazil; [de Oliveira Manoel, Airton Leonardo] Hosp Alemao Oswaldo Cruz, Sao Paulo, Brazil; [Boogaarts, Hieronymus D.] Radboud Univ Nijmegen Med Ctr, Dept Neurosurg, Nijmegen, Netherlands; [Macdonald, R. Loch] Univ Calif San Francisco, Univ Neurosci Inst, Fresno Campus, Fresno, CA USA Icahn School of Medicine at Mount Sinai; University of Minnesota System; University of Minnesota Twin Cities; University of Iowa; University of Iowa; University of Iowa; University of Oxford; Wayne State University; Utrecht University; Utrecht University Medical Center; Chinese University of Hong Kong; Prince of Wales Hospital; Chinese University of Hong Kong; University of Cambridge; James Cook University Hospital; Heinrich Heine University Dusseldorf; Heinrich Heine University Dusseldorf Hospital; Ruprecht Karls University Heidelberg; University of Groningen; University of Toronto; Saint Michaels Hospital Toronto; University of Toronto; Saint Michaels Hospital Toronto; University of Toronto; University of Toronto; Li Ka Shing Knowledge Institute; Saint Michaels Hospital Toronto; University of Toronto; Saint Michaels Hospital Toronto; University of Toronto; Li Ka Shing Knowledge Institute; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Kurashiki Central Hospital; Northwell Health; Hospital Israelita Albert Einstein; Radboud University Nijmegen; University of California System; University of California San Francisco; University of California San Francisco at Fresno Macdonald, RL (corresponding author), UCSF Fresno, Fresno, CA 93701 USA. rlochmacdonald@gmail.com Cusimano, Michael/GWR-1025-2022 35 5 5 2 12 AMER ASSOC NEUROLOGICAL SURGEONS ROLLING MEADOWS 5550 MEADOWBROOK DRIVE, ROLLING MEADOWS, IL 60008 USA 0022-3085 1933-0693 J NEUROSURG J. Neurosurg. JAN 2022.0 136 1 134 147 10.3171/2020.12.JNS203778 0.0 14 Clinical Neurology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Surgery YS8DU 34214980.0 Green Submitted 2023-03-23 WOS:000750903400002 0
J Lu, HB; Li, SH; Ma, MN; Bastrikov, V; Chen, XZ; Ciais, P; Dai, YJ; Ito, A; Ju, WM; Lienert, S; Lombardozzi, D; Lu, XJ; Maignan, F; Nakhavali, M; Quine, T; Schindlbacher, A; Wang, J; Wang, YP; Warlind, D; Zhang, SP; Yuan, WP Lu, Haibo; Li, Shihua; Ma, Minna; Bastrikov, Vladislav; Chen, Xiuzhi; Ciais, Philippe; Dai, Yongjiu; Ito, Akihiko; Ju, Weimin; Lienert, Sebastian; Lombardozzi, Danica; Lu, Xingjie; Maignan, Fabienne; Nakhavali, Mahdi; Quine, Timothy; Schindlbacher, Andreas; Wang, Jun; Wang, Yingping; Warlind, David; Zhang, Shupeng; Yuan, Wenping Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models ENVIRONMENTAL RESEARCH LETTERS English Article benchmark; carbon cycling; global soil respiration; machine learning; terrestrial ecosystem models EARTH SYSTEM MODELS; TEMPERATURE SENSITIVITY; CARBON; VEGETATION; NITROGEN; ROOT; DECOMPOSITION; PRODUCTIVITY; FEEDBACKS; EMISSIONS The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 +/- 40.4 (SD) Pg C yr(-1) for SR, 50.3 +/- 25.0 (SD) Pg C yr(-1) for HR and 35.2 Pg C yr(-1) for AR during 1982-2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr(-1) and 39.8 to 61.7 Pg C yr(-1), respectively. The most discrepancy lays in the estimation of AR, the difference (12.0-42.3 Pg C yr(-1)) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models. [Lu, Haibo; Li, Shihua; Ma, Minna; Chen, Xiuzhi; Dai, Yongjiu; Lu, Xingjie; Zhang, Shupeng; Yuan, Wenping] Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Sch Atmospher Sci, Zhuhai 519082, Guangdong, Peoples R China; [Bastrikov, Vladislav; Ciais, Philippe; Maignan, Fabienne] CEA, CNRS, UVSQ, Lab Sci Climat & EnvironM,LSCE,IPSL, F-91191 Gif Sur Yvette, France; [Ito, Akihiko] Natl Inst Environm Studies, Tsukuba, Ibaraki 3058506, Japan; [Ju, Weimin; Wang, Jun] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China; [Lienert, Sebastian] Univ Bern, Climate & Environm Phys Phys Inst, Bern, Switzerland; [Lienert, Sebastian] Univ Bern, Oeschger Ctr Climate Change Res, Bern, Switzerland; [Lombardozzi, Danica] Natl Ctr Atmospher Res, Climate & Global Dynam Lab, POB 3000, Boulder, CO 80307 USA; [Nakhavali, Mahdi] Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QE, Devon, England; [Quine, Timothy] Univ Exeter, Dept Geog, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon, England; [Schindlbacher, Andreas] Fed Res & Training Ctr Forests Nat Hazards & Land, Dept Forest Ecol, A-1131 Vienna, Austria; [Wang, Jun] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; [Wang, Yingping] CSIRO, Oceans & Atmosphere, Private Bag 1, Aspendale, Vic 3195, Australia; [Wang, Yingping] Chinese Acad Sci, South China Bot Garden, Guangzhou 510650, Peoples R China; [Warlind, David] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden Southern Marine Science & Engineering Guangdong Laboratory; Sun Yat Sen University; UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); National Institute for Environmental Studies - Japan; Nanjing University; University of Bern; University of Bern; National Center Atmospheric Research (NCAR) - USA; University of Exeter; University of Exeter; University System of Maryland; University of Maryland College Park; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Chinese Academy of Sciences; South China Botanical Garden, CAS; Lund University Yuan, WP (corresponding author), Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Sch Atmospher Sci, Zhuhai 519082, Guangdong, Peoples R China. yuanwp3@mail.sysu.edu.cn Wang, Ying/HJI-2509-2023; wang, yingping/A-9765-2011; Wårlind, David/HIK-1179-2022; Ito, Akihiko/P-2624-2017; Maignan, Fabienne/F-5419-2013; Andre Nakhavali/J-1021-2019 wang, yingping/0000-0002-4614-6203; Ito, Akihiko/0000-0001-5265-0791; Maignan, Fabienne/0000-0001-5024-5928; Andre Nakhavali/0000-0003-2675-6730; Li, Shihua/0000-0001-6796-0856 National Science Fund for Distinguished Young Scholars [41925001]; National Key Basic Research Program of China [2018YFA0606104]; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [311020008]; Guangdong Basic and Applied Basic Research Foundation [2020A1515111145]; Fundamental Research Funds for the Central Universities [19lgjc02, 18lgpy09] National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Key Basic Research Program of China(National Basic Research Program of China); Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai); Guangdong Basic and Applied Basic Research Foundation; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This study was supported by the National Science Fund for Distinguished Young Scholars (41925001), National Key Basic Research Program of China (2018YFA0606104), Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (NO.311020008), Guangdong Basic and Applied Basic Research Foundation (2020A1515111145) and Fundamental Research Funds for the Central Universities (19lgjc02, 18lgpy09). 57 9 9 16 69 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-9326 ENVIRON RES LETT Environ. Res. Lett. MAY 2021.0 16 5 54048 10.1088/1748-9326/abf526 0.0 14 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences RX5QK Green Published, gold 2023-03-23 WOS:000647278100001 0
J Huang, YC; Liu, JX; Harkin, J; McDaid, L; Luo, YL Huang, Yongchuang; Liu, Junxiu; Harkin, Jim; McDaid, Liam; Luo, Yuling An memristor-based synapse implementation using BCM learning rule NEUROCOMPUTING English Article Memristor; BCM theory; Spiking neural networks; Learning rule RATE-DEPENDENT PLASTICITY; STABILITY A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed. (C) 2020 Elsevier B.V. All rights reserved. [Huang, Yongchuang; Liu, Junxiu; Luo, Yuling] Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China; [Harkin, Jim; McDaid, Liam] Ulster Univ, Sch Comp Engn & Intelligent Syst, Derry BT48 7JL, Londonderry, North Ireland Guangxi Normal University; Ulster University Liu, JX (corresponding author), Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China. j.liu@ieee.com Harkin, Jim/F-8958-2013 Harkin, Jim/0000-0001-7484-8205 National Natural Science Foundation of China [61976063]; Overseas 100 Talents Program of Guangxi Higher Education National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Overseas 100 Talents Program of Guangxi Higher Education This research is supported by the National Natural Science Foundation of China under Grant 61976063 and the funding of Overseas 100 Talents Program of Guangxi Higher Education. 36 11 11 9 54 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 29 2021.0 423 336 342 10.1016/j.neucom.2020.10.106 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PG7KG Green Published 2023-03-23 WOS:000599909500011 0
C Luo, W; Tang, QR; Fu, CH; Eberhard, P Tan, Y; Shi, Y; Tang, Q Luo, Wei; Tang, Qirong; Fu, Changhong; Eberhard, Peter Deep-Sarsa Based Multi-UAV Path Planning and Obstacle Avoidance in a Dynamic Environment ADVANCES IN SWARM INTELLIGENCE, ICSI 2018, PT II Lecture Notes in Computer Science English Proceedings Paper 9th International Conference on Swarm Intelligence (ICSI) JUL 17-22, 2018 Shanghai, PEOPLES R CHINA Tongji Univ, Sch Mech Engn, Robot & Multi Body Syst Lab,Peking Univ, Computat Intelligence Lab,So Univ Sci & Technol,IEEE Beijing Chapter UAV; Deep-Sarsa; Multi-agent; Dynamic environment This study presents a Deep-Sarsa based path planning and obstacle avoidance method for unmanned aerial vehicles (UAVs). Deep-Sarsa is an on-policy reinforcement learning approach, which gains information and rewards from the environment and helps UAV to avoid moving obstacles as well as finds a path to a target based on a deep neural network. It has a significant advantage over dynamic environment compared to other algorithms. In this paper, a Deep-Sarsa model is trained in a grid environment and then deployed in an environment in ROS-Gazebo for UAVs. The experimental results show that the trained Deep-Sarsa model can guide the UAVs to the target without any collisions. This is the first time that Deep-Sarsa has been developed to achieve autonomous path planning and obstacle avoidance of UAVs in a dynamic environment. [Luo, Wei; Eberhard, Peter] Univ Stuttgart, Inst Engn & Computat Mech, Pfaffenwaldring 9, D-70569 Stuttgart, Germany; [Tang, Qirong; Fu, Changhong] Tongji Univ, Sch Mech Engn, Lab Robot & Multibody Syst, 4800 Cao An Rd, Shanghai 201804, Peoples R China University of Stuttgart; Tongji University Tang, QR (corresponding author), Tongji Univ, Sch Mech Engn, Lab Robot & Multibody Syst, 4800 Cao An Rd, Shanghai 201804, Peoples R China. qirong.tang@outlook.com Fu, Changhong/AAN-2859-2020 Fu, Changhong/0000-0002-9897-6022; Luo, Wei/0000-0003-4016-765X National Natural Science Foundation of China [61603277]; Shanghai Aerospace Science and Technology Innovation Fund [SAST 2016017]; Youth 1000 program project [1000231901]; Key Basic Research Project of Shanghai Science and Technology Innovation Plan [15JC1403300]; 13th-Five-Year-Plan on Common Technology [41412050101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Aerospace Science and Technology Innovation Fund; Youth 1000 program project; Key Basic Research Project of Shanghai Science and Technology Innovation Plan; 13th-Five-Year-Plan on Common Technology This work is supported by the project of National Natural Science Foundation of China (No. 61603277), the 13th-Five-Year-Plan on Common Technology, key project (No. 41412050101), and the Shanghai Aerospace Science and Technology Innovation Fund (SAST 2016017). Meanwhile, this work is also partially supported by the Youth 1000 program project (No. 1000231901), as well as by the Key Basic Research Project of Shanghai Science and Technology Innovation Plan (No. 15JC1403300). All these supports are highly appreciated. 21 13 14 1 22 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-93818-9; 978-3-319-93817-2 LECT NOTES COMPUT SC 2018.0 10942 102 111 10.1007/978-3-319-93818-9_10 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP4GU 2023-03-23 WOS:000552329500010 0
C Beck, C; Nordstrom, J; Tang, BS Assoc Comp Machinery Beck, Chris; Nordstrom, Jakob; Tang, Bangsheng Some Trade-off Results for Polynomial Calculus STOC'13: PROCEEDINGS OF THE 2013 ACM SYMPOSIUM ON THEORY OF COMPUTING English Proceedings Paper 45th Annual ACM Symposium on the Theory of Computing (STOC) JUN 01-04, 2013 Palo Alto, CA Assoc Comp Machinery Special Interest Grp Algorithms & Computat Theory,Assoc Comp Machinery Proof complexity; polynomial calculus; PCR; resolution; trade-offs; size; space; degree; pebble games; pebbling formulas; Tseitin formulas RESOLUTION LOWER BOUNDS; SPACE COMPLEXITY; HARD EXAMPLES; SHORT PROOFS We present size-space trade-offs for the polynomial calculus (PC) and polynomial calculus resolution (PCR) proof systems. These are the first true size-space trade-offs in any algebraic proof system, showing that size and space cannot be simultaneously optimized in these models. We achieve this by extending essentially all known size-space trade-offs for resolution to PC and PCR. As such, our results cover space complexity from constant all the way up to exponential and yield mostly superpolynomial or even exponential size blow-ups. Since the upper bounds in our trade-offs hold for resolution, our work shows that there are formulas for which adding algebraic reasoning on top of resolution does not improve the trade-off properties in any significant way. As byproducts of our analysis, we also obtain trade-offs between space and degree in PC and PCR exactly matching analogous results for space versus width in resolution, and strengthen the resolution trade-offs in [Beame, Beck, and Impagliazzo '12] to apply also to k-CNF formulas. Categories and Subject Descriptors: F.2.3[Analysis of Algorithms and Problem Complexity]: Tradeoffs among Complexity Measures; F.1.3[Computation by Abstract Devices]: Complexity Measures and Classes Relations among complexity measures; I.2.3[Artificial Intelligence]: Deduction and Theorem Proving; F.4.1 [Mathematical Logic and Formal Languages]: Mathematical Logic computational logic [Beck, Chris] Princeton Univ, Princeton, NJ 08544 USA; [Nordstrom, Jakob] KTH Royal Inst Technol, Stockholm, Sweden; [Tang, Bangsheng] Tsinghua Univ, Beijing, Peoples R China Princeton University; Royal Institute of Technology; Tsinghua University Beck, C (corresponding author), Princeton Univ, Princeton, NJ 08544 USA. cbeck@princeton.edu; jakobn@kth.se; bangsheng.tang@gmail.com Nordström, Jakob/C-5924-2018 Nordström, Jakob/0000-0002-2700-4285 NSF [CCF-0832797]; Swedish Research Council [621-2010-4797, 621-2012-5645]; European Research Council under the European Union [279611]; National Basic Research Program of China [2011CBA00300, 2011CBA00301]; NSFC [61033001, 61073174, 61250110577]; Danish National Research Foundation; National Science Foundation of China [61061130540] NSF(National Science Foundation (NSF)); Swedish Research Council(Swedish Research Council); European Research Council under the European Union(European Research Council (ERC)); National Basic Research Program of China(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)); Danish National Research Foundation(Danmarks Grundforskningsfond); National Science Foundation of China(National Natural Science Foundation of China (NSFC)) The research of the first author was supported by NSF grant CCF-0832797. The second author was supported by grants 621-2010-4797 and 621-2012-5645 from the Swedish Research Council and by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no 279611. The third author was supported by National Basic Research Program of China grants 2011CBA00300, 2011CBA00301, NSFC grants no. 61033001, 61073174, 61250110577, and he also acknowledges support from the Danish National Research Foundation and the National Science Foundation of China (under the grant 61061130540) for the Sino-Danish Center for the Theory of Interactive Computation, within which part of this work was performed. 42 25 25 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-2029-0 2013.0 813 822 10 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO0QC 2023-03-23 WOS:000492926700083 0
J Bandyopadhyay, I; Barbarino, M; Bhattacharjee, A; Eidietis, N; Huber, A; Isayama, A; Kim, J; Konovalov, S; Lehnen, M; Nardon, E; Pautasso, G; Rea, C; Sozzi, C; Villone, F; Zeng, L Bandyopadhyay, Indranil; Barbarino, Matteo; Bhattacharjee, Amitava; Eidietis, Nicholas; Huber, Alexander; Isayama, Akihiko; Kim, Jayhyun; Konovalov, Sergey; Lehnen, Michael; Nardon, Eric; Pautasso, Gabriella; Rea, Cristina; Sozzi, Carlo; Villone, Fabio; Zeng, Long Summary of the IAEA technical meeting on plasma disruptions and their mitigation NUCLEAR FUSION English Article plasma disruptions; electromagnetic and heat loads; runaway electrons; disruption prediction and avoidance; plasma control; disruption mitigation; shattered pellet injection This report summarizes the contributions presented at the IAEA technical meeting on plasma disruptions and their mitigation, held virtually, 20-23 July 2020. The meeting brought together more than 120 experts from nuclear fusion research sites worldwide to discuss experimental, theoretical and modelling work in the field of plasma disruptions with special emphasis on developing a solid basis for possible disruption mitigation strategies in ITER and next generation fusion devices. The main topics of the meeting were: (i) disruption consequences, including electromagnetic loads, heat loads, and runaway electrons; (ii) disruption prediction and avoidance, including machine learning and physics-based approaches, and control aspects; and (iii) disruption mitigation, including shattered pellet injection, alternative techniques and general aspects of disruption mitigation. [Bandyopadhyay, Indranil] ITER India, Inst Plasma Res, Gandhinagar 382428, Gujarat, India; [Barbarino, Matteo] IAEA, Vienna Int Ctr, POB 100, A-1400 Vienna, Austria; [Bhattacharjee, Amitava] Princeton Univ, Princeton Plasma Phys Lab, POB 451, Princeton, NJ 08543 USA; [Eidietis, Nicholas] Gen Atom, San Diego, CA USA; [Huber, Alexander] Forschungszentrum Julich GmbH, Inst Energie & Klimaforsch Plasmaphys, D-52425 Julich, Germany; [Isayama, Akihiko] Natl Inst Quantum & Radiol Sci & Technol, Naka, Ibaraki, Japan; [Kim, Jayhyun] Korea Inst Fus Energy KFE, 169-148 Gwahak Ro, Daejeon 34133, South Korea; [Konovalov, Sergey] Natl Res Ctr, Kurchatov Inst, Pl Kurchatova 1, Moscow 123182, Russia; [Lehnen, Michael] ITER Org, Route Vinon Sur Verdon,CS 90 046, F-13067 St Paul Les Durance, France; [Nardon, Eric] CEA, IRFM, F-13108 St Paul Les Durance, France; [Pautasso, Gabriella] Max Planck Inst Plasma Phys, D-85748 Garching, Germany; [Rea, Cristina] MIT, Plasma Sci & Fus Ctr, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Sozzi, Carlo] CNR, Ist Sci & Tecnol Plasmi, Milan, Italy; [Villone, Fabio] Univ Napoli Federico II, Consorzio CREATE, DIETI, Naples, Italy; [Zeng, Long] Chinese Acad Sci, Inst Plasma Phys, POB 1126, Hefei 230031, Peoples R China Institute for Plasma Research (IPR); International Atomic Energy Agency; Princeton University; United States Department of Energy (DOE); Princeton Plasma Physics Laboratory; General Atomics & Affiliated Companies; Helmholtz Association; Research Center Julich; National Institutes for Quantum Science & Technology; National Research Centre - Kurchatov Institute; ITER; CEA; Max Planck Society; Massachusetts Institute of Technology (MIT); Consiglio Nazionale delle Ricerche (CNR); Istituto per la Scienza Tecnologia dei Plasmi (ISTP-CNR); University of Naples Federico II; Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS Barbarino, M (corresponding author), IAEA, Vienna Int Ctr, POB 100, A-1400 Vienna, Austria. m.barbarino@iaea.org Eidietis, Nicholas/0000-0003-0167-5053; Nardon, Eric/0000-0003-0427-2292; Huber, Alexander/0000-0002-3558-8129 IAEA; ITER Organization IAEA(International Atomic Energy Agency); ITER Organization The authors wish to thank all participants of the meeting for their contributions and enormous amount of work done that has made this event successful. The support of the IAEA and the ITER Organization is acknowledged. ITER is the Nuclear Facility INB No. 174. This presentation explores physics processes during the plasma operation of the tokamak when disruptions take place; nevertheless, the nuclear operator is not constrained by the results presented here. The views and opinions expressed herein do not necessarily reflect those of the ITER Organization. 8 1 1 1 12 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0029-5515 1741-4326 NUCL FUSION Nucl. Fusion JUL 2021.0 61 7 77001 10.1088/1741-4326/abfe76 0.0 13 Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Physics SL7TQ Green Submitted 2023-03-23 WOS:000657118000001 0
J Mirasbekov, Y; Zhumakhanova, A; Zhantuyakova, A; Sarkytbayev, K; Malashenkov, DV; Baishulakova, A; Dashkova, V; Davidson, TA; Vorobjev, IA; Jeppesen, E; Barteneva, NS Mirasbekov, Yersultan; Zhumakhanova, Adina; Zhantuyakova, Almira; Sarkytbayev, Kuanysh; Malashenkov, Dmitry, V; Baishulakova, Assel; Dashkova, Veronika; Davidson, Thomas A.; Vorobjev, Ivan A.; Jeppesen, Erik; Barteneva, Natasha S. Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom SCIENTIFIC REPORTS English Article FRESH-WATER; EXTRACELLULAR MICROCYSTIN; HIGH-THROUGHPUT; AERUGINOSA; LAKE; PHYTOPLANKTON; CYANOBACTERIUM; IDENTIFICATION; RECOGNITION; POPULATION A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray-Curtis dissimilarity, and Kullback-Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96-100%) but relatively low intrageneric accuracy (67-78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study. [Mirasbekov, Yersultan; Zhumakhanova, Adina; Zhantuyakova, Almira; Sarkytbayev, Kuanysh; Baishulakova, Assel; Dashkova, Veronika; Vorobjev, Ivan A.; Barteneva, Natasha S.] Nazarbayev Univ, Sch Sci & Humanities, Nur Sultan 010000, Kazakhstan; [Sarkytbayev, Kuanysh; Vorobjev, Ivan A.] Natl Lab Astana, Nur Sultan 010000, Kazakhstan; [Malashenkov, Dmitry, V] Lomonosov Moscow State Univ, Dept Gen Ecol & Hydrobiol, Moscow 119991, Russia; [Dashkova, Veronika] Nazarbayev Univ, Sch Engn & Digital Sci, Nur Sultan 010000, Kazakhstan; [Davidson, Thomas A.; Jeppesen, Erik] Aarhus Univ, Dept Biosci, DK-8600 Silkeborg, Denmark; [Jeppesen, Erik] Middle East Tech Univ, Inst Marine Sci, TR-33731 Mersin, Turkey; [Jeppesen, Erik] Middle East Tech Univ, Dept Biol Sci, Limnol Lab, TR-06800 Ankara, Turkey; [Jeppesen, Erik] Middle East Tech Univ, Ctr Ecosyst Res & Implementat, TR-06800 Ankara, Turkey; [Jeppesen, Erik] Sino Danish Ctr Educ & Res, Beijing 100049, Peoples R China; [Barteneva, Natasha S.] Nazarbayev Univ, Environm Res & Efficiency Cluster EREC, Nur Sultan 010000, Kazakhstan; [Zhantuyakova, Almira] Univ British Columbia, Vancouver, BC, Canada Nazarbayev University; Lomonosov Moscow State University; Nazarbayev University; Aarhus University; Middle East Technical University; Middle East Technical University; Middle East Technical University; Nazarbayev University; University of British Columbia Barteneva, NS (corresponding author), Nazarbayev Univ, Sch Sci & Humanities, Nur Sultan 010000, Kazakhstan.;Barteneva, NS (corresponding author), Nazarbayev Univ, Environm Res & Efficiency Cluster EREC, Nur Sultan 010000, Kazakhstan. natalie.barteneva@nu.edu.kz Malashenkov, Dmitry/ABF-8145-2021; Zhumakhanova, Adina/AGX-9395-2022; Mirasbekov, Yersultan/AAE-3723-2022; Jeppesen, Erik/O-2667-2019 Davidson, Thomas/0000-0003-2326-1564 Nazarbayev University grant, ORAU [110119FD4513]; Ministry of Sciences, Kazakhstan [4350/GF4, AP05134153/GF4]; AQUACOSM (Network of Leading European AQUAtic MesoCOSM Facilities Connecting Mountains); Centre for Water Technology at Aarhus University (WATEC); Sino-Danish Centre for Education and Research; TuBTAK BIDEB-Outstanding Researchers Programme [2232] Nazarbayev University grant, ORAU; Ministry of Sciences, Kazakhstan; AQUACOSM (Network of Leading European AQUAtic MesoCOSM Facilities Connecting Mountains); Centre for Water Technology at Aarhus University (WATEC); Sino-Danish Centre for Education and Research; TuBTAK BIDEB-Outstanding Researchers Programme Funding for this work came from Nazarbayev University grant, ORAU #110119FD4513 to N.S.B, and Ministry of Sciences, Kazakhstan grants MES #4350/GF4 and MES #AP05134153/GF4, to I.A.V. and N.S.B. E.J. and N.S.B. were supported by AQUACOSM (Network of Leading European AQUAtic MesoCOSM Facilities Connecting Mountains to Oceans from the Arctic to the Mediterranean) #IFCPHYTO and #SCPCRTNY. D.V.M is working under #CITIS AAAA-A16-116021660054-4 theme. E.J. was funded from Centre for Water Technology at Aarhus University (WATEC), Sino-Danish Centre for Education and Research, and TuBTAK BIDEB 2232-Outstanding Researchers Programme. We are very thankful to the Core Facilities of Nazarbayev University for access to instrumentation, and to technical members of LMWE Project at Aarhus University (Denmark) for access to mesocosm facilities and technical help. 73 4 4 3 14 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep APR 30 2021.0 11 1 9377 10.1038/s41598-021-88661-2 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics SK8VN 33931681.0 Green Accepted, gold 2023-03-23 WOS:000656497500025 0
C Zhou, YX; Habermann, M; Habibie, I; Tewari, A; Theobalt, C; Xu, F IEEE COMP SOC Zhou, Yuxiao; Habermann, Marc; Habibie, Ikhsanul; Tewari, Ayush; Theobalt, Christian; Xu, Feng Monocular Real-time Full Body Capture with Inter-part Correlations 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF MODEL; SHAPE; TRACKING; POSE We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike previous works, our approach is jointly trained on multiple datasets focusing on hand, body or face separately, without requiring data where all the parts are annotated at the same time, which is much more difficult to create at sufficient variety. The possibility of such multi-dataset training enables superior generalization ability. In contrast to earlier monocular full body methods, our approach captures more expressive 3D face geometry and color by estimating the shape, expression, albedo and illumination parameters of a statistical face model. Our method achieves competitive accuracy on public benchmarks, while being significantly faster and providing more complete face reconstructions. [Zhou, Yuxiao; Xu, Feng] Tsinghua Univ, BNRist & Sch Software, Beijing, Peoples R China; [Habermann, Marc; Habibie, Ikhsanul; Tewari, Ayush; Theobalt, Christian] Max Planck Inst Informat, Saarbrucken, Germany; [Habermann, Marc; Habibie, Ikhsanul; Tewari, Ayush; Theobalt, Christian] Saarland Informat Campus, Saarbrucken, Germany Tsinghua University; Max Planck Society Xu, F (corresponding author), Tsinghua Univ, BNRist & Sch Software, Beijing, Peoples R China. National Key R&D Program of China [2018YFA0704000]; NSFC [61822111, 61727808]; Beijing Natural Science Foundation [JQ19015]; ERC [770784] National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); ERC(European Research Council (ERC)European Commission) This work was supported by the National Key R&D Program of China 2018YFA0704000, the NSFC (No.61822111, 61727808), Beijing Natural Science Foundation (JQ19015), and the ERC Consolidator Grant 4DRepLy (770784). Feng Xu is the corresponding author. 81 1 1 2 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 4809 4820 10.1109/CVPR46437.2021.00478 0.0 12 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5QS Green Submitted 2023-03-23 WOS:000739917305002 0
J Zhang, WW; Xiang, HT; Wang, YX; Bi, X; Zhang, YZ; Zhang, PJ; Chen, J; Wang, L; Zheng, YJ Zhang, Wenwen; Xiang, Hongtao; Wang, Yuanxi; Bi, Xiao; Zhang, Yanzhe; Zhang, Pengju; Chen, Jia; Wang, Lei; Zheng, Yuanjin A Signal Response Visualization Gas Recognition Algorithm Based on a Wavelet Transform Coefficient Map-Capsule Network for Artificial Olfaction IEEE SENSORS JOURNAL English Article Artificial olfaction; capsule network; wavelet coefficient; gas sensor array E-NOSE; IDENTIFICATION; NANOWIRES; ARRAY Conventional gas recognition algorithms used in artificial olfaction rely on manually selected steady-state features of the sensor array signal and ignore the dynamic process signal response features of the sensor array; hence, a large amount of dynamic feature information, which can improve the gas recognition accuracy, is lost. To address this problem, a wavelet transform coefficient map-capsule network (WTCM-CapsNet) model for gas recognition is presented in this paper. It directly uses a raw signal from a sensor array without any signal-processing steps and consists of a wavelet coefficient map and a capsule network (CapsNet) model with two submodels. The WTCM-CapsNet model directly performs a five-scale wavelet transform on the sensor array signal and extracts 4,096 wavelet coefficients to forma 2D 64*64 wavelet coefficient image as the CapsNet input. Compared with conventional gas recognition algorithms, such as the convolutional neural network (CNN) with 99.3% gas recognition accuracy, the K-nearest neighbor (KNN) with 95.74% gas recognition accuracy, support vector machine (SVM) with 96.45% gas recognition accuracy, random forest with 95.74% gas recognition accuracy, naive Bayes with 92.91% gas recognition accuracy and BP neural network (BPNN) algorithms with 97.87% gas recognition accuracy, which extract the steady-state values of the time-domain signal and the wavelet coefficient maps of the whole dynamic response signal in the time-frequency domain as input features, CapsNet uses an iterative routing-by-agreement mechanism that can effectively improve the gas recognition accuracy to approximately 100%, as demonstrated by the dynamic response signal of the gas sensor array collected by our fabricated artificial olfactory system. [Zhang, Wenwen; Zhang, Yanzhe; Zhang, Pengju] Univ Shanghai Sci & Technol, Coll Sci, Inst Deep Sea Adv Equipment Syst, Shanghai, Peoples R China; [Xiang, Hongtao] Shanxi Univ, Coll Automat & Software Engn, Taiyuan, Shanxi, Peoples R China; [Wang, Yuanxi; Wang, Lei] Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China; [Bi, Xiao; Chen, Jia] Tech Univ Munich, Dept Elect & Comp Engn, D-80333 Munich, Germany; [Zheng, Yuanjin] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore University of Shanghai for Science & Technology; Shanxi University; Tongji University; Technical University of Munich; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Zheng, YJ (corresponding author), Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore. wen-wenzhang@usst.edu.cn; 202023603039@email.sxu.edu.cn; 2010146@tongji.edu.cn; xiao.bi@tum.de; zhangyz@usst.edu.cn; zhangpengju@usstedu.cn; jia.chen@tum.de; leiwang@tongji.edu.cn; yjzheng@ntu.edu.sg Bi, Xiao/HGD-5274-2022; Zheng, Yuanjin/A-5133-2011 Bi, Xiao/0000-0002-2733-0451; Chen, Jia/0000-0002-6350-6610 National Key Research and Development Program of China [2018YFE0105000]; Program of China Scholarship Council [201906260029] National Key Research and Development Program of China; Program of China Scholarship Council(China Scholarship Council) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFE0105000 and in part by the Program of China Scholarship Council under Grant 201906260029. 38 1 1 18 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. AUG 1 2022.0 22 15 14717 14726 10.1109/JSEN.2022.3184963 0.0 10 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics 3N0FC 2023-03-23 WOS:000835829900009 0
J Nakoneczny, S; Bilicki, M; Solarz, A; Pollo, A; Maddox, N; Spiniello, C; Brescia, M; Napolitano, NR Nakoneczny, S.; Bilicki, M.; Solarz, A.; Pollo, A.; Maddox, N.; Spiniello, C.; Brescia, M.; Napolitano, N. R. Catalog of quasars from the Kilo-Degree Survey Data Release 3 ASTRONOMY & ASTROPHYSICS English Article catalogs; surveys; quasars: general; large-scale structure of Universe; methods: data analysis; methods: observational DIGITAL-SKY-SURVEY; EFFICIENT PHOTOMETRIC SELECTION; SPECTROSCOPIC TARGET SELECTION; HALO MASSES; WISE We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r < 22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190 000 quasar candidates. Accuracy of 97% (percentage of correctly classified objects), purity of 91% (percentage of true quasars within the objects classified as such), and completeness of 87% (detection ratio of all true quasars), as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from the Wide-field Infrared Survey Explorer (WISE). An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO) > 0.8 is optimal for purity, whereas p(QSO) > 0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey. [Nakoneczny, S.; Solarz, A.; Pollo, A.] Natl Ctr Nucl Res, Astrophys Div, Ul Hoza 69, PL-00681 Warsaw, Poland; [Bilicki, M.] Leiden Univ, Leiden Observ, Pob 9513, NL-2300 RA Leiden, Netherlands; [Bilicki, M.] Polish Acad Sci, Ctr Theoret Phys, Al Lotnikow 32-46, PL-02668 Warsaw, Poland; [Pollo, A.] Jagiellonian Univ, Astron Observ, PL-31007 Krakow, Poland; [Maddox, N.] Ludwig Maximilians Univ Munchen, Fac Phys, Scheinerstr 1, D-81679 Munich, Germany; [Spiniello, C.; Brescia, M.; Napolitano, N. R.] INAF Osservatorio Astron Capodimonte, Salita Moiariello 16, I-80131 Naples, Italy; [Napolitano, N. R.] Sun Yat Sen Univ, Sch Phys & Astron, Zhuhai Campus, Guangzhou 519082, Guangdong, Peoples R China National Centre for Nuclear Research; Leiden University; Leiden University - Excl LUMC; Polish Academy of Sciences; Center for Theoretical Physics - Polish Academy of Sciences; Jagiellonian University; University of Munich; Istituto Nazionale Astrofisica (INAF); Sun Yat Sen University Nakoneczny, S (corresponding author), Natl Ctr Nucl Res, Astrophys Div, Ul Hoza 69, PL-00681 Warsaw, Poland. szymon.nakoneczny@ncbj.gov.pl Spiniello, Chiara/AGV-9974-2022; Solarz, Aleksandra/AAZ-1271-2020; Spiniello, Chiara/AGT-6882-2022; Napolitano, Nicola R./ADU-3813-2022; Brescia, Massimo/J-2774-2019 Solarz, Aleksandra/0000-0001-7432-6562; Spiniello, Chiara/0000-0002-3909-6359; Napolitano, Nicola R./0000-0003-0911-8884; Brescia, Massimo/0000-0001-9506-5680; Pollo, Agnieszka/0000-0003-3358-0665; Bilicki, Maciej/0000-0002-3910-5809 ESO Telescopes at the La Silla Paranal Observatory under programme [177.A-3016, 177.A-3017, 177.A-3018]; NOVA; NWO-M grants; Department of Physics & Astronomy of the University of Padova; Alfred P. Sloan Foundation; National Science Foundation; U.S. Department of Energy Office of Science; University of Arizona; Brazilian Participation Group; Brookhaven National Laboratory; Carnegie Mellon University, University of Florida; French Participation Group; German Participation Group; Harvard University; Instituto de Astrofisica de Canarias; Michigan State/Notre Dame/JINA Participation Group; Johns Hopkins University; Lawrence Berkeley National Laboratory; Max Planck Institute for Astrophysics; Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University; Pennsylvania State University, University of Portsmouth; Princeton University; Spanish Participation Group, University of Tokyo, University of Utah; Yale University; Polish Ministry of Science and Higher Education, MNiSW [DIR/WK/2018/12]; MNiSW [212727/E-78/M/2018]; Netherlands Organization for Scientific Research, NWO [614.001.451]; National Science Centre, Poland [2017/26/A/ST9/00756]; European Union [721463]; MIUR Premiale; 100 Top Talent Program of the Sun Yat-sen University, Guandong Province ESO Telescopes at the La Silla Paranal Observatory under programme; NOVA; NWO-M grants; Department of Physics & Astronomy of the University of Padova; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); National Science Foundation(National Science Foundation (NSF)); U.S. Department of Energy Office of Science(United States Department of Energy (DOE)); University of Arizona; Brazilian Participation Group; Brookhaven National Laboratory(United States Department of Energy (DOE)); Carnegie Mellon University, University of Florida; French Participation Group; German Participation Group; Harvard University; Instituto de Astrofisica de Canarias; Michigan State/Notre Dame/JINA Participation Group; Johns Hopkins University(Johns Hopkins University); Lawrence Berkeley National Laboratory(United States Department of Energy (DOE)); Max Planck Institute for Astrophysics; Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University; Pennsylvania State University, University of Portsmouth; Princeton University(Princeton University); Spanish Participation Group, University of Tokyo, University of Utah; Yale University; Polish Ministry of Science and Higher Education, MNiSW(Ministry of Science and Higher Education, Poland); MNiSW(Ministry of Science and Higher Education, Poland); Netherlands Organization for Scientific Research, NWO(Netherlands Organization for Scientific Research (NWO)); National Science Centre, Poland(National Science Centre, Poland); European Union(European Commission); MIUR Premiale(Ministry of Education, Universities and Research (MIUR)); 100 Top Talent Program of the Sun Yat-sen University, Guandong Province Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017 and 177.A-3018, and on data products produced by Target/OmegaCEN, INAF-OACN, INAF-OAPD and the KiDS production team, on behalf of the KiDS consortium. OmegaCEN and the KiDS production team acknowledge support by NOVA and NWO-M grants. Members of INAF-OAPD and INAF-OACN also acknowledge the support from the Department of Physics & Astronomy of the University of Padova, and of the Department of Physics of Univ. Federico II (Naples). Funding for SDSS-III was provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III website is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. SN, MBi, AS & AP are grateful for support from Polish Ministry of Science and Higher Education, MNiSW, through a grant DIR/WK/2018/12. SN was supported by the MNiSW through grant number 212727/E-78/M/2018. MBi was supported by the Netherlands Organization for Scientific Research, NWO, through grant number 614.001.451. AP was supported by National Science Centre, Poland, through grant No. 2017/26/A/ST9/00756. CS has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie actions grant agreement No 664931. MBr acknowledges the INAF PRIN-SKA 2017 program 1.05.01.88.04 and the funding from MIUR Premiale 2016: MITIC. NRN acknowledges support from the European Union's Horizon 2020 Sundial Innovative Training Network, grant n.721463, and from the 100 Top Talent Program of the Sun Yat-sen University, Guandong Province. The following software was used: TOPCAT (Taylor et al. 2005), Python programming language (Python Software Foundation, https://www.python.org/), Jupyter notebooks (Kluyver et al. 2016), Xtreme Gradient Boosting (XGB, Chen & Guestrin 2016), scikit-learn (Pedregosa et al. 2011), Keras (Chollet 2015) and Tensorflow (Abadi et al. 2015). 84 24 24 0 1 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. APR 1 2019.0 624 A13 10.1051/0004-6361/201834794 0.0 15 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics HR0VQ Green Submitted, Bronze 2023-03-23 WOS:000462847400004 0
J Zhu, DH; Deng, J; Wang, JQ; Wang, S; Zhang, HY; Andert, J; Li, LG Zhu, Denghao; Deng, Jun; Wang, Jinqiu; Wang, Shuo; Zhang, Hongyu; Andert, Jakob; Li, Liguang Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System ENERGIES English Article Ion current; cylinder pressure; cooperative combustion diagnosis and control; field-programmable gate array; artificial neural network CYLINDER PRESSURE; COLD START; IGNITION; ENGINE; TEMPERATURE; MODE; RCCI The application of advanced technologies for engine efficiency improvement and emissions reduction also increase the occurrence possibility of abnormal combustions such as incomplete combustion, misfire, knock or pre-ignition. Novel promising combustion modes, which are basically dominated by chemical reaction kinetics show a major difficulty in combustion control. The challenge in precise combustion control is hard to overcome by the traditional engine map-based control method because it cannot monitor the combustion state of each cycle, hence, real-time cycle-resolved in-cylinder combustion diagnosis and control are required. In the past, cylinder pressure and ion current sensors, as the two most commonly used sensors for in-cylinder combustion diagnosis and control, have enjoyed a seemingly competitive relationship, so all related researches only use one of the sensors. However, these two sensors have their own unique features. In this study, the idea is to combine the information obtained from both sensors. At first, two kinds of ion current detection system are comprehensively introduced and compared at the hardware level and signal level. The most promising variant (the DC-Power ion current detection system) is selected for the subsequent experiments. Then, the concept of ion current/cylinder pressure cooperative combustion diagnosis and control system is illustrated and implemented on the engine prototyping control unit. One application case of employing this system for homogenous charge compression ignition abnormal combustion control and its stability improvement is introduced. The results show that a combination of ion current and cylinder pressure signals can provide richer and also necessary information for combustion control. Finally, ion current and cylinder pressure signals are employed as inputs of artificial neural network (ANN) models for combustion prediction. The results show that the combustion prediction performance is better when the inputs are a combination of both signals, instead of using only one of them. This offline analysis proves the feasibility of using an ANN-based model whose inputs are a combination of ion current and pressure signals for better prediction accuracy. [Zhu, Denghao; Deng, Jun; Wang, Jinqiu; Wang, Shuo; Li, Liguang] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China; [Zhang, Hongyu; Li, Liguang] Tongji Univ, Chines Deutsch Hochschulkolleg, Shanghai 201804, Peoples R China; [Andert, Jakob] Rhein Westfal TH Aachen, Mechatron Syst Combust Engines, D-52074 Aachen, Germany Tongji University; Tongji University; RWTH Aachen University Li, LG (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China.;Li, LG (corresponding author), Tongji Univ, Chines Deutsch Hochschulkolleg, Shanghai 201804, Peoples R China. tj_zdh@outlook.com; eagledeng@tongji.edu.cn; wangjinqiu@tongji.edu.cn; 1831669@tongji.edu.cn; zhanghongyu324@outlook.com; andert@vka.rwth-aachen.de; liguang@tongji.edu.cn Andert, Jakob Lukas/W-4495-2017 Andert, Jakob Lukas/0000-0002-6754-1907; Wang, Jinqiu/0000-0002-5754-6798; Zhu, Denghao/0000-0002-5055-4496 China Natural Science Foundation [52076153, 51761135105]; German Research Association (the joint Sino-German research project Ion-Current Sensor based Closed-Loop Control of Lean Gasoline Combustion with High Compression Ratio) [392430670]; KSPG Professorship Chair of CDHK in Tongji University China Natural Science Foundation(National Natural Science Foundation of China (NSFC)); German Research Association (the joint Sino-German research project Ion-Current Sensor based Closed-Loop Control of Lean Gasoline Combustion with High Compression Ratio); KSPG Professorship Chair of CDHK in Tongji University This research was funded by China Natural Science Foundation (No.52076153 & No.51761135105), German Research Association (the joint Sino-German research project Ion-Current Sensor based Closed-Loop Control of Lean Gasoline Combustion with High Compression Ratio, No.392430670), and KSPG Professorship Chair of CDHK in Tongji University. 55 2 2 2 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies NOV 2020.0 13 21 5656 10.3390/en13215656 0.0 21 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels OQ8SK Green Published, gold 2023-03-23 WOS:000589045700001 0
C Li, X; Ding, DH; Kao, B; Sun, YZ; Mamoulis, N IEEE Li, Xiang; Ding, Danhao; Kao, Ben; Sun, Yizhou; Mamoulis, Nikos Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021) IEEE International Conference on Data Engineering English Proceedings Paper 37th IEEE International Conference on Data Engineering (IEEE ICDE) APR 19-22, 2021 ELECTR NETWORK IEEE heterogeneous information networks; classification; graph neural networks A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types. We study the problem of classifying objects in HINs. Most existing methods perform poorly when given scarce labeled objects as training sets, and methods that improve classification accuracy under such scenarios are often computationally expensive. To address these problems, we propose ConCH, a graph neural network model. ConCH formulates the classification problem as a multi-task learning problem that combines semi-supervised learning with self-supervised learning to learn from both labeled and unlabeled data. ConCH employs meta-paths, which are sequences of object types that capture semantic relationships between objects. ConCH co-derives object embeddings and context embeddings via graph convolution. It also uses the attention mechanism to fuse such embeddings. We conduct extensive experiments to evaluate the performance of ConCH against other 15 classification methods. Our results show that ConCH is an effective and efficient method for HIN classification. [Li, Xiang] East China Normal Univ, Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China; [Ding, Danhao; Kao, Ben] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Sun, Yizhou] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA; [Mamoulis, Nikos] Univ Ioannina, Dept Comp Sci & Engn, Ioannina, Greece East China Normal University; University of Hong Kong; University of California System; University of California Los Angeles; University of Ioannina Li, X (corresponding author), East China Normal Univ, Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China. xiangli@dase.ecnu.edu.cn; dhding2@cs.hku.hk; kao@cs.hku.hk; yzsun@cs.ucla.edu; nikos@cs.uoi.gr Hong Kong Research Grants Council [17253616]; Greek national funds [T1EDK-04810, T2EDK-02848]; NSF [1937599, III-1705169]; DARPA [HR00112090027]; Amazon Research Award; Okawa Foundation Grant; NSF CAREER Award [1741634] Hong Kong Research Grants Council(Hong Kong Research Grants Council); Greek national funds(Greek Ministry of Development-GSRT); NSF(National Science Foundation (NSF)); DARPA(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Amazon Research Award; Okawa Foundation Grant; NSF CAREER Award(National Science Foundation (NSF)NSF - Office of the Director (OD)) This work is supported by Hong Kong Research Grants Council grant 17253616, and partially funded by Greek national funds, under the call Research-Create-Innovate (project codes: T1EDK-04810 and T2EDK-02848), NSF III-1705169, NSF CAREER Award 1741634, NSF #1937599, DARPA HR00112090027, Okawa Foundation Grant, and Amazon Research Award. 63 6 6 1 5 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1084-4627 978-1-7281-9184-3 PROC INT CONF DATA 2021.0 912 923 10.1109/ICDE51399.2021.00084 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS1DL Green Submitted 2023-03-23 WOS:000687830800077 0
J He, LN; Zhou, HR; Wen, YL; He, XF He, Lina; Zhou, Hairui; Wen, Yuanlan; He, Xiufeng Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning SENSORS English Article BeiDou satellite navigation system (BDS); satellite clock prediction; generalized regression neural network; precise point positioning ORBIT; IGS; PRODUCTS Although there are already several real-time precise positioning service providers, unfortunately, not all users can use the correction information due to either cost of the service and limitation of their equipment or out of the service coverage. An alternative way is to enhance the accuracy of the predicted satellite clocks for precise real-time positioning. Based on the study of existing prediction models, an improved model combing the spectrum analysis (SA) and the generalized regression neural network (GRNN) model is proposed especially for BeiDou satellite navigation system (BDS)-2 satellites. The periodic terms and GRNN-related parameters including length and interval of sample data, as well as a smooth factor, are optimized satellite by satellite to consider satellite-specific characteristics for all the fourteen BDS-2 satellites. The improved model is validated by comparing the predicted clocks of existing models and the improved model with precisely estimated ones. The bias of the predicted clock is within +/- 0.5 ns over three hours and better than that of the other models and can be used for several real-time precise applications. The clock prediction is further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic mode for eight IGS (International GNSS Service) MGEX (Multi-GNSS Experiment) stations in the Asia-Pacific region. In the static PPP, the improved model is validated to be effective, and position accuracies of some IGS MGEX stations achieve more than 30.0% improvements on average for each component, which enables us to obtain sub-decimeter positioning. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the position accuracy. The convergence time can be shortened from 1-2 h to 0.5-1 h, while the position accuracy is enhanced by 15.4%, 21.6% and 19.3% on average in east, north and up component, respectively. [He, Lina; Wen, Yuanlan; He, Xiufeng] Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China; [He, Lina] GFZ, German Res Ctr Geosci, D-14473 Potsdam, Germany; [Zhou, Hairui] China Elect Technol Grp Corp, Inst 28, Nanjing 210007, Jiangsu, Peoples R China Hohai University; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences; China Electronics Technology Group He, LN (corresponding author), Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China.;He, LN (corresponding author), GFZ, German Res Ctr Geosci, D-14473 Potsdam, Germany. hlnyh@hhu.edu.cn; zhou_hairui@126.com; wwwgwyl@126.com; xfhe@hhu.edu.cn National Natural Science Foundation of China [41404025, 41830110]; Fundamental Research Funds for the Central Universities of China [2017B16314, 2017B19914, 2018B07414] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) This work is funded by the Program of National Natural Science Foundation of China (No. 41404025, No. 41830110), and it is also supported by the Fundamental Research Funds for the Central Universities of China (No. 2017B16314, No. 2017B19914, No. 2018B07414). 32 8 8 0 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUN 2 2019.0 19 12 2762 10.3390/s19122762 0.0 19 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation IG4GQ 31248220.0 Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000473762500114 0
J Lu, JZ; Chen, DJ; Wang, GX; Kiritsis, D; Torngren, M Lu, Jinzhi; Chen, Dejiu; Wang, Guoxin; Kiritsis, Dimitris; Torngren, Martin Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Artificial intelligence; Decision making; Modeling; Tools; Data models; Unified modeling language; Stakeholders; Automated parameter value selection; cyber-physical systems (CPSs); model-based systems engineering (MBSE) IOT Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain. [Lu, Jinzhi; Kiritsis, Dimitris] Ecole Polytech Fed Lausanne, SCI Dept, CH-1015 Lausanne, Switzerland; [Chen, Dejiu; Torngren, Martin] KTH Royal Inst Technol, Dept Machine Design, S-10044 Stockholm, Sweden; [Wang, Guoxin] Beijing Inst Technol, Dept Mech Engn, Beijing 100081, Peoples R China Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Royal Institute of Technology; Beijing Institute of Technology Wang, GX (corresponding author), Beijing Inst Technol, Dept Mech Engn, Beijing 100081, Peoples R China. wangguoxin@bit.edu.cn Chen, DeJiu/AAO-6363-2021; Lu, Jinzhi/GRJ-9687-2022 Chen, DeJiu/0000-0001-7048-0108; Lu, Jinzhi/0000-0001-5044-2921; Torngren, Martin/0000-0002-4300-885X CN National Key Research and Development Plan [2020YFB1708100]; EU H2020 Project [825030]; QU4LITY Digital Reality in Zero Defect Manufacturing; InnoSwiss IMPULSE Project on Digital Twins CN National Key Research and Development Plan; EU H2020 Project; QU4LITY Digital Reality in Zero Defect Manufacturing; InnoSwiss IMPULSE Project on Digital Twins This work was supported in part by the CN National Key Research and Development Plan under Grant 2020YFB1708100; in part by EU H2020 Project under Grant 825030; in part by QU4LITY Digital Reality in Zero Defect Manufacturing; and in part by InnoSwiss IMPULSE Project on Digital Twins. 44 8 8 6 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. APR 2022.0 52 4 2333 2347 10.1109/TSMC.2020.3048821 0.0 JAN 2021 15 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science 2O3UE hybrid 2023-03-23 WOS:000732109000001 0
J Xiang, L; Zeng, XQ; Wu, SL; Liu, YJ; Yuan, BH Xiang, Lin; Zeng, Xiaoqin; Wu, Shengli; Liu, Yanjun; Yuan, Baohua Computation of CNN's Sensitivity to Input Perturbation NEURAL PROCESSING LETTERS English Article Convolutional neural network; Sensitivity; Additive noise; Input perturbation MULTILAYER PERCEPTRON; NEURAL-NETWORKS; ROBUSTNESS Although Convolutional Neural Networks (CNNs) are considered as being approximately invariant to nuisance perturbations such as image transformation, shift, scaling, and other small deformations, some existing studies show that intense noises can cause noticeable variation to CNNs' outputs. This paper focuses on exploring a method of measuring sensitivity by observing corresponding output variation to input perturbation on CNNs. The sensitivity is statistically defined in a bottom-up way from neuron to layer, and finally to the entire CNN network. An iterative algorithm is proposed for approximating the defined sensitivity. On the basic architecture of CNNs, the theoretically computed sensitivity is verified on the MNIST database with four types of commonly used noise distributions: Gaussian, Uniform, Salt and Pepper, and Rayleigh. Experimental results show the theoretical sensitivity is on the one hand in agreement with the actual output variation what on the maps, layers or entire networks are, and on the other hand an applicable quantitative measure for robust network selection. [Xiang, Lin; Zeng, Xiaoqin; Liu, Yanjun; Yuan, Baohua] Hohai Univ, Inst Intelligence Sci & Technol, Nanjing, Peoples R China; [Xiang, Lin] Huaiyin Inst Technol, Huaian, Peoples R China; [Wu, Shengli] Ulster Univ, Sch Comp, Belfast, Antrim, North Ireland Hohai University; Huaiyin Institute of Technology; Ulster University Zeng, XQ (corresponding author), Hohai Univ, Inst Intelligence Sci & Technol, Nanjing, Peoples R China. xzeng@hhu.edu.cn liu, yan/HCI-5542-2022; liu, yan/HGV-1365-2022; 刘, 严君/GZL-5764-2022 Fundamental Research Funds for the Central Universities [2016B44414, 2018B678X14]; Postgraduate Research and Practice Innovation Program of Jiangsu Province of China [KYCX18_0553]; Science and Technology Project of Huai'an City [HAG201602, HAS201607] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Postgraduate Research and Practice Innovation Program of Jiangsu Province of China; Science and Technology Project of Huai'an City This work is supported by the Fundamental Research Funds for the Central Universities under Grant Nos. 2016B44414 and 2018B678X14, Postgraduate Research and Practice Innovation Program of Jiangsu Province of China under Grant No. KYCX18_0553, and the Science and Technology Project of Huai'an City under Grant No. HAG201602 and HAS201607. 52 2 2 0 7 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1370-4621 1573-773X NEURAL PROCESS LETT Neural Process. Lett. FEB 2021.0 53 1 535 560 10.1007/s11063-020-10420-7 0.0 JAN 2021 26 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science QS7XF Green Submitted 2023-03-23 WOS:000604526500002 0
J Duong, QM; Trinh, QK; Nguyen, VT; Dao, DH; Luong, DM; Hoang, VP; Lin, LY; Deepu, J Duong, Quang-Manh; Trinh, Quang-Kien; Nguyen, Van-Tinh; Dao, Dinh-Ha; Luong, Duy-Manh; Hoang, Van-Phuc; Lin, Longyang; Deepu, John A low-power charge-based integrate-and-fire circuit for binarized-spiking neural network INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS English Article; Early Access binary spiking neural networks; in-memory computing; neuromorphic computing; STT-MRAM This paper presents a charge-based integrate-and-fire (IF) circuit for in-memory binary spiking neural networks (BSNNs). The proposed IF circuit can mimic both addition and subtraction operations that permit better incorporation with in-memory XNOR-based synapses to implement the BSNN processing core. To evaluate the proposed design, we have developed a framework that incorporates the circuit's imperfections effects into the system-level simulation. The array circuits use 2T-2J Spin-Transfer-Torque Magnetoresistive RAM (STT-MRAM) based on a 65-nm commercial CMOS and a fitted magnetic tunnel junction (MTJ). The system model has been described in Pytorch to best fit the extracted parameters from circuit levels, including the cover of device nonidealities and process variations. The simulation results show that the proposed design can achieve a performance of 5.10 fJ/synapse and reaches 82.01% classification accuracy for CIFAR-10 under process variation. [Duong, Quang-Manh; Trinh, Quang-Kien; Nguyen, Van-Tinh; Dao, Dinh-Ha; Luong, Duy-Manh] Le Quy Don Tech Univ, Fac Radioelect Engn, Hanoi, Vietnam; [Hoang, Van-Phuc] Le Quy Don Tech Univ, Inst Syst Integrat, Hanoi, Vietnam; [Lin, Longyang] Southern Univ Sci & Technol, Sch Microelect, Shenzhen, Peoples R China; [Deepu, John] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin, Ireland Le Quy Don Technical University; Le Quy Don Technical University; Southern University of Science & Technology; University College Dublin Trinh, QK (corresponding author), Le Quy Don Tech Univ, Fac Radioelect Engn, Hanoi, Vietnam. kien.trinh@lqdtu.edu.vn; phuchv@lqdtu.edu.vn Hoang, Van-Phuc/0000-0003-0944-8701 Vietnam National Foundation for Science and Technology Development (NAFOSTED) [102.01-2018.310]; Microelectronic Circuit Centre Ireland [MCCI-2020-07]; H2020 FET ChistEra Vietnam National Foundation for Science and Technology Development (NAFOSTED)(National Foundation for Science & Technology Development (NAFOSTED)); Microelectronic Circuit Centre Ireland; H2020 FET ChistEra Vietnam National Foundation for Science and Technology Development (NAFOSTED), Grant/Award Number:102.01-2018.310; Microelectronic Circuit Centre Ireland, Grant/Award Number: MCCI-2020-07; H2020 FET ChistEra 18 0 0 0 0 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0098-9886 1097-007X INT J CIRC THEOR APP Int. J. Circuit Theory Appl. 10.1002/cta.3573 0.0 FEB 2023 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 9J5EB 2023-03-23 WOS:000940208600001 0
J Deehan, EC; Zhang, ZX; Riva, A; Armet, AM; Perez-Munoz, ME; Nguyen, NK; Krysa, JA; Seethaler, B; Zhao, YY; Cole, J; Li, FY; Hausmann, B; Spittler, A; Nazare, JA; Delzenne, NM; Curtis, JM; Wismer, WV; Proctor, SD; Bakal, JA; Bischoff, SC; Knights, D; Field, CJ; Berry, D; Prado, CM; Walter, J Deehan, Edward C.; Zhang, Zhengxiao; Riva, Alessandra; Armet, Anissa M.; Perez-Munoz, Maria Elisa; Nguyen, Nguyen K.; Krysa, Jacqueline A.; Seethaler, Benjamin; Zhao, Yuan-Yuan; Cole, Janis; Li, Fuyong; Hausmann, Bela; Spittler, Andreas; Nazare, Julie-Anne; Delzenne, Nathalie M.; Curtis, Jonathan M.; Wismer, Wendy, V; Proctor, Spencer D.; Bakal, Jeffrey A.; Bischoff, Stephan C.; Knights, Dan; Field, Catherine J.; Berry, David; Prado, Carla M.; Walter, Jens Elucidating the role of the gut microbiota in the physiological effects of dietary fiber MICROBIOME English Article Dietary fiber; Adults; Obesity; Satiety; Insulin resistance; Inflammation; Gut microbiota BETA-CELL FUNCTION; CHAIN FATTY-ACIDS; WHOLE GRAIN RYE; INSULIN SENSITIVITY; METABOLIC SYNDROME; BILE-ACIDS; IN-VITRO; CONCENTRATED ARABINOXYLAN; CARDIOVASCULAR-DISEASE; CARBOHYDRATE QUALITY Background: Dietary fiber is an integral part of a healthy diet, but questions remain about the mechanisms that underlie effects and the causal contributions of the gut microbiota. Here, we performed a 6-week exploratory trial in adults with excess weight (BMI: 25-35 kg/m(2)) to compare the effects of a high-dose (females: 25 g/day; males: 35 g/day) supplement of fermentable corn bran arabinoxylan (AX; n = 15) with that of microbiota-non-accessible microcrystalline cellulose (MCC; n = 16). Obesity-related surrogate endpoints and biomarkers of host-microbiome interactions implicated in the pathophysiology of obesity (trimethylamine N-oxide, gut hormones, cytokines, and measures of intestinal barrier integrity) were assessed. We then determined whether clinical outcomes could be predicted by fecal microbiota features or mechanistic biomarkers. Results: AX enhanced satiety after a meal and decreased homeostatic model assessment of insulin resistance (HOMA-IR), while MCC reduced tumor necrosis factor-a and fecal calprotectin. Machine learning models determined that effects on satiety could be predicted by fecal bacterial taxa that utilized AX, as identified by bioorthogonal noncanonical amino acid tagging. Reductions in HOMA-IR and calprotectin were associated with shifts in fecal bile acids, but correlations were negative, suggesting that the benefits of fiber may not be mediated by their effects on bile acid pools. Biomarkers of host-microbiome interactions often linked to bacterial metabolites derived from fiber fermentation (short-chain fatty acids) were not affected by AX supplementation when compared to non-accessible MCC. Conclusion: This study demonstrates the efficacy of purified dietary fibers when used as supplements and suggests that satietogenic effects of AX may be linked to bacterial taxa that ferment the fiber or utilize breakdown products. Other effects are likely microbiome independent. The findings provide a basis for fiber-type specific therapeutic applications and their personalization. [Deehan, Edward C.; Armet, Anissa M.; Perez-Munoz, Maria Elisa; Nguyen, Nguyen K.; Krysa, Jacqueline A.; Zhao, Yuan-Yuan; Cole, Janis; Li, Fuyong; Curtis, Jonathan M.; Wismer, Wendy, V; Proctor, Spencer D.; Field, Catherine J.; Prado, Carla M.; Walter, Jens] Univ Alberta, Dept Agr Food & Nutr Sci, Edmonton, AB, Canada; [Zhang, Zhengxiao] Univ Alberta, Dept Med, Edmonton, AB, Canada; [Zhang, Zhengxiao] Jimei Univ, Coll Food & Biol Engn, Xiamen, Fujian, Peoples R China; [Riva, Alessandra; Berry, David] Univ Vienna, Ctr Microbiol & Environm Syst Sci, Dept Microbiol & Ecosyst Sci, Div Microbial Ecol, Vienna, Austria; [Krysa, Jacqueline A.; Proctor, Spencer D.] Univ Alberta, Metab & Cardiovasc Dis Lab, Edmonton, AB, Canada; [Seethaler, Benjamin; Bischoff, Stephan C.] Univ Hohenheim, Inst Nutr Med, Stuttgart, Germany; [Hausmann, Bela; Berry, David] Joint Microbiome Facil Med Univ Vienna & Univ Vie, Vienna, Austria; [Hausmann, Bela] Med Univ Vienna, Dept Lab Med, Vienna, Austria; [Spittler, Andreas] Med Univ Vienna, Core Facil Flow Cytometry, Vienna, Austria; [Spittler, Andreas] Med Univ Vienna, Dept Surg, Res Lab, Vienna, Austria; [Nazare, Julie-Anne] Univ Claude Bernard Lyon 1, Univ Lyon, Hosp Civils Lyon,INRA,INSA Lyon,F CRIN FORCE Netw, Ctr Rech Nutr Humaine Rhone Alpes,CarMeN Lab,INSE, Pierre Benite, France; [Delzenne, Nathalie M.] Catholic Univ Louvain, Louvain Drug Res Inst, Metab & Nutr Res Grp, Brussels, Belgium; [Bakal, Jeffrey A.] Univ Alberta, Div Gen Internal Med, Patient Hlth Outcomes Res & Clin Effectiveness Un, Edmonton, AB, Canada; [Knights, Dan] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN USA; [Knights, Dan] Univ Minnesota, BioTechnol Inst, St Paul, MN 55108 USA; [Walter, Jens] Univ Alberta, Dept Biol Sci, Edmonton, AB, Canada; [Walter, Jens] Natl Univ Ireland, Univ Coll Cork, APC Microbiome Ireland, Sch Microbiol, Cork, Ireland; [Walter, Jens] Natl Univ Ireland, Univ Coll Cork, Dept Med, Cork, Ireland University of Alberta; University of Alberta; Jimei University; University of Vienna; University of Alberta; University Hohenheim; Medical University of Vienna; Medical University of Vienna; Medical University of Vienna; CHU Lyon; INRAE; Institut National des Sciences Appliquees de Lyon - INSA Lyon; UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Universite Catholique Louvain; University of Alberta; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities; University of Alberta; University College Cork; University College Cork Deehan, EC; Walter, J (corresponding author), Univ Alberta, Dept Agr Food & Nutr Sci, Edmonton, AB, Canada.;Walter, J (corresponding author), Univ Alberta, Dept Biol Sci, Edmonton, AB, Canada.;Walter, J (corresponding author), Natl Univ Ireland, Univ Coll Cork, APC Microbiome Ireland, Sch Microbiol, Cork, Ireland.;Walter, J (corresponding author), Natl Univ Ireland, Univ Coll Cork, Dept Med, Cork, Ireland. deehan@ualberta.ca; jenswalter@ucc.ie Berry, David/C-8455-2011; Carmen, Team1/Y-7382-2019; Nguyen, Khoi Nguyen/H-5470-2019; ZHAO, YUAN/HCI-5831-2022; Hausmann, Bela/AAE-9558-2020 Berry, David/0000-0002-8997-608X; Carmen, Team1/0000-0003-4234-1746; Nguyen, Khoi Nguyen/0000-0001-8919-3977; Hausmann, Bela/0000-0002-0846-1202; Li, Fuyong/0000-0002-3410-8410; Proctor, Spencer/0000-0002-7597-5262; Deehan, Edward/0000-0001-7697-1418; Riva, Alessandra/0000-0002-5724-7616; Armet, Anissa/0000-0003-4374-3173; Lab, Carmen/0000-0002-5935-3236; Zhang, Zhengxiao/0000-0002-6977-4309 European Research Area and Joint Programming Initiative-A Healthy Diet for a Healthy Life (ERA-HDHL) grant (Biomarkers for Nutrition and Health); Canadian Institutes of Health Research [RN-298871-372173]; European Research Council [741623]; Queen Elizabeth II Graduate Scholarship; Elizabeth Russell MacEachran Scholarship; Anthony Fellowship in Human Nutrition; Science Foundation Ireland Centre [APC/SFI/12/RC/2273_P2]; Science Foundation Ireland Professorship [19/RP/6853] European Research Area and Joint Programming Initiative-A Healthy Diet for a Healthy Life (ERA-HDHL) grant (Biomarkers for Nutrition and Health); Canadian Institutes of Health Research(Canadian Institutes of Health Research (CIHR)); European Research Council(European Research Council (ERC)European Commission); Queen Elizabeth II Graduate Scholarship; Elizabeth Russell MacEachran Scholarship; Anthony Fellowship in Human Nutrition; Science Foundation Ireland Centre(Science Foundation Ireland); Science Foundation Ireland Professorship(Science Foundation Ireland) This study was co-funded by a joint European Research Area and Joint Programming Initiative-A Healthy Diet for a Healthy Life (ERA-HDHL) grant (Biomarkers for Nutrition and Health; The FiberTAG Project; www.healthydie tforhealthylife.eu/) with specific contributions from the Canadian Institutes of Health Research (RN-298871-372173). The ex vivo microbiota analysis was supported by the European Research Council (Starting Grant: FunKeyGut 741623). ECD acknowledges support from the Queen Elizabeth II Graduate Scholarship, Elizabeth Russell MacEachran Scholarship, and Anthony Fellowship in Human Nutrition. JW is funded in part by a Science Foundation Ireland Centre grant to APC Microbiome Ireland (APC/SFI/12/RC/2273_P2) and a Science Foundation Ireland Professorship (19/RP/6853). The funding sources had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. 105 9 9 26 46 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2049-2618 MICROBIOME Microbiome MAY 13 2022.0 10 1 77 10.1186/s40168-022-01248-5 0.0 22 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology 1F4ND 35562794.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000795144500001 0
C Deng, HM; Hua, Y; Song, T; Xue, ZG; Ma, RH; Robertson, N; Guan, HB Assoc Advancement Artificial Intelligence Deng, Hanming; Hua, Yang; Song, Tao; Xue, Zhengui; Ma, Ruhui; Robertson, Neil; Guan, Haibing Reinforcing Neural Network Stability with Attractor Dynamics THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence FEB 07-12, 2020 New York, NY Assoc Advancement Artificial Intelligence Recent approaches interpret deep neural works (DNNs) as dynamical systems. drawing the connection between stability in forward propagation and generalization of DNNs. In this paper, we take a step further to be the first to reinforce this stability of DNNs without changing their original structure and verify the impact of the reinforced stability on the network representation from various aspects. More specifically, we reinforce stability by modeling attractor dynamics of a DNN and propose relu-max attractor network (RMAN), a light-weight module readily to be deployed on state-of-the-art ResNet-like networks. RMAN is only needed during training so as to modify a ResNet's attractor dynamics by minimizing an energy function together with the loss of the original learning task. Through intensive experiments, we show that RMAN-modified attractor dynamics bring a more structured representation space to ResNet and its variants, and more importantly improve the generalization ability of ResNet-like networks in supervised tasks due to reinforced stability. [Deng, Hanming; Song, Tao; Xue, Zhengui; Ma, Ruhui; Guan, Haibing] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Hua, Yang; Robertson, Neil] Queens Univ Belfast, Belfast, Antrim, North Ireland Shanghai Jiao Tong University; Queens University Belfast Song, T (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. denghanmig@sjtu.edu.cn; Y.Hua@qub.ac.uk; songt333@sjtu.edu.cn; zhenguixue@sjtu.edu.cn; ruhuima@sjtu.edu.cn; N.Robertson@qub.ac.uk; hbguan@sjtu.edu.cn hua, yang/GSE-0594-2022; guan, haibing/G-8142-2011 guan, haibing/0000-0002-4714-7400; Hua, Yang/0000-0001-5536-503X; , Tao/0000-0002-5965-3140 37 0 0 1 1 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-835-0 AAAI CONF ARTIF INTE 2020.0 34 3765 3772 8 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Education & Educational Research BR7FR 2023-03-23 WOS:000667722803103 0
J Xia, XH; Yang, L; Wei, XY; Sahli, H; Jiang, DM Xia, Xiaohan; Yang, Le; Wei, Xiaoyong; Sahli, Hichem; Jiang, Dongmei A multi-scale multi-attention network for dynamic facial expression recognition MULTIMEDIA SYSTEMS English Article Facial expression recognition; Multi-scale multi-attention network (MSMA-Net); Spatial attention; Temporal attention MODEL Characterizing spatial information and modelling temporal dynamics of facial images are key challenges for dynamic facial expression recognition (FER). In this paper, we propose an end-to-end multi-scale multi-attention network (MSMA-Net) for dynamic FER. In our model, the spatio-temporal features are encoded at two scales, i.e. the entire face and local facial patches. For each scale, we adopt a 2D convolutional neural network (CNN) to capture frame-based spatial information, and a 3D CNN to depict the short-term dynamics in the temporal sequence. Moreover, we propose a multi-attention mechanism by considering both spatial and temporal attention models. The temporal attention is applied on the image sequence to highlight expressive frames within the whole sequence, and the spatial attention mechanism is applied at the patch level to learn salient facial features. Comprehensive experiments on publicly available datasets (Aff-Wild2, RML, and AFEW) show that the proposed MSMA-Net model automatically highlights salient expressive frames, within which salient facial features are learned, allowing better or very competitive results compared to state-of-the-art methods. [Xia, Xiaohan; Yang, Le; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Key Lab Speech & Image Informat Proc, Youyi Xilu 127, Xian 710072, Peoples R China; [Wei, Xiaoyong] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China; [Wei, Xiaoyong; Jiang, Dongmei] Peng Cheng Lab, Vanke Cloud City Phase 1,Bldg 8,Xili St, Shenzhen 518055, Guangdong, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel VUB, Dept Elect & Informat ETRO, Pl Laan 2, B-1050 Brussels, Belgium; [Sahli, Hichem] Interunivers Microelect Ctr IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Sichuan University; Peng Cheng Laboratory; Vrije Universiteit Brussel; IMEC Xia, XH (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Key Lab Speech & Image Informat Proc, Youyi Xilu 127, Xian 710072, Peoples R China. xiaohanxia@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn Yang, Le/HHZ-4011-2022 Sahli, Hichem/0000-0002-1774-2970 Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14]; National Natural Science Foundation of China [61872256]; VUB Interdisciplinary Research Program through the EMO-App project Shaanxi Provincial International Science and Technology Collaboration Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); VUB Interdisciplinary Research Program through the EMO-App project This work is supported by the Shaanxi Provincial International Science and Technology Collaboration Project (grant 2017KW-ZD-14), the National Natural Science Foundation of China (No. 61872256), and the VUB Interdisciplinary Research Program through the EMO-App project. 46 2 2 6 30 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0942-4962 1432-1882 MULTIMEDIA SYST Multimedia Syst. APR 2022.0 28 2 SI 479 493 10.1007/s00530-021-00849-8 0.0 OCT 2021 15 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0N5WS 2023-03-23 WOS:000703965800001 0
C Liu, JX; Huang, XY; Huang, YC; Luo, YL; Yang, S Tetko, IV; Kurkova, V; Karpov, P; Theis, F Liu, Junxiu; Huang, Xingyue; Huang, Yongchuang; Luo, Yuling; Yang, Su Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I Lecture Notes in Computer Science English Proceedings Paper 28th International Conference on Artificial Neural Networks (ICANN) SEP 17-19, 2019 Tech Univ Munchen, Klinikum Rechts Isar, Munich, GERMANY Tech Univ Munchen, Klinikum Rechts Isar Particle swarm algorithm; Genetic Algorithm; Spiking Neural Networks; Networks-on-Chip FAULT-DETECTION For the Spiking Neuron Network (SNN) systems, the hardware implementation has unique advantages in terms of performance, energy, and scalability. The Networks-on-Chip (NoC) interconnection strategy has been widely used in hardware SNNs as it provides excellent interconnection mechanism for interneuronal communications. However, the mapping between the SNN models and NoC hardware systems remains a research challenge. In this paper, a multi-objective immune genetic algorithm is proposed for the mapping of SNN hardware system, which is based on the Immune Algorithm (IA) and Genetic Algorithm (GA). It can optimize the SNN hardware systems by reducing the energy consumption and communication delays. In the experiments, the spiking astrocyte neuron network model and the Star-Subnet-Based-3D Mesh (3D-SSBM) NoC hardware system are used for testing. Results demonstrate that the proposed algorithm provides an effective mapping solution for hardware SNNs with low energy consumption and communication delay. [Liu, Junxiu; Huang, Xingyue; Huang, Yongchuang; Luo, Yuling] Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China; [Yang, Su] Ulster Univ, Sch Comp Engn & Intelligent Syst, Derry, Londonderry, North Ireland Guangxi Normal University; Ulster University Luo, YL (corresponding author), Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China. yuling0616@mailbox.gxnu.edu.cn National Natural Science Foundation of China [61603104, 61661008]; Guangxi Natural Science Foundation [2016GXNSFCA380017, 2017GXNS-FAA198180]; Overseas 100 Talents Program of Guangxi Higher Education [F-KA16035]; 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program; Innovation Project of Guangxi Graduate Education [YCSW2019076] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Overseas 100 Talents Program of Guangxi Higher Education; 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program; Innovation Project of Guangxi Graduate Education This research was partially supported by the National Natural Science Foundation of China under Grants 61603104 and 61661008, the Guangxi Natural Science Foundation under Grants 2016GXNSFCA380017 and 2017GXNS-FAA198180, the funding of Overseas 100 Talents Program of Guangxi Higher Education under Grant F-KA16035, 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program, and the Innovation Project of Guangxi Graduate Education under Grant YCSW2019076. 28 0 0 2 5 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-30487-4; 978-3-030-30486-7 LECT NOTES COMPUT SC 2019.0 11727 745 757 10.1007/978-3-030-30487-4_58 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP3HE 2023-03-23 WOS:000546494000058 0
C Mai, XY; Couillet, R IEEE Mai, Xiaoyi; Couillet, Romain THE COUNTERINTUITIVE MECHANISM OF GRAPH-BASED SEMI-SUPERVISED LEARNING IN THE BIG DATA REGIME 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) MAR 05-09, 2017 New Orleans, LA IEEE,Inst Elect & Elect Engineers, Signal Proc Soc semi-supervised learning; graphs; performance analysis; random matrix theory In this article, a new approach is proposed to study the performance of graph-based semi-supervised learning methods, under the assumptions that the dimension of data p and their number n grow large at the same rate and that the data arise from a Gaussian mixture model. Unlike small dimensional systems, the large dimensions allow for a Taylor expansion to linearize the weight (or kernel) matrix W, thereby providing in closed form the limiting performance of semi-supervised learning algorithms. This notably allows to predict the classification error rate as a function of the normalization parameters and of the choice of the kernel function. Despite the Gaussian assumption for the data, the theoretical findings match closely the performance achieved with real datasets, particularly here on the popular MNIST database. [Mai, Xiaoyi] Beihang Univ, Beijing, Peoples R China; [Mai, Xiaoyi; Couillet, Romain] Univ Paris Saclay, CentraleSupelec, Gif Sur Yvette, France Beihang University; UDICE-French Research Universities; Universite Paris Saclay Mai, XY (corresponding author), Beihang Univ, Beijing, Peoples R China.;Mai, XY (corresponding author), Univ Paris Saclay, CentraleSupelec, Gif Sur Yvette, France. ANR Project RMT4GRAPH [ANR-14-CE28-0006] ANR Project RMT4GRAPH(French National Research Agency (ANR)) This work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006). 10 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-4117-6 INT CONF ACOUST SPEE 2017.0 2821 2825 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BI7JW Green Submitted 2023-03-23 WOS:000414286202199 0
J Shi, BW; Dong, WH; Zhan, ZC Shi, Bowen; Dong, Weihua; Zhan, Zhicheng AdaFI-FCN: an adaptive feature integration fully convolutional network for predicting driver's visual attention GEO-SPATIAL INFORMATION SCIENCE English Article; Early Access Visual Attention Prediction (VAP); feature integration; Fully Convolutional Network (FCN); driving environment; deep learning SALIENT OBJECT DETECTION; EYE-MOVEMENTS; IMAGES; MODEL Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading. [Shi, Bowen; Dong, Weihua; Zhan, Zhicheng] Beijing Normal Univ, Res Ctr Geospatial Cognit & Visual Analyt, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing, Peoples R China; [Shi, Bowen; Dong, Weihua; Zhan, Zhicheng] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Zhan, Zhicheng] Univ Ghent, Dept Geog, Ghent, Belgium Beijing Normal University; Beijing Normal University; Ghent University Dong, WH (corresponding author), Beijing Normal Univ, Res Ctr Geospatial Cognit & Visual Analyt, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing, Peoples R China.;Dong, WH (corresponding author), Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China. dongweihua@bnu.edu.cn Shi, Bowen/GPX-9383-2022 Shi, Bowen/0000-0002-4753-7572 National Natural Science Foundation of China (NSFC) [42230103]; State Key Laboratory of Geographic Information Engineering; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of the Ministry of Natural Resources [2021-04-03] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Geographic Information Engineering; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of the Ministry of Natural Resources This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 42230103; and the State Key Laboratory of Geographic Information Engineering and the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of the Ministry of Natural Resources Jointly Funded Project under Grant No. 2021-04-03. 85 0 0 5 5 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1009-5020 1993-5153 GEO-SPAT INF SCI Geo-Spat. Inf. Sci. 10.1080/10095020.2022.2147028 0.0 NOV 2022 17 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing 6O2LV gold 2023-03-23 WOS:000890076700001 0
J Oveneke, MC; Zhao, Y; Pei, EC; Berenguer, AD; Jiang, DM; Sahli, H Oveneke, Meshia Cedric; Zhao, Yong; Pei, Ercheng; Berenguer, Abel Diaz; Jiang, Dongmei; Sahli, Hichem Leveraging the Deep Learning Paradigm for Continuous Affect Estimation from Facial Expressions IEEE TRANSACTIONS ON AFFECTIVE COMPUTING English Article Affect estimation; facial expressions; face frontalization; partial least-squares regression; convolutional auto-encoders; neural networks; extended kalman filtering PARTIAL LEAST-SQUARES; CORE AFFECT; EMOTION; RECOGNITION Continuous affect estimation from facial expressions has attracted increased attention in the affective computing research community. This paper presents a principled framework for estimating continuous affect from video sequences. Based on recent developments, we address the problem of continuous affect estimation by leveraging the Bayesian filtering paradigm, i.e., considering affect as a latent dynamical system corresponding to a general feeling of pleasure with a degree of arousal, and recursively estimating its state using a sequence of visual observations. To this end, we advance the state-of-the-art as follows: (i) Canonical face representation (CFR): a novel algorithm for two-dimensional face frontalization, (ii) Convex unsupervised representation learning (CURL): a novel frequency-domain convex optimization algorithm for unsupervised training of deep convolutional neural networks (CNN)s, and (iii) Deep extended Kalman filtering (DEKF): an extended Kalman filtering-based algorithm for affect estimation from a sequence of CNN observations. The performance of the resulting CFR-CURL-DEKF algorithmic framework is empirically evaluated on publicly available benchmark datasets for facial expression recognition (CK+) and continuous affect estimation (AVEC 2012 and 2014). [Oveneke, Meshia Cedric; Zhao, Yong; Pei, Ercheng; Berenguer, Abel Diaz; Sahli, Hichem] Vrije Univ Brussel, Dept Elect & Informat, VUB NPU Joint AVSP Res Lab, Pl Laan 2, B-1050 Brussels, Belgium; [Jiang, Dongmei] Northwestern Polytech Univ, Shaanxi Key Lab Speech & Image Informat Proc, VUB NPU Joint AVSP Res Lab, Youyo Xilu 127, Xian 710072, Peoples R China; [Sahli, Hichem] Interuniv Microelect Ctr, Kapeldreef 75, B-3001 Heverlee, Belgium Vrije Universiteit Brussel; Northwestern Polytechnical University; Interuniversity Microelectronics Centre Oveneke, MC (corresponding author), Vrije Univ Brussel, Dept Elect & Informat, VUB NPU Joint AVSP Res Lab, Pl Laan 2, B-1050 Brussels, Belgium. mcovenek@etrovub.be; yzhao@etrovub.be; epei@etrovub.be; aberengu@etrovub.be; jiangdm@nwpu.edu.cn; hsahli@etrovub.be ZHAO, YONG/0000-0003-2644-952X; Diaz Berenguer, Abel/0000-0003-4970-6517; Sahli, Hichem/0000-0002-1774-2970; Jiang, Dongmei/0000-0002-6238-8499 Agency for Innovation by Science and Technology in Flanders (IWT) [131814]; VUB Interdisciplinary Research Program through the EMO-App project; Chinese Scolarship Council (CSC) [201506290085, 201706290115]; National Natural Science Foundation of China [61273265] Agency for Innovation by Science and Technology in Flanders (IWT)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); VUB Interdisciplinary Research Program through the EMO-App project; Chinese Scolarship Council (CSC); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the Agency for Innovation by Science and Technology in Flanders (IWT) PhD grant nr. 131814, the VUB Interdisciplinary Research Program through the EMO-App project, the Chinese Scolarship Council (CSC) through grants nr. 201506290085 and nr. 201706290115, and the National Natural Science Foundation of China (grant 61273265). 83 0 0 4 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3045 IEEE T AFFECT COMPUT IEEE Trans. Affect. Comput. JAN 2022.0 13 1 426 439 10.1109/TAFFC.2019.2944603 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science ZP2QE 2023-03-23 WOS:000766268600033 0
J Ikram, RMA; Hazarika, BB; Gupta, D; Heddam, S; Kisi, O Ikram, Rana Muhammad Adnan; Hazarika, Barenya Bikash; Gupta, Deepak; Heddam, Salim; Kisi, Ozgur Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study NEURAL COMPUTING & APPLICATIONS English Article; Early Access Streamflow prediction; Extreme learning machine; Empirical wavelet transform; Empirical model decomposition EMPIRICAL MODE DECOMPOSITION Accurate streamflow estimation is crucial for proper water management for irrigation, hydropower, drinking and industrial purposes. The main aim of this study to adopt new data preprocessing techniques (e.g., EMD, EEMD and EWT) to capture the data noise and to enhance the prediction accuracy of machine learning methods for streamflow estimation which is a challenging task in high-altitude basins due to the influence of many external climatic and geographical parameters. The prediction accuracy of support vector regression (SVR), twin support vector machine (T), extreme learning machine (ELM), asymmetric Huber loss function-based ELM (AHELM) and epsilon-insensitive Huber loss function-based ELM (epsilon-AHELM) methods are investigated in monthly streamflow prediction. Among the standalone methods, the epsilon-AHELM performs superior to the SVR, TSVR, ELM, and AHELM in streamflow prediction; improvements in root mean square error are 6.9%, 4.9%, 6% and 4.2%, respectively. The study outcomes reveal that the preprocessing methods considerably improve the prediction accuracy of the implemented standalone models. Among the data preprocessing techniques, it is found that the EWT outperforms the EMD and EEMD techniques by reducing the prediction errors in the best epsilon-AHELM, EMD-epsilon-AHELM and EEMD-epsilon-AHELM models by 68-61.3%, 64.7-63.4% and 59.4-58.6%, respectively. The overall results of the study recommend the use of EWT-epsilon-AHELM in streamflow estimation. [Ikram, Rana Muhammad Adnan] Guangzhou Univ, Sch Econ & Stat, Guangzhou 510006, Peoples R China; [Hazarika, Barenya Bikash] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram 522302, Andhra Pradesh, India; [Gupta, Deepak] Natl Inst Technol, Dept Comp Sci & Engn, Jote 791112, Arunachal Prade, India; [Heddam, Salim] Hydraul Div Univ, Fac Sci, Agron Dept, 20 Aout 1955,Route Hadaik,BP 26, Skikda, Algeria; [Kisi, Ozgur] Tech Univ Lubeck, Dept Civil Engn, D-23562 Lubeck, Germany; [Kisi, Ozgur] Ilia State Univ, Civil Engn Dept, Tbilisi 0162, Georgia Guangzhou University; Koneru Lakshmaiah Education Foundation (K L Deemed to be University); National Institute of Technology (NIT System); National Institute of Technology Arunachal Pradesh; Luebeck University of Applied Sciences; Ilia State University Gupta, D (corresponding author), Natl Inst Technol, Dept Comp Sci & Engn, Jote 791112, Arunachal Prade, India. rana@gzhu.edu.cn; barenya1431@gmail.com; deepak@nitap.ac.in; heddamsalim@yahoo.fr; ozgur.kisi@th-luebeck.de Hazarika, Barenya Bikash/AAL-7514-2021; HEDDAM, SALIM/B-8647-2015 Hazarika, Barenya Bikash/0000-0001-8243-9153; HEDDAM, SALIM/0000-0002-8055-8463 43 0 0 9 9 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. 10.1007/s00521-022-08163-8 0.0 DEC 2022 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7K0SK 2023-03-23 WOS:000904996200002 0
J Lin, XH; Deng, KY; Wu, HY; Du, BS; Viana, B; Li, Y; Hu, YH Lin, Xiaohui; Deng, Kaiyuan; Wu, Haoyi; Du, Bingsheng; Viana, Bruno; Li, Yang; Hu, Yihua Photon energy conversion and management in SrAl12O19: Mn2+, Gd3+ for rewritable optical information storage CHEMICAL ENGINEERING JOURNAL English Article Optical information storage; SrAl12O19; Mn2+; Gd3+; Optical stimulated luminescence LONG PERSISTENT LUMINESCENCE; PHOSPHORS; EMISSION In the current big data era, state-of-the-art optical data storage has become a front-runner in competing data storage technologies. In this work, we propose an information storage prototype using optical stimulated luminescent phosphor of SrAl12O19: Mn2+, Gd3+, which converts the photon into trapped electrons and partially releases them in a form of luminescence when irradiated by infrared irradiation, rendering an optical information storage ability. Information storage can be realized in both graphical and digital formats. In this way, graphical patterns can be stored and rejuvenated by infrared irradiation, and a set of digital data is recorded step by step by a home-make decoding system, completing an optical data reading process. This work provides a practical example for future application of writing-storing-readout-erasing-rewriting integrated optical information storage. [Lin, Xiaohui; Deng, Kaiyuan; Wu, Haoyi; Li, Yang; Hu, Yihua] Guangdong Univ Technol, Sch Phys & Optoelect Engn, Guangzhou 510006, Peoples R China; [Du, Bingsheng] Chongqing Univ, Coll Optoelect Engn, Minist Educ, Key Lab Optoelect Technol & Syst, Chongqing 400044, Peoples R China; [Viana, Bruno] PSL Res Univ, Inst Rech Chim Paris, Chim ParisTech, F-75005 Paris, France Guangdong University of Technology; Chongqing University; UDICE-French Research Universities; Universite PSL; Chimie ParisTech Wu, HY; Li, Y; Hu, YH (corresponding author), Guangdong Univ Technol, Sch Phys & Optoelect Engn, Guangzhou 510006, Peoples R China. manofchina@outlook.com; lychris@sina.com; huyh@gdut.edu.cn Li, Yang/HPC-4054-2023; Li, Yang/AAF-4092-2020 Li, Yang/0000-0002-9544-5840 National Natural Science Foundation of China [51972065] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Grant number 51972065) . 40 4 4 14 96 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1385-8947 1873-3212 CHEM ENG J Chem. Eng. J. SEP 15 2021.0 420 1 129844 10.1016/j.cej.2021.129844 0.0 APR 2021 8 Engineering, Environmental; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering SW1AN Green Submitted 2023-03-23 WOS:000664248700005 0
J Li, ZX; Janardhanan, MN; Nielsen, P; Tang, QH Li, Zixiang; Janardhanan, Mukund Nilakantan; Nielsen, Peter; Tang, Qiuhua Mathematical models and simulated annealing algorithms for the robotic assembly line balancing problem ASSEMBLY AUTOMATION English Article Artificial Intelligence; Assembly line balancing; Integer programming; Robotic assembly line; Simulated annealing ENERGY-CONSUMPTION; GENETIC ALGORITHM; WORKER ASSIGNMENT; CYCLE TIME Purpose - Robots are used in assembly lines because of their higher flexibility and lower costs. The purpose of this paper is to develop mathematical models and simulated annealing algorithms to solve the robotic assembly line balancing (RALB-II) to minimize the cycle time. Design/methodology/approach - Four mixed-integer linear programming models are developed and encoded in CPLEX solver to find optimal solutions for small-sized problem instances. Two simulated annealing algorithms, original simulated annealing algorithm and restarted simulated annealing (RSA) algorithm, are proposed to tackle large-sized problems. The restart mechanism in the RSA methodology replaces the incumbent temperature with a new temperature. In addition, the proposed methods use iterative mechanisms for updating cycle time and a new objective to select the solution with fewer critical workstations. Findings - The comparative study among the tested algorithms and other methods adapted verifies the effectiveness of the proposed methods. The results obtained by these algorithms on the benchmark instances show that 23 new upper bounds out of 32 tested cases are achieved. The RSA algorithm ranks first among the algorithms in the number of updated upper bounds. Originality/value - Four models are developed for RALBP-II and their performance is evaluated for the first time. An RSA algorithm is developed to solve RALBP-II, where the restart mechanism is developed to replace the incumbent temperature with a new temperature. The proposed methods also use iterative mechanisms and a new objective to select the solution with fewer critical workstations. [Li, Zixiang] Wuhan Univ Sci & Technol, Engn Res Ctr Met Automat & Measurement Technol, Minist Educ, Wuhan, Hubei, Peoples R China; [Li, Zixiang] Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control Technol, Wuhan, Hubei, Peoples R China; [Janardhanan, Mukund Nilakantan] Univ Leicester, Dept Engn, Leicester, Leics, England; [Janardhanan, Mukund Nilakantan; Nielsen, Peter] Aalborg Univ, Dept Mat & Prod, Aalborg, Denmark; [Tang, Qiuhua] Key Lab Met Equipment & Control Technol, Wuhan, Hubei, Peoples R China; [Tang, Qiuhua] Wuhan Univ Sci & Technol, Key Lab Mech Transmiss & Mfg Engn, Wuhan, Hubei, Peoples R China Wuhan University of Science & Technology; Wuhan University of Science & Technology; University of Leicester; Aalborg University; Wuhan University of Science & Technology Janardhanan, MN (corresponding author), Univ Leicester, Dept Engn, Leicester, Leics, England.;Janardhanan, MN (corresponding author), Aalborg Univ, Dept Mat & Prod, Aalborg, Denmark. zixiangliwust@gmail.com; mukund.janardhanan@leicester.ac.uk; peter@m-tech.aau.dk; tangqiuhua@wust.edu.cn Li, Zixiang/AAY-6080-2020; Nielsen, Peter/N-5932-2019 Li, Zixiang/0000-0002-8570-8862; Nielsen, Peter/0000-0002-4882-7942 41 15 15 2 35 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0144-5154 1758-4078 ASSEMBLY AUTOM Assem. Autom. 2018.0 38 4 420 436 10.1108/AA-09-2017-115 0.0 17 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering GY2QE Green Submitted 2023-03-23 WOS:000448387000006 0
J Li, CJ; Pan, H; Bousseau, A; Mitra, NJ Li, Changjian; Pan, Hao; Bousseau, Adrien; Mitra, Niloy J. Sketch2CAD: Sequential CAD Modeling by Sketching in Context ACM TRANSACTIONS ON GRAPHICS English Article sketch; CAD modeling; procedural modeling; convolutional neural network We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities between the steps industrial designers follow to draw 3D shapes, and the operations CAD modeling systems offer to create similar shapes. To overcome the strong ambiguity with parsing 2D sketches, we observe that in a sketching sequence, each step makes sense and can be interpreted in the context of what has been drawn before. In our system, this context corresponds to a partial CAD model, inferred in the previous steps, which we feed along with the input sketch to a deep neural network in charge of interpreting how the model should be modified by that sketch. Our deep network architecture then recognizes the intended CAD operation and segments the sketch accordingly, such that a subsequent optimization estimates the parameters of the operation that best fit the segmented sketch strokes. Since there exists no datasets of paired sketching and CAD modeling sequences, we train our system by generating synthetic sequences of CAD operations that we render as line drawings. We present a proof of concept realization of our algorithm supporting four frequently used CAD operations. Using our system, participants are able to quickly model a large and diverse set of objects, demonstrating Sketch2CAD to be an alternate way of interacting with current CAD modeling systems. [Li, Changjian; Mitra, Niloy J.] UCL, 66-72 Gower St, London, England; [Pan, Hao] Microsoft Res Asia, 5 Denting Rd, Beijing, Peoples R China; [Bousseau, Adrien] Univ Cote dAzur, INRIA, 2004 Route Lucioles, Valbonne, France; [Mitra, Niloy J.] Adobe Res, San Jose, CA USA University of London; University College London; Microsoft; Microsoft Research Asia; Inria; UDICE-French Research Universities; Universite Cote d'Azur; Adobe Systems Inc. Li, CJ (corresponding author), UCL, 66-72 Gower St, London, England. changlian.li@ucl.ac.uk; haopan@microsoft.com; adrien.bousseau@inria.fr; n.mitra@cs.ucl.ac.uk ERC Grant [SmartGeometry 335373]; Google Faculty Award; ERC Starting Grant D3 [ERC-2016-STG 714221] ERC Grant; Google Faculty Award(Google Incorporated); ERC Starting Grant D3 The authors would like to thank the reviewers for their valuable and detailed suggestions, the user evaluation participants and Nathan Carr, Yuxiao Guo, Zhiming Cui for the valuable discussions. The work of Niloy was supported by ERC Grant (SmartGeometry 335373), Google Faculty Award and gifts from Adobe, and the work of Adrien was supported by ERC Starting Grant D3 (ERC-2016-STG 714221), research and software donations from Adobe. Finally, Changjian Li wants to thank, in particular, the endless and invaluable love and supports from Huahua Guo over the tough time due to COVID-19. 42 6 6 3 9 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 0730-0301 1557-7368 ACM T GRAPHIC ACM Trans. Graph. DEC 2020.0 39 6 164 10.1145/3414685.3417807 0.0 14 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science PA4DS 2023-03-23 WOS:000595589100004 0
J Abe, K; Haga, Y; Hayato, Y; Hiraide, K; Ieki, K; Ikeda, M; Imaizumi, S; Iyogi, K; Kameda, J; Kanemura, Y; Kataoka, Y; Kato, Y; Kishimoto, Y; Miki, S; Mine, S; Miura, M; Mochizuki, T; Moriyama, S; Nagao, Y; Nakahata, M; Nakajima, T; Nakano, Y; Nakayama, S; Okada, T; Okamoto, K; Orii, A; Sato, K; Sekiya, H; Shiozawa, M; Sonoda, Y; Suzuki, Y; Takeda, A; Takemoto, Y; Takenaka, A; Tanaka, H; Tasaka, S; Tomura, T; Ueno, K; Watanabe, S; Yano, T; Yokozawa, T; Han, S; Irvine, T; Kajita, T; Kametani, I; Kaneyuki, K; Lee, KP; McLachlan, T; Okumura, K; Richard, E; Tashiro, T; Wang, R; Xia, J; Megias, GD; Bravo-Berguno, D; Labarga, L; Zaldivar, B; Goldhaber, M; Blaszczyk, FDM; Gustafson, J; Kachulis, C; Kearns, E; Raaf, JL; Stone, JL; Sulak, LR; Sussman, S; Wan, L; Wester, T; Pointon, BW; Bian, J; Carminati, G; Elnimr, M; Griskevich, NJ; Kropp, WR; Locke, S; Renshaw, A; Smy, MB; Sobel, HW; Takhistov, V; Weatherly, P; Ganezer, KS; Hartfiel, BL; Hill, J; Keig, WE; Hong, N; Kim, JY; Lim, IT; Park, RG; Akiri, T; Bodur, B; Himmel, A; Li, Z; O'Sullivan, E; Scholberg, K; Walter, CW; Wongjirad, T; Bernard, L; Coffani, A; Drapier, O; El Hedri, S; Giampaolo, A; Imber, J; Mueller, TA; Paganini, P; Quilain, B; Ishizuka, T; Nakamura, T; Jang, JS; Choi, K; Learned, JG; Matsuno, S; Smith, SN; Amey, J; Anthony, LHV; Litchfield, RP; Ma, WY; Marin, D; Sztuc, AA; Uchida, Y; Wascko, MO; Berardi, V; Catanesi, MG; Intonti, RA; Radicioni, E; Calabria, NF; De Rosa, G; Machado, LN; Collazuol, G; Iacob, F; Lamoureux, M; Ospina, N; Ludovici, L; Gonin, M; Pronost, G; Maekawa, Y; Nishimura, Y; Cao, S; Friend, M; Hasegawa, T; Ishida, T; Ishii, T; Jakkapu, M; Kobayashi, T; Matsubara, T; Nakadaira, T; Nakamura, K; Oyama, Y; Sakashita, K; Sekiguchi, T; Tsukamoto, T; Boschi, T; Di Lodovico, F; Migenda, J; Sedgwick, SM; Taani, M; Zsoldos, S; Abe, KE; Hasegawa, M; Isobe, Y; Kotsar, Y; Miyabe, H; Ozaki, H; Shiozawa, T; Sugimoto, T; Suzuki, AT; Takeuchi, Y; Yamamoto, S; Ali, A; Ashida, Y; Bronner, C; Feng, J; Hayashino, T; Hiraki, T; Hirota, S; Huang, K; Jiang, M; Kikawa, T; Mori, M; Murakami, A; Nakamura, K; Nakaya, T; Patel, ND; Suzuki, K; Takahashi, S; Tateishi, K; Wendell, RA; Yasutome, K; Fernandez, P; McCauley, N; Mehta, P; Pritchard, A; Tsui, KM; Fukuda, Y; Itow, Y; Menjo, H; Mitsuka, G; Murase, M; Muto, F; Niwa, T; Suzuki, T; Tsukada, M; Frankiewicz, K; Mijakowski, P; Hignight, J; Jiang, J; Jung, CK; Li, X; Palomino, JL; Santucci, G; Vilela, C; Wilking, MJ; Yanagisawa, C; Fukuda, D; Hagiwara, K; Harada, M; Horai, T; Ishino, H; Ito, S; Kayano, T; Kibayashi, A; Kitagawa, H; Koshio, Y; Ma, W; Mori, T; Nagata, H; Piplani, N; Sakai, S; Sakuda, M; Takahira, Y; Xu, C; Yamaguchi, R; Kuno, Y; Barr, G; Barrow, D; Cook, L; Goldsack, A; Samani, S; Simpson, C; Wark, D; Nova, F; Tacik, R; Yang, JY; Cole, A; Jenkins, SJ; Malek, M; McElwee, JM; Stone, O; Thiesse, MD; Thompson, LF; Okazawa, H; Choi, Y; Kim, SB; Yu, I; Ichikawa, AK; Nakamura, K; Ito, K; Nishijima, K; Calland, RG; de Perio, P; Martens, K; Murdoch, M; Vagins, MR; Koshiba, M; Totsuka, Y; Iwamoto, K; Nakajima, Y; Ogawa, N; Suda, Y; Yokoyama, M; Hamabe, D; Izumiyama, S; Kuze, M; Okajima, Y; Tanaka, M; Yoshida, T; Inomoto, M; Ishitsuka, M; Ito, H; Matsumoto, R; Ohta, K; Shinoki, M; Martin, JF; Nantais, CM; Tanaka, HA; Towstego, T; Akutsu, R; Hartz, M; Konaka, A; Prouse, NW; Chen, S; Xu, BD; Zhang, Y; Berkman, S; Tobayama, S; Connolly, K; Wilkes, RJ; Posiadala-Zezula, M; Hadley, D; Richards, B; Jamieson, B; Walker, J; Marti, L; Minamino, A; Okamoto, K; Pintaudi, G; Sano, S; Sasaki, R Abe, K.; Haga, Y.; Hayato, Y.; Hiraide, K.; Ieki, K.; Ikeda, M.; Imaizumi, S.; Iyogi, K.; Kameda, J.; Kanemura, Y.; Kataoka, Y.; Kato, Y.; Kishimoto, Y.; Miki, S.; Mine, S.; Miura, M.; Mochizuki, T.; Moriyama, S.; Nagao, Y.; Nakahata, M.; Nakajima, T.; Nakano, Y.; Nakayama, S.; Okada, T.; Okamoto, K.; Orii, A.; Sato, K.; Sekiya, H.; Shiozawa, M.; Sonoda, Y.; Suzuki, Y.; Takeda, A.; Takemoto, Y.; Takenaka, A.; Tanaka, H.; Tasaka, S.; Tomura, T.; Ueno, K.; Watanabe, S.; Yano, T.; Yokozawa, T.; Han, S.; Irvine, T.; Kajita, T.; Kametani, I; Kaneyuki, K.; Lee, K. P.; McLachlan, T.; Okumura, K.; Richard, E.; Tashiro, T.; Wang, R.; Xia, J.; Megias, G. D.; Bravo-Berguno, D.; Labarga, L.; Zaldivar, B.; Goldhaber, M.; Blaszczyk, F. D. M.; Gustafson, J.; Kachulis, C.; Kearns, E.; Raaf, J. L.; Stone, J. L.; Sulak, L. R.; Sussman, S.; Wan, L.; Wester, T.; Pointon, B. W.; Bian, J.; Carminati, G.; Elnimr, M.; Griskevich, N. J.; Kropp, W. R.; Locke, S.; Renshaw, A.; Smy, M. B.; Sobel, H. W.; Takhistov, V; Weatherly, P.; Ganezer, K. S.; Hartfiel, B. L.; Hill, J.; Keig, W. E.; Hong, N.; Kim, J. Y.; Lim, I. T.; Park, R. G.; Akiri, T.; Bodur, B.; Himmel, A.; Li, Z.; O'Sullivan, E.; Scholberg, K.; Walter, C. W.; Wongjirad, T.; Bernard, L.; Coffani, A.; Drapier, O.; El Hedri, S.; Giampaolo, A.; Imber, J.; Mueller, Th A.; Paganini, P.; Quilain, B.; Ishizuka, T.; Nakamura, T.; Jang, J. S.; Choi, K.; Learned, J. G.; Matsuno, S.; Smith, S. N.; Amey, J.; Anthony, L. H., V; Litchfield, R. P.; Ma, W. Y.; Marin, D.; Sztuc, A. A.; Uchida, Y.; Wascko, M. O.; Berardi, V; Catanesi, M. G.; Intonti, R. A.; Radicioni, E.; Calabria, N. F.; De Rosa, G.; Machado, L. N.; Collazuol, G.; Iacob, F.; Lamoureux, M.; Ospina, N.; Ludovici, L.; Gonin, M.; Pronost, G.; Maekawa, Y.; Nishimura, Y.; Cao, S.; Friend, M.; Hasegawa, T.; Ishida, T.; Ishii, T.; Jakkapu, M.; Kobayashi, T.; Matsubara, T.; Nakadaira, T.; Nakamura, K.; Oyama, Y.; Sakashita, K.; Sekiguchi, T.; Tsukamoto, T.; Boschi, T.; Di Lodovico, F.; Migenda, J.; Sedgwick, S. Molina; Taani, M.; Zsoldos, S.; Abe, K. E.; Hasegawa, M.; Isobe, Y.; Kotsar, Y.; Miyabe, H.; Ozaki, H.; Shiozawa, T.; Sugimoto, T.; Suzuki, A. T.; Takeuchi, Y.; Yamamoto, S.; Ali, A.; Ashida, Y.; Bronner, C.; Feng, J.; Hayashino, T.; Hiraki, T.; Hirota, S.; Huang, K.; Jiang, M.; Kikawa, T.; Mori, M.; Murakami, A.; Nakamura, Ke; Nakaya, T.; Patel, N. D.; Suzuki, K.; Takahashi, S.; Tateishi, K.; Wendell, R. A.; Yasutome, K.; Fernandez, P.; McCauley, N.; Mehta, P.; Pritchard, A.; Tsui, K. M.; Fukuda, Y.; Itow, Y.; Menjo, H.; Mitsuka, G.; Murase, M.; Muto, F.; Niwa, T.; Suzuki, T.; Tsukada, M.; Frankiewicz, K.; Mijakowski, P.; Hignight, J.; Jiang, J.; Jung, C. K.; Li, X.; Palomino, J. L.; Santucci, G.; Vilela, C.; Wilking, M. J.; Yanagisawa, C.; Fukuda, D.; Hagiwara, K.; Harada, M.; Horai, T.; Ishino, H.; Ito, S.; Kayano, T.; Kibayashi, A.; Kitagawa, H.; Koshio, Y.; Ma, W.; Mori, T.; Nagata, H.; Piplani, N.; Sakai, S.; Sakuda, M.; Takahira, Y.; Xu, C.; Yamaguchi, R.; Kuno, Y.; Barr, G.; Barrow, D.; Cook, L.; Goldsack, A.; Samani, S.; Simpson, C.; Wark, D.; Nova, F.; Tacik, R.; Yang, J. Y.; Cole, A.; Jenkins, S. J.; Malek, M.; McElwee, J. M.; Stone, O.; Thiesse, M. D.; Thompson, L. F.; Okazawa, H.; Choi, Y.; Kim, S. B.; Yu, I; Ichikawa, A. K.; Nakamura, K.; Ito, K.; Nishijima, K.; Calland, R. G.; de Perio, P.; Martens, K.; Murdoch, M.; Vagins, M. R.; Koshiba, M.; Totsuka, Y.; Iwamoto, K.; Nakajima, Y.; Ogawa, N.; Suda, Y.; Yokoyama, M.; Hamabe, D.; Izumiyama, S.; Kuze, M.; Okajima, Y.; Tanaka, M.; Yoshida, T.; Inomoto, M.; Ishitsuka, M.; Ito, H.; Matsumoto, R.; Ohta, K.; Shinoki, M.; Martin, J. F.; Nantais, C. M.; Tanaka, H. A.; Towstego, T.; Akutsu, R.; Hartz, M.; Konaka, A.; Prouse, N. W.; Chen, S.; Xu, B. D.; Zhang, Y.; Berkman, S.; Tobayama, S.; Connolly, K.; Wilkes, R. J.; Posiadala-Zezula, M.; Hadley, D.; Richards, B.; Jamieson, B.; Walker, J.; Marti, Ll; Minamino, A.; Okamoto, K.; Pintaudi, G.; Sano, S.; Sasaki, R. Super-Kamiokande Collaboration Neutron tagging following atmospheric neutrino events in a water Cherenkov detector JOURNAL OF INSTRUMENTATION English Article Particle identification methods; Cherenkov detectors; Neutrino detectors; Large detector systems for particle and astroparticle physics We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218 +/- 9 mu s. 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F.] Univ Sheffield, Dept Phys & Astron, Sheffield S3 7RH, S Yorkshire, England; [Okazawa, H.] Shizuoka Univ Welf, Dept Informat Social Welf, Yaizu, Shizuoka 4258611, Japan; [Wark, D.] Harwell Oxford, Rutherford Appleton Lab, STFC, Warrington OX11 0QX, Cheshire, England; [Wark, D.] Daresbury Lab, Warrington OX11 0QX, Cheshire, England; [Choi, Y.; Kim, S. B.; Yu, I] Sungkyunkwan Univ, Dept Phys, Suwon 440746, South Korea; [Ichikawa, A. K.; Nakamura, K.] Tohoku Univ, Fac Sci, Dept Phys, Sendai, Miyagi 9808578, Japan; [Ito, K.; Nishijima, K.] Tokai Univ, Dept Phys, Hiratsuka, Kanagawa 2591292, Japan; [Abe, K.; Hayato, Y.; Hiraide, K.; Kameda, J.; Kataoka, Y.; Kishimoto, Y.; Miura, M.; Moriyama, S.; Nakahata, M.; Nakayama, S.; Shiozawa, M.; Takeda, A.; Takemoto, Y.; Tanaka, H.; Tomura, T.; Kajita, T.; Kaneyuki, K.; Okumura, K.; Kearns, E.; Stone, J. L.; Sobel, H. W.; Takhistov, V; Scholberg, K.; Walter, C. W.; Nakamura, K.; Takeuchi, Y.; Nakaya, T.; Wendell, R. A.; Koshio, Y.; Cook, L.; Goldsack, A.; Simpson, C.; Calland, R. G.; de Perio, P.; Martens, K.; Murdoch, M.; Vagins, M. R.; Nakajima, Y.; Yokoyama, M.] Univ Tokyo, Univ Tokyo Inst Adv Study, Kavli Inst Phys & Math Universe WPI, Kashiwa, Chiba 2778583, Japan; [Koshiba, M.; Totsuka, Y.] Univ Tokyo, Bunkyo Ku, Tokyo 1130033, Japan; [Iwamoto, K.; Nakajima, Y.; Ogawa, N.; Suda, Y.; Yokoyama, M.] Univ Tokyo, Dept Phys, Bunkyo Ku, Tokyo 1130033, Japan; [Hamabe, D.; Izumiyama, S.; Kuze, M.; Okajima, Y.; Tanaka, M.; Yoshida, T.] Tokyo Inst Technol, Dept Phys, Meguro Ku, Tokyo 1528551, Japan; [Inomoto, M.; Ishitsuka, M.; Ito, H.; Matsumoto, R.; Ohta, K.; Shinoki, M.] Tokyo Univ Sci, Fac Sci & Technol, Dept Phys, Noda, Chiba 2788510, Japan; [Martin, J. F.; Nantais, C. M.; Tanaka, H. A.; Towstego, T.] Univ Toronto, Dept Phys, Toronto, ON M5S 1A7, Canada; [Pointon, B. W.; Tacik, R.; Akutsu, R.; Hartz, M.; Konaka, A.; Prouse, N. 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L.; Himmel, A.; Berkman, S.] Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA; [Renshaw, A.] Univ Houston, Dept Phys, Houston, TX 77204 USA; [Li, Z.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China; [O'Sullivan, E.] Uppsala Univ, Dept Phys & Astron, Box 516, S-75120 Uppsala, Sweden; [Wongjirad, T.] Tufts Univ, Dept Phys & Astron, Medford, MA 02155 USA; [Choi, K.] Inst Basic Sci IBS, Dajeon 34126, South Korea; [Litchfield, R. P.] Univ Glasgow, Sch Phys & Astron, Glasgow G12 8QQ, Lanark, Scotland; [Ma, W. Y.] DESY, D-15738 Zeuthen, Germany; [Cao, S.] ICISE, Inst Interdisciplinary Res Sci & Educ, Quy Nhon 55121, Vietnam; [Sedgwick, S. Molina] Univ Valencia, Dept Fis Teor, Paterna 46980, Spain; [Sedgwick, S. Molina] Univ Valencia, Inst Fis Corpuscular, CSIC, Paterna 46980, Spain; [Ali, A.] Univ Winnipeg, Winnipeg, MB R3B 2E9, Canada; [Ashida, Y.] Univ Wisconsin, Dept Phys, Madison, WI 53706 USA; [Ashida, Y.] Univ Wisconsin, Wisconsin IceCube Particle Astrophys Ctr, Madison, WI 53706 USA; [Hiraki, T.] Okayama Univ, Res Inst Interdisciplinary Sci, Okayama, Okayama 7008530, Japan; [Mori, M.] Univ Tokyo, Dept Earth Sci & Astron, Tokyo 1538902, Japan; [Fernandez, P.] Donostia Int Phys Ctr, Donostia San Sebastian 20018, Spain; [Hignight, J.] Univ Alberta, Dept Phys, Edmonton, AB T6G 2E1, Canada; [Palomino, J. L.] IIT, Dept Phys, Chicago, IL 60616 USA; [Santucci, G.] York Univ, Dept Phys & Astron, Toronto, ON, Canada; [Vilela, C.] CERN, EP Dept, CH-1211 Geneva 24, Switzerland; [Yanagisawa, C.] BMCC CUNY, Sci Dept, New York, NY USA University of Tokyo; University of Tokyo; University of Tokyo; Autonomous University of Madrid; United States Department of Energy (DOE); Brookhaven National Laboratory; Boston University; British Columbia Institute of Technology; University of California System; University of California Irvine; California State University System; California State University Dominguez Hills; Chonnam National University; Duke University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Fukuoka Institute of Technology; Gifu University; Gwangju Institute of Science & Technology (GIST); University of Hawaii System; Imperial College London; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; University of Tokyo; Keio University; High Energy Accelerator Research Organization (KEK); University of London; King's College London; Kobe University; Kyoto University; University of Liverpool; Miyagi University Education; Nagoya University; Nagoya University; National Centre for Nuclear Research; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Okayama University; Osaka University; University of Oxford; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Regina; Seoul National University (SNU); University of Sheffield; Shizuoka University; STFC Daresbury Laboratory; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; STFC Daresbury Laboratory; Sungkyunkwan University (SKKU); Tohoku University; Tokai University; University of Tokyo; University of Tokyo; University of Tokyo; Tokyo Institute of Technology; Tokyo University of Science; University of Toronto; University of British Columbia; Tsinghua University; University of British Columbia; University of Washington; University of Washington Seattle; University of Warsaw; University of Warwick; University of Winnipeg; Yokohama National University; Tohoku University; Shanghai Jiao Tong University; University of Tokyo; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; University of Houston System; University of Houston; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Uppsala University; Tufts University; Institute for Basic Science - Korea (IBS); University of Glasgow; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Winnipeg; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; Okayama University; University of Tokyo; University of Alberta; Illinois Institute of Technology; York University - Canada; European Organization for Nuclear Research (CERN); City University of New York (CUNY) System Hayato, Y (corresponding author), Univ Tokyo, Inst Cosm Ray Res, Kamioka Observ, Gifu, Akita 5061205, Japan.;Hayato, Y (corresponding author), Univ Tokyo, Univ Tokyo Inst Adv Study, Kavli Inst Phys & Math Universe WPI, Kashiwa, Chiba 2778583, Japan. hayato@icrr.u-tokyo.ac.jp Ma, Wing Yan/HLW-7686-2023; Megias, Guillermo Daniel/K-9075-2014; Nakano, Yuuki/S-2684-2016; Ospina, Nataly/T-9835-2017 Ma, Wing Yan/0000-0003-1251-5493; Megias, Guillermo Daniel/0000-0002-0890-1343; Nakano, Yuuki/0000-0003-1572-3888; Kearns, Edward/0000-0002-1781-150X; Fernandez, Pablo/0000-0001-9034-1930; Hiraki, Takahiro/0000-0002-6235-5830; Lamoureux, Mathieu/0000-0002-8860-5826; Ospina, Nataly/0000-0002-8404-1808; Migenda, Jost/0000-0002-5350-8049; Hasegawa, Takuya/0000-0002-2967-1954 Japanese Ministry of Education, Culture, Sports, Science and Technology; U.S. Department of Energy; U.S. National Science Foundation; National Research Foundation of Korea - Ministry of Science, ICT, and Future Planning [NRF-2009-0083526]; Ministry of Education [2018R1D1A3B07050696, 2018R1D1A1B07049158]; Japan Society for the Promotion of Science; National Natural Science Foundation of China [11235006]; Spanish Ministry of Science, Universities and Innovation [PGC2018-099388-B-I00]; Natural Sciences and Engineering Research Council (NSERC) of Canada; Scinet and Westgrid consortia of Compute Canada; National Science Centre, Poland [2015/18/E/ST2/00758]; Science and Technology Facilities Council (STFC); GridPP, U.K.; European Union [754496, 822070, 872549] Japanese Ministry of Education, Culture, Sports, Science and Technology(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); U.S. Department of Energy(United States Department of Energy (DOE)); U.S. National Science Foundation(National Science Foundation (NSF)); National Research Foundation of Korea - Ministry of Science, ICT, and Future Planning(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of Korea); Ministry of Education; Japan Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Science, Universities and Innovation(Spanish Government); Natural Sciences and Engineering Research Council (NSERC) of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); Scinet and Westgrid consortia of Compute Canada; National Science Centre, Poland(National Science Centre, Poland); Science and Technology Facilities Council (STFC)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)Science and Technology Development Fund (STDF)); GridPP, U.K.; European Union(European Commission) We gratefully acknowledge the cooperation of the Kamioka Mining and Smelting Company. The Super-Kamiokande experiment has been built and operated from funding by the Japanese Ministry of Education, Culture, Sports, Science and Technology, the U.S. Department of Energy, and the U.S. National Science Foundation. Some of us have been supported by funds from the National Research Foundation of Korea NRF-2009-0083526 (KNRC) funded by the Ministry of Science, ICT, and Future Planning and the Ministry of Education (2018R1D1A3B07050696, 2018R1D1A1B07049158), the Japan Society for the Promotion of Science, the National Natural Science Foundation of China under Grants No. 11235006, the Spanish Ministry of Science, Universities and Innovation (grant PGC2018-099388-B-I00), the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Scinet and Westgrid consortia of Compute Canada, the National Science Centre, Poland (2015/18/E/ST2/00758), the Science and Technology Facilities Council (STFC) and GridPP, U.K., the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement no.754496, H2020-MSCA-RISE2018 JENNIFER2 grant agreement no.822070, and H2020-MSCA-RISE-2019 SK2HK grant agreement no. 872549. 28 0 0 7 7 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-0221 J INSTRUM J. Instrum. OCT 2022.0 17 10 P10029 10.1088/1748-0221/17/10/P10029 0.0 41 Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation 7A8UJ Green Submitted, hybrid, Green Accepted 2023-03-23 WOS:000898723700008 0
J Angeloni, R; Ramos, RC; Catelan, M; Dekany, I; Gran, F; Alonso-Garcia, J; Hempel, M; Navarrete, C; Andrews, H; Aparicio, A; Beamin, JC; Berger, C; Borissova, J; Pena, CC; Cunial, A; de Grijs, R; Espinoza, N; Eyheramendy, S; Lopes, CEF; Fiaschi, M; Hajdu, G; Han, J; Helminiak, KG; Hempel, A; Hidalgo, SL; Ita, Y; Jeon, YB; Jordan, A; Kwon, J; Lee, JT; Martin, EL; Masetti, N; Matsunaga, N; Milone, AP; Minniti, D; Morelli, L; Murgas, F; Nagayama, T; Navarro, C; Ochner, P; Perez, P; Pichara, K; Rojas-Arriagada, A; Roquette, J; Saito, RK; Siviero, A; Sohn, J; Sung, HI; Tamura, M; Tata, R; Tomasella, L; Townsend, B; Whitelock, P Angeloni, R.; Contreras Ramos, R.; Catelan, M.; Dekany, I.; Gran, F.; Alonso-Garcia, J.; Hempel, M.; Navarrete, C.; Andrews, H.; Aparicio, A.; Beamin, J. C.; Berger, C.; Borissova, J.; Contreras Pena, C.; Cunial, A.; de Grijs, R.; Espinoza, N.; Eyheramendy, S.; Ferreira Lopes, C. E.; Fiaschi, M.; Hajdu, G.; Han, J.; Helminiak, K. G.; Hempel, A.; Hidalgo, S. L.; Ita, Y.; Jeon, Y. -B.; Jordan, A.; Kwon, J.; Lee, J. T.; Martin, E. L.; Masetti, N.; Matsunaga, N.; Milone, A. P.; Minniti, D.; Morelli, L.; Murgas, F.; Nagayama, T.; Navarro, C.; Ochner, P.; Perez, P.; Pichara, K.; Rojas-Arriagada, A.; Roquette, J.; Saito, R. K.; Siviero, A.; Sohn, J.; Sung, H-I; Tamura, M.; Tata, R.; Tomasella, L.; Townsend, B.; Whitelock, P. The VVV Templates Project Towards an automated classification of VVV light-curves I. Building a database of stellar variability in the near-infrared ASTRONOMY & ASTROPHYSICS English Article stars: variables: general; surveys; techniques: photometric RR-LYRAE STARS; GALACTIC GLOBULAR-CLUSTERS; PERIODIC VARIABLE-STARS; VISTA SCIENCE ARCHIVES; BAADE-WESSELINK METHOD; GIANT BRANCH STARS; MILKY-WAY BULGE; ABSOLUTE MAGNITUDES; MAGELLANIC CLOUDS; VMC SURVEY Context. The Vista Variables in the Via Lactea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on ESO Visible and Infrared Survey Telescope for Astronomy (VISTA). The VVV survey will eventually deliver a deep near-IR atlas with photometry and positions in five passbands (ZYJHK(s)) and a catalogue of 1-10 million variable point sources - mostly unknown - that require classifications. Aims. The main goal of the VVV Templates Project, which we introduce in this work, is to develop and test the machine-learning algorithms for the automated classification of the VVV light-curves. As VVV is the first massive, multi-epoch survey of stellar variability in the near-IR, the template light-curves that are required for training the classification algorithms are not available. In the first paper of the series we describe the construction of this comprehensive database of infrared stellar variability. Methods. First, we performed a systematic search in the literature and public data archives; second, we coordinated a worldwide observational campaign; and third, we exploited the VVV variability database itself on (optically) well-known stars to gather high-quality infrared light-curves of several hundreds of variable stars. Results. We have now collected a significant (and still increasing) number of infrared template light-curves. This database will be used as a training-set for the machine-learning algorithms that will automatically classify the light-curves produced by VVV. The results of such an automated classification will be covered in forthcoming papers of the series. [Angeloni, R.; Contreras Ramos, R.; Catelan, M.; Dekany, I.; Gran, F.; Alonso-Garcia, J.; Hempel, M.; Navarrete, C.; Andrews, H.; Beamin, J. C.; Espinoza, N.; Hajdu, G.; Jordan, A.; Townsend, B.] Pontificia Univ Catolica Chile, Inst Astrofis, Santiago 7820436, Chile; [Angeloni, R.; Catelan, M.; Jordan, A.] Pontificia Univ Catolica Chile, Ctr Astroingn, Santiago 7820436, Chile; [Angeloni, R.] Max Planck Inst Astron, D-69117 Heidelberg, Germany; [Contreras Ramos, R.; Catelan, M.; Dekany, I.; Gran, F.; Alonso-Garcia, J.; Hempel, M.; Navarrete, C.; Beamin, J. C.; Borissova, J.; Espinoza, N.; Eyheramendy, S.; Hajdu, G.; Jordan, A.; Minniti, D.; Navarro, C.; Pichara, K.; Townsend, B.] Millennium Inst Astrophys, Santiago, Chile; [Berger, C.; Pichara, K.] Pontificia Univ Catolica Chile, Dept Comp Sci, Santiago 7820436, Chile; [Andrews, H.] Leiden Observ, NL-2300 RA Leiden, Netherlands; [Aparicio, A.; Hidalgo, S. L.; Murgas, F.; Tata, R.] IAC, Tenerife 38200, Canary Islands, Spain; [Beamin, J. C.] European So Observ, Santiago 19, Chile; [Borissova, J.; Navarro, C.] Univ Valparaiso, Inst Fis & Astron, Playa Ancha 5030, Casilla, Chile; [Contreras Pena, C.] Univ Hertfordshire, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England; [Cunial, A.; Morelli, L.] Univ Padua, Dipartimento Fis & Astron Galileo Galilei, I-35122 Padua, Italy; [Cunial, A.; Fiaschi, M.; Morelli, L.; Ochner, P.; Siviero, A.; Tomasella, L.] INAF Osservatorio Astron Padova, I-35122 Padua, Italy; [de Grijs, R.] Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China; [de Grijs, R.] Peking Univ, Dept Astron, Beijing 100871, Peoples R China; [Espinoza, N.; Eyheramendy, S.] Pontificia Univ Catolica Chile, Fac Matemat, Santiago 7820436, Chile; [Ferreira Lopes, C. E.] Univ Fed Rio Grande do Norte, BR-59078970 Natal, RN, Brazil; [Han, J.; Lee, J. T.; Sohn, J.] Korea Natl Univ Educ, Cheongwongun 363791, Chungbuk, South Korea; [Helminiak, K. G.] Natl Inst Nat Sci, Natl Astron Observ Japan, Subaru Telescope, Hilo, HI 96720 USA; [Helminiak, K. G.] Nicolaus Copernicus Astron Ctr, Dept Astrophys, PL-87100 Torun, Poland; [Hempel, A.; Minniti, D.] Univ Andres Bello, Dept Ciencias Fis, Santiago, Chile; [Aparicio, A.; Hidalgo, S. L.; Murgas, F.] Univ La Laguna, Dept Astrophys, Tenerife 38200, Canary Islands, Spain; [Ita, Y.] Tohoku Univ, Grad Sch Sci, Astron Inst, Sendai, Miyagi 9808578, Japan; [Jeon, Y. -B.; Sung, H-I] Korea Astron & Space Sci Inst, Taejon 305348, South Korea; [Kwon, J.; Tamura, M.] Natl Astron Observ, Tokyo 181, Japan; [Martin, E. L.] Ctr Astrobiol CSIC INTA, Madrid 28850, Spain; [Masetti, N.] INAF Ist Astrofis Spaziale & Fis Cosm Bologna, I-40129 Bologna, Italy; [Matsunaga, N.; Tamura, M.] Univ Tokyo, Grad Sch Sci, Dept Astron, Tokyo 1130033, Japan; [Milone, A. P.] Australian Natl Univ, Res Sch Astron & Astrophys, Weston, ACT 2611, Australia; [Nagayama, T.] Nagoya Univ, Dept Astrophys, Chikusa Ku, Nagoya, Aichi 4648602, Japan; [Perez, P.] Univ Chile, Dept Ciencias Computac, Santiago, Chile; [Rojas-Arriagada, A.] Univ Nice Sophia Antipolis, CNRS, Observ Cote Azur, Lab Lagrange,UMR 7293, F-06304 Nice, France; [Roquette, J.] Univ Fed Minas Gerais, Dept Fis, ICEx, BR-30270901 Belo Horizonte, MG, Brazil; [Saito, R. K.] Univ Fed Sergipe, Dept Fis, BR-49100000 Sao Cristovao, SE, Brazil; [Whitelock, P.] S African Astron Observ, ZA-7935 Observatory, South Africa; [Whitelock, P.] Univ Cape Town, Dept Astron, Astrophys Cosmol & Grav Ctr, ZA-7701 Rondebosch, South Africa Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Max Planck Society; Pontificia Universidad Catolica de Chile; Leiden University; Leiden University - Excl LUMC; Instituto de Astrofisica de Canarias; European Southern Observatory; Universidad de Valparaiso; University of Hertfordshire; University of Padua; Istituto Nazionale Astrofisica (INAF); Peking University; Peking University; Pontificia Universidad Catolica de Chile; Universidade Federal do Rio Grande do Norte; Korea National University of Education; National Institutes of Natural Sciences (NINS) - Japan; National Astronomical Observatory of Japan (NAOJ); Polish Academy of Sciences; Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences; Universidad Andres Bello; Universidad de la Laguna; Tohoku University; Korea Astronomy & Space Science Institute (KASI); National Institutes of Natural Sciences (NINS) - Japan; National Astronomical Observatory of Japan (NAOJ); Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro de Astrobiologia (INTA); Istituto Nazionale Astrofisica (INAF); University of Tokyo; Australian National University; Nagoya University; Universidad de Chile; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Universite Cote d'Azur; Observatoire de la Cote d'Azur; Universidade Federal de Minas Gerais; Universidade Federal de Sergipe; National Research Foundation - South Africa; South African Astronomical Observatory; University of Cape Town Angeloni, R (corresponding author), Pontificia Univ Catolica Chile, Inst Astrofis, Av Vicuna Mackenna 4860, Santiago 7820436, Chile. rangelon@astro.puc.cl Roquette, Julia/AAS-4696-2020; Minniti, Dante/AAD-7251-2021; Morelli, Lorenzo/AAX-2452-2021; Alonso-García, Javier/I-2723-2015; Hempel, Angela/HKV-6305-2023; Saito, Roberto K./L-6722-2016; Aparicio, Antonio/K-3415-2017; Contreras, Carlos/AAY-5037-2020; Angeloni, Rodolfo/AAA-3136-2020; de Grijs, Richard/AAI-5255-2020; Whitelock, Patricia/AAJ-6959-2021; Lopes, Carlos Eduardo Ferreira/U-9726-2019; Lopes, Carlos Eduardo C. E. Ferreira/E-4228-2016; Hełminiak, Krzysztof Grzegorz/N-6385-2015 Roquette, Julia/0000-0002-0709-4703; Morelli, Lorenzo/0000-0001-6890-3503; Alonso-García, Javier/0000-0003-3496-3772; Saito, Roberto K./0000-0001-6878-8648; Aparicio, Antonio/0000-0002-6054-0004; Angeloni, Rodolfo/0000-0001-7978-7077; de Grijs, Richard/0000-0002-7203-5996; Whitelock, Patricia/0000-0002-4678-4432; Lopes, Carlos Eduardo Ferreira/0000-0002-8525-7977; Lopes, Carlos Eduardo C. E. Ferreira/0000-0002-8525-7977; Hełminiak, Krzysztof Grzegorz/0000-0002-7650-3603; Hajdu, Gergely/0000-0003-0594-9138; Andrews, Heather/0000-0002-6637-2830; Navarrete, Camila/0000-0002-4777-9934; Murgas, Felipe/0000-0001-9087-1245; Tomasella, Lina/0000-0002-3697-2616; Jordan, Andres/0000-0002-5389-3944; Minniti, Dante/0000-0002-7064-099X; Catelan, Marcio/0000-0001-6003-8877; Eyheramendy, Susana/0000-0003-4723-9660; Hidalgo, Sebastian/0000-0002-0002-9298; Beamin, Juan Carlos/0000-0002-8409-6534; Masetti, Nicola/0000-0001-9487-7740; Milone, Antonino P./0000-0001-7506-930X Ministry for the Economy, Development, and Tourism's Programa Iniciativa Cientifica Milenio [IC 120009]; Proyecto Basal [PFB-06/2007]; IAC; Ministry of Science and Competitiveness of the Kingdom of Spain [AYA2010-16717]; Proyectos Fondecyt Regulares [1110326, 1130196, 1141141]; Vicerrectoria de Investigacion (VRI), Pontificia Universidad Catolica de Chile [25/2011]; Proyecto Fondecyt Postdoctoral [3130320, 3130552]; CONICYT-PCHA Magister Nacional [2014-22141509]; CONYCIT-PCHA/Magister Nacional [2012-22121934]; CONICYT-PCHA/Doctorado Nacional [2013-21130248]; FONDECYT [1120601]; National Natural Science Foundation of China (NSFC) [11373010]; CNPq/Brazil [150495/2014-5, 310636/2013-2, 481468/2013-7]; National Astronomical Observatory of Japan; Polish National Science Center [2011/03/N/ST9/01819]; Schmidt Telescope (Asiago, Italy) of INAF - Osservatorio Astronomico di Padova; SA NRF; Grants-in-Aid for Scientific Research [22000005, 14F04023] Funding Source: KAKEN Ministry for the Economy, Development, and Tourism's Programa Iniciativa Cientifica Milenio; Proyecto Basal(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT PIA/BASAL); IAC; Ministry of Science and Competitiveness of the Kingdom of Spain; Proyectos Fondecyt Regulares(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT); Vicerrectoria de Investigacion (VRI), Pontificia Universidad Catolica de Chile; Proyecto Fondecyt Postdoctoral(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT); CONICYT-PCHA Magister Nacional; CONYCIT-PCHA/Magister Nacional; CONICYT-PCHA/Doctorado Nacional; FONDECYT(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); CNPq/Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); National Astronomical Observatory of Japan(National Institutes of Natural Sciences (NINS) - Japan); Polish National Science Center; Schmidt Telescope (Asiago, Italy) of INAF - Osservatorio Astronomico di Padova; SA NRF; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This project is supported by the Ministry for the Economy, Development, and Tourism's Programa Iniciativa Cientifica Milenio through grant IC 120009, awarded to the Millennium Institute of Astrophysics (MAS), by Proyecto Basal PFB-06/2007, and also by the IAC and by the Ministry of Science and Competitiveness of the Kingdom of Spain (grant AYA2010-16717). M. C., I. D., EG., C.N., and J.A.G. acknowledge support by Proyectos Fondecyt Regulares #1110326, #1130196 and #1141141. M. C., S. E., A.J. and K. P. acknowledge support from the Vicerrectoria de Investigacion (VRI), Pontificia Universidad Catolica de Chile (Proyecto Investigacion Interdisciplinaria 25/2011). R. C. R. also acknowledges support by Proyecto Fondecyt Postdoctoral #3130320. F. G. also acknowledges support from CONICYT-PCHA Magister Nacional #2014-22141509. J.A.G. also acknowledges support by Proyecto Fondecyt Postdoctoral #3130552. C.N. also acknowledges financial support from CONYCIT-PCHA/Magister Nacional #2012-22121934. J.C.B. acknowledges support by CONICYT-PCHA/Doctorado Nacional#2013-21130248. J.B. received support from FONDECYT Regular #1120601. R.d.G. acknowledges research support from the National Natural Science Foundation of China (NSFC) through grant #11373010. C. E. F. L. acknowledges support from CNPq/Brazil through projects 150495/2014-5. K. G. H. acknowledges support provided by the National Astronomical Observatory of Japan as Subaru Astronomical Research Fellow, and by the Polish National Science Center through grant 2011/03/N/ST9/01819. We kindly acknowledge the support provided by Schmidt Telescope (Asiago, Italy) of INAF - Osservatorio Astronomico di Padova (Large program 2012-2013 - P.I. Angeloni). R. K. S. acknowledges support from CNPq/Brazil through projects 310636/2013-2 and 481468/2013-7. P. A. W. thanks the SA NRF for a research grant. This research has made use of the International Variable Star Index (VSX) database, operated at AAVSO, Cambridge, Massachusetts, USA. This research has also made use of the SIMBAD database, operated at CDS, Strasbourg, France. 107 27 27 0 13 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. JUL 2014.0 567 A100 10.1051/0004-6361/201423904 0.0 11 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics AO2VU Green Submitted, Green Published, Bronze 2023-03-23 WOS:000341185300130 0
J Wang, YK; Zhao, K; Guirao, JLG; Pan, K; Chen, HT Wang, Yaokun; Zhao, Kun; Guirao, Juan L. G.; Pan, Kai; Chen, Huatao Online intelligent maneuvering penetration methods of missile with respect to unknown intercepting strategies based on reinforcement learning ELECTRONIC RESEARCH ARCHIVE English Article penetration of missile; artificial intelligence; Q -learning; reinforcement learning PROPORTIONAL NAVIGATION This paper considers the maneuvering penetration methods of missile which do not know the intercepting strategies of the interceptor beforehand. Based on reinforcement learning, the online intelligent maneuvering penetration methods of missile are derived. When the missile is locked by the interceptor, in terms of the tracking characteristics of the interceptor, the missile carries out tentative maneuvers which lead to the interceptor makes the responses respectively, in the light of the information on interceptor responses which can be gathered by the missile-borne detectors, online game confrontation learning is employed to increase the miss distance of the interceptor in guidance blind area by reinforcement learning algorithm, the results of which are used to generate maneuvering strategies that make the missile to achieve the successful penetration. The simulation results show that, compared with no maneuvering methods or random maneuvering methods, the methods proposed not only present higher probability of successful penetration, but also need less overload and lower command switching frequency. Moreover, the effectiveness of this maneuvering penetration methods can be realized under the condition of limited number of training. [Wang, Yaokun; Pan, Kai; Chen, Huatao] Shandong Univ Technol, Sch Math & Stat, Div Dynam & Control, Zibo 255000, Peoples R China; [Zhao, Kun] Beijing Electromech Engn Inst, Beijing 100074, Peoples R China; [Guirao, Juan L. G.] Tech Univ Cartagena, Hosp Marina, Dept Appl Math & Stat, Cartagena 30203, Spain; [Guirao, Juan L. G.] King Abdulaziz Univ, Fac Sci, Dept Math, POB 80203, Jeddah 21589, Saudi Arabia Shandong University of Technology; Universidad Politecnica de Cartagena; King Abdulaziz University Chen, HT (corresponding author), Shandong Univ Technol, Sch Math & Stat, Div Dynam & Control, Zibo 255000, Peoples R China. htchencn@sdut.edu.cn National Natural Science Foundation of China; Shandong Provincial Natural Science Foundation; [12072178]; [12002194]; [12202250]; [ZR2020MA054]; [ZR2020QA037] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Natural Science Foundation(Natural Science Foundation of Shandong Province); ; ; ; ; This work have been supported by National Natural Science Foundation of China (No. 12072178; No. 12002194; N0. 12202250) , Project (No. ZR2020MA054; No. ZR2020QA037) supported by Shandong Provincial Natural Science Foundation. 32 0 0 13 13 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES 2688-1594 ELECTRON RES ARCH Electron. Res. Arch. 2022.0 30 12 4366 4381 10.3934/era.2022221 0.0 16 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 5K1KK gold 2023-03-23 WOS:000869491500001 0
J Gadeke, A; Krysanova, V; Aryal, A; Chang, JF; Grillakis, M; Hanasaki, N; Koutroulis, A; Pokhrel, Y; Satoh, Y; Schaphoff, S; Schmied, HM; Stacke, T; Tang, QH; Wada, Y; Thonicke, K Gaedeke, Anne; Krysanova, Valentina; Aryal, Aashutosh; Chang, Jinfeng; Grillakis, Manolis; Hanasaki, Naota; Koutroulis, Aristeidis; Pokhrel, Yadu; Satoh, Yusuke; Schaphoff, Sibyll; Mueller Schmied, Hannes; Stacke, Tobias; Tang, Qiuhong; Wada, Yoshihide; Thonicke, Kirsten Performance evaluation of global hydrological models in six large Pan-Arctic watersheds CLIMATIC CHANGE English Article Global Water Models; Model performance; Model evaluation; Arctic watersheds; Boruta feature selection LAND-SURFACE MODEL; PART 1; RUNOFF; ENVIRONMENT; LATITUDES; ACCURACY; LEVEL; JULES; MASS Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average API(discharge) (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds API(discharge) is 43%. WATERGAP2 and MATSIRO present the highest (API(discharge) > 55%) while ORCHIDEE and JULES-W1 the lowest (API(discharge) <= 25%) performing GWMs over all watersheds. For the high and low flows, average API(extreme) is 35% and 26%, respectively, and over six GWMs API(SWE) is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. [Gaedeke, Anne; Krysanova, Valentina; Aryal, Aashutosh; Schaphoff, Sibyll; Thonicke, Kirsten] Leibniz Assoc, Potsdam Inst Climate Impact Res, D-14412 Potsdam, Germany; [Chang, Jinfeng] Univ Paris Saclay, UVSQ, CEA, CNRS,LSCE,IPSL,Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Chang, Jinfeng; Satoh, Yusuke; Wada, Yoshihide] Int Inst Appl Syst Anal IIASA, Schlosspl 1, A-2361 Laxenburg, Austria; [Chang, Jinfeng] Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China; [Grillakis, Manolis; Koutroulis, Aristeidis] Tech Univ Crete, Sch Environm Engn, Khania 73100, Greece; [Grillakis, Manolis] Fdn Res & Technol Hellas, Inst Mediterranean Studies, Lab Geophys Remote Sensing & Archaeoenvironm, Rethimnon 74100, Greece; [Hanasaki, Naota; Satoh, Yusuke] Natl Inst Environm Studies, 16-2 Onogawa, Tsukuba, Ibaraki 3058506, Japan; [Pokhrel, Yadu] Michigan State Univ, Dept Civil & Environm Engn, E Lansing, MI 48824 USA; [Mueller Schmied, Hannes] Goethe Univ Frankfurt, Inst Phys Geog, D-60438 Frankfurt, Germany; [Mueller Schmied, Hannes] Senckenberg Leibniz Biodivers & Climate Res Ctr S, D-60325 Frankfurt, Germany; [Stacke, Tobias] Helmholtz Zentrum Geesthacht, Inst Coastal Res, D-21502 Geesthacht, Germany; [Tang, Qiuhong] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China Potsdam Institut fur Klimafolgenforschung; UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); International Institute for Applied Systems Analysis (IIASA); Zhejiang University; Technical University of Crete; Foundation for Research & Technology - Hellas (FORTH); National Institute for Environmental Studies - Japan; Michigan State University; Goethe University Frankfurt; Helmholtz Association; Helmholtz-Zentrum Geesthacht - Zentrum fur Material- und Kustenforschung; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Gadeke, A (corresponding author), Leibniz Assoc, Potsdam Inst Climate Impact Res, D-14412 Potsdam, Germany. a.gaedeke@gmail.com Koutroulis, Aristeidis/O-9601-2016; CHANG, Jinfeng/A-4603-2019; Wada, Yoshihide/F-3595-2012; Schmied, Hannes Müller/K-6231-2013; Koutroulis, Aristeidis/H-4393-2019; Hanasaki, Naota/C-2932-2009; Tang, Qiuhong/AAD-9832-2022; Tang, Qiuhong/ABA-2194-2021; Gädeke, Anne/ABG-9630-2021; Pokhrel, Yadu/J-6440-2013; Pokhrel, Yadu/AAC-1776-2021; Grillakis, Manolis/I-3582-2019 Koutroulis, Aristeidis/0000-0002-2999-7575; CHANG, Jinfeng/0000-0003-4463-7778; Wada, Yoshihide/0000-0003-4770-2539; Schmied, Hannes Müller/0000-0001-5330-9923; Koutroulis, Aristeidis/0000-0002-2999-7575; Hanasaki, Naota/0000-0002-5092-7563; Tang, Qiuhong/0000-0002-0886-6699; Gädeke, Anne/0000-0003-0514-2908; Pokhrel, Yadu/0000-0002-1367-216X; Aryal, Aashutosh/0000-0002-2890-4867; Stacke, Tobias/0000-0003-4637-5337 Projekt DEAL Projekt DEAL Open Access funding enabled and organized by Projekt DEAL. 51 8 8 4 20 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0165-0009 1573-1480 CLIMATIC CHANGE Clim. Change DEC 2020.0 163 3 SI 1329 1351 10.1007/s10584-020-02892-2 0.0 NOV 2020 23 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences PT4KO Green Published, Green Accepted, hybrid 2023-03-23 WOS:000592141800001 0
J Hu, Y; Zhang, Y; Gao, XZ; Gong, DW; Song, XF; Guo, YA; Wang, J Hu, Ying; Zhang, Yong; Gao, Xiaozhi; Gong, Dunwei; Song, Xianfang; Guo, Yinan; Wang, Jun A federated feature selection algorithm based on particle swarm optimization under privacy protection KNOWLEDGE-BASED SYSTEMS English Article Particle swarm optimization; Feature selection; Evolutionary computation; Privacy protection DISTRIBUTED FEATURE-SELECTION; GENETIC ALGORITHM; MICROARRAY DATA; CLASSIFICATION Feature selection is an important preprocessing technique in the fields of data mining and machine learning. With the promotion of privacy protection awareness, recently it becomes a very practical and challenging issue to select high-quality feature subsets while ensuring the privacy of all participants. However, there is a lack of research results on this issue, i.e., feature selection under privacy protection. Aiming at the issue, this paper proposes a federated feature selection framework for the first time. In the framework, inspiring by the idea of federated learning, a credible third participant is introduced to process and integrate optimal feature subsets from multiple participants. On the basis of the framework, a federated evolutionary feature selection algorithm based on particle swarm optimization is proposed to effectively solve feature selection problems with multiple participants under privacy protection. Two new operators satisfying the requirement of privacy protection, i.e., the feature assembling strategy with multi-participant cooperation and the swarm initialization strategy guided by assembling solution, are designed to improve the ability of the proposed algorithm. Compared with several typical assembling feature selection algorithms on 15 data sets, experimental results show that the proposed algorithm can significantly improve the classification accuracy of the feature subset selected by each participant, while protecting the privacy of data. (c) 2022 Elsevier B.V. All rights reserved. [Hu, Ying] Anhui Normal Univ, Sch Comp & Informat, Wuhu 241000, Peoples R China; [Hu, Ying; Zhang, Yong; Gong, Dunwei; Song, Xianfang; Guo, Yinan; Wang, Jun] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China; [Gao, Xiaozhi] Univ Eastern Finland, Sch Comp, Kuopio, Finland Anhui Normal University; China University of Mining & Technology; University of Eastern Finland Zhang, Y (corresponding author), China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China.;Gao, XZ (corresponding author), Univ Eastern Finland, Sch Comp, Kuopio, Finland. yongzh401@126.com; xiao.z.gao@gmail.com ZHANG, YONG/O-3660-2019 ZHANG, YONG/0000-0003-0026-8181 65 2 2 5 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JAN 25 2023.0 260 10.1016/j.knosys.2022.110122 0.0 DEC 2022 20 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7M6OA 2023-03-23 WOS:000906773200001 0
C Farnadi, G; Tang, J; De Cock, M; Moens, MF ACM Farnadi, Golnoosh; Tang, Jie; De Cock, Martine; Moens, Marie-Francine User Profiling through Deep Multimodal Fusion WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING English Proceedings Paper 11th ACM International Conference on Web Search and Data Mining FEB 05-09, 2018 Marina Del Rey, CA Assoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Informat Retrieval,Assoc Comp Machinery SIGKDD,Assoc Comp Machinery SIGMOD,Assoc Comp Machinery SIGWEB TEXT User profiling in social media has gained a lot of attention due to its varied set of applications in advertising, marketing, recruiting, and law enforcement. Among the various techniques for user modeling, there is fairly limited work on how to merge multiple sources or modalities of user data - such as text, images, and relations - to arrive at more accurate user profiles. In this paper, we propose a deep learning approach that extracts and fuses information across different modalities. Our hybrid user profiling framework utilizes a shared representation between modalities to integrate three sources of data at the feature level, and combines the decision of separate networks that operate on each combination of data sources at the decision level. Our experimental results on more than 5K Facebook users demonstrate that our approach outperforms competing approaches for inferring age, gender and personality traits of social media users. We get highly accurate results with AUC values of more than 0.9 for the task of age prediction and 0.95 for the task of gender prediction. [Farnadi, Golnoosh] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA; [Farnadi, Golnoosh; De Cock, Martine] Univ Ghent, Ghent, Belgium; [Tang, Jie] Tsinghua Univ, Beijing, Peoples R China; [De Cock, Martine] Univ Washington, Tacoma, WA USA; [Moens, Marie-Francine] Katholieke Univ Leuven, Leuven, Belgium University of California System; University of California Santa Cruz; Ghent University; Tsinghua University; University of Washington; University of Washington Tacoma; KU Leuven Farnadi, G (corresponding author), Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA.;Farnadi, G (corresponding author), Univ Ghent, Ghent, Belgium. gfarnadi@ucsc.edu; jietang@tsinghua.edu.cn; mdecock@uw.edu; sien.moens@cs.kuleuven.be Moens, Marie-Francine/B-8378-2014 De Cock, Martine/0000-0001-7917-0771 34 49 51 0 13 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5581-0 2018.0 171 179 10.1145/3159652.3159691 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL8HW 2023-03-23 WOS:000456363600026 0
J Peng, J; Wang, MH; Sampson, D Peng, Jun; Wang, Minhong; Sampson, Demetrios Visualizing the Complex Process for Deep Learning with an Authentic Programming Project EDUCATIONAL TECHNOLOGY & SOCIETY English Article Visualization; Complex process; Computer programming; Authentic learning; Computer-based learning environment; Project-based learning KNOWLEDGE-CONSTRUCTION; MECHANISMS; EDUCATION; GUIDANCE Project-based learning (PjBL) has been increasingly used to connect abstract knowledge and authentic tasks in educational practice, including computer programming education. Despite its promising effects on improving learning in multiple aspects, PjBL remains a struggle due to its complexity. Completing an authentic programming project involves a complex process of applying programming strategies to design and develop artifacts. Programming strategies are often implicit and hard to capture, but critical for programming performance. This study proposes a visualization-based learning environment that externalizes the complex process of applying programming strategies to the design and development of solutions to an authentic programming project. It aims to make the complex process accessible, trackable, and attainable with the support of technology. Twenty-nine senior college students participated in this study, using the proposed learning environment to complete a PjBL module of ASP.NET. The proposed approach improved students' programming performance and subject knowledge and activated their intrinsic motivation to learn programming. [Peng, Jun; Wang, Minhong] Univ Hong Kong, KM&EL Lab, Fac Educ, Hong Kong, Hong Kong, Peoples R China; [Sampson, Demetrios] Curtin Univ, Sch Educ, Perth, WA, Australia; [Sampson, Demetrios] Univ Piraeus, Dept Digital Syst, Piraeus, Greece University of Hong Kong; Curtin University; University of Piraeus Wang, MH (corresponding author), Univ Hong Kong, KM&EL Lab, Fac Educ, Hong Kong, Hong Kong, Peoples R China. u3002116@connect.hku.hk; magwang@hku.hk; demetrios.sampson@curtin.edu.au Sampson, Demetrios/AAP-6039-2021 Wang, Minhong/0000-0002-1084-6814; Sampson, Demetrios/0000-0002-0174-0137 Hong Kong SAR Government [17201415]; University of Hong Kong [201611159071]; Greek General Secretariat for Research and Technology under the Matching Funds [325123] Hong Kong SAR Government; University of Hong Kong(University of Hong Kong); Greek General Secretariat for Research and Technology under the Matching Funds This research is supported by the General Research Fund (Project No. 17201415) from the Hong Kong SAR Government, and the Seeding Fund for Basic Research (Project No. 201611159071) and the Faculty Research Travel Support for Full-time MPhil and PhD students from the University of Hong Kong. It was partially supported by the Greek General Secretariat for Research and Technology under the Matching Funds 2014-2016 (Project No. 325123). The authors would thank Professor Haijing Jiang for his valuable support for this project. 46 8 8 2 27 NATL TAIWAN NORMAL UNIV, TAIWAN TAIPEI 129, SEC 1, HEPING E RD, EDUCATION BUILDING, RM 611, TAIPEI, 10644, TAIWAN 1176-3647 1436-4522 EDUC TECHNOL SOC Educ. Technol. Soc. OCT 2017.0 20 4 SI 275 287 13 Education & Educational Research Social Science Citation Index (SSCI) Education & Educational Research FK2UJ 2023-03-23 WOS:000413337800024 0
C Liu, SS; Yan, Z IEEE Liu, Shushu; Yan, Zheng Verifiable Edge Computing for Indoor Positioning ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) IEEE International Conference on Communications English Proceedings Paper IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond JUN 07-11, 2020 ELECTR NETWORK IEEE,Huawei,ZTE,Qualcomm fingerprint-based indoor positioning; edge computing; verification; backdoor Edge computing has been widely adopted in many systems, thanks for its advantages to offer low latency and alleviate heavy request loads from end users. Its integration with indoor positioning is one of promising research topics. Different from a traditional positioning system where a user normally query remotely deployed positioning services provided by a Location Information Service Provider (LIS), LIS will outsource its service to an edge device, and the user can obtain the service by directly accessing the edge device in an edge computing-based system. Though the benefits from edge computing, there is still some open issues for service outsourcing. One of them is how to ensure that the outsourced service is executed honestly by the edge device. However, the current literature has not yet seriously studied this issue with a feasible solution. In this paper, we design a verification scheme to solve this open problem for indoor positioning based on edge computing. By injecting some specially designed dataset into a trained machine learning based positioning model, the functionality of outsourced model on edge devices can be verified through this dataset with regard to its prediction accuracy from outsourced model. The verification is successful only when the prediction accuracy can pass a threshold. In experiments, we provide extensive empirical evidence using state-of-the-art positioning models based on realworld datasets to prove the effectiveness of our proposed scheme and meanwhile investigate the effects caused by different factors. [Liu, Shushu; Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Peoples R China Aalto University; Xidian University Liu, SS (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo, Finland. liu.shushu@aalto.fi; zyan@xidian.edu.cn yang, zheng/HGC-7753-2022; zheng, yan/GQY-6668-2022 Academy of Finland [308087, 314203]; National Natural Science Foundation of China [61672410, 61802293]; Key Lab of Information Network Security, Ministry of Public Security [C18614]; open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China [CLDL-20182119]; Shaanxi Innovation Team project [2018TD-007]; 111 project [B16037] Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Lab of Information Network Security, Ministry of Public Security; open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China; Shaanxi Innovation Team project; 111 project(Ministry of Education, China - 111 Project) The work is supported in part by the Academy of Finland under Grants 308087 and 314203, the National Natural Science Foundation of China under Grants 61672410 and 61802293, the Key Lab of Information Network Security, Ministry of Public Security under grant No. C18614, the open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China under grant CLDL-20182119, the Shaanxi Innovation Team project under grant 2018TD-007, and the 111 project under grant B16037. 32 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-3607 978-1-7281-5089-5 IEEE ICC 2020.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BQ5PR 2023-03-23 WOS:000606970301058 0
J Guo, JZ; Lei, Z; Wan, J; Avots, E; Hajarolasvadi, N; Knyazev, B; Kuharenko, A; Jacques, JCS; Baro, X; Demirel, H; Escalera, S; Allik, J; Anbarjafari, G Guo, Jianzhu; Lei, Zhen; Wan, Jun; Avots, Egils; Hajarolasvadi, Noushin; Knyazev, Boris; Kuharenko, Artem; Silveira Jacques Junior, Julio C.; Baro, Xavier; Demirel, Hasan; Escalera, Sergio; Allik, Jueri; Anbarjafari, Gholamreza Dominant and Complementary Emotion Recognition From Still Images of Faces IEEE ACCESS English Article Dominant and complementary emotion recognition; compound emotions; fine-grained face emotion dataset FACIAL EXPRESSION RECOGNITION; EYES Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition. [Guo, Jianzhu; Lei, Zhen; Wan, Jun] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China; [Guo, Jianzhu; Lei, Zhen; Wan, Jun] Univ Chinese Acad Sci, Beijing 101408, Peoples R China; [Avots, Egils; Anbarjafari, Gholamreza] Univ Tartu, Inst Technol, iCV Res Lab, EE-50411 Tartu, Estonia; [Hajarolasvadi, Noushin; Demirel, Hasan] Eastern Mediterranean Univ, Dept Elect & Elect Engn, Mersin 10, Gazimagusa, Turkey; [Knyazev, Boris; Kuharenko, Artem] NTechLab, Moscow 123056, Russia; [Silveira Jacques Junior, Julio C.; Baro, Xavier] Univ Oberta Catalunya, Barcelona 08018, Spain; [Silveira Jacques Junior, Julio C.; Baro, Xavier; Escalera, Sergio] Comp Vis Ctr, Barcelona 08193, Spain; [Escalera, Sergio] Univ Barcelona, Barcelona 08007, Spain; [Allik, Jueri] Univ Tartu, Inst Psychol, EE-50090 Tartu, Estonia; [Anbarjafari, Gholamreza] Hasan Kalyoncu Univ, Dept Elect & Elect Engn, TR-27410 Gaziantep, Turkey Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Tartu; Eastern Mediterranean University; UOC Universitat Oberta de Catalunya; Centre de Visio per Computador (CVC); University of Barcelona; University of Tartu; Hasan Kalyoncu University Wan, J (corresponding author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.;Wan, J (corresponding author), Univ Chinese Acad Sci, Beijing 101408, Peoples R China.;Anbarjafari, G (corresponding author), Univ Tartu, Inst Technol, iCV Res Lab, EE-50411 Tartu, Estonia.;Escalera, S (corresponding author), Comp Vis Ctr, Barcelona 08193, Spain.;Escalera, S (corresponding author), Univ Barcelona, Barcelona 08007, Spain.;Anbarjafari, G (corresponding author), Hasan Kalyoncu Univ, Dept Elect & Elect Engn, TR-27410 Gaziantep, Turkey. jun.wan@nlpr.ia.ac.cn; sergio@maia.ub.es; shb@ut.ee Escalera, Sergio/L-2998-2015; Baró, Xavier/A-4064-2011; Hajarolasvadi, Noushin/T-6209-2019; Demirel, Hasan/H-5769-2013; Allik, Jüri/D-5609-2009 Escalera, Sergio/0000-0003-0617-8873; Baró, Xavier/0000-0001-5338-3007; Allik, Jüri/0000-0002-8358-4747; Hajarolasvadi, Noushin/0000-0002-3120-5370 Estonian Research Council [PUT638, IUT213]; Estonian Center of Excellence in IT through the European Regional Development Fund; Spanish projects (MINECO/FEDER, UE) [TIN2015-66951-C2-2-R, TIN2016-74946-P]; CERCA Programme / Generalitat de Catalunya; European Commission Horizon 2020 granted project SEE.4C [H2020-ICT-2015]; CERCA Programme/Generalitat de Catalunya; National Key Research and Development Plan [2016YFC0801002]; Chinese National Natural Science Foundation [61502491, 61473291, 61572501, 61572536, 61673052, 61773392, 61403405]; Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project [116E097] Estonian Research Council(Estonian Research Council); Estonian Center of Excellence in IT through the European Regional Development Fund; Spanish projects (MINECO/FEDER, UE); CERCA Programme / Generalitat de Catalunya; European Commission Horizon 2020 granted project SEE.4C; CERCA Programme/Generalitat de Catalunya; National Key Research and Development Plan; Chinese National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project This work was supported in part by the Estonian Research Council under Grant PUT638 and Grant IUT213, in part by the Estonian Center of Excellence in IT through the European Regional Development Fund, in part by the Spanish projects (MINECO/FEDER, UE) under Grant TIN2015-66951-C2-2-R and Grant TIN2016-74946-P and CERCA Programme / Generalitat de Catalunya, in part by the European Commission Horizon 2020 granted project SEE.4C under Grant H2020-ICT-2015, in part by the CERCA Programme/Generalitat de Catalunya, in part by the National Key Research and Development Plan under Grant 2016YFC0801002, in part by the Chinese National Natural Science Foundation Projects under Grant 61502491, Grant 61473291, Grant 61572501, Grant 61572536, Grant 61673052, Grant 61773392, and Grant 61403405, and in part by the Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project under Grant 116E097. 51 36 36 1 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 26391 26403 10.1109/ACCESS.2018.2831927 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications GJ0HX Green Published, gold 2023-03-23 WOS:000434935200001 0
J Qin, JS; Zeng, XQ; Wu, SL; Zou, Y Qin, Jisheng; Zeng, Xiaoqin; Wu, Shengli; Zou, Yang Context-sensitive graph representation learning CONNECTION SCIENCE English Article Graph convolutional network; multi-semantic alignment; semantic alignment; GCN NETWORK Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce. In this paper, we propose a Multi-Semantic Aligned Graph Convolutional Network (MSAGCN), which contains two fundamental operations: multi-angle aggregation and semantic alignment, to resolve two challenges simultaneously. The core of MSAGCN is the aggregation of nodes that belong to the same class from three perspectives: nodes, features, and graph structure, and expects the obtained node features to be mapped nearby. Specifically, multi-angle aggregation is applied to extract features from three angles of the labelled nodes, and semantic alignment is utilised to align the semantics in the extracted features to enhance the similar content from different angles. In this way, the problem of over-smoothing and over-fitting for GCN can be alleviated. We perform the node clustering task on three citation datasets, and the experimental results demonstrate that our method outperforms the state-of-the-art (SOTA) baselines. [Qin, Jisheng; Zeng, Xiaoqin; Zou, Yang] Hohai Univ, Inst Intelligence Sci & Technol, Nanjing, Peoples R China; [Wu, Shengli] Ulster Univ, Sch Comp, Belfast, Antrim, North Ireland Hohai University; Ulster University Zeng, XQ (corresponding author), Hohai Univ, Inst Intelligence Sci & Technol, Nanjing, Peoples R China. xzeng@hhu.edu.cn Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX21_0484] Postgraduate Research & Practice Innovation Program of Jiangsu Province This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_0484). 46 2 2 9 9 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0954-0091 1360-0494 CONNECT SCI Connect. Sci. DEC 31 2022.0 34 1 2313 2331 10.1080/09540091.2022.2115010 0.0 19 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4N4WA gold, Green Published 2023-03-23 WOS:000854019200001 0
J Aftab, MU; Munir, Y; Oluwasanmi, A; Qin, ZG; Aziz, MH; Zakria; Son, NT; Iran, V Aftab, Muhammad Umar; Munir, Yasir; Oluwasanmi, Ariyo; Qin, Zhiguang; Aziz, Muhammad Haris; Zakria; Son, Ngo Tung; Iran, Van Dinh A Hybrid Access Control Model With Dynamic COI for Secure Localization of Satellite and IoT-Based Vehicles IEEE ACCESS English Article Access control; hybrid access control; secure vehicle localization; machine learning; neural networks; Internet of Things EFFICIENT ESTIMATOR; TDOA; RBAC Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept. [Aftab, Muhammad Umar; Oluwasanmi, Ariyo; Qin, Zhiguang; Zakria] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China; [Munir, Yasir] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Peoples R China; [Aziz, Muhammad Haris] Univ Engn & Technol Taxila, Ind Engn Dept, Taxila 47050, Pakistan; [Son, Ngo Tung] FPT Univ, Comp Fundamental Dept, Hanoi 10000, Vietnam; [Iran, Van Dinh] Univ Freiburg, Dept Comp Sci, D-79098 Freiburg, Germany University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; University of Engineering & Technology Taxila; FPT University; University of Freiburg Qin, ZG (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China. qinzg@uestc.edu.cn Oluwasanmi, Ariyo/ABD-9254-2021; Aftab, Muhammad Umar/ABF-5617-2020 Oluwasanmi, Ariyo/0000-0001-9853-9554; Tran, Van Dinh/0000-0002-7357-4959; Aftab, Muhammad Umar/0000-0002-9649-7757; Munir, Yasir/0000-0003-2898-6361; Ngo, Tung Son/0000-0003-4098-3147 National Natural Science Foundation of China through the Project Key Theories and Techniques of Privacy and Security Based on Data Life-Cycle [61520106007] National Natural Science Foundation of China through the Project Key Theories and Techniques of Privacy and Security Based on Data Life-Cycle This work was supported by the National Natural Science Foundation of China through the Project Key Theories and Techniques of Privacy and Security Based on Data Life-Cycle under Project 61520106007. 58 15 15 6 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 24196 24208 10.1109/ACCESS.2020.2969715 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LC5XY gold 2023-03-23 WOS:000525406200003 0
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F.; Sun, J.; Wang, F.; Xiao, R.; Xie, W.; Cheng, T.; Dolen, J.; Parashar, N.; Chen, Z.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Kilpatrick, M.; Li, W.; Michlin, B.; Padley, B. P.; Roberts, J.; Rorie, J.; Shi, W.; Tu, Z.; Zabel, J.; Zhang, A.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Taus, R.; Verzetti, M.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Espinosa, T. A. Gomez; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Elayavalli, R. Kunnawalkam; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Heideman, J.; Riley, G.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Luo, S.; Mueller, R.; Patel, R.; Perloff, A.; Pernie, L.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Alvarez, J. D. Ruiz; Sheldon, P.; Tuo, S.; Velkovska, J.; Verweij, M.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Carlsmith, D.; Dasu, S.; Dodd, L.; Gomber, B.; Grothe, M.; Herndon, M.; Herve, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Long, K.; Loveless, R.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Woods, N. CMS Collaboration Measurement of the CP-violating phase phi(s) in the B-s(0) -> J/psi phi(1020) -> mu(+)mu-K+K- channel in proton-proton collisions at root s=13 TeV PHYSICS LETTERS B English Article CMS; Physics; Oscillations; B physics QED RADIATIVE-CORRECTIONS; UNIVERSAL MONTE-CARLO; ANGULAR-DISTRIBUTIONS; PHOTOS The CP-violating weak phase ?s and the decay width difference ??s between the light and heavy B0s mass eigenstates are measured with the CMS detector at the LHC in a sample of 48 500 reconstructed B0s? J/I) d (1020) ?11+11? K+K? events. The measurement is based on a data sample corresponding to an integrated luminosity of 96.4 fb?1, collected in proton-proton collisions at ?s = 13 TeV in 2017?2018. To extract the values of ?s and ??s, a time-dependent and flavor-tagged angular analysis of the 11+11?K+K? final state is performed. The analysis employs a dedicated tagging trigger and a novel opposite-side muon flavor tagger based on machine learning techniques. The measurement yields ?s = ?11 ?50 (stat) ? 10 (syst) mrad and ??s = 0.114 ? 0.014 (stat)? 0.007 (syst) ps?1, in agreement with the standard model predictions. When combined with the previous CMS measurement at ?s = 8 TeV, the following values are obtained: ?s = ?21 ? 44 (stat) ? 10 (syst) mrad, ??s = 0.1032 ? 0.0095 (stat) ? 0.0048 (syst) ps?1, a significant improvement over the 8 TeV result. ? 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license [Sirunyan, A. 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A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van De Klundert, M.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.] Univ Antwerp, Antwerp, Belgium; [Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I.; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.] Vrije Univ Brussel, Brussels, Belgium; [Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. K.; Lenzi, T.; Luetic, J.; Postiau, N.; Starling, E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wang, Q.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Roskas, C.; Trocino, D.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Zaganidis, N.] Univ Ghent, Ghent, Belgium; [Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caputo, C.; David, P.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Saggio, A.; Marono, M. Vidal; Wertz, S.; Zobec, J.] Catholic Univ Louvain, Louvain, Belgium; [Alves, F. L.; Alves, G. A.; Martins Junior, M. Correa; Correia Silva, G.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Melo De Almeida, M.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Sanchez Rosas, L. J.; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Torres Da Silva De Araujo, F.; Vilela Pereira, A.] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Ahuja, S.; Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.] Univ Estadual Paulista, Sao Paulo, Brazil; [Gregores, E. M.; Mercadante, P. G.; Romero Abad, D.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Litov, L.; Pavlov, B.; Petkov, P.] Univ Sofia, Sofia, Bulgaria; [Fang, W.; Gao, X.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhao, J.] Inst High Energy Phys, Beijing, Peoples R China; [Ban, Y.; Chen, G.; Levin, A.; Li, J.; Li, L.; Li, Q.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.] Peking Univ, State Key Lab Nucl Phys Technol, Beijing, Peoples R China; [Wang, Y.] Tsinghua Univ, Beijing, Peoples R China; [Avila, C.; Cabrera, A.; Carrillo Montoya, C. A.; Chaparro Sierra, L. F.; Florez, C.; Gonzalez Hernandez, C. F.; Segura Delgado, M. A.] Univ Los Andes, Bogota, Colombia; [Alvarez, J. D. Ruiz] Univ Antioquia, Medellin, Colombia; [Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I.; Sculac, T.] Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; [Antunovic, Z.; Kovac, M.] Univ Split, Fac Sci, Split, Croatia; [Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.] Inst Rudjer Boskovic, Zagreb, Croatia; [Ather, M. W.; Attikis, A.; Kolosova, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.] Univ Cyprus, Nicosia, Cyprus; [Finger, M.; Finger, M., Jr.] Charles Univ Prague, Prague, Czech Republic; [Ayala, E.] Escuela Politec Nacl, Quito, Ecuador; [Jarrin, E. Carrera] Univ San Francisco Quito, Quito, Ecuador; [Assran, Y.; Elgammal, S.; Mohammed, Y.] Egyptian Network High Energy Phys, Acad Sci Res & Technol Arab Republ Egypt, Cairo, Egypt; [Bhowmik, S.; De Oliveira, A. Carvalho Antunes; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Raidal, M.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Kirschenmann, H.; Pekkanen, J.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Havukainen, J.; Heikkila, J. K.; Jarvinen, T.; Karimaki, V.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Linden, T.; Luukka, P.; Maenpaa, T.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Leloup, C.; Locci, E.; Malcles, J.; Negro, G.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Titov, M.] Univ Paris Saclay, CEA, IRFU, Gif Sur Yvette, France; [Abdulsalam, A.; Amendola, C.; Antropov, I.; Beaudette, F.; Busson, P.; Charlot, C.; de Cassagnac, R. Granier; Kucher, I.; Lobanov, A.; Blanco, J. Martin; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Leiton, A. G. Stahl; Zabi, A.; Zghiche, A.] Univ Paris Saclay, CNRS, Ecole Polytech, Lab Leprince Ringuet,IN2P3, Palaiseau, France; [Agram, J. -L.; Andrea, J.; Bloch, D.; Brom, J. -M.; Chabert, E. C.; Cherepanov, V.; Collard, C.; Conte, E.; Fontaine, J. -C.; Gele, D.; Goerlach, U.; Jansova, M.; Le Bihan, A. -C.; Tonon, N.; Van Hove, P.] Univ Strasbourg, CNRS, UMR 7178, IPHC, Strasbourg, France; [Gadrat, S.] Ctr Calcul Inst Natl Phys Nucl & Phys Particules, CNRS, IN2P3, Villeurbanne, France; [Beauceron, S.; Bernet, C.; Boudoul, G.; Chanon, N.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Finco, L.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lattaud, H.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sordini, V.; Vander Donckt, M.; Viret, S.; Zhang, S.] Univ Lyon, Univ Claude Bernard Lyon 1, CNRS, Inst Phys Nucl Lyon,IN2P3, Villeurbanne, France; [Khvedelidze, A.] Georgian Tech Univ, Tbilisi, Georgia; [Tsamalaidze, Z.] Tbilisi State Univ, Tbilisi, Georgia; [Autermann, C.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Rauch, M. P.; Schomakers, C.; Schulz, J.; Teroerde, M.; Wittmer, B.; Zhukov, V.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Albert, A.; Duchardt, D.; Endres, M.; Erdmann, M.; Esch, T.; Fischer, R.; Ghosh, S.; Gueth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Keller, H.; Knutzen, S.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Schmidt, A.; Teyssier, D.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Fluegge, G.; Hlushchenko, O.; Kress, T.; Kuensken, A.; Muller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.] Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; [Martin, M. Aldaya; Arndt, T.; Asawatangtrakuldee, C.; Babounikau, I.; Beernaert, K.; Behnke, O.; Behrens, U.; Martinez, A. Bermudez; Bertsche, D.; Bin Anuar, A. A.; Borras, K.; Botta, V.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Danilov, V.; De Wit, A.; Defranchis, M. M.; Pardos, C. Diez; Damiani, D. Dominguez; Eckerlin, G.; Eichhorn, T.; Elwood, A.; Eren, E.; Gallo, E.; Geiser, A.; Luyando, J. M. Grados; Grohsjean, A.; Gunnellini, P.; Guthoff, M.; Haranko, M.; Harb, A.; Hauk, J.; Jung, H.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Knolle, J.; Kruecker, D.; Lange, W.; Lelek, A.; Lenz, T.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I. -A.; Meyer, A. B.; Meyer, M.; Missiroli, M.; Mittag, G.; Mnich, J.; Myronenko, V.; Pflitsch, S. K.; Pitzl, D.; Raspereza, A.; Savitskyi, M.; Saxena, P.; Schuetze, P.; Schwanenberger, C.; Shevchenko, R.; Singh, A.; Tholen, H.; Turkot, O.; Vagnerini, A.; Van Onsem, G. P.; Walsh, R.; Wen, Y.; Wichmann, K.; Wissing, C.; Zenaiev, O.] Deutch Elektronen Synchrotron, Hamburg, Germany; [Aggleton, R.; Bein, S.; Benato, L.; Benecke, A.; Blobel, V.; Vignali, M. Centis; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hinzmann, A.; Karavdina, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Kurz, S.; Kutzner, V.; Lange, J.; Marconi, D.; Multhaup, J.; Niedziela, M.; Nowatschin, D.; Perieanu, A.; Reimers, A.; Rieger, O.; Scharf, C.; Schleper, P.; Schumann, S.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Stober, F. M.; Stoever, M.; Troendle, D.; Vanhoefer, A.; Vormwald, B.] Univ Hamburg, Hamburg, Germany; [Akbiyik, M.; Barth, C.; Baselga, M.; Baur, S.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; El Morabit, K.; Faltermann, N.; Freund, B.; Giffels, M.; Harrendorf, M. A.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Kassel, F.; Katkov, I.; Kudella, S.; Mildner, H.; Mitra, S.; Mozer, M. U.; Mueller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Schroder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Woehrmann, C.; Wolf, R.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Paspalaki, G.; Topsis-Giotis, I.] INPP, NCSR Demokritos, Aghia Paraskevi, Greece; [Karathanasis, G.; Kesisoglou, S.; Kontaxakis, P.; Panagiotou, A.; Papavergou, I.; Saoulidou, N.; Tziaferi, E.; Vellidis, K.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Kousouris, K.; Papakrivopoulos, I.; Tsipolitis, G.] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I.; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Mallios, S.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Triantis, F. A.; Tsitsonis, D.] Univ Ioannina, Ioannina, Greece; [Bartok, M.; Csanad, M.; Filipovic, N.; Major, P.; Nagy, M. I.; Pasztor, G.; Suranyi, O.; Veres, G. I.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bencze, G.; Hajdu, C.; Horvath, D.; Hunyadi, A.; Sikler, F.; Vami, T. A.; Veszpremi, V.; Vesztergombi, G.] Wigner Res Ctr Phys, Budapest, Hungary; [Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Raics, P.; Trocsanyi, Z. L.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Choudhury, S.; Komaragiri, J. R.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Kar, C.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Swain, S. K.] Natl Inst Sci Educ Res, HBNI, Bhubaneswar, Orissa, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chauhan, S.; Chawla, R.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, A.; Kaur, M.; Kaur, S.; Kumar, R.; Kumari, P.; Lohan, M.; Mehta, A.; Sandeep, K.; Sharma, S.; Singh, J. B.; Walia, G.] Panjab Univ, Chandigarh, India; [Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Kumar, Ashok; Malhotra, S.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, Aashaq; Sharma, R.] Univ Delhi, Delhi, India; [Bhardwaj, R.; Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhawandeep, U.; Bhowmik, D.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Mondal, K.; Nandan, S.; Purohit, A.; Rout, P. K.; Roy, A.; Chowdhury, S. Roy; Saha, G.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] Saha Inst Nucl Phys, HBNI, Kolkata, India; [Behera, P. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Bhat, M. A.; Dugad, S.; Mohanty, G. B.; Sur, N.; Sutar, B.; Verma, Ravindra Kumar] Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; [Banerjee, S.; Bhattacharya, S.; Chatterjee, S.; Das, P.; Guchait, M.; Jain, Sa.; Karmakar, S.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Sahoo, N.; Sarkar, T.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Chauhan, S.; Dube, S.; Hegde, V.; Kapoor, A.; Kothekar, K.; Pandey, S.; Rane, A.; Sharma, S.] IISER, Pune, Maharashtra, India; [Chenarani, S.; Tadavani, E. Eskandari; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi; Naseri, M.; Hosseinabadi, F. Rezaei; Safarzadeh, B.; Zeinali, M.] Inst Res Fundamental Sci IPM, Tehran, Iran; [Felcini, M.; Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Calabria, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Di Florio, A.; Errico, F.; Fiore, L.; Gelmi, A.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Zito, G.] INFN, Sez Bari, Bari, Italy; [Abbrescia, M.; Calabria, C.; Cristella, L.; De Palma, M.; Di Florio, A.; Errico, F.; Gelmi, A.; Ince, M.; Lezki, S.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Selvaggi, G.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Ciocca, C.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Iemmi, F.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] INFN, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Fanfani, A.; Guiducci, L.; Iemmi, F.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] INFN, Sez Catania, Catania, Italy; [Albergo, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Chatterjee, K.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.] INFN, Sez Firenze, Florence, Italy; [Chatterjee, K.; Ciulli, V.; D'Alessandro, R.; Focardi, E.; Lenzi, P.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.] INFN, Lab Nazl Frascati, Frascati, Italy; [Barbagli, G.; Chatterjee, K.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.] INFN, Sez Genova, Genoa, Italy; [Chatterjee, K.; Ciulli, V.; D'Alessandro, R.; Focardi, E.; Lenzi, P.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V.; Di Guida, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; de Fatis, T. Tabarelli; Zuolo, D.] INFN, Sez Milano Bicocca, Milan, Italy; [Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V.; Di Guida, S.; Dinardo, M. E.; Fiorendi, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Massironi, A.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Cavallo, N.; Di Crescenzo, A.; Fabozzi, F.; Fienga, F.; Galati, G.; Iorio, A. O. M.; Khan, W. A.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Voevodina, E.] INFN, Sez Napoli, Naples, Italy; [Di Crescenzo, A.; Iorio, A. O. M.; Sciacca, C.; Voevodina, E.] Univ Napoli Federico II, Rome, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Boletti, A.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Hoh, S. Y.; Lacaprara, S.; Lujan, P.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tiko, A.; Torassa, E.; Ventura, S.; Zanetti, M.; Zotto, P.; Zumerle, G.] INFN, Sez Padova, Trento, NJ USA; [Boletti, A.; Gasparini, F.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Zanetti, M.; Zotto, P.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.] INFN, Sez Pavia, Pavia, Italy; [Montagna, P.; Ratti, S. P.; Ressegotti, M.; Riccardi, C.; Vai, I.; Vitulo, P.] Univ Pavia, Pavia, Italy; [Solestizi, L. Alunni; Biasini, M.; Bilei, G. M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Manoni, E.; Mantovani, G.; Mariani, V.; Menichelli, M.; Rossi, A.; Santocchia, A.; Spiga, D.] INFN, Sez Perugia, Perugia, Italy; [Solestizi, L. Alunni; Biasini, M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Mariani, V.; Rossi, A.; Santocchia, A.] Univ Perugia, Perugia, Italy; [Androsov, K.; Azzurri, P.; Bagliesi, G.; Bianchini, L.; Boccali, T.; Borrello, L.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fedi, G.; Fiori, F.; Giannini, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Rizzi, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.] INFN, Sez Pisa, Pisa, Italy; [Ciocci, M. A.; Messineo, A.; Rizzi, A.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Fiori, F.; Giannini, L.; Ligabue, F.; Manca, E.; Mandorli, G.] Scuola Normale Super Pisa, Pisa, Italy; [Barone, L.; Cavallari, F.; Cipriani, M.; Daci, N.; Del Re, D.; Di Marco; Diemoz, M.; Gelli, S.; Longo, E.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.] INFN, Sez Roma, Rome, Italy; [Barone, L.; Cipriani, M.; Del Re, D.; Di Marco; Gelli, S.; Longo, E.; Marzocchi, B.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Santanastasio, F.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Soldi, D.; Staiano, A.] INFN, Sez Torino, Novara, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Cenna, F.; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Romero, A.; Sacchi, R.; Shchelina, K.; Solano, A.; Soldi, D.] Univ Torino, Novara, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Vazzoler, F.; Zanetti, A.; Gurrola, A.] INFN, Sez Trieste, Trieste, Italy; [Candelise, V.; Della Ricca, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.; Oh, G.] Chonnam Natl Univ, Inst Univ Elementary Particles, Kwangju, South Korea; [Goh, J.; Kim, T. J.] Hanyang Univ, Seoul, South Korea; [Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.] Korea Univ, Seoul, South Korea; [Kim, H. S.] Sejong Univ, Seoul, South Korea; [Almond, J.; Kim, J.; Kim, J. S.; Lee, H.; Lee, K.; Nam, K.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. H.; Yang, U. K.; Yoo, H. D.; Yu, G. B.] Seoul Natl Univ, Seoul, South Korea; [Jeon, D.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.] Univ Seoul, Seoul, South Korea; [Choi, Y.; Hwang, C.; Lee, J.; Yu, I.] Sungkyunkwan Univ, Suwon, South Korea; [Dudenas, V.; Juodagalvis, A.; Vaitkus, J.] Vilnius Univ, Vilnius, Lithuania; [Ahmed, I.; Ibrahim, Z. A.; Ali, M. A. B. Md; Idris, F. Mohamad; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Hernandez, A. Castaneda; Quijada, J. A. Murillo] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Castilla-Valdez, H.; De La Cruz-Burelo, E.; Duran-Osuna, M. C.; Heredia-De La Cruz, I.; Lopez-Fernandez, R.; Guisao, J. Mejia; Rabadan-Trejo, R. I.; Ramirez-Garcia, M.; Ramirez-Sanchez, G.; Reyes-Almanza, R.; Sanchez-Hernandez, A.] Ctr Invest & Estudios Avanzados IPN, Mexico City, DF, Mexico; [Moreno, S. Carrillo; Barrera, C. Oropeza; Valencia, F. Vazquez] Univ Iberoamer, Mexico City, DF, Mexico; [Eysermans, J.; Pedraza, I.; Ibarguen, H. A. Salazar; Estrada, C. Uribe] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Pineda, A. Morelos] Univ Autonoma San Luis Potosi, San Luis Potosi, Mexico; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Ahmad, M.; Asghar, M. I.; Hassan, Q.; Hoorani, H. R.; Qazi, S.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Nawrocki, K.; Szleper, M.; Traczyk, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Byszuk, A.; Doroba, K.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Pyskir, A.; Walczak, M.] Univ Warsaw, Inst Expt Phys, Fac Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Beirao Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Galinhas, B.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Nemallapudi, M. V.; Seixas, J.; Strong, G.; Toldaiev, O.; Vadruccio, D.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Afanasiev, S.; Alexakhin, V.; Bunin, P.; Gavrilenko, M.; Golunov, A.; Golutvin, I.; Gorbounov, N.; Karjavin, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Savina, M.; Shmatov, S.; Smirnov, V.; Voytishin, N.; Zarubin, A.] Joint Inst Nucl Res, Dubna, Russia; [Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sosnov, D.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst Gatchina, St Petersburg, Russia; [Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.] Inst Nucl Res, Moscow, Russia; [Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Stepennov, A.; Stolin, V.; Toms, M.; Vlasov, E.; Zhokin, A.] Inst Theoret Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys Technol, Moscow, Russia; [Chistov, R.; Danilov, M.; Parygin, P.; Philippov, D.; Polikarpov, S.; Tarkovskii, E.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Rusakov, S. V.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Baskakov, A.; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V.; Dimova, T.; Kardapoltsev, L.; Shtol, D.; Skovpen, Y.] NSU, Novosibirsk, Russia; [Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Godizov, A.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Mandrik, P.; Petrov, V.; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] State Res Ctr Russian Federat, NRC Kurchatov Inst, Inst High Energy Phys, Protvino, Russia; [Babaev, A.; Baidali, S.; Okhotnikov, V.] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.] Vinca Inst Nucl Sci, Belgrade, Serbia; [Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I.; Barrio Luna, M.; Brochero Cifuentes, J. A.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Bedoya, C.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Moran, D.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I.; Romero, L.; Soares, M. S.; Triossi, A.] Ctr Invest Energet Medioambient Tecnol CIEM, Madrid, Spain; [Albajar, C.; de Troconiz, J. F.] Univ Autonoma Madrid, Madrid, Spain; [Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Rodriguez Bouza, V.; Sanchez Cruz, S.; Vischia, P.; Vizan Garcia, J. M.] Univ Oviedo, Oviedo, Spain; [Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Abbaneo, D.; Akgun, B.; Auffray, E.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Botta, C.; Brondolin, E.; Camporesi, T.; Cepeda, M.; Cerminara, G.; Chapon, E.; Chen, Y.; Cucciati, G.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Roeck, A.; Deelen, N.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fallavollita, F.; Fasanella, D.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gilbert, A.; Gill, K.; Glege, F.; Guilbaud, M.; Gulhan, D.; Hegeman, J.; Innocente, V.; Jafari, A.; Janot, P.; Karacheban, O.; Kieseler, J.; Kornmayer, A.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenco, C.; Malgeri, L.; Mannelli, M.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Ngadiuba, J.; Nourbakhsh, S.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pitters, F. M.; Rabady, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schafer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Sharma, A.; Silva, P.; Sphicas, P.; Stakia, A.; Steggemann, J.; Tosi, M.; Treille, D.; Tsirou, A.; Veckalns, V.; Zeuner, W. D.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.] Paul Scherrer Inst, Villigen, Switzerland; [Backhaus, M.; Bani, L.; Berger, P.; Chernyavskaya, N.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Klijnsma, T.; Lustermann, W.; Manzoni, R. A.; Marionneau, M.; Meinhard, M. T.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Pigazzini, S.; Quittnat, M.; Ruini, D.; Becerra, D. A. Sanz; Schonenberger, M.; Shchutska, L.; Tavolaro, V. R.; Theofilatos, K.; Olsson, M. L. Vesterbacka; Wallny, R.; Zhu, D. H.] ETH Zurich Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Aarrestad, T. K.; Amsler, C.; Brzhechko, D.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Neutelings, I.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Schweiger, K.; Seitz, C.; Takahashi, Y.; Zucchetta, A.] Univ Zurich, Zurich, Switzerland; [Chang, Y. H.; Cheng, K. Y.; Doan, T. H.; Jain, Sh.; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W. -S.; Kumar, Arun; Li, Y. y.; Liu, Y. F.; Lu, R. -S.; Paganis, E.; Psallidas, A.; Steen, A.] NTU, Taipei, Taiwan; [Asavapibhop, B.; Srimanobhas, N.; Suwonjandee, N.] Chulalongkorn Univ, Dept Phys, Fac Sci, Bangkok, Thailand; [Bat, A.; Boran, F.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dozen, C.; Dumanoglu, I.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Isik, C.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Dept Phys, Sci & Art Fac, Adana, Turkey; [Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.] Middle East Tech Univ, Dept Phys, Ankara, Turkey; [Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Ozkorucuklu, S.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Kharkov Inst Phys Technol, Natl Sci Ctr, Kharkov, Ukraine; [Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Newbold, D. M.; Paramesvaran, S.; Penning, B.; Sakuma, T.; Smith, D.; Smith, V. J.; Taylor, J.; Titterton, A.] Univ Bristol, Bristol, Avon, England; [Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Linacre, J.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, J.] Ruthetford Appleton Lab, Didcot, Oxon, England; [Auzinger, G.; Bainbridge, R.; Bloch, P.; Borg, J.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; Della Negra, M.; Di Maria, R.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Komm, M.; Laner, C.; Lyons, L.; Magnan, A. -M.; Malik, S.; Martelli, A.; Nash, J.; Nikitenko, A.; Palladino, V.; Pesaresi, M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Singh, G.; Stoye, M.; Strebler, T.; Summers, S.; Tapper, A.; Uchida, K.; Virdee, T.; Wardle, N.; Winterbottom, D.; Wright, J.; Zenz, S. C.] Imperial Coll, London, England; [Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Mackay, C. K.; Morton, A.; Reid, I. D.; Teodorescu, L.; Zahid, S.] Brunel Univ, Uxbridge, Middx, England; [Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Madrid, C.; Mcmaster, B.; Pastika, N.; Smith, C.] Baylor Univ, Waco, TX 76798 USA; [Bartek, R.; Dominguez, A.] Catholic Univ Amer, Washington, DC 20064 USA; [Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.] Univ Alabama, Tuscaloosa, AL USA; [Arcaro, D.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.] Boston Univ, Boston, MA 02215 USA; [Benelli, G.; Coubez, X.; Cutts, D.; Hadley, M.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Lee, J.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Syarif, R.; Usai, E.; Yu, D.] Brown Univ, Providence, RI 02912 USA; [Band, R.; Brainerd, C.; Breedon, R.; Burns, D.; Sanchez, M. Calderon De La Barca; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Ko, W.; Kukral, O.; Lander, R.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Stolp, D.; Taylor, D.; Tos, K.; Tripathi, M.; Wang, Z.; Zhang, F.] Univ Calif Davis, Davis, CA 95616 USA; [Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Regnard, S.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Bouvier, E.; Burt, K.] Univ Calif Los Angeles, Los Angeles, CA USA; [Clare, R.; Gary, J. W.; Shirazi, S. M. A. Ghiasi; Hanson, G.; Karapostoli, G.; Kennedy, E.; Lacroix, F.; Long, O. R.; Negrete, M. Olmedo; Paneva, M. I.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Branson, J. G.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Gilbert, D.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V.; Letts, J.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Tsinghua University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); 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University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida International University; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; CEA; UDICE-French Research Universities; Universite Paris Saclay; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Universite Libre de Bruxelles; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Joint Institute for Nuclear Research - Russia; Egyptian Knowledge Bank (EKB); British University in Egypt; Egyptian Knowledge Bank (EKB); Suez University; Egyptian Knowledge Bank (EKB); Fayoum University; King Abdulaziz University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Lomonosov Moscow State University; European Organization for Nuclear Research (CERN); RWTH Aachen University; University of Hamburg; Brandenburg University of Technology Cottbus; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; Visva Bharati University; University of Science & Technology of Mazandaran; Isfahan University of Technology; Islamic Azad University; University of Siena; Kyung Hee University; International Islamic University Malaysia; Agensi Nuklear Malaysia; Warsaw University of Technology; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Peter the Great St. Petersburg Polytechnic University; State University System of Florida; University of Florida; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; California Institute of Technology; Russian Academy of Sciences; Budker Institute of Nuclear Physics; University of Belgrade; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; University of Belgrade; University of Belgrade; Istituto Nazionale di Fisica Nucleare (INFN); National & Kapodistrian University of Athens; Riga Technical University; University of Zurich; Adiyaman University; Istanbul Aydin University; Mersin University; Piri Reis University; Gaziosmanpasa University; Ozyegin University; Izmir Institute of Technology; Marmara University; Kafkas University; Istanbul University; Istanbul Bilgi University; Hacettepe University; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Utah System of Higher Education; Utah Valley University; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Beykent University; Bingol University; Sinop University; Mimar Sinan Guzel Sanatlar University; Qatar Foundation (QF); Texas A&M University Qatar; Kyungpook National University; University of Hyderabad Sirunyan, AM (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. Hernández, José M./H-9127-2015; Guler, Yalcin/AAG-7833-2019; CAKIR, Altan/ABD-4450-2020; Trejo, Raul Iraq Rabadan/F-7924-2019; Pompili, Alexis/ABG-3556-2021; Lezki, Merve Ince/AAG-5283-2021; Tuo, Shengquan/ABA-9032-2021; Oglakci, Mehmet/AAG-3754-2021; Gupta, Rajat/GVS-7964-2022; Cepeda, Maria/AAF-4992-2019; Zorbilmez, Caglar/ABH-1905-2020; Della Ricca, Giuseppe/B-6826-2013; Sculac, Toni/AAE-4141-2019; Dallavalle, Gaetano Marco/AFW-4654-2022; Moraes, Arthur/F-6478-2010; Danilov, Mikhail/C-5380-2014; Puerta Pelayo, Jesus/L-1632-2014; Battilana, Carlo/AAA-7345-2020; Gozalez-Lopez, Oscar/AAH-3533-2019; Tinoco Mendes, Andre David/D-4314-2011; Singh, J B/ABF-5574-2021; Lowette, Steven/HKV-3341-2023; Andreev, Vladimir F/M-8665-2015; Tsirou, Andromachi/HHC-6869-2022; Gil Da Silveira, Gustavo/N-7279-2014; Cortabitarte, Rocio Vilar/P-8480-2014; Watson, Ian James/AAH-1423-2022; Theofilatos, Konstantinos/AAL-9162-2021; Ferro, Fabrizio/HMP-5516-2023; Ciangottini, Diego/HJH-5914-2023; Mejia Guisao, Jhovanny Andres/ABG-3627-2021; Akgun, Bora/AAM-8311-2021; Fienga, Francesco/HKE-1204-2023; Shchutska, Lesya/M-7052-2017; Duarte, Javier Mauricio/AAA-5414-2020; Fouz, Maria Cruz/AAF-1105-2019; TUVE', Cristina/P-3933-2015; Casarsa, Massimo/L-3623-2018; Lychkovskaya, Natalia/F-8341-2017; Shah, Aashaq/GYI-8983-2022; Androsov, Konstantin/HJA-2332-2022; Kirakosyan, Martin R./N-2701-2015; Blekman, Freya/ABD-7916-2020; Terkulov, Adel/M-8581-2015; Cassese, Antonio/R-1713-2016; Bilki, Burak/GRF-5072-2022; Tcherniaev, Evgueni/G-3453-2016; Strologas, John/GWR-2036-2022; Di Florio, Adriano/ABA-3631-2020; Ozturk, Sertac/AGO-2476-2022; Abdulsalam, Abdulla/B-3386-2019; Pinna Angioni, Gian Luca/AAZ-6834-2021; Garg, Rocky Bala/AAV-9845-2021; Benaglia, Andrea/AGF-5495-2022; Melo da Costa, Eliza Melo/AAX-2394-2021; Peris, Antonio Delgado/AAA-5165-2019; Pásztor, Gabriella/D-9716-2016; Sanchez-Hernandez, Alberto/L-4955-2017; Selvaggi, Michele/CAJ-2129-2022; Yazgan, Efe/C-4521-2014; Ligabue, Franco/ABI-1681-2020; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Sert, H/ABA-5645-2020; Watson, Ian James/AFL-9360-2022; Chenarani, Shirin/AAO-8918-2021; Petkov, Peicho/M-2080-2016; De La Cruz Burelo, Eduard/AAR-9103-2021; Hernández Calama, José María/AAW-6394-2021; Quijada, Javier Alberto Murillo/AAS-4234-2021; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Lange, Torben/HOH-4787-2023; Saka, Halil/GPF-8892-2022; M, F/HGF-3166-2022; Steggemann, Jan/AAL-5700-2020; Azarkin, Maxim/N-2578-2015; Legger, Federica/HNC-2568-2023; Teles, Patricia/ABF-9723-2021; Vai, Ilaria/AAT-5836-2020; Shopova, Mariana Filipova/HKW-0149-2023; Özdemir, Kadri/P-8058-2014; Gerosa, Raffaele/AGL-5921-2022; Hos, ilknur/T-1165-2019; Viliani, Lorenzo/AAU-3462-2021; SEVINC KAYA, OZLEM/AAE-8069-2020; Chaparro Sierra, Luisa Fernanda/AHD-3083-2022; Ogul, Hasan/ABD-2758-2021; Aydogmus, Fatma/I-4879-2014; de Souza Sandro, Fonseca/ABB-8505-2020; Shmatov, Sergei/J-5621-2014; Boimska, Bożena/Z-6088-2019; Felcini, Marta A./S-2684-2019; Lokhtin, Igor/D-7004-2012; Khakzad, Mohsen/ABB-1945-2021; Savina, Maria/HDO-6544-2022; Elwood, Adam/GSN-1799-2022; Dudko, Lev/D-7127-2012; kaya, mithat/D-8062-2011; Benussi, Luigi/O-9684-2014; Calligaris, Luigi/K-2018-2017; Buchanan, James/ABB-6906-2021; Sguazzoni, Giacomo/J-4620-2015; Tiras, Emrah/ABG-2354-2020; Errico, Filippo/AAD-7755-2019; Novaes, Sergio F./D-3532-2012; Bermudez Martinez, Armando/HHN-4625-2022; Kim, Victor T/L-1648-2013; BAT, Ayse/GOK-2363-2022; GULER, E GURPINAR/AAH-4748-2019; Wulz, Claudia-Elisabeth E/H-5657-2011; Kumar, Dr Ashok/HNR-6570-2023; Heredia De La Cruz, Ivan/L-2610-2018; Soares, Mara Senghi/GZM-6676-2022; Moscatelli, Francesco/N-6333-2014; Parygin, Pavel/C-8908-2016; Demiroglu, Zuhal Seyma/A-5168-2016; Trocino, Daniele/AGI-2155-2022; Cepeda, Maria/HNO-9314-2023; Fiorendi, Sara/ABA-3311-2021; Pedraza Morales, Maria Isabel/ABC-7289-2021; Snoeys, Walter J M/K-8259-2015; Iorio, Alberto Orso Maria/GXA-0233-2022; Matorras, Francisco/I-4983-2015; Lezki, Samet/AAG-8277-2019; Seidita, Roberto/AGX-6803-2022; Klyukhin, Vyacheslav I/D-6850-2012; Aarrestad, Thea/GLV-3031-2022; Konecki, Marcin/G-4164-2015; de guio, federico/B-8553-2009; Moscatelli, Francesco/AEL-1878-2022; Polikarpov, Sergey/C-8939-2016; Hoh, Siewyan/AAM-9562-2021; Bortignon, Pierluigi/AAF-6561-2020; Goy Lopez, Silvia/K-9200-2017; Rabady, Dinyar/GSN-9210-2022; Raidal, Martti/F-4436-2012; Azzi, Patrizia/H-5404-2012; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Josa Mutuberria, Isabel/K-5184-2014; Belyaev, Alexander/F-6637-2015; Navarro-Tobar, Alvaro/K-7864-2014; Malik, Sudhir/D-6621-2018; Fernandez Menendez, Javier/B-6550-2014; Grandi, Claudio/B-5654-2015; Popov, Andrey/E-1052-2012; Navarria, Francesco Luigi/ACP-7823-2022; Sagir, Sinan/A-5219-2018; Elkafrawy, Tamer/Q-8786-2016; Palencia Cortezon, Jose Enrique/F-1059-2017; Kyberd, Paul/P-1092-2014; Govoni, Pietro/K-9619-2016; Paganoni, Marco/A-4235-2016; Mora Herrera, Maria Clemencia/L-3893-2016; Duarte-Campderros, Jordi/F-5025-2018; Leonardo, Nuno/M-6940-2016; Fernandez Bedoya, Cristina/K-8066-2014; Rolandi, Luigi (Gigi)/E-8563-2013; Vilela Pereira, Antonio/L-4142-2016; Bernardes, Cesar Augusto/D-2408-2015; Bhatnagar, Vipin/Z-1079-2018; Haddad, Yacine/GRX-5022-2022; Sola, Valentina/E-7726-2015; Lee, Jason/B-9701-2014; Dharmaratna, Welathantri/F-6745-2018; Ivanov, Andrew/A-7982-2013; Rossi, Biagio/F-4137-2018; Ruiz Jimeno, Alberto/E-4473-2011; Gonzalez Caballero, Isidro/E-7350-2010; Paulini, Manfred/N-7794-2014; Gulmez, Erhan/P-9518-2015; Godinovic, Nikola/G-6124-2017; Ragazzi, Stefano/D-2463-2009; Giacomelli, Paolo/B-8076-2009; Dubinin, Mikhail/I-3942-2016; Mundim, Luiz/A-1291-2012; HAJ AHMAD (Vael HAJAHMAD), Wael/E-6738-2016; Sznajder, Andre/L-1621-2016; Padula, Sandra S./G-3560-2012; Spagnolo, Paolo/G-3401-2017; Kim, Tae Jeong/P-7848-2015; Redondo, Ignacio/K-8639-2014; Moon, Chang-Seong/J-3619-2014; ALCARAZ MAESTRE, JUAN/I-5763-2015; Goh, Junghwan/Q-3720-2016 Hernández, José M./0000-0001-6436-7547; Guler, Yalcin/0000-0001-7598-5252; CAKIR, Altan/0000-0002-8627-7689; Trejo, Raul Iraq Rabadan/0000-0002-9787-3910; Lezki, Merve Ince/0000-0001-6907-0195; Oglakci, Mehmet/0000-0003-1096-5087; Cepeda, Maria/0000-0002-6076-4083; Zorbilmez, Caglar/0000-0002-5199-061X; Della Ricca, Giuseppe/0000-0003-2831-6982; Sculac, Toni/0000-0002-9578-4105; Dallavalle, Gaetano Marco/0000-0002-8614-0420; Moraes, Arthur/0000-0002-5157-5686; Danilov, Mikhail/0000-0001-9227-5164; Puerta Pelayo, Jesus/0000-0001-7390-1457; Battilana, Carlo/0000-0002-3753-3068; Gozalez-Lopez, Oscar/0000-0002-4532-6464; Tinoco Mendes, Andre David/0000-0001-5854-7699; Singh, J B/0000-0001-9029-2462; Lowette, Steven/0000-0003-3984-9987; Andreev, Vladimir F/0000-0002-5492-6920; Gil Da Silveira, Gustavo/0000-0003-3514-7056; Cortabitarte, Rocio Vilar/0000-0003-2045-8054; Watson, Ian James/0000-0003-2141-3413; Theofilatos, Konstantinos/0000-0001-8448-883X; Akgun, Bora/0000-0001-8888-3562; Fienga, Francesco/0000-0001-5978-4952; Shchutska, Lesya/0000-0003-0700-5448; Duarte, Javier Mauricio/0000-0002-5076-7096; Fouz, Maria Cruz/0000-0003-2950-976X; TUVE', Cristina/0000-0003-0739-3153; Casarsa, Massimo/0000-0002-1353-8964; Shah, Aashaq/0000-0002-6157-2016; Androsov, Konstantin/0000-0003-2694-6542; Blekman, Freya/0000-0002-7366-7098; Terkulov, Adel/0000-0003-4985-3226; Cassese, Antonio/0000-0003-3010-4516; Tcherniaev, Evgueni/0000-0002-3685-0635; Di Florio, Adriano/0000-0003-3719-8041; Ozturk, Sertac/0000-0001-6533-6144; Abdulsalam, Abdulla/0000-0002-5424-7418; Benaglia, Andrea/0000-0003-1124-8450; Melo da Costa, Eliza Melo/0000-0002-5016-6434; Peris, Antonio Delgado/0000-0002-8511-7958; Pásztor, Gabriella/0000-0003-0707-9762; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; Yazgan, Efe/0000-0001-5732-7950; Ligabue, Franco/0000-0002-1549-7107; Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Watson, Ian James/0000-0003-2141-3413; Chenarani, Shirin/0000-0002-1425-076X; Petkov, Peicho/0000-0002-0420-9480; De La Cruz Burelo, Eduard/0000-0002-7469-6974; Hernández Calama, José María/0000-0001-6436-7547; Quijada, Javier Alberto Murillo/0000-0003-4933-2092; Mohammadi Najafabadi, Mojtaba/0000-0001-6131-5987; Lange, Torben/0000-0001-6242-7331; Steggemann, Jan/0000-0003-4420-5510; Azarkin, Maxim/0000-0002-7448-1447; Legger, Federica/0000-0003-1400-0709; Teles, Patricia/0000-0001-9029-8506; Vai, Ilaria/0000-0003-0037-5032; Shopova, Mariana Filipova/0000-0001-6664-2493; Özdemir, Kadri/0000-0002-0103-1488; Hos, ilknur/0000-0002-7678-1101; Viliani, Lorenzo/0000-0002-1909-6343; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Chaparro Sierra, Luisa Fernanda/0000-0002-6570-9075; Ogul, Hasan/0000-0002-5121-2893; Aydogmus, Fatma/0000-0003-1434-2143; de Souza Sandro, Fonseca/0000-0001-7830-0837; Shmatov, Sergei/0000-0001-5354-8350; Boimska, Bożena/0000-0002-4200-1541; Felcini, Marta A./0000-0002-2051-9331; Khakzad, Mohsen/0000-0002-2212-5715; Savina, Maria/0000-0002-9020-7384; Dudko, Lev/0000-0002-4462-3192; kaya, mithat/0000-0003-2890-4493; Benussi, Luigi/0000-0002-2363-8889; Calligaris, Luigi/0000-0002-9951-9448; Buchanan, James/0000-0001-8207-5556; Sguazzoni, Giacomo/0000-0002-0791-3350; Tiras, Emrah/0000-0002-5628-7464; Errico, Filippo/0000-0001-8199-370X; Novaes, Sergio F./0000-0003-0471-8549; Bermudez Martinez, Armando/0000-0001-8822-4727; Kim, Victor T/0000-0001-7161-2133; BAT, Ayse/0000-0001-5423-4599; GULER, E GURPINAR/0000-0002-6172-0285; Wulz, Claudia-Elisabeth E/0000-0001-9226-5812; Heredia De La Cruz, Ivan/0000-0002-8133-6467; Soares, Mara Senghi/0000-0001-9676-6059; Moscatelli, Francesco/0000-0002-7676-3106; Parygin, Pavel/0000-0001-6743-3781; Demiroglu, Zuhal Seyma/0000-0001-7977-7127; Trocino, Daniele/0000-0002-2830-5872; Cepeda, Maria/0000-0002-6076-4083; Fiorendi, Sara/0000-0003-3273-9419; Pedraza Morales, Maria Isabel/0000-0002-2669-4659; Snoeys, Walter J M/0000-0003-3541-9066; Iorio, Alberto Orso Maria/0000-0002-3798-1135; Matorras, Francisco/0000-0003-4295-5668; Lezki, Samet/0000-0002-6909-774X; Seidita, Roberto/0000-0002-3533-6191; Klyukhin, Vyacheslav I/0000-0002-8577-6531; Aarrestad, Thea/0000-0002-7671-243X; Konecki, Marcin/0000-0001-9482-4841; Moscatelli, Francesco/0000-0002-7676-3106; Polikarpov, Sergey/0000-0001-6839-928X; Hoh, Siewyan/0000-0003-3233-5123; Bortignon, Pierluigi/0000-0002-5360-1454; Rabady, Dinyar/0000-0001-9239-0605; Van Onsem, Gerrit/0000-0002-1664-2337; Dini, Paolo/0000-0001-7375-4899; Elmetenawee, Walaa/0000-0001-7069-0252; Ovtin, Ivan/0000-0002-2583-1412; Lethuillier, Morgan/0000-0001-6185-2045; Raidal, Martti/0000-0001-7040-9491; Caputo, Claudio/0000-0001-7522-4808; Gomez-Ceballos, Guillelmo/0000-0003-1683-9460; Bergauer, Thomas/0000-0002-5786-0293; Ngadiuba, Jennifer/0000-0002-0055-2935; Benitez, J.F./0000-0002-2633-6712; Bhowmik, Sandeep/0000-0003-1260-973X; Beaudette, Florian/0000-0002-1194-8556; Lucchini, Marco Toliman/0000-0002-7497-7450; Nash, Jordan/0000-0003-0607-6519; Moran, Dermot/0000-0002-1941-9333; Malgeri, Luca/0000-0002-0113-7389; Vormwald, Benedikt/0000-0003-2607-7287; Montagna, Paolo Maria/0000-0001-9647-9420; Azzi, Patrizia/0000-0002-3129-828X; Tavolaro, Vittorio Raoul/0000-0003-2518-7521; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226; Josa Mutuberria, Isabel/0000-0002-4985-6964; Belyaev, Alexander/0000-0002-1733-4408; Reis, Thomas/0000-0003-3703-6624; Dalchenko, Mykhailo/0000-0002-0137-136X; Escalante del Valle, Alberto/0000-0002-9702-6359; Colaleo, Anna/0000-0002-0711-6319; Sciacca, Crisostomo/0000-0002-8412-4072; Hangal, Dhanush Anil/0000-0002-3826-7232; Mousa, Jehad/0000-0002-2978-2718; Heikkila, Jaana/0000-0002-0538-1469; Saygin, Kadir/0000-0002-8419-1400; Kasemann, Matthias/0000-0002-0429-2448; Meuser, Danilo/0000-0002-2722-7526; Navarro-Tobar, Alvaro/0000-0003-3606-1780; Elmer, Peter/0000-0001-6830-3356; CHANG, PAO-TI/0000-0003-4064-388X; Malik, Sudhir/0000-0002-6356-2655; Gershtein, Yuri/0000-0002-4871-5449; Krikler, Benjamin/0000-0001-9712-0030; Calzaferri, Simone/0000-0002-1162-2505; Wilson, Graham/0000-0003-0917-4763; Fernandez Menendez, Javier/0000-0002-5213-3708; Ramirez Sanchez, Gabriel/0000-0001-7804-5514; Hall, Geoffrey/0000-0002-6299-8385; siviero, federico/0000-0002-4427-4076; Monaco, Vincenzo/0000-0002-3617-2432; Seidel, Markus/0000-0003-3550-6151; Grandi, Claudio/0000-0001-5998-3070; Popov, Andrey/0000-0002-1207-0984; d'Enterria, David/0000-0002-5754-4303; Ulrich, Ralf/0000-0002-2535-402X; Pradhan, Raghunath/0000-0001-7000-6510; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Martinez Ruiz del Arbol, Pablo/0000-0002-7737-5121; Kiminsu, Ugur/0000-0001-6940-7800; Jabeen, Shabnam/0000-0002-0155-7383; Ruiz, Jose/0000-0002-3306-0363; Cepaitis, Vilius/0000-0002-4809-4056; De Guio, Federico/0000-0001-5927-8865; Trevisani, Nicolo/0000-0002-5223-9342; Amendola, Chiara/0000-0002-4359-836X; Kothekar, Kunal/0000-0001-5102-4326; Papadopoulos, Ioannis/0000-0002-9937-3063; Rinkevicius, Aurelijus/0000-0002-7510-255X; Pigazzini, Simone/0000-0002-8046-4344; Bellora, Andrea/0000-0002-2753-5473; Benato, Lisa/0000-0001-5135-7489; Hinzmann, Andreas/0000-0002-2633-4696; Sarica, Ulascan/0000-0002-1557-4424; Navarria, Francesco Luigi/0000-0001-7961-4889; Ricci-Tam, Francesca/0000-0001-9750-7702; Sharma, Varun/0000-0003-1287-1471; Di Croce, Davide/0000-0002-1122-7919; Wardle, Nicholas/0000-0003-1344-3356; Latino, Giuseppe/0000-0002-4098-3502; Ehataht, Karl/0000-0002-2387-4777; Del Re, Daniele/0000-0003-0870-5796; Lo Meo, Sergio/0000-0003-3249-9208; Schwandt, Joern/0000-0002-0052-597X; Gutsche, Oliver/0000-0002-8015-9622; Claes, Daniel/0000-0003-4198-8919; Zorbakir, Ibrahim Soner/0000-0002-5962-2221; Zuolo, Davide/0000-0003-3072-1020; Abbiendi, Giovanni/0000-0003-4499-7562; Seitova, Diana/0000-0003-2522-7642; Mnich, Joachim/0000-0001-7242-8426; Ghosh, Saranya Samik/0000-0001-6717-0803; Schonenberger, Myriam/0000-0002-6508-5776; Andrea, Jeremy/0000-0002-8298-7560; Garutti, Erika/0000-0003-0634-5539; Sagir, Sinan/0000-0002-2614-5860; Dragicevic, Marko/0000-0003-1967-6783; CUFFIANI, Marco/0000-0003-2510-5039; Verweij, Marta/0000-0002-1504-3420; Haller, Johannes/0000-0001-9347-7657; Mariani, Valentina/0000-0001-7108-8116; Elkafrawy, Tamer/0000-0001-9930-6445; Mitselmakher, Guenakh/0000-0001-5745-3658; Palencia Cortezon, Jose Enrique/0000-0001-8264-0287; Canelli, Florencia/0000-0001-6361-2117; Bhandari, Rohan/0000-0001-5888-955X; Monteno, Marco/0000-0002-3521-6333; Blumenfeld, Barry/0000-0003-1150-1735; Pujahari, Prabhat Ranjan/0000-0002-0994-7212; Kyberd, Paul/0000-0002-7353-7090; Saka, Halil/0000-0001-7616-2573; Cadamuro, Luca/0000-0001-8789-610X; Borg, Johan/0000-0002-7716-7621; Bossini, Edoardo/0000-0002-2303-2588; Gavrilov, Vladimir/0000-0002-9617-2928; Wu, Zhenbin/0000-0003-2165-9501; Flaecher, Henning/0000-0002-5371-941X; Govoni, Pietro/0000-0002-0227-1301; Botta, Cristina/0000-0002-8072-795X; Wallny, Rainer/0000-0001-8038-1613; Chapon, Emilien/0000-0001-6968-9828; Prosper, Harrison Bertrand/0000-0002-4077-2713; Ciangottini, Diego/0000-0002-0843-4108; Garcia, Francisco/0000-0002-4023-7964; Goldstein, Joel/0000-0003-1591-6014; Csanad, Mate/0000-0002-3154-6925; Reimers, Arne Christoph/0000-0002-9438-2059; Schieck, Jochen/0000-0002-1058-8093; Lee, Sung-Won/0000-0002-3388-8339; Giammanco, Andrea/0000-0001-9640-8294; Bluj, Michal/0000-0003-1229-1442; 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ALCARAZ MAESTRE, JUAN/0000-0003-0914-7474; Azzurri, Paolo/0000-0002-1717-5654; Goh, Junghwan/0000-0002-1129-2083; Myronenko, Volodymyr/0000-0002-3984-4732; Pfeiffer, Andreas/0000-0001-5328-448X; Mohamed, Ashraf/0000-0002-6753-7254; Levchuk, Leonid/0000-0001-5889-7410; Kanuganti, Ankush Reddy/0000-0002-0789-1200; Tosi, Silvano/0000-0002-7275-9193 BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MOST (China); NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia); PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Estonia); HIP (Estonia); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); ROSATOM (Russia); RAS (Russia); RFBR (Russia); NRC KI (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); MoSTR(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (USA); NSF (USA); Marie-Curie program (European Union); European Research Council (European Union) [675440, 752730, 765710]; Horizon 2020 Grant (European Union) [675440, 752730, 765710]; Leventis Foundation; A.P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium) under the Excellence of Science -EOS -be.h project [30820817]; FWO (Belgium) under the Excellence of Science -EOS -be.h project [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy [EXC 2121, 390833306]; Lendulet (Momentum) Program of the Hungarian Academy of Sciences; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; New National Excellence Program UNKP; NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; HOMING PLUS program of the Foundation for Polish Science; European Union, Regional Development Fund; Mobility Plus program of the Ministry of Science and Higher Education; National Science Center (Poland) [Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [02.a03.21.0005]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Thalis program - EU-ESF; Aristeia program - EU-ESF; Greek NSRF; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA) BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MOST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); COLCIENCIAS (Colombia)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia)(Estonian Research Council); PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Estonia)(German Research Foundation (DFG)); HIP (Estonia); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); ROSATOM (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MoSTR(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (USA)(United States Department of Energy (DOE)); NSF (USA)(National Science Foundation (NSF)); Marie-Curie program (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); Horizon 2020 Grant (European Union); Leventis Foundation; A.P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium) under the Excellence of Science -EOS -be.h project(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium) under the Excellence of Science -EOS -be.h project(FWO); Beijing Municipal Science AMP; Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy(German Research Foundation (DFG)); Lendulet (Momentum) Program of the Hungarian Academy of Sciences; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences(Hungarian Academy of Sciences); New National Excellence Program UNKP; NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); HOMING PLUS program of the Foundation for Polish Science; European Union, Regional Development Fund(European Commission); Mobility Plus program of the Ministry of Science and Higher Education; National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Thalis program - EU-ESF; Aristeia program - EU-ESF; Greek NSRF(Greek Ministry of Development-GSRT); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (USA) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MOST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, ROSATOM, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MoSTR(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy -EXC 2121 Quantum Universe 390833306; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no. 02.a03.21.; 0005 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA). 33 2 2 16 44 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B MAY 10 2021.0 816 136188 10.1016/j.physletb.2021.136188 0.0 MAR 2021 27 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics RX7TD Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000647421500001 0
J Bartl, D; Drapeau, S; Obloj, J; Wiesel, J Bartl, Daniel; Drapeau, Samuel; Obloj, Jan; Wiesel, Johannes Sensitivity analysis of Wasserstein distributionally robust optimization problems PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES English Article robust stochastic optimization; sensitivity analysis; uncertainty quantification; non-parametric uncertainty; Wasserstein metric RISK MEASURES; CONVERGENCE; AMBIGUITY; DISTANCE We consider sensitivity of a generic stochastic optimization problem to model uncertainty. We take a non-parametric approach and capture model uncertainty using Wasserstein balls around the postulated model. We provide explicit formulae for the first-order correction to both the value function and the optimizer and further extend our results to optimization under linear constraints. We present applications to statistics, machine learning, mathematical finance and uncertainty quantification. In particular, we provide an explicit first-order approximation for square-root LASSO regression coefficients and deduce coefficient shrinkage compared to the ordinary least-squares regression. We consider robustness of call option pricing and deduce a new Black-Scholes sensitivity, a non-parametric version of the so-called Vega. We also compute sensitivities of optimized certainty equivalents in finance and propose measures to quantify robustness of neural networks to adversarial examples. [Bartl, Daniel] Univ Vienna, Dept Math, Oskar Morgenstern Pl 1, A-1090 Vienna, Austria; [Drapeau, Samuel] Shanghai Jiao Tong Univ, Sch Math Sci, 211 West Huaihai Rd, Shanghai 200030, Peoples R China; [Drapeau, Samuel] Shanghai Jiao Tong Univ, Shanghai Adv Inst Finance, 211 West Huaihai Rd, Shanghai 200030, Peoples R China; [Obloj, Jan] Univ Oxford, Math Inst, Woodstock Rd, Oxford OX2 6GG, England; [Wiesel, Johannes] Columbia Univ, Dept Stat, 1255 Amsterdam Ave, New York, NY 10027 USA University of Vienna; Shanghai Jiao Tong University; Shanghai Jiao Tong University; University of Oxford; Columbia University Obloj, J (corresponding author), Univ Oxford, Math Inst, Woodstock Rd, Oxford OX2 6GG, England. jan.obloj@maths.ox.ac.uk Obloj, Jan K/A-4750-2013 Obloj, Jan K/0000-0002-5686-5498; bartl, daniel/0000-0002-1379-2401 European Research Council [7th FP/ERC grant] [335421]; Vienna Science and Technology Fund (WWTF) [MA16-021]; Austrian Science Fund (FWF) [P28661]; National Science Foundation of China [11971310, 11671257] European Research Council [7th FP/ERC grant]; Vienna Science and Technology Fund (WWTF); Austrian Science Fund (FWF)(Austrian Science Fund (FWF)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the European Research Council [7th FP/ERC grant agreement no. 335421], the Vienna Science and Technology Fund (WWTF) [project MA16-021], the Austrian Science Fund (FWF) [project P28661] and the National Science Foundation of China (grant nos 11971310 and 11671257). 55 0 0 1 10 ROYAL SOC LONDON 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND 1364-5021 1471-2946 P ROY SOC A-MATH PHY Proc. R. Soc. A-Math. Phys. Eng. Sci. DEC 22 2021.0 477 2256 20210176 10.1098/rspa.2021.0176 0.0 19 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics XO1IG 35153602.0 Green Submitted, hybrid, Green Accepted 2023-03-23 WOS:000729946200004 0
J Meng, WZ; Jiang, LJ; Choo, WKR; Wang, Y; Jiang, C Meng, Weizhi; Jiang, Lijun; Choo, Kim-Kwang Raymond; Wang, Yu; Jiang, Chong Towards detection of juice filming charging attacks via supervised CPU usage analysis on smartphones COMPUTERS & ELECTRICAL ENGINEERING English Article Charging threat; Android and iOS security; CPU usage analysis; JFC attack; Supervised machine learning Mobile devices, such as Android and iOS devices, are an attractive target for cyber-criminals, due to the amount of private data that can be accessed or stored on such devices. As public charging facilities become more commonplace, phone charging attacks are no longer fiction. Juice filming charging (JFC) attack is a particular phone charging threat, which can capture or infer users' private information by automatically recording screen information from mobile devices during the entire charging process. In this work, we first investigate the types of phone applications users would interact with the most when charging their devices. Then, we propose a detection approach for JFC attacks solely by analyzing CPU usage. In the evaluation, we collect data from a total of 187 participants, and the findings show that our approach using the SVM classifier can achieve better performance than other approaches. Our work complements existing security mechanisms against charging threats. (C) 2019 Elsevier Ltd. All rights reserved. [Meng, Weizhi; Wang, Yu; Jiang, Chong] Guangzhou Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China; [Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Jiang, Lijun] CyberTree, Macau, Peoples R China; [Choo, Kim-Kwang Raymond] Univ Texas San Antonio, Dept Informat Syst & Cyber Secur, San Antonio, TX USA Guangzhou University; Technical University of Denmark; University of Texas System; University of Texas at San Antonio (UTSA) Wang, Y (corresponding author), Guangzhou Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China. weme@dtu.dk; yuwang@gzhu.edu.cn Choo, Kim-Kwang Raymond/A-3634-2009 Choo, Kim-Kwang Raymond/0000-0001-9208-5336; Jiang, Chong/0000-0003-4000-800X; Meng, Weizhi/0000-0003-4384-5786 National Natural Science Foundation of China [61802077]; H2020 SU-ICT-03-2018 CyberSec4Europe National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); H2020 SU-ICT-03-2018 CyberSec4Europe We would like to thank the participants for their hard work in the user study. This work was partially supported by National Natural Science Foundation of China (no. 61802077), and H2020 SU-ICT-03-2018 CyberSec4Europe. 23 7 7 0 1 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. SEP 2019.0 78 230 241 10.1016/j.compeleceng.2019.07.008 0.0 12 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering JA1KA 2023-03-23 WOS:000487574700018 0
J Saxen, H; Gao, CH; Gao, ZW Saxen, Henrik; Gao, Chuanhou; Gao, Zhiwei Data-Driven Time Discrete Models for Dynamic Prediction of the Hot Metal Silicon Content in the Blast Furnace-A Review IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Review Hot metal silicon; prediction; blast furnace; dynamics; time-series models ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINES; SERIES ANALYSIS; IDENTIFICATION; SYSTEM; IRON A review of black-box models for short-term time-discrete prediction of the silicon content of hot metal produced in blast furnaces is presented. The review is primarily focused on work presented in journal papers, but still includes some early conference papers (published before 1990) which have a clear contribution to the field. Linear and nonlinear models are treated separately, and within each group a rough subdivision according to the model type is made. Within each subsection the models are treated (almost) chronologically, presenting the principle behind the modeling approach, the signals used and the main findings in terms of accuracy and usefulness. Finally, in the final section the approaches are discussed and some potential lines of future research are proposed. In an Appendix, a list of commonly used input and output variables in the models is presented. [Saxen, Henrik] Abo Akad Univ, Thermal & Flow Engn Lab, Dept Chem Engn, FI-20500 Turku, Finland; [Gao, Chuanhou] Zhejiang Univ, Dept Math, Hangzhou 310027, Peoples R China; [Gao, Zhiwei] Northumbria Univ, Fac Engn & Environm, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England Abo Akademi University; Zhejiang University; Northumbria University Saxen, H (corresponding author), Abo Akad Univ, Thermal & Flow Engn Lab, Dept Chem Engn, FI-20500 Turku, Finland. hsaxen@abo.fi; gaochou@zju.edu.cn; zhiwei.gao@northumbria.ac.uk Gao, Zhiwei/AAP-2252-2021; Saxen, Henrik/K-5134-2019; Saxen, Henrik/I-8352-2014 Gao, Zhiwei/0000-0001-5464-3288; Saxen, Henrik/0000-0002-5671-8367 Finnish-Chinese Research Program in Computational Sciences and Signal Processing; National Natural Science Foundation of China [11271326, 6091110510]; Public Benefit Technologies R&D Program of Science and Technology Department of Zhejiang Province [2011C21020] Finnish-Chinese Research Program in Computational Sciences and Signal Processing; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Public Benefit Technologies R&D Program of Science and Technology Department of Zhejiang Province Manuscript received May 31, 2012; revised September 07, 2012; accepted October 17, 2012. Date of publication November 12, 2012; date of current version October 14, 2013. This work was supported by the Finnish-Chinese Research Program in Computational Sciences and Signal Processing, the National Natural Science Foundation of China under Grant No. 11271326, 6091110510 and the Public Benefit Technologies R&D Program of Science and Technology Department of Zhejiang Province under Grant No. 2011C21020. Paper no. TII-12-0390. 64 77 87 4 55 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. NOV 2013.0 9 4 2213 2225 10.1109/TII.2012.2226897 0.0 13 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 240RC 2023-03-23 WOS:000326113700040 0
C Xue, R; Zhang, X; Soergel, U Jiang, J; Shaker, A; Zhang, H Xue, R.; Zhang, X.; Soergel, U. URBAN CLASSIFICATION BASED ON TOP-VIEW POINT CLOUD AND SAR IMAGE FUSION WITH SWIN TRANSFORMER XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences English Proceedings Paper 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow JUN 06-11, 2022 Nice, FRANCE Int Soc Photogrammetry & Remote Sensing Deep Learning; Transformer; Feature Fusing; Urban Classification; Synthetic Aperture Radar; Point Cloud LIDAR Urban areas are complex scenarios consisting of objects with various materials. This variety poses a challenge to single-data classification schemes. In this paper, we propose a feature fusion and classification network on RGB top-view point cloud and SAR images with swin-Transformer. In this network, the heterogeneous features are learned separately by an asymmetric encoder, and then they are concatenated along the channel dimension and fed into a fusing encoder. Finally, the fused features are decoded by an UperNet for generating the semantic labels. As data we use the subset of high-resolution 3D point cloud provided by Hessigheim benchmark which are complemented by TerraSAR-X images. The overall precision and the mean intersection over union (mloU) achieves 87.25% and 73.56%, respectively, which outperforms the single-data swin-Transformer by 4.08% and 1.91%, respectively. [Xue, R.] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China; [Xue, R.; Zhang, X.; Soergel, U.] Univ Stuttgart, Inst Photogrammetry, D-70174 Stuttgart, Germany Xidian University; University of Stuttgart Zhang, X (corresponding author), Univ Stuttgart, Inst Photogrammetry, D-70174 Stuttgart, Germany. rhxue@stu.xidian.edu.cn; xinlong.zhang@ifp.uni-stuttgart.de; uwe.soerge@ifp.uni-stuttgart.de China Scholarships Council [202006960021] China Scholarships Council Considerable parts of the research presented in this paper were supported by the China Scholarships Council No. 202006960021. 15 0 0 5 5 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLE 1E, GOTTINGEN, 37081, GERMANY 1682-1750 2194-9034 INT ARCH PHOTOGRAMM 2022.0 43-B3 559 564 10.5194/isprs-archives-XLIII-B3-2022-559-2022 0.0 6 Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Physical Geography; Remote Sensing; Imaging Science & Photographic Technology BT8VW gold 2023-03-23 WOS:000855647800078 0
C Zou, ZQ; Yin, R; Chen, XF; Wu, CLMG IEEE Zou, Zhiqun; Yin, Rui; Chen, Xianfu; Wu, Celimuge Deep Reinforcement Learning for D2D transmission in unlicensed bands 2019 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS IN CHINA (ICCC WORKSHOPS) IEEE International Conference on Communications in China English Proceedings Paper IEEE/CIC International Conference on Communications in China (ICCC) AUG 11-13, 2019 Changchun, PEOPLES R CHINA IEEE,CIC COORDINATION; UNDERLAY In this paper, a reinforcement learning based approach is proposed to realize the distributed power and spectrum allocation for the Device-to-Device (D2D) communications in unlicensed bands, named as D2D-U. To guarantee the harmonious coexistence with the WiFi networks, the conventional duty-cycle muting (DCM) is employed by the D2D-U links. With the proposed learning approach, D2D-U links can optimally select the time fraction on unlicensed channels without knowing the accurate WiFi traffic in a dynamic WiFi working environment. To address the state space explosion during the learning process, the Deep Q-learning network (DQN) is adopted by combining a deep neural network (DNN) with the traditional Q-learning mechanism. After obtaining the available time fraction on unlicensed channels, the spectrum and power allocation on licensed and unlicensed bands can be optimized jointly via the classic convex optimization methods at each D2D-U link. Numerical results are demonstrated to verify the effectiveness of the proposed approach. [Zou, Zhiqun] Zhejiang Univ, Sch Informat Sci & Elect Engn, Hangzhou, Peoples R China; [Yin, Rui] Zhejiang Univ City Coll, Sch Informat Sci & Elect Engn, Hangzhou 310013, Peoples R China; [Chen, Xianfu] VTT Tech Res Ctr Finland, Espoo, Finland; [Wu, Celimuge] Univ Electrocommun, Chofu, Tokyo, Japan Zhejiang University; Zhejiang University City College; VTT Technical Research Center Finland; University of Electro-Communications - Japan Zou, ZQ (corresponding author), Zhejiang Univ, Sch Informat Sci & Elect Engn, Hangzhou, Peoples R China. zouzhiqun@zju.edu.cn; yinrui@zucc.edu.cn; xianfu.chen@ytt.fi; celimuge@uec.ac.jp Wu, Celimuge/P-1232-2019 Wu, Celimuge/0000-0001-6853-5878 12 9 10 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2377-8644 978-1-7281-0738-7 IEEE INT CONF COMMUN 2019.0 42 47 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BR7RJ 2023-03-23 WOS:000669646100008 0
J Lei, D; Qin, K; Pinaya, WHL; Young, J; van Amelsvoort, T; Marcelis, M; Donohoe, G; Mothersill, DO; Corvin, A; Vieira, S; Lui, S; Scarpazza, C; Arango, C; Bullmore, E; Gong, QY; McGuire, P; Mechelli, A Lei, Du; Qin, Kun; Pinaya, Walter H. L.; Young, Jonathan; van Amelsvoort, Therese; Marcelis, Machteld; Donohoe, Gary; Mothersill, David O.; Corvin, Aiden; Vieira, Sandra; Lui, Su; Scarpazza, Cristina; Arango, Celso; Bullmore, Ed; Gong, Qiyong; McGuire, Philip; Mechelli, Andrea Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia SCHIZOPHRENIA BULLETIN English Article neuroimaging; psychosis; machine learning; connectome; graph analysis; magnetic resonance imaging BRAIN NETWORKS; 1ST-EPISODE SCHIZOPHRENIA; CONNECTIVITY; RELIABILITY; PSYCHOSIS; DOPAMINE; CONNECTOME; SYMPTOMS; OUTCOMES; TOOLBOX Background and Hypothesis Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. Study Design We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. Study Results GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis. Conclusions The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia. [Lei, Du; Gong, Qiyong] Sichuan Univ, West China Hosp, Dept Radiol, Huaxi MR Res Ctr HMRRC, Chengdu, Peoples R China; [Lei, Du; Vieira, Sandra; Scarpazza, Cristina; McGuire, Philip; Mechelli, Andrea] Kings Coll London, Inst Psychiat, Dept Psychosis Studies, Psychol Neurosci, London, England; [Lei, Du; Qin, Kun] Univ Cincinnati, Dept Psychiat & Behav Neurosci, Coll Med, Cincinnati, OH USA; [Pinaya, Walter H. L.] Kings Coll London, Sch Biomed Engn Imaging Sci, Dept Biomed Engn, London, England; [Young, Jonathan] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England; [van Amelsvoort, Therese; Marcelis, Machteld] Maastricht Univ, Sch Mental Hlth & Neurosci, Dept Psychiat & Neuropsychol, Med Ctr, Maastricht, Netherlands; [Marcelis, Machteld] Mental Hlth Care Inst Eindhoven GGzE, Eindhoven, Netherlands; [Donohoe, Gary] NUI Galway Univ, Sch Psychol Ctr Neuroimaging & Cognit Genom, Galway, Ireland; [Mothersill, David O.] Natl Coll Ireland, Sch Business, Dept Psychol, Dublin, Ireland; [Corvin, Aiden] Trinity Coll Dublin, Sch Med, Dept Psychiat, Dublin, Ireland; [Scarpazza, Cristina] Univ Padua, Dept Gen Psychol, Padua, Italy; [Scarpazza, Cristina] Univ Padua, Padova Neurosci Ctr, Padua, Italy; [Arango, Celso] Univ Complutense Madrid, Sch Med, Hosp Gen Univ Gregorio Maranon, Dept Child & Adolescent Psychiat,LiSGM,CIBERSAM, Madrid, Spain; [Bullmore, Ed] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England; [Gong, Qiyong] Chinese Acad Med Sci, Res Unit Psychoradiol, Chengdu, Sichuan, Peoples R China; [Gong, Qiyong] Sichuan Univ, Dept Radiol, West China Xiamen Hosp, Xiamen, Fujian, Peoples R China Sichuan University; University of London; King's College London; University System of Ohio; University of Cincinnati; University of London; King's College London; University of London; King's College London; Maastricht University; National College of Ireland; University College Dublin; Trinity College Dublin; University of Padua; University of Padua; CIBER - Centro de Investigacion Biomedica en Red; CIBERSAM; Complutense University of Madrid; General University Gregorio Maranon Hospital; University of Cambridge; Chinese Academy of Medical Sciences - Peking Union Medical College; Sichuan University Gong, QY (corresponding author), Sichuan Univ, West China Hosp, Dept Radiol, Huaxi MR Res Ctr HMRRC, Chengdu, Peoples R China.;Gong, QY (corresponding author), Chinese Acad Med Sci, Res Unit Psychoradiol, Chengdu, Sichuan, Peoples R China.;Gong, QY (corresponding author), Sichuan Univ, Dept Radiol, West China Xiamen Hosp, Xiamen, Fujian, Peoples R China. qiyonggong@hmrrc.org.cn Mechelli, Andrea/B-1114-2011; Gong, Qiyong/W-3052-2019; Donohoe, Gary/J-6481-2013; Vieira, Sandra/HNI-7805-2023; Qin, Kun/GXG-4297-2022; Scarpazza, Cristina/AAA-2306-2022; Pinaya, Walter/A-5791-2018 Gong, Qiyong/0000-0002-5912-4871; Donohoe, Gary/0000-0003-3037-7426; Qin, Kun/0000-0003-1254-6388; Scarpazza, Cristina/0000-0002-4126-426X; Lei, Du/0000-0002-3503-3692; Pinaya, Walter/0000-0003-3739-1087; McGuire, Philip/0000-0003-4381-0532 National Natural Science Foundation of China [81621003, 82027808, 81820108018, 81501452]; European Commission [603196]; Newton international fellowship alumni follow-on funding from the royal society; Wellcome Trust Innovations [WT213038/Z/18/Z]; Wellcome Trust's Psychosis Flagship Innovations [220402/Z/20/Z]; MIUR; Friedrich Wihelm Bessel Research Award of Humboldt Foundation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Commission(European CommissionEuropean Commission Joint Research Centre); Newton international fellowship alumni follow-on funding from the royal society; Wellcome Trust Innovations; Wellcome Trust's Psychosis Flagship Innovations; MIUR(Ministry of Education, Universities and Research (MIUR)); Friedrich Wihelm Bessel Research Award of Humboldt Foundation This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018, and 81501452) and a grant from the European Commission (PSYSCAN - Translating neuroimaging findings from research into clinical practice; ID: 603196). DL was supported by Newton international fellowship alumni follow-on funding from the royal society. WHLP was supported by Wellcome Trust Innovations (WT213038/Z/18/Z). AM was supported by a grant from Wellcome Trust's Psychosis Flagship Innovations (220402/Z/20/Z). The present work was carried out within the scope of the research programme Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padova. SL also acknowledges the support from Friedrich Wihelm Bessel Research Award of Humboldt Foundation. 68 2 2 11 17 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0586-7614 1745-1701 SCHIZOPHRENIA BULL Schizophr. Bull. JUN 21 2022.0 48 4 881 892 10.1093/schbul/sbac047 0.0 MAY 2022 12 Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Psychiatry 2I9DL 35569019.0 Green Accepted 2023-03-23 WOS:000795656600001 0
J Wang, NC; Xiao, D; Veselovskii, I; Wang, Y; Russell, LM; Zhao, CF; Guo, JP; Li, CC; Gross, S; Liu, X; Ni, XQ; Tan, LZ; Liu, YX; Zhang, K; Tong, YC; Wu, LY; Chen, FT; Wang, BY; Liu, C; Chen, WB; Liu, D Wang, Nanchao; Xiao, Da; Veselovskii, Igor; Wang, Yuan; Russell, Lynn M.; Zhao, Chuanfeng; Guo, Jianping; Li, Chengcai; Gross, Silke; Liu, Xu; Ni, Xueqi; Tan, Lizhou; Liu, Yuxuan; Zhang, Kai; Tong, Yicheng; Wu, Lingyun; Chen, Feitong; Wang, Binyu; Liu, Chong; Chen, Weibiao; Liu, Dong This is FAST: multivariate Full-permutAtion based Stochastic foresT method-improving the retrieval of fine-mode aerosol microphysical properties with multi-wavelength lidar REMOTE SENSING OF ENVIRONMENT English Article Multi-wavelength lidar; Fine-mode aerosols; Aerosol microphysical properties; Machine learning method SPECTRAL-RESOLUTION LIDAR; WIDENED MICHELSON INTERFEROMETER; RAMAN LIDAR; TROPOSPHERIC AEROSOL; PARTICLE PARAMETERS; INFORMATION-CONTENT; OPTICAL-PROPERTIES; LINEAR-ESTIMATION; INVERSION; SMOKE Despite the small size and ubiquitous presence, fine-mode aerosols play a vital role in climate change and human health, especially in highly populated regions. Hitherto, some studies have been carried out to retrieve fine-mode aerosol microphysical properties from multi-wavelength lidar measurements. However, there has been a dearth of lidar-based methods with quality retrieval and high time efficiency to adequately quantify the impacts of fine-mode aerosols on Earth's energy budget. The reasons for the gap involve the limitations in current approaches and the nature of the under-determined problem with insufficient information of three backscattering coefficients (beta) and two extinction coefficients (alpha), typically known as the 3 beta + 2 alpha configuration. Furthermore, the latest lidars, especially for spaceborne and airborne applications, are inherently difficult to perform routine diurnal 3 beta + 2 alpha observations with high resolution and require high processing speed for massive data. To overcome these conundrums, we developed a novel unsupervised machine learning method-multivariate Full-permutAtion based Stochastic foresT method (dubbed the FAST method)-to improve the retrieval of fine-mode aerosol microphysical properties. To the best of knowledge, this work is the first time that machine learning algorithms are employed in attempts to retrieve the aerosol microphysical properties with stand-alone multi-wavelength lidar data. The major merits of the FAST method include 1) high accuracy of fine-mode aerosol products with the typical 3 beta + 2 alpha configuration, 2) acceptable performances for fewer input optical channels, and 3) high processing speed for large volume data. Comprehensive simulations have been conducted to investigate the error characteristic of the FAST method under different conditions. We also applied the FAST method to the airborne lidar data acquired during the NASA DISCOVER-AQ field campaign. The retrievals of the FAST method provide high resolution time-height distributions of fine-mode aerosol microphysical properties at 20-s temporal resolution and 45-m vertical resolution. In situ measurements are compared with multi-wavelength lidar retrievals showing good agreements. We achieved 0.010, 0.014 and 0.016 in terms of mean absolute difference for retrieved 532-nm single-scattering albedo with 3 beta + 2 alpha, 3 beta + 1 alpha, and 2 beta + 1 alpha configurations, respectively. The proposed method is expected to represent an important step toward improving microphysical retrievals from multi-wavelength lidar data, especially for airborne and spaceborne lidar missions. [Wang, Nanchao; Xiao, Da; Liu, Yuxuan; Zhang, Kai; Tong, Yicheng; Wu, Lingyun; Chen, Feitong; Wang, Binyu; Liu, Chong; Liu, Dong] Zhejiang Univ, Coll Opt Sci & Engn, State Key Lab Modern Opt Instrumentat, Hangzhou 310000, Peoples R China; [Veselovskii, Igor] Russian Acad Sci, Prokhorov Gen Phys Inst, Moscow, Russia; [Wang, Yuan] Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA; [Russell, Lynn M.] Univ Calif San Diego, Scripps Inst Oceanog, San Diego, CA 92093 USA; [Guo, Jianping] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China; [Zhao, Chuanfeng; Li, Chengcai] Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies, Beijing 100871, Peoples R China; [Gross, Silke] Deutsch Zentrum Luft & Raumfahrt, Inst Phys Atmosphare, Oberpfaffenhofen, Germany; [Liu, Xu; Ni, Xueqi; Tan, Lizhou] Zhejiang Univ, Dept Phys, Hangzhou 310000, Peoples R China; [Liu, Chong; Liu, Dong] Zhejiang Univ, Int Res Ctr Adv Photon, Hangzhou 310000, Peoples R China; [Liu, Chong; Liu, Dong] Jiaxing Key Lab Photon Sensing & Intelligent Imagi, Jiaxing 314000, Peoples R China; [Liu, Chong] Zhejiang Univ, Intelligent Opt & Photon Res Ctr, Jiaxing Res Inst, Jiaxing 314000, Peoples R China; [Chen, Weibiao] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Key Lab Space Laser Commun & Detect Technol, Shanghai 201800, Peoples R China Zhejiang University; Russian Academy of Sciences; Prokhorov General Physics Institute of the Russian Academy of Sciences; Purdue University System; Purdue University; Purdue University West Lafayette Campus; University of California System; University of California San Diego; Scripps Institution of Oceanography; Chinese Academy of Meteorological Sciences (CAMS); Chinese Academy of Sciences; Peking University; Helmholtz Association; German Aerospace Centre (DLR); Zhejiang University; Zhejiang University; Zhejiang University; Chinese Academy of Sciences; Shanghai Institute of Optics & Fine Mechanics Liu, D (corresponding author), Zhejiang Univ, Coll Opt Sci & Engn, State Key Lab Modern Opt Instrumentat, Hangzhou 310000, Peoples R China.;Chen, WB (corresponding author), Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Key Lab Space Laser Commun & Detect Technol, Shanghai 201800, Peoples R China. wbchen@siom.ac.cn; liudongopt@zju.edu.cn LI, Chengcai/B-4654-2012 LI, Chengcai/0000-0001-8860-1916; Wang, Nanchao/0000-0002-5037-4145 Excellent Young Scientist Program of Zhejiang Provincial Natural Science Foundation of China [LR19D050001]; National Key Research and Development Program of China [2016YFC0200700]; Fundamental Research Funds for the Central Universities [2021XZZX019]; State Key Laboratory of Modern Optical Instrumentation Innovation Program [MOI2020ZD02]; Zhejiang Uni- versity Global Partnership Fund; Russian Science Foundation [21-17-00114] Excellent Young Scientist Program of Zhejiang Provincial Natural Science Foundation of China; National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Key Laboratory of Modern Optical Instrumentation Innovation Program; Zhejiang Uni- versity Global Partnership Fund; Russian Science Foundation(Russian Science Foundation (RSF)) This study was supported by Excellent Young Scientist Program of Zhejiang Provincial Natural Science Foundation of China (LR19D050001) , National Key Research and Development Program of China (2016YFC0200700) , Fundamental Research Funds for the Central Universities (2021XZZX019) , State Key Laboratory of Modern Optical Instrumentation Innovation Program (MOI2020ZD02) , Zhejiang Uni- versity Global Partnership Fund, and Russian Science Foundation (project 21-17-00114) . The authors would like to thank the science team from NASA Langley Center for providing the open-accessed lidar data during the DISCOVER-AQ field campaign. 66 1 1 22 22 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. OCT 2022.0 280 113226 10.1016/j.rse.2022.113226 0.0 AUG 2022 16 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology 4R7QY 2023-03-23 WOS:000856954800001 0
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C.; Nabili, S.; Palmer, C.; Seidel, M.; Skuja, A.; Wang, L.; Wong, K.; Abercrombie, D.; Andreassi, G.; Bi, R.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Eysermans, J.; Freer, C.; Ceballos, G. Gomez; Goncharov, M.; Harris, P.; Hu, M.; Klute, M.; Kovalskyi, D.; Krupa, J.; Lee, Y-J; Mironov, C.; Paus, C.; Rankin, D.; Roland, C.; Roland, G.; Shi, Z.; Stephans, G. S. F.; Wang, Z.; Wyslouch, B.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Hiltbrand, J.; Jain, Sh; Krohn, M.; Kubota, Y.; Mans, J.; Revering, M.; Rusack, R.; Saradhy, R.; Schroeder, N.; Strobbe, N.; Wadud, M. A.; Bloom, K.; Bryson, M.; Claes, D. R.; Fangmeier, C.; Finco, L.; Golf, F.; Joo, C.; Kravchenko, I; Musich, M.; Reed, I; Siado, J. E.; Snow, G. R.; Tabb, W.; Yan, F.; Zecchinelli, A. G.; Agarwal, G.; Bandyopadhyay, H.; Hay, L.; Iashvili, I.; Kharchilava, A.; McLean, C.; Nguyen, D.; Pekkanen, J.; Rappoccio, S.; Williams, A.; Alverson, G.; Barberis, E.; Haddad, Y.; Hortiangtham, A.; Li, J.; Madigan, G.; Marzocchi, B.; Morse, D. M.; Nguyen, V; Orimoto, T.; Parker, A.; Skinnari, L.; Tishelman-Charny, A.; Wamorkar, T.; Wang, B.; Wisecarver, A.; Wood, D.; Bueghly, J.; Chen, Z.; Gilbert, A.; Gunter, T.; Hahn, K. A.; Liu, Y.; Odell, N.; Schmitt, M. H.; Velasco, M.; Band, R.; Bucci, R.; Das, A.; Dev, N.; Goldouzian, R.; Hildreth, M.; Anampa, K. Hurtado; Jessop, C.; Lannon, K.; Lawrence, J.; Loukas, N.; Lutton, D.; Marinelli, N.; Mcalister, I; McCauley, T.; Mcgrady, C.; Mohrman, K.; Ruchti, R.; Siddireddy, P.; Townsend, A.; Wayne, M.; Wightman, A.; Zarucki, M.; Zygala, L.; Bylsma, B.; Cardwell, B.; Durkin, L. S.; Francis, B.; Hill, C.; Ornelas, M. Nunez; Wei, K.; Winer, B. L.; Yates, B. R.; Addesa, F. M.; Bonham, B.; Das, P.; Dezoort, G.; Elmer, P.; Frankenthal, A.; Greenberg, B.; Haubrich, N.; Higginbotham, S.; Kalogeropoulos, A.; Kopp, G.; Kwan, S.; Lange, D.; Marlow, D.; Mei, K.; Ojalvo, I; Olsen, J.; Stickland, D.; Tully, C.; Norberg, S.; Bakshi, A. S.; Barnes, V. E.; Chawla, R.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Karmarkar, S.; Kondratyev, D.; Liu, M.; Negro, G.; Neumeister, N.; Paspalaki, G.; Piperov, S.; Purohit, A.; Schulte, J. F.; Stojanovic, M.; Thieman, J.; Wang, F.; Xiao, R.; Xie, W.; Dolen, J.; Parashar, N.; Baty, A.; Decaro, M.; Dildick, S.; Ecklund, K. M.; Freed, S.; Gardner, P.; Geurts, F. J. M.; Li, W.; Padley, B. P.; Redjimi, R.; Shi, W.; Leiton, A. G. Stahl; Zhang, L.; Bodek, A.; de Barbaro, P.; Demina, R.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Hindrichs, O.; Khukhunaishvili, A.; Ranken, E.; Taus, R.; Chiarito, B.; Chou, J. P.; Gandrakota, A.; Gershtein, Y.; Halkiadakis, E.; Hart, A.; Heindl, M.; Karacheban, O.; Laflotte, I; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thayil, S. A.; Thomas, S.; Wang, H.; Acharya, H.; Delannoy, A. G.; Fiorendi, S.; Spanier, S.; Bouhali, O.; Dalchenko, M.; Delgado, A.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Luo, S.; Malhotra, S.; Mueller, R.; Overton, D.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; Hegde, V; Kunori, S.; Lamichhane, K.; Mengke, T.; Muthumuni, S.; Peltola, T.; Volobouev, I; Whitbeck, A.; Appelt, E.; Greene, S.; Gurrola, A.; Johns, W.; Melo, A.; Ni, H.; Padeken, K.; Romeo, F.; Sheldon, P.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Cox, B.; Cummings, G.; Hakala, J.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, A.; Neu, C.; Lara, C. E. Perez; Tannenwald, B.; White, S.; Wolfe, E.; Poudyal, N.; Black, K.; Bose, T.; Caillol, C.; Dasu, S.; De Bruyn, I; Everaerts, P.; Fienga, F.; Galloni, C.; He, H.; Herndon, M.; Herve, A.; Hussain, U.; Lanaro, A.; Loeliger, A.; Loveless, R.; Sreekala, J. Madhusudanan; Mallampalli, A.; Mohammadi, A.; Pinna, D.; Savin, A.; Shang, V; Smith, W. H.; Teague, D.; Trembath-Reichert, S.; Vetens, W. CMS Collaboration Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Beyond Standard Model; Dark matter; Hadron-Hadron scattering (experiments); Higgs physics DARK-MATTER SEARCH; HIGGS A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. 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R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Stylianou, N.] Vrije Univ Brussel, Brussels, Belgium; [Kello, T.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I; Moureaux, L.; Petre, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Wezenbeek, L.; Gao, X.] Univ Libre Bruxelles, Brussels, Belgium; [Cornelis, T.; Dobur, D.; Knolle, J.; Lambrecht, L.; Mestdach, G.; Niedziela, M.; Roskas, C.; Samalan, A.; Skovpen, K.; Tytgat, M.; Vermassen, B.; Vit, M.] Univ Ghent, Ghent, Belgium; [Benecke, A.; Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Jaffel, K.; Jain, Sa; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Tran, T. T.; Vischia, P.; Wertz, S.] Catholic Univ Louvain, Louvain La Neuve, Belgium; [Alves, G. A.; Hensel, C.; Moraes, A.] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; [Alda Junior, W. L.; Gallo Pereira, M. Alves; Ferreira Filho, M. Barroso; Malbouisson, H. Brandao; Carvalho, W.; Chinellato, J.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; De Souza, S. Fonseca; Figueiredo, D. Matos; Herrera, C. Mora; Amarilo, K. Mota; Mundim, L.; Nogima, H.; Teles, P. Rebello; Santoro, A.; Silva Do Amaral, S. M.; Sznajder, A.; Thiel, M.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; [Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.] Univ Estadual Paulista, Sao Paulo, Brazil; [Gregores, E. M.; Mercadante, P. 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A.; Mohammed, Y.] Fayoum Univ, Ctr High Energy Phys CHEP FU, Al Fayyum, Egypt; [Bhowmik, S.; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Nandan, S.; Nielsen, C.; Pata, J.; Raidal, M.; Tani, L.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Bharthuar, S.; Brucken, E.; Garcia, F.; Havukainen, J.; Kim, M. S.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Lehti, S.; Linden, T.; Lotti, M.; Martikainen, L.; Myllymaki, M.; Ott, J.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Luukka, P.; Petrow, H.; Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Amendola, C.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Lenzi, B.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Savoy-Navarro, A.; Titov, M.; Yu, G. B.; Mijuskovic, J.] Univ Paris Saclay, IRFU, CEA, Gif Sur Yvette, France; [Ahuja, S.; Beaudette, F.; Bonanomi, M.; Perraguin, A. Buchot; Busson, P.; Cappati, A.; Charlot, C.; Davignon, O.; Diab, B.; Falmagne, G.; Ghosh, S.; de Cassagnac, R. Granier; Hakimi, A.; Kucher, I; Motta, J.; Nguyen, M.; Ochando, C.; Paganini, P.; Rembser, J.; Salerno, R.; Sarkar, U.; Sauvan, J. B.; Sirois, Y.; Tarabini, A.; Zabi, A.; Zghiche, A.] Ecole Polytech, Lab Leprince Ringuet, Inst Polytech Paris, CNRS IN2P3, Palaiseau, France; [Agram, J-L; Andrea, J.; Apparu, D.; Bloch, D.; Bourgatte, G.; Brom, J-M; Chabert, E. C.; Collard, C.; Darej, D.; Fontaine, J-C; Goerlach, U.; Grimault, C.; Le Bihan, A-C; Nibigira, E.; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; [Asilar, E.; Beauceron, S.; Bernet, C.; Boudoul, G.; Camen, C.; Carle, A.; Chanon, N.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Laktineh, I. 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Pena; Rieger, O.; Schleper, P.; Schroeder, M.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Tews, A.; Zoi, I] Univ Hamburg, Hamburg, Germany; [Bechtel, J.; Brommer, S.; Butz, E.; Caspart, R.; Chwalek, T.; De Boer, W.; Dierlamm, A.; Droll, A.; El Morabit, K.; Faltermann, N.; Giffels, M.; Gosewisch, J. O.; Gottmann, A.; Hartmann, F.; Heidecker, C.; Husemann, U.; Keicher, P.; Koppenhoefer, R.; Maier, S.; Metzler, M.; Mitra, S.; Mueller, Th; Neukum, M.; Nurnberg, A.; Quast, G.; Rabbertz, K.; Rauser, J.; Savoiu, D.; Schnepf, M.; Seith, D.; Shvetsov, I; Simonis, H. J.; Ulrich, R.; Van der Linden, J.; Von Cube, R. F.; Wassmer, M.; Weber, M.; Wieland, S.; Wolf, R.; Wozniewski, S.; Wunsch, S.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Stakia, A.] NCSR Demokritos, Inst Nucl & Particle Phys INPP, Aghia Paraskevi, Greece; [Diamantopoulou, M.; Karasavvas, D.; Karathanasis, G.; Kontaxakis, P.; Koraka, C. 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P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Cassese, A.; Ceccarelli, R.; Ciulli, V; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.] Ist Nazl Fis Nucl, Sez Firenze, Florence, Italy; [Ceccarelli, R.; Ciulli, V; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Seidita, R.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Bozzo, M.; Mulargia, R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Brivio, F.; Cetorelli, F.; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Lucchini, M. T.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Ist Nazl Fis Nucl, Sez Milano Bicocca, Milan, Italy; [Brivio, F.; Cetorelli, F.; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Lucchini, M. T.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Carnevali, F.; Cavallo, N.; De Iorio, A.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Carnevali, F.; De Iorio, A.; Iorio, A. O. M.; Lista, L.; Sciacca, C.; Layer, L.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Layer, L.; Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Re, V; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.; Fallavollita, F.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Riccardi, C.; Vitulo, P.; Fallavollita, F.] Univ Pavia, Pavia, Italy; [Asenov, P.; Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Presilla, M.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Piccinelli, A.; Presilla, M.; Rossi, A.; Santocchia, A.; Tedeschi, T.; Magherini, M.] Univ Perugia, Perugia, Italy; [Azzurri, P.; Bagliesi, G.; Bertacchi, V; Bianchini, L.; Boccali, T.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; D'Amante, V; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Parolia, S.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Bossini, E.; Ciocci, M. A.; Messineo, A.; Parolia, S.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [D'Amante, V; Di Domenico, M. R.; Turini, N.] Univ Siena, Siena, Italy; [Barria, P.; Campana, M.; Cavallari, F.; Del Re, D.; Di Marco, E.; Diemoz, M.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Campana, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ruspa, M.; Shchelina, K.; Siviero, F.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.; Vagnerini, A.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Vagnerini, A.; Rumerio, P.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Candelise, V; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Kim, J.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Francois, B.; Kim, T. J.; Park, J.] Hanyang Univ, Seoul, South Korea; [Lee, K.; Park, J.; Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K. S.; Lim, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Kim, J.; Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Lee, S.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I] Seoul Natl Univ, Seoul, South Korea; [Jang, W.; Kang, D. Y.; Kang, Y.; Kim, S.; Ko, B.; Lee, J. S. H.; Lee, Y.; Park, I. C.; Roh, Y.; Ryu, M. S.; Song, D.; Watson, I. J.; Yang, S.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Lee, H.; Lee, Y.; Choi, M.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Beyrouthy, T.; Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Kuwait; [Torims, T.; Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; De Oliveira, A. Carvalho Antunes; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.] Vilnius Univ, Vilnius, Lithuania; [Bin Norjoharuddeen, N.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Leon Coello, M.; Murillo Quijada, J. A.; Sehrawat, A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; De la Cruz-Burelo, E.; Heredia-De la Cruz, I; Lopez-Fernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez-Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M., I; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Gorski, M.; Kazana, M.; Szleper, M.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Faccioli, P.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Niknejad, T.; Pisano, M.; Seixas, J.; Toldaiev, O.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Finger, M.; Jr, M. Finger; Khvedelidze, A.; Tsamalaidze, Z.; Afanasiev, S.; Budkouski, D.; Golutvin, I; Gorbunov, I; Karjavine, V; Korenkov, V; Lanev, A.; Malakhov, A.; Matveev, V; Palichik, V; Perelygin, V; Savina, M.; Seitova, D.; Shalaev, V; Shmatov, S.; Shulha, S.; Smirnov, V; Teryaev, O.; Voytishin, N.; Yuldashev, B. S.; Zarubin, A.; Zhizhin, I] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V; Ivanov, Y.; Kim, V; Kuznetsova, E.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirpichnikov, D.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Toropin, A.; Musienko, Y.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Nikitenko, A.; Popov, V; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.; Chistov, R.] Moscow Inst Phys & Technol, Moscow, Russia; [Chistov, R.; Bychkova, O.; Danilov, M.; Oskin, A.; Polikarpov, S.; Selivanova, D.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Danilov, M.; Polikarpov, S.; Andreev, V; Azarkin, M.; Dremin, I; Kirakosyan, M.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Belyaev, A.; Boos, E.; Bunichev, V; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V; Kodolova, O.; Lokhtin, I; Obraztsov, S.; Perfilov, M.; Savrin, V; Snigirev, A.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Dimova, T.; Kardapoltsev, L.; Kozyrev, A.; Ovtin, I; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Elumakhov, D.; Kachanov, V; Konstantinov, D.; Mandrik, P.; Petrov, V; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Natl Res Ctr, Inst High Energy Phys, Kurchatov Inst, Protvino, Russia; [Babaev, A.; Okhotnikov, V] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borshch, V; Ivanchenko, V; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.; Kailasapathy, B.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I; Barrio Luna, M.; Bedoya, Cristina F.; Carrillo Montoya, C. A.; Cepeda, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Holgado, J. Leon; Moran, D.; Perez Dengra, C.; Navarro Tobar, A.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Sanchez Navas, S.; Urda Gomez, L.; Willmott, C.] Ctr Invest Energet Medioambientales & Tecnol, Madrid, Spain; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; [de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Ramon Alvarez, C.; Rodriguez Bouza, V; Soto Rodriguez, A.; Trapote, A.; Trevisani, N.; Vico Villalba, C.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Matorras-Cuevas, Pablo; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I; Vizan Garcia, J. M.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Jayananda, M. K.; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, D. D. C.] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Sharma, A.; Stahl, A.; Hartmann, F.; Sphicas, P.; Valsecchi, D.; Meola, S.; Paolucci, P.; Fallavollita, F.; Aarrestad, T. K.; Abbaneo, D.; Alimena, J.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chernyavskaya, N.; Chhibra, S. S.; Cipriani, M.; Cristella, L.; D'Enterria, D.; Dabrowski, A.; David, A.; De Roeck, A.; Defranchis, M. M.; Deile, M.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fasanella, D.; Florent, A.; Franzoni, G.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Haranko, M.; Hegeman, J.; Innocente, V; James, T.; Janot, P.; Kaspar, J.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Lintuluoto, A.; Long, K.; Lourenco, C.; Maier, B.; Malgeri, L.; Mallios, S.; Mannelli, M.; Marini, A. C.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Pitt, M.; Qu, H.; Quast, T.; Rabady, D.; Racz, A.; Gutierrez, G. Reales; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schafer, C.; Schwick, C.; Selvaggi, M.; Silva, P.; Snoeys, W.; Summers, S.; Tatar, K.; Tavolaro, V. R.; Treille, D.; Tropea, P.; Tsirou, A.; Van Onsem, G. P.; Wanczyk, J.; Wozniak, K. A.; Zeuner, W. D.; Virdee, T.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Noehte, L.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Androsov, K.; Backhaus, M.; Berger, P.; Calandri, A.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Eble, F.; Gedia, K.; Glessgen, F.; Espinosa, T. A. Gomez; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A-M; Manzoni, R. A.; Marchese, L.; Perez, C. Martin; Meinhard, M. T.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Stampf, V; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Amsler, C.; Bartschi, P.; Botta, C.; Brzhechko, D.; Canelli, M. F.; Cormier, K.; De Wit, A.; Del Burgo, R.; Heikkila, J. K.; Huwiler, M.; Jin, W.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Liechti, S. P.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I; Reimers, A.; Robmann, P.; Cruz, S. Sanchez; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Caminada, L.; Missiroli, M.; Noehte, L.; Ceard, L.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W-S; Li, Y. Y.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Wu, H. Y.; Yazgan, E.; Yu, P. R.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dumanoglu, I; Eskut, E.; Guler, Y.; Guler, E. Gurpinar; Isik, C.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Cerci, S.; Hos, I; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; [Stylianou, N.; Anthony, D.; Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Krikler, B.; Paramesvaran, S.; El Nasr-Storey, S. Seif; Smith, V. J.; Pass, K. Walkingshaw; White, R.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Bell, K. W.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Cooke, C.; Ellis, K., V; Harder, K.; Harper, S.; Holmberg, M. L.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Nikitenko, A.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); 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National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Hungarian University of Agriculture & Life Sciences; Indian Institute of Science (IISC) - Bangalore; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; Isfahan University of Technology; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; University of Siena; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Kyung Hee University; Sejong University; Seoul National University (SNU); University of Seoul; Yonsei University; Sungkyunkwan University (SKKU); American University of Middle East; Riga Technical University; Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; University of Montenegro; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; AGH University of Science & Technology; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; University of Belgrade; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Universidade do Estado do Amazonas; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade Federal de Mato Grosso do Sul; Nanjing Normal University; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Erzincan Binali Yildirim University; Brandenburg University of Technology Cottbus; Helmholtz Association; Research Center Julich; Egyptian Knowledge Bank (EKB); Assiut University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Sharif University of Technology; University of Science & Technology of Mazandaran; Italian National Agency New Technical Energy & Sustainable Economics Development; Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); Academy of Sciences of Uzbekistan; Peter the Great St. Petersburg Polytechnic University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Eastern University, Sri Lanka; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Sirnak University; Near East University; Konya Technical University; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University; Istanbul University - Cerrahpasa; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Egyptian Knowledge Bank (EKB); Ain Shams University; Bingol University; Sinop University; Erciyes University; Qatar Foundation (QF); Texas A&M University Qatar Tumasyan, A (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. M, F/HGF-3166-2022; Schwarz, Dennis/GYA-4479-2022; Della Ricca, Giuseppe/B-6826-2013; kaya, mithat/D-8062-2011; Shah, Aashaq/GYI-8983-2022; Gil Da Silveira, Gustavo/N-7279-2014; Dallavalle, Gaetano Marco/AFW-4654-2022; Watson, Ian James/AFL-9360-2022; Rabady, Dinyar/GSN-9210-2022; Sguazzoni, Giacomo/J-4620-2015; Casarsa, Massimo/L-3623-2018; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Shopova, Mariana Filipova/HKW-0149-2023; Sert, H/ABA-5645-2020; Legger, Federica/HNC-2568-2023; Donato, Silvio/GPK-2262-2022; Danilov, Mikhail/C-5380-2014; Polikarpov, Sergey/C-8939-2016; de Souza Sandro, Fonseca/ABB-8505-2020; Tinoco Mendes, Andre David/D-4314-2011; Pásztor, Gabriella/D-9716-2016; Tcherniaev, Evgueni/G-3453-2016; Kim, Victor T/L-1648-2013; Trocino, Daniele/AGI-2155-2022; Shmatov, Sergei/J-5621-2014; Ciangottini, Diego/HJH-5914-2023; Cepeda, Maria/HNO-9314-2023; Mejia Guisao, Jhovanny Andres/ABG-3627-2021; Cepeda, Maria/AAF-4992-2019; Selvaggi, Michele/CAJ-2129-2022; Strologas, John/GWR-2036-2022; De Iorio, Agostino/HJH-7155-2023; Puerta Pelayo, Jesus/L-1632-2014; Demiroglu, Zuhal Seyma/A-5168-2016; Goy Lopez, Silvia/K-9200-2017; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Lowette, Steven/HKV-3341-2023; Lange, Torben/HOH-4787-2023; Lezki, Samet/AAG-8277-2019; Moscatelli, Francesco/AEL-1878-2022; Tsirou, Andromachi/HHC-6869-2022; Gozalez-Lopez, Oscar/AAH-3533-2019; Petkov, Peicho/M-2080-2016; Bunichev, Viacheslav/E-2285-2012; Norjoharuddeen, Nurfikri/S-3109-2018; Calligaris, Luigi/K-2018-2017; Yazgan, Efe/C-4521-2014; Bortignon, Pierluigi/AAF-6561-2020; Hos, ilknur/T-1165-2019; Ferro, Fabrizio/HMP-5516-2023; Lokos, Sandor/A-4798-2019; Joo, Changwoo/ABI-4034-2020; Ivanchenko, Vladimir N/L-5254-2017; Bermudez Martinez, Armando/HHN-4625-2022; Ligabue, Franco/ABI-1681-2020; Simone, Federica Maria/HDM-3686-2022; Snoeys, Walter J M/K-8259-2015; Iorio, Alberto Orso Maria/GXA-0233-2022; Kirpichnikov, Dmitry/U-9605-2019; Androsov, Konstantin/HJA-2332-2022; Kirakosyan, Martin R./N-2701-2015; Özdemir, Kadri/P-8058-2014; Pellecchia, Antonello/HNB-9377-2023; Akgun, Bora/AAM-8311-2021; Cassese, Antonio/R-1713-2016; Vai, Ilaria/AAT-5836-2020; Moraes, Arthur/F-6478-2010; Ambrozas, Marijus/F-5551-2018; SIMSEK, Ali Eren/AFO-3050-2022; Lokhtin, Igor/D-7004-2012; Zorbilmez, Caglar/ABH-1905-2020; Iqbal, Muhammad/HJH-2369-2023; Fouz, Maria Cruz/AAF-1105-2019; Aarrestad, Thea/GLV-3031-2022; Khvedelidze, Arsen/AAF-5025-2020; Matorras, Francisco/I-4983-2015; Andreev, Vladimir F/M-8665-2015; Teles, Patricia/ABF-9723-2021; Savina, Maria/HDO-6544-2022; Di Florio, Adriano/ABA-3631-2020; Aimè, Chiara/GXG-4131-2022; Saka, Halil/GPF-8892-2022; Benussi, Luigi/O-9684-2014; Dudko, Lev/D-7127-2012; Watson, Ian James/AAH-1423-2022; Lannon, Kevin/HGU-5755-2022; Guler, Yalcin/AAG-7833-2019; Lychkovskaya, Natalia/F-8341-2017; SEVINC KAYA, OZLEM/AAE-8069-2020; Terkulov, Adel/M-8581-2015; Hernández Calama, José María/AAW-6394-2021; Fiorendi, Sara/ABA-3311-2021; Blekman, Freya/ABD-7916-2020; Fienga, Francesco/HKE-1204-2023; Sunar Cerci, Deniz/AHE-1706-2022; Konecki, Marcin/G-4164-2015; Gupta, Rajat/GVS-7964-2022; CAKIR, Altan/ABD-4450-2020; Duarte, Javier Mauricio/AAA-5414-2020; Benaglia, Andrea/AGF-5495-2022; Lu, Meng/GQZ-7036-2022; Chenarani, Shirin/AAO-8918-2021; Novaes, Sergio F./D-3532-2012; Boimska, Bożena/Z-6088-2019; TUVE', Cristina/P-3933-2015; Giommi, Luca/AGV-7154-2022; Azarkin, Maxim/N-2578-2015; Ragazzi, Stefano/D-2463-2009; Navarria, Francesco Luigi/ACP-7823-2022; Tuominen, Eija/A-5288-2017; Godinovic, Nikola/G-6124-2017; Sola, Valentina/E-7726-2015; Grandi, Claudio/B-5654-2015; Malawski, Maciej/H-9119-2012; Haddad, Yacine/GRX-5022-2022; D'Alessandro, Raffaello/F-5897-2015; Duarte-Campderros, Jordi/F-5025-2018; Grzanka, Leszek/M-9052-2018; Luukka, Panja/S-8956-2018; Gulmez, Erhan/P-9518-2015 Della Ricca, Giuseppe/0000-0003-2831-6982; kaya, mithat/0000-0003-2890-4493; Shah, Aashaq/0000-0002-6157-2016; Gil Da Silveira, Gustavo/0000-0003-3514-7056; Dallavalle, Gaetano Marco/0000-0002-8614-0420; Watson, Ian James/0000-0003-2141-3413; Rabady, Dinyar/0000-0001-9239-0605; Sguazzoni, Giacomo/0000-0002-0791-3350; Casarsa, Massimo/0000-0002-1353-8964; Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Shopova, Mariana Filipova/0000-0001-6664-2493; Legger, Federica/0000-0003-1400-0709; Donato, Silvio/0000-0001-7646-4977; Danilov, Mikhail/0000-0001-9227-5164; Polikarpov, Sergey/0000-0001-6839-928X; de Souza Sandro, Fonseca/0000-0001-7830-0837; Tinoco Mendes, Andre David/0000-0001-5854-7699; Pásztor, Gabriella/0000-0003-0707-9762; Tcherniaev, Evgueni/0000-0002-3685-0635; Kim, Victor T/0000-0001-7161-2133; Trocino, Daniele/0000-0002-2830-5872; Shmatov, Sergei/0000-0001-5354-8350; Cepeda, Maria/0000-0002-6076-4083; Cepeda, Maria/0000-0002-6076-4083; De Iorio, Agostino/0000-0002-9258-1345; Puerta Pelayo, Jesus/0000-0001-7390-1457; Demiroglu, Zuhal Seyma/0000-0001-7977-7127; Lowette, Steven/0000-0003-3984-9987; Lange, Torben/0000-0001-6242-7331; Lezki, Samet/0000-0002-6909-774X; Moscatelli, Francesco/0000-0002-7676-3106; 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Fouz, Maria Cruz/0000-0003-2950-976X; Aarrestad, Thea/0000-0002-7671-243X; Khvedelidze, Arsen/0000-0002-5953-0140; Matorras, Francisco/0000-0003-4295-5668; Andreev, Vladimir F/0000-0002-5492-6920; Teles, Patricia/0000-0001-9029-8506; Savina, Maria/0000-0002-9020-7384; Di Florio, Adriano/0000-0003-3719-8041; Benussi, Luigi/0000-0002-2363-8889; Dudko, Lev/0000-0002-4462-3192; Watson, Ian James/0000-0003-2141-3413; Lannon, Kevin/0000-0002-9706-0098; Guler, Yalcin/0000-0001-7598-5252; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Terkulov, Adel/0000-0003-4985-3226; Hernández Calama, José María/0000-0001-6436-7547; Fiorendi, Sara/0000-0003-3273-9419; Blekman, Freya/0000-0002-7366-7098; Fienga, Francesco/0000-0001-5978-4952; Sunar Cerci, Deniz/0000-0002-5412-4688; Konecki, Marcin/0000-0001-9482-4841; CAKIR, Altan/0000-0002-8627-7689; Duarte, Javier Mauricio/0000-0002-5076-7096; Benaglia, Andrea/0000-0003-1124-8450; Chenarani, Shirin/0000-0002-1425-076X; Novaes, Sergio F./0000-0003-0471-8549; Boimska, Bożena/0000-0002-4200-1541; TUVE', Cristina/0000-0003-0739-3153; Giommi, Luca/0000-0003-3539-4313; Azarkin, Maxim/0000-0002-7448-1447; Bossini, Edoardo/0000-0002-2303-2588; Ragazzi, Stefano/0000-0001-8219-2074; Voutilainen, Mikko/0000-0002-5200-6477; Myllymaki, Mikael/0000-0003-0510-3810; Siikonen, Hannu/0000-0003-2039-5874; Osterberg, Kenneth/0000-0003-4807-0414; Spiga, Daniele/0000-0002-2991-6384; Navarria, Francesco Luigi/0000-0001-7961-4889; Venturi, Andrea/0000-0002-0249-4142; Kirschenmann, Henning/0000-0001-7369-2536; Primavera, Federica/0000-0001-6253-8656; Csanad, Mate/0000-0002-3154-6925; Heikkila, Jaana/0000-0002-0538-1469; Tuominen, Eija/0000-0002-7073-7767; CECCARELLI, RUDY/0000-0003-3232-9380; Tapper, Alexander/0000-0003-4543-864X; Antchev, Gueorgui/0000-0003-3210-5037; Torims, Toms/0000-0002-5167-4844; Martikainen, Laura/0000-0003-1609-3515; Minafra, Nicola/0000-0003-4002-1888; Tuominiemi, Jorma/0000-0003-0386-8633; Vila, Ivan/0000-0002-6797-7209; Godinovic, Nikola/0000-0002-4674-9450; Sola, Valentina/0000-0001-6288-951X; Bhowmik, Sandeep/0000-0003-1260-973X; Covarelli, Roberto/0000-0003-1216-5235; Alessandro, Calandri/0000-0001-7774-0099; Dimova, Tatyana/0000-0002-9560-0660; Bainbridge, Robert/0000-0001-9157-4832; Bharthuar, Shudhashil/0000-0001-5871-9622; Grandi, Claudio/0000-0001-5998-3070; Fasanella, Daniele/0000-0002-2926-2691; Seitova, Diana/0000-0003-2522-7642; Meschi, Emilio/0000-0003-4502-6151; Garcia, Francisco/0000-0002-4023-7964; Brommer, Sebastian/0000-0001-8988-2035; Monteno, Marco/0000-0002-3521-6333; Meyer, Arnd/0000-0001-9598-6623; Viinikainen, Jussi/0000-0003-2530-4265; Moran, Dermot/0000-0002-1941-9333; Malawski, Maciej/0000-0001-6005-0243; Haddad, Yacine/0000-0003-4916-7752; Lampen, Tapio/0000-0002-8398-4249; Donega, Mauro/0000-0001-9830-0412; Ott, Jennifer/0000-0001-9337-5722; Aly, Reham/0000-0001-6808-1335; D'Alessandro, Raffaello/0000-0001-7997-0306; Pugliese, Gabriella/0000-0001-5460-2638; Mohamed, Ashraf/0000-0002-6753-7254; Silvestris, Lucia/0000-0002-8985-4891; Manthos, Nikolaos/0000-0003-3247-8909; Riccardi, Cristina/0000-0003-0165-3962; Duarte-Campderros, Jordi/0000-0003-0687-5214; Kim, Minsuk/0000-0003-0392-8691; Grzanka, Leszek/0000-0002-3599-854X; Lehti, Sami/0000-0003-1370-5598; Dewanjee, Ram Krishna/0000-0001-6645-6244; Dittmann, Jay R./0000-0002-1911-3158; Del Re, Daniele/0000-0003-0870-5796; Safonov, Alexei/0000-0001-9497-5471; siviero, federico/0000-0002-4427-4076; Pujahari, Prabhat Ranjan/0000-0002-0994-7212; Laurila, Santeri/0000-0001-7507-8636; Colaleo, Anna/0000-0002-0711-6319; Gavrilov, Vladimir/0000-0002-9617-2928; Titov, Maxim/0000-0002-1119-6614; Havukainen, Joona Juhani/0000-0003-2898-6900; Santanastasio, Francesco/0000-0003-2505-8359; Grunewald, Martin/0000-0002-5754-0388; Linden, Tomas/0009-0002-4847-8882; Luukka, Panja/0000-0003-2340-4641; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Gulmez, Erhan/0000-0002-6353-518X; Hall, Geoffrey/0000-0002-6299-8385; Ciangottini, Diego/0000-0002-0843-4108; Flix Molina, Jose/0000-0003-2688-8047; Mariani, Valentina/0000-0001-7108-8116 BMBWF; FWF (Austria); FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); MES (Bulgaria); CERN; CAS; MoST; NSFC (China); MINCIENCIAS (Colombia); MSES; CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER; ERC PUT; ERDF (Estonia); Academy of Finland; MEC; HIP (Finland); CEA; CNRS/IN2P3 (France); BMBF; DFG; HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE; DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP; NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE; UM (Malaysia); BUAP; CINVESTAV; CONACYT; LNS; SEP; UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE; NSC (Poland); FCT (Portugal); JINR (Dubna); MON; RosAtom; RAS; RFBR; NRC KI (Russia); MESTD (Serbia); SEIDI; CPAN; PCTI; FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter; IPST; STAR; NSTDA (Thailand); TUBITAK; TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE; NSF (U.S.A.); Marie-Curie program; European Research Council [675440, 724704, 752730, 758316, 765710, 824093]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS and FWO (Belgium) [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; e Deutsche Forschungsgemeinschaft (DFG) [390833306, 400140256 -GRK2497]; Lendulet (Momentum) Program; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; New National Excellence Program UNKP; NKFIA [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; Latvian Council of Science; Ministry of Science and Higher Education; National Science Center (Poland) [2014/15/B/ST2/03998, 2015/19/B/ST2/02861]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [0723-2020-0041]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.) BMBWF; FWF (Austria)(Austrian Science Fund (FWF)); FNRS(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS(Chinese Academy of Sciences); MoST; NSFC (China)(National Natural Science Foundation of China (NSFC)); MINCIENCIAS (Colombia); MSES; CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER; ERC PUT; ERDF (Estonia); Academy of Finland(Academy of Finland); MEC; HIP (Finland); CEA(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF(Federal Ministry of Education & Research (BMBF)); DFG(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP; NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE(Ministry of Higher Education & Scientific Research (MHESR)); UM (Malaysia); BUAP; CINVESTAV; CONACYT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS; SEP; UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE; NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON; RosAtom; RAS(Russian Academy of SciencesRegione Sardegna); RFBR(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI; CPAN; PCTI; FEDER (Spain)(European CommissionSpanish Government); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter; IPST; STAR; NSTDA (Thailand); TUBITAK(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE(United States Department of Energy (DOE)); NSF (U.S.A.)(National Science Foundation (NSF)); Marie-Curie program(European Commission); European Research Council(European Research Council (ERC)European Commission); COST Action(European Cooperation in Science and Technology (COST)); Leventis Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS and FWO (Belgium); Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); e Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Lendulet (Momentum) Program; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences(Hungarian Academy of Sciences); New National Excellence Program UNKP; NKFIA; Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); Latvian Council of Science(Latvian Ministry of Education and Science); Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).; Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306, and under project number 400140256 -GRK2497; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 99 10 10 1 1 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. NOV 19 2021.0 11 153 10.1007/JHEP11(2021)153 0.0 72 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics XB8QD Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000721586700002 0
J Li, N; Naghadehi, MZ; Jimenez, R Li, Ning; Naghadehi, Masoud Zare; Jimenez, Rafael Evaluating short-term rock burst damage in underground mines using a systems approach INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT English Article Rock burst; damage scale index; Rock Engineering Systems (RES); probabilistic analysis; Artificial Neural Network (ANN) NEURAL-NETWORKS; HAZARD; PREDICTION; METHODOLOGY; TOMOGRAPHY; STABILITY; MECHANISM; SUPPORT; DESIGN A new rock burst damage scale index (RDSI) based on the Rock Engineering Systems paradigm and on a BP-ANN is proposed. Seven parameters that are likely to affect rock burst damage are considered to construct the ANN and to objectively code the interaction matrix. A probabilistic analysis is employed to associate damage categories to RDSI values. The methodology is validated using 24 case histories that had not been employed during the training of network. Results suggest that the RDSI can probabilistically predict the damage scale with an accuracy that could be acceptable in engineering, hence simplifying the prediction for real applications. [Li, Ning] Anhui Univ Sci & Technol, Sch Civil Engn & Architecture, Huainan, Anhui, Peoples R China; [Naghadehi, Masoud Zare] Tech Univ Madrid, ETSI Caminos Canales & Puertos, Madrid, Spain; [Li, Ning; Jimenez, Rafael] Univ Nevada, Dept Min & Met Engn, Mackay Sch Earth Sci & Engn, Reno, NV 89557 USA Anhui University of Science & Technology; Universidad Politecnica de Madrid; Nevada System of Higher Education (NSHE); University of Nevada Reno Naghadehi, MZ (corresponding author), Tech Univ Madrid, ETSI Caminos Canales & Puertos, Madrid, Spain. mzare@unr.edu Jiménez, Rafael/AAA-4205-2019 JIMENEZ RODRIGUEZ, RAFAEL/0000-0002-7720-2757 Spanish Ministry of Economy, Industry and Competitiveness [BIA 2015-69152-R]; China Scholarship Council (CSC) Spanish Ministry of Economy, Industry and Competitiveness; China Scholarship Council (CSC)(China Scholarship Council) This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness [BIA 2015-69152-R]; China Scholarship Council (CSC) [N/A]. 62 6 7 6 40 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1748-0930 1748-0949 INT J MIN RECLAM ENV Int. J. Min. Reclam. Environ. SEP 13 2020.0 34 8 531 561 10.1080/17480930.2019.1657654 0.0 SEP 2019 31 Environmental Sciences; Mining & Mineral Processing Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Mining & Mineral Processing MN0RQ 2023-03-23 WOS:000485482400001 0
J Zibar, D; Chin, HM; Tong, YY; Jain, N; Guo, J; Chang, L; Gehring, T; Bowers, JE; Andersen, UL Zibar, Darko; Chin, Hou-Man; Tong, Yeyu; Jain, Nitin; Guo, Joel; Chang, Lin; Gehring, Tobias; Bowers, John E.; Andersen, Ulrik L. Highly-Sensitive Phase and Frequency Noise Measurement Technique Using Bayesian Filtering IEEE PHOTONICS TECHNOLOGY LETTERS English Article Phase noise; lasers; frequency combs; Bayesian filtering; machine learning LINEWIDTH LASERS Spectral purity of laser sources is typically investigated using phase or frequency noise measurements, which require extraction of the optical phase. This is a challenging task if the signal-to-noise-ratio (SNR) of the spectral line or the linewidth-to-noise-ratio (LNR) are not sufficiently high. In this letter, we present a statistically optimal method for optical phase noise measurement that relies on coherent detection and Bayesian filtering. The proposed method offers a record sensitivity, as the optical phase is measured at a signal power of -75 dBm (SNR of -11 dB in 1.1 GHz receiver bandwidth). Practically, this means that the phase noise measurements are, up to a high-degree, not limited by the measurement noise floor. This allows measurements down to -200 dB rad(2)/Hz and up to 10 GHz, which is useful when measuring the Schawlow-Townes (quantum noise limited) laser linewidth. Finally, the estimated optical phase is highly accurate allowing for quantum limited signal demodulation. The method thus holds the potential to become a reference measurement tool. [Zibar, Darko] Tech Univ Denmark, Dept Photon Engn, DK-2800 Lyngby, Denmark; [Chin, Hou-Man; Jain, Nitin; Gehring, Tobias; Andersen, Ulrik L.] Tech Univ Denmark, Dept Phys, Ctr Macroscop Quantum States BigQ, DK-2800 Lyngby, Denmark; [Tong, Yeyu] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Guo, Joel; Chang, Lin; Bowers, John E.] Univ Calif Santa Barbara, Elect & Comp Engn Dept, Santa Barbara, CA 93106 USA Technical University of Denmark; Technical University of Denmark; Chinese University of Hong Kong; University of California System; University of California Santa Barbara Zibar, D (corresponding author), Tech Univ Denmark, Dept Photon Engn, DK-2800 Lyngby, Denmark. dazi@fotonik.dtu.dk Gehring, Tobias/A-8596-2016; Guo, Joel/AFH-7301-2022; Andersen, Ulrik L/A-5965-2011; Bowers, John E/B-3486-2012; Tong, Yeyu/GLU-5441-2022 Gehring, Tobias/0000-0002-4311-2593; Guo, Joel/0000-0003-0203-5170; Andersen, Ulrik L/0000-0002-1990-7687; Bowers, John E/0000-0003-4270-8296; Tong, Yeyu/0000-0002-7867-1918; Zibar, Darko/0000-0003-4182-7488; Jain, Nitin/0000-0001-8555-1773 European Research Council through the ERC-CoG FRECOM Project [771878]; EU project CiViQ [820466]; Innovation Fund Denmark, Quantum Innovation Center (Qubiz); Center for Macroscopic Quantum States (bigQ) [DNRF142] European Research Council through the ERC-CoG FRECOM Project; EU project CiViQ; Innovation Fund Denmark, Quantum Innovation Center (Qubiz); Center for Macroscopic Quantum States (bigQ) This work was supported in part by the European Research Council through the ERC-CoG FRECOM Project under Grant 771878, in part by the EU project CiViQ under Grant 820466, in part by the Innovation Fund Denmark, Quantum Innovation Center (Qubiz), and in part by the Center for Macroscopic Quantum States (bigQ) under Grant DNRF142. 8 4 4 3 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1041-1135 1941-0174 IEEE PHOTONIC TECH L IEEE Photonics Technol. Lett. DEC 1 2019.0 31 23 SI 1866 1869 10.1109/LPT.2019.2945051 0.0 4 Engineering, Electrical & Electronic; Optics; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Optics; Physics KP9ED Green Published, Green Submitted 2023-03-23 WOS:000516533000005 0
J Yang, B; Yang, S; Lv, ZH; Wang, FM; Olofsson, T Yang, Bin; Yang, Shuang; Lv, Zhihan; Wang, Faming; Olofsson, Thomas Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review SENSORS English Review digital twins; Metaverse fluid machinery; fan; pump MAINTENANCE; DESIGN; BLADES; SYSTEM Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification. [Yang, Bin; Yang, Shuang] Tianjin Chengjian Univ, Sch Energy & Safety Engn, Tianjin 300384, Peoples R China; [Yang, Bin; Olofsson, Thomas] Umea Univ, Dept Appl Phys & Elect, SE-90187 Umea, Sweden; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, SE-75105 Uppsala, Sweden; [Wang, Faming] Katholieke Univ Leuven, Dept Biosyst, BE-3001 Leuven, Belgium Tianjin Chengjian University; Umea University; Uppsala University; KU Leuven Lv, ZH (corresponding author), Uppsala Univ, Fac Arts, Dept Game Design, SE-75105 Uppsala, Sweden.;Wang, FM (corresponding author), Katholieke Univ Leuven, Dept Biosyst, BE-3001 Leuven, Belgium. lvzhihan@gmail.com; faming.wang@kuleuven.be Wang, Faming/B-7799-2010; Lv, Zhihan/I-3187-2014 Wang, Faming/0000-0002-2945-4685; Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China; [52278119] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); This work was financially supported by the National Natural Science Foundation of China (project number 52278119). 109 1 1 32 32 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2022.0 22 23 9294 10.3390/s22239294 0.0 27 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 6X4SR 36501994.0 Green Published, gold 2023-03-23 WOS:000896405400001 0
J Chen, L; Li, ZD; Zhang, SQ; Zhang, YH; Huang, T; Cai, YD Chen, Lei; Li, ZhanDong; Zhang, ShiQi; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions BIOMED RESEARCH INTERNATIONAL English Article METHYLATION; M(6)A; IDENTIFICATION; LANDSCAPE Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m(5)C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m(5)C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated. [Chen, Lei; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Li, ZhanDong] Jilin Engn Normal Univ, Coll Food Engn, Changchun, Peoples R China; [Zhang, ShiQi] Univ Copenhagen, Dept Biostat, DK-2099 Copenhagen, Denmark; [Zhang, Yu-Hang] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02115 USA; [Huang, Tao] Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Biomed Big Data Ctr,CAS Key Lab Computat Biol, Shanghai 200031, Peoples R China; [Huang, Tao] Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai 200031, Peoples R China Shanghai University; Shanghai Maritime University; Jilin Engineering Normal University; University of Copenhagen; Harvard University; Brigham & Women's Hospital; Harvard Medical School; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Biomed Big Data Ctr,CAS Key Lab Computat Biol, Shanghai 200031, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai 200031, Peoples R China. chen_lei1@163.com; lizd591@jlenu.edu.cn; shiqi.zhang@outlook.com; zhangyh825@163.com; tohuangtao@126.com; cai_yud@126.com li, zhandong/ABG-6019-2020; Chen, Lei/A-2275-2011 li, zhandong/0000-0002-0171-4473; Chen, Lei/0000-0003-3068-1583; cai, yudong/0000-0001-5664-7979 Strategic Priority Research Program of Chinese Academy of Sciences [XDA26040304, XDB38050200]; National Key R&D Program of China [2018YFC0910403]; Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences [202002] Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); National Key R&D Program of China; Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA26040304 and XDB38050200), National Key R&D Program of China (2018YFC0910403), and the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences (202002). 62 31 31 8 14 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 2314-6133 2314-6141 BIOMED RES INT Biomed Res. Int. JAN 13 2022.0 2022 4035462 10.1155/2022/4035462 0.0 11 Biotechnology & Applied Microbiology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Research & Experimental Medicine 0B8VX 35071593.0 gold 2023-03-23 WOS:000774906800003 0
J Tumasyan, A; Adam, W; Andrejkovic, JW; Bergauer, T; Chatterjee, S; Dragicevic, M; Del Valle, AE; Fruhwirth, R; Jeitler, M; Krammer, N; Lechner, L; Liko, D; Mikulec, I; Paulitsch, P; Pitters, FM; Schieck, J; Schofbeck, R; Spanring, M; Templ, S; Waltenberger, W; Wulz, CE; Chekhovsky, V; Litomin, A; Makarenko, V; Darwish, MR; De Wolf, EA; Janssen, T; Kello, T; Lelek, A; Sfar, HR; Van Mechelen, P; Van Putte, S; Van Remortel, N; Blekman, F; Bols, ES; D'Hondt, J; De Clercq, J; Delcourt, M; El Faham, H; Lowette, S; Moortgat, S; Morton, A; Muller, D; Sahasransu, AR; Tavernier, S; Van Doninck, W; Van Mulders, P; Beghin, D; Bilin, B; Clerbaux, B; De Lentdecker, G; Favart, L; Grebenyuk, A; Kalsi, AK; Lee, K; Mahdavikhorrami, M; Makarenko, I; Moureaux, L; Petre, L; Popov, A; Postiau, N; Starling, E; Thomas, L; Vanden Bemden, M; Vander Velde, C; Vanlaer, P; Vannerom, D; Wezenbeek, L; Cornelis, T; Dobur, D; Knolle, J; Lambrecht, L; Mestdach, G; Niedziela, M; Roskas, C; Samalan, A; Skovpen, K; Tytgat, M; Verbeke, W; Vermassen, B; Vit, M; Bethani, A; Bruno, G; Bury, F; Caputo, C; David, P; Delaere, C; Donertas, IS; Giammanco, A; Jaffel, K; Jain, S; Lemaitre, V; Mondal, K; Prisciandaro, J; Taliercio, A; Teklishyn, M; Tran, TT; Vischia, P; Wertz, S; Alves, GA; Hensel, C; Moraes, A; Alda, WL; Pereira, MAG; Ferreira, MB; Malbouisson, HB; Carvalho, W; Chinellato, J; Da Costa, EM; Da Silveira, GG; Damiao, DD; De Souza, SF; Figueiredo, DM; Herrera, CM; Amarilo, KM; Mundim, L; Nogima, H; Teles, PR; Santoro, A; Do Amaral, SMS; Sznajder, A; Thiel, M; De Araujo, FTD; Pereira, AV; Bernardes, CA; Calligaris, L; Tomei, TRFP; Gregores, EM; Lemos, DS; Mercadante, PG; Novaes, SF; Padula, SS; Aleksandrov, A; Antchev, G; Hadjiiska, R; Iaydjiev, P; Misheva, M; Rodozov, M; Shopova, M; Sultanov, G; Dimitrov, A; Ivanov, T; Litov, L; Pavlov, B; Petkov, P; Petrov, A; Cheng, T; Guo, Q; Javaid, T; Mittal, M; Wang, H; Yuan, L; Ahmad, M; Bauer, G; Dozen, C; Hu, Z; Martins, J; Wang, Y; Yi, K; Chapon, E; Chen, GM; Chen, HS; 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P.; Gandrakota, A.; Gershtein, Y.; Halkiadakis, E.; Hart, A.; Heindl, M.; Karacheban, O.; Laflotte, I; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thayil, S. A.; Thomas, S.; Wang, H.; Acharya, H.; Delannoy, A. G.; Spanier, S.; Bouhali, O.; Dalchenko, M.; Delgado, A.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Kim, H.; Luo, S.; Malhotra, S.; Mueller, R.; Overton, D.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; Hegde, V; Kunori, S.; Lamichhane, K.; Lee, S. W.; Mengke, T.; Muthumuni, S.; Peltola, T.; Volobouev, I; Wang, Z.; Whitbeck, A.; Appelt, E.; Greene, S.; Gurrola, A.; Johns, W.; Melo, A.; Ni, H.; Padeken, K.; Romeo, F.; Sheldon, P.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Cox, B.; Cummings, G.; Hakala, J.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, A.; Neu, C.; Tannenwald, B.; White, S.; Wolfe, E.; Poudyal, N.; Black, K.; Bose, T.; Buchanan, J.; Caillol, C.; Dasu, S.; De Bruyn, I; Everaerts, P.; Fienga, F.; Galloni, C.; He, H.; Herndon, M.; Herve, A.; Hussain, U.; Lanaro, A.; Loeliger, A.; Loveless, R.; Sreekala, J. Madhusudanan; Mallampalli, A.; Mohammadi, A.; Pinna, D.; Savin, A.; Shang, V; Sharma, V; Smith, W. H.; Teague, D.; Trembath-Reichert, S.; Vetens, W. CMS Collaboration A new calibration method for charm jet identification validated with proton-proton collision events at root s=13 TeV JOURNAL OF INSTRUMENTATION English Article Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods FRAGMENTATION Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb(-1) at root s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. 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F.; Padula, Sandra S.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Antchev, G.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Dimitrov, A.; Ivanov, T.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.] Univ Sofia, Sofia, Bulgaria; [Cheng, T.; Guo, Q.; Javaid, T.; Mittal, M.; Wang, H.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bauer, G.; Dozen, C.; Hu, Z.; Martins, J.; Wang, Y.; Yi, K.] Tsinghua Univ, Dept Phys, Beijing, Peoples R China; [Chapon, E.; Chen, G. M.; Chen, H. S.; Chen, M.; Iemmi, F.; Kapoor, A.; Leggat, D.; Liao, H.; Liu, Z-A; Milosevic, V; Monti, F.; Sharma, R.; Tao, J.; Thomas-Wilsker, J.; Wang, J.; Zhang, S.; Zhao, J.; Zhang, H.] Inst High Energy Phys, Beijing, Peoples R China; [Agapitos, A.; An, Y.; Ban, Y.; Chen, C.; Levin, A.; Li, Q.; Lyu, X.; Mao, Y.; Qian, S. J.; Wang, D.; Wang, Q.; Xiao, J.] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing, Peoples R China; [Lu, M.; You, Z.] Sun Yat Sen Univ, Guangzhou, Peoples R China; [Gao, X.; Okawa, H.] Fudan Univ, Inst Modern Phys, Shanghai, Peoples R China; [Gao, X.; Okawa, H.] Fudan Univ, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai, Peoples R China; [Lin, Z.; Xiao, M.] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China; [Avila, C.; Cabrera, A.; Florez, C.; Fraga, J.; Sarkar, A.; Segura Delgado, M. A.] Univ Los Andes, Bogota, Colombia; [Mejia Guisao, J.; Ramirez, F.; Ruiz Alvarez, J. D.; Salazar Gonzalez, C. A.] Univ Antioquia, Medellin, Colombia; [Giljanovic, D.; Godinovic, N.; Lelas, D.; Puljak, I] Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; [Antunovic, Z.; Kovac, M.; Sculac, T.] Univ Split, Fac Sci, Split, Croatia; [Brigljevic, V; Ferencek, D.; Majumder, D.; Roguljic, M.; Starodumov, A.; Susa, T.] Inst Rudjer Boskovic, Zagreb, Croatia; [Attikis, A.; Christoforou, K.; Erodotou, E.; Ioannou, A.; Kole, G.; Kolosova, M.; Konstantinou, S.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Saka, H.] Univ Cyprus, Nicosia, Cyprus; [Finger, M.; Finger Jr, M. Jr; Kveton, A.] Charles Univ Prague, Prague, Czech Republic; [Ayala, E.] Escuela Politec Nacl, Quito, Ecuador; [Carrera Jarrin, E.] Univ San Francisco Quito, Quito, Ecuador; [Abdalla, H.; Assran, Y.] Egyptian Network High Energy Phys, Acad Sci Res & Technol Arab Republ Egypt, Cairo, Egypt; [Lotfy, A.; Mahmoud, M. A.] Fayoum Univ, Ctr High Energy Phys CHEP FU, Al Fayyum, Egypt; [Bhowmik, S.; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Nandan, S.; Nielsen, C.; Pata, J.; Raidal, M.; Tani, L.; Veelken, C.] NICPB, Tallinn, Estonia; [Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.] Univ Helsinki, Dept Phys, Helsinki, Finland; [Bharthuar, S.; Brucken, E.; Garcia, F.; Havukainen, J.; Kim, M. S.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Lehti, S.; Linden, T.; Lotti, M.; Martikainen, L.; Myllymaki, M.; Ott, J.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.] Helsinki Inst Phys, Helsinki, Finland; [Luukka, P.; Petrow, H.; Tuuva, T.] Lappeenranta Univ Technol, Lappeenranta, Finland; [Amendola, C.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Lenzi, B.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Titov, M.; Yu, G. B.; Mijuskovic, J.] Univ Paris Saclay, IRFU, CEA, Gif Sur Yvette, France; [Ahuja, S.; Beaudette, F.; Bonanomi, M.; Perraguin, A. Buchot; Busson, P.; Cappati, A.; Charlot, C.; Davignon, O.; Diab, B.; Falmagne, G.; Ghosh, S.; de Cassagnac, R. Granier; Hakimi, A.; Kucher, I; Motta, J.; Nguyen, M.; Ochando, C.; Paganini, P.; Rembser, J.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Tarabini, A.; Zabi, A.; Zghiche, A.] Ecole Polytech, Lab Leprince Ringuet, Inst Polytech Paris, CNRS IN2P3, Palaiseau, France; [Agram, J-L; Andrea, J.; Apparu, D.; Bloch, D.; Bourgatte, G.; Brom, J-M; Chabert, E. C.; Collard, C.; Darej, D.; Fontaine, J-C; Goerlach, U.; Grimault, C.; Le Bihan, A-C; Nibigira, E.; Van Hove, P.] Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; [Asilar, E.; Beauceron, S.; Bernet, C.; Boudoul, G.; Camen, C.; Carle, A.; Chanon, N.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Laktineh, I. B.; Lattaud, H.; Lesauvage, A.; Lethuillier, M.; Mirabito, L.; Perries, S.; Shchablo, K.; Sordini, V; Torterotot, L.; Touquet, G.; Vander Donckt, M.; Viret, S.] Inst Phys 2 Infinis Lyon IP2I, Villeurbanne, France; [Bagaturia, I; Lomidze, I; Tsamalaidze, Z.; Mestvirishvili, A.] Georgian Tech Univ, Tbilisi, Georgia; [Feld, L.; Klein, K.; Lipinski, M.; Meuser, D.; Pauls, A.; Rauch, M. P.; Roewert, N.; Schulz, J.; Teroerde, M.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Dodonova, A.; Eliseev, D.; Erdmann, M.; Fackeldey, P.; Fischer, B.; Ghosh, S.; Hebbeker, T.; Hoepfner, K.; Ivone, F.; Keller, H.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Mocellin, G.; Mondal, S.; Mukherjee, S.; Noll, D.; Novak, A.; Pook, T.; Pozdnyakov, A.; Rath, Y.; Reithler, H.; Roemer, J.; Schmidt, A.; Schuler, S. C.; Sharma, A.; Vigilante, L.; Wiedenbeck, S.; Zaleski, S.; Borras, K.; Kasem, A.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Dziwok, C.; Fluegge, G.; Ahmad, W. Haj; Hlushchenko, O.; Kress, T.; Pooth, A. NowackO; Roy, D.; Sert, H.; Stahl, A.; Ziemons, T.] Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; [Petersen, H. Aarup; Martin, M. Aldaya; Asmuss, P.; Babounikau, I; Baxter, S.; Behnke, O.; Martinez, A. Bermudez; Bhattacharya, S.; Bin Anuar, A. A.; Borras, K.; Botta, V; Brunner, D.; Campbell, A.; Cardini, A.; Cheng, C.; Colombina, F.; Rodriguez, S. Consuegra; Silva, G. Correia; Danilov, V; Didukh, L.; Eckerlin, G.; Eckstein, D.; Banos, L. I. Estevez; Filatov, O.; Gallo, E.; Geiser, A.; Giraldi, A.; Grohsjean, A.; Guthoff, M.; Jafari, A.; Jomhari, N. Z.; Jung, H.; Kasem, A.; Kasemann, M.; Kaveh, H.; Kleinwort, C.; Krucker, D.; Lange, W.; Lidrych, J.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I-A; Metwally, J.; Meyer, A. B.; Meyer, M.; Mnich, J.; Mussgiller, A.; Otarid, Y.; Adan, D. Perez; Pitzl, D.; Raspereza, A.; Lopes, B. Ribeiro; Rubenach, J.; Saggio, A.; Saibel, A.; Savitskyi, M.; Scham, M.; Scheurer, V; Schwanenberger, C.; Singh, A.; Ricardo, R. E. Sosa; Stafford, D.; Tonon, N.; Turkot, O.; Van De Klundert, M.; Walsh, R.; Walter, D.; Wen, Y.; Wichmann, K.; Wiens, L.; Wissing, C.; Wuchterl, S.; Bakhshiansohi, H.] DESY, Hamburg, Germany; [Gallo, E.; Schwanenberger, C.; Aggleton, R.; Albrecht, S.; Bein, S.; Benato, L.; Benecke, A.; Connor, P.; De Leo, K.; Eich, M.; Feindt, F.; Frohlich, A.; Garbers, C.; Garutti, E.; Gunnellini, P.; Haller, J.; Hinzmann, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kramer, T.; Kutzner, V; Lange, J.; Lange, T.; Lobanov, A.; Malara, A.; Nigamova, A.; Rodriguez, K. J. Pena; Rieger, O.; Schleper, P.; Schroder, M.; Vormwald, J. SchwandtB; Zoi, I; Schwarz, D.; Sonneveld, J.; Stadie, H.; Steinbruck, G.; Tews, A.] Univ Hamburg, Hamburg, Germany; [Bechtel, J.; Berger, T.; Butz, E.; Caspart, R.; Chwalek, T.; De Boer, W.; Dierlamm, A.; Droll, A.; El Morabit, K.; Faltermann, N.; Giffels, M.; Gosewisch, J. O.; Gottmann, A.; Hartmann, F.; Heidecker, C.; Husemann, U.; Katkov, I; Keicher, P.; Koppenhofer, R.; Maier, S.; Metzler, M.; Mitra, S.; Muller, Th; Neukum, M.; Nurnberg, A.; Quast, G.; Rabbertz, K.; Rauser, J.; Savoiu, D.; Schnepf, M.; Seith, D.; Shvetsov, I; Simonis, H. J.; Ulrich, R.; Van der Linden, J.; Von Cube, R. F.; Wassmer, M.; Weber, M.; Wieland, S.; Wolf, R.; Wozniewski, S.; Wunsch, S.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Stakia, A.] NCSR Demokritos, Inst Nucl & Particle Phys INPP, Aghia Paraskevi, Greece; [Diamantopoulou, M.; Karasavvas, D.; Karathanasis, G.; Kontaxakis, P.; Koraka, C. K.; Manousakis-Katsikakis, A.; Panagiotou, A.; Papavergou, I; Saoulidou, N.; Theofilatos, K.; Tziaferi, E.; Vellidis, K.; Vourliotis, E.; Sphicas, P.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Bakas, G.; Kousouris, K.; Papakrivopoulos, I; Tsipolitis, G.; Zacharopoulou, A.] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Manthos, N.; Papadopoulos, I; Strologas, J.] Univ Ioannina, Ioannina, Greece; [Csanad, M.; Farkas, K.; Gadallah, M. M. A.; Major, P.; Mandal, K.; Mehta, A.; Pasztor, G.; Radl, A. J.; Suranyi, O.; Veres, G., I; Trocsanyi, Z. L.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Vesztergombi, G.; Csorgo, T.; Nemes, F.] Wigner Res Ctr Phys, Budapest, Hungary; [Horvath, D.; Czellar, S.; Molnar, J.; Szillasi, Z.; Teyssier, D.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Bartok, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Vesztergombi, G.; Csorgo, T.; Nemes, F.; Novak, T.] MATE Inst Technol, Karoly Robert Campus, Gyongyos, Hungary; [Komaragiri, J. R.; Kumar, D.; Panwar, L.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Kar, C.; Mal, P.; Mishra, T.; Bindhu, V. K. Muraleedharan Nair; Saha, P.; Sur, N.; Swain, S. K.; Vats, D.] HBNI, Natl Inst Sci Educ & Res, Bhubaneswar, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V; Chaudhary, G.; Chauhan, S.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, M.; Kaur, S.; Kumari, P.; Meena, M.; Sandeep, K.; Singh, J. B.; Virdi, A. K.] Panjab Univ, Chandigarh, India; [Ahmed, A.; Bhardwaj, A.; Choudhary, B. C.; Gola, M.; Keshri, S.; Kumar, A.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, A.] Univ Delhi, Delhi, India; [Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhowmik, D.; Dutta, S.; Dutta, S.; Gomber, B.; Maity, M.; Palit, P.; Rout, P. K.; Saha, G.; Sahu, B.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] HBNI, Saha Inst Nucl Phys, Kolkata, India; [Behera, P. K.; Behera, S. C.; Kalbhor, P.; Sikdar, A. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Dutta, D.; Jha, V; Kumar, V; Mishra, D. K.; Naskar, K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Dugad, S.; Kumar, M.; Sarkar, U.] Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; [Banerjee, S.; Chudasama, R.; Guchait, M.; Karmakar, S.; Kumar, S.; Majumder, G.; Mazumdar, K.; Mukherjee, S.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Alpana, K.; Dube, S.; Kansal, B.; Laha, A.; Pandey, S.; Rane, A.; Rastogi, A.; Sharma, S.] Indian Inst Sci Educ & Res IISER, Pune, Maharashtra, India; [Jafari, A.; Bakhshiansohi, H.; Zeinali, M.] Isfahan Univ Technol, Esfahan, Iran; [Chenarani, S.; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi] Inst Res Fundamental Sci IPM, Tehran, Iran; [Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Aly, R.; Aruta, C.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Fiore, L.; Gelmi, A.; Gul, M.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Margjeka, I; Mastrapasqua, V; Merlin, J. A.; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Pugliese, G. A.; Ranieri, A. A.; Selvaggi, G.; Silvestris, L.; Simone, F. M.; Venditti, R.; Verwilligen, P.] INFN Sez Bari, Bari, Italy; [Abbrescia, M.; Aly, R.; Aruta, C.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Fiore, L.; Gelmi, A.; Ince, M.; Lezki, S.; Margjeka, I; Mastrapasqua, V; My, S.; Nuzzo, S.; Pellecchia, A.; Pompili, A.; Selvaggi, G.; Simone, F. M.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G. A.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Cuffiani, M.; Dallavalle, G. M.; Diotalevi, T.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Giommi, L.; Grandi, C.; Guiducci, L.; Lo Meo, S.; Lunerti, L.; Marcellini, S.; Masetti, G.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] INFN Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Campanini, R.; Capiluppi, P.; Castro, A.; Cuffiani, M.; Diotalevi, T.; Fanfani, A.; Giommi, L.; Guiducci, L.; Lunerti, L.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] INFN Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Ceccarelli, R.; Ciulli, V; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.] INFN Sez Firenze, Florence, Italy; [Cassese, A.; Ceccarelli, R.; Ciulli, V; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Seidita, R.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] INFN Lab Nazl Frascati, Frascati, Italy; [Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.] INFN Sez Genova, Genoa, Italy; [Bozzo, M.; Mulargia, R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Brivio, F.; Cetorelli, F.; Ciriolo, V; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] INFN Sez Milano Bicocca, Milan, Italy; [Brivio, F.; Cetorelli, F.; Ciriolo, V; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Carnevali, F.; Cavallo, N.; De Iorio, A.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] INFN Sez Napoli, Naples, Italy; [Carnevali, F.; De Iorio, A.; Lista, L.; Sciacca, C.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Layer, L.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] INFN Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Re, V; Riccardi, C.; Salvini, P.; Vai, I; Vitulo, P.] INFN Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, S. P.; Riccardi, C.; Vitulo, P.] Univ Pavia, Pavia, Italy; [Asenov, P.; Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] INFN Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Lariccia, P.; Magherini, M.; Mantovani, G.; Mariani, V; Piccinelli, A.; Rossi, A.; Santocchia, A.; Tedeschi, T.] Univ Perugia, Perugia, Italy; [Azzurri, P.; Bagliesi, G.; Bertacchi, V; Bianchini, L.; Boccali, T.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; D'Amante, V; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Parolia, S.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] INFN Sez Pisa, Pisa, Italy; [Bossini, E.; Ciocci, M. A.; Messineo, A.; Parolia, S.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [D'Amante, V; Di Domenico, M. R.; Turini, N.] Univ Siena, Siena, Italy; [Campana, M.; Cavallari, F.; Del Re, D.; Di Marco, E.; Diemoz, M.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] INFN Sez Roma, Rome, Italy; [Campana, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ruspa, M.; Shchelina, K.; Siviero, F.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.; Vagnerini, A.] INFN Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Shchelina, K.; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Trocino, D.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] INFN Sez Trieste, Trieste, Italy; [Candelise, V; Da Rold, A.; Della Ricca, G.; Sorrentino, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Kim, J.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.; Kamon, T.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; [Francois, B.; Kim, T. J.; Park, J.; Yoo, J.] Hanyang Univ, Seoul, South Korea; [Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K.; Lee, K. S.; Lim, J.; Park, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I] Seoul Natl Univ, Seoul, South Korea; [Jang, W.; Jeon, D.; Kang, D. Y.; Kang, Y.; Kim, J. H.; Kim, S.; Ko, B.; Lee, J. S. H.; Lee, Y.; Park, I. C.; Roh, Y.; Ryu, M. S.; Song, D.; Watson, I. J.; Yang, S.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Choi, M.; Jeong, Y.; Lee, H.; Lee, Y.; Yu, I] Sungkyunkwan Univ, Suwon, South Korea; [Beyrouthy, T.; Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Dasman, Kuwait; [Torims, T.; Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; De Oliveira, A. Carvalho Antunes; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.] Vilnius Univ, Vilnius, Lithuania; [Bin Norjoharuddeen, N.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Leon Coello, M.; Murillo Quijada, J. A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; De la Cruz-Burelo, E.; Heredia-De la Cruz, I; LopezFernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Ramirez Garcia, M.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M., I; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Gorski, M.; Kazana, M.; Szleper, M.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Leonardo, N.; Niknejad, T.; Pisano, M.; Seixas, J.; Faccioli, P.; Gallinaro, M.; Toldaiev, O.; Varela, J.; Hollar, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Finger, M.; Finger Jr, M. Jr; Tsamalaidze, Z.; Afanasiev, S.; Budkouski, D.; Golutvin, I; Gorbunov, I; Karjavine, V; Korenkov, V; Lanev, A.; Malakhov, A.; Matveev, V; Palichik, V; Perelygin, V; Savina, M.; Seitova, D.; Shalaev, V; Shmatov, S.; Shulha, S.; Smirnov, V; Teryaev, O.; Voytishin, N.; Yuldashev, B. S.; Zarubin, A.; Zhizhin, I] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V; Ivanov, Y.; Kim, V; Kuznetsova, E.; Murzin, V; Oreshkin, V; Smirnov, I; Sosnov, D.; Sulimov, V; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirpichnikov, D.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Tlisov, D.; Toropin, A.] Inst Nucl Res, Moscow, Russia; [Starodumov, A.; Epshteyn, V; Gavrilov, V; Lychkovskaya, N.; Nikitenko, A.; Popov, V; Spiridonov, A.; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys & Technol, Moscow, Russia; [Matveev, V; Bychkova, O.; Chadeeva, M.; Parygin, P.; Popova, E.; Rusinov, V] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Matveev, V; Andreev, Yu; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirpichnikov, D.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Tlisov, D.; Toropin, A.; Bychkova, O.; Chadeeva, M.; Parygin, P.; Popova, E.; Rusinov, V] PN Lebedev Phys Inst, Moscow, Russia; [Katkov, I; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V; Kodolova, O.; Lokhtin, I; Obraztsov, S.; Petrushanko, S.; Savrin, V; Snigirev, A.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V; Dimova, T.; Kardapoltsev, L.; Kozyrev, A.; Ovtin, I; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I; Bayshev, I; Elumakhov, D.; Kachanov, V; Konstantinov, D.; Mandrik, P.; Petrov, V; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Natl Res Ctr, Kurchatov Inst, Inst High Energy Phys, Protvino, Russia; [Babaev, A.; Okhotnikov, V] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borshch, V; Ivanchenko, V; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Maestre, J. Alcaraz; Fernandez, A. Alvarez; Bachiller, I; Luna, M. Barrio; Bedoya, Cristina F.; Montoya, C. A. Carrillo; Cepeda, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Peris, A. Delgado; Ramos, J. P. Fernandez; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M., I; Leon Holgado, J.; Moran, D.; Navarro Tobar, A.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I; Romero, L.; Sanchez Navas, S.; Urda Gomez, L.; Willmott, C.] Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid, Spain; [de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Ramon Alvarez, C.; Ripoll Sau, J.; Rodriguez Bouza, V; Trapote, A.; Trevisani, N.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Matorras Cuevas, P.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I; Vizan Garcia, J. M.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Jayananda, M. K.; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, D. D. C.] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Stahl, A.; Hartmann, F.; Ciriolo, V; Valsecchi, D.; Meola, S.; Paolucci, P.; Aarrestad, T. K.; Abbaneo, D.; Alimena, J.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chhibra, S. S.; Cipriani, M.; Cristella, L.; D'Enterria, D.; Dabrowski, A.; Daci, N.; David, A.; De Roeck, A.; Defranchis, M. M.; Deile, M.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fasanella, D.; Fiorendi, S.; Florent, A.; Franzoni, G.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Haranko, M.; Hegeman, J.; Iiyama, Y.; Innocente, V; James, T.; Janot, P.; Kaspar, J.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Long, K.; Lourenco, C.; Malgeri, L.; Mallios, S.; Mannelli, M.; Marini, A. C.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Pitt, M.; Qu, H.; Quast, T.; Rabady, D.; Racz, A.; Gutierrez, G. Reales; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schafer, C.; Schwick, C.; Selvaggi, M.; Sharma, A.; Silva, P.; Snoeys, W.; Sphicas, P.; Summers, S.; Tatar, K.; Tavolaro, V. R.; Treille, D.; Tsirou, A.; Van Onsem, G. P.; Verzetti, M.; Wanczyk, J.; Wozniak, K. A.; Zeuner, W. D.; Virdee, T.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Androsov, K.; Backhaus, M.; Berger, P.; Calandri, A.; Chernyavskaya, N.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Eble, F.; Gedia, K.; Glessgen, F.; Gomez Espinosa, T. A.; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A-M; Manzoni, R. A.; Martin Perez, C.; Meinhard, M. T.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Schonenberger, M.; Stampf, V; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Caminada, L.; Amsler, C.; Bartschi, P.; Botta, C.; Brzhechko, D.; Canelli, M. F.; Cormier, K.; De Wit, A.; Del Burgo, R.; Heikkila, J. K.; Huwiler, M.; Jin, W.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Liechti, S. P.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I; Reimers, A.; Robmann, P.; Sanchez Cruz, S.; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Ceard, L.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W-S; Li, Y. Y.; Lu, R-S; Paganis, E.; Psallidas, A.; Steen, A.; Wu, H. Y.; Yazgan, E.; Yu, P. R.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dumanoglu, I; Eskut, E.; Guler, Y.; Guler, E. Gurpinar; Hos, I; Isik, C.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Ukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Phys Dept, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Cerci, S.; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; [Anthony, D.; Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Holmberg, M-L; Kreczko, L.; Krikler, B.; Paramesvaran, S.; El Nasr-Storey, S. Seif; Smith, V. J.; Stylianou, N.; Pass, K. Walkingshaw; White, R.] Univ Bristol, Bristol, Avon, England; [Holmberg, M-L; Bell, K. W.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Cooke, C.; Ellis, K., V; Harder, K.; Harper, S.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Nikitenko, A.; Bainbridge, R.; Bloch, P.; Bonomally, S.; Borg, J.; Breeze, S.; Buchmuller, O.; Cepaitis, V; Chahal, G. S.; Colling, D.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Egyptian Knowledge Bank (EKB); Fayoum University; National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Georgian Technical University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Hungarian University of Agriculture & Life Sciences; Indian Institute of Science (IISC) - Bangalore; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; Isfahan University of Technology; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; University of Siena; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Kyung Hee University; Sejong University; Seoul National University (SNU); University of Seoul; Yonsei University; Sungkyunkwan University (SKKU); American University of Middle East; Riga Technical University; Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; University of Montenegro; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; AGH University of Science & Technology; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidade Federal de Mato Grosso do Sul; Nanjing Normal University; Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Suez University; Egyptian Knowledge Bank (EKB); British University in Egypt; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Ilia State University; Erzincan Binali Yildirim University; Brandenburg University of Technology Cottbus; Egyptian Knowledge Bank (EKB); Assiut University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Sharif University of Technology; University of Science & Technology of Mazandaran; Italian National Agency New Technical Energy & Sustainable Economics Development; Consiglio Nazionale delle Ricerche (CNR); Istituto Officina dei Materiali (IOM-CNR); Academy of Sciences of Uzbekistan; Peter the Great St. Petersburg Polytechnic University; California Institute of Technology; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Eastern University, Sri Lanka; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Sirnak University; Near East University; Konya Technical University; Istanbul University - Cerrahpasa; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University; Vrije Universiteit Brussel; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Egyptian Knowledge Bank (EKB); Ain Shams University; Bingol University; Sinop University; Erciyes University; Qatar Foundation (QF); Texas A&M University Qatar Tumasyan, A (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. Simone, Federica Maria/HDM-3686-2022; Lowette, Steven/HKV-3341-2023; Tinoco Mendes, Andre David/D-4314-2011; Schwarz, Dennis/GYA-4479-2022; De Iorio, Agostino/HJH-7155-2023; Rabady, Dinyar/GSN-9210-2022; Goy Lopez, Silvia/K-9200-2017; Legger, Federica/HNC-2568-2023; TUVE', Cristina/P-3933-2015; Shah, Aashaq/GYI-8983-2022; Kim, Victor T/L-1648-2013; Savina, Maria/HDO-6544-2022; Demiroglu, Zuhal Seyma/A-5168-2016; Puerta Pelayo, Jesus/L-1632-2014; Benussi, Luigi/O-9684-2014; Vai, Ilaria/AAT-5836-2020; Blekman, Freya/ABD-7916-2020; Shmatov, Sergei/J-5621-2014; Selvaggi, Michele/CAJ-2129-2022; Guler, Yalcin/AAG-7833-2019; Moscatelli, Francesco/AEL-1878-2022; Lokhtin, Igor/D-7004-2012; Giommi, Luca/AGV-7154-2022; Aarrestad, Thea/GLV-3031-2022; Fouz, Maria Cruz/AAF-1105-2019; Ferro, Fabrizio/HMP-5516-2023; Della Ricca, Giuseppe/B-6826-2013; Androsov, Konstantin/HJA-2332-2022; M, F/HGF-3166-2022; Singh, J B/ABF-5574-2021; Sunar Cerci, Deniz/AHE-1706-2022; Lange, Torben/HOH-4787-2023; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Tsirou, Andromachi/HHC-6869-2022; Gozalez-Lopez, Oscar/AAH-3533-2019; Ivanchenko, Vladimir N/L-5254-2017; Aimè, Chiara/GXG-4131-2022; kaya, mithat/D-8062-2011; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Donato, Silvio/GPK-2262-2022; Yazgan, Efe/C-4521-2014; Calligaris, Luigi/K-2018-2017; Kirpichnikov, Dmitry/U-9605-2019; Akgun, Bora/AAM-8311-2021; Parygin, Pavel/C-8908-2016; Benaglia, Andrea/AGF-5495-2022; Strologas, John/GWR-2036-2022; Petkov, Peicho/M-2080-2016; SEVINC KAYA, OZLEM/AAE-8069-2020; Duarte, Javier Mauricio/AAA-5414-2020; Norjoharuddeen, Nurfikri/S-3109-2018; Tcherniaev, Evgueni/G-3453-2016; Lychkovskaya, Natalia/F-8341-2017; Pellecchia, Antonello/HNB-9377-2023; CAKIR, Altan/ABD-4450-2020; Bortignon, Pierluigi/AAF-6561-2020; Fienga, Francesco/HKE-1204-2023; Saka, Halil/GPF-8892-2022; Dudko, Lev/D-7127-2012; Ligabue, Franco/ABI-1681-2020; Bermudez Martinez, Armando/HHN-4625-2022; Lu, Meng/GQZ-7036-2022; Casarsa, Massimo/L-3623-2018; de Souza Sandro, Fonseca/ABB-8505-2020; Lezki, Samet/AAG-8277-2019; Gupta, Rajat/GVS-7964-2022; Lannon, Kevin/HGU-5755-2022; SIMSEK, Ali Eren/AFO-3050-2022; Boimska, Bożena/Z-6088-2019; Shopova, Mariana Filipova/HKW-0149-2023; Iqbal, Muhammad/HJH-2369-2023; Di Florio, Adriano/ABA-3631-2020; Cassese, Antonio/R-1713-2016; Özdemir, Kadri/P-8058-2014; Matorras, Francisco/I-4983-2015; Zorbilmez, Caglar/ABH-1905-2020; Ambrozas, Marijus/F-5551-2018; Iorio, Alberto Orso Maria/GXA-0233-2022; Tiras, Emrah/ABG-2354-2020; Novaes, Sergio F./D-3532-2012; Ciangottini, Diego/HJH-5914-2023; Haddad, Yacine/GRX-5022-2022; Delgado Peris, Antonio/AAA-5165-2019; Rossi, Biagio/F-4137-2018; Sagir, Sinan/A-5219-2018; Azzi, Patrizia/H-5404-2012; Raidal, Martti/F-4436-2012; Grandi, Claudio/B-5654-2015; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Popov, Andrey/E-1052-2012; Dharmaratna, Welathantri/F-6745-2018; Grzanka, Leszek/M-9052-2018; Spagnolo, Paolo/G-3401-2017; Paulini, Manfred/N-7794-2014; Moraes, Arthur/F-6478-2010; Sanchez-Hernandez, Alberto/L-4955-2017; Redondo, Ignacio/K-8639-2014; Bernardes, Cesar Augusto/D-2408-2015; Lee, Jason/B-9701-2014; Vilela Pereira, Antonio/L-4142-2016; Fernandez Bedoya, Cristina/K-8066-2014; Palencia Cortezon, Jose Enrique/F-1059-2017; Giacomelli, Paolo/B-8076-2009; Fernandez Menendez, Javier/B-6550-2014; Kim, Tae Jeong/P-7848-2015; Belyaev, Alexander/F-6637-2015; Sznajder, Andre/L-1621-2016; Elkafrawy, Tamer/Q-8786-2016; Gonzalez Caballero, Isidro/E-7350-2010; Hernandez Calama, Jose Maria/H-9127-2015; Konecki, Marcin/G-4164-2015; Padula, Sandra S./G-3560-2012; Kyberd, Paul/P-1092-2014; Josa Mutuberria, Isabel/K-5184-2014; Malik, Sudhir/D-6621-2018; Govoni, Pietro/K-9619-2016; HAJ AHMAD (Vael HAJAHMAD), Wael/E-6738-2016; Mundim, Luiz/A-1291-2012; ALCARAZ MAESTRE, JUAN/I-5763-2015; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Dubinin, Mikhail/I-3942-2016; Moon, Chang-Seong/J-3619-2014; Da Silveira, Gustavo Gil/N-7279-2014; Ruiz Jimeno, Alberto/E-4473-2011; Pasztor, Gabriella/D-9716-2016; Ivanov, Andrew/A-7982-2013; Goh, Junghwan/Q-3720-2016; Lokos, Sandor/A-4798-2019; Chernyavskaya, Nadezda/F-2161-2015; Reyes-Almanza, Rogelio/GQJ-8236-2022; Heredia de la Cruz, Ivan/L-2610-2018; Malawski, Maciej/H-9119-2012; Filatov, Oleg/V-9680-2017; Mora Herrera, Maria Clemencia/L-3893-2016; Leonardo, Nuno/M-6940-2016; Snoeys, Walter/K-8259-2015; Navarro-Tobar, Alvaro/K-7864-2014; Rolandi, Luigi (Gigi)/E-8563-2013 Simone, Federica Maria/0000-0002-1924-983X; Lowette, Steven/0000-0003-3984-9987; Tinoco Mendes, Andre David/0000-0001-5854-7699; De Iorio, Agostino/0000-0002-9258-1345; Rabady, Dinyar/0000-0001-9239-0605; Legger, Federica/0000-0003-1400-0709; TUVE', Cristina/0000-0003-0739-3153; Shah, Aashaq/0000-0002-6157-2016; Kim, Victor T/0000-0001-7161-2133; Savina, Maria/0000-0002-9020-7384; Demiroglu, Zuhal Seyma/0000-0001-7977-7127; Puerta Pelayo, Jesus/0000-0001-7390-1457; Benussi, Luigi/0000-0002-2363-8889; Vai, Ilaria/0000-0003-0037-5032; Blekman, Freya/0000-0002-7366-7098; Shmatov, Sergei/0000-0001-5354-8350; Guler, Yalcin/0000-0001-7598-5252; Moscatelli, Francesco/0000-0002-7676-3106; Giommi, Luca/0000-0003-3539-4313; Aarrestad, Thea/0000-0002-7671-243X; Fouz, Maria Cruz/0000-0003-2950-976X; Della Ricca, Giuseppe/0000-0003-2831-6982; Androsov, Konstantin/0000-0003-2694-6542; Singh, J B/0000-0001-9029-2462; Sunar Cerci, Deniz/0000-0002-5412-4688; Lange, Torben/0000-0001-6242-7331; Pérez-Calero Yzquierdo, Antonio/0000-0003-3036-7965; Gozalez-Lopez, Oscar/0000-0002-4532-6464; Ivanchenko, Vladimir N/0000-0002-1844-5433; kaya, mithat/0000-0003-2890-4493; Donato, Silvio/0000-0001-7646-4977; Yazgan, Efe/0000-0001-5732-7950; Calligaris, Luigi/0000-0002-9951-9448; Kirpichnikov, Dmitry/0000-0002-7177-077X; Akgun, Bora/0000-0001-8888-3562; Parygin, Pavel/0000-0001-6743-3781; Benaglia, Andrea/0000-0003-1124-8450; Petkov, Peicho/0000-0002-0420-9480; SEVINC KAYA, OZLEM/0000-0002-8485-3822; Duarte, Javier Mauricio/0000-0002-5076-7096; Norjoharuddeen, Nurfikri/0000-0002-8818-7476; Tcherniaev, Evgueni/0000-0002-3685-0635; Pellecchia, Antonello/0000-0003-3279-6114; CAKIR, Altan/0000-0002-8627-7689; Bortignon, Pierluigi/0000-0002-5360-1454; Fienga, Francesco/0000-0001-5978-4952; Dudko, Lev/0000-0002-4462-3192; Ligabue, Franco/0000-0002-1549-7107; Bermudez Martinez, Armando/0000-0001-8822-4727; Casarsa, Massimo/0000-0002-1353-8964; de Souza Sandro, Fonseca/0000-0001-7830-0837; Lezki, Samet/0000-0002-6909-774X; Lannon, Kevin/0000-0002-9706-0098; SIMSEK, Ali Eren/0000-0002-9074-2256; Boimska, Bożena/0000-0002-4200-1541; Shopova, Mariana Filipova/0000-0001-6664-2493; Di Florio, Adriano/0000-0003-3719-8041; Cassese, Antonio/0000-0003-3010-4516; Özdemir, Kadri/0000-0002-0103-1488; Matorras, Francisco/0000-0003-4295-5668; Zorbilmez, Caglar/0000-0002-5199-061X; Ambrozas, Marijus/0000-0003-2449-0158; Iorio, Alberto Orso Maria/0000-0002-3798-1135; Tiras, Emrah/0000-0002-5628-7464; Novaes, Sergio F./0000-0003-0471-8549; Bhowmik, Sandeep/0000-0003-1260-973X; Goy Lopez, Silvia/0000-0001-6508-5090; Delaere, Christophe/0000-0001-8707-6021; Mnich, Joachim/0000-0001-7242-8426; Fiorendi, Sara/0000-0003-3273-9419; Hall, Geoffrey/0000-0002-6299-8385; Albrecht, Steffen/0000-0002-5960-6803; Reichert, Joseph/0000-0003-2110-8021; Lucchini, Marco Toliman/0000-0002-7497-7450; De La Cruz Burelo, Eduard/0000-0002-7469-6974; Ghosh, Saranya Samik/0000-0001-6717-0803; Heath, Helen/0000-0001-6576-9740; Usai, Emanuele/0000-0001-9323-2107; Viliani, Lorenzo/0000-0002-1909-6343; Stylianou, Nicolas/0000-0002-0113-6829; Sharma, Ram Krishna/0000-0003-1181-1426; Cuevas Maestro, Francisco Javier/0000-0001-5080-0821; Vormwald, Benedikt/0000-0003-2607-7287; Gadallah, Mahmoud Moussa Abdelkhalek/0000-0002-8305-6661; Salvatico, Riccardo/0000-0002-2751-0567; Arneodo, Michele/0000-0002-7790-7132; Missiroli, Marino/0000-0002-1780-1344; Haddad, Yacine/0000-0003-4916-7752; Krintiras, Georgios Konstantinos/0000-0002-0380-7577; Everaerts, Pieter/0000-0003-3848-324X; Delgado Peris, Antonio/0000-0002-8511-7958; Rappoccio, Salvatore/0000-0002-5449-2560; Wardle, Nicholas/0000-0003-1344-3356; Smith, Wesley/0000-0003-3195-0909; Naimuddin, Md/0000-0003-4542-386X; De Guio, Federico/0000-0001-5927-8865; Cadamuro, Luca/0000-0001-8789-610X; Titov, Maxim/0000-0002-1119-6614; Backhaus, Malte/0000-0002-5888-2304; Frankenthal, Andre/0000-0002-2583-5982; Konstantinov, Dmitri/0000-0001-6673-7273; Dominguez, Aaron/0000-0002-7420-5493; Capeans, Mar/0000-0001-7727-9175; Ruiz, Jose/0000-0002-3306-0363; Goldouzian, Reza/0000-0002-0295-249X; Rossi, Biagio/0000-0002-0807-8772; Mondal, Spandan/0000-0003-0153-7590; Lassila-Perini, Kati/0000-0002-5502-1795; d'Enterria, David/0000-0002-5754-4303; Kole, Gouranga/0000-0002-3285-1497; Tao, Junquan/0000-0003-2006-3490; Geurts, Frank/0000-0003-2856-9090; Heikkila, Jaana/0000-0002-0538-1469; Gandrajula, Reddy Pratap/0000-0001-9053-3182; Lawhorn, Jay/0000-0002-8597-9259; Kramer, Tobias Robert Jakob/0000-0002-7004-0214; Belloni, Alberto/0000-0002-1727-656X; Li, Qiang/0000-0002-8290-0517; Conway, John/0000-0003-2719-5779; Sagir, Sinan/0000-0002-2614-5860; Schroder, Matthias/0000-0001-8058-9828; SARKAR, UTTIYA/0000-0002-9892-4601; Sordini, Viola/0000-0003-0885-824X; Nguyen, Thong Q./0000-0003-3954-5131; Blumenfeld, Barry/0000-0003-1150-1735; Pugliese, Gabriella/0000-0001-5460-2638; Azzi, Patrizia/0000-0002-3129-828X; Lange, Clemens/0000-0002-3632-3157; Forthomme, Laurent/0000-0002-3302-336X; Williams, Justin/0000-0002-9810-7097; Kanuganti, Ankush Reddy/0000-0002-0789-1200; Wuchterl, Sebastian/0000-0001-9955-9258; Abbiendi, Giovanni/0000-0003-4499-7562; Mitra, Soureek/0000-0002-3060-2278; Schonenberger, Myriam/0000-0002-6508-5776; YOON, INSEOK/0000-0002-3491-8026; Gomez-Ceballos, Guillelmo/0000-0003-1683-9460; Litov, Leandar/0000-0002-8511-6883; Jafari, Abideh/0000-0001-7327-1870; Alverson, George/0000-0001-6651-1178; Marzocchi, Badder/0000-0001-6687-6214; Staiano, Amedeo/0000-0003-1803-624X; Zghiche, Amina/0000-0002-1178-1450; Starling, Elizabeth/0000-0002-4399-7213; Poudyal, Nabin/0000-0003-4278-3464; Dallavalle, Gaetano Marco/0000-0002-8614-0420; Raidal, Martti/0000-0001-7040-9491; Petrucciani, Giovanni/0000-0003-0889-4726; Dalchenko, Mykhailo/0000-0002-0137-136X; Habibullah, Redwan/0000-0002-3161-8300; Yigitbasi, Efe/0000-0002-9595-2623; CUFFIANI, Marco/0000-0003-2510-5039; Grandi, Claudio/0000-0001-5998-3070; Stepennov, Anton/0000-0001-7747-6582; Diaz, Daniel/0000-0001-6834-1176; Mohammadi Najafabadi, Mojtaba/0000-0001-6131-5987; Faccioli, Pietro/0000-0003-1849-6692; Costa, Salvatore/0000-0001-9919-0569; Wilson, Graham/0000-0003-0917-4763; Popov, Andrey/0000-0002-1207-0984; Landsberg, Greg/0000-0002-4184-9380; Mukherjee, Soumya/0000-0003-3122-0594; Dharmaratna, Welathantri/0000-0002-6366-837X; Giammanco, Andrea/0000-0001-9640-8294; Zhang, Zhicai/0000-0002-1630-0986; Schulte, Jan-Frederik/0000-0003-4421-680X; Sciacca, Crisostomo/0000-0002-8412-4072; Perez Prada, Maximilian/0000-0002-2831-463X; Schweiger, Korbinian/0000-0002-5846-3919; Lattaud, Hugues/0000-0002-8402-3263; Haller, Johannes/0000-0001-9347-7657; Tonelli, Guido Emilio/0000-0003-2606-9156; Sandeep, Kaur/0000-0002-3220-3668; Wallny, Rainer/0000-0001-8038-1613; Saygin, Kadir/0000-0002-8419-1400; Wang, Bingran/0000-0003-0796-2475; Galli Mercadante, Pedro/0000-0001-8333-4302; Grzanka, Leszek/0000-0002-3599-854X; GUZZI, LUCA/0000-0002-3086-8260; Niedziela, Jeremi/0000-0002-9514-0799; , Priyanka/0000-0002-0933-685X; Lee, Sung-Won/0000-0002-3388-8339; Spagnolo, Paolo/0000-0001-7962-5203; Dogra, Sunil Manohar/0000-0002-0812-0758; Moureaux, Louis/0000-0002-2310-9266; Meridiani, Paolo/0000-0002-8480-2259; Chapon, Emilien/0000-0001-6968-9828; Zucchetta, Alberto/0000-0003-0380-1172; Cardini, Andrea/0000-0003-1803-0999; Hatakeyama, Kenichi/0000-0002-6012-2451; Paulini, Manfred/0000-0002-6714-5787; Faltermann, Nils/0000-0001-6506-3107; Moraes, Arthur/0000-0002-5157-5686; PICCINELLI, ANDREA/0000-0003-0386-0527; Mousa, Jehad/0000-0002-2978-2718; Alves, Gilvan/0000-0002-8369-1446; Consuegra Rodriguez, Sandra/0000-0002-1383-1837; You, Zhengyun/0000-0001-8324-3291; Massironi, Andrea/0000-0002-0782-0883; Starodumov, Andrey/0000-0001-9570-9255; De Jesus Damiao, Dilson/0000-0002-3769-1680; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; Janssen, Tahys/0000-0002-3998-4081; Hurtado Anampa, Kenyi/0000-0002-9779-3566; Redondo, Ignacio/0000-0003-3737-4121; Goldstein, Joel/0000-0003-1591-6014; Bernardes, Cesar Augusto/0000-0001-5790-9563; Lee, Jason/0000-0002-2153-1519; Grab, Christophorus/0000-0002-6182-3380; Berenguer Antequera, Jorge/0000-0003-3153-0891; Meuser, Danilo/0000-0002-2722-7526; Vilela Pereira, Antonio/0000-0003-3177-4626; Fernandez Bedoya, Cristina/0000-0001-8057-9152; Pinna, Deborah/0000-0002-0947-1357; Palencia Cortezon, Jose Enrique/0000-0001-8264-0287; Panwar, Lata/0000-0003-2461-4907; Pieri, Marco/0000-0003-3303-6301; Jung, Hannes/0000-0002-2964-9845; Gershtein, Yuri/0000-0002-4871-5449; Ochando, Christophe/0000-0002-3836-1173; Levchuk, Leonid/0000-0001-5889-7410; Van Onsem, Gerrit/0000-0002-1664-2337; Giacomelli, Paolo/0000-0002-6368-7220; Ehataht, Karl/0000-0002-2387-4777; Sharma, Varun/0000-0003-1287-1471; Nash, Jordan/0000-0003-0607-6519; D'Amante, Valeria/0000-0002-7342-2592; Fernandez Menendez, Javier/0000-0002-5213-3708; Carrillo Montoya, Camilo/0000-0002-6245-6535; Melo da Costa, Eliza/0000-0002-5016-6434; Zuolo, Davide/0000-0003-3072-1020; Brigljevic, Vuko/0000-0001-5847-0062; Ecklund, Karl/0000-0002-6976-4637; Lange, Johannes/0000-0001-7513-6330; Gutsche, Oliver/0000-0002-8015-9622; Chauhan, Shubhanshu/0000-0002-6544-5794; Kim, Tae Jeong/0000-0001-8336-2434; Tapper, Alexander/0000-0003-4543-864X; Evdokimov, Olga/0000-0002-1250-8931; Belyaev, Alexander/0000-0002-1733-4408; Sznajder, Andre/0000-0001-6998-1108; Elkafrawy, Tamer/0000-0001-9930-6445; Acosta, Darin/0000-0001-5367-1738; Gerosa, Raffaele/0000-0001-8359-3734; Kasemann, Matthias/0000-0002-0429-2448; Perez Adan, Danyer/0000-0003-3416-0726; Chahal, Gurpreet Singh/0000-0003-0320-4407; Gonzalez Caballero, Isidro/0000-0002-8087-3199; Elmer, Peter/0000-0001-6830-3356; Das, Pallabi/0000-0002-9770-1377; Hernandez Calama, Jose Maria/0000-0001-6436-7547; Konecki, Marcin/0000-0001-9482-4841; Fernandez Ramos, Juan Pablo/0000-0002-0122-313X; Ptochos, Fotios/0000-0002-3432-3452; Caputo, Claudio/0000-0001-7522-4808; Martinez Ruiz del Arbol, Pablo/0000-0002-7737-5121; Vami, Tamas Almos/0000-0002-0959-9211; zhang, Liuyao/0000-0002-5806-6403; Padula, Sandra S./0000-0003-3071-0559; Ince Lezki, Merve/0000-0001-6907-0195; Wittich, Peter/0000-0002-7401-2181; Schieck, Jochen/0000-0002-1058-8093; Kyberd, Paul/0000-0002-7353-7090; Garutti, Erika/0000-0003-0634-5539; Josa Mutuberria, Isabel/0000-0002-4985-6964; Kumar, Arun/0000-0002-5180-6595; Malik, Sudhir/0000-0002-6356-2655; MEHTA, ANKITA/0000-0002-0433-4484; Wu, Zhenbin/0000-0003-2165-9501; Watson, Ian/0000-0003-2141-3413; Tosi, Silvano/0000-0002-7275-9193; Jabeen, Shabnam/0000-0002-0155-7383; Prosper, Harrison Bertrand/0000-0002-4077-2713; Sculac, Toni/0000-0002-9578-4105; Regnard, Simon/0000-0002-9818-6725; Schwandt, Joern/0000-0002-0052-597X; Trevisani, Nicolo/0000-0002-5223-9342; 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Reyes-Almanza, Rogelio/0000-0002-4600-7772; Heredia de la Cruz, Ivan/0000-0002-8133-6467; Andrea, Jeremy/0000-0002-8298-7560; Malawski, Maciej/0000-0001-6005-0243; Taylor, Lucas/0000-0002-6584-2538; Escalante del Valle, Alberto/0000-0002-9702-6359; Tiwari, Praveen Chandra/0000-0002-3667-3843; Beaudette, Florian/0000-0002-1194-8556; Manca, Elisabetta/0000-0001-8946-655X; Savoiu, Daniel/0000-0001-6794-7475; yagil, avi/0000-0002-6108-4004; Matorras-Cuevas, Pablo/0000-0001-7481-7273; Lethuillier, Morgan/0000-0001-6185-2045; Botta, Cristina/0000-0002-8072-795X; Deelen, Nikkie/0000-0003-4010-7155; Filatov, Oleg/0000-0001-9850-6170; Ulrich, Ralf/0000-0002-2535-402X; Pfeiffer, Andreas/0000-0001-5328-448X; sharma, ashish/0000-0002-0688-923X; David, Pieter/0000-0001-9260-9371; Klanner, Robert/0000-0002-7004-9227; Amendola, Chiara/0000-0002-4359-836X; Bury, Florian/0000-0002-3077-2090; Bartok, Marton/0000-0002-4440-2701; Martelli, Arabella/0000-0003-3530-2255; Dini, Paolo/0000-0001-7375-4899; Garcia, Francisco/0000-0002-4023-7964; Alibordi, Muhammad/0000-0002-7535-7149; Zanetti, Marco/0000-0003-4281-4582; Murillo Quijada, Javier Alberto/0000-0003-4933-2092; Feld, Lutz/0000-0001-9813-8646; Xiao, Rui/0000-0001-7292-8527; Colaleo, Anna/0000-0002-0711-6319; Cousins, Robert/0000-0002-5963-0467; Leon Holgado, Jaime/0000-0002-4156-6460; Ahmad, Muhammad/0000-0001-9933-995X; Mora Herrera, Maria Clemencia/0000-0003-3915-3170; Kogler, Roman/0000-0002-5336-4399; Sahasransu, Abanti Ranadhir/0000-0003-1505-1743; Dosselli, Umberto/0000-0001-8086-2863; Leonardo, Nuno/0000-0002-9746-4594; Dragicevic, Marko/0000-0003-1967-6783; Zorbakir, Ibrahim Soner/0000-0002-5962-2221; Snoeys, Walter/0000-0003-3541-9066; Pastrone, Nadia/0000-0001-7291-1979; Robert, Schoefbeck/0000-0002-2332-8784; Collard, Caroline/0000-0002-5230-8387; Elmetenawee, Walaa/0000-0001-7069-0252; Cepaitis, Vilius/0000-0002-4809-4056; Cavallari, Francesca/0000-0002-1061-3877; Navarro-Tobar, Alvaro/0000-0003-3606-1780; Cristella, Leonardo/0000-0002-4279-1221; Dewanjee, Ram Krishna/0000-0001-6645-6244; Di Croce, Davide/0000-0002-1122-7919; Rolandi, Luigi (Gigi)/0000-0002-0635-274X Austrian Federal Ministry of Education, Science and Research; Austrian Science Fund; Belgian Fonds de la Recherche Scientifique; Fonds voor Wetenschappelk Onderzoek; CNPq; CAPES; FAPERJ; FAPERGS; FAPESP; Bulgarian Ministry of Education and Science; Bulgarian National Science Fund; CERN; Chinese Academy of Sciences; Ministry of Science and Technology; National Natural Science Foundation of China; Ministerio de Ciencia Tecnologia e Innovacion (MINCIENCIAS), Colombia; Croatian Ministry of Science, Education and Sport; Croatian Science Foundation; Research and Innovation Foundation, Cyprus; Secretariat for Higher Education, Science, Technology and Innovation, Ecuado; Estonian Research Council [PRG780, PRG803, PRG445]; European Regional Development Fund, Estonia; Helsinki Institute of Physics; Institut National de Physique Nucleaire et de Physique des Particules/CNRS, France; Commissariat a l'Energie Atomique et aux Energies Alternatives/CEA, France; Bundesministerium fur Bildung und Forschung, Germany; Deutsche Forschungsgemeinschaft (DFG), Germany [390833306, 400140256 - GRK2497]; Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; General Secretariat for Research and Innovation, Greece; National Research, Development and Innovation Fund, Hungary; Department of Atomic Energy, India; Department of Science and Technology, India; Institute for Studies in Theoretical Physics and Mathematics, Iran; Science Foundation, Ireland; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Science, ICT and Future Planning, Republic of Korea; National Research Foundation (NRF), Republic of Korea; Ministry of Education and Science of the Republic of Latvia; Lithuanian Academy of Sciences; Ministry of Education (Malaysia); University of Malaya (Malaysia); Ministry of Science of Montenegro; BUAP; CINVESTAV; CONACYT; LNS; SEP; UASLP-FAI; Ministry of Business, Innovation and Employment, New Zealand; Pakistan Atomic Energy Commission; Ministry of Science and Higher Education, Poland; National Science Centre, Poland; Fundacao para a Ciencia e a Tecnologi, Portugal [CERN/FIS-PAR/0025/2019, CERN/FIS-INS/0032/2019]; JINR, Dubna; Ministry of Education and Science of the Russian Federation; Federal Agency of Atomic Energy of the Russian Federation; Russian Foundation for Basic Research; National Research Center Kurchatov Institute; Ministry of Education, Science and Technological Development of Serbia; Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020 [IDI-2018-000174]; Fondo Europeo de Desarrollo Regional, Spain; Ministry of Science, Technology and Research, Sri Lanka; ETH Board; ETH Zurich; PSI; SNF; UniZH; Canton Zurich; SER; Ministry of Science and Technology, Taipei; Thailand Center of Excellence in Physics; Institute for the Promotion of Teaching Science and Technology of Thailand; National Science and Technology Development Agency of Thailand; Scientific and Technical Research Council of Turkey; Turkish Atomic Energy Authority; National Academy of Sciences of Ukraine; Science and Technology Facilities Council, U.K.; US Department of Energy; US National Science Foundation; Marie-Curie programme (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 884104]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 12878, 129058]; Council of Scientific and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [2014/15/B/ST2/03998, 2015/19/B/ST2/02861]; Fundacao para a Ciencia e a Tecnologia [FCT CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [14.W03.31.0026, FSWW-2020-0008]; Russian Foundation for Basic Research (Russia) [19-42-703014]; Programa de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias; Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.); Ministry of Education and Research; Academy of Finland; Finnish Ministry of Education and Culture; Russian Academy of Sciences; Secretaria de Estado de Investigacion, Desarrollo e Innovacion; Programa Consolider-Ingenio 2010; Special Task Force for Activating Research; Fundação para a Ciência e a Tecnologia [CERN/FIS-PAR/0025/2019, CERN/FIS-INS/0032/2019] Funding Source: FCT Austrian Federal Ministry of Education, Science and Research; Austrian Science Fund(Austrian Science Fund (FWF)); Belgian Fonds de la Recherche Scientifique(Fonds de la Recherche Scientifique - FNRS); Fonds voor Wetenschappelk Onderzoek; CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Bulgarian Ministry of Education and Science; Bulgarian National Science Fund(National Science Fund of Bulgaria); CERN; Chinese Academy of Sciences(Chinese Academy of Sciences); Ministry of Science and Technology(Ministry of Science, ICT & Future Planning, Republic of Korea); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministerio de Ciencia Tecnologia e Innovacion (MINCIENCIAS), Colombia; Croatian Ministry of Science, Education and Sport(Ministry of Science, Education and Sports, Republic of Croatia); Croatian Science Foundation; Research and Innovation Foundation, Cyprus; Secretariat for Higher Education, Science, Technology and Innovation, Ecuado; Estonian Research Council(Estonian Research Council); European Regional Development Fund, Estonia; Helsinki Institute of Physics; Institut National de Physique Nucleaire et de Physique des Particules/CNRS, France(Centre National de la Recherche Scientifique (CNRS)); Commissariat a l'Energie Atomique et aux Energies Alternatives/CEA, France(French Atomic Energy Commission); Bundesministerium fur Bildung und Forschung, Germany(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgemeinschaft (DFG), Germany(German Research Foundation (DFG)); Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany(Helmholtz Association); General Secretariat for Research and Innovation, Greece; National Research, Development and Innovation Fund, Hungary(National Research, Development & Innovation Office (NRDIO) - Hungary); Department of Atomic Energy, India(Department of Atomic Energy (DAE)); Department of Science and Technology, India(Department of Science & Technology (India)); Institute for Studies in Theoretical Physics and Mathematics, Iran; Science Foundation, Ireland(Science Foundation Ireland); Istituto Nazionale di Fisica Nucleare, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); Ministry of Science, ICT and Future Planning, Republic of Korea(Ministry of Science, ICT & Future Planning, Republic of Korea); National Research Foundation (NRF), Republic of Korea(National Research Foundation of Korea); Ministry of Education and Science of the Republic of Latvia(Latvian Ministry of Education and Science); Lithuanian Academy of Sciences(Research Council of Lithuania (LMTLT)); Ministry of Education (Malaysia)(Ministry of Education, Malaysia); University of Malaya (Malaysia)(Universiti Malaya); Ministry of Science of Montenegro; BUAP; CINVESTAV; CONACYT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS; SEP; UASLP-FAI; Ministry of Business, Innovation and Employment, New Zealand(New Zealand Ministry of Business, Innovation and Employment (MBIE)); Pakistan Atomic Energy Commission; Ministry of Science and Higher Education, Poland(Ministry of Science and Higher Education, Poland); National Science Centre, Poland(National Science Centre, Poland); Fundacao para a Ciencia e a Tecnologi, Portugal; JINR, Dubna; Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Federal Agency of Atomic Energy of the Russian Federation; Russian Foundation for Basic Research(Russian Foundation for Basic Research (RFBR)); National Research Center Kurchatov Institute; Ministry of Education, Science and Technological Development of Serbia(Ministry of Education, Science & Technological Development, Serbia); Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020; Fondo Europeo de Desarrollo Regional, Spain(European Commission); Ministry of Science, Technology and Research, Sri Lanka; ETH Board; ETH Zurich(ETH Zurich); PSI; SNF; UniZH; Canton Zurich; SER; Ministry of Science and Technology, Taipei; Thailand Center of Excellence in Physics; Institute for the Promotion of Teaching Science and Technology of Thailand; National Science and Technology Development Agency of Thailand; Scientific and Technical Research Council of Turkey(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); Turkish Atomic Energy Authority(Ministry of Energy & Natural Resources - Turkey); National Academy of Sciences of Ukraine; Science and Technology Facilities Council, U.K.(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); US Department of Energy(United States Department of Energy (DOE)); US National Science Foundation(National Science Foundation (NSF)); Marie-Curie programme (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); Horizon 2020 Grant (European Union); COST Action (European Union); Leventis Foundation; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Hungarian Academy of Sciences (Hungary)(Hungarian Academy of Sciences); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Scientific and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); Latvian Council of Science(Latvian Ministry of Education and Science); National Science Center (Poland)(National Science Centre, Poland); Fundacao para a Ciencia e a Tecnologia(Fundacao para a Ciencia e a Tecnologia (FCT)); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Russian Foundation for Basic Research (Russia)(Russian Foundation for Basic Research (RFBR)); Programa de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Stavros Niarchos Foundation (Greece); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.); Ministry of Education and Research; Academy of Finland(Academy of Finland); Finnish Ministry of Education and Culture; Russian Academy of Sciences(Russian Academy of Sciences); Secretaria de Estado de Investigacion, Desarrollo e Innovacion; Programa Consolider-Ingenio 2010; Special Task Force for Activating Research; Fundação para a Ciência e a Tecnologia PN We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses.; Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: the Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappel 1/4k Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of Education and Science, and the Bulgarian National Science Fund; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Ministerio de Ciencia Tecnologia e Innovacion (MINCIENCIAS), Colombia; the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research and Innovation Foundation, Cyprus; the Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Research Council via PRG780, PRG803 and PRG445 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucleaire et de Physique des Particules/CNRS, and Commissariat a l'Energie Atomique et aux Energies Alternatives/CEA, France; the Bundesministerium fur Bildung und Forschung, the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy EXC 2121 Quantum Universe -390833306, and under project number 400140256 - GRK2497, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Innovation, Greece; the National Research, Development and Innovation Fund, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and National Research Foundation (NRF), Republic of Korea; the Ministry of Education and Science of the Republic of Latvia; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Centre, Poland; the Fundacao para a Ciencia e a Tecnologia, grants CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019, Portugal; JINR, Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, the Russian Foundation for Basic Research, and the National Research Center Kurchatov Institute; the Ministry of Education, Science and Technological Development of Serbia; the Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Programa Consolider-Ingenio 2010, Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020, research project IDI-2018-000174 del Principado de Asturias, and Fondo Europeo de Desarrollo Regional, Spain; the Ministry of Science, Technology and Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in P; hysics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine; the Science and Technology Facilities Council, U.K.; the US Department of Energy, and the US National Science Foundation.; Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union) the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science-EOS-be.h project n. 30820817; the Be 1/4 ing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lendulet (Momentum) Programme and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Scientific and Industrial Research, India; the Latvian Council of Science; the National Science Center (Poland), contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861; the Fundacao para a Ciencia e a Tecnologia, grant FCT CEECIND/01334/2018; the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, projects no. 14.W03.31.0026 and no. FSWW-2020-0008, and the Russian Foundation for Basic Research, project No.19-42-703014 (Russia); the Programa de Excelencia Maria de Maeztu, and the Programa Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 61 2 2 11 29 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-0221 J INSTRUM J. Instrum. MAR 2022.0 17 3 P03014 10.1088/1748-0221/17/03/P03014 0.0 65 Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation 0C0IT hybrid, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000775007900007 0
C Gao, G; Lauri, M; Wang, YL; Hu, XL; Zhang, JW; Frintrop, S IEEE Gao, Ge; Lauri, Mikko; Wang, Yulong; Hu, Xiaolin; Zhang, Jianwei; Frintrop, Simone 6D Object Pose Regression via Supervised Learning on Point Clouds 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 31-JUN 15, 2020 ELECTR NETWORK IEEE This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolutional neural networks from color information have been the dominant features to be used for inferring object poses, while depth information receives much less attention. However, depth information contains rich geometric information of the object shape, which is important for inferring the object pose. We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression. We argue that the axis-angle representation is a suitable rotation representation for deep learning, and use a geodesic loss function for rotation regression. Ablation studies show that these design choices outperform alternatives such as the quaternion representation and L2 loss, or regressing translation and rotation with the same network. Our simple yet effective approach clearly outperforms state-of-the-art methods on the YCB-video dataset. [Gao, Ge; Lauri, Mikko; Zhang, Jianwei; Frintrop, Simone] Univ Hamburg, Dept Informat, Hamburg, Germany; [Wang, Yulong; Hu, Xiaolin] Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Dept Comp Sci & Technol, Beijing, Peoples R China University of Hamburg; Tsinghua University Gao, G (corresponding author), Univ Hamburg, Dept Informat, Hamburg, Germany. gao@informatik.uni-hamburg.de; lauri@informatik.uni-hamburg.de; wang-yl15@mails.tsinghua.edu.cn; xlhu@mails.tsinghua.edu.cn; zhang@informatik.uni-hamburg.de; frintrop@informatik.uni-hamburg.de zhang, jian/HPD-1712-2023; Lauri, Mikko/B-4273-2018 Lauri, Mikko/0000-0002-2223-9253 German Science Foundation (DFG) [TRR 169]; National Natural Science Foundation of China (NSFC) [61621136008] German Science Foundation (DFG)(German Research Foundation (DFG)); National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work is partially funded by the German Science Foundation (DFG) in project Crossmodal Learning, TRR 169, and the National Natural Science Foundation of China (NSFC) under Grant No. 61621136008. 33 21 22 3 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-7395-5 IEEE INT CONF ROBOT 2020.0 3643 3649 7 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BS3LB Green Submitted 2023-03-23 WOS:000712319502080 0
J Nie, XB; Min, CA; Pan, YJ; Li, ZX; Krolczyk, G Nie, Xiaobo; Min, Chuan; Pan, Yongjun; Li, Zhixiong; Krolczyk, Grzegorz An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms SENSORS English Article longitudinal-lateral dynamics; vehicle multibody model; deep neural networks; particle swarm optimization; invasive weed optimization We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle's longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles. [Nie, Xiaobo; Min, Chuan; Pan, Yongjun] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China; [Pan, Yongjun] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China; [Li, Zhixiong; Krolczyk, Grzegorz] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland Chongqing University; Dalian University of Technology; Opole University of Technology Pan, YJ (corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China.;Pan, YJ (corresponding author), Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China. xiaobo.nie@cqu.edu.cn; chuan.min@cqu.edu.cn; yongjun.pan@cqu.edu.cn; z.li@po.edu.pl; g.krolczyk@po.opole.pl Krolczyk, Grzegorz/D-6709-2013 Krolczyk, Grzegorz/0000-0002-2967-1719 National Natural Science Foundation of China [12072050, 12211530029]; Research Project of State Key Laboratory of Mechanical System and Vibration [MSV202216]; Fundamental Research Funds for the Central Universities [2021CDJQY-032] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Project of State Key Laboratory of Mechanical System and Vibration; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This study was supported by the National Natural Science Foundation of China (Project No.12072050 and No.12211530029), the Research Project of State Key Laboratory of Mechanical System and Vibration (Project No.MSV202216), and the Fundamental Research Funds for the Central Universities (Project No.2021CDJQY-032). 43 1 1 9 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUL 2022.0 22 13 4676 10.3390/s22134676 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 2V7GG 35808170.0 Green Accepted, gold 2023-03-23 WOS:000824009200001 0
J Zhao, HR; Sun, X; Dong, JY; Dong, ZH; Li, Q Zhao, Haoran; Sun, Xin; Dong, Junyu; Dong, Zihe; Li, Qiong Knowledge distillation via instance-level sequence learning KNOWLEDGE-BASED SYSTEMS English Article Neural networks compression; Knowledge distillation; Computer vision; Deep learning Recently, distillation approaches for extracting general knowledge from a teacher network to guide a student network have been suggested. Most existing methods transfer knowledge from the teacher to the student network by feeding a sequence of random minibatches sampled uniformly from the data. We argue that, instead, a compact student network should be guided gradually using samples ordered in a meaningful sequence. Thus, the gap in feature representation between the teacher and student network can be bridged step by step. In this paper, we provide a curriculum learning knowledge distillation framework via instance-level sequence learning. It employs the student network of the early epoch as a snapshot to create a curriculum for the student network's next training phase. We performed extensive experiments using the CIFAR-10, CIFAR-100, SVHN, and CINIC-10 datasets. When compared with several state-of-the-art methods, our framework achieved the best performance with fewer iterations. (C) 2021 Elsevier B.V. All rights reserved. [Zhao, Haoran; Sun, Xin; Dong, Junyu; Dong, Zihe; Li, Qiong] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China; [Sun, Xin] Tech Univ Munich, Dept Aerosp & Geodesy, Munich, Germany Ocean University of China; Technical University of Munich Sun, X; Dong, JY (corresponding author), Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China. zhaohaoran@stu.ouc.edu.cn; sunxin@ouc.edu.cn; dongjunyu@ouc.edu.cn; dongzihe@stu.ouc.edu.cn; liqiong@stu.ouc.edu.cn Zhao, Haoran/0000-0003-2842-3526; Dong, Zihe/0000-0003-1717-1204 National Natural Science Foundation of China [61971388, U1706218, 41576011, L1824025]; Key Natural Science Foundation of Shandong Province, China [ZR2018ZB0852] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Natural Science Foundation of Shandong Province, China We would like to express our sincere appreciation to anony-mous reviewers. We thank the National Natural Science Founda-tion of China (No. 61971388, U1706218, 41576011, L1824025) , and the Key Natural Science Foundation of Shandong Province, China (No. ZR2018ZB0852). 37 6 7 6 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. DEC 5 2021.0 233 107519 10.1016/j.knosys.2021.107519 0.0 SEP 2021 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WC0HX Green Submitted 2023-03-23 WOS:000703946200001 0
J Phan, AH; Cichocki, A; Uschmajew, A; Tichavsky, P; Luta, G; Mandic, DP Phan, Anh-Huy; Cichocki, Andrzej; Uschmajew, Andre; Tichavsky, Petr; Luta, George; Mandic, Danilo P. Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Tensors; Approximation algorithms; Signal processing algorithms; Matrix decomposition; Signal processing; Data models; Approximation error; Blind source separation; image denoising; nested Tucker; tensor network (TN); tensor train (TT) decomposition; tensorization; Tucker-2 (TK2) decomposition; truncated singular value decomposition (SVD) DECOMPOSITIONS; OPTIMIZATION; COMPLETION; SVD Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model that represents data as an ordered network of subtensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network (TN) decomposition has been long studied in quantum physics and scientific computing. In this article, we present novel algorithms and applications of TN decompositions, with a particular focus on the tensor train (TT) decomposition and its variants. The novel algorithms developed for the TT decomposition update, in an alternating way, one or several core tensors at each iteration and exhibit enhanced mathematical tractability and scalability for large-scale data tensors. For rigor, the cases of the given ranks, given approximation error, and the given error bound are all considered. The proposed algorithms provide well-balanced TT-decompositions and are tested in the classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, achieving superior performance over the widely used truncated algorithms for TT decomposition. [Phan, Anh-Huy] Skolkovo Inst Sci & Technol Skoltech, Ctr Computat & Data Intens Sci & Engn, Moscow 143026, Russia; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol Skoltech, Moscow 143026, Russia; [Phan, Anh-Huy; Cichocki, Andrzej] Tokyo Univ Agr & Technol, Tokyo 1838538, Japan; [Cichocki, Andrzej] Hangzhou Dianzi Univ, Coll Comp Sci, Hangzhou 310018, Peoples R China; [Cichocki, Andrzej] Syst Res Inst, PL-01447 Warsav, Poland; [Uschmajew, Andre] Max Planck Inst Math Sci, D-04103 Leipzig, Germany; [Tichavsky, Petr] Czech Acad Sci, Inst Informat Theory & Automat, Prague 18200, Czech Republic; [Luta, George] Georgetown Univ, Lombardi Comprehens Canc Ctr, Washington, DC 20057 USA; [Mandic, Danilo P.] Imperial Coll, Signal Proc, London SW7 2AZ, England Skolkovo Institute of Science & Technology; Skolkovo Institute of Science & Technology; Tokyo University of Agriculture & Technology; Hangzhou Dianzi University; Max Planck Society; Czech Academy of Sciences; Institute of Information Theory & Automation of the Czech Academy of Sciences; Georgetown University; Imperial College London Phan, AH (corresponding author), Skolkovo Inst Sci & Technol Skoltech, Ctr Computat & Data Intens Sci & Engn, Moscow 143026, Russia. a.phan@skoltech.ru; a.cichocki@skoltech.ru; uschmajew@mis.mpg.de; tichavsk@utia.cas.cz; george.luta@georgetown.edu; d.mandic@imperial.ac.uk Tichavsky, Petr/AAH-6579-2021; Phan, Anh-Huy/G-5333-2013 Tichavsky, Petr/0000-0003-0621-4766; Luta, Gheorghe/0000-0002-4035-7632; Mandic, Danilo/0000-0001-8432-3963; Phan, Anh-Huy/0000-0002-5509-7773 Ministry of Education and Science of the Russian Federation [14.756.31.0001]; Czech Science Foundation [17-00902S] Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Czech Science Foundation(Grant Agency of the Czech Republic) The work of A.-H. Phan and A. Cichocki was supported by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001. The work of P. Tichavsky was supported by the Czech Science Foundation under Project 17-00902S. 39 9 9 5 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. NOV 2020.0 31 11 4622 4636 10.1109/TNNLS.2019.2956926 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OO9NH 32031950.0 2023-03-23 WOS:000587699700017 0
J Boukela, L; Zhang, GX; Bouzefrane, S; Zhou, JL Boukela, Lynda; Zhang, Gongxuan; Bouzefrane, Samia; Zhou, Junlong An outlier ensemble for unsupervised anomaly detection in honeypots data INTELLIGENT DATA ANALYSIS English Article Outlier ensembles; network security; anomaly detection; honeypots FRAMEWORK Nowadays, computers, as well as smart devices, are connected through communication networks making them more vulnerable to attacks. Honeypots are proposed as deception tools but usually used as part of a proactive defense strategy. Hence, this article demonstrates how honeypots data can be analyzed in an active defense strategy. Furthermore, anomaly detection based on unsupervised machine learning techniques allows to build autonomous systems and to detect unknown anomalies without the need for prior knowledge. However, the unsupervised techniques applied for honeypots data analysis do not value the advantages of these tools' data, particularly the high probability that they include a large number of previously unseen anomalies with unexpected and diverse patterns. Therefore, in the present work, the aim is to improve the unsupervised anomaly detection in honeypots data by varying the data feature subset and the parameterization of the anomaly detection algorithm. To this purpose, an outlier ensemble with LOF (Local Outlier Factor) as a base algorithm is proposed. The ensemble outperforms existing solutions as depicted in the experiments where a detection rate higher than 92% is achieved. [Boukela, Lynda; Zhang, Gongxuan; Zhou, Junlong] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, 200 Xiaolingwei St, Nanjing 210094, Peoples R China; [Bouzefrane, Samia] Conservatoire Natl Arts & Metiers, CEDRIC Lab, Paris, France Nanjing University of Science & Technology; heSam Universite; Conservatoire National Arts & Metiers (CNAM); Institut Polytechnique de Paris Boukela, L (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, 200 Xiaolingwei St, Nanjing 210094, Peoples R China. lyndaboukela@njust.edu.cn Zhang, Gongxuan/HKE-1007-2023 National Natural Science Foundation of China [61272420, 61472189] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was funded by the National Natural Science Foundation of China, Grant number 61272420 and 61472189. 27 2 2 2 10 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1088-467X 1571-4128 INTELL DATA ANAL Intell. Data Anal. 2020.0 24 4 743 758 10.3233/IDA-194656 0.0 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science MN8MB Green Submitted 2023-03-23 WOS:000551095000001 0
J Trindade, S; Torres, RD; Zhu, ZQ; da Fonseca, NLS Trindade, Silvana; Torres, Ricardo da S.; Zhu, Zuqing; da Fonseca, Nelson L. S. Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing SENSORS English Article virtual networks; space-division multiplexing; machine learning; control loop; cognitive networks ARCHITECTURE; ALLOCATION; EFFICIENT; ENERGY This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elastic Optical Networks with Space-Division Multiplexing. The proposed solution, called Cognitive control loop (CO-OP), is capable of identifying a set of problems and creating plans to mitigate these problems. The CO-OP comprises four functions that employ learning algorithms to identify problems and plan a series of actions to reduce or eliminate them. The results show that the CO-OP can effectively decrease up to 30% the blocking of requests and up to 50% the crosstalk occurrence compared to existing algorithms. [Trindade, Silvana; da Fonseca, Nelson L. S.] Univ Estadual Campinas, Inst Comp, Albert Einstein 1251, BR-13083852 Campinas, Brazil; [Torres, Ricardo da S.] Wageningen Univ & Res, Farm Technol Grp, NL-6700 AA Wageningen, Netherlands; [Torres, Ricardo da S.] Wageningen Univ & Res, Wageningen Data Competence Ctr, NL-6700 AA Wageningen, Netherlands; [Torres, Ricardo da S.] Norwegian Univ Sci & Technol, Dept ICT & Nat Sci, N-6009 Alesund, Norway; [Zhu, Zuqing] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China Universidade Estadual de Campinas; Wageningen University & Research; Wageningen University & Research; Norwegian University of Science & Technology (NTNU); Chinese Academy of Sciences; University of Science & Technology of China, CAS da Fonseca, NLS (corresponding author), Univ Estadual Campinas, Inst Comp, Albert Einstein 1251, BR-13083852 Campinas, Brazil. silvana@lrc.ic.unicamp.br; ricardo.torres@ntnu.no; zqzhu@ieee.org; nfonseca@ic.unicamp.br Zhu (朱祖勍), Zuqing/J-8431-2017 Zhu (朱祖勍), Zuqing/0000-0002-4251-788X; Torres, Ricardo/0000-0001-9772-263X; Trindade, Silvana/0000-0002-5526-6733 CAPES; CNPq [15/24494-8]; FAPESP CAPES(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)) This work was supported by CAPES, CNPq, and grant 15/24494-8, FAPESP, BRAZIL. 45 0 0 1 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2021.0 21 23 7821 10.3390/s21237821 0.0 21 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation YF5NL 34883831.0 gold, Green Accepted 2023-03-23 WOS:000741853000001 0
J Cheng, JH; Qi, B; Chen, DD; Landry, R Cheng, Jianhua; Qi, Bing; Chen, Daidai; Landry, Rene, Jr. Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes SENSORS English Article IFOG; temperature compensation; temperature product term; RBF ANN; MSD This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements. [Cheng, Jianhua; Qi, Bing; Chen, Daidai] Harbin Engn Univ, Marine Nav Res Inst, Coll Automat, Harbin 150001, Peoples R China; [Cheng, Jianhua; Qi, Bing; Landry, Rene, Jr.] Ecole Technol Super, LASSENA Lab, Montreal, PQ H3C 1K3, Canada; [Chen, Daidai] Aalto Univ, Sch Sci, Dept Biomed Engn & Computat Sci, Espoo 02150, Finland Harbin Engineering University; University of Quebec; Ecole de Technologie Superieure - Canada; Aalto University Qi, B (corresponding author), Harbin Engn Univ, Marine Nav Res Inst, Coll Automat, Harbin 150001, Peoples R China. ins_cheng@163.com; qibing336699@163.com; chen.daidai@aalto.fi; ReneJr.Landry@etsmtl.ca Chen, Daidai/0000-0003-3931-3816 National Nature Science Foundation of China [61374007, 61273081, 61104036]; Fundamental Research Funds for the Central Universities [HEUCFX41309] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) Funding for this work was provided by the National Nature Science Foundation of China under grant No. 61374007, No. 61273081 and No. 61104036, and the Fundamental Research Funds for the Central Universities under the grant of HEUCFX41309. The authors would like to thank all the editors and anonymous reviewers for improving this article. 32 19 21 5 38 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2015.0 15 5 11189 11207 10.3390/s150511189 0.0 19 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation CL7WN 25985163.0 Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000357183100082 0
J Wang, C; Ferrando, M; Causone, F; Jin, X; Zhou, X; Shi, X Wang, Chao; Ferrando, Martina; Causone, Francesco; Jin, Xing; Zhou, Xin; Shi, Xing Data acquisition for urban building energy modeling: A review BUILDING AND ENVIRONMENT English Review Urban building energy modeling; UBEM; Urban energy simulation; Data acquisition; Data science CONVOLUTIONAL NEURAL-NETWORK; PERFORMANCE SIMULATION; RESIDENTIAL BUILDINGS; OCCUPANT BEHAVIOR; STOCHASTIC-MODEL; SHADOW DETECTION; LOAD PROFILE; CITY; RESOLUTION; DEMAND Urban Building Energy Modeling (UBEM) is essential for urban energy-related applications. Its generation mainly requires four data inputs, including geometric data, non-geometric data, weather data, and validation and calibration data. A reliable UBEM depends on the quantity and accuracy of the data inputs. However, the lack of available data and the difficulty in determining stochastic data are two of the main barriers in the development of UBEM. To bridge the research gaps, this paper reviews appropriate acquisition approaches for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science. In addition, detailed evaluations are also conducted in each part of the study, and the performance of the approaches are discussed, as well as the availability and cost of the implemented data. Systematic discussion, multidisciplinary analysis and comprehensive evaluation are the highlights of this review. [Wang, Chao; Jin, Xing; Zhou, Xin] Southeast Univ, Sch Architecture, Nanjing 210096, Peoples R China; [Ferrando, Martina; Causone, Francesco] Politecn Milan, Dept Energy, I-20156 Milan, Italy; [Shi, Xing] Minist Educ, Key Lab Ecol & Energy Saving Study Dense Habitat, Shanghai 200092, Peoples R China; [Shi, Xing] Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China Southeast University - China; Polytechnic University of Milan; Tongji University Shi, X (corresponding author), Minist Educ, Key Lab Ecol & Energy Saving Study Dense Habitat, Shanghai 200092, Peoples R China. 20101@tongji.edu.cn Zhou, Xin/AAA-3990-2022; zhou, Xin/AAA-3990-2022 Zhou, Xin/0000-0002-3135-0260; zhou, Xin/0000-0002-5897-6538; FERRANDO, MARTINA/0000-0003-4060-256X 192 9 9 29 29 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-1323 1873-684X BUILD ENVIRON Build. Environ. JUN 1 2022.0 217 109056 10.1016/j.buildenv.2022.109056 0.0 APR 2022 20 Construction & Building Technology; Engineering, Environmental; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 4P3NT 2023-03-23 WOS:000855304200002 0
J Li, XR; Yao, YT; Yin, GD; Peng, FF; Liu, MX Li, Xiran; Yao, Yitong; Yin, Guodong; Peng, Feifei; Liu, Muxing Forest Resistance and Resilience to 2002 Drought in Northern China REMOTE SENSING English Article drought; resistance; resilience; needleleaf forests; broadleaf forests; northern China GROSS PRIMARY PRODUCTIVITY; TREE; CLIMATE; COVARIATION; PERFORMANCE; MODEL Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling. [Li, Xiran; Peng, Feifei; Liu, Muxing] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China; [Yao, Yitong] Univ Paris Saclay, CEA CNRS UVSQ, LSCE IPSL, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Yin, Guodong] Beijing Normal Univ, Coll Water Sci, Beijing Key Lab Urban Hydrol Cycle & Sponge City, Beijing 100875, Peoples R China Central China Normal University; UDICE-French Research Universities; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Beijing Normal University Liu, MX (corresponding author), Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China. lixiran@mail.ccnu.edu.cn; yitong.yao@lsce.ipsl.fr; guodong_yin@bnu.edu.cn; feifpeng@mail.ccnu.edu.cn; liumuxing@mail.ccnu.edu.cn Li, Xiran/0000-0003-1457-1755; yin, guodong/0000-0003-3926-2609 National Natural Science Foundation of China (NSFC) [41901112]; Natural Science Foundation of Hubei Province of China [ZRMS2019002066] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province of China(Natural Science Foundation of Hubei Province) This research was funded by the National Natural Science Foundation of China (NSFC), grant number 41901112, and the Natural Science Foundation of Hubei Province of China, grant number ZRMS2019002066. 34 4 4 22 58 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13 15 2919 10.3390/rs13152919 0.0 18 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology TW3DF Green Published, gold 2023-03-23 WOS:000682284600001 0
J Alcantud, JCR; Santos-Garcia, G; Peng, XD; Zhan, JM Alcantud, Jose Carlos R.; Santos-Garcia, Gustavo; Peng, Xindong; Zhan, Jianming Dual Extended Hesitant Fuzzy Sets SYMMETRY-BASEL English Article hesitant fuzzy set; extended hesitant fuzzy set; dual hesitant fuzzy set; decision making AGGREGATION OPERATORS; MODEL Hesitant fuzzy sets extend fuzzy sets by considering many-valued sets of membership degrees. Real applications validate this model and decision making approaches of various forms permit to act in a flexible manner. If we can avail ourselves of hesitant information on non-membership degrees too, then dual hesitant fuzzy sets provide a natural extension of both hesitant fuzzy sets and intuitionistic fuzzy sets. This article defines the concept of dual extended hesitant fuzzy set as the combination of extended hesitant fuzzy sets with dual hesitant fuzzy sets. Its basic algebraic properties are set forth, and the model is linked to other successful models in the literature. We also define a comparison law for the prioritization of elements described in this new framework. Moreover, we present an algorithm to solve the dual extended hesitant fuzzy decision making problem by a weight score function. Finally, the feasibility of this approach is demonstrated by the evaluation of big data industries with an effectiveness test. [Alcantud, Jose Carlos R.] Univ Salamanca, BORDA Res Unit, Salamanca 37007, Spain; [Alcantud, Jose Carlos R.; Santos-Garcia, Gustavo] Univ Salamanca, Fac Econ & Empresa, Salamanca 37007, Spain; [Alcantud, Jose Carlos R.; Santos-Garcia, Gustavo] Univ Salamanca, Multidisciplinary Inst Enterprise IME, Salamanca 37007, Spain; [Peng, Xindong] Shaoguan Univ, Dept Internet Things, Sch Informat Sci & Engn, Shaoguan 512005, Peoples R China; [Zhan, Jianming] Hubei Minzu Univ, Coll Sci, Enshi 445000, Peoples R China University of Salamanca; University of Salamanca; University of Salamanca; Shaoguan University; Hubei Minzu University Alcantud, JCR (corresponding author), Univ Salamanca, BORDA Res Unit, Salamanca 37007, Spain.;Alcantud, JCR (corresponding author), Univ Salamanca, Fac Econ & Empresa, Salamanca 37007, Spain.;Alcantud, JCR (corresponding author), Univ Salamanca, Multidisciplinary Inst Enterprise IME, Salamanca 37007, Spain. jcr@usal.es; santos@usal.es; pengxindong@sgu.edu.cn; zhanjianming@hotmail.com Alcantud, José Carlos/E-3258-2010; Santos-Garcia, Gustavo/H-2531-2014 Alcantud, José Carlos/0000-0002-4533-9281; Peng, Xindong/0000-0001-9080-6267; Zhan, Jianming/0000-0003-2510-9515; Santos-Garcia, Gustavo/0000-0001-6609-5493 Spanish MINECO [TRACES TIN2015-67522-C3-3-R, Strongsoft TIN2012-39391-C04-04] Spanish MINECO(Spanish Government) The research of G. Santos-Garcia was funded by the Spanish MINECO projects TRACES TIN2015-67522-C3-3-R and Strongsoft TIN2012-39391-C04-04. 40 12 12 6 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-8994 SYMMETRY-BASEL Symmetry-Basel MAY 2019.0 11 5 714 10.3390/sym11050714 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics IC5ER gold 2023-03-23 WOS:000470990900117 0
C Jia, PQ; Koyuncu, AB; Gaikov, G; Karabutov, A; Alshina, E; Kaup, A IEEE Jia, Panqi; Koyuncu, Ahmet Burakhan; Gaikov, Georgii; Karabutov, Alexander; Alshina, Elena; Kaup, Andre LEARNING-BASED CONDITIONAL IMAGE CODER USING COLOR SEPARATION 2022 PICTURE CODING SYMPOSIUM (PCS) Picture Coding Symposium English Proceedings Paper Picture Coding Symposium (PCS) DEC 07-09, 2022 San Jose, CA Inst Elect & Elect Engineers, Signal Proc Soc Learned Image Compression; Conditional Autoencoder; Deep Learning; Subsampled Color Space Coding; Complexity Reduction Recently, image compression codecs based on Neural Networks (NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG. [Jia, Panqi; Kaup, Andre] Friedrich Alexander Univ, Multimedia Commun & Signal Proc, Erlangen, Germany; [Koyuncu, Ahmet Burakhan] Tech Univ Munich, Chair Media Technol, Munich, Germany; [Jia, Panqi; Koyuncu, Ahmet Burakhan; Gaikov, Georgii; Karabutov, Alexander; Alshina, Elena] Huawei Technol, Shenzhen, Peoples R China University of Erlangen Nuremberg; Technical University of Munich; Huawei Technologies Jia, PQ (corresponding author), Friedrich Alexander Univ, Multimedia Commun & Signal Proc, Erlangen, Germany.;Jia, PQ (corresponding author), Huawei Technol, Shenzhen, Peoples R China. 0 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2330-7935 2472-7822 978-1-6654-9257-7 PICT COD SYMP 2022.0 49 53 10.1109/PCS56426.2022.10018070 0.0 5 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Imaging Science & Photographic Technology BU6OX Green Submitted 2023-03-23 WOS:000926892300009 0
C Deng, HW; Birdal, T; Ilic, S Ferrari, V; Hebert, M; Sminchisescu, C; Weiss, Y Deng, Haowen; Birdal, Tolga; Ilic, Slobodan PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors COMPUTER VISION - ECCV 2018, PT V Lecture Notes in Computer Science English Proceedings Paper 15th European Conference on Computer Vision (ECCV) SEP 08-14, 2018 Munich, GERMANY 3D deep learning; Local features; Descriptors; Rotation invariance We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the foldingbased auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point-set sparsity, is end-to-end, fast, and can extract powerful rotation invariant descriptors. Thanks to a novel feature visualization, its evolution can be monitored to provide interpretable insights. Our extensive experiments demonstrate that despite having six degree-of-freedom invariance and lack of training labels, our network achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present. PPF-FoldNet achieves 9% higher recall on standard benchmarks, 23% higher recall when rotations are introduced into the same datasets and finally, a margin of >35% is attained when point density is significantly decreased. [Deng, Haowen; Birdal, Tolga; Ilic, Slobodan] Tech Univ Munich, Munich, Germany; [Deng, Haowen; Birdal, Tolga; Ilic, Slobodan] Siemens AG, Munich, Germany; [Deng, Haowen] Natl Univ Def Technol, Changsha, Peoples R China Technical University of Munich; Siemens AG; Siemens Germany; National University of Defense Technology - China Birdal, T (corresponding author), Tech Univ Munich, Munich, Germany.;Birdal, T (corresponding author), Siemens AG, Munich, Germany. tolga.birdal@tum.de Birdal, Tolga/H-9173-2019 Birdal, Tolga/0000-0001-7915-7964; Ilic, Slobodan/0000-0002-3413-1936 51 122 129 4 11 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-01228-1; 978-3-030-01227-4 LECT NOTES COMPUT SC 2018.0 11209 620 638 10.1007/978-3-030-01228-1_37 0.0 19 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BQ4RZ Green Submitted 2023-03-23 WOS:000594216400037 0
J Tu, JZ; Liu, C; Qi, P Tu, Jingzhi; Liu, Chun; Qi, Pian Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Adaptive refinement method; crack propagation; machinery manufacturing equipment; physics-informed neural networks (PINNs) ERROR ESTIMATION Crack is one of the critical factors that degrade the performance of machinery manufacturing equipment. Recently, physics-informed neural networks (PINNs) have received attention due to their strong potential in solving physical problems. For fracture problems, PINNs have been used to predict crack paths by minimizing the variational energy of discrete domains where refined meshes are necessary. To obtain refined meshes, posteriori adaptive refinement techniques are commonly used to perform local refinement of the mesh based on errors in the intermediate calculation process; thus, they require pretest calculations. However, it is computationally expensive to precalculate complex problems, especially crack propagation. To solve this problem, we propose a PointNet-based adaptive refinement method to avoid precalculation when constructing the discrete domain. The proposed method is applied to simulate crack propagation using a PINN. Results show that the proposed method can be used to obtain reliable results efficiently when using the PINN framework. [Tu, Jingzhi] China Univ Geosci, Sch Engn & Technolgy, Beijing 100083, Peoples R China; [Liu, Chun] Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China; [Qi, Pian] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, I-80125 Naples, Italy Beijing University of Posts & Telecommunications; University of Naples Federico II Liu, C (corresponding author), Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China. tujingzhi@cugb.edu.cn; chun.liu@bupt.edu.cn; pian.qi@unina.it Fundamental Research Funds for China Central Universities [2652021053] Fundamental Research Funds for China Central Universities This work was supported by the Fundamental Research Funds for China Central Universities under Grant 2652021053. 30 1 1 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2023.0 19 2 2210 2218 10.1109/TII.2022.3201985 0.0 9 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 8Q1HM 2023-03-23 WOS:000926964700106 0
C Liao, YY; Donne, S; Geiger, A IEEE Liao, Yiyi; Donne, Simon; Geiger, Andreas Deep Marching Cubes: Learning Explicit Surface Representations 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-23, 2018 Salt Lake City, UT IEEE,CVF,IEEE Comp Soc Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques. [Liao, Yiyi; Donne, Simon; Geiger, Andreas] MPI Intelligent Syst Tubingen, Autonomous Vis Grp, Tubingen, Germany; [Liao, Yiyi] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou, Zhejiang, Peoples R China; [Donne, Simon] Univ Ghent, IPI, IMEC, Ghent, Belgium; [Geiger, Andreas] Swiss Fed Inst Technol, CVG Grp, Zurich, Switzerland Zhejiang University; Ghent University; IMEC; Swiss Federal Institutes of Technology Domain; ETH Zurich Liao, YY (corresponding author), MPI Intelligent Syst Tubingen, Autonomous Vis Grp, Tubingen, Germany.;Liao, YY (corresponding author), Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou, Zhejiang, Peoples R China. yiyi.liao@tue.mpg.de; simon.donne@tue.mpg.de; andreas.geiger@tue.mpg.de NSFC [U1509210] NSFC(National Natural Science Foundation of China (NSFC)) Yiyi Liao was partially supported by NSFC under grant U1509210. 46 84 85 2 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-5386-6420-9 PROC CVPR IEEE 2018.0 2916 2925 10.1109/CVPR.2018.00308 0.0 10 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL9NZ 2023-03-23 WOS:000457843603006 0
C Deng, HW; Birdal, T; Ilie, S IEEE Deng, Haowen; Birdal, Tolga; Ilie, Slobodan PPFNet: Global Context Aware Local Features for Robust 3D Point Matching 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-23, 2018 Salt Lake City, UT IEEE,CVF,IEEE Comp Soc SURFACE We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to exploit the full sparsity. PPFNet uses a novel N-tuple loss and architecture injecting the global information naturally into the local descriptor. It shows that context awareness also boosts the local feature representation. Qualitative and quantitative evaluations of our network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance. [Deng, Haowen; Birdal, Tolga; Ilie, Slobodan] Tech Univ Munich, Munich, Germany; [Deng, Haowen; Birdal, Tolga; Ilie, Slobodan] Siemens AG Munchen, Munich, Germany; [Deng, Haowen] Natl Univ Def Technol, Changsha, Hunan, Peoples R China Technical University of Munich; Siemens AG; Siemens Germany; National University of Defense Technology - China Deng, HW (corresponding author), Tech Univ Munich, Munich, Germany.;Deng, HW (corresponding author), Siemens AG Munchen, Munich, Germany.;Deng, HW (corresponding author), Natl Univ Def Technol, Changsha, Hunan, Peoples R China. haowen.deng@tum.de; tolga.birdal@tum.de; slobodan.ilic@siemens.com Birdal, Tolga/H-9173-2019 Birdal, Tolga/0000-0001-7915-7964; Ilic, Slobodan/0000-0002-3413-1936 48 193 203 10 42 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-5386-6420-9 PROC CVPR IEEE 2018.0 195 205 10.1109/CVPR.2018.00028 0.0 11 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL9NZ Green Submitted 2023-03-23 WOS:000457843600021 0
C He, ZY; Jin, Z; Xu, X; Luo, L Peng, Y; Hu, SM; Gabbouj, M; Zhou, K; Elad, M; Xu, K He, Zongyao; Jin, Zhi; Xu, Xiao; Luo, Lei Degradation Reconstruction Loss: A Perceptual-Oriented Super-Resolution Framework for Multi-downsampling Degradations IMAGE AND GRAPHICS (ICIG 2021), PT III Lecture Notes in Computer Science English Proceedings Paper 11th International Conference on Image and Graphics (ICIG) AUG 06-08, 2021 China Soc Image & Graph, Haikou, PEOPLES R CHINA Hainan Univ China Soc Image & Graph Image super-resolution; Degradation reconstruction loss; Multiple downsampling degradations; Generative adversarial network Recent years have witnessed the great success of deep learning-based single image super-resolution (SISR) methods. However, most of the existing SR methods assume that low-resolution (LR) images are purely bicubic downsampled from high-resolution (HR) images. Once the actual degradation is not bicubic, their outstanding performance is hard to maintain. Although several SR methods have super-resolved LR images with multiple blur kernels and noise levels, they still follow the bicubic downsampling assumption. To address this issue, we propose a novel degradation reconstruction loss (DRL) to capture the degradation-wise differences between HR images and SR images based on a degradation simulator. By involving the proposed degradation simulator and the loss, a perceptual-oriented SR framework for multi-downsampled images is formed. Extensive experimental results demonstrate that our method outperforms the state-of-the-art perceptual-oriented SR methods on both multi-downsampled datasets and bicubic downsampled datasets. [He, Zongyao; Jin, Zhi] Sun Yat Sen Univ, Shenzhen 518107, Peoples R China; [Xu, Xiao] Tech Univ Munich, Munich, Germany; [Luo, Lei] Chongqing Univ Posts & Telecommun, Chongqing, Peoples R China Sun Yat Sen University; Technical University of Munich; Chongqing University of Posts & Telecommunications Jin, Z (corresponding author), Sun Yat Sen Univ, Shenzhen 518107, Peoples R China. hezy28@mail2.sysu.edu.cn; jinzh26@mail.sysu.edu.cn; xiao.xu@tum.de; luolei@cqupt.edu.cn He, Zongyao/0000-0001-5130-0423; Luo, Lei/0000-0002-7008-4276 National Natural Science Foundation of China [62071500, 61701313] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China (No. 62071500, No. 61701313). 28 0 0 2 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-87361-5; 978-3-030-87360-8 LECT NOTES COMPUT SC 2021.0 12890 437 449 10.1007/978-3-030-87361-5_36 0.0 13 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS7UP 2023-03-23 WOS:000767950300036 0
C Qu, SQ; Chen, G; Ye, CB; Lu, F; Wang, F; Xu, ZC; Ge, YX IEEE Qu, Sanqing; Chen, Guang; Ye, Canbo; Lu, Fan; Wang, Fa; Xu, Zhongcong; Ge, Yixin An Efficient L-Shape Fitting Method for Vehicle Pose Detection with 2D LiDAR 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) English Proceedings Paper IEEE International Conference on Robotics and Biomimetics (ROBIO) DEC 12-15, 2018 Kuala Lumpur, MALAYSIA IEEE,IEEE Robot & Automat Soc,Shenzhen Hans Robot Co Ltd,Guangdong Univ Technol,Guangzhou Univ,Guangdong Inst Intelligent Mfg,Foshan Biowin Robot & Automat Co Ltd TRACKING Detecting vehicles with strong robustness and high efficiency has become one of the key capabilities of fully autonomous driving cars. This topic has already been widely studied by GPU-accelerated deep learning approaches using image sensors and 3D LiDAR, however, few studies seek to address it with a horizontally mounted 2D laser scanner. 2D laser scanner is equipped on almost every autonomous vehicle for its superiorities in the field of view, lighting invariance, high accuracy and relatively low price. In this paper, we propose a highly efficient search-based L-Shape fitting algorithm for detecting positions and orientations of vehicles with a 2D laser scanner. Differing from the approach to formulating L-Shape fitting as a complex optimization problem, our method decomposes the L-Shape fitting into two steps: L-Shape vertexes searching and L-Shape corner localization. Our approach is computationally efficient due to its minimized complexity. In on-road experiments, our approach is capable of adapting to various circumstances with high efficiency and robustness. [Qu, Sanqing; Chen, Guang; Ye, Canbo; Lu, Fan; Wang, Fa; Xu, Zhongcong; Ge, Yixin] Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China; [Chen, Guang] Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Dept Informat, Munich, Germany Tongji University; Technical University of Munich Chen, G (corresponding author), Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Chen, G (corresponding author), Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Dept Informat, Munich, Germany. guang@in.tum.de 19 7 7 1 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-0377-8 2018.0 1159 1164 6 Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Robotics BM8BC Green Submitted 2023-03-23 WOS:000468772200185 0
C Robin, G; Wai, HT; Josse, J; Klopp, O; Moulines, E Bengio, S; Wallach, H; Larochelle, H; Grauman, K; CesaBianchi, N; Garnett, R Robin, Genevieve; Wai, Hoi-To; Josse, Julie; Klopp, Olga; Moulines, Eric Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) Advances in Neural Information Processing Systems English Proceedings Paper 32nd Conference on Neural Information Processing Systems (NIPS) DEC 02-08, 2018 Montreal, CANADA MATRIX COMPLETION Many applications of machine learning involve the analysis of large data frames matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples - with missing values. Low-rank models, as studied by Udell et al. [27], are popular in this framework for tasks such as visualization, clustering and missing value imputation. Yet, available methods with statistical guarantees and efficient optimization do not allow explicit modeling of main additive effects such as row and column, or covariate effects. In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously. We provide statistical guarantees in the form of upper bounds on the estimation error of both components. Then, we introduce a mixed coordinate gradient descent (MCGD) method which provably converges sub-linearly to an optimal solution and is computationally efficient for large scale data sets. We show on simulated and survey data that the method has a clear advantage over current practices. [Robin, Genevieve; Josse, Julie; Moulines, Eric] Ecole Polytech, Ctr Math Appl, XPOP, INRIA, F-91120 Palaiseau, France; [Wai, Hoi-To] Chinese Univ Hong Kong, Dept SE & EM, Shatin, Hong Kong, Peoples R China; [Klopp, Olga] ESSEC Business Sch, CREST, ENSAE, F-95021 Cergy, France Inria; Institut Polytechnique de Paris; Chinese University of Hong Kong; ESSEC Business School; Institut Polytechnique de Paris Robin, G (corresponding author), Ecole Polytech, Ctr Math Appl, XPOP, INRIA, F-91120 Palaiseau, France. genevieve.robin@polytechnique.edu; htwai@se.cuhk.edu.hk; julie.josse@polytechnique.edu; klopp@essec.edu; eric.moulines@polytechnique.edu [NSF CCF-BSF 1714672] The authors would like to thank for the useful comments from three anonymous reviewers. HTW's work was supported by the grant NSF CCF-BSF 1714672. 29 0 0 0 2 NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) LA JOLLA 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA 1049-5258 ADV NEUR IN 2018.0 31 11 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM3CA 2023-03-23 WOS:000461852000004 0
J Braglia, C; Mignani, RP; Belfiore, A; Marelli, M; Israel, GL; Novara, G; De Luca, A; Tiengo, A; Parkinson, PMS Braglia, C.; Mignani, R. P.; Belfiore, A.; Marelli, M.; Israel, G. L.; Novara, G.; De Luca, A.; Tiengo, A.; Parkinson, P. M. Saz A multiwavelength search for black widow and redback counterparts of candidate gamma-ray millisecond pulsars MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article stars: neutron; pulsars: general; gamma-rays: general; X-rays: binaries DISCOVERY The wealth of detections of millisecond pulsars (MSPs) in gamma-rays by Fermi has spurred searches for these objects among the several unidentified gamma-ray sources. Interesting targets are a sub-class of binary MSPs, dubbed 'black widows' (BWs) and 'redbacks' (RBs), which are in orbit with low-mass non-degenerate companions fully or partially ablated by irradiation from the MSP wind. These systems can be easily missed in radio pulsar surveys owing to the eclipse of the radio signal by the intra-binary plasma from the ablated companion star photosphere, making them better targets for multiwavelength observations. We used optical and X-ray data from public data bases to carry out a systematic investigation of all the unidentified gamma-ray sources from the Fermi Large Area Telescope Third Source Catalog, which have been pre-selected as likely MSP candidates according to a machine-learning technique analysis. We tested our procedure by recovering known binary BW/RB identifications and searched for new ones, finding two possible candidates. At the same time, we investigated previously proposed BW/RB identifications and we ruled out one of them based upon the updated gamma-ray source coordinates. [Braglia, C.] Univ Milan, Dept Phys A Pontremoli, Via G Celoria 16, I-20133 Milan, Italy; [Mignani, R. P.; Belfiore, A.; Marelli, M.; Novara, G.; De Luca, A.; Tiengo, A.] INAF Ist Astrofis Spaziale & Fis Cosm Milano, Via A Corti 12, I-20133 Milan, Italy; [Mignani, R. P.] Univ Zielona Gora, Janusz Gil Inst Astron, Ul Szafrana 2, PL-65265 Zielona Gora, Poland; [Israel, G. L.] INAF, Osservatorio Astron Roma, Via Frascati 33, I-00040 Monte Porzio Catone, Italy; [Novara, G.; Tiengo, A.] Scuola Univ Super IUSS Pavia, Piazza Vittoria 15, I-27100 Pavia, Italy; [De Luca, A.; Tiengo, A.] Ist Nazl Fis Nucl, Sez Pavia, Via A Bassi 6, I-27100 Pavia, Italy; [Parkinson, P. M. Saz] Univ Calif Santa Cruz, Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA; [Parkinson, P. M. Saz] Univ Hong Kong, Lab Space Res, Dept Phys, Pokfulam Rd, Hong Kong, Peoples R China University of Milan; Istituto Nazionale Astrofisica (INAF); University of Zielona Gora; Istituto Nazionale Astrofisica (INAF); IUSS PAVIA; Istituto Nazionale di Fisica Nucleare (INFN); University of California System; University of California Santa Cruz; University of Hong Kong Mignani, RP; Belfiore, A; De Luca, A (corresponding author), INAF Ist Astrofis Spaziale & Fis Cosm Milano, Via A Corti 12, I-20133 Milan, Italy.;Mignani, RP (corresponding author), Univ Zielona Gora, Janusz Gil Inst Astron, Ul Szafrana 2, PL-65265 Zielona Gora, Poland.;De Luca, A (corresponding author), Ist Nazl Fis Nucl, Sez Pavia, Via A Bassi 6, I-27100 Pavia, Italy. roberto.mignani@inaf.it; andrea.belfiore@inaf.it; andrea.deluca@inaf.it Mignani, Roberto/0000-0002-8685-583X; Tiengo, Andrea/0000-0002-6038-1090 National Aeronautics and Space Administration [NNG05GF22G, NNX08AR22G]; U.S. National Science Foundation [AST-0909182]; National Science Foundation [AST-1238877]; European Union Seventh Framework Programme [607452] National Aeronautics and Space Administration(National Aeronautics & Space Administration (NASA)); U.S. National Science Foundation(National Science Foundation (NSF)); National Science Foundation(National Science Foundation (NSF)); European Union Seventh Framework Programme(European Commission) We thank the anonymous referee for her/his positive and constructive comments to the manuscript. CB acknowledges hospitality from INAF-IASF, Milan during the period of her MSc thesis work. This research has made use of data obtained from the 3XMM XMMNewton serendipitous source catalogue compiled by the 10 institutes of the XMM-Newton Survey Science Centre selected by ESA. This research has made use of data obtained from the Chandra Source Catalog, provided by the Chandra X-ray Center (CXC) as part of the Chandra Data Archive. The CSS survey is funded by the National Aeronautics and Space Administration under Grant No. NNG05GF22G issued through the Science Mission Directorate Near-Earth Objects Observations Program. The CRTS survey is supported by the U.S. National Science Foundation under Grant No. AST-0909182. The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the PanSTARRS ProjectOffice, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation Grant No. AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. This research has made use of data produced by the EXTraS project, funded by the European Union Seventh Framework Programme under Grant Agreement No. 607452. 61 2 2 0 2 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. OCT 2020.0 497 4 5364 5382 10.1093/mnras/staa2339 0.0 19 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics OP0BC Green Submitted 2023-03-23 WOS:000587739300097 0
J Ruan, JJ; Deng, YQ; Quan, Y; Gong, RH Ruan, Jiangjun; Deng, Yongqing; Quan, Yu; Gong, Ruohan Inversion Detection of Transformer Transient Hot Spot Temperature IEEE ACCESS English Article Oil insulation; Power transformer insulation; Transient analysis; Training; Temperature measurement; Temperature distribution; Load modeling; Transient hot spot temperature; oil-immersed transformer; support vector regression POWER TRANSFORMER; PREDICTION; NETWORK; MODEL This paper proposes an inversion method to estimate a 10 kV oil-immersed transformer transient hot spot temperature (HST). A set of transient feature quantities which can reflect the load change are proposed, those quantities as well as the real time load rate and feature temperature points on the transformer iron shell are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the transformer transient HST. K-fold cross-validation training method and grid search (GS) parameters optimization method are used to find the optimal parameters of the SVR model, the HST inversion results agree well with the transformer temperature rise test data which are conducted with short circuit method, and the HST inversion results outperform the results obtained with GA-BPNN method. The mean absolute percentage error (MAPE) is 1.66 %, and the maximum temperature difference is 2.93 degrees C. [Ruan, Jiangjun; Deng, Yongqing] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China; [Quan, Yu] State Grid Hubei Elect Power Co, Wuhan Power Supply Co, Wuhan 430050, Peoples R China; [Gong, Ruohan] Univ Lille, Arts & Metiers Paris Tech, Lab Electrotech & Elect Puissance, F-59000 Lille, France Wuhan University; State Grid Corporation of China; Arts et Metiers Institute of Technology; Universite de Lille - ISITE; Universite de Lille Ruan, JJ (corresponding author), Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China. ruan308@126.com Deng, Yongqing/0000-0001-8477-1205 Smart Grid Joint Fund of National Natural Science Foundation of China [U2066217] Smart Grid Joint Fund of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Smart Grid Joint Fund of National Natural Science Foundation of China under Grant U2066217. 29 3 4 7 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 7751 7761 10.1109/ACCESS.2021.3049235 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PS9EA gold 2023-03-23 WOS:000608223800001 0
J Huang, YQ; Li, DH; Niu, HY; Conte, D Huang, Yuqi; Li, Dominique H.; Niu, Haoyi; Conte, Donatello Visual identification of oscillatory two-phase flow with complex flow patterns MEASUREMENT English Article Two-phase flow; Pattern identification; Optical flow; Offset vector and sequence; Reynolds number classification; Computational fluid dynamics DENSITY WAVE OSCILLATION; NATURAL CIRCULATION We present an approach based on computer vision and machine learning methods to identify two-phase flow with complex flow patterns in oscillatory conditions. A visualization experiment bench was designed, constructed, and used to simulate the actual reciprocating motion of the cooling gallery inside the piston of low-speed diesel engines. The results of our proposed approach show that the feature vectors extracted from the optical flow images provides a valuable reference for the velocity vectors in two-phase flow. We show that it is possible to identify oscillatory two-phase flow videos with respect to Reynolds numbers from 10568 to 31704 using a Bayesian Network classifier, with the best accuracy of 94%. The approach purposed in this paper can not only be used to present the validating sources for numerical simulation results, but also be widely applied in the visualization of multiphase flow, which is a key area to be developed on the basic research of heat transfer systems. [Huang, Yuqi; Niu, Haoyi] Zhejiang Univ, Coll Energy Engn, Hangzhou 310027, Peoples R China; [Li, Dominique H.; Conte, Donatello] Univ Tours, LIFAT Lab, F-37000 Tours, France Zhejiang University; Universite de Tours Li, DH (corresponding author), Univ Tours, LIFAT Lab, F-37000 Tours, France. huangyuqi@zju.edu.cn; dominique.li@univ-tours.fr; niuhaoyi@zju.edu.cn; donatello.conte@univ-tours.fr CONTE, Donatello/0000-0003-4642-4768 National Natural Science Foundation of China [52076193] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Dr. Y. Huang would like to thank for the financial support by National Natural Science Foundation of China (52076193). 34 4 4 4 8 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement DEC 2021.0 186 110148 10.1016/j.measurement.2021.110148 0.0 SEP 2021 14 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation WK4PM Green Published, hybrid 2023-03-23 WOS:000709709300003 0
C Ding, RY; Yang, JH; Jiang, L; Qi, XJ Avidan, S; Brostow, G; Cisse, M; Farinella, GM; Hassner, T Ding, Runyu; Yang, Jihan; Jiang, Li; Qi, Xiaojuan DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation COMPUTER VISION - ECCV 2022, PT XXVII Lecture Notes in Computer Science English Proceedings Paper 17th European Conference on Computer Vision (ECCV) OCT 23-27, 2022 Tel Aviv, ISRAEL Domain adaptation; 3D semantic segmentation Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3D indoor semantic segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 8 popular Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT -> ScanNet and 3D-FRONT -> S3DIS. Code is available at https://github.com/CVMI-Lab/DODA. [Ding, Runyu; Yang, Jihan; Qi, Xiaojuan] Univ Hong Kong, Hong Kong, Peoples R China; [Jiang, Li] MPI Informat, Saarbrucken, Germany University of Hong Kong; Max Planck Society Qi, XJ (corresponding author), Univ Hong Kong, Hong Kong, Peoples R China. ryding@eee.hku.hk; jhyang@eee.hku.hk; lijiang@mpi-inf.mpg.de; xjqi@eee.hku.hk Hong Kong Research Grant Council - Early Career Scheme [27209621]; HKU Startup Fund; HKU Seed Fund for Basic Research Hong Kong Research Grant Council - Early Career Scheme; HKU Startup Fund; HKU Seed Fund for Basic Research This work has been supported by Hong Kong Research Grant Council - Early Career Scheme (Grant No. 27209621), HKU Startup Fund, and HKU Seed Fund for Basic Research. 73 0 0 2 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-19811-3; 978-3-031-19812-0 LECT NOTES COMPUT SC 2022.0 13687 284 303 10.1007/978-3-031-19812-0_17 0.0 20 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU4MD Green Submitted 2023-03-23 WOS:000903590200017 0
J Zuo, RW; Lv, ML; Li, YH; Nie, HY Zuo, Renwei; Lv, Maolong; Li, Yinghui; Nie, Hongyan Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance INTERNATIONAL JOURNAL OF CONTROL English Article Pure-feedback system; non-affine function; periodic disturbance; robust compensator DYNAMIC SURFACE CONTROL; TIME-DELAY This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme. [Zuo, Renwei; Li, Yinghui] Air Force Engn Univ, Coll Aeronaut Engn, Dept Flight Control & Elect Engn, Xian, Peoples R China; [Lv, Maolong] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands; [Nie, Hongyan] China United Northwest Inst Engn Design & Res Cor, Xian, Peoples R China Air Force Engineering University; Delft University of Technology Lv, ML (corresponding author), Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands. M.Lyu@tudelft.nl 37 0 0 2 6 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0020-7179 1366-5820 INT J CONTROL Int. J. Control SEP 2 2022.0 95 9 2554 2564 10.1080/00207179.2021.1918350 0.0 APR 2021 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems 4F6QU hybrid 2023-03-23 WOS:000648150600001 0
J Scharff, RBN; Doornbusch, RM; Doubrovski, EL; Wu, J; Geraedts, JMP; Wang, CCL Scharff, Rob B. N.; Doornbusch, Rens M.; Doubrovski, Eugeni L.; Wu, Jun; Geraedts, Jo M. P.; Wang, Charlie C. L. Color-Based Proprioception of Soft Actuators Interacting With Objects IEEE-ASME TRANSACTIONS ON MECHATRONICS English Article Actuators; Robot sensing systems; Shape; Strain; Image color analysis; Bellows; Color sensor; pneumatic actuator; shape prediction; sensor fusion; soft robotics STRAIN SENSORS; FEEDBACK; DESIGN Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments. [Scharff, Rob B. N.; Doornbusch, Rens M.; Doubrovski, Eugeni L.; Wu, Jun; Geraedts, Jo M. P.] Delft Univ Technol, Dept Design Engn, NL-2663 Delft, Netherlands; [Geraedts, Jo M. P.] Delft Univ Technol, Robot Inst, NL-2668 Delft, Netherlands; [Wang, Charlie C. L.] Delft Univ Technol, TU Delft, NL-2663 Delft, Netherlands; [Wang, Charlie C. L.] TU Delft Robot Inst, NL-2668 Delft, Netherlands; [Wang, Charlie C. L.] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China Delft University of Technology; Delft University of Technology; Delft University of Technology; Chinese University of Hong Kong Wang, CCL (corresponding author), Delft Univ Technol, TU Delft, NL-2663 Delft, Netherlands.;Wang, CCL (corresponding author), Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China. r.b.n.scharff@tudelft.nl; doornbuschrm@gmail.com; E.L.Doubrovski@tudelft.nl; j.wu-1@tudelft.nl; j.m.p.geraedts@tudelft.nl; charlie.c.l.wang@gmail.com Wu, Jun/L-2487-2017; Doubrovski, Zjenja/GRJ-9889-2022; Wang, Charlie C. L./B-3730-2010; Geraedts, Jo/AAA-7805-2020 Wu, Jun/0000-0003-4237-1806; Wang, Charlie C. L./0000-0003-4406-8480; Geraedts, Jo/0000-0002-1838-9048; Scharff, Rob/0000-0001-7996-4790 IDE faculty of TU Delft IDE faculty of TU Delft The work of C. C. L. Wang (Chair of Advanced Manufacturing) was supported by the IDE faculty of TU Delft. The work (Ph.D.) of R.B.N. Scharff was supported by a scholarship. 36 12 12 3 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1083-4435 1941-014X IEEE-ASME T MECH IEEE-ASME Trans. Mechatron. OCT 2019.0 24 5 1964 1973 10.1109/TMECH.2019.2929818 0.0 10 Automation & Control Systems; Engineering, Manufacturing; Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering JI0RN 2023-03-23 WOS:000493174900006 0
C Xu, JY; Li, ZR; Gao, L; Ma, JY; Liu, Q; Zhao, YA IEEE Xu, Jingyi; Li, Zirui; Gao, Li; Ma, Junyi; Liu, Qi; Zhao, Yanan A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles 2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) IEEE Intelligent Vehicles Symposium English Proceedings Paper 33rd IEEE Intelligent Vehicles Symposium (IEEE IV) JUN 05-09, 2022 Aachen, GERMANY IEEE,Bosch,IEEE Intelligent Transport Syst Soc,RWTH Aachen Univ, IKA,Mercedes Benz The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/ RL- based-Transferable- EMS.git. [Xu, Jingyi; Li, Zirui; Gao, Li; Ma, Junyi; Liu, Qi; Zhao, Yanan] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China; [Li, Zirui] Delft Univ Technol, Dept Transport & Planning, NL-2628 CD Delft, Netherlands Beijing Institute of Technology; Delft University of Technology Zhao, YA (corresponding author), Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China.;Li, ZR (corresponding author), Delft Univ Technol, Dept Transport & Planning, NL-2628 CD Delft, Netherlands. National Key R&D Program of China [2018YFB0105205-02, 2017YFC0804803, 2017YFC0804808] National Key R&D Program of China This work was supported by the National Key R&D Program of China under Grant 2018YFB0105205-02, Grant 2017YFC0804803 and Grant 2017YFC0804808. 35 2 2 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-0587 978-1-6654-8821-1 IEEE INT VEH SYM 2022.0 470 477 10.1109/IV51971.2022.9827042 0.0 8 Automation & Control Systems; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Transportation BT8NW Green Submitted 2023-03-23 WOS:000854106700066 0
J Lin, ML; Liu, MM; Dong, LJ; Caruso, F; Li, SH Lin, Mingli; Liu, Mingming; Dong, Lijun; Caruso, Francesco; Li, Songhai Modeling intraspecific variation in habitat utilization of the Indo-Pacific humpback dolphin using self-organizing map ECOLOGICAL INDICATORS English Article Artificial neural network (ANN); Estuary; Ecotype; Habitat modelling; Marine protected areas (MPAs) ARTIFICIAL NEURAL-NETWORKS; NORTHERN BEIBU GULF; SOUSA-CHINENSIS; SPATIAL-DISTRIBUTION; TURSIOPS-TRUNCATUS; SPECIES RICHNESS; MARINE MAMMALS; HAINAN ISLAND; CALIFORNIA; ECOTYPES Coastal cetaceans are recognized as ecologically important species and have been the target for environmental monitoring programs and conservation strategies. Although supervised models have increasingly been used to better monitor complexity relationships between cetaceans and their habitats, the development of unsupervised learning techniques that have significant advantages in visualizing, grouping and reducing the dimensionality of data has been overlooked. Here, using the unsupervised artificial neural network of self-organizing map (SOM), we examined the intraspecific variation of habitat utilization among three geographically neighboring pop-ulations (waters southwest of Hainan Island abbreviated as WS Hainan, Sanniang Bay, and Zhanjiang waters) of the Indo-Pacific humpback dolphin (Sousa chinensis) in the northern South China Sea. The results showed that the population inhabiting in WS Hainan occupied a largest area with scattered patterns comparing to the other two in Sanniang Bay and Zhanjiang waters. The SOM analysis further showed that the examined populations in different waters have distinct habitat characteristics. S. chinensis in WS Hainan was sighted in deeper water with higher salinity, whereas the population in Sanniang Bay inhabited in estuary with lower pH and salinity. More complicated distributed patterns and environmental heterogeneity were observed in Zhanjiang waters, where the dolphins distributed contractively in a small area at the entrance of Zhanjiang Port and dispersed in another large area in Leizhou Bay. Correspondently, part of the sightings in Zhanjiang waters had similar habitat with those in WS Hainan, while others were reported in more inshore, deeper and muddied waters. Based on these findings, we hypothesized that S. chinensis can be divided into two ecotypes: estuarine and non-estuarine. Here, we confirmed that the SOM can well identify the habitat differentiation in S. chinensis, and therefore suggested it is a powerful modeling tool for cetacean monitoring program. Our findings contribute to a specific habitat conservation strategy that one integrated protected area would be better for S. chinensis in Sanniang Bay and Zhanjiang waters, while several small protected areas with connecting corridors were more suitable for population in WS Hainan. [Lin, Mingli; Liu, Mingming; Dong, Lijun; Caruso, Francesco; Li, Songhai] Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Hainan 572000, Peoples R China; [Caruso, Francesco] Stn Zool Anton Dohrn, Dept Marine Anim Conservat & Publ Engagement, I-80121 Naples, Italy Chinese Academy of Sciences; Institute of Deep-Sea Science & Engineering, CAS; Stazione Zoologica Anton Dohrn di Napoli Li, SH (corresponding author), Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Hainan 572000, Peoples R China. lish@idsse.ac.cn Caruso, Francesco/AAB-4845-2019; li, song hai/A-2652-2012 Caruso, Francesco/0000-0001-6881-3786; li, song hai/0000-0003-4977-1722 National Natural Science Foundation of China [41406182, 41306169, 41422604]; Development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences [E072010101]; Ocean Park Conservation Foundation Hong Kong [AW02-1920] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences; Ocean Park Conservation Foundation Hong Kong We are grateful to Tang Xiaoming, Gong Zining, Xu Xiao, Dong Jianchen, Li Kuan and Xu Wanxue for their assistance with the field survey. This research was financially supported by the National Natural Science Foundation of China (41406182, 41306169 and 41422604), the development project of Institute of Deep-sea Science and Engineering Chinese Academy of Sciences (E072010101) and Ocean Park Conservation Foundation Hong Kong (AW02-1920). This work was performed under an Ethical Statement with the number of IDSSE-SYLL-MMMBL-01. 77 0 0 9 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1470-160X 1872-7034 ECOL INDIC Ecol. Indic. NOV 2022.0 144 109466 10.1016/j.ecolind.2022.109466 0.0 11 Biodiversity Conservation; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology 5K8EA gold 2023-03-23 WOS:000869952500007 0
J Xie, YX; Wang, Y; Hu, CR; Shan, CF; Li, T; Hu, YJ Xie, Yixiang; Wang, Yan; Hu, Chuanrui; Shan, Caifeng; Li, Teng; Hu, Yongjian Cross-Camera Person Re-Identification With Body-Guided Attention Network IEEE SENSORS JOURNAL English Article Person re-identification; convolutional neural network; body-guided attention network Various challenges exist throughout person re-identification (ReID) process, including background clutters, illumination variation, pose variation, occlusion, etc. Addressing these problems, this paper explores the incorporation of human attention mechanism in person ReID and proposes an attention-aware model named Body-guided Attention Network (BANet). The proposed attention is based on the body masked images which are obtained by a reliable pixel-level segmentation strategy. To optimize the feature representation learning so as to pay more attention to the discriminative details of human body, BANet is built. It is composed of three attention branches. In order to guide attention learning layer by layer, these branches are applied to the convolution features of different levels. The proposed BANet aims to fully utilize fine-grained information of body region to guide the final process of feature extraction. Extensive experiments on benchmarks including CUHK03, Market1501 and DukeMTMC-reID show that BANet can achieve state-of-the-art performance, which validates the importance of attention mechanism in person ReID. [Xie, Yixiang; Hu, Chuanrui] Anhui Univ, Sch Elect Engn & Automat, Hefei 230601, Peoples R China; [Wang, Yan] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China; [Shan, Caifeng] Philips Res, High Tech Campus 34, NL-5656 AE Eindhoven, Netherlands; [Li, Teng] Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China; [Hu, Yongjian] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China; [Hu, Yongjian] Sino Singapore Int Joint Res Inst, Guangzhou 510700, Peoples R China Anhui University; Anhui University; Philips; Philips Research; Anhui University; South China University of Technology Li, T (corresponding author), Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China. liteng@ahu.edu.cn; eeyjhu@scut.edu.cn Shan, Caifeng/W-6178-2019; xie, Yixiang/GPT-4111-2022 Shan, Caifeng/0000-0002-2131-1671; xie, yixiang/0000-0001-5759-0169 National Key R&D Program of China [2018YFB1305804]; Science and Technology Foundation of Guangzhou Huangpu Development District [2017GH22] National Key R&D Program of China; Science and Technology Foundation of Guangzhou Huangpu Development District This work was supported by the National Key R&D Program of China under Grant 2018YFB1305804, and in part by the Science and Technology Foundation of Guangzhou Huangpu Development District under Grant 2017GH22. The associate editor coordinating the review of this article and approving it for publication was Dr. Amitava Chatterjee. 49 6 6 0 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. JAN 1 2020.0 20 1 359 368 10.1109/JSEN.2019.2942106 0.0 10 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics KX9XC 2023-03-23 WOS:000522227400041 0
J Zeng, Y; van der Lubbe, JCA; Loog, M Zeng, Y.; van der Lubbe, J. C. A.; Loog, M. Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings MACHINE VISION AND APPLICATIONS English Article Convolutional neural networks; Image reconstruction; Pixel-wise prediction; Van Gogh's drawings RESTORATION This paper investigates the reconstruction of Van Gogh's drawings which have been degraded in the course of time due to aging problems, like ink fading and discoloration. Learning to predict the past and original appearances of degraded drawings can help to envisage how the artist's work may have looked at the time of creation. In this paper, we use reproductions as reference information for the past appearances of drawings and consider the reconstruction of drawings as a pixel-wise prediction problem. We present an approach to automatically predict the past appearances of drawings. This approach brings together methods from multi-resolution image analysis and deep convolutional neural networks (CNNs) for addressing the task of pixel-wise prediction. Our experiments first investigate how scale affects prediction performance of the proposed multi-scale CNN framework and then demonstrate the reconstruction capability of the multi-scale CNN framework. The results demonstrate that the predictive reconstruction of degraded images is a feasible endeavor. [Zeng, Y.] Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Shenzhen, Peoples R China; [Zeng, Y.; van der Lubbe, J. C. A.; Loog, M.] Delft Univ Technol, Delft, Netherlands Southern University of Science & Technology; Delft University of Technology Zeng, Y (corresponding author), Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Shenzhen, Peoples R China.;Zeng, Y (corresponding author), Delft Univ Technol, Delft, Netherlands. zengy3@sustech.edu.cn; J.C.A.vanderLubbe@tudelft.nl; M.Loog@tudelft.nl Netherlands Organization for Scientific Research (NWO) [323.54.004] Netherlands Organization for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)) This research is a part of the REVIGO Project, supported by the Netherlands Organization for Scientific Research (NWO; Grant 323.54.004) in the context of the Science4Arts research program. 32 6 6 1 9 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0932-8092 1432-1769 MACH VISION APPL Mach. Vis. Appl. OCT 2019.0 30 7-8 1229 1241 10.1007/s00138-019-01047-3 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering JB6DW 2023-03-23 WOS:000488655800009 0
J Jin, Z; Huang, JJ; Xiong, AL; Pang, YX; Wang, WJ; Ding, BC Jin, Zhi; Huang, Junjia; Xiong, Aolin; Pang, Yuxian; Wang, Wenjin; Ding, Beichen Attention guided deep features for accurate body mass index estimation PATTERN RECOGNITION LETTERS English Article Deep features; BMI Estimation; Attention Mechanism; Deep Features WEIGHT; CANCER Body Mass Index (BMI) has been widely used as an indicator to evaluate the health condition of individuals, classifying a person as underweight, normal weight, overweight, or obese. Recently, several methods have been proposed to obtain BMI values based on the visual information, e.g., face images or 3D body images. These methods by extrapolating anthropometric features from face images or 3D body images are advanced in BMI estimation accuracy, however, they suffer from the difficulties of obtaining the required data due to the privacy issue or the 3D camera limitations. Moreover, these methods are hard to achieve satisfactory performance when they are directly applied to 2D body images. To tackle these problems, we propose to estimate BMI results from 2D body images by an end-to-end Convolutional Neural Network (CNN) with attention guidance. The proposed method is evaluated on our collected dataset. Extensive experiments confirm that the proposed framework outperforms state-of-the-art approaches in most cases. 0 2022 Elsevier B.V. All rights reserved. [Jin, Zhi; Huang, Junjia; Xiong, Aolin; Pang, Yuxian; Ding, Beichen] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen 518107, Peoples R China; [Jin, Zhi] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China; [Wang, Wenjin] Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands Sun Yat Sen University; Sun Yat Sen University; Eindhoven University of Technology Ding, BC (corresponding author), Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen 518107, Peoples R China. dingbch@mail.sysu.edu.cn National Natural Sci-ence Foundation of China [62071500, 61701313]; Shenzhen Science and Technology Program [GXWD20201231165807008, 2021A26] National Natural Sci-ence Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Science and Technology Program Acknowledgments This work was supported by the National Natural Sci-ence Foundation of China (No. 62071500 , No. 61701313) and Shenzhen Science and Technology Program (Grant No. GXWD20201231165807008, 2021A26) . 38 0 0 2 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. FEB 2022.0 154 22 28 10.1016/j.patrec.2022.01.002 0.0 JAN 2022 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0N9FB 2023-03-23 WOS:000783134200002 0
J Chen, LH; Vroomen, J Chen, Lihan; Vroomen, Jean Intersensory binding across space and time: A tutorial review ATTENTION PERCEPTION & PSYCHOPHYSICS English Review Multisensory Processing; Temporal processing; Spatial localization MODULATE AUDIOVISUAL INTEGRATION; AUDITORY-VISUAL INTERACTION; SUPERIOR TEMPORAL SULCUS; MISMATCH NEGATIVITY MMN; MULTISENSORY INTEGRATION; SOUND LOCALIZATION; SPATIAL ATTENTION; APPARENT MOTION; TACTILE CAPTURE; CROSSMODAL INTERACTION Spatial ventriloquism refers to the phenomenon that a visual stimulus such as a flash can attract the perceived location of a spatially discordant but temporally synchronous sound. An analogous example of mutual attraction between audition and vision has been found in the temporal domain, where temporal aspects of a visual event, such as its onset, frequency, or duration, can be biased by a slightly asynchronous sound. In this review, we examine various-manifestations of spatial and temporal attraction between the senses (both direct effects and aftereffects), and we discuss important constraints on the occurrence of these effects. Factors that potentially modulate ventriloquism-such as attention, synesthetic correspondence, and other cognitive factors-are described. We trace theories and models of spatial and temporal ventriloquism, from the traditional unity assumption and modality appropriateness hypothesis to more recent Bayesian and neural network approaches. Finally, we summarize recent evidence probing the underlying neural mechanisms of spatial and temporal ventriloquism. [Chen, Lihan] Peking Univ, Dept Psychol, Beijing 100871, Peoples R China; [Chen, Lihan] Peking Univ, Minist Educ, Key Lab Machine Percept, Beijing 100871, Peoples R China; [Vroomen, Jean] Tilburg Univ, Dept Psychol, NL-5000 LE Tilburg, Netherlands Peking University; Peking University; Tilburg University Vroomen, J (corresponding author), Tilburg Univ, Dept Psychol, POB 90153, NL-5000 LE Tilburg, Netherlands. clh20000@gmail.com; j.vroomen@uvt.nl Vroomen, Jean/K-1033-2013 Vroomen, Jean/0000-0001-5923-5988; Chen, Lihan/0000-0002-0337-4177 229 25 27 1 78 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1943-3921 1943-393X ATTEN PERCEPT PSYCHO Atten. Percept. Psychophys. JUL 2013.0 75 5 790 811 10.3758/s13414-013-0475-4 0.0 22 Psychology; Psychology, Experimental Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Psychology 194YG 23709064.0 Bronze 2023-03-23 WOS:000322668000002 0
C Guizzo, E; Marinoni, C; Pennese, M; Ren, XL; Zheng, XG; Zhang, C; Masiero, B; Uncini, A; Comminiello, D IEEE Guizzo, Eric; Marinoni, Christian; Pennese, Marco; Ren, Xinlei; Zheng, Xiguang; Zhang, Chen; Masiero, Bruno; Uncini, Aurelio; Comminiello, Danilo L3DAS22 CHALLENGE: LEARNING 3D AUDIO SOURCES IN A REAL OFFICE ENVIRONMENT 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) MAY 22-27, 2022 Singapore, SINGAPORE Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc Grand Challenge; 3D Audio; Ambisonics; Speech Enhancement; Sound Event Localization and Detection SOUND EVENT LOCALIZATION The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the tasks of the L3DAS21 edition(1). We generated a new dataset, which maintains the same general characteristics of L3DAS21 datasets, but with an extended number of data points and adding constrains that improve the baseline model's efficiency and overcome the major difficulties encountered by the participants of the previous challenge. We updated the baseline model of Task 1, using the architecture that ranked first in the previous challenge edition. We wrote a new supporting API, improving its clarity and ease-of-use. In the end, we present and discuss the results submitted by all participants. L3DAS22 Challenge website: www.l3das.com/icassp2022. [Guizzo, Eric; Marinoni, Christian; Pennese, Marco; Uncini, Aurelio; Comminiello, Danilo] Sapienza Univ Rome, DIET Dept, Rome, Italy; [Ren, Xinlei; Zheng, Xiguang; Zhang, Chen] Kuaishou Technol Co, Beijing, Peoples R China; [Masiero, Bruno] Univ Estadual Campinas, DECOM Dept, Campinas, Brazil Sapienza University Rome; Universidade Estadual de Campinas Comminiello, D (corresponding author), Sapienza Univ Rome, DIET Dept, Rome, Italy. danilo.comminiello@uniroma1.it masiero, bruno/D-6364-2014 masiero, bruno/0000-0002-2246-4450 Progetti di Ricerca Grandi of Sapienza University of Rome [RG11916B88E1942F]; Sao Paulo Research Foundation (FAPESP) [2017/08120-6] Progetti di Ricerca Grandi of Sapienza University of Rome; Sao Paulo Research Foundation (FAPESP)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)) This work has been partly supported by Progetti di Ricerca Grandi of Sapienza University of Rome under grant number RG11916B88E1942F and the Sao Paulo Research Foundation (FAPESP) under grant number 2017/08120-6. 26 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-6654-0540-9 INT CONF ACOUST SPEE 2022.0 9186 9190 10.1109/ICASSP43922.2022.9746872 0.0 5 Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering BT9UB Green Submitted 2023-03-23 WOS:000864187909100 0
J Dai, JY; Wu, ZT; Karimi, HR; Jiang, BP; Lu, GQ Dai, Jinyu; Wu, Zhengtian; Karimi, Hamid Reza; Jiang, Baoping; Lu, Guangquan An approximation lagrangian-based algorithm for the maximum clique problem via deterministic annealing neural network JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS English Article BOUND ALGORITHM; LOCAL SEARCH; BRANCH The maximum clique problem is an NP hard combinatorial optimization problem, which is widely used in industrial and management processes. In order to approach the solution of the maximum clique problem, a deterministic annealing algorithm is proposed. When the barrier parameter is reduced from a large positive number to zero, the algorithm can track the minimum path of the obstacle problem to obtain a high-quality solution, which is a continuous method. The global convergence iteration of the Lagrange multiplier can be performed to obtain the minimum point of the obstacle problem in the feasible descent direction for any given positive value of the barrier parameter and has a desired property that it automatically satisfies the upper and lower bounds of variables if the steplength is a number that is between 0 and 1. Numerical results are provided to illustrate the efficiency of the proposed algorithm. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. [Dai, Jinyu; Wu, Zhengtian; Jiang, Baoping] Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Lu, Guangquan] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China Suzhou University of Science & Technology; Polytechnic University of Milan; Guangxi Normal University Wu, ZT (corresponding author), Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China.;Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. wzht8@mail.usts.edu.cn; hamidreza.karimi@polimi.it 51 1 1 4 6 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-0032 1879-2693 J FRANKLIN I J. Frankl. Inst.-Eng. Appl. Math. AUG 2022.0 359 12 6080 6098 10.1016/j.jfranklin.2022.06.009 0.0 JUL 2022 19 Automation & Control Systems; Engineering, Multidisciplinary; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics 3M0MJ 2023-03-23 WOS:000835151700012 0
J Ren, YY; Milo, V; Wang, ZQ; Xu, HY; Ielmini, D; Zhao, XN; Liu, YC Ren, Yanyun; Milo, Valerio; Wang, Zhongqiang; Xu, Haiyang; Ielmini, Daniele; Zhao, Xiaoning; Liu, Yichun Analytical Modeling of Organic-Inorganic CH(3)NH(3)Pbl(3) Perovskite Resistive Switching and its Application for Neuromorphic Recognition ADVANCED THEORY AND SIMULATIONS English Article analytical modeling; neuromorphic computing; organic-inorganic perovskites; resistive switching CROSSBAR ARRAY; MEMORIES This paper presents an analytical model of resistive switching in organic-inorganic CH(3)NH(3)Pbl(3), perovskite. It is interpreted that the resistive switching phenomenon is due to the formation/rupture of iodine vacancy-based conductive filament (CF) propagating in both vertical and lateral directions. Set and reset processes are explained in the model by the evolution of the CF length and radius driven by electrical and thermal forces. The model-based simulation results can describe the experimental results, providing an estimate of several switching parameters such as the activation energy of iodine vacancy migration and the CF diameter. Learning in a two-transistor/one-resistor synapse structure is demonstrated by simulations. Finally, the neuromorphic recognition of multiple patterns is demonstrated through a two-layer neural network consisting of 5625 presynaptic neurons and four postsynaptic neurons. [Ren, Yanyun; Wang, Zhongqiang; Xu, Haiyang; Zhao, Xiaoning; Liu, Yichun] Northeast Normal Univ, Ctr Adv Optoelect Funct Mat Res, Key Lab UV Light Emitting Mat & Technol, Minist Educ, Changchun 130024, Jilin, Peoples R China; [Milo, Valerio; Ielmini, Daniele] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Italian Univ Nanoelect Team, I-20133 Milan, Italy Northeast Normal University - China; Polytechnic University of Milan Wang, ZQ; Xu, HY (corresponding author), Northeast Normal Univ, Ctr Adv Optoelect Funct Mat Res, Key Lab UV Light Emitting Mat & Technol, Minist Educ, Changchun 130024, Jilin, Peoples R China.;Ielmini, D (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Italian Univ Nanoelect Team, I-20133 Milan, Italy. wangzq752@nenu.edu.cn; hyxu@nenu.edu.cn; daniele.ielmini@polimi.it liu, yi/GXE-9662-2022; Milo, Valerio/W-5044-2019 Milo, Valerio/0000-0002-3305-2742; Wang, Zhongqiang/0000-0002-1133-6058 NSFC for Excellent Young Scholars [51422201]; NSFC Program [51732003, 51372035, 61774031, 61574031, 61404026, 11604044]; 111 Project [B13013]; Jilin Province [20160101324JC]; European Research Council (ERC) under the European Union [648635] NSFC for Excellent Young Scholars; NSFC Program(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project); Jilin Province; European Research Council (ERC) under the European Union(European Research Council (ERC)) We thank Dr. Stefano Ambrogio for the helpful discussions on the pattern learning and recognition. This work was supported by the NSFC for Excellent Young Scholars (No. 51422201), the NSFC Program (Nos. 51732003, 51372035, 61774031, 61574031, 61404026, and 11604044), the 111 Project (No. B13013), the Fund from Jilin Province (No. 20160101324JC), and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 648635). 36 35 35 2 55 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 2513-0390 ADV THEOR SIMUL Adv. Theory Simul. APR 2018.0 1 4 1700035 10.1002/adts.201700035 0.0 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics HC6BG Green Submitted 2023-03-23 WOS:000451885600003 0
J Li, YY; Melgani, F; He, BB Li, Youyou; Melgani, Farid; He, Binbin CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Object detection; Convolution; Remote sensing; Feature extraction; Training; Image resolution; Support vector machines; Convolutional neural network (CNN); convolutional support vector machine (CSVM); object detection; remote sensing; very high resolution (VHR) CONVOLUTIONAL NEURAL-NETWORKS; SPATIAL-RESOLUTION; CLASSIFICATION; MODIS Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. [Li, Youyou; He, Binbin] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China; [Melgani, Farid] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy University of Electronic Science & Technology of China; University of Trento He, BB (corresponding author), Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China.;Melgani, F (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy. liyouyou@std.uestc.edu.cn; melgani@disi.unitn.it; binbinhe@uestc.edu.cn He, Binbin/0000-0002-0668-6520 National Natural Science Foundation of China [41671361, 41601373] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 41671361 and Grant 41601373. 46 6 6 3 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEPT 2020.0 58 9 6059 6070 10.1109/TGRS.2020.2972289 0.0 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology NH1SA 2023-03-23 WOS:000564455700005 0
J Chen, H; Yang, KL; Hu, WJ; Bai, J; Wang, KW Chen Hao; Yang Kailun; Hu Weijian; Bai Jian; Wang Kaiwei Semantic Visual Odometry Based on Panoramic Annular Imaging ACTA OPTICA SINICA Chinese Article machine vision; visual odometry; panoramic annular lens; semantic segmentation; pose estimation Visual odometry is commonly used in various applications including intelligent robots and self-driving cars. However, traditional visual odometry algorithms based on the pinhole camera with a limited field of view (FOV) arc usually fragile to moving objects in the environment and fast rotation of the camera, resulting in insufficient robustness and accuracy in practical use. This paper proposes panoramic annular semantic visual odometry as a solution to this problem. Using the panoramic annular imaging system with ultra-wide FOV into visual odometry and coupling semantic information provided by the panoramic annular semantic segmentation based on deep learning into each module of the algorithm, the effect of moving objects and fast rotation is reduced; then, the performance of visual odometry in dealing with these challenging scenarios can be improved. Compared with traditional visual odometry systems, experimental results show that the proposed algorithm achieves more accurate and robust pose estimation in realistic scenarios. [Chen Hao; Hu Weijian; Bai Jian; Wang Kaiwei] Zhejiang Univ, Natl Engn Res Ctr Opt Instrumentat, Hangzhou 310058, Zhejiang, Peoples R China; [Yang Kailun] Karlsruhe Inst Technol, Inst Anthropomat & Robot, D-76131 Karlsruhe, Germany Zhejiang University; Helmholtz Association; Karlsruhe Institute of Technology Wang, KW (corresponding author), Zhejiang Univ, Natl Engn Res Ctr Opt Instrumentat, Hangzhou 310058, Zhejiang, Peoples R China. wangkaiwei@zju.edu.cn 35 1 1 1 3 CHINESE LASER PRESS SHANGHAI PO BOX 800-211, SHANGHAI, 201800, PEOPLES R CHINA 0253-2239 ACTA OPT SIN Acta Opt. Sin. NOV 2021.0 41 22 2215002 10.3788/AOS202141.2215002 0.0 11 Optics Emerging Sources Citation Index (ESCI) Optics 3Q5QK 2023-03-23 WOS:000838285300016 0
J Li, YD; Hu, WD; Zhang, X; Xu, ZH; Ni, JQ; Ligthart, LP Li, Yade; Hu, Weidong; Zhang, Xin; Xu, Zhihao; Ni, Jiaqi; Ligthart, Leo P. Adaptive terahertz image super-resolution with adjustable convolutional neural network OPTICS EXPRESS English Article QUALITY ASSESSMENT During the real-aperture-scanning imaging process, terahertz (THz) images are often plagued with the problem of low spatial resolution. Therefore, an accommodative super-resolution framework for THz images is proposed. Specifically, the 3D degradation model for the imaging system is firstly proposed by incorporating the focused THz beam distribution, which determines the relationship between the imaging range and the corresponding image restoration level. Secondly, an adjustable CNN is introduced to cope with this range dependent super-resolution problem. By simply tuning an interpolation parameter, the network can be adjusted to produce arbitrary restoration levels between the trained fixed levels without extra training. Finally, by selecting the appropriate interpolation coefficient according to the measured imaging range, each THz image can be coped with its matched network and reach the outstanding super-resolution effect. Both the simulated and real tested data, acquired by a 160 similar to 220 GHz imager, have been used to demonstrate the superiority of our method. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement [Li, Yade; Hu, Weidong; Zhang, Xin; Xu, Zhihao; Ni, Jiaqi] Beijing Inst Technol, Beijing Key Lab Millimeter Wave & Terahertz Techn, Beijing 100081, Peoples R China; [Ligthart, Leo P.] Delft Univ Technol, Fac Elect Engn, NL-2600 GA Delft, Netherlands Beijing Institute of Technology; Delft University of Technology Hu, WD (corresponding author), Beijing Inst Technol, Beijing Key Lab Millimeter Wave & Terahertz Techn, Beijing 100081, Peoples R China. hoowind@bit.edu.cn Li, Yade/0000-0003-0632-2898 National Natural Science Foundation of China [61527805, 61731001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China (61527805, 61731001). 36 18 19 9 43 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUL 20 2020.0 28 15 22200 22217 10.1364/OE.394943 0.0 18 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics NC0TW 32752486.0 gold 2023-03-23 WOS:000560926100073 0
J Liu, KX; Zhu, C; Tao, XY; Bruniaux, P; Zeng, XY; Wang, JP Liu, Kaixuan; Zhu, Chun; Tao, Xuyuan; Bruniaux, Pascal; Zeng, Xianyi; Wang, Jianping A novel evaluation technique for human body perception of clothing fit MULTIMEDIA TOOLS AND APPLICATIONS English Article; Early Access Fit evaluation; Decision tree C4; 5; Machine learning; Fashion design; Data learning GARMENT PATTERN; DESIGN; SIZE; OPTIMIZATION; INTENTIONS Fit evaluation plays an important role in garment products development and sales. Effective clothing fit evaluation methods can reduce the development cost of apparel products and the return rate of online apparel sales. In this research, we proposed an intelligent fit evaluation technology to predict clothing fit. The mathematical relationship model between clothing fit levels and indexes reflecting the clothing fit levels was constructed by using decision tree C4.5 algorithm. Then, two experiments were carried out to collect input and output training data. After learning from the collected data, the proposed model can predict clothing fit accurately. Next, we validated our proposed model's prediction accuracy using K-fold cross validation. Finally, we gave two applications of the proposed model for clothing products development and shopping online. Results show that our proposed method has high prediction accuracy and less requirement for the number of learning samples, and can predict clothing fit automatically and rapidly without real try-on. [Liu, Kaixuan; Zhu, Chun] Xian Polytech Univ, Apparel & Art Design Coll, Xian 710048, Peoples R China; [Liu, Kaixuan] Xian Polytech Univ, Shanxi Key Lab Intelligent Clothing Design, Xian 710048, Peoples R China; [Liu, Kaixuan; Tao, Xuyuan; Bruniaux, Pascal; Zeng, Xianyi] GEMTEX Lab, ENSAIT, F-59100 Roubaix, France; [Zhu, Chun; Wang, Jianping] Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China Xi'an Polytechnic University; Xi'an Polytechnic University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Donghua University Liu, KX (corresponding author), Xian Polytech Univ, Apparel & Art Design Coll, Xian 710048, Peoples R China.;Liu, KX (corresponding author), Xian Polytech Univ, Shanxi Key Lab Intelligent Clothing Design, Xian 710048, Peoples R China.;Liu, KX (corresponding author), GEMTEX Lab, ENSAIT, F-59100 Roubaix, France. liukaixuan819@163.com Zeng, Xianyi/0000-0002-3236-6766; liu, kaixuan/0000-0003-4105-8324 National Natural Science Foundation of China [61806161]; Natural Science Basic Research Program of Shaanxi Province, China [2019JQ-848]; Innovation Ability Support Plan of Shaanxi Province-young Science and Technology Star Project, China [2020KJXX-083]; Youth Innovation Team of Shaanxi Universities, China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Program of Shaanxi Province, China; Innovation Ability Support Plan of Shaanxi Province-young Science and Technology Star Project, China; Youth Innovation Team of Shaanxi Universities, China This paper was financially supported by the National Natural Science Foundation of China (No. 61806161), the Natural Science Basic Research Program of Shaanxi Province, China (No. 2019JQ-848), the Innovation Ability Support Plan of Shaanxi Province-young Science and Technology Star Project, China (No. 2020KJXX-083), China and the Youth Innovation Team of Shaanxi Universities, China. 33 0 0 2 2 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. 10.1007/s11042-023-14530-x 0.0 FEB 2023 13 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 8Z1WV 2023-03-23 WOS:000933178200002 0
J Guo, JF; Pan, JF; Guo, JX; Gu, F; Kuusisto, J Guo, Jianfeng; Pan, Jiaofeng; Guo, Jianxin; Gu, Fu; Kuusisto, Jari Measurement framework for assessing disruptive innovations TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE English Article Disruptive innovation; Potential disruptiveness; Multidimensional measurement; Technological feature; Marketplace dynamics; External environment AUGMENTED REALITY; ECONOMIC-GROWTH; BIG DATA; TECHNOLOGIES; EMERGENCE; INDUSTRY; BUSINESS; IDENTIFICATION; DETERMINANTS; CAPABILITIES Assessing potential disruptiveness of innovations is an important but challenging task for incumbents. However, the extant literature focuses only on technological and marketplace aspects, and most of the documented methods tend to be case specific. In this study, we present a multidimensional measurement framework to assess the disruptive potential of product innovations. The framework is designed based on the concept that the nature of disruptive innovations is multidimensional. Three aspects are considered, i.e., technological features, marketplace dynamics and external environment. Ten indicators of the three categories are proposed and then connected based on the conceptual and literature analysis. Three innovations, namely, WeChat (successful), Modularised Mobile Phone (failed) and Virtual Reality/Augmented Reality (ongoing), are selected as case studies. A panel of industrial experts with PhD degree in engineering is surveyed. The survey results are calculated and analysed according to the framework and then compared against the developments of the innovations. We also check the robustness of this framework by surveying other groups of people, and the results are nearly identical to the previous findings. This study enables a systematic assessment of disruptive potential of innovations using the framework, providing insights for decisions in product launch and resource allocation. [Guo, Jianfeng; Pan, Jiaofeng; Guo, Jianxin] Chinese Acad Sci, Inst Sci & Dev, Beijing, Peoples R China; [Guo, Jianfeng; Pan, Jiaofeng] Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing, Peoples R China; [Gu, Fu] Zhejiang Univ, Dept Ind & Syst Engn, Hangzhou, Zhejiang, Peoples R China; [Gu, Fu] Zhejiang Univ, Natl Inst Innovat Management, Hangzhou, Zhejiang, Peoples R China; [Kuusisto, Jari] Univ Vaasa, Vaasa, Finland Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Zhejiang University; Zhejiang University; University of Vaasa Gu, F (corresponding author), Zhejiang Univ, Dept Ind & Syst Engn, Hangzhou, Zhejiang, Peoples R China. gufu@zju.edu.cn Kuusisto, Jari/AAO-6912-2020 Kuusisto, Jari/0000-0002-5269-4134; Gu, Fu/0000-0003-3062-1935 Chinese Key Research Plan Project [2017YFB1400302]; National Natural Science Foundation of China [71832013, 71671180, 71834006] Chinese Key Research Plan Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is financially supported by Chinese Key Research Plan Project (no. 2017YFB1400302), National Natural Science Foundation of China (nos. 71832013, 71671180, and 71834006). 102 71 74 42 176 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0040-1625 1873-5509 TECHNOL FORECAST SOC Technol. Forecast. Soc. Chang. FEB 2019.0 139 250 265 10.1016/j.techfore.2018.10.015 0.0 16 Business; Regional & Urban Planning Social Science Citation Index (SSCI) Business & Economics; Public Administration HI9CR Green Published, hybrid 2023-03-23 WOS:000456753400025 0
C Abbasi-Sureshjani, S; Smit-Ockeloen, I; Zhang, J; Romeny, BT Kamel, M; Campilho, A Abbasi-Sureshjani, Samaneh; Smit-Ockeloen, Iris; Zhang, Jiong; Romeny, Bart Ter Haar Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images IMAGE ANALYSIS AND RECOGNITION (ICIAR 2015) Lecture Notes in Computer Science English Proceedings Paper 12th International Conference on Image Analysis and Recognition (ICIAR) JUL 22-24, 2015 Niagara Falls, CANADA Assoc Image & Machine Intelligence Scanning laser ophthalmoscope; Primary visual cortex; Anisotropic wavelets; Multi-scale; Orientation score; Left-invariant Gaussian derivatives; Blood vessel segmentation; Diabetic retinopathy VESSEL SEGMENTATION; WAVELET We propose a novel Brain-Inspired Multi-Scales and Multi-Orientations (BIMSO) segmentation technique for the retinal images taken with laser ophthalmoscope (SLO) imaging cameras. Conventional retinal segmentation methods have been designed mainly for color RGB images and they often fail in segmenting the SLO images because of the presence of noise in these images. We suppress the noise and enhance the blood vessels by lifting the 2D image to a joint space of positions and orientations (SE(2)) using the directional anisotropic wavelets. Then a neural network classifier is trained and tested using several features including the intensity of pixels, filter response to the wavelet and multi-scale left-invariant Gaussian derivatives jet in SE(2). BIMSO is robust against noise, non-uniform luminosity and contrast variability. In addition to preserving the connections, it has higher sensitivity and detects the small vessels better compared to state-of-the-art methods for both RGB and SLO images. [Abbasi-Sureshjani, Samaneh; Smit-Ockeloen, Iris; Zhang, Jiong; Romeny, Bart Ter Haar] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands; [Romeny, Bart Ter Haar] Northeastern Univ, Biomed & Informat Engn, Shenyang, Peoples R China Eindhoven University of Technology; Northeastern University - China Abbasi-Sureshjani, S (corresponding author), Eindhoven Univ Technol, POB 513, NL-5600 MB Eindhoven, Netherlands. s.abbasi@tue.nl; j.zhang1@tue.nl; B.M.terhaarRomeny@tue.nl Romenij, Bart M. ter Haar/A-5323-2013 Zhang, Jiong/0000-0002-1922-9118 16 33 33 1 6 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 978-3-319-20801-5; 978-3-319-20800-8 LECT NOTES COMPUT SC 2015.0 9164 325 334 10.1007/978-3-319-20801-5_35 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematical & Computational Biology; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematical & Computational Biology; Robotics BD8QH 2023-03-23 WOS:000364183400035 0
J Zhang, ZF; He, MY; Liu, AR; Singh, HK; Ramakrishnan, KR; Hui, D; Shankar, K; Morozov, EV Zhang, Zhifang; He, Mengyue; Liu, Airong; Singh, Hemant Kumar; Ramakrishnan, Karthik Ram; Hui, David; Shankar, Krishna; Morozov, Evgeny V. Vibration-based assessment of delaminations in FRP composite plates COMPOSITES PART B-ENGINEERING English Article Delamination detection; Structural health monitoring; Natural frequency; Composite plates; Genetic algorithm; Artificial neural network NEURAL-NETWORKS; NATURAL FREQUENCIES; INVERSE ALGORITHMS; ACOUSTIC-EMISSION; DAMAGE DETECTION; MODAL-ANALYSIS; BEAMS; PREDICTION Delamination is a frequently occurring type of damage in laminated fibre reinforced polymer (FRP) composites and causes substantial loss in structural stiffness and usable service life. The detection of delaminations in FRP composites is critical for the safe and reliable use of these materials in aeronautical and other industries. Structural Health Monitoring (SHM) techniques based on vibration measurements have proven to be promising towards this end. There have been comprehensive studies of FRP beams with through-width delaminations, but the damage assessment of FRP plates with embedded delaminations using frequency-based detection has not been extensively studied. To solve the inverse problem of determining size and location of delamination from changes in the natural frequencies, this paper presents a new surrogate assisted optimisation (SAO) method for predicting the location and size of delaminations in fibre reinforced composite plates using natural frequency shifts as indicative parameters. The proposed frequency-based delamination assessment method is validated using finite element models of FRP plates with embedded delaminations and by experimental modal analysis. Modal testing was conducted using scanning laser vibrometer on carbon/epoxy and glass/epoxy FRP plates that were manufactured with artificially induced delaminations. The proposed SAO algorithm was compared to an Artificial Neural Network (ANN) method in terms of database size, prediction accuracy and sensitivity to noisy data. The results show that the proposed inverse algorithm can predict the delamination parameters of location and size with good accuracy for numerically simulated frequency shift data but the prediction accuracy was reduced with experimental data. A comparison of the two inverse algorithms show that the SAO method has significant advantages compared to the ANN algorithm for delamination prediction. [Zhang, Zhifang; He, Mengyue; Liu, Airong] Guangzhou Univ, Guangzhou Univ Tamkang Univ Joint Res Ctr Engn St, Guangzhou 510006, Guangdong, Peoples R China; [Singh, Hemant Kumar; Shankar, Krishna; Morozov, Evgeny V.] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia; [Ramakrishnan, Karthik Ram] Tampere Univ Technol, Fac Engn Sci, Tampere 33720, Finland; [Hui, David] Univ New Orleans, Dept Mech Engn, New Orleans, LA 70148 USA Guangzhou University; University of New South Wales Sydney; Tampere University; University of Louisiana System; University of New Orleans Liu, AR (corresponding author), Guangzhou Univ, Guangzhou Univ Tamkang Univ Joint Res Ctr Engn St, Guangzhou 510006, Guangdong, Peoples R China. zfzhang@gzhu.edu.cn; 103796@gzhu.edu.cn; liuar@gzhu.edu.cn; h.singh@adfa.edu.au; karthik.ramakrishnan@tut.fi; DHui@uno.edu; k.shankar@adfa.edu.au; e.morozov@adfa.edu.au Zhang, Zhifang/Q-8889-2019; Morozov, EVGENY/AAG-1594-2019; Singh, Hemant K/B-9861-2015; Liu, Airong/AAM-7082-2020 Singh, Hemant K/0000-0003-1653-232X; Morozov, Evgeny/0000-0003-3094-1269 National Natural Science Funds of China [51508118, 51608135]; Natural Science Foundation of Guangdong Province, China [2016A030310261]; Science and Technology Planning Project of Guangdong Province, China [2016B050501004] National Natural Science Funds of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); Science and Technology Planning Project of Guangdong Province, China The research has been supported by National Natural Science Funds of China (Grant No. 51508118, 51608135); Natural Science Foundation of Guangdong Province, China (Grant No. 2016A030310261); Science and Technology Planning Project of Guangdong Province, China (Grant No. 2016B050501004). 50 41 42 9 75 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1359-8368 1879-1069 COMPOS PART B-ENG Compos. Pt. B-Eng. JUL 1 2018.0 144 254 266 10.1016/j.compositesb.2018.03.003 0.0 13 Engineering, Multidisciplinary; Materials Science, Composites Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science GH7SK 2023-03-23 WOS:000433653500023 0
J Zhang, XR; Liang, YJ; Li, C; Ning, HY; Jiao, LC; Zhou, HY Zhang, Xiangrong; Liang, Yanjie; Li, Chen; Ning Huyan; Jiao, Licheng; Zhou, Huiyu Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Deep learning; hyperspectral image (HSI) classification; recursive autoencoders (RAE); unsupervised feature learning FEATURE-EXTRACTION; SUPPORT For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral data sets, and the results show that our proposed method has a better performance. [Zhang, Xiangrong; Liang, Yanjie; Ning Huyan; Jiao, Licheng] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Shaanxi, Peoples R China; [Li, Chen] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710048, Shaanxi, Peoples R China; [Zhou, Huiyu] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland Xidian University; Xi'an Jiaotong University; Queens University Belfast Zhang, XR (corresponding author), Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Shaanxi, Peoples R China. xrzhang@ieee.org Zhou, Huiyu/O-2692-2014 Zhou, Huiyu/0000-0003-1634-9840 National Natural Science Foundation of China [61772400, 61377011, 61373111]; Program for New Scientific and Technological Star of Shaanxi Province [2014KJXX-45]; UK EPSRC [EP/N508664/1, EP/R007187/1, EP/N011074/1]; Royal Society-Newton Advanced Fellowship [NA160342]; Engineering and Physical Sciences Research Council [EP/N011074/1, EP/R007187/1, EP/N508664/1] Funding Source: researchfish; EPSRC [EP/N508664/1, EP/R007187/1, EP/N011074/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for New Scientific and Technological Star of Shaanxi Province; UK EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society-Newton Advanced Fellowship; Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61772400, Grant 61377011, and Grant 61373111, and in part by the Program for New Scientific and Technological Star of Shaanxi Province under Grant 2014KJXX-45. The work of H. Zhou was supported in part by UK EPSRC under Grant EP/N508664/1, Grant EP/R007187/1, and Grant EP/N011074/1, and in part by Royal Society-Newton Advanced Fellowship under Grant NA160342. 27 51 52 2 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. NOV 2017.0 14 11 1928 1932 10.1109/LGRS.2017.2737823 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology FL1DY Green Submitted, Green Accepted 2023-03-23 WOS:000413955500010 0
J Tian, FY; Zhang, P; Yan, Z Tian, Fengyu; Zhang, Peng; Yan, Zheng A Survey on C-RAN Security IEEE ACCESS English Article Cloud radio access network (C-RAN); security; security threats; 5G; trust USER EMULATION ATTACKS; SECRECY CAPACITY; RADIO While 4G is speeding up its steps toward global markets, 5G has initiated its full development to satisfy an increasing demand on mobile data traffic and big data bandwidth. Centralized data processing, collaborative radio, real-time cloud infrastructure, and cloud radio access network (C-RAN), along with their excellent advantages are being sought by more and more operators to meet end-user requirements. As a promising mobile wireless network architecture, compared with traditional RAN, C-RAN has incomparable advantages in terms of low power consumption, reduced base station (BS) numbers, and economic capital and operating expenditure. It can also improve network capacity and BS utilization rate. Recently, C-RAN security has aroused special attention and concern. However, the literature still lacks an overall review on it in order to guide current and future research. In this paper, we first overview the architecture, deployment scenarios, and special characteristics of C-RAN. We then provide a thorough review on the existing security studies in the field of C-RAN based on its three logic layers and corresponding security threats and attacks. Particularly, we discuss whether the current literature can satisfy the expected security requirements in C-RAN. Based on this, we indicate open research issues and propose future research trends. [Tian, Fengyu; Zhang, Peng; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland Xidian University; Aalto University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland. zhengyan.pz@gmail.com yang, zheng/HGC-7753-2022 Yan, Zheng/0000-0002-9697-2108 National Key Research and Development Program of China [2016YFB0800700]; NSFC [61672410, U1536202]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; 111 Project [B08038, B16037]; Academy of Finland [308087] National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China; 111 Project(Ministry of Education, China - 111 Project); Academy of Finland(Academy of Finland) This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800700, in part by NSFC under Grant 61672410 and Grant U1536202, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Program 2016ZDJC-06, in part by the 111 Project under Grant B08038 and Grant B16037, and in part by the Academy of Finland under Grant 308087. 49 22 22 0 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2017.0 5 13372 13386 10.1109/ACCESS.2017.2717852 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications FI2GW Green Published, gold 2023-03-23 WOS:000411757800003 0
C Liu, HY; Xin, RY; Chen, P; Zhao, ZM Ardagna, CA; Atukorala, N; Buyya, R; Chang, CK; Chang, RN; Damiani, E; Dasgupta, GB; Gagliardi, F; Hagleitner, C; Milojicic, D; Trong, TMH; Ward, R; Xhafa, F; Zhang, J Liu, Hongyun; Xin, Ruyue; Chen, Peng; Zhao, Zhiming Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum 2022 IEEE 15TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (IEEE CLOUD 2022) IEEE International Conference on Cloud Computing English Proceedings Paper 15th IEEE International Conference on Cloud Computing (IEEE CLOUD) / IEEE World Congress on Services (IEEE SERVICES) JUL 11-15, 2022 Barcelona, SPAIN IEEE,IEEE Comp Soc offloading; Meta-Learning; Reinforcement Learning; robustness; multi-objective learning; LSTM COMPUTATION Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical single objective RL-based offloading methods. [Liu, Hongyun; Xin, Ruyue; Zhao, Zhiming] Univ Amsterdam, Multiscale Networked Syst MNS, Amsterdam, Netherlands; [Zhao, Zhiming] LifeWatch ERIC Virtual Lab & Innovat Ctr, Amsterdam, Netherlands; [Chen, Peng] Xihua Univ, Sch Comp & Software Engn, Chengdu, Peoples R China University of Amsterdam; Xihua University Liu, HY (corresponding author), Univ Amsterdam, Multiscale Networked Syst MNS, Amsterdam, Netherlands. h.liu@uva.nl; r.xin@uva.nl; chenpeng@mail.xhu.edu.cn; z.zhao@uva.nl European Union's Horizon 2020 research and innovation program [825134, 862409, 824068]; Chinese Scholarship Council; EU LifeWatch ERIC European Union's Horizon 2020 research and innovation program; Chinese Scholarship Council(China Scholarship Council); EU LifeWatch ERIC This research is funded by the European Union's Horizon 2020 research and innovation program under grant agreements 825134 (ARTICONF project), 862409(BlueCloud project) and 824068 (ENVRI-FAIR project). The research is also supported by the Chinese Scholarship Council, and EU LifeWatch ERIC. 31 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-6182 978-1-6654-8137-3 IEEE INT CONF CLOUD 2022.0 469 478 10.1109/CLOUD55607.2022.00070 0.0 10 Computer Science, Hardware & Architecture; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT9IU Green Submitted 2023-03-23 WOS:000861121600055 0
J Gao, H; Zhao, QT; Ning, CL; Guo, DF; Wu, J; Li, LN Gao, Hao; Zhao, Qingting; Ning, Chuanlin; Guo, Difan; Wu, Jing; Li, Lina Does the COVID-19 Vaccine Still Work That Most of the Confirmed Cases Had Been Vaccinated? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article breakthrough cases; COVID-19 vaccine; sentiment orientation; social media SOCIAL MEDIA; INFORMATION EXPOSURE; TWITTER; COVERAGE; FACEBOOK In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine's effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public's cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues. [Gao, Hao; Zhao, Qingting; Guo, Difan] Nanjing Normal Univ, Sch Journalism & Commun, Nanjing 210097, Peoples R China; [Ning, Chuanlin] Shanghai Jiao Tong Univ, Sch Media & Commun, Shanghai 200240, Peoples R China; [Wu, Jing] Univ Ljubljana, Fac Social Sci, Ljubljana 1000, Slovenia; [Li, Lina] Shanghai Normal Univ, Film Televis & Commun Coll, Shanghai 200234, Peoples R China Nanjing Normal University; Shanghai Jiao Tong University; University of Ljubljana; Shanghai Normal University Li, LN (corresponding author), Shanghai Normal Univ, Film Televis & Commun Coll, Shanghai 200234, Peoples R China. 42396@njnu.edu.cn; huiguaiwandepi@163.com; ningchuanlin@sjtu.edu.cn; guodifan@163.com; sunnyjingya@163.com; lilina@shnu.edu.cn 郭, 迪帆/AFL-5646-2022 郭, 迪帆/0000-0002-0048-985X; Ning, Chuanlin/0000-0002-5545-4910; Gao, Hao/0000-0002-9749-008X; ZHAO, QINGTING/0000-0001-9104-0355 National Social Science Foundation of China [17CXW016] National Social Science Foundation of China FundingThis research was funded by National Social Science Foundation of China, grant number 17CXW016. 61 6 6 12 45 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health JAN 2022.0 19 1 241 10.3390/ijerph19010241 0.0 14 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health YE8TO 35010501.0 Green Accepted, gold 2023-03-23 WOS:000741392200001 0
C Li, M; Gnecco, G; Sanguineti, M Long, G; Yu, X; Wang, S Li, Ming; Gnecco, Giorgio; Sanguineti, Marcello Deeper Insights into Neural Nets with Random Weights AI 2021: ADVANCES IN ARTIFICIAL INTELLIGENCE Lecture Notes in Artificial Intelligence English Proceedings Paper 34th Australasian Joint Conference on Artificial Intelligence (AI) FEB 02-04, 2022 Univ Technol Sydney, ELECTR NETWORK Australian Comp Soc, Natl Comm Artificial Intelligence,Univ New S Wales,Royal Melbourne Inst Technol,CSRIO, Data61,Business Events Sydney,Finity Univ Technol Sydney Neural networks with random weights; Hyperbolic tangent; Polynomial approximation; Effective dimension; Approximate rank NETWORKS In this work, the effective dimension of the output of the hidden layer of a one-hidden-layer neural network with random inner weights of its computational units is investigated. To do this, a polynomial approximation of the sigmoidal activation function of each computational unit is used, whose degree is chosen based both on a desired upper bound on the approximation error and on an estimate of the range of the input to that computational unit. This estimate of the range is parameterized by the number of inputs to the network and by an upper bound both on the size of the random inner weights of the network and on the size of its inputs. The results show that the Root Mean Square Error (RMSE) on the training set is influenced by the effective dimension and by the quality of the features associated with the output of the hidden layer. [Li, Ming] Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zheji, Jinhua 321004, Zhejiang, Peoples R China; [Gnecco, Giorgio] IMT Sch Adv Studies, Axes Res Unit, I-55100 Lucca, Italy; [Sanguineti, Marcello] Univ Genoa, Dept Informat Bioengn Robot & Syst Engn, I-16145 Genoa, Italy Zhejiang Normal University; IMT School for Advanced Studies Lucca; University of Genoa Li, M (corresponding author), Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zheji, Jinhua 321004, Zhejiang, Peoples R China. mingli@zjnu.edu.cn; giorgio.gnecco@imtlucca.it; marcello.sanguineti@unige.it Li, Ming/GYQ-7804-2022; Gnecco, Giorgio/P-7623-2014 Li, Ming/0000-0002-1218-2804; Gnecco, Giorgio/0000-0002-5427-4328; Sanguineti, Marcello/0000-0003-0355-8483 Italian Project ARTES 4.0 -Advanced Robotics and enabling digital TEchnology & Systems 4.0 - Italian Ministry of Economic Development (MISE); National Natural Science Foundation of China [62172370] Italian Project ARTES 4.0 -Advanced Robotics and enabling digital TEchnology & Systems 4.0 - Italian Ministry of Economic Development (MISE); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work of G. Gnecco was supported in part by the Italian Project ARTES 4.0 -Advanced Robotics and enabling digital TEchnology & Systems 4.0, funded by the Italian Ministry of Economic Development (MISE). The work of M. Li was supported in part by the National Natural Science Foundation of China under Grant 62172370. 12 1 1 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-97546-3; 978-3-030-97545-6 LECT NOTES ARTIF INT 2022.0 13151 129 140 10.1007/978-3-030-97546-3_11 0.0 12 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT0FG 2023-03-23 WOS:000787242700011 0
J Yu, Z; Sun, BY; Liu, D; De Dravo, VW; Khokhlova, M; Wu, SY Yu, Zhe; Sun, Bangyong; Liu, Di; de Dravo, Vincent Whannou; Khokhlova, Margarita; Wu, Siyuan STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples JOURNAL OF RENEWABLE MATERIALS English Article Image dehazing; contrast enhancement; high-pass filter; image reconstruction In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is yielded after iterations of the high-pass filter. STRASS can be run directly without any machine learning. Extensive experimental results on data sets prove that STRASS surpass the state-of-the-arts. Image dehazing can be applied in the field of printing and packaging, our method is of great significance for image pre-processing before printing. [Yu, Zhe; Sun, Bangyong; de Dravo, Vincent Whannou] Xian Univ Technol, Sch Printing Packaging Engn & Digital Media, Xian 710048, Peoples R China; [Liu, Di] Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China; [Sun, Bangyong; Wu, Siyuan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China; [Khokhlova, Margarita] Univ Gustave Eiffel, LASTIG, F-69134 Ecully, France Xi'an University of Technology; Beijing Technology & Business University; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS Sun, BY (corresponding author), Xian Univ Technol, Sch Printing Packaging Engn & Digital Media, Xian 710048, Peoples R China.;Sun, BY (corresponding author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China. sunbangyong@xaut.edu.cn National Natural Science Foundation of China [62076199]; Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety [BTBD-2020KF08]; Beijing Technology and Business University; China Postdoctoral Science Foundation [2019M653784]; Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences [LSIT201801D]; Key R&D Project of Shaan'xi Province [2021GY-027] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety; Beijing Technology and Business University; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences; Key R&D Project of Shaan'xi Province This work was supported in part by National Natural Science Foundation of China under Grant 62076199, in part by the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety under Grant BTBD-2020KF08, Beijing Technology and Business University, in part by the China Postdoctoral Science Foundation under Grant 2019M653784, in part by Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences under Grant LSIT201801D, in part by the Key R&D Project of Shaan'xi Province under Grant 2021GY-027. 42 0 0 2 14 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 2164-6325 2164-6341 J RENEW MATER J. Renew. Mater. 2022.0 10 5 1381 1395 10.32604/jrm.2022.018262 0.0 15 Green & Sustainable Science & Technology; Materials Science, Composites; Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science; Polymer Science XW1QP hybrid 2023-03-23 WOS:000735403000014 0
J Quatrini, E; Costantino, F; Li, XC; Mba, D Quatrini, Elena; Costantino, Francesco; Li, Xiaochuan; Mba, David Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions APPLIED SCIENCES-BASEL English Article residual useful life prediction; contribution plot; performance estimation; prognosis; diagnosis; fault detection SAFETY In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector. [Quatrini, Elena; Costantino, Francesco] Sapienza Univ Rome, Dept Mech & Aerosp Engn, Via Eudossiana 18, I-00184 Rome, Italy; [Li, Xiaochuan] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230002, Peoples R China; [Mba, David] Univ Arts London, Inst Creat Comp, London WC1V 7EY, England Sapienza University Rome; Hefei University of Technology; University of Arts London Mba, D (corresponding author), Univ Arts London, Inst Creat Comp, London WC1V 7EY, England. elena.quatrini@uniroma1.it; francesco.costantino@uniroma1.it; xiaochuan.li0309@gmail.com; d.mba@arts.ac.uk Quatrini, Elena/0000-0001-9617-4491 39 0 0 2 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel MAY 2022.0 12 9 4737 10.3390/app12094737 0.0 16 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 1E3PH gold 2023-03-23 WOS:000794404500001 0
J Li, Y; Tondi, B; Barni, M Li, Yue; Tondi, Benedetta; Barni, Mauro Spread-Transform Dither Modulation Watermarking of Deep Neural Network JOURNAL OF INFORMATION SECURITY AND APPLICATIONS English Article ST-DM; Watermarking; DNN DIGITAL WATERMARKING; ALGORITHMS; PROTECTION DNN watermarking is receiving an increasing attention as a suitable mean to protect the Intellectual Property Rights associated to DNN models. Several methods proposed so far are inspired to the popular Spread Spectrum (SS) paradigm according to which the watermark bits are embedded into the projection of the weights of the DNN model onto a pseudorandom sequence. In this paper, we propose a new DNN watermarking algorithm that leverages on the watermarking with side information paradigm to decrease the obtrusiveness of the watermark and increase its payload. In particular, the new scheme exploits the main ideas of ST-DM (Spread Transform Dither Modulation) watermarking to improve the performance of a recently proposed algorithm based on conventional SS. The experiments we carried out by applying the proposed scheme to watermark different models, demonstrate its capability to provide a higher payload with a lower impact on network accuracy than a baseline method based on conventional SS, while retaining a satisfactory level of robustness. [Li, Yue] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, Via Roma 56, I-53100 Siena, Italy Southwest Jiaotong University; University of Siena Li, Y (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China. liyue859000040@my.swjtu.edu.cn; benedettatondi@gmail.com; barni@dii.unisi.it BARNI, Mauro/0000-0002-7368-0866 China Scholarship Council(CSC) [201907000056] China Scholarship Council(CSC)(China Scholarship Council) This work has been partially supported by the China Scholarship Council(CSC), file No. 201907000056. The author would also thank Prof. Hongxia Wang (hxwang@scu.edu.cn) for her valuable advice and help. 30 7 7 1 9 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-2126 2214-2134 J INF SECUR APPL J. Inf. Secur. Appl. DEC 2021.0 63 103004 10.1016/j.jisa.2021.103004 0.0 OCT 2021 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science XG7TV Green Submitted 2023-03-23 WOS:000724953100002 0
J Kwok, KW; Wurdemann, H; Arezzo, A; Menciassi, A; Althoefer, K Kwok, Ka-Wai; Wurdemann, Helge; Arezzo, Alberto; Menciassi, Arianna; Althoefer, Kaspar Soft Robot-Assisted Minimally Invasive Surgery and Interventions: Advances and Outlook PROCEEDINGS OF THE IEEE English Article Robots; Robot sensing systems; Soft robotics; Sensors; Minimally invasive surgery; Endoscopes; Instruments; Endoscopy; flexible structures; machine learning; modeling; robot-assisted minimally invasive surgery (RAMIS); soft robotics; soft sensors; tunable stiffness CONTINUUM ROBOTS; KINEMATIC CONTROL; DYNAMIC-MODEL; DESIGN; DRIVEN; COLONOSCOPY; ALLOY; ARM Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry's appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery. [Kwok, Ka-Wai] Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China; [Wurdemann, Helge] Univ Coll London UCL, Dept Mech Engn, London WC1E 6BT, England; [Arezzo, Alberto] Univ Turin, Dept Surg Sci, I-10124 Turin, Italy; [Menciassi, Arianna] Scuola Super Sant Anna, BioRobot Inst, I-56127 Pisa, Italy; [Althoefer, Kaspar] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England University of Hong Kong; University of London; University College London; University of Turin; Scuola Superiore Sant'Anna; University of London; Queen Mary University London Althoefer, K (corresponding author), Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England. kwokkw@hku.hk; h.wurdemann@ucl.ac.uk; alberto.arezzo@unito.it; a.menciassi@santannapisa.it; k.althoefer@qmul.ac.uk Kwok, Ka-Wai/GQQ-0548-2022; Kwok, Ka-Wai/AAU-9710-2020 Kwok, Ka-Wai/0000-0003-1879-9730; Kwok, Ka-Wai/0000-0003-1879-9730; Arezzo, Alberto/0000-0002-2110-4082; Althoefer, Kaspar/0000-0002-1141-9996; Wurdemann, Helge/0000-0003-3082-146X 137 1 1 24 44 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9219 1558-2256 P IEEE Proc. IEEE JUL 2022.0 110 7 871 892 10.1109/JPROC.2022.3167931 0.0 MAY 2022 22 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 2J4XZ Green Submitted, hybrid 2023-03-23 WOS:000791746100001 0
J Li, ZS; Xiong, G; Wei, ZB; Zhang, Y; Zheng, M; Liu, XL; Tarkoma, S; Huang, M; Lv, YS; Wu, CH Li, Zhishuai; Xiong, Gang; Wei, Zebing; Zhang, Yu; Zheng, Meng; Liu, Xiaoli; Tarkoma, Sasu; Huang, Min; Lv, Yisheng; Wu, Chuheng Trip Purposes Mining From Mobile Signaling Data IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Cellular networks; Trajectory; Semantics; Unsupervised learning; Supervised learning; Resource management; Public transportation; Trip purpose inference; cellular network data; latent Dirichlet allocation; travel behavior; big data PREDICTION; DISCOVERY; PATTERNS With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users' daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method. [Li, Zhishuai; Wei, Zebing; Huang, Min; Lv, Yisheng] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Li, Zhishuai; Xiong, Gang; Wei, Zebing; Lv, Yisheng; Wu, Chuheng] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Xiong, Gang] Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China; [Zhang, Yu; Zheng, Meng] Beijing Municipal Inst City Planning & Design, Beijing 100045, Peoples R China; [Liu, Xiaoli; Tarkoma, Sasu] Univ Helsinki, Dept Comp Sci, Helsinki 00014, Finland Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Helsinki Huang, M; Lv, YS (corresponding author), Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.;Lv, YS (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China. yisheng.lv@ia.ac.cn Li, Zhishuai/0000-0003-3408-6300; xiong, gang/0000-0002-4303-5559; Wei, ZeBing/0000-0001-6168-5179; Liu, Xiaoli/0000-0003-4792-2267 National Key Research and Development Program of China [2020YFB2104001]; National Natural Science Foundation of China [U1811463, 61903363, 61876011, 61603381]; Chinese Guangdong's Science and Technology Project [2019B1515120030] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Guangdong's Science and Technology Project This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB2104001; in part by the National Natural Science Foundation of China under Grant U1811463, Grant 61903363, Grant 61876011, and Grant 61603381; and in part by the Chinese Guangdong's Science and Technology Project under Grant 2019B1515120030. 57 1 1 14 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. AUG 2022.0 23 8 13190 13202 10.1109/TITS.2021.3121551 0.0 OCT 2021 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 4A1EQ 2023-03-23 WOS:000732146400001 0
J Kong, XJ; Liu, XT; Jedari, B; Li, ML; Wan, LT; Xia, F Kong, Xiangjie; Liu, Xiaoteng; Jedari, Behrouz; Li, Menglin; Wan, Liangtian; Xia, Feng Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges IEEE INTERNET OF THINGS JOURNAL English Review Cooperative computing; incentive mechanisms; Internet of Things (IoT); mobile crowdsourcing (MCS); mobility; resource sharing; smart cities; task scheduling INCENTIVE MECHANISM; BIG DATA; INTERNET; SERVICE; OPPORTUNITIES; MANAGEMENT; NETWORKS; SECURITY; QUALITY; PRIVACY Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities. [Kong, Xiangjie; Liu, Xiaoteng; Li, Menglin; Wan, Liangtian; Xia, Feng] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China; [Jedari, Behrouz] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland Dalian University of Technology; Aalto University Xia, F (corresponding author), Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China. xjkong@ieee.org; xt.lau@outlook.com; behrouz.jedari@aalto.fi; cookies.s@outlook.com; wan.liangtian.2015@ieee.org; f.xia@ieee.org Kong, Xiangjie/B-8809-2016; Xia, Feng/Y-2859-2019 Kong, Xiangjie/0000-0003-2698-3319; Li, Menglin/0000-0002-7890-7636; Kong, Xiangjie/0000-0003-2592-6830; Xia, Feng/0000-0002-8324-1859; Liu, Xiaoteng/0000-0002-6617-2361; Wan, Liangtian/0000-0003-0574-8360 Dalian Science and Technology Innovation Fund [2018J12GX048]; Fundamental Research Funds for the Central Universities [DUT18JC09] Dalian Science and Technology Innovation Fund; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the Dalian Science and Technology Innovation Fund under Grant 2018J12GX048, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT18JC09. 163 54 54 7 63 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. OCT 2019.0 6 5 SI 8095 8113 10.1109/JIOT.2019.2921879 0.0 19 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications JF3OB Green Accepted 2023-03-23 WOS:000491295800063 0
C Kang, K; Hu, QY; Cai, YL; Yu, GD; Hoydis, J; Eldar, YC IEEE Kang, Kai; Hu, Qiyu; Cai, Yunlong; Yu, Guanding; Hoydis, Jakob; Eldar, Yonina C. Joint Channel Estimation and Hybrid Beamforming via Deep-Unfolding 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022) European Signal Processing Conference English Proceedings Paper 30th European Signal Processing Conference (EUSIPCO) AUG 29-SEP 02, 2022 Belgrade, SERBIA European Assoc Signa Proc Deep-unfolding; hybrid beamforming; channel estimation; mixed-timescale scheme; massive MIMO MASSIVE MIMO; ALGORITHM In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms. [Kang, Kai; Hu, Qiyu; Cai, Yunlong; Yu, Guanding] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China; [Hoydis, Jakob] NVIDIA, F-06906 Sophia Antipolis, France; [Eldar, Yonina C.] Weizmann Inst Sci, Dept Math & Comp Sci, IL-7610001 Rehovot, Israel Zhejiang University; Weizmann Institute of Science Kang, K (corresponding author), Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China. kangkai@zju.edu.cn; qiyhu@zju.edu.cn; ylcai@zju.edu.cn; yuguanding@zju.edu.cn; jhoydis@nvidia.com; yonina.eldar@weizmann.ac.il National Natural Science Foundation of China [61971376, 61831004]; Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars [LR19F010002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars This work was supported in part by the National Natural Science Foundation of China under Grants 61971376 and 61831004, and in part by the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under Grant LR19F010002. 15 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2076-1465 978-90-827970-9-1 EUR SIGNAL PR CONF 2022.0 658 662 5 Acoustics; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU5UP 2023-03-23 WOS:000918827600130 0
J Bai, S; Bai, X; Liu, WY; Roli, F Bai, Song; Bai, Xiang; Liu, Wenyu; Roli, Fabio Neural shape codes for 3D model retrieval PATTERN RECOGNITION LETTERS English Article Shape retrieval; 3D model; Depth view; CNN ROBUST The paradigm of Convolutional Neural Network (CNN) has already shown its potential for many challenging applications of computer vision, such as image classification, object detection and action recognition. In this paper, the task of 3D model retrieval is addressed by exploiting such promising paradigm. However, 3D models are usually represented with a collection of orderless points, lines and surfaces in a three dimensional space, which makes it difficult to involve the operation of convolution, pooling, etc. Yet, we propose a practical and effective way for applying CNN to 3D model retrieval, by training the network with the depth projections of 3D model. This CNN is regarded as a generic feature extractor for depth image. With large amounts of training data, the learned feature, which is called Neural Shape Codes, can handle various deformation changes that exist in shape analysis. The reported experimental results on several 3D shape benchmark datasets show the superior performance of the proposed method. (C) 2015 Elsevier B.V. All rights reserved. [Bai, Song; Bai, Xiang; Liu, Wenyu] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China; [Roli, Fabio] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy Huazhong University of Science & Technology; University of Cagliari Bai, X (corresponding author), Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China. xiang.bai@gmail.com Liu, Wenyu/AAG-1426-2019 Liu, Wenyu/0000-0002-4582-7488; ROLI, FABIO/0000-0003-4103-9190 National Natural Science Foundation of China (NSFC) [51222308]; NSFC [61173120]; Program for New Century Excellent Talents in University [NCET-12-0217]; Fun-damental Research Funds for the Central Universities [HUST 2013TS115] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)); Program for New Century Excellent Talents in University(Program for New Century Excellent Talents in University (NCET)); Fun-damental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was primarily supported by National Natural Science Foundation of China (NSFC) (no. 51222308), and in part by NSFC (no. 61173120), Program for New Century Excellent Talents in University (no. NCET-12-0217), Fun-damental Research Funds for the Central Universities (no. HUST 2013TS115). 37 20 22 0 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. NOV 1 2015.0 65 15 21 10.1016/j.patrec.2015.06.022 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CS6KH 2023-03-23 WOS:000362187000003 0
J Liu, GJ; Chen, YQ; Zhang, XL; Jiang, WX; Xie, YC; Tian, XJ; Leng, DX; Li, ZX Liu, Guijie; Chen, Yunqing; Zhang, Xiulong; Jiang, Weixiong; Xie, Yingchun; Tian, Xiaojie; Leng, Dingxin; Li, Zhixiong A deep residual shrinkage network based on multi-scale attention module for subsea Christmas tree valve leakage detection MEASUREMENT English Article Valve leakage; Underwater acoustic; Multi-scale attention module; Deep residual shrinkage network FAULT-DIAGNOSIS; GAS EMISSIONS; SYSTEM; FLOW Recently, the valve leakage detection of subsea Christmas tree (SCT) attracts considerable attention in the field of underwater resource exploitation. However, most existing leakage detection methods rely on contact-sensors, which are expensive and troublesome to install and maintain. Additionally, it is still a great challenge to extract sensitive fault features for the non-linear and unsteady signal. To address these issues, a novel remote acoustic detection method based on acoustic sensor and deep learning is proposed in this paper. Firstly, the feasibility of SCT valve leakage detection based on acoustic sensors is theoretically demonstrated by acoustic analysis. Secondly, a multi-scale attention module (MSAM) is proposed to obtain rich feature information according to the characteristics of valve leakage acoustic signals. Subsequently, A deep residual shrinkage network based on multi-scale attention module (MSAM-DRSN) is designed to perform valve leakage detection. The proposed method is evaluated by the valve leakage experiment. The results show that the proposed leakage detection method can obtain sensitive fault features from strong background noise signals and the detection performance is better than existing detection methods. [Liu, Guijie; Chen, Yunqing; Zhang, Xiulong; Jiang, Weixiong; Xie, Yingchun; Tian, Xiaojie; Leng, Dingxin; Li, Zhixiong] Ocean Univ China, Dept Mech Engn, Qingdao 266001, Peoples R China; [Liu, Guijie; Chen, Yunqing; Zhang, Xiulong; Jiang, Weixiong; Xie, Yingchun; Tian, Xiaojie; Leng, Dingxin] Ocean Univ China, Key Lab Ocean Engn Shandong Prov, Qingdao 266100, Peoples R China; [Li, Zhixiong] Opole Univ Technol, Dept Mech Engn, PL-45758 Opole, Poland Ocean University of China; Ocean University of China; Opole University of Technology Liu, GJ (corresponding author), Ocean Univ China, Dept Mech Engn, Qingdao 266001, Peoples R China.;Liu, GJ (corresponding author), Ocean Univ China, Key Lab Ocean Engn Shandong Prov, Qingdao 266100, Peoples R China. liuguijie@ouc.edu.cn National Key Research and Development Program of China [2019YFE0105100]; Fundamental Research Funds for the Central Universities [202165006]; Norwegian Research Council (AutoPRO) [309628] National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Norwegian Research Council (AutoPRO)(Research Council of Norway) Acknowledgment This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFE0105100, the Fundamental Research Funds for the Central Universities under Grant 202165006 and in part by the Norwegian Research Council (AutoPRO) under Grant 309628. 43 0 0 20 33 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement JUL 2022.0 198 110970 10.1016/j.measurement.2022.110970 0.0 MAY 2022 12 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 1U2FD 2023-03-23 WOS:000805232000004 0
J Ali, J; Ashraf, U; Anees, A; Peng, SX; Umar, MU; Thanh, HV; Khan, U; Abioui, M; Mangi, HN; Ali, M; Ullah, J Ali, Jawad; Ashraf, Umar; Anees, Aqsa; Peng, Sanxi; Umar, Muhammad Ubaid; Thanh, Hung Vo; Khan, Umair; Abioui, Mohamed; Mangi, Hassan Nasir; Ali, Muhammad; Ullah, Jar Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling ACS OMEGA English Article; Early Access POTWAR BASIN; TALHAR SHALE; WELL LOGS; CLASSIFICATION; RANGE; AREA The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the Himalayan mountains. The current research aims to delineate the hydrocarbon potential, reservoir zone evaluation, and lithofacies identification through the utilization of seven conventional well logs (M-01, M-08, M-10, M-12, M-13P, and M-17). We employed the advanced unsupervised machine-learning method of self organizing maps for lithofacies identification and the novel Quanti Elan model technique for comprehensive multimineral evaluation. The shale volume, porosity, permeability, and water saturation (petrophysical parameters) of six wells were evaluated to identify the reservoir potential and prospective reservoir zones. Well-logging data and self-organizing maps were used in this study to provide a less costly method for the objective and systematic identification of lithofacies. According to the SOM and Pickett plot analyses, the zone of interest is mostly made up of pure limestone oil zone, whereas the sandy and dolomitic behavior with a mixture of shale content shows non-reservoir oil-water and water zones. The reservoir has good porosity values that range from 0 to 18%, but there is a high water saturation of up to 45% in reservoir production zones. The presence of shale in the entire reservoir interval has a negative effect on the permeability value, but the petrophysical properties of the Meyal oil reservoir are good enough to permit hydrocarbon production. According to the petrophysical estimates, the Meyal oil field ' s Sakesar and Chorgali Formations are promising reservoirs, and new prospects for drilling wells in the southern and central portions of the eastern portion of the research area are recommended. [Ali, Jawad] Shaanxi Univ Sci & Technol, Sch Environm Sci & Engn, Xian 710021, Peoples R China; [Umar, Muhammad Ubaid] China Univ Petr, Coll Geosci, Dept Petr Geol, Beijing 102249, Peoples R China; [Thanh, Hung Vo] Seoul Natl Univ, Sch Earth & Environm Sci, Seoul 08826, South Korea; [Khan, Umair] Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China; [Abioui, Mohamed] Ibn Zohr Univ, Fac Sci, Dept Earth Sci, Agadir 80000, Morocco; [Abioui, Mohamed] Univ Coimbra, Fac Sci & Technol, MARE Marine & Environm Sci Ctr, Dept Earth Sci,Sedimentary Geol Grp, Coimbra, Portugal; [Mangi, Hassan Nasir] Wuhan Inst Technol, Sch XingFa Min Engn, Wuhan 430205, Peoples R China; [Ali, Muhammad; Ullah, Jar] China Univ Geosci, Inst Geophys & Geomatics, Wuhan 430074, Peoples R China; [Ashraf, Umar; Anees, Aqsa] Yunnan Univ, Inst Ecol Res & Pollut Control Plateau Lakes, Sch Ecol & Environm Sci, Kunming 650500, Peoples R China; [Peng, Sanxi] Guilin Univ Technol, Coll Earth Sci, Guilin 541004, Guangxi, Peoples R China Shaanxi University of Science & Technology; China University of Petroleum; Seoul National University (SNU); Central South University; Ibn Zohr University of Agadir; Universidade de Coimbra; Wuhan Institute of Technology; China University of Geosciences; Yunnan University; Guilin University of Technology Anees, A (corresponding author), Yunnan Univ, Inst Ecol Res & Pollut Control Plateau Lakes, Sch Ecol & Environm Sci, Kunming 650500, Peoples R China.;Peng, SX (corresponding author), Guilin Univ Technol, Coll Earth Sci, Guilin 541004, Guangxi, Peoples R China. aqsaanees01@outlook.com; pengsanxi1984@163.com Ashraf, Umar/ABB-7906-2021; Vo, Thanh Hung/AFJ-5727-2022; Anees, Aqsa/ABW-3284-2022 Ashraf, Umar/0000-0003-2402-3605; Vo, Thanh Hung/0000-0002-7094-9380; Anees, Aqsa/0000-0002-1032-0724; Abioui, Mohamed/0000-0002-6331-1442; Khan, Umair/0000-0001-6963-9396 Directorate General of Petroleum Concessions (DGPC) , Pakistan; National Natural Science Foundation of China; Natural Science Foundation of Guangxi; Yunnan Provincial Government Leading Scientist Program; [42062015]; [2018AD19204]; [2015HA024] Directorate General of Petroleum Concessions (DGPC) , Pakistan; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangxi(National Natural Science Foundation of Guangxi Province); Yunnan Provincial Government Leading Scientist Program; ; ; ? ACKNOWLEDGMENTS The authors thank the Directorate General of Petroleum Concessions (DGPC) , Pakistan, for the release of well data. This study was supported by the National Natural Science Foundation of China (No. 42062015) and the Natural Science Foundation of Guangxi (No. 2018AD19204) . The authors also extend their gratitude to Yunnan Provincial Government Leading Scientist Program, No. 2015HA024. 90 2 2 4 4 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2470-1343 ACS OMEGA ACS Omega 10.1021/acsomega.2c05759 0.0 OCT 2022 21 Chemistry, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry 5R1FL 36340099.0 Green Accepted 2023-03-23 WOS:000874263800001 0
J Wang, H; Wang, N; Li, MJ; Mi, SM; Shi, YY Wang, Hui; Wang, Ning; Li, MeiJie; Mi, Simeng; Shi, YaYa Student Physical Health Information Management Model under Big Data Environment SCIENTIFIC PROGRAMMING English Article NETWORK Health is considered an important foundation for students' success. However, with the accelerated pace of life, rising pressure from various parties, weak health awareness, lack of exercise time, and other reasons, students' physical quality is generally declining, the incidence of health diseases is increasing, and the onset age tends to be younger. With the development of the concept of health first, health management continues to expand and extend and students' health management has attracted more attention from many aspects. Due to the late and low starting point of health management research and the lack of professional theoretical support, a complete, mature, and effective health management service system has not been established to deal with the students' health. In order to make student health management more scientific, normative, and effective, this article has proposed big data technology to build the student health information management model. The first step of the approach is to store and analyze the data of students' physical health. It is necessary to combine the data collection, supervision, data analysis, and data application of students' physical health and gradually improve the national monitoring and evaluation system of students' physical health. Student health check-up management platform is mainly used in realizing the school student information management and student health information relationship between system, science, standardization, and automation, and its main task is to use a computer to perform daily management of all previous medical information of students, such as query, modify, add, delete, and enhance the physical health of students information management ability given the large data analysis of useful information. In addition, we have built a doctor recommendation model based on online questions and answers to give specific health recommendations for students of different physiques. [Wang, Hui] Henan Agr Univ, Sports Inst, Zhengzhou 450002, Peoples R China; [Wang, Hui; Wang, Ning] Gdansk Univ Phys Educ & Sport, Fac Phys Culture, PL-80336 Gdansk, Poland; [Li, MeiJie] Huanghe Univ Sci & Technol, Humour Div, Zhengzhou 450063, Peoples R China; [Li, MeiJie; Mi, Simeng; Shi, YaYa] Korea Gangneung Wonju Natl Univ, Sports Inst, Kangnung 25457, South Korea Henan Agricultural University; Gdansk University of Physical Education & Sport Li, MJ (corresponding author), Huanghe Univ Sci & Technol, Humour Div, Zhengzhou 450063, Peoples R China.;Li, MJ (corresponding author), Korea Gangneung Wonju Natl Univ, Sports Inst, Kangnung 25457, South Korea. wanghui@henau.edu.cn; 3757@zzti.edu.cn; 201508113@hhstu.edu.cn; 205910855@kfu.edu.cn; 201101029@jyu.edu.cn 26 10 10 6 46 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1058-9244 1875-919X SCI PROGRAMMING-NETH Sci. Program. JUL 10 2021.0 2021 5795884 10.1155/2021/5795884 0.0 10 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science TM7WD gold 2023-03-23 WOS:000675757200001 0
J Wang, RY; Cao, MQ; Yao, Y; Wu, WJ Wang, Ruoyu; Cao, Mengqiu; Yao, Yao; Wu, Wenjie The inequalities of different dimensions of visible street urban green space provision: A machine learning approach LAND USE POLICY English Article 4?A? framework; Disparity; Visible street urban green space; Street view data; Machine learning; Beijing ENVIRONMENTAL JUSTICE; PARK ACCESSIBILITY; SOCIAL EQUITY; SHANGHAI; ACCESS; DISPARITIES; AVAILABILITY; DISADVANTAGE; ASSOCIATION; INDICATORS Awareness is growing that the uneven provision of street urban green space (UGS) may lead to environmental injustice. Most previous studies have focused on the over-head perspective of street UGS provision. However, only a few studies have evaluated the disparities in visible street UGS provision. While a plethora of studies have focused on a single dimension of visible UGS provision, no previous studies have developed a framework for systematically evaluating visible street UGS provision. This study therefore proposes a novel 4 'A ' framework, and aims to assess different dimensions (namely: availability; accessibility; attractiveness; and aesthetics) of visible street UGS provision, using Beijing as a case study. It investigates inequities in different dimensions of visible street UGS provision. In addition, it also explores the extent to which a neighbourhood's economic level is associated with different dimensions of visible street UGS. Our results show that, in Beijing, the four chosen dimensions of visible street UGS provision significantly differ in terms of spatial distribution and the association between them. Furthermore, we found that the value of the Gini index and Moran's I index for attractiveness and aesthetics are higher than those for availability and accessibility, which indicates a more unequal distribution of visible street UGS from a qualitative perspective. We also found that a community's economic level is positively associated with attractiveness and aesthetics, while no evidence was found to support the claim that the eco-nomic level of a community associated with availability and accessibility. This study suggests that visible street UGS provision is unequal; therefore, urban planning policy should pay more attention to disparities in visible street UGS provision, particularly in urban areas. [Wang, Ruoyu] Queens Univ Belfast, Ctr Publ Hlth, Belfast, North Ireland; [Cao, Mengqiu] Univ Westminster, Sch Architecture & Cities, London NW1 5LS, England; [Cao, Mengqiu] London Sch Econ & Polit Sci, Dept Stat, London WC2A 2AE, England; [Yao, Yao] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China; [Yao, Yao] Univ Tokyo, Ctr Spatial Informat Sci, Chiba, Japan; [Wu, Wenjie] Wuhan Univ, Sch Urban Design, Wuhan 430072, Hubei, Peoples R China; [Wu, Wenjie] Hubei Habitat Environm Res Ctr Engn & Technol, Wuhan 430072, Hubei, Peoples R China Queens University Belfast; University of Westminster; University of London; London School Economics & Political Science; China University of Geosciences; University of Tokyo; Wuhan University Wu, WJ (corresponding author), Wuhan Univ, Sch Urban Design, Wuhan 430072, Hubei, Peoples R China. r.wang@qub.ac.uk; m.cao@westminster.ac.uk; yaoy@cug.edu.cn; caswwj@foxmail.com wu, wenjie/HKM-3623-2023 Wang, Ruoyu/0000-0002-7240-558X National Natural Science Foundation of China [41971194, 41801306, 52278085]; National Office for Philosophy and Social Sciences [20ZDA037]; Fundamental Research Funds for the Central Universities [21621105]; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [18S01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Office for Philosophy and Social Sciences; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University This work was supported by the National Natural Science Foundation of China (41971194 and 41801306, 52278085), the National Office for Philosophy and Social Sciences (20ZDA037), the Fundamental Research Funds for the Central Universities (21621105), and Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (Grant No.18S01). 89 0 0 22 22 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0264-8377 1873-5754 LAND USE POLICY Land Use Pol. DEC 2022.0 123 106410 10.1016/j.lusepol.2022.106410 0.0 9 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology 6F0ZI 2023-03-23 WOS:000883798400001 0
J Jin, C; Jin, SW Jin, Cong; Jin, Shu-Wei Multi-label automatic image annotation approach based on multiple improvement strategies IET IMAGE PROCESSING English Article EXTREME LEARNING-MACHINE; RELEVANCE; MODEL; CLASSIFICATION Currently, multi-label automatic image annotation (MAIA) approach based on machine learning has been widely applied and developed. Since extreme learning machine (ELM) has the advantages of simple structure, fast learning speed, better generalisation ability and so on, it is used for MAIA in this study. In order to enhance the annotation performance and generalisation ability of MAIA, some work is designed and implemented. First of all, a novel distance metric learning method based on cost-sensitive learning for MAIA task is proposed to reduce the impact of class imbalance of samples. Second, an improved ELM approach based on singular value decomposition is proposed for implementing MAIA task. Finally, the selection of training samples (STS) strategy based on error correlation is also proposed to improve the generalisation ability and annotation performance of MAIA. Based on the above work, a novel MAIA approach is implemented. The experimental results confirm that the proposed cost-sensitive DLM, improved cost-sensitive ELM and STS can obtain the good generalisation ability, and achieve better annotation performance than the existing MAIA approaches. [Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China; [Jin, Shu-Wei] Ecole Normale Super, 24 Rue Lhomond, F-75231 Paris 5, France Central China Normal University Jin, C (corresponding author), Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China. jincong@mail.ccnu.edu.cn 62 2 2 0 8 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. MAR 28 2019.0 13 4 623 633 10.1049/iet-ipr.2018.5371 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology HW2HA 2023-03-23 WOS:000466502600010 0
J Butollo, F; Gereffi, G; Yang, C; Krzywdzinski, M Butollo, Florian; Gereffi, Gary; Yang, Chun; Krzywdzinski, Martin Digital transformation and value chains: Introduction GLOBAL NETWORKS-A JOURNAL OF TRANSNATIONAL AFFAIRS English Article global commodity; value chains; global production networks; globalization; governance; ICT; transnational social relations GLOBAL VALUE CHAINS; GOVERNANCE; LABOR; RISE New digital technologies based on the internet of things and artificial intelligence play centre stage in contemporary discussions about the prospects for economic development and the future of work. This article summarizes theoretical and empirical contributions on how these technologies affect global value chains (GVCs). We argue that the leading paradigms that analyse global production - the GVC framework and the related global production networks (GPNs) approach - are in need of some 'technological uprading' themselves. The GVC/GPN approaches acknowledge that technology is constitutive for the emergence of geographically fragmented production, but rarely address directly how technological change affects interfirm relations. The authors provide a framework that acknowledges the key role of technology while situating digital tools and systems in their social embeddedness, that is the role of human agency and institutions in shaping their development and impact. A research agenda is outlined focusing on three topics: the varieties of digitalization approaches in different world regions, the role of data as a specific form of intangible resource and the role of platform business models for industrial ecosystems. These topics are addressed in the special issue of Global Networks on 'Digitalization and Value Chains' introduced by this contribution. [Butollo, Florian; Krzywdzinski, Martin] Berlin Social Sci Ctr, Weizenbaum Inst Networked Soc, Hardenbergstr 32, D-10623 Berlin, Germany; [Gereffi, Gary] Duke Univ, Dept Sociol, Durham, NC 27706 USA; [Yang, Chun] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China Duke University; Hong Kong Baptist University Butollo, F (corresponding author), Berlin Social Sci Ctr, Weizenbaum Inst Networked Soc, Hardenbergstr 32, D-10623 Berlin, Germany. Florian.butollo@wzb.eu Yang, Chun/AFP-6023-2022 Yang, Chun/0000-0002-0169-3449; Butollo, Florian/0000-0003-0749-240X 62 2 2 39 47 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1470-2266 1471-0374 GLOBAL NETW Glob. Netw. OCT 2022.0 22 4 585 594 10.1111/glob.12388 0.0 JUL 2022 10 Anthropology; Geography; Sociology Social Science Citation Index (SSCI) Anthropology; Geography; Sociology 4N8TL Green Published 2023-03-23 WOS:000826099500001 0
C Ouyang, YC; He, W; Li, XJ; Liu, JK; Li, G Ouyang, Yuncheng; He, Wei; Li, Xiajing; Liu, Jin-Kun; Li, Guang Vibration Control Based on Reinforcement Learning for a Single-link Flexible Robotic Manipulator IFAC PAPERSONLINE English Proceedings Paper 20th World Congress of the International-Federation-of-Automatic-Control (IFAC) JUL 09-14, 2017 Toulouse, FRANCE Int Federat Automat Control,Continental Automot,Occitanie Reg,Toulouse Metropole,CNES,Univ Toulouse III,Paul Sabatier,Inria,CNRS,OPTITRACK,MDPI,ISAE Supaero,iCODE,EECI,Int Journal Automat & Comp,IEEE CAA Journal Automatica Sinica,Moveo Neural Networks; Vibration Control; Flexible Robotic Manipulator; Assumed Mode Method; Reinforcement Learning In this paper, we focus on the reinforcement learning control of a single-link flexible manipulator and attempt to suppress the vibration due to its flexibility and lightweight structure. The assumed mode method (AMM) and the Lagranges equation are adopted in modeling to enhance the satisfaction of precision. Two radial basis function neural networks (RBFNNs) are employed in the designed control algorithm, actor neural network (NN) for generating a policy and critic NN for evaluating the cost-function. Rigorous stability of the system has been proven via Lyapunovs direct method. According to the performance of simulation for the proposed control scheme, the superiority and feasibility of the proposed controller is verified. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [Ouyang, Yuncheng] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China; [Ouyang, Yuncheng] Univ Elect Sci & Technol China, Ctr Robot, Chengdu 611731, Sichuan, Peoples R China; [He, Wei] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China; [Li, Xiajing] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands; [Liu, Jin-Kun] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Li, Guang] Queen Mary Univ London, Sch Engn & Mat Sci, Mile End Rd, London E1 4NS, England University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; University of Science & Technology Beijing; Delft University of Technology; Beihang University; University of London; Queen Mary University London He, W (corresponding author), Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China. hewei.ac@gmail.com Li, Guang/AAC-6883-2021 Li, Guang/0000-0003-1068-0479 National Basic Research Program of China (973 Program) [2013CB733100]; National Natural Science Foundation of China [61522302, 6161101273, 61520106009, 61533008]; Newton Advanced Fellowship from The Royal Society, UK [NA160436]; Beijing Natural Science Foundation [4172041]; Fundamental Research Funds for the China Central Universities of USTB [FRF-BD-16-005A, FRF-TP-15-005C1] National Basic Research Program of China (973 Program)(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Newton Advanced Fellowship from The Royal Society, UK; Beijing Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the China Central Universities of USTB This work was supported by the National Basic Research Program of China (973 Program) under Grant 2013CB733100, the National Natural Science Foundation of China under Grant 61522302, 6161101273, 61520106009, 61533008, the Newton Advanced Fellowship from The Royal Society, UK, under Grant NA160436, the Beijing Natural Science Foundation under Grant 4172041, and the Fundamental Research Funds for the China Central Universities of USTB under Grant FRF-BD-16-005A and FRF-TP-15-005C1. 18 2 3 1 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2017.0 50 1 3476 3481 10.1016/j.ifacol.2017.08.932 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems FU6LT gold 2023-03-23 WOS:000423964800077 0
J Liu, ZJ; Liang, JY; Zhang, S; Kayacan, E Liu, Zhijie; Liang, Jinyuan; Zhang, Shuang; Kayacan, Erdal ANN-Based vibration control of an aerial refueling hose system with input nonlinearity and prescribed output constraint JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS English Article BOUNDARY CONTROL; COMPENSATION An adaptive artificial neural network (ANN) boundary control scheme is developed for an autonomous aerial refueling hose system in this study. The objectives of this paper are 1) to guarantee the state of the system uniformly bounded, 2) to make the system output meet the prescribed performance, that is, -chi(a)(t) < y(l(t), t) < chi(b)(t). The proposed novel control law can achieve control objectives by suppressing vibrations of the hose. The unmodeled dynamics of the system are handled by the approximation ability of the designed ANN. The Nussbaum function is employed to construct an auxiliary control signal to deal with the input saturation nonlinearity of the system. By applying the novel control law, the state of the closed-loop system is uniformly bounded which is proved by Lyapunov's direct method. Without violating the asymmetric time-varying prescribed function on system output is obtained by the barrier Lyapunov function. Extensive numerical simulations are performed to confirm the efficiency and efficacy of the proposed novel control strategy which allows multiple control objectives. The simulation results show that the output constraint is never violated, which is guaranteed by the asymmetric Lyapunov function. (C) 2022 Published by Elsevier Ltd on behalf of The Franklin Institute. [Liu, Zhijie; Liang, Jinyuan; Zhang, Shuang] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China; [Liu, Zhijie; Liang, Jinyuan; Zhang, Shuang] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China; [Kayacan, Erdal] Aarhus Univ, Dept Elect & Comp Engn, Artificial Intelligence Robot Lab AiR Lab, DK-8000 Aarhus C, Denmark University of Science & Technology Beijing; University of Science & Technology Beijing; Aarhus University Zhang, S (corresponding author), Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China.;Zhang, S (corresponding author), Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China. liuzhijie@ustb.edu.cn; liangjinyuan@xs.ustb.edu.cn; zhangshuang_ac@ustb.edu.cn; erdal@ece.au.dk Kayacan, Erdal/D-6256-2014; Liu, Zhijie/U-1908-2018 Kayacan, Erdal/0000-0002-4873-2513; Liu, Zhijie/0000-0001-9522-4178 National Natural Science Foundation of China [62073030, U20A20225]; Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB [BK21BF003]; Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB; Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing This work is supported by the National Natural Science Foundation of China under Grant 62073030, U20A20225, Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB under Grant BK21BF003, and Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing. 26 0 0 3 4 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0016-0032 1879-2693 J FRANKLIN I J. Frankl. Inst.-Eng. Appl. Math. APR 2022.0 359 6 2627 2645 10.1016/j.jfranklin.2022.01.006 0.0 MAR 2022 19 Automation & Control Systems; Engineering, Multidisciplinary; Engineering, Electrical & Electronic; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics 2S0JO 2023-03-23 WOS:000821488400011 0
J Sun, T; Dai, ZD; Manoonpong, P Sun, Tao; Dai, Zhendong; Manoonpong, Poramate Distributed-force-feedback-based reflex with online learning for adaptive quadruped motor control NEURAL NETWORKS English Article Reflexes; CPGs; Motor learning; Offset adaptation; Slope terrains; Quadruped robots CENTRAL PATTERN GENERATORS; DYNAMIC WALKING; ROBOTS; LOCOMOTION; CONTACT; CPG Biological motor control mechanisms (e.g., central pattern generators (CPGs), sensory feedback, reflexes, and motor learning) play a crucial role in the adaptive locomotion of animals. However, the interaction and integration of these mechanisms - necessary for generating the efficient, adaptive locomotion responses of legged robots to diverse terrains - have not yet been fully realized. One issue is that of achieving adaptive motor control for fast postural adaptation across various terrains. To address this issue, this study proposes a novel distributed-force-feedback-based reflex with online learning (DFRL). It integrates force-sensory feedback, reflexes, and learning to cooperate with CPGs in producing adaptive motor commands. The DFRL is based on a simple neural network that uses plastic synapses modulated online by a fast dual integral learner. Experimental results on different quadruped robots show that the DFRL can (1) automatically and rapidly adapt the CPG patterns (motor commands) of the robots, enabling them to realize appropriate body postures during locomotion and (2) enable the robots to effectively accommodate themselves to various slope terrains, including steep ones. Consequently, the DFRL-controlled robots can achieve efficient adaptive locomotion, to tackle complex terrains with diverse slopes. (C) 2021 Elsevier Ltd. All rights reserved. [Sun, Tao; Dai, Zhendong; Manoonpong, Poramate] Nanjing Univ Aeronaut & Astronaut, Inst Bioinspired Struct & Surface Engn, Coll Mech & Elect Engn, Nanjing, Peoples R China; [Sun, Tao; Manoonpong, Poramate] Univ Southern Denmark, Embodied AI & Neurorobot Lab, SDU Biorobot, Maersk Mc Kinney Moller, Odense M, Denmark Nanjing University of Aeronautics & Astronautics; University of Southern Denmark Dai, ZD (corresponding author), Nanjing Univ Aeronaut & Astronaut, Inst Bioinspired Struct & Surface Engn, Coll Mech & Elect Engn, Nanjing, Peoples R China. s.tao@nuaa.edu.cn; zddai@nuaa.edu.cn; poma@mmmi.sdu.dk ; Manoonpong, Poramate/Y-8479-2018 Sun, Tao/0000-0002-7249-931X; Manoonpong, Poramate/0000-0002-4806-7576 National key R&D Program of China, Topic 4-NUAA [2020 YFB1313504]; NSFC-DFG Collaborative Research Program of the National Natural Science Foundation of China [51861135 306]; National Natural Science Foundation of China [51435008]; CSC Scholarship, China [CSC201906830012] National key R&D Program of China, Topic 4-NUAA; NSFC-DFG Collaborative Research Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CSC Scholarship, China This work was supported by the National key R&D Program of China, Topic 4-NUAA [Grant No. 2020 YFB1313504 to P.M.] , the NSFC-DFG Collaborative Research Program [Grant No. 51861135 306 to P.M., project Co-PI] of the National Natural Science Foundation of China, the National Natural Science Foundation of China [Grant No. 51435008 to Z.D.] , and a CSC Scholarship, China [Grant No. CSC201906830012 to T.S.] . 63 4 4 5 29 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. OCT 2021.0 142 410 427 10.1016/j.neunet.2021.06.001 0.0 JUN 2021 18 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology UJ9WU 34139657.0 2023-03-23 WOS:000691628800002 0
J Ahmed, I; Ahmad, M; Rodrigues, JJPC; Jeon, G Ahmed, Imran; Ahmad, Misbah; Rodrigues, Joel J. P. C.; Jeon, Gwanggil Edge computing-based person detection system for top view surveillance: Using CenterNet with transfer learning APPLIED SOFT COMPUTING English Article Edge computing; Deep learning; Person detection; Top view; Transfer learning; CenterNet PEOPLE Edge computing significantly expands the range of information technology in smart video surveillance applications in the era of intelligent and connected cities. Edge devices, including Internet of Things based cameras and sensors, produce a large amount of data and have frequently become prominent components for various public surveillance and monitoring applications. The data generated by these smart devices are in the form of videos and images that need to be processed and analyzed in real-time with substantial computation resources. These developed techniques still require large computation resources for real-time surveillance applications. In this regard, Edge computing plays a promising role in order to provide high computation and low-latency requirements. With these motivations, in this work, a real-time top view-based person detection system is presented. We utilize a one-stage deep learning-based object detection algorithm, i.e., CenterNet, for person detection. The model detects the human as a single point, also referred to as its bounding box's center point. The model does a key-point calculation to obtain the center point and regresses all other information regarding the target object's features, size, location, and orientation. Training and testing of the model are performed on a top view data set. The detection results are also compared with conventional detection methods using the same data set. The overall detection accuracy of the model is 95%. (C) 2021 Elsevier B.V. All rights reserved. [Ahmed, Imran; Ahmad, Misbah] Inst Management Sci, Ctr Excellence Informat Technol, 1-A,Sect E-5,Phase 7, Peshawar, Pakistan; [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Postgrad Program Elect Engn PPGEE, BR-64049550 Teresina, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-6201001 Covilha, Portugal; [Jeon, Gwanggil] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China; [Jeon, Gwanggil] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea Universidade Federal do Piaui; Xidian University; Incheon National University Jeon, G (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China.;Jeon, G (corresponding author), Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 22012, South Korea. imran.ahmed@imsciences.edu.pk; misbahahmad4872@gmail.com; joeljr@ieee.org; gjeon@inu.ac.kr Ahmed, Imran/HDL-7255-2022; Rodrigues, Joel J. P. C./A-8103-2013 Rodrigues, Joel J. P. C./0000-0001-8657-3800; Ahmad, Misbah/0000-0001-7013-0159 FCT/MCTES; EU funds [UIDB/50008/2020]; Brazilian National Council for Scientific and Technological Development - CNPq [313036/20209] FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU funds; Brazilian National Council for Scientific and Technological Development - CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work is partially funded by FCT/MCTES through national funds and when applicable cofunded EU funds under the Project UIDB/50008/2020; and by Brazilian National Council for Scientific and Technological DevelopmentCNPq, via Grant No. 313036/20209. 65 19 20 4 31 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. AUG 2021.0 107 107489 10.1016/j.asoc.2021.107489 0.0 MAY 2021 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science SV3RH 2023-03-23 WOS:000663739100003 0
C Wang, SS; Yan, QB; Chen, ZX; Yang, B; Zhao, C; Conti, M IEEE Wang, Shanshan; Yan, Qiben; Chen, Zhenxiang; Yang, Bo; Zhao, Chuan; Conti, Mauro TextDroid: Semantics-based Detection of Mobile Malware Using Network Flows 2017 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) IEEE Conference on Computer Communications Workshops English Proceedings Paper IEEE Conference on Computer Communications (IEEE INFOCOM) MAY 01-04, 2017 Atlanta, GA IEEE The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities. [Wang, Shanshan; Chen, Zhenxiang; Yang, Bo; Zhao, Chuan] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Shandong, Peoples R China; [Yan, Qiben] Univ Nebraska Lincoln, Dept Comp Sci & Engn, Lincoln, NE USA; [Conti, Mauro] Univ Padua, Dipartimento Matemat, Padua, Italy University of Jinan; University of Nebraska System; University of Nebraska Lincoln; University of Padua Chen, ZX (corresponding author), Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Shandong, Peoples R China. czx@ujn.edu.cn Conti, Mauro/F-9145-2012; Yan, Qiben/AAZ-3002-2020 Conti, Mauro/0000-0002-3612-1934; Yan, Qiben/0000-0001-6272-7668 National Natural Science Foundation of China [61672262, 61573166, 61472164, 61572230]; Natural Science Foundation of Shandong Province [ZR2014JL042, ZR2012FM010]; Shandong Provincial Key RD Program [2016GGX101001]; NSF [CNS-1566388]; Direct For Computer & Info Scie & Enginr; Division Of Computer and Network Systems [1566388] Funding Source: National Science Foundation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); Shandong Provincial Key RD Program; NSF(National Science Foundation (NSF)); Direct For Computer & Info Scie & Enginr; Division Of Computer and Network Systems(National Science Foundation (NSF)NSF - Directorate for Computer & Information Science & Engineering (CISE)) This work was supported by the National Natural Science Foundation of China under Grants No. 61672262, No. 61573166, No. 61472164 and No. 61572230, the Natural Science Foundation of Shandong Province under Grants No. ZR2014JL042 and No. ZR2012FM010, the Shandong Provincial Key R&D Program under Grants No. 2016GGX101001. This work is also supported in part by NSF grant CNS-1566388. 13 16 16 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2159-4228 978-1-5386-2784-6 IEEE CONF COMPUT 2017.0 18 23 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BJ1WT 2023-03-23 WOS:000418325100004 0
J Kloeckner, K; Adam, CM; Anerousis, N; Ayachitula, N; Bulut, MF; Dasgupta, G; Deng, Y; Diao, Y; Fuller, N; Gopisetty, S; Hernandez, M; Hwang, J; Iannucci, P; Kalia, AK; Lanfranchi, G; Lanyi, D; Ludwig, H; Mahamuni, A; Mahindru, R; Meng, FJ; Nezhad, HRM; Murthy, K; Nakamura, T; Paradkar, A; Perpetua, D; Pfitzmann, B; Rosu, D; Shwartz, L; Su, Z; Surendra, M; Tao, S; Volzer, H; Vukovic, M; Wiesmann, D; Wozniak, S; Wright, G; Xiao, J; Zeng, S Kloeckner, K.; Adam, C. M.; Anerousis, N.; Ayachitula, N.; Bulut, M. F.; Dasgupta, G.; Deng, Y.; Diao, Y.; Fuller, N.; Gopisetty, S.; Hernandez, M.; Hwang, J.; Iannucci, P.; Kalia, A. K.; Lanfranchi, G.; Lanyi, D.; Ludwig, H.; Mahamuni, A.; Mahindru, R.; Meng, F. J.; Nezhad, H. R. Motahari; Murthy, K.; Nakamura, T.; Paradkar, A.; Perpetua, D.; Pfitzmann, B.; Rosu, D.; Shwartz, L.; Su, Z.; Surendra, M.; Tao, S.; Volzer, H.; Vukovic, M.; Wiesmann, D.; Wozniak, S.; Wright, G.; Xiao, J.; Zeng, S. Building a cognitive platform for the managed IT services lifecycle IBM JOURNAL OF RESEARCH AND DEVELOPMENT English Article In this paper, we present IBM's cognitive strategy for service delivery transformation to a services integration approach for IT service management. This transformation is fueled by three fundamental technological paradigms: cognitive, cloud, and data and digital content. At the foundation of this approach is the IBM Services Platform with Watson-an IBM Cloud and Watson-based platform that uses machine learning, natural language, discovery, and various Watson application programming interfaces to design superior client solutions, achieve an exceptional level of autonomic service management, and facilitate a healthy always-on environment. It continuously learns and optimizes information technology performance to enable enhanced client business outcomes. [Kloeckner, K.] IBM Global Technol Serv, Armonk, NY 10504 USA; [Adam, C. M.; Ayachitula, N.; Bulut, M. F.; Deng, Y.; Diao, Y.; Fuller, N.; Hernandez, M.; Hwang, J.; Kalia, A. K.; Mahindru, R.; Murthy, K.; Nakamura, T.; Paradkar, A.; Rosu, D.; Shwartz, L.; Surendra, M.; Tao, S.; Vukovic, M.; Xiao, J.; Zeng, S.] IBM Res, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA; [Anerousis, N.] IBM Global Technologv Serv GTS, Almaden Res Ctr, San Jose, CA 95120 USA; [Dasgupta, G.] IBM Res, Bangalore 560045, Karnataka, India; [Gopisetty, S.] IBM Res, San Jose, CA 95120 USA; [Iannucci, P.] IBM Global Technol Serv, I-00144 Rome, Italy; [Lanfranchi, G.] IBM Global Technol Serv GTS, I-00144 Rome, Italy; [Lanyi, D.; Pfitzmann, B.; Volzer, H.; Wiesmann, D.; Wozniak, S.] IBM Res, CH-8803 Zurich, Switzerland; [Ludwig, H.; Nezhad, H. R. Motahari] IBM Res, Almaden Res Ctr, San Jose, CA 95120 USA; [Mahamuni, A.] IBM Global Technol Serv GTS, Bangalore 560045, Karnataka, India; [Meng, F. J.; Su, Z.] IBM Res, Beijing 100094, Peoples R China; [Perpetua, D.; Wright, G.] IBM Global Technol Serv Technol Innovat & Automat, Armonk, NY 10504 USA International Business Machines (IBM); International Business Machines (IBM); International Business Machines (IBM); International Business Machines (IBM); International Business Machines (IBM); International Business Machines (IBM) Adam, CM (corresponding author), IBM Res, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA. cmadam@us.ibm.com; nikos@us.ibm.com; mfbulut@us.ibm.com; gaargidasgupta@in.ibm.com; dengy@us.ibm.com; diao@us.ibm.com; nfuller@us.ibm.com; Sandeep.Gopisetty@us.ibm.com; miltonh@us.ibm.com; jinho@us.ibm.com; pietro.iannucci@it.ibm.com; anup.kalia@us.ibm.com; 75823595@it.ibm.com; dla@zurich.ibm.com; hludwig@us.ibm.com; mamol@in.ibm.com; rmahindr@us.ibm.com; mengfj@.cn.ibm.com; motahari@us.ibm.com; kmurthy@us.ibm.com; taiga@us.ibm.com; paradkar@us.ibm.com; perpeden@us.ibm.com; bpf@zurich.ibm.com; drosu@us.ibm.com; lshwart@us.ibm.com; suzhong@cn.ibm.com; suren@us.ibm.com; shutao@us.ibm.com; hvo@zurich.ibm.com; maja@us.ibm.com; dor@zurich.ibm.com; stw@zurich.ibm.com; gjwright@us.ibm.com; jinoaix@us.ibm.com; saizeng@us.ibm.com Bulut, Muhammed/GVS-9099-2022 20 3 3 0 6 IBM CORP ARMONK 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA 0018-8646 2151-8556 IBM J RES DEV IBM J. Res. Dev. JAN-FEB 2018.0 62 1 8 10.1147/JRD.2017.2770642 0.0 11 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GQ0ZV 2023-03-23 WOS:000441352000009 0
J Wang, YD; Li, XP; Ruiz, R Wang, Yadi; Li, Xiaoping; Ruiz, Ruben Feature Selection With Maximal Relevance and Minimal Supervised Redundancy IEEE TRANSACTIONS ON CYBERNETICS English Article; Early Access Redundancy; Mutual information; Correlation; Minimization; Feature extraction; Entropy; Electronic mail; Classification; conditional mutual information; feature selection (FS); mutual information; supervised similarity measure MUTUAL INFORMATION; DEPENDENCY; MACHINE Feature selection (FS) for classification is crucial for large-scale images and bio-microarray data using machine learning. It is challenging to select informative features from high-dimensional data which generally contains many irrelevant and redundant features. These features often impede classifier performance and misdirect classification tasks. In this article, we present an efficient FS algorithm to improve classification accuracy by taking into account both the relevance of the features and the pairwise features correlation in regard to class labels. Based on conditional mutual information and entropy, a new supervised similarity measure is proposed. The supervised similarity measure is connected with feature redundancy minimization evaluation and then combined with feature relevance maximization evaluation. A new criterion max-relevance and min-supervised-redundancy (MRMSR) is introduced and theoretically proved for FS. The proposed MRMSR-based method is compared to seven existing FS approaches on several frequently studied public benchmark datasets. Experimental results demonstrate that the proposal is more effective at selecting informative features and results in better competitive classification performance. [Wang, Yadi] Henan Univ, Sch Comp & Informat Engn, Henan Key Lab Big Data Anal & Proc, Kaifeng 475004, Peoples R China; [Wang, Yadi] Henan Univ, Sch Comp & Informat Engn, Inst Data & Knowledge Engn, Kaifeng 475004, Peoples R China; [Li, Xiaoping] Southeast Univ, Minist Educ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China; [Li, Xiaoping] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 211189, Peoples R China; [Ruiz, Ruben] Univ Politecn Valencia, Grp Sistemas Optimizac Aplicada, Inst Tecnol Informat Ciudad Politecn Innovac, Valencia 46021, Spain Henan University; Henan University; Southeast University - China; Southeast University - China; Universitat Politecnica de Valencia Li, XP (corresponding author), Southeast Univ, Minist Educ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.;Li, XP (corresponding author), Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 211189, Peoples R China. yadiwang@seu.edu.cn; xpli@seu.edu.cn; rruiz@eio.upv.es National Key Research and Development Program of China [2018YFB1402500]; National Natural Science Foundation of China [62106066, 61872077, 61832004]; Collaborative Innovation Center of Wireless Communications Technology and Key Research Projects of Henan Higher Education Institutions [22A520019]; Key Research and Promotion Projects of Henan Province [222102210151]; Spanish Ministry of Science, Innovation, and Universities through the Project OPTEP-Port Terminal Operations Optimization [RTI2018-094940-B-I00]; FEDER Funds National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Collaborative Innovation Center of Wireless Communications Technology and Key Research Projects of Henan Higher Education Institutions; Key Research and Promotion Projects of Henan Province; Spanish Ministry of Science, Innovation, and Universities through the Project OPTEP-Port Terminal Operations Optimization; FEDER Funds(European Commission) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1402500; in part by the National Natural Science Foundation of China under Grant 62106066, Grant 61872077, and Grant 61832004; in part by the Collaborative Innovation Center of Wireless Communications Technology and Key Research Projects of Henan Higher Education Institutions under Grant 22A520019; and in part by the Key Research and Promotion Projects of Henan Province under Grant 222102210151. The work of Ruben Ruiz was supported in part by the Spanish Ministry of Science, Innovation, and Universities through the Project OPTEP-Port Terminal Operations Optimization under Grant RTI2018-094940-B-I00 financed with FEDER Funds. 55 1 1 16 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. 10.1109/TCYB.2021.3139898 0.0 FEB 2022 11 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science YX7KK 35130179.0 2023-03-23 WOS:000754277100001 0
C Hu, T; Yang, PW; Zhang, CL; Yu, G; Mu, YD; Snoek, CGM AAAI Hu, Tao; Yang, Pengwan; Zhang, Chiliang; Yu, Gang; Mu, Yadong; Snoek, Cees G. M. Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence JAN 27-FEB 01, 2019 Honolulu, HI Assoc Advancement Artificial Intelligence Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixel-level annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attention-based Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot selling. [Hu, Tao; Snoek, Cees G. M.] Univ Amsterdam, Amsterdam, Netherlands; [Hu, Tao; Yang, Pengwan; Mu, Yadong] Peking Univ, Beijing, Peoples R China; [Zhang, Chiliang] Tsinghua Univ, Beijing, Peoples R China; [Yu, Gang] Megvii Inc Face, Beijing, Peoples R China University of Amsterdam; Peking University; Tsinghua University Hu, T (corresponding author), Univ Amsterdam, Amsterdam, Netherlands.;Hu, T (corresponding author), Peking Univ, Beijing, Peoples R China. taohu620@gmail.com; yangpengwan2016@gmail.com; zhangcl16@mails.tsinghua.edu.cn; yugang@megvii.com; myd@pku.edu.cn; cgmsnoek@uva.nl NJUST Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information [JYB201701]; Beijing Municipal Commission of Science and Technology [181100008918005] NJUST Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information; Beijing Municipal Commission of Science and Technology This work is supported by NJUST Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information under Grant No. JYB201701 and Beijing Municipal Commission of Science and Technology under Grant No. 181100008918005. 28 52 53 1 8 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-809-1 AAAI CONF ARTIF INTE 2019.0 8441 8448 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BN6ZH 2023-03-23 WOS:000486572502119 0
J Jonauskaite, D; Abu-Akel, A; Dael, N; Oberfeld, D; Abdel-Khalek, AM; Al-Rasheed, AS; Antonietti, JP; Bogushevskaya, V; Chamseddine, A; Chkonia, E; Corona, V; Fonseca-Pedrero, E; Griber, YA; Grimshaw, G; Hasan, AA; Havelka, J; Hirnstein, M; Karlsson, BSA; Laurent, E; Lindeman, M; Marquardt, L; Mefoh, P; Papadatou-Pastou, M; Perez-Albeniz, A; Pouyan, N; Roinishvili, M; Romanyuk, L; Montejo, AS; Schrag, Y; Sultanova, A; Uuskula, M; Vainio, S; Wasowicz, G; Zdravkovic, S; Zhang, M; Mohr, C Jonauskaite, Domicele; Abu-Akel, Ahmad; Dael, Nele; Oberfeld, Daniel; Abdel-Khalek, Ahmed M.; Al-Rasheed, Abdulrahman S.; Antonietti, Jean-Philippe; Bogushevskaya, Victoria; Chamseddine, Amer; Chkonia, Eka; Corona, Violeta; Fonseca-Pedrero, Eduardo; Griber, Yulia A.; Grimshaw, Gina; Hasan, Aya Ahmed; Havelka, Jelena; Hirnstein, Marco; Karlsson, Bodil S. A.; Laurent, Eric; Lindeman, Marjaana; Marquardt, Lynn; Mefoh, Philip; Papadatou-Pastou, Marietta; Perez-Albeniz, Alicia; Pouyan, Niloufar; Roinishvili, Maya; Romanyuk, Lyudmyla; Salgado Montejo, Alejandro; Schrag, Yann; Sultanova, Aygun; Uuskuela, Mari; Vainio, Suvi; Wasowicz, Grazyna; Zdravkovic, Suncica; Zhang, Meng; Mohr, Christine Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity PSYCHOLOGICAL SCIENCE English Article affect; color perception; cross-cultural; universality; cultural relativity; pattern analysis; open data; open materials LANGUAGES; MEANINGS; RED Many of us see red, feel blue, or turn green with envy. Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficientr= .88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design. [Jonauskaite, Domicele; Abu-Akel, Ahmad; Dael, Nele; Antonietti, Jean-Philippe; Pouyan, Niloufar; Schrag, Yann; Mohr, Christine] Univ Lausanne, Inst Psychol, Batiment Geopolis, CH-1015 Lausanne, Switzerland; [Dael, Nele] Univ Lausanne, Dept Org Behav, Lausanne, Switzerland; [Oberfeld, Daniel] Johannes Gutenberg Univ Mainz, Inst Psychol, Mainz, Germany; [Abdel-Khalek, Ahmed M.; Hasan, Aya Ahmed] Alexandria Univ, Fac Arts, Dept Psychol, Alexandria, Egypt; [Al-Rasheed, Abdulrahman S.] King Saud Univ, Dept Psychol, Riyadh, Saudi Arabia; [Bogushevskaya, Victoria] Univ Cattolica Sacro Cuore, Dept Linguist Sci & Foreign Literatures, Milan, Italy; [Chamseddine, Amer] Swiss Fed Inst Technol Lausanne, Sch Comp & Commun Sci, Lausanne, Switzerland; [Chkonia, Eka] Tbilisi State Med Univ, Dept Psychiat, Tbilisi, Georgia; [Corona, Violeta] Univ Panamericana, Escuela Ciencias Econ & Empresari, Mexico City, DF, Mexico; [Corona, Violeta] Univ Politecn Valencia, Business Management Dept, Valencia, Spain; [Fonseca-Pedrero, Eduardo; Perez-Albeniz, Alicia] Univ La Rioja, Dept Educ Sci, La Rioja, Spain; [Griber, Yulia A.] Smolensk State Univ, Dept Sociol & Philosophy, Smolensk, Russia; [Grimshaw, Gina] Victoria Univ Wellington, Sch Psychol, Wellington, New Zealand; [Havelka, Jelena] Univ Leeds, Sch Psychol, Leeds, W Yorkshire, England; [Hirnstein, Marco; Marquardt, Lynn] Univ Bergen, Dept Biol & Med Psychol, Bergen, Norway; [Karlsson, Bodil S. A.] Res Inst Sweden AB, Div Built Environm, Gothenburg, Sweden; [Laurent, Eric] Univ Bourgogne Franche Comte, Lab Psychol, Besancon, France; [Laurent, Eric] CNRS, Maison Sci Homme & Environm, Paris, France; [Laurent, Eric] Univ Franche Comte, Besancon, France; [Lindeman, Marjaana] Univ Helsinki, Dept Psychol & Logoped, Helsinki, Finland; [Mefoh, Philip] Univ Nigeria, Dept Psychol, Nsukka, Nigeria; [Papadatou-Pastou, Marietta] Natl & Kapodistrian Univ Athens, Sch Educ, Athens, Greece; [Papadatou-Pastou, Marietta] Acad Athens, Biomed Res Fdn BRFaa, Athens, Greece; [Roinishvili, Maya] I Beritashvili Ctr Expt Biomed, Lab Vis Physiol, Tbilisi, Georgia; [Romanyuk, Lyudmyla] Taras Shevchenko Natl Univ Kyiv, Fac Psychol, Kiev, Ukraine; [Romanyuk, Lyudmyla] VI Vernadsky Taurida Natl Univ, Dept Psychol, Simferopol, Ukraine; [Romanyuk, Lyudmyla] Kyiv Natl Univ Culture & Arts, Dept Psychol, Kiev, Ukraine; [Salgado Montejo, Alejandro] Univ La Sabana, Escuela Int Ciencias Econ & Adm, Cundinamarca, Colombia; [Salgado Montejo, Alejandro] BI Norwegian Business Sch, Ctr Multisensory Mkt, Oslo, Norway; [Salgado Montejo, Alejandro] Neurosketch, Bogota, Colombia; [Sultanova, Aygun] Natl Mental Hlth Ctr, Minist Hlth, Baku, Azerbaijan; [Uuskuela, Mari] Tallinn Univ, Sch Humanities, Tallinn, Estonia; [Vainio, Suvi] Univ Helsinki, Fac Social Sci, Helsinki, Finland; [Wasowicz, Grazyna] Kozminski Univ, Dept Econ Psychol, Warsaw, Poland; [Zdravkovic, Suncica] Univ Novi Sad, Dept Psychol, Novi Sad, Serbia; [Zdravkovic, Suncica] Univ Belgrade, Lab Expt Psychol, Belgrade, Serbia; [Zhang, Meng] Zhejiang Univ, Dept Psychol & Behav Sci, Hangzhou, Peoples R China University of Lausanne; University of Lausanne; Johannes Gutenberg University of Mainz; Egyptian Knowledge Bank (EKB); Alexandria University; King Saud University; Catholic University of the Sacred Heart; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Tbilisi State Medical University; Universidad Panamericana - Ciudad de Mexico; Universitat Politecnica de Valencia; Universidad de La Rioja; Smolensk State University; Victoria University Wellington; University of Leeds; University of Bergen; Acreo AB; Universite de Franche-Comte; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; University of Helsinki; University of Nigeria; National & Kapodistrian University of Athens; Academy of Athens; Ministry of Education & Science of Ukraine; Taras Shevchenko National University Kiev; Ministry of Education & Science of Ukraine; Taurida National V.I.Vernadsky University; VI Vernadsky Crimean Federal University; Kyiv National University of Culture & Arts; Universidad de La Sabana; BI Norwegian Business School; Tallinn University; University of Helsinki; Kozminski University; University of Novi Sad; University of Belgrade; Zhejiang University Jonauskaite, D (corresponding author), Univ Lausanne, Inst Psychol, Batiment Geopolis, CH-1015 Lausanne, Switzerland. domicele.jonauskaite@unil.ch Fonseca, Eduardo/L-4848-2019; Papadatou-Pastou, Marietta/O-3216-2019; LAURENT, Éric/AAW-4345-2020; Jonauskaite, Domicele/AAF-1166-2019; Chkonia, Eka/F-4517-2014; Chkonia, Eka/AAG-7056-2022; Bogushevskaya, Victoria/A-4998-2018; Griber, Yulia A./AAG-4410-2019; Hirnstein, Marco/D-2450-2013; Romanyuk, Lyudmyla/ABB-7453-2020; Chkonia, Eka/GQQ-1751-2022; Chkonia, Eka/AFL-7032-2022; Grimshaw, Gina/P-1207-2016 Papadatou-Pastou, Marietta/0000-0002-2834-4003; LAURENT, Éric/0000-0002-7112-6765; Jonauskaite, Domicele/0000-0002-7513-9766; Chkonia, Eka/0000-0002-8817-9679; Bogushevskaya, Victoria/0000-0001-8147-3800; Griber, Yulia A./0000-0002-2603-5928; Hirnstein, Marco/0000-0002-6291-0929; Romanyuk, Lyudmyla/0000-0003-0422-1073; Chkonia, Eka/0000-0002-8817-9679; Abu-Akel, Ahmad/0000-0003-3791-8643; Grimshaw, Gina/0000-0002-1291-1368; Mohr, Christine/0000-0002-3720-7115; Lindeman, Marjaana/0000-0003-4176-3736; Oberfeld, Daniel/0000-0002-6710-3309 Swiss National Science Foundation [P0LAP1_ 175055, 100014_182138]; Bergen Research Foundation [BFS2016REK03]; Russian Foundation for Basic Research [17-29-09145]; AkzoNobel; Imperial Chemical Industries Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); Bergen Research Foundation; Russian Foundation for Basic Research(Russian Foundation for Basic Research (RFBR)); AkzoNobel; Imperial Chemical Industries This research was supported by the Swiss National Science Foundation, which provided a Doc.CH fellowship grant to D. Jonauskaite (P0LAP1_ 175055) and a project funding grant to C. Mohr and N. Dael (100014_182138). M. Hirnstein was supported by a research grant from the Bergen Research Foundation (BFS2016REK03). Y. A. Griber was supported by the Russian Foundation for Basic Research (17-29-09145). The initiation of this research was made possible through the support of AkzoNobel, Imperial Chemical Industries, David Elliott and Tom Curwen (Color R& I Team, Slough, United Kingdom), and Stephanie Kraneveld (Sassenheim, The Netherlands). 41 30 31 9 61 SAGE PUBLICATIONS INC THOUSAND OAKS 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA 0956-7976 1467-9280 PSYCHOL SCI Psychol. Sci. OCT 2020.0 31 10 1245 1260 956797620948810 10.1177/0956797620948810 0.0 SEP 2020 16 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology OC7AU 32900287.0 Green Published, Green Accepted 2023-03-23 WOS:000568695200001 0
J Xu, BB; Wang, WS; Guo, LF; Chen, GP; Li, YF; Cao, Z; Wu, SS Xu, Beibei; Wang, Wensheng; Guo, Leifeng; Chen, Guipeng; Li, Yongfeng; Cao, Zhen; Wu, Saisai CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Face recognition; RetinaFace; ArcFace loss; Deep learning; Precision livestock RECOGNITION Cattle identification is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. Traditional non-biometrics methods for cattle identification is not really satisfactory in providing reliability due to theft, fraud, and duplication. In this study, a computer vision technique was proposed to facilitate precision animal management and improve livestock welfare. This paper presents a novel face identification framework by integrating light-weight RetinaFace-mobilenet with Additive Angular Margin Loss (ArcFace), namely CattleFaceNet. RetinaFace-mobilenet is designed for face detection and location, and ArcFace is adopted to strengthen the within-class compactness and also between-class discrepancy during training. Experiments on real-word scenarios dataset prove that RetinaFace-mobilenet achieves superior detection performance and significantly accelerates the computation time against RetinaNet. Three loss functions utilized in human face recognition combined with RetinaFace-mobilenet are compared and results indict that the proposed CattleFaceNet outperforms others with identification accuracy of 91.3% and processing time of 24 frames per second (FPS). This research work demonstrates the potential candidate of CattleFaceNet for livestock identification in real time in practical production scenarios. [Xu, Beibei; Wang, Wensheng; Guo, Leifeng; Li, Yongfeng; Cao, Zhen; Wu, Saisai] Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100086, Peoples R China; [Chen, Guipeng] Jiangxi Acad Agr Sci, Agr Econ & Informat Inst, Nanchang 330200, Jiangxi, Peoples R China; [Cao, Zhen] Wageningen Univ & Res, Informat Technol Grp, NL-6708 PB Wageningen, Netherlands Chinese Academy of Agricultural Sciences; Agriculture Information Institute, CAAS; Wageningen University & Research Xu, BB; Wang, WS (corresponding author), Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100086, Peoples R China. xuxiaobei224@163.com; wangwensheng@caas.cn , Beibei/0000-0001-5804-2906; Cao, Zhen/0000-0002-0652-7706 China Scholarship Council, China [202003250122]; Inner Mongolia Autonomous Region Science and Technology Major Project, China [2020ZD0004]; National Natural Science Foundation of China, China [32060776]; Hebei Province Key Research and Development Plan, China [20327202D, 19220119D]; Youth Science Foundation of Jiangxi Province, China [20192ACBL21023]; Doctoral program of innovation fund of Jiangxi Academy of Agricultural Sciences, China [20181CBS006] China Scholarship Council, China(China Scholarship Council); Inner Mongolia Autonomous Region Science and Technology Major Project, China; National Natural Science Foundation of China, China(National Natural Science Foundation of China (NSFC)); Hebei Province Key Research and Development Plan, China; Youth Science Foundation of Jiangxi Province, China; Doctoral program of innovation fund of Jiangxi Academy of Agricultural Sciences, China This research was supported by China Scholarship Council, China (202003250122) and was funded by Inner Mongolia Autonomous Re-gion Science and Technology Major Project, China (2020ZD0004) , Na-tional Natural Science Foundation of China, China (32060776) , Hebei Province Key Research and Development Plan, China (20327202D, 19220119D) , Youth Science Foundation of Jiangxi Province, China (20192ACBL21023) and Doctoral program of innovation fund of Jiangxi Academy of Agricultural Sciences, China (20181CBS006) . We are also grateful to two private housing farms in China for their kindly support with data collection. 44 8 8 17 40 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. FEB 2022.0 193 106675 10.1016/j.compag.2021.106675 0.0 JAN 2022 10 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science YX7HJ Green Published, hybrid 2023-03-23 WOS:000754269200001 0
J Ma, BJ; Zhang, HP; Chen, GQ; Zhao, YP; Baesens, B Ma, Baojun; Zhang, Huaping; Chen, Guoqing; Zhao, Yanping; Baesens, Bart Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING English Article Software fault prediction; associative classification; prediction performance; comprehensibility; cross project validation STATIC CODE ATTRIBUTES; DEFECT-PREDICTION; CLASSIFIERS; MODELS; RELIABILITY It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in the academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on 12 real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available. [Ma, Baojun] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China; [Zhang, Huaping] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China; [Chen, Guoqing] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China; [Zhao, Yanping] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China; [Baesens, Bart] Katholieke Univ Leuven, Fac Business & Econ, B-3000 Louvain, Belgium Beijing University of Posts & Telecommunications; Beijing Institute of Technology; Tsinghua University; Beijing Institute of Technology; KU Leuven Chen, GQ (corresponding author), Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China. mabaojun@bupt.edu.cn; kevinzhang@bit.edu.cn; chengq@sem.tsinghua.edu.cn; zhaoyp@bit.edu.cn; Bart.Baesens@econ.kuleuven.be MA, Baojun/I-5395-2014; zhao, yan/GWC-9174-2022 MA, Baojun/0000-0002-2274-3089; Zhang, Huaping/0000-0002-0137-4069 National Natural Science Foundation of China [61272362/71372044/71110107027]; Fundamental Research Funds for the Central Universities [2014RC0601]; Doctoral Fund of Ministry of Education of China [20120005120001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Doctoral Fund of Ministry of Education of China(Ministry of Education, China) This work was partly supported by the National Natural Science Foundation of China (61272362/71372044/71110107027), the Fundamental Research Funds for the Central Universities (No. 2014RC0601) and the Doctoral Fund of Ministry of Education of China (No. 20120005120001). We also give thanks to the anonymous reviewers for their thoughtful comments and suggestions. 76 15 16 2 28 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-1940 1793-6403 INT J SOFTW ENG KNOW Int. J. Softw. Eng. Knowl. Eng. FEB 2014.0 24 1 61 90 10.1142/S021819401450003X 0.0 30 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering AI7QX 2023-03-23 WOS:000337091600003 0
J Liao, H; Dong, WH; Zhan, ZC Liao, Hua; Dong, Weihua; Zhan, Zhicheng Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE English Article User identification; pedestrian navigation; machine learning; map reading; individual differences; random forest PEDESTRIAN NAVIGATION; SIMILARITIES; RECOGNITION; ORIENTATION; PERCEPTION; ATTENTION; TRACKING; 2D Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users' eye movements can be used to detect who created them. Specifically, we focus on the use of gaze data for inferring users' identities when users are performing map-based spatial tasks. We collected 32 participants' eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications. [Liao, Hua] Hunan Normal Univ, Sch Geog Sci, Changsha, Peoples R China; [Liao, Hua] Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha, Peoples R China; [Liao, Hua; Dong, Weihua; Zhan, Zhicheng] Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing, Peoples R China; [Liao, Hua; Dong, Weihua; Zhan, Zhicheng] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Zhan, Zhicheng] Univ Ghent, Dept Geog, Ghent, Belgium Hunan Normal University; Hunan Normal University; Beijing Normal University; Beijing Normal University; Ghent University Dong, WH (corresponding author), Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China. dongweihua@bnu.edu.cn Liao, Hua/0000-0002-6304-329X National Natural Science Foundation of China [NSFC] [42001410, 41871366]; Natural Science Foundation of Hunan Province [2021JJ40350]; Scientific Research Project of Education Department of Hunan Province [19B367]; State Key Laboratory of Geo-Information Engineering of MNR, CASM [2021-04-03]; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM [2021-04-03] National Natural Science Foundation of China [NSFC](National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Scientific Research Project of Education Department of Hunan Province; State Key Laboratory of Geo-Information Engineering of MNR, CASM; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM This research is supported by the National Natural Science Foundation of China [NSFC, Grant Nos. 42001410 and 41871366], Natural Science Foundation of Hunan Province [Grant No. 2021JJ40350], Scientific Research Project of Education Department of Hunan Province [Grant No. 19B367] and State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM [Grant No. 2021-04-03]. 79 5 5 5 21 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1523-0406 1545-0465 CARTOGR GEOGR INF SC Cartogr. Geogr. Inf. Sci. JAN 2 2022.0 49 1 50 69 10.1080/15230406.2021.1980435 0.0 OCT 2021 20 Geography Social Science Citation Index (SSCI) Geography XF2KD Green Submitted 2023-03-23 WOS:000705353000001 0
J Chen, G; Wang, F; Li, WJ; Hong, L; Conradt, J; Chen, JN; Zhang, ZY; Lu, YW; Knoll, A Chen, Guang; Wang, Fa; Li, Weijun; Hong, Lin; Conradt, Joerg; Chen, Jieneng; Zhang, Zhenyan; Lu, Yiwen; Knoll, Alois NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Neuromorphics; Vision sensors; Intelligent sensors; Intelligent vehicles; Cameras; Neuromorphic vision; distracted driving; advanced driver assistance system; database and baseline evaluations; event encoding; deep learning NEURAL-NETWORKS; RECOGNITION; SYSTEM; STATE Neuromorphic vision sensors such as the Dynamic and Active-pixel Vision Sensor (DAVIS) using silicon retina are inspired by biological vision, they generate streams of asynchronous events to indicate local log-intensity brightness changes. Their properties of high temporal resolution, low-bandwidth, lightweight computation, and low-latency make them a good fit for many applications of motion perception in the intelligent vehicle. However, as a younger and smaller research field compared to classical computer vision, neuromorphic vision is rarely connected with the intelligent vehicle. For this purpose, we present three novel datasets recorded with DAVIS sensors and depth sensor for the distracted driving research and focus on driver drowsiness detection, driver gaze-zone recognition, and driver hand-gesture recognition. To facilitate the comparison with classical computer vision, we record the RGB, depth and infrared data with a depth sensor simultaneously. The total volume of this dataset has 27360 samples. To unlock the potential of neuromorphic vision on the intelligent vehicle, we utilize three popular event-encoding methods to convert asynchronous event slices to event-frames and adapt state-of-the-art convolutional architectures to extensively evaluate their performances on this dataset. Together with qualitative and quantitative results, this work provides a new database and baseline evaluations named NeuroIV in cross-cutting areas of neuromorphic vision and intelligent vehicle. [Chen, Guang; Wang, Fa; Li, Weijun; Chen, Jieneng; Zhang, Zhenyan; Lu, Yiwen] Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China; [Chen, Guang] State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410012, Peoples R China; [Chen, Guang; Knoll, Alois] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany; [Hong, Lin] Shandong Univ Sci & Technol, Sch Transportat, Qingdao 266510, Peoples R China; [Conradt, Joerg] KTH Royal Inst Technol, Dept Computat Sci & Technol, S-11428 Stockholm, Sweden Tongji University; Technical University of Munich; Shandong University of Science & Technology; Royal Institute of Technology Chen, G (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.;Chen, G (corresponding author), State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410012, Peoples R China.;Chen, G (corresponding author), Tech Univ Munich, Dept Informat, D-80333 Munich, Germany. tj_autodrive@hotmail.com Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X; Fa, Wang/0000-0003-1607-2837 National Natural Science Foundation of China [61906138]; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project [31815005]; European Union [945539]; Shanghai AI Innovation Development Program National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project; European Union(European Commission); Shanghai AI Innovation Development Program This work was supported in part by the National Natural Science Foundation of China under Grant 61906138, in part by the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project under Grant 31815005, in part by the European Union's Horizon 2020 Framework Programme for Research and Innovation (Human Brain Project SGA3) under the Specific Grant 945539, and in part by the Shanghai AI Innovation Development Program 2018. 69 13 13 13 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. FEB 2022.0 23 2 1171 1183 10.1109/TITS.2020.3022921 0.0 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation YR7VL 2023-03-23 WOS:000750200400040 0
J Yu, JS; Zhou, GX; Cichocki, A; Xie, SL Yu, Jinshi; Zhou, Guoxu; Cichocki, Andrzej; Xie, Shengli Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization IEEE ACCESS English Article Nonnegative matrix factorization (NMF); nsNMF; deep nsNMF; face clustering; features learning; sparseness PATTERN DISCOVERY; ALGORITHMS Nonsmooth nonnegative matrix factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data. However, nsNMF as well as other existing NMF methods are incompetent to learn hierarchical features of complex data due to its shallow structure. To fill this gap, we propose a deep nsNMF method coined by the fact that it possesses a deeper architecture compared with standard nsNMF. The deep nsNMF not only gives part-based features due to the nonnegativity constraints but also creates higher level, more abstract features by combing lower level ones. The in-depth description of how deep architecture can help to efficiently discover abstract features in dnsNMF is presented, suggesting that the proposed model inherits the major advantages from both deep learning and NMF. Extensive experiments demonstrate the standout performance of the proposed method in clustering analysis. [Yu, Jinshi; Zhou, Guoxu; Xie, Shengli] Guangdong Univ Technol, Fac Automat, Guangzhou 510006, Guangdong, Peoples R China; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia; [Cichocki, Andrzej] Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland; [Cichocki, Andrzej] Hangzhou Dianzi Univ, Coll Comp Sci, Hangzhou 310018, Zhejiang, Peoples R China Guangdong University of Technology; Skolkovo Institute of Science & Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Hangzhou Dianzi University Zhou, GX (corresponding author), Guangdong Univ Technol, Fac Automat, Guangzhou 510006, Guangdong, Peoples R China. gx.zhou@gdut.edu.cn liu, yi/GXE-9662-2022; Cichocki, Andrzej/AAI-4209-2020; Xie, Shengli/AAZ-6354-2020 National Nature Science Foundation of China (NSFC) [61673124]; Ministry of Education and Science of the Russian Federation [14.756.31.0001]; Polish National Science Center [2016/20/W/N24/00354] National Nature Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Polish National Science Center This work was supported in part by the National Nature Science Foundation of China (NSFC) under Grant 61673124, in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001, and in part by the Polish National Science Center under Grant 2016/20/W/N24/00354. 41 36 36 4 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 58096 58105 10.1109/ACCESS.2018.2873385 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GY9WO Green Submitted, gold 2023-03-23 WOS:000449003300001 0
J Li, W; Shao, W; Ji, SX; Cambria, E Li, Wei; Shao, Wei; Ji, Shaoxiong; Cambria, Erik BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis NEUROCOMPUTING English Article Conversational sentiment analysis; Emotional recurrent unit; Contextual encoding; Dialogue systems KNOWLEDGE; NETWORK Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information that may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases. (c) 2021 Elsevier B.V. All rights reserved. [Li, Wei; Cambria, Erik] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore; [Shao, Wei] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Ji, Shaoxiong] Aalto Univ, Dept Comp Sci, Espoo, Finland Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; City University of Hong Kong; Aalto University Cambria, E (corresponding author), Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore. cambria@ntu.edu.sg Cambria, Erik/C-2103-2013; Li, Yang/HPC-4054-2023; Ji, Shaoxiong/AAI-9628-2020 Cambria, Erik/0000-0002-3030-1280; Ji, Shaoxiong/0000-0003-3281-8002; Shao, Wei/0000-0001-7531-1055 Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme [A18A2b0046] Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme(Agency for Science Technology & Research (A*STAR)) This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046) . 57 79 79 8 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 7 2022.0 467 73 82 10.1016/j.neucom.2021.09.057 0.0 OCT 2021 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WL0QO Green Submitted 2023-03-23 WOS:000710121100007 0
J Han, ZK; Sarker, D; Ouyang, R; Mazheika, A; Gao, Y; Levchenko, SV Han, Zhong-Kang; Sarker, Debalaya; Ouyang, Runhai; Mazheika, Aliaksei; Gao, Yi; Levchenko, Sergey V. Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence NATURE COMMUNICATIONS English Article DENSITY-FUNCTIONAL THEORY; SELECTIVE HYDROGENATION; SCALING RELATIONS; CO OXIDATION; K-FOLD; SURFACE; REACTIVITY; DISCOVERY; ENERGIES; PD Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications. Single-atom metal alloys attract considerable interest as alternative metal hydrogenation catalysts. Here the authors combine first-principles calculations with compressed-sensing data-analytics approaches to develop stability and activity's descriptors for screening single atom alloy catalysts. [Han, Zhong-Kang; Sarker, Debalaya; Levchenko, Sergey V.] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Ctr Energy Sci & Technol, Moscow, Russia; [Ouyang, Runhai] Shanghai Univ, Mat Genome Inst, Shanghai, Peoples R China; [Mazheika, Aliaksei] Tech Univ Berlin, BasCat UniCat BASF JointLab, Berlin, Germany; [Gao, Yi] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai, Peoples R China Skolkovo Institute of Science & Technology; Shanghai University; BASF; Technical University of Berlin; Chinese Academy of Sciences; Shanghai Advanced Research Institute, CAS Levchenko, SV (corresponding author), Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Ctr Energy Sci & Technol, Moscow, Russia.;Mazheika, A (corresponding author), Tech Univ Berlin, BasCat UniCat BASF JointLab, Berlin, Germany.;Gao, Y (corresponding author), Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai, Peoples R China. alex.mazheika@gmail.com; gaoyi@zjlab.org.cn; S.Levchenko@skoltech.ru Mazheika, Aliaksei S./I-3336-2017; gao, yi/HCI-8298-2022; Levchenko, Sergey V/H-1977-2017; Gao, Yi/A-8888-2009; Sarker, Dr Debalaya/ABF-4650-2021; Han, Zhongkang/ABF-1968-2021; Levchenko, Sergey/AAP-3359-2021 Levchenko, Sergey V/0000-0001-5813-8473; Gao, Yi/0000-0001-6015-5694; Levchenko, Sergey/0000-0001-5813-8473 Skolkovo Foundation Grant; RFBR; INSF [20-53-56065]; National Natural Science Foundation of China [11604357, 11574340]; National Key Research and Development Program of China [2018YFB0704400]; Program of Shanghai Youth Oriental Scholars Skolkovo Foundation Grant; RFBR(Russian Foundation for Basic Research (RFBR)); INSF(Iran National Science Foundation (INSF)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Program of Shanghai Youth Oriental Scholars S.V.L. is supported by Skolkovo Foundation Grant. The machine-learning methodology development was funded by RFBR and INSF, project number 20-53-56065. Y.G. is supported by the National Natural Science Foundation of China (11604357, 11574340). R.O. is supported by the National Key Research and Development Program of China (2018YFB0704400) and the Program of Shanghai Youth Oriental Scholars. 68 43 43 33 133 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. MAR 23 2021.0 12 1 1833 10.1038/s41467-021-22048-9 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics TZ8IQ 33758170.0 Green Submitted, gold, Green Published 2023-03-23 WOS:000684714300002 0
J Han, SY; Shao, HD; Huo, ZQ; Yang, XK; Cheng, JS Han, Songyu; Shao, Haidong; Huo, Zhiqiang; Yang, Xingkai; Cheng, Junsheng End-to-end chiller fault diagnosis using fused attention mechanism and dynamic cross-entropy under imbalanced datasets BUILDING AND ENVIRONMENT English Article Smart city systems; Chiller fault diagnosis; Fused attention mechanism; Dynamic cross-entropy; Imbalanced datasets NETWORKS Fault diagnosis techniques play an increasingly important role in the operation and maintenance of smart city systems. Artificial intelligence improves the efficiency of chiller system fault diagnosis, and greatly reduces the energy consumption of urban buildings. The existing intelligent fault diagnosis methods of chiller mostly rely on balanced training datasets; lacking fault samples makes these methods incompetent to extract reliable features to recognize abnormal machine conditions, resulting in the degraded performance. To overcome the deficiencies of reported studies, a new method, called end-to-end chiller fault diagnosis, is proposed using a fused attention mechanism and dynamic cross-entropy. Firstly, a one-dimensional convolution network (1D-CNN) and long-short term memory (LSTM) are combined to capture the spatial-temporal features from the original data directly. Afterwards, a fused attention mechanism is developed to further refine the extracted features to increase the contribution of crucial features and achieve high-quality diagnostic information mining. Finally, the dynamic cross-entropy (DCE) is designed for updating the imbalance factor in real-time, with more focus on the hard-classified types. The experimental analysis results demonstrate the feasibility and superiority of the proposed method in identifying chiller system faults with imbalanced datasets. [Han, Songyu; Shao, Haidong; Cheng, Junsheng] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China; [Shao, Haidong] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Huo, Zhiqiang] UCL, Inst Hlth Informat, London WC1E 6BT, England; [Yang, Xingkai] Univ Alberta, Dept Mech Engn, Edmonton, AB T6G 1H9, Canada Hunan University; Lulea University of Technology; University of London; University College London; University of Alberta Shao, HD (corresponding author), Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China. hdshao@hnu.edu.cn Cheng, Junsheng/HKE-6532-2023; Shao, Haidong/AAE-5814-2022 Shao, Haidong/0000-0002-8018-1774 National Key Research and Development of China [2020YFB1712100]; National Natural Science Foundation of China [51905160]; Natural Science Fund for Excellent Young Scholars of Hunan Province [2021JJ20017] National Key Research and Development of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fund for Excellent Young Scholars of Hunan Province This research is supported by the National Key Research and Development of China (No. 2020YFB1712100), the National Natural Science Foundation of China (No. 51905160), and the Natural Science Fund for Excellent Young Scholars of Hunan Province (No. 2021JJ20017). 48 4 4 22 33 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-1323 1873-684X BUILD ENVIRON Build. Environ. MAR 15 2022.0 212 108821 10.1016/j.buildenv.2022.108821 0.0 FEB 2022 12 Construction & Building Technology; Engineering, Environmental; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 3D4WL 2023-03-23 WOS:000829304100005 0
J Wang, W; Feng, B; Huang, G; Guo, CX; Liao, WL; Chen, Z Wang, Wei; Feng, Bin; Huang, Gang; Guo, Chuangxin; Liao, Wenlong; Chen, Zhe Conformal asymmetric multi-quantile generative transformer for day-ahead wind power interval prediction APPLIED ENERGY English Article Wind power; Interval prediction; Generative transformer; Asymmetric interval; Conformal quantile SPINNING RESERVE; REGRESSION; FRAMEWORK; ALGORITHM; SYSTEMS; MODEL With the rapid increase in the installed capacity of wind power, day-ahead wind power interval prediction is becoming more and more important. To solve such a challenging problem and provide intervals of higher quality, this paper proposes a prediction method based on conformal asymmetric multi-quantile generative transformer. Herein, the multi-quantile generative transformer is a deep learning model that generates multi-quantile forecast results for next day through one forward propagation process. Then, two quantiles, whose width is the smallest while satisfying the nominal confidence constraint, are selected from the predicted sequence as the upper bound and lower bound of the asymmetric interval. Furthermore, we introduce the conformal quantile regression to calibrate the bounds of the prediction interval to ensure that its coverage rate is as close as nominal confidence. The experiments show that the proposed method surpasses the benchmarks by providing narrower prediction intervals with more accurate empirical coverage probability. Under nominal confidence 90%, it gives prediction intervals with average empirical coverage probability of 90.50% and normalized average width of 0.44 on four wind farms. Compared with symmetric prediction intervals given by common benchmark quantile long short term memory network, the average width is reduced by 19.6%. [Wang, Wei; Feng, Bin; Guo, Chuangxin] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China; [Huang, Gang] Zhejiang Lab, Hangzhou 311121, Peoples R China; [Liao, Wenlong; Chen, Zhe] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Zhejiang University; Zhejiang Laboratory; Aalborg University Feng, B (corresponding author), Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China.;Huang, G (corresponding author), Zhejiang Lab, Hangzhou 311121, Peoples R China. fengbinhz@zju.edu.cn; huanggang@zju.edu.cn National Natural Science Foundation of China [U22B2098, 52007173, U19B2042]; Zhejiang Provincial Natural Science Foundation of China and Information Technology Center [LQ20E070002]; State Key Lab of CAD&CG, ZheJiang University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China and Information Technology Center; State Key Lab of CAD&CG, ZheJiang University This work is supported by the National Natural Science Founda-tion of China (Nos. U22B2098, 52007173 and U19B2042) , Zhejiang Provincial Natural Science Foundation of China (No. LQ20E070002) and Information Technology Center and State Key Lab of CAD & CG, ZheJiang University. 41 0 0 5 5 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy MAR 1 2023.0 333 120634 10.1016/j.apenergy.2022.120634 0.0 JAN 2023 15 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 8N6UD 2023-03-23 WOS:000925283700001 0
J Kolb, B; Marshall, P; Zhao, B; Jiang, B; Guo, H Kolb, Brian; Marshall, Paul; Zhao, Bin; Jiang, Bin; Guo, Hua Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes JOURNAL OF PHYSICAL CHEMISTRY A English Article CORRELATED MOLECULAR CALCULATIONS; QUANTUM DYNAMICS; BASIS SETS; SCATTERING; CHEMISTRY Representation of multidimensional global potential energy surfaces suitable for spectral and dynamical calculations from high-level ab initio calculations remains a challenge. Here, we present a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression. Tests for the (3)A '' state of SH2, which facilitates the SH + H <-> S(P-3) + H-2 abstraction reaction and the SH + H' <-> SH' + H exchange reaction, suggest that the Gaussian process is capable of providing a reasonable potential energy surface with a small number (similar to 1 X 10(2)) of ab initio points, but it needs substantially more points (similar to 1 x 10(3)) to converge reaction probabilities. The implications of these observations for construction of potential energy surfaces are discussed. [Kolb, Brian; Marshall, Paul; Zhao, Bin; Guo, Hua] Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA; [Kolb, Brian] MIT, Dept Mech Engn, Cambridge, MA 02139 USA; [Jiang, Bin] Univ Sci & Technol China, Dept Chem Phys, Hefei 230026, Anhui, Peoples R China; [Zhao, Bin] Univ Bielefeld, Fak Chem, Theoret Chem, Univ Str 25, D-33615 Bielefeld, Germany; [Marshall, Paul] Univ North Texas, Dept Chem, Denton, TX 76203 USA University of New Mexico; Massachusetts Institute of Technology (MIT); Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Bielefeld; University of North Texas System; University of North Texas Denton Guo, H (corresponding author), Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA. hguo@unm.edu jiang, bin/F-2391-2014; Guo, H./J-2685-2014 jiang, bin/0000-0003-2696-5436; Guo, H./0000-0001-9901-053X U.S. Air Force Office of Scientific Research [AFOSR-FA9550-15-1-0305]; National Natural Science Foundation of China [91645202, 21573203] U.S. Air Force Office of Scientific Research(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the U.S. Air Force Office of Scientific Research (AFOSR-FA9550-15-1-0305 to H.G.) and by the National Natural Science Foundation of China (91645202 and 21573203 to B.J.). P.M. thanks UNT for development leave. B.J. thanks J. Chen, and H.G. thanks R. Krems for stimulating discussions. 46 52 52 1 56 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1089-5639 J PHYS CHEM A J. Phys. Chem. A APR 6 2017.0 121 13 2552 2557 10.1021/acs.jpca.7b01182 0.0 6 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics ER5YM 28287725.0 Green Published 2023-03-23 WOS:000398880800002 0
J Yang, F; Wang, YX; Herranz, L; Cheng, YM; Mozerov, MG Yang, Fei; Wang, Yaxing; Herranz, Luis; Cheng, Yongmei; Mozerov, Mikhail G. A novel framework for image-to-image translation and image compression NEUROCOMPUTING English Article Image-to-image translation; Autoencoder; Image compression; Communication Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely used approach to image processing problems, such as image synthesis, style transfer, and image restoration. At the same time, neural image compression has emerged as a data-driven alternative to traditional coding approaches in visual communications. In this paper, we study the combination of these two paradigms into a joint I2I compression and translation framework, focusing on multi-domain image synthesis. We first propose distributed I2I translation by integrating quantization and entropy coding into an I2I translation framework (i.e. I2Icodec). In practice, the image compression functionality (i.e. autoencoding) is also desirable, requiring to deploy alongside I2Icodec a regular image codec. Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec. Adaptive residual blocks conditioned on the translation/compression mode provide flexible adaptation to the desired functionality. The experiments show promising results in both I2I translation and image compression using a single model. (C) 2022 Elsevier B.V. All rights reserved. [Yang, Fei; Cheng, Yongmei] Northwestern Polytech Univ, Sch Automat, Xian 710129, Peoples R China; [Yang, Fei; Herranz, Luis; Mozerov, Mikhail G.] Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona 08192, Spain; [Wang, Yaxing] Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China Northwestern Polytechnical University; Autonomous University of Barcelona; Centre de Visio per Computador (CVC); Nankai University Wang, YX (corresponding author), Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China. yaxing@nankai.edu.cn Mozerov, Mikhail/AAN-5362-2021 Huawei Kirin Solution; Spanish Government [RTI2018-102285-AI00, RYC2019-027020-I] Huawei Kirin Solution(Huawei Technologies); Spanish Government(Spanish Government) We acknowledge the support from Huawei Kirin Solution and the Spanish Government funding for projects RTI2018-102285-AI00 and RYC2019-027020-I. 45 0 0 5 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 7 2022.0 508 58 70 10.1016/j.neucom.2022.08.048 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6S3YV Green Submitted 2023-03-23 WOS:000892927500005 0
J Jin, XL; Qi, FY; Wu, QW; Mu, YF; Jia, HJ; Yu, XD; Li, ZY Jin, Xiaolong; Qi, Fengyu; Wu, Qiuwei; Mu, Yunfei; Jia, Hongjie; Yu, Xiaodan; Li, Zhuoyang Integrated optimal scheduling and predictive control for energy management of an urban complex considering building thermal dynamics INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Hierarchical structure; Integrated optimal scheduling and predictive control; Operating cost; Peak-valley load difference; Thermal dynamics; Urban complex NEURAL-NETWORK; SMART BUILDINGS; MODEL; SYSTEM; SIMULATION; OPERATION; SERVICES; WALLS; LOADS; FLOW In this paper, an integrated optimal scheduling and predictive control scheme with a hierarchical structure is proposed for energy management of an urban complex (UC). The proposed scheme consists of a scheduling layer optimizing the energy usage of the UC and a control layer controlling the heating, ventilation, and air conditioning (HVAC) in each individual building. In the control layer, a detailed physical model of the individual building with HVAC system is developed to predict its energy consumption while considering the thermal dynamics of the building envelope with multiple layers of construction material. In the scheduling layer, a multi objective optimal scheduling is formulated based on the predictive energy consumption of the buildings to reduce the peak-valley load difference and minimize the operating cost of the UC. Finally, the optimal control schedules are obtained and issued to the individual HVACs. Numerical results show that the proposed method can reduce the operating cost and reduce the peak-valley load difference for the UC. Meanwhile, the HVACs can be controlled in an optimal way within the limits of indoor temperature. [Jin, Xiaolong; Wu, Qiuwei] Tech Univ Denmark DTU, Dept Elect Engn, Ctr Elect Power & Energy CEE, DK-2800 Lyngby, Denmark; [Qi, Fengyu] State Grid Tianjin Elect Power Corp, Chengxi Dist Supply Co, Tianjin, Peoples R China; [Jin, Xiaolong; Qi, Fengyu; Mu, Yunfei; Jia, Hongjie; Yu, Xiaodan; Li, Zhuoyang] Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 30072, Peoples R China Technical University of Denmark; State Grid Corporation of China; Tianjin University Jin, XL (corresponding author), Tech Univ Denmark DTU, Dept Elect Engn, Ctr Elect Power & Energy CEE, DK-2800 Lyngby, Denmark. xiajin@elektro.dtu.dk; qifengyu@tju.edu.cn; qw@elektro.dtu.dk; yunfeimu@tju.edu.cn; hjjia@tju.edu.cn; yuxd@tju.edu.cn; zhuoyangli@tju.edu.cn WU, QIUWEI/AAA-4012-2021; Jin, Xiaolong/U-5782-2017 Wu, Qiuwei/0000-0001-7935-2567; Jin, Xiaolong/0000-0002-2880-9421; Mu, Yunfei/0000-0002-3238-1501 National Key Research and Development Program of China [2017YFB0903300]; National Natural Science Foundation of China [51677124, 51625702]; Joint Fund Project of National Natural Science Foundation-State Grid Corporation [U1766210]; transnational project Multi-layer aggregator solutions to facilitate optimum demand response and grid flexibility [89029]; TUBITAK (Turkey); SWEA (Sweden); EUDP (Denmark); UEFISCDI (Romania); RCN (Norway) under ERA-Net Smart Energy Systems SG + 2017 Program National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Joint Fund Project of National Natural Science Foundation-State Grid Corporation; transnational project Multi-layer aggregator solutions to facilitate optimum demand response and grid flexibility; TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); SWEA (Sweden); EUDP (Denmark); UEFISCDI (Romania); RCN (Norway) under ERA-Net Smart Energy Systems SG + 2017 Program This work was financially supported by the National Key Research and Development Program of China (Grant no. 2017YFB0903300), the project of National Natural Science Foundation of China (Grant Nos. 51677124, and 51625702), and the Joint Fund Project of National Natural Science Foundation-State Grid Corporation (Grant no. U1766210). This work was also financially supported by the transnational project Multi-layer aggregator solutions to facilitate optimum demand response and grid flexibility (Project No: 89029), co-financed by TUBITAK (Turkey), SWEA (Sweden), EUDP (Denmark), UEFISCDI (Romania), and RCN (Norway) under ERA-Net Smart Energy Systems SG + 2017 Program. 45 17 19 3 30 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. DEC 2020.0 123 106273 10.1016/j.ijepes.2020.106273 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NQ9PR Green Submitted 2023-03-23 WOS:000571198800006 0
J Ma, YM; Wang, HX; Yang, JY; Chen, Z; Yuan, JW; Okoye, MO Ma, Yiming; Wang, Haixin; Yang, Junyou; Chen, Zhe; Yuan, Jingwei; Okoye, Martin Onyeka RNN-Assisted Feature-Extraction VMD for Load Classification in Cloud Computing Platform JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS English Article Cloud-based computing; power system; load classification; variational mode decomposition; feature extraction; demand-side management ALGORITHM Cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods. [Ma, Yiming; Wang, Haixin; Yang, Junyou; Okoye, Martin Onyeka] Shenyang Univ Technol, Sch Elect Engn, Shenliao West Rd 111, Shenyang 110870, Peoples R China; [Chen, Zhe] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Yuan, Jingwei] State Grid Liaoning Elect Power Res Inst, Siping St 55, Shenyang 110006, Peoples R China Shenyang University of Technology; Aalborg University; State Grid Corporation of China Yang, JY (corresponding author), Shenyang Univ Technol, Sch Elect Engn, Shenliao West Rd 111, Shenyang 110870, Peoples R China. lnmayiming@126.com; haixinwang@sut.edu.cn; junyouyang@sut.edu.cn; zch@et.auu.dk; jingwei3150@sina.com; martinokoye@yahoo.com Okoye, Martin Onyeka/GLT-3209-2022 Okoye, Martin/0000-0001-6581-9330; ma, yiming/0000-0001-8389-1424 36 1 1 9 9 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-1266 1793-6454 J CIRCUIT SYST COMP J. Circuits Syst. Comput. APR 2022.0 31 6 2250106 10.1142/S0218126622501067 0.0 20 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 0F9UF 2023-03-23 WOS:000777700600007 0
J Fan, QL; Hsu, YC; Lieli, RP; Zhang, YC Fan, Qingliang; Hsu, Yu-Chin; Lieli, Robert P.; Zhang, Yichong Estimation of Conditional Average Treatment Effects With High-Dimensional Data JOURNAL OF BUSINESS & ECONOMIC STATISTICS English Article Heterogeneous treatment effects; High-dimensional data; Uniform confidence band EFFICIENT SEMIPARAMETRIC ESTIMATION; PROPENSITY SCORE; INFERENCE; MODELS; ECONOMETRICS Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator depending on whether the nuisance functions are estimated over the full sample or over a hold-out sample. Building on Belloni at al. and Chernozhukov et al., we derive functional limit theory for the estimators and provide an easy-to-implement procedure for uniform inference based on the multiplier bootstrap. The empirical application revisits the effect of maternal smoking on a baby's birth weight as a function of the mother's age. [Fan, Qingliang] Chinese Univ Hong Kong, Dept Econ, Shatin, 903 Esther Lee Bldg, Hong Kong, Peoples R China; [Hsu, Yu-Chin] Acad Sinica, Inst Econ, Taipei, Taiwan; [Hsu, Yu-Chin] Natl Cent Univ, Dept Finance, Taoyuan, Taiwan; [Hsu, Yu-Chin] Natl Chengchi Univ, Dept Econ, Taipei, Taiwan; [Lieli, Robert P.] Cent European Univ, Dept Econ & Business, Budapest, Hungary; [Zhang, Yichong] Singapore Management Univ, Sch Econ, Singapore, Singapore Chinese University of Hong Kong; Academia Sinica - Taiwan; National Central University; National Chengchi University; Central European University; Singapore Management University Fan, QL (corresponding author), Chinese Univ Hong Kong, Dept Econ, Shatin, 903 Esther Lee Bldg, Hong Kong, Peoples R China. michaelqfan@gmail.com Fan, Qingliang/J-9205-2019 Fan, Qingliang/0000-0001-9560-3311; Zhang, Yichong/0000-0003-1423-2702 National Natural Science Foundation of China [71671149, 71801183, 71631004]; Ministry of Science and Technology of Taiwan [107-2410-H-001-034-MY3]; Career Development Award of Academia Sinica, Taiwan; Singapore Ministry of Education Tier 2 grant [MOE2018-T2-2-169]; Lee Kong Chian Fellowship National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of Taiwan(Ministry of Science and Technology, Taiwan); Career Development Award of Academia Sinica, Taiwan; Singapore Ministry of Education Tier 2 grant(Ministry of Education, Singapore); Lee Kong Chian Fellowship Fan acknowledges financial support from National Natural Science Foundation of China grants 71671149, 71801183, and 71631004 (Key Project). Hsu acknowledges research support from the Ministry of Science and Technology of Taiwan (107-2410-H-001-034-MY3) and the Career Development Award of Academia Sinica, Taiwan. Zhang acknowledges financial support fromthe Singapore Ministry of Education Tier 2 grant under grant MOE2018-T2-2-169 and the Lee Kong Chian Fellowship. 49 12 12 5 15 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0735-0015 1537-2707 J BUS ECON STAT J. Bus. Econ. Stat. JAN 2 2022.0 40 1 313 327 10.1080/07350015.2020.1811102 0.0 SEP 2020 15 Economics; Social Sciences, Mathematical Methods; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Mathematical Methods In Social Sciences; Mathematics YM7AQ Green Submitted 2023-03-23 WOS:000569774700001 0
J Chen, JJ; Hu, WH; Cao, D; Zhang, M; Huang, Q; Chen, Z; Blaabjerg, F Chen, Jianjun; Hu, Weihao; Cao, Di; Zhang, Man; Huang, Qi; Chen, Zhe; Blaabjerg, Frede Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors IEEE TRANSACTIONS ON ENERGY CONVERSION English Article Feature extraction; Fault detection; Energy management; Fault diagnosis; Convolution; Employee welfare; Computer architecture; Electric machine (EM); intelligent multi-fault detection; capsule network; date-driven; current signal DIAGNOSIS With the steady development of technology, electric motors (EMs) have become one of the most important components in modern industry. To ensure stable industrial production, detecting and classifying the EM faults is crucial. A novel intelligent deep-learning-based multi-fault detection method for EMs under varying working conditions is proposed in this article. This method involves two steps: first, a 2D convolution network without pooling layer is proposed to extract features from raw EM data. In addition, a long short-term memory (LSTM) network is applied to extract the fault features for comparison. Second, a capsule network (Caps-Net) based on a dynamic routing algorithm is used as a classifier to realize intelligent multi-fault detection and improve the generalization performance of the proposed model. The proposed method is applicable to raw physical signals of EMs, which improves the overall efficiency of the fault detection. Moreover, the proposed method has a strong generalization ability. The simulation results demonstrate that the proposed approach can achieve higher accuracy than various benchmark methods. Moreover, its fault detection accuracy is higher than those of other state-of-the-art models under two working conditions, in which the load type and size of the EM are changed, respectively. [Chen, Jianjun; Hu, Weihao; Cao, Di; Zhang, Man; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Power Syst Wide Area Measurement & Control Sichua, Chengdu 611731, Peoples R China; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark University of Electronic Science & Technology of China; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Power Syst Wide Area Measurement & Control Sichua, Chengdu 611731, Peoples R China. chjj@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; zhangman@uestc.edu.cn; hwong@uestc.edu.cn; zch@et.aau.dk; fbl@et.aau.dk Hu, Weihao/AAE-7945-2019; Blaabjerg, Frede/A-5008-2008 Hu, Weihao/0000-0002-7019-7289; Blaabjerg, Frede/0000-0001-8311-7412; Chen, Zhe/0000-0002-2919-4481; Huang, Qi/0000-0002-8637-0269 Sichuan Science and Technology Program [2020JDJQ0037, 2020YFG0312, TEC-00663-2020] Sichuan Science and Technology Program Manuscript received June 30, 2020; revised October 1, 2020 and November 20, 2020; accepted December 19, 2020. Date of publication December 22, 2020; date of current version August 20, 2021. This work was supported by the Sichuan Science and Technology Program under Grants 2020JDJQ0037 and 2020YFG0312, Paper no. TEC-00663-2020. (Corresponding author: Weihao Hu.) 36 11 11 6 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8969 1558-0059 IEEE T ENERGY CONVER IEEE Trans. Energy Convers. SEP 2021.0 36 3 2173 2184 10.1109/TEC.2020.3046642 0.0 12 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering TU8GR Green Published 2023-03-23 WOS:000681269600059 0
J Chen, CY; Ruiz, MT; Delefortrie, G; Mei, TL; Vantorre, M; Lataire, E Chen, Changyuan; Ruiz, Manases Tello; Delefortrie, Guillaume; Mei, Tianlong; Vantorre, Marc; Lataire, Evert Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method OCEAN ENGINEERING English Article Roll model; Shallow water; NLS-SVM; Damping parameters; Parameter identification MOTION EQUATION; ONLINE PREDICTION; IDENTIFICATION; DECAY; FPSO In order to accurately identify the ship's roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms' weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well. [Chen, Changyuan; Ruiz, Manases Tello; Delefortrie, Guillaume; Mei, Tianlong; Vantorre, Marc; Lataire, Evert] Univ Ghent, Ghent, Belgium; [Delefortrie, Guillaume] Flanders Hydraul Res, Antwerp, Belgium; [Mei, Tianlong] Shanghai Jiao Tong Univ, Shanghai, Peoples R China Ghent University; Flanders Hydraulics Research (FHR); Shanghai Jiao Tong University Ruiz, MT (corresponding author), Univ Ghent, Ghent, Belgium. Manases.ruiz@ugent.be Lataire, Evert/AGZ-6878-2022 Lataire, Evert/0000-0002-3551-6686 program of China Scholarships Council [201706570007, 201806230196]; Special Research Fund Cofunding for Chinese candidates holding a CSC-grant [015C8418] program of China Scholarships Council; Special Research Fund Cofunding for Chinese candidates holding a CSC-grant This work was supported by the program of China Scholarships Council (201706570007, 201806230196) and Special Research Fund Cofunding for Chinese candidates holding a CSC-grant (015C8418). 37 5 5 4 30 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 OCEAN ENG Ocean Eng. NOV 1 2019.0 191 106479 10.1016/j.oceaneng.2019.106479 0.0 10 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography JW2LB Green Published 2023-03-23 WOS:000502888100014 0
J Guan, Q; Feng, L; Hou, XJ; Schurgers, G; Zheng, Y; Tang, J Guan, Qi; Feng, Lian; Hou, Xuejiao; Schurgers, Guy; Zheng, Yi; Tang, Jing Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations REMOTE SENSING OF ENVIRONMENT English Article Chlorophyll-a; Algal bloom; Eutrophication; Yangtze Plain; Lakes; MERIS; OLCI; Water quality REMOTE-SENSING REFLECTANCE; OPTICALLY SHALLOW BOTTOMS; ESTIMATING CHLOROPHYLL-A; WATER FISH BIODIVERSITY; TOTAL SUSPENDED MATTER; ATMOSPHERIC CORRECTION; RIVER-BASIN; TAIHU LAKE; SEMIANALYTICAL MODEL; UPWELLING RADIANCES The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions (MEdium Resolution Imaging Spectrometer, or MERIS (2003-2011), and Ocean and Land Color Instrument, or OLCI (2017-2018)), together with in situ data on remote sensing reflectance and chlorophyll-a (Chla) concentrations across various lakes on the Yangtze Plain. A machine learning-based piecewise Chla algorithm was developed in this study, with special considerations to improve algorithm performance under lower Chla conditions. Remotely sensed Chla and algal bloom areas were then used to classify the eutrophication status of 50 large lakes on the Yangtze Plain, and the frequent satellite observations enabled us to estimate the probability of eutrophication occurrence (PEO) for each examined lake. The long-term mean Chla ranged from 17.58 mg m(-3) to 43.86 mg m(-3) on the Yangtze Plain, and severe floating algal blooms were found in 7 lakes. All 50 lakes had high climatological PEO values (50%) during the study period, indicating a generally high probability of eutrophication in lakes on the Yangtze Plain. However, 21 out of 51 lakes exhibited statistically significant (p<.05) decreasing trends in PEO during the observation period, suggesting an overall improvement in the water quality of lakes on the Yangtze Plain in recent years. The methods developed here are expected to contribute to real-time monitoring of drinking water safety for local regions, and the long-term results provide valuable baseline information for future lake conservation and restoration efforts. [Guan, Qi; Feng, Lian; Hou, Xuejiao; Zheng, Yi] Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China; [Guan, Qi; Schurgers, Guy] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark; [Feng, Lian; Zheng, Yi] Southern Univ Sci & Technol, Shenzhen Municipal Engn Lab Environm IoT Technol, Shenzhen 518055, Guangdong, Peoples R China; [Tang, Jing] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; [Tang, Jing] Univ Copenhagen, Dept Biol, Copenhagen, Denmark Southern University of Science & Technology; University of Copenhagen; Southern University of Science & Technology; Lund University; University of Copenhagen Feng, L (corresponding author), Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China. fengl@sustech.edu.cn Schurgers, Guy/K-6543-2012; Guan, Qi/GSI-9131-2022; Tang, Jing/K-3752-2015 Schurgers, Guy/0000-0002-2189-1995; Guan, Qi/0000-0002-6064-134X; Feng, Lian/0000-0002-4590-3022; Tang, Jing/0000-0001-7961-8214 National Key R&D program of China [2016YFC0402806]; National Natural Science Foundation of China [41971304, 41671338, 41890852, 41890851]; High-level Special Funding of the Southern University of Science and Technology [G02296302, G02296402]; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control National Key R&D program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High-level Special Funding of the Southern University of Science and Technology; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control This work was supported by the National Key R&D program of China (NO: 2016YFC0402806), the National Natural Science Foundation of China (NOs: 41971304, 41671338, 41890852 and 41890851), the High-level Special Funding of the Southern University of Science and Technology (Grant No. G02296302, G02296402), and the State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control. We thank to Professor Liqiao Tian of Wuhan University, Guangzhou Water Color Ocean Technology Co., Ltd and Easy Ocean Technology Ltd. for their help in field instrument installment and integration. We also thank US NASA and ESA for providing MERIS and OLCI data, NASA for providing TRMM data, and the China Meteorological Data Sharing Service System for providing meteorological measurements. Two anonymous reviewers provided valuable comments to help improve this manuscript, whose effort is also appreciated. 117 68 77 43 198 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. SEP 1 2020.0 246 111890 10.1016/j.rse.2020.111890 0.0 17 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology LU3XJ 2023-03-23 WOS:000537691800034 0
J Fang, R; Liao, CS; Quan, H; Zeng, LB; Peng, Q Fang, Run; Liao, Chengsheng; Quan, Hong; Zeng, Libo; Peng, Qiao A Hybrid Data-Driven Method to Predict Battery Capacity of Medical Devices and Analyze Component Effects FRONTIERS IN ENERGY RESEARCH English Article data-driven method; artificial intelligence; battery-powered medical devices; battery capacity prediction; component parameter analysis; medical applications MODEL Lithium-ion batteries are currently the most utilized power source in medical devices due to their long service life, high energy performance, and being portable. The performance of battery-powered medical devices is heavily dependent on battery capacity, which would be directly affected by related battery component parameters. To widen the application of battery-powered medical devices, it is vital to effectively monitor battery capacity and analyze the effects of battery component parameters. This article derives a hybrid data-driven method to achieve accurate early predictions of battery capacity and reliable analysis of battery component effects. To be specific, a Gaussian process regression-based data-driven model is first developed to efficiently capture the underlying fitting among four component parameters and battery capacity. Then two effect analysis tools including the automatic relevance determination kernel-based weights and tree-based local interpretable model-agnostic explanation are equipped to quantify and analyze both global and local effects of these four component parameters, respectively. Illustrative results show that the designed hybrid data-driven method is able to provide accurate battery capacity predictions with 0.97 R 2 , while both global effects and local effects of four component parameters are successfully quantified. Due to the merits of data-driven characteristics, the designed hybrid data-driven method is capable of efficiently helping users to monitor/predict battery capacity and analyze/understand the effects of interested component parameters. This could further benefit battery-powered medical devices for higher-performance and longer-lifetime applications. [Fang, Run; Quan, Hong] Wuhan Univ, Sch Phys & Technol, Wuhan, Peoples R China; [Liao, Chengsheng; Zeng, Libo] Wuhan Univ, Elect Informat Sch, Wuhan, Peoples R China; [Peng, Qiao] Queens Univ Belfast, Belfast, North Ireland Wuhan University; Wuhan University; Queens University Belfast Quan, H (corresponding author), Wuhan Univ, Sch Phys & Technol, Wuhan, Peoples R China. 00007962@whu.edu.cn 42 0 0 7 11 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-598X FRONT ENERGY RES Front. Energy Res. JUL 7 2022.0 10 928250 10.3389/fenrg.2022.928250 0.0 9 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 3E2LV gold 2023-03-23 WOS:000829820200001 0
J Wei, DW; Shi, FF; Dhelim, S Wei, Dawei; Shi, Feifei; Dhelim, Sahraoui A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period FUTURE INTERNET English Article unknown Internet traffic identification; self-supervised learning; surge period; clustering The identification of Internet protocols provides a significant basis for keeping Internet security and improving Internet Quality of Service (QoS). However, the overwhelming developments and updating of Internet technologies and protocols have led to large volumes of unknown Internet traffic, which threaten the safety of the network environment a lot. Since most of the unknown Internet traffic does not have any labels, it is difficult to adopt deep learning directly. Additionally, the feature accuracy and identification model also impact the identification accuracy a lot. In this paper, we propose a surge period-based feature extraction method that helps remove the negative influence of background traffic in network sessions and acquire as many traffic flow features as possible. In addition, we also establish an identification model of unknown Internet traffic based on JigClu, the self-supervised learning approach to training unlabeled datasets. It finally combines with the clustering method and realizes the further identification of unknown Internet traffic. The model has been demonstrated with an accuracy of no less than 74% in identifying unknown Internet traffic with the public dataset ISCXVPN2016 under different scenarios. The work provides a novel solution for unknown Internet traffic identification, which is the most difficult task in identifying Internet traffic. We believe it is a great leap in Internet traffic identification and is of great significance to maintaining the security of the network environment. [Wei, Dawei; Shi, Feifei] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Dhelim, Sahraoui] Univ Coll Dublin, Sch Comp Sci, Dublin D04 V1W8, Ireland University of Science & Technology Beijing; University College Dublin Dhelim, S (corresponding author), Univ Coll Dublin, Sch Comp Sci, Dublin D04 V1W8, Ireland. sahraoui.dhelim@hotmail.com Dhelim, Sahraoui/L-5176-2017 Dhelim, Sahraoui/0000-0002-3620-1395 26 2 2 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-5903 FUTURE INTERNET Future Internet OCT 2022.0 14 10 289 10.3390/fi14100289 0.0 16 Computer Science, Information Systems Emerging Sources Citation Index (ESCI) Computer Science 5P1SS gold 2023-03-23 WOS:000872939700001 0
J Chen, BJ; Gao, Y; Xu, LZ; Hong, XP; Zheng, YH; Shi, YQ Chen, Beijing; Gao, Ye; Xu, Lingzheng; Hong, Xiaopeng; Zheng, Yuhui; Shi, Yun-Qing Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field MATHEMATICAL BIOSCIENCES AND ENGINEERING English Article splicing localization; splicing detection; quaternion; fully convolutional network; conditional random field FOURIER-TRANSFORM; FORGERIES Recently, fully convolutional network (FCN) has been successfully used to locate spliced regions in synthesized images. However, all the existing FCN-based algorithms use real-valued FCN to process each channel separately. As a consequence, they fail to capture the inherent correlation between color channels and the integrity of three channels. So, in this paper, quaternion fully convolutional network (QFCN) is proposed to generalize FCN to quaternion domain by replacing real-valued conventional blocks in FCN with quaternion conventional blocks. In addition, a new color image splicing localization algorithm is proposed by combining QFCNs and superpixel (SP)-enhanced pairwise conditional random field (CRF). QFCNs consider three different versions (QFCN32, QFCN16, and QFCN8) with different up-sampling layers. The SP-enhanced pairwise CRF is used to refine the results of QFCNs. Experimental results on three publicly available datasets demonstrate that the proposed algorithm outperforms the existing algorithms including some conventional algorithms and some deep learning-based algorithms. [Chen, Beijing; Gao, Ye; Zheng, Yuhui; Shi, Yun-Qing] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China; [Chen, Beijing; Zheng, Yuhui] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China; [Chen, Beijing] Southeast Univ, Key Lab Comp Network Technol Jiangsu Prov, Nanjing 210096, Jiangsu, Peoples R China; [Xu, Lingzheng] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Sichuan, Peoples R China; [Hong, Xiaopeng] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland; [Shi, Yun-Qing] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Southeast University - China; Sichuan University; University of Oulu; New Jersey Institute of Technology Chen, BJ (corresponding author), Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China.;Chen, BJ (corresponding author), Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China.;Chen, BJ (corresponding author), Southeast Univ, Key Lab Comp Network Technol Jiangsu Prov, Nanjing 210096, Jiangsu, Peoples R China. nbutimage@126.com Zheng, Yuhui/AAF-2420-2019 NSFC [61572258, 61771231, 61772281, 61672294]; PAPD fund; Qing Lan Project NSFC(National Natural Science Foundation of China (NSFC)); PAPD fund; Qing Lan Project(Jiangsu Polytech Institute) This work was supported in part by NSFC under Grants 61572258, 61771231, 61772281, and 61672294, in part by the PAPD fund, and in part by Qing Lan Project. 50 3 4 1 12 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA 1547-1063 1551-0018 MATH BIOSCI ENG Math. Biosci. Eng. 2019.0 16 6 6907 6922 10.3934/mbe.2019346 0.0 16 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology IZ8CY 31698595.0 gold, Green Published 2023-03-23 WOS:000487331700038 0
J Kang, L; Chu, YP; Leng, KJ; Van Nieuwenhuyse, I Kang, Le; Chu, Yeping; Leng, Kaijun; Van Nieuwenhuyse, Inneke Construction of fast retrieval model of e-commerce supply chain information system based on Bayesian network INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT English Article Fast retrieval model; E-commerce supply chain; Bayesian network Bayesian network is a kind of uncertainty knowledge expression and reasoning tool, and it is an effective means to solve problems in related fields such as information retrieval. Considering the characteristics of e-commerce supply chain supply information and Bayesian network, a cognitive big data analysis method for intelligent information system is designed. The model uses a set of information sample documents to describe the query requirements and the documents to be detected. By calculating the similarity between them, the return results of the general search engine are sorted, thereby retrieving the supply chain supply information required by the user. Through numerical results, the precision of the source information retrieval model based on Bayesian network is also significantly higher than that of the trust network model and the inference network model, and the experimental data shows that the Bayesian network model has better retrieval performance than the trust network model and the inference network model. Therefore, when conducting large-scale e-commerce supply chain supply information collection, Bayesian network-based source information retrieval model is effective. [Kang, Le; Chu, Yeping; Leng, Kaijun] Hubei Univ Econ, Sch Business Adm, Wuhan, Peoples R China; [Leng, Kaijun] China Acad Social Sci, Natl Acad Econ Strategy, Beijing, Peoples R China; [Van Nieuwenhuyse, Inneke] Univ Hasselt, Fac Business Econ, Hasselt, Belgium Hubei University of Economics; Chinese Academy of Social Sciences; Hasselt University Chu, YP (corresponding author), Hubei Univ Econ, Sch Business Adm, Wuhan, Peoples R China. chuyeping1963@163.com 19 5 5 3 24 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1617-9846 1617-9854 INF SYST E-BUS MANAG Inf. Syst. E-Bus. Manag. DEC 2020.0 18 4 SI 705 722 10.1007/s10257-018-00392-6 0.0 18 Business; Management Social Science Citation Index (SSCI) Business & Economics PA8KC 2023-03-23 WOS:000595877400014 0
C Huang, JH; Zhang, L; Shen, Y; Zhang, HJ; Zhao, SJ; Yang, YK IEEE Huang, Junhao; Zhang, Lin; Shen, Ying; Zhang, Huijuan; Zhao, Shengjie; Yang, Yukai DMPR-PS: A NOVEL APPROACH FOR PARKING-SLOT DETECTION USING DIRECTIONAL MARKING-POINT REGRESSION 2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) IEEE International Conference on Multimedia and Expo English Proceedings Paper IEEE International Conference on Multimedia and Expo (ICME) JUL 08-12, 2019 Shanghai, PEOPLES R CHINA IEEE,IEEE Signal Proc Soc,IEEE Commun Soc,IEEE Circuits & Syst Soc,IEEE Comp Soc,Kuaishou,JD.COM,MEGVII,iQIYI,Microsoft,DiDi,Alibaba,Horizon Robot,Baidu,Lenovo Res,Unisound,SenseTime,Jiangmen Self-parking system; parking-slot detection; deep convolutional neural network SENSOR The self-parking system plays an important role in autonomous driving, and one of its critical issues is parking-slot detection. Previous studies in this field are mostly based on off-the-shelf models designed for universal purposes, which have various limitations in solving specific problems. In this paper, we propose a parking-slot detection method using directional marking-point regression, namely DMPR-PS. Instead of utilizing multiple off-the-shelf models, DMPR-PS uses a novel CNN-based model specially designed for directional marking-point regression. Given a surround-view image I, the model predicts position, shape and orientation of each marking-point on I. From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. To make the results reproducible, the source code is available at https://github.com/Teoge/DMPR-PS. [Huang, Junhao; Zhang, Lin; Shen, Ying; Zhang, Huijuan; Zhao, Shengjie] Tongji Univ, Sch Software Engn, Shanghai, Peoples R China; [Yang, Yukai] Uppsala Univ, Dept Stat, Uppsala, Sweden Tongji University; Uppsala University Zhang, L; Shen, Y (corresponding author), Tongji Univ, Sch Software Engn, Shanghai, Peoples R China. cslinzhang@tongji.edu.cn; yingshen@tongji.edu.cn Yang, Yukai/0000-0002-2623-8549; Huang, Junhao/0000-0003-3013-2787 Natural Science Foundation of China [61672380]; National Key Research and Development Project [2017YFE0119300]; Fundamental Research Funds for the Central Universities [2100219068] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was funded in part by the Natural Science Foundation of China under Grant No. 61672380, in part by the National Key Research and Development Project under Grant No. 2017YFE0119300, and in part by the Fundamental Research Funds for the Central Universities under Grant No. 2100219068. 20 14 16 3 10 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1945-7871 978-1-5386-9552-4 IEEE INT CON MULTI 2019.0 212 217 10.1109/ICME.2019.00045 0.0 6 Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO1PE 2023-03-23 WOS:000501820600037 0
J Hu, X; Ma, LH; Huang, SC; Huang, TY; Liu, SP Hu, Xing; Ma, Linhua; Huang, Shaocheng; Huang, Tianyu; Liu, Shiping Dynamic Channel Slot Allocation Scheme and Performance Analysis of Cyclic Quorum Multichannel MAC Protocol MATHEMATICAL PROBLEMS IN ENGINEERING English Article In high diversity node situation, multichannel MAC protocol can improve the frequency efficiency, owing to fewer collisions compared with single-channel MAC protocol. And the performance of cyclic quorum-based multichannel (CQM) MAC protocol is outstanding. Based on cyclic quorum system and channel slot allocation, it can avoid the bottleneck that others suffered from and can be easily realized with only one transceiver. To obtain the accurate performance of CQMMAC protocol, a Markov chain model, which combines the channel-hopping strategy of CQM protocol and IEEE 802.11 distributed coordination function (DCF), is proposed. The results of numerical analysis show that the optimal performance of CQM protocol can be obtained in saturation bound situation. And then we obtain the saturation bound of CQM system by bird swarm algorithm. In addition, to improve the performance of CQM protocol in unsaturation situation, a dynamic channel slot allocation of CQM(DCQM) protocol is proposed, based on wavelet neural network. Finally, the performance of CQM protocol and DCQM protocol is simulated by Qualnet platform. And the simulation results show that the analytic and simulation results match very well; the DCQM performs better in unsaturation situation. [Hu, Xing; Ma, Linhua; Huang, Tianyu; Liu, Shiping] Air Force Engn Univ, Sch Aeronaut & Astronaut Engn, Xian 710038, Shaanxi, Peoples R China; [Huang, Shaocheng] Royal Inst Technol, Sch Elect Engn, Stockholm, Sweden Air Force Engineering University; Royal Institute of Technology Hu, X (corresponding author), Air Force Engn Univ, Sch Aeronaut & Astronaut Engn, Xian 710038, Shaanxi, Peoples R China. hustart@yahoo.com Nature Science Foundation of China [61072102]; State Key Laboratory of Xidian University [ISN15-13] Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Xidian University Xing HU wishes to thank Lin-hua MA for their helpful discussions on information theoretic aspects of the subject. This work has been supported by the Nature Science Foundation of China (61072102) and the State Key Laboratory of Xidian University (ISN15-13). 29 1 1 0 4 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. 2017.0 2017 8580913 10.1155/2017/8580913 0.0 16 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics FI5FV Green Submitted, gold 2023-03-23 WOS:000412008700001 0
C Fei, JW; Xia, ZH; Tondi, B; Barni, M IEEE Fei, Jianwei; Xia, Zhihua; Tondi, Benedetta; Barni, Mauro Supervised GAN Watermarking for Intellectual Property Protection 2022 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS) IEEE International Workshop on Information Forensics and Security English Proceedings Paper IEEE International Workshop on Information Forensics and Security (WIFS) DEC 12-16, 2022 ELECTR NETWORK IEEE Intellectual Property Protection; Generative Adversarial Networks; DNN Watermarking; Security of Deep Learning We propose a watermarking method for protecting the Intellectual Property (IP) of Generative Adversarial Networks (GANs). The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature), whose presence inside the image can be checked at a later stage for ownership verification. To achieve this goal, a pre-trained CNN watermarking decoding block is inserted at the output of the generator. The generator loss is then modified by including a watermark loss term, to ensure that the prescribed watermark can be extracted from the generated images. The watermark is embedded via fine-tuning, with reduced time complexity. Results show that our method can effectively embed an invisible watermark inside the generated images. Moreover, our method is a general one and can work with different GAN architectures, different tasks, and different resolutions of the output image. We also demonstrate the good robustness performance of the embedded watermark against several post-processing, among them, JPEG compression, noise addition, blurring, and color transformations. [Fei, Jianwei] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China; [Xia, Zhihua] Jinan Univ, Guangzhou, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Siena, Italy Nanjing University of Information Science & Technology; Jinan University; University of Siena Xia, ZH (corresponding author), Jinan Univ, Guangzhou, Peoples R China. National Key Research and Development Plan of China [2020YFB1005600]; National Natural Science Foundation of China [62122032, U1936118]; Qinglan Project of Jiangsu Province; 333 project of Jiangsu Province; China Scholarship Council (CSC) [202109040029] National Key Research and Development Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Qinglan Project of Jiangsu Province; 333 project of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Scholarship Council (CSC)(China Scholarship Council) This work is supported in part by the National Key Research and Development Plan of China under Grant 2020YFB1005600,in part by the National Natural Science Foundation of China under grant numbers 62122032, and U1936118, in part by Qinglan Project of Jiangsu Province, and 333 project of Jiangsu Province. This work has been partially supported by the China Scholarship Council (CSC), file No.202109040029. 26 0 0 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2157-4766 979-8-3503-0967-6 IEEE INT WORKS INFOR 2022.0 10.1109/WIFS55849.2022.9975409 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BU4TT Green Submitted 2023-03-23 WOS:000907044000014 0
J Zhou, YJ; Wang, YM; Poiesi, F; Qin, Q; Wan, Y Zhou, Youjie; Wang, Yiming; Poiesi, Fabio; Qin, Qi; Wan, Yi Loop Closure Detection Using Local 3D Deep Descriptors IEEE ROBOTICS AND AUTOMATION LETTERS English Article SLAM; computer vision for automation; RGB-D perception We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted from point clouds that are learnt from data using a deep learning algorithm. We propose a novel overlap measure for loop detection by computing the metric error between points that correspond to mutually-nearest-neighbour descriptors after registering the loop candidate point cloud by its estimated relative pose. This novel approach enables us to accurately detect loops and estimate six degrees-of-freedom poses in the case of small overlaps. We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy. Additionally, we embed our loop closure approach in RESLAM, a recent edge-based SLAM system, and perform the evaluation on real-world RGBD-TUM and synthetic ICL datasets. Our approach enables RESLAM to achieve a better localisation accuracy compared to its original loop closure strategy. [Zhou, Youjie; Qin, Qi; Wan, Yi] Shandong Univ, Sch Mech Engn, Jinan 250012, Peoples R China; [Poiesi, Fabio; Qin, Qi] Fdn Bruno Kessler, I-38123 Povo, Italy Shandong University; Fondazione Bruno Kessler Wan, Y (corresponding author), Shandong Univ, Sch Mech Engn, Jinan 250012, Peoples R China. 13455295201@163.com; ywang@fbk.eu; poiesi@fbk.eu; 201933991@mail.sdu.edu.cn; wanyi@sdu.edu.cn Wang, Yiming/0000-0002-5932-4371; /0000-0003-3325-1386 China Government [2019JZZY010112, 2020JMRH0202]; SHIELD Project - European Union China Government; SHIELD Project - European Union This work was supported in part by the China Government under Grants 2019JZZY010112 and 2020JMRH0202, and in part by the SHIELD Project, funded by the European Union's Joint Programming Initiative - Cultural Heritage, Conservation, Protection and Use joint call. 39 2 2 16 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. JUL 2022.0 7 3 6335 6342 10.1109/LRA.2022.3156940 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics 1D4VI Green Submitted 2023-03-23 WOS:000793799400001 0
J Zhang, BW; Tondi, B; Lv, XX; Barni, M Zhang, Bowen; Tondi, Benedetta; Lv, Xixiang; Barni, Mauro Challenging the Adversarial Robustness of DNNs Based on Error-Correcting Output Codes SECURITY AND COMMUNICATION NETWORKS English Article The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defence mechanisms. The use of networks adopting error-correcting output codes (ECOC) has recently been proposed to counter the creation of adversarial examples in a white-box setting. In this paper, we carry out an in-depth investigation of the adversarial robustness achieved by the ECOC approach. We do so by proposing a new adversarial attack specifically designed for multilabel classification architectures, like the ECOC-based one, and by applying two existing attacks. In contrast to previous findings, our analysis reveals that ECOC-based networks can be attacked quite easily by introducing a small adversarial perturbation. Moreover, the adversarial examples can be generated in such a way to achieve high probabilities for the predicted target class, hence making it difficult to use the prediction confidence to detect them. Our findings are proven by means of experimental results obtained on MNIST, CIFAR-10, and GTSRB classification tasks. [Zhang, Bowen; Lv, Xixiang] Xidian Univ, Sch Cyber Engn, Xian 710126, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, I-53100 Siena, Italy Xidian University; University of Siena Lv, XX (corresponding author), Xidian Univ, Sch Cyber Engn, Xian 710126, Peoples R China. bowenzhang.psnl@out-look.com; xxlv@mail.xidian.edu.cn BARNI, Mauro/0000-0002-7368-0866 China Scholarship Council (CSC) [201806960079] China Scholarship Council (CSC)(China Scholarship Council) This work was partially supported by the China Scholarship Council (CSC) (file no. 201806960079). 42 1 1 1 9 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1939-0114 1939-0122 SECUR COMMUN NETW Secur. Commun. Netw. NOV 16 2020.0 2020 8882494 10.1155/2020/8882494 0.0 11 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications PB2KM gold, Green Submitted, Green Published 2023-03-23 WOS:000596156100001 0
J Li, ZJ; Chi, CY; Zhan, YY Li, Zijian; Chi, Chengying; Zhan, Yunyun Corpus Augmentation for Improving Neural Machine Translation CMC-COMPUTERS MATERIALS & CONTINUA English Article Neural machine translation; corpus argumentation; model improvement; deep learning; data cleaning MODEL The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the performance of NMT is greatly reduced, and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model. However, not all languages have large-scale and high-quality bilingual corpus resources available. In these cases, improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results. This paper proposes a new method to improve the quality of data by using data cleaning, data expansion, and other measures to expand the data at the word and sentence-level, thus improving the richness of the bilingual data. The long short-term memory (LSTM) language model is also used to ensure the smoothness of sentence construction in the process of sentence construction. At the same time, it uses a variety of processing methods to improve the quality of the bilingual data. Experiments using three standard test sets are conducted to validate the proposed method; the most advanced fairseq-transformer NMT system is used in the training. The results show that the proposed method has worked well on improving the translation results. Compared with the state-of-the-art methods, the BLEU value of our method is increased by 2.34 compared with that of the baseline. [Li, Zijian; Chi, Chengying] Univ Sci & Technol Liaoning, Anshan 114031, Peoples R China; [Zhan, Yunyun] Technol Univ Dublin, Coll Sci & Hlth, Dublin D08 X622, Ireland University of Science & Technology Liaoning Chi, CY (corresponding author), Univ Sci & Technol Liaoning, Anshan 114031, Peoples R China.;Zhan, YY (corresponding author), Technol Univ Dublin, Coll Sci & Hlth, Dublin D08 X622, Ireland. chichengying@ustl.edu.cn; D16123420@mytudublin.ie National Natural Science Foundation of China (NSFC) [61672138] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This research was supported by the National Natural Science Foundation of China (NSFC) under the grant (No. 61672138). 25 1 1 2 7 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2020.0 64 1 637 650 10.32604/cmc.2020.010265 0.0 14 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science LR5HX 2023-03-23 WOS:000535726900036 0
J Zhang, FR; Zhang, JA; Xu, ZH; Tang, J; Jiang, PY; Zhong, RF Zhang, Furao; Zhang, Jianan; Xu, Zhihong; Tang, Jie; Jiang, Peiyu; Zhong, Ruofei Extracting Traffic Signage by Combining Point Clouds and Images SENSORS English Article lidar point cloud; object detection; panoramic image; convolutional neural network; projection transformation Recognizing traffic signs is key to achieving safe automatic driving. With the decreasing cost of LiDAR, the accurate extraction of traffic signs using point cloud data has received wide attention. In this study, we propose combining point cloud and image traffic sign extraction: firstly, we use the improved YoloV3 model to detect traffic signs in panoramic images. The specific improvement is that the convolution block attention module is added to the algorithm framework, the traditional K-means clustering algorithm is improved, and Focal Loss is introduced as the loss function. It shows higher accuracy on the TT100K dataset, with a 1.4% improvement in accuracy compared to the previous YoloV3. Then, the point cloud of the area where the traffic sign is located is extracted by combining the image detection results. On this basis, the outline of the traffic sign is accurately extracted using the reflection intensity, spatial geometry and other information. Compared with the traditional method, the proposed method can effectively reduce the missed detection rate, narrow the range of point cloud, and improve the detection accuracy by 10.2%. [Zhang, Furao; Zhang, Jianan; Xu, Zhihong; Tang, Jie; Zhong, Ruofei] Capital Normal Univ, Key Lab Informat Acquisit & Applicat 3D, MOE, Beijing 100048, Peoples R China; [Zhang, Furao; Zhang, Jianan; Xu, Zhihong; Tang, Jie; Zhong, Ruofei] Capital Normal Univ, Base State Key Lab Urban Environm Proc & Digital M, Beijing 100048, Peoples R China; [Zhang, Furao; Zhang, Jianan; Xu, Zhihong; Tang, Jie; Zhong, Ruofei] Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China; [Jiang, Peiyu] Uppsala Univ, Dept Stat, S-75120 Uppsala, Sweden Capital Normal University; Capital Normal University; Capital Normal University; Uppsala University Zhong, RF (corresponding author), Capital Normal Univ, Key Lab Informat Acquisit & Applicat 3D, MOE, Beijing 100048, Peoples R China.;Zhong, RF (corresponding author), Capital Normal Univ, Base State Key Lab Urban Environm Proc & Digital M, Beijing 100048, Peoples R China.;Zhong, RF (corresponding author), Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China. zrf@cnu.edu.cn National Key Technologies Research and Development Program of China [2022YFB3904101]; National Natural Science Foundation of China [U22A20568, 42101444] National Key Technologies Research and Development Program of China(National Key Technology R&D Program); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Technologies Research and Development Program of China under Grant 2022YFB3904101, in part by the National Natural Science Foundation of China under Grant U22A20568 and 42101444. 39 0 0 1 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 2023.0 23 4 2262 10.3390/s23042262 0.0 23 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 9J2EN 36850860.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000940007000001 0
J Li, J; Qiu, XY; Wei, YF; Song, M; Wang, XJ Li, Jun; Qiu, Xinyi; Wei, Yifei; Song, Mei; Wang, Xiaojun Online Rail Fastener Detection Based on YOLO Network CMC-COMPUTERS MATERIALS & CONTINUA English Article Fastener detection; deep learning; state recognition; real-time classification CLASSIFICATION; SYSTEM Traveling by high-speed rail and railway transportation have become an important part of people's life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5 (YOLOv5) network, and completes the real-time classification of fastener states. The improved YOLOv5 network proposed contains five sections, which are Input, Backbone, Neck, Head Detector and a read-only Few-shot Example Learning module. Themain purpose of this project is to improve the detection precision and shorten the detection time. Ultimately, the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms. This model achieves on-line fastener detection by completing the sampling-detection-recognition-warning cycle of a single sample before the next image is sampled. Specifically, the mean average precision of model reaches 94.6%. And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU. [Li, Jun; Qiu, Xinyi; Wei, Yifei; Song, Mei] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing Key Lab Work Safety Intelligent Monitorin, Beijing 100876, Peoples R China; [Wang, Xiaojun] Dublin City Univ, Dublin 9, Ireland Beijing University of Posts & Telecommunications; Dublin City University Wei, YF (corresponding author), Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing Key Lab Work Safety Intelligent Monitorin, Beijing 100876, Peoples R China. weiyifei@bupt.edu.cn National Natural Science Foundation of China [61871046] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (61871046, SM, http://www.nsfc.gov.cn/). 27 2 2 7 14 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2022.0 72 3 5955 5967 10.32604/cmc.2022.027947 0.0 13 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science 2P6FU gold 2023-03-23 WOS:000819835200025 0
J Shi, WX; Meng, QY; Zhang, LL; Zhao, MF; Su, C; Jancso, T Shi, Wenxu; Meng, Qingyan; Zhang, Linlin; Zhao, Maofan; Su, Chen; Jancso, Tamas DSANet: A Deep Supervision-Based Simple Attention Network for Efficient Semantic Segmentation in Remote Sensing Imagery REMOTE SENSING English Article convolutional neural network (CNN); deep supervision; lightweight model; remote sensing; semantic segmentation Semantic segmentation for remote sensing images (RSIs) plays an important role in many applications, such as urban planning, environmental protection, agricultural valuation, and military reconnaissance. With the boom in remote sensing technology, numerous RSIs are generated; this is difficult for current complex networks to handle. Efficient networks are the key to solving this challenge. Many previous works aimed at designing lightweight networks or utilizing pruning and knowledge distillation methods to obtain efficient networks, but these methods inevitably reduce the ability of the resulting models to characterize spatial and semantic features. We propose an effective deep supervision-based simple attention network (DSANet) with spatial and semantic enhancement losses to handle these problems. In the network, (1) a lightweight architecture is used as the backbone; (2) deep supervision modules with improved multiscale spatial detail (MSD) and hierarchical semantic enhancement (HSE) losses synergistically strengthen the obtained feature representations; and (3) a simple embedding attention module (EAM) with linear complexity performs long-range relationship modeling. Experiments conducted on two public RSI datasets (the ISPRS Potsdam dataset and Vaihingen dataset) exhibit the substantial advantages of the proposed approach. Our method achieves 79.19% mean intersection over union (mIoU) on the ISPRS Potsdam test set and 72.26% mIoU on the Vaihingen test set with speeds of 470.07 FPS on 512 x 512 images and 5.46 FPS on 6000 x 6000 images using an RTX 3090 GPU. [Shi, Wenxu; Meng, Qingyan; Zhang, Linlin; Zhao, Maofan; Su, Chen] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100049, Peoples R China; [Shi, Wenxu; Meng, Qingyan; Zhang, Linlin; Zhao, Maofan; Su, Chen] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Meng, Qingyan; Zhang, Linlin] Chinese Acad Sci, Hainan Res Inst, Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China; [Jancso, Tamas] Obuda Univ, Alba Regia Tech Fac, Budai Ut 45, H-8001 Szekesfehervar, Hungary Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Obuda University Meng, QY (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100049, Peoples R China.;Meng, QY (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;Meng, QY (corresponding author), Chinese Acad Sci, Hainan Res Inst, Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China. mengqy@radi.ac.cn ; Zhang, Linlin/P-9131-2017 Shi, Wenxu/0000-0001-6229-7995; Zhang, Linlin/0000-0001-5073-1694; Meng, Qingyan/0000-0002-5440-4081 68 0 0 12 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2022.0 14 21 5399 10.3390/rs14215399 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 6F4HV gold 2023-03-23 WOS:000884025000001 0
J Liu, Z; Liu, X; Wang, K; Liang, ZW; Correia, JAFO; De Jesus, AMP Liu, Zheng; Liu, Xin; Wang, Kan; Liang, Zhongwei; Correia, Jose A. F. O.; De Jesus, Abilio M. P. GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades ENERGIES English Article wind turbine blade; full-scale static test; neural networks; strain prediction FATIGUE LIFE PREDICTION; OPTIMIZATION; DESIGN; MODEL This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades' health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades. [Liu, Zheng; Liu, Xin; Liang, Zhongwei] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China; [Wang, Kan] China Gen Certificat Ctr, Beijing 100020, Peoples R China; [Correia, Jose A. F. O.; De Jesus, Abilio M. P.] Univ Porto, INEGI, Fac Engn, P-4200465 Porto, Portugal Guangzhou University; Universidade do Porto Liu, X (corresponding author), Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.;Correia, JAFO (corresponding author), Univ Porto, INEGI, Fac Engn, P-4200465 Porto, Portugal. liu_best@yeah.net; designer_liuxin@163.com; wkbjro@163.com; lzwstalin@126.com; jacorreia@inegi.up.pt; ajesus@fe.up.pt liang, zhongwei/AAN-4151-2020; Oliveira, Jose/D-2303-2009; jesus, abilio/I-1122-2015; Correia, José A.F.O./AAF-2185-2019 Oliveira, Jose/0000-0002-1829-4196; jesus, abilio/0000-0002-1059-715X; Correia, José A.F.O./0000-0002-4148-9426; Liu, Zheng/0000-0002-2879-5859 Guangzhou University Teaching Reform Project [09-18ZX0304]; Innovative Team Project of Guangdong Universities [2017KCXTD025]; Innovative Academic Team Project of Guangzhou Education System [1201610013] Guangzhou University Teaching Reform Project; Innovative Team Project of Guangdong Universities; Innovative Academic Team Project of Guangzhou Education System This research was funded by Guangzhou University Teaching Reform Project (09-18ZX0309), the Guangzhou University Teaching Reform Project (09-18ZX0304), the Innovative Team Project of Guangdong Universities (2017KCXTD025), and the Innovative Academic Team Project of Guangzhou Education System (1201610013). 26 24 25 2 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies MAR 2 2019.0 12 6 1026 10.3390/en12061026 0.0 15 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels HU9LV gold, Green Submitted 2023-03-23 WOS:000465616800047 0
J Wu, J; Hu, W; Wen, Y; Tu, WL; Liu, XM Wu, Jing; Hu, Wei; Wen, Yuan; Tu, WenLi; Liu, XiaoMing Skin Lesion Classification Using Densely Connected Convolutional Networks with Attention Residual Learning SENSORS English Article attention; dermoscopy; residual learning; densely connected convolutional networks Skin lesion classification is an effective approach aided by computer vision for the diagnosis of skin cancer. Though deep learning models presented advantages over traditional methods and brought tremendous breakthroughs, a precise diagnosis is still challenging because of the intra-class variation and inter-class similarity caused by the diversity of imaging methods and clinicopathology. In this paper, we propose a densely connected convolutional network with an attention and residual learning (ARDT-DenseNet) method for skin lesion classification. Each ARDT block consists of dense blocks, transition blocks and attention and residual modules. Compared to a residual network with the same number of convolutional layers, the size of the parameters of the densely connected network proposed in this paper has been reduced by half, while the accuracy of skin lesion classification is preserved. Our improved densely connected network adds an attention mechanism and residual learning after each dense block and transition block without introducing additional parameters. We evaluate the ARDT-DenseNet model with the ISIC 2016 and ISIC 2017 datasets. Our method achieves an ACC of 85.7% and an AUC of 83.7% in skin lesion classification with ISIC 2016 and an average AUC of 91.8% in skin lesion classification with ISIC 2017. The experimental results show that the method proposed in this paper has achieved a significant improvement in skin lesion classification, which is superior to that of the state-of-the-art method. [Wu, Jing; Hu, Wei; Tu, WenLi; Liu, XiaoMing] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan 430081, Peoples R China; [Wen, Yuan] Trinity Coll Dublin, Sch Comp Sci & Stat, Dublin 2, Ireland Wuhan University of Science & Technology; Trinity College Dublin Wu, J (corresponding author), Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan 430081, Peoples R China.;Wen, Y (corresponding author), Trinity Coll Dublin, Sch Comp Sci & Stat, Dublin 2, Ireland. wujingecs@wust.edu.cn; huwei@wust.edu.cn; weny@tcd.ie; toto10241024@163.com; lxmspace@gmail.com National Natural Science Foundation of China [U1803262] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by The National Natural Science Foundation of China, grant number U1803262. 36 10 10 4 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2020.0 20 24 7080 10.3390/s20247080 0.0 15 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation PL7QD 33321864.0 Green Accepted, gold 2023-03-23 WOS:000603310600001 0
J Wu, D; Chen, JY; Lu, BY; Xiong, LN; He, Y; Zhang, Y Wu, Di; Chen, Jianyang; Lu, Baiyi; Xiong, Lina; He, Yong; Zhang, Ying Application of near infrared spectroscopy for the rapid determination of antioxidant activity of bamboo leaf extract FOOD CHEMISTRY English Article Near infrared (NIR) spectroscopy; Extract of bamboo leaves (EBL); Antioxidant activity; Successive projections algorithm (SPA); Variable selection; Regression tool VARIABLE SELECTION; LEAVES; QUANTIFICATION; PREDICTION; CAPACITY; FRESH; FOODS This study was carried out to evaluate the feasibility of using near infrared (NIR) spectroscopy for determining three antioxidant activity indices of the extract of bamboo leaves (EBL), specifically 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing/antioxidant power (FRAP), and 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS). Four different linear and nonlinear regressions tools (i.e. partial least squares (PLS), multiple linear regression (MLR), back-propagation artificial neural network (BP-ANN), and least squares support vector machine (LS-SVM)) were systemically studied and compared in developing the model. Variable selection was first time considered in applying the NIR spectroscopic technique for the determination of antioxidant activity of food or agricultural products. On the basis of these selected optimum wavelengths, the established MLR calibration models provided the coefficients of correlation with a prediction (r(pre)) of 0.863, 0.910, and 0.966 for DPPH, FARP, and ABTS determinations, respectively. The overall results of this study revealed the potential for use of NIB spectroscopy as an objective and non-destructive method to inspect the antioxidant activity of EBL. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved. [Wu, Di; Chen, Jianyang; Lu, Baiyi; Xiong, Lina; He, Yong; Zhang, Ying] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China; [Wu, Di] Natl Univ Ireland Univ Coll Dublin, FRCFT Grp, Dublin 4, Ireland Zhejiang University; University College Dublin Lu, BY (corresponding author), Zhejiang Univ, Coll Biosyst Engn & Food Sci, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, Peoples R China. bylu@zju.edu.cn He, Yong/E-3218-2010; Wu, Di/F-4995-2013; Wu, Di/C-9752-2012; Wu, Di/HNP-3772-2023 He, Yong/0000-0001-6752-1757; Wu, Di/0000-0002-4997-5082; Technologies R&D Program of Zhejiang [2009F70063, 2011C37047]; National Natural Science Foundation of China [30901001] Technologies R&D Program of Zhejiang; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by Technologies R&D Program of Zhejiang (No. 2009F70063 and 2011C37047) and the National Natural Science Foundation of China (No. 30901001) for financial support. We also thank Dr Lijuan Xie for their excellent technical assistance in spectral acquisition. 34 111 116 6 158 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0308-8146 1873-7072 FOOD CHEM Food Chem. DEC 15 2012.0 135 4 2147 2156 10.1016/j.foodchem.2012.07.011 0.0 10 Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Food Science & Technology; Nutrition & Dietetics 020CN 22980783.0 2023-03-23 WOS:000309786400005 0
J Yao, QJ; Jahanshahi, H; Moroz, I; Alotaibi, ND; Bekiros, S Yao, Qijia; Jahanshahi, Hadi; Moroz, Irene; Alotaibi, Naif D.; Bekiros, Stelios Neural Adaptive Fixed-Time Attitude Stabilization and Vibration Suppression of Flexible Spacecraft MATHEMATICS English Article attitude stabilization; flexible spacecraft; neural adaptive control; fixed-time control; vibration suppression; Lyapunov analysis FINITE-TIME; DESIGN; APPENDAGES; CONTROLLER; SATELLITE; DYNAMICS; TRACKING This paper proposes a novel neural adaptive fixed-time control approach for the attitude stabilization and vibration suppression of flexible spacecraft. First, the neural network (NN) was introduced to identify the lumped unknown term involving uncertain inertia, external disturbance, torque saturation, and elastic vibrations. Then, the proposed controller was synthesized by embedding the NN compensation into the fixed-time backstepping control framework. Lyapunov analysis showed that the proposed controller guaranteed the stabilization of attitude and angular velocity to the adjustable small neighborhoods of zero in fixed time. The proposed controller is not only robust against uncertain inertia and external disturbance, but also insensitive to elastic vibrations of the flexible appendages. At last, the excellent stabilization performance and good vibration suppression capability of the proposed control approach were verified through simulations and detailed comparisons. [Yao, Qijia] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China; [Jahanshahi, Hadi] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada; [Moroz, Irene] Univ Oxford, Math Inst, Oxford OX2 6GG, England; [Alotaibi, Naif D.] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Bekiros, Stelios] Univ Malta, Dept Banking & Finance, FEMA, Msida 2080, Malta Beijing Institute of Technology; University of Manitoba; University of Oxford; King Abdulaziz University; University of Malta Bekiros, S (corresponding author), Univ Malta, Dept Banking & Finance, FEMA, Msida 2080, Malta. qijia_yao@126.com; jahanshahi.hadi90@gmail.com; Irene.Moroz@maths.ox.ac.uk; ndalotabi@kau.edu.sa; stelios.bekiros@um.edu.mt Jahanshahi, Hadi/L-5815-2019; Bekiros, Stelios/K-4041-2014 Bekiros, Stelios/0000-0002-9376-8726 Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [FP-085-43] Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia has funded this project, under grant no. (FP-085-43). 40 7 7 11 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics MAY 2022.0 10 10 1667 10.3390/math10101667 0.0 17 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 1R3NJ gold 2023-03-23 WOS:000803279500001 0
J Wei, DSL; Xue, KP; Bruschi, R; Schmid, S Wei, David S. L.; Xue, Kaiping; Bruschi, Roberto; Schmid, Stefan Guest Editorial Leveraging Machine Learning in SDN/NFV-Based Networks IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Editorial Material Akey trend of current network evolution is in the direction of network softwarization and virtualization. These technological paradigms aim to enable a network to be programmable in a way that makes the network more flexible, scalable, and reliable, and in turn leads to agile service deployment and lower capital and operational expenses. So far, two related widely adopted solutions are software defined networks (SDN) and network function virtualization (NFV). There is one main difference between these two new networking paradigms. SDN separates the control plane from the data plane through a well-defined programming interface, such that the centralized controller can have a complete view of the entire network, while NFV decouples network functions from dedicated physical equipment by means of virtualization technology, and runs the virtual network functions (VNFs) in the general purpose physical or virtual network appliances. Both approaches make the network programmable in order to have the aforementioned desired features. SDN and NFV do not depend on each other, and they actually complement each other. They can work well individually and can also work in tandem for performance reasons. Due to such advantages, both SDN and NFV have become key enabling technologies for 5G networks, and have also been used in a wide range of important areas including IoT, mobile edge computing, smart grid, cloud datacenters, and cognition-based networks. [Wei, David S. L.] Fordham Univ, Dept Comp & Informat Sci, Bronx, NY 10458 USA; [Xue, Kaiping] Univ Sci & Technol China, Sch Cyber Secur, Hefei 230027, Peoples R China; [Xue, Kaiping] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China; [Bruschi, Roberto] Italian Natl Consortium Telecommun CNIT, Natl Lab Smart Sustainable & Secure Internet Tech, I-16145 Genoa, Italy; [Schmid, Stefan] Univ Vienna, Fac Comp Sci, A-1010 Vienna, Austria Fordham University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Vienna Wei, DSL (corresponding author), Fordham Univ, Dept Comp & Informat Sci, Bronx, NY 10458 USA. wei@cis.fordham.edu; kpxue@ustc.edu.cn; roberto.bruschi@unige.it; schmiste@gmail.com Wei, David/0000-0002-3839-5576 0 2 3 1 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. FEB 2020.0 38 2 245 247 10.1109/JSAC.2019.2959197 0.0 3 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications KU9RC Bronze, Green Published 2023-03-23 WOS:000520060900001 0
J Chen, ZY; Deng, LI; Luo, YH; Li, DL; Junior, JM; Gonsalves, WN; Nurunnabi, AAM; Li, JAT; Wang, C; Li, DR Chen, Ziyi; Deng, Liai; Luo, Yuhua; Li, Dilong; Marcato Junior, Jose; Gonsalves, Wesley Nunes; Nurunnabi, Abdul Awal Md; Li, Jonathan; Wang, Cheng; Li, Deren Road extraction in remote sensing data: A survey INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Road extraction; Review; 2D and 3D; Remote sensing; Point clouds LASER-SCANNING DATA; FULLY CONVOLUTIONAL NETWORK; CENTERLINE EXTRACTION; LIDAR DATA; LEARNING FRAMEWORK; SATELLITE IMAGES; AUTOMATED EXTRACTION; SHAPE-FEATURES; NEURAL-NETWORK; EDGE-DETECTION Automated extraction of roads from remotely sensed data come forth various usages ranging from digital twins for smart cities, intelligent transportation, urban planning, autonomous driving, to emergency management. Many studies have focused on promoting the progress of methods for automated road extraction from aerial and satellite optical images, synthetic aperture radar (SAR) images, and LiDAR point clouds. In the past 10 years, no a more comprehensive survey on this topic could be found in literature. This paper attempts to provide a comprehensive survey on road extraction methods that use 2D earth observing images and 3D LiDAR point clouds. In this review, we first present a tree-structure that separate the literature into 2D and 3D. Then, further methodologies level classification is demonstrated both in 2D and 3D. In 2D and 3D, we introduce and analyze the literature published in the last ten years. Except for the methodologies, we also review the aspects of data commonly used. Finally, this paper explores the existing challenges and future trends. [Chen, Ziyi; Deng, Liai; Luo, Yuhua; Li, Dilong] Huaqiao Univ, Xiamen Key Lab Comp Vis & Pattern Recognit, Fujian Key Lab Big Data Intelligence & Secur, Dept Comp Sci &Technol, Jimei Rd 668, Xiamen, Fujian, Peoples R China; [Marcato Junior, Jose; Gonsalves, Wesley Nunes] Univ Fed Mato Grosso, Fac Engn, Architecture & Urbanism & Geog, Campo Grande, MS, Brazil; [Nurunnabi, Abdul Awal Md] Univ Luxembourg, Dept Geodesy & Geospatial Engn, L-1359 Luxembourg, Luxembourg; [Li, Jonathan] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada; [Wang, Cheng] Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China; [Li, Deren] Wuhan University, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430079, Peoples R China Huaqiao University; Universidade Federal de Mato Grosso; University of Luxembourg; University of Waterloo; Xiamen University; Wuhan University Li, DL (corresponding author), Huaqiao Univ, Xiamen Key Lab Comp Vis & Pattern Recognit, Fujian Key Lab Big Data Intelligence & Secur, Dept Comp Sci &Technol, Jimei Rd 668, Xiamen, Fujian, Peoples R China. scholar.dll@hqu.edu.cn National Natural Science Foundation of China [61801121, 41971414, 62076107]; Fundamental Research Funds for the Central Universities of Huaqiao University [ZQN-911]; Natural Science Foundation of Fujian Province [2021 J05059]; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [21E01]; Scientific Research Funds of Huaqiao University [605-50Y21026]; CAPES PrInt [88881.311850/2018- 01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of Huaqiao University; Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University; Scientific Research Funds of Huaqiao University; CAPES PrInt(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)) This study was financially supported by National Natural Science Foundation of China (No.62001175) , in part by the Fundamental Research Funds for the Central Universities of Huaqiao University (No. ZQN-911) , in part by the Natural Science Foundation of Fujian Province (No. 2021 J05059) , in part by the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (No.21E01) , in part by the Scientific Research Funds of Huaqiao University (NO. 605-50Y21026) , in part by the National Natural Science Foundation of China (No.61801121, 41971414 and 62076107) and CAPES PrInt (No. 88881.311850/2018- 01) . 246 19 19 21 21 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-8432 1872-826X INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. AUG 2022.0 112 102833 10.1016/j.jag.2022.102833 0.0 24 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing 5R9ZE gold 2023-03-23 WOS:000874860600002 0
C Liu, JY; Li, X; Zhou, JQ; Shen, JX Farkas, I; Masulli, P; Wermter, S Liu, Junyu; Li, Xiang; Zhou, Jiqiang; Shen, Jianxiong Prediction Stability as a Criterion in Active Learning ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II Lecture Notes in Computer Science English Proceedings Paper 29th International Conference on Artificial Neural Networks (ICANN) SEP 15-18, 2020 Bratislava, SLOVAKIA Active learning; Classification; Prediction stability Recent breakthroughs made by deep learning rely heavily on a large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Besides the previous active learning algorithms that only adopted information after training, we propose a new class of methods named sequential-based method based on the information during training. A specific criterion of active learning called prediction stability is proposed to prove the feasibility of sequential-based methods. We design a toy model to explain the principle of our proposed method and pointed out a possible defect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100. [Liu, Junyu] Kyoto Univ, Grad Sch Informat, Kyoto, Japan; [Liu, Junyu; Li, Xiang; Zhou, Jiqiang] Hikvis Res Inst, Hangzhou, Peoples R China; [Shen, Jianxiong] CSIC UPC, Inst Robat & Informat Ind, Barcelona, Spain Kyoto University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Institut de Robotica i Informatica Industrial (IRII) Liu, JY (corresponding author), Kyoto Univ, Grad Sch Informat, Kyoto, Japan. liu.junyu.82w@st.kyoto-u.ac.jp Liu, Junyu/0000-0001-6232-4199 19 1 1 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-61616-8; 978-3-030-61615-1 LECT NOTES COMPUT SC 2020.0 12397 157 167 10.1007/978-3-030-61616-8_13 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS3OJ Green Submitted, Green Published 2023-03-23 WOS:000713797800013 0
J Li, FH; Li, YW; Novoselov, KS; Liang, F; Meng, JS; Ho, SH; Zhao, T; Zhou, H; Ahmad, A; Zhu, YL; Hu, LX; Ji, DX; Jia, LT; Liu, R; Ramakrishna, S; Zhang, XC Li, Fanghua; Li, Yiwei; Novoselov, K. S.; Liang, Feng; Meng, Jiashen; Ho, Shih-Hsin; Zhao, Tong; Zhou, Hui; Ahmad, Awais; Zhu, Yinlong; Hu, Liangxing; Ji, Dongxiao; Jia, Litao; Liu, Rui; Ramakrishna, Seeram; Zhang, Xingcai Bioresource Upgrade for Sustainable Energy, Environment, and Biomedicine NANO-MICRO LETTERS English Review High availability low utilization biomass (HALUB); Circular economy; Machine learning; Energy-efficient conversion; Nano-catalysis CONTROLLED-RELEASE; BIOMASS GASIFICATION; CHEMICAL-COMPOSITION; HYDROTHERMAL CARBONIZATION; LIGNIN DEPOLYMERIZATION; ANAEROBIC-DIGESTION; HYDROGEN-PRODUCTION; NANO-CATALYSTS; CELLULOSE; CARBON We conceptualize bioresource upgrade for sustainable energy, environment, and biomedicine with a focus on circular economy, sustainability, and carbon neutrality using high availability and low utilization biomass (HALUB). We acme energy-efficient technologies for sustainable energy and material recovery and applications. The technologies of thermochemical conversion (TC), biochemical conversion (BC), electrochemical conversion (EC), and photochemical conversion (PTC) are summarized for HALUB. Microalgal biomass could contribute to a biofuel HHV of 35.72 MJ Kg(-)(1) and total benefit of 749 $/ton biomass via TC. Specific surface area of biochar reached 3000 m(2) g(-)(1) via pyrolytic carbonization of waste bean dregs. Lignocellulosic biomass can be effectively converted into bio-stimulants and biofertilizers via BC with a high conversion efficiency of more than 90%. Besides, lignocellulosic biomass can contribute to a current density of 672 mA m(-)(2) via EC. Bioresource can be 100% selectively synthesized via electrocatalysis through EC and PTC. Machine learning, techno-economic analysis, and life cycle analysis are essential to various upgrading approaches of HALUB. Sustainable biomaterials, sustainable living materials and technologies for biomedical and multifunctional applications like nano-catalysis, microfluidic and micro/nanomotors beyond are also highlighted. New techniques and systems for the complete conversion and utilization of HALUB for new energy and materials are further discussed. [Li, Fanghua; Ji, Dongxiao; Ramakrishna, Seeram] Natl Univ Singapore, Ctr Nanofibers & Nanotechnol, Singapore 119260, Singapore; [Li, Fanghua; Ho, Shih-Hsin; Zhao, Tong; Jia, Litao; Liu, Rui] Harbin Inst Technol, State Key Lab Urban Water Resource & Environm, Harbin 150090, Peoples R China; [Li, Yiwei; Meng, Jiashen] MIT, Sch Engn, Cambridge, MA 02139 USA; [Li, Yiwei; Zhang, Xingcai] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA; [Li, Yiwei] Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Key Lab Biomed Photon MOE Wuhan Natl Lab Optoelec, Wuhan Natl Lab Optoelect,Dept Biomed Engn, Wuhan 430074, Hubei, Peoples R China; [Novoselov, K. S.] Natl Univ Singapore, Ctr Adv 2D Mat, Singapore 117546, Singapore; [Novoselov, K. S.] Univ Manchester, Sch Phys & Astron, Manchester M13 9PL, Lancs, England; [Liang, Feng] Massachusetts Gen Hosp, Dept Anesthesia Crit Care & Pain Med, Boston, MA 02114 USA; [Liang, Feng] Harvard Med Sch, Boston, MA 02114 USA; [Zhou, Hui] Tsinghua Univ, Dept Energy & Power Engn, Beijing 100084, Peoples R China; [Ahmad, Awais] Univ Cordoba, Dept Quim Organ, Edif Marie Curie C-3,Ctra Nnal 4-A,Km 396, Cordoba 14014, Spain; [Zhu, Yinlong] Monash Univ, Dept Chem Engn, Clayton, Vic 3800, Australia; [Hu, Liangxing] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore National University of Singapore; Harbin Institute of Technology; Massachusetts Institute of Technology (MIT); Harvard University; Huazhong University of Science & Technology; National University of Singapore; University of Manchester; Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; Tsinghua University; Universidad de Cordoba; Monash University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Zhang, XC (corresponding author), Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA. xingcai@mit.edu Shanghai Jiao Tong University Shanghai Jiao Tong University Open access funding provided by Shanghai Jiao Tong University 308 1 1 106 106 SHANGHAI JIAO TONG UNIV PRESS SHANGHAI SHANGHAI JIAO TONG UNIV, 800 DONGCHUAN RD, SHANGHAI, 200240, PEOPLES R CHINA 2311-6706 2150-5551 NANO-MICRO LETT Nano-Micro Lett. DEC 2023.0 15 1 35 10.1007/s40820-022-00993-4 0.0 50 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science; Physics 7U8JH 36629933.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000912372700002 0
J Zhu, XP; Zhu, XT; Li, MX; Morerio, P; Murino, V; Gong, SG Zhu, Xiangping; Zhu, Xiatian; Li, Minxian; Morerio, Pietro; Murino, Vittorio; Gong, Shaogang Intra-Camera Supervised Person Re-Identification INTERNATIONAL JOURNAL OF COMPUTER VISION English Article Person re-identification; Intra-camera labelling; Cross-camera labelling; Multi-task learning; Multi-label learning Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors. [Zhu, Xiangping] Shenzhen Univ, Sch Comp Sci & Software Engn, Comp Vis Inst, Shenzhen, Peoples R China; [Zhu, Xiangping; Morerio, Pietro; Murino, Vittorio] Ist Italiano Tecnol, Pattern Anal & Comp Vis PAVIS, Genoa, Italy; [Zhu, Xiatian] Vis Semant Ltd, London, England; [Li, Minxian; Gong, Shaogang] Queen Mary Univ London, London, England; [Murino, Vittorio] Huawei Technol Co Ltd, Ireland Res Ctr, Dublin, Ireland; [Murino, Vittorio] Univ Verona, Dept Comp Sci, Verona, Italy Shenzhen University; Istituto Italiano di Tecnologia - IIT; University of London; Queen Mary University London; Huawei Technologies; University of Verona Zhu, XT (corresponding author), Vis Semant Ltd, London, England. xiangping.zhu2010@gmail.com; eddy.zhuxt@gmail.com; m.li@qmul.ac.uk; pietro.morerio@iit.it; vittorio.murino@univr.it; s.gong@qmul.ac.uk Zhu, Xiatian/0000-0002-9284-2955 Vision Semantics Limited; Alan Turing Institute Fellowship Project on Deep Learning for Large-Scale Video Semantic Search; Innovate UK Industrial Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for Public Safety [98111-571149] Vision Semantics Limited; Alan Turing Institute Fellowship Project on Deep Learning for Large-Scale Video Semantic Search; Innovate UK Industrial Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for Public Safety(UK Research & Innovation (UKRI)Innovate UK) This work was partially supported by Vision Semantics Limited, the Alan Turing Institute Fellowship Project on Deep Learning for Large-Scale Video Semantic Search, and the Innovate UK Industrial Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for Public Safety (98111-571149). 0 6 6 6 12 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0920-5691 1573-1405 INT J COMPUT VISION Int. J. Comput. Vis. MAY 2021.0 129 5 1580 1595 10.1007/s11263-021-01440-4 0.0 FEB 2021 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RY0YC hybrid, Green Submitted 2023-03-23 WOS:000622275300004 0
J Cui, EF; Yang, D; Zhang, HK; Gidlund, M Cui, Enfang; Yang, Dong; Zhang, Hongke; Gidlund, Mikael Improving Power Stability of Energy Harvesting Devices With Edge Computing-Assisted Time Fair Energy Allocation IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING English Article Energy harvesting; time fair energy allocation; edge computing Due to the time-varying characteristics of energy harvesting sources, it is a challenge for energy harvesting to provide stable energy output. In this article, the time fair energy allocation (TFEA) problem is investigated, and an utility maximization framework is proposed to guarantee both time fairness and energy efficiency of energy allocation. Then we propose a prediction based energy allocation scheme. First, a deep learning predictor is used to predict the harvested energy. Second, we transform the TFEA problem into an Euclidean shortest path problem and propose a fast time fair energy allocation algorithm (FTF) based on inflection points search. Our algorithm can significantly decrease the iteration number of the shortest path search and reduce the computation time. In addition, we propose an edge computing assisted energy allocation framework, in which the computing tasks are offloaded to edge gateways. The proposed scheme is evaluated in the scenario of metro vehicles health monitoring. Experiment results show that the time consumption of FTF is at least 92.2% lower than traditional algorithms, while the time fairness of FTF is the best. The total time cost and energy cost of our edge computing scheme is also competitive compared with traditional local computing schemes. [Cui, Enfang; Yang, Dong; Zhang, Hongke] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China; [Gidlund, Mikael] Mid Sweden Univ, Dept Informat Syst & Technol, S-85170 Sundsvall, Sweden Beijing Jiaotong University; Mid-Sweden University Yang, D (corresponding author), Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China. enfangcui@bjtu.edu.cn; dyang@bjtu.edu.cn; hkzhang@bjtu.edu.cn; mikael.gidlund@miun.se ; Gidlund, Mikael/L-7643-2018 Cui, Enfang/0000-0002-8134-3640; Gidlund, Mikael/0000-0003-0873-7827 Fundamental Research Funds for the Central Universities [2019YJS007]; National Natural Science Foundation of China [61771040]; Electronic Information Technology Innovation and Cultivation [Z171100001217004] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Electronic Information Technology Innovation and Cultivation This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2019YJS007, in part by the National Natural Science Foundation of China under Grant 61771040, and in part by the Electronic Information Technology Innovation and Cultivation under Grant Z171100001217004. The editor coordinating the review of this article was M. C. Gursoy. 22 2 2 1 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2473-2400 IEEE T GREEN COMMUN IEEE Trans. Green Commun. Netw. MAR 2021.0 5 1 540 551 10.1109/TGCN.2020.3046319 0.0 12 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications ST9QC 2023-03-23 WOS:000662774000040 0
J Shujahat, M; Sousa, MJ; Hussain, S; Nawaz, F; Wang, M; Umer, M Shujahat, Muhammad; Sousa, Maria Jose; Hussain, Saddam; Nawaz, Faisal; Wang, Minhong; Umer, Muhammad Translating the impact of knowledge management processes into knowledge-based innovation: The neglected and mediating role of knowledge-worker productivity JOURNAL OF BUSINESS RESEARCH English Article Knowledge management processes; Innovation; Innovation performance; Knowledge-worker productivity; Knowledge worker HUMAN-RESOURCE PRACTICES; DYNAMIC CAPABILITIES; PERFORMANCE; DETERMINANTS; CREATION This study proposes a novel research model to investigate the neglected and pivotal mediating role of knowledge-worker productivity between knowledge management processes (knowledge generation, knowledge sharing, and knowledge application) and innovation. The data were collected from the 369 knowledge workers in the IT sector of Pakistan and analyzed using the SmartPLS 3 Version 26. The results indicate that knowledge worker productivity mediates between two knowledge management processes (knowledge creation and knowledge utilization) and innovation. However, surprisingly, it does not mediate between knowledge sharing and innovation. The results support the pivotal role of knowledge-worker productivity-the most striking challenge for the management discipline in the 21st century-in knowledge-based innovation. Moreover, the results signify the overarching role of the human and cultural approach to knowledge management over the Big Data and IT and system-based approaches. [Shujahat, Muhammad; Hussain, Saddam; Nawaz, Faisal; Umer, Muhammad] COMSATS Inst Informat Technol, Dept Management Sci, Attock Campus, Islamabad, Pakistan; [Sousa, Maria Jose] Univ Coimbra, Management, Lisbon, Portugal; [Wang, Minhong] Univ Hong Kong, KM&EL Lab, Fac Educ, Hong Kong, Hong Kong, Peoples R China COMSATS University Islamabad (CUI); Universidade de Coimbra; University of Hong Kong Shujahat, M (corresponding author), COMSATS Inst Informat Technol, Dept Management Sci, Attock Campus, Islamabad, Pakistan. Shujahat1993@gmail.com; maria-jose.sousa@universidadeeuropeia.pt; dr.saddam@ciit-attock.edu.pk; magwang@hku.hk Umer, Muhammad/R-9257-2017; Sousa, Maria/HCI-9642-2022; Hussain, Saddam/D-7887-2018; Mir, Faisal Nawaz/D-2600-2017; Sousa, Maria José/I-1818-2014 Umer, Muhammad/0000-0002-5552-4819; Hussain, Saddam/0000-0001-5370-3912; Mir, Faisal Nawaz/0000-0003-1663-895X; Sousa, Maria José/0000-0001-8633-4199 84 144 148 55 290 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0148-2963 1873-7978 J BUS RES J. Bus. Res. JAN 2019.0 94 442 450 10.1016/j.jbusres.2017.11.001 0.0 9 Business Social Science Citation Index (SSCI) Business & Economics HD5MS 2023-03-23 WOS:000452575200041 0
J Zhang, XY; Ma, FN; Yin, SX; Wallace, CD; Soltanian, MR; Dai, ZX; Ritzi, RW; Ma, ZQ; Zhan, CJ; Lu, XS Zhang, Xiaoying; Ma, Funing; Yin, Shangxian; Wallace, Corey; Soltanian, Mohamad Reza; Dai, Zhenxue; Ritzi, Robert W.; Ma, Ziqi; Zhan, Chuanjun; Lu, Xiaoshu Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review APPLIED ENERGY English Review Upscaling; Fluid flow; Reactive transport; Multi-scale heterogeneous media; Geological statistics CRITICAL-PATH-ANALYSIS; 3-DIMENSIONAL STOCHASTIC-ANALYSIS; MINERAL DISSOLUTION RATES; GRID BLOCK PERMEABILITY; FRACTAL POROUS-MEDIA; SOLUTE TRANSPORT; HYDRAULIC CONDUCTIVITY; REACTIVE TRANSPORT; SUBSURFACE HYDROLOGY; CARBON SEQUESTRATION Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive solute transport behaviors in geological formations across scales. From micro pores to regional reservoirs, upscaling has been proven to be a valid approach to estimate large-scale parameters by using data measured at small scales. Upscaling has considerable practical importance in oil and gas production, energy storage, carbon geologic sequestration, contamination remediation, and nuclear waste disposal. This review covers, in a comprehensive manner, the upscaling approaches available in the literature and their applications on various processes, such as advection, dispersion, matrix diffusion, sorption, and chemical reactions. We enclose newly developed approaches and distinguish two main categories of upscaling methodologies, deterministic and stochastic. Volume averaging, one of the deterministic methods, has the advantage of upscaling different kinds of parameters and wide applications by requiring only a few assumptions with improved formulations. Stochastic analytical methods have been extensively developed but have limited impacts in practice due to their requirement for global statistical assumptions. With rapid improvements in computing power, numerical solutions have become more popular for upscaling. In order to tackle complex fluid flow and transport problems, the working principles and limitations of these methods are emphasized. Still, a large gap exists between the approach algorithms and real-world applications. To bridge the gap, an integrated upscaling framework is needed to incorporate in the current upscaling algorithms, uncertainty quantification techniques, data sciences, and artificial intelligence to acquire laboratory and field-scale measurements and validate the upscaled models and parameters with multiscale observations in future geo-energy research. [Zhang, Xiaoying; Ma, Funing; Dai, Zhenxue; Ma, Ziqi; Zhan, Chuanjun] Jilin Univ, Coll Construct Engn, Changchun 130026, Peoples R China; [Yin, Shangxian] North China, Inst Sci & Technol, Langfang 065201, Peoples R China; [Wallace, Corey; Soltanian, Mohamad Reza] Univ Cincinnati, Dept Geol, Cincinnati, OH USA; [Soltanian, Mohamad Reza] Univ Cincinnati, Dept Environm Engn, Cincinnati, OH USA; [Dai, Zhenxue] Jilin Univ, Engn Res Ctr Geothermal Resources Dev Technol & E, Minist Educ, Changchun, Peoples R China; [Ritzi, Robert W.] Wright State Univ, Earth & Environm Sci Dept, Dayton, OH USA; [Lu, Xiaoshu] Univ Vaasa, Dept Elect Engn & Energy Technol, POB 700, FIN-65101 Vaasa, Finland Jilin University; University System of Ohio; University of Cincinnati; University System of Ohio; University of Cincinnati; Jilin University; University System of Ohio; Wright State University Dayton; University of Vaasa Dai, ZX (corresponding author), Jilin Univ, Coll Construct Engn, Changchun 130026, Peoples R China. dzx@jlu.edu.cn Dai, Zhenxue/N-7543-2017 Dai, Zhenxue/0000-0002-0805-7621; Wallace, Corey/0000-0001-7240-0319; Zhan, Chuanjun/0000-0001-7977-3345 National Key Research and Development Program of China [2018YFC1800900]; National Natural Science Foundation of China [41972249, 41772253, 51774136]; Program for Jilin University (JLU) Science and Technology Innovative Research Team [2019TD-35]; Graduate Innovation Fund of Jilin University [101832020CX240]; Natural Science Foundation of Hebei Province of China [D2017508099]; Program of Education Department of Hebei Province [QN219320]; Engineering Research Center of Geothermal Resources Development Technology and Equipment, Ministry of Education, China National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for Jilin University (JLU) Science and Technology Innovative Research Team; Graduate Innovation Fund of Jilin University(Jilin University); Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Program of Education Department of Hebei Province; Engineering Research Center of Geothermal Resources Development Technology and Equipment, Ministry of Education, China(Ministry of Education, China) This work was jointly supported by the National Key Research and Development Program of China (No. 2018YFC1800900), National Natural Science Foundation of China (No: 41972249, 41772253, 51774136), the Program for Jilin University (JLU) Science and Technology Innovative Research Team (No. 2019TD-35), Graduate Innovation Fund of Jilin University (No: 101832020CX240), Natural Science Foundation of Hebei Province of China (D2017508099), and the Program of Education Department of Hebei Province (QN219320). Additional funding was provided by the Engineering Research Center of Geothermal Resources Development Technology and Equipment, Ministry of Education, China. 256 54 54 44 108 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy DEC 1 2021.0 303 117603 10.1016/j.apenergy.2021.117603 0.0 AUG 2021 21 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering WB3ZZ hybrid 2023-03-23 WOS:000703515300001 0
C Liang, DY; Busch, P; Picoto, W Soliman, KS Liang, Danyan; Busch, Peter; Picoto, Winnie A Method for Preservation of Privacy in Data Mining Processes VISION 2020: SUSTAINABLE ECONOMIC DEVELOPMENT AND APPLICATION OF INNOVATION MANAGEMENT English Proceedings Paper 32nd Conference of the International-Business-Information-Management-Association (IBIMA) NOV 15-16, 2018 Seville, SPAIN Int Business Informat Management Assoc Data Mining; Business Process Modelling; Privacy; Visio BIG DATA; ANALYTICS Most ethical issues in data mining have paid more attention to government excesses, while ignoring the critical issue of national security and how data mining can be used to improve it. This paper illustrates how technology can be used to ensure personal privacy is preserved while security agencies use data mining tools to identify behavior patterns of individuals, which may form a security threat to the country. We show that Anonymized data (ANNA) software can be integrated with Non-Obvious Relationship Awareness (NORA) to encrypt data before mining. Understanding the types of users in the process of data mining can help determine privacy concerns of every user and thus develop appropriate measures to ensure privacy is preserved. We also demonstrate how process models can be designed for standardizing the data mining process to track individuals or organizations with access to metadata, retrieve personal profiles and also keep such profiles private. [Liang, Danyan] Shanghai Ouji Imp & Exp Co Ltd, Shanghai, Peoples R China; [Busch, Peter] Macquarie Univ, Dept Comp, N Ryde, NSW 2109, Australia; [Picoto, Winnie] Univ Lisbon, ISEG, P-1200781 Lisbon, Portugal Macquarie University; Universidade de Lisboa Liang, DY (corresponding author), Shanghai Ouji Imp & Exp Co Ltd, Shanghai, Peoples R China. danyanliang26@gmail.com; peter.busch@mq.edu.au; w.picoto@iseg.ulisboa.pt ; Busch, Peter/B-3839-2019 Ng Picoto, Winnie/0000-0002-9232-0858; Busch, Peter/0000-0002-0710-772X 36 0 0 0 0 INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA NORRISTOWN 34 E GERMANTOWN PIKE, NO. 327, NORRISTOWN, PA 19401 USA 978-0-9998551-1-9 2018.0 203 223 21 Business; Economics Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Business & Economics BO2TA 2023-03-23 WOS:000508553200017 0
J Xu, L; Kwan, MP Xu, Li; Kwan, Mei-Po Mining sequential activity-travel patterns for individual-level human activity prediction using Bayesian networks TRANSACTIONS IN GIS English Article ALIGNMENT; SIMILARITY; MODEL In the past decade or so, advances in positioning technologies for capturing individual movement have given rise to a wide range of studies, including transportation, tourism, and public health. In particular, considerable effort has been made to characterize human activity-travel patterns from space-time trajectories. In contrast to visualization, geometric, or statistical methods, we propose an approach based on sequential pattern mining for analyzing human activity-travel patterns. To quantify the differences between individuals and population subgroups, we first develop a single sequential similarity measure for assessing the differences between two activity-travel patterns, then extend it to the group level with the capability to compute on two pattern sets. We also develop and implement three Bayesian network models with specially designed topology to predict the forthcoming activity at the individual level. The proposed method achieves similar or better prediction accuracy and is more robust for exploring imbalanced or sparse datasets when compared to other machine learning algorithms. [Xu, Li] Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA; [Kwan, Mei-Po] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Dept Geog & Resource Management, Hong Kong, Peoples R China; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands University of Illinois System; University of Illinois Urbana-Champaign; Chinese University of Hong Kong; Utrecht University Xu, L (corresponding author), Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA. lixu.pku@gmail.com Xu, Li/ABA-4863-2020; Kwan, Mei-Po/S-4162-2016 Kwan, Mei-Po/0000-0001-8602-9258 43 2 2 5 30 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1361-1682 1467-9671 T GIS Trans. GIS OCT 2020.0 24 5 1341 1358 10.1111/tgis.12635 0.0 MAY 2020 18 Geography Social Science Citation Index (SSCI) Geography OF2JW 2023-03-23 WOS:000536286900001 0
C Kerr, EP; Coleman, SA; Kerr, D; Vance, P; Gardiner, B; Zhang, Y; Wang, F; Wu, C IEEE Kerr, E. P.; Coleman, S. A.; Kerr, D.; Vance, P.; Gardiner, B.; Zhang, Y.; Wang, F.; Wu, C. Sensor-based Vital Sign Monitoring, Analysis and Visualisation for Ageing in Place 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 08-13, 2018 Rio de Janeiro, BRAZIL tactile sensing; cloud computing; data visualisation; health status TIME With the ever-increasing global population and average life expectancy, care homes and care at home services are continuously being stretched beyond capacity. Recent developments in tactile sensing have enabled robot systems to measure human vital signs such as beats per minute (BPM), Respiratory Rate (RR) and Capillary Refill Time (CRT). Using robotic systems to measure vital sign data in the home of an elderly or disabled person would greatly assist medical and health services. This paper proposes the use of a vital sign measuring robotic system together with Cloud computing to intelligently process big data and ascertain the current health status of the service user without the need to expose their identity or burden health professionals. Furthermore, a method that enables medical professionals to visualise the data for a complete geographical region as well as for individual patients is presented and hence we provide details of a closed loop system to support ageing-in-place. [Kerr, E. P.; Coleman, S. A.; Kerr, D.; Vance, P.; Gardiner, B.] Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine, Londonderry, North Ireland; [Zhang, Y.; Wang, F.; Wu, C.] Northeastern Univ, Fac Robot Sci & Engn, Shenyang, Peoples R China Ulster University; Northeastern University - China Kerr, EP (corresponding author), Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine, Londonderry, North Ireland. ep.kerr@ulster.ac.uk; sa.coleman@ulster.ac.uk; d.kerr@ulster.ac.uk; p.vance@ulster.ac.uk; b.gardiner@ulster.ac.uk; zhangyunzhou@mail.neu.edu.cn; wangfei@mail.neu.edu.cn; wuchengdong@mail.neu.edu.cn Coleman, Sonya/0000-0002-4676-7640; Gardiner, Bryan/0000-0001-5642-6850 Ulster University's GCRF pump-priming funding Ulster University's GCRF pump-priming funding The collaborations and research outlined in this paper have been funded by Ulster University's GCRF pump-priming funding. 45 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-5090-6014-6 IEEE IJCNN 2018.0 7 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BQ3NO 2023-03-23 WOS:000585967405036 0
C Yao, Y; Cao, J; Venugopal, S; Benatallah, B; Chen, JJ Chen, J; Yang, LT Yao, Yan; Cao, Jian; Venugopal, Srikumar; Benatallah, Boualem; Chen, Jinjun A Resource Provisioning Strategy for Elastic Analytical Workflows in the Cloud PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS) English Proceedings Paper 18th IEEE International Conference on High Performance Computing and Communications (HPCC) / 14th IEEE International Conference on Smart City (Smart City) / 2nd IEEE International Conference on Data Science and Systems (DSS) DEC 12-14, 2016 Sydney, AUSTRALIA IEEE,IEEE Comp Soc,IEEE Tech Comm Scalable Comp,Univ Technol Sydney cloud computing; resource provisioning; queueing network theory; analytical workflow With the advent of big data era, the data analytical applications have sprung up which can be modeled using workflow. Such workflow applications are subject to continuously arriving requests and have a rigid requirement on response time. When running the analytical workflow in a cloud platform, one of the critical questions arising here is that how to provision resources in a way that the monetary cost can be reduced under the constraint of the response time. In this paper, we use queueing network theory to address this challenge. First, we present the performance analytic model for the elastic analytical workflows based on queueing network theory. Then, we design a resource provision strategy to determine the number of virtual machines for individual component of the applications with response time constraint. Simulation experiments using the real workload trace data show that our proposed approach provides a simple yet powerful solution to provision resources for analytical workflows under dynamic workload conditions. [Yao, Yan; Cao, Jian] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Yao, Yan; Venugopal, Srikumar; Benatallah, Boualem] Univ New South Wales, Sydney, NSW, Australia; [Chen, Jinjun] Univ Technol Sydney, Sydney, NSW, Australia; [Venugopal, Srikumar] IBM Res Ireland, Dublin, Ireland Shanghai Jiao Tong University; University of New South Wales Sydney; University of Technology Sydney Cao, J (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. yaoyan@sjtu.edu.cn; cao-jian@sjtu.edu.cn; srikumarv@ie.ibm.com; boualem@cse.unsw.edu.au; jinjun.chen@uts.edu.au Yao, Yan/GQB-1481-2022; Venugopal, Srikumar/S-5164-2019; Chen, Jinjun/AAP-2361-2020 Yao, Yan/0000-0002-0115-7996; Chen, Jinjun/0000-0003-1677-9525 China National Science Foundation [61272438, 61472253]; Research Funds of Science and Technology Commission of Shanghai Municipality [15411952502, 14511107702]; Cross Research Fund of Biomedical Engineering of Shanghai Jiaotong University [YG2015MS61] China National Science Foundation(National Natural Science Foundation of China (NSFC)); Research Funds of Science and Technology Commission of Shanghai Municipality; Cross Research Fund of Biomedical Engineering of Shanghai Jiaotong University This work is partially supported by China National Science Foundation (Granted Number 61272438,61472253), Research Funds of Science and Technology Commission of Shanghai Municipality (Granted Number 15411952502, 14511107702) and Cross Research Fund of Biomedical Engineering of Shanghai Jiaotong University (YG2015MS61). 15 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-4297-5 2016.0 538 545 10.1109/HPCC-SmartCity-DSS.2016.47 0.0 8 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BH6DH 2023-03-23 WOS:000401700900071 0
J Zhang, M; Yan, L; Lippi, G; Hu, ZD Zhang, Man; Yan, Li; Lippi, Giuseppe; Hu, Zhi-De Pleural biomarkers in diagnostics of malignant pleural effusion: a narrative review TRANSLATIONAL LUNG CANCER RESEARCH English Review Tumor marker; malignant pleural effusion (MPE); review; diagnosis ENDOTHELIAL GROWTH-FACTOR; CELL LUNG-CANCER; TUMOR-MARKERS; MESSENGER-RNA; CARCINOEMBRYONIC ANTIGEN; DIFFERENTIATING BENIGN; CLINICAL-SIGNIFICANCE; POTENTIAL BIOMARKERS; EXOSOMAL MICRORNA; APOLIPOPROTEIN-E Although cytology and pleural biopsy of pleural effusion (PE) are the gold standards for diagnosing malignant pleural effusion (MPE), these tools' diagnostic accuracy is plagued by some limitations such as low sensitivity, considerable inter-observer variation and invasiveness. The assessment of PE biomarkers may hence be seen as an objective and non-invasive diagnostic alternative in MPE diagnostics. In this review, we summarize the characteristics and diagnostic accuracy of available PE biomarkers, including carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), carbohydrate antigens 125 (CA125), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 15-3 (CA15-3), a fragment of cytokeratin 19 (CYFRA 21-1), chitinase-like proteins (CLPs), vascular endothelial growth factor (VEGF) and its soluble receptor, endostatin, calprotectin, cancer ratio, homocysteine, apolipoprotein E (Apo-E), B7 family members, matrix metalloproteinase (MMPs) and tissue-specific inhibitors of metalloproteinases (TIMPs), reactive oxygen species modulator 1 (Romo1), tumor-associated macrophages (TAMs) and monocytes, epigenetic markers (e.g., cell-free microRNA and mRNA). We summarized the evidence from systematic review and meta-analysis for traditional tumor markers' diagnostic accuracy. According to the currently available evidence, we conclude that the traditional tumor markers have high specificity (around 0.90) but low sensitivity (around 0.50). The diagnostic accuracy of novel tumor markers needs to be validated by further studies. None of these tumor biomarkers would have sufficient diagnostic accuracy to confirm or exclude MPE when used alone. A multi-biomarker strategy, also encompassing the use of artificial intelligence algorithms, may be a valuable perspective for improving the diagnostic accuracy of MPE. [Zhang, Man] Inner Mongolia Med Univ, Dept Thorac Surg, Affiliated Hosp, Hohhot, Peoples R China; [Yan, Li] Inner Mongolia Med Univ, Dept Resp & Crit Care Med, Affiliated Hosp, Hohhot, Peoples R China; [Lippi, Giuseppe] Univ Verona, Sect Clin Biochem, Verona, Italy; [Hu, Zhi-De] Inner Mongolia Med Univ, Dept Lab Med, Affiliated Hosp, Hohhot, Peoples R China Inner Mongolia Medical University; Inner Mongolia Medical University; University of Verona; Inner Mongolia Medical University Hu, ZD (corresponding author), Inner Mongolia Med Univ, Dept Lab Med, Affiliated Hosp, Hohhot, Peoples R China. hzdlj81@163.com Hu, Zhi-De/H-5578-2019; Lippi, Giuseppe/X-7198-2018 Hu, Zhi-De/0000-0003-3679-4992; Lippi, Giuseppe/0000-0001-9523-9054 National Natural Science Foundation of China [81860501]; Natural and Science Foundation of Inner Mongolia Autonomous Region for Distinguished Young Scholars [2020JQ07] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural and Science Foundation of Inner Mongolia Autonomous Region for Distinguished Young Scholars This work is supported by the National Natural Science Foundation of China (81860501); and the Natural and Science Foundation of Inner Mongolia Autonomous Region for Distinguished Young Scholars (2020JQ07). 141 10 10 3 18 AME PUBL CO SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2218-6751 2226-4477 TRANSL LUNG CANCER R Transl. Lung Cancer Res. MAR 2021.0 10 3 1557 1570 10.21037/tlcr-20-1111 0.0 14 Oncology; Respiratory System Science Citation Index Expanded (SCI-EXPANDED) Oncology; Respiratory System TF5SO 33889529.0 Green Accepted, gold 2023-03-23 WOS:000670781300028 0
J Ji, YC; Li, N; Cheng, ZM; Fu, XQ; Ao, M; Li, ML; Sun, XG; Chowwanonthapunya, T; Zhang, DW; Xiao, K; Ren, JL; Dey, P; Li, XG; Dong, CF Ji, Yucheng; Li, Ni; Cheng, Zhanming; Fu, Xiaoqian; Ao, Min; Li, Menglin; Sun, Xiaoguang; Chowwanonthapunya, Thee; Zhang, Dawei; Xiao, Kui; Ren, Jingli; Dey, Poulumi; Li, Xiaogang; Dong, Chaofang Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data NPJ MATERIALS DEGRADATION English Article ALUMINUM-ALLOYS; PITTING CORROSION; THICKNESS; MACHINE; SCIENCE Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%. [Ji, Yucheng; Li, Ni; Cheng, Zhanming; Fu, Xiaoqian; Ao, Min; Li, Menglin; Zhang, Dawei; Xiao, Kui; Li, Xiaogang; Dong, Chaofang] Univ Sci & Technol Beijing, Natl Mat Corros & Protect Data Ctr, Inst Adv Mat & Technol, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing, Peoples R China; [Ji, Yucheng; Dey, Poulumi] Delft Univ Technol, Fac Mech Maritime & Mat Engn, Dept Mat Sci & Engn, Delft, Netherlands; [Ji, Yucheng; Li, Ni; Cheng, Zhanming; Fu, Xiaoqian; Ao, Min; Li, Menglin; Zhang, Dawei; Li, Xiaogang; Dong, Chaofang] Univ Sci & Technol Beijing, Shunde Innovat Sch, Foshan, Peoples R China; [Sun, Xiaoguang] CRRC Qingdao Sifang Co Ltd, Tech Engn Dept, Qingdao, Peoples R China; [Chowwanonthapunya, Thee] Kasetsart Univ, Fac Int Maritime Studies, Chon Buri, Thailand; [Ren, Jingli] Zhengzhou Univ, Henan Acad Big Data, Zhengzhou, Peoples R China University of Science & Technology Beijing; Delft University of Technology; University of Science & Technology Beijing; CRRC Corporation; Kasetsart University; Zhengzhou University Dong, CF (corresponding author), Univ Sci & Technol Beijing, Natl Mat Corros & Protect Data Ctr, Inst Adv Mat & Technol, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing, Peoples R China.;Dong, CF (corresponding author), Univ Sci & Technol Beijing, Shunde Innovat Sch, Foshan, Peoples R China. cfdong@ustb.edu.cn Sun, Xiaoguang/C-3028-2018 Sun, Xiaoguang/0000-0002-0463-5519 National Natural Science Foundation of China [52125102]; Fundamental Research Funds for the Central Universities [FRF-TP-2001B2]; Guangdong Basic and Applied Basic Research Foundation [2020B1515120093]; Special Fund Support for Taishan Industrial Leading Talents Project; China Scholarship Council [202106460037] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Guangdong Basic and Applied Basic Research Foundation; Special Fund Support for Taishan Industrial Leading Talents Project; China Scholarship Council(China Scholarship Council) This work was supported by the National Natural Science Foundation of China (No. 52125102), Fundamental Research Funds for the Central Universities (No. FRF-TP-2001B2), Guangdong Basic and Applied Basic Research Foundation (No. 2020B1515120093), and Special Fund Support for Taishan Industrial Leading Talents Project. We are grateful to Mr. Ayden Gramuglia for the language discussion. Yucheng Ji thanks the support from China Scholarship Council #202106460037. 49 0 0 28 28 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2397-2106 NPJ MAT DEGRAD NPJ Mater. Degrad. OCT 7 2022.0 6 1 83 10.1038/s41529-022-00295-5 0.0 10 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science 5D6HQ gold, Green Published 2023-03-23 WOS:000865040600001 0
J Bian, XL; Wei, ZG; Li, WH; Pou, J; Sauer, DU; Liu, LC Bian, Xiaolei; Wei, Zhongbao Gae; Li, Weihan; Pou, Josep; Sauer, Dirk Uwe; Liu, Longcheng State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis IEEE TRANSACTIONS ON POWER ELECTRONICS English Article Discharges (electric); Estimation; Integrated circuit modeling; Aging; Degradation; Current measurement; Computational modeling; Data fusion; incremental capacity analysis (ICA); lithium-ion battery (LIB); open circuit voltage (OCV) model; state of health (SOH) PROGNOSIS; CHARGE; SOC The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness. [Bian, Xiaolei; Liu, Longcheng] KTH Royal Inst Technol, Dept Chem Engn, S-11428 Stockholm, Sweden; [Wei, Zhongbao Gae] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China; [Li, Weihan; Sauer, Dirk Uwe] Rhein Westfal TH Aachen, Inst Power Elect & Elect Drives, D-52062 Aachen, Germany; [Pou, Josep] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Liu, Longcheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Singapore Royal Institute of Technology; Beijing Institute of Technology; RWTH Aachen University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Liu, LC (corresponding author), KTH Royal Inst Technol, Dept Chem Engn, S-11428 Stockholm, Sweden.;Wei, ZG (corresponding author), Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China. xiaoleib@kth.se; weizb@bit.edu.cn; weihan.li@isea.rwth-aachen.de; josep.pou@ieee.org; sauer@isea.rwth-aachen.de; lliu@ket.kth.se Sauer, Dirk Uwe/A-7414-2012; Bian, Xiaolei/GZM-6442-2022; Wei, Zhongbao/AAC-3263-2021; Li, Weihan/ADP-9539-2022; Bian, Xiaolei/AAA-1886-2019 Sauer, Dirk Uwe/0000-0002-5622-3591; Wei, Zhongbao/0000-0003-0051-5648; Bian, Xiaolei/0000-0003-4232-7944 National Natural Science Foundation of China [52072038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 52072038. 45 34 34 67 205 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8993 1941-0107 IEEE T POWER ELECTR IEEE Trans. Power Electron. FEB 2022.0 37 2 2226 2236 10.1109/TPEL.2021.3104723 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering WH3AQ 2023-03-23 WOS:000707555600086 0
J Chen, QY; Hu, Y; Xia, JB; Chen, Z; Tseng, HW Chen, Qinyin; Hu, Y.; Xia, Jingbo; Chen, Z.; Tseng, Hsien-Wei Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS English Article AGGREGATION With the promotion of the latest technologies and the new requirement of humanitarian, the wireless multi-sensor system is applied broadly. This paper studies the data fusion of the industrial wireless sensor networks (IWSNs), in order to acquire more thoughtful data for the prognosis and diagnosis of the monitored device. These authors propose a combination of back propagation neural network (BP NN) and Wavelet Packet algorithm for data fusion. This proposed algorithm is based on each cluster head, which is modelled with a three layers NN. A case study using the ball bearing test data, which is from the Bearing Data Center of the Case Western Reserve University, and to verify the effectiveness of the proposed algorithm. With MATLAB 2016b version, the raw data feature is extracted by the Wavelet Packet and the feature fusion is based on BP NN at sink node. The simulation results show that the proposed algorithm is effective in fault diagnosis of wind turbine. [Chen, Qinyin] Xiamen City Univ, Dept Elect & Informat Engn, 1263 South Qianpu Rd, Xiamen, Peoples R China; [Hu, Y.] Univ Chester, Parkgate Rd, Chester CH1 4BJ, Cheshire, England; [Xia, Jingbo] Xiamen Univ, Tan Kah Kee Coll, Zhangzhou Campus, Xiamen, Peoples R China; [Chen, Z.] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark; [Tseng, Hsien-Wei] Longyan Univ, Sch Informat Engn, Longyan, Peoples R China University of Chester; Xiamen University; Aalborg University; Longyan University Chen, QY (corresponding author), Xiamen City Univ, Dept Elect & Informat Engn, 1263 South Qianpu Rd, Xiamen, Peoples R China. chen.qin.yin@hotmail.com; y.hu@chester.ac.uk; jbxiad@sina.com; zch@et.aau.dk; 507251393@qq.com Chen, Zhe/0000-0002-2919-4481 27 0 0 4 21 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0946-7076 1432-1858 MICROSYST TECHNOL Microsyst. Technol. APR 2021.0 27 4 SI 1187 1199 10.1007/s00542-018-4144-3 0.0 13 Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Science & Technology - Other Topics; Materials Science; Physics RY3FC 2023-03-23 WOS:000647800200017 0
J Saidi, L; Benbouzid, M Saidi, Lotfi; Benbouzid, Mohamed Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends ELECTRONICS English Review data acquisition; health indicator; machine learning; power converter; prognostics and health management; prognostic approach; remaining useful life prediction; wind turbine generators REMAINING USEFUL LIFE; KALMAN FILTER; PREDICTION; NETWORK; MODEL The purpose of this study is to highlight approaches for predicting a system's future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain [Saidi, Lotfi] Univ Tunis, ENSIT Lab Signal Image & Energy Mastery SIME, LR 13ES03, Tunis 1008, Tunisia; [Saidi, Lotfi] Univ Sousse, Higher Sch Sci & Technol Hammam Sousse, Dept Elect & Comp Engn, Sousse 4011, Tunisia; [Saidi, Lotfi; Benbouzid, Mohamed] Univ Brest, Inst Rech Dupuy Lome, UMR CNRS 6027, F-29238 Brest, France; [Benbouzid, Mohamed] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China Universite de Tunis; Universite de Sousse; Universite de Bretagne Occidentale; Shanghai Maritime University Benbouzid, M (corresponding author), Univ Brest, Inst Rech Dupuy Lome, UMR CNRS 6027, F-29238 Brest, France.;Benbouzid, M (corresponding author), Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China. lotfi.saidi@essths.u-sousse.tn; mohamed.benbouzid@univ-brest.fr Saidi, Lotfi/0000-0002-3419-8583 54 3 3 13 51 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics NOV 2021.0 10 22 2732 10.3390/electronics10222732 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics XG5VV gold 2023-03-23 WOS:000724821300001 0
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M.; Falciano, S.; Falke, P. J.; Falke, S.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feickert, M.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, M.; Fenton, M. J.; Fenyuk, A. B.; Feremenga, L.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipcic, A.; Filthaut, F.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, C.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores, L. M.; Castillo, L. R. Flores; Follega, F. M.; Fomin, N.; Forcolin, G. T.; Formica, A.; Foerster, F. A.; Forti, A. C.; Foster, A. 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T.; Shapiro, M.; Sharma, A. S.; Sharma, A.; ; ATLAS Collaboration Search for non-resonant Higgs boson pair production in the bbl nu l nu final state with the ATLAS detector in pp collisions at root s=13 TeV PHYSICS LETTERS B English Article TOP-QUARK PAIR; HADRON COLLIDERS; LHC; EVENTS A search for non-resonant Higgs boson pair production, as predicted by the Standard Model, is presented, where one of the Higgs bosons decays via the H -> bb channel and the other via one of the H -> WW*/ZZ*/tau tau channels. The analysis selection requires events to have at least two b-tagged jets and exactly two leptons (electrons or muons) with opposite electric charge in the final state. Candidate events consistent with Higgs boson pair production are selected using a multi-class neural network discriminant. The analysis uses 139 fb(-1) of pp collision data recorded at a centre-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. An observed (expected) upper limit of 1.2 (0.9(-0.3)(+0.4)) pb is set on the non-resonant Higgs boson pair production cross-section at 95% confidence level, which is equivalent to 40 (29(-9)(+14)) times the value predicted in the Standard Model. (C) 2019 The Author(s). Published by Elsevier B.V. [Banerjee, S.; Dang, N. P.; Duvnjak, D.; Jackson, P.; Kladiva, E.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Cairo, V. M. M.; Jain, V.; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Dehghanian, N.; Gingrich, D. M.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. J.; Falke, S.; Goy, C.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I.; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Mastrandrea, P.; Raspopov, S.; Sauvan, E.; Smart, B. H.; Todorova-Nova, S.; Vallier, A.; Wingerter-Seez, I.] Univ Savoie Mt Blanc, Univ Grenoble Alpes, CNRS, LAPP,IN2P3, Annecy, France; [Abulaiti, Y.; Chekanov, S.; LeCompte, T.; Liu, B. L.; Love, J.; Malon, D.; Metcalfe, J.; Paramonov, A.; Proudfoot, J.; Ryu, S.; Stanek, R. W.; Van Gemmeren, P.; Wang, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Berlendis, S.; Cheu, E.; Johns, K. A.; Jones, S.; Lampl, W.; LeBlanc, M.; Leone, R.; Loch, P.; Nayyar, R.; Varnes, E. W.; Zhou, Y.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Bullock, D.; Darmora, S.; De, K.; Farbin, A.; Feremenga, L.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Little, J. 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Zur] Humboldt Univ, Inst Phys, Berlin, Germany; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Lab High Energy Phys, Bern, Switzerland; [Allport, P. P.; Baca, M. J.; Bracinik, J.; Briglin, D. L.; Broughton, J. H.; Charlton, D. G.; Foster, A. G.; Gonella, L.; Gonnella, F.; Hawkes, C. M.; Hillier, S. J.; Kempster, J. J.; Kendrick, J.; Lindon, J. H.; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Reynolds, E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Vallance, R. A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.; Worm, S. 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C.; Cree, G.; Di Valentino, D.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Oakham, F. G.; Sherafati, N.; Vincter, M. G.; Weber, S. A.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chafaq, A.; Hoummada, A.] Univ Hassan 2, Reseau Univ Phys Hautes Energies, Fac Sci Ain Chock, Casablanca, Morocco; Ctr Natl Energie Sci Tech Nucl CNESTEN, Rabat, Morocco; [El Kacimi, M.; Goujdami, D.] Univ Cadi Ayyad, LPHEA, Fac Sci Semlalia, Marrakech, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; Dahbi, S.; Ezzi, M.; Fassi, F.; Haddad, N.; Idrissi, Z.; Tayalati, Y.] Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Aleksa, M.; Gonzalez, B. Alvarez; Aperio Bella, L.; Armbruster, A. J.; Avolio, G.; Barisits, M-S.; Beermann, T. 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A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stapf, B.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.; Zakharchuk, N.] DESY, Deutsch Elektronen Synchrotron, Zeuthen, Germany; [Burmeister, I.; Cinca, D.; Erdmann, J.; Gessner, G.; Goessling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I.; Weingarten, J.; Zeissner, S. V.] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Hils, M.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Manjarres Ramos, J.; Morgenstern, S.; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, Sch Phys & Astron, SUPA, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becker, K.; Buehrer, F.; Buescher, D.; Coniavitis, E.; Gonella, G.; Gugel, R.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Jakobs, K.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V.; Megy, T. M.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schleicher, K. E.; Schnoor, U.; Schumacher, M.; Tornambe, P.; Weiser, C.; Wiik-Fuchs, L. A. M.; Zaffaroni, E.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Adiguzel, A.; Beck, H. C.; Bindi, M.; Bisanz, T.; Buschmann, E.; Dado, T.; De Maria, A.; Drechsler, E.; Dreyer, T.; Geisen, J.; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lemmer, B.; Mantoani, M.; Mchedlidze, G.; McKay, M. A.; Mellenthin, J. D.; Nellist, C.; Oerdek, S.; Ouyang, Q.; Pokharel, I.; Quadt, A.; Rosien, N-A.; Rzehorz, G. F.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Stolte, P.; Torres, R. E. Ticse; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Akilli, E.; Amrouche, C. S.; Ancu, L. S.; Benoit, M.; Calace, N.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; March, L.; Mermod, P.; Nessi, M.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Pinto, M. Vicente Barreto; Wu, X.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Coccaro, A.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] INFN, Sez Genova, Genoa, Italy; [Dueren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; Hou, S.; O'Shea, V.; Owen, M.; Robson, A.; Shi, L.; Spiteri, D. P.; Thompson, A. S.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Grenoble INP, CNRS, LPSC,IN2P3, Grenoble, France; [Asbah, N.; Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Weber, S. M.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Weber, S. M.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Kondrashova, N.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Shandong, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Kondrashova, N.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Shandong, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kato, C.; Li, L.; Li, S.; Li, X.; Nishu, N.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, SKLPPC, KLPPAC MoE, Sch Phys & Astron, Shanghai, Peoples R China; [Kato, C.; Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V.; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J. I.; Dunford, M.; Franchino, S.; Geisler, M. P.; Giulini, M.; Hanke, P.; Jongmanns, J.; Kugel, A.; Theenhausen, H. Meyer Zu; Napolitano, F.; Villar, D. I. Narrias; Sahinsoy, M.; Schultz-Coulon, H-C.; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; Britzger, D.; de Lima, D. E. Ferreira; Kolb, M.; Lisovyi, M.; Schoening, A.; Sosa, D.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Chan, Y. L.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Lo, C. Y.; Lu, H.; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Orlando, N.; Hernandez, D. Paredes; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Palacino, G.; Roland, C. P. A.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Adiguzel, A.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] INFN, Grp Coll Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Panizzo, G.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Chim Fis & Ambiente, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Primavera, M.; Spagnolo, S.; Ventura, A.] INFN, Sez Lecce, Lecce, Italy; [Bachas, K.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Carra, S.; Cavalli, D.; Citterio, M.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Manzoni, S.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.; Perez, M. Villaplana] INFN, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; Carra, S.; Fanti, M.; Lazzaroni, M.; Manzoni, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V.; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V.; Lavorgna, M.; Massarotti, P.; Merola, L.; Rossi, E.; Sekhniaidze, G.] INFN, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V.; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Fraternali, M.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.; Vercesi, V.] INFN, Sez Pavia, Pavia, Italy; [Farina, E. M.; Fraternali, M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N. V.; Calvetti, M.; Cavasinni, V.; Chiarelli, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.] INFN, Sez Pisa, Pisa, Italy; [Biesuz, N. V.; Calvetti, M.; Cavasinni, V.; De Maria, A.; Francavilla, P.; Roda, C.; Sotiropoulou, C. L.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Gentile, S.; Giagu, S.; Ippolito, V.; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Vari, R.; Veneziano, S.] INFN, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; Gentile, S.; Giagu, S.; Ippolito, V.; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bortolotto, V.; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Liberti, B.; Massa, L.; Pinamonti, M.; Vanadia, M.] INFN, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bortolotto, V.; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Massa, L.; Pinamonti, M.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Baroncelli, A.; Biglietti, M.; Ceradini, F.; Di Micco, B.; Farilla, A.; Iodice, M.; Orestano, D.; Petrucci, F.; Salamanna, G.; Sessa, M.; Stanescu, C.; Vecchio, V.; Verducci, M.] INFN, Sez Roma Tre, Rome, Italy; [Ceradini, F.; Di Micco, B.; Orestano, D.; Petrucci, F.; Salamanna, G.; Sessa, M.; Vecchio, V.; Verducci, M.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Adiguzel, A.; Follega, F. M.; Iuppa, R.; Ricci, E.] INFN, TIFPA, Povo, Italy; [Follega, F. M.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Jakobsen, S.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [Chen, C. H.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Pluth, D.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gongadze, A.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Lyubushkin, V.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Simic, L.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Do Vale, M. A. B.; Gama, R. Goncalves; Manhaes de Andrade Filho, L.; Peralva, B. S.; Oliveira, M. V. Silva] Univ Fed Juiz De Fora UFJF, Dept Engn Eletr, Juiz De Fora, Brazil; [Coutinho, Y. Amaral; Araujo Ferraz, V.; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Freund, W. S.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio De Janeiro, COPPE EE IF, Rio De Janeiro, Brazil; Univ Fed Sao Joao Del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; Navarro, J. L. La Rosa; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Aoki, M.; Hanagaki, K.; Higashino, S.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kondo, T.; Kono, T.; Makida, Y.; Mizukami, A.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Nomidis, I.; Odaka, S.; Okuyama, T.; Sasaki, O.; Shimizu, S.; Suzuki, S.; Takubo, Y.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I.; Unno, Y.; Usui, J.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Kawade, K.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Takeda, K.; Wakamiya, K.; Yamane, F.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I.; Guzik, M. P.; Janus, P. A.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Kremer, J. A.; Mindur, B.; Przybycien, M.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Bahmani, M.; Banas, E.; Bruckman de Renstrom, P. A.; Burka, K.; Chwastowski, J. J.; Czekierda, S.; Derendarz, D.; Dziedzic, B. S.; Godlewski, J.; Kaczmarska, A.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Slawinska, M.; Smykiewicz, A.; Stanecka, E.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; 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Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Shandong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; 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University of Trento; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; Universidade Federal de Juiz de Fora; Universidade Federal do Rio de Janeiro; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; High Energy Accelerator Research Organization (KEK); Kobe University; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Kyoto University; Kyoto University of Education; Kyushu University; Kyushu University; National University of La Plata; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; Lund University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Autonomous University of Madrid; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; City University of New York (CUNY) System; California State University System; California State University East Bay; Council for Scientific & Industrial Research (CSIR) - South Africa; Autonomous University of Barcelona; Universidade de Lisboa; Instituto Superior Tecnico; University of Sharjah; University of Aegean; University of Louisville; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; Stanford University; University of Fribourg; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Ilia State University; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Istanbul University; Manhattan College; Moscow Institute of Physics & Technology; Near East University; Sun Yat Sen University; City University of New York (CUNY) System; Parthenope University Naples Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco. Saraiva, Joao Gentil Mendes/D-1596-2017; Bortfeldt, Jona/AAJ-5370-2021; gaudio, gabriella/AAN-6039-2021; Casado, Pilar/H-1484-2015; Calvetti, Milene/AAQ-1337-2020; Nechaeva, Polina Y/N-1148-2015; Trzebinski, Maciej/W-1748-2018; Ahmadov, Faig N/B-3723-2018; Vaniachine, Alexandre/H-7796-2013; Berger, N/ABE-4064-2020; Sapronov, Andrey/HGB-0392-2022; Barberis, Dario/ABF-1310-2021; CAKIR, Orhan/L-1893-2015; Billoud, Thomas/AAY-1569-2020; D'Onofrio, Adelina/AAT-3903-2020; Hamal, Petr/G-5540-2014; Pereira, Rodrigo Araujo/AFV-9983-2022; Introzzi, Gianluca/K-2497-2015; Cetin, Serkant Ali/AGF-0147-2022; Grabowska-Bołd, Iwona/ABI-7829-2020; Jiménez Peña, Javier/AFY-1817-2022; da via, cinzia/AAS-3978-2021; Kruchonak, Uladzimir/AAN-4371-2020; Banaś, Elżbieta A/W-4583-2017; Lagouri, Theodota/AAC-7358-2021; Bailey, Adam J/T-9828-2017; Kuze, Masahiro/V-4251-2018; Ibáñez, Carlos Escobar/B-3761-2017; Cavaliere, Viviana/CAE-8597-2022; de Almeida, Fernando Marroquim Leao/D-6947-2019; Vandelli, Wainer/ABI-8185-2020; AGHEORGHIESEI, Catalin/B-8596-2014; Llácer, María Moreno/AAU-6582-2020; Sopkova, Filomena/HKV-6270-2023; Cottin, Giovanna/AFR-3846-2022; Kazanin, Vasily F./HHM-2056-2022; Talyshev, Alexey/HGC-6910-2022; Olszewski, Andrzej/AGV-6131-2022; Ozcan, Veysi Erkcan/AAS-4508-2020; Woźniak, Krzysztof/AAQ-6119-2021; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Gonella, Laura/GLR-3838-2022; Capua, Marcella/AAJ-8419-2020; Stabile, Alberto/F-2889-2013; Jiménez, Yesenia Hernández/ABH-1107-2020; Brahimi, Nihal/HNJ-5325-2023; Mindur, Bartosz/A-2253-2017; Barreiro, Fernando/D-9808-2012; Sanchez, Francisca J. Munoz/AAH-3909-2019; Zemaityte, Gabija/AAI-7245-2021; govender, nicolin/CAH-3245-2022; Laycock, Paul/AAL-8261-2021; Herten, Gregor/HNQ-9546-2023; Maj, Klaudia/Q-2724-2019; spagnolo, stefania/A-6359-2012; Orlando, Nicola/AAL-1723-2021; Staszewski, Rafał/AAO-6844-2021; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Kelsey, Daniel/AAG-2652-2020; Ricci, Ester/AAJ-1523-2020; Svatos, Michal/ABA-2041-2020; Beddall, Andrew John/AAE-5820-2022; Alberich, Leonor Cerdá/ABG-8534-2020; Leisos, Antonios/AAJ-2351-2021; Peleganchuk, Sergey V/J-6722-2014; García, Josu Cantero/AAS-9810-2020; Fabbri, Laura/H-3442-2012; Tapia, Sebastian/ABB-6644-2021; Viaux, Nicolas/AAT-5715-2020; Gutierrez, Phillip/C-1161-2011; Jones, Roger WL/H-5578-2011; Wang, Song-Ming/AAP-9832-2021; Malek, Fairouz/E-4410-2018; Carquin, Edson/GLU-9641-2022; Costa, Maria Jose/H-5962-2015; Lee, Lawrence/AAU-6790-2021; Petrucci, Fabrizio/G-8348-2012; Gao, Y./GWZ-3665-2022; Tikhomirov, Vladimir/M-6194-2015; Zanzi, Daniele/AAY-4102-2020; Di Nardo, Roberto/J-4993-2012; Lefebvre, Benoit/ABB-2272-2021; Buttar, Craig/AAG-5392-2021; Kepka, Oldrich/ABF-5327-2021; Oh, Alexander/HHZ-4386-2022; Salt, Jose/F-4928-2016; Artoni, Giacomo/ABA-2164-2020; Perez, Miguel Villaplana/B-2717-2015; Lacasta, Carlos/C-7254-2008; Duarte-Campderrós, Jordi/F-5025-2018; Olszowska, Jolanta/P-2889-2017; Torro Pastor, Emma/AAB-5979-2021; Franchini, Matteo/AAC-9259-2021; Giuli, Francesco/HJI-6649-2023; Parida, Bibhuti/T-3730-2018; Trzupek, Adam/N-2448-2018; Brooks, William K/C-8636-2013; Valero, Alberto/G-9866-2015; Llorente Merino, Javier/AGP-3432-2022; Torregrosa, Esteban Fullana/A-7305-2016; Reznicek, Pavel/C-1989-2017; zhang, jin/GXV-9154-2022; Doyle, Anthony T/C-5889-2009; Suster, Carl J E/L-3714-2016; Garcia, Carmen/W-2465-2018; Smykiewicz, Andrzej/W-6236-2018; Bergmann, Benedikt/L-8368-2019; Smirnova, Oxana/A-4401-2013; do Amaral Coutinho, Yara/AAU-7857-2021; Smirnov, Sergei Y/F-1014-2011; MEONI, Evelin/ABD-9498-2021; Bruscino, Nello/ABA-8980-2021; de Renstrom, Pawel Bruckman/AAG-7725-2021; Iengo, Paolo/AAR-7518-2020; de la Hoz, Santiago Gonzalez/E-2494-2016; Sessa, Marco/AAT-2850-2020; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; YILDIZ, Hatice DURAN/AAN-3727-2021; Chitan, Adrian/AAC-8946-2022; Song, Weimin/AAJ-5415-2020; Mikestikova, Marcela/H-1996-2014; Kharlamova, Tatyana/AAQ-5430-2020; Coccaro, Andrea/AAB-6990-2021; Snesarev, Andrei/H-5090-2013; Wolters, Helmut/M-4154-2013; Alimonti, Gianluca/AAG-4603-2020; Villaplana Perez, Miguel/B-5772-2014; Masik, Jiri/AAE-1203-2021; Darbo, Giovanni/C-8175-2012; Santra, Arka/AEE-4946-2022; Grinstein, Sebastian/ABE-1880-2020; Wozniak, Krzysztof W/P-4475-2017; Dabrowski, Wladyslaw/AAS-6369-2020; Gladilin, Leonid/B-5226-2011; Panizzo, Giancarlo/AAS-2986-2020; Llácer, María Moreno/AAQ-7522-2020; Sopczak, Andre/I-4951-2015; Gonzalez, Barbara Alvarez/ABG-7021-2020; Panizzo, Giancarlo/AAS-2911-2020; Chizhov, Mihail/CAG-2076-2022; Slavíček, Tomáš/GQY-5137-2022; Amoroso, Simone/AAW-4334-2021; Kuleshov, Sergey V/D-9940-2013; Gravila, Paul/HLP-6245-2023; Carquin López, Edson/GZG-4712-2022; Chelkov, Georgy A./G-9934-2019; Norjoharuddeen, Nurfikri/S-3109-2018; Mitsou, Vasiliki A./D-1967-2009; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Bobrovnikov, Victor/AAB-8328-2022; Legger, Federica/HNC-2568-2023; Castro, Nuno/AAB-3648-2019; Koperny, Stefan/ABB-4747-2020; Ippolito, Valerio/L-1435-2016; Schernau, Michael/GRY-3808-2022; Balaji, S/J-1864-2019; Capua, Marcella/A-8549-2015; Betti, Alessandra/AAD-9964-2019; Roy, Ashim/HLV-9248-2023; Buzykaev, Alexey/HIK-0117-2022; Wu, Xin/ABH-1729-2020; perez, miguel/GXF-5077-2022; Ventura, Andrea/CAH-0226-2022; Ferrer, Antonio/H-2942-2015; Burdin, Sergey/AAZ-9062-2021; Bassalat, Ahmed Azzam/HHY-9901-2022; b, m/HPE-0989-2023; Chwastowski, Janusz J./I-4480-2012; Ohm, Christian/AAU-6572-2020; Cavaliere, Viviana/CAG-2384-2022; Bogdanchikov, Alexander/AAB-9414-2022; Zhemchugov, Alexey/N-1717-2017; Romano, Marino/ACW-0715-2022; Derendarz, Dominik/AAO-3512-2021; Tassi, Enrico/AAJ-9661-2020; Trzebinski, Maciej/AAO-6821-2021; Schioppa, Enrico Junior/F-4731-2019; Adiguzel, Aytul/AAC-9049-2020; Padilla, Cristobal/C-3218-2017; Lee, Shih-Chang/AAV-7016-2021; Napolitano, Fabrizio/CAI-2163-2022; Lazzaroni, Massimo/N-3675-2015; Fassi, Farida/F-3571-2016; de la Torre Perez, Hector/ABG-6942-2020; Ye, Sheng/HGA-5474-2022; Olesya, Kuchinskaya/AAF-8437-2020; McKee, Shawn/B-6435-2012; Petersen, Troels/P-5538-2015; D'Auria, Saverio/O-3276-2017 Saraiva, Joao Gentil Mendes/0000-0002-7006-0864; Bortfeldt, Jona/0000-0002-0777-985X; gaudio, gabriella/0000-0002-6833-0933; Casado, Pilar/0000-0002-0394-5646; Calvetti, Milene/0000-0003-0125-2165; Nechaeva, Polina Y/0000-0002-5910-4117; Trzebinski, Maciej/0000-0002-5151-7101; Ahmadov, Faig N/0000-0003-3644-540X; Vaniachine, Alexandre/0000-0002-0367-5666; Berger, N/0000-0002-7963-9725; CAKIR, Orhan/0000-0002-9016-138X; Billoud, Thomas/0000-0002-6280-3306; D'Onofrio, Adelina/0000-0002-0343-6331; Hamal, Petr/0000-0003-3139-7234; Introzzi, Gianluca/0000-0002-1314-2580; Cetin, Serkant Ali/0000-0001-5050-8441; Grabowska-Bołd, Iwona/0000-0001-9159-1210; Jiménez Peña, Javier/0000-0002-8705-628X; da via, cinzia/0000-0001-6154-7323; Kruchonak, Uladzimir/0000-0003-2116-4592; Banaś, Elżbieta A/0000-0001-5325-6040; Bailey, Adam J/0000-0002-3301-2986; Kuze, Masahiro/0000-0001-8858-8440; Ibáñez, Carlos Escobar/0000-0003-4442-4537; Cavaliere, Viviana/0000-0002-4297-8539; de Almeida, Fernando Marroquim Leao/0000-0003-2265-784X; Vandelli, Wainer/0000-0001-6581-9410; AGHEORGHIESEI, Catalin/0000-0003-3695-1847; Llácer, María Moreno/0000-0003-1113-3645; Cottin, Giovanna/0000-0002-5308-5808; Kazanin, Vasily F./0000-0002-4906-5468; Talyshev, Alexey/0000-0001-8560-3756; Olszewski, Andrzej/0000-0003-3368-5475; Ozcan, Veysi Erkcan/0000-0003-4643-6347; Woźniak, Krzysztof/0000-0003-1171-0887; Stabile, Alberto/0000-0002-6868-8329; Jiménez, Yesenia Hernández/0000-0001-9844-6200; Brahimi, Nihal/0000-0003-0992-3509; Mindur, Bartosz/0000-0002-5511-2611; Barreiro, Fernando/0000-0002-3021-0258; Sanchez, Francisca J. Munoz/0000-0002-6374-458X; Herten, Gregor/0000-0001-7661-5122; Maj, Klaudia/0000-0003-4819-9226; spagnolo, stefania/0000-0001-7482-6348; Orlando, Nicola/0000-0003-0616-245X; Staszewski, Rafał/0000-0001-7708-9259; Kelsey, Daniel/0000-0002-2297-1356; Ricci, Ester/0000-0002-4222-9976; Svatos, Michal/0000-0002-7199-3383; Beddall, Andrew John/0000-0002-8451-9672; Alberich, Leonor Cerdá/0000-0002-5567-4278; Leisos, Antonios/0000-0002-8126-3958; Peleganchuk, Sergey V/0000-0003-0907-7592; García, Josu Cantero/0000-0001-8449-1019; Fabbri, Laura/0000-0002-4002-8353; Viaux, Nicolas/0000-0002-5102-9140; Gutierrez, Phillip/0000-0003-2326-3877; Jones, Roger WL/0000-0002-6427-3513; Wang, Song-Ming/0000-0002-5821-4875; Malek, Fairouz/0000-0002-0948-5775; Lee, Lawrence/0000-0002-5590-335X; Petrucci, Fabrizio/0000-0002-5278-2206; Tikhomirov, Vladimir/0000-0002-9634-0581; Zanzi, Daniele/0000-0002-1222-7937; Di Nardo, Roberto/0000-0003-1111-3783; Lefebvre, Benoit/0000-0001-8212-6624; Buttar, Craig/0000-0003-0188-6491; Salt, Jose/0000-0003-4181-2788; Artoni, Giacomo/0000-0002-3477-4499; Perez, Miguel Villaplana/0000-0002-0048-4602; Lacasta, Carlos/0000-0002-2623-6252; Duarte-Campderrós, Jordi/0000-0003-0687-5214; Olszowska, Jolanta/0000-0003-0520-9500; Torro Pastor, Emma/0000-0002-5507-7924; Franchini, Matteo/0000-0002-4554-252X; Trzupek, Adam/0000-0001-6938-5867; Brooks, William K/0000-0001-6161-3570; Valero, Alberto/0000-0002-9776-5880; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Reznicek, Pavel/0000-0003-4017-9829; Doyle, Anthony T/0000-0001-6322-6195; Suster, Carl J E/0000-0001-7021-9380; Garcia, Carmen/0000-0003-1625-7452; Smykiewicz, Andrzej/0000-0001-6088-7094; Bergmann, Benedikt/0000-0002-8076-5614; Smirnova, Oxana/0000-0003-2517-531X; Smirnov, Sergei Y/0000-0002-6778-073X; MEONI, Evelin/0000-0002-6934-3752; de Renstrom, Pawel Bruckman/0000-0002-0206-1160; Iengo, Paolo/0000-0002-5035-1242; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Sessa, Marco/0000-0002-1402-7525; YILDIZ, Hatice DURAN/0000-0003-3469-6045; Song, Weimin/0000-0003-1376-2293; Mikestikova, Marcela/0000-0003-1277-2596; Kharlamova, Tatyana/0000-0002-0387-6804; Coccaro, Andrea/0000-0003-2368-4559; Snesarev, Andrei/0000-0002-9067-8362; Wolters, Helmut/0000-0002-9588-1773; Alimonti, Gianluca/0000-0002-7128-9046; Villaplana Perez, Miguel/0000-0002-0048-4602; Darbo, Giovanni/0000-0003-2165-0638; Santra, Arka/0000-0003-4644-2579; Grinstein, Sebastian/0000-0002-6460-8694; Wozniak, Krzysztof W/0000-0003-1171-0887; Dabrowski, Wladyslaw/0000-0001-9061-9568; Gladilin, Leonid/0000-0001-9422-8636; Llácer, María Moreno/0000-0001-9045-5305; Sopczak, Andre/0000-0001-6981-0544; Gonzalez, Barbara Alvarez/0000-0001-7767-4810; Panizzo, Giancarlo/0000-0002-0352-4833; Chizhov, Mihail/0000-0001-5841-3316; Slavíček, Tomáš/0000-0001-8054-7345; Amoroso, Simone/0000-0001-5450-0447; Kuleshov, Sergey V/0000-0002-3065-326X; Gravila, Paul/0000-0002-0154-577X; Norjoharuddeen, Nurfikri/0000-0002-8818-7476; Mitsou, Vasiliki A./0000-0002-1533-8886; Mikel Eukeni, Pozo Astigarraga/0000-0002-3069-3077; Bobrovnikov, Victor/0000-0002-2003-0261; Legger, Federica/0000-0003-1400-0709; Castro, Nuno/0000-0001-8491-4376; Ippolito, Valerio/0000-0001-5126-1620; Balaji, S/0000-0002-7324-4853; Capua, Marcella/0000-0002-2443-6525; Betti, Alessandra/0000-0003-0839-9311; Roy, Ashim/0000-0002-5622-4260; Buzykaev, Alexey/0000-0002-5458-5564; Wu, Xin/0000-0001-7655-389X; Ventura, Andrea/0000-0002-3368-3413; Ferrer, Antonio/0000-0003-0532-711X; Burdin, Sergey/0000-0003-4831-4132; Bassalat, Ahmed Azzam/0000-0002-0129-1423; Chwastowski, Janusz J./0000-0002-6190-8376; Ohm, Christian/0000-0002-8015-7512; Cavaliere, Viviana/0000-0002-4297-8539; Zhemchugov, Alexey/0000-0002-3360-4965; Romano, Marino/0000-0002-6609-7250; Derendarz, Dominik/0000-0001-5660-3095; Tassi, Enrico/0000-0002-3335-6500; Trzebinski, Maciej/0000-0002-5151-7101; Schioppa, Enrico Junior/0000-0002-1369-9944; Adiguzel, Aytul/0000-0001-6644-0517; Padilla, Cristobal/0000-0001-7951-0166; Lee, Shih-Chang/0000-0002-3353-2658; Napolitano, Fabrizio/0000-0002-8686-5923; Lazzaroni, Massimo/0000-0002-4094-1273; Fassi, Farida/0000-0002-6423-7213; de la Torre Perez, Hector/0000-0002-4516-5269; Ye, Sheng/0000-0003-3229-0746; Olesya, Kuchinskaya/0000-0002-3664-2465; Bold, Tomasz/0000-0002-2432-411X; Cabras, Grazia/0000-0002-8467-8235; Follega, Francesco Maria/0000-0003-2317-9560; Calafiura, Paolo/0000-0002-1692-1678; Di Gregorio, Giulia/0000-0002-7838-576X; McKee, Shawn/0000-0002-4551-4502; Butterworth, Jonathan/0000-0002-5905-5394; Konstantinidis, Nikolaos/0000-0002-4140-6360; Petersen, Troels/0000-0003-0221-3037; Ragusa, Francesco/0000-0002-4064-0489; D'Auria, Saverio/0000-0003-3393-6318; Stanislaus, Beojan/0000-0001-9007-7658; Thiele, Fabian Alexander Jurgen/0000-0002-6620-9734; Sullivan, Matthew/0000-0002-4807-6448; Camplani, Alessandra/0000-0002-6386-9788 ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NRC, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Idex, ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF; Thales programme - EU-ESF; Aristeia programme - EU-ESF; Herakleitos programme - Greek NSRF, Greece; Thales programme - Greek NSRF, Greece; Aristeia programme - Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC [ST/N000277/1, ST/S000860/1, ST/P002439/1, ST/N000331/1, ST/S00095X/1, ST/N000463/1, ST/N000307/1, 1852475, ST/S000879/1, ST/L006162/1, ST/S000747/1, ST/N000234/1] Funding Source: UKRI; Science and Technology Facilities Council [ST/N000331/1, ST/S000879/1] Funding Source: researchfish ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); CFI, Canada(Canada Foundation for Innovation); NRC, Canada; CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France(French National Research Agency (ANR)); Idex, ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme - EU-ESF; Thales programme - EU-ESF(Thales Group); Aristeia programme - EU-ESF; Herakleitos programme - Greek NSRF, Greece; Thales programme - Greek NSRF, Greece(Thales Group); Aristeia programme - Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. 124 4 4 2 54 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B FEB 10 2020.0 801 135145 10.1016/j.physletb.2019.135145 0.0 22 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics KD7BF Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000508018500037 0
J Liu, Y; Wu, FY; Lyu, C; Li, S; Ye, JP; Qu, XB Liu, Yang; Wu, Fanyou; Lyu, Cheng; Li, Shen; Ye, Jieping; Qu, Xiaobo Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW English Article Vehicle dispatching; Deep reinforcement learning; Load balancing NEURAL-NETWORK; DEMAND The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020. [Liu, Yang] Chalmers Univ Technol, Dept Architecture & Civil Engn, Gothenburg, Sweden; [Wu, Fanyou] Purdue Univ, Forestry & Nat Resources Dept, W Lafayette, IN 47907 USA; [Lyu, Cheng] Tech Univ Munich, Dept Civil Geo & Environm Engn, Munich, Germany; [Li, Shen] Tsinghua Univ, Dept Civil Engn, Beijing, Peoples R China; [Ye, Jieping] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA; [Qu, Xiaobo] Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing, Peoples R China Chalmers University of Technology; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Technical University of Munich; Tsinghua University; University of Michigan System; University of Michigan; Tsinghua University Li, S (corresponding author), Tsinghua Univ, Dept Civil Engn, Beijing, Peoples R China. sli299@tsinghua.edu.cn Qu, Xiaobo/AAG-4777-2021 Liu, Yang/0000-0002-3295-2917 European Union [101025896] European Union(European Commission) The authors would like to thank Professor Fei-Yue Wang for valuable discussions pertaining to experiments and paper organization. This study is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101025896. 49 11 11 27 45 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1366-5545 1878-5794 TRANSPORT RES E-LOG Transp. Res. Pt. e-Logist. Transp. Rev. MAY 2022.0 161 102694 10.1016/j.tre.2022.102694 0.0 APR 2022 20 Economics; Engineering, Civil; Operations Research & Management Science; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Engineering; Operations Research & Management Science; Transportation 0Z2TA 2023-03-23 WOS:000790928300001 0
J Nowak, R; Franus, W; Zhang, JR; Zhu, Y; Tian, X; Zhang, ZX; Chen, X; Liu, XY Nowak, Robert; Franus, Wiktor; Zhang, Jiarui; Zhu, Yue; Tian, Xin; Zhang, Zhouxian; Chen, Xu; Liu, Xiaoyu Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles APPLIED SCIENCES-BASEL English Article probabilistic record linkage; fuzzy string matching; text features extraction; supervised learning; DBpedia; All Science Journal Classification (ASJC) We present an algorithm to find corresponding authors of patents and scientific articles. The authors are given as records in Scopus and the Chinese Patents Database. This issue is known as the record linkage problem, defined as finding and linking individual records from separate databases that refer to the same real-world entity. The presented solution is based on a record linkage framework combined with text feature extraction and machine learning techniques. The main challenges were low data quality, lack of common record identifiers, and a limited number of other attributes shared by both data sources. Matching based solely on an exact comparison of authors' names does not solve the records linking problem because many Chinese authors share the same full name. Moreover, the English spelling of Chinese names is not standardized in the analyzed data. Three ideas on how to extend attribute sets and improve record linkage quality were proposed: (1) fuzzy matching of names, (2) comparison of abstracts of patents and articles, (3) comparison of scientists' main research areas calculated using all metadata available. The presented solution was evaluated in terms of matching quality and complexity on approximate to 250,000 record pairs linked by human experts. The results of numerical experiments show that the proposed strategies increase the quality of record linkage compared to typical solutions. [Nowak, Robert; Franus, Wiktor] Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland; [Zhang, Jiarui; Zhu, Yue; Tian, Xin; Zhang, Zhouxian; Chen, Xu; Liu, Xiaoyu] Shanghai Sci & Technol Dev Co Ltd, Shanghai 200233, Peoples R China Warsaw University of Technology Nowak, R (corresponding author), Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland. robert.nowak@pw.edu.pl; wiktor.franus@mion.pw.edu.pl; jrzhang@sstir.cn; yzhu@sgst.cn; xtian@sstir.cn; Cho_Z@outlook.com; xchen@sstir.cn; yjhu@sstir.cn Franus, Wiktor/0000-0003-3980-1107; Nowak, Robert M./0000-0001-7248-6888 Institute of Computer Science Statutory Funds; Shanghai-Warsaw Scientific Joint Lab Institute of Computer Science Statutory Funds; Shanghai-Warsaw Scientific Joint Lab This research was funded by Shanghai-Warsaw Scientific Joint Lab and Institute of Computer Science Statutory Funds. 42 0 0 1 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel SEP 2021.0 11 18 8417 10.3390/app11188417 0.0 17 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics UV2WU gold 2023-03-23 WOS:000699346300001 0
J Zhang, RY; Hummelgard, M; Ortegren, J; Olsen, M; Andersson, H; Yang, Y; Olin, H; Wang, ZL Zhang, Renyun; Hummelgard, Magnus; Ortegren, Jonas; Olsen, Martin; Andersson, Henrik; Yang, Ya; Olin, Hakan; Wang, Zhong Lin Utilising the triboelectricity of the human body for human-computer interactions* NANO ENERGY English Article Triboelectricity; Human body; Human -computer interactions; Text inputs; Graphical input; Mouse functions COMMUNICATION; KEYBOARD Human-computer interaction (HCI) strategies communicate the human mind and machine intelligence based on different devices and technologies. The majority of HCI strategies assume normal physical conditions that limit accessibility for users with disabilities. Certain products, such as Braille keyboards, work fine for people with specific disabilities. However, a more general HCI strategy that can neglect users' physical conditions would enhance the accessibility of these tools for disabled persons. Here, we report an HCI strategy that utilises triboelectricity of the human body (TEHB) for HCI. The TEHB can be generated by many parts of the human body, eliminating the obstacles imposed by physical function disabilities. Such an HCI approach has been used for text inputs, graphical inputs, and mimicked mouse functions. With the assistance of deep learning, an accuracy of approximately 98.4 % is achieved for text inputs obtained directly from handwriting. Our findings provide a new approach for HCI and demonstrate the feasibility of multiple interaction modes. [Zhang, Renyun; Hummelgard, Magnus; Ortegren, Jonas; Olsen, Martin; Olin, Hakan; Wang, Zhong Lin] Mid Sweden Univ, Dept Nat Sci, Holmgatan 10, SE-85170 Sundsvall, Sweden; [Andersson, Henrik] Mid Sweden Univ, Dept Elect Design, Holmgatan 10, SE-85170 Sundsvall, Sweden; [Yang, Ya; Wang, Zhong Lin] Chinese Acad Sci, CAS Ctr Excellence Nanosci, Beijing Inst Nanoenergy & Nanosyst, Beijing Key Lab Micronano Energy & Sensor, Beijing 101400, Peoples R China; [Yang, Ya; Wang, Zhong Lin] Chinese Acad Sci, Beijing Inst Nanoenergy & Nanosyst, Beijing 100083, Peoples R China; [Wang, Zhong Lin] Georgia Inst Technol, Sch Mat Sci & Engn, Atlanta, GA 30332 USA Mid-Sweden University; Mid-Sweden University; Chinese Academy of Sciences; Beijing Institute of Nanoenergy & Nanosystems, CAS; Chinese Academy of Sciences; Beijing Institute of Nanoenergy & Nanosystems, CAS; University System of Georgia; Georgia Institute of Technology Zhang, RY (corresponding author), Mid Sweden Univ, Dept Nat Sci, Holmgatan 10, SE-85170 Sundsvall, Sweden. renyun.zhang@miun.se Yang, Ya/A-7219-2016; Olin, Håkan/A-8175-2008; Wang, Zhong Lin/E-2176-2011 Yang, Ya/0000-0003-0168-2974; Olin, Håkan/0000-0001-7329-3359; Wang, Zhong Lin/0000-0002-5530-0380 45 2 2 10 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2211-2855 2211-3282 NANO ENERGY Nano Energy SEP 2022.0 100 107503 10.1016/j.nanoen.2022.107503 0.0 9 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics 4X3SD 2023-03-23 WOS:000860765200004 0
C Tian, H; Ye, BW; Zheng, XL; Wu, DSD Tian, Hu; Ye, Bowei; Zheng, Xiaolong; Wu, Desheng Dash Robust Graph Neural Networks Against Adversarial Attacks via Jointly Adversarial Training IFAC PAPERSONLINE English Proceedings Paper 3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS) DEC 03-05, 2020 Beijing, PEOPLES R CHINA Int Federat Automat Control, Tech Comm 9 1 Econ, Busines, & Financial Syst,Int Federat Automat Control, Tech Comm 9 2 Social Impact Automat,Int Federat Automat Control, Tech Comm 1 3 Discrete Event & Hybrid Syst,Int Federat Automat Control, Tech Comm 1 4 Stochastic Syst,Int Federat Automat Control, Tech Comm 1 5 Networked Syst,Int Federat Automat Control, Tech Comm 3 1 Comp Control,Int Federat Automat Control, Tech Comm 4 3 Robot,Int Federat Automat Control, Tech Comm 4 5 Human Machine Syst,Int Federat Automat Control, Tech Comm 7 3 Aerosp,Int Federat Automat Control, Tech Comm 9 5 Technol, Culture & Int Stabil Graph neural networks; Robustness; Adversarial Attack; Adversarial Training; Deep Learning Graph neural networks (GNNs) are powerful tools for analyzing graph-structured data. However, recent studies have shown that GNNs are vulnerable to small but intentional perturbations of input features and graph structures in the node classification task. Existing researches focus on enhancing the robustness of GNNs for a single type of perturbation such as graph structure perturbation or node feature perturbation. An ideal graph neural networks model should be able to resist the two kinds of perturbations. For this purpose, we propose a new adversarial training method for graph-structured data named Graph Jointly Adversarial Training (GJAT) which incorporates Graph Structure Adversarial Training (GSAT) and Graph Feature Adversarial Training (GFAT) two components and can resist perturbations from the topological structure and node attribute. Extensive experimental results demonstrate that our proposed method combining two kinds of adversarial training strategies can effectively improve the robustness of graph convolutional networks (GCNs) which is an important subset of GNNs. Copyright (C) 2020 The Authors. [Tian, Hu; Zheng, Xiaolong] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Tian, Hu; Zheng, Xiaolong] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China; [Ye, Bowei] Univ Illinois, Dept Stat, Champaign, IL USA; [Wu, Desheng Dash] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China; [Wu, Desheng Dash] Stockholm Univ, Stockholm Business Sch, SE-10691 Stockholm, Sweden Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Illinois System; University of Illinois Urbana-Champaign; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Stockholm University Tian, H (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.;Tian, H (corresponding author), Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China. tianhu2018@ia.ac.cn; boweiye2@illinois.edu; xiaolong.zheng@ia.ac.cn; dash@risklab.ca Ministry of Health of China [2017ZX10303401-002, 2017YFC1200302]; Natural Science Foundation of China [71472175, 71602184, 71621002]; National Key Research and Development Program of China [2016QY02D0305] Ministry of Health of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work is supported by the Ministry of Health of China under Grant No. 2017ZX10303401-002 and 2017YFC1200302, and the Natural Science Foundation of China under Grant No. 71472175, 71602184 and 71621002, and the National Key Research and Development Program of China under 2016QY02D0305. 23 0 0 1 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53 5 SI 420 425 10.1016/j.ifacol.2021.04.225 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SL0EN gold 2023-03-23 WOS:000656589700073 0
J Alquier, P; Doukhan, P; Fan, XQ Alquier, Pierre; Doukhan, Paul; Fan, Xiequan Exponential inequalities for nonstationary Markov chains DEPENDENCE MODELING English Article Nonstationary Markov chains; Martingales; Exponential inequalities; Time series forecasting; Statistical learning theory; Oracle inequalities; Model selection DEPENDENT RANDOM-VARIABLES; TIME-SERIES PREDICTION; DEVIATION INEQUALITIES; MODEL SELECTION; BOUNDS; FUNCTIONALS; MARTINGALES; SUMS Exponential inequalities are main tools in machine learning theory. To prove exponential inequalities for non i.i.d random variables allows to extend many learning techniques to these variables. Indeed, much work has been done both on inequalities and learning theory for time series, in the past 15 years. However, for the non independent case, almost all the results concern stationary time series. This excludes many important applications: for example any series with a periodic behaviour is nonstationary. In this paper, we extend the basic tools of [19] to nonstationary Markov chains. As an application, we provide a Bernstein type inequality, and we deduce risk bounds for the prediction of periodic autoregressive processes with an unknown period. [Alquier, Pierre] Univ Paris Saclay, ENSAE, CREST, Palaiseau, France; [Doukhan, Paul] Univ Paris Seine, AGM UMR8088, Paris, France; [Doukhan, Paul] Univ Valparaiso, CIMFAV, Valparaiso, Chile; [Fan, Xiequan] Tianjin Univ, CAM, Tianjin, Peoples R China Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Universidad de Valparaiso; Tianjin University Alquier, P (corresponding author), Univ Paris Saclay, ENSAE, CREST, Palaiseau, France. pierre.alquier@ensae.fr fan, xiequan/0000-0001-8824-8032; Alquier, Pierre/0000-0003-4249-7337 GENES; French National Research Agency (ANR) under the grant Labex Ecodec [ANR-11-LABEX-0047]; MME-DII center of excellence [ANR-11-LABEX-0023-01]; PAI-CONICYT MEC [80170072]; National Natural Science Foundation of China [11601375] GENES; French National Research Agency (ANR) under the grant Labex Ecodec(French National Research Agency (ANR)); MME-DII center of excellence; PAI-CONICYT MEC(German Research Foundation (DFG)National Health and Medical Research Council (NHMRC) of Australia); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Pierre Alquier's work has been supported by GENES and by the French National Research Agency (ANR) under the grant Labex Ecodec (ANR-11-LABEX-0047). Paul Doukhan's work has been developed within the MME-DII center of excellence (ANR-11-LABEX-0023-01) & PAI-CONICYT MEC No 80170072. Fan Xiequan has been partially supported by the National Natural Science Foundation of China (Grant No 11601375). 49 5 5 0 4 SCIENDO WARSAW DE GRUYTER POLAND SP Z O O, BOGUMILA ZUGA 32A STR, 01-811 WARSAW, POLAND 2300-2298 DEPEND MODEL Depend. Model. FEB 2019.0 7 1 150 168 10.1515/demo-2019-0007 0.0 19 Statistics & Probability Emerging Sources Citation Index (ESCI) Mathematics ID6XR gold, Green Submitted, Green Published 2023-03-23 WOS:000471826000002 0
J Wang, Q; Nguyen, TT; Huang, JZ; Nguyen, TT Wang, Qiang; Thanh-Tung Nguyen; Huang, Joshua Z.; Thuy Thi Nguyen An efficient random forests algorithm for high dimensional data classification ADVANCES IN DATA ANALYSIS AND CLASSIFICATION English Article Classification; Image classification; High dimensional data; Random forests; Data mining SELECTION; RECOGNITION; ENSEMBLES In this paper, we propose a new random forest (RF) algorithm to deal with high dimensional data for classification using subspace feature sampling method and feature value searching. The new subspace sampling method maintains the diversity and randomness of the forest and enables one to generate trees with a lower prediction error. A greedy technique is used to handle cardinal categorical features for efficient node splitting when building decision trees in the forest. This allows trees to handle very high cardinality meanwhile reducing computational time in building the RF model. Extensive experiments on high dimensional real data sets including standard machine learning data sets and image data sets have been conducted. The results demonstrated that the proposed approach for learning RFs significantly reduced prediction errors and outperformed most existing RFs when dealing with high-dimensional data. [Wang, Qiang; Huang, Joshua Z.] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [Thanh-Tung Nguyen] Thuyloi Univ, Fac Comp Sci & Engn, 175 Tay Son, Hanoi, Vietnam; [Thuy Thi Nguyen] Vietnam Natl Univ Agr, Fac Informat Technol, Hanoi, Vietnam; [Thanh-Tung Nguyen] Sorbonne Univ, Unite Modelisat Math & Informat Syst Complexes, IRD, JEAI WARM,UMMISCO, F-93143 Bondy, France Shenzhen University; Thuyloi University; Vietnam National University of Agriculture (VNUA); UDICE-French Research Universities; Sorbonne Universite; Cadi Ayyad University of Marrakech; Hanoi University of Science & Technology; Institut de Recherche pour le Developpement (IRD); University Cheikh Anta Diop Dakar; University of Yaounde I Nguyen, TT (corresponding author), Thuyloi Univ, Fac Comp Sci & Engn, 175 Tay Son, Hanoi, Vietnam.;Nguyen, TT (corresponding author), Sorbonne Univ, Unite Modelisat Math & Informat Syst Complexes, IRD, JEAI WARM,UMMISCO, F-93143 Bondy, France. wangqiang@szu.edu.cn; tungnt@tlu.edu.vn; zx.huang@szu.edu.cn; ntthuy@vnua.edu.vn Nguyen, Thuy/0000-0002-9358-6201 22 11 11 3 12 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1862-5347 1862-5355 ADV DATA ANAL CLASSI Adv. Data Anal. Classif. DEC 2018.0 12 4 SI 953 972 10.1007/s11634-018-0318-1 0.0 20 Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Mathematics HB2LQ 2023-03-23 WOS:000450873000008 0
J Del Moral, P; Hu, SL; Wu, LM Del Moral, Pierre; Hu, Shulan; Wu, Liming MODERATE DEVIATIONS FOR INTERACTING PROCESSES STATISTICA SINICA English Article Convergence of empirical processes; exponential inequalities; Feynman-Kac particle models; functional central limit theorems; interacting empirical processes; large deviations for projective limits; moderate deviations; sequential Monte Carlo methods EMPIRICAL MEASURES; MARKOV-CHAINS This article is concerned with moderate deviation principles of a class of interacting empirical processes. We derive an explicit description of the rate function, and we illustrate these results with Feynman-Kac particle models arising in nonlinear filtering, statistical machine learning, rare event analysis, and computational physics. We discuss functional moderate deviations of the occupation measures for both the strong tau-topology on the space of finite and bounded measures as well as for the corresponding stochastic processes on some class of functions equipped with the uniform topology, yielding the first results of this type for mean field interacting processes. Our approach is based on an original semigroup analysis combined with Orlicz norm inequalities, stochastic perturbation techniques, and projective limit large deviation methods. [Del Moral, Pierre] Univ New S Wales, Sch Math & Stat, Sydney, NSW, Australia; [Hu, Shulan] Zhongnan Univ Econ & Law, Dept Stat, Wuhan 430073, Peoples R China; [Wu, Liming] Univ Blaise Pascal, CNRS UMR 6620, Lab Math Appl, F-63177 Clermont Ferrand, France University of New South Wales Sydney; Zhongnan University of Economics & Law; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Clermont Auvergne (UCA) Hu, SL (corresponding author), Zhongnan Univ Econ & Law, Dept Stat, Wuhan 430073, Peoples R China. p.del-moral@unsw.edu.au; hu_shulan@yahoo.com; li-ming.wu@math.univ-bpclermont.fr Wu, liming/AAH-5415-2019 NSFC [11201487] NSFC(National Natural Science Foundation of China (NSFC)) This work is supported by NSFC (No. 11201487). 35 4 4 0 9 STATISTICA SINICA TAIPEI C/O DR H C HO, INST STATISTICAL SCIENCE, ACADEMIA SINICA, TAIPEI 115, TAIWAN 1017-0405 1996-8507 STAT SINICA Stat. Sin. JUL 2015.0 25 3 921 951 10.5705/ss.2013.134 0.0 31 Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Mathematics CP5ZB 2023-03-23 WOS:000359963000006 0
J Chevallier, J; Zhu, BZ; Zhang, LY Chevallier, Julien; Zhu, Bangzhu; Zhang, Lyuyuan Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms COMPUTATIONAL ECONOMICS English Article Genetic algorithms; Ensemble empirical mode decomposition; Least squares support vector machine; Grid Search; Particle swarm optimization EMPIRICAL MODE DECOMPOSITION; PARTICLE SWARM OPTIMIZATION; SUPPORT VECTOR MACHINES; TURNING-POINTS; SELECTION; SEARCH; NONSTATIONARY This paper investigates hybrid time series forecasting models, which are based on combinations of ensemble empirical mode decomposition and least squares support vector machines. Several algorithms are considered: the genetic algorithm, the grid search, and particle swarm optimization. Theoretical guarantees of prediction accuracy are tested with sine curves. From a numerical testing perspective, we are interested in showing the superiority of one approach to another based on theoretical prediction and time series applications in finance (S&P 500), commodities (WTI oil price), or cryptocurrencies (Bitcoin). The superiority of hybrid models to soft- and hard-computed models is further assessed through a 'horse race' and trading performance, as well as through fine-tuning of the algorithms. [Chevallier, Julien] IPAG Business Sch, IPAG Lab, 184 Blvd St Germain, F-75006 Paris, France; [Chevallier, Julien] Univ Paris 08, LED, 2 Rue Liberte, F-93526 St Denis, France; [Zhu, Bangzhu] Nanjing Univ Informat Sci & Technol, Sch Business, Nanjing 210044, Peoples R China; [Zhang, Lyuyuan] Univ Melbourne, Sch Math & Stat, Parkville, Vic 3010, Australia IPAG Business School; Universite Paris-VIII; Nanjing University of Information Science & Technology; University of Melbourne Chevallier, J (corresponding author), IPAG Business Sch, IPAG Lab, 184 Blvd St Germain, F-75006 Paris, France.;Chevallier, J (corresponding author), Univ Paris 08, LED, 2 Rue Liberte, F-93526 St Denis, France.;Zhu, BZ (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Business, Nanjing 210044, Peoples R China. julien.chevallier@ipag.fr; wpzbz@126.com Zhang, Lyuyuan/0000-0001-9562-7161 57 6 6 0 6 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0927-7099 1572-9974 COMPUT ECON Comput. Econ. FEB 2021.0 57 2 537 575 10.1007/s10614-019-09966-z 0.0 JAN 2020 39 Economics; Management; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Mathematics QU6RF 2023-03-23 WOS:000574140600001 0
J Rehman, YAU; Po, LM; Komulainen, J Rehman, Yasar Abbas Ur; Po, Lai-Man; Komulainen, Jukka Enhancing deep discriminative feature maps via perturbation for face presentation attack detection IMAGE AND VISION COMPUTING English Article Spoofing; Presentation attack detection; CNN; Attention; Face-biometrics SPOOFING DETECTION; LIVENESS DETECTION; IMAGE QUALITY; TEXTURE Face presentation attack detection (PAD) in unconstrained conditions is one of the key issues in face biometric-based authentication and security applications. In this paper, we propose a perturbation layer - a learnable preprocessing layer for low-level deep features to enhance the discriminative ability of deep features in face PAD. The perturbation layer takes the deep features - of a candidate layer in Convolutional Neural Network (CNN), the corresponding hand-crafted features of an input image, and produces adaptive convolutional weights for the deep features of the candidate layer. These adaptive convolutional weights determine the importance of the pixels in the deep features of the candidate layer for face PAD. The proposed perturbation layer adds very little overhead to the total trainable parameters in the model. We evaluated the proposed perturbation layer with Local Binary Patterns (LBP), with and without color information, on three publicly available face PAD databases, i.e., CASIA, Idiap Replay-Attack, and OULU-NPU databases. Our experimental results show that the introduction of the proposed perturbation layer in the CNN improved the face PAD performance, in both intra-database and cross-database scenarios. Our results also highlight the attention created by the proposed perturbation layer in the deep features and its effectiveness for face PAD in general. (C) 2019 Elsevier B.V. All rights reserved. [Rehman, Yasar Abbas Ur; Po, Lai-Man] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China; [Komulainen, Jukka] Visidon Ltd, Oulu, Finland; [Rehman, Yasar Abbas Ur] TCL Corp Res Hong Kong Co Ltd, Hong Kong Sci Pk, Hong Kong, Peoples R China City University of Hong Kong Rehman, YAU (corresponding author), City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China. yaurehman2-c@my.cityu.edu.hk; eelmpo@my.cityu.edu.hk; yty@iki.fi rehman, yasar/0000-0002-2945-7181; Komulainen, Jukka/0000-0002-0102-7868 City University of Hong Kong [7004430] City University of Hong Kong(City University of Hong Kong) Internal SRC Funding from the City University of Hong Kong substantially supported the work described in this paper under Project No. 7004430. 65 4 4 1 7 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0262-8856 1872-8138 IMAGE VISION COMPUT Image Vis. Comput. FEB 2020.0 94 103858 10.1016/j.imavis.2019.103858 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Optics KU4FH 2023-03-23 WOS:000519664900002 0
J Tam, V; Chen, PK; Yee, A; Solis, N; Klein, T; Kudelko, M; Sharma, R; Chan, WCW; Overall, CM; Haglund, L; Sham, PC; Cheah, KSE; Chan, D Tam, Vivian; Chen, Peikai; Yee, Anita; Solis, Nestor; Klein, Theo; Kudelko, Mateusz; Sharma, Rakesh; Chan, Wilson C. W.; Overall, Christopher M.; Haglund, Lisbet; Sham, Pak C.; Cheah, Kathryn Song Eng; Chan, Danny DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics ELIFE English Article NUCLEUS PULPOSUS; CHONDROCYTE HYPERTROPHY; ANNULUS FIBROSUS; EXPRESSION; OSTEOARTHRITIS; PROTEIN; CELLS; GENE; DIFFERENTIATION; PROGRESSION The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising 94 genome-wide profiles from 17 individuals. To begin with, protein modules defining key directional trends spanning the lateral and anteroposterior axes were derived from high-resolution spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific profiles of regulatory activities and displayed potential paths of deconstruction in the level- and location-matched aged cadaveric discs. Machine learning methods predicted a 'hydration matrisome' that connects extracellular matrix with MRI intensity. Importantly, the static proteome used as point-references can be integrated with dynamic proteome (SILAC/degradome) and transcriptome data from multiple clinical samples, enhancing robustness and clinical relevance. The data, findings, and methodology, available on a web interface (http://www.sbms.hku.hk/dclab/DIPPER/), will be valuable references in the field of IVD biology and proteomic analytics. [Tam, Vivian; Chen, Peikai; Yee, Anita; Kudelko, Mateusz; Chan, Wilson C. W.; Cheah, Kathryn Song Eng; Chan, Danny] Univ Hong Kong, Sch Biomed Sci, Hong Kong, Peoples R China; [Tam, Vivian; Chan, Wilson C. W.; Chan, Danny] Univ Hong Kong, Shenzhen Res Inst & Innovat HKU SIRI, Shenzhen, Peoples R China; [Solis, Nestor; Klein, Theo; Overall, Christopher M.] Univ British Columbia, Fac Dent, Ctr Blood Res, Vancouver, BC, Canada; [Sharma, Rakesh] Univ Hong Kong, Prote & Metabol Core Facil, Hong Kong, Peoples R China; [Chen, Peikai; Chan, Wilson C. W.] HKU Shenzhen Hosp, Dept Orthopaed Surg & Traumatol, Shenzhen, Peoples R China; [Haglund, Lisbet] McGill Univ, Dept Surg, Montreal, PQ, Canada; [Sham, Pak C.] Univ Hong Kong, Ctr PanorOm Sci CPOS, Hong Kong, Peoples R China; [Klein, Theo] Triskelion BV, Zeist, Netherlands University of Hong Kong; University of Hong Kong; University of British Columbia; University of Hong Kong; McGill University; University of Hong Kong Chan, D (corresponding author), Univ Hong Kong, Sch Biomed Sci, Hong Kong, Peoples R China.;Chan, D (corresponding author), Univ Hong Kong, Shenzhen Res Inst & Innovat HKU SIRI, Shenzhen, Peoples R China. chand@hku.hk Haglund, Lisbet/N-3095-2013; Chen, Peikai/ABB-3740-2021; Chan, Danny/C-4203-2009 Haglund, Lisbet/0000-0002-1288-2149; Chen, Peikai/0000-0003-1880-0893; Tam, Vivian/0000-0002-1074-8018; Sharma, Rakesh/0000-0002-5306-3457; Klein, Theo/0000-0001-8061-9353; Chan, Danny/0000-0003-3824-5778; Tam, Vivian/0000-0002-0457-3477 Research Grants Council, University Grants Committee [E-HKU703/18, T12-708/12N, AoE/M-04/04]; Ministry of Science and Technology of the People's Republic of China (973) [2014CB942900]; Canadian Institutes of Health Research [FDN-148408] Research Grants Council, University Grants Committee; Ministry of Science and Technology of the People's Republic of China (973); Canadian Institutes of Health Research(Canadian Institutes of Health Research (CIHR)) Research Grants Council, University Grants Committee E-HKU703/18 Danny Chan; Research Grants Council, University Grants Committee T12-708/12N Kathryn Song Eng Cheah; Research Grants Council, University Grants Committee AoE/M-04/04 Kathryn Song Eng Cheah; Ministry of Science and Technology of the People's Republic of China (973) (2014CB942900) Danny Chan; Canadian Institutes of Health Research FDN-148408 Christopher M Overall; The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. 95 20 20 1 15 eLIFE SCIENCES PUBL LTD CAMBRIDGE SHERATON HOUSE, CASTLE PARK, CAMBRIDGE, CB3 0AX, ENGLAND 2050-084X ELIFE eLife DEC 31 2020.0 9 e64940 10.7554/eLife.64940 0.0 37 Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics QD6UE 33382035.0 gold, Green Submitted, Green Accepted 2023-03-23 WOS:000615649700001 0
J Forster, Y Foerster, Yvonne PAINTING BY NUMBERS: DIGITAL TECHNOLOGY AND THE ART OF LIVING ARBOR-CIENCIA PENSAMIENTO Y CULTURA English Article Technology; Artificial Intelligence; experience; phenomenology; art The digital relies on computation. Programming uses algorithms. Algorithms are sets of rules that solve problems in a finite number of steps. In this sense, the digital world is governed by quantities, numbers, fixed rules. The degree of freedom seems to be very limited. Artistic agency and creativity, on the other hand, rely on openness, freedom, and qualitative experiences. Such experiences are not only vital to artistic expression but also to everyday life. Technological life-worlds as they are represented through current technologies (e.g. smart homes or automated driving) and science fiction do not seem to accommodate such open structures. The philosophy of technology is divided: Many hold that to a large extent technology determines human cognition (such as Mark B.N. Hansen, Bernard Stiegler) and thus subordinate human cognition to mechanical organizations. Others take a different approach and reflect on the creative potential of new technologies (e.g. Erin Manning, Jaime del Val). This article discusses theories that address the human-machine relationship as complex structures that go beyond the dystopian idea of humans being transcended or incorporated by technology. Such approaches are central to the discussion on the future of human beings and cultural-political shaping of life-worlds. To understand how human-machine relationships can be framed as open and creative processes, I present epistemological accounts of embodied cognition, artistic examples of performance strategies with algorithmic set-ups, and finally embed these aspects within a broader picture of conceptualizing technology and human life as a continuum rather than standing in opposition or being determined by the other. [Foerster, Yvonne] Shanxi Univ Taiyuan, Taiyuan, Peoples R China; [Foerster, Yvonne] Leuphana Univ Luneburg, Luneburg, Germany Leuphana University Luneburg Forster, Y (corresponding author), Shanxi Univ Taiyuan, Taiyuan, Peoples R China.;Forster, Y (corresponding author), Leuphana Univ Luneburg, Luneburg, Germany. yvonne.foerster@gmail.com 29 1 1 1 10 CONSEJO SUPERIOR INVESTIGACIONES CIENTIFICAS-CSIC MADRID VITRUVIO 8, 28006 MADRID, SPAIN 0210-1963 1988-303X ARBOR Arbor-Cienc. Pensam. Cult. APR-JUN 2021.0 197 800 a602 10.3989/arbor.2021.800004 0.0 12 Humanities, Multidisciplinary Arts & Humanities Citation Index (A&HCI) Arts & Humanities - Other Topics UI1EV gold, Green Published 2023-03-23 WOS:000690360500005 0
J Zhou, LL; Niu, ZBA; Wang, YQ; Zheng, Y; Zhu, YC; Wang, CX; Gao, XY; Gao, LH; Zhang, W; Zhang, KT; Melino, G; Huang, HY; Wang, XN; Sun, Q Zhou, Lulin; Niu, Zubiao; Wang, Yuqi; Zheng, You; Zhu, Yichao; Wang, Chenxi; Gao, Xiaoyan; Gao, Lihua; Zhang, Wen; Zhang, Kaitai; Melino, Gerry; Huang, Hongyan; Wang, Xiaoning; Sun, Qiang Senescence as a dictator of patient outcomes and therapeutic efficacies in human gastric cancer CELL DEATH DISCOVERY English Article MOLECULAR ANALYSIS; R PACKAGE; CHEMOTHERAPY; SIGNATURE; SUBTYPES Senescence is believed to be a pivotal player in the onset and progression of tumors as well as cancer therapy. However, the guiding roles of senescence in clinical outcomes and therapy selection for patients with cancer remain obscure, largely due to the absence of a feasible senescence signature. Here, by integrative analysis of single cell and bulk transcriptome data from multiple datasets of gastric cancer patients, we uncovered senescence as a veiled tumor feature characterized by senescence gene signature enriched, unexpectedly, in the noncancerous cells, and further identified two distinct senescence-associated subtypes based on the unsupervised clustering. Patients with the senescence subtype had higher tumor mutation loads and better prognosis as compared with the aggressive subtype. By the machine learning, we constructed a scoring system termed as senescore based on six signature genes: ADH1B, IL1A, SERPINE1, SPARC, EZH2, and TNFAIP2. Higher senescore demonstrated robustly predictive capability for longer overall and recurrence-free survival in 2290 gastric cancer samples, which was independently validated by the multiplex staining analysis of gastric cancer samples on the tissue microarray. Remarkably, the senescore signature served as a reliable predictor of chemotherapeutic and immunotherapeutic efficacies, with high-senescore patients benefited from immunotherapy, while low-senescore patients were responsive to chemotherapy. Collectively, we report senescence as a heretofore unrecognized hallmark of gastric cancer that impacts patient outcomes and therapeutic efficacy. [Zhou, Lulin; Wang, Xiaoning] Nankai Univ, Sch Med, 94 Weijin Rd, Tianjin 300071, Peoples R China; [Zhou, Lulin; Niu, Zubiao; Wang, Yuqi; Zheng, You; Zhu, Yichao; Wang, Chenxi; Gao, Lihua; Sun, Qiang] Chinese Acad Med Sci, Inst Biotechnol, Res Unit Cell Death Mech, 20 Dongda St, Beijing 100071, Peoples R China; [Gao, Xiaoyan; Huang, Hongyan] Capital Med Univ, Dept Oncol, Beijing Shijitan Hosp, 10 TIEYI Rd, Beijing 100038, Peoples R China; [Zhang, Wen] Chinese Acad Med Sci & Peking Union Med Coll, Dept Immunol, Natl Canc Ctr, Natl Clin Res Ctr Canc,Canc Hosp, Beijing 100021, Peoples R China; [Zhang, Kaitai] Chinese Acad Med Sci & Peking Union Med Coll, State Key Lab Mol Oncol, Dept Etiol & Carcinogenesis, Natl Canc Ctr,Natl Clin Res Ctr Canc,Canc Hosp, Beijing 100021, Peoples R China; [Melino, Gerry] Univ Roma Tor Vergata, Dept Expt Med, TOR, I-00133 Rome, Italy; [Melino, Gerry] DZNE German Ctr Neurodegenerat Dis, D-53127 Bonn, Germany; [Wang, Xiaoning] Chinese Peoples Liberat Army Gen Hosp, Natl Clin Res Ctr Geriatr Dis, Beijing Key Lab Aging & Geriatr, Inst Geriatr,Med Ctr 2, Beijing 100853, Peoples R China; [Wang, Xiaoning] Southern Med Univ, Sch Lab Med & Biotechnol, Guangzhou 510515, Peoples R China Nankai University; Chinese Academy of Medical Sciences - Peking Union Medical College; Capital Medical University; Chinese Academy of Medical Sciences - Peking Union Medical College; Cancer Institute & Hospital - CAMS; Peking Union Medical College; Chinese Academy of Medical Sciences - Peking Union Medical College; Cancer Institute & Hospital - CAMS; Peking Union Medical College; University of Rome Tor Vergata; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE); Chinese People's Liberation Army General Hospital; Southern Medical University - China Wang, XN (corresponding author), Nankai Univ, Sch Med, 94 Weijin Rd, Tianjin 300071, Peoples R China.;Sun, Q (corresponding author), Chinese Acad Med Sci, Inst Biotechnol, Res Unit Cell Death Mech, 20 Dongda St, Beijing 100071, Peoples R China.;Huang, HY (corresponding author), Capital Med Univ, Dept Oncol, Beijing Shijitan Hosp, 10 TIEYI Rd, Beijing 100038, Peoples R China.;Wang, XN (corresponding author), Chinese Peoples Liberat Army Gen Hosp, Natl Clin Res Ctr Geriatr Dis, Beijing Key Lab Aging & Geriatr, Inst Geriatr,Med Ctr 2, Beijing 100853, Peoples R China.;Wang, XN (corresponding author), Southern Med Univ, Sch Lab Med & Biotechnol, Guangzhou 510515, Peoples R China. hhongy1999@126.com; xnwang88@163.com; sunq@bmi.ac.cn wang, chenxi/HMD-9902-2023; Zhang, Wen/AAX-4530-2021 Zhang, Wen/0000-0003-3150-2751; Sun, Qiang/0000-0002-5342-0637 Beijing Municipal Natural Science Foundation [KZ202110025029]; Beijing Municipal Administration of Hospitals Incubating Program [PX2021033]; National Natural Science Foundation of China [31970685, 31770975] Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Beijing Municipal Administration of Hospitals Incubating Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by Beijing Municipal Natural Science Foundation (KZ202110025029 to HH), Beijing Municipal Administration of Hospitals Incubating Program (PX2021033 to HH), and the National Natural Science Foundation of China (31970685 to QS, 31770975 to XW). 59 6 6 0 4 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2058-7716 CELL DEATH DISCOV Cell Death Discov. JAN 10 2022.0 8 1 13 10.1038/s41420-021-00769-6 0.0 14 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology YE3VE 35013121.0 Green Accepted, Green Submitted, gold 2023-03-23 WOS:000741055400002 0
J Yang, YJ; Sasaki, K; Cheng, L; Liu, XW Yang, Yongjiang; Sasaki, Kuniaki; Cheng, Long; Liu, Xingwei Gender differences in active travel among older adults: Non-linear built environment insights TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT English Article Older adults; Gender; Active travel; Built environment; Non-linear; Machine learning MODE CHOICE; PHYSICAL-ACTIVITY; WALKING; MOBILITY; HEALTH; GAP; ACCESSIBILITY; SINGAPORE; TRANSPORT; BEHAVIOR Although many studies have revealed the relationship between the built environment and active travel among older adults, limited studies offer insights into gender differences. Using data from Chiba (Japan), this study employs a random forest method to fill this gap by revealing the nonlinear associations of the built environment with active travel time among older males and females, and adopts a Shapley Additive exPlanations method to interpret the models. Results show that: (1) The dominant contributions to active travel time are gendered. For example, distance to a cafe/pachinko contributes more to males' active travel time, while for females, population density contributes more. (2) The built environment attributes have non-linear associations with active travel time among older males and females. This, therefore, implies that built environment interventions are only effective to a certain extent. Implications of the findings offer nuanced guidance for a gender-equitable and age-friendly city. [Yang, Yongjiang; Sasaki, Kuniaki; Liu, Xingwei] Waseda Univ, Dept Civil & Environm Engn, Tokyo, Japan; [Cheng, Long] Univ Ghent, Dept Geog, Ghent, Belgium; [Cheng, Long] Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Sch Transportat, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China; [Liu, Xingwei] Nippon Expressway Co Ltd Res Inst, Tokyo, Japan Waseda University; Ghent University; Southeast University - China Sasaki, K (corresponding author), Waseda Univ, Dept Civil & Environm Engn, Shinju Ku, 3-4-1 Okubo, Tokyo 1698555, Japan. yongjiang.yang@fuji.waseda.jp; sasaki.k@waseda.jp; long.cheng@ugent.be; liu@ri-nexco.co.jp Cheng, Long/0000-0003-3720-4093 92 4 4 23 23 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1361-9209 1879-2340 TRANSPORT RES D-TR E Transport. Res. Part D-Transport. Environ. SEP 2022.0 110 103405 10.1016/j.trd.2022.103405 0.0 AUG 2022 15 Environmental Studies; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Transportation 4U5XX hybrid 2023-03-23 WOS:000858867600003 0
J Fu, HY; Jiang, ML; Vierinen, J; Haggstrom, I; Rietveld, MT; Varberg, E; Sato, H; Wu, J; Scales, WA; Jin, YQ Fu, H. Y.; Jiang, M. L.; Vierinen, J.; Haggstrom, I.; Rietveld, M. T.; Varberg, E.; Sato, H.; Wu, J.; Scales, W. A.; Jin, Y. Q. A Stimulated Emission Diagnostic Technique for Electron Temperature of the High Power Radio Wave Modified Ionosphere GEOPHYSICAL RESEARCH LETTERS English Article ionospheric modification; incoherent scatter radar; stimulated Brillouin scattering; electron temperature inversion ELECTROMAGNETIC EMISSION; BRILLOUIN-SCATTERING; PLASMA We report observations of stimulated electromagnetic emission (SEE) induced by high power high frequency (HF) radio waves near the third electron gyroharmonic (3fce ${f}_{\mathrm{ce}}$) at European Incoherent Scatter Radar (EISCAT). It is discovered that stimulated Brillouin scattering (SBS) spectrum behaves similarly as spectral ion lines of the incoherent scatter radar (ISR) for HF pumping frequency above 3fce ${f}_{\mathrm{ce}}$. The SBS spectral width shows correlation with electron to ion temperature ratio T-e/T-i. A new inversion method is proposed by incorporating the SBS spectral width within an artificial neural network approach to achieve electron temperature inversion for ionospheric turbulent plasmas. This work provides a potential new technique to diagnose parameters in the modified ionosphere when the ISR is not available. [Fu, H. Y.; Jin, Y. Q.] Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai, Peoples R China; [Fu, H. Y.; Jiang, M. L.; Jin, Y. Q.] Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China; [Vierinen, J.; Rietveld, M. T.; Varberg, E.] UiT Arctic Univ Norway, Tromso, Norway; [Haggstrom, I.] EISCAT Sci Assoc, Kiruna, Sweden; [Rietveld, M. T.; Varberg, E.] EISCAT Sci Assoc, Tromso, Norway; [Sato, H.] DLR Inst Solar Terr Phys, Neustrelitz, Germany; [Wu, J.] China Res Inst Radiowave Propagat, Qingdao, Peoples R China; [Scales, W. A.] Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 24061 USA Fudan University; Fudan University; UiT The Arctic University of Tromso; Virginia Polytechnic Institute & State University Fu, HY (corresponding author), Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai, Peoples R China.;Fu, HY (corresponding author), Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China. haiyang_fu@fudan.edu.cn Varberg, Erik/0000-0002-8815-4893; , Haiyang/0000-0003-1816-1024; Sato, Hiroatsu/0000-0002-5886-2768; Rietveld, Michael/0000-0003-2331-4718 China (CRIRP); Finland (SA); Japan (NIPR); Japan (ISEE); Norway (NFR); Sweden (VR); UnitedKingdom (UKRI); China National Science Foundation [42074189]; Shanghai Science and Technology Committee [19ZR1403900] China (CRIRP); Finland (SA); Japan (NIPR); Japan (ISEE); Norway (NFR)(Research Council of Norway); Sweden (VR)(Swedish Research Council); UnitedKingdom (UKRI)(UK Research & Innovation (UKRI)); China National Science Foundation(National Natural Science Foundation of China (NSFC)); Shanghai Science and Technology Committee(Shanghai Science & Technology Committee) EISCAT is an international association supported by research organizations in China (CRIRP), Finland (SA), Japan (NIPR and ISEE), Norway (NFR),Sweden (VR), and the UnitedKingdom (UKRI). The authors from Fudan University are also supported by China National Science Foundation (42074189) and Shanghai Science and Technology Committee with Grant 19ZR1403900. 23 0 0 3 3 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. AUG 16 2022.0 49 15 e2022GL099390 10.1029/2022GL099390 0.0 10 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology 3U5TS 2023-03-23 WOS:000841034200001 0
J Liu, FX; Cheng, X; Zhong, SS; Liu, C; Jolkkonen, J; Zhang, XC; Liang, YF; Liu, ZY; Zhao, CS Liu, Fangxi; Cheng, Xi; Zhong, Shanshan; Liu, Chang; Jolkkonen, Jukka; Zhang, Xiuchun; Liang, Yifan; Liu, Zhouyang; Zhao, Chuansheng Communications Between Peripheral and the Brain-Resident Immune System in Neuronal Regeneration After Stroke FRONTIERS IN IMMUNOLOGY English Review brain; peripheral; immune system; gut microbiota; communication; stroke; neuronal regeneration COLONY-STIMULATING FACTOR; ACUTE ISCHEMIC-STROKE; REGULATORY T-CELLS; NEURAL STEM-CELLS; CHAIN FATTY-ACIDS; NF-KAPPA-B; FUNCTIONAL RECOVERY; ALTERNATIVE ACTIVATION; MACROPHAGE ACTIVATION; MODULATOR FINGOLIMOD Cerebral ischemia may cause irreversible neural network damage and result in functional deficits. Targeting neuronal repair after stroke potentiates the formation of new connections, which can be translated into a better functional outcome. Innate and adaptive immune responses in the brain and the periphery triggered by ischemic damage participate in regulating neural repair after a stroke. Immune cells in the blood circulation and gut lymphatic tissues that have been shaped by immune components including gut microbiota and metabolites can infiltrate the ischemic brain and, once there, influence neuronal regeneration either directly or by modulating the properties of brain-resident immune cells. Immune-related signalings and metabolites from the gut microbiota can also directly alter the phenotypes of resident immune cells to promote neuronal regeneration. In this review, we discuss several potential mechanisms through which peripheral and brain-resident immune components can cooperate to promote first the resolution of neuroinflammation and subsequently to improved neural regeneration and a better functional recovery. We propose that new insights into discovery of regulators targeting pro-regenerative process in this complex neuro-immune network may lead to novel strategies for neuronal regeneration. [Liu, Fangxi; Cheng, Xi; Zhong, Shanshan; Liu, Chang; Zhang, Xiuchun; Liang, Yifan; Liu, Zhouyang; Zhao, Chuansheng] China Med Univ, Neurol, Hosp 1, Shenyang, Peoples R China; [Jolkkonen, Jukka] Univ Eastern Finland, AI Virtanen Inst, Kuopio, Finland; [Jolkkonen, Jukka] Univ Eastern Finland, Inst Clin Med Neurol, Kuopio, Finland; [Zhao, Chuansheng] China Med Univ, Stroke Ctr, Hosp 1, Shenyang, Peoples R China China Medical University; University of Eastern Finland; University of Eastern Finland; China Medical University Zhao, CS (corresponding author), China Med Univ, Neurol, Hosp 1, Shenyang, Peoples R China.;Zhao, CS (corresponding author), China Med Univ, Stroke Ctr, Hosp 1, Shenyang, Peoples R China. cszhao@cmu.edu.cn liu, fangxi/0000-0003-3888-7922 Epilepsy Research Fund of China Association against Epilepsy [2016010]; Key Project of Liaoning Key Research and Development Plan [2017225005]; Support Programs for Young Scientific and Technological Innovation Talents of Shenyang [2400018022]; China Medical University High-level Innovation Team Training Plan [2017CXTD02] Epilepsy Research Fund of China Association against Epilepsy; Key Project of Liaoning Key Research and Development Plan; Support Programs for Young Scientific and Technological Innovation Talents of Shenyang; China Medical University High-level Innovation Team Training Plan This work was supported by the following grants: Epilepsy Research Fund of China Association against Epilepsy (No. 2016010), the Key Project of Liaoning Key Research and Development Plan (No. 2017225005), the Support Programs for Young Scientific and Technological Innovation Talents of Shenyang (No. 2400018022), and the China Medical University High-level Innovation Team Training Plan (No. 2017CXTD02). 143 12 12 3 9 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. SEP 18 2020.0 11 1931 10.3389/fimmu.2020.01931 0.0 14 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology NY5XK 33042113.0 gold, Green Accepted 2023-03-23 WOS:000576461800001 0
J Zanchetta, A; Zecchetto, S Zanchetta, Andrea; Zecchetto, Stefano Wind direction retrieval from Sentinel-1 SAR images using ResNet REMOTE SENSING OF ENVIRONMENT English Article Synthetic aperture radar; Wind direction; Convolutional neural network; Deep residual network; Coastal areas SEA; MODEL This paper introduces a novel approach to estimate the wind direction over the sea from Synthetic Aperture Radar (SAR) images without any external information. The method employs deep residual network (ResNet), a variant of Convolutional Neural Network, to obtain high resolution (2 km by 2 km) aliased wind direction fields. Forty-seven SAR images of the European Space Agency satellites Sentinel-1 have been processed with ResNet, previously trained with other fifteen images. The areas of interest are the Mediterranean Sea and the Persian Gulf, two regional seas where the SAR images often present complex patterns associated to the wind field spatial structure reporting traces of the interaction with coastal orography, hence valuable test sites to evaluate the performance of the methodology here proposed. Statistical analysis was carried out comparing the SAR-derived wind directions with those from ECMWF atmospheric model, ASCAT scatterometer and in-situ gauges. It reports biases beta of -1.1 degrees, 2.4 degrees and -4.6 degrees respectively, and centered root mean square difference cRMSd<21 degrees, consistent with the benchmark obtained comparing scatterometer with ECMWF wind directions over the areas imaged by SAR (beta 2.1 degrees, cRMSd=19 degrees). These results are relevant because they include the coastal data not accounted in the benchmark. Analysis of selected cases showed that SAR-derived wind fields reproduce meteorological situations characterized by strong divergence. Notably, our ResNet is able to estimate the wind direction even in the absence of wind streaks on the SAR images and in presence of convective turbulence structures, atmospheric lee waves, and ships. Furthermore, the model is also able to derive the wind field over small areas, as the example of Venice lagoon has shown. Detailed analysis of selected cases raised the issue of the lack of data with true spatial resolution of approximate to 2 km and within half hour from the satellite pass time necessary for exhaustive comparisons. [Zanchetta, Andrea] Hong Kong Baptist Univ, Dept Mus, Kowloon Tong, Sing Tao Bldg, Hong Kong, Peoples R China; [Zecchetto, Stefano] CNR, Ist Sci Polari, Corso Stati Uniti 5, I-35127 Padua, Italy; [Zecchetto, Stefano] Persian Gulf Univ, Dept Elect Engn, Shahid Mahini St, Bushehr 7516913817, Iran Hong Kong Baptist University; Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Polari (ISP-CNR); Persian Gulf University Zecchetto, S (corresponding author), CNR, Ist Sci Polari, Corso Stati Uniti 5, I-35127 Padua, Italy. stefano.zecchetto@cnr.it ZECCHETTO, STEFANO/O-2523-2015 ZECCHETTO, STEFANO/0000-0003-4633-1238; Zanchetta, Andrea/0000-0001-9377-3923 52 13 13 6 20 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. FEB 2021.0 253 112178 10.1016/j.rse.2020.112178 0.0 13 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology PN2OX 2023-03-23 WOS:000604325300007 0
J Liu, KL; Wei, ZB; Zhang, CH; Shang, YL; Teodorescu, R; Han, QL Liu, Kailong; Wei, Zhongbao; Zhang, Chenghui; Shang, Yunlong; Teodorescu, Remus; Han, Qing-Long Towards Long Lifetime Battery: AI-Based Manufacturing and Management IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Review Artificial intelligence; battery health management; battery life diagnostic; battery manufacturing; smart battery LITHIUM-ION-BATTERY; STATE-OF-HEALTH; MODEL-PREDICTIVE CONTROL; CHARGING STRATEGY; ELECTRIC VEHICLES; CELL STATE; TEMPERATURE; OPTIMIZATION; ALGORITHM; SYSTEMS Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels. [Liu, Kailong] Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England; [Wei, Zhongbao] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China; [Zhang, Chenghui; Shang, Yunlong] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China; [Teodorescu, Remus] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Han, Qing-Long] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia University of Warwick; Beijing Institute of Technology; Shandong University; Aalborg University; Swinburne University of Technology Wei, ZB (corresponding author), Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China. Kailong.Liu@warwick.ac.uk; weizb@bit.edu.cn; zchui@sdu.edu.cn; yshang@sdu.edu.cn; ret@et.aau.dk; qhan@swin.edu.au Liu, Kailong/Y-1797-2019 Liu, Kailong/0000-0002-3564-6966 UK HVM Catapult project [8248 CORE]; National Natural Science Foundation of China [52072038, 62122041] UK HVM Catapult project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the UK HVM Catapult project (8248 CORE), the National Natural Science Foundation of China (52072038, 62122041). 154 28 28 45 61 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. JUL 2022.0 9 7 1139 1165 10.1109/JAS.2022.105599 0.0 27 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems 2P6CH 2023-03-23 WOS:000819826100006 0
J Li, WJ; Meng, WZ; Au, MH Li, Wenjuan; Meng, Weizhi; Au, Man Ho Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Collaborative intrusion detection; Semi-supervised learning; False alarm reduction; Detection performance; Internet of things TRUST MANAGEMENT; CLASSIFICATION; NETWORKS; INTERNET; ATTACKS; THINGS Collaborative intrusion detection systems (CIDSs) are developing to improve the detection performance of a single detector in Internet of Things (IoT) networks, through exchanging and sharing data. For anomaly detection, machine learning is an important and essential tool to help identify the deviation between current events and pre-built profile. For a traditional supervised learning classifier, there is a need to provide training examples with ground-truth labels in advance. However, labeled instances are quite limited in real-world loT scenarios, while unlabeled data/instances are widely available. This is because data labeling is a very expensive process that requires huge human efforts and knowledge inputs. To mitigate this issue, the use of semi-supervised learning algorithms is a promising solution, which can leverage unlabeled data to label data automatically without human intervention. In this work, we focus on semi-supervised learning and design DAS-CIDS, by applying disagreement-based semi-supervised learning algorithm for CIDSs. In the evaluation, we investigate the performance of DAS-CIDS using both datasets and in real IoT network environments, in the aspects of both detection performance and false alarm reduction. The experimental results show that as compared with traditional supervised classifiers, our approach is more effective in detecting intrusions and reducing false alarms by automatically leveraging unlabeled data. [Li, Wenjuan; Meng, Weizhi] Guangzhou Univ, Sch Comp Sci, Guangzhou, Peoples R China; [Li, Wenjuan; Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Au, Man Ho] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China Guangzhou University; Technical University of Denmark; University of Hong Kong Meng, WZ (corresponding author), Guangzhou Univ, Sch Comp Sci, Guangzhou, Peoples R China. weme@dtu.dk Li, Wenjuan/0000-0003-3745-5669 National Natural Science Foundation of China [61802077]; H2020-SU-ICT-03-2018: CyberSec4Europe National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); H2020-SU-ICT-03-2018: CyberSec4Europe This work was supported by National Natural Science Foundation of China (No. 61802077), and partially supported by H2020-SU-ICT-03-2018: CyberSec4Europe. 64 44 44 2 26 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 J NETW COMPUT APPL J. Netw. Comput. Appl. JUL 1 2020.0 161 102631 10.1016/j.jnca.2020.102631 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science LP4RV 2023-03-23 WOS:000534307300002 0
J Du, MH; Bi, SL; Zhang, XF; Li, YG; Li, TD; Shi, RJ Du, Minghao; Bi, Shaolan; Zhang, Xianfei; Li, Yaguang; Li, Tanda; Shi, Ruijie Mode angular degree identification in subgiant stars with convolutional neural networks based on power spectrum MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article asteroseismology; methods: data analysis; techniques: image processing; stars: statistics OSCILLATION PARAMETERS; RED GIANTS; SOLAR; ASTEROSEISMOLOGY The identification of the angular degrees l of oscillation modes is essential for asteroseismology and it depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial (l = 0) mode frequencies are distributed linearly in frequency, while non-radial (l >= 1) modes are p-g mixed modes that have a complex distribution in frequency that increases the difficulty of identifying l. In this study, we trained a one-dimensional convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved 95 per cent accuracy for Kepler data. [Du, Minghao; Bi, Shaolan; Zhang, Xianfei; Li, Yaguang; Shi, Ruijie] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China; [Li, Yaguang] Univ Sydney, Sch Phys, Sydney Inst Astron SIfA, Sydney, NSW 2006, Australia; [Li, Yaguang; Li, Tanda] Aarhus Univ, Stellar Astrophys Ctr, Dept Phys & Astron, Ny Munkegade 120, DK-8000 Aarhus C, Denmark; [Li, Tanda] Univ Birmingham, Sch Phys & Astron, Birmingham B15 2TT, W Midlands, England Beijing Normal University; University of Sydney; Aarhus University; University of Birmingham Bi, SL (corresponding author), Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China. bisl@bnu.edu.cn Li, Tanda/HJP-6535-2023 Li, Tanda/0000-0001-6396-2563; Li, Yaguang/0000-0003-3020-4437 National Natural Science Foundation of China (NSFC) [U2031203, U1631236]; Chinese Academy of Sciences (CAS) [U2031203, U1631236]; European Research Council (ERC) under the European Union'sHorizon 2020 research and innovation programme [804752] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS)(Chinese Academy of Sciences); European Research Council (ERC) under the European Union'sHorizon 2020 research and innovation programme(European Research Council (ERC)) This work is supported by the Joint Research Fund in Astronomy (U2031203 and U1631236) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and the Chinese Academy of Sciences (CAS). This work has also received funding from the European Research Council (ERC) under the European Union'sHorizon 2020 research and innovation programme (CartographY GA. 804752). We would also like to thank Jie Yu and the group at the Beijing Normal University for discussions. 59 3 3 1 2 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. FEB 2021.0 501 1 614 622 10.1093/mnras/staa3642 0.0 9 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics PT2UX Green Published, Green Submitted 2023-03-23 WOS:000608474800049 0
C Bonner, S; Atapour-Abarghouei, A; Jackson, PT; Brennan, J; Kureshi, I; Theodoropoulos, G; McGough, AS; Obara, B Baru, C; Huan, J; Khan, L; Hu, XH; Ak, R; Tian, Y; Barga, R; Zaniolo, C; Lee, K; Ye, YF Bonner, Stephen; Atapour-Abarghouei, Amir; Jackson, Philip T.; Brennan, John; Kureshi, Ibad; Theodoropoulos, Georgios; McGough, Andrew Stephen; Obara, Boguslaw Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) IEEE International Conference on Big Data English Proceedings Paper IEEE International Conference on Big Data (Big Data) DEC 09-12, 2019 Los Angeles, CA IEEE Comp Soc,IEEE,Baidu,Very,Ankura representation learning; dynamic link prediction Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently disregarded during the learning process, resulting in suboptimal performance on certain temporal inference tasks. To combat this, we introduce Temporal Neighbourhood Aggregation (TNA), a novel vertex representation model architecture designed to capture both topological and temporal information to directly predict future graph states. Our model exploits hierarchical recurrence at different depths within the graph to enable exploration of changes in temporal neighbourhoods, whilst requiring no additional features or labels to he present. The final vertex representations are created using variational sampling and are optimised to directly predict the next graph in the sequence. Our claims are supported by experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, outperforming them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters. [Bonner, Stephen; Jackson, Philip T.; Brennan, John; Obara, Boguslaw] Univ Durham, Dept Comp Sci, Durham, England; [Atapour-Abarghouei, Amir; McGough, Andrew Stephen] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England; [Theodoropoulos, Georgios] SUSTech, Sch Comp Sci & Engn, Shenzhen, Peoples R China; [Kureshi, Ibad] Inlecom Syst, Brussels, Belgium Durham University; Newcastle University - UK; Southern University of Science & Technology Bonner, S (corresponding author), Univ Durham, Dept Comp Sci, Durham, England. s.a.r.bonner@durham.ac.uk; amir.atapour-abarghouei@newcastle.ac.uk; p.t.g.jackson@durham.ac.uk; j.d.brennan@durham.ac.uk; ibad.kureshi@inlecomsystems.com; georgios@sustec.cdu.cn; stephen.mcgough@newcastle.ac.uk; boguslaw.obara@durham.ac.uk Atapour-Abarghouei, Amir/AAV-8017-2020 Atapour-Abarghouei, Amir/0000-0002-4242-4579; Brennan, John/0000-0002-7274-5920; Obara, Boguslaw/0000-0003-4084-7778; McGough, Stephen/0000-0001-5626-0934; Theodoropoulos, Georgios/0000-0002-7448-5886 NVIDIA Corporation; Engineering and Physical Sciences Research Council UK (EPSRC) NVIDIA Corporation; Engineering and Physical Sciences Research Council UK (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. Additionally we thank the Engineering and Physical Sciences Research Council UK (EPSRC) for funding. 42 10 10 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2639-1589 978-1-7281-0858-2 IEEE INT CONF BIG DA 2019.0 5336 5345 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP4WK Green Accepted, Green Submitted 2023-03-23 WOS:000554828705060 0
J Ke, J; Alieva, T; Oktem, FS; Silveira, PEX; Wetzstein, G; Willomitzer, F Ke, Jun; Alieva, Tatiana; Oktem, Figen S.; Silveira, Paulo E. X.; Wetzstein, Gordon; Willomitzer, Florian Computational optical sensing and imaging 2021: feature issue introduction OPTICS EXPRESS English Article This Feature Issue includes 2 reviews and 34 research articles that highlight recent works in the field of Computational Optical Sensing and Imaging. Many of the works were presented at the 2021 OSA Topical Meeting on Computational Optical Sensing and Imaging, held virtually from July 19 to July 23, 2021. Articles in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue. (C) 2022 Optics Publishing Group under the terms of the Optica Open Access Publishing Agreement [Ke, Jun] Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China; [Alieva, Tatiana] Univ Complutense Madrid, Dept Opt, Madrid 28040, Spain; [Oktem, Figen S.] Middle East Tech Univ METU, Dept Elect Engn, TR-06800 Ankara, Turkey; [Silveira, Paulo E. X.] Occipital Inc, 1155 Canyon Blvd, Boulder, CO 80302 USA; [Wetzstein, Gordon] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Willomitzer, Florian] Northwestern Univ, Dept Elect & Comp Engn, Evanston, IL 60208 USA Beijing Institute of Technology; Complutense University of Madrid; Middle East Technical University; Stanford University; Northwestern University Ke, J (corresponding author), Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China. jke@bit.edu.cn Oktem, Figen S./A-9569-2018; Alieva, Tatiana/K-1319-2014 Oktem, Figen S./0000-0002-7882-5120; Ke, Jun/0000-0001-6027-7659; Alieva, Tatiana/0000-0002-7638-9150 36 0 0 6 22 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express MAR 28 2022.0 30 7 11394 11399 10.1364/OE.456132 0.0 6 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics 0L7QQ 35473085.0 Green Accepted, gold 2023-03-23 WOS:000781665200092 0
C Hagenbuchner, M; Tsoi, AC; Scarselli, F; Zhang, SJ IEEE Hagenbuchner, Markus; Tsoi, Ah Chung; Scarselli, Franco; Zhang, Shu Jia A Fully Recursive Perceptron Network Architecture 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) English Proceedings Paper IEEE Symposium Series on Computational Intelligence (IEEE SSCI) NOV 27-DEC 01, 2017 Honolulu, HI IEEE,IEEE Computat Intelligence Soc Artificial Neural Network; Supervised Learning; Parametric Learning Systems; Recursive Systems DEEP ARCHITECTURES; NEURAL-NETWORKS This paper introduces a fully recursive perceptron network (FRPN) architecture as an alternative to multilayer perceptron (MLP) with multiple hidden layers networks, popularly known as deep neural networks. The FRPN consists of an input layer, an output layer, and only one hidden layer in which the hidden layer neurons are fully connected with algebraic (instantaneous) connections, and not delayed connections. The FRPN is particularly attractive as an alternative to deep MLP since the FRPN eliminates the need of obtaining the number of hidden layers and the number of neurons per hidden layer. Some insight into the operational mechanisms of the FRPN is obtained through an application to a practical learning problem, viz., the handwritten digit recognition problem. [Hagenbuchner, Markus; Zhang, Shu Jia] Univ Wollongong, Sch Comp & IT, Wollongong, NSW, Australia; [Tsoi, Ah Chung] Chu Hai Coll Higher Edu, Dept Comp Sci, Tuen Mun, Hong Kong, Peoples R China; [Scarselli, Franco] Univ Siena, Dep Informat Engn, Siena, Italy; [Tsoi, Ah Chung] Univ Wollongong, Wollongong, NSW, Australia University of Wollongong; University of Siena; University of Wollongong Hagenbuchner, M (corresponding author), Univ Wollongong, Sch Comp & IT, Wollongong, NSW, Australia. markus@uow.edu.au; actsoi@chuhai.edu.hk; franco@ing.unisi.it; stephanie724@gmail.com Tsoi, Ah Chung/0000-0003-2904-7008; Hagenbuchner, Markus/0000-0002-4884-753X Australian Research Council [DP110103376]; Fundo para o Desenvolvimento das Ciencias e da Tecnologia, Macau SAR [059/2010/A3] Australian Research Council(Australian Research Council); Fundo para o Desenvolvimento das Ciencias e da Tecnologia, Macau SAR The authors wish to thank Australian Research Council for partial financial support in the form of a Discovery Project Grant: DP110103376 and the Fundo para o Desenvolvimento das Ciencias e da Tecnologia, Macau SAR, for financial support in the form of a project grant number: 059/2010/A3, which made this research possible. 24 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-2726-6 2017.0 288 295 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ8IF 2023-03-23 WOS:000428251400041 0
J Moorthy, SMK; Bao, YF; Calders, K; Schnitzer, SA; Verbeeck, H Moorthy, Sruthi M. Krishna; Bao, Yunfei; Calders, Kim; Schnitzer, Stefan A.; Verbeeck, Hans Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Automated liana extraction; Lianas; Terrestial LiDAR; Machine learning; Python package; Tropical forests TREE REGENERATION; LASER; DIVERSITY; ABUNDANCE; PROFILES; BIOMASS Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest. [Moorthy, Sruthi M. Krishna; Calders, Kim; Verbeeck, Hans] Univ Ghent, CAVElab Computat & Appl Vegetat Ecol, B-9000 Ghent, Belgium; [Bao, Yunfei] Beijing Inst Space Mech & Elect, 104 Rd Youyi, Beijing 100094, Peoples R China; [Schnitzer, Stefan A.] Marquette Univ, Dept Biol Sci, Milwaukee, WI 53201 USA; [Schnitzer, Stefan A.] Smithsonian Trop Res Inst, Apartado 0843-03092, Balboa, Ancon, Panama Ghent University; Marquette University; Smithsonian Institution; Smithsonian Tropical Research Institute Moorthy, SMK (corresponding author), Univ Ghent, CAVElab Computat & Appl Vegetat Ecol, B-9000 Ghent, Belgium. sruthi.krishnamoorthyparvathi@ugent.be; kim.calders@ugent.be; Hans.Verbeeck@ugent.be Verbeeck, Hans/0000-0003-1490-0168; Krishna Moorthy Parvathi, Sruthi/0000-0002-6838-2880 European Research Council [637643]; National Science Foundation of China [41401410, 41611530544]; National Science Foundation [NSF-IOS 1558093, NSF-DEB 1822473]; BELSPO (Belgian Science Policy Office) [SR/02/355] European Research Council(European Research Council (ERC)European Commission); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Foundation(National Science Foundation (NSF)); BELSPO (Belgian Science Policy Office)(Belgian Federal Science Policy Office) The research leading to these results was funded by the European Research Council Starting Grant 637643 (TREECLIMBERS), National Science Foundation of China grants: 41401410 and 41611530544 and National Science Foundation grants: NSF-IOS 1558093 and NSF-DEB 1822473. KC is funded by BELSPO (Belgian Science Policy Office) in the frame of the STEREO III programme-project 3D-FOREST (SR/02/355). We thank the Smithsonian Tropical Research Institute (STRI) for their support during the fieldwork in Gigante and Jerome Chave, Philippe Gaucher and other members of the Nouragues Ecological Research Station managed by CNRS Guyane, for permitting us to collect data from the plots. In addition, we are grateful to Andreas Mastelinck, Philippe Gaucher, and Florian Jeanne for their help during our stay at the Nouragues research station. The authors declare no conflict of interest. 48 16 16 3 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. AUG 2019.0 154 114 126 10.1016/j.isprsjprs.2019.05.011 0.0 13 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology IQ3SN 31417229.0 Green Published, Green Accepted, hybrid 2023-03-23 WOS:000480671500009 0
J Li, HY; Zhao, HT; Wei, ML; Ruan, HX; Shuang, Y; Cui, TJ; del Hougne, P; Li, LL Li, Hao-Yang; Zhao, Han-Ting; Wei, Meng-Lin; Ruan, Heng-Xin; Shuang, Ya; Cui, Tie Jun; del Hougne, Philipp; Li, Lianlin Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing PATTERNS English Article NEURAL-NETWORK; METASURFACE; METAMATERIALS Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of learned sensing'' applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden. [Li, Hao-Yang; Zhao, Han-Ting; Wei, Meng-Lin; Ruan, Heng-Xin; Shuang, Ya; Li, Lianlin] Peking Univ, Dept Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China; [Cui, Tie Jun] Southeast Univ, State Key Lab Millimeter Waves, Nanjing 210096, Peoples R China; [del Hougne, Philipp] Univ Cote dAzur, Inst Phys Nice, CNRS UMR 7010, F-06108 Nice, France Peking University; Southeast University - China; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); UDICE-French Research Universities; Universite Cote d'Azur Li, LL (corresponding author), Peking Univ, Dept Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China.;Cui, TJ (corresponding author), Southeast Univ, State Key Lab Millimeter Waves, Nanjing 210096, Peoples R China.;del Hougne, P (corresponding author), Univ Cote dAzur, Inst Phys Nice, CNRS UMR 7010, F-06108 Nice, France. tjcui@seu.edu.cn; philipp.delhougne@gmail.com; lianlin.li@pku.edu.cn li, lianlin/0000-0002-2295-4425; Zhao, Hanting/0000-0001-5940-1069 National Key Research and Development Program of China [2017YFA0700201, 2017YFA0700202, 2017YFA0700203]; National Natural Science Foundation of China [61471006, 61631007, 61571117] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China (2017YFA0700201, 2017YFA0700202, and 2017YFA0700203) and in part from the National Natural Science Foundation of China (61471006, 61631007, and 61571117). P.d.H. acknowledges fruitful discussions with the other authors of del Hougne et al.22 50 53 56 14 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns APR 10 2020.0 1 1 100006 10.1016/j.patter.2020.100006 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science SH0HS 33205083.0 Green Accepted, gold, Green Submitted 2023-03-23 WOS:000653819700006 0
J Chen, XY; Moughames, J; Ji, QX; Martinez, JAI; Tan, HF; Adrar, S; Laforge, N; Cote, JM; Euphrasie, S; Ulliac, G; Kadic, M; Laude, V Chen, Xueyan; Moughames, Johnny; Ji, Qingxiang; Martinez, Julio Andres Iglesias; Tan, Huifeng; Adrar, Samia; Laforge, Nicolas; Cote, Jean-Marc; Euphrasie, Sebastien; Ulliac, Gwenn; Kadic, Muamer; Laude, Vincent Optimal isotropic, reusable truss lattice material with near-zero Poisson's ratio EXTREME MECHANICS LETTERS English Article Isotropic composite; Reusable material; Near-zero Poisson's ratio; Optimal design; Truss lattice materials MECHANICAL METAMATERIALS; DESIGN; OPTIMIZATION; ULTRALIGHT; ALGORITHM; BEHAVIOR; SYSTEMS Cork is a natural amorphous material with near-zero Poisson's ratio that is ubiquitously used for sealing glass bottles. It is an anisotropic, transversally isotropic, composite that can hardly be scaled down. Here, we propose a new class of isotropic and reusable cork-like metamaterial that is designed from a hybrid truss-lattice material to show an isotropic Poisson's ratio close to zero. Optimization is conducted using a multi-objective genetic algorithm, assisted by an elliptical basis function neural network, and coupled with finite element simulations. The optimal micro-structured metamaterial, fabricated by two-photon lithography with a lattice constant of 300 mu m, has an almost isotropic Poisson's ratio smaller than 0.08 in all directions. It can recover 96.6% of its original shape after a compressional test exceeding 20% strain. (C) 2020 Published by Elsevier Ltd. [Chen, Xueyan; Ji, Qingxiang; Tan, Huifeng] Harbin Inst Technol, Natl Key Lab Sci & Technol Adv Composites Special, 92 Xidazhi St, Harbin 150001, Peoples R China; [Chen, Xueyan; Moughames, Johnny; Ji, Qingxiang; Martinez, Julio Andres Iglesias; Adrar, Samia; Laforge, Nicolas; Cote, Jean-Marc; Euphrasie, Sebastien; Ulliac, Gwenn; Kadic, Muamer; Laude, Vincent] Univ Bourgogne Franche Comte, CNRS, Inst FEMTO ST, F-25000 Besancon, France Harbin Institute of Technology; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte Tan, HF (corresponding author), Harbin Inst Technol, Natl Key Lab Sci & Technol Adv Composites Special, 92 Xidazhi St, Harbin 150001, Peoples R China. tanhf@hit.edu.cn Kadic, Muamer/J-9478-2015; Laude, Vincent/C-4484-2008 Kadic, Muamer/0000-0002-4692-5696; Laude, Vincent/0000-0001-8930-8797; Euphrasie, Sebastien/0000-0002-9060-4259; jm, cote/0000-0001-6202-8410 Foreign Short-term Visiting Program for Doctoral Students at HIT, PR China; EIPHI Graduate School, France [ANR-17-EURE-0002]; French Investissements d'Avenir program, project ISITE-BFC [ANR-15IDEX-03]; French RENATECH network and its FEMTO-ST technological facility, France Foreign Short-term Visiting Program for Doctoral Students at HIT, PR China; EIPHI Graduate School, France; French Investissements d'Avenir program, project ISITE-BFC(French National Research Agency (ANR)); French RENATECH network and its FEMTO-ST technological facility, France This work was supported by Foreign Short-term Visiting Program for Doctoral Students at HIT, PR China. N.L., S.M., J.M., M.K. and V.L. acknowledge support by the EIPHI Graduate School, France (contract ``ANR-17-EURE-0002'') and the French Investissements d'Avenir program, project ISITE-BFC (contract ANR-15IDEX-03). This work was partly supported by the French RENATECH network and its FEMTO-ST technological facility, France. 55 22 22 14 62 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4316 EXTREME MECH LETT EXTREME MECH. LETT. NOV 2020.0 41 101048 10.1016/j.eml.2020.101048 0.0 9 Engineering, Mechanical; Materials Science, Multidisciplinary; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science; Mechanics OV1MJ Green Submitted 2023-03-23 WOS:000591982700012 0
J Liang, JY; Guo, JL; Liu, X; Lao, SY Liang, Jingyun; Guo, Jinlin; Liu, Xin; Lao, Songyang Fine-Grained Image Classification With Gaussian Mixture Layer IEEE ACCESS English Article Fine-grained image categorization; Gaussian mixture model; convolutional neural network MEANS CLUSTERING-ALGORITHM; SCALE Fine-grained image classification aims at recognizing different subordinates in one basic-level category, for example, distinguishing species of birds. Compared with basic-level classification, it has both low inter-class and high intra-class variances. Therefore, utilization of discriminative parts is crucial for fine-grained classification. In this paper, we propose a Gaussian mixture model, which fuses part features by Gaussian mixture layer. More specifically, it first generates a set of part proposals by selective search. Then, we extract image feature maps from mid-layers of convolutional neural networks. Feature maps and part proposals are used for calculating part features via spatial pyramid pooling. Next, Gaussian mixture layer treats part features as data points and uses several Gaussian components to model their distribution. It finds clusters for input and generates output features based on combination of cluster center. Finally, the output feature can represent the whole image and is used for classification. Training process of the model consists of two loops. The outer loop is the optimization of the whole network, and the inner loop is about the EM algorithm used in Gaussian mixture layer. Experiments demonstrate higher or similar performance on four fine-grained data sets compared with the state-of-the-arts. More discussions on Gaussian mixture layer are also provided. [Liang, Jingyun; Guo, Jinlin; Lao, Songyang] Natl Univ Def Technol, Coll Syst Engn, Changsha 410072, Hunan, Peoples R China; [Liu, Xin] Univ Oulu, Comp Sci & Engn Dept, FIN-90570 Oulu, Finland National University of Defense Technology - China; University of Oulu Liu, X (corresponding author), Univ Oulu, Comp Sci & Engn Dept, FIN-90570 Oulu, Finland. linuxsino@gmail.com Liu, Xin/AAD-5166-2019 Liu, Xin/0000-0002-2242-6139 National Natural Science Foundation of China [61571453] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Project 61571453. 67 6 6 1 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 53356 53367 10.1109/ACCESS.2018.2871621 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GX7WB Green Submitted, gold 2023-03-23 WOS:000447989400001 0
C Chen, JQ; Tandon, N; Hariman, CD; de Melo, G Groth, P; Simperl, E; Gray, A; Sabou, M; Krotzsch, M; Lecue, F; Flock, F; Gil, Y Chen, Jiaqiang; Tandon, Niket; Hariman, Charles Darwis; de Melo, Gerard WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge SEMANTIC WEB - ISWC 2016, PT I Lecture Notes in Computer Science English Proceedings Paper 15th International Semantic Web Conference (ISWC) OCT 17-21, 2016 Kobe, JAPAN Semant Web Sci Assoc,IBM,Semant Software,Oracle,IOS Press,Recruit Technologies Co Ltd,Fujitsu,NTT Resonant,SYSTAP Metaphacts,Hitachi,Rakuten Inst Technol,Yahoo Japan,Google Despite the emergence and growth of numerous large knowledge graphs, many basic and important facts about our everyday world are not readily available on the Web. To address this, we present Web-Brain, a new approach for harvesting commonsense knowledge that relies on joint learning from Web-scale data to fill gaps in the knowledge acquisition. We train a neural network model to learn relations based on large numbers of textual patterns found on the Web. At the same time, the model learns vector representations of general word semantics. This joint approach allows us to generalize beyond the explicitly extracted information. Experiments show that we can obtain representations of words that reflect their semantics, yet also allow us to capture conceptual relationships and commonsense knowledge. [Chen, Jiaqiang] Tsinghua Univ, IIIS, Beijing, Peoples R China; [Tandon, Niket] Allen Inst Artificial Intelligence, Seattle, WA USA; [Hariman, Charles Darwis] Max Planck Inst Informat, Saarbrucken, Germany; [de Melo, Gerard] Rutgers State Univ, Piscataway, NJ 08901 USA Tsinghua University; Max Planck Society; Rutgers State University New Brunswick de Melo, G (corresponding author), Rutgers State Univ, Piscataway, NJ 08901 USA. gdm@demelo.org de Melo, Gerard/P-7886-2014 de Melo, Gerard/0000-0002-2930-2059 37 2 2 1 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-46523-4; 978-3-319-46522-7 LECT NOTES COMPUT SC 2016.0 9981 102 118 10.1007/978-3-319-46523-4_7 0.0 17 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG4TO 2023-03-23 WOS:000389086500007 0
J Cheung, DHT; Wong, KWK; Hannuksela, OA; Li, TGF; Ho, S Cheung, Damon H. T.; Wong, Kaze W. K.; Hannuksela, Otto A.; Li, Tjonnie G. F.; Ho, Shirley Testing the robustness of simulation-based gravitational-wave population inference PHYSICAL REVIEW D English Article METALLICITY; PROGENITORS; INFORMATION; BINARIES Gravitational-wave population studies have become more important in gravitational-wave astronomy because of the rapid growth of the observed catalog. In recent studies, emulators based on different machine learning techniques are used to emulate the outcomes of the population synthesis simulation quickly. In this study, we benchmark the performance of two emulators that learn the truncated power-law phenomenological model by using Gaussian process regression and normalizing flows technique to see which one is a more capable likelihood emulator in the population inference. We benchmark the characteristic of the emulators by comparing their performance in the population inference to the phenomenological model using mock and real observation data. Our results suggest that the normalizing flows emulator can recover the posterior distribution by using the phenomenological model in the population inference with up to 300 mock injections. The normalizing flows emulator also underestimates the uncertainty for some posterior distributions in the population inference on real observation data. On the other hand, the Gaussian process regression emulator has poor performance on the same task and can only be used effectively in low-dimension cases. [Cheung, Damon H. T.; Hannuksela, Otto A.; Li, Tjonnie G. F.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Wong, Kaze W. K.; Ho, Shirley] Flatiron Inst, Ctr Comp Astrophys, New York, NY 10010 USA; [Li, Tjonnie G. F.] Katholieke Univ Leuven, Inst Theoret Phys, Celestijnenlaan 200D, B-3001 Leuven, Belgium; [Li, Tjonnie G. F.] Katholieke Univ Leuven, Dept Elect Engn ESAT, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; [Ho, Shirley] Princeton Univ, Dept Astrophys Sci, Princeton, NJ 08540 USA; [Ho, Shirley] Carnegie Mellon Univ, Phys Dept, Pittsburgh, PA 15213 USA; [Ho, Shirley] NYU, Dept Phys, New York, NY 10012 USA Chinese University of Hong Kong; KU Leuven; KU Leuven; Princeton University; Carnegie Mellon University; New York University Cheung, DHT (corresponding author), Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China. damoncheung@link.cuhk.edu.hk Wong, Kaze W. K./0000-0001-8432-7788 Simons Foundation; Research Grants Council of the Hong Kong; Croucher Foundation of Hong Kong and Research Committee of the Chinese University of Hong Kong; National Science Foundation; French Centre National de Recherche Scientifique (CNRS); Italian Istituto Nazionale della Fisica Nucleare (INFN); NSF's LIGO Laboratory; Dutch Nikhef; Polish institute; Hungarian institute; [CUHK 14306218]; [CUHK 14306419] Simons Foundation; Research Grants Council of the Hong Kong(Hong Kong Research Grants Council); Croucher Foundation of Hong Kong and Research Committee of the Chinese University of Hong Kong; National Science Foundation(National Science Foundation (NSF)); French Centre National de Recherche Scientifique (CNRS)(Centre National de la Recherche Scientifique (CNRS)); Italian Istituto Nazionale della Fisica Nucleare (INFN); NSF's LIGO Laboratory; Dutch Nikhef(Nikhef Theory Group); Polish institute; Hungarian institute; ; K. W. K. W. and S. H. are supported by the Simons Foundation. O. A. H. was partially supported by grants from the Research Grants Council of the Hong Kong (Project No. CUHK 14306218) , T. G. F. L was partially supported by grants from the Research Grants Council of the Hong Kong (Project No. CUHK 14306419) , The Croucher Foundation of Hong Kong and Research Committee of the Chinese University of Hong Kong. This project was conducted using computational resources at the Rusty cluster of the Flatiron Institute. This project made use of the following PYTHON packages: MATPLOTLIB [62] , NumPy [63] , SciPy [64] , SCIKIT-LEARN [46] , CORNER [65] , EMCEE [65] , and PyTorch [66] . This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data, software and/or web tools obtained from the GW Open Science Center [67] , a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS) , the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. 65 0 0 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D OCT 19 2022.0 106 8 83014 10.1103/PhysRevD.106.083014 0.0 12 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 6X6IF Green Submitted 2023-03-23 WOS:000896514600001 0
J Wang, C; Bai, Y; Yuan, H; Liu, J; Fernandez-Ontiveros, JA; Coelho, PRT; Jimenez-Esteban, F; Galarza, CA; Angulo, RE; Cenarro, AJ; Cristobal-Hornillos, D; Dupke, RA; Ederoclite, A; Hernandez-Monteagudo, C; Lopez-Sanjuan, C; Marin-Franch, A; Moles, M; Sodre, L; Ramio, HV; Varela, J Wang, C.; Bai, Y.; Yuan, H.; Liu, J.; Fernandez-Ontiveros, J. A.; Coelho, P. R. T.; Jimenez-Esteban, F.; Galarza, C. A.; Angulo, R. E.; Cenarro, A. J.; Cristobal-Hornillos, D.; Dupke, R. A.; Ederoclite, A.; Hernandez-Monteagudo, C.; Lopez-Sanjuan, C.; Marin-Franch, A.; Moles, M.; Sodre, L., Jr.; Ramio, H. Vazquez; Varela, J. J-PLUS: Support vector regression to measure stellar parameters ASTRONOMY & ASTROPHYSICS English Article methods: data analysis; techniques: spectroscopic; astronomical databases: miscellaneous ATMOSPHERIC PARAMETERS; SEGUE; SPECTRA; APOGEE; STARS Context. Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. Results. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods. [Wang, C.; Bai, Y.; Liu, J.] Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China; [Wang, C.; Liu, J.] Univ Chinese Acad Sci, Coll Astron & Space Sci, Beijing 100049, Peoples R China; [Yuan, H.] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China; [Fernandez-Ontiveros, J. A.; Cenarro, A. J.; Cristobal-Hornillos, D.; Ederoclite, A.; Lopez-Sanjuan, C.; Marin-Franch, A.; Moles, M.; Ramio, H. Vazquez; Varela, J.] Ctr Estudios Fis Cosmos Aragon CEFCA, Unidad Asociada CSIC, Plaza San Juan 1, Teruel 44001, Spain; [Coelho, P. R. T.; Sodre, L., Jr.] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, BR-05508090 Sao Paulo, Brazil; [Jimenez-Esteban, F.] Ctr Astrobiol CSIC INTA, Dept Astrofis, ESAC Campus,Camino Bajo Castillo S-N, Madrid, Spain; [Galarza, C. A.; Dupke, R. A.] Observ Nacl MCTI ON, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, Brazil; [Angulo, R. E.] Donostia Int Phys Ctr DIPC, Paseo Manuel de Lardizabal 4, Donostia San Sebastian 20018, Spain; [Angulo, R. E.] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain; [Dupke, R. A.] Univ Michigan, Dept Astron, 1085 South Univ Ave, Ann Arbor, MI 48109 USA; [Dupke, R. A.] Univ Alabama, Dept Phys & Astron, Gallalee Hall, Tuscaloosa, AL 35401 USA; [Hernandez-Monteagudo, C.] Inst Astrofis Canarias, San Cristobal la Laguna, Spain; [Hernandez-Monteagudo, C.] Univ La Laguna, Dept Astrofis, Tenerife 38206, Spain Chinese Academy of Sciences; National Astronomical Observatory, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Beijing Normal University; Consejo Superior de Investigaciones Cientificas (CSIC); Universidade de Sao Paulo; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro de Astrobiologia (INTA); Basque Foundation for Science; University of Michigan System; University of Michigan; University of Alabama System; University of Alabama Tuscaloosa; Instituto de Astrofisica de Canarias; Universidad de la Laguna Wang, C (corresponding author), Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China.;Wang, C (corresponding author), Univ Chinese Acad Sci, Coll Astron & Space Sci, Beijing 100049, Peoples R China. wangcunshi@nao.cas.cn Villanueva, Rita Guadalupe Angulo/I-8399-2017; Coelho, Paula/B-1592-2015 Villanueva, Rita Guadalupe Angulo/0000-0003-2694-3501; Jimenez-Esteban, Francisco Manuel/0000-0002-6985-9476; Coelho, Paula/0000-0003-1846-4826 National Natural Science Foundation of China (NSFC) [NSFC-11988101/ 11973054/11933004, NSFC-12173007]; National Programs on Key Research and Devel opment Project [2019YFA0405504, 2019YFA0405000]; Strategic Priority Program of the Chinese Academy of Sciences [XDB41000000]; Spanish Ministry of Science and Innovation; European Union - NextGenerationEU [ICTS-MRR-2021-03-CEFCA]; Centro de Astrobiologia (CSIC-INTA), Unidad de Excelencia Maria de Maeztu [PGC2018-101950-B-I00, MDM-2017- 0737]; China Manned Space Project [CMS-CSST-2021-B07]; Government of Spain; Government of Aragon through the Fondo de Inversiones de Teruel; Aragon Government [E96, E103, E16_17R]; Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) [PGC2018-097585-B-C21, PGC2018-097585-B-C22]; Spanish Ministry of Economy and Competitiveness (MINECO) [AYA2015-66211-C2-1- P, AYA2015-66211-C2-2, AYA2012-30789, ICTS-2009-14]; European FEDER funding [FCDD10-4E-867, FCDD13-4E-2685]; Alfred P. Sloan Foundation; U.S. Department of Energy Office of Science; Center for High-Performance Computing at the University of Utah; Brazilian Participation Group; Carnegie Institution for Science; Carnegie Mellon University; Chilean Participation Group; French Participation Group; Harvard-Smithsonian Center for Astrophysics; Instituto de Astrofisica de Canarias; Johns Hopkins University; Kavli Insti tute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo; Lawrence Berkeley National Laboratory; Leibniz Institut fur Astro physik Potsdam (AIP); Max-Planck-Institut fur Astronomie (MPIA Heidelberg); Max- Planck-Institut fur Astrophysik (MPA Garching); Max-Planck-Institut fur Extraterrestrische Physik (MPE); National Astronomical Observatories of China; New Mexico State University; New York University; University of Notre Dame; Observatario Nacional/MCTI; Ohio State University; Pennsylvania State University; Shanghai Astronomical Observatory; United Kingdom Participation Group; Universidad Nacional Autnoma de Mexico; University of Arizona; Uni versity of Colorado Boulder; University of Oxford; University of Portsmouth; University of Utah; University of Virginia; University of Washington; University of Wisconsin; Vanderbilt University; Yale University National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Programs on Key Research and Devel opment Project; Strategic Priority Program of the Chinese Academy of Sciences; Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); European Union - NextGenerationEU; Centro de Astrobiologia (CSIC-INTA), Unidad de Excelencia Maria de Maeztu; China Manned Space Project; Government of Spain(Spanish Government); Government of Aragon through the Fondo de Inversiones de Teruel; Aragon Government(Gobierno de Aragon); Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE)(Spanish Government); Spanish Ministry of Economy and Competitiveness (MINECO)(Spanish Government); European FEDER funding; Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); U.S. Department of Energy Office of Science(United States Department of Energy (DOE)); Center for High-Performance Computing at the University of Utah; Brazilian Participation Group; Carnegie Institution for Science; Carnegie Mellon University; Chilean Participation Group; French Participation Group; Harvard-Smithsonian Center for Astrophysics(Smithsonian InstitutionHarvard-Smithsonian Center for Astrophysics); Instituto de Astrofisica de Canarias; Johns Hopkins University(Johns Hopkins University); Kavli Insti tute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo; Lawrence Berkeley National Laboratory(United States Department of Energy (DOE)); Leibniz Institut fur Astro physik Potsdam (AIP); Max-Planck-Institut fur Astronomie (MPIA Heidelberg); Max- Planck-Institut fur Astrophysik (MPA Garching); Max-Planck-Institut fur Extraterrestrische Physik (MPE); National Astronomical Observatories of China; New Mexico State University; New York University; University of Notre Dame; Observatario Nacional/MCTI; Ohio State University(Ohio State University); Pennsylvania State University; Shanghai Astronomical Observatory; United Kingdom Participation Group; Universidad Nacional Autnoma de Mexico(Universidad Nacional Autonoma de Mexico); University of Arizona; Uni versity of Colorado Boulder; University of Oxford; University of Portsmouth; University of Utah; University of Virginia; University of Washington(University of Washington); University of Wisconsin; Vanderbilt University; Yale University This work was supported by the National Natural Science Foundation of China (NSFC) through grants NSFC-11988101/ 11973054/11933004 and the National Programs on Key Research and Devel opment Project (grant No.2019YFA0405504 and 2019YFA0405000). Strategic Priority Program of the Chinese Academy of Sciences under grant number XDB41000000. H.B. Yuan, acknowledge funding from National Natural Science Foundation of China (NSFC) through grants NSFC-12173007. J.A.F.O. acknowl edges the financial support from the Spanish Ministry of Science and Innova tion and the European Union - NextGenerationEU through the Recovery and Resilience Facility project ICTS-MRR-2021-03-CEFCA. F.J.E. acknowledges financial support by the Spanish grant PGC2018-101950-B-I00 and MDM-2017- 0737 at Centro de Astrobiologia (CSIC-INTA), Unidad de Excelencia Maria de Maeztu. We acknowledge the science research grants from the China Manned Space Project with no. CMS-CSST-2021-B07. Based on observations made with the JAST80 telescope at the Observatorio Astrofisico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Fisica del Cosmos de Aragon (CEFCA). We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing the OAJ data used in this work. Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragon through the Fondo de Inversiones de Teruel; the Aragon Government through the Research Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grants PGC2018-097585-B-C21 and PGC2018-097585-B-C22; the Spanish Ministry of Economy and Competitiveness (MINECO) under AYA2015-66211-C2-1- P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). The Brazilian agencies FINEP, FAPESP, and the National Observatory of Brazil have also contributed to this project. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. .; SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institu tion for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofisica de Canarias, The Johns Hopkins University, Kavli Insti tute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut fur Astro physik Potsdam (AIP), Max-Planck-Institut fur Astronomie (MPIA Heidelberg), Max- Planck-Institut fur Astrophysik (MPA Garching), Max-Planck-Institut fur Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatario Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autnoma de Mexico, University of Arizona, Uni versity of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. 47 1 1 4 6 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. AUG 4 2022.0 664 A38 10.1051/0004-6361/202243130 0.0 15 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 3N1YM hybrid, Green Submitted 2023-03-23 WOS:000835949500018 0
J Zheng, XH; Zhang, XY; Yu, YL; Kechadi, T; Rong, CM Zheng, Xianghan; Zhang, Xueying; Yu, Yuanlong; Kechadi, Tahar; Rong, Chunming ELM-based spammer detection in social networks JOURNAL OF SUPERCOMPUTING English Article Social network; Spammer; Machine learning; Extreme learning machine EXTREME LEARNING-MACHINE Online social networks, such as Facebook, Twitter, and Weibo have played an important role in people's common life. Most existing social network platforms, however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content, malware, viruses, etc. to the legitimate users of the service. The spreading of spam degrades user experience and also negatively impacts server-side functions such as data mining, user behavior analysis, and resource recommendation. In this paper, an extreme learning machine (ELM)-based supervised machine is proposed for effective spammer detection. The work first constructs the labeled dataset through crawling Sina Weibo data and manually classifying corresponding users into spammer and non-spammer categories. A set of features is then extracted from message content and user behavior and applies them to the ELM-based spammer classification algorithm. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99 and 99.95 %, respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM-based approaches. [Zheng, Xianghan; Zhang, Xueying; Yu, Yuanlong] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China; [Zheng, Xianghan; Zhang, Xueying; Yu, Yuanlong] Fujian Key Lab Network Comp & Intelligent Informa, Fuzhou 350108, Peoples R China; [Kechadi, Tahar] Univ Coll Dublin, Sch Comp Sci & Informat, Dublin 4, Ireland; [Rong, Chunming] Univ Stavanger, Dept Elect Engn & Comp Sci, N-4036 Stavanger, Norway Fuzhou University; University College Dublin; Universitetet i Stavanger Yu, YL (corresponding author), Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China.;Yu, YL (corresponding author), Fujian Key Lab Network Comp & Intelligent Informa, Fuzhou 350108, Peoples R China. yu.yuanlong@fzu.edu.cn Rong, Chunming/X-8035-2019; Kechadi, M-Tahar/AAH-3906-2020 KECHADI, Mohand Tahar/0000-0002-0176-6281; Rong, Chunming/0000-0002-8347-0539 National Natural Science Foundation of China [61103175, 11271002]; Chinese Ministry of Education [212086]; Technology Innovation Platform Project of Fujian Province [2009J1007, 2013H6011, 2013J01228]; Key Project Development Foundation of Education Committee of Fujian province [JA11011, JA12016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Ministry of Education(Ministry of Education, China); Technology Innovation Platform Project of Fujian Province; Key Project Development Foundation of Education Committee of Fujian province This paper is supported by the National Natural Science Foundation of China under Grant No. 61103175 and No. 11271002, the Key Project of Chinese Ministry of Education under Grant No. 212086; the Technology Innovation Platform Project of Fujian Province under Grant No. 2009J1007, No. 2013H6011 and 2013J01228; the Key Project Development Foundation of Education Committee of Fujian province under Grand No. JA11011 and JA12016. 32 28 28 0 21 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0920-8542 1573-0484 J SUPERCOMPUT J. Supercomput. AUG 2016.0 72 8 2991 3005 10.1007/s11227-015-1437-5 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering DU1RK 2023-03-23 WOS:000381986200006 0
J Li, YT; Huang, XH; Zhao, GY Li, Yante; Huang, Xiaohua; Zhao, Guoying Micro-expression action unit detection with spatial and channel attention NEUROCOMPUTING English Article Micro-expression analysis; AU detection; Deep learning; Second-order statistics; Covariance matrix Action Unit (AU) detection plays an important role in facial behaviour analysis. In the literature, AU detection has extensive researches in macro-expressions. However, to the best of our knowledge, there is limited research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Due to the small quantity and low intensity of micro-expression databases, micro expression AU detection becomes challenging. To alleviate these problems, in this work, we propose a novel micro-expression AU detection method by utilizing self high-order statistics of spatio-wise and channel-wise features which can be considered as spatial and channel attentions, respectively. Through such spatial attention module, we expect to utilize rich relationship information of facial regions to increase the AU detection robustness on limited micro-expression samples. In addition, considering the low intensity of micro-expression AUs, we further propose to explore high-order statistics for better capturing subtle regional changes on face to obtain more discriminative AU features. Intensive experiments show that our proposed approach outperforms the basic framework by 0.0859 on CASME II, 0.0485 on CASME, and 0.0644 on SAMM in terms of the average F1-score. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Li, Yante; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Huang, Xiaohua] Nanjing Inst Technol, Sch Comp Engn, Nanjing, Peoples R China; [Zhao, Guoying] NorthwestUniv, Sch Informat & Technol, Xian, Peoples R China University of Oulu; Nanjing Institute of Technology Zhao, GY (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland.;Zhao, GY (corresponding author), NorthwestUniv, Sch Informat & Technol, Xian, Peoples R China. Yante.Li@oulu.fi; xiaohuahwang@gmail.com; guoying.zhao@oulu.fi Huang, Xiaohua/A-4878-2011; Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X Infotech Oulu, National Natural Science Foundation of China [61772419, 62076122]; Academy of Finland [316765]; ICT 2023 project [328115]; Jiangsu SpeciallyAppointed Professor Program; Talent Startup project of NJIT [YKJ201982]; Jiangsu joint research project of sinoforeign cooperative education platform and Technology Innovation Project of Nanjing for Oversea Scientist Infotech Oulu, National Natural Science Foundation of China; Academy of Finland(Academy of Finland); ICT 2023 project; Jiangsu SpeciallyAppointed Professor Program; Talent Startup project of NJIT; Jiangsu joint research project of sinoforeign cooperative education platform and Technology Innovation Project of Nanjing for Oversea Scientist This work was supported by Infotech Oulu, National Natural Science Foundation of China (Grant Nos: 61772419, 62076122) , the Academy of Finland for project MiGA (grant 316765) , ICT 2023 project (grant 328115) , the Jiangsu SpeciallyAppointed Professor Program, the Talent Startup project of NJIT (No. YKJ201982) , the Jiangsu joint research project of sinoforeign cooperative education platform and Technology Innovation Project of Nanjing for Oversea Scientist. As well, the authors wish to acknowledge CSC IT Center for Science, Finland, for computational resources. 51 9 11 2 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing MAY 14 2021.0 436 221 231 10.1016/j.neucom.2021.01.032 0.0 FEB 2021 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RC6HO Green Published, hybrid 2023-03-23 WOS:000632900000004 0
J Han, SS; Wang, HP; Tian, Y; Christov, N Han, Shuaishuai; Wang, Haoping; Tian, Yang; Christov, Nicolai Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton ISA TRANSACTIONS English Article Computed torque control; Time-delay estimation; Robust adaptive RBF neural networks; Robotics toolbox; 12 DOF lower limb exoskeleton SLIDING-MODE CONTROL; MOTION CONTROL; TRACKING CONTROL; SYSTEMS; DESIGN A new approach to gait rehabilitation task of a 12 DOF lower limb exoskeleton is proposed combining time-delay estimation (TDE) based computed torque control (CTC) and robust adaptive RBF neural networks. In addition to the conventional advantages of the CTC, TDE technique is integrated to estimate unmodeled dynamics and external disturbance. To realize more accurate tracking, a robust adaptive RBF neural networks compensator is designed to approximate and compensate TDE error. The final asymptotic stability is guaranteed with Lyapunov criteria. To validate the proposed approach, co-simulation experiments are realized using SolidWorks, SimMechanics and MATLAB/Robotics Toolbox. Compared to CTC, sliding mode based CTC and TDE based CTC, the higher performances of the proposed controller are demonstrated by co-simulation. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved. [Han, Shuaishuai; Wang, Haoping; Tian, Yang] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China; [Christov, Nicolai] Univ Lille 1, Res Ctr Comp Sci Signal & Automat Control CRIStAL, Batiment P2, F-59655 Villeneuve Dascq, France Nanjing University of Science & Technology; Universite de Lille - ISITE; Universite de Lille Wang, HP (corresponding author), Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China. hp.wang@njust.edu.cn Han, Shuaishuai/ABA-6949-2021; Christov, Nicolai/B-7135-2008; Wang, Haoping/A-5455-2013 Han, Shuaishuai/0000-0002-3267-0864; Christov, Nicolai/0000-0001-8910-7274; Wang, Haoping/0000-0003-1888-1849 International Science & Technology Cooperation Program of China [2015DFA01710]; Natural Science Foundation of China [61773212, 61304077]; Natural Science Foundation of Jiangsu province, China [BK20170094]; 11th Jiangsu Province Six talent peaks of high level talents, China [2014_ZBZZ_005] International Science & Technology Cooperation Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu province, China(Natural Science Foundation of Jiangsu Province); 11th Jiangsu Province Six talent peaks of high level talents, China This work was partially supported by International Science & Technology Cooperation Program of China (2015DFA01710), by the Natural Science Foundation of China (61773212, 61304077), by the Natural Science Foundation of Jiangsu province, China (BK20170094) and by the 11th Jiangsu Province Six talent peaks of high level talents, China (2014_ZBZZ_005). 49 54 55 9 94 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-0578 1879-2022 ISA T ISA Trans. FEB 2020.0 97 171 181 10.1016/j.isatra.2019.07.030 0.0 11 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation LD0WR 31399252.0 2023-03-23 WOS:000525754600015 0
J Zhou, HY; Zhu, XC; Wang, SS; Zhou, K; Ma, Z; Li, J; Hou, KM; De Vaulx, C Zhou, Haiying; Zhu, Xiancheng; Wang, Sishan; Zhou, Kui; Ma, Zheng; Li, Jian; Hou, Kun-Mean; De Vaulx, Christophe A Novel Cardiac Arrhythmias Detection Approach for Real-Time Ambulatory ECG Diagnosis INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE English Article Cardiac arrhythmias detection; morphological characteristics; statistical features; linear discriminant with confidence interval estimation NEURAL-NETWORK; CLASSIFICATION; FEATURES; CREATION In view of requirements of low-resource consumption and high-efficiency in real-time Ambulatory Electrocardiograph Diagnosis (AED) applications, a novel Cardiac Arrhythmias Detection (CAD) algorithm is proposed. This algorithm consists of three core modules: an automatic-learning machine that models diagnostic criteria and grades the emergency events of cardiac arrhythmias by studying morphological characteristics of ECG signals and experiential knowledge of cardiologists; a rhythm classifier that recognizes and classifies heart rhythms basing on statistical features comparison and linear discriminant with confidence interval estimation; and an arrhythmias interpreter that assesses emergency events of cardia arrhythmias basing on a two rule-relative interpretation mechanisms. The experiential results on off-line MIT-BIH cardiac arrhythmia database as well as online clinical testing explore that this algorithm has 92.8% sensitivity and 97.5% specificity in average, so that it is suitable for real-time cardiac arrhythmias monitoring. [Zhou, Haiying; Zhu, Xiancheng; Wang, Sishan; Zhou, Kui; Ma, Zheng] HuBei Univ Automot Technol, Sch Elect & Informat Engn, Shiyan 442002, Peoples R China; [Li, Jian] HuBei Univ Automot Technol, Sch Econ & Management, Shiyan 442002, Peoples R China; [Hou, Kun-Mean; De Vaulx, Christophe] Univ Blaise Pascal, CNRS, UMR 6158, LIMOS Lab, F-63000 Clermont Ferrand, France Hubei University of Automotive Technology; Hubei University of Automotive Technology; Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA) Li, J (corresponding author), HuBei Univ Automot Technol, Sch Econ & Management, Shiyan 442002, Peoples R China. zhouhy_dy@huat.edu.cn; zhuxc_dy@huat.edu.cn; wangss_dy@huat.edu.cn; zhouk_dy@huat.edu.cn; margaret_mz@163.com; 7120146@qq.com; Kun-mean.hou@isima.fr; Christophe.de_vaulx@univ-bpclermont.fr de Vaulx, Christophe/0000-0003-3459-8568 Sci-technical Pillar program of Hubei Province [2014BHE024]; Natural Science Foundation of HUAT [BK201411]; Outstanding young Sci-technological innovation team plan for Universities of Hubei Province [T201610] Sci-technical Pillar program of Hubei Province; Natural Science Foundation of HUAT; Outstanding young Sci-technological innovation team plan for Universities of Hubei Province The authors would like to thank all colleagues and copartners who have contributed to the study, and also are grateful for the Natural Science Foundation of HUAT (BK201411), the Sci-technical Pillar program of Hubei Province (2014BHE024), and the Outstanding young Sci-technological innovation team plan for Universities of Hubei Province in 2016 (No. T201610) for their supports to this project. 33 2 2 0 30 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-0014 1793-6381 INT J PATTERN RECOGN Int. J. Pattern Recognit. Artif. Intell. OCT 2017.0 31 10 1758004 10.1142/S0218001417580046 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FA3XH 2023-03-23 WOS:000405377600010 0
J Liao, K; Lin, CY; Zhao, Y; Gabbouj, M Liao, Kang; Lin, Chunyu; Zhao, Yao; Gabbouj, Moncef Distortion Rectification From Static to Dynamic: A Distortion Sequence Construction Perspective IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Distortion rectification; deep learning; video processing; dynamic construction CAMERA CALIBRATION Distortion rectification is a fundamental task in the field of computer vision and image processing. Nevertheless, previous methods have regarded distortion rectification as a static problem that learns a mapping function and corrects the distorted image to a unique state. However, this state is generally not the optimal solution, as it would result in an under-rectified or over-rectified structure. In this study, we revisit the classical distortion rectification task with a new perspective and redesign the algorithm, inspired by video processing techniques. Specifically, we regard distortion rectification as a dynamic problem that can be extended to a sequence of different distortion states: the input distorted image (t), under-rectified image (t+1), ideal-rectified image (t+2), and over-rectified image (t+3). We first estimate the residual distortion map (RDM) between the input distorted image and the coarse-rectified (t+1 or t+3) image. Here, RDM indicates the motion difference between two distorted images. Subsequently, the RDM is used to guide the refinement rectification process, aiming to convert the coarse-rectified state into the ideal-rectified state. In addition, the flexible implementation of the proposed refinement process with RDM to improve the rectification results of any method is appealing. The experimental results demonstrate that our method outperforms the state-of-the-art schemes by a significant margin, revealing approximately 40% improvement through quantitative evaluation. [Liao, Kang; Lin, Chunyu; Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Liao, Kang; Lin, Chunyu; Zhao, Yao] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Gabbouj, Moncef] Tampere Univ Technol, Dept Signal Proc, Tampere 33720, Finland Beijing Jiaotong University; Beijing Jiaotong University; Tampere University Lin, CY (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. kang_liao@bjtu.edu.cn; cylin@bjtu.edu.cn; yzhao@bjtu.edu.cn; moncef.gabbouj@tuni.fi Gabbouj, Moncef/G-4293-2014; Liao, Kang/ADB-6353-2022; Lin, Chunyu/AAI-5185-2021 Gabbouj, Moncef/0000-0002-9788-2323; Lin, Chunyu/0000-0003-2847-0349; Zhao, Yao/0000-0002-8581-9554; Liao, Kang/0000-0001-9429-1096 National Natural Science Foundation of China [61772066, 61972028]; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [VRLAB2019B05] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University This work was supported in part by the National Natural Science Foundation of China under Grant 61772066 and Grant 61972028 and in part by the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under Grant VRLAB2019B05. 54 2 2 2 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. NOV 2020.0 30 11 3870 3882 10.1109/TCSVT.2019.2958199 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering OJ1XS 2023-03-23 WOS:000583761300003 0
J Tolosana, R; Vera-Rodriguez, R; Gonzalez-Garcia, C; Fierrez, J; Morales, A; Ortega-Garcia, J; Ruiz-Garcia, JC; Romero-Tapiador, S; Rengifo, S; Caruana, M; Jiang, JJ; Lai, SX; Jin, LW; Zhu, YC; Galbally, J; Diaz, M; Ferrer, MA; Gomez-Barrero, M; Hodashinsky, I; Sarin, K; Slezkin, A; Bardamova, M; Svetlakov, M; Saleem, M; Szcs, CL; Kovari, B; Pulsmeyer, F; Welbi, M; Zanca, D; Ahmad, S; Mishra, S; Jabin, S Tolosana, Ruben; Vera-Rodriguez, Ruben; Gonzalez-Garcia, Carlos; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier; Ruiz-Garcia, Juan Carlos; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel; Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; Zhu, Yecheng; Galbally, Javier; Diaz, Moises; Ferrer, Miguel Angel; Gomez-Barrero, Marta; Hodashinsky, Ilya; Sarin, Konstantin; Slezkin, Artem; Bardamova, Marina; Svetlakov, Mikhail; Saleem, Mohammad; Szcs, Cintia Lia; Kovari, Bence; Pulsmeyer, Falk; Welbi, Mohamad; Zanca, Dario; Ahmad, Sumaiya; Mishra, Sarthak; Jabin, Suraiya SVC-onGoing: Signature verification competition PATTERN RECOGNITION English Article SVC-onGoing; SVC 2021; Biometrics; Handwriting; Signature verification; DeepSignDB; SVC2021_EvalDB ONLINE; BIOMETRICS This article presents SVC-onGoing1, an on-going competition for on-line signature verification where re-searchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB(2) and SVC2021_EvalDB(3), and standard experimen-tal protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to eval-uate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously con-sidered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future stud-ies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) [Tolosana, Ruben; Vera-Rodriguez, Ruben; Gonzalez-Garcia, Carlos; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier; Ruiz-Garcia, Juan Carlos; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel] UAM, Biometr & Data Pattern Analyt Lab, Madrid, Spain; [Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; Zhu, Yecheng] South China Univ Technol, Guangzhou, Peoples R China; [Galbally, Javier] European Commiss, Joint Res Ctr, Rome, Italy; [Diaz, Moises; Ferrer, Miguel Angel] Univ Las Palmas Gran Canaria, Las Palmas Gran Canaria, Spain; [Gomez-Barrero, Marta] Hsch Ansbach, Ansbach, Germany; [Hodashinsky, Ilya; Sarin, Konstantin; Slezkin, Artem; Bardamova, Marina; Svetlakov, Mikhail] Tomsk State Univ Control Syst & Radioelect, Tomsk, Russia; [Saleem, Mohammad; Szcs, Cintia Lia; Kovari, Bence] Budapest Univ Technol & Econ, Budapest, Hungary; [Pulsmeyer, Falk; Welbi, Mohamad; Zanca, Dario] FAU, Machine Learning & Data Analyt Lab, Erlangen, Germany; [Ahmad, Sumaiya; Mishra, Sarthak; Jabin, Suraiya] Jamia Millia Islamia, New Delhi, India Autonomous University of Madrid; South China University of Technology; European Commission Joint Research Centre; Universidad de Las Palmas de Gran Canaria; Tomsk State University of Control Systems & Radioelectronics; Budapest University of Technology & Economics; University of Erlangen Nuremberg Tolosana, R (corresponding author), UAM, Biometr & Data Pattern Analyt Lab, Madrid, Spain. ruben.tolosana@uam.es Bardamova, Marina/U-8884-2017; Sarin, Konstantin/AAB-5058-2020; Diaz, Moises/L-3637-2013; Vera-Rodriguez, Ruben/D-1272-2014; Ferrer, Miguel A/L-3863-2013 Bardamova, Marina/0000-0002-0567-590X; Sarin, Konstantin/0000-0003-3264-7768; Diaz, Moises/0000-0003-3878-3867; Vera-Rodriguez, Ruben/0000-0002-6338-8511; Romero Tapiador, Sergio/0000-0002-8919-8687; Ferrer, Miguel A/0000-0002-2924-1225; Ruiz-Garcia, Juan Carlos/0000-0002-8076-8604; saleem, mohammad/0000-0002-7274-2711; Fierrez, Julian/0000-0002-6343-5656 project: PRIMA (MICINN/FEDER) [H2020-MSCA-ITN-2019-860315]; project: TRESPASS-ETN (MICINN/FEDER) [H2020-MSCA-ITN-2019-860813]; project: INTERACTION (MICINN/FEDER) [PID2021-126521OB-I00]; UAM-Cecabank; [(PID2021-126521OB-I0 0 MICINN/FEDER)] project: PRIMA (MICINN/FEDER); project: TRESPASS-ETN (MICINN/FEDER); project: INTERACTION (MICINN/FEDER); UAM-Cecabank; This work has been supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), INTERACTION (PID2021-126521OB-I00 MICINN/FEDER), Orange Labs, and by UAM-Cecabank. 40 7 7 6 7 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUL 2022.0 127 108609 10.1016/j.patcog.2022.108609 0.0 MAR 2022 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 0P6KC Green Submitted, hybrid, Green Published 2023-03-23 WOS:000784335600001 0
J Zhao, DD; Gao, F; Bouquain, D; Dou, MF; Miraoui, A Zhao, Dongdong; Gao, Fei; Bouquain, David; Dou, Manfeng; Miraoui, Abdellatif Sliding-Mode Control of an Ultrahigh-Speed Centrifugal Compressor for the Air Management of Fuel-Cell Systems for Automotive Applications IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Automotive applications; centrifugal compressors; fuel cells; sliding-mode control; ultrahigh speed CONTROL STRATEGIES; PREDICTIVE CONTROL; STACK; FLOW This paper presents the modeling and control of an ultrahigh-speed centrifugal compressor for the air management of proton exchange membrane fuel-cell (PEMFC) systems. Centrifugal compressors have the advantages of compactness, high efficiency, and low noise, compared with other kinds of displacement compressors. Moreover, ultrahigh-speed technology can also reduce the size and weight of the compressor, which makes it more feasible for automotive applications. However, the adoption of a centrifugal compressor results in control being difficult because of coupling between mass flow and pressure. In this paper, a neural network model of the compressor is developed, and a decentralized sliding-mode controller based on twisting and super twisting algorithms is proposed and tested to control the compressor pressure and mass flow. The experimental results show good dynamic characteristics and faster response compared with conventional proportional-integral (PI) control. [Zhao, Dongdong; Gao, Fei; Miraoui, Abdellatif] Univ Technol Belfort Montbeliard, F-90000 Belfort, France; [Bouquain, David] Univ Technol Belfort Montbeliard, IRTES SeT Lab, F-90000 Belfort, France; [Dou, Manfeng] Northwestern Polytech Univ, Inst Rare Earth Permanent Magnet REPM Elect Mach, Xian 710072, Peoples R China Universite de Technologie de Belfort-Montbeliard (UTBM); Universite de Technologie de Belfort-Montbeliard (UTBM); Northwestern Polytechnical University Zhao, DD (corresponding author), Univ Technol Belfort Montbeliard, F-90000 Belfort, France. dongdong.zhao@utbm.fr; fei.gao@utbm.fr; david.bouquain@utbm.fr; doumf@nwpu.edu.cn; abdellatif.miraoui@utbm.fr Gao, Fei/E-7932-2012; Bouquain, David/AFW-5153-2022 Gao, Fei/0000-0001-9076-9718; zhao, dongdong/0000-0002-3790-0200 44 38 40 8 60 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. JAN 2014.0 63 1 51 61 10.1109/TVT.2013.2274046 0.0 11 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation 295KF 2023-03-23 WOS:000330114300006 0
J Wang, ZH; Jiang, DD; Wang, F; Lv, ZH; Nowak, R Wang, Zhihao; Jiang, Dingde; Wang, Feng; Lv, Zhihan; Nowak, Robert A polymorphic heterogeneous security architecture for edge-enabled smart grids SUSTAINABLE CITIES AND SOCIETY English Article Smart gird; Edge computing; Built-in security; Dynamic heterogeneous redundant; Scheduling algorithm AGGREGATION; CHALLENGES The emergence of edge computing alleviates the network congestion, delay, and packet loss of cloud computing enabled smart grid architecture in big data scenarios, greatly improving the operation efficiency and communication quality of the smart grid. However, edge computing architecture also brings great security risks to the smart grid. This paper proposes a Polymorphic Heterogeneous Security Architecture (PHSA) for edge enabled smart grid, to assure the key services in the smart grid. Based on the dynamic heterogeneous redundancy architecture, the proposed architecture introduces intelligent scheduling of executors, intelligent arbitration of multi-state service response, cleaning and reconstruction of executors, and attack surface management. We establish a closed-loop core control process of decision and processing, adjustment and scheduling, cleaning reconstruction, migration, and recovery. Furthermore, a dynamic scheduling algorithm based on executor credit and heterogeneity is proposed. The simulation results show that, compared with the traditional smart grid security architecture, the PHSA has better defense ability and system reliability, significantly improving the security of edge enabled smart grid system. Besides, compared with the random scheduling strategy, the proposed dynamic executor scheduling algorithm can further improve the security of the system. [Wang, Zhihao; Jiang, Dingde; Wang, Feng] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China; [Wang, Zhihao; Jiang, Dingde] Sichuan Conservatory Mus, Key Lab Sichuan Prov, Digital Media Art, Chengdu 610041, Peoples R China; [Lv, Zhihan] Qingdao Univ, Sch Data Sci & Software Engn, Qingdao 266071, Peoples R China; [Nowak, Robert] Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland University of Electronic Science & Technology of China; Sichuan Conservatory of Music; Qingdao University; Warsaw University of Technology Jiang, DD (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China. jiangdd@uestc.edu.cn; lvzhihan@gmail.com Wang, Zhihao/AAS-6573-2021; Lv, Zhihan/GLR-6000-2022; Lv, Zhihan/I-3187-2014 Wang, Zhihao/0000-0003-1989-3067; Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Nowak, Robert M./0000-0001-7248-6888 National Natural Science Foundation of China [61571104, 61902203]; Sichuan Science and Technology Program [2018JY0539]; Key projects of the Sichuan Provincial Education Department [18ZA0219]; Fundamental Research Funds for the Central Universities [ZYGX2017KYQD170]; CERNET Innovation Project [NGII20190111]; Key Research and Development PlanMajor Scientific and Technological Innovation Projects of ShanDong Province [2019JZZY020101]; [61403110405]; [315075802]; [JZX6Y202001010161]; [2018510007000134] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program; Key projects of the Sichuan Provincial Education Department; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); CERNET Innovation Project; Key Research and Development PlanMajor Scientific and Technological Innovation Projects of ShanDong Province; ; ; ; This work was supported in part by the National Natural Science Foundation of China (No. 61571104) , the Sichuan Science and Technology Program (No. 2018JY0539) , the Key projects of the Sichuan Provincial Education Department (No. 18ZA0219) , the Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170) , the CERNET Innovation Project (No. NGII20190111) , the Fund Project (Nos. 61403110405, 315075802, JZX6Y202001010161) , and the Innovation Funding (No. 2018510007000134) , the National Natural Science Foundation of China (No. 61902203) and Key Research and Development PlanMajor Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101). The authors wish to thank the reviewers for their helpful comments. Dr. Dingde Jiang and Dr. Zhihan Lv are corresponding author of this paper (email: jiangdd@uestc.edu.cn, lvzhihan@gmail.com). 28 17 17 9 23 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. APR 2021.0 67 102661 10.1016/j.scs.2020.102661 0.0 JAN 2021 16 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels RK2JP 2023-03-23 WOS:000638127900004 0
J Chen, HY; Liu, X; Shi, JG; Zhao, GY Chen, Haoyu; Liu, Xin; Shi, Jingang; Zhao, Guoying Temporal Hierarchical Dictionary Guided Decoding for Online Gesture Segmentation and Recognition IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Hidden Markov models; Dictionaries; Entropy; Decoding; Skeleton; Three-dimensional displays; Neural networks; Temporal context; hidden Markov model; hierarchical structure; deep neural network; relative entropy; skeleton-based recognition Online segmentation and recognition of skeletonbased gestures are challenging. Compared with offline cases, the inference of online settings can only rely on the current few frames and always completes before whole temporal movements are performed. However, incompletely performed gestures are ambiguous and their early recognition is easy to fall into local optimum. In this work, we address the problem with a temporal hierarchical dictionary to guide the hidden Markov model (HMM) decoding procedure. The intuition is that, gestures are ambiguous with high uncertainty at early performing phases, and only become discriminate after certain phases. This uncertainty naturally can be measured by entropy. Thus, we propose a measurement called relative entropy map (REM) to encode this temporal context to guide HMM decoding. Furthermore, we introduce a progressive learning strategy with which neural networks could learn a robust recognition of HMM states in an iterative manner. The performance of our method is intensively evaluated on three challenging databases and achieves state-of-the-art results. Our method shows the abilities of both extracting the discriminate connotations and reducing large redundancy in the HMM transition process. It is verified that our framework can achieve online recognition of continuous gesture streams even when they are halfway performed. [Chen, Haoyu; Liu, Xin; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland; [Shi, Jingang] Xi An Jiao Tong Univ, Sch Software, Xian 710049, Peoples R China University of Oulu; Xi'an Jiaotong University Zhao, GY (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland. chen.haoyu@oulu.fi; xin.liu@oulu.fi; jingang.shi@hotmail.com; guoying.zhao@oulu.fi Liu, Xin/AAD-5166-2019; Zhao, Guoying/ABE-7716-2020 Liu, Xin/0000-0002-2242-6139; Zhao, Guoying/0000-0003-3694-206X; Chen, Haoyu/0000-0003-3267-2664 Academy of Finland [316765, 331146]; ICT 2023 Project [328115]; Infotech Oulu; Chinese Scholarship Council; National Natural Science Foundation of China [62002283] Academy of Finland(Academy of Finland); ICT 2023 Project; Infotech Oulu; Chinese Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Academy of Finland for Project MiGA under Grant 316765, ICT 2023 Project under Grant 328115, and Infotech Oulu; and in part by the Chinese Scholarship Council. The work of Xin Liu was supported by the Academy of Finland for Postdoctoral Researcher Project under Grant 331146. The work of Jingang Shi was supported by the National Natural Science Foundation of China under Grant 62002283. 48 3 3 1 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2020.0 29 9689 9702 10.1109/TIP.2020.3028962 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OJ0ZD 33052853.0 Green Accepted 2023-03-23 WOS:000583696200002 0
J Zhou, L; Chen, XJ; Wu, CY; Zhong, QY; Cheng, X; Tang, YB Zhou, Lin; Chen, Xijin; Wu, Chaoyan; Zhong, Qiuyue; Cheng, Xu; Tang, Yibin Speech Enhancement via Mask-Mapping Based Residual Dense Network CMC-COMPUTERS MATERIALS & CONTINUA English Article Mask-mapping-based method; residual dense block; speech enhancement ALGORITHM; NOISE Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal ratio masking (IRM) matrix of consecutive frames. RDN can make full use of feature maps of all layers. Meanwhile, using the global residual learning to combine the shallow features and deep features, RDN obtains the global dense features from the LPS, thereby improves estimated accuracy of the IRM matrix. Simula-tions show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, MM-RDN can still outperform the existing convolutional recurrent network (CRN) method in the measures of perceptual evaluation of speech quality (PESQ) and other evaluation indexes. It indicates that the proposed algorithm is more generalized in untrained conditions. [Zhou, Lin; Chen, Xijin; Wu, Chaoyan; Zhong, Qiuyue] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China; [Cheng, Xu] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland; [Tang, Yibin] Hohai Univ, Coll IOT Engn, Changzhou 213022, Peoples R China Southeast University - China; University of Oulu; Hohai University Zhou, L (corresponding author), Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China. Linzhou@seu.edu.cn 35 0 0 3 3 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2023.0 74 1 1259 1277 10.32604/cmc.2023.027379 0.0 19 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science 6P5QF gold 2023-03-23 WOS:000890984200026 0
J Cao, XL; Yang, B; Huang, CW; Yuen, C; Di Renzo, M; Han, Z; Niyato, D; Poor, HV; Hanzo, L Cao, Xuelin; Yang, Bo; Huang, Chongwen; Yuen, Chau; Di Renzo, Marco; Han, Zhu; Niyato, Dusit; Poor, H. Vincent; Hanzo, Lajos AI-Assisted MAC for Reconfigurable Intelligent-Surface-Aided Wireless Networks: Challenges and Opportunities IEEE COMMUNICATIONS MAGAZINE English Article REFLECTING SURFACE; DESIGN Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve physical layer performance, including wireless coverage, achievable data rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate on centralized, distributed, and hybrid artificial-intelligence-assisted MAC architectures in RIS-aided multi- user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design. [Cao, Xuelin; Yang, Bo; Yuen, Chau] Singapore Univ Technol & Design, Singapore, Singapore; [Huang, Chongwen] Zhejiang Univ, Hangzhou, Peoples R China; [Di Renzo, Marco] Univ Paris Saclay, Paris, France; [Han, Zhu] Univ Houston, Houston, TX 77004 USA; [Han, Zhu] Kyung Hee Univ, Seoul, South Korea; [Niyato, Dusit] Nanyang Technol Univ, Singapore, Singapore; [Poor, H. Vincent] Princeton Univ, Princeton, NJ 08544 USA; [Hanzo, Lajos] Univ Southampton, Southampton, Hants, England Singapore University of Technology & Design; Zhejiang University; UDICE-French Research Universities; Universite Paris Saclay; University of Houston System; University of Houston; Kyung Hee University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Princeton University; University of Southampton Cao, XL (corresponding author), Singapore Univ Technol & Design, Singapore, Singapore. Poor, H. Vincent/S-5027-2016; Han, Zhu/ABG-1222-2021; Yuen, Chau/C-5493-2013 Poor, H. Vincent/0000-0002-2062-131X; Yuen, Chau/0000-0002-9307-2120; Hanzo, Lajos/0000-0002-2636-5214 A*STAR [A19D6a0053]; Fundamental Research Funds for the Central Universities; Fundamental Research Funds for the Central Universities [2021FZZX001-21]; European Commission [871464, 101017011]; NSF [EARS-1839818, CNS1717454, CNS-1731424, CNS-1702850]; National Research Foundation under the AI Singapore Programme (AISG) [AISG-GC-2019-003]; WASP/NTU [M4082187 (4080)]; Singapore Ministry of Education (MOE) Tier 1 [RG16/20]; U.S. National Science Foundation [CCF-1908308]; European Research Council [789028] A*STAR(Agency for Science Technology & Research (A*STAR)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); European Commission(European CommissionEuropean Commission Joint Research Centre); NSF(National Science Foundation (NSF)); National Research Foundation under the AI Singapore Programme (AISG); WASP/NTU; Singapore Ministry of Education (MOE) Tier 1(Ministry of Education, Singapore); U.S. National Science Foundation(National Science Foundation (NSF)); European Research Council(European Research Council (ERC)European Commission) Chau Yuen's work was supported by A*STAR under its RIE2020 AME IAF-PP (Grant No. A19D6a0053). Chongwen Huang's work was supported by the Fundamental Research Funds for the Central Universities. Chongwen Huang's work was supported by the Fundamental Research Funds for the Central Universities, under 2021FZZX001-21. Marco Di Renzo's work was supported in part by the European Commission through the H2020 ARIADNE project (Grant No. 871464) and H2020 RISE6G project (Grant No. 101017011). Zhu Han's work was partially supported by NSF EARS-1839818, CNS1717454, CNS-1731424, and CNS-1702850. Dusit Niyato's work was partially supported by the National Research Foundation under the AI Singapore Programme (AISG Award No: AISG-GC-2019-003), WASP/NTU grant M4082187 (4080), and Singapore Ministry of Education (MOE) Tier 1 (RG16/20). H. Vincent Poor's work was supported by U.S. National Science Foundation Grant CCF-1908308. Lajos Hanzo's work was supported by the European Research Council's Advanced Fellow Grant QuantCom (Grant No. 789028). 24 17 17 2 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0163-6804 1558-1896 IEEE COMMUN MAG IEEE Commun. Mag. JUN 2021.0 59 6 21 27 10.1109/MCOM.001.2001146 0.0 7 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications TF2LZ Green Accepted, Green Submitted 2023-03-23 WOS:000670544800007 0
C Roor, R; Karg, M; Liao, A; Lei, WH; Kirsch, A IEEE Roor, Roman; Karg, Michael; Liao, Andy; Lei, Wenhui; Kirsch, Alexandra Predictive Ridesharing based on Personal Mobility Patterns 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) IEEE Intelligent Vehicles Symposium English Proceedings Paper 28th IEEE Intelligent Vehicles Symposium (IV) JUN 11-14, 2017 Redondo Beach, CA IEEE,Visteon,Intel,IEEE Intelligent Transportat Syst Soc LOCATION PREDICTION; MODEL For digital mobility assistants it is advantageous to know users' mobility habits to be able to infer the most probable departure time and next destination. Different approaches are known to face this challenge, but most of them either have a very static feature model and limited extensibility capabilities or they are very complex and require exponential amount of training data for every added feature. This paper introduces a flexible and extendible mobility model - to represent a user's movement and habits - using a Variable-order Markov Model (VOMM) based on users' mobility patterns enriched with different temporal context information. Since this model uses a tree like data structure, it is possible to find patterns of different lengths in the same training data. Spatio-temporal next location prediction is based on the Prediction by Partial Matching (PPM) algorithm. We examine several classification and regression based machine learning algorithms for probability fusion of next location candidates and possible departure times to obtain the most accurate joint probability for the predicted location. The resulting prediction accuracy is between 60% and 81%. [Roor, Roman; Karg, Michael] BMW AG, Munich, Germany; [Liao, Andy; Lei, Wenhui] BMW Technol Off China, Shanghai, Peoples R China; [Kirsch, Alexandra] Eberhard Karls Univ Tubingen, Dept Comp Sci, Tubingen, Germany BMW AG; Eberhard Karls University of Tubingen Roor, R (corresponding author), BMW AG, Munich, Germany. roman.roor@bmw.de; michael.karg@bmw.de; andy.liao@bmw.com; wenhui.lei@bmw.com; alexandra.kirsch@uni-tuebingen.de 19 2 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-0587 978-1-5090-4804-5 IEEE INT VEH SYM 2017.0 1322 1329 8 Computer Science, Artificial Intelligence; Robotics; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics; Transportation BJ4NE Green Submitted 2023-03-23 WOS:000425212700206 0
J Liu, ZB; Shao, JF; Xu, WY; Chen, HJ; Shi, C Liu, Zaobao; Shao, Jianfu; Xu, Weiya; Chen, Hongjie; Shi, Chong Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches LANDSLIDES English Article Landslide; Displacement prediction; Nonlinear; Computational intelligence; Relevance vector machine; Gaussian process RELEVANCE VECTOR MACHINE; MODELS Landslide displacement is widely obtained to discover landslide behaviors for purpose of event forecasting. This article aims to present a comparative study on landslide nonlinear displacement analysis and prediction using computational intelligence techniques. Three state-of-art techniques, the support vector machine (SVM), the relevance vector machine (RVM), and the Gaussian process (GP), are comparatively presented briefly for modeling landslide displacement series. The three techniques are discussed comparatively for both fitting and predicting the landslide displacement series. Two landslides, the Baishuihe colluvial landslide in China Three Georges and the Super-Sauze mudslide in the French Alps, are illustrated. The results prove that the computational intelligence approaches are feasible and capable of fitting and predicting landslide nonlinear displacement. The Gaussian process, on the whole, performs better than the support vector machine, relevance vector machine, and simple artificial neural network (ANN) with optimized parameter values in predictive analysis of the landslide displacement. [Liu, Zaobao; Shao, Jianfu; Xu, Weiya; Chen, Hongjie; Shi, Chong] Hohai Univ, Geotech Res Inst, Nanjing 210098, Jiangsu, Peoples R China; [Liu, Zaobao; Shao, Jianfu] Univ Lille Sci & Technol 1, Lab Mech Lille, F-59655 Villeneuve Dascq, France Hohai University; Universite de Lille - ISITE; Centrale Lille; Universite de Lille Liu, ZB (corresponding author), Univ Lille Sci & Technol 1, Lab Mech Lille, F-59655 Villeneuve Dascq, France. forward@hhu.edu.cn Liu, Zaobao/R-6767-2016; Liu, Zaobao/AAF-2751-2020; Liu, Zaobao/ACA-9819-2022; shao, jianfu/N-2447-2018 Liu, Zaobao/0000-0002-2047-5463; Liu, Zaobao/0000-0002-2047-5463; Liu, Zaobao/0000-0002-2047-5463; shao, jianfu/0000-0002-6632-8207 China 973 Program for Key Basic Research Project [2011CB013504]; China Natural Science Foundation [11272114] China 973 Program for Key Basic Research Project(National Basic Research Program of China); China Natural Science Foundation(National Natural Science Foundation of China (NSFC)) Financial support from the China 973 Program for Key Basic Research Project (no. 2011CB013504) and the China Natural Science Foundation (no. 11272114) is gratefully acknowledged. The authors are also grateful to the anonymous reviewers for their comments and suggestions. 36 72 80 11 113 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1612-510X 1612-5118 LANDSLIDES Landslides OCT 2014.0 11 5 889 896 10.1007/s10346-013-0443-z 0.0 8 Engineering, Geological; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology AU0OW 2023-03-23 WOS:000345323800011 0
J Xue, Y; Qin, JF; Su, SB; Slowik, A Xue, Yu; Qin, Jiafeng; Su, Shoubao; Slowik, Adam Brain Storm Optimization Based Clustering for Learning Behavior Analysis COMPUTER SYSTEMS SCIENCE AND ENGINEERING English Article Online learning; learning behavior analysis; big data; brain storm optimization; cluster K-MEANS; ALGORITHM Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students' learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a clustering algorithm based on brain storm optimization (CBSO) to categorize students according to their learning behaviors and determine their characteristics. This enables teaching to be tailored to taken into account those results, thereby, improving the education quality over time. Specifically, we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence. The experiments are performed on the 104 students' online learning data, and the results show that CBSO is feasible and efficient. [Xue, Yu; Qin, Jiafeng] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Xue, Yu; Su, Shoubao] Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 211169, Peoples R China; [Slowik, Adam] Koszalin Univ Technol, Dept Elect & Comp Sci, PL-75453 Koszalin, Poland Nanjing University of Information Science & Technology; Jinling Institute of Technology; Koszalin University of Technology Xue, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.;Xue, Y (corresponding author), Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 211169, Peoples R China. xueyu@nuist.edu.cn Su, Shoubao/0000-0003-2036-7646 National Natural Science Foundation of China [61876089, 61876185, 61902281, 61375121]; Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software [2019DS301]; Engineering Research Center of Digital Forensics, Ministry of Education; Key Research and Development Program of Jiangsu Province [BE2020633]; Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software; Engineering Research Center of Digital Forensics, Ministry of Education; Key Research and Development Program of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions This work was partially supported by the National Natural Science Foundation of China (61876089, 61876185, 61902281, 61375121), the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software (No. 2019DS301), the Engineering Research Center of Digital Forensics, Ministry of Education, the Key Research and Development Program of Jiangsu Province (BE2020633), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. 32 2 2 2 19 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 0267-6192 COMPUT SYST SCI ENG Comput. Syst. Sci. Eng. 2021.0 39 2 211 219 10.32604/csse.2021.016693 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science TP3FE hybrid 2023-03-23 WOS:000677478500004 0
J Zhou, PB; Li, K; Wei, W; Wozniak, M; Du, ZM; Li, HA Zhou, Peng-bo; Li, Kang; Wei, Wei; Wozniak, Marcin; Du, Zhuo-ming; Li, Hong-an Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm INFORMATION TECHNOLOGY AND CONTROL English Article Compressed sensing; CT Image; Re-weighted function; Regularized Least-Squares; Sparse representation FRAMEWORK; RECONSTRUCTION; NAVIGATION It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recovery method for compressed sensing CT images. At present, minimizing 0-norm, 1-norm and p-norm is used to recover compressed sensing signals. However, sometimes 0-norm is an NP problem, 1-norm has no solution in theory and p-norm is not a convex function. We introduce a recovery method of compressed sensing signal based on regularized smooth convex optimization. In order to avoid solving the non-convex optimization problems and no solution condition, a convex function is designed as the objective function of optimization to fit 0-norm of signal and a fast iterative shrinkage-thresholding algorithm is proposed to find solution with the convergence speed is quadratic convergence. Experimental results show that our method has a sound recovery effect and is well suitable for processing big data of compressed CT images. [Zhou, Peng-bo] Beijing Normal Univ, Sch Art & Commun, Beijing 100875, Peoples R China; [Li, Kang] Norwest Univ, Sch Comp Sci & Technol, Xian 710048, Peoples R China; [Wei, Wei] Xian Univ Technol, Sch Comp Sci & Engn, Xian 710048, Peoples R China; [Wozniak, Marcin] Silesian Tech Univ, Inst Math, Kaszubska 23, PL-44100 Gliwice, Poland; [Du, Zhuo-ming] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Peoples R China; [Li, Hong-an] Xian Univ Sci & Technol, Coll Comp Sci & Technol, Xian 710054, Peoples R China Beijing Normal University; Xi'an University of Technology; Silesian University of Technology; Nanjing Normal University; Xi'an University of Science & Technology Wei, W (corresponding author), Xian Univ Technol, Sch Comp Sci & Engn, Xian 710048, Peoples R China. zhoupengbo@bnu.edu.cn; 854092@qq.com; weiwei@xaut.edu.cn; Marcin.Wozniak@polsl.pl; duzhuoming423dzm@126.com; an6860@126.com Woźniak, Marcin/L-6640-2013; wei, wei/HHR-8613-2022; Wei, Wei/ABB-8665-2021 Woźniak, Marcin/0000-0002-9073-5347; Wei, Wei/0000-0002-8751-9205 National key R&D Program of China [2018YFB0203901]; National Natural Science Foundation of China [61731015, 61672103, 61402206]; China Postdoctoral Science Foundation [2016M601845, 2016M602941XB]; Shaanxi Provincial Education Bureau [16JK1497]; Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-036] National key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Shaanxi Provincial Education Bureau; Key Research and Development Program of Shaanxi Province This research is supported by the National key R&D Progra m of China under Gra nt NO.2018YFB0203901. This work is also supported by National Natural Science Foundation of China (61731015, 61672103).; This work is supported by the National Natural Science Foundation of China (61402206), the Project Funded by China Postdoctoral Science Foundation (2016M601845, 2016M602941XB) and the Scientific Research Program Funded by Shaanxi Provincial Education Bureau (Program No. 16JK1497). We declare hat there is no conflict of interests regarding the publication of this paper and would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is also supported by The Key Research and Development Program of Shaanxi Province (No.2018ZDXM-GY-036). 34 2 2 1 8 KAUNAS UNIV TECHNOLOGY KAUNAS KAUNAS UNIV TECHNOL, DEPT ELECTRONICS ENGINEERING, STUDENTU STR 50, KAUNAS, LT-51368, LITHUANIA 1392-124X INF TECHNOL CONTROL Inf. Technol. Control 2019.0 48 2 357 365 10.5755/j01.itc.48.2.21864 0.0 9 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science KH1JI gold 2023-03-23 WOS:000510402300012 0
C Kao, YY; He, R; Huang, KQ IEEE Kao, Yueying; He, Ran; Huang, Kaiqi AUTOMATIC IMAGE CROPPING WITH AESTHETIC MAP AND GRADIENT ENERGY MAP 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) MAR 05-09, 2017 New Orleans, LA IEEE,Inst Elect & Elect Engineers, Signal Proc Soc Image cropping; Aesthetic map; Gradient energy map; Convolutional neural networks PHOTO COMPOSITION Image cropping is a fundamental task in image editing to enhance the aesthetic quality of images. In this paper, we propose an automatic image cropping technique based on aesthetic map and gradient energy map. Instead of utilizing aesthetic rules in previous methods, we learn the aesthetic map by a deep convolutional neural network with a large-scale dataset for aesthetic quality assessment. The aesthetic map can highlight the discriminative image regions for high (or low) aesthetic quality category. The gradient energy map presents edge spatial distribution of images and is developed to compute the simplicity of images. Then a composition model is learned with the aesthetic map and gradient energy map to evaluate the quality of composition for crops. Moreover, an aesthetic preservation model is developed to compute the aesthetic information remained in crops to avoid cropping out high aesthetic regions. Experiments show that our approach significantly outperforms state-of-the-art cropping methods. [Kao, Yueying; He, Ran; Huang, Kaiqi] CASIA, CRIPAC & NLPR, Paris, France; [Kao, Yueying; He, Ran; Huang, Kaiqi] Univ Chinese Acad Sci, Beijing, Peoples R China; [He, Ran; Huang, Kaiqi] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Kao, YY (corresponding author), CASIA, CRIPAC & NLPR, Paris, France.;Kao, YY (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. National Natural Science Foundation of China [61322209, 61673375]; International Partnership Program of Chinese Academy of Science [173211KYSB20160008] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); International Partnership Program of Chinese Academy of Science This work is funded by the National Natural Science Foundation of China (Grant No. 61322209, Grant No. 61673375), and the International Partnership Program of Chinese Academy of Science, Grant No. 173211KYSB20160008. 28 19 21 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-4117-6 INT CONF ACOUST SPEE 2017.0 1982 1986 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BI7JW 2023-03-23 WOS:000414286202032 0
J Yin, JH; Bai, X; Fenton, O; Tang, BB; Chen, S; Ma, Y; Zhang, S; Cao, WC; Ding, S; Liu, R; Chen, Q Yin, Junhui; Bai, Xin; Fenton, Owen; Tang, Bingbing; Chen, Shuo; Ma, Yan; Zhang, Shuai; Cao, Wenchao; Ding, Shuai; Liu, Rui; Chen, Qing Extracellular Enzyme Patterns Provide New Insights Regarding Nitrogen Transformation Induced by Alkaline Amendment of Acidic Soil AGRONOMY-BASEL English Article nitrification; mineralization; extracellular enzyme activity; alkaline amendment; acidic soil ORGANIC-MATTER; PH; MINERALIZATION; MICROORGANISMS; NITRIFICATION; ACIDIFICATION; AVAILABILITY; DEPOSITION; DYNAMICS; CALCIUM Nitrogen (N) availability is generally a limiting factor in highly acidic soil, which could be improved by amending these soils with alkaline materials. Soil extracellular enzyme activity (EEA) plays an important role in N transformation; a current knowledge gap is how this occurs in acidic soils amended with alkaline material. The present 45-day incubation experiment was designed to examine the effects of different amounts of alkaline materials (urea and/or calcium-silicon-magnesium-potassium fertilizer (CSMP)) on N transformation. The results show that soil pH significantly increased after the CSMP amendment (1.2 units) and increased soil net N mineralization (R-min), net nitrification (R-nit) rates, and net ammonification (R-amo) rates. CSMP amendment changed the different soil EEA but with differing or opposing effects, e.g., R-nit was positively correlated with the activities of L-leucine aminopeptidase, beta-xylosidase, alpha-glucosidase, and N-acetyl-beta-glucosaminidase but negatively correlated with beta-1, 4-glucosidase and beta-cellobiosidase. A machine learning analysis indicated that the best predictor for R-min and R-amo was soil pH, and for R-nit, it was nitrate. The results of the present study improve our understanding of N availability in acidic soils amended with materials to control soil pH. Such knowledge could lead to more bespoke nutrient management planning at the field scale, leading to better agronomic and environmental outcomes. [Yin, Junhui; Bai, Xin; Tang, Bingbing; Chen, Shuo; Ma, Yan; Zhang, Shuai; Ding, Shuai; Liu, Rui; Chen, Qing] China Agr Univ, Coll Resources & Environm Sci, Beijing Key Lab Farmyard Soil Pollut Prevent Cont, Beijing 100193, Peoples R China; [Fenton, Owen] TEAGASC, Environm Res Ctr, Johnstown Castle Y35 Y521, Wexford, Ireland; [Cao, Wenchao] Weifang Univ Sci & Technol, JIA Sixie Agr Coll, Shouguang 262700, Peoples R China China Agricultural University; Teagasc Liu, R (corresponding author), China Agr Univ, Coll Resources & Environm Sci, Beijing Key Lab Farmyard Soil Pollut Prevent Cont, Beijing 100193, Peoples R China. rliu@cau.edu.cn Fenton, Owen/D-4377-2009 Fenton, Owen/0000-0001-7119-2538 39 0 0 6 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4395 AGRONOMY-BASEL Agronomy-Basel DEC 2022.0 12 12 3015 10.3390/agronomy12123015 0.0 12 Agronomy; Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Plant Sciences 7D3GX gold 2023-03-23 WOS:000900384300001 0
J Jiang, WX; Yu, H; Zhang, JL; Wu, JX; Luo, SB; Ha, YJ Jiang, Weixiong; Yu, Heng; Zhang, Jiale; Wu, Jiaxuan; Luo, Shaobo; Ha, Yajun Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling JOURNAL OF SEMICONDUCTORS English Article CNN; FPGA; DVFS; object detection On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy. [Jiang, Weixiong; Zhang, Jiale; Wu, Jiaxuan; Ha, Yajun] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China; [Jiang, Weixiong; Zhang, Jiale; Wu, Jiaxuan; Ha, Yajun] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China; [Jiang, Weixiong; Zhang, Jiale; Wu, Jiaxuan; Ha, Yajun] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Yu, Heng] Univ Nottingham Ningbo China, Ningbo 315100, Peoples R China; [Luo, Shaobo] Univ Paris Est, F-93162 Paris, France ShanghaiTech University; Chinese Academy of Sciences; Shanghai Institute of Microsystem & Information Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Nottingham Ningbo China Ha, YJ (corresponding author), ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China.;Ha, YJ (corresponding author), Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China.;Ha, YJ (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. hayj@shanghaitech.edu.cn Luo, Shaobo/AAM-9933-2021; Wu, Jiaxuan/HGD-4550-2022 Yu, Heng/0000-0002-0305-2135; jiang, weixiong/0000-0002-6014-6453 39 6 6 0 4 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1674-4926 J SEMICOND J. Semicond. FEB 2020.0 41 2 22406 10.1088/1674-4926/41/2/022406 0.0 10 Physics, Condensed Matter Emerging Sources Citation Index (ESCI) Physics KR3YX Green Accepted 2023-03-23 WOS:000517555800001 0
J Mei, ZN; Gu, X; Chen, HY; Chen, W Mei, Zhenning; Gu, Xiao; Chen, Hongyu; Chen, Wei Automatic Atrial Fibrillation Detection Based on Heart Rate Variability a Spectral Features IEEE ACCESS English Article Atrial fibrillation; ECG; heart rate variability; biomedical signal processing; machine learning Atrial fibrillation (AF) is one of the most common sustained arrhythmias, affecting about 1% of the population around the world. Rapid popularization of portable and wearable devices in recent years makes widespread personalized and mobile healthcare get closer to reality than ever before. This paper presents a method aiming for automatic detection of AF from short single lead electrocardiogram (ECG) recordings. Since AF is a kind of arrhythmia being likely to alter the dynamics of heart rhythms and/or the morphological characteristics in ECG tracings, heart rate variability (HRV)-based metrics and frequency analysis are adopted as feature extractors. We validate our method on a public available data set comprised of short ECG recordings of normal rhythm (N), AF (A), and other arrhythmias (0) by support vector machine and bagging trees. For two-class classification problems (N versus A), accuracy varies from 92.0% to 96.6% under different additional noise levels. For three-class classification problem (N versus A versus 0), accuracy as high as 82.0% is obtained. Experimental results suggest than even for a relatively short ECG recording, nonlinear descriptors of HRV are still efficient and robust for AF detection. [Mei, Zhenning; Gu, Xiao; Chen, Wei] Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Engn, Shanghai 200433, Peoples R China; [Chen, Hongyu] Tech Univ Eindhoven, Dept Ind Design, NL-5600 MB Eindhoven, Netherlands; [Chen, Wei] Fudan Univ, Shanghai Key Lab Med Imaging Comp & Comp Assisted, Shanghai 200032, Peoples R China Fudan University; Eindhoven University of Technology; Fudan University Chen, W (corresponding author), Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Engn, Shanghai 200433, Peoples R China.;Chen, W (corresponding author), Fudan Univ, Shanghai Key Lab Med Imaging Comp & Comp Assisted, Shanghai 200032, Peoples R China. w_chen@fudan.edu.cn chen, hongyu/HHS-4314-2022; Gu, Xiao/K-5191-2019 Gu, Xiao/0000-0002-3015-5818 National Key R&D Program of China [2017YFE0112000]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01] National Key R&D Program of China; Shanghai Municipal Science and Technology Major Project This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0112000 and in part by the Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01. 32 20 22 3 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6 53566 53575 10.1109/ACCESS.2018.2871220 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GX7XB Green Submitted, gold 2023-03-23 WOS:000447992900001 0
J Yang, W; Wang, YW; Morarescu, IC; Liu, XK; Huang, YH Yang, Wu; Wang, Yan-Wu; Morarescu, Irinel-Constantin; Liu, Xiao-Kang; Huang, Yuehua Fixed-Time Synchronization of Competitive Neural Networks With Multiple Time Scales IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Synchronization; Neural networks; Neurons; Process control; Visualization; Perturbation methods; Pattern recognition; Competitive neural network; continuous control method; fixed-time synchronization; multiple time-scale feature DELAYS In this brief, we investigate the fixed-time synchronization of competitive neural networks with multiple time scales. These neural networks play an important role in visual processing, pattern recognition, neural computing, and so on. Our main contribution is the design of a novel synchronizing controller, which does not depend on the ratio between the fast and slow time scales. This feature makes the controller easy to implement since it is designed through well-posed algebraic conditions (i.e., even when the ratio between the time scales goes to 0, the controller gain is well defined and does not go to infinity). Last but not least, the closed-loop dynamics is characterized by a high convergence speed with a settling time which is upper bounded, and the bound is independent of the initial conditions. A numerical simulation illustrates our results and emphasizes their effectiveness. [Yang, Wu; Wang, Yan-Wu; Liu, Xiao-Kang] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China; [Yang, Wu] Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China; [Morarescu, Irinel-Constantin] Univ Lorraine, CNRS, CRAN, F-54000 Nancy, France; [Huang, Yuehua] China Three Gorges Univ, Coll Elect Engn & New Energy, Yichang 443002, Peoples R China Huazhong University of Science & Technology; Guangxi University; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; China Three Gorges University Liu, XK (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China. wangyw@hust.edu.cn; xkliuhust@hotmail.com Morarescu, Constantin/D-8486-2012 Morarescu, Constantin/0000-0002-8950-8112; Wang, yan-wu/0000-0002-4722-1332; Liu, Xiao-Kang/0000-0001-6262-6779 National Natural Science Foundation of China [61903147, 61773172]; Fundamental Research Funds for the Central Universities [2018KFYYXJJ119]; Agence Nationale de la Recherche through Hybrid And Networked Dynamical Systems [ANR-18CE40-0010] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Agence Nationale de la Recherche through Hybrid And Networked Dynamical Systems(French National Research Agency (ANR)) This work is supported in part by National Natural Science Foundation of China under Grants 61903147 and 61773172, in part by the Fundamental Research Funds for the Central Universities (2018KFYYXJJ119), and in part by the Agence Nationale de la Recherche through Hybrid And Networked Dynamical Systems under Grant ANR-18CE40-0010. (Corresponding author: Xiao-Kang Liu.) 26 6 6 14 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. AUG 2022.0 33 8 4133 4138 10.1109/TNNLS.2021.3052868 0.0 FEB 2021 6 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3Z9UL 33556017.0 Green Submitted 2023-03-23 WOS:000732226400001 0
J Zhao, T; Tang, L; Huang, JF; Fu, XM Zhao, Tao; Tang, Lu; Huang, Jinfeng; Fu, Xiaoming Coupled social media content representation for predicting individual socioeconomic status EXPERT SYSTEMS WITH APPLICATIONS English Article Socioeconomic status; Coupled social media content representation; Structure-aware social media text representation; Coupled attribute representation Predicting individual socioeconomic status (SES) from social media content benefits various applications in economic and social fields. Most previous works adopt machine learning methods with predefined features to infer SES. Nevertheless, they ignore some important information of social media content, such as order, struc-ture and relation information, which leads to limited performance. In this paper, we propose a COupled social media content REpresentation model (CORE) for individual SES prediction, which efficiently exploits latent complex couplings of social media content. CORE devises a structure-aware social media text representation method to incorporate the order and the hierarchy of social media text, and leverages a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. Our experiments on a real data set of a Chinese microblogging platform demonstrate that our approach significantly outperforms benchmark methods, which validates its efficiency and robustness. The proposed model could be applied to improve the SES prediction and other user profiling tasks. [Zhao, Tao; Tang, Lu; Huang, Jinfeng] Natl Univ Def Technol, Changsha, Peoples R China; [Fu, Xiaoming] Univ Goettingen, Gottingen, Germany National University of Defense Technology - China; University of Gottingen Zhao, T (corresponding author), Natl Univ Def Technol, Changsha, Peoples R China. taozhao@nudt.edu.cn; lutang@nudt.edu.cn; hjf@nudt.edu.cn; fu@cs.uni-goettingen.de Fu, Xiaoming/AAD-2828-2022 Fu, Xiaoming/0000-0002-8012-4753 European Union [824019] European Union(European Commission) This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 824019. We would like to thank Prof. Jar-der Luo and Prof. Jingming Liu and their graduate students from the Department of Sociology, Tsinghua University, for their assistance in understanding sociological theories and help in obtaining data labels. We also thank anonymous reviewers for their critical comments on early drafts of this paper. 49 0 0 3 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. JUL 15 2022.0 198 116744 10.1016/j.eswa.2022.116744 0.0 MAR 2022 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 1U1AM 2023-03-23 WOS:000805151700003 0
J Xiong, G; Wang, FY; Nyberg, TR; Shang, XQ; Zhou, MC; Shen, Z; Li, SS; Guo, C Xiong, Gang; Wang, Fei-Yue; Nyberg, Timo R.; Shang, Xiuqin; Zhou, Mengchu; Shen, Zhen; Li, Shuangshuang; Guo, Chao From Mind to Products: Towards Social Manufacturing and Service IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Article Big data; cloud computing; intelligent system; social manufacturing; social networks; 3D printing MASS CUSTOMIZATION After reviewing the development of industrial manufacturing, a novel concept called social manufacturing (SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of from mind to products, and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend, and is expected to become popular in more and more industries. First, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies, like Internet of Things (IoT), social networks, cloud computing, 3D printing, and intelligent systems, are introduced and analyzed, and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3D printing are suggested for different user cases. [Xiong, Gang; Wang, Fei-Yue; Shang, Xiuqin] Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Nyberg, Timo R.] Aalto Univ, Sch Sci, FI-00076 Aalto, Finland; [Zhou, Mengchu] New Jersey Inst Technol, Lab Discrete Event Syst, Newark, NJ 07102 USA; [Shen, Zhen] Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China; [Li, Shuangshuang] Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China; [Guo, Chao] Cloud Comp Ctr, Dongguan 523808, Peoples R China Chinese Academy of Sciences; Institute of Automation, CAS; Aalto University; New Jersey Institute of Technology; Chinese Academy of Sciences; Institute of Automation, CAS Shang, XQ (corresponding author), Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China. gang.xiong@ia.ac.cn; feiyue.wang@ia.ac.cn; timo.nyberg@aalto.fi; xiuqin.shang@ia.ac.cn; mengchu.zhou@njit.edu; zheng.shen@ia.ac.cn; shuangshuang.li@ia.ac.cn; guochao2014@ia.ac.cn Shen, Zhen/V-5024-2019 National Natural Science Foundation of China [61233001, 61773381, 71232006, 61304201, 61533019, 61773382]; Finnish TEKES's project SoMa2020: Social Manufacturing [211560]; Chinese Guangdong's ST project [2015B010103001, 2016B090910001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Finnish TEKES's project SoMa2020: Social Manufacturing; Chinese Guangdong's ST project This work was supported in part by the National Natural Science Foundation of China (61233001,61773381,71232006,61304201,61533019,61773382), Finnish TEKES's project SoMa2020: Social Manufacturing (2015-2017,211560), Chinese Guangdong's S&T project (2015B010103001, 2016B090910001). Recommended by Associate Editor Qinglai Wei. 37 51 54 2 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. JAN 2018.0 5 1 47 57 10.1109/JAS.2017.7510742 0.0 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems FR2GP Green Published 2023-03-23 WOS:000418885500005 0
J Shen, JM; Li, W; Deng, SF; Zhang, T Shen, Jianmin; Li, Wei; Deng, Shengfeng; Zhang, Tao Supervised and unsupervised learning of directed percolation PHYSICAL REVIEW E English Article PHASE-TRANSITIONS; FIELD-THEORY; BEHAVIOR Machine learning (ML) has been well applied to studying equilibrium phase transition models by accurately predicating critical thresholds and some critical exponents. Difficulty will be raised, however, for integrating ML into nonequilibrium phase transitions. The extra dimension in a given nonequilibrium system, namely time, can greatly slow down the procedure toward the steady state. In this paper we find that by using some simple techniques of ML, non-steady-state configurations of directed percolation (DP) suffice to capture its essential critical behaviors in both (1+1) and (2+1) dimensions. With the supervised learning method, the framework of our binary classification neural networks can identify the phase transition threshold, as well as the spatial and temporal correlation exponents. The characteristic time t(c), specifying the transition from active phases to absorbing ones, is also a major product of the learning. Moreover, we employ the convolutional autoencoder, an unsupervised learning technique, to extract dimensionality reduction representations and cluster configurations of (1+1) bond DP. It is quite appealing that such a method can yield a reasonable estimation of the critical point. [Shen, Jianmin; Li, Wei; Deng, Shengfeng; Zhang, Tao] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China; [Shen, Jianmin; Li, Wei; Deng, Shengfeng; Zhang, Tao] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China; [Li, Wei] Max Planck Inst Math Sci, D-04103 Leipzig, Germany Central China Normal University; Central China Normal University; Max Planck Society Li, W (corresponding author), Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Li, W (corresponding author), Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Li, W (corresponding author), Max Planck Inst Math Sci, D-04103 Leipzig, Germany. liw@mail.ccnu.edu.cn Deng, Shengfeng/0000-0002-1952-8599 Fundamental Research Funds for the Central Universities, China [CCNU19QN029]; National Natural Science Foundation of China [11505071, 61702207, 61873104]; Programme of Introducing Talents of Discipline to Universities - State Administration of Foreign Experts Affairs under the 111 Project 2.0 [BP0820038]; Programme of Introducing Talents of Discipline to Universities - Ministry of Education, PRC under the 111 Project 2.0 [BP0820038] Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Programme of Introducing Talents of Discipline to Universities - State Administration of Foreign Experts Affairs under the 111 Project 2.0; Programme of Introducing Talents of Discipline to Universities - Ministry of Education, PRC under the 111 Project 2.0 We thank Juan Carrasquilla and Wanzhou Zhang for the helpful computer programs. We thank the two referees for their constructive suggestions and comments. This work was supported in part by the Fundamental Research Funds for the Central Universities, China (Grant No. CCNU19QN029), the National Natural Science Foundation of China (Grants No. 11505071, 61702207, and 61873104), and the Programme of Introducing Talents of Discipline to Universities (sponsored by the State Administration of Foreign Experts Affairs and the Ministry of Education, PRC) under the 111 Project 2.0 with Grant No. BP0820038. 46 5 5 3 12 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0045 2470-0053 PHYS REV E Phys. Rev. E MAY 27 2021.0 103 5 52140 10.1103/PhysRevE.103.052140 0.0 13 Physics, Fluids & Plasmas; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics SK1LA 34134215.0 Green Submitted 2023-03-23 WOS:000655981300004 0
J Liang, XQ; Yan, Z; Deng, RH Liang, Xueqin; Yan, Zheng; Deng, Robert H. Game theoretical study on client-controlled cloud data deduplication COMPUTERS & SECURITY English Article Cloud data deduplication; Free riding; Game theory; Incentive mechanism; Privacy ACCESS-CONTROL; SIDE CHANNELS; EFFICIENT Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Data deduplication not only requires a consummate technological designing, but also involves multiple parties with conflict interests. Thus, how to design incentive mechanisms and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we detail the payoff structure of a client-controlled deduplication scheme and analyze the feasibilities of unified discount and individualized discount under this structure. Through game theoretical study, a privacy-preserving individualized discount-based incentive mechanism is further proposed with detailed implementation algorithms for choosing strategies, setting parameters and granting discounts. After theoretical analysis on the requirements of individual rationality, incentive compatibility, and profitability, we conduct extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed incentive mechanism. (C) 2020 Elsevier Ltd. All rights reserved. [Liang, Xueqin; Yan, Zheng] Xidian Univ, Sch Cyber Engn, Xian, Peoples R China; [Liang, Xueqin; Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Deng, Robert H.] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore Xidian University; Aalto University; Singapore Management University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, Xian, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo, Finland. zyan@xidian.edu.cn; robertdeng@edu.sum.sg yang, zheng/HGC-7753-2022; zheng, yan/GQY-6668-2022; Liang, Xueqin/AAP-5862-2021 Liang, Xueqin/0000-0002-0335-1768; Deng, Robert/0000-0003-3491-8146; Yan, Zheng/0000-0002-9697-2108 National Natural Science Foundation of China [61672410, 61802293]; National Postdoctoral Program for Innovative Talents [BX20180238]; China Postdoctoral Science Foundation [2018M633461]; Academy of Finland [308087, 314203]; Key Lab of Information Network Security, Ministry of Public Security [C18614]; Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China [CLDL-20182119]; Shaanxi Innovation Team project [2018TD-007]; 111 project [B16037] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Academy of Finland(Academy of Finland); Key Lab of Information Network Security, Ministry of Public Security; Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China; Shaanxi Innovation Team project; 111 project(Ministry of Education, China - 111 Project) The work is supported in part by the National Natural Science Foundation of China under Grants 61672410 and 61802293, the National Postdoctoral Program for Innovative Talents under grant BX20180238, the Project funded by China Postdoctoral Science Foundation under grant 2018M633461, the Academy of Finland under Grants 308087 and 314203, the Key Lab of Information Network Security, Ministry of Public Security under grant No. C18614, the open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China under grant CLDL-20182119, the Shaanxi Innovation Team project under grant 2018TD-007, and the 111 project under grant B16037. 42 5 5 0 12 ELSEVIER ADVANCED TECHNOLOGY OXFORD OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0167-4048 1872-6208 COMPUT SECUR Comput. Secur. APR 2020.0 91 101730 10.1016/j.cose.2020.101730 0.0 14 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science KT2TI Green Published, Green Submitted 2023-03-23 WOS:000518868100005 0
C Liu, XW; Liu, J; Bai, Y; Gu, JD; Chen, T; Jia, XJ; Cao, XC Avidan, S; Brostow, G; Cisse, M; Farinella, GM; Hassner, T Liu, Xinwei; Liu, Jian; Bai, Yang; Gu, Jindong; Chen, Tao; Jia, Xiaojun; Cao, Xiaochun Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal COMPUTER VISION - ECCV 2022, PT XIV Lecture Notes in Computer Science English Proceedings Paper 17th European Conference on Computer Vision (ECCV) OCT 23-27, 2022 Tel Aviv, ISRAEL Visible watermark removal; Watermark protection; Adversarial attack As a common security tool, visible watermarking has been widely applied to protect copyrights of digital images. However, recent works have shown that visible watermarks can be removed by DNNs without damaging their host images. Such watermark-removal techniques pose a great threat to the ownership of images. Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good. From the perspective of the adversary, blind watermark-removal networks can be posed as our target models; then we actually optimize an imperceptible adversarial perturbation on the host images to proactively attack against watermark-removal networks, dubbed Watermark Vaccine. Specifically, two types of vaccines are proposed. Disrupting Watermark Vaccine (DWV) induces to ruin the host image along with watermark after passing through watermark-removal networks. In contrast, Inerasable Watermark Vaccine (IWV) works in another fashion of trying to keep the watermark not removed and still noticeable. Extensive experiments demonstrate the effectiveness of our DWV/IWV in preventing watermark removal, especially on various watermark removal networks. [Liu, Xinwei; Jia, Xiaojun; Cao, Xiaochun] Chinese Acad Sci, Inst Informat Engn, SKLOIS, Beijing, Peoples R China; [Liu, Xinwei; Jia, Xiaojun] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China; [Liu, Jian; Chen, Tao] Ant Grp, Beijing, Peoples R China; [Bai, Yang] Tencent Secur Zhuque Lab, Beijing, Peoples R China; [Gu, Jindong] Univ Munich, Munich, Germany; [Cao, Xiaochun] Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China Chinese Academy of Sciences; Institute of Information Engineering, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Munich; Sun Yat Sen University Jia, XJ (corresponding author), Chinese Acad Sci, Inst Informat Engn, SKLOIS, Beijing, Peoples R China.;Jia, XJ (corresponding author), Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China. liuxinwei@iie.ac.cn; rex.lj@antgroup.com; mavisbai@tencent.com; boshan.ct@antgroup.com; jiaxiaojun@iie.ac.cn; caoxiaochun@mail.sysu.edu.cn National Key R&D Program of China [2019YFB 1406500]; Ant Group Security and Risk Management Fund National Key R&D Program of China; Ant Group Security and Risk Management Fund Supported by the National Key R&D Program of China under (Grant 2019YFB 1406500), Sponsored by Ant Group Security and Risk Management Fund. 57 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-19780-2; 978-3-031-19781-9 LECT NOTES COMPUT SC 2022.0 13674 1 17 10.1007/978-3-031-19781-9_1 0.0 17 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU4NL Green Submitted 2023-03-23 WOS:000904096200001 0
J Tran, TK; Vo, QN; Hong, XP; Li, XB; Zhao, GY Tran, Thuong-Khanh; Vo, Quang-Nhat; Hong, Xiaopeng; Li, Xiaobai; Zhao, Guoying Micro-expression spotting: A new benchmark NEUROCOMPUTING English Article Micro-expression spotting; Benchmark; Evaluation protocol Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. Therefore, ME analysis has become an attractive topic for various research areas, such as psychology, law enforcement, and psychotherapy. In the computer vision field, the study of MEs can be divided into two main tasks, spotting and recognition, which are used to identify positions of MEs in videos and determine the emotion category of the detected MEs, respectively. Recently, although much research has been done, no fully automatic system for analyzing MEs has yet been constructed on a practical level for two main reasons: most of the research on MEs only focuses on the recognition part, while abandoning the spotting task; current public datasets for ME spotting are not challenging enough to support developing a robust spotting algorithm. The contributions of this paper are threefold: (1) we introduce an extension of the SMIC-E database, namely the SMIC-E-Long database, which is a new challenging benchmark for ME spotting; (2) we suggest a new evaluation protocol that standardizes the comparison of various ME spotting techniques; (3) extensive experiments with handcrafted and deep learning-based approaches on the SMICE-Long database are performed for baseline evaluation. ? 2021 Elsevier B.V. All rights reserved. [Tran, Thuong-Khanh; Vo, Quang-Nhat; Li, Xiaobai; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Hong, Xiaopeng] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China University of Oulu; Xi'an Jiaotong University Zhao, GY (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland. khanh.tran@oulu.fi; nhat.vo@oulu.fi; hongxiaopeng@mail.xjtu.edu.cn; xiaobai.li@oulu.fi; guoying.zhao@oulu.fi Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X Ministry of Education and Culture of Finland for AI forum project; Academy of Finland [316765]; ICT 2023 project [328115]; Academy of Finland postdoctoral Project 6+E [323287]; Infotech Oulu; Academy of Finland (AKA) [316765] Funding Source: Academy of Finland (AKA) Ministry of Education and Culture of Finland for AI forum project; Academy of Finland(Academy of Finland); ICT 2023 project; Academy of Finland postdoctoral Project 6+E; Infotech Oulu; Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported by Infotech Oulu, Ministry of Education and Culture of Finland for AI forum project, the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115) and Academy of Finland postdoctoral Project 6+E (grant 323287). As well, the authors wish to acknowledge CSC IT Center for Science, Finland, for computational resources. 51 6 7 5 20 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUL 5 2021.0 443 356 368 10.1016/j.neucom.2021.02.022 0.0 APR 2021 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RX4PH Green Published, hybrid, Green Submitted 2023-03-23 WOS:000647206400010 0
J Dong, LM; Zeng, WZ; Wu, LF; Lei, GQ; Chen, HR; Srivastava, AK; Gaiser, T Dong, Liming; Zeng, Wenzhi; Wu, Lifeng; Lei, Guoqing; Chen, Haorui; Srivastava, Amit Kumar; Gaiser, Thomas Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm WATER English Article pan evaporation; machine learning; bat algorithm; CatBoost; random forest SUPPORT VECTOR MACHINE; GLOBAL SOLAR-RADIATION; REFERENCE EVAPOTRANSPIRATION; REGRESSION; MODELS; CLASSIFICATION; TEMPERATURE; CLIMATES; ANN Accurate estimation of pan evaporation (E-p) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling Bat algorithm (Bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006-2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (T-min, T-max), relative humidity (RH), wind speed (U), and global solar radiation (R-s), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed Bat-CB model (RMSE = 0.859-2.227 mm center dot d(-1); MAE = 0.540-1.328 mm center dot d(-1); NSE = 0.625-0.894; MAPE = 0.162-0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897-2.754 mm center dot d(-1); MAE = 0.531-1.77 mm center dot d(-1); NSE = 0.147-0.869; MAPE = 0.161-0.421) slightly outperformed RF (RMSE = 1.005-3.604 mm center dot d(-1); MAE = 0.644-2.479 mm center dot d(-1); NSE = -1.242-0.894; MAPE = 0.176-0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of Bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, Bat-CB has high accuracy, robust stability, and huge potential for E-p estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries. [Dong, Liming; Zeng, Wenzhi; Lei, Guoqing] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Wu, Lifeng] Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China; [Chen, Haorui] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China; [Srivastava, Amit Kumar; Gaiser, Thomas] Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Crop Sci Grp, Katzenburgweg 5, D-53115 Bonn, Germany Wuhan University; Nanchang Institute Technology; China Institute of Water Resources & Hydropower Research; University of Bonn Zeng, WZ (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China.;Wu, LF (corresponding author), Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China. lmdong1997@whu.edu.cn; zengwenzhi1989@whu.edu.cn; lifengwu@nit.edu.cn; leiguoqing1001@whu.edu.cn; chenhr@iwhr.com; amit.srivastava@uni-bonn.de; tgaiser@uni-bonn.de Srivastava, Amit K/D-9159-2018; Zeng, Wen/GPS-8058-2022; dong, li/HMW-0948-2023; Chen, Haorui/Z-1670-2018 Srivastava, Amit K/0000-0001-8219-4854; Chen, Haorui/0000-0001-7820-9793; Gaiser, Thomas/0000-0002-5820-2364 National Natural Science Foundation of China (NSFC) [51879196, 51790533]; mobility program by the Joint Committee of the SinoGerman Center for Research Promotion (SGC) [M-0009]; DFG [031B0151A] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); mobility program by the Joint Committee of the SinoGerman Center for Research Promotion (SGC); DFG(German Research Foundation (DFG)) This research was funded by the National Natural Science Foundation of China (NSFC) (Grant Nos. 51879196 and 51790533), the mobility program by the Joint Committee of the SinoGerman Center for Research Promotion (SGC) (Grant No. M-0009), and the DFG Mobility and BonaRes-Soil3 (Grant No. 031B0151A). 47 13 14 5 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water FEB 2021.0 13 3 256 10.3390/w13030256 0.0 17 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources QD6FN Green Published, gold 2023-03-23 WOS:000615611600001 0
J Jentzen, A; Riekert, A Jentzen, Arnulf; Riekert, Adrian Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS English Article Artificial neural networks; Nonconvex optimization; Gradient flow; Nonsmooth optimization; Machine learning Gradient descent (GD) type optimization schemes are the standard methods to train artificial neural networks (ANNs) with rectified linear unit (ReLU) activation. Such schemes can be considered as discretizations of the corresponding gradient flows (GFs). In this work we analyze GF processes in the training of ANNs with ReLU activation and three layers. In particular, in this article we prove in the case where the distribution of the input data is absolutely continuous with respect to the Lebesgue measure that the risk of every bounded GF trajectory converges to the risk of a critical point. In addition, we show in the case of a 1-dimensional affine target function and a uniform input distribution that the risk of every bounded GF trajectory converges to zero if the initial risk is sufficiently small. Finally, we show that the boundedness assumption can be removed if the hidden layer consists of only one neuron. (c) 2022 Elsevier Inc. All rights reserved. [Jentzen, Arnulf] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen, Peoples R China; [Jentzen, Arnulf] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China; [Jentzen, Arnulf; Riekert, Adrian] Univ Munster, Appl Math Inst Anal & Numer, Fac Math & Comp Sci, Munster, Germany Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen; University of Munster Riekert, A (corresponding author), Univ Munster, Appl Math Inst Anal & Numer, Fac Math & Comp Sci, Munster, Germany. ajentzen@cuhk.edu.cn; ariekert@uni-muenster.de Riekert, Adrian/0000-0002-1458-3388 26 1 1 3 3 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0022-247X 1096-0813 J MATH ANAL APPL J. Math. Anal. Appl. JAN 15 2023.0 517 2 10.1016/j.jmaa.2022.126601 0.0 43 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 5V7BT Green Submitted 2023-03-23 WOS:000877382300002 0
J Bai, Y; He, JH; Xia, XY; Wang, Y; Yang, Y; Di, HB; Li, XL; Ziemann, U Bai, Yang; He, Jianghong; Xia, Xiaoyu; Wang, Yong; Yang, Yi; Di, Haibo; Li, Xiaoli; Ziemann, Ulf Spontaneous transient brain states in EEG source space in disorders of consciousness NEUROIMAGE English Article Transient state; Hidden Markov model; Electroencephalography; Disorder of consciousness RESTING-STATE; DEFAULT MODE; CONNECTIVITY; NETWORK; MEG; LEVEL; ORGANIZATION; DYNAMICS Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment. [Bai, Yang; Di, Haibo] Hangzhou Normal Univ, Int Vegetat State & Consciousness Sci Inst, Hangzhou, Peoples R China; [Bai, Yang; Ziemann, Ulf] Univ Tubingen, Dept Neurol & Stroke, Hoppe Seyler Str 3, D-72076 Tubingen, Germany; [Bai, Yang; Ziemann, Ulf] Univ Tubingen, Hertie Inst Clin Brain Res, Tubingen, Germany; [He, Jianghong; Yang, Yi] Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China; [Xia, Xiaoyu] Chinese Peoples Liberat Army Gen Hosp, Dept Neurosurg, Beijing, Peoples R China; [Wang, Yong; Li, Xiaoli] Beijing Normal Univ, McGovern Inst Brain Res, IDG, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China Hangzhou Normal University; Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Capital Medical University; Chinese People's Liberation Army General Hospital; Beijing Normal University Ziemann, U (corresponding author), Univ Tubingen, Dept Neurol & Stroke, Hoppe Seyler Str 3, D-72076 Tubingen, Germany.;Li, XL (corresponding author), Beijing Normal Univ, McGovern Inst Brain Res, IDG, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China. xiaoli@bnu.edu.cn; ulf.ziemann@uni-tuebingen.de He, Jianghong/GZG-4676-2022; li, xiao/HKV-8405-2023; li, xiao/HJP-5134-2023; Li, Xiaoli/GYQ-7384-2022 He, Jianghong/0000-0001-9470-9966; National Natural Science Foundation of China [61901155]; Medicine and Health Science and Technology Project of Zhejiang Province [2019RC254]; Alexander-vonHumboldt foundation [3.5 -1203399 -CHN -HFST-P] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Medicine and Health Science and Technology Project of Zhejiang Province; Alexander-vonHumboldt foundation(Alexander von Humboldt Foundation) This work was supported by the National Natural Science Foundation of China (61901155); Medicine and Health Science and Technology Project of Zhejiang Province (2019RC254); and the Alexander-vonHumboldt foundation (3.5 -1203399 -CHN -HFST-P). 71 9 9 9 32 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage OCT 15 2021.0 240 118407 10.1016/j.neuroimage.2021.118407 0.0 JUL 2021 13 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging UM2VG 34280527.0 gold 2023-03-23 WOS:000693193300001 0
J Kim, D; Tao, Y; Kim, S; Zeller, A Kim, Dongsun; Tao, Yida; Kim, Sunghun; Zeller, Andreas Where Should We Fix This Bug? A Two-Phase Recommendation Model IEEE TRANSACTIONS ON SOFTWARE ENGINEERING English Article Bug reports; machine learning; patch file prediction PROBABILISTIC RANKING; PREDICTING FAULTS; IMPACT ANALYSIS; ERRORS; TOOL To support developers in debugging and locating bugs, we propose a two-phase prediction model that uses bug reports' contents to suggest the files likely to be fixed. In the first phase, our model checks whether the given bug report contains sufficient information for prediction. If so, the model proceeds to predict files to be fixed, based on the content of the bug report. In other words, our two-phase model speaks up only if it is confident of making a suggestion for the given bug report; otherwise, it remains silent. In the evaluation on the Mozilla Firefox and Core packages, the two-phase model was able to make predictions for almost half of all bug reports; on average, 70 percent of these predictions pointed to the correct files. In addition, we compared the two-phase model with three other prediction models: the Usual Suspects, the one-phase model, and BugScout. The two-phase model manifests the best prediction performance. [Kim, Dongsun; Tao, Yida; Kim, Sunghun] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China; [Zeller, Andreas] Univ Saarlandes Informat, D-66123 Saarbrucken, Germany Hong Kong University of Science & Technology Kim, D (corresponding author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Clear Water Bay Rd, Kowloon, Hong Kong, Peoples R China. darkrsw@cse.ust.hk; idagoo@cse.ust.hk; humkim@cse.ust.hk; zeller@acm.org Kim, Dongsun/B-4856-2015 Kim, Dongsun/0000-0003-0272-6860; Zeller, Andreas/0000-0003-4719-8803 66 106 114 0 22 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0098-5589 1939-3520 IEEE T SOFTWARE ENG IEEE Trans. Softw. Eng. NOV 2013.0 39 11 1597 1610 10.1109/TSE.2013.24 0.0 14 Computer Science, Software Engineering; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 245YN 2023-03-23 WOS:000326501200009 0
C Zhang, MX; Ren, ZC; Wang, ZH; Ren, PJ; Chen, ZM; Hu, PF; Zhang, Y ASSOC COMP MACHINERY Zhang, Minxing; Ren, Zhaochun; Wang, Zihan; Ren, Pengjie; Chen, Zhumin; Hu, Pengfei; Zhang, Yang Membership Inference Attacks Against Recommender Systems CCS '21: PROCEEDINGS OF THE 2021 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY English Proceedings Paper ACM SIGSAC Conference on Computer and Communications Security (ACM CCS) NOV 15-19, 2021 ELECTR NETWORK ACM SIGSAC,Assoc Comp Machinery membership inference attack; recommender system; membership leakage Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems. In this paper, we make the first attempt on quantifying the privacy leakage of reconunender systems through the lens of membership inference. In contrast with traditional membership inference against machine learning classifiers, our attack faces two main differences. First, our attack is on the user-level but not on the data sample-level. Second, the adversary can only observe the ordered recommended items from a recommender system instead of prediction results in the form of posterior probabilities. To address the above challenges, we propose a novel method by representing users from relevant items. Moreover, a shadow recommender is established to derive the labeled training data for training the attack model. Extensive experimental results show that our attack framework achieves a strong performance. In addition, we design a defense mechanism to effectively mitigate the membership inference threat of recommender systems.(1) [Zhang, Minxing; Ren, Zhaochun; Wang, Zihan; Ren, Pengjie; Chen, Zhumin; Hu, Pengfei] Shandong Univ, Jinan, Peoples R China; [Zhang, Minxing; Zhang, Yang] CISPA Helmholtz Ctr Informat Secur, Saarbrucken, Germany Shandong University Zhang, Y (corresponding author), CISPA Helmholtz Ctr Informat Secur, Saarbrucken, Germany. ; Hu, Pengfei/N-5744-2018 Wang, Zihan/0000-0003-0493-2668; Hu, Pengfei/0000-0002-7935-886X; Ren, Zhaochun/0000-0002-9076-6565 Natural Science Foundation of China [61902219, 61972234, 62072279, 62102234]; Helmholtz Association within the project Trustworthy Federated Data Analytics (TFDA) [ZT-I-OO1 4]; National Key R&D Program of China [2020YFB1406704]; Key Scientific and Technological Innovation Program of Shandong Province [2019JZZY010129]; Shandong University multidisciplinary research and innovation team of young scholars [2020QNQT017]; Tencent WeChat Rhino-Bird Focused Research Program [JR-WXG2021411] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Helmholtz Association within the project Trustworthy Federated Data Analytics (TFDA); National Key R&D Program of China; Key Scientific and Technological Innovation Program of Shandong Province; Shandong University multidisciplinary research and innovation team of young scholars; Tencent WeChat Rhino-Bird Focused Research Program We thank the anonymous reviewers for their insightful feedback. This work was supported by the Natural Science Foundation of China (61902219, 61972234, 62072279, 62102234), the Helmholtz Association within the project Trustworthy Federated Data Analytics (TFDA) (funding number ZT-I-OO1 4), the National Key R&D Program of China with grant No. 2020YFB1406704, the Key Scientific and Technological Innovation Program of Shandong Province (2019JZZY010129), Shandong University multidisciplinary research and innovation team of young scholars (No. 2020QNQT017), and the Tencent WeChat Rhino-Bird Focused Research Program (JR-WXG2021411). All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. 52 2 2 6 7 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8454-4 2021.0 864 879 10.1145/3460120.3484770 0.0 16 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BS7XR Green Submitted 2023-03-23 WOS:000768478300054 0
J Sabin, L; Guerrero, MA; Ramos-Larios, G; Boumis, P; Zijlstra, AA; Iskandar, DNFA; Barlow, MJ; Toala, JA; Parker, QA; Corradi, RML; Morris, RAH Sabin, L.; Guerrero, M. A.; Ramos-Larios, G.; Boumis, P.; Zijlstra, A. A.; Iskandar, D. N. F. Awang; Barlow, M. J.; Toala, J. A.; Parker, Q. A.; Corradi, R. M. L.; Morris, R. A. H. First deep images catalogue of extended IPHAS PNe MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article stars: mass-loss; stars: winds, outflows; planetary nebulae PLANETARY-NEBULA We present the first instalment of a deep imaging catalogue containing 58 True, Likely, and Possible extended PNe detected with the Isaac Newton Telescope Photometric H alpha Survey (IPHAS). The three narrow-band filters in the emission lines of H alpha, [N II] lambda 6584 angstrom, and [0111] lambda 5007 angstrom used for this purpose allowed us to improve our description of the morphology and dimensions of the nebulae. In some cases even the nature of the source has been reassessed. We were then able to unveil new macro- and micro-structures, which will without a doubt contribute to a more accurate analysis of these PNe. It has been also possible to perform a primary classification of the targets based on their ionization level. A Deep Learning classification tool has also been tested. We expect that all the PNe from the IPHAS catalogue of new extended planetary nebulae will ultimately be part of this deep H alpha, [N II], and [OIII] imaging catalogue. [Sabin, L.] Univ Nacl Autonoma Mexico, Inst Astron, Apdo Postal 106, Ensenada 22800, Baja California, Mexico; [Guerrero, M. A.] Inst Astrofis Andalucia IAA CSIC, Glorieta Astron S-N, Granada 18008, Spain; [Ramos-Larios, G.] Univ Guadalajara, Inst Astron & Meteorol, CUCEI, Av Vallarta 2602, Guadalajara 44130, Jalisco, Mexico; [Boumis, P.] Natl Observ Athens, Inst Agron Astrophys Space Applicat & Remote Sens, Athens 15236, Greece; [Zijlstra, A. A.] Univ Manchester, Jodrell Bank Ctr Astrophys, Oxford Rd, Manchester M13 9PL, Lancs, England; [Zijlstra, A. A.] Univ Hong Kong, Lab Space Res, Pokfulam Rd, Hong Kong, Peoples R China; [Iskandar, D. N. F. Awang] Univ Malaysia Sarawak, Fac Comp Sci & Informat Technol, Sarawak 94300, Malaysia; [Barlow, M. J.] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England; [Toala, J. A.] UNAM, Inst Radioastron & Astrofis, Campus Morelia, Morelia 58089, Michoacan, Mexico; [Parker, Q. A.] Univ Hong Kong, Dept Phys, Chong Yuet Ming Phys Bldg Pokfulam Rd, Hong Kong, Peoples R China; [Corradi, R. M. L.] GRANTECAN, Cuesta San Jose S-N, E-38712 Brena Baja, La Palma, Spain; [Corradi, R. M. L.] Inst Astrofis Canarias, E-38200 Tenerife, Spain; [Morris, R. A. H.] Univ Bristol, Sch Phys, Tyndall Ave, Bristol BS8 1TL, Avon, England Universidad Nacional Autonoma de Mexico; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Astrofisica de Andalucia (IAA); Universidad de Guadalajara; National Observatory of Athens; University of Manchester; Jodrell Bank Centre for Astrophysics; University of Hong Kong; University of Malaysia Sarawak; University of London; University College London; Universidad Nacional Autonoma de Mexico; University of Hong Kong; Instituto de Astrofisica de Canarias; University of Bristol Sabin, L (corresponding author), Univ Nacl Autonoma Mexico, Inst Astron, Apdo Postal 106, Ensenada 22800, Baja California, Mexico. lsabin@astro.unam.mx Boumis, Panos/K-4653-2013; Barlow, Michael J/A-5638-2009; Sabin, Laurence/HDO-7492-2022; Guerrero, Martin/L-1482-2014 Boumis, Panos/0000-0002-4260-940X; Barlow, Michael J/0000-0002-3875-1171; Awang Iskandar, D.N.F/0000-0002-0909-5670; Parker, Quentin/0000-0002-2062-0173; Guerrero, Martin/0000-0002-7759-106X; Zijlstra, Albert/0000-0002-3171-5469 PAPIIT (Mexico) [IN-101819]; grant AYA [PGC2018102184-B-I00]; FEDER funds; CONACyT [263373]; PRODEP (Mexico); Direccion General deAsuntos del Personal Academico of the Universidad Nacional Autonoma deM'exico (DGAPA, UNAM) project [IA100720]; Marcos Moshinsky Fundation (Mexico); STFC [ST/T000414/1, ST/J001333/1]; Newton grant [ST/R006768/1]; Hung Hing Ying visiting professorship at the University of HongKong; Newton-Ungku Omar Fund [F08/STFC/1792/2018] PAPIIT (Mexico)(Programa de Apoyo a Proyectos de Investigacion e Innovacion Tecnologica (PAPIIT)); grant AYA; FEDER funds(European Commission); CONACyT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); PRODEP (Mexico); Direccion General deAsuntos del Personal Academico of the Universidad Nacional Autonoma deM'exico (DGAPA, UNAM) project; Marcos Moshinsky Fundation (Mexico); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Newton grant; Hung Hing Ying visiting professorship at the University of HongKong; Newton-Ungku Omar Fund We thank the referee for the his/her comments which help improving the paper. LS acknowledges support from PAPIIT grant IN-101819 (Mexico). MAG acknowledges support from grant AYA PGC2018102184-B-I00 co-funded with FEDER funds. GR-L acknowledges support from CONACyT (grant 263373) and PRODEP (Mexico). JAT acknowledges funding by Direcci ' on General deAsuntos del Personal Academico of the Universidad Nacional Aut ' onoma deM ' exico (DGAPA, UNAM) project IA100720 and the Marcos Moshinsky Fundation (Mexico). AAZ acknowledges funding from STFC under grant number ST/T000414/1 and Newton grant ST/R006768/1, and from a Hung Hing Ying visiting professorship at the University of HongKong. DNFAI acknowledges funding under `Deep Learning for Classification of Astronomical Archives' from the Newton-Ungku Omar Fund: F08/STFC/1792/2018.; This work is partially based on observations made with the Nordic Optical Telescope, operated by the Nordic Optical Telescope Scientific Association installed in the Spanish Observatorio de El Roque de los Muchachos of the Instituto de Astrofisica de Canarias in La Palma, Spain, and the Aristarchos telescope, which is operated at the Helmos Observatory by the IAASARS of the National Observatory of Athens. This paper makes use of data obtained as part of the INT Photometric Ha Survey of the Northern Galactic Plane (IPHAS, www.iphas.org) carried out at the Isaac Newton Telescope (INT). The INT is operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica deCanarias. All IPHASdata are processed by the Cambridge Astronomical Survey Unit, at the Institute of Astronomy in Cambridge. The bandmerged DR2 catalogue was assembled at the Centre for Astrophysics Research, University of Hertfordshire, supported by STFC grant ST/J001333/1. This work has made extensive use of NASA's Astrophysics Data System. 16 4 4 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. DEC 2021.0 508 2 1599 1617 10.1093/mnras/stab2477 0.0 OCT 2021 19 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics WQ4SF Green Submitted, Green Published 2023-03-23 WOS:000713807500005 0
J Biswas, K; Shivakumara, P; Pal, U; Lu, T; Blumenstein, M; Llados, J Biswas, Kunal; Shivakumara, Palaiahnakote; Pal, Umapada; Lu, Tong; Blumenstein, Michael; Llados, Josep Classification of aesthetic natural scene images using statistical and semantic features MULTIMEDIA TOOLS AND APPLICATIONS English Article; Early Access Scene images; Personality traits; Person behaviour; Color features; Deep learning; Aesthetic images QUALITY ASSESSMENT Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. [Biswas, Kunal; Pal, Umapada] Indian Stat Inst, Comp Vis & Pattern Recognit Unit, Kolkata, India; [Shivakumara, Palaiahnakote] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia; [Lu, Tong] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Peoples R China; [Blumenstein, Michael] Univ Technol Sydney, Sydney, NSW, Australia; [Llados, Josep] Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona, Spain Indian Statistical Institute; Indian Statistical Institute Kolkata; Universiti Malaya; Nanjing University; University of Technology Sydney; Autonomous University of Barcelona; Centre de Visio per Computador (CVC) Shivakumara, P (corresponding author), Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. biswaskunal29@gmail.com; shiva@um.edu.my; umapada@isical.ac.in; lutong@nju.edu.cn; michael.blumenstein@uts.edu.au; josep@cvc.uab.es Palaiahnakote, Shivakumara/B-6261-2013 Shivakumara, Palaiahnakote/0000-0001-9026-4613 University of Malaya, Malaysia [GPF096A-2020, GPF096B-2020, GPF096C-2020]; TIH; ISI University of Malaya, Malaysia(Universiti Malaya); TIH; ISI Palaiahnakote Shivakumara received partial support for this work from the Faculty Grant: GPF096A-2020, GPF096B-2020 and GPF096C-2020, University of Malaya, Malaysia. This work is also partly supported by TIH, ISI. 37 0 0 6 6 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. 10.1007/s11042-022-13924-7 0.0 SEP 2022 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4S2LQ 2023-03-23 WOS:000857279400002 0
J Liu, YZ; Lin, WJ; Xu, PF; Zhang, DD; Luo, YJ Liu, Yunzhe; Lin, Wanjun; Xu, Pengfei; Zhang, Dandan; Luo, Yuejia Neural Basis of Disgust Perception in Racial Prejudice HUMAN BRAIN MAPPING English Article racial prejudice; disgust; face perception; insula; amygdala; anterior cingulate cortex INDIVIDUAL-DIFFERENCES; SENSITIVITY PREDICTS; DISEASE AVOIDANCE; RACE BIAS; AMYGDALA; NEUROSCIENCE; MODULATION; RESPONSES; GENDER; FACES Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. (C) 2015 Wiley Periodicals, Inc. [Liu, Yunzhe; Lin, Wanjun] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China; [Liu, Yunzhe; Lin, Wanjun] Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China; [Xu, Pengfei; Zhang, Dandan; Luo, Yuejia] Shenzhen Univ, Inst Affect & Social Neurosci, Shenzhen 518060, Peoples R China; [Xu, Pengfei] Univ Groningen, Univ Med Ctr Groningen, Neuroimaging Ctr, Groningen, Netherlands Beijing Normal University; Beijing Normal University; Shenzhen University; University of Groningen Zhang, DD (corresponding author), Shenzhen Univ, Inst Affect & Social Neurosci, Room B412,Normal Sch Bldg,3688 Nanhai Ave, Shenzhen 518060, Peoples R China. zhangdd05@gmail.com National Natural Science Foundation of China [31300867, 81471376]; National Key Basic Research Program of China (973 Program) [2014CB744600] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Basic Research Program of China (973 Program)(National Basic Research Program of China) Contract grant sponsor: National Natural Science Foundation of China; Contract grant numbers: 31300867, 81471376; Contract grant sponsor: National Key Basic Research Program of China (973 Program); Contract grant number: 2014CB744600. 61 17 18 3 48 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. DEC 2015.0 36 12 5275 5286 10.1002/hbm.23010 0.0 12 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging DB1QF 26417673.0 Green Published 2023-03-23 WOS:000368283100039 0
J Yang, JB; Zhu, H; Tian, X Yang, Jinbiao; Zhu, Hao; Tian, Xing Group-Level Multivariate Analysis in EasyEEG Toolbox: Examining the Temporal Dynamics Using Topographic Responses FRONTIERS IN NEUROSCIENCE English Article EEG; EEG/MEG; methodology; EEG signal processing; toolbox; topography; multivariate analysis; machine learning Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices-using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication. [Yang, Jinbiao; Zhu, Hao; Tian, Xing] New York Univ Shanghai, Neural & Cognit Sci, Shanghai, Peoples R China; [Yang, Jinbiao; Zhu, Hao; Tian, Xing] East China Normal Univ, Sch Psychol & Cognit Sci, Shanghai Key Lab Brain Funct Genom, Minist Educ, Shanghai, Peoples R China; [Yang, Jinbiao; Zhu, Hao; Tian, Xing] New York Univ Shanghai, Inst Brain & Cognit Sci, NYU ECNU, Shanghai, Peoples R China; [Yang, Jinbiao] Max Planck Inst Psycholinguist Nijmegen, Nijmegen, Netherlands; [Yang, Jinbiao] Radboud Univ Nijmegen, Ctr Language Studies Nijmegen, Nijmegen, Netherlands NYU Shanghai; East China Normal University; NYU Shanghai; Max Planck Society; Radboud University Nijmegen Yang, JB; Tian, X (corresponding author), New York Univ Shanghai, Neural & Cognit Sci, Shanghai, Peoples R China.;Yang, JB; Tian, X (corresponding author), East China Normal Univ, Sch Psychol & Cognit Sci, Shanghai Key Lab Brain Funct Genom, Minist Educ, Shanghai, Peoples R China.;Yang, JB; Tian, X (corresponding author), New York Univ Shanghai, Inst Brain & Cognit Sci, NYU ECNU, Shanghai, Peoples R China.;Yang, JB (corresponding author), Max Planck Inst Psycholinguist Nijmegen, Nijmegen, Netherlands.;Yang, JB (corresponding author), Radboud Univ Nijmegen, Ctr Language Studies Nijmegen, Nijmegen, Netherlands. jinbiao.yang@mpi.nl; xing.tian@nyu.edu Yang, Jinbiao/ABC-9682-2020 Yang, Jinbiao/0000-0001-9337-0243 National Natural Science Foundation of China [31500914]; Major Program of Science and Technology Commission of Shanghai Municipality (STCSM) [15JC1400104, 17JC1404104]; Program of Introducing Talents of Discipline to Universities [B16018]; New York University Global Seed Grants for Collaborative Research [85-65701-G0757-R4551]; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Program of Science and Technology Commission of Shanghai Municipality (STCSM); Program of Introducing Talents of Discipline to Universities(Ministry of Education, China - 111 Project); New York University Global Seed Grants for Collaborative Research; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai This study was supported by National Natural Science Foundation of China 31500914, Major Program of Science and Technology Commission of Shanghai Municipality (STCSM) 15JC1400104 and 17JC1404104, Program of Introducing Talents of Discipline to Universities, Base B16018, a grant from the New York University Global Seed Grants for Collaborative Research(85-65701-G0757-R4551), and the JRI Seed Grants for Research Collaboration from NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai to XT. 26 5 5 0 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-453X FRONT NEUROSCI-SWITZ Front. Neurosci. JUL 17 2018.0 12 468 10.3389/fnins.2018.00468 0.0 12 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology GN4DZ 30065624.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000438968300001 0
J Deng, JQ; Song, WQ; Zio, E Deng, Juequan; Song, Wanqing; Zio, Enrico A Discrete Increment Model for Electricity Price Forecasting Based on Fractional Brownian Motion IEEE ACCESS English Article Forecasting; Predictive models; Time series analysis; Brownian motion; Stochastic processes; Autoregressive processes; Maximum likelihood estimation; Electricity price forecasting; fractional Brownian motion; long-range dependence; discrete increment model; maximum likelihood estimation LONG-RANGE DEPENDENCE; NEURAL-NETWORK; HYBRID MODEL; ARMA Electricity price forecasting is essential for all participants of the power consumption market. However, electricity price series has complex properties such as high volatility and non-stationarity that make forecasting turn out to be very difficult. In this study, we aim to forecast electricity price series by the discrete increment model of fractional Brownian motion (fBm). A specific feature of the fBm is that it represents a typical non-stationary stochastic process with long-range dependent (LRD) characteristics. Analysis of electricity price series has LRD characteristics, and the Hurst exponent can be calculated. The Hurst exponent is the key parameter of fBm, and it is a measure of self-similarity. The stochastic differential equation driven by fBm is discretized into the discrete increment model for electricity price forecasting. Other parameters of the discrete increment model can be evaluated by the maximum likelihood estimation (MLE). The performance of the proposed method is demonstrated by using the data from the U.S. Energy Information Administration. The validity of the proposed model was compared with other methods. [Deng, Juequan; Song, Wanqing] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China; [Zio, Enrico] PSL Res Univ, MINES ParisTech, CRC, F-75006 Paris, France; [Zio, Enrico] Politecn Milan, Energy Dept, I-20156 Milan, Italy Shanghai University of Engineering Science; UDICE-French Research Universities; Universite PSL; MINES ParisTech; Polytechnic University of Milan Song, WQ (corresponding author), Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China. swqls@126.com wanqing, song/0000-0002-0561-3258 28 2 2 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 130762 130770 10.1109/ACCESS.2020.3008797 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications MQ6EG gold, Green Submitted 2023-03-23 WOS:000552985500001 0
J Choe, Y; Fesenmaier, DR Choe, Yeongbae; Fesenmaier, Daniel R. Designing an advanced system for destination management: a case study of Northern Indiana INDUSTRIAL MANAGEMENT & DATA SYSTEMS English Article Smart tourism; Spatial-temporal behavior; Clickstream data; Destination management system ST-GALLEN CONSENSUS; TOURISM DESTINATIONS; INFORMATION SEARCH; MARKETING ORGANIZATIONS; CONSUMER-BEHAVIOR; BIG DATA; TRAVEL; FRAMEWORK; WEB; FOUNDATIONS Purpose The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics. Design/methodology/approach This study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure. Findings Key demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana. Originality/value The process and data used in this study provide a proof of concept for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization. [Choe, Yeongbae] Univ Macau, Taipa, Macao, Peoples R China; [Fesenmaier, Daniel R.] Modul Univ Vienna, Vienna, Austria University of Macau Choe, Y (corresponding author), Univ Macau, Taipa, Macao, Peoples R China. ychoe@um.edu.mo; Daniel.fesenmaier@modul.ac.at Choe, Yeongbae/E-1525-2015; Fesenmaier, Daniel R./HDM-0912-2022 Choe, Yeongbae/0000-0003-3225-469X; Fesenmaier, Daniel R./0000-0003-4829-8056 Northern Indiana Tourism Association Northern Indiana Tourism Association The authors wish to recognize the Northern Indiana Tourism Association for their financial support of the initial data collection effort. 81 3 3 3 15 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0263-5577 1758-5783 IND MANAGE DATA SYST Ind. Manage. Data Syst. JUN 7 2021.0 121 6 SI 1167 1190 10.1108/IMDS-02-2020-0063 0.0 NOV 2020 24 Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SR2NO 2023-03-23 WOS:000590845300001 0
J Wang, FL; Yang, M; Ma, LF; Zhang, T; Qin, WL; Li, W; Zhang, YH; Sun, ZC; Wang, ZM; Li, F; Yu, K Wang, Falv; Yang, Mao; Ma, Longfei; Zhang, Tong; Qin, Weilong; Li, Wei; Zhang, Yinghua; Sun, Zhencai; Wang, Zhimin; Li, Fei; Yu, Kang Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV REMOTE SENSING English Article unmanned aerial vehicle; winter wheat biomass; vegetation indices; machine learning; random forest regression; partial least squares regression; multiple growth stages LEAF-AREA INDEX; HYPERSPECTRAL VEGETATION INDEXES; DIFFERENT GROWTH-STAGES; CHLOROPHYLL CONTENT; REMOTE ESTIMATION; SPECTRAL INDEXES; NITROGEN CONCENTRATION; SENSITIVITY-ANALYSIS; SQUARES REGRESSION; PADDY RICE One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R-2 values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R-2 = 0.74~0.92, RMSE = 0.95~2.87 t/ha, MAE = 0.75~2.18 t/ha, and RPD = 2.00~3.67) and validation dataset (R-2 = 0.50~0.75, RMSE = 1.56~2.57 t/ha, MAE = 1.44~2.05 t/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R-2 = 0.95~0.97, RMSE = 0.58~1.08 t/ha, MAE = 0.46~0.89 t/ha, and RPD = 3.95~6.35) and validation dataset (R-2 = 0.83~0.93, RMSE = 0.93~2.34 t/ha, MAE = 0.72~2.01 t/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models. [Wang, Falv; Yang, Mao; Ma, Longfei; Zhang, Tong; Qin, Weilong; Zhang, Yinghua; Sun, Zhencai; Wang, Zhimin] China Agr Univ, Coll Agron & Biotechnol, Beijing 100193, Peoples R China; [Zhang, Tong] China Agr Univ, Coll Hort, Beijing 100193, Peoples R China; [Li, Wei] China Agr Univ, Coll Biol Sci, Beijing 100193, Peoples R China; [Zhang, Yinghua; Sun, Zhencai; Wang, Zhimin] Engn Technol Res Ctr Agr Low Plain Areas, Cangzhou 061000, Peoples R China; [Li, Fei] Inner Mongolia Agr Univ, Coll Grassland Resources & Environm, Hohhot 010011, Peoples R China; [Yu, Kang] Tech Univ Munich, Sch Life Sci, D-85354 Freising Weihenstephan, Germany China Agricultural University; China Agricultural University; China Agricultural University; Inner Mongolia Agricultural University; Technical University of Munich Yu, K (corresponding author), Tech Univ Munich, Sch Life Sci, D-85354 Freising Weihenstephan, Germany. s20203010032@cau.edu.cn; s20193010034@cau.edu.cn; malf032@cau.edu.cn; s20203172657@cau.edu.cn; bs20203010068@cau.edu.cn; weilion@cau.edu.cn; yhzhang@cau.edu.cn; zhencai_sun@cau.edu.cn; wangzm@cau.edu.cn; lifei@imau.edu.cn; kang.yu@tum.de Zhang, Yinghua/HGE-7633-2022; Yu, Kang/G-8306-2011 Zhang, Yinghua/0000-0002-1356-6084; Longfei, Ma/0000-0003-4753-0263; Yu, Kang/0000-0002-0686-6783 118 14 14 25 43 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2022.0 14 5 1251 10.3390/rs14051251 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology ZT0YP gold 2023-03-23 WOS:000768883200001 0
J Greitemann, J; Liu, K; Pollet, L Greitemann, Jonas; Liu, Ke; Pollet, Lode The view of TK-SVM on the phase hierarchy in the classical kagome Heisenberg antiferromagnet JOURNAL OF PHYSICS-CONDENSED MATTER English Article machine learning; TK-SVM; multipolar order; classical spin liquid CONTINUOUS SYMMETRY GROUP; LONG-RANGE ORDER; 2-DIMENSIONAL SYSTEMS; DESTRUCTION; TRANSITIONS We illustrate how the tensorial kernel support vector machine (TK-SVM) can probe the hidden multipolar orders and emergent local constraint in the classical kagome Heisenberg antiferromagnet. We show that TK-SVM learns the finite-temperature phase diagram in an unsupervised way. Moreover, in virtue of its strong interpretability, it identifies the tensorial quadrupolar and octupolar orders, which define a biaxial D-3h spin nematic, and the local constraint that underlies the selection of coplanar states. We then discuss the disorder hierarchy of the phases, which can be inferred from both the analytical order parameters and an SVM bias parameter. For completeness we mention that the machine also picks up the leading 3x30.7). Conclusions Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders. [Iacucci, Marietta; Gkoutos, Georgios, V; Ghosh, Subrata] Univ Birmingham, Natl Inst Hlth Res, Birmingham Biomed Res Ctr, Birmingham, England; [Iacucci, Marietta; Jeffery, Louisa; Zardo, Davide; Shivaji, Uday N.; Ghosh, Subrata] Univ Hosp Birmingham NHS Fdn Trust, Queen Elizabeth Hosp, Gastroenterol, Birmingham, England; [Iacucci, Marietta; Jeffery, Louisa; Nardone, Olga M.; Smith, Samuel C. L.; Labarile, Nunzia; Ungar, Bella; Hunter, Stuart; Cannatelli, Rosanna; Shivaji, Uday N.; Parigi, Tommaso Lorenzo; Gkoutos, Georgios, V; Ghosh, Subrata] Univ Birmingham, Inst Immunol & Immunotherapy, Birmingham, England; [Acharjee, Animesh; Reynolds, Gary M.; Gkoutos, Georgios, V] Univ Birmingham, Inst Canc & Genom Sci, Birmingham, England; [Acharjee, Animesh] Queen Elizabeth Hosp Birmingham, Natl Inst Hlth Res Surg Reconstruct, Birmingham, England; [Grisan, Enrico] Univ Padua, Dept Informat Engn, Padua, Italy; [Grisan, Enrico] London South Bank Univ, Sch Engn Comp Sci & Informat, London, England; [Buda, Andrea] S Maria del Prato Hosp, Dept Gastrointestinal Oncol Surg, Gastroenterol Unit, Feltre, Italy; [Mao, Ren] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Gastroenterol, Guangzhou, Peoples R China; [Ghosh, Subrata] Univ Coll Cork, Coll Med & Hlth, APC Microbiome Ireland, Cork, Ireland; [Iacucci, Marietta] Univ Hosp Birmingham NHS Fdn Trust, Inst Translat Med, Heritage Bldg Res & Dev, Birmingham B15 2TT, W Midlands, England University of Birmingham; University of Birmingham; University of Birmingham; University of Birmingham; University of Birmingham; University of Padua; London South Bank University; Sun Yat Sen University; University College Cork; University of Birmingham Iacucci, M (corresponding author), Univ Hosp Birmingham NHS Fdn Trust, Inst Translat Med, Heritage Bldg Res & Dev, Birmingham B15 2TT, W Midlands, England. m.iacucci@bham.ac.uk Acharjee, Animesh/AAC-7637-2019; Parigi, Tommaso Lorenzo/AFJ-6307-2022 Acharjee, Animesh/0000-0003-2735-7010; Parigi, Tommaso Lorenzo/0000-0002-3398-0231; Buda, Andrea/0000-0002-3099-8612; Nardone, Olga Maria/0000-0002-9554-4785; Gkoutos, Georgios/0000-0002-2061-091X 53 0 0 0 0 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 1078-0998 1536-4844 INFLAMM BOWEL DIS Inflamm. Bowel Dis. 10.1093/ibd/izac233 0.0 NOV 2022 12 Gastroenterology & Hepatology Science Citation Index Expanded (SCI-EXPANDED) Gastroenterology & Hepatology 6H8GG 36378498.0 Green Accepted 2023-03-23 WOS:000885670100001 0
C Zhao, XL; Lin, SF; Huang, ZS Siuly, S; Lee, I; Huang, Z; Zhou, R; Wang, H; Xiang, W Zhao, Xiaoli; Lin, Shaofu; Huang, Zhisheng Extraction of Semantic Relations from Medical Literature Based on Semantic Predicates and SVM HEALTH INFORMATION SCIENCE (HIS 2018) Lecture Notes in Computer Science English Proceedings Paper 7th International Conference on Health Information Science (HIS) OCT 05-07, 2018 James Cook Univ, Cairns, AUSTRALIA James Cook Univ Relation extraction; Semantic technology; SVM; Multi-classification; RDF The relationship of biomedical entity is the cornerstone of acquiring biomedical knowledge. It is of great significance to the construction of related databases in the biomedical field and the management of medical literature. How to quickly and accurately extract the required relationships of biomedical entity from massive unstructured literature is an important research. In order to improve accuracy, we use support vector machine (SVM) which is a machine learning algorithm based on feature vectors to extract relationships of entities. We extract the five main relationships in medical literature, including ISA, PART_OF, CAUSES, TREATS and DIAGNOSES. First of all, related topics are used to search medical literature from PubMed database, such as disease-drug, cause-disease. These documents are used as experimental data and then processed to form a corpus. In selection of features, the method of information gain is used to select the influential entities' own features and entities' context features. On this basis, semantic predicates are added as a feature to improve accuracy. The experimental results show that the accuracy of extraction is increased by 5%-10%. In the end, Resource Description Framework (RDF) is used to store extracted relationships from the corresponding documents, and it provides support for the subsequent retrieval of related documents. [Zhao, Xiaoli; Lin, Shaofu] Beijing Univ Technol, Coll Software, Beijing, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands Beijing University of Technology; Vrije Universiteit Amsterdam Zhao, XL (corresponding author), Beijing Univ Technol, Coll Software, Beijing, Peoples R China. zhaoxiaoli@emails.bjut.edu.cn; linshaofu@bjut.edu.cn; huang@cs.vu.nl 14 1 1 2 8 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-01078-2; 978-3-030-01077-5 LECT NOTES COMPUT SC 2018.0 11148 17 24 10.1007/978-3-030-01078-2_2 0.0 8 Computer Science, Theory & Methods; Medical Informatics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Medical Informatics BN2UM 2023-03-23 WOS:000477763900002 0
J Lin, J; Wan, C; Song, YH; Huang, RL; Chen, XS; Guo, WF; Zong, Y; Shi, Y Lin, Jin; Wan, Can; Song, Yonghua; Huang, Renle; Chen, Xiaoshuang; Guo, Wanfang; Zong, Yi; Shi, You Situation Awareness of Active Distribution Network: Roadmap, Technologies, and Bottlenecks CSEE JOURNAL OF POWER AND ENERGY SYSTEMS English Article Active distribution network; distribution automation system; roadmap; situation awareness; technology bottleneck ENERGY-STORAGE SYSTEMS; COST-BENEFIT ANALYSES; PREDICTION INTERVALS; DROOP CONTROL; POWER; MANAGEMENT; DEMAND; IMPACT With the rapid development of local generation and demand response, the active distribution network (ADN), which aggregates and manages miscellaneous distributed resources, has moved from theory to practice. Secure and optimal operations now require an advanced situation awareness (SA) system so that operators are aware of operation states and potential risks. Current solutions in distribution supervisory control and data acquisition (DSCADA) as well as the distribution automation system (DAS) generally are not able to meet the technology requirements of SA. In this paper, the authors' participation in the project of developing an SA system as the basic component of a practical active distribution management system (ADMS) deployed in Beijing, China, is presented. This paper reviews the ADN's development roadmap by illustrating the changes that are made in elements, topology, structure, and control scheme. Taking into consideration these hardware changes, a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN. The SA system's implementation bottlenecks are also presented, including, but not limited to issues in big data platform, distribution forecasting, and security evaluation. Potential technology solutions are also provided. [Lin, Jin; Wan, Can; Song, Yonghua; Chen, Xiaoshuang; Guo, Wanfang] Tsinghua Univ, State Key Lab Control & Simulat Power Syst & Gene, Dept Elect Engn, Beijing 100084, Peoples R China; [Huang, Renle] State Grid Beijing Elect Power Co, Beijing 102200, Peoples R China; [Zong, Yi; Shi, You] Tech Univ Denmark, Ctr Elect Power & Energy, Elektrovej 325, DK-2800 Lyngby, Denmark Tsinghua University; State Grid Corporation of China; Technical University of Denmark Lin, J (corresponding author), Tsinghua Univ, State Key Lab Control & Simulat Power Syst & Gene, Dept Elect Engn, Beijing 100084, Peoples R China. linjin@tsinghua.edu.cn Zong, Yi/ABB-7863-2020; chen, xia/GYR-3948-2022; chen, xia/GXM-5435-2022; You, Shi/ABB-8041-2020; chen, xi/GXH-3653-2022; Zong, Yi/G-8555-2017 You, Shi/0000-0003-3371-9647; Zong, Yi/0000-0003-1262-3558 46 34 42 7 46 CHINA ELECTRIC POWER RESEARCH INST BEIJING 15, QINGHE XIAOYING DONG LU, HAIDIAN-QU, BEIJING, 100192, PEOPLES R CHINA 2096-0042 CSEE J POWER ENERGY CSEE J. Power Energy Syst. SEP 2016.0 2 3 35 42 10.17775/CSEEJPES.2016.00033 0.0 8 Energy & Fuels; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering EC0FW gold 2023-03-23 WOS:000387775000005 0
S Peng, SW; Mamitsuka, H; Zhu, SF Mamitsuka, H Peng, Shengwen; Mamitsuka, Hiroshi; Zhu, Shanfeng MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing DATA MINING FOR SYSTEMS BIOLOGY, 2 EDITION: Methods and Protocols Methods in Molecular Biology English Article; Book Chapter MeSH indexing; Text categorization; Multi-label classification; Medical subject headings; MEDLINE; Machine learning QUERY EXPANSION The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) ( see Note 1) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28,000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing learning to rank (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at http://datamining-iip.fudan.edu.cn/deepmesh. [Peng, Shengwen; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Peng, Shengwen; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Mamitsuka, Hiroshi] Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Japan; [Mamitsuka, Hiroshi] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Zhu, Shanfeng] Fudan Univ, Ctr Computat Syst Biol, Shanghai, Peoples R China Fudan University; Fudan University; Kyoto University; Aalto University; Fudan University Peng, SW (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Peng, SW (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China. Mamitsuka, Hiroshi/R-1110-2016 Mamitsuka, Hiroshi/0000-0002-6607-5617 National Natural Science Foundation of China [61572139]; MEXT KAKENHI [16H02868]; FiDiPro by Tekes; Grants-in-Aid for Scientific Research [16H02868] Funding Source: KAKEN National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MEXT KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); FiDiPro by Tekes; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868 and FiDiPro by Tekes. 22 1 1 0 2 HUMANA PRESS INC TOTOWA 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA 1064-3745 1940-6029 978-1-4939-8561-6; 978-1-4939-8560-9 METHODS MOL BIOL Methods Mol. Biol. 2018.0 1807 203 209 10.1007/978-1-4939-8561-6_15 0.0 10.1007/978-1-4939-8561-6 7 Biochemical Research Methods; Biology; Mathematical & Computational Biology Book Citation Index – Science (BKCI-S) Biochemistry & Molecular Biology; Life Sciences & Biomedicine - Other Topics; Mathematical & Computational Biology BS0SX 30030813.0 Green Accepted 2023-03-23 WOS:000684257200016 0
J Zhang, YL; Wang, HB; Li, C; Meriaudeau, F Zhang, Yonglin; Wang, Haibin; Li, Chao; Meriaudeau, Fabrice Data augmentation aided complex-valued network for channel estimation in underwater acoustic orthogonal frequency division multiplexing system JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA English Article NEURAL-NETWORKS; COMMUNICATION; OFDM; PROPAGATION In this paper, a data augmentation aided complex-valued network is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel estimations, wherein empirical mode decomposition based data augmentation is proposed to solve the current dilemma in the deep learning embedded UWA-OFDM communications: data scarcity and data-sampling difficulties in real-world applications. In addition, the significance of high-frequency component augmentation for the UWA channel and how it positively influences the following model training are discussed in detail and demonstrated experimentally in this paper. In addition, the complex-valued network is specially designed for the complex-formatted UWA-OFDM signal, which can fully utilize the relationship between its real and imaginary parts with half of the spatial resources of its real-valued counterparts. The experiments with the at-sea-measured WATERMARK dataset indicate that the proposed method can perform a near-optimal channel estimation, and its low resource requirements (on dataset and model) make it more adaptable to real-world UWA applications. (C) 2022 Acoustical Society of America. [Zhang, Yonglin; Wang, Haibin; Li, Chao] Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China; [Zhang, Yonglin; Meriaudeau, Fabrice] Univ Bourgogne Franche Comte, Lab ImViA, F-21078 Dijon, France; [Zhang, Yonglin; Wang, Haibin; Li, Chao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Chinese Academy of Sciences; Institute of Acoustics, CAS; Universite de Bourgogne; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Wang, HB (corresponding author), Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China. whb@mail.ioa.ac.cn ZHANG, YONGLIN/0000-0001-5339-6502 China Scholarship Council; Chinese Academy of Sciences; National Natural Science Foundation of China [62171440]; National Key Research and Development Program of China [2021YFC3101403] China Scholarship Council(China Scholarship Council); Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This research was supported by the China Scholarship Council, the Chinese Academy of Sciences, the National Natural Science Foundation of China (Grant No. 62171440), and the National Key Research and Development Program of China (Grant No. 2021YFC3101403). 38 0 0 11 14 ACOUSTICAL SOC AMER AMER INST PHYSICS MELVILLE STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA 0001-4966 1520-8524 J ACOUST SOC AM J. Acoust. Soc. Am. JUN 2022.0 151 6 4150 4164 10.1121/10.0011674 0.0 15 Acoustics; Audiology & Speech-Language Pathology Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Audiology & Speech-Language Pathology 5H5UO 35778218.0 2023-03-23 WOS:000867744400001 0
J Long, T; Zhang, YX; Fortunato, NM; Shen, C; Dai, M; Zhang, HB Long, Teng; Zhang, Yixuan; Fortunato, Nuno M.; Shen, Chen; Dai, Mian; Zhang, Hongbin Inverse design of crystal structures for multicomponent systems ACTA MATERIALIA English Article Inverse design; Machine learning; Generative adversarial network; Crystal structures CALYPSO We developed an inverse design framework, constrained crystal deep convolutional generative adversarial networks (CCDCGAN), enabling automated generation of stable multicomponent crystal structures. Their formation energy can be optimized in the latent space based on reversible crystal images with continuous representation. After training by 52,615 crystal structures from Materials Project, the CCDCGAN model is able to generate new crystal structure. It is demonstrated that when 9160 crystal structures are generated by CCDCGAN, 8310 of them have not been reported in the training set. Detailed analysis on 15 selected materials systems of the generated structures reveals that unreported crystal structures below the convex hull can be discovered in 6 of them. Moreover, the generation efficiency can be further improved by considering extra hypothetical structures in the training. This paves the way to perform inverse design of multicomponent materials with possible multi-objective optimization.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) [Long, Teng] Shandong Univ, Sch Mat Sci & Engn, Jingshi Rd 17923, Jinan 250061, Peoples R China; [Long, Teng; Zhang, Yixuan; Fortunato, Nuno M.; Shen, Chen; Dai, Mian; Zhang, Hongbin] Tech Univ Darmstadt, Inst Mat Sci, Otto Berndt Str 3, D-64287 Darmstadt, Germany Shandong University; Technical University of Darmstadt Long, T (corresponding author), Shandong Univ, Sch Mat Sci & Engn, Jingshi Rd 17923, Jinan 250061, Peoples R China.;Zhang, HB (corresponding author), Tech Univ Darmstadt, Inst Mat Sci, Otto Berndt Str 3, D-64287 Darmstadt, Germany. tenglong@tmm.tu-darmstadt.de; hongbin.zhang@tu-darmstadt.de Zhang, Hongbin/A-1986-2017 Zhang, Hongbin/0000-0002-1035-8861; Shen, Chen/0000-0002-8538-9873; Fortunato, Nuno/0000-0002-9166-1626 China Scholarship Council [CSC201806220067]; Fulbright-Cottrell Foundation; European Research Council (ERC) under the European Union [743116]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [405553726 -TRR 270] China Scholarship Council(China Scholarship Council); Fulbright-Cottrell Foundation; European Research Council (ERC) under the European Union(European Research Council (ERC)); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) Teng Long thanks the financial support from the China Scholarship Council [CSC201806220067]. Yixuan Zhang thanks the financial support from Fulbright-Cottrell Foundation. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [Grant No. 743116-project Cool Innov] and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Project-ID 405553726 -TRR 270]. 41 1 1 16 30 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1359-6454 1873-2453 ACTA MATER Acta Mater. JUN 1 2022.0 231 117898 10.1016/j.actamat.2022.117898 0.0 APR 2022 10 Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Metallurgy & Metallurgical Engineering 0Z8LR hybrid, Green Submitted 2023-03-23 WOS:000791323500004 0
J Liu, Y; Wang, Y; Ni, YQ; Cheung, CKY; Lam, KSL; Wang, Y; Xia, ZY; Ye, DW; Guo, J; Tse, MA; Panagiotou, G; Xu, AM Liu, Yan; Wang, Yao; Ni, Yueqiong; Cheung, Cynthia K. Y.; Lam, Karen S. L.; Wang, Yu; Xia, Zhengyuan; Ye, Dewei; Guo, Jiao; Tse, Michael Andrew; Panagiotou, Gianni; Xu, Aimin Gut Microbiome Fermentation Determines the Efficacy of Exercise for Diabetes Prevention CELL METABOLISM English Article INSULIN SENSITIVITY; INTESTINAL MICROBIOTA; WEIGHT-LOSS; SERUM METABOLOME; ADIPOSE-TISSUE; FATTY-ACID; IDENTIFICATION; OBESITY; CELL; RESTRICTION Exercise is an effective strategy for diabetes management but is limited by the phenomenon of exercise resistance (i.e., the lack of or the adverse response to exercise on metabolic health). Here, in 39 medication-naive men with prediabetes, we found that exercise-induced alterations in the gut microbiota correlated closely with improvements in glucose homeostasis and insulin sensitivity entry (clinicaltrials.gov entry NCT03240978). The microbiome of responders exhibited an enhanced capacity for biosynthesis of short-chain fatty acids and catabolism of branched-chain amino acids, whereas those of non-responders were characterized by increased production of metabolically detrimental compounds. Fecal microbial transplantation from responders, but not non-responders, mimicked the effects of exercise on alleviation of insulin resistance in obese mice. Furthermore, a machine-learning algorithm integrating baseline microbial signatures accurately predicted personalized glycemic response to exercise in an additional 30 subjects. These findings raise the possibility of maximizing the benefits of exercise by targeting the gut microbiota. [Liu, Yan; Wang, Yao; Cheung, Cynthia K. Y.; Lam, Karen S. L.; Wang, Yu; Xia, Zhengyuan; Xu, Aimin] Univ Hong Kong, State Key Lab Pharmaceut Biotechnol, Hong Kong, Peoples R China; [Liu, Yan; Cheung, Cynthia K. Y.; Lam, Karen S. L.; Xu, Aimin] Univ Hong Kong, Dept Med, Hong Kong, Peoples R China; [Wang, Yao; Wang, Yu; Xu, Aimin] Univ Hong Kong, Dept Pharmacol & Pharm, Hong Kong, Peoples R China; [Ni, Yueqiong; Panagiotou, Gianni] Univ Hong Kong, Sch Biol Sci, Fac Sci, Syst Biol & Bioinformat Grp, Hong Kong, Peoples R China; [Ni, Yueqiong; Panagiotou, Gianni] Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Jena, Germany; [Ye, Dewei; Guo, Jiao] Guangdong Pharmaceut Univ, Joint Lab Guangdong & Hong Kong Metab Dis, Guangzhou, Peoples R China; [Tse, Michael Andrew] Univ Hong Kong, Ctr Sports & Exercise, Act Hlth Clin, Hong Kong, Peoples R China; [Panagiotou, Gianni] Univ Hong Kong, Li Ka Shing Fac Med, Dept Microbiol, Hong Kong, Peoples R China University of Hong Kong; University of Hong Kong; University of Hong Kong; University of Hong Kong; Hans Knoll Institute (HKI); Guangdong Pharmaceutical University; University of Hong Kong; University of Hong Kong Xu, AM (corresponding author), Univ Hong Kong, State Key Lab Pharmaceut Biotechnol, Hong Kong, Peoples R China.;Xu, AM (corresponding author), Univ Hong Kong, Dept Med, Hong Kong, Peoples R China.;Xu, AM (corresponding author), Univ Hong Kong, Dept Pharmacol & Pharm, Hong Kong, Peoples R China.;Panagiotou, G (corresponding author), Univ Hong Kong, Sch Biol Sci, Fac Sci, Syst Biol & Bioinformat Grp, Hong Kong, Peoples R China.;Panagiotou, G (corresponding author), Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, Jena, Germany.;Tse, MA (corresponding author), Univ Hong Kong, Ctr Sports & Exercise, Act Hlth Clin, Hong Kong, Peoples R China.;Panagiotou, G (corresponding author), Univ Hong Kong, Li Ka Shing Fac Med, Dept Microbiol, Hong Kong, Peoples R China. matse@hku.hk; gianni.panagiotou@hki-jena.de; amxu@hku.hk Ye, Dewei/AFS-1276-2022 WANG, Yao/0000-0003-0010-2819; Liu, Yan/0000-0001-6358-4056 National Key Research and Development Program of China [2016YFC1305003, 2015CB553603]; Hong Kong Research Grants Council/Area of Excellence [AoE/M/707-18]; Collaborative Research Fund [C7037-17W]; General Research Fund [17128115]; National Natural Science Foundation of China [81600660]; Deutsche Forschungsgemeinschaft (DFG) [CRC/Transregio 124]; Innovative Training Networks, H2020-MSCA-ITN-2018 [813781]; DFG under Germany's Excellence Strategy [EXC 2051, 390713860]; Marie Skoldowska-Curie Actions (MSCA) National Key Research and Development Program of China; Hong Kong Research Grants Council/Area of Excellence; Collaborative Research Fund; General Research Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Innovative Training Networks, H2020-MSCA-ITN-2018; DFG under Germany's Excellence Strategy(German Research Foundation (DFG)); Marie Skoldowska-Curie Actions (MSCA) The authors thank Alan Wong and Glen Joe of the Active Health Clinic, Centre for Sports and Exercise for their contributions to the exercise intervention. This study was financially supported by grants from the National Key Research and Development Program of China (2016YFC1305003 and 2015CB553603), Hong Kong Research Grants Council/Area of Excellence (AoE/M/707-18), Collaborative Research Fund (C7037-17W), General Research Fund (17128115), National Natural Science Foundation of China (81600660), the Deutsche Forschungsgemeinschaft (DFG) CRC/Transregio 124 Pathogenic fungi and their human host: networks of interaction'' subproject B5 & INF, the Marie Skoldowska-Curie Actions (MSCA), and Innovative Training Networks, H2020-MSCA-ITN-2018 813781 BestTreat.'' G.P. would like to thank the DFG under Germany's Excellence Strategy - EXC 2051 - Project ID 390713860 for intellectual input. 77 130 138 21 213 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 1550-4131 1932-7420 CELL METAB Cell Metab. JAN 7 2020.0 31 1 77 + 10.1016/j.cmet.2019.11.001 0.0 20 Cell Biology; Endocrinology & Metabolism Science Citation Index Expanded (SCI-EXPANDED) Cell Biology; Endocrinology & Metabolism KA7UE 31786155.0 2023-03-23 WOS:000506002900012 0
C Alippi, C; Bu, L; Zhao, DB IEEE Alippi, Cesare; Bu, Li; Zhao, Dongbin A Prior-Free Encode-Decode Change Detection Test to Inspect Datastreams for Concept Drift 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) AUG 04-09, 2013 Dallas, TX Int Neural Network Soc,IEEE Computat Intelligence Soc TIME ADAPTIVE CLASSIFIERS; MODEL Online change detection in datastreams has attracted many researchers and is becoming a very hot topic whose relevance will further increase with research on Big Data. Concept drift is induced by changes in stationarity of the process generating the data caused by faults, time variance of the environment and inaccuracy of the change detection mechanism. Here, we propose a recurrent auto-associative Encode-Decode machine trained to reconstruct input data. The generated residual is then inspected for structural changes with a Change Detection Test (CDT). Although any CDT can be used, in the paper we focus the attention on the Hierarchical Intersection of Confidence Intervals change detection test for its capability of controlling false positives with a two layered test and an online version of the Lepage Change Point Model. Once concept drift is detected, the designed Encode-Decode machine, globally acting as an Encode-Decode CDT, is retrained on new data to detect subsequent changes. [Alippi, Cesare] Politecn Milan, Milan, Italy; [Alippi, Cesare] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100864, Peoples R China; [Bu, Li; Zhao, Dongbin] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China Polytechnic University of Milan; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; Institute of Automation, CAS Alippi, C (corresponding author), Politecn Milan, Milan, Italy. cesare.alippi@polimi.it; bulipolly@gmail.com; dongbin.zhao@ia.ac.cn 18 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-4673-6129-3; 978-1-4673-6128-6 IEEE IJCNN 2013.0 6 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BC0TZ 2023-03-23 WOS:000349557200129 0
J He, X; Chen, YS; Ghamisi, P He, Xin; Chen, Yushi; Ghamisi, Pedram Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Training; Kernel; Hyperspectral imaging; Data mining; Task analysis; Shape; Classification; convolutional neural network (CNN); dual graph convolutional network (DGCN); hyperspectral image (HSI); label distribution learning SPATIAL CLASSIFICATION; SPARSE-REPRESENTATION; FRAMEWORK; PROFILES; FUSION Due to powerful feature extraction capability, convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. However, because of a large number of parameters that need to be trained, sufficient training samples are usually required for deep CNN-based methods. Unfortunately, limited training samples are a common issue in the remote sensing community. In this study, a dual graph convolutional network (DGCN) is proposed for the supervised classification of HSI with limited training samples. The first GCN fully extracts features existing in and among HSI samples, while the second GCN utilizes label distribution learning, and thus, it potentially reduces the number of required training samples. The two GCNs are integrated through several iterations to decrease interclass distances, which leads to a more accurate classification step. Moreover, a new idea entitled multiscale feature cutout is proposed as a regularization technique for HSI classification (DGCN-M). Different from the regularization methods (e.g., dropout and DropBlock), the proposed multiscale feature cutout could randomly mask out multiscale region sizes in a feature map, which further reduces the overfitting problem and yields consistent improvement. Experimental results on the four popular hyperspectral data sets (i.e., Salinas, Indian Pines, Pavia, and Houston) indicate that the proposed method obtains good classification performance compared to state-of-the-art methods, which shows the potential of GCN for HSI classification. [He, Xin; Chen, Yushi] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol, D-01328 Dresden, Germany; [Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, A-1030 Vienna, Austria Harbin Institute of Technology; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Chen, YS (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China. 19B905007@stu.hit.edu.cn; chenyushi@hit.edu.cn; p.ghamisi@gmail.com Chen, Yushi/0000-0003-2421-0996; He, Xin/0000-0003-0455-4230 Natural Science Foundation of China [61971164] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Natural Science Foundation of China under Grant 61971164. 62 17 18 37 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5502418 10.1109/TGRS.2021.3061088 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology XL6PS 2023-03-23 WOS:000728266600065 0
J Yi, HL; Tian, JX; Mulero, A; Cachadin, I Yi, Huili; Tian, Jianxiang; Mulero, A.; Cachadin, I. Weibull-type correlation for the surface tension of common fluids JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY English Article Surface tension; Temperature; Correlation; Weibull distribution function ARTIFICIAL NEURAL-NETWORK; DENSITY GRADIENT THEORY; SCALED EQUATION; BINARY-MIXTURES; PURE COMPOUNDS; PREDICTION; INTERFACE; SIMULATIONS; ALCOHOLS The Weibull model is a well-known probability distribution function that uses only two adjustable coefficients, which have a certain mathematical meaning, and can represent a lot of different analytical behaviors. In this paper, we show that the Weibull model can be used to correlate the surface tension values of liquids. For that, we compare this model with the results given by the REFPROP computer program for 117 liquids. While the REFPROP program uses two to six adjustable coefficients depending on the fluid in question, what we propose here is the use of only one or two adjustable coefficients. We show that for 112 out of the 117 fluids the new correlation using two adjustable coefficients deviates by <2 % (absolute average mean deviation, AAD) from the correlations used in REFPROP. We also show that it is possible to apply just one adjustable coefficient for 63 fluids, with the AAD being below 1.7 % and the maximum deviation below 6 %. [Yi, Huili; Tian, Jianxiang] Qufu Normal Univ, Dept Phys, Shandong Prov Key Lab Laser Polarizat & Informat, Qufu 273165, Peoples R China; [Tian, Jianxiang] Dalian Univ Technol, Dept Phys, Dalian 116024, Peoples R China; [Mulero, A.; Cachadin, I.] Univ Extremadura, Dept Fis Aplicada, Badajoz 06006, Spain Qufu Normal University; Dalian University of Technology; Universidad de Extremadura Tian, JX (corresponding author), Qufu Normal Univ, Dept Phys, Shandong Prov Key Lab Laser Polarizat & Informat, Qufu 273165, Peoples R China.;Tian, JX (corresponding author), Dalian Univ Technol, Dept Phys, Dalian 116024, Peoples R China.;Mulero, A (corresponding author), Univ Extremadura, Dept Fis Aplicada, Badajoz 06006, Spain. jxtian@dlut.edu.cn; mulero@unex.es Mulero, Angel/AAE-5609-2020; Mulero, Angel/N-8849-2019 Mulero, Angel/0000-0003-2148-0915; Tian, Jianxiang/0000-0001-6373-6953 National Natural Science Foundation of China [11274200]; Natural Science Foundation of Shandong Province [ZR2011AM017]; QFNU; DUT; Gobierno de Extremadura''; European Union (FEDER) [GR15146] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province); QFNU; DUT; Gobierno de Extremadura''; European Union (FEDER)(European Commission) The National Natural Science Foundation of China under Grant No. 11274200, the Natural Science Foundation of Shandong Province under Grant No. ZR2011AM017, and the QFNU and DUT foundations have supported this work (J.T.). It was also partially supported by the Gobierno de Extremadura'' and the European Union (FEDER) through Project GR15146 (A.M. and I.C.). 36 12 12 1 9 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1388-6150 1588-2926 J THERM ANAL CALORIM J. Therm. Anal. Calorim. DEC 2016.0 126 3 1603 1613 10.1007/s10973-016-5656-7 0.0 11 Thermodynamics; Chemistry, Analytical; Chemistry, Physical Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Chemistry EC1GK 2023-03-23 WOS:000387852700061 0
C Zhao, ZT; Yang, GY DeBruijne, M; Cattin, PC; Cotin, S; Padoy, N; Speidel, S; Zheng, Y; Essert, C Zhao, Ziteng; Yang, Guanyu Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT II Lecture Notes in Computer Science English Proceedings Paper International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 27-OCT 01, 2021 ELECTR NETWORK Self-supervised learning; Unsupervised contrastive learning; Radiomics; Tumor classification Tumor classification is important for decision support of precision medicine. Computer-aided diagnosis by convolutional neural networks relies on a large amount of annotated dataset, which is costly sometimes. To solve the poor predictive ability caused by tumor heterogeneity and inadequate labeled image data, a self-supervised learning method combined with radiomics is proposed to learn rich visual representation about tumors without human supervision. A self-supervised pretext task, namely Radiomics-Deep Feature Correspondence, is formulated to maximize agreement between radiomics view and deep learning view of the same sample in the latent space. The presented self-supervised model is evaluated on two public medical image datasets of thyroid nodule and kidney tumor and achieves high score on linear evaluations. Furthermore, fine-tuning the pre-trained network leads to a better score than the train-from-scratch models on the tumor classification task and shows label-efficient performance using small training datasets. This shows injecting radiomics prior knowledge about tumors into the representation space can build a more powerful self-supervised method. [Zhao, Ziteng; Yang, Guanyu] Southeast Univ, Minist Educ, LIST, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China; [Yang, Guanyu] Ctr Rech Informat Biomed Sino Francais CRIBs, Rennes, France Southeast University - China; Universite de Rennes Yang, GY (corresponding author), Southeast Univ, Minist Educ, LIST, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.;Yang, GY (corresponding author), Ctr Rech Informat Biomed Sino Francais CRIBs, Rennes, France. yang.list@seu.edu.cn National Natural Science Foundation [31571001, 61828101] National Natural Science Foundation(National Natural Science Foundation of China (NSFC)) This research was supported by National Natural Science Foundation under grants (31571001, 61828101). We thank the Big Data Center of Southeast University for providing the GPUs to support the numerical calculations in this paper. 23 3 3 3 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-87196-3; 978-3-030-87195-6 LECT NOTES COMPUT SC 2021.0 12902 252 261 10.1007/978-3-030-87196-3_24 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Biomedical; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging BS3JX 2023-03-23 WOS:000712020700024 0
C Gao, Z; Wei, XH; Zhang, H; Li, WS; Ge, GJ; Wang, Y; Reviriego, P Dilillo, L; Psarakis, M; Siddiqua, T Gao, Zhen; Wei, Xiaohui; Zhang, Han; Li, Wenshuo; Ge, Guangjun; Wang, Yu; Reviriego, Pedro Reliability Evaluation of Pruned Neural Networks against Errors on Parameters 2020 33RD IEEE INTERNATIONAL SYMPOSIUM ON DEFECT AND FAULT TOLERANCE IN VLSI AND NANOTECHNOLOGY SYSTEMS (DFT) IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems English Proceedings Paper 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) OCT 19-21, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,IEEE Test Technol Tech Council,TCFT,IEEE Fault Tolerant Comp Tech Comm Convolutional Neural Networks (CNNs); Pruning; Reliability; Parameter Error Convolutional Neural Networks (CNNs) are widely used in image classification tasks. To fit the application of CNNs on resource-limited embedded systems, pruning is a popular technique to reduce the complexity of the network. In this paper, the robustness of the pruned network against errors on the network parameters is examined with VGG16 as a case study. The effects of errors on the weights, bias, and batch normalization (BN) parameters are evaluated for the network with different pruning rates based on error injection experiments. The results show that in general networks with more weights pruned are more robust for a given error rate. The effect of multiple errors on bias or BN parameters is almost the same for the networks with different pruning rates that are lower than 90%. Further experiments are performed to explain the bimodal phenomenon of the network performance with errors on the parameters, to find that only errors on 6% of the parameter bits will cause large degradation of the neural network performance. [Gao, Zhen; Wei, Xiaohui; Zhang, Han] Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China; [Li, Wenshuo; Ge, Guangjun; Wang, Yu] Tsinghua Univ, Sch Elect Engn, Beijing, Peoples R China; [Reviriego, Pedro] Univ Carlos III Madrid, Dept Ingn Telemat, Madrid, Spain Tianjin University; Tsinghua University; Universidad Carlos III de Madrid Gao, Z (corresponding author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China. Gao, Zhen/0000-0002-2709-0216 Natural Science Funds of China [61501321]; Spanish Ministry of Science and Innovation [PID2019-104207RB-I00]; Madrid Community research project TAPIR-CM [P2018/TCS-4496] Natural Science Funds of China(National Natural Science Foundation of China (NSFC)); Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Madrid Community research project TAPIR-CM This work is supported by Natural Science Funds of China (61501321), and is partially supported by the ACHILLES project PID2019-104207RB-I00 funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM (P2018/TCS-4496). 26 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-5774 978-1-7281-9457-8 INT SYM DEFEC FAU TO 2020.0 6 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS4JB 2023-03-23 WOS:000719567900009 0
J Zhou, XK; Deng, YS; Xia, HY; Wu, SC; Bennis, M Zhou, Xiaokang; Deng, Yansha; Xia, Huiyun; Wu, Shaochuan; Bennis, Mehdi Time-Triggered Federated Learning Over Wireless Networks IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS English Article Federated learning; resource allocation; convergence analysis RESOURCE-ALLOCATION The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead. [Zhou, Xiaokang; Xia, Huiyun; Wu, Shaochuan] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China; [Deng, Yansha] Kings Coll London, Dept Engn, London WC2R, England; [Bennis, Mehdi] Univ Oulu, Ctr Wireless Commun CWC, Oulu 90570, Finland Harbin Institute of Technology; University of Oulu Wu, SC (corresponding author), Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China.;Deng, YS (corresponding author), Kings Coll London, Dept Engn, London WC2R, England. kangsenneo@sina.com; yansha.deng@kcl.ac.uk; summerxiahy@163.com; scwu@hit.edu.cn; mehdi.bennis@oulu.fi ; Bennis, Mehdi/ABE-5838-2020 Deng, Yansha/0000-0003-1001-7036; Wu, Shaochuan/0000-0002-2362-3126; Bennis, Mehdi/0000-0003-0261-0171 Natural Science Foundation of China [61671173, 62171163, 61831002]; Engineering and Physical Sciences Research Council (EPSRC), U.K. [EP/W004348/1]; China Scholarship Council Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Engineering and Physical Sciences Research Council (EPSRC), U.K.(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); China Scholarship Council(China Scholarship Council) This work was supported in part by the Natural Science Foundation of China under Grant 61671173, Grant 62171163, and Grant 61831002; and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/W004348/1. The workof Xiaokang Zhou was supported by the China Scholarship Council, and was performed during his visit at King's College London 36 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1276 1558-2248 IEEE T WIREL COMMUN IEEE Trans. Wirel. Commun. DEC 2022.0 21 12 11066 11079 10.1109/TWC.2022.3189601 0.0 14 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 7W8ZJ Green Accepted, Green Submitted 2023-03-23 WOS:000913795700066 0
J Aaboud, M; Aad, G; Abbott, B; Abbott, DC; Abdinov, O; Abeloos, B; Abhayasinghe, DK; Abidi, SH; AbouZeid, OS; Abraham, NL; Abramowicz, H; Abreu, H; Abulaiti, Y; Acharya, BS; Adachi, S; Adam, L; Adamczyk, L; Adamek, L; Adelman, J; Adersberger, M; Adiguzel, A; Adye, T; Affolder, AA; Afik, Y; Agheorghiesei, C; Aguilar-Saavedra, JA; Ahmadov, F; Aielli, G; Akatsuka, S; Akesson, TPA; Akilli, E; Akimov, AV; Alberghi, GL; Albert, J; Albicocco, P; Verzini, MJA; Alderweireldt, S; Aleksa, M; Aleksandrov, IN; Alexa, C; Alexandre, D; Alexopoulos, T; Alhroob, M; Ali, B; Alimonti, G; Alison, J; Alkire, SP; Allaire, C; Allbrooke, BMM; Allen, BW; Allport, PP; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Alshehri, AA; Alstaty, MI; Gonzalez, BA; Piqueras, DA; Alviggi, MG; Amadio, BT; Coutinho, YA; Ambler, A; Ambroz, L; Amelung, C; Amidei, D; Dos Santos, SPA; Amoroso, S; Amrouche, CS; An, F; Anastopoulos, C; Ancu, LS; Andari, N; Andeen, T; Anders, CF; Anders, JK; Anderson, KJ; Andreazza, A; Andrei, V; Anelli, CR; Angelidakis, S; Angelozzi, I; Angerami, A; Anisenkov, AV; Annovi, A; Antel, C; Anthony, MT; Antonelli, M; Antrim, DJA; Anulli, F; Aoki, M; Pozo, JAA; Bella, LA; Arabidze, G; Araque, JP; Ferraz, VA; Pereira, RA; Arce, ATH; Ardell, RE; Arduh, FA; Arguin, JF; Argyropoulos, S; Arling, JH; Armbruster, AJ; Armitage, LJ; Armstrong, A; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Asimakopoulou, EM; Asquith, L; Assamagan, K; Astalos, R; Atkin, RJ; Atkinson, M; Atlay, NB; Augsten, K; Avolio, G; Avramidou, R; Ayoub, MK; Azoulay, AM; Azuelos, G; Baas, AE; Baca, MJ; Bachacou, H; Bachas, K; Backes, M; Bagnaia, P; Bahmani, M; Bahrasemani, H; Bailey, AJ; Bailey, VR; Baines, JT; Bajic, M; Bakalis, C; Baker, OK; Bakker, PJ; Gupta, DB; Balaji, S; Baldin, EM; Balek, P; Balli, F; Balunas, WK; Balz, J; Banas, E; Bandyopadhyay, A; Banerjee, S; Bannoura, AAE; Barak, L; Barbe, WM; Barberio, EL; Barberis, D; Barbero, M; Barillari, T; Barisits, MS; 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Berta, P; Bertella, C; Bertoli, G; Bertram, IA; Besjes, GJ; Bylund, OB; Bessner, M; Besson, N; Bethani, A; Bethke, S; Betti, A; Bevan, AJ; Beyer, J; Bi, R; Bianchi, RM; Biebel, O; Biedermann, D; Bielski, R; Bierwagen, K; Biesuz, NV; Biglietti, M; Billoud, TRV; Bindi, M; Bingul, A; Bini, C; Biondi, S; Birman, M; Bisanz, T; Biswal, JP; Bittrich, C; Bjergaard, DM; Black, JE; Black, KM; Blazek, T; Bloch, I; Blocker, C; Blue, A; Blumenschein, U; Blunier, D; Bobbink, GJ; Bobrovnikov, VS; Bocchetta, SS; Bocci, A; Boerner, D; Bogavac, D; Bogdanchikov, AG; Bohm, C; Boisvert, V; Bokan, P; Bold, T; Boldyrev, AS; Bolz, AE; Bomben, M; Bona, M; Bonilla, JS; Boonekamp, M; Borecka-Bielska, HM; Borisov, A; Borissov, G; Bortfeldt, J; Bortoletto, D; Bortolotto, V; Boscherini, D; Bosman, M; Sola, JDB; Bouaouda, K; Boudreau, J; Bouhova-Thacker, EV; Boumediene, D; Bourdarios, C; Boutle, SK; Boveia, A; Boyd, J; Boye, D; Boyko, IR; Bozson, AJ; Bracinik, J; Brahimi, N; Brandt, A; Brandt, G; Brandt, O; Braren, F; 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F-W; Sadykov, R.; Tehrani, F. Safai; Saha, P.; Sahinsoy, M.; Sahu, A.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakharov, A.; Salamani, D.; Salamanna, G.; Salazar Loyola, J. E.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Samarati, J.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sanchez, J.; Pineda, A. Sanchez; Sandaker, H.; Sander, C. O.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sano, Y.; Sansoni, A.; Santoni, C.; Santos, H.; Castillo, I. Santoyo; Santra, A.; Sapronov, A.; Saraiva, J. G.; Sasaki, O.; Sato, K.; Sauvan, E.; Savard, P.; Savic, N.; Sawada, R.; Sawyer, C.; Sawyer, L.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaeffer, J.; Schaepe, S.; Schaefer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharmberg, N.; Schegelsky, V. A.; Scheirich, D.; Schenck, F.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillaci, Z. M.; Schioppa, E. J.; Schioppa, M.; Schleicher, K. E.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schmoeckel, J. C.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schulte, A.; Schultz-Coulon, H-C; Schumacher, M.; Schumm, B. A.; Schune, Ph; Schwartzman, A.; Schwarz, T. A.; Schwemling, Ph; Schwienhorst, R.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Scyboz, L. M.; Sebastiani, C. D.; Seema, P.; Seidel, S. C.; Seiden, A.; Seiss, T.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Sen, S.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Severini, H.; Sforza, F.; Sfyrla, A.; Shabalina, E.; ; ATLAS Collaboration Measurements of inclusive and differential fiducial cross-sections of t(t)over-bar gamma production in leptonic final states at root s=13 TeV in ATLAS EUROPEAN PHYSICAL JOURNAL C English Article Inclusive and differential cross-sections for the production of a top-quark pair in association with a photon are measured with proton-proton collision data corresponding to an integrated luminosity of 36.1fb-1, collected by the ATLAS detector at the LHC in 2015 and 2016 at a centre-of-mass energy of 13TeV. The measurements are performed in single-lepton and dilepton final states in a fiducial volume. Events with exactly one photon, one or two leptons, a channel-dependent minimum number of jets, and at least one b-jet are selected. Neural network algorithms are used to separate the signal from the backgrounds. The fiducial cross-sections are measured to be 521 +/- 9(stat.)+/- 41(sys.)fb and 69 +/- 3(stat.)+/- 4(sys.) fb for the single-lepton and dilepton channels, respectively. The differential cross-sections are measured as a function of photon transverse momentum, photon absolute pseudorapidity, and angular distance between the photon and its closest lepton in both channels, as well as azimuthal opening angle and absolute pseudorapidity difference between the two leptons in the dilepton channel. All measurements are in agreement with the theoretical predictions. [Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Cairo, V. M. M.; Jain, V; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Dehghanian, N.; Gingrich, D. M.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Costanza, F.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. J.; Falke, S.; Goy, C.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Raspopov, S.; Sauvan, E.; Smart, B. H.; Todorova-Nova, S.; Vallier, A.; Wingerter-Seez, I] Univ Grenoble Alpes, Univ Savoie Mt Blanc, IN2P3, CNRS, Annecy, France; [Abulaiti, Y.; Benjamin, D. P.; Chekanov, S.; LeCompte, T.; Liu, B. L.; Love, J.; Malon, D.; Metcalfe, J.; Paramonov, A.; Proudfoot, J.; Ryu, S.; Stanek, R. W.; Van Gemmeren, P.; Wang, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Berlendis, S.; Cheu, E.; Delitzsch, C. M.; Johns, K. A.; Jones, S.; Lampl, W.; LeBlanc, M.; Leone, R.; Loch, P.; Nayyar, R.; Rutherfoord, J. P.; Varnes, E. W.; Zhou, Y.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Gupta, D. Bakshi; Brandt, A.; Bullock, D.; Burghgrave, B.; Darmora, S.; De, K.; Farbin, A.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Little, J. D.; Ozturk, N.; Usai, G.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA; [Bellos, P.; Fassouliotis, D.; Gkialas, I; Kourkoumelis, C.; Papageorgiou, K.; Tsirintanis, N.] Natl & Kapodistrian Univ Athens, Phys Dept, Athens, Greece; [Alexopoulos, T.; Bakalis, C.; Benekos, N.; Gazis, E. N.; Gkountoumis, P.; Karentzos, E.; Koulouris, A.; Maltezos, S.; Moschovakos, P.; Panagoulias, I; Papadopoulou, T. D.; Vlachos, S.; Zacharis, G.] Natl Tech Univ Athens, Phys Dept, Zografos, Greece; [Andeen, T.; Narayan, R.; Nikiforou, N.; Onyisi, P. U. E.; Potti, H.; Webb, A. F.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Beddall, A. J.] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Celebi, E.; Cetin, S. A.] Istanbul Bilgi Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Adiguzel, A.; Gurbuz, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Phys Engn, Gaziantep, Turkey; [Abdinov, O.; Ahmadov, F.; Huseynov, N.; Javadov, N.; Khalil-Zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan; [Bosman, M.; Casado, M. P.; Cavallaro, E.; Cavalli-Sforza, M.; Fischer, C.; Forster, F. A.; Gerbaudo, D.; Gkougkousis, E. L.; Glatzer, J.; Grinstein, S.; Juste Rozas, A.; Korolkov, I; Martinez, M.; Mir, L. M.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I; Rizzi, C.; Rodriguez Perez, A.; Rosten, R.; Terzo, S.; Van Daalen, T. R.; Vazquez Furelos, D.; Volpi, G.; Zaidan, R.] Barcelona Inst Sci & Technol, Inst Fis Altes Energies, Barcelona, Spain; [Ayoub, M. K.; da Costa, J. Barreiro Guimaraes; Bertella, C.; Cheng, H. J.; Fang, Y.; Pascual, J. A. Garcia; Han, S.; Huang, Y.; Kurth, M. G.; Li, M.; Li, Q.; Liang, Z.; Liu, Y.; Merino, J. Llorente; Lou, X.; Lyu, F.; Ouyang, Q.; Peng, C.; Ran, K.; Ren, H.; Shan, L. Y.; Xu, D.; Zhang, Y.; Zhou, M. S.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Chen, X.; Li, B.; Ran, K.; Zhang, G.] Tsinghua Univ, Phys Dept, Beijing, Peoples R China; [Chen, S. J.; Jin, S.; Wang, W.; Zhang, H.; Zhang, L.; Zhang, P.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Cheng, H. J.; Han, S.; Kurth, M. G.; Li, Q.; Liu, Y.; Zhang, Y.; Zhou, M. S.] Univ Chinese Acad Sci, Beijing, Peoples R China; [Krstic, J.; Sijacki, Dj; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Djuvsland, J., I; Eigen, G.; Fomin, N.; Lipniacka, A.; Maeland, S.; Latour, B. Martin Dit; Stugu, B.; Yang, Z.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Barnett, R. M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Jeanty, L.; Ju, X.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; McCormack, W. P.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Poley, A.; Shapiro, M.; Tsulaia, V; Viel, S.; Wang, H.; Yang, H. T.; Yao, W-M] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA USA; [Amadio, B. T.; Barnett, R. M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Jeanty, L.; Ju, X.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; McCormack, W. P.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Poley, A.; Shapiro, M.; Tsulaia, V; Viel, S.; Wang, H.; Yang, H. T.; Yao, W-M] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Alexandre, D.; Biedermann, D.; Dietrich, J.; Grancagnolo, S.; Herbert, G. H.; Hristova, I; Kind, O. M.; Lacker, H.; Lohse, T.; Mergelmeyer, S.; Ng, Y. S.; Peri, F.; Rehnisch, L.; Schenck, F.; Seema, P.; Sperlich, D.; Stamm, S.; Zur Nedden, M.] Humboldt Univ, Inst Phys, Berlin, Germany; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Merlassino, C.; Miucci, A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Merlassino, C.; Miucci, A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Lab High Energy Phys, Bern, Switzerland; [Allport, P. P.; Baca, M. J.; Bracinik, J.; Briglin, D. L.; Broughton, J. H.; Charlton, D. G.; Chisholm, A. S.; Foster, A. G.; Gonella, L.; Gonnella, F.; Hawkes, C. M.; Hillier, S. J.; Kempster, J. J.; Kendrick, J.; Lindon, J. H.; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Reynolds, E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Turner, R. J.; Vallance, R. A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.; Worm, S. D.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Fac Ciencias, Bogota, Colombia; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Ctr Invest, Bogota, Colombia; [Alberghi, G. L.; Biondi, S.; Cabras, G.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giangiacomi, N.; Manghi, F. Lasagni; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Ist Nazl Fis Nucl, Dipartimento Fis, Bologna, Italy; [Alberghi, G. L.; Biondi, S.; Cabras, G.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giangiacomi, N.; Manghi, F. Lasagni; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Univ Bologna, Bologna, Italy; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Cabras, G.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giangiacomi, N.; Giorgi, F. M.; Manghi, F. Lasagni; Negrini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Spighi, R.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Arslan, O.; Bandyopadhyay, A.; Bechtle, P.; Beisiegel, F.; Bernlochner, F. U.; Betti, A.; Brock, I; Caudron, J.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Gaycken, G.; Ghneimat, M.; Gottardo, C. A.; Grefe, C.; Hageboeck, S.; Hansen, M. C.; Heer, S.; Hinterkeuser, F.; Huebner, M.; Huegging, F.; Jacobs, R. M.; Janssen, J.; Klingl, T.; Koenig, P. T.; Kostyukhin, V. V.; Krueger, H.; Lantzsch, K.; Lenz, T.; Maettig, P.; Melzer, A.; Moles-Valls, R.; Pohl, D.; Ricken, O.; Schildgen, L. K.; Sciandra, A.; Vergis, C.; von Toerne, E.; Wagner, P.; Wermes, N.; Yuen, S. P. Y.; Zhang, R.] Univ Bonn, Phys Inst, Bonn, Germany; [Black, K. M.; Butler, J. M.; Dell'Asta, L.; Finelli, K. D.; Shank, J. T.; Sherman, A. D.; Yan, Z.; Yigitbasi, E.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Bensinger, J. R.; Bergsten, L. J.; Blocker, C.; Dhaliwal, S.; Dodsworth, D.; Goblirsch-Kolb, M.; Herde, H.; O'Connor, K.; Schillaci, Z. M.; Sciolla, G.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Tulbure, T. T.] Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Caprini, I; Caprini, M.; Chitan, A.; Constantinescu, S.; Dobre, M.; Ducu, O. A.; Dumitriu, A. E.; Galea, C.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Rotaru, M.; Stoicea, G.; Tarna, G.; Tudorache, A.; Tudorache, V] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Agheorghiesei, C.] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Phys Dept, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Bartos, P.; Blazek, T.; Dado, T.; Dubovsky, M.; Majersky, O.; Melo, M.; Smiesko, J.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Kladiva, E.; Murin, P.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Inst Expt Phys, Dept Subnucl Phys, Kosice, Slovakia; [Assamagan, K.; Barone, G.; Begel, M.; Cavaliere, V; Chen, H.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Hu, Q.; Iakovidis, G.; Klimentov, A.; Kouskoura, V; Lancon, E.; Lanni, F.; Lee, C. A.; Liu, H. B.; Lynn, D.; Ma, H.; Maeno, T.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Bossio Sola, J. D.; Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Bawa, H. S.; Gao, Y. S.; Grimm, K.; Moss, J.] Calif State Univ, Long Beach, CA USA; [Arratia, M.; Batley, J. R.; Brunt, B. H.; Chapman, J. D.; Cottin, G.; Cowley, J. W.; Fawcett, W. J.; Hill, J. C.; Hommels, L. B. A. H.; Lester, C. G.; Lin, C. Y.; Malone, C.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Williams, S.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Hamilton, A.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; [Boye, D.; Casadei, D.; Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; [Jimenez, Y. Hernandez; Hlaluku, D. R.; Jivan, H.; Kar, D.; Lagouri, T.; Mdhluli, J. E.; Garcia, B. R. Mellado; Reed, R. G.; Roy, D.; Ruan, X.; Haddad, E. Sideras; Tomiwa, K. G.; Von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Cree, G.; Di Valentino, D.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Oakham, F. G.; Park, T. H.; Sherafati, N.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chafaq, A.; Hoummada, A.] Univ Hassan 2, Reseau Univ Phys Hautes Energies, Fac Sci Ain Chock, Casablanca, Morocco; Univ Ibn Tofail, Fac Sci, Kenitra, Morocco; [El Kacimi, M.; Goujdami, D.] Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA Marrakech, Marrakech, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; Dahbi, S.; Ezzi, M.; Fassi, F.; Haddad, N.; Idrissi, Z.; Tayalati, Y.] Univ Mohammed, Fac Sci, Rabat, Morocco; [Aleksa, M.; Gonzalez, B. Alvarez; Bella, L. Aperio; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Bielski, R.; Bortfeldt, J.; Boyd, J.; Buat, Q.; Butti, P.; Buttinger, W.; Calace, N.; Camarda, S.; Camincher, C.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Cattai, A.; Chelstowska, M. A.; Chisholm, A. S.; Cortes-Gonzalez, A.; Czodrowski, P.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Dittus, F.; Dudarev, A.; Duhrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Froidevaux, D.; Goossens, L.; Gorini, B.; Govender, N.; Guenther, J.; Guindon, S.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Hrdinka, J.; Huhtinen, M.; Iengo, P. I.; Javurek, T.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Kuehn, S.; Kuwertz, E. S.; Langenberg, R. J.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Malyukov, S.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Berlingen, J. Montejo; Llacer, M. Moreno; Morley, A. K.; Mornacchi, G.; Mountricha, E.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pandini, C. E.; Pani, P.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Polifka, R.; Pontecorvo, L.; Poveda, J.; Astigarraga, M. E. Pozo; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Salzburger, A.; Samarati, J.; Schaepe, S.; Schioppa, E. J.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sidiropoulou, O.; Sanchez, C. A. Solans; Spigo, G.; Stelzer, H. J.; Stewart, G. A.; Stockton, M. C.; Ten Kate, H.; Unal, G.; Vandelli, W.; Schroeder, T. Vazquez; Venturi, N.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Young, C. J. S.; Zanzi, D.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Gardner, R. W.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Merritt, F. S.; Miller, D. W.; Olsson, M. J. R.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Swiatlowski, M.; Vukotic, I; Wu, M.; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Angelidakis, S.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Gris, Ph; Morales, F. A. Jimenez; Madar, R.; Marjanovic, M.; Nibigira, E.; Pallin, D.; Santoni, C.; Senkin, S.; Vazeille, F.] Univ Clermt Auvergne, IN2P3, CNRS, LPC, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Carbone, R. M.; Chen, J.; Clark, M. R.; Cole, B.; Emerman, A.; Havener, L. B.; Iordanidou, K.; Mahon, D. J.; Mohapatra, S.; Ochoa, I; Parsons, J. A.; Rybar, M.; Tian, Y.; Tuts, P. M.] Columbia Univ, Nevis Lab, Irvington, NY USA; [AbouZeid, O. S.; Alonso, A.; Bajic, M.; Besjes, G. J.; Camplani, A.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Monk, J.; Nielsen, D. S.; Petersen, T. C.; Stark, S. H.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Ist Nazl Fis Nucl, Grp Collegato Cosenza, Lab Nazl Frascati, Frascati, Italy; [Deiana, A. M.; Feickert, M.; Kama, S.; Kehoe, R.; Lo Sterzo, F.; McKay, M. A.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Wang, P.; Ye, J.] Southern Methodist Univ, Phys Dept, Dallas, TX USA; [Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Phys Dept, Richardson, TX 75083 USA; [Bertoli, G.; Bohm, C.; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Bertoli, G.; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Amoroso, S.; Arling, J-H; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I; Braren, F.; Brendlinger, K.; Brenner, L.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Hamburg, Germany; [Amoroso, S.; Arling, J-H; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I; Braren, F.; Brendlinger, K.; Brenner, L.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Robinson, J. E. M.; Rossi, E.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] Deutsch Elektronen Synchrotron DESY, Zeuthen, Germany; [Burmeister, I; Cinca, D.; Erdmann, J.; Freundlich, E. M.; Gessner, G.; Goessling, C.; Homann, M.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Ucchielli, G.; Weingarten, J.; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Herrmann, T.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Maerker, M.; Ramos, J. Manjarres; Morgenstern, S.; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Palazzo, S.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becker, K.; Buehrer, F.; Coniavitis, E.; Garcia-Argos, C.; Gargiulo, S.; Gonella, G.; Gugel, R.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hohn, D.; Jakobs, K.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V; Megy, T.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schleicher, K. E.; Schnoor, U.; Schumacher, M.; Tornambe, P.; Weiser, C.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wollrath, J.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Beck, H. C.; Bindi, M.; Bisanz, T.; Bokan, P.; Buschmann, E.; Dado, T.; Dreyer, T.; Geisen, J.; Gama, R. Goncalves; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lange, J. C.; Mchedlidze, G.; Mellenthin, J. D.; Nellist, C.; Oerdek, S.; Pokharel, I; Quadt, A.; Rosien, N-A; Rzehorz, G. F.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Sohns, F.; Stolte, P.; Torres, R. E. Ticse; Veatch, J.; Volkel, B.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Akilli, E.; Amrouche, C. S.; Ancu, L. S.; Benoit, M.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; March, L.; Mermod, P.; Nessi, M.; Nindhito, H. R.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Pinto, M. Vicente Barreto; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Fumagalli, E.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Fumagalli, E.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Dueren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P. J.; Buttar, C. M.; Callea, G.; Crawley, S. J.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; O'Shea, V; Owen, M.; Robson, A.; Spiteri, D. P.; Thompson, A. S.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Grenoble INP, IN2P3, CNRS, Grenoble, France; [Asbah, N.; Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Shandong, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Shandong, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kato, C.; Kondrashova, N.; Li, L.; Li, S.; Li, X.; Mittal, M.; Nishu, N.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, KLPPAC MoE, Shanghai, Peoples R China; [Kato, C.; Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Antel, C.; Baas, A. E.; Brandt, O.; Dunford, M.; Franchino, S.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kugel, A.; Theenhausen, H. Meyer Zu; Napolitano, F.; Villar, D. I. Narrias; Sahinsoy, M.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; de Lima, D. E. Ferreira; Giulini, M.; Kolb, M.; Lisovyi, M.; Schoening, A.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Cheng, H. C.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Lu, H.; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Lo, C. Y.; Orlando, N.; Hernandez, D. Paredes; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Meyer, C.; Palacino, G.; Roland, C. P. A.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] Ist Nazl Fis Nucl, Grp Collegato Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Politecn Ingn & Architettura, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lccce, Lecce, Italy; [Bachas, K.; Gorini, E.; Gravili, F. G.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Carra, S.; Cavalli, D.; Citterio, M.; D'Auria, S.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Manzoni, S.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R. T.; Perez, M. Villaplana] Ist Nazl Fis Nucl, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; Carra, S.; D'Auria, S.; Fanti, M.; Lazzaroni, M.; Manzoni, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.; Sekhniaidze, G.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.; Vercesi, V.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Farina, E. M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; De Maria, A.; Di Gregorio, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; De Maria, A.; Di Gregorio, G.; Francavilla, P.; Roda, C.; Sotiropoulou, C. L.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Vari, R.; Veneziano, S.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Liberti, B.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Vanadia, M.] Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Di Ciaccio, A.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Baroncelli, A.; Biglietti, M.; Ceradini, F.; Di Micco, B.; Farilla, A.; Iodice, M.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Stanescu, C.; Vecchio, V.; Verducci, M.] Ist Nazl Fis Nucl, Sez Roma Tre, Rome, Italy; [Ceradini, F.; Di Micco, B.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Vecchio, V.; Verducci, M.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Ist Nazl Fis Nucl, TIFPA, Povo, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Beermann, T. A.; Jakobsen, S.; Kneringer, E.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [An, F.; Chen, C. H.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Liu, B.; Pluth, D.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M., V; Dedovich, D., V; Demichev, M.; Gongadze, A.; Gostkin, M., I; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V; Kulchitsky, Y.; Ladygin, E.; Lyubushkin, V; Lyubushkina, T.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Tsiareshka, P., V; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I; Zhemchugov, A.; Zimine, N., I] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.; Peralva, B. S.; Silva Oliveira, M., V] Univ Fed Juiz de Fora, Dept Engn Eletr, Juiz De Fora, Brazil; [Amaral Coutinho, Y.; Araujo Ferraz, V; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Freund, W. S.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio de Janeiro, COPPE EE IF, Rio De Janeiro, Brazil; [Do Vale, M. A. B.] Univ Fed Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Aoki, M.; Hanagaki, K.; Higashino, S.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Makida, Y.; Mizukami, A.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Odaka, S.; Okuyama, T.; Sasaki, O.; Shimizu, S.; Suzuki, S.; Takubo, Y.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I; Unno, Y.; Usui, J.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Tsinghua University; Nanjing University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; Humboldt University of Berlin; University of Bern; University of Bern; University of Birmingham; Universidad Antonio Narino; 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Mohammed V University in Rabat; European Organization for Nuclear Research (CERN); University of Chicago; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Columbia University; University of Copenhagen; Niels Bohr Institute; University of Calabria; Istituto Nazionale di Fisica Nucleare (INFN); Southern Methodist University; University of Texas System; University of Texas Dallas; Stockholm University; Oskar Klein Centre; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Shandong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; National Tsing Hua University; Indiana University System; Indiana University Bloomington; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); 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University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; New York University; Ochanomizu University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; City University of New York (CUNY) System; Council for Scientific & Industrial Research (CSIR) - South Africa; Autonomous University of Barcelona; University of Sharjah; University of Aegean; University of Louisville; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; Stanford University; University of Fribourg; Lomonosov Moscow State University; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Bulgarian Academy of Sciences; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Ilia State University; Istanbul University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Manhattan College; Moscow Institute of Physics & Technology; Moscow Institute of Physics & Technology; Sun Yat Sen University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. Rosati, Stefano/AAF-6968-2019; Fullana Torregrosa, Esteban/AAC-9079-2022; Caprini, Irinel/AAP-3760-2020; Dabrowski, Wladyslaw/AAS-6369-2020; Woźniak, Krzysztof/AAQ-6119-2021; Kruchonak, Uladzimir/AAN-4371-2020; Fassi, Farida/F-3571-2016; Cetin, Serkant Ali/AGF-0147-2022; Kuze, Masahiro/V-4251-2018; Bobrovnikov, Victor/AAB-8328-2022; Ibáñez, Carlos Escobar/B-3761-2017; Kazanin, Vasily F./HHM-2056-2022; Gurbuz, Saime/AAG-5583-2019; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Ahmadov, Faig N/B-3723-2018; Oh, Alexander/HHZ-4386-2022; Wu, Xin/ABH-1729-2020; Ippolito, Valerio/L-1435-2016; Franchini, Matteo/HNI-8314-2023; Mitsou, Vasiliki A./D-1967-2009; Parodi, Fabrizio/AHE-5089-2022; Santra, Arka/AEE-4946-2022; Doyle, Anthony T/C-5889-2009; Gabrielli, Alessandro/H-4931-2012; Beddall, Andrew John/AAE-5820-2022; Amoroso, Simone/AAW-4334-2021; Tassi, Enrico/AAJ-9661-2020; Barberis, Dario/ABF-1310-2021; Sopczak, Andre/I-4951-2015; Fabbri, Laura/H-3442-2012; Calvetti, Milene/AAQ-1337-2020; Kurashige, Hisaya/H-4916-2012; Klimentov, Alexei A/I-4270-2013; Hejbal, Jiri/H-1358-2014; Sadykov, Renat/AAN-9602-2020; Burdin, Sergey/AAZ-9062-2021; do Amaral Coutinho, Yara/AAU-7857-2021; Lee, Lawrence/AAU-6790-2021; Svatos, Michal/ABA-2041-2020; Stabile, Alberto/F-2889-2013; Smirnova, Oxana/A-4401-2013; Olszowska, Jolanta/P-2889-2017; Giagu, Stefano/H-6455-2013; Sopkova, Filomena/HKV-6270-2023; Fiorini, Luca/W-6250-2018; Franchini, Matteo/AAC-9259-2021; Padilla, Cristobal/C-3218-2017; Bruscino, Nello/ABA-8980-2021; Staszewski, Rafał/AAO-6844-2021; D'Onofrio, Adelina/AAT-3903-2020; Ozcan, Veysi Erkcan/AAS-4508-2020; Cavaliere, Viviana/CAE-8597-2022; de Almeida, Fernando Marroquim Leao/D-6947-2019; Gonzalez, Barbara Alvarez/ABG-7021-2020; Cottin, Giovanna/AFR-3846-2022; Vaniachine, Alexandre/H-7796-2013; KHODINOV, ALEKSANDR/D-6269-2015; b, m/HPE-0989-2023; Olszewski, Andrzej J/W-1825-2018; Chizhov, Mihail/CAG-2076-2022; CAKIR, Orhan/L-1893-2015; de Renstrom, Pawel Bruckman/AAG-7725-2021; de la Hoz, Santiago Gonzalez/E-2494-2016; Betti, Alessandra/AAD-9964-2019; Peleganchuk, Sergey V/J-6722-2014; Alimonti, Gianluca/AAG-4603-2020; Trzebinski, Maciej/AAO-6821-2021; banerjee, swagato/AAN-3165-2021; Brooks, William K/C-8636-2013; Gao, Y./GWZ-3665-2022; Balaji, S/J-1864-2019; Palka, Marek/GXW-2506-2022; Chitan, Adrian/AAC-8946-2022; Bassalat, Ahmed Azzam/HHY-9901-2022; Tikhomirov, Vladimir/M-6194-2015; Ventura, Andrea/CAH-0226-2022; Petrucci, Fabrizio/G-8348-2012; Zhemchugov, Alexey/N-1717-2017; Koperny, Stefan/ABB-4747-2020; Luci, Claudio/HKW-0143-2023; Wolters, Helmut/M-4154-2013; Ferrer, Antonio/H-2942-2015; AGHEORGHIESEI, Catalin/B-8596-2014; Viaux, Nicolas/AAT-5715-2020; Camarero, Daniel/Z-1924-2019; Maevskiy, Artem/P-4101-2016; Glasman, Claudia/Y-8858-2019; Gutierrez, Phillip/C-1161-2011; Pereira, Rodrigo Araujo/AFV-9983-2022; Alberich, Leonor Cerdá/ABG-8534-2020; Bailey, Adam J/T-9828-2017; Introzzi, Gianluca/K-2497-2015; Nechaeva, Polina Y/N-1148-2015; Benekos, Nektarios/I-9928-2017; Wang, Song-Ming/AAP-9832-2021; Parida, Bibhuti/T-3730-2018; Lagouri, Theodota/AAC-7358-2021; Kroll, Jiri/C-8465-2018; Grinstein, Sebastian/ABE-1880-2020; Smykiewicz, Andrzej/W-6236-2018; Villaplana Perez, Miguel/B-5772-2014; ezzi, mohammed/AAD-7543-2020; Pages, Andres Pacheco/C-5353-2011; Maj, Klaudia/Q-2724-2019; Sultansoy, Saleh/AAV-6853-2021; Chwastowski, Janusz J./I-4480-2012; Romano, Marino/ACW-0715-2022; Kartvelishvili, Vakhtang G/K-2312-2013; Francavilla, Paolo/HKE-1206-2023; De, Kaushik/N-1953-2013; Darbo, Giovanni/C-8175-2012; Banaś, Elżbieta A/W-4583-2017; Berger, N/ABE-4064-2020; Tapia, Sebastian/ABB-6644-2021; Castro, Nuno/AAB-3648-2019; Godlewski, Jan/AAI-3300-2021; Adiguzel, Aytul/AAC-9049-2020; Schioppa, Enrico Junior/F-4731-2019; Lacasta, Carlos/C-7254-2008; spagnolo, stefania/A-6359-2012; Giuli, Francesco/HJI-6649-2023; Bergmann, Benedikt/L-8368-2019; Fassouliotis, Dimitrios/AAL-7081-2021; Sapronov, Andrey/HGB-0392-2022; Zanzi, Daniele/AAY-4102-2020; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; MEONI, Evelin/ABD-9498-2021; Malek, Fairouz/E-4410-2018; Hunter, Robert/AAG-8342-2019; Trzupek, Adam/N-2448-2018; Gravila, Paul/HLP-6245-2023; Perez, Miguel Villaplana/B-2717-2015; Buttar, Craig/AAG-5392-2021; Chelkov, Georgy A./G-9934-2019; Dubinin, Filipp/M-9546-2015; govender, nicolin/CAH-3245-2022; Carquin, Edson/GLU-9641-2022; Masik, Jiri/AAE-1203-2021; da via, cinzia/AAS-3978-2021; gaudio, gabriella/AAN-6039-2021; Orlando, Nicola/AAL-1723-2021; Llorente Merino, Javier/AGP-3432-2022; Buzykaev, Alexey/HIK-0117-2022; Casado, Pilar/H-1484-2015; Lefebvre, Benoit/ABB-2272-2021; Llácer, María Moreno/AAQ-7522-2020; Huseynov, Nazim/AAE-4663-2019; Jiménez, Yesenia Hernández/ABH-1107-2020; Grabowska-Bołd, Iwona/ABI-7829-2020; zhang, jin/GXV-9154-2022; Gonella, Laura/GLR-3838-2022; Smirnov, Sergei Y/F-1014-2011; Mikestikova, Marcela/H-1996-2014; Cavaliere, Viviana/CAG-2384-2022; Kharlamova, Tatyana/AAQ-5430-2020; Carquin López, Edson/GZG-4712-2022; Norjoharuddeen, Nurfikri/S-3109-2018; Snesarev, Andrei/H-5090-2013; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Hamal, Petr/G-5540-2014; Jones, Roger WL/H-5578-2011; Artoni, Giacomo/ABA-2164-2020; Sultansoy, Saleh/AAA-8267-2019; Billoud, Thomas/AAY-1569-2020; Schernau, Michael/GRY-3808-2022; Kuleshov, Sergey V/D-9940-2013; Dyndal, Mateusz/AAB-1528-2020; Kepka, Oldrich/ABF-5327-2021; Napolitano, Fabrizio/CAI-2163-2022; Pascual, Juan Antonio Garcia/B-3540-2019; Cheng, Hok-Chuen/GNP-8341-2022; Bortfeldt, Jona/AAJ-5370-2021; Derendarz, Dominik/AAO-3512-2021; García, Josu Cantero/AAS-9810-2020; Brahimi, Nihal/HNJ-5325-2023; Torro Pastor, Emma/AAB-5979-2021; de la Torre Perez, Hector/ABG-6942-2020; Gustavino, Giuliano/AAK-6591-2020; Gladilin, Leonid/B-5226-2011; Torregrosa, Esteban Fullana/A-7305-2016; Trzebinski, Maciej/W-1748-2018; Marcisovsky, Michal/AAM-2404-2020; Panizzo, Giancarlo/AAS-2911-2020; Song, Weimin/AAJ-5415-2020; Sanchez, Francisca J. Munoz/AAH-3909-2019; Garcia, Carmen/W-2465-2018; Berta, Peter/AAL-7109-2020; YILDIZ, Hatice DURAN/AAN-3727-2021; perez, miguel/GXF-5077-2022; Lee, Shih-Chang/AAV-7016-2021; Zemaityte, Gabija/AAI-7245-2021; Ohm, Christian/AAU-6572-2020; Reznicek, Pavel/C-1989-2017; Legger, Federica/HNC-2568-2023; Leisos, Antonios/AAJ-2351-2021; Saraiva, Joao Gentil Mendes/D-1596-2017; Suster, Carl J E/L-3714-2016; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Bachas, Konstantinos/C-8101-2019; Valero, Alberto/G-9866-2015; Annovi, Alberto/AAA-8638-2020; Camarri, Paolo/AAF-9629-2020; Llácer, María Moreno/AAU-6582-2020; Barreiro, Fernando/D-9808-2012; Sessa, Marco/AAT-2850-2020; Herten, Gregor/HNQ-9546-2023; Capua, Marcella/A-8549-2015; Coccaro, Andrea/AAB-6990-2021; Vandelli, Wainer/ABI-8185-2020; Bogdanchikov, Alexander/AAB-9414-2022; Dziedzic, Bartosz/Q-4189-2017; Wozniak, Krzysztof W/P-4475-2017; Talyshev, Alexey/HGC-6910-2022; Panizzo, Giancarlo/AAS-2986-2020; Di Nardo, Roberto/J-4993-2012; Maneira, Jose/D-8486-2011; Capua, Marcella/AAJ-8419-2020; Costa, Maria Jose/H-5962-2015; Albert, Justin/J-4152-2017; Maeda, Junpei/B-8131-2018; Bosman, Martine/J-9917-2014; cerri, alessandro/Q-6884-2016; Carquin, Edson/G-5221-2015; xella, stefania/E-6752-2015; Mamuzic, Judita/U-3509-2017; La Rosa, Alessandro/I-1856-2013; Blue, Andrew/C-9882-2016; Kukla, Romain/P-9760-2016; LIVAN, Michele/D-7531-2012; Carli, Ina/C-2189-2017; Goncalo, Jose/M-3153-2016; Worm, Steven/I-3575-2012; Sykora, Ivan/T-5252-2018; Aguilar Saavedra, Juan Antonio/F-1256-2016; Vos, Marcel/G-8123-2015; Monzani, Simone/D-6328-2017; McKee, Shawn/B-6435-2012; MARTINEZ, MARIO/I-3549-2015; Korcyl, Krzysztof/W-2111-2018; Juste, Aurelio/I-2531-2015; Davidek, Tomas/P-2697-2017; Grancagnolo, Sergio/J-3957-2015; D'Eramo, Louis/Q-5816-2017; Garcia Navarro, Jose Enrique/H-6339-2015; Britton, David/F-2602-2010; Baldin, Evgenii/A-6186-2014; Chu, Ming-chung/M-2655-2018; Konig, Adriaan/B-6439-2013; Lazzaroni, Massimo/N-3675-2015; Rebuzzi, Daniela Marcella/D-9727-2018; Hansen, Peter Henrik/C-2098-2015; Penc, Ondrej/H-3032-2014; Wolter, Marcin/A-7412-2012; Petersen, Troels/P-5538-2015; Schultz-Coulon, Hans-Christian/X-5006-2018; Vranjes Milosavljevic, Marija/F-9847-2016; Buckley, Andy/B-8362-2014; Faltova, Jana/P-6842-2017; Sioli, Maximiliano/Q-1597-2016; Kaczmarska, Anna/B-2753-2019; Giordani, Mario/Q-6211-2018; Kodys, Peter/P-2636-2017; Rodriguez Bosca, Sergi/HPC-6167-2023; Ungaro, Francesca/G-4554-2018; Lysak, Roman/H-2995-2014; Monk, James/A-9021-2017; Zenis, Tibor/T-5270-2018; Li, Liang/O-1107-2015 Rosati, Stefano/0000-0003-0838-5980; Fullana Torregrosa, Esteban/0000-0003-3082-621X; Caprini, Irinel/0000-0003-3343-3200; Dabrowski, Wladyslaw/0000-0001-9061-9568; Woźniak, Krzysztof/0000-0003-1171-0887; Kruchonak, Uladzimir/0000-0003-2116-4592; Fassi, Farida/0000-0002-6423-7213; Cetin, Serkant Ali/0000-0001-5050-8441; Kuze, Masahiro/0000-0001-8858-8440; Bobrovnikov, Victor/0000-0002-2003-0261; Ibáñez, Carlos Escobar/0000-0003-4442-4537; Kazanin, Vasily F./0000-0002-4906-5468; Mikel Eukeni, Pozo Astigarraga/0000-0002-3069-3077; Ahmadov, Faig N/0000-0003-3644-540X; Wu, Xin/0000-0001-7655-389X; Ippolito, Valerio/0000-0001-5126-1620; Mitsou, Vasiliki A./0000-0002-1533-8886; Santra, Arka/0000-0003-4644-2579; Doyle, Anthony T/0000-0001-6322-6195; Gabrielli, Alessandro/0000-0001-5346-7841; Beddall, Andrew John/0000-0002-8451-9672; Amoroso, Simone/0000-0001-5450-0447; Tassi, Enrico/0000-0002-3335-6500; Sopczak, Andre/0000-0001-6981-0544; Fabbri, Laura/0000-0002-4002-8353; Calvetti, Milene/0000-0003-0125-2165; Kurashige, Hisaya/0000-0003-3932-016X; Hejbal, Jiri/0000-0002-4600-3659; Sadykov, Renat/0000-0002-9157-6819; Burdin, Sergey/0000-0003-4831-4132; Lee, Lawrence/0000-0002-5590-335X; Svatos, Michal/0000-0002-7199-3383; Stabile, Alberto/0000-0002-6868-8329; Smirnova, Oxana/0000-0003-2517-531X; Olszowska, Jolanta/0000-0003-0520-9500; Giagu, Stefano/0000-0001-9192-3537; Fiorini, Luca/0000-0002-5070-2735; Franchini, Matteo/0000-0002-4554-252X; Padilla, Cristobal/0000-0001-7951-0166; Staszewski, Rafał/0000-0001-7708-9259; D'Onofrio, Adelina/0000-0002-0343-6331; Ozcan, Veysi Erkcan/0000-0003-4643-6347; Cavaliere, Viviana/0000-0002-4297-8539; de Almeida, Fernando Marroquim Leao/0000-0003-2265-784X; Gonzalez, Barbara Alvarez/0000-0001-7767-4810; Cottin, Giovanna/0000-0002-5308-5808; Vaniachine, Alexandre/0000-0002-0367-5666; KHODINOV, ALEKSANDR/0000-0003-3551-5808; Olszewski, Andrzej J/0000-0003-3368-5475; Chizhov, Mihail/0000-0001-5841-3316; CAKIR, Orhan/0000-0002-9016-138X; de Renstrom, Pawel Bruckman/0000-0002-0206-1160; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Betti, Alessandra/0000-0003-0839-9311; Peleganchuk, Sergey V/0000-0003-0907-7592; Alimonti, Gianluca/0000-0002-7128-9046; Trzebinski, Maciej/0000-0002-5151-7101; banerjee, swagato/0000-0001-8852-2409; Brooks, William K/0000-0001-6161-3570; Balaji, S/0000-0002-7324-4853; Palka, Marek/0000-0002-7185-3540; Bassalat, Ahmed Azzam/0000-0002-0129-1423; Tikhomirov, Vladimir/0000-0002-9634-0581; Ventura, Andrea/0000-0002-3368-3413; Petrucci, Fabrizio/0000-0002-5278-2206; Zhemchugov, Alexey/0000-0002-3360-4965; Wolters, Helmut/0000-0002-9588-1773; Ferrer, Antonio/0000-0003-0532-711X; AGHEORGHIESEI, Catalin/0000-0003-3695-1847; Viaux, Nicolas/0000-0002-5102-9140; Camarero, Daniel/0000-0002-2855-7738; Maevskiy, Artem/0000-0003-1652-8005; Glasman, Claudia/0000-0003-2025-3817; Gutierrez, Phillip/0000-0003-2326-3877; Alberich, Leonor Cerdá/0000-0002-5567-4278; Bailey, Adam J/0000-0002-3301-2986; Introzzi, Gianluca/0000-0002-1314-2580; Nechaeva, Polina Y/0000-0002-5910-4117; Benekos, Nektarios/0000-0001-7831-8762; Wang, Song-Ming/0000-0002-5821-4875; Kroll, Jiri/0000-0001-6215-3326; Grinstein, Sebastian/0000-0002-6460-8694; Smykiewicz, Andrzej/0000-0001-6088-7094; Villaplana Perez, Miguel/0000-0002-0048-4602; ezzi, mohammed/0000-0001-7944-2554; Pages, Andres Pacheco/0000-0001-8210-1734; Maj, Klaudia/0000-0003-4819-9226; Chwastowski, Janusz J./0000-0002-6190-8376; Romano, Marino/0000-0002-6609-7250; Kartvelishvili, Vakhtang G/0000-0002-1957-3787; Francavilla, Paolo/0000-0003-1164-6870; De, Kaushik/0000-0002-5647-4489; Darbo, Giovanni/0000-0003-2165-0638; Banaś, Elżbieta A/0000-0001-5325-6040; Berger, N/0000-0002-7963-9725; Castro, Nuno/0000-0001-8491-4376; Godlewski, Jan/0000-0002-5666-2965; Adiguzel, Aytul/0000-0001-6644-0517; Schioppa, Enrico Junior/0000-0002-1369-9944; Lacasta, Carlos/0000-0002-2623-6252; spagnolo, stefania/0000-0001-7482-6348; Bergmann, Benedikt/0000-0002-8076-5614; Zanzi, Daniele/0000-0002-1222-7937; MEONI, Evelin/0000-0002-6934-3752; Malek, Fairouz/0000-0002-0948-5775; Hunter, Robert/0000-0002-6839-7775; Trzupek, Adam/0000-0001-6938-5867; Gravila, Paul/0000-0002-0154-577X; Perez, Miguel Villaplana/0000-0002-0048-4602; Buttar, Craig/0000-0003-0188-6491; da via, cinzia/0000-0001-6154-7323; gaudio, gabriella/0000-0002-6833-0933; Orlando, Nicola/0000-0003-0616-245X; Buzykaev, Alexey/0000-0002-5458-5564; Casado, Pilar/0000-0002-0394-5646; Lefebvre, Benoit/0000-0001-8212-6624; Llácer, María Moreno/0000-0001-9045-5305; 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Morvaj, Ljiljana/0000-0003-2061-2904; Shi, Liaoshan/0000-0001-9532-5075; Fray, Antony/0000-0002-9433-8648; Wiglesworth, Craig/0000-0001-6219-8946; Di Gregorio, Giulia/0000-0002-7838-576X ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia, Russian Federation; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme; Thales programme; Aristeia programme; EU-ESF; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC [ST/S000747/1, ST/N000234/1, ST/N000277/1, ST/S000860/1, ST/P002439/1, ST/S00095X/1, ST/N000463/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia, Russian Federation(Russian Federation); NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme; Thales programme(Thales Group); Aristeia programme; EU-ESF(European Commission); Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [64]. 63 16 16 2 30 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C MAY 3 2019.0 79 5 382 10.1140/epjc/s10052-019-6849-6 0.0 41 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics HW8UI Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000466965600004 0
J Zhou, XQ; Wu, RJ; Zeng, YX; Qi, ZY; Ferraro, S; Xu, L; Zheng, XX; Li, JL; Fu, MN; Yao, SX; Kendrick, KM; Becker, B Zhou, Xinqi; Wu, Renjing; Zeng, Yixu; Qi, Ziyu; Ferraro, Stefania; Xu, Lei; Zheng, Xiaoxiao; Li, Jialin; Fu, Meina; Yao, Shuxia; Kendrick, Keith M.; Becker, Benjamin Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers COMMUNICATIONS BIOLOGY English Article SEX-DIFFERENCES; GRAY-MATTER; RELIABILITY; VOLUME; MASS Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results. Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation. [Zhou, Xinqi; Wu, Renjing; Zeng, Yixu; Qi, Ziyu; Ferraro, Stefania; Xu, Lei; Zheng, Xiaoxiao; Fu, Meina; Yao, Shuxia; Kendrick, Keith M.; Becker, Benjamin] Univ Elect Sci & Technol China, High Field Magnet Resonance Imaging Key Lab Sichu, MOE Key Lab Neuroinformat,Ctr Psychosomat Med, Sichuan Prov Peoples Hosp,Sichuan Prov Ctr Mental, Chengdu, Peoples R China; [Zhou, Xinqi; Xu, Lei] Sichuan Normal Univ, Inst Brain & Psychol Sci, Chengdu, Peoples R China; [Ferraro, Stefania; Li, Jialin] Fdn Inst Neurol Carlo Besta, Neuroradiol Dept, Milan, Italy Sichuan Provincial People's Hospital; University of Electronic Science & Technology of China; Sichuan Normal University; IRCCS Istituto Neurologico Besta Zhou, XQ; Becker, B (corresponding author), Univ Elect Sci & Technol China, High Field Magnet Resonance Imaging Key Lab Sichu, MOE Key Lab Neuroinformat,Ctr Psychosomat Med, Sichuan Prov Peoples Hosp,Sichuan Prov Ctr Mental, Chengdu, Peoples R China.;Zhou, XQ (corresponding author), Sichuan Normal Univ, Inst Brain & Psychol Sci, Chengdu, Peoples R China. xinqizhou.uestc@outlook.com; den_becker@gmx.de Zhou, Xinqi/R-3616-2019 Zhou, Xinqi/0000-0002-5032-9141; Yao, Shuxia/0000-0002-5259-6374 China MOST2030 Brain Project [2022ZD0208500]; National Key Research and Development Program of China [2018YFA0701400]; National Natural Science Foundation of China (NSFC) [91632117, 31700998, 31530032] China MOST2030 Brain Project; National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported by the China MOST2030 Brain Project No. 2022ZD0208500, National Key Research and Development Program of China (Grant No. 2018YFA0701400), National Natural Science Foundation of China (NSFC, No 91632117, 31700998, 31530032). 67 0 0 3 4 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2399-3642 COMMUN BIOL Commun. Biol. SEP 6 2022.0 5 1 913 10.1038/s42003-022-03880-1 0.0 12 Biology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Science & Technology - Other Topics 4J5GD 36068295.0 gold, Green Accepted 2023-03-23 WOS:000851290400002 0
J Sui, XD; Zheng, YJ; He, YL; Jia, WK Sui, Xiaodan; Zheng, Yuanjie; He, Yunlong; Jia, Weikuan Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images JOURNAL OF HEALTHCARE ENGINEERING English Article HAMMER Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results. [Sui, Xiaodan; Zheng, Yuanjie; Jia, Weikuan] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China; [Zheng, Yuanjie] Shandong Normal Univ, Shandong Prov Key Lab Novel Distributed Comp Soft, Inst Biomed Sci, Key Lab Intelligent Comp & Informat Secur Univ Sh, Jinan 250358, Peoples R China; [He, Yunlong] Univ Lyon, CNRS, INSERM, INSA Lyon, F-69621 Villeurbanne, France Shandong Normal University; Shandong Normal University; Centre National de la Recherche Scientifique (CNRS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Zheng, YJ; Jia, WK (corresponding author), Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.;Zheng, YJ (corresponding author), Shandong Normal Univ, Shandong Prov Key Lab Novel Distributed Comp Soft, Inst Biomed Sci, Key Lab Intelligent Comp & Informat Secur Univ Sh, Jinan 250358, Peoples R China. suixiaodan521@gmail.com; yjzheng@sdnu.edu.cn; yunlong.he@insa-lyon.fr; wkjia@sdnu.edu.cn Jia, Weikuan/0000-0001-6242-3269; Zheng, Yuanjie/0000-0002-5786-2491 National Natural Science Foundation of China [81871508, 61773246]; Major Program of Shandong Province Natural Science Foundation [ZR2019ZD04, ZR2018ZB0419]; Taishan Scholar Program of Shandong Province of China [TSHW201502038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Program of Shandong Province Natural Science Foundation; Taishan Scholar Program of Shandong Province of China This work was supported by the National Natural Science Foundation of China (Grants nos. 81871508 and 61773246), the Major Program of Shandong Province Natural Science Foundation (Grant nos. ZR2019ZD04 and ZR2018ZB0419), and the Taishan Scholar Program of Shandong Province of China (Grant no. TSHW201502038). 35 1 1 2 7 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 2040-2295 2040-2309 J HEALTHC ENG J. Healthc. Eng. APR 23 2021.0 2021 5520196 10.1155/2021/5520196 0.0 8 Health Care Sciences & Services Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services SX1PU 33976754.0 gold 2023-03-23 WOS:000664984800001 0
J Cachadina, I; Mulero, A; Tian, JX Cachadina, I.; Mulero, A.; Tian, Jianxiang Surface tension of refrigerants: A new correlation using the boiling point as reference FLUID PHASE EQUILIBRIA English Article Correlation; Refrigerants; Surface tension; Boiling point; Corresponding-states ARTIFICIAL NEURAL-NETWORK; LIQUID VISCOSITY; THERMODYNAMIC PROPERTIES; SCALED EQUATION; PURE COMPOUNDS; VAPOR; TEMPERATURE; DENSITIES; MODEL A new general corresponding-states correlation model for the calculation of the surface tension of refrigerants is proposed. The main difference with respect to other models available in the literature is that, in addition to the critical point temperature, the proposed model includes the temperature and surface tension values at the boiling point as input properties to define the reduced (non-dimensional) properties. Only three general adjustable coefficients are needed, which were obtained by a fitting procedure to the data presently available for 80 refrigerants. Averaged absolute deviations below 9.5% are found in all cases, being below 5% for 67 out of the 80 fluids considered. Results for another four corresponding states models available in the literature are also obtained. The correlation model proposed here gives clearly better overall results, and it is the only one including exclusively temperature and surface tension values as inputs. (C) 2017 Elsevier B.V. All rights reserved. [Cachadina, I.; Mulero, A.] Univ Extremadura, Dept Fis Aplicada, Badajoz 06006, Spain; [Tian, Jianxiang] Qufu Normal Univ, Dept Phys, Shandong Prov Key Lab Laser Polarizat & Informat, Qufu 273165, Peoples R China; [Tian, Jianxiang] Dalian Univ Technol, Dept Phys, Dalian 116024, Peoples R China Universidad de Extremadura; Qufu Normal University; Dalian University of Technology Tian, JX (corresponding author), Qufu Normal Univ, Dept Phys, Shandong Prov Key Lab Laser Polarizat & Informat, Qufu 273165, Peoples R China. jxtian@dlut.edu.cn Mulero, Angel/AAE-5609-2020; Mulero, Angel/N-8849-2019; Cachadiña, Isidro/AFL-6752-2022; Cachadiña, Isidro/AAG-6898-2022 Mulero, Angel/0000-0003-2148-0915; Cachadiña, Isidro/0000-0002-1139-1284; Cachadiña, Isidro/0000-0002-1139-1284; Tian, Jianxiang/0000-0001-6373-6953 Junta de Extremadura; FEDER [GR15146]; National Natural Science Foundation of China [11274200, A040215]; Natural Science Foundation of Shandong Province [ZR2011AM017, ZR2015AM004] Junta de Extremadura; FEDER(European Commission); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work was supported by the Junta de Extremadura and FEDER through project GR15146 (I. C. and A. M.), the National Natural Science Foundation of China, projects 11274200 and A040215 (J. Tian), and the Natural Science Foundation of Shandong Province, projects ZR2011AM017 and ZR2015AM004 (J. Tian). 33 15 15 1 9 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0378-3812 1879-0224 FLUID PHASE EQUILIBR Fluid Phase Equilib. JUN 25 2017.0 442 68 80 10.1016/j.fluid.2017.03.017 0.0 13 Thermodynamics; Chemistry, Physical; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Chemistry; Engineering EU7KO 2023-03-23 WOS:000401213900008 0
C Haut, JM; Liu, Y; Paoletti, ME; Xu, X; Plaza, J; Plaza, A IEEE Haut, Juan M.; Liu, Yi; Paoletti, Mercedes E.; Xu, Xiong; Plaza, Javier; Plaza, Antonio EVALUATION OF DIFFERENT REGULARIZATION METHODS FOR THE EXTREME LEARNING MACHINE APPLIED TO HYPERSPECTRAL IMAGES IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 22-27, 2018 Valencia, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc,European Space Agcy REGRESSION During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the the original ELM in classification scenarios. [Haut, Juan M.; Paoletti, Mercedes E.; Plaza, Javier; Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain; [Liu, Yi] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, N-7034 Trondheim, Norway; [Xu, Xiong] Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China Universidad de Extremadura; Norwegian University of Science & Technology (NTNU); Tongji University Haut, JM (corresponding author), Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain. juanmariohaut@unex.es; y.liu@ntnu.no; mpaolett@unex.es; xvxiong@tongji.edu.cn; jplaza@unex.es; aplaza@unex.es Haut, Juan M./AAD-9028-2019; Plaza, Antonio/C-4455-2008; Paoletti, Mercedes Eugenia/AAE-1946-2019; Plaza, Javier/C-4452-2008 Haut, Juan M./0000-0001-6701-961X; Plaza, Antonio/0000-0002-9613-1659; Paoletti, Mercedes Eugenia/0000-0003-1030-3729; Plaza, Javier/0000-0002-2384-9141 Ministerio de Educacion; Junta de Extremadura [297/2014, GR15005] Ministerio de Educacion; Junta de Extremadura This work has been supported by Ministerio de Educacion (Resolucion de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaria de Estado de Educacion, Formacion Profesional y Universidades, por la que se convocan ayudas para la formacion de profesorado universitario, de los subprogramas de Formacion y de Movilidad incluidos en el Programa Estatal de Promocion del Talento y su Empleabilidad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016. This work has also been supported by Junta de Extremadura (decreto 297/2014, ayudas para la realizacion de actividades de investigacion y desarrollo tecnologico, de divulgacion y de transferencia de conocimiento por los Grupos de Investigacion de Extremadura, Ref. GR15005). 16 2 2 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-7150-4 INT GEOSCI REMOTE SE 2018.0 3603 3606 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BL4XL 2023-03-23 WOS:000451039803146 0
J Sun, QQ; Liu, XF; Bourennane, S; Liu, B Sun, Qiaoqiao; Liu, Xuefeng; Bourennane, Salah; Liu, Bin Multiscale denoising autoencoder for improvement of target detection INTERNATIONAL JOURNAL OF REMOTE SENSING English Article Target detection; hyperspectral image; denoising autoencoder; spectrum Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the performance of detectors, especially when the target size is small. Therefore, improving the detection performance of small targets and noisy targets is a key task. Considering the great feature extraction and representation ability of deep learning models, denoising autoencoder (DAE) is introduced to reconstruct spectrums and exploit the invariant information for target detection. To fully extract the features from the original spectrums, a multiscale denoising autoencoder (MSDAE) model is designed to incorporate complementary informationin in this paper. The final spectrum is fused by reconstructed spectrums from different scales representations, which provides more complex information and more robust features for subsequent spectral identification. Results on simulated hyperspectral images (HSIs) and real-world HSIs demonstrate that the proposed MSDAE model can effectively remove noise interference and lead to great improvements of the target detection. In addition, the proposed method shows significant potential in preserving small targets. [Sun, Qiaoqiao; Bourennane, Salah] Aix Marseille Univ, Inst Fresnel, Cent Marseille, CNRS, 52 Ave Escadrille Normandie Niemen, F-13013 Marseille, France; [Sun, Qiaoqiao; Liu, Xuefeng; Liu, Bin] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao, Peoples R China Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Aix-Marseille Universite; Qingdao University of Science & Technology Bourennane, S (corresponding author), Aix Marseille Univ, Inst Fresnel, Cent Marseille, CNRS, 52 Ave Escadrille Normandie Niemen, F-13013 Marseille, France. salah.bourennane@fresnel.fr 于, 于增臣/AAH-4657-2021 /0000-0003-2383-369X China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) The first author would like to thank the China Scholarship Council (CSC) for a Ph.D. grant. All authors would like to thank the reviewers and editors for their careful reading and helpful comments which improve the quality of this paper. 23 7 7 3 54 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. APR 18 2021.0 42 8 3002 3016 10.1080/01431161.2020.1856960 0.0 15 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology PT0PZ 2023-03-23 WOS:000608324800001 0
J Zhang, F; Chan, PPK; Biggio, B; Yeung, DS; Roli, F Zhang, Fei; Chan, Patrick P. K.; Biggio, Battista; Yeung, Daniel S.; Roli, Fabio Adversarial Feature Selection Against Evasion Attacks IEEE TRANSACTIONS ON CYBERNETICS English Article Adversarial learning; classifier security; evasion attacks; feature selection; malware detection; spam filtering MULTIPLE CLASSIFIER SYSTEMS; SECURITY EVALUATION; MUTUAL INFORMATION; FUSION Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary-aware classifiers, we propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection. [Zhang, Fei; Chan, Patrick P. K.; Yeung, Daniel S.] S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China; [Biggio, Battista; Roli, Fabio] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy South China University of Technology; University of Cagliari Chan, PPK (corresponding author), S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China. patrickchan@ieee.org Biggio, Battista/M-5931-2016; Biggio, Battista/HCI-4766-2022 Biggio, Battista/0000-0001-7752-509X; Biggio, Battista/0000-0001-7752-509X National Natural Science Foundation of China [61003171, 61272201, 61003172]; Regione Autonoma della Sardegna [CRP-18293] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Regione Autonoma della Sardegna(Regione Sardegna) This work was supported in part by the National Natural Science Foundation of China under Grant 61003171, Grant 61272201, and Grant 61003172, and in part by Regione Autonoma della Sardegna under Grant CRP-18293, L. R. 7/2007, Bando 2009. This paper was recommended by Associate Editor M. Last. (Corresponding author: Patrick P. K. Chan.) 68 66 70 3 44 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. MAR 2016.0 46 3 766 777 10.1109/TCYB.2015.2415032 0.0 12 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science DE9NC 25910268.0 Green Submitted 2023-03-23 WOS:000370963500016 0
C Arcamone, F; Tu, YL; Yang, Y Wei, Y; Li, M; Skums, P; Cai, Z Arcamone, Francesco; Tu, Yanlun; Yang, Yang Prediction of Protein Subcellular Localization from Microscopic Images via Few-Shot Learning BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2021 Lecture Notes in Computer Science English Proceedings Paper 17th International Symposium on Bioinformatics Research and Applications (ISBRA) NOV 26-28, 2021 Shenzhen, PEOPLES R CHINA Protein subcellular localization; Few-shot learning; ArcNet; Immunofluorescence images AUTOMATED-ANALYSIS Benefitting from the breakthrough development of microscopy imaging techniques, various bio-microscopic images have accumulated rapidly for the past decade. Using computer vision and machine learning methods, biological activities and molecular functions can be interpreted from these images, thus image analysis has become more and more important in current life science research. A prominent difficulty in biological image analysis is the lack of annotation, and the test set even contains data from unseen classes, i.e. the open-set issue. The image-based protein subcellular localization is a typical open-set problem. There are tens of subcellular compartments in cells, while the labeled data may only consist of proteins from several major organelles. Till now, the open-set problem has been rarely studied for biomedical image data. The main goal of this study is to train a few-shot learningmodel for the recognition of protein subcellular localization from immunofluorescence images. We conduct experiments on a data set collected from Human Protein Atlas (HPA) and the results show that the introduced system can provide accurate results even with a small handful of images for an unknown class in a multi-instance learning scenario. [Arcamone, Francesco; Tu, Yanlun; Yang, Yang] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Ctr Brain Like Comp & Machine Intelligence, 800 Dong Chuan Rd, Shanghai 200240, Peoples R China; [Arcamone, Francesco] Politecn Milan, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy; [Yang, Yang] Key Lab Shanghai Educ Commiss Intelligent Interac, Shanghai 200240, Peoples R China Shanghai Jiao Tong University; Polytechnic University of Milan Yang, Y (corresponding author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Ctr Brain Like Comp & Machine Intelligence, 800 Dong Chuan Rd, Shanghai 200240, Peoples R China.;Yang, Y (corresponding author), Key Lab Shanghai Educ Commiss Intelligent Interac, Shanghai 200240, Peoples R China. yangyang@cs.sjtu.edu.cn National Natural Science Foundation of China [61972251] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (No. 61972251). 17 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-91414-1; 978-3-030-91415-8 LECT NOTES COMPUT SC 2021.0 13064 241 253 10.1007/978-3-030-91415-8_21 0.0 13 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Mathematical & Computational Biology BU6DL 2023-03-23 WOS:000922632800021 0
J Chen, H; Lei, S; Zhang, SG; Wang, MM; Tang, Y Chen Han; Lei Shen; Zhang Shaogeng; Wang Mingming; Tang Ying Man-algorithm Cooperation Intelligent Design of Clothing Products in Multi Links FIBRES & TEXTILES IN EASTERN EUROPE English Article intelligent algorithm; cooperation; clothing design; machine learning; efficiency PARAMETRIC DESIGN The changes in technology have led to a synchronous change in the clothing design method, as well as media and artistic aesthetics in the same period. The intelligence algorithm is constantly increasing its participation in development and production in the clothing industry. In this study, a variety of intelligent algorithms, including the parameterised number state algorithm, Generative Adversarial Networks, and style transfer were introduced into the multi-links of clothing product design and development, such as clothing shape, print pattern, texture craft, product vision, and so on. Men, an innovative clothing design method based on the cooperation of the intelligent algorithm and various human functional roles was constructed. The method improves the efficiency of the multiple links of clothing design, such as generating 10000 printing patterns every 72.12 seconds, and completing the migration of 92.7 frames of the garment process style every second. To a certain extent, this study realizes the scale economy of clothing design and reduces its marginal cost through the unlimited computing power brought about by Moore's law of digital technology, which provides a reference for the exploration of clothing design in the era of industry 4.0. [Chen Han; Lei Shen; Zhang Shaogeng] Jiangnan Univ, Sch Design, Wuxi 214122, Jiangsu, Peoples R China; [Wang Mingming] Fudan Univ, Sch Comp Sci & Technol, Shanghai 200125, Peoples R China; [Tang Ying] Politecn Milan, Sch Design, I-20158 Milan, Italy Jiangnan University; Fudan University; Polytechnic University of Milan Chen, H (corresponding author), Jiangnan Univ, Sch Design, Wuxi 214122, Jiangsu, Peoples R China. chenhanisaac@163.com; 71952573@qq.com Key Special Projects of 2019 National Key RD Plan [2019YFB1405704]; 2020 Humanities and Social Sciences Planning Fund Project of the Ministry of Education [20YJAZH087]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_1891] Key Special Projects of 2019 National Key RD Plan; 2020 Humanities and Social Sciences Planning Fund Project of the Ministry of Education; Postgraduate Research & Practice Innovation Program of Jiangsu Province This work was supported by the Key Special Projects of 2019 National Key R&D Plan (grant number 2019YFB1405704), 2020 Humanities and Social Sciences Planning Fund Project of the Ministry of Education (grant number 20YJAZH087), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (grant number KYCX20_1891). The authors acknowledge the above financial support. 18 2 2 15 21 INST CHEMICAL FIBRES LODZ UL M SKLODOWSKIEJ-CURIE 19/27, 90-570 LODZ, POLAND 1230-3666 FIBRES TEXT EAST EUR Fibres Text. East. Eur. JAN-FEB 2022.0 30 1 59 66 10.5604/01.3001.0015.6462 0.0 8 Materials Science, Textiles Science Citation Index Expanded (SCI-EXPANDED) Materials Science 2E6IX gold 2023-03-23 WOS:000812331700008 0
J He, B; Chen, C; Lin, SR; Yuan, WP; Chen, HW; Chen, DL; Zhang, YF; Guo, LL; Zhao, X; Liu, XB; Piao, SL; Zhong, ZQ; Wang, R; Tang, R He, Bin; Chen, Chen; Lin, Shangrong; Yuan, Wenping; Chen, Hans W.; Chen, Deliang; Zhang, Yafeng; Guo, Lanlan; Zhao, Xiang; Liu, Xuebang; Piao, Shilong; Zhong, Ziqian; Wang, Rui; Tang, Rui Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks NATIONAL SCIENCE REVIEW English Article vapor pressure deficit; net ecosystem production; gross vegetation production; carbon dioxide concentration CO2 GROWTH-RATE; SENSITIVITY; WATER; ARIDITY; DEMAND; CYCLE Atmospheric vapor pressure deficit has worldwide impacts on the interannual variability of terrestrial carbon sinks and atmospheric carbon dioxide concentrations. Interannual variability of the terrestrial ecosystem carbon sink is substantially regulated by various environmental variables and highly dominates the interannual variation of atmospheric carbon dioxide (CO2) concentrations. Thus, it is necessary to determine dominating factors affecting the interannual variability of the carbon sink to improve our capability of predicting future terrestrial carbon sinks. Using global datasets derived from machine-learning methods and process-based ecosystem models, this study reveals that the interannual variability of the atmospheric vapor pressure deficit (VPD) was significantly negatively correlated with net ecosystem production (NEP) and substantially impacted the interannual variability of the atmospheric CO2 growth rate (CGR). Further analyses found widespread constraints of VPD interannual variability on terrestrial gross primary production (GPP), causing VPD to impact NEP and CGR. Partial correlation analysis confirms the persistent and widespread impacts of VPD on terrestrial carbon sinks compared to other environmental variables. Current Earth system models underestimate the interannual variability in VPD and its impacts on GPP and NEP. Our results highlight the importance of VPD for terrestrial carbon sinks in assessing ecosystems' responses to future climate conditions. [He, Bin; Zhang, Yafeng; Guo, Lanlan; Liu, Xuebang; Zhong, Ziqian; Wang, Rui; Tang, Rui] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Earth Surface Proc & Res Ecol, Beijing 100875, Peoples R China; [Chen, Chen] Twenty First Century Aerosp Technol Co Ltd, Dept Applicat Res, Beijing 100723, Peoples R China; [Lin, Shangrong; Yuan, Wenping] Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Sch Atmospher Sci, Zhuhai 519082, Peoples R China; [Chen, Hans W.] Lund Univ, Dept Phys Geog & Ecosyst Sci, S-22364 Lund, Sweden; [Chen, Deliang] Univ Gothenburg, Dept Earth Sci, Reg Climate Grp, S-40530 Gothenburg, Sweden; [Guo, Lanlan] Beijing Normal Univ, Sch Geog, Acad Disaster Reduct & Emergency Management, Beijing 100875, Peoples R China; [Zhao, Xiang] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Piao, Shilong] Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China Beijing Normal University; Southern Marine Science & Engineering Guangdong Laboratory; Sun Yat Sen University; Lund University; University of Gothenburg; Beijing Normal University; Beijing Normal University; Peking University He, B (corresponding author), Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Earth Surface Proc & Res Ecol, Beijing 100875, Peoples R China.;Yuan, WP (corresponding author), Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Sch Atmospher Sci, Zhuhai 519082, Peoples R China. hebin@bnu.edu.cn; yuanwp3@mail.sysu.edu.cn Chen, Deliang/A-5107-2013; Chen, Hans/GZL-6840-2022; Liu, Xuebang/GZL-5503-2022 Chen, Deliang/0000-0003-0288-5618; Chen, Hans/0000-0002-8601-6024; Guo, Lanlan/0000-0001-5371-9973 National Key Scientific Research and Development Program of China [2017YFA0603601, 2018YFC1508901]; Strategic Priority Research Program of Chinese Academy of Sciences [XDA20060402]; Swedish Formas [2017-01408] National Key Scientific Research and Development Program of China; Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); Swedish Formas(Swedish Research Council Formas) This work was supported by the National Key Scientific Research and Development Program of China (2017YFA0603601 and 2018YFC1508901), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060402) and Swedish Formas (2017-01408). 35 14 14 77 111 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2095-5138 2053-714X NATL SCI REV Natl. Sci. Rev. APR 4 2022.0 9 4 10.1093/nsr/nwab150 0.0 JAN 2022 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 0G8PY 35386922.0 gold 2023-03-23 WOS:000772871500001 0
J Zhao, JQ; Wang, LZ; Tao, J; Chen, JJ; Sun, WY; Ranjan, R; Kolodziej, J; Streit, A; Georgakopoulos, D Zhao, Jiaqi; Wang, Lizhe; Tao, Jie; Chen, Jinjun; Sun, Weiye; Ranjan, Rajiv; Kolodziej, Joanna; Streit, Achim; Georgakopoulos, Dimitrios A security framework in G-Hadoop for big data computing across distributed Cloud data centres JOURNAL OF COMPUTER AND SYSTEM SCIENCES English Article Cloud computing; Massive data processing; Data-intensive computing; Security model; Map Reduce MAPREDUCE; SYSTEM MapReduce is regarded as an adequate programming model for large-scale data-intensive applications. The Hadoop framework is a well-known MapReduce implementation that runs the MapReduce tasks on a cluster system. G-Hadoop is an extension of the Hadoop MapReduce framework with the functionality of allowing the MapReduce tasks to run on multiple clusters. However, G-Hadoop simply reuses the user authentication and job submission mechanism of Hadoop, which is designed for a single cluster. This work proposes a new security model for G-Hadoop. The security model is based on several security solutions such as public key cryptography and the SSL protocol, and is dedicatedly designed for distributed environments. This security framework simplifies the users authentication and job submission process of the current G-Hadoop implementation with a single-sign-on approach. In addition, the designed security framework provides a number of different security mechanisms to protect the G-Hadoop system from traditional attacks. (c) 2014 Elsevier Inc. All rights reserved. [Zhao, Jiaqi] Changchun Univ Technol, Sch Basic Sci, Changchun, Peoples R China; [Wang, Lizhe] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100864, Peoples R China; [Tao, Jie; Sun, Weiye; Streit, Achim] Karlsruhe Inst Technol, Steinbuch Ctr Comp, D-76021 Karlsruhe, Germany; [Chen, Jinjun] Univ Technol Sydney, Fac Engn & IT, Sydney, NSW 2007, Australia; [Ranjan, Rajiv; Georgakopoulos, Dimitrios] CSIRO ICT Ctr, Informat Engn Lab, Marsfield, NSW, Australia; [Kolodziej, Joanna] Cracow Univ Technol, Inst Comp Sci, Krakow, Poland Changchun University of Technology; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Helmholtz Association; Karlsruhe Institute of Technology; University of Technology Sydney; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Cracow University of Technology Wang, LZ (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100864, Peoples R China. Lizhe.Wang@gmail.com; jinjun.chen@gmail.com Ranjan, Rajiv/F-4700-2011; Wang, Lizhe/L-7453-2014; Chen, Jinjun/AAP-2361-2020 Ranjan, Rajiv/0000-0002-6610-1328; Wang, Lizhe/0000-0003-2766-0845; Georgakopoulos, Dimitrios/0000-0001-7880-2140; Kolodziej, Joanna/0000-0002-5181-8713; Chen, Jinjun/0000-0003-1677-9525 27 72 74 3 263 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0022-0000 1090-2724 J COMPUT SYST SCI J. Comput. Syst. Sci. AUG 2014.0 80 5 SI 994 1007 10.1016/j.jcss.2014.02.006 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science AE7CV hybrid 2023-03-23 WOS:000334155200007 0
J Kustner, T; Pan, JZ; Gilliam, C; Qi, HK; Cruz, G; Hammernik, K; Blu, T; Rueckert, D; Botnar, R; Prieto, C; Gatidis, S Kuestner, Thomas; Pan, Jiazhen; Gilliam, Christopher; Qi, Haikun; Cruz, Gastao; Hammernik, Kerstin; Blu, Thierry; Rueckert, Daniel; Botnar, Rene; Prieto, Claudia; Gatidis, Sergios Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING English Article Motion-compensated image reconstruction; Magnetic Resonance; Imaging; Image registration; Deep learning reconstruction ACCELERATED DYNAMIC MRI; ANGIOGRAPHY; SPARSITY; ARTIFACTS; SENSE Respiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with motion by fast imaging sequences under free-movement conditions followed by motion binning based on motion traces. These acquisitions yield sub-Nyquist sampled and motion-resolved k-space data. Motion states are linked to each other by non-rigid deformation fields. Usually, motion registration is formulated in image space which can however be impaired by aliasing artifacts or by estimation from low-resolution images. Subsequently, any motion-corrected reconstruction can be biased by errors in the deformation fields. In this work, we propose a deep-learning based motion-corrected 4D (3D spatial + time) image reconstruction which combines a non-rigid registration network and a 4D reconstruction network. Non-rigid motion is estimated in k-space and incorporated into the reconstruction network. The proposed method is evaluated on in-vivo 4D motion-resolved magnetic resonance images of patients with suspected liver or lung metastases and healthy subjects. The proposed approach provides 4D motion-corrected images and deformation fields. It enables a similar to 14x accelerated acquisition with a 25fold faster reconstruction than comparable approaches under consistent preservation of image quality for changing patients and motion patterns. [Kuestner, Thomas; Gatidis, Sergios] Univ Tubingen Hosp, Med Image & Data Anal MIDAS lab, Dept Diagnost & Intervent Radiol, Tubingen, Germany; [Pan, Jiazhen; Rueckert, Daniel] Tech Univ Munich, Lab Artificial Intelligence Med, Munich, Germany; [Gilliam, Christopher] RMIT Univ, Melbourne, Australia; [Qi, Haikun] ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China; [Cruz, Gastao; Botnar, Rene; Prieto, Claudia] St Thomas Hosp, Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England; [Hammernik, Kerstin; Rueckert, Daniel] Imperial Coll London, Biomed Image Anal Grp, Dept Comp, London, England; [Blu, Thierry] Chinese Univ Hong Kong, Hong Kong, Peoples R China; [Botnar, Rene; Prieto, Claudia] Pontificia Univ Catolica Chile, Escuela Ingn, Santiago, Chile Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Technical University of Munich; Royal Melbourne Institute of Technology (RMIT); ShanghaiTech University; Guy's & St Thomas' NHS Foundation Trust; University of London; King's College London; Imperial College London; Chinese University of Hong Kong; Pontificia Universidad Catolica de Chile Kustner, T (corresponding author), Univ Tubingen Hosp, Med Image & Data Anal MIDAS lab, Dept Diagnost & Intervent Radiol, Tubingen, Germany. thomas.kuestner@med.uni-tuebingen.de Botnar, Rene/E-6875-2012; Gilliam, Christopher/B-6606-2018 Botnar, Rene/0000-0003-2811-2509; Cruz, Gastao/0000-0002-7397-9104; Gilliam, Christopher/0000-0002-6730-8419 77 0 0 2 2 NOW PUBLISHERS INC HANOVER PO BOX 1024, HANOVER, MA 02339, UNITED STATES 2048-7703 APSIPA TRANS SIGNAL APSIPA Trans. Signal Inf. Proc. 2022.0 11 1 e12 10.1561/116.00000039 0.0 27 Engineering, Electrical & Electronic Emerging Sources Citation Index (ESCI) Engineering 8A7XH gold, Green Published 2023-03-23 WOS:000916447600009 0
J Lou, XY; Suykens, JAK Lou, Xuyang; Suykens, Johan A. K. Hybrid Coupled Local Minimizers IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS English Article Global optimization; hybrid coupled local minimizers; Lagrange programming network; Newton-based trust region method; pulse-coupling NEURAL-NETWORK; STABILITY This paper proposes an improved global optimization technique, named Hybrid Coupled Local Minimizers (HCLM), which is inspired on the method of coupled local minimizers (CLM). The HCLM method uses a set of search points, called particles, initially spread over the search space, that are occasionally impulsively coupled, instead of permanently coupled. This approach leads to a much better optimization efficiency, because it combines the fast convergence (due to a Newton-based method that is used for each particle) with the capability of global optimization (resulting from the hybrid interaction which enables a parallel strategy). Such parallel and distributed computing process embedded with a trust region strategy relies on the hybrid interconnections of particles rather than individual particles, which is able to fully exploit the parallel nature of the computation and produce results which are more globally optimal. It is worth mentioning that the stability of each particle and the synchronization of all particles are also derived and proved by means of the contraction theory. The HCLM method is illustrated on several test functions with many local minima and applied to a problem of static nonlinear regression with multilayer perceptrons (MLPs) from given noisy measurement data. [Lou, Xuyang] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China; [Suykens, Johan A. K.] Katholieke Univ Leuven, Dept Elect Engn ESAT SCD SISTA, B-3001 Louvain, Belgium Jiangnan University; KU Leuven Lou, XY (corresponding author), Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China. Xuyang.Lou@gmail.com; Johan.Suykens@esat.kuleuven.be Suykens, Johan A.K./C-9781-2014 Suykens, Johan A.K./0000-0002-8846-6352 Central Universities [JUSRP51322B]; 111 Project [B12018]; Research Council KUL [GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM]; Flemish Government: FWO [G0226.06, G0321.06, G.0302.07, G.0320.08, G.0558.08, G.0557.08, G.0588.09, G.0377.09]; EU: ERNSI; FP7-HD-MPC [INFSO-ICT-223854]; COST intelliCIS, FP7-EMBOCON [ICT-248940] Central Universities; 111 Project(Ministry of Education, China - 111 Project); Research Council KUL(KU Leuven); Flemish Government: FWO(FWO); EU: ERNSI; FP7-HD-MPC; COST intelliCIS, FP7-EMBOCON This work is supported by Research Council KUL: GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM, several Ph.D./postdoc & fellow grants; Flemish Government: FWO: Ph.D./postdoc grants, projects: G0226.06 (cooperative systems and optimization), G0321.06 (Tensors), G.0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08 (Glycemia2), G.0588.09 (Brain- machine) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC), IWT: Ph. D. Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O & O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelliCIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; ERC AdG A-DATADRIVE-B; Other: Helmholtz: viCERP, ACCM. This work is also partially supported by National Natural Science Foundation of China (61174021, 61104155), the Fundamental Research Funds for the Central Universities (JUSRP51322B) and the 111 Project (B12018). This paper was recommended by Associate Editor M. Frasca. 26 4 4 0 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-8328 1558-0806 IEEE T CIRCUITS-I IEEE Trans. Circuits Syst. I-Regul. Pap. FEB 2014.0 61 2 542 551 10.1109/TCSI.2013.2278352 0.0 10 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering AA6EO 2023-03-23 WOS:000331191800020 0
J Zhang, BC; Perina, A; Li, C; Ye, QX; Murino, V; Del Bue, A Zhang, Baochang; Perina, Alessandro; Li, Ce; Ye, Qixiang; Murino, Vittorio; Del Bue, Alessio Manifold constraint transfer for visual structure-driven optimization PATTERN RECOGNITION English Article Manifold; Transfer learning; Alternating direction method of multipliers; Object tracking; Augmented lagrange multiplier; Image tracking; Image recognition; Object categorization FACE RECOGNITION; OBJECT TRACKING; REPRESENTATION; CLASSIFICATION In this paper, we leverage the manifold structure of visual data in order to improve performance in general optimization problems subject to linear constraints. As the main theoretical result, we show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated. We also show that the resulting optimization problem can be solved with an efficient alternating direction method of multipliers that can consistently integrate the manifold constraints during the optimization process. This leads to a simplification of the approach, which instead of directly optimizing on the manifold, we can iteratively recast the problem as the projection over the manifold via an embedding method. The proposed method is extremely versatile since it can be applied to different problems including Kernel Ridge Regression (KRR) and sparse coding which have numerous applications in machine learning and computer vision. In particular, we apply the methods to different problems such as tracking, object recognition and categorization showing a consistent increase of performance with respect to the state of the art. (C) 2017 Elsevier Ltd. All rights reserved. [Zhang, Baochang] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; [Perina, Alessandro; Murino, Vittorio] IIT, Pattern Anal & Comp Vis PAVIS, Genoa, Italy; [Del Bue, Alessio] IIT, Visual Geometry & Modelling VGM Lab, Genoa, Italy; [Ye, Qixiang] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China; [Li, Ce] China Univ Min & Technol, Dept Comp Sci, Beijing, Peoples R China Beihang University; Istituto Italiano di Tecnologia - IIT; Istituto Italiano di Tecnologia - IIT; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; China University of Mining & Technology Zhang, BC (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China. bczhang@buaa.edu.cn Murino, Vittorio/0000-0002-8645-2328 Natural Science Foundation of China [61601466, 61672079, 61473086]; National Laboratory of Pattern Recognition Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Laboratory of Pattern Recognition The work was supported in part by the Natural Science Foundation of China under Contract 61601466, 61672079 and 61473086. This work is supported by the Open Projects Program of National Laboratory of Pattern Recognition. 68 3 3 1 25 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. MAY 2018.0 77 87 98 10.1016/j.patcog.2017.11.006 0.0 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering FX6UM 2023-03-23 WOS:000426222800008 0
J Castiglione, A; Umer, M; Sadiq, S; Obaidat, MS; Vijayakumar, P Castiglione, Aniello; Umer, Muhammad; Sadiq, Saima; Obaidat, Mohammad S.; Vijayakumar, Pandi The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic IEEE INTERNET OF THINGS JOURNAL English Article Internet of Things; COVID-19; Diseases; Monitoring; Pandemics; Thermometers; Sensors; COVID-19; Internet of Things (IoT); IoT applications; sensors IOT; 5G; AI Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide. [Castiglione, Aniello] Univ Naples Parthenope, Ctr Direz Napoli, Dept Sci & Technol, I-80143 Naples, Italy; [Umer, Muhammad; Sadiq, Saima] Khwaja Fareed Univ Engn & Informat Technol, Dept Comp Sci, Rahim Yar Khan 64200, Pakistan; [Umer, Muhammad] Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur 63100, Pakistan; [Obaidat, Mohammad S.] Univ Sharjah, Coll Comp & Informat, Sharjah 27272, U Arab Emirates; [Obaidat, Mohammad S.] Univ Jordan, King Abdullah II Sch Informat Technol, Amman 11942, Jordan; [Obaidat, Mohammad S.] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Vijayakumar, Pandi] Univ Coll Engn Tindivanam, Dept Comp Sci & Engn, Tindivanam 604001, India Parthenope University Naples; Telecom Italia; Khwaja Fareed University of Engineering & Information Technology, Pakistan; Islamia University of Bahawalpur; University of Sharjah; University of Jordan; University of Science & Technology Beijing Castiglione, A (corresponding author), Univ Naples Parthenope, Ctr Direz Napoli, Dept Sci & Technol, I-80143 Naples, Italy. castiglione@ieee.org; umersabir1996@gmail.com; s.kamrran@gmail.com; msobaidat@gmail.com; vijibond2000@gmail.com Castiglione, Aniello/F-1034-2011; Vijayakumar, Pandi/Y-4636-2019 Castiglione, Aniello/0000-0003-0571-1074; Vijayakumar, Pandi/0000-0001-5451-8946; Obaidat, Mohammad S./0000-0002-1569-9657; Sadiq, Saima/0000-0002-2611-3738; Umer, Muhammad/0000-0002-6015-9326 PR of China Ministry of Education Distinguished Possessor Grant [MS2017BJKJ003] PR of China Ministry of Education Distinguished Possessor Grant The work of Mohammad S. Obaidat was supported in part by the PR of China Ministry of Education Distinguished Possessor Grant under Grant MS2017BJKJ003. 60 16 16 3 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. NOV 1 2021.0 8 21 16072 16082 10.1109/JIOT.2021.3070306 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications WN5LI 35782179.0 Bronze, Green Accepted 2023-03-23 WOS:000711808500041 0
J Tham, YC; Anees, A; Zhang, L; Goh, JHL; Rim, TH; Nusinovici, S; Hamzah, H; Chee, ML; Tjio, G; Li, SH; Xu, XX; Goh, R; Tang, FY; Cheung, CYL; Wang, YX; Nangia, V; Jonas, JB; Gopinath, B; Mitchell, P; Husain, R; Lamoureux, E; Sabanayagam, C; Wang, JJ; Aung, T; Liu, Y; Wong, TY; Cheng, CY Tham, Yih-Chung; Anees, Ayesha; Zhang, Liang; Goh, Jocelyn Hui Lin; Rim, Tyler Hyungtaek; Nusinovici, Simon; Hamzah, Haslina; Chee, Miao-Li; Tjio, Gabriel; Li, Shaohua; Xu, Xinxing; Goh, Rick; Tang, Fangyao; Cheung, Carol Yim-Lui; Wang, Ya Xing; Nangia, Vinay; Jonas, Jost B.; Gopinath, Bamini; Mitchell, Paul; Husain, Rahat; Lamoureux, Ecosse; Sabanayagam, Charumathi; Wang, Jie Jin; Aung, Tin; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study LANCET DIGITAL HEALTH English Article DIABETIC-RETINOPATHY; EYE DISEASES; VISION IMPAIRMENT; ACUITY; ADULTS; METHODOLOGY; POPULATION; PREVALENCE; VALIDATION; GUIDELINES Background In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. Methods In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any diseaserelated visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). Findings In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94.2% (95% CI 93.0-95.3; sensitivity 90.7% [87.0-93.6]; specificity 86.8% [85.6-87.9]). The AUC for moderate or worse disease-related visual impairment was 93.9% (95% CI 92.2-95.6; sensitivity 94.6% [89.6-97.6]; specificity 81.3% [80.0-82.5]). Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86.6% (83.4-89.7; sensitivity 87.5% [80.7-92.5]; specificity 70.0% [66.7-73.1]) and 93.6% (92.4-94.8; sensitivity 87.8% [84.1-90.9]; specificity 87.1% [86.2-88.0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85.9% (81.8-90.1; sensitivity 84.7% [73.0-92.8]; specificity 74.4% [71.4-77.2]) and 93.5% (91.7-95.3; sensitivity 90.3% [84.2-94.6]; specificity 84.2% [83.2-85.1]). Interpretation This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd. [Tham, Yih-Chung; Zhang, Liang; Goh, Jocelyn Hui Lin; Rim, Tyler Hyungtaek; Nusinovici, Simon; Hamzah, Haslina; Chee, Miao-Li; Husain, Rahat; Lamoureux, Ecosse; Sabanayagam, Charumathi; Aung, Tin; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Tham, Yih-Chung; Rim, Tyler Hyungtaek; Nusinovici, Simon; Husain, Rahat; Lamoureux, Ecosse; Sabanayagam, Charumathi; Wang, Jie Jin; Aung, Tin; Liu, Yong; Wong, Tien Yin; Cheng, Ching-Yu] Duke NUS Med Sch, Singapore, Singapore; [Anees, Ayesha; Tjio, Gabriel; Li, Shaohua; Xu, Xinxing; Goh, Rick; Liu, Yong] ASTAR, Inst High Performance Comp, Singapore, Singapore; [Goh, Jocelyn Hui Lin] Nanyang Technol Univ, Sch Chem & Biomed Engn, Div Bioengn, Singapore, Singapore; [Tang, Fangyao; Cheung, Carol Yim-Lui] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China; [Wang, Ya Xing] Capital Med Univ, Beijing Inst Ophthalmol, Beijing Ophthalmol & Visual Sci Key Lab, Beijing Tongren Eye Ctr,Beijing Tongren Hosp, Beijing, Peoples R China; [Nangia, Vinay] Suraj Eye Inst, Nagpur, Maharashtra, India; [Jonas, Jost B.] Ruprecht Karis Univ Heidelberg, Dept Ophthalmol, Med Fac Mannheim, Mannheim, Germany; [Gopinath, Bamini; Mitchell, Paul] Univ Sydney, Westmead Hosp, Ctr Vis Res, Dept Ophthalmol,Westmead Inst, Westmead, NSW, Australia; [Aung, Tin; Wong, Tien Yin; Cheng, Ching-Yu] Natl Univ Singapore, Dept Ophthalmol, Yong Loo Lin Sch Med, Singapore, Singapore National University of Singapore; Singapore National Eye Center; National University of Singapore; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Chinese University of Hong Kong; Capital Medical University; Suraj Eye Institute; Ruprecht Karls University Heidelberg; University of Sydney; Westmead Institute for Medical Research; National University of Singapore Cheng, CY (corresponding author), Singapore Eye Res Inst, Discovery Tower, Singapore 169856, Singapore. chingyu.cheng@duke-nus.edu.sg Sabanayagam, Charumathi/C-1294-2011; Wong, Tien Yin/AAC-9724-2020; Cheng, Ching-Yu/Y-2229-2019; wang, YA XING/K-9671-2016; Chung, Yih/AIF-2414-2022; Li, Shaohua/HDO-3502-2022; Wang, Ying/HJI-2509-2023 Sabanayagam, Charumathi/0000-0002-4042-4719; Wong, Tien Yin/0000-0002-8448-1264; Cheng, Ching-Yu/0000-0003-0655-885X; wang, YA XING/0000-0003-2749-7793; Chung, Yih/0000-0002-6752-797X; Zhang, Liang/0000-0001-9264-170X; Rim, Tyler Hyungtaek/0000-0001-6465-2620; Li, Shaohua/0000-0002-2792-8277 National Medical Research Council, Singapore National Medical Research Council, Singapore(National Medical Research Council, SingaporeUK Research & Innovation (UKRI)Medical Research Council UK (MRC)) National Medical Research Council, Singapore. 53 11 11 2 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2589-7500 LANCET DIGIT HEALTH Lancet Digit. Health JAN 2021.0 3 1 E29 E40 12 Medical Informatics; Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) Medical Informatics; General & Internal Medicine PN3NE 33735066.0 gold 2023-03-23 WOS:000604388400009 0
J Zhai, GY; Zheng, Y; Xu, ZW; Kong, X; Liu, Y; Busam, B; Ren, Y; Navab, N; Zhang, ZY Zhai, Guangyao; Zheng, Yu; Xu, Ziwei; Kong, Xin; Liu, Yong; Busam, Benjamin; Ren, Yi; Navab, Nassir; Zhang, Zhengyou DA(2) Dataset: Toward Dexterity-Aware Dual-Arm Grasping IEEE ROBOTICS AND AUTOMATION LETTERS English Article Perception for grasping and manipulation; dual-arm manipulation; deep learning in grasping and manipulation In this paper, we introduce DA(2), the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9 M pairs of parallel jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-to-end dual-arm grasp evaluation model trained on the rendered scenes from this dataset. We utilize the evaluation model as our baseline to show the value of this novel and nontrivial dataset by both online analysis and real robot experiments. [Zhai, Guangyao; Busam, Benjamin; Navab, Nassir] Tech Univ Munich, Chair Comp Aided Med Procedures & Augmented Real, D-80333 Munich, Germany; [Zheng, Yu; Xu, Ziwei; Ren, Yi; Zhang, Zhengyou] Tencent, Tencent Robot X, Shenzhen 518000, Peoples R China; [Kong, Xin] Imperial Coll London, Dept Comp, London SW7 2BX, England; [Liu, Yong] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou 310027, Peoples R China; [Navab, Nassir] Johns Hopkins Univ, Lab Computat Sensing & Robot, Baltimore, MD 21218 USA Technical University of Munich; Tencent; Imperial College London; Zhejiang University; Johns Hopkins University Ren, Y (corresponding author), Tencent, Tencent Robot X, Shenzhen 518000, Peoples R China. guangyao.zhai@tum.de; petezheng@tencent.com; ziwei.xu@tum.de; xinkong@zju.edu.cn; yongliu@iipc.zju.edu.cn; b.busam@tum.de; evanyren@tencent.com; navab@cs.tum.edu; zhengyou@tencent.com Busam, Benjamin/ADC-5306-2022; Ren, Yi/HGB-1013-2022 Busam, Benjamin/0000-0002-0620-5774; Ren, Yi/0000-0002-0541-3985; Zhai, Guangyao/0000-0002-6702-8302; Xu, Ziwei/0000-0001-7912-1662; Zhang, Zhengyou/0000-0002-6606-2525 43 0 0 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. OCT 2022.0 7 4 8941 8948 10.1109/LRA.2022.3189959 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics 3Q9UG Green Submitted 2023-03-23 WOS:000838567100049 0
J Duan, GH; Zhang, SF; Lu, MZ; Okinda, C; Shen, MX; Norton, T Duan, Guanghui; Zhang, Shengfu; Lu, Mingzhou; Okinda, Cedric; Shen, Mingxia; Norton, Tomas Short-term feeding behaviour sound classification method for sheep using LSTM networks INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING English Article sheep behaviour; short-term feeding behaviour; acoustic analysis; Mel-frequency cepstral coefficients; long-short term memory networks RESTING BEHAVIOR; SYSTEM; RECOGNITION; TIME; ENHANCEMENT A deep learning approach using long-short term memory (LSTM) networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep, including biting, chewing, bolus regurgitation, and rumination chewing. The original acoustic signal was split into sound episodes using an endpoint detection method, where the thresholds of short-term energy and average zero-crossing rate were utilized. A discrete wavelet transform (DWT), Mel-frequency cepstral, and principal-component analysis (PCA) were integrated to extract the dimensionally reduced DWT based Mel-frequency cepstral coefficients (denoted by PW_ MFCC) for each sound episode. Then, LSTM networks were employed to train classifiers for sound episode category classification. The performances of the LSTM classifiers with original Mel-frequency cepstral coefficients (MFCC), DWT based MFCC (denoted by W_MFCC), and PW_MFCC as the input feature coefficients were compared. Comparison results demonstrated that the introduction of DWT improved the classifier performance effectively, and PCA reduced the computational overhead without degrading classifier performance. The overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97% and 97.41%, respectively. The classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern. [Duan, Guanghui; Lu, Mingzhou; Okinda, Cedric; Shen, Mingxia] Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China; [Zhang, Shengfu] Qinghai Nationalities Univ, Comp Coll, Xining 810007, Peoples R China; [Norton, Tomas] Katholieke Univ Leuven, M3 BIORES Measure Model & Manage Bioresponses, B-3001 Heverlee, Belgium Nanjing Agricultural University; Qinghai Nationalities University; KU Leuven Lu, MZ (corresponding author), Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China. 2018812092@njau.edu.cn; zhangsf@qhmu.edu.cm; lmz@njau.edu.cn; cedsean@hotmail.com; mingxia@njau.edu.cn; tomas.norton@kuleuven.be Okinda, Cedric/L-2087-2019; Norton, Tomas/Q-3803-2017 Okinda, Cedric/0000-0001-7887-4216; Norton, Tomas/0000-0002-0161-3189; Lu, Mingzhou/0000-0002-7909-7962 Basic Research Project of the Science and Technology Department of Qinghai province, China [2020-ZJ-716]; Key Research and Development Project of the Science and Technology Department of Jiangsu province, China [BE2018433]; Key Research and Development Project of the Science and Technology Department of Qinghai Province, China [2017-HZ-813] Basic Research Project of the Science and Technology Department of Qinghai province, China; Key Research and Development Project of the Science and Technology Department of Jiangsu province, China; Key Research and Development Project of the Science and Technology Department of Qinghai Province, China This work was supported by the Basic Research Project of the Science and Technology Department of Qinghai province, China (Grant No. 2020-ZJ-716), the Key Research and Development Project of the Science and Technology Department of Jiangsu province, China (Grant No. BE2018433), and the Key Research and Development Project of the Science and Technology Department of Qinghai Province, China (Grant No. 2017-HZ-813). 45 5 6 5 15 CHINESE ACAD AGRICULTURAL ENGINEERING BEIJING RM 506, NO 41, MAIZIDIAN ST, CHAOYANG DISTRICT, BEIJING, 100125, PEOPLES R CHINA 1934-6344 1934-6352 INT J AGR BIOL ENG Int. J. Agric. Biol. Eng. MAR 2021.0 14 2 43 54 10.25165/j.ijabe.20211402.6081 0.0 12 Agricultural Engineering Science Citation Index Expanded (SCI-EXPANDED) Agriculture RQ0BB gold, Green Accepted 2023-03-23 WOS:000642084200006 0
J Tian, G; Tang, FR; Yang, CH; Zhang, WF; Bergquist, J; Wang, B; Mi, J; Zhang, JD Tian, Geng; Tang, Fangrong; Yang, Chunhua; Zhang, Wenfeng; Bergquist, Jonas; Wang, Bin; Mi, Jia; Zhang, Jiandi Quantitative dot blot analysis (QDB), a versatile high throughput immunoblot method ONCOTARGET English Article quantitative dot blot; high throughput; proteomics; immunoblot analysis; western blot LINKED-IMMUNOSORBENT-ASSAY; ELECTROPHORETIC TRANSFER; POLYACRYLAMIDE GELS; IMMUNOGLOBULIN-G; PROSTATE-CANCER; COATED TUBES; ANTIBODIES; PROTEINS; ANTIGEN; NITROCELLULOSE Lacking access to an affordable method of high throughput immunoblot analysis for daily use remains a big challenge for scientists worldwide. We proposed here Quantitative Dot Blot analysis (QDB) to meet this demand. With the defined linear range, QDB analysis fundamentally transforms traditional immunoblot method into a true quantitative assay. Its convenience in analyzing large number of samples also enables bench scientists to examine protein expression levels from multiple parameters. In addition, the small amount of sample lysates needed for analysis means significant saving in research sources and efforts. This method was evaluated at both cellular and tissue levels with unexpected observations otherwise would be hard to achieve using conventional immunoblot methods like Western blot analysis. Using QDB technique, we were able to observed an age-dependent significant alteration of CAPG protein expression level in TRAMP mice. We believe that the adoption of QDB analysis would have immediate impact on biological and biomedical research to provide much needed high-throughput information at protein level in this Big Data era. [Tian, Geng; Yang, Chunhua; Wang, Bin; Mi, Jia; Zhang, Jiandi] Binzhou Med Univ, Med & Pharm Res Ctr, Yantai, Peoples R China; [Tang, Fangrong; Zhang, Wenfeng; Zhang, Jiandi] Yantai Zestern Biotech Co LTD, Yantai, Peoples R China; [Bergquist, Jonas; Mi, Jia] Uppsala Univ, BMC, Dept Chem, Uppsala, Sweden Binzhou Medical University; Uppsala University Mi, J; Zhang, JD (corresponding author), Binzhou Med Univ, Med & Pharm Res Ctr, Yantai, Peoples R China.;Zhang, JD (corresponding author), Yantai Zestern Biotech Co LTD, Yantai, Peoples R China.;Mi, J (corresponding author), Uppsala Univ, BMC, Dept Chem, Uppsala, Sweden. jiajiami@gmail.com; Jiandi.zhang@zestern.net Bergquist, Jonas/0000-0002-4597-041X; zhang, jiandi/0000-0002-9282-9443; Tang, Fangrong/0000-0003-4425-4443 Taishan Scholars Construction Engineering; Shandong Excellent Young Scientist Award [ZR2016JL026]; National Natural Science Foundation of China [81400771, 31671139]; Shandong provincial natural science foundation [ZR2014HL073]; Shandong provincial science and technology Plan [J14LE01, J15LK03]; Yantai science and technology plan [2015ZH083]; Binzhou Medical University Scientific Research Funds [BY2013KYQD14, BY2013KYQD17, BY2013KYQD18]; Swedish Research Council grant [621-2011-4423, 2015-4870]; Yantai Double Hundred Talent Plan; Yantai Hi-Tech Zone Blue Ocean Talent Plan Taishan Scholars Construction Engineering; Shandong Excellent Young Scientist Award; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong provincial natural science foundation(Natural Science Foundation of Shandong Province); Shandong provincial science and technology Plan; Yantai science and technology plan; Binzhou Medical University Scientific Research Funds; Swedish Research Council grant(Swedish Research Council); Yantai Double Hundred Talent Plan; Yantai Hi-Tech Zone Blue Ocean Talent Plan This work is supported by Taishan Scholars Construction Engineering (G.T.), The Shandong Excellent Young Scientist Award (ZR2016JL026 to G.T.), National Natural Science Foundation of China (81400771, 31671139), Shandong provincial natural science foundation (ZR2014HL073), Shandong provincial science and technology Plan(J14LE01 and J15LK03), Yantai science and technology plan(2015ZH083) and Binzhou Medical University Scientific Research Funds (BY2013KYQD14, BY2013KYQD17, BY2013KYQD18), the Swedish Research Council grant 621-2011-4423 and 2015-4870. This work is also sponsored by Yantai Double Hundred Talent Plan and Yantai Hi-Tech Zone Blue Ocean Talent Plan. 17 23 25 0 10 IMPACT JOURNALS LLC ORCHARD PARK 6666 E QUAKER ST, STE 1, ORCHARD PARK, NY 14127 USA 1949-2553 ONCOTARGET Oncotarget AUG 29 2017.0 8 35 58553 58562 10.18632/oncotarget.17236 0.0 10 Oncology; Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Oncology; Cell Biology FF4RD 28938578.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000408941900053 0
J Zhou, XY; Nattino, F; Zhang, YL; Chen, J; Kroes, GJ; Guo, H; Jiang, B Zhou, Xueyao; Nattino, Francesco; Zhang, Yaolong; Chen, Jun; Kroes, Geert-Jan; Guo, Hua; Jiang, Bin Dissociative chemisorption of methane on Ni(111) using a chemically accurate fifteen dimensional potential energy surface PHYSICAL CHEMISTRY CHEMICAL PHYSICS English Article GENERALIZED GRADIENT APPROXIMATION; AUGMENTED-WAVE METHOD; QUANTUM MONTE-CARLO; METAL-SURFACES; REACTIVE SCATTERING; BOND SELECTIVITY; MODE SPECIFICITY; BARRIER HEIGHTS; LATTICE MOTION; REACTION H-2 A fifteen-dimensional global potential energy surface for the dissociative chemisorption of methane on the rigid Ni(111) surface is developed by a high fidelity fit of similar to 200 000 DFT energy points computed using a specific reaction parameter density functional designed to reproduce experimental data. The permutation symmetry and surface periodicity are rigorously enforced using the permutation invariant polynomial-neural network approach. The fitting accuracy of the potential energy surface is thoroughly investigated by examining both static and dynamical attributes of CHD3 dissociation on the frozen surface. This potential energy surface is expected to be chemically accurate as after correction for surface temperature effects it reproduces the measured initial sticking probabilities of CHD3 on Ni(111) for various incidence conditions. [Zhou, Xueyao; Zhang, Yaolong; Jiang, Bin] Univ Sci & Technol China, Dept Chem Phys, Hefei 230026, Anhui, Peoples R China; [Nattino, Francesco; Kroes, Geert-Jan] Leiden Univ, Leiden Inst Chem, Gorlaeus Labs, POB 9502, NL-2300 RA Leiden, Netherlands; [Chen, Jun] Xiamen Univ, Collaborat Innovat Ctr Chem Energy Mat iChEM, Coll Chem & Chem Engn, Xiamen 361005, Fujian, Peoples R China; [Guo, Hua] Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA Chinese Academy of Sciences; University of Science & Technology of China, CAS; Leiden University; Leiden University - Excl LUMC; Xiamen University; University of New Mexico Jiang, B (corresponding author), Univ Sci & Technol China, Dept Chem Phys, Hefei 230026, Anhui, Peoples R China.;Kroes, GJ (corresponding author), Leiden Univ, Leiden Inst Chem, Gorlaeus Labs, POB 9502, NL-2300 RA Leiden, Netherlands.;Guo, H (corresponding author), Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA. g.j.kroes@chem.leidenuniv.nl; hguo@unm.edu; bjiangch@ustc.edu.cn CHEN, Jun/A-6437-2014; jiang, bin/F-2391-2014; Guo, H./J-2685-2014 CHEN, Jun/0000-0002-8021-7458; jiang, bin/0000-0003-2696-5436; Guo, H./0000-0001-9901-053X; Zhou, Xueyao/0000-0002-7038-9360; Kroes, Geert-Jan/0000-0002-4913-4689 National Key R&D Program of China [2017YFA0303500]; National Natural Science Foundation of China [91645202, 21722306, 21573203]; Fundamental Research Funds for the Central Universities of China [WK2060190082, WK2340000078]; U. S. National Science Foundation [CHE-1462019]; ERC [338580]; NWO-CW TOP; China Postdoctoral Science Foundation [2016M600500]; National Postdoctoral Program for Innovative Talents National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); U. S. National Science Foundation(National Science Foundation (NSF)); ERC(European Research Council (ERC)European Commission); NWO-CW TOP; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Postdoctoral Program for Innovative Talents This work was supported by National Key R&D Program of China (2017YFA0303500 to B. J.), National Natural Science Foundation of China (91645202, 21722306, and 21573203 to B. J.), Fundamental Research Funds for the Central Universities of China (WK2060190082 and WK2340000078 to B. J.), U. S. National Science Foundation (CHE-1462019 to H. G.), and an ERC2013 advanced grant (No. 338580) and a NWO-CW TOP grant to G. J. K., J. C. is thankful for the support of China Postdoctoral Science Foundation (Grant No. 2016M600500) and National Postdoctoral Program for Innovative Talents. Part of the calculations have been done at the Supercomputing Center of USTC and National Supercomputing Center in Guangzhou. B. J. thanks Prof. Bret Jackson and Dr Han Guo for their kind help on the models to account for lattice effects, and we thank Mr Davide Migliorini for performing part of the single-point DFT calculations needed to fit the PES. 94 39 39 3 36 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1463-9076 1463-9084 PHYS CHEM CHEM PHYS Phys. Chem. Chem. Phys. DEC 7 2017.0 19 45 30540 30550 10.1039/c7cp05993k 0.0 11 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics FN5NJ 29115358.0 Green Submitted 2023-03-23 WOS:000416054400025 0
J Jin, X; Tang, P; Houet, T; Corpetti, T; Alvarez-Vanhard, EG; Zhang, Z Jin, Xing; Tang, Ping; Houet, Thomas; Corpetti, Thomas; Alvarez-Vanhard, Emilien Gence; Zhang, Zheng Sequence Image Interpolation via Separable Convolution Network REMOTE SENSING English Article sequence image interpolation; separable convolution network; separable convolution kernel; UAV dataset; Landsat-8 dataset DATA SET Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information. [Jin, Xing; Tang, Ping; Zhang, Zheng] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Jin, Xing] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China; [Houet, Thomas; Corpetti, Thomas; Alvarez-Vanhard, Emilien Gence] Univ Rennes 2, CNRS, UMR 6554, LETG, Pl Recteur Henri le Moal, F-35000 Rennes, France Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Rennes; Universite Rennes 2 Tang, P (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China. jinxing@radi.ac.cn; tangping@aircas.ac.cn; thomas.houet@univ-rennes2.fr; thomas.corpetti@univ-rennes2.fr; emilien.alvarez-vanhard@univ-rennes2.fr; zhangzheng@aircas.ac.cn Alvarez-Vanhard, Emilien/AAV-6612-2021 Alvarez-Vanhard, Emilien/0000-0002-7589-2352; Houet, Thomas/0000-0001-5890-6145; Corpetti, Thomas/0000-0002-0257-138X; Zhang, Zheng/0000-0002-4549-3502; Jin, Xing/0000-0002-5343-0800 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080301]; National Natural Science Foundation of China [41701399] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19080301) and the National Natural Science Foundation of China (grant no. 41701399). 31 0 0 2 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2021.0 13 2 296 10.3390/rs13020296 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology PX7TN Green Published, gold 2023-03-23 WOS:000611557000001 0
J Zhao, S; Oh, SK; Kim, JY; Fu, ZW; Pedrycz, W Zhao, Shengmin; Oh, Sung-Kwun; Kim, Jin-Yul; Fu, Zunwei; Pedrycz, Witold Motion-blurred image restoration framework based on parameter estimation and fuzzy radial basis function neural networks PATTERN RECOGNITION English Article Motion-blurred image restoration; framework; Point spread function; Blur parameter estimation based on the; particle swarm optimization; Polynomial-based radial basis function; neural network; Image Quality Assessment The restoration of motion-blurred images has always been a complex problem in image restoration. The current single blurred image algorithm cannot very well solve the estimation error of motion blur param-eters. A comprehensive motion-blurred image restoration framework is proposed, which includes motion -blurred data generation, blur parameter estimation, and image quality assessment of restored images. First, we designed and used four image data sets with different degrees of blurring. We innovatively pro-pose a blur parameter estimation algorithm based on the particle swarm optimization (B-PSO) algorithm. The Naturalness Image Quality Evaluator (NIQE) is used as the fitness function of the PSO algorithm. The framework also introduces a polynomial-based radial basis function neural network (P-RBFNN) as a new image quality assessment (IQA) method, with good image classification performance. Test results from public datasets show that the proposed framework can accurately estimate blur parameters. The peak signal-to-noise ratio (PSNR) reaches 29.976 dB, the structural similarity (SSIM) reaches 0.9044, and the classification rate is 96%. The proposed restoration framework produces the best image restoration results.(c) 2022 Published by Elsevier Ltd. [Zhao, Shengmin; Fu, Zunwei] Univ Suwon, Dept Comp, San 2-2 Wau Ri, Hwaseong Si 18323, Gyeonggi Do, South Korea; [Oh, Sung-Kwun; Kim, Jin-Yul] Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea; [Oh, Sung-Kwun; Fu, Zunwei] Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland Suwon University; Suwon University; Linyi University; University of Alberta; King Abdulaziz University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Fu, ZW (corresponding author), Univ Suwon, Dept Comp, San 2-2 Wau Ri, Hwaseong Si 18323, Gyeonggi Do, South Korea.;Oh, SK; Kim, JY (corresponding author), Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea.;Oh, SK; Fu, ZW (corresponding author), Linyi Univ, Res Ctr Big Data & Artificial Intelligence, Linyi 276005, Peoples R China. ohsungkwun@hanmail.net; jykim@suwon.ac.kr; fuzunwei@lyu.edu.cn National Research Founda- tion of Korea (NRF) - Korea Government (MSIT) [NRF-2021R1F1A1056102, NRF-2021R1A2C1095739] National Research Founda- tion of Korea (NRF) - Korea Government (MSIT)(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of KoreaMinistry of Science & ICT (MSIT), Republic of Korea) Acknowledgements This work was supported by the National Research Founda- tion of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102 and NRF-2021R1A2C1095739) . 34 0 0 20 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. DEC 2022.0 132 108983 10.1016/j.patcog.2022.108983 0.0 AUG 2022 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 4F6HC 2023-03-23 WOS:000848609900003 0
J Yang, JX; Xiao, L; Zhao, YQ; Chan, JCW Yang, Jingxiang; Xiao, Liang; Zhao, Yong-Qiang; Chan, Jonathan Cheung-Wai Variational Regularization Network With Attentive Deep Prior for Hyperspectral-Multispectral Image Fusion IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Degradation; Data models; Training data; Spatial resolution; Feature extraction; TV; Bandwidth; Attention; deep; enhancement; fusion; hyperspectral; multispectral; nonlocal LAND-COVER; RANK; SUPERRESOLUTION; QUALITY Hyperspectral-multispectral image (HSI-MSI) fusion relies on a robust degradation model and data prior, where the former describes the degeneration of HSI in the spectral and spatial domains, and the latter reveals the latent statistics of the expected high-resolution (HR) HSI. In practice, the degradation model is often unknown, and the data prior is usually too complicated to be expressed analytically. In this study, we propose a variational network for HSI-MSI fusion (VaFuNet), in which the degradation model and data prior are implicitly represented by a deep learning network and jointly learned from the training data. A variational fusion model regularized by deep prior is first proposed, and then, it is optimized via a half-quadratic splitting and unfolded into a deep network. The deep prior is implicitly represented by a proximity operator. Due to the structural self-similarity, HSI possesses structural recurrences across different scales. To exploit such nonlocal prior and enhance the representability of network, we also propose a multiscale nonlocal attention and embed it into the deep prior proximity. The degradation model and deep prior proximity are jointly learned via end-to-end training. Experimental results on simulated and real-life HSI datasets demonstrate the effectiveness of the proposed VaFuNet HSI-MSI fusion method. [Yang, Jingxiang; Xiao, Liang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China; [Zhao, Yong-Qiang] Northwestern Poly Tech Univ, Sch Automat, Xian 710129, Peoples R China; [Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Nanjing University of Science & Technology; Vrije Universiteit Brussel Xiao, L (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. yang123jx@njust.edu.cn; xiaoliang@njust.edu.cn; zhaoyq@nwpu.edu.cn; jcheungw@etrovub.be xiao, liang/G-2968-2010 xiao, liang/0000-0003-0178-9384 National Natural Science Foundation of China [62001226, 61871226, 61771391]; Natural Science Foundation of Jiangsu Province [BK20200465]; Jiangsu Provincial Social Developing Project [BE2018727]; Fundamental Research Funds for the Central Universities [30920021134] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Jiangsu Provincial Social Developing Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 62001226, Grant 61871226, and Grant 61771391; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200465, in part by the Jiangsu Provincial Social Developing Project under Grant BE2018727, and in part by the Fundamental Research Funds for the Central Universities under Grant 30920021134. (Corresponding author: Liang Xiao.) 53 20 20 4 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 10.1109/TGRS.2021.3080697 0.0 MAY 2021 17 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YD8DY 2023-03-23 WOS:000732809600001 0
J Gao, C; Ghodrati, V; Shih, SF; Wu, HH; Liu, YK; Nickel, MD; Vahle, T; Dale, B; Sai, V; Felker, E; Surawech, C; Miao, Q; Finn, JP; Zhong, XD; Hu, P Gao, Chang; Ghodrati, Vahid; Shih, Shu-Fu; Wu, Holden H.; Liu, Yongkai; Nickel, Marcel Dominik; Vahle, Thomas; Dale, Brian; Sai, Victor; Felker, Ely; Surawech, Chuthaporn; Miao, Qi; Finn, J. Paul; Zhong, Xiaodong; Hu, Peng Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network MAGNETIC RESONANCE IMAGING English Article Deep learning; Artifact suppression; Radial acquisition; Deep adversarial network; Abdominal MRI FAT; QUANTIFICATION; COMBINATION Purpose: Stack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acqui-sition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness.Methods: A 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of -radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists.Results: The 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p < 0.0001) compared to U-Net. ROI analysis showed GAN removed streaks in both the background air and the tissue and was not significantly different from the reference mean and variations. Radiologists' scores showed GAN had a significant improvement of 1.6 point (p = 0.004) on a 4-point scale in streaking score while no significant difference in sharpness score compared to the input.Conclusion: 3D GAN removes streaking artifacts and preserves perceptual image details. [Gao, Chang; Ghodrati, Vahid; Shih, Shu-Fu; Wu, Holden H.; Liu, Yongkai; Sai, Victor; Felker, Ely; Surawech, Chuthaporn; Miao, Qi; Finn, J. Paul; Hu, Peng] Univ Calif Los Angeles, Dept Radiol Sci, Los Angeles, CA USA; [Gao, Chang; Ghodrati, Vahid; Wu, Holden H.; Liu, Yongkai; Finn, J. Paul; Hu, Peng] Univ Calif Los Angeles, Interdept Grad Program Phys & Biol Med, Los Angeles, CA USA; [Shih, Shu-Fu; Wu, Holden H.] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA USA; [Nickel, Marcel Dominik; Vahle, Thomas] Siemens Healthcare GmbH, MR Applicat Predev, Erlangen, Germany; [Dale, Brian] Siemens Med Solut USA Inc, MR R&D Collaborat, Cary, NC USA; [Surawech, Chuthaporn] Chulalongkorn Univ, Fac Med, Dept Radiol, Div Diagnost Radiol, Bangkok, Thailand; [Surawech, Chuthaporn] King Chulalongkorn Mem Hosp, Bangkok, Thailand; [Miao, Qi] China Med Univ, Dept Radiol, Affiliated Hosp 1, Shenyang, Liaoning, Peoples R China; [Zhong, Xiaodong] Siemens Med Solut USA Inc, MR R&D Collaborat, Los Angeles, CA USA University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; Siemens AG; Siemens AG; Chulalongkorn University; Chulalongkorn University; China Medical University; Siemens AG Hu, P (corresponding author), Univ Calif Los Angeles, David Geffen Sch Med, 300 UCLA Med Plaza,Suite B119, Los Angeles, CA 90095 USA. peng.hu@gmail.com Nickel, Dominik/0000-0002-0360-7233; Wu, Holden/0000-0002-2585-5916 Siemens Medical Solutions USA, Inc. Siemens Medical Solutions USA, Inc. This study was supported by Siemens Medical Solutions USA, Inc. The authors MDN, TV, BD and XZ are Siemens employees. 59 0 0 3 3 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0730-725X 1873-5894 MAGN RESON IMAGING Magn. Reson. Imaging JAN 2023.0 95 70 79 10.1016/j.mri.2022.10.010 0.0 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 6R4DX 36270417.0 2023-03-23 WOS:000892257000003 0
C Lin, J; Morere, O; Veillard, A; Duan, LY; Goh, H; Chandrasekhar, V ACM Lin, Jie; Morere, Olivier; Veillard, Antoine; Duan, Ling-Yu; Goh, Hanlin; Chandrasekhar, Vijay DeepHash for Image Instance Retrieval: Getting Regularization, Depth and Fine-Tuning Right PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR'17) English Proceedings Paper ACM International Conference on Multimedia Retrieval (ICMR) JUN 06-09, 2017 Bucharest, ROMANIA Assoc Comp Machinery,ACM SIGMM RBM; Hashing; Regularization; Siamese Network; Fisher Vectors; CNN; Image Instance Retrieval CODES; FEATURES This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations - regularization, depth and fine-tuning - each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme outperforms state-of-the-art methods over several benchmark datasets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 8.5% over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features - a remarkable 512x compression. [Lin, Jie; Goh, Hanlin] Inst Infocomm Res, Singapore, Singapore; [Morere, Olivier; Veillard, Antoine] Univ Paris 06, Paris, France; [Duan, Ling-Yu] Peking Univ, Sch EE&CS, Beijing, Peoples R China; [Chandrasekhar, Vijay] Nanyang Technol Univ, Inst Infocomm Res, Singapore, Singapore Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); UDICE-French Research Universities; Sorbonne Universite; Peking University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Lin, J (corresponding author), Inst Infocomm Res, Singapore, Singapore. lin-j@i2r.a-star.edu.sg; olivier.morere@gmail.com; antoine.veillard@gmail.com; lingyu@pku.edu.cn; gohhanlin@gmail.com; vijay@i2r.a-star.edu.sg National Natural Science Foundation of China [U1611461, 61661146005]; National Hightech R&D Program of China [2015AA016302] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Hightech R&D Program of China(National High Technology Research and Development Program of China) This work was partially supported by grants from National Natural Science Foundation of China (U1611461, 61661146005) and National Hightech R&D Program of China (2015AA016302). 49 9 10 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-4701-3 2017.0 138 146 10.1145/3078971.3078983 0.0 9 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ6AZ 2023-03-23 WOS:000610413000023 0
J Ma, N; Szilagyi, J; Jozsa, J Ma, Ning; Szilagyi, Jozsef; Jozsa, Janos Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM JOURNAL OF HYDROLOGY English Article Complementary relationship; FLUXCOM; Large-scale evapotranspiration; Benchmarking; Water balance TERRESTRIAL EVAPOTRANSPIRATION; DATA SETS; WATER; EVAPORATION; PRECIPITATION; PRODUCTS; MACHINE; FLUXES; MODEL; SOIL Accurately quantifying large-scale terrestrial evapotranspiration (ET) remains hampered by poor parameterization of the physical processes that relate to ET. Previous studies suggested that the calibration-free complementary relationship (CR) method that requires only routine meteorological data performed better than main-stream atmospheric reanalyses, land surface or remote sensing models in estimating large-scale ET. Here we simultaneously evaluate the latest machine learning-based upscaling of eddy-covariance measurements (FLUXCOM) and the CR estimates against the water-balance derived ET rates of 18 large Hydrologic Unit Code-2 (HUC2) and 327 medium HUC6 basins across the conterminous United States. Overall, CR and FLUXCOM perform comparably in representing the multiyear mean and temporal variations in annual ET at both, HUC2 and HUC6, scales for the 1979-2013 period. Such equally good skills also hold true for the 2003-2015 period, during which FLUXCOM was driven solely by remote sensing data. However, the CR generally captures the longterm linear tendencies in annual ET rates somewhat better than FLUXCOM. Because of its minimal data requirement, the calibration-free version of the CR may continue to serve as a benchmarking tool for large-scale ET simulations. [Ma, Ning] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China; [Ma, Ning] Chinese Acad Sci, State Key Lab Cryospher Sci, Lanzhou, Peoples R China; [Ma, Ning] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing, Peoples R China; [Szilagyi, Jozsef; Jozsa, Janos] Budapest Univ Technol & Econ, Dept Hydraul & Water Resources Engn, Budapest, Hungary; [Szilagyi, Jozsef] Univ Nebraska, Sch Nat Resources, Conservat & Survey Div, Lincoln, NE USA; [Jozsa, Janos] Budapest Univ Technol & Econ, MTA BME Water Management Res Grp, Budapest, Hungary Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS; Budapest University of Technology & Economics; University of Nebraska System; University of Nebraska Lincoln; Budapest University of Technology & Economics Ma, N (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China.;Ma, N (corresponding author), Chinese Acad Sci, State Key Lab Cryospher Sci, Lanzhou, Peoples R China.;Ma, N (corresponding author), Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing, Peoples R China. ma.n2007@aliyun.com szilagyi, jozsef/AAQ-5969-2021; Ma, Ning/AEN-5541-2022 Ma, Ning/0000-0003-4580-0661 National Key Research and Development Program of China [2017YFA0603101]; National Natural Science Foundation of China [41801047]; State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, CAS [SKLCS-OP-2020-11]; China Postdoctoral Science Foundation [2019T120139]; BMEWater Sciences & Disaster Prevention FIKP grant of EMMI (BME FIKP-VIZ) National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, CAS; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); BMEWater Sciences & Disaster Prevention FIKP grant of EMMI (BME FIKP-VIZ) The manuscript benefitted greatly from the comments by Martin Jung. This research was jointly supported by the National Key Research and Development Program of China (2017YFA0603101), National Natural Science Foundation of China (41801047), Open Research Fund Program of State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, CAS (SKLCS-OP-2020-11), China Postdoctoral Science Foundation (2019T120139), and BMEWater Sciences & Disaster Prevention FIKP grant of EMMI (BME FIKP-VIZ). We thank the two anonymous reviewers for their comments that improved an earlier version of the manuscript. All data used in this study can be freely accessed from the websites as follows: USGS HUC2 and HUC6 runoff (https://waterwatch.usgs.gov/?id=wwds_runoff); PRISM data (http://prism.oregonstate.edu/); NARR data (www.esrl.noaa.gov/psd/data/gridded/data.narr.html); GRACE (http://grace.jpl.nasa.gov), GRACE_REC (https://doi.org/10.6084/m9.figshare.7670849).The FLUXCOM ET products are available from the MPI-BGC data portal (https://www.bgc-jena.mpg.de/geodb/projects/Home.php). 78 15 16 9 33 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. NOV 2020.0 590 125221 10.1016/j.jhydrol.2020.125221 0.0 14 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources PG5DE Bronze 2023-03-23 WOS:000599754500023 0
J Lu, NJ; Elsheikh, A; Rozema, JJ; Hafezi, N; Aslanides, IM; Hillen, M; Eckert, D; Funck, C; Koppen, C; Cui, LL; Hafezi, F Lu, Nan-Ji; Elsheikh, Ahmed; Rozema, Jos J.; Hafezi, Nikki; Aslanides, Ioannis M.; Hillen, Mark; Eckert, Daniel; Funck, Christian; Koppen, Carina; Cui, Le-Le; Hafezi, Farhad Combining Spectral-Domain OCT and Air-Puff Tonometry Analysis to Diagnose Keratoconus JOURNAL OF REFRACTIVE SURGERY English Article OPTICAL COHERENCE TOMOGRAPHY; ECTASIA; IDENTIFICATION PURPOSE: To investigate the diagnostic capacity of spectral-domain optical coherence tomography (SD-OCT) combined with air-puff tonometry using artificial intelligence (AI) in dif-ferentiating between normal and keratoconic eyes. METHODS: Patients who had either undergone uneventful la-ser vision correction with at least 3 years of stable follow-up or those who had forme fruste keratoconus (FFKC), early keratoconus (EKC), or advanced keratoconus (AKC) were included. SD-OCT and biomechanical information from air-puff tonometry was divided into training and validation sets. AI models based on random forest or neural networks were trained to distinguish eyes with FFKC from normal eyes. Model accuracy was independently tested in eyes with FFKC and normal eyes. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity values. RESULTS: A total of 223 normal eyes from 223 patients, 69 FFKC eyes from 69 patients, 72 EKC eyes from 72 patients, and 258 AKC eyes from 258 patients were included. The top AUC ROC values (normal eyes compared with AKC and EKC) were Pentacam Random Forest Index (AUC = 0.985 and 0.958), Tomographic and Biomechanical Index (AUC = 0.983 and 0.925), and Belin-Ambr??sio Enhanced Ectasia Total De-viation Index (AUC = 0.981 and 0.922). When SD-OCT and air-puff tonometry data were combined, the random forest AI model provided the highest accuracy with 99% AUC for FFKC (75% sensitivity; 94.74% specificity). CONCLUSIONS: Currently, AI parameters accurately diagnose AKC and EKC, but have a limited ability to diagnose FFKC. AI-assisted diagnostic technology that uses both SD-OCT and air-puff tonometry may overcome this limitation, leading to improved treatment of patients with keratoconus. [J Refract Surg. 2022;38(6):374-380.] [Lu, Nan-Ji; Aslanides, Ioannis M.; Cui, Le-Le; Hafezi, Farhad] Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Eye Hosp, 270 West Xueyuan Rd, Wenzhou, Zhejiang, Peoples R China; [Lu, Nan-Ji; Aslanides, Ioannis M.; Cui, Le-Le; Hafezi, Farhad] Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Sch Ophthalmol & Optometry, 270 West Xueyuan Rd, Wenzhou, Zhejiang, Peoples R China; [Lu, Nan-Ji; Rozema, Jos J.; Hafezi, Nikki; Koppen, Carina] Univ Antwerp, Fac Med & Hlth Sci, Antwerp, Belgium; [Lu, Nan-Ji; Rozema, Jos J.; Koppen, Carina] Antwerp Univ Hosp, Dept Ophthalmol, Edegem, Belgium; [Lu, Nan-Ji; Hafezi, Nikki; Hafezi, Farhad] ELZA Inst, Dietikon, Switzerland; [Lu, Nan-Ji; Aslanides, Ioannis M.] Ernmetropia Mediterranean Eye Inst, Iraklion, Greece; [Elsheikh, Ahmed] Univ Liverpool, Sch Engn, Liverpool, Merseyside, England; [Elsheikh, Ahmed] Moorfields Eye Hosp NHS Fdn Trust, NIHR Biomed Res Ctr Ophthalmol, London, England; [Elsheikh, Ahmed] UCL Inst Ophthalmol, London, England; [Elsheikh, Ahmed] Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, Beijing, Peoples R China; [Eckert, Daniel; Funck, Christian; Hafezi, Farhad] Univ Zurich, Ctr Appl Biotechnol & Mol Med, Lab Ocular Cell Biol, Zurich, Switzerland; [Hafezi, Farhad] Univ Geneva, Fac Med, Geneva, Switzerland; [Hafezi, Farhad] Univ Southern Calif, USC Roski Eye Inst, Los Angeles, CA 90007 USA Wenzhou Medical University; Wenzhou Medical University; University of Antwerp; University of Antwerp; University of Liverpool; University of London; University College London; Moorfields Eye Hospital NHS Foundation Trust; University of London; University College London; Beihang University; University of Zurich; University of Geneva; University of Southern California Cui, LL (corresponding author), Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Eye Hosp, 270 West Xueyuan Rd, Wenzhou, Zhejiang, Peoples R China.;Cui, LL (corresponding author), Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Sch Ophthalmol & Optometry, 270 West Xueyuan Rd, Wenzhou, Zhejiang, Peoples R China. cll@eye.ac.cn Zhejiang Provincial Natural Science Foundation of China [LY22H120007]; Chinese Scholarship Council award [202008330323] Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); Chinese Scholarship Council award Supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LY22H120007) and a Chinese Scholarship Council award (Grant No. 202008330323) (N-JL) . 23 0 0 2 3 SLACK INC THOROFARE 6900 GROVE RD, THOROFARE, NJ 08086 USA 1081-597X 1938-2391 J REFRACT SURG J. Refractive Surg. JUN 2022.0 38 6 374 + 10.3928/1081597X-20220414-02 0.0 JUN 2022 10 Ophthalmology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology; Surgery 2G3OH 35686708.0 hybrid, Green Published 2023-03-23 WOS:000810750700001 0
J Sun, Z; Ielmini, D Sun, Zhong; Ielmini, Daniele Invited Tutorial: Analog Matrix Computing With Crosspoint Resistive Memory Arrays IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS English Article Voltage; Eigenvalues and eigenfunctions; Transfer functions; Tutorials; Digital computers; Circuit stability; Time complexity; Analog computing; matrix; in-memory computing; resistive memory CONTINUOUS-TIME; RRAM Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a heavy workload in many data-intensive applications, e.g., machine learning, scientific computing, and wireless communications. For fast, efficient matrix computations, analog computing with resistive memory arrays has been proven to be a promising solution. In this Tutorial, we present analog matrix computing (AMC) circuits based on crosspoint resistive memory arrays. AMC circuits are able to carry out basic matrix computations, including matrix multiplication, matrix inversion, pseudoinverse and eigenvector computation, all with one single operation. We describe the main design principles of the AMC circuits, such as local/global or negative/positive feedback configurations, with/without external inputs. Mapping strategies for matrices containing negative values will be presented. The underlying requirements for circuit stability will be described via the transfer function analysis, which also defines time complexity of the circuits towards steady-state results. Lastly, typical applications, challenges, and future trends of AMC circuits will be discussed. [Sun, Zhong] Peking Univ, Inst Artificial Intelligence, Beijing 100871, Peoples R China; [Sun, Zhong] Peking Univ, Sch Integrated Circuits, Beijing 100871, Peoples R China; [Ielmini, Daniele] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy Peking University; Peking University; Polytechnic University of Milan Ielmini, D (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy. zhong.sun@pku.edu.cn; daniele.ielmini@polimi.it Sun, Zhong/AHE-9747-2022; Ielmini, Daniele/N-3477-2015 Sun, Zhong/0000-0003-1856-0279; Ielmini, Daniele/0000-0002-1853-1614 National Key Research and Development Program of China [2020YFB2206001]; National Natural Science Foundation of China (NSFC) [62004002, 92064004, 61927901]; 111 Project [B18001] National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project) This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB2206001; in part by the National Natural Science Foundation of China (NSFC) under Grant 62004002, Grant 92064004, and Grant 61927901; and in part by the 111 Project under Grant B18001. 35 0 0 4 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-7747 1558-3791 IEEE T CIRCUITS-II IEEE Trans. Circuits Syst. II-Express Briefs JUL 2022.0 69 7 3024 3029 10.1109/TCSII.2022.3174920 0.0 6 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 2O2EJ 2023-03-23 WOS:000818877500004 0
J Li, C; Zhang, XC; Chrysostomou, D; Yang, HJ Li, Chen; Zhang, Xiaochun; Chrysostomou, Dimitrios; Yang, Hongji ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots IEEE ACCESS English Article Robots; Service robots; Natural language processing; Oral communication; Human-robot interaction; User experience; Artificial intelligence; Natural language processing; interactive systems; data collection; neural networks; human-robot interaction Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots. One stumbling block is the lack of a domain-specific discourse corpus for training these systems. Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates. When designing a dialogue system for social robots, the user experience is prioritized above industrial robots. To overcome these challenges, we provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ), a fully-labeled discourse dataset covering four robotics domains. It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings. Small talk concepts and human-to-human conversation strategies are provided to support human-like answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement. Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset. We performed a series of trials to validate ToD4IR's performance in a real manufacturing context. Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset. Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IR for reproducible research. [Li, Chen; Chrysostomou, Dimitrios] Aalborg Univ, Dept Mat & Prod, DK-9220 Aalborg, Denmark; [Zhang, Xiaochun] Anhui Univ Finance & Econ, Sch Management Sci & Comp, Bengbu 233030, Anhui, Peoples R China; [Yang, Hongji] Univ Leicester, Sch Comp & Math Sci, Leicester LE1 7RH, Leics, England Aalborg University; Anhui University of Finance & Economics; University of Leicester Zhang, XC (corresponding author), Anhui Univ Finance & Econ, Sch Management Sci & Comp, Bengbu 233030, Anhui, Peoples R China. xiaochun.zhang@aufe.edu.cn Chrysostomou, Dimitrios/H-7077-2013 Chrysostomou, Dimitrios/0000-0002-6114-8944; Li, Chen/0000-0001-6249-8957; Yang, Hongji/0000-0001-6561-3631 Natural Science Foundation of the Education Department of Anhui Province [KJ2020A0012]; Program of School Scientific Research of Anhui University of Finance and Economics [ACKYC22092]; EU's SMART EUREKA Programme [S0218-chARmER]; Innovation Fund Denmark [9118-00001B]; H2020-WIDESPREAD Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing-R2P2 [857061] Natural Science Foundation of the Education Department of Anhui Province(Natural Science Foundation of Anhui Province); Program of School Scientific Research of Anhui University of Finance and Economics; EU's SMART EUREKA Programme; Innovation Fund Denmark; H2020-WIDESPREAD Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing-R2P2 This work was supported in part by the Natural Science Foundation of the Education Department of Anhui Province under Grant KJ2020A0012, in part by the Program of School Scientific Research of Anhui University of Finance and Economics under Grant ACKYC22092, in part by the EU's SMART EUREKA Programme under Grant S0218-chARmER, in part by the Innovation Fund Denmark under Grant 9118-00001B, and in part by the H2020-WIDESPREAD Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing-R2P2 under Project 857061. 60 1 1 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2022.0 10 91631 91649 10.1109/ACCESS.2022.3202554 0.0 19 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 4I8SQ Green Submitted, gold 2023-03-23 WOS:000850844700001 0
J Zhao, XD; Zhang, MM; Tao, R; Li, W; Liao, WZ; Tian, LF; Philips, W Zhao, Xudong; Zhang, Mengmeng; Tao, Ran; Li, Wei; Liao, Wenzhi; Tian, Lianfang; Philips, Wilfried Fractional Fourier Image Transformer for Multimodal Remote Sensing Data Classification IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Feature extraction; Transformers; Laser radar; Data mining; Discrete Fourier transforms; Visualization; Semantics; Fractional Fourier image transformer (FrIT); hyperspectral image (HSI); light detection and ranging (LiDAR); multimodal data FEATURE-EXTRACTION; LIDAR; FUSION; MULTISOURCE With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, the limited receptive field restricted the convolutional neural networks (CNNs) to represent global contextual and sequential attributes, while visual image transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier image transformer (FrIT) as a backbone network to extract both global and local contexts effectively. In the proposed FrIT framework, HSI and LiDAR data are first fused at the pixel level, and both multisource feature and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contextual and sequential feature extraction. Unlike the attention-based representations in classic VIT, FrIT is capable of speeding up the transformer architectures massively and learning valuable contextual information effectively and efficiently. More significantly, to reduce redundancy and loss of information from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. Five HSI and LiDAR scenes including one newly labeled benchmark are utilized for extensive experiments, showing improvement over both CNNs and VITs. [Zhao, Xudong; Zhang, Mengmeng; Tao, Ran; Li, Wei] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China; [Zhao, Xudong; Philips, Wilfried] Univ Ghent, Image Proc & Interpretat IPI Grp, IMEC Res Grp, B-9000 Ghent, Belgium; [Liao, Wenzhi] Flemish Inst Technol Res VITO, Sustainable Mat Management Unit, B-2400 Mol, Belgium; [Liao, Wenzhi] Univ Ghent, Dept TELIN, B-9000 Ghent, Belgium; [Tian, Lianfang] South China Univ Technol, Sch Automat, Guangzhou 510640, Peoples R China Beijing Institute of Technology; Ghent University; VITO; Ghent University; South China University of Technology Tao, R (corresponding author), Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China. zhaoxudong@bit.edu.cn; 7520200002@bit.edu.cn; rantao@bit.edu.cn; liwei089@ieee.org; wenzhi.liao@vito.be; tianliangfang@scut.edu.cn; wilfried.philips@ugent.be LI, WEI/ABD-5001-2021 Zhao, Xudong/0000-0002-6942-136X; zhang, mengmeng/0000-0002-5724-9785 National Natural Science Foundation of China [62001023, 61922013]; Beijing Natural Science Foundation [JQ20021] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 62001023 and Grant 61922013, and in part by the Beijing Natural Science Foundation under Grant JQ20021. 52 3 3 25 36 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3189994 0.0 JUL 2022 13 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3D3EA 35839200.0 2023-03-23 WOS:000829188000001 0
J Sharma, VS; Fossati, A; Ciuffa, R; Buljan, M; Williams, EG; Chen, Z; Shao, WG; Pedrioli, PGA; Purcell, AW; Martinez, MR; Song, JN; Manica, M; Aebersold, R; Li, C Sharma, Varun S.; Fossati, Andrea; Ciuffa, Rodolfo; Buljan, Marija; Williams, Evan G.; Chen, Zhen; Shao, Wenguang; Pedrioli, Patrick G. A.; Purcell, Anthony W.; Martinez, Maria Rodriguez; Song, Jiangning; Manica, Matteo; Aebersold, Ruedi; Li, Chen PCfun: a hybrid computational framework for systematic characterization of protein complex function BRIEFINGS IN BIOINFORMATICS English Article protein complex function; machine learning; gene ontology; natural language processing INTERACTION NETWORKS; MASS-SPECTROMETRY; PREDICTION; SUBUNITS In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes-or macromolecular machines-are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function. [Sharma, Varun S.] Ctr Mol Med Vienna, Vienna, Austria; [Fossati, Andrea] Univ Calif San Francisco, Quantitat Biosci Inst, San Francisco, CA 94143 USA; [Fossati, Andrea] J David Gladstone Inst, San Francisco, CA USA; [Ciuffa, Rodolfo] Swiss Fed Inst Technol, Lab Ruedi Aeriersoir, Zurich, Switzerland; [Buljan, Marija] Swiss Inst Bioinformat, Swiss Fed Lab Mat Sci & Technol, Zurich, Switzerland; [Williams, Evan G.] Univ Luxembourg, Luxerrboug Ctr Syst Biomed, Luxembourg, Luxembourg; [Chen, Zhen] Henan Agr Univ, Collaborat Innovat Ctr Henan Grain Crops, Zhengzhou, Peoples R China; [Shao, Wenguang] Shanghai Jiao Tong Univ, Sch Life Sci & Biotechnol, Shanghai, Peoples R China; [Pedrioli, Patrick G. A.] Swiss Fed Inst Technol, Personalized Health & Related Techrtri PHRT, Clin Proteotype Analys Ctr, Zurich, Switzerland; [Purcell, Anthony W.; Song, Jiangning; Li, Chen] Monash Univ, Monash Biomed Discovery Inst, Clayton, Vic, Australia; [Purcell, Anthony W.; Song, Jiangning; Li, Chen] Monash Univ, Dept Biochem & Mol Biol, Clayton, Vic, Australia; [Martinez, Maria Rodriguez] Swiss Fed Inst Technol, Dept Biol, Computat Syst Biol IBM Res Europe Switzerland, Zurich, Switzerland; [Manica, Matteo] Swiss Fed Inst Technol, Inst Mol Syst, Zurich, Switzerland; [Aebersold, Ruedi] Swiss Fed Inst Technol, Mole Syst Biol, Zurich, Switzerland; [Song, Jiangning; Li, Chen] Monash Univ, Monash Biomed Discovery Inst, Melbourne, Vic 3800, Australia; [Song, Jiangning; Li, Chen] Monash Univ, Dept Biochem & Mol Biol, Melbourne, Vic 3800, Australia; [Manica, Matteo] IBM Res Europe, Zurich, Switzerland; [Aebersold, Ruedi; Li, Chen] Swiss Fed Inst Technol, Inst Mol Syst Biol, Dept Biol, Zurich, Switzerland; [Aebersold, Ruedi] Univ Zurich, Fac Sci, Zurich, Switzerland University of California System; University of California San Francisco; University of California System; University of California San Francisco; The J David Gladstone Institutes; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Institute of Bioinformatics; University of Luxembourg; Henan Agricultural University; Shanghai Jiao Tong University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Monash University; Monash University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; Monash University; Monash University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich Song, JN; Li, C (corresponding author), Monash Univ, Monash Biomed Discovery Inst, Melbourne, Vic 3800, Australia.;Song, JN; Li, C (corresponding author), Monash Univ, Dept Biochem & Mol Biol, Melbourne, Vic 3800, Australia.;Manica, M (corresponding author), IBM Res Europe, Zurich, Switzerland.;Aebersold, R; Li, C (corresponding author), Swiss Fed Inst Technol, Inst Mol Syst Biol, Dept Biol, Zurich, Switzerland.;Aebersold, R (corresponding author), Univ Zurich, Fac Sci, Zurich, Switzerland. VSharma@cemm.oeaw.ac.at; andrea.fossati@ucsf.edu; rodolfo.ciuffa2@gmail.com; Marija.Buljan@empa.ch; evan.williams@uni.lu; chenzhen-win2009@163.com; wshao@sjtu.edu.cn; pedrioli@gmail.com; anthony.purcell@monash.edu; MRM@zurich.ibm.com; Jiangning.Song@monash.edu; TTE@zurich.ibm.com; aebersold@imsb.biol.ethz.ch; Chen.Li@monash.edu Song, Jiangning/F-9787-2010 Song, Jiangning/0000-0001-8031-9086; Williams, Evan/0000-0002-9746-376X European Research Council [ERC-20140AdG 670821]; Swiss National Science Foundation [31003A_166435]; National Health and Medicine Research Council of Australia (NHMRC) CJ Martin Early Career Research Fellowship [1143366] European Research Council(European Research Council (ERC)European Commission); Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); National Health and Medicine Research Council of Australia (NHMRC) CJ Martin Early Career Research Fellowship(National Health and Medical Research Council (NHMRC) of Australia) European Research Council (ERC-20140AdG 670821), and the Swiss National Science Foundation (grant 31003A_166435) to R.A. C.L. was supported by a National Health and Medicine Research Council of Australia (NHMRC) CJ Martin Early Career Research Fellowship (1143366). We thank Fabian Frommelt, Federico Uliana, Michiel Karrenbelt and Shreyans Jain from ETH Zurich for their critical comments and insightful suggestions. 67 0 0 3 5 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. JUL 18 2022.0 23 4 10.1093/bib/bbac239 0.0 JUN 2022 15 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology 3M0JJ 35724564.0 Green Published, Green Accepted, hybrid 2023-03-23 WOS:000813284600001 0
J Tang, QY; Ren, WT; Wang, J; Kaneko, K Tang, Qian-Yuan; Ren, Weitong; Wang, Jun; Kaneko, Kunihiko The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database MOLECULAR BIOLOGY AND EVOLUTION English Article protein evolution; protein structure and dynamics; evolution of plasticity and complexity; scaling analysis; normal mode analysis SELF-ORGANIZED CRITICALITY; NORMAL-MODE ANALYSIS; DYNAMICS; COMPLEXITY; SEQUENCE; PHYSICS; SPECIFICITY; NETWORKS; ORIGIN The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives. [Tang, Qian-Yuan] RIKEN, Lab Neural Computat & Adaptat, Ctr Brain Sci, 2-1 Hirosawa, Wako, Saitama 3510106, Japan; [Ren, Weitong] RIKEN Cluster Pioneering Res, Theoret Mol Sci Lab, 2-1 Hirosawa, Wako, Saitama 3510198, Japan; [Wang, Jun] Nanjing Univ, Natl Lab Solid State Microstruct, Sch Phys, Nanjing 210093, Peoples R China; [Wang, Jun] Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China; [Kaneko, Kunihiko] Univ Tokyo, Universal Biol Inst, Ctr Complex Syst Biol, Meguro Ku, Tokyo 1538902, Japan; [Kaneko, Kunihiko] Univ Copenhagen, Niels Bohr Inst, Blegdamsvej 17, DK-2100 Copenhagen, Denmark RIKEN; RIKEN; Nanjing University; Nanjing University; University of Tokyo; University of Copenhagen; Niels Bohr Institute Tang, QY (corresponding author), RIKEN, Lab Neural Computat & Adaptat, Ctr Brain Sci, 2-1 Hirosawa, Wako, Saitama 3510106, Japan.;Kaneko, K (corresponding author), Univ Tokyo, Universal Biol Inst, Ctr Complex Syst Biol, Meguro Ku, Tokyo 1538902, Japan.;Kaneko, K (corresponding author), Univ Copenhagen, Niels Bohr Inst, Blegdamsvej 17, DK-2100 Copenhagen, Denmark. tangqianyuan@gmail.com; kaneko@complex.c.u-tokyo.ac.jp Tang, Qian-Yuan/0000-0002-2821-0494; Kaneko, Kunihiko/0000-0001-6400-8587 Brain/MINDS from Japan Agency for Medical Research and Development [JP21dm0207001]; National Natural Science Foundation of China [11774157]; Ministry of Education, Culture, Sports, Science and Technology of Japan [17H06386]; Japanese Society for the Promotion of Science [(A)20H00123]; Novo Nordisk Fonden Brain/MINDS from Japan Agency for Medical Research and Development(Japan Agency for Medical Research and Development (AMED)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Education, Culture, Sports, Science and Technology of Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Japanese Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Novo Nordisk Fonden(Novo Nordisk Foundation) We gratefully thank Taro Toyoizumi, Xiangze Zeng, Hisao Moriya, Haobo Wang, Haiguang Liu, Zhengqi He, Sida Chen, Weiyi Qiu, Wenfei Li, Zeke Xie, Yingnan Li, Xinhong Liu, Lei-Han Tang, and Xuefei Li for participating in stimulating discussions. This work was supported by Brain/MINDS from Japan Agency for Medical Research and Development (grant number JP21dm0207001), National Natural Science Foundation of China (grant number 11774157), a Grant-in-Aid for Scientific Research on Innovative Areas (grant number 17H06386) from the Ministry of Education, Culture, Sports, Science and Technology of Japan, a Grant-in-Aid for Scientific Research (grant number (A)20H00123) from the Japanese Society for the Promotion of Science, and Novo Nordisk Fonden (to K.K.). 105 1 1 10 10 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0737-4038 1537-1719 MOL BIOL EVOL Mol. Biol. Evol. OCT 7 2022.0 39 10 msac197 10.1093/molbev/msac197 0.0 13 Biochemistry & Molecular Biology; Evolutionary Biology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Evolutionary Biology; Genetics & Heredity 5E7TR 36108094.0 Green Submitted, Green Accepted 2023-03-23 WOS:000865826400004 0
J Dong, XC; Zhang, YC; Li, HW; Yan, YT; Li, JQ; Song, J; Wang, K; Jakobi, M; Yetisen, AK; Koch, AW Dong, Xingchen; Zhang, Yucheng; Li, Hongwei; Yan, Yuntian; Li, Jianqing; Song, Jian; Wang, Kun; Jakobi, Martin; Yetisen, Ali K.; Koch, Alexander W. Microscopic Image Deblurring by a Generative Adversarial Network for 2D Nanomaterials: Implications for Wafer-Scale Semiconductor Characterization ACS APPLIED NANO MATERIALS English Article 2D semiconductors; deep learning; generative adversarial network; image deblurring; layer number identification; wafer-scale characterization THICKNESS; INTEGRATION Wafer-scale two-dimensional (2D) semiconductors with atomically thin layers are promising materials for fabricating optic and photonic devices. Bright-field microscopy is a widely utilized method for large-area characterization, layer number identification, and quality assessment of 2D semiconductors based on optical contrast. Out-of-focus microscopic images caused by instrumental focus drifts contained blurred and degraded structural and color information, hindering the reliability of automated layer number identification of 2D nanosheets. To achieve automated restoration and accurate characterization, deep-learning-based microscopic imagery deblurring (MID) was developed. Specifically, a generative adversarial network with an improved loss function was employed to recover both the structural and color information of out-of-focus low-quality images. 2D MoS2 grown by the chemical vapor deposition on a SiO2/Si substrate was characterized. Quantitative indexes including structural similarity (SSIM), peak signalto-noise ratio, and CIE 1931 color space were studied to evaluate the performance of MID for deblurring of out-of-focus images, with a minimum value of SSIM over 90% of deblurred images. Further, a pre-trained U-Net model with an average accuracy over 80% was implemented to segment and predict the layer number distribution of 2D nanosheet categories (monolayer, bilayer, trilayer, multi-layer, and bulk). The developed automated microscopic image deblurring using MID and the layer number identification by the U-Net model allow for on-site, accurate, and large-area characterization of 2D semiconductors for analyzing local optical properties. This method may be implemented in wafer-scale industrial manufacturing and quality monitoring of 2D photonic devices. [Dong, Xingchen; Zhang, Yucheng; Yan, Yuntian; Li, Jianqing; Wang, Kun; Jakobi, Martin; Koch, Alexander W.] Tech Univ Munich, Inst Measurement Syst & Sensor Technol, Dept Elect & Comp Engn, D-80333 Munich, Germany; [Li, Hongwei] Tech Univ Munich, Dept Comp Sci, D-85748 Garching, Germany; [Li, Hongwei] Univ Zurich, Dept Quant Biomed, CH-8057 Zurich, Switzerland; [Song, Jian] Sun Yat sen Univ, Sch Biomed Engn, Shenzhen 518107, Peoples R China; [Yetisen, Ali K.] Imperial Coll London, Dept Chem Engn, London SW7 2AZ, England Technical University of Munich; Technical University of Munich; University of Zurich; Sun Yat Sen University; Imperial College London Dong, XC (corresponding author), Tech Univ Munich, Inst Measurement Syst & Sensor Technol, Dept Elect & Comp Engn, D-80333 Munich, Germany. xingchen.dong@tum.de Yetisen, Ali K./HOI-0302-2023; Li, Hongwei Bran/HJH-5317-2023 Li, Hongwei Bran/0000-0002-5328-6407; Song, Jian/0000-0002-2924-6640; Song, Jian/0000-0002-2856-2473; Yetisen, Ali K./0000-0003-0896-267X; Dong, Xingchen/0000-0001-6734-7568 57 1 1 6 6 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2574-0970 ACS APPL NANO MATER ACS Appl. Nano Mater. SEP 23 2022.0 5 9 12855 12864 10.1021/acsanm.2c02725 0.0 SEP 2022 10 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science 4X3IB 2023-03-23 WOS:000858445200001 0
J Hernandez-Garcia, L; Aramendia-Vidaurreta, V; Bolar, DS; Dai, WY; Fernandez-Seara, MA; Guo, J; Madhuranthakam, AJ; Mutsaerts, H; Petr, J; Qin, Q; Schollenberger, J; Suzuki, Y; Taso, M; Thomas, DL; van Osch, MJP; Woods, J; Yan, LR; Wang, Z; Zhao, L; Zhao, MY; Okell, TW Hernandez-Garcia, Luis; Aramendia-Vidaurreta, Veronica; Bolar, Divya S.; Dai, Weiying; Fernandez-Seara, Maria A.; Guo, Jia; Madhuranthakam, Ananth J.; Mutsaerts, Henk; Petr, Jan; Qin, Qin; Schollenberger, Jonas; Suzuki, Yuriko; Taso, Manuel; Thomas, David L.; van Osch, Matthias J. P.; Woods, Joseph; Yan, Lirong; Wang, Ze; Zhao, Li; Zhao, Moss Y.; Okell, Thomas W. Recent Technical Developments in ASL: A Review of the State of the Art MAGNETIC RESONANCE IN MEDICINE English Review arterial spin labeling; CBF; MR imaging; perfusion; technical advances; vascular imaging WHITE-MATTER PERFUSION; T-2-RELAXATION-UNDER-SPIN-TAGGING TRUST MRI; CONCURRENT FUNCTIONAL PERFUSION; CEREBRAL-BLOOD-FLOW; HUMAN BRAIN; ARTERIOVENOUS-MALFORMATION; ALZHEIMERS-DISEASE; FREE PRECESSION; DYNAMIC MRA; HIGH-FIELD This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations. [Hernandez-Garcia, Luis; Schollenberger, Jonas] Univ Michigan, FMRI Lab, Ann Arbor, MI 48109 USA; [Aramendia-Vidaurreta, Veronica; Fernandez-Seara, Maria A.] Clin Univ Navarra, Dept Radiol, Pamplona, Spain; [Bolar, Divya S.] Univ Calif San Diego, Ctr Funct Magnet Resonance Imaging, Dept Radiol, San Diego, CA 92103 USA; [Dai, Weiying] SUNY Binghamton, Dept Comp Sci, Binghamton, NY USA; [Guo, Jia] Univ Calif Riverside, Dept Bioengn, Riverside, CA 92521 USA; [Madhuranthakam, Ananth J.] UT Southwestern Med Ctr, Dept Radiol, Dallas, TX USA; [Mutsaerts, Henk] Amsterdam Univ Med Ctr, Amsterdam Neurosci, Dept Radiol & Nucl Med, Amsterdam, Netherlands; [Petr, Jan] Inst Radiopharmaceut Canc Res, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany; [Qin, Qin] Johns Hopkins Univ, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD USA; [Suzuki, Yuriko; Woods, Joseph; Okell, Thomas W.] Univ Oxford, Nuffield Dept Clin Neurosci, FMRIB, Wellcome Ctr Integrat Neuroimaging, Oxford, England; [Taso, Manuel] Beth Israel Deaconess Med Ctr, Radiol, Div MRI Res, Boston, MA 02215 USA; [Taso, Manuel] Harvard Med Sch, Boston, MA 02115 USA; [Thomas, David L.] UCL Queen Sq Inst Neurol, Dept Brain Repair & Rehabil, London, England; [van Osch, Matthias J. P.] Leiden Univ, Med Ctr, Dept Radiol, CJ Gorter Ctr High Field MRI, Leiden, Netherlands; [Woods, Joseph] Univ Calif La Jolla, Dept Radiol, La Jolla, CA USA; [Yan, Lirong] Stanford Univ, Dept Radiol, Stanford, CA 94305 USA; [Wang, Ze] Northwestern Univ, Feinberg Sch Med, Dept Radiol, Chicago, IL 60611 USA; [Zhao, Li] Univ Maryland, Sch Med, Dept Diagnost Radiol & Nucl Med, Baltimore, MD 21201 USA; [Zhao, Moss Y.] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Minist Educ, Key Lab Biomed Engn, Hangzhou, Zhejiang, Peoples R China University of Michigan System; University of Michigan; University of Navarra; University of California System; University of California San Diego; State University of New York (SUNY) System; State University of New York (SUNY) Binghamton; University of California System; University of California Riverside; University of Texas System; University of Texas Southwestern Medical Center Dallas; Vrije Universiteit Amsterdam; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Johns Hopkins University; University of Oxford; Harvard University; Beth Israel Deaconess Medical Center; Harvard University; Harvard Medical School; University of London; University College London; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; University of California System; University of California San Diego; Stanford University; Northwestern University; Feinberg School of Medicine; University System of Maryland; University of Maryland Baltimore; Zhejiang University Hernandez-Garcia, L (corresponding author), Univ Michigan, FMRI Lab, Ann Arbor, MI 48109 USA. hernan@umich.edu Taso, Manuel/HHS-7192-2022; Woods, Joseph G/GRR-8307-2022; Petr, Jan/B-5309-2013; Zhuo, Xianglong/HNO-9030-2023; Qin, Qin/GVT-5910-2022; Fernandez-Seara, Maria A./K-1246-2014; hernandez-garcia, luis/G-6509-2013; Aramendia-Vidaurreta, Veronica/D-1817-2017 Woods, Joseph G/0000-0002-0329-824X; Petr, Jan/0000-0002-3201-6002; Qin, Qin/0000-0002-6432-2944; Fernandez-Seara, Maria A./0000-0001-8536-6295; Guo, Jia/0000-0003-3371-5857; Zhao, Moss/0000-0002-0210-7739; Mutsaerts, Henk/0000-0003-0894-0307; hernandez-garcia, luis/0000-0003-3002-0304; Thomas, David/0000-0003-1491-1641; Aramendia-Vidaurreta, Veronica/0000-0001-9834-2866; Bolar, Divya/0000-0002-5968-2304 American Heart Association [826254]; EU Joint Program for Neurodegenerative Disease Research [ASPIRE E!113701]; Hartstichting [2020T049]; National Institutes of Health [NS108042, NS112233, R01EB025220-02]; Netherlands Organisation for Scientific Research (NWO) [016.160.351]; Research and Development and Alzheimer Nederland [JPND2020-568-106]; Rijksdienst voor Ondernemend Nederland [ASPIRE E!113701]; UCL LeonardWolfson Experimental Neurology Centre [PR/ylr/18575]; UCLH NIHR Biomedical Research Centre; Wellcome Trust [203139/Z/16/Z, 220204/Z/20/Z, 539208] American Heart Association(American Heart Association); EU Joint Program for Neurodegenerative Disease Research; Hartstichting; National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); Research and Development and Alzheimer Nederland; Rijksdienst voor Ondernemend Nederland; UCL LeonardWolfson Experimental Neurology Centre; UCLH NIHR Biomedical Research Centre; Wellcome Trust(Wellcome Trust) American Heart Association, Grant/Award Number: 826254; EU Joint Program for Neurodegenerative Disease Research, Grant/Award Number: ASPIRE E!113701; Hartstichting, Grant/Award Number: 2020T049; National Institutes of Health, Grant/Award Numbers: NS108042, NS112233, R01EB025220-02; Netherlands Organisation for Scientific Research (NWO), Grant/Award Number: 016.160.351; Research and Development and Alzheimer Nederland, Grant/Award Number: JPND2020-568-106; Rijksdienst voor Ondernemend Nederland, Grant/Award Number: ASPIRE E!113701; UCL LeonardWolfson Experimental Neurology Centre, Grant/Award Number: PR/ylr/18575; UCLH NIHR Biomedical Research Centre; Wellcome Trust, Grant/Award Numbers: 203139/Z/16/Z, 220204/Z/20/Z, 539208 187 4 4 12 12 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0740-3194 1522-2594 MAGN RESON MED Magn. Reson. Med. NOV 2022.0 88 5 2021 2042 10.1002/mrm.29381 0.0 AUG 2022 22 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 3Y4RC 35983963.0 hybrid, Green Published, Green Accepted 2023-03-23 WOS:000841818100001 0
C Yao, KS; Zweig, G; Hwang, MY; Shi, YY; Yu, D Bimbot, F; Cerisara, C; Fougeron, C; Gravier, G; Lamel, L; Pellegrino, F; Perrier, P Yao, Kaisheng; Zweig, Geoffrey; Hwang, Mei-Yuh; Shi, Yangyang; Yu, Dong Recurrent Neural Networks for Language Understanding 14TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2013), VOLS 1-5 Interspeech English Proceedings Paper 14th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2013) AUG 25-29, 2013 Lyon, FRANCE Int Speech Commun Assoc,europa org,amazon,Microsoft,Google,TcL SYTRAL,European Language Resources Assoc,ouaero,imaginove,VOCAPIA res,acapela,speech ocean,ALDEBARAN,orange,vecsys,IBM Res,Raytheon BBN Technol,voxygen Recurrent Neural Networks; Spoken Language Understanding Recurrent Neural Network Language Models (RNN-LMs) have recently shown exceptional performance across a variety of applications. In this paper, we modify the architecture to perform Language Understanding, and advance the state-of-the-art for the widely used ATIS dataset. The core of our approach is to take words as input as in a standard RNN-LM, and then to predict slot labels rather than words on the output side. We present several variations that differ in the amount of word context that is used on the input side, and in the use of non-lexical features. Remarkably, our simplest model produces state-of-the-art results, and we advance state-of-the-art through the use of bag-of-words, word embedding, named-entity, syntactic, and word class features. Analysis indicates that the superior performance is attributable to the task-specific word representations learned by the RNN. [Yao, Kaisheng; Hwang, Mei-Yuh] Microsoft, Online Serv Div, Beijing, Peoples R China; [Zweig, Geoffrey; Yu, Dong] Microsoft Res, Washington, DC USA; [Shi, Yangyang] Delft Univ Technol, Interact Intelligence Grp, Delft, Netherlands Microsoft; Delft University of Technology Yao, KS (corresponding author), Microsoft, Online Serv Div, Beijing, Peoples R China. kaisheny@microsoft.com; gzweig@microsoft.com; mehwang@microsoft.com; dongyu@microsoft.com 41 119 121 0 0 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X 978-1-62993-443-3 INTERSPEECH 2013.0 2523 2527 5 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BH0HX 2023-03-23 WOS:000395050001043 0
J Jiang, P; Klemes, JJ; Fan, YV; Fu, XJ; Tan, RR; You, SM; Foley, AM Jiang, Peng; Klemes, Jiri Jaromir; Fan, Yee Van; Fu, Xiuju; Tan, Raymond R.; You, Siming; Foley, Aoife M. Energy, environmental, economic and social equity (4E) pressures of COVID-19 vaccination mismanagement: A global perspective ENERGY English Article Vaccination; Vaccines; COVID-19; Energy-environment-economy-equity; Cold chain logistics; Sustainable management VACCINES Vaccination now offers a way to resolve the COVID-19 pandemic. However, it is critical to recognise the full energy, environmental, economic and social equity (4E) impacts of the vaccination life cycle. The full 4E impacts include the design and trials, order management, material preparation, manufacturing, cold chain logistics, low-temperature storage, crowd management and end-of-life waste management. A life cycle perspective is necessary for sustainable vaccination management because a prolonged immunisation campaign for COVID-19 is likely. The impacts are geographically dispersed across sectors and regions, creating real and virtual 4E footprints that occur at different timescales. Decision-makers in industry and governments have to act, unify, resolve, and work together to implement more sustainable COVID-19 vaccination management globally and locally to minimise the 4E footprints. Potential practices include using renewable energy in production, storage, transportation and waste treatment, using better product design for packaging, using the Internet of Things (IoT) and big data analytics for better logistics, using real-time database management for better tracking of deliveries and public vaccination programmes, and using coordination platforms for more equitable vaccine access. These practices raise global challenges but suggest solutions with a 4E perspective, which could mitigate the impacts of global vaccination campaigns and prepare sustainably for future pandemics and global warming. (c) 2021 Elsevier Ltd. All rights reserved. [Jiang, Peng] Sichuan Univ, Business Sch, Dept Ind Engn & Engn Management, Chengdu 610064, Peoples R China; [Klemes, Jiri Jaromir; Fan, Yee Van] Brno Univ Technol VUT Brno, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab SPIL, Tech 2896-2, Brno 61669, Czech Republic; [Fu, Xiuju] ASTAR, Inst High Performance Comp IHPC, Dept Syst Sci, Singapore 138632, Singapore; [Tan, Raymond R.] De La Salle Univ, Chem Engn Dept, 2401 Taft Ave, Manila 0922, Philippines; [You, Siming] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Foley, Aoife M.] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AH, Antrim, North Ireland; [Foley, Aoife M.] Univ Dublin, Trinity Coll Dublin, Civil Struct & Environm Engn, Dublin, Ireland Sichuan University; Brno University of Technology; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); De La Salle University; University of Glasgow; Queens University Belfast; Trinity College Dublin Klemes, JJ (corresponding author), Brno Univ Technol VUT Brno, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab SPIL, Tech 2896-2, Brno 61669, Czech Republic. jiri.klemes@vutbr.cz Fan, Yee Van/H-1088-2019; Jiang, Peng/AAP-4117-2020; Foley, Aoife/N-4404-2017; Klemes, Jiri Jaromir/B-7291-2009; Fan, Yee Van/H-1088-2019 Fan, Yee Van/0000-0002-4591-0038; Jiang, Peng/0000-0002-0502-113X; Foley, Aoife/0000-0001-6491-2592; Klemes, Jiri Jaromir/0000-0002-7450-7029; Fan, Yee Van/0000-0001-5514-0260; FU, XIUJU/0000-0002-6673-1098 EU [CZ.02.1.01/0.0/0.0/15_003/0000456]; Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research; De La Salle University, Philippines; National Natural Science Foundation of China [72042007]; Fundamental Research Funds for the Central Universities [YJ202138] EU(European Commission); Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research; De La Salle University, Philippines; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The financial support from the EU supported project Sustainable Process Integration Laboratory e SPIL funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research is acknowledged under the collaboration agreement with De La Salle University, Philippines. This study was also partially supported by the National Natural Science Foundation of China [Grant No. 72042007] and the Fundamental Research Funds for the Central Universities [Grant No. YJ202138]. 62 17 17 5 18 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy NOV 15 2021.0 235 121315 10.1016/j.energy.2021.121315 0.0 JUL 2021 6 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels WC3EO 34226789.0 Green Accepted 2023-03-23 WOS:000704143300005 0
J Zhang, LL; Zhang, Z; Tao, F; Luo, YC; Zhang, J; Cao, J Zhang, Liangliang; Zhang, Zhao; Tao, Fulu; Luo, Yuchuan; Zhang, Jing; Cao, Juan Adapting to climate change precisely through cultivars renewal for rice production across China: When, where, and what cultivars will be required? AGRICULTURAL AND FOREST METEOROLOGY English Article Agriculture; Climate change impact; Adaptation; Cultivar ideotype; Food security LEAF-AREA INDEX; MAJOR CROPS; WHEAT YIELD; MODEL; GRAIN; ADAPTATION; CO2; SIMULATION; EXTREMES; IMPACTS Climate change is projected to have an important impact on crop productivity at broad regions of the world. Crop breeders across the globe have continuously been working on development of crop cultivars to adapt to climate change, the detailed information is necessary on when and where the adaptability of current cultivars will be broken and what cultivar traits will be required. Here, we developed a novel hybrid crop modelling approach that coupled a process-based crop model with machine learning and systematically assessed the impacts of climate change on rice productivity for 36 representative cultivars in China. We identified when, where, and what cultivars would be required for rice production to adapt to climate change precisely across China. We showed substantial differences in climate change impacts amongst cultivars. Current cultivars replacement could only alleviate but not offset the potential yield loss due to climate change in the future. Without adaptations and CO2 effect, nearly 67% of single rice and 46% of double rice cultivation areas would require cultivar renewal before 2050. Only two decades of leading time remain in the mid-lower reaches of the Yangtze River. The cultivars with a medium growth cycle, long grain-filling period, high photosynthetic capacity, and less spikelets should be breeding targets to adapt to climate change, although the ideotypic traits could be different for a specific environment and rice type. [Zhang, Liangliang; Zhang, Zhao; Luo, Yuchuan; Zhang, Jing; Cao, Juan] Beijing Normal Univ, Acad Disaster Reduct & Emergency Management Minsi, Sch Natl Safety & Emergency Management, Beijing, Peoples R China; [Tao, Fulu] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; [Tao, Fulu] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; [Tao, Fulu] Nat Resources Inst Finland Luke, Helsinki, Finland Beijing Normal University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Natural Resources Institute Finland (Luke) Zhang, Z (corresponding author), Beijing Normal Univ, Acad Disaster Reduct & Emergency Management Minsi, Sch Natl Safety & Emergency Management, Beijing, Peoples R China. sunny_zhang@bnu.edu.cn Luo, Yuchuan/GSN-5985-2022 Tao, F/0000-0001-8574-0080 National Science Foundation of China [42061144003, 31761143006, 41977405]; National Key Research & Development Programme of China [2020YFA0608201] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research & Development Programme of China Acknowledgments This study was funded by the National Science Foundation of China (No. 42061144003, 31761143006, 41977405) , and the National Key Research & Development Programme of China (No. 2020YFA0608201) . 65 6 6 31 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0168-1923 1873-2240 AGR FOREST METEOROL Agric. For. Meteorol. APR 1 2022.0 316 108856 10.1016/j.agrformet.2022.108856 0.0 FEB 2022 14 Agronomy; Forestry; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Forestry; Meteorology & Atmospheric Sciences 1G5PU 2023-03-23 WOS:000795900600005 0
C Ding, RZ; Wang, L; Zhang, QL; Niu, ZX; Zheng, NN; Hua, G IEEE Ding, Rizhi; Wang, Le; Zhang, Qilin; Niu, Zhenxing; Zheng, Nanning; Hua, Gang FINE-GRAINED GIANT PANDA IDENTIFICATION 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing MAY 04-08, 2020 Barcelona, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Signal Proc Soc Fine-grained recognition; panda identification; feature fusion; patch detector The image-based fine-grained identification of individual giant pandas (Ailuropoda melanoleuca) is an emerging technology, and it is extraordinarily challenging due to the extremely subtle visual differences between individual giant pandas and limited annotated training data. To address these challenges, we propose the Feature-Fusion Convolutional Neural Network with Patch Detector (FFCNN-PD) algorithm, which exploits the discriminative local patches and builds a hierarchical representation generated by fusing both global and local features. Specifically, an attentional cross-channel pooling is embedded in the FFCNN-PD to improve the class-specific patch detectors. In addition, we propose a new giant panda identification dataset (iPanda-30) to establish a bench-mark. Experiments on the proposed iPanda-30 dataset and other fine-grained recognition datasets demonstrate the effectiveness of the FFCNN-PD algorithm against the existing state-of-the-arts. [Ding, Rizhi; Wang, Le; Zheng, Nanning] Xi An Jiao Tong Univ, Xian, Peoples R China; [Zhang, Qilin] HERE Technol, Eindhoven, Netherlands; [Niu, Zhenxing] Alibaba Grp, Hangzhou, Peoples R China; [Hua, Gang] Wormpex AI Res, Bellevue, WA USA Xi'an Jiaotong University; Alibaba Group Wang, L (corresponding author), Xi An Jiao Tong Univ, Xian, Peoples R China. Zhang, Qilin/B-5171-2018; Zhang, Qilin/HOF-0365-2023 Zhang, Qilin/0000-0002-7917-9749; National Key R&D Program of China [2018AAA0101400]; NSFC [61629301, 61773312, 61976171]; China Postdoctoral Science Foundation [2019M653642]; Young Elite Scientists Sponsorship Program by CAST [2018QNRC001] National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Young Elite Scientists Sponsorship Program by CAST This work was supported partly by National Key R&D Program of China Grant 2018AAA0101400, NSFC Grants 61629301, 61773312, and 61976171, China Postdoctoral Science Foundation Grant 2019M653642, and Young Elite Scientists Sponsorship Program by CAST Grant 2018QNRC001. 25 3 3 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-6631-5 INT CONF ACOUST SPEE 2020.0 2108 2112 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BQ7HU 2023-03-23 WOS:000615970402070 0
J Li, LX; Ye, F; Zhan, YZ; Kumar, A; Schiavone, F; Li, YN Li, Lixu; Ye, Fei; Zhan, Yuanzhu; Kumar, Ajay; Schiavone, Francesco; Li, Yina Unraveling the performance puzzle of digitalization: Evidence from manufacturing firms JOURNAL OF BUSINESS RESEARCH English Article Digitalization; Knowledge inertia; Organizational integration mechanisms; Firm performance; China BIG DATA ANALYTICS; KNOWLEDGE INERTIA; DYNAMIC CAPABILITIES; MEDIATING ROLE; INNOVATION; MANAGEMENT; IMPACT; CHAIN; DETERMINANTS; PRODUCT The COVID-19 pandemic has boosted firms' investments in digital technologies, and digitalization is booming. However, it remains unclear how and when digitalization leads to superior performance. To demystify this phenomenon, we develop a moderated moderation model to investigate the combined effects of digitalization, knowledge inertia, and organizational integration mechanisms on firm performance. Based on survey data from 192 Chinese manufacturing firms with different degrees of digitalization, we find that although digitalization has a positive relationship with firm performance, that relationship is negatively moderated by knowledge inertia. More interestingly, a formal organizational integration mechanism, but not an informal organizational integration mechanism, mitigates the negative moderation effect of knowledge inertia. We contribute to the literature by articulating how knowledge inertia and organizational integration mechanisms jointly determine the effect of digitalization on firm performance. Our study also provides implications for firms to modify their practices to prosper in the digital revolution. [Li, Lixu] Xian Univ Technol, Sch Econ & Management, Xian 710054, Peoples R China; [Ye, Fei] South China Univ Technol, Sch Business Adm, Guangzhou 510640, Peoples R China; [Zhan, Yuanzhu] Univ Birmingham, Birmingham Business Sch, Birmingham, W Midlands, England; [Zhan, Yuanzhu; Kumar, Ajay] Emlyon Business Sch, Ecully, France; [Kumar, Ajay; Schiavone, Francesco] Univ Parthenope, Naples, Italy; [Schiavone, Francesco] Paris Sch Business, Paris, France; [Li, Yina] South China Univ Technol, Guangzhou 510640, Peoples R China Xi'an University of Technology; South China University of Technology; University of Birmingham; EMLYON Business School; Parthenope University Naples; South China University of Technology Ye, F (corresponding author), South China Univ Technol, Sch Business Adm, Guangzhou 510640, Peoples R China. li.lixu@foxmail.com; yefei@scut.edu.cn; y.zhan@bham.ac.uk; akumar@em-lyon.com; francesco.schiavone@uniparthenope.it; bmliyina@scut.edu.cn zhan, yuanzhu/0000-0002-8585-8828; ye, fei/0000-0002-0749-7393; Li, Lixu/0000-0002-1927-0604 89 9 9 120 140 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0148-2963 1873-7978 J BUS RES J. Bus. Res. OCT 2022.0 149 54 64 10.1016/j.jbusres.2022.04.071 0.0 MAY 2022 11 Business Social Science Citation Index (SSCI) Business & Economics 3H0ID 2023-03-23 WOS:000831726800005 0
J Yu, X; Liang, ZT; Wang, YJ; Yin, HS; Liu, XW; Yu, WL; Huang, YQ Yu, Xiao; Liang, Zhongting; Wang, Youjie; Yin, Hongshen; Liu, Xiaowen; Yu, Wanli; Huang, Yanqiu A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions MEASUREMENT English Article Wavelet packet transform; Fault diagnosis; Residual network; Multi-kernel maximum mean discrepancy; Domain adaptation method NETWORK Deep learning has achieved significant advances in the fault diagnosis of rotating machinery. However, it still suffers many challenges such as various working conditions, large environmental noise interference and insufficient effective data samples. Signal time-frequency analysis and feature transfer learning methods can help solve these problems. Combining wavelet packet transform (WPT) and multi-kernel maximum mean discrepancy (MK-MMD), this paper proposes a novel residual network (ResNet)-based deep transfer diagnosis model for bearing faults. Firstly, this paper devises a distinctive WPT time-frequency feature map (WPT-TFFM) construction method using WPT for time-frequency analysis on nonlinear and non-stationary vibration signals. Then, a modified multi-group parallel ResNet network is structured to extract the depth features of WPT-TFFM for the characteristics of small size and feature dispersion. Then, MK-MMD is further applied to evaluate the distribution difference between the depth features of the source and target domain data. Combining with the classification loss of the sample set with the source domain, the depth features extraction network is optimized to achieve better cross-domain invariance and fault state differentiation capability of the depth features. To evaluate the proposed method, this work conducts comparative experiments on two test rigs under different working loads and speeds. The results reveal that the proposed method offers excellent fault diagnosis and noise prevention capability for working condition transfer tasks. [Yu, Xiao; Liang, Zhongting; Wang, Youjie; Yin, Hongshen] China Univ Min & Technol, IOT Percept Mine Res Ctr, Xuzhou 221000, Peoples R China; [Yu, Xiao; Liang, Zhongting; Wang, Youjie; Yin, Hongshen; Liu, Xiaowen] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221000, Peoples R China; [Liu, Xiaowen] China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221000, Peoples R China; [Yu, Wanli] Univ Bremen, Inst Electrodynam & Microelect, D-28359 Bremen, Germany; [Huang, Yanqiu] Univ Twente, Fac Elect Engn Math & Comp Sci, NL-7522 NH Enschede, Netherlands China University of Mining & Technology; China University of Mining & Technology; China University of Mining & Technology; University of Bremen; University of Twente Liu, XW (corresponding author), China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221000, Peoples R China. xwliucumt@126.com ; Yu, Wanli/L-8410-2018 Huang, Yanqiu/0000-0003-0718-6322; Yu, Wanli/0000-0003-1299-6223 National Natural Science Foundation of China [52074273]; Youth Science and Technology Fund of China University of Mining and Technology [2021QN1093]; Smart Mine Key Technology R&D Open Fund of China University of Mining and Technology; Zibo Mining Group Co., Ltd [2019LH08]; National Key R&D Program of China [2017YFC0804401] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Science and Technology Fund of China University of Mining and Technology; Smart Mine Key Technology R&D Open Fund of China University of Mining and Technology; Zibo Mining Group Co., Ltd; National Key R&D Program of China The present work is supported by the National Natural Science Foundation of China (No 52074273) , Youth Science and Technology Fund of China University of Mining and Technology (No 2021QN1093) , the Smart Mine Key Technology R&D Open Fund of China University of Mining and Technology and Zibo Mining Group Co., Ltd (No 2019LH08) , National Key R&D Program of China (No 2017YFC0804401) . 43 2 2 35 48 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement SEP 30 2022.0 201 111597 10.1016/j.measurement.2022.111597 0.0 AUG 2022 14 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3X8HT 2023-03-23 WOS:000843276400003 0
C Zhang, WB; Zhang, ML; Zhang, J; Liu, Z; Chen, ZY; Wang, JW; Raff, E; Messina, E Alamaniotis, M; Pan, S Zhang, Wenbin; Zhang, Mingli; Zhang, Ji; Liu, Zhen; Chen, Zhiyuan; Wang, Jianwu; Raff, Edward; Messina, Enza Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams 2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) Proceedings-International Conference on Tools With Artificial Intelligence English Proceedings Paper 32nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI) NOV 09-11, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,Biol & Artificial Intelligence Fdn AI fairness; online classification; flexible fairness Artificial intelligence (AI)-based decision-making systems are employed nowadays in an ever growing number of online as well as offline services-some of great importance. Depending on sophisticated learning algorithms and available data, these systems are increasingly becoming automated and data-driven. However, these systems can impact individuals and communities with ethical or legal consequences. Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design. However, these methods assume the underlying data distribution is stationary without drift, which is counterfactual in many real-world applications. In addition, their focus has been largely on minimizing discrimination while maximizing prediction performance without necessary flexibility in customizing the trade-off according to different applications. To this end, we propose a learning algorithm for fair classification that also adapts to evolving data streams and further allows for a flexible control on the degree of accuracy and fairness. The positive results on a set of discriminated and non-stationary data streams demonstrate the effectiveness and flexibility of this approach. [Zhang, Wenbin; Chen, Zhiyuan; Wang, Jianwu] Univ Maryland Baltimore Cty, Baltimore, MD 21228 USA; [Zhang, Mingli] McGill Univ, Montreal, PQ, Canada; [Zhang, Ji] Univ Southern Queensland, Toowoomba, Qld, Australia; [Liu, Zhen] Guangdong Pharmaceut Univ, Guangzhou, Peoples R China; [Raff, Edward] Booz Allen Hamilton, Riverdale, MD USA; [Messina, Enza] Univ Milano Bicocca, Milan, Italy University System of Maryland; University of Maryland Baltimore County; McGill University; University of Southern Queensland; Guangdong Pharmaceutical University; Booz Allen Hamilton Holding Corporation; University of Milano-Bicocca Zhang, WB (corresponding author), Univ Maryland Baltimore Cty, Baltimore, MD 21228 USA. wenbinzhang@umbc.edu; mingli.zhang@mcgill.ca; ji.zhang@usq.edu.au; liu.zhen@gdpu.edu.cn; zhchen@umbc.edu; jianwu@umbc.edu; raff_edward@bah.com; enza.messina@unimib.it Wang, Jianwu/0000-0002-9933-1170 47 2 2 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1082-3409 978-1-7281-9228-4 PROC INT C TOOLS ART 2020.0 399 406 10.1109/ICTAI50040.2020.00069 0.0 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR4AM Green Published 2023-03-23 WOS:000649734800059 0
J Li, XM; Wan, JF; Dai, HN; Imran, M; Xia, M; Celesti, A Li, Xiaomin; Wan, Jiafu; Dai, Hong-Ning; Imran, Muhammad; Xia, Min; Celesti, Antonio A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Edge computing; industry 4.0; resource scheduling; smart manufacturing INDUSTRIAL INTERNET; THINGS; CLOUD; EFFICIENT; ALGORITHM; FUTURE; IOT At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. [Li, Xiaomin] Zhongkai Univ Agr & Engn, Sch Mech Engn, Guangzhou 510225, Guangdong, Peoples R China; [Wan, Jiafu] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China; [Dai, Hong-Ning] Macau Univ Sci & Technol, Coll Informat Technol, Macau, Peoples R China; [Imran, Muhammad] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11451, Saudi Arabia; [Imran, Muhammad; Xia, Min] Univ British Columbia, Dept Mech Engn, Vancouver, BC V1V 1V7, Canada; [Celesti, Antonio] Univ Messina, MIFT Dept, I-98122 Messina, Italy Zhongkai University of Agriculture & Engineering; South China University of Technology; Macau University of Science & Technology; King Saud University; University of British Columbia; University of Messina Wan, JF (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China. lixiaomin@zhku.edu.cn; mejwan@scut.edu.cn; hndai@ieee.org; dr.m.imran@ieee.org; minxia@mech.ubc.ca; acelesti@unime.it Dai, Hong-Ning/B-1931-2012; li, xiaomin/ABC-3188-2020; Imran, Muhammad/Q-2375-2017; Xia, Min/AAL-3935-2020; Wan, Jiafu/I-3059-2016 Dai, Hong-Ning/0000-0001-6165-4196; li, xiaomin/0000-0001-7587-0543; Imran, Muhammad/0000-0002-6946-2591; Xia, Min/0000-0001-8057-9654; Wan, Jiafu/0000-0001-9188-4179 National Key Research and Development Project of China [2017YFE0101000]; National Natural Science Foundation of China [U1801264]; Guangdong Province [U1801264]; Natural Science Foundation of Guangdong Province [2017B030311008]; Science and Technology Programof Guangzhou, China [201802030005]; King Saud University [RG-1435-051] National Key Research and Development Project of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Province; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Science and Technology Programof Guangzhou, China; King Saud University(King Saud University) This work was supported in part by the National Key Research and Development Project of China under Grant 2017YFE0101000, in part by the Joint Fund of the National Natural Science Foundation of China and Guangdong Province under Grant U1801264, in part by the Key Program of Natural Science Foundation of Guangdong Province under Grant 2017B030311008, and in part by the Science and Technology Programof Guangzhou, China, under Grant 201802030005. The work of M. Imran was supported by the Deanship of Scientific Research, King Saud University through research group number RG-1435-051. Paper no. TII-18-3244. R1. (Corresponding author: Jiafu Wan.) 35 94 97 10 114 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. JUL 2019.0 15 7 4225 4234 10.1109/TII.2019.2899679 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering IH6SB 2023-03-23 WOS:000474628100044 0
J Zheng, Q; Guo, CJ; Ding, J; Akinsolu, MO; Liu, B; Vandenbosch, GAE Zheng, Qi; Guo, Chenjiang; Ding, Jun; Akinsolu, Mobayode O.; Liu, Bo; Vandenbosch, Guy A. E. A Wideband Low-RCS Metasurface-Inspired Circularly Polarized Slot Array Based on AI-Driven Antenna Design Optimization Algorithm IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION English Article Antenna array; circular polarization; metasurface (MS); parallel surrogate model-assisted hybrid differential evolution for antenna optimization (PSADEA); radar cross section (RCS) EFFICIENT GLOBAL OPTIMIZATION; BROAD-BAND; REDUCTION A metasurface (MS)-inspired low-profile circularly polarized (CP) slot array with a wide CP band and broadband low radar cross section (RCS) is proposed in this communication. The slot array consists of four element antennas, four grounded substrates, and a sequential rotated feeding network. In terms of radiation performance, the array yields a wide CP band resulting from the CP element antenna and the sequential-rotated feeding network. The CP element antenna is achieved due to the polarization conversion property of the MS-based superstrate. The feeding network with multiple related design parameters is optimized by an artificial intelligence (AI)-driven antenna design method to find the widest bandwidth. In terms of scattering performance, broadband RCS reduction is achieved by using a hybrid RCS reduction technique that combines two destructive interference principles. The vertical bar S-11 vertical bar < -10 dB bandwidth reaches 53.2%, the AR < 3 dB bandwidth reaches 50%, and the RCS reduction bandwidth reaches 147.8% for a low-profile structure with a relatively low number of MS unit cells. A prototype was fabricated and measured. The measured and simulated results are in good agreement. [Zheng, Qi] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China; [Guo, Chenjiang; Ding, Jun] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710060, Peoples R China; [Akinsolu, Mobayode O.] Wrexham Glyndwr Univ, Fac Arts Sci & Technol, Wrexham LL11 2AW, Wales; [Liu, Bo] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Vandenbosch, Guy A. E.] Katholieke Univ Leuven, ESAT WaveCoRE Res Div, Dept Elect Engn, B-3001 Leuven, Belgium Shanghai University; Northwestern Polytechnical University; University of Glasgow; KU Leuven Zheng, Q (corresponding author), Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China.;Liu, B (corresponding author), Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland.;Vandenbosch, GAE (corresponding author), Katholieke Univ Leuven, ESAT WaveCoRE Res Div, Dept Elect Engn, B-3001 Leuven, Belgium. qizheng@shu.edu.cn; cjguo@nwpu.edu.cn; dingjun@nwpu.edu.cn; m.o.akinsolu@ieee.org; bo.liu@glasgow.ac.uk; guy.vandenbosch@kuleuven.be Liu, Bo/0000-0002-3093-4571; Akinsolu, Mobayode O./0000-0003-1164-5501; Zheng, Qi/0000-0002-7748-8994 Natural Science Foundation of Shanghai [21ZR1424300]; China Scholarship Council [201806290108] Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); China Scholarship Council(China Scholarship Council) This work was supported in part by the Natural Science Foundation of Shanghai under Grant 21ZR1424300 and in part by the China Scholarship Council under Grant 201806290108. 29 0 0 18 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-926X 1558-2221 IEEE T ANTENN PROPAG IEEE Trans. Antennas Propag. SEP 2022.0 70 9 8584 8589 10.1109/TAP.2022.3161389 0.0 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 5F7RX Green Accepted 2023-03-23 WOS:000866510000122 0
J Mei, XC; Cui, Z; Sheng, Q; Zhou, J; Li, CQ Mei, Xiancheng; Cui, Zhen; Sheng, Qian; Zhou, Jian; Li, Chuanqi Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material MATERIALS English Article aseismic concrete material; strength; energy absorption property; improved POA algorithm; tunnel OPTIMIZATION ALGORITHM; COMPRESSIVE STRENGTH; IMPACT BEHAVIOR The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R-2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R-2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering. [Mei, Xiancheng; Cui, Zhen; Sheng, Qian] Chinese Acad Sci, Inst Rock & Soil Mech, Wuhan 430071, Peoples R China; [Mei, Xiancheng; Cui, Zhen; Sheng, Qian] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Zhou, Jian] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China; [Li, Chuanqi] Grenoble Alpes Univ, CNRS, UMR 5521, Lab 3SR, F-38000 Grenoble, France Chinese Academy of Sciences; Wuhan Institute of Rock & Soil Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Central South University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA) Li, CQ (corresponding author), Grenoble Alpes Univ, CNRS, UMR 5521, Lab 3SR, F-38000 Grenoble, France. chuanqi.li@univ-grenoble-alpes.fr Zhou, Jian/M-2461-2018 Zhou, Jian/0000-0003-4769-4487; CUI, Zhen/0000-0003-4119-8108 National Natural Science Foundation of China [51779253, 41902288]; National Basic Research Program of China [51991393]; CRSRI Open Research Program [SN: CKWV2019746/KY]; Youth Innovation Promotion Association CAS National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Basic Research Program of China(National Basic Research Program of China); CRSRI Open Research Program; Youth Innovation Promotion Association CAS The study reported here is financially supported by the National Natural Science Foundation of China (51779253, 41902288), the National Basic Research Program of China (51991393), CRSRI Open Research Program (Program SN: CKWV2019746/KY) and the Youth Innovation Promotion Association CAS. The authors want to thank all the members who give us lots of help and cooperation. 64 1 1 4 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials FEB 2023.0 16 3 1286 10.3390/ma16031286 0.0 19 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics 8T9XE 36770291.0 gold 2023-03-23 WOS:000929604900001 0
J Maurelli, F; Krupinski, S; Xiang, XB; Petillot, Y Maurelli, Francesco; Krupinski, Szymon; Xiang, Xianbo; Petillot, Yvan AUV localisation: a review of passive and active techniques INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS English Review Localisation; Estimation; Filtering; Underwater; Motion planning; Navigation GLOBAL LOCALIZATION; MOBILE ROBOT; UNDERWATER ROBOT; KALMAN FILTER; NAVIGATION; SLAM; COMMUNICATION; LANDMARKS; TRACKING; VEHICLES Localisation, i.e. estimation of one's position in a given environment is a crucial element of many mobile systems, manned and unmanned. Due to the high demand for autonomous exploration, patrolling and inspection services and a rapid improvement of batteries, sensors and machine learning algorithms, the quality of localisation becomes even more important for smart robotic systems. The underwater domain is a very challenging environment due to the water blocking most of the signals over short distances. Recent results in localisation techniques for underwater vehicles are summarised in two principal categories: passive techniques, which strive to provide the best estimation of the vehicle's position (global or local) given the past and current information from sensors, and active techniques, which additionally produce guidance output that is expected to minimise the uncertainty of estimated position. [Maurelli, Francesco; Krupinski, Szymon] Jacobs Univ Bremen, Coll Ring 1, D-28759 Bremen, Germany; [Xiang, Xianbo] Huazhong Univ Sci & Technol, Luoyu Rd 1037, Wuhan, Peoples R China; [Petillot, Yvan] Heriot Watt Univ, Edinburgh EH14 4AS, Midlothian, Scotland Jacobs University; Huazhong University of Science & Technology; Heriot Watt University Maurelli, F (corresponding author), Jacobs Univ Bremen, Coll Ring 1, D-28759 Bremen, Germany. f.maurelli@jacobs-university.de; s.krupinski@jacobs-university.de; xbxiang@bust.edu.cn; y.r.petillot@hw.ac.uk Xiang, Xianbo/O-7613-2019 Xiang, Xianbo/0000-0002-6215-9864; Maurelli, Francesco/0000-0002-5265-3666 Projekt DEAL Projekt DEAL Open Access funding enabled and organized by Projekt DEAL. 150 10 10 15 43 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2366-5971 2366-598X INT J INTELL ROBOT Int. J. Intell. Robot. JUN 2022.0 6 2 SI 246 269 10.1007/s41315-021-00215-x 0.0 NOV 2021 24 Robotics Emerging Sources Citation Index (ESCI) Robotics 1S4RT hybrid 2023-03-23 WOS:000721657300001 0
J Micchelli, CA; Pontil, M; Wu, Q; Zhou, DX Micchelli, Charles A.; Pontil, Massimiliano; Wu, Qiang; Zhou, Ding-Xuan Error bounds for learning the kernel ANALYSIS AND APPLICATIONS English Article Kernel; regularization; error decomposition; least square; support vector machine; Gaussians SUPPORT VECTOR MACHINES; SOFT MARGIN CLASSIFIERS The problem of learning the kernel function has received considerable attention in machine learning. Much of the work has focused on kernel selection criteria, particularly on minimizing a regularized error functional over a prescribed set of kernels. Empirical studies indicate that this approach can enhance statistical performance and is computationally feasible. In this paper, we present a theoretical analysis of its generalization error. We establish for a wide variety of classes of kernels, such as the set of all multivariate Gaussian kernels, that this learning method generalizes well and, when the regularization parameter is appropriately chosen, it is consistent. A central role in our analysis is played by the interaction between the sample error and the approximation error. [Micchelli, Charles A.] SUNY Albany, Dept Math & Stat, 1400 Washington Ave, Albany, NY 12222 USA; [Pontil, Massimiliano] Ist Italiano Tecnol, Via Morego 30, I-16163 Genoa, Italy; [Pontil, Massimiliano] UCL, Dept Comp Sci, Gower St, London WC1E, England; [Wu, Qiang] Middle Tennessee State Univ, Dept Math Sci, 1301 E Main St, Murfreesboro, TN 37132 USA; [Zhou, Ding-Xuan] City Univ Hong Kong, Dept Math, 83 Tat Chee Ave, Kowloon, Hong Kong, Peoples R China State University of New York (SUNY) System; State University of New York (SUNY) Albany; Istituto Italiano di Tecnologia - IIT; University of London; University College London; Middle Tennessee State University; City University of Hong Kong Micchelli, CA (corresponding author), SUNY Albany, Dept Math & Stat, 1400 Washington Ave, Albany, NY 12222 USA. charles_micchelli@hotmail.com; massimiliano.pontil@iit.it; qwu@mtsu.edu; mazhou@cityu.edu.hk Wu, Qiang/B-1620-2008; Zhou, Ding-Xuan/B-3160-2013 Wu, Qiang/0000-0002-4698-6966; Zhou, Ding-Xuan/0000-0003-0224-9216 National Science Foundation of the United States (NSF) [DMS-1522339]; EPSRC [P/M006093/1, EP/P009069/1]; National Natural Science Foundation of China [11671171, 11471292, 11461161006]; Research Grants Council of Hong Kong [CityU 104113] National Science Foundation of the United States (NSF)(National Science Foundation (NSF)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Grants Council of Hong Kong(Hong Kong Research Grants Council) We acknowledge that Charles A. Micchelli was partially supported by the National Science Foundation of the United States (NSF Grant No. DMS-1522339), Massimiliano Pontil was partially supported by EPSRC (No. P/M006093/1 and EP/P009069/1), Qiang Wu was partially supported by the National Natural Science Foundation of China (No. 11671171), Ding-Xuan Zhou was partially supported by the Research Grants Council of Hong Kong (Project No. CityU 104113) and the National Natural Science Foundation of China (No. 11471292 and 11461161006). 43 9 9 0 6 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0219-5305 1793-6861 ANAL APPL Anal. Appl. NOV 2016.0 14 6 SI 849 868 10.1142/S0219530516400054 0.0 20 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics EB1MZ 2023-03-23 WOS:000387117900006 0
J Challita, U; Ferdowsi, A; Chen, MZ; Saad, W Challita, Ursula; Ferdowsi, Aidin; Chen, Mingzhe; Saad, Walid MACHINE LEARNING FOR WIRELESS CONNECTIVITY AND SECURITY OF CELLULAR-CONNECTED UAVS IEEE WIRELESS COMMUNICATIONS English Article Cellular-connected UAVs will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable reliable wireless connectivity for the UAVs as well as secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this article, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, ANN-based solution schemes are introduced. The introduced approaches enable UAVs to adaptively exploit wireless system resources while guaranteeing secure operation in real time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use cases. [Challita, Ursula] Ericsson Res, Stockholm, Sweden; [Challita, Ursula] Univ Edinburgh, Edinburgh, Midlothian, Scotland; [Ferdowsi, Aidin] Virginia Tech, Bradley Dept Elect & Comp Engn, Blacksburg, VA USA; [Saad, Walid] Virginia Tech, Dept Elect & Comp Engn, Blacksburg, VA USA; [Chen, Mingzhe] Beijing Univ Posts & Telecommun, Informat & Commun Engn Dept, Beijing, Peoples R China Ericsson; University of Edinburgh; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Beijing University of Posts & Telecommunications Challita, U (corresponding author), Ericsson Res, Stockholm, Sweden. Ferdowsi, Aidin/AAN-8892-2020; Chen, Mingzhe/U-3377-2019; Saad, Walid/C-7978-2018 Chen, Mingzhe/0000-0003-2570-703X; Saad, Walid/0000-0003-2247-2458 Army Research Office (ARO) [W911NF-17-1-0593]; U.S. National Science Foundation [OAC-1541105, IIS-1633363] Army Research Office (ARO); U.S. National Science Foundation(National Science Foundation (NSF)) This work was supported by the Army Research Office (ARO) under Grant W911NF-17-1-0593 and in part by the U.S. National Science Foundation under Grants OAC-1541105 and IIS-1633363. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of ARO or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein. 15 109 112 6 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. FEB 2019.0 26 1 28 35 10.1109/MWC.2018.1800155 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HL6LC Green Submitted 2023-03-23 WOS:000458844600007 0
J Kueffner, R; Zach, N; Bronfeld, M; Norel, R; Atassi, N; Balagurusamy, V; Di Camillo, B; Chio, A; Cudkowicz, M; Dillenberger, D; Garcia-Garcia, J; Hardiman, O; Hoff, B; Knight, J; Leitner, ML; Li, G; Mangravite, L; Norman, T; Wang, LX; Alkallas, R; Anghel, C; Avril, J; Bacardit, J; Balser, B; Balser, J; Bar-Sinai, Y; Ben-David, N; Ben-Zion, E; Bliss, R; Cai, J; Chernyshev, A; Chiang, JH; Chicco, D; Corriveau, BAN; Dai, JQ; Deshpande, Y; Desplats, E; Durgin, JS; Espiritu, SMG; Fan, F; Fevrier, P; Fridley, BL; Godzik, A; Golinska, A; Gordon, J; Graw, S; Guo, YL; Herpelinck, T; Hopkins, J; Huang, B; Jacobsen, J; Jahandideh, S; Jeon, J; Ji, WK; Jung, K; Karanevich, A; Koestler, DC; Kozak, M; Kurz, C; Lalansingh, C; Larrieu, T; Lazzarini, N; Lerner, B; Lesinski, W; Liang, XT; Lin, XH; Lowe, J; Mackey, L; Meier, R; Min, WW; Mnich, K; Nahmias, V; Noel-MacDonnell, J; O'Donnell, A; Paadre, S; Park, J; Polewko-Klim, A; Raghavan, R; Rudnicki, W; Saghapour, E; Salomond, JB; Sankaran, K; Sendorek, D; Sharan, V; Shiah, YJ; Sirois, JK; Sumanaweera, DN; Usset, J; Vang, YS; Vens, C; Wadden, D; Wang, D; Wong, WC; Xie, XH; Xu, ZQ; Yang, HT; Yu, X; Zhang, HC; Zhang, L; Zhang, SH; Zhu, SF; Xiao, JF; Fang, WC; Peng, J; Yang, C; Chang, HJ; Stolovitzky, G Kueffner, Robert; Zach, Neta; Bronfeld, Maya; Norel, Raquel; Atassi, Nazem; Balagurusamy, Venkat; Di Camillo, Barbara; Chio, Adriano; Cudkowicz, Merit; Dillenberger, Donna; Garcia-Garcia, Javier; Hardiman, Orla; Hoff, Bruce; Knight, Joshua; Leitner, Melanie L.; Li, Guang; Mangravite, Lara; Norman, Thea; Wang, Liuxia; Alkallas, Rached; Anghel, Catalina; Avril, Jeanne; Bacardit, Jaume; Balser, Barbara; Balser, John; Bar-Sinai, Yoav; Ben-David, Noa; Ben-Zion, Eyal; Bliss, Robin; Cai, Jialu; Chernyshev, Anatoly; Chiang, Jung-Hsien; Chicco, Davide; Corriveau, Bhavna Ahuja Nicole; Dai, Junqiang; Deshpande, Yash; Desplats, Eve; Durgin, Joseph S.; Espiritu, Shadrielle Melijah G.; Fan, Fan; Fevrier, Philippe; Fridley, Brooke L.; Godzik, Adam; Golinska, Agnieszka; Gordon, Jonathan; Graw, Stefan; Guo, Yuelong; Herpelinck, Tim; Hopkins, Julia; Huang, Barbara; Jacobsen, Jeremy; Jahandideh, Samad; Jeon, Jouhyun; Ji, Wenkai; Jung, Kenneth; Karanevich, Alex; Koestler, Devin C.; Kozak, Michael; Kurz, Christoph; Lalansingh, Christopher; Larrieu, Thomas; Lazzarini, Nicola; Lerner, Boaz; Lesinski, Wojciech; Liang, Xiaotao; Lin, Xihui; Lowe, Jarrett; Mackey, Lester; Meier, Richard; Min, Wenwen; Mnich, Krzysztof; Nahmias, Violette; Noel-MacDonnell, Janelle; O'Donnell, Adrienne; Paadre, Susan; Park, Ji; Polewko-Klim, Aneta; Raghavan, Rama; Rudnicki, Witold; Saghapour, Ehsan; Salomond, Jean-Bernard; Sankaran, Kris; Sendorek, Dorota; Sharan, Vatsal; Shiah, Yu-Jia; Sirois, Jean-Karl; Sumanaweera, Dinithi N.; Usset, Joseph; Vang, Yeeleng S.; Vens, Celine; Wadden, Dave; Wang, David; Wong, Wing Chung; Xie, Xiaohui; Xu, Zhiqing; Yang, Hsih-Te; Yu, Xiang; Zhang, Haichen; Zhang, Li; Zhang, Shihua; Zhu, Shanfeng; Xiao, Jinfeng; Fang, Wen-Chieh; Peng, Jian; Yang, Chen; Chang, Huan-Jui; Stolovitzky, Gustavo ALS Stratification Consortium Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach SCIENTIFIC REPORTS English Article BODY-MASS INDEX; DISEASE PROGRESSION; GENETIC-HETEROGENEITY; OUTCOME MEASURES; DOUBLE-BLIND; CREATININE; SURVIVAL; TRIAL; PREDICTORS; DEXPRAMIPEXOLE Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from > 10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development. [Kueffner, Robert] Icahn Sch Med Mt Sinai, New York, NY 10029 USA; [Zach, Neta] Teva Pharmaceut, Netanyah, Israel; [Bronfeld, Maya] Prize4Life, Haifa, Israel; [Norel, Raquel; Balagurusamy, Venkat; Dillenberger, Donna; Knight, Joshua; Stolovitzky, Gustavo] IBM Res, Yorktown Hts, NY USA; [Atassi, Nazem; Cudkowicz, Merit] Massachusetts Gen Hosp, Boston, MA 02114 USA; [Di Camillo, Barbara] Univ Padua, Dept Informat Engn, Padua, Italy; [Chio, Adriano] Univ Turin, Turin, Italy; [Garcia-Garcia, Javier] Pompeu Fabra Univ, Barcelona, Spain; [Hardiman, Orla] Trinity Coll Dublin, Inst Neurosci, Dublin, Ireland; [Hoff, Bruce; Mangravite, Lara; Norman, Thea] Sage Bionetworks, Seattle, WA USA; [Leitner, Melanie L.] Accelerating NeuroVentures, Boston, MA USA; [Li, Guang] Amazon, Seattle, WA USA; [Wang, Liuxia] Zillow, Seattle, WA USA; [Xiao, Jinfeng; Peng, Jian] Univ Illinois, Dept Comp Sci, Champaign, IL USA; [Fang, Wen-Chieh] Natl Univ, Dept Informat & Learning Technol, Tainan, Taiwan; [Yang, Chen] Natl Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan; [Chang, Huan-Jui] Monash Univ, Fac Informat Technol, Clayton, Vic, Australia; [Alkallas, Rached] McGill Univ, Dept Human Genet, Montreal, PQ, Canada; [Anghel, Catalina; Espiritu, Shadrielle Melijah G.; Fan, Fan; Hopkins, Julia; Huang, Barbara; Jeon, Jouhyun; Lalansingh, Christopher; Lin, Xihui; Sendorek, Dorota; Shiah, Yu-Jia; Wang, David] OICR, Toronto, ON, Canada; [Avril, Jeanne; Larrieu, Thomas; Nahmias, Violette] Ecole Polytech, Dept Econ, Paris, France; [Bacardit, Jaume; Lazzarini, Nicola] Newcastle Univ, Interdisciplinary Comp & Complex BioSyst ICOS Res, Tyne, England; [Balser, Barbara; Balser, John; Bliss, Robin; Cai, Jialu; Corriveau, Bhavna Ahuja Nicole; Desplats, Eve; Lowe, Jarrett; O'Donnell, Adrienne; Paadre, Susan; Sirois, Jean-Karl; Xu, Zhiqing; Zhang, Li] Veristat Inc, Southborough, MA USA; [Bar-Sinai, Yoav] Med Res, Kfar Malal, Israel; [Ben-David, Noa] Ben Gurion Univ Negev, Dept Comp Sci, Beer Sheva, Israel; [Ben-Zion, Eyal; Lerner, Boaz] Ben Gurion Univ Negev, Dept Ind Engn & Management, Negev, Israel; [Chernyshev, Anatoly] Analyt Labs, Hamilton, New Zealand; [Chiang, Jung-Hsien; Yang, Hsih-Te] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan; [Chicco, Davide] Princess Margaret Canc Ctr, Toronto, ON, Canada; [Dai, Junqiang; Graw, Stefan; Karanevich, Alex; Koestler, Devin C.; Meier, Richard; Raghavan, Rama; Usset, Joseph] Univ Kansas, Med Ctr, Dept Biostat, Kansas City, KS 66103 USA; [Deshpande, Yash] MIT, Dept Math, Cambridge, MA 02139 USA; [Durgin, Joseph S.] Univ Penn, Perelman Sch Med, Philadelphia, PA 19104 USA; [Fevrier, Philippe] Ctr Rech Econ & Stat, Paris, France; [Fridley, Brooke L.] H Lee Moffitt Canc Ctr & Res Inst, Dept Biostat & Bioinformat, Tampa, FL USA; [Godzik, Adam] Sanford Burnham Prebys Med Discovery Inst, Program Bioinformat & Syst Biol, La Jolla, CA USA; [Golinska, Agnieszka; Polewko-Klim, Aneta; Rudnicki, Witold] Univ Bialystok, Inst Informat, Bialystok, Poland; [Gordon, Jonathan] Univ Cambridge, Dept Engn, Cambridge, England; [Guo, Yuelong] RTI Int, Res Triangle Pk, NC USA; [Herpelinck, Tim; Vens, Celine] Katholieke Univ Leuven, Dept Publ Hlth & Primary Care, Kortrijk, Belgium; [Jacobsen, Jeremy] Univ Colorado, Dept Biochem, Boulder, CO USA; [Jahandideh, Samad] Origent Data Sci Inc, Vienna, VA USA; [Ji, Wenkai; Zhang, Shihua] Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Huairou, Peoples R China; [Jung, Kenneth] Stanford Univ, Ctr Biomed Informat Res, Stanford, CA 94305 USA; [Kozak, Michael] Tel Aviv Univ, Dept Stat, Tel Aviv, Israel; [Kurz, Christoph] Helmholtz Zentrum Munchen, Inst Hlth Econ & Hlth Care Management, Munich, Germany; [Lesinski, Wojciech] Univ Bialystok, Dept Bioinformat, Bialystok, Poland; [Liang, Xiaotao; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Liang, Xiaotao; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Mackey, Lester] Microsoft Res New England, Cambridge, MA USA; [Min, Wenwen] Wuhan Univ, Sch Comp, Wuhan, Hubei, Peoples R China; [Mnich, Krzysztof; Rudnicki, Witold] Univ Bialystok, Computat Ctr, Bialystok, Poland; [Noel-MacDonnell, Janelle] Childrens Mercy Hosp, Kansas City, MO 64108 USA; [Park, Ji] LinkedIn, Sunnyville, CA USA; [Rudnicki, Witold] Univ Warsaw, Interdisciplinary Ctr Math & Computat Modelling, Warsaw, Poland; [Saghapour, Ehsan] Isfahan Univ Med Sci, Sch Adv Technol Med, Dept Biomed Engn, Esfahan, Iran; [Salomond, Jean-Bernard] Ceremade Univ Paris Dauphine, Paris, France; [Salomond, Jean-Bernard] Univ Paris Est, Lab Anal & Math Appliquees, Creteil, France; [Sankaran, Kris] Stanford Univ, Dept Stat, Stanford, CA 94305 USA; [Sharan, Vatsal] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Sumanaweera, Dinithi N.] Univ Moratuwa, Dept Comp Sci & Engn, Moratuwa, Sri Lanka; [Vang, Yeeleng S.; Xie, Xiaohui] Univ Calif Irvine, Dept Comp Sci, Irvine, CA USA; [Wadden, Dave] Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA; [Wong, Wing Chung] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Xie, Xiaohui] Univ Calif Irvine, Dept Comp Sci, Bren Sch Informat & Comp Sci, Irvine, CA USA; [Yu, Xiang] Univ Penn, Philadelphia, PA 19104 USA; [Zhang, Haichen] Univ Maryland, Baltimore, MD 21201 USA; [Zhu, Shanfeng] Fudan Univ, ISTBI, Ctr Computat Syst Biol, Shanghai, Peoples R China Icahn School of Medicine at Mount Sinai; Teva Pharmaceutical Industries; International Business Machines (IBM); Harvard University; Massachusetts General Hospital; University of Padua; University of Turin; Pompeu Fabra University; Trinity College Dublin; Amazon.com; University of Illinois System; University of Illinois Urbana-Champaign; Monash University; McGill University; Newcastle University - UK; Ben Gurion University; Ben Gurion University; National Cheng Kung University; University of Toronto; University Health Network Toronto; Princess Margaret Cancer Centre; University of Kansas; University of Kansas Medical Center; Massachusetts Institute of Technology (MIT); University of Pennsylvania; Pennsylvania Medicine; H Lee Moffitt Cancer Center & Research Institute; Sanford Burnham Prebys Medical Discovery Institute; University of Bialystok; University of Cambridge; Research Triangle Institute; KU Leuven; University of Colorado System; University of Colorado Boulder; Chinese Academy of Sciences; Stanford University; Tel Aviv University; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Bialystok; Fudan University; Fudan University; Microsoft; Wuhan University; University of Bialystok; Children's Mercy Hospital; University of Warsaw; Interdisciplinary Centre for Mathematical & Computational Modelling; Isfahan University Medical Science; Universite Gustave-Eiffel; Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Stanford University; Stanford University; University Moratuwa; University of California System; University of California Irvine; University of Washington; University of Washington Seattle; City University of Hong Kong; University of California System; University of California Irvine; University of Pennsylvania; University System of Maryland; University of Maryland Baltimore; Fudan University Kueffner, R (corresponding author), Icahn Sch Med Mt Sinai, New York, NY 10029 USA.;Zach, N (corresponding author), Teva Pharmaceut, Netanyah, Israel. r.m.kueffner@gmail.com; netazach@gmail.com Hardiman, Orla/Y-3441-2019; LERNER, BOAZ/AAJ-4064-2020; Wang, Lai-Xi/E-8755-2019; Herpelinck, Tim/AAQ-3756-2021; Wang, Xin/GYU-1129-2022; Golińska, Agnieszka Kitlas/AAR-2097-2020; Min, Wenwen/GPF-8094-2022; Kurz, Christoph/AAL-7578-2020; Bacardit, Jaume/F-9293-2014; Wang, Xin/AAN-8435-2021; Noel-MacDonnell, Janelle/ABF-1499-2020; Kurz, Christoph/HPD-4539-2023; wang, xl/Y-8251-2019; Golińska, Agnieszka Kitlas/V-9677-2018; CUDKOWICZ, MERIT/GWM-6585-2022; Rudnicki, Witold R/B-6670-2012; Norel, Raquel/ABB-3422-2021; Jack, Fang/GVT-3928-2022; Polewko-Klim, Aneta/T-1045-2018; Guo, Yue-Liang Leon/ABF-2314-2020; Meier, Richard/HLH-8362-2023; Godzik, Adam/AAW-1467-2020; Di Camillo, Barbara/L-3202-2013; Chicco, Davide/C-7058-2017 Hardiman, Orla/0000-0003-2610-1291; Herpelinck, Tim/0000-0002-4500-4676; Min, Wenwen/0000-0002-2558-2911; Bacardit, Jaume/0000-0002-2692-7205; Wang, Xin/0000-0002-4457-7376; Kurz, Christoph/0000-0001-9498-8002; Golińska, Agnieszka Kitlas/0000-0001-8737-765X; CUDKOWICZ, MERIT/0000-0002-7075-1681; Rudnicki, Witold R/0000-0002-7928-4944; Norel, Raquel/0000-0001-7737-4172; Jack, Fang/0000-0002-3647-2232; Polewko-Klim, Aneta/0000-0003-1987-7374; Godzik, Adam/0000-0002-2425-852X; Di Camillo, Barbara/0000-0001-8415-4688; Mangravite, Lara/0000-0001-7841-3612; Chernyshev, Anatoly/0000-0002-1628-9318; Peng, Jian/0000-0002-1736-2978; Chio, Adriano/0000-0001-9579-5341; Usset, Joseph/0000-0002-5820-9881; Xie, Xiaohui/0000-0002-5479-6345; Lesinski, Wojciech/0000-0001-6628-9466; Mackey, Lester/0000-0002-1102-0387; Sankaran, Kris/0000-0002-9415-1971; YANG, HSIH-TE/0000-0002-4851-1374; Chiang, Jung-Hsien/0000-0002-4657-6705; Chicco, Davide/0000-0001-9655-7142 57 26 26 1 8 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2045-2322 SCI REP-UK Sci Rep JAN 24 2019.0 9 690 10.1038/s41598-018-36873-4 0.0 14 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics HI6HI 30679616.0 Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000456554600129 0
C Campbell, A; Chen, WL; Stimper, V; Hernandez-Lobato, JM; Zhang, YCA Meila, M; Zhang, T Campbell, Andrew; Chen, Wenlong; Stimper, Vincent; Hernandez-Lobato, Jose Miguel; Zhang, Yichuan A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 Proceedings of Machine Learning Research English Proceedings Paper International Conference on Machine Learning (ICML) JUL 18-24, 2021 ELECTR NETWORK Hamiltonian Monte Carlo (HMC) is one of the most successful sampling methods in machine learning. However, its performance is significantly affected by the choice of hyperparameter values. Existing approaches for optimizing the HMC hyperparameters either optimize a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that can be very loose in practice. Instead, we propose to optimize an objective that quantifies directly the speed of convergence to the target distribution. Our objective can be easily optimized using stochastic gradient descent. We evaluate our proposed method and compare to baselines on a variety of problems including sampling from synthetic 2D distributions, reconstructing sparse signals, learning deep latent variable models and sampling molecular configurations from the Boltzmann distribution of a 22 atom molecule. We find that our method is competitive with or improves upon alternative baselines in all these experiments. [Campbell, Andrew] Univ Oxford, Dept Stat, Oxford, England; [Chen, Wenlong] Baidu Inc, Beijing, Peoples R China; [Stimper, Vincent; Hernandez-Lobato, Jose Miguel] Univ Cambridge, Dept Engn, Cambridge, England; [Stimper, Vincent] Max Planck Inst Intelligent Syst, Stuttgart, Germany; [Zhang, Yichuan] Boltzbit Ltd, London, England University of Oxford; Baidu; University of Cambridge; Max Planck Society Campbell, A (corresponding author), Univ Oxford, Dept Stat, Oxford, England. campbell@stats.ox.ac.uk EPSRC CDT in Modern Statistics and Statistical Machine Learning [EP/S023151/1]; Turing AI Fellowship [EP/V023756/1]; Samsung Research, Samsung Electronics Co., Seoul, Republic of Korea; Boltzbit Ltd. EPSRC CDT in Modern Statistics and Statistical Machine Learning; Turing AI Fellowship; Samsung Research, Samsung Electronics Co., Seoul, Republic of Korea(Samsung); Boltzbit Ltd. Andrew Campbell acknowledges support from the EPSRC CDT in Modern Statistics and Statistical Machine Learning (EP/S023151/1). Jos ' andez Lobato acknowl-e Miguel Hern ' edges support from a Turing AI Fellowship under grant EP/V023756/1. Yichuan Zhang acknowledges support from Samsung Research, Samsung Electronics Co., Seoul, Republic of Korea and from Boltzbit Ltd. 34 0 0 0 1 JMLR-JOURNAL MACHINE LEARNING RESEARCH SAN DIEGO 1269 LAW ST, SAN DIEGO, CA, UNITED STATES 2640-3498 PR MACH LEARN RES 2021.0 139 11 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS0JP 2023-03-23 WOS:000683104601022 0
J Wang, F; Yang, DK; Zhang, B; Li, WQ; Darrozes, J Wang, Feng; Yang, Dongkai; Zhang, Bo; Li, Weiqiang; Darrozes, Jose Wind Speed Retrieval Using Coastal Ocean-Scattered GNSS Signals IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Coherent averaging; incoherent averaging; scattered GNSS signals; wind speed SURFACE; REFLECTIONS This paper aims to propose a new coastal observable based on the computation of the ratio between the coherent and incoherent averaging as an alternative to the coherent time and the effectiveness of incoherent averaging proposed by previous works. Experimental data have been processed to develop the relationship between wind speed and the proposed observable. The influence on fitting results of elevation angle, averaging samples, and the delay lag to align the local replica to the scattered signals are analyzed to obtain an optimal retrieval. When the number of averaged samples is less than 150, and the delay range relative to the specular reflection is from -0.4 to 0.4, fitting results with a root mean square error (RMSE) less than 2.0 m/s and a correction coefficient larger than 0.8 for signals from BeiDou GEO and GPS implemented compensation of an elevation angle are obtained. Finally, the feasibility of multisatellite observation using multiple regression and neural networks is demonstrated. Neural networks can get better fitting results than multiple regression for both BeiDou GEO and GPS. In addition, for GPS satellites, when the elevation angle of GPS is considered as an input of a neural network, the influence of the elevation angle is greatly weakened, so that the RMSE of 1.03 m/s and the correlation coefficient of 0.96 can be obtained. [Wang, Feng; Yang, Dongkai; Zhang, Bo] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Li, Weiqiang] Inst Estudis Espacials Catalunya, Earth Observat Res Grp, Barcelona 08191, Spain; [Darrozes, Jose] Univ Paul Sabatier, CNRS, IRD, GET OMP, F-31400 Toulouse, France Beihang University; Institut d'Estudis Espacials de Catalunya (IEEC); Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Toulouse; Universite Toulouse III - Paul Sabatier Zhang, B (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China. wangf.19@163.com; yangdongkai@sina.com; bozhang@buaa.edu.com; weiqiang.li.buaa@gmail.com; jose.darrozes@get.obs-mip.fr Darrozes, Jose/H-8618-2019; Li, Weiqiang/B-8829-2017 Li, Weiqiang/0000-0002-6215-7607; darrozes, jose/0000-0003-1750-2819 National Science Foundation of China [61171070]; National High Technology Research and Development Program (863 Program) [SS2013AA120602]; Beihang University National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National High Technology Research and Development Program (863 Program)(National High Technology Research and Development Program of China); Beihang University This work was supported in part by the National Science Foundation of China under Grant 61171070, in part by the National High Technology Research and Development Program (863 Program) under Grant SS2013AA120602, and in part by Dr. Excellent Development Fund of Beihang University. (Corresponding author: Bo Zhang.) 36 17 17 1 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. NOV 2016.0 9 11 SI 5272 5283 10.1109/JSTARS.2016.2611598 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology ED5DJ 2023-03-23 WOS:000388871500037 0
J Shahrour, I; Bian, HB; Xie, XY; Zhang, ZX Shahrour, Isam; Bian, Hanbing; Xie, Xiongyao; Zhang, Zixin Use of Smart Technology to Improve Management of Utility Tunnels APPLIED SCIENCES-BASEL English Article utility tunnel; challenges; security; governance; management; smart technology; monitoring; maintenance; data analysis; artificial intelligence This paper presents a smart solution for the utility tunnel, which allows hosting a wide range of water and energy utilities in an accessible underground space. It is well known that utility tunnels offer major advantages such as the possibility to inspect, maintain, and easily extend urban utilities without excavation, thereby eliminating disturbances related to urban excavation such as traffic jams, noise, pollution, and pavement degradation. However, despite these advantages, the effective development of this facility remains below expectations, because of serious challenges related to the security and governance of this shared space. The solution presented in this paper is based on discussions with experts and companies involved in the design, construction, and management of utility tunnels, as well as on the authors' experiences in the design and implementation of smart solutions for urban utilities. The paper firstly presents the major challenges of utility tunnels and then discusses how theb smart technology could help in coping with these challenges. The paper presents the architecture of this solution, as well as the main layers of monitoring, information system data analysis, and system control. It also presents the methodology to be followed for the implementation of this smart solution. Finally, the paper discusses two major issues for utility tunnels: fire risk and risk assessment. The paper shows that the use of smart technology allows developing a comprehensive digital solution, which uses advanced monitoring system to collect real-time data about the tunnel environment and functioning. These data can be easily shared by authorized staffs. Analysis of these data allows improving the utility tunnel security and performances. [Shahrour, Isam; Xie, Xiongyao; Zhang, Zixin] Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China; [Shahrour, Isam; Bian, Hanbing] Univ Lille, Lab Genie Civil & Geoenvironm, F-59000 Lille, France Tongji University; Universite de Lille - ISITE; Universite de Lille Shahrour, I (corresponding author), Tongji Univ, Sch Civil Engn, Shanghai 200092, Peoples R China.;Shahrour, I (corresponding author), Univ Lille, Lab Genie Civil & Geoenvironm, F-59000 Lille, France. Isam.Shahrour@Univ-Lille.fr; hanbing.bian@univ-lille.fr; xiexiongyao@tongji.edu.cn; zxzhang@tongji.edu.cn xie, xiongyao/AAH-3550-2022; Shahrour, Isam/I-7151-2019; xie, xiongyao/AFM-0926-2022; Shahrour, isam/A-6865-2008 Shahrour, Isam/0000-0001-7279-8005; 26 11 11 13 51 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JAN 2020.0 10 2 711 10.3390/app10020711 0.0 14 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics KY4LI Green Published, gold 2023-03-23 WOS:000522540400287 0
C Amrouch, H; Gao, D; Hu, XS; Kazemi, A; Laguna, AF; Ni, K; Niemier, M; Sharifi, MM; Thomann, S; Yin, XZ; Zhuo, C IEEE Amrouch, Hussam; Gao, Di; Hu, Xiaobo Sharon; Kazemi, Arman; Laguna, Ann Franchesca; Ni, Kai; Niemier, Michael; Sharifi, Mohammad Mehdi; Thomann, Simon; Yin, Xunzhao; Zhuo, Cheng ICCAD Tutorial Session Paper Ferroelectric FET Technology and Applications: From Devices to Systems 2021 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN (ICCAD) ICCAD-IEEE ACM International Conference on Computer-Aided Design English Proceedings Paper 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD) NOV 01-04, 2021 ELECTR NETWORK IEEE,ACM,IEEE CAS,IEEE Council Elect Design Automat,ACM SIGDA,IEEE Electron Devices Soc CONTENT-ADDRESSABLE MEMORY; DESIGN; FEFET The rapidly increasing volume and complexity of data is demanding the relentless scaling of computing power. With transistor feature size approaching physical limits, the benefits that CMOS technology can provide is diminishing. For future energy efficient computing systems, researchers aim to exploit various emerging nanotechnologies to replace conventional CMOS technology. In particular, ferroelectric FETs (FeFETs) appear to be a promising candidate to continue improving energy efficiency for data-intensive applications. Advances in FeFET scalability and FeFET compatibility with CMOS have sparked growing interest in device, circuit, and system communities. While FeFET is still evolving, many researchers and developers are already cautiously optimistic about its future. This paper provides a review on FeFET's recent technology advances, challenges, and opportunities, with a particular emphasis upon device modeling and circuit design of FeFET content addressable memory, as well as their applications in machine learning. [Amrouch, Hussam; Thomann, Simon] Univ Stuttgart, Inst Comp Architecture & Comp Engn, Stuttgart, Germany; [Gao, Di; Yin, Xunzhao; Zhuo, Cheng] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China; [Hu, Xiaobo Sharon; Kazemi, Arman; Laguna, Ann Franchesca; Niemier, Michael; Sharifi, Mohammad Mehdi] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 USA; [Ni, Kai] Rochester Inst Technol, Dept Microsyst Engn, Rochester, NY 14623 USA University of Stuttgart; Zhejiang University; University of Notre Dame; Rochester Institute of Technology Amrouch, H (corresponding author), Univ Stuttgart, Inst Comp Architecture & Comp Engn, Stuttgart, Germany. amrouch@iti.uni-stuttgart.de; shu@nd.edu; kai.ni@rit.edu; xzyin1@zju.edu.cn; czhuo@zju.edu.cn Laguna, Ann Franchesca/GLV-3028-2022; Amrouch, Hussam/AFH-5124-2022; Zhuo, Cheng/HLV-8134-2023; Hu, Xiaobo/B-9367-2018 Laguna, Ann Franchesca/0000-0001-8267-1040; Hu, Xiaobo/0000-0002-6636-9738; Sharifi, Mohammad Mehdi/0000-0001-6345-0242; Thomann, Simon/0000-0002-7902-9353 National Key R&D Program of China [2018YFE0126300]; Zhejiang Provincial NSF [LD21F040003]; German Research Foundation (DFG); Applications and Systems-Driven Center for Energy-Efficient Integrated NanoTechnologies (ASCENT) - DARPA National Key R&D Program of China; Zhejiang Provincial NSF(Natural Science Foundation of Zhejiang Province); German Research Foundation (DFG)(German Research Foundation (DFG)); Applications and Systems-Driven Center for Energy-Efficient Integrated NanoTechnologies (ASCENT) - DARPA This work is partially supported by National Key R&D Program of China (Grant No. 2018YFE0126300), Zhejiang Provincial NSF (Grant No. LD21F040003); the German Research Foundation (DFG) through the project ACCROSS: Approximate Computing aCROss the System Stack; and Applications and Systems-Driven Center for Energy-Efficient Integrated NanoTechnologies (ASCENT), one of six centers in JUMP, a SRC program sponsored by DARPA. The corresponding authors are Xunzhao Yin and Cheng Zhuo. 46 3 3 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1933-7760 978-1-6654-4507-8 ICCAD-IEEE ACM INT 2021.0 10.1109/ICCAD51958.2021.9643578 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Manufacturing; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS6FI 2023-03-23 WOS:000747493600140 0
J Yu, S; Albarran-Arriagada, F; Retamal, JC; Wang, YT; Liu, W; Ke, ZJ; Meng, Y; Li, ZP; Tang, JS; Solano, E; Lamata, L; Li, CF; Guo, GC Yu, Shang; Albarran-Arriagada, Francisco; Carlos Retamal, Juan; Wang, Yi-Tao; Liu, Wei; Ke, Zhi-Jin; Meng, Yu; Li, Zhi-Peng; Tang, Jian-Shun; Solano, Enrique; Lamata, Lucas; Li, Chuan-Feng; Guo, Guang-Can Reconstruction of a Photonic Qubit State with Reinforcement Learning ADVANCED QUANTUM TECHNOLOGIES English Article quantum optics; quantum state reconstruction; reinforcement learning An experiment is performed to reconstruct an unknown photonic quantum state with a limited amount of copies. A semiquantum reinforcement learning approach is employed to adapt one qubit state, an agent, to an unknown quantum state, an environment, by successive single-shot measurements and feedback, in order to achieve maximum overlap. The experimental learning device herein, composed of a quantum photonics setup, can adjust the corresponding parameters to rotate the agent system based on the measurement outcomes 0 or 1 in the environment (i.e., reward/punishment signals). The results show that, when assisted by such a quantum machine learning technique, fidelities of the deterministic single-photon agent states can achieve over 88% under a proper reward/punishment ratio within 50 iterations. This protocol offers a tool for reconstructing an unknown quantum state when only limited copies are provided, and can also be extended to higher dimensions, multipartite, and mixed quantum state scenarios. [Yu, Shang; Wang, Yi-Tao; Liu, Wei; Ke, Zhi-Jin; Meng, Yu; Li, Zhi-Peng; Tang, Jian-Shun; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China; [Yu, Shang; Wang, Yi-Tao; Liu, Wei; Ke, Zhi-Jin; Meng, Yu; Li, Zhi-Peng; Tang, Jian-Shun; Li, Chuan-Feng; Guo, Guang-Can] Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phy, Hefei 230026, Peoples R China; [Albarran-Arriagada, Francisco; Carlos Retamal, Juan] Univ Santiago Chile USACH, Dept Fis, Ave Ecuador 3493, Santiago 9170124, Chile; [Albarran-Arriagada, Francisco; Carlos Retamal, Juan] Ctr Dev Nanosci & Nanotechnol, Estn Cent, Santiago 9170124, Chile; [Solano, Enrique; Lamata, Lucas] Univ Basque Country, UPV EHU, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Solano, Enrique] Basque Fdn Sci, Ikerbasque, Maria Diaz de Haro 3, Bilbao 48013, Spain; [Solano, Enrique] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universidad de Santiago de Chile; University of Basque Country; Basque Foundation for Science; Shanghai University Tang, JS; Li, CF (corresponding author), Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China.;Tang, JS; Li, CF (corresponding author), Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phy, Hefei 230026, Peoples R China. tjs@ustc.edu.cn; cfli@ustc.edu.cn Lamata, Lucas/B-2439-2009; Lamata, Lucas/CAG-6488-2022; Retamal, Juan Carlos/G-6425-2010 Lamata, Lucas/0000-0002-9504-8685; Lamata, Lucas/0000-0002-9504-8685; Retamal, Juan Carlos/0000-0002-7174-7879; Albarran-Arriagada, Francisco/0000-0001-8899-3673; Wang, Yi-Tao/0000-0003-3028-0923; Yu, Shang/0000-0002-6463-952X National Key Research and Development Program of China [2017YFA0304100]; National Natural Science Foundation of China [61327901, 11674304, 11822408, 61490711, 11774335, 11821404]; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences [QYZDY-SSW-SLH003]; Youth Innovation Promotion Association of Chinese Academy of Sciences [2017492]; Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences [YJKYYQ20170032]; Anhui Initiative in Quantum Information Technologies [AHY020100, AHY060300]; National Postdoctoral Program for Innovative Talents [BX20180293]; China Postdoctoral Science Foundation [2018M640587]; Fundamental Research Funds for the Central Universities [WK2470000026]; Centro Basal [FB0807]; Ramon y Cajal Grant [RYC-2012-11391]; MINECO/FEDER [FIS2015-69983-P]; Basque Government [IT986-16]; project OpenSuperQ of the EU Flagship on Quantum Technologies [820363]; project QMiCS of the EU Flagship on Quantum Technologies [820505] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences of the Chinese Academy of Sciences; Youth Innovation Promotion Association of Chinese Academy of Sciences; Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences; Anhui Initiative in Quantum Information Technologies; National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Centro Basal; Ramon y Cajal Grant(Spanish Government); MINECO/FEDER(Spanish Government); Basque Government(Basque Government); project OpenSuperQ of the EU Flagship on Quantum Technologies; project QMiCS of the EU Flagship on Quantum Technologies The authors acknowledge Nora Tischler for helpful discussions and helping us to make corrections in the early version. This work is supported by the National Key Research and Development Program of China (No. 2017YFA0304100), the National Natural Science Foundation of China (Grant Nos. 61327901, 11674304, 11822408, 61490711, 11774335, and 11821404), the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (Grant No. QYZDY-SSW-SLH003), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2017492), the Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences (No. YJKYYQ20170032), Anhui Initiative in Quantum Information Technologies (AHY020100, AHY060300), the National Postdoctoral Program for Innovative Talents (Grant No. BX20180293), China Postdoctoral Science Foundation (Grant No. 2018M640587), the Fundamental Research Funds for the Central Universities (No. WK2470000026), Centro Basal FB0807, Ramon y Cajal Grant RYC-2012-11391, MINECO/FEDER FIS2015-69983-P, Basque Government IT986-16, and the projects OpenSuperQ (820363) and QMiCS (820505) of the EU Flagship on Quantum Technologies. 41 37 37 5 17 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2511-9044 ADV QUANTUM TECHNOL Adv. Quantum Technol. AUG 2019.0 2 7-8 SI UNSP 1800074 10.1002/qute.201800074 0.0 6 Quantum Science & Technology; Optics Science Citation Index Expanded (SCI-EXPANDED) Physics; Optics MJ4RK Green Submitted 2023-03-23 WOS:000548079200002 0
J Luo, W; Wu, TF; Chen, BQ; Liang, M; Zou, HW Luo, Wei; Wu, Tongfei; Chen, Biqiong; Liang, Mei; Zou, Huawei Highly Stretchable Conductors Based on Expanded Graphite Macroconfined in Tubular Rubber ACS APPLIED MATERIALS & INTERFACES English Article stretchable conductor; strain sensor; confined packing; expanded graphite STRAIN SENSORS; GRAPHENE; TRANSPARENT; COMPOSITES; LIQUID; SKIN; CONDUCTIVITY; ELECTRONICS; TRANSISTORS; EFFICIENT Highly stretchable and durable conductors are significant to the development of wearable devices, robots, human-machine interfaces, and other artificial intelligence products. Although many respectable methods have been reported, it is still a challenge to fabricate stretchable conductors with a large elastic limit, high conductivity, and excellent reliability in rapid, effective, and economic ways. Herein, a facile method is offered to fabricate high-performance stretchable tubular conductors (TCs) based on a macroconfined structure of expanded graphite (EG) in rubber tubing by simply physical packing. The maximum original electrical conductivity of TCs reached a high value of 160.6 s/cm. Meanwhile, TCs showed more insensitive response of conductivity to increasing tensile strain compared to the TCs encapsulated with liquid metal or ionic liquid. The conductivity and effective stretchability of TCs can be adjusted by varying the packing density of EG. A low gauge factor below 3 was reached even under 400% stretching for TCs with a packing density of 1.233 g/cm(3). The excellent resilience and good stability of conductivity of TCs during dynamic stretching-releasing cycles are attributed to the stable and rapid reconstruction of the percolation network of EG particles. The combination of high conductivity, tunable stretchability, and good reliability renders potential applications to TCs, such as highly stretchable interconnects or strain sensors, in human motion detection. [Luo, Wei; Liang, Mei; Zou, Huawei] Sichuan Univ, Polymer Res Inst, State Key Lab Polymer Mat Engn, Chengdu 610065, Sichuan, Peoples R China; [Luo, Wei; Wu, Tongfei; Chen, Biqiong] Univ Sheffield, Dept Mat Sci & Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England; [Chen, Biqiong] Queens Univ Belfast, Sch Mech & Aerosp Engn, Stranmillis Rd, Belfast BT9 5AH, Antrim, North Ireland Sichuan University; University of Sheffield; Queens University Belfast Zou, HW (corresponding author), Sichuan Univ, Polymer Res Inst, State Key Lab Polymer Mat Engn, Chengdu 610065, Sichuan, Peoples R China.;Chen, BQ (corresponding author), Univ Sheffield, Dept Mat Sci & Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England.;Chen, BQ (corresponding author), Queens Univ Belfast, Sch Mech & Aerosp Engn, Stranmillis Rd, Belfast BT9 5AH, Antrim, North Ireland. b.chen@qub.ac.uk; hwzou@163.com Wu, Tongfei/K-1469-2014; Chen, Biqiong/C-3385-2017 Wu, Tongfei/0000-0002-0060-4408; Chen, Biqiong/0000-0002-6465-2871 Chinese Scholarship Council; European Commission [656467-SUPRONICS-H2020-MSCA-IF-2014] Chinese Scholarship Council(China Scholarship Council); European Commission(European CommissionEuropean Commission Joint Research Centre) The authors would like to thank the Chinese Scholarship Council and European Commission's Horizon 2020 Research and Innovation Program for funding this research (grant number: 656467-SUPRONICS-H2020-MSCA-IF-2014). 69 13 13 7 98 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1944-8244 1944-8252 ACS APPL MATER INTER ACS Appl. Mater. Interfaces DEC 13 2017.0 9 49 43239 43249 10.1021/acsami.7b08866 0.0 11 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science FQ2RG 29137456.0 Green Accepted 2023-03-23 WOS:000418204300091 0
J Zhao, HT; Hu, SG; Zhang, HR; Wang, Z; Dong, H; del Hougne, P; Cui, TJ; Li, LL Zhao, Hanting; Hu, Shengguo; Zhang, Hongrui; Wang, Zhuo; Dong, Hao; del Hougne, Philipp; Cui, Tie Jun; Li, Lianlin Intelligent indoor metasurface robotics NATIONAL SCIENCE REVIEW English Article; Early Access intelligent metasurfaces; intelligent robotics; edge devices; IoT; 6G COMMUNICATION Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs (motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at similar to 2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics. An intelligent indoor metasurface robotic is empowered on the physical layer by programmable metasurfaces and on the cyber layer by artificial-intelligence tools. [Zhao, Hanting; Hu, Shengguo; Zhang, Hongrui; Wang, Zhuo; Li, Lianlin] Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China; [Dong, Hao] Peking Univ, Ctr Frontiers Comp Studies, Sch Comp Sci, Beijing 100871, Peoples R China; [del Hougne, Philipp] University, French Natl Ctr Sci Res CNRS, F-35000 Rennes, France; [Cui, Tie Jun] Southeast Univ, State Key Lab Millimeter Waves, Nanjing 210096, Peoples R China Peking University; Peking University; Centre National de la Recherche Scientifique (CNRS); Southeast University - China Li, LL (corresponding author), Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China.;del Hougne, P (corresponding author), University, French Natl Ctr Sci Res CNRS, F-35000 Rennes, France.;Cui, TJ (corresponding author), Southeast Univ, State Key Lab Millimeter Waves, Nanjing 210096, Peoples R China. philipp.delhougne@gmail.com; tjcui@seu.edu.cn; lianlin.li@pku.edu.cn National Key Research and Development Program of China [2021YFA1401002, 2017YFA0700201, 2017YFA0700202, 2017YFA0700203]; Overseas Expertise Introduction Project for Discipline Innovation (111 Project) [111-2-05] National Key Research and Development Program of China; Overseas Expertise Introduction Project for Discipline Innovation (111 Project) This work was supported by the National Key Research and Development Program of China (2021YFA1401002, 2017YFA0700201, 2017YFA0700202 and 2017YFA0700203) and the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) (111-2-05). 45 0 0 15 15 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2095-5138 2053-714X NATL SCI REV Natl. Sci. Rev. 10.1093/nsr/nwac266 0.0 NOV 2022 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 7U2JC 2023-03-23 WOS:000911959800001 0
J Zou, HQ; Lu, G; Liu, Y; Bauer, R; Tao, O; Gong, JT; Zhao, LY; Li, JH; Ren, ZY; Yan, YH Zou, Hui-Qin; Lu, Gang; Liu, Yong; Bauer, Rudolf; Tao, Ou; Gong, Jian-Ting; Zhao, Li-Ying; Li, Jia-Hui; Ren, Zhi-Yu; Yan, Yong-Hong Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms? JOURNAL OF FOOD AND DRUG ANALYSIS English Article artificial neural networks; Asteraceae family; Chinese herbal medicine; discriminative model; electronic nose QUALITY; AUTHENTICATION Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose. Copyright (C) 2015, Food and Drug Administration, Taiwan. Published by Elsevier Taiwan LLC. All rights reserved. [Zou, Hui-Qin; Lu, Gang; Liu, Yong; Tao, Ou; Gong, Jian-Ting; Zhao, Li-Ying; Li, Jia-Hui; Ren, Zhi-Yu; Yan, Yong-Hong] Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing 100029, Peoples R China; [Bauer, Rudolf] Graz Univ, Inst Pharmaceut Sci, Graz, Austria Beijing University of Chinese Medicine; University of Graz Yan, YH (corresponding author), Beijing Univ Chinese Med, Sch Chinese Mat Med, 11 Beisanhuan Donglu, Beijing 100029, Peoples R China. lxdyyh@yeah.net Bauer, Rudolf/F-8468-2010; Zou, Huiqin/N-4215-2014 Bauer, Rudolf/0000-0002-0057-5547; Zou, Huiqin/0000-0003-3066-6180 National Natural Science Foundation of China [81403054]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This study was supported by grants from National Natural Science Foundation of China (No. 81403054). As well, we would like to send our gratitude to China Scholarship Council providing scholarship to one of our team members and sponsoring the abroad research in University of Graz in Austria. 19 13 14 2 27 FOOD & DRUG ADMINSTRATION TAIPEI 161-2 KUNYANG STREET, NANGANG, TAIPEI, 00000, TAIWAN 1021-9498 J FOOD DRUG ANAL J. Food Drug Anal. DEC 2015.0 23 4 788 794 10.1016/j.jfda.2015.07.001 0.0 7 Food Science & Technology; Pharmacology & Pharmacy Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology; Pharmacology & Pharmacy CZ6LA 28911496.0 hybrid, Green Accepted 2023-03-23 WOS:000367211600022 0
J Naik, B; Obaidat, MS; Nayak, J; Pelusi, D; Vijayakumar, P; Islam, SKH Naik, Bighnaraj; Obaidat, Mohammad S.; Nayak, Janmenjoy; Pelusi, Danilo; Vijayakumar, Pandi; Islam, S. K. Hafizul Intelligent Secure Ecosystem Based on Metaheuristic and Functional Link Neural Network for Edge of Things IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Neural networks; Optimization; Intrusion detection; Ecosystems; Servers; Image edge detection; Edge computing; elitism; functional link artificial neural network (FLANN); Internet of Things (IoT); intrusion detection system (IDS); metaheuristic INTRUSION-DETECTION; OPTIMIZATION Internet of Things (IoT) has evolved for building smart environments in a distributed system, where the data produced by IoT devices are transmitted through Edge computing devices to streamline the flow of traffic from IoT devices to a distributed network. In such a scenario, the attacker introduces many attacks to the edge before forwarding them to distributed servers. This necessitates intrusion detection systems for such environments to mitigate security attacks. This paper has projected a basis for characterization of intrusive behaviors in a distributed system based on the functional link neural nets response weighted-average and teaching-learning metaheuristic with elitism on weight-space. The proposed technique makes use of teaching-learning metaheuristic optimization to obtain suitable parameters for the functional link neural net. Furthermore, the processing of duplicate parameters is successfully avoided by using mutation operation. In addition to this, in this paper the proposed method is found to be more efficient in terms of computational burden. [Naik, Bighnaraj] Veer Surendra Sai Univ Technol, Dept Comp Applicat, Burla 768018, India; [Obaidat, Mohammad S.] Nazarbayev Univ, Dept Elect & Comp Engn, Nur Sultan 010000, Kazakhstan; [Obaidat, Mohammad S.] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Obaidat, Mohammad S.] Univ Jordan, Amman 11942, Jordan; [Obaidat, Mohammad S.] Univ Sci & Technol, Beijing 100091, Peoples R China; [Obaidat, Mohammad S.] Amity Univ, Noida 201313, India; [Nayak, Janmenjoy] Sri Sivani Coll Engn, Dept Comp Sci & Engn, Srikakulam 532410, India; [Pelusi, Danilo] Univ Teramo, Fac Commun Sci, I-64100 Teramo, Italy; [Vijayakumar, Pandi] Univ Coll Engn Tindivanam, Dept Comp Sci & Engn, Tindivanam 604001, India; [Islam, S. K. Hafizul] Indian Inst Informat Technol Kalyani, Dept Comp Sci & Engn, Kalyani 741235, W Bengal, India Veer Surendra Sai University of Technology; Nazarbayev University; Korea Advanced Institute of Science & Technology (KAIST); University of Jordan; Amity University Noida; University of Teramo Vijayakumar, P (corresponding author), Univ Coll Engn Tindivanam, Dept Comp Sci & Engn, Tindivanam 604001, India. mailtobnaik@gmail.com; msobaidat@gmail.com; mailforjnayak@srisivani.com; dpelusi@unite.it; vijibond2000@gmail.com; hafi786@gmail.com Naik, Bighnaraj/A-1212-2016; Vijayakumar, Pandi/Y-4636-2019 Naik, Bighnaraj/0000-0002-9761-8389; Vijayakumar, Pandi/0000-0001-5451-8946; Obaidat, Mohammad S./0000-0002-1569-9657 Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi, Government of India [EEQ/2017/000355]; People Republic (PR) of China Ministry of Education Distinguished Possessor [MS2017BJKJ003] Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi, Government of India; People Republic (PR) of China Ministry of Education Distinguished Possessor This work was supported in part by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi, Government of India (Sanction Order No EEQ/2017/000355) and in part by People Republic (PR) of China Ministry of Education Distinguished Possessor under Grant MS2017BJKJ003. Paper no. TII-19-1184. 30 13 13 1 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. MAR 2020.0 16 3 1947 1956 10.1109/TII.2019.2920831 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering KH8LX 2023-03-23 WOS:000510903200047 0
J Zhang, RB; Kranz, GS; Zou, WJ; Deng, Y; Huang, XJ; Lin, KG; Lee, TMC Zhang, Ruibin; Kranz, Georg S.; Zou, Wenjin; Deng, Yue; Huang, Xuejun; Lin, Kangguang; Lee, Tatia M. C. Rumination network dysfunction in major depression: A brain connectome study PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY English Article Rumination; Depression; Attentional control; Graph theory; Functional connectivity STATE FUNCTIONAL CONNECTIVITY; DEFAULT-MODE; DISENGAGEMENT; ADOLESCENTS; PREDICTION; PATTERNS; DISORDER; AMYGDALA; HEALTHY; MODULES Background: Rumination is a central feature of major depressive disorder (MDD). Knowledge of the neural structures that underpin rumination offers significant insight into depressive pathophysiology and may help to develop potential intervention strategies for MDD, a mental illness that has become the leading cause of disability worldwide. Methods: Using resting-state fMRI and graph theory, this study adopted a connectome approach to examine the functional topological organization of the neural network associated with rumination in MDD. Data from 96 participants were analyzed, including 51 patients with MDD and 45 healthy controls. Results: We found altered functional integration and segregation of neural networks associated with depressive rumination as indicated by reduced global and local efficiency in MDD patients compared with controls. Interestingly, these metrics correlated positively with depression severity, as measured by the Hamilton Depression Rating Scale. Moreover, mediation analysis indicated that the association between network metrics and depression severity was mediated by the ruminative tendency of patients. Disrupted nodal centralities were located in regions associated with emotional processing, visual mental imagery, and attentional control. Conclusion: Our results highlight rumination as a two-edged sword that reflects a disease-specific neuropathology but also points to a functionality of depressive symptoms with evolutionary meaning. [Zhang, Ruibin; Kranz, Georg S.; Lee, Tatia M. C.] Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China; [Zhang, Ruibin; Lee, Tatia M. C.] Univ Hong Kong, Lab Social Cognit & Affect Neurosci, Hong Kong, Peoples R China; [Zhang, Ruibin] Southern Med Univ, Sch Publ Hlth, Guangdong Prov Key Lab Trop Dis Res, Dept Psychol, Guangzhou, Guangdong, Peoples R China; [Zhang, Ruibin] Southern Med Univ, Zhujiang Hosp, Dept Psychiat, Guangzhou 510282, Guangdong, Peoples R China; [Kranz, Georg S.] Hong Kong Polytech Univ, Dept Rehabil Sci, Hong Kong, Peoples R China; [Kranz, Georg S.] Med Univ Vienna, Dept Psychiat & Psychotherapy, Vienna, Austria; [Zou, Wenjin] Guangzhou Med Univ, Dept Radiol, Affiliated Hosp, Guangzhou Huiai Hosp, Guangzhou, Guangdong, Peoples R China; [Deng, Yue; Huang, Xuejun] Guangzhou Med Univ, Dept Psychiat, Affiliated Hosp 2, Guangzhou, Guangdong, Peoples R China; [Lin, Kangguang] Guangzhou Med Univ, Affiliated Hosp, Guangzhou Huiai Hosp, Dept Affect Disorders, Guangzhou, Guangdong, Peoples R China; [Lin, Kangguang; Lee, Tatia M. C.] Guangzhou Med Univ, Affiliated Hosp, Lab Emot & Cognit, Guangzhou, Guangdong, Peoples R China; [Lee, Tatia M. C.] Guangdong Hong Kong Macao Greater Bay Area, Ctr Brain Sci & Brain Inspired Intelligence, Guangzhou, Guangdong, Peoples R China University of Hong Kong; University of Hong Kong; Southern Medical University - China; Southern Medical University - China; Hong Kong Polytechnic University; Medical University of Vienna; Guangzhou Medical University; Guangzhou Medical University; Guangzhou Medical University; Guangzhou Medical University Lee, TMC (corresponding author), Univ Hong Kong, Lab Neuropsychol, Room 656,Jockey Club Tower,Pokfulam Rd, Hong Kong, Peoples R China.;Lin, KG (corresponding author), Guangzhou Brain Hosp, Dept Affect Disorder, 36 MingxinLoad, Guangzhou 510370, Guangdong, Peoples R China. linkangguang@163.com; tmclee@hku.hk University of Hong Kong May Endowed Professorship in Neuropsychology; Science and Technology Program of Guangdong [2018B030334001]; National Natural Science Foundation of China [NSFC: 81671347, 31900806]; National Key R&D Program of China [2016YFC1306702] University of Hong Kong May Endowed Professorship in Neuropsychology; Science and Technology Program of Guangdong; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China This study was supported by The University of Hong Kong May Endowed Professorship in Neuropsychology, the Science and Technology Program of Guangdong (2018B030334001), the National Natural Science Foundation of China (NSFC: 81671347 and 31900806), and the National Key R&D Program of China (2016YFC1306702). 67 24 25 1 45 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0278-5846 1878-4216 PROG NEURO-PSYCHOPH Prog. Neuro-Psychopharmacol. Biol. Psychiatry MAR 2 2020.0 98 109819 10.1016/j.pnpbp.2019.109819 0.0 9 Clinical Neurology; Neurosciences; Pharmacology & Pharmacy; Psychiatry Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Pharmacology & Pharmacy; Psychiatry JV7FV 31734293.0 hybrid 2023-03-23 WOS:000502527600025 0
J Zhao, YT; Jiang, YJ; Zheng, YM; Li, ZY; Zhang, YL; Zheng, BD; Lo, YM; Miao, S; Tian, YT Zhao, Yingting; Jiang, Yajun; Zheng, Yimei; Li, Zhiyu; Zhang, Yaling; Zheng, Baodong; Lo, Y. Martin; Miao, Song; Tian, Yuting Effects of Microwave Vacuum Drying on Macroscopic Properties and Microstructure of Lotus (Nelumbo nucifera Gaertn.) Seeds INTERNATIONAL JOURNAL OF FOOD ENGINEERING English Article drying characteristics; fractal analysis; lotus seeds; microwave vacuum drying; structure-quality relationship RESPONSE-SURFACE METHODOLOGY; FRACTAL DIMENSION; NEURAL-NETWORK; KINETICS; PRETREATMENT; TEXTURE; QUALITY; SLICES; AIR; OPTIMIZATION The structural characteristics of lotus (Nelumbo nucifera Gaertn.) seeds preserved by microwave vacuum drying (MVD) were investigated under various drying parameters, including microwave power density and vacuum degree. Dried lotus seeds were examined for microstructure by field emission scanning electron microscopy. Fractal dimension of the microscopic images was calculated by the box counting method. The apparent physical changes of the seeds, namely shrinkage ratio, rehydration rate, and hardness index, were determined to correlate well with their microstructural changes computed by the normalized changes of the fractal dimension (Delta FD/FD0). The samples at -90 kPa, 15 W/g exhibited a lower shrinkage ratio (46.2 %), higher rehydration rate (187.5 %) and lower hardness (3692.4 N). Although the physical and microstructural changes of the samples prepared by different drying methods (MVD, microwave drying, and hot air drying) varied, the changes of the Delta FD/FD0 of the dried samples exhibited the same trends. [Zhao, Yingting; Jiang, Yajun; Zheng, Yimei; Li, Zhiyu; Zhang, Yaling; Zheng, Baodong; Tian, Yuting] Fujian Agr & Forestry Univ, Coll Food Sci, Fuzhou 350002, Fujian, Peoples R China; [Zheng, Baodong; Tian, Yuting] Fujian Prov Key Lab Qual Sci & Proc Technol Speci, Fuzhou 350002, Fujian, Peoples R China; [Lo, Y. Martin] Biointellipro LLC, Ashton, MD 20861 USA; [Miao, Song] Teagasc Food Res Ctr, Fermoy, Cork, Ireland; [Zheng, Baodong; Miao, Song] Fujian Agr & Forestry Univ, China Ireland Int Cooperat Ctr Food Mat Sci & Str, Fuzhou 350002, Fujian, Peoples R China Fujian Agriculture & Forestry University; Teagasc; Fujian Agriculture & Forestry University Tian, YT (corresponding author), Fujian Agr & Forestry Univ, Coll Food Sci, Fuzhou 350002, Fujian, Peoples R China.;Tian, YT (corresponding author), Fujian Prov Key Lab Qual Sci & Proc Technol Speci, Fuzhou 350002, Fujian, Peoples R China. etingtian@hotmail.com MIAO, SONG/C-3919-2018; 赵, 莹婷/W-2689-2017; Zhao, Yingting/AAC-5788-2019 MIAO, SONG/0000-0002-8385-3513; 赵, 莹婷/0000-0003-0316-6869; Zhao, Yingting/0000-0003-0316-6869 National Natural Science Foundation of China [31401616, 31772039]; Specialized Research Fund for the Doctoral Program of Higher Education [20133515120016]; Funds for Distinguished Young Scientists [xjq201418]; University-Industry Cooperation Project in Fujian Province [2015N5001]; Scientific and Technological Innovation Team Support Plan of FAFU [cxtd12009]; High Level University Construction Project of FAFU [612014042] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); Funds for Distinguished Young Scientists(Xi'an Jiaotong University); University-Industry Cooperation Project in Fujian Province; Scientific and Technological Innovation Team Support Plan of FAFU; High Level University Construction Project of FAFU Projects of the National Natural Science Foundation of China (Grant No. 31401616, 31772039), the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20133515120016), the Funds for Distinguished Young Scientists (Grant No. xjq201418), the University-Industry Cooperation Project in Fujian Province (Grant No. 2015N5001), the Scientific and Technological Innovation Team Support Plan of FAFU (Grant No. cxtd12009) and the High Level University Construction Project of FAFU (Grant No. 612014042) supported this research financially. 32 2 2 4 66 WALTER DE GRUYTER GMBH BERLIN GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY 2194-5764 1556-3758 INT J FOOD ENG Int. J. Food Eng. MAR 2018.0 14 3 20170313 10.1515/ijfe-2017-0313 0.0 13 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology GE7BG 2023-03-23 WOS:000431384400001 0
J Lin, HQ; Yan, Z; Fu, YL Lin, Huaqing; Yan, Zheng; Fu, Yulong Adaptive security-related data collection with context awareness JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Security-related data; Adaptive data collection; Heterogeneous network; Network context NETWORKS; MOBILE; SYSTEMS The huge economic loss resulting from network attacks and intrusions has led to an intensive study on network security. The network security is usually reflected by some relevant data that can be collected in a network system. By learning and analyzing such data, which are called security-related data, we can detect the intrusions to the network system and further measure its security level. Clearly, the first step of detecting network intrusions is to collect security-related data. However, in the context of 5G and big data, there are a number of challenges in collecting these data due to the heterogeneity of network and ever-growing amount of data. Therefore, traditional data collection methods cannot be applied in the next generation network systems directly, especially for security-related data. This paper presents the design and implementation of an adaptive security-related data collector based on network context in heterogeneous networks. The proposed collector solves the issue of heterogeneity of network system by designing a Security-related Data Description Language (SDDL) to instruct security related data collection in various networking contexts. It also applies adaptive sampling algorithms to reduce the amount of collected data. Furthermore, performance evaluation based on a prototype implementation shows the effectiveness of the adaptive security-related data collector in terms of a number of pre-defined design requirements. [Lin, Huaqing; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China; [Lin, Huaqing; Yan, Zheng; Fu, Yulong] Xidian Univ, Sch Cyber Engn, Xian, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo, Finland Xidian University; Xidian University; Aalto University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China. zyan@xidian.edu.cn yang, zheng/HGC-7753-2022 Yan, Zheng/0000-0002-9697-2108 National Key Research and Development Program of China [2016YFB0800704]; National Natural Science Foundation of China [61672410, 61802293, U1536202]; Academy of Finland [308087]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; National Postdoctoral Program for Innovative Talents of China [BX20180238]; China Postdoctoral Science Foundation [2018M633461]; China 111 project [B16037, B08038] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Natural Science Basic Research Plan in Shaanxi Province of China; National Postdoctoral Program for Innovative Talents of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); China 111 project(Ministry of Education, China - 111 Project) This work is sponsored by the National Key Research and Development Program of China (grant 2016YFB0800704), the National Natural Science Foundation of China (grants 61672410, 61802293 and U1536202), the Academy of Finland (grant 308087), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), the National Postdoctoral Program for Innovative Talents of China (grant BX20180238), the Project funded by China Postdoctoral Science Foundation (grant 2018M633461), and the China 111 project (grants B16037 and B08038). 35 11 11 1 9 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 J NETW COMPUT APPL J. Netw. Comput. Appl. JAN 15 2019.0 126 88 103 10.1016/j.jnca.2018.11.002 0.0 16 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science HI9HC hybrid, Green Published 2023-03-23 WOS:000456764900007 0
J Liu, C; Musharraf, M; Li, F; Kujala, P Liu, Cong; Musharraf, Mashrura; Li, Fang; Kujala, Pentti A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters OCEAN ENGINEERING English Article Maritime safety; Data mining; Escort operations; Convoy operations; Ship performance in ice; Ice-covered waters NEMO-NORDIC 1.0; WINTER NAVIGATION; ESCORT OPERATIONS; RISK ANALYSIS; SHIP ESCORT; MODEL; SEA; DENSITY; TOOL Icebreaker assistance is a common but complex operation in ice-infested regions. Currently, the operational decision-making and the decisions regarding the safety indicators are primarily based on expert knowledge, resulting in subjectivity and the ad hoc nature of icebreaker assistance. This can negatively impact both the navigational efficiency of icebreaker services and the productivity of port services. This paper proposes a data mining method to automatically identify icebreaker assistance cases from big data. The identified cases are then used to statistically analyze the safety indicators. The data used in the paper include navigational data obtained from the Automatic Identification System (AIS) and sea ice data in the Baltic Sea area. A multi-step clustering method is adopted to cluster similar trajectories of merchant vessels and icebreakers, identifying assistance events automatically. The results show that the proposed method can automatically identify icebreaker assis-tance cases with an accuracy of 99.6%, precision of 87%, and recall of 78.3%. The automatic identification along with the statistical analysis can assist in the development of an intelligent decision-making system for safe and efficient winter navigation. [Liu, Cong; Musharraf, Mashrura; Li, Fang; Kujala, Pentti] Aalto Univ, Dept Mech Engn, Marine Technol, Espoo, Finland; [Li, Fang] Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China; [Li, Fang] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai 200240, Peoples R China; [Liu, Cong] Otakaari 4, Koneteknikka 1, Espoo 02150, Finland Aalto University; Shanghai Jiao Tong University; Shanghai Jiao Tong University Liu, C (corresponding author), Otakaari 4, Koneteknikka 1, Espoo 02150, Finland. cong.1.liu@aalto.fi Li, Fang/B-4861-2018; Kujala, Pentti JS/B-3780-2011 Li, Fang/0000-0003-0589-9644; Academy of Finland, Finland: Towards human centered intelligent ships for winter navigation [351491]; AI-based simulation grant for intelligent ice navigation - Winter Navigation Research Board [W22-1 SIMNAV]; Winter Naviga- tion Research Board Academy of Finland, Finland: Towards human centered intelligent ships for winter navigation(Academy of Finland); AI-based simulation grant for intelligent ice navigation - Winter Navigation Research Board; Winter Naviga- tion Research Board This work was supported by the Academy of Finland, Finland: Towards human centered intelligent ships for winter navigation (Decision number: 351491), and the AI-based simulation grant for intelligent ice navigation (Grant number: W22-1 SIMNAV) funded by Winter Navigation Research Board. The authors also thank Ketki Kulkarni from Aalto University, Jarkko Toivola and Tuomas Taivi from FTIA, and FMI for providing practical knowledge and data. 66 0 0 12 12 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 1873-5258 OCEAN ENG Ocean Eng. DEC 15 2022.0 266 2 112914 10.1016/j.oceaneng.2022.112914 0.0 OCT 2022 16 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 5Z5DD Green Published, hybrid 2023-03-23 WOS:000879992500001 0
J Chandio, A; Shen, Y; Bendechache, M; Inayat, I; Kumar, T Chandio, Aisha; Shen, Yao; Bendechache, Malika; Inayat, Irum; Kumar, Teerath AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition APPLIED SCIENCES-BASEL English Article audio classification; baseline classification accuracy; digit recognition; speech processing; Urdu dataset classification; Urdu digit dataset CLASSIFICATION The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy. [Chandio, Aisha; Shen, Yao] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China; [Bendechache, Malika] Dublin City Univ, Sch Comp, ADAPT Ctr, Dublin 9, Ireland; [Bendechache, Malika] Dublin City Univ, Sch Comp, Lero Res Ctr, Dublin 9, Ireland; [Inayat, Irum; Kumar, Teerath] Natl Univ Comp & Emerging Sci, Sch Comp, Dept Software Engn, Islamabad 44000, Pakistan Shanghai Jiao Tong University; Dublin City University; Dublin City University Shen, Y (corresponding author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. aishabatool5512@gmail.com; yshen@cs.sjtu.edu.cn; malika.bendechache@dcu.ie; irum.inayat@nu.edu.pk; tchandani142@gmail.com Bendechache, Malika/0000-0003-0069-1860; Inayat, Irum/0000-0001-5576-6212 Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China [201802005]; National Natural Science Foundation of China [61932014]; Pudong New Area Science & Technology Development Fundation [PKX2019-R02] Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Pudong New Area Science & Technology Development Fundation This work was partially supported by the Research and Development Projects of Applied Technology of Inner Mongolia Autonomous Region, China under Grant No. 201802005, the Key Program of the National Natural Science Foundation of China under Grant No. 61932014, and Pudong New Area Science & Technology Development Fundation under Grant No. PKX2019-R02. Yao Shen is the corresponding author. 42 11 11 0 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel OCT 2021.0 11 19 8842 10.3390/app11198842 0.0 13 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics WF7ZT Green Accepted, gold 2023-03-23 WOS:000706521400001 0
J Sbert, M; Chen, M; Poch, J; Bardera, A Sbert, Mateu; Chen, Min; Poch, Jordi; Bardera, Anton Some Order Preserving Inequalities for Cross Entropy and Kullback-Leibler Divergence ENTROPY English Article cross entropy; Kullback-Leibler divergence; likelihood; Kolmogorov mean; generalized mean; weighted mean; stochastic dominance; stochastic order Cross entropy and Kullback-Leibler (K-L) divergence are fundamental quantities of information theory, and they are widely used in many fields. Since cross entropy is the negated logarithm of likelihood, minimizing cross entropy is equivalent to maximizing likelihood, and thus, cross entropy is applied for optimization in machine learning. K-L divergence also stands independently as a commonly used metric for measuring the difference between two distributions. In this paper, we introduce new inequalities regarding cross entropy and K-L divergence by using the fact that cross entropy is the negated logarithm of the weighted geometric mean. We first apply the well-known rearrangement inequality, followed by a recent theorem on weighted Kolmogorov means, and, finally, we introduce a new theorem that directly applies to inequalities between K-L divergences. To illustrate our results, we show numerical examples of distributions. [Sbert, Mateu] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China; [Sbert, Mateu; Poch, Jordi; Bardera, Anton] Univ Girona, Graph & Imaging Lab, Campus Montilivi, Girona 17003, Spain; [Chen, Min] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England Tianjin University; Universitat de Girona; University of Oxford Sbert, M (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.;Sbert, M (corresponding author), Univ Girona, Graph & Imaging Lab, Campus Montilivi, Girona 17003, Spain. mateusbert@mac.com; min.chen@oerc.ox.ac.uk; poch@imae.udg.edu; anton.bardera@ima.udg.edu Poch, Jordi/G-6731-2011; Bardera, Anton/AAU-7878-2020; Sbert, Mateu/G-6711-2011; Bardera, Anton/M-7088-2018 Poch, Jordi/0000-0002-8205-842X; Sbert, Mateu/0000-0003-2164-6858; Bardera, Anton/0000-0001-9648-0624 National Natural Science Foundation of China [61471261, 61771335]; Spanish Government [TIN2016-75866-C3-3-R] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Spanish Government(Spanish Government) Mateu Sbert acknowledges the funding of National Natural Science Foundation of China under grants No. 61471261 and No. 61771335, and by grant TIN2016-75866-C3-3-R from Spanish Government, Jordi Poch and Anton Bardera acknowledge the funding of TIN2016-75866-C3-3-R from Spanish Government. 10 3 3 1 6 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy DEC 2018.0 20 12 959 10.3390/e20120959 0.0 10 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics HF5OM 33266683.0 Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000454282000067 0
J Lopez, G; Martin-Marquez, V; Wang, F; Xu, HK Lopez, Genaro; Martin-Marquez, Victoria; Wang, Fenghui; Xu, Hong-Kun Forward-Backward Splitting Methods for Accretive Operators in Banach Spaces ABSTRACT AND APPLIED ANALYSIS English Article STRONG-CONVERGENCE THEOREMS; NONEXPANSIVE-MAPPINGS; MONOTONE-OPERATORS; FIXED-POINTS; HILBERT-SPACE; APPROXIMATION METHODS; ITERATIVE ALGORITHMS; NONLINEAR OPERATORS; FEASIBILITY PROBLEM; FINITE FAMILY Splitting methods have recently received much attention due to the fact that many nonlinear problems arising in applied areas such as image recovery, signal processing, and machine learning are mathematically modeled as a nonlinear operator equation and this operator is decomposed as the sum of two (possibly simpler) nonlinear operators. Most of the investigation on splitting methods is however carried out in the framework of Hilbert spaces. In this paper, we consider these methods in the setting of Banach spaces. We shall introduce two iterative forward-backward splitting methods with relaxations and errors to find zeros of the sum of two accretive operators in the Banach spaces. We shall prove the weak and strong convergence of these methods under mild conditions. We also discuss applications of these methods to variational inequalities, the split feasibility problem, and a constrained convex minimization problem. [Xu, Hong-Kun] Natl Sun Yat Sen Univ, Dept Appl Math, Kaohsiung 80424, Taiwan; [Lopez, Genaro; Martin-Marquez, Victoria] Univ Seville, Dept Math Anal, Fac Matemat, E-41080 Seville, Spain; [Wang, Fenghui] Luoyang Normal Univ, Dept Math, Luoyang 471022, Peoples R China; [Xu, Hong-Kun] Natl Sun Yat Sen Univ, Dept Appl Math, Kaohsiung 80424, Taiwan National Sun Yat Sen University; University of Sevilla; Luoyang Normal University; National Sun Yat Sen University Xu, HK (corresponding author), Natl Sun Yat Sen Univ, Dept Appl Math, Kaohsiung 80424, Taiwan. xuhk@math.nsysu.edu.tw Wang, Fenghui/K-8568-2012; Lopez, Genaro/G-7473-2015; Martín-Márquez, Victoria/H-1047-2015 Lopez, Genaro/0000-0002-8303-7858; Basic and Frontier Project of Henan [122300410268]; Peiyu Project of Luoyang Normal University [2011-PYJJ-002]; Plan Andaluz de Investigacin de la Junta de Andaluca [FQM-127, P08-FQM-03543]; NSC (Taiwan) [100-2115-M-110-003-MY2]; Scientific Research at King Saud University; [MTM2009-10696-C02-01] Basic and Frontier Project of Henan; Peiyu Project of Luoyang Normal University; Plan Andaluz de Investigacin de la Junta de Andaluca; NSC (Taiwan)(Ministry of Science and Technology, Taiwan); Scientific Research at King Saud University(King Saud University); The work of G. Lopez, V. Martin-Marquez, and H.-K. Xu was supported by Grant MTM2009-10696-C02-01. This work was carried out while F. Wang was visiting Universidad de Sevilla under the support of this grant. He was also supported by the Basic and Frontier Project of Henan 122300410268 and the Peiyu Project of Luoyang Normal University 2011-PYJJ-002. The work of G. Lopez and V. Martin-Marquez was also supported by the Plan Andaluz de Investigacin de la Junta de Andaluca FQM-127 and Grant P08-FQM-03543. The work of H.-K. Xu was also supported in part by NSC 100-2115-M-110-003-MY2 (Taiwan). He extended his appreciation to the Deanship of Scientific Research at King Saud University for funding the work through a visiting professor-ship program (VPP) 47 99 99 1 12 HINDAWI PUBLISHING CORPORATION NEW YORK 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA 1085-3375 ABSTR APPL ANAL Abstract Appl. Anal. 2012.0 109236 10.1155/2012/109236 0.0 25 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 989QF Green Published, gold, Green Submitted 2023-03-23 WOS:000307574600001 0
J Luan, SY; Gu, ZH; Freidovich, LB; Jiang, LL; Zhao, QL Luan, Siyu; Gu, Zonghua; Freidovich, Leonid B.; Jiang, Lili; Zhao, Qingling Out-of-Distribution Detection for Deep Neural Networks With Isolation Forest and Local Outlier Factor IEEE ACCESS English Article Runtime; Monitoring; Uncertainty; Training; Safety; Feature extraction; Neurons; Out-of-distribution; deep neural networks; runtime monitoring; outlier detection; isolation forest; local outlier factor Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical autonomous systems thanks to their excellent performance. However, they are known to make mistakes unpredictably, e.g., a DNN may misclassify an object if it is used for perception, or issue unsafe control commands if it is used for planning and control. One common cause for such unpredictable mistakes is Out-of-Distribution (OOD) input samples, i.e., samples that fall outside of the distribution of the training dataset. We present a framework for OOD detection based on outlier detection in one or more hidden layers of a DNN with a runtime monitor based on either Isolation Forest (IF) or Local Outlier Factor (LOF). Performance evaluation indicates that LOF is a promising method in terms of both the Machine Learning metrics of precision, recall, F1 score and accuracy, as well as computational efficiency during testing. [Luan, Siyu; Gu, Zonghua; Freidovich, Leonid B.] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Freidovich, Leonid B.] Sirius Univ Sci & Technol, Dept Informat Technol & AI, Soci 354349, Russia; [Jiang, Lili] Umea Univ, Dept Comp Sci, S-90187 Umea, Sweden; [Zhao, Qingling] Nanjing Univ Sci & Technol, Coll Comp Sci & Technol, Nanjing 210094, Peoples R China Umea University; Umea University; Nanjing University of Science & Technology Zhao, QL (corresponding author), Nanjing Univ Sci & Technol, Coll Comp Sci & Technol, Nanjing 210094, Peoples R China. ada_zhao@njust.edu.cn Gu, Zonghua/0000-0003-4228-2774 NSFC [61902185]; Jiangsu Provincial NSF [BK20190448] NSFC(National Natural Science Foundation of China (NSFC)); Jiangsu Provincial NSF This work was supported in part by the NSFC under Grant 61902185, and in part by Jiangsu Provincial NSF under Grant BK20190448. 34 0 0 0 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9 132980 132989 10.1109/ACCESS.2021.3108451 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications UZ9TW gold, Green Submitted 2023-03-23 WOS:000702542000001 0
J Zhang, JD; Lin, C; Ogiela, U; Nedjah, N; Sangaiah, AK; Wang, X Zhang, Jindan; Lin, Chuan; Ogiela, Urszula; Nedjah, Nadia; Sangaiah, Arun K.; Wang, Xuan Improved publicly verifiable auditing protocol for cloud storage CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE English Article attack; auditing protocol; cloud storage; tag generation PROVABLE DATA POSSESSION; BIG DATA-STORAGE; SCHEME; INTEGRITY Outsourcing data to cloud servers is a popular service for data owners, however, how to check the integrity and freshness of the outsourced data is very challenge. Recently, Jin et al. proposed a cloud auditing protocol with full integrity and freshness support for cloud data, unfortunately in this article, we show their proposal is not secure. Concretely, the cloud servers can forge the authentication tag and thus has the ability to forge proof of data possession, which obviously invalidates their cloud auditing protocol. We also give a new cloud auditing protocol and analysis its security and performance. The results show our protocol is more efficient and secure. [Zhang, Jindan] Xianyang Vocat Tech Coll, Dept Elect Informat, Xianyang, Peoples R China; [Zhang, Jindan; Lin, Chuan; Wang, Xuan] Guizhou Univ, Guizhou Prov Key Lab Publ Big Data, Guiyang, Peoples R China; [Ogiela, Urszula] Pedag Univ Krakow, Krakow, Poland; [Nedjah, Nadia] Univ Estado Rio De Janeiro, Fac Engn, Dept Elect Engn & Telecommun, Rio De Janeiro, Brazil; [Sangaiah, Arun K.] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India Guizhou University; Pedagogical University of Cracow; Universidade do Estado do Rio de Janeiro; Universidade Federal de Juiz de Fora; Vellore Institute of Technology (VIT); VIT Vellore Zhang, JD (corresponding author), Xianyang Vocat Tech Coll, Xianyang, Peoples R China. zhangjindan83@163.com Sangaiah, Arun Kumar/U-6785-2019 Sangaiah, Arun Kumar/0000-0002-0229-2460; Wang, Xu An/0000-0003-2070-4913; Zhang, Jindan/0000-0002-1499-8460 National Cryptography Development Fund of China [MMJJ20170112]; Guizhou Provincial Key Laboratory of Public Big Data [2019BDKFJJ008]; Pedagogical University of Krakow research [BN.610-28/PBU/2020] National Cryptography Development Fund of China; Guizhou Provincial Key Laboratory of Public Big Data; Pedagogical University of Krakow research National Cryptography Development Fund of China, Grant/Award Number: Grants No. MMJJ20170112; Open Project from Guizhou Provincial Key Laboratory of Public Big Data, Grant/Award Number: No. 2019BDKFJJ008; Pedagogical University of Krakow research, Grant/Award Number: No. BN.610-28/PBU/2020. 35 2 2 2 13 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1532-0626 1532-0634 CONCURR COMP-PRACT E Concurr. Comput.-Pract. Exp. DEC 10 2021.0 33 23 SI e6049 10.1002/cpe.6049 0.0 OCT 2020 12 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science WX0FA 2023-03-23 WOS:000576046200001 0
J Reynolds, MP; Lewis, JM; Ammar, K; Basnet, BR; Crespo-Herrera, L; Crossa, J; Dhugga, KS; Dreisigacker, S; Juliana, P; Karwat, H; Kishii, M; Krause, MR; Langridge, P; Lashkari, A; Mondal, S; Payne, T; Pequeno, D; Pinto, F; Sansaloni, C; Schulthess, U; Singh, RP; Sonder, K; Sukumaran, S; Xiong, W; Braun, HJ Reynolds, Matthew P.; Lewis, Janet M.; Ammar, Karim; Basnet, Bhoja R.; Crespo-Herrera, Leonardo; Crossa, Jose; Dhugga, Kanwarpal S.; Dreisigacker, Susanne; Juliana, Philomin; Karwat, Hannes; Kishii, Masahiro; Krause, Margaret R.; Langridge, Peter; Lashkari, Azam; Mondal, Suchismita; Payne, Thomas; Pequeno, Diego; Pinto, Francisco; Sansaloni, Carolina; Schulthess, Urs; Singh, Ravi P.; Sonder, Kai; Sukumaran, Sivakumar; Xiong, Wei; Braun, Hans J. Harnessing translational research in wheat for climate resilience JOURNAL OF EXPERIMENTAL BOTANY English Review Abiotic; big data; breeding; climate resilience; environment; genetic resources; genomics; international collaboration; phenomics; physiology SPRING BREAD WHEAT; SYNTHETIC HEXAPLOID WHEAT; DROUGHT-ADAPTIVE TRAITS; CANOPY TEMPERATURE DEPRESSION; TOLERANCE LOCUS FR-A(M)2; TRITICUM-AESTIVUM L; GENOMIC SELECTION; GRAIN-YIELD; PHYSIOLOGICAL TRAITS; SPECTRAL REFLECTANCE Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area. [Reynolds, Matthew P.; Lewis, Janet M.; Ammar, Karim; Basnet, Bhoja R.; Crespo-Herrera, Leonardo; Crossa, Jose; Dhugga, Kanwarpal S.; Dreisigacker, Susanne; Juliana, Philomin; Karwat, Hannes; Kishii, Masahiro; Krause, Margaret R.; Mondal, Suchismita; Payne, Thomas; Pequeno, Diego; Pinto, Francisco; Sansaloni, Carolina; Singh, Ravi P.; Sonder, Kai; Sukumaran, Sivakumar; Braun, Hans J.] Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco, Mexico; [Langridge, Peter] Univ Adelaide, Sch Agr Food & Wine, Waite Campus,PMB1, Glen Osmond, SA 5064, Australia; [Langridge, Peter] Julius Kuhn Inst, Wheat Initiat, Konigin Luise Str 19, D-14195 Berlin, Germany; [Lashkari, Azam; Schulthess, Urs; Xiong, Wei] Henan Agr Univ, CIMMYT Henan Collaborat Innovat Ctr, Zhengzhou 450002, Peoples R China CGIAR; International Maize & Wheat Improvement Center (CIMMYT); University of Adelaide; Julius Kuhn-Institut; Henan Agricultural University Reynolds, MP (corresponding author), Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco, Mexico. m.reynolds@cgiar.org Sukumaran, Sivakumar/AFO-2436-2022; PINTO, FAUSTO J/GQA-4584-2022; Pinto, Fausto/G-9363-2015; sukumaran, sivakumar/AFO-2409-2022; Lashkari, Azam/A-3293-2015 Sukumaran, Sivakumar/0000-0003-4088-4624; PINTO, FAUSTO J/0000-0002-8034-4529; Pinto, Fausto/0000-0002-8034-4529; Lashkari, Azam/0000-0002-2080-7413; Juliana, Philomin/0000-0001-6922-0173; BASNET, BHOJA/0000-0002-1693-4807; Dreisigacker, Susanne/0000-0002-3546-5989; Dhugga, Kanwarpal/0000-0003-2477-3422; Lewis, Janet/0000-0001-6680-5061; Karwat, Hannes/0000-0002-4339-1676; Crespo Herrera, Leonardo Abdiel/0000-0003-0506-4700; Crossa, Jose/0000-0001-9429-5855; Krause, Margaret/0000-0003-1154-337X Foundation for Food and Agriculture Research [DFs19-0000000013]; CGIAR Research Program on Wheat (CRP-WHEAT); Government of Australia; Government of Belgium; Government of Canada; Government of China; Government of France; Government of India; Government of Japan; Government of Korea; Government of Netherlands; Government of New Zealand; Government of Norway; Government of Sweden; Government of Switzerland; Government of UK; Government of USA; World Bank; Secretariat of Agriculture and Rural Development (SADER, Government of Mexico); Bill and Melinda Gates Foundation; Foreign Commonwealth and Development Office [INV-003439]; United States Agency for International Development [9 MTO 069033] Foundation for Food and Agriculture Research; CGIAR Research Program on Wheat (CRP-WHEAT)(CGIAR); Government of Australia; Government of Belgium(CGIAR); Government of Canada(CGIAR); Government of China; Government of France; Government of India; Government of Japan; Government of Korea(Korean Government); Government of Netherlands(Netherlands Government); Government of New Zealand; Government of Norway; Government of Sweden; Government of Switzerland; Government of UK; Government of USA; World Bank(The World Bank India); Secretariat of Agriculture and Rural Development (SADER, Government of Mexico); Bill and Melinda Gates Foundation(Bill & Melinda Gates Foundation); Foreign Commonwealth and Development Office; United States Agency for International Development(United States Agency for International Development (USAID)) The HeDWIC is supported by the following funders: Foundation for Food and Agriculture Research under award number -Grant ID: DFs19-0000000013; CGIAR Research Program on Wheat (CRP-WHEAT), which receives support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Netherlands, New Zealand, Norway, Sweden, Switzerland, UK., USA, and the World Bank; The Sustainable Modernization of Traditional Agriculture (MasAgro) initiative from the Secretariat of Agriculture and Rural Development (SADER, Government of Mexico); the Accelerating Genetic Gains for Maize and Wheat project, which is supported by the Bill and Melinda Gates Foundation and the Foreign Commonwealth and Development Office [INV-003439 BMGF/FCDO Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)] and the United States Agency for International Development (Amend. No. 9 MTO 069033, USAIDCIMMYT Wheat/AGGMW). The authors would like to acknowledge all HeDWIC collaborators (www.hedwic.org), and especially highlight the contributions and collaborations of the following individuals in light of this publication: Malcolm Bennett (The University of Nottingham), Phil Bruckner (Montana State University), Jason Cook (Montana State University), Kate Dreher (CIMMYT), Jorge Dubcovsky (University of California, Davis), and Jesse Poland (Kansas State University). In addition, we acknowledge Victor Kommerell (CIMMYT) for funding advice, and Simon Griffiths (John Innes Centre) and Bernd Hackauf (Julius Kuhn-Institut) for germplasm sharing and discussions on springxwinter crossing strategies and establishing a HeDWIC winter wheat hub. We also acknowledge the following groups as important to the success of the HeDWIC:The International Wheat Improvement Network (IWIN), The Wheat Initiative (including the Alliance for Wheat Adaptation to Heat and Drought, AHEAD), the International Wheat Yield Partnership (IWYP), the Buckler Lab and the Institute for Genomic Diversity (Cornell University), and the CIMMYT Academy. 219 10 10 8 28 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0022-0957 1460-2431 J EXP BOT J. Exp. Bot. JUL 10 2021.0 72 14 SI 5134 5157 10.1093/jxb/erab256 0.0 JUN 2021 24 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences TZ0FS 34139769.0 hybrid 2023-03-23 WOS:000684152600004 0
J Cai, Q; Guo, Y; Li, P; Bogris, A; Shore, KA; Zhang, YM; Wang, YC Cai, Qiang; Guo, Ya; Li, Pu; Bogris, Adonis; Shore, K. Alan; Zhang, Yamei; Wang, Yuncai Modulation format identification in fiber communications using single dynamical node-based photonic reservoir computing PHOTONICS RESEARCH English Article SEMICONDUCTOR-LASER SUBJECT; DOUBLE OPTICAL FEEDBACK; NEURAL-NETWORK; PERFORMANCE; CHAOS; RECOGNITION; PREDICTION We present a simple approach based on photonic reservoir computing (P-RC) for modulation format identification (MFI) in optical fiber communications. Here an optically injected semiconductor laser with self-delay feedback is trained with the representative features from the asynchronous amplitude histograms of modulation signals. Numerical simulations are conducted for three widely used modulation formats (on-off keying, differential phase-shift keying, and quadrature amplitude modulation) for various transmission situations where the optical signal-to-noise ratio varies from 12 to 26 dB, the chromatic dispersion varies from -500 to 500 ps/nm, and the differential group delay varies from 0 to 20 ps. Under these situations, final simulation results demonstrate that this technique can efficiently identify all those modulation formats with an accuracy of >95% after optimizing the control parameters of the P-RC layer such as the injection strength, feedback strength, bias current, and frequency detuning. The proposed technique utilizes very simple devices and thus offers a resource-efficient alternative approach to MFI. (C) 2020 Chinese Laser Press [Cai, Qiang; Guo, Ya; Li, Pu] Taiyuan Univ Technol, Key Lab Adv Transducers & Intelligent Control Sys, Minist Educ, Taiyuan 030024, Peoples R China; [Guo, Ya] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China; [Li, Pu; Wang, Yuncai] Guangdong Univ Technol, Sch Informat Engn, Guangzhou 510006, Peoples R China; [Li, Pu] Shanghai Univ, Key Lab Specialty Fiber Opt & Opt Access Networks, Shanghai 200444, Peoples R China; [Bogris, Adonis] Univ West Attica, Dept Informat & Comp Engn, Athens 12243, Greece; [Shore, K. Alan] Bangor Univ, Sch Elect Engn, Bangor LL57 1UT, Gwynedd, Wales; [Zhang, Yamei] Nanjing Univ Aeronaut & Astronaut, Key Lab Radar Imaging & Microwave Photon, Minist Educ, Nanjing 210016, Peoples R China Taiyuan University of Technology; Northwestern Polytechnical University; Guangdong University of Technology; Shanghai University; University of West Attica; Bangor University; Nanjing University of Aeronautics & Astronautics Li, P (corresponding author), Taiyuan Univ Technol, Key Lab Adv Transducers & Intelligent Control Sys, Minist Educ, Taiyuan 030024, Peoples R China.;Li, P (corresponding author), Guangdong Univ Technol, Sch Informat Engn, Guangzhou 510006, Peoples R China.;Li, P (corresponding author), Shanghai Univ, Key Lab Specialty Fiber Opt & Opt Access Networks, Shanghai 200444, Peoples R China. lipu8603@126.com Shore, Alan/AAT-6367-2021; Bogris, Adonis/O-4808-2018 Bogris, Adonis/0000-0002-5740-6285 National Natural Science Foundation of China [61775158, 61961136002, 61927811, U19A2076, 61705159, 61805168, 17174343, 11904157]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Program for the Top Young Academic Leaders of High Learning Institutions of Shanxi; National Cryptography Development Fund [MMJJ20170127]; China Postdoctoral Science Foundation [2018M630283, 2019T120197]; Natural Science Foundation of Shanxi Province [201901D211116]; STCSM [SKLSFO2018-03]; Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing University of Aeronautics and Astronautics), Ministry of Education [RIMP2019002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Program for the Top Young Academic Leaders of High Learning Institutions of Shanxi; National Cryptography Development Fund; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation of Shanxi Province(Natural Science Foundation of Shanxi Province); STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing University of Aeronautics and Astronautics), Ministry of Education National Natural Science Foundation of China (61775158, 61961136002, 61927811, U19A2076, 61705159, 61805168, 17174343, 11904157); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Program for the Top Young Academic Leaders of High Learning Institutions of Shanxi; National Cryptography Development Fund (MMJJ20170127); China Postdoctoral Science Foundation (2018M630283, 2019T120197); Natural Science Foundation of Shanxi Province (201901D211116); STCSM (SKLSFO2018-03); Project of Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing University of Aeronautics and Astronautics), Ministry of Education (RIMP2019002). 50 29 29 8 51 CHINESE LASER PRESS SHANGHAI PO BOX 800-211, SHANGHAI, 201800, PEOPLES R CHINA 2327-9125 PHOTONICS RES Photonics Res. JAN 1 2021.0 9 1 B1 B8 10.1364/PRJ.409114 0.0 8 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics PM1WV 2023-03-23 WOS:000603599400002 0
J Jiang, XH; Juste, A; Li, YY; Liu, T Jiang, Xu-Hui; Juste, Aurelio; Li, Ying-Ying; Liu, Tao Detecting new physics as novelty - Complementarity matters JOURNAL OF HIGH ENERGY PHYSICS English Article Specific BSM Phenomenology; Anomalous Higgs Couplings; Supersymmetry Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in ref. [1], between isolationbased and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a t (t) over bar gamma gamma final state: t (t) over bar th, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging. [Jiang, Xu-Hui; Liu, Tao] Hong Kong Univ Sci & Technol, Dept Phys, Hong Kong, Peoples R China; [Juste, Aurelio] Univ Autonoma Barcelona, Fac Ciencies, Inst Fis Altes Energies IFAE, Edifici Cn, E-08193 Barcelona, Spain; [Juste, Aurelio] Inst Catalana Recerca & Estudis Avancats ICREA, E-08010 Barcelona, Spain; [Li, Ying-Ying] Theoret Phys Dept, Fermi Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA Hong Kong University of Science & Technology; Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); ICREA; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory Jiang, XH (corresponding author), Hong Kong Univ Sci & Technol, Dept Phys, Hong Kong, Peoples R China. xjiangaj@connect.ust.hk; Aurelio.Juste.Rozas@cern.ch; yingying@fnal.gov; taoliu@ust.hk Li, Ying-Ying/AFQ-6466-2022 Li, Ying-Ying/0000-0003-2580-1974 General Research Fund [16304315]; Fermi Research Alliance, LLC [DE-AC02-07CH11359]; Spanish Ministerio de Economia y Competitividad [RTI2018-096930-B-I00]; Centro de Excelencia Severo Ochoa [SEV-2016-0588]; United States Department of Energy General Research Fund; Fermi Research Alliance, LLC; Spanish Ministerio de Economia y Competitividad(Spanish Government); Centro de Excelencia Severo Ochoa; United States Department of Energy(United States Department of Energy (DOE)) We would like to thank Yanjun Tu for useful discussions. T.L. was supported by the General Research Fund under Grant No. 16304315, which was issued by the Research Grants Council of Hong Kong S.A.R. Fermilab is operated by Fermi Research Alliance, LLC under contract number DE-AC02-07CH11359 with the United States Department of Energy. A.J. was supported in part by the Spanish Ministerio de Economia y Competitividad under projects RTI2018-096930-B-I00 and Centro de Excelencia Severo Ochoa SEV-2016-0588. 60 0 0 6 6 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. OCT 13 2022.0 10 85 10.1007/JHEP10(2022)085 0.0 31 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics 5I5FU Green Submitted, gold 2023-03-23 WOS:000868383100001 0
J Zhang, ZY; Huang, JL; Duan, SW; Huang, YL; Cai, JT; Bian, J Zhang, Zhenyu; Huang, Jinliang; Duan, Shuiwang; Huang, Yaling; Cai, Juntao; Bian, Jing Use of interpretable machine learning to identify the factors influencing the nonlinear linkage between land use and river water quality in the Chesapeake Bay watershed ECOLOGICAL INDICATORS English Article Land-use; Water quality; Nonlinearity; Threshold effect; Potomac River basin MODELS; URBAN; CALIBRATION; NUTRIENTS; POLLUTION; IMPACTS Understanding the relationship between land use and water quality is essential for effective watershed management. However, it remains challenging to identify such a relationship owing to its nonlinearity. We developed an interpretable machine learning method that integrated the random forest regression (RFR) model with the Shapley Additive exPlanations (SHAP) method to explore the relationship between water quality and land use in the Potomac River Basin (PRB), the second largest tributary entering Chesapeake Bay from 2006 to 2019. The water quality of the 26 sub-watersheds, classified into five types (natural, forested, agricultural, mixed, and urbanized), was investigated using statistical methods and scenario analysis. The results showed that the models employed were effective in predicting the water quality. The mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS), R-2 coefficient of determination (R-2), and Kling-Gupta efficiency (KGE) were 0.011-0.159 mg/L, 0.019-0.219 mg/L, 0.14-0.64%, 0.79-0.99, and 0.69-0.98, respectively, during the training period, which were 0.010-0.201 mg/L, 0.017-0.292 mg/L, 1.87-0.41%, 0.82-0.99, and 0.80-0.97, respectively, during the testing period. The threshold effects of land use patterns were obvious for water quality indicators with high concentrations (i.e., TN and NH4+-N). In contrast, the water quality at low concentrations (i. e., TP and NO3--N) may be more sensitive to wetland or barren land with changing climate. Agricultural activities and urbanization could be the dominant factors determining nutrient export to the PRB. Meanwhile, the typical 'sink' for the nutrient such as wetland may change into the 'source' for different nutrient. This study provides an in-depth understanding of how riverine nutrient export responds to the land use gradient in the Chesapeake Bay watershed. [Zhang, Zhenyu; Huang, Jinliang; Cai, Juntao; Bian, Jing] Xiamen Univ, Fujian Inst Sustainable Oceans, Xiamen 361102, Peoples R China; [Zhang, Zhenyu] Univ Kiel, Dept Hydrol & Water Resource Management, D-24118 Kiel, Germany; [Duan, Shuiwang] Univ Maryland, Dept Geol & Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20740 USA; [Huang, Yaling] Minjiang Univ, Inst Oceanog, Fuzhou 350108, Peoples R China Xiamen University; University of Kiel; University System of Maryland; University of Maryland College Park; Minjiang University Huang, JL (corresponding author), Xiamen Univ, Fujian Inst Sustainable Oceans, Xiamen 361102, Peoples R China. jlhuang@xmu.edu.cn zhang, zhenyu/HOA-8440-2023 National Natural Science Foundation of China [41971231]; Provincial Natural Science Foundation of Fujian Province [2019J01039]; Chinese Scholarship Council (CSC) scholarship [201506315023] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Provincial Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); Chinese Scholarship Council (CSC) scholarship We thank the National Natural Science Foundation of China [Grant No. 41971231] and the Provincial Natural Science Foundation of Fujian Province [Grant No. 2019J01039] for providing financial support. This research was also supported by Chinese Scholarship Council (CSC) scholarship (CSC No. 201506315023) . Anonymous reviewers supplied constructive feedback that helped to improve this paper. 47 2 2 16 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1470-160X 1872-7034 ECOL INDIC Ecol. Indic. JUL 2022.0 140 108977 10.1016/j.ecolind.2022.108977 0.0 MAY 2022 10 Biodiversity Conservation; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology 1T9OJ gold 2023-03-23 WOS:000805052600001 0
J Guo, ZW; Yu, KP; Lv, ZH; Choo, KKR; Shi, P; Rodrigues, JJPC Guo, Zhiwei; Yu, Keping; Lv, Zhihan; Choo, Kim-Kwang Raymond; Shi, Peng; Rodrigues, Joel J. P. C. Deep Federated Learning Enhanced Secure POI Microservices for Cyber-Physical Systems IEEE WIRELESS COMMUNICATIONS English Article Training; Privacy; Microservice architectures; Systems architecture; Optimal scheduling; Cyber-physical systems; Collaborative work; Network security An essential consideration in cyber-physical systems (CPS) is the ability to support secure communication services, such as points of interest (POI) microservices. Existing approaches to support secure POI microservices generally rely on anonymity and/or differential privacy technologies. There are, however, a number of known limitations with such approaches. Hence, this work presents a deep-federated-learning-based framework for securing POI microservices in CPS. In order to enhance data security, the system architecture is designed to isolate the cloud center from accessing user data on edge nodes, and an interactive training mechanism is introduced between the cloud center and edge nodes. Specifically, edge nodes pre-train reliable deep-learning-based models for users, and the cloud server coordinates parameter updating via federated learning. The connected and isolated structure between cloud center and edges facilitates deep federated learning. Finally, we implement and evaluate the performance of our proposed approach using two real-world POI-related datasets. The results show that our proposed approach achieves optimal scheduling performance and demonstrates its practical utility. [Guo, Zhiwei] Chongqing Technol & Business Univ, Chongqing, Peoples R China; [Yu, Keping] Hosei Univ, Tokyo, Japan; [Yu, Keping] RIKEN Ctr Adv Intelligence Project, Tokyo, Japan; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden; [Choo, Kim-Kwang Raymond] Univ Texas San Antonio, San Antonio, TX USA; [Shi, Peng] Univ Adelaide, Adelaide, SA, Australia; [Rodrigues, Joel J. P. C.] China Univ Petr East China, Coll Comp Sci & Technol, Dongying, Peoples R China; [Rodrigues, Joel J. P. C.] Inst Telecomun, Lisbon, Portugal Chongqing Technology & Business University; Hosei University; RIKEN; Uppsala University; University of Texas System; University of Texas at San Antonio (UTSA); University of Adelaide; China University of Petroleum; Universidade de Lisboa Yu, KP (corresponding author), Hosei Univ, Tokyo, Japan.;Yu, KP (corresponding author), RIKEN Ctr Adv Intelligence Project, Tokyo, Japan. Yu, Keping/GVT-7847-2022; Lv, Zhihan/GLR-6000-2022; Rodrigues, Joel J. P. C./A-8103-2013; Lv, Zhihan/I-3187-2014 Yu, Keping/0000-0001-5735-2507; Lv, Zhihan/0000-0003-2525-3074; Rodrigues, Joel J. P. C./0000-0001-8657-3800; Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China [62106029]; Ministry of Education [21YJC630036]; National Language Commission Research Program of China [YB135-121]; Chongqing Natural Science Foundation of China [cstc2019jcyj-msxmX0747]; Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN202000805]; Japan Society for the Promotion of Science [JP18K18044, JP21K17736]; High-Level Talents Research Project of CTBU [1953013]; FCT/MCTES; EU funds [UIDB/50008/2020]; Brazilian National Council for Scientific and Technological Development - CNPq [313036/2020-9]; Cloud Technology Endowed Professorship National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Education; National Language Commission Research Program of China; Chongqing Natural Science Foundation of China; Science and Technology Research Program of Chongqing Municipal Education Commission; Japan Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); High-Level Talents Research Project of CTBU; FCT/MCTES(Fundacao para a Ciencia e a Tecnologia (FCT)); EU funds; Brazilian National Council for Scientific and Technological Development - CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Cloud Technology Endowed Professorship This work was supported in part by the National Natural Science Foundation of China under grant 62106029, in part by the Humanities and Social Science Research Project of the Ministry of Education under grant 21YJC630036, in part by the National Language Commission Research Program of China under grant YB135-121, in part by the Chongqing Natural Science Foundation of China under grant cstc2019jcyj-msxmX0747, in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000805, in part by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) under Grants JP18K18044 and JP21K17736, in part by the High-Level Talents Research Project of CTBU under Grant 1953013, in part by FCT/MCTES through national funds and when applicable co-funded by EU funds under Project UIDB/50008/2020, and in part by the Brazilian National Council for Scientific and Technological Development - CNPq, via Grant No. 313036/2020-9. The research of K.-K. R. Choo was supported only by the Cloud Technology Endowed Professorship. 15 17 17 4 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. APR 2022.0 29 2 22 29 10.1109/MWC.002.2100272 0.0 8 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 2I0NE 2023-03-23 WOS:000814680900016 0
J Li, P; Ding, XC; Sun, HF; Zhao, SQ; Cajo, R Li, Peng; Ding, Xiangcheng; Sun, Hongfang; Zhao, Shiquan; Cajo, Ricardo Research on Dynamic Path Planning of Mobile Robot Based on Improved DDPG Algorithm MOBILE INFORMATION SYSTEMS English Article Aiming at the problems of low success rate and slow learning speed of the DDPG algorithm in path planning of a mobile robot in a dynamic environment, an improved DDPG algorithm is designed. In this article, the RAdam algorithm is used to replace the neural network optimizer in DDPG, combined with the curiosity algorithm to improve the success rate and convergence speed. Based on the improved algorithm, priority experience replay is added, and transfer learning is introduced to improve the training effect. Through the ROS robot operating system and Gazebo simulation software, a dynamic simulation environment is established, and the improved DDPG algorithm and DDPG algorithm are compared. For the dynamic path planning task of the mobile robot, the simulation results show that the convergence speed of the improved DDPG algorithm is increased by 21%, and the success rate is increased to 90% compared with the original DDPG algorithm. It has a good effect on dynamic path planning for mobile robots with continuous action space. [Li, Peng; Ding, Xiangcheng; Zhao, Shiquan] Harbin Engn Univ, Coll Intelligent Sci & Engn, Harbin, Heilongjiang, Peoples R China; [Sun, Hongfang] Harbin Engn Univ, Qingdao Ship Sci & Technol Co Ltd, Harbin, Shandong, Peoples R China; [Cajo, Ricardo] Univ Ghent, Dept Elect Syst & Met Engn, Ghent, Belgium; [Cajo, Ricardo] Escuela Super Politecn Litoral ESPOL, Fac Ingn Elect & Computac, Guayaquil, Ecuador Harbin Engineering University; Harbin Engineering University; Ghent University; Escuela Superior Politecnica del Litoral Ding, XC (corresponding author), Harbin Engn Univ, Coll Intelligent Sci & Engn, Harbin, Heilongjiang, Peoples R China. dingxiangcheng@hrbeu.edu.cn Diaz, Ricardo Cajo/T-3585-2019 Diaz, Ricardo Cajo/0000-0002-0371-2439 Fundamental Research Funds for the Central Universities [3072021CFJ0408] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the Fundamental Research Funds for the Central Universities (no. 3072021CFJ0408). 21 6 6 42 70 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1574-017X 1875-905X MOB INF SYST Mob. Inf. Syst. NOV 12 2021.0 2021 5169460 10.1155/2021/5169460 0.0 10 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications ZP9YF Green Published, gold 2023-03-23 WOS:000766771000005 0
J Wang, M; Yu, JZ; Tan, M; Zhang, JW Wang Ming; Yu JunZhi; Tan Min; Zhang JianWei Multimodal swimming control of a robotic fish with pectoral fins using a CPG network CHINESE SCIENCE BULLETIN English Article bio-inspired control; central pattern generator (CPG); neural network; robotic fish; swimming control CENTRAL PATTERN GENERATOR; LOCOMOTION; DESIGN; SYSTEM The neural-based approaches inspired by biological neural mechanisms of locomotion are becoming increasingly popular in robot control. This paper investigates a systematic method to formulate a Central Pattern Generator (CPG) based control model for multimodal swimming of a multi-articulated robotic fish with flexible pectoral fins. A CPG network is created to yield diverse swimming in three dimensions by coupling a set of nonlinear neural oscillators using nearest-neighbor interactions. In particular, a sensitivity analysis of characteristic parameters and a stability proof of the CPG network are given. Through the coordinated control of the joint CPG, caudal fin CPG, and pectoral fin CPG, a diversity of swimming modes are defined and successfully implemented. The latest results obtained demonstrate the effectiveness of the proposed method. It is also confirmed that the CPG-based swimming control exhibits better dynamic invariability in preserving rhythm than the conventional body wave method. [Wang Ming; Yu JunZhi; Tan Min] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Wang Ming] Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Peoples R China; [Zhang JianWei] Univ Hamburg, Dept Informat, D-22527 Hamburg, Germany Chinese Academy of Sciences; Institute of Automation, CAS; Shandong Jianzhu University; University of Hamburg Yu, JZ (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China. junzhi.yu@ia.ac.cn Yu, Junzhi/A-7876-2010 Yu, Junzhi/0000-0002-6347-572X National Natural Science Foundation of China [60775053, 61075102]; Beijing Natural Science Foundation [4102063, 4122084] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work was supported by the National Natural Science Foundation of China (60775053, 61075102) and in part by the Beijing Natural Science Foundation (4102063, 4122084) 25 17 18 2 38 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1001-6538 1861-9541 CHINESE SCI BULL Chin. Sci. Bull. APR 2012.0 57 10 1209 1216 10.1007/s11434-012-5005-6 0.0 8 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 919BC 2023-03-23 WOS:000302294600015 0
J Lin, QD; Zhang, YL; Zhao, B; Jiang, B Lin, Qidong; Zhang, Yaolong; Zhao, Bin; Jiang, Bin Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy JOURNAL OF CHEMICAL PHYSICS English Article QUANTUM DYNAMICS; DISSOCIATIVE CHEMISORPTION; RESONANCES; SCATTERING; PLUS An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The configuration space is gradually and globally covered without the need to run classical trajectory (or equivalently molecular dynamics) simulations. Through refitting the available analytical PESs of H-3 and OH3 reactive systems, we demonstrate the efficiency and robustness of this new strategy, which enables fast convergence of the reactive PESs with respect to the number of points in terms of quantum scattering probabilities. [Lin, Qidong; Zhang, Yaolong; Jiang, Bin] Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Key Lab Surface & Interface Chem & Energy Catalys, Dept Chem Phys,Anhui Higher Educ Inst, Hefei 230026, Anhui, Peoples R China; [Zhao, Bin] Univ Bielefeld, Theoret Chem, Fak Chem, Univ Str 25, D-33615 Bielefeld, Germany Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Bielefeld Jiang, B (corresponding author), Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Key Lab Surface & Interface Chem & Energy Catalys, Dept Chem Phys,Anhui Higher Educ Inst, Hefei 230026, Anhui, Peoples R China. bjiangch@ustc.edu.cn jiang, bin/F-2391-2014 jiang, bin/0000-0003-2696-5436 National Key RAMP;D Program of China [2017YFA0303500]; National Natural Science Foundation of China [21722306, 91645202]; Anhui Initiative in Quantum Information Technologies [AHY090200] National Key RAMP;D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Anhui Initiative in Quantum Information Technologies This work was supported by the National Key R&D Program of China (Grant No. 2017YFA0303500), the National Natural Science Foundation of China (Grant Nos. 21722306 and 91645202), and the Anhui Initiative in Quantum Information Technologies (Grant No. AHY090200). 52 25 25 3 31 AMER INST PHYSICS MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0021-9606 1089-7690 J CHEM PHYS J. Chem. Phys. APR 21 2020.0 152 15 154104 10.1063/5.0004944 0.0 9 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics LI1GK 32321263.0 Green Submitted 2023-03-23 WOS:000529233100002 0
J Yin, XY; Spatschek, R; Menzler, NH; Huter, C Yin, Xiaoyan; Spatschek, Robert; Menzler, Norbert H.; Hueter, Claas A Pragmatic Transfer Learning Approach for Oxygen Vacancy Formation Energies in Oxidic Ceramics MATERIALS English Article solid oxide cells; oxygen vacancy formation energy; transfer learning; artificial neural networks; Bayesian analysis ARTIFICIAL NEURAL-NETWORKS; CHROMIUM DEPOSITION; FUEL-CELLS; CATHODE; DEGRADATION; MECHANISM; INSIGHT; DENSITY; STACK Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO(3) perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials. [Yin, Xiaoyan; Spatschek, Robert; Hueter, Claas] Forschungszentrum Julich, Inst Energy & Climate Res IEK 2, D-52425 Julich, Germany; [Menzler, Norbert H.] Forschungszentrum Julich, Inst Energy & Climate Res IEK 1, D-52425 Julich, Germany; [Yin, Xiaoyan] China DaTang Corp Sci & Technol Gen Res Inst, Beijing 100040, Peoples R China; [Hueter, Claas] Deutsch Telekom Tech GmbH, Landgrabenweg 151, D-53227 Bonn, Germany Helmholtz Association; Research Center Julich; Helmholtz Association; Research Center Julich Spatschek, R (corresponding author), Forschungszentrum Julich, Inst Energy & Climate Res IEK 2, D-52425 Julich, Germany. xiaoyan.yin11@gmail.com; r.spatschek@fz-juelich.de; n.h.menzler@fz-juelich.de; claashueter@posteo.de Spatschek, Robert/0000-0002-0087-677X; Menzler, Norbert H./0000-0001-7091-0980 BMBF via the programme-oriented funding (PoF); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [491111487]; BMBF projectWirLebenSOFC [FKZ: 03SF0622] BMBF via the programme-oriented funding (PoF)(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)); BMBF projectWirLebenSOFC(Federal Ministry of Education & Research (BMBF)) R. Spatschek and N. H. Menzler appreciate financial support from the BMBF via the programme-oriented funding (PoF) and by the funding via the BMBF projectWirLebenSOFC (FKZ: 03SF0622) based on a resolution of the German parliament. The publication of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-491111487. 42 0 0 9 12 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials APR 2022.0 15 8 2879 10.3390/ma15082879 0.0 18 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics 0S5UT 35454572.0 gold, Green Accepted 2023-03-23 WOS:000786339200001 0
C Sun, L; Wang, J; Yang, KL; Wu, KK; Zhou, XD; Wang, KW; Bai, J Behnke, S; Menegatti, E; McCool, C Sun, Lei; Wang, Jia; Yang, Kailun; Wu, Kaikai; Zhou, Xiangdong; Wang, Kaiwei; Bai, Jian Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos 10TH EUROPEAN CONFERENCE ON MOBILE ROBOTS (ECMR 2021) English Proceedings Paper 10th European Conference on Mobile Robots (ECMR) AUG 31-SEP 03, 2021 Univ Bonn, ELECTR NETWORK IEEE,IEEE Robot & Automat Soc Univ Bonn DESIGN; NETWORK Aerial pixel-wise scene perception of the surrounding environment is an important task for UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional pinhole cameras or fisheye cameras as the imaging device. However, these imaging systems cannot achieve large Field of View (FoV), small size, and lightweight at the same time. To this end, we design a UAV system with a Panoramic Annular Lens (PAL), which has the characteristics of small size, low weight, and a 360 degrees annular FoV. A lightweight panoramic annular semantic segmentation neural network model is designed to achieve high-accuracy and real-time scene parsing. In addition, we present the first drone-perspective panoramic scene segmentation dataset Aerial-PASS, with annotated labels of track, field, and others. A comprehensive variety of experiments shows that the designed system performs satisfactorily in aerial panoramic scene parsing. In particular, our proposed model strikes an excellent trade-off between segmentation performance and inference speed, validated on both public street-scene and our established aerial-scene datasets. [Sun, Lei; Wang, Jia; Wu, Kaikai; Zhou, Xiangdong; Wang, Kaiwei; Bai, Jian] Zhejiang Univ, State Key Lab Opt Instrumentat, Hangzhou, Peoples R China; [Yang, Kailun] Karlsruhe Inst Technol, Inst Anthropomat & Robot, Karlsruhe, Germany Zhejiang University; Helmholtz Association; Karlsruhe Institute of Technology Wang, KW (corresponding author), Zhejiang Univ, State Key Lab Opt Instrumentat, Hangzhou, Peoples R China. wangkaiwei@zju.edu.cn; bai@zju.edu.cn Yang, Kailun/U-2491-2019 Yang, Kailun/0000-0002-1090-667X ZJU-Sunny Photonics Innovation Center [2020-03]; Federal Ministry of Labor and Social Affairs (BMAS) [01KM151112]; Hangzhou SurImage Technology Company Ltd.; Hangzhou SurImage Technology Company Ltd ZJU-Sunny Photonics Innovation Center; Federal Ministry of Labor and Social Affairs (BMAS); Hangzhou SurImage Technology Company Ltd.; Hangzhou SurImage Technology Company Ltd This research was granted from ZJU-Sunny Photonics Innovation Center (No. 2020-03). This research was also funded in part through the AccessibleMaps project by the Federal Ministry of Labor and Social Affairs (BMAS) under the Grant No. 01KM151112. This research was also supported in part by Hangzhou HuanJun Technology Company Ltd. and Hangzhou SurImage Technology Company Ltd. 37 7 7 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-1213-1 2021.0 10.1109/ECMR50962.2021.9568802 0.0 6 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BT2LQ Green Submitted 2023-03-23 WOS:000810510000018 0
C Yu, H; Tan, ZH; Ma, ZY; Guo, J Int Speech Commun Assoc Yu, Hong; Tan, Zheng-Hua; Ma, Zhanyu; Guo, Jun Adversarial Network Bottleneck Features for Noise Robust Speaker Verification 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION Interspeech English Proceedings Paper 18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017) AUG 20-24, 2017 Stockholm, SWEDEN Int Speech Commun Assoc,Stockholm Univ,KTH Royal Inst Technol,Karolinska Inst,Amazon Alexa,DiDi,Furhat Robot,Microsoft,EZ Alibaba Grp,CIRRUS LOGIC,CVTE,Google,Baidu,IBM Res,YAHOO Japan,Nuance,Voice Provider,ASM Solut Ltd,Mitsubishi Elect Res Lab,Yandex speaker verification; STSA-MMSE; DNN based speech enhancement; adversarial training; bottleneck features In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Melfrequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition. [Yu, Hong; Ma, Zhanyu; Guo, Jun] Beijing Univ Posts & Telecommun, Beijing, Peoples R China; [Yu, Hong; Tan, Zheng-Hua] Aalborg Univ, Aalborg, Denmark Beijing University of Posts & Telecommunications; Aalborg University Ma, ZY (corresponding author), Beijing Univ Posts & Telecommun, Beijing, Peoples R China. hongyu@bupt.edu.cn; zt@es.aau.dk; mazhanyu@bupt.edu.cn; guojun@bupt.edu.cn NSFC [61402047, 61273217, 61401259]; Beijing National Science Foundation [4162044]; Scientific Research Foundation for Returned Scholars, Ministry of Education of China; Chinese 111 program of Advanced Intelligence, Network Service Grant [B08004]; OCTAVE Project - Research European Agency (REA) of the European Commission [647850] NSFC(National Natural Science Foundation of China (NSFC)); Beijing National Science Foundation; Scientific Research Foundation for Returned Scholars, Ministry of Education of China(Scientific Research Foundation for the Returned Overseas Chinese ScholarsMinistry of Education, China); Chinese 111 program of Advanced Intelligence, Network Service Grant; OCTAVE Project - Research European Agency (REA) of the European Commission This work is partly supported by Project 61402047, 61273217, 61401259 supported by NSFC, Beijing National Science Foundation No. 4162044, Scientific Research Foundation for Returned Scholars, Ministry of Education of China, Chinese 111 program of Advanced Intelligence, Network Service Grant B08004. OCTAVE Project (No. 647850), funded by the Research European Agency (REA) of the European Commission, in its framework programme Horizon 2020. The authors would like to thank Morten Kolbaek for his assistance and software used for the DNN speech enhancement baseline systems. 36 17 17 0 0 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X 978-1-5108-4876-4 INTERSPEECH 2017.0 1492 1496 10.21437/Interspeech.2017-883 0.0 5 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL9IK Green Submitted 2023-03-23 WOS:000457505000309 0
J Zhu, LN; Xiong, X; Dong, XX; Zhao, Y; Kawczynski, A; Chen, AG; Wang, W Zhu, Lina; Xiong, Xuan; Dong, Xiaoxiao; Zhao, Yi; Kawczynski, Adam; Chen, Aiguo; Wang, Wei Working memory network plasticity after exercise intervention detected by task and resting-state functional MRI JOURNAL OF SPORTS SCIENCES English Article Working memory; neural network plasticity; deaf children; exercise intervention; functional MRI The current study examined the effects of an 11-week exercise intervention on brain activity during a working memory (WM) task and resting-state functional network connectivity in deaf children. Twenty-six deaf children were randomly assigned to either an 11-week exercise intervention or control conditions. Before and after the exercise intervention, all participants were scanned with functional magnetic resonance imaging (fMRI) during N-back task performance and a resting state. The behavioural results showed that the exercise intervention improved WM performance. Task activation analyses showed an increase in the parietal, occipital, and temporal gyri and hippocampus and hippocampus (HIP). In addition, WM performance improvements were associated with greater activation in the left HIP region. Resting-state functional connectivity (Rs-FC) between HIP and certain other brain areas shown a significant interaction of group (exercise versus no exercise) and time (pre- and postintervention). Moreover, connectivity between the left HIP and left middle frontal gyrus was related to improved WM performance. These data extend current knowledge by indicating that an exercise intervention can improve WM in deaf children, and these enhancements may be related to the WM network plasticity changes induced by exercise. [Zhu, Lina; Xiong, Xuan; Dong, Xiaoxiao; Chen, Aiguo] Yangzhou Univ, Coll Phys Educ, Yangzhou 225127, Jiangsu, Peoples R China; [Zhao, Yi; Wang, Wei] Yangzhou Univ, Affiliated Hosp, Dept Med Imaging, Yangzhou, Jiangsu, Peoples R China; [Kawczynski, Adam] Univ Sch Phys Educ, Dept Paralymp Sports, Wroclaw, Poland Yangzhou University; Yangzhou University Chen, AG (corresponding author), Yangzhou Univ, Coll Phys Educ, Yangzhou 225127, Jiangsu, Peoples R China. agchen@yzu.edu.cn Kawczynski, Adam/AAK-3031-2021 Kawczynski, Adam/0000-0001-7840-3799; chen, aiguo/0000-0003-1483-5028 National Natural Science Foundation of China [31771243]; Fok Ying Tung Education Foundation [141113] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fok Ying Tung Education Foundation(Fok Ying Tung Education Foundation) This research was supported partly by grants received by Aiguo Chen from the National Natural Science Foundation of China [31771243] and the Fok Ying Tung Education Foundation [141113]. 0 2 3 5 32 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0264-0414 1466-447X J SPORT SCI J. Sports Sci. JUL 18 2021.0 39 14 1621 1632 10.1080/02640414.2021.1891722 0.0 FEB 2021 12 Sport Sciences Science Citation Index Expanded (SCI-EXPANDED) Sport Sciences TC6CB 33629647.0 2023-03-23 WOS:000621759700001 0
J Yang, M; Yang, YYX; Xie, CX; Ni, M; Liu, J; Yang, HM; Mu, F; Wang, J Yang, Meng; Yang, Yueyuxiao; Xie, Chenxi; Ni, Ming; Liu, Jian; Yang, Huanming; Mu, Feng; Wang, Jian Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale NATURE MACHINE INTELLIGENCE English Article Single-cell datasets continue to grow in size, posing computational challenges for dealing with expanded scale, extended modality and inevitable batch effects. Deep learning-based approaches have recently emerged to address these points by deriving nonlinear cell embeddings. Here we present contrastive learning of cell representations, Concerto, which leverages a self-supervised distillation framework to model multimodal single-cell atlases. Simply by discriminating each cell from the others, Concerto can be adapted to various downstream tasks such as automatic cell type classification, data integration and especially reference mapping. Unlike current mainstream packages, Concerto's contrastive setting well supports operating on all genes to preserve biological variations. Concerto can flexibly generalize to multiomics to obtain unified cell representations. Benchmarking on both simulated and real datasets, Concerto substantially outperforms competing methods. By mapping to a comprehensive reference, Concerto recapitulates differential immune responses and discovers disease-specific cell states in patients with COVID-19. Concerto is easily parallelizable and efficiently scalable to build a 10-million-cell reference within 1.5 h and query 10,000 cells within 8 s. Overall, Concerto will facilitate biomedical research by enabling iteratively constructing single-cell reference atlases and rapidly mapping novel dataset against them to transfer relevant cell annotations. Single-cell datasets continue to grow in size and complexity, calling for computational tools to process and analyse data. Yang et al. present a contrastive learning framework to learn cell representations from single-cell multiomics datasets. [Yang, Meng; Yang, Yueyuxiao; Xie, Chenxi; Liu, Jian; Mu, Feng; Wang, Jian] BGI Shenzhen, MGI, Shenzhen, Peoples R China; [Yang, Meng] Univ Copenhagen, Dept Biol, Copenhagen, Denmark; [Ni, Ming] BGI Shenzhen, MGI QingDao, Qingdao, Peoples R China; [Yang, Huanming; Wang, Jian] BGI Shenzhen, Shenzhen, Peoples R China Beijing Genomics Institute (BGI); University of Copenhagen; Beijing Genomics Institute (BGI); Beijing Genomics Institute (BGI) Yang, M; Mu, F; Wang, J (corresponding author), BGI Shenzhen, MGI, Shenzhen, Peoples R China.;Yang, M (corresponding author), Univ Copenhagen, Dept Biol, Copenhagen, Denmark.;Wang, J (corresponding author), BGI Shenzhen, Shenzhen, Peoples R China. yangmeng1@mgi-tech.com; mufeng@mgi-tech.com; wangjian@genomics.cn Mu, Feng/0000-0002-0496-4996 National Key R&D Program of China [2021YFF1200105] National Key R&D Program of China This research is supported by National Key R&D Program of China (grant no. 2021YFF1200105). 73 1 1 13 16 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2522-5839 NAT MACH INTELL Nat. Mach. Intell. AUG 2022.0 4 8 696 + 10.1038/s42256-022-00518-z 0.0 AUG 2022 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4E4MO Bronze, Green Submitted 2023-03-23 WOS:000844590000002 0
J Koungang, BMG; Mbouendeu, JOT; Ndapeu, DD; Zhao, Z; Tchemou, G; Michel, F; Njeugna, EE; Messan, A; Courard, L Koungang, B. M. Ganou; Mbouendeu, J. O. Tchamdjou; Ndapeu, D. D.; Zhao, Z.; Tchemou, G.; Michel, F.; Njeugna, E. E.; Messan, A.; Courard, L. Experimental thermophysical dependent mechanical analysis of earth bricks with Canarium schweinfurthii and Cocos nucifera bio-aggregates-A case study in Cameroon COGENT ENGINEERING English Article compressed earth bricks; Cocos nucifera; Canarium schweinfurthii; compressive strength; durability; thermal conductivity and machine learning models UNIAXIAL COMPRESSIVE STRENGTH; CALCIUM CARBIDE RESIDUE; BUILDING-MATERIALS; PREDICTION; WOOD; STABILIZATION; CONCRETE; BEHAVIOR; BLOCKS; CEMENT In order to meet sustainable development goals, global policies are strongly oriented towards the recovery of local materials such as agricultural waste. In this context, a new biosourced compressed earth brick (CEB) has been produced with bio-shell aggregates of Cocos nucifera (CN) and Canarium schweinfurthii (CS), added in equal proportions for various mixtures. The main objective is to study, on one hand, the effect of CN and CS aggregates on the mechanical behaviour and the thermo-physical properties of the CEB. On the other hand, durability of CEB was investigated in terms of hydric stability, abrasion resistance and hygro-thermo-mechanical behaviour. Mixtures were prepared using earthen material and 0, 5, 10 and 15 wt% CNCS, with 8 wt% cement CEM II-B-LL 42.5, respectively. Wet and dry compressive strengths are evaluated versus compaction pressure (2.5, 5, 7 and 10 MPa). The increase of CNCS aggregate content induced a reduction of the compressive strength in both wet a\nd dry situations whatever the compaction pressure. The samples with 5 wt % CNCS and 8 wt % cement, however, offered performances close to reference samples without CNCS aggregates. The reverse trend was found for thermal conductivity: thermal conductivity of the bio-sourced CEB is below 1 W center dot m(-2)center dot K-1 which means good insulating performances. It is noted that the modified CEB have relatively low water absorption, which contribute to a good durability. Hygro- and thermo-mechanical tests showed however a quasi-linear decrease of compressive strength when the humidity of the environment increases but also when the temperature rises. At the end, to obtain more accurate properties by prediction, some machine learning models were used. [Koungang, B. M. Ganou; Mbouendeu, J. O. Tchamdjou; Ndapeu, D. D.; Tchemou, G.; Njeugna, E. E.] Univ Douala, Dept Mech Engn, Enset Douala, LAb Mecan & Mat Adaptes LAMMA, Douala, Cameroon; [Koungang, B. M. Ganou; Michel, F.; Courard, L.] Univ Liege Uliege, GeMMe Bldg Mat, Urban & Environm Engn, Liege, Belgium; [Mbouendeu, J. O. Tchamdjou; Ndapeu, D. D.] Univ Dschang, Dept Phys, Res Unit Mech & Phys Syst Modelling UR2MSP, Dschang, Cameroon; [Zhao, Z.] Tongji Univ, Coll Civil Engn, Dept Struct Engn, Shanghai, Peoples R China; [Messan, A.] Inst Int Ingn Eau et Environm Inst 2iE, Lab Ecomat & Habitats Durables LEMHaD, Rue Sci, Ouagadougou, Burkina Faso Tongji University Koungang, BMG (corresponding author), Univ Douala, Dept Mech Engn, Enset Douala, LAb Mecan & Mat Adaptes LAMMA, Douala, Cameroon. morinoganou@yahoo.fr ; messan, adamah/AEG-9709-2022 COURARD, Luc/0000-0001-6573-6631; GANOU KOUNGANG, Bernard Morino/0000-0002-2630-4999; messan, adamah/0000-0003-1925-1750; ZHAO, Zengfeng/0000-0002-7059-9085 78 0 0 0 0 TAYLOR & FRANCIS AS OSLO KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY 2331-1916 COGENT ENG Cogent Eng. DEC 31 2023.0 10 1 2159159 10.1080/23311916.2022.2159159 0.0 30 Engineering, Multidisciplinary Emerging Sources Citation Index (ESCI) Engineering 8X5MI gold 2023-03-23 WOS:000932057100001 0
C Wang, SJ; Flohr, FB; Xiong, H; Wen, TP; Wang, BF; Yang, M; Yang, DG IEEE Wang, Sijia; Flohr, Fabian B.; Xiong, Hui; Wen, Tuopu; Wang, Baofeng; Yang, Mengmeng; Yang, Diange Leverage of Limb Detection in Pose Estimation for Vulnerable Road Users 2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper IEEE Intelligent Transportation Systems Conference (IEEE-ITSC) OCT 27-30, 2019 Auckland, NEW ZEALAND IEEE,Intel,Griffith Univ,Auckland Tourism Events & Econ Dev Application of human pose estimation in intelligent driving has been gradually popular in recent years, as human pose could be informative in depiction of their actions and intentions. The focus of this paper is pose estimation for vulnerable road users with relatively small amounts of data. We present an improved framework, transferring general human pose estimation algorithms based on convolutional neural network to intelligent vehicle domain. Limb detection is introduced as an auxiliary task to allow explicit appearance-based modeling of spatial relationships among human keypoints. The network for keypoint detection is initialized with the parameters in limb detection task. Besides, the produced limb heatmaps are incorporated with keypoint heatmaps as extra source to gain the final keypoint locations. Concerning practical application, we adapt the widely used pose estimation networks to our proposed framework and evaluate them on the newly introduced TDCB-Pose dataset. The experiment results validate our method, and demonstrate the leverage of limb detection in human pose estimation without abundant data. [Wang, Sijia; Xiong, Hui; Wen, Tuopu; Yang, Mengmeng; Yang, Diange] Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China; [Flohr, Fabian B.] Daimler AG Res & Dev, Environm Percept Dept, D-70565 Stuttgart, Germany; [Wang, Baofeng] Daimler Greater China Ltd, Res & Dev Autonomous Driving & Safety, Beijing, Peoples R China Tsinghua University Yang, DG (corresponding author), Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China. Flohr, Fabian/0000-0002-1499-3790 Tsinghua-Daimler Joint Research Center for Sustainable Transportation [20183910018]; National Natural Science Foundation of China [61773234, U1864203]; National Key Research and Development Program of China [2018YFB0105000]; Tsinghua University; Beijing Municipal Science and Technology Program [D171100005117001, Z181100005918001]; Toyota Joint Research Center for AI Technology of Automated Vehicle [TT2018-02] Tsinghua-Daimler Joint Research Center for Sustainable Transportation; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Tsinghua University; Beijing Municipal Science and Technology Program; Toyota Joint Research Center for AI Technology of Automated Vehicle This work was supported by the Tsinghua-Daimler Joint Research Center for Sustainable Transportation (20183910018). It was also supported in part by the National Natural Science Foundation of China (61773234 and U1864203), in part by the National Key Research and Development Program of China (2018YFB0105000), in part by the Project of Tsinghua University and Toyota Joint Research Center for AI Technology of Automated Vehicle (TT2018-02). The software used in this work was developed in the Beijing Municipal Science and Technology Program (D171100005117001 and Z181100005918001). 35 3 3 5 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-5386-7024-8 IEEE INT C INTELL TR 2019.0 528 534 7 Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Transportation BO6LS 2023-03-23 WOS:000521238100085 0
J Chen, WJ; Jin, J; Gerontitis, D; Qiu, LX; Zhu, JC Chen, Weijie; Jin, Jie; Gerontitis, Dimitrios; Qiu, Lixin; Zhu, Jingcan Improved Recurrent Neural Networks for Text Classification and Dynamic Sylvester Equation Solving NEURAL PROCESSING LETTERS English Article; Early Access Text classification; Novel activation functions; Recurrent neural networks; Dynamic Sylvester equation; Dynamic matrix inversion; Robot manipulator FINITE-TIME CONVERGENCE; ACTIVATION FUNCTIONS; DESIGN; ROBOT; STRATEGY; MODELS The solution of the text classification and time-varying problems are two basic practical problems frequently encountered in the fields of science and engineering, and most of the text classification and dynamic problems solving are realized by recurrent neural networks (RNN), therefore, the improvement on the convergence and robustness of the RNN models becomes increasingly important. Based on this fact, two novel activation functions (NAF) are proposed to improve the performance of each RNN formula for text classification, dynamic problems solving and dynamic matrix inversion in this work. Firstly, the first NAF (NAF(1)) is applied to the two-layer simple RNN model, long short-term memory RNN model and gated recurrent unit RNN model for text classification. Comparing with the above three RNN models activated by reported activation functions (rectified linear unit (ReLU) function, leak relu (LReLU), exponential linear unit (ELU), scaled ELU], the NAF(1)-activated RNN models achieve higher accuracy in text classification. In addition, based on the second NAF (NAF(2)), an improved fixed-time convergent recurrent neural network (IFTCRNN) model for time-varying problems solving is constructed. The NAF(2)-based IFTCRNN model achieves fixed-time convergence and strong robustness to noises in time-varying Sylvester matrix equation solving, dynamic matrix inversion and robot manipulator trajectory tracking. [Chen, Weijie; Jin, Jie; Qiu, Lixin; Zhu, Jingcan] Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Peoples R China; [Gerontitis, Dimitrios] Int Hellenic Univ, Dept Informat & Elect Engn, Thessaloniki, Greece Hunan University of Science & Technology Chen, WJ; Jin, J (corresponding author), Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Peoples R China. 1437758712@qq.com; jj67123@hnust.edu.cn National Natural Science Foundation of China [62273141]; Natural Science Foundation of Hunan Province [2020JJ4315]; Scientific Research Fund of Hunan Provincial Education Department [20B216] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Scientific Research Fund of Hunan Provincial Education Department(Hunan Provincial Education Department) AcknowledgementsThis work is supported by the National Natural Science Foundation of China (Grant No. 62273141), Natural Science Foundation of Hunan Province (Grant No. 2020JJ4315), Scientific Research Fund of Hunan Provincial Education Department (Grant No. 20B216). 51 0 0 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1370-4621 1573-773X NEURAL PROCESS LETT Neural Process. Lett. 10.1007/s11063-023-11176-6 0.0 MAR 2023 30 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 9N9GC 2023-03-23 WOS:000943215500001 0
J Zhang, B; Hu, WH; Xu, X; Li, T; Zhang, ZY; Chen, Z Zhang, Bin; Hu, Weihao; Xu, Xiao; Li, Tao; Zhang, Zhenyuan; Chen, Zhe Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach RENEWABLE ENERGY English Article Neural network; Deep reinforcement learning; Energy management; Microgrid; Renewable energy OPTIMIZATION; ALGORITHM; NETWORK Renewable-based microgrid (MG) is recognized as an eco-friendly solution in the development of renewable energy (RE). Moreover, the MG energy management with high RE penetration faces complicate uncertainties due to the inaccuracy of predictions. Besides, the growing participation of electric vehicles (EVs) makes the tradi-tional model-based methods even more infeasible. Considering uncertainties associated with RE, EVs, and electricity price, a model-free deep reinforcement learning (DRL), namely twin delayed deep deterministic policy gradient (TD3), is employed to develop an optimized control strategy to minimize operating costs and satisfy charging expectations. The energy management problem is first formulated as a Markov decision process. Then, TD3 solely relies on the limited observation to find the optimal continuous control strategy. The proposed method flexibly adjusts the operating and charging strategies of components according to RE output and elec-tricity price. Its real-time optimized performance on three consecutive days along with electricity price is evaluated, indicating its practical potential for future application. Additionally, comparison results demonstrate that the proposed method reduces the total costs up to 15.27% and 4.24%, respectively, compared to traditional optimization and other DRL methods, which illustrates the superiority of the TD3 method on optimizing total costs of the considered MG. [Zhang, Bin; Chen, Zhe] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark; [Hu, Weihao; Zhang, Zhenyuan] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China; [Xu, Xiao] Sichuan Univ, Coll Elect Engn, Chengdu, Peoples R China; [Li, Tao] State Grid Yibin Power Supply Co, Yibin, Peoples R China Aalborg University; University of Electronic Science & Technology of China; Sichuan University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China. bzh@et.aau.dk; whu@uestc.edu.cn; xxiao_scu@163.com; tli94@foxmail.com; zhangzhenyuan@uestc.edu.cn; zch@et.aau.dk Zhang, Zhenyuan/ABR-9488-2022 56 0 0 13 13 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-1481 1879-0682 RENEW ENERG Renew. Energy NOV 2022.0 200 433 448 10.1016/j.renene.2022.09.125 0.0 OCT 2022 16 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels 5K4UM 2023-03-23 WOS:000869722800003 0
J Liu, ZH; Wu, K; Guo, JW; Wang, YH; Deussen, O; Cheng, ZL Liu, Zhihao; Wu, Kai; Guo, Jianwei; Wang, Yunhai; Deussen, Oliver; Cheng, Zhanglin Single Image Tree Reconstruction via Adversarial Network GRAPHICAL MODELS English Article tree reconstruction; single image reconstruction; adversarial network MODELS; SKETCHES; SHAPE ABS T R A C T Realistic 3D tree reconstruction is still a tedious and time-consuming task in the graphics community. In this paper, we propose a simple and efficient method for reconstructing 3D tree models with high fidelity from a single image. The key to single image-based tree reconstruction is to recover 3D shape information of trees via a deep neural network learned from a set of synthetic tree models. We adopted a conditional generative adversarial network (cGAN) to infer the 3D silhouette and skeleton of a tree respectively from edges extracted from the image and simple 2D strokes drawn by the user. Based on the predicted 3D silhouette and skeleton, a realistic tree model that inherits the tree shape in the input image can be generated using a procedural modeling tech-nique. Experiments on varieties of tree examples demonstrate the efficiency and effectiveness of the proposed method in reconstructing realistic 3D tree models from a single image. [Liu, Zhihao; Wu, Kai; Deussen, Oliver; Cheng, Zhanglin] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China; [Liu, Zhihao; Wu, Kai; Guo, Jianwei; Cheng, Zhanglin] Univ Chinese Acad Sci, Beijing, Peoples R China; [Guo, Jianwei] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China; [Wang, Yunhai] Shandong Univ, Jinan, Peoples R China; [Deussen, Oliver] Univ Konstanz, Constance, Germany Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Shandong University; University of Konstanz Cheng, ZL (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.;Guo, JW; Cheng, ZL (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China.;Guo, JW (corresponding author), Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China. liuzh96@outlook.com; kai.wu@siat.ac.cn; jianwei.guo@nlpr.ia.ac.cn; cloudseawang@gmail.com; oliver.deussen@uni-konstanz.de; zl.cheng@siat.ac.cn Deussen, Oliver/HKF-2004-2023 Cheng, Zhanglin/0000-0002-3360-2679; Liu, Zhihao/0000-0003-1099-5745; Guo, Jianwei/0000-0002-3376-1725 NSFC [61972388, 61761003, 61802406]; Shenzhen Basic Research Program [JCYJ20180507182222355]; Leading Talents of Guangdong Program [00201509]; CAS grant [GJHZ1862] NSFC(National Natural Science Foundation of China (NSFC)); Shenzhen Basic Research Program; Leading Talents of Guangdong Program; CAS grant Y This work is partially funded by the NSFC (61972388, 61761003, 61802406) , Shenzhen Basic Research Program (JCYJ20180507182222355) , the Leading Talents of Guangdong Pro-gram (00201509) , and the CAS grant (GJHZ1862) . 55 3 3 11 27 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1524-0703 1524-0711 GRAPH MODELS Graph. Models SEP 2021.0 117 101115 10.1016/j.gmod.2021.101115 0.0 JUL 2021 13 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science UU2ON 2023-03-23 WOS:000698640800001 0
C Li, XS; De Rijke, M; Liu, YQ; Mao, JX; Ma, WZ; Zhang, M; Ma, SP ASSOC COMP MACHINERY Li, Xiangsheng; de Rijke, Maarten; Liu, Yiqun; Mao, Jiaxin; Ma, Weizhi; Zhang, Min; Ma, Shaoping Learning Better Representations for Neural Information Retrieval with Graph Information CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT English Proceedings Paper 29th ACM International Conference on Information and Knowledge Management (CIKM) OCT 19-23, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGIR,ACM SIGWEB Neural ranking; Network embedding; Graph neural network Neural ranking models have recently gained much attention in information retrieval (IR) community and obtain good ranking performance. However, most of these retrieval models focus on capturing the textual matching signals between query and document but do not consider user behavior information that may be helpful for the retrieval task. Specifically, users' click and query reformulation behavior can be represented by a click-through bipartite graph and a session-flow graph, respectively. Such graph representations contain rich user behavior information and may help us better understand users' search intent beyond the textual information. In this study, we aim to incorporate this rich information encoded in these two graphs into existing neural ranking models. We present two graph-based neural ranking models (EmbRanker and AggRanker) to enrich learned text representations with graph information that captures rich users' interaction behavior information. Experimental results on a large-scale publicly available benchmark dataset show that the two models outperform most existing neural ranking models that only consider textual information, which illustrates the effectiveness of integrating graph information with textual information. Further analyses show how graph information complements text matching signals and examine whether these two models can be adopted in practical applications. [Li, Xiangsheng; Liu, Yiqun; Mao, Jiaxin; Ma, Weizhi; Zhang, Min; Ma, Shaoping] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Inst Artificial Intelligence, Dept Comp Sci & Technol, Beijing, Peoples R China; [de Rijke, Maarten] Univ Amsterdam, Amsterdam, Netherlands; [de Rijke, Maarten] Ahold Delhaize, Zaandam, Netherlands Tsinghua University; University of Amsterdam Liu, YQ (corresponding author), Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Inst Artificial Intelligence, Dept Comp Sci & Technol, Beijing, Peoples R China. m.derijke@uva.nl; yiqunliu@tsinghua.edu.cn de Rijke, Maarten/0000-0002-1086-0202 National Key Research and Development Program of China [2018YFC0831700]; Natural Science Foundation of China [61732008, 61532011, 61902209]; Beijing Academy of Artificial Intelligence (BAAI); Tsinghua University Guoqiang Research Institute; China Postdoctoral Science Foundation; Shuimu Tsinghua Scholar Program National Key Research and Development Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Academy of Artificial Intelligence (BAAI); Tsinghua University Guoqiang Research Institute; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Shuimu Tsinghua Scholar Program This work is supported by the National Key Research and Development Program of China (2018YFC0831700), Natural Science Foundation of China (Grant No. 61732008, 61532011, 61902209), Beijing Academy of Artificial Intelligence (BAAI) and Tsinghua University Guoqiang Research Institute. This project is also funded by China Postdoctoral Science Foundation and DrWeizhi Ma has been supported by Shuimu Tsinghua Scholar Program. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. 42 5 5 3 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6859-9 2020.0 795 804 10.1145/3340531.3411957 0.0 10 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LW Green Published 2023-03-23 WOS:000749561300082 0
J Li, YZ; Zhou, HL; Lin, ZL; Wang, YF; Chen, SJ; Liu, C; Wang, ZY; Gifu, D; Xia, JB Li, Youzhu; Zhou, Huiling; Lin, Zhonglong; Wang, Yifan; Chen, Shunjie; Liu, Chang; Wang, Zhouyang; Gifu, Daniela; Xia, Jingbo Investigation in the influences of public opinion indicators on vegetable prices by corpora construction and WeChat article analysis FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Corpora design; Vegetable price; Socio-economic indicators; Social media; Prediction algorithms MODEL; PREDICTION; SERVICE; SYSTEM In China, vegetable production plays a critical role directly to national economics and social stability. With the rapid development of social media, public opinions through the Internet is transmitted to the vegetable market in a direct way Previously, focus of most systems was to investigate whether large scale network public opinion is capable of affecting or predicting vegetable price changes. In this paper, we analysed the impact of network public opinions based on a hybrid research strategy. The strategy combined natural language processing (NLP), convolutional neural network (CNN) and classic economic methods. First, we designed corpora indicating different domains of the vegetable market, including supply, demand, natural environment, and government policy; second, we used CNN to perform the topic modelling upon public opinions in a large scale; Third, we investigated vegetable prices volatility with Granger causality test on account of time-lag effects, and results show the correlation between vegetable prices and three public opinion indicators, i.e. demand, supply and natural environment. A subsequent multiple linear regression model augmented the results. Eventually, two linear and three nonlinear predictive models were presented. Results suggested that there is a weak linear correlation between vegetable prices and these three public opinion indicators. This indicated that network public opinions have a potential impact on vegetable prices volatility and can be treated as potential factor to predict vegetable prices. (C) 2019 Elsevier B.V. All rights reserved. [Li, Youzhu; Zhou, Huiling; Lin, Zhonglong; Wang, Yifan; Chen, Shunjie; Liu, Chang; Wang, Zhouyang] Huazhong Agr Univ, Coll Publ Adm, Wuhan 430070, Hubei, Peoples R China; [Zhou, Huiling] Hunan Univ, Business Sch, Changsha 410082, Hunan, Peoples R China; [Gifu, Daniela] Alexandru Ioan Cuza Univ, Fac Comp Sci, Iasi, Romania; [Gifu, Daniela] Romanian Acad, Inst Comp Sci, Iasi Branch, Iasi, Romania; [Gifu, Daniela] Cognos Business Consulting SRL, Bucharest, Romania; [Xia, Jingbo] Huazhong Agr Univ, Coll Informat, Wuhan 430070, Hubei, Peoples R China Huazhong Agricultural University; Hunan University; Alexandru Ioan Cuza University; Romanian Academy of Sciences; Huazhong Agricultural University Xia, JB (corresponding author), Huazhong Agr Univ, Coll Informat, Wuhan 430070, Hubei, Peoples R China. xjb@mail.hzau.edu.cn Gifu, Daniela/D-1805-2015; Xia, Jingbo/E-6796-2011 Gifu, Daniela/0000-0001-8116-053X; Xia, Jingbo/0000-0002-7285-588X Fundamental Research Funds for the Central Universities [2662017PY046, 2662018PY096]; National Social Science Foundation of China [13CJY104]; Earmarked Fund for Modern Agro-industry Technology Research System [CARS-23-F01, CARS-21] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Social Science Foundation of China; Earmarked Fund for Modern Agro-industry Technology Research System This work was supported partly by the Fundamental Research Funds for the Central Universities (Project No. 2662017PY046 and No. 2662018PY096), the National Social Science Foundation of China (Project No. 13CJY104) and the Earmarked Fund for Modern Agro-industry Technology Research System (Project No. CARS-23-F01 and and No. CARS-21). We would also like to express our high gratitude for reviewers for their illuminative suggestions. 38 12 12 15 61 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JAN 2020.0 102 876 888 10.1016/j.future.2019.07.016 0.0 13 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science JU8RD 2023-03-23 WOS:000501936300071 0
J Shi, XH; Zheng, ZG; Zhou, YL; Jin, H; He, LG; Liu, B; Hua, QS Shi, Xuanhua; Zheng, Zhigao; Zhou, Yongluan; Jin, Hai; He, Ligang; Liu, Bo; Hua, Qiang-Sheng Graph Processing on GPUs: A Survey ACM COMPUTING SURVEYS English Article Graph processing; GPU; graph datasets; parallelism; BSP model; GAS model In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future. [Shi, Xuanhua; Zheng, Zhigao; Jin, Hai; Liu, Bo; Hua, Qiang-Sheng] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Serv Comp Technol & Syst Lab, Big Data Technol & Syst Lab, 1037,Luoyu Rd, Wuhan, Hubei, Peoples R China; [Zhou, Yongluan] Univ Copenhagen, Dept Comp Sci, Univ Pk 5, DK-2100 Copenhagen, Denmark; [He, Ligang] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England Huazhong University of Science & Technology; University of Copenhagen; University of Warwick Shi, XH (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Serv Comp Technol & Syst Lab, Big Data Technol & Syst Lab, 1037,Luoyu Rd, Wuhan, Hubei, Peoples R China. xhshi@hust.edu.cn; zhengzhigao@hust.edu.cn; zhou@di.ku.dk; hjin@hust.edu.cn; Ligang.He@warwick.ac.uk; borenaliu@hust.edu.cn; qshua@hust.edu.cn NSFC [61370104, 61433019, U1435217]; International Science and Technology Cooperation Program of China [2015DFE12860]; Outstanding Youth Foundation of Hubei Province [2016CFA032]; EPSRC [EP/R007195/1] Funding Source: UKRI; Engineering and Physical Sciences Research Council [EP/R007195/1] Funding Source: researchfish NSFC(National Natural Science Foundation of China (NSFC)); International Science and Technology Cooperation Program of China; Outstanding Youth Foundation of Hubei Province; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work is partly supported by three grants from the NSFC (No. 61370104, No. 61433019, and No. U1435217), a grant from the International Science and Technology Cooperation Program of China (No. 2015DFE12860), and a grant from the Outstanding Youth Foundation of Hubei Province (No. 2016CFA032). 92 49 52 1 14 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0360-0300 1557-7341 ACM COMPUT SURV ACM Comput. Surv. JAN 2018.0 50 6 81 10.1145/3128571 0.0 35 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science FS6BA Green Submitted 2023-03-23 WOS:000419881700004 0
J Zhang, ZW; Liu, Z; Martin, A; Liu, ZG; Zhou, K Zhang, Zuo-wei; Liu, Zhe; Martin, Arnaud; Liu, Zhun-ga; Zhou, Kuang Dynamic evidential clustering algorithm KNOWLEDGE-BASED SYSTEMS English Article Dynamic evidential clustering; Credal partition; Uncertainty; Belief functions; Unsupervised classification In this paper, a dynamic evidential clustering algorithm (DEC) is introduced to address the computational burden of existing methods. To derive such a solution, an FCM-like objective function is first employed and minimized to obtain the support levels of the real singletons (specific) clusters to which the query objects belong, and then the query objects is initially adaptively assigned to outlier, precise or imprecise one via a new rule-based on the conflicts between the different support levels. For each imprecise object, it is finally reassigned to the singleton clusters or related meta-cluster by partial credal redistribution with the corresponding dynamic edited framework to reduce the computational burden. The proposed method can reduce the complexity to the level similar to that of the fuzzy and possibilistic clustering, which can effectively extend the application of evidential clustering, especially in big data. The effectiveness of the DEC method is tested by four experiments with artificial and real datasets. (C) 2020 Elsevier B.V. All rights reserved. [Zhang, Zuo-wei; Liu, Zhun-ga] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China; [Zhang, Zuo-wei; Martin, Arnaud] Univ Rennes, CNRS, IRISA, Rue E Branly, F-22300 Lannion, France; [Liu, Zhe] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian, Peoples R China; [Zhou, Kuang] Northwestern Polytech Univ, Dept Appl Math, Xian, Peoples R China Northwestern Polytechnical University; Centre National de la Recherche Scientifique (CNRS); Xi'an University of Architecture & Technology; Northwestern Polytechnical University Liu, ZG (corresponding author), Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China. liuzhunga@gmail.com Liu, Zhe/GRS-6410-2022 Liu, Zhe/0000-0002-8580-9655; Zhang, Zuowei/0000-0002-4855-5924 National Natural Science Foundation of China [U20B2067, 61790552, 61790554, 61701409]; Shaanxi Science Fund for Distinguished Young Scholars, China [2018JC-006]; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University, China [CX201953] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shaanxi Science Fund for Distinguished Young Scholars, China; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University, China This work has been partially supported by the National Natural Science Foundation of China (No. U20B2067, No. 61790552, No. 61790554, No. 61701409), Shaanxi Science Fund for Distinguished Young Scholars, China (No. 2018JC-006), and Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University, China (No. CX201953). 43 9 9 2 21 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. FEB 15 2021.0 213 106643 10.1016/j.knosys.2020.106643 0.0 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science QC2CY Green Submitted 2023-03-23 WOS:000614643400010 0
C Xia, B; Wang, HW; Emilion, R; Diday, E Qiang, G; Wu, D Xia, Bang; Wang, Huiwen; Emilion, Richard; Diday, Edwin EM algorithm For Dirichlet Samples And Its Application To Movie Data PROCEEDINGS OF 2017 SYMPOSIUM ON SERVICE: INNOVATION IN BIG DATA ERA English Proceedings Paper Symposium on Service Innovation in Big Data Era NOV 04-05, 2017 Hainan Univ, Serv Syst Engn Dept, Hainan, PEOPLES R CHINA IEEE Analyt & Risk Techn Comm,SES,Univ Chinese Acad Sci, Soc Syst Engn China,IEEE,Soc Management Sci & Engn China, Intelligent Decis Inst,Univ Chinese Acad Sci, Sch Econ & Management Hainan Univ, Serv Syst Engn Dept Dirichlet distribution; EM algorithm; Iteration; Social media MAXIMUM-LIKELIHOOD; DATA AUGMENTATION; MODEL Dirichlet samples are common in economics and many other fields though it has a relatively complex distribution form. Especially in the era of big data, many datasets can be transformed into compositional data which conforms to Dirichlet distributions. The present work first explores EM algorithm for Dirichlet samples and describes its mathematical iteration procedure. Then, the finite sample performance of the proposed method is evaluated using extensive simulations, and the results indicate that the proposed procedure is successful. Moreover, we apply the method to expert rating data from an official movie website, which is an important social media in China. Results show that EM algorithm has a good performance on categorizing the movies. All the movies are classified into two extremely different clusters, of which one represents movies with high quality and the other represents movies with hybrid styles. Further research will extend the method from independent Dirichlet distributions to dependent distributions which are more common situations in real world. [Xia, Bang; Wang, Huiwen] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China; [Wang, Huiwen] Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China; [Emilion, Richard] Univ Orleans, Lab MAPMO, F-45067 Orleans 2, France; [Diday, Edwin] Univ Dauphine, CEREMADE, F-75775 Paris 16, France Beihang University; Universite de Orleans Xia, B (corresponding author), Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China. xiabang@buaa.edu.cn National Natural Science Foundation of China [71420107025] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China, No. 71420107025. 25 0 0 0 1 UNIVERSE ACADEMIC PRESS TORONTO TORONTO 1 SPADINA CRESCENT, ROOM 205, TORONTO, ONTARIO M5S 3G3, CANADA 978-1-926642-18-5 2017.0 201 209 9 Business, Finance; Management Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Business & Economics BJ3NA 2023-03-23 WOS:000424018000017 0
J Liu, YW; Zhang, SW; Ding, ZG; Schober, R; Al-Dhahir, N; Hossain, E; Shen, XM Liu, Yuanwei; Zhang, Shuowen; Ding, Zhiguo; Schober, Robert; Al-Dhahir, Naofal; Hossain, Ekram; Shen, Xuemin Special Issue on Next Generation Multiple Access-Part I IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Editorial Material As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems. [Liu, Yuanwei] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Zhang, Shuowen] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Peoples R China; [Ding, Zhiguo] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England; [Schober, Robert] Friedrich Alexander Univ Erlangen Nurnberg FAU, Inst Digital Commun, D-91054 Erlangen, Germany; [Al-Dhahir, Naofal] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA; [Hossain, Ekram] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB R2M 2J8, Canada; [Shen, Xuemin] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada University of London; Queen Mary University London; Hong Kong Polytechnic University; University of Manchester; University of Erlangen Nuremberg; University of Texas System; University of Texas Dallas; University of Manitoba; University of Waterloo Liu, YW (corresponding author), Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England. yuanwei.liu@qmul.ac.uk; shuowen.zhang@polyu.edu.hk; zhiguo.ding@manchester.ac.uk; robert.schober@fau.de; aldhahir@utdallas.edu; ekram.hossain@umanitoba.ca; sshen@uwaterloo.ca Shen, Xuemin/AAH-2564-2020; Aldhahir, Naofal/HCH-5192-2022; Liu, Yuanwei/AAA-2444-2020 Shen, Xuemin/0000-0002-4140-287X; Liu, Yuanwei/0000-0002-6389-8941; Al-Dhahir, Naofal/0000-0001-9894-8382 21 0 0 17 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. APR 2022.0 40 4 1031 1036 10.1109/JSAC.2021.3139485 0.0 6 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications ZU7AW Bronze 2023-03-23 WOS:000769993400004 0
J Rizwan, M; Wan, WG; Gwiazdzinski, L Rizwan, Muhammad; Wan, Wanggen; Gwiazdzinski, Luc Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article geolocation data; social media; LBSN; activities behavior; Shanghai; KDE; GWR; SDE SOCIAL MEDIA ANALYTICS; ACTIVITY SPACE; BIG DATA; PRIVACY; CHINA; INFORMATION; MOTIVATIONS; SECURITY; SCIENCE; GENDER Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as geolocation) in the form of a tweet (also referred to as a check-in), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user's activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user's daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time. [Rizwan, Muhammad; Wan, Wanggen] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China; [Rizwan, Muhammad; Wan, Wanggen] Shanghai Univ, Inst Smart City, Shanghai 200444, Peoples R China; [Gwiazdzinski, Luc] Univ Grenoble Alpes, IGA, F-38100 Grenoble, France Shanghai University; Shanghai University; Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA) Rizwan, M (corresponding author), Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China.;Rizwan, M (corresponding author), Shanghai Univ, Inst Smart City, Shanghai 200444, Peoples R China. rizwan@shu.edu.cn; wanwg@staffshu.edu.cn; lucmarg@gmail.com Muhammad, Dr. Rizwan/R-5074-2016 Muhammad, Dr. Rizwan/0000-0001-8903-5426; Wan, Wanggen/0000-0002-5065-9650 National Natural Science Foundation of China [61711530245]; Shanghai Science and Technology Commission [18510760300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Science and Technology Commission(Shanghai Science & Technology CommitteeScience & Technology Commission of Shanghai Municipality (STCSM)) This research was funded by the National Natural Science Foundation of China (61711530245), partially supported by the project of the Shanghai Science and Technology Commission (18510760300). 84 11 13 3 18 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT GEO-INF ISPRS Int. Geo-Inf. FEB 2020.0 9 2 137 10.3390/ijgi9020137 0.0 34 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Physical Geography; Remote Sensing KY3CX gold 2023-03-23 WOS:000522449700077 0
J Shen, GR; Kurths, J; Yuan, Y Shen, Guorui; Kurths, Juergen; Yuan, Ye Sequence-to-sequence prediction of spatiotemporal systems CHAOS English Article WAVE We propose a novel type of neural networks known as attention-based sequence-to-sequence architecture for a model-free prediction of spatiotemporal systems. This architecture is composed of an encoder and a decoder in which the encoder acts upon a given input sequence and then the decoder yields another output sequence to make a multistep prediction at a time. In order to demonstrate the potential of this approach, we train the neural network using data numerically sampled from the Korteweg-de Vries equation-which describes the interaction between solitary waves-and then predict its future evolution. Furthermore, we validate the applicability of the approach on datasets sampled from the chaotic Lorenz system and three other partial differential equations. The results show that the proposed method can achieve good performance in predicting the evolutionary behavior of studied spatiotemporal dynamics. To the best of our knowledge, this work is the first attempt at applying attention-based sequence-to-sequence architecture to the prediction task of solitary waves. Published under license by AIP Publishing. [Shen, Guorui; Yuan, Ye] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Kurths, Juergen] Humboldt Univ, Dept Phys, D-12489 Berlin, Germany Huazhong University of Science & Technology; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin Yuan, Y (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China. yye@hust.edu.cn Kurths, Juergen/0000-0002-5926-4276 33 5 5 3 13 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1054-1500 1089-7682 CHAOS Chaos FEB 2020.0 30 2 23102 10.1063/1.5133405 0.0 10 Mathematics, Applied; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics KS9EB 32113238.0 2023-03-23 WOS:000518609600002 0
J Teng, J; Tang, DH; Zhang, X; Hu, WH; Said, S; Rohrmann, RG Teng, Jun; Tang, De-Hui; Zhang, Xiao; Hu, Wei-Hua; Said, Samir; Rohrmann, Rolf. G. Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases SENSORS English Article automated modal analysis (AMA); system model order; density-based spatial clustering of applications with noise (DBSCAN); continuous dynamic monitoring; temperature effect CABLE TENSION; IDENTIFICATION; PARAMETERS; ALGORITHM; FORMULAS The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions. [Teng, Jun; Tang, De-Hui; Zhang, Xiao; Hu, Wei-Hua] Harbin Inst Technol Shenzhen, Sch Civil & Environm Engn, Shenzhen 518055, Peoples R China; [Said, Samir] BAM Fed Inst Mat Res & Testing, D-12205 Berlin, Germany; [Rohrmann, Rolf. G.] SABM GbR, D-10965 Berlin, Germany Harbin Institute of Technology; Federal Institute for Materials Research & Testing Hu, WH (corresponding author), Harbin Inst Technol Shenzhen, Sch Civil & Environm Engn, Shenzhen 518055, Peoples R China. tengj@hit.edu.cn; tangdehui@stu.hit.edu.cn; 16S055247@stu.hit.edu.cn; huweihua@hit.edu.cn; samir.said@bam.de; rolf.rohrmann@gmx.de National Natural Science Foundation of China [51878226]; Research Project of Knowledge Innovation Plan in Shenzhen [JCYJ20160531193340540]; Guangdong Provincial Natural Science Foundation [2017A030313292]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Project of Knowledge Innovation Plan in Shenzhen; Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research is supported by the grants from the National Natural Science Foundation of China: 51878226, the Research Project of Knowledge Innovation Plan in Shenzhen: JCYJ20160531193340540, the Guangdong Provincial Natural Science Foundation: 2017A030313292 and the Fundamental Research Funds for the Central Universities. 32 10 12 3 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 2 2019.0 19 4 927 10.3390/s19040927 0.0 23 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HO3ND 30813288.0 Green Accepted, Green Submitted, Green Published, gold 2023-03-23 WOS:000460829200178 0
J Liu, J; Zhang, H; Yu, T; Ren, LK; Ni, DY; Yang, QH; Lu, BQ; Zhang, L; Axmacher, N; Xue, G Liu, Jing; Zhang, Hui; Yu, Tao; Ren, Liankun; Ni, Duanyu; Yang, Qinhao; Lu, Baoqing; Zhang, Liang; Axmacher, Nikolai; Xue, Gui Transformative neural representations support long-term episodic memory SCIENCE ADVANCES English Article WORKING-MEMORY; PATTERN SIMILARITY; SEMANTIC REPRESENTATIONS; MAINTENANCE CONTRIBUTES; REINSTATEMENT; RETRIEVAL; HIPPOCAMPAL; INFORMATION; REPLAY; CONSOLIDATION Memory is often conceived as a dynamic process that involves substantial transformations of mental representations. However, the neural mechanisms underlying these transformations and their role in memory formation and retrieval have only started to be elucidated. Combining intracranial EEG recordings with deep neural network models, we provide a detailed picture of the representational transformations from encoding to short-term memory maintenance and long-term memory retrieval that underlie successful episodic memory. We observed substantial representational transformations during encoding. Critically, more pronounced semantic representational formats predicted better subsequent long-term memory, and this effect was mediated by more consistent item-specific representations across encoding events. The representations were further transformed right after stimulus offset, and the representations during long-term memory retrieval were more similar to those during short-term maintenance than during encoding. Our results suggest that memory representations pass through multiple stages of transformations to achieve successful long-term memory formation and recall. [Liu, Jing; Yang, Qinhao; Lu, Baoqing; Zhang, Liang; Axmacher, Nikolai; Xue, Gui] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China; [Liu, Jing; Yang, Qinhao; Lu, Baoqing; Zhang, Liang; Axmacher, Nikolai; Xue, Gui] Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China; [Zhang, Hui; Axmacher, Nikolai] Ruhr Univ Bochum, Fac Psychol, Inst Cognit Neurosci, Dept Neuropsychol, D-44801 Bochum, Germany; [Yu, Tao; Ni, Duanyu] Capital Med Univ, Xuanwu Hosp, Beijing Inst Funct Neurosurg, Beijing 100053, Peoples R China; [Ren, Liankun] Capital Med Univ, Xuanwu Hosp, Comprehens Epilepsy Ctr Beijing, Dept Neurol, Beijing 100053, Peoples R China Beijing Normal University; Beijing Normal University; Ruhr University Bochum; Capital Medical University; Capital Medical University Xue, G (corresponding author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.;Xue, G (corresponding author), Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China. gxue@bnu.edu.cn Xue, Gui/A-1762-2009; Zhang, Liang/GRO-0808-2022 Xue, Gui/0000-0001-7891-8151; Axmacher, Nikolai/0000-0002-0475-6492; Zhang, Liang/0000-0001-9041-1150; Zhang, Hui/0000-0002-0843-399X; Ren, Liankun/0000-0001-5147-3068; Liu, Jing/0000-0002-9167-6190 National Science Foundation of China [31730038]; China-Israel collaborative research grant [NSFC 31861143040]; Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant [2016ZT06S220]; Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) [316803389-SFB 1280, 122679504-SFB 874, AX 82/3] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); China-Israel collaborative research grant; Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant; Deutsche Forschungsgemeinschaft (DFG; German Research Foundation)(German Research Foundation (DFG)) G.X. was supported by the National Science Foundation of China (31730038), the China-Israel collaborative research grant (NSFC 31861143040), and the Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant #2016ZT06S220. N.A. received funding from the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation)-Projektnummer 316803389-SFB 1280, via Projektnummer 122679504-SFB 874, and via DFG grant AX 82/3. 88 5 5 14 45 AMER ASSOC ADVANCEMENT SCIENCE WASHINGTON 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA 2375-2548 SCI ADV Sci. Adv. OCT 2021.0 7 41 eabg9715 10.1126/sciadv.abg9715 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics WD4KW 34623910.0 gold, Green Accepted 2023-03-23 WOS:000704912700012 0
J Xu, HS; Wu, QW; Wen, JY; Yang, ZH Xu, Hongsheng; Wu, Qiuwei; Wen, Jinyu; Yang, Zhihong Joint bidding and pricing for electricity retailers based on multi-task deep reinforcement learning INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Deep reinforcement learning Multi-task learning Electricity market Demand response; Bidding; Pricing INCENTIVE-BASED DEMAND; STRATEGY; SYSTEM; MARKET; AGENT; NETWORKS; BEHAVIOR The single-task deep reinforcement learning (STDRL)-based methods solve the joint bidding and pricing problem for the electricity retailer in a hierarchical electricity market by defining a bidding policy and a pricing policy separately, which may suffer from low learning efficiency, time-consuming training and local optimization. To deal with these issues, this paper proposes a novel Multi-task Deep reinforcement learning approach for Joint Bidding and Pricing (MDJBP) optimization model. MDJBP can deal with the bidding and pricing tasks concur-rently through a shared long short-term memory (LSTM) representation network to distill meaningful temporal characteristics from high-dimensional environment states. Furthermore, we develop a deep neural network (DNN) structure consisting a regression branch for bidding task and a soft actor-critic (SAC) branch for pricing task with automating entropy adjustment and adaptive loss weighting to implement MDJBP. The proposed multi- task deep reinforcement learning (MTDRL)-based method is tested with the IEEE 30-bus system. Numerical re-sults show that the proposed methodology succeeds in giving an optimal joint bidding and pricing policy by fully exploiting commonalities and differences between bidding task and pricing task, and thereby boosts the profit, improves learning efficiency, reduces training time, and enhances stability. [Xu, Hongsheng; Yang, Zhihong] NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 211000, Peoples R China; [Xu, Hongsheng; Wen, Jinyu] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China; [Wu, Qiuwei] Tech Univ Denmark, Ctr Elect Power & Energy, Dept Elect Engn, DK-2800 Lyngby, Denmark; [Wen, Jinyu] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China Nari Group Corp; State Grid Corporation of China; Huazhong University of Science & Technology; Technical University of Denmark; Huazhong University of Science & Technology Xu, HS (corresponding author), NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 211000, Peoples R China.;Xu, HS (corresponding author), Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China. xuhongsheng@sgepri.sgcc.com.cn Natural Science Foundation of Jiangsu~Province [BK20210002]; State Grid Corporation of China [1400-202140341A-0-0-00] Natural Science Foundation of Jiangsu~Province(Natural Science Foundation of Jiangsu Province); State Grid Corporation of China This work was supported by Natural Science Foundation of Jiangsu Province (grant number BK20210002) and State Grid Corporation of China (project number 1400-202140341A-0-0-00) 69 3 3 13 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. JUN 2022.0 138 107897 10.1016/j.ijepes.2021.107897 0.0 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 1C5EV 2023-03-23 WOS:000793143200012 0
J Li, C; Tao, Y; Ao, WG; Yang, S; Bai, Y Li, Chuan; Tao, Ying; Ao, Wengang; Yang, Shuai; Bai, Yun Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition ENERGY English Article Electricity consumption; Forecast; Ensemble empirical mode decomposition; Fast Fourier transformation; Random forest SUPPORT VECTOR REGRESSION; ENERGY-CONSUMPTION; DEMAND; GAS; PREDICTION; ALGORITHM; MACHINE; CHINA; EEMD; TRANSFORM The forecast of electricity consumption plays an essential role in marketing management. In this study, a random forest (RF) model coupled with ensemble empirical mode decomposition (EEMD) named EEM-DRF is presented for forecasting the daily electricity consumption of general enterprises. The candidate data is first decomposed into several intrinsic mode functions (IMFs) by the EEMD. Through fast Fourier transformation, the features in each IMF are extracted in the time-frequency domain, then simulated and predicted by the RF model. Finally, the results of each IMF are integrated into the overall trend of the daily electricity consumption for those enterprises. The proposed method was applied to two enterprises located in the Jiangsu High-Tech Zone, and the period of collected data was from January 1, 2015 to May 3, 2016. To show the applicability and superiority of the EEMD-RF approach, two basic models (a back propagation neural network (BPNN) and least squares support vector regression (LSSVM) and five model experiments (EEMD-BPNN, EEMD-LSSVM, RF, BPNN and LSSVM) were selected for comparison. Among these approaches, the proposed model exhibited the best forecast performance in terms of mean absolute error, mean absolute percentage error, and root-mean-square error. (C) 2018 Elsevier Ltd. All rights reserved. [Li, Chuan; Tao, Ying; Ao, Wengang; Yang, Shuai; Bai, Yun] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China; [Li, Chuan] Univ Algarve, CEOT, Faro, Portugal Chongqing Technology & Business University; Universidade do Algarve Bai, Y (corresponding author), Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China. yunbai@foxmail.com Bai, Yun/0000-0003-2710-7994; Li, Chuan/0000-0003-0004-1497 National Key Research and Development Program of China [2016YFE0132200]; National Natural Science Foundation of China [71801044]; Program of Chongqing Municipal Education Commission [KJZH17123]; Research Start-Up Funds of Chongqing Technology and Business University [1756012] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program of Chongqing Municipal Education Commission; Research Start-Up Funds of Chongqing Technology and Business University This research was supported in part by the National Key Research and Development Program of China (2016YFE0132200), the National Natural Science Foundation of China (71801044), the Program of Chongqing Municipal Education Commission (KJZH17123), and the Research Start-Up Funds of Chongqing Technology and Business University (1756012). 41 90 90 4 70 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy DEC 15 2018.0 165 B 1220 1227 10.1016/j.energy.2018.10.113 0.0 8 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels HE2MM 2023-03-23 WOS:000453112200012 0
J Ram, J; Zhang, CY Ram, Jiwat; Zhang, Changyu Examining the Role of Social Media Analytics in Providing Competitive Intelligence: The Impacts and Limitations JOURNAL OF GLOBAL INFORMATION MANAGEMENT English Article Business Intelligence; Competitive Intelligence; Social Media BUSINESS INTELLIGENCE; BIG DATA; MANAGEMENT; CREATION; HOTELS This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT, and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs, and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors' strengths and weaknesses and customers' dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents' views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis. [Ram, Jiwat] Excelia, La Rochelle Business Sch, La Rochelle, France; [Zhang, Changyu] IBBS Suzhou, Suzhou, Jiangsu, Peoples R China Ram, J (corresponding author), Excelia, La Rochelle Business Sch, La Rochelle, France. Ram, Jiwat/AGH-4904-2022 Ram, Jiwat/0000-0002-3495-9223 40 5 5 11 33 IGI GLOBAL HERSHEY 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA 1062-7375 1533-7995 J GLOB INF MANAG J. Glob. Inf. Manag. NOV-DEC 2021.0 29 6 15 10.4018/JGIM.20211101.oa15 0.0 18 Information Science & Library Science Social Science Citation Index (SSCI) Information Science & Library Science TL6JK gold 2023-03-23 WOS:000674965300003 0
J Qian, TT; Yang, SC; Geng, J; Wang, SH Qian, Tiantian; Yang, Shengchun; Geng, Jian; Wang, Shenghe Probabilistic optimal power flow calculation method considering sequential features and correlations of multi-uncertain source-load scenarios JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY English Article GENERATION; OPERATION; SYSTEM; MODEL In order to accurately evaluate the influence of the correlation and sequential feature of wind power, PV power, and load on the economic operation of a power system, a sequential probabilistic optimal power flow calculation method based on multi-uncertain source-load scenarios generated by a multi-generative adversarial network (MGAN) is proposed. First, the historical datasets of wind power, PV power, and load in adjacent areas were collected, and their probability distributions in 24 time periods were analyzed. The results showed that the probability distribution of different periods had obvious differences, which were defined as time sequence differences in this paper. Second, a novel neural network called MGAN for scenario generation was designed. Based on the historical data of each time period, the correlated wind power, photovoltaic (PV) power, and load scenarios were generated by MGAN. Finally, in the modified IEEE 118-bus system, in which two wind power sources, two PV power sources, and two loads with correlation were integrated, probabilistic optimal power flow calculations were carried out for 24 periods with the goal of the lowest power generation cost of thermal power units. The experimental results showed that the power generation cost fluctuation ranges that considered the time sequence difference were reduced by 60% on average compared with the nonconsidered. The proposed method can effectively reduce the power generation cost budget. [Qian, Tiantian; Yang, Shengchun; Geng, Jian] China Elect Power Res Inst Nanjing, Nanjing 210003, Peoples R China; Tech Univ Denmark, DK-2800 Lyngby, Denmark; [Wang, Shenghe] State Grid Anhui Elect Power Co Ltd, Hefei 230009, Anhui, Peoples R China Technical University of Denmark; State Grid Corporation of China Qian, TT (corresponding author), China Elect Power Res Inst Nanjing, Nanjing 210003, Peoples R China. zoe2121@126.com science and technology project of state grid corporation of China Research On Power Grid Static Security Analysis Method Considering Strong Uncertainty Of Power Source Side [SGAH0000KJJS2000675] science and technology project of state grid corporation of China Research On Power Grid Static Security Analysis Method Considering Strong Uncertainty Of Power Source Side This work was supported by the science and technology project of state grid corporation of China Research On Power Grid Static Security Analysis Method Considering Strong Uncertainty Of Power Source Side (Grant No. SGAH0000KJJS2000675). 23 0 0 11 17 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1941-7012 J RENEW SUSTAIN ENER J. Renew. Sustain. Energy JUL 2021.0 13 4 45503 10.1063/5.0053687 0.0 10 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels TT8ZM 2023-03-23 WOS:000680631600001 0
J Yang, C; Guo, XQ; Zhu, ML; Ibragimov, B; Yuan, YX Yang, Chen; Guo, Xiaoqing; Zhu, Meilu; Ibragimov, Bulat; Yuan, Yixuan Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Prototypes; Image segmentation; Image reconstruction; Semantics; Feature extraction; Adaptation models; Colonoscopy; Polyp segmentation; domain adaptation; prototype; self-training; reconstruction IMAGE Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when applied to new data acquired from different scanners or sequences (target domain). As manual annotation is tedious and labor-intensive for new target domain, leveraging knowledge learned from the labeled source domain to promote the performance in the unlabeled target domain is highly demanded. In this work, we propose a mutual-prototype adaptation network to eliminate domain shifts in multi-centers and multi-devices colonoscopy images. We first devise a mutual-prototype alignment (MPA) module with the prototype relation function to refine features through self-domain and cross-domain information in a coarse-to-fine process. Then two auxiliary modules: progressive self-training (PST) and disentangled reconstruction (DR) are proposed to improve the segmentation performance. The PST module selects reliable pseudo labels through a novel uncertainty guided self-training loss to obtain accurate prototypes in the target domain. The DR module reconstructs original images jointly utilizing prediction results and private prototypes to maintain semantic consistency and provide complement supervision information. We extensively evaluate the proposed model in polyp segmentation performance on three conventional colonoscopy datasets: CVC-DB, Kvasir-SEG, and ETIS-Larib. The comprehensive experimental results demonstrate that the proposed model outperforms state-of-the-art methods. [Yang, Chen; Guo, Xiaoqing; Zhu, Meilu; Yuan, Yixuan] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Ibragimov, Bulat] Univ Copenhagen, Dept Comp Sci, DK-2100 Copenhagen, Denmark City University of Hong Kong; University of Copenhagen Yuan, YX (corresponding author), City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China. cyang.ee@my.cityu.edu.hk; xqguo.ee@my.cityu.edu.hk; meiluzhu2@cityu.edu.hk; ibbulat@gmail.com; yxyuan.ee@cityu.edu.hk Zhu, Meilu/CAI-0127-2022 Yuan, Yixuan/0000-0002-0853-6948; Zhu, Meilu/0000-0002-5563-7282; GUO, Xiaoqing/0000-0002-9476-521X; YANG, Chen/0000-0001-7841-5300; Ibragimov, Bulat/0000-0001-7739-7788 Hong Kong Research Grants Council (RGC) Early Career Scheme [21207420 (CityU 9048179)]; Hong Kong RGC Collaborative Research Fund [C4063-18 G (CityU 8739029)]; National Natural Science Foundation of China [62001410] Hong Kong Research Grants Council (RGC) Early Career Scheme; Hong Kong RGC Collaborative Research Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by Hong Kong Research Grants Council (RGC) Early Career Scheme under Grant 21207420 (CityU 9048179), in part by Hong Kong RGC Collaborative Research Fund under Grant C4063-18 G (CityU 8739029), and in part by the National Natural Science Foundation of China under Grant 62001410. 42 6 6 2 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. OCT 2021.0 25 10 3886 3897 10.1109/JBHI.2021.3077271 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics WC2SH 33945490.0 2023-03-23 WOS:000704111100025 0
J Dong, JX; Pan, JS Dong, Jiangxin; Pan, Jinshan Deep Outlier Handling for Image Deblurring IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Image deblurring; outlier handling; deep convolutional neural networks CAMERA Outlier handling has attracted considerable attention recently but remains challenging for image deblurring. Existing approaches mainly depend on iterative outlier detection steps to explicitly or implicitly reduce the influence of outliers on image deblurring. However, these outlier detection steps usually involve heuristic operations and iterative optimization processes, which are complex and time-consuming. In contrast, we propose to learn a deep convolutional neural network to directly estimate the confidence map, which can identify reliable inliers and outliers from the blurred image and thus facilitates the following deblurring process. We analyze that the proposed algorithm incorporated with the learned confidence map is effective in handling outliers and does not require ad-hoc outlier detection steps which are critical to existing outlier handling methods. Compared to existing approaches, the proposed algorithm is more efficient and can be applied to both non-blind and blind image deblurring. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy and efficiency. [Dong, Jiangxin] Max Planck Inst Informat, D-66123 Saarbrucken, Germany; [Pan, Jinshan] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China Max Planck Society; Nanjing University of Science & Technology Pan, JS (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. dongjxjx@gmail.com; sdluran@gmail.com Pan, Jinshan/AAO-2258-2021 National Natural Science Foundation of China [61872421, 61922043]; National Key Research and Development Program of China [2018AAA0102001]; Natural Science Foundation of Jiangsu Province [BK20180471] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) This work was supported in part by the National Natural Science Foundation of China under Grant 61872421 and Grant 61922043, in part by the National Key Research and Development Program of China under Grant 2018AAA0102001, and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180471. 55 4 4 4 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2021.0 30 1799 1811 10.1109/TIP.2020.3048679 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering PT5WI 33417555.0 2023-03-23 WOS:000608685000009 0
J Zhao, J; Yuan, LJ; Sun, K; Huang, H; Guan, PB; Jia, C Zhao, Jie; Yuan, Linjiang; Sun, Kun; Huang, Han; Guan, Panbo; Jia, Ce Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks SUSTAINABILITY English Article Chinese regions; variable selection; meteorological factors; BiLSTM; prediction PM2.5 CONCENTRATIONS; CHINA Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM2.5. However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM2.5/0-12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM2.5 concentration prediction is 3.73, 9.33, and 12.68 mu g/m(3) for Beijing, 1.33, 3.38, and 4.60 mu g/m(3) for Guangzhou, 1.37, 4.19, and 6.35 mu g/m(3) for Xi'an, and 2.20, 7.75, and 10.07 mu g/m(3) for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM2.5 concentrations from 1 to 12 h post. [Zhao, Jie; Yuan, Linjiang] Xian Univ Architecture & Technol, Sch Environm & Municipal Engn, 13 Yanta Rd, Xian 710055, Peoples R China; [Zhao, Jie; Yuan, Linjiang] Xian Univ Architecture & Technol, Key Lab Northwest Water Resource Environm & Ecol, MOE, 13 Yanta Rd, Xian 710055, Peoples R China; [Zhao, Jie; Yuan, Linjiang] Shaanxi Key Lab Environm Engn, Xian 710055, Peoples R China; [Sun, Kun] Univ Southern Denmark, Dept Green Technol, SDU Life Cycle Engn, DK-5230 Odense, Denmark; [Huang, Han] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Jiangsu, Peoples R China; [Guan, Panbo] 714 Res Inst CSSC, Dept Energy Conservat & Green Dev, Beijing 100101, Peoples R China; [Jia, Ce] Renmin Univ China, Sch Environm & Nat Resources, Beijing 100872, Peoples R China Xi'an University of Architecture & Technology; Xi'an University of Architecture & Technology; University of Southern Denmark; China University of Mining & Technology; Renmin University of China Yuan, LJ (corresponding author), Xian Univ Architecture & Technol, Sch Environm & Municipal Engn, 13 Yanta Rd, Xian 710055, Peoples R China.;Yuan, LJ (corresponding author), Xian Univ Architecture & Technol, Key Lab Northwest Water Resource Environm & Ecol, MOE, 13 Yanta Rd, Xian 710055, Peoples R China.;Yuan, LJ (corresponding author), Shaanxi Key Lab Environm Engn, Xian 710055, Peoples R China. zhaojie19870930@126.com; yuanlinjiang@xauat.edu.cn; ksu@igt.sdu.dk; hanhuang1224@hotmail.com; guanpanbo@163.com; jiace1020@foxmail.com HUANG, HAN/GSN-4694-2022 Sun, Kun/0000-0003-1234-6781; Huang, Han/0000-0003-2867-128X 38 1 1 4 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability AUG 2022.0 14 15 9430 10.3390/su14159430 0.0 18 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology 3R6HV Green Published, gold 2023-03-23 WOS:000839012200001 0
J Zhang, D; Huang, F; Khansari, M; Berendschot, TTJM; Xu, XY; Dashtbozorg, B; Sun, Y; Zhang, J; Tan, T Zhang, Dan; Huang, Fan; Khansari, Maziyar; Berendschot, Tos T. J. M.; Xu, Xiayu; Dashtbozorg, Behdad; Sun, Yue; Zhang, Jiong; Tan, Tao Automatic corneal nerve fiber segmentation and geometric biomarker quantification EUROPEAN PHYSICAL JOURNAL PLUS English Article CONFOCAL-MICROSCOPY; DIABETIC-RETINOPATHY; DIAGNOSIS; TORTUOSITY; IMAGES; DAMAGE Geometric and topological features of corneal nerve fibers in confocal microscopy images are important indicators for the diagnosis of common diseases such as diabetic neuropathy. Quantitative analysis of these important biomarkers requires an accurate segmentation of the nerve fiber network. Currently, most of the analysis are performed based on manual annotations of the nerve fiber segments, while a fully automatic corneal nerve fiber extraction and analysis framework is still needed. In this paper, we establish a fully convolutional network method to precisely enhance and segment corneal nerve fibers in microscopy images. Based on the segmentation results, automatic tortuosity measurement and branching detection modules are established to extract valuable geometric and topological biomarkers. The proposed segmentation method is validated on a dataset with 142 images. The experimental results show that our deep learning-based framework outperforms state-of-the-art segmentation approaches. The biomarker extraction methods are validated on two different datasets, demonstrating high effectiveness and reliability of the proposed methods. [Zhang, Dan; Tan, Tao] Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands; [Huang, Fan] Eindhoven Univ Technol, Dept Biomed Engn, Eindhoven, Netherlands; [Khansari, Maziyar; Zhang, Jiong] Univ Southern Calif, Keck Sch Med, USC Stevens Neuroimaging & Informat Inst, Lab Neuro Imaging LONI, Los Angeles, CA 90007 USA; [Berendschot, Tos T. J. M.] Univ Eye Clin Maastricht, Maastricht, Netherlands; [Xu, Xiayu] Xi An Jiao Tong Univ, Sch Life Sci & Technol, Minist Educ, Key Lab Biomed Informat Engn, Xian, Peoples R China; [Dashtbozorg, Behdad] Netherlands Canc Inst, Dept Surg, Amsterdam, Netherlands; [Sun, Yue] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands Eindhoven University of Technology; Eindhoven University of Technology; University of Southern California; Maastricht University; Maastricht University Medical Centre (MUMC); Xi'an Jiaotong University; Netherlands Cancer Institute; Eindhoven University of Technology Tan, T (corresponding author), Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands. t.tan1@tue.nl Berendschot, Tos TJM/M-8509-2016 Berendschot, Tos TJM/0000-0002-8101-939X National Natural Science Foundation of China [81801776]; Natural Science Research Foundation of Jiangsu Province [BK20170256]; Shanghai Key Lab of Digital Media Processing and Transmission [STCSM 18DZ2270700] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Research Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Shanghai Key Lab of Digital Media Processing and Transmission This work was supported in part by National Natural Science Foundation of China (No. 81801776), Natural Science Research Foundation of Jiangsu Province (No. BK20170256) and Shanghai Key Lab of Digital Media Processing and Transmission (STCSM 18DZ2270700). 44 6 6 1 9 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2190-5444 EUR PHYS J PLUS Eur. Phys. J. Plus FEB 20 2020.0 135 2 266 10.1140/epjp/s13360-020-00127-y 0.0 16 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics KQ9XK hybrid, Green Published 2023-03-23 WOS:000517274900001 0
C Vieira, A; Ribeiro, B; Sobral, H; Coimbra, AP; Crisostomo, M; Ferreira, JP; Liu, T IEEE Vieira, Alexandra; Ribeiro, Bernardete; Sobral, Heloisa; Coimbra, A. Paulo; Crisostomo, Manuel; Ferreira, Joao P.; Liu, Tao Dynamic Human Gait VGRF Reference Profile Generation via Extreme Learning Machine 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 08-13, 2018 Rio de Janeiro, BRAZIL BNN; Dynamic VGRF; ELM; Gait Pattern; MSVR GROUND REACTION FORCES; PREDICTION; CLASSIFICATION; REGRESSION; KINEMATICS Vertical Ground Reaction Forces (VGRF) reference profiles of human gait are important in medicine, for the recognition of gait disorders and in the assessment of rehabilitation treatments. A walking human is a dynamic varying system and in spite of the VGRF reference dependence on several patient's variables, doctors traditionally use the same static reference for all patients. The purpose of this study is to find out if an Extreme Learning Machine (ELM) is adequate to generate the dynamic VGRF reference profiles of healthy people depending on the subject's age, weight, height and stride duration. The ELM is compared with two other baseline Computational Intelligence (CI) methods, the Backpropagation Neural Network (BNN) and Multioutput Support Vector Regression (MSVR). Data from 28 healthy males walking at five different stride durations, collected using instrumented shoes, were used to train and test the CI models. The results showed that ELM is a well suited method to generate the dynamic VGRF reference profile for both dominant and non- dominant limbs, showing the lowest root mean square errors for the test set, 0.0201 and 0.0243 (fraction of body weight) for the dominant and non-dominant limbs respectively. This study reveals a promising methodology that can be implemented in real time gait analysis, allowing doctors to find a specific reference gait pattern for the gait analysis of an unhealthy person by specifying the age, weight, height and stride duration. [Vieira, Alexandra; Ribeiro, Bernardete] Univ Coimbra, CISUC, Dept Informat Engn, Coimbra, Portugal; [Sobral, Heloisa; Coimbra, A. Paulo; Crisostomo, Manuel] Univ Coimbra, Inst Syst & Robot, Dept Elect & Comp Engn, Coimbra, Portugal; [Ferreira, Joao P.] Univ Coimbra, Inst Syst & Robot, Dept Elect Engn, Super Inst Engn Coimbra, Coimbra, Portugal; [Liu, Tao] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China Universidade de Coimbra; Universidade de Coimbra; Universidade de Coimbra; Zhejiang University Vieira, A (corresponding author), Univ Coimbra, CISUC, Dept Informat Engn, Coimbra, Portugal. Ferreira, João Paulo/L-5034-2019; Ribeiro, Bernardete/A-8010-2016; Ferreira, João P/E-3816-2016 Ferreira, João Paulo/0000-0003-0143-9421; Ribeiro, Bernardete/0000-0002-9770-7672; Ferreira, João P/0000-0003-0143-9421 Fundacao para a Ciencia e a Tecnologia (FCT) [PTDC/EEI-AUT/5141/2014]; COMPETE 2020 program [PTDC/EEI-AUT/5141/2014]; Fundação para a Ciência e a Tecnologia [PTDC/EEI-AUT/5141/2014] Funding Source: FCT Fundacao para a Ciencia e a Tecnologia (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); COMPETE 2020 program; Fundação para a Ciência e a Tecnologia The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT) and COMPETE 2020 program for the financial support to the project Automatic Adaptation of an Humanoid Robot Gait to Different Floor-Robot Friction Coefficients (PTDC/EEI-AUT/5141/2014). 28 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-5090-6014-6 IEEE IJCNN 2018.0 7 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BQ3NO 2023-03-23 WOS:000585967403108 0
C Han, J; Qian, K; Song, MS; Yang, ZJ; Ren, Z; Liu, S; Liu, J; Zheng, HY; Ji, W; Koike, T; Li, X; Zhang, ZX; Yamamoto, Y; Schuller, BW Int Speech Commun Assoc Han, Jing; Qian, Kun; Song, Meishu; Yang, Zijiang; Ren, Zhao; Liu, Shuo; Liu, Juan; Zheng, Huaiyuan; Ji, Wei; Koike, Tomoya; Li, Xiao; Zhang, Zixing; Yamamoto, Yoshiharu; Schuller, Bjoern W. An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety INTERSPEECH 2020 Interspeech English Proceedings Paper Interspeech Conference OCT 25-29, 2020 Shanghai, PEOPLES R CHINA COVID-19 diagnosis; speech analysis; computational paralinguistics RECOGNITION; EMOTION The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease. [Han, Jing; Song, Meishu; Yang, Zijiang; Ren, Zhao; Liu, Shuo; Schuller, Bjoern W.] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany; [Qian, Kun; Koike, Tomoya; Yamamoto, Yoshiharu] Univ Tokyo, Educ Physiol Lab, Tokyo, Japan; [Liu, Juan] Huazhong Univ Sci & Technol, Tongji Med Coll, Cent Hosp Wuhan, Dept Plast Surg, Wuhan, Peoples R China; [Zheng, Huaiyuan; Ji, Wei] Huazhong Univ Sci & Technol, Wuhan Union Hosp, Tongji Med Coll, Dept Hand Surg, Wuhan, Peoples R China; [Li, Xiao] Chongqing Med Univ, Childrens Hosp, Dept Neurol, Chongqing, Peoples R China; [Zhang, Zixing; Schuller, Bjoern W.] Imperial Coll London, GLAM Grp Language Audio & Mus, London, England University of Augsburg; University of Tokyo; Huazhong University of Science & Technology; Huazhong University of Science & Technology; Chongqing Medical University; Imperial College London Qian, K (corresponding author), Univ Tokyo, Educ Physiol Lab, Tokyo, Japan.;Liu, J (corresponding author), Huazhong Univ Sci & Technol, Tongji Med Coll, Cent Hosp Wuhan, Dept Plast Surg, Wuhan, Peoples R China.;Zheng, HY; Ji, W (corresponding author), Huazhong Univ Sci & Technol, Wuhan Union Hosp, Tongji Med Coll, Dept Hand Surg, Wuhan, Peoples R China. jing.han@informatik.uni-augsburg.de; gian@p.u-tokyo.ac.jp; liujuan_1018@126.com; zhenghuaiyuan@126.com; jiwei1230@foxmail.com Qian, Kun/0000-0002-1918-6453 Zhejiang Lab's International Talent Fund for Young Professionals (Project HANAMI), P. R. China; JSPS Postdoctoral Fellowship for Research in Japan from the Japan Society for the Promotion of Science (JSPS), Japan [P19081]; Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan [19F19081, 17H00878] Zhejiang Lab's International Talent Fund for Young Professionals (Project HANAMI), P. R. China; JSPS Postdoctoral Fellowship for Research in Japan from the Japan Society for the Promotion of Science (JSPS), Japan; Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)) We express our deepest sorrow for those who left us due to COVID19; they are lives, not numbers. We further express our highest gratitude and respect to the clinicians and scientists, and anyone else these days helping to fight against COVID-19, and at the same time help us maintain our daily lives. This work was partially supported by the Zhejiang Lab's International Talent Fund for Young Professionals (Project HANAMI), P. R. China, the JSPS Postdoctoral Fellowship for Research in Japan (ID No. P19081) from the Japan Society for the Promotion of Science (JSPS), Japan, and the Grants-in-Aid for Scientific Research (No. 19F19081 and No. 17H00878) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. 32 14 14 2 2 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2020.0 4946 4950 10.21437/Interspeech.2020-2223 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4QT Green Submitted, Green Published 2023-03-23 WOS:000833594105017 0
J Sodhro, AH; Pirbhulal, S; Luo, ZW; Muhammad, K; Zahid, N Sodhro, Ali Hassan; Pirbhulal, Sandeep; Luo, Zongwei; Muhammad, Khan; Zahid, Noman Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems IEEE INTERNET OF THINGS JOURNAL English Article Energy-efficient framework; IoT; sixth generation (6G); smart automation; user terminal (UT) devices MANAGEMENT; FRAMEWORK; CHALLENGES Energy-efficient communication has become the center of attention from various interdisciplinary fields, such as industrial automation, healthcare, and transportation, among others. Besides, proliferation in the artificial intelligence (AI)-based sixth-generation (6G) technology for achieving the smart automation system has caught the attention of both academia and industry. Most of the intelligent automation systems are formed by IoT-based user terminal (UT) devices for multimedia (i.e., video, audio, image, and text) content delivery with high clarity and efficiency. Customer satisfaction/perception, i.e., Quality of Experience (QoE), is an essential factor to be analyzed because the Quality of Service (QoS) is not a suitable candidate to portray the feelings and expectations of users during multimedia transmission. Therefore, the energy-efficient, entropy-aware communication, and QoE analysis through IoT devices are the dire need. This article focuses on how the energy-efficient communication and user's QoE level can be captured through the UT device during multimedia transmission. Thus first, QoS-based joint energy and entropy optimization (QJEEO) algorithm is proposed. Second, the 6G-driven multimedia data structure model and framework are developed for modeling and evaluation of QoE with acquisition time. Third, the relationship between subjective test score (i.e., surveyed data) and objective performance metrics with mobility/speed of IoT-based devices for multimedia service is established. Fourth, the correlation model is proposed for integrating QoS parameters with estimated QoE perceptions. The experimental results indicate that QoE is modeled and evaluated with acquisition time and correlated with QoS parameter, i.e., packet loss ratio (PLR), and average transfer delay during energy-efficient multimedia transmission in 6G-based networks to improve the satisfaction level of customers. [Sodhro, Ali Hassan] Linkoping Univ, Comp & Informat Sci Dept, S-58183 Linkoping, Sweden; [Sodhro, Ali Hassan] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Sodhro, Ali Hassan; Zahid, Noman] Sukkur IBA Univ, Elect Engn Dept, Sukkur 65200, Pakistan; [Pirbhulal, Sandeep] Univ Beira Interior, Dept Comp Sci, P-6201001 Covilha, Portugal; [Luo, Zongwei] Beijing Normal Univ Zhuhai, BNU HKBU UIC, BNU UIC Inst AI & Future Networks, Zhuhai 519087, Peoples R China; [Muhammad, Khan] Sejong Univ, Dept Software, Seoul 143747, South Korea Linkoping University; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Sukkur IBA University; Universidade da Beira Interior; Beijing Normal University; Beijing Normal University Zhuhai; Sejong University Pirbhulal, S (corresponding author), Univ Beira Interior, Dept Comp Sci, P-6201001 Covilha, Portugal.;Luo, ZW (corresponding author), Beijing Normal Univ Zhuhai, BNU HKBU UIC, BNU UIC Inst AI & Future Networks, Zhuhai 519087, Peoples R China.;Muhammad, K (corresponding author), Sejong Univ, Dept Software, Seoul 143747, South Korea. ali.hassan.sodhro@liu.se; sandeep.hemnani28@gmail.com; lzwqhk@outlook.com; khan.muhammad@ieee.org; noman.mece17@iba-suk.edu.pk Sodhro, Ali Hassan/ABE-1955-2021; Zahid, Noman/ADI-9651-2022; Pirbhulal, Sandeep/X-7191-2019; Muhammad, Khan/L-9059-2016; Sodhro, Ali Hassan/ABE-1975-2021; Hassan, Ali/O-7769-2017 Zahid, Noman/0000-0002-7304-398X; Pirbhulal, Sandeep/0000-0003-0843-8974; Muhammad, Khan/0000-0003-4055-7412; Hassan, Ali/0000-0001-5502-530X; Muhammad, Khan/0000-0002-5302-1150 CAS President's International Fellowship Initiative Project China [2020VBC0002]; Sukkur IBA University, Sukkur, Pakistan; BNU Startup Research Fund; UIC Startup Research Fund CAS President's International Fellowship Initiative Project China; Sukkur IBA University, Sukkur, Pakistan; BNU Startup Research Fund; UIC Startup Research Fund This work was supported in part by CAS President's International Fellowship Initiative Project China under Grant 2020VBC0002; in part by Sukkur IBA University, Sukkur, Pakistan; in part by BNU Startup Research Fund; and in part by UIC Startup Research Fund. 28 38 38 2 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. APR 1 2021.0 8 7 5141 5148 10.1109/JIOT.2020.3024715 0.0 8 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications RD4FS 2023-03-23 WOS:000633436600004 0
C Vitek, M; Das, A; Pourcenoux, Y; Missler, A; Paumier, C; Das, S; De Ghosh, I; Lucio, DR; Zanlorensi, LA; Menotti, D; Boutros, F; Damer, N; Grebe, JH; Kuijper, A; Hu, J; He, Y; Wang, C; Liu, H; Wang, Y; Sun, Z; Osorio-Roig, D; Rathgeb, C; Busch, C; Tapia, J; Valenzuela, A; Zampoukis, G; Tsochatzidis, L; Pratikakis, I; Nathan, S; Suganya, R; Mehta, V; Dhall, A; Raja, K; Gupta, G; Khiarak, JN; Akbari-Shahper, M; Jaryani, F; Asgari-Chenaghlu, M; Vyas, R; Dakshit, S; Peer, P; Pal, U; Struc, V IEEE Vitek, M.; Das, A.; Pourcenoux, Y.; Missler, A.; Paumier, C.; Das, S.; De Ghosh, I; Lucio, D. R.; Zanlorensi Jr, L. A.; Menotti, D.; Boutros, F.; Damer, N.; Grebe, J. H.; Kuijper, A.; Hu, J.; He, Y.; Wang, C.; Liu, H.; Wang, Y.; Sun, Z.; Osorio-Roig, D.; Rathgeb, C.; Busch, C.; Tapia, J.; Valenzuela, A.; Zampoukis, G.; Tsochatzidis, L.; Pratikakis, I; Nathan, S.; Suganya, R.; Mehta, V; Dhall, A.; Raja, K.; Gupta, G.; Khiarak, J. N.; Akbari-Shahper, M.; Jaryani, F.; Asgari-Chenaghlu, M.; Vyas, R.; Dakshit, S.; Peer, P.; Pal, U.; Struc, V SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment IEEE/IAPR INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2020) English Proceedings Paper IEEE/IAPR International Joint Conference on Biometrics (IJCB) SEP 28-OCT 01, 2020 ELECTR NETWORK IEEE,IAPR,IEEE Biometr Council,Google,Qualcomm,NSF RECOGNITION The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. [Vitek, M.; Struc, V] Univ Ljubljana UL, Ljubljana, Slovenia; [Das, A.; Pal, U.] Indian Stat Inst ISI, Bengaluru, India; [Pourcenoux, Y.] Grenoble Inst Technol INP, Grenoble, France; [Missler, A.; Paumier, C.] Ecole Natl Super Elect & Ses Applicat ENSEA, Cergy, France; [Das, S.] Univ Engn & Management UEM, Kolkata, India; [De Ghosh, I] Barrackpore Rastraguru Surendranath Coll BRSNC, Kolkata, India; [Lucio, D. R.; Zanlorensi Jr, L. A.; Menotti, D.] Fed Univ Parana UFPR, Curitiba, Parana, Brazil; [Boutros, F.; Damer, N.; Grebe, J. H.; Kuijper, A.] Fraunhofer Inst Comp Graph Res IGD, Darmstadt, Germany; [Boutros, F.; Damer, N.; Kuijper, A.] Tech Univ Darmstadt TUD, Darmstadt, Germany; [Hu, J.; He, Y.; Liu, H.; Wang, Y.; Sun, Z.] Chinese Acad Sci, Beijing, Peoples R China; [Wang, C.] Beijing Univ Civil Engn & Architecture BUCEA, Beijing, Peoples R China; [Osorio-Roig, D.; Rathgeb, C.; Busch, C.] Hsch Darmstadt HDA, Darmstadt, Germany; [Tapia, J.] Univ Santiago USACH, Santiago, Chile; [Valenzuela, A.] TOC Biometr, Santiago, Chile; [Zampoukis, G.; Tsochatzidis, L.; Pratikakis, I] Democritus Univ Thrace DUTH, Athens, Greece; [Nathan, S.] Couger Inc, Tokyo, Japan; [Suganya, R.] Thiagarajar Coll Engn TCE, Chennai, Tamil Nadu, India; [Mehta, V; Dhall, A.] Indian Inst Technol Ropar IIT RPR, Ropar, India; [Dhall, A.] Monash Univ MU, Melbourne, Vic, Australia; [Raja, K.; Gupta, G.] Norwegian Univ Sci & Technol NTNU, Trondheim, Norway; [Khiarak, J. N.] Warsaw Univ Technol WUT, Warsaw, Poland; [Akbari-Shahper, M.; Asgari-Chenaghlu, M.] Tabriz Univ TU, Tabriz, Iran; [Jaryani, F.] Arak Univ AU, Arak, Iran; [Vyas, R.] Bennet Univ BU, Greater Noida, India; [Dakshit, S.] Maulana Abul Kalam Univ Technol MAKAUT, Calcutta Inst Engn & Management, Kolkata, India; [Peer, P.] Univ Texas Dallas UTD, Dallas, TX USA Indian Statistical Institute; Indian Statistical Institute Bangalore; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universidade Federal do Parana; Fraunhofer Gesellschaft; Technical University of Darmstadt; Chinese Academy of Sciences; Hochschule Darmstadt; Universidad de Santiago de Chile; Thiagarajar College of Engineering; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Ropar; Norwegian University of Science & Technology (NTNU); Maulana Abul Kalam Azad University of Technology Vitek, M (corresponding author), Univ Ljubljana UL, Ljubljana, Slovenia. Menotti, David/M-6205-2014; Lucio, Diego Rafael/GLT-4634-2022 Menotti, David/0000-0003-2430-2030; Zanlorensi Junior, Luiz Antonio/0000-0003-2545-0588; nathan, sabari/0000-0003-2621-0690; Gupta, Gourav/0000-0003-3122-1976 ARRS [P2-0250, P2-0214]; European Union [860813]; German Federal Ministry of Education and Research; Hessen State Ministry for Higher Education, Research and the Arts within National Research Center for Applied Cybersecurity ATHENE ARRS(Slovenian Research Agency - Slovenia); European Union(European Commission); German Federal Ministry of Education and Research(Federal Ministry of Education & Research (BMBF)); Hessen State Ministry for Higher Education, Research and the Arts within National Research Center for Applied Cybersecurity ATHENE Supported in parts by the ARRS Research Programmes P2-0250 (B) Metrology and Biometric Systems, P2-0214 (A) Computer Vision and the ARRS young researcher program, by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 860813 - TReSPAsS-ETN and the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. 47 13 13 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-9186-7 2020.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematical & Computational Biology BS4XQ 2023-03-23 WOS:000723870900028 0
J Wang, JR; Liu, Y; Li, PG; Lin, ZX; Sindakis, S; Aggarwal, S Wang, Jingran; Liu, Yi; Li, Peigong; Lin, Zhenxing; Sindakis, Stavros; Aggarwal, Sakshi Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality JOURNAL OF THE KNOWLEDGE ECONOMY English Article; Early Access Big data analytics; Data quality; Decision-making; Open data; Economic growth; IT-based resources DECISION-MAKING; INFORMATION-SYSTEMS; MANAGEMENT Competition in the business world is fierce, and poor decisions can bring disaster to firms, especially in the big data era. Decision quality is determined by data quality, which refers to the degree of data usability. Data is the most valuable resource in the twenty-first century. The open data (OD) movement offers publicly accessible data for the growth of a knowledge-based society. As a result, the idea of OD is a valuable information technology (IT) instrument for promoting personal, societal, and economic growth. Users must control the level of OD in their practices in order to advance these processes globally. Without considering data conformity with norms, standards, and other criteria, what use is it to use data in science or practice only for the sake of using it? This article provides an overview of the dimensions, subdimensions, and metrics utilized in research publications on OD evaluation. To better understand data quality, we review the literature on data quality studies in information systems. We identify the data quality dimensions, antecedents, and their impacts. In this study, the notion of Data Analytics Competency is developed and validated as a five-dimensional formative measure (i.e., data quality, the bigness of data, analytical skills, domain knowledge, and tool sophistication) and its effect on corporate decision-making performance is experimentally examined (i.e., decision quality and decision efficiency). By doing so, we provide several research suggestions, which information system (IS) researchers can leverage when investigating future research in data quality. [Li, Peigong; Lin, Zhenxing] Shanghai Lixin Univ Accounting & Finance, Sch Accounting, 2800 Wenxiang Rd, Shanghai 201620, Peoples R China; [Wang, Jingran] Sogang Univ, Sogang Business Sch, 35 Baekbeom Ro, Seoul, South Korea; [Liu, Yi] Univ Incarnate Word, H E B Sch Business & Adm, 4301 Broadway, San Antonio, TX 78209 USA; [Sindakis, Stavros] Hellen Open Univ, Sch Social Sci, 18 Aristotelous St, Patras 26335, Greece; [Aggarwal, Sakshi] Inst Strategy Entrepreneurship & Educ Growth, Athens, Greece Shanghai Lixin University of Accounting & Finance; Sogang University; Hellenic Open University Lin, ZX (corresponding author), Shanghai Lixin Univ Accounting & Finance, Sch Accounting, 2800 Wenxiang Rd, Shanghai 201620, Peoples R China. jrwang1@sina.com; yiliu5@uiwtx.edu; pgli@xmu.edu.cn; linzx@lixin.edu.cn; sindakis.stavros@ac.eap.gr; s.aggarwal@iseeg.org H-E-B School of Business Administration; University of the Incarnate Word; Social Science Foundation of the Ministry of Education of China [16YJA630025] H-E-B School of Business Administration; University of the Incarnate Word; Social Science Foundation of the Ministry of Education of China(Ministry of Education, China) The paper was supported by the H-E-B School of Business & Administration, the University of the Incarnate Word, and the Social Science Foundation of the Ministry of Education of China (16YJA630025). 68 0 0 0 0 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1868-7865 1868-7873 J KNOWL ECON J. Knowl. Econ. 10.1007/s13132-022-01096-6 0.0 FEB 2023 20 Economics Social Science Citation Index (SSCI) Business & Economics 8Y4RG Bronze 2023-03-23 WOS:000932685700002 0
J Chen, L; Pan, XY; Zeng, T; Zhang, YH; Zhang, YH; Huang, T; Cai, YD Chen, Lei; Pan, XiaoYong; Zeng, Tao; Zhang, Yu-Hang; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY English Article cancer subtype; expression rule; immunosignature; multi-class classification; feature selection CELL; IDENTIFICATION; ANGIOGENESIS; GENES; TRANSPLANTATION; METHYLATION; PREDICTION; DIAGNOSIS; PATHWAYS; NETWORK Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the immunosignature of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed. [Chen, Lei; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China; [Chen, Lei] East China Normal Univ, Shanghai Key Lab Pure Math & Math Practice PMMP, Shanghai, Peoples R China; [Pan, XiaoYong] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Key Lab Syst Control & Informat Proc, Minist Educ China, Shanghai, Peoples R China; [Pan, XiaoYong] Univ Ghent, IDLab, Dept Elect & Informat Syst, Ghent, Belgium; [Zeng, Tao] Chinese Acad Sci, Inst Biochem & Cell Biol, Key Lab Syst Biol, Shanghai, Peoples R China; [Zhang, Yu-Hang; Huang, Tao] Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai, Peoples R China; [Zhang, YunHua] Anhui Agr Univ, Anhui Prov Key Lab Farmland Ecol Conservat & Poll, Sch Resources & Environm, Hefei, Anhui, Peoples R China Shanghai University; Shanghai Maritime University; East China Normal University; Ministry of Education, China; Shanghai Jiao Tong University; Ghent University; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; Shanghai Institutes for Biological Sciences, CAS; Anhui Agricultural University Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai, Peoples R China. tohuangtao@126.com; cai_yud@126.com Zhang, Yu-Hang/AAQ-9012-2021; Zeng, Tao/A-1711-2012; Chen, Lei/A-2275-2011; Zeng, Tao/AAC-9375-2020; Zhang, Yu-Hang/O-9774-2015 Zhang, Yu-Hang/0000-0003-3825-0796; Zeng, Tao/0000-0002-0295-3994; Chen, Lei/0000-0003-3068-1583; Zeng, Tao/0000-0002-0295-3994; Zhang, Yu-Hang/0000-0003-3825-0796 Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]; National Key RAMP;D Program of China [2018YFC0910403]; National Natural Science Foundation of China [31701151, 31872418]; Natural Science Foundation of Shanghai [17ZR1412500]; Shanghai Sailing Program [16YF1413800]; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]; key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]; Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000] Shanghai Municipal Science and Technology Major Project; National Key RAMP;D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS); key Laboratory of Stem Cell Biology of Chinese Academy of Sciences; Science and Technology Commission of Shanghai Municipality (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)) This study was supported by the Shanghai Municipal Science and Technology Major Project [2017SHZDZX01], National Key R&D Program of China [2018YFC0910403], National Natural Science Foundation of China [31701151, 31872418], Natural Science Foundation of Shanghai [17ZR1412500], Shanghai Sailing Program [16YF1413800], the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245], the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703], Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000]. 81 7 7 2 12 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-4185 FRONT BIOENG BIOTECH Front. Bioeng. Biotechnol. NOV 29 2019.0 7 370 10.3389/fbioe.2019.00370 0.0 13 Biotechnology & Applied Microbiology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Science & Technology - Other Topics JW0FJ 31850330.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000502734900001 0
J Long, Y; Xia, GS; Li, SY; Yang, W; Yang, MY; Zhu, XX; Zhang, LP; Li, DR Long, Yang; Xia, Gui-Song; Li, Shengyang; Yang, Wen; Yang, Michael Ying; Zhu, Xiao Xiang; Zhang, Liangpei; Li, Deren On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Remote sensing; Benchmark testing; Task analysis; Earth; Semantics; Annotations; Machine learning algorithms; Annotation; benchmark datasets; Million Aerial Image Dataset (Million-AID); remote sensing (RS) image interpretation; scene classification CONVOLUTIONAL NEURAL-NETWORKS; REMOTE-SENSING IMAGES; OBJECT DETECTION; SEMANTIC SEGMENTATION; SCENE CLASSIFICATION; HYPERSPECTRAL IMAGES; VEHICLE DETECTION; TARGET DETECTION; RANDOM FOREST; ALGORITHMS The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones. [Long, Yang; Xia, Gui-Song; Yang, Wen; Zhang, Liangpei; Li, Deren] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China; [Xia, Gui-Song] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China; [Li, Shengyang] Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing 100094, Peoples R China; [Yang, Wen] Wuhan Univ, Sch Elect Informat, Wuhan 430079, Peoples R China; [Yang, Michael Ying] Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7522 NB Enschede, Netherlands; [Zhu, Xiao Xiang] German Aerosp Ctr DLR, Remote Sensing Technol, D-82234 Wessling, Germany; [Zhu, Xiao Xiang] Tech Univ Munich, D-80333 Munich, Germany Wuhan University; Wuhan University; Chinese Academy of Sciences; Technology & Engineering Center for Space Utilization, CAS; Wuhan University; University of Twente; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich Xia, GS (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China. longyang@whu.edu.cn; guisong.xia@whu.edu.cn; shyli@csu.ac.cn; yangwen@whu.edu.cn; michael.yang@utwente.nl; xiaoxiang.zhu@dlr.de; zlp62@whu.edu.cn; drli@whu.edu.cn Xia, Gui-Song/HII-9232-2022; Yang, Michael/AAC-6698-2019 Xia, Gui-Song/0000-0001-7660-6090; Yang, Michael/0000-0002-0649-9987; Yang, Wen/0000-0002-3263-8768; Zhu, Xiao Xiang/0000-0001-5530-3613; li, sheng yang/0000-0002-9888-9869 National Natural Science Foundation of China (NSFC) [61922065, 61771350, 41820104006, 61871299, 92038301]; Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) [2019AEA170]; German Federal Ministry of Education, and Research (BMBF) of the International Future AI Lab AI4EO -Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics, and Beyond National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies); German Federal Ministry of Education, and Research (BMBF) of the International Future AI Lab AI4EO -Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics, and Beyond(Federal Ministry of Education & Research (BMBF)) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61922065, Grant 61771350, Grant 41820104006, Grant 61871299, and Grant 92038301, in part by the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant 2019AEA170, and in part by the German Federal Ministry of Education, and Research (BMBF) in the framework of the International Future AI Lab AI4EO -Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics, and Beyond. 213 26 28 8 43 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14 4205 4230 10.1109/JSTARS.2021.3070368 0.0 26 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology RX6NE gold, Green Accepted, Green Submitted 2023-03-23 WOS:000647337300002 0
J Zhou, N; Cheng, XD; Xia, YQ; Liu, YJ Zhou, Ning; Cheng, Xiaodong; Xia, Yuanqing; Liu, Yanjun Fully Adaptive-Gain-Based Intelligent Failure-Tolerant Control for Spacecraft Attitude Stabilization Under Actuator Saturation IEEE TRANSACTIONS ON CYBERNETICS English Article Space vehicles; Actuators; Attitude control; Adaptation models; Robustness; Fault tolerance; Adaptive control; attitude stabilization; failure-tolerant control; finite-time control; input saturation TRACKING CONTROL; TIME STABILIZATION; RIGID SPACECRAFT; OBSERVER; FEEDBACK This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples. [Zhou, Ning] Hebei Univ Sci & Technol, Sch Elect Engn, Shijiazhuang 050018, Hebei, Peoples R China; [Cheng, Xiaodong] Eindhoven Univ Technol, Control Syst Grp, NL-5600 MB Eindhoven, Netherlands; [Xia, Yuanqing] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China; [Liu, Yanjun] Liaoning Univ Technol, Coll Sci, Jinzhou 121001, Peoples R China Hebei University of Science & Technology; Eindhoven University of Technology; Beijing Institute of Technology; Liaoning University of Technology Cheng, XD (corresponding author), Eindhoven Univ Technol, Control Syst Grp, NL-5600 MB Eindhoven, Netherlands. zhouning2010@gmail.com; x.cheng@tue.nl; xia_yuanqing@bit.edu.cn; liuyanjun@live.com 刘, 严君/GZL-5764-2022; liu, yan/HCI-5542-2022; liu, yan/HGV-1365-2022 , Ning/0000-0002-7457-5048 National Natural Science Foundation of China [61603095, 61972093, 61720106010, 61973147]; Science and Technology on Space Intelligent Control Laboratory [KGJZDSYS-2018-05] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology on Space Intelligent Control Laboratory This work was supported in part by the National Natural Science Foundation of China under Grant 61603095, Grant 61972093, Grant 61720106010, and Grant 61973147. The work of Yuanqing Xia was supported in part by the Science and Technology on Space Intelligent Control Laboratory under Grant KGJZDSYS-2018-05. 47 18 18 26 85 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JAN 2022.0 52 1 344 356 10.1109/TCYB.2020.2969281 0.0 13 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science YG0IU 32149666.0 Green Submitted 2023-03-23 WOS:000742182700033 0
J Meng, WZ; Wang, Y; Wong, DS; Wen, S; Xiang, Y Meng, Weizhi; Wang, Yu; Wong, Duncan S.; Wen, Sheng; Xiang, Yang TouchWB: Touch behavioral user authentication based on web browsing on smartphones JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Behavioral authentication; Touch gesture; Smartphone security; Access control; Touchscreen; Machine learning Modern mobile devices especially smartphones have rapidly evolved and are widely adopted by people of different ages. Smartphones can assist users in a variety of activities, i.e., from social networking to online shopping, but also have become an attractive target for cyber-criminals due to the stored personal data and sensitive information. The traditional authentication mechanisms like PIN suffer from well-known limitations and drawbacks in the security community; thus, touch behavioral authentication has recently received much attention. Intuitively, authentication based on free touches would be hard to build a stand-alone system. In this work, we advocate that such authentication can consider users' actions under certain phone applications like web browser, and then propose a touch gesture-based authentication scheme, called TouchWB, with 21 features that can be extracted from web browsing gestures. For evaluation, we implemented the scheme on Android phones and conducted a user study involving 48 participants. Experimental results demonstrated that our approach could reduce the touch behavioral deviation by nearly half and achieve an average error rate of about 2.4% by using a combined classifier of PSO-RBFN. [Meng, Weizhi; Wang, Yu] Guangzhou Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China; [Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Wong, Duncan S.] Hong Kong Appl Sci & Technol Res Inst, Hong Kong, Hong Kong, Peoples R China; [Wen, Sheng] Swinburne Univ Technol, Hawthorn, Vic, Australia; [Xiang, Yang] Swinburne Univ Technol, Digital Res & Innovat Capabil Platform, Hawthorn, Vic, Australia Guangzhou University; Technical University of Denmark; Hong Kong Applied Science & Technology Research Institute Company Limited (ASTRI); Swinburne University of Technology; Swinburne University of Technology Wang, Y (corresponding author), Guangzhou Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China. weme@dtu.dk; yuwang@gzhu.edu.cn Meng, Weizhi/N-9638-2019; Xiang, Yang/M-3527-2019; xiang, yang/HKN-0533-2023; Wen, Sheng/P-6699-2017 Meng, Weizhi/0000-0003-4384-5786; Xiang, Yang/0000-0001-5252-0831; Wen, Sheng/0000-0003-0655-666X National Natural Science Foundation of China [61472091] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We would like to thank the participants for their hard work in the user study. This work was partially supported by National Natural Science Foundation of China (No. 61472091). 39 37 37 0 18 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 J NETW COMPUT APPL J. Netw. Comput. Appl. SEP 1 2018.0 117 1 9 10.1016/j.jnca.2018.05.010 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science GM3LF 2023-03-23 WOS:000438005400001 0
J Yu, TH; Cao, JD; Rutkowski, L; Luo, YP Yu, Tianhu; Cao, Jinde; Rutkowski, Leszek; Luo, Yi-Ping Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks Via Hybrid Control IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Synchronization; Biological neural networks; Stability criteria; Multi-layer neural network; Perturbation methods; Neurons; Memristors; Complex-valued memristive neural networks (CVMNNs); differential inequality; finite-time stability (FTS); finite-time synchronization; impulsive control TO-STATE STABILITY; GLOBAL STABILITY; DISSIPATIVITY; DELAY The finite-time synchronization problem is investigated for the master-slave complex-valued memristive neural networks in this article. A novel Lyapunov-function based finite-time stability criterion with impulsive effects is proposed and utilized to design the decentralized finite-time synchronization controller. Not only the settling time but also the attractive domain with respect to the impulsive gain and average impulsive interval, as well as initial values is derived according to the sufficient synchronization condition. Two examples are outlined to illustrate the validity of our hybrid control strategy. [Yu, Tianhu] Luoyang Normal Univ, Dept Math, Luoyang 471934, Peoples R China; [Yu, Tianhu; Cao, Jinde] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China; [Cao, Jinde] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Rutkowski, Leszek] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland; [Rutkowski, Leszek] Univ Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland; [Luo, Yi-Ping] Hunan Inst Engn, Coll Elect & Informat Engn, Xiangtan 411104, Peoples R China Luoyang Normal University; Southeast University - China; Yonsei University; Technical University Czestochowa; University of Social Sciences; Hunan Institute of Engineering Cao, JD (corresponding author), Southeast Univ, Sch Math, Nanjing 210096, Peoples R China. yuthjianyang@163.com; jdcao@seu.edu.cn; leszek.rutkowski@pcz.pl; lyp@hnie.edu.cn Cao, Jinde/D-1482-2012 Cao, Jinde/0000-0003-3133-7119; Rutkowski, Leszek/0000-0001-6960-9525; Yi-ping, Luo/0000-0002-2963-7270; Yu, Tianhu/0000-0002-2031-9030 National Natural Science Foundation of China [11902137]; China Postdoctoral Science Foundation [2019M651633]; Program for Science and Technology Innovation Talents in Universities of Henan Province [20HASTIT023]; Key Project of Natural Science Foundation of China [61833005]; Polish National Science Center [2017/27/B/ST6/02852] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Program for Science and Technology Innovation Talents in Universities of Henan Province; Key Project of Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Polish National Science Center The work of Tianhu Yu was supported in part by the National Natural Science Foundation of China under 11902137, in part by the China Postdoctoral Science Foundation under Grant 2019M651633, and in part by the Program for Science and Technology Innovation Talents in Universities of Henan Province under Grant 20HASTIT023. The work of Jinde Cao was supported in part by the Key Project of Natural Science Foundation of China under Grant 61833005. The work of Leszek Rutkowski was supported by the Polish National Science Center under Grant 2017/27/B/ST6/02852. 39 16 16 19 71 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. AUG 2022.0 33 8 3938 3947 10.1109/TNNLS.2021.3054967 0.0 FEB 2021 10 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3Z9UL 33566775.0 2023-03-23 WOS:000732397600001 0
C Abbasi-Sureshjani, S; Dashtbozorg, B; Romeny, BMT; Fleuret, F Tavares, JMRS; Jorge, RMN Abbasi-Sureshjani, Samaneh; Dashtbozorg, Behdad; Romeny, Bart M. ter Haar; Fleuret, Francois Exploratory Study on Direct Prediction of Diabetes Using Deep Residual Networks VIPIMAGE 2017 Lecture Notes in Computational Vision and Biomechanics English Proceedings Paper 6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE) OCT 18-20, 2017 Porto, PORTUGAL ECCOMAS Retinal images; Diabetes; Diabetic retinopathy; Deep learning; ResNet Diabetes is threatening the health of many people in the world. People may be diagnosed with diabetes only when symptoms or complications such as diabetic retinopathy start to appear. Retinal images reflect the health of the circulatory system and they are considered as a cheap and patient-friendly source of information for diagnosis purposes. Convolutional neural networks have enhanced the performance of conventional image processing techniques significantly by neglecting inconsistent feature extraction pipelines and learning informative features automatically from data. In this work we explore the possibility of using the deep residual networks as one of the state-of-the-art convolutional networks to diagnose diabetes directly from retinal images, without using any blood glucose information. The results indicate that convolutional networks are able to capture informative differences between healthy and diabetic patients and it is possible to differentiate between these two groups using only the retinal images. The performance of the proposed method is significantly higher than human experts. [Abbasi-Sureshjani, Samaneh; Dashtbozorg, Behdad; Romeny, Bart M. ter Haar] Eindhoven Univ Technol, Dept Biomed Engn, Box 513, NL-5600 MB Eindhoven, Netherlands; [Romeny, Bart M. ter Haar] Northeastern Univ, Dept Biomed & Informat Engn, Shenyang 110000, Liaoning, Peoples R China; [Fleuret, Francois] Idiap Res Inst, Machine Learning Grp, Rue Marconi 19, CH-1920 Martigny, Switzerland Eindhoven University of Technology; Northeastern University - China Abbasi-Sureshjani, S (corresponding author), Eindhoven Univ Technol, Dept Biomed Engn, Box 513, NL-5600 MB Eindhoven, Netherlands. S.Abbasi@tue.nl; B.Dasht.Bozorg@tue.nl; Bart.Romeny@outlook.com; Francois.Fleuret@idiap.ch Romenij, Bart M. ter Haar/A-5323-2013; Dashtbozorg, Behdad/H-8728-2019 Dashtbozorg, Behdad/0000-0003-4443-1614 European Union's Seventh Framework Programme, Marie Curie Actions-Initial Training Network [607643] European Union's Seventh Framework Programme, Marie Curie Actions-Initial Training Network This project has received funding from the European Union's Seventh Framework Programme, Marie Curie Actions-Initial Training Network, under grant agreement No. 607643, Metric Analysis For Emergent Technologies (MAnET). The authors would like to thank Dr. Tos Berendschot and Dr. Jan Schouten from University Eye Clinic Maastricht for providing the fundus images and clinical data used during this research. 15 7 7 0 7 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2212-9391 978-3-319-68195-5; 978-3-319-68194-8 L N COMPUT VIS BIOME 2018.0 27 797 802 10.1007/978-3-319-68195-5_86 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging BK4NW 2023-03-23 WOS:000437032100086 0
J Ma, J; Sun, DW; Pu, HB; Wei, QY; Wang, XM Ma, Ji; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi; Wang, Xiaomei Protein content evaluation of processed pork meats based on a novel single shot (snapshot) hyperspectral imaging sensor JOURNAL OF FOOD ENGINEERING English Article Single shot; Snapshot; Hyperspectral imaging; Pork; Protein content; Processed meats TVB-N CONTENT; CARP CTENOPHARYNGODON-IDELLA; SQUARES REGRESSION PLSR; MOISTURE-CONTENT; COMPUTER VISION; FRESHNESS EVALUATION; ELECTRONIC NOSE; NONDESTRUCTIVE PREDICTION; CHEMICAL-COMPOSITION; CHEMOMETRIC ANALYSIS Protein is an important nutrient for people diet, and protein content is one of the most vital properties in quality assessment of pork meat. The potential of line-scanning based hyperspectral imaging (HSI) has been proved to be able to assess the protein content of meat, however quality of images obtained from line-scanning HSI is affected by sample transformation conditions. A novel single shot (snapshot) HSI sensor was employed to evaluate the protein content of numerous processed pork meat by using back propagation - neural network (BP-NN) and partial least squares regression (PLSR) predictive models. The results of BP-NN were better than that of PLSR. The best spectral profile was selected from reflectance, absorbance, and Kubelka-Munk (K-M) spectra by comparing the performances of their BP-NN models. The BP-NN model combined with absorbance spectra showed the best performance for evaluating the protein content of various processed pork meats with determination coefficient of cross-validation set (R-CV(2)) = 0.8318, and root mean square error of cross-validation set (RMSECV) = 8.38 mg/g, respectively. Results indicated the feasibility of determining the protein content of pork meats by means of single shot HSI. [Ma, Ji; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi; Wang, Xiaomei] South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China; [Ma, Ji; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi; Wang, Xiaomei] South China Univ Technol, Guangzhou Higher Educ Mega Ctr, Acad Contemporary Food Engn, Guangzhou 510006, Guangdong, Peoples R China; [Ma, Ji; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi; Wang, Xiaomei] Guangzhou Higher Educ Mega Ctr, Engn & Technol Res Ctr Guangdong Prov Intelligent, Guangzhou 510006, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, FRCFT, Dublin 4, Ireland South China University of Technology; South China University of Technology; University College Dublin Sun, DW (corresponding author), South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China.;Wei, QY (corresponding author), South China Univ Technol, Guangzhou Higher Educ Mega Ctr, Acad Contemporary Food Engn, Guangzhou 510006, Guangdong, Peoples R China. dawen.sun@ucd.ie; feweiqingyi@scut.edu.cn Ma, Ji/U-7550-2019; Sun, Da-Wen/B-1899-2012; Hongbin, Pu/ABE-8433-2020 Sun, Da-Wen/0000-0002-3634-9963; Hongbin, Pu/0000-0002-7977-1902; Ma, Ji/0000-0002-1437-464X National Key R&D Program of China [2017YFD0400404]; Collaborative Innovation Major Special Projects of Guangzhou City [201604020007]; Guangdong Provincial Science and Technology Plan Projects [2015A020209016, 2016A040403040]; Fundamental Research Funds for the Central Universities [2017MS067, 2017MS075]; International and Hong Kong Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology Equipment [2015KGJHZ001]; Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products [2016LM2154]; Innovation Centre of Guangdong Province for Modern Agricultural Science and Technology on Intelligent Sensing and Precision Control of Agricultural Product Qualities National Key R&D Program of China; Collaborative Innovation Major Special Projects of Guangzhou City; Guangdong Provincial Science and Technology Plan Projects; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); International and Hong Kong Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology Equipment; Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products; Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products; Innovation Centre of Guangdong Province for Modern Agricultural Science and Technology on Intelligent Sensing and Precision Control of Agricultural Product Qualities The authors are grateful to the National Key R&D Program of China (2017YFD0400404) for its support. This research was also supported by the Collaborative Innovation Major Special Projects of Guangzhou City (201604020007), the Guangdong Provincial Science and Technology Plan Projects (2015A020209016, 2016A040403040), the Fundamental Research Funds for the Central Universities (2017MS067, 2017MS075), the International and Hong Kong Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology & Equipment (2015KGJHZ001), the Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products, the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154) and the Innovation Centre of Guangdong Province for Modern Agricultural Science and Technology on Intelligent Sensing and Precision Control of Agricultural Product Qualities. 75 34 36 16 137 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0260-8774 1873-5770 J FOOD ENG J. Food Eng. JAN 2019.0 240 207 213 10.1016/j.jfoodeng.2018.07.032 0.0 7 Engineering, Chemical; Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Food Science & Technology GX1LV 2023-03-23 WOS:000447479700023 0
J Shi, WX; Wang, C; Jiang, Y; Li, Q; Shen, GB; Muntean, GM Shi, Wanxin; Wang, Chao; Jiang, Yong; Li, Qing; Shen, Gengbiao; Muntean, Gabriel-Miro CoLEAP: Cooperative Learning-Based Edge Scheme With Caching and Prefetching for DASH Video Delivery IEEE TRANSACTIONS ON MULTIMEDIA English Article Streaming media; Prefetching; Quality of experience; Bit rate; Servers; Optimization; Bandwidth; DASH; caching; prefetching; QoE; edge computing MANAGEMENT The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE. [Shi, Wanxin; Jiang, Yong; Shen, Gengbiao] Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China; [Wang, Chao; Li, Qing] Peng Cheng Lab PCL, PCL Res Ctr Networks & Commun, Shenzhen 518066, Peoples R China; [Wang, Chao] Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Shenzhen 518055, Peoples R China; [Jiang, Yong] Peng Cheng Lab PCL, Shenzhen 518066, Peoples R China; [Li, Qing] Southern Univ Sci & Technol, Shenzhen 518055, Peoples R China; [Muntean, Gabriel-Miro] Dublin City Univ, Sch Elect Engn, Performance Engn Lab, Galsnevin Campus, Dublin D09, Ireland Tsinghua University; Southern University of Science & Technology; Dublin City University Li, Q (corresponding author), Southern Univ Sci & Technol, Shenzhen 518055, Peoples R China. shiwx17@mails.tsinghua.edu.cn; wangchao17@mails.tsinghua.edu.cn; jiangy@sz.tsinghua.edu.cn; liq8@sustech.edu.cn; sgb16@mails.tsinghua.edu.cn; gabriel.muntean@dcu.ie Li, Qing/U-8995-2018 Li, Qing/0000-0002-6071-473X; , Wanxin/0000-0001-5394-1478; Muntean, Gabriel-Miro/0000-0002-9332-4770 National Natural Science Foundation of China [61972189]; Guangdong Province Key Area RD Program [2018B010113001]; Project PCL Future Regional Network Facilities for Large-scale Experiments and Applications [PCL2018KP001]; Shenzhen Key Laboratory of Software Defined Networking [ZDSYS20140509172959989] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Province Key Area RD Program; Project PCL Future Regional Network Facilities for Large-scale Experiments and Applications; Shenzhen Key Laboratory of Software Defined Networking This work is supported in part by the National Natural Science Foundation of China under Grant 61972189, in part by Guangdong Province Key Area R&D Program under Grant 2018B010113001, in part by the Project PCL Future Regional Network Facilities for Large-scale Experiments and Applications (PCL2018KP001), and in part by the Shenzhen Key Laboratory of Software Defined Networking underGrant ZDSYS20140509172959989. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sanjeev Mehrotra. (Corresponding author: Qing Li.) 57 5 5 3 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia 2021.0 23 3631 3645 10.1109/TMM.2020.3029893 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications WJ5OB 2023-03-23 WOS:000709093100016 0
J Hong, YS; Chen, YY; Shen, RL; Chen, SC; Xu, G; Cheng, H; Guo, L; Wei, ZS; Yang, J; Liu, YL; Shi, Z; Mouazen, AM Hong, Yongsheng; Chen, Yiyun; Shen, Ruili; Chen, Songchao; Xu, Gang; Cheng, Hang; Guo, Long; Wei, Zushuai; Yang, Jian; Liu, Yaolin; Shi, Zhou; Mouazen, Abdul M. Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy ENVIRONMENTAL POLLUTION English Article Urban and suburban soil Cd contamination; Visible-to-near-infrared spectroscopy; Boruta algorithm; Synthetic minority over-sampling technique; Machine learning DIFFUSE-REFLECTANCE SPECTROSCOPY; POTENTIALLY TOXIC ELEMENTS; HEAVY-METAL CONCENTRATIONS; NIR SPECTROSCOPY; ORGANIC-CARBON; QUANTITATIVE-ANALYSIS; RANDOM FOREST; HUMAN HEALTH; RIVER DELTA; PREDICTION Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas. [Hong, Yongsheng; Chen, Yiyun; Cheng, Hang; Liu, Yaolin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China; [Hong, Yongsheng; Mouazen, Abdul M.] Univ Ghent, Dept Environm, Coupure Links 653, B-9000 Ghent, Belgium; [Shen, Ruili] Hubei Acad Environm Sci, Wuhan 430072, Peoples R China; [Chen, Songchao; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China; [Xu, Gang] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Guo, Long] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China; [Wei, Zushuai; Yang, Jian] Minist Ecol & Environm, South China Inst Environm Sci, Guangzhou 510530, Peoples R China Wuhan University; Ghent University; Zhejiang University; Wuhan University; Huazhong Agricultural University Chen, YY (corresponding author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China. chenyy@whu.edu.cn CHEN, YIYUN/AAD-9501-2019; shi, Zhou/M-7845-2019; Mouazen, Abdul M./AAO-9709-2020; Chen, Songchao/S-7982-2019; Xu, Gang/B-1816-2018 CHEN, YIYUN/0000-0002-7442-3239; shi, Zhou/0000-0003-3914-5402; Mouazen, Abdul M./0000-0002-0354-0067; Chen, Songchao/0000-0003-1245-0482; Xu, Gang/0000-0001-5731-5036 National Key R&D Program of China [2019YFC1803404]; National Natural Science Foundation of China [41771440, 818346] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Key R&D Program of China (2019YFC1803404) and the National Natural Science Foundation of China (grant number: 41771440) . Authors also acknowledged the financial support received from the European Commission H2020-SFS-38-2018 call for proposals for SIEUSOIL project, under Grant Agreement No. 818346. 104 15 15 12 56 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0269-7491 1873-6424 ENVIRON POLLUT Environ. Pollut. DEC 15 2021.0 291 118128 10.1016/j.envpol.2021.118128 0.0 SEP 2021 11 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology UR8HH 34530244.0 Green Published 2023-03-23 WOS:000696982800003 0
J Ahmed, M; Hayat, R; Ahmad, M; Ul-Hassan, M; Kheir, AMS; Ul-Hassan, F; Ur-Rehman, MH; Shaheen, FA; Raza, MA; Ahmad, S Ahmed, Mukhtar; Hayat, Rifat; Ahmad, Munir; Ul-Hassan, Mahmood; Kheir, Ahmed M. S.; Ul-Hassan, Fayyaz; Ur-Rehman, Muhammad Habib; Shaheen, Farid Asif; Raza, Muhammad Ali; Ahmad, Shakeel Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need INTERNATIONAL JOURNAL OF PLANT PRODUCTION English Review Cropping systems; Climate change; Precision agriculture; Management options; Diversification SOIL CARBON SEQUESTRATION; WATER-USE EFFICIENCY; HIGH-TEMPERATURE; CROPPING SYSTEM; ELEVATED CO2; CONSERVATION AGRICULTURE; SUSTAINABLE INTENSIFICATION; NITROGEN DYNAMICS; CHANGE MITIGATION; CHANGE SCENARIOS Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future. [Ahmed, Mukhtar; Ul-Hassan, Fayyaz] PMAS Arid Agr Univ, Dept Agron, Rawalpindi 46300, Pakistan; [Ahmed, Mukhtar] Swedish Univ Agr Sci, Dept Agr Res Northern Sweden, S-90183 Umea, Sweden; [Hayat, Rifat] PMAS Arid Agr Univ, Dept Soil Sci & Soil Water Conservat, Rawalpindi 46300, Pakistan; [Ahmad, Munir; Ul-Hassan, Mahmood] PMAS Arid Agr Univ, Dept Plant Breeding & Genet, Rawalpindi 46300, Pakistan; [Kheir, Ahmed M. S.] Chinese Acad Trop Agr Sci CATAS, Haikou Expt Stn, Haikou, Hainan, Peoples R China; [Kheir, Ahmed M. S.] Agr Res Ctr, Soils Water & Environm Res Inst, 9 Cairo Univ St, Giza, Egypt; [Ur-Rehman, Muhammad Habib] INRES Univ, Inst Crop Sci & Resource Conservat, D-53115 Bonn, Germany; [Ur-Rehman, Muhammad Habib] Muhammad Nawaz Shareef Agr Univ, Dept Agron, Multan 60800, Pakistan; [Shaheen, Farid Asif] PMAS Arid Agr Univ, Dept Entomol, Rawalpindi 46300, Pakistan; [Raza, Muhammad Ali] Sichuan Agr Univ, Coll Agron, Chengdu 611130, Sichuan, Peoples R China; [Ahmad, Shakeel] Bahauddin Zakariya Univ, Dept Agron, Multan 60800, Pakistan Arid Agriculture University; Swedish University of Agricultural Sciences; Arid Agriculture University; Arid Agriculture University; Chinese Academy of Tropical Agricultural Sciences; Egyptian Knowledge Bank (EKB); Agricultural Research Center - Egypt; Arid Agriculture University; Sichuan Agricultural University; Bahauddin Zakariya University Ahmed, M (corresponding author), PMAS Arid Agr Univ, Dept Agron, Rawalpindi 46300, Pakistan.;Ahmed, M (corresponding author), Swedish Univ Agr Sci, Dept Agr Res Northern Sweden, S-90183 Umea, Sweden. ahmadmukhtar@uaar.edu.pk Kheir, Ahmed/M-9292-2016; Ahmad, Shakeel/F-3108-2011; Ahmed, Mukhtar/HIR-3483-2022 Kheir, Ahmed/0000-0001-9569-5420; Ahmad, Shakeel/0000-0002-5848-2773; HEC Pakistan [NRPU-6132] HEC Pakistan(Higher Education Commission of Pakistan) The current study was funded by HEC Pakistan (Project# NRPU-6132). 197 7 7 15 33 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1735-6814 1735-8043 INT J PLANT PROD Int. J. Plant Prod. SEP 2022.0 16 3 341 363 10.1007/s42106-022-00197-1 0.0 MAY 2022 23 Agronomy Science Citation Index Expanded (SCI-EXPANDED) Agriculture 3O6XS 35614974.0 Green Accepted, Bronze 2023-03-23 WOS:000800807800001 0
J Zeng, D; Veldhuis, R; Spreeuwers, L; Arendsen, R Zeng, Dan; Veldhuis, Raymond; Spreeuwers, Luuk; Arendsen, Richard Occlusion-invariant face recognition using simultaneous segmentation IET BIOMETRICS English Article ROBUST; REPRESENTATION When using convolutional neural network (CNN) models to extract features of an occluded face, the occluded part will inevitably be embedded into the representation just as with other facial regions. Existing methods deal with occluded face recognition either by augmenting the training dataset with synthesized occluded faces or by segmenting occlusions first and subsequently recognize the face based on unoccluded facial regions. Instead, simultaneous occlusion segmentation and face recognition is developed to make the most of these correlated two tasks. This is inspired by the phenomenon that features corrupted by occlusion are traceable within a CNN trained to segment occluded parts in face images. Specifically, a simultaneous occlusion invariant deep network (SOIDN) is proposed that contains simultaneously operating face recognition and occlusion segmentation networks coupled with an occlusion mask adaptor module as their bridge to learn occlusion invariant features. The training of SOIDN is jointly supervised by classification and segmentation losses aiming to obtain (1) occlusion invariant features, (2) occlusion segmentation, and (3) an occlusion feature mask that weighs the reliability of features. Experiments on synthesized occluded dataset (e.g. LFW-occ) and real occluded face dataset (e.g. AR) demonstrate that SOIDN outperforms state of the art methods for face verification and identification. [Zeng, Dan] Southern Univ Sci & Technol, Shenzhen, Peoples R China; [Zeng, Dan; Veldhuis, Raymond; Spreeuwers, Luuk] Univ Twente, Enschede, Netherlands; [Arendsen, Richard] 20face BV, Overijssel, Netherlands Southern University of Science & Technology; University of Twente Zeng, D (corresponding author), Southern Univ Sci & Technol, Shenzhen, Peoples R China. zengd@sustech.edu.cn Zeng, Dan/0000-0002-9036-7791 56 1 1 5 19 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2047-4938 2047-4946 IET BIOMETRICS IET Biom. NOV 2021.0 10 6 679 691 10.1049/bme2.12036 0.0 APR 2021 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WF9IR gold 2023-03-23 WOS:000642249300001 0
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CMS Collaboration Search for anomalous Wtb couplings and flavour-changing neutral currents in t-channel single top quark production in pp collisions at root s=7 and 8 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Flavour Changing Neutral Currents; Hadron-Hadron scattering (experiments); Top physics STANDARD; HIGGS; DECAYS; MODELS Single top quark events produced in the t channel are used to set limits on anomalous Wtb couplings and to search for top quark flavour-changing neutral current (FCNC) interactions. The data taken with the CMS detector at the LHC in proton-proton collisions at and 8 TeV correspond to integrated luminosities of 5.0 and 19.7 fb(-1), respectively. The analysis is performed using events with one muon and two or three jets. A Bayesian neural network technique is used to discriminate between the signal and backgrounds, which are observed to be consistent with the standard model prediction. The 95% confidence level (CL) exclusion limits on anomalous right-handed vector, and left- and right-handed tensor Wtb couplings are measured to be vertical bar f (V) (R) vertical bar < 0.16,aEuro,vertical bar f (T) (L) vertical bar < 0.057, and - 0.049 < f (T) (R) < 0.048, respectively. For the FCNC couplings kappa (tug) and kappa (tcg), the 95% CL upper limits on coupling strengths are vertical bar kappa (tug)vertical bar/I > < 4.1 x 10(- 3) TeV-1 and vertical bar kappa (tcg)vertical bar/Lambda < 1.8 x 10(- 2) TeV-1, where I > is the scale for new physics, and correspond to upper limits on the branching fractions of 2.0 x 10(-5) and 4.1 x 10(-4) for the decays t -> ug and t -> cg, respectively. [Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.] Yerevan Phys Inst, Yerevan 375036, Armenia; [Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Eroe, J.; Flechl, M.; Friedl, M.; Fruehwirth, R.; Ghete, V. 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B.; Keshri, S.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.] Univ Delhi, Delhi, India; [Ghosh, S.; Bhattacharya, R.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutt, S.; Dutta, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Roy, A.; Roy, D.; Chowdhury, S. Roy; Sarkar, S.; Sharan, M.; Thakur, S.] Saha Inst Nucl Phys, Kolkata, India; [Behera, P. K.] Indian Inst Technol, Madras, Tamil Nadu, India; [Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.; Topkar, A.] Bhabha Atom Res Ctr, Bombay, Maharashtra, India; [Aziz, T.; Dugad, S.; Kole, G.; Mahakud, B.; Mitra, S.; Mohanty, G. B.; Sur, N.; Sutar, B.] Tata Inst Fundamental Res A, Bombay, Maharashtra, India; [Banerjee, S.; Bhowmik, S.; Dewanjee, R. K.; Ganguly, S.; Guchait, M.; Jain, Sa.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Parida, B.; Sarkar, T.; Wickramage, N.] Tata Inst Fundamental Res B, Bombay, Maharashtra, India; [Chauhan, S.; Dube, S.; Kapoor, A.; Kothekar, K.; Rane, A.; Sharma, S.] Indian Inst Sci Educ & Res, Pune, Maharashtra, India; [Behnamian, H.; Chenarani, S.; Tadavani, E. Eskandari; Etesami, S. M.; Fahim, A.; Khakzad, M.; Najafabadi, M. Mohammadi; Naseri, M.; Mehdiabadi, S. Paktinat; Hosseinabadi, F. Rezaei; Safarzadeh, B.; Zeinali, M.] Inst Res Fundamental Sci IPM, Tehran, Iran; [Felcini, M.; Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Venditti, R.; Verwilligen, P.] Ist Nazl Fis Nucl, Sez Bari, Bari, Italy; [Abbrescia, M.; Calabria, C.; Caputo, C.; Cristella, L.; De Palma, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Radogna, R.; Selvaggi, G.; Venditti, R.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Fanfani, A.; Fasanella, D.; Guiducci, L.; Navarria, F. L.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] Univ Bologna, Bologna, Italy; [Albergo, S.; Chiorboli, M.; Costa, S.; Di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy; [Albergo, S.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Viliani, L.] Ist Nazl Fis Nucl, Sez Firenze, Florence, Italy; [Ciulli, V.; D'Alessandro, R.; Focardi, E.; Gori, V.; Lenzi, P.; Viliani, L.] Univ Florence, Florence, Italy; [Fabbri, F.; Benussi, L.; Bianco, S.; Piccolo, D.; Primavera, F.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy; [Calvelli, V.; Ferro, F.; Lo Vetere, M.; Monge, M. R.; Robutti, E.; Tosi, S.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Calvelli, V.; Lo Vetere, M.; Monge, M. R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Brianza, L.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malvezzi, S.; Manzoni, R. A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Pigazzini, S.; Ragazzi, S.; de Fatis, T. Tabarelli] Ist Nazl Fis Nucl, Sez Milano Bicocca, Milan, Italy; [Brianza, L.; Dinardo, M. E.; Fiorendi, S.; Ghezzi, A.; Govoni, P.; Manzoni, R. A.; Marzocchi, B.; Paganoni, M.; Pigazzini, S.; Ragazzi, S.; de Fatis, T. Tabarelli] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Cavallo, N.; De Nardo, G.; Di Guida, S.; Esposito, M.; Fabozzi, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.] Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy; [De Nardo, G.; Esposito, M.; Iorio, A. O. M.; Sciacca, C.; Thyssen, F.] Univ Naples Federico II, Naples, Italy; [Cavallo, N.; De Nardo, G.; Fabozzi, F.; Thyssen, F.] Univ Basilicata, Potenza, Italy; [De Nardo, G.; Meola, S.; Thyssen, F.; De Guio, F.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; De Oliveira, A. Carvalho Antunes; Checchia, P.; Dall'Osso, M.; Manzano, P. De Castro; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Ist Nazl Fis Nucl, Sez Padova, Padua, Italy; [Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; De Oliveira, A. Carvalho Antunes; Dall'Osso, M.; Gasparini, F.; Gasparini, U.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; [Zanetti, M.] Univ Trento, Trento, Italy; [Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.] Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; [Magnani, A.; Montagna, P.; Ratti, S. P.; Riccardi, C.; Vai, I.; Vitulo, P.] Univ Pavia, Pavia, Italy; [Solestizi, L. Alunni; Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.] Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; [Solestizi, L. Alunni; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Santocchia, A.] Univ Perugia, Perugia, Italy; [Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; [Fedi, G.; Martini, L.; Messineo, A.; Rizzi, A.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Donato, S.; Fedi, G.; Ligabue, F.] Scuola Normale Super Pisa, Pisa, Italy; [Barone, L.; Cavallari, F.; Cipriani, M.; D'imperio, G.; Del Re, D.; Diemoz, M.; Gelli, S.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.] Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; [Barone, L.; Cipriani, M.; D'imperio, G.; Del Re, D.; Gelli, S.; Longo, E.; Margaroli, F.; Organtini, G.; Preiato, F.; Rahatlou, S.; Santanastasio, F.] Univ Rome, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.] Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Finco, L.; Kiani, B.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Ravera, F.; Romero, A.; Sacchi, R.; Shchelina, K.; Solano, A.; Traczyk, P.] Univ Turin, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; La Licata, C.; Schizzi, A.; Zanetti, A.] Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; [Della Ricca, G.; La Licata, C.; Schizzi, A.] Univ Trieste, Trieste, Italy; [Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, S.; Lee, S. W.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Kamon, T.] Kyungpook Natl Univ, Daegu, South Korea; [Lee, A.] Chonbuk Natl Univ, Jeonju, South Korea; [Cifuentes, J. A. Brochero; Kim, T. J.] Hanyang Univ, Seoul, South Korea; [Lee, S.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, B.; Lee, K.; Lee, K. S.; Park, S. K.; Roh, Y.; Kim, J.] Korea Univ, Seoul, South Korea; [Almond, J.; Kim, J.; Oh, S. B.; Seo, S. H.; Yang, U. K.; Yoo, H. D.; Yu, G. B.] Seoul Natl Univ, Seoul, South Korea; [Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.] Univ Seoul, Seoul, South Korea; [Choi, Y.; Goh, J.; Hwang, C.; Lee, J.; Yu, I.] Sungkyunkwan Univ, Suwon, South Korea; [Dudenas, V.; Juodagalvis, A.; Vaitkus, J.] Vilnius Univ, Vilnius, Lithuania; [Ahmed, I.; Ibrahim, Z. A.; Komaragiri, J. R.; Ali, M. A. B. Md; Idris, F. Mohamad; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Castilla-Valdez, H.; De la Cruz-Burelo, E.; la Cruz, I. Heredia-De; Hernandez-Almada, A.; Lopez-Fernandez, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.] IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Morelos Pineda, A.] Univ Autonoma San Luis Potosi, San Luis Potosi, Mexico; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, M.; Ahmad, A.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Walczak, M.] Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; [Bargassa, P.; Da Cruz E Silva, C. Beirao; Di Francesco, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. V.; Rodrigues Antunes, J.; Seixas, J.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Vischia, P.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Tsamalaidze, Z.; Golunov, A.; Golutvin, I.; Gorbounov, N.; Kamenev, A.; Karjavin, V.; Korenkov, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Mitsyn, V. V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Tikhonenko, E.; Zarubin, A.] Joint Inst Nucl Res, Dubna, Russia; [Chtchipounov, L.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Murzin, V.; Oreshkin, V.; Sulimov, V.; Vorobyev, A.] Petersburg Nucl Phys Inst, Gatchina, Russia; [Matveev, V.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.] Inst Nucl Res, Moscow, Russia; [Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Toms, M.; Vlasov, E.; Zhokin, A.; Starodumov, A.; Nikitenko, A.] Inst Theoret & Expt Phys, Moscow, Russia; [Matveev, V.; Chadeeva, M.; Danilov, M.; Markin, O.] Natl Res Nucl Univ, Moscow Engn Phys Inst, Moscow, Russia; [Chadeeva, M.; Danilov, M.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Rusakov, S. V.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Popov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Klyukhin, V.; Kodolova, O.; Korneeva, N.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Volkov, P.; Vorotnikov, G.] Moscow MV Lomonosov State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] State Res Ctr Russian Federat, Inst High Energy Phys, Protvino, Russia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Milosevic, J.; Rekovic, V.; Milenovic, P.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Milosevic, J.; Rekovic, V.] Univ Belgrade, Vinca Inst Nucl Sci, Belgrade, Serbia; [Alcaraz Maestre, J.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De la Cruz, B.; Peris, A. Delgado; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.] CIEMAT, Madrid, Spain; [de Troconiz, J. F.; Missiroli, M.; Moran, D.] Univ Autonoma Madrid, Madrid, Spain; [Cuevas, J.; Fernandez Menendez, J.; Gonzalez Caballero, I.; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Suarez Andres, I.; Vizan Garcia, J. M.] Univ Oviedo, Oviedo, Spain; [Cabrillo, I. J.; Calderon, A.; Castineiras De Saa, J. R.; Curras, E.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.] Univ Cantabria, CSIC, Inst Fis Cantabria IFCA, Santander, Spain; [Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Bloch, P.; Bocci, A.; Bonato, A.; Botta, C.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Guio, F.; De Roeck, A.; Di Marco, E.; Dobson, M.; Dordevic, M.; Dorney, B.; du Pree, T.; Duggan, D.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Fartoukh, S.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gill, K.; Girone, M.; Glege, F.; Gulhan, D.; Gundacker, S.; Guthoff, M.; Hammer, J.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kirschenmann, H.; Knunz, V.; Kornmayer, A.; Kortelainen, M. J.; Kousouris, K.; Krammer, M.; Lecoq, P.; Lourenco, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Ruan, M.; Sakulin, H.; Sauvan, J. B.; Schafer, C.; Schwick, C.; Seidel, M.; Sharma, A.; Silva, P.; Simon, M.; Sphicas, P.; Steggemann, J.; Stoye, M.; Takahashi, Y.; Tosi, M.; Treille, D.; Triossi, A.; Tsirou, A.; Veckalns, V.; Veres, G. I.; Wardle, N.; Wohri, H. K.; Zagozdzinska, A.; Zeuner, W. D.] CERN, European Org Nucl Res, Geneva, Switzerland; [Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Bachmair, F.; Bani, L.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donega, M.; Eller, P.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lecomte, P.; Lustermann, W.; Mangano, B.; Marionneau, M.; del Arbol, P. Martinez Ruiz; Masciovecchio, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Rossini, M.; Schonenberger, M.; Starodumov, A.; Takahashi, M.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.] Swiss Fed Inst Technol, Inst Particle Phys, Zurich, Switzerland; [Aarrestad, T. K.; Amsler, C.; Caminada, L.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Yang, Y.] Univ Zurich, Zurich, Switzerland; [Candelise, V.; Doan, T. H.; Jain, Sh.; Khurana, R.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Pozdnyakov, A.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Kumar, Arun; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Fiori, F.; Hou, W. -S.; Hsiung, Y.; Liu, Y. F.; Lu, R. -S.; Moya, M. Minano; Paganis, E.; Psallidas, A.; Tsai, J. f.; Tzeng, Y. M.] Natl Taiwan Univ, Taipei, Taiwan; [Asavapibhop, B.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.] Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; [Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Dumanoglu, I.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Tali, B.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Adana, Turkey; [Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.] Middle E Tech Univ, Dept Phys, Ankara, Turkey; [Gulmez, E.; Kaya, M.; Kaya, O.; Yetkin, E. A.; Yetkin, T.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.; Sorokin, P.] Kharkov Inst Phys & Technol, Ctr Nat Sci, Kharkov, Ukraine; [Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; El Nasr-Storey, S. Seif; Smith, D.; Smith, V. J.] Univ Bristol, Bristol, Avon, England; [Belyaev, A.; Newbold, D. M.; Bell, K. W.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, H.; Thea, A.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Baber, M.; Bainbridge, R.; Buchmuller, O.; Bundock, A.; Burton, D.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Dunne, P.; Elwood, A.; Futyan, D.; Haddad, Y.; Hall, G.; Iles, G.; Laner, C.; Lucas, R.; Lyons, L.; Magnan, A. -M.; Malik, S.; Mastrolorenzo, L.; Nash, J.; Nikitenko, A.; Pela, J.; Penning, B.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Seez, C.; Tapper, A.; Uchida, K.; Acosta, M. Vazquez; Virdee, T.; Zenz, S. C.] Univ London Imperial Coll Sci Technol & Med, London, England; [Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leslie, D.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.] Brunel Univ, Uxbridge, Middx, England; [Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.] Baylor Univ, Waco, TX 76798 USA; [Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.] Univ Alabama, Tuscaloosa, AL 35487 USA; [Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.] Boston Univ, Boston, MA 02215 USA; [Benelli, G.; Berry, E.; Cutts, D.; Garabedian, A.; Hakala, J.; Heintz, U.; Hogan, J. M.; Jesus, O.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Spencer, E.; Syarif, R.; Clarida, W.] Brown Univ, Providence, RI 02912 USA; [Chauhan, S.; Burns, D.; Breedon, R.; Breto, G.; Sanchez, M. Calderon De la Barca; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.] Univ Calif Davis, Davis, CA 95616 USA; [Weber, M.; Cousins, R.; Everaerts, P.; Florent, A.; Hauser, J.; Ignatenko, M.; Saltzberg, D.; Takasugi, E.; Valuev, V.] Univ Calif Los Angeles, Los Angeles, CA 90095 USA; [Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Malberti, M.; Negrete, M. Olmedo; Paneva, M. I.; Shrinivas, A.; Wei, H.; Wimpenny, S.; Yates, B. R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Sharma, V.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Holzner, A.; Klein, D.; Krutelyov, V.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Welke, C.; Wood, J.; Wurthwein, F.; Yagil, A.; Della Porta, G. Zevi] Univ Calif San Diego, La Jolla, CA 92093 USA; [Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Flowers, K.; Sevilla, M. Franco; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Heller, R.; Incandela, J.; Mccoll, N.; Mullin, S. D.; Ovcharova, A.; Richman, J.; Stuart, D.; Suarez, I.; West, C.; Yoo, J.] Univ Calif Santa Barbara, Dept Phys, Santa Barbara, CA 93106 USA; [Chen, Y.; Dubinin, M.; Anderson, D.; Apresyan, A.; Bendavid, J.; Bornheim, A.; Bunn, J.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.] CALTECH, Pasadena, CA 91125 USA; [Andrews, M. B.; Azzolini, V.; Carlson, B.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Stenson, K.; ; Yerevan Physics Institute; Belarusian State University; National Center of Particles & High Energy Physics - Belarus; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; University of Mons; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Universidad de los Andes (Colombia); University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Universite de Haute-Alsace (UHA); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Georgian Technical University; Ivane Javakhishvili Tbilisi State University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Jeonbuk National University; Hanyang University; Korea University; Seoul National University (SNU); University of Seoul; Sungkyunkwan University (SKKU); Vilnius University; Universiti Malaya; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Instituto Politecnico Nacional - Mexico; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; Universidad Autonoma de San Luis Potosi; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; Fairfield University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida International University; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Purdue University System; Purdue University; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Universidade Estadual de Campinas; Egyptian Knowledge Bank (EKB); British University in Egypt; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; Egyptian Knowledge Bank (EKB); Fayoum University; Egyptian Knowledge Bank (EKB); Ain Shams University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Brandenburg University of Technology Cottbus; Indian Institute of Science Education & Research (IISER) - Bhopal; Institute of Physics Bhubaneswar (IOPB); Visva Bharati University; University Ruhuna; Isfahan University of Technology; University of Tehran; Islamic Azad University; University of Siena; International Islamic University Malaysia; Agensi Nuklear Malaysia; Warsaw University of Technology; Peter the Great St. Petersburg Polytechnic University; National Technical University of Athens; Istituto Nazionale di Fisica Nucleare (INFN); Riga Technical University; University of Bern; Gaziosmanpasa University; Adiyaman University; Mersin University; Cag University - Turkey; Piri Reis University; Ozyegin University; Izmir Institute of Technology; Marmara University; Kafkas University; Istanbul Bilgi University; Yildiz Technical University; Hacettepe University; University of Southampton; Instituto de Astrofisica de Canarias; Utah System of Higher Education; Utah Valley University; Sapienza University Rome; United States Department of Energy (DOE); Argonne National Laboratory; Erzincan Binali Yildirim University; Mimar Sinan Guzel Sanatlar University; Qatar Foundation (QF); Texas A&M University Qatar Khachatryan, V (corresponding author), Yerevan Phys Inst, Yerevan 375036, Armenia. 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Bernardes, Cesar Augusto/D-2408-2015; Torassa, Ezio/I-1788-2012; Ligabue, Franco/ABI-1681-2020; Dudko, Lev/D-7127-2012; Vai, Ilaria/AAT-5836-2020; Canelli, Florencia/O-9693-2016; Андреев, Юрий/AAK-9717-2020; Soares, Mara Senghi/GZM-6676-2022; Duarte, Javier Mauricio/AAA-5414-2020; Pinna Angioni, Gian Luca/AAZ-6834-2021; Paoletti, Simone/R-2295-2016; Yazgan, Efe/C-4521-2014; Oglakci, Mehmet/AAG-3754-2021; Mousa, Jehad/I-8019-2019; BARRILLON, Stephanie/ABA-6873-2021; Kumar, Ramandeep/AAY-6388-2020; Gokbulut, Gul/G-2141-2018; Hernández, José M./H-9127-2015; Pompili, Alexis/ABG-3556-2021; Sanchez-Hernandez, Alberto/AAH-3473-2020; Gundacker, Stefan/FBD-4491-2022; Moraes, Arthur/F-6478-2010; Sguazzoni, Giacomo/J-4620-2015; Seo, Seon-Hee/M-1189-2019; Viliani, Lorenzo/AAU-3462-2021; Safronov, Grigory/F-8494-2017; Chinellato, Jose Augusto A/I-7972-2012; Herrera, Maria Clemencia Mora/AAD-6466-2021; Khakzad, Mohsen/ABB-1945-2021; yagil, avi/I-3278-2018; Malvezzi, Sandra/B-8524-2009; Ferro, Fabrizio/HMP-5516-2023; LECOQ, Paul/S-7246-2019; Yetkin, Taylan/ABH-2774-2021; Romero, Luciano/B-8917-2018; Yagil, Avi/GMX-0958-2022; Flix, Josep/G-5414-2012; Novaes, Sergio F./D-3532-2012; Raidal, Martti/F-4436-2012; Vischia, Pietro/AAB-7811-2021; Andreev, Vladimir F/M-8665-2015; Mejia Guisao, Jhovanny Andres/ABG-3627-2021; Bedoya, Cristina F/K-8066-2014; Ogul, Hasan/S-7951-2016; Klyukhin, Vyacheslav I/D-6850-2012; ABDULLAH, WAN AHMAD TAJUDDIN WAN/B-5439-2010; Verwilligen, Piet/M-2968-2014; Adiguzel, Aytul/AAC-9049-2020; Asavapibhop, Burin/X-1477-2019; Battilana, Carlo/AAA-7345-2020; Dorigo, Tommaso/AAY-6673-2020; Cimmino, Anna/AAA-1673-2020; Moon, Chang-Seong/J-3619-2014; Lokhtin, Igor/D-7004-2012; Manganote, Edmilson J.T./K-8251-2013; Blekman, Freya/ABD-7916-2020; Abdulsalam, Abdulla/B-3386-2019; Herrera, Maria Clemencia Mora/L-3893-2016; Korenkov, Vladimir V/H-7665-2017; Isildak, Bora/AAW-4994-2020; Fouz, Maria Cruz/AAF-1105-2019; Elwood, Adam/GSN-1799-2022; Banerjee, Swagato/AAN-3326-2021; Cadamuro, Luca/AAO-8637-2020; Tiras, Emrah/ABG-2354-2020; Marchesini, Ivan/G-7852-2018; Anampa, Kenyi Hurtado/ABA-2626-2020; Boimska, Bożena/Z-6088-2019; Amapane, Nicola/HDN-9630-2022; Arneodo, Michele/ABF-7197-2020; De La Cruz Burelo, Eduard/AAR-9103-2021; MAESTRE, JUAN ALCARAZ/I-5763-2015; Trocino, Daniele/AGI-2155-2022; Singh, J B/ABF-5574-2021; Montagna, Paolo Maria/AAY-8669-2021; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Malbouisson, H./N-6733-2015; Amapane, Nicola/J-3683-2012; Ferencek, Dinko/G-6411-2015; Heredia De La Cruz, Ivan/L-2610-2018; Melo da Costa, Eliza Melo/AAX-2394-2021; Sanchez-Hernandez, Alberto/L-4955-2017; de Paula Carvalho, Wagner/V-3343-2019; Danilov, Mikhail/C-5380-2014; Sagir, Sinan/A-5219-2018; Barria, Patrizia/AAT-3894-2020; Shmatov, Sergei/J-5621-2014; celik, Ali/A-3563-2019; MANJHVAR, AMIT KUMAR/HNB-6459-2023; Gerosa, Raffaele/AGL-5921-2022; Fabozzi, Francesco/T-6468-2019; Fiori, Francesco/H-1454-2018; Mussgiller, Andreas/AAF-6933-2019; Rovelli, Tiziano/K-4432-2015; De La Cruz Burelo, Eduard/Q-6021-2019; 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Schieck, Jochen/0000-0002-1058-8093; Wang, Ren-Jie/0000-0002-5059-8456; Bachtis, Michail/0000-0003-3110-0701; Bansal, Sunil/0000-0003-1992-0336; Tapper, Alexander/0000-0003-4543-864X; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226; Mooney, Michael/0000-0001-8348-4167; Cole, Joanne/0000-0001-5638-7599; Venturi, Andrea/0000-0002-0249-4142; Silvestris, Lucia/0000-0002-8985-4891; Fernandez Menendez, Javier/0000-0002-5213-3708; Piotrzkowski, Krzysztof/0000-0002-6226-957X; Ciangottini, Diego/0000-0002-0843-4108; cartiglia, nicolo/0000-0002-0548-9189; Ahmed, Ijaz/0000-0002-3210-8302; Collard, Caroline/0000-0002-5230-8387; Martelli, Arabella/0000-0003-3530-2255; Gori, Valentina/0000-0003-0215-4022; Sordini, Viola/0000-0003-0885-824X; Rabbertz, Klaus/0000-0001-7040-9846; Kunnawalkam Elayavalli, Raghav/0000-0002-9202-1516; Popov, Andrey/0000-0002-1207-0984; Li, Hengne/0000-0002-2366-9554; Zalewski, Piotr/0000-0003-4429-2888; Arcidiacono, Roberta/0000-0001-5904-142X; Rolandi, Luigi (Gigi)/0000-0002-0635-274X; Tonelli, Guido Emilio/0000-0003-2606-9156; Gonzalez Suarez, Rebeca/0000-0002-6126-7230; Ciulli, Vitaliano/0000-0003-1947-3396; Ivanov, Andrew/0000-0002-9270-5643; Pierini, Maurizio/0000-0003-1939-4268; Alderweireldt, Sara/0000-0002-8224-7036; Robert, Schoefbeck/0000-0002-2332-8784; Costa, Salvatore/0000-0001-9919-0569; Massironi, Andrea/0000-0002-0782-0883; Sciacca, Crisostomo/0000-0002-8412-4072; Brigljevic, Vuko/0000-0001-5847-0062; Zevi Della Porta, Giovanni/0000-0003-0495-6061; Godinovic, Nikola/0000-0002-4674-9450; CONTE, Eric/0000-0003-3154-3123; Barbieri, Richard/0000-0002-7945-005X; Autermann, Christian/0000-0002-0057-0033; Wang, Michael/0000-0002-4713-9646; Nishu, Nishu/0000-0002-9048-1332; Ahmad, Muhammad/0000-0001-9933-995X; Jacob, Jeson/0000-0001-6895-5493; Pozzobon, Nicola/0000-0002-9674-0809; Riccardi, Cristina/0000-0003-0165-3962; Gorski, Maciej/0000-0003-2146-187X; Malgeri, Luca/0000-0002-0113-7389; Zghiche, Amina/0000-0002-1178-1450; Lucchini, Marco Toliman/0000-0002-7497-7450; Weber, Hannsjorg/0000-0002-5074-0539; Lethuillier, Morgan/0000-0001-6185-2045; Meyer, Arnd/0000-0001-9598-6623; Tao, Junquan/0000-0003-2006-3490; Libeiro, Terence/0000-0003-0623-7909; ORTONA, Giacomo/0000-0001-8411-2971; Stober, Fred/0000-0003-2620-3159; Kyberd, Paul/0000-0002-7353-7090; Krolikowski, Jan/0000-0002-3055-0236; Carrera Jarrin, Edgar/0000-0002-0857-8507; Kumar, Ajay/0000-0002-8477-9079; Shi, Xin/0000-0001-9910-9345; Lacroix, Florent/0000-0002-4304-8843; Wei, Hua/0000-0002-6021-8189; Ruiz, Jose/0000-0002-3306-0363; Buontempo, Salvatore/0000-0001-9526-556X; Gavrilov, Vladimir/0000-0002-9617-2928; Dinardo, Mauro Emanuele/0000-0002-8575-7250; Papadopoulos, Ioannis/0000-0002-9937-3063; Govoni, Pietro/0000-0002-0227-1301 Austrian Federal Ministry of Science, Research and Economy; Austrian Science Fund; Belgian Fonds de la Recherche Scientifique; Brazilian Funding Agency CNPq; Brazilian Funding Agency CAPES; Brazilian Funding Agency FAPERJ; Brazilian Funding Agency FAPESP; Fonds voor Wetenschappelijk Onderzoek; Bulgarian Ministry of Education and Science; CERN; Chinese Academy of Sciences, Ministry of Science and Technology; National Natural Science Foundation of China; Colombian Funding Agency (COLCIENCIAS); Croatian Ministry of Science, Education and Sport; Croatian Science Foundation; Research Promotion Foundation, Cyprus; Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; Ministry of Education and Research, Estonian Research Council, Estonia [IUT23-4, IUT23-6]; European Regional Development Fund, Estonia; Academy of Finland, Finnish Ministry of Education and Culture; Institut National de Physique Nucleaire et de Physique des Particules / CNRS, France; Bundesministerium fur Bildung und Forschung, Germany; Deutsche Forschungsgemeinschaft, Germany; Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; General Secretariat for Research and Technology, Greece; National Scientific Research Foundation, Hungary; National Innovation Office, Hungary; Department of Atomic Energy, India; Institute for Studies in Theoretical Physics and Mathematics, Iran; Science Foundation, Ireland; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Science, ICT and Future Planning, Republic of Korea; National Research Foundation (NRF), Republic of Korea; Lithuanian Academy of Sciences; Ministry of Education, (Malaysia); BUAP; CINVESTAV; CONACYT; LNS; SEP; UASLP-FAI; Ministry of Business, Innovation and Employment, New Zealand; Pakistan Atomic Energy Commission; Ministry of Science and Higher Education, Poland; National Science Centre, Poland; Fundacao para a Ciencia e a Tecnologia, Portugal; JINR, Dubna; Ministry of Education and Science of the Russian Federation; Federal Agency of Atomic Energy of the Russian Federation; Russian Academy of Sciences; Russian Foundation for Basic Research; Ministry of Education, Science and Technological Development of Serbia; Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Spain; Programa Consolider-Ingenio, Spain; ETH Board; ETH Zurich; PSI; SNF; UniZH; Canton Zurich; SER; Ministry of Science and Technology, Taipei; Thailand Center of Excellence in Physics; Institute for the Promotion of Teaching Science and Technology of Thailand; Special Task Force for Activating Research; National Science and Technology Development Agency of Thailand; Scientific and Technical Research Council of Turkey; Turkish Atomic Energy Authority; National Academy of Sciences of Ukraine, Ukraine; State Fund for Fundamental Researches, Ukraine; Science and Technology Facilities Council, U.K.; US Department of Energy; US National Science Foundation; Marie-Curie programme (European Union); European Research Council (European Union); EPLANET (European Union); Leventis Foundation; A.P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Council of Science and Industrial Research, India; HOMING PLUS programme of the Foundation for Polish Science; European Union; Regional Development Fund; Mobility Plus programme of the Ministry of Science and Higher Education; National Science Center (Poland) [Harmonia 2014/14/M/ST2/00428, Opus 2013/11/B/ST2/04202, 2014/13/B/ST2/02543, 2014/15/B/ST2/03998]; Thalis programme - EU-ESF; Greek NSRF; National Priorities Research Program by Qatar National Research Fund; Programa Clarin-COFUND del Principado de Asturias; Rachadapisek Sompot Fund for Postdoctoral Fellowship (Thailand); Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Welch Foundation [C-1845]; Department of Science and Technology, India; University of Malaya (Malaysia); Sonata-bis [2012/07/E/ST2/01406]; Aristeia programme - EU-ESF; Helsinki Institute of Physics; Commissariat a l'Energie Atomique et aux Energies Alternatives / CEA, France; Direct For Mathematical & Physical Scien; Division Of Physics [1606321, 1508869, 1151640, 1624356, 1506168, 1211067] Funding Source: National Science Foundation; Division Of Physics; Direct For Mathematical & Physical Scien [1506130] Funding Source: National Science Foundation; Science and Technology Facilities Council [ST/K001256/1, ST/N000250/1, GRIDPP, CMS] Funding Source: researchfish Austrian Federal Ministry of Science, Research and Economy; Austrian Science Fund(Austrian Science Fund (FWF)); Belgian Fonds de la Recherche Scientifique(Fonds de la Recherche Scientifique - FNRS); Brazilian Funding Agency CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Brazilian Funding Agency CAPES(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); Brazilian Funding Agency FAPERJ(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); Brazilian Funding Agency FAPESP(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Fonds voor Wetenschappelijk Onderzoek(FWO); Bulgarian Ministry of Education and Science; CERN; Chinese Academy of Sciences, Ministry of Science and Technology(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Colombian Funding Agency (COLCIENCIAS)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); Croatian Ministry of Science, Education and Sport(Ministry of Science, Education and Sports, Republic of Croatia); Croatian Science Foundation; Research Promotion Foundation, Cyprus(Research and Innovation Foundation (RIF)); Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; Ministry of Education and Research, Estonian Research Council, Estonia; European Regional Development Fund, Estonia; Academy of Finland, Finnish Ministry of Education and Culture(Academy of Finland); Institut National de Physique Nucleaire et de Physique des Particules / CNRS, France(Centre National de la Recherche Scientifique (CNRS)); Bundesministerium fur Bildung und Forschung, Germany(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgemeinschaft, Germany(German Research Foundation (DFG)); Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany(Helmholtz Association); General Secretariat for Research and Technology, Greece(Greek Ministry of Development-GSRT); National Scientific Research Foundation, Hungary; National Innovation Office, Hungary; Department of Atomic Energy, India(Department of Atomic Energy (DAE)); Institute for Studies in Theoretical Physics and Mathematics, Iran; Science Foundation, Ireland(Science Foundation Ireland); Istituto Nazionale di Fisica Nucleare, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); Ministry of Science, ICT and Future Planning, Republic of Korea(Ministry of Science, ICT & Future Planning, Republic of Korea); National Research Foundation (NRF), Republic of Korea(National Research Foundation of Korea); Lithuanian Academy of Sciences(Research Council of Lithuania (LMTLT)); Ministry of Education, (Malaysia)(Ministry of Education, Malaysia); BUAP; CINVESTAV; CONACYT(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS; SEP; UASLP-FAI; Ministry of Business, Innovation and Employment, New Zealand(New Zealand Ministry of Business, Innovation and Employment (MBIE)); Pakistan Atomic Energy Commission; Ministry of Science and Higher Education, Poland(Ministry of Science and Higher Education, Poland); National Science Centre, Poland(National Science Centre, Poland); Fundacao para a Ciencia e a Tecnologia, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR, Dubna; Ministry of Education and Science of the Russian Federation(Ministry of Education and Science, Russian Federation); Federal Agency of Atomic Energy of the Russian Federation; Russian Academy of Sciences(Russian Academy of Sciences); Russian Foundation for Basic Research(Russian Foundation for Basic Research (RFBR)); Ministry of Education, Science and Technological Development of Serbia(Ministry of Education, Science & Technological Development, Serbia); Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Spain; Programa Consolider-Ingenio, Spain(Spanish Government); ETH Board; ETH Zurich(ETH Zurich); PSI; SNF; UniZH; Canton Zurich; SER; Ministry of Science and Technology, Taipei; Thailand Center of Excellence in Physics; Institute for the Promotion of Teaching Science and Technology of Thailand; Special Task Force for Activating Research; National Science and Technology Development Agency of Thailand; Scientific and Technical Research Council of Turkey(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); Turkish Atomic Energy Authority(Ministry of Energy & Natural Resources - Turkey); National Academy of Sciences of Ukraine, Ukraine; State Fund for Fundamental Researches, Ukraine(State Fund for Fundamental Research (SFFR)); Science and Technology Facilities Council, U.K.(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); US Department of Energy(United States Department of Energy (DOE)); US National Science Foundation(National Science Foundation (NSF)); Marie-Curie programme (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); EPLANET (European Union); Leventis Foundation; A.P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); HOMING PLUS programme of the Foundation for Polish Science; European Union(European Commission); Regional Development Fund; Mobility Plus programme of the Ministry of Science and Higher Education; National Science Center (Poland)(National Science Centre, Poland); Thalis programme - EU-ESF; Greek NSRF(Greek Ministry of Development-GSRT); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Programa Clarin-COFUND del Principado de Asturias(Principality of Asturias); Rachadapisek Sompot Fund for Postdoctoral Fellowship (Thailand); Chulalongkorn University (Thailand)(Chulalongkorn University); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Welch Foundation(The Welch Foundation); Department of Science and Technology, India(Department of Science & Technology (India)); University of Malaya (Malaysia)(Universiti Malaya); Sonata-bis; Aristeia programme - EU-ESF; Helsinki Institute of Physics; Commissariat a l'Energie Atomique et aux Energies Alternatives / CEA, France(French Atomic Energy Commission); Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses.; r Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the Austrian Federal Ministry of Science, Research and Economy and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of Education and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Research Council via IUT23-4 and IUT23-6 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucleaire et de Physique des Particules / CNRS, and Commissariat a l'Energie Atomique et aux Energies Alternatives / CEA, France; the Bundesministerium fur Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Technology, Greece; the National Scientific Research Foundation, and National Innovation Office, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and National Research Foundation (NRF), Republic of Korea; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya (Malaysia); the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Centre, Poland; the Fundacao para a Ciencia e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, and the Russian Foundation for Basic Research; the Ministry of Education, Science and Technological Development of Serbia; the Secretaria de Estado de Investigacion, Desarrollo e Innovacion and Programa Consolider-Ingenio 2010, Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science and Technology Facilities Council, U.K.; the US Department of Energy, and the US National Science Foundation.r Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A.P.; Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus programme of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2013/11/B/ST2/04202, 2014/13/B/ST2/02543 and 2014/15/B/ST2/03998, Sonata-bis 2012/07/E/ST2/01406; the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarin-COFUND del Principado de Asturias; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. 79 27 27 4 63 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. FEB 7 2017.0 2 28 10.1007/JHEP02(2017)028 0.0 45 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics EL6RO Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000394750000001 0
J Wang, L; Liu, D; Pu, HB; Sun, DW; Gao, WH; Xiong, ZJ Wang, Lu; Liu, Dan; Pu, Hongbin; Sun, Da-Wen; Gao, Wenhong; Xiong, Zhenjie Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice FOOD ANALYTICAL METHODS English Article Hyperspectral imaging; Rice; Discriminate; Variety; Chalkiness; Shape feature COMPUTER VISION SYSTEM; ISOTHERM EQUATIONS; ANALYTICAL TOOL; FOOD-PRODUCTS; COLOR; WHEAT; PREDICTION; CLASSIFICATION; CALIBRATION; INSPECTION This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', length-width ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice. [Wang, Lu; Liu, Dan; Pu, Hongbin; Sun, Da-Wen; Gao, Wenhong; Xiong, Zhenjie] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Agr & Food Sci Ctr, Univ Coll Dublin, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), Natl Univ Ireland, Agr & Food Sci Ctr, Univ Coll Dublin, Dublin 4, Ireland. dawen.sun@ucd.ie Gao, Wenhong/AAP-9166-2020; WANG, LU/AAO-9121-2020; Hongbin, Pu/ABE-8433-2020; Sun, Da-Wen/B-1899-2012 Hongbin, Pu/0000-0002-7977-1902; Sun, Da-Wen/0000-0002-3634-9963 Guangdong Province Government (China) through program of 'Leading Talent of Guangdong Province'; National Key Technologies RD Program [2014BAD08B09]; China Postdoctoral Science Foundation [2013M530366] Guangdong Province Government (China) through program of 'Leading Talent of Guangdong Province'; National Key Technologies RD Program(National Key Technology R&D Program); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors are grateful to the Guangdong Province Government (China) for support through the program of 'Leading Talent of Guangdong Province (Da-Wen Sun)'. This research was also supported by National Key Technologies R&D Program (2014BAD08B09) and China Postdoctoral Science Foundation (2013M530366). 50 65 75 3 101 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1936-9751 1936-976X FOOD ANAL METHOD Food Anal. Meth. FEB 2015.0 8 2 515 523 10.1007/s12161-014-9916-5 0.0 9 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology AY6MT 2023-03-23 WOS:000347681100028 0
J Defourny, P; Bontemps, S; Bellemans, N; Cara, C; Dedieu, G; Guzzonato, E; Hagolle, O; Inglada, J; Nicola, L; Rabaute, T; Savinaud, M; Udroiu, C; Valero, S; Begue, A; Dejoux, JF; El Harti, A; Ezzahar, J; Kussul, N; Labbassi, K; Lebourgeois, V; Miao, Z; Newby, T; Nyamugama, A; Salh, N; Shelestov, A; Simonneaux, V; Traore, PS; Traore, SS; Koetz, B Defourny, Pierre; Bontemps, Sophie; Bellemans, Nicolas; Cara, Cosmin; Dedieu, Gerard; Guzzonato, Eric; Hagolle, Olivier; Inglada, Jordi; Nicola, Laurentiu; Rabaute, Thierry; Savinaud, Mickael; Udroiu, Cosmin; Valero, Silvia; Begue, Agnes; Dejoux, Jean-Francois; El Harti, Abderrazak; Ezzahar, Jamal; Kussul, Nataliia; Labbassi, Kamal; Lebourgeois, Valentine; Miao, Zhang; Newby, Terrence; Nyamugama, Adolph; Salh, Norakhan; Shelestov, Andrii; Simonneaux, Vincent; Traore, Pierre Sibiry; Traore, Souleymane S.; Koetz, Benjamin Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world REMOTE SENSING OF ENVIRONMENT English Article Agriculture monitoring; Cloud computing; Machine learning; Sentinel-2; Crop type mapping; Cropland EARTH OBSERVATION REQUIREMENTS; BIDIRECTIONAL REFLECTANCE; CANOPY; MODELS; SET; EXTRACTION; SENESCENCE; VARIABLES; SEASON The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives. [Defourny, Pierre; Bontemps, Sophie; Bellemans, Nicolas] Catholic Univ Louvain, Earth & Life Inst, 2 Croix Sud Bte L7-05-16, B-1348 Louvain La Neuve, Belgium; [Cara, Cosmin; Nicola, Laurentiu; Udroiu, Cosmin] CS Romania SA, 29 Str Pacii, Craiova 200692, Romania; [Dedieu, Gerard; Hagolle, Olivier; Inglada, Jordi; Valero, Silvia; Dejoux, Jean-Francois; Simonneaux, Vincent] Univ Toulouse, Ctr Etud Spatiales BIOsphere CESBIO, CNES, CNRS,IRD,UPS, 18 Ave Edouard Belin, F-31401 Toulouse, France; [Guzzonato, Eric; Rabaute, Thierry; Savinaud, Mickael] CS Syst Informat, 5 Rue Brindejonc Moulinais, F-31506 Toulouse, France; [Begue, Agnes; Lebourgeois, Valentine] CIRAD, UMR TETIS, Maison Teledetect,500 Rue JF Breton, F-34093 Montpellier, France; [Begue, Agnes; Lebourgeois, Valentine] Univ Montpellier, AgroParisTech, CNRS, TETIS,CIRAD,Irstea, Montpellier, France; [El Harti, Abderrazak] Sultan Moulay Slimane Univ, Fac Sci & Tech, POB 523, Beni Mellal, Morocco; [Ezzahar, Jamal] Univ Cadi Ayyad, Ecole Natl Sci Appl Safi, Safi 46000, Morocco; [Kussul, Nataliia; Shelestov, Andrii] Natl Acad Sci Ukraine, Space Res Inst, 40 Prosp Glushkov,Build 4-1, UA-03680 Kiev, Ukraine; [Kussul, Nataliia; Shelestov, Andrii] State Space Agcy Ukraine, 40 Prosp Glushkov,Build 4-1, UA-03680 Kiev, Ukraine; [Labbassi, Kamal] Univ Chouaib Doukkali, El Jadida, Morocco; [Miao, Zhang] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Olymp Village Sci Pk,West Beichen Rd, Beijing 100101, Peoples R China; [Newby, Terrence; Nyamugama, Adolph] Agr Res Council South Africa, Private Bag X79, ZA-0001 Pretoria, South Africa; [Salh, Norakhan] Minist Agr, Juba, Sudan; [Ezzahar, Jamal; Simonneaux, Vincent] Fac Sci Semlalia, Ctr Geber, Lab Mixte Int TREMA, Marrakech 40000, Morocco; [Traore, Pierre Sibiry] Int Crops Res Inst Semi Arid Trop, POB 320, Bamako, Mali; [Traore, Souleymane S.] Inst Econ Rurale, Bamako, Mali; [Koetz, Benjamin] ESA, ESRIN, Via Galileo Galilei,Casella Postale 64, I-00044 Rome, Italy Universite Catholique Louvain; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); AgroParisTech; CIRAD; AgroParisTech; Centre National de la Recherche Scientifique (CNRS); CIRAD; INRAE; Universite de Montpellier; Sultan Moulay Slimane University of Beni Mellal; National Academy of Sciences Ukraine; State Space Agency of Ukraine; Space Research Institute of NAS of Ukraine & SSA of Ukraine; State Space Agency of Ukraine; Chouaib Doukkali University of El Jadida; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Cadi Ayyad University of Marrakech; CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); European Space Agency Bontemps, S (corresponding author), Catholic Univ Louvain, Earth & Life Inst, 2 Croix Sud Bte L7-05-16, B-1348 Louvain La Neuve, Belgium. sophie.bontemps@uclouvain.be Ezzahar, Jamal/AAF-9758-2020; Harti, Abderrazak El/Q-5503-2019; Kussul, Nataliia/N-8649-2014; DEDIEU, Gérard/G-6836-2016; begue, agnes/A-5718-2011; Newby, Terence s/H-1249-2015; Nyamugama, Adolph/W-4505-2019; Hagolle, Olivier/AAT-2050-2021 Kussul, Nataliia/0000-0002-9704-9702; DEDIEU, Gérard/0000-0002-8383-6465; begue, agnes/0000-0002-9289-1052; Newby, Terence s/0000-0003-4850-1659; TRAORE, Souleymane S./0000-0002-8373-7205; El HARTI, Abderrazak/0000-0003-3976-4588; Simonneaux, Vincent/0000-0002-4245-5383 European Space Agency Data User Element program through the Sen2-Agri project [ESRIN 400109979/14/I-AM] European Space Agency Data User Element program through the Sen2-Agri project(European Space Agency) This research was supported by the European Space Agency Data User Element program through the Sen2-Agri project (under contrat noESRIN 400109979/14/I-AM) carried out by a consortium led by UCL and including CESBIO, CS-France and CS-Romania. The authors are very thankful for the Sen2-Agri Champions Users and to all the members of the national and JECAM teams who collected and quality controlled in situ data and provided feedbacks to the Sen2-Agri products through the different national workshops locally organized. 55 158 163 7 79 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. FEB 2019.0 221 551 568 10.1016/j.rse.2018.11.007 0.0 18 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology HI7MQ Green Accepted, Green Published, hybrid 2023-03-23 WOS:000456640700041 0
C Zhu, YG; Zhang, L; Manoonpong, P IEEE Zhu, Yaguang; Zhang, Liang; Manoonpong, Poramate Virtual Motoneuron Activation for Goal-directed Locomotion of a Hexapod Robot 2020 5TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2020) English Proceedings Paper 5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) DEC 18-21, 2020 Shenzhen, PEOPLES R CHINA IEEE,IEEE Syst, Man & Cybernet Soc,IEEE Robot & Automat Soc,IEEE SMC Tech Comm Bio Mechatron & Bio Robot Syst,IEEE RAS Tech Comm Neuro Robot Syst,Shenzhen Univ,Artificial Intelligence & Robot Assoc Guangdong Prov,Anhui Robot Soc,IEEE CAA Journal Automatica Sinica,Deltason PATTERN GENERATION; WALKING; DRIVEN Walking animals have shown energy efficiency and versatile locomotion ability when adapting to their environments. Different methods, including machine learning, model control and biologically inspired control, have been applied in artificial legged locomotion systems to imitate these natural characteristics. In this paper, a virtual motoneuron activation module (VMAM) is built to convert goal-directed behavioral instructions into nerve signal strength, which leads to animal-like signal processing mechanisms and behavioral responses. The system is composed of a reticulospinal neuron module, a central pattern generator (CPG) and a virtual motoneuron network (VMN). The VMN receives the rhythm signals from the CPG and generates different motions that are controlled by global and local factors of muscle activation and goal-directed points; then, through an inverse kinematics module, the signals for driving the joint motors of the robot are obtained. Finally, four groups of experiments with different global and local factors of muscle activation are tested on a bionic hexapod robot to validate the effectiveness. The results prove that the method can generate active adaption and emergent behavior according to signal feedback and improve the locomotion agility, coordination, and diversity of the robot. [Zhu, Yaguang; Zhang, Liang] Changan Univ, Key Lab Rd Construct Technol & Equipment MOE, Xian 710064, Peoples R China; [Zhu, Yaguang] Univ Southern Denmark, Ctr BioRobot, Maersk Mc Kinney Moller Inst, Embodied AI & Neurorobot Lab, DK-5230 Odense, Denmark; [Manoonpong, Poramate] Univ Southern Denmark, SDU Biorobot, Maersk Mc Kinney Moller Inst, Embodied Artificial Intelligence & Neurorobot Lab, DK-5230 Odense, Denmark; [Manoonpong, Poramate] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China; [Manoonpong, Poramate] Vidyasirimedhi Inst Sci & Technol, Sch Informat Sci & Technol, Rayong 21210, Thailand Chang'an University; University of Southern Denmark; University of Southern Denmark; Nanjing University of Aeronautics & Astronautics; Vidyasirimedhi Institute of Science & Technology Zhu, YG (corresponding author), Changan Univ, Key Lab Rd Construct Technol & Equipment MOE, Xian 710064, Peoples R China.;Zhu, YG (corresponding author), Univ Southern Denmark, Ctr BioRobot, Maersk Mc Kinney Moller Inst, Embodied AI & Neurorobot Lab, DK-5230 Odense, Denmark. zhuyaguang@chd.edu.cn; liangzhang@chd.edu.cn; poma@mmmi.sdu.dk National Natural Science Foundation of China [51605039]; Thirteenth Five-Year Plan Equipment Pre-research Field Fund [61403120407]; China Postdoctoral Science Foundation [2018T111005]; Shaanxi International Science and Technology Cooperation Project [2020KW-064]; Fundamental Research Funds for the Central Universities [300102259308, 300102259401]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Thirteenth Five-Year Plan Equipment Pre-research Field Fund; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Shaanxi International Science and Technology Cooperation Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) This work was supported in part by National Natural Science Foundation of China under Grant 51605039, in part by Thirteenth Five-Year Plan Equipment Pre-research Field Fund under Grant 61403120407, in part by the China Postdoctoral Science Foundation under Grant 2018T111005, in part by the Shaanxi International Science and Technology Cooperation Project under Grant 2020KW-064, in part by the Fundamental Research Funds for the Central Universities under Grant 300102259308 and 300102259401, and in part by the China Scholarship Council. (Corresponding author: Yaguang Zhu) 31 0 1 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-6479-3 2020.0 380 386 7 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BS5DA Green Submitted 2023-03-23 WOS:000728183800068 0
C He, YF; Peemen, M; Luc, WJ; Diken, E; Fiumara, M; Rauwerda, G; Corporaal, H; Geng, T Najjar, W; Gerstlauer, A He, Yifan; Peemen, Maurice; Luc Waeijen; Diken, Erkan; Fiumara, Mattia; Rauwerda, Gerard; Corporaal, Henk; Geng, Tong A Configurable SIMD Architecture with Explicit Datapath for Intelligent Learning PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON EMBEDDED COMPUTER SYSTEMS: ARCHITECTURES, MODELING AND SIMULATION (SAMOS) English Proceedings Paper International Conference on Embedded Computer Systems - Architectures, Modeling and Simulation (SAMOS) JUL 17-21, 2016 Samos, GREECE IEEE Circuits & Syst Soc,IEEE Solid State Circuits Soc Configurable; Wide SIMD; Explicit Datapath; CNN; MAC; Low Power HIGH-PERFORMANCE; XETAL-II; DSP; PROCESSOR; ENERGY The use of a wide Single-Instruction-Multiple-Data (SIMD) architecture is a promising approach to build energyefficient high performance embedded processors. In this paper, based on our design framework for low-power SIMD processors, we propose a multiply-accumulate (MAC) unit with variable number of accumulator registers. The proposed MAC unit exploits both the merits of merged operation and register tiling. A Convolutional Neural Network (CNN) is a popular learning based algorithm due to its flexibility and high accuracy. However, a CNN-based application is often computationally intensive as it applies convolution operations extensively on a large data set. In this work, a CNN-based intelligent learning application is analyzed and mapped in the context of SIMD architectures. Experimental results show that the proposed architecture is efficient. In a 64-PE instance, the proposed SIMD processor with MAC4reg achieves an effective performance of 63.2 GOPS. Compared to the two baseline SIMD processors without MAC4reg, the proposed design brings 54.0% and 32.1% reduction in execution time, and 20.5% and 35.1% reduction in energy consumption respectively. [He, Yifan] Xiamen Univ Technol, Xiamen, Peoples R China; [Peemen, Maurice; Luc Waeijen; Diken, Erkan; Fiumara, Mattia; Corporaal, Henk; Geng, Tong] Eindhoven Univ Technol, Eindhoven, Netherlands; [Rauwerda, Gerard] Recore Syst BV, Enschede, Netherlands Xiamen University of Technology; Eindhoven University of Technology He, YF (corresponding author), Xiamen Univ Technol, Xiamen, Peoples R China. y.he@xmut.edu.cn; m.c.j.peemen@tue.nl; l.j.w.waeijen@tue.nl; e.diken@tue.nl; m.fiumara@student.tue.nl; gerard.rauwerda@recoresystems.com; h.corporaal@tue.nl; t.geng@tue.nl Sensor Technology Applied in Reconfigurable Systems (STARS) Consortium, The Netherlands; STARS [RS1501A]; Affordable Safe & Secure Mobility Evolution (ASSUME) [ITEA3 14014] Sensor Technology Applied in Reconfigurable Systems (STARS) Consortium, The Netherlands; STARS; Affordable Safe & Secure Mobility Evolution (ASSUME) This work is supported by the Sensor Technology Applied in Reconfigurable Systems (STARS) Consortium, The Netherlands, project STARS RS1501A and Affordable Safe & Secure Mobility Evolution (ASSUME), project ITEA3 14014 ASSUME. 31 3 5 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-3076-7 2016.0 156 163 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BH2RD 2023-03-23 WOS:000399143000021 0
J Zhu, BZ; Ye, SX; Wang, P; Chevallier, J; Wei, YM Zhu, Bangzhu; Ye, Shunxin; Wang, Ping; Chevallier, Julien; Wei, Yi-Ming Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels JOURNAL OF FORECASTING English Article least squares support vector machine; machine learning; mixture kernels; multi-objective fitness function; particle swarm optimization MODE DECOMPOSITION; HYBRID ARIMA; DYNAMICS; INTELLIGENCE; PREDICTION; NETWORKS; PARADIGM For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO2 assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance. [Zhu, Bangzhu; Ye, Shunxin; Wang, Ping] Jinan Univ, Sch Management, Guangzhou 510632, Peoples R China; [Zhu, Bangzhu] Nanjing Univ Informat Sci & Technol, Sch Business, Nanjing, Peoples R China; [Chevallier, Julien] IPAG Business Sch, IPAG Lab, Finance Dept, 184 Blvd St Germain, F-75006 Paris, France; [Chevallier, Julien] Univ Paris 8 LED, Econ Dept, St Denis, France; [Wei, Yi-Ming] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing, Peoples R China Jinan University; Nanjing University of Information Science & Technology; IPAG Business School; Beijing Institute of Technology Zhu, BZ (corresponding author), Jinan Univ, Sch Management, Guangzhou 510632, Peoples R China.;Chevallier, J (corresponding author), IPAG Business Sch, IPAG Lab, Finance Dept, 184 Blvd St Germain, F-75006 Paris, France.;Chevallier, J (corresponding author), Univ Paris 8 LED, Econ Dept, St Denis, France. wpzbz@126.com; julien.chevallier@ipag.fr Guangzhou key Base of Humanities and Social Science-Centre for Low Carbon Economic Research; Guangdong Key Base of Humanities and Social Science-Enterprise Development Research; Guangdong Young Zhujiang Scholar [[2016]95]; National Philosophy and Social Science Foundation of China [16ZZD049]; National Natural Science Foundation of China [71771105, 71974077, 72074120] Guangzhou key Base of Humanities and Social Science-Centre for Low Carbon Economic Research; Guangdong Key Base of Humanities and Social Science-Enterprise Development Research; Guangdong Young Zhujiang Scholar; National Philosophy and Social Science Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Guangzhou key Base of Humanities and Social Science-Centre for Low Carbon Economic Research; Guangdong Key Base of Humanities and Social ScienceEnterprise Development Research; Guangdong Young Zhujiang Scholar, Grant/Award Number: [2016]95; National Philosophy and Social Science Foundation of China, Grant/Award Number: 16ZZD049; National Natural Science Foundation of China, Grant/Award Numbers: 71771105, 71974077 : 72074120 43 36 36 6 52 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0277-6693 1099-131X J FORECASTING J. Forecast. JAN 2022.0 41 1 100 117 10.1002/for.2784 0.0 JUN 2021 18 Economics; Management Social Science Citation Index (SSCI) Business & Economics XH1JE 2023-03-23 WOS:000658256200001 0
J Deng, YQ; Ruan, JJ; Quan, Y; Gong, RH; Huang, DC; Duan, CH; Xie, YM Deng, Yongqing; Ruan, Jiangjun; Quan, Yu; Gong, Ruohan; Huang, Daochun; Duan, Cihan; Xie, Yiming A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer IEEE ACCESS English Article Hot spot temperature; oil-immersed transformer; support vector regression; multi-physical field analysis PARTICLE SWARM OPTIMIZATION; SUPPORT VECTOR REGRESSION; POWER; MODEL This paper proposed a prediction method to predict a 10-kV oil-immersed transformer hot spot temperature (HST). A set of feature temperature points on the transformer iron shell is proposed based on fluid-thermal field calculation. These feature points, as well as transformer load rate, are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the HST. This model is trained by nine samples selected by L-9(3(4)) orthogonal array and applied to predict the HST of 20 test samples. The training samples are all obtained by simulation, and the test samples have consisted of simulation and transformer temperature rise test results. With effective parameter optimization of the SVR model, the predicted results agree well with the experimental and simulation data, the mean absolute percentage error (MAPE) is 1.55%, and the maximum temperature difference is less than 3 degrees C. The results validated the validity and the generalization performance of the prediction model. [Deng, Yongqing; Ruan, Jiangjun; Huang, Daochun; Duan, Cihan; Xie, Yiming] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Hubei, Peoples R China; [Quan, Yu] Wuhan Power Supply Co, State Grid Hubei Elect Power Co, Wuhan 430050, Hubei, Peoples R China; [Gong, Ruohan] Univ Lille, EA 2697 L2EP Lab Electrotech & Electron Puissan, Arts & Metiers Paris Tech, Cent Lille,HEI, F-59000 Lille, France Wuhan University; State Grid Corporation of China; Arts et Metiers Institute of Technology; Universite de Lille - ISITE; Centrale Lille; Universite de Lille Huang, DC (corresponding author), Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Hubei, Peoples R China.;Gong, RH (corresponding author), Univ Lille, EA 2697 L2EP Lab Electrotech & Electron Puissan, Arts & Metiers Paris Tech, Cent Lille,HEI, F-59000 Lille, France. rhgong@whu.edu.cn; huangdc99@163.com Deng, Yongqing/0000-0001-8477-1205; Xie, Yiming/0000-0002-1604-4273 29 13 15 9 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 107380 107388 10.1109/ACCESS.2019.2924709 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IS2GX gold 2023-03-23 WOS:000481972100170 0
J Manni, F; van der Sommen, F; Zinger, S; Shan, CF; Holthuizen, R; Lai, M; Bustrom, G; Hoveling, RJM; Edstrom, E; Elmi-Terander, A; de With, PHN Manni, Francesca; van der Sommen, Fons; Zinger, Svitlana; Shan, Caifeng; Holthuizen, Ronald; Lai, Marco; Bustrom, Gustav; Hoveling, Richelle J. M.; Edstrom, Erik; Elmi-Terander, Adrian; de With, Peter H. N. Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery APPLIED SCIENCES-BASEL English Article hyperspectral imaging; feature detection; spine surgery; markerless tracking; deep local features PEDICLE SCREW PLACEMENT; AUGMENTED REALITY; BAND SELECTION; ACCURACY; LUMBAR; INSTRUMENTATION; FEASIBILITY; TECHNOLOGY; 3D Featured Application Current spinal navigation systems rely on optical reference markers to detect the patient's position. To bypass the use of current reference marker solutions, our work aims at a reliable and simple skin feature detection technology for improving clinical workflow during spine surgery. Unfortunately, reference markers or reference frames can be displaced or obscured during the surgical procedures. We present a solution by applying hyperspectral imaging (HSI) to directly detect skin features for navigation. The initial results demonstrate that HSI has the potential to replace marker-based solutions and can serve as a platform for the further development of markerless tracking. In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers. [Manni, Francesca; van der Sommen, Fons; Zinger, Svitlana; de With, Peter H. N.] Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands; [Shan, Caifeng] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China; [Holthuizen, Ronald] Philips Healthcare, NL-5684 PC Best, Netherlands; [Lai, Marco] Philips Res, HTC 34, NL-5656 AE Eindhoven, Netherlands; [Bustrom, Gustav; Edstrom, Erik; Elmi-Terander, Adrian] Karolinska Univ Hosp, Dept Neurosurg, SE-17146 Stockholm, Sweden; [Bustrom, Gustav; Edstrom, Erik; Elmi-Terander, Adrian] Karolinska Inst, Dept Clin Neurosci, SE-17146 Stockholm, Sweden; [Hoveling, Richelle J. M.] Quest Med Imaging BV, NL-1775 PW Middenmeer, Netherlands Eindhoven University of Technology; Shandong University of Science & Technology; Philips; Philips Healthcare; Philips; Philips Research; Karolinska Institutet; Karolinska University Hospital; Karolinska Institutet Manni, F (corresponding author), Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands. f.manni@tue.nl; fvdsommen@tue.nl; s.zinger@tue.nl; Caifeng.Shan@gmail.com; ronald.holthuizen@philips.com; marco.lai@philips.com; gustav.burstrom@ki.se; richelle.hoveling@quest-innovations.com; erik.edstrom@sll.se; adrian.elmi-terander@sll.se; P.H.N.de.With@tue.nl Edström, Erik/AAH-6539-2020; Elmi-Terander, Adrian/AAC-7997-2019; Shan, Caifeng/W-6178-2019 Elmi-Terander, Adrian/0000-0002-3776-6136; Shan, Caifeng/0000-0002-2131-1671; Edstrom, Erik/0000-0002-8781-1169; Holthuizen, Ronaldus/0000-0002-1704-9789; Manni, Francesca/0000-0003-0470-2299; van der Sommen, Fons/0000-0002-3593-2356 H2020-ECSEL Joint Undertaking [692470] H2020-ECSEL Joint Undertaking The research activity leading to the results of this paper was funded by the H2020-ECSEL Joint Undertaking under Grant Agreement No. 692470 (ASTONISH Project). 47 14 14 2 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel JUN 2020.0 10 12 4078 10.3390/app10124078 0.0 19 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics ML4QS gold 2023-03-23 WOS:000549453100001 0
C Meng, WZ; Li, WJ; Jiang, LJ; Zhou, JY Dhillon, G; Karlsson, F; Hedstrom, K; Zuquete, A Meng, Weizhi; Li, Wenjuan; Jiang, Lijun; Zhou, Jianying SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications ICT SYSTEMS SECURITY AND PRIVACY PROTECTION, SEC 2019 IFIP Advances in Information and Communication Technology English Proceedings Paper 34th IFIP TC 11 International Conference on ICT Systems Security and Privacy Protection (SEC) JUN 25-27, 2019 Lisbon, PORTUGAL Int Federat Informat Proc, Tech Comm 11 Informat Secur & Privacy Protect Informat Proc Syst Behavioral user authentication; Touch gestures; Usable security; Smartphone security; Social networking; Machine learning Modern smartphones expressed an exponential growth and have become a personal assistant in people's daily lives, i.e., keeping connected with peers. Users are willing to store their personal data even sensitive information on the phones, making these devices an attractive target for cyber-criminals. Due to the limitations of traditional authentication methods like Personal Identification Number (PIN), research has been moved to the design of touch behavioral authentication on smartphones. However, how to design a robust behavioral authentication in a long-term period remains a challenge due to behavioral inconsistency. In this work, we advocate that touch gestures could become more consistent when users interact with specific applications. In this work, we focus on social networking applications and design a touch behavioral authentication scheme called SocialAuth. In the evaluation, we conduct a user study with 50 participants and demonstrate that touch behavioral deviation under our scheme could be significantly decreased and kept relatively stable even after a long-term period, i.e., a single SVM classifier could achieve an average error rate of about 3.1% and 3.7% before and after two weeks, respectively. [Meng, Weizhi; Li, Wenjuan] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Li, Wenjuan] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China; [Jiang, Lijun] CyberTree Res Inst, Hong Kong, Peoples R China; [Zhou, Jianying] Singapore Univ Technol & Design, Singapore, Singapore Technical University of Denmark; City University of Hong Kong; Singapore University of Technology & Design Meng, WZ (corresponding author), Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark. weme@dtu.dk; jianying_zhou@sutd.edu.sg Li, Wenjuan/AAE-4027-2019 Li, Wenjuan/0000-0003-3745-5669; Zhou, Jianying/0000-0003-0594-0432 SUTD [SRG-ISTD-2017-124] SUTD(Singapore University of Technology & Design) We would like to thank all anonymous reviewers for their helpful comments, and Jianying Zhou was supported by SUTD start-up research grant SRG-ISTD-2017-124. 16 7 7 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1868-4238 1868-422X 978-3-030-22312-0; 978-3-030-22311-3 IFIP ADV INF COMM TE 2019.0 562 180 193 10.1007/978-3-030-22312-0_13 0.0 14 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP6QE Green Published, Green Submitted 2023-03-23 WOS:000560392300013 0
J Gomez-Canon, JS; Cano, E; Eerola, T; Herrera, P; Hu, X; Yang, YH; Gomez, E Gomez-Canon, Juan Sebastian; Cano, Estefania; Eerola, Tuomas; Herrera, Perfecto; Hu, Xiao; Yang, Yi-Hsuan; Gomez, Emilia Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications IEEE SIGNAL PROCESSING MAGAZINE English Article CHALLENGES; MODEL Emotion is one of the main reasons why people engage and interact with music [1]. Songs can express our inner feelings, produce goosebumps, bring us to tears, share an emotional state with a composer or performer, or trigger specific memories. Interest in a deeper understanding of the relationship between music and emotion has motivated researchers from various areas of knowledge for decades [2], including computational researchers. Imagine an algorithm capable of predicting the emotions that a listener perceives in a musical piece, or one that dynamically generates music that adapts to the mood of a conversation in a film-a particularly fascinating and provocative idea. These algorithms typify music emotion recognition (MER), a computational task that attempts to automatically recognize either the emotional content in music or the emotions induced by music to the listener [3]. To do so, emotionally relevant features are extracted from music. The features are processed, evaluated, and then associated with certain emotions. MER is one of the most challenging high-level music description problems in music information retrieval (MIR), an interdisciplinary research field that focuses on the development of computational systems to help humans better understand music collections. MIR integrates concepts and methodologies from several disciplines, including music theory, music psychology, neuroscience, signal processing, and machine learning. [Gomez-Canon, Juan Sebastian; Herrera, Perfecto; Gomez, Emilia] Univ Pompeu Fabra, Mus Technol Grp, Barcelona 08018, Spain; [Cano, Estefania] AudioSourceRE, Cork, Ireland; [Eerola, Tuomas] Univ Durham, Mus Cognit, Durham DH1 3DA, England; [Hu, Xiao] Univ Hong Kong, Hong Kong, Peoples R China; [Yang, Yi-Hsuan] Acad Sinica, Res Ctr It Innovat, New Taipei 22142, Taiwan; [Yang, Yi-Hsuan] Taiwan AI Labs, New Taipei 22142, Taiwan; [Gomez, Emilia] European Commiss, Joint Res Ctr, Seville 41092, Spain Pompeu Fabra University; Durham University; University of Hong Kong Gomez-Canon, JS (corresponding author), Univ Pompeu Fabra, Mus Technol Grp, Barcelona 08018, Spain. juansebastian.gomez@upf.edu; estefania.cano@songquito.com; tuomas.eerola@durham.ac.uk; perfecto.herrera@upf.edu; xiaoxhu@hku.hk; yang@citi.sinica.edu.tw; emilia.gomez@upf.edu Cano, Estefania/0000-0002-1056-5844 European Commission under the TROMPA project [H2020 770376] European Commission under the TROMPA project Juan Gomez-Canon is partially supported by the European Commission under the TROMPA project (H2020 770376). We would like to thank anonymous reviewers that helped us improve the article with constructive feedback. 36 8 8 9 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-5888 1558-0792 IEEE SIGNAL PROC MAG IEEE Signal Process. Mag. NOV 2021.0 38 6 106 114 10.1109/MSP.2021.3106232 0.0 9 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering WN4CT Green Accepted 2023-03-23 WOS:000711718500019 0
J Li, ZW; Liu, XF; Dai, JH; Chen, JL; Fujita, H Li, Zhaowen; Liu, Xiaofeng; Dai, Jianhua; Chen, Jiaolong; Fujita, Hamido Measures of uncertainty based on Gaussian kernel for a fully fuzzy information system KNOWLEDGE-BASED SYSTEMS English Article Fully fuzzy information system; Uncertainty measure; Fuzzy information structure; Gaussian kernel GRANULARITY MEASURES; ENTROPY MEASURES; KNOWLEDGE GRANULATION; ROUGH SETS; SELECTION; MODEL; APPROXIMATION; ALGORITHM; INTERVAL The uncertainty of information plays an important role in practical applications, so how to capture the uncertainty of information systems becomes more and more popular. Uncertainty measures can supply new viewpoints for processing information systems, and they can help us in disclosing the substantive characteristics of information. Fuzzy information systems are important research objects in artificial intelligence. As a special kind of fuzzy information system, fully fuzzy information system (FFIS) is worth studying. This article is devoted to search indicators for measuring uncertainty in a FFIS according to fuzzy information structures in view of Gaussian kernel, and the fuzzy information structures can be viewed as granular structures under granular computing. Firstly, by employing Gaussian kernel for calculating similarities among objects in a FFIS, the fuzzy Tcos-similarity relation is obtained. Then, based on this relation, fuzzy information structures in a FFIS are introduced. Next, according to the information structures, granulation measure of a given FFIS is advanced. Moreover, entropy measure is also considered for a given FFIS. Finally, two numerical experiments are conducted to interpret the realistic significance and potential applications for measuring uncertainty in a FFIS. Theoretical research, numerical experiments and validity analysis make clear that the proposed measures are efficacious and applicable for a FFIS. (C) 2020 Elsevier B.V. All rights reserved. [Li, Zhaowen] Yulin Normal Univ, Key Lab Complex Syst Optimizat & Big Data Proc De, Yulin 537000, Guangxi, Peoples R China; [Liu, Xiaofeng; Dai, Jianhua] Hunan Normal Univ, Sch Math & Stat, Key Lab Comp & Stochast Math, Minist Educ, Changsha 410081, Hunan, Peoples R China; [Liu, Xiaofeng; Dai, Jianhua; Chen, Jiaolong] Hunan Normal Univ, Hunan Prov Lab Intelligent Comp & Language Inform, Changsha 410081, Hunan, Peoples R China; [Liu, Xiaofeng; Dai, Jianhua; Chen, Jiaolong] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Hunan, Peoples R China; [Fujita, Hamido] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain; [Fujita, Hamido] Iwate Prefectural Univ, Fac Software & Informat Sci, Takizawa, Iwate, Japan Yulin Normal University; Hunan Normal University; Hunan Normal University; Hunan Normal University; University of Granada; Iwate Prefectural University Dai, JH (corresponding author), Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Hunan, Peoples R China. jhdai@hunnu.edu.cn Fujita, Hamido/D-6249-2012 Fujita, Hamido/0000-0001-5256-210X; Liu, Xiaofeng/0000-0001-5024-9187 National Natural Science Foundation of China [61976089, 11971420]; Natural Science Foundation of Guangxi, China [2016GXNSFAA380045, 2016GXNSFAA380 282, 2016GXNSFAA380286]; Key Laboratory of Optimization Control and Engineering Calculation in Department of Guangxi Education, China; Special Funds of Guangxi Distinguished Experts Construction Engineering, China; Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, China; Hunan Provincial Science & Technology Project Foundation, China [2018TP1018, 2018RS3065]; Construct Program of the Key Discipline in Hunan Province, China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangxi, China(National Natural Science Foundation of Guangxi Province); Key Laboratory of Optimization Control and Engineering Calculation in Department of Guangxi Education, China; Special Funds of Guangxi Distinguished Experts Construction Engineering, China; Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, China; Hunan Provincial Science & Technology Project Foundation, China; Construct Program of the Key Discipline in Hunan Province, China This work is supported by the National Natural Science Foundation of China (61976089, 11971420), Natural Science Foundation of Guangxi, China (2016GXNSFAA380045, 2016GXNSFAA380 282, 2016GXNSFAA380286), Key Laboratory of Optimization Control and Engineering Calculation in Department of Guangxi Education, China, Special Funds of Guangxi Distinguished Experts Construction Engineering, China, Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, China, the Hunan Provincial Science & Technology Project Foundation, China (2018TP1018, 2018RS3065) and the Construct Program of the Key Discipline in Hunan Province, China. 61 35 35 0 17 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAY 21 2020.0 196 105791 10.1016/j.knosys.2020.105791 0.0 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LF3DO 2023-03-23 WOS:000527301700022 0
C Deng, RF; Pan, L; Clavel, C IEEE Deng, Ruofan; Pan, Li; Clavel, Chloe Domain Adaptation for Stance Detection towards Unseen Target on Social Media 2022 10TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII) International Conference on Affective Computing and Intelligent Interaction English Proceedings Paper 10th International Conference on Affective Computing and Intelligent Interaction (ACII) OCT 18-21, 2022 Nara, JAPAN stance detection; domain adaptation; feature disentanglement Stance detection aims at identifying people's standpoint towards a given target. New targets are constantly appearing on social media, making previous annotated data unusable by stance detection models relying on classical supervised machine learning. Thus, cross-target stance detection which uses labeled data from source targets to learn a model that can be adapted to the destination new target, has become a prevailing research direction. However, previous methods rely on manually chosen similar source-destination target pairs and lack generalization to unseen targets with no explicit relation to known ones. To this end, we investigate the problem from a domain adaptation perspective and further propose a novel Unified Target-aware Domain Adaptation method (UTDA) that leverages knowledge transfer capability of transformer-based language model. The proposed method can effectively extract critical target-shared features for detecting stance by feature disentanglement and automatically learn to identify target relations. UTDA can easily be applied to a new unseen target since it does not rely on any pre-defined target pairs. Experimental results on two benchmark stance datasets demonstrate that our method achieves better performance than strong baselines. [Deng, Ruofan; Pan, Li] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China; [Deng, Ruofan; Clavel, Chloe] Telecom Paris, Inst Polytech Paris, LTCI, Palaiseau, France Shanghai Jiao Tong University; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Deng, RF (corresponding author), Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.;Deng, RF (corresponding author), Telecom Paris, Inst Polytech Paris, LTCI, Palaiseau, France. henri16@sjtu.edu.cn; panli@sjtu.edu.cn; chloe.clavel@telecom-paris.fr National Natural Science Foundation of China [62172278]; Telecom Paris research chair on Data Science and Artificial Intelligence for Digitalized Industry and Services (DSAIDIS) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Telecom Paris research chair on Data Science and Artificial Intelligence for Digitalized Industry and Services (DSAIDIS) This work is supported by National Natural Science Foundation of China (62172278) and T ' el ' ecom Paris research chair on Data Science and Artificial Intelligence for Digitalized Industry and Services (DSAIDIS). 34 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-8103 978-1-6654-5908-2 INT CONF AFFECT 2022.0 10.1109/ACII55700.2022.9953818 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU3WK 2023-03-23 WOS:000893674200007 0
J Liu, X; Chen, GX; Li, YG; Chen, L; Meng, QL; Mehdi-Souzani, C Liu, Xu; Chen, Gengxiang; Li, Yingguang; Chen, Lu; Meng, Qinglu; Mehdi-Souzani, Charyar Sampling via the aggregation value for data-driven manufacturing NATIONAL SCIENCE REVIEW English Article data-driven modelling; intelligent manufacturing; data sampling; data value STATE Data-driven modelling has shown promising potential in many industrial applications, while the expensive and time-consuming labelling of experimental and simulation data restricts its further development. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. In this research, the aggregation value is defined using a novel representation for the value of a sample set by modelling the invisible redundant information as the overlaps of neighbouring values. The sampling problem is hence converted to the maximisation of the submodular function over the aggregation value. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods. The research outcome also indicates its appealing potential to reduce labelling efforts for more data-scarcity scenarios. A new concept, aggregation value, is proposed to evaluate the real value of sample set, thus high-value sampling can be ensured which will greatly reduce the labelling effort for machine learning tasks. [Liu, Xu] Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing 211816, Peoples R China; [Chen, Gengxiang; Li, Yingguang; Chen, Lu; Meng, Qinglu] Nanjing Univ Aeronaut & Astronaut, Coll Mech Elect Engn, Nanjing 210016, Peoples R China; [Mehdi-Souzani, Charyar] Univ Paris Saclay, Univ Res Lab Automated Prod, Univ prime Sorbonne Paris Nord, prime Ecole Normale Superieure Paris Sacl, F-91190 Gif Sur Yvette, France Nanjing Tech University; Nanjing University of Aeronautics & Astronautics; UDICE-French Research Universities; Universite Paris Saclay Li, YG (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Mech Elect Engn, Nanjing 210016, Peoples R China. liyingguang@nuaa.edu.cn Li, Yingguang/0000-0003-4425-8073 National Science Fund for Distinguished Young Scholars [51925505]; Major Program of the National Natural Science Foundation of China [52090052]; Science Fund for Creative Research Groups of the National Natural Science Foundation of China [51921003] National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Major Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Fund for Creative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Science Fund for Distinguished Young Scholars (51925505), the Major Program of the National Natural Science Foundation of China (52090052) and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (51921003). 35 0 0 6 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2095-5138 2053-714X NATL SCI REV Natl. Sci. Rev. NOV 9 2022.0 9 11 nwac201 10.1093/nsr/nwac201 0.0 11 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 6A7YZ 36381214.0 gold 2023-03-23 WOS:000880867600001 0
J Ma, R; Li, ZL; Breaz, E; Liu, C; Bai, H; Briois, P; Gao, F Ma, Rui; Li, Zhongliang; Breaz, Elena; Liu, Chen; Bai, Hao; Briois, Pascal; Gao, Fei Data-Fusion Prognostics of Proton Exchange Membrane Fuel Cell Degradation IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS English Article; Proceedings Paper IEEE Transportation and Electrification Conference and Expo (ITEC) JUN 13-15, 2018 Long Beach, CA IEEE Degradation; fuel cell; modeling; machine learning STATE; PERFORMANCE; LIFE Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under various fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications. [Ma, Rui] Northwestern Polytech Univ, Xian 710072, Shaanxi, Peoples R China; [Li, Zhongliang] Aix Marseille Univ, LSIS Lab, UMR CNRS 7296, F-13397 Marseille, France; [Breaz, Elena; Liu, Chen; Bai, Hao; Briois, Pascal; Gao, Fei] Univ Bourgogne Franche Comte, FCLAB, F-90010 Belfort, France; [Breaz, Elena; Liu, Chen; Bai, Hao; Briois, Pascal; Gao, Fei] Univ Bourgogne Franche Comte, FEMTO ST Inst, F-90010 Belfort, France Northwestern Polytechnical University; UDICE-French Research Universities; Aix-Marseille Universite; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM) Ma, R (corresponding author), Northwestern Polytech Univ, Xian 710072, Shaanxi, Peoples R China. rui.ma@nwpu.edu.cn; zhongliang.li@lsis.org; elena.breaz@utbm.fr; chen.liu@utbm.fr; hao.bai@utbm.fr; pascal.briois@utbm.fr; fei.gao@utbm.fr Gao, Fei/E-7932-2012; Pascal, BRIOIS/J-2923-2013 Gao, Fei/0000-0001-9076-9718; Pascal, BRIOIS/0000-0002-2060-2477 European Commission H2020 grant ESPESA (H2020-TWINN-2015), EU [692224]; European Commission H2020 grant PANDA (H2020-LC-GV-2018), EU [824256] European Commission H2020 grant ESPESA (H2020-TWINN-2015), EU; European Commission H2020 grant PANDA (H2020-LC-GV-2018), EU This work was supported in part by European Commission H2020 grant ESPESA (H2020-TWINN-2015), EU Grant agreement 692224 and in part by European Commission H2020 grant PANDA (H2020-LC-GV-2018), EU Grant agreement 824256. (Corresponding author: Rui Ma.) 38 39 39 11 66 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0093-9994 1939-9367 IEEE T IND APPL IEEE Trans. Ind. Appl. JUL-AUG 2019.0 55 4 4321 4331 10.1109/TIA.2019.2911846 0.0 11 Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering IH5VZ 2023-03-23 WOS:000474562900096 0
J Ma, LQ; Georgoulis, S; Jia, X; Van Gool, L Ma, Liqian; Georgoulis, Stamatios; Jia, Xu; Van Gool, Luc FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty IEEE ROBOTICS AND AUTOMATION LETTERS English Article Computer vision for automation; deep learning for visual perception; visual learning The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and robots. It allows them to plan early and decide accordingly. Motivated by this observation, in this letter we utilize information from a video sequence with a narrow field-of-view to infer the scene at a wider field-of-view. To this end, we propose a temporally consistent field-of-view extrapolation framework, namely FoV-Net, that: (1) leverages 3D information to propagate the observed scene parts from past frames; (2) aggregates the propagated multi-frame information using an attention-based feature aggregation module and a gated self-attention module, simultaneously hallucinating any unobserved scene parts; and (3) assigns an interpretable uncertainty value at each pixel. Extensive experiments show that FoV-Net does not only extrapolate the temporally consistent wide field-of-view scene better than existing alternatives, but also provides the associated uncertainty which may benefit critical decision-making downstream applications. Project page is at http://charliememory.github.io/RAL21_FoV. [Ma, Liqian; Van Gool, Luc] Katholieke Univ Leuven, Dept Elect Engn, B-3001 Leuven, Belgium; [Georgoulis, Stamatios] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, CH-8092 Zurich, Switzerland; [Jia, Xu] Dalian Univ Technol, Sch Artificial Intelligence, Dalian 116024, Peoples R China; [Van Gool, Luc] ETH Zilch, Dept Informat Technol & Elect Engn, CH-8092 Zurich, Switzerland KU Leuven; Swiss Federal Institutes of Technology Domain; ETH Zurich; Dalian University of Technology Ma, LQ (corresponding author), Katholieke Univ Leuven, Dept Elect Engn, B-3001 Leuven, Belgium. ligian.ma@esat.kuleuven.be; stamatios.georgoulis@vision.ee.ethz.ch; xjia@dlut.edu.cn; vangool@vision.ee.ethz.ch Van Gool, Luc/0000-0002-3445-5711; Ma, Liqian/0000-0002-0814-9876; Jia, Xu/0000-0003-3168-3505 Toyota Motors Europe Toyota Motors Europe Manuscript received October 14, 2020; accepted February 18, 2021. Date of publication March 23, 2021; date of current version April 9, 2021. This letter was recommended for publication by Associate Editor Ho Seok Ahn and Editor D. Kulic upon evaluation of the reviewers' comments. This work was supported by Toyota Motors Europe. 67 0 0 4 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. JUL 2021.0 6 3 4321 4328 10.1109/LRA.2021.3067848 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics RM6KJ Green Submitted 2023-03-23 WOS:000639767800010 0
J Shi, YF; Yu, ZW; Chen, CLP; You, JN; Wong, HS; Wang, YD; Zhang, J Shi, Yifan; Yu, Zhiwen; Chen, C. L. Philip; You, Jane; Wong, Hau-San; Wang, Yide; Zhang, Jun Transfer Clustering Ensemble Selection IEEE TRANSACTIONS ON CYBERNETICS English Article Clustering algorithms; Partitioning algorithms; Task analysis; Micromechanical devices; Redundancy; Indexes; Cybernetics; Clustering ensemble selection (CES); machine learning; multiobjective; transfer learning FRAMEWORK; DIVERSITY; CONSENSUS; QUALITY; SCHEME Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results. [Shi, Yifan; Yu, Zhiwen; Zhang, Jun] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China; [Chen, C. L. Philip] Univ Macau, Dept Comp & Informat Sci, Taipa 999078, Macao, Peoples R China; [You, Jane] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Wong, Hau-San] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Wang, Yide] Univ Nantes, Dept Elect & Digital Technol, Ecole Polytech, F-44000 Nantes, France South China University of Technology; University of Macau; Hong Kong Polytechnic University; City University of Hong Kong; Nantes Universite Yu, ZW (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China. zhwyu@scut.edu.cn Zhang, jun/E-9359-2011 Zhang, jun/0000-0001-7835-9871; WONG, Hau-San/0000-0002-1530-7529; You, Jane/0000-0002-8181-4836; Shi, Yifan/0000-0001-6961-5888; WANG, Yide/0000-0002-1461-2003; Yu, Zhiwen/0000-0002-0935-5890 Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme 2018; NSFC [61722205, 61751205, 61751202, 61572199, 61502174, 61872148]; Natural Science Foundation of Guangdong Province [2017A030313355]; Guangzhou Science and Technology Planning Project [201704030051]; Research Grants Council of the Hong Kong [CityU 11300715]; Hong Kong General Research [152202/14E]; Hong Kong Polytechnic University [G-YM05, G-YN39] Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme 2018; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Guangzhou Science and Technology Planning Project; Research Grants Council of the Hong Kong(Hong Kong Research Grants Council); Hong Kong General Research; Hong Kong Polytechnic University(Hong Kong Polytechnic University) This work was supported in part by the Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme 2018, in part by NSFC under Grant 61722205, Grant 61751205, Grant 61751202, Grant 61572199, Grant 61502174, and Grant 61872148, in part by the Natural Science Foundation of Guangdong Province under Grant 2017A030313355, in part by the Guangzhou Science and Technology Planning Project under Grant 201704030051, in part by the Research Grants Council of the Hong Kong under Grant CityU 11300715, in part by the Hong Kong General Research under Grant 152202/14E, and in part by the Hong Kong Polytechnic University under Grant G-YM05 and Grant G-YN39. This paper was recommended by Associate Editor P. N. Suganthan. 73 18 18 4 34 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JUN 2020.0 50 6 2872 2885 10.1109/TCYB.2018.2885585 0.0 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science LS3PF 30596592.0 2023-03-23 WOS:000536299200047 0
J Feng, DW; Germain, C Feng, Dawei; Germain, Cecile Fault Monitoring with Sequential Matrix Factorization ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS English Article Grids and Clouds; Machine Learning; Fault inference; matrix factorization; active learning For real-world distributed systems, the knowledge component at the core of the MAPE-K loop has to be inferred, as it cannot be realistically assumed to be defined a priori. Accordingly, this paper considers fault monitoring as a latent factors discovery problem. In the context of end-to-end probing, the goal is to devise an efficient sampling policy that makes the best use of a constrained sampling budget. Previous work addresses fault monitoring in a collaborative prediction framework, where the information is a snapshot of the probes outcomes. Here, we take into account the fact that the system dynamically evolves at various time scales. We propose and evaluate Sequential Matrix Factorization (SMF) that exploits both the recent advances in matrix factorization for the instantaneous information and a new sampling heuristics based on historical information. The effectiveness of the SMF approach is exemplified on datasets of increasing difficulty and compared with state of the art history-based or snapshot-based methods. In all cases, strong adaptivity under the specific flavor of active learning is required to unleash the full potential of coupling the most confident and the most uncertain sampling heuristics, which is the cornerstone of SMF. [Feng, Dawei] Natl Univ Def Technol, Sch Comp Sci, Natl Key Lab Parallel & Distributed Proc, Changsha 410073, Hunan, Peoples R China; [Feng, Dawei] Univ Paris 11, F-91405 Orsay, France; [Germain, Cecile] Univ Paris 11, LRI 660, F-91405 Orsay, France; [Feng, Dawei; Germain, Cecile] CNRS, F-75700 Paris, France National University of Defense Technology - China; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS) Feng, DW (corresponding author), Natl Univ Def Technol, Sch Comp Sci, Natl Key Lab Parallel & Distributed Proc, Changsha 410073, Hunan, Peoples R China. davyfeng.c@qq.com; cecile.germain@lri.fr Chinese Scholarship Council; French cooperative project TIMCO, Pole de Competitivite Systematic [FUI 13]; European Infrastructure Project EGI-InsPIRE [INFSO-RI-261323] Chinese Scholarship Council(China Scholarship Council); French cooperative project TIMCO, Pole de Competitivite Systematic; European Infrastructure Project EGI-InsPIRE D. Feng was supported by the Chinese Scholarship Council. C. Germain was supported by the French cooperative project TIMCO, Pole de Competitivite Systematic (FUI 13). This work was supported by The European Infrastructure Project EGI-InsPIRE INFSO-RI-261323. 46 3 3 0 5 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 1556-4665 1556-4703 ACM T AUTON ADAP SYS ACM Trans. Auton. Adapt. Syst. OCT 2015.0 10 3 20 10.1145/2797141 0.0 25 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science CU6WD Green Submitted 2023-03-23 WOS:000363674700007 0
J Zeng, JY; Caplliure-Giner, EM; Adame-Sanchez, C Zeng, Juying; Caplliure-Giner, Eva-Maria; Adame-Sanchez, Consolacion Individualized evaluation of health cost and health risks JOURNAL OF BUSINESS RESEARCH English Article Health cost; Health risks; Conduction channel; Haze pollution; Ordinal evaluation; Quantile evaluation FOREIGN DIRECT-INVESTMENT; WILLINGNESS-TO-PAY; AIR-POLLUTION; ECONOMIC-GROWTH; CARDIOVASCULAR-DISEASE; SOCIAL-RESPONSIBILITY; PARTICULATE MATTER; BIG DATA; MORTALITY; QUALITY This paper presents an individualized evaluation of the health costs and health risks of haze pollution by conduction channels and conduction strengths. Ordinal probit model and quantile regression are used to analyze data on individuals in Hangzhou, an environmental friendly city in China. The analyses yield four findings. First, in Hangzhou, haze pollution increases citizens' health spending by > 2.49 billion US dollars. Second, health costs are mainly driven by increased risks of respiratory infection. Third, the quantile analysis of health risks shows the conduction channels and corresponding strengths vary for different population groups by age and health status. Lastly, respondents were aware that they suffered haze-related health problems, while showed a surprising lack of interest in learning about the effects of haze pollution on health and finances. The findings suggest the need to increase government actions to raise citizens' awareness of the value of improving air quality throughout China. [Zeng, Juying] Zhejiang Univ Finance & Econ, Sch Data Sci, Xiasha Higher Educ Zone, 18 Xueyuan St, Hangzhou 310018, Zhejiang, Peoples R China; [Caplliure-Giner, Eva-Maria] Univ Valencia, Fac Econ, Dept Comercializac & Invest Mercados, Avda Naranjos S-N, Valencia 46022, Spain; [Adame-Sanchez, Consolacion] Univ Valencia, Fac Econ, Dept Direcc Empresas Juan Jose Renau, Avda Naranjos S-N, Valencia 46022, Spain Zhejiang University of Finance & Economics; University of Valencia; University of Valencia Zeng, JY (corresponding author), Zhejiang Univ Finance & Econ, New Type Key Think Tank Zhejiang Prov Res Inst Re, Sch Data Sci, Xiasha Higher Educ Zone, 511,Bldg 6,18,Xueyuan St, Hangzhou 310018, Zhejiang, Peoples R China. riverzjy@163.com; eva.caplliure@uv.es; consolacion.adame@uv.es Adame, Consolacion/0000-0002-4310-8869 Youth Project of National Social Science Foundation in China [16CTJ010] Youth Project of National Social Science Foundation in China This work is supported by the Youth Project of National Social Science Foundation in China (16CTJ010). The authors also thank Ms. Sulan Ding for her valuable discussion and help for the research. 44 8 8 3 20 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0148-2963 1873-7978 J BUS RES J. Bus. Res. AUG 2019.0 101 828 835 10.1016/j.jbusres.2018.12.003 0.0 8 Business Social Science Citation Index (SSCI) Business & Economics IF8YK 2023-03-23 WOS:000473379000083 0
J Ma, ZF; Liu, Z; Zhang, YR; Song, L; He, JH Ma, Zongfang; Liu, Zhe; Zhang, Yiru; Song, Lin; He, Jihuan Credal Transfer Learning With Multi-Estimation for Missing Data IEEE ACCESS English Article Training; Task analysis; Machine learning algorithms; Licenses; Cognition; Transfer learning; missing data; belief function; Credal classification; uncertainty PATTERN-CLASSIFICATION; IMPUTATION METHOD; RULE Transfer learning (TL) has grown popular in recent years. It is effective to improve the classification accuracy in the target domain by using the training knowledge in the related domain (called source domain). However, the classification of missing data (or incomplete data) is a challenging task for TL because different strategies of imputation may have strong impacts on learning models. To address this problem, we propose credal transfer learning (CTL) with multi-estimation for missing data based on belief function theory by introducing uncertainty and imprecision in data imputation procedure. CTL mainly consists of three steps: Firstly, the query patterns are reasonably mapped into multiple versions in source domain to characterize the uncertainty caused by missing values. Afterwards, the multiple mapping patterns are classified in the source domain to obtain the corresponding outputs with different discounting factors. Finally, the discounted outputs, represented by the basic belief assignments (BBAs), are submitted to a new belief-based fusion system to get the final classification result for the query patterns. Three comparative experiments are given to illustrate the interests and potentials of CTL method. [Ma, Zongfang; Liu, Zhe; Song, Lin; He, Jihuan] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian 710055, Peoples R China; [Zhang, Yiru] Univ Rennes 1, Inst Rech Informat & Syst Alcatoires, F-35380 Rennes, France; [He, Jihuan] Soochow Univ, Natl Engn Lab Modern Silk, Suzhou 215006, Peoples R China Xi'an University of Architecture & Technology; Universite de Rennes; Soochow University - China Zhang, YR (corresponding author), Univ Rennes 1, Inst Rech Informat & Syst Alcatoires, F-35380 Rennes, France. yiru.zhang@univ-rennes1.fr He, Ji-Huan/K-8504-2013; Liu, Zhe/GRS-6410-2022 He, Ji-Huan/0000-0002-1636-0559; Liu, Zhe/0000-0002-8580-9655; Zhang, Yiru/0000-0002-2132-3226 47 2 3 1 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8 70316 70328 10.1109/ACCESS.2020.2983319 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications MM0DD gold 2023-03-23 WOS:000549829900019 0
C Zhang, JY; Hao, JY; Fogelman-Soulie, F Lauw, HW; Wong, RCW; Ntoulas, A; Lim, EP; Ng, SK; Pan, SJ Zhang, Jianyu; Hao, Jianye; Fogelman-Soulie, Francoise Cross-data Automatic Feature Engineering via Meta-learning and Reinforcement Learning ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT I Lecture Notes in Artificial Intelligence English Proceedings Paper 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) MAY 11-14, 2020 ELECTR NETWORK Singapore Tourism Board Feature Engineering (FE) is one of the most beneficial, yet most difficult and time-consuming tasks of machine learning projects, and requires strong expert knowledge. It is thus significant to design generalized ways to perform FE. The primary difficulties arise from the multiform information to consider, the potentially infinite number of possible features and the high computational cost of feature generation and evaluation. We present a framework called Cross-data Automatic Feature Engineering Machine (CAFEM), which formalizes the FE problem as an optimization problem over a Feature Transformation Graph (FTG). CAFEM contains two components: a FE learner (FeL) that learns finegrained FE strategies on one single dataset by Double Deep Q-learning (DDQN) and a Cross-data Component (CdC) that speeds up FE learning on an unseen dataset by the generalized FE policies learned by MetaLearning on a collection of datasets. We compare the performance of FeL with several existing state-of-the-art automatic FE techniques on a large collection of datasets. It shows that FeL outperforms existing approaches and is robust on the selection of learning algorithms. Further experiments also show that CdC can not only speed up FE learning but also increase learning performance. [Zhang, Jianyu; Hao, Jianye] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China; [Fogelman-Soulie, Francoise] Hub France IA, Paris, France Tianjin University Zhang, JY (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China. edzhang@tju.edu.cn; jianye.hao@tju.edu.cn; francoise.soulie@hub-franceia.fr National Natural Science Foundation of China [61702362, U1836214] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work is supported by the National Natural Science Foundation of China (Grant Nos.: 61702362, U1836214). 15 0 0 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-47426-3; 978-3-030-47425-6 LECT NOTES ARTIF INT 2020.0 12084 818 829 10.1007/978-3-030-47426-3_63 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS3YZ Bronze 2023-03-23 WOS:000716986400063 0
J Xiao, B; Chen, C; Yin, XF Xiao, Bo; Chen, Chen; Yin, Xianfei Recent advancements of robotics in construction AUTOMATION IN CONSTRUCTION English Review Robotics in Construction (RiC); Literature Review; Bibliometric Review; Qualitative Review; Construction Industry SCIENCE MAPPING APPROACH; SYSTEM; TRACKING; DESIGN; INSPECTION; NETWORKS; BEHAVIOR; SAFETY In the past two decades, robotics in construction (RiC) has become an interdisciplinary research field that in-tegrates a large number of urgent technologies (e.g., additive manufacturing, deep learning, building information modelling (BIM)), resulting in the related literature being both fragmented and vast. This paper has explored the advances in RiC in the past two decades using a mixed quantitative-qualitative review method. Initially, 940 related articles (170 journal articles and 770 conference papers) were identified by keyword-searching in Scopus and then fed into a bibliometric analysis to build science maps. Following this, a qualitative discussion highlights recent achievements in RiC across three dimensions: tasks, algorithms, and collaborations. Moreover, four future research directions are proposed: 1) in-depth integration of BIM and robotics; 2) near-site robotic fabrication; 3) deep reinforcement learning for flexible environment adaption; and 4) high-level robot-to-robot collaboration. The contributions of this research are twofold: 1) identifying the latest research topics and trends concerning robotic technologies in construction; and 2) providing in-depth insights into the future direction of RiC. The findings from this research can serve both academia and industry in terms of promoting robotic algorithms, hardware, and applications in construction industry. [Xiao, Bo] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hung Hom, Kowloon, Hong Kong, Peoples R China; [Chen, Chen] Zhejiang Univ Sci & Technol, Sch Civil Engn & Architecture, Hangzhou 310023, Peoples R China; [Yin, Xianfei] Univ Twente, Dept Construct Management & Engn, Enschede, Netherlands Hong Kong Polytechnic University; Zhejiang University of Science & Technology; University of Twente Yin, XF (corresponding author), Univ Twente, Dept Construct Management & Engn, Enschede, Netherlands. eric.xiao@polyu.edu.hk; chen.c@zust.edu.cn; x.yin@utwente.nl Xiao, Bo/0000-0003-0798-8018 118 3 3 48 48 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0926-5805 1872-7891 AUTOMAT CONSTR Autom. Constr. DEC 2022.0 144 104591 10.1016/j.autcon.2022.104591 0.0 16 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 5K9SI hybrid 2023-03-23 WOS:000870057800004 0
J Castro-Schez, JJ; Murillo, JM; Miguel, R; Luo, XD Castro-Schez, Jose J.; Murillo, Jose M.; Miguel, Raul; Luo, Xudong Knowledge acquisition based on learning of maximal structure fuzzy rules KNOWLEDGE-BASED SYSTEMS English Article Knowledge acquisition; Inductive learning; Fuzzy rules; Classification; Maximal rules DECISION-MAKING; SYSTEMS; INDUCTION; UNCERTAIN; ALGORITHM; MODELS; LOGIC; CLASSIFICATION; IDENTIFICATION; METHODOLOGY There are a lot of systems that make decisions or classifications on the basis of a number of rules. This set of rules that govern such a system is called the rule base. When a new system of this kind is being developed, setting up its rule base is a time-consuming and expensive process because the rule base contains the knowledge of the outside world, which could be acquired from experts or produced from previous experiences. In this latter case, machine-learning algorithms can help. In fact, many methods have been proposed to generate rules from training instances. The aim of this paper is to present a new fuzzy learning algorithm to generate IF-THEN rules, for classifying instances in one application domain. This algorithm can improve the results offered by a previously presented algorithm. In addition, the more common classification problems of the original algorithm are presented and a measure to determine the conflicts among generated rules is introduced. Moreover, we study the classification stage of that inductive fuzzy learning algorithm and an improvement is suggested to obtain better classification results. (C) 2013 Elsevier B.V. All rights reserved. [Castro-Schez, Jose J.; Murillo, Jose M.] Univ Castilla La Mancha, Escuela Super Informat, E-13071 Ciudad Real, Spain; [Luo, Xudong] Sun Yat Sen Univ, Inst Log & Cognit, Guangzhou, Peoples R China Universidad de Castilla-La Mancha; Sun Yat Sen University Castro-Schez, JJ (corresponding author), Univ Castilla La Mancha, Escuela Super Informat, E-13071 Ciudad Real, Spain. JoseJesus.Castro@uclm.es Castro-Schez, Jose Jesus/AAH-7344-2021; Castro-Schez, Jose Jesus/E-8934-2012; Luo, Xudong/GRX-4351-2022 Castro-Schez, Jose Jesus/0000-0002-0201-7653; Castro-Schez, Jose Jesus/0000-0002-0201-7653; 49 8 9 1 20 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 KNOWL-BASED SYST Knowledge-Based Syst. MAY 2013.0 44 112 120 10.1016/j.knosys.2013.01.033 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 135YO 2023-03-23 WOS:000318326800011 0
J Quan, DY; Feng, W; Dauphin, G; Wang, XF; Huang, WJ; Xing, MD Quan, Daying; Feng, Wei; Dauphin, Gabriel; Wang, Xiaofeng; Huang, Wenjiang; Xing, Mengdao A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data REMOTE SENSING English Article classification; remote sensing; hyperspectral image; imbalance learning; data sampling IMAGE CLASSIFICATION; NETWORK; SMOTE The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm. [Quan, Daying; Wang, Xiaofeng] China Jiliang Univ, Coll Informat Engn, Key Lab Electromagnet Wave Informat Technol & Met, Hangzhou 310018, Peoples R China; [Feng, Wei; Xing, Mengdao] Xidian Univ, Hangzhou Inst Technol, Hangzhou 311200, Peoples R China; [Dauphin, Gabriel] Univ Paris XIII, Inst Galilee, Lab Informat Proc & Transmiss, L2TI, F-93430 Villetaneuse, France; [Huang, Wenjiang] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China China Jiliang University; Xidian University; Universite Paris 13; Chinese Academy of Sciences Feng, W (corresponding author), Xidian Univ, Hangzhou Inst Technol, Hangzhou 311200, Peoples R China. wfeng@xidian.edu.cn Dauphin, Gabriel/0000-0002-0677-6702; Quan, Daying/0000-0002-4898-9167; , huangwenjiang/0000-0003-1710-8301 Natural Science Foundation of Zhejiang Province [LQ20F020021] Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province) This research was funded by Natural Science Foundation of Zhejiang Province (LQ20F020021). 57 0 0 10 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2022.0 14 15 3765 10.3390/rs14153765 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 3S8OV gold 2023-03-23 WOS:000839849800001 0
J Liu, H; Chen, J; Hissel, D; Hou, M; Shao, ZG Liu, Hao; Chen, Jian; Hissel, Daniel; Hou, Ming; Shao, Zhigang A multi-scale hybrid degradation index for proton exchange membrane fuel cells JOURNAL OF POWER SOURCES English Article Degradation index; Proton exchange membrane fuel cells; Electrochemical surface area; Membrane thickness CATALYST LAYERS; PROGNOSTICS; MODEL; LIFE; PERFORMANCE; TESTS An accurate degradation index can help users understand the aging state of proton exchange membrane fuel cells and take appropriate maintenance measures to improve durability. The degradation of membranes and electrodes is one of the most important causes leading to the performance degradation of proton exchange membrane fuel cells. This paper proposes a multi-scale hybrid degradation index that combines the degradation of membranes and electrodes to estimate the aging state of proton exchange membrane fuel cells. Firstly, different degradation models are utilized to estimate the degradation indexes such as average radius of Pt particles, electrochemical surface area, and membrane thickness. A multi-sale hybrid degradation index is then designed to fuse all the previous indexes based on the quantified effects of electrode and membrane degradation. Finally, based on the automatic machine learning algorithm and the aging experimental data of proton exchange membrane fuel cells, the proposed hybrid degradation index is verified. Experimental results show that the proposed hybrid degradation index can accurately predict the future aging states and the remaining useful life of proton exchange membrane fuel cells. [Liu, Hao; Chen, Jian] Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China; [Liu, Hao; Chen, Jian] Zhejiang Univ, Ningbo Res Inst, Ningbo 315100, Zhejiang, Peoples R China; [Hissel, Daniel] Univ Bourgogne Franche Comte, CNRS, FEMTO ST, FCLAB, Rue Thierry Mieg, F-90010 Belfort, France; [Hou, Ming; Shao, Zhigang] Chinese Acad Sci, Dalian Inst Chem Phys, Fuel Cell Syst & Engn Lab, 457 Zhongshan Rd, Dalian 116023, Peoples R China Zhejiang University; Zhejiang University; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS Chen, J (corresponding author), Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China.;Chen, J (corresponding author), Zhejiang Univ, Ningbo Res Inst, Ningbo 315100, Zhejiang, Peoples R China. jchen@zju.edu.cn Hissel, Daniel/AFY-3459-2022 Hissel, Daniel/0000-0003-0657-3320 National Natural Science Foundation of China [61433013]; Fundamental Research Funds for the Central Universities of China [2019QNA5012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) This work was supported by the National Natural Science Foundation of China (61433013) and the Fundamental Research Funds for the Central Universities of China (2019QNA5012). 47 16 17 14 97 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-7753 1873-2755 J POWER SOURCES J. Power Sources OCT 15 2019.0 437 226916 10.1016/j.jpowsour.2019.226916 0.0 13 Chemistry, Physical; Electrochemistry; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels; Materials Science IY4IT 2023-03-23 WOS:000486355400040 0
J Xia, QL; Nyberg, T Xia, Qingling; Nyberg, Tobias Inhibition of cortical neural networks using infrared laser JOURNAL OF BIOPHOTONICS English Article heating model; infrared laser; inhibition; neural activity; safe STIMULATION; TEMPERATURE; CORTEX; CELLS; AXON The aim of the present study is to optimize parameters for inhibiting neuronal activity safely and investigating thermal inhibition of rat cortex neural networks in vitro by continuous infrared (IR) laser. Rat cortex neurons were cultured on multi-electrode arrays until neural networks were formed with spontaneous neural activity. Neurons were then irradiated to inhibit the activity of the networks using different powers of 1550 nm IR laser light. A finite element heating model, calibrated by the open glass pipette method, was used to calculate temperature increases at different laser irradiation intensities. A damage signal ratio (DSR) was evaluated to avoid excessive heating that may damage cells. The DSR predicted that cortex neurons should be safe at temperatures up to 49.6 degrees C for 30 seconds, but experiments suggested that cortex neurons should not be exposed to temperatures over 46 degrees C for 30 seconds. Neural response experiments showed that the inhibition of neural activity is temperature dependent. The normal neural activity could be inhibited safely with an inhibition degree up to 80% and induced epileptiform activity could be suppressed. These results show that continuous IR laser radiations provide a possible way to safely inhibit the neural network activity. [Xia, Qingling] Chongqing Univ, Key Lab Biorheol Sci & Technol, Minist Educ, Chongqing 400044, Peoples R China; [Xia, Qingling; Nyberg, Tobias] KTH Royal Inst Technol, Div Neuron Engn, Dept Biomed Engn & Hlth Syst, S-14157 Stockholm, Sweden Chongqing University; Royal Institute of Technology Xia, QL (corresponding author), Chongqing Univ, Key Lab Biorheol Sci & Technol, Minist Educ, Chongqing 400044, Peoples R China.;Nyberg, T (corresponding author), KTH Royal Inst Technol, Div Neuron Engn, Dept Biomed Engn & Hlth Syst, S-14157 Stockholm, Sweden. qingling@kth.se; tnybe@kth.se Xia, Qingling/0000-0002-2990-6100 KTH-CSC Programme; Division of Neuronic Engineering KTH-CSC Programme; Division of Neuronic Engineering The authors are grateful for the financial support provided by the KTH-CSC Programme and by the Division of Neuronic Engineering of Prof. Svein Kleiven. The first author would like to thank Ke Lu for providing suggestions for the signal processing and thank Zhou Zhou, Shiyang Meng, and Dr. Xiaogai Li who critically reviewed the manuscript. 35 19 19 1 23 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1864-063X 1864-0648 J BIOPHOTONICS J. Biophotonics JUL 2019.0 12 7 e201800403 10.1002/jbio.201800403 0.0 12 Biochemical Research Methods; Biophysics; Optics Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biophysics; Optics IM0MW 30859700.0 2023-03-23 WOS:000477683300017 0
J Peng, F; Xu, QY; Li, YF; Zheng, MX; Su, H Peng, Fang; Xu, Qinyi; Li, Yifei; Zheng, Maoxi; Su, Hang Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Target tracking; Filtering algorithms; Grasping; Robots; Kernel; Correlation; Training; 3-D reconstruction; kernelized correlation filters; scale adaptation; secondary detection; tracking and grasping NEURAL-NETWORK; MANIPULATOR Tracking and grasping moving objects are hot topics in the field of robots, which provides great potential in the industrial scene and human-computer cooperation. Based on the kernel correlation filter and vision 3-D reconstruction, this article proposes a visual-based tracking and grasping method for moving targets. An improved algorithm based on the kernel correlation filter is proposed for object tracking. A scale pool is constructed, and a scale filter is trained to solve the problem of algorithm scale adaptation. At the same time, the judgment mechanism of tracking results, secondary detection, and modification update mechanisms are added to improve the robustness of the algorithm. Combined with an RGB-D camera, the target is reconstructed to obtain the 3-D pose of the target. The tracking and intercepting strategy is adopted to grasp the moving target. The proposed method is proven to have good performance through dataset comparison tests and experiments on real robot systems. [Peng, Fang; Zheng, Maoxi] Univ Elect Sci & Technol China, Zhongshan Inst, Sch Mech & Elect Engn, Zhongshan 528402, Peoples R China; [Xu, Qinyi] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China; [Li, Yifei] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China; [Su, Hang] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy University of Electronic Science & Technology of China; Guangdong University of Technology; University of Electronic Science & Technology of China; Polytechnic University of Milan Su, H (corresponding author), Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy. fion_peng@163.com; xu13632273658@163.com; 13693996604@163.com; zhmx1984@126.com; hang.su@ieee.org Li, Yifei/0000-0002-3509-2254 Science and Technology Foundation of Guangdong Province [2021A0101180005]; Special Project in Key Areas of Colleges and Universities in Guangdong Province [2020ZDZX2037] Science and Technology Foundation of Guangdong Province; Special Project in Key Areas of Colleges and Universities in Guangdong Province This work was supported in part by the Science and Technology Foundation of Guangdong Province under Grant 2021A0101180005 and in part by the Special Project in Key Areas of Colleges and Universities in Guangdong Province under Grant 2020ZDZX2037. 34 0 0 14 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71 7504112 10.1109/TIM.2022.3195258 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3V6WS 2023-03-23 WOS:000841802700008 0
J Yao, W; Lian, C; Bruzzone, L Yao, Wei; Lian, Cheng; Bruzzone, Lorenzo ClusterCNN: Clustering-Based Feature Learning for Hyperspectral Image Classification IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Feature extraction; Clustering algorithms; Task analysis; Training; Hyperspectral imaging; Clustering; convolutional neural network (CNN); feature learning; hyperspectral image (HSI) classification; remote sensing NETWORK Convolutional neural networks (CNNs) are widely used in the field of remote sensing images. However, the applications of CNNs and related techniques often ignore the properties of remote sensing data. In our study, we focus on the hyperspectral image (HSI) classification problem, and address the issue of including the very rich spectral information present in HSIs in CNN-based models to produce highly accurate classification results. We propose a two-step classification technique, ClusterCNN. The first step divides HSI pixels into different clusters, to form a material map which can be considered as a compressed expression of the original spectral features. The second step trains a CNN that can extract spatial features from the material map, and then exploits these spatial features to classify HSI pixels. The proposed approach follows a strict hierarchy to exploit both the spectral and spatial features in HSIs. Experimental results show the effectiveness of ClusterCNN as compared to the much more complicated state-of-the-art approaches. [Yao, Wei] South Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China; [Yao, Wei] South Cent Univ Nationalities, Hubei Prov Engn Res Ctr Intelligent Management Mf, Wuhan 430074, Peoples R China; [Yao, Wei; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy; [Lian, Cheng] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Peoples R China South Central Minzu University; South Central Minzu University; University of Trento; Wuhan University of Technology Yao, W (corresponding author), South Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China. wyao@mail.scuec.edu.cn; chenglian@whut.edu.cn; lorenzo.bruzzone@unitn.it Lian, Cheng/HHS-1095-2022; Bruzzone, Lorenzo/A-2076-2012 Lian, Cheng/0000-0002-9016-832X; Bruzzone, Lorenzo/0000-0002-6036-459X Natural Science Foundation of China [61976226, 61673188, 61876219]; State Scholarship Fund of China [201908420071]; Fundamental Research Funds for the Central Universities; South-Central University for Nationalities [CZT20021]; Fundamental Research Funds for the Central Universities, Wuhan University of Technology [2020III044] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Scholarship Fund of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); South-Central University for Nationalities; Fundamental Research Funds for the Central Universities, Wuhan University of Technology(Fundamental Research Funds for the Central Universities) This work was supported in part by the Natural Science Foundation of China under Grant 61976226, Grant 61673188, and Grant 61876219, in part by the State Scholarship Fund of China under Grant 201908420071, in part by the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities under Grant CZT20021, and in part by the Fundamental Research Funds for the Central Universities, Wuhan University of Technology under Grant 2020III044. 12 7 7 3 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. NOV 2021.0 18 11 1991 1995 10.1109/LGRS.2020.3010837 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology WN5SU 2023-03-23 WOS:000711828000030 0
J Huang, F; Tan, T; Dashtbozorg, B; Zhou, Y; Romeny, BMT Huang, Fan; Tan, Tao; Dashtbozorg, Behdad; Zhou, Yi; Romeny, Bart M. Ter Haar From Local to Global: A Graph Framework for Retinal Artery/Vein Classification IEEE TRANSACTIONS ON NANOBIOSCIENCE English Article Retina; Arteries; Veins; Image edge detection; Biomedical imaging; Feature extraction; Fundus images; artery; vein classification; deep learning; graph convolutional networks; GCNet; prediabetes; diabetes; retinopathy BLOOD-VESSELS; DIABETIC-RETINOPATHY; IMAGES Fundus photography has been widely used for inspecting eye disorders by ophthalmologists or computer algorithms. Biomarkers related to retinal vessels plays an essential role to detect early diabetes. To quantify vascular biomarkers or the corresponding changes, an accurate artery and vein classification is necessary. In this work, we propose a new framework to boost local vessel classification with a global vascular network model using graph convolution. We compare our proposed method with two traditional state-of-the-art methods on a testing dataset of 750 images from the Maastricht Study. After incorporating global information, our model achieves the best accuracy of 86.45% compared to 85.5% from convolutional neural networks (CNN) and 82.9% from handcrafted pixel feature classification (HPFC). Our model also obtains the best area under receiver operating characteristic curve (AUC) of 0.95, compared to 0.93 from CNN and 0.90 from HPFC. The new classification framework has the advantage of easy deployment on top of local classification features. It corrects the local classification error by minimizing global classification error and it brings free additional classification performance. [Huang, Fan; Tan, Tao; Dashtbozorg, Behdad; Romeny, Bart M. Ter Haar] Eindhoven Univ Technol, Dept Biomed Engn, NL-5612 AP Eindhoven, Netherlands; [Zhou, Yi] Sun Yat Sen Univ, Zhongshan Sch Med, Dept Biomed Engn, Guangzhou 510085, Peoples R China Eindhoven University of Technology; Sun Yat Sen University Tan, T (corresponding author), Eindhoven Univ Technol, Dept Biomed Engn, NL-5612 AP Eindhoven, Netherlands. t.tan1@tue.nl Dashtbozorg, Behdad/0000-0003-4443-1614; ter Haar Romeny, Bart/0000-0003-3442-3207 Netherlands Organization for Scientific Research (NWO) [629.001.003]; Science and Technology Development Project of GuangZhou City [201604020016]; Natural Science Foundation of China [61876194]; Shanghai Key Lab of Digital Media Processing and Transmission [STCSM 18DZ2270700] Netherlands Organization for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); Science and Technology Development Project of GuangZhou City; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Key Lab of Digital Media Processing and Transmission This work was supported in part by The Netherlands Organization for Scientific Research (NWO) under Grant 629.001.003, in part by the Science and Technology Development Project of GuangZhou City under Grant 201604020016, in part by the Natural Science Foundation of China under Grant 61876194, and in part by the Shanghai Key Lab of Digital Media Processing and Transmission under Grant STCSM 18DZ2270700. 42 1 1 0 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1241 1558-2639 IEEE T NANOBIOSCI IEEE Trans. Nanobiosci. OCT 2020.0 19 4 589 597 10.1109/TNB.2020.3004481 0.0 9 Biochemical Research Methods; Nanoscience & Nanotechnology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Science & Technology - Other Topics NW1AD 32746331.0 2023-03-23 WOS:000574739200001 0
J Xie, WY; Xu, ZS; Ren, ZL; Viedma, EH Xie, Wanying; Xu, Zeshui; Ren, Zhiliang; Herrera Viedma, Enrique Restoring incomplete PUMLPRs for evaluating the management way of online public opinion INFORMATION SCIENCES English Article Online public opinion; IPUMLPRs; Repairing; Consistency; Possibility degree GROUP DECISION-MAKING; LINGUISTIC PREFERENCE RELATIONS; TERM SETS; AGGREGATION OPERATORS; CONSISTENCY; COMPATIBILITY; INFORMATION; CONSENSUS; FUSION; MODEL In the age of big data explosion, the management of online public opinion has encountered great challenges. Which way can effectively manage online public opinion has become a decision-making question for us to think about. Probabilistic uncertain multiplicative linguistic preference relations (PUMLPRs) are a remarkable instrument to solve uncertain evaluation problems. This paper uses the PUMLPRs to assess the management ways of the online public opinion. Owing to the intricacy of decision-making domain, the PUMLPRs are not always complete. We get the complete PUMLPRs in two steps: the repair for uncertain multiplicative linguistic variables and the repair for probability. Moreover, the consistency of the complete PUMLPRs is researched. Then the final priorities are obtained by the proposed possibility degree formula. After that, the numerical example that helps assess the valid way to manage online public opinion is performed to check the feasibility of the proposed decision-making procedure. (C) 2019 Elsevier Inc. All rights reserved. [Xie, Wanying; Xu, Zeshui; Ren, Zhiliang] Southeast Univ, Sch Econ & Management, Nanjing 211189, Jiangsu, Peoples R China; [Xu, Zeshui] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China; [Xie, Wanying; Herrera Viedma, Enrique] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain Southeast University - China; Sichuan University; University of Granada Xu, ZS (corresponding author), Southeast Univ, Sch Econ & Management, Nanjing 211189, Jiangsu, Peoples R China.;Xu, ZS (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China.;Viedma, EH (corresponding author), Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain. xuzeshui@263.net; viedma@decsai.ugr.es Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_0199]; FEDER [TIN2016-75850-R]; Scientific Research Foundation of Graduate School of Southeast University [YBJJ1832] Postgraduate Research & Practice Innovation Program of Jiangsu Province; FEDER(European Commission); Scientific Research Foundation of Graduate School of Southeast University This work was supported the Postgraduate Research & Practice Innovation Program of Jiangsu Province [Nos. KYCX18_0199], the FEDER financial support from the Project TIN2016-75850-R, and the Scientific Research Foundation of Graduate School of Southeast University [Nos. YBJJ1832]. 47 9 9 9 72 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. APR 2020.0 516 72 88 10.1016/j.ins.2019.12.030 0.0 17 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science KO3FB 2023-03-23 WOS:000515432200005 0
C Huang, DM; Du, YL; He, Q; Song, W; Liotta, A Fortino, G; Zhou, MC; Lukszo, Z; Vasilakos, AV; Basile, F; Palau, C; Liotta, A; Fanti, MP; Guerrieri, A; Vinci, A Huang, Dongmei; Du, Yanling; He, Qi; Song, Wei; Liotta, Antonio DeepEddy: A Simple Deep Architecture for Mesoscale Oceanic Eddy Detection in SAR Images PROCEEDINGS OF THE 2017 IEEE 14TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2017) IEEE International Conference on Networking Sensing and Control English Proceedings Paper 14th IEEE International Conference on Networking, Sensing and Control (ICNSC) MAY 16-18, 2017 Calabria, ITALY IEEE deep learning; feature learning; mesoscale oceanic eddies; automatic detection ANTICYCLONIC EDDIES; SATELLITE; IDENTIFICATION; VORTICES; SEA Automatic detection of mesoscale oceanic eddies is in great demand to monitor their dynamics which play a significant role in ocean current circulation and marine climate change. Traditional methods of eddies detection using remotely sensed data are usually based on physical parameters, geometrics, handcrafted features or expert knowledge, they face a great challenge in accuracy and efficiency due to the high variability of oceanic eddies and our limited understanding of their physical process, especially for rich and large remotely sensed data. In this paper, we propose a simple deep architecture DeepEddy to detect oceanic eddies automatically and be free of expert knowledge. DeepEddy can learn high-level and invariant features of oceanic eddies hierarchically. It is designed with two principal component analysis (PCA) convolutional layers for eddies feature learning, a binary hashing layer for non-linear transformation, a feature pooling layer using block-wise histograms and spatial pyramid pooling to resolve the complicated structures and poses of oceanic eddies, and a classifier for the final eddies identification. We verify the accuracy of the architecture with comprehensive experiments on high spatial resolution Synthetic Aperture Radar (SAR) images. We achieve the state-of-the-art accuracy of 96.68%. [Huang, Dongmei; Du, Yanling; He, Qi; Song, Wei] Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China; [Liotta, Antonio] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands Shanghai Ocean University; Eindhoven University of Technology Huang, DM (corresponding author), Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China. dmhuang@shou.edu.cn; yldu@shou.edu.cn; qihe@shou.edu.cn; wsong@shou.edu.cn; a.liotta@tue.nl Liotta, Antonio/G-9532-2014 Liotta, Antonio/0000-0002-2773-4421; Yanling, Du/0000-0003-1613-4022; He, Qile/0000-0001-9906-2508 National Natural Science Foundation of China (NSFC) [41671431]; Capacity Development for Local College Project [15590501900]; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [TP2016038, 201405031-05] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Capacity Development for Local College Project; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning This work is supported by the National Natural Science Foundation of China (NSFC) Grant 41671431, the Capacity Development for Local College Project Grant 15590501900, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning No. TP2016038, and polar public welfare project 201405031-05. 33 22 25 1 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1810-7869 978-1-5090-4429-0 IEEE INT C NETW SENS 2017.0 673 678 6 Automation & Control Systems; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering BM4LQ 2023-03-23 WOS:000463480000114 0
J Che, HJ; Wang, J; Cichocki, A Che, Hangjun; Wang, Jun; Cichocki, Andrzej Bicriteria Sparse Nonnegative Matrix Factorization via Two-Timescale Duplex Neurodynamic Optimization IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Optimization; Neurodynamics; Sparse matrices; Matrix converters; Linear programming; Search problems; Recurrent neural networks; Mixed-integer optimization; sparse nonnegative matrix factorization (SNMF); two-timescale duplex neurodynamics RECURRENT NEURAL-NETWORK; PARTICLE SWARM OPTIMIZATION; ALGORITHMS; INTEGER; SUBJECT In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation of $l_{0}$ matrix norm. The binary constraints of the problem are then equivalently replaced with bilinear constraints to convert the problem to a biconvex problem. The reformulated biconvex problem is finally solved by using a two-timescale duplex neurodynamic approach consisting of two recurrent neural networks (RNNs) operating collaboratively at two timescales. A Gaussian score (GS) is defined as to integrate the bicriteria of factorization errors and sparsity of resulting matrices. The performance of the proposed neurodynamic approach is substantiated in terms of low factorization errors, high sparsity, and high GS on four benchmark datasets. [Che, Hangjun] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China; [Wang, Jun] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China; [Wang, Jun] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China; [Cichocki, Andrzej] Skolkovo Inst Sci & Technol, Moscow 143026, Russia; [Cichocki, Andrzej] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland Southwest University - China; City University of Hong Kong; City University of Hong Kong; Skolkovo Institute of Science & Technology; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Wang, J (corresponding author), City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.;Wang, J (corresponding author), City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China. hjche123@swu.edu.cn; jwang.cs@cityu.edu.hk; a.cichocki@skoltech.ru Wang, Jun/A-2807-2011; Cichocki, Andrzej/A-1545-2015 Wang, Jun/0000-0002-1305-5735; Cichocki, Andrzej/0000-0002-8364-7226 Fundamental Research Funds for the Central Universities [SWU020006]; National Natural Science Foundation of China [62003281]; Research Grants Council of the Hong Kong Special Administrative Region of China [11202318]; Ministry of Science and Higher Education of the Russian Federation [075-10-2021-068] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Grants Council of the Hong Kong Special Administrative Region of China(Hong Kong Research Grants Council); Ministry of Science and Higher Education of the Russian Federation This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant SWU020006, in part by the National Natural Science Foundation of China under Grant 62003281, in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Grant 11202318, and in part by the Ministry of Science and Higher Education of the Russian Federation under Grant 075-10-2021-068. 71 13 13 7 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2021.3125457 0.0 NOV 2021 11 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering XR2WC 34788223.0 hybrid 2023-03-23 WOS:000732095100001 0
J Lei, YX; Karimi, HR; Cen, LH; Chen, XF; Xie, YF Lei, Yongxiang; Karimi, Hamid Reza; Cen, Lihui; Chen, Xiaofang; Xie, Yongfang Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application CONTROL ENGINEERING PRACTICE English Article Plant-wide control; Soft model; Stacked autoencoder (SAE); Wavelet kernel function; Extreme learning method NEURAL-NETWORK Data-driven soft modeling has been extensively used for industrial processes to estimate key quality indicators which are hard to measure by some physical devices. However,the existing deep soft methods faces the challenge of training efficiency, gradient diminishing and explosion. Constructing an accurate and robust soft model is still a challenging topic from an application point of view. This paper develops an effective and efficient soft method (SAE-WELM) for processes modeling. First, a stacked autoencoder (SAE) is used to extract the deep features. Then, a top-layer extreme learning machine (ELM) is further applied to a plant-wide industrial aluminum production process. The activation function is wavelet kernel. Finally, the approximation and convergence of the proposed SAE-WELM are theoretically proved. The industrial case demonstrates that SAE-WELM captures the deep features faster than other iterative-based neural networks, and the accuracy and robustness outperform the existing state-of-the-art methods. [Lei, Yongxiang; Cen, Lihui; Chen, Xiaofang; Xie, Yongfang] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Lei, Yongxiang] Res Ctr Ind Intelligence, Peng Cheng Lab, Shenzhen 518000, Peoples R China Central South University; Polytechnic University of Milan; Peng Cheng Laboratory Cen, LH (corresponding author), Cent South Univ, Sch Automat, Changsha 410083, Peoples R China.;Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. leiyongxiang@csu.edu.cn; hamidreza.karimi@polimi.it; lhcen@csu.edu.cn; xiaofangchen@csu.edu.cn; yfxie@csu.edu.cn 于, 于增臣/AAH-4657-2021 General Program of the National Natural Science Foundation of China [61773405]; National Natural Science Foundation of China [61725306]; Chinese Scholarship Commite fellowship [202006370101] General Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Scholarship Commite fellowship This research has been supported in part by the General Program of the National Natural Science Foundation of China under Grant No. 61773405, the National Natural Science Foundation of China under Grant No. 61725306, and the Chinese Scholarship Commite fellowship under grant No. 202006370101. The authors would like to thank Mrs. Liu Fang for her technical support to Yongxiang Lei during his master thesis study. 40 15 16 2 21 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0967-0661 1873-6939 CONTROL ENG PRACT Control Eng. Practice MAR 2021.0 108 104706 10.1016/j.conengprac.2020.104706 0.0 JAN 2021 9 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering PV3SK 2023-03-23 WOS:000609910700004 0
C She, Q; Feng, F; Hao, XY; Yang, QH; Lan, CL; Lomonaco, V; Shi, XS; Wang, ZW; Guo, Y; Zhang, YM; Qiao, F; Chan, RHM IEEE She, Qi; Feng, Fan; Hao, Xinyue; Yang, Qihan; Lan, Chuanlin; Lomonaco, Vincenzo; Shi, Xuesong; Wang, Zhengwei; Guo, Yao; Zhang, Yimin; Qiao, Fei; Chan, Rosa H. M. OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 31-JUN 15, 2020 ELECTR NETWORK IEEE The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non -varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while naive incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset (OpenLORISObject) collected via RGB-D cameras. The dataset embeds the challenges faced by a robot in the real -life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of 9 stateof-the-art lifelong learning algorithms. Each of them involves 18 tasks with 1 evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at https://lifelong-robotic-vision.githubio/dataset/object. [She, Qi; Shi, Xuesong; Zhang, Yimin] Intel Labs, Robot Innovat Lab, Beijing, Peoples R China; [Feng, Fan; Yang, Qihan; Lan, Chuanlin; Chan, Rosa H. M.] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Hao, Xinyue; Qiao, Fei] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China; [Hao, Xinyue] Beijing Univ Posts & Telecommun, Beijing, Peoples R China; [Lan, Chuanlin] Wuhan Univ, Sch Elect Informat, Wuhan, Peoples R China; [Lomonaco, Vincenzo] Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy; [Wang, Zhengwei] Dublin City Univ, Insight Ctr Data Analyt, Dublin, Ireland; [Guo, Yao] Imperial Coll London, Hamlyn Ctr Robot Surg, London, England Intel Corporation; City University of Hong Kong; Tsinghua University; Beijing University of Posts & Telecommunications; Wuhan University; University of Bologna; Dublin City University; Imperial College London She, Q (corresponding author), Intel Labs, Robot Innovat Lab, Beijing, Peoples R China.;Chan, RHM (corresponding author), City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China. qi.she@intel.com; rosachan@cityu.edu.hk FENG, FAN/0000-0002-3043-2360; LOMONACO, VINCENZO/0000-0001-8308-6599 Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 11215618] Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council) The work was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11215618). The authors would like to thank Hong Pong Ho from Intel RealSense Team for the technical support of RealSense cameras for recording the high-quality RGB-D data sequences. Thank Yang Peng, Kelvin Yu, Dion Gavin Mascarenhas for data collection and labeling. 44 15 15 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-7395-5 IEEE INT CONF ROBOT 2020.0 4767 4773 7 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BS3LB 2023-03-23 WOS:000712319502123 0
J Chen, G; Zhang, J; Fu, QT; Taly, V; Tan, F Chen, Geng; Zhang, Jing; Fu, Qiaoting; Taly, Valerie; Tan, Fei Integrative analysis of multi-omics data for liquid biopsy BRITISH JOURNAL OF CANCER English Review CIRCULATING TUMOR DNA; HIGH-LEVEL FEATURES; CLONAL HEMATOPOIESIS; CANCER DETECTION; BREAST-CANCER; LUNG-CANCER; PLASMA; PCR; MUTATIONS; BLOOD The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement. [Chen, Geng; Zhang, Jing; Tan, Fei] Tongji Univ, Shanghai Skin Dis Hosp, Sch Med, Shanghai 200443, Peoples R China; [Chen, Geng] East China Normal Univ, Ctr Bioinformat & Computat Biol, Sch Life Sci, Shanghai 200241, Peoples R China; [Fu, Qiaoting; Tan, Fei] Anhui Med Univ, Shanghai Skin Dis Clin Coll, Clin Med Coll 5, Shanghai 200443, Peoples R China; [Taly, Valerie] Univ Paris, Equipe Labelisee Ligue Natl Canc, Ctr Rech Cordeliers, UMR S1138,CNRS SNC5096, Paris, France Tongji University; East China Normal University; Anhui Medical University; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite Chen, G; Tan, F (corresponding author), Tongji Univ, Shanghai Skin Dis Hosp, Sch Med, Shanghai 200443, Peoples R China.;Chen, G (corresponding author), East China Normal Univ, Ctr Bioinformat & Computat Biol, Sch Life Sci, Shanghai 200241, Peoples R China.;Tan, F (corresponding author), Anhui Med Univ, Shanghai Skin Dis Clin Coll, Clin Med Coll 5, Shanghai 200443, Peoples R China.;Taly, V (corresponding author), Univ Paris, Equipe Labelisee Ligue Natl Canc, Ctr Rech Cordeliers, UMR S1138,CNRS SNC5096, Paris, France. chengeng66666@outlook.com; valerie.taly@parisdescartes.fr; tanfeitrue@126.com ; TALY, VALERIE/N-9766-2014 Chen, Geng/0000-0002-3452-9434; TALY, VALERIE/0000-0002-3116-8602 Shanghai Municipal Health Commission [2020YJZX0108]; Scientific Research Project of Education Department of Anhui Province [YJS20210323]; Ministere de l'Enseignement Superieur et de la Recherche; Universite de Paris Cite; Centre National de la Recherche Scientifique (CNRS); Institut National de la Sante et de la Recherche Medicale (INSERM); SIRIC CARPEM; ligue nationale contre le cancer (LNCC) Shanghai Municipal Health Commission; Scientific Research Project of Education Department of Anhui Province; Ministere de l'Enseignement Superieur et de la Recherche(Estonian Research Council); Universite de Paris Cite; Centre National de la Recherche Scientifique (CNRS)(Centre National de la Recherche Scientifique (CNRS)); Institut National de la Sante et de la Recherche Medicale (INSERM)(Institut National de la Sante et de la Recherche Medicale (Inserm)); SIRIC CARPEM; ligue nationale contre le cancer (LNCC)(Ligue nationale contre le cancer) This work was supported by the Shanghai Municipal Health Commission (2020YJZX0108) and the Scientific Research Project of Education Department of Anhui Province (YJS20210323). VT acknowledge the support of the Ministere de l'Enseignement Superieur et de la Recherche, the Universite de Paris Cite, the Centre National de la Recherche Scientifique (CNRS), the Institut National de la Sante et de la Recherche Medicale (INSERM), the SIRIC CARPEM, and the ligue nationale contre le cancer (LNCC). 130 0 0 3 3 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 0007-0920 1532-1827 BRIT J CANCER Br. J. Cancer FEB 16 2023.0 128 4 505 518 10.1038/s41416-022-02048-2 0.0 NOV 2022 14 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 9L8ML 36357703.0 2023-03-23 WOS:000882270000002 0
J Zhao, HT; Jiang, P; Chen, Z; Ezeh, CI; Hong, YD; Guo, YS; Zheng, CH; Dzapo, H; Gao, X; Wu, T Zhao, Haitao; Jiang, Peng; Chen, Zhe; Ezeh, Collins, I; Hong, Yuanda; Guo, Yishan; Zheng, Chenghang; Dzapo, Hrvoje; Gao, Xiang; Wu, Tao Improvement of fuel sources and energy products flexibility in coal power plants via energy-cyber-physical-systems approach APPLIED ENERGY English Article Energy flexibility; e-CPS; Coal; Sewage sludge; Co-combustion; CCHP; Aspen PLUS; ANN CHEMICAL-LOOPING COMBUSTION; ARTIFICIAL NEURAL-NETWORKS; CARBON CAPTURE; TECHNOECONOMIC ANALYSIS; OPERATIONAL FLEXIBILITY; EXERGY ANALYSIS; HG-0 CAPTURE; CO2 CAPTURE; GASIFICATION; BIOMASS The fluctuating energy requirement from time-varying demand changes require flexible power system to respond to the dynamic load balance. In certain scenarios, flexible power systems are necessary to provide high system efficiencies and increase the long-term economic benefits. Therefore, it is important to identify the relationship between energy requirement of a city and/or industrial zone with the dynamics of a flexible power system. In this paper, we evaluate the possible benefits of adopting the energy-cyber-physical-systems (e-CPSs) concept in realization of a flexible closed-loop control power system in case of coal-sewage sludge co-combustion. The case study performance analysis of flexibility, efficiency and economics (FEE) for a 356 MW coal-fired power plant was conducted by means of simulation in Aspen PLUS software. The analysis was based on a specific multifarious model of a dynamic power system with two energy sources and four energy products. The results confirmed that substantial advantages including a more flexible, efficient and economically viable system can be achieved by using e-CPSs based solution in different case scenarios. Moreover, following the analyzed data from the proposed case scenarios, a forecasting model for optimization of the power plant parameters based on the use of artificial neural network (ANN) was established. The results showed that ANN-based approach for predictive model is capable of modelling the complex process within an acceptable prediction accuracy. The study demonstrates that the proposed flexible closed-loop control power system based on e-CPSs concept is able to achieve higher efficiency and economic benefits compared to the traditional approaches. [Zhao, Haitao; Chen, Zhe; Guo, Yishan; Zheng, Chenghang; Gao, Xiang] Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China; [Zhao, Haitao; Jiang, Peng; Wu, Tao] Univ Nottingham Ningbo China, Municipal Key Lab Clean Energy Convers Technol, Ningbo 315100, Zhejiang, Peoples R China; [Zhao, Haitao; Jiang, Peng; Wu, Tao] Univ Nottingham Ningbo China, New Mat Inst, Ningbo 315100, Zhejiang, Peoples R China; [Ezeh, Collins, I; Hong, Yuanda] Univ Nottingham Ningbo China, Dept Chem & Environm Engn, Ningbo 315100, Zhejiang, Peoples R China; [Dzapo, Hrvoje] Univ Zagreb, Fac Elect Engn & Comp, Unska 3, HR-10000 Zagreb, Croatia Zhejiang University; University of Nottingham Ningbo China; University of Nottingham Ningbo China; University of Nottingham Ningbo China; University of Zagreb Gao, X (corresponding author), Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China.;Wu, T (corresponding author), Univ Nottingham Ningbo China, Municipal Key Lab Clean Energy Convers Technol, Ningbo 315100, Zhejiang, Peoples R China. xgao@zju.edu.cn; tao.wu@nottingham.edu.cn Hong, Yuanda/GLS-7641-2022; Wu, Tao/ABD-5166-2020; Wu, Tao/C-5515-2014; Zheng, Chenghang/N-2893-2017 Wu, Tao/0000-0001-6469-9613; Wu, Tao/0000-0001-6469-9613; Zheng, Chenghang/0000-0003-0410-2007 National Key Research and Development Projects [2017YFB0603202]; National Natural Science Foundation of China [51706114, 51836006]; Ningbo 'Science and Technology Innovation 2025' Major Project; Ningbo Natural Science Foundation [2017A610060]; China Postdoctoral Science Foundation National Key Research and Development Projects; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ningbo 'Science and Technology Innovation 2025' Major Project; Ningbo Natural Science Foundation; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) Following funding bodies are acknowledged for partially sponsoring this research: National Key Research and Development Projects (2017YFB0603202), National Natural Science Foundation of China (51706114 and 51836006), Ningbo 'Science and Technology Innovation 2025' Major Project, Ningbo Natural Science Foundation (2017A610060) and the project funded by China Postdoctoral Science Foundation as well. Authors 1 and 2 contributed equally to this work. 55 15 15 4 39 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy NOV 15 2019.0 254 113554 10.1016/j.apenergy.2019.113554 0.0 18 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering JP0PR 2023-03-23 WOS:000497974600010 0
J Cai, ZC; Li, XP; Ruiz, R; Li, QM Cai, Zhicheng; Li, Xiaoping; Ruiz, Ruben; Li, Qianmu A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Cloud computing; Scheduling; Workflow; Bag of tasks; Stochastic RESOURCE; PREDICTION; NETWORKS Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved. [Cai, Zhicheng; Li, Qianmu] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Li, Xiaoping] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Ruiz, Ruben] Acc B Univ Politecn Valencia, Inst Tecnol Informat, Valencia, Spain; [Cai, Zhicheng] Key Lab Image & Video Understanding Social Safety, Nanjing, Peoples R China; [Cai, Zhicheng] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Jiangsu, Peoples R China Nanjing University of Science & Technology; Southeast University - China; Southeast University - China Cai, ZC (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China. caizhicheng@njust.edu.cn; xpli@seu.edu.cn; rruiz@eio.upv.es; qianmu@njust.edu.cn García, Rubén Ruiz/L-5381-2019; Li, Xiaoping/I-8389-2012; Ruiz, Rubén/G-3929-2015 García, Rubén Ruiz/0000-0003-3295-3888; Li, Xiaoping/0000-0003-3201-0038; Ruiz, Rubén/0000-0003-3295-3888; Li, Qianmu/0000-0002-0998-1517; Cai, Zhicheng/0000-0002-8702-6216 National Natural Science Foundation of China [61602243, 61572127]; Natural Science Foundation ofJiangsu Province [BK20160846]; Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) [30916014107]; Fundamental Research Funds for the Central University [30916015104]; Spanish Ministry of Economy and Competitiveness - FEDER [DP12015-65895-R] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation ofJiangsu Province(Natural Science Foundation of Jiangsu Province); Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology); Fundamental Research Funds for the Central University; Spanish Ministry of Economy and Competitiveness - FEDER(Spanish Government) The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and 61572127), the Natural Science Foundation ofJiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30916014107), the Fundamental Research Funds for the Central University (Grant No. 30916015104). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project SCHEYARD (No. DP12015-65895-R) co-financed by FEDER funds. 49 45 45 1 43 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUN 2017.0 71 57 72 10.1016/j.future.2017.01.020 0.0 16 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science EO8OW Green Published 2023-03-23 WOS:000396950100005 0
J Feng, W; Dauphin, G; Huang, WJ; Quan, YH; Bao, WX; Wu, MQ; Li, Q Feng, Wei; Dauphin, Gabriel; Huang, Wenjiang; Quan, Yinghui; Bao, Wenxing; Wu, Mingquan; Li, Qiang Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article; Proceedings Paper 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 22-27, 2018 Valencia, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc,European Space Agcy Ensemble learning; hyperspectral image classification; imbalance learning; rotation forest (RoF) ENSEMBLES; SELECTION; SMOTE Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial attention due to its performance in hyperspectral data classification. Multi-class imbalance learning is one of the biggest challenges in machine learning and remote sensing. The standard technique for constructing RoF ensemble tends to increase the overall accuracy; RoF has difficulty to sufficiently recognize the minority class. This paper proposes a novel dynamic SMOTE (synthetic minority oversampling technique)-based RoF algorithm for the multi-class imbalance problem. The main idea of the proposed method is to dynamically balance the class distribution before building each rotation decision tree. A resampling rate is set in each iteration (ranging from 10% in the first iteration to 100% in the last) and this ratio defines the number of minority class instances randomly resampled (with replacement) from the original dataset in each iteration. The rest of the minority class instances are generated by the SMOTE method. The reported results on three real hyperspectral datasets show that the proposed method can get better performance than random forest, RoF, and some popular data sampling methods. [Feng, Wei; Huang, Wenjiang] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Feng, Wei] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China; [Dauphin, Gabriel] Univ Paris 13, Inst Galilee, L2TI, Lab Informat Proc & Transmiss, F-93430 Villetaneuse, France; [Quan, Yinghui] Xidian Univ, Key Lab Radar Signal Proc, Xian 710071, Shaanxi, Peoples R China; [Bao, Wenxing] Beifang Univ, Sch Comp Sci & Engn, Yinchuan 750021, Peoples R China; [Wu, Mingquan] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Li, Qiang] Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China Chinese Academy of Sciences; Xidian University; Universite Paris 13; Xidian University; North Minzu University; Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Theoretical Physics, CAS Feng, W (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. fengwei@radi.ac.cn; gabriel.dauphin@univ-paris13.fr; huangwj@radi.ac.cn; yhquan@mail.xidian.edu.cn; bwx71@163.com; wumq@radi.ac.cn; liruo@itp.ac.cn Feng, Wei/W-7050-2019; tt, bwx/AAF-7406-2020; huang, wenjiang/AAM-6844-2021; Bao, Wenxing/GSE-6238-2022; Dauphin, Gabriel/AAE-5606-2020; FENG, Wei/T-5851-2019 Dauphin, Gabriel/0000-0002-0677-6702; Bao, Wenxing/0000-0002-0267-5824 National Key R&D Program of China [2016YFB0501501]; Hainan Provincial Key R&D Program of China [ZDYF2018073]; National Natural Science Foundation of China [41601466, 41871339, 61461003] National Key R&D Program of China; Hainan Provincial Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China under Grant 2016YFB0501501, in part by Hainan Provincial Key R&D Program of China under Grant ZDYF2018073, and in part by the National Natural Science Foundation of China under Grants 41601466, 41871339, and 61461003. 40 35 37 3 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. JUL 2019.0 12 7 SI 2159 2169 10.1109/JSTARS.2019.2922297 0.0 11 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology IP9FE 2023-03-23 WOS:000480354800015 0
J Hu, ZY; Ji, KF; Chen, J; Deng, YY; Su, JT; Bai, XY; Liu, S; Guo, JJ; Liu, JJ; Wintoft, P Hu, Ziyao; Ji, Kaifan; Chen, Jie; Deng, Yuanyong; Su, Jiangtao; Bai, Xianyong; Liu, Suo; Guo, Jingjing; Liu, Jiajia; Wintoft, Peter Calibration scheme for space-borne full-disk vector magnetograph under the influence of orbiter velocity ASTRONOMY & ASTROPHYSICS English Article magnetic fields; Sun; magnetic fields MAGNETIC-FIELD; FARADAY-ROTATION; INVERSION; HMI; TELESCOPE Context. The Full-disk Vector MagnetoGraph (FMG) is one of the three payloads on the Advanced Space-based Solar Observatory (ASO-S). The FMG is set to observe the full disk vector magnetic field at a single wavelength point. The magnetograph in orbit will encounter the wavelength shift problem caused by the Doppler effect in the magnetic field, which mainly comes from the Sun's rotation velocity and the satellite-sun relative velocity. Aims. We look to use neural networks for single-wavelength calibration to solve the wavelength shift problem. Methods. We used the existing data from the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO). To simulate plausible single-wavelength observations, we used the Stokes polarization image from the HMI at a single wavelength point. We also input the satellite orbital velocity given by the HMI data file and the solar rotation velocity to the network. We developed a set of data preprocessing methods before entering the network and we trained the network to get the calibration model. Results. By analyzing and comparing the prediction of the neural network with the target magnetogram, we believe that our network model has learned a single-wavelength full-disk calibration model. The mean absolute error (MAE) of the longitudinal field and the transverse field of the full disk are 3.68 G and 28.08 G, respectively. The MAE error of the azimuth angle of pixels above 300 G is 12.29 degrees. [Hu, Ziyao; Chen, Jie; Deng, Yuanyong; Su, Jiangtao; Bai, Xianyong; Liu, Suo] Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China; [Ji, Kaifan] Chinese Acad Sci, Yunnan Observ, Kunming 650216, Yunnan, Peoples R China; [Hu, Ziyao; Deng, Yuanyong; Su, Jiangtao; Bai, Xianyong] Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 101408, Peoples R China; [Guo, Jingjing] Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan 030024, Peoples R China; [Liu, Jiajia] Queens Univ Belfast, Astrophys Res Ctr, Sch Math & Phys, Belfast BT7 1NN, Antrim, North Ireland; [Wintoft, Peter] Swedish Inst Space Phys, Scheelevagen 17, S-22370 Lund, Sweden Chinese Academy of Sciences; National Astronomical Observatory, CAS; Chinese Academy of Sciences; Yunnan Astronomical Observatory, NAOC, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Taiyuan University of Technology; Queens University Belfast Hu, ZY (corresponding author), Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China.;Hu, ZY (corresponding author), Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 101408, Peoples R China. hzy@nao.cas.cn; chenjie@bao.ac.cn Liu, Jiajia/AAX-7996-2020 Liu, Jiajia/0000-0003-2569-1840; Chen, Jie/0000-0001-7472-5539 National Natural Science Foundation of China [12073077, 11427901, 11773038, U1831107, 1210030183]; Strategic Priority Research Program on Space Science, the Chinese Academy of Sciences; Chinese Academy of Sciences [XDA15320302, XDA15052200, XDA15320102]; Beijing Natural Science Foundation [1222029]; National Key R&D Program of China [2021YFA1600500] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program on Space Science, the Chinese Academy of Sciences; Chinese Academy of Sciences(Chinese Academy of Sciences); Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Key R&D Program of China This work is supported by National Natural Science Foundation of China (Grant Nos. 12073077, 11427901, 11773038, U1831107, 1210030183), and the Strategic Priority Research Program on Space Science, the Chinese Academy of Sciences (Grant Nos. XDA15320302, XDA15052200, XDA15320102), and Beijing Natural Science Foundation(Grant No.1222029), and National Key R&D Program of China (Grant No. 2021YFA1600500). 39 0 0 1 1 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. OCT 13 2022.0 666 A93 10.1051/0004-6361/202142920 0.0 14 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 5G6FC Green Submitted, hybrid 2023-03-23 WOS:000867091700003 0
J Barros, P; Eppe, M; Parisi, GI; Liu, X; Wermter, S Barros, Pablo; Eppe, Manfred; Parisi, German, I; Liu, Xun; Wermter, Stefan Expectation Learning for Stimulus Prediction Across Modalities Improves Unisensory Classification FRONTIERS IN ROBOTICS AND AI English Article multisensory binding; deep learning; autoencoder; unsupervised learning; online learning SOUND; ENHANCEMENT; ATTENTION Expectation learning is a unsupervised learning process which uses multisensory bindings to enhance unisensory perception. For instance, as humans, we learn to associate a barking sound with the visual appearance of a dog, and we continuously fine-tune this association over time, as we learn, e.g., to associate high-pitched barking with small dogs. In this work, we address the problem of developing a computational model that addresses important properties of expectation learning, in particular focusing on the lack of explicit external supervision other than temporal co-occurrence. To this end, we present a novel hybrid neural model based on audio-visual autoencoders and a recurrent self-organizing network for multisensory bindings that facilitate stimulus reconstructions across different sensory modalities. We refer to this mechanism as stimulus prediction across modalities and demonstrate that the proposed model is capable of learning concept bindings by evaluating it on unisensory classification tasks for audio-visual stimuli using the 43,500 Youtube videos from the animal subset of the AudioSet corpus. [Barros, Pablo; Eppe, Manfred; Parisi, German, I; Wermter, Stefan] Univ Hamburg, Dept Informat, Knowledge Technol, Hamburg, Germany; [Liu, Xun] Univ CAS, Dept Psychol, Beijing, Peoples R China University of Hamburg Barros, P (corresponding author), Univ Hamburg, Dept Informat, Knowledge Technol, Hamburg, Germany. barros@informatik.uni-hamburg.de Eppe, Manfred/ABE-9392-2021; Liu, Xun/C-2400-2009 Eppe, Manfred/0000-0002-5473-3221; Barros, Pablo/0000-0002-6517-682X; Wermter, Stefan/0000-0003-1343-4775; Liu, Xun/0000-0003-1366-8926 German Research Foundation (DFG) [CML (TRR 169)]; NSFC [61621136008]; China Scholarship Council; Volkswagen Stiftung German Research Foundation (DFG)(German Research Foundation (DFG)); NSFC(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Volkswagen Stiftung(Volkswagen) This work was partially supported by the German Research Foundation (DFG) under project CML (TRR 169) and the NSFC (61621136008) and the China Scholarship Council. ME acknowledges support by the Volkswagen Stiftung. 50 3 3 0 0 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-9144 FRONT ROBOT AI Front. Robot. AI DEC 11 2019.0 6 137 10.3389/frobt.2019.00137 0.0 11 Robotics Emerging Sources Citation Index (ESCI) Robotics JY1UR 33501152.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000504208000001 0
C Ran, YH; He, B; Hui, K; Xu, JG; Sun, L Hu, XH; Shyu, CR; Bromberg, Y; Gao, J; Gong, Y; Korkin, D; Yoo, I; Zheng, JH Ran, Yanhua; He, Ben; Hui, Kai; Xu, Jungang; Sun, Le A Document-Based Neural Relevance Model for Effective Clinical Decision Support 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) IEEE International Conference on Bioinformatics and Biomedicine-BIBM English Proceedings Paper Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) NOV 13-16, 2017 Kansas City, MI IEEE,IEEE Comp Soc,IEEE Tech Comm Computat Life Sci Clinical Decision Support (CDS) can be regarded as an information retrieval (IR) task, where medical records are used to retrieve the full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modeled, resulting in non-optimal results in that a medial record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a document-based neural relevance model (DNRM), addressing the mismatch by utilizing the content of relevant articles to complement the medical records. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model. [Ran, Yanhua; He, Ben; Xu, Jungang] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China; [Hui, Kai] Saarbrucken Grad Sch Comp Sci, Max Planck Inst Informat, Saarbrucken, Germany; [Sun, Le] Chinese Acad Sci, Inst Software, Beijing, Peoples R China Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Max Planck Society; Chinese Academy of Sciences; Institute of Software, CAS Ran, YH (corresponding author), Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China. ranyanhua16@mails.ucas.ac.cn; benhe@ucas.ac.cn; khui@mpi-inf.mpg.de; xujg@ucas.ac.cn; sunle@iscas.ac.cn Hui, Kai/AAM-2600-2020 Hui, Kai/0000-0002-3110-7404 National Natural Science Foundation of China [61472391/61433015/61572477] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China (61472391/61433015/61572477). 30 10 10 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-1125 2156-1133 978-1-5090-3050-7 IEEE INT C BIOINFORM 2017.0 798 804 7 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematical & Computational Biology BJ6DG 2023-03-23 WOS:000426504100149 0
C Deng, J; Gao, CH; Feng, Q; Xu, XZ; Chen, ZP IEEE Deng, Jun; Gao, Chunhui; Feng, Qian; Xu, Xinzhou; Chen, Zhaopeng ADAPTIVE GENERALIZED CROSS-ENTROPY LOSS FOR SOUND EVENT CLASSIFICATION WITH NOISY LABELS 2021 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA) IEEE Workshop on Applications of Signal Processing to Audio and Acoustics English Proceedings Paper IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) OCT 17-20, 2021 New Paltz, NY IEEE Sound event classification; label noise; cross-entropy; deep learning Considering the high cost of manually annotated large-scale datasets for superior sound event classifier performance, the data collection process has shifted to using the Internet, which facilitates easier user-contributed audio and metadata collection. However, label noise is inevitable. To address the problems caused by label noise, several types of noise-robust loss functions have been proposed recently as alternatives to the commonly categorical cross-entropy (CCE) loss, one of which is the generalized cross-entropy (GCE) loss, which demonstrates state-of-the-art performance. However, GCE cannot realize sufficient noise robustness and satisfactory accuracy simultaneously. Thus, we propose adaptive GCE loss, which automatically adapts to noisy labels in every batch to achieve adequate noise robustness and sufficient accuracy. We conducted experiments and found that the classification accuracy of the proposed loss demonstrated 4.7% and 1.2% absolute improvement over the CCE and GCE baselines, respectively. We also demonstrate that clean data consumption in the proposed loss is dramatically reduced by more than 75% compared with CCE. [Deng, Jun; Gao, Chunhui; Feng, Qian; Chen, Zhaopeng] Agile Robots AG, Deep Learning Dept, D-81477 Munich, Germany; [Xu, Xinzhou] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing, Peoples R China; [Chen, Zhaopeng] Univ Hamburg, Dept Informat, TAMS Tech Aspects Multimodal Syst, Hamburg, Germany Nanjing University of Posts & Telecommunications; University of Hamburg Deng, J (corresponding author), Agile Robots AG, Deep Learning Dept, D-81477 Munich, Germany. jun.deng@agile-robots.com; xinzhou.xu@njupt.edu.cn German Research Foundation (DFG); National Science Foundation of China (NSFC) [DFG TRR-169/NSFC 61621136008]; European project H2020 STEP2DYNA [691154]; European project ULTRACEPT [778602]; Natural Science Foundation of China [61801241] German Research Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); European project H2020 STEP2DYNA; European project ULTRACEPT; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Tbis research has received funding from the German Research Foundation (DFG) and the National Science Foundation of China (NSFC) in project Crossmodal Learning, DFG TRR-169/NSFC 61621136008, partially supported by European projects H2020 STEP2DYNA (691154) and ULTRACEPT (778602), as well as Natural Science Foundation of China under Grant No. 61801241 30 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-1168 978-1-6654-4870-3 IEEE WORK APPL SIG 2021.0 256 260 10.1109/WASPAA52581.2021.9632679 0.0 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BS7NL 2023-03-23 WOS:000765269300052 0
J Gu, XH; Wang, XK; Liang, S Gu, Xianhui; Wang, Xiaokan; Liang, Shuang Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm CMC-COMPUTERS MATERIALS & CONTINUA English Article Employment quality; fuzzy c-means clustering algorithm; grey correlation analysis method; evaluation model; index system; comparative test GREY RELATIONAL ANALYSIS; COMPREHENSIVE EVALUATION; NEURAL-NETWORK; OPTIMIZATION In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality evaluation models. The test results show that the employment quality evaluation accuracy of the design model exceeds 93%, the employment quality evaluation error can meet the requirements of practical application, and the employment quality evaluation effect is much better than the comparison model. The comparison test verifies the superiority of the model. [Gu, Xianhui; Wang, Xiaokan] Henan Mech Elect Vocat Coll, Xinzheng 451191, Hennan, Peoples R China; [Liang, Shuang] Univ Florence, I-50041 Florence, Italy University of Florence Gu, XH (corresponding author), Henan Mech Elect Vocat Coll, Xinzheng 451191, Hennan, Peoples R China. xianhuigu@163.com 29 0 0 10 10 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2023.0 74 1 131 139 10.32604/cmc.2023.028756 0.0 9 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science 6P5QF gold 2023-03-23 WOS:000890984200004 0
J Zeng, H; Yang, C; Zhang, H; Wu, ZH; Zhang, JM; Dai, GJ; Babiloni, F; Kong, WZ Zeng, Hong; Yang, Chen; Zhang, Hua; Wu, Zhenhua; Zhang, Jiaming; Dai, Guojun; Babiloni, Fabio; Kong, Wanzeng A LightGBM-Based EEG Analysis Method for Driver Mental States Classification COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article CONVOLUTIONAL NEURAL-NETWORKS; HEART-RATE-VARIABILITY; PASSIVE BCI; FATIGUE Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI). [Zeng, Hong; Yang, Chen; Zhang, Hua; Wu, Zhenhua; Zhang, Jiaming; Dai, Guojun; Kong, Wanzeng] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China; [Zeng, Hong; Babiloni, Fabio] Univ Roma La Sapienza, Ind NeuroSci Lab, I-00161 Rome, Italy Hangzhou Dianzi University; Sapienza University Rome Kong, WZ (corresponding author), Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China. kongwanzeng@hdu.edu.cn Zeng, Hong/ABH-9072-2020; Babiloni, Fabio/E-5169-2015 Zeng, Hong/0000-0002-5821-0540; Babiloni, Fabio/0000-0002-4962-176X; Kong, Wanzeng/0000-0002-0113-6968 NSFC [61671193]; National Key R&D Program of China MOST [2017YFE0118200, 2017YFE0116800]; Key Science and Technology Program of Zhejiang Province [2018C04012]; International Joint Research Center for Brain-Machine Collaborative Intelligence of Hangzhou Dianzi University; BrainSigns srl; Industrial Neuroscience Lab of University of Rome La Sapienza NSFC(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China MOST; Key Science and Technology Program of Zhejiang Province; International Joint Research Center for Brain-Machine Collaborative Intelligence of Hangzhou Dianzi University; BrainSigns srl; Industrial Neuroscience Lab of University of Rome La Sapienza , is study was supported by NSFC (61671193), the National Key R&D Program of China MOST (2017YFE0118200 and 2017YFE0116800), Key Science and Technology Program of Zhejiang Province (2018C04012), International Joint Research Center for Brain-Machine Collaborative Intelligence of Hangzhou Dianzi University, and Industrial Neuroscience Lab of University of Rome La Sapienza., e authors also thank BrainSigns srl for the support. 54 46 48 6 50 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. SEP 9 2019.0 2019 3761203 10.1155/2019/3761203 0.0 11 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology JA2WR 31611912.0 gold, Green Published 2023-03-23 WOS:000487680100002 0
J Tian, Y; Zhang, XY; He, C; Tan, KC; Jin, YC Tian, Ye; Zhang, Xingyi; He, Cheng; Tan, Kay Chen; Jin, Yaochu Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics CHINESE JOURNAL OF ELECTRONICS English Article Deep learning; Metaheuristics; Reinforcement learning; Benchmark testing; Search problems; Robustness; Behavioral sciences; Evolutionary computation; Swarm intelligence; Variation operator; Transformation invariance; Automated design DIFFERENTIAL EVOLUTION; OPTIMIZATION; ADAPTATION; ALGORITHM; SEARCH A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems. This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables. [Tian, Ye] Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Inst Phys Sci & Informat Technol, Minist Educ, Hefei 230601, Peoples R China; [Zhang, Xingyi] Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China; [He, Cheng] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China; [Tan, Kay Chen] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Jin, Yaochu] Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany Anhui University; Anhui University; Huazhong University of Science & Technology; Hong Kong Polytechnic University; University of Bielefeld Zhang, XY (corresponding author), Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China. field910921@gmail.com; xyzhanghust@gmail.com; chenghehust@gmail.com; kctan@polyu.edu.hk; yaochu.jin@uni-bielefeld.de National Key R&D Program of China [2018AAA0100100]; National Natural Science Foundation of China [61876162, 61906001, 61903178, 62136008, U20A20306, U21A20512]; Federal Ministry of Education and Research, Germany National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Federal Ministry of Education and Research, Germany(Federal Ministry of Education & Research (BMBF)) This work was supported in part by the National Key R & D Program of China (2018AAA0100100), the National Natural Science Foundation of China (61876162, 61906001, 61903178, 62136008, U20A20306, U21A20512), and an Alexander von Humboldt Professorship for Artificial Intelligence funded by the Federal Ministry of Education and Research, Germany. 44 0 0 0 0 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1022-4653 2075-5597 CHINESE J ELECTRON Chin. J. Electron. JAN 2023.0 32 1 111 129 10.23919/cje.2022.00.100 0.0 19 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 8X8SM 2023-03-23 WOS:000932278600001 0
C Qiu, D; Zeng, J; Ke, ZH; Sun, WX; Yang, CX IEEE Qiu, Di; Zeng, Jin; Ke, Zhanghan; Sun, Wenxiu; Yang, Chengxi Towards Geometry Guided Neural Relighting with Flash Photography 2020 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2020) International Conference on 3D Vision English Proceedings Paper 8th International Conference on 3D Vision (3DV) NOV 25-28, 2020 ELECTR NETWORK Facebook Real Labs,Microsoft,Huawei,Ocado Technol,Omron,Cyber Agent AI Diu,Denso IT Lab,Google,Sensetime,Tencent AI Lab,Toppan,Apple REFLECTANCE Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs non-trivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images under different lighting conditions, little has been done to utilize geometric information that crucially influences the high-frequency features in the images, such as glossy highlight and cast shadow. We therefore propose a framework for image relighting from a single flash photograph with its corresponding depth map using deep learning. By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map. Moreover, the single-image based setup greatly simplifies the data capture process. We experimentally validate the advantage of our geometry guided approach over state-of-the-art image-based approaches in intrinsic image decomposition and image relighting, and also demonstrate our performance on real mobile phone photo examples. [Qiu, Di] Chinese Univ Hong Kong, Hong Kong, Peoples R China; [Zeng, Jin; Ke, Zhanghan; Sun, Wenxiu] SenseTime Res, Munich, Germany; [Yang, Chengxi] Alibaba Grp, Beijing, Peoples R China Chinese University of Hong Kong; Alibaba Group Qiu, D (corresponding author), Chinese Univ Hong Kong, Hong Kong, Peoples R China. sylvesterqiu@gmail.com; zengjin@tetras.ai; kezhanghan@sensetime.com; sunwenxiu@sensetime.com; streamycx@yahoo.com Ke, Zhanghan/GWU-5885-2022 35 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2378-3826 2475-7888 978-1-7281-8128-8 INT CONF 3D VISION 2020.0 1137 1146 10.1109/3DV50981.2020.00124 0.0 10 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BR4SK Green Submitted 2023-03-23 WOS:000653085200115 0
J Tian, JM; Vanderstraeten, J; Matthyssens, P; Shen, L Tian, Jiamian; Vanderstraeten, Johanna; Matthyssens, Paul; Shen, Lei Developing and leveraging platforms in a traditional industry: An orchestration and co-creation perspective INDUSTRIAL MARKETING MANAGEMENT English Article Platform; Value co-creation; Co-evolution; Orchestration capability INTENSIVE BUSINESS SERVICES; INNOVATION PERFORMANCE; NETWORK ORCHESTRATION; DYNAMIC CAPABILITIES; CONCEPTUAL-FRAMEWORK; EMPIRICAL-ANALYSIS; BIG DATA; KNOWLEDGE; SERVITIZATION; TECHNOLOGY In the current fast-changing and digitalizing world, ever more firms active in traditional industries are transforming themselves into 'Smart Factories'. Within their value chains, manufacturers are shifting from working at arm's length with other firms, to creating integrated platforms. These are composed of co-creating and collaborating actors. Even though the opportunities and competitive advantages of platforms for industrial applications have been proven, insights in the lead actors' efforts to ensure co-evolution of the platform and the platform actors' contributions to value co-creation are scarce. That way, the full potential of collaborative firm settings might not be realized. Addressing this gap, we explore through a multiple case study how manufacturers in the traditional Chinese textile industry gradually adopt a platform-based logic. This results in an integrative framework unfolding the lead actor's orchestration capabilities to ensure co-evolution, and the prerequisites for value co-creation in a platform. As such, we adapt and extend the co-evolution concept mostly examined in a dyadic setting to a multiple actors' logic and answer calls for additional research on the micro-foundations of value co-creation in platforms. [Tian, Jiamian; Shen, Lei] Donghua Univ, Glorious Sun Sch Business & Management, Shanghai, Peoples R China; [Vanderstraeten, Johanna; Matthyssens, Paul] Univ Antwerp, Antwerp, Belgium; [Matthyssens, Paul] Antwerp Management Sch, Antwerp, Belgium; [Shen, Lei] Donghua Univ, Innovat Ctr Text Sci & Technol, Shanghai, Peoples R China Donghua University; University of Antwerp; University of Antwerp; Donghua University Shen, L (corresponding author), Donghua Univ, Glorious Sun Sch Business & Management, Shanghai, Peoples R China. slei@dhu.edu.cn Matthyssens, Paul/0000-0002-9138-7911; Tian, Jiamian/0000-0002-2264-9359 China Scholarship Council [201806630047]; Chinese Academy of Engineering [2016-XZ-17] China Scholarship Council(China Scholarship Council); Chinese Academy of Engineering We gratefully acknowledge the financial support of the China Scholarship Council (file number 201806630047) and Chinese Academy of Engineering (file number 2016-XZ-17) to pursue part of this study abroad. 140 13 13 22 57 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-8501 1873-2062 IND MARKET MANAG Ind. Mark. Manage. JAN 2021.0 92 14 33 10.1016/j.indmarman.2020.10.007 0.0 20 Business; Management Social Science Citation Index (SSCI) Business & Economics QU5DW 2023-03-23 WOS:000627301400002 0
J Wei, ZB; Ruan, HK; Li, Y; Li, JW; Zhang, CZ; He, HW Wei, Zhongbao; Ruan, Haokai; Li, Yang; Li, Jianwei; Zhang, Caizhi; He, Hongwen Multistage State of Health Estimation of Lithium-Ion Battery With High Tolerance to Heavily Partial Charging IEEE TRANSACTIONS ON POWER ELECTRONICS English Article Integrated circuits; Batteries; Estimation; Feature extraction; Data mining; Aging; Power electronics; Health indicators (HIs); lithium-ion battery (LIB); partial charging; state of health (SOH) INCREMENTAL CAPACITY ANALYSIS; ONLINE STATE; APPROXIMATE ENTROPY; SAMPLE ENTROPY; MODEL; DEGRADATION; TEMPERATURE; PROGNOSIS; DIAGNOSIS State of health (SOH) is critical to the management of lithium-ion batteries (LIBs) due to its deep insight into health diagnostic and protection. However, the lack of complete charging data is common in practice, which poses a challenge for the charging-based SOH estimators. This article proposes a multistage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily partial charging. In particular, different sets of health indicators (HIs), covering both the morphological incremental capacity features and the voltage entropy information, are extracted from the partial constant-current charging data with different initial charging voltages to characterize the aging status. Following this endeavor, artificial neural network based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types. [Wei, Zhongbao; Ruan, Haokai; Li, Jianwei; He, Hongwen] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Sch Mech Engn, Beijing 100081, Peoples R China; [Li, Yang] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden; [Zhang, Caizhi] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China Beijing Institute of Technology; Chalmers University of Technology; Chongqing University Li, Y (corresponding author), Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden. weizb@bit.edu.cn; 3220190306@bit.edu.cn; yangli@ieee.org; lijianw@bit.edu.cn; czzhang@cqu.edu.cn; hwhebit@bit.edu.cn He, Hongwen/Y-2860-2019; Li, Yang/A-7000-2011 Li, Yang/0000-0002-9497-3051; Wei, Zhongbao/0000-0003-0051-5648; Zhang, Caizhi/0000-0002-2060-4245; , Hongwen/0000-0003-2874-1858 National Natural Science Foundation of China [52072038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 52072038. Recommended for publication by Associate Editor W. Cao. 45 24 24 17 82 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8993 1941-0107 IEEE T POWER ELECTR IEEE Trans. Power Electron. JUN 2022.0 37 6 7432 7442 10.1109/TPEL.2022.3144504 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering ZB5OG 2023-03-23 WOS:000756890700102 0
C Yang, CG; Yang, RX; Na, J; Chen, F IEEE Yang, Chenguang; Yang, Runxian; Na, Jing; Chen, Fei Adaptive RBFNN Control of Robot Manipulators with Finite-time Convergence PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY IEEE Industrial Electronics Society English Proceedings Paper 42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON) OCT 24-27, 2016 Florence, ITALY IEEE Ind Elect Soc,Inst Elect & Elect Engineers NONLINEAR-SYSTEMS In this paper, the position tracking control with finite-time convergence has been studied for a class of nonliear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies. [Yang, Chenguang; Yang, Runxian] Swansea Univ, Zienkiewicz Ctr Computat Engn, Swansea, W Glam, Wales; [Yang, Runxian] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing, Jiangsu, Peoples R China; [Na, Jing] Univ Bristol, Dept Mech Engn, Bristol, Avon, England; [Chen, Fei] Ist Italiano Tecnol, Dept Adv Robot, Genoa, Italy Swansea University; Nanjing University of Aeronautics & Astronautics; University of Bristol; Istituto Italiano di Tecnologia - IIT Yang, CG (corresponding author), Swansea Univ, Zienkiewicz Ctr Computat Engn, Swansea, W Glam, Wales. cyang@theiet.org Yang, Chenguang/AAJ-2509-2020 Na, Jing/0000-0002-3067-1580; Chen, Fei/0000-0003-4397-0931 Engineering and Physical Sciences Research Council (EPSRC) [EP/L026856/2]; Marie Curie Intra-European Fellowships Project AECE [FP7-PEOPLE-2013-IEF-62553174]; EPSRC [EP/L026856/2, EP/L026856/1] Funding Source: UKRI Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Marie Curie Intra-European Fellowships Project AECE; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/L026856/2, and Marie Curie Intra-European Fellowships Project AECE under Grant FP7-PEOPLE-2013-IEF-62553174. 22 2 2 1 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1553-572X 978-1-5090-3474-1 IEEE IND ELEC 2016.0 42 47 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BH2LH Green Submitted 2023-03-23 WOS:000399031200008 0
J Li, SF; Zheng, P; Fan, JM; Wang, LH Li, Shufei; Zheng, Pai; Fan, Junming; Wang, Lihui Toward Proactive Human-Robot Collaborative Assembly: A Multimodal Transfer-Learning-Enabled Action Prediction Approach IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Robots; Three-dimensional displays; Collaboration; Service robots; Skeleton; Videos; Visualization; Action recognition; human-robot collaboration; multimodal intelligence; transfer learning NEURAL-NETWORK Human-robot collaborative assembly (HRCA) is vital for achieving high-level flexible automation for mass personalization in today's smart factories. However, existing works in both industry and academia mainly focus on the adaptive robot planning, while seldom consider human operator's intentions in advance. Hence, it hinders the HRCA transition toward a proactive manner. To overcome the bottleneck, this article proposes a multimodal transfer-learning-enabled action prediction approach, serving as the prerequisite to ensure the proactive HRCA. First, a multimodal intelligence-based action recognition approach is proposed to predict ongoing human actions by leveraging the visual stream and skeleton stream with short-time input frames. Second, a transfer-learning-enabled model is adapted to transfer learnt knowledge from daily activities to industrial assembly operations rapidly for online operator intention analysis. Third, a dynamic decision-making mechanism, including robotic decision and motion control, is described to allow mobile robots to assist operators in a proactive manner. Finally, an aircraft bracket assembly task is demonstrated in the laboratory environment, and the comparative study result shows that the proposed approach outperforms other state-of-the-art ones for efficient action prediction. [Li, Shufei; Zheng, Pai; Fan, Junming] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China; [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, S-11428 Stockholm, Sweden Hong Kong Polytechnic University; Royal Institute of Technology Zheng, P (corresponding author), Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China. shufei.li@connect.polyu.hk; pai.zheng@polyu.edu.hk; jun-ming.fan@connect.polyu.hk; lihuiw@kth.se ZHENG, PAI/K-7989-2012; Li, Shufei/GZB-2215-2022; Fan, Junming/ABE-3269-2020; Wang, Lihui/O-3907-2014 ZHENG, PAI/0000-0002-2329-8634; Fan, Junming/0000-0001-9317-0428; Wang, Lihui/0000-0001-8679-8049; Li, Shufei/0000-0002-5684-6756 Research Committee of The Hong Kong Polytechnic University under Departmental General Research Fund under Grant G-UAHH; Human Subjects Ethics Sub-Committee at the Hong Kong Polytechnic University [HSEARS20201110002]; Laboratory for Artificial Intelligence in Design, under Hong Kong Special Administrative Region [RP2-1] Research Committee of The Hong Kong Polytechnic University under Departmental General Research Fund under Grant G-UAHH; Human Subjects Ethics Sub-Committee at the Hong Kong Polytechnic University; Laboratory for Artificial Intelligence in Design, under Hong Kong Special Administrative Region This work was supported in part by the Laboratory for Artificial Intelligence in Design under Project Code: RP2-1, Hong Kong Special Administrative Region, in part by the Research Committee of The Hong Kong Polytechnic University under Departmental General Research Fund under Grant G-UAHH, and in part by the Human Subjects Ethics Sub-Committee at the Hong Kong Polytechnic University under Grant HSEARS20201110002. 31 13 13 54 102 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. AUG 2022.0 69 8 8579 8588 10.1109/TIE.2021.3105977 0.0 10 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation ZN2OU Green Accepted 2023-03-23 WOS:000764880700100 0
J Ying, XG; Zinnai, A; Venturi, F; Sanmartin, C; Deng, SG Ying, Xiaoguo; Zinnai, Angela; Venturi, Francesca; Sanmartin, Chiara; Deng, Shanggui Freshness evaluation of grass carp (Ctenopharyngodon idellus) by electronic nose JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION English Article Grass carp; Freshness ranking method; Electronic nose; Non-linear; Eigen value CHROMATOGRAPHY-MASS SPECTROMETRY; INDOOR AIR CONTAMINANTS; GAS-CHROMATOGRAPHY; NEURAL-NETWORK; GC-MS; TONGUE; FRUIT; CLASSIFICATION; IDENTIFICATION; SPECTROSCOPY A novel grass carp (Ctenopharyngodon idellus) freshness rapid evaluation method using electronic nose (EN) was proposed in this paper. Total viable counts (TVC) and gas chromatography-mass spectrometry (GC-MS) were examined. EN responses to grass carp samples stored at 4 degrees C were continuously measured for 8 days. Microbial growth model was developed based on modified Gompertz regression. Results of TVC and GC-MS provided freshness references for EN analysis. Principal component analysis (PCA) and stochastic resonance (SR) were utilized in EN measurement data analysis. PCA qualitatively discriminated grass carp samples in different freshness. SR output signal-to-noise ratio (SNR) eigenvalues (SNRmax) quantitatively discriminated freshness for all samples. Grass carp freshness evaluation model was developed by non-linear regression between SNRmax values and storage time. Validation experiments results demonstrated that the proposed grass carp freshness rapid evaluation method presented good forecasting accuracy. The proposed method is promising in aquatic product freshness analysis. [Ying, Xiaoguo; Zinnai, Angela; Venturi, Francesca; Sanmartin, Chiara] Pisa Univ, DAFE, Via Borghetto 80, I-56124 Pisa, Italy; [Ying, Xiaoguo; Deng, Shanggui] Zhejiang Ocean Univ, Coll Food & Pharm, Zhejiang Prov Key Lab Hlth Risk Factors Seafood, Lincheng New Area, 1 Rd South Haida, Zhoushan 316022, Zhejiang, Peoples R China University of Pisa; Zhejiang Ocean University Deng, SG (corresponding author), Zhejiang Ocean Univ, Coll Food & Pharm, Zhejiang Prov Key Lab Hlth Risk Factors Seafood, Lincheng New Area, 1 Rd South Haida, Zhoushan 316022, Zhejiang, Peoples R China. yingxiaoguo@126.com; dengshanggui@zjou.edu.cn xiaoguo, ying/AAG-3337-2021; Sanmartin, Chiara/F-7524-2017; Venturi, Francesca/O-8348-2016; ying, xiaoguo/T-6861-2017 Sanmartin, Chiara/0000-0002-7559-1955; Zinnai, Angela/0000-0001-6321-8785; Venturi, Francesca/0000-0002-7844-3253; ying, xiaoguo/0000-0003-4006-7085 National Natural Science Foundation of China [31471609] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (No. 31471609). 39 8 8 3 39 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 2193-4126 2193-4134 J FOOD MEAS CHARACT J. Food Meas. Charact. SEP 2017.0 11 3 1026 1034 10.1007/s11694-017-9478-5 0.0 9 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology FB3DR 2023-03-23 WOS:000406023300012 0
J Randall, M; Fensholt, R; Zhang, YY; Jensen, MB Randall, Mark; Fensholt, Rasmus; Zhang, Yongyong; Jensen, Marina Bergen Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale WATER English Article Sponge City; Low Impact Development; urban hydrology; SWMM; remote sensing; Geographic Object Based Image Analysis CLASSIFICATION; IMPACT China's Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation. [Randall, Mark; Jensen, Marina Bergen] Univ Copenhagen, Dept Geosci & Nat Resources Management, Sect Landscape Architecture & Planning, DK-1958 Frederiksberg, Denmark; [Randall, Mark; Zhang, Yongyong; Jensen, Marina Bergen] Chinese Acad Sci, Sino Danish Ctr Educ & Res, Beijing 101408, Peoples R China; [Fensholt, Rasmus] Univ Copenhagen, Dept Geosci & Nat Resources Management, Sect Geog, DK-1350 Copenhagen, Denmark; [Zhang, Yongyong] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100875, Peoples R China University of Copenhagen; Chinese Academy of Sciences; University of Copenhagen; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Randall, M (corresponding author), Univ Copenhagen, Dept Geosci & Nat Resources Management, Sect Landscape Architecture & Planning, DK-1958 Frederiksberg, Denmark.;Randall, M (corresponding author), Chinese Acad Sci, Sino Danish Ctr Educ & Res, Beijing 101408, Peoples R China. mtr@ign.ku.dk; rf@ign.ku.dk; zhangyy003@igsnrr.ac.cn; mbj@ign.ku.dk Fensholt, Rasmus/L-7951-2014; Randall, Mark/AAD-6869-2022; Zhang, Yongyong/I-9367-2014; Jensen, Marina Bergen/C-9841-2015 Fensholt, Rasmus/0000-0003-3067-4527; Zhang, Yongyong/0000-0001-9159-9626; Randall, Mark/0000-0003-4976-2893; Jensen, Marina Bergen/0000-0003-0202-0366 Sino-Danish Center Sino-Danish Center This research and the APC were funded by the Sino-Danish Center. 36 10 10 7 30 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water JUN 2019.0 11 6 10.3390/w11061133 0.0 17 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources II7BC Green Published, gold 2023-03-23 WOS:000475346300021 0
J Guo, SZ; Wang, J; Chen, Z; Li, YB; Lu, ZH Guo, Shize; Wang, Jian; Chen, Zhe; Li, Yubai; Lu, Zhonghai Securing IoT Space via Hardware Trojan Detection IEEE INTERNET OF THINGS JOURNAL English Article Trojan horses; Hardware; Integrated circuits; Steady-state; Logic gates; Internet of Things; Power demand; Hardware Trojan (HT) detection; integrated circuit (IC); Internet-of-Things (IoT) security; thermal side channel INTERNET; THINGS Hardware Trojan (HT) is a malicious modification in the chip circuitry, which may lead to undesired chip function changing or sensitive information leaking once activated. As recently studied, HT has become one of the main threats for Internet-of-Things (IoT) security, and therefore, protecting IoT against the HT attack attracts growing attention from IoT researchers. In this article, we propose an HT detection technique which makes use of chip temporal thermal information and self-organizing map (SOM) neural network to automatically isolate the Trojan-infected chips with the Trojan-free ones, and meanwhile, confirm the Trojan location at the infected chips. The experimental results reveal that our method is effective. Specifically, for the Trust-hub benchmarks, it can detect HTs which increase only 0.02% power consumption of the original design and localize the Trojan positions precisely without any error. In addition, we demonstrate the advantages of our method over two existing HT detection methods, namely, the thermal and power map (TPM) and ring oscillator net (RON), and make a thorough discussion on how the thermal image resolution, chip technology, and clustering algorithm affect the Trojan detection results. [Guo, Shize; Wang, Jian; Chen, Zhe; Li, Yubai] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-16440 Stockholm, Sweden University of Electronic Science & Technology of China; Royal Institute of Technology Wang, J (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China. wangjian3630@uestc.edu.cn Lu, Zhonghai/0000-0003-0061-3475 National Nature Science Foundation of China [61671110] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Nature Science Foundation of China under Grant 61671110. 35 8 8 3 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. NOV 2020.0 7 11 11115 11122 10.1109/JIOT.2020.2994627 0.0 8 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications OT3MP 2023-03-23 WOS:000590754700031 0
J Huang, J; Xu, Y; Peng, XY; Hu, L; Yang, JK Huang, Jing; Xu, Ying; Peng, Xiaoyan; Hu, Lin; Yang, Jikuang Development of a human head and neck muscle activation control model based on BPNN JOURNAL OF INTELLIGENT & FUZZY SYSTEMS English Article; Proceedings Paper International Conference on Telecommunication, Power Analysis and Computing Techniques APR 06-08, 2017 Chennai, INDIA Active muscle; BPNN control model; feedback control; Simulink; human head and neck model TUBE-TYPE AIRBAG; FEEDBACK-CONTROL; MOVEMENTS; IMPACTS With the development of vehicle active safety technology and its combination with passive safety technologies, there is a need for an updated tool to assess the performance of vehicle safety systems in both the in-crash and pre-crash phases. Consequently, developing human body models with active muscles is critical to achieve improved bio-fidelity simulation results to evaluate the effects of safety systems. In the present study, an innovative human active muscle control model based on a back-propagation neural network (BPNN) controller was developed, and the active muscle response was modelled using a feedback control strategy with the BPNN algorithm implemented in a human head and neck Sim Mechanics model. The BPNN controller was used to dynamically control the muscle activation level, the most complicated and essential factor in active muscle force calculation based on the Hill equation. Then, the active muscle control model was evaluated by simulating the human response in volunteer sled tests. The results validated the proposed active muscle control method as successful and easily realized. Achieving muscle active control would further improve the bio-fidelity of the human body model. [Huang, Jing; Xu, Ying; Peng, Xiaoyan] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China; [Hu, Lin] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha, Hunan, Peoples R China; [Yang, Jikuang] Chalmers Univ Technol, Dept Appl Mech, Gothenburg, Sweden Hunan University; Changsha University of Science & Technology; Chalmers University of Technology Huang, J (corresponding author), Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China. huangjing926@hnu.edu.cn Yang, Jikuang/Y-2391-2018; hu, lin/AAT-5814-2020 Yang, Jikuang/0000-0003-4737-511X; hu, lin/0000-0002-2658-8629 Natural Science Foundation of Hunan Province [2017JJ2034]; National Natural Science Foundation of China [51775178, 51575167] Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors acknowledge the Natural Science Foundation of Hunan Province (Grant No. 2017JJ2034), the National Natural Science Foundation of China (Grant No. 51775178, 51575167). 23 7 7 0 21 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1064-1246 1875-8967 J INTELL FUZZY SYST J. Intell. Fuzzy Syst. 2018.0 34 2 1161 1167 10.3233/JIFS-169410 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science FX7FL 2023-03-23 WOS:000426252600041 0
J Asteris, PG; Mamou, A; Ferentinou, M; Tran, TT; Zhou, J Asteris, Panagiotis G.; Mamou, Anna; Ferentinou, Maria; Tran, Trung-Tin; Zhou, Jian Predicting clay compressibility using a novel Manta ray foraging optimization-based extreme learning machine model TRANSPORTATION GEOTECHNICS English Article Clay compressibility; Manta ray foraging optimization; Extreme learning machine; Soft ground treatment; Soft computing ENGINEERING PROPERTIES; GROUND VIBRATION; SOILS; CONSOLIDATION; DISPLACEMENT; EXCAVATION; PARAMETERS; STRENGTH; BEHAVIOR Designing infrastructure founded on very soft deposits requires soil improvement to reduce the compressibility of clay so as to prevent the development of unacceptably high differential settlements. Assessing the likely compression index of clay is critical in determining the extend of the required soil improvement. This research presents extreme learning machine (ELM) models developed using Manta ray foraging optimization (MRFO) for the prediction of the compressibility of clay for soft ground improvement. The results show that the developed MRFO-ELM model predicts the compressibility of clay with less than +/- 20 % deviation of the data for 67 % of the specimen and outperforms the prediction accuracy of simple or multiple regression correlations and advanced neural network models currently reported in the literature. [Asteris, Panagiotis G.; Mamou, Anna] Sch Pedag & Technol Educ, Computat Mech Lab, Athens 14121, Greece; [Ferentinou, Maria] Liverpool John Moores Univ, Sch Civil Engn & Built Environm, Liverpool, Merseyside, England; [Tran, Trung-Tin] FPT Univ, Dept Informat Technol, Danang 550000, Vietnam; [Zhou, Jian] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China ASPETE - School of Pedagogical & Technological Education; Liverpool John Moores University; FPT University; Central South University Asteris, PG (corresponding author), Sch Pedag & Technol Educ, Computat Mech Lab, Athens 14121, Greece. panagiotisasteris@gmail.com Ferentinou, Maria/O-6219-2019 Ferentinou, Maria/0000-0001-6892-7919; Tran, Trung Tin/0000-0002-8362-0795 73 3 3 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-3912 TRANSP GEOTECH Transp. Geotech. NOV 2022.0 37 100861 10.1016/j.trgeo.2022.100861 0.0 16 Engineering, Civil; Engineering, Geological Science Citation Index Expanded (SCI-EXPANDED) Engineering 6M4AM 2023-03-23 WOS:000888812100003 0
C Lin, SB; Chen, L; Zou, Q; Tian, W IEEE Lin, Shaobo; Chen, Long; Zou, Qin; Tian, Wei HIGH-RESOLUTION DRIVING SCENE SYNTHESIS USING STACKED CONDITIONAL GANS AND SPECTRAL NORMALIZATION 2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) IEEE International Conference on Multimedia and Expo English Proceedings Paper IEEE International Conference on Multimedia and Expo (ICME) JUL 08-12, 2019 Shanghai, PEOPLES R CHINA IEEE,IEEE Signal Proc Soc,IEEE Commun Soc,IEEE Circuits & Syst Soc,IEEE Comp Soc,Kuaishou,JD.COM,MEGVII,iQIYI,Microsoft,DiDi,Alibaba,Horizon Robot,Baidu,Lenovo Res,Unisound,SenseTime,Jiangmen Conditional generative adversarial networks; Image-to-image translation; Stack structure; Spectral normalization Large-scale dataset plays a key role in the driving scene understanding for deep learning based-autonomous driving tasks. Due to the fact that the annotation for a large number of images is extremely labor-intensive and time-consuming, many researchers turn to using image-synthesis techniques for automatic construction of training data. However, traditional methods often have difficulties in producing high definition driving scene images. To tackle this problem, in this paper, we propose a novel deep model hdCGAN for high-definition image-to-image translation. The hdCGAN is built on a conditional GAN in combination with a spectral normalization. Moreover, we improve the hdCGAN by using a stacked network architecture and the enhanced model is called stack-hdCGAN. With the guidance of multi-scale discriminators and the constraint of spectral normalization in the training procedure, the learned models can generate high-resolution and high-quality driving scene images from corresponding semantic segmentation maps. Quantitative and qualitative evaluations on the Cityscapes dataset demonstrate the effectiveness of the proposed models. [Lin, Shaobo; Chen, Long] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China; [Zou, Qin] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China; [Tian, Wei] Karlsruhe Inst Technol, Karlsruhe, Germany Sun Yat Sen University; Wuhan University; Helmholtz Association; Karlsruhe Institute of Technology Chen, L (corresponding author), Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China. linshb9@mail2.sysu.edu.cn; chenl46@mail.sysu.edu.cn; qzou@whu.edu.cn; wei.tian@kit.edu Zou, Qin/GVU-2237-2022 Tian, Wei/0000-0002-5085-7219; Zou, Qin/0000-0001-7955-0782 National Key R&D Program of China [2018YFB1305002]; National Natural Science Foundation of China [61872277, 41571437] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Key R&D Program of China under Grant 2018YFB1305002, the National Natural Science Foundation of China under Grant 61773414 and the National Natural Science Foundation of China (61872277, 41571437). 19 2 2 1 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1945-7871 978-1-5386-9552-4 IEEE INT CON MULTI 2019.0 1330 1335 10.1109/ICME.2019.00231 0.0 6 Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO1PE 2023-03-23 WOS:000501820600223 0
J Lu, P; Chen, PP; Tian, Y; He, Y; Mo, DW; Yang, RX; Lasaponara, R; Masini, N Lu, Peng; Chen, Panpan; Tian, Yan; He, Yang; Mo, Duowen; Yang, Ruixia; Lasaponara, Rosa; Masini, Nicola Reconstructing settlement evolution from neolithic to Shang dynasty in Songshan mountain area of central China based on self-organizing feature map JOURNAL OF CULTURAL HERITAGE English Article Songshan Mountain Region; Prehistoric settlements distribution; Size-grade division; Spatial pattern; Evolution characteristic QINGHAI PROVINCES; ARCHAEOLOGY; GANSU; BASIN; SITE; SIZE; GIS The Self-Organizing Feature Map (SOFM) is one of the most popular neural network models, recently also adopted in archaeology to improve and enhance, on the basis of the availability of information and archaeological records, our understanding of the long-term human settlements and their evolution. In this paper, SOFM has been applied to classify prehistoric settlement size-grade in the Songshan Mountain Region in China, mainly focusing on the following four periods: Peiligang (9000-7000aBP), Yangshao (7000-5000aBP), Longshan (5000-4000aBP) and Xia-Shang (4000-3000aBP). Outputs from the SOFM analysis enabled us to capture the spatial relation between higher and lower grade settlements and to identify specific morphological patterns. This brought new light on the human settlements and their evolution in relations with the nature, environmental features, and cultural attitude in the Songshan Mountain Region where the Chinese civilization emerged and developed. (C) 2018 Elsevier Masson SAS. All rights reserved. [Lu, Peng; Chen, Panpan; Tian, Yan; He, Yang] Henan Acad Sci, Inst Geog, Zhengzhou 450052, Henan, Peoples R China; [Lu, Peng; Chen, Panpan; Tian, Yan; He, Yang; Yang, Ruixia] UNESCO, Zhengzhou Base, Int Ctr Space Technol Nat & Cultural Heritage Aus, Zhengzhou 450052, Henan, Peoples R China; [Mo, Duowen] Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; [Yang, Ruixia] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Lasaponara, Rosa] CNR, Inst Methodol Environm Anal, I-85050 Tito, PZ, Italy; [Masini, Nicola] CNR, Inst Archaeol & Architectural Heritage, I-85050 Tito, PZ, Italy Peking University; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Consiglio Nazionale delle Ricerche (CNR) Lu, P; Chen, PP (corresponding author), Henan Acad Sci, Inst Geog, Zhengzhou 450052, Henan, Peoples R China. bulate_0@163.com; cppbed@163.com Masini, Nicola/AFK-3635-2022; Masini, Nicola/H-3889-2012; Lu, Peng/HNR-4786-2023; Nicola, Masini/ABG-8663-2021; Lasaponara, Rosa/AAI-6160-2020 Masini, Nicola/0000-0002-8804-5718; Lasaponara, Rosa/0000-0002-1287-646X National Natural Science Foundation of China [41671014, 41701014]; Major Program of the National Social Science Foundation of China [11, ZD183]; Major Project of Songshan Mountain Culture Research Society; Project of Digital Environment Archaeology Specially-appointed Researcher of Henan, China; Project of Environment Archaeology Researcher in Zhengzhou National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Program of the National Social Science Foundation of China; Major Project of Songshan Mountain Culture Research Society; Project of Digital Environment Archaeology Specially-appointed Researcher of Henan, China; Project of Environment Archaeology Researcher in Zhengzhou The study is funded by the National Natural Science Foundation of China (Grant No. 41671014, 41701014), Major Program of the National Social Science Foundation of China (Grant No. 11 and ZD183), the Major Project of Songshan Mountain Culture Research Society, The Project of Digital Environment Archaeology Specially-appointed Researcher of Henan, China and The Project of Environment Archaeology Researcher in Zhengzhou. 58 10 10 7 36 ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER ISSY-LES-MOULINEAUX 65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE 1296-2074 1778-3674 J CULT HERIT J. Cult. Herit. MAR-APR 2019.0 36 23 31 10.1016/j.culher.2018.08.006 0.0 9 Archaeology; Art; Chemistry, Analytical; Geosciences, Multidisciplinary; Materials Science, Multidisciplinary; Spectroscopy Science Citation Index Expanded (SCI-EXPANDED); Arts & Humanities Citation Index (A&HCI) Archaeology; Art; Chemistry; Geology; Materials Science; Spectroscopy HN6QW 2023-03-23 WOS:000460311800003 0
J Wang, S; Bi, J; Wu, JP; Vasilakos, AV; Fan, QL Wang, Sen; Bi, Jun; Wu, Jianping; Vasilakos, Athanasios V.; Fan, Qilin VNE-TD: A virtual network embedding algorithm based on temporal-difference learning COMPUTER NETWORKS English Article Network virtualization; Virtual network embedding; Temporal-difference learning NODE Recently, network virtualization is considered as a promising solution for the future Internet which can help to overcome the resistance of the current Internet to fundamental changes. The problem of embedding Virtual Networks (VN) in a Substrate Network (SN) is the main resource allocation challenge in network virtualization. The major challenge of the Virtual Network Embedding (VNE) problem lies in the contradiction between making online embedding decisions and pursuing a long-term objective. Most previous works resort to balancing the SN workload with various methods to deal with this contradiction. Rather than passive balancing, we try to overcome it by learning actively and making online decisions based on previous experiences. In this article, we model the VNE problem as Markov Decision Process (MDP) and develop a neural network to approximate the value function of VNE states. Further, a VNE algorithm based on Temporal-Difference Learning (one kind of Reinforcement Learning methods), named VNE-TD, is proposed. In VNE-TD, multiple embedding candidates of node-mapping are generated probabilistically, and TD Learning is involved to evaluate the long-run potential of each candidate. Extensive simulation results show that VNE-TD outperforms previous algorithms significantly in terms of both block ratio and revenue. (C) 2019 Published by Elsevier B.V. [Wang, Sen; Fan, Qilin] Chongqing Univ, Sch Software Engn, Chongqing, Peoples R China; [Bi, Jun] Tsinghua Univ, Inst Network Sci & Cyberspace, Network Architecture & IPv6 Res Div, Beijing, Peoples R China; [Wu, Jianping] Tsinghua Univ, Network Res Ctr, Beijing, Peoples R China; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Lulea, Sweden Chongqing University; Tsinghua University; Tsinghua University; Lulea University of Technology Wang, S (corresponding author), Chongqing Univ, Sch Software Engn, Chongqing, Peoples R China. wangsen@cqu.edu.cn Vasilakos, Athanasios/0000-0003-1902-9877 Fundamental Research Funds for the Central Universities [106112015CDJXY090003]; National Science Foundation of China [61602071] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the Fundamental Research Funds for the Central Universities (No. 106112015CDJXY090003) and the National Science Foundation of China (No. 61602071). Sen Wang is the corresponding author. 41 20 21 2 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. OCT 9 2019.0 161 251 263 10.1016/j.comnet.2019.05.004 0.0 13 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IX7HQ 2023-03-23 WOS:000485854800021 0
J Wang, Y; Huang, RJ; Xu, W; Zhong, HB; Duan, J; Lin, CS; Gu, YF; Wang, T; Li, YJ; Ovadnevaite, J; Ceburnis, D; O'Dowd, C Wang, Ying; Huang, Ru-Jin; Xu, Wei; Zhong, Haobin; Duan, Jing; Lin, Chunshui; Gu, Yifang; Wang, Ting; Li, Yongjie; Ovadnevaite, Jurgita; Ceburnis, Darius; O'Dowd, Colin Staggered-peak production is a mixed blessing in the control of particulate matter pollution NPJ CLIMATE AND ATMOSPHERIC SCIENCE English Article AIR-QUALITY; CHEMICAL-COMPOSITION; METEOROLOGICAL NORMALIZATION; ANTHROPOGENIC EMISSIONS; PM2.5 CONCENTRATIONS; HAZE EVENTS; AEROSOL; CHINA; WINTERTIME; IMPACT Staggered-peak production (SP)-a measure to halt industrial production in the heating season-has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM1 composition in Beijing during the SP period in the 2016 heating season (SPhs) with those in the normal production (NP) periods during the 2015 heating season (NPhs) and 2016 non-heating season (NPnhs) to investigate the effectiveness of SP. The PM1 mass concentration decreased from 70.0 +/- 54.4 mu g m(-3) in NPhs to 53.0 +/- 56.4 mu g m(-3) in SPhs, with prominent reductions in primary emissions. However, the fraction of nitrate during SPhs (20.2%) was roughly twice that during NPhs (12.7%) despite a large decrease of NOx, suggesting an efficient transformation of NOx to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NPhs (22.5%) to SPhs (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM1 loading was similar between SPhs (53.0 +/- 56.4 mu g m(-3)) and NPnhs (50.7 +/- 49.4 mu g m(-3)), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species. [Wang, Ying; Huang, Ru-Jin; Xu, Wei; Zhong, Haobin; Duan, Jing; Lin, Chunshui; Gu, Yifang; Wang, Ting] Inst Earth Environm, Chinese Acad Sci, Ctr Excellence Quaternary Sci & Global Change, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China; [Wang, Ying] Beijing Normal Univ, Interdisciplinary Res Ctr Earth Sci Frontier IRCES, Beijing 100875, Peoples R China; [Huang, Ru-Jin] Laoshan Lab, Qingdao 266061, Peoples R China; [Huang, Ru-Jin; Gu, Yifang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Xu, Wei; Ovadnevaite, Jurgita; Ceburnis, Darius; O'Dowd, Colin] Univ Galway, Ryan Inst Ctr Climate & Air Pollut Studies, Sch Nat Sci, Phys Unit, Univ Rd, Galway H91CF50, Ireland; [Li, Yongjie] Univ Macau, Fac Sci & Technol, Dept Civil & Environm Engn, Macau 999078, Peoples R China Chinese Academy of Sciences; Institute of Earth Environment, CAS; Beijing Normal University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Macau Huang, RJ (corresponding author), Inst Earth Environm, Chinese Acad Sci, Ctr Excellence Quaternary Sci & Global Change, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China.;Huang, RJ (corresponding author), Laoshan Lab, Qingdao 266061, Peoples R China.;Huang, RJ (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. rujin.huang@ieecas.cn Xu, Wei/P-1277-2017 Xu, Wei/0000-0002-9590-1906 National Natural Science Foundation of China (NSFC); Strategic Priority Research Program of Chinese Academy of Sciences; Chinese Academy of Sciences; Cross Innovative Team fund from the State Key Laboratory of Loess and Quaternary Geology; [41925015]; [XDB40000000]; [ZDBS-LY-DQC001]; [SKLLQGTD1801] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); Chinese Academy of Sciences(Chinese Academy of Sciences); Cross Innovative Team fund from the State Key Laboratory of Loess and Quaternary Geology; ; ; ; AcknowledgementsThis work was supported by the National Natural Science Foundation of China (NSFC) under Grant no. 41925015, the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB40000000), the Chinese Academy of Sciences (No. ZDBS-LY-DQC001), and the Cross Innovative Team fund from the State Key Laboratory of Loess and Quaternary Geology (No. SKLLQGTD1801). 63 0 0 4 4 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2397-3722 NPJ CLIM ATMOS SCI npj Clim. Atmos. Sci. DEC 10 2022.0 5 1 99 10.1038/s41612-022-00322-x 0.0 9 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences 6X6AL 36530483.0 Green Accepted, gold 2023-03-23 WOS:000896493800001 0
J Niu, B; Li, H; Qin, T; Karimi, HR Niu, Ben; Li, Huan; Qin, Tian; Karimi, Hamid Reza Adaptive NN Dynamic Surface Controller Design for Nonlinear Pure-Feedback Switched Systems With Time-Delays and Quantized Input IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Dynamic surface control (DSC); input quantization; neural network (NN); pure-feedback systems; switched systems; time-delay ABSOLUTE EXPONENTIAL STABILITY; FUZZY TRACKING CONTROL; NEURAL-NETWORKS; STABILIZATION This paper deals with the problem of robust stabilization for a class of uncertain nonlinear pure-feedback switched time-delay systems with quantized input signal. In order to overcome the design difficulty caused by the quantization and unpredictable switchings, a nonlinear decomposition strategy for quantizer is employed in advance. In the controller design procedure, the unknown time-delay terms are compensated by utilizing Lyapunov-Krasovskii functionals, the desired virtual stabilizing functions and desired actual control input are approximated by applying radial basis function neural networks, and dynamic surface control technology are used to handle the issue of explosion of complexity in the traditional backstepping procedure. Furthermore, it is shown that the resulting closed-loop system are stable in the sense of semi-global uniformly ultimately bounded. Finally, to verify the effectiveness and applicability of the presented control scheme, an example is given to construct an adaptive neural controller for an electromechanical system. [Niu, Ben; Li, Huan; Qin, Tian] Bohai Univ, Coll Math & Phys, Jinzhou 121013, Peoples R China; [Niu, Ben] Dalian Univ Technol, Coll Control Sci & Engn, Dalian 116024, Peoples R China; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy Bohai University; Dalian University of Technology; Polytechnic University of Milan Niu, B (corresponding author), Bohai Univ, Coll Math & Phys, Jinzhou 121013, Peoples R China. niubenzg@gmail.com; lihuanbhu@163.com; qintianbhu@163.com; hamidreza.karimi@polimi.it Niu, Ben/ABW-8384-2022; Karimi, Hamid Reza/Y-2403-2019 Niu, Ben/0000-0002-3680-5594 National Natural Science Foundation of China [61673073]; Liaoning Provincial Natural Science Foundation, China [201602009] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Liaoning Provincial Natural Science Foundation, China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61673073, and in part by the Liaoning Provincial Natural Science Foundation, China, under Grant 201602009. 50 96 97 0 83 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. OCT 2018.0 48 10 1676 1688 10.1109/TSMC.2017.2696710 0.0 13 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science GT8YH 2023-03-23 WOS:000444825500003 0
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R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rubinskiy, I.; Ruckert, B.; Ruckstuhl, N.; Rud, V. I.; Rudolph, C.; Rudolph, G.; Ruehr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rumiantsev, V.; Rumyantsev, L.; Runge, K.; Rurikova, Z.; Rusakovich, N. A.; Rutherfoord, J. P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y. F.; Ryadovikov, V.; Ryan, P.; Rybar, M.; Rybkin, G.; Ryder, N. C.; Rzaeva, S.; Saavedra, A. F.; Sadeh, I.; Sadrozinski, H. F-W.; Sadykov, R.; Tehrani, F. Safai; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.; Salek, D.; Salihagic, D.; Salnikov, A.; Salt, J.; Ferrando, B. M. Salvachua; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sampsonidis, D.; Samset, B. H.; Sanchez, A.; Sanchez Martinez, V.; Sandaker, H.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, T.; Sandoval, C.; Sandstroem, R.; Sandvoss, S.; Sankey, D. P. C.; Sansoni, A.; Rios, C. Santamarina; Santoni, C.; Santonico, R.; Santos, H.; Saraiva, J. G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sartisohn, G.; Sasaki, O.; Sasao, N.; Satsounkevitch, I.; ; ATLAS Collaboration Measurement of the t-channel single top-quark production cross section in pp collisions at root s=7 TeV with the ATLAS detector PHYSICS LETTERS B English Article We report a measurement of the cross section of single top-quark production in the t-channel using 1.04 fb(-1) of pp collision data at root s = 7 TeV recorded with the ATLAS detector at the LHC. Selected events feature one electron or muon, missing transverse momentum, and two or three jets, exactly one of them identified as originating from a b quark. The cross section is measured by fitting the distribution of a multivariate discriminant constructed with a neural network, yielding sigma(t) = 83 +/- 4 (stat.)(-19)(+20) (syst.) pb, which is in good agreement with the prediction of the Standard Model. Using the ratio of the measured to the theoretically predicted cross section and assuming that the top-quark-related CKM matrix elements obey the relation vertical bar V-tb vertical bar >> vertical bar V-ts vertical bar, vertical bar V-td vertical bar, the coupling strength at the W-t-b vertex is determined to be vertical bar V-tb vertical bar = 1.13(-0.13)(+0.14). If it is assumed that vertical bar V-tb vertical bar <= 1 a lower limit of vertical bar V-tb vertical bar > 0.75 is obtained at the 95% confidence level. (C) 2012 CERN. Published by Elsevier B.V. All rights reserved. [Aad, G.; Ahles, F.; Barber, T.; Bernhard, R.; Bitenc, U.; Bruneliere, R.; Christov, A.; Consorti, V.; Fehling-Kaschek, M.; Flech, M.; Glatzer, J.; Hartert, J.; Herten, G.; Horner, S.; Jakobs, K.; Janus, M.; Kollefrath, M.; Kononov, A. I.; Kuehn, S.; Lai, S.; Landgraf, U.; Lohwasser, K.; Ludwig, I.; Ludwig, J.; Lumb, D.; Mahboubi, K.; Mohr, W.; Nilsen, H.; Parzefall, U.; Rammensee, M.; Rave, T. C.; Runge, K.; Rurikova, Z.; Sansoni, A.; Schmidt, E.; Schumacher, M.; Siegert, F.; Stoerig, K.; Sundermann, J. E.; Temming, K. K.; Thoma, S.; Tsiskaridze, V.; Venturi, M.; Vivarelli, I.; von Radziewski, H.; Anh, T. Vu; Warsinsky, M.; Weiser, C.; Werner, M.; Wiik-Fuchs, L. A. M.; Winkelmann, S.; Xie, S.; Zimmermann, S.] Univ Freiburg, Fak Math & Phys, Hugstetter Str 55, D-79106 Freiburg, Germany; [Alam, M. S.; Ernstl, J.; Rojo, V.] SUNY Albany, Albany, NY 12222 USA; [Bahinipati, S.; Buchanan, N. J.; Chan, K.; Gingrich, D. M.; Kim, M. S.; Moore, R. W.; Pinfold, J. L.; Soni, N.; Subramania, H. S.; Vague, F. Vives] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Ciftci, A. K.; Ciftci, R.; Yildiz, H. Duran; Kuday, S.; Persembe, S.] Ankara Univ, Dept Phys, TR-06100 Ankara, Turkey; Dumlupinar Univ, Dept Phys, Kutahya, Turkey; [Yilmaz, M.] Gazi Univ, Dept Phys, Ankara, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Cakir, I. Turk] Turkish Atom Energy Commiss, Ankara, Turkey; [Bella, L. Aperio; Aubert, B.; Berger, N.; Colas, J.; Delmastro, M.; Di Ciaccio, L.; Doan, T. K. O.; Elles, S.; Goy, C.; Helary, L.; Hryn'ova, T.; Jeremie, A.; Jezeque, S.; Kataoka, M.; Labbe, J.; Lafaye, R.; Leveque, J.; Lombardo, V. P.; Massol, N.; Perrodo, P.; Petit, E.; Przysiezniak, H.; Richter-Was, E.; Sauvage, G.; Sauvan, E.; Schwoerer, M.; Todorov, T.; Tsionou, D.; Wingerter-Seez, I.; Zitoun, R.] CNRS IN2P3, LAPP, Annecy Le Vieux, France; [Bella, L. Aperio; Aubert, B.; Berger, N.; Colas, J.; Delmastro, M.; Di Ciaccio, L.; Doan, T. K. O.; Elles, S.; Goy, C.; Helary, L.; Hryn'ova, T.; Jeremie, A.; Jezeque, S.; Kataoka, M.; Labbe, J.; Lafaye, R.; Leveque, J.; Lombardo, V. P.; Massol, N.; Perrodo, P.; Petit, E.; Przysiezniak, H.; Richter-Was, E.; Sauvage, G.; Sauvan, E.; Schwoerer, M.; Todorov, T.; Tsionou, D.; Wingerter-Seez, I.; Zitoun, R.] Univ Savoie, Annecy Le Vieux, France; [Asquith, L.; Blair, R. E.; Chekanov, S.; Dawson, J. W.; Fellmann, D.; Goshaw, A. T.; Guarino, V. J.; Hill, D.; Hill, N.; LeCompte, T.; Malon, D.; May, E. N.; Nodulman, L.; Paramonov, A.; Price, L. E.; Proudfoot, J.; Ferrando, B. M. Salvachua; Schlereth, J. L.; Stanek, R. W.; Underwood, D. G.; van Gemmeren, P.; Vaniachine, A.; Yoshida, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Cheu, E.; Johns, K. A.; Kaushik, V.; Lampen, C. L.; Lampl, W.; Lei, X.; Loch, P.; Paleari, C. P.; Ruehr, F.; Rutherfoord, J. P.; Shaver, L.; Shupe, M. A.; Varnes, E. W.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Brown, H.; De, K.; Farbin, A.; Heelan, L.; Hernandez, C. M.; Marchiori, G.; Nilsson, P.; Ozturk, N.; Pravahan, R.; Sarkisyan-Grinbaum, E.; Sosebee, M.; Spurlock, B.; Stradling, A. R.; Usai, G.; Vartapetian, A.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, Arlington, TX 76019 USA; [Antonaki, A.; Fassouliotis, D.; Giakoumopoulou, V.; Giokaris, N.; Ioannou, P.; Kourkoumelis, C.; Manousakis-Katsikakis, A.; Tzanakos, G.; Vellidis, C.] Univ Athens, Dept Phys, Athens, Greece; [Alexopoulos, T.; Avramidou, R.; Dris, M.; Filippas, A.; Fokitis, M.; Gazis, E. N.; Iakovidis, G.; Katsoufis, E.; Leontsinis, S.; Maltezos, S.; Mountricha, E.; Panagiotopoulou, E.; Papadopoulou, Th. D.; Tsipolitis, G.; Vlachos, S.] Natl Tech Univ Athens, Dept Phys, Zografos, Greece; [Abdinov, O.; Aliyev, M.; Huseynov, N.; Khalil-Zada, F.; Rzaeva, S.] Azerbaijan Acad Sci, Inst Phys, Baku 370143, Azerbaijan; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Rozas, A. Juste; Korolkov, I.; Le Menedeu, E.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I.; Rossetti, V.; Rubbo, F.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] Univ Autonoma Barcelona, Inst Fis Altes Energies, E-08193 Barcelona, Spain; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Rozas, A. Juste; Korolkov, I.; Le Menedeu, E.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I.; Rossetti, V.; Rubbo, F.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] Univ Autonoma Barcelona, Dept Fis, E-08193 Barcelona, Spain; [Abdallah, J.; Bosman, M.; Casado, M. P.; Cavalli-Sforza, M.; Conidi, M. C.; Demirkoz, B.; Dosil, M.; Espinal Curull, X.; Francavilla, P.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Rozas, A. Juste; Korolkov, I.; Le Menedeu, E.; Martinez, M.; Meoni, E.; Mir, L. M.; Miralles Verge, L.; Nadal, J.; Osuna, C.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I.; Rossetti, V.; Rubbo, F.; Segura, E.; Succurro, A.; Sushkov, S.; Vorwerk, V.] ICREA, Barcelona, Spain; [Borjanovic, I.; Krstic, J.; Popovic, D. S.; Sijacki, Dj.; Simic, Lj.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Bozovic-Jelisavcic, I.; Jovin, T.; Mamuzic, J.; Mudrinic, M.] Univ Belgrade, Vinca Inst Nucl Sci, Belgrade, Serbia; [Buanes, T.; Burgess, T.; Chouridou, S.; Dawson, I.; Eigen, G.; Johansen, L. G.; Kastanas, A.; Liebig, W.; Lipniacka, A.; Rosendahl, P. L.; Sandaker, H.; Sjursen, T. B.; Stugu, B.; Tonoyan, A.; Ugland, M.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Arguin, J-F.; Bach, A. M.; Galtieri, A. Barbaro; Barnett, R. M.; Beringer, J.; Biesiada, J.; Calafiura, P.; Caminada, L. M.; Ciocio, A.; Cooke, M.; Copic, K.; Dube, S.; Einsweiler, K.; Gaponenko, A.; Garcia-Sciveres, M.; Haber, C.; Hance, M.; Heinemann, B.; Hinchliffe, I.; Hsu, S. -C.; Hurwitz, M.; Joseph, J.; Lavrijsen, W.; Leggett, C.; Loscutoff, P.; Lys, J.; Madaras, R. J.; Ovcharova, A.; Griso, S. Pagan; Pranko, A.; Quarrie, D. R.; Ruwiedel, C.; Shapiro, M.; Skinnari, L. A.; Tatarkhanov, M.; Tibbetts, M. J.; Tsulaia, V.; Vahsen, S.; Varouchas, D.; Virzi, J.; Yao, Y.; Zenz, S.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA 94720 USA; [Aliev, M.; Giorgi, F. M.; Grancagnolo, S.; Herrberg, R.; Hristova, I.; Kind, O.; Kolanoski, H.; Kwee, R.; Lacker, H.; Leyton, M.; Lohse, T.; Mandrysch, R.; Nikiforov, A.; Schulz, H.; Wendland, D.; Zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Ancu, L. S.; Battaglia, A.; Beck, H. P.; Borer, C.; Ereditato, A.; Martin, T. Fonseca; Gallo, V.; Haug, S.; Kabana, S.; Kruker, T.; Pretzl, K.; Topfel, C.; Weber, M. S.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Ancu, L. S.; Battaglia, A.; Beck, H. P.; Borer, C.; Ereditato, A.; Martin, T. Fonseca; Gallo, V.; Haug, S.; Kabana, S.; Kruker, T.; Pretzl, K.; Topfel, C.; Weber, M. S.] Univ Bern, High Energy Phys Lab, Bern, Switzerland; [Allbrooke, B. M. M.; Bansil, H. S.; Bracinik, J.; Charlton, D. G.; Chisholm, A. S.; Collins, N. J.; Curtis, C. J.; Garvey, J.; Hadley, D. R.; Harrison, K.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Mahout, G.; Martin, T. A.; Mclaughlan, T.; Newman, P. R.; O'Neale, S. W.; Palmer, J. D.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Akdogan, T.; Arik, E.; Arik, M.; Istin, S.; Ozcan, V. E.; Rador, T.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Cetin, S. A.] Dogus Univ, Div Phys, Istanbul, Turkey; [Beddall, A. J.; Beddall, A.; Bingul, A.; Diblen, F.] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkey; Istanbul Tech Univ, Dept Phys, TR-80626 Istanbul, Turkey; [Bertin, A.; Bindi, M.; Caforio, D.; De Castro, S.; Di Sipio, R.; Fabbri, L.; Massa, I.; Mengarelli, A.; Monzani, S.; Piccinini, M.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Univ Bologna, Dipartimento Fis, Bologna, Italy; [Bellagambala, L.; Bertin, A.; Bindi, M.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Caforio, D.; Ciocca, C.; Corradi, M.; De Castro, S.; Di Sipio, R.; Fabbri, L.; Giacobbe, B.; Giusti, P.; Jha, M. K.; Massa, I.; Mengarelli, A.; Monzani, S.; Piccinini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Spighi, R.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Univ Bologna, INFN Sez Bologna, Bologna, Italy; [Alhroob, M.; Anders, C. F.; Arutinov, D.; Backhaus, M.; Barbero, M.; Bechtle, R.; Brock, I.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Fischer, P.; Gaycken, G.; Geich-Gimbel, Ch.; Gonella, L.; Havranek, M.; Hellmich, D.; Hillert, S.; Huegging, F.; Ince, T.; Karagounis, M.; Khoriauli, G.; Koevesarki, R.; Kokott, T.; Kostyukhin, V. V.; Kraus, J. K.; Kroseberg, J.; Krueger, H.; Kruth, A.; Lapoire, C.; Lehmacher, M.; Leyko, A. M.; Limbach, C.; Loddenkoetter, T.; Mazur, M.; Moeser, N.; Mueller, K.; Nanava, G.; Nattermann, T.; Nuncio-Quiroz, A. -E.; Poghosyan, T.; Psoroulas, S.; Radics, B.; Schaepe, S.; Schmieden, K.; Schmitz, M.; Schultens, M. J.; Schumacher, J. W.; Schwindt, T.; Stillings, J. A.; Therhaag, J.; Tsung, J-W.; Uchida, K.; Uhlenbrock, M.; Vlasov, N.; Vogel, A.; von Toerne, E.; Wang, T.; Wermes, N.; Wienemann, P.; Zendler, C.; Zimmermann, R.; Zimmermann, S.] Univ Bonn, Inst Phys, Bonn, Germany; [Ahlen, S. P.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Hazen, E.; Love, J.; Nation, N. R.; Posch, C.; Shank, J. T.; Whitaker, S. P.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, Boston, MA 02215 USA; [Aefsky, S.; Amelung, C.; Bensinger, J. R.; Blocker, C.; Daya-Ishmukhametova, R. K.; Gozpinar, S.; Kirsch, L. E.; Pomeroy, D.; Sciolla, G.; Skvorodnev, N.; Wellenstein, H.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Caloba, L. P.; Coura Torres, R.; Da Silva, P. V. M.; Maidantchik, C.; Manhaes de Andrade Filho, L.; Marroquim, F.; Nepomuceno, A. A.; Perantoni, M.; Seixas, J. M.] Univ Fed Rio De Janeiro COPPE EE IF, Rio De Janeiro, Brazil; [Cerqueir, A. S.] Fed Univ Juiz de Fora UFJF, Juiz De Fora, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, BR-01498 Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Baker, M. D.; Begel, M.; Bernius, C.; Chen, H.; Chernyatin, V.; Salgado, P. E. De Castro Faria; Debbe, R.; Dhullipudi, R.; Ernst, M.; Gadfort, T.; Gibbard, B.; Gordon, H. A.; Greenwood, Z. D.; Klimentov, A.; Lanni, F.; Lissauer, D.; Lynn, D.; Ma, H.; Maeno, T.; Majewski, S.; Nevski, P.; Nikolopoulos, K.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Park, W.; Pleier, M. -A.; Poblaguev, A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Rahm, D.; Rajagopalan, S.; Redlinger, G.; Sawyer, L.; Sircar, A.; Snyder, S.; Steinberg, P.; Stumer, I.; Takai, H.; Tamsett, M. C.; Trivedi, A.; Undrus, A.; Wenaus, T.; Ye, S.; Yu, D.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Alexa, C.; Badescu, E.; Boldea, V.; Buda, S. I.; Caprini, I.; Caprini, M.; Ciubancan, M.; Constantinescu, S.; Cuciuc, C. -M.; Dita, P.; Dita, S.; Micu, L.; Olariu, A.; Pantea, D.; Popeneciu, G. A.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Darlea, G. L.] Univ Politehn Bucuresti, Bucharest, Romania; W Univ Timisoara, Timisoara, Romania; [Gonzalez Silva, M. L.; Otero y Garzon, G.; Piegaia, R.; Romeo, G.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Ask, S.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Buttinger, W.; Carter, J. R.; Chapman, J. D.; Cowden, C.; French, S. T.; Frost, J. A.; Hill, J. C.; Kaneti, S.; Khoo, T. J.; Lester, C. G.; Moeller, V.; Parker, M. A.; Robinson, D.; Sandoval, T.; Thomson, M.; Ward, C. P.] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England; [Gillberg, D.; Koffas, T.; Liu, C.; Marchand, J. F.; McCarthy, T. G.; Oakham, F. G.; Randrianarivony, K.; Tarrade, F.; Ueno, R.; Vincter, M. G.; Whalen, K.] Carleton Univ, Dept Phys, Ottawa, ON K1S 5B6, Canada; [Aleksa, M.; Amaral, R.; Anastopoulos, C.; Anghinolfi, F.; Baak, M. A.; Bachas, K.; Banfi, D.; Battistin, M.; Bellina, F.; Bellomo, M.; Beltramello, O.; Berge, D.; Bertinelli, F.; Bianchi, R. M.; Blanchot, G.; Bogaerts, J. A.; Boyd, J.; Braem, A.; Bremer, J.; Burckhart, H.; Butin, F.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Cerri, A.; Barajas, C. A. Chavez; Childers, J. T.; Chromek-Burckhart, D.; Cook, J.; Cote, D.; Danielsson, H. O.; Dell'Acqua, A.; Delruelle, N.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Dittus, F.; Dobinson, R.; Dobos, D.; Dobson, E.; Dopke, J.; Drevermann, H.; Dudarev, A.; Duehrssen, M.; Dunford, M.; Dydak, F.; Ellis, N.; Elsing, M.; Fabre, C.; Farthouat, P.; Fassnacht, P.; Foussat, A.; Francis, D.; Franz, S.; Froeschl, R.; Froidevaux, D.; Torregrosa, E. Fullana; Gabaldon, C.; Garelli, N.; Garonne, V.; Gayde, J-C.; Gianotti, F.; Gibson, S. M.; Godlewski, J.; Gonidec, A.; Goossens, L.; Gorini, B.; Grafstrom, R.; Gray, H. M.; Haas, S.; Hahn, F.; Haider, S.; Hatch, M.; Hauschild, M.; Hawkings, R. J.; Heller, M.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Inigo-Golfin, J.; Jaekel, M. R.; Jansen, H.; Jenni, R.; Jonsson, O.; Joram, C.; Jungst, R. M.; Kaneda, M.; Kaplon, J.; Kerschen, N.; Klioutchnikova, T.; Knobloch, J.; Koeneke, K.; Kotamaeki, M. J.; Lamanna, M.; Lasseur, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Lichard, P.; Magnoni, L.; Malaescu, B.; Malyukov, S.; Mapelli, A.; Mapelli, L.; Marshal, Z.; Martin, B.; Maugain, J. M.; McLaren, R. A.; Menot, C.; Messina, A.; Meyer, T. C.; Michal, S.; Molina-Perez, J.; Morley, A. K.; Mornacchi, G.; Muenstermann, D.; Nairz, A. M.; Nakahama, Y.; Negri, G.; Nessi, M.; Nicquevert, B.; Niinikoski, T.; Nordberg, M.; Palestini, S.; Pauly, T.; Pernegger, H.; Peters, K.; Petersen, B. A.; Petersen, J.; Piacquadio, G.; Pirotte, O.; Pommes, K.; Poppleton, A.; Bueso, X. Portell; Poulard, G.; Prasad, S.; Pribyl, L.; Price, M. J.; Raymond, M.; Rembser, C.; Dos Santos, D. Roda; Roe, S.; Salek, D.; Salzburger, A.; Savu, D. O.; Schlenker, S.; Schott, M.; Schuler, G.; Sfyrla, A.; Shimizu, S.; Sloper, J.; Spigo, G.; Spiwoks, R.; Stewart, G. A.; Szeless, B.; Tappern, G. P.; Ten Kate, H.; Viegas, F. J. Tique Aires; Torchiani, I.; Tremblet, L.; Tricoli, A.; Tsarouchas, C.; Unal, G.; van der Ster, D.; Vandelli, W.; Vandoni, G.; Rodriguez, F. Varela; Veness, R.; Vinek, E.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Winklmeier, F.; Wotschack, J.; Zajacova, Z.; Zsenei, A.; Zwalinski, L.] CERN, Geneva, Switzerland; [Anderson, K. J.; Boveia, A.; Canelli, F.; Choudalakis, G.; Costin, T.; Feng, E. I. J.; Fiascaris, M.; Gardner, R. W.; Gupta, A.; Plante, I. Jen-La; Kapliy, A.; Melachrinos, C.; Merritt, F. S.; Meyer, C.; Miller, D. W.; Onyisi, P. U. E.; Oreglia, M. J.; Penning, B.; Pilcher, J. E.; Shochet, M. J.; Tompkins, L.; Tuggle, J. M.] Univ Chicago, Enrico Fermi Inst, Chicago, IL 60637 USA; [Diaz, M. A.; Olivares Pino, S. A.; Panes, B.; Quinonez, F.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Carquin, E.; Kuleshov, S.; Pezoa, R.; Prokoshin, F.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Bai, Y.; Cheng, S.; Han, H.; Jin, S.; Lu, F.; Ouyang, Q.; Ruan, X.; Shan, L. Y.; Tong, G.; Xie, Y.; Xu, G.; Yang, Y.; Yuan, L.; Zheng, S.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Han, L.; Jiang, Y.; Jin, G.; Li, S.; Liu, M.; Liu, Y.; Peng, H.; Wang, H.; Wu, Y.; Xu, C.; Zhang, D.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Chen, S.; Chen, T.; Ping, J.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Feng, C.; Ge, P.; He, M.; Li, H.; Meng, Z.; Miao, J.; Zhan, Z.; Zhang, X.; Zhu, C. G.] Shandong Univ, Sch Phys, Jinan, Shandong, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] Clermont Univ, Phys Corpusculaire Lab, Aubiere, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] Univ Blaise Pascal, Aubiere, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Cinca, D.; Donini, J.; Febbraro, R.; Ghodbane, N.; Guicheney, C.; Liao, H.; Pallin, D.; Hernandez, D. Paredes; Podlyski, F.; Santoni, C.; Says, L. P.; Vazeille, F.] CNRS IN2P3, Aubiere, France; [Andeen, T.; Angerami, A.; Brooijmans, G.; Dodd, J.; Grau, N.; Guo, J.; Hughes, E. W.; Leltchouk, M.; Nikiforou, N.; Parsons, J. A.; Penson, A.; Perez, K.; Reale, V. Perez; Scherzer, M. I.; Thompson, E. N.; Tian, F.; Tuts, P. M.; Urbaniec, D.; Williams, E.; Willis, W.; Wulf, E.; Zivkovic, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Boelaert, N.; Dam, M.; Driouichi, C.; Gregersen, K.; Hansen, J. R.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Heisterkamp, S.; Jakobsen, S.; Jez, R.; Joergensen, M. D.; Kadlecik, P.; Klinkby, E. B.; Lundquist, J.; Mackeprang, R.; Mehlhase, S.; Petersen, T. C.; Simonyan, M.; Thomsen, L. A.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Crosetti, G.; Fazio, S.; La Rotonda, L.; Lavorini, V.; Mastroberardino, A.; Morello, G.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, INFN Grp Collegato Cosenza, Arcavacata Di Rende, Italy; [Capua, M.; Crosetti, G.; Fazio, S.; La Rotonda, L.; Mastroberardino, A.; Morello, G.; Policicchio, A.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Dwuznik, M.; Grabowska-Bold, I.; Jelen, K.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Mindur, B.; Przybycien, M.; Toczek, B.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Banas, E.; Blocki, J.; de Renstrom, P. A. Bruckman; Derendarz, D.; Gornicki, E.; Hajduk, Z.; Iwanski, W.; Kaczmarska, A.; Korcyl, K.; Malecki, Pa.; Malecki, P.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Trzebinski, M.; Trzupek, A.; Turala, M.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.; Zemla, A.] Polish Acad Sci, Henryk Niewodniczanski Inst Nucl Phys, Krakow, Poland; [Yagci, K. Dindar; Firan, A.; Hadavand, H. K.; Hoffman, J.; Ilchenko, Y.; Ishmukhametov, R.; Joffe, D.; Kama, S.; Kehoe, R.; Randle-Conde, A. S.; Rios, R. R.; Sekula, S. J.; Stroynowski, R.; Ye, J.] So Methodist Univ, Dept Phys, Dallas, TX 75275 USA; [Ahsan, M.; Izen, J. M.; Lou, X.; Reeves, K.; Wong, W. C.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Kuutmann, E. Bergeaas; Boehler, M.; Dietrich, J.; Ehrenfeld, W.; Ferrara, V.; Fischer, G.; Glazov, A.; Goebel, M.; Fajardo, L. S. Gomez; Da Costa, J. Goncalves Pinto Firmino; Gosdzik, B.; Grahn, K-J.; Gregor, I. M.; Hiller, K. H.; Huettmann, A.; Husemann, U.; Belenguer, M. Jimenez; Johnert, S.; Karnevskiy, M.; Katzy, J.; Kono, T.; Kuhl, T.; Lange, C.; Lobodzinska, E.; Ludwig, D.; Maettig, S.; Medinnis, M.; Mijovic, L.; Moenig, K.; Naumann, T.; Cavalcanti, T. Perez; Petschull, D.; Piec, S. M.; Qin, Z.; Radescu, V.; Rubinskiy, I.; Sedov, G.; Stanescu-Bellu, M.; Styles, N. A.; Tackmann, K.; Vankov, P.; Viti, M.; Wasicki, C.; Wild, M. A.; Zhu, H.] DESY, D-2000 Hamburg, Germany; [Kuutmann, E. Bergeaas; Boehler, M.; Dietrich, J.; Ehrenfeld, W.; Ferrara, V.; Fischer, G.; Glazov, A.; Goebel, M.; Fajardo, L. S. Gomez; Da Costa, J. Goncalves Pinto Firmino; Gosdzik, B.; Grahn, K-J.; Gregor, I. M.; Hiller, K. H.; Huettmann, A.; Husemann, U.; Belenguer, M. Jimenez; Johnert, S.; Karnevskiy, M.; Katzy, J.; Kono, T.; Kuhl, T.; Lange, C.; Lobodzinska, E.; Ludwig, D.; Maettig, S.; Medinnis, M.; Mijovic, L.; Moenig, K.; Naumann, T.; Cavalcanti, T. Perez; Petschull, D.; Piec, S. M.; Qin, Z.; Radescu, V.; Rubinskiy, I.; Sedov, G.; Stanescu-Bellu, M.; Styles, N. A.; Tackmann, K.; Vankov, P.; Viti, M.; Wasicki, C.; Wild, M. A.; Zhu, H.] DESY, Zeuthen, Germany; [Bunse, M.; Goessling, C.; Hirsch, F.; Jung, C. A.; Klingenberg, R.; Reisinger, I.; Wunstorf, R.] Tech Univ Dortmund, Inst Expt Phys 4, Dortmund, Germany; [Czodrowski, P.; Friedrich, F.; Goepfert, T.; Kar, D.; Kobel, M.; Leonhardt, K.; Ludwig, A.; Mader, W. F.; Morgenstern, M.; Prudent, X.; Rudolph, C.; Schwierz, R.; Seifert, F.; Steinbach, P.; Straessner, A.; Vest, A.] Tech Univ Dresden, Inst Kern & Teilchenphys, D-01062 Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bocci, A.; Ebenstein, W. L.; Fowler, A. J.; Ko, B. R.; Kotwal, A.; Oh, S. H.; Wang, C.; Yamaoka, J.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bhimji, W.; Buckley, A. G.; Clark, P. J.; Debenedetti, C.; Harrington, R. D.; Martin, V. J.; O'Brien, B. J.; Selbach, K. E.; Smart, B. H.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; Fachhsch Wiener Neustadt, A-2700 Wiener Neustadt, Austria; [Annovi, A.; Antonelli, M.; Bilokon, H.; Cerutti, F.; Curatolo, M.; Di Nardo, R.; Esposito, B.; Ferrer, M. L.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Testa, M.; Vilucchi, E.; Volpi, G.] INFN Lab Nazl Frascati, Frascati, Italy; [Abdelalim, A. A.; Alexandre, G.; Backes, M.; Barone, G.; Bell, P. J.; Bell, W. H.; Noccioli, E. Benhar; Blondel, A.; Bucci, F.; Clark, A.; Dao, V.; Doglioni, C.; Ferrere, D.; Gadomski, S.; Gonzalez-Sevilla, S.; Goulette, M. P.; Iacobucci, G.; La Rosa, A.; Leger, A.; Lister, A.; Latour, B. Martin Dit; Mermod, O. P.; Herrera, C. Mora; Nektarijevic, S.; Nessi, M.; Nikolics, K.; Pasztor, G.; Picazio, A.; Pohl, M.; Rosbach, K.; Rosselet, L.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Beccherle, R.; Caso, C.; Dameri, M.; Darbo, G.; Parodi, A. Ferretto; Gagliardi, G.; Gemme, C.; Morettini, P.; Olcese, M.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Schiavi, C.] Univ Genoa, INFN Sez Genova, Genoa, Italy; [Barberis, D.; Caso, C.; Dameri, M.; Parodi, A. Ferretto; Gagliardi, G.; Osculati, B.; Parodi, F.; Schiavi, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Chikovani, L.; Tskhadadze, E. G.] Tbilisi State Univ, E Andronikashvili Inst Phys, GE-380086 Tbilisi, Georgia; [Djobava, T.; Khubua, J.; Mchedlidze, G.; Mosidze, M.] Tbilisi State Univ, Inst High Energy Phys, Tbilisi, Georgia; [Astvatsatourov, A.; Dueren, M.; Gemmell, A.; Stenzel, H.] Univ Giessen, Inst Phys 2, D-6300 Giessen, Germany; [Allwood-Spiers, S. E.; Bates, R. L.; Britton, D.; Bussey, P.; Buttar, C. M.; Collins-Tooth, C.; D'Auria, S.; Doherty, T.; Doyle, A. T.; Edwards, N. C.; Ferrag, S.; Ferrando, J.; de Lima, D. E. Ferreira; Kenyon, M.; McGlone, O. H.; Moraes, A.; O'Shea, V.; Barrera, C. Oropeza; Robson, A.; Saxon, D. H.; Smith, K. M.; St Denis, R. D.; Steele, G.; Thompson, A. S.; Wraight, K.; Wright, C.; Wright, M.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Ay, C.; Bierwagen, K.; Blumenschein, U.; Brandt, O.; Erdmann, O. J.; Evangelakou, D.; George, M.; Grosse-Knetter, J.; Guindon, S.; Haller, J.; Hamer, M.; Henrichs, A.; Hensel, C.; Keil, M.; Knue, A.; Kohn, F.; Krieger, N.; Kroeninger, K.; Lemmer, B.; Magradze, E.; Mann, A.; Meyer, J.; Morel, J.; Pashapour, S.; Quadt, A.; Roe, A.; Serkin, L.; Shabalina, E.; Uhrmacher, M.; Schroeder, T. Vazquez; Weber, P.; Weingarten, J.] Univ Gottingen, Inst Phys 2, Gottingen, Germany; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Dechenaux, B.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, E.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] Univ Grenoble 1, Lab Phys Subatom & Cosmol, Grenoble, France; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Dechenaux, B.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, E.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] Inst Natl Polytech Grenoble, F-38031 Grenoble, France; [Albrand, S.; Andrieux, M-L.; Buat, Q.; Clement, B.; Collot, J.; Crepe-Renaudin, S.; Dechenaux, B.; Delemontex, T.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y.; Laisne, E.; Ledroit-Guillon, E.; Lleres, A.; Lucotte, A.; Malek, F.; Martin, Ph.; Polci, F.; Stark, J.; Sun, X.; Trocme, B.; Wang, J.; Weydert, C.] CNRS IN2P3, Grenoble, France; [Addy, T. N.; Harvey, A.; McFarlane, K. W.; Shin, T.; Vassilakopoulos, V. I.] Hampton Univ, Dept Phys, Hampton, VA 23668 USA; [da Costa, J. Barreiro Guimaraes; Belloni, A.; Brandenburg, G. W.; Conti, G.; Franklin, M.; Hurst, R.; Huth, J.; Jeanty, L.; Kagan, M.; Mateos, D. Lopez; Outschoorn, V. Martinez; Mercurio, K. M.; Mills, C.; Moed, S.; Morii, M.; Skottowe, H. P.; Smith, B. C.; Della Porta, G. Zevi] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Anders, G.; Andrei, V.; Davygora, Y.; Dietzsch, T. A.; Geweniger, C.; Hanke, P.; Henke, M.; Khomich, A.; Kluge, E. -E.; Lendermann, V.; Meier, K.; Mueller, F.; Poddar, S.; Scharf, V.; Schultz-Coulon, H. -C.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Kasieczka, G.; Narayan, R.; Schaetzel, S.; Schmitt, S.; Schoening, A.; Stamen, R.] Heidelberg Univ, Inst Phys, D-6900 Heidelberg, Germany; [Kugel, A.; Maenner, R.; Schroer, N.] Heidelberg Univ, ZITI Inst Tech Informat, D-6800 Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Brunet, S.; Cwetanski, P.; Evans, H.; Gagnon, P.; Jain, V.; Luehring, F.; Ogren, H.; Penwell, J.; Price, D.; Whittington, D.; Yang, Y.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Epp, B.; Jussel, P.; Kneringer, E.; Kuhn, D.; Lukas, W.; Rudolph, G.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Behera, P. K.; Limper, M.; Mallik, U.; Pylypchenko, Y.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; Dudziak, F.; Krumnack, N.; Lavorini, V.; Mete, A. S.; Meyer, W. T.; Prell, S.; Rosenberg, E. I.; Ruiz-Martinez, A.; Salvatore, D.; Shrestha, S.; Yamamoto, K.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Aleksandrov, I. N.; Barashkou, A.; Bardin, D. Y.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chepurnov, V. F.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Glonti, G. L.; Gostkin, M. I.; Grigalashvili, N.; Gusakov, Y.; Huseynov, N.; Kalinovskaya, L. V.; Kazarinov, M. Y.; Kekelidze, G. D.; Kharchenko, D.; Khovanskiy, N.; Khramov, E.; Kolesnikov, V.; Kotov, V. M.; Kruchonak, U.; Krumshteyn, Z. V.; Kukhtin, V.; Ladygin, E.; Lazarev, A. B.; Manjavidze, I. D.; Minashvili, I. A.; Mineev, M.; Nikolaev, K.; Olchevski, A. G.; Peshekhonov, V. D.; Plotnikova, E.; Pozdnyakov, V.; Romanov, V. M.; Rumyantsev, L.; Rusakovich, N. A.; Sadykov, R.; Shiyakova, M.; Sisakyan, A. N.; Topilin, N. D.; Vinogradov, V. B.; Zhemchugov, A.] Joint Inst Nucl Res Dubna, Dubna, Russia; [Amako, K.; Arai, Y.; Doi, Y.; Haruyama, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kohriki, T.; Kondo, T.; Makida, Y.; Manabe, A.; Mitsui, S.; Nagano, K.; Nozaki, M.; Odaka, S.; Sasaki, O.; Suzuki, Y.; Takubo, Y.; Tanaka, S.; Terada, S.; Tojo, J.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Yamada, M.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Akiyama, A.; Hayakawa, T.; Homma, Y.; Ichimiya, R.; Ishikawa, A.; Kawagoe, K.; King, M.; Kishimoto, T.; Kurashige, H.; Matsushita, T.; Miyazaki, K.; Nishiyama, T.; Ochi, A.; Okada, S.; Omachi, C.; Suita, K.; Suzuki, Y.; Takeda, H.; Tani, K.; Tokunaga, K.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo 657, Japan; [Ishino, M.; Sasao, N.; Sumida, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto 612, Japan; [Anduaga, X. S.; Dova, M. T.; Monticelli, F.; Tripiana, M. F.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Argentina; [Anduaga, X. S.; Dova, M. T.; Monticelli, F.; Tripiana, M. F.] Consejo Nacl Invest Cient & Tecn, La Plata, Argentina; [Barton, A. E.; Borissov, G.; Bouhova-Thacker, E. V.; Brodbeck, T. J.; Chilingarov, A.; Davidson, R.; de Mora, L.; Dearnaley, W. J.; Fox, H.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Ratoff, P. N.; Smizanska, M.; Walder, J.] Univ Lancaster, Dept Phys, Lancaster, England; [Bianco, M.; Cataldi, G.; Chiodini, G.; Crupi, R.; Gorini, E.; Grancagnolo, F.; Orlando, N.; Perrino, R.; Primavera, M.; Spagnolo, S.; Ventura, A.] Univ Salento, INFN Sez Lecce, Lecce, Italy; [Bianco, M.; Crupi, R.; Gorini, E.; Guida, A.; Orlando, N.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Fis, Lecce, Italy; [Allport, P. P.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Greenshaw, T.; Gwilliam, C. B.; Hayward, H. S.; Houlden, M. A.; Jackson, J. N.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kluge, T.; Kretzschmar, J.; Laycock, P.; Mahmoud, S.; Maxfield, S. J.; Mehta, A.; Migas, S.; Price, J.; Sellers, G.; Vossebeld, J. H.; Waller, P.; Wrona, B.] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 3BX, Merseyside, England; [Cindro, V.; Deliyergiyev, M.; Dolenc, I.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Tykhonov, A.] Univ Ljubljana, Ljubljana, Slovenia; [Cindro, V.; Deliyergiyev, M.; Dolenc, I.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Tykhonov, A.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Adragna, P.; Bona, M.; Carter, A. A.; Cerrito, L.; Eisenhandler, E.; Ellis, K.; Goddard, J. R.; Landon, M. P. J.; Lloyd, S. L.; Morin, J.; Morris, J. D.; Piccaro, E.; Poll, J.; Rizvi, E.; Salamanna, G.; Stevenson, K.; Castanheira, M. Teixeira Dias; Wiglesworth, C.] Queen Mary Univ London, Sch Phys & Astron, London, England; [Alam, M. A.; Berry, T.; Boisvert, V.; Cowan, G.; Edwards, C. A.; George, S.; Goncalo, R.; Hayden, D.; Misiejuk, A.; Pastore, Fr; Rose, M.; Spano, F.; Strong, J. A.; Teixeira-Dias, R.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Baker, S.; Bernat, R.; Bieniek, S. R.; Butterworth, J. M.; Campanelli, M.; Chislett, R. T.; Christidi, I. A.; Cooper, B. D.; Davison, A. R.; Dean, S.; Dobson, E.; Hesketh, G. G.; Jansen, E.; Jones, T. W.; Konstantinidis, N.; Lambourne, L.; Monk, J.; Nash, M.; Nurse, E.; Prabhu, R.; Richards, A.; Robinson, J. E. M.; Sherwood, P.; Simmons, B.; Taylor, C.; Waugh, B. M.; Wijeratne, P. A.] UCL, Dept Phys & Astron, London, England; [Beau, T.; Bomben, M.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bomben, M.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] Univ Paris Diderot, Paris, France; [Beau, T.; Bomben, M.; Bordoni, S.; Calderini, G.; Cavalleri, P.; Chareyre, E.; De Cecco, S.; Derue, F.; Krasny, M. W.; Kuna, M.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Nikolic-Audit, I.; Ocariz, J.; Ridel, M.; Roos, L.; Schwemling, Ph.; Theveneaux-Pelzer, T.; Torres, H.; Trincaz-Duvoid, S.; Trinh, T. N.; Vannucci, F.; Yuan, L.] CNRS IN2P3, Paris, France; [Akesson, T. P. A.; Alonso, A.; Bocchetta, S. S.; Floderus, A.; Hawkins, A. D.; Hedberg, V.; Jarlskog, G.; Lundberg, B.; Lytken, E.; Meirose, B.; Mjornmark, J. U.; Smirnova, O.] Lund Univ, Fysiska Inst, Lund, Sweden; [Arnal, V.; Barreiro, F.; Cantero, J.; De la Torre, H.; Del Peso, J.; Glasman, C.; Labarga, L.; Lagouri, T.; Llorente Merino, J.; March, L.; Nebot, E.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor C15, Madrid, Spain; [Aharrouche, M.; Arnaez, O.; Bendel, M.; Blum, W.; ; University of Freiburg; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Dumlupinar University; Gazi University; TOBB Ekonomi ve Teknoloji University; Gaziantep University; Ministry of Energy & Natural Resources - Turkey; Turkish Energy, Nuclear & Mineral Research Agency (TENMAK); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; 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Institute of Physics of the Czech Academy of Sciences; Charles University Prague; Czech Technical University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Regina; Ritsumeikan University; Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; University of Rome Tor Vergata; University of Rome Tor Vergata; Roma Tre University; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; CEA; University of California System; University of California Santa Cruz; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Comenius University Bratislava; Slovak Academy of Sciences; University of Johannesburg; University of Witwatersrand; Stockholm University; Oskar Klein Centre; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; Tufts University; Universidad Antonio Narino; University of California System; University of California Irvine; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; University of Illinois System; University of Illinois Urbana-Champaign; Uppsala University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Valencia; University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Barcelona (IMB-CNM); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); University of British Columbia; University of Victoria; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universidade de Lisboa; Universidade de Lisboa; California State University System; California State University Fresno; Novosibirsk State University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; Universidade de Coimbra; Parthenope University Naples; Middle East Technical University; University of Louisiana System; Louisiana Technical University; University of Cape Town; University of Hamburg; Manhattan College; Sun Yat Sen University; Academia Sinica - Taiwan; Universidade do Minho; University of South Carolina System; University of South Carolina Columbia; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; California Institute of Technology; Jagiellonian University Aad, G (corresponding author), Univ Freiburg, Fak Math & Phys, Hugstetter Str 55, D-79106 Freiburg, Germany. Uhlen, Mathias/B-3262-2016; Kumar, Dr Ashok/HNR-6570-2023; Kostyukhin, Vadim/F-3171-2019; Lee, Lawrence/AAU-6790-2021; Benslama, Kamal/AAO-2056-2021; Cascella, Michele/B-6156-2013; Moraes, Arthur/F-6478-2010; Trzebinski, Maciej/AAO-6821-2021; beddall, ayda/AAG-6531-2020; Di Nardo, Roberto/J-4993-2012; Ferrer, Antonio/H-2942-2015; Ozcan, Veysi Erkcan/AAS-4508-2020; Fuster, Juan/W-6189-2018; Ros, Eduardo/P-2625-2018; Pásztor, Gabriella/D-9716-2016; Muino, Patricia Conde/F-7696-2011; Brooks, William K/C-8636-2013; Zhang, He/GXN-0028-2022; Milosavljevic, Marija Vranjes/F-9847-2016; Ohm, Christian/AAU-6572-2020; Kalinovskaya, Lidia/AAO-1352-2020; MANJHVAR, AMIT KUMAR/HNB-6459-2023; Kartvelishvili, Vakhtang G/K-2312-2013; Staroba, Pavel/G-8850-2014; Strube, Jan/T-6675-2019; Mir, Lluïsa-Maria/G-7212-2015; Jiménez, Yesenia Hernández/ABH-1107-2020; Lagouri, Theodota/AAC-7358-2021; Ruggieri, Federico/T-5300-2019; Canelli, Florencia/O-9693-2016; Ishikawa, Akimasa/G-6916-2012; Zivkovic, Lidija/HGA-8150-2022; Annovi, Alberto/AAA-8638-2020; Olshevskiy, Alexander/I-1580-2016; Donadelli, Marisilvia/H-3710-2016; Kuutmann, Elin Bergeaas/A-5204-2013; Carquin, Edson/GLU-9641-2022; CAKIR, Orhan/L-1893-2015; Busato, Emmanuel/AAS-5459-2020; Alimonti, Gianluca/AAG-4603-2020; Francavilla, Paolo/HKE-1206-2023; Benekos, Nektarios/I-9928-2017; Barreiro, Fernando/D-9808-2012; Gutierrez, Phillip/C-1161-2011; Górnicki, Edward/C-4075-2019; Lee, Shih-Chang/AAV-7016-2021; Grancagnolo, Sergio/J-3957-2015; Marti-Garcia, Salvador/F-3085-2011; Trzebinski, Maciej/W-1748-2018; Irles, Adrian/AAW-1879-2021; Ventura, Andrea/A-9544-2015; Wolter, Marcin/A-7412-2012; de Renstrom, Pawel Bruckman/AAG-7725-2021; Nevski, Pavel/M-6292-2015; Malecki, Piotr/O-2434-2018; Lokajicek, Milos/G-7800-2014; Carquin López, Edson/GZG-4712-2022; Canelli, Florencia/AAI-1616-2021; Solfaroli Camillocci, Elena/J-1596-2012; Nemecek, Stanislav/G-5931-2014; Lee, Jason SH/B-9701-2014; Prokoshin, Fedor/E-2795-2012; Robson, Aidan/G-1087-2011; Kostyukhin, Vadim/ABA-9033-2020; Hansen, John R/B-9058-2015; Kabana, Sonia/ABF-5974-2020; Sultansoy, Saleh/AAV-6853-2021; Coccaro, Andrea/AAB-6990-2021; Herrera, Maria Clemencia Mora/AAD-6466-2021; Yang, Haijun/O-1055-2015; Giagu, Stefano/H-6455-2013; Lange, Clemens/AAL-6635-2020; spagnolo, stefania/A-6359-2012; de Groot, Nicolo/A-2675-2009; Capua, Marcella/A-8549-2015; Messina, Andrea/C-2753-2013; Castillo, Luis Roberto Flores/W-3928-2018; Kumar, Amit/HMP-4853-2023; Ciftci, Rena/O-6517-2018; Moorhead, Gareth F/B-6634-2009; Banaś, Elżbieta A/W-4583-2017; Kuday, Sinan/CAF-3048-2022; Ippolito, Valerio/L-1435-2016; Guo, Jun/O-5202-2015; Casadei, Diego/I-1785-2013; Petrucci, Fabrizio/G-8348-2012; Boyko, Igor R/J-3659-2013; Kaczmarska, Anna/B-2753-2019; Parodi, Fabrizio/AHE-5089-2022; Stoicea, Gabriel/L-5602-2019; Gonzalez, Barbara Alvarez/ABG-7021-2020; Torregrosa, Esteban Fullana/A-7305-2016; Kepka, Oldrich/ABF-5327-2021; Castro, Nuno/AAB-3648-2019; Bates, Richard L/D-6596-2013; Wozniak, Krzysztof W/P-4475-2017; Casado, Pilar/H-1484-2015; Santos, Denis R./F-4015-2018; Sadykov, Renat/AAN-9602-2020; rolf jenni, MSEE/B-2334-2015; Anjos, Nuno/I-3918-2013; Demirköz, Bilge M/C-8179-2014; Perrino, Roberto/B-4633-2010; Radics, Balint/ABE-7286-2020; Branchini, Paolo/AAY-8607-2020; Llorente Merino, Javier/AGP-3432-2022; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Camarri, Paolo/M-7979-2015; Masik, Jiri/AAE-1203-2021; Solodkov, Alexander A/B-8623-2017; BESSON, NATHALIE/L-6250-2015; Balaji, S/J-1864-2019; Huseynov, Nazim/AAE-4663-2019; Darbo, Giovanni/C-8175-2012; b, m/HPE-0989-2023; Amorim, Antonio/C-8460-2013; Psoroulas, Serena/Q-3929-2019; Chudoba, Jiri/G-7737-2014; Pedraza Morales, Maria Isabel/ABC-7289-2021; Kupco, Alexander/G-9713-2014; MEONI, Evelin/ABD-9498-2021; Vandelli, Wainer/ABI-8185-2020; Testa, Marianna/L-9768-2019; Berge, David/P-2179-2017; Snesarev, Andrei/H-5090-2013; Benekos, Nektarios/J-8629-2017; Ríos, Cibrán Santamarina/K-4686-2014; Grabowska-Bołd, Iwona/ABI-7829-2020; Doyle, Anthony T/C-5889-2009; Ferrando, James E/A-9192-2012; Christov, Asen/S-4508-2018; Klok, Peter/AAF-7393-2019; Veneziano, Stefano/J-1610-2012; Urbán, Susana Cabrera/H-1376-2015; Leyton, Michael/G-2214-2016; Bachas, Konstantinos/C-8101-2019; Gorelov, Igor V/J-9010-2015; Trzupek, Adam/N-2448-2018; Ibáñez, Carlos Escobar/B-3761-2017; Maneira, Jose/D-8486-2011; Coadou, Yann/G-2263-2010; Stoicea, Gabriel/B-6717-2011; Smirnov, Sergei Y/F-1014-2011; da via, cinzia/AAS-3978-2021; Warburton, Andreas/N-8028-2013; Coccaro, Andrea/P-5261-2016; Llácer, María Moreno/AAU-6582-2020; Fazio, Salvatore/G-5156-2010; Castro, Nuno/D-5260-2011; Sukharev, Andrey/A-6470-2014; Bosman, Martine/J-9917-2014; Oh, Alexander/HHZ-4386-2022; Yigit, Yenal Can/S-4099-2017; Paganis, Stathes/J-8413-2017; Price, Darren D/E-6162-2012; Laycock, Paul/AAL-8261-2021; Gavrilenko, Igor/M-8260-2015; Passeri, Antonio/AAJ-8429-2020; Talyshev, Alexey/HGC-6910-2022; Watson, Ian James/AFL-9360-2022; Kuze, Masahiro/V-4251-2018; Martinez, Mario/I-3549-2015; Camarri, Paolo/AAF-9629-2020; La Rosa, Alessandro/I-1856-2013; Wolters, Helmut/M-4154-2013; Gao, Y./GWZ-3665-2022; Kantserov, Vadim/M-9761-2015; SULIN, VLADIMIR V/N-2793-2015; Mamuzic, Judita/U-3509-2017; Orlando, Nicola/AAL-1723-2021; Behera, Prafulla Kumar/AAK-8686-2020; Alexa, Calin/F-6345-2010; Nevski, Pavel/AAH-1839-2019; Giordani, Mario P/Q-6211-2018; Dawson, Ian/K-6090-2013; Wemans, Andre/A-6738-2012; Saraiva, Joao Gentil Mendes/D-1596-2017; Romaniouk, Anatoli/Q-6674-2017; Schernau, Michael/GRY-3808-2022; Pomarede, Daniel/AAH-7438-2020; Herten, Gregor/HNQ-9546-2023; Lacasta, Carlos/C-7254-2008; Cavalli-Sforza, Matteo/H-7102-2015; Salamon, Andrea/AGN-6159-2022; de Almeida, Fernando Marroquim Leao/D-6947-2019; Lambourne, Luke/AAG-7298-2020; Perrino, Roberto/AAA-5752-2022; Jones, Roger WL/H-5578-2011; Joergensen, Morten D/E-6847-2015; Tudorache, Valentina I/D-2743-2012; Mitsou, Vasiliki A./D-1967-2009; Chekulaev, Sergey V/O-1145-2015; Price, Darren/AAB-5991-2021; Albert, Justin/J-4152-2017; Malek, Fairouz/E-4410-2018; Costa, Maria Jose/H-5962-2015; BLOCKI, Jacek/AAP-3256-2021; Niinikoski, Tapio/AAA-1901-2021; Della Pietra, Massimo/J-5008-2012; Kramarenko, Victor V.A./E-1781-2012; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Busato, Emmanuel/AAZ-4423-2020; Fassi, Farida/F-3571-2016; Legger, Federica/HNC-2568-2023; Glasman, Claudia/Y-8858-2019; Dabrowski, Wladyslaw/AAS-6369-2020; KHODINOV, ALEKSANDR/D-6269-2015; Villa, Mauro/C-9883-2009; de la Hoz, Santiago Gonzalez/E-2494-2016; Nordberg, Markus/L-4255-2017; Nechaeva, Polina Y/N-1148-2015; Grinstein, Sebastian/ABE-1880-2020; Andreazza, Attilio/E-5642-2011; Annovi, Alberto/G-6028-2012; Peleganchuk, Sergey V/J-6722-2014; Llácer, María Moreno/AAQ-7522-2020; Monzani, Simone/D-6328-2017; Fassouliotis, Dimitrios/AAL-7081-2021; Gonçalo, Ricardo/M-3153-2016; Berger, N/ABE-4064-2020; Fabbri, Laura/H-3442-2012; Herrera, Maria Clemencia Mora/L-3893-2016; Torro Pastor, Emma/AAB-5979-2021; García, Josu Cantero/AAS-9810-2020; gaudio, gabriella/AAN-6039-2021; Pages, Andres Pacheco/C-5353-2011; do Vale, Maria Aline Barros/D-2357-2019; Nessi, Marzio/L-5194-2017; Kepka, Oldrich/G-6375-2014; Klimentov, Alexei A/I-4270-2013; Rosati, Stefano/AAF-6968-2019; Klinkby, Esben/AAN-1630-2020; Garcia, Jose E/H-6339-2015; Burdin, Sergey/AAZ-9062-2021; perez, miguel/GXF-5077-2022; Šolc, Jaroslav/AAC-5868-2021; Tassi, Enrico/AAJ-9661-2020; Capua, Marcella/AAJ-8419-2020; Kodys, Peter/P-2636-2017; Garcia, Carmen/W-2465-2018; O'Shea, Val/G-1279-2010; Gauzzi, Paolo/D-2615-2009; arik, metin/T-4193-2019; de la Torre Perez, Hector/ABG-6942-2020; Kuday, Sinan/C-8528-2014; Koperny, Stefan/ABB-4747-2020; Zaitsev, Alexandre M/B-8989-2017; Sýkora, Tomáš/Q-3174-2017; della Volpe, Domenico/B-4482-2012; Strube, Jan F/E-9310-2015; Olszewski, Andrzej J/W-1825-2018; Svatos, Michal/G-8437-2014; valente, paolo/A-6640-2010; Vaniachine, Alexandre/H-7796-2013; Padilla, Cristobal/C-3218-2017; Buttar, Craig/AAG-5392-2021; Dolejší, Jiří/P-1509-2017; Ma, Hong/F-2725-2011; Cetin, Serkant Ali/AGF-0147-2022; Giordano, Raffaele/J-3695-2012; Svatos, Michal/ABA-2041-2020; Samset, Bjørn H./H-6828-2016; Watson, Ian James/AAH-1423-2022; Vanadia, Marco/K-5870-2016; De, Kaushik/N-1953-2013; Woźniak, Krzysztof/AAQ-6119-2021; Rozen, Yoram/H-1880-2017; Carvalho, Joao/M-4060-2013; Smirnova, Oxana/A-4401-2013; Zenin, Oleg/E-6116-2018; Pina, Joao/C-4391-2012; Derendarz, Dominik/AAO-3512-2021; Alexopoulos, Theodoros/AAC-9633-2022; Mashinistov, Ruslan/M-8356-2015; Tsybychev, Dmitri/J-3733-2017; Tikhomirov, Vladimir/M-6194-2015; Ciftci, Abbas Kenan/AAC-5487-2022; Gladilin, Leonid/B-5226-2011; Kuleshov, Sergey V/D-9940-2013; Tudorache, Alexandra/L-3557-2013; Booth, Christopher N/B-5263-2016; Villaplana Perez, Miguel/B-5772-2014; Shmeleva, Alevtina/M-6199-2015; SULIN, Vladimir V/J-6966-2014; García, Elena Oliver/AAZ-1125-2020; Mikestikova, Marcela/H-1996-2014; Weigell, Philipp/I-9356-2012; Radics, Balint/AAN-2721-2020; Livan, Michele M/D-7531-2012; Ventura, Andrea/CAH-0226-2022; Perez, Miguel Villaplana/B-2717-2015; Zhemchugov, Alexey/N-1717-2017; Saavedra, Juan Antonio Aguilar/F-1256-2016; Sbarra, Carla/M-9669-2018; Sultansoy, Saleh/AAA-8267-2019; Artoni, Giacomo/ABA-2164-2020; Olszowska, Jolanta/P-2889-2017; Chelkov, Georgy A./G-9934-2019; Fiorini, Luca/W-6250-2018; Marcisovsky, Michal/AAM-2404-2020; Gonella, Laura/GLR-3838-2022; YILDIZ, Hatice DURAN/AAN-3727-2021; Chizhov, Mihail/CAG-2076-2022; Leitner, Rupert/C-2004-2017; Drasal, Zbynek/Q-7165-2017; Hamal, Petr/G-5540-2014; Soldatov, Evgeny/E-3990-2017; la rotonda, laura/B-4028-2016; Grancagnolo, Francesco/K-2857-2015; Malecki, Pawel/W-1710-2018; Vos, Marcel/G-8123-2015; Izen, Joseph/A-4392-2018; D'Auria, Saverio/O-3276-2017; Petersen, Troels/P-5538-2015; Zabinski, Bartlomiej/Q-6483-2017; Karyukhin, Andrey/J-3904-2014; Dolezal, Zdenek/K-6861-2017; Reznicek, Pavel/C-1989-2017; Dam, Mogens/C-2081-2015; Korol, Aleksandr/A-6244-2014; Tartarelli, Giuseppe Francesco/A-5629-2016 Uhlen, Mathias/0000-0002-4858-8056; Kostyukhin, Vadim/0000-0002-0490-9209; Lee, Lawrence/0000-0002-5590-335X; Benslama, Kamal/0000-0002-2532-2415; Cascella, Michele/0000-0003-2091-2501; Moraes, Arthur/0000-0002-5157-5686; Trzebinski, Maciej/0000-0002-5151-7101; beddall, ayda/0000-0001-8730-9649; Di Nardo, Roberto/0000-0003-1111-3783; Ferrer, Antonio/0000-0003-0532-711X; Ozcan, Veysi Erkcan/0000-0003-4643-6347; Ros, Eduardo/0000-0003-2812-9554; Pásztor, Gabriella/0000-0003-0707-9762; Muino, Patricia Conde/0000-0002-9187-7478; Brooks, William K/0000-0001-6161-3570; Milosavljevic, Marija Vranjes/0000-0003-4477-9733; Ohm, Christian/0000-0002-8015-7512; MANJHVAR, AMIT KUMAR/0000-0002-9577-6295; Kartvelishvili, Vakhtang G/0000-0002-1957-3787; Staroba, Pavel/0000-0001-6009-6321; Mir, Lluïsa-Maria/0000-0002-4276-715X; Jiménez, Yesenia Hernández/0000-0001-9844-6200; Ruggieri, Federico/0000-0002-1013-3929; Canelli, Florencia/0000-0001-6361-2117; Zivkovic, Lidija/0000-0003-4236-8930; Olshevskiy, Alexander/0000-0002-8902-1793; Donadelli, Marisilvia/0000-0001-8329-4240; CAKIR, Orhan/0000-0002-9016-138X; Busato, Emmanuel/0000-0003-2174-8512; Alimonti, Gianluca/0000-0002-7128-9046; Francavilla, Paolo/0000-0003-1164-6870; Benekos, Nektarios/0000-0001-7831-8762; Barreiro, Fernando/0000-0002-3021-0258; Gutierrez, Phillip/0000-0003-2326-3877; Górnicki, Edward/0000-0001-6243-9121; Lee, Shih-Chang/0000-0002-3353-2658; Grancagnolo, Sergio/0000-0001-8490-8304; Trzebinski, Maciej/0000-0002-5151-7101; Irles, Adrian/0000-0001-5668-151X; Ventura, Andrea/0000-0002-3368-3413; Wolter, Marcin/0000-0001-9184-2921; de Renstrom, Pawel Bruckman/0000-0002-0206-1160; Malecki, Piotr/0000-0002-7991-5902; Canelli, Florencia/0000-0001-6361-2117; Solfaroli Camillocci, Elena/0000-0002-5347-7764; Lee, Jason SH/0000-0002-2153-1519; Prokoshin, Fedor/0000-0001-6389-5399; Kostyukhin, Vadim/0000-0002-0490-9209; Hansen, John R/0000-0002-8422-5543; Kabana, Sonia/0000-0003-0568-5750; Coccaro, Andrea/0000-0003-2368-4559; Herrera, Maria Clemencia Mora/0000-0003-3915-3170; Yang, Haijun/0000-0001-7367-1380; Giagu, Stefano/0000-0001-9192-3537; Lange, Clemens/0000-0002-3632-3157; spagnolo, stefania/0000-0001-7482-6348; Capua, Marcella/0000-0002-2443-6525; Messina, Andrea/0000-0003-1195-6780; Castillo, Luis Roberto Flores/0000-0003-1551-5974; Ciftci, Rena/0000-0003-4461-1252; Moorhead, Gareth F/0000-0002-9299-9549; Banaś, Elżbieta A/0000-0001-5325-6040; Ippolito, Valerio/0000-0001-5126-1620; Guo, Jun/0000-0001-8125-9433; Petrucci, Fabrizio/0000-0002-5278-2206; Boyko, Igor R/0000-0002-3355-4662; Kaczmarska, Anna/0000-0002-8880-4120; Gonzalez, Barbara Alvarez/0000-0001-7767-4810; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Castro, Nuno/0000-0001-8491-4376; Wozniak, Krzysztof W/0000-0003-1171-0887; Casado, Pilar/0000-0002-0394-5646; Santos, Denis R./0000-0001-7992-9602; Sadykov, Renat/0000-0002-9157-6819; Demirköz, Bilge M/0000-0001-7171-5976; Perrino, Roberto/0000-0002-5764-7337; Camarri, Paolo/0000-0002-5732-5645; Solodkov, Alexander A/0000-0002-2737-8674; Balaji, S/0000-0002-7324-4853; Darbo, Giovanni/0000-0003-2165-0638; Amorim, Antonio/0000-0003-0638-2321; Psoroulas, Serena/0000-0001-7576-3238; Chudoba, Jiri/0000-0002-6425-2579; Pedraza Morales, Maria Isabel/0000-0002-2669-4659; MEONI, Evelin/0000-0002-6934-3752; Vandelli, Wainer/0000-0001-6581-9410; Berge, David/0000-0002-2918-1824; Snesarev, Andrei/0000-0002-9067-8362; Ríos, Cibrán Santamarina/0000-0002-9810-1816; Grabowska-Bołd, Iwona/0000-0001-9159-1210; Doyle, Anthony T/0000-0001-6322-6195; Ferrando, James E/0000-0002-1007-7816; Christov, Asen/0000-0001-8121-5860; Veneziano, Stefano/0000-0002-2598-2659; Leyton, Michael/0000-0002-0727-8107; Bachas, Konstantinos/0000-0002-9047-6517; Gorelov, Igor V/0000-0001-5570-0133; Trzupek, Adam/0000-0001-6938-5867; Ibáñez, Carlos Escobar/0000-0003-4442-4537; Maneira, Jose/0000-0002-3222-2738; Coadou, Yann/0000-0001-8195-7004; Stoicea, Gabriel/0000-0002-7511-4614; Smirnov, Sergei Y/0000-0002-6778-073X; da via, cinzia/0000-0001-6154-7323; Warburton, Andreas/0000-0002-2298-7315; Coccaro, Andrea/0000-0003-2368-4559; Llácer, María Moreno/0000-0003-1113-3645; Fazio, Salvatore/0000-0002-4321-1946; Castro, Nuno/0000-0001-8491-4376; Bosman, Martine/0000-0002-7290-643X; Paganis, Stathes/0000-0002-1950-8993; Passeri, Antonio/0000-0003-4864-3411; Talyshev, Alexey/0000-0001-8560-3756; Watson, Ian James/0000-0003-2141-3413; Kuze, Masahiro/0000-0001-8858-8440; Martinez, Mario/0000-0002-3135-945X; Camarri, Paolo/0000-0002-5732-5645; La Rosa, Alessandro/0000-0001-6291-2142; Wolters, Helmut/0000-0002-9588-1773; Kantserov, Vadim/0000-0001-8255-416X; SULIN, VLADIMIR V/0000-0003-3943-2495; Mamuzic, Judita/0000-0002-3203-4243; Orlando, Nicola/0000-0003-0616-245X; Behera, Prafulla Kumar/0000-0002-1527-2266; Giordani, Mario P/0000-0002-0792-6039; Wemans, Andre/0000-0002-9669-9500; Saraiva, Joao Gentil Mendes/0000-0002-7006-0864; Pomarede, Daniel/0000-0003-2038-0488; Herten, Gregor/0000-0001-7661-5122; Lacasta, Carlos/0000-0002-2623-6252; de Almeida, Fernando Marroquim Leao/0000-0003-2265-784X; Lambourne, Luke/0000-0002-7001-7575; Perrino, Roberto/0000-0002-5764-7337; Jones, Roger WL/0000-0002-6427-3513; 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MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; Lundbeck Foundation, Denmark; EPLANET, European Union; ERC, European Union; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, Germany; DFG, Germany; HGF, Germany; MPG, Germany; AvH Foundation, Germany; GSRT, Greece; ISF, Israel; MINERVA, Israel; GIF, Israel; DIP, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; RCN, Norway; MNiSW, Poland; GRICES, Portugal; FCT, Portugal; MERYS (MECTS), Romania; MES of Russia, Russian Federation; ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, United Kingdom; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; DOE, United States of America; NSF, United States of America; STFC [ST/H00100X/1, ST/I005811/1, ST/H001042/1, PP/E000355/1, ST/J004928/1, ST/G502320/1, ST/I006080/1, ST/L001195/1, ST/H001093/1, ST/K501840/1, ST/H001026/1, ST/I003142/1, ST/M000753/1, PP/E002757/1, ST/K000705/1, ST/K00140X/1, ST/F007337/1, ST/H001042/2, ST/J004944/1, PP/E003699/2, ST/M000761/1, ST/J005576/1, ST/H001069/2, ST/K001361/1, ST/I003525/1, ST/I005803/1, ST/J005460/1, PP/E003087/1, ST/I000186/1, ST/J002798/1, ST/K001329/1, ST/M001431/1, ST/H001093/2, ST/K001248/1, ST/K00073X/1, ST/M001512/1, PP/E003699/1, ST/H001026/2, ST/K001337/1, PP/E000347/1, ST/H00100X/2, ST/I003517/1, ST/L001144/1] Funding Source: UKRI; Science and Technology Facilities Council [ST/M001512/1, ST/M001431/1, PP/E003699/1, ST/G502320/1, ST/K501840/1 GRIDPP, ST/H001093/2, ST/J004944/1, ST/K001418/1, ATLAS, ST/K00140X/1, ST/K001310/1 LHCb, ST/J004928/1 ATLAS Upgrade, ST/K001361/1 LHCb, ST/H00100X/1, ST/J002798/1, ST/K001248/1 ATLAS, PP/E000355/1, ST/K001361/1 ATLAS Upgrades, ST/K00140X/1 LHCb, ST/F007337/1, ST/K001310/1 ATLAS Upgrades, ST/K00073X/1, ST/J005460/1, GRIDPP, PP/E003699/2, ST/K001310/1, ST/L001144/1, ST/H00100X/2, ST/I003517/1, PP/E003087/1, ST/J004928/1, ST/I006080/1, ST/K001337/1 ATLAS, ST/I000186/1, ST/K001310/1 LHCb Upgrades, ST/J005576/1, ST/K000705/1, ST/I005811/1, PP/E000347/1, ST/K001361/1 LHCb Upgrades, ST/H001042/1, ST/H001042/2, ST/H001093/1, ST/H001026/1, ST/H001069/2, ST/K001361/1 MINOS/MINOS+, ST/K001310/1 ATLAS, ST/H001026/2, ST/I005803/1, ST/M001474/1, ST/I003142/1, PP/E002757/1, ST/I003525/1, ST/L001195/1, ST/K00140X/1 ATLAS, ST/K001361/1 ATLAS, ST/K001248/1, ST/M000761/1, ST/I005803/1 GRIDPP, ST/K001264/1 ATLAS, ST/K001337/1, ST/K501840/1, ST/K001361/1] Funding Source: researchfish; Division Of Physics; Direct For Mathematical & Physical Scien [1119200] Funding Source: National Science Foundation; ICREA Funding Source: Custom ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWF, Austria; ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); Lundbeck Foundation, Denmark(Lundbeckfonden); EPLANET, European Union; ERC, European Union(European Research Council (ERC)); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); DFG, Germany(German Research Foundation (DFG)); HGF, Germany; MPG, Germany(Max Planck Society); AvH Foundation, Germany(Alexander von Humboldt Foundation); GSRT, Greece(Greek Ministry of Development-GSRT); ISF, Israel(Israel Science Foundation); MINERVA, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); DIP, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); GRICES, Portugal; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MERYS (MECTS), Romania; MES of Russia, Russian Federation(Russian Federation); ROSATOM, Russian Federation(Russian Federation); JINR; MSTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); ICREA(ICREA) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China: COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET and ERC, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT, Greece; ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW, Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. 49 136 136 2 148 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B OCT 31 2012.0 717 4-5 330 350 10.1016/j.physletb.2012.09.031 0.0 21 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 034HK Green Published, Green Accepted, Green Submitted, hybrid 2023-03-23 WOS:000310863300008 0
J Sannigrahi, S; Pilla, F; Zhang, Q; Chakraborti, S; Wang, Y; Basu, B; Basu, AS; Joshi, PK; Keesstra, S; Roy, PS; Sutton, PC; Bhatt, S; Rahmat, S; Jha, S; Singh, LK Sannigrahi, Srikanta; Pilla, Francesco; Zhang, Qi; Chakraborti, Suman; Wang, Ying; Basu, Bidroha; Basu, Arunima Sarkar; Joshi, P. K.; Keesstra, Saskia; Roy, P. S.; Sutton, Paul C.; Bhatt, Sandeep; Rahmat, Shahid; Jha, Shouvik; Singh, Laishram Kanta Examining the effects of green revolution led agricultural expansion on net ecosystem service values in India using multiple valuation approaches JOURNAL OF ENVIRONMENTAL MANAGEMENT English Article Ecosystem service value; Land use change; Ecology; Value transfer; Green revolution; India LAND-USE CHANGES; NEURAL-NETWORK; COVER CHANGES; DYNAMICS; IMPACTS; CONSERVATION; INEQUALITY; LANDSCAPE; CROPLAND; FORESTS Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The 'Green Revolution' (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work - 'the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 1970'. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%-90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being. [Sannigrahi, Srikanta; Pilla, Francesco; Basu, Bidroha; Basu, Arunima Sarkar] Univ Coll Dublin, Sch Architecture Planning & Environm Policy, Dublin D14 E099, Ireland; [Zhang, Qi] Boston Univ, Frederick S Pardee Sch Global Studies, Frederick S Pardee Ctr Study Longer Range Future, Boston, MA 02215 USA; [Chakraborti, Suman] Jawaharlal Nehru Univ, Ctr Study Reg Dev CSRD, New Delhi 110067, India; [Wang, Ying] China Univ Geosci, Sch Publ Adm, Wuhan 430074, Peoples R China; [Joshi, P. K.] Jawaharlal Nehru Univ JNU, Sch Environm Sci SES, Spatial Anal & Informat Lab SAIL, New Delhi 110067, India; [Keesstra, Saskia] Wageningen Univ & Res, Soil Water & Land Use Team, Droevendaalsesteeg3, NL-6708 PB Wageningen, Netherlands; [Keesstra, Saskia] Univ Newcastle, Civil Surveying & Environm Engn, Callaghan, NSW 2308, Australia; [Roy, P. S.] Int Crops Res Inst Semi Arid Trop, Innovat Syst Drylands ISD, Hyderabad 502324, India; [Sutton, Paul C.] Univ Denver, Dept Geog & Environm, 2050 East Iliff Ave, Denver, CO 80208 USA; [Bhatt, Sandeep] Indian Inst Technol, Dept Earth Sci, Roorkee 247667, Uttar Pradesh, India; [Rahmat, Shahid] Indian Inst Technol Kharagpur, Dept Architecture & Reg Planning, Kharagpur, W Bengal, India; [Jha, Shouvik] Indian Ctr Climate & Societal Impacts Res ICCSIR, Kachchh 370465, Gujarat, India; [Singh, Laishram Kanta] Indian Inst Technol Kharagpur, Agr & Food Engn Dept, Kharagpur 721302, W Bengal, India University College Dublin; Boston University; Jawaharlal Nehru University, New Delhi; China University of Geosciences; Jawaharlal Nehru University, New Delhi; Wageningen University & Research; University of Newcastle; CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); University of Denver; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Roorkee; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur Sannigrahi, S (corresponding author), Univ Coll Dublin, Sch Architecture Planning & Environm Policy, Dublin D14 E099, Ireland. srikanta.sannigrahi@ucd.ie Zhang, Qi/I-9741-2016; Rahmat, Shahid/ABC-7190-2020; wang, ying/ABG-8215-2020; Sannigrahi, Srikanta/ABF-3806-2020 Zhang, Qi/0000-0002-4242-7614; wang, ying/0000-0001-5868-5661; Sannigrahi, Srikanta/0000-0002-3835-386X; Basu, Bidroha/0000-0002-8822-7167; Pilla, Francesco/0000-0002-1535-1239; Sutton, Paul/0000-0001-6972-3256 Microsoft AI for Earth Azure Compute grant Microsoft AI for Earth Azure Compute grant(Microsoft) The authors express their sincere gratitude to the anonymous reviewers and the Editorial Board for fruitful and constructive comments to enhance the quality of the paper. Qi Zhang is supported by a Microsoft AI for Earth Azure Compute grant. 63 12 12 9 39 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0301-4797 1095-8630 J ENVIRON MANAGE J. Environ. Manage. JAN 1 2021.0 277 111381 10.1016/j.jenvman.2020.111381 0.0 16 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology OY0VX 33011421.0 hybrid, Green Submitted, Green Accepted 2023-03-23 WOS:000593973700006 0
J Sun, YX; Tang, H; Tang, YT; Zheng, J; Dong, DB; Chen, XH; Liu, FQ; Bai, L; Ge, WJ; Xin, LM; Pu, HY; Peng, Y; Luo, J Sun, Yuanxi; Tang, Hao; Tang, Yuntao; Zheng, Jia; Dong, Dianbiao; Chen, Xiaohong; Liu, Fuqiang; Bai, Long; Ge, Wenjie; Xin, Liming; Pu, Huayan; Peng, Yan; Luo, Jun Review of Recent Progress in Robotic Knee Prosthesis Related Techniques: Structure, Actuation and Control JOURNAL OF BIONIC ENGINEERING English Review bionic; robotic knee prosthesis; prosthetic knee; mechanism; actuation; control TRANSFEMORAL PROSTHESIS; DESIGN; JOINT; PREDICTION; STANCE; MOVEMENT; DAMPER As the essential technology of human-robotics interactive wearable devices, the robotic knee prosthesis can provide above-knee amputations with functional knee compensations to realize their physical and psychological social regression. With the development of mechanical and mechatronic science and technology, the fully active knee prosthesis that can provide subjects with actuating torques has demonstrated a better wearing performance in slope walking and stair ascent when compared with the passive and the semi-active ones. Additionally, with intelligent human-robotics control strategies and algorithms, the wearing effect of the knee prosthesis has been greatly enhanced in terms of stance stability and swing mobility. Therefore, to help readers to obtain an overview of recent progress in robotic knee prosthesis, this paper systematically categorized knee prostheses according to their integrated functions and introduced related research in the past ten years (2010-2020) regarding (1) mechanical design, including uniaxial, four-bar, and multi-bar knee structures, (2) actuating technology, including rigid and elastic actuation, and (3) control method, including mode identification, motion prediction, and automatic control. Quantitative and qualitative analysis and comparison of robotic knee prosthesis-related techniques are conducted. The development trends are concluded as follows: (1) bionic and lightweight structures with better mechanical performance, (2) bionic elastic actuation with energy-saving effect, (3) artificial intelligence-based bionic prosthetic control. Besides, challenges and innovative insights of customized lightweight bionic knee joint structure, highly efficient compact bionic actuation, and personalized daily multi-mode gait adaptation are also discussed in-depth to facilitate the future development of the robotic knee prosthesis. [Sun, Yuanxi; Liu, Fuqiang; Bai, Long; Luo, Jun] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Tang, Hao] Tianjin Univ, Sch Mech Engn, Tianjin 300072, Peoples R China; [Tang, Yuntao; Chen, Xiaohong] Chongqing Univ, Coll Mech Engn, Chongqing 400044, Peoples R China; [Zheng, Jia] Chongqing Univ Posts & Telecommun, Sch Adv Mfg Engn, Chongqing 400065, Peoples R China; [Dong, Dianbiao; Ge, Wenjie] Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Peoples R China; [Dong, Dianbiao] Vrije Univ Brussel, Dept Mech Engn, B-1050 Brussels, Belgium; [Xin, Liming] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 2E8, Canada; [Xin, Liming; Pu, Huayan; Peng, Yan] Shanghai Univ, Res Inst Unmanned Surface Vessel USV Engn, Shanghai 200444, Peoples R China Chongqing University; Tianjin University; Chongqing University; Chongqing University of Posts & Telecommunications; Northwestern Polytechnical University; Vrije Universiteit Brussel; University of Toronto; Shanghai University Sun, YX (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China. sunyuanxi@cqu.edu.cn chen, xia/GXM-5435-2022; Chen, Xiao/GQA-8928-2022; chen, xi/GXH-3653-2022; chen, xia/GYR-3948-2022 Chen, Xiao/0000-0002-9797-8384; Liu, Fuqiang/0000-0002-8466-2794; Chen, Xiaohong/0000-0002-5176-2668; BAI, LONG/0000-0002-9410-5107 National Natural Science Foundation of China [62003060, 51975070, 62033001]; National Key Research and Development Program of China [2020YFB1313000] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work was supported by the National Natural Science Foundation of China (Grant Nos. 62003060, 51975070 and 62033001), and the National Key Research and Development Program of China under Grant 2020YFB1313000. 69 5 5 13 65 SPRINGER SINGAPORE PTE LTD SINGAPORE #04-01 CENCON I, 1 TANNERY RD, SINGAPORE 347719, SINGAPORE 1672-6529 2543-2141 J BIONIC ENG J. Bionic Eng. JUL 2021.0 18 4 764 785 10.1007/s42235-021-0065-4 0.0 22 Engineering, Multidisciplinary; Materials Science, Biomaterials; Robotics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science; Robotics TU3UN hybrid 2023-03-23 WOS:000680965200002 0
J Zhao, JQ; Jiao, LC; Liu, F; Fernandes, VB; Yevseyeva, I; Xia, SX; Emmerich, MTM Zhao, Jiaqi; Jiao, Licheng; Liu, Fang; Fernandes, Vitor Basto; Yevseyeva, Iryna; Xia, Shixiong; Emmerich, Michael T. M. 3D fast convex-hull-based evolutionary multiobjective optimization algorithm APPLIED SOFT COMPUTING English Article Convex hull; Area under ROC; Indicator-based evolutionary algorithm; Multiobjective optimization; ROC analysis PARTICLE SWARM OPTIMIZATION; SELECTION The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves have been widely used in the machine learning community to analyze the performance of classifiers. The area (or volume) under the convex hull has been used as a scalar indicator for the performance of a set of classifiers in ROC and DET space. Recently, 3D convex-hull-based evolutionary multiobjective optimization algorithm (3DCH-EMOA) has been proposed to maximize the volume of convex hull for binary classification combined with parsimony and three-way classification problems. However, 3DCH-EMOA revealed high consumption of computational resources due to redundant convex hull calculations and a frequent execution of nondominated sorting. In this paper, we introduce incremental convex hull calculation and a fast replacement for non-dominated sorting. While achieving the same high quality results, the computational effort of 3DCH-EMOA can be reduced by orders of magnitude. The average time complexity of 3DCH-EMOA in each generation is reduced from 0(n(2) log n) to 0(n log n) per iteration, where n is the population size. Six test function problems are used to test the performance of the newly proposed method, and the algorithms are compared to several state-of-the-art algorithms, including NSGA-III, RVEA, etc., which were not compared to 3DCH-EMOA before. Experimental results show that the new version of the algorithm (3DFCH-EMOA) can speed up 3DCH-EMOA for about 30 times for a typical population size of 300 without reducing the performance of the method. Besides, the proposed algorithm is applied for neural networks pruning, and several UCI datasets are used to test the performance. (C) 2018 Elsevier B.V. All rights reserved. [Zhao, Jiaqi; Xia, Shixiong] China Univ Min & Technol, Sch Comp Sci & Technol, 1 Daxue Rd, Xuzhou 221116, Jiangsu, Peoples R China; [Jiao, Licheng; Liu, Fang] Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Int Res Ctr Intelligent Percept & Computat,Minist, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi, Peoples R China; [Fernandes, Vitor Basto] IUL, ISTAR, ISCTE, Univ Inst Lisbon, Av Forcas Armadas, P-1649026 Lisbon, Portugal; [Fernandes, Vitor Basto] Polytech Inst Leiria, Sch Technol & Management, Comp Sci & Commun Res Ctr, P-2411901 Leiria, Portugal; [Yevseyeva, Iryna] De Montfort Univ, Fac Technol, Gateway House 5-33, Leicester LE1 9BH, Leics, England; [Emmerich, Michael T. M.] Leiden Univ, LIACS, Multicriteria Optimizat Design & Analyt Grp, Niels Bohrweg 1, NL-2333 CA Leiden, Netherlands China University of Mining & Technology; Xidian University; Instituto Universitario de Lisboa; De Montfort University; Leiden University; Leiden University - Excl LUMC Zhao, JQ (corresponding author), China Univ Min & Technol, Sch Comp Sci & Technol, 1 Daxue Rd, Xuzhou 221116, Jiangsu, Peoples R China. jiaqizhao88@126.com; xiasx@cumt.edu.cn Emmerich, Michael/AAD-4791-2021; Basto-Fernandes, Vitor/N-1891-2016 Basto-Fernandes, Vitor/0000-0003-4269-5114; Zhao, Jiaqi/0000-0002-3564-5090; Emmerich, Michael/0000-0002-7342-2090 National Key Research and Development Plan [2016YFC0600908]; National Natural Science Foundation of China [U1610124, 61572505, 61772530]; National Natural Science Foundation of Jiangsu Province [BK20171192] National Key Research and Development Plan; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) This work was partially supported by the National Key Research and Development Plan (No. 2016YFC0600908), the National Natural Science Foundation of China (No. U1610124, 61572505 and 61772530), and the National Natural Science Foundation of Jiangsu Province (No. BK20171192). We also thank the authors of PlatEMO [53] and LightNet [58]. 59 12 12 0 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. JUN 2018.0 67 322 336 10.1016/j.asoc.2018.03.005 0.0 15 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science GF4EC Green Accepted, Green Submitted 2023-03-23 WOS:000431913000024 0
J Bo, W; Jinping, L; Alassafid, MO; FawazE, A; Hadi, J; Bekiros, S Bo, Wang; Jinping, Liu; Alassafid, Madini O.; FawazE., Alsaadi; Hadi, Jahanshahi; Bekiros, Stelios Intelligent parameter identification and prediction of variable time fractional derivative and application in a symmetric chaotic financial system CHAOS SOLITONS & FRACTALS English Article Chaotic financial system; Identification of fractional derivative; Prediction; Differential Evolution; Gaussian process; Neural networks; Recurrent mechanism SLIDING MODE CONTROL; NEURAL-NETWORKS; STRATEGIES; DYNAMICS Identifying parameters of financial and economic models with chaotic dynamics is an important, yet daunting challenge because of the complexities there exist in these chaotic systems. Although several studies have been devoted to understanding the mechanism of financial systems, the application of most state-of-the-art methods to these systems is completely ignored. To the best of our knowledge, no study identifies and predicts fractional derivatives of economic models. The current study has been motivated by this issue. Employing the Differential Evolution algorithm, Gaussian process regression, and neural networks, we propose a novel algorithm to identify and predict parameters of a symmetric chaotic fractional financial model. In the first step, a combination of Differential Evolution and the Gaussian process is utilized to identify time-varying fractional-order derivatives. Then, through numerical simulation, it is demonstrated that although this method provides bright results for estimation and interpolation purposes, it fails to extrapolate and predict time-varying parameters. Hence, in the next step, by taking advantage of a recurrent neural network, the proposed method is promoted and modified for extrapolation. Numerical simulations firmly confirm the excellent performance of the improved algorithm for both interpolation and extrapolation purposes. (C) 2021 Elsevier Ltd. All rights reserved. [Bo, Wang] Xihua Univ, Sch Elect & Informat Engn, Chengdu 610039, Peoples R China; [Bo, Wang] Sch Elect Informat & Automat, Aba Teachers University 623002, Sichuan, Peoples R China; [Jinping, Liu] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Hunan, Peoples R China; [Alassafid, Madini O.; FawazE., Alsaadi] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah, Saudi Arabia; [Hadi, Jahanshahi] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada; [Bekiros, Stelios] Univ Malta, Dept Banking & Finance, FEMA, Msida 2080, MSD, Malta; [Bekiros, Stelios] European Univ Inst, Dept Econ, Via Fontanelle,18, I-50014 Florence, Italy Xihua University; Hunan Normal University; King Abdulaziz University; University of Manitoba; University of Malta; European University Institute Alassafi, Madini O./AGY-4104-2022; Liu, Jinping/AAM-7723-2021; Bekiros, Stelios/K-4041-2014 Alassafi, Madini O./0000-0001-9919-8368; Liu, Jinping/0000-0002-8669-882X; Alsaadi, Fawaz/0000-0003-0041-3158; Bekiros, Stelios/0000-0002-9376-8726 83 11 11 8 12 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-0779 1873-2887 CHAOS SOLITON FRACT Chaos Solitons Fractals JAN 2022.0 154 111590 10.1016/j.chaos.2021.111590 0.0 8 Mathematics, Interdisciplinary Applications; Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics 0P8JA 2023-03-23 WOS:000784474000002 0
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F.; Sabato, G.; Sacerdoti, S.; Saddique, A.; Sadrozinski, H. F-W.; Sadykov, R.; Tehrani, F. Safai; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Loyola, J. E. Salazar; Saleem, M.; Salek, D.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sanchez, J.; Sanchez Martinez, V.; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Castillo, I. Santoyo; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. 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C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Serre, T.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shamim, M.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Shushkevich, S.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silver, Y.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sisakyan, A. N.; Sivoklokov, S. Yu.; Sjoelin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; ; ATLAS Collaboration Search for single top-quark production via flavour-changing neutral currents at 8TeV with the ATLAS detector EUROPEAN PHYSICAL JOURNAL C English Article PARTON DISTRIBUTIONS; DECAYS; MODELS; COUPLINGS; STANDARD; PHYSICS A search for single top-quark production via flavour-changing neutral current processes from gluon plus up- or charm-quark initial states in proton-proton collisions at the LHC is presented. Data collected with the ATLAS detector in 2012 at a centre-of-mass energy of 8TeV and corresponding to an integrated luminosity of 20.3 fb(-1) are used. Candidate events for a top quark decaying into a lepton, a neutrino and a jet are selected and classified into signal- and background-like candidates using a neural network. No signal is observed and an upper limit on the production cross-section multiplied by the t -> Wb branching fraction is set. The observed 95% CL limit is sigma(qg -> t) x B(t -> Wb) < 3.4 pb and the expected 95% CL limit is sigma(qg -> t) x B(t -> Wb) < 2.9 pb. The observed limit can be interpreted as upper limits on the coupling constants of the flavour-changing neutral current interactions divided by the scale of new physics kappa(ugt)/Lambda < 5.8 x 10(-3) TeV-1 and kappa(cgt)/Lambda < 13x10(-3) TeV and on the branching fractions B(t -> ug) < 4.0 x 10(-5) and B(t -> cg) < 20 x 10(-5). [Aleksa, M.; Gonzalez, B. Alvarez; Anders, G.; Anghinolfi, F.; Armbruster, A. J.; Arnaez, O.; Avolio, G.; Baak, M. A.; Backes, M.; Backhaus, M.; Barak, L.; Beermann, T. A.; Beltramello, O.; Bianco, M.; Bogaerts, J. A.; Boveia, A.; Boyd, J.; Burckhart, H.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Carrillo-Montoya, G. D.; Catinaccio, A.; Cattai, A.; Cerv, M.; Chromek-Burckhart, D.; Conti, G.; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Girolamo, B.; Dittus, F.; Dobos, D.; Dudarev, A.; Duehrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Farthouat, P.; Fassnacht, P.; Feigl, S.; Fernandez Perez, S.; Fiascaris, M.; Francis, D.; Froidevaux, D.; Gadatsch, S.; Gillberg, D.; Glatzer, J.; Goossens, L.; Gorini, B.; Gray, H. M.; Hawkings, R. J.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Jaekel, M. R.; Jakobsen, S.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Lapoire, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Lichard, P.; Macina, D.; Malyukov, S.; Mandelli, B.; Mapelli, L.; Marzin, A.; Milic, A.; Mornacchi, G.; Nairz, A. M.; Nakahama, Y.; Nessi, M.; Nicquevert, B.; Nordberg, M.; Oide, H.; Palestini, S.; Pauly, T.; Pernegger, H.; Peters, K.; Petersen, B. A.; Pommes, K.; Poppleton, A.; Poulard, G.; Poveda, J.; Prasad, S.; Rammensee, M.; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Ruiz-Martinez, A.; Salzburger, A.; Schaefer, D.; Schlenker, S.; Schmieden, K.; Serfon, C.; Sforza, F.; Sfyrla, A.; Solans, C. A.; Spigo, G.; Stelzer, H. J.; Teischinger, F. A.; Ten Kate, H.; Tremblet, L.; Tricoli, A.; Tsarouchas, C.; Unal, G.; van Woerden, M. C.; Vandelli, W.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Wotschack, J.; Young, C. J. S.; Zwalinski, L.; ATLAS Collaboration] CERN, CH-1211 Geneva 23, Switzerland; [Jackson, P.; Lee, L.; Petridis, A.; Soni, N.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Bouffard, J.; Edson, W.; Ernst, J.; Fischer, A.; Guindon, S.; Jain, V.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Butt, A. I.; Czodrowski, P.; Dassoulas, J.; Gingrich, D. M.; Jabbar, S.; Karamaoun, A.; Moore, R. W.; Pinfold, J. L.; Saddique, A.; Vaque, F. Vives] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Ciftci, A. K.; Yildiz, H. Duran] Ankara Univ, Dept Phys, TR-06100 Ankara, Turkey; [Kuday, S.] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Barnovska, Z.; Berger, N.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Hryn'ova, T.; Jezequel, S.; Koletsou, I.; Lafaye, R.; Leveque, J.; Massol, N.; Sauvage, G.; Sauvan, E.; Simard, O.; Todorov, T.; Wingerter-Seez, I.; Yatsenko, E.] CNRS, IN2P3, LAPP, Annecy Le Vieux, France; [Barnovska, Z.; Berger, N.; Delmastro, M.; Di Ciaccio, L.; Elles, S.; Hryn'ova, T.; Jezequel, S.; Koletsou, I.; Lafaye, R.; Leveque, J.; Massol, N.; Sauvage, G.; Sauvan, E.; Simard, O.; Todorov, T.; Wingerter-Seez, I.; Yatsenko, E.] Univ Savoie Mont Blanc, Annecy Le Vieux, France; [Blair, R. E.; Chekanov, S.; Feng, E. J.; LeCompte, T.; Love, J.; Malon, D.; Nguyen, D. H.; Paramonov, A.; Price, L. E.; Proudfoot, J.; van Gemmeren, P.; Vaniachine, A.; Wang, R.; Yoshida, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Cheu, E.; Johns, K. A.; Lampen, C. L.; Lampl, W.; Lei, X.; Leone, R.; Loch, P.; Nayyar, R.; O'grady, F.; Rutherfoord, J. P.; Shupe, M. A.; Varnes, E. W.; Veatch, J.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Bullock, D.; Cote, D.; Darmora, S.; De, K.; Farbin, A.; Feremenga, L.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Kim, H. Y.; Ozturk, N.; Schovancova, J.; Sosebee, M.; Stradling, A. R.; Usai, G.; Vartapetian, A.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA; [Angelidakis, S.; Chouridou, S.; Fassouliotis, D.; Giokaris, N.; Ioannou, P.; Kourkoumelis, C.; Manousakis-Katsikakis, A.; Tsirintanis, N.] Univ Athens, Dept Phys, Athens, Greece; [Alexopoulos, T.; Benekos, N.; Dris, M.; Gazis, E. N.; Karakostas, K.; Karastathis, N.; Karentzos, E.; Leontsinis, S.; Maltezos, S.; Ntekas, K.; Panagiotopoulou, E.; Papadopoulou, Th. D.; Tsipolitis, G.; Vlachos, S.] Natl Tech Univ Athens, Dept Phys, GR-15773 Zografos, Greece; [Abdinov, O.; Ahmadov, F.; Huseynov, N.; Javadov, N.; Khalil-zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku 370143, Azerbaijan; [Anjos, N.; Bosman, M.; Casado, M. P.; Casolino, M.; Cavalli-Sforza, M.; Cortes-Gonzalez, A.; Farooque, T.; Fischer, C.; Fracchia, S.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Rozas, A. Juste; Korolkov, I.; Lange, J. C.; Le Menedeu, E.; Paz, I. Lopez; Martinez, M.; Mir, L. M.; Berlingen, J. Montejo; Pages, A. Pacheco; Aranda, C. Padilla; Riu, I.; Sorin, V.; Succurro, A.; Tripiana, M. F.; Tsiskaridze, S.; Valery, L.] Univ Autonoma Barcelona, Inst Fis Altes Energies, E-08193 Barcelona, Spain; [Anjos, N.; Bosman, M.; Casado, M. P.; Casolino, M.; Cavalli-Sforza, M.; Cortes-Gonzalez, A.; Farooque, T.; Fischer, C.; Fracchia, S.; Giangiobbe, V.; Gonzalez Parra, G.; Grinstein, S.; Helsens, C.; Rozas, A. Juste; Korolkov, I.; Lange, J. C.; Le Menedeu, E.; Paz, I. Lopez; Martinez, M.; Mir, L. M.; Berlingen, J. Montejo; Pages, A. Pacheco; Aranda, C. Padilla; Riu, I.; Sorin, V.; Succurro, A.; Tripiana, M. F.; Tsiskaridze, S.; Valery, L.] Univ Autonoma Barcelona, Dept Fis, E-08193 Barcelona, Spain; [Agatonovic-Jovin, T.; Bogavac, D.; Bozic, I.; Dimitrievska, A.; Krstic, J.; Marjanovic, M.; Popovic, D. S.; Sijacki, Dj.; Simic, Lj.; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Dale, O.; Eigen, G.; Kastanas, A.; Liebig, W.; Lipniacka, A.; Maeland, S.; Latour, B. Martin dit; Rosendahl, P. L.; Sjursen, T. B.; Smestad, L.; Stugu, B.; Ugland, M.; Zalieckas, J.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Bhimji, W.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Einsweiler, K.; Farrell, S.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Hance, M.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Ovcharova, A.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Quarrie, D. R.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA 94720 USA; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Bhimji, W.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Einsweiler, K.; Farrell, S.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Hance, M.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Ovcharova, A.; Griso, S. Pagan; Polifka, R.; Potamianos, K.; Pranko, A.; Quarrie, D. R.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Biedermann, D.; Dietrich, J.; Giorgi, F. M.; Grancagnolo, S.; Herbert, G. H.; Herrberg-Schubert, R.; Hristova, I.; Kind, O. M.; Kolanoski, H.; Lacker, H.; Lohse, T.; Nikiforov, A.; Rehnisch, L.; Rieck, P.; Schulz, H.; Sperlich, D.; Stamm, S.; zur Nedden, M.] Humboldt Univ, Dept Phys, Invalidenstr 110, Berlin, Germany; [Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Marti, L. F.; Meloni, F.; Mullier, G. A.; Sciacca, F. G.; Stramaglia, M. E.; Stucci, S. A.; Weber, M. S.] Univ Bern, Albert Einstein Ctr, Fundamental Phys & Lab High Energy Phys, Bern, Switzerland; [Bella, L. Aperio; Baca, M. J.; Bansil, H. S.; Bracinik, J.; Charlton, D. G.; Chisholm, A. S.; Daniells, A. C.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Levy, M.; Mudd, R. D.; Quijada, J. A. Murillo; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Arik, M.; Istin, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A. J.; Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Phys, Gaziantep, Turkey; [Cetin, S. A.] Dogus Univ, Dept Phys, Istanbul, Turkey; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Corradi, M.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giorgi, F. M.; Grafstroem, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Negrini, M.; Piccinini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Spighi, R.; Tupputi, S. A.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Ist Nazl Fis Nucl, Sez Bologna, I-40126 Bologna, Italy; [Alberghi, G. L.; Biondi, S.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Grafstroem, P.; Manghi, F. Lasagni; Massa, I.; Massa, L.; Mengarelli, A.; Piccinini, M.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Tupputi, S. A.; Valentinetti, S.; Villa, M.; Zoccoli, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Arslan, O.; Bechtle, P.; Bernlochner, F. U.; Brock, I.; Bruscino, N.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Ehrenfeld, W.; Gaycken, G.; Geich-Gimbel, Ch.; Gonella, L.; Grefe, C.; Haefner, P.; Hageboeck, S.; Hansen, M. C.; Hellmich, D.; Hohn, D.; Huegging, F.; Janssen, J.; Kostyukhin, V. V.; Kraus, J. K.; Kroseberg, J.; Kruger, H.; Lantzsch, K.; Lenz, T.; Leyko, A. M.; Liebal, J.; Limbach, C.; Mergelmeyer, S.; Mijovic, L.; Moles-Valls, R.; Obermann, T.; Pohl, D.; Ricken, O.; Sarrazin, B.; Schaepe, S.; Schopf, E.; Schultens, M. J.; Schwindt, T.; Scutti, F.; Seema, P.; Stillings, J. A.; Tannoury, N.; Velz, T.; von Toerne, E.; Wagner, P.; Wang, T.; Wermes, N.; Wienemann, P.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wong, K. H. Yau; Yuen, S. P. Y.] Univ Bonn, Inst Phys, Bonn, Germany; [Ahlen, S. P.; Bernard, C.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Helary, L.; Kruskal, M.; Long, B. A.; Shank, J. T.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Amundsen, G.; Artoni, G.; Barone, G.; Bensinger, J. R.; Bianchini, L.; Blocker, C.; Coffey, L.; Dhaliwal, S.; Fitzgerald, E. A.; Sciolla, G.; Venturini, A.; Zengel, K.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Amaral Coutinho, Y.; Caloba, L. P.; Maidantchik, C.; Marroquim, F.; Nepomuceno, A. A.; Seixas, J. M.] Univ Fed Rio de Janeiro, COPPE, EE, IF, Rio De Janeiro, Brazil; [Cerqueira, A. S.; de Andrade Filho, L. Manhaes] Fed Univ Juiz De Fora UFJF, Elect Circuits Dept, Juiz De Fora, Brazil; [do Vale, M. A. B.] Fed Univ Sao Joao Del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; Navarro, J. L. La Rosa; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, CP 20516, BR-01498 Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Begel, M.; Buttinger, W.; Chen, H.; Chernyatin, V.; Debbe, R.; Ernst, M.; Gibbard, B.; Gordon, H. A.; Iakovidis, G.; Klimentov, A.; Kouskoura, V.; Kravchenko, A.; Lanni, F.; Lissauer, D.; Lynn, D.; Ma, H.; Maeno, T.; Metcalfe, J.; Mountricha, E.; Nevski, P.; Nilsson, P.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Perepelitsa, D. V.; Pleier, M. -A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Radeka, V.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Takai, H.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Alexa, C.; Boldea, V.; Buda, S. I.; Caprini, I.; Caprini, M.; Chitan, A.; Ciubancan, M.; Constantinescu, S.; Dita, P.; Dita, S.; Dobre, M.; Ducu, O. A.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Pantea, D.; Rotaru, M.; Stoicea, G.; Tudorache, A.; Tudorache, V.] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest 077125, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Dept Phys, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; West Univ Timisoara, Timisoara, Romania; [Otero Y Garzon, G.; Piegaia, R.; Reisin, H.; Sacerdoti, S.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Carter, J. R.; Chapman, J. D.; Cottin, G.; French, S. T.; Gillam, T. P. S.; Hill, J. C.; Kaneti, S.; Khoo, T. J.; Lester, C. G.; Mueller, T.; Parker, M. A.; Robinson, D.; Rosten, J. H. N.; Thomson, M.; Ward, C. P.; Yusuff, I.] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England; [Bellerive, A.; Cree, G.; Di Valentino, D.; Koffas, T.; Lacey, J.; Leight, W. A.; McCarthy, T. G.; Nomidis, I.; Oakham, F. G.; Pasztor, G.; Tarrade, F.; Ueno, R.; Vincter, M. G.] Carleton Univ, Dept Phys, Ottawa, ON K1S 5B6, Canada; [Alison, J.; Anderson, K. J.; Toro, R. Camacho; Cheng, Y.; Dandoy, J. R.; Facini, G.; Gardner, R. W.; Ilchenko, Y.; Kapliy, A.; Kim, Y. K.; Krizka, K.; Li, H. L.; Merritt, F. S.; Miller, D. W.; Narayan, R.; Okumura, Y.; Onyisi, P. U. E.; Oreglia, M. J.; Penning, B.; Pilcher, J. E.; Saxon, J.; Shochet, M. J.; Vukotic, I.; Webster, J. S.; Wu, M.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Carquin, E.; Diaz, M. A.; Ochoa-Ricoux, J. P.; Vogel, M.] Pontificia Univ Catolica Chile, Dept Fis, Alameda 340, Santiago, Chile; [Brooks, W. K.; Kuleshov, S.; Pezoa, R.; Prokoshin, F.; Loyola, J. E. Salazar; White, R.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Bai, Y.; Fang, Y.; Jin, S.; Lou, X.; Ouyang, Q.; Peng, C.; Ren, H.; Shan, L. Y.; Sun, X.; Wang, J.; Xu, D.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Gao, J.; Guo, Y.; Han, L.; Hu, Q.; Jiang, Y.; Li, B.; Liu, J. B.; Liu, M.; Liu, Y.; Peng, H.; Song, H. Y.; Zhang, R.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Hefei, Anhui, Peoples R China; [Li, Y.; Zhang, H.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Chen, L.; Feng, C.; Ge, P.; Liu, B.; Ma, L. L.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Sch Phys, Jinan, Shandong, Peoples R China; [Bret, M. Cano; Chen, S.; Guo, J.; Li, L.; Yang, H.] Shanghai Jiao Tong Univ, Dept Phys & Astron, Shanghai Key Lab Particle Phys & Cosmol, Shanghai 200030, Peoples R China; [Chen, X.; Zhou, N.] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] Univ Clermont Ferrand 2, Phys Corpusculaire Lab, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] Univ Blaise Pascal, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Donini, J.; Dubreuil, E.; Gilles, G.; Gris, Ph.; Liao, H.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Theveneaux-Pelzer, T.; Vazeille, F.] CNRS, IN2P3, Clermont Ferrand, France; [Alkire, S. P.; Altheimer, A.; Andeen, T.; Angerami, A.; Bain, T.; Brooijmans, G.; Cole, B.; Hu, D.; Hughes, E. W.; Iordanidou, K.; Klein, M. H.; Mohapatra, S.; Nikiforou, N.; Parsons, J. A.; Smith, M. N. K.; Smith, R. W.; Thompson, E. N.; Tuts, P. M.; Wang, T.; Zhou, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Besjes, G. J.; Dam, M.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Joergensen, M. D.; Loevschall-Jensen, A. E.; Monk, J.; Mortensen, S. S.; Pedersen, L. E.; Petersen, T. C.; Pingel, A.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Cairo, V. M.; Capua, M.; Crosetti, G.; La Rotonda, L.; Mastroberardino, A.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, Grp Coll Cosenza, POB 13, I-00044 Frascati, Italy; [Cairo, V. M.; Capua, M.; Crosetti, G.; La Rotonda, L.; Mastroberardino, A.; Policicchio, A.; Salvatore, D.; Scarfone, V.; Schioppa, M.; Susinno, G.; Tassi, E.] Univ Calabria, Dipartmento Fis, I-87036 Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Dyndal, M.; Gach, G. P.; Grabowska-Bold, I.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Mindur, B.; Przybycien, M.; Zemla, A.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, PL-30059 Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Banas, E.; de Renstrom, P. A. Bruckman; Chwastowski, J. J.; Derendarz, D.; Godlewski, J.; Gornicki, E.; Hajduk, Z.; Iwanski, W.; Kaczmarska, A.; Knapik, J.; Korcyl, K.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Cao, T.; Firan, A.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Sekula, S. J.; Stroynowski, R.; Turvey, A. J.; Varol, T.; Wang, H.; Ye, J.; Zhao, X.; Zhou, L.] So Methodist Univ, Dept Phys, Dallas, TX 75275 USA; [Izen, J. M.; Leyton, M.; Meirose, B.; Namasivayam, H.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Asbah, N.; Bessner, M.; Bloch, I.; Borroni, S.; Britzger, D.; Camarda, S.; Deterre, C.; Eckardt, C.; Filipuzzi, M.; Flaschel, N.; Glazov, A.; Grahn, K-J.; Gregor, I. M.; Grohsjean, A.; Haleem, M.; Hamnett, P. G.; Hengler, C.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E.; Lohwasser, K.; Mamuzic, J.; Medinnis, M.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; Peschke, R.; Petit, E.; Pirumov, H.; Poley, A.; Radescu, V.; Robinson, J. E. M.; Rubinskiy, I.; Schaefer, R.; Schmitt, S.; Sedov, G.; Shushkevich, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Wang, J.; Wasicki, C.; Yildirim, E.] DESY, Notkestr 85, Hamburg, Germany; [Asbah, N.; Bessner, M.; Bloch, I.; Borroni, S.; Britzger, D.; Camarda, S.; Deterre, C.; Eckardt, C.; Filipuzzi, M.; Flaschel, N.; Glazov, A.; Grahn, K-J.; Gregor, I. M.; Grohsjean, A.; Haleem, M.; Hamnett, P. G.; Hengler, C.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E.; Lohwasser, K.; Mamuzic, J.; Medinnis, M.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; Peschke, R.; Petit, E.; Pirumov, H.; Poley, A.; Radescu, V.; Robinson, J. E. M.; Rubinskiy, I.; Schaefer, R.; Schmitt, S.; Sedov, G.; Shushkevich, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Wang, J.; Wasicki, C.; Yildirim, E.] DESY, Zeuthen, Germany; [Burmeister, I.; Erdmann, J.; Esch, H.; Gossling, C.; Homann, M.; Jentzsch, J.; Jung, C. A.; Klingenberg, R.; Kroeninger, K.] Tech Univ Dortmund, Inst Expt Phys 4, D-44221 Dortmund, Germany; [Anger, P.; Duschinger, D.; Friedrich, F.; Grohs, J. P.; Gumpert, C.; Gutschow, C.; Hauswald, L.; Kobel, M.; Mader, W. F.; Novgorodova, O.; Rudolph, C.; Schnoor, U.; Siegert, F.; Socher, F.; Staerz, S.; Straessner, A.; Vest, A.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, D-01062 Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bocci, A.; Cerio, B. C.; Goshaw, A. T.; Kajomovitz, E.; Kotwal, A.; Kruse, M. C.; Li, L.; Li, S.; Liu, M.; Oh, S. H.; Zhou, C.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bristow, T. M.; Clark, P. J.; Dias, F. A.; Edwards, N. C.; Gao, Y.; Walls, F. M. Garay; Glaysher, P. C. F.; Harrington, R. D.; Leonidopoulos, C.; Martin, V. J.; Mills, C.; O'Brien, B. J.; Pino, S. A. Olivares; Proissl, M.; Smart, B. H.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA, Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Di Nardo, R.; Esposito, B.; Gatti, C.; Giromini, P.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Ist Nazl Fis Nucl, Lab Nazl Frascati, POB 13, I-00044 Frascati, Italy; [Amoroso, S.; Arnold, H.; Betancourt, C.; Boehler, M.; Bruneliere, R.; Buehrer, F.; Burgard, C. D.; Buecher, D.; Cardillo, F.; Coniavitis, E.; Consorti, V.; Dang, N. P.; Dao, V.; Di Simone, A.; Giuliani, C.; Herten, G.; Jakobs, K.; Javuerek, T.; Jenni, P.; Kiss, F.; Koeneke, K.; Kopp, A. K.; Kuehn, S.; Landgraf, U.; Mahboubi, K.; Mohr, W.; Pagacova, M.; Parzefall, U.; Ronzani, M.; Rosbach, K.; Ruhr, F.; Rurikova, Z.; Ruthmann, N.; Sammel, D.; Schillo, C.; Schumacher, M.; Sommer, P.; Sundermann, J. E.; Ta, D.; Temming, K. K.; Tsiskaridze, V.; Ungaro, F. C.; von Radziewski, H.; Weiser, C.; Werner, M.; Zhang, L.; Zimmermann, S.] Univ Freiburg, Fak Math & Phys, Hugstetter Str 55, D-79106 Freiburg, Germany; [Ancu, L. S.; Bell, W. H.; Noccioli, E. Benhar; De Mendizabal, J. Bilbao; Calace, N.; Clark, A.; Coccaro, A.; Delitzsch, C. M.; della Volpe, D.; Ferrere, D.; Gadomski, S.; Golling, T.; Gonzalez-Sevilla, S.; Gramling, J.; Guescini, F.; Iacobucci, G.; Katre, A.; Mermod, P.; Miucci, A.; Muenstermann, D.; Paolozzi, L.; Picazio, A.; Ristic, B.; Schramm, S.; Tykhonov, A.; Vallecorsa, S.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Darbo, G.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Guido, E.; Morettini, P.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.] Ist Nazl Fis Nucl, Sez Genova, Via Dodecaneso 33, I-16146 Genoa, Italy; [Barberis, D.; Favareto, A.; Parodi, A. Ferretto; Gagliardi, G.; Gaudiello, A.; Guido, E.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Jejelava, J.; Tskhadadze, E. G.] Iv Javakhishvili Tbilisi State Univ, E Andronikashvili Inst Phys, Tbilisi, Georgia; [Djobava, T.; Durglishvili, A.; Khubua, J.; Mosidze, M.] Tbilisi State Univ, Inst High Energy Phys, GE-380086 Tbilisi, Georgia; [Bates, R. L.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Buzatu, A.; Cinca, D.; D'Auria, S.; Doyle, A. T.; Ferrando, J.; de Lima, D. E. Ferreira; Gul, U.; Knue, A.; Morton, A.; Mullen, P.; O'Shea, V.; Barrera, C. Oropeza; Owen, M.; Pollard, C. S.; Qin, G.; Quilty, D.; Ravenscroft, T.; Robson, A.; St Denis, R. D.; Stewart, G. A.; Thompson, A. S.] Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow, Lanark, Scotland; [Dueren, M.; Kreutzfeldt, K.; Stenzel, H.] Univ Giessen, Inst Phys 2, D-35390 Giessen, Germany; [Agricola, J.; Bindi, M.; Blumenschein, U.; Brandt, G.; Drechsler, E.; George, M.; Graber, L.; Grosse-Knetter, J.; Janus, M.; Kareem, M. J.; Kawamura, G.; Lai, S.; Lemmer, B.; Magradze, E.; Mantoani, M.; Mchedlidze, G.; Llacer, M. Moreno; Musheghyan, H.; Nackenhorst, O.; Nadal, J.; Quadt, A.; Rieger, J.; Schorlemmer, A. L. S.; Shabalina, E.; Stolte, P.; Weingarten, J.; Zinonos, Z.] Univ Gottingen, Inst Phys 2, Gottingen, Germany; [Albrand, S.; Brown, J.; Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Gabaldon, C.; Genest, M. H.; Hostachy, J-Y.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Monini, C.; Stark, J.; Trocme, B.; Wu, M.] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, Grenoble, France; [McFarlane, K. W.] Hampton Univ, Dept Phys, Hampton, VA 23668 USA; [da Costa, J. Barreiro Guimaraes; Catastini, P.; Clark, B. L.; Franklin, M.; Huth, J.; Ippolito, V.; Lazovich, T.; Mateos, D. Lopez; Mercurio, K. M.; Morii, M.; Skottowe, H. P.; Spearman, W. R.; Sun, S.; Tolley, E.; Tuna, A. N.; Yen, A. L.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Andrei, V.; Baas, A. E.; Brandt, O.; Davygora, Y.; Djuvsland, J. I.; Dunford, M.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kluge, E. -E.; Lang, V. S.; Meier, K.; Theenhausen, H. Meyer Zu; Villar, D. I. Narrias; Sahinsoy, M.; Scharf, V.; Schultz-Coulon, H. -C.; Stamen, R.; Starovoitov, P.; Wessels, M.] Heidelberg Univ, Kirchhoff Inst Phys, Bergheimer Str 58, D-69115 Heidelberg, Germany; [Anders, C. F.; Giulini, M.; Lisovyi, M.; Schaetzel, S.; Schmitt, S.; Schoening, A.; Sosa, D.] Heidelberg Univ, Inst Phys, Philosophenweg 12, Heidelberg, Germany; [Colombo, T.; Kretz, M.; Kugel, A.] Heidelberg Univ, ZITI Inst Tech Informat, Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Bortolotto, V.; Castillo, L. R. Flores; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Bortolotto, V.] Univ Hong Kong, Dept Phys, Pokfulam, Hong Kong, Peoples R China; [Bortolotto, V.; Prokofiev, K.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Hong Kong, Peoples R China; [Choi, K.; Dattagupta, A.; Evans, H.; Gagnon, P.; Lammers, S.; Martinez, N. Lorenzo; Luehring, F.; Ogren, H.; Penwell, J.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Jansky, R.; Jussel, P.; Kneringer, E.; Lukas, W.; Usanova, A.; Vigne, R.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Mallik, U.; Mandrysch, R.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; De Lorenzi, F.; Krumnack, N.; Pluth, D.; Prell, S.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kotov, V. M.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Minashvili, I. A.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Pozdnyakov, V.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Sisakyan, A. N.; Soloshenko, A.; Vinogradov, V. B.; Yeletskikh, I.; Zhemchugov, A.; Zimine, N. I.] Joint Inst Nucl Res Dubna, Dubna, Russia; [Amako, K.; Aoki, M.; Arai, Y.; Hanagaki, K.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kohriki, T.; Kondo, T.; Kono, T.; Makida, Y.; Nagano, K.; Nakamura, K.; Nozaki, M.; Odaka, S.; Okuyama, T.; Sasaki, O.; Suzuki, S.; Takubo, Y.; Tanaka, S.; Terada, S.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Yamada, M.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Hasegawa, M.; Kido, S.; Kishimoto, T.; Kurashige, H.; Maeda, J.; Ochi, A.; Shimizu, S.; Takeda, H.; Yakabe, R.; Yamazaki, Y.; Yuan, L.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo 657, Japan; [Ishino, M.; Kunigo, T.; Monden, R.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto 612, Japan; [Kawagoe, K.; Oda, S.; Otono, H.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka 812, Japan; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, RA-1900 La Plata, Buenos Aires, Argentina; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Monticelli, F.; Wahlberg, H.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Borissov, G.; Bouhova-Thacker, E. V.; Dearnaley, W. J.; Fox, H.; Grimm, K.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Maddocks, H. J.; Skinner, M. B.; Smizanska, M.; Walder, J.; Wharton, A. M.] Univ Lancaster, Dept Phys, Lancaster, England; [Chiodini, G.; Gorini, E.; Primavera, M.; Spagnolo, S.; Ventura, A.] Ist Nazl Fis Nucl, Sez Lecce, I-73100 Lecce, Italy; [Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Affolder, A. A.; Allport, P. P.; Anders, J. K.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Gwilliam, C. B.; Hayward, H. S.; Jackson, M.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Lehan, A.; Maxfield, S. J.; Mehta, A.; Readioff, N. P.; Schnellbach, Y. J.; Sowden, B. C.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool L69 3BX, Merseyside, England; [Cindro, V.; Deliyergiyev, M.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Sfiligoj, T.; Sokhrannyi, G.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Cindro, V.; Deliyergiyev, M.; Filipcic, A.; Gorisek, A.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Sfiligoj, T.; Sokhrannyi, G.] Univ Ljubljana, Ljubljana, Slovenia; [Alpigiani, C.; Bevan, A. J.; Bona, M.; Cerrito, L.; Fletcher, G.; Goddard, J. R.; Hays, J. M.; Hickling, R.; Landon, M. P. J.; Lloyd, S. L.; Morris, J. D.; Nooney, T.; Piccaro, E.; Rizvi, E.; Sandbach, R. L.; Snidero, G.; Castanheira, M. Teixeira Dias] Queen Mary Univ London, Sch Phys & Astron, London, England; [Berry, T.; Blanco, J. E.; Boisvert, V.; Brooks, T.; Connelly, I. A.; Cowan, G.; Duguid, L.; Giannelli, M. Faucci; George, S.; Gibson, S. M.; Kempster, J. J.; Vazquez, J. G. Panduro; Pastore, Fr.; Savage, G.; Sowden, B. C.; Spano, F.; Teixeira-Dias, P.; Thomas-Wilsker, J.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Bieniek, S. P.; Butterworth, J. M.; Campanelli, M.; Casadei, D.; Chislett, R. T.; Christodoulou, V.; Cooper, B. D.; Davison, P.; Falla, R. J.; Freeborn, D.; Gregersen, K.; Ortiz, N. G. Gutierrez; Hesketh, G. G.; Jansen, E.; Jiggins, S.; Konstantinidis, N.; Korn, A.; Kucuk, H.; Lambourne, L.; Leney, K. J. C.; Martyniuk, A. C.; Mcfayden, J. A.; Nurse, E.; Ochoa, I.; Richter, S.; Scanlon, T.; Sherwood, P.; Simmons, B.; Wardrope, D. R.; Waugh, B. M.] UCL, Dept Phys & Astron, London, England; [Greenwood, Z. D.; Grossi, G. C.; Jana, D. K.; Sawyer, L.; Subramaniam, R.] Louisiana Tech Univ, Ruston, LA 71270 USA; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] Univ Paris Diderot, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Vannucci, F.; Varouchas, D.] CNRS, IN2P3, Paris, France; [Akesson, T. P. A.; Bocchetta, S. S.; Bryngemark, L.; Doglioni, C.; Floderus, A.; Hawkins, A. D.; Hedberg, V.; Ivarsson, J.; Jarlskog, G.; Lytken, E.; Mjornmark, J. U.; Smirnova, O.; Viazlo, O.] Lund Univ, Inst Fys, Lund, Sweden; [Arnal, V.; Barreiro, F.; Cantero, J.; De la Torre, H.; Del Peso, J.; Glasman, C.; Llorente Merino, J.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor C 15, Madrid, Spain; [Becker, M.; Bertella, C.; Blum, W.; Buecher, V.; Caputo, R.; Caudron, J.; Endner, O. C.; Ertel, E.; Fiedler, F.; Torregrosa, E. Fullana; Heck, T.; Hohlfeld, M.; Huelsing, T. A.; Karnevskiy, M.; Kleinknecht, K.; Koepke, L.; Lin, T. H.; Masetti, L.; Mattmann, J.; Meyer, C.; Moritz, S.; Rave, S.; Sander, H. G.; Schaeffer, J.; Schaefer, U.; Schmitt, C.; Schott, M.; Schroeder, C.; Schuh, N.; Simioni, E.; Tapprogge, S.; Urrejola, P.; Valderanis, C.; Wollstadt, S. J.; Zimmermann, C.; Zinser, M.] Johannes Gutenberg Univ Mainz, Inst Phys, D-55122 Mainz, Germany; [Balli, F.; Barnes, S. L.; Cox, B. E.; Da Via, C.; Forti, A.; Ponce, J. M. Iturbe; Joshi, K. D.; Keoshkerian, H.; Li, X.; Loebinger, F. K.; Marsden, S. P.; Masik, J.; Neep, T. J.; Oh, A.; Ospanov, R.; Pater, J. R.; Peters, R. F. Y.; Pilkington, A. D.; Price, D.; Qin, Y.; Queitsch-Maitland, M.; Schwanenberger, C.; Schweiger, H.; Shaw, S. M.; Thompson, R. J.; Tomlinson, L.; Watts, S.; Webb, S.; Woudstra, M. J.; Wyatt, T. R.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Aad, G.; Alio, L.; Barbero, M.; Chen, L.; Coadou, Y.; Diaconu, C.; Diglio, S.; Djama, F.; Ducu, O. A.; Feligioni, L.; Gao, J.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Mochizuki, K.; Monnier, E.; Muanza, S.; Nagai, Y.; Nagy, E.; Pralavorio, P.; Rozanov, A.; Serre, T.; Talby, M.; Torres, R. E. Ticse; Tiouchichine, E.; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.; ; European Organization for Nuclear Research (CERN); University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Autonomous University of Barcelona; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Autonomous University of Barcelona; University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Humboldt University of Berlin; University of Bern; University of Birmingham; Bogazici University; Gaziantep University; Dogus University; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; University of Bonn; Boston University; Brandeis University; Universidade Federal do Rio de Janeiro; Universidade Federal de Juiz de Fora; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; United States Department of Energy (DOE); Brookhaven National Laboratory; Horia Hulubei National Institute of Physics & Nuclear Engineering; Babes Bolyai University from Cluj; National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; Polytechnic University of Bucharest; West University of Timisoara; University of Buenos Aires; University of Cambridge; Carleton University; University of Chicago; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Nanjing University; Shandong University; Shanghai Jiao Tong University; Tsinghua University; Universite Clermont Auvergne (UCA); Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Columbia University; University of Copenhagen; Niels Bohr Institute; Istituto Nazionale di Fisica Nucleare (INFN); University of Calabria; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Southern Methodist University; University of Texas System; University of Texas Dallas; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Geneva; Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; University of Glasgow; Justus Liebig University Giessen; University of Gottingen; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Hampton University; Harvard University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; Indiana University System; Indiana University Bloomington; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; High Energy Accelerator Research Organization (KEK); Kobe University; Kyoto University; Kyoto University of Education; Kyushu University; National University of La Plata; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; Istituto Nazionale di Fisica Nucleare (INFN); University of Salento; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; UDICE-French Research Universities; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Lund University; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; Istituto Nazionale di Fisica Nucleare (INFN); University of Milan; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; National Center of Particles & High Energy Physics - Belarus; Massachusetts Institute of Technology (MIT); Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; New York University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Osaka University; University of Oslo; University of Oxford; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade Nova de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade Nova de Lisboa; Universidade do Minho; University of Granada; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Comenius University Bratislava; Slovak Academy of Sciences; University of Cape Town; University of Johannesburg; University of Witwatersrand; Stockholm University; Oskar Klein Centre; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; Tufts University; Universidad Antonio Narino; University of California System; University of California Irvine; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; University of Illinois System; University of Illinois Urbana-Champaign; Uppsala University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Valencia; University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Barcelona (IMB-CNM); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); University of British Columbia; University of Victoria; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); University of London; King's College London; Novosibirsk State University; California State University System; California State University Fresno; University of Fribourg; Universidade do Porto; Tomsk State University; Parthenope University Naples; Peter the Great St. Petersburg Polytechnic University; University of Louisiana System; Louisiana Technical University; ICREA; Osaka University; National Tsing Hua University; University of Texas System; University of Texas Austin; Ilia State University; Georgian Technical University; Manhattan College; Hellenic Open University; Academia Sinica - Taiwan; Moscow Institute of Physics & Technology; University of Geneva; International School for Advanced Studies (SISSA); University of South Carolina System; University of South Carolina Columbia; Sun Yat Sen University; Lomonosov Moscow State University; Stanford University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Universiti Malaya Aad, G (corresponding author), CNRS, IN2P3, Marseille, France. Bobrovnikov, Victor/AAB-8328-2022; Carquin, Edson/GLU-9641-2022; do Amaral Coutinho, Yara/AAU-7857-2021; Annovi, Alberto/AAA-8638-2020; Luci, Claudio/HKW-0143-2023; Castro, Nuno/AAB-3648-2019; Fabbri, Laura/H-3442-2012; Donadelli, Marisilvia/H-3710-2016; Pásztor, Gabriella/D-9716-2016; zhang, jin/GXV-9154-2022; Prokoshin, Fedor/E-2795-2012; Chitan, Adrian/AAC-8946-2022; Sbarra, Carla/M-9669-2018; Paganis, Stathes/J-8413-2017; Cheng, Hok-Chuen/GNP-8341-2022; Donadelli, Marisilvia/ABD-5330-2021; Svatos, Michal/ABA-2041-2020; Price, Darren/AAB-5991-2021; de Groot, Nicolo/A-2675-2009; Price, Darren D/E-6162-2012; Buttar, Craig/AAG-5392-2021; Giagu, Stefano/H-6455-2013; Stabile, Alberto/F-2889-2013; Cetin, Serkant Ali/AGF-0147-2022; Ros, Eduardo/P-2625-2018; Vanadia, Marco/K-5870-2016; Fuster, Juan/W-6189-2018; Gavrilenko, Igor/M-8260-2015; Bassalat, Ahmed Azzam/HHY-9901-2022; O'Shea, Val/G-1279-2010; Trzebinski, Maciej/W-1748-2018; Di Domenico, Antonio/G-6301-2011; Ohm, Christian/AAU-6572-2020; Palka, Marek/GXW-2506-2022; b, m/HPE-0989-2023; Franchini, Matteo/AAC-9259-2021; Jiménez, Yesenia Hernández/ABH-1107-2020; Chelkov, Georgy A./G-9934-2019; Smirnov, Sergei Y/F-1014-2011; Bogdanchikov, Alexander/AAB-9414-2022; Busato, Emmanuel/AAZ-4423-2020; Camarri, Paolo/M-7979-2015; Guo, Jun/O-5202-2015; Padilla, Cristobal/C-3218-2017; Llácer, María Moreno/AAQ-7522-2020; Gabrielli, Alessandro/H-4931-2012; Urbán, Susana Cabrera/H-1376-2015; Ahmadov, Faig N/B-3723-2018; Jiménez Peña, Javier/AFY-1817-2022; Franchini, Matteo/HNI-8314-2023; Albert, Justin/J-4152-2017; Ciftci, Abbas Kenan/AAC-5487-2022; Berger, N/ABE-4064-2020; Snesarev, Andrei/H-5090-2013; Perez, Miguel Villaplana/B-2717-2015; de Almeida, Fernando Marroquim Leao/D-6947-2019; Ferrer, Antonio/H-2942-2015; Yang, Haijun/O-1055-2015; Della Pietra, Massimo/J-5008-2012; Tsybychev, Dmitri/J-3733-2017; Garcia, Jose E/H-6339-2015; de Renstrom, Pawel Bruckman/AAG-7725-2021; Owen, Mark/Q-8268-2016; li, li/GPX-3938-2022; Gonzalez, Barbara Alvarez/ABG-7021-2020; Carquin López, Edson/GZG-4712-2022; Alexa, Calin/F-6345-2010; Monticeli, Francisco Maciel/AAO-9697-2020; Petrucci, Fabrizio/G-8348-2012; Grabowska-Bołd, Iwona/ABI-7829-2020; Kamenshchikov, Andrey/R-1112-2017; SULIN, Vladimir V/J-6966-2014; Glasman, Claudia/Y-8858-2019; Torregrosa, Esteban Fullana/A-7305-2016; Jones, Roger WL/H-5578-2011; Zhukov, Konstantin I/M-6027-2015; Koperny, Stefan/ABB-4747-2020; Camarri, Paolo/AAF-9629-2020; Orlando, Nicola/AAL-1723-2021; Pages, Andres Pacheco/C-5353-2011; Vaniachine, Alexandre/H-7796-2013; Sultansoy, Saleh/AAA-8267-2019; Sadykov, Renat/AAN-9602-2020; Coccaro, Andrea/AAB-6990-2021; Huseynov, Nazim/AAE-4663-2019; Oh, Alexander/HHZ-4386-2022; Kodys, Peter/P-2636-2017; Coadou, Yann/G-2263-2010; Banaś, Elżbieta A/W-4583-2017; de la Hoz, Santiago Gonzalez/E-2494-2016; Sultansoy, Saleh/AAV-6853-2021; Nevski, Pavel/M-6292-2015; Messina, Andrea/C-2753-2013; Monzani, Simone/D-6328-2017; Stoicea, Gabriel/B-6717-2011; Gramstad, Eirik/AAV-9229-2021; Schernau, Michael/GRY-3808-2022; Casado, Pilar/H-1484-2015; García, Elena Oliver/AAZ-1125-2020; Fassi, Farida/F-3571-2016; Kuday, Sinan/CAF-3048-2022; Benekos, Nektarios/J-8629-2017; Černý, Karel/AAK-7746-2021; CAKIR, Orhan/L-1893-2015; della Volpe, Domenico/B-4482-2012; Doyle, Anthony T/C-5889-2009; Gao, Y./GWZ-3665-2022; Burdin, Sergey/AAZ-9062-2021; Negrini, Matteo/C-8906-2014; Irles, Adrian/AAW-1879-2021; Peleganchuk, Sergey V/J-6722-2014; Iengo, Paolo/AAR-7518-2020; Castillo, Luis Roberto Flores/W-3928-2018; Villa, Mauro/C-9883-2009; Mamuzic, Judita/U-3509-2017; Potamianos, Karolos/V-2525-2019; Li, Hao/GPS-9834-2022; Saraiva, Joao Gentil Mendes/D-1596-2017; Fullana Torregrosa, Esteban/AAC-9079-2022; Kostyukhin, Vadim/ABA-9033-2020; do Vale, Maria Aline Barros/D-2357-2019; Klimentov, Alexei A/I-4270-2013; Gauzzi, Paolo/D-2615-2009; Mikestikova, Marcela/H-1996-2014; Berta, Peter/AAL-7109-2020; Talyshev, Alexey/HGC-6910-2022; Bruscino, Nello/ABA-8980-2021; Lacasta, Carlos/C-7254-2008; Smirnova, Oxana/A-4401-2013; Cottin, Giovanna/AFR-3846-2022; Valero, Alberto/G-9866-2015; Giordani, Mario P/Q-6211-2018; Vandelli, Wainer/ABI-8185-2020; Costa, Maria Jose/H-5962-2015; Romaniouk, Anatoli/Q-6674-2017; Chizhov, Mihail/CAG-2076-2022; Lagouri, Theodota/AAC-7358-2021; Leisos, Antonios/AAJ-2351-2021; Muino, Patricia Conde/F-7696-2011; Rosati, Stefano/AAF-6968-2019; Zenin, Oleg/E-6116-2018; Woźniak, Krzysztof/AAQ-6119-2021; Artoni, Giacomo/ABA-2164-2020; Introzzi, Gianluca/K-2497-2015; Gustavino, Giuliano/AAK-6591-2020; Buzykaev, Alexey/HIK-0117-2022; Kosek, Tomáš/Q-1906-2017; Andreazza, Attilio/E-5642-2011; Derendarz, Dominik/AAO-3512-2021; Grinstein, Sebastian/ABE-1880-2020; Hamal, Petr/G-5540-2014; Masik, Jiri/AAE-1203-2021; Darbo, Giovanni/C-8175-2012; Amoroso, Simone/AAW-4334-2021; Boyko, Igor R/J-3659-2013; Staszewski, Rafał/AAO-6844-2021; perez, miguel/GXF-5077-2022; Gonella, Laura/GLR-3838-2022; Cheremushkina, Evgeniya/E-9881-2018; Solodkov, Alexander A/B-8623-2017; Lambourne, Luke/AAG-7298-2020; Fiorini, Luca/W-6250-2018; Brooks, William K/C-8636-2013; Dyndal, Mateusz/AAB-1528-2020; Chekulaev, Sergey V/O-1145-2015; da via, cinzia/AAS-3978-2021; Alexopoulos, Theodoros/AAC-9633-2022; Salamon, Andrea/AGN-6159-2022; Stoicea, Gabriel/L-5602-2019; Crispin-Ortuzar, Mireia/AAN-7108-2020; Sopczak, Andre/I-4951-2015; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Kartvelishvili, Vakhtang G/K-2312-2013; Mashinistov, Ruslan/M-8356-2015; Nagai, Yoshikazu/AAG-6157-2021; Llácer, María Moreno/AAU-6582-2020; Maleev, Victor/R-4140-2016; Hrabovsky, Milan/H-4413-2014; Staroba, Pavel/G-8850-2014; Ungaro, Francesca C/G-4554-2018; Bin Yusuff, Imran/ABA-5986-2020; Maevskiy, Artem/P-4101-2016; Bates, Richard L/D-6596-2013; Evangelos, Gazis/L-3966-2017; Zanzi, Daniele/AAY-4102-2020; Kuday, Sinan/C-8528-2014; beddall, ayda/AAG-6531-2020; Dolejší, Jiří/P-1509-2017; Sýkora, Tomáš/Q-3174-2017; Lysak, Roman/H-2995-2014; Ozcan, Veysi Erkcan/AAS-4508-2020; Laycock, Paul/AAL-8261-2021; Bachas, Konstantinos/C-8101-2019; García, Josu Cantero/AAS-9810-2020; Rozen, Yoram/H-1880-2017; Trzupek, Adam/N-2448-2018; Cavaliere, Viviana/CAG-2384-2022; Villaplana Perez, Miguel/B-5772-2014; Testa, Marianna/L-9768-2019; spagnolo, stefania/A-6359-2012; Tassi, Enrico/AAJ-9661-2020; Wozniak, Krzysztof W/P-4475-2017; Garcia, Carmen/W-2465-2018; Alimonti, Gianluca/AAG-4603-2020; Capua, Marcella/AAJ-8419-2020; Beddall, Andrew John/AAE-5820-2022; Legger, Federica/HNC-2568-2023; Fassouliotis, Dimitrios/AAL-7081-2021; Olszowska, Jolanta/P-2889-2017; Benekos, Nektarios/I-9928-2017; Kepka, Oldrich/ABF-5327-2021; Herten, Gregor/HNQ-9546-2023; Berge, David/P-2179-2017; Grancagnolo, Sergio/J-3957-2015; Gutierrez, Phillip/C-1161-2011; Sessa, Marco/AAT-2850-2020; Mitsou, Vasiliki A./D-1967-2009; Lee, Lawrence/AAU-6790-2021; Malek, Fairouz/E-4410-2018; Nevski, Pavel/AAH-1839-2019; Maneira, Jose/D-8486-2011; Wu, Xin/ABH-1729-2020; Llorente Merino, Javier/AGP-3432-2022; YILDIZ, Hatice DURAN/AAN-3727-2021; Tikhomirov, Vladimir/M-6194-2015; gaudio, gabriella/AAN-6039-2021; Ventura, Andrea/CAH-0226-2022; Ventura, Andrea/A-9544-2015; Barreiro, Fernando/D-9808-2012; De, Kaushik/N-1953-2013; Górnicki, Edward/C-4075-2019; Trzebinski, Maciej/AAO-6821-2021; Gladilin, Leonid/B-5226-2011; Capua, Marcella/A-8549-2015; Dam, Mogens/C-2081-2015; Francavilla, Paolo/HKE-1206-2023; Zhemchugov, Alexey/N-1717-2017; Kukla, Romain/P-9760-2016; Mindur, Bartosz/A-2253-2017; Bosman, Martine/J-9917-2014; Tykhonov, Andrii/Q-8688-2018; Kostyukhin, Vadim/F-3171-2019; Warburton, Andreas/N-8028-2013; Amorim, Antonio/C-8460-2013; Lee, Shih-Chang/AAV-7016-2021; Dabrowski, Wladyslaw/AAS-6369-2020; Marcisovsky, Michal/AAM-2404-2020; Cavaliere, Viviana/CAE-8597-2022; Livan, Michele M/D-7531-2012; MEONI, Evelin/ABD-9498-2021; Nordberg, Markus/L-4255-2017; Kantserov, Vadim/M-9761-2015; Ippolito, Valerio/L-1435-2016; Chwastowski, Janusz J./I-4480-2012; Hejbal, Jiri/H-1358-2014; de la Torre Perez, Hector/ABG-6942-2020; Watson, Ian James/AAH-1423-2022; Quijada, Javier Alberto Murillo/AAS-4234-2021; Di Nardo, Roberto/J-4993-2012; kucuk, hilal/AAM-5530-2020; Kuze, Masahiro/V-4251-2018; Šolc, Jaroslav/AAC-5868-2021; Zaitsev, Alexandre M/B-8989-2017; Sapronov, Andrey/HGB-0392-2022; Caprini, Irinel/AAP-3760-2020; Olszewski, Andrzej J/W-1825-2018; Torro Pastor, Emma/AAB-5979-2021; Kazanin, Vasily F./HHM-2056-2022; Watson, Ian James/AFL-9360-2022; Ibáñez, Carlos Escobar/B-3761-2017; Li, Liang/O-1107-2015; Balaji, S/J-1864-2019; Kaczmarska, Anna/B-2753-2019; Nessi, Marzio/L-5194-2017; Busato, Emmanuel/AAS-5459-2020; Nechaeva, Polina Y/N-1148-2015; Faltova, Jana/P-6842-2017; cerri, alessandro/Q-6884-2016; Vranjes Milosavljevic, Marija/F-9847-2016; D'Auria, Saverio/O-3276-2017; Wosiek, Barbara/K-5811-2017; Schultz-Coulon, Hans-Christian/X-5006-2018; Maeda, Junpei/B-8131-2018; Carli, Ina/C-2189-2017; Leitner, Rupert/C-2004-2017; Slawinska, Magdalena/W-2551-2018; Vos, Marcel/G-8123-2015; Sykora, Ivan/T-5252-2018; Davidek, Tomas/P-2697-2017; Monk, James/A-9021-2017; Konig, Adriaan/B-6439-2013; Gorelov, Igor/J-9010-2015; Zenis, Tibor/T-5270-2018; Wolter, Marcin/A-7412-2012; Penc, Ondrej/H-3032-2014; Reznicek, Pavel/C-1989-2017; Carquin, Edson/G-5221-2015; Izen, Joseph/A-4392-2018; Carvalho, Joao/M-4060-2013; Rebuzzi, Daniela Marcella/D-9727-2018; Hansen, Peter Henrik/C-2098-2015; Korcyl, Krzysztof/W-2111-2018; MARTINEZ, MARIO/I-3549-2015; Tartarelli, Giuseppe Francesco/A-5629-2016; Malecki, Pawel/W-1710-2018; Sioli, Maximiliano/Q-1597-2016; Zabinski, Bartlomiej/Q-6483-2017; Goncalo, Jose/M-3153-2016; xella, stefania/E-6752-2015; Baldin, Evgenii/A-6186-2014; Turecek, Daniel/N-4149-2017; Vykydal, Zdenek/H-6426-2016; Britton, David/F-2602-2010; McKee, Shawn/B-6435-2012; Wolters, Helmut/M-4154-2013; Hrabovsky, Miroslav/G-6714-2014; La Rosa, Alessandro/I-1856-2013; Aguilar Saavedra, Juan Antonio/F-1256-2016; Petersen, Troels/P-5538-2015; Soldatov, Evgeny/E-3990-2017; , Alex/N-8715-2016; Stanecka, Ewa/V-6242-2018; Juste, Aurelio/I-2531-2015 Bobrovnikov, Victor/0000-0002-2003-0261; Castro, Nuno/0000-0001-8491-4376; Fabbri, Laura/0000-0002-4002-8353; Donadelli, Marisilvia/0000-0001-8329-4240; Pásztor, Gabriella/0000-0003-0707-9762; Prokoshin, Fedor/0000-0001-6389-5399; Sbarra, Carla/0000-0002-8236-5251; Paganis, Stathes/0000-0002-1950-8993; Cheng, Hok-Chuen/0000-0002-8912-4389; Svatos, Michal/0000-0002-7199-3383; Price, Darren/0000-0003-2750-9977; Buttar, Craig/0000-0003-0188-6491; Giagu, Stefano/0000-0001-9192-3537; Stabile, Alberto/0000-0002-6868-8329; Cetin, Serkant Ali/0000-0001-5050-8441; Ros, Eduardo/0000-0003-2812-9554; Vanadia, Marco/0000-0003-2684-276X; Bassalat, Ahmed Azzam/0000-0002-0129-1423; O'Shea, Val/0000-0001-7183-1205; Trzebinski, Maciej/0000-0002-5151-7101; Di Domenico, Antonio/0000-0001-8078-2759; Ohm, Christian/0000-0002-8015-7512; Palka, Marek/0000-0002-7185-3540; Franchini, Matteo/0000-0002-4554-252X; Jiménez, Yesenia Hernández/0000-0001-9844-6200; Smirnov, Sergei Y/0000-0002-6778-073X; Busato, Emmanuel/0000-0003-2174-8512; Camarri, Paolo/0000-0002-5732-5645; Guo, Jun/0000-0001-8125-9433; Padilla, Cristobal/0000-0001-7951-0166; Llácer, María Moreno/0000-0001-9045-5305; Gabrielli, Alessandro/0000-0001-5346-7841; Ahmadov, Faig N/0000-0003-3644-540X; Jiménez Peña, Javier/0000-0002-8705-628X; Albert, Justin/0000-0003-0253-2505; Berger, N/0000-0002-7963-9725; Snesarev, Andrei/0000-0002-9067-8362; Perez, Miguel Villaplana/0000-0002-0048-4602; de Almeida, Fernando Marroquim Leao/0000-0003-2265-784X; Ferrer, Antonio/0000-0003-0532-711X; Yang, Haijun/0000-0001-7367-1380; Della Pietra, Massimo/0000-0003-4446-3368; Garcia, Jose E/0000-0002-0279-0523; de Renstrom, Pawel Bruckman/0000-0002-0206-1160; Owen, Mark/0000-0001-6820-0488; Gonzalez, Barbara Alvarez/0000-0001-7767-4810; Monticeli, Francisco Maciel/0000-0002-0814-8160; Petrucci, Fabrizio/0000-0002-5278-2206; Grabowska-Bołd, Iwona/0000-0001-9159-1210; SULIN, Vladimir V/0000-0003-3943-2495; Glasman, Claudia/0000-0003-2025-3817; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Jones, Roger WL/0000-0002-6427-3513; Zhukov, Konstantin I/0000-0003-2468-9634; Camarri, Paolo/0000-0002-5732-5645; Orlando, Nicola/0000-0003-0616-245X; Pages, Andres Pacheco/0000-0001-8210-1734; Vaniachine, Alexandre/0000-0002-0367-5666; Sadykov, Renat/0000-0002-9157-6819; Coccaro, Andrea/0000-0003-2368-4559; Kodys, Peter/0000-0002-8644-2349; Coadou, Yann/0000-0001-8195-7004; Banaś, Elżbieta A/0000-0001-5325-6040; de la Hoz, Santiago Gonzalez/0000-0001-5304-5390; Messina, Andrea/0000-0003-1195-6780; Monzani, Simone/0000-0002-0479-2207; Stoicea, Gabriel/0000-0002-7511-4614; Casado, Pilar/0000-0002-0394-5646; García, Elena Oliver/0000-0002-9214-9384; Fassi, Farida/0000-0002-6423-7213; Černý, Karel/0000-0003-0683-2177; CAKIR, Orhan/0000-0002-9016-138X; Doyle, Anthony T/0000-0001-6322-6195; Burdin, Sergey/0000-0003-4831-4132; Negrini, Matteo/0000-0003-0101-6963; Irles, Adrian/0000-0001-5668-151X; Peleganchuk, Sergey V/0000-0003-0907-7592; Iengo, Paolo/0000-0002-5035-1242; Castillo, Luis Roberto Flores/0000-0003-1551-5974; Villa, Mauro/0000-0002-9181-8048; Mamuzic, Judita/0000-0002-3203-4243; Potamianos, Karolos/0000-0001-7839-9785; Saraiva, Joao Gentil Mendes/0000-0002-7006-0864; Fullana Torregrosa, Esteban/0000-0003-3082-621X; Kostyukhin, Vadim/0000-0002-0490-9209; do Vale, Maria Aline Barros/0000-0002-0836-6483; Gauzzi, Paolo/0000-0003-4841-5822; Mikestikova, Marcela/0000-0003-1277-2596; Berta, Peter/0000-0003-0780-0345; Talyshev, Alexey/0000-0001-8560-3756; Lacasta, Carlos/0000-0002-2623-6252; Smirnova, Oxana/0000-0003-2517-531X; Cottin, Giovanna/0000-0002-5308-5808; Valero, Alberto/0000-0002-9776-5880; Giordani, Mario P/0000-0002-0792-6039; Vandelli, Wainer/0000-0001-6581-9410; Chizhov, Mihail/0000-0001-5841-3316; Leisos, Antonios/0000-0002-8126-3958; Muino, Patricia Conde/0000-0002-9187-7478; Rosati, Stefano/0000-0003-0838-5980; Woźniak, Krzysztof/0000-0003-1171-0887; Artoni, Giacomo/0000-0002-3477-4499; Introzzi, Gianluca/0000-0002-1314-2580; Gustavino, Giuliano/0000-0002-5938-4921; Buzykaev, Alexey/0000-0002-5458-5564; Kosek, Tomáš/0000-0003-1639-5564; Andreazza, Attilio/0000-0001-5161-5759; Derendarz, Dominik/0000-0001-5660-3095; 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NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMTCR, Czech Republic; MPOCR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; Lundbeck Foundation, Denmark; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI; SNSF; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UK; DOE, USA; NSF, USA; BCKDF; Canada Council; CANARIE; CRC; Compute Canada; FQRNT; Ontario Innovation Trust, Canada; EPLANET; ERC; FP7; Horizon; Marie SklodowskaCurie Actions, European Union; Investissements d'Avenir Labex; Idex, ANR, Region Auvergne; Fondation Partager le Savoir, France; DFG, Germany; AvH Foundation, Germany; EU-ESF; Greek NSRF; BSF, Israel; GIF, Israel; Minerva, Israel; BRF, Norway; Royal Society; Leverhulme Trust, UK; Direct For Mathematical & Physical Scien; Division Of Physics [1119200] Funding Source: National Science Foundation; Division Of Physics; Direct For Mathematical & Physical Scien [1410972] Funding Source: National Science Foundation; STFC [ST/H00100X/2, PP/E000347/1, ST/I006056/1, ST/I005846/1, PP/E000355/1, ST/M006417/1, PP/D002915/1, PP/E002846/1, ST/I00372X/1, ST/L001179/1, ST/L003414/1, ST/L001195/1, ST/H001093/1, ST/J005487/1, ST/J005525/1, ST/K000705/1, ST/M001407/1, ST/M006980/1, ST/N000463/1, ST/J00474X/1, ST/M001504/1, ST/N000420/1, ST/K00140X/1, ST/K001302/1, ST/L003511/1, ST/M002306/1, ST/L00352X/1, ST/M000761/1, ST/K001361/1, ST/K003666/1, ST/N000277/1, ST/L003112/1, ST/I000178/1, ST/J00488X/1, ST/M001733/1, ST/K001329/1, ST/M001431/1, ST/H001093/2, ST/G50228X/1, ST/K001248/1, ST/K00073X/1, ST/K00137X/1, ST/L005662/1, ST/L001144/1, ST/H00100X/1] Funding Source: UKRI; ICREA Funding Source: Custom ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMTCR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPOCR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); Lundbeck Foundation, Denmark(Lundbeckfonden); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI; SNSF(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, UK(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, USA(United States Department of Energy (DOE)); NSF, USA(National Science Foundation (NSF)); BCKDF; Canada Council; CANARIE; CRC(Australian GovernmentDepartment of Industry, Innovation and ScienceCooperative Research Centres (CRC) Programme); Compute Canada; FQRNT(FQRNT); Ontario Innovation Trust, Canada; EPLANET; ERC(European Research Council (ERC)European Commission); FP7; Horizon; Marie SklodowskaCurie Actions, European Union; Investissements d'Avenir Labex(French National Research Agency (ANR)); Idex, ANR, Region Auvergne(French National Research Agency (ANR)); Fondation Partager le Savoir, France; DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); BSF, Israel(US-Israel Binational Science Foundation); GIF, Israel(German-Israeli Foundation for Scientific Research and Development); Minerva, Israel; BRF, Norway; Royal Society(Royal Society of London); Leverhulme Trust, UK(Leverhulme Trust); Direct For Mathematical & Physical Scien; Division Of Physics(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); ICREA(ICREA) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMTCR, MPOCR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UK; DOE and NSF, USA. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie SklodowskaCurie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, UK. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFNCNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide. 91 37 38 1 60 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1434-6044 1434-6052 EUR PHYS J C Eur. Phys. J. C JAN 29 2016.0 76 2 55 10.1140/epjc/s10052-016-3876-4 0.0 30 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics DK9UU 26900343.0 Green Published, Green Submitted, gold, Green Accepted 2023-03-23 WOS:000375277900001 0
J Jentzen, A; Salimova, D; Welti, T Jentzen, Arnulf; Salimova, Diyora; Welti, Timo A PROOF THAT DEEP ARTIFICIAL NEURAL NETWORKS OVERCOME THE CURSE OF DIMENSIONALITY IN THE NUMERICAL APPROXIMATION OF KOLMOGOROV PARTIAL DIFFERENTIAL EQUATIONS WITH CONSTANT DIFFUSION AND NONLINEAR DRIFT COEFFICIENTS COMMUNICATIONS IN MATHEMATICAL SCIENCES English Article Curse of dimensionality; partial differential equations; numerical approximation; Feynman-Kac; deep neural networks MULTILAYER FEEDFORWARD NETWORKS; UNIVERSAL APPROXIMATION; ERROR-BOUNDS; DERIVATIVES; ALGORITHM; SMOOTH In recent years deep artificial neural networks (DNNs) have been successfully employed in numerical simulations for a multitude of computational problems including, for example, object and face recognition, natural language processing, fraud detection, computational advertisement, and numerical approximations of partial differential equations (PDEs). These numerical simulations indicate that DNNs seem to have the fundamental flexibility to overcome the curse of dimensionality in the sense that the number of real parameters used to describe the DNN grows at most polynomially in both the reciprocal of the prescribed approximation accuracy epsilon > 0 and the dimension d is an element of N of the function which the DNN aims to approximate in such computational problems. There is also a large number of rigorous mathematical approximation results for artificial neural networks in the scientific literature but there are only a few special situations where results in the literature can rigorously justify the success of DNNs to approximate high-dimensional functions. The key contribution of this article is to reveal that DNNs do overcome the curse of dimensionality in the numerical approximation of Kolmogorov PDEs with constant diffusion and nonlinear drift coefficients. We prove that the number of parameters used to describe the employed DNN grows at most polynomially in both the reciprocal of the prescribed approximation accuracy epsilon > 0 and the PDE dimension d is an element of N. A crucial ingredient in our proof is the fact that the artificial neural network used to approximate the solution of the PDE is indeed a deep artificial neural network with a large number of hidden layers. [Jentzen, Arnulf; Salimova, Diyora; Welti, Timo] Swiss Fed Inst Technol, Seminar Appl Math, Dept Math, Ramistr 101, CH-8092 Zurich, Switzerland; [Jentzen, Arnulf] Univ Munster, Appl Math Inst Anal & Numer, Fac Math & Comp Sci, Orleansring 10, D-48149 Munster, Germany; [Jentzen, Arnulf] Chinese Univ Hong Kong, Sch Data Sci, 2001 Longxiang Rd, Shenzhen 518172, Peoples R China; [Jentzen, Arnulf] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, 2001 Longxiang Rd, Shenzhen 518172, Peoples R China; [Salimova, Diyora] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Chair Math Informat Sci, Sternwartstr 7, CH-8092 Zurich, Switzerland; [Welti, Timo] D ONE Solut AG, Sihlfeldstr 58, CH-8003 Zurich, Switzerland Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Munster; Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen; Swiss Federal Institutes of Technology Domain; ETH Zurich Jentzen, A (corresponding author), Swiss Fed Inst Technol, Seminar Appl Math, Dept Math, Ramistr 101, CH-8092 Zurich, Switzerland.;Jentzen, A (corresponding author), Univ Munster, Appl Math Inst Anal & Numer, Fac Math & Comp Sci, Orleansring 10, D-48149 Munster, Germany.;Jentzen, A (corresponding author), Chinese Univ Hong Kong, Sch Data Sci, 2001 Longxiang Rd, Shenzhen 518172, Peoples R China.;Jentzen, A (corresponding author), Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, 2001 Longxiang Rd, Shenzhen 518172, Peoples R China. ajentzen@uni-muenster.de; sdiyora@ethz.ch; contact@twelti.org Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2044-390685587]; Swiss National Science Foundation (SNSF) [200020 175699]; ETH Foundations of Data Science (ETHFDS); ETH [ETH-47 15-2] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy(German Research Foundation (DFG)); Swiss National Science Foundation (SNSF)(Swiss National Science Foundation (SNSF)); ETH Foundations of Data Science (ETHFDS); ETH(ETH Zurich) David Ko~er is gratefully acknowledged for his useful comments regarding the a priori estimates in Subsections 4.1{4.2. This work has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC 2044-390685587, Mathematics Munster: Dynamics-Geometry-Structure, by the Swiss National Science Foundation (SNSF) through the research grant 200020 175699, by ETH Foundations of Data Science (ETHFDS), and through the ETH Research Grant ETH-47 15-2 \Mild stochastic calculus and numerical approximations for nonlinear stochastic evolution equations with Levy noise. 70 10 10 0 2 INT PRESS BOSTON, INC SOMERVILLE PO BOX 43502, SOMERVILLE, MA 02143 USA 1539-6746 COMMUN MATH SCI Commun. Math. Sci. 2021.0 19 5 1167 1205 39 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics TU4ZW 2023-03-23 WOS:000681047500001 0
J Wei, ZH; Zhou, Z; Wang, P; Ren, J; Yin, YZ; Pedersen, GF; Shen, M Wei, Zhaohui; Zhou, Zhao; Wang, Peng; Ren, Jian; Yin, Yingzeng; Pedersen, Gert Frolund; Shen, Ming Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modeling for Antenna Array Decoupling IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION English Article Antenna arrays; Metasurfaces; Data models; Training data; Training; Estimation; Optimization; Decoupling metasurface (DCMS); deep neural networks (DNNs); design automation; reinforcement learning (RL) ARTIFICIAL NEURAL-NETWORKS; MICROWAVE CIRCUITS; BROAD-BAND; DEEP; OPTIMIZATION; METASURFACE; GAME; GO Modern electromagnetic (EM) device design generally relies on extensive iterative optimizations by designers using simulation software (e.g., CST), which is a very time-consuming and tedious process. To relieve human engineers and boost productivity, we proposed a machine learning (ML) framework to solve the problem of automated design for EM tasks. The proposed approach combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs) in an attempt to simulate the decision-making process of human designers to realize automation learning. Specifically, the RL-based agent can interact with the EM design software without engaging human designers, allowing for automated design. Besides, the data accumulated during EM software simulation in the early design stage are reused as training data to build a DNN surrogate model to replace the time-consuming EM simulation and further accelerate the training of RL to achieve better optimization of EM design. Two types of antenna array decoupling including 1 x 2 and 1 x 4 arrays working at 3.5 GHz are used as test vehicles to validate the proposed method. The decoupling metasurfaces designed by the proposed fully automated method based on RL showed satisfactory results comparable to the results achievable by human designers. This indicates that the proposed method can be used to build powerful tools to boost the design efficiency of EM devices. [Wei, Zhaohui; Zhou, Zhao; Wang, Peng; Pedersen, Gert Frolund; Shen, Ming] Aalborg Univ, Dept Elect Syst, Aalborg, Denmark; [Ren, Jian; Yin, Yingzeng] Xidian Univ, Natl Key Lab Antennas & Microwave Technol, Xian 710071, Peoples R China Aalborg University; Xidian University Shen, M (corresponding author), Aalborg Univ, Dept Elect Syst, Aalborg, Denmark.;Ren, J (corresponding author), Xidian Univ, Natl Key Lab Antennas & Microwave Technol, Xian 710071, Peoples R China. renjian@xidian.edu.cn; mish@es.aau.dk Shen, Ming/0000-0002-9388-3513; Pedersen, Gert F./0000-0002-6570-7387 China Scholar Council; National Natural Science Foundation of China [61901316] China Scholar Council; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the China Scholar Council and in part by the National Natural Science Foundation of China under Grant 61901316. 55 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-926X 1558-2221 IEEE T ANTENN PROPAG IEEE Trans. Antennas Propag. JAN 2023.0 71 1 660 671 10.1109/TAP.2022.3221613 0.0 12 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 8K4IH Green Published 2023-03-23 WOS:000923067100064 0
J Li, B; Jiang, W; Peng, JY; Li, XB Li, Bin; Jiang, Wei; Peng, Jinye; Li, Xiaobai Deep learning-based remote-photoplethysmography measurement from short-time facial video PHYSIOLOGICAL MEASUREMENT English Article remote photoplethysmography; heart rate estimation; spatial-temporal convolution; fusion attention mechanism; short-time monitoring HEART-RATE ESTIMATION Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model. Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets. Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively. Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time. [Li, Bin; Jiang, Wei; Peng, Jinye] Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China; [Li, Xiaobai] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland Northwest University Xi'an; University of Oulu Peng, JY (corresponding author), Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China. pjy@nwu.edu.cn Jiang, Wei/0000-0003-4521-2886 China Postdoctoral Science Foundation; [2020M683696XB] China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); AcknowledgmentsThe authors have confirmed that any identifiable participants in this study have given their consent for publication. The dataset collection has been approved by the local ethics committee (Shaanxi Provincial People's Hospital), and all the subjects signed an informed consent form. This work was supported by the China Postdoctoral Science Foundation (Program No. 2020M683696XB). 47 0 0 22 22 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0967-3334 1361-6579 PHYSIOL MEAS Physiol. Meas. NOV 30 2022.0 43 11 115003 10.1088/1361-6579/ac98f1 0.0 16 Biophysics; Engineering, Biomedical; Physiology Science Citation Index Expanded (SCI-EXPANDED) Biophysics; Engineering; Physiology 5Z6ZA 36215976.0 2023-03-23 WOS:000880117900001 0
J Li, X; Chen, DS; Xu, WP; Chen, HH; Li, JJ; Mo, F Li, Xun; Chen, Dongsheng; Xu, Weipan; Chen, Haohui; Li, Junjun; Mo, Fan Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS English Article MIGRATION Artificial intelligence (AI) has become frequently used in data and knowledge production in diverse domain studies. Scholars began to reflect on the plausibility of AI models that learn unexplained tacit knowledge, spawning the emerging research field, eXplainable AI (XAI). However, superior XAI approaches have yet to emerge that can explain the tacit knowledge acquired by AI models into human-understandable explicit knowledge. This paper proposes a novel eXplainable Dimensionality Reduction (XDR) framework, which aims to effectively translate the high-dimensional tacit knowledge learned by AI into explicit knowledge that is understandable to domain experts. We present a case study of recognizing the ethnic styles of village dwellings in Guangdong, China, via an AI model that can recognize the building footprints from satellite imagery. We find that the patio, size, length, direction and asymmetric shape of the village dwellings are the key to distinguish Canton, Hakka, Teochew or their mixed styles. The data-derived results, including key features, proximity relationships and geographical distribution of the styles are consistent with the findings of existing field studies. Moreover, an evidence of Hakka migration was also found in our results, complementing existing knowledge in architectural and historical geography. This proposed XDR framework can assist experts in diverse fields to further expand their domain knowledge. [Li, Xun; Xu, Weipan; Li, Junjun; Mo, Fan] Sun Yat Sen Univ, Guangzhou, Peoples R China; [Chen, Dongsheng] Tech Univ Munich, Munich, Germany; [Chen, Haohui] CSIRO, Canberra, Australia Sun Yat Sen University; Technical University of Munich; Commonwealth Scientific & Industrial Research Organisation (CSIRO) Li, X (corresponding author), Sun Yat Sen Univ, Guangzhou, Peoples R China. lixun@mail.sysu.edu.cn 43 0 0 1 1 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-9992 HUM SOC SCI COMMUN Hum. Soc. Sci. Commun. JAN 26 2023.0 10 1 35 10.1057/s41599-023-01505-4 0.0 13 Humanities, Multidisciplinary; Social Sciences, Interdisciplinary Social Science Citation Index (SSCI); Arts & Humanities Citation Index (A&HCI) Arts & Humanities - Other Topics; Social Sciences - Other Topics 8H2JR gold 2023-03-23 WOS:000920862100001 0
J Shao, WZ; Bao, BK; Li, HB Shao, Wen-Ze; Bao, Bing-Kun; Li, Hai-Bo Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE English Article Super-resolution; dictionary learning; deep learning; blind deblurring; CNN SPARSE; REGULARIZATION This paper aims to propose a candidate solution to the challenging task of single-image blind super-resolution (SR), via extensively exploring the potentials of learning-based SR schemes in the literature. The task is formulated into an energy functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The functional includes a so-called convolutional consistency term which incorporates a nonblind learning-based SR result to better guide the kernel estimation process, and a bi-L0-L2-norm regularization imposed on both the super-resolved sharp image and the nonparametric blur-kernel. A numerical algorithm is deduced via coupling the splitting augmented Lagrangian (SAL) and the conjugate gradient (CG) method. With the estimated blur-kernel, the final SR image is reconstructed using a simple TV-based nonblind SR method. The proposed blind SR approach is demonstrated to achieve better performance than [T. Michaeli and M. Irani, Nonparametric Blind Super-resolution, in Proc. IEEE Conf. Comput. Vision (IEEE Press, Washington, 2013), pp. 945-952.] in terms of both blur-kernel estimation accuracy and image ehancement quality. In the meanwhile, the experimental results demonstrate surprisingly that the local linear regression-based SR method, anchored neighbor regression (ANR) serves the proposed functional more appropriately than those harnessing the deep convolutional neural networks. [Shao, Wen-Ze; Bao, Bing-Kun; Li, Hai-Bo] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China; [Shao, Wen-Ze] Nanjing Univ Posts & Telecommun, Natl Engn Res Ctr Commun & Networking, Nanjing, Jiangsu, Peoples R China; [Li, Hai-Bo] KTH Royal Inst Technol, Sch Comp Sci & Commun, Stockholm, Sweden Nanjing University of Posts & Telecommunications; Nanjing University of Posts & Telecommunications; Royal Institute of Technology Shao, WZ (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China.;Shao, WZ (corresponding author), Nanjing Univ Posts & Telecommun, Natl Engn Res Ctr Commun & Networking, Nanjing, Jiangsu, Peoples R China. shaowenze@njupt.edu.cn; bingkunbao@njupt.edu.cn; haiboli@kth.se Natural Science Foundation (NSF) of China [61771250, 61402239, 61602257, 61502244]; NSF of Jiangsu Province [GBK20160904, BK20150859]; NSF [16KJB520035]; Open Fund of National Engineering Research Center of Communications and Networking in Nanjing University of Posts and Telecommunications [TXKY17008] Natural Science Foundation (NSF) of China(National Natural Science Foundation of China (NSFC)); NSF of Jiangsu Province; NSF(National Science Foundation (NSF)); Open Fund of National Engineering Research Center of Communications and Networking in Nanjing University of Posts and Telecommunications We would like to thank Dr. Tomer Michaeli for his kind help in running their blind image SR approach, 21 enabling the reported comparison between the proposed method and Ref. 21. Wenze Shao is thankful to Prof. Michael Elad for his invitation of conducting postdoc research in Technion and as well his efforts of polishing the early version of the paper. Wenze Shao also shows great gratitudes to Prof. Zhi-Hui Wei, Prof. Yi-Zhong Ma, Dr. Min Wu, and Mr. Ya-Tao Zhang for their kind supports in the past years. This work was supported in part by the Natural Science Foundation (NSF) of China (Nos. 61771250, 61402239, 61602257, and 61502244), the NSF of Jiangsu Province (Nos. GBK20160904 and BK20150859), the NSF for Jiangsu Institutions (No. 16KJB520035), and the Open Fund of National Engineering Research Center of Communications and Networking in Nanjing University of Posts and Telecommunications (No. TXKY17008). 43 5 5 1 10 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-0014 1793-6381 INT J PATTERN RECOGN Int. J. Pattern Recognit. Artif. Intell. JUN 30 2019.0 33 7 1940007 10.1142/S021800141940007X 0.0 21 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science IC3OY 2023-03-23 WOS:000470871900007 0
J Petropoulos, F; Apiletti, D; Assimakopoulos, V; Babai, MZ; Barrow, DK; Ben Taieb, S; Bergmeir, C; Bessa, RJ; Bijak, J; Boylan, JE; Browell, J; Carnevale, C; Castle, JL; Cirillo, P; Clements, MP; Cordeiro, C; Oliveira, FLC; De Baets, S; Dokumentov, A; Ellison, J; Fiszeder, P; Franses, PH; Frazier, DT; Gilliland, M; Gonul, MS; Goodwin, P; Grossi, L; Grushka-Cockayne, Y; Guidolin, M; Guidolin, M; Gunter, U; Guo, XJ; Guseo, R; Harvey, N; Hendry, DF; Hollyman, R; Januschowski, T; Jeon, J; Jose, VRR; Kang, YF; Koehler, AB; Kolassa, S; Kourentzes, N; Leva, S; Li, F; Litsiou, K; Makridakis, S; Martin, GM; Martinez, AB; Meeran, S; Modis, T; Nikolopoulos, K; Onkal, D; Paccagnini, A; Panagiotelis, A; Panapakidis, I; Pavia, JM; Pedio, M; Pedregal, DJ; Pinson, P; Ramos, P; Rapach, DE; Reade, JJ; Rostami-Tabar, B; Rubaszek, M; Sermpinis, G; Shang, HL; Spiliotis, E; Syntetos, AA; Talagala, PD; Talagala, TS; Tashman, L; Thomakos, D; Thorarinsdottir, T; Todini, E; Arenas, JRT; Wang, XQ; Winkler, RL; Yusupova, A; Ziel, F Petropoulos, Fotios; Apiletti, Daniele; Assimakopoulos, Vassilios; Babai, Mohamed Zied; Barrow, Devon K.; Ben Taieb, Souhaib; Bergmeir, Christoph; Bessa, Ricardo J.; Bijak, Jakub; Boylan, John E.; Browell, Jethro; Carnevale, Claudio; Castle, Jennifer L.; Cirillo, Pasquale; Clements, Michael P.; Cordeiro, Clara; Oliveira, Fernando Luiz Cyrino; De Baets, Shari; Dokumentov, Alexander; Ellison, Joanne; Fiszeder, Piotr; Franses, Philip Hans; Frazier, David T.; Gilliland, Michael; Gonul, M. Sinan; Goodwin, Paul; Grossi, Luigi; Grushka-Cockayne, Yael; Guidolin, Mariangela; Guidolin, Massimo; Gunter, Ulrich; Guo, Xiaojia; Guseo, Renato; Harvey, Nigel; Hendry, David F.; Hollyman, Ross; Januschowski, Tim; Jeon, Jooyoung; Jose, Victor Richmond R.; Kang, Yanfei; Koehler, Anne B.; Kolassa, Stephan; Kourentzes, Nikolaos; Leva, Sonia; Li, Feng; Litsiou, Konstantia; Makridakis, Spyros; Martin, Gael M.; Martinez, Andrew B.; Meeran, Sheik; Modis, Theodore; Nikolopoulos, Konstantinos; Onkal, Dilek; Paccagnini, Alessia; Panagiotelis, Anastasios; Panapakidis, Ioannis; Pavia, Jose M.; Pedio, Manuela; Pedregal, Diego J.; Pinson, Pierre; Ramos, Patricia; Rapach, David E.; Reade, J. James; Rostami-Tabar, Bahman; Rubaszek, Michal; Sermpinis, Georgios; Shang, Han Lin; Spiliotis, Evangelos; Syntetos, Aris A.; Talagala, Priyanga Dilini; Talagala, Thiyanga S.; Tashman, Len; Thomakos, Dimitrios; Thorarinsdottir, Thordis; Todini, Ezio; Arenas, Juan Ramon Trapero; Wang, Xiaoqian; Winkler, Robert L.; Yusupova, Alisa; Ziel, Florian Forecasting: theory and practice INTERNATIONAL JOURNAL OF FORECASTING English Review Review; Encyclopedia; Methods; Applications; Principles; Time series; Prediction CRUDE-OIL PRICE; VALUE-AT-RISK; FUNCTIONAL TIME-SERIES; INTEGRATING MANAGEMENT JUDGMENT; ARTIFICIAL NEURAL-NETWORK; AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY; PREDICTIVE UNCERTAINTY ASSESSMENT; RENEWABLE ENERGY TECHNOLOGIES; PARTICLE SWARM OPTIMIZATION; TUNING PARAMETER SELECTION Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. (C) 2021 The Author( s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. [Petropoulos, Fotios; Goodwin, Paul; Hollyman, Ross; Meeran, Sheik] Univ Bath, Sch Management, Bath, Avon, England; [Apiletti, Daniele] Politecn Torino, Turin, Italy; [Assimakopoulos, Vassilios; Spiliotis, Evangelos] Natl Tech Univ Athens, Sch Elect & Comp Engn, Forecasting & Strategy Unit, Athens, Greece; [Babai, Mohamed Zied] Kedge Business Sch, Talence, France; [Barrow, Devon K.] Univ Birmingham, Birmingham Business Sch, Dept Management, Birmingham, W Midlands, England; [Ben Taieb, Souhaib] Univ Mons UMONS, Big Data & Machine Learning Lab, Mons, Belgium; [Bergmeir, Christoph] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia; [Bessa, Ricardo J.] INESC TEC Inst Syst & Comp Engn Technol & Sci, Porto, Portugal; [Bijak, Jakub; Ellison, Joanne] Univ Southampton, Dept Social Stat & Demog, Southampton, Hants, England; [Boylan, John E.; Kolassa, Stephan; Kourentzes, Nikolaos; Yusupova, Alisa] Univ Lancaster, Lancaster Univ Management Sch, Ctr Mkt Analyt & Forecasting, Lancaster, England; [Browell, Jethro] Univ Glasgow, Sch Math & Stat, Glasgow, Lanark, Scotland; [Carnevale, Claudio] Univ Brescia, Dept Mech & Ind Engn, Brescia, Italy; [Castle, Jennifer L.] Univ Oxford, Magdalen Coll, Oxford, England; [Cirillo, Pasquale] Zurich Univ Appl Sci, ZHAW Sch Management & Law, Zurich, Switzerland; [Clements, Michael P.] Univ Reading, ICMA Ctr, Henley Business Sch, Reading, Berks, England; [Cordeiro, Clara] Univ Algarve, Fac Ciencias & Tecnol, Faro, Portugal; [Cordeiro, Clara] Univ Lisbon, Fac Ciencias, CEAUL, Lisbon, Portugal; [Oliveira, Fernando Luiz Cyrino] Pontifical Catholic Univ Rio de Janeiro PUC Rio, Rio De Janeiro, Brazil; [De Baets, Shari] Univ Ghent, Fac Econ & Business Adm, Dept Business Informat & Operat Management, Ghent, Belgium; [Dokumentov, Alexander] Lets Forecast, Sydney, NSW, Australia; [Fiszeder, Piotr] Nicolaus Copernicus Univ Torun, Fac Econ Sci & Management, Torun, Poland; [Franses, Philip Hans] Erasmus Sch Econ, Econometr Inst, Rotterdam, Netherlands; [Frazier, David T.; Martin, Gael M.] Monash Univ, Dept Econometr & Business Stat, Melbourne, Vic, Australia; [Gilliland, Michael] SAS, Cary, NC USA; [Gonul, M. Sinan; Onkal, Dilek] Northumbria Univ, Newcastle Business Sch, Newcastle Upon Tyne, Tyne & Wear, England; [Grossi, Luigi; Guidolin, Mariangela; Guseo, Renato] Univ Padua, Dept Stat Sci, Padua, Italy; [Grushka-Cockayne, Yael] Univ Virginia, Darden Sch Business, Charlottesville, VA 22903 USA; [Guidolin, Massimo] Bocconi Univ, Finance Dept, Milan, Italy; [Guidolin, Massimo] Baffi CAREFIN Ctr, Milan, Italy; [Gunter, Ulrich] MODUL Univ Vienna, Dept Tourism & Serv Management, Vienna, Austria; [Guo, Xiaojia] Univ Maryland, Robert H Smith Sch Business, College Pk, MD 20742 USA; [Harvey, Nigel] UCL, Dept Expt Psychol, London, England; [Hendry, David F.] Univ Oxford, Nuffield Coll, Oxford Martin Sch, Oxford, England; [Hendry, David F.] Univ Oxford, Inst New Econ Thinking, Oxford Martin Sch, Oxford, England; [Januschowski, Tim] Amazon Res, Munich, Germany; [Jeon, Jooyoung] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Jose, Victor Richmond R.] Georgetown Univ, McDonough Sch Business, Washington, DC 20057 USA; [Kang, Yanfei; Wang, Xiaoqian] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China; [Koehler, Anne B.] Miami Univ, Oxford, OH 45056 USA; [Kolassa, Stephan] Sap, Zurich, Switzerland; [Kourentzes, Nikolaos] Univ Skovde, Sch Informat, Skovde Artificial Intelligence Lab, Skovde, Sweden; [Leva, Sonia] Politecn Milan, Dept Energy, Milan, Italy; [Li, Feng] Cent Univ Finance & Econ, Sch Stat & Math, Beijing, Peoples R China; [Litsiou, Konstantia] Manchester Metropolitan Univ, Business Sch, Manchester, Lancs, England; [Makridakis, Spyros] Univ Nicosia, M Open Forecasting Ctr, Nicosia, Cyprus; [Makridakis, Spyros] Univ Nicosia, Inst Future, Nicosia, Cyprus; [Martinez, Andrew B.] US Dept Treasury, Off Macroecon Anal, Washington, DC 20226 USA; [Martinez, Andrew B.] GWU Res Program Forecasting, Washington, DC USA; [Modis, Theodore] Growth Dynam, Lugano, Switzerland; [Nikolopoulos, Konstantinos] Univ Durham, Durham Univ Business Sch, Durham, England; [Paccagnini, Alessia] Univ Coll Dublin, Michael Smurfit Business Sch, Dublin, Ireland; [Paccagnini, Alessia] Ctr Appl Macroecon Anal, Canberra, ACT, Australia; [Panagiotelis, Anastasios] Univ Sydney, Discipline Business Analyt, Business Sch, Sydney, NSW, Australia; [Panapakidis, Ioannis] Univ Thessaly, Dept Elect & Comp Engn, Volos, Greece; [Pavia, Jose M.] Univ Valencia, Dept Appl Econ, UMMICS, GIPEyOP, Valencia, Spain; [Pedio, Manuela] Univ Bristol, Sch Accounting & Finance, Bristol, Avon, England; [Pedio, Manuela] Bocconi Univ, Baffi CAREFIN Ctr, Milan, Italy; [Pedregal, Diego J.; Arenas, Juan Ramon Trapero] Univ Castilla La Mancha, ETSI Ind, Ciudad Real, Spain; [Pinson, Pierre] Tech Univ Denmark, Dept Technol Management & Econ, Lyngby, Denmark; [Ramos, Patricia] Polytech Porto, Porto Accounting & Business Sch, Porto, Portugal; [Rapach, David E.] St Louis Univ, Dept Econ, St Louis, MO 63103 USA; [Reade, J. James] Univ Reading, Sch Polit Econ & Int Relat, Dept Econ, Reading, Berks, England; [Rostami-Tabar, Bahman; Syntetos, Aris A.] Cardiff Univ, Cardiff Business Sch, Cardiff, S Glam, Wales; [Rubaszek, Michal] SGH Warsaw Sch Econ, Coll Econ Anal, Warsaw, Poland; [Sermpinis, Georgios] Univ Glasgow, Adam Smith Business Sch, Glasgow, Lanark, Scotland; [Shang, Han Lin] Macquarie Univ, Dept Actuarial Studies & Business Analyt, N Ryde, NSW, Australia; [Talagala, Priyanga Dilini] Univ Moratuwa, Dept Computat Math, Moratuwa, Sri Lanka; [Talagala, Thiyanga S.] Univ Sri Jayewardenepura, Fac Appl Sci, Dept Stat, Nugegoda, Sri Lanka; [Tashman, Len] Int Inst Forecasters, Foresight, Medford, MA USA; [Thomakos, Dimitrios] Natl & Kapodistrian Univ Athens, Sch Econ & Polit Sci, Athens, Greece; [Thorarinsdottir, Thordis] Norwegian Comp Ctr, Oslo, Norway; [Todini, Ezio] Univ Bologna, Bologna, Italy; [Todini, Ezio] Italian Hydrol Soc, Bologna, Italy; [Winkler, Robert L.] Duke Univ, Fuqua Sch Business, Durham, NC 27706 USA; [Ziel, Florian] Univ Duisburg Essen, House Energy Markets & Finance, Duisburg, Germany University of Bath; Polytechnic University of Turin; National Technical University of Athens; Kedge Business School; University of Birmingham; University of Mons; Monash University; INESC TEC; University of Southampton; Lancaster University; University of Glasgow; University of Brescia; University of Oxford; Zurich University of Applied Sciences; University of Reading; Universidade do Algarve; Universidade de Lisboa; Pontificia Universidade Catolica do Rio de Janeiro; Ghent University; Nicolaus Copernicus University; Erasmus University Rotterdam; Monash University; Newcastle University - UK; Northumbria University; University of Padua; University of Virginia; Bocconi University; University System of Maryland; University of Maryland College Park; University of London; University College London; University of Oxford; University of Oxford; Korea Advanced Institute of Science & Technology (KAIST); Georgetown University; Beihang University; University System of Ohio; Miami University; University of Skovde; Polytechnic University of Milan; Central University of Finance & Economics; Manchester Metropolitan University; University of Nicosia; University of Nicosia; United States Department of the Treasury; Durham University; University College Dublin; University of Sydney; University of Thessaly; University of Valencia; University of Bristol; Bocconi University; Universidad de Castilla-La Mancha; Technical University of Denmark; Polytechnic Institute of Porto; Saint Louis University; University of Reading; Cardiff University; Warsaw School of Economics; University of Glasgow; Macquarie University; University Moratuwa; University Sri Jayewardenepura; National & Kapodistrian University of Athens; University of Bologna; Duke University; University of Duisburg Essen Petropoulos, F (corresponding author), Univ Bath, Sch Management, Bath, Avon, England. f.petropoulos@bath.ac.uk Cordeiro, Clara/L-4123-2013; Oliveira, Fernando Luiz Cyrino/B-4187-2015; Jeon, Jooyoung/GQZ-2560-2022; De Baets, Shari/P-6859-2017; GROSSI, Luigi/HOH-7776-2023; Pinson, Pierre/GPW-6451-2022; Fiszeder, Piotr/D-3979-2014; Guidolin, Mariangela/GRS-3871-2022; Bessa, Ricardo/G-5762-2017; Gregorio Ramos, Patricia Alexandra/B-2728-2017 Cordeiro, Clara/0000-0002-1026-6078; Oliveira, Fernando Luiz Cyrino/0000-0003-1870-9440; De Baets, Shari/0000-0003-2399-3378; GROSSI, Luigi/0000-0003-3671-7348; Pinson, Pierre/0000-0002-1480-0282; Fiszeder, Piotr/0000-0002-9777-2239; Guidolin, Mariangela/0000-0002-4607-0915; Bergmeir, Christoph/0000-0002-3665-9021; Shang, Han Lin/0000-0003-1769-6430; Ziel, Florian/0000-0002-2974-2660; Bessa, Ricardo/0000-0002-3808-0427; Panagiotelis, Anastasios/0000-0001-8678-7294; Petropoulos, Fotios/0000-0003-3039-4955; Bijak, Jakub/0000-0002-2563-5040; Ellison, Joanne/0000-0002-6973-8797; ROSTAMI TABAR, Bahman/0000-0002-3730-0045; Gregorio Ramos, Patricia Alexandra/0000-0002-0959-8446 European Union [870299]; FCT -Fundacao para a Ciencia e a Tecnologia, Portugal [UIDB/00006/2020]; Coordination for the Improvement of Higher Level Personnel (CAPES), Brazil [001]; Brazilian National Council for Scientific and Technological Development (CNPq) [307403/2019-0]; Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ) [202.673/2018, 211.086/2019]; FWO Research Foundation Flanders; ESRC FertilityTrends project [ES/S009477/1]; ESRC Centre for Population Change [ES/R009139/1]; National Science Centre, Poland [2016/21/B/HS4/00662, 2019/33/B/HS4/01923]; Australian Research Council (ARC) [DP200101414, DE200101070, DP170100729]; University of Padua, Italy [BIRD188753/18]; Robertson Foundation, USA; Nuffield College, UK; National Natural Science Foundation of China [11701022]; National Key Research and Development Program, China [2019YFB1404600]; COST Action [CA19130]; Spanish Ministry of Science, Innovation and Universities; Spanish Agency of Research; FEDER funds [ECO2017-87245-R]; Conselleria d'Innovacio, Universitats, Ciencia i Societat Digital, Generalitat Valenciana [AICO/2019/053]; European Regional Development Fund [SBPLY/19/180501/000151]; Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) [SBPLY/19/180501/000151]; Vicerrectorado de Investigacion y Politica Cientifica from UCLM, Spain through the research group fund program PREDILAB [DOCM 26/02/2020 [2020-GRIN-28770]]; Australian Research Council [DP200101414, DE200101070] Funding Source: Australian Research Council European Union(European Commission); FCT -Fundacao para a Ciencia e a Tecnologia, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); Coordination for the Improvement of Higher Level Personnel (CAPES), Brazil(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); Brazilian National Council for Scientific and Technological Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FWO Research Foundation Flanders(FWO); ESRC FertilityTrends project; ESRC Centre for Population Change(UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)); National Science Centre, Poland(National Science Centre, Poland); Australian Research Council (ARC)(Australian Research Council); University of Padua, Italy; Robertson Foundation, USA; Nuffield College, UK; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program, China; COST Action(European Cooperation in Science and Technology (COST)); Spanish Ministry of Science, Innovation and Universities(Spanish Government); Spanish Agency of Research; FEDER funds(European Commission); Conselleria d'Innovacio, Universitats, Ciencia i Societat Digital, Generalitat Valenciana(Center for Forestry Research & Experimentation (CIEF)); European Regional Development Fund(European Commission); Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE); Vicerrectorado de Investigacion y Politica Cientifica from UCLM, Spain through the research group fund program PREDILAB; Australian Research Council(Australian Research Council) Fotios Petropoulos would like to thank all the coauthors of this article for their very enthusiastic response and participation in this initiave. He would also like to thank Pierre Pinson for inviting this paper to be submitted to the International Journal of Forecasting. The constructive comments and suggestions from this advisory board were vital in improving the paper. He also thanks Artur Tarassow for offering a list of Gretl's software functionalities.; Jakub Bijak's work received funding from the European Union's Horizon 2020 research and innovation programme, grant 870299 QuantMig: Quantifying Migration Scenarios for Better Policy.; Clara Cordeiro is partially financed by national funds through FCT -Fundacao para a Ciencia e a Tecnologia, Portugal under the project UIDB/00006/2020.; Fernando Luiz Cyrino Oliveira acknowledges the support of the Coordination for the Improvement of Higher Level Personnel (CAPES), Brazil -grant number 001, the Brazilian National Council for Scientific and Technological Development (CNPq) -grant number 307403/2019-0, and the Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ) -grant numbers 202.673/2018 and 211.086/2019.; Shari De Baets was funded by the FWO Research Foundation Flanders.; Joanne Ellison acknowledges the support of the ESRC FertilityTrends project (grant number ES/S009477/1) and the ESRC Centre for Population Change (grant number ES/R009139/1).; Piotr Fiszeder was supported by the National Science Centre, Poland project number 2016/21/B/HS4/00662 entitled ``Multivariate volatility models -the application of low and high prices''.; David T. Frazier has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414, and ARC Early Career Researcher Award DE200101070.; Mariangela Guidolin acknowledges the support of the University of Padua, Italy, through the grant BIRD188753/18. David F. Hendry gratefully acknowledges funding from the Robertson Foundation, USA and Nuffield College, UK.; Yanfei Kang acknowledges the support of the National Natural Science Foundation of China (number 11701022) and the National Key Research and Development Program, China (number 2019YFB1404600).; Stephan Kolassa would like to thank Tilmann Gneiting for some very helpful tips.; Gael M. Martin has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414.; Alessia Paccagnini acknowledges the research support by COST Action ``Fintech and Artificial Intelligence in Finance -Towards a transparent financial industry'' (FinAI) CA19130.; Jose M. Pavia acknowledges the support of the Spanish Ministry of Science, Innovation and Universities and the Spanish Agency of Research, co-funded with FEDER funds, grant ECO2017-87245-R, and of Conselleria d'Innovacio, Universitats, Ciencia i Societat Digital, Generalitat Valenciana -grant number AICO/2019/053.; Diego J. Pedregal and Juan Ramon Trapero Arenas acknowledge the support of the European Regional Development Fund and Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) under the project SBPLY/19/180501/000151 and by the Vicerrectorado de Investigacion y Politica Cientifica from UCLM, Spain through the research group fund program PREDILAB; DOCM 26/02/2020 [2020-GRIN-28770].; Michal Rubaszek is thankful for the financial support provided by the National Science Centre, Poland, grant No. 2019/33/B/HS4/01923 entitled ``Predictive content of equilibrium exchange rate models''. 2132 49 49 124 131 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0169-2070 1872-8200 INT J FORECASTING Int. J. Forecast. JUL-SEP 2022.0 38 3 705 871 10.1016/j.ijforecast.2021.11.001 0.0 JUN 2022 167 Economics; Management Social Science Citation Index (SSCI) Business & Economics 3O4KG Green Submitted, Green Accepted, Green Published, hybrid 2023-03-23 WOS:000836807400001 0
J Gao, H; Guo, DF; Wu, J; Zhao, QT; Li, LA Gao, Hao; Guo, Difan; Wu, Jing; Zhao, Qingting; Li, Lina Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts FRONTIERS IN PUBLIC HEALTH English Article COVID-19; vaccines; vaccination; official announcement; public attitude; China PREFERENCES Introduction: On December 31, 2020, the Chinese government announced that the domestic coronavirus disease-2019 (COVID-19) vaccines have obtained approval for conditional marketing and are free for vaccination. This release drove the attention of the public and intense debates on social media, which reflected public attitudes to the domestic vaccine. This study examines whether the public concerns and public attitudes to domestic COVID-19 vaccines changed after the official announcement.Methods: This article used big data analytics in the research, including semantic network and sentiment analysis. The purpose of the semantic network is to obtain the public concerns about domestic vaccines. Sentiment analysis reflects the sentiments of the public to the domestic vaccines and the emotional changes by comparing the specific sentiments shown on the posts before and after the official announcement.Results: There exists a correlation between the public concerns about domestic vaccines before and after the official announcement. According to the semantic network analysis, the public concerns about vaccines have changed after the official announcement. The public focused on the performance issues of the vaccines before the official approval, but they cared more about the practical issues of vaccination after that. The sentiment analysis showed that both positive and negative emotions increased among the public after the official announcement. Good was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. Fear was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines.Conclusion: The official announcement of the approval for marketing improved the Chinese public acceptance of the domestic COVID-19 vaccines. In addition, safety and effectiveness are vital factors influencing public vaccine hesitancy. [Gao, Hao; Guo, Difan; Zhao, Qingting] Nanjing Normal Univ, Sch Journalism & Commun, Nanjing, Peoples R China; [Wu, Jing] Univ Ljubljana, Fac Social Sci, Ljubljana, Slovenia; [Li, Lina] Shanghai Normal Univ, Film Televis & Commun Coll, Shanghai, Peoples R China Nanjing Normal University; University of Ljubljana; Shanghai Normal University Li, LA (corresponding author), Shanghai Normal Univ, Film Televis & Commun Coll, Shanghai, Peoples R China. lilina@shnu.edu.cn 郭, 迪帆/AFL-5646-2022 郭, 迪帆/0000-0002-0048-985X; Lina, Li/0000-0003-1484-5883; Gao, Hao/0000-0002-9749-008X; ZHAO, QINGTING/0000-0001-9104-0355 National Social Science Foundation of China [17CXW016] National Social Science Foundation of China Funding This work was supported by grants from the National Social Science Foundation of China (17CXW016). 45 6 6 10 43 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-2565 FRONT PUBLIC HEALTH Front. Public Health NOV 11 2021.0 9 723015 10.3389/fpubh.2021.723015 0.0 11 Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Public, Environmental & Occupational Health XT7AR 34858918.0 Green Accepted, gold 2023-03-23 WOS:000733736200001 0
J Tang, Y; Yen, GRY; Kurths, J Tang, Yang; Yen, Gary; Kurths, Juergen Special Issue on Computational Intelligence for Perception and Decision-Making of Autonomous Systems IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE English Editorial Material Special issues and sections; Artificial intelligence; Computational intelligence; Decision making; Autonomous systems; Sensor systems; Real-time systems; Neural networks; Natural language processing; Intelligent sensors; Evolutionary computation The papers in this special section focus on computational intelligence for perception and decision making of autonomous systems. Due to powerful capabilities in environmental perception, real-time computing, and intelligent decision-making, autonomous systems have demonstrated their great potential to efficiently accomplish a variety of complex tasks that humans cannot. Hence, autonomous systems are able to facilitate the development of almost every walk of life and have attracted increasing attention from both academia and industry. However, given high dimensional, heterogeneous, unstructured, and unpredictable data sampled from different modalities of sensors, autonomous systems with conventional algorithms may fail to acquire the accurate information related to the environment, and make the appropriate decision to complete assigned tasks. Notice that recent advanced computational intelligence algorithms including deep neural networks and evolutionary algorithms have the unique ability to efficiently extract useful information from the multi-source heterogeneous data, and thus have been successfully applied in the fields of computer vision, natural language processing, and so on. Therefore, it is promising to have a thorough and tight integration between computational intelligence and autonomous systems by upgrading advanced and innovative computational intelligence algorithms to ensure high-level environmental perception and decision-making of autonomous systems. [Tang, Yang] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, 130 Meilong Rd, Shanghai, Peoples R China; [Yen, Gary] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA; [Kurths, Juergen] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany East China University of Science & Technology; Oklahoma State University System; Oklahoma State University - Stillwater; Humboldt University of Berlin Tang, Y (corresponding author), East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, 130 Meilong Rd, Shanghai, Peoples R China. tangtany@gmail.com; gyen@okstate.edu; juergen.kurths@pik-potsdam.de Yen, Gary/0000-0001-8851-5348; Kurths, Juergen/0000-0002-5926-4276 0 0 0 4 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2471-285X IEEE TETCI IEEE Trans. Emerg. Top. Comput. Intell. DEC 2022.0 6 6 1287 1289 10.1109/TETCI.2022.3219539 0.0 3 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6S3XF Bronze 2023-03-23 WOS:000892923300001 0
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D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Velicanu, D.; Wang, J.; Wang, T. W.; Wyslouch, B.; Zhaozhong, S.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Kalafut, S.; Kubota, Y.; Lesko, Z.; Mans, J.; Ruckstuhl, N.; Rusack, R.; Turkewitz, J.; Wadud, M. A.; Acosta, G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Golf, F.; Suarez, R. Gonzalez; Kamalieddin, R.; Kravchenko, I.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kharchilava, A.; Mclean, C.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Freer, C.; Hortiangtham, A.; Morse, D. M.; Orimoto, T.; De Lima, R. Teixeira; Wamorkar, T.; Wang, B.; Wisecarver, A.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Mucia, N.; Odell, N.; Schmitt, M. H.; Sevova, S.; Sung, K.; Trovato, M.; Velasco, M.; Bucci, R.; Dev, N.; Hildreth, M.; Anampa, K. Hurtado; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Li, W.; Loukas, N.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Siddireddy, P.; Smith, G.; Taroni, S.; Wayne, M.; Wightman, A.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Ji, W.; Ling, T. Y.; Luo, W.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Elmer, P.; Hardenbrook, J.; Higginbotham, S.; Kalogeropoulos, A.; Lange, D.; Lucchini, M. T.; Luo, J.; Marlow, D.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroue, P.; Salfeld-Nebgen, J.; Stickland, D.; Tully, C.; Malik, S.; Norberg, S.; Barker, A.; Barnes, V. E.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Khatiwada, A.; Mahakud, B.; Miller, D. H.; Neumeister, N.; Peng, C. C.; Qiu, H.; Schulte, J. F.; Sun, J.; Wang, F.; Xiao, R.; Xie, W.; Cheng, T.; Dolen, J.; Parashar, N.; Chen, Z.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Kilpatrick, M.; Li, W.; Michlin, B.; Padley, B. P.; Roberts, J.; Rorie, J.; Shi, W.; Tu, Z.; Zabel, J.; Zhang, A.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Taus, R.; Verzetti, M.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Espinosa, T. A. Gomez; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Elayavalli, R. Kunnawalkam; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Heideman, J.; Riley, G.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Luo, S.; Mueller, R.; Patel, R.; Perloff, A.; Pernie, L.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Alvarez, J. D. Ruiz; Sheldon, P.; Tuo, S.; Velkovska, J.; Verweij, M.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Carlsmith, D.; Dasu, S.; Dodd, L.; Gomber, B.; Grothe, M.; Herndon, M.; Herve, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Long, K.; Loveless, R.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Woods, N.; Kim, Y. CMS Collaboration Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at root s=13 TeV JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron scattering (experiments); Higgs physics SYMMETRIES; ALGORITHMS; MASS A search for standard model Higgs bosons (H) produced with transverse momentum (p(T)) greater than 450 GeV and decaying to bottom quark-antiquark pairs (b (b) over bar) is performed using proton-proton collision data collected by the CMS experiment at the LHC at root s = 13 TeV. The data sample corresponds to an integrated luminosity of 137 fb(-1). The search is inclusive in the Higgs boson production mode. Highly Lorentz-boosted Higgs bosons decaying to b (b) over bar are reconstructed as single large-radius jets, and are identified using jet substructure and a dedicated b tagging technique based on a deep neural network. The method is validated with Z -> b (b) over bar decays. For a Higgs boson mass of 125 GeV, an excess of events above the background assuming no Higgs boson production is observed with a local significance of 2.5 standard deviations (sigma), while the expectation is 0.7. The corresponding signal strength and local significance with respect to the standard model expectation are mu(H) = 3.7 +/- 1.2(stat)(-0.7)(+0.8)(syst)(-0.5)(+0.8)(theo) and 1.9 sigma. Additionally, an unfolded differential cross section as a function of Higgs boson p(T) for the gluon fusion production mode is presented, assuming the other production modes occur at the expected rates. [Sirunyan, A. M.; Tumasyan, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Dragicevic, M.; Eroe, J.; Del Valle, A. Escalante; Flechl, M.; Fruehwirth, R.; Ghete, V. M.; Hrubec, J.; Jeitler, M.; Krammer, N.; Kraetschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Rad, N.; Rohringer, H.; Schieck, J.; Schoefbeck, R.; Spanring, M.; Spitzbart, D.; Taurok, A.; Waltenberger, W.; Wittmann, J.; Wulz, C. -E.; Zarucki, M.] Inst Hochenergiephys, Vienna, Austria; [Chekhovsky, V.; Mossolov, V.; Gonzalez, J. Suarez] Inst Nucl Problems, Minsk, BELARUS; [De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van De Klundert, M.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.] Univ Antwerp, Antwerp, Belgium; [Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I.; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.] Vrije Univ Brussel, Brussels, Belgium; [Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. 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G.; Romero Abad, D.] Univ Fed ABC, Sao Paulo, Brazil; [Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; [Litov, L.; Pavlov, B.; Petkov, P.] Univ Sofia, Sofia, Bulgaria; [Fang, W.; Gao, X.; Yuan, L.] Beihang Univ, Beijing, Peoples R China; [Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhao, J.] Inst High Energy Phys, Beijing, Peoples R China; [Ban, Y.; Chen, G.; Levin, A.; Li, J.; Li, L.; Li, Q.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.] Peking Univ, State Key Lab Nucl Phys Technol, Beijing, Peoples R China; [Wang, Y.] Tsinghua Univ, Beijing, Peoples R China; [Avila, C.; Cabrera, A.; Carrillo Montoya, C. A.; Chaparro Sierra, L. F.; Florez, C.; Gonzalez Hernandez, C. F.; Segura Delgado, M. A.] Univ Los Andes, Bogota, Colombia; [Alvarez, J. D. Ruiz] Univ Antioquia, Medellin, Colombia; [Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I.; Sculac, T.] Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; [Antunovic, Z.; Kovac, M.] Univ Split, Fac Sci, Split, Croatia; [Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.] Inst Rudjer Boskovic, Zagreb, Croatia; [Ather, M. W.; Attikis, A.; Kolosova, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.] Univ Cyprus, Nicosia, Cyprus; [Finger, M.; Finger, M., Jr.] Charles Univ Prague, Prague, Czech Republic; [Ayala, E.] Escuela Politec Nacl, Quito, Ecuador; [Jarrin, E. Carrera] Univ San Francisco Quito, Quito, Ecuador; [Assran, Y.; Elgammal, S.; Mohammed, Y.] Egyptian Network High Energy Phys, Acad Sci Res & Technol Arab Republ Egypt, Cairo, Egypt; [Bhowmik, S.; De Oliveira, A. Carvalho Antunes; Dewanjee, R. 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Centis; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hinzmann, A.; Karavdina, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Kurz, S.; Kutzner, V.; Lange, J.; Marconi, D.; Multhaup, J.; Niedziela, M.; Nowatschin, D.; Perieanu, A.; Reimers, A.; Rieger, O.; Scharf, C.; Schleper, P.; Schumann, S.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Stober, F. M.; Stoever, M.; Troendle, D.; Vanhoefer, A.; Vormwald, B.] Univ Hamburg, Hamburg, Germany; [Akbiyik, M.; Barth, C.; Baselga, M.; Baur, S.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; El Morabit, K.; Faltermann, N.; Freund, B.; Giffels, M.; Harrendorf, M. A.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Kassel, F.; Katkov, I.; Kudella, S.; Mildner, H.; Mitra, S.; Mozer, M. U.; Mueller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Schroder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Woehrmann, C.; Wolf, R.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Paspalaki, G.; Topsis-Giotis, I.] INPP, NCSR Demokritos, Aghia Paraskevi, Greece; [Karathanasis, G.; Kesisoglou, S.; Kontaxakis, P.; Panagiotou, A.; Papavergou, I.; Saoulidou, N.; Tziaferi, E.; Vellidis, K.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Kousouris, K.; Papakrivopoulos, I.; Tsipolitis, G.] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I.; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Mallios, S.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Triantis, F. A.; Tsitsonis, D.] Univ Ioannina, Ioannina, Greece; [Bartok, M.; Csanad, M.; Filipovic, N.; Major, P.; Nagy, M. I.; Pasztor, G.; Suranyi, O.; Veres, G. I.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bencze, G.; Hajdu, C.; Horvath, D.; Hunyadi, A.; Sikler, F.; Vami, T. A.; Veszpremi, V.; Vesztergombi, G.] Wigner Res Ctr Phys, Budapest, Hungary; [Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Raics, P.; Trocsanyi, Z. L.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Choudhury, S.; Komaragiri, J. R.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Kar, C.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Swain, S. K.] Natl Inst Sci Educ Res, HBNI, Bhubaneswar, Orissa, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chauhan, S.; Chawla, R.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, A.; Kaur, M.; Kaur, S.; Kumar, R.; Kumari, P.; Lohan, M.; Mehta, A.; Sandeep, K.; Sharma, S.; Singh, J. B.; Walia, G.] Panjab Univ, Chandigarh, India; [Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Kumar, Ashok; Malhotra, S.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, Aashaq; Sharma, R.] Univ Delhi, Delhi, India; [Bhardwaj, R.; Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhawandeep, U.; Bhowmik, D.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Mondal, K.; Nandan, S.; Purohit, A.; Rout, P. K.; Roy, A.; Chowdhury, S. Roy; Saha, G.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] Saha Inst Nucl Phys, HBNI, Kolkata, India; [Behera, P. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Bhat, M. A.; Dugad, S.; Mohanty, G. B.; Sur, N.; Sutar, B.; Verma, Ravindra Kumar] Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; [Banerjee, S.; Bhattacharya, S.; Chatterjee, S.; Das, P.; Guchait, M.; Jain, Sa.; Karmakar, S.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Sahoo, N.; Sarkar, T.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Chauhan, S.; Dube, S.; Hegde, V.; Kapoor, A.; Kothekar, K.; Pandey, S.; Rane, A.; Sharma, S.] IISER, Pune, Maharashtra, India; [Chenarani, S.; Tadavani, E. Eskandari; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi; Naseri, M.; Hosseinabadi, F. Rezaei; Safarzadeh, B.; Zeinali, M.] Inst Res Fundamental Sci IPM, Tehran, Iran; [Felcini, M.; Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Calabria, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Di Florio, A.; Errico, F.; Fiore, L.; Gelmi, A.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Zito, G.] INFN, Sez Bari, Bari, Italy; [Abbrescia, M.; Calabria, C.; Cristella, L.; De Palma, M.; Di Florio, A.; Errico, F.; Gelmi, A.; Ince, M.; Lezki, S.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Selvaggi, G.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Ciocca, C.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Iemmi, F.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] INFN, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Fanfani, A.; Guiducci, L.; Iemmi, F.; Navarria, F. L.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] INFN, Sez Catania, Catania, Italy; [Albergo, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Chatterjee, K.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.] INFN, Sez Firenze, Florence, Italy; [Chatterjee, K.; Ciulli, V.; D'Alessandro, R.; Focardi, E.; Lenzi, P.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.] INFN, Lab Nazl Frascati, Frascati, Italy; [Barbagli, G.; Chatterjee, K.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.] INFN, Sez Genova, Genoa, Italy; [Chatterjee, K.; Ciulli, V.; D'Alessandro, R.; Focardi, E.; Lenzi, P.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V.; Di Guida, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; de Fatis, T. Tabarelli; Zuolo, D.] INFN, Sez Milano Bicocca, Milan, Italy; [Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V.; Di Guida, S.; Dinardo, M. E.; Fiorendi, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Massironi, A.; Paganoni, M.; Ragazzi, S.; de Fatis, T. Tabarelli] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Cavallo, N.; Di Crescenzo, A.; Fabozzi, F.; Fienga, F.; Galati, G.; Iorio, A. O. M.; Khan, W. A.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Voevodina, E.] INFN, Sez Napoli, Naples, Italy; [Di Crescenzo, A.; Iorio, A. O. M.; Sciacca, C.; Voevodina, E.] Univ Napoli Federico II, Rome, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Boletti, A.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Hoh, S. Y.; Lacaprara, S.; Lujan, P.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tiko, A.; Torassa, E.; Ventura, S.; Zanetti, M.; Zotto, P.; Zumerle, G.] INFN, Sez Padova, Trento, NJ USA; [Boletti, A.; Gasparini, F.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Zanetti, M.; Zotto, P.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.] INFN, Sez Pavia, Pavia, Italy; [Montagna, P.; Ratti, S. P.; Ressegotti, M.; Riccardi, C.; Vai, I.; Vitulo, P.] Univ Pavia, Pavia, Italy; [Solestizi, L. Alunni; Biasini, M.; Bilei, G. M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Manoni, E.; Mantovani, G.; Mariani, V.; Menichelli, M.; Rossi, A.; Santocchia, A.; Spiga, D.] INFN, Sez Perugia, Perugia, Italy; [Solestizi, L. Alunni; Biasini, M.; Cecchi, C.; Ciangottini, D.; Fano, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Mariani, V.; Rossi, A.; Santocchia, A.] Univ Perugia, Perugia, Italy; [Androsov, K.; Azzurri, P.; Bagliesi, G.; Bianchini, L.; Boccali, T.; Borrello, L.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fedi, G.; Fiori, F.; Giannini, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Rizzi, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.] INFN, Sez Pisa, Pisa, Italy; [Ciocci, M. A.; Messineo, A.; Rizzi, A.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Fiori, F.; Giannini, L.; Ligabue, F.; Manca, E.; Mandorli, G.] Scuola Normale Super Pisa, Pisa, Italy; [Barone, L.; Cavallari, F.; Cipriani, M.; Daci, N.; Del Re, D.; Di Marco, E.; Diemoz, M.; Gelli, S.; Longo, E.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.] INFN, Sez Roma, Rome, Italy; [Barone, L.; Cipriani, M.; Del Re, D.; Di Marco, E.; Gelli, S.; Longo, E.; Marzocchi, B.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Santanastasio, F.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Soldi, D.; Staiano, A.] INFN, Sez Torino, Novara, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Cenna, F.; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, G. L. Pinna; Romero, A.; Sacchi, R.; Shchelina, K.; Solano, A.; Soldi, D.] Univ Torino, Novara, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Vazzoler, F.; Zanetti, A.; Gurrola, A.] INFN, Sez Trieste, Trieste, Italy; [Candelise, V.; Della Ricca, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.; Oh, G.] Chonnam Natl Univ, Inst Univ Elementary Particles, Kwangju, South Korea; [Goh, J.; Kim, T. J.] Hanyang Univ, Seoul, South Korea; [Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.] Korea Univ, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Almond, J.; Kim, J.; Kim, J. S.; Lee, H.; Lee, K.; Nam, K.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. H.; Yang, U. K.; Yoo, H. D.; Yu, G. B.] Seoul Natl Univ, Seoul, South Korea; [Jeon, D.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.] Univ Seoul, Seoul, South Korea; [Choi, Y.; Hwang, C.; Lee, J.; Yu, I.] Sungkyunkwan Univ, Suwon, South Korea; [Dudenas, V.; Juodagalvis, A.; Vaitkus, J.] Vilnius Univ, Vilnius, Lithuania; [Ahmed, I.; Ibrahim, Z. A.; Ali, M. A. B. Md; Idris, F. Mohamad; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Hernandez, A. Castaneda; Quijada, J. A. Murillo] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Castilla-Valdez, H.; De La Cruz-Burelo, E.; Duran-Osuna, M. C.; Heredia-De La Cruz, I.; Lopez-Fernandez, R.; Guisao, J. Mejia; Rabadan-Trejo, R. I.; Ramirez-Garcia, M.; Ramirez-Sanchez, G.; Reyes-Almanza, R.; Sanchez-Hernandez, A.] Ctr Invest & Estudios Avanzados IPN, Mexico City, DF, Mexico; [Moreno, S. Carrillo; Barrera, C. Oropeza; Valencia, F. Vazquez] Univ Iberoamer, Mexico City, DF, Mexico; [Eysermans, J.; Pedraza, I.; Ibarguen, H. A. Salazar; Estrada, C. Uribe] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Pineda, A. Morelos] Univ Autonoma San Luis Potosi, San Luis Potosi, Mexico; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Ahmad, M.; Asghar, M. I.; Hassan, Q.; Hoorani, H. R.; Qazi, S.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Nawrocki, K.; Szleper, M.; Traczyk, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Byszuk, A.; Doroba, K.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Pyskir, A.; Walczak, M.] Univ Warsaw, Inst Expt Phys, Fac Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Beirao Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Galinhas, B.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Nemallapudi, M. V.; Seixas, J.; Strong, G.; Toldaiev, O.; Vadruccio, D.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Afanasiev, S.; Alexakhin, V.; Bunin, P.; Gavrilenko, M.; Golunov, A.; Golutvin, I.; Gorbounov, N.; Karjavin, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Savina, M.; Shmatov, S.; Smirnov, V.; Voytishin, N.; Zarubin, A.] Joint Inst Nucl Res, Dubna, Russia; [Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sosnov, D.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst Gatchina, St Petersburg, Russia; [Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.] Inst Nucl Res, Moscow, Russia; [Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Stepennov, A.; Stolin, V.; Toms, M.; Vlasov, E.; Zhokin, A.] Inst Theoret Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys Technol, Moscow, Russia; [Chistov, R.; Danilov, M.; Parygin, P.; Philippov, D.; Polikarpov, S.; Tarkovskii, E.] Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Rusakov, S. V.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Baskakov, A.; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V.; Dimova, T.; Kardapoltsev, L.; Shtol, D.; Skovpen, Y.] NSU, Novosibirsk, Russia; [Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Godizov, A.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Mandrik, P.; Petrov, V.; Ryutin, R.; Slabospitskii, S.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] State Res Ctr Russian Federat, NRC Kurchatov Inst, Inst High Energy Phys, Protvino, Russia; [Babaev, A.; Baidali, S.; Okhotnikov, V.] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.] Univ Belgrade, Fac Phys, Belgrade, Serbia; [Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.] Vinca Inst Nucl Sci, Belgrade, Serbia; [Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I.; Barrio Luna, M.; Brochero Cifuentes, J. A.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Bedoya, C.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Moran, D.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I.; Romero, L.; Soares, M. S.; Triossi, A.] Ctr Invest Energet Medioambient Tecnol CIEM, Madrid, Spain; [Albajar, C.; de Troconiz, J. F.] Univ Autonoma Madrid, Madrid, Spain; [Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Gonzalez Fernandez, J. R.; Palencia Cortezon, E.; Rodriguez Bouza, V.; Sanchez Cruz, S.; Vischia, P.; Vizan Garcia, J. M.] Univ Oviedo, Oviedo, Spain; [Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.] Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; [Abbaneo, D.; Akgun, B.; Auffray, E.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Botta, C.; Brondolin, E.; Camporesi, T.; Cepeda, M.; Cerminara, G.; Chapon, E.; Chen, Y.; Cucciati, G.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Roeck, A.; Deelen, N.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fallavollita, F.; Fasanella, D.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gilbert, A.; Gill, K.; Glege, F.; Guilbaud, M.; Gulhan, D.; Hegeman, J.; Innocente, V.; Jafari, A.; Janot, P.; Karacheban, O.; Kieseler, J.; Kornmayer, A.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenco, C.; Malgeri, L.; Mannelli, M.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Ngadiuba, J.; Nourbakhsh, S.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pitters, F. M.; Rabady, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schafer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Sharma, A.; Silva, P.; Sphicas, P.; Stakia, A.; Steggemann, J.; Tosi, M.; Treille, D.; Tsirou, A.; Veckalns, V.; Zeuner, W. D.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.] Paul Scherrer Inst, Villigen, Switzerland; [Backhaus, M.; Bani, L.; Berger, P.; Chernyavskaya, N.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Klijnsma, T.; Lustermann, W.; Manzoni, R. A.; Marionneau, M.; Meinhard, M. T.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Pigazzini, S.; Quittnat, M.; Ruini, D.; Becerra, D. A. Sanz; Schonenberger, M.; Shchutska, L.; Tavolaro, V. R.; Theofilatos, K.; Olsson, M. L. Vesterbacka; Wallny, R.; Zhu, D. H.] ETH Zurich Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Aarrestad, T. K.; Amsler, C.; Brzhechko, D.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Neutelings, I.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Schweiger, K.; Seitz, C.; Takahashi, Y.; Zucchetta, A.] Univ Zurich, Zurich, Switzerland; [Chang, Y. H.; Cheng, K. Y.; Doan, T. H.; Jain, Sh.; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W. -S.; Kumar, Arun; Li, Y. y.; Liu, Y. F.; Lu, R. -S.; Paganis, E.; Psallidas, A.; Steen, A.] NTU, Taipei, Taiwan; [Asavapibhop, B.; Srimanobhas, N.; Suwonjandee, N.] Chulalongkorn Univ, Dept Phys, Fac Sci, Bangkok, Thailand; [Bat, A.; Boran, F.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Isik, C.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Dept Phys, Sci & Art Fac, Adana, Turkey; [Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.] Middle East Tech Univ, Dept Phys, Ankara, Turkey; [Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Ozkorucuklu, S.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Kharkov Inst Phys Technol, Natl Sci Ctr, Kharkov, Ukraine; [Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Newbold, D. M.; Paramesvaran, S.; Penning, B.; Sakuma, T.; Smith, D.; Smith, V. J.; Taylor, J.; Titterton, A.] Univ Bristol, Bristol, Avon, England; [Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Linacre, J.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, J.] Ruthetford Appleton Lab, Didcot, Oxon, England; [Auzinger, G.; Bainbridge, R.; Bloch, P.; Borg, J.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; Della Negra, M.; Di Maria, R.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Komm, M.; Laner, C.; Lyons, L.; Magnan, A. -M.; Malik, S.; Martelli, A.; Nash, J.; Nikitenko, A.; Palladino, V.; Pesaresi, M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Singh, G.; Stoye, M.; Strebler, T.; Summers, S.; Tapper, A.; Uchida, K.; Virdee, T.; Wardle, N.; Winterbottom, D.; Wright, J.; Zenz, S. C.] Imperial Coll, London, England; [Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Mackay, C. K.; Morton, A.; Reid, I. D.; Teodorescu, L.; Zahid, S.] Brunel Univ, Uxbridge, Middx, England; [Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Madrid, C.; Mcmaster, B.; Pastika, N.; Smith, C.] Baylor Univ, Waco, TX 76798 USA; [Bartek, R.; Dominguez, A.] Catholic Univ Amer, Washington, DC 20064 USA; [Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.] Univ Alabama, Tuscaloosa, AL USA; [Arcaro, D.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.] Boston Univ, Boston, MA 02215 USA; [Benelli, G.; Coubez, X.; Cutts, D.; Hadley, M.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Lee, J.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Syarif, R.; Usai, E.; Yu, D.] Brown Univ, Providence, RI 02912 USA; [Band, R.; Brainerd, C.; Breedon, R.; Burns, D.; Sanchez, M. Calderon De La Barca; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Ko, W.; Kukral, O.; Lander, R.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Stolp, D.; Taylor, D.; Tos, K.; Tripathi, M.; Wang, Z.; Zhang, F.] Univ Calif Davis, Davis, CA 95616 USA; [Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Regnard, S.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Bouvier, E.; Burt, K.] Univ Calif Los Angeles, Los Angeles, CA USA; [Clare, R.; Gary, J. W.; Shirazi, S. M. A. Ghiasi; Hanson, G.; Karapostoli, G.; Kennedy, E.; Lacroix, F.; Long, O. R.; Negrete, M. Olmedo; Paneva, M. I.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Branson, J. G.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Gilbert, D.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V.; Letts, J.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Universidade Federal do ABC (UFABC); Bulgarian Academy of Sciences; University of Sofia; Beihang University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Tsinghua University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; Rudjer Boskovic Institute; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Georgian Technical University; Ivane Javakhishvili Tbilisi State University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Indian Institute of Science (IISC) - Bangalore; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Sejong University; Seoul National University (SNU); University of Seoul; Sungkyunkwan University (SKKU); Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; Universidad Autonoma de San Luis Potosi; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Tomsk Polytechnic University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida International University; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; CEA; UDICE-French Research Universities; Universite Paris Saclay; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Universite Libre de Bruxelles; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Joint Institute for Nuclear Research - Russia; Egyptian Knowledge Bank (EKB); British University in Egypt; Egyptian Knowledge Bank (EKB); Suez University; Egyptian Knowledge Bank (EKB); Fayoum University; King Abdulaziz University; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Lomonosov Moscow State University; European Organization for Nuclear Research (CERN); RWTH Aachen University; University of Hamburg; Brandenburg University of Technology Cottbus; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; Visva Bharati University; Isfahan University of Technology; Islamic Azad University; University of Siena; Kyung Hee University; International Islamic University Malaysia; Agensi Nuklear Malaysia; Warsaw University of Technology; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Peter the Great St. Petersburg Polytechnic University; State University System of Florida; University of Florida; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; California Institute of Technology; Russian Academy of Sciences; Budker Institute of Nuclear Physics; University of Belgrade; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; University of Belgrade; University of Belgrade; Istituto Nazionale di Fisica Nucleare (INFN); National & Kapodistrian University of Athens; Riga Technical University; University of Zurich; Adiyaman University; Istanbul Aydin University; Mersin University; Piri Reis University; Gaziosmanpasa University; Ozyegin University; Izmir Institute of Technology; Marmara University; Kafkas University; Istanbul University; Istanbul Bilgi University; Hacettepe University; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Utah System of Higher Education; Utah Valley University; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Beykent University; Bingol University; Sinop University; Mimar Sinan Guzel Sanatlar University; Qatar Foundation (QF); Texas A&M University Qatar; Kyungpook National University; University of Hyderabad; University of Science & Technology of Mazandaran Sirunyan, AM (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Moon, Chang-Seong/J-3619-2014; Matorras, Francisco/I-4983-2015; Rabady, Dinyar/GSN-9210-2022; Quijada, Javier Alberto Murillo/AAS-4234-2021; Shchutska, Lesya/M-7052-2017; Fallavollita, Francesco/AAL-8850-2020; Novaes, Sergio F./D-3532-2012; Mejia Guisao, Jhovanny Andres/ABG-3627-2021; Hos, ilknur/T-1165-2019; Shah, Aashaq/GYI-8983-2022; Ozturk, Sertac/AGO-2476-2022; Aydogmus, Fatma/I-4879-2014; Chaparro Sierra, Luisa Fernanda/AHD-3083-2022; Missiroli, Marino/AAA-9072-2021; Khvedelidze, Arsen/AAF-5025-2020; Pásztor, Gabriella/D-9716-2016; Della Ricca, Giuseppe/B-6826-2013; Ciangottini, Diego/HJH-5914-2023; De La Cruz Burelo, Eduard/AAR-9103-2021; Isildak, Bora/AAW-4994-2020; Cortabitarte, Rocio Vilar/P-8480-2014; Lannon, Kevin/HGU-5755-2022; Selvaggi, Michele/CAJ-2129-2022; Bartosik, Nazar/ABF-2326-2020; Konecki, Marcin/G-4164-2015; Gokbulut, Gul/G-2141-2018; Pinna Angioni, Gian Luca/AAZ-6834-2021; Demiroglu, Zuhal Seyma/A-5168-2016; Zorbilmez, Caglar/ABH-1905-2020; Garg, Rocky Bala/AAV-9845-2021; Theofilatos, Konstantinos/AAL-9162-2021; Hoh, Siewyan/AAM-9562-2021; Strologas, John/GWR-2036-2022; Snoeys, Walter J M/K-8259-2015; Hernández Calama, José María/AAW-6394-2021; Montanari, Alessandro/J-2420-2012; Cepeda, Maria/AAF-4992-2019; Khakzad, Mohsen/ABB-1945-2021; Fouz, Maria Cruz/AAF-1105-2019; Androsov, Konstantin/HJA-2332-2022; Lezki, Samet/AAG-8277-2019; Felcini, Marta A./S-2684-2019; Moraes, Arthur/F-6478-2010; Lowette, Steven/HKV-3341-2023; Petkov, Peicho/M-2080-2016; Guler, Yalcin/AAG-7833-2019; M, F/HGF-3166-2022; Blekman, Freya/ABD-7916-2020; Gozalez-Lopez, Oscar/AAH-3533-2019; Goy Lopez, Silvia/K-9200-2017; Gerosa, Raffaele/AGL-5921-2022; GULER, E GURPINAR/AAH-4748-2019; Bunichev, Viacheslav/E-2285-2012; Elwood, Adam/GSN-1799-2022; Kumar, Dr Ashok/HNR-6570-2023; Melo da Costa, Eliza Melo/AAX-2394-2021; Lange, Torben/HOH-4787-2023; Canelli, Florencia/AAI-1616-2021; Tcherniaev, Evgueni/G-3453-2016; Bermudez Martinez, Armando/HHN-4625-2022; Voevodina, Elena/AAI-4805-2021; Ogul, Hasan/S-7951-2016; Watson, Ian James/AAH-1423-2022; Watson, Ian James/AFL-9360-2022; Buchanan, James/ABB-6906-2021; Sguazzoni, Giacomo/J-4620-2015; de Jesus Damiao, Dilson/G-6218-2012; Cassese, Antonio/R-1713-2016; Donato, Silvio/GPK-2262-2022; Saka, Halil/GPF-8892-2022; de guio, federico/B-8553-2009; Viliani, Lorenzo/AAU-3462-2021; Vai, Ilaria/AAT-5836-2020; Steggemann, Jan/AAL-5700-2020; Aarrestad, Thea/GLV-3031-2022; Fienga, Francesco/HKE-1204-2023; Tiras, Emrah/ABG-2354-2020; Trocino, Daniele/AGI-2155-2022; SEVINC KAYA, OZLEM/AAE-8069-2020; Tsirou, Andromachi/HHC-6869-2022; Puerta Pelayo, Jesus/L-1632-2014; Bortignon, Pierluigi/AAF-6561-2020; Sculac, Toni/AAE-4141-2019; Calligaris, Luigi/K-2018-2017; Trejo, Raul Iraq Rabadan/F-7924-2019; Tuo, Shengquan/ABA-9032-2021; MAESTRE, JUAN ALCARAZ/I-5763-2015; Battilana, Carlo/AAA-7345-2020; Parygin, Pavel/C-8908-2016; Pedraza Morales, Maria Isabel/ABC-7289-2021; Herrera, Maria Clemencia Mora/AAD-6466-2021; Shmatov, Sergei/J-5621-2014; Tinoco Mendes, Andre David/D-4314-2011; TUVE', Cristina/P-3933-2015; Peris, Antonio Delgado/AAA-5165-2019; Agaras, Merve Nazlim/AAB-5221-2021; Di Florio, Adriano/ABA-3631-2020; Chenarani, Shirin/AAO-8918-2021; Benaglia, Andrea/AGF-5495-2022; Gupta, Rajat/GVS-7964-2022; Yazgan, Efe/C-4521-2014; Pompili, Alexis/ABG-3556-2021; de Souza Sandro, Fonseca/ABB-8505-2020; Benussi, Luigi/O-9684-2014; Lokhtin, Igor/D-7004-2012; Ligabue, Franco/ABI-1681-2020; Dallavalle, Gaetano Marco/AFW-4654-2022; Klyukhin, Vyacheslav I/D-6850-2012; Hernández, José M./H-9127-2015; Dudko, Lev/D-7127-2012; Errico, Filippo/AAD-7755-2019; Sanchez-Hernandez, Alberto/L-4955-2017; Al-bataineh, Ayman Ahmad/AAK-1197-2021; Wulz, Claudia-Elisabeth E/H-5657-2011; Akgun, Bora/AAM-8311-2021; Duarte-Campderrós, Jordi/F-5025-2018; kaya, mithat/D-8062-2011; Moscatelli, Francesco/AEL-1878-2022; Bilki, Burak/GRF-5072-2022; Moscatelli, Francesco/N-6333-2014; Özdemir, Kadri/P-8058-2014; Seidita, Roberto/AGX-6803-2022; Casarsa, Massimo/L-3623-2018; Ferro, Fabrizio/HMP-5516-2023; Lezki, Merve Ince/AAG-5283-2021; polatoz, ayse/AAV-1190-2020; Shopova, Mariana Filipova/HKW-0149-2023; Polatoz, Ayse/AAF-6722-2021; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Terkulov, Adel/M-8581-2015; Teles, Patricia/ABF-9723-2021; Iorio, Alberto Orso Maria/GXA-0233-2022; Singh, J B/ABF-5574-2021; Azarkin, Maxim/N-2578-2015; Savina, Maria/HDO-6544-2022; ., MOHD. 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Rossi, Biagio/F-4137-2018; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Vilela Pereira, Antonio/L-4142-2016; Gonzalez Caballero, Isidro/E-7350-2010; My, Salvatore/I-5160-2015; Kyberd, Paul/P-1092-2014; Sagir, Sinan/A-5219-2018; Rolandi, Luigi (Gigi)/E-8563-2013; Haddad, Yacine/GRX-5022-2022; Ragazzi, Stefano/D-2463-2009; Redondo, Ignacio/K-8639-2014; Malik, Sudhir/D-6621-2018; Josa Mutuberria, Isabel/K-5184-2014; Popov, Andrey/E-1052-2012; Lee, Jason/B-9701-2014; Fernandez Menendez, Javier/B-6550-2014; Navarro-Tobar, Alvaro/K-7864-2014; Goh, Junghwan/Q-3720-2016; Piperov, Stefan/Q-1980-2017; Navarria, Francesco Luigi/ACP-7823-2022; Elkafrawy, Tamer/Q-8786-2016; Giacomelli, Paolo/B-8076-2009; Paganoni, Marco/A-4235-2016; Paulini, Manfred/N-7794-2014; Sznajder, Andre/L-1621-2016; Bernardes, Cesar Augusto/D-2408-2015; Palencia Cortezon, Jose Enrique/F-1059-2017; Ruiz Jimeno, Alberto/E-4473-2011; Padula, Sandra S./G-3560-2012; Dharmaratna, Welathantri/F-6745-2018; Spagnolo, Paolo/G-3401-2017; Kim, Tae Jeong/P-7848-2015; Bhatnagar, Vipin/Z-1079-2018; Raidal, Martti/F-4436-2012; D'Alessandro, Raffaello/F-5897-2015; Ivanov, Andrew/A-7982-2013; HAJ AHMAD (Vael HAJAHMAD), Wael/E-6738-2016 Moon, Chang-Seong/0000-0001-8229-7829; Matorras, Francisco/0000-0003-4295-5668; Rabady, Dinyar/0000-0001-9239-0605; Quijada, Javier Alberto Murillo/0000-0003-4933-2092; Shchutska, Lesya/0000-0003-0700-5448; Fallavollita, Francesco/0000-0003-2315-2499; Novaes, Sergio F./0000-0003-0471-8549; Hos, ilknur/0000-0002-7678-1101; Shah, Aashaq/0000-0002-6157-2016; Ozturk, Sertac/0000-0001-6533-6144; Aydogmus, Fatma/0000-0003-1434-2143; Chaparro Sierra, Luisa Fernanda/0000-0002-6570-9075; Missiroli, Marino/0000-0002-1780-1344; Khvedelidze, Arsen/0000-0002-5953-0140; Pásztor, Gabriella/0000-0003-0707-9762; Della Ricca, Giuseppe/0000-0003-2831-6982; De La Cruz Burelo, Eduard/0000-0002-7469-6974; Isildak, Bora/0000-0002-0283-5234; Cortabitarte, Rocio Vilar/0000-0003-2045-8054; Lannon, Kevin/0000-0002-9706-0098; 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Kiminsu, Ugur/0000-0001-6940-7800; Santanastasio, Francesco/0000-0003-2505-8359; Gutsche, Oliver/0000-0002-8015-9622; Bhatnagar, Vipin/0000-0002-8392-9610; Smith, Wesley/0000-0003-3195-0909; Heath, Helen/0000-0001-6576-9740; Zanetti, Marco/0000-0003-4281-4582; Haller, Johannes/0000-0001-9347-7657; Palladino, Vito/0000-0002-9786-9620; Lange, Johannes/0000-0001-7513-6330; Csanad, Mate/0000-0002-3154-6925; Pelliccioni, Mario/0000-0003-4728-6678; Hamel de Monchenault, Gautier/0000-0002-3872-3592; Galli Mercadante, Pedro/0000-0001-8333-4302; Petrucciani, Giovanni/0000-0003-0889-4726; Bloom, Kenneth/0000-0002-4272-8900; Consuegra Rodriguez, Sandra/0000-0002-1383-1837; Ptochos, Fotios/0000-0002-3432-3452; Raidal, Martti/0000-0001-7040-9491; Grab, Christophorus/0000-0002-6182-3380; Hall, Geoffrey/0000-0002-6299-8385; Belforte, Stefano/0000-0001-8443-4460; Caminada, Lea/0000-0001-5677-6033; Azzurri, Paolo/0000-0002-1717-5654; Ulrich, Ralf/0000-0002-2535-402X; Ngadiuba, Jennifer/0000-0002-0055-2935; Robert, Schoefbeck/0000-0002-2332-8784; Goldstein, Joel/0000-0003-1591-6014; Primavera, Federica/0000-0001-6253-8656; Flaecher, Henning/0000-0002-5371-941X; Ricci-Tam, Francesca/0000-0001-9750-7702; Zghiche, Amina/0000-0002-1178-1450; Cadamuro, Luca/0000-0001-8789-610X; Cappati, Alessandra/0000-0003-4386-0564; Kanuganti, Ankush Reddy/0000-0002-0789-1200; Pfeiffer, Andreas/0000-0001-5328-448X; Geurts, Frank/0000-0003-2856-9090; D'Alessandro, Raffaello/0000-0001-7997-0306; Trevisani, Nicolo/0000-0002-5223-9342; Pieri, Marco/0000-0003-3303-6301; Ivanov, Andrew/0000-0002-9270-5643; David, Pieter/0000-0001-9260-9371; HAJ AHMAD (Vael HAJAHMAD), Wael/0000-0003-1491-0446; Staiano, Amedeo/0000-0003-1803-624X; Das, Pallabi/0000-0002-9770-1377; Tosi, Silvano/0000-0002-7275-9193; Elmetenawee, Walaa/0000-0001-7069-0252; Reimers, Arne Christoph/0000-0002-9438-2059; Covarelli, Roberto/0000-0003-1216-5235; Di Croce, Davide/0000-0002-1122-7919; Li, Qiang/0000-0002-8290-0517 BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia); PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia); RFBR (Russia); NRC KI (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (U.S.A.); NSF (U.S.A.); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 752730, 765710]; Leventis Foundation; A.P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy-EXC 2121 Quantum Universe [390833306]; Hungarian Academy of Sciences (Hungary); New National Excellence Program UNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; HOMING PLUS program of the Foundation for Polish Science; European Union, Regional Development Fund; Mobility Plus program of the Ministry of Science and Higher Education; National Science Center (Poland) [Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998,, 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [02.a03.21.0005]; Tomsk Polytechnic University Competitiveness Enhancement Program; Nauka Project (Russia) [FSWW-2020-0008]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Thalis program; Aristeia program; EU-ESF; Greek NSRF; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.); STFC [ST/K001639/1, ST/H000925/2, ST/S000739/1, ST/K003542/1, ST/L005603/1, ST/M004775/1, ST/N001273/1] Funding Source: UKRI; Science and Technology Facilities Council [ST/K003542/1, ST/H000925/2, ST/M004775/1, ST/N001273/1, ST/K001639/1, ST/L005603/1] Funding Source: researchfish BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MoST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); COLCIENCIAS (Colombia)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC IUT (Estonia)(Estonian Research Council); PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); RosAtom (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MOSTR (Sri Lanka); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (U.S.A.)(United States Department of Energy (DOE)); NSF (U.S.A.)(National Science Foundation (NSF)); Marie-Curie program (European Union); European Research Council (European Union)(European Research Council (ERC)European Commission); Horizon 2020 Grant (European Union); Leventis Foundation; A.P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium)(FWO); Beijing Municipal Science AMP; Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy-EXC 2121 Quantum Universe(German Research Foundation (DFG)); Hungarian Academy of Sciences (Hungary)(Hungarian Academy of Sciences); New National Excellence Program UNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); HOMING PLUS program of the Foundation for Polish Science; European Union, Regional Development Fund(European Commission); Mobility Plus program of the Ministry of Science and Higher Education; National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Tomsk Polytechnic University Competitiveness Enhancement Program; Nauka Project (Russia); Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Thalis program; Aristeia program; EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (U.S.A.); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science -EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy-EXC 2121 Quantum Universe -390833306; the Lendulet (Momentum) Program and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no. 02.a03.21.; 0005 (Russia); the Tomsk Polytechnic University Competitiveness Enhancement Program and Nauka Project FSWW-2020-0008 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.). 106 2 2 7 43 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. DEC 11 2020.0 12 85 10.1007/JHEP12(2020)085 0.0 49 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics PG5EA Green Published, Green Accepted, gold, Green Submitted 2023-03-23 WOS:000599756700003 0
J Li, YJ; Qiao, G; Popov, S; Cui, XB; Florinsky, IV; Yuan, XH; Wang, LJ Li, Yanjun; Qiao, Gang; Popov, Sergey; Cui, Xiangbin; Florinsky, Igor V.; Yuan, Xiaohan; Wang, Lijuan Unmanned Aerial Vehicle Remote Sensing for Antarctic Research: A review of progress, current applications, and future use cases IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE English Review; Early Access Antarctica; Autonomous aerial vehicles; Satellites; Ocean temperature; Artificial intelligence; Remote sensing; Monitoring; Climate change Antarctica has been significantly influenced by global climate change. Owing to the spatiotemporal limitations of existing datasets, budgetary constraints, logistical challenges, and adverse temperature and climatic conditions of Antarctica, researchers face great challenges. Unmanned aerial vehicles (UAVs) have helped to solve this issue because they can collect high-resolution spatiotemporal data and conduct operations in inaccessible locations at a low cost and with ease compared with in situ observation and conventional spaceborne and airborne remote sensing. The development and testing of UAVs for use in polar environments mainly focus on enhancing UAV performance in extreme Antarctic conditions by improving their endurance, wind resistance, and aerial photography stability. The equipped multisensors, flexible data collection and operation window, and high-spatiotemporal resolution all contribute to making UAVs the most powerful platform for cryospheric research. In recent years, a series of UAV-related studies on the cryosphere have been published. However, a thorough review that explicitly details the scientific progress and possibilities of using UAVs in Antarctic polar research is lacking. In this era of rapid global and regional climate change, it is becoming increasingly necessary to employ UAVs to investigate the finer changes in the Antarctic ice sheet (AIS) and ice shelves. This work investigates the use of UAVs in the monitoring of the glacial microtopography (including rifts and crevasses, surface subsidence, and melting ponds), ice surface landforms, atmosphere, flora and fauna, sea ice, subglacial environment, and other aspects of Antarctic glaciology investigation, and it speculates on their future use in multidisciplinary research. [Li, Yanjun; Wang, Lijuan] Tongji Univ, Shanghai 20009, Peoples R China; [Li, Yanjun] Univ Libre Bruxelles, Brussels, Belgium; [Qiao, Gang] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 20009, Peoples R China; [Popov, Sergey] St Petersburg State Univ, Polar Marine Geosurvey Expedit, St Petersburg 198412, Russia; [Cui, Xiangbin] Polar Res Inst China, Shanghai 200136, Peoples R China; [Florinsky, Igor V.] Russian Acad Sci, Inst Math Problems Biol, Keldysh Inst Appl Math, Pushchino 142290, Russia Tongji University; Universite Libre de Bruxelles; Tongji University; Saint Petersburg State University; Polar Research Institute of China; Russian Academy of Sciences; Keldysh Institute of Applied Mathematics; Pushchino Scientific Center for Biological Research (PSCBI) of the Russian Academy of Sciences Qiao, G (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 20009, Peoples R China. 1710992@tongji.edu.cn; qiaogang@tongji.edu.cn; spopov67@yandex.ru; cuixiangbin@pric.gov.cn; iflor@mail.ru; 1996yuanxiaohan@tongji.edu.cn; lijuan_wang@tongji.edu.cn Popov, Sergey/I-2319-2013 Popov, Sergey/0000-0002-1830-8658 National Key Research and Development Program of China [2017YFA0603100, 2021YFB3900105]; National Natural Science Foundation of China [42276249, 41941006, 42111530185, 42011530088]; Fundamental Research Funds for the Central Universities National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) Constructive comments from the editors and two reviewers are greatly appreciated. We thank the 35th and 36th CHI-NARE for supporting our team members to conduct field surveys in the Zhongshan Station region, Antarctica. This study was supported by the National Key Research and Development Program of China (grants 2017YFA0603100 and 2021YFB3900105), the National Natural Science Foundation of China (grants 42276249, 41941006, 42111530185, and 42011530088), and Fundamental Research Funds for the Central Universities. This article contains supplementary materials, provided by the authors, available at https://www.doi.org/10.1109/MGRS.2022.3227056. Gang Qiao is the corresponding author. 0 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2473-2397 2168-6831 IEEE GEOSC REM SEN M IEEE Geosci. Remote Sens. Mag. 10.1109/MGRS.2022.3227056 0.0 JAN 2023 22 Geochemistry & Geophysics; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Remote Sensing; Imaging Science & Photographic Technology 8H5ZM 2023-03-23 WOS:000921111400001 0
J Sun, TR; Hu, L; Zhang, SB; Li, XY; Meng, KL; Wu, XF; Wang, LF; Castro-Tirado, AJ Sun, Tianrui; Hu, Lei; Zhang, Songbo; Li, Xiaoyan; Meng, Kelai; Wu, Xuefeng; Wang, Lifan; Castro-Tirado, A. J. Pipeline for the Antarctic Survey Telescope 3-3 in Yaoan, Yunnan FRONTIERS IN ASTRONOMY AND SPACE SCIENCES English Article data analysis-astrometry-instrumentation; image processing; photometric-(stars); transient detection; convolutional neural networks-CNN IMAGE; REPRESENTATIONS; SOFTWARE AST3-3 is the third robotic facility of the Antarctic Survey Telescopes (AST3) for transient surveys to be deployed at Dome A, Antarctica. Due to the current pandemic, the telescope has been currently deployed at the Yaoan Observation Station in China, starting the commissioning observation and a transient survey. This article presented a fully automatic data processing system for AST3-3 observations. The transient detection pipeline uses state-of-the-art image subtraction techniques optimized for GPU devices. Image reduction and transient photometry are accelerated by concurrent task methods. Our Python-based system allows for transient detection from wide-field data in a real-time and accurate way. A ResNet-based rotational-invariant neural network was employed to classify the transient candidates. As a result, the system enables the auto-generation of transients and their light curves. [Sun, Tianrui; Hu, Lei; Zhang, Songbo; Meng, Kelai; Wu, Xuefeng] Chinese Acad Sci, Purple Mt Observ, Nanjing, Peoples R China; [Sun, Tianrui; Wu, Xuefeng] Univ Sci & Technol China, Sch Astron & Space Sci, Hefei, Peoples R China; [Li, Xiaoyan] Chinese Acad Sci, Nanjing Inst Astron Opt Instruments, Nanjing, Peoples R China; [Wang, Lifan] Texas A&M Univ, George P & Cynthia Woods Mitchell Inst Fundamental, Dept Phys & Astron, College Stn, TX USA; [Castro-Tirado, A. J.] Inst Astrofis Andalucia, Stellar Phys Dept, Granada, Spain Chinese Academy of Sciences; Nanjing Institute of Astronomical Optics & Technology, NAOC, CAS; Purple Mountain Observatory, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; Texas A&M University System; Texas A&M University College Station; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Astrofisica de Andalucia (IAA) Wu, XF (corresponding author), Chinese Acad Sci, Purple Mt Observ, Nanjing, Peoples R China.;Wu, XF (corresponding author), Univ Sci & Technol China, Sch Astron & Space Sci, Hefei, Peoples R China. xfwu@pmo.ac.cn National Natural Science Foundation of China [11725314, 12041306]; Major Science and Technology Project of Qinghai Province [2019-ZJ-A10]; ACAMAR Postdoctoral Fellow; China Postdoctoral Science Foundation [2020M681758]; Natural Science Foundation of Jiangsu Province [BK20210998]; State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa [SEV-2017-0709]; China Scholarship Council (CSC) [202006340174] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Science and Technology Project of Qinghai Province; ACAMAR Postdoctoral Fellow; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa(Spanish Government); China Scholarship Council (CSC)(China Scholarship Council) This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 11725314 and 12041306), the Major Science and Technology Project of Qinghai Province (2019-ZJ-A10), the ACAMAR Postdoctoral Fellow, the China Postdoctoral Science Foundation (Grant No. 2020M681758) and the Natural Science Foundation of Jiangsu Province (grant No. BK20210998). TS and AC also acknowledge financial support from the State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709). TS acknowledges the China Scholarship Council (CSC) for funding his PhD scholarship (202006340174). 70 0 0 2 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-987X FRONT ASTRON SPACE Front. Astron. Space Sci. JUL 7 2022.0 9 897100 10.3389/fspas.2022.897100 0.0 14 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 3E0FZ gold, Green Published, Green Submitted 2023-03-23 WOS:000829669500001 0
J Sun, YJ; Li, J; Chen, SM; Andrienko, G; Andrienko, N; Zhang, K Sun, Yongjian; Li, Jie; Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Zhang, Kang A learning-based approach for efficient visualization construction VISUAL INFORMATICS English Article Learned index; Neural network; Visualization index; Interactive exploration; Spatiotemporal visualization REAL-TIME EXPLORATION; LARGE-SCALE GRAPHS; VISUAL ANALYTICS; LAYOUT We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user's interactions. Instead, LVI directly predicts aggregates of interest for the user's data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. [Sun, Yongjian; Li, Jie] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China; [Chen, Siming] Fudan Univ, Sch Data Sci, Shanghai, Peoples R China; [Andrienko, Gennady; Andrienko, Natalia] Fraunhofer Inst IAIS, Berlin, Germany; [Andrienko, Gennady; Andrienko, Natalia] City Univ London, London, England; [Zhang, Kang] Beijing Normal Univ Hong Kong Baptist Univ United, Beijing, Peoples R China Tianjin University; Fudan University; Fraunhofer Gesellschaft; City University London; Beijing Normal University - Hong Kong Baptist University United International College; Hong Kong Baptist University Li, J (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China. jie.li@tju.edu.cn National Key R&D Program of China [2018YFC0831700]; NSFC project [61972278]; Natural Science Foundation of Tianjin [20JCQNJC01620]; Browser Project [CEIEC-2020-ZM02-0132] National Key R&D Program of China; NSFC project(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Tianjin(Natural Science Foundation of Tianjin); Browser Project This work is supported by National Key R&D Program of China (2018YFC0831700), NSFC project (61972278), Natural Science Foundation of Tianjin (20JCQNJC01620), and the Browser Project (CEIEC-2020-ZM02-0132). 55 1 1 3 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2468-502X VIS INFORM Vis. Inform. MAR 2022.0 6 1 14 25 10.1016/j.visinf.2022.01.001 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Emerging Sources Citation Index (ESCI) Computer Science 2S0ZC Green Accepted, gold 2023-03-23 WOS:000821528800002 0
J Xie, JJ; Zhong, YJ; Xiao, T; Wang, Z; Zhang, JG; Wang, TW; Schuller, BW Xie, Jiangjian; Zhong, Yujie; Xiao, Tong; Wang, Zheng; Zhang, Junguo; Wang, Tuowai; Schuller, Bjoern W. A multi-information fusion model for short term load forecasting of an architectural complex considering spatio-temporal characteristics ENERGY AND BUILDINGS English Article Architectural complex; Short term load forecasting; Pearson correlation analysis; Graph convolutional neural network; Long -short term memory network; Attention mechanism NEURAL-NETWORK Building load forecasting is critical for energy saving and carbon emission reduction, as building loads account for a rising percentage of energy consumption. Existing load forecasting methods only target a single building and do not consider the spatial characteristics of different building loads in an architec-tural complex, making it impossible to effectively forecast loads for all buildings in an architectural com-plex simultaneously. In this paper, considering the spatial correlation among building loads in an architectural complex, a novel short-term load forecasting method is proposed to improve the load fore-casting accuracy for the architectural complex. First, Pearson correlation analysis is carried out to deter-mine the key influencing factors of load, which are treated as multi-information. Then, to realize the load forecasting of all buildings in an architectural complex at the same time, a multi-information fusion model combined with a graph convolutional network and a long-short term memory network is pro-posed, and spatio-temporal attention modules are included to extract the dynamic spatio-temporal cor-relations of load. The performance of the proposed model is evaluated using actual load data from a public university in Beijing. When compared to other current methods, the experimental results show that the proposed model not only achieves the best results for architectural complex load forecasting (the largest MAPE is only 6.86%, the smallest R2 and Accuracy reach 0.87 and 0.85, respectively), but also maintains forecasting stability to some extent over a longer forecast period. The proposed method is a meaningful step towards achieving fast and accurate architectural complex load forecasting.(c) 2022 Elsevier B.V. All rights reserved. [Xie, Jiangjian; Zhong, Yujie; Xiao, Tong; Zhang, Junguo; Wang, Tuowai] Beijing Forestry Univ, Sch Technol, Beijing, Peoples R China; [Xie, Jiangjian; Schuller, Bjoern W.] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany; [Wang, Zheng] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing, Peoples R China; [Schuller, Bjoern W.] Imperial Coll London, GLAM, London, England Beijing Forestry University; University of Augsburg; North China Electric Power University; Imperial College London Zhang, JG (corresponding author), Beijing Forestry Univ, Sch Technol, Beijing, Peoples R China. zhangjunguo@bjfu.edu.cn jiangjian, Xie/0000-0003-1367-324X; Schuller, Bjorn/0000-0002-6478-8699 Beijing Municipal Natural Science Foundation [6214040]; Fundamental Research Funds for the Central Universities [2021ZY70]; China Scholarship Council [202106515010] Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) Acknowledgements We would like to express our gratitude to the Beijing Municipal Natural Science Foundation (No. 6214040) , the Fundamental Research Funds for the Central Universities (No. 2021ZY70) and China Scholarship Council (202106515010) . 35 0 0 6 6 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7788 1872-6178 ENERG BUILDINGS Energy Build. DEC 15 2022.0 277 112566 10.1016/j.enbuild.2022.112566 0.0 11 Construction & Building Technology; Energy & Fuels; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Energy & Fuels; Engineering 8Z4LE 2023-03-23 WOS:000933351000002 0
J Chen, MZ; Mozaffari, M; Saad, W; Yin, CC; Debbah, M; Hong, CS Chen, Mingzhe; Mozaffari, Mohammad; Saad, Walid; Yin, Changchuan; Debbah, Merouane; Hong, Choong Seon Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Article CRAN; UAV; unmanned aerial vehciles; drones; QoE; caching; machine learning; conceptor-based echo state networks NETWORKS In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs. [Chen, Mingzhe; Yin, Changchuan] Beijing Univ Posts & Telecommun, Beijing Lab Adv Informat Network, Beijing 100876, Peoples R China; [Chen, Mingzhe; Yin, Changchuan] Beijing Univ Posts & Telecommun, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China; [Mozaffari, Mohammad; Saad, Walid] Virginia Polytech Inst & State Univ, Elect & Comp Engn Dept, Wireless VT, Blacksburg, VA 24061 USA; [Saad, Walid; Hong, Choong Seon] Kyung Hee Univ, Dept Comp Sci & Engn, Yongin 02447, South Korea; [Debbah, Merouane] Univ Paris Saclay, Cent Supelec, Large Networks & Syst Grp, F-91192 Gif Sur Yvette, France; [Debbah, Merouane] Huawei France Res & Dev, Math & Algorithm Sci Lab, Paris, France Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Virginia Polytechnic Institute & State University; Kyung Hee University; UDICE-French Research Universities; Universite Paris Saclay; Huawei Technologies Saad, W (corresponding author), Virginia Polytech Inst & State Univ, Elect & Comp Engn Dept, Wireless VT, Blacksburg, VA 24061 USA. chenmingzhe@bupt.edu.cn; mmozaff@vt.edu; walids@vt.edu; ccyin@ieee.org; merouane.debbah@huawei.com; cshong@khu.ac.kr Hong, Choong Seon/ABF-5527-2020; Saad, Walid/C-7978-2018; Debbah, Merouane/B-6261-2011; Chen, Mingzhe/U-3377-2019; Yin, Changchuan/AAB-7284-2021 Saad, Walid/0000-0003-2247-2458; Chen, Mingzhe/0000-0003-2570-703X; Hong, Choong Seon/0000-0003-3484-7333 National Research Foundation of Korea [21A20131612192] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS) National Research Foundation of Korea(National Research Foundation of Korea) 35 504 512 13 89 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. MAY 2017.0 35 5 1046 1061 10.1109/JSAC.2017.2680898 0.0 16 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications EW0BG Green Submitted, hybrid 2023-03-23 WOS:000402152400003 0
C Dong, ZP; Gao, Y; Ren, QY; Yan, YH; Chen, F Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Dong, Zhipeng; Gao, Yi; Ren, Qinyuan; Yan, Yunhui; Chen, Fei Depth Generation Network: Estimating Real World Depth from Stereo and Depth Images 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster In this work, we propose the Depth Generation Network (DGN) to address the problem of dense depth estimation by exploiting the variational method and the deep-learning technique. In particular, we focus on improving the feasibility of depth estimation under complex scenarios given stereo RGB images, where the stereo pairs and/or depth ground-truth captured by real sensors may be deteriorated; the stereo setting parameters may be unavailable or unreliable, hence hamper efforts to establish the correspondence between image pairs via supervision learning or epipolar geometric cues. Instead of relying on real data, we supervise the training of our model using synthetic depth maps generated by the simulator, which deliver complex scenes and reliable data with ease. Two non-trivial challenges, i.e., (i) attaining reasonable amount yet realistic samples for training, and (ii) developing a model that adapts to both synthetic and real scenes arise, whereas in this work we mainly deal with the later one yet leveraging state-of-the-art Falling Things (FAT) dataset to overcome the first. Experiments on FAT and KITTI datasets demonstrate that our model estimates relative dense depth in fine details, potentially generalizable to real scenes without knowing the stereo geometric and optic settings. [Dong, Zhipeng; Gao, Yi; Yan, Yunhui] Northeastern Univ, Sch Mech Engn & Automat, 3-11 Wenhua Rd, Shenyang, Liaoning, Peoples R China; [Ren, Qinyuan] Zhejiang Univ, Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China; [Chen, Fei] Ist Italiano Tecnol, Dept Adv Robot, Act Percept & Robot Interact Learning Lab, Via Morego 30, I-16163 Genoa, Italy Northeastern University - China; Zhejiang University; Istituto Italiano di Tecnologia - IIT Dong, ZP (corresponding author), Northeastern Univ, Sch Mech Engn & Automat, 3-11 Wenhua Rd, Shenyang, Liaoning, Peoples R China. zhipengdong@stumail.neu.edc.cn; yigaoyi@stumail.neu.eda.cn; latepat@gmail.com; yanyh@mail.neu.edu.cn; fei.chen@iit.it Yan, Yunhui/HDL-7343-2022 Yan, Yunhui/0000-0001-7121-2367 National Key Research and Development Program of China [2017YFB0304200]; Fundamental Research Funds for the Central Universities [N150308001, N170304014]; Shenzhen Peacock Plan [KQTD2016112515134654] National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Shenzhen Peacock Plan This work was supported by the National Key Research and Development Program of China (2017YFB0304200), the Fundamental Research Funds for the Central Universities (N150308001, N170304014) and Shenzhen Peacock Plan (KQTD2016112515134654) 27 0 0 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 7201 7206 6 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN 2023-03-23 WOS:000494942305038 0
J Monti, L; Tse, R; Tang, SK; Mirri, S; Delnevo, G; Maniezzo, V; Salomoni, P Monti, Lorenzo; Tse, Rita; Tang, Su-Kit; Mirri, Silvia; Delnevo, Giovanni; Maniezzo, Vittorio; Salomoni, Paola Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context SENSORS English Article Internet of Things; smart buildings; smart environments; deep learning; transfer learning; occupancy detection; smart sensing; ambient intelligence COUNTING PEOPLE; TRACKING Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23. [Monti, Lorenzo] INAF Ist Radioastron, I-40127 Bologna, Italy; [Tse, Rita; Tang, Su-Kit] Macao Polytech Univ, Fac Appl Sci, Macau, Peoples R China; [Mirri, Silvia; Delnevo, Giovanni; Maniezzo, Vittorio; Salomoni, Paola] Univ Bologna, Dept Comp Sci & Engn, I-40126 Bologna, Italy Istituto Nazionale Astrofisica (INAF); University of Bologna Mirri, S (corresponding author), Univ Bologna, Dept Comp Sci & Engn, I-40126 Bologna, Italy. lorenzo.monti@inaf.it; ritatse@ipm.edu.mo; sktang@ipm.edu.mo; silvia.mirri@unibo.it; giovanni.delnevo2@unibo.it; vittorio.maniezzo@unibo.it; paola.salomoni@unibo.it ; Mirri, Silvia/I-7983-2016 maniezzo, vittorio/0000-0002-1220-1235; Delnevo, Giovanni/0000-0001-6640-5746; Mirri, Silvia/0000-0002-5385-4734; Monti, Lorenzo/0000-0002-2087-0535; Tang, Su-Kit/0000-0001-8104-7887 Macao Polytechnic University, through the Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities program [RP/ESCA01/2020] Macao Polytechnic University, through the Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities program This work was supported in part by the Macao Polytechnic University, through the Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities program (RP/ESCA01/2020). 54 1 1 4 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2022.0 22 10 3692 10.3390/s22103692 0.0 16 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 1T7GW 35632101.0 gold, Green Published 2023-03-23 WOS:000804897700001 0
J Zhang, BW; Tondi, B; Barni, M Zhang, Bowen; Tondi, Benedetta; Barni, Mauro Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability COMPUTER VISION AND IMAGE UNDERSTANDING English Article Adversarial examples; Anti-spoofing; Physical domain adversarial examples; Presentation attack; Face authentication In the race of arms between attackers, trying to build more and more realistic face replay attacks, and defenders, deploying spoof detection modules with ever-increasing capabilities, CNN-based methods have shown outstanding detection performance thus raising the bar for the construction of realistic replay attacks against face-based authentication systems. Rather than trying to rebroadcast even more realistic faces, we show that attackers can successfully fool a face authentication system equipped with a deep learning spoof detection module, by exploiting the vulnerabilities of CNNs to adversarial perturbations. We first show that mounting such an attack is not a trivial task due to the unique features of spoofing detection modules. Then, we propose a method to craft adversarial images that can be successfully exploited to build an effective replay attack. Experiments conducted on the REPLAY-MOBILE database demonstrate that our attacked images achieve good performance against a face recognition system equipped with CNN-based anti-spoofing, in that they are able to pass the face detection, spoof detection and face recognition modules of the authentication chain. [Zhang, Bowen] Xidian Univ, Sch Cyber Engn, 266 Xinglong Sect Xifeng Rd, Xian 710126, Shaanxi, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, Via Roma 56, I-53100 Siena, Italy Xidian University; University of Siena Zhang, BW (corresponding author), Xidian Univ, Sch Cyber Engn, 266 Xinglong Sect Xifeng Rd, Xian 710126, Shaanxi, Peoples R China. bowenzhang.psnl@outlook.com China Scholarship Council (CSC) [201806960079] China Scholarship Council (CSC)(China Scholarship Council) This work has been partially supported by the China Scholarship Council (CSC), file No. 201806960079. The author would also thank Prof. Xixiang Lv (xxlv@mail.xidian.edu.cn) for her valuable advice and help. 45 15 15 2 14 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1077-3142 1090-235X COMPUT VIS IMAGE UND Comput. Vis. Image Underst. AUG 2020.0 197 102988 10.1016/j.cviu.2020.102988 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering MX3RI 2023-03-23 WOS:000557642000004 0
C Wu, YF; Guo, H; Li, ZB; Ma, Q; Zhao, YY; Pezzuolo, A IEEE Wu, Yufeng; Guo, Hao; Li, Zhenbo; Ma, Qin; Zhao, Yuanyang; Pezzuolo, Andrea Body Condition Score for Dairy Cows Method Based on Vision Transformer 2021 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY (IEEE METROAGRIFOR 2021) English Proceedings Paper 1st IEEE International Workshop on Metrology for the Agriculture and Forestry (IEEE MetroAgriFor) NOV 03-05, 2021 Polytechn Univ Marche, ELECTR NETWORK IEEE,Athena,Ministero Poliitiche Agricole Alimentari & Forestali,Univ Trento,Unibz Polytechn Univ Marche body condition score; vision transformer; deep learning; CNN SHAPE Body condition score or body condition assessment, which is a quantitative analysis of the fat content in an animal's body. The transformer model is widely used in computer vision, transformer does not need to rely on the structure of CNN, can achieve good results on image classification tasks, and is suitable for transfer learning. In this paper, we introduce the transformer structure into the field of body condition score for dairy cows. We collected 918 images for scoring the body condition of the dairy cows. On the collected data sets, we use traditional CNN, including ResNext-50, ResNet-50, ECA_NFNet, EfficientNet, EfficientNetV2, ResNext101, HRNet, to score cow body condition, and compare their accuracy with Swin transformer and Vision Transformer. Vision Transformer has achieved good estimation results in comparison with other traditional CNN in the area. Overall accuracy of BCS estimations within 0.25 units of difference from true values was 97.842%, while overall accuracy within 0.50 units was 99.640%. The transformer model for body condition score of dairy cows will be used in the farms gradually. [Wu, Yufeng; Li, Zhenbo; Ma, Qin; Zhao, Yuanyang] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China; [Guo, Hao] China Agr Univ, Coll Informat & Elect Engn, Coll Land Sci & Technol, Beijing 100083, Peoples R China; [Pezzuolo, Andrea] Univ Padua, Dept Land Environm Agr & Forestry, Viale Univ 16, I-35020 Legnaro, PD, Italy China Agricultural University; China Agricultural University; University of Padua Wu, YF (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. wuyufeng@cau.edu.cn; guohaolys@cau.edu.cn; lizb@cau.edu.cn; sockline@163.com; zhaoyyqx@163.com; andrea.pezzuolo@unipd.it National Natural Science Foundation of China [42071449, 41601491] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors wish to thank everyone who participated in the method assessment. This research is supported by the National Natural Science Foundation of China [grant numbers 42071449, 41601491]. 18 0 0 8 14 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-0533-1 2021.0 37 41 10.1109/MetroAgriFor52389.2021.9628437 0.0 5 Automation & Control Systems; Agricultural Engineering; Forestry; Instruments & Instrumentation; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Agriculture; Forestry; Instruments & Instrumentation; Remote Sensing BT0YU 2023-03-23 WOS:000794138700008 0
S Ju, Y; Hsu, WC; Radke, JD; Fourt, W; Lang, W; Hoes, O; Foster, H; Biging, GS; Schmidt-Poolman, M; Neuhausler, R; Alruheil, A; Maier, W Thakuriah, P; Tilahun, N; Zellner, M Ju, Yang; Hsu, Wei-Chen; Radke, John D.; Fourt, William; Lang, Wei; Hoes, Olivier; Foster, Howard; Biging, Gregory S.; Schmidt-Poolman, Martine; Neuhausler, Rosanna; Alruheil, Amna; Maier, William Planning for the Change: Mapping Sea Level Rise and Storm Inundation in Sherman Island Using 3Di Hydrodynamic Model and LiDAR SEEING CITIES THROUGH BIG DATA: RESEARCH, METHODS AND APPLICATIONS IN URBAN INFORMATICS Springer Geography English Article; Book Chapter Sea level rise; Mapping; 3Di hydrodynamic model; LiDAR; Sherman island In California, one of the greatest concerns of global climate change is sea level rise (SLR) associated with extreme storm events. Several studies were conducted to statically map SLR and storm inundation, while its dynamic was less studied. This study argues it is important to conduct dynamic simulation with high resolution data, and employs a 3Di hydrodynamic model to simulate the inundation of Sherman Island, California. The big data, high resolution digital surface model (DSM) from Light Detection and Ranging (LiDAR), was used to model the ground surface. The results include a series of simulated inundation, which show that when the sea level rises more than 1 m, there are major impacts on Sherman Island. In all, this study serves as a fine database for better planning, management, and governance to understand future scenarios. [Ju, Yang; Hsu, Wei-Chen; Radke, John D.; Fourt, William; Alruheil, Amna] Univ Calif Berkeley, Dept Landscape Architecture & Environm Planning, Berkeley, CA 94720 USA; [Radke, John D.; Neuhausler, Rosanna] Univ Calif Berkeley, Dept City & Reg Planning, Berkeley, CA 94720 USA; [Lang, Wei] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Kowloon, Hong Kong, Peoples R China; [Hoes, Olivier] Delft Univ Technol, Fac Civil Engn & Geosci, Stevinweg 1, NL-2628 CN Delft, Netherlands; [Foster, Howard; Schmidt-Poolman, Martine] Univ Calif Berkeley, Ctr Catastroph Risk Management, Berkeley, CA 94720 USA; [Biging, Gregory S.; Neuhausler, Rosanna; Maier, William] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA University of California System; University of California Berkeley; University of California System; University of California Berkeley; Hong Kong Polytechnic University; Delft University of Technology; University of California System; University of California Berkeley; University of California System; University of California Berkeley Ju, Y (corresponding author), Univ Calif Berkeley, Dept Landscape Architecture & Environm Planning, Berkeley, CA 94720 USA. yangju90@berkeley.edu Ju, Yang/AAI-8472-2021; Ju, Yang/L-6808-2019 Ju, Yang/0000-0002-1947-7533; 22 6 6 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-315X 978-3-319-40902-3; 978-3-319-40900-9 SPRING GEOGR 2017.0 313 329 10.1007/978-3-319-40902-3_18 0.0 10.1007/978-3-319-40902-3 17 Management; Social Sciences, Mathematical Methods; Transportation; Urban Studies Book Citation Index – Social Sciences & Humanities (BKCI-SSH) Business & Economics; Mathematical Methods In Social Sciences; Transportation; Urban Studies BH4IT 2023-03-23 WOS:000400367100019 0
J Chen, JY; Lecue, F; Pan, JZ; Deng, SM; Chen, HJ Chen, Jiaoyan; Lecue, Freddy; Pan, Jeff Z.; Deng, Shumin; Chen, Huajun Knowledge graph embeddings for dealing with concept drift in machine learning JOURNAL OF WEB SEMANTICS English Article Ontology; Knowledge graph embedding; Stream learning; Concept drift Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift,1 as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams. (C) 2021 Elsevier B.V. All rights reserved. [Chen, Jiaoyan] Univ Oxford, Dept Comp Sci, Oxford, England; [Lecue, Freddy] INRIA, Le Chesnay Rocquencourt, France; [Lecue, Freddy] Thales, CortAIx, Montreal, PQ, Canada; [Pan, Jeff Z.] Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland; [Pan, Jeff Z.] Huawei, Knowledge Graph Lab, Reading, Berks, England; [Deng, Shumin; Chen, Huajun] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China; [Deng, Shumin; Chen, Huajun] ZJU Alibaba Joint Lab Knowledge Engine, Hangzhou, Peoples R China University of Oxford; Inria; University of Aberdeen; Huawei Technologies; Zhejiang University Lecue, F (corresponding author), INRIA, Le Chesnay Rocquencourt, France.;Lecue, F (corresponding author), Thales, CortAIx, Montreal, PQ, Canada. jiaoyan.chen@cs.ox.ac.uk; freddy.lecue@inria.fr; jeff.z.pan@abdn.ac.uk; 231sm@zju.edu.cn; huajunsir@zju.edu.cn Deng, Shumin/AAP-7003-2021; Huajun, Chen/B-6340-2013 Huajun, Chen/0000-0001-5496-7442 SIRIUS Centre for Scalable Data Access (Research Council of Norway) [237889]; Royal Society, UK; EPSRC project DBOnto; EPSRC project MaSI3; EPSRC project ED3; EU Marie Curie, UK IAPP K-Drive project [286348]; [NSFC91846204] SIRIUS Centre for Scalable Data Access (Research Council of Norway); Royal Society, UK(Royal Society of London); EPSRC project DBOnto(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC project MaSI3(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC project ED3(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EU Marie Curie, UK IAPP K-Drive project; This work is partially funded by the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project 237889), the Royal Society, UK, EPSRC projects DBOnto, MaSI3, ED3, NSFC91846204, and the EU Marie Curie, UK IAPP K-Drive project (286348). 57 2 2 6 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1570-8268 1873-7749 J WEB SEMANT J. Web Semant. FEB 2021.0 67 100625 10.1016/j.websem.2020.100625 0.0 JAN 2021 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science QW9TA Bronze 2023-03-23 WOS:000628989400001 0
J Zhai, YK; Deng, WB; Xu, Y; Ke, QR; Gan, JY; Sun, B; Zeng, JY; Piuri, V Zhai, Yikui; Deng, Wenbo; Xu, Ying; Ke, Qirui; Gan, Junying; Sun, Bing; Zeng, Junying; Piuri, Vincenzo Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L-2-Regularization COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article MODEL; ALGORITHM Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L-2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L-2-Regularization is trained to extract robust features, in which the L-2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets. [Zhai, Yikui; Deng, Wenbo; Xu, Ying; Ke, Qirui; Gan, Junying; Zeng, Junying] Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China; [Sun, Bing] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Piuri, Vincenzo] Univ Milan, Dipartimento Informat, Via Celoria 18, I-20133 Milan, Italy Wuyi University; Beihang University; University of Milan Xu, Y (corresponding author), Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China. xuying117@163.com deng, wenbo/AAI-3947-2020 deng, wenbo/0000-0003-2038-5844; Zhai, Yikui/0000-0003-0154-9743; Zeng, Junying/0000-0002-7559-0637 National Natural Science Foundation of China [61771347]; Characteristic Innovation Project of Guangdong Province [2017KTSCX181]; Young Innovative Talents Project of Guangdong Province [2017KQNCX206]; Jiangmen Science and Technology Project (2017) [268]; 2017 Guangdong Science and Technology Plan Project [2017A010101019]; Youth Foundation of Wuyi University [2015zk11]; Opening Project of GuangDong Province Key Laboratory of Information Security Technology [2017B030314131]; 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Characteristic Innovation Project of Guangdong Province; Young Innovative Talents Project of Guangdong Province; Jiangmen Science and Technology Project (2017); 2017 Guangdong Science and Technology Plan Project; Youth Foundation of Wuyi University; Opening Project of GuangDong Province Key Laboratory of Information Security Technology; 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing This work was supported by the National Natural Science Foundation of China (no. 61771347); Characteristic Innovation Project of Guangdong Province (no. 2017KTSCX181); Young Innovative Talents Project of Guangdong Province (2017KQNCX206); Jiangmen Science and Technology Project ((2017) no. 268); 2017 Guangdong Science and Technology Plan Project (No. 2017A010101019); Youth Foundation of Wuyi University (no. 2015zk11); the Opening Project of GuangDong Province Key Laboratory of Information Security Technology (Grant no. 2017B030314131); and the 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing. 50 6 7 2 18 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. NOV 15 2019.0 2019 9140167 10.1155/2019/9140167 0.0 13 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology JQ8AM 31915430.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000499161400001 0
J Peng, H; Wang, J; Shi, P; Perez-Jimenez, MJ; Riscos-Nunez, A Peng, Hong; Wang, Jun; Shi, Peng; Perez-Jimenez, Mario J.; Riscos-Nunez, Agustin Fault diagnosis of power systems using fuzzy tissue-like P systems INTEGRATED COMPUTER-AIDED ENGINEERING English Article Power systems; fault diagnosis; tissue-like P systems; fuzzy tissue-like P systems NEURAL-NETWORK; SECTION ESTIMATION; EXPERT-SYSTEM Fuzzy tissue-like P systems (FTPS), as a new variant of tissue-like P systems (TPS), is proposed in this paper. FTPS inherits the advantages of TPS and has the ability of dealing with incomplete and uncertain information. FTPS is very suitable to model the causal relationship between a fault and its protective devices in power systems. A forward reasoning algorithm based on FTPS is developed, and then the corresponding fault diagnosis model is discussed. In order to evaluate the availability and effectiveness of the proposed fault diagnosis model, three case studies of fault diagnosis of a transmission system are discussed and analyzed, including simple fault, complex faults and multiple faults with uncertain status information. The results of case studies demonstrate that FTPS can be used to diagnose faulty sections in power systems accurately and effectively. [Peng, Hong] Xihua Univ, Sch Comp & Software Engn, Chengdu, Sichuan, Peoples R China; [Wang, Jun] Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Sichuan, Peoples R China; [Shi, Peng] Univ Adelaide, Sch Elect & Elect Engn, Adelaide, SA, Australia; [Perez-Jimenez, Mario J.; Riscos-Nunez, Agustin] Univ Seville, Dept Comp Sci & Artificial Intelligence, Res Grp Nat Comp, Seville, Spain Xihua University; Xihua University; University of Adelaide; University of Sevilla Wang, J (corresponding author), Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Sichuan, Peoples R China. wj.xhu@hotmail.com Shi, Peng/H-5906-2012; Jiménez, Mario J. Pérez/B-2114-2008; Peng, Hong/C-8705-2012; Núñez, Agustín Riscos/A-7389-2013 Shi, Peng/0000-0002-1358-2367; Peng, Hong/0000-0002-4736-0164; Núñez, Agustín Riscos/0000-0002-5409-3578; Shi, Peng/0000-0001-8218-586X National Natural Science Foundation of China [61472328]; Chunhui Project Foundation of the Education Department of China [Z2016143, Z2016148]; Research Foundation of the Education Department of Sichuan province, China [17TD0034] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chunhui Project Foundation of the Education Department of China; Research Foundation of the Education Department of Sichuan province, China This work was partially supported by the National Natural Science Foundation of China (No. 61472328), Chunhui Project Foundation of the Education Department of China (Nos. Z2016143 and Z2016148), and Research Foundation of the Education Department of Sichuan province (No. 17TD0034), China. 33 40 40 4 41 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1069-2509 1875-8835 INTEGR COMPUT-AID E Integr. Comput.-Aided Eng. 2017.0 24 4 401 411 10.3233/ICA-170552 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering FI4ZG 2023-03-23 WOS:000411988700006 0
J Liu, JQ; Dai, GZ; Zhang, YM; Bian, XY Liu, Jiaqi; Dai, Guangzhi; Zhang, Youming; Bian, Xiaoying Microbial chassis engineering drives heterologous production of complex secondary metabolites BIOTECHNOLOGY ADVANCES English Review Microbial chassis; Synthetic biology; Genome engineering; Secondary metabolites; Heterologous expression BIOSYNTHETIC GENE-CLUSTER; COMPARATIVE GENOMIC ANALYSIS; BACILLUS-SUBTILIS GENOME; DE-NOVO PRODUCTION; SYNTHETIC BIOLOGY; YARROWIA-LIPOLYTICA; PSEUDOMONAS-PUTIDA; ESCHERICHIA-COLI; NATURAL-PRODUCTS; HOMOLOGOUS RECOMBINATION The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nature compounds. The two juxtaposed conventional model microorganisms, Escherichia coli, Saccharomyces cerevisiae, have been harnessed as microbial chassis to produce a bounty of secondary metabolites with the help of certain host engineering. In last decade, engineering non-model microbes to efficiently biosynthesize secondary metabolites has received increasing attention due to their peculiar advantages in metabolic networks and/or biosynthesis. The state-of-the-art synthetic biology tools lead the way in operating genetic manipulation in non-model microorganisms for phenotypic optimization or yields improvement of desired secondary metabolites. In this review, we firstly discuss the pros and cons of several model and non-model microbial chassis, as well as the importance of developing broader non-model microorganisms as alternative programmable heterologous hosts to satisfy the desperate needs of biosynthesis study and industrial production. Then we highlight the lately advances in the synthetic biology tools and engineering strategies for optimization of non-model microbial chassis, in particular, the successful applications for efficient heterologous production of multifarious complex secondary metabolites, e.g., polyketides, nonribosomal peptides, as well as ribosomally synthesized and post-translationally modified peptides. Lastly, emphasis is on the perspectives of chassis cells development to access the ideal cell factory in the artificial intelligence-driven genome era. [Liu, Jiaqi; Dai, Guangzhi; Zhang, Youming; Bian, Xiaoying] Shandong Univ, State Key Lab Microbial Technol, Shandong Univ Helmholtz Inst Biotechnol, Helmholtz Int Lab for Antiinfect, Qingdao 266237, Shandong, Peoples R China; [Liu, Jiaqi] Saarland Univ, Helmholtz Ctr Infect Res HZI, Helmholtz Inst Pharmaceut Res Saarland HIPS, D-66123 Saarbrucken, Germany Shandong University; Helmholtz Association; Helmholtz-Center for Infection Research; Saarland University Bian, XY (corresponding author), Shandong Univ, State Key Lab Microbial Technol, Shandong Univ Helmholtz Inst Biotechnol, Helmholtz Int Lab for Antiinfect, Qingdao 266237, Shandong, Peoples R China. bianxiaoying@sdu.edu.cn Bian, Xiaoying/A-7007-2015 Bian, Xiaoying/0000-0002-1356-3211 National Key RAMP;D Program of China [2021YFC2100500, 2019YFA0905700]; National Natural Science Foundation of China [32070060, 32161133013]; Shandong Provincial Natural Science Foundation [ZR2019JQ11, ZR2019ZD18] National Key RAMP;D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Natural Science Foundation(Natural Science Foundation of Shandong Province) This work was supported by the National Key R&D Program of China (Grant nos. 2021YFC2100500, 2019YFA0905700), National Natural Science Foundation of China (Grant nos. 32070060, 32161133013), Shandong Provincial Natural Science Foundation (Grant no. ZR2019JQ11, ZR2019ZD18). 264 8 8 49 97 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0734-9750 1873-1899 BIOTECHNOL ADV Biotechnol. Adv. OCT 2022.0 59 107966 10.1016/j.biotechadv.2022.107966 0.0 MAY 2022 20 Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology 1E0SC 35487394.0 2023-03-23 WOS:000794206100004 0
J Ge, JD; Huang, YY; Shen, XY; Li, CAY; Hu, W Ge, Jidong; Huang, Yunyun; Shen, Xiaoyu; Li, Chuanyi; Hu, Wei Learning Fine-Grained Fact-Article Correspondence in Legal Cases IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Law; Task analysis; Speech processing; Pattern matching; Feature extraction; Artificial intelligence; Annotations; NLP in law; law article recommendation; neural networks; text matching CRIMINAL CHARGES Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be time-consuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated fact-article correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream task of legal decision prediction, increasing the F1 score by up to 12.7%. Code and dataset are available at https://github.com/gjdnju/MLMN. [Ge, Jidong; Huang, Yunyun; Li, Chuanyi; Hu, Wei] Nanjing Univ, Software Inst, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China; [Shen, Xiaoyu] Amazon, Alexa AI, D-52078 Berlin, Germany Nanjing University; Amazon.com Li, CAY (corresponding author), Nanjing Univ, Software Inst, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China. gjdnju@163.com; oli_yun@163.com; isky1994@gmail.com; lcy@nju.edu.cn; whu@nju.edu.cn National Key R&D Program of China [2016YFC0800803] National Key R&D Program of China This work was supported by the National Key R&D Program of China under Grant 2016YFC0800803. 40 1 1 1 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 2329-9304 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. 2021.0 29 3694 3706 10.1109/TASLP.2021.3130992 0.0 13 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering XP8YB Green Submitted, hybrid 2023-03-23 WOS:000731144400001 0
J Dong, HJ; Zhou, ML; Che, DW; Zhang, HY; Bodomo, A Dong, Hongjie; Zhou, Minli; Che, Dewei; Zhang, Huiying; Bodomo, Adams Analysis of COVID-19 Name Varieties in Chinese Mass and Social Media INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article COVID-19; severe viral pneumonia; new coronavirus epidemic; disease name varieties; distribution across media; usage timelines; daily frequencies; word forms The sudden appearance of a new epidemic disease in China created the need for names identifying that disease. Between December 2019 and January 2020, a variety of severe pneumonia-related disease names suddenly appeared, and more name varieties kept coming up afterwards. To better understand the introduction and spread of these names, 16 different COVID-19-related name varieties were selected covering the period from the end of December 2019, when the epidemic started, to mid-March 2020, a moment at which the term competition had stabilized. By way of big data analysis, the initiation and distribution of the 16 names across the media landscape was traced with regard to the impact of different media platforms, while the distribution frequency of each of the selected terms was mapped, resulting in a distinction of three groups of disease names, each with a different media and time profile. The results were discussed based on the hypotheses of disease confusion by name variety and management failures in absence of clear language governance at the national and global levels. The analysis of the data led to a refutation of both hypotheses. Based on this discussion, the study offers empirically based suggestions for the WHO in their naming practices and further research. [Dong, Hongjie] Xian Univ, Sch Liberal Arts, Xian 710065, Peoples R China; [Zhou, Minli] Guangdong Univ Educ, Dept Liberal Arts, Guangzhou 510303, Peoples R China; [Che, Dewei; Bodomo, Adams] Univ Vienna, Fac Philol & Cultural Studies, A-1090 Vienna, Austria; [Zhang, Huiying] Shaanxi Normal Univ, Sch Liberal Arts, Xian 710062, Peoples R China Xi'an University; University of Vienna; Shaanxi Normal University Che, DW (corresponding author), Univ Vienna, Fac Philol & Cultural Studies, A-1090 Vienna, Austria. donghongjie@xawl.edu.cn; minlizhou@163.com; dewei.che@univie.ac.at; zhanghuiying@snnu.edu.cn; adams.bodomo@univie.ac.at Bodomo, Adams/A-8403-2010 Bodomo, Adams/0000-0002-3807-3681; Dong, Hongjie/0000-0003-3233-3865 Ministry of Education of the People's Republic of China [19YJC740007] Ministry of Education of the People's Republic of China(Ministry of Education, China) This research was funded by Ministry of Education of the People's Republic of China, grant number 19YJC740007. 16 1 1 1 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health SEP 2021.0 18 18 9850 10.3390/ijerph18189850 0.0 21 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health UV9KC 34574774.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000699787300001 0
J Xu, ZG; Wei, T; Easa, S; Zhao, XM; Qu, XB Xu, Zhigang; Wei, Tao; Easa, Said; Zhao, Xiangmo; Qu, Xiaobo Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING English Article FUNDAMENTAL DIAGRAM; PREDICTION; ALGORITHM; EMISSIONS; SPEED In this research, by taking advantage of dynamic fuel consumption-speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers' driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well-recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT-Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy-efficient driving behaviors in the soon-to-come era of connected and automated vehicles. [Xu, Zhigang; Wei, Tao; Zhao, Xiangmo] Changan Univ, Sch Informat Engn, Xian, Shaanxi, Peoples R China; [Easa, Said] Ryerson Univ, Dept Civil Engn, Toronto, ON, Canada; [Qu, Xiaobo] Chalmers Univ Technol, Dept Architecture & Civil Engn, Gothenburg, Sweden Chang'an University; Toronto Metropolitan University; Chalmers University of Technology Qu, XB (corresponding author), Chalmers Univ Technol, Dept Architecture & Civil Engn, Gothenburg, Sweden. drxiaoboqu@gmail.com tao, wei/GXN-4445-2022; Qu, Xiaobo/AAG-4777-2021; Easa, Said/AAA-1755-2019; Xu, Zhigang/AAC-7839-2019; Qu, Xiaobo/C-4182-2013 XU, ZHIGANG/0000-0002-8479-4973; Qu, Xiaobo/0000-0003-0973-3756 National Natural Science Foundation of China [51278058]; Fundamental Application Research Program of China Ministry of Transport [S2013JC9397]; 111 Project [B14043]; Funds for Key Scientific and Technological Innovation Team of the Shaanxi Province [2017KCT-29]; Joint Laboratory of Internet of Vehicles - Ministry of Education; China Mobile National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Application Research Program of China Ministry of Transport; 111 Project(Ministry of Education, China - 111 Project); Funds for Key Scientific and Technological Innovation Team of the Shaanxi Province; Joint Laboratory of Internet of Vehicles - Ministry of Education; China Mobile This research is jointly supported by the National Natural Science Foundation of China (51278058), the Fundamental Application Research Program of China Ministry of Transport (no. S2013JC9397), the 111 Project (no. B14043), the Funds for Key Scientific and Technological Innovation Team of the Shaanxi Province (no. 2017KCT-29), and the Joint Laboratory of Internet of Vehicles sponsored by Ministry of Education and China Mobile. The authors are grateful for the data support by Shaanxi Automotive Group. The authors appreciate six anonymous reviewers for their thorough and most helpful comments, and for the journal's rigorous review process. 46 45 50 15 124 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1093-9687 1467-8667 COMPUT-AIDED CIV INF Comput.-Aided Civil Infrastruct. Eng. MAR 2018.0 33 3 209 219 10.1111/mice.12344 0.0 11 Computer Science, Interdisciplinary Applications; Construction & Building Technology; Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Construction & Building Technology; Engineering; Transportation FV4DE Green Submitted 2023-03-23 WOS:000424522900003 0
J Smyl, D; Tallman, TN; Black, JA; Hauptmann, A; Liu, D Smyl, Danny; Tallman, Tyler N.; Black, Jonathan A.; Hauptmann, Andreas; Liu, Dong Learning and correcting non-Gaussian model errors JOURNAL OF COMPUTATIONAL PHYSICS English Article Finite element method; Inverse problems; Model errors; Neural networks; Non-linearity; Tomography APPROXIMATION ERRORS; INVERSE PROBLEMS; ELEMENT; REDUCTION All discretized numerical models contain modeling errors - this reality is amplified when reduced-order models are used. The ability to accurately approximate modeling errors informs statistics on model confidence and improves quantitative results from frameworks using numerical models in prediction, tomography, and signal processing. Further to this, the compensation of highly nonlinear and non-Gaussian modeling errors, arising in many ill-conditioned systems aiming to capture complex physics, is a historically difficult task. In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems. The viability of the approach is demonstrated using simulated and experimental data arising from differing physical direct and inverse problems. (C) 2021 Elsevier Inc. All rights reserved. [Smyl, Danny; Black, Jonathan A.] Univ Sheffield, Dept Civil & Struct Engn, Sheffield, S Yorkshire, England; [Tallman, Tyler N.] Purdue Univ, Sch Aeronaut & Astronaut, W Lafayette, IN 47907 USA; [Hauptmann, Andreas] Univ Oulu, Res Unit Math Sci, Oulu, Finland; [Hauptmann, Andreas] UCL, Dept Comp Sci, London, England; [Liu, Dong] Univ Sci & Technol China USTC, CAS Key Lab Microscale Magnet Resonance, Hefei 230026, Peoples R China; [Liu, Dong] Univ Sci & Technol China USTC, Dept Modern Phys, Hefei 230026, Peoples R China; [Liu, Dong] USTC, Hefei Natl Lab Phys Sci Microscale, Hefei, Peoples R China; [Liu, Dong] USTC, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei, Peoples R China University of Sheffield; Purdue University System; Purdue University; Purdue University West Lafayette Campus; University of Oulu; University of London; University College London; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS Smyl, D (corresponding author), Univ Sheffield, Dept Civil & Struct Engn, Sheffield, S Yorkshire, England.;Liu, D (corresponding author), Univ Sci & Technol China USTC, CAS Key Lab Microscale Magnet Resonance, Hefei 230026, Peoples R China.;Liu, D (corresponding author), Univ Sci & Technol China USTC, Dept Modern Phys, Hefei 230026, Peoples R China.;Liu, D (corresponding author), USTC, Hefei Natl Lab Phys Sci Microscale, Hefei, Peoples R China.;Liu, D (corresponding author), USTC, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei, Peoples R China. d.smyl@sheffield.ac.uk; ttallman@purdue.edu; j.a.black@sheffield.ac.uk; andreas.hauptmann@oulu.fi; dong2016@ustc.edu.cn Smyl, Danny/AAC-6942-2022; Black, Jonathan/E-5502-2019 Hauptmann, Andreas/0000-0002-3756-8121; Black, Jonathan/0000-0002-3529-6708 National Natural Science Foundation of China [61871356]; Academy of Finland [336796]; EPSRC [EP/K040316/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) DL was supported by National Natural Science Foundation of China (Grant No. 61871356). This work was partially supported by The Academy of FinlandProject 336796 (Finnish Centre of Excellence in Inverse Modelling and Imaging, 2018-2025). 34 9 9 1 6 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0021-9991 1090-2716 J COMPUT PHYS J. Comput. Phys. MAY 1 2021.0 432 110152 10.1016/j.jcp.2021.110152 0.0 FEB 2021 18 Computer Science, Interdisciplinary Applications; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physics RI0DM Green Submitted, Green Accepted 2023-03-23 WOS:000636580800002 0
J Bian, C; Yang, SK; Liu, J; Zio, E Bian, Chong; Yang, Shunkun; Liu, Jie; Zio, Enrico Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks APPLIED SOFT COMPUTING English Article State-of-charge estimation; Deep learning; Multiscale feature learning; Bidirectional feature learning OPEN-CIRCUIT VOLTAGE; MANAGEMENT-SYSTEM Due to the lack of multiscale feature extraction and bidirectional feature learning abilities, the existing deep state-of-charge (SOC) estimators are difficult to capture: (1) localized invariant characteristics hidden in battery measurement perturbations at multiple scales; and (2) intercorrelations among measurements in both time and reverse-time orders. If these situations are not considered, it will lead to large fluctuations in estimated SOC and accumulation of estimated errors at continuous time steps To solve these problems, an estimator combining multichannel convolutional and bidirectional recurrent neural networks (MCNN-BRNN) is proposed for SOC estimation. Specifically, MCNN can extract multiscale local robust features that are invariant to perturbations from measurements on different input paths reducing the estimation fluctuations and enhancing the robustness of the estimator. Moreover, a global convolutional layer is designed to learn the intercorrelations of multiscale features and preserve their temporal coherence. By this means, BRNN can capture the effective time varying information of intercorrelated features in the forward and reverse directions to sequentially estimate SOC, thus alleviating the error accumulation and improving the overall estimation accuracy. Experiments results reveal that MCNN-BRNN outperforms the state-of-the-art estimators in terms of robustness and accuracy under the situations where multiscale perturbations and their comovements exist in measurements. (C) 2021 Elsevier B.V. All rights reserved. [Bian, Chong; Liu, Jie] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Yang, Shunkun; Liu, Jie] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China; [Zio, Enrico] Univ Paris, Chair Syst Sci & Energet Challenges, EDF Fdn, Lab Genie Ind,Cent Supelec, 3 Rue Joliot Curie, F-91190 Saclay, France; [Zio, Enrico] Politecn Milan, Energy Dept, Piazza Leonardo Vinci 32, I-20133 Milan, Italy; [Zio, Enrico] Beihang Univ, Sino French Risk Sci & Engn Lab, Beijing 100191, Peoples R China Beihang University; Beihang University; UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; Polytechnic University of Milan; Beihang University Yang, SK (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China. ysk@buaa.edu.cn Zio, Enrico/0000-0002-7108-637X 44 5 4 4 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. FEB 2022.0 116 108401 10.1016/j.asoc.2021.108401 0.0 JAN 2022 17 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZS1AO 2023-03-23 WOS:000768204300008 0
C Bernard, J; Chang, TW; Popescu, E; Graf, S Conati, C; Heffernan, N; Mitrovic, A; Verdejo, MF Bernard, Jason; Chang, Ting-Wen; Popescu, Elvira; Graf, Sabine Using Artificial Neural Networks to Identify Learning Styles ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015 Lecture Notes in Artificial Intelligence English Proceedings Paper 17th International Conference on Artificial Intelligence in Education (AIED) JUN 22-26, 2015 Univ Natl Educac Distancia, Madrid, SPAIN Pearson,Carnegie Learning,Carney Inc,Univ Complutense Madrid Univ Natl Educac Distancia Artificial neural network; Felder-silverman learning style model; Identification of learning styles SYSTEMS Adaptive learning systems may be used to provide personalized content to students based on their learning styles which can improve students' performance and satisfaction, or reduce the time to learn. Although typically questionnaires exist to identify students' learning styles, there are several disadvantages when using such questionnaires. In order to overcome these disadvantages, research has been conducted on automatic approaches to identify learning styles. However, this line of research is still in an early stage and the accuracy levels of current approaches leave room for improvement before they can be effectively used in adaptive systems. In this paper, we introduce an approach which uses artificial neural networks to identify students' learning styles. The approach has been evaluated with data from 75 students and found to outperform current state of the art approaches. By increasing the accuracy level of learning style identification, more accurate advice can be provided to students, either by adaptive systems or by teachers who are informed about students' learning styles, leading to benefits for students such as higher performance, greater learning satisfaction and less time required to learn. [Bernard, Jason; Graf, Sabine] Athabasca Univ, Athabasca, AB, Canada; [Chang, Ting-Wen] Beijing Normal Univ, Beijing 100875, Peoples R China; [Popescu, Elvira] Univ Craiova, Craiova, Romania Athabasca University; Beijing Normal University; University of Craiova Bernard, J (corresponding author), Athabasca Univ, Athabasca, AB, Canada. c.j.bernard@ieee.org; tingwenchang@bnu.edu.cn; popescu_elvira@software.ucv.ro; sabineg@athabascau.ca Popescu, Elvira/A-2220-2013 Popescu, Elvira/0000-0002-6575-4212; Graf, Sabine/0000-0002-9898-4145 12 14 14 0 8 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 1611-3349 978-3-319-19773-9; 978-3-319-19772-2 LECT NOTES ARTIF INT 2015.0 9112 541 544 10.1007/978-3-319-19773-9_57 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BD9OU 2023-03-23 WOS:000365041100057 0
J Wamba, SF; Queiroz, MM; Wu, LW; Sivarajah, U Wamba, Samuel Fosso; Queiroz, Maciel M.; Wu, Lunwen; Sivarajah, Uthayasankar Big data analytics-enabled sensing capability and organizational outcomes: assessing the mediating effects of business analytics culture ANNALS OF OPERATIONS RESEARCH English Article; Early Access Data analytics; Dynamic capabilities; Data-driven culture; Organizational outcomes; Sensing capabilities RESOURCE-BASED VIEW; PARTIAL LEAST-SQUARES; FIRM PERFORMANCE; SUPPLY CHAIN; PREDICTIVE ANALYTICS; DYNAMIC CAPABILITIES; PLS-SEM; COLLABORATIVE PERFORMANCE; OPERATIONS MANAGEMENT; DECISION-MAKING With the emergence of information and communication technologies, organizations worldwide have been putting in meaningful efforts towards developing and gaining business insights by combining technology capability, management capability and personnel capability to explore data potential, which is known as big data analytics (BDA) capability. In this context, variables such as sensing capability-which is related to the organization's ability to explore the market and develop opportunities-and analytics culture-which refers to the organization's practices and behavior patterns of its analytical principles-play a fundamental role in BDA initiatives. However, there is a considerable literature gap concerning the effects of BDA-enabled sensing capability and analytics culture on organizational outcomes (i.e., customer linking capability, financial performance, market performance, and strategic business value) and on how important the organization's analytics culture is as a mediator in the relationship between BDA-enabled sensing capability and organizational outcomes. Therefore, this study aims to investigate these relationships. And to attain this goal, we developed a conceptual model supported by dynamics capabilities, BDA, and analytics culture. We then validated our model by applying partial least squares structural equation modeling. The findings showed not only the positive effect of the BDA-enabled sensing capability and analytics culture on organizational outcomes but also the mediation effect of the analytics culture. Such results bring valuable theoretical implications and contributions to managers and practitioners. [Wamba, Samuel Fosso] TBS Business Sch, 1 Pl Alphonse Jourdain, F-31068 Toulouse, France; [Queiroz, Maciel M.] Paulista Univ UNIP, Postgrad Program Business Adm, BR-04026002 Sao Paulo, Brazil; [Wu, Lunwen] Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 610074, Sichuan, Peoples R China; [Sivarajah, Uthayasankar] Univ Bradford, Fac Management Law & Social Sci, Richmond Rd, Bradford BD7 1DP, W Yorkshire, England Universidade Paulista; Southwestern University of Finance & Economics - China; University of Bradford Wu, LW (corresponding author), Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 610074, Sichuan, Peoples R China. s.fosso-wamba@tbs-education.fr; maciel.queiroz@docente.unip.br; wulunwen@swufe.edu.cn; U.Sivarajah@bradford.ac.uk Queiroz, Maciel M./F-1274-2014; Wamba, Samuel Fosso/AAB-4953-2019; Queiroz, Maciel M./U-8499-2019 Queiroz, Maciel M./0000-0002-6025-9191; Wamba, Samuel Fosso/0000-0002-1073-058X; 85 15 15 16 93 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0254-5330 1572-9338 ANN OPER RES Ann. Oper. Res. 10.1007/s10479-020-03812-4 0.0 OCT 2020 20 Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science NX6BI Green Accepted 2023-03-23 WOS:000575793800002 0
J Li, MY; Kwan, MP; Wang, FH; Wang, J Li, Mengya; Kwan, Mei-Po; Wang, Fahui; Wang, Jun Using points-of-interest data to estimate commuting patterns in central Shanghai, China JOURNAL OF TRANSPORT GEOGRAPHY English Article Commuting pattern; Gravity model; POIs data; Central urban area; Shanghai ATLANTA METROPOLITAN-AREA; BIG DATA; RESIDENTIAL LOCATION; GIS ENVIRONMENT; TRAVEL PATTERNS; SOCIAL MEDIA; URBAN; GENDER; ACCESSIBILITY; TIME Commuting is an essential part of urban life. Long commutes have negative impacts on individuals and society, such as stress, loss of productivity, traffic congestion and air pollution. However, researchers often face the challenge of lack of data such as commute distance, duration, departure/arrival time, and origins/destinations in countries such as China. This study uses points of interest (POIs) to estimate employment locations, and implements a gravity-based model to estimate interzonal commuting patterns in central Shanghai, China. The results reveal a busy corridor in the west of the central city, especially during the morning peak hours. This pattern corresponds well with reported real-time traffic conditions in Shanghai. Our methodology offers a promising alternative for studying commuting patterns when such data are limited. [Li, Mengya; Wang, Jun] East China Normal Univ, Sch Geog Sci, Minist Educ, Key Lab Geog Informat Sci, 500 Dongchuan Rd, Shanghai 200241, Peoples R China; [Li, Mengya; Kwan, Mei-Po] Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3508 TC Utrecht, Netherlands; [Wang, Fahui] Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70810 USA East China Normal University; University of Illinois System; University of Illinois Urbana-Champaign; Utrecht University; Louisiana State University System; Louisiana State University Wang, J (corresponding author), East China Normal Univ, Sch Geog Sci, Minist Educ, Key Lab Geog Informat Sci, 500 Dongchuan Rd, Shanghai 200241, Peoples R China. fwang@lsu.edu; jwang@geo.ecnu.edu.cn Wang, Fahui/AAF-4700-2022; Wang, Fahui/H-4302-2013; Wang, Fahui/AFJ-5920-2022; Kwan, Mei-Po/S-4162-2016 Wang, Fahui/0000-0001-7765-3024; Wang, Fahui/0000-0001-7765-3024; Wang, Fahui/0000-0001-7765-3024; Kwan, Mei-Po/0000-0001-8602-9258; Li, Mengya/0000-0002-0646-1866 National Natural Science Foundation of China [41671095, 71373084, 51761135024, 41529101]; National Key Research and Development Program of China [2017YFE0107400]; EPSRC [EP/R034214/1] Funding Source: UKRI National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research was supported by the National Natural Science Foundation of China (Grant No. 41671095, 71373084, 51761135024, 41529101), and the National Key Research and Development Program of China (No. 2017YFE0107400). 41 24 24 11 88 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0966-6923 1873-1236 J TRANSP GEOGR J. Transp. Geogr. OCT 2018.0 72 201 210 10.1016/j.jtrangeo.2018.09.004 0.0 10 Economics; Geography; Transportation Social Science Citation Index (SSCI) Business & Economics; Geography; Transportation HA3EY 2023-03-23 WOS:000450134100017 0
J Nabipour, N; Mosavi, A; Hajnal, E; Nadai, L; Shamshirband, S; Chau, KW Nabipour, Narjes; Mosavi, Amir; Hajnal, Eva; Nadai, Laszlo; Shamshirband, Shahaboddin; Chau, Kwok-Wing Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS English Article Wind turbine; adaptive neuro-fuzzy inference system (ANFIS); dynamical downscaling; regional climate change model; renewable energy; machine learning SUPPORT VECTOR REGRESSION; ENERGY; EVAPORATION; PREDICTION; TURBINE; FLOW Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken into consideration. An adaptive neuro-fuzzy inference system (ANFIS)-based post-processing technique was used to match the power outputs of the regional climate model (RCM) with those obtained from reference data. The near-surface wind data obtained from an RCM were used to investigate climate change impacts on the wind power resources in the Caspian Sea. After converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results were investigated to reveal mean annual power, seasonal and monthly variability for 20 year historical (1981-2000) and future (2081-2100) periods. The results revealed that climate change does not notably affect the wind climate over the study area. However, a small decrease was projected in the future simulation, revealing a slight decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution, with winter and summer having the highest values. The results indicate that the middle and northern parts of the Caspian Sea have the highest values of wind power. However, the results of the post-processing technique using the ANFIS model showed that the real potential of wind power in the area is lower than that projected in the RCM. [Nabipour, Narjes] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Mosavi, Amir; Nadai, Laszlo] Obuda Univ, Kalman Kando Fac Elect Engn, Budapest, Hungary; [Mosavi, Amir] Oxford Brookes Univ, Sch Built Environm, Oxford, England; [Mosavi, Amir] J Selye Univ, Dept Math & Informat, Komarno, Slovakia; [Hajnal, Eva] Obuda Univ, Alba Regia Tech Fac, Szekesfehervar, Hungary; [Shamshirband, Shahaboddin] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; [Shamshirband, Shahaboddin] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam; [Chau, Kwok-Wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China Duy Tan University; Obuda University; Oxford Brookes University; J. Selye University; Obuda University; Ton Duc Thang University; Ton Duc Thang University; Hong Kong Polytechnic University Shamshirband, S (corresponding author), Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam.;Shamshirband, S (corresponding author), Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam. shahaboddin.shamshirband@tdtu.edu.vn Hajnal, Éva/ABH-8784-2020; Mosavi, Amir/I-7440-2018; S.Band, Shahab/AAD-3311-2021; S.Band, Shahab/ABI-7388-2020; Chau, Kwok-wing/E-5235-2011 Hajnal, Éva/0000-0002-9498-5052; Mosavi, Amir/0000-0003-4842-0613; S.Band, Shahab/0000-0002-8963-731X; Chau, Kwok-wing/0000-0001-6457-161X European Union [EFOP-3.6.1-16-2016-00010, 2017-1.3.1-VKE-2017-00025] European Union(European Commission) We acknowledge the financial support of this research by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-00025 project. 39 35 35 1 12 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 1994-2060 1997-003X ENG APPL COMP FLUID Eng. Appl. Comp. Fluid Mech. JAN 1 2020.0 14 1 491 506 10.1080/19942060.2020.1722241 0.0 16 Engineering, Multidisciplinary; Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics KO4MO Green Published, Green Submitted, gold 2023-03-23 WOS:000515525400001 0
J Kaleem, Z; Coleri, S; Deng, YS; Yuen, C; Debbah, M Kaleem, Zeeshan; Coleri, Sinem; Deng, Yansha; Yuen, Chau; Debbah, Merouane ARTIFICIAL INTELLIGENCE ENABLED INTERNET OF UAVS FOR EMERGENCY COMMUNICATIONS IEEE WIRELESS COMMUNICATIONS English Editorial Material Special issues and sections; Ultra reliable low latency communication; Ubiquitous computing; Throughput; Reliability engineering; Real-time systems; Sensors; Millimeter wave technology The unprecedented demands for high-quality and ubiquitous wireless services impose enormous challenges to the existing cellular networks to meet the challenges of connectivity, reliability, etc. The targeted applications in designing the beyond fifth generation (BSG) systems include enhanced mobile broadband (eMBB), ultra-reliable, low latency communications (URLLC), and massive machine-type communications (mMTC). Recently to meet those diverse requirements, the latest technologies like millimeter wave (mmWave) communications, ultra-dense networks (UDN), and massive multiple-input and multiple-output (MIMO) are under consideration. Moreover, we also require an evolution in an architectural platform that performs joint communications, sensing, localization, and computing while ensuring ultra-high throughput, ultra-low latency, and ultra-high reliability for real-time emergency communications. [Kaleem, Zeeshan] COMSATS Univ Islamabad, Elect & Comp Engn Dept, Wah Campus, Wah, Pakistan; [Coleri, Sinem] Koc Univ, Dept Elect & Elect Engn, Istanbul, Turkey; [Deng, Yansha] Kings Coll London, Dept Engn, London, England; [Yuen, Chau] Hong Kong Polytech Univ, Hong Kong, Peoples R China; [Yuen, Chau] Inst Infocomm Res I2R, Singapore, Singapore; [Yuen, Chau] Singapore Univ Technol & Design SUTD, Singapore, Singapore; [Debbah, Merouane] Technol Innovat Inst, Abu Dhabi, U Arab Emirates; [Debbah, Merouane] Mohamed Bin Zayed Univ Artificial Intelligence, Dept Machine Learning, Abu Dhabi, U Arab Emirates; [Debbah, Merouane] Inst Eurecom, Mobile Commun Dept, Sophia Antipolis, France; [Debbah, Merouane] Cent Supelec, Gif Sur Yvette, France COMSATS University Islamabad (CUI); Koc University; University of London; King's College London; Hong Kong Polytechnic University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); Singapore University of Technology & Design; Technology Innovation Institute; Mohamed Bin Zayed University of Artificial Intelligence; IMT - Institut Mines-Telecom; EURECOM; UDICE-French Research Universities; Universite Paris Saclay Kaleem, Z (corresponding author), COMSATS Univ Islamabad, Elect & Comp Engn Dept, Wah Campus, Wah, Pakistan. zeeshankaleem@gmail.com; scoleri@ku.edu.tr; yansha.deng@kcl.ac.uk; yuenchau@sutd.edu.sg; merouane.debbah@tii.ae Yuen, Chau/C-5493-2013 Yuen, Chau/0000-0002-9307-2120 0 0 0 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. AUG 2022.0 29 4 8 9 10.1109/MWC.2022.9920740 0.0 2 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5L9KW Bronze 2023-03-23 WOS:000870727800011 0
J Zeng, H; Zhang, JM; Zakaria, W; Babiloni, F; Gianluca, B; Li, XF; Kong, WZ Zeng, Hong; Zhang, Jiaming; Zakaria, Wael; Babiloni, Fabio; Gianluca, Borghini; Li, Xiufeng; Kong, Wanzeng InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection SENSORS English Article Electroencephalogram (EEG); InstanceEasyTL; fatigue driving; cross-subject; transfer learning SEIZURE DETECTION; DRIVER FATIGUE; BCI Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc. [Zeng, Hong; Zhang, Jiaming; Li, Xiufeng; Kong, Wanzeng] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China; [Zeng, Hong; Kong, Wanzeng] Key Lab Brain Machine Collaborat Intelligence Zhe, Hangzhou 310018, Peoples R China; [Zakaria, Wael] Ain Shams Univ, Fac Sci, Dept Math Comp Sci, Cairo 11435, Egypt; [Babiloni, Fabio; Gianluca, Borghini] Univ Roma La Sapienza, Ind NeuroSci Lab, I-00161 Rome, Italy Hangzhou Dianzi University; Egyptian Knowledge Bank (EKB); Ain Shams University; Sapienza University Rome Babiloni, F (corresponding author), Univ Roma La Sapienza, Ind NeuroSci Lab, I-00161 Rome, Italy. jivon@hdu.edu.cn; 182050179@hdu.edu.cn; wael.zakaria@sci.asu.edu.eg; Correspondencefabio.babiloni@uniroma1.it; gianluca.borghini@uniroma1.it; lixiufengcn@hdu.edu.cn; kongwanzeng@hdu.edu.cn Borghini, Gianluca/AAA-4687-2019; Zakaria, Wael/AAU-7837-2021; Babiloni, Fabio/E-5169-2015 Borghini, Gianluca/0000-0001-8560-5671; Zakaria, Wael/0000-0001-6718-0980; Babiloni, Fabio/0000-0002-4962-176X; Kong, Wanzeng/0000-0002-0113-6968 National Key R&D Program of China [2017YFE0118200]; NSFC [62076083]; Excellent Dissertation Cultivation Fund of Zhejiang Province [GK208802299013-101]; Graduate Scientific Research Foundation of Hangzhou Dianzi University [CXJJ2020086]; Fundamental Research Funds for the Provincial Universities of Zhejiang [GK209907299001-008] National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Excellent Dissertation Cultivation Fund of Zhejiang Province; Graduate Scientific Research Foundation of Hangzhou Dianzi University; Fundamental Research Funds for the Provincial Universities of Zhejiang This research was funded by the National Key R&D Program of China with grant No. 2017YFE0118200, NSFC with grant No. 62076083, Excellent Dissertation Cultivation Fund of Zhejiang Province with grant No. GK208802299013-101, the Graduate Scientific Research Foundation of Hangzhou Dianzi University with grant No. CXJJ2020086 and the Fundamental Research Funds for the Provincial Universities of Zhejiang with grant No. GK209907299001-008. 42 10 11 27 89 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2020.0 20 24 7251 10.3390/s20247251 0.0 17 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation PL7LC 33348823.0 Green Accepted, gold, Green Submitted 2023-03-23 WOS:000603297500001 0
J Bokde, N; Feijoo, A; Al-Ansari, N; Tao, SY; Yaseen, ZM Bokde, Neeraj; Feijoo, Andres; Al-Ansari, Nadhir; Tao, Siyu; Yaseen, Zaher Mundher The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling ENERGIES English Article hybrid intelligent model; time-series; wind speed; wind power; wind energy TIME-SERIES; NEURAL-NETWORK; PREDICTION; IMPLEMENTATION; SIMULATION; SPECTRUM In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package 'decomposedPSF' which is discussed in the Appendix. [Bokde, Neeraj] Aarhus Univ, Dept Engn Renewable Energy & Thermodynam, Aarhus, Denmark; [Feijoo, Andres] Univ Vigo, Dept Enxerieria Elect, Campus Lagoas Marcosende, Vigo, Spain; [Al-Ansari, Nadhir] Lulea Univ Technol, Civil Environm & Nat Resources Engn, Lulea, Sweden; [Tao, Siyu] Southeast Univ, Sch Elect Engn, Nanjing, Peoples R China; [Yaseen, Zaher Mundher] Ton Duc Thang Univ, Sustainable Dev Civil Engn Res Grp, Fac Civil Engn, Ho Chi Minh City, Vietnam Aarhus University; Universidade de Vigo; Lulea University of Technology; Southeast University - China; Ton Duc Thang University Yaseen, ZM (corresponding author), Ton Duc Thang Univ, Sustainable Dev Civil Engn Res Grp, Fac Civil Engn, Ho Chi Minh City, Vietnam. neerajdhanraj@eng.au.dk; afeijoo@uvigo.es; nadhir.alansari@ltu.se; 230188145@seu.edu.cn; yaseen@tdtu.edu.vn Yaseen, Zaher Mundher/G-7029-2018; Tao, Siyu/W-6387-2019; Feijóo-Lorenzo, Andrés Elías/D-3866-2019; Bokde, Neeraj/I-2621-2016 Yaseen, Zaher Mundher/0000-0003-3647-7137; Tao, Siyu/0000-0003-4466-2535; Feijóo-Lorenzo, Andrés Elías/0000-0003-3172-7037; Bokde, Neeraj/0000-0002-3493-9302; Al-Ansari, Nadhir/0000-0002-6790-2653 70 18 18 7 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies APR 2020.0 13 7 1666 10.3390/en13071666 0.0 23 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels LU3WB gold, Green Submitted 2023-03-23 WOS:000537688400135 0
J Lu, JF; Liu, HB; Zhang, Z; Wang, JT; Goudos, SK; Wan, SH Lu, Jianfeng; Liu, Haibo; Zhang, Zhao; Wang, Jiangtao; Goudos, Sotirios K.; Wan, Shaohua Toward Fairness-Aware Time-Sensitive Asynchronous Federated Learning for Critical Energy Infrastructure IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Critical energy infrastructure; fairness-aware; federated learning; task allocation; time-sensitive Critical energy infrastructure (CEI) systems are vital to underpin the national economy and social development, but vulnerable to cyber attack and data privacy leakage when distributed machine learning technologies are deployed on them. Although federated learning (FL) has promoted distributed collaborative learning while keeping natural compliance with the privacy protection, it is tremendously difficult to schedule edge nodes of CEI collaboratively when asynchronous FL tasks are applied in CEI system, since the CEI system must make an irrevocable immediate decision on whether to hire a participant who arrives and departs dynamically without knowing future information. In this article, we tackle this issue by designing fairness-aware and time-sensitive task allocation mechanisms in asynchronous FL for CEI. First, we design an optimal multidimensional contract to guarantee the reliability, honesty, and fairness, and maximize the learning accuracy for the fixed deadline scenario. Second, we design a multimetric participant recruitment mechanism to control time consumption for the limited budget scenario, prove that the problem of optimizing this mechanism is NP-hard, and propose an e-approximation algorithm accordingly. Finally, extensive experiments using both real-world data and simulated data further demonstrate the effectiveness and efficiency of our proposed mechanisms compared to the state-of-the-art approaches. [Lu, Jianfeng] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430065, Peoples R China; [Lu, Jianfeng; Liu, Haibo; Zhang, Zhao] Zhejiang Normal Univ, Dept Comp Sci & Engn, Jinhua 321004, Zhejiang, Peoples R China; [Wang, Jiangtao] Coventry Univ, Ctr Intelligent Healthcare, Coventry CV1 5FB, W Midlands, England; [Goudos, Sotirios K.] Aristotle Univ Thessaloniki, Dept Phys, ELEDIA AUTH, Thessaloniki 54124, Greece; [Wan, Shaohua] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China Wuhan University of Science & Technology; Zhejiang Normal University; Coventry University; Aristotle University of Thessaloniki; Zhongnan University of Economics & Law Wan, SH (corresponding author), Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China. lujian-feng@wust.edu.cn; lhbzjnu@163.com; zhaozhang@zjnu.cn; jiang-tao.wang@coventry.ac.uk; sgoudo@physics.auth.gr; shaohua.wan@ieee.org Goudos, Sotirios K./HJA-6146-2022; Wan, Shaohua/B-9243-2014 Goudos, Sotirios K./0000-0001-5981-5683; Wan, Shaohua/0000-0001-7013-9081; Lu, Jianfeng/0000-0001-6834-1539; Wang, Jiangtao/0000-0002-8704-502X National Natural Science Foundation of China [62072411, 62172438, U20A2068, 11771013]; Zhejiang Provincial Natural Science Foundation of China [LR21F020001, LD19A010001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work was supported in part by the National Natural Science Foundation of China under Grant 62072411, Grant 62172438, Grant U20A2068, and Grant 11771013 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR21F020001 and Grant LD19A010001. 23 4 4 2 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. MAY 2022.0 18 5 3462 3472 10.1109/TII.2021.3117861 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering YU4MK Green Published 2023-03-23 WOS:000752019100059 0
J Peng, JY; Kimmig, A; Niu, ZB; Wang, JH; Liu, XF; Ovtcharova, J Peng, Jieyang; Kimmig, Andreas; Niu, Zhibin; Wang, Jiahai; Liu, Xiufeng; Ovtcharova, Jivka A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework APPLIED ENERGY English Article Urban energy; Spatio-temporal demand forecasting; Visual analysis; Spatial mapping; Pattern recognition ELECTRICITY CONSUMPTION; RESIDENTIAL SECTOR; NEURAL-NETWORK; TIME-SERIES; POWER Understanding urban demand profiles is an important determinant for energy dispatch and the optimization of the electric energy supply. For the design of the energy supply system, an important consideration is, to express the characteristics of urban household energy demand as a function of space and time. However, the focus of most research activities is only on the modeling of time series data. High-resolution forecasting models for the spatial-temporal distribution of energy were rarely reported in current literature. In this paper, we propose a spatio-temporal forecasting model based on potential-flow for urban energy demand forecasting. Compared with previous studies, potential-flow can describe energy migration in space with a high resolution. Based on the orientation of vectors, our model can predict the direction and intensity of spatial migrations in energy demand and identify energy transfer events. Extensive experiments on real-world data sets verify that our approach can achieve a better prediction accuracy compared with traditional methods. In further empirical studies, we find that the temporal electricity demand flow shows locally concentrating behavior for different regions of the city. In addition to temporal factors such as seasons, peaks and valleys, such clustering behavior also depend on local populations and major industries (financial, commercial, residential, etc.). Finally, we use entropy to quantitatively describe the intensity of this clustering phenomenon and explore its relationship with meteorological factors. Our research demonstrates a unified visual prediction approach to support exploratory demand analysis. We anticipate that the process will be expanded to support more forms of energy in the future. [Peng, Jieyang; Wang, Jiahai] Tongji Univ, Adv Mfg Technol Ctr, Shanghai 200092, Peoples R China; [Peng, Jieyang; Kimmig, Andreas; Ovtcharova, Jivka] Karlsruhe Inst Technol, D-76133 Karlsruhe, Germany; [Niu, Zhibin; Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark; [Niu, Zhibin] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300354, Peoples R China Tongji University; Helmholtz Association; Karlsruhe Institute of Technology; Technical University of Denmark; Tianjin University Wang, JH (corresponding author), Tongji Univ, Adv Mfg Technol Ctr, Shanghai 200092, Peoples R China.;Niu, ZB (corresponding author), Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark.;Niu, ZB (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin 300354, Peoples R China. mind3str@gmail.com; jhwang@tongji.edu.cn Niu, Zhibin/AAE-7765-2020; Tomm, He/E-5457-2013 Niu, Zhibin/0000-0002-5171-7648; Liu, Xiufeng/0000-0001-5133-6688; PENG, jieyang/0000-0001-6544-1825 National Key R&D Program of China [2017YFE0101400]; National Natural Science Foundation of China [61802278]; EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant [754462]; Heat4.0 project - Innovation Fund Denmark [809000046A]; Flexibility for Smart Urban Energy Systems project (Flex-SUS) - European Union [91352, 775970] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant; Heat4.0 project - Innovation Fund Denmark; Flexibility for Smart Urban Energy Systems project (Flex-SUS) - European Union The research is supported by National Key R&D Program of China (No. 2017YFE0101400) , National Natural Science Foundation of China (No. 61802278) , EU H2020 Research and Innovation Program un-der the Marie Sklodowska-Curie Grant Agreement (No.754462) , the Heat4.0 project (No.809000046A) funded by Innovation Fund Denmark, and the Flexibility for Smart Urban Energy Systems project (Flex-SUS) (91352) funded by the European Union's Horizon 2020 research and innovation programme under grant agreement (No.775970) . 64 3 3 4 12 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy OCT 1 2021.0 299 117321 10.1016/j.apenergy.2021.117321 0.0 JUL 2021 14 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering UA2BU Green Submitted 2023-03-23 WOS:000684970300004 0
J Huang, XH; Dhall, A; Goecke, R; Pietikainen, M; Zhao, GY Huang, Xiaohua; Dhall, Abhinav; Goecke, Roland; Pietikainen, Matti; Zhao, Guoying Analyzing Group-Level Emotion with Global Alignment Kernel based Approach IEEE TRANSACTIONS ON AFFECTIVE COMPUTING English Article Kernel; Emotion recognition; Face recognition; Computer vision; Mood; Computational modeling; Group-level emotion recognition; global alignment kernels; multiple kernel learning; facial expression analysis; convolution neural network TEXTURE CLASSIFICATION; RECOGNITION; DYNAMICS From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been received increasingly interest in affective computing community. The variability in group size makes difficulty for group-level emotion recognition to straightforwardly measure the feature distance of two group-level images. Recent works attempted to resolve the preceding problem by using feature encoding. However, the early works lack of efficiency. To alleviate this problem, this article aims to design a new method to effectively analyze the group behavior from a group-level image. Motivated by time-series kernel approaches explored in dynamic facial expression classification, this article mainly concentrates on global alignment kernel and design support vector machine with the combined global alignment kernels (SVM-CGAK) to better recognize group-level emotion. Specifically, we first propose to use global alignment kernel to explicitly measure the distance of two group-level images. For improving the performance of global alignment kernel, we use the global weight sort scheme based on their spatial relation information to sort the faces from group-level image, making an efficient data structure to the global alignment kernel. With this new global alignment kernel, we construct the backbone of SVM-CGAK, namely, support vector machine with global alignment kernel. Furthermore, considering the challenging environment, we construct two global alignment kernels based on Reisz-based Volume Local Binary Pattern and deep convolutional neural network features, respectively. Lastly, to make the robustness of group-level emotion recognition, we propose SVM-CGAK combining both global alignment kernels with multiple kernel learning approach. It can enhance the discriminative ability of each global alignment kernel. Intensive experiments are conducted on three challenging group-level emotion databases. The experimental results demonstrate that the proposed approach achieves promising performance for group-level emotion recognition compared with the recent state-of-the-art methods. [Huang, Xiaohua] Nanjing Inst Technol, Sch Comp Engn, Nanjing 211167, Peoples R China; [Huang, Xiaohua] Univ Oulu, Oulu 90014, Finland; [Dhall, Abhinav] Monash Univ, Fac Informat Technol, Clayton, Vic, Australia; [Dhall, Abhinav] Indian Inst Technol Roper, Rupnagar 140001, Punjab, India; [Goecke, Roland] Univ Canberra, Human Ctr Technol Res Ctr, Bruce, ACT 2617, Australia; [Pietikainen, Matti; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland Nanjing Institute of Technology; University of Oulu; Monash University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Ropar; University of Canberra; University of Oulu Huang, XH (corresponding author), Nanjing Inst Technol, Sch Comp Engn, Nanjing 211167, Peoples R China. xiaohua.huang@njit.edu.cn; abhinav.dhall@monash.edu; roland.goecke@ieee.org; mkp@ee.oulu.fi; guoying.zhao@oulu.fi Huang, Xiaohua/A-4878-2011; Goecke, Roland/F-7499-2013 Huang, Xiaohua/0000-0003-4150-0477; Goecke, Roland/0000-0003-2279-7041; Zhao, Guoying/0000-0003-3694-206X; Pietikainen, Matti/0000-0003-2263-6731 National Natural Science Foundation of China [62076122]; Jiangsu SpeciallyAppointed Professor Program [3051107219003]; Jorma Ollila Grant of Nokia Foundation; Central Fund of the Finnish Cultural Foundation; Natural Science Foundation of Jiangsu Higher Education Institutions of China [17KJB520010]; Tekes Fidipro program [849/31/2015]; Business Finland project [3116/31/2017]; Academy of Finland; NVIDIA Corporation; Infotech Oulu; Talent Startup project ofNJIT [YKJ201982] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu SpeciallyAppointed Professor Program; Jorma Ollila Grant of Nokia Foundation(Nokia Corporation); Central Fund of the Finnish Cultural Foundation; Natural Science Foundation of Jiangsu Higher Education Institutions of China; Tekes Fidipro program(Finnish Funding Agency for Technology & Innovation (TEKES)); Business Finland project; Academy of Finland(Academy of Finland); NVIDIA Corporation; Infotech Oulu; Talent Startup project ofNJIT This work was supported by National Natural Science Foundation of China under Grant 62076122, the Jiangsu SpeciallyAppointed Professor Program under Grant 3051107219003, the Talent Startup project ofNJIT under Grant YKJ201982, the Jorma Ollila Grant of Nokia Foundation, and the Central Fund of the Finnish Cultural Foundation. It is also supported in part by the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant 17KJB520010, the Tekes Fidipro program under Grant 849/31/2015, and the Business Finland project under Grant 3116/31/2017, Infotech Oulu, Academy of Finland. This work was also supported by NVIDIA Corporation with the donation of the Quadro P6000 GPUused for this research. 59 0 0 2 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3045 IEEE T AFFECT COMPUT IEEE Trans. Affect. Comput. APR-JUN 2022.0 13 2 713 728 10.1109/TAFFC.2019.2953664 0.0 16 Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1T3OS Green Accepted 2023-03-23 WOS:000804643000013 0
J Kong, Y; Genchev, GZ; Wang, XL; Zhao, HY; Lu, H Kong, Yan; Genchev, Georgi Z.; Wang, Xiaolei; Zhao, Hongyu; Lu, Hui Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY English Article nuclei segmentation; histopathological image; Stacked U-Nets; attention generation mechanism; deep learning Nuclei segmentation is a fundamental but challenging task in histopathological image analysis. One of the main problems is the existence of overlapping regions which increases the difficulty of independent nuclei separation. In this study, to solve the segmentation of nuclei and overlapping regions, we introduce a nuclei segmentation method based on two-stage learning framework consisting of two connected Stacked U-Nets (SUNets). The proposed SUNets consists of four parallel backbone nets, which are merged by the attention generation model. In the first stage, a Stacked U-Net is utilized to predict pixel-wise segmentation of nuclei. The output binary map together with RGB values of the original images are concatenated as the input of the second stage of SUNets. Due to the sizable imbalance of overlapping and background regions, the first network is trained with cross-entropy loss, while the second network is trained with focal loss. We applied the method on two publicly available datasets and achieved state-of-the-art performance for nuclei segmentation-mean Aggregated Jaccard Index (AJI) results were 0.5965 and 0.6210, and F1 scores were 0.8247 and 0.8060, respectively; our method also segmented the overlapping regions between nuclei, with average AJI = 0.3254. The proposed two-stage learning framework outperforms many current segmentation methods, and the consistent good segmentation performance on images from different organs indicates the generalized adaptability of our approach. [Kong, Yan; Genchev, Georgi Z.; Wang, Xiaolei; Lu, Hui] Shanghai Jiao Tong Univ, SJTU Yale Joint Ctr Biostat & Data Sci, Dept Bioinformat & Biostat, Sch Life Sci & Biotechnol, Shanghai, Peoples R China; [Genchev, Georgi Z.; Lu, Hui] Shanghai Childrens Hosp, Shanghai Engn Res Ctr Big Data Pediat Precis Med, Ctr Biomed Informat, Shanghai, Peoples R China; [Genchev, Georgi Z.] Bulgarian Inst Genom & Precis Med, Sofia, Bulgaria; [Zhao, Hongyu] Yale Univ, Dept Biostat, New Haven, CT 06520 USA Shanghai Jiao Tong University; Shanghai Jiao Tong University; Yale University Wang, XL; Lu, H (corresponding author), Shanghai Jiao Tong Univ, SJTU Yale Joint Ctr Biostat & Data Sci, Dept Bioinformat & Biostat, Sch Life Sci & Biotechnol, Shanghai, Peoples R China.;Lu, H (corresponding author), Shanghai Childrens Hosp, Shanghai Engn Res Ctr Big Data Pediat Precis Med, Ctr Biomed Informat, Shanghai, Peoples R China.;Zhao, HY (corresponding author), Yale Univ, Dept Biostat, New Haven, CT 06520 USA. thundawner@gmail.com; hongyu.zhao@yale.edu; huilu@sjtu.edu.cn National Key R&D Program of China [2018YFC0910500]; SJTU-Yale Collaborative Research Seed Fund; Neil Shen's SJTU Medical Research Fund National Key R&D Program of China; SJTU-Yale Collaborative Research Seed Fund; Neil Shen's SJTU Medical Research Fund This work was supported by the National Key R&D Program of China 2018YFC0910500 (to HL), SJTU-Yale Collaborative Research Seed Fund (to YK and HL), and the Neil Shen's SJTU Medical Research Fund (to YK and HL). 33 8 8 0 6 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-4185 FRONT BIOENG BIOTECH Front. Bioeng. Biotechnol. OCT 26 2020.0 8 573866 10.3389/fbioe.2020.573866 0.0 7 Biotechnology & Applied Microbiology; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Science & Technology - Other Topics OO4XF 33195135.0 gold 2023-03-23 WOS:000587382700001 0
J Feng, CS; Liu, B; Yu, KP; Goudos, SK; Wan, SH Feng, Chaosheng; Liu, Bin; Yu, Keping; Goudos, Sotirios K.; Wan, Shaohua Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Blockchains; Authentication; Industrial Internet of Things; Training; Data models; Drones; Informatics; 5G-enabled unmanned aerial vehicles (UAVs); cross-domain authentication; federated learning (FL); privacy preservation; smart contract Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy. [Feng, Chaosheng; Liu, Bin] Sichuan Normal Univ, Coll Comp Sci, Chengdu 610101, Peoples R China; [Yu, Keping] Waseda Univ, Global Informat & Telecommun Inst, Shinju Ku, Tokyo 1698050, Japan; [Goudos, Sotirios K.] Aristotle Univ Thessaloniki, Dept Phys, Radiocommun Lab, ELEDIA AUTH, Thessaloniki 54124, Greece; [Wan, Shaohua] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China Sichuan Normal University; Waseda University; Aristotle University of Thessaloniki; Zhongnan University of Economics & Law Yu, KP (corresponding author), Waseda Univ, Global Informat & Telecommun Inst, Shinju Ku, Tokyo 1698050, Japan.;Wan, SH (corresponding author), Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China. csfenggy@126.com; liubin10@foxmail.com; keping.yu@aoni.waseda.jp; sgoudos@gmail.com; shaohua.wan@ieee.org Goudos, Sotirios K./HJA-6146-2022; Yu, Keping/GVT-7847-2022; Feng, Chaosheng/HLQ-6463-2023; Wan, Shaohua/B-9243-2014 Goudos, Sotirios K./0000-0001-5981-5683; Yu, Keping/0000-0001-5735-2507; Feng, Chaosheng/0000-0002-1797-5280; Liu, Bin/0000-0002-2949-832X; Wan, Shaohua/0000-0001-7013-9081 National Natural Science Foundation of China [61373163]; Japan Society for the Promotion of Science (JSPS) [JP18K18044, JP21K17736, TII-21-2779] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Japan Society for the Promotion of Science (JSPS)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science) This work was supported in part by the National Natural Science Foundation of China under Grant 61373163 and in part by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044 and Grant JP21K17736. Paper no. TII-21-2779. 27 67 67 75 131 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. MAY 2022.0 18 5 3582 3592 10.1109/TII.2021.3116132 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering YU4MK 2023-03-23 WOS:000752019100072 0
J Zhong, RY; Putnik, GD; Newman, ST Zhong, Ray Y.; Putnik, Goran D.; Newman, Stephen T. A Heterogeneous Data Analytics Framework for RFID-Enabled Factories IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Radiofrequency identification; Data analysis; Smart manufacturing; Production facilities; Real-time systems; Data analytics; framework; heterogeneity; radio-frequency identification (RFID); smart manufacturing BIG DATA ANALYTICS; MANUFACTURING EXECUTION SYSTEM; SUPPLY CHAIN MANAGEMENT; OF-THE-ART; CHALLENGES; SERVICE; TIME As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data. [Zhong, Ray Y.] Univ Hong Kong, Ind & Mfg Syst Engn, Hong Kong, Peoples R China; [Putnik, Goran D.] Univ Minho, Dept Prod & Syst Engn, P-4800058 Guimaraes, Portugal; [Putnik, Goran D.] Univ Minho, Res Ctr ALGORITMI, P-4800058 Guimaraes, Portugal; [Newman, Stephen T.] Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England University of Hong Kong; Universidade do Minho; Universidade do Minho; University of Bath Zhong, RY (corresponding author), Univ Hong Kong, Ind & Mfg Syst Engn, Hong Kong, Peoples R China. zhongzry@hku.hk; putnikgd@dps.uminho.pt; s.t.newman@bath.ac.uk Newman, Stephen/A-1246-2009; Putnik, Goran/B-1428-2014 Newman, Stephen/0000-0003-3163-1908; Putnik, Goran/0000-0003-3378-6866 Seed Fund for Basic Research in HKU [201906159001]; HKU KE Impact Project Scheme [KE-IP-2019/20-31] Seed Fund for Basic Research in HKU; HKU KE Impact Project Scheme This work was supported in part by the Seed Fund for Basic Research in HKU under Grant 201906159001, and in part by HKU KE Impact Project Scheme under Grant KE-IP-2019/20-31. 38 4 4 0 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. SEP 2021.0 51 9 5567 5576 10.1109/TSMC.2019.2956201 0.0 10 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science UB5MV 2023-03-23 WOS:000685890800035 0
C Wang, SS; Yan, QB; Chen, ZX; Wang, L; Spolaor, R; Yang, B; Conti, M Beyah, R; Chang, B; Li, Y; Zhu, S Wang, Shanshan; Yan, Qiben; Chen, Zhenxiang; Wang, Lin; Spolaor, Riccardo; Yang, Bo; Conti, Mauro Lexical Mining of Malicious URLs for Classifying Android Malware SECURITY AND PRIVACY IN COMMUNICATION NETWORKS, SECURECOMM 2018, PT I Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering English Proceedings Paper 14th European-Alliance-for-Innovation (EAI) International Conference on Security and Privacy in Communication Networks (SecureComm) AUG 08-10, 2018 Singapore, SINGAPORE European Alliance Innovat The prevalence of mobile malware has become a growing issue given the tight integration of mobile systems with our daily life. Most malware programs use URLs inside network traffic to forward commands to launch malicious activities. Therefore, the detection of malicious URLs can be essential in deterring such malicious activities. Traditional methods construct blacklists with verified URLs to identify malicious URLs, but their effectiveness is impaired by unknown malicious URLs. Recently, machine learning-based methods have been proposed for malware detection with improved performance. In this paper, we propose a novel URL detection method based on Floating Centroids Method (FCM), which integrates supervised classification and unsupervised clustering in a coherent manner. The proposed method uses the lexical features of a URL to effectively identify malicious URLs while grouping similar URLs into the same cluster. Our experimental results show that a URL cluster exhibits unique behavioral patterns that can be used for malware detection with high accuracy. Moreover, the proposed behavioral clustering method facilitates the identification of malicious URL categories and unseen malware variants. [Wang, Shanshan; Chen, Zhenxiang; Wang, Lin; Yang, Bo] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China; [Yan, Qiben] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA; [Spolaor, Riccardo; Conti, Mauro] Univ Padua, Dept Math, Padua, Italy University of Jinan; University of Nebraska System; University of Nebraska Lincoln; University of Padua Chen, ZX (corresponding author), Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China. czx@ujn.edu.cn Conti, Mauro/F-9145-2012 Conti, Mauro/0000-0002-3612-1934; Yan, Qiben/0000-0001-6272-7668 National Natural Science Foundation of China [61672262, 61573166, 61572230]; Shandong Provincial Key RD Program [2016GGX101001, 2018CXGC0706]; CERNET Next Generation Internet Technology Innovation Project [NGII20160404]; NSF [CNS-1566388] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Key RD Program; CERNET Next Generation Internet Technology Innovation Project; NSF(National Science Foundation (NSF)) This work was supported by the National Natural Science Foundation of China under Grants No. 61672262, No. 61573166 and No. 61572230, the Shandong Provincial Key R&D Program under Grant No. 2016GGX101001 and No. 2018CXGC0706, CERNET Next Generation Internet Technology Innovation Project under Grant No. NGII20160404. This work is also supported in part by NSF grant CNS-1566388. 19 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1867-8211 1867-822X 978-3-030-01701-9; 978-3-030-01700-2 L N INST COMP SCI SO 2018.0 254 248 263 10.1007/978-3-030-01701-9_14 0.0 16 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BS8BA 2023-03-23 WOS:000769954500014 0
J Khan, MA; Zafar, A; Akbar, A; Javed, MF; Mosavi, A Khan, Mohsin Ali; Zafar, Adeel; Akbar, Arslan; Javed, Muhammad Faisal; Mosavi, Amir Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete MATERIALS English Article artificial intelligence; gene expression programming; fly ash; waste materials; geopolymer; regression analysis; building materials; sustainable construction materials; smart cities; sustainable concrete; cement For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc ' of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc ' experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume (%AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc ' of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations. [Khan, Mohsin Ali; Zafar, Adeel] Natl Univ Sci & Technol NUST, Mil Coll Engn MCE, Dept Struct Engn, Islamabad 44000, Pakistan; [Akbar, Arslan] City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Hong Kong 999077, Peoples R China; [Javed, Muhammad Faisal] Comsats Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan; [Mosavi, Amir] Tech Univ Dresden, Fac Civil Engn, D-01069 Dresden, Germany; [Mosavi, Amir] Norwegian Univ Life Sci, Sch Econ & Business, N-1430 As, Norway; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary; [Mosavi, Amir] Oxford Brookes Univ, John Sch Built Environm, Oxford OX3 0BP, England National University of Sciences & Technology - Pakistan; City University of Hong Kong; COMSATS University Islamabad (CUI); Technische Universitat Dresden; Norwegian University of Life Sciences; Obuda University; Oxford Brookes University Akbar, A (corresponding author), City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Hong Kong 999077, Peoples R China.;Mosavi, A (corresponding author), Tech Univ Dresden, Fac Civil Engn, D-01069 Dresden, Germany.;Mosavi, A (corresponding author), Norwegian Univ Life Sci, Sch Econ & Business, N-1430 As, Norway.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary.;Mosavi, A (corresponding author), Oxford Brookes Univ, John Sch Built Environm, Oxford OX3 0BP, England. moak.pg18mce@student.nust.edu.pk; adeel.zafar@mce.nust.edu.pk; aakbar4-c@my.cityu.edu.hk; arbabfaisal@cuiatd.edu.pk; amir.mosavi@mailbox.tu-dresden.de Javed, Muhammad Faisal/AFD-8149-2022; Akbar, Arslan/AAZ-2674-2020; Mosavi, Amir/I-7440-2018; Javed, Muhammad/AAM-8277-2021 Javed, Muhammad Faisal/0000-0001-5478-9324; Mosavi, Amir/0000-0003-4842-0613; Khan, Mohsin Ali/0000-0002-7260-5557; AKBAR, Arslan/0000-0003-0676-5242 National University of Science and Technology (NUST), Islamabad National University of Science and Technology (NUST), Islamabad This study was supported by the National University of Science and Technology (NUST), Islamabad. 109 46 46 3 15 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials MAR 2021.0 14 5 1106 10.3390/ma14051106 0.0 21 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics QW0TX 33652972.0 Green Accepted, Green Submitted, Green Published, gold 2023-03-23 WOS:000628373200001 0
J Yan, J; Moehrlen, C; Goecmen, T; Kelly, M; Wessel, A; Giebel, G Yan, Jie; Moehrlen, Corinna; Goecmen, Tuhfe; Kelly, Mark; Wessel, Arne; Giebel, Gregor Uncovering wind power forecasting uncertainty sources and their the whole chain RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Article Wind power; Forecast uncertainty; Modelling chain NUMERICAL WEATHER PREDICTION; PROBABILISTIC-FORECASTS; ENSEMBLE FORECASTS; GAUSSIAN-PROCESSES; FEATURE-EXTRACTION; SPEED PREDICTION; NEURAL-NETWORK; MISSING DATA; MODEL; ENERGY Wind power forecasting has supported operational decision-making for power system and electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of deterministic or probabilistic wind power forecasts are continuously exerted by academics and industries. Forecast errors and associated uncertainties propagating through the whole forecasting chain, from weather provider to end user, cannot be eliminated completely. Therefore, understanding the uncertainty sources and how these uncertainties propagate through-out the modelling chain is significant to implement more rational and targeted uncertainty mitigation strategies and standardise the forecast and uncertainty validation. This paper presents a qualitative review on wind power forecasting uncertainty. First, the definition of uncertainty sources throughout the forecast modelling chain acts as a guiding line for checking and evaluating the uncertainty of a wind power forecast system/model. For each of the types of uncertainty sources, uncertainty mitigation strategies are provided, starting from the planning phase of wind farms, the establishment of a forecasting system through the operational phase and market phase. Our review finalises with a discussion on uncertainty validation with an example on ramp forecast validation. Highlights are a qualitative review and discussion including: (1) forecasting uncertainty exists and propagates everywhere throughout the entire modelling chain, from the planning phase to the market phase; (2) the mitigation efforts should be exerted in every modelling step; (3) standardised uncertainty validation practice, including why global data samples are required for forecasters to improve model performance and for forecast users to select and evaluate forecast model outputs. [Yan, Jie] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewable, Beijing, Peoples R China; [Moehrlen, Corinna] WEPROG, Dreijervaenget 8, Assens 5610, Denmark; [Goecmen, Tuhfe; Kelly, Mark; Giebel, Gregor] Tech Univ Denmark, Dept Wind & Energy Syst, Frederiksborgvej 399, Roskilde 4000, Denmark; [Wessel, Arne] Fraunhofer Inst Energy Econ & Energy Syst Technol, Kassel, Germany North China Electric Power University; Technical University of Denmark Giebel, G (corresponding author), Tech Univ Denmark, Dept Wind & Energy Syst, Frederiksborgvej 399, Roskilde 4000, Denmark. grgi@dtu.dk yan, jie/HNJ-0097-2023; Kelly, Mark/G-9672-2015; Giebel, Gregor/C-9476-2011 Kelly, Mark/0000-0003-2882-4450; Yan, Jie/0000-0002-9412-0999; Giebel, Gregor/0000-0002-4453-8756; Gocmen, Tuhfe/0000-0003-2825-518X 208 7 7 14 21 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. SEP 2022.0 165 112519 10.1016/j.rser.2022.112519 0.0 MAY 2022 31 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels 2D3CW Green Published 2023-03-23 WOS:000811430600005 0
J Khundrakpam, B; Bhutani, N; Vainik, U; Gong, JN; Al-Sharif, N; Dagher, A; White, T; Evans, AC Khundrakpam, Budhachandra; Bhutani, Neha; Vainik, Uku; Gong, Jinnan; Al-Sharif, Noor; Dagher, Alain; White, Tonya; Evans, Alan C. C. A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD MOLECULAR PSYCHIATRY English Article; Early Access CORTICAL THICKNESS; FUNCTIONAL CONNECTIVITY; GENERAL-POPULATION; PREFRONTAL CORTEX; SOCIAL-PERCEPTION; TRAITS; CONNECTOME; CHILDREN; ANATOMY; HUBS Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD. [Khundrakpam, Budhachandra; Bhutani, Neha; Al-Sharif, Noor; Evans, Alan C. C.] McGill Univ, Montreal Neurol Inst, McGill Ctr Integrat Neurosci, Montreal, PQ H3A 2B4, Canada; [Vainik, Uku; Dagher, Alain] McGill Univ, Montreal Neurol Inst, Montreal, PQ H3A 2B4, Canada; [Vainik, Uku] Univ Tartu, Inst Psychol, Fac Social Sci, Naituse 2-216, EE-50409 Tartu, Estonia; [Gong, Jinnan] Chengdu Univ, Sch Comp Sci, Chengdu, Peoples R China; [White, Tonya] Erasmus MC Sophia, Kamer KP 2869, Postbus 2060, Kamer KP 2869, Postbus 2060, NL-3000 CB Rotterdam, Netherlands McGill University; McGill University; University of Tartu; Chengdu University Khundrakpam, B (corresponding author), McGill Univ, Montreal Neurol Inst, McGill Ctr Integrat Neurosci, Montreal, PQ H3A 2B4, Canada. budhachandra.khundrakpam@mcgill.ca Evans, Alan C/B-2373-2013 Evans, Alan C/0000-0003-3841-6098; KHUNDRAKPAM, BUDHACHANDRA/0000-0001-8095-5656; Dagher, Alain/0000-0002-0945-5779 Brain & Behavior Research Foundation (BBRF) - NARSAD [29492]; CIHR Grant [254573]; Estonian Research Council [PSG656]; National Nature Science Foundation of China [62003058] Brain & Behavior Research Foundation (BBRF) - NARSAD(NARSAD); CIHR Grant(Canadian Institutes of Health Research (CIHR)); Estonian Research Council(Estonian Research Council); National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)) BK was supported by a Brain & Behavior Research Foundation (BBRF) - NARSAD 2020 YI Grant (Grant Number 29492). ACE was supported by a CIHR Grant (Fund 254573). UV has been funded by Estonian Research Council's personal research funding start-up grant PSG656. JG was supported by National Nature Science Foundation of China (Grant Number 62003058). 82 0 0 1 1 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1359-4184 1476-5578 MOL PSYCHIATR Mol. Psychiatr. 10.1038/s41380-022-01916-w 0.0 DEC 2022 9 Biochemistry & Molecular Biology; Neurosciences; Psychiatry Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Neurosciences & Neurology; Psychiatry 7K0YD 36575304.0 hybrid, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000905011500003 0
J Wang, J; Charron, N; Husic, B; Olsson, S; Noe, F; Clementi, C Wang, Jiang; Charron, Nicholas; Husic, Brooke; Olsson, Simon; Noe, Frank; Clementi, Cecilia Multi-body effects in a coarse-grained protein force field JOURNAL OF CHEMICAL PHYSICS English Article ASSOCIATION KINETICS; MODEL; WATER; POTENTIALS; SIMULATION; DISTRIBUTIONS; PREDICTION; MECHANISM; SURFACE; SITES The use of coarse-grained (CG) models is a popular approach to study complex biomolecular systems. By reducing the number of degrees of freedom, a CG model can explore long time- and length-scales inaccessible to computational models at higher resolution. If a CG model is designed by formally integrating out some of the system's degrees of freedom, one expects multi-body interactions to emerge in the effective CG model's energy function. In practice, it has been shown that the inclusion of multi-body terms indeed improves the accuracy of a CG model. However, no general approach has been proposed to systematically construct a CG effective energy that includes arbitrary orders of multi-body terms. In this work, we propose a neural network based approach to address this point and construct a CG model as a multi-body expansion. By applying this approach to a small protein, we evaluate the relative importance of the different multi-body terms in the definition of an accurate model. We observe a slow convergence in the multi-body expansion, where up to five-body interactions are needed to reproduce the free energy of an atomistic model. [Wang, Jiang; Charron, Nicholas; Noe, Frank; Clementi, Cecilia] Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA; [Wang, Jiang; Noe, Frank; Clementi, Cecilia] Rice Univ, Dept Chem, Houston, TX 77005 USA; [Wang, Jiang] Guizhou Inst Technol, Coll Sci, Guiyang 550000, Guizhou, Peoples R China; [Charron, Nicholas; Clementi, Cecilia] Rice Univ, Dept Phys, Houston, TX 77005 USA; [Charron, Nicholas; Noe, Frank; Clementi, Cecilia] Free Univ Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany; [Husic, Brooke; Olsson, Simon; Noe, Frank] Free Univ Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany; [Olsson, Simon] Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden Rice University; Rice University; Guizhou Institute of Technology; Rice University; Free University of Berlin; Free University of Berlin; Chalmers University of Technology Clementi, C (corresponding author), Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA.;Clementi, C (corresponding author), Rice Univ, Dept Chem, Houston, TX 77005 USA.;Clementi, C (corresponding author), Rice Univ, Dept Phys, Houston, TX 77005 USA.;Clementi, C (corresponding author), Free Univ Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany. frank.noe@fu-berlin.de; cecilia.clementi@fu-berlin.de Clementi, Cecilia/0000-0001-9221-2358; Noe, Frank/0000-0003-4169-9324 National Science Foundation [CHE-1738990, CHE-1900374, PHY-1427654]; Welch Foundation [C-1570]; German MATH+ Excellence Cluster [AA1-6, EF1-2]; Deutsche Forschungsgemeinschaft [SFB 1114/C03, SFB/TRR 186/A12, SFB 1078/C7]; European Commission [ERC CoG 772230]; Einstein Foundation Berlin; Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF) [CNS-1338099] National Science Foundation(National Science Foundation (NSF)); Welch Foundation(The Welch Foundation); German MATH+ Excellence Cluster; Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); European Commission(European CommissionEuropean Commission Joint Research Centre); Einstein Foundation Berlin; Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF) We thank Francesco Paesani for insightful discussions and Gianni de Fabritiis and Adria Perez for the chignolin atomistic trajectories. This work was supported by the National Science Foundation (Grant Nos. CHE-1738990, CHE-1900374, and PHY-1427654), the Welch Foundation (Grant No. C-1570), the German MATH+ Excellence Cluster (Grant Nos. AA1-6 and EF1-2), the Deutsche Forschungsgemeinschaft (Grant Nos. SFB 1114/C03, SFB/TRR 186/A12, and SFB 1078/C7), the European Commission (Grant No. ERC CoG 772230 ScaleCell), and the Einstein Foundation Berlin. Simulations have been performed on the computer clusters of the Center for Research Computing at Rice University, supported, in part, by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF, Grant No. CNS-1338099). 96 16 16 4 11 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0021-9606 1089-7690 J CHEM PHYS J. Chem. Phys. APR 28 2021.0 154 16 164113 10.1063/5.0041022 0.0 11 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics UL7GC 33940848.0 Green Published, hybrid 2023-03-23 WOS:000692814300005 0
J Zhang, ZH; Murtagh, F; Van Poucke, S; Lin, S; Lan, P Zhang, Zhongheng; Murtagh, Fionn; Van Poucke, Sven; Lin, Su; Lan, Peng Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R ANNALS OF TRANSLATIONAL MEDICINE English Article Hierarchical cluster analysis (HCA); dendrogram; clinical research; heat map TRIALS; HEALTH Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work. [Zhang, Zhongheng] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Emergency Med, Hangzhou 310016, Zhejiang, Peoples R China; [Murtagh, Fionn] Univ Derby, Big Data Lab, Derby, England; [Murtagh, Fionn] Goldsmiths Univ London, London, England; [Van Poucke, Sven] Ziekenhuis Oost Limburg, Dept Anesthesia Crit Care Emergency Med & Pain Th, B-3600 Genk, Belgium; [Lin, Su] Fujian Med Univ, Affiliated Hosp 1, Liver Res Ctr, Fuzhou 350005, Peoples R China; [Lan, Peng] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Crit Care Med, Hangzhou 310016, Zhejiang, Peoples R China Zhejiang University; University of Derby; University of London; Goldsmiths University London; East Limburg Hospital; Fujian Medical University; Zhejiang University Zhang, ZH (corresponding author), 3 East Qinchun Rd, Hangzhou 310016, Zhejiang, Peoples R China. zh_zhang1984@hotmail.com Zhang, Zhongheng/E-1282-2011; Van Poucke, Sven/K-5999-2016 Zhang, Zhongheng/0000-0002-2336-5323; Van Poucke, Sven/0000-0001-8070-8786 19 71 72 1 18 AME PUBLISHING COMPANY SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2305-5839 2305-5847 ANN TRANSL MED ANN. TRANSL. MED. FEB 2017.0 5 4 75 10.21037/atm.2017.02.05 0.0 11 Oncology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Oncology; Research & Experimental Medicine EV0OY 28275620.0 Green Published, Green Accepted 2023-03-23 WOS:000401442400009 0
J Zou, L; Zhan, XX; Sun, J; Hanjalic, A; Wang, HJ Zou, Li; Zhan, Xiu-Xiu; Sun, Jie; Hanjalic, Alan; Wang, Huijuan Temporal Network Prediction and Interpretation IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING English Article Random forests; Prediction algorithms; Time series analysis; Machine learning algorithms; Training; Network topology; Epidemics; Temporal network; link prediction; prediction backbone network LINK PREDICTION Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods motivates us to explore what intrinsic properties/mechanisms facilitate the prediction of temporal networks. We use interpretable learning algorithms, Lasso Regression and Random Forest, to predict, based on the current activities (i.e., connected or not) of all links, the activity of each link at the next time step. From the coefficients learned from each algorithm, we construct the prediction backbone network that presents the influence of all links in determining each link's future activity. Analysis of the backbone, its relation to the link activity time series and to the time aggregated network reflects which properties of temporal networks are captured by the learning algorithms. Via six real-world contact networks, we find that the next step activity of a particular link is mainly influenced by (a) its current activity and (b) links strongly correlated in the time series to that particular link and close in distance (in hops) in the aggregated network. [Zou, Li; Hanjalic, Alan; Wang, Huijuan] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, NL-2628 CD Delft, Netherlands; [Zhan, Xiu-Xiu] Hangzhou Normal Univ, Alibaba Res Ctr Complex Sci, Hangzhou 311121, Peoples R China; [Sun, Jie] Huawei Technol Co Ltd, Theory Lab, Cent Res Inst, Hong Kong 999077, Peoples R China Delft University of Technology; Hangzhou Normal University; Huawei Technologies Wang, HJ (corresponding author), Delft Univ Technol, Fac Elect Engn Math & Comp Sci, NL-2628 CD Delft, Netherlands.;Zhan, XX (corresponding author), Hangzhou Normal Univ, Alibaba Res Ctr Complex Sci, Hangzhou 311121, Peoples R China. l.zou@tudelft.nl; zhanxiuxiu@hznu.edu.cn; riosun@gmail.com; a.hanjalic@tudelft.nl; h.wang@tudelft.nl Hanjalic, Alan/0000-0002-5771-2549; Zhan, Xiuxiu/0000-0002-9013-7590; Zou, Li/0000-0001-6226-590X Netherlands Organisation for Scientific Research NWO [612.001.802]; Zhejiang Provincial Natural Science Foundation of China [LQ22F030008]; Scientific Research Foundation for Scholars of HZNU [2021QDL030]; China Scholarship Council Netherlands Organisation for Scientific Research NWO(Netherlands Organization for Scientific Research (NWO)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); Scientific Research Foundation for Scholars of HZNU; China Scholarship Council(China Scholarship Council) This work was supported in part by the Netherlands Organisation for Scientific Research NWO under Grant 612.001.802, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ22F030008, in part by the Scientific Research Foundation for Scholars of HZNU under Grant 2021QDL030, and in part by the China Scholarship Council. Recommended for acceptance by Prof. Xiang Li. 56 0 0 5 6 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 2327-4697 IEEE T NETW SCI ENG IEEE Trans. Netw. Sci. Eng. MAY-JUN 2022.0 9 3 1215 1224 10.1109/TNSE.2021.3138643 0.0 10 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics 1M8FH Green Published 2023-03-23 WOS:000800200900025 0
J Yang, LH; Ye, FF; Liu, J; Wang, YM Yang, Long-Hao; Ye, Fei-Fei; Liu, Jun; Wang, Ying-Ming Belief rule-base expert system with multilayer tree structure for complex problems modeling EXPERT SYSTEMS WITH APPLICATIONS English Article Belief rule base; Multilayer tree structure; Expert system; Complex problems; Combinatorial explosion problem CLASSIFICATION SYSTEMS; MULTICLASS PROBLEMS; RISK-ASSESSMENT; METHODOLOGY; PREDICTION; REPRESENTATION; OPTIMIZATION Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS-BRB is able to overcome the combinatorial explosion problem of the con-ventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologies. [Yang, Long-Hao; Wang, Ying-Ming] Fuzhou Univ, Decis Sci Inst, Fuzhou 350108, Peoples R China; [Ye, Fei-Fei] Fujian Normal Univ, Sch Cultural Tourism & Publ Adm, Fuzhou 350117, Peoples R China; [Liu, Jun] Ulster Univ, Sch Comp, Belfast Campus, York St, Belfast BT15 1ED, North Ireland Fuzhou University; Fujian Normal University; Ulster University Ye, FF (corresponding author), Fujian Normal Univ, Sch Cultural Tourism & Publ Adm, Fuzhou 350117, Peoples R China. 13075810934@163.com National Natural Science Foundation of China [72001043, 61773123, 72001042]; Humanities and Social Science Foundation of the Ministry of Education of China [20YJC630188]; National Science Foundation of Fujian Province of China [2020J05122, 2022J01178] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Science Foundation of the Ministry of Education of China(Ministry of Education, China); National Science Foundation of Fujian Province of China This research was supported by the National Natural Science Foundation of China (Nos. 72001043, 61773123, and 72001042), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188), and the National Science Foundation of Fujian Province of China (Nos. 2020J05122 and 2022J01178). 58 0 0 3 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. MAY 1 2023.0 217 119567 10.1016/j.eswa.2023.119567 0.0 JAN 2023 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 8Q9NV 2023-03-23 WOS:000927526000001 0
J Lan, GJ; Gao, ZY; Tong, LY; Liu, T Lan, Gongjin; Gao, Zhenyu; Tong, Lingyao; Liu, Ting Class binarization to neuroevolution for multiclass classification NEURAL COMPUTING & APPLICATIONS English Article Multiclass classification; Binary classification; Error correcting output codes; NEAT; One-vs-one; One-vs-all NEURAL-NETWORKS; BINARY Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that are used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets of Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness. [Lan, Gongjin] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China; [Gao, Zhenyu; Liu, Ting] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands; [Tong, Lingyao] Vrije Univ Amsterdam, Dept Clin Neuro & Dev Psychol, Amsterdam, Netherlands Southern University of Science & Technology; Vrije Universiteit Amsterdam; Vrije Universiteit Amsterdam Gao, ZY; Liu, T (corresponding author), Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands. langj@sustech.edu.cn; vu.gaozhy@gmail.com; l.tong@vu.nl; t.liu@vu.nl Liu, Ting/0000-0002-8208-3430; Lan, Gongjin/0000-0003-2020-8186; Tong, Lingyao/0000-0002-5023-6949; Gao, Zhenyu/0000-0001-9456-8059 Guangdong Natural Science Funds for Young Scholar [2021A1515110641]; National Natural Science Foundation of China [61773197]; Shenzhen Fundamental Research Program [JCYJ20200109141622964] Guangdong Natural Science Funds for Young Scholar; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Fundamental Research Program This work was partially supported by the Guangdong Natural Science Funds for Young Scholar (No: 2021A1515110641), the National Natural Science Foundation of China (No: 61773197), the Shenzhen Fundamental Research Program (No: JCYJ20200109141622964). 30 0 0 2 2 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0941-0643 1433-3058 NEURAL COMPUT APPL Neural Comput. Appl. NOV 2022.0 34 22 SI 19845 19862 10.1007/s00521-022-07525-6 0.0 JUL 2022 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5M0NF hybrid 2023-03-23 WOS:000822478900001 0
J Liu, D; Kankare, E; Laukkanen, AM; Alku, P Liu, Dong; Kankare, Elina; Laukkanen, Anne-Maria; Alku, Paavo Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Article Contact quotient (CQ); Normalized amplitude quotient (NAQ); Perceived firmness; Voice training and therapy VOCAL FOLD CONTACT; GLOTTAL VOICE SOURCE; CLOSED QUOTIENT; IMPACT STRESS; WAVE-FORM; AIR-FLOW; FEMALE; GENDER; QUANTIFICATION; PARAMETERS This study compared for the electroglottographic (EGG) signal how well six earlier presented and two new parameters distinguish between normal, breathy and pressed phonation and how well they correlate with perceptual evaluation. The results were compared with those obtained for nine parameters describing the glottal flow waveform obtained through inverse filtering of the acoustic speech pressure signal. Acoustic and dual-channel EGG signals were recorded for twenty female and twenty male subjects with healthy voices phonating sustained samples of the vowel [a:] in their habitual normal voice and in simulated breathy (hypofunctional) and pressed (hyperfunctional) phonation. The samples were perceptually evaluated by five voice specialists and rated for firmness of phonation. The best examples from 12 females and 12 males were used for the analyses. Few earlier studies have ranked the behavior of this many EGG and glottal flow parameters from this large speech data. Although the parameters differed in their ranking order, contact quotient calculated with a criterion level at 50% both from the EGG and the inverse filtered signal was strong in correlating with perception and in distinguishing phonation types in cases where fundamental frequency and sound pressure level also varied. When this variation was taken into account, the normalized amplitude quotient NAQ still had an effect in predicting voice quality. The results will have applicability in voice training and therapy and in development of machine learning-based classification methods. (C) 2017 Elsevier Ltd. All rights reserved. [Liu, Dong] Univ Sci & Technol China, CAS Key Lab Microscale Magnet Resonance, Hefei 230026, Peoples R China; [Liu, Dong] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China; [Liu, Dong] Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Peoples R China; [Kankare, Elina] Tampere Univ Hosp, Dept Phoniatr, Ear & Oral Dis, Tampere, Finland; [Laukkanen, Anne-Maria] Univ Tampere, Speech & Voice Res Lab, Tampere, Finland; [Alku, Paavo] Aalto Univ, Dept Signal Proc & Acoust, Helsinki, Finland Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Tampere University; Tampere University Hospital; Tampere University; Aalto University Laukkanen, AM (corresponding author), Univ Tampere, Speech & Voice Res Lab, Tampere, Finland. Anne-Maria.Laukkanen@uta.fi Alku, Paavo/E-2400-2012; Kankare, Elina/AAN-5043-2021; Laukkanen, Anne-Maria/GRS-5359-2022 Laukkanen, Anne-Maria/0000-0003-4836-2513; Liu, Dong/0000-0002-9645-7683; Alku, Paavo/0000-0002-8173-9418; Kankare, Eliina/0000-0002-9232-7240 Academy of Finland [1128095, 134868, 284671]; Anhui Provincial Natural Science foundation [1708085MA25]; Academy of Finland (AKA) [134868] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); Anhui Provincial Natural Science foundation(Natural Science Foundation of Anhui Province); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This study was supported by the Academy of Finland (grants No. 1128095, 134868 and 284671). The work of DL has also been supported by Anhui Provincial Natural Science foundation (1708085MA25). We thank Ms Paivi Svard for her assistance with the figures and Mr Matthew James for the language correction. 82 5 5 0 9 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control JUL 2017.0 36 183 193 10.1016/j.bspc.2017.04.001 0.0 11 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering EW2SW Green Accepted 2023-03-23 WOS:000402347300019 0
J Goodchild, MF; Guo, HD; Annoni, A; Bian, L; de Bie, K; Campbell, F; Craglia, M; Ehlers, M; van Genderen, J; Jackson, D; Lewis, AJ; Pesaresi, M; Remetey-Fulopp, G; Simpson, R; Skidmore, A; Wang, CL; Woodgate, P Goodchild, Michael F.; Guo, Huadong; Annoni, Alessandro; Bian, Ling; de Bie, Kees; Campbell, Frederick; Craglia, Max; Ehlers, Manfred; van Genderen, John; Jackson, Davina; Lewis, Anthony J.; Pesaresi, Martino; Remetey-Fueloepp, Gabor; Simpson, Richard; Skidmore, Andrew; Wang, Changlin; Woodgate, Peter Next-generation Digital Earth PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA English Article scientific communication; visualization A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of big data has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public's access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation. [Goodchild, Michael F.] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA; [Guo, Huadong; Wang, Changlin] Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100094, Peoples R China; [Annoni, Alessandro; Craglia, Max; Pesaresi, Martino] Commiss European Communities, Joint Res Ctr, I-21027 Ispra, Italy; [Bian, Ling] SUNY Buffalo, Dept Geog, Buffalo, NY 14261 USA; [de Bie, Kees; van Genderen, John] Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7500 AE Enschede, Netherlands; [Campbell, Frederick; Skidmore, Andrew] Fred Campbell Consulting, Ottawa, ON K2H 5G8, Canada; [Ehlers, Manfred] Univ Osnabruck, Inst Geoinformat & Remote Sensing, D-49076 Osnabruck, Germany; [Jackson, Davina] D City Network, Newtown, Tas 2042, Australia; [Lewis, Anthony J.] Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA; [Remetey-Fueloepp, Gabor] Hungarian Assoc Geoinformat, H-1122 Budapest, Hungary; [Simpson, Richard] Nextspace, Auckland 1542, New Zealand; [Woodgate, Peter] Cooperat Res Ctr Spatial Informat, Carlton, Vic 3053, Australia University of California System; University of California Santa Barbara; Chinese Academy of Sciences; European Commission Joint Research Centre; EC JRC ISPRA Site; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; University of Twente; University Osnabruck; Louisiana State University System; Louisiana State University Goodchild, MF (corresponding author), Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA. good@geog.ucsb.edu Skidmore, Andrew K/C-7441-2011; van Genderen, John L./F-9390-2016; Jackson, Davina/J-1744-2015; Guo, Huadong/G-9388-2017; de Bie, CAJM/G-3388-2011 Skidmore, Andrew K/0000-0002-7446-8429; Jackson, Davina/0000-0003-0187-7755; Guo, Huadong/0000-0003-0337-1862; Pesaresi, Martino/0000-0003-0620-439X; van Genderen, John/0000-0002-8900-4774; Annoni, Alessandro/0000-0002-8940-4356; de Bie, Kees/0000-0002-3944-8924 Direct For Social, Behav & Economic Scie [0849910] Funding Source: National Science Foundation; Division Of Behavioral and Cognitive Sci [0849910] Funding Source: National Science Foundation Direct For Social, Behav & Economic Scie(National Science Foundation (NSF)NSF - Directorate for Social, Behavioral & Economic Sciences (SBE)); Division Of Behavioral and Cognitive Sci(National Science Foundation (NSF)NSF - Directorate for Social, Behavioral & Economic Sciences (SBE)) 19 182 199 8 185 NATL ACAD SCIENCES WASHINGTON 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA 0027-8424 P NATL ACAD SCI USA Proc. Natl. Acad. Sci. U. S. A. JUL 10 2012.0 109 28 11088 11094 10.1073/pnas.1202383109 0.0 7 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 977FP 22723346.0 Green Accepted, Green Submitted, Green Published, Bronze 2023-03-23 WOS:000306642100020 0
J Chen, XH; Fang, Z; Zhao, YK; Cheng, WB; Chen, HL; Li, GR; Xu, J; Deng, JL; Cai, X; Zhuang, JH; Yin, Y Chen, Xiaohan; Fang, Zhuo; Zhao, Yike; Cheng, Wenbin; Chen, Honglin; Li, Genru; Xu, Jin; Deng, Jiale; Cai, Xiao; Zhuang, Jianhua; Yin, You Sleep Parameters and Plasma Biomarkers for Cognitive Impairment Evaluation in Patients With Cerebral Small Vessel Disease JOURNALS OF GERONTOLOGY SERIES B-PSYCHOLOGICAL SCIENCES AND SOCIAL SCIENCES English Article Cognitive evaluation; Machine learning; Vascular dementia SERUM NEUROFILAMENT LIGHT; NEUROCOGNITIVE FUNCTION; CHINESE VERSION; P-TAU; APNEA; MICROBLEEDS; DIAGNOSIS; MEMORY; IMPACT Objectives Cognitive impairment caused by cerebrovascular disease accounts for more than half of vascular dementia. However, neuropsychological tests are limited by their subjectivity. Additional effective approaches to evaluate cognitive impairment in patients with cerebrovascular disease are necessary. Method One hundred and thirty-two patients with cerebrovascular disease were recruited. One hundred participants met the criteria and completed neuropsychological scales. Sixty-nine participants proceeded with polysomnography, and 63 of them had their peripheral blood biomarkers measured. According to Mini-Mental State Examination scores, patients were divided into cognitively impaired and cognitively normal groups. The differences in biomarkers and sleep parameters between the groups were compared, and decision tree models were constructed to evaluate the evaluation ability of these indicators on cognitive decline. Results The integrated decision tree model of sleep parameters yielded an area under curve (AUC) of 0.952 (95% confidence interval [CI]: 0.911-0.993), while that of plasma biomarkers yielded an AUC of 0.872 (95% CI: 0.810-0.935) in the assessment of cognition status. Then the participants were automatically clustered into mild and severe cognitive impairment groups by multiple neuropsychological test results. The integrated plasma biomarker model showed an AUC of 0.928 (95% CI: 0.88-0.977), and the integrated sleep parameter model showed an AUC of 0.851 (95% CI: 0.783-0.919) in the assessment of mild/severe cognitive impairment. Discussion Integrated models which consist of sleep parameters and plasma biomarkers can accurately evaluate dementia status and cognitive impairment in patients with cerebral small vessel disease. This innovative study may facilitate drug development, early screening, clinical diagnosis, and prognosis evaluation of the disease. [Chen, Xiaohan; Cheng, Wenbin; Li, Genru; Xu, Jin; Zhuang, Jianhua; Yin, You] Naval Med Univ, Affiliated Hosp 2, Dept Neurol, Shanghai, Peoples R China; [Fang, Zhuo; Zhao, Yike; Deng, Jiale; Cai, Xiao] WuXi Diagnost, Dept Data & Analyt, Shanghai, Peoples R China; [Chen, Honglin] Univ Eastern Finland, Dept Social Sci, Kuopio, Finland; [Yin, You] 15 Fengyang Rd, Shanghai 200003, Peoples R China Naval Medical University; University of Eastern Finland Yin, Y (corresponding author), 15 Fengyang Rd, Shanghai 200003, Peoples R China. yinyou179@163.com 50 0 0 0 0 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 1079-5014 1758-5368 J GERONTOL B-PSYCHOL J. Gerontol. Ser. B-Psychol. Sci. Soc. Sci. FEB 19 2023.0 78 2 210 219 10.1093/geronb/gbac137 0.0 SEP 2022 10 Geriatrics & Gerontology; Gerontology; Psychology; Psychology, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Geriatrics & Gerontology; Psychology 9B6IS 36107008.0 2023-03-23 WOS:000891544700001 0
J Mai, ZJ; Hu, GS; Chen, DX; Shen, FM; Shen, HT Mai, Zhijun; Hu, Guosheng; Chen, Dexiong; Shen, Fumin; Shen, Heng Tao MetaMixUp: Learning Adaptive Interpolation Policy of MixUp With Metalearning IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Interpolation; Training; Optimization; Neural networks; Task analysis; Manifolds; Adaptation models; Deep learning; metalearning; MixUp; regularization MixUp is an effective data augmentation method to regularize deep neural networks via random linear interpolations between pairs of samples and their labels. It plays an important role in model regularization, semisupervised learning (SSL), and domain adaption. However, despite its empirical success, its deficiency of randomly mixing samples has poorly been studied. Since deep networks are capable of memorizing the entire data set, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks. To overcome overfitting to corrupted samples, inspired by metalearning (learning to learn), we propose a novel technique of learning to a mixup in this work, namely, MetaMixUp. Unlike the vanilla MixUp that samples interpolation policy from a predefined distribution, this article introduces a metalearning-based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way (learning to learn better). The validation set performance via metalearning captures the noisy degree, which provides optimal directions for interpolation policy learning. Furthermore, we adapt our method for pseudolabel-based SSL along with a refined pseudolabeling strategy. In our experiments, our method achieves better performance than vanilla MixUp and its variants under SL configuration. In particular, extensive experiments show that our MetaMixUp adapted SSL greatly outperforms MixUp and many state-of-the-art methods on CIFAR-10 and SVHN benchmarks under the SSL configuration. [Mai, Zhijun] Univ Elect Sci & Technol China, Dept Comp Sci & Technol, Chengdu 611731, Peoples R China; [Hu, Guosheng] AnyVis, Belfast BT3 9DT, Antrim, North Ireland; [Chen, Dexiong] Inria Grenoble Rhone Alpes, F-38330 Montbonnot St Martin, France; [Shen, Fumin; Shen, Heng Tao] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China University of Electronic Science & Technology of China; University of Electronic Science & Technology of China Hu, GS (corresponding author), AnyVis, Belfast BT3 9DT, Antrim, North Ireland.;Shen, FM (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China. fumin.shen@gmail.com; huguosheng100@gmail.com zhijun, mai/0000-0001-8375-4403 National Natural Science Foundation of China [62072080]; Sichuan Science and Technology Program, China [2019ZDZX0008, 2019YFG0533, 2019YFG0003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program, China This work was supported in part by the National Natural Science Foundation of China under Grant 62072080; and in part by the Sichuan Science and Technology Program, China, under Grant 2019ZDZX0008, Grant 2019YFG0533, and Grant 2019YFG0003. 63 4 4 1 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JUL 2022.0 33 7 3050 3064 10.1109/TNNLS.2020.3049011 0.0 FEB 2021 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3Q7SS 33646956.0 Green Submitted 2023-03-23 WOS:000732421100001 0
J Zhang, ZH; Chen, P; Jamet, C; Dionisi, D; Hu, YX; Lu, XM; Pan, DL Zhang, Zhenhua; Chen, Peng; Jamet, Cedric; Dionisi, Davide; Hu, Yongxiang; Lu, Xiaomei; Pan, Delu Retrieving bbp and POC from CALIOP: A deep neural network approach REMOTE SENSING OF ENVIRONMENT English Article Lidar; Polarization; CALIOP; DNN; bbp; POC SUBSURFACE PHYTOPLANKTON LAYER; VOLUME SCATTERING FUNCTION; LIDAR MEASUREMENTS; AIRBORNE LIDAR; OPTICAL-PROPERTIES; SPACEBORNE LIDAR; OCEAN SUBSURFACE; PLANKTON LAYERS; COASTAL WATERS; COLOR The CALIOP instrument has demonstrated tremendous potential in terms of oceanic detection. Nighttime mea-surements from spaceborne lidar increase knowledge of the diurnal variability of ocean optical properties, while measurements in polar regions fill the gaps that passive measurements of ocean color may leave in these areas. However, due to non-ideal instrumental characteristics, errors may occur during retrieval of the ocean optical properties that are derived directly from CALIOP's measured signals. These errors are caused by the transient response (TR) function of the detectors and the polarization crosstalk (CT) between the polarization beam splitters. However, there is no study that integrates and explicitly describes both the required corrections. In addition, the published physics-based methods for retrieving the particulate backscatter coefficient (bbp) from CALIOP use an empirical linear relationship between bbp and the backscatter coefficient at 180 degrees b(pi) without considering the spatial and temporal variability associated with the conversion coefficient. In this study, after both effects are eliminated through TR deconvolution and CT correction, an innovative deep neural network (DNN) approach is proposed to retrieve bbp and particulate organic carbon (POC) from CALIOP. The DNN is trained with the CALIOP total ocean column-integrated depolarization ratio and subsurface column-integrated perpendicular backscatter data, which are combined with the collocated daytime MODIS bbp and POC prod-ucts. Once the DNN has been trained with the daytime MODIS product, measurements can be derived from CALIOP in conditions not covered by MODIS data, such as at night or in polar regions. Compared to published physics-based methods, this approach does not require diffuse attenuation coefficients, nor are additional as-sumptions regarding the conversion coefficient between bbp and the backscatter coefficient at 180 degrees b(pi) neces-sary. A comparison of global total depolarization ratios, which are calculated before and after TR deconvolution and CT correction, indicates the necessity and efficacy of correction, and the preliminary results show that the oceanic properties derived from CALIOP via this DNN-based approach agree better with the MODIS products than those derived with the traditional physics-based methods. The use of lidar measurements to derive bbp and POC without relying on passively acquired data is of great interest to the ocean color community. [Zhang, Zhenhua; Chen, Peng; Pan, Delu] Southern Marine Sci & Engn Guangdong Lab Guangzhou, 1119 Haibin Rd, Guangzhou 511458, Peoples R China; [Zhang, Zhenhua; Chen, Peng; Pan, Delu] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, 36 Bochubeilu, Hangzhou 310012, Peoples R China; [Jamet, Cedric] Univ Littoral Cote dOpale, Univ Lille, CNRS, IRD,UMR 8187,Lab Oceanol & Geosci LOG, F-62930 Wimereux, France; [Dionisi, Davide] Italian Natl Res Council CNR, Inst Marine Sci ISMAR, Rome, Italy; [Hu, Yongxiang; Lu, Xiaomei] NASA Langley Res Ctr, Hampton, VA 23681 USA Southern Marine Science & Engineering Guangdong Laboratory; Ministry of Natural Resources of the People's Republic of China; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); Institut de Recherche pour le Developpement (IRD); Universite de Lille - ISITE; Universite de Lille; Universite du Littoral-Cote-d'Opale; Consiglio Nazionale delle Ricerche (CNR); Istituto di Neuroscienze (IN-CNR); Istituto di Scienze Marine (ISMAR-CNR); National Aeronautics & Space Administration (NASA); NASA Langley Research Center Chen, P (corresponding author), Southern Marine Sci & Engn Guangdong Lab Guangzhou, 1119 Haibin Rd, Guangzhou 511458, Peoples R China. chenp@sio.org.cn Zhang, Zhenhua/GSE-4691-2022; CHEN, Peng/GSD-2999-2022 Zhang, Zhenhua/0000-0001-9818-5346; CHEN, Peng/0000-0002-0635-9220 National Key Research and Development Program of China [2022YFB3901703]; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) [GML2019ZD0602]; National Natural Science Foun-dation [41901305, 61991453]; Key Research and Development Program of Zhejiang Province [2020C03100] National Key Research and Development Program of China; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); National Natural Science Foun-dation(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Zhejiang Province Funding National Key Research and Development Program of China (2022YFB3901703) , Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0602) , the National Natural Science Foun-dation (41901305; 61991453) , and the Key Research and Development Program of Zhejiang Province (2020C03100) . 87 0 0 7 7 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. MAR 15 2023.0 287 113482 10.1016/j.rse.2023.113482 0.0 JAN 2023 22 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology 8N8GU 2023-03-23 WOS:000925385300001 0
J Cheng, G; Zhou, PC; Han, JW; Guo, L; Han, JG Cheng, Gong; Zhou, Peicheng; Han, Junwei; Guo, Lei; Han, Jungong Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images IET COMPUTER VISION English Article EFFECTIVE FEATURE-EXTRACTION; OBJECT DETECTION; EFFICIENT Effective representation and classification of scenes using very high resolution (VHR) remote sensing images cover a wide range of applications. Although robust low-level image features have been proven to be effective for scene classification, they are not semantically meaningful and thus have difficulty to deal with challenging visual recognition tasks. In this study, the authors propose a new and effective auto-encoder-based method to learn a shared mid-level visual dictionary. This dictionary serves as a shared and universal basis to discover mid-level visual elements. On the one hand, the mid-level visual dictionary learnt using machine learning technique is more discriminative and contains rich semantic information, compared with the traditional low-level visual words. On the other hand, the mid-level visual dictionary is more robust to occlusions and image clutters. In the authors' scene-classification scheme, they use discriminative mid-level visual elements, rather than individual pixels or low-level image features, to represent images. This new image representation is able to capture much of the high-level meaning and contents of the image, facilitating challenging remote sensing image scene-classification tasks. Comprehensive evaluations on a challenging VHR remote sensing images data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of their study. [Cheng, Gong; Zhou, Peicheng; Han, Junwei; Guo, Lei] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China; [Han, Jungong] Civolut Technol, Eindhoven, Netherlands Northwestern Polytechnical University Han, JW (corresponding author), Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China. junweihan2010@gmail.com Cheng, Gong/I-9551-2019; Han, Jungong/ABE-6812-2020 Cheng, Gong/0000-0001-5030-0683; National Science Foundation of China [91120005, 61473231, 61401357, 61333017]; China Postdoctoral Science Foundation [2014M552491] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was partially supported by the National Science Foundation of China under grants 91120005, 61473231, 61401357 and 61333017 and the China Postdoctoral Science Foundation under grant 2014M552491. 28 55 61 1 32 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-9632 1751-9640 IET COMPUT VIS IET Comput. Vis. OCT 2015.0 9 5 SI 639 647 10.1049/iet-cvi.2014.0270 0.0 9 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering CR5EG 2023-03-23 WOS:000361362400003 0
C Yu, SC; Liu, WQ; O'Neill, M IEEE Computer Society Yu, Shichao; Liu, Weiqiang; O'Neill, Maire An Improved Automatic Hardware Trojan Generation Platform 2019 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2019) IEEE Computer Society Annual Symposium on VLSI English Proceedings Paper 18th IEEE-Computer-Society Annual Symposium on VLSI (ISVLSI) JUL 15-17, 2019 Miami, FL IEEE Comp Soc,IEEE Comp Soc Tech Comm VLSI,NSF Hardware Trojans; benchmarks; automatic generation; highly configurable; transition probability Over the past 10 years, various Hardware Trojan (HT) detection techniques have been proposed by the research comnumity. However, the development of HT benchmark suites for testing and evaluating HT detection techniques lags behind. The number of HT-infected circuits available in current public HT benchmarks is somewhat limited and the circuits lack diversity in structure. Therefore, this paper proposes a new method to generate HTs using a highly configurable generation platform based on transition probability. The generation platform is highly configurable in terms of the HT trigger condition, trigger type, payload type and in the number and variety of HT-infected circuits that can be generated. In this research the transition probability of netlists is employed to identify rarely activated internal nodes to target for HT insertion rather than functional simulation as utilised in previous research. The authors believe transition probability provides a more realistic reflection of the netlist activity for use in determining the appropriate position for HT insertion. Finally, the generated HT-infected circuits are tested by a machine learning (ML)-based HT detection technique, which is known as Controllability and Observability for HT Detection (COTD). The resulting false positive and false negative rates illustrate the feasibility of the benchmark suite. [Yu, Shichao; O'Neill, Maire] Queens Univ Belfast, CSIT, ECIT, Belfast, Antrim, North Ireland; [Liu, Weiqiang] Nanjing Univ Aero & Astro, Coll EIE, Nanjing, Peoples R China Queens University Belfast; Nanjing University of Aeronautics & Astronautics Yu, SC (corresponding author), Queens Univ Belfast, CSIT, ECIT, Belfast, Antrim, North Ireland. syu08@qub.ac.uk; liuweiqiang@nuaa.edu.cn; m.oneill@ecit.qub.ac.uk EPSRC [EP/N508664/1, EP/R007187/1, EP/K004379/1, EP/R011494/1, 1943868] Funding Source: UKRI EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) 14 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2159-3469 2159-3477 978-1-7281-3391-1 IEEE COMP SOC ANN 2019.0 303 308 10.1109/ISVLSI.2019.00062 0.0 6 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP0YC Green Accepted 2023-03-23 WOS:000538332100053 0
J Wang, ZY; Novack, T; Yan, YW; Zipf, A Wang, Zhiyong; Novack, Tessio; Yan, Yingwei; Zipf, Alexander Quiet Route Planning for Pedestrians in Traffic Noise Polluted Environments IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Roads; Urban areas; Estimation; Solid modeling; Planning; Pollution; Routing; Route planning; volunteered geographic information; traffic noise pollution; routing algorithm EXPOSURE; INFORMATION; COUNTY; FLOW Noise pollution is one of the main stressors in urban environments, having negative impacts on people's quality of life and health. For some groups of citizens, such as school children, patients, and elders, there is a need to support them in finding pedestrian routes in noise polluted areas of cities. In this paper, we focus on the estimation of traffic noise, and present an approach to provide quiet routing services, taking into account the estimated noise levels of roads. By combining Volunteered Geographic Information, official socio-economic data, and open-access GPS trajectory data, we develop a set of traffic related variables, and apply machine learning methods to perform traffic volume estimations. Given the estimated traffic information, an existing traffic noise model is then employed to derive the noise polluted areas. For generation of quiet routes, a new routing algorithm is proposed based on the Dijkstra algorithm. It minimizes the exposure of pedestrians to traffic noise pollution while taking into account the route distance constraint. We apply our quiet routing approach to the city of Heidelberg (Germany). The application results demonstrate the efficacy of our algorithms in the generation of quiet routes customized to pedestrian preferences. [Wang, Zhiyong] Univ Utrecht, Fac Geosci, Dept Human Geog & Spatial Planning, NL-3584 CB Utrecht, Netherlands; [Wang, Zhiyong; Novack, Tessio; Zipf, Alexander] Heidelberg Univ, GISci Res Grp, Inst Geog, D-69120 Heidelberg, Germany; [Yan, Yingwei] Guangzhou Inst Geog, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Peoples R China; [Yan, Yingwei] Guangzhou Inst Geog, Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Peoples R China; [Yan, Yingwei] Natl Univ Singapore, Dept Geog, Singapore 117570, Singapore Utrecht University; Ruprecht Karls University Heidelberg; Guangdong Academy of Sciences; Guangzhou Institute of Geography, Guangdong Academy of Sciences; Guangdong Academy of Sciences; Guangzhou Institute of Geography, Guangdong Academy of Sciences; National University of Singapore Wang, ZY (corresponding author), Univ Utrecht, Fac Geosci, Dept Human Geog & Spatial Planning, NL-3584 CB Utrecht, Netherlands. z.wang2@uu.nl 王, 志勇/B-6670-2009 Wang, Zhiyong/0000-0003-2219-5542; Yan, Yingwei/0000-0002-3487-9792 47 3 4 4 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. DEC 2021.0 22 12 7573 7584 10.1109/TITS.2020.3004660 0.0 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Transportation XD4ZF 2023-03-23 WOS:000722718400024 0
J Cao, LL; Sun, FC; Liu, XL; Huang, WB; Kotagiri, R; Li, HB Cao, Lele; Sun, Fuchun; Liu, Xiaolong; Huang, Wenbing; Kotagiri, Ramamohanarao; Li, Hongbo End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble COGNITIVE COMPUTATION English Article Recognition; Tactile sensors; Feature extraction; Residual learning; Convolution neural networks; Tactile flow OBJECT RECOGNITION; NETWORKS; ARRAY Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Our approach uses stacks of raw frames and tactile flow as dual input, and incorporates the strength of multi-layer OTConvs (orthogonal tiling convolutions) organized in a residual learning paradigm. We empirically demonstrate that OTConvs have adjustable invariance capability to different input transformations such as translation, rotation, and scaling. To effectively capture multi-scale global context, a pyramid convolution structure is attached to the concatenated output of two residual OTConv pathways. The extensive experimental evaluations show that ROTConvPCE-mv outperforms several state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance. Practical suggestions and hints are summarized throughout this paper to facilitate the effective recognition using tactile sensory data. [Cao, Lele; Sun, Fuchun; Liu, Xiaolong; Huang, Wenbing; Li, Hongbo] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Cao, Lele; Kotagiri, Ramamohanarao] Univ Melbourne, Dept Comp & Informat Syst, Melbourne, Vic 3010, Australia; [Cao, Lele] Activis Blizzard Inc ATVI, King Digital Entertainment Plc, S-11134 Stockholm, Sweden Tsinghua University; University of Melbourne Cao, LL (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.;Cao, LL (corresponding author), Univ Melbourne, Dept Comp & Informat Syst, Melbourne, Vic 3010, Australia.;Cao, LL (corresponding author), Activis Blizzard Inc ATVI, King Digital Entertainment Plc, S-11134 Stockholm, Sweden. caolele@gmail.com; fcsun@mail.tsinghua.edu.cn; liuxl12@mail.tsinghua.edu.cn; huangwb12@mail.tsinghua.edu.cn; kotagiri@unimelb.edu.au; hbli@mail.tsinghua.edu.cn Cao, Lele/HLG-7011-2023; Huang, Wenbing/AHB-1846-2022; Huang, Wenbing/AAI-7943-2021 Huang, Wenbing/0000-0002-2566-4159; Huang, Wenbing/0000-0002-2566-4159; KOTAGIRI, RAMAMOHANARAO/0000-0003-3304-9268; Cao, Lele/0000-0002-5680-9031 China National Natural Science Foundation [61327809, 61210013]; State Scholarship Fund [201406210275] China National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); State Scholarship Fund This work was supported by grants from the China National Natural Science Foundation (Nos. 61327809 and 61210013). Lele Cao is also supported by the State Scholarship Fund under file number 201406210275. 62 5 5 2 18 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1866-9956 1866-9964 COGN COMPUT Cogn. Comput. OCT 2018.0 10 5 718 736 10.1007/s12559-018-9568-7 0.0 19 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology GW0DK 2023-03-23 WOS:000446532900003 0
J Zhang, Y; Dwight, RP; Schmelzer, M; Gomez, JF; Han, ZH; Hickel, S Zhang, Yu; Dwight, Richard P.; Schmelzer, Martin; Gomez, Javier F.; Han, Zhong-hua; Hickel, Stefan Customized data-driven RANS closures for bi-fidelity LES-RANS optimization JOURNAL OF COMPUTATIONAL PHYSICS English Article Turbulence modelling; Algebraic stress model; Reynolds-averaged Navier-Stokes; Large-eddy simulation; Multi-fidelity optimization Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low-and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for turbulent fluid-flow applications with Large-Eddy Simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) as the high-and low-fidelity models within a hierarchical-Kriging surrogate modelling framework. Since the LES-RANS correlation is often poor, we use the full LES flow-field at a single point in the design space to derive a custom-tailored RANS closure model that reproduces the LES at that point. This is achieved with machine-learning techniques, specifically sparse regression to obtain high corrections of the turbulence anisotropy tensor and the production of turbulence kinetic energy as functions of the RANS mean flow. The LES-RANS correlation is dramatically improved throughout the design-space. We demonstrate the effectivity and efficiency of our method in a proof-of-concept shape optimization of the well-known periodic-hill case. Standard RANS models perform poorly in this case, whereas our method converges to the LES-optimum with only two LES samples. (C) 2021 The Author(s). Published by Elsevier Inc. [Zhang, Yu; Han, Zhong-hua] Northwestern Polytech Univ, Inst Aerodynam & Multidisciplinary Design Optimiz, Sch Aeronaut, Natl Key Lab Sci & Technol Aerodynam Design & Res, Youyi West Rd 127, Xian 710072, Peoples R China; [Dwight, Richard P.; Schmelzer, Martin; Gomez, Javier F.; Hickel, Stefan] Delft Univ Technol, Fac Aerosp Engn, Aerodynam Grp, Kluyverweg 2, NL-2629 HS Delft, Netherlands Northwestern Polytechnical University; Delft University of Technology Dwight, RP (corresponding author), Delft Univ Technol, Fac Aerosp Engn, Aerodynam Grp, Kluyverweg 2, NL-2629 HS Delft, Netherlands. zhangyu91@mail.nwpu.edu.cn; r.p.dwight@tudelft.nl; m.schmelzer@tudelft.nl; jfatoug@gmail.com; hanzh@nwpu.edu.cn; s.hickel@tudelft.nl Hickel, Stefan/H-4022-2011; Han, Zhonghua/AFS-6071-2022 Hickel, Stefan/0000-0002-7463-9531; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201801]; China Scholarship Council [201706290083] Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University; China Scholarship Council(China Scholarship Council) This document is the results of the research project funded by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under grant No. CX201801 and China Scholarship Council No. 201706290083. 40 6 6 3 17 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0021-9991 1090-2716 J COMPUT PHYS J. Comput. Phys. MAY 1 2021.0 432 110153 10.1016/j.jcp.2021.110153 0.0 FEB 2021 16 Computer Science, Interdisciplinary Applications; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physics QT5YL Green Submitted, Green Published, hybrid 2023-03-23 WOS:000626663200001 0
J Zhang, TY; El Ali, A; Wang, C; Hanjalic, A; Cesar, P Zhang, Tianyi; El Ali, Abdallah; Wang, Chen; Hanjalic, Alan; Cesar, Pablo CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors SENSORS English Article emotion recognition; video; physiological signals; machine learning SYSTEM; TECHNOLOGY; FRAMEWORK; SIGNALS; CONTEXT; SET Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1-4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (<= 64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance. [Zhang, Tianyi; Hanjalic, Alan; Cesar, Pablo] Delft Univ Technol, Multimedia Comp Grp, NL-2600 AA Delft, Netherlands; [Zhang, Tianyi; El Ali, Abdallah; Cesar, Pablo] Ctr Wiskunde & Informat CWI, NL-1098XG Amsterdam, Netherlands; [Wang, Chen] Xinhuanet, Future Media & Convergence Inst, Beijing 100000, Peoples R China; [Wang, Chen] Xinhua News Agcy, State Key Lab Media Convergence Prod Technol & Sy, Beijing 100000, Peoples R China Delft University of Technology Zhang, TY; Cesar, P (corresponding author), Delft Univ Technol, Multimedia Comp Grp, NL-2600 AA Delft, Netherlands.;Zhang, TY; Cesar, P (corresponding author), Ctr Wiskunde & Informat CWI, NL-1098XG Amsterdam, Netherlands. T.Zhang-5@tudelft.nl; abdallah.el.ali@cwi.nl; wangchen@news.cn; A.Hanjalic@tudelft.nl; P.S.CesarGarcia@tudelft.nl Cesar, Pablo/0000-0003-1752-6837; Zhang, Tianyi/0000-0001-6293-881X; El Ali, Abdallah/0000-0002-9954-4088; Hanjalic, Alan/0000-0002-5771-2549 Xinhuanet; Centrum Wiskunde Informatica Xinhuanet; Centrum Wiskunde Informatica This work is fund by the Joint PhD Program between Xinhuanet and Centrum Wiskunde & Informatica. 123 14 14 9 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JAN 2021.0 21 1 52 10.3390/s21010052 0.0 25 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Chemistry; Engineering; Instruments & Instrumentation PP8HC 33374281.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000606096100001 0
J Feng, CL; Wang, L; Li, T; Xu, PF Feng, Chunliang; Wang, Li; Li, Ting; Xu, Pengfei Connectome-based individualized prediction of loneliness SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE English Article loneliness; connectome-based predictive modeling; resting-state functional connectivity PERCEIVED SOCIAL-ISOLATION; CROSS-LAGGED ANALYSES; RESTING-STATE; FUNCTIONAL CONNECTIVITY; ENVIRONMENTAL CONTRIBUTIONS; HUMAN BRAIN; NETWORK; SELF; ATTENTION; CORTEX Loneliness is an increasingly prevalent condition linking with enhanced morbidity and premature mortality. Despite recent proposal on medicalization of loneliness, so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Here, we applied a machine-learning approach to decode loneliness from whole-brain resting-state functional connectivity (RSFC). The relationship between whole-brain RSFC and loneliness was examined in a linear predictive model. The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively. Key nodes that contributed to the prediction model comprised regions previously implicated in loneliness, including the dorsolateral prefrontal cortex, lateral orbital frontal cortex, ventromedial prefrontal cortex, caudate, amygdala and temporal regions. Our findings also demonstrated that both loneliness and associated neural substrates are modulated by levels of neuroticism and extraversion. The current data-driven approach provides the first evidence on the predictive brain features of loneliness based on organizations of intrinsic brain networks. Our work represents initial efforts in the direction of making individualized prediction of loneliness that could be useful for diagnosis, prognosis and treatment. [Xu, Pengfei] Shenzhen Univ, Ctr Brain Disorders & Cognit Sci, Shenzhen Key Lab Affect & Social Neurosci, Shenzhen 518060, Peoples R China; [Feng, Chunliang] South China Normal Univ, Ctr Studies Psychol Applicat, Guangdong Prov Key Lab Mental Hlth & Cognit Sci, Sch Psychol, Guangzhou 510631, Guangdong, Peoples R China; [Wang, Li; Li, Ting] Beijing Normal Univ, Collaborat Innovat Ctr Assessment Basic Educ Qual, Beijing 100875, Peoples R China; [Xu, Pengfei] Shenzhen Inst Neurosci, Ctr Neuroimaging, Shenzhen 518057, Peoples R China; [Xu, Pengfei] Univ Groningen, Univ Med Ctr Groningen, Dept Neurosci, NL-9713 AW Groningen, Netherlands Shenzhen University; South China Normal University; Beijing Normal University; University of Groningen Xu, PF (corresponding author), Shenzhen Univ, Shenzhen Key Lab Affect & Social Neurosci, Shenzhen 518060, Peoples R China. xupf@szu.edu.cn National Natural Science Foundation of China [31871137, 31530031, 31700959, 31671133, 31500920]; National Postdoctoral Program for Innovative Talents [BX201600019]; China Postdoctoral Science Foundation [2017 M610055]; Natural Science Foundation of Shenzhen University [85303-00000275]; Shenzhen Science and Technology Research Funding Program [JCYJ20170412164413575]; Shenzhen Peacock Program [827-000235, KQTD2015033016104926]; Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant [2016ZT06S220] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation of Shenzhen University; Shenzhen Science and Technology Research Funding Program; Shenzhen Peacock Program; Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant This work was supported by the National Natural Science Foundation of China (31871137, 31530031, 31700959, 31671133 and 31500920); the National Postdoctoral Program for Innovative Talents (BX201600019); the China Postdoctoral Science Foundation (2017 M610055); Natural Science Foundation of Shenzhen University (85303-00000275); Shenzhen Science and Technology Research Funding Program (JCYJ20170412164413575), Shenzhen Peacock Program (827-000235 and KQTD2015033016104926) and the Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team grant (2016ZT06S220). 97 34 34 7 28 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1749-5016 1749-5024 SOC COGN AFFECT NEUR Soc. Cogn. Affect. Neurosci. APR 2019.0 14 4 353 365 10.1093/scan/nsz020 0.0 13 Neurosciences; Psychology; Psychology, Experimental Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Psychology IB1XS 30874805.0 Green Published, Green Submitted, gold 2023-03-23 WOS:000470060000002 0
J Ghayvat, H; Awais, M; Gope, P; Pandya, S; Majumdar, S Ghayvat, Hemant; Awais, Muhammad; Gope, Prosanta; Pandya, Sharnil; Majumdar, Shubhankar ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence SUSTAINABLE CITIES AND SOCIETY English Article Cross-path; GPS location; Backtracking; Outbreak; Pandemic; AI; IoT; Edge computing IOT Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject?s infection, backpropagation through the subjects? location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects. [Ghayvat, Hemant] Technol Univ Denmark, Innovat Dept, Lyngby, Denmark; [Awais, Muhammad] Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Technol, Dept Elect Engn, Shanghai 200433, Peoples R China; [Gope, Prosanta] Univ Sheffield, Sheffield, S Yorkshire, England; [Pandya, Sharnil] Symbiosis Int Deemed Univ, Symbiosis Ctr Appl Artificial Intelligence, Pune, Maharashtra, India; [Pandya, Sharnil] Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune, Maharashtra, India; [Majumdar, Shubhankar] Natl Inst Technol Meghalaya, Dept Elect & Commun Engn, Shillong 793003, Meghalaya, India Technical University of Denmark; Fudan University; University of Sheffield; Symbiosis International University; Symbiosis International University; Symbiosis Institute of Technology (SIT); National Institute of Technology (NIT System); National Institute of Technology Meghalaya Ghayvat, H (corresponding author), Technol Univ Denmark, Innovat Dept, Lyngby, Denmark. hemant.ghayvat@lnu.se Pandya, Sharnil/P-7479-2019; ghayvat, hemant/E-5873-2015; MAJUMDAR, SHUBHANKAR/N-1410-2015 Pandya, Sharnil/0000-0002-4507-1844; MAJUMDAR, SHUBHANKAR/0000-0002-3703-4904; Gope, Prosanta/0000-0003-2786-0273 Eurotech MSCA H2020 [754462]; ARC ITRH for Digital Enhances Living [IH170100013]; REACH [690425]; Australian Research Council [IH170100013] Funding Source: Australian Research Council Eurotech MSCA H2020; ARC ITRH for Digital Enhances Living; REACH; Australian Research Council(Australian Research Council) This work is supported by Eurotech MSCA H2020 (Grant No. 754462), ARC ITRH for Digital Enhances Living (Grant No. IH170100013), and REACH (Grant No. 690425). 25 21 21 2 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. JUN 2021.0 69 102798 10.1016/j.scs.2021.102798 0.0 MAR 2021 11 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels RY2HT 36568858.0 Green Published, Green Accepted 2023-03-23 WOS:000647739000001 0
J Yang, LH; Liu, J; Wang, YM; Martinez, L Yang, Long-Hao; Liu, Jun; Wang, Ying-Ming; Martinez, Luis Extended belief-rule-based system with new activation rule determination and weight calculation for classification problems APPLIED SOFT COMPUTING English Article Extended belief-rule-based system; Classification problem; Activation rule determination; Activation weight calculation; Multi-class FEATURE-SELECTION; DECISION-ANALYSIS; NEURAL-NETWORK; INFERENCE; REPRESENTATION; RECOGNITION; ALGORITHM Among many rule-based systems employed to deal with classification problems, the extended beliefrule-based (EBRB) system is an effective and efficient tool and also has potentials in handing both quantitative and qualitative information under uncertainty. Despite many advantages, several drawbacks must be overcome for better applying the conventional EBRB system, including counterintuitive individual matching degrees, insensitivity to the calculation of individual matching degrees, and the inconsistency problem. Accordingly, by constructing the activation region of extended belief rules and revising the calculation formula of activation weights, the new procedures of activation rule determination and weight calculation are proposed to improve the conventional EBRB system, while the original procedures of rule inference and class estimation are retained from the conventional EBRB system. Nineteen classification datasets with different numbers of classes are studied to validate the efficiency and effectiveness of the proposed EBRB classification system compared with existing works. The comparison results demonstrate that the proposed EBRB classification system not only obtains a high accuracy better than the conventional EBRB system, but also has an excellent response time for classification. More importantly, the results derived from multi-class datasets show the significant performance of the proposed EBRB classification system compared with some state of art classification tools. (C) 2018 Elsevier B.V. All rights reserved. [Yang, Long-Hao; Wang, Ying-Ming] Fuzhou Univ, Decis Sci Inst, Fuzhou, Fujian, Peoples R China; [Wang, Ying-Ming] Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou, Fujian, Peoples R China; [Yang, Long-Hao; Martinez, Luis] Univ Jaen, Dept Comp Sci, Jaen, Spain; [Liu, Jun] Ulster Univ, Sch Comp, Jorhanstown Campus, Coleraine, North Ireland Fuzhou University; Fuzhou University; Universidad de Jaen; Ulster University Wang, YM (corresponding author), Fuzhou Univ, Decis Sci Inst, Fuzhou, Fujian, Peoples R China.;Wang, YM (corresponding author), Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou, Fujian, Peoples R China. ymwang@fzu.edu.cn Wang, Ying/HJI-2509-2023; Liu, Jun/C-1338-2011; wang, yan/GSE-6489-2022; wang, yingying/GRS-3058-2022; Martinez, Luis/A-1746-2009 Liu, Jun/0000-0001-8859-5405; Martinez, Luis/0000-0003-4245-8813 National Natural Science Foundation of China [61773123, 71371053, 71501047, 71601180]; Humanities and Social Science Foundation of the Ministry of Education [14YJC630056]; Natural Science Foundation of Fujian Province, China [2015J01248] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanities and Social Science Foundation of the Ministry of Education; Natural Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province) This research was supported by the National Natural Science Foundation of China (Nos. 61773123, 71371053, 71501047 and 71601180), the Humanities and Social Science Foundation of the Ministry of Education under Grant (No. 14YJC630056), and the Natural Science Foundation of Fujian Province, China (No. 2015J01248). 34 13 15 2 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. NOV 2018.0 72 261 272 10.1016/j.asoc.2018.08.004 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science GY7RZ Green Submitted 2023-03-23 WOS:000448813100018 0
J Xie, WC; Li, T; Tiraferri, A; Drioli, E; Figoli, A; Crittenden, JC; Liu, BC Xie, Wancen; Li, Tong; Tiraferri, Alberto; Drioli, Enrico; Figoli, Alberto; Crittenden, John C.; Liu, Baicang Toward the Next Generation of Sustainable Membranes from Green Chemistry Principles ACS SUSTAINABLE CHEMISTRY & ENGINEERING English Article Membrane technology; Biobased polymers; Green solvents; End-of-life management; Sustainability INDUCED PHASE-SEPARATION; DEEP EUTECTIC SOLVENTS; HOLLOW-FIBER MEMBRANES; BIODEGRADABLE BLEND MEMBRANES; MICROPOROUS PVDF MEMBRANES; TRIETHYL PHOSPHATE TEP; WASTE-WATER TREATMENT; ULTRAFILTRATION MEMBRANES; CELLULOSE-ACETATE; NANOFILTRATION MEMBRANES Large-scale membrane technology has been widely implemented and rapidly growing for roughly 40 years. However, considering its entire life cycle, there are aspects being characterized by low sustainability, and this industry certainly cannot be defined as green. In the membrane manufacturing process, raw materials mainly rely on nonbiodegradable petroleum-based polymers and hazardous solvents. These materials are thus associated with the energy crisis and with disposal burdens at the end of their lifetime, and they pose risks to workers and the environment. Therefore, biobased polymers and green solvents should be employed within the membrane preparation process and replace traditional ones. Moreover, the wastewater generated from membrane fabrication processes contains an important amount of organic solvents and should be efficiently treated or recycled before discharge. The application of artificial intelligence in membrane manufacturing and use processes can also improve efficiency significantly. Finally, a large number of spent membrane elements should also be reused and recovered, rather than landfilled. This review critically evaluates the recent advances in methods to improve the sustainability of membrane technology, specifically emphasizing the progresses made, with regard to the above aspects. This review thus analyzes the needs for membrane industry transformations in the light of circular economy. [Xie, Wancen; Liu, Baicang] Sichuan Univ, Coll Architecture & Environm, Inst New Energy & Low Carbon Technol, Key Lab Deep Earth Sci & Engn,Minist Educ, Chengdu 610207, Sichuan, Peoples R China; [Xie, Wancen; Liu, Baicang] Sichuan Univ, Yibin Inst Ind Technol, Chengdu 610207, Sichuan, Peoples R China; [Li, Tong] Guangzhou Univ, Inst Environm Res Greater Bay, Key Lab Water Qual & Conservat Pearl River Delta, Minist Educ, Guangzhou 510006, Guangdong, Peoples R China; [Tiraferri, Alberto] Politecn Torino, Dept Environm Land & Infrastruct Engn, I-10129 Turin, Italy; [Drioli, Enrico; Figoli, Alberto] Inst Membrane Technol ITM CNR, C, I-87030 Cosenza, Italy; [Crittenden, John C.] Georgia Inst Technol, Brook Byers Inst Sustainable Syst, Sch Civil & Environm Engn, Atlanta, GA 30332 USA Sichuan University; Sichuan University; Guangzhou University; Polytechnic University of Turin; Consiglio Nazionale delle Ricerche (CNR); Istituto per la Tecnologia delle Membrane (ITM-CNR); University System of Georgia; Georgia Institute of Technology Liu, BC (corresponding author), Sichuan Univ, Coll Architecture & Environm, Inst New Energy & Low Carbon Technol, Key Lab Deep Earth Sci & Engn,Minist Educ, Chengdu 610207, Sichuan, Peoples R China.;Liu, BC (corresponding author), Sichuan Univ, Yibin Inst Ind Technol, Chengdu 610207, Sichuan, Peoples R China. bcliu@scu.edu.cn Liu, Baicang/V-6193-2017; Tiraferri, Alberto/HMP-6954-2023 Liu, Baicang/0000-0003-3219-1924; Tiraferri, Alberto/0000-0001-9859-1328; Drioli, Enrico/0000-0003-1092-5368; Li, Tong/0000-0003-2508-8339; Crittenden, John/0000-0002-9048-7208 National Natural Science Foundation of China [51678377, 52070134]; Sichuan University and Yibin City People's Government strategic cooperation project [2019CDYB-25] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan University and Yibin City People's Government strategic cooperation project The work was supported by the National Natural Science Foundation of China (Nos. 51678377, 52070134), and Sichuan University and Yibin City People's Government strategic cooperation project (No. 2019CDYB-25). 252 54 54 52 165 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2168-0485 ACS SUSTAIN CHEM ENG ACS Sustain. Chem. Eng. JAN 11 2021.0 9 1 50 75 10.1021/acssuschemeng.0c07119 0.0 26 Chemistry, Multidisciplinary; Green & Sustainable Science & Technology; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Engineering PW7CH 2023-03-23 WOS:000610827900003 0
J Javadpour, A; Wang, GJ; Rezaei, S Javadpour, Amir; Wang, Guojun; Rezaei, Samira Resource Management in a Peer to Peer Cloud Network for IoT WIRELESS PERSONAL COMMUNICATIONS English Article Software-Defined Resource management; SDIoT Internet of Things (IoT); Peer to Peer Fog Computing; Cloud Big Data; Bottlenecks of cloud networks DISCOVERY; SERVICE; INTERNET; THINGS; MODEL; MIDDLEWARE; SYSTEMS Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale of data centers, heterogeneity issues and their interconnections have made the resource management a hard problem specially when there are different actors in cloud system with different needs. Resource management is a vital requirement to achieve robust networks specially with facing continuously increasing amount of heterogeneous resources and devices to the network. The goal of this paper is reviews to address IoT resource management issues in cloud computing services. We discuss the bottlenecks of cloud networks for IoT services such as mobility. We review Fog computing in IoT services to solve some of these issues. It provides a comprehensive literature review of around one hundred studies on resource management in Peer to Peer Cloud Networks and IoT. It is very important to find a robust design to efficiently manage and provision requests and available resources. We also reviewed different search methodologies to help clients find proper resources to answer their needs. [Javadpour, Amir; Wang, Guojun] Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China; [Rezaei, Samira] Univ Groningen, Bernoulli Inst Math & Comp Sci, Groningen, Netherlands Guangzhou University; University of Groningen Wang, GJ (corresponding author), Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China. a_javadpour@e.gzhu.edu.cn; csgjwang@gzhu.edu.cn; s.rezaei.badafshani@rug.nl National Natural Science Foundation of China [61632009, 61472451]; Guangdong Provincial Natural Science Foundation [2017A030308006]; High-Level Talents Program of Higher Education in Guangdong Province [2016ZJ01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); High-Level Talents Program of Higher Education in Guangdong Province This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 & 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006 and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01. 78 33 33 1 3 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0929-6212 1572-834X WIRELESS PERS COMMUN Wirel. Pers. Commun. DEC 2020.0 115 3 2471 2488 10.1007/s11277-020-07691-7 0.0 AUG 2020 18 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications OU3IU Green Published 2023-03-23 WOS:000558130800010 0
J Huang, X; Zhang, L; Wang, H; Li, L Huang, X.; Zhang, L.; Wang, H.; Li, L. Improving the performance of solar flare prediction using active longitudes information ASTRONOMY & ASTROPHYSICS English Article Sun: flares; Sun: activity; Sun: magnetic topology MAGNETIC-FIELD PROPERTIES; MICHELSON DOPPLER IMAGER; FORECASTING METHODS; SINGULAR POINTS; SUNSPOT GROUPS; LARGE-SCALE; REGIONS; CLASSIFICATION; MAGNETOGRAMS; SAMPLE Context. Solar flare prediction models normally depend on properties of active regions, such as sunspot area, McIntosh classifications, Mount Wilson classifications, and various measures of the magnetic field. Nevertheless, the positional information of active regions has not been used. Aims. We define a metric, DARAL (distance between active regions and predicted active longitudes), to depict the positional relationship between active regions and predicted active longitudes and add DARAL to our solar flare prediction model to improve its performance. Methods. Combining DARAL with other solar magnetic field parameters, we build a solar flare prediction model with the instance-based learning method, which is a simple and effective algorithm in machine learning. We extracted 70 078 active region instances from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) magnetograms containing 1055 National Oceanic and Atmospheric Administration (NOAA) active regions within 30 degrees of the solar disk center from 1996 to 2007 and used them to train and test the solar flare prediction model. Results. Using four performance measures (true positive rate, true negative rate, true skill statistic, and Heidke skill score), we compare performances of the solar flare prediction model with and without DARAL. True positive rate, true negative rate, true skill statistic, and Heidke skill score increase by 6.7% +/- 1.3%, 4.2% +/- 0.5%, 10.8% +/- 1.4% and 8.7% +/- 1.0%, respectively. Conclusions. The comparison indicates that the metric DARAL is beneficial to performances of the solar flare prediction model. [Huang, X.; Zhang, L.; Wang, H.; Li, L.] Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing, Peoples R China; [Zhang, L.] Univ Oulu, Dept Phys, Oulu, Finland Chinese Academy of Sciences; National Astronomical Observatory, CAS; University of Oulu Huang, X (corresponding author), Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing, Peoples R China. xhuang@bao.ac.cn Zhang, Liyun/0000-0003-3959-6895 Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences; National Basic Research Program of China (973 Program) [2011CB811406]; National Natural Science Foundation of China [11273031, 10733020, 10921303, 11003026, 11078010] Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences(Chinese Academy of Sciences); National Basic Research Program of China (973 Program)(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank the SOHO consortium for the data. SOHO is a project of international cooperation between ESA and NASA. This work is supported by the Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences, the National Basic Research Program of China (973 Program, Grant No. 2011CB811406), and the National Natural Science Foundation of China (Grant Nos. 11273031, 10733020, 10921303, 11003026, and 11078010). Xin Huang especially thanks Prof. Daren Yu and Qinghua Hu for the helpful discussions. This paper has benefited from comments of the anonymous reviewer. 45 19 20 0 10 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 ASTRON ASTROPHYS Astron. Astrophys. JAN 2013.0 549 A127 10.1051/0004-6361/201219742 0.0 6 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 073LR Bronze, Green Submitted 2023-03-23 WOS:000313745000127 0
C Valsecchi, F; Tassi, R; Kilina, E Rau, PLP Valsecchi, Francesca; Tassi, Roberta; Kilina, Elena A First Speculation on Cultural Experiments as Design Research Methods CROSS-CULTURAL DESIGN Lecture Notes in Computer Science English Proceedings Paper 9th International Conference on Cross-Cultural Design (CCD) Held as Part of 19th International Conference on Human-Computer Interaction (HCI International) JUL 09-14, 2017 Vancouver, CANADA Transculturality; Cultures; Design research; Cultural experiments This paper offers a reflection on design research and its forms and methods of cultural interventions, with the aim of contextualising it into the frame of complexity. The paper seeks to restate culture as a necessary element in the construction of a qualitative analytic that informs design discipline, in a contemporary moment where the strategic insights and discoveries valuable to innovation are more and more associated with artificial intelligence in its variety of forms, and cumbersome data processes. In the first part, we discuss the definitions of transculturality, context, and story, within the frame of design and complexity and, specifically, in the methods and tools we use to conduct design research, both in the way of developing the research process and conducting original design inquiries in the field. In the second part, we discuss culture practices in design research, presenting four experiments that we have been conducting in a transcultural context, and framing which use of culture is necessary to the production of research insights. The examples are discussed as ethnographic exercises in which transculturality is a process of subjective negotiation, when relative and progressive framing defines the context, and where stories include pluralism and allow diversity of interpretations. Because we aim to transform these cases of cultural exploration into structured research methods, we will discuss how ultimately they help to inform if and how the design that we do might be genuinely necessary. [Valsecchi, Francesca; Tassi, Roberta; Kilina, Elena] Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China; [Valsecchi, Francesca; Tassi, Roberta; Kilina, Elena] Politecn Milan, Milan, Italy; [Valsecchi, Francesca; Tassi, Roberta; Kilina, Elena] Univ Estadual Campinas, Campinas, SP, Brazil Tongji University; Polytechnic University of Milan; Universidade Estadual de Campinas Valsecchi, F (corresponding author), Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.;Valsecchi, F (corresponding author), Politecn Milan, Milan, Italy.;Valsecchi, F (corresponding author), Univ Estadual Campinas, Campinas, SP, Brazil. francesca@tongji.edu.cn; roberta.tassi@polimi.it; lena.kilina@gmail.com Valsecchi, Francesca/Z-3633-2019 Valsecchi, Francesca/0000-0002-6453-4605 15 0 0 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-57931-3; 978-3-319-57930-6 LECT NOTES COMPUT SC 2017.0 10281 76 93 10.1007/978-3-319-57931-3_7 0.0 18 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL5QJ 2023-03-23 WOS:000452457700007 0
J Chen, YW; Sun, J; Wang, LC; Shi, S; Gong, W; Wang, SQ; Tagesson, T Chen, Yuwen; Sun, Jia; Wang, Lunche; Shi, Shuo; Gong, Wei; Wang, Shaoqiang; Tagesson, Torbern Optimized Estimation of Leaf Mass per Area with a 3D Matrix of Vegetation Indices REMOTE SENSING English Article leaf mass per area; vegetation index; PROSPECT-D model; 3D matrix HYPERSPECTRAL INDEXES; BIOCHEMICAL-PROPERTIES; ATMOSPHERIC CORRECTION; SPECTRAL REFLECTANCE; CHLOROPHYLL CONTENT; PROSPECT; TRAITS; WATER; RETRIEVAL; SELECTION Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was constructed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R-2 = 0.76 and 0.78, RMSE = 0.0016 g/cm(2) and 0.0017 g/cm(2), respectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations. [Chen, Yuwen; Sun, Jia; Wang, Lunche; Wang, Shaoqiang] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China; [Sun, Jia; Tagesson, Torbern] Lund Univ, Dept Phys Geog & Ecosyst Sci, S-22100 Lund, Sweden; [Shi, Shuo; Gong, Wei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China China University of Geosciences; Lund University; Wuhan University Sun, J (corresponding author), China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.;Sun, J (corresponding author), Lund Univ, Dept Phys Geog & Ecosyst Sci, S-22100 Lund, Sweden. yuwen@cug.edu.cn; sunjia@cug.edu.cn; wang@cug.edu.cn; shishuo@whu.edu.cn; weigong@whu.edu.cn; sqwang@igsnrr.ac.cn; torbern.tagesson@nateko.lu.se Tagesson, Torbern/AGG-5627-2022; Tagesson, Torbern/E-5429-2015 Wang, Lunche/0000-0001-7783-5725; Tagesson, Torbern/0000-0003-3011-1775 National Key R&D Program of China [2018YFB0504500]; National Natural Science Foundation of China [42001314]; Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University [20R02]; Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan [111-G1323520290]; SNSA [Dnr 96/16]; EU-Aid-funded CASSECS project National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University; Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan(Fundamental Research Funds for the Central Universities); SNSA; EU-Aid-funded CASSECS project This research was funded by the National Key R&D Program of China (2018YFB0504500); National Natural Science Foundation of China (42001314); Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University (grant number 20R02), and Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan (grant number 111-G1323520290). T.T. was funded by SNSA (Dnr 96/16) and the EU-Aid-funded CASSECS project. 54 1 1 6 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. SEP 2021.0 13 18 3761 10.3390/rs13183761 0.0 15 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology UZ2HF gold 2023-03-23 WOS:000702030500001 0
J Huang, K; Chen, S; Li, BW; Claesen, L; Yao, H; Chen, JJ; Jiang, XW; Liu, ZL; Xiong, DL Huang, Kai; Chen, Siang; Li, Bowen; Claesen, Luc; Yao, Hao; Chen, Junjian; Jiang, Xiaowen; Liu, Zhili; Xiong, Dongliang Structured precision skipping: Accelerating convolutional neural networks with budget-aware dynamic precision selection JOURNAL OF SYSTEMS ARCHITECTURE English Article Convolutional neural networks; Algorithm-architecture co-design; Model compression and acceleration; Dynamic quantization Despite the remarkable advancement in various intelligence tasks achieved by Convolutional Neural Networks, the massive computation and storage consumption limit applications on resource-constrained devices. Existing works explore to reduce computation cost by leveraging the input-dependent redundancy at runtime. The irregular dynamic sparsity distribution, however, limits the real speedup for dynamic models deployed in traditional neural network accelerators. To solve this problem, we propose an algorithm-architecture co-design, named structured precision skipping (SPS), to exploit the dynamic precision redundancy in statically quantized models. SPS computes most neurons in a lower precision and only a small portion of important neurons in a higher precision to preserve performance. Specifically, we first propose the structured dynamic block to exploit the dynamic sparsity in a structured manner. Based on the block, we then apply a budget-aware training method by inducing a budget regularization to learn the precision skipping under a target resource constraint. Finally, we present an architecture design based on the bit-serial architecture with support for SPS models, where only a predict controller module with small overhead is introduced. Extensive evaluation results demonstrate that SPS can achieve up to 1.5x speedup and 1.4x energy saving on various models and datasets with marginal accuracy loss. [Huang, Kai; Chen, Siang; Li, Bowen; Jiang, Xiaowen; Xiong, Dongliang] Zhejiang Univ, Inst VLSI Design, Hangzhou 310030, Peoples R China; [Claesen, Luc] Univ Hasselt, Engn Technol Elect ICT Dept, B-3590 Diepenbeek, Belgium; [Yao, Hao; Chen, Junjian] CSG, Digital Grid Res Inst, Guangzhou 510670, Peoples R China; [Liu, Zhili] Sec chip, Hangzhou 310030, Peoples R China Zhejiang University; Hasselt University Liu, ZL (corresponding author), Sec chip, Hangzhou 310030, Peoples R China. huangk@zju.edu.cn; chensiang@zju.edu.cn; libw@zju.edu.cn; luc.claesen@uhasselt.be; yaohao@csg.cn; chensiang@zju.edu.cn; xiaowen_jiang@zju.edu.cn; liuzhili@sec-chip.com; xiongdl@zju.edu.cn National Key R&D Program of China [2020YFB0906000, 2020YFB0906001] National Key R&D Program of China Acknowledgment This work is supported by the National Key R&D Program of China (2020YFB0906000, 2020YFB0906001) . 53 1 1 3 6 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1383-7621 1873-6165 J SYST ARCHITECT J. Syst. Architect. MAR 2022.0 124 102403 10.1016/j.sysarc.2022.102403 0.0 FEB 2022 13 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0N0YF 2023-03-23 WOS:000782573200004 0
J Iranmehr, H; Aazami, R; Tavoosi, J; Shirkhani, M; Azizi, AR; Mohammadzadeh, A; Mosavi, AH; Guo, W Iranmehr, Hamid; Aazami, Rahmat; Tavoosi, Jafar; Shirkhani, Mohammadamin; Azizi, Amir-Reza; Mohammadzadeh, Ardashir; Mosavi, Amir H.; Guo, Wei Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies FRONTIERS IN ENERGY RESEARCH English Article renewable energy; emergency capacity; reserve market; transmission lines; distribution network; machine learning The law of free access to the transmission network obliges the transmission network to be in orbit, and on the other hand, the high loads in the transmission network, and economic uncertainties cause that the owners of transmission companies, don't have sufficient motivation and resources to rebuild and develop the network. The main objective of this paper is the modeling the price of emergency power transmission lines in the reserve markets. This paper presents a method for calculating the reference price that a transmission line owner uses to bid on a price in excess of the nominal capacity of the transmission line under his ownership. For this purpose, first, the effects of operating a transmission line at a power greater than the rated power are described. After that, the reduction rate of the transmission line due to operation in these conditions is calculated, and finally the price determination is calculated based on the reduction rate of the generated life. In the next stage, this excess capacity is entered the two-stage model of energy market and reservation considering renewable energy sources as a price offer function. Numerical results of 6-Shin network show that the entry of renewable energy sources reduces energy costs, but the costs of the reserve market increases due to uncertainty. However, despite the emergency capacity, these costs are reduced due to the use of cheap resources in the network. [Iranmehr, Hamid; Aazami, Rahmat; Tavoosi, Jafar; Shirkhani, Mohammadamin; Azizi, Amir-Reza] Ilam Univ, Dept Elect Engn, Ilam, Iran; [Mohammadzadeh, Ardashir] Univ Bonab, Dept Elect Engn, Bonab, Iran; [Mosavi, Amir H.] Tech Univ Dresden, Fac Civil Engn, Dresden, Germany; [Mosavi, Amir H.] Univ Publ Serv, Inst Informat Soc, Budapest, Hungary; [Mosavi, Amir H.] Obuda Univ, John Neumann Fac Informat, Budapest, Hungary; [Guo, Wei] Guangdong Univ Finance, Sch Credit Management, Guangzhou, Peoples R China Ilam University; University of Bonab; Technische Universitat Dresden; University of Public Service; Obuda University; Guangdong University of Finance Aazami, R (corresponding author), Ilam Univ, Dept Elect Engn, Ilam, Iran.;Mohammadzadeh, A (corresponding author), Univ Bonab, Dept Elect Engn, Bonab, Iran.;Mosavi, AH (corresponding author), Tech Univ Dresden, Fac Civil Engn, Dresden, Germany.;Mosavi, AH (corresponding author), Univ Publ Serv, Inst Informat Soc, Budapest, Hungary.;Mosavi, AH (corresponding author), Obuda Univ, John Neumann Fac Informat, Budapest, Hungary. r.aazami@ilam.ac.ir; a.mzadeh@ubonab.ac.ir; amir.mosavi@mailbox.tu-dresden.de Mosavi, Amir/I-7440-2018; Shirkhani, Mohammadamin/AAD-3079-2022; Mohammadzadeh, Ardashir/AEN-2013-2022 Mosavi, Amir/0000-0003-4842-0613; Mohammadzadeh, Ardashir/0000-0001-5173-4563; Shirkhani, Mohammadamin/0000-0001-9945-5635; Aazami, Rahmat/0000-0002-1585-029X TU Dresden TU Dresden Open Access Funding by the Publication Fund of the TU Dresden. 26 11 11 0 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-598X FRONT ENERGY RES Front. Energy Res. JAN 13 2022.0 9 792418 10.3389/fenrg.2021.792418 0.0 8 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels YO5TA gold 2023-03-23 WOS:000748002500001 0
J Rezaie, M; Ross, AJ; Seo, HJ; Mueller, EM; Percival, WJ; Merz, G; Katebi, R; Bunescu, RC; Bautista, J; Brownstein, JR; Burtin, E; Dawson, K; Gil-Marin, H; Hou, JM; Lyke, EB; de la Macorra, A; Rossi, G; Schneider, DP; Zarrouk, P; Zhao, GB Rezaie, Mehdi; Ross, Ashley J.; Seo, Hee-Jong; Mueller, Eva-Maria; Percival, Will J.; Merz, Grant; Katebi, Reza; Bunescu, Razvan C.; Bautista, Julian; Brownstein, Joel R.; Burtin, Etienne; Dawson, Kyle; Gil-Marin, Hector; Hou, Jiamin; Lyke, Eleanor B.; de la Macorra, Axel; Rossi, Graziano; Schneider, Donald P.; Zarrouk, Pauline; Zhao, Gong-Bo Primordial non-Gaussianity from the completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey - I: Catalogue preparation and systematic mitigation MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article inflation; large-scale structure of the Universe LARGE-SCALE STRUCTURE; POWER-SPECTRUM ANALYSIS; LY-ALPHA ABSORPTION; ACOUSTIC-OSCILLATIONS; DENSITY PERTURBATIONS; DATA RELEASE; REDSHIFT MEASUREMENT; FIBER COLLISIONS; GALAXIES; EMISSION We investigate the large-scale clustering of the final spectroscopic sample of quasars from the recently completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). The sample contains 343 708 objects in the redshift range 0.8 < z < 2.2 and 72 667 objects with redshifts 2.2 < z < 3.5, covering an effective area of 4699 deg(2). We develop a neural network-based approach to mitigate spurious fluctuations in the density field caused by spatial variations in the quality of the imaging data used to select targets for follow-up spectroscopy. Simulations are used with the same angular and radial distributions as the real data to estimate covariance matrices, perform error analyses, and assess residual systematic uncertainties. We measure the mean density contrast and cross-correlations of the eBOSS quasars against maps of potential sources of imaging systematics to address algorithm effectiveness, finding that the neural network-based approach outperforms standard linear regression. Stellar density is one of the most important sources of spurious fluctuations, and a new template constructed using data from the Gaia spacecraft provides the best match to the observed quasar clustering. The end-product from this work is a new value-added quasar catalogue with the improved weights to correct for non-linear imaging systematic effects, which will be made public. Our quasar catalogue is used to measure the local-type primordial non-Gaussianity in a companion paper. [Rezaie, Mehdi; Seo, Hee-Jong; Merz, Grant; Katebi, Reza] Ohio Univ, Dept Phys & Astron, Athens, OH 45701 USA; [Ross, Ashley J.] Ohio State Univ, Ctr Cosmol & AstroParticle Phys, Columbus, OH 43210 USA; [Mueller, Eva-Maria] Univ Oxford, Dept Phys, Denys Wilkinson Bldg,Keble Rd, Oxford OX1 3RH, England; [Percival, Will J.] Univ Waterloo, Waterloo Ctr Astrophys, Waterloo, ON N2L 3G1, Canada; [Percival, Will J.] Univ Waterloo, Dept Phys & Astron, Waterloo, ON N2L 3G1, Canada; [Percival, Will J.] Perimeter Inst Theoret Phys, 31 Caroline St North, Waterloo, ON N2L 2Y5, Canada; [Bunescu, Razvan C.] Univ N Carolina, Coll Comp & Informat, Dept Comp Sci, Charlotte, NC 28223 USA; [Bautista, Julian] Univ Portsmouth, Inst Cosmol & Gravitat, Dennis Sciama Bldg, Portsmouth PO1 3FX, Hants, England; [Brownstein, Joel R.; Dawson, Kyle] Univ Utah, Dept Phys & Astron, 115 S 1400 E, Salt Lake City, UT 84112 USA; [Burtin, Etienne; Zarrouk, Pauline] Univ Paris Saclay, CEA, IRFU, F-91191 Gif Sur Yvette, France; [Gil-Marin, Hector] Univ Barcelona, Inst Ciencies Cosmos, ICCUB, Marti & Franques 1, E-08028 Barcelona, Spain; [Gil-Marin, Hector] Inst Estudis Espacials Catalunya IEEC, E-08034 Barcelona, Spain; [Hou, Jiamin] Max Planck Inst Extraterr Phys, Postfach 1312,Giessenbachstr, D-85748 Garching, Germany; [Lyke, Eleanor B.] Univ Wyoming, Dept Phys & Astron, Laramie, WY 82071 USA; [de la Macorra, Axel] Univ Nacl Autonoma Mexico, Inst Fis, Apdo Postal 20-364, Ciudad De Mexico, Mexico; [Rossi, Graziano] Sejong Univ, Dept Phys & Astron, Seoul 143747, South Korea; [Schneider, Donald P.] Penn State Univ, Dept Astron & Astrophys, University Pk, PA 16802 USA; [Schneider, Donald P.] Penn State Univ, Inst Gravitat & Cosmos, University Pk, PA 16802 USA; [Zarrouk, Pauline] Univ Durham, Inst Computat Cosmol, Dept Phys, South Rd, Durham DH1 3LE, England; [Zhao, Gong-Bo] Chinese Acad Sci, Natl Astron Observ, Beijing 100101, Peoples R China; [Zhao, Gong-Bo] Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China University System of Ohio; Ohio University; University System of Ohio; Ohio State University; University of Oxford; University of Waterloo; University of Waterloo; Perimeter Institute for Theoretical Physics; University of North Carolina; University of North Carolina Charlotte; University of Portsmouth; Utah System of Higher Education; University of Utah; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of Barcelona; Institut d'Estudis Espacials de Catalunya (IEEC); Max Planck Society; University of Wyoming; Universidad Nacional Autonoma de Mexico; Sejong University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Durham University; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Rezaie, M (corresponding author), Ohio Univ, Dept Phys & Astron, Athens, OH 45701 USA. mr095415@ohio.edu ; Gil Marin, Hector/B-2013-2017 Seo, Hee-Jong/0000-0002-6588-3508; Gil Marin, Hector/0000-0003-0265-6217; Ross, Ashley/0000-0002-7522-9083; Percival, Will/0000-0002-0644-5727; Hou, Jiamin/0000-0001-6083-1947 U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-SC0014329, DE-SC0019091]; National Research Foundation of Korea (NRF) [2017R1E1A1A01077508, 2020R1A2C1005655]; Korean Ministry of Education, Science and Technology (MoEST); Alfred P. Sloan Foundation; U.S. Department of Energy, Office of Science; Center for High Performance Computing at the University of Utah; National Energy Research Scientific Computing Center (NERSC); NERSC is a U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231]; Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard; Chilean Participation Group; French Participation Group; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo; Korean Participation Group; Lawrence Berkeley National Laboratory; Max-Planck-Institut fur Astronomie (MPIA Heidelberg); Max-Planck-Institut fur Extraterrestrische Physik (MPE); NewMexico State University, New York University; Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Aut PRIME; Yale University U.S. Department of Energy, Office of Science, Office of High Energy Physics(United States Department of Energy (DOE)); National Research Foundation of Korea (NRF)(National Research Foundation of Korea); Korean Ministry of Education, Science and Technology (MoEST)(Ministry of Education, Science & Technology (MEST), Republic of Korea); Alfred P. Sloan Foundation(Alfred P. Sloan Foundation); U.S. Department of Energy, Office of Science(United States Department of Energy (DOE)); Center for High Performance Computing at the University of Utah; National Energy Research Scientific Computing Center (NERSC); NERSC is a U.S. Department of Energy Office of Science User Facility(United States Department of Energy (DOE)); Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard; Chilean Participation Group; French Participation Group; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo; Korean Participation Group; Lawrence Berkeley National Laboratory(United States Department of Energy (DOE)); Max-Planck-Institut fur Astronomie (MPIA Heidelberg); Max-Planck-Institut fur Extraterrestrische Physik (MPE); NewMexico State University, New York University; Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Aut PRIME; Yale University We thank the anonymous referee for their insightful comments and suggestions. MR is supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under DE-SC0014329. H-JS is supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under DE-SC0014329 and DE-SC0019091. We acknowledge the support and resources from the Ohio Supercomputer Center (OSC; Center 2016). Specifically, this work utilized more than 359000 core hours of the Owens cluster. MR is grateful for help from Xia Wang, Antonio Marcum, and Yu Feng. GR acknowledges support from the National Research Foundation of Korea (NRF) through Grant No. 2017R1E1A1A01077508 and Grant No. 2020R1A2C1005655 funded by the Korean Ministry of Education, Science and Technology (MoEST). We would like to appreciate the open-source software and modules that were invaluable to this research: PYTORCH, NBODYKIT, HEALPIX, FITSIO, SCIKIT-LEARN, NUMPY, SCIPY, PANDAS, IPYTHON, JUPYTER, and GITHUB.; Funding for the SDSS IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy, Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org.This work also relied on resources provided to the eBOSS Collaboration by the National Energy Research Scientific Computing Center (NERSC). NERSC is a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.; SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofisica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut fur Astrophysik Potsdam (AIP), Max-Planck-Institut fur Astronomie (MPIA Heidelberg), Max-Planck-Institut fur Astrophysik (MPA Garching), Max-Planck-Institut fur Extraterrestrische Physik (MPE), National Astronomical Observatories of China, NewMexico State University, New York University, University of Notre Dame, Observatario Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autonoma de Mexico, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. 118 5 5 1 3 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. SEP 2021.0 506 3 3439 3454 10.1093/mnras/stab1730 0.0 JUL 2021 16 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics UA6BM Green Accepted, Green Submitted, Bronze 2023-03-23 WOS:000685245200025 0
J Kim, J; Zeng, H; Ghadiyaram, D; Lee, S; Zhang, L; Bovik, AC Kim, Jongyoo; Zeng, Hui; Ghadiyaram, Deepti; Lee, Sanghoon; Zhang, Lei; Bovik, Alan C. Deep Convolutional Neural Models for Picture-Quality Prediction Challenges and solutions to data-driven image quality assessment IEEE SIGNAL PROCESSING MAGAZINE English Article PERCEPTUAL IMAGE Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database. [Kim, Jongyoo; Lee, Sanghoon] Yonsei Univ, Dept Elect & Elect Engn, Seoul, South Korea; [Zeng, Hui; Zhang, Lei] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China; [Lee, Sanghoon] IEEE IVMSP TC, Aristi Village, Greece; [Lee, Sanghoon] APSIPA IVM TC, Kowloon, Hong Kong, Peoples R China; [Bovik, Alan C.] Univ Texas Austin, Austin, TX 78712 USA Yonsei University; Hong Kong Polytechnic University; University of Texas System; University of Texas Austin Kim, J (corresponding author), Yonsei Univ, Dept Elect & Elect Engn, Seoul, South Korea. jongky@yonsei.ac.kr; cshzeng@comp.polyu.edu.hk; deepti@cs.utexas.edu; slee@yonsei.ac.kr; cslzhang@comp.polyu.edu.hk; bovik@ece.utexas.edu Lee, Sanghoon/A-3430-2019 Lee, Sanghoon/0000-0001-9895-5347; Zhang, Lei/0000-0002-2078-4215; Zeng, Hui/0000-0001-6862-6964 35 170 176 2 35 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-5888 1558-0792 IEEE SIGNAL PROC MAG IEEE Signal Process. Mag. NOV 2017.0 34 6 130 141 10.1109/MSP.2017.2736018 0.0 12 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering FM6SP 2023-03-23 WOS:000415188500015 0
J Novak, M; Xie, HT; Dragicevic, T; Wang, FX; Rodriguez, J; Blaabjerg, F Novak, Mateja; Xie, Haotian; Dragicevic, Tomislav; Wang, Fengxiang; Rodriguez, Jose; Blaabjerg, Frede Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN) IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Cost function; Torque; Measurement; Voltage control; Complexity theory; Stators; Artificial neural network (ANN); drives; model-predictive torque control; voltage source converter (VSC); weighting factor design INDUCTION-MOTOR DRIVE; WEIGHTING FACTORS; CONVERTERS The use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive. [Novak, Mateja; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Xie, Haotian] Tech Univ Munich, Inst Elect Drive Syst & Power Elect, D-80333 Munich, Germany; [Dragicevic, Tomislav] Tech Univ Denmark, Ctr Elect Power & Energy, DK-2800 Lyngby, Denmark; [Wang, Fengxiang] Chinese Acad Sci, Ouanzhou Inst Equipment Mfg, Haixi Inst, Jinjiang 362200, Peoples R China; [Rodriguez, Jose] Univ Andres Bello, Fac Engn, Santiago 8370146, Chile Aalborg University; Technical University of Munich; Technical University of Denmark; Chinese Academy of Sciences; Universidad Andres Bello Novak, M (corresponding author), Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark. nov@et.aau.dk; haotian.xie@tum.de; tomdr@elektro.dtu.dk; fengxiang.wang@fjirsm.ac.cn; jose.rodriguez@unab.cl; fbl@et.aau.dk Blaabjerg, Frede/A-5008-2008; Rodriguez-Perez, Jose/A-2534-2013 Blaabjerg, Frede/0000-0001-8311-7412; Rodriguez-Perez, Jose/0000-0002-1410-4121; Novak, Mateja/0000-0001-8142-0872 National Natural Science Funds of China [51877207]; Chilean National Agency for Research and Development [FB0008, ACT192013, 1170167] National Natural Science Funds of China(National Natural Science Foundation of China (NSFC)); Chilean National Agency for Research and Development This work was supported in part by the National Natural Science Funds of China under Grant 51877207 and in part by the Chilean National Agency for Research and Development through projects FB0008, ACT192013, and 1170167. 28 25 25 8 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. AUG 2021.0 68 8 7309 7319 10.1109/TIE.2020.3009607 0.0 11 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation RX8QT Green Published, Green Submitted 2023-03-23 WOS:000647484000085 0
J Wang, ZX; Liu, YX; Yan, WY; Wei, W; Li, JH; Zhang, DY; Alfandi, O Wang, Zhixiao; Liu, Yanxiao; Yan, Wenyao; Wei, Wei; Li, Junhuai; Zhang, Deyun; Alfandi, Omar Cheating detection and cheater identification in (t, n) secret sharing scheme COMPUTER SYSTEMS SCIENCE AND ENGINEERING English Article Secret sharing; cheating detection; cheater identification; voting Secret sharing technology is being again on the agenda when the security of big data and cloud computing is becoming a thorny problem. In a (t, n) secret sharing scheme, a secret data is distributed by a mutually trusted dealer to n shareholders by this means that the secret could not be obtained and reconstructed by less than t shareholders unless the number is no less than t. However, dishonest shareholders can fool other honest shareholders by presenting fake shares in the secret reconstruction phase. Therefore, cheating detection and cheater identification are very important to achieving fair secret reconstruction. In this paper, we propose a (t, n) detecting and identifying secret sharing scheme (DISS) based on the symmetric bivariate polynomial. In our (t, n) DISS, we use the property of symmetry to detect cheating, and use voting to identify cheaters. Furthermore, detection and identification abilities, and the thresholds in voting are discussed under three types of attack. [Wang, Zhixiao; Zhang, Deyun] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China; [Wang, Zhixiao; Liu, Yanxiao; Wei, Wei; Li, Junhuai] Xian Univ Technol, Shaanxi Key Lab Network Comp & Secur, Xian, Peoples R China; [Yan, Wenyao] Yanan Univ, Xian Innovat Coll, Xian, Peoples R China; [Alfandi, Omar] Univ Gottingen, Inst Comp Sci, D-37073 Gottingen, Germany Xi'an Jiaotong University; Xi'an University of Technology; Yanan University; University of Gottingen Wang, ZX (corresponding author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China. maplewzx@gmail.com; liuyanxiao@xaut.edu.cn; weny.yan@gmail.com; weiwei@xaut.edu.cn; lijunhuai@xaut.edu.cn; alfandi@cs.uni-goettingen.de Wei, Wei/ABB-8665-2021; wei, wei/HHR-8613-2022; Alfandi, Omar/S-7011-2019 Wei, Wei/0000-0002-8751-9205; Alfandi, Omar/0000-0002-9581-401X National Development and Reform Commission Program of China CNGI [CNGI-09-02-07]; NSFC [61172018, 60602025]; Education Department of Shaanxi [12JK0944, 2013JK1120, 2013JK1139]; Xi'an Sci-tech Plan; Beilin District High-tech Plan, Xi'an, China [GX1211] National Development and Reform Commission Program of China CNGI; NSFC(National Natural Science Foundation of China (NSFC)); Education Department of Shaanxi; Xi'an Sci-tech Plan; Beilin District High-tech Plan, Xi'an, China This work is partially supported by National Development and Reform Commission Program of China CNGI Grant No. CNGI-09-02-07, NSFC Grant No. 61172018 and No. 60602025, Education Department of Shaanxi Grant No. 12JK0944, No. 2013JK1120, No. 2013JK1139, 2014 Xi'an Sci-tech Plan and Beilin District 2012 High-tech Plan No. GX1211, Xi'an, China. The authors also would like to thank the anonymous reviewers for their constructive feedback and valuable input. 21 0 0 0 15 C R L PUBLISHING LTD LEICESTER 5 WEIR RD, KIBWORTH BEAUCHAMP, LEICESTER LE8 0LQ, ENGLAND 0267-6192 COMPUT SYST SCI ENG Comput. Syst. Sci. Eng. JAN 2014.0 29 1 SI 87 93 7 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science AD4PL 2023-03-23 WOS:000333232900008 0
J Wen, WL; Ren, WQ; Shi, YH; Nie, YF; Zhang, JG; Cao, XC Wen, Weilei; Ren, Wenqi; Shi, Yinghuan; Nie, Yunfeng; Zhang, Jingang; Cao, Xiaochun Video Super-Resolution via a Spatio-Temporal Alignment Network IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Superresolution; Motion compensation; Estimation; Integrated optics; Optical imaging; Image reconstruction; Feature extraction; Video super-resolution; temporal consistency; spatio-temporal adaptive filters Deep convolutional neural network based video super-resolution (SR) models have achieved significant progress in recent years. Existing deep video SR methods usually impose optical flow to wrap the neighboring frames for temporal alignment. However, accurate estimation of optical flow is quite difficult, which tends to produce artifacts in the super-resolved results. To address this problem, we propose a novel end-to-end deep convolutional network that dynamically generates the spatially adaptive filters for the alignment, which are constituted by the local spatio-temporal channels of each pixel. Our method avoids generating explicit motion compensation and utilizes spatio-temporal adaptive filters to achieve the operation of alignment, which effectively fuses the multi-frame information and improves the temporal consistency of the video. Capitalizing on the proposed adaptive filter, we develop a reconstruction network and take the aligned frames as input to restore the high-resolution frames. In addition, we employ residual modules embedded with channel attention as the basic unit to extract more informative features for video SR. Both quantitative and qualitative evaluation results on three public video datasets demonstrate that the proposed method performs favorably against state-of-the-art super-resolution methods in terms of clearness and texture details. [Wen, Weilei] Nankai Univ, Coll Comp Sci, TKLNDST, Tianjin 300350, Peoples R China; [Ren, Wenqi; Cao, Xiaochun] Sun Yat Sen Univ Shenzhen, Sch Cyber Sci & Technol, Shenzhen 518107, Peoples R China; [Ren, Wenqi; Shi, Yinghuan] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China; [Nie, Yunfeng] Vrije Univ Brussel, Brussels Photon, Dept Appl Phys & Photon, B-1050 Brussels, Belgium; [Zhang, Jingang] Univ Chinese Acad Sci, Intelligent Imaging Ctr, Beijing 100049, Peoples R China Nankai University; Sun Yat Sen University; Nanjing University; Vrije Universiteit Brussel; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Ren, WQ (corresponding author), Sun Yat Sen Univ Shenzhen, Sch Cyber Sci & Technol, Shenzhen 518107, Peoples R China.;Zhang, JG (corresponding author), Univ Chinese Acad Sci, Intelligent Imaging Ctr, Beijing 100049, Peoples R China. wenwlmail@163.com; rwq.renwenqi@gmail.com; syh@nju.edu.cn; ynie@b-phot.org; zhangjg@ucas.ac.cn; caoxiaochun@iie.ac.cn Zhang, JinGang/0000-0002-4913-9906 National Key Research and Development Program of China [2018AAA0102503]; National Natural Science Foundation of China [62172409, 61971016, U1803264] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2018AAA0102503; and in part by the National Natural Science Foundation of China under Grant 62172409, Grant 61971016, and Grant U1803264. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Nikolaos Mitianoudis. 63 5 5 6 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2022.0 31 1761 1773 10.1109/TIP.2022.3146625 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YW5FK 35104218.0 2023-03-23 WOS:000753438500005 0
J Chen, ZY; Gryllias, K; Li, WH Chen, Zhuyun; Gryllias, Konstantinos; Li, Weihua Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Fault diagnosis; Convolutional Neural Network; Extreme Learning Machine; Wavelet Transform FEATURE-EXTRACTION; AUTO-ENCODER In the era of the so called 4th industrial revolution, the Factory of the Future and the Industrial Internet of Things, the industrial mechanical systems become continuously more intelligent and more complex. Therefore, there is a clear need for research and development on data driven methodologies and condition monitoring techniques which are able to achieve fast, reliable and high-quality diagnosis in an automatic manner. In this paper, a novel fault diagnosis approach integrating Convolutional Neural Networks (CNN) and Extreme Learning Machine (ELM) is proposed, consisting of three main stages. Firstly, the Continuous Wavelet Transform (CWT) is employed in order to obtain pre-processed representations of raw vibration signals. Secondly, a CNN with a square pooling architecture is constructed to extract high-level features. The model does not require extra training and fine-tuning, which can effectively reduce computational cost. Finally, ELM as a strong classifier is further utilized to improve the classification performance on the diagnosis framework. Two datasets, including a gearbox dataset and a motor bearing dataset, have been collected and used to verify the effectiveness of the proposed method. A comprehensive comparison and analysis with widely used algorithms is also performed. The results demonstrate that the proposed method can detect different fault types and outperforms other methods in terms of classification accuracy. (C) 2019 Elsevier Ltd. All rights reserved. [Chen, Zhuyun; Li, Weihua] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Guangdong, Peoples R China; [Chen, Zhuyun; Gryllias, Konstantinos] Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300, B-3001 Leuven, Belgium; [Gryllias, Konstantinos] Flanders Make, Dynam Mech & Mechatron Syst, Lommel, Belgium South China University of Technology; KU Leuven Li, WH (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Guangdong, Peoples R China. whlee@scut.edu.cn LI, Weihua/AAE-6294-2022; Gryllias, Konstantinos/F-4989-2017 Gryllias, Konstantinos/0000-0002-8703-8938; Li, Weihua/0000-0002-7493-1399 National Key Research and Development Program of China [2018YFB1702402]; National Natural Science Foundation of China [51875208]; Guangzhou Elite Overseas Study Program National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangzhou Elite Overseas Study Program This work is supported by National Key Research and Development Program of China (Grant No. 2018YFB1702402), National Natural Science Foundation of China (Grant No. 51875208) and Guangzhou Elite Overseas Study Program. 42 167 173 31 264 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. NOV 1 2019.0 133 106272 10.1016/j.ymssp.2019.106272 0.0 21 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering JD0TQ Green Accepted 2023-03-23 WOS:000489689600036 0
J Li, JP; Huang, RY; Chen, ZY; He, GL; Gryllias, KC; Li, WH Li, Jipu; Huang, Ruyi; Chen, Zhuyun; He, Guolin; Gryllias, Konstantinos C.; Li, Weihua Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions ADVANCED ENGINEERING INFORMATICS English Article Continual learning; Transfer learning; Industrial streaming data; Fault diagnosis; Rotating machinery NEURAL-NETWORK Catastrophic forgetting of learned knowledges and distribution discrepancy of different data are two key problems within fault diagnosis fields of rotating machinery. However, existing intelligent fault diagnosis methods generally tackle either the catastrophic forgetting problem or the domain adaptation problem. In complex industrial environments, both the catastrophic forgetting problem and the domain adaptation problem will occur simultaneously, which is termed as continual transfer problem. Therefore, it is necessary to investigate a more practical and challenging task where the number of fault categories are constantly increasing with industrial streaming data under varying operation conditions. To address the continual transfer problem, a novel framework named deep continual transfer learning network with dynamic weight aggregation (DCTLN-DWA) is proposed in this study. The DWA module is used to retain the diagnostic knowledge learned from previous phases and learn new knowledge from the new samples. The adversarial training strategy is applied to eliminate the data distribution discrepancy between source and target domains. The effectiveness of the proposed framework is investigated on an automobile transmission dataset. The experimental results demonstrate that the proposed framework can effectively handle the industrial streaming data under different working conditions and can be utilized as a promising tool for solving actual industrial problem. [Li, Jipu; Chen, Zhuyun; He, Guolin; Li, Weihua] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Peoples R China; [Huang, Ruyi; Li, Weihua] South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou 510006, Peoples R China; [Li, Jipu; Gryllias, Konstantinos C.] Katholieke Univ Leuven, Dept Mech Engn, B-3001 Leuven, Belgium South China University of Technology; South China University of Technology; KU Leuven Li, JP (corresponding author), South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Peoples R China.;Li, JP (corresponding author), Katholieke Univ Leuven, Dept Mech Engn, B-3001 Leuven, Belgium. National Key R & D Program of China [2018YFB1702400]; National Natural Science Foundation of China [51875208, 52275111, 52075182, 52205101, 52205100]; China Scholarship Council [202106150060] National Key R & D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This research was supported by National Key R & D Program of China under Grant 2018YFB1702400, National Natural Science Foundation of China under Grant 51875208, Grant 52275111, Grant 52075182, Grant 52205101, Grant 52205100, and China Scholarship Council (202106150060) . 42 0 0 5 5 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1474-0346 1873-5320 ADV ENG INFORM Adv. Eng. Inform. JAN 2023.0 55 101883 10.1016/j.aei.2023.101883 0.0 FEB 2023 12 Computer Science, Artificial Intelligence; Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 8Q8BG 2023-03-23 WOS:000927425300001 0
J Huang, HJ; Song, YD; Peng, X; Ding, SX; Zhong, WM; Du, W Huang, Haojie; Song, Yedong; Peng, Xin; Ding, Steven X.; Zhong, Weimin; Du, Wei A Sparse Nonstationary Trigonometric Gaussian Process Regression and Its Application on Nitrogen Oxide Prediction of the Diesel Engine IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Kernel; Gaussian processes; Informatics; Computational complexity; Standards; Sparse representation; Diesel engines; Gaussian process regression (GPR); nonstationarity; sparse Gaussian process regression Gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity. However, the stationarity of this method is a strict assumption for data and usually mismatches the industrial processes. In this article, we proposed a sparse nonstationary GPR, which can deal with the nonstationary relationship among samples and make the model more flexible, to settle the aforementioned problems. Furthermore, the performance of the proposed method is evaluated using three public datasets and a sampled diesel engine dataset, and the results show the superiority of our proposed method in terms of accuracy. [Huang, Haojie; Peng, Xin; Zhong, Weimin; Du, Wei] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China; [Song, Yedong] Weichai Power Co Ltd, Shanghai 200000, Peoples R China; [Ding, Steven X.] Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany East China University of Science & Technology; University of Duisburg Essen Peng, X; Zhong, WM (corresponding author), East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China. haojie.huang@mail.ecust.edu.cn; songyd@weichai.com; Xinpeng@ecust.edu.cn; steven.ding@uni-due.de; wmzhong@ecust.edu.cn; duwei0203@gmail.com Ding, Steven X./ABF-2356-2020 Peng, Xin/0000-0001-9277-8415; Ding, Steven/0000-0002-5149-5918; huang, haojie/0000-0002-9473-4851 National Natural Science Foundation of China (Basic Science Center Program) [61988101, 61925305, 61890930-3]; Optimal Short-term Operation of Petroleum Refineries International (Regional) Cooperation and Exchange Project [61720106008, TII-204752] National Natural Science Foundation of China (Basic Science Center Program)(National Natural Science Foundation of China (NSFC)); Optimal Short-term Operation of Petroleum Refineries International (Regional) Cooperation and Exchange Project This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, Grant 61925305, and Grant 61890930-3 and in part by the Optimal Short-term Operation of Petroleum Refineries International (Regional) Cooperation and Exchange Project under Grant 61720106008. Paper no. TII-204752. 29 3 3 5 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. DEC 2021.0 17 12 8367 8377 10.1109/TII.2021.3068288 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering UI9TV 2023-03-23 WOS:000690940600048 0
J Zhou, DM; Al-Durra, A; Zhang, K; Ravey, A; Gao, F Zhou, Daming; Al-Durra, Ahmed; Zhang, Ke; Ravey, Alexandre; Gao, Fei Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology JOURNAL OF POWER SOURCES English Article Remaining useful lifetime (RUL) estimation; Proton exchange membrane fuel cell (PEMFC); Autoregressive and moving average (ARMA) model; Nonlinear pattern of stationary time series ARTIFICIAL NEURAL-NETWORK; DEGRADATION PREDICTION; SOLAR IRRADIANCE; ION BATTERY; MODEL; PROGNOSTICS; RADIATION; PERFORMANCE; FILTER; ARIMA This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The linear component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonlinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters linear component), the remaining nonlinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack. [Zhou, Daming; Zhang, Ke] Northwestern Polytech Univ, Sch Astronaut, Xian 710072, Shaanxi, Peoples R China; [Zhou, Daming; Ravey, Alexandre; Gao, Fei] UTBM, Univ Bourgogne Franche Comte, CNRS, FCLAB,FR 3539, Rue Thierry Mieg, F-90010 Belfort, France; [Al-Durra, Ahmed] Khalifa Univ Sci & Technol, Dept Elect & Comp Engn, Sas Al Nakhl Campus, Abu Dhabi, U Arab Emirates Northwestern Polytechnical University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Khalifa University of Science & Technology Zhou, DM (corresponding author), Northwestern Polytech Univ, Sch Astronaut, Xian 710072, Shaanxi, Peoples R China. daming.zhou.ubfc@gmail.com; ahmed.aldurra@ku.ac.ae; zhangke@nwpu.edu.cn; alexandre.ravey@utbm.fr; fei.gao@utbm.fr Ravey, Alexandre/E-6690-2017; Zhou, Daming/GYD-4796-2022; Gao, Fei/E-7932-2012 Gao, Fei/0000-0001-9076-9718; Ravey, Alexandre/0000-0002-3377-2408; Al-Durra, Ahmed/0000-0002-6629-5134; Zhou, Daming/0000-0002-4304-6709 European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU [692224]; Franche-Comte regional council grant PEMFC aging Regional Grant [2013C-5588/92] European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU; Franche-Comte regional council grant PEMFC aging Regional Grant This work is supported by: 1) European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU Grant agreement No: 692224; 2) Franche-Comte regional council grant PEMFC aging Regional Grant agreement No: 2013C-5588/92. 48 81 83 16 108 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0378-7753 1873-2755 J POWER SOURCES J. Power Sources SEP 30 2018.0 399 314 328 10.1016/j.jpowsour.2018.06.098 0.0 15 Chemistry, Physical; Electrochemistry; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels; Materials Science GU5CJ 2023-03-23 WOS:000445302400038 0
J Li, XB; Liu, XL; Yue, CX; Liu, SY; Zhang, BW; Li, RY; Liang, SVY; Wang, LH Li, Xuebing; Liu, Xianli; Yue, Caixu; Liu, Shaoyang; Zhang, Bowen; Li, Rongyi; Liang, Steven Y.; Wang, Lihui A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion MEASUREMENT English Article Tool wear monitoring; Radar map feature fusion; Tool health indicator; Adaboost-DT; SBiLSTM FRACTAL ANALYSIS; VECTOR MACHINE; CUTTING FORCE; VIBRATION; SIGNALS; CLASSIFICATION; REGRESSION; MODEL Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A datadriven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes. [Li, Xuebing; Liu, Xianli; Yue, Caixu; Liu, Shaoyang; Zhang, Bowen; Li, Rongyi] Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China; [Liang, Steven Y.] Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA; [Wang, Lihui] KTH Royal Inst Technol, S-25175 Stockholm, Sweden Harbin University of Science & Technology; University System of Georgia; Georgia Institute of Technology; Royal Institute of Technology Liu, XL (corresponding author), Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China. Xianli.liu@hrbust.edu.cn Li, Xuebing/AGG-0723-2022; Wang, Lihui/O-3907-2014 Li, Xuebing/0000-0003-3519-4303; Wang, Lihui/0000-0001-8679-8049 Projects of International Cooperation and Exchanges NSFC [51720105009]; National Key Research and Development Project [19YFB1704800] Projects of International Cooperation and Exchanges NSFC; National Key Research and Development Project This research was funded by Projects of International Cooperation and Exchanges NSFC (Grant No.51720105009) and the National Key Research and Development Project (Grant No.19YFB1704800) . Besides, the authors want to thank all editors and reviewers for their help. 50 11 11 22 60 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement NOV 2021.0 185 110072 10.1016/j.measurement.2021.110072 0.0 AUG 2021 14 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation WK1DG 2023-03-23 WOS:000709473100003 0
J Hu, XJ; Li, CF; Wang, Q; Wu, XY; Chen, ZY; Xia, F; Cai, P; Zhang, LD; Fan, YF; Ma, KS Hu, Xiaojun; Li, Changfeng; Wang, Qiang; Wu, Xueyun; Chen, Zhiyu; Xia, Feng; Cai, Ping; Zhang, Leida; Fan, Yingfang; Ma, Kuansheng Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma DIAGNOSTICS English Article radiomics; magnetic resonance imaging; pathologic grade; gadoxetic acid; hepatocellular carcinoma; machine learning GD-EOB-DTPA; LIVER-BIOPSY; FEATURES Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images. Three classifiers, logistic regression (LR), support vector machine, and Adaboost were adopted for modeling. The areas under the curve of the three models were 0.70, 0.67, and 0.61, respectively, in the external test cohort. Alpha-fetoprotein (AFP) was the only significant clinicopathological variable associated with HCC grading (odds ratio: 2.75). When combining AFP, the LR+AFP model showed the best performance, with an AUC of 0.71 (95%CI: 0.59-0.82) in the external test cohort. A radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades. Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels. [Hu, Xiaojun; Wu, Xueyun; Fan, Yingfang] Southern Med Univ, Zhujiang Hosp, Dept Gen Surg & Hepatobiliary Surg, Guangzhou 510280, Peoples R China; [Hu, Xiaojun] Southern Med Univ, Dept Hepatobiliary Surg, Affiliated Hosp 5, Guangzhou 510920, Peoples R China; [Li, Changfeng; Chen, Zhiyu; Xia, Feng; Zhang, Leida; Ma, Kuansheng] Army Med Univ, Southwest Hosp, Inst Hepatobiliary Surg, Chongqing 400038, Peoples R China; [Wang, Qiang] Karolinska Inst, Dept Clin Sci, Div Med Imaging & Technol, Intervent & Technol CLINTEC, S-14152 Stockholm, Sweden; [Wang, Qiang] Karolinska Univ Hosp Huddinge, Dept Radiol, S-14186 Stockholm, Sweden; [Cai, Ping] Army Med Univ, Southwest Hosp, Dept Radiol, Chongqing 400038, Peoples R China Southern Medical University - China; Southern Medical University - China; Army Medical University; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; Army Medical University Fan, YF (corresponding author), Southern Med Univ, Zhujiang Hosp, Dept Gen Surg & Hepatobiliary Surg, Guangzhou 510280, Peoples R China.;Ma, KS (corresponding author), Army Med Univ, Southwest Hosp, Inst Hepatobiliary Surg, Chongqing 400038, Peoples R China. fanxifan@smu.edu.cn; kuanshengma@outlook.com Wang, Qiang/0000-0001-6686-6630 Science and Technology Program of Guangzhou, China [202206010093]; 5G+ Medical and Health Application Pilot Project of Ministry of Industry and Information Technology; 5G+ Medical and Health Application Pilot Project of Ministry of Industry and Information Technology; National Health Commission of P.R. China; National Natural Science Foundation of China [82073346]; Famous Teachers section of the Chongqing Talents Program [4246ZP112]; Guangdong Province Key Research and Development Project, China [2020B010165004] Science and Technology Program of Guangzhou, China; 5G+ Medical and Health Application Pilot Project of Ministry of Industry and Information Technology; 5G+ Medical and Health Application Pilot Project of Ministry of Industry and Information Technology; National Health Commission of P.R. China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Famous Teachers section of the Chongqing Talents Program; Guangdong Province Key Research and Development Project, China This research was funded by the Science and Technology Program of Guangzhou, China (202206010093), the 5G+ Medical and Health Application Pilot Project of Ministry of Industry and Information Technology and National Health Commission of P.R. China (2021), the National Natural Science Foundation of China (No. 82073346), the Famous Teachers section of the Chongqing Talents Program (4246ZP112), and the Guangdong Province Key Research and Development Project, China (2020B010165004). 43 0 0 2 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2075-4418 DIAGNOSTICS Diagnostics FEB 2023.0 13 3 413 10.3390/diagnostics13030413 0.0 12 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine 9A1KF 36766518.0 gold 2023-03-23 WOS:000933823200001 0
J Li, Z; Cen, WR; Robinson, A; Moore, DC; Wen, LJ; Odian, A; Al Kharusi, S; Anton, G; Arnquist, IJ; Badhrees, I; Barbeau, PS; Beck, D; Belov, V; Bhatta, T; Brodsky, JP; Brown, E; Brunner, T; Caden, E; Cao, GF; Cao, L; Chambers, C; Chana, B; Charlebois, SA; Chiu, M; Cleveland, B; Coon, M; Craycraft, A; Dalmasson, J; Daniels, T; Darroch, L; Daugherty, SJ; De St Croix, A; Mesrobian-Kabakian, AD; DeVoe, R; Di Vacri, ML; Dilling, J; Ding, YY; Dolinski, MJ; Dragone, A; Echevers, J; Elbeltagi, M; Fabris, L; Fairbank, D; Fairbank, W; Farine, J; Ferrara, S; Feyzbakhsh, S; Fontaine, R; Fucarino, A; Gallina, G; Gautam, P; Giacomini, G; Goeldi, D; Gornea, R; Gratta, G; Hansen, EV; Heffner, M; Hoppe, EW; Hossl, J; House, A; Hughes, M; Iverson, A; Jamil, A; Jewell, MJ; Jiang, XS; Karelin, A; Kaufman, LJ; Kodroff, D; Koffas, T; Krucken, R; Kuchenkov, A; Kumar, KS; Lan, Y; Larson, A; Leach, KG; Lenardo, BG; Leonard, DS; Li, G; Li, S; Licciardi, C; Lin, YH; Lv, P; MacLellan, R; McElroy, T; Medina-Peregrina, M; Michel, T; Mong, B; Murray, K; Nakarmi, P; Natzke, CR; Newby, RJ; Ning, Z; Njoya, O; Nolet, F; Nusair, O; Odgers, K; Oriunno, M; Orrell, JL; Ortega, GS; Ostrovskiy, I; Overman, CT; Parent, S; Piepke, A; Pocar, A; Pratte, JF; Radeka, V; Raguzin, E; Rescia, S; Retiere, F; Richman, M; Rossignol, T; Rowson, PC; Roy, N; Runge, J; Saldanha, R; Sangiorgio, S; Skarpaas, K; Soma, AK; St-Hilaire, G; Stekhanov, V; Stiegler, T; Sun, XL; Tarka, M; Todd, J; Tolba, T; Totev, TI; Tsang, R; Tsang, T; Vachon, F; Veeraraghavan, V; Viel, S; Visser, G; Vivo-Vilches, C; Vuilleumier, JL; Wagenpfeil, M; Walent, M; Wang, Q; Ward, M; Watkins, J; Weber, M; Wei, W; Wichoski, U; Wu, SX; Wu, WH; Wu, X; Xia, Q; Yang, H; Yang, L; Yen, YR; Zeldovich, O; Zhao, J; Zhou, Y; Ziegler, T Li, Z.; Cen, W. R.; Robinson, A.; Moore, D. C.; Wen, L. J.; Odian, A.; Al Kharusi, S.; Anton, G.; Arnquist, I. J.; Badhrees, I.; Barbeau, P. S.; Beck, D.; Belov, V.; Bhatta, T.; Brodsky, J. P.; Brown, E.; Brunner, T.; Caden, E.; Cao, G. F.; Cao, L.; Chambers, C.; Chana, B.; Charlebois, S. A.; Chiu, M.; Cleveland, B.; Coon, M.; Craycraft, A.; Dalmasson, J.; Daniels, T.; Darroch, L.; Daugherty, S. J.; De St Croix, A.; Mesrobian-Kabakian, A. Der; DeVoe, R.; Di Vacri, M. L.; Dilling, J.; Ding, Y. Y.; Dolinski, M. J.; Dragone, A.; Echevers, J.; Elbeltagi, M.; Fabris, L.; Fairbank, D.; Fairbank, W.; Farine, J.; Ferrara, S.; Feyzbakhsh, S.; Fontaine, R.; Fucarino, A.; Gallina, G.; Gautam, P.; Giacomini, G.; Goeldi, D.; Gornea, R.; Gratta, G.; Hansen, E. V.; Heffner, M.; Hoppe, E. W.; Hoessl, J.; House, A.; Hughes, M.; Iverson, A.; Jamil, A.; Jewell, M. J.; Jiang, X. S.; Karelin, A.; Kaufman, L. J.; Kodroff, D.; Koffas, T.; Krucken, R.; Kuchenkov, A.; Kumar, K. S.; Lan, Y.; Larson, A.; Leach, K. G.; Lenardo, B. G.; Leonard, D. S.; Li, G.; Li, S.; Licciardi, C.; Lin, Y. H.; Lv, P.; MacLellan, R.; McElroy, T.; Medina-Peregrina, M.; Michel, T.; Mong, B.; Murray, K.; Nakarmi, P.; Natzke, C. R.; Newby, R. J.; Ning, Z.; Njoya, O.; Nolet, F.; Nusair, O.; Odgers, K.; Oriunno, M.; Orrell, J. L.; Ortega, G. S.; Ostrovskiy, I.; Overman, C. T.; Parent, S.; Piepke, A.; Pocar, A.; Pratte, J. -F.; Radeka, V.; Raguzin, E.; Rescia, S.; Retiere, F.; Richman, M.; Rossignol, T.; Rowson, P. C.; Roy, N.; Runge, J.; Saldanha, R.; Sangiorgio, S.; Skarpaas, K.; Soma, A. K.; St-Hilaire, G.; Stekhanov, V.; Stiegler, T.; Sun, X. L.; Tarka, M.; Todd, J.; Tolba, T.; Totev, T. I.; Tsang, R.; Tsang, T.; Vachon, F.; Veeraraghavan, V.; Viel, S.; Visser, G.; Vivo-Vilches, C.; Vuilleumier, J. -L.; Wagenpfeil, M.; Walent, M.; Wang, Q.; Ward, M.; Watkins, J.; Weber, M.; Wei, W.; Wichoski, U.; Wu, S. X.; Wu, W. H.; Wu, X.; Xia, Q.; Yang, H.; Yang, L.; Yen, Y. -R.; Zeldovich, O.; Zhao, J.; Zhou, Y.; Ziegler, T. nEXO Collaboration Simulation of charge readout with segmented tiles in nEXO JOURNAL OF INSTRUMENTATION English Article Double-beta decay detectors; Noble liquid detectors (scintillation, ionization, double-phase) LIQUID XENON; SEARCH nEXO is a proposed experiment to search for the neutrino-less double beta decay (0 nu beta beta) of Xe-136 in a tonne-scale liquid xenon time projection chamber (TPC). The nEXO TPC will be equipped with charge collection tiles to form the anode. In this work, the charge reconstruction performance of this anode design is studied with a dedicated simulation package. A multi-variate method and a deep neural network are developed to distinguish simulated 0 nu beta beta signals from backgrounds arising from trace levels of natural radioactivity in the detector materials. These simulations indicate that the nEXO TPC with charge-collection tiles shows promising capability to discriminate the 0 nu beta beta signal from backgrounds. The estimated half-life sensitivity for 0 nu beta beta decay is improved by similar to 20 (32)% with the multi-variate (deep neural network) methods considered here, relative to the sensitivity estimated in the nEXO pre-conceptual design report. [Badhrees, I.] King Abdulaziz City Sci & Technol, Riyadh, Saudi Arabia; [Caden, E.; Cleveland, B.] SNOLAB, Sudbury, ON, Canada; [Cao, G. F.] Univ Chinese Acad Sci, Beijing, Peoples R China; [Chambers, C.] McGill Univ, Dept Phys, Montreal, PQ, Canada; [Kaufman, L. J.] Indiana Univ, Bloomington, IN USA; [Kumar, K. S.; Tarka, M.] Univ Massachusetts, Dept Phys, Amherst, MA 01003 USA; [Tolba, T.] Forschungszentrum Julich, IKP, Julich, Germany; [Ward, M.] Queens Univ, Dept Phys, Kingston, ON, Canada; [Li, Z.; Moore, D. C.; Jamil, A.; Xia, Q.] Yale Univ, Dept Phys, Wright Lab, New Haven, CT 06511 USA; [Cen, W. R.; Wen, L. J.; Cao, G. F.; Ding, Y. Y.; Jiang, X. S.; Lv, P.; Ning, Z.; Sun, X. L.; Tolba, T.; Wei, W.; Wu, W. H.; Zhao, J.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China; [Robinson, A.; Caden, E.; Cleveland, B.; Mesrobian-Kabakian, A. Der; Farine, J.; Licciardi, C.; Walent, M.; Wichoski, U.] Laurentian Univ, Dept Phys, Sudbury, ON P3E 2C6, Canada; [Odian, A.; Dragone, A.; Kaufman, L. J.; Mong, B.; Oriunno, M.; Rowson, P. C.; Skarpaas, K.] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA; [Al Kharusi, S.; Brunner, T.; Darroch, L.; McElroy, T.; Medina-Peregrina, M.; Murray, K.; Totev, T. I.] McGill Univ, Dept Phys, Montreal, PQ H3A 2T8, Canada; [Anton, G.; Hoessl, J.; Michel, T.; Wagenpfeil, M.; Ziegler, T.] Friedrich Alexander Univ Erlangen Nurnberg, ECAP, D-91058 Erlangen, Germany; [Arnquist, I. J.; Di Vacri, M. L.; Ferrara, S.; Hoppe, E. W.; Orrell, J. L.; Ortega, G. S.; Overman, C. T.; Saldanha, R.; Tsang, R.] Pacific Northwest Natl Lab, Richland, WA 99352 USA; [Badhrees, I.; Chana, B.; Elbeltagi, M.; Goeldi, D.; Gornea, R.; Koffas, T.; Viel, S.; Vivo-Vilches, C.; Watkins, J.] Carleton Univ, Dept Phys, Ottawa, ON K1S 5B6, Canada; [Barbeau, P. S.; Runge, J.] Duke Univ, Dept Phys, Durham, NC 27708 USA; [Barbeau, P. S.; Runge, J.] TUNL, Durham, NC 27708 USA; [Beck, D.; Coon, M.; Echevers, J.; Li, S.; Yang, L.] Univ Illinois, Dept Phys, 1110 W Green St, Urbana, IL 61801 USA; [Belov, V.; Karelin, A.; Kuchenkov, A.; Stekhanov, V.; Zeldovich, O.] AI Alikhanov Natl Res Ctr, Kurchatov Inst, Inst Theoret & Expt Phys, Moscow 117218, Russia; [Bhatta, T.; Larson, A.; MacLellan, R.] Univ South Dakota, Dept Phys, Vermillion, SD 57069 USA; [Brodsky, J. P.; Heffner, M.; House, A.; Sangiorgio, S.; Stiegler, T.] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA; [Brown, E.; Fucarino, A.; Odgers, K.] Rensselaer Polytech Inst, Dept Phys Appl Phys & Astron, Troy, NY 12180 USA; [Brunner, T.; De St Croix, A.; Dilling, J.; Gallina, G.; Gornea, R.; Krucken, R.; Lan, Y.; Retiere, F.; Ward, M.] TRIUMF, Vancouver, BC V6T 2A3, Canada; [Cao, L.; Wang, Q.; Wu, X.; Yang, H.; Zhou, Y.] Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China; [Chambers, C.; Craycraft, A.; Fairbank, D.; Fairbank, W.; Iverson, A.; Todd, J.] Colorado State Univ, Dept Phys, Ft Collins, CO 80523 USA; [Charlebois, S. A.; Fontaine, R.; Nolet, F.; Parent, S.; Pratte, J. -F.; Rossignol, T.; Roy, N.; St-Hilaire, G.; Vachon, F.] Univ Sherbrooke, Sherbrooke, PQ J1K 2R1, Canada; [Chiu, M.; Giacomini, G.; Radeka, V.; Raguzin, E.; Rescia, S.; Tsang, T.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Dalmasson, J.; DeVoe, R.; Gratta, G.; Jewell, M. J.; Lenardo, B. G.; Li, G.; Weber, M.; Wu, S. X.] Stanford Univ, Dept Phys, Stanford, CA 94305 USA; [Daniels, T.] Univ N Carolina, Dept Phys & Phys Oceanog, Wilmington, NC 28403 USA; [Daugherty, S. J.; Visser, G.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Daugherty, S. J.; Visser, G.] Indiana Univ, CEEM, Bloomington, IN 47405 USA; [De St Croix, A.; Dilling, J.; Gallina, G.; Krucken, R.; Lan, Y.] Univ British Columbia, Dept Phys & Astron, Vancouver, BC V6T 1Z1, Canada; [Dolinski, M. J.; Gautam, P.; Hansen, E. V.; Lin, Y. H.; Richman, M.; Yen, Y. -R.] Drexel Univ, Dept Phys, Philadelphia, PA 19104 USA; [Fabris, L.; Newby, R. J.] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA; [Feyzbakhsh, S.; Kodroff, D.; Pocar, A.] Univ Massachusetts, Amherst Ctr Fundamental Interact, Amherst, MA 01003 USA; [Feyzbakhsh, S.; Kodroff, D.; Pocar, A.] Univ Massachusetts, Dept Phys, Amherst, MA 01003 USA; [Hughes, M.; Nakarmi, P.; Nusair, O.; Ostrovskiy, I.; Piepke, A.; Soma, A. K.; Veeraraghavan, V.] Univ Alabama, Dept Phys & Astron, Tuscaloosa, AL 35487 USA; [Kumar, K. S.; Njoya, O.; Tarka, M.] SUNY Stony Brook, Dept Phys & Astron, Stony Brook, NY 11794 USA; [Leach, K. G.; Natzke, C. R.] Colorado Sch Mines, Dept Phys, Golden, CO 80401 USA; [Leonard, D. S.] IBS Ctr Underground Phys, Daejeon 34126, South Korea; [Vuilleumier, J. -L.] Univ Bern, Albert Einstein Ctr, LHEP, CH-3012 Bern, Switzerland King Abdulaziz City for Science & Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; McGill University; Indiana University System; Indiana University Bloomington; University of Massachusetts System; University of Massachusetts Amherst; Helmholtz Association; Research Center Julich; Queens University - Canada; Yale University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Laurentian University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; McGill University; University of Erlangen Nuremberg; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Carleton University; Duke University; University of North Carolina; University of North Carolina Chapel Hill; Duke University; North Carolina State University; University of Illinois System; University of Illinois Urbana-Champaign; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; University of South Dakota; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; Rensselaer Polytechnic Institute; University of British Columbia; Chinese Academy of Sciences; Institute of Microelectronics, CAS; Colorado State University; University of Sherbrooke; United States Department of Energy (DOE); Brookhaven National Laboratory; Stanford University; University of North Carolina; University of North Carolina Wilmington; Indiana University System; Indiana University Bloomington; Indiana University System; Indiana University Bloomington; University of British Columbia; Drexel University; United States Department of Energy (DOE); Oak Ridge National Laboratory; University of Massachusetts System; University of Massachusetts Amherst; University of Massachusetts System; University of Massachusetts Amherst; University of Alabama System; University of Alabama Tuscaloosa; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Colorado School of Mines; Institute for Basic Science - Korea (IBS); University of Bern Li, Z (corresponding author), Yale Univ, Dept Phys, Wright Lab, New Haven, CT 06511 USA. zepeng.li@yale.edu Orrell, John Laurence/E-9313-2015; Dragone, Angelo/AAQ-2936-2021; Li, Zepeng/GPT-0864-2022; Michel, Thilo/AAT-9705-2020; ding, yy/HHS-9589-2022; Wang, Qidong/G-5568-2010; Charlebois, Serge/ABE-7121-2020; Leach, Kyle/AAT-5216-2020; Kruecken, Reiner/N-9449-2019; Anton, Gisela/C-4840-2013; Moore, David C/Q-2208-2017; Newby, Robert/AAK-5209-2021; Li, Zepeng/ABB-9232-2021; Soma, Arun Kumar/ABG-1170-2020; Yang, Liang/AAT-9994-2020; Gallina, Giacomo/AAA-8103-2020; Brunner, Thomas/CAF-7872-2022; Sangiorgio, Samuele/F-4389-2014 Orrell, John Laurence/0000-0001-7968-4051; Dragone, Angelo/0000-0002-0795-2579; Li, Zepeng/0000-0001-6252-4625; Michel, Thilo/0000-0002-2960-0611; Wang, Qidong/0000-0001-9176-5583; Charlebois, Serge/0000-0001-7857-5056; Leach, Kyle/0000-0002-4751-1698; Kruecken, Reiner/0000-0002-2755-8042; Anton, Gisela/0000-0003-2039-4724; Moore, David C/0000-0002-2358-4761; Newby, Robert/0000-0003-3571-1067; Yang, Liang/0000-0001-5272-050X; MacLellan, Ryan/0000-0003-2479-5277; Brunner, Thomas/0000-0002-3131-8148; Pratte, Jean-Francois/0000-0002-8327-3842; Saldanha, Richard/0000-0003-2771-3281; Nusair, Omar/0000-0002-4841-5286; Goeldi, Damian/0000-0001-5231-9919; Sangiorgio, Samuele/0000-0002-4792-7802; de St. Croix, Austin/0000-0002-6517-420X; Gallina, Giacomo/0000-0002-9878-6499; Viel, Simon/0000-0001-9554-4059; Vivo-Vilches, Carlos/0000-0002-4972-4072; Fontaine, Rejean/0000-0002-5453-0168; Tarka, Michal/0000-0002-7099-2119; Gautam, Prakash/0000-0003-3934-775X; Jewell, Michael/0000-0002-5260-7416; Dolinski, Michelle/0000-0002-7716-2126; Brodsky, Jason/0000-0002-7498-6461; Barbeau, Phillip/0000-0002-8891-8988; Ostrovskiy, Igor/0000-0003-4939-0225; Soma, Arun Kumar/0000-0002-0771-0406; Heffner, Michael/0000-0003-0909-9871 Offices of Nuclear and High Energy Physics within DOE's Office of Science; NSF in the United States; CFI; FRQNT, NRC; McDonald Institute (CFREF) in Canada; IBS in Korea; RFBR in Russia [18-02-00550]; CAS; NSFC in China; Laboratory Directed Research and Development (LDRD) programs at Brookhaven National Laboratory (BNL); Lawrence Livermore National Laboratory (LLNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL) Offices of Nuclear and High Energy Physics within DOE's Office of Science(United States Department of Energy (DOE)); NSF in the United States(National Science Foundation (NSF)); CFI(Canada Foundation for Innovation); FRQNT, NRC; McDonald Institute (CFREF) in Canada; IBS in Korea; RFBR in Russia(Russian Foundation for Basic Research (RFBR)); CAS(Chinese Academy of Sciences); NSFC in China(National Natural Science Foundation of China (NSFC)); Laboratory Directed Research and Development (LDRD) programs at Brookhaven National Laboratory (BNL); Lawrence Livermore National Laboratory (LLNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL) This work has been supported by the Offices of Nuclear and High Energy Physics within DOE's Office of Science, and NSF in the United States, by NERSC, CFI, FRQNT, NRC, and the McDonald Institute (CFREF) in Canada, by IBS in Korea, by RFBR (18-02-00550) in Russia, and by CAS and NSFC in China. This work was supported in part by Laboratory Directed Research and Development (LDRD) programs at Brookhaven National Laboratory (BNL), Lawrence Livermore National Laboratory (LLNL), Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL). 27 7 7 1 9 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1748-0221 J INSTRUM J. Instrum. SEP 2019.0 14 P09020 10.1088/1748-0221/14/09/P09020 0.0 25 Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Instruments & Instrumentation JP8TN Green Submitted 2023-03-23 WOS:000498531700003 0
C Cai, YH; Lin, J; Hu, XW; Wang, HQ; Yuan, X; Zhang, YL; Timofte, R; Van Gool, L Avidan, S; Brostow, G; Cisse, M; Farinella, GM; Hassner, T Cai, Yuanhao; Lin, Jing; Hu, Xiaowan; Wang, Haoqian; Yuan, Xin; Zhang, Yulun; Timofte, Radu; Van Gool, Luc Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction COMPUTER VISION - ECCV 2022, PT XVII Lecture Notes in Computer Science English Proceedings Paper 17th European Conference on Computer Vision (ECCV) OCT 23-27, 2022 Tel Aviv, ISRAEL Compressive imaging; Transformer; Image restoration CLASSIFICATION; SUPERRESOLUTION; DESIGN Many learning-based algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show limitations in capturing long-range dependencies. Previous Transformer-based methods densely sample tokens, some of which are uninformative, and calculate multi-head self-attention (MSA) between some tokens that are unrelated in content. In this paper, we propose a novel Transformerbased method, coarse-to-fine sparse Transformer (CST), firstly embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing. Comprehensive experiments show that our CST significantly outperforms stateof-the-art methods while requiring cheaper computational costs. https://github.com/caiyuanhao1998/MST [Cai, Yuanhao; Lin, Jing; Hu, Xiaowan; Wang, Haoqian] Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Peoples R China; [Cai, Yuanhao; Lin, Jing; Hu, Xiaowan; Wang, Haoqian] Shenzhen Inst Future Media Technol, Shenzhen, Peoples R China; [Yuan, Xin] Westlake Univ, Hangzhou, Peoples R China; [Zhang, Yulun; Timofte, Radu; Van Gool, Luc] Swiss Fed Inst Technol, Zurich, Switzerland; [Timofte, Radu] Univ Wurzburg, Wurzburg, Germany Tsinghua University; Westlake University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Wurzburg Wang, HQ (corresponding author), Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Peoples R China.;Wang, HQ (corresponding author), Shenzhen Inst Future Media Technol, Shenzhen, Peoples R China. wanghaoqian@tsinghua.edu.cn Timofte, Radu/0000-0002-1478-0402 NSFC fund [61831 014]; Shenzhen Science and Technology Project [JSGG20 210802153150005, CJGJZD20200617102601004]; Westlake Foundation [2021B1 501-2] NSFC fund(National Natural Science Foundation of China (NSFC)); Shenzhen Science and Technology Project; Westlake Foundation This work is partially supported by the NSFC fund (61831 014), the Shenzhen Science and Technology Project under Grant (JSGG20 210802153150005, CJGJZD20200617102601004), and the Westlake Foundation (2021B1 501-2). Xin Yuan would like to thank the funding from Lochn Optics. 107 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-19789-5; 978-3-031-19790-1 LECT NOTES COMPUT SC 2022.0 13677 686 704 10.1007/978-3-031-19790-1_41 0.0 19 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU4NS Green Submitted 2023-03-23 WOS:000904106100041 0
J Hong, YL; Chen, JH; Cheng, Y; Han, YS; Van Reeth, F; Claesen, L; Liu, WY Hong, Yuanlin; Chen, Junhong; Cheng, Yu; Han, Yishi; Van Reeth, Frank; Claesen, Luc; Liu, Wenyin ClueDepth Grasp: Leveraging positional clues of depth for completing depth of transparent objects FRONTIERS IN NEUROROBOTICS English Article depth completion; transparent objects; grasping; deep learning; robot NETWORK Obtaining accurate depth information is key to robot grasping tasks. However, for transparent objects, RGB-D cameras have difficulty perceiving them owing to the objects' refraction and reflection properties. This property makes it difficult for humanoid robots to perceive and grasp everyday transparent objects. To remedy this, existing studies usually remove transparent object areas using a model that learns patterns from the remaining opaque areas so that depth estimations can be completed. Notably, this frequently leads to deviations from the ground truth. In this study, we propose a new depth completion method [i.e., ClueDepth Grasp (CDGrasp)] that works more effectively with transparent objects in RGB-D images. Specifically, we propose a ClueDepth module, which leverages the geometry method to filter-out refractive and reflective points while preserving the correct depths, consequently providing crucial positional clues for object location. To acquire sufficient features to complete the depth map, we design a DenseFormer network that integrates DenseNet to extract local features and swin-transformer blocks to obtain the required global information. Furthermore, to fully utilize the information obtained from multi-modal visual maps, we devise a Multi-Modal U-Net Module to capture multiscale features. Extensive experiments conducted on the ClearGrasp dataset show that our method achieves state-of-the-art performance in terms of accuracy and generalization of depth completion for transparent objects, and the successful employment of a humanoid robot grasping capability verifies the efficacy of our proposed method. [Hong, Yuanlin; Chen, Junhong; Cheng, Yu; Han, Yishi; Liu, Wenyin] Guangdong Univ Technol, Guangzhou, Peoples R China; [Chen, Junhong; Van Reeth, Frank; Claesen, Luc] Hasselt Univ, Hasselt, Belgium Guangdong University of Technology; Hasselt University Han, YS; Liu, WY (corresponding author), Guangdong Univ Technol, Guangzhou, Peoples R China. yshan@gdut.edu.cn; liuwy@gdut.edu.cn National Natural Science Foundation of China; Guangdong Basic and Applied Basic Research Foundation; Science and Technology Program of Guangzhou; Guangdong Innovative Research Team Program; Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation; Key-Area Research and Development Program of Guangdong Province; Science and Technology Planning Project of Guangdong Province; [91748107]; [62076073]; [61902077]; [2020A1515010616]; [202102020524]; [2014ZT05G157]; [pdjh2020a0173]; [2019B010136001]; [LZC0023] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Science and Technology Program of Guangzhou; Guangdong Innovative Research Team Program; Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation; Key-Area Research and Development Program of Guangdong Province; Science and Technology Planning Project of Guangdong Province; ; ; ; ; ; ; ; ; Funding This work is supported by the National Natural Science Foundation of China (No. 91748107, No. 62076073, No. 61902077), the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010616), Science and Technology Program of Guangzhou (No. 202102020524), the Guangdong Innovative Research Team Program (No. 2014ZT05G157), Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (pdjh2020a0173), and the Key-Area Research and Development Program of Guangdong Province (2019B010136001), and the Science and Technology Planning Project of Guangdong Province LZC0023. 38 0 0 12 12 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5218 FRONT NEUROROBOTICS Front. Neurorobotics NOV 8 2022.0 16 1041702 10.3389/fnbot.2022.1041702 0.0 13 Computer Science, Artificial Intelligence; Robotics; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Robotics; Neurosciences & Neurology 6N8KT 36425928.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000889800400001 0
J Teng, S; Chen, XD; Chen, GF; Cheng, L; Bassir, D Teng, Shuai; Chen, Xuedi; Chen, Gongfa; Cheng, Li; Bassir, David Structural damage detection based on convolutional neural networks and population of bridges MEASUREMENT English Article Structural damage detection; Population of bridges; Convolutional neural network; Bridge structure; Vibration signal NOMINALLY IDENTICAL STRUCTURES; IDENTIFICATION The CNN-based detection methods have been widely used in the field of structural health monitoring (SHM), however, they can only be used for individual structures under certain conditions; for the structures in-service, damage detection will be affected by a variety of external factors (unknown/uncertain load and geometric dimension, etc.). Therefore, in order to improve the applicability of the CNNs, their compatibility and robustness need to be thoroughly investigated. In this paper, a large number of random models were produced to establish a population of bridge structures, the damage features of the population were extracted by the CNN; subsequently, the CNN was applied to damage detection of the new randomly-created models. The results show that the best detection results (99.4% accuracy) can be obtained by using the acceleration signals as the CNN input. This demonstrates that the proposed method will expand the detection ability of the CNN beyond an individual structure. [Teng, Shuai; Chen, Xuedi; Chen, Gongfa] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China; [Cheng, Li] Hong Kong Polytech Univ, Dept Mech Engn, Hung Hom, Kowloon, Hong Kong 999077, Peoples R China; [Bassir, David] Univ Paris Saclay, CNRS, CMLA, ENS Cachan,Ctr Borelli, F-94235 Cachan, France Guangdong University of Technology; Hong Kong Polytechnic University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Chen, GF (corresponding author), Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China. gongfa.chen@gdut.edu.cn BASSIR, David/D-9637-2018 BASSIR, David/0000-0002-5364-9992 34 2 2 15 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement OCT 2022.0 202 111747 10.1016/j.measurement.2022.111747 0.0 AUG 2022 12 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 4H6BF 2023-03-23 WOS:000849961500002 0
J Huang, FN; Li, WQ; Shen, WH; Seferlis, P; Man, Y; Corriou, JP Huang, Feini; Li, Wenqing; Shen, Wenhao; Seferlis, Panagiotis; Man, Yi; Corriou, Jean-Pierre A hierarchical intelligent control strategy for greenhouse gas reduction in wastewater treatment process of paper mill JOURNAL OF CLEANER PRODUCTION English Article Wastewater treatment; Greenhouse gas; Dissolved oxygen; Optimization; Control; Paper mill TREATMENT PLANTS; EMISSIONS; SIMULATION; BENCHMARK; GENERATION; OPERATION; PULP Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dis-solved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the estab-lished integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity con-sumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs. [Huang, Feini; Li, Wenqing; Shen, Wenhao; Man, Yi] South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510641, Peoples R China; [Seferlis, Panagiotis] Aristotle Univ Thessaloniki, Dept Mech Engn, POB 484, Thessaloniki 54124, Greece; [Corriou, Jean-Pierre] Lorraine Univ, ENSIC, Lab React & Genie Procedes, UMR 7274 CNRS, 1, rue Grandville BP 20451, F-54001 Nancy, France South China University of Technology; Aristotle University of Thessaloniki; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Lorraine Shen, WH; Man, Y (corresponding author), South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510641, Peoples R China. ppwhshen@scut.edu.cn Man, Yi/B-4243-2019; Corriou, Jean-Pierre/G-2123-2014 Man, Yi/0000-0002-3745-9128; Corriou, Jean-Pierre/0000-0003-4780-3313; SHEN, Wenhao/0000-0002-9828-1052 Joint Research for INTERNATIONAL COOPERATION on Scientific and Technological Innovation by MOST [2021A1515010327]; National Natural Science Foundation of Guangdong Province, China [52000078]; National Natural Science Foundation of China [T7?KI-00426]; European Union; Greek national funds through the Operational Program ?; Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation; [2017YFE0184900] Joint Research for INTERNATIONAL COOPERATION on Scientific and Technological Innovation by MOST; National Natural Science Foundation of Guangdong Province, China(National Natural Science Foundation of Guangdong Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union(European Commission); Greek national funds through the Operational Program ?; Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation; This work was supported by Joint Research for INTERNATIONAL COOPERATION on Scientific and Technological Innovation by MOST (2017YFE0184900), National Natural Science Foundation of Guangdong Province, China (2021A1515010327), and the National Natural Science Foundation of China (52000078). This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation under the call Bilateral Scientific and Technological Cooperation between Greece and P. R. China (project code: T7?KI-00426). 39 0 0 15 15 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. DEC 15 2022.0 379 2 134818 10.1016/j.jclepro.2022.134818 0.0 OCT 2022 15 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 5Z4WR 2023-03-23 WOS:000879975700002 0
J Awan, U; Sroufe, R; Shahbaz, M Awan, Usama; Sroufe, Robert; Shahbaz, Muhammad Industry 4.0 and the circular economy: A literature review and recommendations for future research BUSINESS STRATEGY AND THE ENVIRONMENT English Review circular economy; Industry 4.0; internet of things; manufacturing firms; recycling; reuse; stakeholders SUSTAINABLE SUPPLY CHAIN; BIG DATA ANALYTICS; VALUE-APPROPRIATION; THINGS APPLICATIONS; VALUE-CREATION; SMART; TECHNOLOGIES; IMPLEMENTATION; INTERNET; MANAGEMENT Research regarding the Fourth Industrial Revolution (Industry 4.0) and the circular economy has gained traction since 2014. Paralleling the growth in the internet of things (IoT), the circular economy poses both risks and opportunities to various stakeholders in its development. This literature review aims to identify Industry 4.0 stakeholders' interests and expectations regarding how the IoT can be part of circular economy management. Contributions include identifying various IoT tools for dealing with circular economy challenges while also addressing implementation best practices. The transition of the circular economy within Industry 4.0 requires a better understanding of government, suppliers, international organizational interests, and expectations regarding the IoT. This study enables future research on circular economy practices and their potential sustainability benefits for manufacturing firms. This study's findings allow practitioners and researchers to understand the literature and critical elements of the transition toward a more circular economy. [Awan, Usama] Lappeenrant Lathi Univ Technol, Ind Engn & Management, Lappeenranta, Finland; [Sroufe, Robert] Duquesne Univ, Donahue Grad Sch Business, 820 Rockwell Hall,600 Forbes Ave, Pittsburgh, PA 15219 USA; [Shahbaz, Muhammad] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing, Peoples R China Duquesne University; Beijing Institute of Technology Sroufe, R (corresponding author), Duquesne Univ, Donahue Grad Sch Business, 820 Rockwell Hall,600 Forbes Ave, Pittsburgh, PA 15219 USA. sroufer@duq.edu Awan, Usama/K-5538-2013 Awan, Usama/0000-0002-6185-9594 140 127 127 78 348 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0964-4733 1099-0836 BUS STRATEG ENVIRON Bus. Strateg. Environ. MAY 2021.0 30 4 2038 2060 10.1002/bse.2731 0.0 JAN 2021 23 Business; Environmental Studies; Management Social Science Citation Index (SSCI) Business & Economics; Environmental Sciences & Ecology RW0FZ 2023-03-23 WOS:000606992400001 0
C Zhou, J; Peng, Y; Gui, G; Lin, Y; Adebisi, B; Gacanin, H; Sari, H IEEE Zhou, Jie; Peng, Yang; Gui, Guan; Lin, Yun; Adebisi, Bamidele; Gacanin, Haris; Sari, Hikmet A Novel Radio Frequency Fingerprint Identification Method Using Incremental Learning 2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL) IEEE Vehicular Technology Conference Proceedings English Proceedings Paper IEEE 96th Vehicular Technology Conference (VTC-Fall) SEP 26-29, 2022 London, ECUADOR IEEE Radio frequency fingerprint; heat constellation trace figure; physical layer security; incremental learning Radio frequency fingerprint (RFF) is regarded as a key technology in physical layer security in various wireless communications systems. Deep learning (DL) has achieved great success in the field of signal identification, particularly in improving performance and eliminating manual feature extraction. However, the training cost of these DL-based methods is usually large. It is unwise to retrain the network with whole data when it comes to new data. Therefore, we propose a novel RFF identification method based on incremental learning (IL), which uses continuous data stream to update the identification model, constantly. Experimental results show that with the increase of increment times, the accuracy of the proposed IL-based method gradually approaches the performance of joint training, and finally reaches 96.79%, which is only 1.9% lower than the performance upper bound. [Zhou, Jie; Peng, Yang; Gui, Guan; Sari, Hikmet] NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China; [Lin, Yun] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin, Peoples R China; [Adebisi, Bamidele] Manchester Metropolitan Univ, Dept Engn, Manchester, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, Aachen, Germany Nanjing University of Posts & Telecommunications; Harbin Engineering University; Manchester Metropolitan University; RWTH Aachen University Zhou, J (corresponding author), NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China. Adebisi, Bamidele/0000-0001-9071-9120 26 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2577-2465 978-1-6654-5468-1 IEEE VTS VEH TECHNOL 2022.0 10.1109/VTC2022-Fall57202.2022.10012703 0.0 5 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications; Transportation BU6QI 2023-03-23 WOS:000927580600011 0
J Huang, K; Chen, S; Li, BW; Claesen, L; Yao, H; Chen, JJ; Jiang, XW; Liu, ZL; Xiong, DL Huang, Kai; Chen, Siang; Li, Bowen; Claesen, Luc; Yao, Hao; Chen, Junjian; Jiang, Xiaowen; Liu, Zhili; Xiong, Dongliang Acceleration-Aware Fine-Grained Channel Pruning for Deep Neural Networks via Residual Gating IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS English Article Tensors; Computational modeling; Residual neural networks; Optimization; Logic gates; Biological neural networks; Task analysis; Deep learning system; model compression and acceleration; neural networks; pruning Deep neural networks have achieved remarkable advancement in various intelligence tasks. However, the massive computation and storage consumption limit applications on resource-constrained devices. While channel pruning has been widely applied to compress models, it is challenging to reach very deep compressions for such a coarse-grained pruning structure without significant performance degradation. In this article, we propose an acceleration-aware fine-grained channel pruning (AFCP) framework for accelerating neural networks, which optimizes trainable gate parameters by estimating residual errors between pruned and original channels with hardware characteristics. Our fine-grained concept consists of both algorithm and structure levels. Different from existing methods that leverage a predefined pruning criterion, AFCP explicitly considers both zero-out and similar criteria for each channel, and adaptively selects the suitable one via residual gate parameters. For structure level, AFCP adopts a fine-grained channel pruning strategy for residual neural networks and a decomposition-based structure, which further extends the pruning optimization space. Moreover, instead of using theoretical computation costs, such as floating-point operations, we propose the hardware predictor that bridges the gap between realistic acceleration and pruning procedure to guide the learning of pruning, which improves the efficiency of model pruning when deployed on accelerators. Extensive evaluation results demonstrate that AFCP outperforms state-of-the-art methods, and achieves a favorable balance between model performance and computation cost. [Huang, Kai; Chen, Siang; Li, Bowen; Jiang, Xiaowen; Xiong, Dongliang] Zhejiang Univ, Inst VLSI Design, Hangzhou 310030, Peoples R China; [Claesen, Luc] Univ Hasselt, Engn Techno Elect ICT Dept, B-3590 Diepenbeek, Belgium; [Yao, Hao; Chen, Junjian] CSG, Digital Grid Res Inst, Guangzhou 510670, Peoples R China; [Liu, Zhili] Sec Chip, Prod Ctr Dept, Hangzhou 310030, Peoples R China Zhejiang University; Hasselt University Xiong, DL (corresponding author), Zhejiang Univ, Inst VLSI Design, Hangzhou 310030, Peoples R China. huangk@zju.edu.cn; chensiang@zju.edu.cn; libw@zju.edu.cn; luc.claesen@uhasselt.be; yaohao@csg.cn; chenjj4@csg.cn; xiaowen_jiang@zju.edu.cn; liuzhili@sec-chip.com; xiongdl@zju.edu.cn Claesen, Luc/0000-0003-0405-6290; Li, Bowen/0000-0001-7525-9672 National Key Research and Development Program of China [2020YFB0906000, 2020YFB0906001] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2020YFB0906000 and Grant 2020YFB0906001. This article was recommended by Associate Editor H. Li. 57 2 2 8 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0070 1937-4151 IEEE T COMPUT AID D IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. JUN 2022.0 41 6 1902 1915 10.1109/TCAD.2021.3093835 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 1L9UF Green Submitted 2023-03-23 WOS:000799624800028 0
J Jiao, F; Huang, L; Song, RJ; Huang, HF Jiao, Feng; Huang, Lei; Song, Rongjia; Huang, Haifeng An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic SENSORS English Article daily bus passenger flow prediction; hybrid model; deep learning; COVID-19 TRAFFIC FLOW; DECOMPOSITION; INFORMATION The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people's travel and public transport companies' management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction. [Jiao, Feng; Huang, Lei; Song, Rongjia; Huang, Haifeng] Beijing Jiaotong Univ, Sch Econ & Management, Dept Informat Management, Beijing 100044, Peoples R China; [Song, Rongjia] Katholieke Univ Leuven, Dept Decis Sci & Informat Management, Fac Econ & Business, B-3000 Leuven, Belgium Beijing Jiaotong University; KU Leuven Huang, L (corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Dept Informat Management, Beijing 100044, Peoples R China. 15120625@bjtu.edu.cn; lhuang@bjtu.edu.cn; rjsong@bjtu.edu.cn; 17113151@bjtu.edu.cn huang, lei/GQP-8739-2022 Song, Rongjia/0000-0001-8594-5641 National Natural Science Foundation of China [52172311] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by National Natural Science Foundation of China, grant number 52172311. 41 13 13 16 67 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2021.0 21 17 5950 10.3390/s21175950 0.0 18 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation UO2OH 34502841.0 Green Accepted, gold 2023-03-23 WOS:000694538700001 0
C Shi, MJ; Yang, ZH; Xu, C; Chen, QJ IEEE Comp Soc Shi, Miaojing; Yang, Zhaohui; Xu, Chao; Chen, Qijun Revisiting Perspective Information for Efficient Crowd Counting 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 16-20, 2019 Long Beach, CA IEEE,CVF,IEEE Comp Soc Crowd counting is the task of estimating people numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions. A major challenge of this task lies in the perspective distortion, which results in drastic person scale change in an image. Density regression on the small person area is in general very hard. In this work, we propose a perspective-aware convolutional neural network (PACNN) for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image. Ground truth perspective maps are firstly generated for training;PACNN is then specifically designed to predict multi-scale perspective maps, and encode them as perspective-aware weighting layers in the network to adaptively combine the outputs of multi-scale density maps. The weights are learned at every pixel of the maps such that the final density combination is robust to the perspective distortion. We conduct extensive experiments on the ShanghaiTech, WorldExpo'10, UCF_CC_50,and UCSD datasets, and demonstrate the effectiveness and efficiency of PACNN over the state-of-the-art. [Shi, Miaojing] Univ Rennes, INRIA, CNRS, IRISA, Rennes, France; [Yang, Zhaohui; Xu, Chao] Peking Univ, Cooperat Medianet Innovat Ctr, Key Lab Machine Percept, Beijing, Peoples R China; [Chen, Qijun] Tongji Univ, Dept Control Sci & Engn, Shanghai, Peoples R China Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Rennes; Peking University; Tongji University Shi, MJ (corresponding author), Univ Rennes, INRIA, CNRS, IRISA, Rennes, France. NSFC [61876007, 61872012] NSFC(National Natural Science Foundation of China (NSFC)) This work was supported by NSFC 61828602 and 61733013. Zhaohui Yang and Chao Xu were supported by NSFC 61876007 and 61872012. We thank Dr. Yannis Avrithis for the discussion on perspective geometry and Dr. Holger Caesar for proofreading. 48 79 79 3 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1063-6919 978-1-7281-3293-8 PROC CVPR IEEE 2019.0 7271 7280 10.1109/CVPR.2019.00745 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP2IB Green Submitted 2023-03-23 WOS:000542649300074 0
J Li, CY; Xie, HB; Mengersen, K; Fan, XH; Da Xu, RY; Sisson, SA; Van Huffel, S Li, Caoyuan; Xie, Hong-Bo; Mengersen, Kerrie; Fan, Xuhui; Da Xu, Richard Yi; Sisson, Scott A.; Van Huffel, Sabine Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Dirichlet process; nonnegative matrix factorization; nonparametric Bayesian methods; Gaussian mixture model; variational Bayes BCI COMPETITION 2003; EEG Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process nonnegative matrix factorization (DPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. Moreover, the model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. We first test the model on synthetic data sets contaminated by Gaussian, sparse and mixed noise. We then apply it to extract muscle synergies from the electromyographic (EMG) signal and to select discriminative features for motor imagery single-trial electroencephalogram (EEG) classification. Experimental results demonstrate that DPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods. [Li, Caoyuan] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China; [Li, Caoyuan; Da Xu, Richard Yi] Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia; [Xie, Hong-Bo; Mengersen, Kerrie] Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, Brisbane, Qld 4001, Australia; [Fan, Xuhui; Sisson, Scott A.] Univ New South Wales, Sch Math & Stat, Sydney, NSW 2033, Australia; [Van Huffel, Sabine] Katholieke Univ Leuven, ESAT Stadius Div, Dept Elect Engn, B-3001 Leuven, Belgium Beijing Institute of Technology; University of Technology Sydney; Queensland University of Technology (QUT); University of New South Wales Sydney; KU Leuven Xie, HB (corresponding author), Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, Brisbane, Qld 4001, Australia. caoyuan.li@student.uts.edu.au; hongbo.xie@qut.edu.au; k.mengersen@qut.edu.au; xuhui.fan@unsw.edu.au; YiDa.Xu@uts.edu.au; scott.sisson@unsw.edu.au; sabine.VanHuffel@esat.kuleuven.be Fan, Xu/GSE-2196-2022; Mengersen, Kerrie/AHC-4096-2022; Van Huffel, Sabine/T-2282-2017 Van Huffel, Sabine/0000-0001-5939-0996; Xu, Richard Yi Da/0000-0003-2080-4762; Sisson, Scott/0000-0001-8943-067X; Fan, Xuhui/0000-0002-7558-7200; XIE, HONGBO/0000-0002-7739-3047 Australian Research Council (ARC) Laureate Program [FL150100150, CE140100049, DP160102544, FT170100079]; Centre of Excellence and Discovery Project Schemes [FL150100150, CE140100049, DP160102544, FT170100079]; Australian Research Council [FT170100079] Funding Source: Australian Research Council Australian Research Council (ARC) Laureate Program(Australian Research Council); Centre of Excellence and Discovery Project Schemes; Australian Research Council(Australian Research Council) This work was supported in part by the Australian Research Council (ARC) Laureate Program and in part by Centre of Excellence and Discovery Project Schemes. under Grants FL150100150, CE140100049, DP160102544, and FT170100079. 45 3 3 3 38 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2020.0 68 3860 3870 10.1109/TSP.2020.3003120 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering MF6IW 2023-03-23 WOS:000545444500012 0
J Sun, RB; Yang, ZB; Yang, LD; Qiao, BJ; Chen, XF; Gryllias, K Sun, Ruo-Bin; Yang, Zhi-Bo; Yang, Liang-Dong; Qiao, Bai-Jie; Chen, Xue-Feng; Gryllias, Konstantinos Planetary gearbox spectral modeling based on the hybrid method of dynamics and LSTM MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Planetary gearbox fault diagnosis; Spectral modeling; Long short-term memory network; Power spectral density estimation CONVOLUTIONAL NEURAL-NETWORK; FAULT-DIAGNOSIS; MESH STIFFNESS; MACHINERY; TRANSFORM; FREQUENCY; VIBRATION; FRICTION; SYSTEMS It is the prerequisite for mechanical diagnosis to clarify the mapping relations between vibrations and damage conditions based on the dynamic model analyses and the systematic experiments. However, there is a certain deviation from the actual measurements in the existing planetary dynamic modeling methods which utilizes linear models or just considering simple non-linear factors. To solve the problem, the hybrid spectral modeling method based on the fault dynamics analysis and the long short-term memory (LSTM) network is proposed in this paper. Firstly, the response of several parts in planetary sets are simulated by the classical dynamic method. Then, as an exploratory attempt, an LSTM network is used to rectify the predicted spectral deviation caused by the over-simplification and parameter errors of the dynamic models. From the inputs of simulated spectral sequences of the several components, the LSTM successfully predicts the spectral sequence of the observation. Finally, by the overlapping weighted average, the power spectral density of the planetary system is estimated through the Welch method. The effectiveness and superiority of the proposed method are verified via the experimental data of a planetary gearbox test rig. (C) 2019 Elsevier Ltd. All rights reserved. [Sun, Ruo-Bin; Yang, Zhi-Bo; Yang, Liang-Dong; Qiao, Bai-Jie; Chen, Xue-Feng] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China; [Yang, Zhi-Bo; Yang, Liang-Dong; Qiao, Bai-Jie; Chen, Xue-Feng] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China; [Sun, Ruo-Bin; Gryllias, Konstantinos] Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300, B-3001 Leuven, Belgium; [Gryllias, Konstantinos] Flanders Make, Dynam Mech & Mechatron Syst, Lommel, Belgium Xi'an Jiaotong University; Xi'an Jiaotong University; KU Leuven Yang, ZB (corresponding author), Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China.;Yang, ZB (corresponding author), Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China. phdapple@mail.xjtu.edu.cn; konstantinos.gryllias@kuleuven.be Qiao, Baijie/HGB-4427-2022; Gryllias, Konstantinos/F-4989-2017; Qiao, Baijie/AAD-1099-2019 Qiao, Baijie/0000-0003-1883-0523; Qiao, Baijie/0000-0003-1883-0523; Gryllias, Konstantinos/0000-0002-8703-8938; Sun, Ruo-Bin/0000-0002-9128-9035; Yang, Liangdong/0000-0002-8669-7966 National Natural Science Foundation of China [51875433]; Young Talent fund of University Association for Science and Technology in Shaanxi of China [20170502]; Key Research and Development Program of Shanxi Province [2017ZDCXL-GY-02-01, 2017ZDCXL-GY-02-02]; Fundamental Research Funds for the Central Universities; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Young Talent fund of University Association for Science and Technology in Shaanxi of China; Key Research and Development Program of Shanxi Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) We thank the supports given by the National Natural Science Foundation of China (No. 51875433), the Young Talent fund of University Association for Science and Technology in Shaanxi of China (No. 20170502), the Key Research and Development Program of Shanxi Province (Nos. 2017ZDCXL-GY-02-01 & 2017ZDCXL-GY-02-02), the Fundamental Research Funds for the Central Universities, and the China Scholarship Council. 33 16 16 6 46 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. APR 2020.0 138 106611 10.1016/j.ymssp.2019.106611 0.0 13 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering KR8HE 2023-03-23 WOS:000517855800043 0
J Li, SZ; Wang, HP; Tian, Y; Christov, N; Aitouche, A Li, Shanzhi; Wang, Haoping; Tian, Yang; Christov, Nicolai; Aitouche, Abdel NNPID-based Stator Voltage Oriented Vector Control for DFIG based Wind Turbine Systems STUDIES IN INFORMATICS AND CONTROL English Article wind turbine system; maximum power point tracking; DFIG; NNPID SPEED; SIMULATION Doubly fed induction generators are widely adopted for the wind turbine systems since it is cheap and reliable. Based on the traditional stator voltage oriented vector control method, the Performance of the proposed vector control is largely influenced by the variations of the DFIG parameters. And the classical PID algorithm cannot achieve the maximum power point tracking (MPPT) in time (owing to the transient wind). Hence, in this paper, to eliminate parameters variations on the power output and capture the MPPT rapidly, we propose a stator voltage oriented vector control which is based on a Neural Network PID (NNPID) technology. The weights which are being similar to the PID coefficients are adapted by Hebb rule to decrease the power error online according to the error gradient descent method, while the classical PID coefficients will be a constant. The effectiveness of the proposed method is demonstrated by corresponding simulation results: even in the case of wind mutation change, the proposed NNPID can track the variation of the wind energy, and robust to the DFIG parameters variations. [Li, Shanzhi; Wang, Haoping; Tian, Yang] NUST, Sino French Int Joint Lab Automat Control & Signa, Nanjing 210094, Jiangsu, Peoples R China; [Christov, Nicolai; Aitouche, Abdel] Univ Lille Nord France, LaFCAS, CNRS, UMR 8219,LAGIS, F-59600 Lille, France Centre National de la Recherche Scientifique (CNRS); Universite de Lille - ISITE; Centrale Lille; Universite de Lille Wang, HP (corresponding author), NUST, Sino French Int Joint Lab Automat Control & Signa, Nanjing 210094, Jiangsu, Peoples R China. Christov, Nicolai/B-7135-2008; Wang, Haoping/A-5455-2013; Popescu, Dumitru/E-6433-2019 Christov, Nicolai/0000-0001-8910-7274; Wang, Haoping/0000-0003-1888-1849; Popescu, Dumitru/0000-0001-6261-628X National Natural Science Foundation of China [61304077, 61203115]; Natural Science Foundation of Jiangsu Province [BK20130765]; Specialized Research Fund for the Doctoral Program of Higher Education of China [20123219120038]; Chinese Ministry of Education Project of Humanities and Social Sciences [13YJCZH171]; Fundamental Research Funds for the Central Universities [30920130111014]; Zijin Intelligent Program of Nanjing University of Science and Technology [2013_ZJ_0105] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Specialized Research Fund for the Doctoral Program of Higher Education of China(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); Chinese Ministry of Education Project of Humanities and Social Sciences; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Zijin Intelligent Program of Nanjing University of Science and Technology This work was partially supported by the National Natural Science Foundation of China (61304077, 61203115), by the Natural Science Foundation of Jiangsu Province (BK20130765), by the Specialized Research Fund for the Doctoral Program of Higher Education of China (20123219120038), by the Chinese Ministry of Education Project of Humanities and Social Sciences (13YJCZH171), by the Fundamental Research Funds for the Central Universities (30920130111014), and by the Zijin Intelligent Program of Nanjing University of Science and Technology (2013_ZJ_0105). 20 5 5 0 14 NATL INST R&D INFORMATICS-ICI BUCHAREST PUBL DEPT, 8-10 AVERESCU BLVD, SECTOR 1, BUCHAREST, 011455, ROMANIA 1220-1766 STUD INFORM CONTROL Stud. Inform. Control MAR 2014.0 23 1 5 12 8 Automation & Control Systems; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Operations Research & Management Science AE0MX 2023-03-23 WOS:000333661500001 0
J Wang, XM; Shi, JH; Fujita, H; Zhao, YL Wang, Xingmei; Shi, Jiahao; Fujita, Hamido; Zhao, Yilin Aggregate attention module for fine-grained image classification JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING English Article; Early Access Deep learning; Fine-grained image classification; Attention mechanism; Loss function According to huge intra-class diversity and inter-class differences, fine-grained image classification has been a difficult topic for a long time. Attention mechanism has proven to be useful to aggregate features and discover discriminative local details. However, one issue is that the increasing parameters lead to unnecessary computation. In this paper, an attention mechanism named aggregate attention module is proposed to classify fine-grained images accurately with fewer parameters. Specifically, to balance the trade-off between performance and complexity, the proposed attention module combines channel attention with spatial attention in parallel, which effectively learns the key features and can be extended to other neural models easily. Meanwhile, we design cross-channel loss to explore discriminative fine-grained categories. Compared with state-of-the-art models, experiments reveal that our proposed model can achieve superior accuracy using different fine-grained image benchmarks (CUB-200-2011, FGVC Aircraft and Stanford Cars). To verify the effectiveness, we further evaluate our method through ablation study and visualization. [Wang, Xingmei; Shi, Jiahao; Zhao, Yilin] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China; [Fujita, Hamido] Ho Chi Minh City Univ Technol HUTECH, Fac Informat Technol, Ho Chi Minh City, Vietnam; [Fujita, Hamido] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain; [Fujita, Hamido] Harbin Engn Univ, Coll Math Sci, Harbin 150001, Peoples R China Harbin Engineering University; Ho Chi Minh City University of Technology (HUTECH); Vietnam National University Hochiminh City; University of Granada; Harbin Engineering University Fujita, H (corresponding author), Ho Chi Minh City Univ Technol HUTECH, Fac Informat Technol, Ho Chi Minh City, Vietnam.;Fujita, H (corresponding author), Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain.;Fujita, H (corresponding author), Harbin Engn Univ, Coll Math Sci, Harbin 150001, Peoples R China. h.fujita@hutech.edu.vn Fujita, Hamido/D-6249-2012 Fujita, Hamido/0000-0001-5256-210X National Natural Science Foundation of China [41876110] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China (Grant No. 41876110). 41 0 0 3 20 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-5137 1868-5145 J AMB INTEL HUM COMP J. Ambient Intell. Humaniz. Comput. 10.1007/s12652-021-03599-7 0.0 NOV 2021 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications XA5RJ 2023-03-23 WOS:000720703400003 0
C Li, Z; Guo, J; Huang, DQ IEEE Li, Zhan; Guo, Jing; Huang, Deqing Bidirectional joint torque prediction with EMG of multiple channels: both agonist and antagonist muscles are necessary PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) Chinese Control and Decision Conference English Proceedings Paper 28th Chinese Control and Decision Conference MAY 28-30, 2016 Yinchuan, PEOPLES R CHINA NE Univ, State Key Lab Synthet Automat Proc Ind,IEEE Ind Elect Chapter,Beifang Univ Nationalities,IEEE Control Syst Soc,Syst Engn Soc China,Chinese Assoc Artificial Intelligence,Chinese Assoc Artificial Intelligence, Tech Comm Control Theory Torque prediction; electromyography (EMG); agonist and antagonist muscles NEURAL-NETWORK; ACTIVATIONS; REGRESSION; MODEL In numerous rehabilitation applications, joint motion is frequently required to be estimated through human muscle activity for motor control paradigm to impaired patients. Predicting joint torque in different degrees of freedom involves measurement of voluntary electromyography (EMG) on multiple muscle groups. From viewpoint of kinesiological discipline, agonist and antagonist muscle groups are respectively in charge of joint movement in different flexion/ rotation directions. As a result, modeling and estimation of bidirectional joint motion follows to measure EMG signals of both agonist and antagonist muscles. Due to high possibility of drop off of EMG electrodes during motion, acquisition of some EMG channels may be missed and predicting joint torque with rest EMG channels occurs. This paper discusses the bidirectional joint torque prediction issue with complete and incomplete channels of EMG signals. The prediction results indicate that, EMG of both agonist and antagonist muscles are necessary to fulfill a promising bidirectional torque prediction, otherwise the prediction would be much degraded owing to lack of EMG on either agonist or antagonist muscle group. [Li, Zhan] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China; [Guo, Jing] Univ Montpellier, LIRMM, Robot Dept, F-34090 Montpellier, France; [Huang, Deqing] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China University of Electronic Science & Technology of China; Centre National de la Recherche Scientifique (CNRS); Universite Paul-Valery; Universite Perpignan Via Domitia; Universite de Montpellier; Southwest Jiaotong University Li, Z (corresponding author), Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China. zhan.li@uestc.edu.cn; jing.guo@lirmm.fr; elehd2012@gmail.com 18 2 2 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1948-9439 978-1-4673-9714-8 CHIN CONT DECIS CONF 2016.0 4226 4231 6 Automation & Control Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering BF6JW 2023-03-23 WOS:000383222304087 0
J Wang, SC; Qiang, Q; Xiang, LJ; Fernie, AR; Yang, J Wang, Shouchuang; Qiang, Qi; Xiang, Lijun; Fernie, Alisdair R.; Yang, Jun Targeted approaches to improve tomato fruit taste HORTICULTURE RESEARCH English Review STEROIDAL GLYCOALKALOID BIOSYNTHESIS; GENOME-WIDE ASSOCIATION; FLAVOR VOLATILES; BRANCHED-CHAIN; SOLANUM-LYCOPERSICON; ORGANIC-ACIDS; METABOLOMICS; LOCI; IDENTIFICATION; DOMESTICATION Tomato (Solanum lycopersicum) is the most valuable fruit and horticultural crop species worldwide. Compared with the fruits of their progenitors, those of modern tomato cultivars are, however, often described as having unsatisfactory taste or lacking flavor. The flavor of a tomato fruit arises from a complex mix of tastes and volatile metabolites, including sugars, acids, amino acids, and various volatiles. However, considerable differences in fruit flavor occur among tomato varieties, resulting in mixed consumer experiences. While tomato breeding has traditionally been driven by the desire for continual increases in yield and the introduction of traits that provide a long shelf-life, consumers are prepared to pay a reasonable premium for taste. Therefore, it is necessary to characterize preferences of tomato flavor and to define its underlying genetic basis. Here, we review recent conceptual and technological advances that have rendered this more feasible, including multi-omics-based QTL and association analyses, along with the use of trained testing panels, and machine learning approaches. This review proposes how the comprehensive datasets compiled to date could allow a precise rational design of tomato germplasm resources with improved organoleptic quality for the future. [Wang, Shouchuang; Yang, Jun] Hainan Univ, Sanya Nanfan Res Inst, Hainan Yazhou Bay Seed Lab, Sanya, Peoples R China; [Wang, Shouchuang; Qiang, Qi; Xiang, Lijun; Yang, Jun] Hainan Univ, Coll Trop Crops, Haikou 570228, Peoples R China; [Fernie, Alisdair R.] Max Planck Inst Mol Plant Physiol, D-14476 Potsdam, Germany Hainan University; Hainan University; Max Planck Society Wang, SC; Yang, J (corresponding author), Hainan Univ, Sanya Nanfan Res Inst, Hainan Yazhou Bay Seed Lab, Sanya, Peoples R China.;Wang, SC; Yang, J (corresponding author), Hainan Univ, Coll Trop Crops, Haikou 570228, Peoples R China.;Fernie, AR (corresponding author), Max Planck Inst Mol Plant Physiol, D-14476 Potsdam, Germany. shouchuang.wang@hainanu.edu.cn; fernie@mpimp-golm.mpg.de; yang9yj@hainanu.edu.cn Wang, Shouchuang/HLW-0824-2023 Wang, Shouchuang/0000-0002-5568-816X Hainan Province Science and Technology Special Fund [ZDYF2022XDNY144]; National Natural Science Foundation of China [32100212]; National Key R&D Program of China [2021YFA0909600]; Young Elite Scientists Sponsorship Program by CAST [2019QNRC001]; Hainan Provincial Academician Innovation Platform Project [HD-YSZX-202004]; Hainan University Startup Fund [KYQD (ZR) 1916, KYQD (ZR) 21025]; Innovation Project of Postgraduates of Hainan Province [Qhys2021-171] Hainan Province Science and Technology Special Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Young Elite Scientists Sponsorship Program by CAST; Hainan Provincial Academician Innovation Platform Project; Hainan University Startup Fund; Innovation Project of Postgraduates of Hainan Province We apologize to those authors whose work could not be discussed due to space limitations. This work was supported by the Hainan Province Science and Technology Special Fund(ZDYF2022XDNY144), the National Natural Science Foundation of China (No. 32100212), the National Key R & D Program of China (2021YFA0909600), the Young Elite Scientists Sponsorship Program by CAST (No. 2019QNRC001), the Hainan Provincial Academician Innovation Platform Project (No. HD-YSZX-202004),the Hainan University Startup Fund [Nos. KYQD (ZR) 1916 and KYQD (ZR) 21025] and the Innovation Project of Postgraduates of Hainan Province (No. Qhys2021-171). 116 0 0 6 6 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 2662-6810 2052-7276 HORTIC RES-ENGLAND Hortic. Res.-England JAN 4 2023.0 10 1 10.1093/hr/uhac229 0.0 12 Plant Sciences; Genetics & Heredity; Horticulture Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences; Genetics & Heredity; Agriculture 7W4GP 36643745.0 gold 2023-03-23 WOS:000913472300015 0
J Chen, BB; Gao, Y; Guo, YB; Liu, YZ; Zhao, HH; Liao, HJ; Wang, L; Xiang, T; Li, W; Xie, ZY Chen, Bin-Bin; Gao, Yuan; Guo, Yi-Bin; Liu, Yuzhi; Zhao, Hui-Hai; Liao, Hai-Jun; Wang, Lei; Xiang, Tao; Li, Wei; Xie, Z. Y. Automatic differentiation for second renormalization of tensor networks PHYSICAL REVIEW B English Article Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a uniform framework of differentiable TRG (partial derivative TRG) that can be applied to improve various TRG methods, in an automatic fashion. partial derivative TRG systematically extends the essential concept of second renormalization [Phys. Rev. Lett. 103. 160601 (2009)] where the tensor environment is computed recursively in the backward iteration. Given the forward TRG process, partial derivative TRG automatically finds the gradient of local tensors through backpropagation, with which one can deeply train the tensor networks. We benchmark partial derivative TRG in solving the square-lattice Ising model, and we demonstrate its power by simulating one- and two-dimensional quantum systems at finite temperature. The global optimization as well as GPU acceleration renders partial derivative TRG a highly efficient and accurate many-body computation approach. [Chen, Bin-Bin; Gao, Yuan; Li, Wei] Beihang Univ, Sch Phys, Minist Educ, Key Lab Micronano Measurement Manipulat & Phys, Beijing 100191, Peoples R China; [Chen, Bin-Bin] Ludwig Maximilians Unive, Arnold Sommerfeld Ctr Theoret Phys, Phys Dept, Theresienstr 37, D-80333 Munich, Germany; [Chen, Bin-Bin] Ludwig Maximilians Unive, Ctr NanoSci, Theresienstr 37, D-80333 Munich, Germany; [Guo, Yi-Bin; Liao, Hai-Jun; Wang, Lei; Xiang, Tao] Chinese Acad Sci, Inst Phys, POB 603, Beijing 100190, Peoples R China; [Guo, Yi-Bin; Xiang, Tao] Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China; [Liu, Yuzhi] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Zhao, Hui-Hai] Alibaba Grp, Alibaba Quantum Lab, Beijing, Peoples R China; [Liao, Hai-Jun; Wang, Lei] Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China; [Xiang, Tao] Univ Chinese Acad Sci, Kavli Inst Theoret Sci, Beijing 100190, Peoples R China; [Xie, Z. Y.] Renmin Univ China, Dept Phys, Beijing 100872, Peoples R China Beihang University; University of Munich; University of Munich; Chinese Academy of Sciences; Institute of Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Indiana University System; Indiana University Bloomington; Alibaba Group; Songshan Lake Materials Laboratory; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Renmin University of China Li, W (corresponding author), Beihang Univ, Sch Phys, Minist Educ, Key Lab Micronano Measurement Manipulat & Phys, Beijing 100191, Peoples R China. w.li@buaa.edu.cn; qingtaoxie@ruc.edu.cn Liao, Hai-jun/ABG-8583-2021; Xiang, Tao/C-8899-2011; Xie, Zhiyuan/AAG-7197-2021 Xiang, Tao/0000-0001-5998-7338; Xie, Zhiyuan/0000-0002-7756-3552; Zhao, Hui-Hai/0000-0001-7075-8325; gao, yuan/0000-0002-6960-3171; Li, Wei/0000-0003-0474-9337; Chen, Bin-Bin/0000-0001-8896-6173; Guo, YiBin/0000-0003-0435-1476 National Natural Science Foundation of China [11774420, 11834014, 11974036, 11774398]; National R&D Program of China [2016YFA0300503, 2017YFA0302900]; German Research Foundation under Germany's Excellence Strategy [DFG WE4819/3-1, EXC-2111 - 390814868]; Renmin University of China [20XNLG19] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National R&D Program of China; German Research Foundation under Germany's Excellence Strategy; Renmin University of China We are indebted to Jian Cui, Jan von Delft, Yannick Meurice, and AndreasWeichselbaum for helpful discussions. This work was supported by the National Natural Science Foundation of China (Grants No. 11774420, No. 11834014, No. 11974036, and No. 11774398), the National R&D Program of China (Grants No. 2016YFA0300503 and No. 2017YFA0302900), German Research Foundation (DFG WE4819/3-1) under Germany's Excellence Strategy - EXC-2111 - 390814868 and by the Research Funds of Renmin University of China (Grants No. 20XNLG19). Our code implementation in PyTorch is publicly available [33]. 52 13 13 7 26 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9950 2469-9969 PHYS REV B Phys. Rev. B JUN 23 2020.0 101 22 220409 10.1103/PhysRevB.101.220409 0.0 6 Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Physics MA6VS Green Submitted 2023-03-23 WOS:000542052700001 0
J Chen, HY; Zhang, ZB; Karamanakos, P; Rodriguez, J Chen, Haoyu; Zhang, Zhenbin; Karamanakos, Petros; Rodriguez, Jose Digital Twin Techniques for Power Electronics-Based Energy Conversion Systems: A Survey of Concepts, Application Scenarios, Future Challenges, and Trends IEEE INDUSTRIAL ELECTRONICS MAGAZINE English Article; Early Access Data models; Solid modeling; Real-time systems; Maintenance engineering; Analytical models; Software; Sensors BIG DATA; MODEL The steady increase in energy demands has led to ever-increasing energy generation This, coupled with the need for higher efficiency, flexibility, and reliability, has boosted the use of power electronics in power and energy systems. Therefore, power electronics-based energy conversion systems (PEECSs) have become prominent in power generation, power transmission, and end user applications. Given the relevance of such systems, and by considering their trend of digitalization, it is crucial to establish digital and intelligent PEECSs. To this end, digital twins (DTs) can be adopted, as they integrate many cuttingedge information techniques to realize the life cycle management of complex systems by constructing real-time mappings of them. In this article, existing DT techniques for PEECSs are reviewed. The concept, system layers, and key technologies of DTs are described first. Some application cases of DTs are then elaborated. Finally, future trends and challenges of DTs are discussed to provide a valuable reference for subsequent research. [Chen, Haoyu] Shandong Univ, Sch Elect Engn, Lab More Power Elect Energy Syst, Jinan 250000, Peoples R China; [Zhang, Zhenbin] Shandong Univ, Jinan 250000, Peoples R China; [Karamanakos, Petros] Tampere Univ, Fac Informat Technol & Commun Sci, Tampere 33101, Finland; [Rodriguez, Jose] Univ San Sebastian, Concepcion 8420524, Chile Shandong University; Shandong University; Tampere University; Universidad San Sebastian Chen, HY (corresponding author), Shandong Univ, Sch Elect Engn, Lab More Power Elect Energy Syst, Jinan 250000, Peoples R China. haoyu.chen@mail.sdu.edu.cn; zbz@sdu.edu.cn; p.karamanakos@ieee.org; jose.rodriguez@unab.cl Chen, Haoyu/HJA-4950-2022 Chen, Haoyu/0000-0002-3041-6555 75 0 0 7 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4529 1941-0115 IEEE IND ELECTRON M IEEE Ind. Electron. Mag. 10.1109/MIE.2022.3216719 0.0 NOV 2022 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 6J5KU 2023-03-23 WOS:000886862400001 0
J Mou, NX; Zheng, YH; Makkonen, T; Yang, TF; Tang, JW; Song, Y Mou, Naixia; Zheng, Yunhao; Makkonen, Teemu; Yang, Tengfei; Tang, Jinwen (Jimmy); Song, Yan Tourists' digital footprint: The spatial patterns of tourist flows in Qingdao, China TOURISM MANAGEMENT English Article Tourist flow; Spatial pattern; Digital footprint; Qingdao; Online travel diary; Social network analysis SOCIAL NETWORK ANALYSIS; MOVEMENT PATTERNS; TRACKING DATA; BIG DATA; DESTINATION; BEHAVIOR; ONLINE; REPRESENTATION; IMAGE Spatial patterns of tourist flows represent the movement of tourists and show differences in tourism resources giving advice for promoting balanced and sustainable tourism development. This paper proposes a novel framework for analyzing these patterns based on tourists' digital footprint data collected from online travel diaries. Based on illustrative case study data from Qingdao (China), the framework, combining traditional quantitative and social network analysis, is able to pinpoint: (1) The influence of distance decay and attractions' popularity on the spatial patterns of tourist flows; (2) The uneven distribution of the core tourist nodes and the existence of the structural hole phenomenon, which form a network pattern with unbalanced power and intense internal competition; (3) The formation of the core area for tourism along the coastline - as is typical for coastal tourism cities. This difference of tourism resources between coastal and inland areas, thus, remains a challenge for future tourism development in Qingdao. [Mou, Naixia; Zheng, Yunhao] Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China; [Makkonen, Teemu] Univ Eastern Finland, Karelian Inst, FI-80101 Joensuu, Finland; [Yang, Tengfei] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Tang, Jinwen (Jimmy)] South China Univ Technol, Sch Econ & Commerce, Guangzhou 510006, Peoples R China; [Tang, Jinwen (Jimmy)] Univ Surrey, Sch Hospitality & Tourism Management, Guildford GU2 7XH, Surrey, England; [Song, Yan] Shandong Univ Sci & Technol, Coll Foreign Languages, Qingdao 266590, Peoples R China Shandong University of Science & Technology; University of Eastern Finland; Chinese Academy of Sciences; South China University of Technology; University of Surrey; Shandong University of Science & Technology Mou, NX; Zheng, YH (corresponding author), Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China. mounx@lreis.ac.cn; zhengyh1996@126.com MOU, Naixia/AAA-9625-2022; TANG, Jimmy/AAE-4377-2022 MOU, Naixia/0000-0003-1700-5943; TANG, Jimmy/0000-0003-2563-1004; ZHENG, Yunhao/0000-0001-5156-0719; Makkonen, Teemu/0000-0002-1065-1806 National Natural Science Foundation of China [41771476] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported in part by the National Natural Science Foundation of China (No. 41771476). 50 50 54 35 234 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0261-5177 1879-3193 TOURISM MANAGE Tourism Manage. DEC 2020.0 81 104151 10.1016/j.tourman.2020.104151 0.0 18 Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Social Sciences - Other Topics; Business & Economics MM4UO 2023-03-23 WOS:000550153800015 0
C Khan, KN; Hoque, MA; Niemi, T; Ou, ZH; Nurminen, JK Assoc Comp Machinery Khan, Kashif Nizam; Hoque, Mohammad Ashraful; Niemi, Tapio; Ou, Zhonghong; Nurminen, Jukka K. Energy Efficiency of Large Scale Graph Processing Platforms UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING English Proceedings Paper ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / 20th ACM International Symposium on Wearable Computers (ISWC) SEP 12-16, 2016 Heidelberg, GERMANY Assoc Comp Machinery,ACM SIGCHI,ACM SIGMOBILE Big Data; Energy efficiency; Distributed Computing; Graph Processing; GraphX; Giraph; RAPL; Hadoop; Spark A number of graph processing platforms have emerged recently as a result of the growing demand on graph data analytics with complex and large-scale graph structured datasets. These platforms have been tailored for iterative graph computations and can offer an order of magnitude performance gain over generic data-flow frameworks like Apache Hadoop and Spark. Nevertheless, the increasing availability of such platforms and their functionality overlap necessitates a comparative study on various aspects of the platforms. including applications, performance and energy efficiency. In this work, we focus on the energy efficiency aspect of some large scale graph processing platforms. Specifically, we select two representatives, e.g., Apache Giraph and Spark GraphX, for the comparative study. We compare and analyze the energy consumption of these two platforms with PageRank, Strongly Connected Component and Single Source Shortest Path algorithms over five different realistic graphs. Our experimental results demonstrate that GraphX outperforms Giraph in terms of energy consumption. Specifically, Giraph consumes 1.71 times more energy than GraphX on average for the mentioned algorithms. [Khan, Kashif Nizam; Niemi, Tapio; Nurminen, Jukka K.] Aalto Univ, Helsinki Inst Phys, Helsinki, Finland; [Khan, Kashif Nizam] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Hoque, Mohammad Ashraful] Univ Helsinki, Helsinki, Finland; [Niemi, Tapio] CERN, Geneva, Switzerland; [Ou, Zhonghong] Beijing Univ Posts & Telecommun, Beijing, Peoples R China Aalto University; Helsinki Institute of Physics; Aalto University; University of Helsinki; European Organization for Nuclear Research (CERN); Beijing University of Posts & Telecommunications Khan, KN (corresponding author), Aalto Univ, Helsinki Inst Phys, Helsinki, Finland.;Khan, KN (corresponding author), Aalto Univ, Dept Comp Sci, Espoo, Finland. kashif.khan@aalto.fi; mohoque@cs.helsinki.fi; tapio.niemi@cern.ch; zhonghong.ou@bupt.edu.cn; jukka.k.nurminen@aalto.fi Nurminen, Jukka/0000-0001-5083-1927 21 2 2 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-4462-3 2016.0 1287 1294 10.1145/2968219.2968296 0.0 8 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM8JE 2023-03-23 WOS:000469272600247 0
C Chen, ZH; Xu, C; Soto, J; Markl, V; Qian, WN; Zhou, AY ASSOC COMP MACHINERY Chen, Zihao; Xu, Chen; Soto, Juan; Markl, Volker; Qian, Weining; Zhou, Aoying Hybrid Evaluation for Distributed Iterative Matrix Computation SIGMOD '21: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA International Conference on Management of Data English Proceedings Paper ACM SIGMOD International Conference on Management of Data (SIGMOD) JUN 20-25, 2021 ELECTR NETWORK ACM SIGMOD,Assoc Comp Machinery Matrix Computation; Hybrid Evaluation; Iteration Distributed matrix computation is common in large-scale data processing and machine learning applications. Existing systems that support distributed matrix computation already explore incremental evaluation for iterative-convergent algorithms. However, they are oblivious to the fact that non-zero increments are scattered in different blocks in a distributed environment. Additionally, we observe that incremental evaluation does not always outperform full evaluation. To address these issues, we propose matrix reorganization to optimize the physical layout upon the state-of-art optimized partition schemes, and thereby accelerate the incremental evaluation. More importantly, we propose a hybrid evaluation to efficiently interleave full and incremental evaluation during the iterative process. In particular, it employs a cost model to compare the overhead costs of two types of evaluations and a selective comparison mechanism to reduce the overhead incurred by comparison itself. To demonstrate the efficiency of our techniques, we implement HyMAC, a hybrid matrix computation system based on SystemML. Our experiments show that HyMAC reduces execution time on large datasets by 23% on average in comparison to the state-of-art optimization technique and consequently outperforms SystemML, ScaLAPACK, and SciDB by an order of magnitude. [Chen, Zihao; Xu, Chen; Qian, Weining; Zhou, Aoying] East China Normal Univ, DaSE, Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China; [Soto, Juan; Markl, Volker] Tech Univ Berlin, DIMA, Berlin, Germany East China Normal University; Technical University of Berlin Xu, C (corresponding author), East China Normal Univ, DaSE, Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China. zhchen@stu.ecnu.edu.cn; cxu@dase.ecnu.edu.cn; juan.soto@tu-berlin.de; volker.markl@tu-berlin.de; wnqian@dase.ecnu.edu.cn; ayzhou@dase.ecnu.edu.cn Chen, zihao/HGE-3792-2022 Soto, Juan/0000-0002-5821-4533 National Key Research and Development Program of China [2018YFB1003400]; National Natural Science Foundation of China [61902128]; Shanghai Sailing Program [19YF1414200]; German Federal Ministry of Education and Research (BMBF) [01IS18025A] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Sailing Program; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) This work was supported by the National Key Research and Development Program of China (No. 2018YFB1003400), National Natural Science Foundation of China (No. 61902128), Shanghai Sailing Program (No. 19YF1414200), and the German Federal Ministry of Education and Research (BMBF) as BIFOLD (funding mark 01IS18025A). 30 2 2 1 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 0730-8078 978-1-4503-8343-1 INT CONF MANAGE DATA 2021.0 300 312 10.1145/3448016.3452843 0.0 13 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6FP 2023-03-23 WOS:000747673800029 0
J Kong, C; Gao, M; Xu, C; Fu, YB; Qian, WN; Zhou, AY Kong, Chao; Gao, Ming; Xu, Chen; Fu, Yunbin; Qian, Weining; Zhou, Aoying EnAli: entity alignment across multiple heterogeneous data sources FRONTIERS OF COMPUTER SCIENCE English Article entity alignment; exponential family; locality sensitive hashing; EM-algorithm RECORD LINKAGE; RESOLUTION; RULES Entity alignment is the problem of identifying which entities in a data source refer to the same real-world entity in the others. Identifying entities across heterogeneous data sources is paramount to many research fields, such as data cleaning, data integration, information retrieval and machine learning. The aligning process is not only overwhelmingly expensive for large data sources since it involves all tuples from two or more data sources, but also need to handle heterogeneous entity attributes. In this paper, we propose an unsupervised approach, called EnAli, to match entities across two or more heterogeneous data sources. EnAli employs a generative probabilistic model to incorporate the heterogeneous entity attributes via employing exponential family, handle missing values, and also utilize the locality sensitive hashing schema to reduce the candidate tuples and speed up the aligning process. EnAli is highly accurate and efficient even without any ground-truth tuples. We illustrate the performance of EnAli on re-identifying entities from the same data source, as well as aligning entities across three real data sources. Our experimental results manifest that our proposed approach outperforms the comparable baseline. [Kong, Chao; Gao, Ming; Fu, Yunbin; Qian, Weining; Zhou, Aoying] East China Normal Univ, Sch Data Sci & Engn, Shanghai 200062, Peoples R China; [Xu, Chen] Tech Univ Berlin, D-10623 Berlin, Germany East China Normal University; Technical University of Berlin Gao, M (corresponding author), East China Normal Univ, Sch Data Sci & Engn, Shanghai 200062, Peoples R China. mgao@sei.ecnu.edu.cn Kong, Chao/0000-0002-4671-9825 National Key Research and Development Program of China [2016YFB1000905]; National Natural Science Foundation of China [U1401256, 61402177, 61672234, 61402180, 61232002]; NSF of Shanghai [14ZR1412600] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NSF of Shanghai This work has been supported by the National Key Research and Development Program of China (2016YFB1000905), the National Natural Science Foundation of China (Grant Nos. U1401256, 61402177, 61672234, 61402180 and 61232002). This work was also supported by NSF of Shanghai (14ZR1412600). 39 8 10 4 25 HIGHER EDUCATION PRESS BEIJING CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R CHINA 2095-2228 2095-2236 FRONT COMPUT SCI-CHI Front.. Comput. Sci. FEB 2019.0 13 1 157 169 10.1007/s11704-017-6561-3 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HJ9ND 2023-03-23 WOS:000457525200012 0
C Shafi, R; Shuai, W; Gong, H; Younus, MU IEEE Shafi, Rabia; Shuai, Wan; Gong, Hao; Younus, Muhammad Usman Head Movement Prediction using FCNN 2021 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) Asia-Pacific Signal and Information Processing Association Annual Summit and Conference English Proceedings Paper Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC) DEC 14-17, 2021 Tokyo, JAPAN Asia Pacific Signal & Informat Proc Assoc,IEEE Signal Proc Soc Viewport Prediction; FCNN; EWMA Viewport adaptive streaming of 360-dgree videos relies on accurate prediction of the viewport, while the user generally suffers from significant quality degradation under long delay settings. To deal with this issue, advanced methods for long-term viewport prediction are highly desired to improve viewport prediction accuracy. To more accurately capture the non-linear relationship between the future and past viewpoints, this paper proposes a Fully Connected Neural Network (FCNN) model to make future predictions, which is light in computation. The input data such as yaw values, pitch values, Estimated Weighted Moving Average (EWMA) of yaw values, and EWMA of pitch values, are transformed into sine and cosine angles before feeding into the encoding layer of the FCNN model by considering the roll angle to zero. After transforming the data input into the proposed FCNN model, a long-term prediction length of up to 4 seconds has been explored, to capture the nonlinear and long-term dependent relation between past and future viewport positions more accurately. Experimental results show that the proposed scheme performs well for the large size prediction window. [Shafi, Rabia; Shuai, Wan; Gong, Hao] Northwestern Polytech Univ, Xian, Peoples R China; [Younus, Muhammad Usman] Univ Toulouse, Ecole Math Informat Telecommun Toulouse, Toulouse, France Northwestern Polytechnical University; Universite de Toulouse Shuai, W (corresponding author), Northwestern Polytech Univ, Xian, Peoples R China. rabibabi@mail.nwpu.edu.cn; swan@nwpu.edu.cn; gong_h@mail.nwpu.edu.cn; usman1644@gmail.com Younus, Muhammad/GZL-1617-2022 27 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2309-9402 978-988-14768-9-0 ASIAPAC SIGN INFO PR 2021.0 1458 1464 7 Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS9LQ 2023-03-23 WOS:000782454900242 0
J Zeng, R; Wu, JS; Shao, ZH; Senhadji, L; Shu, HZ Zeng, Rui; Wu, Jiasong; Shao, Zhuhong; Senhadji, Lotfi; Shu, Huazhong Quaternion softmax classifier ELECTRONICS LETTERS English Article COLOR IMAGES; FOURIER-TRANSFORMS; HYPERCOMPLEX For the feature extraction of red-blue-green (RGB) colour images, researchers usually deal with R, G and B channels separately to obtain three feature vectors, and then combine them together to obtain a long real feature vector. This approach does not exploit the relationships between the three channels of the colour images. Recently, attention has been paid to quaternion features, which take the relationships between channels into consideration and seem to be more suitable for representing colour images. However, there are only a few quaternion classifiers for dealing with quaternion features. To meet this requirement, a new quaternion classifier, namely, the quaternion softmax classifier is proposed, which is an extended version of the conventional softmax classifier generally defined in the complex (or real) domain. The proposed quaternion softmax classifier is applied to two of the most common quaternion features, that is, the quaternion principal components analysis feature and the colour image pixel feature. The experimental results show that the proposed method performs better than the quaternion back propagation neural network in terms of accuracy and convergence rate. [Zeng, Rui; Wu, Jiasong; Shao, Zhuhong; Shu, Huazhong] Southeast Univ, Key Lab Comp Network & Informat Integrat, LIST, Nanjing 210096, Jiangsu, Peoples R China; [Zeng, Rui; Wu, Jiasong; Shao, Zhuhong; Senhadji, Lotfi; Shu, Huazhong] INSERM, U1099, F-35000 Rennes, France; [Zeng, Rui; Wu, Jiasong; Shao, Zhuhong; Senhadji, Lotfi; Shu, Huazhong] Univ Rennes 1, LTSI, F-35000 Rennes, France; [Zeng, Rui; Wu, Jiasong; Shao, Zhuhong; Senhadji, Lotfi; Shu, Huazhong] Ctr Rech Informat Biomed Sinofrancais, F-35000 Rennes, France Southeast University - China; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Rennes; Universite de Rennes Wu, JS (corresponding author), Southeast Univ, Key Lab Comp Network & Informat Integrat, LIST, Nanjing 210096, Jiangsu, Peoples R China. jswu@seu.edu.cn Senhadji, Lotfi/E-5903-2013 Senhadji, Lotfi/0000-0001-9434-6341 National Basic Research Program of China [2011CB707904]; NSFC [61201344, 61271312, 11301074]; SRFDP [20110092110023, 20120092120036]; SRF for ROCS, SEM; Natural Science Foundation of Jiangsu Province [BK2012329]; Qing Lan Project National Basic Research Program of China(National Basic Research Program of China); NSFC(National Natural Science Foundation of China (NSFC)); SRFDP(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); SRF for ROCS, SEM(Scientific Research Foundation for the Returned Overseas Chinese Scholars); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project(Jiangsu Polytech Institute) This work was supported by the National Basic Research Program of China under grant 2011CB707904, by the NSFC under grants 61201344, 61271312 and 11301074 and by the SRFDP under grants 20110092110023 and 20120092120036. The project was sponsored by SRF for ROCS, SEM and by the Natural Science Foundation of Jiangsu Province under grant BK2012329 and by the Qing Lan Project. 10 30 33 1 23 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 0013-5194 1350-911X ELECTRON LETT Electron. Lett. DEC 4 2014.0 50 25 1929 1930 10.1049/el.2014.2526 0.0 2 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering AW0PH Green Submitted 2023-03-23 WOS:000345994700022 0
J Zeng, XL; Holck, EN; Westra, J; Hu, FK; Huang, JY; Emori, H; Kubo, T; Wijns, W; Chen, LL; Tu, SX Zeng, Xiaoling; Holck, Emil Nielsen; Westra, Jelmer; Hu, Fukang; Huang, Jiayue; Emori, Hiroki; Kubo, Takashi; Wijns, William; Chen, Lianglong; Tu, Shengxian Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging CARDIOVASCULAR DIAGNOSIS AND THERAPY English Article Atherosclerosis; coronary plaque morphology; fractional flow reserve (FFR); optical coherence tomography (OCT); coronary physiology ENDOTHELIAL DYSFUNCTION; DIAGNOSTIC-ACCURACY; INTERVENTION; ANGIOGRAPHY; ATHEROSCLEROSIS; VALIDATION; PATIENT; STRESS; BURDEN; RATIO Background: Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels. Methods: This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention between August 2011 and October 2018 at Wakayama Medical University Hospital were included. All frames from OCT pullbacks were analyzed using a deep learning algorithm to obtain coronary plaque morphology including thin-cap fibroatheroma (TCFA), lipidic plaque volume (LPV), fibrous plaque volume (FPV), and calcific plaque volume (CPV). The interrogated vessels were stratified into three subgroups: the overestimation group with the numerical difference between the optical flow ratio (OFR) and FFR >0.05, the reference group with the difference >=-0.05 and <= 0.05, and the underestimation group with the difference <-0.05. Results: In total 230 vessels with intermediate coronary artery stenosis from 193 patients were analyzed. The mean FFR was 0.82 +/- 0.10. Among them, 21, 179, and 30 vessels were in the overestimation, the reference, and the underestimation group, respectively. TCFA was higher in the underestimation group (60%) compared with reference (36.3%) and overestimation group (19%). Besides, it was not associated with numerical difference between OFR and FFR (NDOF) after multilevel linear regression. LPV was associated with NDOF as OFR underestimated FFR with -0.028 [95% confidence interval (CI): -0.047, -0.009] for every 100 mm(3) increase in LPV. Conclusions: High lipid burden underestimates FFR when OFR is used to assess the hemodynamic importance of intermediate coronary artery stenosis. TCFA, FPV, and CPV were not independent predictors of NDOI. [Zeng, Xiaoling; Chen, Lianglong; Tu, Shengxian] Fujian Med Univ Union Hosp, Dept Cardiol, Fujian Heart Med Ctr, Fuzhou 350001, Peoples R China; [Holck, Emil Nielsen; Westra, Jelmer] Aarhus Univ Hosp, Dept Cardiol, Aarhus, Denmark; [Hu, Fukang; Tu, Shengxian] Shanghai Jiao Tong Univ, Biomed Instrument Inst, Sch Biomed Engn, Shanghai 200030, Peoples R China; [Huang, Jiayue; Wijns, William] Natl Univ Ireland Galway, Lambe Inst Translat Med & Curam, Galway, Ireland; [Emori, Hiroki; Kubo, Takashi] Wakayama Med Univ, Dept Cardiovasc Med, Wakayama, Japan Aarhus University; Shanghai Jiao Tong University; Ollscoil na Gaillimhe-University of Galway; Wakayama Medical University Chen, LL (corresponding author), Fujian Med Univ Union Hosp, Dept Cardiol, Fujian Heart Med Ctr, Fuzhou 350001, Peoples R China.;Tu, SX (corresponding author), Shanghai Jiao Tong Univ, Biomed Instrument Inst, Sch Biomed Engn, Shanghai 200030, Peoples R China. lianglongchen@126.com; sxtu@sjtu.edu.cn Westra, Jelmer/O-7712-2018 Westra, Jelmer/0000-0002-1926-0844 National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality [19DZ1930600]; Natural Science Foundation of China [82020108015, 81871460]; Science Foundation Ireland [15/RP/2765] National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation Ireland(Science Foundation Ireland) This work was supported by the National Key Research and Development Program of China; and the Science and Technology Commission of Shanghai Municipality (grant number 19DZ1930600 to ST) ; and the Natural Science Foundation of China (grant number 82020108015 and 81871460 to ST) ; and the Science Foundation Ireland (15/RP/2765 to WW and JH) . 29 4 4 3 5 AME PUBLISHING COMPANY SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2223-3652 2223-3660 CARDIOVASC DIAGN THE Cardiovisc. Diagn. Ther. APR 2022.0 12 2 155 165 10.21037/cdt-21-505 0.0 11 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology 0V0FP 35433350.0 gold 2023-03-23 WOS:000788022200001 0
C Zhang, L; Shi, MJ; Chen, QB IEEE Zhang, Lu; Shi, Miaojing; Chen, Qiaobo Crowd counting via scale-adaptive convolutional neural network 2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) IEEE Winter Conference on Applications of Computer Vision English Proceedings Paper 18th IEEE Winter Conference on Applications of Computer Vision (WACV) MAR 12-15, 2018 NV IEEE,IEEE Comp Soc,IEEE Biometr Council,Cognex,Google,Honeywell,Kitware,Netflix,SAP,Amazon,Percept Automata,Verisk Analyt HUMANS The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fields. We extract feature maps from multiple layers and adapt them to have the same output size; we combine them to produce the final density map. The number of people is computed by integrating the density map. We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on. We conduct extensive experiments on the ShanghaiTech, UCF CC 50 and WorldExpo'10 datasets as well as a new dataset SmartCity that we collect for crowd scenes with few people. The results demonstrate significant improvements of SaCNN over the state-of-the-art. [Zhang, Lu; Shi, Miaojing] Tencent Youtu, Shenzhen, Peoples R China; [Shi, Miaojing] Inria Rennes, Rennes, France; [Chen, Qiaobo] Shanghai Jiao Tong Univ, Shanghai, Peoples R China Universite de Rennes; Shanghai Jiao Tong University Zhang, L (corresponding author), Tencent Youtu, Shenzhen, Peoples R China. v_youyzhang@tencent.com; miaojing.shi@inria.fr; chenqiaobo@sjtu.edu.cn 35 147 161 2 18 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2472-6737 978-1-5386-4886-5 IEEE WINT CONF APPL 2018.0 1113 1121 10.1109/WACV.2018.00127 0.0 9 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BK2ZI Green Published, Green Submitted 2023-03-23 WOS:000434349200121 0
J Li, SM; Yang, M; Ji, L; Fan, H Li, Shenming; Yang, Min; Ji, Lei; Fan, Hua A multi-omics machine learning framework in predicting the recurrence and metastasis of patients with pancreatic adenocarcinoma FRONTIERS IN MICROBIOLOGY English Article pancreatic adenocarcinoma; multi-omics; microbial community; random forest; local recurrence; distant metastasis CANCER Local recurrence and distant metastasis are the main causes of death in patients with pancreatic adenocarcinoma (PDAC). Microbial content in PDAC metastasis is still not well-characterized. Here, the tissue microbiome was comprehensively compared between metastatic and non-metastatic PDAC patients. We found that the pancreatic tissue microbiome of metastatic patients was significantly different from that of non-metastatic patients. Further, 10 potential bacterial biomarkers (Kurthia, Gulbenkiania, Acetobacterium and Planctomyces etc.) were identified by differential analysis. Meanwhile, significant differences in expression patterns across multiple omics (lncRNA, miRNA, and mRNA) of PDAC patients were found. The highest accuracy was achieved when these 10 bacterial biomarkers were used as features to predict recurrence or metastasis in PDAC patients, with an AUC of 0.815. Finally, the recurrence and metastasis in PDAC patients were associated with reduced survival and this association was potentially driven by the 10 biomarkers we identified. Our studies highlight the association between the tissue microbiome and recurrence or metastasis of pancreatic adenocarcioma patients, as well as the survival of patients. [Li, Shenming; Fan, Hua] Capital Med Univ, Beijing Chaoyang Hosp, Dept Hepatobiliary & Pancreaticosplen Surg, Beijing, Peoples R China; [Li, Shenming] Univ Duisburg Essen, Essen Univ Hosp, Dept Nephrol, Essen, Germany; [Yang, Min] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan, Anhui, Peoples R China; [Yang, Min; Ji, Lei] Genesis Beijing Co Ltd, Beijing, Peoples R China Capital Medical University; University of Duisburg Essen; Anhui University of Technology Fan, H (corresponding author), Capital Med Univ, Beijing Chaoyang Hosp, Dept Hepatobiliary & Pancreaticosplen Surg, Beijing, Peoples R China. fanhua@medmail.com.cn FAN, HUA/HNP-8526-2023 39 0 0 2 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-302X FRONT MICROBIOL Front. Microbiol. NOV 3 2022.0 13 1032623 10.3389/fmicb.2022.1032623 0.0 10 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology 6I2DE 36406449.0 Green Accepted, gold 2023-03-23 WOS:000885938700001 0
J Li, QH; Tian, X; Ng, WWY; Pelillo, M Li, Qihua; Tian, Xing; Ng, Wing W. Y.; Pelillo, Marcello Hashing-based affinity matrix for dominant set clustering NEUROCOMPUTING English Article Dominant set clustering; Hashing; Affinity matrix; Semi-supervised Dominant set clustering has been widely used to solve a variety of problems such as image segmentation, video analysis, and image retrieval. However, the key problem of it is the need of a given affinity matrix to provide similarity for every pair of data points. The affinity matrix is either user-given or computed using a pairwise computation, which is impractical for big data situations. Therefore, this work proposes a semi-supervised hashing-based affinity matrix computation method (HAM) for dominant set clustering (HAM-DSC) for cases of semi-supervised clustering where labels are available to a portion of data. The HAM computes affinity matrix efficiently based on Hamming distance between learned hash codes, which preserve semantic similarities effectively. To deal with unsupervised clustering problems, an unsupervised extension of the HAM-DSC is also given in this work. Experimental results on 7 real-world datasets show that the HAM-DSC yields an overall better performance together with lower time costs compared to dominant set clustering using other affinity matrix computation methods. (c) 2022 Elsevier B.V. All rights reserved. [Li, Qihua; Tian, Xing; Ng, Wing W. Y.] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China; [Pelillo, Marcello] Univ Venice, Dept Environm Sci Informat & Stat, I-30172 Venice, Italy South China University of Technology; Universita Ca Foscari Venezia Tian, X; Ng, WWY (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China. liqihua1999@163.com; shawntian123@gmail.com; wingng@ieee.org; pelillo@unive.it Ng, Wing W. Y./0000-0003-0783-3585 National Natural Science Foundation of China [61876066]; 67th Chinese Postdoctoral Science Foundation [2020M672631]; Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) [2019A050510006] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); 67th Chinese Postdoctoral Science Foundation; Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) This work was supported in part by the National Natural Science Foundation of China under Grant 61876066, in part by the 67th Chinese Postdoctoral Science Foundation (2020M672631), and in part by Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) 2019A050510006. 36 0 0 2 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing AUG 28 2022.0 501 544 554 10.1016/j.neucom.2022.06.067 0.0 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4N5LC 2023-03-23 WOS:000854059700016 0
J Guo, Y; Davy, A; Facciolo, G; Morel, JM; Jin, QY Guo, Yu; Davy, Axel; Facciolo, Gabriele; Morel, Jean-Michel; Jin, Qiyu Fast, Nonlocal and Neural: A Lightweight High Quality Solution to Image Denoising IEEE SIGNAL PROCESSING LETTERS English Article Noise reduction; Image restoration; Signal processing algorithms; Training; Mobile handsets; Neural networks; Convolution; BM3D; convolutional neural network; image denoising; nonlocal methods NETWORK With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they are computationally demanding, which makes their deployment especially difficult for mobile terminals. Second, experimental evidence shows that CNNs often over-smooth regular textures present in images, in contrast to traditional non-local models. In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN. s solution gives full latitude to the advantages of both models. We apply this framework to two GPU implementations of classic nonlocal algorithms (NLM and BM3D) and observe a substantial gain in both cases, performing better than the state-of-the-art with low computational requirements. Our solution is between 10 and 20 times faster than CNNs with equivalent performance and attains higher PSNR. In addition the final method shows a notable gain on images containing complex textures like the ones of the MIT Moire dataset. [Guo, Yu; Jin, Qiyu] Inner Mongolia Univ, Sch Math Sci, Hohhot 010020, Peoples R China; [Davy, Axel; Facciolo, Gabriele; Morel, Jean-Michel] ENS Paris Saclay, CNRS, Ctr Borelli, F-91190 Gif Sur Yvette, France Inner Mongolia University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Jin, QY (corresponding author), Inner Mongolia Univ, Sch Math Sci, Hohhot 010020, Peoples R China. yuguomath@aliyun.com; axel.davy@normalesup.org; gabriele.facciolo@ens-paris-saclay.fr; moreljeanmichel@gmail.com; qyjin2015@aliyun.com Jin, Qiyu/HGD-8010-2022 Guo, Yu/0000-0002-5031-3502; Jin, Qiyu/0000-0001-8639-233X National Natural Science Foundation of China [12061052]; Natural Science Fund of Inner Mongolia Autonomous Region [2020MS01002]; network information center of Inner Mongolia University; Office of Naval research [N00014-17-1-2552, N00014-20-S-B001]; DGA Defals challenge [ANR-16-DEFA-0004-01]; MENRT; Fondation Mathematique Jacques Hadamard; Prof. Guoqing Chen's 111 project of higher education talent training in Inner Mongolia Autonomous Region National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fund of Inner Mongolia Autonomous Region; network information center of Inner Mongolia University; Office of Naval research(Office of Naval Research); DGA Defals challenge; MENRT; Fondation Mathematique Jacques Hadamard; Prof. Guoqing Chen's 111 project of higher education talent training in Inner Mongolia Autonomous Region This work was supported in part by the National Natural Science Foundation of China under Grant 12061052, in part by the Natural Science Fund of Inner Mongolia Autonomous Region under Grant 2020MS01002, in part by Prof. Guoqing Chen's 111 project of higher education talent training in Inner Mongolia Autonomous Region and the network information center of Inner Mongolia University, in part by the Office of Naval research under Grants N00014-17-1-2552 and N00014-20-S-B001, in part by DGA Defals challenge under Grant ANR-16-DEFA-0004-01, and in part by MENRT and Fondation Mathematique Jacques Hadamard. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Sheng Li. 35 1 1 2 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2021.0 28 1515 1519 10.1109/LSP.2021.3099963 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering TY7VF Green Submitted 2023-03-23 WOS:000683987700003 0
J Meng, QY; Hickson, KM; Shao, KJ; Loisonc, JC; Zhang, DH Meng, Qingyong; Hickson, Kevin M.; Shao, Kejie; Loisonc, Jean-Christophe; Zhang, Dong H. Theoretical and experimental investigations of rate coefficients of O(D-1) + CH4 at low temperature PHYSICAL CHEMISTRY CHEMICAL PHYSICS English Article POLYMER MOLECULAR-DYNAMICS; CHEMICAL-REACTION RATES; ATMOSPHERIC CHEMISTRY; PHOTOCHEMICAL DATA The rate coefficients of the barrierless O(D-1) + CH4 reaction are determined both theoretically and experimentally at 50-296 K. For the calculations, ring polymer molecular dynamics (RPMD) simulations are performed on the basis of a new neural network potential energy surface (PES) in the reactant asymptotic part. Only the reactant asymptotic part of the PES is constructed because of its barrierless and exothermic properties. Experimentally, the reaction rate coefficients are measured using a supersonic flow reactor. Pulsed laser photolysis of O-3 molecules is used as the source of O(D-1) atoms, which are detected directly through vacuum ultraviolet laser induced fluorescence at 115 nm. The branching ratio for H atom production is measured by comparing the H atom yields of the O(D-1) + CH4 and O(D-1) + H-2 reactions. At T >= 75 K, good agreement between theoretical and experimental rate coefficients is found, while at 50 K, the larger difference is discussed in detail. [Meng, Qingyong] Northwestern Polytech Univ, Dept Appl Chem, Youyi West Rd 127, Xian 710072, Peoples R China; [Meng, Qingyong; Shao, Kejie; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Zhongshan Rd 457, Dalian 116023, Peoples R China; [Meng, Qingyong; Shao, Kejie; Zhang, Dong H.] Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Zhongshan Rd 457, Dalian 116023, Peoples R China; [Hickson, Kevin M.; Loisonc, Jean-Christophe] Univ Bordeaux, CNRS UMR 5255, Inst Mol Sci, Batiment A12,351 Cours Liberat, F-33400 Talence, France Northwestern Polytechnical University; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Chemistry (INC); UDICE-French Research Universities; Universite de Bordeaux Meng, QY (corresponding author), Northwestern Polytech Univ, Dept Appl Chem, Youyi West Rd 127, Xian 710072, Peoples R China.;Meng, QY; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Zhongshan Rd 457, Dalian 116023, Peoples R China.;Meng, QY; Zhang, DH (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret & Computat Chem, Zhongshan Rd 457, Dalian 116023, Peoples R China.;Hickson, KM (corresponding author), Univ Bordeaux, CNRS UMR 5255, Inst Mol Sci, Batiment A12,351 Cours Liberat, F-33400 Talence, France. qingyong.meng@nwpu.edu.cn; km.hickson@ism.u-bordeaux1.fr; zhangdh@dicp.ac.cn Hickson, Kevin M/A-2443-2012; Hickson, Kevin/AAA-4572-2019; Loison, Jean-Christophe/S-8249-2018 Hickson, Kevin M/0000-0001-8317-2606; Hickson, Kevin/0000-0001-8317-2606; Loison, Jean-Christophe/0000-0001-8063-8685 CNRS program PCMI; CNRS program PNP; Ministry of Science and Technology of P. R. China [2013CB834601]; National Natural Science Foundation of China [21433009, 21503214]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB17010200]; Northwestern Polytechnical University CNRS program PCMI; CNRS program PNP; Ministry of Science and Technology of P. R. China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Northwestern Polytechnical University K. M. H. and J.-C. L. thank the CNRS programs PCMI and PNP for their support. Q. M., K. S., and D. H. Z. thank Ministry of Science and Technology of P. R. China (Grant No. 2013CB834601), the National Natural Science Foundation of China (Grant No. 21433009 and 21503214), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB17010200). Q. M. also acknowledges the start-up fund from Northwestern Polytechnical University. Q. M. wishes to thank Dr B. Fu for discussing on the dynamics of the O(1D) + CH4 system. We thank Prof. Dr. P. Honvault for providing their 3D TIQM results. 34 28 28 2 37 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1463-9076 1463-9084 PHYS CHEM CHEM PHYS Phys. Chem. Chem. Phys. NOV 14 2016.0 18 42 29286 29292 10.1039/c6cp05517f 0.0 7 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics EB0HI 27731878.0 Green Submitted 2023-03-23 WOS:000387024300030 0
C Wang, J; Zhang, XY; Tang, BH; Wu, H; Li, ZL IEEE Wang, Jiao; Zhang, Xiao-Yu; Tang, Bo-Hui; Wu, Hua; Li, Zhao-Liang ESTIMATION OF NET SURFACE LONGWAVE RADIATION FOR THE TIBETAN PLATEAU REGION USING MODIS DATA 2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 21-26, 2013 Melbourne, AUSTRALIA IEEE,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Net surface longwave radiation; Downwelling surface longwave radiation; MODIS; clear-sky conditions This paper proposed two methods to estimate the instantaneous downwelling surface longwave radiation (R-L,R- D) using the MODIS measurements observed at the top of the atmosphere (TOA) over the Tibetan plateau region for clear-sky conditions. One is the method proposed by [2] and refined in this work, and the other is an artificial neural network (ANN) method. The upwelling surface longwave radiation (R-L,R- U) was estimated using the Stefan-Boltzmann law with MODIS surface temperature/emissivity products (MOD11_L2). The two methods are all based on the atmospheric transfer simulation. The net surface longwave radiation (R-n,R- l) can then be obtained by differing the R-L,R- D and the R-L,R- U. The results showed that the RMSEs of the estimated R-L,R- D and measured R-L,R- D with the first method are smaller than those of with the ANN method for the sites over the Tibetan plateau region. [Wang, Jiao; Tang, Bo-Hui; Wu, Hua] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Wang, Jiao; Zhang, Xiao-Yu] Shanxi Univ, Sch Environm & Resources, Taiyuan 030006, Peoples R China; [Li, Zhao-Liang] Chinese Acad Agr Sci, Minsit Agr, Inst Agr Resources Regional Planning, Key Lab Agri Informat, Beijing 100081, Peoples R China; [Li, Zhao-Liang] CNRS, UdS, LSIIT, F-67412 Illkirch Graffenstaden, France Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Shanxi University; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg Tang, BH (corresponding author), Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China. tangbh@igsnrr.ac.cn zhang, xiaoyu/HJI-4374-2023; ZHU, XIAOYU/HJY-7240-2023 Chinese Academy of Sciences; Kezhen Distinguished Young Scholar in IGSNRR; CAS [2012RC101] Chinese Academy of Sciences(Chinese Academy of Sciences); Kezhen Distinguished Young Scholar in IGSNRR; CAS(Defence Research & Development Organisation (DRDO)Chinese Academy of Sciences) This work was jointly supported by the special foundation of excellent doctoral dissertations of the Chinese Academy of Sciences, and the Excellent Young Talent Funds for Kezhen Distinguished Young Scholar in IGSNRR, CAS under grant 2012RC101. 6 0 0 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-4799-1114-1 INT GEOSCI REMOTE SE 2013.0 477 480 10.1109/IGARSS.2013.6721196 0.0 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BB7JO 2023-03-23 WOS:000345638900117 0
J Ding, H; Velasco, C; Ye, HH; Lindner, T; Grech-Sollars, M; O'Callaghan, J; Hiley, C; Chouhan, M; Niendorf, T; Koh, DM; Prieto, C; Adeleke, S Ding, Hao; Velasco, Carlos; Ye, Huihui; Lindner, Thomas; Grech-Sollars, Matthew; O'Callaghan, James; Hiley, Crispin; Chouhan, Manil; Niendorf, Thoralf; Koh, Dow-Mu; Prieto, Claudia; Adeleke, Sola Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer CANCERS English Review magnetic resonance imaging; multiparametric magnetic resonance imaging; prostatic neoplasms; brain neoplasms; abdominal neoplasms; radiotherapy; image-guided; deep learning HIGH-RESOLUTION T-1; PROSTATE-CANCER; TUMOR PROGRESSION; BRAIN-TUMORS; MRI; HETEROGENEITY; BIOPSY; DIFFERENTIATION; QUANTIFICATION; RECONSTRUCTION Simple Summary: Magnetic resonance fingerprinting (MRF) is a framework for acquiring co-registered multiparametric magnetic resonance mapping with increased scan efficiency. Many studies have explored the use of MRF for cancer management. A review on the current developments in this area has not yet been written but is needed to keep both clinicians and researchers updated. This review summarises recent studies detecting and characterising tumours using MRF, with a focus on brain tumours, prostate cancers, and abdominal/pelvic cancers. Advances in MRF for radiotherapy planning are also mentioned. The principles and limitations of MRF have been simplified to increase accessibility to clinicians with minimal radiological backgrounds. Future oncological applications of MRF are explored, including integrating MRF and deep learning, as well as the use of MRF in assessing disease heterogeneity. We propose further research that needs to take place before MRF can provide a credible means for assessing tumour biomarkers or be accepted by clinicians. Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment. [Ding, Hao] Imperial Coll London, Imperial Coll, Fac Med, Sch Med, London SW7 2AZ, England; [Velasco, Carlos; Prieto, Claudia] Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London SE1 7EH, England; [Ye, Huihui] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Peoples R China; [Lindner, Thomas] Univ Hosp Hamburg Eppendorf, Dept Diagnost & Intervent Neuroradiol, D-20246 Hamburg, Germany; [Grech-Sollars, Matthew] Royal Surrey NHS Fdn Trust, Dept Med Phys, Surrey GU2 7XX, England; [Grech-Sollars, Matthew] Imperial Coll London, Dept Surg & Canc, London SW7 2AZ, England; [O'Callaghan, James; Chouhan, Manil] UCL, UCL Ctr Med Imaging, Div Med, London W1W 7TS, England; [Hiley, Crispin] UCL, Lung Canc Ctr Excellence, Canc Res UK, Canc Inst, London WC1E 6DD, England; [Niendorf, Thoralf] Helmholtz Assoc, Max Delbrueck Ctr Mol Med, Berlin Ultrahigh Field Facil BUFF, D-13125 Berlin, Germany; [Koh, Dow-Mu] Inst Canc Res, Div Radiotherapy & Imaging, London SM2 5NG, England; [Koh, Dow-Mu] Royal Marsden Hosp, Dept Radiol, London SW3 6JJ, England; [Adeleke, Sola] UCL, High Dimens Neurol Grp, Queens Sq Inst Neurol, London WC1N 3BG, England; [Adeleke, Sola] Guys & St Thomas Hosp, Dept Oncol, London SE1 9RT, England; [Adeleke, Sola] Kings Coll London, Sch Canc & Pharmaceut Sci, London WC2R 2LS, England Imperial College London; Guy's & St Thomas' NHS Foundation Trust; University of London; King's College London; Zhejiang University; University of Hamburg; University Medical Center Hamburg-Eppendorf; Imperial College London; University of London; University College London; Cancer Research UK; University of London; University College London; Helmholtz Association; Max Delbruck Center for Molecular Medicine; Royal Marsden NHS Foundation Trust; University of London; Institute of Cancer Research - UK; Royal Marsden NHS Foundation Trust; University of London; University College London; Guy's & St Thomas' NHS Foundation Trust; University of London; King's College London Adeleke, S (corresponding author), UCL, High Dimens Neurol Grp, Queens Sq Inst Neurol, London WC1N 3BG, England.;Adeleke, S (corresponding author), Guys & St Thomas Hosp, Dept Oncol, London SE1 9RT, England.;Adeleke, S (corresponding author), Kings Coll London, Sch Canc & Pharmaceut Sci, London WC2R 2LS, England. hao.ding19@imperial.ac.uk; carlos.velasco@kd.ac.uk; yehuihui@zju.edu.cn; t.lindner@uke.de; m.grech-sollars@imperial.ac.uk; james.ocallaghan@ud.ac.uk; crispin.hiley@ucl.ac.uk; m.chouhan@ucl.ac.uk; thoralf.niendorf@mdc-berlin.de; mu.koh@icr.ac.uk; claudia.prieto@kcl.ac.uk; s.adeleke@ucl.ac.uk Adeleke, Sola/GWM-4958-2022; Grech-Sollars, Matthew/ABG-4740-2021; Niendorf, Thoralf/H-7738-2013 Grech-Sollars, Matthew/0000-0003-3881-4870; Lindner, Thomas/0000-0002-4408-2429; Velasco Jimeno, Carlos/0000-0002-1226-3436; Adeleke, Sola/0000-0002-4388-8754; Niendorf, Thoralf/0000-0001-7584-6527; Chouhan, Manil/0000-0001-5903-1002; Ding, Hao/0000-0002-9812-2385; Ye, Huihui/0000-0002-7563-0352 National Physics Laboratory's ISCF Medical Imaging Accelerator programme - Department for Business, Energy and Industrial Strategy National Physics Laboratory's ISCF Medical Imaging Accelerator programme - Department for Business, Energy and Industrial Strategy MGS was part funded by the National Physics Laboratory's ISCF Medical Imaging Accelerator programme, financed by the Department for Business, Energy and Industrial Strategy's Industrial Strategy Challenge Fund. According to UK research councils' Common Principles on Data Policy, Wellcome Trust's Policy on data, software and materials management and sharing, all data supporting this study will be openly available at https://doi.org/10.3390/cancers13194742. 120 2 2 1 2 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6694 CANCERS Cancers OCT 2021.0 13 19 4742 10.3390/cancers13194742 0.0 20 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 0A4JQ 34638229.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000773923000003 0
J Liu, XT; Huang, JW; Lai, JH; Zhang, JW; Senousi, AM; Zhao, PX Liu, Xintao; Huang, Jianwei; Lai, Jianhui; Zhang, Junwei; Senousi, Ahmad M.; Zhao, Pengxiang Analysis of urban agglomeration structure through spatial network and mobile phone data TRANSACTIONS IN GIS English Article BIG DATA; CHINA; PATTERNS; GOVERNANCE; DYNAMICS Urban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy-makers and planners. In this study, the Beijing-Tianjin-Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight-directed spatial interaction network based on an origin-destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich-club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four-level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over-dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top-down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed. [Liu, Xintao; Zhang, Junwei; Senousi, Ahmad M.] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China; [Huang, Jianwei] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Kowloon, Hong Kong, Peoples R China; [Lai, Jianhui] Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China; [Zhao, Pengxiang] Lund Univ, GIS Ctr, Dept Phys Geog & Ecosyst Sci, Lund, Sweden Hong Kong Polytechnic University; Chinese University of Hong Kong; Beijing University of Technology; Lund University Huang, JW (corresponding author), Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Kowloon, Hong Kong, Peoples R China. huangjianwei1119@gmail.com Huang, jianwei/HGE-9266-2022; LIU, Xintao/F-6744-2017 Zhang, Junwei/0000-0003-2228-8247; LIU, Xintao/0000-0002-7323-9878; Zhao, Pengxiang/0000-0002-5279-9331; Senousi, Ahmad Muhamad/0000-0002-2578-795X; Huang, Jianwei/0000-0002-2230-3446 RGC Early Career Scheme [1-PP5Q] RGC Early Career Scheme This research was supported by a grant from RGC Early Career Scheme (Grant no. 1-PP5Q) 68 7 7 13 51 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1361-1682 1467-9671 T GIS Trans. GIS AUG 2021.0 25 4 1949 1969 10.1111/tgis.12755 0.0 MAY 2021 21 Geography Social Science Citation Index (SSCI) Geography UK5JB 2023-03-23 WOS:000656117900001 0
J Yang, QC; Wang, YL; Zhang, JN; Martin, JD Yang, Qingchun; Wang, Yanli; Zhang, Jianing; Delgado Martin, Jordi Stochastic simulation of groundwater dynamics based on grey theory and seasonal decomposition model in a coastal aquifer of South China JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA English Article coastal aquifer; grey theory; groundwater level; seasonal decomposition model; simulation NEURAL-NETWORK; LEVEL; PREDICTION Accurate and reliable prediction of groundwater level is a critical component in coastal water management. It is important to find a suitable model with acceptable accuracy. This paper presents a comparative study with seasonal decomposition method of time series analysis and GM (1,1) method, which are applied to test the model accuracy by simulating groundwater levels of two representative wells located at a coastal aquifer in Fujian Province, South China. The average monthly groundwater level was monitored during 2006-2011, the data set from 2006-2010 was used for model establishment, and that of 2011 used for predicting the dynamic change of groundwater level. The results indicate that the multiplicative model fits better than the additive model for seasonal decomposition method. Finally, the effectiveness of the model and prediction accuracy was evaluated based on root mean square error (RMSE) and regression coefficient (R-2). From the obtained results, it is concluded that the GM (1,1) method can be a promising tool to simulate and forecast groundwater level and serve as an alternative physically based model. [Yang, Qingchun; Wang, Yanli; Zhang, Jianing] Jilin Univ, Minist Educ, Key Lab Groundwater Resources & Environm, Changchun 130021, Peoples R China; [Delgado Martin, Jordi] Univ A Coruna, Escuela Ingenieros Caminos, La Coruna 15192, Spain Jilin University; Universidade da Coruna Yang, QC (corresponding author), Jilin Univ, Minist Educ, Key Lab Groundwater Resources & Environm, Changchun 130021, Peoples R China. qyang@udc.es Delgado Martin, Jordi/H-7819-2015 Delgado Martin, Jordi/0000-0002-8261-8087 National Natural Science Foundation of China [41402202]; Specialized Research Fund for the Doctoral Program of Higher Education [20130061120084] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)) This research was financially supported by National Natural Science Foundation of China (grant number 41402202), Specialized Research Fund for the Doctoral Program of Higher Education (20130061120084). Special gratitude is offered to Rita Henderson for her continuous efforts in dealing with and evaluating the work, and the valuable comments from the other two anonymous reviewers are also greatly acknowledged. 32 2 3 2 30 IWA PUBLISHING LONDON ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND 0003-7214 1365-2087 J WATER SUPPLY RES T J. Water Supply Res Technol.-Aqua 2015.0 64 8 947 957 10.2166/aqua.2015.047 0.0 11 Engineering, Civil; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Water Resources DJ6IT 2023-03-23 WOS:000374315800009 0
J Wang, YZ; Fan, ZY; Qian, P; Ala-Nissila, T; Caro, MA Wang, Yanzhou; Fan, Zheyong; Qian, Ping; Ala-Nissila, Tapio; Caro, Miguel A. Structure and Pore Size Distribution in Nanoporous Carbon CHEMISTRY OF MATERIALS English Article FUNCTIONALIZATION; POTENTIALS; ALGORITHMS We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csanyi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm(3)) with structures containing 131 072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials. [Wang, Yanzhou; Qian, Ping] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Dept Phys, Beijing 100083, Peoples R China; [Wang, Yanzhou; Fan, Zheyong; Ala-Nissila, Tapio] Aalto Univ, QTF Ctr Excellence, Dept Appl Phys, FIN-00076 Espoo, Finland; [Fan, Zheyong] Bohai Univ, Coll Phys Sci & Technol, Jinzhou 121013, Peoples R China; [Ala-Nissila, Tapio] Loughborough Univ, Interdisciplinary Ctr Math Modelling, Loughborough LE11 3TU, Leics, England; [Ala-Nissila, Tapio] Loughborough Univ, Dept Math Sci, Loughborough LE11 3TU, Leics, England; [Caro, Miguel A.] Aalto Univ, Dept Elect Engn & Automat, FIN-02150 Espoo, Finland University of Science & Technology Beijing; Aalto University; Bohai University; Loughborough University; Loughborough University; Aalto University Qian, P (corresponding author), Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Dept Phys, Beijing 100083, Peoples R China.;Ala-Nissila, T (corresponding author), Aalto Univ, QTF Ctr Excellence, Dept Appl Phys, FIN-00076 Espoo, Finland.;Ala-Nissila, T (corresponding author), Loughborough Univ, Interdisciplinary Ctr Math Modelling, Loughborough LE11 3TU, Leics, England.;Ala-Nissila, T (corresponding author), Loughborough Univ, Dept Math Sci, Loughborough LE11 3TU, Leics, England.;Caro, MA (corresponding author), Aalto Univ, Dept Elect Engn & Automat, FIN-02150 Espoo, Finland. qianping@ustb.edu.cn; tapio.ala-nissila@aalto.fi; mcaroba@gmail.com Ala-Nissila, Tapio/B-4723-2017; Wang, Yanzhou/GMW-5690-2022; Fan, Zheyong/G-8235-2012 Ala-Nissila, Tapio/0000-0002-3210-3181; Wang, Yanzhou/0000-0003-1509-4364; Fan, Zheyong/0000-0002-2253-8210 Academy of Finland [321713, 330488, 312298]; China Scholarship Council [CSC202006460064]; Academy of Finland (AKA) [330488, 321713] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); China Scholarship Council(China Scholarship Council); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) The authors acknowledge funding from the Academy of Finland, under projects 321713 (M.A.C. and Y.W.), 330488 (M.A.C.), 312298/QTF Center of Excellence program (T.A.-N., Z.F., and Y.W.), and the China Scholarship Council under Grant No. CSC202006460064 (Y.W.). The authors also acknowledge computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project. 53 10 10 8 25 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0897-4756 1520-5002 CHEM MATER Chem. Mat. JAN 25 2022.0 34 2 617 628 10.1021/acs.chemmater.1c03279 0.0 JAN 2022 12 Chemistry, Physical; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science ZL1LY Green Published, Green Submitted, hybrid 2023-03-23 WOS:000741090900001 0
J Bennett, AA; Mahood, EH; Fan, K; Moghe, GD Bennett, Alexandra A.; Mahood, Elizabeth H.; Fan, Kai; Moghe, Gaurav D. Untargeted metabolomics of purple and orange-fleshed sweet potatoes reveals a large structural diversity of anthocyanins and flavonoids SCIENTIFIC REPORTS English Article ACYLATED ANTHOCYANINS; NATURAL COLORANTS; STRESS TOLERANCE; FOOD; QUANTITATION; PHENOLICS; STORAGE; HEALTH Anthocyanins are economically valuable phytochemicals of significant relevance to human health. Industrially extracted from multiple fruit and vegetable sources, anthocyanin yield and profiles can vary between sources and growing conditions. In this study, we focused on three purple-fleshed and one orange-fleshed cultivars of sweet potato-a warm-weather, nutritious crop of substantial interest to growers in northern, cooler latitudes-to determine the yield and diversity of anthocyanins and flavonoids. Acidified ethanol extraction of lyophilized roots yielded similar to 800 mg average anthocyanins/100 g dry weight from all three cultivars. UHPLC-DAD-Orbitrap analysis of sweet potato extracts identified 18 high-confidence, mostly acylated peonidin and cyanidin derivatives contributing to > 90% of the total anthocyanin signal. Further assessment of the untargeted Liquid Chromatography-Tandem Mass Spectrometry data using deep learning and molecular networking identified over 350 flavonoid peaks with variable distributions in different sweet potato cultivars. These results provide a novel insight into anthocyanin content of purple-fleshed sweet potatoes grown in the northern latitudes, and reveal the large structural diversity of anthocyanins and flavonoids in this popular crop. [Bennett, Alexandra A.; Mahood, Elizabeth H.; Moghe, Gaurav D.] Cornell Univ, Plant Biol Sect, Sch Integrat Plant Sci, Ithaca, NY 14853 USA; [Fan, Kai] Fujian Agr & Forestry Univ, Coll Agr, Key Lab, Minist Educ Genet Breeding & Multiple Utilizat Cr, Fuzhou 350002, Peoples R China; [Bennett, Alexandra A.] Univ Bodenkultur Wien, Inst Analyt Chem, Dept Chem, A-1190 Vienna, Austria Cornell University; Fujian Agriculture & Forestry University; University of Natural Resources & Life Sciences, Vienna Moghe, GD (corresponding author), Cornell Univ, Plant Biol Sect, Sch Integrat Plant Sci, Ithaca, NY 14853 USA. gdm67@cornell.edu Moghe, Gaurav/0000-0002-8761-064X; Mahood, Elizabeth/0000-0002-7087-6580; Bennett, Alexandra/0000-0002-5403-0382 USDA-NIFA Hatch award [1021130]; China Scholarship Council [201708350002] USDA-NIFA Hatch award; China Scholarship Council(China Scholarship Council) This work was supported by a USDA-NIFA Hatch award #1021130 to GM and China Scholarship Council award #201708350002 to KF. 51 8 8 4 18 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep AUG 12 2021.0 11 1 16408 10.1038/s41598-021-95901-y 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics UD0LT 34385537.0 gold, Green Accepted 2023-03-23 WOS:000686909600039 0
J Cao, C; Liao, JX; Hou, ZM; Wang, G; Feng, WT; Fang, YL Cao, Cheng; Liao, Jianxing; Hou, Zhengmeng; Wang, Gui; Feng, Wentao; Fang, Yanli Parametric uncertainty analysis for CO2 sequestration based on distance correlation and support vector regression JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING English Article CO2 storage; Uncertainty quantification; Caprock integrity; Distance correlation; Support vector regression CARBON-DIOXIDE INJECTION; SENSITIVITY-ANALYSIS; STORAGE; QUANTIFICATION; SANDSTONE; EQUATION; CAPTURE; MACHINE; PROJECT; MODELS The uncertainty of reservoir and operating parameters challenges the accuracy of risk assessment, as well as the efficiency of optimization in carbon capture and storage (CCS) operation. To quantitatively analyze the role of uncertainty parameters on the response of CO2 injection, the effects of geomechanical and hydrogeological parameters on CCS are investigated using the approaches of distance correlation and machine learning support vector regression (SVR). In addition, a risk factor is introduced as a combination of brittleness and stress increment to assess the potential risk of caprock integrity. Using quantitative analysis, the order of importance of the parameters on the fluid pressure in the reservoir and the caprock, and the formation deformation at the ground surface and the bottom and top of the caprock are obtained. Compared to formation deformation, the pressure change can provide more valuable information regarding the assessment of the integrity of the caprock. The trained SVR surrogate model based on SVR can predict both the pressure change as well as formation deformation with reliable accuracy. [Cao, Cheng; Liao, Jianxing; Hou, Zhengmeng; Feng, Wentao; Fang, Yanli] Tech Univ Clausthal, Energy Res Ctr Lower Saxony, D-38640 Goslar, Germany; [Cao, Cheng; Liao, Jianxing; Hou, Zhengmeng; Feng, Wentao; Fang, Yanli] Tech Univ Clausthal, Inst Petr Engn, D-38678 Clausthal Zellerfeld, Germany; [Wang, Gui] Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Peoples R China TU Clausthal; TU Clausthal; Southwest Petroleum University Liao, JX (corresponding author), Tech Univ Clausthal, Energy Res Ctr Lower Saxony, D-38640 Goslar, Germany. jianxing.liao@tu-clausthal.de Liao, Jianxing/AAV-1516-2020; Hou, Michael Zhengmeng/AAF-7940-2019; Feng, Wentao/GRJ-3315-2022; Cao, Cheng/AAG-3503-2020 Hou, Michael Zhengmeng/0000-0002-8544-7172; Feng, Wentao/0000-0001-8109-5117; Cao, Cheng/0000-0003-0329-6188; liao, jianxing/0009-0002-5474-4163; Wang, Gui/0000-0002-6418-2195 China Scholarship Council (CSC) [201808080067, 201708080145] China Scholarship Council (CSC)(China Scholarship Council) The authors are grateful for the editor and the two anonymous reviewers, whose comments and suggestions are very beneficial for the improvement of the paper. Cheng Cao and Jianxing Liao would like to thank the China Scholarship Council (CSC No. 201808080067, 201708080145) for fellowship support. 68 9 9 4 21 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1875-5100 2212-3865 J NAT GAS SCI ENG J. Nat. Gas Sci. Eng. MAY 2020.0 77 103237 10.1016/j.jngse.2020.103237 0.0 16 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering LF4HG 2023-03-23 WOS:000527379600016 0
C Paoletti, ME; Haut, JM; Plaza, J; Plaza, A; Liu, Q; Hang, R IEEE Paoletti, M. E.; Haut, J. M.; Plaza, J.; Plaza, A.; Liu, Q.; Hang, R. MULTICORE IMPLEMENTATION OF THE MULTI-SCALE ADAPTIVE DEEP PYRAMID MATCHING MODEL FOR REMOTELY SENSED IMAGE CLASSIFICATION 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 23-28, 2017 Fort Worth, TX Institute of Elect & Electron Engineers Geoscience & Remote Sensing Soc,IEEE,IEEE GRSS Convolutional neural network (CNN); adaptive deep pyramid matching (ADPM); convolutional features; multi-core implementation; image classification Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM) model [1] takes advantage of the features from all of the convolutional layers. This model allows the optimal fusing weights for different convolutional layers be learned from the data itself. In addition, the combination of CNNs with spatial pyramid pooling (SPP-net) to create the basic deep network allows the use of images with multiple scales, which results in better learning process thanks to the complementary information. The original ADPM method is divided in two parts: the multi-scale deep feature extraction and the ADPM core. In this paper we present a computational improvement of the ADPM core, coding a parallel-multicore version. This strategy is shown to significantly enhance performance in the analysis of remotely sensed data. [Paoletti, M. E.; Haut, J. M.; Plaza, J.; Plaza, A.] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres, Spain; [Liu, Q.; Hang, R.] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Big Data Anal Technol, Nanjing, Jiangsu, Peoples R China Universidad de Extremadura; Nanjing University of Information Science & Technology Plaza, A (corresponding author), Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres, Spain. Paoletti, Mercedes Eugenia/AAE-1946-2019; Plaza, Javier/C-4452-2008; Plaza, Antonio/C-4455-2008; Haut, Juan M./AAD-9028-2019 Paoletti, Mercedes Eugenia/0000-0003-1030-3729; Plaza, Javier/0000-0002-2384-9141; Plaza, Antonio/0000-0002-9613-1659; Haut, Juan M./0000-0001-6701-961X Ministerio de Educacion; Junta de Extremadura [297/2014, GR15005] Ministerio de Educacion; Junta de Extremadura This work has been supported by Ministerio de Educacion (Resolucion de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaria de Estado de Educacion, Formacion Profesional y Universidades, por la que se convocan ayudas para la formacion de profesorado universitario, de los subprogramas de Formacion y de Movilidad incluidos en el Programa Estatal de Promocion del Talento y su Empleabilidad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016. This work has also been supported by Junta de Extremadura (decreto 297/2014, ayudas para la realizacion de actividades de investigacion y desarrollo tecnologico, de divulgacion y de transferencia de conocimiento por los Grupos de Investigacion de Extremadura, Ref. GR15005). 18 4 5 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5090-4951-6 INT GEOSCI REMOTE SE 2017.0 2247 2250 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BJ6TY 2023-03-23 WOS:000426954602092 0
J Ji, ZY; Song, XJ; Guo, XX; Wang, FS; Armendariz-Inigo, JE Ji, Zhenyan; Song, Xiaojun; Guo, Xiaoxuan; Wang, Fangshi; Armendariz-Inigo, Jose Enrique Real-time monocular depth estimation with adaptive receptive fields JOURNAL OF REAL-TIME IMAGE PROCESSING English Article Monocular depth estimation; Adaptive receptive field; Real-time performance; Convolutional neural network Monocular depth estimation is a popular research topic in the field of autonomous driving. Nowadays many models are leading in accuracy but performing poorly in a real-time scenario. To effectively increase the depth estimation efficiency, we propose a novel model combining a multi-scale pyramid architecture for depth estimation together with adaptive receptive fields. The pyramid architecture reduces the trainable parameters from dozens of mega to less than 10 mega. Adaptive receptive fields are more sensitive to objects at different depth/distances in images, leading to better accuracy. We have adopted stacked convolution kernels instead of raw kernels to compress the model. Thus, the model that we proposed performs well in both real-time performance and estimation accuracy. We provide a set of experiments where our model performs better in terms of Eigen split than other previously known models. Furthermore, we show that our model is also better in runtime performance in regard to the depth estimation to the rest of models but the Pyd-Net model. Finally, our model is a lightweight depth estimation model with state-of-the-art accuracy. [Ji, Zhenyan; Song, Xiaojun; Guo, Xiaoxuan; Wang, Fangshi] Beijing Jiaotong Univ, Sch Software Engn, Beijing 100044, Peoples R China; [Armendariz-Inigo, Jose Enrique] Univ Publ Navarra, Dept Stat Comp Sci & Math, Pamplona 31006, Spain Beijing Jiaotong University; Universidad Publica de Navarra Ji, ZY (corresponding author), Beijing Jiaotong Univ, Sch Software Engn, Beijing 100044, Peoples R China. jzhenyan@hotmail.com Armendariz-Inigo, Jose Enrique/0000-0002-2666-7191; , zhenyan/0000-0002-6566-9464 National Key Research and Development Program of China [2018YFC0831903]; Major Project of National Natural Science Foundation of China [51935002] National Key Research and Development Program of China; Major Project of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Key Research and Development Program of China (No. 2018YFC0831903) and Major Project of National Natural Science Foundation of China (No. 51935002). 26 0 0 1 13 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1861-8200 1861-8219 J REAL-TIME IMAGE PR J. Real-Time Image Process. AUG 2021.0 18 4 SI 1369 1381 10.1007/s11554-020-01036-0 0.0 OCT 2020 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology UC7WK 2023-03-23 WOS:000582309300001 0
J Zhan, YF; Xia, YQ; Vasilakos, AV Zhan, Yufeng; Xia, Yuanqing; Vasilakos, Athanasios V. Future directions of networked control systems: A combination of cloud control and fog control approach COMPUTER NETWORKS English Article Networked control systems (NCSs); Cloud computing; Fog computing; Cloud control systems TELEOPERATION; INTERNET; DESIGN Currently, we have witnessed that networked control technology has played a key role in Internet of Things (IoT). However, the volume, variety and velocity properties of big data from loT make the traditional networked control systems (NCSs) can not meet the current requirements. Due to this, cloud control systems have emerged as a new control paradigm which bring lots of benefits and have played a key role in current loT society. Despite cloud control systems have tremendous advantages, there are still lots of tough challenges such as latency, network congestion and etc., which hinder the development of cloud control systems. For these challenges, we extend the cloud control systems to the cloud fog control systems which bring the fog computing into the NCSs design. First, some recent studies of fog computing have been surveyed. Second, a new architecture of NCSs based on cloud computing and fog computing has been proposed. Then, an incentive mechanism has been designed for the cloud fog control systems. In the end, the cases of control tasks offloading and a simple platform of cloud fog control systems have been studied. (C) 2019 Published by Elsevier B.V. [Zhan, Yufeng; Xia, Yuanqing] Beijing Inst Technol, Key Lab Intelligent Control & Decis Complex Syst, Sch Automat, Beijing 100081, Peoples R China; [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden Beijing Institute of Technology; Lulea University of Technology Xia, YQ (corresponding author), Beijing Inst Technol, Key Lab Intelligent Control & Decis Complex Syst, Sch Automat, Beijing 100081, Peoples R China. zhanyf1989@gmail.com; xia_yuanqing@bit.edu.cn; vasilako@ath.forthnet.gr Vasilakos, Athanasios/0000-0003-1902-9877 National Key Research and Development Program of China [2018YFB1003700]; National Nature Science Foundation [61836001]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621063] National Key Research and Development Program of China; National Nature Science Foundation(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Key Research and Development Program of China under Grant 2018YFB1003700, the National Nature Science Foundation under Grant 61836001, the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621063. 35 11 12 2 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. OCT 9 2019.0 161 235 248 10.1016/j.comnet.2019.07.004 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IX7HQ 2023-03-23 WOS:000485854800019 0
J Long, LF; Lugu, RK; Xiong, X; Liu, Q; Peng, H; Wang, J; Orellana-Martin, D; Perez-Jimenez, MJ Long, Lifan; Lugu, Rikong; Xiong, Xin; Liu, Qian; Peng, Hong; Wang, Jun; Orellana-Martin, David; Perez-Jimenez, Mario J. Echo spiking neural P systems KNOWLEDGE-BASED SYSTEMS English Article Nonlinear spiking neural P systems; Echo state network; Echo spiking neural P systems; Time-series forecasting STATE NETWORKS Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing models that abstract the nonlinear spiking mechanisms of biological neurons. Echo state network (ESN) is a new type of recurrent neural network (RNN) that can overcome the disadvantages of traditional RNN. Inspired by the structure of ESN, this study proposes a new variant of NSNP systems, called echo spiking neural P (ESNP) systems, or the ESNP model. An ESNP system is essentially a specialized NSNP system equipped with an input layer and a readout layer. The ESNP system can also be viewed as a variant of ESN, where a specialized NSNP system is used as its reservoir. Owing to the use of spiking neurons, the state equation of ESNP model is different from the state equation of the ESN model. The proposed ESNP model is a recurrent-like model, and we used time-series forecasting as a case study to prove its capabilities. Experimental results demonstrate the effectiveness of the proposed ESNP model for time-series forecasting. (C) 2022 Elsevier B.V. All rights reserved. [Long, Lifan; Lugu, Rikong; Xiong, Xin; Liu, Qian; Peng, Hong] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China; [Wang, Jun] Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Peoples R China; [Orellana-Martin, David; Perez-Jimenez, Mario J.] Univ Seville, Dept Comp Sci & Artificial Intelligence, Res Grp Nat Comp, Seville 41012, Spain Xihua University; Xihua University; University of Sevilla Peng, H (corresponding author), Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China. ph.xhu@hotmail.com Perez Jimenez, Mario J./B-2114-2008; Orellana-Martin, David/C-6346-2016; Peng, Hong/C-8705-2012 Perez Jimenez, Mario J./0000-0002-5055-0102; Orellana-Martin, David/0000-0002-2892-6775; Peng, Hong/0000-0002-4736-0164 National Natural Science Foun-dation of China [62176216, 62076206]; European Social Fund; Junta de Andalucia National Natural Science Foun-dation of China(National Natural Science Foundation of China (NSFC)); European Social Fund(European Social Fund (ESF)); Junta de Andalucia(Junta de Andalucia) This work was partially supported by the National Natural Science Foun-dation of China (No. 62176216 and No. 62076206) , China. D. O.-M. also acknowledges Contrataci?n de Personal Investigador Doctor, the European Social Fund, and Junta de Andalucia (Paidi 2020) . 46 3 3 30 30 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. OCT 11 2022.0 253 109568 10.1016/j.knosys.2022.109568 0.0 AUG 2022 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4Y0EQ 2023-03-23 WOS:000861208200015 0
C Chen, YJ; Bai, YJ; Mitev, R; Wang, KB; Sadeghi, AR; Xu, WY ASSOC COMP MACHINERY Chen, Yanjiao; Bai, Yijie; Mitev, Richard; Wang, Kaibo; Sadeghi, Ahmad-Reza; Xu, Wenyuan FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants CCS '21: PROCEEDINGS OF THE 2021 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY English Proceedings Paper ACM SIGSAC Conference on Computer and Communications Security (ACM CCS) NOV 15-19, 2021 ELECTR NETWORK ACM SIGSAC,Assoc Comp Machinery voice assistants; fuzzy words; interpretable machine learning; security In the area of Internet of Things (IoT), voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles. To save power and protect user privacy, voice assistants send commands to the cloud only if a small set of pre-registered wake-up words are detected. However, voice assistants are shown to be vulnerable to the FakeWake phenomena, whereby they are inadvertently triggered by innocent-sounding fuzzy words. In this paper, we present a systematic investigation of the FakeWake phenomena from three aspects. To start with, we design the first fuzzy word generator to automatically and efficiently produce fuzzy words instead of searching through a swarm of audio materials. We manage to generate 965 fuzzy words covering 8 most popular English and Chinese smart speakers. To explain the causes underlying the FakeWake phenomena, we construct an interpretable tree-based decision model, which reveals phonetic features that contribute to false acceptance of fuzzy words by wake-up word detectors. Finally, we propose remedies to mitigate the effect of FakeWake. The results show that the strengthened models are not only resilient to fuzzy words but also achieve better overall performance on original training datasets. [Chen, Yanjiao; Bai, Yijie; Wang, Kaibo; Xu, Wenyuan] Zhejiang Univ, Hangzhou, Peoples R China; [Mitev, Richard; Sadeghi, Ahmad-Reza] Tech Univ Darmstadt, Darmstadt, Germany Zhejiang University; Technical University of Darmstadt Chen, YJ (corresponding author), Zhejiang Univ, Hangzhou, Peoples R China. chenyanjiao@zju.edu.cn; baiyj@zju.edu.cn; richard.mitev@trust.tu-darmstadt.de; kaibo@zju.edu.cn; ahmad.sadeghi@trust.tu-darmstadt.de; xuwenyuan@zju.edu.cn China NSFC [61925109, 61941120]; German Research Foundation (DFG) [CRC 1119 CROSSING]; Intel Private AI Center China NSFC(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG)(German Research Foundation (DFG)); Intel Private AI Center We thank all anonymous reviewers for their insightful comments on this paper. This work is funded in part by China NSFC Grant 61925109 and 61941120, as well as the German Research Foundation (DFG) within CRC 1119 CROSSING (S2 and P3) and the Intel Private AI Center. 48 1 1 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8454-4 2021.0 1861 1883 10.1145/3460120.3485365 0.0 23 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BS7XR Green Submitted 2023-03-23 WOS:000768478301050 0
J Romano, P; Ceol, A; Drager, A; Fiannaca, A; Giugno, R; La Rosa, M; Milanesi, L; Pfeffer, U; Rizzo, R; Shin, SY; Xia, JF; Urso, A Romano, Paolo; Ceol, Arnaud; Draeger, Andreas; Fiannaca, Antonino; Giugno, Rosalba; La Rosa, Massimo; Milanesi, Luciano; Pfeffer, Ulrich; Rizzo, Riccardo; Shin, Soo-Yong; Xia, Junfeng; Urso, Alfonso The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes INTRODUCTION BMC BIOINFORMATICS English Editorial Material CANCER-RISK; CYTOSCAPE The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was Methods, tools and platforms for Personalised Medicine in the Big Data Era, but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting. [Romano, Paolo; Pfeffer, Ulrich] IRCCS Osped Policlin San Martino, Largo Rosanna Benzi 10, I-16132 Genoa, Italy; [Ceol, Arnaud] European Inst Oncol IRCCS, I-20141 Milan, Italy; [Draeger, Andreas] Ctr Bioinformat Tubingen ZBIT, Computat Syst Biol Infect & Antimicrobial Resista, D-72074 Tubingen, Germany; [Draeger, Andreas] Univ Tubingen, Dept Comp Sci, D-72074 Tubingen, Germany; [Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso] Natl Res Council Italy, ICAR CNR, Inst High Performance Comp & Networking, I-90146 Palermo, Italy; [Giugno, Rosalba] Univ Verona, Dept Comp Sci, I-37134 Verona, Italy; [Milanesi, Luciano] Natl Res Council Italy, Inst Biomed Technol, ITB CNR, I-20090 Segrate, MI, Italy; [Shin, Soo-Yong] Sungkyunkwan Univ, SAIHST, Dept Digital Hlth, Seoul 03063, South Korea; [Xia, Junfeng] Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Anhui, Peoples R China IRCCS European Institute of Oncology (IEO); Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Consiglio Nazionale delle Ricerche (CNR); Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR-CNR); University of Verona; Consiglio Nazionale delle Ricerche (CNR); Istituto di Tecnologie Biomediche (ITB-CNR); Sungkyunkwan University (SKKU); Anhui University Romano, P (corresponding author), IRCCS Osped Policlin San Martino, Largo Rosanna Benzi 10, I-16132 Genoa, Italy. paolo.romano@hsanmartino.it FIANNACA, ANTONINO/AAY-4703-2020; Milanesi, Luciano/AAE-7230-2019; Dräger, Andreas/F-5620-2015; Romano, Paolo/M-8669-2013; URSO, ALFONSO/AAY-1060-2020; Ceol, Arnaud/H-3386-2012 FIANNACA, ANTONINO/0000-0002-0114-4984; Milanesi, Luciano/0000-0002-1201-3939; Dräger, Andreas/0000-0002-1240-5553; Romano, Paolo/0000-0003-4694-3883; Ceol, Arnaud/0000-0001-9533-4246 NETTAB NETTAB Publication of this article has been funded by the organizers of the NETTAB 2017 meeting by the budget of the event. 49 2 2 2 7 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics APR 18 2019.0 20 4.0 125 10.1186/s12859-019-2681-0 0.0 9 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology HU2OC 30999855.0 gold, Green Published 2023-03-23 WOS:000465109600001 0
J Badia-Melis, R; Qian, JP; Fan, BL; Hoyos-Echevarria, P; Ruiz-Garcia, L; Yang, XT Badia-Melis, R.; Qian, J. P.; Fan, B. L.; Hoyos-Echevarria, P.; Ruiz-Garcia, L.; Yang, X. T. Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples FOOD AND BIOPROCESS TECHNOLOGY English Article Cold chain; Temperature estimation; Artificial neural networks; Thermal image; Food monitoring FRUIT; QUALITY The inability to correctly implement and safeguard a product cold chain leads to premature product spoilage and increased product waste. Special care is required to both implement and monitor the cold chain for perishable goods in order to preserve them. Many technologies are available on the market today with varying levels of success. This article presents a new technique, namely thermal imaging predicts surface temperature over a pallet of apples whilst comparing packaging (plastic boxes and cardboard boxes). This temperature data was then introduced as an input in artificial neural network (ANN) software to estimate the temperature across the entire pallet. Results obtained (root mean squared error [RMSE]) indicate that the estimation with plastic boxes has an error of 0.41 A degrees C whilst the error, taking as a reference the surface temperature, would be RMSE 2.14 A degrees C. In the case of cardboard boxes, the estimation error is 0.086 A degrees C whilst only taking into account the thermal image, data would be RMSE 3.56 A degrees C. This article proves the concept of the possibility of temperature monitoring by ANN through thermal imaging technology. [Badia-Melis, R.; Ruiz-Garcia, L.] Univ Politecn Madrid, ETSI Agron, Dept Ingn Agroforestal, Edificio Motores,Avda Complutense 3, E-28040 Madrid, Spain; [Qian, J. P.; Fan, B. L.; Yang, X. T.] Beijing Acad Agr & Forestry Sci, NERCITA, Beijing 100097, Peoples R China; [Hoyos-Echevarria, P.] Univ Politecn Madrid, EUIT Agr, Dept Prod Agr, Avda Complutense S-N, E-28040 Madrid, Spain Universidad Politecnica de Madrid; Beijing Academy of Agriculture & Forestry Sciences (BAAFS); Universidad Politecnica de Madrid Badia-Melis, R (corresponding author), Univ Politecn Madrid, ETSI Agron, Dept Ingn Agroforestal, Edificio Motores,Avda Complutense 3, E-28040 Madrid, Spain. ricardobadia@live.com Ruiz-Garcia, Luis/AAF-9877-2019 Ruiz-Garcia, Luis/0000-0001-6656-6507 32 20 20 1 25 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1935-5130 1935-5149 FOOD BIOPROCESS TECH Food Bioprocess Technol. JUL 2016.0 9 7 1089 1099 10.1007/s11947-016-1700-7 0.0 11 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology DN9WQ 2023-03-23 WOS:000377431800002 0
J Zhong, YC; Wang, T; Pu, YF; Moreno, R Zhong, Yichen; Wang, Ting; Pu, Yanfeng; Moreno, Ramon An adaptive bilateral impedance control based on nonlinear disturbance observer for different flexible targets grasping COMPUTERS & ELECTRICAL ENGINEERING English Article Adaptive impedance control; Non-linear disturbance observer; Sliding mode control; Force tracking; Soft targets; Manipulator NEURAL-NETWORK CONTROL; ADMITTANCE CONTROL; FORCE TRACKING; MANIPULATORS The uncertainty of environmental parameters and time-varied characteristics usually generate contact force errors resulting the inaccuracy or even task failed in many tele-operation systems. In order to solve the problem, a novel composite bilateral tele-operation controller is proposed in this paper combining adaptive impedance control with nonlinear disturbance observer-based sliding mode controller. An adaptive impedance controller is constructed to achieve high -precision tracking of desired contact forces and ensure the safety of grasping various flexible targets. A sliding mode controller on the basis of a nonlinear disturbance observer is designed, so as to accurately estimate the composite disturbance signals as well as to ensure the stability and the high accuracy of the trajectory tracking. The stability of the system is proved by the Lyapunov function. Numerical simulations and real robot experiments are performed. Results shows that our method can greatly enhance the safety of the flexible target in the process, minimize the influence of disturbance signals on the system, and improve the robustness of the system. [Zhong, Yichen; Wang, Ting] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Peoples R China; [Pu, Yanfeng] Nanjing Customs Dist PR China, 360 Longpanzhong Rd, Nanjing, Peoples R China; [Moreno, Ramon] Grp Antolin, Dept Adv Mfg 4 0, Burgos, Spain Nanjing Tech University Wang, T (corresponding author), Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Peoples R China. wangting0310@njtech.edu.cn; puyanfeng@customs.gov.cn; ramon.m@outlook.es Moreno, Ramon/0000-0002-6610-9581 National Natural Science Foundation of China; Nanjing Customs Project; [61906086]; [2022KJ03] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Nanjing Customs Project; ; Acknowledgments This work was supported by the National Natural Science Foundation of China [grant numbers No. 61906086] and Nanjing Customs Project [2022KJ03] . 25 0 0 3 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-7906 1879-0755 COMPUT ELECTR ENG Comput. Electr. Eng. OCT 2022.0 103 108388 10.1016/j.compeleceng.2022.108388 0.0 13 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 6N6XX 2023-03-23 WOS:000889698500003 0
C Xu, JL; Nie, YM; Wang, P; Lopez, AM Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Xu, Jiaolong; Nie, Yiming; Wang, Peng; Lopez, Antonio M. Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural networks (BWNs) are the extreme case which quantizes the float-point into just 1 bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet- and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. [Xu, Jiaolong; Nie, Yiming] Natl Innovat Inst Def Technol, Unmanned Syst Res Ctr, Beijing, Peoples R China; [Lopez, Antonio M.] Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona, Spain Autonomous University of Barcelona; Centre de Visio per Computador (CVC) Xu, JL (corresponding author), Natl Innovat Inst Def Technol, Unmanned Syst Res Ctr, Beijing, Peoples R China. jiaolong_xu@126.com; antonio@cvc.uab.es National Natural Science Foundation of China (NSFC) [61601508, 61790565, 61803380]; Spanish project (Ministerio de Economia, Industria y Competitividad) [TIN2017-88709-R]; Generalitat de Catalunya CERCA Program; ACCIO agency; ICREA under the ICREA Academia program National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Spanish project (Ministerio de Economia, Industria y Competitividad); Generalitat de Catalunya CERCA Program; ACCIO agency; ICREA under the ICREA Academia program This work is supported by the National Natural Science Foundation of China (NSFC, NO. 61601508, 61790565 and 61803380). Antonio M. Lopez acknowledges the support of the Spanish project TIN2017-88709-R (Ministerio de Economia, Industria y Competitividad), and the Generalitat de Catalunya CERCA Program and its ACCIO agency. Antonio M. Lopez gratefully acknowledges the financial support by ICREA under the ICREA Academia program. 30 9 10 3 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 2379 2384 6 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN Green Submitted 2023-03-23 WOS:000494942301117 0
C Scaioni, M; Barazzetti, L; Gianinetto, M IEEE Scaioni, Marco; Barazzetti, Luigi; Gianinetto, Marco THE CONCEPT OF RELIABILITY APPLIED TO CO-REGISTRATION OF MULTIPLE SATELLITE TIME-SERIES OF IMAGES 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 26-31, 2015 Milan, ITALY IEEE Automation; Block adjustment; Registration; Reliability This paper wants to explore the application of the theory of reliability, which has become largely popular in the analysis of geodetic and photogrammetric networks, to the multiple co-registration of satellite time-series of images through block adjustment. Indeed the current and future availability of long series of satellite imagery can be exploited for the purpose of change detection and soil monitoring along time. Due to the large overlap between images, the multiple co-registration is a feasible task that may result in an increased reliability of the observations. On the other hand, to deal with such big data sets, automatic procedures have been developed for co-registration of satellite time-series. Due to the presence of residual errors in such procedures, the error analysis during the final block adjustment plays a key-role. Consequently, the background on the theory of reliability has been introduced here, and three simulated examples have been created in order to check the efficiency of the reliability analysis when dealing with time-series of medium resolution satellite imagery. [Scaioni, Marco] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Scaioni, Marco; Barazzetti, Luigi; Gianinetto, Marco] Politecn Milan, Dept ABC, I-20133 Milan, Italy Tongji University; Polytechnic University of Milan Scaioni, M (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. Gianinetto, Marco/D-6251-2011; Scaioni, Marco/Y-4398-2018 Gianinetto, Marco/0000-0002-6526-3093; BARAZZETTI, LUIGI/0000-0002-7859-8677 11 3 3 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-4799-7929-5 INT GEOSCI REMOTE SE 2015.0 4420 4423 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BE4GM 2023-03-23 WOS:000371696704125 0
C Zeng, ZQ; Bai, RB; Wang, L; Liu, S IEEE Zeng, Zhuoqi; Bai, Rubing; Wang, Lei; Liu, Steven NLOS identification and mitigation based on CIR with particle filter 2019 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) IEEE Wireless Communications and Networking Conference English Proceedings Paper IEEE Wireless Communications and Networking Conference (WCNC) APR 15-19, 2019 Marrakech, MOROCCO IEEE Localization; UWB; NLOS identification; CIR; probability density functions; correlation coefficient; weighted particle filter ALGORITHMS As key factors to guarantee accurate localization for ultra-wide band system (UWB), Non-line-of-sight (NLOS) identification and mitigation attract lots of attentions. One of the most effective methods for NLOS detection is based on the different characters of channel impulse response (CIR) under Line-of-sight (LOS) and NLOS condition. Features (such as kurtosis, standard deviation, energy, etc.) extracted from CIR are used for classification with the help of machine learning algorithm. Different from existing approaches, the NLOS and LOS probability density functions (PDF) of the correlation coefficient are calculated with the training data. The probability that the CIR is measured under LOS or NLOS is determined based on the PDF. A weighted particle filter is proposed to reduce the localization error, caused by NLOS. The weights for the proposed approach are obtained with the help of the measured variance of the LOS error and the NLOS/LOS probability. The weighted least squares (WLS) and standard particle filter are used as comparison. The real field test results show that the proposed weighted particle filter has better accuracy compared to the standard particle filter and the WLS. [Zeng, Zhuoqi; Bai, Rubing] Bosch China Investment Ltd, CR RTC5 AP, Shanghai, Peoples R China; [Wang, Lei] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China; [Liu, Steven] Univ Kaiserslautern, Inst Control Syst, Kaiserslautern, Germany Bosch; Tongji University; University of Kaiserslautern Zeng, ZQ (corresponding author), Bosch China Investment Ltd, CR RTC5 AP, Shanghai, Peoples R China. Zhuoqi.ZENG@cn.bosch.com; nedu_brb@hotmail.com; leiwang@tongji.edu.cn; sliu@eit.uni-kl.de Liu, Steven/0000-0002-3407-2745 National Key R&D Program of China [2017YFE0100900] National Key R&D Program of China This paper is supported by National Key R&D Program of China: No. 2017YFE0100900. 10 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1525-3511 978-1-5386-7646-2 IEEE WCNC 2019.0 6 Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Telecommunications BO5YG 2023-03-23 WOS:000519086302087 0
J Xue, Z; Zeng, X; Koehl, L Xue, Z.; Zeng, X.; Koehl, L. An intelligent method for the evaluation and prediction of fabric formability for men's suits TEXTILE RESEARCH JOURNAL English Article fabric formability; men's suit; sensory evaluation; Kawabata Evaluation System; principal component analysis; Adaptive Neuro-fuzzy Inference System MECHANICAL-PROPERTIES; SENSORY EVALUATION; QUALITY; HAND; IDENTIFICATION; PERCEPTIONS; CONSISTENCY; INFORMATION; DESIGN; FIBER Sixty-six commonly used suitings were selected as the experimental samples of the current study. The Kawabata Evaluation System was used to measure the mechanical properties of the samples. Each sample fabric was made into a shoulder-back as a part of a men's suit. In order to study the appropriateness of the samples for making good shaped men's suits, which is known as fabric formability, sensory evaluation methods have been applied to obtain panelists' assessments on the shape of the shoulder-backs. During data analysis, principal component analysis was initially adopted to reduce the complexity of the system by extracting a small number of important mechanical properties. Then, a fuzzy neural network was developed to model the underlying relations between the samples' formability and their mechanical properties. Finally, a number of testing samples were used to verify the effectiveness of the proposed predictive model. [Xue, Z.] Jiangnan Univ, Sch Text & Clothing, Key Lab Ecotext, Minist Educ, Wuxi, Jiangsu, Peoples R China; [Xue, Z.; Zeng, X.; Koehl, L.] ENSAIT, GEMTEX, Roubaix, France Jiangnan University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT) Xue, Z (corresponding author), Jiangnan Univ, Sch Text & Clothing, 1800 Lihu Ave, Wuxi 214122, Jiangsu, Peoples R China. zhebin.xue@hotmail.com Zeng, Xianyi/0000-0002-3236-6766 National Natural Science Foundation of China [61503154]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Fundamental Research Funds for the Central Universities [JUSRP11503] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant number 61503154), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Fundamental Research Funds for the Central Universities (grant number JUSRP11503). 46 3 3 2 14 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0040-5175 1746-7748 TEXT RES J Text. Res. J. FEB 2018.0 88 4 438 452 10.1177/0040517516681956 0.0 15 Materials Science, Textiles Science Citation Index Expanded (SCI-EXPANDED) Materials Science FS8PA 2023-03-23 WOS:000422673300007 0
C Liu, K; Roemer, F; da Costa, JPCL; Xiong, J; Yan, YS; Wang, WQ; Del Galdo, G IEEE Liu, Kefei; Roemer, Florian; da Costa, Joao Paulo C. L.; Xiong, Jie; Yan, Yi-Sheng; Wang, Wen-Qin; Del Galdo, Giovanni Tensor-Based Sparsity Order Estimation for Big Data Applications 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) European Signal Processing Conference English Proceedings Paper 25th European Signal Processing Conference (EUSIPCO) AUG 28-SEP 02, 2017 GREECE European Assoc Signal Proc,IEEE Signal Proc Soc,Inst Commun & Comp Syst,Natl Tech Univ Athens,Plaisio,Mitsubishi Electr Res Lab,MathWorks,Blue Star Ferries,Moto Island Bicycle Rentals Compressed sensing; sparsity order; order selection; tensor decomposition DATA ANALYTICS; NUMBER; COMPONENTS; SIGNALS; MODELS In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections into a low-dimensional space. In this case, the degree of sparsity, referred to as sparsity order, is of high interest. It has recently been shown that if the measurement matrix obey certain structural constraints, one can estimate the sparsity order directly from the compressed data. The rich structure of the measurement matrix allows to rearrange the multiple-snapshot measurement vectors into a fourth-order tensor with rank equal to the desired sparsity order. In this paper, we exploit the multilinear structure of the data for accurate sparsity order estimation with improved identifiability. We discuss the choice of the parameters, i.e., the block size, block offset, and number of blocks, to maximize the sparsity order that can be inferred from a certain number of observations, and compare state-of-the-art order selection algorithms for sparsity order estimation under the chosen parameter settings. By performing an extensive campaign of simulations, we show that the discriminant function based method and the random matrix theory algorithm outperform other approaches in small and large snapshot-number scenarios, respectively. [Liu, Kefei] Indiana Univ Sch Med, Indianapolis, IN 46202 USA; [Roemer, Florian; Del Galdo, Giovanni] Ilmenau Univ Technol, Inst Informat Technol, Ilmenau, Germany; [da Costa, Joao Paulo C. L.] Univ Brasilia, Dept Elect Engn, Brasilia, DF, Brazil; [Xiong, Jie; Yan, Yi-Sheng; Wang, Wen-Qin] Univ Elect Sci & Technol China, Sch Commun Engn, Chengdu, Sichuan, Peoples R China; [Del Galdo, Giovanni] Fraunhofer Inst Integrated Circuits IIS, Ilmenau, Germany Indiana University System; Indiana University Bloomington; Technische Universitat Ilmenau; Universidade de Brasilia; University of Electronic Science & Technology of China; Fraunhofer Gesellschaft Liu, K (corresponding author), Indiana Univ Sch Med, Indianapolis, IN 46202 USA. Roemer, Florian/J-8508-2013; Javidi da Costa, João Paulo/C-7382-2013; Del Galdo, Giovanni/AAD-8822-2019 Roemer, Florian/0000-0002-6403-6489; Javidi da Costa, João Paulo/0000-0002-8616-4924; Del Galdo, Giovanni/0000-0002-7195-4253 Carl-Zeiss Foundation under the postdoctoral scholarship project EMBiCoS; Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF) [01/2017]; DPI/DPG/UnB [01/2017]; Fundacao de Empreendimentos Cientificos e Tecnologicos (Finatec) [01/2017]; CNPq [303905/ 2014-0] Carl-Zeiss Foundation under the postdoctoral scholarship project EMBiCoS; Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)); DPI/DPG/UnB; Fundacao de Empreendimentos Cientificos e Tecnologicos (Finatec); CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work was partially supported by the Carl-Zeiss Foundation under the postdoctoral scholarship project EMBiCoS. The authors would also like to thank the Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF) (call no. 01/2017), the DPI/DPG/UnB and Fundacao de Empreendimentos Cientificos e Tecnologicos (Finatec) (call no. 01/2017), and the CNPq (Grant no. 303905/ 2014-0) for their financial supports. 21 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2076-1465 978-0-9928-6267-1 EUR SIGNAL PR CONF 2017.0 648 652 5 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BJ6ZZ Green Submitted 2023-03-23 WOS:000426986000131 0
J He, NJ; Fang, LY; Li, ST; Plaza, A; Plaza, J He, Nanjun; Fang, Leyuan; Li, Shutao; Plaza, Antonio; Plaza, Javier Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature fusion; multilayer feature maps; pre-trained convolutional neural networks (CNN); remote sensing scene classification FEATURES; REPRESENTATION This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following three steps. First, a pretrained CNN model is used to extract multilayer feature maps. Then, the feature maps are stacked together, and a covariance matrix is calculated for the stacked features. Each entry of the resulting covariance matrix stands for the covariance of two different feature maps, which provides a natural and innovative way to exploit the complementary information provided by feature maps coming from different layers. Finally, the extracted covariance matrices are used as features for classification by a support vector machine. The experimental results, conducted on three challenging data sets, demonstrate that the proposed MSCP method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods. [He, Nanjun; Fang, Leyuan; Li, Shutao] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [He, Nanjun; Plaza, Antonio; Plaza, Javier] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, E-10003 Caceres, Spain Hunan University; Universidad de Extremadura Fang, LY (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. henanjun@hnu.edu.cn; fangleyuan@gmail.com; shutao_li@hnu.edu.cn; aplaza@unex.es; jplaza@unex.es Li, Shutao/Y-3102-2019; Plaza, Antonio/C-4455-2008; Plaza, Javier/C-4452-2008 Li, Shutao/0000-0002-0585-9848; Plaza, Antonio/0000-0002-9613-1659; Plaza, Javier/0000-0002-2384-9141; Fang, Leyuan/0000-0003-2351-4461 National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]; National Natural Science Foundation for Young Scientist of China [61501180]; National Natural Science Foundation [61771192, 61471167]; Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]; China Postdoctoral Science Foundation [2017T100597]; China Scholarship Council National Natural Science Fund of China for International Cooperation and Exchanges; National Natural Science Foundation for Young Scientist of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Fund of Hunan Province for Science and Technology Plan Project; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); China Scholarship Council(China Scholarship Council) This work was supported in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, in part by the National Natural Science Foundation for Young Scientist of China under Grant 61501180, in part by the National Natural Science Foundation under Grant 61771192 and Grant 61471167, in part by the Fund of Hunan Province for Science and Technology Plan Project under Grant 2017RS3024, in part by the China Postdoctoral Science Foundation under Project 2017T100597, and in part by the China Scholarship Council. 66 175 177 9 57 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing DEC 2018.0 56 12 6899 6910 10.1109/TGRS.2018.2845668 0.0 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology HC2GT 2023-03-23 WOS:000451621000003 0
J Li, D; Aubry, A; De Maio, A; Fu, YW; Marano, S Li, Dan; Aubry, Augusto; De Maio, Antonio; Fu, Yaowen; Marano, Stefano Detection by Block- and Band-Permuted Data IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Unlabeled detection; shuffled bits; block permutation; banded permutation; detection algorithms ASSIGNMENT; ALGORITHMS; DECISIONS; SEQUENCE Unlabeled detection is an important problem in large sensor networks faced with big-data applications. The focus of this paper is on decision problems in which the data vector received at the fusion center (FC), tasked to perform the final inference, undergoes an unknown permutation. Two representative permutation models are considered, whose structure is imposed by practical considerations. The first is the arc-block permutation model, where the observations are split into different blocks of size arc, and independent data permutations affect each block. The second is the r-banded permutation model, in which each sample of the vector received by the FC lies at most r positions ahead or behind its original location. To solve these challenging detection tasks, three detectors for both permutation models are proposed. The first is inspired by the uniformly most powerful invariant principle; the second relies on a distribution-averaged strategy; and the third is designed according the generalized likelihood ratio approach. The performance of these detectors is investigated by computer experiments and their relative merits are discussed. [Li, Dan; Fu, Yaowen] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China; [Aubry, Augusto; De Maio, Antonio] Univ Naples Federico II, DIETI, I-80125 Naples, Italy; [Marano, Stefano] Univ Salerno, DIEM, I-84084 Fisciano, Italy National University of Defense Technology - China; University of Naples Federico II; University of Salerno Marano, S (corresponding author), Univ Salerno, DIEM, I-84084 Fisciano, Italy. lidan_nudt@hotmail.com; augusto.aubry@unina.it; ademaio@unina.it; fuyaowen@nudt.edu.cn; marano@unisa.it MARANO, Stefano/0000-0002-5307-0980; Fu, Yaowen/0000-0001-7081-266X 62 0 0 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2022.0 70 5778 5790 10.1109/TSP.2022.3224646 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 7F8IS 2023-03-23 WOS:000902084900003 0
J Wang, SY; Mei, G; Cuomo, S Wang, Shuangyan; Mei, Gang; Cuomo, Salvatore A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE English Article complex network analysis; data mining; GPS trajectory; human mobility The mining of human mobility can be exploited to support the design of traffic planning, route recommendations, urban planning, emergency management, and land use. Currently, various methods such as the machine learning algorithms, statistical methods, and semantic analysis are widely applied to identify and extract human mobility patterns. In this paper, we propose a simple and generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis. The essential ideas behind the proposed paradigm mainly include (1) creating weighted complex networks of GPS trajectories and (2) extracting the human mobility patterns by analyzing the structures and metrics of the created complex networks of GPS trajectories. To evaluate the performance of the proposed paradigm, we design five groups of experiments and identify the mobility patterns of the selected five persons based on the selected five network analysis metrics. Experimental results indicate that (1) the proposed paradigm is effective and (2) the quite interesting potential information about the human mobility can be mined easily. The proposed paradigm is simple and generic, which can be employed to rapidly identify the human mobility patterns based on the GPS trajectory data. [Wang, Shuangyan; Mei, Gang] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China; [Cuomo, Salvatore] Univ Naples Federico II, Dept Math & Applicat, Naples, Italy China University of Geosciences; University of Naples Federico II Mei, G (corresponding author), China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China. gang.mei@cugb.edu.cn Mei, Gang/C-9124-2016; Cuomo, Salvatore/Q-1365-2016 Mei, Gang/0000-0003-0026-5423; Cuomo, Salvatore/0000-0003-4128-2588 33 2 2 3 30 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1532-0626 1532-0634 CONCURR COMP-PRACT E Concurr. Comput.-Pract. Exp. FEB 25 2021.0 33 4 SI e5335 10.1002/cpe.5335 0.0 18 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science QA8NY 2023-03-23 WOS:000613700700010 0
C Yang, KL; Bergasa, LM; Romera, E; Wang, J; Wang, KW; Lopez, E IEEE Yang, Kailun; Bergasa, Luis M.; Romera, Eduardo; Wang, Juan; Wang, Kaiwei; Lopez, Elena Perception framework of water hazards beyond traversability for real-world navigation assistance systems 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) English Proceedings Paper IEEE International Conference on Robotics and Biomimetics (ROBIO) DEC 12-15, 2018 Kuala Lumpur, MALAYSIA IEEE,IEEE Robot & Automat Soc,Shenzhen Hans Robot Co Ltd,Guangdong Univ Technol,Guangzhou Univ,Guangdong Inst Intelligent Mfg,Foshan Biowin Robot & Automat Co Ltd Traversability perception constitutes an important task for robotics and visually impaired people, which aims to detect obstacle-free paths that allow individuals to ambulate with suitable navigational aids. However, approaches that help prevent stepping into water areas are rare in state-of-art autonomous or assisted navigation systems. To address water hazard detection, this paper proposes a pRGB-D-SS perception framework, which incorporates: Polarization imaging (p), RGB-D sensory awareness and real-time Semantic Segmentation (SS). More specifically, as large water areas and small water puddles exhibit different characteristics, the detection of these two kinds of hazards pursue different pipelines. In our contribution, large water areas are detected together with traversable areas through pixel-wise semantic segmentation. Comparatively, the detection of water puddles extends the convolutional neural network based segmentation by using polarized RGB-D information as the primary cue. Beyond enhanced traversability awareness, it enables a unified framework of water hazard detection, which has been evaluated in a comprehensive variety of real-world surroundings on two wearable systems including a pair of commercial intelligent spectacles and a customized prototype, demonstrating the speeded-up efficiency and augmented sensorial/environmental robustness. [Yang, Kailun; Wang, Juan; Wang, Kaiwei] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Zhejiang, Peoples R China; [Bergasa, Luis M.; Romera, Eduardo; Lopez, Elena] Univ Alcala, Dept Elect, Madrid, Spain Zhejiang University; Universidad de Alcala Yang, KL (corresponding author), Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Zhejiang, Peoples R China. elnino@zju.edu.cn; luism.bergasa@uah.es; eduardo.romera@edu.uah.es; zjuoptwj@zju.edu.cn; wangkaiwei@zju.edu.cn; elena@depeca.uah.es Yang, Kailun/U-2491-2019; Bergasa, Luis M./H-9810-2013 Yang, Kailun/0000-0002-1090-667X; Bergasa, Luis M./0000-0002-0087-3077 Spanish MINECO/FEDER through the SmartElderlyCar project [TRA2015-70501-C2-1-R]; RoboCity2030-III-CM project - Programas de actividades I+D (CAM) [S2013/MIT-2748]; EU Structural Funds Spanish MINECO/FEDER through the SmartElderlyCar project(Spanish Government); RoboCity2030-III-CM project - Programas de actividades I+D (CAM); EU Structural Funds This work has been partially funded by the Spanish MINECO/FEDER through the SmartElderlyCar project (TRA2015-70501-C2-1-R) and from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds. 20 3 3 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-0377-8 2018.0 186 191 6 Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Robotics BM8BC 2023-03-23 WOS:000468772200029 0
J Huyan, N; Quan, D; Zhang, XR; Liang, XF; Chanussot, J; Jiao, LC Huyan, Ning; Quan, Dou; Zhang, Xiangrong; Liang, Xuefeng; Chanussot, Jocelyn; Jiao, Licheng Unsupervised Outlier Detection Using Memory and Contrastive Learning IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Feature extraction; Prototypes; Image reconstruction; Training; Memory modules; Anomaly detection; Detectors; Anomaly detection; contrastive learning; memory module; deep clustering; unsupervised learning SUPPORT Outlier detection is to separate anomalous data from inliers in the dataset. Recently, the most deep learning methods of outlier detection leverage an auxiliary reconstruction task by assuming that outliers are more difficult to recover than normal samples (inliers). However, it is not always true in deep auto-encoder (AE) based models. The auto-encoder based detectors may recover certain outliers even if outliers are not in the training data, because they do not constrain the feature learning. Instead, we think outlier detection can be done in the feature space by measuring the distance between outliers' features and the consistency feature of inliers. To achieve this, we propose an unsupervised outlier detection method using a memory module and a contrastive learning module (MCOD). The memory module constrains the consistency of features, which merely represent the normal data. The contrastive learning module learns more discriminative features, which boosts the distinction between outliers and inliers. Extensive experiments on four benchmark datasets show that our proposed MCOD performs well and outperforms eleven state-of-the-art methods. [Huyan, Ning; Quan, Dou] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China; [Zhang, Xiangrong] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China; [Liang, Xuefeng] Xidian Univ, Sch AI, Xian 710071, Peoples R China; [Chanussot, Jocelyn] Grenoble Inst Technol, F-38402 Grenoble, France; [Jiao, Licheng] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Int Res Ctr Intelligent Percept & Computat, Xian 710071, Peoples R China Xidian University; Xidian University; Xidian University; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Xidian University Zhang, XR (corresponding author), Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China.;Liang, XF (corresponding author), Xidian Univ, Sch AI, Xian 710071, Peoples R China. xrzhang@mail.xidian.edu.cn; xliang@xidian.edu.cn Huyan, Ning/AAM-1882-2020 Huyan, Ning/0000-0002-6123-8659; Chanussot, Jocelyn/0000-0003-4817-2875; liang, xuefeng/0000-0002-1448-0477; Quan, Dou/0000-0001-6943-4657 National Natural Science Foundation of China [61801351, 61871306, 61802295]; Key Research and Development Program in the Shaanxi Province of China [2019ZDLGY03-08]; 111 Project [B07048] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program in the Shaanxi Province of China; 111 Project(Ministry of Education, China - 111 Project) This work was supported in part by the National Natural Science Foundation of China under Grant 61801351, Grant 61871306, and Grant 61802295; in part by the Key Research and Development Program in the Shaanxi Province of China under Grant 2019ZDLGY03-08; and in part by the 111 Project under Grant B07048. 57 1 1 9 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2022.0 31 6440 6454 10.1109/TIP.2022.3211476 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5M3XM 36215361.0 Green Submitted 2023-03-23 WOS:000871032400002 0
J Yang, L; Lin, LN; Fan, L; Liu, N; Huang, LY; Xu, YS; Mertikas, SP; Jia, YJ; Lin, MS Yang, Lei; Lin, Lina; Fan, Long; Liu, Na; Huang, Lingyong; Xu, Yongsheng; Mertikas, Stelios P.; Jia, Yongjun; Lin, Mingsen Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis REMOTE SENSING English Review satellite altimetry; scientometrics; bibliometrics; meta-analysis SIGNIFICANT WAVE HEIGHT; SEA-LEVEL BUDGET; BACKSCATTER COEFFICIENTS; GEOPHYSICAL APPLICATIONS; MESOSCALE VARIABILITY; GENERAL-CIRCULATION; OCEAN CIRCULATION; RADAR ALTIMETER; ICE-SHEET; CHINA Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry's overall development and research front. [Yang, Lei; Lin, Lina; Liu, Na] Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China; [Yang, Lei] Minist Nat Resource, Key Lab Marine Environm Survey Technol & Applicat, Guangzhou 510300, Peoples R China; [Yang, Lei; Xu, Yongsheng] Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China; [Yang, Lei; Lin, Lina; Liu, Na; Xu, Yongsheng] Qingdao Natl Lab Marine Sci & Technol, Qingdao 266373, Peoples R China; [Yang, Lei; Xu, Yongsheng] Univ Chinese Acad Sci, Beijing 100864, Peoples R China; [Yang, Lei; Lin, Lina; Liu, Na] Minsitry Nat Resources, Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China; [Fan, Long] Naval Res Acad, Tianjin 300061, Peoples R China; [Huang, Lingyong] State Key Lab Geoinformat Engn, Xian 710054, Peoples R China; [Mertikas, Stelios P.] Tech Univ Crete, Geodesy & Geomat Engn Lab, GR-73100 Khania, Greece; [Jia, Yongjun; Lin, Mingsen] Natl Satellite Ocean Applicat Serv, Beijing 100081, Peoples R China Ministry of Natural Resources of the People's Republic of China; Chinese Academy of Sciences; Institute of Oceanology, CAS; Qingdao National Laboratory for Marine Science & Technology; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Technical University of Crete; National Satellite Ocean Application Service Fan, L (corresponding author), Naval Res Acad, Tianjin 300061, Peoples R China. leiyang@fio.org.cn; linln@fio.org.cn; fl_arc@fio.org.cn; liun@fio.org.cn; huanglingyong@nssc.ac.cn; yongsheng.xu@qdio.ac.cn; smertikas@tuc.gr; jiayongjun@mail.nsoas.org.cn; mslin@mail.nsoas.org.cn Xu, Yongsheng/GZA-6022-2022 Xu, Yongsheng/0000-0002-5064-9284; Yang, Lei/0000-0002-6503-0505; LIN, Mingsen/0000-0001-5468-1591 Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resource [MESTA-2020-B010]; National Natural Science Foundation of China [41806214]; Global Change and Air-Sea Interaction [ZY0722031]; Dragon 5 Cooperation (2020-2024) Project of the European Space Agency; National Remote Sensing Center of China (NRSCC, under MOST) Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resource; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Global Change and Air-Sea Interaction; Dragon 5 Cooperation (2020-2024) Project of the European Space Agency; National Remote Sensing Center of China (NRSCC, under MOST) This research was funded by the Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resource (MESTA-2020-B010), National Natural Science Foundation of China (41806214) and Global Change and Air-Sea Interaction (ZY0722031). We acknowledge the support of the Dragon 5 Cooperation (2020-2024) Project of the European Space Agency and the National Remote Sensing Center of China (NRSCC, under MOST) with Principal Investigators Prof. S. P. Mertikas (Technical University of Crete, Chania, Greece) and Prof. Mingsen Lin (NSOAS, Beijing, China). 140 2 2 18 26 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUL 2022.0 14 14 3332 10.3390/rs14143332 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 3G7ND gold 2023-03-23 WOS:000831535700001 0
J Lv, ZH; Piccialli, F Lv, Zhihan; Piccialli, Francesco The Security of Medical Data on Internet Based on Differential Privacy Technology ACM TRANSACTIONS ON INTERNET TECHNOLOGY English Article DP; k-anonymity; privacy leakage risk; information loss; medical data HEALTH-CARE; BIG DATA; FDA APPROVAL; SAFETY; MANAGEMENT; EFFICACY The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security. [Lv, Zhihan] Qingdao Univ, Sch Data Sci & Software Engn, 308 Ningxia Rd, Qingdao, Peoples R China; [Piccialli, Francesco] Univ Naples Federico II, Dept Matemath & Applicat Renato Caccioppoli, Via Cinthia, I-80126 Naples, Italy Qingdao University; University of Naples Federico II Lv, ZH (corresponding author), Qingdao Univ, Sch Data Sci & Software Engn, 308 Ningxia Rd, Qingdao, Peoples R China. lvzhihan@gmail.com; francesco.piccialli@unina.it Lv, Zhihan/I-3187-2014; Lv, Zhihan/GLR-6000-2022 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China [61902203]; Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province [2019JZZY020101] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province This work was supported by the National Natural Science Foundation of China (61902203) and the Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101). Authors' addresses: Z. Lv (corresponding author), School of Data Science and Software Engineering, Qingdao University, Qingdao, 308 Ningxia Road, Qingdao, China; email: lvzhihan@gmail.com;F.Piccialli, Department of Matemathics and Applications Renato Caccioppoli, University of Naples Federico II, Via Cinthia, 80126, Naples, Italy; email: francesco.piccialli@unina.it. 53 18 18 2 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1533-5399 1557-6051 ACM T INTERNET TECHN ACM Trans. Internet. Technol. AUG 2021.0 21 3 55 10.1145/3382769 0.0 18 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science WQ2BW 2023-03-23 WOS:000713626400003 0
J Shen, J; Wang, C; Castiglione, A; Liu, DZ; Esposito, C Shen, Jian; Wang, Chen; Castiglione, Aniello; Liu, Dengzhi; Esposito, Christian Trustworthiness Evaluation-Based Routing Protocol for Incompletely Predictable Vehicular Ad Hoc Networks IEEE TRANSACTIONS ON BIG DATA English Article Incompletely predicable networks; VANETs; trustworthiness evaluation; routing protocol; self-configured networks TRUST EVALUATION; CLOUD; ALGORITHM; MODELS; DTN Incompletely predictable vehicular ad hoc networks is a type of networks where vehicles move in a certain range or just in a particular tendency, which is very similar to some circumstances in reality. However, how to route in such type of networks more efficiently according to the node motion characteristics and related historical big data is still an open issue. In this paper, we propose a novel routing protocol named trustworthiness evaluation-based routing protocol (TERP). In our protocol, trustworthiness of each individual is calculated by the cloud depending on the attribute parameters uploaded by the corresponding vehicle. In addition, according to the trustworthiness provided by the cloud, vehicles in the network choose reliable forward nodes and complete the entire route. The analysis shows that our protocol can effectively improve the fairness of the trustworthiness judgement. In the simulation, our protocol has a good performance in terms of the packet delivery ratio, normalized routing overhead and average end-to-end delay. [Shen, Jian] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Nanjing 210044, Peoples R China; [Shen, Jian] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China; [Shen, Jian] Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100864, Peoples R China; [Wang, Chen; Liu, Dengzhi] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Castiglione, Aniello; Esposito, Christian] Univ Salerno, Dept Comp Sci, I-84084 Fisciano, SA, Italy Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Institute of Information Engineering, CAS; Nanjing University of Information Science & Technology; University of Salerno Esposito, C (corresponding author), Univ Salerno, Dept Comp Sci, I-84084 Fisciano, SA, Italy. s_shenjian@126.com; wangchennuist@126.com; castiglione@ieee.org; liudzdh@qq.com; esposito@unisa.it Castiglione, Aniello/F-1034-2011; ESPOSITO, Christiancarmine/AAI-4626-2020; Wang, Chen/GZL-4309-2022 Castiglione, Aniello/0000-0003-0571-1074; ESPOSITO, Christiancarmine/0000-0002-0085-0748; Wang, Chen/0000-0002-3951-3159; Shen, Jian/0000-0003-0519-9058 National Natural Science Foundation of China [61672295, 61300237, U1405254]; State Key Laboratory of Information Security [2017-MS-10]; 2015 Project of Six Personnel in Jiangsu Province [R2015L06]; CICAEET fund; PAPD fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Information Security; 2015 Project of Six Personnel in Jiangsu Province; CICAEET fund; PAPD fund This work is supported by the National Natural Science Foundation of China under Grant No. 61672295, No. 61300237, No. U1405254, the State Key Laboratory of Information Security under Grant No. 2017-MS-10, the 2015 Project of Six Personnel in Jiangsu Province under Grant No. R2015L06, the CICAEET fund, and the PAPD fund. 78 6 6 0 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2332-7790 IEEE T BIG DATA IEEE Trans. Big Data FEB 1 2022.0 8 1 48 59 10.1109/TBDATA.2017.2710347 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science YG8HU 2023-03-23 WOS:000742723200005 0
J Wang, ZL; Wu, DH; Gravina, R; Fortino, G; Jiang, YM; Tang, K Wang, Zhelong; Wu, Donghui; Gravina, Raffaele; Fortino, Giancarlo; Jiang, Yongmei; Tang, Kai Kernel fusion based extreme learning machine for cross-location activity recognition INFORMATION FUSION English Article Human activity recognition; Extreme learning machine; Inertial sensors; Mixed kernel; Machine learning BODY SENSOR NETWORKS; TRIAXIAL ACCELEROMETER; FEEDFORWARD NETWORKS; FEATURE-SELECTION; WEARABLE SENSORS; NEURAL-NETWORKS; DISPLACEMENT; ROBUSTNESS; CLASSIFIER; ALGORITHM Fixed placements of inertial sensors have been utilized by previous human activity recognition algorithms to train the classifier. However, the distribution of sensor data is seriously affected by the sensor placement. The performance will be degraded when the model trained on one placement is used in others. In order to tackle this problem, a fast and robust human activity recognition model called TransM-RKELM (Transfer learning mixed and reduced kernel Extreme Learning Machine) is proposed in this paper; It uses a kernel fusion method to reduce the influence by the'choice of kernel function and the reduced kernel is utilized to reduce the computational cost. After realizing initial activity recognition model by mixed and reduced kernel extreme learning model (M-RKELM), in the online phase M-RKELM is utilized to classify the activity and adapt the model to new locations based on high confident recognition results in real time. Experimental results show that the proposed model can adapt the classifier to new sensor locations quickly and obtain good recognition performance. (C) 2017 Elsevier B.V. All rights reserved. [Wang, Zhelong; Wu, Donghui] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China; [Wu, Donghui] Zhengzhou Univ Light Ind, Fac Elect Informat & Elect Engn, Zhengzhou 450045, Henan, Peoples R China; [Gravina, Raffaele; Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst, Via P Bucci, I-87036 Arcavacata Di Rende, CS, Italy; [Jiang, Yongmei] Dalian Med Univ, Affiliated Hosp 2, Dalian 1160272, Liaoning, Peoples R China; [Tang, Kai] Dalian Med Univ, Affiliated Hosp 1, Dalian 116044, Liaoning, Peoples R China Dalian University of Technology; Zhengzhou University of Light Industry; University of Calabria; Dalian Medical University; Dalian Medical University Wu, DH (corresponding author), Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China.;Wu, DH (corresponding author), Zhengzhou Univ Light Ind, Fac Elect Informat & Elect Engn, Zhengzhou 450045, Henan, Peoples R China. w_donghui@163.com Fortino, Giancarlo/J-2950-2017; Wu, Donghui/AAE-9238-2020; wang, z/A-4607-2016 Fortino, Giancarlo/0000-0002-4039-891X; wang, z/0000-0003-4959-3372 National Natural Science Foundation [61473058]; Fundamental Research Funds for the Central Universities [DUT15213114] National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by National Natural Science Foundation of China under Grant No.61473058, and Fundamental Research Funds for the Central Universities (DUT15213114). 40 61 63 0 93 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion SEP 2017.0 37 1 9 10.1016/j.inffus.2017.01.004 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science ES4PV 2023-03-23 WOS:000399518100001 0
J Xiao, HB; Zhu, JT; Fu, LP; Zhang, SH; Fan, JH Xiao, Hubing; Zhu, Jingtian; Fu, Liping; Zhang, Shaohua; Fan, Junhui The radio dichotomy of active galactic nuclei PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF JAPAN English Article galaxies: active; galaxies: nuclei; quasars: general BLACK-HOLE MASS; BRIGHT QUASAR SURVEY; QUIET; LOUD; EMISSION; SPECTRA; LUMINOSITY; ACCRETION; ORIGIN The question of radio dichotomy in active galactic nuclei (AGNs) is still in debate, even though it has been discussed for more than 40 years. In order to solve this old riddle, we collect a sample of AGNs with optical B band and radio 6 cm wavelength data to analyze the radio loudness log R. Our results indicate a separation of log R = 1.37 +/- 0.02 between radio-loud (RL) AGNs and radio-quiet (RQ) AGNs, suggesting the existence of an RL/RQ dichotomy. For the first time, we suggest combining radio luminosity and radio loudness as a double criterion to divide AGNs into RLs and RQs to avoid misclassification problems that may happen in the single-criterion scenario; we propose the double-criterion dividing line log L-6 cm = -2.7log R + 44.3 by using a machine-learning method. In addition, the key point of the RL/RQ dichotomy is the origin of radio emission for the two classes, and we suggest the radio emission from RLs and RQs share the same origin, e.g., jets and mini-jets (aborted-jet or outflow), through a correlation study between radio 6 cm luminosity and optical B-band luminosity. [Xiao, Hubing; Fu, Liping; Zhang, Shaohua] Shanghai Normal Univ, Shanghai Key Lab Astrophys, Shanghai 200234, Peoples R China; [Zhu, Jingtian; Fan, Junhui] Guangzhou Univ, Ctr Astrophys, Guangzhou 510006, Peoples R China; [Zhu, Jingtian] Univ Padua, Dept Phys & Astron G Galilei, I-35131 Padua, PD, Italy; [Zhu, Jingtian] Ist Nazl Fis Nucl, I-35131 Padua, PD, Italy; [Fan, Junhui] Key Lab Astron Observat & Technol Guangzhou, Guangzhou 510006, Peoples R China; [Fan, Junhui] Dept Educ Guangdong Prov, Astron Sci & Technol Res Lab, Guangzhou 510006, Peoples R China Shanghai Normal University; Guangzhou University; University of Padua; Istituto Nazionale di Fisica Nucleare (INFN) Zhu, JT; Fan, JH (corresponding author), Guangzhou Univ, Ctr Astrophys, Guangzhou 510006, Peoples R China.;Zhu, JT (corresponding author), Univ Padua, Dept Phys & Astron G Galilei, I-35131 Padua, PD, Italy.;Zhu, JT (corresponding author), Ist Nazl Fis Nucl, I-35131 Padua, PD, Italy.;Fan, JH (corresponding author), Key Lab Astron Observat & Technol Guangzhou, Guangzhou 510006, Peoples R China.;Fan, JH (corresponding author), Dept Educ Guangdong Prov, Astron Sci & Technol Res Lab, Guangzhou 510006, Peoples R China. jingtian.zhu@studenti.unipd.it; fjh@gzhu.edu.cn key laboratory for astrophysics of Shanghai; National Natural Science Foundation of China (NSFC) [11933002]; STCSM [18590780100, 19590780100]; SMEC Innovation Program [2019-01-07-00-02-E00032]; Shuguang Program [19SG41]; Natural Science Foundation of Shanghai [20ZR1473600]; NSFC [NSFC U2031201, NSFC 11733001]; China Manned Space [CMS-CSST-2021-A06] key laboratory for astrophysics of Shanghai; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); STCSM(Science & Technology Commission of Shanghai Municipality (STCSM)); SMEC Innovation Program; Shuguang Program; Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); NSFC(National Natural Science Foundation of China (NSFC)); China Manned Space We thank the support from our laboratory, the key laboratory for astrophysics of Shanghai. Meanwhile, L.P.F acknowledges the support from the National Natural Science Foundation of China (NSFC) grants 11933002, STCSM grants 18590780100, 19590780100, SMEC Innovation Program 2019-01-07-00-02-E00032 and Shuguang Program 19SG41. S.H.Z acknowledges the support from by Natural Science Foundation of Shanghai (20ZR1473600). J.H.F acknowledges the support by the NSFC (NSFC U2031201, NSFC 11733001) and the science research grants from the China Manned Space Project with NO. CMS-CSST-2021-A06. 42 1 1 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0004-6264 2053-051X PUBL ASTRON SOC JPN Publ. Astron. Soc. Jpn. APR 4 2022.0 74 2 239 246 10.1093/pasj/psab121 0.0 JAN 2022 8 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 0G0TA Green Submitted 2023-03-23 WOS:000768368400001 0
C Yang, KL; Cheng, RQ; Bergasa, LM; Romera, E; Wang, KW; Long, NB IEEE Yang, Kailun; Cheng, Ruiqi; Bergasa, Luis M.; Romera, Eduardo; Wang, Kaiwei; Long, Ningbo Intersection perception through real-time semantic segmentation to assist navigation of visually impaired pedestrians 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) English Proceedings Paper IEEE International Conference on Robotics and Biomimetics (ROBIO) DEC 12-15, 2018 Kuala Lumpur, MALAYSIA IEEE,IEEE Robot & Automat Soc,Shenzhen Hans Robot Co Ltd,Guangdong Univ Technol,Guangzhou Univ,Guangdong Inst Intelligent Mfg,Foshan Biowin Robot & Automat Co Ltd Intersection navigation comprises one of the major ingredient of Intelligent Transportation Systems ( ITS) for Visually Impaired Pedestrians ( VIP), who are the most vulnerable road users that should be protected with a high priority in metropolitan areas. Robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors or depth sensors. These dividual solutions have reached impressive detectable range and accuracy with relatively short running time, and enhanced the intersection perception to a large degree. However, simultaneously enabling all detectors incurs a long delay and becomes computationally prohibitive on wearable embedded systems. In this work, we propose to seize CNN-based per-pixel semantic segmenter to cover navigational perception needs in a unified way. This is not only critical to perceive crosswalk position ( where to cross roads), traffic light signal ( when to cross roads), but also to analyze the states of other pedestrians and vehicles ( whether safe to cross roads). At the centroid of our unification proposal is a deep learning architecture, aspired to attain efficient and robust semantic understanding. A comprehensive variety of experiments demonstrates the advanced accuracy over state-of-art algorithms/segmenters while maintaining high inference speed on a real-world navigation assistance system. [Yang, Kailun; Cheng, Ruiqi; Wang, Kaiwei; Long, Ningbo] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Zhejiang, Peoples R China; [Bergasa, Luis M.; Romera, Eduardo] Univ Alcala, Dept Elect, Madrid, Spain Zhejiang University; Universidad de Alcala Yang, KL (corresponding author), Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou, Zhejiang, Peoples R China. elnino@zju.edu.cn; rickycheng@zju.edu.cn; luism.bergasa@uah.es; eduardo.romera@edu.uah.es; wangkaiwei@zju.edu.cn; longningbo@zju.edu.cn Bergasa, Luis M./H-9810-2013; Yang, Kailun/U-2491-2019 Bergasa, Luis M./0000-0002-0087-3077; Yang, Kailun/0000-0002-1090-667X; long, ningbo/0000-0001-7279-4258 Spanish MINECO/FEDER through the SmartElderlyCar project [TRA2015-70501-C2-1-R]; DGT through the SERMON project [SPIP2017-02305]; RoboCity2030-III-CM project - Programas de actividades I+D (CAM) [S2013/MIT-2748]; EU Structural Funds Spanish MINECO/FEDER through the SmartElderlyCar project(Spanish Government); DGT through the SERMON project; RoboCity2030-III-CM project - Programas de actividades I+D (CAM); EU Structural Funds This work has been partially funded by the Spanish MINECO/FEDER through the SmartElderlyCar project (TRA2015-70501-C2-1-R), the DGT through the SERMON project (SPIP2017-02305), and from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds. 18 12 12 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-0377-8 2018.0 1034 1039 6 Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Robotics BM8BC 2023-03-23 WOS:000468772200165 0
C Pawliczek, P; Dzwinel, W; Yuen, DA Rutkowski, L; Korytkowski, M; Scherer, R; Tadeusiewicz, R; Zadeh, LA; Zurada, JM Pawliczek, Piotr; Dzwinel, Witold; Yuen, David A. Visual Exploration of Data with Multithread MIC Computer Architectures ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II (ICAISC 2015) Lecture Notes in Computer Science English Proceedings Paper 14th International Conference on Artificial Intelligence and Soft Computing (ICAISC) JUN 14-18, 2015 Zakopane, POLAND Polish Neural Network Soc,Univ Social Sci,Czestochowa Univ Technol, Inst Computat Intelligence,IEEE Computat Intelligence Soc, Poland Chapter Interactive data visualization; Many integrated core architecture (MIC); Multidimensional scaling; Method of particles GPU Knowledge mining from immense datasets requires fast, reliable and affordable tools for their visual and interactive exploration. Multidimensional scaling (MDS) is a good candidate for embedding of high-dimensional data into visually perceived 2-D and 3-D spaces. We focus here on the way to increase the computational performance of MDS in the context of interactive, hierarchical, visualization of big data. To this end we propose a parallel implementation of MDS on the modern Intel Many Integrated Core Architecture (MIC). We compare the timings obtained for MIC architecture to GPU and standard multi-core CPU implementations of MDS. We conclude that despite 30-40% lower computational performance comparing to GPU/CUDA tuned MDS codes, the MIC solution is still competitive due to dramatically shorter code production and tuning time. The integration of MIC with CPU will make this architecture very competitive with more volatile on technological changes GPU solutions. [Pawliczek, Piotr] Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA; [Dzwinel, Witold] AGH Univ Sci & Technol, Inst Comp Sci, PL-30059 Krakow, Poland; [Yuen, David A.] China Univ Geosci, Sch Environm Sci, Wuhan 430074, Peoples R China; [Yuen, David A.] Univ Minnesota, Dept Earth Sci, Minneapolis, MN 55455 USA; [Yuen, David A.] Univ Minnesota, Minnesota Supercomp Inst, Minneapolis, MN 55455 USA Baylor College of Medicine; AGH University of Science & Technology; China University of Geosciences; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities Dzwinel, W (corresponding author), AGH Univ Sci & Technol, Inst Comp Sci, PL-30059 Krakow, Poland. pppawliczek@gmail.com; dzwinel@agh.edu.pl; daveyuen@gmail.com dzwinel, witold/0000-0001-8321-5928 Division Of Earth Sciences; Directorate For Geosciences [1348066] Funding Source: National Science Foundation Division Of Earth Sciences; Directorate For Geosciences(National Science Foundation (NSF)NSF - Directorate for Geosciences (GEO)) 14 1 1 0 4 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 0302-9743 978-3-319-19369-4 LECT NOTES COMPUT SC 2015.0 9120 25 35 10.1007/978-3-319-19369-4_3 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BD7KZ 2023-03-23 WOS:000363254600003 0
J Lv, ZH; Guo, JK; Singh, AK; Lv, HB Lv, Zhihan; Guo, Jinkang; Singh, Amit Kumar; Lv, Haibin Digital Twins Based VR Simulation for Accident Prevention of Intelligent Vehicle IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Logic gates; Solid modeling; Predictive models; Training; Computational modeling; Visualization; Accidents; Digital Twins; Artificial Fish Swarm; Traffic accident video analysis system; LSTM TRANSPORTATION This work aims to prevent Traffic Accident (TA) and ensure drivers' and pedestrians' life and property safety. A TA prevention and prediction system is established based on Digital Twins (DTs) and Artificial Intelligence (AI). Firstly, the double-scale decomposition equation decomposes the original TA Time Series Data (TSD) into multiple sub-layers. The Long-Short Term Memory (LSTM) network is used to predict the low-frequency sub-layers. Then, the double-scale LSTM network prediction model is constructed based on the prediction results. Secondly, a Particle Filter (PF) is proposed based on target block tracking and improved resampling against the possible occlusion problem in target tracking. The proposed PF can improve particle dilution. Finally, the proposed target tracking algorithm and DTs are combined and applied to TA processing, and a motor vehicle road TA-oriented video analysis system is designed. Then, the proposed system is tested. The results corroborate that the proposed research model can effectively predict the TSD of TA compared with other models and has strong robustness. Compared with the original LSTM model and Stacked Auto Encoders (SAEs) prediction model, the prediction accuracy of the proposed model is improved by 6% and 8%, respectively. Besides, the training and prediction time of the proposed model is less than the original LSTM and SAEs models. The optimized Particle Swarm Optimization (PSO) model makes the target identification easier. Additionally, the proposed model has good generalization performance. In short, the proposed system can effectively improve the efficiency of TA handling and ensure accuracy and fairness, which provides some data support for applying DTs in intelligent transportation. [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, S-75236 Uppsala, Sweden; [Guo, Jinkang] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Shandong, Peoples R China; [Singh, Amit Kumar] Natl Inst Technol, Dept Comp Sci & Ngineering, Patna 800005, Bihar, India; [Lv, Haibin] Minist Nat Resources, North China Sea Offshore Engn Survey Inst, North Sea Bur, Qingdao 266061, Peoples R China Uppsala University; Qingdao University; National Institute of Technology (NIT System); National Institute of Technology Patna; Ministry of Natural Resources of the People's Republic of China Lv, HB (corresponding author), Minist Nat Resources, North China Sea Offshore Engn Survey Inst, North Sea Bur, Qingdao 266061, Peoples R China. lvzhihan@gmail.com; 2020025951@qdu.edu.cn; amit_245singh@yahoo.com; lvhaibinsoa@gmail.com Lv, Zhihan/GLR-6000-2022; Lv, Zhihan/I-3187-2014; Singh, Amit Kumar/D-1300-2015 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Jinkang, Guo/0000-0003-2633-6314; Singh, Amit Kumar/0000-0001-7359-2068 33 1 1 16 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. APR 2022.0 71 4 3414 3428 10.1109/TVT.2022.3152597 0.0 15 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation 0Z1HM 2023-03-23 WOS:000790830700006 0
J Vu, K; Poirion, PL; Liberti, L Vu, Ky; Poirion, Pierre-Louis; Liberti, Leo Gaussian random projections for Euclidean membership problems DISCRETE APPLIED MATHEMATICS English Article Johnson-Lindenstrauss lemma; Machine learning; Euclidean distance geometry; Clustering JOHNSON-LINDENSTRAUSS We discuss the application of Gaussian random projections to the fundamental problem of deciding whether a given point in a Euclidean space belongs to a given set. In particular, we consider the two cases when the target set is either at most countable or of low doubling dimension. We show that, under a number of different assumptions, the feasibility (or infeasibility) of this problem is preserved almost surely when the problem data is projected to a lower dimensional space. We also consider the threshold version of this problem, in which we require that the projected point and the projected set are separated by a certain distance error. As a consequence of these results, we are able to improve the bound of lndyk-Naor on the Nearest Neigbour preserving embeddings. Our results are applicable to any algorithmic setting which needs to solve Euclidean membership problems in a high-dimensional space. (C) 2018 Elsevier B.V. All rights reserved. [Vu, Ky] Chinese Univ Hong Kong, ITCSC, Hong Kong, Peoples R China; [Poirion, Pierre-Louis] RIKEN, Ctr Adv Intelligence Project, Tokyo, Japan; [Liberti, Leo] Ecole Polytech, CNRS LIX, F-91128 Palaiseau, France Chinese University of Hong Kong; RIKEN; Institut Polytechnique de Paris Liberti, L (corresponding author), Ecole Polytech, CNRS LIX, F-91128 Palaiseau, France. vukhacky@gmail.com; pierre-louis.poirion@riken.jp; liberti@lix.polytechnique.fr Liberti, Leo/AAE-6459-2019 Liberti, Leo/0000-0003-3139-6821 Siebel Energy Institute, USA Siebel Energy Institute, USA The authors are grateful to the Siebel Energy Institute, USA for a seed grant. 22 7 8 1 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0166-218X 1872-6771 DISCRETE APPL MATH Discret Appl. Math. JAN 30 2019.0 253 SI 93 102 10.1016/j.dam.2018.08.025 0.0 10 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics HG4WR Green Submitted, Bronze 2023-03-23 WOS:000454976200009 0
J Wang, YX; Liu, JQ; Chang, XL; Wang, JH; Rodriguez, RJ Wang, Yixiang; Liu, Jiqiang; Chang, Xiaolin; Wang, Jianhua; Rodriguez, Ricardo J. AB-FGSM: AdaBelief optimizer and FGSM-based approach to generate adversarial examples JOURNAL OF INFORMATION SECURITY AND APPLICATIONS English Article Adversarial Examples; Deep Learning; Generalization; Optimization; Security; Transferability Deep neural networks (DNNs) can be misclassified by adversarial examples, which are legitimate inputs integrated with imperceptible perturbations at the testing stage. Extensive research has made progress for white-box adversarial attacks to craft adversarial examples with a high success rate. However, these crafted examples have a low success rate in misleading black-box models with defensive mechanisms. To tackle this problem, we design an AdaBelief based iterative Fast Gradient Sign Method (AB-FGSM) to generalize adversarial examples. By integrating the AdaBelief optimizer into the iterative-FGSM (I-FGSM), the generalization of adversarial examples is boosted, considering that the AdaBelief method can find the transferable adversarial point in the epsilon ball around the legitimate input on different optimization surfaces. We carry out white-box and black-box attacks on various adversarially trained models and ensemble models to verify the effectiveness and transferability of the adversarial examples crafted by AB-FGSM. Our experimental results indicate that the proposed AB-FGSM can efficiently and effectively craft adversarial examples in the white-box setting compared with state-of-the-art attacks. In addition, the transfer rate of adversarial examples is 4% to 21% higher than that of state-of-the-art attacks in the black-box manner. [Wang, Yixiang; Liu, Jiqiang; Chang, Xiaolin; Wang, Jianhua] Beijing Jiaotong Univ, Beijing Key Lab Secur & Privacy Intelligent Transp, Beijing 100044, Peoples R China; [Rodriguez, Ricardo J.] Univ Zaragoza, Dept Comp Sci & Syst Engn, Zaragoza, Spain Beijing Jiaotong University; University of Zaragoza Liu, JQ (corresponding author), Beijing Jiaotong Univ, Beijing Key Lab Secur & Privacy Intelligent Transp, Beijing 100044, Peoples R China. 18112047@bjtu.edu.cn; jqliu@bjtu.edu.cn; xlchang@bjtu.edu.cn; 20112051@bjtu.edu.cn; rjrodriguez@unizar.es Rodriguez, Ricardo/GXF-0596-2022; Rodriguez, Ricardo J./L-8958-2014 Rodriguez, Ricardo J./0000-0001-7982-0359; Wang, Jianhua/0000-0002-2773-3429 Fundamental Research Funds for the Central Universities of China [2021YJS023]; National Key R&D Program of China [2020YFB2103802]; Beijing Municipal Natural Science Foundation [M22037]; Aragonese Government under Programa de Proyectos Estrategicos de Grupos de Investigacion [T21-20R] Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); National Key R&D Program of China; Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Aragonese Government under Programa de Proyectos Estrategicos de Grupos de Investigacion We would like to thank the editors and anonymous reviewers for their helpful comments. The work of Y. Wang, J. Liu, and X. Chang was supported by the Fundamental Research Funds for the Central Universities of China under Grants 2021YJS023, by National Key R&D Program of China, under Grant 2020YFB2103802, and by Beijing Municipal Natural Science Foundation (No. M22037). The research of R J. Rodriguez was supported in part by the Aragonese Government under Programa de Proyectos Estrat ' egicos de Grupos de Investigaci ' on (project reference T21-20R). 40 0 0 48 48 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-2126 2214-2134 J INF SECUR APPL J. Inf. Secur. Appl. AUG 2022.0 68 103227 10.1016/j.jisa.2022.103227 0.0 JUN 2022 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2T7XA 2023-03-23 WOS:000822681700003 0
J He, L; Zhu, JW; Li, J; Plaza, A; Chanussot, J; Yu, ZL He, Lin; Zhu, Jiawei; Li, Jun; Plaza, Antonio; Chanussot, Jocelyn; Yu, Zhuliang CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Spatial resolution; Pansharpening; Task analysis; Training; Image reconstruction; Hyperspectral imaging; Convolutional neural networks; Arbitrary resolution HS (ARHS) pansharpening; convolutional neural networks (CNNs); dynamic convolution; hyperspectral (HS) pansharpening; spectral fidelity PAN-SHARPENING METHOD; FUSION TECHNIQUE; QUALITY; IMAGES; VALIDATION; MS Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method. [He, Lin; Zhu, Jiawei; Yu, Zhuliang] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China; [Plaza, Antonio] Univ Extremadura, Hyperspectral Comp Lab, Caceres 10003, Spain; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, INRIA, LJK,CNRS, F-38000 Grenoble, France South China University of Technology; Sun Yat Sen University; Universidad de Extremadura; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria He, L (corresponding author), South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China. helin@scut.edu.cn; lijun48@mail.sysu.edu.cn; aplaza@unex.es; jocelyn.chanussot@gipsa-lab.grenoble-inp.fr Li, Jun/AAD-7233-2020; Plaza, Antonio/C-4455-2008 Li, Jun/0000-0003-1613-9448; Plaza, Antonio/0000-0002-9613-1659; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [62071184, 61571195, 61836003, 61771496, 61633010]; Guangdong Provincial Natural Science Foundation [2016A030313516, 2017A030313382]; Guangzhou Science and Technology Program [202002030395]; Key Realm Research and Development Program of Guangzhou, China [202007030007] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); Guangzhou Science and Technology Program; Key Realm Research and Development Program of Guangzhou, China This work was supported in part by the National Natural Science Foundation of China under Grant 62071184, Grant 61571195, Grant 61836003, Grant 61771496, and Grant 61633010; in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313516 and Grant 2017A030313382; in part by the Guangzhou Science and Technology Program under Grant 202002030395; and in part by the Key Realm Research and Development Program of Guangzhou, China, under Grant 202007030007. 71 6 6 11 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60 5518821 10.1109/TGRS.2021.3132997 0.0 21 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZH9FP 2023-03-23 WOS:000761235500017 0
C Zhou, AC; Gong, YF; He, BS; Zhai, JD Assoc Comp Machinery Zhou, Amelie Chi; Gong, Yifan; He, Bingsheng; Zhai, Jidong Efficient Process Mapping in Geo-Distributed Cloud Data Centers SC'17: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS English Proceedings Paper International Conference for High Performance Computing, Networking, Storage and Analysis (SC) NOV 12-17, 2017 Denver, CO Process Mapping; Geo-distributed Data Centers; Cloud Computing OPTIMIZATION Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways to map parallel processes to physical nodes (i.e., process mapping) could significantly impact the performance of the applications because of non-uniform communication cost in such geo-distributed environments. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environments. In this paper, we propose a novel model to formulate the geo-distributed process mapping problem and develop a new method to efficiently find the near optimal solution. Our algorithm considers both the network communication performance of geo-distributed data centers as well as the communication matrix of the target application. Evaluation results with real experiments on Amazon EC2 and simulations demonstrate that our proposal achieves significant performance improvement (50% on average) compared to the state-of-the-art algorithms. [Zhou, Amelie Chi] Shenzhen Univ, Shenzhen, Peoples R China; [Gong, Yifan] TuSimple, Beijing, Peoples R China; [He, Bingsheng] Natl Univ Singapore, Singapore, Singapore; [Zhai, Jidong] Tsinghua Univ, Beijing, Peoples R China; [Zhou, Amelie Chi] INRIA, Rennes, France Shenzhen University; National University of Singapore; Tsinghua University; Inria Zhou, AC (corresponding author), Shenzhen Univ, Shenzhen, Peoples R China. Zhou, Amelie Chi/HNC-1421-2023 He, Bingsheng/0000-0001-8618-4581 MoE AcRF Tier 1 grant in Singapore [T1 251RES1610]; Microsoft Research Asia; NSFC in China [61628204]; National Key Research and Development Program of China [2016YFB0200100]; NSFC [61472201]; HPC Group of TuSimple; NSFC-Guangdong [U1301252] MoE AcRF Tier 1 grant in Singapore(Ministry of Education, Singapore); Microsoft Research Asia(Microsoft); NSFC in China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); HPC Group of TuSimple; NSFC-Guangdong(National Natural Science Foundation of Guangdong Province) We would like to thank Prof. Amy Apon for shepherding the paper and anonymous reviewers from SC17. This work is supported by a MoE AcRF Tier 1 grant (T1 251RES1610) in Singapore, a collaborative grant from Microsoft Research Asia and NSFC Project 61628204 in China. Jidong Zhai's research is partly supported by National Key Research and Development Program of China 2016YFB0200100 and NSFC Project 61472201. Yifan Gong's research is partly supported by the HPC Group of TuSimple. Amelie Chi Zhou's work is partly supported by grant NSFC-Guangdong U1301252. Amelie Chi Zhou, Bingsheng He and Jidong Zhai are corresponding authors of the paper. 50 9 9 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5114-0 2017.0 10.1145/3126908.3126913 0.0 12 Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL9RL 2023-03-23 WOS:000458161700016 0
J Shi, Y; Jin, L; Li, S; Li, J; Qiang, JP; Gerontitis, DK Shi, Yang; Jin, Long; Li, Shuai; Li, Jian; Qiang, Jipeng; Gerontitis, Dimitrios K. Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Mathematical model; Linear matrix inequalities; Numerical models; Computational modeling; Recurrent neural networks; Analytical models; Manipulators; Convergence; discrete-form time-variant multi-augmented Sylvester matrix problems; discrete-time recurrent neural networks (RNNs); discretization formula; robot manipulator application ZNN MODELS; CONVERGENCE; EQUATION; OPTIMIZATION; ALGORITHMS; REFLEXIVE; ZFS In this article, the discrete-form time-variant multi-augmented Sylvester matrix problems, including discrete-form time-variant multi-augmented Sylvester matrix equation (MASME) and discrete-form time-variant multi-augmented Sylvester matrix inequality (MASMI), are formulated first. In order to solve the above-mentioned problems, in continuous time-variant environment, aided with the Kronecker product and vectorization techniques, the multi-augmented Sylvester matrix problems are transformed into simple linear matrix problems, which can be solved by using the proposed discrete-time recurrent neural network (RNN) models. Second, the theoretical analyses and comparisons on the computational performance of the recently developed discretization formulas are presented. Based on these theoretical results, a five-instant discretization formula with superior property is leveraged to establish the corresponding discrete-time RNN (DTRNN) models for solving the discrete-form time-variant MASME and discrete-form time-variant MASMI, respectively. Note that these DTRNN models are zero stable, consistent, and convergent with satisfied precision. Furthermore, illustrative numerical experiments are given to substantiate the excellent performance of the proposed DTRNN models for solving discrete-form time-variant multi-augmented Sylvester matrix problems. In addition, an application of robot manipulator further extends the theoretical research and physical realizability of RNN methods. [Shi, Yang; Qiang, Jipeng] Yangzhou Univ, Sch Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China; [Jin, Long] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China; [Li, Shuai] Hong Kong Polytech Univ, Dept Comp, Hung Hom, Hong Kong, Peoples R China; [Li, Jian] Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China; [Gerontitis, Dimitrios K.] Aristotle Univ Thessaloniki, Sch Informat, Thessaloniki 54124, Greece Yangzhou University; Chinese Academy of Sciences; Chongqing Institute of Green & Intelligent Technology, CAS; Hong Kong Polytechnic University; Xinyang Normal University; Aristotle University of Thessaloniki Shi, Y (corresponding author), Yangzhou Univ, Sch Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China.;Jin, L (corresponding author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China. shiy@yzu.edu.cn; jinlongsysu@foxmail.com; shuaili@ieee.org; lijianxynu@foxmail.com; jpqiang@yzu.edu.cn; dimitrios_gerontitis@yahoo.gr Jin, Long/HHS-5672-2022 Li, Jian/0000-0003-1033-842X; , Jipeng/0000-0001-8036-9550; Jin, Long/0000-0002-5329-5098 National Natural Science Foundation of China [61906164, 61703189, 61703362]; Natural Science Foundation of Jiangsu Province of China [BK20190875, BK20170513]; Natural Science Foundation of Chongqing, China [cstc2020jcyjzdxm0047]; Light of West China Program, Chinese Academy of Sciences (CAS) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province of China(Natural Science Foundation of Jiangsu Province); Natural Science Foundation of Chongqing, China(Natural Science Foundation of Chongqing); Light of West China Program, Chinese Academy of Sciences (CAS) This work was supported in part by the National Natural Science Foundation of China under Grant 61906164, Grant 61703189, and Grant 61703362; in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20190875 and Grant BK20170513, in part by the Light of West China Program, Chinese Academy of Sciences (CAS), and in part by the Natural Science Foundation of Chongqing, China, under Grant cstc2020jcyjzdxm0047. 44 17 17 8 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. FEB 2022.0 33 2 587 599 10.1109/TNNLS.2020.3028136 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YU4LL 33074831.0 2023-03-23 WOS:000752016400014 0
J An, S; Zhu, HG; Wei, D; Tsintotas, KA; Gasteratos, A An, Shan; Zhu, Haogang; Wei, Dong; Tsintotas, Konstantinos A.; Gasteratos, Antonios Fast and incremental loop closure detection with deep features and proximity graphs JOURNAL OF FIELD ROBOTICS English Article learned-based features; loop closure detection; mapping; navigable small-world graph indexing; visual-based navigation VISUAL PLACE RECOGNITION; BAG-OF-WORDS; SIMULTANEOUS LOCALIZATION; IMAGE FEATURES; LARGE-SCALE; FAB-MAP; CNN; NAVIGATION; SCENE; SHAPE In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance-based loop closure detection pipeline named Fast and Incremental Loop closure Detection (FILD++). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small-world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image-to-image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available data sets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52,480 images) compared to other state-of-the-art approaches. [An, Shan; Zhu, Haogang] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China; [Wei, Dong] JDCOM Inc, Tech & Data Ctr, Beijing, Peoples R China; [Tsintotas, Konstantinos A.; Gasteratos, Antonios] Democritus Univ Thrace, Dept Prod & Management Engn, Xanthi, Greece Beihang University; Democritus University of Thrace Zhu, HG (corresponding author), Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China. haogangzhu@buaa.edu.cn Tsintotas, Konstantinos A./AAG-2765-2022; Tsintotas, Konstantinos A./ACG-1650-2022; Gasteratos, Antonios/AAI-4740-2021 Tsintotas, Konstantinos A./0000-0002-1808-2601; Tsintotas, Konstantinos A./0000-0002-1808-2601; Gasteratos, Antonios/0000-0002-5421-0332; An, Shan/0000-0001-7796-6952 Ministry of Science and Technology of the People's Republic of China Ministry of Science and Technology of the People's Republic of China(Ministry of Science and Technology, China) Ministry of Science and Technology of the People's Republic of China 88 12 12 11 18 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1556-4959 1556-4967 J FIELD ROBOT J. Field Robot. JUN 2022.0 39 4 473 493 10.1002/rob.22060 0.0 JAN 2022 21 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics 0X0AQ Green Submitted 2023-03-23 WOS:000749638500001 0
J Fang, WD; Xu, M; Zhu, CS; Han, WL; Zhang, WX; Rodrigues, JJPC Fang, Weidong; Xu, Ming; Zhu, Chunsheng; Han, Weili; Zhang, Wuxiong; Rodrigues, Joel J. P. C. FETMS: Fast and Efficient Trust Management Scheme for Information-Centric Networking in Internet of Things IEEE ACCESS English Article Information-centric networking (ICN); Internet of Things; cyber security; trust management; on-off attacks SENSOR-CLOUD; BIG DATA As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threats from internal attacks. The trust management technology is an effective approach for defending against internal attacks. In this paper, our contributions lie in our investigating the requirements of cybersecurity in ICN and analyzing the typical attack behaviors and defense schemes. Then, we propose a fast and efficient trust management scheme (FETMS) for defending against the on-off attack, which is an intelligent internal attack. The simulation results show that FETMS can efficiently detect and remove the malicious node in a short period of time; meanwhile, it can achieve lower latency and add its mobility. [Fang, Weidong; Zhang, Wuxiong] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 201899, Peoples R China; [Xu, Ming; Han, Weili] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Zhu, Chunsheng] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T IZ4, Canada; [Zhang, Wuxiong] Shanghai Res Ctr Wireless Commun, Shanghai 201210, Peoples R China; [Rodrigues, Joel J. P. C.] Natl Inst Telecommun, BR-37540000 Santa Rita Do Sapucai, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecommun, P-1049001 Lisbon, Portugal; [Rodrigues, Joel J. P. C.] Univ Fortaleza, BR-68011905 Fortaleza, Ceara, Brazil Chinese Academy of Sciences; Shanghai Institute of Microsystem & Information Technology, CAS; Fudan University; University of British Columbia; Instituto Nacional de Telecomunicacoes (INATEL); Instituto de Telecomunicacoes; Universidade de Lisboa; Universidade Fortaleza Han, WL (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China. awlhan@fudan.edu.cn Rodrigues, Joel J. P. C./A-8103-2013; Zhu, Chunsheng/P-2182-2019 Rodrigues, Joel J. P. C./0000-0001-8657-3800; Zhu, Chunsheng/0000-0001-8041-0197 National Natural Science Foundation of China [61571004]; Shanghai Natural Science Foundation [17ZR1429100]; Science and Technology Innovation Program of Shanghai [17DZ1200302, 17DZ2292000, 16510711600]; Fundacao para a Ciencia e a Tecnologia [UID/EEA/500008/2013]; Finep; Funttel, through the Radiocommunication Reference Center project of the National Institute of Telecommunications, Brazil [01.14.0231.00]; Brazilian National Council for Research and Development (CNPq) [309335/2017-5] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Natural Science Foundation(Natural Science Foundation of Shanghai); Science and Technology Innovation Program of Shanghai; Fundacao para a Ciencia e a Tecnologia(Fundacao para a Ciencia e a Tecnologia (FCT)); Finep(Financiadora de Inovacao e Pesquisa (Finep)); Funttel, through the Radiocommunication Reference Center project of the National Institute of Telecommunications, Brazil; Brazilian National Council for Research and Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work was supported in part by the National Natural Science Foundation of China under Grant 61571004, in part by the Shanghai Natural Science Foundation under Grant 17ZR1429100, in part by the Science and Technology Innovation Program of Shanghai under Grant 17DZ1200302, Grant 17DZ2292000, and Grant 16510711600, in part by the National Funding from the Fundacao para a Ciencia e a Tecnologia under Project UID/EEA/500008/2013, in part by Finep, with resources from Funttel, through the Radiocommunication Reference Center project of the National Institute of Telecommunications, Brazil, under Grant 01.14.0231.00, and in part by the Brazilian National Council for Research and Development (CNPq) under Grant 309335/2017-5. 26 23 23 4 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7 13476 13485 10.1109/ACCESS.2019.2892712 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HL5UP gold 2023-03-23 WOS:000458796400016 0
J Chitour, Y; Liao, ZY; Couillet, R Chitour, Yacine; Liao, Zhenyu; Couillet, Romain A GEOMETRIC APPROACH OF GRADIENT DESCENT ALGORITHMS IN LINEAR NEURAL NETWORKS MATHEMATICAL CONTROL AND RELATED FIELDS English Article; Early Access Deep learning theory; gradient descent; normal hyperbolicity CONVERGENCE In this paper, we propose a geometric framework to analyze the convergence properties of gradient descent trajectories in the context of linear neural networks. We translate a well-known empirical observation of linear neural nets into a conjecture that we call the overfitting conjecture which states that, for almost all training data and initial conditions, the trajectory of the corresponding gradient descent system converges to a global minimum. This would imply that the solution achieved by vanilla gradient descent algorithms is equivalent to that of the least-squares estimation, for linear neural networks of an arbitrary number of hidden layers. Built upon a key invariance property induced by the network structure, we first establish convergence of gradient descent trajectories to critical points of the square loss function in the case of linear networks of arbitrary depth. Our second result is the proof of the overfitting conjecture in the case of single-hidden-layer linear networks with an argument based on the notion of normal hyperbolicity and under a generic property on the training data (i.e., holding for almost all training data). [Chitour, Yacine] Univ Paris Saclay, Cent Supelec, Lab Signaux & Syst, Gif Sur Yvette, France; [Liao, Zhenyu] Huazhong Univ Sci & Technol, EIC, Wuhan, Peoples R China; [Liao, Zhenyu; Couillet, Romain] Univ Grenoble Alpes, INRIA, CNRS, Grenoble INP,LIG, F-38000 Grenoble, France UDICE-French Research Universities; Universite Paris Saclay; Huazhong University of Science & Technology; Inria; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Chitour, Y (corresponding author), Univ Paris Saclay, Cent Supelec, Lab Signaux & Syst, Gif Sur Yvette, France. yacine.chitour@l2s.centralesupelec.fr; zhenyu_liao@hust.edu.cn; romain.couillet@univ-grenoble-alpes.fr LIAO, Zhenyu/0000-0002-1915-8559 French National Research Agency (ANR); National Natural Science Foundation of China [NSFC-12141107]; Fundamental Research Funds for the Central Universities of China [2021XXJS110]; Key Research and Development Program of Hubei [2021BAA037, 20210008, ANR-19-P3IA0003]; IDEX Paris-Saclay [ANR-11-IDEX-000302] French National Research Agency (ANR)(French National Research Agency (ANR)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); Key Research and Development Program of Hubei; IDEX Paris-Saclay The work of YC is supported by a public grant overseen by the French National Research Agency (ANR) as part of the \Investissement d'Avenirprogram, through the iCODE project funded by the IDEX Paris-Saclay, ANR-11-IDEX-000302. ZL would like to acknowledge the National Natural Science Foundation of China (NSFC-12141107), the Fundamental Research Funds for the Central Universities of China (2021XXJS110), the Key Research and Development Program of Hubei (2021BAA037), and the CCF-Hikvision Open Fund (20210008) for providing partial support. RC would like to acknowledge the MIAI LargeDATA chair (ANR-19-P3IA0003) at University Grenobles-Alpes as well as the HUAWEI LarDist project for providing partial support of this work. 30 1 1 0 0 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES 2156-8472 2156-8499 MATH CONTROL RELAT F Math. Control Relat. Fields 10.3934/mcrf.2022021 0.0 APR 2022 28 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 1L2RA gold, Green Submitted 2023-03-23 WOS:000799138900001 0
J Langenfeld, F; Peng, YX; Lai, YK; Rosin, PL; Aderinwale, T; Terashi, G; Christoffer, C; Kihara, D; Benhabiles, H; Hammoudi, K; Cabani, A; Windal, F; Melkemi, M; Giachetti, A; Mylonas, S; Axenopoulos, A; Daras, P; Otu, E; Zwiggelaar, R; Hunter, D; Liu, YH; Montes, M Langenfeld, Florent; Peng, Yuxu; Lai, Yu-Kun; Rosin, Paul L.; Aderinwale, Tunde; Terashi, Genki; Christoffer, Charles; Kihara, Daisuke; Benhabiles, Halim; Hammoudi, Karim; Cabani, Adnane; Windal, Feryal; Melkemi, Mahmoud; Giachetti, Andrea; Mylonas, Stelios; Axenopoulos, Apostolos; Daras, Petros; Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David; Liu, Yonghuai; Montes, Matthieu SHREC 2020: Multi-domain protein shape retrieval challenge COMPUTERS & GRAPHICS-UK English Article 3D shape analysis; 3D shape descriptor; 3D shape retrieval; 3D shape matching; Protein shape; SHREC STRUCTURAL CLASSIFICATION Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the protein and species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost. (C) 2020 The Author(s). Published by Elsevier Ltd. [Langenfeld, Florent; Montes, Matthieu] HESAM Univ, Lab Genom Bioinformat & Chim Mol GBCM, Conservatoire Natl Arts & Metiers, EA 7528, 2 Rue Conte, F-75003 Paris, France; [Peng, Yuxu] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Hunan, Peoples R China; [Lai, Yu-Kun; Rosin, Paul L.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales; [Aderinwale, Tunde; Christoffer, Charles; Kihara, Daisuke] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA; [Terashi, Genki; Kihara, Daisuke] Purdue Univ, Dept Biol Sci, W Lafayette, IN 47907 USA; [Benhabiles, Halim; Windal, Feryal] Univ Lille, Univ Polytech Hauts de France, Yncrea Hauts de France, Cent Lille,CNRS,UMR 8520,IEMN, F-59000 Lille, France; [Hammoudi, Karim; Melkemi, Mahmoud] Univ Haute Alsace, Dept Comp Sci, IRIMAS, F-68100 Mulhouse, France; [Hammoudi, Karim; Melkemi, Mahmoud] Univ Strasbourg, Strasbourg, France; [Cabani, Adnane] Normandie Univ, IRSEEM, ESIGELEC, UNIROUEN, F-76000 Rouen, France; [Giachetti, Andrea] Univ Verona, Dept Comp Sci, Verona, Italy; [Mylonas, Stelios; Axenopoulos, Apostolos; Daras, Petros] Ctr Res & Technol Hellas, Informat Technol Inst, Ptolemaida, Greece; [Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David] Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3DB, Dyfed, Wales; [Liu, Yonghuai] Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England heSam Universite; Conservatoire National Arts & Metiers (CNAM); Changsha University of Science & Technology; Cardiff University; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Lille - ISITE; Centrale Lille; Universite de Lille; Universite Polytechnique Hauts-de-France; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Universite de Rouen Normandie; University of Verona; Centre for Research & Technology Hellas; Aberystwyth University; Edge Hill University Langenfeld, F (corresponding author), HESAM Univ, Lab Genom Bioinformat & Chim Mol GBCM, Conservatoire Natl Arts & Metiers, EA 7528, 2 Rue Conte, F-75003 Paris, France. florent.langenfeld@cnam.fr; pengyuxupjl@csust.edu.cn; LaiY4@cardiff.ac.uk; RosinPL@cardiff.ac.uk; taderinw@purdue.edu; gterashi@purdue.edu; christ35@purdue.edu; dkihara@purdue.edu; halim.benhabiles@yncrea.fr; karim.hammoudi@uha.fr; adnane.cabani@esigelec.fr; feryal.windal@yncrea.fr; mahmoud.melkemi@uha.fr; andrea.giachetti@univr.it; smylonas@iti.gr; axenop@iti.gr; daras@iti.gr; eko@aber.ac.uk; rrz@aber.ac.uk; dah56@aber.ac.uk; liuyo@edgehill.ac.uk; matthieu.montes@cnam.fr Liu, Yonghuai/ABF-3794-2020; Zwiggelaar, Reyer/HGA-3089-2022 Otu, Ekpo/0000-0002-5616-6902; Montes, Matthieu/0000-0001-5921-460X; Kihara, Daisuke/0000-0003-4091-6614; Langenfeld, Florent/0000-0002-3184-3401 Young teachers growth plan project - Changsha University of Science Technology [2019QJCZ014]; ATXN1-MED15 PPI project - GSRT Hellenic Foundation for Research and Innovation; European Research Council Executive Agency [640283] Young teachers growth plan project - Changsha University of Science Technology; ATXN1-MED15 PPI project - GSRT Hellenic Foundation for Research and Innovation; European Research Council Executive Agency Yuxu Peng was supported by the Young teachers growth plan project (2019QJCZ014) funded by Changsha University of Science & Technology; Stelios Mylonas, Apostolos Axenopoulos and Petros Daras were supported by the ATXN1-MED15 PPI project funded by the GSRT Hellenic Foundation for Research and Innovation.; Matthieu Montes and Florent Langenfeld were supported by the European Research Council Executive Agency under the research grant number 640283. 35 7 7 0 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0097-8493 1873-7684 COMPUT GRAPH-UK Comput. Graph.-UK OCT 2020.0 91 189 198 10.1016/j.cag.2020.07.013 0.0 10 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science NZ9QR Green Published, Bronze, Green Accepted 2023-03-23 WOS:000577434300016 0
S Doborjeh, MG; Doborjeh, ZG; Gollahalli, AR; Kumarasinghe, K; Breen, V; Sengupta, N; Ramos, JIE; Hartono, R; Capecci, E; Kawano, H; Othman, M; Zhou, L; Yang, J; Bose, P; Ge, CJ Sgurev, V; Jotsov, V; Kacprzyk, J Doborjeh, Maryam Gholami; Doborjeh, Zohreh Gholami; Gollahalli, Akshay Raj; Kumarasinghe, Kaushalya; Breen, Vivienne; Sengupta, Neelava; Ramos, Josafath Israel Espinosa; Hartono, Reggio; Capecci, Elisa; Kawano, Hideaki; Othman, Muhaini; Zhou, Lei; Yang, Jie; Bose, Pritam; Ge, Chenjie From von Neumann Architecture and Atanasoff's ABC to Neuromorphic Computation and Kasabov's NeuCube. Part II: Applications PRACTICAL ISSUES OF INTELLIGENT INNOVATIONS Studies in Systems Decision and Control English Article; Book Chapter SPIKING NEURAL-NETWORKS; WEATHER Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the hidden interactions and interrelationship among multivariate data, is vital in SSTD investigation. This chapter describes a number of prominent applications built using the Kasabov's NeuCube-based Spiking Neural Network (SNN) architecture for mapping, learning, visualization, classification/regression and better understanding and interpretation of SSTD. [Doborjeh, Maryam Gholami; Doborjeh, Zohreh Gholami; Gollahalli, Akshay Raj; Kumarasinghe, Kaushalya; Breen, Vivienne; Sengupta, Neelava; Ramos, Josafath Israel Espinosa; Hartono, Reggio; Capecci, Elisa] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand; [Kawano, Hideaki] Kyushu Inst Technol, Kitakyushu, Fukuoka, Japan; [Othman, Muhaini] Univ Tun Hussein Onn Malaysia, Parit Raja, Malaysia; [Zhou, Lei; Yang, Jie] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China; [Bose, Pritam] Univ Trento, Trento, Italy; [Ge, Chenjie] Shanghai Jiao Tong Univ, Shanghai, Peoples R China Auckland University of Technology; Kyushu Institute of Technology; University of Tun Hussein Onn Malaysia; Shanghai Jiao Tong University; University of Trento; Shanghai Jiao Tong University Doborjeh, MG (corresponding author), Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand. maryam.gholami.doborjeh@aut.ac.nz Othman, Muhaini/AAQ-8548-2021 Bose, Pritam/0000-0002-0511-3050; Doborjeh, Zohreh/0000-0003-1431-8258 30 2 2 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2198-4182 2198-4190 978-3-319-78437-3; 978-3-319-78436-6 STUD SYST DECIS CONT 2018.0 140 17 36 10.1007/978-3-319-78437-3_2 0.0 10.1007/978-3-319-78437-3 20 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Book Citation Index – Science (BKCI-S) Automation & Control Systems; Computer Science; Engineering BM6GS 2023-03-23 WOS:000466552900003 0
J Ni, HB; Wu, S; Abdulrazak, B; Zhang, DQ; Ma, XJ; Zhou, XS Ni, Hongbo; Wu, Shu; Abdulrazak, Bessam; Zhang, Daqing; Ma, Xiaojuan; Zhou, Xingshe Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment FRONTIERS OF COMPUTER SCIENCE English Article sleep pattern; elder-care; pressure sensor; UWB tags; Naive Bayes; Random Forest The quality of sleep may be a reflection of an elderly individual's health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novelmulti-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user's sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user's sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users' sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper. [Ni, Hongbo; Zhou, Xingshe] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China; [Wu, Shu] Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit Inst Automat, Beijing 100090, Peoples R China; [Abdulrazak, Bessam] Univ Sherbrooke, Dept Comp, Sherbrooke, PQ JIK 2R1, Canada; [Zhang, Daqing] Inst Telecom SudParis, Handicom Lab, F-91011 Evry, France; [Ma, Xiaojuan] Huawei Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China Northwestern Polytechnical University; Chinese Academy of Sciences; University of Sherbrooke; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Huawei Technologies Ni, HB (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China. nihb@nwpu.edu.cn zhou, xt/GWZ-9212-2022 Fond Nature of Technologies, MELS Program, Quebec, Canada; Key Project of National Found of Science of China [61332013]; NWPU [3102015JSJ0010] Fond Nature of Technologies, MELS Program, Quebec, Canada; Key Project of National Found of Science of China; NWPU We would like to thank the reviewers for the valuable comments and the time spent on the improvement of the paper. This work was supported by the Fond Nature of Technologies, MELS Program, Quebec, Canada, the Key Project of National Found of Science of China (61332013) and Fundamental Research Grant of NWPU (3102015JSJ0010). 36 2 2 1 14 HIGHER EDUCATION PRESS BEIJING CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R CHINA 2095-2228 2095-2236 FRONT COMPUT SCI-CHI Front.. Comput. Sci. DEC 2015.0 9 6 966 979 10.1007/s11704-015-4404-7 0.0 14 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science DB5XY 2023-03-23 WOS:000368588400011 0
J Zhang, L; Wang, C; Zhang, DQ Zhang, Lei; Wang, Cong; Zhang, Daqing Wi-PIGR: Path Independent Gait Recognition With Commodity Wi-Fi IEEE TRANSACTIONS ON MOBILE COMPUTING English Article Legged locomotion; Wireless fidelity; Gait recognition; Feature extraction; Mobile computing; Wearable sensors; Transceivers; Channel state information (CSI); gait recognition; fresnel model; device-free sensing WALKING SPEED; AUTHENTICATION Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15 percent, when the number of subjects is 50. [Zhang, Lei; Wang, Cong] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300050, Peoples R China; [Zhang, Lei; Wang, Cong] Tianjin Key Lab Adv Network Technol & Applicat, Tianjin 300050, Peoples R China; [Zhang, Lei] Henan Univ Technol, Minist Educ, Key Lab Grain Informat Proc & Control, Zhengzhou 450001, Henan, Peoples R China; [Wang, Cong] China Elect Technol Grp Corp, Res Inst 54, Shijiazhuang 050081, Hebei, Peoples R China; [Zhang, Daqing] Telecom SudParis, Inst Mines Telecom, IP Paris, F-91011 Paris, France Tianjin University; Henan University of Technology; China Electronics Technology Group; IMT - Institut Mines-Telecom; IMT Atlantique; Institut Polytechnique de Paris Wang, C (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin 300050, Peoples R China.;Wang, C (corresponding author), China Elect Technol Grp Corp, Res Inst 54, Shijiazhuang 050081, Hebei, Peoples R China.;Zhang, DQ (corresponding author), Telecom SudParis, Inst Mines Telecom, IP Paris, F-91011 Paris, France. lzhang@tju.edu.cn; congwang@tju.edu.cn; daqing.zhrang@telecom-sudparis.eu Zhang, Lei/0000-0001-5699-1024 NSFC [61772364]; National Instrument Program [2013YQ030915]; EUCHISTCERARADIOSENSE Project NSFC(National Natural Science Foundation of China (NSFC)); National Instrument Program; EUCHISTCERARADIOSENSE Project This workwas supported by theNSFC under Grant 61772364, National Instrument Program under Grant 2013YQ030915, and the EUCHISTCERARADIOSENSE Project. 60 2 2 7 9 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1536-1233 1558-0660 IEEE T MOBILE COMPUT IEEE. Trans. Mob. Comput. SEPT 1 2022.0 21 9 3414 3427 10.1109/TMC.2021.3052314 0.0 14 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 3O1VO Green Published 2023-03-23 WOS:000836627600026 0
C Fang, W; Yu, ZF; Chen, YQ; Huang, TJ; Masquelier, T; Tian, Y Ranzato, M; Beygelzimer, A; Dauphin, Y; Liang, PS; Vaughan, JW Fang, Wei; Yu, Zhaofei; Chen, Yanqi; Huang, Tiejun; Masquelier, Timothee; Tian, Yonghong Deep Residual Learning in Spiking Neural Networks ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) Advances in Neural Information Processing Systems English Proceedings Paper 35th Conference on Neural Information Processing Systems (NeurIPS) DEC 06-14, 2021 ELECTR NETWORK Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatialtemporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spikeelement-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible. Our codes are available at https: //github.com/fangwei123456/Spike-Element-Wise- ResNet. [Fang, Wei; Yu, Zhaofei; Chen, Yanqi; Huang, Tiejun; Tian, Yonghong] Peking Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Fang, Wei; Yu, Zhaofei; Chen, Yanqi; Huang, Tiejun; Tian, Yonghong] Peng Cheng Lab, Shenzhen 518055, Peoples R China; [Masquelier, Timothee] Univ Toulouse 3, Ctr Rech Cerveau & Cognit, UMR5549 CNRS, Toulouse, France Peking University; Peng Cheng Laboratory; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Centre National de la Recherche Scientifique (CNRS) Yu, ZF; Tian, Y (corresponding author), Peking Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.;Yu, ZF; Tian, Y (corresponding author), Peng Cheng Lab, Shenzhen 518055, Peoples R China. National Natural Science Foundation of China [62027804, 61825101, 62088102] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by grants from the National Natural Science Foundation of China under contracts No.62027804, No.61825101, and No.62088102. 65 0 0 1 1 NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) LA JOLLA 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA 1049-5258 ADV NEUR IN 2021.0 34 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU4JS 2023-03-23 WOS:000901616407003 0
J Yuan, XL; Zhou, XT; Pan, YQ; Kosonen, R; Cai, H; Gao, Y; Wang, Y Yuan, Xiaolei; Zhou, Xuetao; Pan, Yiqun; Kosonen, Risto; Cai, Hao; Gao, Yang; Wang, Yu Phase change cooling in data centers: A review ENERGY AND BUILDINGS English Review Data center; Phase change cooling; Heat pipe system; Operation safety; Heat transfer enhancement; Energy saving SEPARATE HEAT-PIPE; ENERGY-STORAGE SYSTEMS; AIR-FLOW DISTRIBUTION; THERMAL MANAGEMENT; INTEGRATED-SYSTEM; THERMOSIPHON; PERFORMANCE; LOOP; REFRIGERATION; EFFICIENCY With the advent of big data era, data centers (DCs) related energy use accounts for approximately 3% of the global electric power consumption. As the augmentation of data-processing performance and thermal density in DCs, its energy use will undoubtedly continuously burst. Meanwhile, the potential safety haz-ards, caused by IT equipment local overheating, threaten the safe operation and restrict the further devel-opment of DCs. Thus, efficient cooling approaches should be applied in DCs to ensure its safe operation and optimize its thermal environment and cooling efficiency. Phase change cooling (PCC) technology is regarded as one of the effective and widely-used cooling methods, which have been applied in DCs for several years. In this paper, the up-to-date PCC technologies are reviewed and summarized, as well as the latest progress in DC cooling field. Four main PCC technologies are discussed in this paper, namely independent heat pipe cooling, integrated heat pipe cooling, two-phase immersion cooling, and cold stor-age systems. Finally, the shortcomings of the current researches on the PCC methods are summarized, while some suggestions for future researches are provided to promote the application of PCC technologies and achieve safe operations and energy savings in data centers. (c) 2021 Elsevier B.V. All rights reserved. [Yuan, Xiaolei; Pan, Yiqun] Tongji Univ, Sch Mech Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China; [Yuan, Xiaolei; Kosonen, Risto] Aalto Univ, Sch Engn, Dept Mech Engn, Espoo, Finland; [Zhou, Xuetao; Kosonen, Risto; Cai, Hao; Wang, Yu] Nanjing Tech Univ, Coll Urban Construct, Nanjing 210009, Peoples R China; [Gao, Yang] Nanjing Yangtze River Urban Architectural Design, Nanjing 210002, Peoples R China Tongji University; Aalto University; Nanjing Tech University Pan, YQ (corresponding author), Tongji Univ, Sch Mech Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China. yiqunpan@tongji.edu.cn GAO, Yang/HMO-8142-2023; Yuan, xiaolei/GVT-2711-2022 Yuan, Xiaolei/0000-0002-4354-5575 National Natural Science Foundation of China [51978481] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This paper has received funding from the National Natural Science Foundation of China (Grant No. 51978481) . 132 19 19 25 118 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7788 1872-6178 ENERG BUILDINGS Energy Build. APR 1 2021.0 236 110764 10.1016/j.enbuild.2021.110764 0.0 FEB 2021 19 Construction & Building Technology; Energy & Fuels; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Energy & Fuels; Engineering RF5MP Green Published 2023-03-23 WOS:000634882700006 0
J Tong, Z; Yuan, DD; Gao, J; Wei, YF; Dou, H Tong, Zheng; Yuan, Dongdong; Gao, Jie; Wei, Yongfeng; Dou, Hui Pavement-distress detection using ground-penetrating radar and network in networks CONSTRUCTION AND BUILDING MATERIALS English Article Pavement distress detection; Nondestructive testing; Network in network; Ground penetrating radar; Signal analysis NEURAL-NETWORKS; GPR; RECOGNITION; SUBGRADE; OBJECTS This study proposes a nondestructive testing technique for pavement distress detection using ground-penetrating radar and network in networks. Ground-penetrating radar signals are imported into two network-in-network structure as input data directly. The network in networks are used as deep learning models to distinguish abnormal signals, recognize distress types, and measure distress locations and sizes. A database with information from four highways is generated by a ground-penetrating radar with different transmitting frequencies and numbers of samples per trace. Then, the database is used to train, validate, and test the network in networks. The results show that the proposed method detects cracks, water-damage pits, and uneven settlements with 85.17% accuracy, 2.15 mm location errors, and reasonable stability. The proposed method was superior to other state-of-the-art techniques in terms of classification accuracy, location error, and stability. Additionally, the results show that this method overcomes the negative effect of transmitting frequencies in pavement distress detection using GPR data. (C) 2019 Elsevier Ltd. All rights reserved. [Tong, Zheng; Wei, Yongfeng; Dou, Hui] Gansu Rd & Bridge Construct Grp, Res & Dev Ctr Transport Ind Technol Mat & Equipme, Lanzhou 730030, Gansu, Peoples R China; [Yuan, Dongdong; Gao, Jie] Changan Univ, Xian 710064, Peoples R China; [Gao, Jie] East China Jiaotong Univ, Sch Transportat & Logist, Nanchang 330013, Jiangxi, Peoples R China; [Tong, Zheng] Univ Technol Compiegne, Sorbonne Univ, CNRS, UMR Heudiasyc 7253, CS 60319-60203, Compiegne, France Chang'an University; East China Jiaotong University; Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; UDICE-French Research Universities; Sorbonne Universite Tong, Z (corresponding author), Gansu Rd & Bridge Construct Grp, Res & Dev Ctr Transport Ind Technol Mat & Equipme, Lanzhou 730030, Gansu, Peoples R China.;Gao, J (corresponding author), Changan Univ, Xian 710064, Peoples R China.;Tong, Z (corresponding author), Univ Technol Compiegne, Sorbonne Univ, CNRS, UMR Heudiasyc 7253, CS 60319-60203, Compiegne, France. zheng.tong@hds.utc.fr; highway-gaojie@st.chd.edu.cn Gao, Jie/AAM-9632-2020 Gao, Jie/0000-0002-2296-907X; Tong, Zheng/0000-0001-6894-3521 Opening Foundation of Research and Development Center of Transport Industry of Technologies, Materials and Equipments of Highway Construction and Maintenance (Gansu Road & Bridge Construction Group) [GLKF201811]; China Scholarship Council [CSC201801810108] Opening Foundation of Research and Development Center of Transport Industry of Technologies, Materials and Equipments of Highway Construction and Maintenance (Gansu Road & Bridge Construction Group); China Scholarship Council(China Scholarship Council) The authors gratefully acknowledge the financial support provided by Opening Foundation of Research and Development Center of Transport Industry of Technologies, Materials and Equipments of Highway Construction and Maintenance (Gansu Road & Bridge Construction Group) (No. GLKF201811). This work is also supported by Co-operation Program with the UTs and INSAs (France) funded by the China Scholarship Council (No. CSC201801810108). 44 41 42 15 117 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. FEB 10 2020.0 233 117352 10.1016/j.conbuildmat.2019.117352 0.0 12 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science KL2YS 2023-03-23 WOS:000513295100066 0
C Li, T; Alhilal, A; Zhang, AL; Hoque, MA; Chatzopoulos, D; Xiao, Z; Li, Y; Hui, P IEEE Li, Tong; Alhilal, Ahmad; Zhang, Anlan; Hoque, Mohammad A.; Chatzopoulos, Dimitris; Xiao, Zhu; Li, Yong; Hui, Pan Driving Big Data: A First Look at Driving Behavior Via a Large-scale Private Car Dataset 2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2019) IEEE International Conference on Data Engineering Workshop English Proceedings Paper IEEE 35th International Conference on Data Engineering (ICDE) APR 08-11, 2019 Macau, PEOPLES R CHINA IEEE,IEEE Comp Soc Privately owned vehicles; Data collection; Trajectories; Smart cities; Fuel efficiency; Vehicular communications The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars. [Li, Tong; Alhilal, Ahmad; Chatzopoulos, Dimitris; Hui, Pan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Zhang, Anlan] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China; [Hoque, Mohammad A.; Hui, Pan] Univ Helsinki, Dept Comp Sci, Helsinki, Finland; [Xiao, Zhu] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Peoples R China; [Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China Hong Kong University of Science & Technology; Beihang University; University of Helsinki; Hunan University; Tsinghua University Li, T (corresponding author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China. t.li@connect.ust.hk; aalhilal@ust.hk; zal1506@buaa.edu.cn; mohammad.a.hoque@helsinki.fi; dcab@cse.ust.hk; zhxiao@hnu.edu.cn; liyong07@tsinghua.edu.cn; panhui@cse.ust.hk , zhxiao@hnu.edu.cn/AFT-5052-2022; Chatzopoulos, Dimitris/AAV-8599-2020; ALHILAL, Ahmad/HMV-5885-2023; li, yong/HDN-3885-2022; Li, Tong/AAL-7761-2020; Hui, Pan/AAK-6660-2020 Chatzopoulos, Dimitris/0000-0002-4765-5085; ALHILAL, Ahmad/0000-0002-2575-1391; Li, Tong/0000-0002-4343-703X; Hui, Pan/0000-0001-6026-1083 Research Grants Council of Hong Kong [26211515, 16214817, G-HKUST604/16]; Academy of Finland Research Grants Council of Hong Kong(Hong Kong Research Grants Council); Academy of Finland(Academy of Finland) This research has been supported in part by projects 26211515, 16214817 and G-HKUST604/16 from the Research Grants Council of Hong Kong, and 5GEAR project from the Academy of Finland. 25 9 9 3 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1943-2895 978-1-7281-0890-2 I C DATA ENGIN WORKS 2019.0 61 68 10.1109/ICDEW.2019.00-34 0.0 8 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ3DJ Green Accepted 2023-03-23 WOS:000584318800011 0
C Mu, ZH; Castro, FM; Marin-Jimenez, MJ; Guil, N; Li, YR; Yu, SQ IEEE Mu, Zihao; Castro, Francisco M.; Marin-Jimenez, Manuel J.; Guil, Nicolas; Li, Yan-Ran; Yu, Shiqi iLGaCo: Incremental Learning of Gait Covariate Factors IEEE/IAPR INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2020) English Proceedings Paper IEEE/IAPR International Joint Conference on Biometrics (IJCB) SEP 28-OCT 01, 2020 ELECTR NETWORK IEEE,IAPR,IEEE Biometr Council,Google,Qualcomm,NSF RECOGNITION Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical 'training-from-scratch' approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment. [Mu, Zihao; Li, Yan-Ran] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Guangdong, Peoples R China; [Castro, Francisco M.; Guil, Nicolas] Univ Malaga, Dept Comp Architecture, Malaga, Spain; [Marin-Jimenez, Manuel J.] Univ Cordoba, Dept Comp & Numer Anal, Cordoba, Spain; [Li, Yan-Ran] Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen, Guangdong, Peoples R China; [Yu, Shiqi] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China Shenzhen University; Universidad de Malaga; Universidad de Cordoba; Southern University of Science & Technology Mu, ZH (corresponding author), Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Guangdong, Peoples R China. muzihao2018@email.szu.edu.cn; fcastro@ema.es; mjmarin@uco.es; nguil@uma.es; lyran@szu.edu.cn; yusq@sustech.edu.cn Yu, Shiqi/GZM-1998-2022 Spanish Ministry of Science and Technology [TIN2016-80920R, RED2018102511-T]; Andalusian regional government [P18-FR3130]; National Natural Science Foundation of China [61976144] Spanish Ministry of Science and Technology(Ministry of Science and Innovation, Spain (MICINN)Spanish Government); Andalusian regional government; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work has been funded by the Spanish Ministry of Science and Technology (TIN2016-80920R and RED2018102511-T), Andalusian regional government (P18-FR3130) and the National Natural Science Foundation of China (Grant No. 61976144). 46 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-9186-7 2020.0 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematical & Computational Biology BS4XQ 2023-03-23 WOS:000723870900004 0
C Ge, FP; Li, KH; Wu, B; Siniscalchi, SM; Yan, YH; Lee, CH Int Speech Commun Assoc Ge, Fengpei; Li, Kehuang; Wu, Bo; Siniscalchi, Sabato Marco; Yan, Yonghong; Lee, Chin-Hui Joint Training of Multi-channel-condition Dereverberation and Acoustic Modeling of Microphone Array Speech for Robust Distant Speech Recognition 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION Interspeech English Proceedings Paper 18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017) AUG 20-24, 2017 Stockholm, SWEDEN Int Speech Commun Assoc,Stockholm Univ,KTH Royal Inst Technol,Karolinska Inst,Amazon Alexa,DiDi,Furhat Robot,Microsoft,EZ Alibaba Grp,CIRRUS LOGIC,CVTE,Google,Baidu,IBM Res,YAHOO Japan,Nuance,Voice Provider,ASM Solut Ltd,Mitsubishi Elect Res Lab,Yandex distant speech recognition; reverberant speech recognition; multi-condition; joint training We propose a novel data utilization strategy, called multichannel-condition learning, leveraging upon complementary information captured in microphone array speech to jointly train dereverberation and acoustic deep neural network (DNN) models for robust distant speech recognition. Experimental results, with a single automatic speech recognition (ASR) system, on the REVERB2014 simulated evaluation data show that, on 1-channel testing, the baseline joint training scheme attains a word error rate (WER) of 7.47%, reduced from 8.72% for separate training. The proposed multi-channel-condition learning scheme has been experimented on different channel data combinations and usage showing many interesting implications. Finally, training on all 8-channel data and with DNN-based language model rescoring, a state-of-the-art WER of 4.05% is achieved. We anticipate an even lower WER when combining more top ASR systems. [Ge, Fengpei; Yan, Yonghong] Inst Acoust, Key Lab Speech Acoust & Content Understanding, Beijing, Peoples R China; [Li, Kehuang; Lee, Chin-Hui] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA; [Wu, Bo] Xidian Univ, Natl Lab Radar Signal Proc, Xian, Shaanxi, Peoples R China; [Siniscalchi, Sabato Marco] Univ Enna Kore, Fac Comp & Telecommun Engn, Enna, Italy Chinese Academy of Sciences; Institute of Acoustics, CAS; University System of Georgia; Georgia Institute of Technology; Xidian University; Universita Kore di ENNA Ge, FP (corresponding author), Inst Acoust, Key Lab Speech Acoust & Content Understanding, Beijing, Peoples R China. gefengpei@gq.com; kehlekernel@gmail.com; rambowu11@gmail.com; siniscalchi77.19@gmail.com; yonghong.yan@hccl.ioa.ac.cn; chl@ece.gatech.edu Siniscalchi, Sabato Marco Marco/I-3423-2012 Siniscalchi, Sabato Marco Marco/0000-0002-0770-0507 China Scholarship Council; National Natural Science Foundation of China [11461141004, 61271426, U153611, 711504406, 115907 74]; National 863 Program [2015AA016306] China Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National 863 Program(National High Technology Research and Development Program of China) The first author was supported by a grant from the China Scholarship Council and this work was partially supported by the National Natural Science Foundation of China (Nos. 11461141004, 61271426, U153611, 711504406, 115907 74) and National 863 Program (No. 2015AA016306). 38 2 2 0 4 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X 978-1-5108-4876-4 INTERSPEECH 2017.0 3847 3851 10.21437/Interspeech.2017-579 0.0 5 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL9IK 2023-03-23 WOS:000457505000798 0
J Li, PF; Lu, H; Zheng, Q; Li, SJ; Pan, G Li, Pengfei; Lu, Hua; Zheng, Qian; Li, Shijian; Pan, Gang HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Twitter; Feature extraction; Semisupervised learning; Data models; Computer science; Location awareness; Twitter; POI; co-location judgement; semi-supervised learning PREDICTION Enabled by smartphones, social media users are increasingly going mobile. This trend fosters various location based services on social media platforms (e.g., Twitter). Many services like friends notification and community detection benefit from co-location judgement, i.e., to decide whether two Twitter users are co-located in some point-of-interest (POI). This problem is challenging due to the limited information in tweets and the lack of explicit geo-tags in tweets that can be used as labeled data. Our approach to this problem is based on a novel concept of HisRect features extracted from users' historical visits and recent tweets: The former has impacts on where a user visits in general, whereas the latter gives more hints about where a user is currently. In practice, labeled data is scarce. Therefore, we design a semi-supervised learning (SSL) framework that leverages unlabeled data to extract HisRect features. Moreover, we employ an embedding neural network layer to process HisRect features of two users, which decides co-location based on the embedding difference between the two features. Our model is extensively evaluated on two large sets of real Twitter data from more than one million users. The experimental results demonstrate that our HisRect features and SSL framework are highly effective at deciding co-locations. In terms of multiple metrics, our approach clearly outperforms alternative approaches using state-of-the-art techniques. [Li, Pengfei; Zheng, Qian; Li, Shijian; Pan, Gang] Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Peoples R China; [Lu, Hua] Aalborg Univ, Dept Comp Sci, DK-9100 Aalborg, Denmark; [Pan, Gang] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310027, Peoples R China Zhejiang University; Aalborg University; Zhejiang University Li, SJ (corresponding author), Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Peoples R China.;Lu, H (corresponding author), Aalborg Univ, Dept Comp Sci, DK-9100 Aalborg, Denmark. pfl@zju.edu.cn; luhua@cs.aau.dk; csqianzheng@gmail.com; shijianli@zju.edu.cn; gpan@zju.edu.cn Natural Science Foundation of China [61772460]; Ten Thousand Talent Program of Zhejiang Province [2018R52039] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ten Thousand Talent Program of Zhejiang Province This research is supported by Natural Science Foundation of China (No.61772460), Ten Thousand Talent Program of Zhejiang Province (No.2018R52039). 60 1 1 2 8 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAR 1 2021.0 33 3 1005 1018 10.1109/TKDE.2019.2934686 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering QC7WD Green Published 2023-03-23 WOS:000615042700014 0
J Kang, J; Fernandez-Beltran, R; Ye, Z; Tong, XH; Plaza, A Kang, Jian; Fernandez-Beltran, Ruben; Ye, Zhen; Tong, Xiaohua; Plaza, Antonio Deep Hashing Based on Class-Discriminated Neighborhood Embedding IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Feature extraction; Semantics; Remote sensing; Measurement; Binary codes; Earth; Quantization (signal); Content-based image retrieval; deep hashing; deep learning; fast similarity search; hashing; remote sensing IMAGE RETRIEVAL; CHALLENGES; BENCHMARK; SEARCH Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community. [Kang, Jian] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China; [Fernandez-Beltran, Ruben] Univ Jaume 1, Inst New Imaging Technol, Castellon De La Plana 12071, Spain; [Ye, Zhen; Tong, Xiaohua] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10003, Spain Soochow University - China; Universitat Jaume I; Tongji University; Universidad de Extremadura Ye, Z (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. kangjian_1991@outlook.com; rufernan@uji.es; yezhen0402@126.com; xhtong@tongji.edu.cn; aplaza@unex.es Fdz, Ruben/ABH-6669-2020; Plaza, Antonio/C-4455-2008; kang, jian/V-3055-2019 Plaza, Antonio/0000-0002-9613-1659; kang, jian/0000-0001-6284-3044; Ye, Zhen/0000-0002-7178-9817 National Key Research, and Development Project of China [2018YFB0505400, 2017YFB0502700]; National Natural Science Foundation of China [41631178]; Ministry of Science, Innovation, and Universities of Spain [RTI2018-098651-B-C54]; Valencian Government of Spain [GV/2020/167]; FEDER-Junta de Extremadura [GR18060]; European Union under the H2020 EOXPOSURE Project [734541] National Key Research, and Development Project of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science, Innovation, and Universities of Spain(Spanish Government); Valencian Government of Spain; FEDER-Junta de Extremadura(European Commission); European Union under the H2020 EOXPOSURE Project This work was supported in part by the National Key Research, and Development Project of China under Grant 2018YFB0505400 and Grant 2017YFB0502700, in part by the National Natural Science Foundation of China under Grant 41631178, in part by the Ministry of Science, Innovation, and Universities of Spain (RTI2018-098651-B-C54), in part by the Valencian Government of Spain (GV/2020/167), FEDER-Junta de Extremadura (Ref. GR18060), and in part by the European Union under the H2020 EOXPOSURE Project (734541). 56 6 6 0 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2020.0 13 5998 6007 10.1109/JSTARS.2020.3027954 0.0 10 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology OA6FQ Green Published, gold 2023-03-23 WOS:000577878900006 0
J Di, XY; Xue, ZH; Zhang, MX Di, Xiyao; Xue, Zhaohui; Zhang, Mengxue Active Learning-Driven Siamese Network for Hyperspectral Image Classification REMOTE SENSING English Article hyperspectral image (HSI) classification; limited labeled samples problem; active learning; siamese network SPECTRAL-SPATIAL CLASSIFICATION Hyperspectral image (HSI) classification has recently been successfully explored by using deep learning (DL) methods. However, DL models rely heavily on a large number of labeled samples, which are laborious to obtain. Therefore, finding a way to efficiently embed DL models in limited labeled samples is a hot topic in the field of HSI classification. In this paper, an active learning-based siamese network (ALSN) is proposed to solve the limited labeled samples problem in HSI classification. First, we designed a dual learning-based siamese network (DLSN), which consists of a contrastive learning module and a classification module. Secondly, in view of the problem that active learning is difficult to effectively sample under the extremely limited labeling cost, we proposed an adversarial uncertainty-based active learning (AUAL) method to query valuable samples, and to promote DLSN to learn a more complete feature distribution by fine-tuning. Finally, an active learning architecture, based on inter-class uncertainty (ICUAL), is proposed to construct a lightweight sample pair training set, fully extracting the inter-class information of sample pairs and improving classification accuracy. Experiments on three generic HSI datasets strongly demonstrated the effectiveness of ALSN for HSI classification, with performance improvements over other related DL methods. [Di, Xiyao; Xue, Zhaohui] Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China; [Zhang, Mengxue] Univ Valencia, Image & Signal Proc ISP Grp, Valencia 46980, Spain Hohai University; University of Valencia Xue, ZH (corresponding author), Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China. zhaohui.xue@hhu.edu.cn 47 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. FEB 2023.0 15 3 752 10.3390/rs15030752 0.0 25 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 8W0FU gold, Green Published 2023-03-23 WOS:000930999100001 0
C Zhang, HX; Sanin, C; Szczerbicki, E Nguyen, NT; Trawinski, B; Kosala, R Zhang, Haoxi; Sanin, Cesar; Szczerbicki, Edward Experience-Oriented Enhancement of Smartness for Internet of Things INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II Lecture Notes in Artificial Intelligence English Proceedings Paper 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS) MAR 23-25, 2015 Bali, INDONESIA Bina Nusantara Univ,Wroclaw Univ Technol,Ton Duc Thang Univ,Quang Binh Univ,IEEE Indonesia Sect,IEEE SMC Tech Comm Computat Collect Intelligence Knowledge representation; Decisional DNA; Machine learning; Intelligent systems; Internet of things KNOWLEDGE STRUCTURE; DECISIONAL DNA; IOT DIn this paper, we propose a novel approach, the Experience-Oriented Smart Things that allows experiential knowledge discovery, storage, involving, and sharing for Internet of Things. The main features, architecture, and initial experiments of this approach are introduced. Rather than take all the data produced by Internet of Things, this approach focuses on acquiring only interesting data for its knowledge discovery process. By catching decision events, this approach gathers its own daily operation experience, which is the interesting data, and uses such experience for knowledge discovery. An initial experiment was made at the end of this paper, by applying this approach to a sensors-equipped bicycle, the bicycle is able to learn user's physical features and recognize its user out of other riders. Customized version of Decisional DNA is used in this approach as the knowledge representation technique. Decisional DNA is a domain-independent, and flexible, and standard experiential knowledge repository solution that allows knowledge to be acquired, reused, evolved and shared easily. The presented conceptual approach demonstrates how knowledge can be discovered through its domain's experiences and stored as Decisional DNA. [Zhang, Haoxi] Chengdu Univ Informat Technol, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China; [Sanin, Cesar] Univ Newcastle, Fac Engn & Built Environm, Sch Engn, Callaghan, NSW 2308, Australia; [Szczerbicki, Edward] Gdansk Univ Technol, Gdansk, Poland Chengdu University of Information Technology; University of Newcastle; Fahrenheit Universities; Gdansk University of Technology Zhang, HX (corresponding author), Chengdu Univ Informat Technol, 24 Block 1,Xuefu Rd, Chengdu 610225, Peoples R China. Haoxi@cuit.edu.cn; Cesar.Sanin@newcastle.edu.au; Edward.Szczerbicki@zie.pg.gda.pl Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912 36 4 4 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-15705-4; 978-3-319-15704-7 LECT NOTES ARTIF INT 2015.0 9012 506 515 10.1007/978-3-319-15705-4_49 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG5HH 2023-03-23 WOS:000389499300049 0
J Gao, C; Li, Y; Zhou, J; Pedrycz, W; Lai, ZH; Wan, J; Lu, JL Gao, Can; Li, Yong; Zhou, Jie; Pedrycz, Witold; Lai, Zhihui; Wan, Jun; Lu, Jianglin Global structure-guided neighborhood preserving embedding for dimensionality reduction INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Dimensionality reduction; Neighborhood preserving embedding; Global structure; Principal component analysis; Structured sparsity LAPLACIAN EIGENMAPS; FACE-RECOGNITION; REGRESSION; PROJECTIONS; SELECTION Graph embedding is one of the most efficient dimensionality reduction methods in machine learning and pattern recognition. Many local or global graph embedding methods have been proposed and impressive results have been achieved. However, little attention has been paid to the methods that integrate both local and global structural information without constructing complex graphs. In this paper, we propose a simple and effective global structure guided neighborhood preserving embedding method for dimensionality reduction called GSGNPE. Specifically, instead of constructing global graph, principal component analysis (PCA) projection matrix is first introduced to extract the global structural information of the original data, and then the induced global information is integrated with local neighborhood preserving structure to generate a discriminant projection. Moreover, the L-2,L-1-norm regularization is employed in our method to enhance the robustness to occlusion. Finally, we propose an iterative optimization algorithm to solve the proposed problem, and its convergence is also theoretically analyzed. Extensive experiments on four face and six non-face benchmark data sets demonstrate the competitive performance of our proposed method in comparison with the state-of-the-art methods. [Gao, Can; Li, Yong; Zhou, Jie; Lai, Zhihui; Wan, Jun; Lu, Jianglin] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [Gao, Can; Li, Yong; Zhou, Jie; Lai, Zhihui; Wan, Jun; Lu, Jianglin] Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China; [Gao, Can; Li, Yong; Zhou, Jie; Lai, Zhihui; Wan, Jun; Lu, Jianglin] Shenzhen Inst Artificial Intelligence & Robot Soc, SZU Branch, Shenzhen 518060, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland Shenzhen University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Zhou, J (corresponding author), Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.;Zhou, J (corresponding author), Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China.;Zhou, J (corresponding author), Shenzhen Inst Artificial Intelligence & Robot Soc, SZU Branch, Shenzhen 518060, Peoples R China. jie_jpu@163.com Zhou, Jie/0000-0001-5882-3649 National Natural Science Foundation of China [61806127, 62076164, 61976145]; Guangdong Basic and Applied Basic Research Foundation [2021A1515011861]; Shenzhen Institute of Artificial Intelligence and Robotics for Society; Shenzhen Science and Technology Program [JCYJ20210324094601005]; Bureau of Education of Foshan [2019XJZZ05] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Basic and Applied Basic Research Foundation; Shenzhen Institute of Artificial Intelligence and Robotics for Society; Shenzhen Science and Technology Program; Bureau of Education of Foshan The author would like to thank the Editor-in-Chief, editors and anonymous reviewers for their kind help and valuable comments. The work was supported in part by the National Natural Science Foundation of China (Nos. 61806127, 62076164, 61976145), in part by Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011861), in part by Shenzhen Institute of Artificial Intelligence and Robotics for Society, in part by Shenzhen Science and Technology Program (No. JCYJ20210324094601005), and in part by the Bureau of Education of Foshan (Nos. 2019XJZZ05). 52 1 1 9 20 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. JUL 2022.0 13 7 2013 2032 10.1007/s13042-021-01502-6 0.0 JAN 2022 20 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1M3SR 2023-03-23 WOS:000741281100001 0
J Li, Y; Yang, HT; Wang, JY; Zhang, CG; Liu, ZJ; Chen, H Li, Yang; Yang, Haitao; Wang, Jinyu; Zhang, Changgong; Liu, Zhengjun; Chen, Hang An Image Fusion Method Based on Special Residual Network and Efficient Channel Attention ELECTRONICS English Article codec network; deep learning; image fusion; attention mechanism MULTI-FOCUS; PERFORMANCE; INFORMATION; NEST This paper presents an image fusion network based on a special residual network and attention mechanism. Compared with the traditional fusion network, the image fusion network has the advantages of an end-to-end network and integrates the feature extraction advantages of the attention mechanism residual network. It overcomes the shortcomings of the traditional network that need complex design rules and manual operation. In this method, hierarchical feature fusion is used to achieve effective fusion. A combined loss function is designed to optimize training results and improve image fusion quality. This paper uses many qualitative and quantitative experimental analyses on different data sets. The results show that, compared with the comparison algorithm, the method in this paper has a stronger retention ability of infrared and visible light information and better indexes. 72% of eleven indexes compared with some images in the public TNO data set are optimal or sub-optimal, and 80% are optimal or suboptimal in the RoadScene data set, which is much higher than other algorithms. The overall fusion effect is more in line with human visual perception. [Li, Yang; Wang, Jinyu; Zhang, Changgong; Chen, Hang] Space Engn Univ, Sch Space Informat, Beijing 101416, Peoples R China; [Yang, Haitao] Space Engn Univ, Space Secur Res Ctr, Beijing 101416, Peoples R China; [Liu, Zhengjun] Harbin Inst Technol, Sch Phys, Harbin 150001, Peoples R China; [Chen, Hang] Univ Lorraine, CNRS, CRAN UMR 7039, F-54000 Nancy, France Aerospace Engineering University; Aerospace Engineering University; Harbin Institute of Technology; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine Chen, H (corresponding author), Space Engn Univ, Sch Space Informat, Beijing 101416, Peoples R China.;Chen, H (corresponding author), Univ Lorraine, CNRS, CRAN UMR 7039, F-54000 Nancy, France. hangchen@alu.hit.edu.cn Liu, Zhengjun/A-2665-2010 Liu, Zhengjun/0000-0003-2886-8496 National Natural Science Foundation of China [62005320, 61975044] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Grant 62005320, 61975044). 44 0 0 10 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics OCT 2022.0 11 19 3140 10.3390/electronics11193140 0.0 19 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics 5F9TH gold 2023-03-23 WOS:000866652100001 0
C Jiu, MY; Sahbi, H; Qi, L IEEE Jiu, Mingyuan; Sahbi, Hichem; Qi, Lin Deep Context Networks for Image Annotation 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 24th International Conference on Pattern Recognition (ICPR) AUG 20-24, 2018 Chinese Acad Sci, Inst Automat, Beijing, PEOPLES R CHINA Int Assoc Pattern Recognit,Chinese Assoc Automat Chinese Acad Sci, Inst Automat Context plays an important role in visual pattern recognition as it provides complementary clues for different learning tasks including image classification and annotation. In the particular scenario of kernel learning, the general recipe of context-based kernel design consists in learning positive semidefinite similarity functions that return high values not only when data share similar content but also similar context. However, in spite of having a positive impact on performance, the use of context in these kernel design methods has not been fully explored; indeed, context has been handcrafted instead of being learned. In this paper, we introduce a novel context-aware kernel design framework based on deep learning. Our method discriminatively learns spatial geometric context as the weights of a deep network (DN). The architecture of this network is fully determined by the solution of an objective function that mixes content, context and regularization, while the parameters of this network determine the most relevant (discriminant) parts of the learned context. We apply this context and kernel learning framework to image classification using the challenging ImageCLEF Photo Annotation benchmark; the latter shows that our deep context learning provides highly effective kernels for image classification as corroborated through extensive experiments. [Jiu, Mingyuan; Qi, Lin] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China; [Sahbi, Hichem] UPMC Sorbonne Univ, CNRS LIP6, F-75005 Paris, France Zhengzhou University; UDICE-French Research Universities; Sorbonne Universite Jiu, MY (corresponding author), Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China. iemyjiu@zzu.edu.cn; hichem.sahbi@lip6.fr; ielqi@zzu.edu.cn research agency ANR (Agence Nationale de la Recherche) under the MLVIS project (Machine Learning for Visual Annotation in Social-media) [ANR-11-B502-0017]; Startup Research Foundation of Zhengzhou University research agency ANR (Agence Nationale de la Recherche) under the MLVIS project (Machine Learning for Visual Annotation in Social-media)(French National Research Agency (ANR)); Startup Research Foundation of Zhengzhou University This work was partially supported by a grant from the research agency ANR (Agence Nationale de la Recherche) under the MLVIS project (Machine Learning for Visual Annotation in Social-media, ANR-11-B502-0017). Mingyuan Jiu was with Telecom ParisTech as a postdoc, working with Hichem Sahbi, when this work was discussed and part of it achieved and written (under the MLVIS project). Mingyuan Jiu is also supported by a grant from Startup Research Foundation of Zhengzhou University. 35 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1051-4651 978-1-5386-3788-3 INT C PATT RECOG 2018.0 2422 2427 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BL7LC 2023-03-23 WOS:000455146802071 0
J Zhou, MX; Xu, W; Zhang, WP; Jiang, QQ Zhou, Mengxi; Xu, Wei; Zhang, Wenping; Jiang, Qiqi Leverage knowledge graph and GCN for fine-grained-level clickbait detection WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS English Article Knowledge graph; Graph convolutional network; Graph attention network; Clickbait detection MODEL Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability. [Zhou, Mengxi; Xu, Wei; Zhang, Wenping] Renmin Univ China, Sch Informat, Beijing, Peoples R China; [Jiang, Qiqi] Copenhagen Business Sch, Dept Digitalizat, Frederiksberg, Denmark Renmin University of China; Copenhagen Business School Zhang, WP (corresponding author), Renmin Univ China, Sch Informat, Beijing, Peoples R China. zhoumx0808@ruc.edu.cn; weixu@ruc.edu.cn; wpzhang@ruc.edu.cn; qj.digi@cbs.dk ZHANG, Wenping/0000-0002-0183-4504 Fundamental Research Funds for the Central Universities; Research Funds of Renmin University of China [21XNA035] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Research Funds of Renmin University of China This work is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 21XNA035). 48 1 1 3 5 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1386-145X 1573-1413 WORLD WIDE WEB World Wide Web MAY 2022.0 25.0 3 SI 1243 1258 10.1007/s11280-022-01032-3 0.0 MAR 2022 16 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1D5UO 35308295.0 Bronze, Green Accepted 2023-03-23 WOS:000769845700002 0
J Chen, ZL; Wu, XW Chen, Zhuolun; Wu, Xiaowei Research on regional energy efficiency based on GIS technology and image quality processing JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Deep learning; GIS technology; Image quality analysis; Model; Quality CHINA; POLICY; INNOVATION; VEHICLES; INDUSTRY; IMPACT Economic development requires energy support, but the economy that relies too much on high energy consumption and high pollution is not sustainable. However, due to blind investment and low-level expansion of some new energy companies, some new energy industry has overcapacity and low production efficiency, slowing down the pace of new energy industry development, and limiting the development space of new energy companies. Thus, this paper is based on the GIS technology and image quality processing design improvement, around this idea, completed the new system simulation analysis, system development, experimental testing and so on. This paper focuses on the evaluation of the distribution efficiency of new energy industry based on image processing. Natural images have their own unique statistical properties, and interference can cause them to deviate from the original natural statistical state. According to the image itself and the characteristics of the human visual system, the expression of the image is adjusted, which is more conducive to the transmission, storage and understanding of information. Therefore, this paper proposes a new energy industry distribution efficiency evaluation method based on GIS technology and image processing from three aspects of the nature of natural images, the expression of image information and the characteristics of human visual system. The research shows that the method can objectively and effectively evaluate the distribution efficiency of the new energy industry, and has certain practicability, which can effectively alleviate the bottleneck problems faced above. (C) 2019 Published by Elsevier Inc. [Chen, Zhuolun; Wu, Xiaowei] South China Univ Technol, Architectural Design & Res Inst, Sch Architecture, Guangzhou, Guangdong, Peoples R China; [Chen, Zhuolun] Tech Univ Denmark, Dept Technol Management & Econ, Copenhagen Ctr Energy Efficiency, UNEP DTU Partnership, Copenhagen, Denmark South China University of Technology; Technical University of Denmark Wu, XW (corresponding author), South China Univ Technol, Architectural Design & Res Inst, Sch Architecture, Guangzhou, Guangdong, Peoples R China. arzlchen@scut.edu.cn; xwwu@scut.edu.cn Chen, Zhuolun/0000-0001-5348-1117 National Scientific Fund [51108185, 51778235]; Province natural science fund of Guangdong [2016A030313513]; State Key Laboratory of Subtropical Building Science [2019ZA04, 2013KB24]; Guangzhou development and reform commission energy -saving special fund project (Technology research of regional energy based on the energy cascade utilization), Basic scientific research fund of South China University of Technology [2015ZM013]; fund of Guangdong modern architectural creation and engineering technology research centre [2016AZ28] National Scientific Fund; Province natural science fund of Guangdong; State Key Laboratory of Subtropical Building Science; Guangzhou development and reform commission energy -saving special fund project (Technology research of regional energy based on the energy cascade utilization), Basic scientific research fund of South China University of Technology; fund of Guangdong modern architectural creation and engineering technology research centre The research of this paper is funded by National Scientific Fund (No. 51108185 and No. 51778235), Province natural science fund of Guangdong (No. 2016A030313513), State Key Laboratory of Subtropical Building Science (NO. 2019ZA04 and No. 2013KB24), Guangzhou development and reform commission energy -saving special fund project (Technology research of regional energy based on the energy cascade utilization), Basic scientific research fund of South China University of Technology (No. 2015ZM013) and fund of Guangdong modern architectural creation and engineering technology research centre (No. 2016AZ28). 26 2 2 0 8 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. JUL 2019.0 62.0 410 417 10.1016/j.jvcir.2019.06.008 0.0 8 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science IL0CB Green Submitted 2023-03-23 WOS:000476962600038 0
J Liang, YS; Zhou, SZ; Zhang, YJ; Cai, XL; Wang, Y; Cheung, EFC; Lui, SSY; Yu, X; Madsen, KH; Ma, YT; Chan, RCK Liang, Yun-si; Zhou, Shu-zhe; Zhang, Yi-jing; Cai, Xin-lu; Wang, Yi; Cheung, Eric F. C.; Lui, Simon S. Y.; Yu, Xin; Madsen, Kristoffer H.; Ma, Yan-tao; Chan, Raymond C. K. Altered empathy-related resting-state functional connectivity in patients with bipolar disorder EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE English Article Cognitive empathy; Affective empathy; Theory of mind; Bipolar disorder; Resting-state functional connectivity COGNITIVE EMPATHY; SOCIAL COGNITION; ALE METAANALYSIS; NEURAL-NETWORK; RATING-SCALE; I DISORDER; MIND; SCHIZOPHRENIA; BRAIN; QUESTIONNAIRE Empathy is the ability to generate emotional responses (i.e., cognitive empathy) and to make cognitive inferences (i.e., affective empathy) to other people's emotions. Empirical evidence suggests that patients with bipolar disorder (BD) exhibit impairment in cognitive empathy, but findings on affective empathy are inconsistent. Few studies have examined the neural mechanisms of cognitive and affective empathy in patients with BD. In this study, we examined the empathy-related resting-state functional connectivity (rsFC) in BD patients. Thirty-seven patients with BD and 42 healthy controls completed the self-report Questionnaires of Cognitive and Affective Empathy (QCAE), the Yoni behavioural task, and resting-sate fMRI brain scans. Group comparison of empathic ability was conducted. The interactions between group and empathic ability on seed-based whole brain rsFC were examined. BD patients scored lower on the Online Simulation subscale of the QCAE and showed positive correlations between cognitive empathy and the rsFC of the dorsal Medial Prefrontal Cortex (dmPFC) with the lingual gyrus. The correlations between cognitive empathy and the rsFC of the temporal-parietal junction (TPJ) with the fusiform gyrus, the cerebellum and the parahippocampus were weaker in BD patients than that in healthy controls. These findings highlight the underlying neural mechanisms of empathy impairments in BD patients. [Liang, Yun-si; Zhang, Yi-jing; Cai, Xin-lu; Wang, Yi; Chan, Raymond C. K.] Chinese Acad Sci, Inst Psychol, Neuropsychol & Appl Cognit Neurosci Lab, CAS Key Lab Mental Hlth, 16 Lincui Rd, Beijing 100101, Peoples R China; [Liang, Yun-si; Cai, Xin-lu; Chan, Raymond C. K.] Univ Chinese Acad Sci, Sino Danish Coll, Beijing, Peoples R China; [Liang, Yun-si; Cai, Xin-lu; Madsen, Kristoffer H.; Chan, Raymond C. K.] Sino Danish Ctr Educ & Res, Beijing, Peoples R China; [Liang, Yun-si; Zhang, Yi-jing; Cai, Xin-lu; Wang, Yi; Chan, Raymond C. K.] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China; [Zhou, Shu-zhe; Yu, Xin; Ma, Yan-tao] Peking Univ, Hosp 6, 51 Huayuan Rd, Beijing 100191, Peoples R China; [Zhou, Shu-zhe; Yu, Xin; Ma, Yan-tao] Peking Univ, Inst Mental Hlth, Beijing, Peoples R China; [Zhou, Shu-zhe; Yu, Xin; Ma, Yan-tao] Peking Univ, Key Lab Mental Hlth, Minist Hlth, Beijing, Peoples R China; [Zhou, Shu-zhe; Yu, Xin; Ma, Yan-tao] Peking Univ, Natl Clin Res Ctr Mental Disorders, Hosp 6, Beijing, Peoples R China; [Cheung, Eric F. C.; Lui, Simon S. Y.] Castle Peak Hosp, Hong Kong, Peoples R China; [Lui, Simon S. Y.] Univ Hong Kong, Dept Psychiat, Hong Kong, Peoples R China; [Madsen, Kristoffer H.] Copenhagen Univ Hosp, Danish Res Ctr Magnet Resonance, Ctr Funct & Diagnost Imaging & Res, Hvidovre, Denmark; [Madsen, Kristoffer H.] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Peking University; Peking University; Peking University; Peking University; University of Hong Kong; University of Copenhagen; Technical University of Denmark Chan, RCK (corresponding author), Chinese Acad Sci, Inst Psychol, Neuropsychol & Appl Cognit Neurosci Lab, CAS Key Lab Mental Hlth, 16 Lincui Rd, Beijing 100101, Peoples R China.;Chan, RCK (corresponding author), Univ Chinese Acad Sci, Sino Danish Coll, Beijing, Peoples R China.;Chan, RCK (corresponding author), Sino Danish Ctr Educ & Res, Beijing, Peoples R China.;Chan, RCK (corresponding author), Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China.;Ma, YT (corresponding author), Peking Univ, Hosp 6, 51 Huayuan Rd, Beijing 100191, Peoples R China.;Ma, YT (corresponding author), Peking Univ, Inst Mental Hlth, Beijing, Peoples R China.;Ma, YT (corresponding author), Peking Univ, Key Lab Mental Hlth, Minist Hlth, Beijing, Peoples R China.;Ma, YT (corresponding author), Peking Univ, Natl Clin Res Ctr Mental Disorders, Hosp 6, Beijing, Peoples R China. mayantao@bjmu.edu.cn; rckchan@psych.ac.cn Madsen, Kristoffer Hougaard/M-9547-2014; ZHOU, SHUZHE/GXM-9748-2022 Madsen, Kristoffer Hougaard/0000-0001-8606-7641 Beijing Municipal Science & Technology Commission [Z161100000216138]; Beijing Training Project for the Leading Talents in Science and Technology [Z151100000315020]; Beijing Municipal Science and Technology Commission [Z171100001017086]; National Natural Science Foundation of China [31871114, 31400884]; CAS Key Laboratory of Mental Health, Institute of Psychology Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Beijing Training Project for the Leading Talents in Science and Technology; Beijing Municipal Science and Technology Commission(Beijing Municipal Science & Technology Commission); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CAS Key Laboratory of Mental Health, Institute of Psychology This study was supported by grants from Beijing Municipal Science & Technology Commission Grant (Z161100000216138), Beijing Training Project for the Leading Talents in Science and Technology (Z151100000315020), Beijing Municipal Science and Technology Commission (Z171100001017086), National Natural Science Foundation of China (31871114; 31400884), and the CAS Key Laboratory of Mental Health, Institute of Psychology. These funding agents had no further role in the study design; in the collection, analysis and interpretation of the data; in the writing of the manuscript; and in the decision to submit the paper for publication. 49 1 1 8 21 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0940-1334 1433-8491 EUR ARCH PSY CLIN N Eur. Arch. Psych. Clin. Neurosci. AUG 2022.0 272.0 5 839 848 10.1007/s00406-021-01305-4 0.0 JUL 2021 10 Clinical Neurology; Psychiatry Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychiatry 2W6CZ 34282469.0 2023-03-23 WOS:000674535600001 0
J Cheng, JH; Sun, DW; Pu, HB; Chen, XH; Liu, YL; Zhang, H; Li, JL Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hong-Bin; Chen, Xinghai; Liu, Yelin; Zhang, Hong; Li, Jiang-Lin Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets JOURNAL OF FOOD ENGINEERING English Article Imaging spectroscopy; Frozen-thawed; Spectral pre-processing; Grass carp fish NEAR-INFRARED SPECTROSCOPY; CARP CTENOPHARYNGODON-IDELLA; SUPPORT VECTOR MACHINES; COOKED BEEF PRODUCT; SALMON SALMO-SALAR; COMPUTER VISION; QUALITY EVALUATION; DESORPTION ISOTHERMS; VARIABLE SELECTION; VOLATILE COMPOUNDS The investigation of visible and near infrared hyperspectral imaging (400-1000 nm) coupled with classifiers and spectral pre-processing techniques was conducted to discriminate fresh from cold-stored (4 degrees C for 7 days) and frozen-thawed (-20 degrees C and 40 degrees C for 30 days) grass carp fish fillets. Four classifiers with four spectral pretreatment methods were applied to establish the classification models. Compared with the original models established using the full wavelengths, the classification models with three classifiers of soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM) and probabilistic neural network (PNN) in tandem with the first derivative pretreatment showed the best classification performance and the highest correct classification rate (CCR) of 94.29%. In addition, in order to reduce the high dimensionality of hyperspectral images, seven optimal wavelengths were selected by successive projections algorithm (SPA) and used to simplify the classification models. The simplified model obtained by the LS-SVM classifier coupled with the first derivative pre-processing method also presented good prediction accuracy with the CCR of 91.43%. The results demonstrated that the integration of hyperspectral imaging and classifiers analysis had a great potential for on-line detection and was feasible to rapidly and non-invasively discriminate fresh and frozen-thawed fish fillets. (C) 2015 Elsevier Ltd. All rights reserved. [Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hong-Bin; Li, Jiang-Lin] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, FRCFT, Dublin 4, Ireland; [Chen, Xinghai; Liu, Yelin] Zolix Instruments Co Ltd, Beijing 101102, Peoples R China; [Zhang, Hong] Zhejiang Ruiyang Technol Co Ltd, Hangzhou 310052, Zhejiang, Peoples R China South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Hongbin, Pu/ABE-8433-2020; Sun, Da-Wen/B-1899-2012; Cheng, Jun-Hu/AAW-9490-2020; Cheng, Jun-Hu/I-9574-2016 Hongbin, Pu/0000-0002-7977-1902; Sun, Da-Wen/0000-0002-3634-9963; Cheng, Jun-Hu/0000-0003-3928-1770; Cheng, Jun-Hu/0000-0003-3928-1770 Guangdong Province Government (China); National Key Technologies RD Program [2015BAD19B03]; International S&T Cooperation Programme of China [2015DFA71150]; International S&T Cooperation Projects of Guangdong Province [2013B051000010]; Natural Science Foundation of Guangdong Province [2014A030313244] Guangdong Province Government (China); National Key Technologies RD Program(National Key Technology R&D Program); International S&T Cooperation Programme of China; International S&T Cooperation Projects of Guangdong Province; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province) The authors were grateful to the Guangdong Province Government (China) for support through the program of Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by the National Key Technologies R&D Program (2015BAD19B03), the International S&T Cooperation Programme of China (2015DFA71150), the International S&T Cooperation Projects of Guangdong Province (2013B051000010) and the Natural Science Foundation of Guangdong Province (2014A030313244). 63 36 38 6 85 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0260-8774 1873-5770 J FOOD ENG J. Food Eng. SEP 2015.0 161.0 33 39 10.1016/j.jfoodeng.2015.03.011 0.0 7 Engineering, Chemical; Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Food Science & Technology CI8SM 2023-03-23 WOS:000355042000005 0
J Chen, XQ; Qi, L; Yang, YS; Luo, Q; Postolache, O; Tang, JJ; Wu, HF Chen, Xinqiang; Qi, Lei; Yang, Yongsheng; Luo, Qiang; Postolache, Octavian; Tang, Jinjun; Wu, Huafeng Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis JOURNAL OF ADVANCED TRANSPORTATION English Article TRACKING; IDENTIFICATION; COLLISIONS; FUSION Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety. [Chen, Xinqiang; Qi, Lei; Yang, Yongsheng] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China; [Luo, Qiang] Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China; [Postolache, Octavian] Lisbon Univ Inst, ISCTE Inst Univ Lisboa, Lisbon, Portugal; [Tang, Jinjun] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China; [Wu, Huafeng] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China Shanghai Maritime University; Guangzhou University; Instituto Universitario de Lisboa; Central South University; Shanghai Maritime University Tang, JJ (corresponding author), Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China. chenxinqiang@stu.shmtu.edu.cn; 201830510017@stu.shmtu.edu.cn; yangys@shmtu.edu.cn; luoq_yan@gzhu.edu.cn; octavian.adrian.postolache@iscte-iul.pt; jinjuntang@csu.edu.cn; hfwu@shmtu.edu.cn Postolache, Octavian Adrian/ADC-7853-2022; Lang, Ming/HIK-0758-2022; Postolache, Octavian/AAE-1343-2021 Postolache, Octavian Adrian/0000-0001-5055-6347; National Natural Science Foundation of China [51579143, 51509167]; Shanghai Committee of Science and Technology China [18040501700, 18295801100, 17595810300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Committee of Science and Technology China(Shanghai Science & Technology Committee) This work was supported in part by the National Natural Science Foundation of China (51579143, 51509167), Shanghai Committee of Science and Technology China (18040501700, 18295801100, and 17595810300). 34 76 76 24 109 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 0197-6729 2042-3195 J ADV TRANSPORT J. Adv. Transp. JAN 20 2020.0 2020.0 7194342 10.1155/2020/7194342 0.0 12 Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation KH8ED gold 2023-03-23 WOS:000510880900010 0
J Liu, J; Zio, E Liu, Jie; Zio, Enrico A scalable fuzzy support vector machine for fault detection in transportation systems EXPERT SYSTEMS WITH APPLICATIONS English Article Prognostics and health management; High-speed train; Fuzzy SVM; Fuzzy membership calculation; Imbalanced data BIG DATA; PREDICTION; ALGORITHMS; DIAGNOSIS; TRAIN Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. The proposed method is verified by comparison with several benchmark methods on five public datasets. Then, a real case study concerning fault detection in a braking system of a high-speed train is considered. (C) 2018 Published by Elsevier Ltd. [Liu, Jie] Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing, Peoples R China; [Zio, Enrico] Univ Paris Saclay, Cent Supelec, Lab Genie Ind, Chair Syst Sci & Energet Challenges,EDF Fdn, 3 Rue Joliot Curie, F-91190 Gif Sur Yvette, France; [Zio, Enrico] Politecn Milan, Energy Dept, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy; [Zio, Enrico] Beihang Univ, Sinofrench Risk Sci & Engn Lab, 37 Xueyuan Rd, Beijing, Peoples R China Beihang University; UDICE-French Research Universities; Universite Paris Saclay; Polytechnic University of Milan; Beihang University Zio, E (corresponding author), Univ Paris Saclay, Cent Supelec, Lab Genie Ind, Chair Syst Sci & Energet Challenges,EDF Fdn, 3 Rue Joliot Curie, F-91190 Gif Sur Yvette, France. liujie805@buaa.edu.cn; enrico.zio@centralesupelec.fr Liu, Jie/0000-0003-0895-7598 42 28 30 2 96 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. JUL 15 2018.0 102.0 36 43 10.1016/j.eswa.2018.02.017 0.0 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science GD8PP Green Submitted 2023-03-23 WOS:000430774900004 0
J Wang, NN; Yang, YC; Sun, DW; Pu, HB; Zhu, ZW Wang, Nan-Nan; Yang, Yi-Chao; Sun, Da-Wen; Pu, Hongbin; Zhu, Zhiwei Shelf-Life Prediction of 'Gros Michel' Bananas with Different Browning Levels Using Hyperspectral Reflectance Imaging FOOD ANALYTICAL METHODS English Article Artificial neural network; ANN; Banana browning; Hyperspectral imaging; Principal component analysis; PCA; Optimal wavelengths; Shelf life prediction NONDESTRUCTIVE DETERMINATION; AMBIENT-TEMPERATURE; QUALITY ATTRIBUTES; ISOTHERM EQUATIONS; MUSA-CAVENDISHII; COMPUTER VISION; FRUIT-QUALITY; FRESH BEEF; COLOR; SELECTION A hyperspectral imaging system was developed in the spectral region between 400 and 1,100 nm to investigate its potential for predicting the shelf life of bananas with different browning levels. Principal component analysis (PCA) was conducted for reducing data dimensionality and selecting optimal wavelengths. Five optimal wavelengths (454, 486, 559, 686, and 728 nm) were found. Among all principal component (PC) images, PC-4 was selected to segregate browning area from normal surface using a simple threshold algorithm for extracting image features of browning area. The average spectra were obtained by calculating the mean of the spectra of all browning areas in the hyperspectral images of bananas. Then, image features and average spectra in five optimal wavelengths were used to develop classification models for predicting the shelf life of banana samples by determining their browning levels using back propagation (BP), radial basis function (RBF), and self-organizing feature maps (SOM) networks. Results indicated that the classification models using both image features and average spectra were obviously superior to those models using image features or average spectra alone. Among all classifiers, BP classifier had the best performance with the best classification rates of 95.6 % for training set and 90.5 % for testing set, respectively. The results demonstrated that hyperspectral imaging has great potential in predicting banana shelf life based on combination of image features and average spectra. [Wang, Nan-Nan; Yang, Yi-Chao; Sun, Da-Wen; Pu, Hongbin; Zhu, Zhiwei] S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China; [Sun, Da-Wen] Univ Coll Dublin, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol, Dublin 4, Ireland South China University of Technology; University College Dublin Sun, DW (corresponding author), S China Univ Technol, Coll Light Ind & Food Sci, Guangzhou 510641, Guangdong, Peoples R China. dawen.sun@ucd.ie Wang, Nan/HLV-7836-2023; Sun, Da-Wen/B-1899-2012; Wang, Nan/GRY-3150-2022; Hongbin, Pu/ABE-8433-2020 Sun, Da-Wen/0000-0002-3634-9963; Hongbin, Pu/0000-0002-7977-1902 Guangdong Province Government (China); National Key Technologies RD Program [2014BAD08B09] Guangdong Province Government (China); National Key Technologies RD Program(National Key Technology R&D Program) The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program Leading Talent of Guangdong Province (Da-Wen Sun). This research was also supported by National Key Technologies R&D Program (2014BAD08B09). 49 21 22 5 55 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1936-9751 1936-976X FOOD ANAL METHOD Food Anal. Meth. MAY 2015.0 8.0 5 1173 1184 10.1007/s12161-014-9960-1 0.0 12 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology CF6EA 2023-03-23 WOS:000352648300011 0
C Zhu, F; Halimi, A; Honeine, P; Chen, BD; Zheng, NN IEEE Zhu, Fei; Halimi, Abderrahim; Honeine, Paul; Chen, Badong; Zheng, Nanning ADMM for Maximum Correntropy Criterion 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 24-29, 2016 Vancouver, CANADA IEEE,IEEE Computat Intelligence Soc,Int Nueral Network Soc,Evolutionary Programming Soc,IET,IEEE BigData,Gulf Univ Sci & Technol Correntropy; maximum correntropy estimation; alternating direction method of multipliers; hyperspectral image; unmixing problem HYPERSPECTRAL IMAGES; SIGNAL; ALGORITHM The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra called endmembers. The coefficients of the combination need to be estimated subject to the nonnegativity and the sum-to-one constraints. In practice, some spectral bands suffer from low signal-to-noise ratio due to acquisition noise and atmospheric effects, thus requiring robust techniques for the unmixing problem. In this work, we cast the unmixing problem as the maximization of a correntropy criterion, and provide a relevant solution using the alternating direction method of multipliers (ADMM) method. Finally, the relevance of the proposed approach is validated on synthetic and real hyperspectral images, demonstrating that the correntropybased unmixing is robust to outlier bands. [Zhu, Fei] Univ Technol Troyes, Inst Charles Delaunay CNRS, Troyes, France; [Halimi, Abderrahim] Heriot Watt Univ, Sch Engn & Phys Sci, Edinburgh, Midlothian, Scotland; [Honeine, Paul] Univ Rouen, LITIS Lab, Rouen, France; [Chen, Badong; Zheng, Nanning] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Peoples R China Universite de Technologie de Troyes; Heriot Watt University; Universite de Rouen Normandie; Xi'an Jiaotong University Zhu, F (corresponding author), Univ Technol Troyes, Inst Charles Delaunay CNRS, Troyes, France. fei.zhu@utt.fr; a.halimi@hw.ac.uk; paul.honeine@univ-rouen.fr; chenbd@mail.xjtu.edu.cn; nnzheng@mail.xjtu.edu.cn Honeine, Paul/M-3230-2019; HALIMI, Abderrahim/AAI-8802-2020; zhu, fei/AAD-2458-2019 Honeine, Paul/0000-0002-3042-183X; HALIMI, Abderrahim/0000-0002-8112-5352; zhu, fei/0000-0002-8113-3707 French ANR [HYPANEMA: ANR-12BS03-0033] French ANR(French National Research Agency (ANR)) This work was supported by the French ANR, grant HYPANEMA: ANR-12BS03-0033. 38 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-5090-0619-9 IEEE IJCNN 2016.0 1420 1427 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BH3QM Green Submitted 2023-03-23 WOS:000399925501082 0
J Wen, PF; Li, Y; Chen, SW; Zhao, S Wen, Pengfei; Li, Yong; Chen, Shaowei; Zhao, Shuai Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources IEEE INTERNET OF THINGS JOURNAL English Article Feature extraction; Estimation; Sensors; Uncertainty; Industrial Internet of Things; Degradation; Data models; Industrial Internet of Things (IIoT); information fusion; multiple sources; prognostics; remaining useful life (RUL) RECURRENT NEURAL-NETWORK; HEALTH INDICATOR; MODEL; PROGNOSTICS; ENSEMBLE; STATE Industrial Internet of Things has significantly boosted predictive maintenance for complex industrial systems, where the accurate prediction of remaining useful life (RUL) with high-level confidence is challenging. By aggregating multiple informative sources of system degradation, information fusion can be applied to improve the prediction accuracy and reduce the uncertainty. It can be performed on the data-level, feature-level, and decision-level. To fully exploit the available degradation information, this article proposes a hybrid fusion method on both the data level and decision level to predict the RUL. On the data level, genetic programming (GP) is adopted to integrate physical sensor sources into a composite health indicator (HI), resulting in an explicit nonlinear data-level fusion model. Subsequently, the predictions of the RUL based on each physical sensor and the developed composite HI are synthesized in the framework of belief functions theory, as the decision-level fusion method. Moreover, the decision-level method is flexible for incorporating other statistical data-driven methods with explicit estimations of the RUL. The proposed method is verified via a case study on NASA's C-MAPSS data set. Compared to the single-level fusion methods, the results confirm the superiority of the proposed method for higher accuracy and certainty of predicting the RUL. [Wen, Pengfei; Li, Yong; Chen, Shaowei] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China; [Zhao, Shuai] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Northwestern Polytechnical University; Aalborg University Zhao, S (corresponding author), Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark. wenpengfei@mail.nwpu.edu.cn; ruikel@nwpu.edu.cn; cgong@nwpu.edu.cn; szh@et.aau.dk Wen, Pengfei/ABB-1421-2021; Zhao, Shuai/AAP-2135-2020 Wen, Pengfei/0000-0002-9384-3845; Zhao, Shuai/0000-0001-7441-5434 47 9 9 12 54 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. JUN 1 2021.0 8.0 11 9045 9058 10.1109/JIOT.2021.3055977 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications SF5MA Green Published 2023-03-23 WOS:000652798400026 0
C Dong, DW; Ye, ZW; Su, J; Xie, SW; Cao, Y; Kochan, R IEEE Dong, Dawei; Ye, Zhiwei; Su, Jun; Xie, Shiwei; Cao, Yu; Kochan, Roman A Malware Detection Method Based on Improved Fireworks Algorithm and Support Vector Machine 15TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET - 2020) English Proceedings Paper 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) FEB 25-29, 2020 Lviv, UKRAINE Natl Univ Lviv Polytechn,IEEE,IEEE Ukraine Sect,Mil Univ Technol Fireworks algorithm; Levy Flights; Support vector machine; Malware detection The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms. [Dong, Dawei; Ye, Zhiwei; Su, Jun; Xie, Shiwei; Cao, Yu] Hubei Univ Technol, Sch Comp Sci, Wuhan, Peoples R China; [Kochan, Roman] Univ Bielsko Biala, Dept Comp Sci & Automat, Bielsko Biala, Poland; [Kochan, Roman] Lviv Polytech Natl Univ, Dept Specialized Comp Syst, Lvov, Ukraine Hubei University of Technology; University of Bielsko-Biala; Ministry of Education & Science of Ukraine; Lviv Polytech National University Ye, ZW (corresponding author), Hubei Univ Technol, Sch Comp Sci, Wuhan, Peoples R China. weizhiye121@163.com; kochan.roman@gmail.com China University Students Innovation and Entrepreneurship Training Program - Fujian Provincial Key Laboratory of Data Intensive Computing [201810500001]; Key Laboratory of Intelligent Computing and Information Processing, Fujian [BD201801]; State Key Laboratory of Geo-Information Engineering [SKLGIE2014-M-3-3] China University Students Innovation and Entrepreneurship Training Program - Fujian Provincial Key Laboratory of Data Intensive Computing; Key Laboratory of Intelligent Computing and Information Processing, Fujian; State Key Laboratory of Geo-Information Engineering This work is funded by the China University Students Innovation and Entrepreneurship Training Program No.201810500001, funded by Fujian Provincial Key Laboratory of Data Intensive Computing and Key Laboratory of Intelligent Computing and Information Processing, Fujian No.BD201801 and funded by State Key Laboratory of Geo-Information Engineering, no. SKLGIE2014-M-3-3. (The author marked with * is Corresponding author. Tel.: +86 18971593240. E-mail address: weizhiye121@163.com.) 19 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-5566-1 2020.0 846 851 10.1109/TCSET49122.2020.235556 0.0 6 Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BQ1XG 2023-03-23 WOS:000578041000177 0
J Yao, B; Ge, XL; Xie, D; Li, ST; Zhang, YC; Wang, HM; Wang, HR Yao, Bo; Ge, Xinglai; Xie, Dong; Li, Songtao; Zhang, Yichi; Wang, Huimin; Wang, Haoran Electrothermal Stress Analysis and Lifetime Evaluation of DC-Link Capacitor Banks in the Railway Traction Drive System IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS English Article Capacitors; Rail transportation; Reliability; Thermal stresses; Stress; Thermal analysis; Analytical models; DC-link capacitor; electrothermal stress; lifetime; multioperation conditions; railway traction drive system METALLIZED FILM CAPACITORS; RELIABILITY EVALUATION; POWER; MODEL In the railway traction drive system, the reliability of DC-link capacitor banks faces enormous challenges due to the multioperation conditions, the complex physical structures, and the safe operation requirements. Considering these issues, this article proposes a reliability evaluation method of the DC-link capacitors banks to provide a guideline for preventive maintenance in the traction drive system. In the electrothermal stress analysis, different from the conventional methods, the dynamic power loss profile is obtained by the sliding-window discrete Fourier transform (DFT), and the equivalent series resistance is fit by the neural network. Afterward, a bidirectional thermal model is established to estimate the thermal stress under the dynamic power losses, considering the impact of thermal capacity, thermal coupling, and air-cooling heat dissipation. In addition, according to the stability analysis and the rated temperature limitation, an end-of-lifetime standard that meets the safe operation requirement of the train is first proposed, thereby evaluating the lifetime bottleneck of the DC-link capacitor banks. The evaluation results can provide a reference to use the low-cost on-condition maintenance instead of scheduled maintenance in the railway traction drive system. Furthermore, a prototype experiment is performed to verify the effectiveness of the electrothermal stress model. [Yao, Bo; Ge, Xinglai; Xie, Dong; Li, Songtao; Zhang, Yichi; Wang, Huimin] Southwest Jiaotong Univ, Minist Educ Key, Lab Magnet Suspens Technol & Maglev Vehicle, Chengdu 610031, Sichuan, Peoples R China; [Wang, Haoran] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Southwest Jiaotong University; Aalborg University Ge, XL (corresponding author), Southwest Jiaotong Univ, Minist Educ Key, Lab Magnet Suspens Technol & Maglev Vehicle, Chengdu 610031, Sichuan, Peoples R China. yaobo1666@my.swjtu.edu.cn; xlgee@163.com; xiedong@my.swjtu.edu.cn; songtaoli@my.swjtu.edu.cn; yichizhang@my.swjtu.edu.cn; wanghuimin@my.swjtu.edu.cn; hao@et.aau.dk wang, hui/GRS-4730-2022; Wang, Hui/HMU-9512-2023; wang, huimin/HDM-8421-2022 Li, Songtao/0000-0001-5474-8039; Xie, Dong/0000-0002-2314-6980; zhang, yichi/0000-0002-7160-8430; Yao, Bo/0000-0001-6463-5468 High-Speed Railway Joint Funds of the National Natural Science Foundation of China [U1934204]; National Natural Science Foundation of China [51677156, 61733015] High-Speed Railway Joint Funds of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Project Supported by High-Speed Railway Joint Funds of the National Natural Science Foundation of China under Grant U1934204 and in part by the Project Supported by the National Natural Science Foundation of China under Grant 51677156 and Grant 61733015. Recommended for publication by Associate Editor Yongheng Yang. 45 5 5 2 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-6777 2168-6785 IEEE J EM SEL TOP P IEEE J. Emerg. Sel. Top. Power Electron. AUG 2021.0 9.0 4 4269 4284 10.1109/JESTPE.2020.3000130 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering TS3IY Green Published 2023-03-23 WOS:000679548500042 0
J Tedja, MS; Haarman, AEG; Meester-Smoor, MA; Kaprio, J; Mackey, DA; Guggenheim, JA; Hammond, CJ; Verhoeven, VJM; Klaver, CCW; Bailey-Wilson, JE; Baird, PN; Veluchamy, AB; Biino, G; Burdon, KP; Campbell, H; Chen, LJ; Cheng, CY; Chew, EY; Craig, JE; Cumberland, PM; Deangelis, MM; Delcourt, C; Ding, XH; van Duijn, CM; Evans, DM; Fan, Q; Fossarello, M; Foster, PJ; Gharahkhani, P; Iglesias, AI; Guol, XB; Haller, T; Han, XK; Hayward, C; He, MG; Hewitt, AW; Hoang, Q; Hysi, PG; Igo, RP; Iyengar, SK; Jonas, JB; Kahonen, M; Khawaja, AP; Klein, BE; Klein, R; Lass, JH; Lee, K; Lehtimaki, T; Lewis, D; Li, Q; Li, SM; Lyytikainen, LP; MacGregor, S; Martin, NG; Meguro, A; Metspalu, A; Middlebrooks, C; Miyake, M; Mizuki, N; Musolf, A; Nickels, S; Oexle, K; Pang, CP; Parssinen, O; Paterson, AD; Pfeiffer, N; Polasek, O; Rahi, JS; Raitakari, O; Rudan, I; Sahebjada, S; Saw, SM; Stambolian, D; Simpson, CL; Tai, ES; Tideman, JWL; Tsujikawa, A; Verhoeven, VJM; Vitart, V; Wang, NL; Wedenoja, J; Wei, WB; Williams, C; Williams, KM; Wilson, JF; Wojciechowski, R; Wang, YX; Yamashiro, K; Yam, JCS; Yap, MKH; Yazar, S; Yip, SP; Young, TL; Zhou, XT Tedja, Milly S.; Haarman, Annechien E. G.; Meester-Smoor, Magda A.; Kaprio, Jaakko; Mackey, David A.; Guggenheim, Jeremy A.; Hammond, Christopher J.; Verhoeven, Virginie J. M.; Klaver, Caroline C. W.; Bailey-Wilson, Joan E.; Baird, Paul Nigel; Veluchamy, Amutha Barathi; Biino, Ginevra; Burdon, Kathryn P.; Campbell, Harry; Chen, Li Jia; Cheng, Ching-Yu; Chew, Emily Y.; Craig, Jamie E.; Cumberland, Phillippa M.; Deangelis, Margaret M.; Delcourt, Cecile; Ding, Xiaohu; van Duijn, Cornelia M.; Evans, David M.; Fan, Qiao; Fossarello, Maurizio; Foster, Paul J.; Gharahkhani, Puya; Iglesias, Adriana I.; Guol, Xiaobo; Haller, Toomas; Han, Xikun; Hayward, Caroline; He, Mingguang; Hewitt, Alex W.; Hoang, Quan; Hysi, Pirro G.; Igo, Robert P., Jr.; Iyengar, Sudha K.; Jonas, Jost B.; Kahonen, Mika; Khawaja, Anthony P.; Klein, Barbara E.; Klein, Ronald; Lass, Jonathan H.; Lee, Kris; Lehtimaki, Terho; Lewis, Deyana; Li, Qing; Li, Shi-Ming; Lyytikainen, Leo-Pekka; MacGregor, Stuart; Martin, Nicholas G.; Meguro, Akira; Metspalu, Andres; Middlebrooks, Candace; Miyake, Masahiro; Mizuki, Nobuhisa; Musolf, Anthony; Nickels, Stefan; Oexle, Konrad; Pang, Chi Pui; Parssinen, Olavi; Paterson, Andrew D.; Pfeiffer, Norbert; Polasek, Ozren; Rahi, Jugnoo S.; Raitakari, Olli; Rudan, Igor; Sahebjada, Srujana; Saw, Seang-Mei; Stambolian, Dwight; Simpson, Claire L.; Tai, E-Shyong; Tideman, J. Willem L.; Tsujikawa, Akitaka; Verhoeven, Virginie J. M.; Vitart, Veronique; Wang, Ningli; Wedenoja, Juho; Wei, Wen Bin; Williams, Cathy; Williams, Katie M.; Wilson, James F.; Wojciechowski, Robert; Wang, Ya Xing; Yamashiro, Kenji; Yam, Jason C. S.; Yap, Maurice K. H.; Yazar, Seyhan; Yip, Shea Ping; Young, Terri L.; Zhou, Xiangtian CREAM Consortium IMI - Myopia Genetics Report INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE English Article myopia; refractive error; genetics; GWAS; GxE interactions GENOME-WIDE ASSOCIATION; HEPATOCYTE GROWTH-FACTOR; HIGH-GRADE MYOPIA; ONSET HIGH MYOPIA; REFRACTIVE ERROR; SUSCEPTIBILITY LOCUS; CORNEAL CURVATURE; AXIAL LENGTH; PAX6 GENE; MENDELIAN RANDOMIZATION The knowledge on the genetic background of refractive error and myopia has expanded dramatically in the past few years. This white paper aims to provide a concise summary of current genetic findings and defines the direction where development is needed. We performed an extensive literature search and conducted informal discussions with key stakeholders. Specific topics reviewed included common refractive error, any and high myopia, and myopia related to syndromes. To date, almost 200 genetic loci have been identified for refractive error and myopia, and risk variants mostly carry low risk but are highly prevalent in the general population. Several genes for secondary syndromic myopia overlap with those for common myopia. Polygenic risk scores show overrepresentation of high myopia in the higher deciles of risk. Annotated genes have a wide variety of functions, and all retinal layers appear to be sites of expression. The current genetic findings offer a world of new molecules involved in myopiagenesis. As the missing heritability is still large, further genetic advances are needed. This Committee recommends expanding large-scale, in-depth genetic studies using complementary big data analytics, consideration of gene-environment effects by thorough measurement of environmental exposures, and focus on subgroups with extreme phenotypes and high familial occurrence. Functional characterization of associated variants is simultaneously needed to bridge the knowledge gap between sequence variance and consequence for eye growth. [Tedja, Milly S.; Haarman, Annechien E. G.; Meester-Smoor, Magda A.; Verhoeven, Virginie J. M.; Klaver, Caroline C. W.; Iglesias, Adriana I.; Tideman, J. Willem L.; Verhoeven, Virginie J. M.] Erasmus MC, Dept Ophthalmol, Rotterdam, Netherlands; [Tedja, Milly S.; Haarman, Annechien E. G.; Meester-Smoor, Magda A.; Verhoeven, Virginie J. M.; Klaver, Caroline C. W.; van Duijn, Cornelia M.; Iglesias, Adriana I.; Tideman, J. Willem L.; Verhoeven, Virginie J. M.] Erasmus MC, Dept Epidemiol, Rotterdam, Netherlands; [Kaprio, Jaakko] Univ Helsinki, Inst Mol Med, Helsinki, Finland; [Kaprio, Jaakko; Wedenoja, Juho] Univ Helsinki, Dept Publ Hlth, Helsinki, Finland; [Mackey, David A.] Univ Melbourne, Ctr Eye Res Australia, Royal Victorian Eye & Ear Hosp, Ophthalmol,Dept Surg, Melbourne, Vic, Australia; [Mackey, David A.; Burdon, Kathryn P.; Hewitt, Alex W.] Univ Tasmania, Dept Ophthalmol, Menzies Inst Med Res, Hobart, Tas, Australia; [Mackey, David A.] Univ Western Australia, Ctr Ophthalmol & Visual Sci, Lions Eye Inst, Perth, WA, Australia; [Guggenheim, Jeremy A.] Cardiff Univ, Sch Optometry & Vis Sci, Cardiff, S Glam, Wales; [Hammond, Christopher J.; Hysi, Pirro G.; Williams, Katie M.] Kings Coll London, Sect Acad Ophthalmol, Sch Life Course Sci, London, England; [Verhoeven, Virginie J. M.; Iglesias, Adriana I.; Verhoeven, Virginie J. M.] Erasmus MC, Dept Clin Genet, Rotterdam, Netherlands; [Klaver, Caroline C. W.] Radboud Univ Nijmegen, Med Ctr, Dept Ophthalmol, Nijmegen, Netherlands; [Bailey-Wilson, Joan E.; Lewis, Deyana; Musolf, Anthony; Rahi, Jugnoo S.; Simpson, Claire L.] NHGRI, Computat & Stat Genom Branch, NIH, Bethesda, MD 20892 USA; [Mackey, David A.; Baird, Paul Nigel; He, Mingguang; Hewitt, Alex W.; Sahebjada, Srujana] Univ Melbourne, Ctr Eye Res Australia, Royal Victorian Eye & Ear Hosp, Ophthalmol,Dept Surg, Melbourne, Vic, Australia; [Veluchamy, Amutha Barathi; Hoang, Quan] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore; [Veluchamy, Amutha Barathi] DUKE NUS Med Sch, Singapore, Singapore; [Veluchamy, Amutha Barathi; Rahi, Jugnoo S.] Natl Univ Singapore, Dept Ophthalmol, Natl Univ Hlth Syst, Singapore, Singapore; [Biino, Ginevra] Natl Res Council Italy, Inst Mol Genet, Pavia, Italy; [Campbell, Harry; Guol, Xiaobo; Rudan, Igor; Wilson, James F.] Univ Edinburgh, Ctr Global Hlth Res, Usher Inst Populat Hlth Sci & Informat, Edinburgh, Midlothian, Scotland; [Chen, Li Jia; Pang, Chi Pui; Yam, Jason C. S.] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong Eye Hosp, Kowloon, Hong Kong, Peoples R China; [Cheng, Ching-Yu] Natl Univ Singapore, Dept Ophthalmol, Yong Loo Lin Sch Med, Singapore, Singapore; [Cheng, Ching-Yu] Singapore Natl Eye Ctr, Ocular Epidemiol Res Grp, Singapore Eye Res Inst, Singapore, Singapore; [Cheng, Ching-Yu] DUKE NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program Eye ACP, Singapore, Singapore; [Chew, Emily Y.] NEI, Div Epidemiol & Clin Applicat, NIH, Bethesda, MD 20892 USA; [Craig, Jamie E.] Flinders Univ S Australia, Dept Ophthalmol, Adelaide, SA, Australia; [Cumberland, Phillippa M.] UCL, Great Ormond St Inst Child Hlth, London, England; [Deangelis, Margaret M.] Univ Utah, Dept Ophthalmol & Visual Sci, John Moran Eye Ctr, Salt Lake City, UT USA; [Delcourt, Cecile] Univ Bordeaux, Bordeaux Populat Hlth Res Ctr, Team LEHA, INSERM,UMR 1219, F-33000 Bordeaux, France; [Ding, Xiaohu; He, Mingguang] Sun Yat Sen Univ, State Key Lab Ophthalmol, Zhongshan Ophthalm Ctr, Guangzhou, Guangdong, Peoples R China; [Evans, David M.] Univ Queensland, Diamantina Inst, Translat Res Inst, Brisbane, Qld, Australia; [Evans, David M.] Univ Bristol, MRC Integrat Epidemiol Unit, Bristol, Avon, England; [Evans, David M.; Williams, Cathy] Univ Bristol Sch Med, Dept Populat Hlth Sci, Bristol, Avon, England; [Fan, Qiao] DUKE Natl Univ Singapore, Ctr Quantitat Med, Singapore, Singapore; [Fossarello, Maurizio] Univ Hosp San Giovanni di Dio, Cagliari, Italy; [Foster, Paul J.; Khawaja, Anthony P.; Rahi, Jugnoo S.] Moorfields Eye Hosp NHS Fdn Trust, NIHR Biomed Res Ctr, London, England; [Foster, Paul J.; Khawaja, Anthony P.; Rahi, Jugnoo S.] UCL Inst Ophthalmol, London, England; [Gharahkhani, Puya; Han, Xikun; MacGregor, Stuart] QIMR Berghofer Med Res Inst, Stat Genet, Brisbane, Qld, Australia; [Guol, Xiaobo] Sun Yat Sen Univ, Dept Stat Sci, Sch Math, Guangzhou, Guangdong, Peoples R China; [Haller, Toomas; Metspalu, Andres] Univ Tartu, Inst Genom, Tartu, Estonia; [Hayward, Caroline; Vitart, Veronique; Wilson, James F.] Univ Edinburgh, MRC Human Genet Unit, MRC Inst Genet & Mol Med, Edinburgh, Midlothian, Scotland; [Mackey, David A.; Hewitt, Alex W.; Yazar, Seyhan] Univ Western Australia, Ctr Ophthalmol & Visual Sci, Lions Eye Inst, Perth, WA, Australia; [Hoang, Quan] Columbia Univ, Dept Ophthalmol, New York, NY 10027 USA; [Igo, Robert P., Jr.; Iyengar, Sudha K.; Lass, Jonathan H.] Case Western Reserve Univ, Dept Populat & Quantitat Hlth Sci, Cleveland, OH 44106 USA; [Iyengar, Sudha K.; Lass, Jonathan H.] Case Western Reserve Univ, Dept Ophthalmol & Visual Sci, Cleveland, OH 44106 USA; [Iyengar, Sudha K.; Lass, Jonathan H.] Univ Hosp Eye Inst, Cleveland, OH USA; [Iyengar, Sudha K.] Case Western Reserve Univ, Dept Genet, Cleveland, OH 44106 USA; [Jonas, Jost B.] Heidelberg Univ, Dept Ophthalmol, Med Fac Mannheim, Mannheim, Germany; [Jonas, Jost B.; Li, Shi-Ming; Wang, Ningli; Wang, Ya Xing] Capital Med Univ, Beijing Tongren Eye Ctr, Beijing Key Lab Ophthalmol & Visual Sci, Beijing Tongren Hosp,Beijing Inst Ophthalmol, Beijing, Peoples R China; [Kahonen, Mika] Tampere Univ Hosp, Dept Clin Physiol, Tampere, Finland; [Kahonen, Mika] Univ Tampere, Sch Med, Tampere, Finland; [Kahonen, Mika] Univ Tampere, Finnish Cardiovasc Res Ctr, Fac Med & Life Sci, Tampere, Finland; [Kaprio, Jaakko] Univ Helsinki, Inst Mol Med Finland FIMM, HiLIFE Unit, Helsinki, Finland; [Khawaja, Anthony P.] Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge, England; [Klein, Barbara E.; Klein, Ronald; Lee, Kris; Young, Terri L.] Univ Wisconsin, Dept Ophthalmol & Visual Sci, Madison, WI USA; [Lehtimaki, Terho; Lyytikainen, Leo-Pekka] Univ Tampere, Dept Clin Chem, Finnish Cardiovasc Res Ctr Tampere, Fac Med & Life Sci, Tampere, Finland; [Lehtimaki, Terho; Lyytikainen, Leo-Pekka] Univ Tampere, Dept Clin Chem, Fimlab Labs, Tampere, Finland; [Li, Qing] NHGRI, NIH, Baltimore, MD USA; [Martin, Nicholas G.] QIMR Berghofer Med Res Inst, Genet Epidemiol, Brisbane, Qld, Australia; [Meguro, Akira; Mizuki, Nobuhisa] Yokohama City Univ, Dept Ophthalmol, Sch Med, Yokohama, Kanagawa, Japan; [Miyake, Masahiro; Tsujikawa, Akitaka] Kyoto Univ, Dept Ophthalmol & Visual Sci, Grad Sch Med, Kyoto, Japan; [Nickels, Stefan; Pfeiffer, Norbert] Johannes Gutenberg Univ Mainz, Dept Ophthalmol, Univ Med Ctr, Mainz, Germany; [Oexle, Konrad] Helmholtz Zentrum Munchen, Inst Neurogen, German Res Ctr Environm Hlth, Neuherberg, Germany; [Parssinen, Olavi] Cent Hosp Cent Finland, Dept Ophthalmol, Jyvaskyla, Finland; [Parssinen, Olavi] Univ Jyvaskyla, Gerontol Res Ctr, Fac Sport & Hlth Sci, Jyvaskyla, Finland; [Paterson, Andrew D.] Hosp Sick Children, Program Genet & Genome Biol, Toronto, ON, Canada; [Paterson, Andrew D.] Univ Toronto, Program Genet & Genome Biol, Toronto, ON, Canada; [Polasek, Ozren] Gen Info Ltd, Zagreb, Croatia; [Polasek, Ozren] Univ Split, Sch Med, Soltanska 2, Split, Croatia; [Rahi, Jugnoo S.] UCL, Ulverscroft Vis Res Grp, London, England; [Raitakari, Olli] Univ Turku, Res Ctr Appl & Prevent Cardiovasc Med, Turku, Finland; [Raitakari, Olli] Turku Univ Hosp, Dept Clin Physiol & Nucl Med, Turku, Finland; [Saw, Seang-Mei] Singapore Natl Eye Ctr, Myopia Res Grp, Singapore Eye Res Inst, Singapore, Singapore; [Saw, Seang-Mei; Tai, E-Shyong] Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Natl Univ Hlth Syst, Singapore, Singapore; [Stambolian, Dwight] Univ Penn, Dept Ophthalmol, Philadelphia, PA 19104 USA; [Simpson, Claire L.] Univ Tennessee, Ctr Hlth Sci, Dept Genet Genom & Informat, Memphis, TN 38163 USA; [Wedenoja, Juho] Univ Helsinki, Dept Ophthalmol, Helsinki, Finland; [Wedenoja, Juho] Helsinki Univ Hosp, Helsinki, Finland; [Wei, Wen Bin] Capital Med Univ, Beijing Key Lab Intraocular Tumor Diag & Treatmen, Beijing Ophthalmol & Visual Sci Key Lab, Beijing Tongren Eye Ctr,Beijing Tongren Hosp, Beijing, Peoples R China; [Wojciechowski, Robert] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol & Med, Baltimore, MD USA; [Wojciechowski, Robert] Johns Hopkins Med Inst, Wilmer Eye Inst, Baltimore, MD 21205 USA; [Yamashiro, Kenji] Otsu Red Cross Hosp, Dept Ophthalmol, Nagara, Japan; [Yap, Maurice K. H.] Hong Kong Polytech Univ, Ctr Myopia Res, Sch Optometry, Hong Kong, Peoples R China; [Yip, Shea Ping] Hong Kong Polytech Univ, Dept Hlth Technol & Informat, Hong Kong, Peoples R China; [Zhou, Xiangtian] Wenzhou Med Univ, Sch Ophthalmol & Optometry, Hosp Eye, Wenzhou, Peoples R China Erasmus University Rotterdam; Erasmus MC; Erasmus University Rotterdam; Erasmus MC; University of Helsinki; University of Helsinki; Centre for Eye Research Australia; Royal Victorian Eye & Ear Hospital; University of Melbourne; University of Tasmania; Menzies Institute for Medical Research; Lions Eye Institute; University of Western Australia; Cardiff University; University of London; King's College London; Erasmus University Rotterdam; Erasmus MC; Radboud University Nijmegen; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI); Centre for Eye Research Australia; Royal Victorian Eye & Ear Hospital; University of Melbourne; National University of Singapore; Singapore National Eye Center; National University of Singapore; National University of Singapore; Consiglio Nazionale delle Ricerche (CNR); Istituto di Genetica Molecolare (IGM-CNR); University of Edinburgh; Chinese University of Hong Kong; National University of Singapore; National University of Singapore; Singapore National Eye Center; National University of Singapore; National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI); Flinders University South Australia; University of London; University College London; Utah System of Higher Education; University of Utah; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite de Bordeaux; Sun Yat Sen University; University of Queensland; University of Bristol; University of Bristol; National University of Singapore; Presidio Ospedaliero San Giovanni di Dio; University of London; University College London; Moorfields Eye Hospital NHS Foundation Trust; University of London; University College London; QIMR Berghofer Medical Research Institute; Sun Yat Sen University; University of Tartu; University of Edinburgh; Lions Eye Institute; University of Western Australia; Columbia University; Case Western Reserve University; Case Western Reserve University; Case Western Reserve University; Ruprecht Karls University Heidelberg; Capital Medical University; Tampere University; Tampere University Hospital; Tampere University; Tampere University; University of Helsinki; University of Cambridge; University of Wisconsin System; University of Wisconsin Madison; Tampere University; Tampere University; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI); NIH National Institute on Aging (NIA); QIMR Berghofer Medical Research Institute; Yokohama City University; Kyoto University; Johannes Gutenberg University of Mainz; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Central Finland Central Hospital; University of Jyvaskyla; University of Toronto; Hospital for Sick Children (SickKids); University of Toronto; University of Split; University of London; University College London; University of Turku; University of Turku; National University of Singapore; Singapore National Eye Center; National University of Singapore; University of Pennsylvania; University of Tennessee System; University of Tennessee Health Science Center; University of Helsinki; University of Helsinki; Helsinki University Central Hospital; Capital Medical University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine; Hong Kong Polytechnic University; Hong Kong Polytechnic University; Wenzhou Medical University Klaver, CCW (corresponding author), Erasmus MC, Room Na 2808,POB 2040, NL-3000 CA Rotterdam, Netherlands. c.c.w.klaver@erasmusmc.nl Gharahkhani, Puya/AAA-6912-2020; Campbell, Harry/E-2959-2010; wei, wei/HHR-8613-2022; Macgregor, Stuart/C-6442-2009; Fossarello, Maurizio/AAD-8235-2021; Yam, Jason C./AAI-2522-2020; /S-1190-2019; Haller, Toomas/N-3797-2019; Mackey, David A/H-5340-2014; Polasek, Ozren/B-6002-2011; Wilson, James F/A-5704-2009; Paterson, Andrew/AAQ-7511-2020; Pfeiffer, Norbert/AAO-7586-2020; Lehtimäki, Terho/AAD-1094-2022; Foster, Paul/V-7288-2019; Burdon, Kathryn/AAD-2334-2022; Raitakari, Olli/AAQ-7389-2021; Kaprio, Jaakko/A-1820-2008; Chen, Li Jia/I-5078-2014; Yam, Jason/I-5682-2014; Rudan, Igor/I-1467-2012; Cheng, Ching-Yu/Y-2229-2019; He, Mingguang/AAY-5239-2020; wang, YA XING/K-9671-2016; Wedenoja, Juho/AAX-7008-2020; Miyake, Masahiro/V-1261-2019; Yap, Maurice KH/K-4927-2016; Han, Xikun/AAN-9012-2020; zhou, xt/GWZ-9212-2022; Wang, Ying/HJI-2509-2023; Yazar, Seyhan/U-4032-2018 Gharahkhani, Puya/0000-0002-4203-5952; Campbell, Harry/0000-0002-6169-6262; Macgregor, Stuart/0000-0001-6731-8142; Yam, Jason C./0000-0002-2156-1486; /0000-0001-7488-250X; Haller, Toomas/0000-0002-5069-6523; Mackey, David A/0000-0001-7914-4709; Polasek, Ozren/0000-0002-5765-1862; Wilson, James F/0000-0001-5751-9178; Paterson, Andrew/0000-0002-9169-118X; Lehtimäki, Terho/0000-0002-2555-4427; Foster, Paul/0000-0002-4755-177X; Burdon, Kathryn/0000-0001-8217-1249; Kaprio, Jaakko/0000-0002-3716-2455; Chen, Li Jia/0000-0003-3500-5840; Yam, Jason/0000-0002-2156-1486; Rudan, Igor/0000-0001-6993-6884; Cheng, Ching-Yu/0000-0003-0655-885X; He, Mingguang/0000-0002-6912-2810; wang, YA XING/0000-0003-2749-7793; Wedenoja, Juho/0000-0002-6155-0378; Miyake, Masahiro/0000-0001-7410-3764; Yap, Maurice KH/0000-0003-4687-4101; Han, Xikun/0000-0002-3823-7308; Verhoeven, Virginie/0000-0001-7359-7862; Guggenheim, Jeremy/0000-0001-5164-340X; Kahonen, Mika/0000-0002-4510-7341; Hammond, Christopher/0000-0002-3227-2620; Baird, Paul/0000-0002-1305-3502; Yazar, Seyhan/0000-0003-0994-6196 European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme [648268]; Netherlands Organisation for Scientific Research (NWO) [91815655, 91617076]; National Eye Institute [R01EY020483]; Oogfonds; ODAS; Uitzicht [2017-28]; Uitzicht (Oogfonds); Uitzicht (Landelijke Stichting voor Blinden en Slechtzienden); Uitzicht (MaculaFonds); International Myopia Institute; Medical Research Council [MC_UU_00007/10] Funding Source: researchfish European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme(European Research Council (ERC)); Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)); National Eye Institute(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Eye Institute (NEI)); Oogfonds; ODAS; Uitzicht; Uitzicht (Oogfonds); Uitzicht (Landelijke Stichting voor Blinden en Slechtzienden); Uitzicht (MaculaFonds); International Myopia Institute; Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) Supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant 648268); the Netherlands Organisation for Scientific Research (NWO, grant 91815655 and grant 91617076); the National Eye Institute (grant R01EY020483); Oogfonds; ODAS; and Uitzicht (grant 2017-28; Oogfonds; Landelijke Stichting voor Blinden en Slechtzienden; MaculaFonds). The International Myopia Institute provided funds to cover publication costs. 177 87 91 4 32 ASSOC RESEARCH VISION OPHTHALMOLOGY INC ROCKVILLE 12300 TWINBROOK PARKWAY, ROCKVILLE, MD 20852-1606 USA 0146-0404 1552-5783 INVEST OPHTH VIS SCI Invest. Ophthalmol. Vis. Sci. FEB 2019.0 60.0 3 SI M89 M105 10.1167/iovs.18-25965 0.0 17 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology HN4BB 30817828.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000460128100005 0
J Li, SC; Hu, WH; Cao, D; Zhang, ZY; Huang, Q; Chen, Z; Blaabjerg, F Li, Sichen; Hu, Weihao; Cao, Di; Zhang, Zhenyuan; Huang, Qi; Chen, Zhe; Blaabjerg, Frede A Multiagent Deep Reinforcement Learning Based Approach for the Optimization of Transformer Life Using Coordinated Electric Vehicles IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Junctions; Modulation; Reactive power; Power quality; Temperature control; Temperature; Switching loss; Electric vehicle (EV) charging management; multiagent deep reinforcement learning (MADRL); transformer life CO-OPTIMIZATION; ENERGY; MANAGEMENT; ALGORITHM; IMPACT The uncertainties of charging behavior of electric vehicle (EV) owners have a negative impact on the loss of life (LOL) of distribution transformer. This article proposes a decentralized EV charging framework for optimization of the LOL of distribution transformer considering the dissatisfactions of EV owners. Specifically, long-short-term memory (LSTM) neural network is first utilized to capture the uncertainties caused by the load demand and electricity price. After that, each EV is modeled as an intelligent agent and a multiagent deep reinforcement learning approach is applied to solve the coordinated charging problem based on the forecasting information by the LSTM network. All the agents are trained in a centralized manner to develop coordinated control strategies while informing decisions based on local information when finishing the training process. The proposed approach can achieve coordinated charging management of EVs based on local information, which helps preserve the privacy of EV owners, reduce the cost induced by the deployment of communication devices, and avoid single-point failure. In addition, the parameter space noise and deep dense architecture in reinforcement learning are introduced to overcome premature convergence, training instability, and inefficiency due to the large action space of multiagent scenario. Comparative tests are carried out among several benchmarks utilizing real-world data to illustrate the effectiveness of the proposed approach. [Li, Sichen; Hu, Weihao; Cao, Di; Zhang, Zhenyuan; Huang, Qi; Chen, Zhe] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China; [Huang, Qi] Chengdu Univ Technol, Coll Energy, Chengdu 610059, Peoples R China; [Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark University of Electronic Science & Technology of China; Chengdu University of Technology; Aalborg University Cao, D (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China. sichenli@std.uestc.edu.cn; whu@uestc.edu.cn; caodi@std.uestc.edu.cn; zhangzhenyuan@uestc.edu.cn; hwong@uestc.edu.cn; zch@et.aau.dk; fbl@et.aau.dk Zhang, Zhenyuan/ABR-9488-2022; Blaabjerg, Frede/A-5008-2008 ZHANG, ZHENYUAN/0000-0003-3356-0111; Chen, Zhe/0000-0002-2919-4481; Li, Sichen/0000-0001-8313-9574; Blaabjerg, Frede/0000-0001-8311-7412; Hu, Weihao/0000-0002-7019-7289 National Key Research and Development Program of China [2018YFE0127600] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2018YFE0127600. 50 3 3 11 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. NOV 2022.0 18.0 11 7639 7652 10.1109/TII.2021.3139650 0.0 14 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 4Q5TK 2023-03-23 WOS:000856145200028 0
J Wang, XW; Wu, GH; Xing, LN; Pedrycz, W Wang, Xinwei; Wu, Guohua; Xing, Lining; Pedrycz, Witold Agile Earth Observation Satellite Scheduling Over 20 Years: Formulations, Methods, and Future Directions IEEE SYSTEMS JOURNAL English Article Task analysis; Satellites; Orbits; Earth; Earth Observing System; Scheduling; Agile earth observation satellite; aerospace engineering; review; earth observing system; scheduling; space systems OPTIMIZATION; ALGORITHMS; MODEL; GRAPH; FRAMEWORK; TRACKING Agile satellites with advanced attitude maneuvering capability are the new generation of earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this article are, thus, to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multiobjective function and autonomous model. A detailed literature review from 1997-2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic, and machine learning. Finally, we discuss a number of topics worth pursuing in the future. [Wang, Xinwei; Wu, Guohua] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410083, Peoples R China; [Wang, Xinwei] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Xing, Lining] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, PL-02543 Warsaw, Poland; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-02543 Warsaw, Poland Central South University; University of London; Queen Mary University London; National University of Defense Technology - China; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Wu, GH (corresponding author), Cent South Univ, Sch Traff & Transportat Engn, Changsha 410083, Peoples R China. xinwei.wang@qmul.ac.uk; guohuawu.nudt@gmail.com; xinglining@gmail.com; wpedrycz@ualberta.ca Wang, Xinwei/AAT-8080-2021; XING, Lining/GRO-1108-2022; Wang, Xinwei/ABE-2627-2020 Wang, Xinwei/0000-0003-4988-222X; Wu, Guohua/0000-0003-1552-9620 Natural Science Fund for Distinguished Young Scholars of Hunan Province [2019JJ20026]; National Natural Science Foundation of China [61603404] Natural Science Fund for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Natural Science Fund for Distinguished Young Scholars of Hunan Province under Grant 2019JJ20026 and in part by the National Natural Science Foundation of China under Grant 61603404. 113 48 50 1 287 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-8184 1937-9234 IEEE SYST J IEEE Syst. J. SEP 2021.0 15.0 3 3881 3892 10.1109/JSYST.2020.2997050 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Operations Research & Management Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science; Telecommunications UJ0ON Green Submitted 2023-03-23 WOS:000690994700078 0
J Serban, RD; Serban, M; He, RX; Jin, HJ; Li, Y; Li, XY; Wang, XB; Li, GY Serban, Raul-David; Serban, Mihaela; He, Ruixia; Jin, Huijun; Li, Yan; Li, Xinyu; Wang, Xinbin; Li, Guoyu 46-Year (1973-2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification REMOTE SENSING English Article land use and cover changes (LUCC); object-based image analysis (OBIA); Support Vector Machine (SVM); permafrost; Northeast China LAND-COVER CLASSIFICATION; SUPPORT VECTOR MACHINE; WATER INDEX NDWI; FOREST BIOMASS; IMAGE-ANALYSIS; TIME-SERIES; VEGETATION; FIELD; CLIMATE; AREAS Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km(2)) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km(2) in 1973), declined by 88 km(2) (11.8%) from 1973 to 1986 but had a recovery of 78 km(2) (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km(2) in 1973), increased by 86 km(2) (46.5%) between 1973 and 1986 and decreased by 90 km(2) (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km(2) (1973) to 37 km(2) (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China. [Serban, Raul-David; Serban, Mihaela; He, Ruixia; Jin, Huijun; Li, Yan; Wang, Xinbin; Li, Guoyu] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Frozen Soils Engn, Lanzhou 730000, Peoples R China; [Serban, Raul-David; Serban, Mihaela] West Univ Timisoara, Appl Geomorphol & Interdisciplinary Res Ctr, Dept Geog, Timis 300223, Romania; [Jin, Huijun] Northeast Forestry Univ, Minist Educ, Sch Civil Engn, Inst Cold Reg Sci & Engn, Harbin 150040, Peoples R China; [Jin, Huijun] Northeast Forestry Univ, Minist Educ, Northeast China Observ & Res Stn Permafrost Geol, Harbin 150040, Peoples R China; [Li, Yan] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Li, Xinyu] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China Chinese Academy of Sciences; Northeast Forestry University - China; Northeast Forestry University - China; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Harbin Institute of Technology He, RX (corresponding author), Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Frozen Soils Engn, Lanzhou 730000, Peoples R China. davidserban@lzb.ac.cn; mihaelaserban@lzb.ac.cn; ruixiahe@lzb.ac.cn; hjjin@nefu.edu.cn; liyan1@nieer.ac.cn; 18B933025@stu.hit.edu.cn; wangxinbin@lzb.ac.cn; guoyuli@lzb.ac.cn Serban, Raul-David/0000-0003-2924-2735; Wang, Xinbin/0000-0003-4544-584X; Yan, Li/0000-0002-3332-8040; Li, Guoyu/0000-0002-4651-6251 National Nature Science Foundation of China [41871052, U20A2082]; State Key Laboratory of Frozen Soils Engineering Open Fund Project [SKLFSE201811] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Frozen Soils Engineering Open Fund Project This research was funded by the National Nature Science Foundation of China (Grant Nos. 41871052 and U20A2082) and the State Key Laboratory of Frozen Soils Engineering Open Fund Project (SKLFSE201811). 69 11 11 7 36 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAY 2021.0 13.0 10 1910 10.3390/rs13101910 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology ST6EX gold 2023-03-23 WOS:000662535200001 0
J Tang, YH; Hu, WH; Xiao, J; Lu, ZD; Li, ZQ; Huang, Q; Chen, Z; Blaabjerg, F Tang, Yuanhong; Hu, Weihao; Xiao, Jian; Lu, Zhengdong; Li, Zhuoqiang; Huang, Qi; Chen, Zhe; Blaabjerg, Frede RL-ANN-Based Minimum-Current-Stress Scheme for the Dual-Active-Bridge Converter With Triple-Phase-Shift Control IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS English Article Optimization; Zero voltage switching; Training; Power electronics; Artificial neural networks; Stress; Bridge circuits; Artificial neural networks (ANNs); current stress; Q-learning; reinforcement learning (RL); triple phase shift (TPS) DC-DC CONVERTER; MULTILAYER PERCEPTRON; MINIMIZATION; OPTIMIZATION; PERFORMANCE; MODULATION; PARAMETER; SYSTEM Aiming to reduce the current stress and improve the power efficiency of the dual-active-bridge (DAB) converter, this article proposes a reinforcement learning (RL) + artificial neural network (ANN)-based minimum-current-stress scheme. In the first stage, Q-learning as a typical algorithm of the RL method is adopted for offline training. The aim of the first stage is to solve the optimized control strategy based on the triple-phase-shift (TPS) control. More specifically, the zero-voltage-switching (ZVS) constraints and each effective operation mode are taken into consideration during the training process of the Q-learning algorithm. Therefore, the minimum-current-stress scheme while maintaining the soft switching can be obtained after the first stage. In the second stage, the training results of the Q-learning algorithm are used to train an ANN, in order to reduce the computational time and memory allocation. After that, the trained agent of the ANN, which likes an implicit function, can provide optimal phase-shift-angle online in real time under the entire continuous operation range. Finally, the detailed simulation and experimental results are given to demonstrate the effectiveness of the proposed optimized scheme. [Tang, Yuanhong; Hu, Weihao; Lu, Zhengdong; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Power Syst Wide Area Measurement & Control Sichua, Chengdu 611731, Peoples R China; [Xiao, Jian] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China; [Xiao, Jian] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Quzhou 324000, Peoples R China; [Li, Zhuoqiang] United Automot Elect Syst Co Ltd, Shanghai 201206, Peoples R China; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Power Syst Wide Area Measurement & Control Sichua, Chengdu 611731, Peoples R China. yhtang@std.uestc.edu.cn; whu@uestc.edu.cn; 202011012223@std.uestc.edu.cn; 201821040301@std.uestc.edu.cn; zhuoqiang.li@uaes.com; hwong@uestc.edu.cn; zch@et.aau.dk; fbl@et.aau.dk ; Blaabjerg, Frede/A-5008-2008 xiao, Jian/0000-0003-1354-2258; Hu, Weihao/0000-0002-7019-7289; Blaabjerg, Frede/0000-0001-8311-7412; Chen, Zhe/0000-0002-2919-4481 Sichuan Science and Technology Program [2020JDJQ0037, 2020YFG0312] Sichuan Science and Technology Program This work was supported by the Sichuan Science and Technology Program under Grant 2020JDJQ0037 and Grant 2020YFG0312. Recommended for publication by Associate Editor Mario Pacas. (Corresponding author: Weihao Hu.) 41 5 5 7 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-6777 2168-6785 IEEE J EM SEL TOP P IEEE J. Emerg. Sel. Top. Power Electron. FEB 2022.0 10.0 1 673 689 10.1109/JESTPE.2021.3071724 0.0 17 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering YR9UK 2023-03-23 WOS:000750333100060 0
J Zhou, B; Duan, HR; Wu, QW; Wang, HZ; Or, SW; Chan, KW; Meng, YF Zhou, Bin; Duan, Haoran; Wu, Qiuwei; Wang, Huaizhi; Or, Siu Wing; Chan, Ka Wing; Meng, Yunfan Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Generative adversarial network; Semi-supervised regression; Wind power forecasting; Wind power ramp event; Renewable energy NEURAL-NETWORK; HYBRID MODEL; SPEED; REGRESSION Short-term predictions of wind power and its ramp events play a critical role in economic operation and risk management of smart grid. This paper proposes a hybrid forecasting model based on semi-supervised generative adversarial network (GAN) to solve the short-term wind power outputs and ramp event forecasting problems. In the proposed model, the original time series of wind energy data can be decomposed into several sub-series characterized by intrinsic mode functions (IMFs) with different frequencies, and the semi-supervised regression with label learning is employed for data augmentation to extract non-linear and dynamic behaviors from each IMF. Then, the GAN generative model is used to obtain unlabeled virtual samples for capturing data distribution characteristics of wind power outputs, while the discriminative model is redesigned with a semi-supervised regression layer to perform the point prediction of wind power. These two GAN models form a min-max game so as to improve the sample generation quality and reduce forecasting errors. Moreover, a self-tuning forecasting strategy with multi-label classifier is proposed to facilitate the forecasting of wind power ramp events. Finally, the real data of a wind farm from Belgium is collected in the case study to demonstrate the superior performance of the proposed approach compared with other forecasting algorithms. [Zhou, Bin; Duan, Haoran; Meng, Yunfan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China; [Wu, Qiuwei] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark; [Wang, Huaizhi] Shenzhen Univ, Guangdong Key Lab Electromagnet Control & Intelli, Shenzhen 518060, Peoples R China; [Or, Siu Wing; Chan, Ka Wing] Hong Kong Polytech Univ, Dept Elect Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China; [Or, Siu Wing] Natl Rail Transit Electrificat & Automat Engn Tec, Hong Kong Branch, Hong Kong, Peoples R China Hunan University; Technical University of Denmark; Shenzhen University; Hong Kong Polytechnic University Zhou, B (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China.;Wang, HZ (corresponding author), Shenzhen Univ, Guangdong Key Lab Electromagnet Control & Intelli, Shenzhen 518060, Peoples R China.;Or, SW (corresponding author), Hong Kong Polytech Univ, Dept Elect Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China. binzhou@hnu.edu.cn; wanghz@szu.edu.cn; eeswor@polyu.edu.hk OR, Siu Wing/AAC-3122-2021 OR, Siu Wing/0000-0003-2536-5658; CHAN, Ka Wing/0000-0001-7462-0753; Wu, Qiuwei/0000-0001-7935-2567 Research Grants Council of the HKSAR Government [R5020-18]; Innovation and Technology Commission of the HKSAR Government [K-BBY1]; National Natural Science Foundation of China [51877072]; Huxiang Young Talents programme of Hunan Province [2019RS2018] Research Grants Council of the HKSAR Government(Hong Kong Research Grants Council); Innovation and Technology Commission of the HKSAR Government; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Huxiang Young Talents programme of Hunan Province This work was jointly supported by the Research Grants Council of the HKSAR Government (Grant No. R5020-18), the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant No. K-BBY1), the National Natural Science Foundation of China (51877072), and Huxiang Young Talents programme of Hunan Province under Grant 2019RS2018. 38 25 25 16 73 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. FEB 2021.0 125.0 106411 10.1016/j.ijepes.2020.106411 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering OZ1KE 2023-03-23 WOS:000594692800001 0
J Zhang, TF; Lv, CF; Ma, FM; Zhao, KW; Wang, HK; O'Hare, GMP Zhang, Tengfei; Lv, Chaofeng; Ma, Fumin; Zhao, Kewei; Wang, Haikuan; O'Hare, Gregory M. P. A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform NEUROCOMPUTING English Article Photovoltaic power; Very-short-term forecasting; Dendritic neural network; Wavelet transform SOLAR-RADIATION; TERM; FUZZY; OUTPUT; IRRADIANCE; PREDICTION; SYSTEM The ever increasing proportion of photovoltaic (PV), which is, in effect, a random and intermittent energy source, makes PV power forecasting increasingly important for power grid stability. Artificial neural networks (ANN) have become one of the commonly utilized methods in PV power prediction. Since there is no ideal theoretical guidance as yet on the determination of the number of hidden layers and hidden units, there are always abundant neurons in traditional neural networks in order to learn as many data characteristics as possible, which often results in overfitting and high computational costs. The dendritic model proposed in recent years has the characteristics of simple structure, fast convergence and better fitting ability. This paper proposes a PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy. In order to better extract characteristics of different frequencies of the input data, the approach introduces a wavelet transform. Firstly, the data is decomposed into high-frequency and low-frequency components via a wavelet transform. Thereafter, the input data of different frequencies obtained by the decomposition are transmitted respectively to different sub-models. Finally, the results of sub-models are reconstructed to obtain the final output. The proposed PV power forecasting model was tested upon actual photovoltaic datasets. Results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency. (C) 2020 Elsevier B.V. All rights reserved. [Zhang, Tengfei; Lv, Chaofeng; Zhao, Kewei] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Peoples R China; [Zhang, Tengfei; Lv, Chaofeng; Zhao, Kewei] Nanjing Univ Posts & Telecommun, Coll Artificial Intelligence, Nanjing 210023, Peoples R China; [Ma, Fumin] Nanjing Univ Finance & Econ, Coll Informat Engn, Nanjing 210023, Peoples R China; [Wang, Haikuan] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China; [O'Hare, Gregory M. P.] Univ Coll Dublin UCD, Sch Comp Sci, Dublin 4, Ireland Nanjing University of Posts & Telecommunications; Nanjing University of Posts & Telecommunications; Nanjing University of Finance & Economics; Shanghai University; University College Dublin Zhang, TF (corresponding author), Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Peoples R China.;Zhang, TF (corresponding author), Nanjing Univ Posts & Telecommun, Coll Artificial Intelligence, Nanjing 210023, Peoples R China.;Ma, FM (corresponding author), Nanjing Univ Finance & Econ, Coll Informat Engn, Nanjing 210023, Peoples R China. zhangtf@njupt.edu.cn; fuminma@nufe.edu.cn O'Hare, Gregory M.P./AAA-7756-2022; Zhang, Tengfei/R-4485-2018 O'Hare, Gregory M.P./0000-0002-5124-1686; Zhang, Tengfei/0000-0002-2503-7024 National Natural Science Foundation of China [61833011, 61973151]; Natural Science Foundation of Jiangsu Province, China [BK20191376, BK20191406]; Major Program of the Natural Science Foundation of Jiangsu Province Education Commission, China [17KJA120 0 01]; 1311 Talent Plan of Nanjing University of Posts and Telecommunications (NY2018); Postgraduate Research & Practice Innovation Program of Jiangsu Province, China [KYCX18_0930]; Six Talent Peaks Project of Jiangsu Province, China [XNY-038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province, China(Natural Science Foundation of Jiangsu Province); Major Program of the Natural Science Foundation of Jiangsu Province Education Commission, China; 1311 Talent Plan of Nanjing University of Posts and Telecommunications (NY2018); Postgraduate Research & Practice Innovation Program of Jiangsu Province, China; Six Talent Peaks Project of Jiangsu Province, China This work was supported by the National Natural Science Foundation of China (61833011, 61973151), the Natural Science Foundation of Jiangsu Province, China (BK20191376, BK20191406), the Major Program of the Natural Science Foundation of Jiangsu Province Education Commission, China (17KJA120 0 01), the 1311 Talent Plan of Nanjing University of Posts and Telecommunications (NY2018), the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (KYCX18_0930), and the Six Talent Peaks Project of Jiangsu Province, China (XNY-038). 40 28 28 3 43 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUL 15 2020.0 397.0 438 446 10.1016/j.neucom.2019.08.105 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LR8AJ 2023-03-23 WOS:000535918100006 0
J Niu, HY; Bonati, L; Piaggi, PM; Parrinello, M Niu, Haiyang; Bonati, Luigi; Piaggi, Pablo M.; Parrinello, Michele Ab initio phase diagram and nucleation of gallium NATURE COMMUNICATIONS English Article CRYSTAL-NUCLEATION; LIQUID GALLIUM; PREDICTION; CRYSTALLIZATION; TRANSITIONS; SIMULATIONS Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal-multibaric simulations. Here we show that the relative equilibrium between liquid gallium, alpha-Ga, beta-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into alpha-Ga and beta-Ga are studied. We find that the formation of metastable beta-Ga is kinetically favored over the thermodinamically stable alpha-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga. [Niu, Haiyang] Northwestern Polytech Univ, Int Ctr Mat Discovery, Sch Mat Sci & Engn, State Key Lab Solidificat Proc, Xian 710072, Peoples R China; [Niu, Haiyang; Piaggi, Pablo M.; Parrinello, Michele] Swiss Fed Inst Technol, Dept Chem & Appl Biosci, USI Campus,Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland; [Niu, Haiyang; Bonati, Luigi; Piaggi, Pablo M.; Parrinello, Michele] Univ Svizzera Italiana USI, Fac Informat, Inst Sci Computat, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland; [Niu, Haiyang; Bonati, Luigi; Piaggi, Pablo M.; Parrinello, Michele] Univ Svizzera Italiana USI, Natl Ctr Computat Design & Discovery Novel Mat Ma, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland; [Bonati, Luigi] Univ Svizzera Italiana, Dept Phys, ETH Zurich, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland; [Parrinello, Michele] Ist Italiano Tecnol, Via Morego 30, I-16163 Genoa, Italy Northwestern Polytechnical University; Universita della Svizzera Italiana; Universita della Svizzera Italiana; Universita della Svizzera Italiana; Istituto Italiano di Tecnologia - IIT Niu, HY (corresponding author), Northwestern Polytech Univ, Int Ctr Mat Discovery, Sch Mat Sci & Engn, State Key Lab Solidificat Proc, Xian 710072, Peoples R China.;Niu, HY; Parrinello, M (corresponding author), Swiss Fed Inst Technol, Dept Chem & Appl Biosci, USI Campus,Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland.;Niu, HY; Parrinello, M (corresponding author), Univ Svizzera Italiana USI, Fac Informat, Inst Sci Computat, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland.;Niu, HY; Parrinello, M (corresponding author), Univ Svizzera Italiana USI, Natl Ctr Computat Design & Discovery Novel Mat Ma, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland.;Parrinello, M (corresponding author), Ist Italiano Tecnol, Via Morego 30, I-16163 Genoa, Italy. haiyang.niu@nwpu.edu.cn; parrinello@phys.chem.ethz.ch Bonati, Luigi/AAE-6938-2021; Piaggi, Pablo/HKE-3760-2023 Bonati, Luigi/0000-0002-9118-6239; Piaggi, Pablo/0000-0003-0225-0081; Parrinello, Michele/0000-0001-6550-3272 NCCR MARVEL - Swiss National Science Foundation; European Union [ERC-2014-AdG-670227/VARMET]; Research Fund of the State Key Laboratory of Solidification Processing (NPU), China [2019-TS-08]; Early Postdoc. Mobility fellowship from the Swiss National Science Foundation NCCR MARVEL - Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); European Union(European Commission); Research Fund of the State Key Laboratory of Solidification Processing (NPU), China; Early Postdoc. Mobility fellowship from the Swiss National Science Foundation(Swiss National Science Foundation (SNSF)) The authors would like to thank Dr. Tarak Karmakar for carefully reading the manuscript. This research was supported by the NCCR MARVEL, funded by the Swiss National Science Foundation, and the European Union Grant No. ERC-2014-AdG-670227/VARMET. H.N. would like to thank the support by the Research Fund of the State Key Laboratory of Solidification Processing (NPU), China(Grant No.2019-TS-08). P.M.P was also supported by an Early Postdoc. Mobility fellowship from the Swiss National Science Foundation. The calculations were performed in Swiss National Supercomputing Center (CSCS) cluster Piz Daint and International Center for Materials Discovery (ICMD) cluster of NPU. 53 59 59 17 79 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2041-1723 NAT COMMUN Nat. Commun. MAY 27 2020.0 11.0 1 2654 10.1038/s41467-020-16372-9 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics LU8WZ 32461573.0 gold, Green Published 2023-03-23 WOS:000538030400010 0
J Peng, F; Wen, HY; Zhang, C; Xu, BG; Li, JH; Su, H Peng, Fang; Wen, Haiyang; Zhang, Cheng; Xu, Bugong; Li, Jiehao; Su, Hang Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory APPLIED SCIENCES-BASEL English Article active prosthetic knee; time delay estimation; sliding mode control; fuzzy neural network SLIDING-MODE CONTROL; DESIGN; EXOSKELETON; ACTUATOR; JOINT; LEG Featured Application Active prosthetic knees are integrated functional applications used clinically to empower and assist natural human rehabilitation for the elderly or disabled users. In this paper, an adaptive robust force position control for flexible active prosthetic knee using gait trajectory is proposed to empower and assist natural human motion, providing an optimized approach for further development of prosthetic knee. Abstract Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in +/- 0.5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human-machine hybrid control model. [Peng, Fang; Xu, Bugong] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China; [Peng, Fang; Wen, Haiyang] Univ Elect Sci & Technol China, Zhongshan Inst, Zhongshan 528402, Peoples R China; [Zhang, Cheng] Waseda Univ, Dept Comp Sci & Commun Engn, Tokyo 1698050, Japan; [Li, Jiehao; Su, Hang] Politecn Milan, Dept Elect Informat & Bioengn, I-20133 Milan, Italy South China University of Technology; University of Electronic Science & Technology of China; Waseda University; Polytechnic University of Milan Zhang, C (corresponding author), Waseda Univ, Dept Comp Sci & Commun Engn, Tokyo 1698050, Japan. fion_peng@163.com; why19950717@outlook.com; cheng.zhang@akane.waseda.jp; jiehao.li@ieee.org; hang.su@ieee.org Science and Technology Planning Project of Guangdong Province [2016A020220003]; Zhongshan Science and Technology Plan Project [2019B2066] Science and Technology Planning Project of Guangdong Province; Zhongshan Science and Technology Plan Project This work was supported by Science and Technology Planning Project of Guangdong Province under Grant 2016A020220003, and the Zhongshan Science and Technology Plan Project under Grant 2019B2066. 46 3 3 6 28 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel FEB 2020.0 10.0 8 2755 10.3390/app10082755 0.0 20 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics LO0XE gold 2023-03-23 WOS:000533352100119 0
J Mount, SM; Avsec, Z; Carmel, L; Casadio, R; Celik, MH; Chen, K; Cheng, J; Cohen, NE; Fairbrother, WG; Fenesh, T; Gagneur, J; Gotea, V; Holzer, T; Lin, CF; Martelli, PL; Naito, T; Nguyen, TYD; Savojardo, C; Unger, R; Warig, R; Yang, YD; Zhao, HY Mount, Stephen M.; Avsec, Ziga; Carmel, Liran; Casadio, Rita; Celik, Muhammed Hasan; Chen, Ken; Cheng, Jun; Cohen, Noa E.; Fairbrother, William G.; Fenesh, Tzila; Gagneur, Julien; Gotea, Valer; Holzer, Tamar; Lin, Chiao-Feng; Martelli, Pier Luigi; Naito, Tatsuhiko; Thi Yen Duong Nguyen; Savojardo, Castrense; Unger, Ron; Warig, Robert; Yang, Yuedong; Zhao, Huiying Assessing predictions of the impact of variants on splicing in CAGI5 HUMAN MUTATION English Article CAGI experiment; machine learning; mutation; splicing; variant interpretation MISSENSE MUTATIONS; GENETIC-VARIATION; RNA; MECHANISMS Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes. [Mount, Stephen M.] Univ Maryland, Dept Cell Biol & Mol Genet, College Pk, MD 20742 USA; [Avsec, Ziga; Celik, Muhammed Hasan; Cheng, Jun; Gagneur, Julien; Thi Yen Duong Nguyen] Tech Univ Munich, Dept Informat, Garching, Germany; [Carmel, Liran; Cohen, Noa E.] Hebrew Univ Jerusalem, Alexander Silberman Inst Life Sci, Dept Genet, Jerusalem, Israel; [Casadio, Rita; Martelli, Pier Luigi; Savojardo, Castrense] Univ Bologna, Dept Pharm & Biotechnol, Biocomp Grp, Bologna, Italy; [Chen, Ken; Yang, Yuedong] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Cohen, Noa E.] Hebrew Univ Jerusalem, Sch Comp Sci & Engn, Integrated Program Comp Sci & Computat Biol, Jerusalem, Israel; [Fairbrother, William G.] Brown Univ, Dept Mol Biol Cell Biol & Biochem, Ctr Computat Biol, Providence, RI 02912 USA; [Fenesh, Tzila; Holzer, Tamar; Unger, Ron] Bar Ilan Univ, Mina & Everard Goodman Fac Life Sci, Ramat Gan, Israel; [Gotea, Valer] NHGRI, NIH, Bethesda, MD 20892 USA; [Lin, Chiao-Feng] DNAnexus, Translat Informat, Mountain View, CA USA; [Naito, Tatsuhiko] Univ Tokyo, Grad Sch Med, Dept Neurol, Tokyo, Japan; [Warig, Robert] Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA; [Warig, Robert] Univ Calif Berkeley, Dept Plant & Mol Biol, Berkeley, CA 94720 USA; [Zhao, Huiying] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Guangdong Prov Key Lab Malignant Tumor Epigenet &, Guangzhou, Guangdong, Peoples R China University System of Maryland; University of Maryland College Park; Technical University of Munich; Hebrew University of Jerusalem; University of Bologna; Sun Yat Sen University; Hebrew University of Jerusalem; Brown University; Bar Ilan University; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI); University of Tokyo; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Sun Yat Sen University Mount, SM (corresponding author), Univ Maryland, Dept Cell Biol & Mol Genet, College Pk, MD 20742 USA. smount@umd.edu Savojardo, Castrense/ADK-4436-2022; Yang, Yuedong/C-9394-2009; Cheng, Jun/AAN-2923-2020; Martelli, Pier Luigi/I-8824-2012; Casadio, Rita/K-3747-2017; Brenner, Steven E/A-8729-2008; Savojardo, Castrense/J-3441-2012 Savojardo, Castrense/0000-0002-7359-0633; Yang, Yuedong/0000-0002-6782-2813; Cheng, Jun/0000-0001-5573-9791; Martelli, Pier Luigi/0000-0002-0274-5669; Brenner, Steven E/0000-0001-7559-6185; Gotea, Valer/0000-0001-7857-3309; Celik, Muhammed Hasan/0000-0001-7185-3711; Wang, Robert/0000-0003-2614-5956; Mount, Steve/0000-0003-2748-8205; Chen, Ken/0000-0001-5701-1438; Naito, Tatsuhiko/0000-0002-2779-4600; Cohen, Noa E./0000-0002-4638-5407 National Human Genome Research Institute [U41 HG007346, R13 HG006650]; National Science Foundation, Division of BiologicalInfrastructure [ABI 1564785] National Human Genome Research Institute(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Human Genome Research Institute (NHGRI)); National Science Foundation, Division of BiologicalInfrastructure(National Science Foundation (NSF)) National Human Genome Research Institute, Grant/Award Numbers: U41 HG007346, R13 HG006650; National Science Foundation, Division of BiologicalInfrastructure, Grant/Award Number: ABI 1564785 33 8 8 1 3 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1059-7794 1098-1004 HUM MUTAT Hum. Mutat. SEP 2019.0 40.0 9 SI 1215 1224 10.1002/humu.23869 0.0 AUG 2019 10 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity IX9FW 31301154.0 2023-03-23 WOS:000482099500001 0
J Du, XD; Carpentier, L; Teng, GH; Liu, ML; Wang, CY; Norton, T Du, Xiaodong; Carpentier, Lenn; Teng, Guanghui; Liu, Mulin; Wang, Chaoyuan; Norton, Tomas Assessment of Laying Hens' Thermal Comfort Using Sound Technology SENSORS English Article animal vocalisation; THI; SVM; laying hens; animal welfare CLASSIFICATION; VOCALIZATIONS; RECOGNITION; CHICKENS; BEHAVIOR; FEATURES; NOISE; PARAMETERS; DISEASE; STRESS Heat stress is one of the most important environmental stressors facing poultry production and welfare worldwide. The detrimental effects of heat stress on poultry range from reduced growth and egg production to impaired health. Animal vocalisations are associated with different animal responses and can be used as useful indicators of the state of animal welfare. It is already known that specific chicken vocalisations such as alarm, squawk, and gakel calls are correlated with stressful events, and therefore, could be used as stress indicators in poultry monitoring systems. In this study, we focused on developing a hen vocalisation detection method based on machine learning to assess their thermal comfort condition. For extraction of the vocalisations, nine source-filter theory related temporal and spectral features were chosen, and a support vector machine (SVM) based classifier was developed. As a result, the classification performance of the optimal SVM model was 95.1 +/- 4.3% (the sensitivity parameter) and 97.6 +/- 1.9% (the precision parameter). Based on the developed algorithm, the study illustrated that a significant correlation existed between specific vocalisations (alarm and squawk call) and thermal comfort indices (temperature-humidity index, THI) (alarm-THI, R = -0.414, P = 0.01; squawk-THI, R = 0.594, P = 0.01). This work represents the first step towards the further development of technology to monitor flock vocalisations with the intent of providing producers an additional tool to help them actively manage the welfare of their flock. [Du, Xiaodong; Teng, Guanghui; Liu, Mulin; Wang, Chaoyuan] China Agr Univ, Coll Water Resources & Civil Engn, Beijing 100083, Peoples R China; [Carpentier, Lenn; Norton, Tomas] Katholieke Univ Leuven, Dept Biosyst, Div Model & Mange Bioresponses, Kasteelpk Arenberg 30, B-3001 Heverlee, Belgium China Agricultural University; KU Leuven Teng, GH (corresponding author), China Agr Univ, Coll Water Resources & Civil Engn, Beijing 100083, Peoples R China.;Norton, T (corresponding author), Katholieke Univ Leuven, Dept Biosyst, Div Model & Mange Bioresponses, Kasteelpk Arenberg 30, B-3001 Heverlee, Belgium. duxiaodong@cau.edu.cn; lenn.carpentier@kuleuven.be; futong@cau.edu.cn; b20193090700@cau.edu.cn; gotowchy@cau.edu.cn; tomas.norton@kuleuven.be Norton, Tomas/Q-3803-2017; Wang, Chaoyuan/AAN-3145-2020 Norton, Tomas/0000-0002-0161-3189; Du, Xiaodong/0000-0002-9631-4201 National Key Research and Development Program of China [2017YFD0701602, 2016YFD0700204]; China Scholarship Council [201806350182] National Key Research and Development Program of China; China Scholarship Council(China Scholarship Council) This research was funded by the National Key Research and Development Program of China (grant number: 2017YFD0701602) and (grant number: 2016YFD0700204), and the China Scholarship Council (grant number: 201806350182). 45 18 19 13 37 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JAN 2020.0 20.0 2 473 10.3390/s20020473 0.0 14 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation KR7IT 31947639.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000517790100147 0
J Wang, YT; Su, Z; Zhang, N; Benslimane, A Wang, Yuntao; Su, Zhou; Zhang, Ning; Benslimane, Abderrahim Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING English Article AI security; blockchain; federated learning; local differential privacy; reinforcement learning; UAV BLOCKCHAIN; INTERNET; SCHEME Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs' sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a secure federated learning framework for UAV-assisted MCS. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs' privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs' high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes. [Wang, Yuntao; Su, Zhou] Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian 710049, Peoples R China; [Su, Zhou] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China; [Zhang, Ning] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON, Canada; [Benslimane, Abderrahim] Univ Avignon, Comp Sci & Engn, Avignon, France Xi'an Jiaotong University; Shanghai University; University of Windsor; Avignon Universite Su, Z (corresponding author), Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian 710049, Peoples R China.;Su, Z (corresponding author), Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China. wyt2426697973@163.com; zhousu@ieee.org; ning.zhang@tamucc.edu; abderrahim.benslimane@univ-avignon.fr Zhang, Ning/T-5396-2018 Zhang, Ning/0000-0002-8781-4925; wang, yuntao/0000-0003-3810-7076 NSFC [U1808207, 91746114]; Project of Shanghai Municipal Science and Technology Commission [18510761000] NSFC(National Natural Science Foundation of China (NSFC)); Project of Shanghai Municipal Science and Technology Commission Manuscript received March 10, 2020; revised June 1, 2020; accepted July 11, 2020. Date of publication August 5, 2020; date of current version July 7, 2021. This work was supported in part by NSFC (nos. U1808207, 91746114), and the Project of Shanghai Municipal Science and Technology Commission, 18510761000. Recommended for acceptance by Dr. Mohsen Guizani 54 45 47 21 74 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 2327-4697 IEEE T NETW SCI ENG IEEE Trans. Netw. Sci. Eng. APR-JUN 2021.0 8.0 2 1055 1069 10.1109/TNSE.2020.3014385 0.0 15 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics TU2SY 2023-03-23 WOS:000680892400022 0
J Yue, NH; Li, LL; Morandi, A; Zhao, Y Yue, Naihua; Li, Lingling; Morandi, Alessandro; Zhao, Yang A metamodel-based multi-objective optimization method to balance thermal comfort and energy efficiency in a campus gymnasium ENERGY AND BUILDINGS English Article NSGA-II; MLPANN; Multi-objective optimization; Energy consumption; Thermal comfort; Gymnasium ARTIFICIAL NEURAL-NETWORK; GENETIC ALGORITHM; BUILDING DESIGN; PERFORMANCE; VENTILATION; RETROFIT; HOT Performing multi-objective optimization for actual public building design has become one of the most challenging subjects in buildings energy efficiency area. Gymnasium is a large energy consumer in public buildings. This study efforts to put forward a novel approach to tackle multi-objective optimization problems for building performance of Qingdao University (QUT) Gymnasium using a new metamodel method. For this purpose, the Nondominated Sorting Genetic Algorithm-II (NSGA-II) was dynamically combined with Multilayer Perception Artificial Neural Network (MLPANN) metamodel, which was previously trained with the co-simulation results conducted using EnergyPlus and Eppy. The new research method also proposes an optimal algorithm coupling Latin Hypercube Sample (LHS) with Principal Component Analysis (PCA) to minimize the total training samples, and guarantees the accuracy of optimization results. The most influential design factors like internal and external wall types, roof types, solar absorptance, windows shading as well as night ventilation (NV) strategy and displacement ventilation (DV) air conditioning system of the gymnasium were considered in three cases of 4 x 10(8) possibilities to obtain the optimal trade-off results (Pareto front) between energy consumption and thermal comfort. Finally, a normalized minimum distance decision method was adopted to choose the optimal design configuration from the Pareto front. The optimization results of the study cases showed that reductions were achieved not only in the normalized objectives (88.0% less f(h) and 85.3% less f(c)) but also in the sub-objectives: up to 78.2% fewer heating energy and 71.3% fewer cooling energy in air conditioning seasons, and up to 97.7% less heating degree-hours and 99.2% less cooling degree-hours in naturally-ventilated seasons, compared to the original configuration by using optimal design takes simultaneous advantage of NV and DV strategies. The method was confirmed to be an efficient and robust tool for gymnasium design, it could reduce the calculation time of whole optimization process from 10 months to 2 days. (C) 2021 Elsevier B.V. All rights reserved. [Yue, Naihua] Qingdao Univ Technol, Sch Architecture & Urban Planning, 11 Fushun Rd, Qingdao 262011, Shandong, Peoples R China; [Li, Lingling; Zhao, Yang] Harbin Inst Technol, Sch Architecture, 66 Xidazhi St, Harbin 150000, Peoples R China; [Morandi, Alessandro] Univ Padua, Dept Civil Environm & Architectural Engn, Via 8 Febbraio, I-235122 Padua, Italy; [Zhao, Yang] Natl Univ Singapore, Dept Built Environm, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore Qingdao University of Technology; Harbin Institute of Technology; University of Padua; National University of Singapore Yue, NH (corresponding author), Qingdao Univ Technol, Sch Architecture & Urban Planning, 11 Fushun Rd, Qingdao 262011, Shandong, Peoples R China. yuenaihua@qut.edu.cn National Natural Science Foundation of China Project [51178130, 51678176] National Natural Science Foundation of China Project(National Natural Science Foundation of China (NSFC)) The research was supported by the National Natural Science Foundation of China Project (No. 51178130 and 51678176). The assistance of Mr. Wen Yuan and Mr. Giovanni Gazzi during the site measurements and program coding process is greatly appreciated. 46 7 7 18 43 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7788 1872-6178 ENERG BUILDINGS Energy Build. DEC 15 2021.0 253.0 111513 10.1016/j.enbuild.2021.111513 0.0 OCT 2021 15 Construction & Building Technology; Energy & Fuels; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Energy & Fuels; Engineering WF3KV 2023-03-23 WOS:000706208400011 0
J Grainge, K; Alachkar, B; Amy, S; Barbosa, D; Bommineni, M; Boven, P; Braddock, R; Davis, J; Diwakar, P; Francis, V; Gabrielczyk, R; Gamatham, R; Garrington, S; Gibbon, T; Gozzard, D; Gregory, S; Guo, Y; Gupta, Y; Hammond, J; Hindley, D; Horn, U; Hughes-Jones, R; Hussey, M; Lloyd, S; Mammen, S; Miteff, S; Mohile, V; Muller, J; Natarajan, S; Nicholls, J; Oberland, R; Pearson, M; Rayner, T; Schediwy, S; Schilizzi, R; Sharma, S; Stobie, S; Tearle, M; Wang, B; Wallace, B; Wang, L; Warange, R; Whitaker, R; Wilkinson, A; Wingfield, N Grainge, K.; Alachkar, B.; Amy, Shaun; Barbosa, D.; Bommineni, M.; Boven, P.; Braddock, R.; Davis, J.; Diwakar, P.; Francis, V.; Gabrielczyk, R.; Gamatham, R.; Garrington, S.; Gibbon, T.; Gozzard, D.; Gregory, S.; Guo, Y.; Gupta, Y.; Hammond, J.; Hindley, D.; Horn, U.; Hughes-Jones, R.; Hussey, M.; Lloyd, S.; Mammen, S.; Miteff, S.; Mohile, V.; Muller, J.; Natarajan, S.; Nicholls, J.; Oberland, R.; Pearson, M.; Rayner, T.; Schediwy, S.; Schilizzi, R.; Sharma, S.; Stobie, S.; Tearle, M.; Wang, B.; Wallace, B.; Wang, L.; Warange, R.; Whitaker, R.; Wilkinson, A.; Wingfield, N. Square Kilometre Array: The radio telescope of the XXI century ASTRONOMY REPORTS English Article The Square Kilometre Array (SKA) will be the world's largest and most sensitive radio telescope. It will address fundamental unanswered questions about our Universe including how the first stars and galaxies formed after the Big Bang, how dark energy is accelerating the expansion of theUniverse, the role of magnetism in the cosmos, the nature of gravity, and the search for life beyond Earth. This project envisages the construction of 133 15-m antennas in South Africa and 131072 log-periodic antennas in Australia, together with the associated infrastructure in the two desert sites. In addition, the SKA is an exemplar Big Data project, with data rates of over 10 Tbps being transported from the telescope to HPC/HTC facilities. [Grainge, K.; Alachkar, B.; Braddock, R.; Gabrielczyk, R.; Garrington, S.; Hammond, J.; Hussey, M.; Lloyd, S.; Muller, J.; Nicholls, J.; Oberland, R.; Pearson, M.; Schilizzi, R.; Tearle, M.; Whitaker, R.; Wilkinson, A.; Wingfield, N.] Univ Manchester, Sch Phys & Astron, Jodrell Bank Ctr Astrophys, Alan Turing Bldg, Manchester, Lancs, England; [Amy, Shaun] CSIRO Astron & Space Sci, Australia Telescope Natl Facil, Epping, NSW, Australia; [Barbosa, D.] Inst Telecomunicacoes, Grp Radio Astron Basic Sci & Enabling Technol, Campus Univ Santiago, Aveiro, Portugal; [Bommineni, M.; Diwakar, P.; Mohile, V.; Sharma, S.; Warange, R.] Persistent Syst Ltd, Pune, Maharashtra, India; [Boven, P.] Joint Inst VLBI ERIC JIVE, Dwingeloo, Netherlands; [Davis, J.; Hindley, D.] Natl Phys Lab, Teddington, Middx, England; [Francis, V.] Rutherford Appleton Lab, STFC, Harwell Campus, Didcot, Oxon, England; [Gamatham, R.; Wallace, B.] SKA South Africa, Pinelands, South Africa; [Gibbon, T.] Nelson Mandela Metropolitan Univ, Dept Phys, Port Elizabeth, South Africa; [Gozzard, D.; Schediwy, S.; Stobie, S.] Univ Western Australia, Sch Phys, Perth, WA, Australia; [Gregory, S.] Daresbury Lab, STFC, Warrington, Cheshire, England; [Guo, Y.; Wang, B.; Wang, L.] Tsinghua Univ, Joint Inst Measurement Sci, State Key Lab Precis Measurement Technol & Instru, Dept Precis Instruments, Beijing, Peoples R China; [Gupta, Y.] Tata Inst Fundamental Res, Natl Ctr Radio Astrophys, Pune, Maharashtra, India; [Horn, U.; Miteff, S.] CSIR Meraka Inst, South African Natl Res Network, Pretoria, South Africa; [Hughes-Jones, R.] GEANT, Cambridge, England; [Mammen, S.; Natarajan, S.] Tata Consultancy Serv, Mumbia, Maharashtra, India; [Rayner, T.] AARNet, N Ryde, NSW, Australia University of Manchester; Jodrell Bank Centre for Astrophysics; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Universidade de Aveiro; National Physical Laboratory - UK; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; SKA South Africa; Nelson Mandela University; University of Western Australia; STFC Daresbury Laboratory; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); Tsinghua University; Tata Institute of Fundamental Research (TIFR); National Centre for Radio Astrophysics (NCRA), Pune; Tata Sons; Tata Consultancy Services Limited (TCS) Grainge, K (corresponding author), Univ Manchester, Sch Phys & Astron, Jodrell Bank Ctr Astrophys, Alan Turing Bldg, Manchester, Lancs, England. boch@sai.msu.ru Wang, Bo/ABB-6187-2021; Wang, Bo/K-7928-2012 Wang, Bo/0000-0002-2918-1912; Wang, Bo/0000-0002-2918-1912; Gozzard, David/0000-0001-8828-6469; Barbosa, Domingos/0000-0002-5191-7826; Schediwy, Sascha/0000-0001-9928-1364; Tearle, Mark/0000-0002-8905-5416 Science and Technology Facilities Council [ST/P000649/1] Funding Source: researchfish; STFC [ST/M001482/1, ST/P003478/1, ST/M007561/1, ST/P005764/1, ST/N00292X/1, ST/I002847/1, ST/P000649/1, ST/L000032/1] Funding Source: UKRI Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) 9 21 21 2 12 MAIK NAUKA/INTERPERIODICA/SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013-1578 USA 1063-7729 1562-6881 ASTRON REP+ Astron. Rep. APR 2017.0 61.0 4 288 296 10.1134/S1063772917040059 0.0 9 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics ES8TS Green Submitted 2023-03-23 WOS:000399830400006 0
J Zhang, ZJ; Xu, XB; Zhang, XL; Xu, XJ; Ye, ZF; Wang, GD; Dustdar, S Zhang, Zhenjie; Xu, Xiaobin; Zhang, Xuelin; Xu, Xiaojian; Ye, Zifa; Wang, Guodong; Dustdar, Schahram Intelligent identification for vertical track irregularity based on multi-level evidential reasoning rule model APPLIED INTELLIGENCE English Article Vertical track irregularity; Evidential reasoning rule; Multi-level; Sample imbalance; Identification model AXLE-BOX; ACCELERATION Vertical track irregularity is one of the most significant indicators to evaluate track health. Accurate identification of vertical track irregularity is beneficial to achieve precise maintenance of the track and thus avoid accidents. However, the continuous variation of the track irregularity and the imbalance of the abnormal/normal data samples make it difficult to guarantee the accuracy of identification models. Therefore, by considering the interaction between train and track, a multi-level evidential reasoning (M-ER) rule model is proposed to build the nonlinear causal relationship of vibration signals and vertical track irregularity. In the modeling process of M-ER, the referential evidence matrix (REM) and fusion parameters (i.e., reliability factors and importance weights) are determined and optimized. In the model, the reliability factor of evidence is determined through trend analysis, while the importance weights of evidence and REM are optimized by sequential quadratic programming (SQP). In the inference process of M-ER, sample expansion strategy and two-level evidence fusion mechanism are designed. Specifically, in the first level, samples on each vibration signal are fused with their nearest neighboring historical samples obtained by K-Nearest Neighbor(K-NN) method. In the second-level, the results generated in the first-level are integrated by ER rule. We evaluate the M-ER rule model with an actual data set from China railway. The experimental results show that the model can identify the vertical track irregularity more accurately compared with the single-level ER rule model and other typical machine learning based models. [Zhang, Zhenjie; Xu, Xiaobin; Xu, Xiaojian] Hangzhou Dianzi Univ, China Austria Belt & Rd Joint Lab Artificial Inte, Hangzhou, Zhejiang, Peoples R China; [Zhang, Zhenjie; Xu, Xiaobin; Zhang, Xuelin; Xu, Xiaojian; Ye, Zifa] Hangzhou Dianzi Univ, Sch Automat, Sch Artificial Intelligence, Hangzhou, Zhejiang, Peoples R China; [Wang, Guodong] Sino Austria Res Inst Intelligent Ind Nanjing Co, Gupinggang Rd 4, Nanjing, Peoples R China; [Dustdar, Schahram] TU Wien, Distributed Syst Grp, Vienna, Austria Hangzhou Dianzi University; Hangzhou Dianzi University; Technische Universitat Wien Xu, XB (corresponding author), Hangzhou Dianzi Univ, China Austria Belt & Rd Joint Lab Artificial Inte, Hangzhou, Zhejiang, Peoples R China.;Xu, XB (corresponding author), Hangzhou Dianzi Univ, Sch Automat, Sch Artificial Intelligence, Hangzhou, Zhejiang, Peoples R China. xuxiaobin1980@163.com NSFC [61903108, 62103121]; Zhejiang Province Outstanding Youth Fund [LR21F030001]; Zhejiang Province Public Welfare Technology Application Research Project [LGF20H270004, LGF19H180018, LGG22F020023]; Zhejiang Province Key RD projects [2021C03015]; Open Fund of National Engineering Research Centre for Water Transport Safety, China [A2020002]; Key project of Zhejiang Provincial Medical and health Science and Technology Plan [WKJ-ZJ-2038] NSFC(National Natural Science Foundation of China (NSFC)); Zhejiang Province Outstanding Youth Fund; Zhejiang Province Public Welfare Technology Application Research Project; Zhejiang Province Key RD projects; Open Fund of National Engineering Research Centre for Water Transport Safety, China; Key project of Zhejiang Provincial Medical and health Science and Technology Plan We acknowledge financial support from NSFC (62103121), Zhejiang Province Outstanding Youth Fund (LR21F030001), Zhejiang Province Public Welfare Technology Application Research Project (LGG22F020023), NSFC (61903108), Zhejiang Province Key R&D projects (2021C03015), Open Fund of National Engineering Research Centre for Water Transport Safety, China (A2020002), Zhejiang Province Public Welfare Technology Application Research Project (LGF20H270004, LGF19H180018), Key project of Zhejiang Provincial Medical and health Science and Technology Plan (WKJ-ZJ-2038). 26 0 0 5 9 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-669X 1573-7497 APPL INTELL Appl. Intell. NOV 2022.0 52.0 14 16555 16571 10.1007/s10489-021-03114-7 0.0 MAR 2022 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6H0HG 2023-03-23 WOS:000780306800005 0
J Soomro, TA; Afifi, AJ; Shah, AA; Soomro, S; Baloch, GA; Zheng, LH; Yin, M; Gao, JB Soomro, Toufique Ahmed; Afifi, Ahmed J.; Shah, Ahmed Ali; Soomro, Shafiullah; Baloch, Gulsher Ali; Zheng, Lihong; Yin, Ming; Gao, Junbin Impact of Image Enhancement Technique on CNN Model for Retinal Blood Vessels Segmentation IEEE ACCESS English Article Retinal; segmentation; vessels; morphological operation; CLAHE; FCM MATCHED-FILTER; NEURAL-NETWORK In this paper, we have developed a new method of accurate detection of retinal blood vessels based on a deep convolutional neural network (CNN) model. This method plays an important role in the observation of many eye diseases. Retinal Images have many issues that make the process of vessels segmentation very hard. We treat each issue of the retina image with the greatest observation to obtain a well-segmented image. The first step is to apply a pre-processing method based on fuzzy logic and image processing tactics. In a second step, in order to generate the segmented images, we propose a strided encoder-decoder CNN model. This network is trained and optimized using the Dice Loss function that supports the class imbalance problem that is in the database. The proposed model has a U-Net shape, but it is deeper and the pooling layers are replaced with strided convolutional layers in the encoder. This modification allows for a more precise segmentation of vessels and accelerates the training process. The last step is post-processing for removing the noisy pixels as well as the shadow of the optic disc. The performance of the proposed method was evaluated on DRIVE and STARE databases. The proposed method gives a sensitivity of 0.802 and 0.801 respectively on DRIVE and STARE, with an accuracy of 0.959 and 0.961 respectively. We focused on sensitivity and accuracy measurements that represent the accuracy of the model, especially tiny vessels. According to the results, the model outperforms many other proposed methods, especially in the above-mentioned measures. [Soomro, Toufique Ahmed; Yin, Ming] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China; [Afifi, Ahmed J.] Tech Univ Berlin, Comp Vis & Remote Sensing, D-10623 Berlin, Germany; [Shah, Ahmed Ali; Baloch, Gulsher Ali] Sukkur IBA Univ, Eletr Engn Dept, Sukkur 65200, Pakistan; [Soomro, Shafiullah] Quaid E Awam Univ Engn & Technol, Basic Sci & Related Study Dept, Sindh 67450, Pakistan; [Zheng, Lihong] Charles Sturt Univ, Sch Comp & Math, Bathurst, NSW 2795, Australia; [Gao, Junbin] Univ Sydney, Business Sch, Camperdown, NSW 2006, Australia Guangdong University of Technology; Technical University of Berlin; Sukkur IBA University; Charles Sturt University; University of Sydney Soomro, TA; Yin, M (corresponding author), Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China. etoufique@yahoo.com; yiming@gdut.edu.cn Baloch, Gulsher/ABW-4713-2022; Afifi, Ahmed J./T-5073-2019; Zheng, Lihong/ACI-9716-2022; , Toufique/AAO-8905-2020; Gao, Junbin/C-6566-2008 Afifi, Ahmed J./0000-0001-6782-6753; Zheng, Lihong/0000-0001-5728-4356; , Toufique/0000-0002-8560-0026; Ali, Ahmed/0000-0002-2645-7258; , Gulsher/0000-0002-7346-6077; Yin, Ming/0000-0002-7037-1048 National Science Foundation of China (NSF) China [61876042]; Science and Technology Planning Project of Guangdong Province [2017A010101024] National Science Foundation of China (NSF) China; Science and Technology Planning Project of Guangdong Province The work of M. Yin was supported in part by the National Science Foundation of China (NSF) China under Grant 61876042, and in part by the Science and Technology Planning Project of Guangdong Province under Grant 2017A010101024. 74 35 36 3 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 158183 158197 10.1109/ACCESS.2019.2950228 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications JQ1RC gold 2023-03-23 WOS:000498729300001 0
C Li, J; Zhong, JP; Chen, F; Yang, CG IEEE Li, Jie; Zhong, Junpei; Chen, Fei; Yang, Chenguang An Incremental Learning Framework for Skeletal-based Hand Gesture Recognition with Leap Motion 2019 9TH IEEE ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (IEEE-CYBER 2019) IEEE Annual International Conference on Cyber Technology in Automation Control and Intelligent Systems English Proceedings Paper 9th IEEE Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER) JUL 29-AUG 02, 2019 Suzhou, PEOPLES R CHINA IEEE,RA Hand gesture recognition has become the focus of researchers lately because of its manifold applications in various fields. Leap Motion (LM) is a device to obtain useful and accurate information of the hand action, which is suitable for collecting the three-dimensional (3D) human hand gesture. In this paper, a novel framework which consists of an incremental learning (IL) algorithm without deep structure is proposed and applied to hand gestures classification that explicitly aimed to the LM data. The same datasets are used to train the proposed framework and the conventional Long Short Term Memory Recurrent Neural Network (LSTM-RNN). Due to the structural advantage of the proposed model, the recognition performance is improved distinctly in robustness and training time than the LSTM network. Moreover, convincing experiment results are given to illustrate that the solution is more efficient in static gesture classification. [Li, Jie; Yang, Chenguang] South China Univ Technol, Sch Automat Sci & Engn, Key Lab Autonomous Syst & Networked Control, Guangzhou 510640, Peoples R China; [Zhong, Junpei] Nottingham Trent Univ, Sch Sci & Technol, Nottingham NG11 8NS, England; [Chen, Fei] Ist Italiano Tecnol, Dept Adv Robot, Via Morego 30, I-16163 Genoa, Italy; [Yang, Chenguang] Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England South China University of Technology; Nottingham Trent University; Istituto Italiano di Tecnologia - IIT; University of Bristol; University of West England Yang, CG (corresponding author), South China Univ Technol, Sch Automat Sci & Engn, Key Lab Autonomous Syst & Networked Control, Guangzhou 510640, Peoples R China.;Yang, CG (corresponding author), Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England. cyang@ieee.org Zhong, Junpei/0000-0001-7642-2961 National Nature Science Foundation (NSFC) [61861136009, 61811530281] National Nature Science Foundation (NSFC)(National Natural Science Foundation of China (NSFC)) This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61861136009 and 61811530281. 22 2 2 0 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2379-7711 978-1-7281-0770-7 IEEE ANN INT CONF CY 2019.0 13 18 6 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science BP9LN 2023-03-23 WOS:000569550300003 0
J Panda, B; Leite, M; Biswal, BB; Niu, XD; Garg, A Panda, Biranchi; Leite, Marco; Biswal, Bibhuti Bhusan; Niu, Xiaodong; Garg, Akhil Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures MEASUREMENT English Article 3D printing (3DP); Cellular structures; Computational intelligence (CI); Mechanical Properties SURFACE-ROUGHNESS; DEPOSITION; OPTIMIZATION; PARAMETERS; TEMPERATURE; GEOPOLYMER; STRENGTH In recent years, 3-D printing experts have laid emphasis on designing and printing the cellular structures, since the key advantages (high strength to weight ratio, thermal and acoustical insulation properties) offered by these structures makes them highly versatile to be used in aerospace and automotive industries. In the present work, an experimental study is firstly conducted to study the effects of the design parameters (wall thickness and cell size) on the mechanical properties i.e yield strength and modulus of elasticity (stiffness) of honeycomb cellular structures printed by fused deposition modelling (FDM) process. Further, three promising numerical modelling methods based on computational intelligence (CI) such as genetic programming (GP), automated neural network search (ANS) and response surface regression (RSR) were applied and their performances were compared while formulating models for the two mechanical properties. Statistical analysis concluded that the ANS model performed the best followed by GP and RSR models. The experimental findings were validated by performing the 2-D, 3-D surface analysis on formulated models based on ANS. [Panda, Biranchi; Leite, Marco] Univ Lisbon, Inst Super Tecn, IDMEC, Av Rovisco Pais, P-1040001 Lisbon, Portugal; [Biswal, Bibhuti Bhusan] Natl Inst Technol, Dept Ind Design, Rourkela, India; [Niu, Xiaodong; Garg, Akhil] Shantou Univ, Intelligent Mfg Key Lab, Minist Educ, Shantou, Peoples R China Universidade de Lisboa; Instituto Superior Tecnico; National Institute of Technology (NIT System); National Institute of Technology Rourkela; Shantou University Garg, A (corresponding author), Shantou Univ, Intelligent Mfg Key Lab, Minist Educ, Shantou, Peoples R China. akhil@stu.edu.cn GARG, AKHIL/GWC-5056-2022; Leite, Marco/A-5188-2013; Garg, Akhil/E-5454-2019; Garg, Akhil/AAK-5240-2020; Panda, Biranchi/U-3270-2019 Leite, Marco/0000-0002-0319-4475; Garg, Akhil/0000-0001-5731-4105; Panda, Biranchi/0000-0002-3563-7744 FCT, through IDMEC, under LAETA [UID/EMS/50022/2013]; Shantou University Scientific Research Fund [NTF 16002]; International Science AMP; Technology Collaboration project between China and Israel [2017A050501061] FCT, through IDMEC, under LAETA; Shantou University Scientific Research Fund; International Science AMP; Technology Collaboration project between China and Israel This work was supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013 and Shantou University Scientific Research Fund (NTF 16002). This work is also supported by a grant of an International Science & Technology Collaboration project between China and Israel (No: 2017A050501061) founded by Guangdong Science and Technology Department. 35 62 63 4 65 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement FEB 2018.0 116.0 495 506 10.1016/j.measurement.2017.11.037 0.0 12 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation GD4DI 2023-03-23 WOS:000430452700054 0
J Di, BF; Zhang, HY; Liu, YY; Li, JR; Chen, NS; Stamatopoulos, CA; Luo, YZ; Zhan, Y Di, Baofeng; Zhang, Hanyue; Liu, Yongyao; Li, Jierui; Chen, Ningsheng; Stamatopoulos, Constantine A.; Luo, Yuzhou; Zhan, Yu Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine SCIENTIFIC REPORTS English Article HAZARD ASSESSMENT; WENCHUAN EARTHQUAKE; SICHUAN PROVINCE; MEDIUM-SCALE; RISK; AREA; GIS; PREDICTION; INTENSITY; MODEL A gradient boosting machine (GBM) was developed to model the susceptibility of debris flow in Sichuan, Southwest China for risk management. A total of 3839 events of debris flow during 1949-2017 were compiled from the Sichuan Geo-Environment Monitoring program, field surveys, and satellite imagery interpretation. In the cross-validation, the GBM showed better performance, with the prediction accuracy of 82.0% and area under curve of 0.88, than the benchmark models, including the Logistic Regression, the K-Nearest Neighbor, the Support Vector Machine, and the Artificial Neural Network. The elevation range, precipitation, and aridity index played the most important role in determining the susceptibility. In addition, the water erosion intensity, road construction, channel gradient, and human settlement sites also largely contributed to the formation of debris flow. The susceptibility map produced by the GBM shows that the spatial distributions of high-susceptibility watersheds were highly coupled with the locations of the topographical extreme belt, fault zone, seismic belt, and dry valleys. This study provides critical information for risk mitigating and prevention of debris flow. [Di, Baofeng] Hongkong Polytech Univ, Inst Disaster Management & Reconstruct, Sichuan Univ, Chengdu 610200, Sichuan, Peoples R China; [Di, Baofeng; Zhang, Hanyue; Liu, Yongyao; Li, Jierui; Zhan, Yu] Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China; [Chen, Ningsheng] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China; [Stamatopoulos, Constantine A.] Stamatopoulos & Associates Co, Athens 11471, Greece; [Stamatopoulos, Constantine A.] Hellen Open Univ, Athens 11471, Greece; [Luo, Yuzhou] Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA; [Zhan, Yu] Sichuan Univ, Sinogerman Ctr Water & Hlth Res, Chengdu 610065, Sichuan, Peoples R China; [Zhan, Yu] Sichuan Univ, Med Big Data Ctr, Chengdu 610041, Sichuan, Peoples R China Hong Kong Polytechnic University; Sichuan University; Sichuan University; Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS; Hellenic Open University; University of California System; University of California Davis; Sichuan University; Sichuan University Zhan, Y (corresponding author), Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China.;Zhan, Y (corresponding author), Sichuan Univ, Sinogerman Ctr Water & Hlth Res, Chengdu 610065, Sichuan, Peoples R China.;Zhan, Y (corresponding author), Sichuan Univ, Med Big Data Ctr, Chengdu 610041, Sichuan, Peoples R China. yzhan@scu.edu.cn Luo, Yuzhou/A-7223-2010 Luo, Yuzhou/0000-0003-0879-9622 Natural Science Foundation of China [41977245]; National Key R&D Program of China [2017YFC1502903]; Fundamental Research Funds for the Central Universities [YJ201765]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA23090502] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences) This study was supported by the Natural Science Foundation of China (Grant No. 41977245), the National Key R&D Program of China (Grant No. 2017YFC1502903), the Fundamental Research Funds for the Central Universities (YJ201765), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA23090502). 56 28 30 1 24 NATURE RESEARCH BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep AUG 29 2019.0 9.0 12532 10.1038/s41598-019-48986-5 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics IT6WN 31467342.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000483017100011 0
J Cui, HT; Lu, YL; Zhou, YQ; He, GZ; Song, S; Yang, SJ; Wang, R; Wang, SY; Han, GX; Yi, XJ; Du, D; Stenseth, NC; Hessen, DO; Chen, DL; Cheng, YY Cui, Haotian; Lu, Yonglong; Zhou, Yunqiao; He, Guizhen; Song, Shuai; Yang, Shengjie; Wang, Rui; Wang, Siyu; Han, Guoxiang; Yi, Xiaojie; Du, Di; Stenseth, Nils Chr; Hessen, Dag O.; Chen, Deliang; Cheng, Yinyi Carbon flow through continental-scale ground logistics transportation ISCIENCE English Article CO2; EMISSIONS; TARGETS; IMPACTS The flourishing logistics in both developed and emerging economies leads to huge greenhouse gas (GHG) emissions; however, the emission fluxes are poorly constrained. Here, we constructed a spatial network of logistic GHG emissions based on multisource big data at continental scale. GHG emissions related to logistics transportation reached 112.14Mt CO2-equivalents (CO(2)e), with seven major urban agglomerations contributing 63% of the total emissions. Regions with short transport distances and well-developed road infrastructure had relatively high emission efficiency. Underlying value flow of the commodities is accompanied by logistics carbon flow along the supply chain. The main driving factors affecting GHG emissions are driving speed and gross domestic product. It may mitigate GHGemissions by 27.50-1162.75 Mt CO(2)e in 15 years if a variety of energy combinations or the appropriate driving speed (65-70 km/h) is adopted. The estimations are of great significance to make integrated management policies for the global logistics sector. [Cui, Haotian; Lu, Yonglong] Xiamen Univ, State Key Lab Marine Environm Sci, Minist Educ Coastal Wetland Ecosyst, Coll Environm & Ecol, Xiamen 361102, Fujian, Peoples R China; [Cui, Haotian; Lu, Yonglong] Xiamen Univ, Key Lab, Minist Educ Coastal Wetland Ecosyst, Coll Environm & Ecol, Xiamen 361102, Fujian, Peoples R China; [Cui, Haotian; Lu, Yonglong; He, Guizhen; Song, Shuai; Yang, Shengjie; Wang, Rui; Han, Guoxiang; Yi, Xiaojie; Du, Di] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China; [Cui, Haotian; He, Guizhen; Song, Shuai; Wang, Rui; Wang, Siyu; Han, Guoxiang; Yi, Xiaojie; Du, Di; Cheng, Yinyi] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Zhou, Yunqiao] Chinese Acad Sci, State Key Lab Tibetan Plateau Earth Syst Resource, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China; [Yang, Shengjie] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China; [Wang, Siyu] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; [Stenseth, Nils Chr] Univ Oslo, Ctr Ecol & Evolutionary Synth, N-031603 Oslo, Norway; [Hessen, Dag O.] Univ Oslo, Ctr Biogeochem Anthropocene, Sect Aquat Biol & Toxicol, N-031603 Oslo, Norway; [Stenseth, Nils Chr] Tsinghua Univ, Minist Educ, Dept Earth Syst Sci, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China; [Chen, Deliang] Univ Gothenburg, Dept Earth Sci, Reg Climate Grp, S-40530 Gothenburg, Sweden Xiamen University; Xiamen University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS; Central China Normal University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; University of Oslo; University of Oslo; Tsinghua University; University of Gothenburg Lu, YL (corresponding author), Xiamen Univ, State Key Lab Marine Environm Sci, Minist Educ Coastal Wetland Ecosyst, Coll Environm & Ecol, Xiamen 361102, Fujian, Peoples R China.;Lu, YL (corresponding author), Xiamen Univ, Key Lab, Minist Educ Coastal Wetland Ecosyst, Coll Environm & Ecol, Xiamen 361102, Fujian, Peoples R China.;Lu, YL (corresponding author), Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China. yllu@xmu.edu.cn Lu, Yonglong/0000-0002-3338-4210 National Natural Science Foundation of China [71761147001, 42030707]; National Key RAMP;D Program of China [2019YFC0507505]; International Partnership Program by the Chinese Academy of Sciences [121311KYSB20190029]; Fundamental Research Fund for the Central Universities [20720210083] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key RAMP;D Program of China; International Partnership Program by the Chinese Academy of Sciences; Fundamental Research Fund for the Central Universities This study was supported by the National Natural Science Foundation of China (Grant No. 71761147001, No. 42030707), the National Key R&D Program of China (2019YFC0507505), International Partnership Program by the Chinese Academy of Sciences (121311KYSB20190029), and the Fundamental Research Fund for the Central Universities (No.20720210083). We are grateful to G7 Company for generous provision of basic logistics data. 54 0 0 12 12 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 2589-0042 ISCIENCE iScience JAN 20 2023.0 26.0 1 105792 10.1016/j.isci.2022.105792 0.0 18 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 8O0GM 36594022.0 gold 2023-03-23 WOS:000925522700001 0
C Wang, YX; Li, K; Gan, SJ; Cameron, C; Zheng, M IEEE Wang, Yanxia; Li, Kang; Gan, Shaojun; Cameron, Che; Zheng, Min Data augmentation for intelligent manufacturing with generative adversarial framework 2019 1ST INTERNATIONAL CONFERENCE ON INDUSTRIAL ARTIFICIAL INTELLIGENCE (IAI 2019) English Proceedings Paper 1st International Conference on Industrial Artificial Intelligence (IAI) JUL 22-26, 2019 Shenyang, PEOPLES R CHINA CAA,NE Univ,Shenyang Assoc Sci & Technol,IEEE Computat Intelligence Soc,IES monitoring system; data augmentation; generative adversarial framework; optimization algorithm MULTIPLE IMPUTATION; ENERGY The global economy is greatly shaped by the unprecedented booming of ICT and artificial intelligence technologies. Their applications in manufacturing has led to the advent of intelligent manufacturing and industry 4.0. Data has become a precious asset for modern industry. This paper first introduces an energy monitoring and data acquisition system namely the Point Energy Technology, which has been developed by the team and installed in several industrial partners, including a local bakery. The lack of data always exists due to various reasons, such as measurement or transmission errors at data collection and transmission stage, leading to the loss of varied length of data samples that are key for process monitoring and control. To solve this problem, we introduce a generative adversarial framework which is based on a game theory for data augmentation. This framework consists of two multi layer perceptron networks, namely generator and discriminator. An improved framework with Q-net that extracts the latent variables from real data is also proposed, in which the Q-net shares the structure with discriminator except for the last layer. In addition, the two optimization methods, namely mini-batch gradient descent and adaptive moment estimation are adopted to tune the parameters. To evaluate the performance of these algorithms, energy consumption data collected from a bakery process is used in the experiment. The experimental results confirm that the latent generative adversarial framework with adaptive moment estimation could generate good quality data samples to compensate the random loss of samples in time series data. [Wang, Yanxia; Li, Kang; Gan, Shaojun] Univ Leeds, Sch Elect & Elect Engn, Leeds, W Yorkshire, England; [Cameron, Che] Queends Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland; [Zheng, Min] Shanghai Univ, Sch Mechatron & Automat, Shanghai, Peoples R China University of Leeds; Shanghai University Wang, YX (corresponding author), Univ Leeds, Sch Elect & Elect Engn, Leeds, W Yorkshire, England. wyxdsky@gmail.com; k.li1@leeds.ac.uk; cqugsj@gmail.com; ccameron07@qub.ac.uk; zhengmin203@shu.edu.cn UK Engineering and Physical Sciences Research Council (EPSRC) [EP/P004636/1] UK Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research is financially supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/P004636/1 'Optimising Energy Management in Industry - OPTEMIN'. 27 3 3 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-3593-9 2019.0 6 Computer Science, Artificial Intelligence; Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP8EH 2023-03-23 WOS:000564554900018 0
J Wang, Y; Wei, X; Shen, H; Tang, X; Yu, H Wang, Yong; Wei, Xian; Shen, Hao; Tang, Xuan; Yu, Hui Adaptive model updating for robust object tracking SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Hierarchical convolutional feature; Correlation filter; Object tracking; Adaptive model updating In this paper, we exploit features extracted from convolutional neural network (CNN) to be better utilized for visual tracking. It is observed that CNN features in higher levels provide semantic information which is robust to appearance variations. Thus we integrate the hierarchical features in different layers of a deep model to correlation filter tracking framework. More specifically, correlation filters are learned on each layer to encode the object appearance. The peak-to-sidelobe ratio (PSR) is employed to measure the differences between image patches. To leverage the robustness of our model, we develop an adaptive model updating scheme to train the correlation filters according to different response maps. Extensive experimental results on three large scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods. [Wang, Yong] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada; [Wei, Xian; Tang, Xuan; Yu, Hui] Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Fujian, Peoples R China; [Shen, Hao] Tech Univ Munich, Munich, Germany; [Shen, Hao] Fortiss GmbH, Munich, Germany University of Ottawa; Chinese Academy of Sciences; Fujian Institute of Research on the Structure of Matter, CAS; Technical University of Munich; fortiss Wei, X (corresponding author), Chinese Acad Sci, Fujian Inst Res Struct Matter, Fuzhou, Fujian, Peoples R China. xian.wei@tum.de; shen@fortiss.de shen, hao/HGF-0551-2022 German Research Foundation (DFG) [KL 2189/9-1]; CAS Pioneer Hundred Talents Program (Type C), Canada [2017-122]; National Science Found for Young Scholars, China [61806186] German Research Foundation (DFG)(German Research Foundation (DFG)); CAS Pioneer Hundred Talents Program (Type C), Canada; National Science Found for Young Scholars, China This work has been supported by the German Research Foundation (DFG) under Grant No. KL 2189/9-1, the CAS Pioneer Hundred Talents Program (Type C), Canada under Grant No. 2017-122, and the National Science Found for Young Scholars, China under Grant No. 61806186. 46 2 2 5 24 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. FEB 2020.0 80.0 115656 10.1016/j.image.2019.115656 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering JU0UN 2023-03-23 WOS:000501394800016 0
J Yang, J; You, XH; Wu, GX; Hassan, MM; Almogren, A; Guna, J Yang, Jun; You, Xinghui; Wu, Gaoxiang; Hassan, Mohammad Mehedi; Almogren, Ahmad; Guna, Joze Application of reinforcement learning in UAV cluster task scheduling FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Reinforcement learning; UAV cluster; Task scheduling BIG DATA; OPTIMIZATION; PREDICTION Recently, unmanned aerial vehicle (UAV) clusters have been widely used in various applications due to its high flexibility, large coverage and reliable transmission efficiency. In order to achieve the collaboration of multiple UAV tasks within a UAV duster, we propose a task-scheduling algorithm based on reinforcement learning in this paper, which enables the UAV to adjust its task strategy automatically and dynamically using its calculation of task performance efficiency. As the UAV needs to perform real-time tasks while working in a dynamic environment without centralized control, it needs to learn tasks according to real time data. Reinforcement learning has the ability to carry out real-time learning and decision making based on the environment, which is an appropriate and feasible method for the task scheduling of UAV clusters. From this perspective, we discuss reinforcement learning that solves the channel allocation problem existing in UAV cluster task scheduling. Finally, this paper also discusses several research problems that may be faced by the further application of UAV cluster task scheduling. (C) 2018 Elsevier B.V. All rights reserved. [Yang, Jun; You, Xinghui; Wu, Gaoxiang] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China; [Hassan, Mohammad Mehedi; Almogren, Ahmad] King Saud Univ, Chair Pervas & Mobile Comp, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia; [Guna, Joze] Univ Ljubljana, Fac Elect Engn, Ljubljana 1000, Slovenia Huazhong University of Science & Technology; King Saud University; University of Ljubljana You, XH; Wu, GX (corresponding author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China. junyang_cs@hust.edu.cn; xinghuiepic@hust.edu.cn; gaoxiangwu@hust.edu.cn; mmhassan@ksu.edu.sa; ahalmogren@ksu.edu.sa; joze.guna@fe.uni-lj.si Hassan, Mohammad/GZA-7507-2022; Hassan, Mohammad Mehedi/D-4946-2016; Guna, Jože/AAO-8714-2020; Almogren, Ahmad S/F-1365-2014 Hassan, Mohammad/0000-0002-1712-0004; Hassan, Mohammad Mehedi/0000-0002-3479-3606; Guna, Jože/0000-0002-5161-7751; Almogren, Ahmad S/0000-0002-8253-9709 King Saud University, Saudi Arabia King Saud University, Saudi Arabia(King Saud University) This paper is financially supported by King Saud University, Saudi Arabia through the Vice Deanship of Research Chairs: Chair of Pervasive and Mobile Computing. 28 42 48 50 182 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUN 2019.0 95.0 140 148 10.1016/j.future.2018.11.014 0.0 9 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HU8CJ 2023-03-23 WOS:000465509600013 0
J Zhang, Y; Feng, ZY; Moustafa, H; Ye, F; Javaid, U; Cui, CF Zhang, Yan; Feng, Zhiyong; Moustafa, Hassnaa; Ye, Feng; Javaid, Usman; Cui, Chunfen GUEST EDITORIAL: EDGE INTELLIGENCE FOR BEYOND 5G NETWORKS IEEE WIRELESS COMMUNICATIONS English Editorial Material Beyond fifth-generation (B5G) networks, or so-called 6G, is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g., self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/VR services. By integrating AI functions into edge networks, radio networks become service-aware and resource-aware to have a full insight into the operating environment and can adapt resource allocation/orchestration in a dynamic manner. Despite the potential of edge intelligence, however, many challenges also need to be addressed in this new paradigm. Until now, limited research efforts have been made on edge intelligence for B5G networks. [Zhang, Yan] Univ Oslo, Dept Informat, Oslo, Norway; [Zhang, Yan] IEEE Commun Soc Tech Comm Green Commun & Soc Tech, Oslo, Norway; [Feng, Zhiyong] Minist Educ, Key Lab Univ Wireless Commun, Beijing, Peoples R China; [Feng, Zhiyong] Chinese Inst Commun CIC, Beijing, Peoples R China; [Moustafa, Hassnaa] Edge Solut IoT Segments & Network & Edge Infrastr, Beijing, Peoples R China; [Moustafa, Hassnaa] Univ Paris 11, Comp & Networks, Paris, France; [Ye, Feng] Univ Dayton, Dept Elect & Comp Engn, Dayton, OH USA; [Cui, Chunfen] Beijing Univ Posts Telecommun, Beijing, Peoples R China University of Oslo; UDICE-French Research Universities; Universite Paris Saclay; University of Dayton; Beijing University of Posts & Telecommunications Zhang, Y (corresponding author), Univ Oslo, Dept Informat, Oslo, Norway.;Zhang, Y (corresponding author), IEEE Commun Soc Tech Comm Green Commun & Soc Tech, Oslo, Norway. Zhang, Yan/AFK-8566-2022 0 1 1 0 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-1284 1558-0687 IEEE WIREL COMMUN IEEE Wirel. Commun. APR 2021.0 28.0 2 10 11 10.1109/MWC.2021.9430853 0.0 2 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications TI4AZ Bronze 2023-03-23 WOS:000672740400003 0
J Rakkiyappan, R; Latha, VP; Zhu, QX; Yao, ZS Rakkiyappan, R.; Latha, V. Preethi; Zhu, Quanxin; Yao, Zhangsong Exponential synchronization of Markovian jumping chaotic neural networks with sampled-data and saturating actuators NONLINEAR ANALYSIS-HYBRID SYSTEMS English Article Saturating actuator; Exponential synchronization; Markovian jumping neural network; Chaotic system; Sampled-data; Linear matrix inequality TIME-VARYING DELAYS; COMPLEX DYNAMICAL NETWORKS; ADAPTIVE SYNCHRONIZATION; ASYMPTOTIC STABILITY; SYSTEMS SUBJECT; LINEAR-SYSTEMS; INPUT DELAY; DISCRETE; CRITERIA; DESIGN This paper deals with the problem of exponential synchronization of Markovian jumping chaotic neural networks with saturating actuators using a sampled-data controller. By constructing a proper Lyapunov-Krasovskii functional (LKF) with triple integral terms, and employing Jensen's inequality, some new sufficient conditions for the exponential synchronization of considered chaotic neural networks are derived in terms of linear matrix inequalities (LMIs). The obtained LMIs can be easily solved by any of the available software. Finally, the numerical examples are provided to demonstrate the effectiveness of our theoretical results. (c) 2016 Elsevier Ltd. All rights reserved. [Rakkiyappan, R.; Latha, V. Preethi] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India; [Zhu, Quanxin] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany; [Yao, Zhangsong] Nanjing Xiaozhuang Univ, Sch Math & Informat Technol, Nanjing 211171, Jiangsu, Peoples R China Bharathiar University; Nanjing Normal University; Nanjing Normal University; University of Bielefeld; Nanjing Xiaozhuang University Zhu, QX (corresponding author), Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China.;Zhu, QX (corresponding author), Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China. 05401@njnu.edu.cn Zhu, Quanxin/X-3230-2018; Rakkiyappan, R./D-6737-2013 Zhu, Quanxin/0000-0003-3130-4923; Rakkiyappan, R./0000-0003-0809-2782 Alexander von Humboldt Foundation of Germany [CHN/1163390]; National Natural Science Foundation of China [61374080]; Natural Science Foundation of Jiangsu Province [BK20161552]; Qing Lan Project of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions Alexander von Humboldt Foundation of Germany(Alexander von Humboldt Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions This work was jointly supported by the Alexander von Humboldt Foundation of Germany (Fellowship CHN/1163390), the National Natural Science Foundation of China (61374080), the Natural Science Foundation of Jiangsu Province (BK20161552), Qing Lan Project of Jiangsu Province and the Priority Academic Program Development of Jiangsu Higher Education Institutions. 46 81 84 1 86 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1751-570X 1878-7460 NONLINEAR ANAL-HYBRI Nonlinear Anal.-Hybrid Syst. MAY 2017.0 24.0 28 44 10.1016/j.nahs.2016.10.004 0.0 17 Automation & Control Systems; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Mathematics ES4OJ 2023-03-23 WOS:000399514300003 0
J Zhao, ZP; Li, QF; Zhang, ZX; Cummins, N; Wang, HS; Tao, JH; Schuller, BW Zhao, Ziping; Li, Qifei; Zhang, Zixing; Cummins, Nicholas; Wang, Haishuai; Tao, Jianhua; Schuller, Bjoern W. Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition NEURAL NETWORKS English Article Speech emotion recognition; Parallel 2D CNN; Connectionist temporal classification; Residual dilated network; Self-attention CONVOLUTIONAL NEURAL-NETWORKS; REPRESENTATIONS; RECURRENT A challenging issue in the field of the automatic recognition of emotion from speech is the efficient modelling of long temporal contexts. Moreover, when incorporating long-term temporal dependencies between features, recurrent neural network (RNN) architectures are typically employed by default. In this work, we aim to present an efficient deep neural network architecture incorporating Connectionist Temporal Classification (CTC) loss for discrete speech emotion recognition (SER). Moreover, we also demonstrate the existence of further opportunities to improve SER performance by exploiting the properties of convolutional neural networks (CNNs) when modelling contextual information. Our proposed model uses parallel convolutional layers (PCN) integrated with Squeeze-and-Excitation Network (SEnet), a system herein denoted as PCNSE, to extract relationships from 3D spectrograms across timesteps and frequencies; here, we use the log-Mel spectrogram with deltas and delta-deltas as input. In addition, a self-attention Residual Dilated Network (SADRN) with CTC is employed as a classification block for SER. To the best of the authors' knowledge, this is the first time that such a hybrid architecture has been employed for discrete SER. We further demonstrate the effectiveness of our proposed approach on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and FAU-Aibo Emotion corpus (FAU-AEC). Our experimental results reveal that the proposed method is well-suited to the task of discrete SER, achieving a weighted accuracy (WA) of 73.1% and an unweighted accuracy (UA) of 66.3% on IEMOCAP, as well as a UA of 41.1% on the FAU-AEC dataset. (C) 2021 Elsevier Ltd. All rights reserved. [Zhao, Ziping; Li, Qifei; Wang, Haishuai; Schuller, Bjoern W.] Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin, Peoples R China; [Zhang, Zixing; Schuller, Bjoern W.] Imperial Coll London, GLAM Grp Language Audio & Mus, London, England; [Cummins, Nicholas; Schuller, Bjoern W.] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany; [Cummins, Nicholas] Kings Coll London, Dept Biostat & Hlth Informat, IoPPN, London, England; [Wang, Haishuai] Fairfield Univ, Dept Comp Sci & Engn, Fairfield, CT 06430 USA; [Tao, Jianhua] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China; [Tao, Jianhua] Chinese Acad Sci, Natl Lab Pattern Recognit, Beijing, Peoples R China; [Tao, Jianhua] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China Tianjin Normal University; Imperial College London; University of Augsburg; University of London; King's College London; Fairfield University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences Tao, JH (corresponding author), Chinese Acad Sci, Natl Lab Pattern Recognit, Beijing, Peoples R China. jhtao@nlpr.ia.ac.cn Wang, Haishuai/ABW-2609-2022 Wang, Haishuai/0000-0003-1617-0920; Zhao, Ziping/0000-0002-8719-6389 National Natural Science Foundation of China [62071330, 61702370]; National Science Fund for Distinguished Young Scholars, China [61425017]; Key Program of the National Natural Science Foundation of China [61831022]; Natural Science Foundation of Tianjin, China [18JCZDJC36300]; technology plan of Tianjin, China [18ZXRHSY00100] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Fund for Distinguished Young Scholars, China(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Key Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Tianjin, China(Natural Science Foundation of Tianjin); technology plan of Tianjin, China The work presented in this article was substantially supported by the National Natural Science Foundation of China (Grant No. 62071330,61702370) , the National Science Fund for Distinguished Young Scholars, China (Grant No: 61425017) , the Key Program of the National Natural Science Foundation of China (Grant No: 61831022) , the Key Program of the Natural Science Foundation of Tianjin, China (Grant No. 18JCZDJC36300) , the technology plan of Tianjin, China (Grant No: 18ZXRHSY00100) . 52 29 31 6 23 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. SEP 2021.0 141.0 52 60 10.1016/j.neunet.2021.03.013 0.0 APR 2021 9 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology TS9KG 33866302.0 2023-03-23 WOS:000679966600005 0
J Ahmad, M; Ahmad, F; Wroblewski, P; Al-Mansob, RA; Olczak, P; Kaminski, P; Safdar, M; Rai, P Ahmad, Mahmood; Ahmad, Feezan; Wroblewski, Piotr; Al-Mansob, Ramez A.; Olczak, Piotr; Kaminski, Pawel; Safdar, Muhammad; Rai, Partab Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach APPLIED SCIENCES-BASEL English Article cohesionless soil; machine learning; Gaussian process regression; shallow foundation; ultimate bearing capacity MODEL This study examines the potential of the soft computing technique-namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (gamma), and internal friction angle (phi). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (q(u)). This study shows that the developed GPR is a robust model for the q(u) prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter. [Ahmad, Mahmood; Al-Mansob, Ramez A.] Int Islamic Univ Malaysia, Fac Engn, Dept Civil Engn, Jalan Gombak, Bangi 50728, Selangor, Malaysia; [Ahmad, Mahmood] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Bannu 28100, Pakistan; [Ahmad, Feezan] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116024, Peoples R China; [Wroblewski, Piotr] Univ Technol & Econ H Chodkowska Warsaw, Fac Engn, Jutrzenki 135, PL-02231 Warsaw, Poland; [Wroblewski, Piotr] Mil Univ Technol, Fac Mechatron Armament & Aerosp, Sylwestra Ka Liskiego 2, PL-00908 Warsaw, Poland; [Olczak, Piotr] Polish Acad Sci, Mineral & Energy Econ Res Inst, Wybickiego St 7A, PL-31261 Krakow, Poland; [Kaminski, Pawel] AGH Univ Sci & Technol, Fac Min & Geoengn, PL-30059 Krakow, Poland; [Safdar, Muhammad] Univ Engn & Technol Peshawar, Earthquake Engn Ctr, Peshawar 25000, Pakistan; [Rai, Partab] Southwest Jiaotong Univ, Key Lab Transportat Tunnel Engn, Minist Educ, Sch Civil Engn, Chengdu 610031, Peoples R China International Islamic University Malaysia; Dalian University of Technology; Military University of Technology in Warsaw; Polish Academy of Sciences; Mineral & Energy Economy Research Institute of the Polish Academy of Sciences; AGH University of Science & Technology; University of Peshawar; Southwest Jiaotong University Al-Mansob, RA (corresponding author), Int Islamic Univ Malaysia, Fac Engn, Dept Civil Engn, Jalan Gombak, Bangi 50728, Selangor, Malaysia. ahmadm@iium.edu.my; ahmadf@mail.dlut.edu.cn; piotr.wroblewski@wat.edu.pl; ramez@iium.edu.my; olczak@min-pan.krakow.pl; pkamin@agh.edu.pl; drsafdar@uetpeshawar.edu.pk; partabrai@my.swjtu.edu.cn Kamiński, Paweł/T-7756-2017; Ahmad, Mahmood/HHD-1034-2022; Wróblewski, Piotr/AAP-9921-2021; Olczak, Piotr/AAF-4891-2020; Rai, Partab/AAK-2755-2021 Kamiński, Paweł/0000-0002-1450-5881; Ahmad, Mahmood/0000-0002-1139-1831; Wróblewski, Piotr/0000-0001-5811-8333; Olczak, Piotr/0000-0002-4926-0845; Rai, Partab/0000-0002-1198-0951; Al-Mansob, Ramez/0000-0003-1702-4191; Safdar, Muhammad/0000-0001-6129-0265 54 13 13 3 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel NOV 2021.0 11.0 21 10317 10.3390/app112110317 0.0 14 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics WZ5TY gold 2023-03-23 WOS:000720030500001 0
J Fu, GZ; Jia, SK; Zhu, WB; Yang, JX; Cao, YL; Yang, MY; Cao, YP Fu, Guizhong; Jia, Shukai; Zhu, Wenbin; Yang, Jiangxin; Cao, Yanlong; Yang, Michael Ying; Cao, Yanpeng Fusion of multi-light source illuminated images for effective defect inspection on highly reflective surfaces MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Surface inspection; Defect classification; Convolutional neural network; Image fusion; Multi-light source illumination LEARNING-BASED APPROACH; CLASSIFICATION; SYSTEM; MODEL It is observed that a human inspector can obtain better visual observations of surface defects via changing the lighting/viewing directions from time to time. Accordingly, we first build a multi light source illumination/acquisition system to capture images of workpieces under individual lighting directions and then propose a multi-stream CNN model to process multi-light source illuminated images for high-accuracy surface defect classification on highly reflective metal. Moreover, we present two effective techniques including individual stream deep supervision and channel attention (CA) based feature re-calibration to generate and select the most discriminative features on multi-light source illuminated images for the subsequent defect classification task. Comparative evaluation results demonstrate that our proposed method is capable of generating more accurate recognition results via the fusion of complementary features extracted on images illuminated by multi-light sources. Furthermore, our proposed light-weight CNN model can process more than 20 input frames per second on a single NVIDIA Quadro P6000 GPU (24G RAM) and is faster than a human inspector. Source codes and the newly constructed multi-light source illuminated dataset will be accessible to the public. [Fu, Guizhong; Jia, Shukai; Zhu, Wenbin; Yang, Jiangxin; Cao, Yanlong; Cao, Yanpeng] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China; [Fu, Guizhong] Suzhou Univ Sci & Technol, Sch Mech Engn, Suzhou, Peoples R China; [Fu, Guizhong; Jia, Shukai; Zhu, Wenbin; Yang, Jiangxin; Cao, Yanlong; Cao, Yanpeng] Zhejiang Univ, Sch Mech Engn, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou, Peoples R China; [Yang, Michael Ying] Univ Twente, Scene Understanding Grp, Hengelosestr 99, NL-7514 AE Enschede, Netherlands Zhejiang University; Suzhou University of Science & Technology; Zhejiang University; University of Twente Cao, YP (corresponding author), Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China.;Cao, YP (corresponding author), Zhejiang Univ, Sch Mech Engn, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou, Peoples R China. caoyp@zju.edu.cn Fu, Guizhong/HDN-6319-2022; Zhu, Wenbin/HCH-7312-2022 Fu, Guizhong/0000-0002-9587-4135; Zhu, Wenbin/0000-0001-9158-335X National Natural Science Foundation of China [52105526, 52075485]; National Key Research and Development Program of China [2020YFB1711400] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China Acknowledgments This research was supported by the National Natural Science Foundation of China (No. 52105526 and No. 52075485) and the National Key Research and Development Program of China (2020YFB1711400) . 42 2 2 12 18 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. AUG 1 2022.0 175.0 109109 10.1016/j.ymssp.2022.109109 0.0 APR 2022 13 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 1P7YN Green Published 2023-03-23 WOS:000802221200003 0
J Zhang, WQ; Dong, WH; Lv, ML; Liu, ZC; Zhou, Y; Feng, HM Zhang, Wenqian; Dong, Wenhan; Lv, Maolong; Liu, Zongcheng; Zhou, Yang; Feng, Haoming Barrier Lypunov functions-based nonsingular fixed-time switching control for strict-feedback nonlinear dynamics with full state constraints INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL English Article adaptive backstepping control; fixed-time stability; switching control; time-varying state constraints FAULT-TOLERANT CONTROL; LYAPUNOV FUNCTIONS; BACKSTEPPING CONTROL; ADAPTIVE-CONTROL; TRACKING CONTROL; SYSTEMS; DESIGN; STABILIZATION This work proposes a nonsingular adaptive fixed-time switching control method for a class of strict-feedback nonlinear dynamics subject to full state constraints. The peculiarity of this design lies in overcoming the singularity issue that typically appears in the existing backstepping-based fixed-time control methods caused by the iterative differentiation of fractional power terms as tracking errors approach to zero, while guaranteeing the nonviolation of full state constraints. Crucial in solving such singularity issue is to skillfully introduce a smooth switching between fractional power and integer power terms, which guarantees that fractional power term is confined within a positive interval all the time. An asymmetric time-varying barrier Lyapunov function is delicately incorporated into control design, rendering state variables to be within prescribed time-varying bounds. Besides, radial basis function neural network is employed to handle system unknown nonlinearities. It is rigorously proved that all the closed-loop signals eventually converge to small regions around origin within fixed-time. Comparative simulation results are finally given to validate the effectiveness and superiority of the proposed control strategy. [Zhang, Wenqian; Dong, Wenhan; Liu, Zongcheng; Zhou, Yang; Feng, Haoming] Air Force Engn Univ, Aeronaut Engn Coll, Xian, Peoples R China; [Lv, Maolong] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands; [Liu, Zongcheng] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian, Peoples R China Air Force Engineering University; Delft University of Technology; Northwestern Polytechnical University Liu, ZC (corresponding author), Air Force Engn Univ, Aeronaut Engn Coll, Xian, Peoples R China. liu434853780@163.com Zhang, Wenqian/0000-0002-9350-4122; Zhou, Yang/0000-0003-0498-1013 Natural Science Basic Research Program of Shaanxi [2019JQ-711]; Young Talent Promotion Program of Association for Science and Technology in Xi'an [095920201309] Natural Science Basic Research Program of Shaanxi; Young Talent Promotion Program of Association for Science and Technology in Xi'an Natural Science Basic Research Program of Shaanxi, Grant/Award Number: 2019JQ-711; Young Talent Promotion Program of Association for Science and Technology in Xi'an, Grant/Award Number: 095920201309 46 0 0 6 44 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1049-8923 1099-1239 INT J ROBUST NONLIN Int. J. Robust Nonlinear Control NOV 10 2021.0 31.0 16 7862 7885 10.1002/rnc.5720 0.0 AUG 2021 24 Automation & Control Systems; Engineering, Electrical & Electronic; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Mathematics WH0MF 2023-03-23 WOS:000680046800001 0
J Xu, HY; Akbari, MK; Verpoort, F; Zhuiykov, S Xu, Hongyan; Akbari, Mohammad Karbalaei; Verpoort, Francis; Zhuiykov, Serge Nano-engineering and functionalization of hybrid Au-MexOy-TiO2 (Me = W, Ga) hetero-interfaces for optoelectronic receptors and nociceptors NANOSCALE English Article MEMORY; CONVERSION; MEMRISTOR; VOLTAGE Bio-inspired nano-electronic devices are key instruments for the development of advanced artificial intelligence systems, which will shape the future of humanoid nano-robotics. An emerging demand is realized for an accurate reception of environmental stimuli via visual perception, processing and realization of optical signals. The present study demonstrates the capability of functionalized all-oxide heterostructured two-dimensional (2D) plasmonic devices for the self-adaptive recognition of visual optical pulses. Specifically, the nano-engineering of the metal/semiconductor interface and co-modulation of heterostructured 2D semiconductor hetero-interfaces of Au/WO3 : TiO2 and Au/Ga2O3 : TiO2 facilitated the receptive and nociceptive detection of visible light pulses. A decrease in the dark current of the Au/ WO3 : TiO2 unit resulted in the development of sensitive visible light photoreceptors. Furthermore, the modulation of charge transfers at the Au/Ga2O3 : TiO2 hetero-interfaces were the key parameter to determine the optical reception characteristics and nociceptive performance of all-oxide optoelectronic devices. Specifically, the rapid thermal annealing (RTA) of 2D Ga2O3 in N-2 atmosphere ensured the modulation of charge transfer at Au/Ga2O3 : TiO2 hetero-interfaces in plasmonic devices. Thus, hetero-interface engineering enabled the effective control of charge transfer at 2D hetero-interfaces for an adaptive perception of visible optical pulses. Consequently, the fabricated sensitive Au/Ga2O3 (N2) : TiO2 bio-inspired unit emulated the optical functionalities of corneal nociceptors. [Xu, Hongyan; Zhuiykov, Serge] North Univ China, Sch Mat Sci & Engn, Taiyuan 030051, Shanxi, Peoples R China; [Akbari, Mohammad Karbalaei; Verpoort, Francis; Zhuiykov, Serge] Univ Ghent, Ctr Environm & Energy Res, Global Campus, Incheon 21985, South Korea; [Akbari, Mohammad Karbalaei; Zhuiykov, Serge] Univ Ghent, Dept Solid State Sci, Fac Sci, B-9000 Ghent, Belgium; [Verpoort, Francis] Wuhan Univ Technol, State Key Lab Adv Technol Mat Synth & Proc, Wuhan 630070, Peoples R China North University of China; Ghent University; Ghent University; Wuhan University of Technology Zhuiykov, S (corresponding author), North Univ China, Sch Mat Sci & Engn, Taiyuan 030051, Shanxi, Peoples R China.;Akbari, MK; Zhuiykov, S (corresponding author), Univ Ghent, Ctr Environm & Energy Res, Global Campus, Incheon 21985, South Korea.;Akbari, MK; Zhuiykov, S (corresponding author), Univ Ghent, Dept Solid State Sci, Fac Sci, B-9000 Ghent, Belgium. Mohammad.akbari@ugent.be; serge.zhuiykov@ugent.be Verpoort, Francis/I-7822-2013 Verpoort, Francis/0000-0002-5184-5500; Karbalaei akbari, Mohammad/0000-0001-9367-8475 100 Talents program of the Shanxi province of PR China; Research & Development Program of the Ghent University Global Campus, South Korea 100 Talents program of the Shanxi province of PR China; Research & Development Program of the Ghent University Global Campus, South Korea S. Z. acknowledges the support from the 100 Talents program of the Shanxi province of PR China. The work was partially supported by Research & Development Program of the Ghent University Global Campus, South Korea. The work performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victoria Node of the Australian National Fabrication Facility (ANFF). 50 15 15 14 53 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2040-3364 2040-3372 NANOSCALE Nanoscale OCT 21 2020.0 12.0 39 20177 20188 10.1039/d0nr02184a 0.0 12 Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics OD5FF 32697233.0 2023-03-23 WOS:000579877200048 0
J Varshney, RK; Bohra, A; Roorkiwal, M; Barmukh, R; Cowling, WA; Chitikineni, A; Lam, HM; Hickey, LT; Croser, JS; Bayer, PE; Edwards, D; Crossa, J; Weckwerth, W; Millar, H; Kumar, A; Bevan, MW; Siddique, KHM Varshney, Rajeev K.; Bohra, Abhishek; Roorkiwal, Manish; Barmukh, Rutwik; Cowling, Wallace A.; Chitikineni, Annapurna; Lam, Hon-Ming; Hickey, Lee T.; Croser, Janine S.; Bayer, Philipp E.; Edwards, David; Crossa, Jose; Weckwerth, Wolfram; Millar, Harvey; Kumar, Arvind; Bevan, Michael W.; Siddique, Kadambot H. M. Fast-forward breeding for a food-secure world TRENDS IN GENETICS English Review MASS-SPECTROMETRY; CROP; TRAITS; POPULATIONS; PREDICTION; PROTEOMICS; GENOMICS; DISEASE; PLANTS; YIELD Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people. [Varshney, Rajeev K.; Roorkiwal, Manish; Barmukh, Rutwik; Chitikineni, Annapurna] Int Crops Res Inst Semi Arid Trop, Ctr Excellence Genom & Syst Biol, Hyderabad 502324, India; [Varshney, Rajeev K.] Murdoch Univ, State Agr Biotechnol Ctr, Ctr Crop & Food Innovat, Food Futures Inst, Murdoch, WA 6150 USA; [Varshney, Rajeev K.; Roorkiwal, Manish; Cowling, Wallace A.; Croser, Janine S.; Bayer, Philipp E.; Edwards, David; Millar, Harvey; Siddique, Kadambot H. M.] Univ Western Australia, UWA Inst Agr, Perth, WA 6009, Australia; [Bohra, Abhishek] ICAR Indian Inst Pulses Res IIPR, Kanpur, Uttar Pradesh, India; [Lam, Hon-Ming] Chinese Univ Hong Kong, Sch Life Sci, Shatin, Hong Kong, Peoples R China; [Lam, Hon-Ming] Chinese Univ Hong Kong, Ctr Soybean Res, State Key Lab Agrobiotechnol, Shatin, Hong Kong, Peoples R China; [Hickey, Lee T.] Univ Queensland, Queensland Alliance Agr & Food Innovat, Brisbane, Qld, Australia; [Bayer, Philipp E.; Edwards, David] Univ Western Australia, Sch Biol Sci, Crawley, WA, Australia; [Crossa, Jose] Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco, Mexico; [Weckwerth, Wolfram] Univ Vienna, Dept Ecogen & Syst Biol, Vienna Metabol Ctr VIME, Vienna, Austria; [Millar, Harvey] Univ Western Australia, ARC Ctr Excellence Plant Energy Biol, Crawley, WA, Australia; [Kumar, Arvind] Int Crops Res Inst Semi Arid Trop, Deputy Director Gen Off, Hyderabad 502324, India; [Bevan, Michael W.] John Innes Ctr, Norwich Res Pk, Norwich, Norfolk, England CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); Murdoch University; University of Western Australia; Indian Council of Agricultural Research (ICAR); ICAR - Indian Institute of Pulses Research; Chinese University of Hong Kong; Chinese University of Hong Kong; University of Queensland; University of Western Australia; CGIAR; International Maize & Wheat Improvement Center (CIMMYT); University of Vienna; University of Western Australia; CGIAR; International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT); UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); John Innes Center Varshney, RK (corresponding author), Int Crops Res Inst Semi Arid Trop, Ctr Excellence Genom & Syst Biol, Hyderabad 502324, India.;Varshney, RK (corresponding author), Murdoch Univ, State Agr Biotechnol Ctr, Ctr Crop & Food Innovat, Food Futures Inst, Murdoch, WA 6150 USA.;Varshney, RK (corresponding author), Univ Western Australia, UWA Inst Agr, Perth, WA 6009, Australia. r.k.varshney@cgiar.org Barmukh, Rutwik/HGU-0813-2022; Millar, A. Harvey/A-5452-2008; Hickey, Lee Thomas/AEA-2000-2022; Cowling, Wallace/B-3879-2009; Varshney, Rajeev K/C-5295-2014; Roorkiwal, Manish/E-9822-2011; Siddique, Kadambot H.M./B-3462-2011; Edwards, David/H-3710-2015; Bayer, Philipp/L-4481-2018 Barmukh, Rutwik/0000-0002-2740-8992; Millar, A. Harvey/0000-0001-9679-1473; Cowling, Wallace/0000-0002-3101-7695; Varshney, Rajeev K/0000-0002-4562-9131; Roorkiwal, Manish/0000-0001-6595-281X; Siddique, Kadambot H.M./0000-0001-6097-4235; Edwards, David/0000-0001-7599-6760; Weckwerth, Wolfram/0000-0002-9719-6358; bohra, abhishek/0000-0003-4569-8900; Bayer, Philipp/0000-0001-8530-3067; Hickey, Lee/0000-0001-6909-7101; Croser, Janine/0000-0002-1020-9460 University of Western Australia; Bill & Melinda Gates Foundation; Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India; Hong Kong Research Grants Council Area of Excellence Scheme [AoE/M-403/16] University of Western Australia; Bill & Melinda Gates Foundation(Bill & Melinda Gates FoundationCGIAR); Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India; Hong Kong Research Grants Council Area of Excellence Scheme(Hong Kong Research Grants Council) The authors are thankful to Deputy Vice-Chancellor Research, The University of Western Australia and Director General, ICRISAT, to provide support for organizing an international workshop at Perth (Australia) for brainstorming on the topic of the article. R.K.V. acknowledges support from the Bill & Melinda Gates Foundation for undertaking research on the topic of the article at ICRISAT and to the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India for the JC Bose National Fellowship. H.M.L. acknowledges support from the Hong Kong Research Grants Council Area of Excellence Scheme (AoE/M-403/16). 95 31 31 5 21 ELSEVIER SCIENCE LONDON LONDON 84 THEOBALDS RD, LONDON WC1X 8RR, ENGLAND 0168-9525 1362-4555 TRENDS GENET Trends Genet. DEC 2021.0 37.0 12 1124 1136 10.1016/j.tig.2021.08.002 0.0 NOV 2021 13 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity WW7PQ 34531040.0 hybrid, Green Published 2023-03-23 WOS:000718104000011 0
J Zhang, Q; Garlaschelli, D Zhang, Qi; Garlaschelli, Diego Strong ensemble nonequivalence in systems with local constraints NEW JOURNAL OF PHYSICS English Article local constraints; ensemble nonequivalence; matrices ensemble INFORMATION The asymptotic equivalence of canonical and microcanonical ensembles is a central concept in statistical physics, with important consequences for both theoretical research and practical applications. However, this property breaks down under certain circumstances. The most studied violation of ensemble equivalence requires phase transitions, in which case it has a 'restricted' (i.e. confined to a certain region in parameter space) but 'strong' (i.e. characterized by a difference between the entropies of the two ensembles that is of the same order as the entropies themselves) form. However, recent research on networks has shown that the presence of an extensive number of local constraints can lead to ensemble nonequivalence (EN) even in the absence of phase transitions. This occurs in a 'weak' (i.e. leading to a subleading entropy difference) but remarkably 'unrestricted' (i.e. valid in the entire parameter space) form. Here we look for more general manifestations of EN in arbitrary ensembles of matrices with given margins. These models have widespread applications in the study of spatially heterogeneous and/or temporally nonstationary systems, with consequences for the analysis of multivariate financial and neural time-series, multi-platform social activity, gene expression profiles and other big data. We confirm that EN appears in 'unrestricted' form throughout the entire parameter space due to the extensivity of local constraints. Surprisingly, at the same time it can also exhibit the 'strong' form. This novel, simultaneously 'strong and unrestricted' form of nonequivalence is very robust and imposes a principled choice of the ensemble. We calculate the proper mathematical quantities to be used in real-world applications. [Zhang, Qi] Jiangsu Univ Sci & Technol, Sch Sci, Zhenjiang 212100, Jiangsu, Peoples R China; [Zhang, Qi; Garlaschelli, Diego] Leiden Univ, Lorentz Inst Theoret Phys, Niels Bohrweg 2, NL-2333 CA Leiden, Netherlands; [Garlaschelli, Diego] IMT Sch Adv Studies, Piazza San Francesco 19, I-55100 Lucca, Italy Jiangsu University of Science & Technology; Leiden University; Leiden University - Excl LUMC; IMT School for Advanced Studies Lucca Zhang, Q (corresponding author), Jiangsu Univ Sci & Technol, Sch Sci, Zhenjiang 212100, Jiangsu, Peoples R China.;Zhang, Q; Garlaschelli, D (corresponding author), Leiden Univ, Lorentz Inst Theoret Phys, Niels Bohrweg 2, NL-2333 CA Leiden, Netherlands.;Garlaschelli, D (corresponding author), IMT Sch Adv Studies, Piazza San Francesco 19, I-55100 Lucca, Italy. Qi.Zhang@just.edu.cn; garlaschelli@lorentz.leienuniv.nl Garlaschelli, Diego/O-4862-2014 Garlaschelli, Diego/0000-0001-6035-1783; Zhang, Qi/0000-0002-6692-0737 Chinese Scholarship Council [201606990001]; Dutch Econophysics Foundation (Stichting Econophysics, Leiden, the Netherlands); Netherlands Organization for Scientific Research (NWO/OCW); EU project SoBigData++ [871042] Chinese Scholarship Council(China Scholarship Council); Dutch Econophysics Foundation (Stichting Econophysics, Leiden, the Netherlands)(Netherlands Government); Netherlands Organization for Scientific Research (NWO/OCW)(Netherlands Organization for Scientific Research (NWO)); EU project SoBigData++ This work is supported by the Chinese Scholarship Council (No. 201606990001), the Dutch Econophysics Foundation (Stichting Econophysics, Leiden, the Netherlands), the Netherlands Organization for Scientific Research (NWO/OCW) and the EU project SoBigData++, Contract No. 871042. 27 2 2 0 2 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1367-2630 NEW J PHYS New J. Phys. APR 1 2022.0 24.0 4 43011 10.1088/1367-2630/ac5e89 0.0 44 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics 0J4HT Green Published, Green Submitted, gold 2023-03-23 WOS:000780066200001 0
J Pan, XY; Zhu, L; Fan, YX; Yan, JC Pan, Xiaoyong; Zhu, Lin; Fan, Yong-Xian; Yan, Junchi Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection COMPUTATIONAL BIOLOGY AND CHEMISTRY English Article Protein-RNA interaction site; Sample imbalance; Evolution information; Submodularity subset selection; Random forest BINDING-SITES; SECONDARY STRUCTURE; TARGET RECOGNITION; CLIP; SPECIFICITIES; PROPENSITY; DATABASE; AREA Protein-RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein-RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis. (C) 2014 Elsevier Ltd. All rights reserved. [Pan, Xiaoyong] Univ Copenhagen, Sect Anim Genet Bioinformat & Breeding, Ctr Noncoding RNA Technol & Hlth, DK-1168 Copenhagen, Denmark; [Pan, Xiaoyong] Univ Copenhagen, Dept Dis Syst Biol, Novo Nordisk Fdn Ctr Prot Res, DK-1168 Copenhagen, Denmark; [Zhu, Lin] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai, Peoples R China; [Fan, Yong-Xian] Guilin Univ Elect Technol, Sch Comp Sci & Engn, Guilin, Peoples R China; [Yan, Junchi] Shanghai Jiao Tong Univ, Shanghai 200030, Peoples R China University of Copenhagen; University of Copenhagen; Shanghai University of Electric Power; Guilin University of Electronic Technology; Shanghai Jiao Tong University Pan, XY (corresponding author), Univ Copenhagen, Sect Anim Genet Bioinformat & Breeding, Ctr Noncoding RNA Technol & Hlth, DK-1168 Copenhagen, Denmark. xypan172436@gmail.com Pan, Xiaoyong/0000-0001-5010-464X Faculty of Health and Medical Sciences, University of Copenhagen; National Natural Science Foundation of China [61462018, 61403247, 61272437, 61373152, 61305094]; YangFan Project of Shanghai Municipal Science and Technology Commission [14YF1411000]; Innovation Program of Shanghai Municipal Education Commission [14YZ131]; Scientific Research Foundation of Shanghai University of Electric Power [K2014-032] Faculty of Health and Medical Sciences, University of Copenhagen; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); YangFan Project of Shanghai Municipal Science and Technology Commission; Innovation Program of Shanghai Municipal Education Commission(Innovation Program of Shanghai Municipal Education Commission); Scientific Research Foundation of Shanghai University of Electric Power This research is partly supported by Fellowship from Faculty of Health and Medical Sciences, University of Copenhagen, the National Natural Science Foundation of China (Grant no. 61462018, 61403247, 61272437, 61373152, and 61305094), YangFan Project (Grant no. 14YF1411000) of Shanghai Municipal Science and Technology Commission, the Innovation Program of Shanghai Municipal Education Commission (Grant no. 14YZ131) and the Scientific Research Foundation of Shanghai University of Electric Power, the Science Research Foundation of Shanghai University of Electric Power (Grant no. K2014-032). 47 29 31 2 21 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1476-9271 1476-928X COMPUT BIOL CHEM Comput. Biol. Chem. DEC 2014.0 53.0 B 324 330 10.1016/j.compbiolchem.2014.11.002 0.0 7 Biology; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science AY4YK 25462339.0 2023-03-23 WOS:000347580700015 0
C Lin, Y; Chu, L; Hu, JC; Zhang, YJ; Hou, ZR IEEE Lin, Yang; Chu, Liang; Hu, Jincheng; Zhang, Yuanjian; Hou, Zhuoran DRL-ECMS: An Adaptive Hierarchical Equivalent Consumption Minimization Strategy Based on Deep Reinforcement Learning 2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) IEEE Intelligent Vehicles Symposium English Proceedings Paper 33rd IEEE Intelligent Vehicles Symposium (IEEE IV) JUN 05-09, 2022 Aachen, GERMANY IEEE,Bosch,IEEE Intelligent Transport Syst Soc,RWTH Aachen Univ, IKA,Mercedes Benz Reinforcement learning; energy management; equivalent consumption minimization strategy HYBRID ELECTRIC VEHICLE; ENERGY MANAGEMENT With the rise of machine learning, reinforcement learning (RL) is gradually applied to the energy management strategy (EMS) of plug-in hybrid electric vehicle (PHEV). Some old algorithms have also achieved better results by combining with reinforcement learning. In order to learn from the advantages of previous algorithms and explore the application potential of reinforcement learning algorithm, this paper proposes an adaptive hierarchical management strategy combining equivalent consumption minimization strategy (ECMS) knowledge with proximal policy optimization (PPO). This system is an advanced data-driven RL algorithm at present. For a more comprehensive comparison, this paper compares the proposed EMS with dynamic programming (DP), ECMS with constant equivalence factor and q-learning. The results show that the fuel consumption of the proposed control strategy is very close to that of the DP-based control strategy and the performance is better than the other two strategies. It shows that deep reinforcement learning can help ECMS solve the problem of dynamic factor planning and DRL-ECMS has the potential of deployment in real-time system. [Lin, Yang; Chu, Liang; Hou, Zhuoran] Jilin Univ, State Key Lab Automot Dynam Simulat & Control, Changchun, Peoples R China; [Hu, Jincheng] Univ Glasgow, Sch Comp Sci, Glasgow, Lanark, Scotland; [Zhang, Yuanjian] Queens Univ Belfast, W Tech Ctr, Belfast, Antrim, North Ireland Jilin University; University of Glasgow; Queens University Belfast Lin, Y (corresponding author), Jilin Univ, State Key Lab Automot Dynam Simulat & Control, Changchun, Peoples R China. Zhang, Yuanjian/HKN-4832-2023 Zhang, Yuanjian/0000-0001-5563-8480 16 0 0 5 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1931-0587 978-1-6654-8821-1 IEEE INT VEH SYM 2022.0 235 240 10.1109/IV51971.2022.9827234 0.0 6 Automation & Control Systems; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Transportation BT8NW 2023-03-23 WOS:000854106700034 0
J Ling, HF; Wu, J; Li, P; Shen, JL Ling, Hefei; Wu, Jia; Li, Ping; Shen, Jialie Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition CMC-COMPUTERS MATERIALS & CONTINUA English Article Gait recognition; latent semantic analysis; attention mechanism; attention-aware neural network; clothing-invariant; feature fusion Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches. [Ling, Hefei; Wu, Jia; Li, Ping] Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China; [Shen, Jialie] Queens Univ, Belfast, Antrim, North Ireland Huazhong University of Science & Technology; Queens University Belfast Li, P (corresponding author), Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China. lpshome@hust.edu.cn Shen, Jialie/AAX-6851-2020 Natural Science Foundation of China [U1536203]; National key research and development program of China [2016QY01W0200]; Major Scientific and Technological Project of Hubei Province [2018AAA068] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National key research and development program of China; Major Scientific and Technological Project of Hubei Province This work was supported in part by the Natural Science Foundation of China under Grant U1536203, in part by the National key research and development program of China (2016QY01W0200), in part by the Major Scientific and Technological Project of Hubei Province (2018AAA068). 26 6 6 2 4 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2019.0 60.0 3 1041 1054 10.32604/cmc.2019.05605 0.0 14 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science KH2BX gold 2023-03-23 WOS:000510452200012 0
J Kang, K; Shakouri, K; Kroes, GJ; Kleyn, AW; Meyer, J Kang, Kai; Shakouri, Khosrow; Kroes, Geert-Jan; Kleyn, Aart W.; Meyer, Jorg Three-dimensional Langevin dynamics of N atom scattering from N-covered Ag(111) CHEMICAL PHYSICS English Article Gas-surface dynamics; Atom scattering; Molecular dynamics; Generalised Langevin oscillator TOTAL-ENERGY CALCULATIONS; MOLECULAR-DYNAMICS; DISSIPATION; EXCITATION; ADSORPTION; SURFACES; METALS; N-2 We analyzed the dynamics of N atom scattering from N-covered Ag(1 1 1), using the generalized Langevin oscillator (GLO) method and a three-dimensional neural network potential energy surface. Two values for the mass of the surface oscillator were considered in the GLO model to account for the energy exchange with the surface, i.e., that of Ag and that of N. For these mass values different trends were found for the dependence of the ratio of the average final divided by the average initial translational energy on the scattering angle. Using the nitrogen mass gives a trend closer to the experimental results for the dependence of the energy of the in-plane scattered atoms on the scattering angle, but worse agreement with experiment for the angular distribution of the in-plane scattered atoms. Two different algorithms are applied to integrate the stochastic equations of motion in the presence of energy dissipation. Our calculations show that a trivially extended Bulirsch-Stoer algorithm is more efficient than an algorithm based on a Langevin Liouville operator splitting technique, in that it yields the same results with a much larger time step. [Kang, Kai] Chengde Med Univ, Dept Biomed Engn, Chengde 067000, Peoples R China; [Kang, Kai; Kleyn, Aart W.] China Acad Engn Phys, Inst Mat, Chengdu 610200, Sichuan, Peoples R China; [Shakouri, Khosrow; Kroes, Geert-Jan; Meyer, Jorg] Leiden Univ, Leiden Inst Chem, Gorlaeus Labs, POB 9502, NL-2300 RA Leiden, Netherlands Chengde Medical University; Chinese Academy of Engineering Physics; Leiden University; Leiden University - Excl LUMC Meyer, J (corresponding author), Leiden Univ, Leiden Inst Chem, Gorlaeus Labs, POB 9502, NL-2300 RA Leiden, Netherlands. j.meyer@chem.leidenuniv.nl Meyer, Joerg/E-7638-2012 Meyer, Joerg/0000-0003-0146-730X National Natural Science Foundation of China [11547181]; NWO vidi [723.014.009] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NWO vidi(Netherlands Organization for Scientific Research (NWO)) This work was supported by the National Natural Science Foundation of China (No. 11547181) for KK, NWO vidi grant 723.014.009 for JM. K. K. thanks Gernot Fchsel and Linsen Zhou for useful discussions. 44 0 0 3 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0301-0104 1873-4421 CHEM PHYS Chem. Phys. AUG 1 2022.0 560.0 111557 10.1016/j.chemphys.2022.111557 0.0 MAY 2022 8 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics 1T2GO hybrid, Green Published 2023-03-23 WOS:000804554000001 0
J Lin, YP; Vosselman, G; Cao, YP; Yang, MY Lin, Yaping; Vosselman, George; Cao, Yanpeng; Yang, Michael Ying Local and global encoder network for semantic segmentation of Airborne laser scanning point clouds ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Point clouds; Semantic segmentation; Global context; Attention models CONVOLUTIONAL NEURAL-NETWORK; LIDAR; CLASSIFICATION Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps. In this paper, we present a local and global encoder network (LGENet) for semantic segmentation of ALS point clouds. Adapting the KPConv network, we first extract features by both 2D and 3D point convolutions to allow the network to learn more representative local geometry. Then global encoders are used in the network to exploit contextual information at the object and point level. We design a segment-based Edge Conditioned Convolution to encode the global context between segments. We apply a spatial-channel attention module at the end of the network, which not only captures the global interdependencies between points but also models interactions between channels. We evaluate our method on two ALS datasets namely, the ISPRS benchmark dataset and DCF2019 dataset. For the ISPRS benchmark dataset, our model achieves state-of-the-art results with an overall accuracy of 0.845 and an average F1 score of 0.737. With regards to the DFC2019 dataset, our proposed network achieves an overall accuracy of 0.984 and an average F1 score of 0.834. [Lin, Yaping; Vosselman, George; Yang, Michael Ying] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands; [Cao, Yanpeng] Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou, Peoples R China University of Twente; Zhejiang University Yang, MY (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands. ylin@utwente.nl; george.vosselman@utwente.nl; caoyp@zju.edu.cn; michael.yang@utwente.nl Vosselman, George/D-3985-2009; Yang, Michael/AAC-6698-2019 Vosselman, George/0000-0001-8813-8028; Yang, Michael/0000-0002-0649-9987 69 9 9 7 25 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. JUN 2021.0 176.0 151 168 10.1016/j.isprsjprs.2021.04.016 0.0 APR 2021 18 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology SJ4AV Green Published, hybrid 2023-03-23 WOS:000655474600012 0
J Yang, HX; Shan, CF; Bouwman, A; Dekker, LRC; Kolen, AF; de With, PHN Yang, Hongxu; Shan, Caifeng; Bouwman, Arthur; Dekker, Lukas R. C.; Kolen, Alexander F.; de With, Peter H. N. Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Instrument segmentation; 3D ultrasound; semi-supervised learning; Dual-UNet Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requiresmuch less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches. [Yang, Hongxu; de With, Peter H. N.] Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands; [Shan, Caifeng] Shandong Univ Sci & Technol, Qingdao 266590, Peoples R China; [Bouwman, Arthur; Dekker, Lukas R. C.] Catharina Hosp, NL-5623 EJ Eindhoven, Netherlands; [Kolen, Alexander F.] Philips Res, NL-5656 AE Eindhoven, Netherlands Eindhoven University of Technology; Shandong University of Science & Technology; Catharina Hospital; Philips; Philips Research Yang, HX (corresponding author), Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands.;Shan, CF (corresponding author), Shandong Univ Sci & Technol, Qingdao 266590, Peoples R China. h.yang@tue.nl; caifeng.shan@gmail.com; arthur.bouwman@catharinaziekenhuis.nl; lukas.dekker@catharinaziekenhuis.nl; alex.kolen@philips.com; p.h.n.de.with@tue.nl Shan, Caifeng/W-6178-2019 Shan, Caifeng/0000-0002-2131-1671; Dekker, Lukas/0000-0002-8166-3716; Bouwman, R. Arthur/0000-0002-2051-5947; Yang, Hongxu/0000-0003-2447-094X 34 2 2 5 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. FEB 2022.0 26.0 2 762 773 10.1109/JBHI.2021.3101872 0.0 12 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics ZY1DZ 34347611.0 Green Submitted 2023-03-23 WOS:000772331200026 0
J Zhu, ZQ; Chen, B; Qiu, SH; Wang, RX; Wang, YP; Ma, L; Qiu, XG Zhu, Zhengqiu; Chen, Bin; Qiu, Sihang; Wang, Rongxiao; Wang, Yiping; Ma, Liang; Qiu, Xiaogang A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster ROYAL SOCIETY OPEN SCIENCE English Article Bayesian maximum entropy; multi-objective optimization model; air quality monitoring network; atmospheric dispersion simulation system ARTIFICIAL NEURAL-NETWORK; OPTIMIZATION; STATIONS; INTERPOLATION; METHODOLOGY The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency. [Zhu, Zhengqiu; Chen, Bin; Wang, Rongxiao; Ma, Liang; Qiu, Xiaogang] Natl Univ Def Technol, Coll Syst Engn, 109 Deya Rd, Changsha 410073, Hunan, Peoples R China; [Qiu, Sihang] Delft Univ Technol, Fac Elect Engn, Web Informat Syst, Math & Comp Sci, NL-2628 XE Delft, Netherlands; [Wang, Yiping] Naval 902 Factory, Shanghai, Peoples R China National University of Defense Technology - China; Delft University of Technology Chen, B (corresponding author), Natl Univ Def Technol, Coll Syst Engn, 109 Deya Rd, Changsha 410073, Hunan, Peoples R China. nudtcb9372@gmail.com Chen, Bin/0000-0002-2962-9254 National Key Research & Development (RD) Plan [2018YFC0806900]; National Natural Science Foundation of China [71673292, 71673294, 61673388]; National Social Science Foundation of China [17CGL047]; Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion National Key Research & Development (RD) Plan; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Social Science Foundation of China; Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion This study is supported by National Key Research & Development (R&D) Plan under grant no. 2018YFC0806900 and the National Natural Science Foundation of China under grant nos. 71673292, 71673294, 61673388, National Social Science Foundation of China under grant no. 17CGL047 and Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion. 45 4 5 0 6 ROYAL SOC LONDON 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND 2054-5703 ROY SOC OPEN SCI R. Soc. Open Sci. SEP 2018.0 5.0 9 180889 10.1098/rsos.180889 0.0 21 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics GV6WO 30839708.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000446259700048 0
J Krohne, LG; Wang, Y; Hinrich, JL; Moerup, M; Chan, RCK; Madsen, KH Krohne, Laerke Gebser; Wang, Yi; Hinrich, Jesper L.; Moerup, Morten; Chan, Raymond C. K.; Madsen, Kristoffer H. Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task HUMAN BRAIN MAPPING English Article archetypical analysis; decomposition; functional connectivity; social anhedonia; support vector classification ULTRA-HIGH-RISK; FUNCTIONAL CONNECTIVITY; PSYCHOMETRIC SCHIZOTYPY; PSYCHOSIS-PRONENESS; ARCHETYPAL ANALYSIS; NEURAL BASIS; INDIVIDUALS; MIND; SCHIZOPHRENIA; EMPATHY Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia. [Krohne, Laerke Gebser; Hinrich, Jesper L.; Moerup, Morten; Madsen, Kristoffer H.] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Krohne, Laerke Gebser; Madsen, Kristoffer H.] Copenhagen Univ Hosp, Danish Res Ctr Magnet Resonance, Ctr Funct & Diagnost Imaging & Res, Kettegard Alle 30, DK-2650 Hvidovre, Denmark; [Krohne, Laerke Gebser; Wang, Yi; Chan, Raymond C. K.] Chinese Acad Sci, Neuropsychol & Appl Cognit Neurosci Lab, CAS Key Lab Mental Hlth, Inst Psychol, Beijing, Peoples R China; [Krohne, Laerke Gebser; Chan, Raymond C. K.; Madsen, Kristoffer H.] Univ Chinese Acad Sci, Sinodanish Coll, Beijing, Peoples R China; [Wang, Yi; Chan, Raymond C. K.] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China Technical University of Denmark; University of Copenhagen; Chinese Academy of Sciences; Institute of Psychology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Madsen, KH (corresponding author), Copenhagen Univ Hosp, Danish Res Ctr Magnet Resonance, Ctr Funct & Diagnost Imaging & Res, Kettegard Alle 30, DK-2650 Hvidovre, Denmark.;Chan, RCK (corresponding author), Chinese Acad Sci, Inst Psychol, 16 Lincui Rd, Beijing 100101, Peoples R China. rckchan@psych.ac.cn; kristofferm@drcmr.dk wang, yi/AAI-8689-2020; Madsen, Kristoffer Hougaard/M-9547-2014; Chan, Raymond CK/B-6717-2009; Hinrich, Jesper Løve/AGJ-5196-2022 wang, yi/0000-0001-6880-5831; Chan, Raymond CK/0000-0002-3414-450X; Hinrich, Jesper Løve/0000-0003-0258-7151; Krohne, Laerke/0000-0001-5354-1482; Morup, Morten/0000-0003-4985-4368; Madsen, Kristoffer Hougaard/0000-0001-8606-7641 Beijing Municipal Science & Technology Commission [Z161100000216138]; Lundbeckfonden [R105-9813]; National Basic Research Programme of China [2016YFC0906402]; NVIDIA Corporation; NVIDIA GPU grant Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Lundbeckfonden(Lundbeckfonden); National Basic Research Programme of China(National Basic Research Program of China); NVIDIA Corporation; NVIDIA GPU grant Beijing Municipal Science & Technology Commission, Grant/Award Number: Z161100000216138; Lundbeckfonden, Grant/Award Number: R105-9813; National Basic Research Programme of China, Grant/Award Number: 2016YFC0906402; NVIDIA Corporation, Grant/Award Number: NVIDIA GPU grant 81 4 4 4 20 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. DEC 1 2019.0 40.0 17 4965 4981 10.1002/hbm.24751 0.0 AUG 2019 17 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging KW4QR 31403748.0 Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000480936300001 0
J Li, YT; Huang, XH; Zhao, GY Li, Yante; Huang, Xiaohua; Zhao, Guoying Joint Local and Global Information Learning With Single Apex Frame Detection for Micro-Expression Recognition IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Micro-expression; 3D FFT; facial expression recognition; multi-instance learning; deep learning SYSTEM Micro-expressions (MEs) are rapid and subtle facial movements that are difficult to detect and recognize. Most recent works have attempted to recognize MEs with spatial and temporal information from video clips. According to psychological studies, the apex frame conveys the most emotional information expressed in facial expressions. However, it is not clear how the single apex frame contributes to micro-expression recognition. To alleviate that problem, this paper firstly proposes a new method to detect the apex frame by estimating pixel-level change rates in the frequency domain. With frequency information, it performs more effectively on apex frame spotting than the currently existing apex frame spotting methods based on the spatio-temporal change information. Secondly, with the apex frame, this paper proposes a joint feature learning architecture coupling local and global information to recognize MEs, because not all regions make the same contribution to ME recognition and some regions do not even contain any emotional information. More specifically, the proposed model involves the local information learned from the facial regions contributing major emotion information, and the global information learned from the whole face. Leveraging the local and global information enables our model to learn discriminative ME representations and suppress the negative influence of unrelated regions to MEs. The proposed method is extensively evaluated using CASME, CASME II, SAMM, SMIC, and composite databases. Experimental results demonstrate that our method with the detected apex frame achieves considerably promising ME recognition performance, compared with the state-of-the-art methods employing the whole ME sequence. Moreover, the results indicate that the apex frame can significantly contribute to micro-expression recognition. [Li, Yante; Zhao, Guoying] Univ Oulu, Ctr Machine Vis & Signal Anal, FI-90014 Oulu, Finland; [Huang, Xiaohua] Nanjing Inst Technol, Sch Comp Engn, Nanjing 211167, Peoples R China; [Zhao, Guoying] Northwest Univ, Sch Informat & Technol, Xian 710069, Peoples R China University of Oulu; Nanjing Institute of Technology; Northwest University Xi'an Zhao, GY (corresponding author), Northwest Univ, Sch Informat & Technol, Xian 710069, Peoples R China. yante.li@oulu.fi; xiaohua.huang@njit.edu.cn; guoying.zhao@oulu.fi Huang, Xiaohua/A-4878-2011; Zhao, Guoying/ABE-7716-2020 Zhao, Guoying/0000-0003-3694-206X; Huang, Xiaohua/0000-0003-4150-0477 Infotech Oulu; National Natural Science Foundation of China [61772419, 62076122]; Academy of Finland [316765]; ICT 2023 Project [328115]; Jiangsu Specially-Appointed Professor Program [3051107219003]; Talent Startup Project of NJIT [YKJ201982] Infotech Oulu; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); ICT 2023 Project; Jiangsu Specially-Appointed Professor Program; Talent Startup Project of NJIT This work was supported by Infotech Oulu, the National Natural Science Foundation of China under Grant 61772419 and Grant 62076122, the Academy of Finland for Project MiGA under Grant 316765, and ICT 2023 Project under Grant 328115, and in part by the Jiangsu Specially-Appointed Professor Program under Grant 3051107219003, and the Talent Startup Project of NJIT under Grant YKJ201982. 52 33 36 10 58 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2021.0 30.0 249 263 10.1109/TIP.2020.3035042 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering OU9GC 33156789.0 Green Accepted 2023-03-23 WOS:000591830600019 0
J Tang, SN; Li, TH; Cong, PS; Xiong, WW; Wang, ZH; Sun, JM Tang, Shengnan; Li, Tonghua; Cong, Peisheng; Xiong, Wenwei; Wang, Zhiheng; Sun, Jiangming PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif NUCLEIC ACIDS RESEARCH English Article AMINO-ACID; GENE ONTOLOGY; SVM; LOCATION; PROFILE Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/. [Tang, Shengnan; Li, Tonghua; Cong, Peisheng; Wang, Zhiheng] Tongji Univ, Dept Chem, Shanghai 200092, Peoples R China; [Xiong, Wenwei] Montclair State Univ, Dept Biol & Mol Biol, Montclair, NJ 07043 USA; [Sun, Jiangming] Lund Univ, Ctr Diabet, Units Mol Metab, SE-20502 Malmo, Sweden Tongji University; Montclair State University; Lund University Li, TH (corresponding author), Tongji Univ, Dept Chem, Siping Rd 1239, Shanghai 200092, Peoples R China. lith@tongji.edu.cn Xiong, Wenwei/G-5010-2015; Sun, Jiangming/AFT-1203-2022 Xiong, Wenwei/0000-0002-1790-7384; Sun, Jiangming/0000-0001-6814-1297 National Natural Science Foundation of China (NSFC) [20705024, 21275108]; NSFC National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China (NSFC) [20705024, 21275108]. Funding for open access charge: NSFC. 32 12 12 1 14 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0305-1048 1362-4962 NUCLEIC ACIDS RES Nucleic Acids Res. JUL 2013.0 41.0 W1 W441 W447 10.1093/nar/gkt428 0.0 7 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 207MC 23729470.0 Green Accepted, Green Submitted, gold, Green Published 2023-03-23 WOS:000323603200070 0
J Chen, X; Gao, W; Li, J; You, DF; Yu, ZL; Zhang, MZ; Shao, F; Wei, YY; Zhang, RY; Lange, T; Wang, QH; Chen, F; Lu, X; Zhao, Y Chen, Xin; Gao, Wei; Li, Jie; You, Dongfang; Yu, Zhaolei; Zhang, Mingzhi; Shao, Fang; Wei, Yongyue; Zhang, Ruyang; Lange, Theis; Wang, Qianghu; Chen, Feng; Lu, Xiang; Zhao, Yang A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers BRIEFINGS IN BIOINFORMATICS English Article COVID-19; longitudinal data; dynamic risk prediction; time-to-event ASSOCIATION Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level. [Chen, Xin; You, Dongfang; Yu, Zhaolei; Zhang, Mingzhi; Shao, Fang; Wei, Yongyue; Zhang, Ruyang; Chen, Feng; Zhao, Yang] Nanjing Med Univ, Sch Publ Hlth, Nanjing, Peoples R China; [Gao, Wei; Lu, Xiang] Nanjing Med Univ, Sir Run Run Hosp, Nanjing, Peoples R China; [Li, Jie; Wang, Qianghu] Nanjing Med Univ, Dept Bioinformat, Nanjing, Peoples R China; [Lange, Theis] Univ Copenhagen, Fac Hlth & Med Sci, Dept Publ Hlth, Copenhagen, Denmark Nanjing Medical University; Nanjing Medical University; Nanjing Medical University; University of Copenhagen Wang, QH; Chen, F; Zhao, Y (corresponding author), 101 Longmian Ave, Nanjing 211166, Jiangsu, Peoples R China.;Lu, X (corresponding author), 109 Longmian Ave, Nanjing 211166, Jiangsu, Peoples R China. wangqh@njmu.edu.cn; fengchen@njmu.edu.cn; luxiang66@njmu.edu.cn; yzhao@njmu.edu.cn zhao, yang/GXF-4424-2022; Wang, Qianghu/HJI-9741-2023; Wei, Yongyue/GYV-5660-2022 Wei, Yongyue/0000-0002-1132-1796; Chen, Feng/0000-0002-2699-7190; Lange, Theis/0000-0001-6807-8347 National Natural Science Foundation of China [81872709, 82041024, 81973142, 81970217, 81770440, 81970218] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This study was supported by the National Natural Science Foundation of China (81872709 to Y.Z., 82041024 to F.C., 81973142 to Y.W., 81970217 to W.G. and 81770440 and 81970218 to X.L.). 43 4 4 5 17 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. NOV 2021.0 22.0 6 bbab206 10.1093/bib/bbab206 0.0 11 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology XT0ZO 34081102.0 Bronze, Green Accepted 2023-03-23 WOS:000733325700076 0
J Chen, ZJ; Lu, Z; Chen, QS; Zhong, HL; Zhang, YS; Xue, J; Wu, CZ Chen, Zhijun; Lu, Zhe; Chen, Qiushi; Zhong, Hongliang; Zhang, Yishi; Xue, Jie; Wu, Chaozhong Spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism INFORMATION SCIENCES English Article Short-term traffic flow prediction; Graph convolution; Intelligent connected transportation; Deep learning; Group vehicle movement prediction NETWORKS Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road sections are usually distinct in real life and are difficult to analyze manually. The traditional GCN mechanism, which relies on a manually set adjacency matrix, is unable to dynamically learn such spatial patterns during training. To address this drawback, this study proposes a novel location graph convolutional network (location-GCN). The location-GCN solves this problem by adding a new learnable matrix to the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Subsequently, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, trigonometric function encoding was used in this study to enable the short-term input sequence to convey long-term periodic information. Finally, the proposed model was compared with the baseline models and evaluated on two real-world traffic flow datasets. The results show that our model is more accurate and robust than the other representative traffic prediction models. (C) 2022 Elsevier Inc. All rights reserved. [Chen, Zhijun; Wu, Chaozhong] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China; [Lu, Zhe] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan 430063, Peoples R China; [Chen, Qiushi; Zhong, Hongliang] Wuhan Univ Technol, Sch Comp Sci & Artificial Intelligence, Wuhan 430070, Peoples R China; [Zhang, Yishi] Wuhan Univ Technol, Sch Management, Wuhan 430070, Peoples R China; [Zhang, Yishi] Wuhan Univ Technol, Res Inst Digital Governance & Management Decis In, Wuhan 430070, Peoples R China; [Xue, Jie] Delft Univ Technol, Fac Technol Policy & Management, Safety & Secur Sci Grp S3G, NL-2628 BX Delft, Netherlands Wuhan University of Technology; Wuhan University of Technology; Wuhan University of Technology; Wuhan University of Technology; Wuhan University of Technology; Delft University of Technology Wu, CZ (corresponding author), Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.;Zhang, YS (corresponding author), Wuhan Univ Technol, Sch Management, Wuhan 430070, Peoples R China. yszhang@whut.edu.cn; wucz@whut.edu.cn Zhang, Yishi/0000-0001-6269-1239 50 3 3 29 29 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 2022.0 611.0 522 539 10.1016/j.ins.2022.08.080 0.0 AUG 2022 18 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6D5AO Green Submitted 2023-03-23 WOS:000882703500014 0
J Daneshjou, R; Wang, YR; Bromberg, Y; Bovo, S; Martelli, PL; Babbi, G; Di Lena, P; Casadio, R; Edwards, M; Gifford, D; Jones, DT; Sundaram, L; Bhat, RR; Li, XL; Pal, LR; Kundu, K; Yin, YZ; Moult, J; Jiang, YX; Pejaver, V; Pagel, KA; Li, B; Mooney, SD; Radivojac, P; Shah, S; Carraro, M; Gasparini, A; Leonardi, E; Giollo, M; Ferrari, C; Tosatto, SCE; Bachar, E; Azaria, JR; Ofran, Y; Unger, R; Niroula, A; Vihinen, M; Chang, B; Wang, MH; Franke, A; Petersen, BS; Pirooznia, M; Zandi, P; McCombie, R; Potash, JB; Altman, RB; Klein, TE; Hoskins, RA; Repo, S; Brenner, SE; Morgan, AA Daneshjou, Roxana; Wang, Yanran; Bromberg, Yana; Bovo, Samuele; Martelli, Pier L.; Babbi, Giulia; Di Lena, Pietro; Casadio, Rita; Edwards, Matthew; Gifford, David; Jones, David T.; Sundaram, Laksshman; Bhat, Rajendra Rana; Li, Xiaolin; Pal, Lipika R.; Kundu, Kunal; Yin, Yizhou; Moult, John; Jiang, Yuxiang; Pejaver, Vikas; Pagel, Kymberleigh A.; Li, Biao; Mooney, Sean D.; Radivojac, Predrag; Shah, Sohela; Carraro, Marco; Gasparini, Alessandra; Leonardi, Emanuela; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C. E.; Bachar, Eran; Azaria, Johnathan R.; Ofran, Yanay; Unger, Ron; Niroula, Abhishek; Vihinen, Mauno; Chang, Billy; Wang, Maggie H.; Franke, Andre; Petersen, Britt-Sabina; Pirooznia, Mehdi; Zandi, Peter; McCombie, Richard; Potash, James B.; Altman, Russ B.; Klein, Teri E.; Hoskins, Roger A.; Repo, Susanna; Brenner, Steven E.; Morgan, Alexander A. Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges HUMAN MUTATION English Article bipolar disorder; Crohn's disease; exomes; machine learning; phenotype prediction; warfarin INFLAMMATORY-BOWEL-DISEASE; BIPOLAR DISORDER; WIDE ASSOCIATION; GENETICS; VARIANTS; TOOL Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships. [Daneshjou, Roxana; Altman, Russ B.; Klein, Teri E.] Stanford Sch Med, Dept Genet, Stanford, CA USA; [Wang, Yanran; Bromberg, Yana] Rutgers State Univ, Dept Biochem & Microbiol, New Brunswick, NJ USA; [Bovo, Samuele; Martelli, Pier L.; Babbi, Giulia; Casadio, Rita] Univ Bologna, Biocomp Grp, BiGeA CIG, Luigi Galvani Interdept Ctr Integrated Studies Bi, Bologna, Italy; [Di Lena, Pietro] Univ Bologna, Dept Comp Sci & Engn, Biocomp Group, Bologna, Italy; [Casadio, Rita] Univ Bologna, Giorgio Prodi Interdept Ctr Canc Res, Bologna, Italy; [Edwards, Matthew; Gifford, David] MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Jones, David T.] UCL, Dept Comp Sci, Bioinformat Grp, London, England; [Sundaram, Laksshman; Bhat, Rajendra Rana; Li, Xiaolin] Univ Florida, NSF Ctr Big Learning, Large Scale Intelligent Syst Lab, Gainesville, FL USA; [Pal, Lipika R.; Kundu, Kunal; Yin, Yizhou; Moult, John] Univ Maryland, Inst Biosci & Biotechnol Res, Rockville, MD USA; [Kundu, Kunal; Yin, Yizhou] Univ Maryland, Biol Sci Grad Program, Computat Biol Bioinformat & Gen, College Pk, MD 20742 USA; [Moult, John] Univ Maryland, Dept Cell Biol & Mol Genet, College Pk, MD 20742 USA; [Jiang, Yuxiang; Pejaver, Vikas; Pagel, Kymberleigh A.; Radivojac, Predrag] Indiana Univ, Dept Comp Sci & Informat, Bloomington, IN USA; [Pejaver, Vikas; Mooney, Sean D.] Univ Washington, Dept Biomed Informat & Med Educ, Seattle, WA USA; [Li, Biao] Gilead Sci, Foster City, CA USA; [Shah, Sohela] Qiagen Bioinformat, Redwood City, CA USA; [Carraro, Marco; Gasparini, Alessandra; Giollo, Manuel; Tosatto, Silvio C. E.] Univ Padua, Dept Biomed Sci, Padua, Italy; [Gasparini, Alessandra; Leonardi, Emanuela] Univ Padua, Dept Woman & Child Hlth, Padua, Italy; [Giollo, Manuel; Ferrari, Carlo] Univ Padua, Dept Informat Engn, Padua, Italy; [Tosatto, Silvio C. E.] CNR, Inst Neurosci, Padua, Italy; [Bachar, Eran; Azaria, Johnathan R.; Ofran, Yanay; Unger, Ron] Bar Ilan Univ, Mina & Everard Goodman Fac Life Sci, Ramat Gan, Israel; [Niroula, Abhishek; Vihinen, Mauno] Lund Univ, Dept Expt Med Sci, Protein Struct & Bioinformat Grp, Lund, Sweden; [Chang, Billy; Wang, Maggie H.] Chinese Univ Hong Kong, Div Biostat, Shatin, Hong Kong, Peoples R China; [Chang, Billy; Wang, Maggie H.] Chinese Univ Hong Kong, Ctr Clin Res & Biostat, JC Sch Publ Hlth & Primary Care, Shatin, Hong Kong, Peoples R China; [Wang, Maggie H.] CUHK Shenzhen Res Inst, Shenzhen, Peoples R China; [Franke, Andre; Petersen, Britt-Sabina] Christian Albrechts Univ Kiel, Inst Clin Mol Biol, Kiel, Germany; [Pirooznia, Mehdi] Johns Hopkins Univ, Sch Med, Dept Psychiat, Baltimore, MD 21205 USA; [Zandi, Peter] Johns Hopkins Bloomberg, Sch Publ Hlth, Dept Mental Hlth, Baltimore, MD USA; [McCombie, Richard] Cold Spring Harbor Lab, New York, NY USA; [Potash, James B.] Univ Iowa, Dept Psychiat, Iowa City, IA 52242 USA; [Hoskins, Roger A.; Repo, Susanna; Brenner, Steven E.] Univ Calif Berkeley, Dept Plant & Microbial Biol, Berkeley, CA 94720 USA; [Morgan, Alexander A.] Stanford Sch Med, Stanford, CA USA Stanford University; Rutgers State University New Brunswick; University of Bologna; University of Bologna; University of Bologna; Massachusetts Institute of Technology (MIT); University of London; University College London; State University System of Florida; University of Florida; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; Indiana University System; Indiana University Bloomington; University of Washington; University of Washington Seattle; Gilead Sciences; University of Padua; University of Padua; University of Padua; Consiglio Nazionale delle Ricerche (CNR); Bar Ilan University; Lund University; Chinese University of Hong Kong; Chinese University of Hong Kong; University of Kiel; Johns Hopkins University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Cold Spring Harbor Laboratory; University of Iowa; University of California System; University of California Berkeley; Stanford University Daneshjou, R (corresponding author), Stanford Sch Med, Dept Genet, Stanford, CA USA. roxanad@stanford.edu Babbi, Giulia/AAO-4603-2020; Niroula, Abhishek/G-4865-2017; Ferrari, Carlo/AAU-8107-2021; Ray, Lipika/N-1245-2019; Young, Richard A/F-6495-2012; leonardi, emanuela/AAN-2392-2020; Franke, Andre/B-2151-2010; Pirooznia, Mehdi/AAG-1878-2019; Vihinen, Mauno/A-8452-2012; Martelli, Pier Luigi/I-8824-2012; Bromberg, Yana/AAA-7801-2020; Yu, Chen-Hsin Albert/ABG-2121-2021; Niroula, Abhishek/AAB-3998-2022; Brenner, Steven E/A-8729-2008; Tosatto, Silvio/B-2840-2009; Bovo, Samuele/M-4179-2019; Casadio, Rita/K-3747-2017; Daneshjou, Roxana/ABE-7764-2021; Casadio, Rita/K-4814-2015 Babbi, Giulia/0000-0002-9816-4737; Niroula, Abhishek/0000-0002-5904-0635; Ray, Lipika/0000-0002-3390-110X; Young, Richard A/0000-0001-8855-8647; leonardi, emanuela/0000-0001-8486-8461; Franke, Andre/0000-0003-1530-5811; Pirooznia, Mehdi/0000-0002-4210-6458; Vihinen, Mauno/0000-0002-9614-7976; Martelli, Pier Luigi/0000-0002-0274-5669; Bromberg, Yana/0000-0002-8351-0844; Yu, Chen-Hsin Albert/0000-0002-5662-3026; Niroula, Abhishek/0000-0002-5904-0635; Brenner, Steven E/0000-0001-7559-6185; Tosatto, Silvio/0000-0003-4525-7793; Bovo, Samuele/0000-0002-5712-8211; Altman, Russ/0000-0003-3859-2905; Klein, Teri/0000-0001-5527-6475; Susanna, Repo/0000-0003-3488-4767; MOONEY, SEAN D/0000-0003-2654-0833; Yin, Yizhou/0000-0002-5365-2294; Daneshjou, Roxana/0000-0001-7988-9356; Casadio, Rita/0000-0002-7462-7039; Li, Xiaolin/0000-0002-3368-159X NIH [U41 HG007446, R13 HG006650, U01 GM115486, U24 MH068457]; Informatics Research Starter grant from the PhRMA Foundation; National Science Foundation of China [81473035, 31401124]; Israeli Science Foundation [772/13] NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Informatics Research Starter grant from the PhRMA Foundation; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Israeli Science Foundation(Israel Science Foundation) Contract grant sponsors: NIH (U41 HG007446, R13 HG006650, U01 GM115486, U24 MH068457); Informatics Research Starter grant from the PhRMA Foundation; National Science Foundation of China (81473035, 31401124); Israeli Science Foundation (772/13). 40 28 28 0 17 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1059-7794 1098-1004 HUM MUTAT Hum. Mutat. SEP 2017.0 38.0 9 SI 1182 1192 10.1002/humu.23280 0.0 11 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity FD9RZ 28634997.0 Green Accepted, Green Published, Green Submitted, gold 2023-03-23 WOS:000407861100014 0
J Wei, SQ; Ji, SP; Lu, M Wei, Shiqing; Ji, Shunping; Lu, Meng Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Building extraction; fully convolutional network (FCN); polygon regularization; segmentation CONVOLUTIONAL NETWORKS; EXTRACTION This study proposes an automatic building footprint extraction framework that consists of a convolutional neural network (CNN)-based segmentation and an empirical polygon regularization that transforms segmentation maps into structured individual building polygons. The framework attempts to replace part of the manual delineation of building footprints that are involved in surveying and mapping field with algorithms. First, we develop a scale robust fully convolutional network (FCN) by introducing multiple scale aggregation of feature pyramids from convolutional layers. Two postprocessing strategies are introduced to refine the segmentation maps from the FCN. The refined segmentation maps are vectorized and polygonized. Then, we propose a polygon regularization algorithm consisting of a coarse and fine adjustment, to translate the initial polygons into structured footprints. Experiments on a large open building data set including 181 000 buildings showed that our algorithm reached a high automation level where at least 50% of individual buildings in the test area could be delineated to replace manual work. Experiments on different data sets demonstrated that our FCN-based segmentation method outperformed several most recent segmentation methods, and our polygon regularization algorithm is robust in challenging situations with different building styles, image resolutions, and even low-quality segmentation. [Wei, Shiqing; Ji, Shunping] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Lu, Meng] Univ Utrecht, Dept Phys Geog, NL-3584 CE Utrecht, Netherlands Wuhan University; Utrecht University Ji, SP (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China. wei_sq@whu.edu.cn; jishunping@whu.edu.cn; m.lu@uu.nl Lu, Meng/AAF-1711-2019 Lu, Meng/0000-0002-6850-581X; Ji, Shunping/0000-0002-3088-1481 National Key Research and Development Program of China [2018YFB0505003] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2018YFB0505003. 32 79 82 16 49 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing MAR 2020.0 58.0 3 2178 2189 10.1109/TGRS.2019.2954461 0.0 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology KU3GZ 2023-03-23 WOS:000519598700052 0
J Song, W; Wiesniak, M; Liu, N; Pawlowski, M; Lee, J; Kim, J; Bang, J Song, Wooyeong; Wiesniak, Marcin; Liu, Nana; Pawlowski, Marcin; Lee, Jinhyoung; Kim, Jaewan; Bang, Jeongho Tangible reduction in learning sample complexity with large classical samples and small quantum system QUANTUM INFORMATION PROCESSING English Article Quantum machine learning; Probably Approximately Correct (PAC) learning; Classical-quantum hybrid query; Sample complexity DNF Quantum computation requires large classical datasets to be embedded into quantum states in order to exploit quantum parallelism. However, this embedding requires considerable resources in general. It would therefore be desirable to avoid it, if possible, for noisy intermediate-scale quantum (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows large classical input data, with a relatively small-scale quantum system. This hybrid architecture is used to implement a sampling oracle. It is shown that in the presence of noise in the hybrid oracle, the effects of internal noise can cancel each other out and thereby improve the query success rate. It is also shown that such an immunity of the hybrid oracle to noise directly and tangibly reduces the sample complexity in the framework of computational learning theory. This NISQ-compatible learning advantage is attributed to the oracle's ability to handle large input features. [Song, Wooyeong; Lee, Jinhyoung] Hanyang Univ, Dept Phys, Seoul 04763, South Korea; [Wiesniak, Marcin] Univ Gdansk, Fac Math Phys & Informat, Inst Theoret Phys & Astrophys, Ul Wita Stwosza 57, PL-80306 Gdansk, Poland; [Wiesniak, Marcin; Pawlowski, Marcin] Univ Gdansk, Int Ctr Theory Quantum Technol, Ul Wita Stwosza 63, PL-80308 Gdansk, Poland; [Liu, Nana] Shanghai Jiao Tong Univ, Inst Nat Sci, Shanghai 200240, Peoples R China; [Kim, Jaewan] Korea Inst Adv Study, Sch Computat Sci, Seoul 02455, South Korea; [Bang, Jeongho] Elect & Telecommun Res Inst, Daejeon 34129, South Korea; [Song, Wooyeong] Korea Inst Sci & Technol KIST, Ctr Quantum Informat, Seoul 02792, South Korea; [Liu, Nana] Shanghai Jiao Tong Univ, Key Lab Sci & Engn Comp, Minist Educ, Shanghai 200240, Peoples R China; [Liu, Nana] Univ Michigan Shanghai Jiao Tong Univ Joint Inst, Shanghai 200240, Peoples R China Hanyang University; Fahrenheit Universities; University of Gdansk; Fahrenheit Universities; University of Gdansk; Shanghai Jiao Tong University; Korea Institute for Advanced Study (KIAS); Electronics & Telecommunications Research Institute - Korea (ETRI); Korea Institute of Science & Technology (KIST); Shanghai Jiao Tong University; Shanghai Jiao Tong University Lee, J (corresponding author), Hanyang Univ, Dept Phys, Seoul 04763, South Korea.;Kim, J (corresponding author), Korea Inst Adv Study, Sch Computat Sci, Seoul 02455, South Korea.;Bang, J (corresponding author), Elect & Telecommun Res Inst, Daejeon 34129, South Korea. hyoung@hanyang.ac.kr; jaewan@kias.re.kr; jbang@etri.re.kr Bang, Jeongho/0000-0001-9263-9394 National Research Foundation of Korea (NRF) - MSIP (Ministry of Science, ICT and Future Planning) of the Korea government [2019R1A2C2005504, NRF-2019M3E4A1079666, 2021M3E4A1038213]; Institute of Information and Communications Technology Planning and Evaluation - Korea government [2020-0-00890]; research project on developing quantum machine learning and quantum algorithm by ETRI affiliated research institute [2018-104]; KIST research program [2E31021]; Shanghai Pujiang Talent Grant [20PJ1408400]; NSFC International Young Scientists Project [12050410230]; Innovation Program of the Shanghai Municipal Education Commission [2021-01-07-00-02-E00087]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0102]; Natural Science Foundation of Shanghai [21ZR1431000]; EU [2018/MAB/5]; NCN [2015/19/B/ST2/01999, 2017/26/E/ST2/01008]; FNP [First Team/2016-1/5]; KIAS Advanced Research Program [CG014604]; KIAS Individual Grant [CG061003] National Research Foundation of Korea (NRF) - MSIP (Ministry of Science, ICT and Future Planning) of the Korea government(National Research Foundation of Korea); Institute of Information and Communications Technology Planning and Evaluation - Korea government; research project on developing quantum machine learning and quantum algorithm by ETRI affiliated research institute; KIST research program(Korea Institute of Science & Technology (KIST)); Shanghai Pujiang Talent Grant; NSFC International Young Scientists Project; Innovation Program of the Shanghai Municipal Education Commission(Innovation Program of Shanghai Municipal Education Commission); Shanghai Municipal Science and Technology Major Project; Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); EU(European Commission); NCN; FNP(Foundation for Polish Science); KIAS Advanced Research Program; KIAS Individual Grant W.S., N.L., and J.B. are grateful to Gahyun Choi and Yonuk Chong for the valuable discussions on superconducting-qubit experiments. W.S., J.L., and J.B. acknowledge the financial support of the National Research Foundation of Korea (NRF) Grants (No. 2019R1A2C2005504, No. NRF-2019M3E4A1079666, and No. 2021M3E4A1038213), funded by the MSIP (Ministry of Science, ICT and Future Planning) of the Korea government. J.B. also acknowledge the support of the Institute of Information and Communications Technology Planning and Evaluation Grant funded by the Korea government (Grant No. 2020-0-00890, Development of trusted node core and interfaces for the interoperability among QKD protocols ). W.S. and J.B. acknowledge the research project on developing quantum machine learning and quantum algorithm (No. 2018-104) by the ETRI affiliated research institute. W.S. acknowledge the KIST research program (2E31021). N. L. acknowledges funding from the Shanghai Pujiang Talent Grant (no. 20PJ1408400) and the NSFC International Young Scientists Project (no. 12050410230). N. L. is also supported by the Innovation Program of the Shanghai Municipal Education Commission (no. 2021-01-07-00-02-E00087), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102) and the Natural Science Foundation of Shanghai grant 21ZR1431000. M.W. and M.P. acknowledge the ICTQT IRAP project of FNP (Contract No. 2018/MAB/5), financed by structural funds of EU. M.W. was supported by NCN Grants 2015/19/B/ST2/01999 and 2017/26/E/ST2/01008. M.P. was supported under FNP Grant First Team/2016-1/5. J.K. was supported in part by KIAS Advanced Research Program (No. CG014604). J.B. was supported by a KIAS Individual Grant (No. CG061003). 37 0 0 1 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1570-0755 1573-1332 QUANTUM INF PROCESS Quantum Inf. Process. AUG 2021.0 20.0 8 275 10.1007/s11128-021-03217-7 0.0 18 Quantum Science & Technology; Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics UE1SL Green Submitted 2023-03-23 WOS:000687675900001 0
J Ye, T; Zhang, J; Zhang, ZH; Zhang, Y; Zhou, FQ; Gao, XZ Ye Tao; Zhang Jun; Zhang Zhi-hao; Zhang Yi; Zhou Fu-qiang; Gao Xiao-zhi Fault detection of train mechanical parts using multi-mode aggregation feature enhanced convolution neural network INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Attention-guided detail feature enhancement module; CIoU Loss; Receptive field enhancement module; Two-step adjustment structure INSPECTION Faults in train mechanical parts pose a significant safety hazard to railway transportation. Although some image detection methods have replaced manual fault detection of train mechanical parts, the detection effect on small mechanical parts under low illumination conditions is not ideal. To improve the accuracy and efficiency of the detection of train faults under different environments, we propose a multi-mode aggregation feature enhanced network (MAFENet) based on a single-stage detector (SSD). This network uses the idea of a two-step adjustment structure from coarse to fine and uses the K-means algorithm to design anchors. The receptive field enhancement module (RFEM) is designed to obtain the fusion features of different receptive fields. The attention-guided detail feature enhancement module (ADEM) is designed to complement the detailed features of deep-level feature maps. Meanwhile, the complete intersection over union (CIoU) loss is used to obtain more accurate bounding boxes. The experimental results on the train mechanical parts fault (TMPF) dataset showed that the detection performance of MAFENet is better than those of other SSD models. MAFENet with an input size of 320 x 320 pixels can achieve a mean average precision (mAP) of 0.9787 and a detection speed of 33 frames per second (FPS), which indicates that it can realize real-time detection, has good robustness to images under different environmental conditions, and can be used to improve the efficiency of the detection of faulty train parts. [Ye Tao; Zhang Jun; Zhang Yi] China Univ Min & Technol, Sch Mech & Informat Engn, Ding 11 Xueyuan Rd, Beijing 100083, Peoples R China; [Ye Tao; Zhang Jun] State Key Lab Coal Min & Clean Utilizat, Beijing 100083, Peoples R China; [Ye Tao; Zhang Jun] Minist Emergency Management, Key Lab Intelligent Min & Robot, Beijing 100083, Peoples R China; [Zhang Zhi-hao] China Acad Aerosp Elect Technol, China Aerosp Sci & Technol Corp Ltd, Unmanned Syst Dept, Acad 9, Beijing 100094, Peoples R China; [Zhou Fu-qiang] Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China; [Gao Xiao-zhi] Univ Eastern Finland, Dept Elect Engn & Automat, Kuopio, Finland China University of Mining & Technology; Beihang University; University of Eastern Finland Ye, T (corresponding author), China Univ Min & Technol, Sch Mech & Informat Engn, Ding 11 Xueyuan Rd, Beijing 100083, Peoples R China.;Ye, T (corresponding author), State Key Lab Coal Min & Clean Utilizat, Beijing 100083, Peoples R China.;Ye, T (corresponding author), Minist Emergency Management, Key Lab Intelligent Min & Robot, Beijing 100083, Peoples R China. ayetao198715@163.com National Natural Science Foundation of China [52075027]; Fundamental Research Funds for the Central Universities [2020XJJD03]; State Key Laboratory of Coal Mining and Clean Utilization, China [2021-CMCU-KF012] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Key Laboratory of Coal Mining and Clean Utilization, China We thank China University of Mining and Technology (Beijing) for providing the experimental hardware platform. This work was supported by the National Natural Science Foundation of China (No. 52075027), the Fundamental Research Funds for the Central Universities (2020XJJD03), and the State Key Laboratory of Coal Mining and Clean Utilization, China (2021-CMCU-KF012). 37 2 2 5 12 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. JUN 2022.0 13.0 6 1781 1794 10.1007/s13042-021-01488-1 0.0 JAN 2022 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0U0EG 2023-03-23 WOS:000739230500001 0
J Meng, G; Rasmussen, SK; Christensen, CSL; Fan, WY; Torp, AM Meng, Geng; Rasmussen, Soren K.; Christensen, Cecilie S. L.; Fan, Weiyao; Torp, Anna Maria Molecular breeding of barley for quality traits and resilience to climate change FRONTIERS IN GENETICS English Review anthocyanin; lignin; grain protein; Hordeum vulgare; mutation; seed; food; feed GENOME-WIDE ASSOCIATION; HORDEUM-VULGARE L.; DIHYDROFLAVONOL REDUCTASE; ANTHOCYANIN BIOSYNTHESIS; TRANSCRIPTION FACTOR; GENE FAMILY; MYB DOMAIN; LIGNIN; GRAIN; ACCUMULATION Barley grains are a rich source of compounds, such as resistant starch, beta-glucans and anthocyanins, that can be explored in order to develop various products to support human health, while lignocellulose in straw can be optimised for feed in husbandry, bioconversion into bioethanol or as a starting material for new compounds. Existing natural variations of these compounds can be used to breed improved cultivars or integrated with a large number of mutant lines. The technical demands can be in opposition depending on barley's end use as feed or food or as a source of biofuel. For example beta-glucans are beneficial in human diets but can lead to issues in brewing and poultry feed. Barley breeders have taken action to integrate new technologies, such as induced mutations, transgenics, marker-assisted selection, genomic selection, site-directed mutagenesis and lastly machine learning, in order to improve quality traits. Although only a limited number of cultivars with new quality traits have so far reached the market, research has provided valuable knowledge and inspiration for future design and a combination of methodologies to achieve the desired traits. The changes in climate is expected to affect the quality of the harvested grain and it is already a challenge to mitigate the unpredictable seasonal and annual variations in temperature and precipitation under elevated [CO2] by breeding. This paper presents the mutants and encoded proteins, with a particular focus on anthocyanins and lignocellulose, that have been identified and characterised in detail and can provide inspiration for continued breeding to achieve desired grain and straw qualities. [Meng, Geng; Rasmussen, Soren K.; Christensen, Cecilie S. L.; Fan, Weiyao; Torp, Anna Maria] Univ Copenhagen, Dept Plant & Environm Sci, Frederiksberg, Denmark; [Meng, Geng] Henan Agr Univ, Coll Hort, Zhengzhou, Peoples R China University of Copenhagen; Henan Agricultural University Rasmussen, SK (corresponding author), Univ Copenhagen, Dept Plant & Environm Sci, Frederiksberg, Denmark. skr@plen.ku.dk Rasmussen, Soren K./F-6728-2014 Rasmussen, Soren K./0000-0001-6169-2504 102 0 0 3 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. JAN 5 2023.0 13.0 1039996 10.3389/fgene.2022.1039996 0.0 8 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity 8C3LG 36685930.0 Green Accepted, gold 2023-03-23 WOS:000917513500001 0
J Zhang, WQ; Luo, GP; Chen, CB; Ochege, FU; Hellwich, O; Zheng, HW; Hamdi, R; Wu, SX Zhang, Wenqiang; Luo, Geping; Chen, Chunbo; Ochege, Friday U.; Hellwich, Olaf; Zheng, Hongwei; Hamdi, Rafiq; Wu, Shixin Quantifying the contribution of climate change and human activities to biophysical parameters in an arid region ECOLOGICAL INDICATORS English Article Climate change; Human activities; Biophysical parameters; The Northern slope of the Tianshan Mountains; Random Forest model; Machine learning SURFACE-TEMPERATURE; TIANSHAN MOUNTAINS; VEGETATION COVER; NORTHERN SLOPE; RIVER-BASIN; MODIS; REGRESSION; EVAPOTRANSPIRATION; COEFFICIENT; PRODUCTS Quantifying the variation of biophysical parameters and their driving mechanisms is essential for monitoring land surface environmental changes and for understanding the land-atmosphere interaction in the arid region. Due to the complexity of human activities, most researches are limited to climate change, whereas the response analysis of human activities to changes in biophysical parameters are still lacking or not comprehensively considered. Therefore, large biases and uncertainties still exist in the estimates of regional responses to global change. Firstly, we specifically quantified the main human activities related to land use/land cover change (LULCC) in the northern Tianshan Mountains (NTM), and identified the spatiotemporal changes of primary biophysical parameters, including Albedo, leaf area index (LAI), land surface temperature (LST), and Normalized Difference Vegetation Index (NDVI). Then, we tested the performance of the five models used, including multiple linear regression (MLR), random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), and K-nearest neighbor (KNN). RF outperformed others and was used to quantify and disaggregate the contribution of climate change and human activities to land surface parameters in the NTM. We found a strong spatial heterogeneity in the spatial variation of all biophysical parameters. Except for LST, the annual maximum Albedo, LAI, and NDVI showed a significant increasing trend in the NTM from 2000 to 2019 (p < 0.05). Generally, climate change contributed more to the biophysical parameters than human activities. However, the contribution of human activities to NDVI was 0.51, which was greater than that of climate change during 2000-2015. This study provides new insight on the impact of climate change and human activities on biophysical parameters and a scientific basis for model parameterization in the arid region. [Zhang, Wenqiang; Luo, Geping; Chen, Chunbo; Ochege, Friday U.; Zheng, Hongwei; Hamdi, Rafiq; Wu, Shixin] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Zhang, Wenqiang; Luo, Geping; Ochege, Friday U.; Zheng, Hongwei] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Luo, Geping] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Peoples R China; [Hellwich, Olaf] Tech Univ Berlin, Dept Comp Vis & Remote Sensing, D-10587 Berlin, Germany; [Hamdi, Rafiq] Royal Meteorol Inst, B-1180 Brussels, Belgium; [Hamdi, Rafiq] Univ Ghent, Dept Phys & Astron, B-9000 Ghent, Belgium Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Technical University of Berlin; Royal Meteorological Institute of Belgium; Ghent University Luo, GP; Wu, SX (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China. luogp@ms.xjb.ac.cn; wushixin@ms.xjb.ac.cn Ochege, Friday Uchenna/AAF-6958-2021; hamdi, Rafiq/AAA-5719-2022 Ochege, Friday Uchenna/0000-0002-6661-0966; zhang, wen qiang/0000-0001-7316-6456 High-End Foreign Experts Project; National Natural Science Foundation of China [41877012, 41671108]; Chinese Academy of Sciences [2018-YDYLTD-002] High-End Foreign Experts Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences(Chinese Academy of Sciences) This work was supported by High-End Foreign Experts Project; the National Natural Science Foundation of China under Grant [grant numbers: No. 41877012 and 41671108]; the team project of the Chinese Academy of Sciences under Grant [grant numbers: 2018-YDYLTD-002]. 61 4 4 12 50 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1470-160X 1872-7034 ECOL INDIC Ecol. Indic. OCT 2021.0 129.0 107996 10.1016/j.ecolind.2021.107996 0.0 JUL 2021 13 Biodiversity Conservation; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology TV4LL gold 2023-03-23 WOS:000681693800006 0
J Li, GQ; Zhang, M; Wang, JY; Weng, DP; Corporaal, H Li, Guoqing; Zhang, Meng; Wang, Jiuyang; Weng, Dongpeng; Corporaal, Henk SCWC: Structured channel weight sharing to compress convolutional neural networks INFORMATION SCIENCES English Article Convolutional neural network; Image classification; Network compression; Kernel sharing Convolutional neural networks (CNNs) have surpassed humans in many computer vision areas. However, the redundancy of CNNs inhibits its application in the embedded device. In this paper, a simple shared channel weight convolution (SCWC) approach is proposed to reduce the number of parameters. Multiplication is much more complex than accumulation, so reducing multiplication is more significant than reducing accumulation in CNNs. The proposed SCWC can reduce the number of multiplications by the distributive property due to the structured channel parameter sharing. Furthermore, the fully trained CNN can be directly compressed without tedious training from scratch. To evaluate the performance of the proposed SCWC, five competitive benchmark datasets for image classification and object detection, CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011 and PASCAL VOC are adopted. Experiments demonstrate that the proposed SCWC can reduce about 50% of parameters and multiplications for ResNet50 with only 0.13% accuracy loss, and reduce about 70% of parameters and multiplications for VGG16 with 1.64% accuracy loss on ImageNet, which is better than many other pruning methods for CNNs. The accuracy loss is very small because of the soft parameter sharing. Moreover, the proposed SCWC is also effective for object detection and fine-grained image classification tasks.(c) 2021 Elsevier Inc. All rights reserved. [Li, Guoqing; Zhang, Meng] Southeast Univ, Sch Elect Sci & Engn, Natl ASIC Res Ctr, Nanjing 210096, Peoples R China; [Wang, Jiuyang; Weng, Dongpeng] Southeast Univ, Sch Microelect, Wuxi 214000, Jiangsu, Peoples R China; [Corporaal, Henk] Eindhoven Univ Technol, Dept Elect Engn, Elect Syst Grp, POB 513, NL-5600 MB Eindhoven, Netherlands Southeast University - China; Southeast University - China; Eindhoven University of Technology Zhang, M (corresponding author), Southeast Univ, Sch Elect Sci & Engn, Natl ASIC Res Ctr, Nanjing 210096, Peoples R China. zmeng@seu.edu.cn National Key R&D Program of China [2018YFB2202703]; Key R&D Program of Guangdong Province [2021B1101270006]; Natural Science Foundation of Jiangsu Province [BK20201145] National Key R&D Program of China; Key R&D Program of Guangdong Province; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) Acknowledgments This research work was partly supported by National Key R&D Program of China (Project No. 2018YFB2202703) and the Key R&D Program of Guangdong Province (Project No. 2021B1101270006) , and the Natural Science Foundation of Jiangsu Province (Project No. BK20201145) . 49 3 3 4 9 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. MAR 2022.0 587.0 82 96 10.1016/j.ins.2021.12.020 0.0 15 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZV7DZ 2023-03-23 WOS:000770687500005 0
J Cao, HL; Peng, JH; Fan, J; Yong, WY; Wu, DC Cao, Hailin; Peng, Junhui; Fan, Jin; Yong, Wai Yan; Wu, Decheng Gradient-Guided Attentional Network for Radio Transient Localization With the Cluster-Feed Telescope IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Location awareness; Radio astronomy; Feeds; Transient analysis; Telescopes; Computed tomography; Three-dimensional displays; 3-D focal field distribution (3DFFD); attentional network; localization of radio transients (RTs); single-dish radio telescope; wide field of view (FoV) Large, single-dish radio telescopes with high sensitivities are ideal for detecting faint radio transients (RTs). However, single-dish radio telescopes possess a limited angular resolution, which limits their accuracy in localizing objects. In this article, we propose to improve the localization accuracy of the RT by exploring the 3-D focal field distributions (3DFFDs) of the dish reflector with a gradient-guided attentional network (GGAN). The LSTM-based attention block of the GGAN achieves the task-oriented adaptive recalibration of 3DFFD features by exploring the significant properties and spatial dependencies of 3DFFD. In addition, a gradient-guided approach is being developed to improve the attention block performance under varying incident angles. The proposed attention mechanism is applied to the convolutional neural network in order to reconstruct 3DFFDs and perceive RT positions based on the reconstructed results. Simulation results indicate that the technique can enable the precise localization of RTs. Moreover, the proposed solution improves the telescope's instantaneous field of view (FOV) compared to a sky survey with the traditional cluster feed telescope. [Cao, Hailin; Peng, Junhui] Chongqing Univ, Chongqing Key Lab Space Informat Network & Intell, Chongqing 400044, Peoples R China; [Fan, Jin] Natl Astron Observ China, Key Lab Radio Astron, Beijing 100012, Peoples R China; [Yong, Wai Yan] Univ Twente, Dept Elect Engn, NL-7522 NB Enschede, Netherlands; [Wu, Decheng] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing 400065, Peoples R China Chongqing University; Chinese Academy of Sciences; National Astronomical Observatory, CAS; University of Twente; Chongqing University of Posts & Telecommunications Fan, J (corresponding author), Natl Astron Observ China, Key Lab Radio Astron, Beijing 100012, Peoples R China. hailincao@cqu.edu.cn; junhuipeng@cqu.edu.cn; jfan@bao.ac.cn; w.y.yongwaiyan@utwente.nl; wudc@cqupt.edu.cn Cao, Hailin/0000-0002-7944-3327; /0000-0001-7317-1634; Yong, Wai Yan/0000-0002-8902-4855 National Natural Science Foundation of China (NSFC) [51877015, U1831117, U1931129, U20A20157, 11403054, 11611130023]; Natural Science Foundation of Chongqing [cstc2021jcyj-bsh0198]; Fundamental Research Funds for the Central Universities [106112017CDJXSYY0002] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Chongqing(Natural Science Foundation of Chongqing); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 51877015, Grant U1831117, Grant U1931129, Grant U20A20157, Grant 11403054, and Grant 11611130023; in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-bsh0198; and in part by the Fundamental Research Funds for the Central Universities under Project 106112017CDJXSYY0002. 31 1 1 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 4601511 10.1109/TGRS.2022.3167762 0.0 11 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 1D7GB 2023-03-23 WOS:000793964300006 0
J Ding, T; Li, C; Huang, C; Yang, YH; Li, FX; Blaabjerg, F Ding, Tao; Li, Cheng; Huang, Can; Yang, Yongheng; Li, Fangxing; Blaabjerg, Frede A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting IEEE SYSTEMS JOURNAL English Article Curve fitting; decomposition; hierarchical modeling; joint networks; reactive power optimization PARTICLE SWARM OPTIMIZATION; VOLTAGE CONTROL; SYSTEM; DECOMPOSITION; ALGORITHM In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master-slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second, the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods. [Ding, Tao; Li, Cheng] Xi An Jiao Tong Univ, State Key Lab Elect Insulat & Power Equipment, Sch Elect Engn, Xian 710049, Peoples R China; [Huang, Can] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA; [Yang, Yongheng; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark; [Li, Fangxing] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA Xi'an Jiaotong University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; Aalborg University; University of Tennessee System; University of Tennessee Knoxville Ding, T (corresponding author), Xi An Jiao Tong Univ, State Key Lab Elect Insulat & Power Equipment, Sch Elect Engn, Xian 710049, Peoples R China. tding15@mail.xjtu.edu.cn; jack-cleebest@hotmail.com; can7huang@gmail.com; yoy@et.aau.dk; fli6@utk.edu; fbl@et.aau.dk Blaabjerg, Frede/A-5008-2008; Yang, Yongheng/N-7735-2015; Huang, Can/N-9205-2016; Li, Fangxing/E-6023-2013 Blaabjerg, Frede/0000-0001-8311-7412; Yang, Yongheng/0000-0002-1488-4762; Huang, Can/0000-0002-5159-4908; Li, Fangxing/0000-0003-1060-7618 National Key Research and Development Program of China [2016YFB0901904]; National Natural Science Foundation of China [51607137]; China Postdoctoral Science Foundation [2015M580847]; Natural Science Basis Research Plan in Shaanxi Province of China [2016JQ5015]; Project of the State Key Laboratory of Electrical Insulation; Power Equipment in Xi'an Jiaotong University [EIPE16301]; U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344, LLNL-JRNL-716920] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Basis Research Plan in Shaanxi Province of China; Project of the State Key Laboratory of Electrical Insulation; Power Equipment in Xi'an Jiaotong University; U.S. Department of Energy by Lawrence Livermore National Laboratory(United States Department of Energy (DOE)) This work was supported in part by the National Key Research and Development Program of China (2016YFB0901904), in part by the National Natural Science Foundation of China under Grant 51607137, in part by the China Postdoctoral Science Foundation (2015M580847), in part by the Natural Science Basis Research Plan in Shaanxi Province of China (2016JQ5015), in part by the Project of the State Key Laboratory of Electrical Insulation, and in part by the Power Equipment in Xi'an Jiaotong University (EIPE16301). This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 with Release Number LLNL-JRNL-716920. 35 56 62 2 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-8184 1937-9234 IEEE SYST J IEEE Syst. J. SEP 2018.0 12.0 3 2739 2748 10.1109/JSYST.2016.2642218 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Operations Research & Management Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science; Telecommunications GR9EP Green Submitted, Green Published 2023-03-23 WOS:000443049900066 0
J Liu, XM; Tong, X; Zhu, J; Tian, L; Jie, ZY; Zou, YQ; Lin, XQ; Liang, HW; Li, WX; Ju, YM; Qin, YW; Zou, LY; Lu, HR; Zhu, SD; Jin, X; Xu, X; Yang, HM; Wang, J; Zong, Y; Liu, WB; Hou, Y; Jia, HJ; Zhang, T Liu, Xiaomin; Tong, Xin; Zhu, Jie; Tian, Liu; Jie, Zhuye; Zou, Yuanqiang; Lin, Xiaoqian; Liang, Hewei; Li, Wenxi; Ju, Yanmei; Qin, Youwen; Zou, Leying; Lu, Haorong; Zhu, Shida; Jin, Xin; Xu, Xun; Yang, Huanming; Wang, Jian; Zong, Yang; Liu, Weibin; Hou, Yong; Jia, Huijue; Zhang, Tao Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome CELL DISCOVERY English Article HUMAN GUT MICROBIOME; READ ALIGNMENT; HOST GENETICS; SERVICE; AGE The oral microbiota contains billions of microbial cells, which could contribute to diseases in many body sites. Challenged by eating, drinking, and dental hygiene on a daily basis, the oral microbiota is regarded as highly dynamic. Here, we report significant human genomic associations with the oral metagenome from more than 1915 individuals, for both the tongue dorsum (n = 2017) and saliva (n = 1915). We identified five genetic loci associated with oral microbiota at study-wide significance (p < 3.16 x 10(-11)). Four of the five associations were well replicated in an independent cohort of 1439 individuals: rs1196764 at APPL2 with Prevotella jejuni, Oribacterium uSGB 3339 and Solobacterium uSGB 315; rs3775944 at the serum uric acid transporter SLC2A9 with Oribacterium uSGB 1215, Oribacterium uSGB 489 and Lachnoanaerobaculum umeaense; rs4911713 near OR11H1 with species F0422 uSGB 392; and rs36186689 at LOC105371703 with Eggerthia. Further analyses confirmed 84% (386/455 for tongue dorsum) and 85% (391/466 for saliva) of host genome-microbiome associations including six genome-wide significant associations mutually validated between the two niches. As many of the oral microbiome-associated genetic variants lie near miRNA genes, we tentatively validated the potential of host miRNAs to modulate the growth of specific oral bacteria. Human genetics accounted for at least 10% of oral microbiome compositions between individuals. Machine learning models showed that polygenetic risk scores dominated over oral microbiome in predicting risk of dental diseases such as dental calculus and gingival bleeding. These findings indicate that human genetic differences are one explanation for a stable or recurrent oral microbiome in each individual. [Liu, Xiaomin; Tong, Xin; Zhu, Jie; Tian, Liu; Jie, Zhuye; Zou, Yuanqiang; Lin, Xiaoqian; Liang, Hewei; Li, Wenxi; Ju, Yanmei; Qin, Youwen; Zou, Leying; Zhu, Shida; Jin, Xin; Xu, Xun; Yang, Huanming; Wang, Jian; Zong, Yang; Liu, Weibin; Hou, Yong; Jia, Huijue; Zhang, Tao] BGI Shenzhen, Shenzhen, Guangdong, Peoples R China; [Liu, Xiaomin; Ju, Yanmei] Univ Chinese Acad Sci, Coll Life Sci, Beijing, Peoples R China; [Jie, Zhuye; Zou, Yuanqiang; Zhang, Tao] Univ Copenhagen, Dept Biol, Univ Pk 13, Copenhagen, Denmark; [Zou, Yuanqiang] BGI Shenzhen, Qingdao Europe Adv Inst Life Sci, Qingdao, Shandong, Peoples R China; [Liang, Hewei; Li, Wenxi] South China Univ Technol, Sch Biosci & Biotechnol, Guangzhou, Guangdong, Peoples R China; [Lu, Haorong] BGI Shenzhen, China Natl Genebank, Shenzhen, Guangdong, Peoples R China; [Yang, Huanming; Wang, Jian] James D Watson Inst Genome Sci, Hangzhou, Zhejiang, Peoples R China; [Jia, Huijue] BGI Shenzhen, Shenzhen Key Lab Human Commensal Microorganisms &, Shenzhen, Guangdong, Peoples R China Beijing Genomics Institute (BGI); Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Copenhagen; Beijing Genomics Institute (BGI); South China University of Technology; Beijing Genomics Institute (BGI); Beijing Genomics Institute (BGI) Liu, XM; Jia, HJ; Zhang, T (corresponding author), BGI Shenzhen, Shenzhen, Guangdong, Peoples R China.;Liu, XM (corresponding author), Univ Chinese Acad Sci, Coll Life Sci, Beijing, Peoples R China. liuxiaomin@genomics.cn; jiahuijue@genomics.cn; tao.zhang@genomics.cn Jia, Huijue/M-3991-2015; Zou, leying/GPS-7329-2022; Qin, Youwen/AAY-1194-2021 Jia, Huijue/0000-0002-3592-126X; Qin, Youwen/0000-0003-3956-4711; Liu, Xiaomin/0000-0003-2155-3627; Zhang, Tao/0000-0003-2765-2802 China National Gene Bank China National Gene Bank We sincerely thank the support provided by the China National Gene Bank. We thank all the volunteers for their time and for self-collecting the oral samples using our kit. 66 10 10 7 19 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2056-5968 CELL DISCOV Cell Discov. DEC 7 2021.0 7.0 1 117 10.1038/s41421-021-00356-0 0.0 16 Cell Biology Science Citation Index Expanded (SCI-EXPANDED) Cell Biology XK2JI 34873157.0 Green Submitted, Green Accepted, gold 2023-03-23 WOS:000727297600001 0
J Song, JK; Guo, YY; Gao, LL; Li, XL; Hanjalic, A; Shen, HT Song, Jingkuan; Guo, Yuyu; Gao, Lianli; Li, Xuelong; Hanjalic, Alan; Shen, Heng Tao From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Recurrent neural network (RNN); uncertainty; video captioning PERSON REIDENTIFICATION; IMAGE Video captioning, in essential, is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, and so on. In this paper, we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multimodal stochastic recurrent neural networks (MS-RNNs), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning and generate multiple sentences to describe a video considering different random factors. Specifically, a multimodal long short-term memory (LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging data sets, microsoft video description and microsoft research videoto-text, show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks. [Song, Jingkuan; Guo, Yuyu; Gao, Lianli; Shen, Heng Tao] Univ Elect Sci & Technol China, Ctr Future Media, Chengdu 611731, Sichuan, Peoples R China; [Li, Xuelong] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China; [Hanjalic, Alan] Delft Univ Technol, Dept Intelligent Syst, Delft, Netherlands University of Electronic Science & Technology of China; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Delft University of Technology Gao, LL; Shen, HT (corresponding author), Univ Elect Sci & Technol China, Ctr Future Media, Chengdu 611731, Sichuan, Peoples R China. lianli.gao@uestc.edu.cn; shenhengtao@hotmail.com Li, Xuelong/ABF-3381-2020; Shen, Heng Tao/ABD-5331-2021 Li, Xuelong/0000-0002-0019-4197; song, jingkuan/0000-0002-2549-8322; Hanjalic, Alan/0000-0002-5771-2549 Fundamental Research Funds for the Central Universities [ZYGX2014J063, ZYGX2014Z007]; National Natural Science Foundation of China [61772116, 61502080, 61632007, 61602049, 61761130079]; National Key Research and Development Program of China [2018YFB1107400]; 111 Project [B17008] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; 111 Project(Ministry of Education, China - 111 Project) This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2014J063 and Grant ZYGX2014Z007, in part by the National Natural Science Foundation of China under Grant 61772116, Grant 61502080, Grant 61632007, Grant 61602049, and Grant 61761130079, in part by the National Key Research and Development Program of China under Grant 2018YFB1107400, and in part by the 111 Project under Grant B17008. 66 168 174 11 108 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. OCT 2019.0 30.0 10 3047 3058 10.1109/TNNLS.2018.2851077 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IZ6NL 30130235.0 Green Submitted 2023-03-23 WOS:000487199000014 0
J Li, L; Xia, ZQ; Hadid, A; Jiang, XY; Zhang, HX; Feng, XY Li, Lei; Xia, Zhaoqiang; Hadid, Abdenour; Jiang, Xiaoyue; Zhang, Haixi; Feng, Xiaoyi Replayed Video Attack Detection Based on Motion Blur Analysis IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Replayed video attack; motion blur analysis; 1D CNN; local similar pattern FACE SPOOFING DETECTION; IMAGE QUALITY; LIVENESS DETECTION; RECOGNITION Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. That is because the replayed fake faces contain a variety of aliveness signals, such as eye blinking and facial expression changes. Replayed video attacks occur when attackers try to invade biometric systems by presenting face videos in front of the cameras, and these videos are often launched by a liquid-crystal display (LCD) screen. Due to the smearing effects and movements of LCD, videos captured from the real and replayed fake faces present different motion blurs, which are reflected mainly in blur intensity variation and blur width. Based on these descriptions, a motion blur analysis-based method is proposed to deal with the replayed video attack problem. We first present a 1D convolutional neural network (CNN) for motion blur intensity variation description in the time domain, which consists of a serial of 1D convolutional and pooling filters. Then, a local similar pattern (LSP) feature is introduced to extract blur width. Finally, features extracted from ID CNN and LSP are fused to detect the replayed video attacks. Extensive experiments on two standard face PAD databases, i.e., relay-attack and OULU-NPU, indicate that our proposed method based on the motion blur analysis significantly outperforms the state-of-the-art methods and shows excellent generalization capability. [Li, Lei; Xia, Zhaoqiang; Hadid, Abdenour; Jiang, Xiaoyue; Zhang, Haixi; Feng, Xiaoyi] Northwestern Polytech Univ, Xian 710129, Shaanxi, Peoples R China; [Hadid, Abdenour] Univ Oulu, Oulu 90014, Finland Northwestern Polytechnical University; University of Oulu Xia, ZQ; Feng, XY (corresponding author), Northwestern Polytech Univ, Xian 710129, Shaanxi, Peoples R China. lilei_upu@mail.nwpu.edu.cn; zxia@nwpu.edu.cn; hadid@ee.oulu.fi; xjiang@nwpu.edu.cn; dennisbang@live.cn; fengxiao@nwpu.edu.cn Zhang, Haixi/HGB-7131-2022; Xia, Zhaoqiang/AAC-4021-2019 Xia, Zhaoqiang/0000-0003-0630-3339; Hadid, Abdenour/0000-0001-9092-735X; Li, Lei/0000-0003-4498-6126 National Natural Science Foundation of China [61702419]; Natural Science Basic Research Plan of Shaanxi Province of China [2018JQ6090]; Fundamental Research Funds for the Central Universities [3102015BJ(II) ZS016] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan of Shaanxi Province of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61702419, in part by the Natural Science Basic Research Plan of Shaanxi Province of China under Grant 2018JQ6090, and in part by the Fundamental Research Funds for the Central Universities under Grant 3102015BJ(II) ZS016. 69 17 19 2 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. SEP 2019.0 14.0 9 2246 2261 10.1109/TIFS.2019.2895212 0.0 16 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IA8VP Green Accepted 2023-03-23 WOS:000469836900001 0
J Huang, X; Zhu, ZR; Li, YS; Wu, B; Yang, M Huang, Xin; Zhu, Zerun; Li, Yansheng; Wu, Bo; Yang, Michael Tea Garden Detection from High-Resolution Imagery Using a Scene-Based Framework PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING English Article TIME-SERIES; CLASSIFICATION; MEGACITIES; FEATURES Tea cultivation has a long history in China, and it is one of the pillar industries of the Chinese agricultural economy. It is therefore necessary to map tea gardens for their ongoing management. However, the previous studies have relied on fieldwork to achieve this task, which is time-consuming. In this paper, we propose a framework to map tea gardens using high-resolution remotely sensed imagery, including three scene-based methods: the bag-of-visual-words (BOVW) model, supervised latent Dirichlet allocation (sLDA), and the unsupervised convolutional neural network (UCNN). These methods can develop direct and holistic semantic representations for tea garden scenes composed of multiple sub-objects, thus they are more suitable than the traditional pixel-based or object-based methods, which focus on the local characteristics of pixels or objects. In the experiments undertaken in this study, the three different methods were tested on four datasets from Longyan (Oolong tea), Hangzhou (Longjing tea), and Puer (Puer tea). All the methods achieved a good performance, both quantitatively and visually, and the UCNN outperformed the other methods. Moreover, it was found that the addition of textural features improved the accuracy of the BOVW and sr-DA models, but had no effect on the UCNN. [Huang, Xin; Li, Yansheng] Wuhan Univ, Sch Remote Sensing Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China; [Huang, Xin; Zhu, Zerun] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China; [Wu, Bo] Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330022, Jiangxi, Peoples R China; [Yang, Michael] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands Wuhan University; Wuhan University; Jiangxi Normal University; University of Twente Huang, X (corresponding author), Wuhan Univ, Sch Remote Sensing Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China.;Huang, X (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China. xhuang@whu.edu.cn Li, Yansheng/AAU-6392-2021; Yang, Michael/AAC-6698-2019 Li, Yansheng/0000-0001-8203-1246; Yang, Michael/0000-0002-0649-9987; Wu, Bo/0000-0002-8911-1625 National Natural Science Foundation of China [41522110, 41771360]; Hubei Provincial Natural Science Foundation of China [2017CFA029]; National Key Research and Development Program of China [2016YFB0501403] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hubei Provincial Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China The research was supported by the National Natural Science Foundation of China under Grants 41522110 and 41771360, the Hubei Provincial Natural Science Foundation of China under Grant 2017CFA029, and the National Key Research and Development Program of China under Grant 2016YFB0501403. 30 5 6 4 27 AMER SOC PHOTOGRAMMETRY BETHESDA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 USA 0099-1112 2374-8079 PHOTOGRAMM ENG REM S Photogramm. Eng. Remote Sens. NOV 2018.0 84.0 11 723 731 10.14358/PERS.84.11.723 0.0 9 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology HH7ZN 2023-03-23 WOS:000455950700007 0
C Wang, S; Lee, M; Hosseinalipour, S; Morabito, R; Chiang, M; Brinton, CG IEEE Wang, Su; Lee, Mengyuan; Hosseinalipour, Seyyedali; Morabito, Roberto; Chiang, Mung; Brinton, Christopher G. Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2021) IEEE INFOCOM English Proceedings Paper 40th IEEE Conference on Computer Communications (IEEE INFOCOM) MAY 10-13, 2021 ELECTR NETWORK IEEE SUM The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, while (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using this result, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and resulting offloading that maximizes FedL accuracy. Through evaluation on real-world datasets and network measurements from our IoT testbed, we find that our methodology while sampling less than 5% of all devices outperforms conventional FedL substantially both in terms of trained model accuracy and required resource utilization. [Wang, Su; Hosseinalipour, Seyyedali; Chiang, Mung; Brinton, Christopher G.] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA; [Lee, Mengyuan] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China; [Morabito, Roberto] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA; [Morabito, Roberto] Ericsson Res, Stockholm, Sweden Purdue University System; Purdue University; Purdue University West Lafayette Campus; Zhejiang University; Princeton University; Ericsson Wang, S (corresponding author), Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. wang2506@purdue.edu; mengyuan_lee@zju.edu.cn; hosseina@purdue.edu; roberto.morabito@princeton.edu; chiang@purdue.edu; cgb@purdue.edu 47 3 3 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0743-166X 978-0-7381-1281-7 IEEE INFOCOM SER 2021.0 10.1109/INFOCOM42981.2021.9488906 0.0 10 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS2JU Green Submitted 2023-03-23 WOS:000702210400239 0
C Duan, YC; Wang, YZ; Wei, JP; Kattepur, A; Du, WC Hu, X; Lin, TY; Raghavan, V; Wah, B; BaezaYates, R; Fox, G; Shahabi, C; Smith, M; Yang, Q; Ghani, R; Fan, W; Lempel, R; Nambiar, R Duan, Yucong; Wang, Yongzhi; Wei, Jinpeng; Kattepur, Ajay; Du, Wencai Constructing E-Tourism Platform Based on Service Value Broker: A Knowledge Management Perspective 2013 IEEE INTERNATIONAL CONFERENCE ON BIG DATA IEEE International Conference on Big Data English Proceedings Paper IEEE International Conference on Big Data (Big Data) OCT 06-09, 2013 Santa Clara, CA IEEE,IEEE Comp Soc,CCF,Yahoo Labs,CISCO knowledge management; service value; value added; Big Data; broker In our previous work, we have introduced various service value broker (SVB) patterns which integrate business modeling, knowledge management and economic analysis. In this paper, working towards the target of maximizing the potential usage of available resource to achieve the optimization of the satisfaction on both the service provider side and the service consumer side under the guidance of the public administrative, we propose to build the E-Tourism platform based on SVB. This paper demonstrates the mechanism for SVB based E-Tourism framework. The advantages of employing SVB include that the SVB can help to increase the value added in a realtime and balanced manner which conforms to the economical goal of both long run and short run. An experiment is shown using a personnel recommendation system. [Duan, Yucong; Du, Wencai] Hainan Univ, Haikou, Peoples R China; [Wang, Yongzhi; Wei, Jinpeng] Florida Int Univ, Miami, FL USA; [Kattepur, Ajay; Du, Wencai] INRIA Paris Rocquencouri, Paris, France Hainan University; State University System of Florida; Florida International University Duan, YC (corresponding author), Hainan Univ, Haikou, Peoples R China. duanyucong@hotmail.com; ywang032@cis.fiu.edu; weijp@cis.fiu.edu; ajay.kattepur@inria.fr; wencai@hainu.edu.cn Du, Wencai/AAY-4121-2021; Wang, Yongzhi/AAF-3917-2021; Du, Wencai/AAY-2039-2021 Du, Wencai/0000-0003-0428-0057 NSFC [61162010, 61363007]; Hainan University [KYQD1242, HDSF-A01] NSFC(National Natural Science Foundation of China (NSFC)); Hainan University This paper was supported in part by NSFC grant 61162010 and 61363007 and by Hainan University grant KYQD1242 and HDSF-A01.** represents corresponding author. 29 0 0 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2639-1589 978-1-4799-1292-6; 978-1-4799-1293-3 IEEE INT CONF BIG DA 2013.0 8 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BA0BK 2023-03-23 WOS:000330831300235 0
J Wang, XS; Zhang, GH; Wang, Y; Yang, L; Liang, ZH; Cong, FY Wang, Xiaoshuang; Zhang, Guanghui; Wang, Ying; Yang, Lin; Liang, Zhanhua; Cong, Fengyu One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG INTERNATIONAL JOURNAL OF NEURAL SYSTEMS English Article Epilepsy; seizure prediction; intracranial electroencephalogram (iEEG); convolutional neural network (CNN); channel selection EPILEPTIC SEIZURES; FEATURES; FRAMEWORK Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30 min and seizure prediction horizon (SPH) of 5 min, 98.60% accuracy, 98.85% sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60 min and SPH of 5 min, 98.32% accuracy, 98.48% sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance. [Wang, Xiaoshuang; Zhang, Guanghui; Cong, Fengyu] Dalian Univ Technol, Fac Elect Informat & Elect, Sch Biomed Engn, Dalian 116024, Peoples R China; [Wang, Xiaoshuang; Zhang, Guanghui; Cong, Fengyu] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland; [Wang, Ying; Yang, Lin; Liang, Zhanhua] DaLian Med Univ, Affiliated Hosp 1, Dept Neurol & Psychiat, Dalian, Peoples R China; [Cong, Fengyu] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Artificial Intelligence, Dalian 116024, Peoples R China; [Cong, Fengyu] Dalian Univ Technol, Key Lab Integrated Circuit & Biomed Elect Syst Li, Dalian 116024, Peoples R China Dalian University of Technology; University of Jyvaskyla; Dalian Medical University; Dalian University of Technology; Dalian University of Technology Cong, FY (corresponding author), Dalian Univ Technol, Fac Elect Informat & Elect, Sch Biomed Engn, Dalian 116024, Peoples R China.;Cong, FY (corresponding author), Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland.;Liang, ZH (corresponding author), DaLian Med Univ, Affiliated Hosp 1, Dept Neurol & Psychiat, Dalian, Peoples R China.;Cong, FY (corresponding author), Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Artificial Intelligence, Dalian 116024, Peoples R China.;Cong, FY (corresponding author), Dalian Univ Technol, Key Lab Integrated Circuit & Biomed Elect Syst Li, Dalian 116024, Peoples R China. zhanhualiang@163.com; cong@dlut.edu.cn Cong, Fengyu/0000-0003-0058-2429 National Natural Science Foundation of China [9174 8105]; National Foundation in China [JCKY 2019110B009, 2020-JCJQ-JJ-252]; China Scholarship Council [201806060166, 201806060168]; Fundamental Research Funds for the Central Universities [DUT20LAB303, DUT20LAB308]; Dalian University of Technology in China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Foundation in China; China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Dalian University of Technology in China This work was supported by National Natural Science Foundation of China (Grant No. 9174 8105), National Foundation in China (Nos. JCKY 2019110B009 and 2020-JCJQ-JJ-252), the scholarships from China Scholarship Council (Nos. 201806060166 and 201806060168) and the Fundamental Research Funds for the Central Universities [DUT20LAB303 and DUT20LAB308] in Dalian University of Technology in China. This study is to memorize Prof. Tapani Ristaniemi for his great help to the authors, Xiaoshuang Wang, Guanghui Zhang, Ying Wang, Lin Yang, Zhanhua Liang and Fengyu Cong. 64 4 4 11 53 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0129-0657 1793-6462 INT J NEURAL SYST Int. J. Neural Syst. FEB 2022.0 32.0 2 2150048 10.1142/S0129065721500489 0.0 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science YK2ST 34635034.0 Green Accepted 2023-03-23 WOS:000745070100005 0
J Pan, S; Pei, F; Wei, YX; Wang, HW; Liu, JP; Zhang, XX; Li, GQ; Gu, YX Pan, Song; Pei, Fei; Wei, Yixuan; Wang, Hongwei; Liu, Jiaping; Zhang, Xingxing; Li, Guoqing; Gu, Yaxiu Design and experimental study of a novel air conditioning system using evaporative condenser at a subway station in Beijing, China SUSTAINABLE CITIES AND SOCIETY English Article Air conditioning; Performance evaluation; Energy conservation; Evaporative condenser; Metro station ARTIFICIAL NEURAL-NETWORK; VENTILATION PERFORMANCE; COOLING PERFORMANCE; COOLED CONDENSER; HEAT; SIMULATIONS; IMPROVEMENT; STRATEGIES; TUNNEL; MODEL Air conditioning system (AC) contributes significantly to the energy consumption of underground metros. In China, most metro stations are designed with water-cooling centralized air conditioning (WC-AC) system, it has been found that several serious problems are brought by this conventional system, such as large space occupying, water leaking, cooling tower noise and low system efficiency. In order to solve these problems, a novel energy-efficient AC system incorporating an independent evaporative condenser (EC) has been proposed and installed at Eutong metro station in Beijing, China. A series of pilot measurements were conducted to analyze the cooling performance and energy consumption of this novel EC-AC system. During the testing period, the average refrigeration efficiency of COP, SCOP and ACOP in A and B side is up to 3.8/3.9, 3.4/3.4 and 2.5/2.3. At the same time, some operation problems such as unbalanced working condition have been identified during measurement. The research indicates that such EC-AC system could be a feasible solution to enhance the energy efficiency and reduce the operational costs and carbon emission in metro stations. [Pan, Song; Pei, Fei; Wang, Hongwei; Liu, Jiaping] Beijing Univ Technol, Beijing Key Lab Green Built Environm & Energy Eff, Beijing 100124, Peoples R China; [Wei, Yixuan] Univ Nottingham, Res Ctr Fluids & Thermal Engn D, Dept Architectural & Built Environm, Ningbo 315100, Zhejiang, Peoples R China; [Zhang, Xingxing] Dalarna Univ, Sch Ind Technol & Business Studies, S-79188 Falun, Sweden; [Li, Guoqing] Beijing Inst Residential Bldg Design & Res Co LTD, Beijing 100005, Peoples R China; [Gu, Yaxiu] Changan Univ, Coll Architectural Engn, Res Lab HVAC, Xian 710061, Shaanxi, Peoples R China Beijing University of Technology; University of Nottingham Ningbo China; Dalarna University; Chang'an University Wei, YX (corresponding author), Univ Nottingham, Res Ctr Fluids & Thermal Engn D, Dept Architectural & Built Environm, Ningbo 315100, Zhejiang, Peoples R China.;Zhang, XX (corresponding author), Dalarna Univ, Sch Ind Technol & Business Studies, S-79188 Falun, Sweden. yixuan.wei@nottingham.edu.cn; xza@du.se li, jiawei/HOA-5023-2023; Li, JW/HNC-1743-2023; liu, jia/HKE-9796-2023; Zhang, Xingxing/D-1021-2018 Zhang, Xingxing/0000-0002-2369-0169 National Natural Science Foundation of China [51578011, 51590912, 51378025]; IDIC (International Doctoral Innovation Centre) program of the University of Nottingham (China campus); Beijing University of Technology Science and Innovation Service Capacity Building-Scientific Research Base-Beijing Laboratory-Urban Railway Transportation Beijing Laboratory National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); IDIC (International Doctoral Innovation Centre) program of the University of Nottingham (China campus); Beijing University of Technology Science and Innovation Service Capacity Building-Scientific Research Base-Beijing Laboratory-Urban Railway Transportation Beijing Laboratory The authors would acknowledge sincere appreciation to the financial supports from National Natural Science Foundation of China (Grant No. 51578011), National Natural Science Foundation of China (Major Program No. 51590912), National Natural Science Foundation of China (General Program No. 51378025), the IDIC (International Doctoral Innovation Centre) program of the University of Nottingham (China campus) and Beijing University of Technology Science and Innovation Service Capacity Building-Scientific Research Base-Beijing Laboratory-Urban Railway Transportation Beijing Laboratory(participate, municipal). 40 16 16 5 65 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. NOV 2018.0 43.0 550 562 10.1016/j.scs.2018.09.013 0.0 13 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels GY5KG 2023-03-23 WOS:000448613300048 0
J Zhao, JQ; Qian, XL; Zhang, YZ; Shan, DX; Liu, XZ; Coleman, S; Kerr, D Zhao, Jiaqi; Qian, Xiaolong; Zhang, Yunzhou; Shan, Dexing; Liu, Xiaozheng; Coleman, Sonya; Kerr, Dermot A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification JOURNAL OF INTELLIGENT MANUFACTURING English Article; Early Access Few-shot learning; Defect classification; Multi-scale feature encoder; Cross-domain; Knowledge distillation Surface defect classification plays a very important role in industrial production and mechanical manufacturing. However, there are currently some challenges hindering its use. The first is the similarity of different defect samples makes classification a difficult task. Second, the lack of defect samples leads to poor accuracies when using deep learning methods. In this paper, we first design a novel backbone network, ResMSNet, which draws on the idea of multi-scale feature extraction for small discriminative regions in defect samples. Then, we introduce few-shot learning for defect classification and propose a Relation-Prototypical network (RPNet), which combines the characteristics of ProtoNet and RelationNet and provides classification by linking the prototypes distances and the nonlinear relation scores. Next, we consider a more realistic scenario where the base dataset for training the model and target defect dataset for applying the model are usually obtained from domains with large differences, called cross-domain few-shot learning. Hence, we further improve RPNet to KD-RPNet inspired by knowledge distillation methods. Through extensive comparative experiments and ablation experiments, we demonstrate that either our ResMSNet or RPNet proves its effectiveness and KD-RPNet outperforms other state-of-the-art approaches for few-shot defect classification. [Zhao, Jiaqi; Qian, Xiaolong; Zhang, Yunzhou; Shan, Dexing; Liu, Xiaozheng] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China; [Coleman, Sonya; Kerr, Dermot] Univ Ulster, Intelligent Syst Res Ctr, Londonderry, North Ireland Northeastern University - China; Ulster University Qian, XL (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China. 2000925@stu.neu.edu.cn; qianxiaolong@ise.neu.edu.cn National Natural Science Foundation of China [61973066, 61471110]; Major Science and Technology Projects of Liaoning [2021JH1/10400049]; Fundation of Key Laboratory of Aerospace System Simulation [6142002200301]; Fundation of Key Laboratory of Equipment Reliability [WD2C20205500306]; Open Research Projects of Zhejiang Lab [2019KD0AD01/006]; Major Science and Technology Innovation Engineering Projects of Shandong Province [2019JZZY010128] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Science and Technology Projects of Liaoning; Fundation of Key Laboratory of Aerospace System Simulation; Fundation of Key Laboratory of Equipment Reliability; Open Research Projects of Zhejiang Lab; Major Science and Technology Innovation Engineering Projects of Shandong Province Funding was provided by National Natural Science Foundation of China (Grant Nos. 61973066 and 61471110), Major Science and Technology Projects of Liaoning (Grant No. 2021JH1/10400049), Fundation of Key Laboratory of Aerospace System Simulation (Grant No. 6142002200301), Fundation of Key Laboratory of Equipment Reliability (Grant No. WD2C20205500306), Open Research Projects of Zhejiang Lab (Grant No. 2019KD0AD01/006), and Major Science and Technology Innovation Engineering Projects of Shandong Province (Grant No. 2019JZZY010128). 61 0 0 1 1 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0956-5515 1572-8145 J INTELL MANUF J. Intell. Manuf. 10.1007/s10845-023-02080-w 0.0 FEB 2023 17 Computer Science, Artificial Intelligence; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 8O8TO 2023-03-23 WOS:000926104700001 0
J Bai, N; Nourian, P; Roders, AP; Bunschoten, R; Huang, WX; Wang, L Bai, Nan; Nourian, Pirouz; Roders, Ana Pereira; Bunschoten, Raoul; Huang, Weixin; Wang, Lu Investigating rural public spaces with cultural significance using morphological, cognitive and behavioural data ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE English Article Rural planning; space syntax; cognition; big data; activity space SYNTAX; IMAGE; METHODOLOGY; ISOVISTS; GEOMETRY; URBANISM During the rural [re]vitalization process in China, national strategies required rural public spaces with cultural significance to be identified before planning decision-making. However, places identified as culturally significant by planners and visitors can differ from the ones mostly used and valued by locals. Even if there is a growing interest in integrating local perspectives and experiences in planning, studies seldom discuss and compare openly the adequacy of spatial configuration, cognition and behaviour to support it. This study took Anyi Historic Village Cluster as a case study to empirically investigate rural public spaces with three distinct, yet related approaches: (1) Morphological: spatial network centralities based on space syntax; (2) Cognitive: Lynchian village images with semi-structured interviews; (3) Behavioural: spatiotemporal occupation patterns using Wi-Fi positioning tracking. Significant places valued and used by locals and non-locals were detected with the multi-source data. Furthermore, multivariant regression models managed to characterize the relationship among different aspects of investigated rural public spaces, which also helped diagnose places of interest to prioritize in planning, demonstrating the advantage of integrating the sources of information in practice instead of studying them apart. Results can also assist rural planning on how to identify what to preserve, what to enhance, and how to develop such spaces, without overlooking the local needs or losing the rural identity. [Bai, Nan; Roders, Ana Pereira] Delft Univ Technol, UNESCO, Chair Heritage & Values Heritage & Reshaping Urba, Dept Architectural Engn & Technol, Delft, Netherlands; [Nourian, Pirouz] Delft Univ Technol, Design Informat, Dept Architectural Engn & Technol, Delft, Netherlands; [Bunschoten, Raoul] TU Berlin, Sustainable Urban Planning & Urban Design, Berlin, Germany; [Huang, Weixin; Wang, Lu] Tsinghua Univ, Sch Architecture, S400C, Beijing 100084, Peoples R China Delft University of Technology; IHE Delft Institute for Water Education; Delft University of Technology; Technical University of Berlin; Tsinghua University Huang, WX (corresponding author), Tsinghua Univ, Sch Architecture, S400C, Beijing 100084, Peoples R China. huangwx@mail.tsinghua.edu.cn Bai, Nan/GYU-0194-2022; Nourian, Pirouz/AAL-8313-2020; Pereira Roders, Ana/G-3836-2012 Bai, Nan/0000-0001-7637-3629; Nourian, Pirouz/0000-0002-3817-7931; Pereira Roders, Ana/0000-0003-2571-9882 National Key Research and Development Program of China [2018YFD1100303] National Key Research and Development Program of China The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by National Key Research and Development Program of China (NO. 2018YFD1100303) Digital Simulation and Evaluation Model for Reconstruction of Town and Village Settlements Space. 74 1 1 26 34 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 2399-8083 2399-8091 ENVIRON PLAN B-URBAN Env. Plan. B-Urban Anal. City Sci. JAN 2023.0 50.0 1 94 116 23998083211064288 10.1177/23998083211064290 0.0 JUN 2022 23 Environmental Studies; Geography; Regional & Urban Planning; Urban Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Geography; Public Administration; Urban Studies 7V4BX Green Published 2023-03-23 WOS:000811013400001 0
J He, L; Tiwari, P; Lv, CH; Wu, WS; Guo, LY He, Lang; Tiwari, Prayag; Lv, Chonghua; Wu, WenShuai; Guo, Liyong Reducing noisy annotations for depression estimation from facial images NEURAL NETWORKS English Article Depression; Self-adaptation network (SAN); Affective computing; Noisy labels RECOGNITION; APPEARANCE Depression has been considered the most dominant mental disorder over the past few years. To help clinicians effectively and efficiently estimate the severity scale of depression, various automated systems based on deep learning have been proposed. To estimate the severity of depression, i.e., the depression severity score (Beck Depression Inventory-II), various deep architectures have been designed to perform regression using the Euclidean loss. However, they do not consider the label distribution, and they do not learn the relationships between the facial images and BDI-II scores, which can be resulting in the noisy labeling for automatic depression estimation (ADE). To mitigate this problem, we propose an automated deep architecture, namely the self-adaptation network (SAN), to improve this uncertain labeling for ADE. Specifically, the architecture consists of four modules: (1) ResNet-18 and ResNet-50 are adopted in the deep feature extraction module (DFEM) to extract informative deep features; (2) a self-attention module (SAM) is adopted to learn the weights from the mini-batch; (3) a square ranking regularization module (SRRM) to create high partitions and low partitions is proposed; and (4) a re-label module (RM) is used to re-label the uncertain annotations for ADE in the low partitions. We conduct extensive experiments on depression databases (i.e., AVEC2013 and AVEC2014) and obtain a performance comparable to the performances of other ADE methods in assessing the severity of depression. More importantly, the proposed method can learn valuable depression patterns from facial videos and obtain a performance comparable to the performances of other methods for depression recognition. (C) 2022 The Author(s). Published by Elsevier Ltd. [He, Lang] Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Xian 710121, Shaanxi, Peoples R China; [He, Lang] Xian Univ Posts & Telecommun, Shaanxi Key Lab Network Data Anal & Intelligent Pr, Xian 710121, Shaanxi, Peoples R China; [He, Lang] Xian Univ Posts & Telecommun, Xian Key Lab Big Data & Intelligent Comp, Xian 710121, Shaanxi, Peoples R China; [Tiwari, Prayag] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Lv, Chonghua] Xidian Univ, Sch Artificial Intelligence, Xian 710121, Shaanxi, Peoples R China; [Wu, WenShuai] Xian Univ Posts & Telecommun, Sch Telecommun & Informat Engn, Xian 710121, Shaanxi, Peoples R China; [Guo, Liyong] Xiaomi Technol, Beijing, Peoples R China Xi'an University of Posts & Telecommunications; Xi'an University of Posts & Telecommunications; Xi'an University of Posts & Telecommunications; Aalto University; Xidian University; Xi'an University of Posts & Telecommunications Tiwari, P (corresponding author), Aalto Univ, Dept Comp Sci, Espoo, Finland. langhe@xupt.edu.cn; prayag.tiwari@aalto.fi; 21171213803@stu.xidian.edu.cn Tiwari, Prayag/N-6261-2017 Tiwari, Prayag/0000-0002-2851-4260 Shaanxi Provincial Social Sci-ence Foundation [2021K015]; Shaanxi Provincial Natural Science Foundation [2021JQ-824, 2022JM-380]; Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education; Shaanxi Provincial Education Department [20JG030, 19JS028]; Academy of Finland [336033, 315896]; Business Finland [884/31/2018]; EUH2020 [101016775] Shaanxi Provincial Social Sci-ence Foundation; Shaanxi Provincial Natural Science Foundation; Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education; Shaanxi Provincial Education Department; Academy of Finland(Academy of Finland); Business Finland; EUH2020 This work is supported by the Shaanxi Provincial Social Sci-ence Foundation (grant 2021K015) , the Shaanxi Provincial Natu-ral Science Foundation (grant 2021JQ-824) , the Shaanxi Provincial Natural Science Foundation (grant 2022JM-380) , the Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education, the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 19JS028) , and the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 20JG030) . This work was also supported by the Academy of Finland (grants 336033, 315896) , Business Finland (grant 884/31/2018) , and EUH2020 (grant 101016775) . 50 0 0 7 11 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. SEP 2022.0 153.0 120 129 10.1016/j.neunet.2022.05.025 0.0 10 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology 2U4BJ 35717754.0 Green Published 2023-03-23 WOS:000823104800004 0
J Lei, L; Wu, B; Fang, X; Chen, L; Wu, H; Liu, W Lei, Lei; Wu, Bing; Fang, Xin; Chen, Li; Wu, Hao; Liu, Wei A dynamic anomaly detection method of building energy consumption based on data mining technology ENERGY English Article Building energy consumption; Dynamic anomaly detection; Semi -supervised algorithm; Particle swarm optimization; K-medoids algorithm; KNN algorithm FAULT-DETECTION ANALYSIS; NEURAL-NETWORK; PREDICTION; DIAGNOSIS; FRAMEWORK; PATTERNS; CLUSTER Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study pro-poses a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algo-rithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algo-rithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy con-sumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies. [Lei, Lei] Zhejiang Sci Tech Univ, Sch Civil Engn & Architecture, Hangzhou 310018, Peoples R China; [Wu, Bing] Guangxi Vocat & Tech Coll Commun, Coll Civil Engn & Architecture, 1258 Kunlun Ave, Nanning 530216, Peoples R China; [Fang, Xin; Chen, Li; Wu, Hao] Alibaba Grp, Alibaba Cloud, 969 West Wen Yi Rd, Hangzhou 311121, Peoples R China; [Liu, Wei] KTH Royal Inst Technol, Dept Civil & Architectural Engn, Div Sustainable Bldg, Brinellvagen 23, S-10044 Stockholm, Sweden Zhejiang Sci-Tech University; Alibaba Group; Royal Institute of Technology Liu, W (corresponding author), KTH Royal Inst Technol, Dept Civil & Architectural Engn, Div Sustainable Bldg, Brinellvagen 23, S-10044 Stockholm, Sweden. weiliu2@kth.se National Natural Science Foundation of China [51708146, 51808487]; Alibaba-Zhejiang University Joint Institute of Frontier Technologies (AZFT), China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alibaba-Zhejiang University Joint Institute of Frontier Technologies (AZFT), China This research was supported by the National Natural Science Foundation of China (Grant No.: 51708146 and 51808487) and Alibaba-Zhejiang University Joint Institute of Frontier Technologies (AZFT), China. 33 0 0 16 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy JAN 15 2023.0 263.0 A 125575 10.1016/j.energy.2022.125575 0.0 19 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 5I4HG hybrid 2023-03-23 WOS:000868319200001 0
J Zhou, Z; Wei, ZH; Ren, J; Yin, YZ; Pedersen, GF; Shen, M Zhou, Zhao; Wei, Zhaohui; Ren, Jian; Yin, Yingzeng; Pedersen, Gert Frolund; Shen, Ming Representation Learning-Driven Fully Automated Framework for the Inverse Design of Frequency-Selective Surfaces IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES English Article; Early Access Topology; Optimization; Data models; Metasurfaces; Training; Representation learning; Geometry; Autoevolution; autoselection; frequency-selective surface (FSS); fully automated; inverse design; representation learning Frequency-selective surfaces (FSSs) refer to planar structures that behave with specific electromagnetic (EM) responses within a frequency range and are widely applied in wireless propagation systems. Given the fact that different EM responses correspond to distinguished topologies, conventional inverse design methods of FSSs are usually labor-intensive, as they rely on experienced human engineers to determine the topology and then rationally tune its structures. There have been great attempts using optimization algorithms (e.g., genetic algorithms) or machine learning to automate the second tuning stage after the initial EM topologies are determined by human engineers. However, the first topology selection stage still requires engagements with experienced engineers. This article proposes a fully automated framework for the inverse design of FSSs. We achieved a fully automated inverse design by establishing a machine-friendly mapping flow. The mapping flow derives its continuity and compactness from representation learning, which enables both autoselection of the topology and autoevolution of the unit cell based on the topology. The autoselection stage automatically determines the appropriate topology by compressing the EM constraints through the principal component analysis (PCA) and classifying the topology using the support vector machine (SVM). Afterward, the autoevolution system can efficiently evolve until it yields an optimal unit cell. We developed a self-monitor strategy to control the evolution and maximize the evolution efficiency by adaptively tuning the three modules within the autoevolution system. We validated the presented framework with four FSS designs. The results proved its potential as a highly efficient fully automated tool for the inverse design of FSSs. [Zhou, Zhao; Wei, Zhaohui; Pedersen, Gert Frolund; Shen, Ming] Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark; [Ren, Jian; Yin, Yingzeng] Xidian Univ, Natl Key Lab Antennas & Microwave Technol, Xian 710071, Peoples R China Aalborg University; Xidian University Shen, M (corresponding author), Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark.;Ren, J (corresponding author), Xidian Univ, Natl Key Lab Antennas & Microwave Technol, Xian 710071, Peoples R China. zhazho@es.aau.dk; zw@es.aau.dk; renjianroy@gmail.com; yzyin@mail.xidian.edu.cn; gfp@es.aau.dk; mish@es.aau.dk Zhou, Zhao/HGV-1742-2022 Zhou, Zhao/0000-0001-7895-8129; Shen, Ming/0000-0002-9388-3513; Pedersen, Gert F./0000-0002-6570-7387 China Scholarship Council [202006960011]; National Natural Science Foundation of China [62071351, 61901316]; 111 Project of China China Scholarship Council(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); 111 Project of China(Ministry of Education, China - 111 Project) This work was supported in part by the China Scholarship Council under Grant 202006960011; in part by the National Natural Science Foundation of China under Grant 62071351 and Grant 61901316; and in part by the 111 Project of China. 39 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9480 1557-9670 IEEE T MICROW THEORY IEEE Trans. Microw. Theory Tech. 10.1109/TMTT.2023.3235066 0.0 JAN 2023 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 8G7UD Green Published 2023-03-23 WOS:000920548200001 0
J Zhang, LW; Lin, J; Shao, HD; Zhang, ZC; Yan, XH; Long, JY Zhang, Liangwei; Lin, Jing; Shao, Haidong; Zhang, Zhicong; Yan, Xiaohui; Long, Jianyu End-to-end unsupervised fault detection using a flow-based model RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Prognostics and health management; Fault detection; Deep learning; Unsupervised learning; Flow-based models SVM Fault detection has been extensively studied in both academia and industry. The rareness of faulty samples in the real world restricts the use of many supervised models, and the reliance on domain expertise for feature engineering raises other barriers. For this reason, this paper proposes an unsupervised, end-to-end approach to fault detection based on a flow-based model, the Nonlinear Independent Components Estimation (NICE) model. A NICE model models a target distribution via a sequence of invertible transformations to a prior distribution in the latent space. We prove that, under certain conditions, the L-2-norm of normal samples' latent codes in a trained NICE model is Chi-distributed. This facilitates the use of hypothesis testing for fault detection purpose. Concretely, we first apply Zero-phase Component Analysis to decorrelate the data of normal states. The whitened data are fed to a NICE model for training, in a maximum likelihood sense. At the testing stage, samples whose L-2-norm of latent codes fail in the hypothesis testing are suspected of being generated by different mechanisms and hence regarded as potential faults. The proposed approach was validated on two datasets of vibration signals; it proved superior to several alternatives. We also show the use of NICE, a type of generative model, can produce real-like vibration signals because of the model's bijective nature. [Zhang, Liangwei; Zhang, Zhicong; Yan, Xiaohui; Long, Jianyu] Dongguan Univ Technol, Dept Ind Engn, Dongguan 523808, Peoples R China; [Lin, Jing; Shao, Haidong] Lulea Univ Technol, Div Operat & Maintenance, S-97187 Lulea, Sweden; [Shao, Haidong] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China Dongguan University of Technology; Lulea University of Technology; Hunan University Lin, J (corresponding author), Lulea Univ Technol, Div Operat & Maintenance, S-97187 Lulea, Sweden. janet.lin@ltu.se Long, Jianyu/ABG-2133-2021; Yan, Xiaohui/HGC-6427-2022; Lin, Jing/B-3076-2015; Shao, Haidong/AAE-5814-2022 Lin, Jing/0000-0002-7458-6820; Shao, Haidong/0000-0002-8018-1774 National Natural Science Foundation of China [71801045, 71801046, 51905160]; Research start-up funds of DGUT [GC300502-46]; Natural Science Foundation of Hunan Province [2020JJ5072]; National Key Research and Development Program of China [2020YFB1712103]; Fundamental Research Funds for the Central Universities [531118010335] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research start-up funds of DGUT; Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors wish to thank the CRRC-LTU joint research centre and CSSC-LTU joint smart Maintenance Lab, Sweden. The research was partially supported by the National Natural Science Foundation of China (71801045, 71801046, 51905160), the Research start-up funds of DGUT (GC300502-46), the Natural Science Foundation of Hunan Province (2020JJ5072), the National Key Research and Development Program of China (2020YFB1712103), and the Fundamental Research Funds for the Central Universities (531118010335). 40 9 9 10 26 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. NOV 2021.0 215.0 107805 10.1016/j.ress.2021.107805 0.0 JUN 2021 14 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science UI0BI hybrid 2023-03-23 WOS:000690283800019 0
J Liu, JP; Kadzinski, M; Liao, XW; Mao, XX; Wang, Y Liu, Jiapeng; Kadzinski, Milosz; Liao, Xiuwu; Mao, Xiaoxin; Wang, Yao A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples EUROPEAN JOURNAL OF OPERATIONAL RESEARCH English Article Decision analysis; Multiple criteria sorting; Preference learning; Additive value function; Valued decision examples; Class priority ROBUSTNESS ANALYSIS; DECISION; DISAGGREGATION; SET We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows considering valued assignment examples in which a reference alternative can be classified into multiple classes with respective credibility degrees. We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives. To improve the predictive ability of the constructed model on new instances, we employ the regularization techniques. Moreover, to enhance the capability of addressing large-scale datasets, we introduce a state-of-the-art algorithm that is widely used in the machine learning community to solve the proposed optimization model in a computationally efficient way. Using the constructed additive value model, we determine both crisp and valued assignments for non-reference alternatives. Moreover, we allow the Decision Maker to prioritize the importance of classes and give the method a flexibility to adjust classification performance across classes according to the specified priorities. The practical usefulness of the analytical framework is demonstrated on a real-world dataset by comparing it to several existing sorting methods. (C) 2020 Elsevier B.V. All rights reserved. [Liu, Jiapeng; Liao, Xiuwu; Mao, Xiaoxin; Wang, Yao] Xi An Jiao Tong Univ, Sch Management, Ctr Intelligent Decis Making & Machine Learning, Xian 710049, Shaanxi, Peoples R China; [Kadzinski, Milosz] Poznan Univ Tech, Inst Comp Sci, Piotrowo 2, PL-60965 Poznan, Poland Xi'an Jiaotong University; Poznan University of Technology Liu, JP (corresponding author), Xi An Jiao Tong Univ, Sch Management, Ctr Intelligent Decis Making & Machine Learning, Xian 710049, Shaanxi, Peoples R China. jiapengliu@mail.xjtu.edu.cn; milosz.kadzinski@cs.put.poznan.pl; liaoxium@mail.xjtu.edu.cn; maoxiaoxin29@stu.xjtu.edu.cn; yao.s.wang@gmail.com Kadziński, Miłosz/L-8304-2014 Kadziński, Miłosz/0000-0003-1806-3715 National Natural Science Foundation of China [71701160, 91546119, 91846110, 91746111]; Youth Innovation Team of Shaanxi Universities Big data and Business Intelligent Innovation Team; Polish National Science Center under the SONATA BIS project [DEC-2019/34/E/HS4/00045] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Innovation Team of Shaanxi Universities Big data and Business Intelligent Innovation Team; Polish National Science Center under the SONATA BIS project The work of Jiapeng Liu and Xiuwu Liao is supported by the National Natural Science Foundation of China (No.71701160, No.91546119, No.91846110, No.91746111) and the Youth Innovation Team of Shaanxi Universities Big data and Business Intelligent Innovation Team. Milosz Kadzi ' nski acknowledges financial support from the Polish National Science Center under the SONATA BIS project (Grant No. DEC-2019/34/E/HS4/00045). We are grateful to the anonymous reviewers for their constructive and detailed comments, which helped us to improve the previous versions of the paper. 50 19 19 5 54 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0377-2217 1872-6860 EUR J OPER RES Eur. J. Oper. Res. NOV 1 2020.0 286.0 3 963 985 10.1016/j.ejor.2020.04.013 0.0 23 Management; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Business & Economics; Operations Research & Management Science LV6WI Green Submitted 2023-03-23 WOS:000538576200013 0
J Govindan, K; Jafarian, A; Azbari, ME; Choi, TM Govindan, Kannan; Jafarian, Ahmad; Azbari, Mostafa E.; Choi, Tsan-Ming Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management IEEE TRANSACTIONS ON CYBERNETICS English Article Meta-heuristic algorithms; multiobjective optimization; reliability optimization; systems risk management SERIES-PARALLEL SYSTEMS; MULTIOBJECTIVE GENETIC ALGORITHM; OPTIMIZATION PROBLEMS; MULTIPLE OBJECTIVES; ASSEMBLY LINES; SEARCH; MODEL; STRATEGIES; CHOICE; CONSTRAINTS In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multi-objective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management. [Govindan, Kannan] Univ Southern Denmark, Dept Econ & Business, DK-5000 Odense, Denmark; [Jafarian, Ahmad] Allameh Tabatabai Univ, Sch Business, Fac Management & Accounting, Tehran 1461674751, Iran; [Azbari, Mostafa E.] Univ Guilan, Dept Management, Fac Humanities, Rasht 416353988, Iran; [Choi, Tsan-Ming] Hong Kong Polytech Univ, Inst Text & Clothing, Business Div, Hong Kong, Hong Kong, Peoples R China University of Southern Denmark; Allameh Tabataba'i University; University of Guilan; Hong Kong Polytechnic University Choi, TM (corresponding author), Hong Kong Polytech Univ, Inst Text & Clothing, Business Div, Hong Kong, Hong Kong, Peoples R China. Choi, Tsan-Ming/P-6065-2014; Govindan, Kannan/M-5996-2017 Choi, Tsan-Ming/0000-0003-3865-7043; Govindan, Kannan/0000-0002-6204-1196; Ebrahimpour, Mostafa/0000-0003-0410-6374 Forsknings-og Innovationsstyrelsen [4070-00139B]; Hong Kong Polytechnic University Forsknings-og Innovationsstyrelsen; Hong Kong Polytechnic University(Hong Kong Polytechnic University) Kannan Govindan's research was supported by Forsknings-og Innovationsstyrelsen for project 4070-00139B. The work of T.-M. Choi was supported by The Hong Kong Polytechnic University under Grant G-UA1Q. 70 35 35 0 41 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. AUG 2016.0 46.0 8 1735 1748 10.1109/TCYB.2014.2382666 0.0 14 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science DR6AW 25622333.0 2023-03-23 WOS:000379984500003 0
C Hu, W; Sheng, HH; Wu, J; Li, YN; Liu, TY; Wang, YH; Wen, Y IEEE Hu, Wei; Sheng, Huanhuan; Wu, Jing; Li, Yining; Liu, Tianyi; Wang, Yonghao; Wen, Yuan Generative Adversarial Training for Weakly Supervised Nuclei Instance Segmentation 2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) IEEE International Conference on Systems Man and Cybernetics Conference Proceedings English Proceedings Paper IEEE International Conference on Systems, Man, and Cybernetics (SMC) OCT 11-14, 2020 ELECTR NETWORK IEEE,IEEE Syst Man & Cybernet Soc,IEEE Brain,Intheon,Guger Technologies image processing; nuclei segmentation; weak supervision; GAN Nuclei segmentation occupies an important position in medical image analysis, which helps to predict and diagnose diseases. With the further research of deep learning, the task of nuclei segmentation has been automated. However, most existing methods require a great deal of manually marked full masks for training, which is time-consuming and labor-intensive, and can only be done by professional personnel. For the purpose of reducing the cost of labeling, we propose a weakly supervised method using generative adversarial training for segmentation of nucleus. In the case of no boundary, but only the centroid of the nucleus, the proposed method segmented the nucleus region with blurred boundaries. We first use the generative adversarial network(GAN) to generate the likelihood map of the nuclear centroid, then use Guided Backpropagation to visualize the pixels that contributes to the detection of the centroid of each nucleus, and finally obtain the segmentation mask of the nucleus by graphcut. In addition, for the purpose of training the network better, we performed stain normalization on each pathological image. We have verified the proposed method on a multi-organ nuclei dataset. The final experiment results show that our advanced method achieves better segmentation performance than other weakly supervised methods, and can even reach the level of full supervision. [Hu, Wei; Sheng, Huanhuan; Wu, Jing; Li, Yining; Liu, Tianyi] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China; [Wang, Yonghao] Birmingham City Univ, Digital Media Technol Lab, Birmingham, W Midlands, England; [Wen, Yuan] Trinity Coll Dublin, Dublin, Ireland Wuhan University of Science & Technology; Birmingham City University; Trinity College Dublin Hu, W (corresponding author), Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China. huwei@wust.edu.cn; shenghh1996@163.com; wujingecs@gmail.com; wustlyn@gmail.com; WustLty@outlook.com; yonghao.wang@bcu.ac.uk; weny@tcd.ie Wang, Yonghao/0000-0002-4924-2508 Key Project of Hubei Provincial Department of Education [D20181103] Key Project of Hubei Provincial Department of Education This work was sponsored by Key Project of Hubei Provincial Department of Education under Granted No. D20181103. 34 3 3 1 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-922X 978-1-7281-8526-2 IEEE SYS MAN CYBERN 2020.0 3649 3654 6 Computer Science, Cybernetics; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS1DD 2023-03-23 WOS:000687430603106 0
J Guan, RC; Wang, X; Marchese, M; Yang, MQ; Liang, YC; Yang, C Guan, Renchu; Wang, Xu; Marchese, Maurizio; Yang, Mary Qu; Liang, Yanchun; Yang, Chen Feature space learning model JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING English Article Feature space learning; Semi-supervised learning; Affinity Propagation; k-means CLASSIFICATION With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces. [Guan, Renchu; Wang, Xu; Liang, Yanchun] Jilin Univ, Coll Comp Sci & Technol, Natl Educ Minist, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China; [Marchese, Maurizio] Univ Trento, Dept Engn & Comp Sci, I-38123 Povo 9, Italy; [Yang, Mary Qu] Univ Arkansas, MidSouth Bioinformat Ctr & Joint Bioinformat, Little Rock, AR 72204 USA; [Yang, Mary Qu] Univ Arkansas Med Sci, Little Rock, AR 72204 USA; [Yang, Chen] Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China; [Liang, Yanchun] Jilin Univ, Zhuhai Coll, Minist Educ, Zhuhai Lab Key Lab Symbol Computat & Knowledge, Zhuhai 519041, Peoples R China Jilin University; University of Trento; University of Arkansas System; University of Arkansas Fayetteville; University of Arkansas Little Rock; University of Arkansas System; University of Arkansas Medical Sciences; Jilin University; Jilin University Yang, C (corresponding author), Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China. yangc616@jlu.edu.cn Guan, Renchu/S-5503-2019; Guan, Renchu/K-5223-2015 Guan, Renchu/0000-0002-7162-7826; Guan, Renchu/0000-0002-7162-7826 National Natural Science Foundation of China [61572228, 61472158, 61300147, 61602207]; United States National Institutes of Health (NIH) Academic Research Enhancement Award [1R15GM114739]; Science Technology Development Project from Jilin Province [20160101247JC]; Zhuhai Premier-Discipline Enhancement Scheme National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); United States National Institutes of Health (NIH) Academic Research Enhancement Award(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Science Technology Development Project from Jilin Province; Zhuhai Premier-Discipline Enhancement Scheme The authors are grateful for the support of the National Natural Science Foundation of China (Nos. 61572228, 61472158, 61300147, 61602207), United States National Institutes of Health (NIH) Academic Research Enhancement Award (No. 1R15GM114739), the Science Technology Development Project from Jilin Province (No. 20160101247JC), Zhuhai Premier-Discipline Enhancement Scheme and Guangdong Premier Key-Discipline Enhancement Scheme.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 33 3 3 3 15 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-5137 1868-5145 J AMB INTEL HUM COMP J. Ambient Intell. Humaniz. Comput. MAY 2019.0 10.0 5 SI 2029 2040 10.1007/s12652-018-0805-4 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications HR4XS 31068980.0 hybrid, Green Published, Green Accepted 2023-03-23 WOS:000463151400029 0
J Yan, SR; Aly, AA; Felemban, BF; Gheisarnejad, M; Tian, MW; Khooban, MH; Mohammadzadeh, A; Mobayen, S Yan, Shurong; Aly, Ayman A.; Felemban, Bassem F.; Gheisarnejad, Meysam; Tian, Manwen; Khooban, Mohammad Hassan; Mohammadzadeh, Ardashir; Mobayen, Saleh A New Event-Triggered Type-3 Fuzzy Control System for Multi-Agent Systems: Optimal Economic Efficient Approach for Actuator Activating ELECTRONICS English Article fuzzy control; type-3 fuzzy systems; machine learning; multi-agent systems; uncertain upper; lower bounds of uncertainty; event-triggered CONSENSUS CONTROL; TRACKING CONTROL; DISCRETE; HYBRID; FILTER This study presents a new approach for multi-agent systems (MASs). The agent dynamics are approximated by the suggested type-3 (T3) fuzzy logic system (FLS). Some sufficient conditions based on the event-triggered scheme are presented to ensure the stability under less activation of the actuators. New tuning rules are obtained for T3-FLSs form the stability and robustness examination. The effect of perturbations, actuator failures and approximation errors are compensated by the designed adaptive compensators. Simulation results show that the output of all agents well converged to the leader agent under disturbances and faulty conditions. Additionally, it is shown that the suggested event-triggered scheme is effective and the actuators are updated about 20-40% of total sample times. [Yan, Shurong; Tian, Manwen] Jiangxi Univ Engn, Natl Key Project Lab, Xinyu 338000, Peoples R China; [Aly, Ayman A.; Felemban, Bassem F.] Taif Univ, Coll Engn, Dept Mech Engn, POB 11099, At Taif 21944, Saudi Arabia; [Gheisarnejad, Meysam; Khooban, Mohammad Hassan] Aarhus Univ, Dept Elect & Comp Engn, DK-8200 Aarhus, Denmark; [Mobayen, Saleh] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan Taif University; Aarhus University; National Yunlin University Science & Technology Mobayen, S (corresponding author), Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan. tmw168@126.com; Aymanaly@tu.edu.sa; B.felemban@tu.edu.sa; me.gheisarnejad@gmail.com; manwen@126.com; khooban@ece.au.dk; a.mzadeh@ieee.org; mobayens@yuntech.edu.tw Aly, Ayman A./AAZ-9459-2021; Mohammadzadeh, Ardashir/AEN-2013-2022; Mobayen, Saleh/N-3724-2018 Aly, Ayman A./0000-0002-8383-8014; Mohammadzadeh, Ardashir/0000-0001-5173-4563; Mobayen, Saleh/0000-0002-5676-1875; Felemban, Bassem/0000-0003-0196-0209 Taif University Researchers Supporting Project [TURSP-2020/260] Taif University Researchers Supporting Project Taif University Researchers Supporting Project, number (TURSP-2020/260). 35 5 5 2 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics DEC 2021.0 10.0 24 3122 10.3390/electronics10243122 0.0 22 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics YK5TL gold 2023-03-23 WOS:000745274700001 0
J Lello, L; Avery, SG; Tellier, L; Vazquez, AI; de los Campos, G; Hsu, SDH Lello, Louis; Avery, Steven G.; Tellier, Laurent; Vazquez, Ana I.; de los Campos, Gustavo; Hsu, Stephen D. H. Accurate Genomic Prediction of Human Height GENETICS English Article investigation; complex traits; genomic prediction; GWAS; heritability; penalized regression; GenPred HERITABILITY; ARCHITECTURE; LOCI; GCTA We construct genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). The constructed predictors explain, respectively, similar to 40, 20, and 9% of total variance for the three traits, in data not used for training. For example, predicted heights correlate similar to 0.65 with actual height; actual heights of most individuals in validation samples are within a few centimeters of the prediction. The proportion of variance explained for height is comparable to the estimated common SNP heritability from genome-wide complex trait analysis (GCTA), and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for SNPs. Thus, our results close the gap between prediction R-squared and common SNP heritability. The similar to 20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common variants. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier genome-wide association studies (GWAS) for out-of-sample validation of our results. [Lello, Louis; Avery, Steven G.; Tellier, Laurent; Hsu, Stephen D. H.] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA; [Vazquez, Ana I.; de los Campos, Gustavo] Michigan State Univ, Dept Epidemiol & Biostat, E Lansing, MI 48824 USA; [de los Campos, Gustavo] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA; [Tellier, Laurent; Hsu, Stephen D. H.] BGI Shenzhen, China Natl GeneBank, Shenzhen Key Lab Neurogen, Cognit Genom Lab, Shenzhen 518083, Peoples R China; [Tellier, Laurent] Univ Copenhagen, Dept Biol, Funct Genet, DK-2200 Copenhagen, Denmark Michigan State University; Michigan State University; Michigan State University; Beijing Genomics Institute (BGI); University of Copenhagen Hsu, SDH (corresponding author), Hannah Adm Bldg,426 Auditorium Rd, E Lansing, MI 48824 USA. hsu@msu.edu de los Campos, Gustavo/0000-0001-5692-7129; Melchior Tellier, Laurent Christian Asker/0000-0003-3240-4926 Office of the Vice-President for Research at Michigan State University (MSU); MSU; National Institutes of Health (NIH) [R01GM099992, R01GM101219]; National Science Foundation (NSF) [IOS-1444543, UFDSP00010707]; Shenzhen Key Laboratory of Neurogenomics [CXB201108250094A] Office of the Vice-President for Research at Michigan State University (MSU); MSU; National Institutes of Health (NIH)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Science Foundation (NSF)(National Science Foundation (NSF)); Shenzhen Key Laboratory of Neurogenomics L.L., S.G.A., and S.D.H.H. acknowledge support from the Office of the Vice-President for Research at Michigan State University (MSU). This work was supported in part by MSU through computational resources provided by the Institute for Cyber-Enabled Research. The authors are grateful for useful correspondence and discussion with Alexander Grueneberg and Hwasoon Kim. We also acknowledge support from the National Institutes of Health (NIH) Grants R01GM099992 and R01GM101219, and National Science Foundation (NSF) Grant IOS-1444543, subaward UFDSP00010707. L.T. acknowledges the additional support of Shenzhen Key Laboratory of Neurogenomics (CXB201108250094A). The authors acknowledge acquisition of datasets via UK Biobank Main Application 15326. 36 66 68 1 18 GENETICS SOCIETY AMERICA BETHESDA 9650 ROCKVILLE AVE, BETHESDA, MD 20814 USA 0016-6731 1943-2631 GENETICS Genetics OCT 2018.0 210.0 2 477 497 10.1534/genetics.118.301267 0.0 21 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity GZ4UK 30150289.0 Green Published, Green Submitted 2023-03-23 WOS:000449398800008 0
J Choi, J; Toumanidis, L; Yeum, CM; Charalampos, P; Lenjani, A; Liu, XY; Kasnesis, P; Ortiz, R; Jiang, NJ; Dyke, SJ Choi, Jongseong; Toumanidis, Lazaros; Yeum, Chul Min; Charalampos, Patrikakis; Lenjani, Ali; Liu, Xiaoyu; Kasnesis, Panagiotis; Ortiz, Ricardo; Jiang, Ning-Jun; Dyke, Shirley J. Automated Graffiti Detection: A Novel Approach to Maintaining Historical Architecture in Communities APPLIED SCIENCES-BASEL English Article graffiti; cultural heritage assessment; convolutional neural network; orthophoto generation; object detection; citizen science Graffiti is common in many communities and even affects our historical and heritage structures. This leads to a decrease in the revenue associated with commercial activities or services (e.g., shops, restaurants, residences), and potentially reduces tourism in a region. Visual data, in the form of photographs, is becoming an efficient mechanism to record information. Photographs can be quickly captured, and are already frequently posted online by ordinary citizens (e.g., tourists, residents, visitors). Exploiting image data through automation and computer vision provides a new opportunity to simplify the current manual graffiti-monitoring processes, enabling automated detection, localization, and quantification of such markings. In this study, we developed a vision-based graffiti-detection technique using a convolutional neural network. Images collected from historical structures of interest within a community can be utilized to automatically inspect for graffiti markings. In the case in which citizens collect and contribute data, there is a high degree of duplication and repetition, and potentially a lack of GPS information. These hinder the direct use of the images for automating the process. To address these challenges, we built high-resolution, single-view facade images (orthophotos) before applying our robust graffiti detector. The robust graffiti detector was built using a database with 1022 images of damaged or contaminated structures gathered during a recent European Union project, entitled Safeguarding Cultural Heritage through Technical and Organisational Resources Management (STORM). A total of 818 images were used for training (10% of the training set was randomly chosen for the validation set), achieving 88% accuracy among the remaining 204 samples for testing. Using the trained detector, the technique developed was demonstrated using data collected from the Church of Agios Nikolaos (Leontariou), Kantza, Greece. [Choi, Jongseong; Ortiz, Ricardo] State Univ New York, Dept Mech Engn, SUNY Korea, Incheon 21985, South Korea; [Choi, Jongseong] SUNY Stony Brook, Dept Mech Engn, Stony Brook, NY 11794 USA; [Toumanidis, Lazaros; Charalampos, Patrikakis; Kasnesis, Panagiotis] Univ West Attica, Dept Elect & Elect Engn, Egaleo 12241, Greece; [Yeum, Chul Min] Univ Waterloo, Dept Civil & Environm Engn, Waterloo, ON N2L 2G1, Canada; [Lenjani, Ali] Stanford Univ, Sch Med, Stanford, CA 94305 USA; [Liu, Xiaoyu; Dyke, Shirley J.] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47906 USA; [Jiang, Ning-Jun] Southeast Univ, Inst Geotech Engn, Nanjing 211189, Peoples R China; [Dyke, Shirley J.] Purdue Univ, Lyles Sch Civil Engn, W Lafayette, IN 47906 USA State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of West Attica; University of Waterloo; Stanford University; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Southeast University - China; Purdue University System; Purdue University; Purdue University West Lafayette Campus Choi, J (corresponding author), State Univ New York, Dept Mech Engn, SUNY Korea, Incheon 21985, South Korea.;Choi, J (corresponding author), SUNY Stony Brook, Dept Mech Engn, Stony Brook, NY 11794 USA. jongseong.choi@stonybrook.edu; laztoum@uniwa.gr; cmyeum@uwaterloo.ca; bpatr@uniwa.gr; alenjani@stanford.edu; liu1787@purdue.edu; pkasnesis@uniwa.gr; ricardo.ortiz@stonybrook.edu; jiangn@seu.edu.cn; sdyke@purdue.edu ; Patrikakis, Charalampos/U-2154-2018 Jiang, Ning-Jun/0000-0001-6070-4307; Kasnesis, Panagiotis/0000-0003-3607-8187; Patrikakis, Charalampos/0000-0003-1921-4466; Toumanidis, Lazaros/0000-0002-3221-0745; Dyke, Shirley/0000-0003-3697-992X Civil, Mechanical and Manufacturing Innovation (CMMI) Program of the National Science Foundation [NSF 1645047]; European Union's Horizon 2020 research program under the STORM project [700191]; National Research Foundation of Korea - Korean government (MSIT) [NRF-2021R1G1A1012298] Civil, Mechanical and Manufacturing Innovation (CMMI) Program of the National Science Foundation; European Union's Horizon 2020 research program under the STORM project; National Research Foundation of Korea - Korean government (MSIT)(National Research Foundation of KoreaMinistry of Science & ICT (MSIT), Republic of Korea) YYYYYY This work supported by the Civil, Mechanical and Manufacturing Innovation (CMMI) Program of the National Science Foundation (Grant No. NSF 1645047), the European Union's Horizon 2020 research program under the STORM project (Grant No. 700191), and the National Research Foundation of Korea funded by the Korean government (MSIT) (Grant No. NRF-2021R1G1A1012298). 48 2 2 0 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel MAR 2022.0 12.0 6 2983 10.3390/app12062983 0.0 16 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics 0D5GI gold 2023-03-23 WOS:000776023200001 0
J Liu, LZ; Cui, JJ; Huan, YX; Zou, Z; Hu, XM; Zheng, LR Liu, Lizheng; Cui, Jianjun; Huan, Yuxiang; Zou, Zhuo; Hu, Xiaoming; Zheng, Lirong A Design of Smart Unmanned Vending Machine for New Retail Based on Binocular Camera and Machine Vision IEEE CONSUMER ELECTRONICS MAGAZINE English Article Cameras; Image stitching; Servers; Image sensors; Feature extraction; Face recognition; Containers The smart unmanned vending machine using machine vision technology suffers from the sharp decrease of detection accuracy due to the incomplete image collection of items by monocular camera in complex environment, and the lack of obvious features in dense stacking of items. In this article, a binocular camera system is designed to effectively solve the problems of distortion and coverage caused by monocular camera. Besides, an image-stitching algorithm is developed to splice the images captured by the camera, which reliefs the burden of computation for back-end recognition processing brought by the binocular camera. A new neural network structure-the YOLOv3-TinyE is proposed based on YOLOv3-tiny model. Based on the dataset of 21,000 images captured in real scenarios containing 20 different type of beverages, the comparison experimental results show that YOLOv3-TinyE model achieves the mean average precision of 99.15%, and the inference speed is 2.91 times faster than that of YOLOv3 model, and the detection accuracy of YOLOv3-TinyE model based on binocular vision is higher than that based on monocular vision. The results suggest that the designed method achieves the goal in terms of inference speed and average precision, that is, it is able to satisfy the requirements for real-world applications. [Liu, Lizheng; Cui, Jianjun; Huan, Yuxiang; Zheng, Lirong] Fudan Univ, Shanghai, Peoples R China; [Zou, Zhuo] Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China; [Liu, Lizheng; Hu, Xiaoming] Royal Inst Technol, Stockholm, Sweden Fudan University; Fudan University; Royal Institute of Technology Liu, LZ (corresponding author), Fudan Univ, Shanghai, Peoples R China.;Liu, LZ (corresponding author), Royal Inst Technol, Stockholm, Sweden. lzliu@fudan.edu.cn; jjcui16@fudan.edu.cn; yxhuan13@fudan.edu.cn; zhuo@fudan.edu.cn; hu@kth.se; lrzheng@fudan.edu.cn ZHENG, Lirong/ABF-9274-2020; liu, lizheng/HHS-3632-2022; Hu, Xiaoming/A-8341-2018 ZHENG, Lirong/0000-0001-9588-0239; Liu, Lizheng/0000-0002-6554-2041; Hu, Xiaoming/0000-0003-0177-1993 China Postdoctoral Science Foundation [2020M670993]; Pilot project on the basic theory and key technologies of ternary spatial swarm intelligence [19511132000] China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Pilot project on the basic theory and key technologies of ternary spatial swarm intelligence This Project has been supported by China Postdoctoral Science Foundation 2020M670993, and the Pilot project on the basic theory and key technologies of ternary spatial swarm intelligence (19511132000). 21 3 3 5 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-2248 2162-2256 IEEE CONSUM ELECTR M IEEE Consum. Electron. Mag. JUL 1 2022.0 11.0 4 21 31 10.1109/MCE.2021.3060722 0.0 11 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 2A3GX 2023-03-23 WOS:000809395000011 0
J Zhao, CR; Wang, CT; Hu, GS; Chen, HN; Liu, C; Tang, JH Zhao, Cairong; Wang, Chutian; Hu, Guosheng; Chen, Haonan; Liu, Chun; Tang, Jinhui ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Deepfakes; Transformers; Visualization; Task analysis; Electronic mail; Shape; Robustness; Deepfake detection; video transformer; deep learning interpretability With the rapid development of Deepfake synthesis technology, our information security and personal privacy have been severely threatened in recent years. To achieve a robust Deepfake detection, researchers attempt to exploit the joint spatial-temporal information in the videos, like using recurrent networks and 3D convolutional networks. However, these spatial-temporal models remain room to improve. Another general challenge for spatial-temporal models is that people do not clearly understand what these spatial-temporal models really learn. To address these two challenges, in this paper, we propose an Interpretable Spatial-Temporal Video Transformer (ISTVT), which consists of a novel decomposed spatial-temporal self-attention and a self-subtract mechanism to capture spatial artifacts and temporal inconsistency for robust Deepfake detection. Thanks to this decomposition, we propose to interpret ISTVT by visualizing the discriminative regions for both spatial and temporal dimensions via the relevance (the pixel-wise importance on the input) propagation algorithm. We conduct extensive experiments on large-scale datasets, including FaceForensics++, FaceShifter, DeeperForensics, Celeb-DF, and DFDC datasets. Our strong performance of intra-dataset and cross-dataset Deepfake detection demonstrates the effectiveness and robustness of our method, and our visualization-based interpretability offers people insights into our model. [Zhao, Cairong] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China; [Hu, Guosheng] Oosto, Belfast 38330, North Ireland; [Chen, Haonan] Alibaba Grp, Hangzhou 310052, Peoples R China; [Liu, Chun] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Tang, Jinhui] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China Tongji University; Alibaba Group; Tongji University; Nanjing University of Science & Technology Zhao, CR (corresponding author), Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China. zhaocairong@tongji.edu.cn; huguosheng100@gmail.com; haolan.chn@alibaba-inc.com; liuchun@tongji.edu.cn; jinhuitang@njust.edu.cn National Natural Science Fund of China [62076184, 61976158, 61976160, 62076182]; Shanghai Innovation Action Project of Science and Technology [20511100700]; Shanghai Natural Science Foundation [22ZR1466700]; Fundamental Research Funds for the Central Universities and the State Key Laboratory of Integrated Services Networks, Xidian University National Natural Science Fund of China(National Natural Science Foundation of China (NSFC)); Shanghai Innovation Action Project of Science and Technology; Shanghai Natural Science Foundation(Natural Science Foundation of Shanghai); Fundamental Research Funds for the Central Universities and the State Key Laboratory of Integrated Services Networks, Xidian University This work was supported in part by the National Natural Science Fund of China under Grant 62076184,Grant 61976158, Grant 61976160, and Grant 62076182; in part by the Shanghai Innovation Action Project of Science and Technology under Grant 20511100700; in part by the Shanghai Natural Science Foundation under Grant 22ZR1466700; and in part by the Fundamental Research Funds for the Central Universities and the State Key Laboratory of Integrated Services Networks, Xidian University. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Bin Li.(Cairong Zhao and Chutian Wang contributed equally to this work.) 59 0 0 4 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2023.0 18.0 1335 1348 10.1109/TIFS.2023.3239223 0.0 14 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 8O8IZ 2023-03-23 WOS:000926076800003 0
J Li, YS; Zong, SX; Shen, YF; Pu, ZQ; Gomez, MA; Cui, YX Li, Yuesen; Zong, Shouxin; Shen, Yanfei; Pu, Zhiqiang; Gomez, Miguel-Angel; Cui, Yixiong Characterizing player's playing styles based on player vectors for each playing position in the Chinese Football Super League JOURNAL OF SPORTS SCIENCES English Article Soccer; match analysis; style of play; performance profile; machine learning NETWORK ANALYSIS; SOCCER; TEAMS; BEHAVIOR; SPANISH Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players. [Li, Yuesen; Zong, Shouxin; Shen, Yanfei; Cui, Yixiong] Beijing Sport Univ, Sch Sports Engn, Informat Rd 48, Beijing 100084, Peoples R China; [Pu, Zhiqiang] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China; [Gomez, Miguel-Angel] Univ Politecn Madrid, Fac Actividad Fis & Deporte INEF, Madrid, Spain; [Cui, Yixiong] Beijing Sport Univ, Sch Sports Engn, AI Sports Engn Lab, Beijing, Hebei, Peoples R China Beijing Sport University; Chinese Academy of Sciences; Institute of Automation, CAS; Universidad Politecnica de Madrid; Beijing Sport University Shen, YF; Cui, YX (corresponding author), Beijing Sport Univ, Sch Sports Engn, Informat Rd 48, Beijing 100084, Peoples R China. syf@bsu.edu.cn; amtf000cui@gmail.com Li, Yuesen/0000-0002-5218-9672; CUI, YIXIONG/0000-0002-1755-9631 National Key Research and Development Program of China [2020AAA0103404]; National Natural Science Foundation of China [72071018, 72101032]; Fundamental Research Funds for the Central Universities of China [2021TD008] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development Program of China under grants 2020AAA0103404, and by National Natural Science Foundation of China under grants 72071018 and 72101032. The corresponding author (Y.C.) was supported by the Fundamental Research Funds for the Central Universities of China (2021TD008) 47 0 0 7 9 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0264-0414 1466-447X J SPORT SCI J. Sports Sci. JUL 18 2022.0 40.0 14 1629 1640 10.1080/02640414.2022.2096771 0.0 JUL 2022 12 Sport Sciences Science Citation Index Expanded (SCI-EXPANDED) Sport Sciences 3X1RN 35793267.0 Green Submitted 2023-03-23 WOS:000821440000001 0
J Zhang, X; Zou, Q; Rodriguez-Paton, A; Zeng, XX Zhang, Xuan; Zou, Quan; Rodriguez-Paton, Alfonso; Zeng, Xiangxiang Meta-Path Methods for Prioritizing Candidate Disease miRNAs IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS English Article Systems biology; miRNA; disease related miRNA; biological network; meta-path method MICRORNA; ASSOCIATIONS; SIMILARITY; PREDICTION MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Numerous experimental studies indicate that alterations and dysregulations in miRNAs are associated with important complex diseases, especially cancers. Predicting potential miRNA-disease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNA-disease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA-target association and disease-miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/. [Zhang, Xuan; Zeng, Xiangxiang] Xiamen Univ, Dept Comp Sci, Xiamen 361005, Peoples R China; [Zou, Quan] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China; [Rodriguez-Paton, Alfonso] Univ Politecn Madrid, Campus Montegancedo S-N, E-28660 Madrid, Spain Xiamen University; Tianjin University; Universidad Politecnica de Madrid Zeng, XX (corresponding author), Xiamen Univ, Dept Comp Sci, Xiamen 361005, Peoples R China. xuanzhang@foxmail.com; zouquan@nclab.net; arpaton@fi.upm.es; xzeng@xmu.edu.cn Rodríguez-Patón, Alfonso/H-1003-2011; Zou, Quan/A-7801-2015; zeng, xiangxiang/H-3771-2014 Rodríguez-Patón, Alfonso/0000-0001-7289-2114; zeng, xiangxiang/0000-0003-1081-7658; Zou, Quan/0000-0001-6406-1142; Zhang, Xuan/0000-0002-3089-9809 National Natural Science Foundation of China [61472333, 61772441, 61272152]; Natural Science Foundation of the Higher Education Institutions of Fujian Province [JZ160400]; President Fund of Xiamen University [20720170054]; [FP7-FET 612146] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of the Higher Education Institutions of Fujian Province; President Fund of Xiamen University; This work was supported by the National Natural Science Foundation of China (no. 61472333, 61772441 and 61272152), Natural Science Foundation of the Higher Education Institutions of Fujian Province (no. JZ160400), President Fund of Xiamen University (no. 20720170054), and the European project funded under FP7-FET 612146. 35 107 108 7 46 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5963 1557-9964 IEEE ACM T COMPUT BI IEEE-ACM Trans. Comput. Biol. Bioinform. JAN-FEB 2019.0 16.0 1 283 291 10.1109/TCBB.2017.2776280 0.0 9 Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematics, Interdisciplinary Applications; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Computer Science; Mathematics HK7UJ 29990255.0 2023-03-23 WOS:000458194900028 0
J Chen, H; Jiang, L; Yang, HP; Lu, ZH; Fu, YX; Li, L; Yu, ZG Chen, Hui; Jiang, Lin; Yang, Heping; Lu, Zhonghai; Fu, Yuxiang; Li, Li; Yu, Zongguang An Efficient Hardware Architecture with Adjustable Precision and Extensible Range to Implement Sigmoid and Tanh Functions ELECTRONICS English Article sigmoid and tanh; hyperbolic and linear CORDIC; carry-save method; efficient hardware; adjustable precision; high speed The efficient and precise hardware implementations of tanh and sigmoid functions play an important role in various neural network algorithms. Different applications have different requirements for accuracy. However, it is difficult for traditional methods to achieve adjustable precision. Therefore, we propose an efficient-hardware, adjustable-precision and high-speed architecture to implement them for the first time. Firstly, we present two methods to implement sigmoid and tanh functions. One is based on the rotation mode of hyperbolic CORDIC and the vector mode of linear CORDIC (called RHC-VLC), another is based on the carry-save method and the vector mode of linear CORDIC (called CSM-VLC). We validate the two methods by MATLAB and RTL implementations. Synthesized under the TSMC 40 nm CMOS technology, we find that a special case AR divide VR(3,0), based on RHC-VLC method, has the area of 4290.98 mu m2 and the power of 1.69 mW at the frequency of 1.5 GHz. However, under the same frequency, AR divide VC(3) (a special case based on CSM-VLC method) costs 3196.36 mu m2 area and 1.38 mW power. They are both superior to existing methods for implementing such an architecture with adjustable precision. [Chen, Hui; Jiang, Lin; Yang, Heping; Fu, Yuxiang; Li, Li; Yu, Zongguang] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, S-16440 Stockholm, Sweden Nanjing University; Royal Institute of Technology Fu, YX; Li, L (corresponding author), Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China. winnerchan@smail.nju.edu.cn; jianglin@smail.nju.edu.cn; peace_yang@smail.nju.edu.cn; zhonghai@kth.se; yuxiangfu@nju.edu.cn; lili@nju.edu.cn; yuzg58@163.com National Nature Science Foundation of China [61176024]; Nanjing University Technology Innovation Fund National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Nanjing University Technology Innovation Fund This work was supported by the National Nature Science Foundation of China under Grant No. 61176024, Nanjing University Technology Innovation Fund. 25 5 5 3 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics OCT 2020.0 9.0 10 1739 10.3390/electronics9101739 0.0 23 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics ON8RF gold 2023-03-23 WOS:000586960000001 0
C Kibalya, G; Serrat, J; Gorricho, JL; Pasquini, R; Yao, HP; Zhang, PY Lutfiyya, H; Diao, YX; Zincir-Heywood, N; Badonnel, R; Madeira, E Kibalya, Godfrey; Serrat, Joan; Gorricho, Juan-Luis; Pasquini, Rafael; Yao, Haipeng; Zhang, Peiying A Reinforcement Learning Based Approach for 5G Network Slicing Across Multiple Domains 2019 15TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) International Conference on Network and Service Management English Proceedings Paper 15th Int Conf on Network and Serv Management (CNSM) / 1st Int Workshop on Analyt for Serv and Application Management (AnServApp) / Int Workshop on High-Precision Networks Operat and Control, Segment Routing and Serv Function Chaining (HiPNet+SR/SFC) OCT 21-25, 2019 Halifax, CANADA IFIP,IEEE Comp Soc,IEEE Commun Soc,ACM,Dalhousie Univ,Western Univ,Juniper Networks,Mitacs,2Keys Corp,Moogsoft,GoSecure,Discover Halifax,Murphys,Digital Nova Scotia,Cisco Multi-substrate VNE; Reinforcement learning; Multi-domain slicing; 5G Network Function Virtualization (NFV) and Machine Learning (ML) are envisioned as possible techniques for the realization of a flexible and adaptive 5G network. ML will provide the network with experiential intelligence to forecast, adapt and recover from temporal network fluctuations. On the other hand, NFV will enable the deployment of slice instances meeting specific service requirements. Moreover, a single slice instance may require to be deployed across multiple substrate networks; however, existing works on multi-substrate Virtual Network Embedding fall short on addressing the realistic slice constraints such as delay, location, etc., hence they are not suited for applications transcending multiple domains. In this paper, we address the multi-substrate slicing problem in a coordinated manner, and we propose a Reinforcement Learning (RL) algorithm for partitioning the slice request to the different candidate substrate networks. Moreover, we consider realistic slice constraints such as delay, location, etc. Simulation results show that the RL approach results into a performance comparable to the combinatorial solution, with more than 99% of time saving for the processing of each request. [Kibalya, Godfrey; Serrat, Joan; Gorricho, Juan-Luis] Univ Politecn Cataluna, Dept Network Engn, Barcelona, Spain; [Pasquini, Rafael] Univ Fed Uberlandia, Comp Fac, Uberlandia, MG, Brazil; [Yao, Haipeng] Beinjing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol Bein, Beijing, Peoples R China; [Zhang, Peiying] China Univ Petr East China, Coll Comp & Commun Engn, Qingdao, Peoples R China Universitat Politecnica de Catalunya; Universidade Federal de Uberlandia; China University of Petroleum Kibalya, G (corresponding author), Univ Politecn Cataluna, Dept Network Engn, Barcelona, Spain. Godfrey.mirondo.Kibalya@upc.edu; serrat@tsc.upc.edu; juanluis@entel.upc.edu; rafael.pasquini@ufu.br; yaohaipeng@bupt.edu.cn; 25640521@qq.com European Union [777067]; MINECO/FEDER [TEC201571329-C2-2-R] European Union(European Commission); MINECO/FEDER This work has received funding from the European Union's Horizon 2020 re-search and innovation programme under grant agreement No 777067 (NECOS pro-ject). This work is also funded by the national project TEC201571329-C2-2-R (MINECO/FEDER). 10 4 4 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2165-9605 978-3-903176-24-9 INT CONF NETW SER 2019.0 5 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BP4GE 2023-03-23 WOS:000552229800016 0
C Tallon-Ballesteros, AJ; Fong, S; Leal-Diaz, R Garcia, HP; Gonzalez, LS; Limas, MC; Pardo, HQ; Rodriguez, EC Tallon-Ballesteros, Antonio J.; Fong, Simon; Leal-Diaz, Rocio Does the Order of Attributes Play an Important Role in Classification? HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019 Lecture Notes in Artificial Intelligence English Proceedings Paper 14th International Conference on Hybrid Artificial Intelligence Systems (HAIS) SEP 04-06, 2019 Leon, SPAIN Startup OLE,Univ Leon,Univ Salamanca Data mining; Feature sorting; Feature ranking; Feature selection; Data preparation; Knowledge Discovery in Databases This paper proposes a methodology to feature sorting in the context of supervised machine learning algorithms. Feature sorting is defined as a procedure to order the initial arrangement of the attributes according to any sorting algorithm to assign an ordinal number to every feature, depending on its importance; later the initial features are sorted following the ordinal numbers from the first to the last, which are provided by the sorting method. Feature ranking has been chosen as the representative technique to fulfill the sorting purpose inside the feature selection area. This contribution aims at introducing a new methodology where all attributes are included in the data mining task, following different sortings by means of different feature ranking methods. The approach has been assessed in ten binary and multiple class problems with a number of features lower than 37 and a number of instances below than 106 up to 28056; the test-bed includes one challenging data set with 21 labels and 23 attributes where previous works were not able to achieve an accuracy of at least a fifty percent. ReliefF is a strong candidate to be applied in order to re-sort the initial characteristic space and C4.5 algorithm achieved a promising global performance; additionally, PART -a rule-based classifierand Support Vector Machines obtained acceptable results. [Tallon-Ballesteros, Antonio J.] Univ Huelva, Dept Elect Comp Syst & Automat Engn, Huelva, Spain; [Fong, Simon] Univ Macau, Dept Comp & Informat Sci, Taipa, Macao, Peoples R China; [Leal-Diaz, Rocio] Univ Seville, Higher Sch Comp Sci, Seville, Spain Universidad de Huelva; University of Macau; University of Sevilla Tallon-Ballesteros, AJ (corresponding author), Univ Huelva, Dept Elect Comp Syst & Automat Engn, Huelva, Spain. antonio.tallon.diesia@zimbra.uhu.es Tallón-Ballesteros, Antonio J/HNB-6529-2023; Fong, Simon/C-9388-2009 Fong, Simon/0000-0002-1848-7246 Spanish Inter-Ministerial Commission of Science and Technology (MICYT) [TIN2014-55894-C2-R, TIN2017-88209-C2-2-R]; FEDER funds; Junta de Andalucia (Spain) [P11-TIC-7528] Spanish Inter-Ministerial Commission of Science and Technology (MICYT); FEDER funds(European Commission); Junta de Andalucia (Spain)(Junta de AndaluciaEuropean Commission) This work has been partially subsidised by TIN2014-55894-C2-R and TIN2017-88209-C2-2-R projects of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7528 project of the Junta de Andalucia (Spain). 41 0 0 1 2 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-29859-3; 978-3-030-29858-6 LECT NOTES ARTIF INT 2019.0 11734.0 370 380 10.1007/978-3-030-29859-3_32 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BP6ZO 2023-03-23 WOS:000561045500032 0
J Adeel, M; Tejedor, J; Macias-Guarasa, J; Zhu, K Adeel, Muhammad; Tejedor, Javier; Macias-Guarasa, Javier; Zhu, Kun Undiscovered issues and solutions for direct detected phi-OTDR systems OPTICAL FIBER TECHNOLOGY English Article Distributed acoustic sensing; phi-OTDR; Machine learning; Detection; Recognition SURVEILLANCE SYSTEM; SENSING SYSTEM; VIBRATION; RECOGNITION; RESOLUTION; TRANSFORM Time and spatial domains in phi-OTDR perturbation detection and recognition for pipeline and border security applications in very long fiber under test (FUT) environments have not been properly analyzed so far. We propose in this paper several issues and the corresponding solutions that should be considered in both domains when developing those applications. Solutions are based on: (1) the importance of differential signals in direct detected phi-OTDR systems; (2) dealing with the useless data acquired in the time domain; (3) dealing with the zero and ghost energy points along the FUT which will undoubtedly lead to hiding the real perturbation and reducing the potential of finding the exact location of the perturbation, respectively. Experimental results show that: (1) setting the appropriate experimental conditions (i.e., increasing the intensity of the perturbation and sampling at the minimum sampling rate that satisfies the Nyquist criterion) is a must; (2) the Kendall correlation based technique on differential signals allows obtaining more significant data traces both in time and spatial domains; (3) combining both methods obtains further gains; (4) S-Scan algorithm accurately provides the time instant of the real perturbation by increasing the probability of detecting zero energy points and avoiding ghost energy points in further processing. [Adeel, Muhammad] Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China; [Tejedor, Javier] Fdn Univ San Pablo, CEU, Madrid, Spain; [Macias-Guarasa, Javier] Univ Alcala, Madrid, Spain; [Zhu, Kun] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China Hong Kong Polytechnic University; Universidad de Alcala; City University of Hong Kong Zhu, K (corresponding author), City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China. m.adeel@connect.polyu.hk; javier.tejedornoguerales@ceu.es; javier.maciasguarasa@uah.es; kunzhu@cityu.edu.hk Tejedor, Javier/L-2075-2015; Macias-Guarasa, Javier/J-4625-2012 Tejedor, Javier/0000-0001-7699-5620; Macias-Guarasa, Javier/0000-0002-3303-3963 Spanish Ministry of Economy and Competitiveness [TIN2016-75982-C2-1-R, TIN2016-80939-R]; University of Alcala [CCG2018/EXP-019, CCG19/IA-024] Spanish Ministry of Economy and Competitiveness(Spanish Government); University of Alcala This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under projects HEIMDAL-UAH (TIN2016-75982-C2-1-R) and ARTEMISA (TIN2016-80939-R) and by the University of Alcala under projects ACERCA (CCG2018/EXP-019) and ACUFANO (CCG19/IA-024). 51 1 1 4 21 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1068-5200 1095-9912 OPT FIBER TECHNOL Opt. Fiber Technol. DEC 2020.0 60.0 102346 10.1016/j.yofte.2020.102346 0.0 13 Engineering, Electrical & Electronic; Optics; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Optics; Telecommunications PE1QR 2023-03-23 WOS:000598146200004 0
J Gonzalez-Raya, T; Cheng, XH; Egusquiza, IL; Chen, X; Sanz, M; Solano, E Gonzalez-Raya, Tasio; Cheng, Xiao-Hang; Egusquiza, Inigo L.; Chen, Xi; Sanz, Mikel; Solano, Enrique Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons PHYSICAL REVIEW APPLIED English Article NOISE-INDUCED SYNCHRONIZATION; COHERENCE RESONANCE; RELIABILITY; BEHAVIOR; NETWORK The Hodgkin-Huxley model describes the behavior of the cell membrane in neurons, treating each part of it as an electric circuit element; namely, capacitors, memristors, and voltage sources. We focus on the activation channel of potassium ions, due to its simplicity, while keeping most of the features displayed by the original model. This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor. We consider a quantized Hodgkin-Huxley model based on a quantum-memristor formalism. We compare the behavior of the membrane voltage and the potassium-channel conductance when the circuit is subjected to ac sources, in both the classical realm and the quantum realm. Numerical simulations show an expected adaptation of the considered channel conductance depending on the signal history in all regimes. Remarkably, the computation of higher moments of the voltage shows purely quantum features related to the circuit zero-point energy. Finally, we study the implementation of the Hodgkin-Huxley quantum memristor as an asymmetric rf superconducting quantum-interference device in superconducting circuits. This study may allow the construction of quantum neuron networks inspired by the brain function, as well as the design of neuromorphic quantum architectures for quantum machine learning. [Gonzalez-Raya, Tasio; Cheng, Xiao-Hang; Chen, Xi; Sanz, Mikel; Solano, Enrique] Univ Basque Country, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain; [Cheng, Xiao-Hang; Chen, Xi; Solano, Enrique] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Cheng, Xiao-Hang; Chen, Xi; Solano, Enrique] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China; [Egusquiza, Inigo L.] Univ Basque Country, Dept Theoret Phys & Hist Sci, Apartado 644, Bilbao 48080, Spain; [Solano, Enrique] Basque Fdn Sci, Ikerbasque, Maria Diaz de Haro 3, Bilbao 48013, Spain University of Basque Country; Shanghai University; Shanghai University; University of Basque Country; Basque Foundation for Science Sanz, M (corresponding author), Univ Basque Country, Dept Phys Chem, Apartado 644, Bilbao 48080, Spain. mikel.sanz@ehu.eus chen, xi/GXH-3653-2022; Sanz, Mikel/W-8637-2019; Egusquiza, Íñigo Luis Egusquiza/C-8952-2014; Chen, Xi/ABE-6575-2020; Egusquiza, Íñigo Luis Egusquiza/ABF-3277-2020 Sanz, Mikel/0000-0003-1615-9035; Egusquiza, Íñigo Luis Egusquiza/0000-0002-5827-8027; Chen, Xi/0000-0003-4221-4288; Egusquiza, Íñigo Luis Egusquiza/0000-0002-5827-8027; Gonzalez-Raya, Tasio/0000-0001-9645-9681 Spanish Government (MCIU/AEI/FEDER, UE) [PGC2018-095113-B-I00]; Ramon y Cajal Grant [RYC-2017-22482]; Basque Government [IT986-16]; Shanghai Municipal Science and Technology Commission [18010500400, 18ZR1415500]; Shanghai Program for Eastern Scholar; EU Flagship on Quantum Technologies [820505, 820363]; EU [828826]; U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR) [ERKJ333] Spanish Government (MCIU/AEI/FEDER, UE); Ramon y Cajal Grant(Spanish Government); Basque Government(Basque Government); Shanghai Municipal Science and Technology Commission(Science & Technology Commission of Shanghai Municipality (STCSM)); Shanghai Program for Eastern Scholar; EU Flagship on Quantum Technologies; EU(European Commission); U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR) The authors acknowledge support from Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE) and Ramon y Cajal Grant RYC-2017-22482, and Basque Government IT986-16, as well as Shanghai Municipal Science and Technology Commission (18010500400 and 18ZR1415500), and the Shanghai Program for Eastern Scholar. The authors also acknowledge support from the projects QMiCS (820505) and Open-SuperQ (820363) of the EU Flagship on Quantum Technologies, as well as EU FET Open Grant Quromorphic (828826). This material is also based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR), under field work proposal number ERKJ333. 51 6 6 1 23 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2331-7019 PHYS REV APPL Phys. Rev. Appl. JUL 22 2019.0 12.0 1 14037 10.1103/PhysRevApplied.12.014037 0.0 13 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics IK6LI Green Submitted, hybrid 2023-03-23 WOS:000476697000001 0
C Liang, HZ; Ma, XJ; Li, S; Gorner, M; Tang, S; Fang, B; Sun, FC; Zhang, JW Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Liang, Hongzhuo; Ma, Xiaojian; Li, Shuang; Gorner, Michael; Tang, Song; Fang, Bin; Sun, Fuchun; Zhang, Jianwei PointNetGPD: Detecting Grasp Configurations from Point Sets 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud. Compared to recent grasp evaluation metrics that are based on handcrafted depth features and a convolutional neural network (CNN), our proposed PointNetGPD is lightweight and can directly process the 3D point cloud that locates within the gripper for grasp evaluation. Taking the raw point cloud as input, our proposed grasp evaluation network can capture the complex geometric structure of the contact area between the gripper and the object even if the point cloud is very sparse. To further improve our proposed model, we generate a large-scale grasp dataset with 350k real point cloud and grasps with the YCB object set for training. The performance of the proposed model is quantitatively measured both in simulation and on robotic hardware. Experiments on object grasping and clutter removal show that our proposed model generalizes well to novel objects and outperforms state-of-the-art methods. Code and video are available at https://lianghongzhuo.github.io/PointNetGPD. [Liang, Hongzhuo; Li, Shuang; Gorner, Michael; Tang, Song; Zhang, Jianwei] Univ Hamburg, Dept Informat, TAMS, Hamburg, Germany; [Ma, Xiaojian; Fang, Bin; Sun, Fuchun] Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, TNList, Beijing, Peoples R China University of Hamburg; Tsinghua University Sun, FC (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, TNList, Beijing, Peoples R China. fsun@tsinghua.edu.cn zhang, jian/HPD-1712-2023 National Science Foundation of China (NSFC) [NSFC 61621136008]; German Research Foundation (DFG) [DFG TRR-169]; National Science Foundation of China [91848206, U1613212]; project STEP2DYNA [691154] National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); project STEP2DYNA This research was funded jointly by the National Science Foundation of China (NSFC) and the German Research Foundation (DFG) in project Cross Modal Learning, NSFC 61621136008/DFG TRR-169. It was also partially supported by National Science Foundation of China (Grant No.91848206, U1613212) and project STEP2DYNA (691154). We would like to thank Chao Yang and Professor Huaping Liu for their generous help and insightful advice. 27 96 100 7 45 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 3629 3635 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN Green Submitted 2023-03-23 WOS:000494942302096 0
J Hu, YN; Ying, D; Sun, ZY; Li, B; Zhou, HX; Wang, S; Hu, XM; He, K; Qu, M; Chen, WX; Chen, Z; Yang, WX Hu, Yaning; Ying, Dou; Sun, Zhiyi; Li, Bing; Zhou, Hexin; Wang, Shuo; Hu, Xuemin; He, Kuan; Qu, Ming; Chen, Wenxing; Chen, Zhuo; Yang, Wenxiu Rational design, application and dynamic evolution of Cu-N-C single-atom catalysts JOURNAL OF MATERIALS CHEMISTRY A English Review GRAPHITIC CARBON NITRIDE; OXYGEN REDUCTION; DOPED GRAPHENE; SITE CATALYST; NANOPARTICLES; COPPER; OXIDATION; CONVERSION; AMMONIA; ELECTROCATALYSIS Benefiting from the unique electronic and structural characteristics, transition metal and nitrogen co-doped carbon-based (M-N-C) nanomaterials are considered to be one of the most promising catalysts. The combination of uniformly dispersed Cu sites and N-doped carbon materials has enabled Cu-N-C SACs to show outstanding performances in many catalytic fields. However, the adjustment of their coordination environment, the influence of carrier materials and their dynamic evolution in the catalytic process still remain unclear, which limits the application of Cu-N-C SACs. Thus, it is of great significance and urgent to deeply understand the reaction mechanism of Cu-N-C SACs in different catalytic fields. This review focuses on the preparation strategy, regulation mechanism of the coordination environment, catalytic performance and dynamic evolution process of Cu-N-C SACs. Combined with a series of in situ/operando characterization methods, the changes in their coordination and structure in the catalytic process is highlighted. Finally, the prospects of active machine learning, coordination structure identification and determination of real active sites are presented. This review aims to provide a reference for the rational design of Cu-N-C SACs and the exploration of their catalytic mechanism. [Hu, Yaning; Li, Bing; Zhou, Hexin; Wang, Shuo; Hu, Xuemin; Yang, Wenxiu] Hebei Univ Sci & Technol, Coll Text & Garments, Innovat Ctr Textile & Garment Technol, Shijiazhuang 050018, Hebei, Peoples R China; [Ying, Dou; Sun, Zhiyi; Chen, Wenxing; Chen, Zhuo] Beijing Inst Technol, Sch Mat Sci & Engn, Energy & Catalysis Ctr, Beijing 100081, Peoples R China; [He, Kuan] Univ Paris Dauphine PSL, Paris, France; [Qu, Ming] China Univ Petr, Unconvent Petr Res Inst, Beijing 102249, Peoples R China Hebei University of Science & Technology; Beijing Institute of Technology; UDICE-French Research Universities; Universite PSL; Universite Paris-Dauphine; China University of Petroleum Yang, WX (corresponding author), Hebei Univ Sci & Technol, Coll Text & Garments, Innovat Ctr Textile & Garment Technol, Shijiazhuang 050018, Hebei, Peoples R China.;Chen, Z (corresponding author), Beijing Inst Technol, Sch Mat Sci & Engn, Energy & Catalysis Ctr, Beijing 100081, Peoples R China.;Qu, M (corresponding author), China Univ Petr, Unconvent Petr Res Inst, Beijing 102249, Peoples R China. zchen@bit.edu.cn; wenxiuyang-hbust@outlook.com Chen, Wenxing/AAE-9886-2020 Chen, Wenxing/0000-0001-9669-4358 National Natural Science Foundation of China [21801015]; Beijing Institute of Technology Research Fund Program for Young Scholars [3090012221909]; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hebei Province [B2021208001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Institute of Technology Research Fund Program for Young Scholars; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Natural Science Foundation of Hebei Province(Natural Science Foundation of Hebei Province) This work was supported by the National Natural Science Foundation of China (Grant No. 21801015) and Beijing Institute of Technology Research Fund Program for Young Scholars (3090012221909) and Fundamental Research Funds for the Central Universities and the Natural Science Foundation of Hebei Province (B2021208001). 208 0 0 98 98 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2050-7488 2050-7496 J MATER CHEM A J. Mater. Chem. A OCT 25 2022.0 10.0 41 21769 21796 10.1039/d2ta06050g 0.0 SEP 2022 28 Chemistry, Physical; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Energy & Fuels; Materials Science 5N2YO 2023-03-23 WOS:000864392900001 0
J Wang, T; Ji, XJ; Song, AG; Madani, K; Chohra, A; Lu, HM; Monero, R Wang, Ting; Ji, Xiangjun; Song, Aiguo; Madani, Kurosh; Chohra, Amine; Lu, Huimin; Monero, Ramon Output-Bounded and RBFNN-Based Position Tracking and Adaptive Force Control for Security Tele-Surgery ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS English Article Security tele-surgery; RBFNN; bilateral position control; force control IMPEDANCE CONTROL; ROBOT In security e-health brain neurosurgery, one of the important processes is to move the electrocoagulation to the appropriate position in order to excavate the diseased tissue.(1) However, it has been problematic for surgeons to freely operate the electrocoagulation, as the workspace is very narrow in the brain. Due to the precision, vulnerability, and important function of brain tissues, it is essential to ensure the precision and safety of brain tissues surrounding the diseased part. The present study proposes the use of a robot-assisted tele-surgery system to accomplish the process. With the aim to achieve accuracy, an output-bounded and RBF neural network-based bilateral position control method was designed to guarantee the stability and accuracy of the operation process. For the purpose of accomplishing a minimal amount of bleeding and damage, an adaptive force control of the slave manipulator was proposed, allowing it to be appropriate to contact the susceptible vessels, nerves, and brain tissues. The stability was analyzed, and the numerical simulation results revealed the high performance of the proposed controls. [Wang, Ting] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing 211816, Peoples R China; [Wang, Ting; Song, Aiguo] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China; [Ji, Xiangjun] Jingling Hosp, Dept Neurosurg, 305 East Zhongshan Rd, Nanjing 210002, Peoples R China; [Madani, Kurosh; Chohra, Amine] Paris Est Univ, Signals Images & Intelligent Syst Lab LISSI EA 39, Lieusaint, France; [Lu, Huimin] Kyushu Inst Technol, Sch Engn, Kitakyushu, Fukuoka 8048550, Japan; [Monero, Ramon] IK4 Res Alliance, LORTEK, Arranomendi Kalea 4A, Ordizia 20240, Gipuzkoa, Spain Nanjing Tech University; Southeast University - China; Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Kyushu Institute of Technology Wang, T (corresponding author), Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing 211816, Peoples R China.;Wang, T (corresponding author), Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China. Chohra, Amine/ABG-3479-2021 Chohra, Amine/0000-0002-1145-1371 National Natural Science Foundation of China; China Postdoctoral Science Foundation National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The authors would like to thank the National Natural Science Foundation of China and the China Postdoctoral Science Foundation for their support of this research. 17 1 1 0 10 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 1551-6857 1551-6865 ACM T MULTIM COMPUT ACM Trans. Multimed. Comput. Commun. Appl. JUN 2021.0 17.0 2 S SI 61 10.1145/3394920 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science TA7ZH 2023-03-23 WOS:000667464100004 0
C Mu, GD; Huang, D; Hu, GS; Sun, J; Wang, YH IEEE Comp Soc Mu, Guodong; Huang, Di; Hu, Guosheng; Sun, Jia; Wang, Yunhong Led3D: A Lightweight and Efficient Deep Approach to Recognizing Low-quality 3D Faces 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 16-20, 2019 Long Beach, CA IEEE,CVF,IEEE Comp Soc Due to the intrinsic invariance to pose and illumination changes, 3D Face Recognition (FR) has a promising potential in the real world. 3D FR using high-quality faces, which are of high resolutions and with smooth surfaces, have been widely studied. However research on that with low-quality input is limited, although it involves more applications. In this paper we focus on 3D FR using low-quality data, targeting an efficient and accurate deep learning solution. To achieve this, we work on two aspects: (1) designing a lightweight yet powerful CNN; (2) generating finer and bigger training data. For(1), we propose a Multi-Scale Feature Fusion (MSFF) module and a Spatial Attention Vectorization (SAV) module to build a compact and discriminative CNN. For (2), we propose a data processing system including point-cloud recovery, surface refinement, and data augmentation (with newly proposed shape jittering and shape scaling). We conduct extensive experiments on Lock3DFace and achieve state-of-the-art results, outperforming many heavy CNNs such as VGG-16 and ResNet-34. In addition, our model can operate at a very high speed (136 fps) on Jetson TX2, and the promising accuracy and efficiency reached show its great applicability on edge/mobile devices. [Mu, Guodong; Huang, Di; Sun, Jia; Wang, Yunhong] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China; [Hu, Guosheng] Anyvision, Queens Rd, Belfast, Antrim, North Ireland Beihang University Huang, D (corresponding author), Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China. muyouhang@buaa.edu.cn; dhuang@buaa.edu.cn; huguosheng100@gmail.com; sunjia@buaa.edu.cn; yhwang@buaa.edu.cn National Natural Science Foundation of China [61673033] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is funded by the National Natural Science Foundation of China under Grant 61673033. 40 19 23 1 3 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-7281-3293-8 PROC CVPR IEEE 2019.0 5766 5775 10.1109/CVPR.2019.00592 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO8XD 2023-03-23 WOS:000529484005097 0
C Shi, XH; Zhang, YP; Huang, H; Hu, ZY; Jin, H; Shen, H; Zhou, YL; He, BS; Li, RB; Zhou, KY IEEE Shi, Xuanhua; Zhang, Yipeng; Huang, Hong; Hu, Zhenyu; Jin, Hai; Shen, Huan; Zhou, Yongluan; He, Bingsheng; Li, Ruibo; Zhou, Keyong Maxson: Reduce Duplicate Parsing Overhead on Raw Data 2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) IEEE International Conference on Data Engineering English Proceedings Paper IEEE 36th International Conference on Data Engineering (ICDE) APR 20-24, 2020 Dallas, TX IEEE,IEEE Comp Soc JSON parsing; semi-structured format; data analytics system JSON is a very popular data format in many applications in Web and enterprise. Recently, many data analytical systems support the loading and querying JSON data. However, JSON parsing can be costly, which dominates the execution time of querying JSON data. Many previous studies focus on building efficient parsers to reduce this parsing cost, and little work has been done on how to reduce the occurrences of parsing. In this paper, we start with a study with a real production workload in Alibaba, which consists of over 3 million queries on JSON. Our study reveals significant temporal and spatial correlations among those queries, which result in massive redundant parsing operations among queries. Instead of repetitively parsing the JSON data, we propose to develop a cache system named Maxson for caching the JSON query results (the values evaluated from JSONPath) for reuse. Specifically, we develop effective machine learning-based predictor with combining LSTM (long short-term memory) and CRF (conditional random field) to determine the JSONPaths to cache given the space budget. We have implemented Maxson on top of SparkSQL. We experimentally evaluate Maxson and show that 1) Maxson is able to eliminate the most of duplicate JSON parsing overhead, 2) Maxson improves end-to-end workload performance by 1.5-6.5x. [Shi, Xuanhua; Zhang, Yipeng; Huang, Hong; Hu, Zhenyu; Jin, Hai; Shen, Huan] Huazhong Univ Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab, Wuhan, Hubei, Peoples R China; [Zhou, Yongluan] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark; [He, Bingsheng] Natl Univ Singapore, Singapore, Singapore; [Li, Ruibo; Zhou, Keyong] Alibaba Grp, Hangzhou, Peoples R China Huazhong University of Science & Technology; University of Copenhagen; National University of Singapore; Alibaba Group Huang, H (corresponding author), Huazhong Univ Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab, Wuhan, Hubei, Peoples R China. xhshi@hust.edu.cn; jinyu_zyp@hust.edu.cn; honghuang@hust.edu.cn; cszhenyuhu@hust.edu.cn; hjin@hust.edu.cn; huanshen@hust.edu.cn; Tzhou@di.ku.dk; hebs@comp.nus.edu.sg; ruibo.lirb@alibaba-inc.com; zky.zhoukeyong@alibaba-inc.com Zhou, Yongluan/0000-0002-7578-8117; He, Bingsheng/0000-0001-8618-4581 National Key Research and Development Plan [2017YFC0803700]; NSFC [61772218, 61832006, 61802140]; Hubei Provincial Natural Science Foundation [2018CFB200]; Alibaba Innovative Research (AIR) program National Key Research and Development Plan; NSFC(National Natural Science Foundation of China (NSFC)); Hubei Provincial Natural Science Foundation; Alibaba Innovative Research (AIR) program We thank Lu Lu and Yufen Zong for their valuable comments. This work is supported by the National Key Research and Development Plan (No. 2017YFC0803700), NSFC (No. 61772218, No. 61832006 and No. 61802140), Hubei Provincial Natural Science Foundation (No. 2018CFB200) and Alibaba Innovative Research (AIR) program. 29 2 2 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1084-4627 978-1-7281-2903-7 PROC INT CONF DATA 2020.0 1621 1632 10.1109/ICDE48307.2020.00144 0.0 12 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BQ3CR 2023-03-23 WOS:000584252700137 0
J Chen, BP; Wu, HF; Zhou, HW; Sun, DF Chen, Baiping; Wu, Huifeng; Zhou, Hongwei; Sun, Danfeng EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism APPLIED SCIENCES-BASEL English Article mold protection; toggle mechanism; EKF; servo system; self-adaptive SENSORLESS VALVE CONTROL; NEURAL-NETWORK CONTROL; OBSERVER; INTEGRATION; MACHINE; QUALITY; VISION Nowadays, the plastic injection molding industry is ever-growing, crucial, and its plastic products can be seen everywhere. However, the mold damage problem still frustrates operators because of its high maintenance price and time-consuming maintenance process. This damage is commonly caused by foreign bodies in mold area, and the conventional mold protection method is insufficient for high-performance injection molding machines because of the uncertainty from many setting parameters. To improve detection precision of mold protection driven by a toggle mechanism (TM), this paper puts forward EMP, i.e., an extended Kalman filter (EKF) based self-adaptive mold protection method, wherein the EKF is used in current curve optimization, and the self-adaptive method (SAM) is proposed to gain an safety range of current curve. The EMP was verified in a 140-ton electric injection molding machine. Compared with a general method, the proposed method decreases the detected distance of mold protection by 22% under different thickness foreign bodies. [Chen, Baiping; Wu, Huifeng] Hangzhou Dianzi Univ, Inst Intelligent & Software Technol, Hangzhou 310018, Peoples R China; [Zhou, Hongwei] Teder Machinery Co Ltd, Hangzhou 310018, Peoples R China; [Sun, Danfeng] Inst Automat & Kommunikat eV, D-39106 Magdeburg, Germany Hangzhou Dianzi University Sun, DF (corresponding author), Inst Automat & Kommunikat eV, D-39106 Magdeburg, Germany. chenbp@hdu.edu.cn; whf@hdu.edu.cn; zhou_hongwei@tederic-cn.com; danfeng.sun@ifak.eu Wu, Huifeng/HKE-9650-2023; Sun, Danfeng/J-9406-2019 Sun, Danfeng/0000-0002-7332-1169; Baiping, Chen/0000-0001-9730-3398 National Natural Science Foundation of China [U1609211] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China (U1609211). 37 2 2 2 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel FEB 2020.0 10.0 3 940 10.3390/app10030940 0.0 13 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics LC4PI gold 2023-03-23 WOS:000525305900211 0
J Liu, SJ; Xin, JM; Wu, JY; Deng, YY; Su, RS; Niessen, WJ; Zheng, NN; van Walsum, T Liu, Sijie; Xin, Jingmin; Wu, Jiayi; Deng, Yangyang; Su, Ruisheng; Niessen, Wiro J.; Zheng, Nanning; van Walsum, Theo Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification NEUROCOMPUTING English Article IVOCT; TCFA; Plaque identification; Multi -view learning; Transformer VULNERABLE PLAQUE; AUTOMATIC CLASSIFICATION; OCT Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view information or incorporate prior domain knowledge. In this paper, we pro-pose a multi-view contour-constrained transformer network (MVCTN) for TCFA identification in IVOCT images. Inspired by the diagnosis process of cardiologists, we use contour constrained self-attention modules (CCSM) to emphasize features corresponding to salient regions (i.e., vessel walls) in an unsuper-vised manner and enhance the visual interpretability based on class activation mapping (CAM). Moreover, we exploit transformer modules (TM) to build global-range relations between two views (i.e., polar and Cartesian views) to effectively fuse features at multiple feature scales. Experimental results on a semi-public dataset and an in-house dataset demonstrate that the proposed MVCTN outperforms other single-view and multi-view methods. Lastly, the proposed MVCTN can also provide meaningful visualization for cardiologists via CAM.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Liu, Sijie; Xin, Jingmin; Wu, Jiayi; Zheng, Nanning] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China; [Deng, Yangyang] Xi An Jiao Tong Univ, Affiliated Hosp 1, Med Coll, Cardiovasc Dept, Xian 710049, Peoples R China; [Liu, Sijie; Su, Ruisheng; Niessen, Wiro J.; van Walsum, Theo] Erasmus MC, Dept Radiol & Nucl Med, Biomed Imaging Grp Rotterdam, Rotterdam, Netherlands; [Niessen, Wiro J.] Delft Univ Technol, Fac Appl Sci, Delft, Netherlands Xi'an Jiaotong University; Xi'an Jiaotong University; Erasmus University Rotterdam; Erasmus MC; Delft University of Technology Xin, JM (corresponding author), Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China. jxin@mail.xjtu.edu.cn van Walsum, Theo/ABM-1912-2022 van Walsum, Theo/0000-0001-8257-7759 National Key Research and Development Program of China [2017YFA0700800]; National Natural Science Foundation of China [62088102, 82000336] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Acknowledgements This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFA0700800, and the National Natural Science Foundation of China under Grant 62088102 and 82000336. We thank Professor Bo Yu from Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China for help in building the Optical coherence tomography Plaque Recognition Database (OPRD) . 38 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing FEB 28 2023.0 523.0 224 234 10.1016/j.neucom.2022.12.041 0.0 DEC 2022 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8K5DH Green Published 2023-03-23 WOS:000923121800001 0
J Witteman, J; Van IJzendoorn, MH; Rilling, JK; Bos, PA; Schiller, NO; Bakermans-Kranenburg, MJ Witteman, J.; Van IJzendoorn, M. H.; Rilling, J. K.; Bos, P. A.; Schiller, N. O.; Bakermans-Kranenburg, M. J. Towards a neural model of infant cry perception NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS English Review Cry; Perception; Auditory; Brain; Emotion FUNCTIONAL CONNECTIVITY; CINGULATE CORTEX; BRAIN; METAANALYSIS; RESPONSES; OXYTOCIN; AMYGDALA; CEREBELLUM; FMRI; ATTACHMENT Previous work suggests that infant cry perception is supported by an evolutionary old neural network consisting of the auditory system, the thalamocingulate circuit, the frontoinsular system, the reward pathway and the medial prefrontal cortex. Furthermore, gender and parenthood have been proposed to modulate processing of infant cries. The present meta-analysis (N = 350) confirmed involvement of the auditory system, the thalamocingulate circuit, the dorsal anterior insula, the pre-supplementary motor area and dorsomedial prefrontal cortex and the inferior frontal gyrus in infant cry perception, but not of the reward pathway. Structures related to motoric processing, possibly supporting the preparation of a parenting response, were also involved. Finally, females (more than males) and parents (more than non-parents) recruited a cortico-limbic sensorimotor integration network, offering a neural explanation for previously observed enhanced processing of infant cries in these sub-groups. Based on the results, an updated neural model of infant cry perception is presented. [Witteman, J.; Schiller, N. O.; Bakermans-Kranenburg, M. J.] Leiden Univ, Ctr Linguist, Leiden Inst Brain & Cognit, Van Wijkplaats 2,R2-02b, NL-2311 BV Leiden, Netherlands; [Van IJzendoorn, M. H.] Capital Normal Univ Beijing, 83 Xi San Huan Bei Lu, Haidian 100089, Beijing Beijing, Peoples R China; [Van IJzendoorn, M. H.] Erasmus Univ, Mandeville Bldg,Room T15-10,POB 1738, NL-3000 DR Rotterdam, Netherlands; [Rilling, J. K.] Emory Coll Arts & Sci, Dept Anthropol, 1462 Clifton Rd, Atlanta, GA 30329 USA; [Bos, P. A.] Univ Utrecht, Fac Social Sci, Heidelberglaan 1, NL-3584 CS Utrecht, Netherlands; [Bakermans-Kranenburg, M. J.] Vrije Univ Amsterdam, Clin Child & Family Studies, Van Boechorststr 7, NL-1081 BT Amsterdam, Netherlands Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Erasmus University Rotterdam; Utrecht University; Vrije Universiteit Amsterdam Witteman, J (corresponding author), Leiden Univ, Ctr Linguist, Leiden Inst Brain & Cognit, Van Wijkplaats 2,R2-02b, NL-2311 BV Leiden, Netherlands. j.witteman@hum.leidenuniv.nl van IJzendoorn, Marinus/P-4907-2019; Schiller, Niels O./A-9481-2008; Bakermans-Kranenburg, Marian/AAF-7746-2020 van IJzendoorn, Marinus/0000-0003-1144-454X; Schiller, Niels O./0000-0002-0392-7608; Witteman, Jurriaan/0000-0003-3990-7569; Bos, Peter A/0000-0001-8944-0181 European Research Council [ERC AdG 669249] European Research Council(European Research Council (ERC)European Commission) We thank Joaquim Radua and his colleagues for the excellent support on the SDM support website. This work was supported by a European Research Council grant (ERC AdG 669249) awarded to M.J.B-K and a Spinoza prize to M.H-vIJ. 70 27 29 1 18 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0149-7634 1873-7528 NEUROSCI BIOBEHAV R Neurosci. Biobehav. Rev. APR 2019.0 99.0 23 32 10.1016/j.neubiorev.2019.01.026 0.0 10 Behavioral Sciences; Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Behavioral Sciences; Neurosciences & Neurology HP5OZ 30710581.0 hybrid, Green Published 2023-03-23 WOS:000461727700004 0
J Zhao, H; Li, LX; Peng, HP; Kurths, J; Xiao, JJ; Yang, YX; Li, A Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Juergen; Xiao, Jinghua; Yang, Yixian; Li, Ang A new approach of analyzing time-varying dynamical equation via an optimal principle MODERN PHYSICS LETTERS B English Article Time-varying dynamical equation; an optimal design principle; convergence performance; sensitivity to additive noise RECURRENT NEURAL-NETWORK; MATRIX-INVERSION; SYLVESTER EQUATIONS; EIGENSTRUCTURE ASSIGNMENT; SYSTEMS In this paper, an innovative design approach is proposed to solve time-varying dynamical equation, including matrix inverse equation and Sylvester equation. Based on the precondition of the existing solution of time-varying dynamical equation, different from previous approach to solve unknown matrix, an optimal design principle is used to solve the unknown variables. A performance index is introduced based on the inherent properties of the time-varying dynamical equation and Euler equation. The solution of time-varying dynamical equation is converted to an optimal problem of performance index. Furthermore, convergence and sensitivity to additive noise are also analyzed, and simulation results confirm that the method is feasible and effective. Especially, in simulations we design a tunable positive parameter in the dynamic optimization model. The tunable parameter is not only helpful to accelerate its convergence but also reduce its sensitivity to additive noise. Meanwhile the comparative simulation results are shown for the convergence accuracy and robustness of this method. [Zhao, Hui; Xiao, Jinghua] Beijing Univ Posts & Telecommun, Sch Sci, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China; [Li, Lixiang; Peng, Haipeng; Yang, Yixian] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Yang, Yixian] State Key Lab Publ Big Data, Guizhou 550025, Peoples R China; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Li, Ang] Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications; Potsdam Institut fur Klimafolgenforschung; Beijing University of Posts & Telecommunications Li, LX (corresponding author), Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. li_lixiang2006@163.com jinghua, xiao/F-3710-2012; Li, lixiang/G-6222-2011 Li, lixiang/0000-0001-9949-8731; Li, Lixiang/0000-0001-8541-308X National Natural Science Foundation of China [61472045, 61573067]; National Key Research and Development Program [2016YFB0800602, 2016YFB0800604]; Beijing City Board of Education Science and Technology Key Project [KZ201510015015]; Beijing City Board of Education Science and Technology Project [KM201510015009] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program; Beijing City Board of Education Science and Technology Key Project; Beijing City Board of Education Science and Technology Project This paper is supported by the National Natural Science Foundation of China (Grant Nos. 61472045 and 61573067), the National Key Research and Development Program (Grant Nos. 2016YFB0800602 and 2016YFB0800604), the Beijing City Board of Education Science and Technology Key Project (Grant No. KZ201510015015), and the Beijing City Board of Education Science and Technology Project (Grant No. KM201510015009). 35 3 3 1 7 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0217-9849 1793-6640 MOD PHYS LETT B Mod. Phys. Lett. B MAR 20 2017.0 31.0 8 1750084 10.1142/S0217984917500841 0.0 19 Physics, Applied; Physics, Condensed Matter; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics EQ4JQ 2023-03-23 WOS:000398043300010 0
J Chen, YN; Sun, RL; Gao, Y; Leopold, J Chen, Yanni; Sun, Ronglei; Gao, Yuan; Leopold, Juergen A nested-ANN prediction model for surface roughness considering the effects of cutting forces and tool vibrations MEASUREMENT English Article Surface roughness; ANN; RSM; Turning TI-6AL-4V ALLOY; METHODOLOGY RSM; PARAMETERS; OPTIMIZATION; WEAR; ALGORITHM; TAGUCHI This paper demonstrates a nested-ANN (Artificial Neural Network) mddel predicting surface roughness (Ra). The special ANN includes enclosed-ANNs and an output-ANN. The enclosed-ANN models use cutting parameters as inputs to predict the values of cutting forces and tool vibrations respectively, and then forward all outputs to the output-ANN model. Subsequently, the output-ANN adopts the forward values and cutting parameters as inputs to predict R. To verify the effectiveness of the nested-ANN model, it is compared with mathematical and statistical models based on conventional ANN and RSM (Response Surface Methodology) using the same experimental data. The results show that the nested-ANN uses less input variables to obtain superior prediction accuracy than other models. Additionally, the statistical analyses show that Ra is mostly affected by the feed rate and has a signification correlation with the feed rate, the cutting force in both radial and tangential directions as well as the tool vibrations. (C) 2016 Elsevier Ltd. All rights reserved. [Chen, Yanni; Sun, Ronglei; Gao, Yuan] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China; [Leopold, Juergen] TBZ PARIV GmbH, D-09126 Chemnitz, Germany Huazhong University of Science & Technology Sun, RL (corresponding author), Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China. ronglei@hust.edu.cn Sun, Ronglei/0000-0001-6295-6583 National Basic Research Program of China (973 Program) [2013CB035805] National Basic Research Program of China (973 Program)(National Basic Research Program of China) This work was supported by the National Basic Research Program of China (973 Program) with granted No. 2013CB035805. 31 40 44 3 32 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement FEB 2017.0 98.0 25 34 10.1016/j.measurement.2016.11.027 0.0 10 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation EJ5EU 2023-03-23 WOS:000393241200004 0
J Guo, CP; Pierson, JM; Song, J; Herzog, C Guo, Chaopeng; Pierson, Jean-Marc; Song, Jie; Herzog, Christina Hot-N-Cold model for energy aware cloud databases JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING English Article Energy efficiency; Hot-N-Cold; DVFS; Cloud database; Bounded problem CONSUMPTION A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time. (C) 2018 Elsevier Inc. All rights reserved. [Guo, Chaopeng; Pierson, Jean-Marc] Univ Paul Sabatier, IRIT, 118 Route Narbonne, F-31062 Toulouse 9, France; [Song, Jie] Northeastern Univ, Software Coll, 159 ChuangXin Rd, Shenyang 110169, Liaoning, Peoples R China; [Herzog, Christina] Univ Pau & Pays Adour, LIUPPA, Bayonne, France Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Northeastern University - China; Universite de Pau et des Pays de l'Adour Guo, CP (corresponding author), Univ Paul Sabatier, IRIT, 118 Route Narbonne, F-31062 Toulouse 9, France. chaopeng.guo@irit.fr Pierson, Jean-Marc/V-2290-2019; PIERSON, Jean-Marc/AAQ-4009-2020 PIERSON, Jean-Marc/0000-0001-8948-0474; Guo, Chaopeng/0000-0001-5022-5919 National Natural Science Foundation of China [61672143, U1435216]; China Scholarship Council (CSC) [201506080029]; CNRS; RENATER; Inria National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC)(China Scholarship Council); CNRS(Centre National de la Recherche Scientifique (CNRS)); RENATER; Inria This paper is supported by the National Natural Science Foundation of China under Grant No. 61672143, U1435216. This research also is supported by the scholarship from China Scholarship Council (CSC, see www.campuschina.org) under the Grant CSC No 201506080029. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see www.grid5000.fr). 24 3 3 1 8 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0743-7315 1096-0848 J PARALLEL DISTR COM J. Parallel Distrib. Comput. JAN 2019.0 123.0 130 144 10.1016/j.jpdc.2018.09.012 0.0 15 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HB5NZ Green Submitted 2023-03-23 WOS:000451108900012 0
C Wu, Y; Li, L; Li, MZ; Zhang, M; Sun, H; Sygrimis, N; Lai, WF Wu, Yong; Li, Li; Li, Minzan; Zhang, Man; Sun, Hong; Sygrimis, Nikolaos; Lai, Wangfeng Remote-Control System for Greenhouse Based on Open Source Hardware IFAC PAPERSONLINE English Proceedings Paper 6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL) DEC 04-06, 2019 Sydney, AUSTRALIA Int Federat Automat Control,Int Federat Automat Control Tech Comm 8 1 Control Agr,Int Federat Automat Control Tech Comm 3 1 Comp Control,Int Federat Automat Control Tech Comm 4 2 Mechatron Syst,Int Federat Automat Control Tech Comm 7 5 Intelligent Autonomous Vehicles,Int Federat Automat Control Tech Comm 8 4 Biosystems & Bioprocesses,Australias Grains Res & Dev Corp,UNSW Sydney,Univ Technol Sydney Greenhouse control; Python; Java; PID; LoRa With the development of technologies such as the Internet of Things and Big Data, agriculture is entering the digital age. Modern information technology and intelligent equipment are the development direction of agriculture. In this paper, a remote-control system for greenhouse environment was developed based on Raspberry Pi, MySQL and Android. The system consists of a greenhouse control system (subsystem A), an environment monitoring system (subsystem B), a MySQL remote database (subsystem C), and an Android monitoring software (subsystem D). The subsystem A is used to implement automatic fertigation and collect data from subsystem B, which will be uploaded to cloud server database (subsystem C). The subsystem B is used to monitor the environmental parameters in the greenhouse such as air temperature/humidity, soil temperature/humidity etc. The subsystem C is used for storage of greenhouse environment data and communication between subsystem B and subsystem D. The subsystem D is used for data visualization including real-time display, storage and analysis of greenhouse environmental data, and remote control of fertigation system including the EC/pH control of the fertigation solution, irrigation scheduling, and fertigation settings. A proportional-integral-derivative (PID) control algorithm is used for the fertigation process of the subsystem A. The low-power LoRa technology is used for subsystem B to achieve low-power long-distance data transmission, which is especially suitable to greenhouses. All monitoring settings can be operated remotely by users. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [Wu, Yong; Li, Minzan; Zhang, Man; Sun, Hong] China Agr Univ, Key Lab Modern Precis Agr Syst Integrat Res, Minist Educ, Beijing 100083, Peoples R China; [Li, Li] China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr, Beijing 100083, Peoples R China; [Sygrimis, Nikolaos] Agr Univ Athens, Dept Agr Engn, Athens 7511855, Greece; [Lai, Wangfeng] Beijing Clesun Sci & Technol Corp, Beijing 100083, Peoples R China China Agricultural University; China Agricultural University; Ministry of Agriculture & Rural Affairs Wu, Y (corresponding author), China Agr Univ, Key Lab Modern Precis Agr Syst Integrat Res, Minist Educ, Beijing 100083, Peoples R China. takeyourcap@foxmail.com; lily@cau.edu.cn; limz@cau.edu.cn; cauzm@cau.edu.cn; sunhong@cau.edu.cn; ns@aua.gr; laiwangfeng@clesun.com Guangxi Science and Technology Major Project [GUIKE AA18118037]; Yunnan Academician Expert Workstation Program [20170907] Guangxi Science and Technology Major Project; Yunnan Academician Expert Workstation Program This study was supported by the Guangxi Science and Technology Major Project (GUIKE AA18118037) and the Yunnan Academician Expert Workstation Program (Li Minzan, Grant No. 20170907). 15 7 9 3 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2019.0 52.0 30 178 183 10.1016/j.ifacol.2019.12.518 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems JZ4FW gold 2023-03-23 WOS:000505058200032 0
J Xu, WR; Li, CL; Lee, MH; Zhang, C Xu, Weiran; Li, Chenliang; Lee, Minghao; Zhang, Chi Multi-task learning for abstractive text summarization with key information guide network EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING English Article Deep learning; Reinforcement learning; Text summarization; Multi-task learning; Attention mechanism Neural networks based on the attentional encoder-decoder model have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. And some key information, such as time, place, and people, is indispensable for humans to understand the main content. In this paper, we propose a key information guide network for abstractive text summarization based on a multi-task learning framework. The core idea is to automatically extract the key information that people need most in an end-to-end way and use it to guide the generation process, so as to get a more human-compliant summary. In our model, the document is encoded into two parts: results of the normal document encoder and the key information encoding, and the key information includes the key sentences and the keywords. A multi-task learning framework is introduced to get a more sophisticated end-to-end model. To fuse the key information, we propose a novel multi-view attention guide network to obtain the dynamic representations of the source text and the key information. In addition, the dynamic representations are incorporated into the abstractive module to guide the process of summary generation. We evaluate our model on the CNN/Daily Mail dataset and experimental results show that our model leads to significant improvements. [Xu, Weiran; Li, Chenliang] Beijing Univ Posts & Telecommun, Xitucheng Rd, Beijing 100876, Peoples R China; [Lee, Minghao] China Gen Technol Res Inst, Beijing, Peoples R China; [Zhang, Chi] Royal Inst Technol, Energy Proc Div, Teknikringen 42, SE-10044 Stockholm, Sweden Beijing University of Posts & Telecommunications; Royal Institute of Technology Xu, WR (corresponding author), Beijing Univ Posts & Telecommun, Xitucheng Rd, Beijing 100876, Peoples R China. xuweiran@bupt.edu.cn ZHANG, CHI/0000-0003-0034-843X Beijing Natural Science Foundation [4174098]; National Natural Science Foundation of China [61703234]; Fundamental Research Funds for the Central Universities [2017RC02] Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) We thank the reviewers for useful comments. This work was supported by Beijing Natural Science Foundation (4174098), National Natural Science Foundation of China (61702047), National Natural Science Foundation of China (61703234), and the Fundamental Research Funds for the Central Universities (2017RC02). 25 7 7 3 12 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1687-6180 EURASIP J ADV SIG PR EURASIP J. Adv. Signal Process. APR 19 2020.0 2020.0 1 16 10.1186/s13634-020-00674-7 0.0 11 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering LF2TC gold 2023-03-23 WOS:000527274500001 0
J Perez-Garcia, M; Diaz-Ordonez, M Perez-Garcia, Manuel; Diaz-Ordonez, Manuel GECEM Project Database: A digital humanities solution to analyse complex historical realities in early modern China and Europe DIGITAL SCHOLARSHIP IN THE HUMANITIES English Article; Early Access The GECEM Project Database stands out as a new Digital Humanities solution to accurately order and analyse the new historical Big Data gathered in Chinese and European historical archives. Traditional challenges such as capture, storage, analysis, data curation, searching, sharing, transfer, visualization, querying, updating, and information privacy are being tackled and solved within the design of this new multi-relational database. The implementation of this database has as its main innovative elements the capability of coding and cross-referring historical data in Chinese and Western languages. Thus, we present a new database as a digital solution to solve the long-lasting problem in historical research: the maximization and optimization of data collection when analysing historical sources and how to make use of a large amount of registers and information when developing case studies and work hypotheses for historical research. In this paper, we present a solution for data collection and analysis of probate inventories and trade records which is a relevant source when determining changes in patterns of consumption and global trade through the introduction of Chinese goods into Europe and vice versa during the early modern period. [Perez-Garcia, Manuel] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Diaz-Ordonez, Manuel] Univ Seville, Seville, Spain Shanghai Jiao Tong University; University of Sevilla Perez-Garcia, M (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. manuel.perez@alumni.eui.eu Perez-Garcia, Manuel/L-6257-2014; Diaz-Ordonez, Manuel/A-5215-2015 Perez-Garcia, Manuel/0000-0003-4520-3760; Diaz-Ordonez, Manuel/0000-0002-3492-6210 GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille) [16801840]; ERC (European Research Council) Starting-Grant under the European Union's Horizon 2020 Research and Innovation Programme [679371]; H2020 European Research Council GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille); ERC (European Research Council) Starting-Grant under the European Union's Horizon 2020 Research and Innovation Programme; H2020 European Research Council This research has been sponsored and financially supported by GECEM (Global Encounters between China and Europe: Trade Networks, Consumption and Cultural Exchanges in Macau and Marseille, 16801840), a project hosted by the Pablo de Olavide University (UPO) of Seville (Spain). The GECEM project is funded by the ERC (European Research Council) Starting-Grant, ref. 679371, under the European Union's Horizon 2020 Research and Innovation Programme, www.gecem.eu.The Principal Investigator is Professor Manuel Perez-Garcia (Distinguished Researcher at UPO). This work was supported by H2020 European Research Council. This research has also been part of the academic activities of the Global History Network in China www.globalhistorynetwork.com. 29 0 0 9 13 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2055-7671 2055-768X DIGIT SCHOLARSH HUM Digit. Scholarsh. Humanit. 10.1093/llc/fqac046 0.0 AUG 2022 17 Humanities, Multidisciplinary; Linguistics Social Science Citation Index (SSCI); Arts & Humanities Citation Index (A&HCI) Arts & Humanities - Other Topics; Linguistics 3S5EA 2023-03-23 WOS:000839617900001 0
J Alonso-Almeida, MDM; Borrajo-Millan, F; Yi, L Alonso-Almeida, Maria-del-Mar; Borrajo-Millan, Fernando; Yi, Liu Are Social Media Data Pushing Overtourism? The Case of Barcelona and Chinese Tourists SUSTAINABILITY English Article overtourism; social media; overcrowding WORD-OF-MOUTH; DESTINATION CHOICE; SENTIMENT ANALYSIS; HOSPITALITY; INTERNET; REVIEWS; TRAVEL Overtourism spoils the good economic and social results produced by the tourism sector, causing reductions in the quality of service of the tourist destination and rejection by the local population. Previous literature has suggested that social networks and new electronic channels could be accelerators of the process of overcrowding destinations; however, this link has not been established. For this reason, in this exploratory study, the influence of social networks on overtourism is analysed using Barcelona as a base, as Barcelona is a massively popular destination in the country that is second in the world in reception of tourists to Spain. This study is also focused on Chinese tourism, which brings large numbers of tourists and presents great economic potential. Two types of study have been used: big data techniques applied to social media with sentimental analysis, and analysis of travel packages offered in China to travel to Spain. Relevant results are obtained to understand the influence of social networks on the travel behaviour of tourists, possible contributions to overtourism, and recommendations for the management of tourism. [Alonso-Almeida, Maria-del-Mar; Borrajo-Millan, Fernando] Autonomous Univ Madrid, Business Org Dept, Fac Econ & Business Adm, E-28049 Madrid, Spain; [Yi, Liu] Sun Yat Sen Univ, Sch Tourism Management, Guangzhou 510275, Guangdong, Peoples R China Autonomous University of Madrid; Sun Yat Sen University Alonso-Almeida, MDM (corresponding author), Autonomous Univ Madrid, Business Org Dept, Fac Econ & Business Adm, E-28049 Madrid, Spain. mar.alonso@uam.es; fernando.borrajo@uam.es; liuyi89@mail.sysu.edu.cn Program Autonomous of Madrid University-Santander Bank [2017/Asia01]; Humanity and Social Science Foundation of Ministry of Education of China [19YJAZH060] Program Autonomous of Madrid University-Santander Bank; Humanity and Social Science Foundation of Ministry of Education of China The research development was funding by the Program Autonomous of Madrid University-Santander Bank, grant number 2017/Asia01. Data mining and processing works was funding by Humanity and Social Science Foundation of Ministry of Education of China (19YJAZH060). 44 25 25 5 54 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JUN 2 2019.0 11.0 12 3356 10.3390/su11123356 0.0 17 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology IG4DG Green Submitted, Green Published, gold 2023-03-23 WOS:000473753700126 0
J Jia, XL; Fu, TT; Hu, BF; Shi, Z; Zhou, LQ; Zhu, YW Jia, Xiaolin; Fu, Tingting; Hu, Bifeng; Shi, Zhou; Zhou, Lianqing; Zhu, Youwei Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory JOURNAL OF HAZARDOUS MATERIALS English Article Heavy metal pollution; Source-Sink theory; Machine learning methods; Cluster analysis; Potential risk areas CLASSIFICATION; DELTA; SCALE; SOUTH; GIS From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R-2 were 4.84 mg kg(-1) and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale. [Jia, Xiaolin; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Key Lab Environm Remediat & Ecol Hlth, Minist Educ, Hangzhou 31005, Zhejiang, Peoples R China; [Jia, Xiaolin; Fu, Tingting; Shi, Zhou; Zhou, Lianqing] Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Yuhangtang Rd 866, Hangzhou 31005, Zhejiang, Peoples R China; [Hu, Bifeng] INRA, Unite Rech Sci Sol, F-45075 Orleans, France; [Hu, Bifeng] Orleans Univ, Sci Terre & Univers, F-45067 Orleans, France; [Zhu, Youwei] Bur Agr, Protect & Monitoring Stn Agr Environm, Hangzhou 310020, Zhejiang, Peoples R China Zhejiang University; Zhejiang University; INRAE Shi, Z (corresponding author), Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Yuhangtang Rd 866, Hangzhou 31005, Zhejiang, Peoples R China. 11814013@zju.edu.cn; ftt321@zju.edu.cn; bifeng.hu@inra.fr; shizhou@zju.edu.cn; lianqing@zju.edu.cn; 13018941333@163.com shi, Zhou/M-7845-2019; Hu, Bifeng/G-2190-2018 shi, Zhou/0000-0003-3914-5402; Hu, Bifeng/0000-0001-9353-0307 National Key Research and Development Program of China [2018YFC1800201]; Key Research and Development Project of Zhejiang Province [2015C02011] National Key Research and Development Program of China; Key Research and Development Project of Zhejiang Province This work was supported by the National Key Research and Development Program of China [grant number 2018YFC1800201] and Key Research and Development Project of Zhejiang Province [2015C02011]. 35 82 87 59 257 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3894 1873-3336 J HAZARD MATER J. Hazard. Mater. JUL 5 2020.0 393.0 122424 10.1016/j.jhazmat.2020.122424 0.0 8 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology LN3GB 32143165.0 2023-03-23 WOS:000532829200080 0
J Wang, S; Song, T; Jarolin, K; Dymala, T; Dosta, M; Heinrich, S; Shen, LH Wang, Shen; Song, Tao; Jarolin, Kolja; Dymala, Timo; Dosta, Maksym; Heinrich, Stefan; Shen, Laihong Mixing evolution behavior of raw and gasified biomass pellets in a fluidized bed reactor CHEMICAL ENGINEERING SCIENCE English Article Biomass pellet; Gasification; Fluidized bed; Binary mixing evolution; Convolutional neural networks SEGREGATION BEHAVIOR; OXYGEN CARRIER; PARTICLES; GASIFICATION; COMBUSTION; FREQUENCY; SIZE Due to a large particle size and a small specific surface, a homogeneous mixing of biomass pellets with bed materials during gasification plays a critical role in the devolatilization and carbon conversion. In this work, the mixing evolution behavior of biomass pellets at different gasification stages is investigated for the first time. Two bubbling fluidized beds are established to perform the preparation of biomass samples undergoing different gasification times and visualized mixing experiments, respectively. An image processing technique is introduced for the determination of the real-time distribution of biomass pellets. The vertical and lateral migration paths of biomass pellets at different gasification stages are revealed. The improvement of binary mixing by adjusting the operating conditions as well as the adaptability to different biomass loadings are discussed. A convolutional neural network is developed to validate the influence of fluidization velocity on the resulting flow and classify the fluidization behavior. (C) 2022 Published by Elsevier Ltd. [Wang, Shen; Shen, Laihong] Southeast Univ, Minist Educ, Key Lab Energy Thermal Convers & Control, Nanjing 210096, Peoples R China; [Song, Tao] Nanjing Normal Univ, Sch Energy & Mech Engn, Nanjing 210023, Peoples R China; [Jarolin, Kolja; Dymala, Timo; Dosta, Maksym; Heinrich, Stefan] Hamburg Univ Technol, Inst Solids Proc Engn & Particle Technol, D-21073 Hamburg, Germany Southeast University - China; Nanjing Normal University; Hamburg University of Technology Shen, LH (corresponding author), Southeast Univ, Minist Educ, Key Lab Energy Thermal Convers & Control, Nanjing 210096, Peoples R China. lhshen@seu.edu.cn National Natural Science Foundation of China [52076044]; German Research Foundation [392123414] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); German Research Foundation(German Research Foundation (DFG)) This work was supported by the National Natural Science Foundation of China (52076044) and the German Research Foundation (392123414). 49 0 0 5 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0009-2509 1873-4405 CHEM ENG SCI Chem. Eng. Sci. DEC 31 2022.0 264.0 118161 10.1016/j.ces.2022.118161 0.0 OCT 2022 14 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 6P3UG 2023-03-23 WOS:000890856800011 0
J He, L; Guo, CG; Tiwari, P; Pandey, HM; Dang, W He, Lang; Guo, Chenguang; Tiwari, Prayag; Pandey, Hari Mohan; Dang, Wei Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS English Article 3D‐ CNN; depression; industrial intelligent system; pattern recognition; vector of local aggregated descriptors As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems in the pattern recognition field for audiovisual depression detection. This paper presents an end-to-end trainable intelligent system to generate high-level representations over the entire video clip. Specifically, a three-dimensional (3D) convolutional neural network equipped with a module spatiotemporal feature aggregation module (STFAM) is trained from scratch on audio/visual emotion challenge (AVEC)2013 and AVEC2014 data, which can model the discriminative patterns closely related to depression. In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP-NetVLAD, are integrated to learn the compact characteristic based on the feature maps. Extensive experiments on the two databases (i.e., AVEC2013 and AVEC2014) are illustrated that the proposed intelligent system can efficiently model the underlying depression patterns and obtain better performances over the most video-based depression recognition approaches. Case studies are presented to describes the applicability of the proposed intelligent system for industrial intelligence. [He, Lang] Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Comp Sci, Xian, Shaanxi, Peoples R China; [He, Lang] Xian Univ Posts & Telecommun, Shaanxi Key Lab Network Data Anal & Intelligent P, Xian, Shaanxi, Peoples R China; [Guo, Chenguang] Northwestern Polytech Univ, Sch Elect & Informat, Dept Elect & Informat Engn, Xian 710072, Peoples R China; [Tiwari, Prayag] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Pandey, Hari Mohan] Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England; [Dang, Wei] Shaanxi Mental Hlth Ctr, Xian, Shaanxi, Peoples R China Xi'an University of Posts & Telecommunications; Xi'an University of Posts & Telecommunications; Northwestern Polytechnical University; Aalto University; Edge Hill University Guo, CG (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Dept Elect & Informat Engn, Xian 710072, Peoples R China.;Pandey, HM (corresponding author), Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England. guochg@nwpu.edu.cn; pandeyh@edgehill.ac.uk Guo, Chenguang/HLH-8704-2023; Tiwari, Prayag/N-6261-2017; Pandey, Hari Mohan/M-9658-2015 Tiwari, Prayag/0000-0002-2851-4260; Pandey, Hari Mohan/0000-0002-9128-068X Shaanxi Provincial Education Department [20JG030]; Key Disciplines of Shaanxi Provincial Higher Education Shaanxi Provincial Education Department; Key Disciplines of Shaanxi Provincial Higher Education Shaanxi Provincial Education Department, Grant/Award Number: 20JG030; Key Disciplines of Shaanxi Provincial Higher Education 38 10 10 9 21 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0884-8173 1098-111X INT J INTELL SYST Int. J. Intell. Syst. DEC 2022.0 37.0 12 10140 10157 10.1002/int.22426 0.0 APR 2021 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7K9PV Green Submitted, gold 2023-03-23 WOS:000638001800001 0
C Gao, SP; Zhong, JQ; Cui, YL; Gao, C; Li, XH Liu, Y; Zhao, L; Cai, G; Xiao, G; Li, KL; Wang, L Gao, Shupeng; Zhong, Jiaqi; Cui, Yali; Gao, Chao; Li, Xianghua A novel pheromone initialization strategy of ACO algorithms for solving TSP 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) English Proceedings Paper 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) JUL 29-31, 2017 Guilin, PEOPLES R CHINA IEEE,IEEE Circuits & Syst Soc,Guilin Univ Elect Technol,Hunan Univ,Guilin Univ Technol,Guilin Univ Aerosp Technol Ant Colony Optimization; TSP; Pheromone Initialization Strategy ANT COLONY OPTIMIZATION; NEURAL-NETWORK MODEL; GENETIC ALGORITHMS Travelling salesman problem (TSP), as a famous combinational optimization problem, has promoted the generation of a large number of algorithms. However, the existing algorithms, such as ant colony optimization (ACO) algorithms, still need to be enhanced further in terms of their robustness and the quality of the solution. In this paper, a novel pheromone initialization (NPI) strategy of ACO algorithms has been proposed for solving TSP, which shows a better efficiency in both robustness and the quality of the solution. Combining NPI strategy with a typical ACO algorithm like ant colony system (ACS) algorithm, a novel algorithm, called NPI-ACS algorithm, is put forward to strengthen the efficiency of ACS. Meanwhile, seven different scale datasets related to TSP are used to estimate the performance of NPI strategy. Afterwards, the experimental results show that there is a remarkable improvement in terms of robustness and the quality of the solution. Moreover, the proposed NPI strategy is flexible enough to be combined with multifarious ACO algorithms for solving TSP because of its independence in operation details. [Gao, Shupeng; Zhong, Jiaqi; Gao, Chao; Li, Xianghua] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China; [Cui, Yali] Guangdong Coll Business & Technol, Zhaoqing 526000, Peoples R China; [Li, Xianghua] Potsdam Inst Climate Impact Res PIK, D-14473 Potsdam, Germany Southwest University - China; Potsdam Institut fur Klimafolgenforschung Li, XH (corresponding author), Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China.;Li, XH (corresponding author), Potsdam Inst Climate Impact Res PIK, D-14473 Potsdam, Germany. li_xianghua@163.com National Natural Science Foundation of China [61403315, 61402379]; National Training Programs of Innovation and Entrepreneurship for Undergraduates [201610635011] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Training Programs of Innovation and Entrepreneurship for Undergraduates The corresponding author of the paper is Dr Xianghua Li. Jiaqi Zhong is the co-first author because of her contribution to this paper. National Natural Science Foundation of China (Nos. 61403315, 61402379) and the National Training Programs of Innovation and Entrepreneurship for Undergraduates (201610635011) have surpported this work. 33 2 2 2 9 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-2165-3 2017.0 243 248 6 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK4TF 2023-03-23 WOS:000437355300041 0
C Li, MD; Weber, C; Kerzel, M; Lee, JH; Zeng, ZN; Liu, ZY; Wermter, S IEEE Li, Mengdi; Weber, Cornelius; Kerzel, Matthias; Lee, Jae Hee; Zeng, Zheni; Liu, Zhiyuan; Wermter, Stefan Robotic Occlusion Reasoning for Efficient Object Existence Prediction 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) SEP 27-OCT 01, 2021 ELECTR NETWORK IEEE,RSJ Reasoning about potential occlusions is essential for robots to efficiently predict whether an object exists in an environment. Though existing work shows that a robot with active perception can achieve various tasks, it is still unclear if occlusion reasoning can be achieved. To answer this question, we introduce the task of robotic object existence prediction: when being asked about an object, a robot needs to move as few steps as possible around a table with randomly placed objects to predict whether the queried object exists. To address this problem, we propose a novel recurrent neural network model that can be jointly trained with supervised and reinforcement learning methods using a curriculum training strategy. Experimental results show that 1) both active perception and occlusion reasoning are necessary to successfully achieve the task; 2) the proposed model demonstrates a good occlusion reasoning ability by achieving a similar prediction accuracy to an exhaustive exploration baseline while requiring only about 10% of the baseline's number of movement steps on average; and 3) the model generalizes to novel object combinations with a moderate loss of accuracy. [Li, Mengdi; Weber, Cornelius; Kerzel, Matthias; Lee, Jae Hee; Wermter, Stefan] Univ Hamburg, Dept Informat, Knowledge Technol, D-22527 Hamburg, Germany; [Zeng, Zheni; Liu, Zhiyuan] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China University of Hamburg; Tsinghua University Li, MD (corresponding author), Univ Hamburg, Dept Informat, Knowledge Technol, D-22527 Hamburg, Germany. mli@informatik.unihamburg.de; weber@informatik.unihamburg.de; kerzel@informatik.unihamburg.de; lee@informatik.unihamburg.de; zzn20@mails.tsinghua.edu.cn; liuzy@tsinghua.edu.cn; wermter@informatik.unihamburg.de Li, Mengdi/HPC-9714-2023 Lee, Jae Hee/0000-0001-9840-780X; Liu, Zhiyuan/0000-0002-7709-2543 China Scholarship Council (CSC); German Research Foundation DFG [CML (TRR 169)] China Scholarship Council (CSC)(China Scholarship Council); German Research Foundation DFG(German Research Foundation (DFG)) tThe authors gratefully acknowledge support from the China Scholarship Council (CSC) and the German Research Foundation DFG under project CML (TRR 169). We would like to thank Erik Strahl for his support with the experimental setup. 21 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-6654-1714-3 IEEE INT C INT ROBOT 2021.0 2686 2692 10.1109/IROS51168.2021.9635947 0.0 7 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Computer Science; Engineering; Robotics BS6ZK Green Submitted 2023-03-23 WOS:000755125502031 0
C Bouzaraa, F; Urfalioglu, O IEEE Bouzaraa, Fand; Urfalioglu, Onay CNN BASED NON-LOCAL COLOR MAPPING PROCEEDINGS OF 2016 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM) IEEE International Symposium on Multimedia-ISM English Proceedings Paper 18th IEEE International Symposium on Multimedia (IEEE ISM) DEC 11-13, 2016 San Jose, CA IEEE,IEEE Comp Soc Color mapping is a fundamental task for many important computer vision applications such as High Dynamic Range Imaging (HDRI), Stereo Matching, Camera Calibration and various other tasks. Typically, the task of color mapping is to transfer the colors of an image to a reference distribution. For example, this way, it is possible to simulate different camera exposures using a single image, e. g., by transforming a dark image to a brighter image showing the same scene. Most approaches for color mapping are local in the sense that they just apply a pixel-wise (local) mapping to generate the color mapped image. In this paper, we empirically show that this approach yields sub-optimal results and we propose a non-local mapping based on learned features directly from the image-texture, using a Convolutional Neural Network. This way, we learn to generate an image which would have been captured by a certain factor of the actual exposure time. We demonstrate our method using various applications in the HDR domain and compare our results against other state-of-the-art methods where we obtain excellent results, both visually as well as numerically. [Bouzaraa, Fand] Tech Univ Munich, Munich, Germany; [Urfalioglu, Onay] Huawei Technol Co Ltd, European Res Ctr, Shenzhen, Peoples R China Technical University of Munich; Huawei Technologies Bouzaraa, F (corresponding author), Tech Univ Munich, Munich, Germany. 12 0 0 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-4571-6 IEEE INT SYM MULTIM 2016.0 313 316 10.1109/ISM.2016.62 0.0 4 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BH2RN 2023-03-23 WOS:000399166000058 0
C Zhao, JP; Guo, WW; Liu, B; Zhang, ZH; Yu, WX; Cui, SY IEEE Zhao, Juanping; Guo, Weiwei; Liu, Bin; Zhang, Zenghui; Yu, Wenxian; Cui, Shiyong PRELIMINARY EXPLORATION OF SAR IMAGE LAND COVER CLASSIFICATION WITH NOISY LABELS 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience & Remote Sensing Symposium JUL 23-28, 2017 Fort Worth, TX Institute of Elect & Electron Engineers Geoscience & Remote Sensing Soc,IEEE,IEEE GRSS SAR image land cover classification; Noisy labels; Deep feature; Probability Transition model Synthetic Aperture Radar (SAR) image land cover classification is an important task in SAR image interpretation. Supervised learning, such as Convolutional Neural Network (CNN), demands instances which are accurately labeled. However, a large amount of accurately labeled SAR images are difficult to produce. In this paper, a Probability Transition CNN (PTCNN) is proposed for patch-level SAR image land cover classification with noisy labels. Firstly, deep features are extracted by a CNN model, followed by a probabilistic transition model, where true labels are treated as hidden variables and the posterior probabilities of true labels are transferred into their noisy versions. The whole network is trained with Caffe in a uniform fashion and a land cover database is used to produce noisy labels, which are randomly chosen with various proportions. Experimental results demonstrate that the proposed PTCNN model is robust to noise and gives a promising classification performance. Therefore, the PTCNN model may lower the standards for the quality of image labels, and shows its availability in practical applications. [Zhao, Juanping; Guo, Weiwei; Liu, Bin; Zhang, Zenghui; Yu, Wenxian] Shanghai Jiao Tong Univ, Shanghai Key Lab Intelligent Sensing & Recognit, Shanghai, Peoples R China; [Cui, Shiyong] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany Shanghai Jiao Tong University; Helmholtz Association; German Aerospace Centre (DLR) Zhao, JP (corresponding author), Shanghai Jiao Tong Univ, Shanghai Key Lab Intelligent Sensing & Recognit, Shanghai, Peoples R China. zhang, zenghui/GVS-8983-2022; Zhang, Zenghui/T-9406-2019 State Key Program of National Natural Science Foundation of China [61331015]; China Postdoctoral Science Foundation [2015M581618] State Key Program of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) Thanks to the State Key Program of National Natural Science Foundation of China (61331015) and the China Postdoctoral Science Foundation (2015M581618) for funding. 7 7 7 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5090-4951-6 INT GEOSCI REMOTE SE 2017.0 3274 3277 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BJ6TY 2023-03-23 WOS:000426954603091 0
J Ren, B; Hou, B; Chanussot, J; Jiao, LC Ren, Bo; Hou, Biao; Chanussot, Jocelyn; Jiao, Licheng Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Tensors; Synthetic aperture radar; Feature extraction; Task analysis; Backscatter; Speckle; Measurement; Land cover classification; modified tensor distance (MTD); multiview spectral embedding (MSE); polarimetric synthetic aperture radar (PolSAR) UNSUPERVISED CLASSIFICATION; FEATURES This letter proposes a novel method for combining multiview features in polarimetric synthetic aperture radar (PolSAR) for land cover classification. It is well-known that feature extraction and classifier design are two significant steps in machine learning methods for PolSAR data interpretation. Each PolSAR pixel can be represented in different feature spaces, such as polarimetric data scattering, or the polarimetric target decomposition spaces. In this letter, a tensor-based multiview embedding algorithm is proposed to fuse those features from different spaces in order to obtain a distinctive set of features for the subsequent classification. Based on the pixel-based classification tasks, a modified tensor distance (MTD) is designed to accurately calculate the distance between tensors. It emphasizes the importance of the central pixel, and decreases the influence of the neighbors in the feature patch when calculating tensor distance. Furthermore, the complementary properties of different views are exploited by an MTD measured tensor multiview spectral embedding method, so as to obtain relevant low-dimensional features. Compared with state-of-the-art methods, the validation and effectiveness of the proposed method is demonstrated on two real PolSAR data sets. [Ren, Bo; Hou, Biao; Jiao, Licheng] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, INRIA, CNRS,LJK, F-38000 Grenoble, France Ministry of Education, China; Xidian University; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria Hou, B (corresponding author), Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China. asdwer2046@126.com; avcodec@163.com; jocelyn.chanussot@gipsa-lab.grenoble-inp.fr; lchjiao@mail.xidian.edu.cn Jiao, Licheng/0000-0003-3354-9617; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [61671350, 61836009]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005]; Key Research and Development Program in Shaanxi Province of China [2019ZDLGY03-05]; China Postdoctoral Science Foundation [2018M633468] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program in Shaanxi Province of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 61671350 and Grant 61836009, in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005, in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY03-05, and in part by the China Postdoctoral Science Foundation Funded Project under Grant 2018M633468. 16 8 8 2 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. DEC 2020.0 17.0 12 2095 2099 10.1109/LGRS.2019.2962185 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology OZ0NY 2023-03-23 WOS:000594634800015 0
J Song, J; He, HY; Wang, Z; Yu, G; Pierson, JM Song, Jie; He, HongYan; Wang, Zhi; Yu, Ge; Pierson, Jean-Marc Modulo Based Data Placement Algorithm for Energy Consumption Optimization of MapReduce System JOURNAL OF GRID COMPUTING English Article Big data; Data placement; Energy consumption optimization; MapReduce CLUSTERS With the explosion of data production, the efficiency of data management and analysis has been concerned by both industry and academia. Meanwhile, more and more energy is consumed by the IT infrastructure especially the larger scale distributed systems. In this paper, a novel idea for optimizing the Energy Consumption (EC for short) of MapReduce system is proposed. We argue that a fair data placement is helpful to save energy, and then we propose three goals of data placement, and a modulo based Data Placement Algorithm (DPA for short) which achieves these goals. Afterwards, the correctness of the proposed DPA is proved from both theoretical and experimental perspectives. Three different systems which implement MapReduce model with different DPAs are compared in our experiments. Our algorithm is proved to optimize EC effectively, without introducing the additional costs and delaying data loading. With the help of our DPA, the EC for the WordCount can be reduced by 10.9 %, 8.3 % and 17 % respectively, and time consumption is reduced by 7 %, 6.3 % and 7 % respectively. [Song, Jie; He, HongYan; Wang, Zhi] Northeastern Univ, Software Coll, Shenyang, Liaoning, Peoples R China; [Yu, Ge] Northeastern Univ, Sch Informat Sci & Engn, Shenyang, Liaoning, Peoples R China; [Pierson, Jean-Marc] Paul Sabatier Univ, IRIT, F-31062 Toulouse, France Northeastern University - China; Northeastern University - China; Universite de Toulouse; Universite Toulouse III - Paul Sabatier Song, J (corresponding author), Northeastern Univ, Software Coll, Shenyang, Liaoning, Peoples R China. songjie@mail.neu.edu.cn Yu, Ge/AAN-8191-2021; PIERSON, Jean-Marc/AAQ-4009-2020; Pierson, Jean-Marc/V-2290-2019 PIERSON, Jean-Marc/0000-0001-8948-0474; National Natural Science Foundation of China [61202088, 61433008]; Fundamental Research Funds for the Central Universities [N120817001, N130417001]; Science Foundation of China Postdoctor [2013M540232]; National Research Foundation for the Doctoral Program of Higher Education of China [20120042110028] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Science Foundation of China Postdoctor; National Research Foundation for the Doctoral Program of Higher Education of China(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)) Supported by the National Natural Science Foundation of China under Grant No. 61202088 and 61433008; the Fundamental Research Funds for the Central Universities under Grant No. N120817001 and N130417001; the Science Foundation of China Postdoctor under Grant No. 2013M540232; the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20120042110028. 26 11 12 1 16 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1570-7873 1572-9184 J GRID COMPUT J. Comput. SEP 2018.0 16.0 3 409 424 10.1007/s10723-016-9370-2 0.0 16 Computer Science, Information Systems; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GQ6WU Green Submitted 2023-03-23 WOS:000441870400002 0
J Zhang, JZ; Zhang, X; Jiang, S; de Pablos, PO; Sun, YQ Zhang, Jinzhuo; Zhang, Xi; Jiang, Shan; Ordonez de Pablos, Patricia; Sun, Yongqiang Mapping the study of learning analytics in higher education BEHAVIOUR & INFORMATION TECHNOLOGY English Article Higher education; learning analytics; documentary analysis; library analytics BIG DATA; COGNITION; PATTERNS; SCIENCE; CONTEXT; SYSTEMS; ISSUES; MODEL; TOOL In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field. [Zhang, Jinzhuo; Zhang, Xi; Jiang, Shan] TianJin Univ, Coll Management & Econ, Tianjin, Peoples R China; [Ordonez de Pablos, Patricia] Univ Oviedo, Dept Business Adm & Accountabil Oviedo, Oviedo, Spain; [Sun, Yongqiang] Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China Tianjin University; University of Oviedo; Wuhan University Zhang, X (corresponding author), TianJin Univ, Coll Management & Econ, Tianjin, Peoples R China. jackyzhang@tju.edu.cn Zhang, Jacky/HHS-9302-2022; Ordóñez de Pablos, Patricia/AAC-9329-2022 zhang, jinzhuo/0000-0002-5531-4117; Zhang, Xi/0000-0002-1105-9417; Ordonez de Pablos, Patricia/0000-0002-8388-6382; Sun, Yongqiang/0000-0001-8753-9268 National Natural Science Foundation of China [71722005, 71571133, 71790594] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China [grant number 71722005; 71571133; 71790594]. 80 14 14 3 63 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0144-929X 1362-3001 BEHAV INFORM TECHNOL Behav. Inf. Technol. NOV 2 2018.0 37.0 10-11 SI 1142 1155 10.1080/0144929X.2018.1529198 0.0 14 Computer Science, Cybernetics; Ergonomics Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering GX5TL 2023-03-23 WOS:000447813400015 0
J Wang, WN; Qin, Y; Li, XF; Wang, D; Chen, HQ Wang, Weina; Qin, Yu; Li, Xiaofei; Wang, Di; Chen, Huiqiang Comparisons of Faulting-Based Pavement Performance Prediction Models ADVANCES IN MATERIALS SCIENCE AND ENGINEERING English Article Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy. [Wang, Weina] Chongqing Jiaotong Univ, Sch Civil Engn, State & Local Engn Lab Civil Engn Mat, Xuefu Ave 66, Chongqing, Peoples R China; [Qin, Yu] CREEC Chongqing Survey Design & Res Co Ltd, Liangjiang New Area, Kunlun Ave 46, Chongqing, Peoples R China; [Li, Xiaofei; Chen, Huiqiang] Chongqing Jiaotong Univ, Sch Civil Engn, Xuefu Ave 66, Chongqing, Peoples R China; [Wang, Di] Tech Univ Carolo Wilhelmina Braunschweig, Pavement Engn Ctr, Raum 104,Beethovenstr 51 B, Braunschweig, Germany Chongqing Jiaotong University; Chongqing Jiaotong University; Braunschweig University of Technology Qin, Y (corresponding author), CREEC Chongqing Survey Design & Res Co Ltd, Liangjiang New Area, Kunlun Ave 46, Chongqing, Peoples R China. qinyubridge@163.com wang, di/HKD-8820-2023; Wang, Di/V-2805-2019; Li, xiaofei/GXF-7187-2022 Wang, Di/0000-0001-9018-0719; National Natural Science Foundation of China [51508064, 51408083]; China Post-doctoral Science Foundation [2014M562287]; Chongqing Science and Technology Commission [cstc2014jcyjA30018, cstc2016jcyjA0128]; Department of Human Resource and Social Security of Chongqing [Xm2014094]; State and Local Engineering Laboratory for Civil Engineering Material of Chongqing Jiaotong University [LHSYS-2016-01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Post-doctoral Science Foundation(China Postdoctoral Science Foundation); Chongqing Science and Technology Commission(Natural Science Foundation Project of CQ CSTC); Department of Human Resource and Social Security of Chongqing; State and Local Engineering Laboratory for Civil Engineering Material of Chongqing Jiaotong University This paper is sponsored by National Natural Science Foundation of China [51508064, 51408083], China Post-doctoral Science Foundation [2014M562287], Chongqing Science and Technology Commission [cstc2014jcyjA30018, cstc2016jcyjA0128], Department of Human Resource and Social Security of Chongqing [Xm2014094], and State and Local Engineering Laboratory for Civil Engineering Material of Chongqing Jiaotong University [LHSYS-2016-01]. 46 4 4 3 19 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-8434 1687-8442 ADV MATER SCI ENG Adv. Mater. Sci. Eng. 2017.0 2017.0 6845215 10.1155/2017/6845215 0.0 9 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science FI4VR Green Submitted, gold 2023-03-23 WOS:000411977000001 0
J Duntsch, I; Gediga, G Duntsch, Ivo; Gediga, Guenther Indices for rough set approximation and the application to confusion matrices INTERNATIONAL JOURNAL OF APPROXIMATE REASONING English Article Rough set approximation; Confusion matrix; Precision indices; Odds ratios; Error estimation CLASSIFICATION Confusion matrices and their associated statistics are a well established tool in machine learning to evaluate the accuracy of a classifier. In the present study, we define a rough confusion matrix based on a very general classifier, and derive various statistics from it which are related to common rough set estimators. In other words, we perform a rough set-like analysis on a confusion matrix, which is the converse of the usual procedure; in particular, we consider upper approximations. A suitable index for measuring the tightness of the upper bound uses a ratio of odds. Odds ratios offer a symmetric interpretation of lower and upper precision, and remove the bias in the upper approximation. We investigate rough odds ratios of the parameters obtained from the confusion matrix; to guard against undue random influences, we also approximate their standard errors. (C) 2019 Elsevier Inc. All rights reserved. [Duntsch, Ivo] Fujian Normal Univ, Coll Maths Informat, Fuzhou, Peoples R China; [Duntsch, Ivo] Brock Univ, Dept Comp Sci, St Catharines, ON, Canada; [Gediga, Guenther] Inst Evaluat & Marktanalysen, Brinkstr 19, D-49143 Jeggen, Germany Fujian Normal University; Brock University Duntsch, I (corresponding author), Fujian Normal Univ, Coll Maths Informat, Fuzhou, Peoples R China.;Duntsch, I (corresponding author), Brock Univ, Dept Comp Sci, St Catharines, ON, Canada. duentsch@brocku.ca; gediga@eval-institut.de National Natural Science Foundation of China [61976053] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank the anonymous reviewers for careful reading and helpful suggestions. I. Duntsch gratefully acknowledges support by the National Natural Science Foundation of China, Grant No. 61976053. 35 12 12 1 10 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0888-613X 1873-4731 INT J APPROX REASON Int. J. Approx. Reasoning MAR 2020.0 118.0 155 172 10.1016/j.ijar.2019.12.008 0.0 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KM2ZB 2023-03-23 WOS:000513990200009 0
J Liu, JL; Lughofer, E; Zeng, XY Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi Could linear model bridge the gap between low-level statistical features and aesthetic emotions of visual textures? NEUROCOMPUTING English Article Visual texture; Feature extraction; Dimensionality reduction; Model building; Aesthetic emotion; Affective computing INTRINSIC DIMENSIONALITY ESTIMATION; NEUROAESTHETICS; REDUCTION; COLORS; ART; NEUROPSYCHOLOGY; PREFERENCE; DYNAMICS; BEAUTY Aesthetic perceptions are common in everyday life, even though we are not aware of these natural behavior and ability. The exploration of the processing pathways in human aesthetic perception of visual textures by modeling is vital and valuable in important domains such as product design and architecture. After conducting semantic differential rating experiments, we model the relationship between low-level texture features and aesthetic properties involved in human aesthetic texture perception. In this paper, we use multiple linear regression models to bridge the gap between computation texture features and aesthetic emotions that are more interpretable and clearer in structure than artificial neural network models. In feature extraction stage, four different categories of low level features are used to objectively represent the characteristics of visual textures. To reduce the information redundancy of the feature set and the complexity of the prediction model, the dimensionality reduction of the extracted feature set using stochastic neighbor embedding is implanted. In psychological experiment stage, the semantic differential rating experiment is conducted to collect the aesthetic perceptions of selected texture stimuli from participants, and then the aesthetic properties are assigned to three different model layers based on the neural mechanisms of aesthetic perception. Before model building, an evaluation of the presentation ability of feature subsets is firstly carried out to investigate whether there is a potential relationship between 8 high level aesthetic properties and the feature set including 106 texture features. Next, we describe the generation of a multiple linear regression model for aesthetic prediction by taking a reduced feature set as dependent variables. Experimental results indicate that the hierarchical feed-forward layer model of aesthetic texture perception proposed in our research can link computational texture features with aesthetic texture properties quite well. (C) 2015 Elsevier B.V. All rights reserved. [Liu, Jianli] Jiangnan Univ, Coll Text & Clothing, Wuxi 214122, Peoples R China; [Lughofer, Edwin] Johannes Kepler Univ Linz, Dept Knowledge Based Math Syst, A-4040 Linz, Austria; [Zeng, Xianyi] Univ Lille Nord France, F-59000 Lille, France; [Zeng, Xianyi] GEMTEX, ENSAIT, F-59056 Roubaix, France Jiangnan University; Johannes Kepler University Linz; Universite de Lille - ISITE; Universite de Lille; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT) Liu, JL (corresponding author), Jiangnan Univ, Coll Text & Clothing, Wuxi 214122, Peoples R China. jian-li.liu@hotmail.cm Lughofer, Edwin/0000-0003-1560-5136; Zeng, Xianyi/0000-0002-3236-6766 National Natural Science Foundation of China [61203364]; China Postdoctoral Science Foundation [20110490098, 2012T50754]; Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM); Austrian federal government; federal state of Upper Austria National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM); Austrian federal government; federal state of Upper Austria The authors would like to thank the anonymous reviewers and the handling associate editor for their insightful comments. This project was supported by National Natural Science Foundation of China [Grant number 61203364], China Postdoctoral Science Foundation [grant number 20110490098], and Special Financial Grant from the China Postdoctoral Science Foundation [Grant number 2012T50754]. The authors also acknowledge the support of the Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM), which is funded by the Austrian federal government and the federal state of Upper Austria. We thank Dr. Honglin Xu and Dr. Xueyan Wei for their helps on the algorithm design and the psychological experiment. This publication reflects only the authors' views. 79 14 15 1 50 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing NOV 30 2015.0 168.0 947 960 10.1016/j.neucom.2015.05.030 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science CO4XU 2023-03-23 WOS:000359165000093 0
C Yu, ZJ; Wang, H; Liu, Y; Bohm, C; Shao, JM Plant, C; Wang, H; Cuzzocrea, A; Zaniolo, C; Wu, X Yu, Zhongjing; Wang, Han; Liu, Yang; Bohm, Christian; Shao, Junming Community Attention Network for Semi-supervised Node Classification 20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) IEEE International Conference on Data Mining English Proceedings Paper 20th IEEE International Conference on Data Mining (ICDM) NOV 17-20, 2020 ELECTR NETWORK IEEE,IEEE Comp Soc,Univ Calabria,Mininglamp Technol Semi-supervised Learning; Node Classification; Attention Network; Network Embedding; Graph Neural Network REPRESENTATION Graph neural networks (GNNs) have achieved great success for semi-supervised node classification by embedding node representation into a low-dimensional space.. However, existing approaches usually ignore one intrinsic property of graphs: community structure, where the formation of distinct communities in graphs is often driven by different subset of attributes. In this paper, we introduce a new method, called Community Attention Network (CAT), aiming to extract community-specific features and then enhance node embeddings for classification. To learn such community-specific information, we design a new loss function to ensure the nodes in the same community should share similar attributes (i.e., low covariance), and any unlabelled node should belong to only one class with high probability i.e.,( low community distribution entropy) in a community attention network. Extensive experimental results demonstrate the effectiveness of CAT and its advantages over many state-of-the-art approaches. To further illustrate the benefits of CAT to capture the community information, a case study is given and discussed. [Yu, Zhongjing; Wang, Han; Liu, Yang; Shao, Junming] Univ Elect Sci & Technol China, Chengdu, Peoples R China; [Bohm, Christian] Ludwig Maximilians Univ Munchen, Munich, Germany University of Electronic Science & Technology of China; University of Munich Shao, JM (corresponding author), Univ Elect Sci & Technol China, Chengdu, Peoples R China. zhongjing.yu@outlook.com; wanghan0612@std.uestc.edu.cn; yangliu.cs@outlook.com; boehm@dbs.ifi.lmu.de; junmshao@uestc.edu.cn Böhm, Christian/HDN-5925-2022 National atural Science Foundation of China [61976044, 52079026]; Fundamental Research Funds for the Central Universities [ZYGX2019Z014]; Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Instjtutions of China [161062]; National key research and development program [2016YFB0502300]; Belt and Road und on Water and Sustainability of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering [20l9nkzd02]; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin [IWHR-SKL-20 1911]; Sichuan Science and Technology Program [2020YFH0037] National atural Science Foundation of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Instjtutions of China; National key research and development program; Belt and Road und on Water and Sustainability of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin; Sichuan Science and Technology Program This work is supported by the National atural Science Foundation of China (61976044,52079026), Fundamental Research Funds for the Central Universities (ZYGX2019Z014), Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Instjtutions of China (161062), National key research and development program (2016YFB0502300), The Belt and Road und on Water and Sustainability of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (20l9nkzd02), State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (IWHR-SKL-20 1911),Sichuan Science and Technology Program (2020YFH0037). 18 1 1 1 4 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1550-4786 978-1-7281-8316-9 IEEE DATA MINING 2020.0 1382 1387 10.1109/ICDM50108.2020.00181 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR0QL 2023-03-23 WOS:000630177700169 0
J Feng, WQ; Sui, HG; Hua, L; Xu, C; Ma, GR; Huang, WM Feng, Wenqing; Sui, Haigang; Hua, Li; Xu, Chuan; Ma, Guorui; Huang, Weiming Building extraction from VHR remote sensing imagery by combining an improved deep convolutional encoder-decoder architecture and historical land use vector map INTERNATIONAL JOURNAL OF REMOTE SENSING English Article SEGMENTATION; NETWORKS Building extraction has attracted considerable attention in the field of remote sensing image analysis. Fully convolutional network modelling is a recently developed technique that is capable of significantly enhancing building extraction accuracy. It is a prominent branch of deep learning and uses advanced state-of-the-art techniques, especially with regard to building segmentation. In this paper, we present an enhanced deep convolutional encoder-decoder (DCED) network by incorporating historical land use vector maps (HVMs) customized for building extraction. The approach combines enhanced DCED architecture with multi-scale image pyramid for pixel-wise building segmentation. The improved DCED network, together with symmetrical dense-shortcut connection structures, is employed to establish the encoders for automatic extraction of building features. The feature maps from early layers were fused with more discriminative feature maps from the deeper layers through 'Res path' skip connections for superior building extraction accuracy. To further reduce the occurrence of falsely segmented buildings, and to sharpen the buildings' boundaries, the new temporal testing image is segmented under the constraints of an HVM. A majority voting strategy is employed to ensure the homogeneity of the building objects as the post-processing method. Experimental results indicate that the proposed approach exhibits competitive quantitative and qualitative performance, effectively alleviating the salt-and-pepper phenomenon and block effects, and retaining the edge structures of buildings. Compared with other state-of-the-art methods, our method demonstrably achieves the optimal final accuracies. [Feng, Wenqing; Sui, Haigang; Xu, Chuan; Ma, Guorui] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China; [Sui, Haigang] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China; [Hua, Li] Huazhong Agr Univ, Coll Resources & Environm, Wuhan, Peoples R China; [Huang, Weiming] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden Wuhan University; Wuhan University; Huazhong Agricultural University; Lund University Sui, HG (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China. haigang_sui@263.net Huang, Weiming/0000-0002-3208-4208 National Natural Science Foundation of China [41771457, 91738301, 41601443]; National Key Research and Development Program of China [2016YFB0502603] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work was supported by the National Natural Science Foundation of China under Grant Nos. [41771457, 91738301 and 41601443]; and the National Key Research and Development Program of China [No. 2016YFB0502603]. 43 9 12 3 44 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. SEP 1 2020.0 41.0 17 6595 6617 10.1080/01431161.2020.1742944 0.0 23 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology ME1ZJ 2023-03-23 WOS:000544459300001 0
J Zhao, Q; Chang, Z; Min, GY Zhao, Qiang; Chang, Zheng; Min, Geyong Anomaly Detection and Classification of Household Electricity Data: A Time Window and Multilayer Hierarchical Network Approach IEEE INTERNET OF THINGS JOURNAL English Article Feature extraction; Smart grids; Time series analysis; Training; Meters; Nonhomogeneous media; Correlation; Anomaly detection; autoencoder; classification; feedforward network; household electricity; multilayer hierarchical network (MLHN) With the increasing popularity of the smart grid, huge volumes of data are gathered from numerous sensors. How to classify, store, and analyze massive data sets to facilitate the development of the smart grid has recently attracted much attention. In particular, with the popularity of household smart meters and electricity monitoring sensors, a large amount of data can be obtained to analyze household electricity usage so as to better diagnose the leakage and theft behaviors, identify man-made tampering and data fraud, and detect powerline loss. In this article, the time window method is first proposed to obtain the features and potential periodicity of household electricity data. Combining the denoising ability of the autoencoder and the induction ability of the feedforward neural network, a multilayer hierarchical network (MLHN) is then established to detect anomalies in single sensor data and classify multiple groups of sensor data, respectively. The experimental results show that the accuracy of detecting abnormal data and data classification is significantly improved compared with the presented scheme. [Zhao, Qiang] Shanxi Univ, Dept Software Engn, Taiyuan 037006, Peoples R China; [Chang, Zheng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China; [Chang, Zheng] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland; [Min, Geyong] Univ Exeter, Coll Engn Math & Phys Sci, Dept Comp Sci, Exeter EX4 4QF, Devon, England Shanxi University; University of Electronic Science & Technology of China; University of Jyvaskyla; University of Exeter Chang, Z (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China. hmoe@sxu.edu.cn; zheng.chang@jyu.fi Chang, Zheng/G-2873-2018 Chang, Zheng/0000-0003-3766-820X; Zhao, Qiang/0000-0002-9506-4140 NSFC [62071105] NSFC(National Natural Science Foundation of China (NSFC)) This work was supported in part by NSFC under Grant 62071105. 29 1 1 5 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAR 1 2022.0 9.0 5 3704 3716 10.1109/JIOT.2021.3098735 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications ZH8YN Green Accepted 2023-03-23 WOS:000761217100046 0
J Yang, B; Cheng, XG; Dai, DX; Olofsson, T; Li, HB; Meier, A Yang, Bin; Cheng, Xiaogang; Dai, Dengxin; Olofsson, Thomas; Li, Haibo; Meier, Alan Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings BUILDING AND ENVIRONMENT English Article Contactless measurement; Thermal discomfort; Human pose; Machine learning SKIN TEMPERATURE; COMFORT; PREDICTION; OCCUPANCY; MODEL Individual thermal discomfort perception gives important feedback signals for energy efficient control of building heating, ventilation and air conditioning systems. However, there is few effective method to measure thermal discomfort status of occupants in a real-time and contactless way. A novel method based on contactless measurements of human thermal discomfort status was presented. Images of occupant poses, which are related to thermoregulation mechanisms, were captured by a digital camera and the corresponding 2D coordinates were obtained. These poses were converted into skeletal configurations. An algorithm was developed to recognize different poses related to thermal discomfort, such as hugging oneself or wiping sweat off the brow. The algorithm could recognize twelve thermal discomfort related human poses. These poses were derived from a questionnaire survey of 369 human subjects. Some other human subjects participated in the validation experiments of the proposed method. All twelve thermal discomfort related poses can be recognized effectively. [Yang, Bin] Xian Univ Architecture & Technol, Sch Bldg Serv Sci & Engn, Xian 710055, Shaanxi, Peoples R China; [Cheng, Xiaogang] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China; [Yang, Bin; Olofsson, Thomas] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Cheng, Xiaogang; Dai, Dengxin] Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland; [Cheng, Xiaogang; Li, Haibo] Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden; [Meier, Alan] Univ Calif Davis, Energy & Efficiency Inst, Davis, CA 95616 USA Xi'an University of Architecture & Technology; Nanjing University of Posts & Telecommunications; Umea University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Royal Institute of Technology; University of California System; University of California Davis Cheng, XG (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China. chengxg@njupt.edu.cn Yang, Bin/GQA-9324-2022 Yang, Bin/0000-0002-1579-7978; Meier, Alan/0000-0002-1260-2151; Olofsson, Thomas/0000-0002-8704-8538 International Postdoctoral Fellowship Program from China Postdoctoral Council [20160022]; National Natural Science Foundation of China [61401236]; Jiangsu Postdoctoral Science Foundation [1601039B]; Key Research Project of Jiangsu Science and Technology Department [BE2016001-3] International Postdoctoral Fellowship Program from China Postdoctoral Council; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Key Research Project of Jiangsu Science and Technology Department The authors thanks to Yaser Sheikh's research group (Carnegie Mellon University) for providing his Openpose code. This study was supported by the International Postdoctoral Fellowship Program from China Postdoctoral Council (No. 20160022), the National Natural Science Foundation of China (No. 61401236), the Jiangsu Postdoctoral Science Foundation (No. 1601039B), the Key Research Project of Jiangsu Science and Technology Department (No. BE2016001-3). 43 38 38 6 29 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-1323 1873-684X BUILD ENVIRON Build. Environ. SEP 2019.0 162.0 106284 10.1016/j.buildenv.2019.106284 0.0 10 Construction & Building Technology; Engineering, Environmental; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering IV8KM Green Submitted 2023-03-23 WOS:000484514400018 0
J Huang, CW; Bai, H; Yao, X Huang, Changwu; Bai, Hao; Yao, Xin Online algorithm configuration for differential evolution algorithm APPLIED INTELLIGENCE English Article Automatic algorithm configuration; Differential evolution algorithm; Adaptive parameter control; Multi-armed bandit; Kernel density estimation; Machine learning PARAMETER CONTROL; OPTIMIZATION; ADAPTATION; STRATEGIES The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance. [Huang, Changwu; Yao, Xin] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China; [Bai, Hao] INSA Rouen Normandie, Lab Mech Normandy LMN, F-76000 Rouen, France Southern University of Science & Technology Yao, X (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China. huangcw3@sustech.edu.cn; hao.bai@insa-rouen.fr; xiny@sustech.edu.cn Huang, Changwu/ABC-8082-2021 BAI, Hao/0000-0003-3546-8802 Guangdong Basic and Applied Basic Research Foundation [2019A1515110575]; Guangdong Provincial Key Laboratory [2020B121201001]; Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X386]; Shenzhen Science and Technology Program [KQTD2016112514355531]; Shenzhen Basic Research Program [JCYJ20180504165652917]; Program for University Key Laboratory of Guangdong Province [2017KSYS008] Guangdong Basic and Applied Basic Research Foundation; Guangdong Provincial Key Laboratory; Program for Guangdong Introducing Innovative and Enterpreneurial Teams; Shenzhen Science and Technology Program; Shenzhen Basic Research Program; Program for University Key Laboratory of Guangdong Province This work was supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110575), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), Shenzhen Basic Research Program (No. JCYJ20180504165652917), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008). 51 0 0 1 10 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-669X 1573-7497 APPL INTELL Appl. Intell. JUN 2022.0 52.0 8 9193 9211 10.1007/s10489-021-02752-1 0.0 JAN 2022 19 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1U7XE 2023-03-23 WOS:000737094100001 0
J Jin, C; Jin, SW Jin, Cong; Jin, Shu-Wei Content-based image retrieval model based on cost sensitive learning JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Content-based image retrieval; Distance metric learning; Cost sensitive learning; Classification performance; Misclassification cost; Class imbalance DISTANCE; CLASSIFICATION; ANNOTATION How to retrieve the desired images quickly and accurately from the large scale image database has become a hot topic in the field of multimedia research. Many content-based image retrieval (CBIR) technologies already exist, but they are not always satisfactory. In many applications, the CBIR model based on machine learning relies heavily on the distance metric between samples. Although the traditional distance metric methods are simple and convenient, it is not always appropriate for CBIR tasks. In this paper, a novel distance metric learning (DML) method based on cost sensitive learning (CSL) is studied, and then it is used in a large margin distribution learning machine (LDM) to replace the traditional kernel functions. The improved LDM also takes into account CSL, and which is called CS-DLDM. Finally, CS-DLDM model is applied to CBIR tasks for implementation classification. We compare the proposed CS-DLDM model with other classifiers based on CSL. The experimental results show that the proposed CS-DLDM model not only has satisfactory classification performance but also the lowest misclassification cost, can effectively avoid the class imbalance of sample. (C) 2018 Elsevier Inc. All rights reserved. [Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China; [Jin, Shu-Wei] Ecole Normale Super, Dept Phys, 24 Rue Lhomond, F-75231 Paris 5, France Central China Normal University; UDICE-French Research Universities; Universite PSL; Ecole Normale Superieure (ENS) Jin, C (corresponding author), Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China. jinc26@aliyun.com , Cong/0000-0002-3703-359X 53 4 4 0 3 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. AUG 2018.0 55.0 720 728 10.1016/j.jvcir.2018.08.009 0.0 9 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science GU5IB 2023-03-23 WOS:000445318100062 0
J Li, YS; Ma, RQ; Goncalves, B; Gong, BN; Cui, YX; Shen, YF Li, Yuesen; Ma, Runqing; Goncalves, Bruno; Gong, Bingnan; Cui, Yixiong; Shen, Yanfei Data-driven team ranking and match performance analysis in Chinese Football Super League CHAOS SOLITONS & FRACTALS English Article Soccer; Match analysis; Ranking; Predictive modeling; Big data GAME-RELATED STATISTICS; PROFESSIONAL SOCCER; POSSESSION Recent years have seen an increasing body of research into the evaluation of the team-level technical tactical performance in association football using match events data. However, most studies used mono dimensional approach and modeled the influence of each performance aspects on match result in isolation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classifier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams' match performance and prediction of teams' league standings were highly correlated with their actual ranking. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their counterparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season. (C) 2020 Elsevier Ltd. All rights reserved. [Li, Yuesen; Ma, Runqing; Cui, Yixiong; Shen, Yanfei] Beijing Sport Univ, Sch Sports Engn, Informat Rd 48, Beijing 100084, Peoples R China; [Goncalves, Bruno] Univ Evora, Dept Desporto & Saude, Escola Ciencias & Tecnol, Evora, Portugal; [Goncalves, Bruno] Univ Evora, Comprehens Hlth Res Ctr CHRC, Evora, Portugal; [Goncalves, Bruno] Portuguese Football Federat, Portugal Football Sch, Oeiras, Portugal; [Gong, Bingnan] Univ Politecn Madrid, Fac Phys Act & Sports Sci, Madrid, Spain; [Cui, Yixiong] Beijing Sport Univ, Sch Sports Engn, AI Sports Engn Lab, Beijing, Peoples R China Beijing Sport University; University of Evora; University of Evora; Universidad Politecnica de Madrid; Beijing Sport University Cui, YX; Shen, YF (corresponding author), Beijing Sport Univ, Sch Sports Engn, Informat Rd 48, Beijing 100084, Peoples R China. cuiyixiong@bsu.edu.cn; syf@bsu.edu.cn Gonçalves, Bruno/M-5361-2013 Gonçalves, Bruno/0000-0001-7874-4104; Li, Yuesen/0000-0002-5218-9672; CUI, YIXIONG/0000-0002-1755-9631; GONG, BINGNAN/0000-0003-2646-093X National Key Research and Development Program of China [2020AAA0103404, 2018YFC2000600]; China Postdoctoral Science Foundation [2020T130067]; Fundamental Research Funds for the Central Universities [2019QD033] National Key Research and Development Program of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Key Research and Development Program of China under grants 2020AAA0103404 and 2018YFC2000600. The corresponding author (Y. Cui) was supported by the China Postdoctoral Science Foundation (2020T130067) and the Fundamental Research Funds for the Central Universities (2019QD033). 48 9 9 4 32 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-0779 1873-2887 CHAOS SOLITON FRACT Chaos Solitons Fractals DEC 2020.0 141.0 110330 10.1016/j.chaos.2020.110330 0.0 9 Mathematics, Interdisciplinary Applications; Physics, Multidisciplinary; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics PC8CV Green Published 2023-03-23 WOS:000597223300013 0
J He, CX; Duan, L; Dong, GZ; Nummenmaa, J; Wang, TT; Pang, TH He, Chengxin; Duan, Lei; Dong, Guozhu; Nummenmaa, Jyrki; Wang, Tingting; Pang, Tinghai Efficient mining of concept-hierarchy aware distinguishing sequential patterns KNOWLEDGE-BASED SYSTEMS English Article Distinguishing sequential pattern; Contrast mining; Domain knowledge; Concept hierarchy FUZZY ASSOCIATION RULES; FRAMEWORK Distinguishing sequential patterns are sequential patterns that have much higher frequencies in one target group of sequences (concerning a given phenomenon of interest) than in a contrasting group of sequences. Distinguishing sequential patterns are useful for many machine learning tasks, as well as for the explanation and characterization of the phenomenon underlying the target group of sequences. However, previous studies on mining distinguishing sequential patterns did not consider the hierarchical relationship among elements in sequences. To fill the gap, this paper investigates the mining of distinguishing sequential patterns in the presence of concept hierarchies among sequence elements. The associated patterns will be called concept-hierarchy aware distinguishing sequential patterns (hDSPs). After presenting the challenges on mining hDSPs, we present hDSP-Miner, a method with effective pruning techniques, for mining hDSPs. Our empirical study using real-world protein sequences demonstrates that hDSP-Miner is effective and efficient, and it can discover more novel distinguishing sequential patterns than previous algorithms for mining distinguishing sequential patterns. (c) 2022 Elsevier B.V. All rights reserved. [He, Chengxin; Duan, Lei; Pang, Tinghai] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China; [Dong, Guozhu] Wright State Univ, Dept Comp Sci & Engn, Dayton, OH 45435 USA; [Nummenmaa, Jyrki] Univ Tampere, Fac Informat Technol & Commun Sci, Tampere 33014, Fin, Finland; [Wang, Tingting] RMIT Univ, Sch Comp Technol, Melbourne, Vic 3001, Australia Sichuan University; University System of Ohio; Wright State University Dayton; Tampere University; Royal Melbourne Institute of Technology (RMIT) Duan, L (corresponding author), Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China. leiduan@scu.edu.cn Nummenmaa, Jyrki/0000-0002-7476-7840 National Natural Science Foundation of China [61972268]; Sichuan Province Science and Technology Support Program, China [2020YFG0034]; Academy of Finland [327352]; Academy of Finland (AKA) [327352] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Province Science and Technology Support Program, China; Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) Acknowledgments This work was supported in part by the National Natural Science Foundation of China (61972268) , the Sichuan Province Science and Technology Support Program, China (2020YFG0034) , and the Academy of Finland (327352) . 50 0 0 7 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. NOV 14 2022.0 255.0 109710 10.1016/j.knosys.2022.109710 0.0 SEP 2022 16 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4X0WV 2023-03-23 WOS:000860573400003 0
J Chen, C; Richter, F; Zhang, JL; Guerrero-Sanchez, C; Traeger, A; Schubert, US; Feng, AC; Thang, SH Chen, Chao; Richter, Friederike; Zhang, Junliang; Guerrero-Sanchez, Carlos; Traeger, Anja; Schubert, Ulrich S.; Feng, Anchao; Thang, San H. Synthesis of functional miktoarm star polymers in an automated parallel synthesizer EUROPEAN POLYMER JOURNAL English Article LIVING RADICAL POLYMERIZATION; HIGH-THROUGHPUT SYNTHESIS; RAFT PROCESS; MOLECULAR-WEIGHT; COPOLYMERS; DELIVERY; SIRNA; SYSTEM; AGENT; CELLS A feasibility study for the synthesis of well-defined miktoarm star polymers containing peptide arms in an automated parallel synthesizer is reported. A series of linear homo- and block (co-)polymers were prepared in the presence of commercial or peptide-functionalized reversible addition-fragmentation chain transfer (RAFT) agents. Thereafter, miktoarm star polymers were obtained by cross-linking different combinations of the abovementioned linear polymers using N, N'-bis(acryloyl) cystamine (BAC) as a redox responsive cross-linker. The described automated synthesis can be regarded as a proof-of-principle technique that next to emerging automated polymer purification and machine learning approaches could be useful to potentiate the development of autonomous methods for accessing systematic and comprehensive (peptide-)functionalized star polymer libraries. With these approaches hundreds of candidates of these complex polymer architectures could be rapidly synthesized, screened and optimized for different applications. [Chen, Chao; Feng, Anchao] Beijing Univ Chem Technol, Coll Mat Sci & Engn, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China; [Richter, Friederike; Guerrero-Sanchez, Carlos; Traeger, Anja; Schubert, Ulrich S.] Friedrich Schiller Univ Jena, Lab Organ & Macromol Chem IOMC, Humboldtstr 10, D-07743 Jena, Germany; [Zhang, Junliang] Northwestern Polytech Univ, Sch Chem & Chem Engn, Shaanxi Key Lab Macromol Sci & Technol, Xian 710072, Shaanxi, Peoples R China; [Guerrero-Sanchez, Carlos; Traeger, Anja; Schubert, Ulrich S.] Friedrich Schiller Univ Jena, Jena Ctr Soft Matter JCSM, Philosophenweg 7, D-07743 Jena, Germany; [Thang, San H.] Monash Univ, Sch Chem, Clayton Campus, Clayton, Vic 3800, Australia Beijing University of Chemical Technology; Friedrich Schiller University of Jena; Northwestern Polytechnical University; Friedrich Schiller University of Jena; Monash University Feng, AC (corresponding author), Beijing Univ Chem Technol, Coll Mat Sci & Engn, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China.;Guerrero-Sanchez, C (corresponding author), Friedrich Schiller Univ Jena, Lab Organ & Macromol Chem IOMC, Humboldtstr 10, D-07743 Jena, Germany.;Guerrero-Sanchez, C (corresponding author), Friedrich Schiller Univ Jena, Jena Ctr Soft Matter JCSM, Philosophenweg 7, D-07743 Jena, Germany.;Thang, SH (corresponding author), Monash Univ, Sch Chem, Clayton Campus, Clayton, Vic 3800, Australia. carlos.guerrero.sanchez@uni-jena.de; fengac@mail.buct.edu.cn; san.thang@monash.edu Traeger, Anja/G-3862-2012; Schubert, Ulrich S/B-5777-2009; Schubert, Ulrich/M-8136-2019 Traeger, Anja/0000-0001-7734-2293; Schubert, Ulrich S/0000-0003-4978-4670; Schubert, Ulrich/0000-0003-4978-4670; Thang, San/0000-0003-2629-3895 National Natural Science Foundation of China [21704001]; Beijing Advanced Innovation Center for Soft Matter Science and Engineering; Bundesministerium fur Bildung und Forschung (BMBF, Germany) [13XP5034A PolyBioMik]; Deutsche Forschungsgemeinschaft (DFG, Germany) through the Collaborative Research Centre Poly-Target [316213987, SFB 1278] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Advanced Innovation Center for Soft Matter Science and Engineering; Bundesministerium fur Bildung und Forschung (BMBF, Germany)(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgemeinschaft (DFG, Germany) through the Collaborative Research Centre Poly-Target(German Research Foundation (DFG)) This work was supported by the National Natural Science Foundation of China (Grant No. 21704001) and the Beijing Advanced Innovation Center for Soft Matter Science and Engineering. Furthermore, the authors gratefully acknowledge the Bundesministerium fur Bildung und Forschung (BMBF, Germany, #13XP5034A PolyBioMik) and the support of the Deutsche Forschungsgemeinschaft (DFG-funded, Germany) through the Collaborative Research Centre Poly-Target (Project No. 316213987, SFB 1278, Projects B01, B02 and Z01). 59 4 4 5 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0014-3057 1873-1945 EUR POLYM J Eur. Polym. J. NOV 5 2021.0 160.0 110777 10.1016/j.eurpolymj.2021.110777 0.0 OCT 2021 7 Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Polymer Science WI7WA hybrid 2023-03-23 WOS:000708566500003 0
C Jin, ZR; Zhang, TJ; Jiang, TX; Vivone, G; Deng, LJ Assoc Advancement Artificial Intelligence Jin, Zi-Rong; Zhang, Tian-Jing; Jiang, Tai-Xiang; Vivone, Gemine; Deng, Liang-Jian LAGConv: Local-Context Adaptive Convolution Kernels with Global Harmonic Bias for Pansharpening THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence FEB 22-MAR 01, 2022 ELECTR NETWORK Assoc Advancement Artificial Intelligence FUSION; RESOLUTION; NETWORK Pansharpening is a critical yet challenging low-level vision task that aims to obtain a higher-resolution image by fusing a multispectral (MS) image and a panchromatic (PAN) image. While most pansharpening methods are based on convolutional neural network (CNN) architectures with standard convolution operations, few attempts have been made with context-adaptive/dynamic convolution, which delivers impressive results on high-level vision tasks. In this paper, we propose a novel strategy to generate local-context adaptive (LCA) convolution kernels and introduce a new global harmonic (GH) bias mechanism, exploiting image local specificity as well as integrating global information, dubbed LAGCony. The proposed LAGConv can replace the standard convolution that is context-agnostic to fully perceive the particularity of each pixel for the task of remote sensing pansharpening. Furthermore, by applying the LAGConv, we provide an image fusion network architecture, which is more effective than conventional CNN-based pansharpening approaches. The superiority of the proposed method is demonstrated by extensive experiments implemented on a wide range of datasets compared with state-of-the-art pansharpening methods. Besides, more discussions testify that the proposed LAGConv outperforms recent adaptive convolution techniques for pansharpening. [Jin, Zi-Rong; Zhang, Tian-Jing; Deng, Liang-Jian] Univ Elect Sci & Technol China, Chengdu, Peoples R China; [Jiang, Tai-Xiang] Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Chengdu, Peoples R China; [Vivone, Gemine] CNR, Inst Methodol Environm Anal, Rome, Italy University of Electronic Science & Technology of China; Southwestern University of Finance & Economics - China; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR) Deng, LJ (corresponding author), Univ Elect Sci & Technol China, Chengdu, Peoples R China. 2018051403016@std.uestc.edu.cn; zhangtianjinguestc@163.com; taixiangjiang@gmail.com; gemine.vivone@gmail.com; liangjian.deng@uestc.edu.cn NSFC [12171072]; Key Projects of Applied Basic Research in Sichuan Province [2020YJ0216]; National Key Research and Development Program of China [2020YFA0714001] NSFC(National Natural Science Foundation of China (NSFC)); Key Projects of Applied Basic Research in Sichuan Province; National Key Research and Development Program of China This research was supported by by NSFC (12171072), Key Projects of Applied Basic Research in Sichuan Province (2020YJ0216), and National Key Research and Development Program of China (2020YFA0714001). 39 2 2 1 1 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-876-3 AAAI CONF ARTIF INTE 2022.0 1113 1121 9 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU3UU 2023-03-23 WOS:000893636201021 0
J Ren, L; Reyes, CM; Friess, H; Demir, IE Ren, Lei; Mota Reyes, Carmen; Friess, Helmut; Demir, Ihsan Ekin Neoadjuvant therapy in pancreatic cancer: what is the true oncological benefit? LANGENBECKS ARCHIVES OF SURGERY English Review Pancreatic cancer; Neoadjuvant therapy; Arterial resection; Immunotherapy CHEMORADIATION THERAPY; ADENOCARCINOMA; RESECTION; IMMUNOTHERAPY; SURGERY; CHEMOTHERAPY; EFFICACY; OUTCOMES; CELLS Background Neoadjuvant therapies (neoTx) have revolutionized the treatment of borderline resectable (BR) and locally advanced (LA) pancreatic cancer (PCa) by significantly increasing the rate of R0 resections, which remains the only curative strategy for these patients. However, there is still room for improvement of neoTx in PCa. Purpose Here, we aimed to critically analyze the benefits of neoTx in LA and BR PCa and its potential use on patients with resectable PCa. We also explored the feasibility of arterial resection (AR) to increase surgical radicality and the incorporation of immunotherapy to optimize neoadjuvant approaches in PCa. Conclusion For early stage, i.e., resectable, PCa, there is not enough scientific evidence for routinely recommending neoTx. For LA and BR PCa, optimization of neoadjuvant therapy necessitates more sophisticated complex surgical resections, machine learning and radiomic approaches, integration of immunotherapy due to the high antigen load, standardized histopathological assessment, and improved multidisciplinary communication. [Ren, Lei; Mota Reyes, Carmen; Friess, Helmut; Demir, Ihsan Ekin] Tech Univ Munich, Klinikum Rechts Isar, Sch Med, Dept Surg, Ismaninger Str 22, D-81675 Munich, Germany; [Ren, Lei] Southwest Med Univ, Affiliated Hosp, Dept Gen Surg Gastrointestinal Surg, Luzhou, Sichuan, Peoples R China; [Friess, Helmut; Demir, Ihsan Ekin] German Canc Consortium DKTK, Partner Site Munich, Munich, Germany; [Friess, Helmut; Demir, Ihsan Ekin] CRC 1321 Modelling & Targeting Pancreat Canc, Munich, Germany; [Demir, Ihsan Ekin] Acibadem Mehmet Ali Aydinlar Univ, Sch Med, HPB Unit, Dept Gen Surg, Istanbul, Turkey Technical University of Munich; Southwest Medical University; Helmholtz Association; German Cancer Research Center (DKFZ); Acibadem University Demir, IE (corresponding author), Tech Univ Munich, Klinikum Rechts Isar, Sch Med, Dept Surg, Ismaninger Str 22, D-81675 Munich, Germany.;Demir, IE (corresponding author), German Canc Consortium DKTK, Partner Site Munich, Munich, Germany.;Demir, IE (corresponding author), CRC 1321 Modelling & Targeting Pancreat Canc, Munich, Germany.;Demir, IE (corresponding author), Acibadem Mehmet Ali Aydinlar Univ, Sch Med, HPB Unit, Dept Gen Surg, Istanbul, Turkey. ekin.demir@tum.de Demir, Ihsan Ekin/0000-0001-8241-1871 Projekt DEAL; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [329628492-SFB 1321] Projekt DEAL; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) Open Access funding provided by Projekt DEAL. IED was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-project ID 329628492-SFB 1321. 53 4 5 0 0 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1435-2443 1435-2451 LANGENBECK ARCH SURG Langenbecks Arch. Surg. NOV 2020.0 405.0 7 879 887 10.1007/s00423-020-01946-4 0.0 AUG 2020 9 Surgery Science Citation Index Expanded (SCI-EXPANDED) Surgery NX6YZ 32776259.0 Green Accepted, hybrid, Green Published 2023-03-23 WOS:000558172100001 0
J Cui, YB; Du, YT; Wang, X; Wang, H; Su, C Cui, Yunbo; Du, Youtian; Wang, Xue; Wang, Hang; Su, Chang Leveraging attention-based visual clue extraction for image classification IET IMAGE PROCESSING English Article Deep learning-based approaches have made considerable progress in image classification tasks, but most of the approaches lack interpretability, especially in revealing the decisive information causing the categorization of images. This paper seeks to answer the question of what clues encode the discriminative visual information between image categories and can help improve the classification performance. To this end, an attention-based clue extraction network (ACENet) is introduced to mine the decisive local visual information for image classification. ACENet constructs a clue-attention mechanism, that is global-local attention, between the image and visual clue proposals extracted from it and then introduces a contrastive loss defined over the achieved discrete attention distribution to increase the discriminability of clue proposals. The loss encourages considerable attention to be devoted to discriminative clue proposals, that is those similar within the same category and dissimilar across categories. The experimental results for the Negative Web Image (NWI) dataset and the public ImageNet2012 dataset demonstrate that ACENet can extract true clues to improve the image classification performance and outperforms the baselines and the state-of-the-art methods. [Cui, Yunbo; Du, Youtian; Wang, Xue; Wang, Hang] Xi An Jiao Tong Univ, Fac Elect & Informat Engn, 28 Xianning West Rd, Xian, Peoples R China; [Wang, Xue] Leiden Univ, Inst Adv Comp Sci, Leiden, Netherlands; [Su, Chang] Cornell Univ, Weill Cornell Med, Dept Healthcare Policy & Res, Ithaca, NY USA Xi'an Jiaotong University; Leiden University; Leiden University - Excl LUMC; Cornell University; Weill Cornell Medicine Du, YT (corresponding author), Xi An Jiao Tong Univ, Fac Elect & Informat Engn, 28 Xianning West Rd, Xian, Peoples R China. duty@mail.xjtu.edu.cn Wang, Hang/HHR-9240-2022 Wang, Xue/0000-0002-5719-7794; WANG, HANG/0000-0001-9876-8285; Du, Youtian/0000-0002-1714-3433 National Key Research and Development Program of China [2018AAA0101501] National Key Research and Development Program of China National Key Research and Development Program of China, Grant/AwardNumber: 2018AAA0101501 39 1 1 1 6 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. OCT 2021.0 15.0 12 2937 2947 10.1049/ipr2.12280 0.0 JUN 2021 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology UN4RK 2023-03-23 WOS:000660808200001 0
J Ren, JZ; Gao, SZ; Tan, SY; Dong, LC Ren, Jingzheng; Gao, Suzhao; Tan, Shiyu; Dong, Lichun Prediction of the yield of biohydrogen under scanty data conditions based on GM(1,N) INTERNATIONAL JOURNAL OF HYDROGEN ENERGY English Article Yield; Prediction; Biohydrogen; Grey theory Biohydrogen technology is regarded as one of the most promising ways for hydrogen production with the considerations of economic priority and environmental performance. In this study, grey model is used to predict the yield of biohydrogen under scanty data condition. An illustrative case has been studied by the proposed method, and pH, glucose and iron sulfate concentration are used as the independent variables, the yield of biohydrogen is used as dependent variable in the grey prediction model, and 9 groups of data are used as the training samples and another 2 groups of data are used as the test samples, the results show that the proposed method is feasible to predict the yield of biohydrogen under scanty data condition and the effect of the influencing factors on the yield could also be identified. According to the comparison with the results predicted by artificial neural network, it could be concluded that grey model has better predictability with scanty data. This method could be popularized to other biohydrogen systems. Copyright (C) 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. [Ren, Jingzheng; Gao, Suzhao; Tan, Shiyu; Dong, Lichun] Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China; [Ren, Jingzheng] Univ Padua, Dept Ind Engn, CESQA Qual & Environm Res Ctr, I-35100 Padua, Italy Chongqing University; University of Padua Dong, LC (corresponding author), Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China. lcdong72@cqu.edu.cn Ren, Jingzheng/ABF-5240-2020; dong, li/HMW-0948-2023 Ren, Jingzheng/0000-0002-9690-5183 18 21 21 0 24 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-3199 1879-3487 INT J HYDROGEN ENERG Int. J. Hydrog. Energy OCT 8 2013.0 38.0 30 13198 13203 10.1016/j.ijhydene.2013.07.081 0.0 6 Chemistry, Physical; Electrochemistry; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels 242CE 2023-03-23 WOS:000326214200009 0
J Yang, HR; Dou, QY; Liu, K; Liu, ZT; Francese, R; Yang, XM Yang, Haoran; Dou, Qingyu; Liu, Kai; Liu, Zitao; Francese, Rita; Yang, Xiaomin Efficient local cascading residual network for real-time single image super-resolution JOURNAL OF REAL-TIME IMAGE PROCESSING English Article Lightweight; Super-resolution; Real-time; Attention; Convolutional neural network In the past decade, single image super-resolution (SISR) based on convolutional neural networks (CNNs) has been represented remarkable performance. Powerful characterization of CNN is important for recent methods to learn an intricate non-linear mapping between high-resolution and corresponding low-resolution images. However, a deeper and wider network structure brings superior performance while increasing the number of network parameters and calculations so that it is difficult to handle the real-time information. Hence, it can be embedded in mobile devices with difficulty. Inspired by the above motivation, a lightweight network for the real-time SISR is proposed by stacking efficient cascading residual blocks, which consist of several concatenate effective modules with wide activation. To further improve the network performance, with the increase of a slight number of parameters, the proposed network cooperates with a lightweight residual efficient channel attention module to capture feature interaction between channels. Extensive experiments provide significant demonstrations that the proposed network obtains the superior trade-off between performance and parameters compared with other current methods. The lightweight trait of our method allows it to implement real-time image processing and can be embedded in mobile devices. [Yang, Haoran; Yang, Xiaomin] Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China; [Yang, Haoran; Yang, Xiaomin] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu, Peoples R China; [Liu, Kai] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Sichuan, Peoples R China; [Dou, Qingyu] Sichuan Univ, West China Hosp, Ctr Gerontol & Geriatr, Chengdu, Peoples R China; [Liu, Zitao] TAL Educ Grp, Beijing, Peoples R China; [Francese, Rita] Univ Salerno, Dept Comp Sci, Via Giovanni Paolo II, I-13284084 Fisciano, SA, Italy Sichuan University; Sichuan University; Sichuan University; Sichuan University; University of Salerno Liu, K (corresponding author), Sichuan Univ, Coll Elect Engn, Chengdu 610065, Sichuan, Peoples R China.;Dou, QY (corresponding author), Sichuan Univ, West China Hosp, Ctr Gerontol & Geriatr, Chengdu, Peoples R China. haoran__yang@outlook.com; ddqqking@126.com; kailiu@scu.edu.cn; liuzitao@100tal.com; francese@unisa.it; arielyang@scu.edu.cn yang, xiao/HJI-7815-2023 Dou, Qingyu/0000-0001-8849-3716 National Key R and D Program of China [2020AAA0-104500]; Sichuan University [2020SCUNG205] National Key R and D Program of China; Sichuan University(Sichuan University) The research in our paper is sponsored by National Key R and D Program of China, under Grant No. 2020AAA0-104500. The funding is from Sichuan University under grant 2020SCUNG205. 36 3 3 1 8 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1861-8200 1861-8219 J REAL-TIME IMAGE PR J. Real-Time Image Process. AUG 2021.0 18.0 4 SI 1235 1246 10.1007/s11554-021-01134-7 0.0 JUN 2021 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology UC7WK 2023-03-23 WOS:000658062800001 0
J Jovic, D; Liang, X; Zeng, H; Lin, L; Xu, FP; Luo, YL Jovic, Dragomirka; Liang, Xue; Zeng, Hua; Lin, Lin; Xu, Fengping; Luo, Yonglun Single-cell RNA sequencing technologies and applications: A brief overview CLINICAL AND TRANSLATIONAL MEDICINE English Review atlas; big data; precision medicine; regenerative medicine; RNA sequencing; single cell GENOME-WIDE EXPRESSION; GENE-EXPRESSION; MESSENGER-RNA; BETA-CELL; PHENOTYPIC HETEROGENEITY; SPATIAL TRANSCRIPTOMICS; STROMAL CELLS; SEQ ANALYSIS; ATLAS; REVEALS Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment. [Jovic, Dragomirka; Liang, Xue; Xu, Fengping; Luo, Yonglun] Qingdao Europe Adv Inst Life Sci, Lars Bolund Inst Regenerat Med, Qingdao, Peoples R China; [Jovic, Dragomirka; Liang, Xue; Xu, Fengping; Luo, Yonglun] BGI Shenzhen, Shenzhen, Peoples R China; [Liang, Xue] Univ Copenhagen, Dept Biol, Copenhagen, Denmark; [Zeng, Hua] Nanjing Univ Chinese Med, Nanjing, Peoples R China; [Lin, Lin; Luo, Yonglun] Aarhus Univ, Dept Biomed, Aarhus, Denmark; [Lin, Lin; Luo, Yonglun] Aarhus Univ Hosp, Steno Diabet Ctr Aarhus, Aarhus, Denmark Beijing Genomics Institute (BGI); University of Copenhagen; Nanjing University of Chinese Medicine; Aarhus University; Aarhus University Jovic, D; Luo, YL (corresponding author), Qingdao Europe Adv Inst Life Sci, Lars Bolund Inst Regenerat Med, Qingdao, Peoples R China. dragomirkajovic@gmail.com; alun@biomed.au.dk ; Luo, Yonglun/C-4708-2013 Zeng, Hua/0000-0003-4821-1870; Luo, Yonglun/0000-0002-0007-7759 Qingdao-Europe Advanced Institute for Life Sciences; Independent Research Fund Denmark (DFF) [8048-00072A]; China National GeneBank Qingdao-Europe Advanced Institute for Life Sciences; Independent Research Fund Denmark (DFF); China National GeneBank We would like to thank Fred Dubee, Lars Bolund and Huanming Yang for their critical comments to the manuscript. The single-cell project was partially supported by the Qingdao-Europe Advanced Institute for Life Sciences. L.L. is supported by the Independent Research Fund Denmark (DFF, Sapere Aude Starting grant 8048-00072A). We thank the China National GeneBank for the support of executing the project under the framework of Genome Read and Write. 177 15 15 31 65 JOHN WILEY & SONS LTD CHICHESTER THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND 2001-1326 CLIN TRANSL MED Clin. Transl. Med. MAR 2022.0 12.0 3 e694 10.1002/ctm2.694 0.0 21 Oncology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Oncology; Research & Experimental Medicine 0E0AY 35352511.0 Green Accepted 2023-03-23 WOS:000776350200001 0
J Hu, Z; Borsje, BW; Belzen, J; Willemsen, PWJM; Wang, H; Peng, YS; Yuan, L; De Dominicis, M; Wolf, J; Temmerman, S; Bouma, TJ Hu, Zhan; Borsje, Bas W.; Belzen, Jim; Willemsen, Pim W. J. M.; Wang, Heng; Peng, Yisheng; Yuan, Lin; De Dominicis, Michela; Wolf, Judith; Temmerman, Stijn; Bouma, Tjeerd J. Mechanistic Modeling of Marsh Seedling Establishment Provides a Positive Outlook for Coastal Wetland Restoration Under Global Climate Change GEOPHYSICAL RESEARCH LETTERS English Article SEA-LEVEL RISE; TIDAL FLATS; LONG-TERM; ECOSYSTEM SERVICES; INTERTIDAL FLATS; YANGTZE ESTUARY; RANGE EXPANSION; SALT-MARSHES; WIND-SPEED; SEDIMENT While many studies focus on the persistence of coastal wetlands under climate change, similar predictions are lacking for new wetland establishment, despite being critical to restoration. Recent experiments revealed that marsh seedling establishment is driven by a balance between physical disturbance of bed-level dynamics and seedling root stability. Using machine learning, we quantitatively translate such finding in a new biogeomorphic model to assess marsh establishment extent. This model was validated against multiyear observations of natural seedling-expansion events at typical sites in the Netherlands and China. Subsequently, synthetic modeling experiments underscored that seedling expansion was primarily determined by controllable local conditions (e.g., sediment supply, local wave height, and tidal flat bathymetry) rather than uncontrollable climate change factors (e.g., change in sea-level and global wave regime). Thus, science-based local management measures can facilitate coastal wetland restoration, despite global climate change, shedding hope for managing a variety of coastal ecosystems under similar stresses. Plain Language Summary Vulnerability of coastal wetlands under climate changes is of global concern due to their valuable services. Thus, restoring coastal wetlands is of widespread importance. However, the key processes controlling wetland vegetation establishment are not clear until recent field and laboratory experiments using marshes as an exemplary system. In the current study, we translate the insights of these recent studies into a new biogeomorphic model to predict marsh establishment extent on a landscape scale. Synthetical model analysis underlines that controllable local conditions are much more important than uncontrollable climate change stressors, thus providing a positive outlook for future coastal wetland restorations. [Hu, Zhan] Sun Yat Sen Univ, Sch Marine Sci, Zhuhai, Peoples R China; [Hu, Zhan] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Peoples R China; [Hu, Zhan] Guangdong Prov Key Lab Marine Res & Coastal Engn, Guangzhou, Peoples R China; [Hu, Zhan] Minist Educ, Pearl River Estuary Marine Ecosyst Res Stn, Zhuhai, Peoples R China; [Borsje, Bas W.; Willemsen, Pim W. J. M.] Univ Twente, Water Engn & Management, Enschede, Netherlands; [Belzen, Jim; Bouma, Tjeerd J.] Royal Netherlands Inst Sea Res NIOZ, Dept Estuarine & Delta Syst, Yerseke, Netherlands; [Wang, Heng] MOT, Natl Engn Lab Port Hydraul Construct Technol, Tianjin Res Inst Water Transport Engn, Tianjin, Peoples R China; [Peng, Yisheng] Sun Yat Sen Univ, Sch Environm Sci & Engn, Guangzhou, Peoples R China; [Yuan, Lin] East China Normal Univ, State Key Lab Estuarine & Coastal Res, Inst Estuarine & Coastal Res, Inst Ecochongming, Shanghai, Peoples R China; [De Dominicis, Michela; Wolf, Judith] Natl Oceanog Ctr, Liverpool, Merseyside, England; [Temmerman, Stijn] Univ Antwerp, Ecosyst Management Res Grp, Antwerp, Belgium; [Bouma, Tjeerd J.] Univ Utrecht, Fac Geosci, Dept Phys Geog, Utrecht, Netherlands; [Bouma, Tjeerd J.] HZ Univ Appl Sci, Bldg Nat Grp, Vlissingen, Netherlands Sun Yat Sen University; Southern Marine Science & Engineering Guangdong Laboratory; University of Twente; Utrecht University; Royal Netherlands Institute for Sea Research (NIOZ); Tianjin Research Institute for Water Transport Engineering; Sun Yat Sen University; East China Normal University; NERC National Oceanography Centre; University of Antwerp; Utrecht University Wang, H (corresponding author), MOT, Natl Engn Lab Port Hydraul Construct Technol, Tianjin Res Inst Water Transport Engn, Tianjin, Peoples R China.;Peng, YS (corresponding author), Sun Yat Sen Univ, Sch Environm Sci & Engn, Guangzhou, Peoples R China. wangheng3@mail.sysu.edu.cn; pyish@mail.sysu.edu.cn Temmerman, Stijn/C-5521-2009; Hu, Zhan/L-7695-2019 Hu, Zhan/0000-0002-2809-3718; Wolf, Judith/0000-0003-4129-8221; van Belzen, Jim/0000-0003-2099-1545; De Dominicis, Michela/0000-0003-0544-7939; Borsje, Bas/0000-0002-0357-6270; Peng, Yisheng/0000-0002-8331-3429 NSFC [51761135022]; Sustainable Deltas, Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [311021004]; Guangdong Provincial Department of Science and Technology [2019ZT08G090]; Fundamental Research Funds for the Central Universities of China [20lgzd16]; 111 Project [B21018]; NWO [ALWSD.2016.026]; EPSRC [EP/R024537/1] NSFC(National Natural Science Foundation of China (NSFC)); Sustainable Deltas, Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai); Guangdong Provincial Department of Science and Technology; Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); 111 Project(Ministry of Education, China - 111 Project); NWO(Netherlands Organization for Scientific Research (NWO)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) The authors thank Haobing Cao for providing detailed information of marsh seedling experiments, and Mark Schuerch for providing data on global marsh distribution and dynamics. The authors gratefully acknowledge financial support from the Joint Research Project: NSFC (No. 51761135022)-NWO (No. ALWSD.2016.026)-EPSRC (No. EP/R024537/1): Sustainable Deltas, Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021004), Guangdong Provincial Department of Science and Technology (2019ZT08G090), Fundamental Research Funds for the Central Universities of China (20lgzd16), and 111 Project (B21018). 73 22 22 15 44 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. NOV 28 2021.0 48.0 22 e2021GL095596 10.1029/2021GL095596 0.0 12 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology XE0RV Green Published, Green Accepted 2023-03-23 WOS:000723105600001 0
J Hoque, MA; Finley, B; Rao, AS; Kumar, A; Hui, P; Ammar, M; Tarkoma, S Hoque, Mohammad A.; Finley, Benjamin; Rao, Ashwin; Kumar, Abhishek; Hui, Pan; Ammar, Mostafa; Tarkoma, Sasu Context-driven encrypted multimedia traffic classification on mobile devices PERVASIVE AND MOBILE COMPUTING English Article Encrypted traffic classification; Mobile context; Multimedia applications; Mobile components The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, net-work operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device's detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [Hoque, Mohammad A.; Finley, Benjamin; Rao, Ashwin; Kumar, Abhishek; Hui, Pan; Tarkoma, Sasu] Univ Helsinki, Helsinki, Finland; [Kumar, Abhishek; Tarkoma, Sasu] Univ Oulu, Oulu, Finland; [Ammar, Mostafa] Georgia Inst Technol, Atlanta, GA USA; [Hui, Pan] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China; [Hui, Pan] Hong Kong Univ Sci & Technol Guangzhou, Guangzhou, Peoples R China University of Helsinki; University of Oulu; University System of Georgia; Georgia Institute of Technology; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology (Guangzhou) Finley, B (corresponding author), Univ Helsinki, Helsinki, Finland. mohammad.a.hoque@helsinki.fi; benjamin.finley@helsinki.fi; ashwin.rao@helsinki.fi; abhishek.kumar@oulu.fi; panhui@ust.hk; ammar@cc.gatech.edu; sasu.tarkoma@helsinki.fi Academy of Finland, Finland [335934, 319669, 325570, 346208]; Nokia Foundation, Finland; Walter Ahlstroem Foundation, Finland; GP INDFICORE, Finland EFFICACY Menot [24650101115]; NSF, USA grant NETS [1909040]; Finnish Computing Competence Infrastructure (FCCI) Academy of Finland, Finland(Academy of Finland); Nokia Foundation, Finland(Nokia Corporation); Walter Ahlstroem Foundation, Finland; GP INDFICORE, Finland EFFICACY Menot; NSF, USA grant NETS; Finnish Computing Competence Infrastructure (FCCI) The work was supported by the Academy of Finland, Finland IDEA-MILL project (Grant Number 335934), Academy of Finland, Finland 5GEAR project (Grant Number 319669), Nokia Foundation, Finland Grant, Walter Ahlstroem Foundation, Finland Grant, Academy of Finland, Finland FIT project (Grant Number 325570), Academy of Finland, Finland 6G Flagship Program (Grant Number 346208), and GP INDFICORE, Finland EFFICACY Menot (Grant Number 24650101115). Mostafa Ammar's work was partially supported by NSF, USA grant NETS (Grant Number 1909040). In addition, the authors wish to thank the Finnish Computing Competence Infrastructure (FCCI) for supporting this project with computational and data storage resources. 46 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1574-1192 1873-1589 PERVASIVE MOB COMPUT Pervasive Mob. Comput. JAN 2023.0 88.0 101737 10.1016/j.pmcj.2022.101737 0.0 DEC 2022 18 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications 7V1AQ Green Accepted, Green Published, hybrid 2023-03-23 WOS:000912553800001 0
C Wu, XT; Granger, E; Kinnunen, T; Feng, XY; Hadid, A IEEE Wu, Xiaoting; Granger, Eric; Kinnunen, Tomi; Feng, Xiaoyi; Hadid, Abdenour Audio-Visual Kinship Verification in the Wild 2019 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) International Conference on Biometrics English Proceedings Paper International Conference on Biometrics (ICB) JUN 04-07, 2019 Crete, GREECE LOCAL BINARY PATTERNS; SPEAKER VERIFICATION; FACE Kinship verification is a challenging problem, where recognition systems are trained to establish a kin relation between two individuals based on facial images or videos. However due to variations in capture conditions (background,pose, expression, illumination and occlusion), state-of-the-art systems currently provide a low level of accuracy. As in many visual recognition and affective computing applications, kinship verification may benefit from a combination of discriminant information extracted from both video and audio signals. In this paper we investigate for the first time the fusion audio-visual information from both face and voice modalities to improve kinship verification accuracy. First, we propose a new multi-modal kinship dataset called TALking KINship (TALKIN), that is comprised of several pairs of video sequences with subjects talking. State-of-the-art conventional and deep learning models are assessed and compared for kinship verification using this dataset. Finally,we propose a deep Siamese network for multi-modal fusion of kinship relations. Experiments with the TALKIN dataset indicate that the proposed Siamese network provides a significantly higher level of accuracy over baseline uni-modal and multi-modal fusion techniques for kinship verification. Results also indicate that audio (vocal) information is complementary and useful for kinship verification problem. [Wu, Xiaoting; Hadid, Abdenour] Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; [Wu, Xiaoting; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China; [Granger, Eric] Ecole Technol Super, Lab Imagerie Vis & Intelligence Artificielle LIVI, Dept Syst Engn, Montreal, PQ, Canada; [Kinnunen, Tomi] Univ Eastern Finland, Sch Comp, Joensuu, Finland University of Oulu; Northwestern Polytechnical University; University of Quebec; Ecole de Technologie Superieure - Canada; University of Eastern Finland Wu, XT (corresponding author), Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland.;Wu, XT (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China. China Scholarship Council [201706290103]; Academy of Finland; Natural Sciences and Engineering Research Council of Canada; CSC-IT Center for Science, Finland China Scholarship Council(China Scholarship Council); Academy of Finland(Academy of Finland); Natural Sciences and Engineering Research Council of Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)CGIAR); CSC-IT Center for Science, Finland This work is partially supported by the China Scholarship Council (grant 201706290103), the Academy of Finland, and the Natural Sciences and Engineering Research Council of Canada. The authors wish to acknowledge CSC-IT Center for Science, Finland, for the computational resources. The initial help from Dr. Miguel Bordallo Lopez and Dr. Elhocine Boutellaa is also acknowledged. 39 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-4201 978-1-7281-3640-0 INT CONF BIOMETR 2019.0 8 Computer Science, Theory & Methods; Engineering, Multidisciplinary; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematical & Computational Biology BP2EI 2023-03-23 WOS:000542138900006 0
J He, XL; Gong, QY; Chen, Y; Zhang, Y; Wang, X; Fu, XM He, Xinlei; Gong, Qingyuan; Chen, Yang; Zhang, Yang; Wang, Xin; Fu, Xiaoming DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING English Article Social networking (online); Neural networks; Feature extraction; Blogs; User-generated content; Safety; Privacy; Dating apps; malicious account detection; deep learning; attention mechanism; text analytics Dating apps have gained tremendous popularity during the past decade. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. In this article, we focus on malicious user detection in dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to limited detection performance. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. We evaluate DatingSec on a real-world dataset collected from Momo, a widely used dating app with more than 180 million users. Experimental results show that DatingSec outperforms state-of-the-art methods and achieves an F1-score of 0.857 and AUC of 0.940. [He, Xinlei; Gong, Qingyuan; Chen, Yang; Wang, Xin] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [He, Xinlei; Gong, Qingyuan; Chen, Yang; Wang, Xin] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [He, Xinlei; Gong, Qingyuan; Chen, Yang; Wang, Xin] Peng Cheng Lab, Shenzhen 518066, Peoples R China; [Zhang, Yang] CISPA Helmholtz Ctr Informat Secur, Saarland Informat Campus, D-66123 Saarbrucken, Germany; [Fu, Xiaoming] Univ Goettingen, Inst Comp Sci, D-37073 Gottingen, Germany Fudan University; Fudan University; Peng Cheng Laboratory; University of Gottingen Chen, Y (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China.;Chen, Y (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China. xlhe17@fudan.edu.cn; gongqingyuan@fudan.edu.cn; chenyang@fudan.edu.cn; zhang@cispa.de; xinw@fudan.edu.cn; fu@cs.uni-goettingen.de Fu, Xiaoming/AAD-2828-2022; Fu, Xiaoming/B-7208-2016 Fu, Xiaoming/0000-0002-8012-4753; Fu, Xiaoming/0000-0002-8012-4753 National Natural Science Foundation of China [62072115, 71731004, 61602122, 61971145]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant [824019]; project PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications'' [LZC0019] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant; project PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications'' This work was supported by the National Natural Science Foundation of China (No. 62072115, No. 71731004, No. 61602122, and No. 61971145), the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant No. 824019, and the project PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications (LZC0019). 74 8 8 0 5 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1545-5971 1941-0018 IEEE T DEPEND SECURE IEEE Trans. Dependable Secur. Comput. SEPT 1 2021.0 18.0 5 2193 2208 10.1109/TDSC.2021.3068307 0.0 16 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science UI2JP 2023-03-23 WOS:000690440600012 0
J Wu, TF; Bilbie, FD; Paun, A; Pan, LQ; Neri, F Wu, Tingfang; Bilbie, Florin-Daniel; Paun, Andrei; Pan, Linqiang; Neri, Ferrante Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request INTERNATIONAL JOURNAL OF NEURAL SYSTEMS English Article Bio-inspired computing; membrane computing; spiking neural network; spiking neural P system; computation power COMPUTATIONAL POWER; NETWORKS; NEURONS; RECOGNITION; ALGORITHM; CLASSIFICATION; IMPLEMENTATION; MACHINES; DESIGN; SPIKES Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal. [Wu, Tingfang; Pan, Linqiang] Huazhong Univ Sci & Technol, Sch Automat, Educ Minist China, Key Lab Image Informat Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China; [Bilbie, Florin-Daniel; Paun, Andrei] Univ Bucharest, Fac Math & Comp Sci, Dept Comp Sci, Str Acad 14,Sect 1, Bucharest 010014, Romania; [Paun, Andrei] Natl Inst Res & Dev Biol Sci, Dept Bioinformat, Splaiul Independentei 296,Sect 6, Bucharest, Romania; [Pan, Linqiang] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Henan, Peoples R China; [Neri, Ferrante] De Montfort Univ, Sch Comp Sci & Informat, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England Huazhong University of Science & Technology; University of Bucharest; National Research Institute for Biological Sciences; Zhengzhou University of Light Industry; De Montfort University Pan, LQ (corresponding author), Huazhong Univ Sci & Technol, Sch Automat, Educ Minist China, Key Lab Image Informat Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China.;Pan, LQ (corresponding author), Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Henan, Peoples R China. lqpan@mail.hust.edu.cn Neri, Ferrante/U-7036-2019; Paun, Andrei/A-5292-2008 Neri, Ferrante/0000-0002-6100-6532; Paun, Andrei/0000-0002-1644-8198 National Natural Science Foundation of China [61320106005, 61772214]; Innovation Scientists and Technicians Troop Construction Projects of Henan Province [154200510012]; ANCS [POC P 37 257] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Scientists and Technicians Troop Construction Projects of Henan Province; ANCS This work was supported by National Natural Science Foundation of China (61320106005 and 61772214), the Innovation Scientists and Technicians Troop Construction Projects of Henan Province (154200510012), and ANCS under the Award No. POC P 37 257 (MoDASyS). 74 87 87 6 34 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0129-0657 1793-6462 INT J NEURAL SYST Int. J. Neural Syst. OCT 2018.0 28.0 8 1850013 10.1142/S0129065718500132 0.0 19 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GR6OZ 29759014.0 Green Submitted 2023-03-23 WOS:000442776900004 0
J Pascucci, S; Pignatti, S; Casa, R; Darvishzadeh, R; Huang, WJ Pascucci, Simone; Pignatti, Stefano; Casa, Raffaele; Darvishzadeh, Roshanak; Huang, Wenjiang Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation REMOTE SENSING English Editorial Material hyperspectral remote sensing for soil and crops in agriculture; hyperspectral imaging for vegetation; plant traits; high-resolution spectroscopy for agricultural soils and vegetation; hyperspectral databases for agricultural soils and vegetation; hyperspectral data as input for modelling soil; crop; and vegetation; product validation; new hyperspectral technologies; future hyperspectral missions RADIATIVE-TRANSFER MODELS; SOIL TEXTURE; RESOLUTION; RETRIEVAL; IMAGERY The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. [Pascucci, Simone; Pignatti, Stefano] CNR, Inst Methodol Environm Anal CNR IMAA, I-85050 Tito, PZ, Italy; [Casa, Raffaele] Tuscia Univ, Dept Agr & Forestry Sci DAFNE, Via San Camillo de Lellis, I-01100 Viterbo, Italy; [Darvishzadeh, Roshanak] Univ Twente, Dept Nat Resources, ITC Fac Geoinformat Sci & Earth Observat, POB 217, NL-7500 AE Enschede, Netherlands; [Huang, Wenjiang] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Tuscia University; University of Twente; Chinese Academy of Sciences Pascucci, S (corresponding author), CNR, Inst Methodol Environm Anal CNR IMAA, I-85050 Tito, PZ, Italy. simone.pascucci@cnr.it; stefano.pignatti@imaa.cnr.it; rcasa@unitus.it; r.darvish@utwente.nl; huangwj@aircas.ac.cn huang, wenjiang/AAM-6844-2021; Pignatti Morano, Stefano/A-4640-2008 CASA, Raffaele/0000-0003-3091-7680; Pascucci, Simone/0000-0002-8311-8615; Pignatti Morano, Stefano/0000-0002-0587-8926; Darvishzadeh, Roshanak/0000-0001-7512-0574 35 11 11 21 76 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2020.0 12.0 21 3665 10.3390/rs12213665 0.0 7 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology OR3XW gold, Green Published 2023-03-23 WOS:000589408300001 0
J Yi, JZ; Mao, X; Chen, LJ; Xue, YL; Compare, A Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli; Compare, Angelo Facial expression recognition considering individual differences in facial structure and texture IET COMPUTER VISION English Article DIMENSIONALITY REDUCTION Facial expression recognition (FER) plays an important role in human-computer interaction. The recent years have witnessed an increasing trend of various approaches for the FER, but these approaches usually do not consider the effect of individual differences to the recognition result. When the face images change from neutral to a certain expression, the changing information constituted of the structural characteristics and the texture information can provide rich important clues not seen in either face image. Therefore it is believed to be of great importance for machine vision. This study proposes a novel FER algorithm by exploiting the structural characteristics and the texture information hiding in the image space. Firstly, the feature points are marked by an active appearance model. Secondly, three facial features, which are feature point distance ratio coefficient, connection angle ratio coefficient and skin deformation energy parameter, are proposed to eliminate the differences among the individuals. Finally, a radial basis function neural network is utilised as the classifier for the FER. Extensive experimental results on the Cohn-Kanade database and the Beihang University (BHU) facial expression database show the significant advantages of the proposed method over the existing ones. [Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China; [Compare, Angelo] Univ Bergamo, Dept Human Sci, Bergamo, Italy Beihang University; University of Bergamo Yi, JZ (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China. moukyoucn@yahoo.com.cn Compare, Prof. Angelo/H-3519-2014 Compare, Prof. Angelo/0000-0002-3336-7920 Specialized Research Fund for the Doctoral Program of Higher Education [30400002012102002]; National Natural Science Foundation of China [61103097] Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Specialized Research Fund for the Doctoral Program of Higher Education (Grant no. 30400002012102002), and the National Natural Science Foundation of China (Grant no. 61103097). The authors would like to thank the associate editor and the anonymous reviewers for their valuable suggestions. This paper also utilises the Cohn-Kanade database and the BHU facial expression database. The authors would like to thank the providers of these databases. 38 21 21 0 14 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-9632 1751-9640 IET COMPUT VIS IET Comput. Vis. OCT 2014.0 8.0 5 429 440 10.1049/iet-cvi.2013.0171 0.0 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering AZ4LY 2023-03-23 WOS:000348193900006 0
C Wang, NX; Wang, ZY; He, Z; Huang, BJ; Zhou, LG; Han, Z IEEE Wang, Nanxi; Wang, Zhongyuan; He, Zheng; Huang, Baojin; Zhou, Liguo; Han, Zhen A TILT-ANGLE FACE DATASET AND ITS VALIDATION 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) IEEE International Conference on Image Processing ICIP English Proceedings Paper IEEE International Conference on Image Processing (ICIP) SEP 19-22, 2021 ELECTR NETWORK IEEE,Inst Elect & Elect Engineers Signal Proc Soc Face dataset; overhead view; tilt-angle face; face detection; face recognition Since the surveillance cameras are usually mounted at a high position to overlook targets, tilt-angle faces on overhead view are common in the public video surveillance environment. Face recognition approaches based on deep learning models have achieved excellent performance, but there remains a large gap for the overlooking surveillance scenarios. The results of face recognition depend not only on the structure of the model, but also on the completeness and diversity of the training samples. The existing multi-pose face datasets do not cover complete top-view face samples, and the models trained by them thus cannot provide satisfactory accuracy. To this end, this paper pioneers a multi-view tilt-angle face dataset (TFD), which is collected with an elaborately devised overhead capture equipment. TFD contains 11,124 face images from 927 subjects, covering a variety of tilt angles on the overhead view. To verify the validity of the constructed dataset, we further conduct comprehensive face detection and recognition experiments using the corresponding models trained by WiderFace, Webface and our TFD, respectively. Experimental results show that our TFD substantially promotes the face detection and recognition accuracy under the top-view situation. [Wang, Nanxi; Wang, Zhongyuan; He, Zheng; Huang, Baojin; Han, Zhen] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China; [Zhou, Liguo] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany Wuhan University; Technical University of Munich Wang, ZY (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China. wzy_hope@163.com National Natural Science Foundation of China [U1903214,62071339, 62072347,62072350]; Hubei Province Key RD Project [2020BAB018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hubei Province Key RD Project This research was funded by National Natural Science Foundation of China (U1903214,62071339, 62072347,62072350), and Hubei Province Key R&D Project (2020BAB018). 21 2 2 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1522-4880 978-1-6654-4115-5 IEEE IMAGE PROC 2021.0 894 898 10.1109/ICIP42928.2021.9506052 0.0 5 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology BT3GP 2023-03-23 WOS:000819455101004 0
C Yu, ZT; Shen, YM; Shi, JG; Zhao, HS; Torr, P; Zhao, GY IEEE COMP SOC Yu, Zitong; Shen, Yuming; Shi, Jingang; Zhao, Hengshuang; Torr, Philip; Zhao, Guoying PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-24, 2022 New Orleans, LA IEEE,CVF,IEEE Comp Soc Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications. Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. Furthermore, we also propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. One highlight is that, unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community. The codes are available at https://github.com/ZitongYu/PhysFormer. [Yu, Zitong; Zhao, Guoying] Univ Oulu, CMVS, Oulu, Finland; [Shen, Yuming; Zhao, Hengshuang; Torr, Philip] Univ Oxford, TVG, Oxford, England; [Shi, Jingang] Xi An Jiao Tong Univ, Xian, Peoples R China; [Zhao, Hengshuang] Univ Hong Kong, Hong Kong, Peoples R China University of Oulu; University of Oxford; Xi'an Jiaotong University; University of Hong Kong Zhao, GY (corresponding author), Univ Oulu, CMVS, Oulu, Finland. Academy of Finland [336116, 345122, 328115]; Ministry of Education and Culture of Finland; National Natural Science Foundation of China [62002283]; EPSRC grant: Turing AI Fellowship [EP/W002981/1]; EPSRC/MURI [EP/N019474/1]; Royal Academy of Engineering; FiveAI Academy of Finland(Academy of Finland); Ministry of Education and Culture of Finland; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EPSRC grant: Turing AI Fellowship(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC/MURI(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Academy of Engineering(Royal Academy of Engineering - UK); FiveAI This work was supported by the Academy of Finland for Academy Professor project EmotionAI (grants 336116, 345122), and ICT 2023 project (grant 328115), by Ministry of Education and Culture of Finland for AI forum project, the National Natural Science Foundation of China (Grant No. 62002283), the EPSRC grant: Turing AI Fellowship: EP/W002981/1, EPSRC/MURI grant EP/N019474/1. We would also like to thank the Royal Academy of Engineering and FiveAI. As well, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. 73 1 1 10 10 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-6946-3 PROC CVPR IEEE 2022.0 4176 4186 10.1109/CVPR52688.2022.00415 0.0 11 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU0EQ Green Submitted 2023-03-23 WOS:000867754204043 0
J Mehrizi, AA; Jafarzadeh, H; Lashkenari, MS; Naddafi, M; Le, V; Tran, VA; Dragoi, EN; Vasseghian, Y Mehrizi, Abbasali Abouei; Jafarzadeh, Hamed; Lashkenari, Mohammad Soleimani; Naddafi, Mastoureh; Van Thuan Le; Vy Anh Tran; Dragoi, Elnea-Niculina; Vasseghian, Yasser Artificial neural networks modeling ethanol oxidation reaction kinetics catalyzed by polyaniline-manganese ferrite supported platinum-ruthenium nanohybrid electrocatalyst CHEMICAL ENGINEERING RESEARCH & DESIGN English Article Ethanol oxidation reaction; Tafel plots; Artificial neural network; Differential evolution algorithm PERFORMANCE; WATER The electrochemical and physicochemical properties of the anode catalyst used in alcohol fuel cells affect the efficiency of the fuel cell due to both its effect on the cell potential and its direct relationship with the reaction stoichiometry. It is possible to determine these parameters from polarization curves (current vs. cell potential) of the cells. At low potentials, Tafel plots offer kinetic information, whereas currents at high potentials reveal the average number of electrons released per ethanol molecule and their potential dependency. Herein, bearing the chemical engineering research and design aspects, it was aimed to model the kinetics of ethanol electrooxidation reaction catalyzed by PANI-MnFe2O4/Pt/Ru nanocomposite by an artificial neural network (ANN) approach, specifically by Differential Evolution (DE) algorithm for the first time. The different parts of the Tafel plots were condensed into a single and efficient model to illustrate the generalization potential of ANN. The findings demonstrated that the best model with a single hidden layer with 18 neurons offered the highest correlation metrics of 0.997476, and the lowest mean-square-error value of 0.000428 at the testing phase. Furthermore, the average absolute error was calculated, with 0.773% in the training phase and 0.66% in the testing phase. These outstanding results indicated that the best model is capable of accurately capturing the dynamics of all instances analyzed. (C) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. [Mehrizi, Abbasali Abouei] Univ Elect Sci & Technol China, Dept Phys, Chengdu 611731, Peoples R China; [Jafarzadeh, Hamed] Quchan Univ Technol, Dept Phys, Quchan, Iran; [Lashkenari, Mohammad Soleimani] Amol Univ Special Modern Technol, Fac Engn Modern Technol, Fuel Cell Electrochem & Adv Mat Res Lab, Amol 4616849767, Iran; [Van Thuan Le] Duy Tan Univ, Ctr Adv Chem, Inst Res & Dev, 03 Quang Trung, Da Nang 550000, Vietnam; [Van Thuan Le] Duy Tan Univ, Fac Nat Sci, 03 Quang Trung, Da Nang 550000, Vietnam; [Vy Anh Tran] Gachon Univ, Dept Chem & Biochem Engn, 1342 Seongnamdaero, Seongnam Si 13120, South Korea; [Dragoi, Elnea-Niculina] Gheorghe Asachi Tech Univ, Fac Chem Engn & Environm Protect Cristofor Simion, Bld Mangeron 73, Iasi 700050, Romania; [Vasseghian, Yasser] Soongsil Univ, Dept Chem, Seoul 06978, South Korea; [Vasseghian, Yasser] SIMATS, Dept Sustainable Engn, Saveetha Sch Engn, Chennai 602105, Tamil Nadu, India; [Naddafi, Mastoureh] Univ Elect Sci & Technol China, Sch Resources & Environm, POB 611731,Xiyuan Ave, Chengdu, Peoples R China University of Electronic Science & Technology of China; Amol University of Special Modern Technologies - Iran; Duy Tan University; Duy Tan University; Gachon University; Soongsil University; Saveetha Institute of Medical & Technical Science; Saveetha School of Engineering; University of Electronic Science & Technology of China Mehrizi, AA (corresponding author), Univ Elect Sci & Technol China, Dept Phys, Chengdu 611731, Peoples R China.;Lashkenari, MS (corresponding author), Amol Univ Special Modern Technol, Fac Engn Modern Technol, Fuel Cell Electrochem & Adv Mat Res Lab, Amol 4616849767, Iran.;Naddafi, M (corresponding author), Univ Elect Sci & Technol China, Sch Resources & Environm, POB 611731,Xiyuan Ave, Chengdu, Peoples R China. abbasabouei@gmail.com; m.soleimani@ausmt.ac.ir; Mastooreh67@gmail.com Tran, Vy/GWC-8836-2022; abouei mehrizi, abbasali/F-8571-2011; Vasseghian, Yasser/AAN-4675-2020 abouei mehrizi, abbasali/0000-0003-0169-6612; Vasseghian, Yasser/0000-0001-6225-627X; Anh Vy, Tran/0000-0001-5533-8381 51 5 5 5 6 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0263-8762 1744-3563 CHEM ENG RES DES Chem. Eng. Res. Des. AUG 2022.0 184.0 72 78 10.1016/j.cherd.2022.05.046 0.0 JUN 2022 7 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering 3O4CR 2023-03-23 WOS:000836782700002 0
J Tlili, A; Zhang, JJ; Papamitsiou, Z; Manske, S; Huang, RH; Kinshuk; Hoppe, HU Tlili, Ahmed; Zhang, Jingjing; Papamitsiou, Zacharoula; Manske, Sven; Huang, Ronghuai; Kinshuk; Hoppe, H. Ulrich Towards utilising emerging technologies to address the challenges of using Open Educational Resources: a vision of the future ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT English Article Open educational resources (OER); Challenges; Open education; Emerging technologies The rapid advancements in online education have pointed to a new open learning approach using Open Educational Resources (OER). In this approach, educators and learners can freely access or redistribute educational resources that have been released online in the public domain under an open licence. Whereas this approach looks appealing in reducing learning costs, as well as in enhancing learning quality and facilitating knowledge sharing, several challenges might hinder the adoption of OER, such as locating and selecting the most appropriate resources among the thousands that are published and that are available online, and trusting them. This paper elaborates on those challenges and suggests an emerging technologies-based perspective for addressing the efficient inclusion of OER. To this end, this paper discusses how the integration of emerging yet essential technologies, such as Artificial Intelligence (AI) and blockchain, with big learning data and educational data mining algorithms could have a profound impact on enhancing OER-based learning and teaching. The dynamics of incorporating these technologies to solve several OER challenges are demonstrated through numerous examples, and the potential limitations are also discussed. The paper concludes with visions of the future, possible research challenges and directions. [Tlili, Ahmed; Huang, Ronghuai] Beijing Normal Univ, Smart Learning Inst, 12F Block A,Jingshi Technol Bldg,12 Xueyuan South, Beijing 100082, Peoples R China; [Zhang, Jingjing] Beijing Normal Univ, Big Data Ctr Technol Mediated Educ, Room 1302,8 Li Yun Bldg, Beijing 100875, Peoples R China; [Papamitsiou, Zacharoula] Norwegian Univ Sci & Technol NTNU, Fac Informat Technol & Elect Engn, Dept Comp Sci, 142 Gloshaugen,Sem Saelands Vei 9, N-7034 Trondheim, Norway; [Manske, Sven; Hoppe, H. Ulrich] Univ Duisburg Essen, Fac Engn, Lotharstr 65, D-47057 Duisburg, Germany; [Kinshuk] Univ North Texas, Coll Informat, 3940 North Elm St, Denton, TX 76207 USA; [Hoppe, H. Ulrich] Univ Duisburg Essen, Dept Computat & Cognit Sci, Lotharstr 63-65, D-47058 Duisburg, Germany Beijing Normal University; Beijing Normal University; Norwegian University of Science & Technology (NTNU); University of Duisburg Essen; University of North Texas System; University of North Texas Denton; University of Duisburg Essen Zhang, JJ (corresponding author), Beijing Normal Univ, Big Data Ctr Technol Mediated Educ, Room 1302,8 Li Yun Bldg, Beijing 100875, Peoples R China. jingjing.zhang@bnu.edu.cn Huang, Ronghuai/0000-0003-4651-5248; Papamitsiou, Zacharoula/0000-0002-0982-3623 Department of science and technology of Guangxi Zhuang Autonomous Region: Construction and application of innovative design method system of 'Internet +' education service industry [AB18126068]; International Joint Research Project of Huiyan International College, Faculty of Education, Beijing Normal University Department of science and technology of Guangxi Zhuang Autonomous Region: Construction and application of innovative design method system of 'Internet +' education service industry; International Joint Research Project of Huiyan International College, Faculty of Education, Beijing Normal University This paper was funded by Department of science and technology of Guangxi Zhuang Autonomous Region: Construction and application of innovative design method system of 'Internet +' education service industry (Project no. AB18126068), and the International Joint Research Project of Huiyan International College, Faculty of Education, Beijing Normal University. 74 13 13 2 32 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1042-1629 1556-6501 ETR&D-EDUC TECH RES ETR&D-Educ. Tech. Res. Dev. APR 2021.0 69.0 2 515 532 10.1007/s11423-021-09993-4 0.0 APR 2021 18 Education & Educational Research Social Science Citation Index (SSCI) Education & Educational Research RY0WH Green Submitted 2023-03-23 WOS:000641656600001 0
J Ran, YH; He, B; Hui, K; Xu, JG; Sun, L Ran, Yanhua; He, Ben; Hui, Kai; Xu, Jungang; Sun, Le Neural relevance model using similarities with elite documents for effective clinical decision support INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS English Article information retrieval; clinical decision support; biomedical search; neural IR model Clinical Decision Support (CDS) is regarded as an information retrieval (IR) task, where medical records are used to retrieve full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modelled, resulting in non-optimal results in that a medical record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a neural relevance model (DNRM) based on similarities to a set of elite documents, addressing the information mismatch by utilising the content of relevant articles as a complete picture of the given medical record. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model. [Ran, Yanhua; He, Ben; Xu, Jungang] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China; [Hui, Kai] Max Planck Inst Informat, Saarbrucken, Germany; [Sun, Le] Chinese Acad Sci, Inst Software, Beijing, Peoples R China Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Max Planck Society; Chinese Academy of Sciences; Institute of Software, CAS He, B (corresponding author), Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China. ranyanhua16@mails.ucas.ac.cn; benhe@ucas.ac.cn; khui@mpi-inf.mpg.de; xujg@ucas.ac.cn; sunle@iscas.ac.cn Hui, Kai/AAM-2600-2020 Hui, Kai/0000-0002-3110-7404 National Natural Science Foundation of China [61433015/61472391] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Natural Science Foundation of China (61433015/61472391). 27 2 2 0 7 INDERSCIENCE ENTERPRISES LTD GENEVA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND 1748-5673 1748-5681 INT J DATA MIN BIOIN Int. J. Data Min. Bioinform. 2018.0 20.0 2 91 108 10.1504/IJDMB.2018.093680 0.0 18 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology GT6AS 2023-03-23 WOS:000444593200001 0
J Liu, ZJ; Zhou, QX; Yin, H; Xu, W; Yang, XY; Gao, J Liu, Zhijian; Zhou, Qingxu; Yin, Hang; Xu, Wei; Yang, Xinyan; Gao, Jun Indoor environmental quality and energy consumption real-time assessment: A field measurement of a nearly zero-energy building in cold region of China ENERGY AND BUILDINGS English Article Quality of indoor environment; Energy use; NZE buildings; Big data and measurements; Cold zones; China PASSIVE HOUSE CONCEPT; THERMAL COMFORT; AIR-QUALITY; PERFORMANCE; OPTIMIZATION; EFFICIENCY; SYSTEMS; DESIGN; PM2.5; ZONES The paper presents a two-year field measurement and assessment of a nearly zero-energy Building in cold zones of China. These parameters of indoor environment quality (IEQ) and electricity consumption for HVAC are recorded. According to the measurement data, the most of indoor temperature was controlled in the range of 23 degrees C similar to 26 degrees C and relative humidity (RH) mainly fluctuates within the range of 30% similar to 60%. The concentrations of carbon dioxide (CO2) and particulate matter (PM2.5) are lower than the relevant standard requirements in most of the time. The average electricity consumption for HVAC was 33.05kWh/(m(2).a) and the heating and cooling demands could be reduced by 55.2% and 54.0%, respectively, compared with existing buildings. In general, the NZEB can provide indoor comfortable thermal environment and indoor air quality with less energy demand, but the overheating problem in summer and effect of occupant behavior should be especially paid attention during operation. Overall, the experience from the actual operation of NZEBs will help China to reach its goals of reducing carbon emissions and addressing climate change through realizing the energy-saving potential of NZEBs. (C) 2021 Elsevier B.V. All rights reserved. [Liu, Zhijian; Zhou, Qingxu] North China Elect Power Univ, Dept Power Engn, Baoding 071003, Hebei, Peoples R China; [Yin, Hang] Tech Univ Denmark, Dept Civil Engn, DK-2800 Lyngby, Denmark; [Xu, Wei; Yang, Xinyan] China Acad Bldg Res, China Inst Bldg Environm & Energy, Beijing 100013, Peoples R China; [Gao, Jun] Tongji Univ, Sch Mech Engn, Shanghai 200092, Peoples R China North China Electric Power University; Technical University of Denmark; Tongji University Liu, ZJ (corresponding author), North China Elect Power Univ, Dept Power Engn, Baoding 071003, Hebei, Peoples R China.;Gao, J (corresponding author), Tongji Univ, Sch Mech Engn, Shanghai 200092, Peoples R China. zhijianliu@ncepu.edu.cn; gaojun-hvac@tongji.edu.cn yang, xin/HGU-9981-2022 Liu, Zhijian/0000-0003-1841-2671; Yin, Hang/0000-0002-3614-2655 National Key R&D Program of China-Technical System and Key Technology Development ofNearly Zero Energy Building [2017YFC0702600]; Key R&D Program of Hebei province [20374501D] National Key R&D Program of China-Technical System and Key Technology Development ofNearly Zero Energy Building; Key R&D Program of Hebei province This work was supported by National Key R&D Program of China-Technical System and Key Technology Development ofNearly Zero Energy Building (No. 2017YFC0702600) and Key R&D Program of Hebei province [20374501D]. 57 9 9 5 20 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0378-7788 1872-6178 ENERG BUILDINGS Energy Build. SEP 1 2021.0 246.0 111093 10.1016/j.enbuild.2021.111093 0.0 MAY 2021 16 Construction & Building Technology; Energy & Fuels; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Energy & Fuels; Engineering TK9JZ 2023-03-23 WOS:000674472400006 0
J Chen, GS; Zou, WT; Jing, WP; Wei, W; Scherer, RF Chen, Guangsheng; Zou, Weitao; Jing, Weipeng; Wei, Wei; Scherer, Rafal Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Spatial databases; Smart cities; Indexes; Environmental management; Distributed databases; Data processing; Sparks; Distributed system; enterprise management system (EMS); smart city; spatial index; spatial partition; spatial query DATA-MANAGEMENT; BIG DATA The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is a spatially enabled framework for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases. [Chen, Guangsheng; Zou, Weitao; Jing, Weipeng] Northeast Forestry Univ, Coll Informat Sci & Technol, Harbin 150040, Peoples R China; [Wei, Wei] Xian Univ Technol, Sch Comp Sci & Engn, Xian 710048, Peoples R China; [Scherer, Rafal] Czestochowa Tech Univ, Dept Intelligent Comp Syst, PL-42201 Czestochowa 69, Poland Northeast Forestry University - China; Xi'an University of Technology; Technical University Czestochowa Jing, WP (corresponding author), Northeast Forestry Univ, Coll Informat Sci & Technol, Harbin 150040, Peoples R China. icec@nefu.edu.cn; zouweitao1996@gmail.com; jwp@nefu.edu.cn; wei-wei@xaut.edu.cn; rafal.scherer@pcz.pl Zou, Weitao/HNR-5425-2023; Scherer, Rafal/F-6745-2012 Zou, Weitao/0000-0003-2834-4289; Scherer, Rafal/0000-0001-9592-262X Fundamental Research Funds for the Central Universities [Grant2572018AB33, 2572017PZ04]; National Natural Science Foundation of China [32171777]; Heilongjiang Province Applied Technology Research and Development Program Major Project [GA20A301] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Heilongjiang Province Applied Technology Research and Development Program Major Project This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant2572018AB33 and Grant 2572017PZ04, in part by the National Natural Science Foundation of China under Grant 32171777, and in part by the Heilongjiang Province Applied Technology Research and Development Program Major Project under Grant GA20A301. 31 1 1 10 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. JAN 2023.0 19.0 1 594 604 10.1109/TII.2022.3194056 0.0 11 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 6A4VJ 2023-03-23 WOS:000880654600060 0
J Wei, PC; Hansch, R Wei, Pengchao; Hansch, Ronny Random Ferns for Semantic Segmentation of PolSAR Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Semantics; Image segmentation; Radio frequency; Random forests; Covariance matrices; Task analysis; Ensemble classifier; fern optimization; PolSAR image; random ferns (RFes); semantic segmentation RANDOM FORESTS; CLASSIFICATION; FEATURES; COVER Random ferns-as a less known example of ensemble learning-have been successfully applied in many computer vision applications ranging from keypoint matching to object detection. This article extends the random fern framework to the semantic segmentation of polarimetric synthetic aperture radar images. By using internal projections that are defined over the space of Hermitian matrices, the proposed classifier can be directly applied to the polarimetric covariance matrices without the need to explicitly compute predefined image features. Furthermore, two distinct optimization strategies are proposed: the first based on preselection and grouping of internal binary features before the creation of the classifier and the second based on iteratively improving the properties of a given random fern. Both strategies are able to boost the performance by filtering features that are either redundant or have a low information content and by grouping correlated features to best fulfill the independence assumptions made by the random fern classifier. Experiments show that results can be achieved, which is similar to a more complex random forest model and competitive to a deep learning baseline. [Wei, Pengchao] Shanghai Jiao Tong Univ SJTU, Sch Elect Informat & Elect Engn, Elect Engn Dept, Shanghai 200240, Peoples R China; [Hansch, Ronny] German Aerosp Ctr DLR, SAR Technol Dept, D-82234 Oberpfaffenhofen, Germany Shanghai Jiao Tong University; Helmholtz Association; German Aerospace Centre (DLR) Hansch, R (corresponding author), German Aerosp Ctr DLR, SAR Technol Dept, D-82234 Oberpfaffenhofen, Germany. ronny.haensch@dlr.de Hansch, Ronny/0000-0002-2936-6765 65 1 1 3 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5218212 10.1109/TGRS.2021.3131418 0.0 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZH9FN Green Accepted, Green Submitted 2023-03-23 WOS:000761235300027 0
J Liu, J; Wang, J Liu, Jun; Wang, Jing Social data governance: From reflective practices to comparative synthesis BIG DATA & SOCIETY English Editorial Material Social data governance; datafication; contextualization; social dynamics; comparison; digitalization BIG DATA; DATAFICATION; TECHNOLOGY This special theme brings together reflections and deliberations regarding the design, implementation, and development of data governance. By addressing social data governance as the keyword of the special theme, we aim to further the discussion on a contextual understanding of both the governing foundations and effects of data, dataism, and datafication in different societies. Such a discussion reminds us to pay particular attention to-and thus account for-the social dynamics that underpin and contextualize the design, operation, and promotion of quantified governing mechanisms in which information on social behaviors is collected, datafied, manipulated, and represented. Essentially, the social dynamics of data governance have existed for a long time and in many forms, ranging from credit bureaus' scrutiny, evaluation, and labeling of their customers to internet-enabled massive data collection and scoring systems used by governments, and to automated contact tracing techniques as a centerpiece of dataveillance and infection control amid the COVID-19 pandemic. Nevertheless, scholarly work from a wide range of disciplines like law, mathematics, and business and with diverse geographical foci has not yet been comparatively and reflectively articulated. Being rich and diverse, the special theme advances such a requisite understanding of the status and relevance of social dynamics of data governance mechanisms based on a wide range of empirical cases around the globe. To scrutinize the social dynamics helps illuminate and contrast divergent manifestations of data governance and their underlying mechanisms. [Liu, Jun] Univ Copenhagen, Ctr Tracking & Soc, Copenhagen, Denmark; [Liu, Jun] Univ Copenhagen, Dept Commun, Copenhagen, Denmark; [Wang, Jing] New York Univ Shanghai, Interact Media & Businesses, Shanghai, Peoples R China University of Copenhagen; University of Copenhagen; NYU Shanghai Liu, J (corresponding author), Univ Copenhagen, Ctr Tracking & Soc, Copenhagen, Denmark.;Liu, J (corresponding author), Univ Copenhagen, Dept Commun, Copenhagen, Denmark. liujun@hum.ku.dk Independent Research Fund Denmark-Sapere Aude Starting Grant [1055-00011B]; Carlsberg Foundation [CF20-0705] Independent Research Fund Denmark-Sapere Aude Starting Grant; Carlsberg Foundation(Carlsberg Foundation) The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by The Independent Research Fund Denmark-Sapere Aude Starting Grant (grant number 1055-00011B), The Carlsberg Foundation Grant (grant number CF20-0705) 44 0 0 2 2 SAGE PUBLICATIONS INC THOUSAND OAKS 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA 2053-9517 BIG DATA SOC Big Data Soc. JUL 2022.0 9.0 2 20539517221139784 10.1177/20539517221139786 0.0 5 Social Sciences, Interdisciplinary Social Science Citation Index (SSCI) Social Sciences - Other Topics 8R5SC gold 2023-03-23 WOS:000927953700001 0
J Shen, J; Sun, N; Zens, P; Kunzke, T; Buck, A; Prade, VM; Wang, J; Wang, Q; Hu, RG; Feuchtinger, A; Berezowska, S; Walch, A Shen, Jian; Sun, Na; Zens, Philipp; Kunzke, Thomas; Buck, Achim; Prade, Verena M.; Wang, Jun; Wang, Qian; Hu, Ronggui; Feuchtinger, Annette; Berezowska, Sabina; Walch, Axel Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients CANCER COMMUNICATIONS English Article cancer metabolism; machine learning; mass spectrometry imaging; metabolic classifier; Non-small cell lung cancer; prognosis; spatial metabolomics; treatment response FATTY-ACID-METABOLISM; MASS-SPECTROMETRY; CHEMOTHERAPY; METAANALYSIS; EXPRESSION; CARCINOMA; FRUCTOSE; SURVIVAL; SURGERY; PROFILE Background The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning. Methods Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naive patients with NSCLC (n = 85). Results The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716-8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004-4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445-1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977-1.550) were not independent prognostic factors. Using Kaplan-Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non-responders (P < 0.001). Conclusions Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response. [Shen, Jian; Sun, Na; Kunzke, Thomas; Buck, Achim; Prade, Verena M.; Wang, Jun; Wang, Qian; Feuchtinger, Annette; Walch, Axel] Helmholtz Zentrum Munchen, Res Unit Analyt Pathol, German Res Ctr Environm Hlth, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany; [Zens, Philipp; Berezowska, Sabina] Univ Bern, Inst Pathol, CH-3008 Bern, Switzerland; [Zens, Philipp] Univ Bern, Grad Sch Hlth Sci, Mittelstr 43, CH-3012 Bern, Switzerland; [Hu, Ronggui] Chinese Acad Sci, Ctr Excellence Mol Cell Sci, Shanghai 200031, Peoples R China; [Berezowska, Sabina] Lausanne Univ Hosp, Inst Pathol, Dept Lab Med & Pathol, Bugnon 25, CH-1011 Lausanne, Switzerland; [Berezowska, Sabina] Univ Lausanne, Bugnon 25, CH-1011 Lausanne, Switzerland Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Bern; University of Bern; Chinese Academy of Sciences; University of Lausanne; Centre Hospitalier Universitaire Vaudois (CHUV); University of Lausanne Walch, A (corresponding author), Helmholtz Zentrum Munchen, Res Unit Analyt Pathol, German Res Ctr Environm Hlth, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany.;Berezowska, S (corresponding author), Lausanne Univ Hosp, Inst Pathol, Dept Lab Med & Pathol, Bugnon 25, CH-1011 Lausanne, Switzerland.;Berezowska, S (corresponding author), Univ Lausanne, Bugnon 25, CH-1011 Lausanne, Switzerland. sabina.berezowska@chuv.ch; axel.walch@helmholtz-muenchen.de Sun, Na/ABC-7810-2020; Walch, Axel/B-4554-2012; Buck, Achim/A-5092-2017 Sun, Na/0000-0003-0155-1763; Walch, Axel/0000-0001-5578-4023; Berezowska, Sabina/0000-0001-5442-9791 Ministry of Education and Research of the Federal Republic of Germany (BMBF) [01ZX1610B, 01KT1615]; Deutsche Forschungsgmeinschaft [SFB 824 C4, CRC/Transregio 205/1]; Deutsche Krebshilfe [70112617]; Stiftung zur Krebsbekampfung [SKB425]; Cancer Research Switzerland [KFS-4694-02-2019, MD-PhD-5088-06-2020] Ministry of Education and Research of the Federal Republic of Germany (BMBF)(Federal Ministry of Education & Research (BMBF)); Deutsche Forschungsgmeinschaft; Deutsche Krebshilfe(Deutsche Krebshilfe); Stiftung zur Krebsbekampfung; Cancer Research Switzerland The authors thank Ulrike Buchholz, Claudia-Mareike Pfluger, Cristina Hubner Freitas, Andreas Voss, and Elenore Samson for excellent technical assistance. The study was supported by the Ministry of Education and Research of the Federal Republic of Germany (BMBF; 01ZX1610B and 01KT1615); the Deutsche Forschungsgmeinschaft (SFB 824 C4, CRC/Transregio 205/1); the Deutsche Krebshilfe (70112617) to A. Walch; Stiftung zur Krebsbekampfung (SKB425); Cancer Research Switzerland (KFS-4694-02-2019) to S. Berezowska and Cancer Research Switzerland (MD-PhD-5088-06-2020) to P. Zens. 72 3 3 13 24 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2523-3548 CANCER COMMUN Cancer Commun. JUN 2022.0 42.0 6 517 535 10.1002/cac2.12310 0.0 MAY 2022 19 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 2C3RM 35593195.0 Green Published, Green Accepted 2023-03-23 WOS:000797845600001 0
C Wang, HT; Chen, G; Li, ZJ; Peng, JY; Liu, ZF; Wu, Y IEEE Wang, Haitao; Chen, Guang; Li, Zhijun; Peng, Jianyi; Liu, Zhengfa; Wu, Ya Context-aware Method for Small Object Segmentation in Road Scenes 2022 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2022) English Proceedings Paper 7th IEEE International Conference on Advanced Robotics and Mechatronics JUL 09-11, 2022 Guilin, PEOPLES R CHINA Guangxi Univ,Foshan Univ,So Univ Sci & Technol,IEEE Robot & Automat Soc,IEEE Syst Man & Cybernet Soc,IEEE RAS Tech Comm Neuro Robot Syst,IEEE SMC Tech Comm Bio Mechatron & Bio Robot Syst,Chinese Assoc Automat, Tech Comm Robot Intelligence,Beijing Inst Technol Press,MDPI, Machines,Springer,MDPI, Actuators,Gaitech Robot,Beijing Nokov Sci & Technol Co Ltd Thanks to the rapid development of deep learning algorithms, autonomous driving perception systems are now able to handle most common road scenes. But the perception in some corner scenes is still a great challenge. In this paper, we focus on the perception of small road objects. Perception of small objects at long distance is diffcult due to their low resolution in images. In order to address this issue, we firstly build a large dataset for small object segmentation in road scenes, which could be a data support for deep learningbased method. Then, we propose a method that contains three modules: transformer-based attention module, feature fusion module and atrous convolution-based auxiliary loss module to divide small obstacles from road area. The transformer-based attention module converts images to sequences and exploits global context information adequately. Feature fusion module is designed to combine texture information and semantic information. Auxiliary loss module takes atrous convolution with multiple dilation rates to catch context information as much as possible. Finally, experiments are conducted on both self-collected dataset and generic segmentation dataset, which all show the superiority of our method in segmenting small objects. [Wang, Haitao] State Key Lab Vehicle NVH & Safety Technol, China 401122, Peoples R China; [Wang, Haitao; Chen, Guang; Peng, Jianyi; Liu, Zhengfa; Wu, Ya] Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China; [Chen, Guang] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany; [Li, Zhijun] Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China Tongji University; Technical University of Munich; Chinese Academy of Sciences; University of Science & Technology of China, CAS Chen, G (corresponding author), Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China. guangchen@tongji.edu.cn 2020 Open Foundation of State Key Laboratory of Vehicle NVH and Safety Technology [NVHSKL-202009]; National Natural Science Foundation of China [61906138]; Shanghai Rising Star Program [21QC1400900] 2020 Open Foundation of State Key Laboratory of Vehicle NVH and Safety Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Rising Star Program This work was supported, by 2020 Open Foundation of State Key Laboratory of Vehicle NVH and Safety Technology (Project No.NVHSKL-202009), by the National Natural Science Foundation of China under Grant 61906138, and by Shanghai Rising Star Program (No. 21QC1400900). 22 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-8306-3 2022.0 238 243 10.1109/ICARM54641.2022.9959713 0.0 6 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BU6NB 2023-03-23 WOS:000926398000039 0
J Chen, J; Alnowibet, K; Annuk, A; Mohamed, MA Chen, Jian; Alnowibet, Khalid; Annuk, Andres; Mohamed, Mohamed A. An effective distributed approach based machine learning for energy negotiation in networked microgrids ENERGY STRATEGY REVIEWS English Article Energy negotiation; Distributed optimization approach; Networked microgrid; Uncertainty; Reinforcement learning; Energy management DEMAND RESPONSE; SMART GRIDS; MANAGEMENT; OPTIMIZATION; BLOCKCHAIN; DECISION In recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the centralized frameworks. In this sense, this work proposes an effective energy management framework for a distributed networked microgrid (DNM) based on the primal-dual method of multipliers (PDMM). The networked microgrid is comprised of a wind turbine (WT), battery unit and photovoltaic (PV) system which supply the demands of the networked microgrid. The power negotiation process among the networked microgrid' segments will continue until obtaining an adequate solution to achieve the best performance of the proposed microgrid. A Reinforcement Learning (RL) approach is proposed that can increase the accuracy of modeling uncertainty of the parameters of the DNM compared to other manners. To make the secured communication platform, a secured energy transaction structure based on the directed acyclic graph (DAG) approach, is proposed as one of the other aims of this paper. The results indicate the effectiveness of the proposed model and its applicability for the networked microgrid. [Chen, Jian] Robot Sch Fuzhou Polytech, Fuzhou 350108, Peoples R China; [Alnowibet, Khalid] King Saud Univ, Coll Sci, Stat & Operat Res Dept, Riyadh 11451, Saudi Arabia; [Annuk, Andres] Estonian Univ Life Sci, Inst Technol, Chair Energy Applicat Engn, EE-51006 Tartu, Estonia; [Mohamed, Mohamed A.] Minia Univ, Fac Engn, Elect Engn Dept, Al Minya 61519, Egypt King Saud University; Estonian University of Life Sciences; Egyptian Knowledge Bank (EKB); Minia University Mohamed, MA (corresponding author), Minia Univ, Fac Engn, Elect Engn Dept, Al Minya 61519, Egypt. 7823828@qq.com; knowibet@ksu.edu.sa; andres.annuk@emu.ee; dr.mohamed.abdelaziz@mu.edu.eg Annuk, A./AAJ-3903-2020; Mohamed, Mohamed A./AAD-3846-2021 Mohamed, Mohamed A./0000-0001-8700-0270; Annuk, Andres/0000-0001-5464-0415 King Saud University, Riyadh, Saudi Arabia [RSP-2021/305]; Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE - European Regional Development Fund [TK146]; Scientific research project of Fuzhou Polytechnic [FZYKJRCQD202101]; Fujian Key Laboratory of New Energy Generation and Power Conversion [KLIF-202102]; Key research topics of educational reform of Fuzhou Polytechnic [2021jgkt001] King Saud University, Riyadh, Saudi Arabia(King Saud University); Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE - European Regional Development Fund; Scientific research project of Fuzhou Polytechnic; Fujian Key Laboratory of New Energy Generation and Power Conversion; Key research topics of educational reform of Fuzhou Polytechnic The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number (RSP-2021/305) , King Saud University, Riyadh, Saudi Arabia. Furthermore, the authors would like to thank the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant TK146, funded by the European Regional Development Fund to supportthis research. This work was supported in part by 1) Scientific research project of Fuzhou Polytechnic under Grant FZYKJRCQD202101 2) Fujian Key Laboratory of New Energy Generation and Power Conversion under Grant KLIF-202102 3) Key research topics of educational reform of Fuzhou Polytechnic under Grant 2021jgkt001. 42 18 18 6 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2211-467X 2211-4688 ENERGY STRATEG REV Energy Strateg. Rev. NOV 2021.0 38.0 100760 10.1016/j.esr.2021.100760 0.0 NOV 2021 12 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels YE7SL gold 2023-03-23 WOS:000741320700011 0
J Qiu, QH; Zuo, YF; Zhang, M Qiu, Qihang; Zuo, Yifan; Zhang, Mu Can Live Streaming Save the Tourism Industry from a Pandemic? A Study of Social Media ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article live streaming; tourism; social media; big data; content analysis; semantic network analysis PLATFORMS As a reflection of shifting and fluid experiences in time and space, live streaming can reduce losses in the tourism industry associated with travel restrictions during the COVID-19 pandemic. Compared with the use of live streaming activities in entertainment, shopping, sport, e-sport, religious, educational, and academic settings, the tourism context has yet to be explored. This study takes China as a case to examine tourism practices related to live streaming. Specifically, 48,114 social media posts were subjected to systematic content analysis. The dataset contained live streaming content related to 147 countries and 34 Chinese provincial administrative regions between 2010 and 2021. Findings revealed the following: (1) the development of tourism live streaming in China can be classified into germination, exploration, and opportunity stages; (2) live content mainly evoked positive emotions, whereas negative sentiment resulted from illegal or boring content; (3) users' perceptions of tourism live streaming content involved institutions, live streaming tools, live streaming attractions, the live streaming economy, people, facilities and information, time, and regions; and (4) live streaming tools and attractions constituted the core of the identified semantic network and had the strongest regulation capabilities in tourism live streaming activities. Findings shed light on latent cultural meanings in social media communications, where tourism live streaming features high-frequency linguistic signs. [Qiu, Qihang] Adam Mickiewicz Univ, Fac Human Geog & Planning, PL-61680 Poznan, Poland; [Zuo, Yifan] Jinan Univ, Sch Management, Guangzhou 510632, Peoples R China; [Zuo, Yifan; Zhang, Mu] Jinan Univ, Shenzhen Tourism Coll, Shenzhen 518053, Peoples R China Adam Mickiewicz University; Jinan University; Jinan University Zhang, M (corresponding author), Jinan Univ, Shenzhen Tourism Coll, Shenzhen 518053, Peoples R China. qihang.qiu@amu.edu.pl; yifanzuo@stu2019.jnu.edu.cn; zhangmu@jnu.edu.cn Zuo, Yifan/GQA-8356-2022; Qiu, Qihang/GMW-4485-2022 Zuo, Yifan/0000-0002-9546-6860; Zhang, Mu/0000-0003-4682-8156; Qiu, Qihang/0000-0002-7821-8670 Shenzhen Philosophy and Social Science Planning Project [SZ2021D003] Shenzhen Philosophy and Social Science Planning Project This research was funded by: The Shenzhen Philosophy and Social Science Planning Project in 2021 (No. SZ2021D003). 53 11 11 55 161 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. SEP 2021.0 10.0 9 595 10.3390/ijgi10090595 0.0 17 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Physical Geography; Remote Sensing UW0MD gold 2023-03-23 WOS:000699860500001 0
C Wei, W; Xu, HS; Alpers, J; Zhang, TB; Wang, L; Rak, M; Hansen, C IEEE Wei, Wei; Xu, Haishan; Alpers, Julian; Zhang Tianbao; Wang, Lei; Rak, Marko; Hansen, Christian FAST REGISTRATION FOR LIVER MOTION COMPENSATION IN ULTRASOUND-GUIDED NAVIGATION 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) IEEE International Symposium on Biomedical Imaging English Proceedings Paper 16th IEEE International Symposium on Biomedical Imaging (ISBI) APR 08-11, 2019 Venice, ITALY Inst Elect & Elect Engineers,IEEE Engn Med & Biol Soc,IEEE Signal Proc Soc,Canon Med Res Europe Ltd,UAI, United Imaging Intelligence,Baidu,GSK,Kitware U-Net; CMA-ES; CUDA; Registration ABLATION; FUSION; TUMORS In recent years, image-guided thermal ablations have become a considerable treatment method for cancer patients, including support through navigational systems. One of the most critical challenges in these systems is the registration between the intraoperative :images and the preoperative volume. The motion secondary to inspiration makes registration even more difficult. In this work, we propose a coarse-fine fast patient registration technique to solve the problem of motion compensation. In contrast to other state-of-the-art methods, we focus on improving the convergence range of registration. To this end, we make use of a Deep Learning 2D U-Net framework to extract the vessels and liver borders from intraoperative ultrasound images and employ the segmentation results as regions of interest in the registration. After an initial 3D-3D registration during breath hold, the following motion compensation is achieved using a 2D-3D registration. Our approach yields a convergence rate of over 70% with an accuracy of 1.97 +/- 1.07 mm regarding the target registration error. The 2D-3D registration is GPU-accelerated with a time cost of less than 200 ms. [Wei, Wei; Alpers, Julian; Rak, Marko; Hansen, Christian] Univ Magdeburg, Fac Comp Sci, Magdeburg, Germany; [Wei, Wei; Alpers, Julian; Rak, Marko; Hansen, Christian] Univ Magdeburg, Res Campus Stimulate, Magdeburg, Germany; [Xu, Haishan; Zhang Tianbao; Wang, Lei] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Hangzhou, Zhejiang, Peoples R China Otto von Guericke University; Otto von Guericke University; Zhejiang University Wei, W (corresponding author), Univ Magdeburg, Fac Comp Sci, Magdeburg, Germany.;Wei, W (corresponding author), Univ Magdeburg, Res Campus Stimulate, Magdeburg, Germany. WANG, Lei/C-8138-2018 WANG, Lei/0000-0002-0336-7241; Hansen, Christian/0000-0002-5734-7529 EU; federal state of Saxony-Anhalt, Germany as part of the initiative Sachsen-Anhalt WISSENSCHAFT Schwerpunkte [ZS/2016/10/81684, ZS/2016/04/78123]; Zhejiang Provincial Natural Science Foundation of China [SY19H180037] EU(European Commission); federal state of Saxony-Anhalt, Germany as part of the initiative Sachsen-Anhalt WISSENSCHAFT Schwerpunkte; Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work has been funded by the EU and the federal state of Saxony-Anhalt, Germany under grant number ZS/2016/10/81684 and ZS/2016/04/78123 as part of the initiative Sachsen-Anhalt. WISSENSCHAFT Schwerpunkte, and the Zhejiang Provincial Natural Science Foundation of China (SY19H180037). 18 7 7 0 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1945-7928 978-1-5386-3641-1 I S BIOMED IMAGING 2019.0 1132 1136 5 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Radiology, Nuclear Medicine & Medical Imaging BN6AZ 2023-03-23 WOS:000485040000240 0
J Stergiou, GS; Mukkamala, R; Avolio, A; Kyriakoulis, KG; Mieke, S; Murray, A; Parati, G; Schutte, AE; Sharman, JE; Asmar, R; McManus, RJ; Asayama, K; De la Sierra, A; Head, G; Kario, K; Kollias, A; Myers, M; Niiranen, T; Ohkubo, T; Wang, JG; Wuerzner, G; O'Brien, E; Kreutz, R; Palatini, P Stergiou, George S.; Mukkamala, Ramakrishna; Avolio, Alberto; Kyriakoulis, Konstantinos G.; Mieke, Stephan; Murray, Alan; Parati, Gianfranco; Schutte, Aletta E.; Sharman, James E.; Asmar, Roland; McManus, Richard J.; Asayama, Kei; De la Sierra, Alejandro; Head, Geoffrey; Kario, Kazuomi; Kollias, Anastasios; Myers, Martin; Niiranen, Teemu; Ohkubo, Takayoshi; Wang, Jiguang; Wuerzner, Gregoire; O'Brien, Eoin; Kreutz, Reinhold; Palatini, Paolo European Soc Hypertension Working Cuffless blood pressure measuring devices: review and statement by the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability JOURNAL OF HYPERTENSION English Article accuracy; calibration; continuous; cuffless blood pressure measurement; cuffless blood pressure monitoring; photoplethysmography; smartwatch; technology; validation; wearable MEDICAL INSTRUMENTATION/EUROPEAN SOCIETY; OPTICAL AKTIIA BRACELET; HYPERTENSION/INTERNATIONAL ORGANIZATION; UNIVERSAL STANDARD; VALIDATION; PHOTOPLETHYSMOGRAPHY; LESS; TIME; ADVANCEMENT; ASSOCIATION Background: Many cuffless blood pressure (BP) measuring devices are currently on the market claiming that they provide accurate BP measurements. These technologies have considerable potential to improve the awareness, treatment, and management of hypertension. However, recent guidelines by the European Society of Hypertension do not recommend cuffless devices for the diagnosis and management of hypertension. Objective: This statement by the European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability presents the types of cuffless BP technologies, issues in their validation, and recommendations for clinical practice. Statements: Cuffless BP monitors constitute a wide and heterogeneous group of novel technologies and devices with different intended uses. Cuffless BP devices have specific accuracy issues, which render the established validation protocols for cuff BP devices inadequate for their validation. In 2014, the Institute of Electrical and Electronics Engineers published a standard for the validation of cuffless BP devices, and the International Organization for Standardization is currently developing another standard. The validation of cuffless devices should address issues related to the need of individual cuff calibration, the stability of measurements post calibration, the ability to track BP changes, and the implementation of machine learning technology. Clinical field investigations may also be considered and issues regarding the clinical implementation of cuffless BP readings should be investigated. Conclusion: Cuffless BP devices have considerable potential for changing the diagnosis and management of hypertension. However, fundamental questions regarding their accuracy, performance, and implementation need to be carefully addressed before they can be recommended for clinical use. [Stergiou, George S.; Kyriakoulis, Konstantinos G.; Kollias, Anastasios] Natl & Kapodistrian Univ Athens, Sotiria Hosp, Hypertens Ctr Stride 7, Dept Med 3,Sch Med, 152 Mesog Ave, Athens 11527, Greece; [Mukkamala, Ramakrishna] Univ Pittsburgh, Dept Bioengn, Pittsburgh, PA USA; [Mukkamala, Ramakrishna] Univ Pittsburgh, Dept Anesthesiol & Perioperat Med, Pittsburgh, PA USA; [Avolio, Alberto] Macquarie Univ, Fac Med Hlth & Human Sci, Macquarie Med Sch, Sydney, NSW, Australia; [Mieke, Stephan] Phys Tech Bundesanstalt, Berlin, Germany; [Murray, Alan] Newcastle Univ, Sch Engn, Newcastle Upon Tyne, Tyne & Wear, England; [Murray, Alan] Newcastle Univ, Med Fac, Newcastle Upon Tyne, Tyne & Wear, England; [Parati, Gianfranco] San Luca Hosp, Dept Cardiol, Ist Auxol Italiano, IRCCS, Milan, Italy; [Parati, Gianfranco] Univ Milano Bicocca, Dept Med & Surg, Milan, Italy; [Schutte, Aletta E.] Univ New South Wales, Sch Populat Hlth, George Inst Global Hlth, Sydney, NSW, Australia; [Sharman, James E.] Univ Tasmania, Coll Hlth & Med, Menzies Inst Med Res, Hobart, Tas, Australia; [Asmar, Roland] Fdn Med Res Inst, Geneva, Switzerland; [McManus, Richard J.] Univ Oxford, Nuffield Dept Primary Care Hlth Sci, Oxford, England; [Asayama, Kei; Ohkubo, Takayoshi] Teikyo Univ, Dept Hyg & Publ Hlth, Sch Med, Tokyo, Japan; [De la Sierra, Alejandro] Univ Barcelona, Hosp Mutua Terrassa, Dept Internal Med, Hypertens Unit, Barcelona, Spain; [Head, Geoffrey] Baker Heart & Diabet Inst, Melbourne, Vic, Australia; [Kario, Kazuomi] Jichi Med Univ, Dept Med, Div Cardiovasc Med, Sch Med, Shimotsuke, Tochigi, Japan; [Myers, Martin] Univ Toronto, Sunnybrook Hlth Sci Ctr, Toronto, ON, Canada; [Niiranen, Teemu] Turku Univ Hosp, Dept Med, Turku, Finland; [Niiranen, Teemu] Univ Turku, Turku, Finland; [Niiranen, Teemu] Finnish Inst Hlth & Welf, Dept Publ Hlth Solut, Turku, Finland; [Wang, Jiguang] Shanghai Jiao Tong Univ, Ruijin Hosp, Shanghai Inst Hypertens, Sch Med, Shanghai, Peoples R China; [Wuerzner, Gregoire] Lausanne Univ Hosp, Serv Nephrol & Hypertens, Lausanne, Switzerland; [Wuerzner, Gregoire] Univ Lausanne, CHUV, Lausanne, Switzerland; [O'Brien, Eoin] Univ Coll Dublin, Conway Inst, Dublin, Ireland; [Kreutz, Reinhold] Charite Univ Med Berlin, Berlin, Germany; [Kreutz, Reinhold] Free Univ Berlin, Berlin, Germany; [Kreutz, Reinhold] Humboldt Univ, Berlin, Germany; [Kreutz, Reinhold] Berlin Inst Hlth, Dept Clin Pharmacol & Toxicol, Berlin, Germany; [Kreutz, Reinhold] Charite, Berlin, Germany; [Palatini, Paolo] Univ Padua, Dept Med, Padua, Italy Athens Medical School; National & Kapodistrian University of Athens; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Macquarie University; Physikalisch-Technische Bundesanstalt (PTB); Newcastle University - UK; Newcastle University - UK; IRCCS Istituto Auxologico Italiano; University of Milano-Bicocca; George Institute for Global Health; University of New South Wales Sydney; University of Sydney; University of Tasmania; Menzies Institute for Medical Research; University of Oxford; Teikyo University; Hospital Universitario Mutua Terrassa; University of Barcelona; Baker Heart and Diabetes Institute; Jichi Medical University; University of Toronto; Sunnybrook Health Science Center; Sunnybrook Research Institute; University of Turku; University of Turku; Shanghai Jiao Tong University; University of Lausanne; Centre Hospitalier Universitaire Vaudois (CHUV); University of Lausanne; Centre Hospitalier Universitaire Vaudois (CHUV); University College Dublin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; University of Padua Stergiou, GS (corresponding author), Natl & Kapodistrian Univ Athens, Sotiria Hosp, Hypertens Ctr Stride 7, Dept Med 3,Sch Med, 152 Mesog Ave, Athens 11527, Greece. stergiougs@gmail.com ; Schutte, Aletta/E-5126-2018 Wuerzner, Gregoire/0000-0002-6424-7630; Schutte, Aletta/0000-0001-9217-4937 UK Engineering and Physical Sciences Research Council; National Health and Medical Research Council [S0026615] UK Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Health and Medical Research Council(National Health and Medical Research Council (NHMRC) of Australia) G.S.S., G.P., K.K., J.W., E.O.B., and P.P. conducted validation studies for various manufacturers of blood pressure measurement technologies and advised manufacturers on device and software development. R.M. holds NIH grants and patents on cuffless blood pressure measurement. Some of the patents have been licensed or optioned to Digitouch Health and Samsung Advanced Institute of Technology. A. A., K.G.K., A.K. contributed to validation studies by various manufacturers of blood pressure measurement technologies. S.M. is the former convenor of the ISO/IEC JWG Noninvasive Sphygmomanometer. A.M. is the Principal Investigator in blood pressure measurement research funded by the UK Engineering and Physical Sciences Research Council. A.E.S., R.A., G.W. contributed to validation studies by manufacturers of blood pressure measurement technologies and received speaker honoraria from device manufacturers. J.E.S. served as a consultant on blood pressure device validation for HEARTS in the Americas. He conducted independent validation studies of central blood pressure devices. He is the Principal investigator of a National Health and Medical Research Council partnership grant (S0026615) on cardiovascular disease risk assessment, which includes a medical technology company that manufactures a central blood pressure monitor. R.J.M.: BP monitors for research from Omron. His institution (Oxford University) receives fees from his telemonitoring work from Omron and Sensyne. K.A. and T.O. received research grant from Omron Healthcare. A.S., G.H., M.M., T.N., and R. K.: none. 79 11 11 12 15 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA 0263-6352 1473-5598 J HYPERTENS J. Hypertens. AUG 2022.0 40.0 8 1449 1460 10.1097/HJH.0000000000003224 0.0 12 Peripheral Vascular Disease Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology 3L3BA 35708294.0 2023-03-23 WOS:000834638400002 0
J Wang, FJ; Sahana, M; Pahlevanzadeh, B; Pal, SC; Shit, PK; Piran, MJ; Janizadeh, S; Band, SS; Mosavi, A Wang, Fengjie; Sahana, Mehebub; Pahlevanzadeh, Bahareh; Pal, Subodh Chandra; Shit, Pravat Kumar; Piran, Md Jalil; Janizadeh, Saeid; Band, Shahab S.; Mosavi, Amir Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility ALEXANDRIA ENGINEERING JOURNAL English Article Head-cut gully erosion; Resampling algorithms; Bootstrap; K-fold cross validation; Machine learning; Boosted regression tree; Support vector machine; Random forest LOGISTIC-REGRESSION; PERFORMANCE; ENSEMBLE; ALGORITHM; REGION Gully erosion is one of the advanced forms of water erosion. Identifying the effective fac-tors and gully erosion predicting is one of the important tools to control and manage such phe-nomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Opti-mism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. that...>The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. [Wang, Fengjie] Donghua Univ, Coll Sci, Shanghai 201620, Peoples R China; [Sahana, Mehebub] Univ Manchester, Sch Environm Educ & Dev, Manchester, Lancs, England; [Pahlevanzadeh, Bahareh] Reg Informat Ctr Sci & Technol RICeST, Res Dept Design & Syst Operat, Shiraz, Fars, Iran; [Pal, Subodh Chandra] Univ Burdwan, Dept Geog, Barddhaman 713104, W Bengal, India; [Shit, Pravat Kumar] Dept Geog & Environm Management, Medinipur, India; [Piran, Md Jalil] Sejeong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea; [Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115111, Iran; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan; [Mosavi, Amir] Oxford Brookes Univ, Sch Built Environm, Oxford OX3 0BP, England; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary Donghua University; University of Manchester; University of Burdwan; Tarbiat Modares University; National Yunlin University Science & Technology; Oxford Brookes University; Obuda University Pahlevanzadeh, B (corresponding author), Reg Informat Ctr Sci & Technol RICeST, Res Dept Design & Syst Operat, Shiraz, Fars, Iran.;Piran, MJ (corresponding author), Sejeong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea.;Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan.;Mosavi, A (corresponding author), Oxford Brookes Univ, Sch Built Environm, Oxford OX3 0BP, England.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary. 2191748@mail.dhu.edu.cn; pahlevanzadeh@ricest.ac.ir; piran@sejong.ac.kr; shamshirbands@yuntech.edu.tw; amir.mosavi@kvk.uni-obuda.hu S. Band, Shahab/ABB-2469-2020; Mosavi, Amir/I-7440-2018 S. Band, Shahab/0000-0001-6109-1311; Mosavi, Amir/0000-0003-4842-0613; Pal, Subodh Chandra/0000-0003-0805-8007; Sahana, Mehebub/0000-0002-3166-5916 52 19 20 0 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1110-0168 2090-2670 ALEX ENG J Alex. Eng. J. DEC 2021.0 60.0 6 5813 5829 10.1016/j.aej.2021.04.026 0.0 JUN 2021 17 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering WA4IL gold, Green Published 2023-03-23 WOS:000702850300001 0
J Peak, CM; Wesolowski, A; zu Erbach-Schoenberg, E; Tatem, AJ; Wetter, E; Lu, X; Power, D; Weidman-Grunewald, E; Ramos, S; Moritz, S; Buckee, CO; Bengtsson, L Peak, Corey M.; Wesolowski, Amy; zu Erbach-Schoenberg, Elisabeth; Tatem, Andrew J.; Wetter, Erik; Lu, Xin; Power, Daniel; Weidman-Grunewald, Elaine; Ramos, Sergio; Moritz, Simon; Buckee, Caroline O.; Bengtsson, Linus Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data INTERNATIONAL JOURNAL OF EPIDEMIOLOGY English Article Ebola epidemic; travel restrictions; quarantine; mobile phone data; big data; human mobility; population mobility; Sierra Leone VIRUS DISEASE; WEST-AFRICA; INFLUENZA; SPREAD; DYNAMICS Background: Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures. Methods: We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis. Results: Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted. Conclusions: The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment. [Peak, Corey M.; Buckee, Caroline O.] Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Ctr Communicable Dis Dynam, Boston, MA 02115 USA; [Peak, Corey M.; zu Erbach-Schoenberg, Elisabeth; Tatem, Andrew J.; Wetter, Erik; Lu, Xin; Power, Daniel; Bengtsson, Linus] Flowminder Fdn, Stockholm, Sweden; [Wesolowski, Amy] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA; [zu Erbach-Schoenberg, Elisabeth; Tatem, Andrew J.] Univ Southampton, Dept Geog & Environm, WorldPop, Southampton, Hants, England; [Wetter, Erik] Stockholm Sch Econ, Misum, Stockholm, Sweden; [Wetter, Erik] Univ Makati, Asian Inst Management, Manila, Philippines; [Lu, Xin] Cent South Univ, Sch Business, Changsha, Hunan, Peoples R China; [Lu, Xin] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China; [Weidman-Grunewald, Elaine; Ramos, Sergio; Moritz, Simon] Ericsson, Erisson Technol & Emerging Business, Stockholm, Sweden; [Bengtsson, Linus] Karolinska Inst, Dept Epidemiol & Publ Hlth Sci, Stockholm, Sweden Harvard University; Harvard T.H. Chan School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of Southampton; Stockholm School of Economics; Asian Institute of Management; Central South University; National University of Defense Technology - China; Ericsson; Karolinska Institutet Buckee, CO (corresponding author), Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Ctr Communicable Dis Dynam, Boston, MA 02115 USA. cbuckee@hsph.harvard.edu lu, xin/C-1940-2009; Peak, Corey M/H-5998-2019 lu, xin/0000-0002-3547-6493; Peak, Corey M/0000-0001-6150-5318; Tatem, Andrew/0000-0002-7270-941X National Institute of General Medical Sciences [U54GM088558]; National Research Service Award [T32AI007535-16A]; Wellcome Trust [106866/Z/15/Z]; Swedish Research Council [D0313701]; Burroughs Wellcome Fund; Natural Science Foundation of China [71522014] National Institute of General Medical Sciences(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS)); National Research Service Award(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Wellcome Trust(Wellcome Trust); Swedish Research Council(Swedish Research Council); Burroughs Wellcome Fund(Burroughs Wellcome Fund); Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Institute of General Medical Sciences [U54GM088558]; National Research Service Award [T32AI007535-16A]; Wellcome Trust Sustaining Health Grant [106866/Z/15/Z]; and the Swedish Research Council [D0313701]. A.W. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. X.L. was partially supported by the Natural Science Foundation of China [71522014]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences nor of the National Institutes of Health. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. 37 66 67 0 22 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0300-5771 1464-3685 INT J EPIDEMIOL Int. J. Epidemiol. OCT 2018.0 47.0 5 1562 1570 10.1093/ije/dyy095 0.0 9 Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED) Public, Environmental & Occupational Health HA3TH 29947788.0 hybrid, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000450177900030 0
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H.; Raymond, M.; Read, A. L.; Readioff, N. P.; Reale, M.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reikher, D.; Reiss, A.; Rej, A.; Rembser, C.; Renda, M.; Rescigno, M.; Resconi, S.; Resseguie, E. D.; Rettie, S.; Reynolds, E.; Rezanova, O. L.; Reznicek, P.; Ricci, E.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ripellino, G.; Riu, I; Vergara, J. C. Rivera; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Roberts, R. T.; Robertson, S. H.; Robin, M.; Robinson, D.; Robinson, J. E. M.; Robles Gajardo, C. M.; Robson, A.; Rocchi, A.; Rocco, E.; Roda, C.; Rodriguez Bosca, S.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Rodriguez Vera, A. M.; Roe, S.; Rohne, O.; Roehrig, R.; Rojas, R. A.; Roland, C. P. A.; Roloff, J.; Romaniouk, A.; Romano, M.; Rompotis, N.; Ronzani, M.; Roos, L.; Rosati, S.; Rosbach, K.; Rosin, G.; Rosser, B. J.; Rossi, E.; Rossi, L. P.; Rossini, L.; Rosten, R.; Rotaru, M.; Rothberg, J.; Rousseau, D.; Rovelli, G.; Roy, A.; Roy, D.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Ruehr, F.; Ruiz-Martinez, A.; Rummler, A.; Rurikova, Z.; Rusakovich, N. A.; Russell, H. L.; Rustige, L.; Rutherfoord, J. P.; Rttinger, E. M.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Rye, E. B.; Ryzhov, A.; Sabatini, P.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F-W; Sadykov, R.; Tehrani, F. Safai; Samani, B. Safarzadeh; Saha, P.; Saha, S.; Sahinsoy, M.; Sahu, A.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakharov, A.; Salamani, D.; Salamanna, G.; Salazar Loyola, J. E.; De Bruin, P. H. Sales; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Samarati, J.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sanchez, J.; Pineda, A. Sanchez; Sandaker, H.; Sander, C. O.; Sanderswood, I. G.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sano, Y.; Sansoni, A.; Santoni, C.; Santos, H.; Santpur, S. N.; Santra, A.; Sapronov, A.; Saraiva, J. G.; Sasaki, O.; Sato, K.; Sauerburger, F.; Sauvan, E.; Savard, P.; Savic, N.; Sawada, R.; Sawyer, C.; Sawyer, L.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaeffer, J.; Schaepe, S.; Schaefer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharmberg, N.; Schegelsky, V. A.; Scheirich, D.; Schenck, F.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillaci, Z. M.; Schioppa, E. J.; Schioppa, M.; Schleicher, K. E.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schmoeckel, J. C.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Scholer, P. G.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schroeder, F.; Schulte, A.; Schultz-Coulon, H-C; Schumacher, M.; Schumm, B. A.; Schune, Ph; Schwartzman, A.; Schwarz, T. A.; Schwemling, Ph; Schwienhorst, R.; Sciandra, A.; Sciolla, G.; Scodeggio, M.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Scyboz, L. M.; Sebastiani, C. D.; Seema, P.; Seidel, S. C.; ; ATLAS Collaboration Search for flavour-changing neutral currents in processes with one top quark and a photon using 81 fb(-1) of pp collisions at root s=13 TeV with the ATLAS experiment PHYSICS LETTERS B English Article PLUS PLUS; MODELS A search for flavour-changing neutral current (FCNC) events via the coupling of a top quark, a photon, and an up or charm quark is presented using 81 fb(-1) of proton-proton collision data taken at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Events with a photon, an electron or muon, a b-tagged jet, and missing transverse momentum are selected. A neural network based on kinematic variables differentiates between events from signal and background processes. The data are consistent with the background-only hypothesis, and limits are set on the strength of the tq gamma coupling in an effective field theory. These are also interpreted as 95% CL upper limits on the cross section for FCNC t gamma production via a left-handed (right-handed) tu gamma coupling of 36 fb (78 fb) and on the branching ratio for t -> gamma u of 2.8 x 10(-5) (6.1 x 10(-5)). In addition, they are interpreted as 95% CL upper limits on the cross section for FCNC t gamma production via a left-handed (right-handed) tc.coupling of 40 fb (33 fb) and on the branching ratio for t -> gamma c of 22 x 10(-5) (18 x 10(-5)). (C) 2019 The Author(s). Published by Elsevier B.V. [Deliot, F.; Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Jain, V; Swift, S. P.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Gingrich, D. M.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Bourdarios, C. Adam; Berger, N.; Costanza, F.; Cueto, A.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. J.; Falke, S.; Goy, C.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Portales, L.; Raspopov, S.; Sauvan, E.; Todorova-Nova, S.; Wingerter-Seez, I] Univ Grenoble Alpes, Univ Savoie Mt Blanc, IN2P3, CNRS,LAPP, Annecy, France; [Abulaiti, Y.; Benjamin, D. P.; Chekanov, S.; Hopkins, W. H.; LeCompte, T.; Liu, B. L.; Love, J.; Malon, D.; Metcalfe, J.; Paramonov, A.; Proudfoot, J.; Van Gemmeren, P.; Wang, R.; Zhang, J.] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA; [Berlendis, S.; Cheu, E.; Delitzsch, C. M.; Johns, K. 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M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Ju, X.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; McCormack, W. P.; Muskinja, M.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Poley, A.; Santpur, S. N.; Shapiro, M.; Tsulaia, V; Wang, H.; Yang, H. T.; Yao, W-M] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA USA; [Ai, X.; Barnett, R. M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Ju, X.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; McCormack, W. P.; Muskinja, M.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Poley, A.; Santpur, S. N.; Shapiro, M.; Tsulaia, V; Wang, H.; Yang, H. 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E.] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Agheorghiesei, C.] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Phys Dept, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Bartos, P.; Blazek, T.; Dado, T.; Dubovsky, M.; Hyrych, S.; Majersky, O.; Melo, M.; Racko, M.; Smiesko, J.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Murin, P.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Inst Expt Phys, Dept Subnucl Phys, Kosice, Slovakia; [Assamagan, K.; Barone, G.; Begel, M.; Cavaliere, V; Chen, H.; D'amen, G.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Hu, Q.; Iakovidis, G.; Klimentov, A.; Kouskoura, V; Lancon, E.; Lanni, F.; Lee, C. A.; Liu, H. B.; Lynn, D.; Ma, H.; Maeno, T.; Nagle, J. L.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Ouellette, J.; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Seidlitz, B. D.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; [Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Bawa, H. S.; Gao, Y. S.; Grimm, K.; Moss, J.; Parker, A. J.] Calif State Univ, Long Beach, CA USA; [Batley, J. R.; Brandt, O.; Brunt, B. H.; Chapman, J. D.; Cowley, J. W.; Fawcett, W. J.; Hill, J. C.; Hommels, L. B. A. H.; Lester, C. G.; Lin, C. Y.; Malone, C.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson, D.; Ward, C. P.; Williams, S.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; [Boye, D.; Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; [Hernandez Jimenez, Y.; Hlaluku, D. R.; Jivan, H.; Kar, D.; Lagouri, T.; Mdhluli, J. E.; Garcia, B. R. Mellado; Roy, D.; Ruan, X.; Shrif, E. M.; Haddad, E. Sideras; Tomiwa, K. G.; von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Di Valentino, D.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Laurier, A.; Lee, S. J.; Oakham, F. G.; Sherafati, N.; Vincter, M. G.; Weber, S. A.; Zakharchuk, N.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chadi, Z.; Hoummada, A.] Univ Hassan 2, Reseau Univ Phys Hautes Energies, Fac Sci Ain Chock, Casablanca, Morocco; [Gouighri, M.] Univ Ibn Tofail, Fac Sci, Kenitra, Morocco; [El Kacimi, M.; Goujdami, D.] Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA, Marrakech, Morocco; [Assahsah, J.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Assahsah, J.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; Cherkaoui El Moursli, R.; Dahbi, S.; El Jarrari, H.; Fassi, F.; Haddad, N.; Hamdaoui, H.; Idrissi, Z.; Ngair, B.; Tayalati, Y.; Zaazoua, M.] Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Alderweireldt, S.; Aleksa, M.; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Bielski, R.; Bortfeldt, J.; Boyd, J.; Buat, Q.; Butti, P.; Buttinger, W.; Calace, N.; Camarda, S.; Camincher, C.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Catinaccio, A.; Cattai, A.; Chelstowska, M. A.; Coimbra, A. E. C.; Cortes-Gonzalez, A.; Czodrowski, P.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Dittus, F.; Dudarev, A.; Duhrssen, M.; Dyndal, M.; Eifert, T.; Ellis, N.; Elsing, M.; Errenst, M.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Froidevaux, D.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Hageboeck, S.; Hawkings, R. J.; Heinrich, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P. I.; Javurek, T.; Jenni, P.; Jovicevic, J.; Klioutchnikova, T.; Kohler, N. M.; Krasznahorkay, A.; Kuehn, S.; Kuwertz, E. S.; Langenberg, R. J.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Longo, L.; Malyukov, S.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Meehan, S.; Meng, L.; Berlingen, J. Montejo; Llacer, M. Moreno; Morley, A. K.; Mornacchi, G.; Moschovakos, P.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pandini, C. E.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Pontecorvo, L.; Poveda, J.; Astigarraga, M. E. Pozo; Raymond, M.; Rembser, C.; Rizzi, C.; Roe, S.; Rummler, A.; Salzburger, A.; Samarati, J.; Schaepe, S.; Schioppa, E. J.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sidiropoulou, O.; Sanchez, C. A. Solans; Spigo, G.; Stewart, G. A.; Stockton, M. C.; Ten Kate, H.; Unal, G.; Vallier, A.; Vandelli, W.; Schroeder, T. Vazquez; Venturi, N.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Wilkens, H. G.; Young, C. J. S.; Zanzi, D.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Bryant, P.; Gardner, R. W.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Miller, D. W.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Swiatlowski, M.; Vukotic, I; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Agaras, M. N.; Angelidakis, S.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Morales, F. A. Jimenez; Madar, R.; Nibigira, E.; Pallin, D.; Rustige, L.; Santoni, C.; Senkin, S.; Vazeille, F.] Univ Clermont Auvergne, IN2P3, CNRS, LPC, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Chen, J.; Clark, M. R.; Cole, B.; Emerman, A.; Havener, L. B.; Mahon, D. J.; Mohapatra, S.; Ochoa, I; Parsons, J. A.; Rybar, M.; Tuts, P. M.] Columbia Univ, Nevis Lab, Irvington, NY USA; [AbouZeid, O. S.; Alonso, A.; Bajic, M.; Camplani, A.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Monk, J.; Nielsen, D. S.; Petersen, T. C.; Stark, S. H.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Capua, M.; Carducci, G.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Capua, M.; Carducci, G.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] INFN, Lab Nazl Frascati, Grp Collegato Cosenza, Frascati, Italy; [Deiana, A. M.; Feickert, M.; Kehoe, R.; Leney, K. J. C.; Lo Sterzo, F.; McKay, M. A.; Narayan, R.; Sekula, S. J.; Stroynowski, R.; Thomas, J. O.; Wang, P.; Ye, J.] Southern Methodist Univ, Phys Dept, Dallas, TX USA; [Izen, J. M.; Meirose, B.; Parajuli, S.; Reeves, K.] Univ Texas Dallas, Phys Dept, Richardson, TX 75083 USA; [Fanourakis, G.; Geralis, T.; Stavropoulos, G.] Natl Ctr Sci Res Demokritos, Aghia Paraskevi, Greece; [Backman, F.; Navarro, L. Barranco; Bohm, C.; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Pasuwan, P.; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Backman, F.; Navarro, L. Barranco; Carney, R. M. D.; Clement, C.; Hellman, S.; Jon-And, K.; Kastanas, A.; Milstead, D. A.; Moa, T.; Pasuwan, P.; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Amoroso, S.; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Berge, D.; Bloch, I; Boerner, D.; Braren, F.; Brendlinger, K.; Brenner, L.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuechler, J. T.; Kuhl, T.; Lang, V. S.; Leight, W. A.; Li, X.; Li, Y.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Potamianos, K.; Pownall, G.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Rimoldi, M.; Robin, M.; Robinson, J. E. M.; Rossi, E.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; Scodeggio, M.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] DESY, Hamburg, Germany; [Amoroso, S.; Arling, J-H; Basalaev, A.; Becot, C.; Behr, J. K.; Berge, D.; Bloch, I; Boerner, D.; Braren, F.; Brendlinger, K.; Brenner, L.; Burr, J. T. P.; Carra, S.; Chen, Y-H; Daubney, T.; David, C.; Diaz, Y. Delabat; Deterre, C.; Cornell, S. Diez; Dutta, B.; Ferrando, J.; Gaycken, G.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Gupta, R.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Jeong, N.; Katzy, J.; Kitali, V; Kuechler, J. T.; Kuhl, T.; Lang, V. S.; Leight, W. A.; Li, X.; Li, Y.; Lobodzinska, E. M.; Lou, X.; Meloni, F.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; Nechansky, F.; Pani, P.; Peters, K.; Pollard, C. S.; Potamianos, K.; Pownall, G.; Queitsch-Maitland, M.; Rauch, D. M.; Richter, S.; Rifki, O.; Rimoldi, M.; Robin, M.; Robinson, J. E. M.; Rossi, E.; Saimpert, M.; Sander, C. O.; Schmitt, S.; Schmoeckel, J. C.; Scodeggio, M.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.] DESY, Zeuthen, Germany; [Cinca, D.; Erdmann, J.; Freundlich, E. M.; Gessner, G.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Rustige, L.; Ucchielli, G.; Weingarten, J.; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Herrmann, T.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Maerker, M.; Ramos, J. Manjarres; Morgenstern, S.; Nag, A. Nag; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Beacham, J. B.; Bocci, A.; Davis, D. R.; Eggleston, M. G.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Pachal, K.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Farrington, S. M.; Giannelli, M. Faucci; Gao, Y.; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Palazzo, S.; Sogaard, A.; Strubig, A.; Takeva, E. P.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Frascati, Italy; [Antonelli, M.; Arcangeletti, C.; Beretta, M.; Chiarella, V; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becherer, F.; Becker, K.; Buehrer, F.; Coniavitis, E.; Garcia-Argos, C.; Gargiulo, S.; Gonella, G.; Gugel, R.; Guth, M.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Hirose, S.; Hohn, D.; Jakobs, K.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Kuprash, O.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V; Megy, T.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Sauerburger, F.; Schleicher, K. E.; Schnoor, U.; Scholer, P. G.; Schumacher, M.; Sperlich, D.; Weiser, C.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wollrath, J.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Abeling, K.; Achkar, B.; Beck, H. C.; Bindi, M.; Bisanz, T.; Bokan, P.; Buschmann, E.; Dreyer, T.; Geisen, J.; Gerlach, L. O.; Gama, R. Goncalves; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lange, J. C.; Mellenthin, J. D.; Nellist, C.; Oerdek, S.; Peiffer, T.; Pokharel, I; Quadt, A.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Sohns, F.; Torres, R. E. Ticse; Veatch, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Adorni, S.; Akilli, E.; Amrouche, C. S.; Benoit, M.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; Mermod, P.; Nessi, M.; Nindhito, H. R.; Paolozzi, L.; Poggi, R.; Raine, J. A.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Pinto, M. Vicente Barreto; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl & Corpusculaire, Geneva, Switzerland; [Barberis, D.; Fumagalli, E.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Parodi, F.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Fumagalli, E.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Sforza, F.; Varni, C.] INFN, Sez Genova, Genoa, Italy; [Caforio, D.; Dren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P. J.; Buttar, C. M.; Callea, G.; Connelly, I. A.; Crawley, S. J.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; Jamieson, J.; OShea, V; Owen, M.; Robson, A.; Spiteri, D. P.; Thompson, A. S.; Warrack, N.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Quintero, D. M. Portillo; Readioff, N. P.; Stark, J.; Trocme, B.; Yang, X.] Univ Grenoble Alpes, Grenoble INP, IN2P3, CNRS,LPSC, Grenoble, France; [Asbah, N.; Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Baroncelli, A.; Chen, C.; Chen, J.; Gao, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; He, F.; Huang, Y.; Jiang, Y.; Krasnopevtsev, D.; Li, C-Q; Li, H.; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Sessa, M.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, L.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, D.; Duan, Y.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Ma, Y.; Tariq, K.; Wang, C.; Xu, Z.; Yang, X.; Yuan, R.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Shandong, Peoples R China; [De Sousa, M. J. Da Cunha Sargedas; Du, D.; Duan, Y.; Feng, C.; Li, H.; Li, Z.; Ma, L. L.; Ma, Y.; Tariq, K.; Wang, C.; Xu, Z.; Yang, X.; Yuan, R.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao, Shandong, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Dong, B.; Guo, J.; Hu, S.; Kato, C.; Kondrashova, N.; Li, F.; Li, J.; Li, L.; Li, S.; Mittal, M.; Nishu, N.; Wang, C.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, KLPPAC MoE, Shanghai, Peoples R China; [Kato, C.; Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Antel, C.; Dunford, M.; Franchino, S.; Geisler, M. P.; Jongmanns, J.; Klassen, M.; Kugel, A.; Zu Theenhausen, H. Meyer; Napolitano, F.; Ott, P. S.; Sahinsoy, M.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; de Lima, D. E. Ferreira; Helary, L.; Kolb, M.; Krishnan, A.; Lisovyi, M.; Schoening, A.; Vigani, L.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Cheng, H. C.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Salvucci, A.; Wang, J.; Zhang, Z.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Lo, C. Y.; Paredes Hernandez, D.; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Lie, K.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Meyer, C.; Palacino, G.; Roland, C. P. A.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Faraj, M.; Giordani, M. P.; Giugliarelli, G.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] INFN, Grp Collegato Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Faraj, M.; Giordani, M. P.; Giugliarelli, G.; Panizzo, G.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Politecn Ingn & Architettura, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Gravili, F. G.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] INFN, Sez Lecce, Lecce, Italy; [Bachas, K.; Gorini, E.; Gravili, F. G.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Citterio, M.; D'Auria, S.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R. T.; Perez, M. Villaplana] INFN, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; D'Auria, S.; Fanti, M.; Lazzaroni, M.; Murrone, A.; Perini, L.; Ragusa, F.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Giannini, A.; Izzo, V; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.; Sekhniaidze, G.] INFN, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Giannini, A.; Lavorgna, M.; Massarotti, P.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Abud, A. Abed; Farina, E. M.; Ferrari, R.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Pezzotti, L.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.; Vercesi, V.] INFN, Sez Pavia, Pavia, Italy; [Abud, A. Abed; Farina, E. M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Pezzotti, L.; Rebuzzi, D. M.; Rimoldi, A.; Rovelli, G.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; Di Gregorio, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Mastrandrea, P.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.; Verducci, M.] INFN, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; Di Gregorio, G.; Francavilla, P.; Mastrandrea, P.; Roda, C.; Sotiropoulou, C. L.; Verducci, M.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Chomont, A. R.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Francescato, S.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Policicchio, A.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Vannicola, D.; Vari, R.; Veneziano, S.] INFN, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Chomont, A. R.; Corradi, M.; De Cecco, S.; Francescato, S.; Gentile, S.; Giagu, S.; Ippolito, V; Kado, M.; Lacava, F.; Luci, C.; Messina, A.; Policicchio, A.; Sebastiani, C. D.; Vannicola, D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Liberti, B.; Loffredo, S.; Marcoccia, L.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] INFN, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; De Santis, M.; Dell'Asta, L.; Di Ciaccio, A.; Giuli, F.; Loffredo, S.; Marcoccia, L.; Massa, L.; Pinamonti, M.; Pizzimento, L.; Rocchi, A.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Biglietti, M.; Ceradini, F.; D'Amico, V; Di Micco, B.; Farilla, A.; Iodice, M.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Vecchio, V.] INFN, Sez Roma Tre, Rome, Italy; [Ceradini, F.; D'Amico, V; Di Micco, B.; Martinelli, L.; Orestano, D.; Petrucci, F.; Rossi, E.; Salamanna, G.; Vecchio, V.] Univ Roma Tre, Dipartimento Matemat & Fis, Rome, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] INFN TIFPA, Trento, Italy; [Follega, F. M.; Forcolin, G. T.; Iuppa, R.; Ricci, E.] Univ Trento, Trento, Italy; [Beermann, T. A.; Hrdinka, J.; Jakobsen, S.; Kneringer, E.; Manousos, A.; Serfon, C.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Ghosh, A.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [An, F.; Chen, B.; Chen, C. H.; Cochran, J.; Heidorn, W. D.; Jiang, H.; Krumnack, N.; Lee, S.; Liu, B.; Magini, N.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M., V; Dedovich, D., V; Demichev, M.; Gongadze, A.; Gostkin, M., I; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V; Kulchitsky, Y.; Ladygin, E.; Lyubushkin, V; Lyubushkina, T.; Mineev, M.; Plotnikova, E.; Potrap, I. N.; Prokoshin, F.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Tsiareshka, P., V; Turchikhin, S.; Yeletskikh, I; Zhemchugov, A.; Zimine, N., I] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Manhaes de Andrade Filho, L.; Peralva, B. S.; Silva Oliveira, M., V] Univ Fed Juiz de Fora, Dept Engn Eletr, Juiz de Fora, Brazil; [Amaral Coutinho, Y.; Araujo Ferraz, V; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Pinto, J. V. Da Fonseca; Freund, W. S.; Maidantchik, C.; Seixas, J. M.] Univ Fed Rio De Janeiro, EE, COPPE, IF, Rio De Janeiro, Brazil; [Do Vale, M. A. B.] Univ Fed Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, SP, Brazil; [Aoki, M.; Hanagaki, K.; Higashino, S.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Tsinghua University; Nanjing University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley; Humboldt University of Berlin; University of Bern; University of Bern; University of Birmingham; Universidad Antonio Narino; Universidad Antonio Narino; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Bonn; Boston University; Brandeis University; Transylvania University of Brasov; Horia Hulubei National Institute of Physics & Nuclear Engineering; Alexandru Ioan Cuza University; National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; 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University of Texas System; University of Texas Dallas; National Centre of Scientific Research Demokritos; Stockholm University; Oskar Klein Centre; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Shandong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; National Tsing Hua University; Indiana University System; Indiana University Bloomington; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; Istituto Nazionale di Fisica Nucleare (INFN); University of Salento; Istituto Nazionale di Fisica Nucleare (INFN); University of Milan; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; Universidade Federal de Juiz de Fora; Universidade Federal do Rio de Janeiro; Universidade Federal de Sao Joao del-Rei; Universidade de Sao Paulo; High Energy Accelerator Research Organization (KEK); Kobe University; AGH University of Science & Technology; Jagiellonian University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Kyoto University; Kyoto University of Education; Kyushu University; Kyushu University; National University of La Plata; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Lancaster University; University of Liverpool; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana; University of London; Queen Mary University London; University College London; University of London; Royal Holloway University London; University of London; University College London; University of Louisiana System; Louisiana Technical University; Lund University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Autonomous University of Madrid; Autonomous University of Madrid; Johannes Gutenberg University of Mainz; University of Manchester; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Aix-Marseille Universite; University of Massachusetts System; University of Massachusetts Amherst; McGill University; University of Melbourne; University of Michigan System; University of Michigan; Michigan State University; National Academy of Sciences of Belarus (NASB); B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus; Belarusian State University; Universite de Montreal; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Lomonosov Moscow State University; University of Munich; Max Planck Society; Nagasaki Institute of Applied Science; Nagoya University; Nagoya University; University of New Mexico; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; FOM National Institute for Subatomic Physics; University of Amsterdam; Northern Illinois University; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Novosibirsk State University; Russian Academy of Sciences; Novosibirsk State University; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; New York University; Ochanomizu University; University System of Ohio; Ohio State University; Okayama University; University of Oklahoma System; University of Oklahoma - Norman; Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; Autonomous University of Barcelona; Universidade de Lisboa; Instituto Superior Tecnico; University of Sharjah; University of Aegean; University of Louisville; California State University System; California State University Fresno; California State University System; California State University Sacramento; University of London; King's College London; Peter the Great St. Petersburg Polytechnic University; Stanford University; University of Fribourg; Lomonosov Moscow State University; Giresun University; Hellenic Open University; ICREA; University of Hamburg; Bulgarian Academy of Sciences; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Ilia State University; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Manhattan College; Moscow Institute of Physics & Technology; An Najah National University; University of South Africa; Sun Yat Sen University; City University of New York (CUNY) System; City College of New York (CUNY); Parthenope University Naples Aad, G (corresponding author), Aix Marseille Univ, IN2P3, CNRS, CPPM, Marseille, France. 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Suster, Carl J E/L-3714-2016; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Černý, Karel/AAK-7746-2021; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; D'Onofrio, Adelina/AAT-3903-2020; Buttar, Craig/AAG-5392-2021; Artoni, Giacomo/ABA-2164-2020; Bogdanchikov, Alexander/AAB-9414-2022; Bruscino, Nello/ABA-8980-2021; Luci, Claudio/HKW-0143-2023; Wu, Xin/ABH-1729-2020; Chwastowski, Janusz J./I-4480-2012; Wolters, Helmut/M-4154-2013; gaudio, gabriella/AAN-6039-2021; Oh, Alexander/HHZ-4386-2022; Ozcan, Veysi Erkcan/AAS-4508-2020; Song, Weimin/AAJ-5415-2020; Wang, Song-Ming/AAP-9832-2021; Alimonti, Gianluca/AAG-4603-2020; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Padilla, Cristobal/C-3218-2017; banerjee, swagato/AAN-3165-2021; Hamal, Petr/G-5540-2014; Trzebinski, Maciej/AAO-6821-2021; Fullana Torregrosa, Esteban/AAC-9079-2022; beddall, ayda/AAG-6531-2020; Torregrosa, Esteban Fullana/A-7305-2016; Burdin, Sergey/AAZ-9062-2021; Gutierrez, Phillip/C-1161-2011; Meshkov, Oleg/AAM-8539-2021; Chitan, Adrian/AAC-8946-2022; 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Grinstein, Sebastian/ABE-1880-2020; Camarero, Daniel/Z-1924-2019; Kroll, Jiri/C-8465-2018; Adiguzel, Aytul/AAC-9049-2020; Giuli, Francesco/HJI-6649-2023; Maj, Klaudia/Q-2724-2019; Norjoharuddeen, Nurfikri/S-3109-2018; Gurbuz, Saime/AAG-5583-2019; Fassi, Farida/F-3571-2016; Hunter, Robert/AAG-8342-2019; AGHEORGHIESEI, Catalin/B-8596-2014; Amoroso, Simone/AAW-4334-2021; Ahmadov, Faig N/B-3723-2018; Mindur, Bartosz/A-2253-2017; Valero, Alberto/G-9866-2015; Franchini, Matteo/AAC-9259-2021; Chelkov, Georgy A./G-9934-2019; Perez, Miguel Villaplana/B-2717-2015; Yang, Haijun/O-1055-2015; Mikestikova, Marcela/H-1996-2014; Lacasta, Carlos/C-7254-2008; de Renstrom, Pawel Bruckman/AAG-7725-2021; Panizzo, Giancarlo/AAS-2986-2020; Camarri, Paolo/AAF-9629-2020; Iengo, Paolo/AAR-7518-2020; Agaras, Merve Nazlim/AAB-5221-2021; Brahimi, Nihal/HNJ-5325-2023; Castro, Nuno/AAB-3648-2019; Lefebvre, Benoit/ABB-2272-2021; Lee, Shih-Chang/AAV-7016-2021; YILDIZ, Hatice DURAN/AAN-3727-2021; Smirnov, Sergei Y/F-1014-2011; Sanchez, Francisca J. 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Shojaii, Seyed Ruhollah/0000-0002-9449-0412; Krauss, Dominik/0000-0002-6419-7602; MARTINEZ, MARIO/0000-0002-3135-945X; Filthaut, Frank/0000-0003-3338-2247; D'Auria, Saverio/0000-0003-3393-6318; Stanislaus, Beojan/0000-0001-9007-7658; Murrone, Alessia/0000-0001-5399-2478; Tzovara, Eftychia/0000-0002-0410-0055; Coniavitis, Elias/0000-0002-2148-8012; Morley, Anthony/0000-0003-0373-1346; Villa, Mauro/0000-0002-9181-8048; Schleicher, Katharina E./0000-0002-2917-7032; Chiodini, Gabriele/0000-0002-2458-9513; Vincter, Manuella/0000-0002-5338-8972; Citron, Zvi/0000-0003-1831-6452; Zhulanov, Vladimir/0000-0002-0306-9199; govender, nicolin/0000-0002-5068-5429; Anastopoulos, Christos/0000-0003-1587-5830; Bouhova-Thacker, Evelina/0000-0002-5103-1558; Maeda, Junpei/0000-0002-9084-3305; Hassani, Samira/0000-0002-2834-5110; Biesuz, Nicolo Vladi/0000-0003-3004-0946; Pottgen, Ruth/0000-0002-3304-0987; Benjamin, TROCME/0000-0001-9500-2487; Mkrtchyan, Tigran/0000-0002-5786-3136; Winter, Benedict Tobias/0000-0001-9606-7688; Trofymov, Artur/0000-0001-7688-5165; Haas, Andrew/0000-0002-4832-0455; Mijovic, Liza/0000-0003-0162-2891; Queitsch-Maitland, Michaela/0000-0003-4643-515X; Vickey Boeriu, Oana/0000-0002-6497-6809; Lysak, Roman/0000-0003-2990-1673; Cristinziani, Markus/0000-0003-3893-9171; Atkin, Ryan/0000-0002-1972-1006; Biondi, Silvia/0000-0002-1492-6715; Buckley, Andy/0000-0001-8355-9237; Bianchi, Riccardo Maria/0000-0001-7345-7798; Held, Alexander/0000-0002-8924-5885; PERINI, LAURA/0000-0003-3715-0523; Walkowiak, Wolfgang/0000-0002-0385-3784; Robson, Aidan/0000-0002-1659-8284; Hill, John/0000-0002-8650-2807; Montejo Berlingen, Javier/0000-0001-9213-904X; Wardrope, David/0000-0002-8208-2964; Cheremushkina, Evgeniya/0000-0002-8772-0961; Azuelos, Georges/0000-0003-4241-022X; Albert, Justin/0000-0003-0253-2505; Bosman, Martine/0000-0002-7290-643X; Grivaz, Jean-Francois/0000-0003-4793-7995; Sebastiani, Cristiano/0000-0003-1073-035X; Akesson, Torsten/0000-0003-4141-5408; Dao, Valerio/0000-0003-1645-8393; Senkin, Sergey/0000-0001-5848-005X; Camarda, Stefano/0000-0003-0479-7689; Pleier, Marc-Andre/0000-0002-9461-3494; Gagnon, Louis-Guillaume/0000-0003-3000-8479; Wengler, Thorsten/0000-0002-4375-5265; Schmitt, Christian/0000-0003-1471-690X; Schmitt, Stefan/0000-0001-8387-1853; Ellert, Mattias/0000-0001-5265-3175; Danninger, Matthias/0000-0002-7807-7484; Little, Jared/0000-0002-9372-0730; Oda, Susumu/0000-0001-5836-768X; Wanotayaroj, Chaowaroj/0000-0002-8178-5705; Zenis, Tibor/0000-0001-8265-6916; Hanagaki, Kazunori/0000-0003-0676-0441; Cardillo, Fabio/0000-0002-4478-3524; Argyropoulos, Spyridon/0000-0001-7748-1429; URQUIJO, PHILLIP/0000-0002-0887-7953; Hays, Chris/0000-0003-2371-9723; Bruscino, Nello/0000-0002-6168-689X; Hansen, Peter Henrik/0000-0002-6764-4789; Grummer, Aidan/0000-0003-2752-1183; Sotiropoulou, Calliope-Louisa/0000-0001-9851-1658; Weber, Michele/0000-0002-2770-9031; Whalen, Kathleen/0000-0002-9383-8763; Hill, Ewan/0000-0002-1725-7414; Cranmer, Kyle/0000-0002-5769-7094; ; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NRC, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex, France; Idex, ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece; Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSFNSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; Science and Technology Facilities Council [ST/S000879/1, ST/N000331/1] Funding Source: researchfish; STFC [ST/N000277/1, ST/S000860/1, ST/N000331/1, ST/S00095X/1, ST/N000463/1, ST/N000307/1, 1852475, ST/S000879/1, ST/L006162/1, ST/P002439/1, ST/N000234/1, ST/S000747/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); CFI, Canada(Canada Foundation for Innovation); NRC, Canada; CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canarie, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex, France(French National Research Agency (ANR)); Idex, ANR, France(French National Research Agency (ANR)); DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece(Thales Group); Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSFNSF, Israel; GIF, Israel(German-Israeli Foundation for Scientific Research and Development); CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSFNSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. 75 8 8 2 32 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B JAN 10 2020.0 800.0 135082 10.1016/j.physletb.2019.135082 0.0 21 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics JX6HB Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000503832500051 0
J Hao, SY; Wang, W; Ye, YX; Nie, TY; Bruzzone, L Hao, Siyuan; Wang, Wei; Ye, Yuanxin; Nie, Tingyuan; Bruzzone, Lorenzo Two-Stream Deep Architecture for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Class-specific fusion; convolutional neural networks (CNNs); deep learning; hyperspectral image (HSI) classification; remote sensing; stacked denoising autoencoder (SdAE); two-stream architecture FUSION Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the spectral values of the input channels. However, the spatial context around a pixel is also very important and can enhance the classification performance. In order to effectively exploit and fuse both the spatial context and spectral structure, we propose a novel two-stream deep architecture for HSI classification. The proposed method consists of a two-stream architecture and a novel fusion scheme. In the two-stream architecture, one stream employs the stacked denoising autoencoder to encode the spectral values of each input pixel, and the other stream takes as input the corresponding image patch and deep convolutional neural networks are employed to process the image patch. In the fusion scheme, the prediction probabilities from two streams are fused by adaptive class-specific weights, which can be obtained by a fully connected layer. Finally, a weight regularizer is added to the loss function to alleviate the overfitting of the class-specific fusion weights. Experimental results on real HSIs demonstrate that the proposed two-stream deep architecture can achieve competitive performance compared with the state-of-the-art methods. [Hao, Siyuan; Nie, Tingyuan] Qingdao Univ Technol, Coll Commun & Elect Engn, Qingdao 266520, Peoples R China; [Wang, Wei; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38122 Trento, Italy; [Ye, Yuanxin] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China Qingdao University of Technology; University of Trento; Southwest Jiaotong University Ye, YX (corresponding author), Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China. lemonbananan@163.com; wei.wang@unitn.it; yeyuanxin@home.swjtu.edu.cn; tynie@qut.edu.cn; lorenzo.bruzzone@ing.unitn.it Wang, Wei/AAK-5521-2021; Bruzzone, Lorenzo/A-2076-2012 Wang, Wei/0000-0002-5477-1017; Bruzzone, Lorenzo/0000-0002-6036-459X National Natural Science Foundation of China [61572269, 61701272]; Fundamental Research Funds for the Central Universities [2682016CX083]; Natural Science Foundation of Shandong Province [ZR2017PF004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Natural Science Foundation of Shandong Province(Natural Science Foundation of Shandong Province) This work was supported in part by the National Natural Science Foundation of China under Grant 61572269 and Grant 61701272, in part by the Fundamental Research Funds for the Central Universities under Grant 2682016CX083, and in part by the Natural Science Foundation of Shandong Province under Grant ZR2017PF004. 58 81 82 3 53 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing APR 2018.0 56.0 4 2349 2361 10.1109/TGRS.2017.2778343 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology GA9PR 2023-03-23 WOS:000428673800038 0
J Blachowski, B; An, YH; Spencer, BF; Ou, JP Blachowski, Bartlomiej; An, Yonghui; Spencer, Billie F., Jr.; Ou, Jinping Axial Strain Accelerations Approach for Damage Localization in Statically Determinate Truss Structures COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING English Article WAVELET NEURAL-NETWORK; WIRELESS SENSOR NETWORK; PARAMETRIC IDENTIFICATION; SYSTEM-IDENTIFICATION; MODAL DATA; BRIDGE; MODEL; TEMPERATURE This work proposes an efficient and reliable method for damage localization in truss structures. The damage is localized on the basis of measured acceleration signals of the structure followed by simple statistical signal processing. It has three main advantages over many existing methods. First, it can be directly applied to real engineering structures without the need of identifying modal parameters or solving any global optimization problem. Second, the proposed method has higher sensitivity to damage than some other frequently used methods and allows to localize damage as small as a few percentages. Third, it is a model-freemethod, which does not require precise finite element model development or updating. Validation of the method has been conducted on numerical examples and laboratory-scale trusses. Two types of frequently used trusses have been selected for this study, namely, Howe and Bailey trusses. The presented experimental validation of the method shows its efficiency and robustness for damage localization in truss structures. [Blachowski, Bartlomiej] Polish Acad Sci, Inst Fundamental Technol Res, Warsaw, Poland; [An, Yonghui; Ou, Jinping] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dept Civil Engn, Dalian, Peoples R China; [An, Yonghui] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dept Civil Engn, Dalian, Peoples R China; [Spencer, Billie F., Jr.] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL USA Polish Academy of Sciences; Institute of Fundamental Technological Research of the Polish Academy of Sciences; Dalian University of Technology; Dalian University of Technology; University of Illinois System; University of Illinois Urbana-Champaign An, YH (corresponding author), Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dept Civil Engn, Dalian, Peoples R China.;An, YH (corresponding author), Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dept Civil Engn, Dalian, Peoples R China. anyh@dlut.edu.cn BLACHOWSKI, Bartlomiej/P-9583-2016 BLACHOWSKI, Bartlomiej/0000-0001-6021-0374; An, Yonghui/0000-0001-7407-8706 National Natural Science Foundation of China [51508070]; National Key Basic Research Program of China [2015CB060000]; Polish Ministry of Science and Higher Education; Fundamental Research Funds for the Central Universities [DUT16YQ101]; State Key Laboratory of Structural Analysis for Industrial Equipment [GZ1601]; [MNiSW, 634/MOB/2011/0] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Basic Research Program of China(National Basic Research Program of China); Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Key Laboratory of Structural Analysis for Industrial Equipment; The authors would like to express their gratitude for financial support from the National Natural Science Foundation of China (51508070), the National Key Basic Research Program of China (2015CB060000), the Polish Ministry of Science and Higher Education, program Mobility Plus no. MNiSW, 634/MOB/2011/0, the Fundamental Research Funds for the Central Universities (DUT16YQ101), and the opening fund of State Key Laboratory of Structural Analysis for Industrial Equipment (GZ1601). 53 23 23 1 39 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1093-9687 1467-8667 COMPUT-AIDED CIV INF Comput.-Aided Civil Infrastruct. Eng. APR 2017.0 32.0 4 304 318 10.1111/mice.12258 0.0 15 Computer Science, Interdisciplinary Applications; Construction & Building Technology; Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Construction & Building Technology; Engineering; Transportation EQ1TD 2023-03-23 WOS:000397851600003 0
J Qiu, JR; Mousavi, N; Gustavsson, K; Xu, CX; Mehlig, B; Zhao, LH Qiu, Jingran; Mousavi, Navid; Gustavsson, Kristian; Xu, Chunxiao; Mehlig, Bernhard; Zhao, Lihao Navigation of micro-swimmers in steady flow: the importance of symmetries JOURNAL OF FLUID MECHANICS English Article micro-organism dynamics; active matter; machine learning DIEL VERTICAL MIGRATION; ESCAPE BEHAVIOR; PLANKTONIC COPEPODS; MARINE COPEPODS; OYSTER LARVAE; RED-TIDE; TURBULENCE; PREDATOR; MOTION; PREY Marine micro-organisms must cope with complex flow patterns and even turbulence as they navigate the ocean. To survive they must avoid predation and find efficient energy sources. A major difficulty in analysing possible survival strategies is that the time series of environmental cues in nonlinear flow is complex and that it depends on the decisions taken by the organism. One way of determining and evaluating optimal strategies is reinforcement learning. In a proof-of-principle study, Colabrese et al. (Phys. Rev. Lett., vol. 118, 2017, 158004) used this method to find out how a micro-swimmer in a vortex flow can navigate towards the surface as quickly as possible, given a fixed swimming speed. The swimmer measured its instantaneous swimming direction and the local flow vorticity in the laboratory frame, and reacted to these cues by swimming either left, right, up or down. However, usually a motile micro-organism measures the local flow rather than global information, and it can only react in relation to the local flow because, in general, it cannot access global information (such as up or down in the laboratory frame). Here we analyse optimal strategies with local signals and actions that do not refer to the laboratory frame. We demonstrate that symmetry breaking is required to find such strategies. Using reinforcement learning, we analyse the emerging strategies for different sets of environmental cues that micro-organisms are known to measure. [Qiu, Jingran; Xu, Chunxiao; Zhao, Lihao] Tsinghua Univ, Dept Engn Mech, AML, Beijing 100084, Peoples R China; [Mousavi, Navid; Gustavsson, Kristian; Mehlig, Bernhard] Univ Gothenburg, Dept Phys, SE-41296 Gothenburg, Sweden Tsinghua University; University of Gothenburg Zhao, LH (corresponding author), Tsinghua Univ, Dept Engn Mech, AML, Beijing 100084, Peoples R China. zhaolihao@mail.tsinghua.edu.cn zhao, lihao/GVU-8502-2022 Mousavi, Navid/0000-0001-5412-5528; , Qiu/0000-0003-0064-3797; Gustafsson, Kristian/0000-0002-6613-3821 Knut and Alice Wallenberg Foundation [2019.0079]; VR grant [2017-3865]; Vetenskapsradet [2018-03974]; joint China-Sweden mobility programme (National Natural Science Foundation of China (NSFC)-Swedish Foundation for International Cooperation in Research and Higher Education (STINT)) [11911530141, CH2018-7737]; Institute for Guo Qiang of Tsinghua University [2019GQG1012]; NSFC [91752205] Knut and Alice Wallenberg Foundation(Knut & Alice Wallenberg Foundation); VR grant; Vetenskapsradet(Swedish Research Council); joint China-Sweden mobility programme (National Natural Science Foundation of China (NSFC)-Swedish Foundation for International Cooperation in Research and Higher Education (STINT)); Institute for Guo Qiang of Tsinghua University; NSFC(National Natural Science Foundation of China (NSFC)) K.G. and B.M. are supported in part by a grant from the Knut and Alice Wallenberg Foundation, grant no. 2019.0079, and in part by VR grant no. 2017-3865. K.G. is supported by Vetenskapsradet, Grant No. 2018-03974. B.M. and L.Z. are supported by a collaboration grant from the joint China-Sweden mobility programme (National Natural Science Foundation of China (NSFC)-Swedish Foundation for International Cooperation in Research and Higher Education (STINT)), grant nos. 11911530141 (NSFC) and CH2018-7737 (STINT). J.Q. and L.Z. acknowledge the support from the Institute for Guo Qiang of Tsinghua University (Grant No. 2019GQG1012). C.X. acknowledges the support from NSFC (Grant No. 91752205). 74 10 10 7 19 CAMBRIDGE UNIV PRESS NEW YORK 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA 0022-1120 1469-7645 J FLUID MECH J. Fluid Mech. DEC 2 2021.0 932.0 A10 10.1017/jfm.2021.978 0.0 21 Mechanics; Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Physics XG9HL Green Submitted 2023-03-23 WOS:000725056900001 0
J Li, F; Zurada, JM; Liu, Y; Wu, W Li, Feng; Zurada, Jacek M.; Liu, Yan; Wu, Wei Input Layer Regularization of Multilayer Feedforward Neural Networks IEEE ACCESS English Article Multilayer feedforward neural network; autoencoder; compressive sensing; regularization of input layer; L-1 and L-1/2 regularization SMOOTHING L-1/2 REGULARIZATION; GRADIENT-METHOD; CONVERGENCE; SELECTION Multilayer feedforward neural networks (MFNNs) have been widely used for classification or approximation of nonlinear mappings described by a data set consisting of input and output samples. In many MFNN applications, a common compressive sensing task is to find the redundant dimensions of the input data. The aim of a regularization technique presented in this paper is to eliminate the redundant dimensions and to achieve compression of the input layer. It is achieved by introducing an L-1 or L-1/2 regularizer to the input layer weights training. As a comparison, in the existing references, a regularization method is usually applied to the hidden layer for a better representation of the dataset and sparsification of the network. Gradient-descent method is used for solving the resulting optimization problem. Numerical experiments including a simulated approximation problem and three classification problems (Monk, Sonar, and the MNIST data set) have been used to illustrate the algorithm. [Li, Feng; Wu, Wei] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China; [Li, Feng; Zurada, Jacek M.] Univ Louisville, Dept Elect & Comp Engn, Louisville, KY 40208 USA; [Zurada, Jacek M.] Univ Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland; [Liu, Yan] Dalian Polytech Univ, Sch Informat Sci & Engn, Dalian 116034, Peoples R China Dalian University of Technology; University of Louisville; University of Social Sciences; Dalian Polytechnic University Wu, W (corresponding author), Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China. wuweiw@dlut.edu.cn National Science Foundation of China [61473059, 61403056]; Fundamental Research Funds for the Central Universities of China; China Scholarship Council National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) This work was supported in part by the National Science Foundation of China under Grant 61473059 and Grant 61403056 and in part by the Fundamental Research Funds for the Central Universities of China and China Scholarship Council. 32 25 27 0 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2017.0 5.0 10979 10985 10.1109/ACCESS.2017.2713389 0.0 7 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications EZ1QW gold 2023-03-23 WOS:000404486900011 0
J Lei, YX; Liu, F; Karimi, HR; Chen, XF Lei, Yongxiang; Liu, Fang; Karimi, Hamid Reza; Chen, Xiaofang Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING English Article Cell temperature identification; soft modeling; extreme learning machine; manifold semi-supervised learning; Hessian regularization FAULT-DIAGNOSIS; REPRESENTATION; SYSTEM The aim of this article is to develop a soft approach for a real-time cell temperature prediction in the aluminum electrolysis reduction. Under the limited labeled data constraint, Laplacian semi-supervised learning methods, which can fully utilize the underlying structure of the data distribution and further extract information contained in all available data, has recently received extensive attention in the field of soft sensor modeling. Since the Laplacian underlying manifold is a constant, it remains a challenging task to improve the extrapolating ability for the case that only a few labeled samples are available. This study presents a soft modeling method based on a semi-supervised deep learning structure, which was developed from the hierarchical autoencoders with extreme learning machine. Furthermore, a Laplacian-Hessian semi-supervised extreme learning machine is built to learn all the geometric distribution information. The Laplacian-Hessian semi-supervised extreme learning machine method is applied to estimate the cell temperature in an aluminum reduction process. The experimental results demonstrate the performance and robustness of the proposed algorithm are superior to those of the existing state-of-the-art methods. [Lei, Yongxiang; Karimi, Hamid Reza; Chen, Xiaofang] Politecn Milan, Dept Mech Engn, I-410083 Milan, Italy; [Liu, Fang] Cent South Univ, Sch Phys & Elect, Changsha, Peoples R China Polytechnic University of Milan; Central South University Lei, YX (corresponding author), Politecn Milan, Dept Mech Engn, I-410083 Milan, Italy. leiyongxiang@csu.edu.cn Lei, Yongxiang/0000-0001-6375-8474 General Program of the National Natural Science Foundation of China [61773405]; National Natural Science Foundation of China [61533020]; Key Projects for emergency management of the National Natural Science Foundation [61751312]; China Scholarship Council (CSC) [202006370101] General Program of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Projects for emergency management of the National Natural Science Foundation; China Scholarship Council (CSC)(China Scholarship Council) The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been supported, in part, by the General Program of the National Natural Science Foundation of China under Grant No. 61773405, the Key Program of the National Natural Science Foundation of China under Grant No. 61533020, the Key Projects for emergency management of the National Natural Science Foundation under Grant No. 61751312 and the China Scholarship Council (CSC) under Grant No. 202006370101. 34 2 2 2 12 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0959-6518 2041-3041 P I MECH ENG I-J SYS Proc. Inst. Mech. Eng. Part I-J Syst Control Eng. JUL 2022.0 236.0 6 1109 1118 9596518221082856 10.1177/09596518221082857 0.0 MAR 2022 10 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems 1M4BP 2023-03-23 WOS:000772856900001 0
J Hao, SY; Wu, B; Zhao, K; Ye, YX; Wang, W Hao, Siyuan; Wu, Bin; Zhao, Kun; Ye, Yuanxin; Wang, Wei Two-Stream Swin Transformer with Differentiable Sobel Operator for Remote Sensing Image Classification REMOTE SENSING English Article remote sensing; scene classification; deep learning; swin transformer; feature fusion; edge detection OBJECT DETECTION; SCENE CLASSIFICATION; INVARIANT; ATTENTION; NETWORKS Remote sensing (RS) image classification has attracted much attention recently and is widely used in various fields. Different to natural images, the RS image scenes consist of complex backgrounds and various stochastically arranged objects, thus making it difficult for networks to focus on the target objects in the scene. However, conventional classification methods do not have any special treatment for remote sensing images. In this paper, we propose a two-stream swin transformer network (TSTNet) to address these issues. TSTNet consists of two streams (i.e., original stream and edge stream) which use both the deep features of the original images and the ones from the edges to make predictions. The swin transformer is used as the backbone of each stream given its good performance. In addition, a differentiable edge Sobel operator module (DESOM) is included in the edge stream which can learn the parameters of Sobel operator adaptively and provide more robust edge information that can suppress background noise. Experimental results on three publicly available remote sensing datasets show that our TSTNet achieves superior performance over the state-of-the-art (SOTA) methods. [Hao, Siyuan; Wu, Bin; Zhao, Kun] Qingdao Univ Technol, Coll Commun & Elect Engn, Qingdao 266520, Peoples R China; [Ye, Yuanxin] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Peoples R China; [Wang, Wei] Univ Trento, Dept Informat Engn & Comp Sci, Via Sommar, I-38123 Trento, Italy Qingdao University of Technology; Southwest Jiaotong University; University of Trento Zhao, K (corresponding author), Qingdao Univ Technol, Coll Commun & Elect Engn, Qingdao 266520, Peoples R China. haosiyuan@qut.edu.cn; wubin970623@gmail.com; zhaokun@qut.edu.cn; yeyuanxin@home.swjtu.edu.cn; wei.wang@unitn.it Ye, Yuanxin/0000-0001-6843-6722; Zhao, Kun/0000-0001-9949-4693 National Natural Science Foundation of China [62171247, 41921781] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 62171247 and 41921781. 62 3 3 24 62 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAR 2022.0 14.0 6 1507 10.3390/rs14061507 0.0 23 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 0D9KR gold 2023-03-23 WOS:000776307900001 0
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A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burdin, S.; Burgard, C. D.; Burger, A. M.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I; Burr, J. T. P.; Buescher, D.; Buescher, V; Buschmann, E.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabras, G.; Cabrera Urban, S.; Caforio, D.; Cai, H.; Cairo, V. M. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Calvetti, M.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V; Bret, M. Cano; Cantero, J.; Cao, T.; Cao, Y.; Garrido, M. D. M. Capeans; Caprini, I; Caprini, M.; Capua, M.; Carbone, R. M.; Cardarelli, R.; Cardillo, F. C.; Carli, I; Carli, T.; Carlino, G.; Carlson, B. T.; Carminati, L.; Carney, R. M. D.; Caron, S.; Carquin, E.; Carra, S.; Carrillo-Montoya, G. D.; Casadei, D.; Casado, M. P.; Casha, A. F.; Casolino, M.; Casper, D. W.; Castelijn, R.; Castillo, F. L.; Castillo Gimenez, V; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V; Celebi, E.; Ceradini, F.; Cerda Alberich, L.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, W. S.; Chan, Y. L.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S., V; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, C. H.; Chen, H.; Chen, J.; Chen, S.; Chen, S. J.; Chen, X.; Chen, Y.; Chen, Y-H; Cheng, H. C.; Cheng, H. J.; Cheplakov, A.; Cheremushkina, E.; El Moursli, R. Cherkaoui; Cheu, E.; Cheung, K.; Chevalier, L.; Chiarella, V; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chiu, I; Chiu, Y. H.; Chizhov, M., V; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, Y. S.; Christodoulou, V; Chu, M. C.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Cinca, D.; Cindro, V; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Clark, A.; Clark, M. R.; Clark, P. J.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coimbra, A. E. C.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Conde Muino, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Constantinescu, S.; Conventi, F.; Cooper-Sarkar, A. M.; Cormier, F.; Cormier, K. J. R.; Corradi, M.; Corrigan, E. E.; Corriveau, F.; Cortes-Gonzalez, A.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Crane, J.; Cranmer, K.; Crawley, S. J.; Creager, R. A.; Cree, G.; Crepe-Renaudin, S.; Crescioli, F.; Cristinziani, M.; Croft, V; Crosetti, G.; Cueto, A.; Donszelmann, T. Cuhadar; Cukierman, A. R.; Curatolo, M.; Cuth, J.; Czekierda, S.; Czodrowski, P.; Da Cunha Sargedas De Sousa, M. J.; Da Via, C.; Dabrowski, W.; Dado, T.; Dahbi, S.; Dai, T.; Dallaire, F.; Dallapiccola, C.; Dam, M.; D'amen, G.; Damp, J.; Dandoy, J. R.; Daneri, M. F.; Dang, N. P.; Dann, N. D.; Danninger, M.; Dao, V; Darbo, G.; Darmora, S.; Dartsi, O.; Dattagupta, A.; Daubney, T.; D'Auria, S.; Davey, W.; David, C.; Davidek, T.; Davis, D. R.; Dawe, E.; Dawson, I; De, K.; De Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Maria, A.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; Corga, K. De Vasconcelos; De Regie, J. B. De Vivie; Debenedetti, C.; Dedovich, D., V; Dehghanian, N.; Del Gaudio, M.; Del Peso, J.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Della Pietra, M.; Della Volpe, D.; Dell'Acqua, A.; Dell'Asta, L.; Delmastro, M.; Delporte, C.; Delsart, P. A.; DeMarco, D. A.; Demers, S.; Demichev, M.; Denisov, S. P.; Denysiuk, D.; D'Eramo, L.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Bello, F. A.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Micco, B.; Di Nardo, R.; Di Petrillo, K. F.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Dias Do Vale, T.; Diaz, M. A.; Dickinson, J.; Diehl, E. B.; Dietrich, J.; Cornell, S. Diez; Dimitrievska, A.; Dingfelder, J.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J., I; Do Vale, M. A. B.; Dobre, M.; Dodsworth, D.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donini, J.; D'onofrio, A.; D'Onofrio, M.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dreyer, E.; Dreyer, T.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubinin, F.; Dubovsky, M.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. C.; Duffield, E. M.; Duflot, L.; Duhrssen, M.; Duelsen, C.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Duperrin, A.; Yildiz, H. Duran; Dueren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Duvnjak, D.; Dyndal, M.; Dysch, S.; Dziedzic, B. S.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; El Kosseifi, R.; Ellajosyula, V; Ellert, M.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Ennis, J. S.; Epland, M. B.; Erdmann, J.; Ereditato, A.; Errede, S.; Escalier, M.; Escobar, C.; Esposito, B.; Estrada Pastor, O.; Etienvre, A., I; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Fabiani, V; Facini, G.; Faisca Rodrigues Pereira, R. M.; Fakhrutdinov, R. M.; Falciano, S.; Falke, P. J.; Falke, S.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feickert, M.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, M.; Fenton, M. J.; Fenyuk, A. B.; Feremenga, L.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipcic, A.; Filthaut, F.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, C.; Fisher, W. C.; Flaschel, N.; Fleck, I; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores, L. M.; Castillo, L. R. Flores; Fomin, N.; Forcolin, G. T.; Formica, A.; Forster, F. A.; Forti, A. C.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Franchino, S.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Freund, B.; Freund, W. S.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fusayasu, T.; Fuster, J.; Gabizon, O.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadow, P.; Gagliardi, G.; Gagnon, L. G.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Goni, R. Gamboa; Gan, K. K.; Ganguly, S.; Gao, Y.; Gao, Y. S.; Garcia, C.; Garcia Navarro, J. E.; Pascual, J. A. Garcia; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V; Gasnikova, K.; Gaudiello, A.; Gaudio, G.; Gavrilenko, I. L.; Gavrilyuk, A.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gee, C. N. P.; Geisen, J.; Geisen, M.; Geisler, M. P.; Gellerstedt, K.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; Gentsos, C.; George, S.; Gerbaudo, D.; Gessner, G.; Ghasemi, S.; Bostanabad, M. Ghasemi; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giangiacomi, N.; Giannetti, P.; Gibson, S. M.; Gignac, M.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giordani, M. P.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugliarelli, G.; Giugni, D.; Giuli, F.; Giulini, M.; Gkaitatzis, S.; Gkialas, I; Gkougkousis, E. L.; Gkountoumis, P.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Goncalves Gama, R.; Goncalo, R.; Gonella, G.; Gonella, L.; Gongadze, A.; Gonnella, F.; Gonski, J. L.; Gonzalez de la Hoz, S.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorini, B.; Gorini, E.; Goriaek, A.; Goshaw, A. T.; Goessling, C.; Gostkin, M., I; Gottardo, C. A.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Goy, C.; Gozani, E.; Grabowska-Bold, I; Gradin, P. O. J.; Graham, E. C.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V; Gravila, P. M.; Gray, C.; Gray, H. M.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph; Grivaz, J-F; Groh, S.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Grud, C.; Grummer, A.; Guan, L.; Guan, W.; Guenther, J.; Guerguichon, A.; Guescini, F.; Guest, D.; Gugel, R.; Gui, B.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, W.; Guo, Y.; Guo, Z.; Gupta, R.; Gurbuz, S.; Gustavino, G.; Gutelman, B. J.; Gutierrez, P.; Gutschow, C.; Guyot, C.; Guzik, M. P.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Hageboeck, S.; Hagihara, M.; Hakobyan, H.; Haleem, M.; Haley, J.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Han, K.; Han, L.; Han, S.; Hanagaki, K.; Hance, M.; Handl, D. M.; Haney, B.; Hankache, R.; Hanke, P.; Hansen, E.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Harkusha, S.; Harrison, P. F.; Hartmann, N. M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havener, L. B.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hayden, D.; Hayes, C.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Heath, M. P.; Hedberg, V; Heelan, L.; Heer, S.; Heidegger, K. K.; Heilman, J.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, J. J.; Heinrich, L.; Heinz, C.; Hejbal, J.; Helary, L.; Held, A.; Hellesund, S.; Hellman, S.; Helsens, C.; Henderson, R. C. W.; Heng, Y.; Henkelmann, S.; Correia, A. M. Henriques; Herbert, G. H.; Herde, H.; Herget, V; Jimenez, Y. Hernandez; Herr, H.; Herten, G.; Hertenberger, R.; Hervas, L.; Herwig, T. C.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Higashino, S.; Higon-Rodriguez, E.; Hildebrand, K.; Hill, E.; Hill, J. C.; Hill, K. K.; Hiller, K. H.; Hillier, S. J.; Hils, M.; Hinchliffe, I; Hirose, M.; Hirschbuehl, D.; Hiti, B.; Hladik, O.; Hlaluku, D. R.; Hoad, X.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohn, D.; Hohov, D.; Holmes, T. R.; Holzbock, M.; Homann, M.; Honda, S.; Honda, T.; Hong, T. M.; Hoenle, A.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horn, P.; Horton, A. J.; Horyn, L. A.; Hostachy, J-Y; Hostiuc, A.; Hou, S.; Hoummada, A.; Howarth, J.; Hoya, J.; Hrabovsky, M.; Hrdinka, J.; Hristova, I; Hrivnac, J.; Hrynevich, A.; Hryn'ova, T.; Hsu, P. J.; Hsu, S-C; Hu, Q.; Hu, S.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huebner, M.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Huhtinen, M.; Hunter, R. F. H.; Huo, P.; Hupe, A. M.; Huseynov, N.; Huston, J.; Huth, J.; Hyneman, R.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Idrissi, Z.; Iengo, P.; Ignazzi, R.; Igonkina, O.; Iguchi, R.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Iliadis, D.; Ilic, N.; Iltzsche, F.; Introzzi, G.; Iodice, M.; Iordanidou, K.; Ippolito, V; Isacson, M. F.; Ishijima, N.; Ishino, M.; Ishitsuka, M.; Islam, W.; Issever, C.; Istin, S.; Ito, F.; Ponce, J. M. Iturbe; Iuppa, R.; Ivina, A.; Iwasaki, H.; Izen, J. M.; Izzo, V; Jabbar, S.; Jacka, P.; Jackson, P.; Jacobs, R. M.; Jain, V; Jaekel, G.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansky, R.; Janssen, J.; Janus, M.; Janus, P. A.; Jarlskog, G.; Javadov, N.; Javurek, T.; Javurkova, M.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jelinskas, A.; Jenni, P.; Jeong, J.; Jeske, C.; Jezequel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiang, Z.; Jiggins, S.; Morales, F. A. Jimenez; Jimenez Pena, J.; Jin, S.; Jinaru, A.; Jinnouchi, O.; Jivan, H.; Johansson, P.; Johns, K. A.; Johnson, C. A.; Johnson, W. J.; Jon-And, K.; Jones, R. W. L.; Jones, S. D.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Junggeburth, J. J.; Juste Rozas, A.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kaji, T.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanjir, L.; Kano, Y.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kar, D.; Kareem, M. J.; Karentzos, E.; Karpov, S. N.; Karpova, Z. M.; Kartvelishvili, V; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katzy, J.; Kawade, K.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kay, E. F.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kellermann, E.; Kempster, J. J.; Kendrick, J.; Kepka, O.; Kersten, S.; Kersevan, B. P.; Keyes, R. A.; Khader, M.; Khalil-Zada, F.; Khanov, A.; Kharlamov, A. G.; Kharlamova, T.; Khodinov, A.; Khoo, T. J.; Khramov, E.; Khubua, J.; Kido, S.; Kiehn, M.; Kilby, C. R.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; Kirchmeier, D.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kitali, V; Kivernyk, O.; Kladiva, E.; Klapdor-Kleingrothaus, T.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klingl, T.; Klioutchnikova, T.; Klitzner, F. 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J.; Shojaii, S.; ; ATLAS Collaboration Search for pair production of heavy vectorlike quarks decaying into hadronic final states in pp collisions at root s=13 TeV with the ATLAS detector PHYSICAL REVIEW D English Article PARTON DISTRIBUTIONS; BROKEN SYMMETRIES; BREAKING; MASS; BOSON A search is presented for the pair production of heavy vectorlike quarks, T (T) over bar or B (B) over bar, that decay into final states with jets and no reconstructed leptons. Jets in the final state are classified using a deep neural network as arising from hadronically decaying W/Z bosons, Higgs bosons, top quarks, or background. The analysis uses data from the ATLAS experiment corresponding to 36.1 fb(-1) of proton-proton collisions with a center-of-mass energy of root s = 13 TeV delivered by the Large Hadron Collider in 2015 and 2016. No significant deviation from the Standard Model expectation is observed. Results are interpreted assuming the vectorlike quarks decay into a Standard Model boson and a third-generation-quark, T -> Wb, Ht, Zt or B -> Wt, Hb, Zb, for a variety of branching ratios. At 95% confidence level, the observed (expected) lower limit on the vectorlike B-quark mass for a weak-isospin doublet (B, Y) is 950 (890) GeV, and the lower limits on the masses for the pure decays B -> Hb and T -> Ht, where these results are strongest, arc 1010 (970) GeV and 1010 (1010) GeV, respectively. [Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Cairo, V. M. M.; Jain, V; Swift, S. P.] SUNY Albany, Phys Dept, Albany, NY 12222 USA; [Dehghanian, N.; Gingrich, D. M.; Jabbar, S.; Moore, R. W.; Pinfold, J. L.; Sun, X.; Wang, H.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakir, O.; Yildiz, H. Duran] Ankara Univ, Dept Phys, Ankara, Turkey; [Kuday, S.; Cakir, I. Turk] Istanbul Aydin Univ, Istanbul, Turkey; [Sultansoy, S.] TOBB Univ Econ & Technol, Div Phys, Ankara, Turkey; [Berger, N.; Burger, A. M.; Dartsi, O.; Delmastro, M.; Di Ciaccio, L.; Falke, P. J.; Falke, S.; Goy, C.; Guillemin, T.; Hryn'ova, T.; Jezequel, S.; Kivernyk, O.; Koletsou, I; Lafaye, R.; Leveque, J.; Martinez, N. Lorenzo; Mastrandrea, P.; Raspopov, S.; Sauvan, E.; Smart, B. H.; Todorova-Nova, S.; Vallier, A.; Wingerter-Seez, I] Univ Grenoble Alpes, Univ Savoie Mont Blanc, LAPP, CNRS,IN2P3, Annecy, France; [Abulaiti, Y.; Blair, R. E.; Chekanov, S.; LeCompte, T.; Liu, B. L.; Love, J.; Malon, D.; Metcalfe, J.; Paramonov, A.; Proudfoot, J.; Ryu, S.; Stanek, R. W.; Van Gemmeren, P.; Wang, R.; Webster, J. S.; Zhang, J.] Argonne Natl Lab, High Energy Phys Div, 9700 S Cass Ave, Argonne, IL 60439 USA; [Berlendis, S.; Cheu, E.; Delitzsch, C. M.; Johns, K. A.; Jones, S.; Lampl, W.; LeBlanc, M.; Leone, R.; Loch, P.; Nayyar, R.; Rutherfoord, J. P.; Varnes, E. W.; Zhou, Y.] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Brandt, A.; Bullock, D.; Darmora, S.; De, K.; Farbin, A.; Feremenga, L.; Griffiths, J.; Hadavand, H. K.; Heelan, L.; Little, J. D.; Ozturk, N.; Usai, G.; White, A.; Yu, J.] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA; [Bellos, P.; Fassouliotis, D.; Gkialas, I; Kourkoumelis, C.; Papageorgiou, K.; Tsirintanis, N.] Natl & Kapodistrian Univ Athens, Phs Dept, Athens, Greece; [Alexopoulos, T.; Bakalis, C.; Benekos, N.; Dris, M.; Gazis, E. N.; Gkountoumis, P.; Karentzos, E.; Koulouris, A.; Maltezos, S.; Moschovakos, P.; Panagoulias, I; Papadopoulou, T. D.; Vlachos, S.; Zacharis, G.] Natl Tech Univ Athens, Phys Dept, Zografos, Greece; [Andeen, T.; Narayan, R.; Nikiforou, N.; Onyisi, P. U. E.; Potti, H.; Webb, A. F.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Beddall, A. J.] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Celebi, E.; Cetin, S. A.] Istanbul Bilgi Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Adiguzel, A.; Gurbuz, S.; Istin, S.; Ozcan, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Phys Engn, Gaziantep, Turkey; [Abdinov, O.; Ahmadov, F.; Huseynov, N.; Javadov, N.; Khalil-Zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan; [Bosman, M.; Casado, M. P.; Casolino, M.; Cavallaro, E.; Cavalli-Sforza, M.; Fischer, C.; Forster, F. A.; Gerbaudo, D.; Gkougkousis, E. L.; Glatzer, J.; Grinstein, S.; Juste Rozas, A.; Korolkov, I; Lange, J. C.; Lopez Paz, I; Martinez, M.; Mir, L. M.; Pacheco Pages, A.; Padilla Aranda, C.; Riu, I; Rizzi, C.; Rodriguez Perez, A.; Rosten, R.; Terzo, S.; Tripiana, M. F.; Van Daalen, T. R.; Vazquez Furelos, D.; Volpi, G.; Zaidan, R.] Barcelona Inst Sci & Technol, IFAE, Barcelona, Spain; [Ayoub, M. K.; da Costa, J. Barreiro Guimaraes; Bertella, C.; Fang, Y.; Pascual, J. A. Garcia; Huang, Y.; Liang, Z.; Liu, Y.; Merino, J. Llorente; Lou, X.; Lyu, F.; Mansour, J. D.; Ouyang, Q.; Shan, L. Y.; Xu, D.; Zhu, H.; Zhuang, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Chen, X.] Tsinghua Univ, Phys Dept, Beijing, Peoples R China; [Chen, S. J.; Jin, S.; Wang, W.; Zhang, H.; Zhang, P.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Cheng, H. J.; Han, S.; Kurth, M. G.; Li, Q.; Peng, C.; Ren, H.; Zhang, Y.; Zhou, M. S.] UCAS, Beijing, Peoples R China; [Krstic, J.; Sijacki, Dj; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Eigen, G.; Fomin, N.; Lipniacka, A.; Maeland, S.; Latour, B. Martin Dit; Stugu, B.; Yang, Z.; Zalieckas, J.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Jeanty, L.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Shapiro, M.; Tsulaia, V; Viel, S.; Wang, H.; Yang, H. T.; Yao, W-M] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA USA; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Dickinson, J.; Dimitrievska, A.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gray, H. M.; Haber, C.; Heim, T.; Hinchliffe, I; Jeanty, L.; Krizka, K.; Leggett, C.; Liu, P.; Marshall, Z.; Nachman, B. P.; Griso, S. Pagan; Pianori, E.; Shapiro, M.; Tsulaia, V; Viel, S.; Wang, H.; Yang, H. T.; Yao, W-M] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Biedermann, D.; Dietrich, J.; Grancagnolo, S.; Herbert, G. H.; Hristova, I; Kind, O. M.; Lacker, H.; Lohse, T.; Mergelmeyer, S.; Ng, Y. S.; Peri, F.; Rehnisch, L.; Schenck, F.; Sperlich, D.; Stamm, S.; Zur Nedden, M.] Humboldt Univ, Inst Phys, Berlin, Germany; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Meloni, F.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Anders, J. K.; Beck, H. P.; Ereditato, A.; Haug, S.; Meloni, F.; Merlassino, C.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Weber, M. S.; Weston, T. D.] Univ Bern, Lab High Energy Phys, Bern, Switzerland; [Allport, P. P.; Andari, N.; Baca, M. J.; Bracinik, J.; Briglin, D. L.; Broughton, J. H.; Charlton, D. G.; Foster, A. G.; Gonella, L.; Gonnella, F.; Hawkes, C. M.; Hillier, S. J.; Kempster, J. J.; Kendrick, J.; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Reynolds, E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Vallance, R. A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.; Worm, S. D.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Ctr Invest, Bogota, Colombia; [Alberghi, G. L.; Biondi, S.; Cabras, G.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giangiacomi, N.; Manghi, F. Lasagni; Mengarelli, A.; Romano, M.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Cabras, G.; Cervelli, A.; D'amen, G.; De Castro, S.; Fabbri, L.; Franchini, M.; Gabrielli, A.; Giacobbe, B.; Giangiacomi, N.; Giorgi, F. M.; Manghi, F. Lasagni; Mengarelli, A.; Negrini, M.; Polini, A.; Rinaldi, L.; Romano, M.; Sbarra, C.; Sbrizzi, A.; Semprini-Cesari, N.; Sidoti, A.; Sioli, M.; Spighi, R.; Ucchielli, G.; Valentinetti, S.; Villa, M.; Vittori, C.; Zoccoli, A.] INFN, Sez Bologna, Bologna, Italy; [Arslan, O.; Bandyopadhyay, A.; Bechtle, P.; Bernlochner, F. U.; Betti, A.; Brock, I; Caudron, J.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Gaycken, G.; Ghneimat, M.; Gottardo, C. A.; Grefe, C.; Hageboeck, S.; Hansen, M. C.; Heer, S.; Hohn, D.; Huebner, M.; Huegging, F.; Jacobs, R. M.; Janssen, J.; Klingl, T.; Kostyukhin, V. V.; Krueger, H.; Lantzsch, K.; Lenz, T.; Melzer, A.; Moles-Valls, R.; Obermann, T.; Pohl, D.; Ricken, O.; Schildgen, L. K.; Schopf, E.; Sciandra, A.; Seema, P.; Von Toerne, E.; Wagner, P.; Wermes, N.; Winter, B. T.; Yuen, S. P. Y.; Zhang, R.] Univ Bonn, Phys Inst, Bonn, Germany; [Black, K. M.; Butler, J. M.; Dell'Asta, L.; Finelli, K. D.; Long, B. A.; Shank, J. T.; Sherman, A. D.; Yan, Z.; Yigitbasi, E.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Barone, G.; Bensinger, J. R.; Bergsten, L. J.; Blocker, C.; Dhaliwal, S.; Dodsworth, D.; Goblirsch-Kolb, M.; Herde, H.; Loew, K. M.; O'Connor, K.; Schillaci, Z. M.; Sciolla, G.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Tulbure, T. T.] Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Caprini, I; Caprini, M.; Chitan, A.; Constantinescu, S.; Dobre, M.; Ducu, O. A.; Dumitriu, A. E.; Galea, C.; Jinaru, A.; Martoiu, V. S.; Maurer, J.; Olariu, A.; Rotaru, M.; Stoicea, G.; Tarna, G.; Tudorache, A.; Tudorache, V] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Agheorghiesei, C.] Alexandru Ioan Cuza Univ, Dept Phys, Iasi, Romania; [Popeneciu, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Phys Dept, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravila, P. M.] West Univ Timisoara, Timisoara, Romania; [Astalos, R.; Bartos, P.; Blazek, T.; Dado, T.; Dubovsky, M.; Majersky, O.; Melo, M.; Smiesko, J.; Sykora, I; Tokar, S.; Zenis, T.] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia; [Bruncko, D.; Kladiva, E.; Murin, P.; Sopkova, F.; Strizenec, P.; Urban, J.] Slovak Acad Sci, Dept Subnucl Phys, Inst Expt Phys, Kosice, Slovakia; [Assamagan, K.; Begel, M.; Cavaliere, V; Chen, H.; Elmsheuser, J.; Gordon, H. A.; Hill, K. K.; Hu, Q.; Iakovidis, G.; Klimentov, A.; Kouskoura, V; Lancon, E.; Lanni, F.; Lee, C. A.; Liu, H. B.; Lynn, D.; Ma, H.; Maeno, T.; Nilsson, P.; Nomura, M. A.; Damazio, D. Oliveira; Perepelitsa, D., V; Pleier, M-A; Polychronakos, V; Protopopescu, S.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Phys Dept, Upton, NY 11973 USA; [Bossio Sola, J. D.; Daneri, M. F.; Devesa, M. R.; Marceca, G.; Otero y Garzon, G.; Piegaia, R.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brunt, B. H.; Chapman, J. D.; Cottin, G.; Hill, J. C.; Lester, C. G.; Lin, C. Y.; Malone, C.; Pacey, H. A.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Williams, S.; Yusuff, I] Univ Cambridge, Cavendish Lab, Cambridge, England; [Atkin, R. J.; Hamilton, A.; Mwewa, C.; Yacoob, S.] Univ Cape Town, Dept Phys, Cape Town, South Africa; [Casadei, D.; Connell, S. H.; Govender, N.; Truong, L.] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa; [Jimenez, Y. Hernandez; Hlaluku, D. R.; Jivan, H.; Kar, D.; Lagouri, T.; Mdhluli, J. E.; Garcia, B. R. Mellado; Reed, R. G.; Roy, D.; Ruan, X.; Haddad, E. Sideras; Tomiwa, K. G.; von Buddenbrock, S. E.] Univ Witwatersrand, Sch Phys, Johannesburg, South Africa; [Bellerive, A.; Chau, C. C.; Cree, G.; Di Valentino, D.; Gillberg, D.; Heilman, J.; Hunter, R. F. H.; Hupe, A. M.; Keller, J. S.; Koffas, T.; Oakham, F. G.; Ruiz-Martinez, A.; Sherafati, N.; Vincter, M. G.; Weber, S. A.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Benchekroun, D.; Bouaouda, K.; Chafaq, A.; Hoummada, A.] Univ Hassan 2, Fac Sci Ain Chock, Reseau Univ Phys Hautes Energies, Casablanca, Morocco; Ctr Natl Energie Sci Tech Nucl CNESTEN, Rabat, Morocco; [El Kacimi, M.; Goujdami, D.] Univ Cadi Ayyad, Fac Sci Semlalia, LPHEA Marrakech, Marrakech, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] Univ Mohamed Premier, Fac Sci, Oujda, Morocco; [Aaboud, M.; Derkaoui, J. E.; Ouchrif, M.] LPTPM, Oujda, Morocco; [Batlamous, S.; El Moursli, R. Cherkaoui; Dahbi, S.; Ezzi, M.; Fassi, F.; Haddad, N.; Idrissi, Z.; Tayalati, Y.] Univ Mohammed 5, Fac Sci, Rabat, Morocco; [Aleksa, M.; Gonzalez, B. Alvarez; Amoroso, S.; Bella, L. Aperio; Armbruster, A. J.; Avolio, G.; Barisits, M-S; Beermann, T. A.; Bortfeldt, J.; Boyd, J.; Buat, Q.; Butti, P.; Buttinger, W.; Camarda, S.; Camincher, C.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Carrillo-Montoya, G. D.; Catinaccio, A.; Cattai, A.; Chelstowska, M. A.; Chisholm, A. S.; Cortes-Gonzalez, A.; Czodrowski, P.; Dao, V; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Nardo, R.; Dittus, F.; Dudarev, A.; Duhrssen, M.; Eifert, T.; Ellis, N.; Elsing, M.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Fressard-Batraneanu, S. M.; Froidevaux, D.; Goossens, L.; Gorini, B.; Guenther, J.; Guindon, S.; Gumpert, C.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Hrdinka, J.; Huhtinen, M.; Iengo, P.; Jenni, P.; Klioutchnikova, T.; Krasznahorkay, A.; Kuehn, S.; Langenberg, R. J.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Malyukov, S.; Tobon, C. A. Marin; Marzin, A.; Mcfayden, J. A.; Berlingen, J. Montejo; Llacer, M. Moreno; Morgenstern, S.; Morley, A. K.; Mornacchi, G.; Mountricha, E.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pandini, C. E.; Pani, P.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Polifka, R.; Poveda, J.; Astigarraga, M. E. Pozo; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Ruthmann, N.; Salzburger, A.; Schaepe, S.; Schioppa, E. J.; Schlenker, S.; Schmieden, K.; Schovancova, J.; Sanchez, C. A. Solans; Spigo, G.; Starz, S.; Stelzer, H. J.; Ten Kate, H.; Unal, G.; Vandelli, W.; Venturi, N.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Young, C. J. S.; Zanzi, D.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Gardner, R. W.; Hildebrand, K.; Holmes, T. R.; Horyn, L. A.; Kim, Y. K.; Merritt, F. S.; Miller, D. W.; Olsson, M. J. R.; Oreglia, M. J.; Pilcher, J. E.; Schaefer, D.; Seiss, T.; Shochet, M. J.; Stark, G. H.; Swiatlowski, M.; Vukotic, I; Wu, M.; Zou, R.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Angelidakis, S.; Barbe, W. M.; Boumediene, D.; Calvet, D.; Calvet, S.; Donini, J.; Gris, Ph; Morales, F. A. Jimenez; Madar, R.; Marjanovic, M.; Nibigira, E.; Pallin, D.; Santoni, C.; Senkin, S.; Vazeille, F.] Univ Clermont Auvergne, LPC, CNRS, IN2P3, Clermont Ferrand, France; [Angerami, A.; Brooijmans, G.; Carbone, R. M.; Chen, J.; Clark, M. R.; Cole, B.; Havener, L. B.; Hughes, E. W.; Iordanidou, K.; Mohapatra, S.; Ochoa, I; Parsons, J. A.; Smith, M. N. K.; Smith, R. W.; Tian, Y.; Tuts, P. M.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Bajic, M.; Besjes, G. J.; Camplani, A.; Dam, M.; Dias, F. A.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Ignazzi, R.; Monk, J.; Petersen, T. C.; Stark, S. H.; Thiele, F.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Callea, G.; Capua, M.; Crosetti, G.; Del Gaudio, M.; La Rotonda, L.; La Ruffa, F.; Mastroberardino, A.; Meoni, E.; Palazzo, S.; Policicchio, A.; Salvatore, D.; Schioppa, M.; Scornajenghi, M.; Tassi, E.] INFN, Grp Collegato Cosenza, Lab Nazl Frascati, Frascati, Italy; [Feickert, M.; Gupta, R.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Lo Sterzo, F.; McKay, M. A.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Wang, P.; Ye, J.; Zhao, X.; Zhou, L.] Southern Methodist Univ, Phys Dept, Dallas, TX USA; [Izen, J. M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Phys Dept, Richardson, TX 75083 USA; [Bertoli, G.; Bylund, O. Bessidskaia; Bohm, C.; Carney, R. M. D.; Clement, C.; Gellerstedt, K.; Hellman, S.; Jon-And, K.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Silverstein, S. B.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Stockholm Univ, Dept Phys, Stockholm, Sweden; [Bertoli, G.; Bylund, O. Bessidskaia; Carney, R. M. D.; Clement, C.; Gellerstedt, K.; Hellman, S.; Jon-And, K.; Milstead, D. A.; Moa, T.; Molander, S.; Pasuwan, P.; Shaikh, N. W.; Sjolin, J.; Strandberg, S.; Ughetto, M.; Santurio, E. Valdes; Wallangen, V] Oskar Klein Ctr, Stockholm, Sweden; [Asbah, N.; Becot, C.; Behr, J. K.; Berge, D.; Bessner, M.; Bloch, I; Braren, F.; Brendlinger, K.; Brenner, L.; Bryngemark, L.; Chen, Y-H; Daubney, T.; David, C.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Flaschel, N.; Gasnikova, K.; Glaysher, P. C. F.; Glazov, A.; Gregor, I. M.; Grevtsov, K.; Heim, S.; Heinemann, B.; Hiller, K. H.; Jeong, J.; Katzy, J.; Kitali, V; Kuhl, T.; Lacey, J.; Lang, V. S.; Leight, W. A.; Lobodzinska, E. M.; Lou, X.; Madsen, A.; Moenig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Parker, K. A.; Peters, K.; Poley, A.; Pollard, C. S.; Potamianos, K.; Queitsch-Maitland, M.; Rauch, D. M.; Rifki, O.; Robinson, J. E. M.; Ruettinger, E. M.; Saimpert, M.; Sander, C. O.; Schmitt, S.; South, D.; Stanitzki, M. M.; Stegler, M.; Styles, N. A.; Tackmann, K.; Theveneaux-Pelzer, T.; Tsai, F.; Valery, L.; Vishwakarma, A.; Wanotayaroj, C.; Yap, Y. C.; Zakharchuk, N.] DESY, Hamburg, Germany; [Burmeister, I; Cinca, D.; Erdmann, J.; Gessner, G.; Goessling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.; Kupfer, T.; Nackenhorst, O.; Nitsche, I; Zeissner, S., V] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Bittrich, C.; Duschinger, D.; Hauswald, L.; Hils, M.; Horn, P.; Iltzsche, F.; Kirchmeier, D.; Kobel, M.; Mader, W. F.; Madysa, N.; Ramos, J. Manjarres; Novgorodova, O.; Siegert, F.; Straessner, A.; Todt, S.; Torres, H.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Davis, D. R.; Epland, M. B.; Feng, M.; Goshaw, A. T.; Kotwal, A.; Kruse, M. C.; Oh, S. H.; Sen, S.; Zhao, P.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Clark, P. J.; Giannelli, M. Faucci; Hasib, A.; Heath, M. P.; Hoad, X.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Sogaard, A.; Strubig, A.; Taylor, A. J.; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Curatolo, M.; Esposito, B.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Frascati, Italy; [Albicocco, P.; Antonelli, M.; Beretta, M.; Chiarella, V; Curatolo, M.; Esposito, B.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] Lab Nazl Frascati, Frascati, Italy; [Becker, K.; Buehrer, F.; Buescher, D.; Cardillo, F. C.; Coniavitis, E.; Di Simone, A.; Gonella, G.; Gugel, R.; Heidegger, K. K.; Heinemann, B.; Herten, G.; Jakobs, K.; Javurek, T.; Javurkova, M.; Jenni, P.; Jiggins, S.; Klapdor-Kleingrothaus, T.; Knue, A.; Koeneke, K.; Landgraf, U.; Loesle, A.; Luedtke, C.; Magerl, V; Megy, T. M.; Mogg, P.; Nagel, M.; Parzefall, U.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schleicher, K. E.; Schnoor, U.; Schumacher, M.; Tornambe, P.; Weiser, C.; Wiik-Fuchs, L. A. M.; Zhang, L.; Zimmermann, S.] Albert Ludwigs Univ Freiburg, Phys Inst, Freiburg, Germany; [Beck, H. C.; Bindi, M.; Bisanz, T.; Bokan, P.; Buschmann, E.; Dado, T.; De Maria, A.; Drechsler, E.; Dreyer, T.; Geisen, J.; Grosse-Knetter, J.; Janus, M.; Kawamura, G.; Lai, S.; Lemmer, B.; Mantoani, M.; Mchedlidze, G.; Mellenthin, J. D.; Nellist, C.; Pokharel, I; Quadt, A.; Rosien, N-A; Rzehorz, G. F.; Sabatini, P.; Shabalina, E.; Smith, J. W.; Stolte, P.; Torres, R. E. Ticse; Veatch, J.; Weingarten, J.; Zoch, K.] Georg August Univ Gottingen, Phys Inst 2, Gottingen, Germany; [Akilli, E.; Amrouche, C. S.; Ancu, L. S.; Benoit, M.; Calace, N.; Clark, A.; Della Volpe, D.; Di Bello, F. A.; Dubreuil, A.; Fawcett, W. J.; Ferrere, D.; Gadatsch, S.; Golling, T.; Gonzalez-Sevilla, S.; Iacobucci, G.; Iizawa, T.; Jansky, R.; Khoo, T. J.; Kiehn, M.; Lanfermann, M. C.; Lionti, A. L.; March, L.; Mermod, P.; Nessi, M.; Paolozzi, L.; Poggi, R.; Salamani, D.; Schramm, S.; Sfyrla, A.; Sultan, D. M. S.; Valente, M.; Wu, X.; Zaffaroni, E.] Univ Geneva, Dept Phys Nucl Corpusculaire, Geneva, Switzerland; [Barberis, D.; Coccaro, A.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Lapertosa, A.; Oide, H.; Osculati, B.; Parodi, F.; Sannino, M.; Schiavi, C.; Varni, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Barberis, D.; Coccaro, A.; Darbo, G.; Favareto, A.; Gagliardi, G.; Gaudiello, A.; Gemme, C.; Lapertosa, A.; Morettini, P.; Oide, H.; Osculati, B.; Parodi, F.; Passaggio, S.; Rossi, L. P.; Sannino, M.; Schiavi, C.; Varni, C.] INFN, Sez Genova, Genoa, Italy; [Dueren, M.; Heinz, C.; Stenzel, H.] Justus Liebig Univ Giessen, Phys Inst 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Duncan, A. K.; Fabbri, F.; Fenton, M. J.; Gray, C.; Gul, U.; Mullen, P.; O'Shea, V; Owen, M.; Robson, A.; Spiteri, D. P.; Stewart, G. A.; Thompson, A. S.; Wraight, K.; Zaripovas, D. R.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Genest, M. H.; Hostachy, J-Y; Kuna, M.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Meideck, T.; Petit, E.; Readioff, N. P.; Stark, J.; Trocme, B.] Univ Grenoble Alpes, Grenoble INP, CNRS, LPSC,IN2P3, Grenoble, France; [Chan, S. K.; Di Petrillo, K. F.; Franklin, M.; Giromini, P.; Gonski, J. L.; Huth, J.; Lee, L.; Morii, M.; Rogan, C. S.; Roloff, J.; Tong, B.; Tuna, A. N.; Wang, A. M.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Li, C-Q; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Avramidou, R.; Barnovska-Blenessy, Z.; Chen, C.; Chen, J.; Guo, Y.; Hadef, A.; Han, K.; Han, L.; Jiang, Y.; Li, C-Q; Li, Q. Y.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y. W.; Lu, M.; Ospanov, R.; Peng, H.; Wang, R.; Wang, W. T.; Wang, W. X.; Wang, Y.; Wu, Y.; Xu, H.; Zhang, G.; Zhang, L.; Zhang, R.; Zhao, Z.; Zhu, H. L.; Zhu, Y.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei, Anhui, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Inst Frontier & Interdisciplinary Sci, Qingdao, Peoples R China; [Da Cunha Sargedas De Sousa, M. J.; Du, Y.; Feng, C.; Li, H.; Ma, L. L.; Ma, Y.; Wang, C.; Zhang, D. F.; Zhang, X.; Zhao, Y.; Zhu, C. G.] Shandong Univ, Key Lab Particle Phys & Particle Irradiat, MOE, Qingdao, Peoples R China; [Barnes, S. L.; Bret, M. Cano; Guo, J.; Hu, S.; Kondrashova, N.; Li, L.; Li, S.; Li, X.; Nishu, N.; Parida, B.; Wang, Z.; Yang, H. J.; Yatsenko, E.; Zhou, N.] Shanghai Jiao Tong Univ, Sch Phys & Astron, SKLPPC, KLPPAC MoE, Shanghai, Peoples R China; [Li, S.; Yang, H. J.; Yatsenko, E.] Tsung Dao Lee Inst, Shanghai, Peoples R China; [Andrei, V; Antel, C.; Baas, A. E.; Brandt, O.; Djuvsland, J., I; Dunford, M.; Franchino, S.; Geisler, M. P.; Hanke, P.; Jongmanns, J.; Kugel, A.; Theenhausen, H. Meyer Zu; Napolitano, F.; Villar, D. I. Narrias; Sahinsoy, M.; Schultz-Coulon, H-C; Spieker, T. M.; Stamen, R.; Starovoitov, P.; Suchek, S.; Weber, S. M.; Wessels, M.; Yue, X.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; Bolz, A. E.; Britzger, D.; de Lima, D. E. Ferreira; Giulini, M.; Kolb, M.; Lisovyi, M.; Schoening, A.; Sosa, D.; Wang, J.] Heidelberg Univ, Phys Inst, Heidelberg, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Chan, Y. L.; Chu, M. C.; Castillo, L. R. Flores; Ponce, J. M. Iturbe; Lau, T. S.; Lu, H.; Salvucci, A.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Bortolotto, V; Lo, C. Y.; Orlando, N.; Hernandez, D. Paredes; Salvucci, A.; Tu, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Hong Kong, Peoples R China; [Bortolotto, V; Lie, K.; Ng, T. Y.; Prokofiev, K.; Salvucci, A.] Hong Kong Univ Sci & Technol, Dept Phys & Inst Adv Study, Kowloon, Hong Kong, Peoples R China; [Cheung, K.; Hsu, P. J.; Lu, Y. J.] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan; [Calfayan, P.; Choi, K.; Evans, H.; Johnson, C. A.; Kopeliansky, R.; Lammers, S.; Linck, R. A.; Luehring, F.; Palacino, G.; Roland, C. P. A.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Acharya, B. S.; Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Serkin, L.; Soualah, R.] INFN, Grp Collegato Udine, Sez Trieste, Udine, Italy; [Acharya, B. S.; Serkin, L.] Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy; [Cobal, M.; Giordani, M. P.; Giugliarelli, G.; Kimura, N.; Panizzo, G.; Pineda, A. Sanchez; Soualah, R.] Univ Udine, Dipartimento Chim Fis & Ambiente, Udine, Italy; [Bachas, K.; Chiodini, G.; Gorini, E.; Longo, L.; Mirto, A.; Primavera, M.; Reale, M.; Spagnolo, S.; Ventura, A.] INFN, Sez Lecce, Lecce, Italy; [Bachas, K.; Gorini, E.; Longo, L.; Mirto, A.; Reale, M.; Spagnolo, S.; Ventura, A.] Univ Salento, Dipartimento Matemat & Fis, Lecce, Italy; [Alimonti, G.; Andreazza, A.; Carminati, L.; Carra, S.; Cavalli, D.; Citterio, M.; Fanti, M.; Giugni, D.; Lari, T.; Lazzaroni, M.; Manzoni, S.; Meroni, C.; Monzani, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Resconi, S.; Rossini, L.; Stabile, A.; Tartarelli, G. F.; Troncon, C.; Turra, R.; Perez, M. Villaplana] INFN, Sez Milano, Milan, Italy; [Andreazza, A.; Carminati, L.; Carra, S.; Fanti, M.; Lazzaroni, M.; Manzoni, S.; Murrone, A.; Perini, L.; Ragusa, F.; Ratti, M. G.; Rossini, L.; Stabile, A.; Perez, M. Villaplana] Univ Milan, Dipartimento Fis, Milan, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Carlino, G.; Cirotto, F.; Conventi, F.; De Asmundis, R.; Della Pietra, M.; Di Donato, C.; Doria, A.; Izzo, V; Merola, L.; Perrella, S.; Rossi, E.; Sekhniaidze, G.] INFN, Sez Napoli, Naples, Italy; [Aloisio, A.; Alviggi, M. G.; Canale, V; Cirotto, F.; Della Pietra, M.; Di Donato, C.; Merola, L.; Perrella, S.; Rossi, E.] Univ Napoli, Dipartimento Fis, Naples, Italy; [Farina, E. M.; Ferrari, R.; Fraternali, M.; Gaudio, G.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Lanza, A.; Livan, M.; Negri, A.; Polesello, G.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.; Vercesi, V.] INFN, Sez Pavia, Pavia, Italy; [Farina, E. M.; Fraternali, M.; Introzzi, G.; Kourkoumeli-Charalampidi, A.; Livan, M.; Negri, A.; Rebuzzi, D. M.; Rimoldi, A.; Sottocornola, S.] Univ Pavia, Dipartimento Fis, Pavia, Italy; [Annovi, A.; Biesuz, N., V; Calvetti, M.; Cavasinni, V; Chiarelli, G.; Francavilla, P.; Giannetti, P.; Leone, S.; Roda, C.; Scuri, F.; Sotiropoulou, C. L.] INFN, Sez Pisa, Pisa, Italy; [Biesuz, N., V; Calvetti, M.; Cavasinni, V; De Maria, A.; Francavilla, P.; Roda, C.; Sotiropoulou, C. L.] Univ Pisa, Dipartimento Fis E Fermi, Pisa, Italy; [Anulli, F.; Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; De Pedis, D.; De Salvo, A.; Falciano, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Luminari, L.; Messina, A.; Nisati, A.; Pasqualucci, E.; Petrolo, E.; Pontecorvo, L.; Rescigno, M.; Rosati, S.; Tehrani, F. Safai; Sebastiani, C. D.; Vari, R.; Veneziano, S.] INFN, Sez Roma, Rome, Italy; [Bagnaia, P.; Bauce, M.; Bini, C.; Corradi, M.; De Cecco, S.; Gentile, S.; Giagu, S.; Ippolito, V; Lacava, F.; Luci, C.; Messina, A.; Sebastiani, C. D.] Sapienza Univ Roma, Dipartimento Fis, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cardarelli, R.; Cerrito, L.; De Sanctis, U.; Di Ciaccio, A.; Liberti, B.; Massa, L.; Pinamonti, M.; Vanadia, M.] INFN, Sez Roma Tor Vergata, Rome, Italy; [Aielli, G.; Bortolotto, V; Bruno, S.; Camarri, P.; Cerrito, L.; De Sanctis, U.; Di Ciaccio, A.; Massa, L.; Pinamonti, M.; Vanadia, M.] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy; [Baroncelli, A.; Biglietti, M.; Ceradini, F.; Di Micco, B.; Farilla, A.; Iodice, M.; Orestano, D.; Petrucci, F.; Salamanna, G.; Sessa, M.; Stanescu, C.; Vecchio, V.; Verducci, M.] INFN, Sez Roma Tre, Rome, Italy; [Ceradini, F.; Di Micco, B.; Orestano, D.; Petrucci, F.; Salamanna, G.; Sessa, M.; Vecchio, V.; Verducci, M.] Univ Roma Tre, Dipartimento Matemat Fis, Rome, Italy; [Iuppa, R.] INFN, TIFPA, Rome, Italy; [Iuppa, R.] Univ Trento, Trento, Italy; [Jakobsen, S.; Kneringer, E.; Lukas, W.; Manousos, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Yang, S.] Univ Iowa, Iowa City, IA USA; [Chen, C. H.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Liu, B.; Pluth, D.; Prell, S.; Infante, C. M. Vergel; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M., V; Dedovich, D., V; Demichev, M.; Gongadze, A.; Gostkin, M., I; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V; Ladygin, E.; Lyubushkin, V; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Simic, L.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I; Zhemchugov, A.; Zimine, N., I] Joint Inst Nucl Res, Dubna, Russia; [Cerqueira, A. S.; Goncalves Gama, R.; Manhaes de Andrade Filho, L.; Peralva, B. S.; Silva Oliveira, M., V] Univ Fed Juiz de Fora UFJF, Dept Engn Eletr, Juiz De Fora, Brazil; [Amaral Coutinho, Y.; Araujo Ferraz, V; Araujo Pereira, R.; Begalli, M.; Caloba, L. P.; Freund, W. S.; Maidantchik, C.; Marroquim, F.; Seixas, J. M.] Univ Fed Rio de Janeiro COPPE EE IF, Rio De Janeiro, Brazil; [Do Vale, M. A. B.] Univ Fed Sao Joao del Rei UFSJ, Sao Joao Del Rei, Brazil; [Donadelli, M.; La Rosa Navarro, J. L.; Leite, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Aoki, M.; Arai, Y.; Hanagaki, K.; Higashino, S.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Mizukami, A.; Nagano, K.; Nakamura, K.; Ninomiya, Y.; Nozaki, M.; Odaka, S.; Okuyama, T.; Sasaki, O.; Suzuki, S.; Takubo, Y.; Terada, S.; Tokushuku, K.; Tsuno, S.; Ueda, I; Unno, Y.; Usui, J.; Yamamoto, A.; Yasu, Y.] High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki, Japan; [Chen, Y.; Kawade, K.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Shimizu, S.; Takeda, K.; Tanioka, R.; Wakamiya, K.; Yamane, F.; Yamazaki, Y.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Adamczyk, L.; Bold, T.; Dabrowski, W.; Gach, G. P.; Grabowska-Bold, I; Guzik, M. P.; Janus, P. A.; Kisielewska, D.; Koperny, S.; Kowalski, T. Z.; Kremer, J. A.; Mindur, B.; Przybycien, M.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palka, M.; Richter-Was, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Bahmani, M.; Banas, E.; de Renstrom, P. A. Bruckman; Burka, K.; Chwastowski, J. J.; Czekierda, S.; Derendarz, D.; Dziedzic, B. S.; Godlewski, J.; Kaczmarska, A.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa; Olszewski, A.; Olszowska, J.; Slawinska, M.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Akatsuka, S.; Kunigo, T.; Noguchi, Y.; Okazaki, Y.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto, Japan; [Kawagoe, K.; Kobayashi, D.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Res Ctr Adv Particle Phys, Fukuoka, Fukuoka, Japan; [Kawagoe, K.; Kobayashi, D.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka, Fukuoka, Japan; [Alconada Verzini, M. J.; Alonso, F.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Bertram, I. A.; Borissov, G.; Bouhova-Thacker, E., V; Fox, H.; Henderson, R. C. W.; Jones, R. W. L.; Kartvelishvili, V; Long, R. E.; Love, P. A.; Muenstermann, D.; Parker, A. J.; Ristic, B.; Skinner, M. B.; Smizanska, M.; Tee, A. S.; Walder, J.; Wharton, A. M.; Whitmore, B. W.] Univ Lancaster, Phys Dept, Lancaster, England; [Burdin, S.; Dervan, P.; D'Onofrio, M.; Gao, Y.; Graham, E. C.; Gwilliam, C. B.; Hayward, H. S.; Jones, T. J.; Kay, E. F.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Maxfield, S. J.; Mehta, A.; Meng, L.; Rompotis, N.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool, Merseyside, England; [Cindro, V; Filipcic, A.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Bahcesehir University; Istanbul Bilgi University; Bogazici University; Gaziantep University; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; 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Comenius University Bratislava; Slovak Academy of Sciences; United States Department of Energy (DOE); Brookhaven National Laboratory; University of Buenos Aires; University of Cambridge; University of Cape Town; University of Johannesburg; University of Witwatersrand; Carleton University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; European Organization for Nuclear Research (CERN); University of Chicago; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite Clermont Auvergne (UCA); Columbia University; University of Copenhagen; Niels Bohr Institute; University of Calabria; Istituto Nazionale di Fisica Nucleare (INFN); Southern Methodist University; University of Texas System; University of Texas Dallas; Stockholm University; Oskar Klein Centre; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Dortmund University of Technology; Technische Universitat Dresden; Duke University; University of Edinburgh; Istituto Nazionale di Fisica Nucleare (INFN); University of Freiburg; University of Gottingen; University of Geneva; University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); Justus Liebig University Giessen; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Harvard University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Shandong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Hiroshima Institute of Technology; Chinese University of Hong Kong; University of Hong Kong; Hong Kong University of Science & Technology; National Tsing Hua University; Indiana University System; Indiana University Bloomington; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; Istituto Nazionale di Fisica Nucleare (INFN); University of Salento; Istituto Nazionale di Fisica Nucleare (INFN); University of Milan; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of Innsbruck; University of Iowa; Iowa State University; Joint Institute for Nuclear Research - Russia; 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Oklahoma State University System; Oklahoma State University - Stillwater; Palacky University Olomouc; University of Oregon; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Osaka University; University of Oslo; University of Oxford; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; University of Pennsylvania; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Universidade de Lisboa; Universidade de Coimbra; Universidade de Lisboa; Universidade do Minho; University of Granada; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; Pontificia Universidad Catolica de Chile; Universidad Tecnica Federico Santa Maria; University of Washington; University of Washington Seattle; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Ivane Javakhishvili Tbilisi State University; Ivane Javakhishvili Tbilisi State University; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of British Columbia; University of Victoria; University of Wurzburg; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wuppertal; Yale University; Yerevan Physics Institute; University of London; King's College London; Istanbul University; University of Louisville; California State University System; California State University Fresno; University of Fribourg; Autonomous University of Barcelona; Moscow Institute of Physics & Technology; Parthenope University Naples; Peter the Great St. Petersburg Polytechnic University; City University of New York (CUNY) System; University of Aegean; Council for Scientific & Industrial Research (CSIR) - South Africa; ICREA; Near East University; Ilia State University; Stanford University; Manhattan College; Hellenic Open University; City University of New York (CUNY) System; City College of New York (CUNY); California State University System; California State University Sacramento; Sun Yat Sen University; University of Sharjah; University of Hamburg; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Giresun University; Universiti Malaya Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. de la Hoz, Santiago Gonzalez/AHC-7576-2022; govender, nicolin/CAH-3245-2022; Fabbri, Laura/H-3442-2012; Franchini, Matteo/HNI-8314-2023; Bortfeldt, Jona/AAJ-5370-2021; Billoud, Thomas/AAY-1569-2020; Gonzalez, Barbara Alvarez/ABG-7021-2020; Tapia, Sebastian/ABB-6644-2021; Gladilin, Leonid/B-5226-2011; Kroll, Jiri/C-8465-2018; Marcisovsky, Michal/AAM-2404-2020; Zanzi, Daniele/AAY-4102-2020; Artoni, Giacomo/ABA-2164-2020; Cavaliere, Viviana/CAE-8597-2022; Palka, Marek/GXW-2506-2022; Adiguzel, Aytul/AAC-9049-2020; Cervelli, Alberto/X-7416-2018; Duarte-Campderrós, Jordi/F-5025-2018; Stabile, Alberto/F-2889-2013; Cottin, Giovanna/AFR-3846-2022; do Vale, Maria Aline Barros/D-2357-2019; Olszowska, Jolanta/P-2889-2017; della Volpe, Domenico/B-4482-2012; Bogdanchikov, Alexander/AAB-9414-2022; Gonella, Laura/GLR-3838-2022; Tassi, Enrico/AAJ-9661-2020; Sadykov, Renat/AAN-9602-2020; Kantserov, Vadim/M-9761-2015; Llácer, María Moreno/AAU-6582-2020; Trzupek, Adam/N-2448-2018; Lee, Shih-Chang/AAV-7016-2021; Negrini, Matteo/C-8906-2014; Llácer, María Moreno/AAQ-7522-2020; beddall, ayda/AAG-6531-2020; Buttar, Craig/AAG-5392-2021; b, m/HPE-0989-2023; Warburton, Andreas/N-8028-2013; Maneira, Jose/D-8486-2011; Ippolito, Valerio/L-1435-2016; Giordani, Mario P/Q-6211-2018; Chizhov, Mihail/CAG-2076-2022; de Renstrom, Pawel Bruckman/AAG-7725-2021; Perez, Miguel Villaplana/B-2717-2015; Rosati, Stefano/AAF-6968-2019; Gabrielli, Alessandro/H-4931-2012; Schernau, Michael/GRY-3808-2022; Song, Weimin/AAJ-5415-2020; Gonçalo, Ricardo/M-3153-2016; Gao, Y./GWZ-3665-2022; Alimonti, Gianluca/AAG-4603-2020; Brahimi, Nihal/HNJ-5325-2023; Mindur, Bartosz/A-2253-2017; Muino, Patricia Conde/F-7696-2011; Glasman, Claudia/Y-8858-2019; Coadou, Yann/G-2263-2010; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Bobrovnikov, Victor/AAB-8328-2022; Zhukov, Konstantin I/M-6027-2015; Cetin, Serkant Ali/AGF-0147-2022; Chitan, Adrian/AAC-8946-2022; Kruchonak, Uladzimir/AAN-4371-2020; Schioppa, Enrico Junior/F-4731-2019; Burdin, Sergey/AAZ-9062-2021; Pages, Andres Pacheco/C-5353-2011; Snesarev, Andrei/H-5090-2013; Lysak, Roman/H-2995-2014; CAKIR, Orhan/L-1893-2015; Wolters, Helmut/M-4154-2013; Zemaityte, Gabija/AAI-7245-2021; Kharlamova, Tatyana/AAQ-5430-2020; Gravila, Paul/HLP-6245-2023; Gustavino, Giuliano/AAK-6591-2020; Svatos, Michal/ABA-2041-2020; Romano, Marino/ACW-0715-2022; Bailey, Adam J/T-9828-2017; Derendarz, Dominik/AAO-3512-2021; Caprini, Irinel/AAP-3760-2020; Castro, Nuno/AAB-3648-2019; Trzebinski, Maciej/AAO-6821-2021; Prokoshin, Fedor/E-2795-2012; do Amaral Coutinho, Yara/AAU-7857-2021; Malek, Fairouz/E-4410-2018; Giagu, Stefano/H-6455-2013; Zhemchugov, Alexey/N-1717-2017; Hejbal, Jiri/H-1358-2014; Trzebinski, Maciej/W-1748-2018; Livan, Michele M/D-7531-2012; Barberis, Dario/ABF-1310-2021; Oh, Alexander/HHZ-4386-2022; Llorente Merino, Javier/AGP-3432-2022; Sopczak, Andre/I-4951-2015; Introzzi, Gianluca/K-2497-2015; Balaji, S/J-1864-2019; Darbo, Giovanni/C-8175-2012; Napolitano, Fabrizio/CAI-2163-2022; Lefebvre, Benoit/ABB-2272-2021; Kepka, Oldrich/ABF-5327-2021; Sopkova, Filomena/HKV-6270-2023; Garcia, Carmen/W-2465-2018; De, Kaushik/N-1953-2013; Bin Yusuff, Imran/ABA-5986-2020; Capua, Marcella/A-8549-2015; Pascual, Juan Antonio Garcia/B-3540-2019; Casado, Pilar/H-1484-2015; Annovi, Alberto/AAA-8638-2020; Franchini, Matteo/AAC-9259-2021; Herten, Gregor/HNQ-9546-2023; Ohm, Christian/AAU-6572-2020; Lazzaroni, Massimo/N-3675-2015; Capua, Marcella/AAJ-8419-2020; Smirnov, Sergei Y/F-1014-2011; Bruscino, Nello/ABA-8980-2021; Della Pietra, Massimo/J-5008-2012; Ferrer, Antonio/H-2942-2015; Viaux, Nicolas/AAT-5715-2020; Sbarra, Carla/M-9669-2018; D'Onofrio, Adelina/AAT-3903-2020; Ahmadov, Faig N/B-3723-2018; Carquin López, Edson/GZG-4712-2022; Buzykaev, Alexey/HIK-0117-2022; Jiménez, Yesenia Hernández/ABH-1107-2020; Panizzo, Giancarlo/AAS-2986-2020; Petrucci, Fabrizio/G-8348-2012; Klimentov, Alexei A/I-4270-2013; Talyshev, Alexey/HGC-6910-2022; Padilla, Cristobal/C-3218-2017; Godlewski, Jan/AAI-3300-2021; Gurbuz, Saime/AAG-5583-2019; Huseynov, Nazim/AAE-4663-2019; Laycock, Paul/AAL-8261-2021; Hamal, Petr/G-5540-2014; perez, miguel/GXF-5077-2022; YILDIZ, Hatice DURAN/AAN-3727-2021; Amoroso, Simone/AAW-4334-2021; Nechaeva, Polina Y/N-1148-2015; Mitsou, Vasiliki A./D-1967-2009; Maj, Klaudia/Q-2724-2019; Barreiro, Fernando/D-9808-2012; Grinstein, Sebastian/ABE-1880-2020; AGHEORGHIESEI, Catalin/B-8596-2014; Alberich, Leonor Cerdá/ABG-8534-2020; Dabrowski, Wladyslaw/AAS-6369-2020; Berger, N/ABE-4064-2020; Donadelli, Marisilvia/H-3710-2016; de la Hoz, Santiago Gonzalez/E-2494-2016; Kostyukhin, Vadim/F-3171-2019; Doyle, Anthony T/C-5889-2009; Ozcan, Veysi Erkcan/AAS-4508-2020; Ventura, Andrea/A-9544-2015; Bachas, Konstantinos/C-8101-2019; Fassouliotis, Dimitrios/AAL-7081-2021; Costa, Maria Jose/H-5962-2015; Gongadze, Alexi/AAY-5201-2021; Fiorini, Luca/W-6250-2018; TÜRK ÇAKIR, İLKAY/HNI-7509-2023; Grabowska-Bołd, Iwona/ABI-7829-2020; Parodi, Fabrizio/AHE-5089-2022; Lacasta, Carlos/C-7254-2008; Maevskiy, Artem/P-4101-2016; Coccaro, Andrea/AAB-6990-2021; Kazanin, Vasily F./HHM-2056-2022; Smirnova, Oxana/A-4401-2013; Dubinin, Filipp/M-9546-2015; Torro Pastor, Emma/AAB-5979-2021; Ventura, Andrea/CAH-0226-2022; Francavilla, Paolo/HKE-1206-2023; Tikhomirov, Vladimir/M-6194-2015; Chwastowski, Janusz J./I-4480-2012; Bassalat, Ahmed Azzam/HHY-9901-2022; Vaniachine, Alexandre/H-7796-2013; Sapronov, Andrey/HGB-0392-2022; Sultansoy, Saleh/AAA-8267-2019; Valero, Alberto/G-9866-2015; da via, cinzia/AAS-3978-2021; KHODINOV, ALEKSANDR/D-6269-2015; Banaś, Elżbieta A/W-4583-2017; Donadelli, Marisilvia/ABD-5330-2021; Chelkov, Georgy A./G-9934-2019; Leisos, Antonios/AAJ-2351-2021; Hunter, Robert/AAG-8342-2019; Begalli, M/C-4121-2017; Giuli, Francesco/HJI-6649-2023; Sessa, Marco/AAT-2850-2020; Kuleshov, Sergey V/D-9940-2013; Vandelli, Wainer/ABI-8185-2020; Kostyukhin, Vadim/ABA-9033-2020; Carquin, Edson/GLU-9641-2022; Kaczmarska, Anna/B-2753-2019; Orlando, Nicola/AAL-1723-2021; Sanchez, Francisca J. Munoz/AAH-3909-2019; Norjoharuddeen, Nurfikri/S-3109-2018; Peleganchuk, Sergey V/J-6722-2014; Camarri, Paolo/AAF-9629-2020; Brooks, William K/C-8636-2013; Calvetti, Milene/AAQ-1337-2020; Grancagnolo, Sergio/J-3957-2015; Ibáñez, Carlos Escobar/B-3761-2017; Peralva, Bernardo Sotto-Maior/AAD-2086-2022; Castillo, Luis Roberto Flores/W-3928-2018; Kurashige, Hisaya/H-4916-2012; Benekos, Nektarios/I-9928-2017; Gutierrez, Phillip/C-1161-2011; Woźniak, Krzysztof/AAQ-6119-2021; zhang, jin/GXV-9154-2022; Betti, Alessandra/AAD-9964-2019; Cavaliere, Viviana/CAG-2384-2022; Mikestikova, Marcela/H-1996-2014; Parida, Bibhuti/T-3730-2018; Sultansoy, Saleh/AAV-6853-2021; Saraiva, Joao Gentil Mendes/D-1596-2017; Suster, Carl J E/L-3714-2016; de la Torre Perez, Hector/ABG-6942-2020; Gongadze, Alexi/T-9162-2017; Dziedzic, Bartosz/Q-4189-2017; Fassi, Farida/F-3571-2016; Reznicek, Pavel/C-1989-2017; Berta, Peter/AAL-7109-2020; Bosman, Martine/J-9917-2014; Kuze, Masahiro/V-4251-2018; Fuster, Juan/W-6189-2018; 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CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia, Russian Federation; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada; Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union; ERDF, European Union; FP7, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, France; ANR, France; Region Auvergne, France; Fondation Partager le Savoir, France; DFG, Germany; AvH Foundation, Germany; EU-ESF; Greek NSRF; BSF, Israel; GIF, Israel; Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC [ST/H001158/1, ST/L003449/1, ST/N000234/1, ST/N000277/1, ST/P002439/1, ST/N000420/1, ST/M000753/1, ST/N000307/1, ST/J005533/1, ST/J004804/1, ST/L006162/1, ST/N000447/1] Funding Source: UKRI; Division Of Physics; Direct For Mathematical & Physical Scien [1624739] Funding Source: National Science Foundation ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia, Russian Federation(Russian Federation); NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Cantons of Bern and Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada(FQRNT); Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union(European Research Council (ERC)); ERDF, European Union; FP7, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, France(French National Research Agency (ANR)); ANR, France(French National Research Agency (ANR)); Region Auvergne, France(Region Auvergne-Rhone-Alpes); Fondation Partager le Savoir, France; DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); BSF, Israel(US-Israel Binational Science Foundation); GIF, Israel(German-Israeli Foundation for Scientific Research and Development); Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain(Center for Forestry Research & Experimentation (CIEF)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)) We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [101]. 100 29 29 2 37 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D NOV 9 2018.0 98.0 9 92005 10.1103/PhysRevD.98.092005 0.0 36 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics GZ8YS Green Published, Green Accepted, hybrid, Green Submitted 2023-03-23 WOS:000449782800001 0
J Guo, HY; Pedrycz, W; Liu, XD Guo, Hongyue; Pedrycz, Witold; Liu, Xiaodong Hidden Markov Models Based Approaches to Long-Term Prediction for Granular Time Series IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Dynamic time warping (DTW); hidden Markov models (HMM); information granule; long-term forecasting; time series forecasting FORECASTING-MODEL; NEURAL-NETWORK; HYBRIDIZATION; SEGMENTATION; ARIMA In time series forecasting, a challenging and important task is to realize long-term forecasting that is both accurate and transparent. In this study, we propose a long-term prediction approach by transforming the original numerical data into some meaningful and interpretable entities following the principle of justifiable granularity. The obtained sequences exhibiting sound semantics may have different lengths, which bring some difficulties when carrying out predictions. To equalize these temporal sequences, we propose to adjust their lengths by involving the dynamic time warping (DTW) distance. Two theorems are included to ensure the correctness of the proposed equalization approach. Finally, we exploit hidden Markov models (HMM) to derive the relations existing in the granular time series. A series of experiments using publicly available data are conducted to assess the performance of the proposed prediction method. The comparative analysis demonstrates the performance of the prediction delivered by the proposed model. [Guo, Hongyue] Dalian Maritime Univ, Coll Shipping Econ & Management, Dalian 116026, Peoples R China; [Guo, Hongyue] Dalian Maritime Univ, Collaborat Innovat Ctr Transport Studies, Dalian 116026, Peoples R China; [Guo, Hongyue] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China; [Guo, Hongyue; Liu, Xiaodong] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland; [Liu, Xiaodong] Dalian Maritime Univ, Coll Sci, Dalian 116026, Peoples R China Dalian Maritime University; Dalian Maritime University; Dalian University of Technology; Dalian University of Technology; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Dalian Maritime University Guo, HY (corresponding author), Dalian Maritime Univ, Coll Shipping Econ & Management, Dalian 116026, Peoples R China. hyguo@mail.dlut.edu.cn; wpedrycz@ualberta.ca; xdli-uros@dlut.edu.cn , Hongyue/0000-0003-1454-4087 Natural Science Foundation of China [61673082, 61773352, 61533005]; China Scholarship Council [201606060027] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported by the Natural Science Foundation of China under Grant 61673082, Grant 61773352, and Grant 61533005. The work of H. Guo was supported by the China Scholarship Council under Grant 201606060027. 36 29 30 7 53 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. OCT 2018.0 26.0 5 2807 2817 10.1109/TFUZZ.2018.2802924 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering GW1XI 2023-03-23 WOS:000446675400026 0
J Niu, YK; Tondi, B; Zhao, Y; Ni, RR; Barni, M Niu, Yakun; Tondi, Benedetta; Zhao, Yao; Ni, Rongrong; Barni, Mauro Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Image coding; Transform coding; Location awareness; Quantization (signal); Splicing; Forgery; Estimation; Image forensics; double JPEG compression; image forgery localization; deep learning based image forensics; primary quantization matrix; spectral clustering; normalized mutual information (NMI) COMPRESSION Detection of inconsistencies of double JPEG artifacts across different image regions is often used to detect local image manipulations, like image splicing, and to localize them. In this paper, we move one step further, proposing an end-to-end system that, in addition to detecting and localizing spliced regions, can also distinguish regions coming from different donor images. We assume that both the spliced regions and the background image have undergone a double JPEG compression, and use a local estimate of the primary quantization matrix to distinguish between spliced regions taken from different sources. To do so, we cluster the image blocks according to the estimated primary quantization matrix and refine the result by means of morphological reconstruction. The proposed method can work in a wide variety of settings including aligned and non-aligned double JPEG compression, and regardless of whether the second compression is stronger or weaker than the first one. We validated the proposed approach by means of extensive experiments showing its superior performance with respect to baseline methods working in similar conditions. [Niu, Yakun; Zhao, Yao; Ni, Rongrong] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Niu, Yakun; Zhao, Yao; Ni, Rongrong] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, I-53100 Siena, Italy Beijing Jiaotong University; Beijing Jiaotong University; University of Siena Zhao, Y (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. niuyakun@bjtu.edu.cn; benedettatondi@gmail.com; yzhao@bjtu.edu.cn; rrni@bjtu.edu.cn; barni@dii.unisi.it BARNI, Mauro/0000-0002-7368-0866; Zhao, Yao/0000-0002-8581-9554; Niu, Yakun/0000-0002-2793-2823 National Natural Science Foundation of China [U1936212, 62120106009]; PREMIER Project [PRIN 2017 2017Z595XS-001]; Italian Ministry of University and Research; Defense Advanced Research Projects Agency (DARPA); Air Force Research Laboratory (AFRL) [FA875020-2-1004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); PREMIER Project; Italian Ministry of University and Research(Ministry of Education, Universities and Research (MIUR)); Defense Advanced Research Projects Agency (DARPA)(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Air Force Research Laboratory (AFRL)(United States Department of DefenseUS Air Force Research Laboratory) This work was supported in part by the National Natural Science Foundation of China under Grant U1936212 and Grant 62120106009, in part by the PREMIER Project under Contract PRIN 2017 2017Z595XS-001, in part by the Italian Ministry of University and Research, and in part by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under Agreement FA875020-2-1004. The associate editor coordinating the review of this manuscript and approving it for publication was Mr. Remi Cogranne. (Corresponding author: Yao Zhao.) 48 1 1 5 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2021.0 16.0 5397 5412 10.1109/TIFS.2021.3129654 0.0 16 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering XL4TS Green Submitted 2023-03-23 WOS:000728138100005 0
J Zhang, F; Fleyeh, H; Wang, XR; Lu, MH Zhang, Fan; Fleyeh, Hasan; Wang, Xinru; Lu, Minghui Construction site accident analysis using text mining and natural language processing techniques AUTOMATION IN CONSTRUCTION English Article Construction site accident analysis; Machine learning; Text mining; Natural language processing; Sequential quadratic programming; Optimization TRAJECTORY OPTIMIZATION; CODING CAUSATION; REGRESSION; NARRATIVES; SAFETY Workplace safety is a major concern in many countries. Among various industries, construction sector is identified as the most hazardous work place. Construction accidents not only cause human sufferings but also result in huge financial loss. To prevent reoccurrence of similar accidents in the future and make scientific risk control plans, analysis of accidents is essential. In construction industry, fatality and catastrophe investigation summary reports are available for the past accidents. In this study, text mining and natural language process (NLP) techniques are applied to analyze the construction accident reports. To be more specific, five baseline models, support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB) and an ensemble model are proposed to classify the causes of the accidents. Besides, Sequential Quadratic Programming (SQP) algorithm is utilized to optimize weight of each classifier involved in the ensemble model. Experiment results show that the optimized ensemble model outperforms rest models considered in this study in terms of average weighted F1 score. The result also shows that the proposed approach is more robust to cases of low support. Moreover, an unsupervised chunking approach is proposed to extract common objects which cause the accidents based on grammar rules identified in the reports. As harmful objects are one of the major factors leading to construction accidents, identifying such objects is extremely helpful to mitigate potential risks. Certain limitations of the proposed methods are discussed and suggestions and future improvements are provided. [Zhang, Fan; Fleyeh, Hasan] Dalarna Univ, Dept Comp Engn, S-79188 Falun, Sweden; [Wang, Xinru] Univ Nottingham Ningbo, Res Ctr Fluids & Thermal Engn, Ningbo 315100, Zhejiang, Peoples R China; [Lu, Minghui] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China Dalarna University; University of Nottingham Ningbo China; Shanghai Jiao Tong University Zhang, F (corresponding author), Dalarna Univ, Dept Comp Engn, S-79188 Falun, Sweden. fzh@du.se Wang, Xin/GYU-1129-2022; Wang, Xin/AAN-8435-2021; Fleyeh, Hasan/AAB-6415-2020 Wang, Xin/0000-0002-4457-7376; Fleyeh, Hasan/0000-0002-1429-2345; zhang, fan/0000-0002-1792-5195 57 111 112 32 185 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0926-5805 1872-7891 AUTOMAT CONSTR Autom. Constr. MAR 2019.0 99.0 238 248 10.1016/j.autcon.2018.12.016 0.0 11 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Construction & Building Technology; Engineering HI9EZ 2023-03-23 WOS:000456759400020 0
J Ji, SP; Dai, PY; Lu, M; Zhang, YJ Ji, Shunping; Dai, Peiyu; Lu, Meng; Zhang, Yongjun Simultaneous Cloud Detection and Removal From Bitemporal Remote Sensing Images Using Cascade Convolutional Neural Networks IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Clouds; Remote sensing; Image reconstruction; Machine learning; Image restoration; Image segmentation; Support vector machines; Convolutional neural networks (CNNs); cloud detection; cloud removal; multitemporal remote sensing images SHADOW DETECTION; THICK CLOUDS; CLASSIFICATION Clouds and cloud shadows heavily affect the quality of the remote sensing images and their application potential. Algorithms have been developed for detecting, removing, and reconstructing the shaded regions with the information from the neighboring pixels or multisource data. In this article, we propose an integrated cloud detection and removal framework using cascade convolutional neural networks, which provides accurate cloud and shadow masks and repaired images. First, a novel fully convolutional network (FCN), embedded with multiscale aggregation and the channel-attention mechanism, is developed for detecting clouds and shadows from a cloudy image. Second, another FCN, with the masks of the detected cloud and shadow, the cloudy image, and a temporal image as the input, is used for the cloud removal and missing-information reconstruction. The reconstruction is realized through a self-training strategy that is designed to learn the mapping between the clean-pixel pairs of the bitemporal images, which bypasses the high demand of manual labels. Experiments showed that our proposed framework can simultaneously detect and remove the clouds and shadows from the images and the detection accuracy surpassed several recent cloud-detection methods; the effects of image restoring outperform the mainstream methods in every indicator by a large margin. The data set used for cloud detection and removal is made open. [Ji, Shunping; Dai, Peiyu; Zhang, Yongjun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China; [Lu, Meng] Univ Utrecht, Dept Phys Geog, NL-3584 CE Utrecht, Netherlands Wuhan University; Utrecht University Ji, SP (corresponding author), Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China. jishunping@whu.edu.cn; pydai@whu.edu.cn; m.lu@uu.nl; zhangyj@whu.edu.cn Lu, Meng/AAF-1711-2019; Zhang, Yongjun/HCH-7076-2022 Lu, Meng/0000-0002-6850-581X; Zhang, Yongjun/0000-0001-9845-4251; Dai, Peiyu/0000-0002-1061-8926; Ji, Shunping/0000-0002-3088-1481 National Key Research and Development Program of China [2018YFB0505003] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2018YFB0505003. 58 30 30 12 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JAN 2021.0 59.0 1 732 748 10.1109/TGRS.2020.2994349 0.0 17 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology PL4FE 2023-03-23 WOS:000603079000054 0
J Gan, NF; Sun, ZY; Zhang, ZS; Xu, SQ; Liu, P; Qin, ZA Gan, Naifeng; Sun, Zhenyu; Zhang, Zhaosheng; Xu, Shiqi; Liu, Peng; Qin, Zian Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles IEEE TRANSACTIONS ON POWER ELECTRONICS English Article Batteries; Fault diagnosis; Voltage; Circuit faults; Discharges (electric); Voltage measurement; Data acquisition; Electric vehicle (EVS); extreme gradient boosting (XGboost); fault diagnosis; lithium-ion battery (LIB); overdischarge EXTERNAL SHORT-CIRCUIT; MANAGEMENT; MECHANISM; SYSTEM; ISSUES The overdischarge can significantly degrade a lithium-ion (Li-ion) batteryx0027;s lifetime. Therefore, it is important to detect the overdischarge and prevent severe damage of the Li-ion battery. Depending on the battery technology, there is a minimum voltage (cutoff voltage) that the battery is allowed to be discharged in common practice. Once the battery voltage is below the cutoff voltage, it is considered as overdischarge. However, overdischarge will not lead to immediate failure of the battery, and if it is not detected, the battery voltage can increase above the cutoff voltage during charging process. How to detect an overdischarge has happened, while the current voltage is larger than the cutoff voltage, thus becomes very challenging. In this article, a machine learning based two-layer overdischarge fault diagnosis strategy for Li-ion batteries in electric vehicles is proposed. The first layer is to detect the overdischarge by comparing the battery voltage with cutoff voltage, like what is utilized in common practice. If the battery voltage is larger than the cutoff voltage, the second layer, which is a detection approach based on eXtreme Gradient Boosting algorithm, is triggered. The second layer is employed to detect the previous overdischarge. The proposed method is validated by real electric vehicle data. [Gan, Naifeng; Sun, Zhenyu; Zhang, Zhaosheng; Xu, Shiqi; Liu, Peng] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China; [Qin, Zian] Delft Univ Technol, Dept Elect Sustainable Energy, NL-2628 CD Delft, Netherlands Beijing Institute of Technology; Delft University of Technology Liu, P (corresponding author), Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China.;Qin, ZA (corresponding author), Delft Univ Technol, Dept Elect Sustainable Energy, NL-2628 CD Delft, Netherlands. gannaifeng@163.com; bitzhenyu@163.com; zhangzhaosheng@bit.edu.cn; 18910440672@163.com; roc726@126.com; zqi@ieee.org Qin, Zian/AAG-6157-2019 Qin, Zian/0000-0002-7408-7706; Gan, Naifeng/0000-0003-4710-3683; Sun, Zhenyu/0000-0002-7513-229X National Key R&D Program of China [2019YFE0107900] National Key R&D Program of China Manuscript received May 12, 2021; revised September 19, 2021; accepted October 16, 2021. Date of publication October 21, 2021; date of current version December 31, 2021. This work was supported by the National Key R&D Program of China under Grant 2019YFE0107900. Recommended for publication by Associate Editor H. Wang. (Zhenyu Sun contributed equally to this work.) (Corresponding authors: Peng Liu; Zian Qin.) 47 11 11 41 117 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0885-8993 1941-0107 IEEE T POWER ELECTR IEEE Trans. Power Electron. APR 2022.0 37.0 4 4575 4588 10.1109/TPEL.2021.3121701 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering XU0JQ Green Published 2023-03-23 WOS:000733963100084 0
J He, MS; Wang, XJ; Zhou, JH; Xi, YY; Jin, L; Wang, XL He, Mingshu; Wang, Xiaojuan; Zhou, Junhua; Xi, Yuanyuan; Jin, Lei; Wang, Xinlei Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection SECURITY AND COMMUNICATION NETWORKS English Article CLASSIFICATION; MACHINE With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly detection for the small number of abnormal behavior samples. This paper proposes an anomaly detection model with a few abnormal samples to solve the problem in few-shot detection based on convolutional neural networks (CNN) and autoencoder (AE). This model mainly consists of the CNN-based supervised pretraining module and the AE-based data reconstruction module. Only a few abnormal samples are utilized to the pretrain module to build the structure of extracting deep features. The data reconstruction module simply chooses the deep features of normal samples as training data. There also exist some effective attention mechanisms in the pretraining module. Through the pretraining of small samples, the accuracy of abnormal detection is improved compared with merely training normal samples with AE. The simulation results prove that this solution can solve the above problems occurring in network behavior anomaly detection. In comparison to the original AE model and other clustering methods, the proposed model advances the detection results in a visible way. [He, Mingshu; Wang, Xiaojuan; Jin, Lei; Wang, Xinlei] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China; [Zhou, Junhua] Beijing Inst Elect Syst Engn, State Key Lab Intelligent Mfg Syst Technol, Beijing 100854, Peoples R China; [Xi, Yuanyuan] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden Beijing University of Posts & Telecommunications; Royal Institute of Technology Wang, XJ (corresponding author), Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China. hemingshu@bupt.edu.cn; wj2718@163.com; zjh@163.com; yuanxi@kth.se; jinlei@bupt.edu.cn; ann96125@126.com , XINLEI WANG/0000-0002-7383-0852; Jin, Lei/0000-0003-4855-2464; HE, MINGSHU/0000-0002-2896-4595 National Natural Science Foundation of China [62071056] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) /e authors acknowledge ICN&CAD Laboratory of School of Electronic Engineering, Beijing University of Posts and Telecommunications, for the experimental environment. Special thanks are due to Lv Tianqi, Xiao Yabo, Wu Lingrui, Fan Yiqi, Keiven, and Zhang Yu for their great help in this work./is work was supported by the National Natural Science Foundation of China (Grant no. 62071056). 41 10 10 5 36 WILEY-HINDAWI LONDON ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND 1939-0114 1939-0122 SECUR COMMUN NETW Secur. Commun. Netw. MAR 27 2021.0 2021.0 6659022 10.1155/2021/6659022 0.0 13 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications RJ6SW gold 2023-03-23 WOS:000637731400001 0
J Sun, M; Zhang, XW; Van hamme, H; Zheng, TF Sun, Meng; Zhang, Xiongwei; Van hamme, Hugo; Zheng, Thomas Fang Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Deep auto encoder; source separation; speech enhancement; unseen noise compensation HMM Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines. [Sun, Meng; Zhang, Xiongwei] PLA Univ Sci & Technol, Lab Intelligent Informat Proc, Nanjing 210007, Jiangsu, Peoples R China; [Van hamme, Hugo] Katholieke Univ Leuven, Elect Engn Dept ESAT, Speech Proc Res Grp, B-3000 Louvain, Belgium; [Zheng, Thomas Fang] Tsinghua Univ, Res Inst Informat Technol, Beijing 100084, Peoples R China Army Engineering University of PLA; KU Leuven; Tsinghua University Sun, M (corresponding author), PLA Univ Sci & Technol, Lab Intelligent Informat Proc, Nanjing 210007, Jiangsu, Peoples R China. sunmengccjs@gmail.com Van hamme, Hugo/D-6581-2012 Van hamme, Hugo/0000-0003-1331-5186; Sun, Meng/0000-0002-7435-3752 Natural Science Foundation of China [61471394, 61402519]; Natural Science Foundation of Jiangsu Province [BK20140071, BK20140074, BK2012510]; KULeuven research grant [GOA/14/005]; National Natural Science Foundation of China [61271389]; National Basic Research Program (973 Program) of China [2013CB329302] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); KULeuven research grant; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Basic Research Program (973 Program) of China(National Basic Research Program of China) The work of M. Sun and X. Zhang was supported in part by the Natural Science Foundation of China under Grants 61471394 and 61402519 and in part by the Natural Science Foundation of Jiangsu Province under Grants BK20140071, BK20140074, and BK2012510. The work of H. Van hamme was supported by the KULeuven research grant GOA/14/005 (CAMETRON). The work of T. F. Zheng was supported in part by the National Natural Science Foundation of China under Grant 61271389 and in part by the National Basic Research Program (973 Program) of China under Grant 2013CB329302. 26 46 55 3 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 2329-9304 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. JAN 2016.0 24.0 1 93 104 10.1109/TASLP.2015.2498101 0.0 12 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering CW8AD Green Accepted 2023-03-23 WOS:000365219900002 0
J Wang, H; Liu, ZL; Ge, YP; Peng, DD Wang, Huan; Liu, Zhiliang; Ge, Yipei; Peng, Dandan Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data KNOWLEDGE-BASED SYSTEMS English Article Machinery fault diagnosis; Convolutional neural network; Self-supervised learning BEARINGS Recently, convolutional neural networks (CNNs) have achieved remarkable success in machinery fault diagnosis. However, these methods usually require mass of manually labeled data, which is expensive and impractical. To this end, this paper explores the application of self-supervised learning (SS-Learning) paradigm in the field of machinery fault diagnosis, and proposes a new fault diagnosis framework based on self-supervised representation learning. This method can directly learn representative features that can be used for signal classification from unlabeled signals. In addition, it enables the network to have a deeper semantic understanding of vibration signals. In this way, the proposed method can significantly improve the performance of the diagnostic model in the case of limited labeled data. Furthermore, this paper deeply analyzes the mechanism behind the SS-Learning algorithm and the reasons for its excellent performance. The proposed SS-Learning algorithm is verified on three real fault diagnosis datasets high-speed train (HST) wheelset bearing dataset, CWRU dataset and motor bearing dataset). When there are only 50 labeled samples, the proposed SS-Learning algorithm achieves an accuracy of 85% on the motor dataset, which is 17.86% higher than the ordinary CNN. It is proven that the proposed method can provide a powerful supervision signal for feature learning of unlabeled samples and obtain quite competitive fault diagnosis performance with limited labeled samples. (c) 2021 Elsevier B.V. All rights reserved. [Wang, Huan; Liu, Zhiliang] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China; [Wang, Huan] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China; [Liu, Zhiliang; Ge, Yipei] Univ Elect Sci & Technol China, Glasgow Coll, Chengdu, Peoples R China; [Peng, Dandan] Katholieke Univ Leuven, Dept Mech Engn, Leuven, Belgium; [Peng, Dandan] Flanders Make, Dynam Mech & Mechatron Syst, Lommel, Belgium University of Electronic Science & Technology of China; Tsinghua University; University of Electronic Science & Technology of China; KU Leuven Liu, ZL (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China. wh.huanwang@gmail.com; zhiliang_liu@uestc.edu.cn; gyp.uestc@gmail.com; dandan.peng@kuleuven.be Wang, Huan/0000-0002-1403-5314 National Key Research and Development Program of China [2018YFB1702400] National Key Research and Development Program of China This work was supported by National Key Research and Development Program of China under Grant 2018YFB1702400. 41 5 5 15 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAR 5 2022.0 239.0 107978 10.1016/j.knosys.2021.107978 0.0 JAN 2022 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 0V9BI 2023-03-23 WOS:000788633300009 0
J Chen, HF; Jiang, DM; Sahli, H Chen, Haifeng; Jiang, Dongmei; Sahli, Hichem Transformer Encoder With Multi-Modal Multi-Head Attention for Continuous Affect Recognition IEEE TRANSACTIONS ON MULTIMEDIA English Article Emotion recognition; Context modeling; Feature extraction; Correlation; Computational modeling; Visualization; Redundancy; Multi modal affective state recognition; self-attention; temporal dependency; multi-modal multi-head attention; inter-modality interaction EMOTION RECOGNITION; NETWORKS Continuous affect recognition is becoming an increasingly attractive research topic in affective computing. Previous works mainly focused on modelling the temporal dependency within a sensor modality, or adopting early or late fusion for multi-modal affective state recognition. However, early fusion suffers from the curse of dimensionality, and late fusion ignores the complementarity and redundancy between multiple modal streams. In this paper, we first introduce the transformer-encoder with a self-attention mechanism and propose a Convolutional Neural Network-Transformer Encoder (CNN-TE) framework to model the temporal dependency for single modal affect recognition. Further, to effectively consider the complementarity and redundancy between multiple streams we propose a Transformer Encoder with Multi-modal Multi-head Attention (TEMMA) for multi-modal affect recognition. TEMMA allows to progressively and simultaneously refine the inter-modality interactions and intra-modality temporal dependency. The learned multi-modal representations are fed to an Inference Sub-network with fully connected layers to estimate the affective state. The proposed framework is trained in a nutshell and demonstrates its effectiveness on the AVEC2016 and AVEC2019 datasets. Compared to state-of-the-art models, our approach obtains remarkable improvements on both arousal and valence in terms of concordance correlation coefficient (CCC) reaching 0.583 for arousal and 0.564 for valence on the AVEC2019 test set. [Chen, Haifeng; Jiang, Dongmei] Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, NPU VUB Joint AVSP Res Lab, Sch Comp Sci,Shaanxi Key Lab Speech & Image Infor, Xian 710072, Peoples R China; [Chen, Haifeng; Jiang, Dongmei] Peng Cheng Lab, Shenzhen 518055, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel, Dept Elect & Informat, VUB NPU Joint AVSP Res Lab, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Peng Cheng Laboratory; Vrije Universiteit Brussel; Interuniversity Microelectronics Centre Jiang, DM (corresponding author), Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, NPU VUB Joint AVSP Res Lab, Sch Comp Sci,Shaanxi Key Lab Speech & Image Infor, Xian 710072, Peoples R China. chenhaifeng2018@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn; hsahli@etrovub.be Jiang, Dongmei/0000-0002-6238-8499; Chen, Haifeng/0000-0001-7164-5840; Sahli, Hichem/0000-0002-1774-2970 Shaanxi Provincial Key RD Program [2017KW-ZD-14]; 111 Project [B16039]; Flemish Government (AI Research Program) Shaanxi Provincial Key RD Program; 111 Project(Ministry of Education, China - 111 Project); Flemish Government (AI Research Program) This work was supported in part by the Shaanxi Provincial Key R&D Program under Grant 2017KW-ZD-14, in part by 111 Project under Grant B16039, and in part by Flemish Government (AI Research Program). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Elisa Ricci. 68 8 8 12 39 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia 2021.0 23.0 4171 4183 10.1109/TMM.2020.3037496 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications XA2YU 2023-03-23 WOS:000720519900020 0
J Arif, MH; Li, JX; Iqbal, M; Liu, KX Arif, Muhammad Hassan; Li, Jianxin; Iqbal, Muhammad; Liu, Kaixu Sentiment analysis and spam detection in short informal text using learning classifier systems SOFT COMPUTING English Article Sentiment analysis; Spam detection; Learning classifier systems; High-dimensional; Sparseness REAL Sentiment analysis of public views and spam detection from social media text messages are two challenging data analysis tasks due to short informal text. This paper investigates the performance of learning classifier systems (LCS), which are rule-based machine learning techniques, in sentiment analysis of twitter messages and movie reviews, and spam detection from SMS and email data sets. In this study, an existing LCS technique is extended by introducing a novel encoding scheme to represent classifier rules in order to handle the sparseness in feature vectors, which are generated using the term frequency inverse document frequency of word n-grams and sentiment lexicons. The obtained results show that the proposed encoding scheme smoothed the learning process and generated consistently good results in all experiments conducted in this study. [Arif, Muhammad Hassan; Li, Jianxin] Beihang Univ BUAA, Sch Comp Sci & Engn, Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China; [Iqbal, Muhammad] Xtracta Ltd, Auckland 1061, New Zealand; [Liu, Kaixu] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy Beihang University; University of Pavia Li, JX (corresponding author), Beihang Univ BUAA, Sch Comp Sci & Engn, Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China. mhassanarif@act.buaa.edu.cn; lijx@act.buaa.edu.cn; iqbal.muhammad@xtracta.com; kaixu.liu01@universitadipavia.it NSFC [61472022, 61421003, SKLSDE-2016ZX-11]; Beijing Advanced Innovation Center for Big Data and Brain Computing NSFC(National Natural Science Foundation of China (NSFC)); Beijing Advanced Innovation Center for Big Data and Brain Computing This work is supported by NSFC program (Nos. 61472022, 61421003), SKLSDE-2016ZX-11 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing. 55 38 38 4 36 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1432-7643 1433-7479 SOFT COMPUT Soft Comput. NOV 2018.0 22.0 21 SI 7281 7291 10.1007/s00500-017-2729-x 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science GV8SE 2023-03-23 WOS:000446414900023 0
J Wang, LF; Wang, LG; Wang, H; Wang, XY; Bruzzone, L Wang, Lifeng; Wang, Liguo; Wang, Heng; Wang, Xiaoyi; Bruzzone, Lorenzo SPCNet: A Subpixel Convolution-Based Change Detection Network for Hyperspectral Images With Different Spatial Resolutions IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Spatial resolution; Feature extraction; Convolution; Task analysis; Interpolation; Residual neural networks; Tensors; Change detection (CD); deep learning; hyperspectral images (HSIs); multiscale images; remote sensing; residual network; subpixel convolution UNSUPERVISED CHANGE DETECTION; LAND-COVER TRANSITIONS; FUSION; URBAN The very high spectral resolution in hyperspectral images (HSIs) offers an opportunity to detect subtle land-cover changes. However, the availability of HSIs acquired from different platforms requires the development of change detection (CD) methods capable of processing HSIs with different spatial resolutions. In this article, we propose a general end-to-end subpixel convolution-based residual network (SPCNet) to accomplish the CD task between high spatial resolution (HR) and low spatial resolution (LR) HSIs. To effectively tackle the resolution matching issue, a super-resolution (SR) block with an efficient subpixel convolution layer is introduced to upscale the LR feature maps into HR maps. The subpixel convolution layer can fully explore the subpixel context information by learning an array of upscaling filters. Moreover, the designed SPC module is embedded into the LR branch to generate more discriminative representations. More importantly, the SPC module as a plug-and-play unit has the potential to be embedded into other baseline networks to enhance the feature learning capability. Experimental results on four HSI datasets demonstrate the effectiveness of the proposed SPCNet. [Wang, Lifeng; Wang, Liguo; Wang, Heng; Wang, Xiaoyi] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150000, Peoples R China; [Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy Harbin Engineering University; University of Trento Wang, LG (corresponding author), Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150000, Peoples R China. wanglifeng_2016@163.com; wangliguo@hrbeu.edu.cn ; Bruzzone, Lorenzo/A-2076-2012 Wang, Heng/0000-0002-5678-7186; Bruzzone, Lorenzo/0000-0002-6036-459X National Natural Science Foundation of China [62071084] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 62071084. 45 0 0 20 23 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5533314 10.1109/TGRS.2022.3189188 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3Q8TL 2023-03-23 WOS:000838496600008 0
J Wang, LZ; Pijanowski, B; Yang, WS; Zhai, RX; Omrani, H; Li, K Wang, Lingzhi; Pijanowski, Bryan; Yang, Weishi; Zhai, Ruixue; Omrani, Hichem; Li, Ke Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China HABITAT INTERNATIONAL English Article Multiple land use transitions; Urbanization; Artificial neural networks; Land use management; Urban planning; China CELLULAR-AUTOMATA; LOGISTIC-REGRESSION; LONG-TERM; EXPANSION; GROWTH; MODEL; VALIDATION; SPRAWL; MAPS; AREA Numerous machine learning-based land change models have been presented by researchers over the last two decades. To date, however, far less have simulated multiple land use classes and specific land use subclasses at the same time. In some areas of the world, it is important to simulate both of these dynamics to understand fully the drivers and consequences of land change. One important example is the process of urbanization in China, as urban policies have been developed that guide urban expansion, rural protections, and urban subclass development. This paper presents a new model integrating geographic information systems (GIS) with artificial neural networks (ANNs) to predict multiple transitions among land use types and urban subclasses in the Zhanggong District of Ganzhou city in China. We show that the model produces satisfactory goodness of fit values-based on location, quantity and spatial configuration-between simulated and observed land use maps for 2015. Our simulated future maps produced by the model for 2020 and 2025 demonstrate that transitions from farmland and forest to urban will remain the main pathway of urbanization although we predict that the rate will slow after 2025. The goals of urban planning should be aligned with land use planning according to Combining multiple laws and regulations in China. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our model can be adapted to other areas to benefit land use management and urban planning in China. [Wang, Lingzhi] Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China; [Wang, Lingzhi] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Wang, Lingzhi; Pijanowski, Bryan] Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47906 USA; [Yang, Weishi] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China; [Yang, Weishi] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; [Zhai, Ruixue] Zhejiang Huanke Environm Consultancy Co Ltd, Hangzhou 311100, Zhejiang, Peoples R China; [Omrani, Hichem] LISER, Urban Dev & Mobil Dept, Luxembourg, Luxembourg; [Li, Ke] Jilin Jianzhu Univ, Minist Educ, Key Lab Songliao Aquat Environm, Changchun, Jilin, Peoples R China Jilin University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Sun Yat Sen University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Jilin Jianzhu University Wang, LZ (corresponding author), Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China. wanglingz@jlu.edu.cn; bpijanow@purdue.edu; 43106363@qq.com; vchuju000@163.com; hichem.omrani@liser.lu; like123@163.com Omrani, Hichem/C-9647-2013; Omrani, Hichem/AAP-8294-2021 Omrani, Hichem/0000-0003-1484-3028; Pijanowski, Bryan/0000-0002-7089-1959 Jilin Province Science and Technology Development Plan Project [20180418111FG]; Jilin Provincial Department of Education 13th Five-Year Science and Technology Project [JJKH20180163KJ]; Key Program of National Natural Science Foundation of China [41731286]; Major Science and Technology Program for Water Pollution Control and Treatment [2010ZX07320-003-004]; Jilin Science Foundation for Excellent Young Scholars [20180520169JH] Jilin Province Science and Technology Development Plan Project; Jilin Provincial Department of Education 13th Five-Year Science and Technology Project; Key Program of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Science and Technology Program for Water Pollution Control and Treatment; Jilin Science Foundation for Excellent Young Scholars This work was supported by Jilin Province Science and Technology Development Plan Project (Grant No. 20180418111FG); Jilin Provincial Department of Education 13th Five-Year Science and Technology Project (Grant No. JJKH20180163KJ), Key Program of National Natural Science Foundation of China (Grant No. 41731286); The last author was supported by the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No.2010ZX07320-003-004); Jilin Science Foundation for Excellent Young Scholars (Grant No. 20180520169JH); The land use data and soil fertility data were provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). 78 32 33 11 94 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0197-3975 1873-5428 HABITAT INT Habitat Int. DEC 2018.0 82.0 48 61 10.1016/j.habitatint.2018.08.007 0.0 14 Development Studies; Environmental Studies; Regional & Urban Planning; Urban Studies Social Science Citation Index (SSCI) Development Studies; Environmental Sciences & Ecology; Public Administration; Urban Studies HD5JJ 2023-03-23 WOS:000452565000005 0
J Shen, KQ; Yang, XY; Lolli, S; Vivone, G Shen, Kangqing; Yang, Xiaoyuan; Lolli, Simone; Vivone, Gemine A continual learning-guided training framework for pansharpening ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Continual learning; Convolutional neural networks; Deep learning; Pansharpening; Image fusion; Remote sensing PAN-SHARPENING METHOD; IMAGE FUSION; NETWORK; CONTRAST; PCA Supervised learning-based methods for pansharpening were criticized since their appearance because they rely on scale-shift assumption, i.e. those methods usually perform much better at reduced resolution than at full resolution. To address this issue, here we propose a general training framework for supervised learning-based pansharpening. Our training process consists of two stages: the first one is a conventional supervised method, which is applied to the reduced resolution dataset to obtain the converged model, while in the second model, obtained from stage one, is trained through an unsupervised learning scheme. Moreover, we developed a novel loss function made up of two terms that guarantees model high performance, both at reduced and full resolution. To the best of our knowledge, this is the first attempt to introduce the continual learning concept into pansharpening. The proposed framework is general and can be applied to any supervised learning-based pansharpening network. Also, the proposed method shows robustness with respect to the changing of the satellite sensor used to provide the data to be fused. Extensive tests on images from QuickBird, GaoFen-2, WorldView-3, and WorldView-2 show the effectiveness of the proposed methodology. [Shen, Kangqing; Yang, Xiaoyuan] Beihang Univ, Sch Math Sci, Beijing 102206, Peoples R China; [Lolli, Simone; Vivone, Gemine] CNR IMAA, Inst Methodol Environm Anal, I-85050 Tito, Italy Beihang University; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR) Yang, XY (corresponding author), Beihang Univ, Sch Math Sci, Beijing 102206, Peoples R China. xiaoyuanyang@vip.163.com Vivone, Gemine/0000-0001-9542-0638; Shen, Kangqing/0000-0002-6136-5594 National Natural Science Founda-tion of China [61671002]; Supercomputing Platform of School of Mathematical Sciences at Beihang University National Natural Science Founda-tion of China(National Natural Science Foundation of China (NSFC)); Supercomputing Platform of School of Mathematical Sciences at Beihang University Acknowledgments This work was supported by the National Natural Science Founda-tion of China under grant 61671002. This research was supported by the high performance computing (HPC) resources at Beihang University and the Supercomputing Platform of School of Mathematical Sciences at Beihang University. Our special thanks go to the authors of (Zhou et al., 2021a) for providing their source images and codes through https://github.com /zhysora/PSGan-Family. 64 0 0 8 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. FEB 2023.0 196.0 45 57 10.1016/j.isprsjprs.2022.12.015 0.0 JAN 2023 13 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology 7Z9MC 2023-03-23 WOS:000915874000001 0
C Guan, RC; Wang, X; Marchese, M; Liang, YC; Yang, C IEEE Guan, Renchu; Wang, Xu; Marchese, Maurizio; Liang, Yanchun; Yang, Chen A Feature Space Learning Model Based on Semi-Supervised Clustering 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1 IEEE International Conference on Computational Science and Engineering English Proceedings Paper 20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC) JUL 21-24, 2017 Guangzhou, PEOPLES R CHINA IEEE,IEEE Comp Soc,IFIP,IEEE Tech Comm Scalable Comp,Univ Porto,Xilinx,Univ Guangzhou Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and useful features but also adaptively span new feature spaces (e.g. only 13.4% - 41.3% of the original feature space) and update feature values; (2) Using two classical clustering methods (k-means and affinity propagation), four feature space learning (FSL) algorithms are proposed based on a semi-supervised setting. (3) FSL can provide a better data understanding and a descriptive learned feature space that facilitates learning without the tough training for deep architectures. The experimental results on the benchmark data sets demonstrate that FSLbased models are better at promoting the learning performance than the classical unsupervised and semi-supervised clustering models, such as k-means, affinity propagation, and semisupervised affinity propagation, e. g. 13.8% higher F-measure than SK-means and 73.6% higher than k-means. With the carefully designed risk control strategy, the FSL model can dynamically disentangle explanatory factors, depress the noise accumulation and semantic shift, construct easy-to-understand feature spaces. [Guan, Renchu; Wang, Xu; Liang, Yanchun] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Natl Educ Minist, Changchun 130012, Jilin, Peoples R China; [Marchese, Maurizio] Univ Trento, Dept Engn & Comp Sci, I-38123 Povo 9, Italy; [Liang, Yanchun] Jilin Univ, Zhuhai Coll, Minist Educ, Zhuhai Lab Key Lab Symbol Computat & Knowledge En, Zhuhai 519041, Peoples R China; [Yang, Chen] Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China Jilin University; University of Trento; Jilin University; Jilin University Yang, C (corresponding author), Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China. yangc616@jlu.edu.cn Guan, Renchu/S-5503-2019; Guan, Renchu/K-5223-2015 Guan, Renchu/0000-0002-7162-7826; Guan, Renchu/0000-0002-7162-7826 19 1 1 0 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1949-0828 978-1-5386-3221-5 IEEE INT C COMPUT 2017.0 403 409 10.1109/CSE-EUC.2017.79 0.0 7 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL5BB 2023-03-23 WOS:000451195900067 0
J Cheng, LL; Tong, ZF; Xie, SJ; Kersemans, M Cheng, Liangliang; Tong, Zongfei; Xie, Shejuan; Kersemans, Mathias IRT-GAN: A generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography COMPOSITE STRUCTURES English Article Deep learning; GAN; Infrared thermography; Composite; Defect detection; Non-destructive testing; Image fusion ENHANCEMENT InfraRed Thermography (IRT) is a valuable diagnostic tool to non-destructively detect defects in fiber reinforced polymers. Often, a range of processing techniques are applied, e.g. principal component analysis, Fourier transformation, and thermographic signal reconstruction, in an attempt to enhance the defect detectability. Still, for the actual defect detection and evaluation, the interpretation by an expert operator is required which thus limits the (industrial) application potential of infrared thermography.This study proposes a Generative Adversarial Network (GAN) framework, termed IRT-GAN, to create a single unique thermal-image-to-segmentation translation of defects in composite materials. A large augmented numerical dataset has been simulated for a range of composite materials with different defects in order to train the IRT-GAN model. Integrated with the Spatial Group-wise Enhance layer, the IRT-GAN takes six pre-processed thermal images, thermographic signal reconstruction images in our case, as input and progressively fuses them via a multi-headed fusion strategy in the Generator. As such, this proposed IRT-GAN framework leads to the automated generation of a unique defect segmentation image.The high performance of the IRT-GAN, trained on the virtual dataset, is demonstrated on experimental data of both glass and carbon fiber reinforced polymers with various defect types, sizes, and depths. In addition, it is investigated how early, middle, and late-stage feature fusion in the GAN influences the segmentation performance. [Cheng, Liangliang; Tong, Zongfei; Kersemans, Mathias] Univ Ghent, Mech Mat & Struct MMS, Technol Pk 46, B-9052 Zwijnaarde, Belgium; [Tong, Zongfei; Xie, Shejuan] Xi An Jiao Tong Univ, State Key Lab Strength & Vibrat Mech Struct, Xian 710049, Peoples R China Ghent University; Xi'an Jiaotong University Cheng, LL; Kersemans, M (corresponding author), Univ Ghent, Mech Mat & Struct MMS, Technol Pk 46, B-9052 Zwijnaarde, Belgium. liangliang.cheng@ugent.be; mathias.kersemans@ugent.be Kersemans, Mathias/GLQ-7503-2022 Cheng, Liangliang/0000-0002-7789-9849 Bijzonder OnderzoeksFonds Ghent University [BOF 01N01719]; China Scholarship Council [202006280481] Bijzonder OnderzoeksFonds Ghent University(Ghent University); China Scholarship Council(China Scholarship Council) This work has been funded by Bijzonder OnderzoeksFonds Ghent University (BOF 01N01719). Zongfei Tong is a joint Ph.D. student between Ghent University and Xi'an Jiaotong University and is supported by the China Scholarship Council by grant number 202006280481. 48 2 2 10 19 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-8223 1879-1085 COMPOS STRUCT Compos. Struct. JUN 15 2022.0 290.0 115543 10.1016/j.compstruct.2022.115543 0.0 APR 2022 16 Mechanics; Materials Science, Composites Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Materials Science 1C9PV Green Published 2023-03-23 WOS:000793445300003 0
J Jiang, H; Popov, T; Jylanki, P; Bi, K; Yao, Z; Lu, Q; Jensen, O; van Gerven, MAJ Jiang, H.; Popov, T.; Jylanki, P.; Bi, K.; Yao, Z.; Lu, Q.; Jensen, O.; van Gerven, M. A. J. Predictability of depression severity based on posterior alpha oscillations CLINICAL NEUROPHYSIOLOGY English Article MEG; Resting state; Major depressive disorder; Bayesian linear regression GAMMA-BAND ACTIVITY; NEURONAL OSCILLATIONS; MENTAL-DISORDERS; HUMAN BRAIN; POWER; ASYMMETRY; FREQUENCY; CONNECTIVITY; PERCEPTION; NETWORKS Objective: We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. Methods: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. Results: In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r = 0.68, p = 0.0005) and beta power (r = 0.56, p = 0.007) respectively. Conclusions: Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. Significance: The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process. (C) 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. [Jiang, H.; Yao, Z.] Nanjing Med Univ, Acad Dept Psychiat, Nanjing Brain Hosp, 249 Guangzhou Rd, Nanjing 210029, Jiangsu, Peoples R China; [Jiang, H.; Popov, T.; Jylanki, P.; Jensen, O.; van Gerven, M. A. J.] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6500 HE Nijmegen, Netherlands; [Bi, K.; Lu, Q.] Southeast Univ, Key Lab Child Dev & Learning Sci, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R China; [Yao, Z.] Nanjing Univ, Coll Med, 22 Hankou Rd, Nanjing 210093, Jiangsu, Peoples R China Nanjing Medical University; Radboud University Nijmegen; Southeast University - China; Nanjing University Yao, Z; Lu, Q (corresponding author), Nanjing Med Univ, Acad Dept Psychiat, Nanjing Brain Hosp, 249 Guangzhou Rd, Nanjing 210029, Jiangsu, Peoples R China. zjyao@njmu.edu.cn; luq@seu.edu.cn Jensenq, Ole/D-2120-2010; van Gerven, Marcel A. J./D-7800-2012; Jiang, Haiteng/F-3197-2011; Popov, Todor A/Q-9928-2016 Jensenq, Ole/0000-0001-8193-8348; van Gerven, Marcel A. J./0000-0002-2206-9098; Jiang, Haiteng/0000-0003-0739-8413; Popov, Todor A/0000-0001-5052-5866 National High Technology Research and Development Program of China (863 Program) [2015AA020509]; National Natural Science Foundation of China [81371522, 61372032]; Jiangsu Clinical Medicine Technology Foundation [BL2012052, BL2014009] National High Technology Research and Development Program of China (863 Program)(National High Technology Research and Development Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Clinical Medicine Technology Foundation The work was supported by the grants of: National High Technology Research and Development Program of China (863 Program) (2015AA020509); The National Natural Science Foundation of China (81371522, 61372032); Jiangsu Clinical Medicine Technology Foundation (BL2012052, BL2014009). 40 30 31 3 29 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 1388-2457 1872-8952 CLIN NEUROPHYSIOL Clin. Neurophysiol. APR 2016.0 127.0 4 2108 2114 10.1016/j.clinph.2015.12.018 0.0 7 Clinical Neurology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology DJ4JS 26821982.0 2023-03-23 WOS:000374171800027 0
J Mei, ZN; Zhao, X; Chen, HY; Chen, W Mei, Zhenning; Zhao, Xian; Chen, Hongyu; Chen, Wei Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review SENSORS English Review epileptic seizure; non-stationary signal processing; nonlinear dynamics; complex network; machine learning ILAE COMMISSION; TIME-SERIES; AUTOMATIC RECOGNITION; INTERNATIONAL-LEAGUE; APPROXIMATE ENTROPY; DETECTION ALGORITHM; DRY ELECTRODES; POSITION PAPER; EEG; CLASSIFICATION Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications. [Mei, Zhenning; Zhao, Xian; Chen, Wei] Fudan Univ, Sch Informat Sci & Engn, CIME, Shanghai 200433, Peoples R China; [Chen, Hongyu] Eindhoven Univ Technol, Dept Ind Design, POB 513, NL-5600 MB Eindhoven, Netherlands; [Chen, Wei] Shanghai Key Lab Med Imaging Comp & Comp Assisted, Shanghai 200032, Peoples R China Fudan University; Eindhoven University of Technology Chen, W (corresponding author), Fudan Univ, Sch Informat Sci & Engn, CIME, Shanghai 200433, Peoples R China.;Chen, W (corresponding author), Shanghai Key Lab Med Imaging Comp & Comp Assisted, Shanghai 200032, Peoples R China. znmei16@fudan.edu.cn; xzhao17@fudan.edu.cn; Hongyu.Chen@tue.nl; w_chen@fudan.edu.cn chen, hongyu/HHS-4314-2022 China Minister of Science and Technology and EU Commission [2017YFE0112000]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01] China Minister of Science and Technology and EU Commission; Shanghai Municipal Science and Technology Major Project This work was funded by China Minister of Science and Technology and EU Commission collaborative project (Grant No. 2017YFE0112000) and Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01). 122 13 13 4 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUN 2018.0 18.0 6 1720 10.3390/s18061720 0.0 27 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GL0KD 29861451.0 Green Submitted, gold, Green Accepted, Green Published 2023-03-23 WOS:000436774300054 0
C Xu, WQ; Ji, XF; Zhang, CW; Zhang, BC; Wang, Y; Wang, XJ; Wang, YS; Wang, JP; Liu, B IEEE Xu, Wenquan; Ji, Xuefeng; Zhang, Chuwen; Zhang, Beichuan; Wang, Yu; Wang, Xiaojun; Wang, Yunsheng; Wang, Jianping; Liu, Bin PQR: Prediction-supported Quality-aware Routing for Uninterrupted Vehicle Communication 2021 IEEE/ACM 29TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS) International Workshop on Quality of Service English Proceedings Paper 29th IEEE/ACM International Symposium on Quality of Service (IWQOS) JUN 25-28, 2021 ELECTR NETWORK IEEE,Assoc Comp Machinery,IEEE Commun Soc,Natl Inst Informat & Commun Technol,IEICE Tech Comm Commun Qual,Cisco Syst ALGORITHM Vehicle to Vehicle (V2V) communication opens a new way to make vehicles directly communicate with each other, providing faster responses for time-sensitive tasks than cellular networks. Effective V2V routing protocols are essential yet challenging, as the high dynamic road environment makes communication easy to break. Many prediction methods proposed in the existing protocols to address this issue are either flawed or have a poor effect. In this paper, to cope with the two aspects of the problems that cause communication interrupt, i.e., link breaks and route quality degradation, we design an acceleration-based trajectory prediction algorithm to estimate the link lifetime, and a machine learning model to predict route quality. Based on the prediction algorithms, we propose PQR, a Prediction-supported Quality-aware Routing protocol, which can proactively switch to a better route before the current link breaks or the route quality degrades. Especially, considering the limitations of the current routing protocols, we elaborate a new hybrid routing protocol that integrates the topology-based method and location-based method to achieve instant communication. Simulation results show that PQR outperforms the existing protocols in Packet Delivery Ratio (PDR), Roundtrip Time (RTT), and Normalized Routing Overhead (NRO). Specifically, we have also implemented a vehicular testbed to demonstrate PQR's real-world performance, and results show that PQR achieves almost no packet loss with latency less than 10ms during route handoff for topology change. [Xu, Wenquan; Ji, Xuefeng; Zhang, Chuwen; Liu, Bin] Tsinghua Univ, Beijing, Peoples R China; [Zhang, Beichuan] Univ Arizona, Tucson, AZ 85721 USA; [Wang, Yu] Temple Univ, Philadelphia, PA 19122 USA; [Wang, Xiaojun] Dublin City Univ, Dublin, Ireland; [Wang, Yunsheng] Kettering Univ, Flint, MI USA; [Wang, Jianping] City Univ Hong Kong, Hong Kong, Peoples R China Tsinghua University; University of Arizona; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Dublin City University; Kettering University; City University of Hong Kong Liu, B (corresponding author), Tsinghua Univ, Beijing, Peoples R China. lmyujie@gmail.com Guangdong Basic and Applied Basic Research Foundation [2019B1515120031]; NSFC [61872213, 62032013]; Tsinghua University (Department of Computer Science and Technology)-Siemens Ltd.; Hong Kong Research Grant Council under NSFC/RGC [CityU 140/20]; China Joint Research Center for Industrial Intelligence and Internet of Things Guangdong Basic and Applied Basic Research Foundation; NSFC(National Natural Science Foundation of China (NSFC)); Tsinghua University (Department of Computer Science and Technology)-Siemens Ltd.; Hong Kong Research Grant Council under NSFC/RGC; China Joint Research Center for Industrial Intelligence and Internet of Things This paper is supported in part by Guangdong Basic and Applied Basic Research Foundation under Grant (2019B1515120031), NSFC (61872213,62032013), Tsinghua University (Department of Computer Science and Technology)-Siemens Ltd., China Joint Research Center for Industrial Intelligence and Internet of Things, and Hong Kong Research Grant Council under NSFC/RGC N CityU 140/20. Corresponding Authors: Bin Liu (lmyujie@gmail.com). 31 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1548-615X 978-1-6654-1494-4 INT WORKSH QUAL SERV 2021.0 10.1109/IWQOS52092.2021.9521294 0.0 10 Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS3CA 2023-03-23 WOS:000709411600034 0
J Yang, HR; Zhang, QR; Helbich, M; Lu, Y; He, DS; Ettema, D; Chen, L Yang, Haoran; Zhang, Qinran; Helbich, Marco; Lu, Yi; He, Dongsheng; Ettema, Dick; Chen, Long Examining non-linear associations between built environments around workplace and adults' walking behaviour in Shanghai, China TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE English Article Walking behaviors; Workplace; Built environment; Non-linear associations; Machine learning PHYSICAL-ACTIVITY; NEIGHBORHOOD; HEALTH; TIME; UTILITARIAN; PERCEPTIONS; TRAVEL Considering that most working adults spend nearly half their waking time at work, creating a supportive built environment around workplaces could be a feasible approach to maintain adequate levels of physical activity. However, the extent to which the built environment around workplaces influences walking behaviors in working adults remains unclear. Using survey data of 1009 full-time employees in Shanghai, China, this study assessed the nonlinear relationships between the built environment characteristics around workplaces and three domains of walking behaviors (commuting, utilitarian, and recreational walking). Using gradient boosting decision trees, our results showed that the built environment around workplaces is crucial for higher levels of walking behaviors, but built environment features tended to have distinctive associations with different domains of walking behaviors. Specifically, the number of physical activity facilities was positively associated with all three domains of walking behaviors, while a high floor area ratio was negatively associated with different domains of walking behaviors to some extent. Furthermore, several built environment characteristics, such as land use entropy, street view greenery, distance from home to the city center, and distance between the city center and workplaces had distinctive associations with different domains of walking behaviors. The findings of this study could provide nuanced guidance for creating pedestrian-friendly environments around workplaces to promote walking behaviors and overall physical activity levels in the working population. [Yang, Haoran; Zhang, Qinran; He, Dongsheng] East China Normal Univ, Res Ctr China Adm Div, Shanghai, Peoples R China; [Yang, Haoran; Zhang, Qinran; He, Dongsheng] East China Normal Univ, Ctr Modern Chinese City Studies, Shanghai, Peoples R China; [Yang, Haoran; Zhang, Qinran; He, Dongsheng] East China Normal Univ, Sch Urban & Reg Sci, Shanghai, Peoples R China; [Yang, Haoran; Zhang, Qinran; He, Dongsheng] East China Normal Univ, Future City Lab, Shanghai, Peoples R China; [Helbich, Marco; Ettema, Dick] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands; [Lu, Yi; Chen, Long] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China; [He, Dongsheng] Univ Hong Kong, Dept Urban Planning & Design, Hong Kong, Peoples R China; [He, Dongsheng] Univ Hong Kong, Urban Analyt & Intervent Res Lab, Hong Kong, Peoples R China East China Normal University; East China Normal University; East China Normal University; East China Normal University; Utrecht University; City University of Hong Kong; University of Hong Kong; University of Hong Kong He, DS (corresponding author), East China Normal Univ, Res Ctr China Adm Div, Shanghai, Peoples R China.;He, DS (corresponding author), East China Normal Univ, Ctr Modern Chinese City Studies, Shanghai, Peoples R China.;He, DS (corresponding author), East China Normal Univ, Sch Urban & Reg Sci, Shanghai, Peoples R China.;He, DS (corresponding author), East China Normal Univ, Future City Lab, Shanghai, Peoples R China. haoranyang0119@126.com; Tsingreychang@outlook.com; m.helbich@uu.nl; yilu24@cityu.edu.hk; hedsh3@connect.hku.hk; d.f.ettema@uu.nl; lochen6-c@my.cityu.edu.hk Yang, Haoran/GXH-6567-2022; Chen, Long/ABG-6218-2021; LU, Yi/AAD-7750-2020 Chen, Long/0000-0001-6291-9950; LU, Yi/0000-0001-7614-6661; Yang, Haoran/0000-0001-5572-9390 National Natural Science of China [51778552, 42001123]; Strategic Priority Research Program (A) of Chinese Academy of Sciences [XDA19040402]; Fundamental Research Funds for the Central Universities; Academy of Humanities & Social Science in ECNU National Natural Science of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program (A) of Chinese Academy of Sciences; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Academy of Humanities & Social Science in ECNU The research is supported by the National Natural Science of China [Project No. 51778552 and No. 42001123] , the grants from the Strategic Priority Research Program (A) of Chinese Academy of Sciences (Project No. XDA19040402) , and the Fundamental Research Funds for the Central Universities. The authors appreciate Academy of Humanities & Social Science in ECNU for funding the data collection. 58 12 12 26 41 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0965-8564 1879-2375 TRANSPORT RES A-POL Transp. Res. Pt. A-Policy Pract. JAN 2022.0 155.0 234 246 10.1016/j.tra.2021.11.017 0.0 13 Economics; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Transportation 1B7SG 2023-03-23 WOS:000792632500015 0
J Hu, WY; Wang, W; Wang, YY; Chen, YF; Shang, XW; Liao, H; Huang, Y; Bulloch, G; Zhang, SR; Kiburg, K; Zhang, XL; Tang, SL; Yu, HH; Yang, XH; He, MG; Zhu, ZT Hu, Wenyi; Wang, Wei; Wang, Yueye; Chen, Yifan; Shang, Xianwen; Liao, Huan; Huang, Yu; Bulloch, Gabriella; Zhang, Shiran; Kiburg, Katerina; Zhang, Xueli; Tang, Shulin; Yu, Honghua; Yang, Xiaohong; He, Mingguang; Zhu, Zhuoting Retinal age gap as a predictive biomarker of future risk of Parkinson's disease AGE AND AGEING English Article retinal age; Parkinson's disease; prediction; older people NERVE-FIBER LAYER; TELOMERE LENGTH; BLOOD Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 +/- 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD. [Hu, Wenyi; Huang, Yu; Zhang, Xueli; Tang, Shulin; Yu, Honghua; Yang, Xiaohong; He, Mingguang; Zhu, Zhuoting] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Ophthalmol, Guangzhou, Peoples R China; [Hu, Wenyi; Shang, Xianwen; Bulloch, Gabriella; Kiburg, Katerina; He, Mingguang; Zhu, Zhuoting] Univ Melbourne, Ctr Eye Res Australia, Ophthalmol, Melbourne, Vic, Australia; [Hu, Wenyi; He, Mingguang] Univ Melbourne, Dept Surg, Ophthalmol, Melbourne, Vic, Australia; [Wang, Wei; Wang, Yueye; Zhang, Shiran; He, Mingguang] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China; [Chen, Yifan] Oxford Univ Hosp NHS Fdn Trust, John Radcliffe Hosp, Oxford, England; [Liao, Huan] Univ Bonn, Inst Reconstruct Neurobiol, Neural Regenerat Grp, Bonn, Germany Guangdong Academy of Medical Sciences & Guangdong General Hospital; Centre for Eye Research Australia; University of Melbourne; University of Melbourne; Sun Yat Sen University; Oxford University Hospitals NHS Foundation Trust; University of Oxford; University of Bonn Yu, HH; Yang, XH; He, MG; Zhu, ZT (corresponding author), Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Ophthalmol, Guangzhou, Peoples R China.;He, MG; Zhu, ZT (corresponding author), Univ Melbourne, Ctr Eye Res Australia, Ophthalmol, Melbourne, Vic, Australia.;He, MG (corresponding author), Univ Melbourne, Dept Surg, Ophthalmol, Melbourne, Vic, Australia.;He, MG (corresponding author), Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China. yuhonghua@gdph.org.cn; syyangxh@scut.edu.cn; mingguang.he@unimelb.edu.au; zhuoting_zhu@hotmail.com yang, xiao/HJI-7815-2023 Shang, Xianwen/0000-0002-2362-3222; Bulloch, Gabriella/0000-0001-9901-7088; Hu, Wenyi/0000-0001-7366-3874; Zhang, Xueli/0000-0001-5963-9261; Shang, Xianwen/0000-0001-6143-9349 National Natural Science Foundation of China [82000901, 82101173, 81870663, 82171075]; Fundamental Research Funds of the State Key Laboratory of Ophthalmology; NHMRC [2010072]; Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital [KJ012019087]; Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province [KJ012019457]; Talent Introduction Fund of Guangdong Provincial People's Hospital [Y012018145]; Science and Technology Program of Guangzhou, China [202002020049]; Project of Special Research on Cardiovascular Diseases [2020XXG007]; Research Foundation of Medical Science and Technology of Guangdong Province [B2021237]; University of Melbourne at Research Accelerator Program; CERA Foundation; National Health and Medical Research Council of Australia [2010072] Funding Source: NHMRC National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds of the State Key Laboratory of Ophthalmology; NHMRC(National Health and Medical Research Council (NHMRC) of Australia); Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital; Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province; Talent Introduction Fund of Guangdong Provincial People's Hospital; Science and Technology Program of Guangzhou, China; Project of Special Research on Cardiovascular Diseases; Research Foundation of Medical Science and Technology of Guangdong Province; University of Melbourne at Research Accelerator Program; CERA Foundation; National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia) The present work was supported by Fundamental Research Funds of the State Key Laboratory of Ophthalmology, National Natural Science Foundation of China (82000901, 82101173, 81870663, 82171075), NHMRC Investigator Grants (2010072) Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital (KJ012019087), Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province (KJ012019457), Talent Introduction Fund of Guangdong Provincial People's Hospital (Y012018145), Science and Technology Program of Guangzhou, China (202002020049), Project of Special Research on Cardiovascular Diseases (2020XXG007) and Research Foundation of Medical Science and Technology of Guangdong Province (B2021237). Prof. Mingguang He receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organisation had no role in the design or conduct of this research. 51 2 2 2 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0002-0729 1468-2834 AGE AGEING Age Ageing MAR 1 2022.0 51.0 3 afac062 10.1093/ageing/afac062 0.0 9 Geriatrics & Gerontology Science Citation Index Expanded (SCI-EXPANDED) Geriatrics & Gerontology 0C3OJ 35352798.0 hybrid, Green Accepted 2023-03-23 WOS:000775226400006 0
J Yan, J; Rodriguez-Martinez, X; Pearce, D; Douglas, H; Bili, D; Azzouzi, M; Eisner, F; Virbule, A; Rezasoltani, E; Belova, V; Dorling, B; Few, S; Szumska, AA; Hou, XY; Zhang, GCA; Yip, HL; Campoy-Quiles, M; Nelson, J Yan, Jun; Rodriguez-Martinez, Xabier; Pearce, Drew; Douglas, Hana; Bili, Danai; Azzouzi, Mohammed; Eisner, Flurin; Virbule, Alise; Rezasoltani, Elham; Belova, Valentina; Dorling, Bernhard; Few, Sheridan; Szumska, Anna A.; Hou, Xueyan; Zhang, Guichuan; Yip, Hin-Lap; Campoy-Quiles, Mariano; Nelson, Jenny Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics ENERGY & ENVIRONMENTAL SCIENCE English Article CLEAN ENERGY PROJECT; SOLAR-CELLS; QUANTUM-CHEMISTRY; POLYMERS; DESCRIPTORS; CANDIDATES; EFFICIENCY; STABILITY; DISCOVERY; SPECTRA Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of their high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of similar to 500 pi-conjugated molecules. The calculations are first validated by comparison with experimental measurements in solution and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient (epsilon(d,max)) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic pi-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest algorithm to draw predictions for epsilon(d,max) using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in pi-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost. [Yan, Jun; Pearce, Drew; Douglas, Hana; Bili, Danai; Azzouzi, Mohammed; Eisner, Flurin; Virbule, Alise; Rezasoltani, Elham; Few, Sheridan; Szumska, Anna A.; Hou, Xueyan; Nelson, Jenny] Imperial Coll London, Dept Phys, London SW7 2AZ, England; [Rodriguez-Martinez, Xabier] Linkoping Univ, Dept Phys Chem & Biol IFM, Elect & Photon Mat EFM, Linkoping, Sweden; [Rodriguez-Martinez, Xabier; Belova, Valentina; Dorling, Bernhard; Campoy-Quiles, Mariano] ICMAB CSIC, Inst Ciencia Mat Barcelona, Campus UAB, Bellaterra 08193, Spain; [Zhang, Guichuan; Yip, Hin-Lap] South China Univ Technol, Inst Polymer Optoelect Mat & Devices, State Key Lab Luminescent Mat & Devices, Guangzhou 510640, Peoples R China; [Yip, Hin-Lap] City Univ Hong Kong, Dept Mat Sci & Engn, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China; [Few, Sheridan] Univ Leeds, Sustainabil Res Inst, Sch Earth & Environm, Leeds LS2 9JT, W Yorkshire, England Imperial College London; Linkoping University; Autonomous University of Barcelona; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Ciencia de Materiales de Barcelona (ICMAB); South China University of Technology; City University of Hong Kong; University of Leeds Nelson, J (corresponding author), Imperial Coll London, Dept Phys, London SW7 2AZ, England.;Rodriguez-Martinez, X (corresponding author), Linkoping Univ, Dept Phys Chem & Biol IFM, Elect & Photon Mat EFM, Linkoping, Sweden.;Campoy-Quiles, M (corresponding author), ICMAB CSIC, Inst Ciencia Mat Barcelona, Campus UAB, Bellaterra 08193, Spain. xabier.rodriguez.martinez@liu.se; mcampoy@icmab.es; jenny.nelson@imperial.ac.uk Rodríguez-Martínez, Xabier/HGU-5910-2022; Yan, Jun/AAD-2292-2021; Yip, Hin-Lap/A-3637-2012 Rodríguez-Martínez, Xabier/0000-0002-6715-4392; Yan, Jun/0000-0001-9966-4357; Yip, Hin-Lap/0000-0002-5750-9751; Few, Sheridan/0000-0002-0876-158X; Eisner, Flurin/0000-0001-5507-4961; Rein, Valentina/0000-0002-8142-2090 European Research Council under the European Union's Horizon 2020 research and innovation program [742708, 648901]; Spanish Ministry of Science and Innovation through the Severo Ochoa'' Program for Centers of Excellence in RD [CEX2019-000917-S, PGC2018-095411-B-I00]; Fonds de Recherche du Quebec-Nature et technologies (FRQNT); European Cooperation in Science and Technology; Engineering and Physical Sciences Research Council (EPSRC); Knut and Allice Wallenberg Foundation; Guangdong Major Project of Basic and Applied Basic Research [2019B030302007] European Research Council under the European Union's Horizon 2020 research and innovation program; Spanish Ministry of Science and Innovation through the Severo Ochoa'' Program for Centers of Excellence in RD; Fonds de Recherche du Quebec-Nature et technologies (FRQNT); European Cooperation in Science and Technology(European Cooperation in Science and Technology (COST)); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Knut and Allice Wallenberg Foundation(Knut & Alice Wallenberg Foundation); Guangdong Major Project of Basic and Applied Basic Research J. N., J. Y., D. P., M. A., F. E., and E. R. thank the European Research Council for support under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 742708 and 648901). The authors at ICMAB acknowledge financial support from the Spanish Ministry of Science and Innovation through the Severo Ochoa'' Program for Centers of Excellence in R&D (No. CEX2019-000917-S), and project PGC2018-095411-B-I00. E. R. is grateful to the Fonds de Recherche du Quebec-Nature et technologies (FRQNT) for a postdoctoral fellowship and acknowledges financial support from the European Cooperation in Science and Technology. M. A. thanks the Engineering and Physical Sciences Research Council (EPSRC) for support via doctoral studentships. F. E. thanks the Engineering and Physical Sciences Research Council (EPSRC) for support via the Post-Doctoral Prize Fellowship. X. R.-M. acknowledges Prof. Olle Inganas and the Knut and Allice Wallenberg Foundation for funding of his current postdoctoral position. H.-L. Yip thanks the support from Guangdong Major Project of Basic and Applied Basic Research (2019B030302007). The TOC figure and Fig. 5a in the manuscript include freely available resources from Flaticon.com. J. Y. thank Xiaodan Ge for her support. 95 7 7 15 26 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1754-5692 1754-5706 ENERG ENVIRON SCI Energy Environ. Sci. JUL 13 2022.0 15.0 7 2958 2973 10.1039/d2ee00887d 0.0 MAY 2022 16 Chemistry, Multidisciplinary; Energy & Fuels; Engineering, Chemical; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Energy & Fuels; Engineering; Environmental Sciences & Ecology 2V4KF 35923416.0 Green Published, Green Accepted, hybrid, Green Submitted 2023-03-23 WOS:000803559300001 0
J Tsilimigras, DI; Mehta, R; Moris, D; Sahara, K; Bagante, F; Paredes, AZ; Moro, A; Guglielmi, A; Aldrighetti, L; Weiss, M; Bauer, TW; Alexandrescu, S; Poultsides, GA; Maithel, SK; Marques, HP; Martel, G; Pulitano, C; Shen, F; Soubrane, O; Koerkamp, BG; Endo, I; Pawlik, TM Tsilimigras, Diamantis, I; Mehta, Rittal; Moris, Dimitrios; Sahara, Kota; Bagante, Fabio; Paredes, Anghela Z.; Moro, Amika; Guglielmi, Alfredo; Aldrighetti, Luca; Weiss, Matthew; Bauer, Todd W.; Alexandrescu, Sorin; Poultsides, George A.; Maithel, Shishir K.; Marques, Hugo P.; Martel, Guillaume; Pulitano, Carlo; Shen, Feng; Soubrane, Olivier; Koerkamp, Bas Groot; Endo, Itaru; Pawlik, Timothy M. A Machine-Based Approach to Preoperatively Identify Patients with the Most and Least Benefit Associated with Resection for Intrahepatic Cholangiocarcinoma: An International Multi-institutional Analysis of 1146 Patients ANNALS OF SURGICAL ONCOLOGY English Article LONG-TERM SURVIVAL; CHEMOTHERAPY; CARCINOMA; NOMOGRAM; OUTCOMES; GRADE Background Accurate risk stratification and patient selection is necessary to identify patients who will benefit the most from surgery or be better treated with other non-surgical treatment strategies. We sought to identify which patients in the preoperative setting would likely derive the most or least benefit from resection of intrahepatic cholangiocarcinoma (ICC). Methods Patients who underwent curative-intent resection for ICC between 1990 and 2017 were identified from an international multi-institutional database. A machine-based classification and regression tree (CART) was used to generate homogeneous groups of patients relative to overall survival (OS) based on preoperative factors. Results Among 1146 patients, CART analysis revealed tumor number and size, albumin-bilirubin (ALBI) grade and preoperative lymph node (LN) status as the strongest prognostic factors associated with OS among patients undergoing resection for ICC. In turn, four groups of patients with distinct outcomes were generated through machine learning: Group 1 (n = 228): single ICC, size <= 5 cm, ALBI grade I, negative preoperative LN status; Group 2 (n = 708): (1) single tumor > 5 cm, (2) single tumor <= 5 cm, ALBI grade 2/3, and (3) single tumor <= 5 cm, ALBI grade 1, metastatic/suspicious LNs; Group 3 (n = 150): 2-3 tumors; Group 4 (n = 60): >= 4 tumors. 5-year OS among Group 1, 2, 3, and 4 patients was 60.5%, 35.8%, 27.5%, and 3.8%, respectively (p < 0.001). Similarly, 5-year disease-free survival (DFS) among Group 1, 2, 3, and 4 patients was 47%, 27.2%, 6.8%, and 0%, respectively (p < 0.001). Conclusions The machine-based CART model identified distinct prognostic groups of patients with distinct outcomes based on preoperative factors. Survival decision trees may be useful as guides in preoperative patient selection and risk stratification. [Tsilimigras, Diamantis, I; Mehta, Rittal; Moris, Dimitrios; Sahara, Kota; Bagante, Fabio; Paredes, Anghela Z.; Moro, Amika; Pawlik, Timothy M.] Ohio State Univ, Wexner Med Ctr, Dept Surg, Div Surg Oncol, Columbus, OH 43210 USA; [Tsilimigras, Diamantis, I; Mehta, Rittal; Moris, Dimitrios; Sahara, Kota; Bagante, Fabio; Paredes, Anghela Z.; Moro, Amika; Pawlik, Timothy M.] Ohio State Univ, James Comprehens Canc Ctr, Columbus, OH 43210 USA; [Guglielmi, Alfredo] Univ Verona, Dept Surg, Verona, Italy; [Aldrighetti, Luca] Osped San Raffaele, Dept Surg, Milan, Italy; [Weiss, Matthew] Johns Hopkins Univ Hosp, Dept Surg, Baltimore, MD 21287 USA; [Bauer, Todd W.] Univ Virginia, Dept Surg, Charlottesville, VA USA; [Alexandrescu, Sorin] Fundeni Clin Inst, Dept Surg, Bucharest, Romania; [Poultsides, George A.] Stanford Univ, Dept Surg, Stanford, CA 94305 USA; [Maithel, Shishir K.] Emory Univ, Dept Surg, Atlanta, GA 30322 USA; [Marques, Hugo P.] Curry Cabral Hosp, Dept Surg, Lisbon, Portugal; [Martel, Guillaume] Univ Ottawa, Dept Surg, Ottawa, ON, Canada; [Pulitano, Carlo] Univ Sydney, Royal Prince Alfred Hosp, Dept Surg, Sydney, NSW, Australia; [Shen, Feng] Eastern Hepatobiliary Surg Hosp, Dept Surg, Shanghai, Peoples R China; [Soubrane, Olivier] Beaujon Hosp, AP HP, Dept Hepatobiliopancreat Surg & Liver Transplanta, Clichy, France; [Koerkamp, Bas Groot] Erasmus MC, Dept Surg, Rotterdam, Netherlands; [Endo, Itaru] Yokohama City Univ, Sch Med, Dept Gastroenterol Surg, Yokohama, Kanagawa, Japan; [Pawlik, Timothy M.] Ohio State Univ, Wexner Med Ctr, Urban Meyer III & Shelley Meyer Chair Canc Res, Dept Surg, Columbus, OH 43210 USA University System of Ohio; Ohio State University; James Cancer Hospital & Solove Research Institute; University System of Ohio; Ohio State University; University of Verona; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele; Johns Hopkins University; Johns Hopkins Medicine; University of Virginia; Institutul Clinic Fundeni; Stanford University; Emory University; University of Ottawa; University of Sydney; Naval Medical University; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Beaujon - APHP; Erasmus University Rotterdam; Erasmus MC; Yokohama City University; University System of Ohio; Ohio State University Pawlik, TM (corresponding author), Ohio State Univ, Wexner Med Ctr, Dept Surg, Div Surg Oncol, Columbus, OH 43210 USA.;Pawlik, TM (corresponding author), Ohio State Univ, James Comprehens Canc Ctr, Columbus, OH 43210 USA.;Pawlik, TM (corresponding author), Ohio State Univ, Wexner Med Ctr, Urban Meyer III & Shelley Meyer Chair Canc Res, Dept Surg, Columbus, OH 43210 USA. tim.pawlik@osumc.edu Soubrane, Olivier/ABB-4434-2021; ALEXANDRESCU, SORIN/AAJ-3660-2021; Shen, Feng/GSD-2508-2022; Mehta, Rittal/AAK-7539-2020; Pulitano, Carlo/HPB-9047-2023; Pawlik, Timothy/AAO-9335-2020; Groot koerkamp, Bas/ABD-1621-2020 ALEXANDRESCU, SORIN/0000-0002-2783-2769; Moris, Dimitrios/0000-0002-5276-0699; Mehta, Rittal/0000-0002-0807-4763; Pinto Marques, Hugo/0000-0003-3540-0488; Bagante, Fabio/0000-0002-5386-0958; Tsilimigras, Diamantis I./0000-0002-3676-9263; Groot Koerkamp, Bas/0000-0003-1917-6973; Pulitano, Carlo/0000-0002-4420-4107 33 23 24 0 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1068-9265 1534-4681 ANN SURG ONCOL Ann. Surg. Oncol. APR 2020.0 27.0 4 1110 1119 10.1245/s10434-019-08067-3 0.0 10 Oncology; Surgery Science Citation Index Expanded (SCI-EXPANDED) Oncology; Surgery KT1YN 31728792.0 Green Published 2023-03-23 WOS:000518809300031 0
J Dong, LX; Tang, SH; Min, M; Veroustraete, F; Cheng, J Dong, Lixin; Tang, Shihao; Min, Min; Veroustraete, Frank; Cheng, Jie Aboveground forest biomass based on OLSR and an ANN model integrating LiDAR and optical data in a mountainous region of China INTERNATIONAL JOURNAL OF REMOTE SENSING English Article LEAF-AREA INDEX; TROPICAL RAIN-FOREST; WAVE-FORM LIDAR; CANOPY STRUCTURE; FOOTPRINT LIDAR; ETM PLUS; LANDSAT; RADAR; HEIGHT; VALIDATION Aboveground forest biomass (B-agf) and height of forest canopy (H-fc) are of great significance for the determination of carbon sources and sinks, carbon cycling and global change research. In this paper, B-agf of coniferous and broadleaf forest in the Chinese Three Gorges region is estimated by integrating light detection and ranging (LiDAR) and Landsat derived data. For a better B-agf estimation, a synergetic extrapolation method for regional H-fc is explored based on a specific relationship between LiDAR footprint H-fc and optical data such as vegetation index (VI), leaf area index (LAI) and forest vegetation cover (FVC). Then, an ordinary least squares regression (OLSR) and a back propagation neural network (BP-NN) model for regional B-agf estimation from synergetic LiDAR and optical data are developed and compared. Validation results show that the OLSR can achieve higher accuracy of H-fc estimation for all forest types (R-2 = 0.751, Root mean square error (RMSE) = 5.74 m). The OLSR estimated B-agf shows a good agreement with field measurements. The accuracy of regional B-agf estimated by the BP-NN model (RMSE = 12.23 t ha(-1)) is superior to that estimated by the OLSR method (RMSE = 17.77 t ha(-1)) especially in areas with complex topography. [Dong, Lixin; Tang, Shihao; Min, Min] China Meteorol Adm, Key Lab Radiometr Calibrat & Validat Environm Sat, Beijing, Peoples R China; [Dong, Lixin; Tang, Shihao; Min, Min] China Meteorol Adm, Natl Satellites Meteorol Ctr, Beijing, Peoples R China; [Veroustraete, Frank] Univ Antwerp, Dept Biosci Engn, Fac Sci, Antwerp, Belgium; [Cheng, Jie] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China China Meteorological Administration; China Meteorological Administration; University of Antwerp; Beijing Normal University Dong, LX (corresponding author), Natl Satellites Meteorol Ctr, 46 Zhongguancun South St, Beijing 100081, Peoples R China. dlxwater.student@sina.com Cheng, Jie/G-2039-2011 Cheng, Jie/0000-0002-7620-4507; Dong, lixin/0000-0001-7945-9073 Climate change special project of China [QHBH2014]; CMA Special Fund for Scientific Research in the Public Interest 'Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III)' [GYHY201406001-01]; National Key Research and Development Programme of China [2016YFA0600101]; Three Gorges Project Construction Committee of the State Council Project [SX2002-004] Climate change special project of China; CMA Special Fund for Scientific Research in the Public Interest 'Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III)'; National Key Research and Development Programme of China; Three Gorges Project Construction Committee of the State Council Project The leading author is supported by Climate change special project of China 'Remote sensing evaluation of the effects of climate and CO2 concentration change on alpine vegetation (QHBH2014)', the CMA Special Fund for Scientific Research in the Public Interest 'Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) (GYHY201406001-01)', the National Key Research and Development Programme of China (NO. 2016YFA0600101) and Three Gorges Project Construction Committee of the State Council Project (SX2002-004). Thanks go to the NSIDC User Services of University of Colorado for the GLAS DATA that was downloading free from http://icesat.gsfc.nasa.gov/intro.html.The Landsat ETM images were purchased from a commercial company. 74 6 7 12 57 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. AUG 3 2019.0 40.0 15 6059 6083 10.1080/01431161.2019.1587201 0.0 25 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology HX9VJ 2023-03-23 WOS:000467756400019 0
J Tong, ZF; Hedayatrasa, S; Cheng, LL; Pei, CX; Chen, ZM; Xie, SJ; Kersemans, M Tong, Zongfei; Hedayatrasa, Saeid; Cheng, Liangliang; Pei, Cuixiang; Chen, Zhenmao; Xie, Shejuan; Kersemans, Mathias An efficient parametrized optical infrared thermography 3D finite element framework for computer vision applications NDT & E INTERNATIONAL English Article Infrared thermography; Fiber reinforced polymer; Finite element method; Interface element; Computer vision; Virtual dataset Matching infrared thermography (IRT) with deep learning-based computer vision has recently gained a lot of interest for automated defect assessment in materials. One of the remaining bottlenecks concerns the necessity of a large and diverse experimental and/or virtual training dataset in order to achieve a sufficiently generalizable computer vision algorithm. This paper presents a parametrized 3D finite element (FE) framework, implemented in Fortran90, for efficiently simulating optical infrared thermographic inspection of multi-layer anisotropic media and establishing large-scale virtual dataset with sufficient diversity. The interface element is introduced for the modelling of an imperfect thermal contact, allowing to simulate a variety of defect types. The flexibility of the interface element makes it possible to simulate delaminations with different thickness using the same dis-cretized model. Validation is done for two benchmark cases which are representative for a fiber reinforced polymer laminate with delamination-like defects. In order to achieve true-to-nature thermographic simulation data, non-uniform heating conditions are adopted from experiment, and a stochastic morphology generator is introduced for modelling realistic irregular defect geometries. To demonstrate the added value of a large, diverse and true-to-nature virtual database for computer vision applications, a Faster-RCNN model was trained on a generated virtual dataset for the detection of delamination-like defects in fiber reinforced polymer laminates. Application of the trained Faster-RCNN on experimental thermographic data yields excellent inference results, illustrating the high generalization ability of the virtually trained object detector. [Tong, Zongfei; Hedayatrasa, Saeid; Cheng, Liangliang; Kersemans, Mathias] Univ Ghent, Dept Mat Text & Chem Engn MaTCh, Mech Mat & Struct UGent MMS, B-9052 Zwijnaarde, Belgium; [Tong, Zongfei; Pei, Cuixiang; Chen, Zhenmao; Xie, Shejuan] Xi An Jiao Tong Univ, Shaanxi Engn Res Ctr Nondestruct Testing & Struct, State Key Lab Strength & Vibrat Mech Struct, Xian 710049, Peoples R China Ghent University; Xi'an Jiaotong University Kersemans, M (corresponding author), Univ Ghent, Dept Mat Text & Chem Engn MaTCh, Mech Mat & Struct UGent MMS, B-9052 Zwijnaarde, Belgium.;Xie, SJ (corresponding author), Xi An Jiao Tong Univ, Shaanxi Engn Res Ctr Nondestruct Testing & Struct, State Key Lab Strength & Vibrat Mech Struct, Xian 710049, Peoples R China. xiesj2014@mail.xjtu.edu.cn; Mathias.Kersemans@UGent.be Kersemans, Mathias/GLQ-7503-2022 Kersemans, Mathias/0000-0002-9325-0556; Cheng, Liangliang/0000-0002-7789-9849 China Scholarship Council (CSC) [202006280481]; Bijzonder OnderzoeksFonds (BOF) of Ghent University [01N01719, BOF21/PDO/041] China Scholarship Council (CSC)(China Scholarship Council); Bijzonder OnderzoeksFonds (BOF) of Ghent University(Ghent University) Zongfei Tong acknowledges the financial support from China Scholarship Council (CSC, grant number 202006280481) and from Bijzonder OnderzoeksFonds (BOF) of Ghent University under grant number 01N01719. Saeid Hedayatrasa acknowledges funding from Bijzonder OnderzoeksFonds (BOF) of Ghent University under grant number BOF21/PDO/041. 0 0 0 4 4 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0963-8695 1879-1174 NDT&E INT NDT E Int. APR 2023.0 135.0 102787 10.1016/j.ndteint.2023.102787 0.0 JAN 2023 16 Materials Science, Characterization & Testing Science Citation Index Expanded (SCI-EXPANDED) Materials Science 8N5YR 2023-03-23 WOS:000925226800001 0
J Zhu, ZT; Chen, YF; Wang, W; Wang, YY; Hu, WY; Shang, XW; Liao, H; Shi, DL; Huang, Y; Ha, J; Tan, Z; Kiburg, KV; Zhang, XL; Tang, SL; Yu, HH; Yang, XH; He, MG Zhu, Zhuoting; Chen, Yifan; Wang, Wei; Wang, Yueye; Hu, Wenyi; Shang, Xianwen; Liao, Huan; Shi, Danli; Huang, Yu; Ha, Jason; Tan, Zachary; Kiburg, Katerina, V; Zhang, Xueli; Tang, Shulin; Yu, Honghua; Yang, Xiaohong; He, Mingguang Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease STROKE English Article aging; cardiovascular disease; retina; vascular stiffness CORONARY-HEART-DISEASE; ALL-CAUSE MORTALITY; RISK-FACTORS; AORTIC STIFFNESS; VASCULAR CALIBER; UNITED-KINGDOM; VESSEL CALIBER; GLOBAL BURDEN; EVENTS; RETINOPATHY Background: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). Methods: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. Results: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (beta=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall P-nonlinear=0.0681). Conclusions: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events. [Zhu, Zhuoting; Shang, Xianwen; Huang, Yu; Zhang, Xueli; Tang, Shulin; Yu, Honghua; Yang, Xiaohong; He, Mingguang] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Ophthalmol, Guangzhou, Peoples R China; [Zhu, Zhuoting; Hu, Wenyi; Ha, Jason; Tan, Zachary; Kiburg, Katerina, V; He, Mingguang] Univ Melbourne, Ctr Eye Res Australia, Dept Ophthalmol, Melbourne, Vic, Australia; [Zhu, Zhuoting; Hu, Wenyi; He, Mingguang] Univ Melbourne, Dept Surg, Melbourne, Vic, Australia; [Chen, Yifan] Oxford Univ Hosp NHS Fdn Trust, John Radcliffe Hosp, Oxford, England; [Wang, Wei; Wang, Yueye; Shi, Danli; He, Mingguang] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China; [Liao, Huan] Univ Bonn, Inst Reconstruct Neurobiol, Neural Regenerat Grp, Bonn, Germany Guangdong Academy of Medical Sciences & Guangdong General Hospital; Centre for Eye Research Australia; University of Melbourne; University of Melbourne; Oxford University Hospitals NHS Foundation Trust; University of Oxford; Sun Yat Sen University; University of Bonn Zhu, ZT; He, MG (corresponding author), Ctr Eye Res Australia, Level 7,32 Gisborne St, Melbourne, Vic, Australia.;Zhu, ZT; He, MG (corresponding author), Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Ophthalmol, Guangzhou, Peoples R China.;Zhu, ZT; He, MG (corresponding author), Univ Melbourne, Ctr Eye Res Australia, Dept Ophthalmol, Melbourne, Vic, Australia. lisa.zhu@unimelb.edu.au; mingguang.he@unimelb.edu.au yang, xiao/HJI-7815-2023 Ha, Jason/0000-0003-3013-1469; Shang, Xianwen/0000-0002-2362-3222; Hu, Wenyi/0000-0001-7366-3874; Shang, Xianwen/0000-0001-6143-9349 Fundamental Research Funds of the State Key Laboratory of Ophthalmology, National Natural Science Foundation of China [82000901, 82101173, 81870663, 82171075]; NHMRC Investigator Grants [2010072]; Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital [KJ012019087]; Distinguished Young Scholars in Guangdong Province [KJ012019457]; Talent Introduction Fund of Guangdong Provincial People's Hospital [Y012018145]; Science and Technology Program of Guangzhou, China [202002020049]; Project of Special Research on Cardiovascular Diseases [2020XXG007]; Research Foundation of Medical Science and Technology of Guangdong Province [B2021237]; University of Melbourne at Research Accelerator Program; CERA Foundation; Victorian State Government; Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents Fundamental Research Funds of the State Key Laboratory of Ophthalmology, National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NHMRC Investigator Grants(National Health and Medical Research Council (NHMRC) of Australia); Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital; Distinguished Young Scholars in Guangdong Province; Talent Introduction Fund of Guangdong Provincial People's Hospital; Science and Technology Program of Guangzhou, China; Project of Special Research on Cardiovascular Diseases; Research Foundation of Medical Science and Technology of Guangdong Province; University of Melbourne at Research Accelerator Program; CERA Foundation; Victorian State Government; Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents The present work was supported by Fundamental Research Funds of the State Key Laboratory of Ophthalmology, National Natural Science Foundation of China (82000901, 82101173, 81870663, 82171075), NHMRC Investigator Grants (2010072), Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital (KJ012019087), Guangdong Provincial People's Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province (KJ012019457), Talent Introduction Fund of Guangdong Provincial People's Hospital (Y012018145), Science and Technology Program of Guangzhou, China (202002020049), Project of Special Research on Cardiovascular Diseases (2020XXG007), and Research Foundation of Medical Science and Technology of Guangdong Province (B2021237). Dr He receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organization had no role in the design or conduct of this research. 70 0 0 7 9 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA 0039-2499 1524-4628 STROKE Stroke NOV 2022.0 53.0 11 3320 3328 10.1161/STROKEAHA.122.038809 0.0 9 Clinical Neurology; Peripheral Vascular Disease Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Cardiovascular System & Cardiology 5Q8QE 35880520.0 hybrid, Green Submitted 2023-03-23 WOS:000874088700022 0
J Chen, XL; Su, ZB; Ma, YM; Middleton, EM Chen, Xuelong; Su, Zhongbo; Ma, Yaoming; Middleton, Elizabeth M. Optimization of a remote sensing energy balance method over different canopy applied at global scale AGRICULTURAL AND FOREST METEOROLOGY English Article Remote sensing energy balance; Parameter optimization; Surface energy balance system; Heat roughness length; Fluxnetwork; Particle swarm optimization PARTICLE SWARM OPTIMIZATION; SYSTEM SEBS; MOMENTUM-TRANSFER; EVAPOTRANSPIRATION; MODEL; FLUX; EVAPORATION; LAND; ROUGHNESS; ALGORITHM Parameterization methods which calculate turbulent heat and water fluxes with thermal remote sensing data were evaluated in the revised remote sensing surface energy balance system (SEBS) model (Chen et al., 2013). The model calculates sensible heat (H) based on the Monin-Obukhov similarity theory (MOST) and determines latent heat (LE) as the residual of energy balance. We examined the uncertainties of H and LE in the SEBS model due to five key parameters at the local station point scale. Observations at 27 flux towers located in seven land cover types (needle-leaf forest, broadleaf forest, shrub, savanna, grassland, cropland, and sparsely vegetated land) and an artificial intelligence particle swarm optimization (PSO) algorithm was combined to calibrate the five parameters (leaf drag coefficient, leaf heat transfer coefficients, roughness length for soil, and two parameters for ground heat calculation) in the SEBS model. The root-mean-square error at the site scale was reduced by 9Wm(-2) for H, and 92Wm(-2) for LE, and their correlation coefficients were increased by 0.07 (H) and 0.11 (LE) after using the calibrated parameters. The updated model validation was further conducted globally for the remotely sensed evapotranspiration (ET) calculations. Overestimation of SEBS global ET was significantly improved by using the optimized values of the parameters. The results suggested PSO was able to consistently locate the global optimum of the SEBS model, and appears to be capable of solving the ET model optimization problem. [Chen, Xuelong; Ma, Yaoming] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing, Peoples R China; [Chen, Xuelong; Ma, Yaoming] Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China; [Su, Zhongbo] Univ Twente, Fac Geoinformat Sci & Earth Observat, Enschede, Netherlands; [Ma, Yaoming] Univ Chinese Acad Sci, Beijing, Peoples R China; [Middleton, Elizabeth M.] NASA, Biospher Sci Lab, GSFC, Greenbelt, MD USA; [Su, Zhongbo] Changan Univ, Sch Environm Sci & Engn, Xian, Shaanxi, Peoples R China Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS; Chinese Academy of Sciences; University of Twente; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; Chang'an University Chen, XL (corresponding author), Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing, Peoples R China.;Chen, XL (corresponding author), Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China. x.chen@itpcas.ac.cn Chen, Xuelong/C-2748-2014 Chen, Xuelong/0000-0003-3892-5298; Su, Zhongbo/0000-0003-2096-1733 CAS Pioneer Hundred Talents Program CAS Pioneer Hundred Talents Program Xuelong Chen was supported by CAS Pioneer Hundred Talents Program. We acknowledge the following AmeriFlux sites: site Us-NC2, US-GLE, US-MRf, US-NR1, US-UMB, US-ChR, US-MOz, US-MMS, US-WBW, US-WCr, US-Aud, US-Me6, US-Fmf, Us-Br1, US-Ctn, US-Wkg, the following TERN OzFlux sites: Gingin, Calperum, Ti Tree East, Riggs Creek, Daly Pasture, and Arcturus, for providing us with their data sets. AmeriFlux, OzFlux, European Fluxes Database Cluster, ChinaFlux are acknowledged for the in-situ data. The remote sensing ET V2 data in this study is freely open on the web: http://en.tpedatabase.cn/portal/MetaDataInfo.jsp?MetaDataId=249454 55 14 15 0 51 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0168-1923 1873-2240 AGR FOREST METEOROL Agric. For. Meteorol. DEC 15 2019.0 279.0 107633 10.1016/j.agrformet.2019.107633 0.0 15 Agronomy; Forestry; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Forestry; Meteorology & Atmospheric Sciences JS3GO Green Published 2023-03-23 WOS:000500197400049 0
J Lei, WX; Pang, ZB; Wen, H; Hou, WJ; Han, W Lei, Wenxin; Pang, Zhibo; Wen, Hong; Hou, Wenjing; Han, Wen FDI Attack Detection at the Edge of Smart Grids Based on Classification of Predicted Residuals IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Smart grids; Image edge detection; Current measurement; Pollution measurement; Real-time systems; Computational modeling; Noise measurement; Attack detection; edge computing; false data injection (FDI); machine learning; smart grid security FALSE DATA INJECTION; STATE ESTIMATION The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids. [Lei, Wenxin; Wen, Hong; Hou, Wenjing; Han, Wen] Univ Elect Sci & Technol, Sch Aeronaut & Astronaut, Chengdu 610054, Peoples R China; [Pang, Zhibo] ABB Corp Res Sweden, Dept Automat Technol, S-72178 Vasteras, Sweden; [Pang, Zhibo] Royal Inst Technol KTH, Dept Intelligent Syst, S-11428 Stockholm, Sweden University of Electronic Science & Technology of China; ABB; Royal Institute of Technology Wen, H (corresponding author), Univ Elect Sci & Technol, Sch Aeronaut & Astronaut, Chengdu 610054, Peoples R China. leiwenxin@std.uestc.edu.cn; pang.zhibo@se.abb.com; sunlike@uestc.edu.cn; uestc_hwj@126.com; 202022100408@std.uestc.edu.cn Lei, Wenxin/0000-0002-9559-9890 NSFC [61901089]; National Key Research and Development Program of China [2019YFB1803204, TII-21-4036] NSFC(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China This work was supported in part by NSFC under Grant 61901089 and in part by the National Key Research and Development Program of China under Grant 2019YFB1803204. Paper no. TII-21-4036. 26 2 2 19 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. DEC 2022.0 18.0 12 9302 9311 10.1109/TII.2022.3174159 0.0 10 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 4Z8CP 2023-03-23 WOS:000862429800096 0
J Xu, SQ; Yang, X; Liu, WH; Jonsson, J; Qian, RB; Konda, PC; Zhou, KC; Kreiss, L; Wang, HQ; Dai, QH; Berrocal, E; Horstmeyer, R Xu, Shiqi; Yang, Xi; Liu, Wenhui; Jonsson, Joakim; Qian, Ruobing; Konda, Pavan Chandra; Zhou, Kevin C.; Kreiss, Lucas; Wang, Haoqian; Dai, Qionghai; Berrocal, Edouard; Horstmeyer, Roarke Imaging Dynamics Beneath Turbid Media via Parallelized Single-Photon Detection ADVANCED SCIENCE English Article deep imaging; dynamic scattering; single-photon avalanche diode array CEREBRAL-BLOOD-FLOW; DIFFUSE OPTICAL TOMOGRAPHY; CORRELATION SPECTROSCOPY; SCATTERING; TISSUE; LIGHT; RECONSTRUCTION; MICROSCOPY; RAT Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels. [Xu, Shiqi; Yang, Xi; Liu, Wenhui; Qian, Ruobing; Konda, Pavan Chandra; Zhou, Kevin C.; Kreiss, Lucas; Horstmeyer, Roarke] Duke Univ, Dept Biomed Engn, Durham, NC 27708 USA; [Liu, Wenhui; Dai, Qionghai] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China; [Jonsson, Joakim; Berrocal, Edouard] Lund Univ, Dept Phys, Div Combust Phys, S-22100 Lund, Sweden; [Kreiss, Lucas] Friedrich Alexander Univ Erlangen Nurnberg FAU, Inst Med Biotechnol, D-91054 Erlangen, Germany; [Wang, Haoqian] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China; [Horstmeyer, Roarke] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA; [Horstmeyer, Roarke] Duke Univ, Dept Phys, Durham, NC 27708 USA Duke University; Tsinghua University; Lund University; University of Erlangen Nuremberg; Tsinghua University; Duke University; Duke University Horstmeyer, R (corresponding author), Duke Univ, Dept Biomed Engn, Durham, NC 27708 USA.;Horstmeyer, R (corresponding author), Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA.;Horstmeyer, R (corresponding author), Duke Univ, Dept Phys, Durham, NC 27708 USA. roarke.w.horstmeyer@duke.edu Xu, Shiqi/0000-0002-8450-9001; Qian, Ruobing/0000-0001-8465-683X National Institute of Neurological Disorders and Stroke of the National Institutes of Health [RF1NS113287]; Duke-Coulter Translational Partnership; Kernel Inc.; China Scholarship Council; Hartwell Foundation Individual Biomedical Researcher Award; Air Force Office of Scientific Research [FA9550] National Institute of Neurological Disorders and Stroke of the National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS)); Duke-Coulter Translational Partnership; Kernel Inc.; China Scholarship Council(China Scholarship Council); Hartwell Foundation Individual Biomedical Researcher Award; Air Force Office of Scientific Research(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)) Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number RF1NS113287, as well as the Duke-Coulter Translational Partnership. The authors also want to thank Kernel Inc. for their generous support. W.L. acknowledges the support from the China Scholarship Council. R.H. acknowledges support from a Hartwell Foundation Individual Biomedical Researcher Award, and Air Force Office of Scientific Research under award number FA9550. In addition, the authors would like to express our great appreciation to Dr. Haowen Ruan for editing and inspirational discussion. 69 1 1 9 10 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2198-3844 ADV SCI Adv. Sci. AUG 2022.0 9.0 24 2201885 10.1002/advs.202201885 0.0 JUN 2022 11 Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science 3Z0WC 35748188.0 Green Submitted, gold, Green Accepted 2023-03-23 WOS:000815022700001 0
J Ru, BB; 't Hoen, PAC; Nie, FL; Lin, H; Guo, FB; Huang, JA Ru, Beibei; 't Hoen, Peter A. C.; Nie, Fulei; Lin, Hao; Guo, Feng-Biao; Huang, Jian PhD7FASTER: PREDICTING CLONES PROPAGATING FASTER FROM THE Ph.D.-7 PHAGE DISPLAY PEPTIDE LIBRARY JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY English Article Phage display; target-unrelated peptides; PrTUPs; support vector machine; reduced dipeptide composition PROTEIN-INTERACTION; IDENTIFICATION; AMPLIFICATION; MIMODB Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl. [Ru, Beibei; Nie, Fulei; Lin, Hao; Guo, Feng-Biao; Huang, Jian] Univ Elect Sci & Technol China, Ctr Bioinformat COBI, Key Lab NeuroInformat, Minist Educ, Chengdu 610054, Peoples R China; ['t Hoen, Peter A. C.] Leiden Univ, Med Ctr, Ctr Human & Clin Genet, Dept Human Genet, NL-2300 RC Leiden, Netherlands University of Electronic Science & Technology of China; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC Huang, JA (corresponding author), Univ Elect Sci & Technol China, Ctr Bioinformat COBI, Key Lab NeuroInformat, Minist Educ, Chengdu 610054, Peoples R China. rubeibei1988@gmail.com; p.a.c.hoen@lumc.nl; niefulei@126.com; hlin@uestc.edu.cn; fbguo@uestc.edu.cn; hj@uestc.edu.cn Ru, Beibei/F-3830-2011; Guo, Feng-Biao/C-2761-2015; Huang, Jian/G-8437-2011; Hoen, Peter A 't/D-3950-2018; Lin, Hao/B-2066-2012 Guo, Feng-Biao/0000-0002-1129-5233; Huang, Jian/0000-0003-3282-8892; Hoen, Peter A 't/0000-0003-4450-3112; Lin, Hao/0000-0001-6265-2862 National Natural Science Foundation of China [61071177]; Program for New Century Excellent Talents in University [NCET-12-0088] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for New Century Excellent Talents in University(Program for New Century Excellent Talents in University (NCET)) The authors are grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this paper. This work was supported in part by the National Natural Science Foundation of China under the Grant 61071177 and the Program for New Century Excellent Talents in University (NCET-12-0088). 33 33 34 2 21 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0219-7200 1757-6334 J BIOINF COMPUT BIOL J. Bioinform. Comput. Biol. FEB 2014.0 12.0 1 1450005 10.1142/S021972001450005X 0.0 13 Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Computer Science; Mathematical & Computational Biology AZ6RI 24467763.0 2023-03-23 WOS:000348346200012 0
J Hong, DF; Gao, LR; Yao, J; Zhang, B; Plaza, A; Chanussot, J Hong, Danfeng; Gao, Lianru; Yao, Jing; Zhang, Bing; Plaza, Antonio; Chanussot, Jocelyn Graph Convolutional Networks for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Convolution; Hyperspectral imaging; Task analysis; Symmetric matrices; Fourier transforms; Hyperspectral (HS) classification; convolutional neural networks (CNNs); graph convolutional networks (GCNs); deep learning (DL); fusion FRAMEWORK Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature representations. Nevertheless, their ability in modeling relations between the samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or nongrid) data representation and analysis. In this article, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new minibatch GCN (called miniGCN hereinafter), which allows to train large-scale GCNs in a minibatch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without retraining networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batchwise network training (enabling the combination of CNNs and GCNs), we explore three fusion strategies: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS data sets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regard to the single CNN or GCN models. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility. [Hong, Danfeng] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France; [Gao, Lianru; Zhang, Bing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Yao, Jing] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China; [Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Plaza, Antonio] Univ Extremadura, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Escuela Politecn, Caceres 10003, Spain; [Chanussot, Jocelyn] Univ Grenoble Alpes, CNRS, INRIA, LJK,Grenoble INP, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; Xi'an Jiaotong University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidad de Extremadura; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Chinese Academy of Sciences Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. hongdanfeng1989@gmail.com; gaolr@aircas.ac.cn; jasonyao@stu.xjtu.edu.cn; zb@radi.ac.cn; aplaza@unex.es; jocelyn@hi.is Hong, Danfeng/U-6082-2019; Plaza, Antonio/C-4455-2008; Yao, Jing/AAA-5843-2021 Hong, Danfeng/0000-0002-3212-9584; Plaza, Antonio/0000-0002-9613-1659; Yao, Jing/0000-0003-1301-9758; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [41722108, 91638201]; AXA Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); AXA Research Fund(AXA Research Fund) This work was supported in part by the National Natural Science Foundation of China under Grant 41722108 and Grant 91638201, and in part by the AXA Research Fund. 44 592 596 150 374 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUL 2021.0 59.0 7 5966 5978 10.1109/TGRS.2020.3015157 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology SX4HH Green Submitted 2023-03-23 WOS:000665167500041 0
J Hong, DF; Han, Z; Yao, J; Gao, LR; Zhang, B; Plaza, A; Chanussot, J Hong, Danfeng; Han, Zhu; Yao, Jing; Gao, Lianru; Zhang, Bing; Plaza, Antonio; Chanussot, Jocelyn SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Transformers; Feature extraction; Task analysis; Data mining; Hyperspectral imaging; Encoding; Convolutional neural networks; Convolutional neural networks; deep learning; hyperspectral (HS) image classification; local contextual information; remote sensing; sequence data; skip fusion; transformer FEATURE-EXTRACTION Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called SpectralFormer. Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse soft residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility. [Hong, Danfeng; Han, Zhu; Yao, Jing; Gao, Lianru; Zhang, Bing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Han, Zhu; Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10003, Spain; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, CNRS, GIPSA Lab, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universidad de Extremadura; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. hongdf@aircas.ac.cn; hanzhu19@mails.ucas.ac.cn; yaojing@aircas.ac.cn; gaolr@aircas.ac.cn; zb@radi.ac.cn; aplaza@unex.es; jocelyn@hi.is Han, Zhu/ABH-5886-2022; Plaza, Antonio/C-4455-2008 Han, Zhu/0000-0002-8602-864X; Plaza, Antonio/0000-0002-9613-1659; hong, danfeng/0000-0002-3212-9584; Chanussot, Jocelyn/0000-0003-4817-2875; Yao, Jing/0000-0003-1301-9758 National Key R&D Program of China [2021YFB3900502]; National Natural Science Foundation of China [42030111]; MIAI@Grenoble Alpes [ANR-19-P3IA-0003]; AXA Research Fund; Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2020-0-01819-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS) National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MIAI@Grenoble Alpes; AXA Research Fund(AXA Research Fund); Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea(Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea) This work was supported by the National Key R&D Program of China (Grant No. 2021YFB3900502), the National Natural Science Foundation of China under Grant 42030111, and by the MIAI@Grenoble Alpes (ANR-19-P3IA-0003) and the AXA Research Fund. 40 240 241 195 291 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5518615 10.1109/TGRS.2021.3130716 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZH9FN Green Submitted 2023-03-23 WOS:000761235300021 0
J Zhang, AR; Xu, J; Luo, XY; Cao, XB; Zhen, XT Zhang, Anran; Xu, Jun; Luo, Xiaoyan; Cao, Xianbin; Zhen, Xiantong Cross-Domain Attention Network for Unsupervised Domain Adaptation Crowd Counting IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Feature extraction; Task analysis; Adversarial machine learning; Adaptation models; Decoding; Training; Labeling; Crowd counting; attention; domain adaptation; unsupervised learning DENSITY-ESTIMATION; PEOPLE Unsupervised domain adaptation crowd counting (UDACC) has been studied with practical research utility by getting rid of the labeling burden on large-scale dense crowds in the target domain. Current methods generalize well within the specific domain gap by directly aligning domain distributions or translating synthetic data to realistic images. However, it is difficult to define domain gaps among complex real-world datasets, in which the images vary greatly in style, density level and/or content. To tackle this problem, in this paper, we propose a Cross-Domain Attention Network (CDANet), which can effectively generalize the model to the unlabeled domain on both unsupervised synthetic-to-realistic and realistic-to-realistic crowd counting. Specifically, we propose a Cross-Domain Attention Module (CDAM) to learn domain-related information between the source and target domain, which extracts relations in cross-domain attentive information, thus enhancing crowd-informative features. Moreover, to make our CDAM invariant to domain shifts, we introduce a consistency penalty to ensure that the attention maps are consistent before and after the domain shifting. Thus our CDANet can pay attention to the shared counting information across domains, while remaining its invariant ability during domain adaptation. Extensive experiments on several common benchmarks for UDACC demonstrate that our CDANet gets competitive results on both unsupervised synthetic-to-realistic and realistic-to-realistic UDACC tasks. [Zhang, Anran; Cao, Xianbin] Beihang Univ, Key Lab Adv Technol Near Space Informat Syst, Beijing 100190, Peoples R China; [Xu, Jun] Nankai Univ, Sch Stat & Data Sci, Tianjin 300071, Peoples R China; [Luo, Xiaoyan] Beihang Univ, Sch Astronaut, Beijing 100190, Peoples R China; [Zhen, Xiantong] Univ Amsterdam, AIM Lab, NL-1012 WX Amsterdam, Netherlands Beihang University; Nankai University; Beihang University; University of Amsterdam Cao, XB (corresponding author), Beihang Univ, Key Lab Adv Technol Near Space Informat Syst, Beijing 100190, Peoples R China. zhanganran@buaa.edu.cn; xbcao@buaa.edu.cn National Key Scientific Instrument and Equipment Development Project [61827901]; Natural Science Foundation of China [91738301, 62002176, 62088101, U1833117] National Key Scientific Instrument and Equipment Development Project; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Scientific Instrument and Equipment Development Project under Grant 61827901; and in part by the Natural Science Foundation of China under Grant 91738301, Grant 62002176, Grant 62088101, and Grant U1833117. This article was recommended by Associate Editor L. Li. 77 0 0 13 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. OCT 2022.0 32.0 10 6686 6699 10.1109/TCSVT.2022.3179824 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 5C3XQ 2023-03-23 WOS:000864197600018 0
J Wu, SL; Di, BF; Ustin, SL; Stamatopoulos, CA; Li, JR; Zuo, Q; Wu, X; Ai, NS Wu, Shaolin; Di, Baofeng; Ustin, Susan L.; Stamatopoulos, Constantine A.; Li, Jierui; Zuo, Qi; Wu, Xiao; Ai, Nanshan Classification and detection of dominant factors in geospatial patterns of traditional settlements in China JOURNAL OF GEOGRAPHICAL SCIENCES English Article rural settlements; geographical pattern; rural revitalization; traditional wisdom RURAL-DEVELOPMENT; NORTHEAST CHINA; TIBETAN PLATEAU; LAND-USE; PROVINCE; HUMIDITY; STRATEGY; YUNNAN; AGRICULTURE; VALIDATION The geospatial distribution pattern in traditional Chinese settlements (TCSs) reflects the traditional harmony between humans and nature, which has been learned over centuries. However, TCSs have experienced serious disturbances by urbanization and migration. It is crucial to explore the local wisdom of geospatial patterns and dominant factors for TCSs at the national scale in China. This study sought to determine the geospatial wisdom of traditional settlements to enrich our future settlement development with the aim of establishing Chinese settlement values for modern living. Herein, a dataset of 4000 TCSs were analyzed and clustered for environmental factors that affect their geospatial patterns by machine learning algorithms. We concluded that (1) five geospatial patterns of TCSs were clustered on a national scale, and the threshold of environmental factors of TCS groups was detected. (2) Environmental conditions and settlement concepts interacted and determined the similarities and differences among TCS groups. (3) The key boundary for TCSs and the dominant factors for each zone were determined, and topographical conditions and hydrologic resources played significant roles in all five TCS zones. This study provides a better understanding of the adaptability of the environment in relation to the TCSs and aids in planning TCS conservation and rural revitalization. [Wu, Shaolin; Wu, Xiao; Ai, Nanshan] Sichuan Univ, Coll Architecture & Environm, Chengdu 610065, Peoples R China; [Di, Baofeng; Li, Jierui; Zuo, Qi] Hong Kong Polytech Univ, Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610207, Peoples R China; [Ustin, Susan L.] Univ Calif Davis, John Muir Inst Environm, Davis, CA 95616 USA; [Stamatopoulos, Constantine A.] Stamatopoulos & Associates Co, Athens 11471, Greece; [Stamatopoulos, Constantine A.] Hellen Open Univ, Athens 11471, Greece Sichuan University; Hong Kong Polytechnic University; Sichuan University; University of California System; University of California Davis; Hellenic Open University Di, BF (corresponding author), Hong Kong Polytech Univ, Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610207, Peoples R China. wushaolin@stu.scu.edu.cn; dibaofeng@scu.edu.cn Doyle, Susan L./HMO-4879-2023; Zuo, Qiting/B-3022-2017 Doyle, Susan L./0000-0001-8551-0461; Zuo, Qiting/0000-0002-9760-2360; , Shaolin Wu/0000-0001-9376-3784 National Key Research and Development Program of China [2020YFD1100701]; Social Science Research 14th Five-Year Plan 2021 Project of Sichuan Province; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA23090502] National Key Research and Development Program of China; Social Science Research 14th Five-Year Plan 2021 Project of Sichuan Province; Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences) National Key Research and Development Program of China, No.2020YFD1100701; Social Science Research 14th Five-Year Plan 2021 Project of Sichuan Province, No.SC21ST001; Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA23090502 99 2 2 23 35 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1009-637X 1861-9568 J GEOGR SCI J. Geogr. Sci. MAY 2022.0 32.0 5 873 891 10.1007/s11442-022-1976-7 0.0 19 Geography, Physical Science Citation Index Expanded (SCI-EXPANDED) Physical Geography 0U3AF 2023-03-23 WOS:000787524700005 0
C Gelli, F; Uricchio, T; He, XN; Del Bimbo, A; Chua, TS ACM Gelli, Francesco; Uricchio, Tiberio; He, Xiangnan; Del Bimbo, Alberto; Chua, Tat-Seng Learning Subjective Attributes of Images from Auxiliary Sources PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) English Proceedings Paper 27th ACM International Conference on Multimedia (MM) OCT 21-25, 2019 Nice, FRANCE Assoc Comp Machinery,ACM SIGMM Subjective Attributes; Probabilistic Modeling; Computational Marketing; Personality Psychology Recent years have seen unprecedented research on using artificial intelligence to understand the subjective attributes of images and videos. These attributes are not objective properties of the content but are highly dependent on the perception of the viewers. Subjective attributes are extremely valuable in many applications where images are tailored to the needs of a large group, which consists of many individuals with inherently different ideas and preferences. For instance, marketing experts choose images to establish specific associations in the consumers' minds, while psychologists look for pictures with adequate emotions for therapy. Unfortunately, most of the existing frameworks either focus on objective attributes or rely on large scale datasets of annotated images, making them costly and unable to clearly measure multiple interpretations of a single input. Meanwhile, we can see that users or organizations often interact with images in a multitude of real-life applications, such as the sharing of photographs by brands on social media or the re-posting of image microblogs by users. We argue that these aggregated interactions can serve as auxiliary information to infer image interpretations. To this end, we propose a probabilistic learning framework capable of transferring such subjective information to the image-level labels based on a known aggregated distribution. We use our framework to rank images by subjective attributes from the domain knowledge of social media marketing and personality psychology. Extensive studies and visualizations show that using auxiliary information is a viable line of research for the multimedia community to perform subjective attributes prediction. [Gelli, Francesco; Chua, Tat-Seng] Natl Univ Singapore, Singapore, Singapore; [Uricchio, Tiberio; Del Bimbo, Alberto] Univ Firenze, Florence, Italy; [He, Xiangnan] Univ Sci & Technol China, Hefei, Peoples R China National University of Singapore; University of Florence; Chinese Academy of Sciences; University of Science & Technology of China, CAS Gelli, F (corresponding author), Natl Univ Singapore, Singapore, Singapore. francesco.gelli@u.nus.edu; tiberio.uricchio@unifi.it; xiangnanhe@gmail.com; alberto.delbimbo@unifi.it; chuats@comp.nus.edu.sg National Research Foundation, Prime Minister's Office, Singapore under its IRC@SG Funding Initiative National Research Foundation, Prime Minister's Office, Singapore under its IRC@SG Funding Initiative(National Research Foundation, Singapore) NExT research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its IRC@SG Funding Initiative. 46 5 5 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6889-6 2019.0 2263 2271 10.1145/3343031.3350574 0.0 9 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO2ZT 2023-03-23 WOS:000509743400285 0
J Liu, Y; Ji, YJ; Feng, T; Timmermans, H Liu, Yang; Ji, Yanjie; Feng, Tao; Timmermans, Harry Understanding the determinants of young commuters' metro-bikeshare usage frequency using big data TRAVEL BEHAVIOUR AND SOCIETY English Article Young commuter; Metro-bikeshare integration; Transfer frequency; Negative binomial regression; Smart card data TRAVEL BEHAVIOR; BUILT ENVIRONMENT; BICYCLE; MODE; TRANSIT; CHINA; TRIP; CITY; INTEGRATION; MELBOURNE This paper examines the determinants of young commuters' frequency of using public bikes as a feeder mode to/ from metro. Using three-week metroand public bikesmart card data from Nanjing, 1,154 metro-bikeshare commuters aged 18-35 were extracted. As possible factors influencing the use of the combined mode, individual and household socio-demographics, travel-related attributes and built environment characteristics were extracted from multi-source data. A negative binomial regression model was used to examine the effects of these factors on usage frequency. We found that young commuters are the biggest group using metro-bikeshare system. They use public bikes frequently to transfer to/from metro when the cycling time is less than 10 min and the transfer happens during the morning peak. Built environment characteristics also influence usage frequencies, with high-density bike facilities being related to higher cycling rates in inner areas, and residential /employment locations related to lower rates of cycling in the core areas. This suggests that different measures and policies designed to encourage the integrated use of metro-bikeshare should be put forward for different regions. [Liu, Yang; Ji, Yanjie] Southeast Univ, Sch Transportat, Dongnandaxue Rd 2, Nanjing, Jiangsu, Peoples R China; [Liu, Yang; Ji, Yanjie] Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Sch Transportat, Jiangsu Key Lab Urban ITS, Dongnandaxue Rd 2, Nanjing, Jiangsu, Peoples R China; [Feng, Tao; Timmermans, Harry] Eindhoven Univ Technol, Dept Urban Sci & Syst, Urban Planning Grp, POB 513, NL-5600 Eindhoven, Netherlands; [Timmermans, Harry] Nanjing Univ Aeronaut & Astronaut, Dept Air Transportat Management, Jiangjun Ave, Nanjing 211106, Jiangsu, Peoples R China Southeast University - China; Southeast University - China; Eindhoven University of Technology; Nanjing University of Aeronautics & Astronautics Ji, YJ (corresponding author), Southeast Univ, Sch Transportat, Dongnandaxue Rd 2, Nanjing, Jiangsu, Peoples R China.;Ji, YJ (corresponding author), Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Sch Transportat, Jiangsu Key Lab Urban ITS, Dongnandaxue Rd 2, Nanjing, Jiangsu, Peoples R China. kmliuyang@outlook.com; jiyanjie@seu.edu.cn; T.Feng@tue.nl; H.J.P.Timmermans@tue.nl Feng, Tao/ABF-7705-2020; Feng, Tao/ABD-7177-2020 Feng, Tao/0000-0002-5759-3164 National Key RAMP;D Program of China [2018YFB1600900]; Research Foundation of Graduated School of Southeast University [YBJJ1842]; Fundamental Research Funds for the Central Universities [2242020K40063]; China Scholarship Council (CSC) [201806090205] National Key RAMP;D Program of China; Research Foundation of Graduated School of Southeast University; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council (CSC)(China Scholarship Council) This research is funded by the National Key R&D Program of China (Grant No. 2018YFB1600900), the Research Foundation of Graduated School of Southeast University (Grant No. YBJJ1842), the Fundamental Research Funds for the Central Universities (No. 2242020K40063) and the China Scholarship Council (CSC) (Grant No. 201806090205). We are grateful for the comments and suggestions from the editor and the anonymous reviewers who helped improve the paper. 68 19 19 5 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-367X 2214-3688 TRAVEL BEHAV SOC Travel Behav. Soc. OCT 2020.0 21.0 121 130 10.1016/j.tbs.2020.06.007 0.0 10 Transportation Social Science Citation Index (SSCI) Transportation NJ0MD Green Published 2023-03-23 WOS:000565737600012 0
J Gong, WJ; Zhang, XN; Gonzalez, J; Sobral, A; Bouwmans, T; Tu, CH; Zahzah, E Gong, Wenjuan; Zhang, Xuena; Gonzalez, Jordi; Sobral, Andrews; Bouwmans, Thierry; Tu, Changhe; Zahzah, El-hadi Human Pose Estimation from Monocular Images: A Comprehensive Survey SENSORS English Article human pose estimation; human body models; generative methods; discriminative methods; top-down methods; bottom-up methods 3D HUMAN POSE; POINT SET REGISTRATION; HUMAN MOTION ANALYSIS; ACTIVE SHAPE MODELS; OBJECT RECOGNITION; BODY POSE; PICTORIAL STRUCTURES; FLEXIBLE MIXTURES; TRACKING; CAPTURE Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. [Gong, Wenjuan; Zhang, Xuena] China Univ Petr, Dept Comp Sci & Technol, Qingdao 266580, Peoples R China; [Gonzalez, Jordi] Univ Autonoma Barcelona, Comp Vis Ctr, Catalonia 08193, Spain; [Sobral, Andrews; Bouwmans, Thierry] Univ La Rochelle, Lab MIA, F-17042 La Rochelle, France; [Sobral, Andrews; Zahzah, El-hadi] Univ La Rochelle, Lab L3i, F-17042 La Rochelle, France; [Tu, Changhe] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China China University of Petroleum; Autonomous University of Barcelona; Centre de Visio per Computador (CVC); La Rochelle Universite; La Rochelle Universite; Shandong University Gong, WJ (corresponding author), China Univ Petr, Dept Comp Sci & Technol, Qingdao 266580, Peoples R China. wenjuangong@upc.edu.cn; xuena_zhanghh@163.com; poal@cvc.uab.es; andrews.sobral@univ-lr.fr; thierry.bouwmans@univ-lr.fr; chtu@sdu.edu.cn; ezahzah@univ-lr.fr ZAHZAH, El-hadi/AAT-6894-2021; Gonzàlez, Jordi/I-1812-2015; BOUWMANS, Thierry/H-7041-2017 Gonzàlez, Jordi/0000-0001-8033-0306; BOUWMANS, Thierry/0000-0003-4018-8856; Sobral, Andrews/0000-0002-1047-3755; Gong, Wenjuan/0000-0001-7805-3629 Natural Science Foundation of Shandong [ZR2015FL015]; Qingdao Technology Plan [15-9-1-69-jch]; Ministry of Science and Technology of China [2015IM010300]; MINECO/FEDER [TIN2015-65464-R]; COST Action iVAMP;L Net (European Network on Integrating Vision and Language) [IC1307]; COST (European Cooperation in Science and Technology) Natural Science Foundation of Shandong(Natural Science Foundation of Shandong Province); Qingdao Technology Plan; Ministry of Science and Technology of China(Ministry of Science and Technology, China); MINECO/FEDER(Spanish Government); COST Action iVAMP;L Net (European Network on Integrating Vision and Language); COST (European Cooperation in Science and Technology)(European Cooperation in Science and Technology (COST)) his work has been supported by the Natural Science Foundation of Shandong with project number ZR2015FL015, the Qingdao Technology Plan with project number 15-9-1-69-jch, the Ministry of Science and Technology of China with project number 2015IM010300, the Spanish project TIN2015-65464-R (MINECO/FEDER) and the COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language), supported by COST (European Cooperation in Science and Technology). 297 54 54 3 39 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2016.0 16.0 12 1966 10.3390/s16121966 0.0 39 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation EG8JM 27898003.0 Green Submitted, gold, Green Accepted, Green Published 2023-03-23 WOS:000391303000001 0
C Pei, EC; Yang, L; Jiang, DM; Sahli, H IEEE Pei, Ercheng; Yang, Le; Jiang, Dongmei; Sahli, Hichem Multimodal Dimensional Affect Recognition Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks 2015 INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII) International Conference on Affective Computing and Intelligent Interaction English Proceedings Paper 6th AAAC Affective Computing and Intelligent Interaction International Conference (ACII) SEP 21-24, 2015 Xian, PEOPLES R CHINA AAAC DBLSTM-RNN; DBLSTM-MA; DBLSTM-DBLSTM-MA; affect recognition In this paper we propose the deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) based single modal and multi-modal affect recognition frameworks. In the single modal framework DBLSTM with moving average (MA), audio or visual features are input into the DBLSTM-RNN model, whose output estimations of a dimension are smoothed by the moving average filter. After the smoothed estimations are expanded to the frame rate of the ground truth labels, another MA is adopted for smoothing the final results. In the multi-modal framework DBLSTM-DBLSTM-MA, the initial estimations from the audio and visual modalities via the first layer of DBLSTM-RNNs are input into a second layer of DBLSTM-RNN, whose outputs are smoothed by MA. The smoothed estimations are then expanded to the frame rate of the ground truth labels and smoothed again by another MA. Affect recognition experiments are carried out on the training set and development set of the AVEC2014 database, results show that the proposed DBLSTM-MA framework outperforms linear regression, support vector regression (SVR), and BLSTM for single modal dimension estimation. For audio visual multi modal affect recognition, DBLSTM-DBLSTM-MA obtains better or comparable performance than the state of the art results in the competition of AVEC2014, with the average correlation coefficient (COR) reaches 0.599 on the Freeform database, 0.630 on the Northwind database, and 0.615 on the Freeform-Northwind database. [Pei, Ercheng; Yang, Le; Jiang, Dongmei] Northwestern Polytech Univ, NPU VUB Joint AVSP Res Lab, Sch Comp Sci, Shaanxi Key Lab Speech & Image Informat Proc, 127 Youyi Xilu, Xian 710072, Peoples R China; [Sahli, Hichem] Vrije Univ Brussel, NPU VUB Joint AVSP Res Lab, Dept Elect & Informat ETRO, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel; IMEC Pei, EC (corresponding author), Northwestern Polytech Univ, NPU VUB Joint AVSP Res Lab, Sch Comp Sci, Shaanxi Key Lab Speech & Image Informat Proc, 127 Youyi Xilu, Xian 710072, Peoples R China. jiangdm@nwpu.edu.cn; hsahli@vub.ac.be Sahli, Hichem/0000-0002-1774-2970 34 17 18 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-8103 978-1-4799-9953-8 INT CONF AFFECT 2015.0 208 214 7 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BE9NN 2023-03-23 WOS:000377887000031 0
J Lim, CF; Chen, CC; Smail, I; Wang, WH; Tee, WL; Lin, YT; Scott, D; Toba, Y; Chang, YY; Ao, YP; Babul, A; Bunker, A; Chapman, SC; Clements, DL; Conselice, CJ; Gao, Y; Greve, TR; Ho, LC; Hong, SE; Hwang, HS; Koprowski, M; Michalowski, MJ; Shim, H; Shu, XW; Simpson, JM Lim, Chen-Fatt; Chen, Chian-Chou; Smail, Ian; Wang, Wei-Hao; Tee, Wei-Leong; Lin, Yen-Ting; Scott, Douglas; Toba, Yoshiki; Chang, Yu-Yen; Ao, YiPing; Babul, Arif; Bunker, Andy; Chapman, Scott C.; Clements, David L.; Conselice, Christopher J.; Gao, Yu; Greve, Thomas R.; Ho, Luis C.; Hong, Sungwook E.; Hwang, Ho Seong; Koprowski, Maciej; Michalowski, Michal J.; Shim, Hyunjin; Shu, Xinwen; Simpson, James M. SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies ASTROPHYSICAL JOURNAL English Article Clustering; Submillimeter astronomy; High-redshift galaxies; Galaxy evolution; Large-scale structure of the universe; Galaxy formation COSMOLOGY LEGACY SURVEY; STAR-FORMING GALAXIES; LYMAN-BREAK GALAXIES; REDSHIFT SURVEY; MU-M; ALMA SURVEY; LARGE-SCALE; LUMINOSITY DEPENDENCE; BOLOMETER CAMERA; PASSIVE GALAXIES We analyze an extremely deep 450 mu m image (1 sigma = 0.56 mJy beam(-1)) of a similar or equal to 300 arcmin(2) area in the CANDELS/COSMOS field as part of the Sub-millimeter Common User Bolometric Array-2 Ultra Deep Imaging EAO Survey. We select a robust (signal-to-noise ratio >= 4) and flux-limited (>= 4 mJy) sample of 164 submillimeter galaxies (SMGs) at 450 mu m that have K-band counterparts in the COSMOS2015 catalog identified from radio or mid-infrared imaging. Utilizing this SMG sample and the 4705 K-band-selected non-SMGs that reside within the noise level <= 1 mJy beam(-1) region of the 450 mu m image as a training set, we develop a machine-learning classifier using K-band magnitude and color-color pairs based on the 13-band photometry available in this field. We apply the trained machine-learning classifier to the wider COSMOS field (1.6 deg(2)) using the same COSMOS2015 catalog and identify a sample of 6182 SMG candidates with similar colors. The number density, radio and/or mid-infrared detection rates, redshift and stellar-mass distributions, and the stacked 450 mu m fluxes of these SMG candidates, from the S2COSMOS observations of the wide field, agree with the measurements made in the much smaller CANDELS field, supporting the effectiveness of the classifier. Using this SMG candidate sample, we measure the two-point autocorrelation functions from z = 3 down to z = 0.5. We find that the SMG candidates reside in halos with masses of similar or equal to (2.0 +/- 0.5) x 10(13) h(-1) M-circle dot across this redshift range. We do not find evidence of downsizing that has been suggested by other recent observational studies. [Lim, Chen-Fatt; Chen, Chian-Chou; Wang, Wei-Hao; Tee, Wei-Leong; Lin, Yen-Ting; Toba, Yoshiki; Chang, Yu-Yen; Simpson, James M.] Acad Sinica, Inst Astron & Astrophys ASIAA, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan; [Lim, Chen-Fatt] Natl Taiwan Univ, Grad Inst Astrophys, Taipei 10617, Taiwan; [Chen, Chian-Chou] European Southern Observ, Karl Schwarzschild Str 2, Garching, Germany; [Smail, Ian] Univ Durham, Ctr Extragalact Astron, Dept Phys, South Rd, Durham DH1 3LE, England; [Tee, Wei-Leong] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA; [Scott, Douglas; Chapman, Scott C.] Univ British Columbia, Dept Phys & Astron, 6225 Agr Rd, Vancouver, BC V6T 1Z1, Canada; [Toba, Yoshiki] Kyoto Univ, Dept Astron, Sakyo Ku, Kitashirakawa Oiwake Cho, Kyoto 6068502, Japan; [Toba, Yoshiki] Ehime Univ, Res Ctr Space & Cosm Evolut, 2-5 Bunkyo Cho, Matsuyama, Ehime 7908577, Japan; [Ao, YiPing; Gao, Yu] Chinese Acad Sci, Purple Mt Observ, Nanjing 210033, Peoples R China; [Ao, YiPing; Gao, Yu] Chinese Acad Sci, Key Lab Radio Astron, Nanjing 210033, Peoples R China; [Babul, Arif] Univ Victoria, Dept Phys & Astron, Elliott Bldg,3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada; [Bunker, Andy] Univ Oxford, Dept Phys, Denys Wilkinson Bldg,Keble Rd, Oxford OX1 3RH, England; [Chapman, Scott C.] Natl Res Council Canada, Herzberg Astron & Astrophys, 5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada; [Chapman, Scott C.] Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS B3H 4R2, Canada; [Clements, David L.] Imperial Coll, Dept Phys, Blackett Lab, Prince Consort Rd, London SW7 2AZ, England; [Conselice, Christopher J.] Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England; [Gao, Yu] Xiamen Univ, Dept Astron, Xiamen 361005, Fujian, Peoples R China; [Greve, Thomas R.] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England; [Greve, Thomas R.] Cosm Dawn Ctr, Copenhagen, Denmark; [Ho, Luis C.] Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China; [Ho, Luis C.] Peking Univ, Sch Phys, Dept Astron, Beijing 100871, Peoples R China; [Hong, Sungwook E.] Univ Seoul, Nat Sci Res Inst, 163 Seoulsiripdaero, Seoul 02504, South Korea; [Hwang, Ho Seong] Korea Astron & Space Sci Inst, 776 Daedeokdae Ro, Daejeon 34055, South Korea; [Koprowski, Maciej] Nicolaus Copernicus Univ, Fac Phys Astron & Informat, Inst Astron, Grudziadzka 5, PL-87100 Torun, Poland; [Michalowski, Michal J.] Adam Mickiewicz Univ, Fac Phys, Astron Observ Inst, PL-60286 Poznan, Poland; [Shim, Hyunjin] Kyungpook Natl Univ, Dept Earth Sci Educ, Deagu 41566, South Korea; [Shu, Xinwen] Anhui Normal Univ, Dept Phys, Wuhu 241000, Anhui, Peoples R China; [Bunker, Andy] Univ Tokyo, Inst Adv Study, Kavli Inst Phys & Math Universe WPI, Tokyo, Japan Academia Sinica - Taiwan; National Taiwan University; European Southern Observatory; Durham University; University of Arizona; University of British Columbia; Kyoto University; Ehime University; Chinese Academy of Sciences; Nanjing Institute of Astronomical Optics & Technology, NAOC, CAS; Purple Mountain Observatory, CAS; Chinese Academy of Sciences; University of Victoria; University of Oxford; National Research Council Canada; Dalhousie University; Imperial College London; University of Nottingham; Xiamen University; University of London; University College London; Peking University; Peking University; University of Seoul; Korea Astronomy & Space Science Institute (KASI); Nicolaus Copernicus University; Adam Mickiewicz University; Kyungpook National University; Anhui Normal University; University of Tokyo Lim, CF (corresponding author), Acad Sinica, Inst Astron & Astrophys ASIAA, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan.;Lim, CF (corresponding author), Natl Taiwan Univ, Grad Inst Astrophys, Taipei 10617, Taiwan. chenfatt.lim@gmail.com 林彥廷, Yen-Ting Lin/ABC-4256-2020; CHEN, Chian-Chou (TC) 陳建州/ABB-2819-2020; HWANG, Ho Seong/AAS-6010-2020; Martínez-Pagán, Pedro/M-1491-2014; Shu, Xinwen/ABG-3953-2020; Michałowski, Michał/AAQ-4789-2021; Wang, Wei-Hao/ABD-9942-2020; Chang, Yu-Yen/GLR-5642-2022; Koprowski, Maciej/M-7525-2019; Babul, Arif/E-2730-2010; Hong, Sungwook E./J-1533-2019; Smail, Ian/M-5161-2013 林彥廷, Yen-Ting Lin/0000-0001-7146-4687; CHEN, Chian-Chou (TC) 陳建州/0000-0002-3805-0789; HWANG, Ho Seong/0000-0003-3428-7612; Martínez-Pagán, Pedro/0000-0002-3308-3768; Wang, Wei-Hao/0000-0003-2588-1265; Chang, Yu-Yen/0000-0002-6720-8047; Babul, Arif/0000-0003-1746-9529; Hong, Sungwook E./0000-0003-4923-8485; Tee, Wei Leong/0000-0003-0747-1780; Michalowski, Michal/0000-0001-9033-4140; Lim, Chen-Fatt/0000-0003-1213-9360; Smail, Ian/0000-0003-3037-257X; Greve, Thomas R./0000-0002-2554-1837; Koprowski, Maciej/0000-0001-5785-1154 Ministry of Science and Technology of Taiwan [MOST 109-2112-M-001-016-MY3, 105-2112-M-001-029-MY3, 108-2112-M-001-014-]; SSDF-(ST) [18/27/E]; STFC [ST/P000541/1]; National Science Foundation of China [11721303, 11991052]; National Key RAMP;D Program of China [2016YFA0400702, 2017YFA0402700]; National Science Centre, Poland through the SONATA BIS grant [2018/30/E/ST9/00208]; First TEAM grant of the Foundation for Polish Science [POIR.04.04.00-00-5D21/18-00]; Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education [2018R1A6A1A06024977]; NSFC [11933011]; National Key Basic Research and Development Program of China [2017YFA0402704]; National Natural Science Foundation of China [11861131007, 11420101002]; Chinese Academy of Sciences Key Research Program of Frontier Sciences [QYZDJSSWSLH008]; Science and Technology Facilities Council of the United Kingdom; S2COSMOS program [M16AL002]; STUDIES program [M16AL006]; S2CLS program [MJLSC01]; PI program [M11BH11A, M12AH11A, M12BH21A]; STFC [ST/L005042/1, ST/R001464/1] Funding Source: UKRI Ministry of Science and Technology of Taiwan(Ministry of Science and Technology, Taiwan); SSDF-(ST); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key RAMP;D Program of China; National Science Centre, Poland through the SONATA BIS grant; First TEAM grant of the Foundation for Polish Science; Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education(Ministry of Education (MOE), Republic of KoreaNational Research Foundation of Korea); NSFC(National Natural Science Foundation of China (NSFC)); National Key Basic Research and Development Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences Key Research Program of Frontier Sciences; Science and Technology Facilities Council of the United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); S2COSMOS program; STUDIES program; S2CLS program; PI program; STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We thank the JCMT/EAO staff for observational support and the data/survey management and the anonymous referee for comments that significantly improved the manuscript. C.C.C. acknowledges support from the Ministry of Science and Technology of Taiwan (MOST 109-2112-M-001-016-MY3). C.F.L. and C.C.C. acknowledge grant support from SSDF-(ST) 18/27/E. C.F.L., W.H.W., and Y.Y.C. acknowledge grant support from the Ministry of Science and Technology of Taiwan (105-2112-M-001-029-MY3 and 108-2112-M-001-014-). I.R.S. acknowledges support from STFC (ST/P000541/1). L.C.H. acknowledges support from the National Science Foundation of China (11721303 and 11991052) and National Key R&D Program of China (2016YFA0400702). M.J.M. acknowledges the support of the National Science Centre, Poland through the SONATA BIS grant 2018/30/E/ST9/00208. M.P.K. acknowledges support from the First TEAM grant of the Foundation for Polish Science No. POIR.04.04.00-00-5D21/18-00. S.E.H. is supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2018R1A6A1A06024977). Y.A. acknowledges financial support by NSFC grant 11933011. Y.G.'s research is supported by National Key Basic Research and Development Program of China (grant No. 2017YFA0402704), National Natural Science Foundation of China (grant Nos. 11861131007 and 11420101002), and Chinese Academy of Sciences Key Research Program of Frontier Sciences (grant No. QYZDJSSWSLH008).r The James Clerk Maxwell Telescope is operated by the East Asian Observatory on behalf of the National Astronomical Observatory of Japan; the Academia Sinica Institute of Astronomy and Astrophysics; the Korea Astronomy and Space Science Institute; and the Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments, budgeted from the Ministry of Finance (MOF) of China and administrated by the Chinese Academy of Sciences (CAS), as well as the National Key R&D Program of China (No. 2017YFA0402700). Additional funding support is provided by the Science and Technology Facilities Council of the United Kingdom and participating universities in the United Kingdom and Canada. The submillimeter observations used in this work include the S2COSMOS program (program code M16AL002), STUDIES program (program code M16AL006), S2CLS program (program code MJLSC01) and the PI program of (Casey et al. 2013, program codes M11BH11A, M12AH11A, and M12BH21A). 183 8 8 0 4 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. JUN 2020.0 895.0 2 104 10.3847/1538-4357/ab8eaf 0.0 18 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics LW7VH Green Accepted, Bronze, Green Submitted, Green Published 2023-03-23 WOS:000539351400001 0
C Miskelly, J; Gu, CY; Ma, QQ; Cui, YJ; Liu, WQ; O'Neill, M IEEE Miskelly, Jack; Gu, Chongyan; Ma, Qingqing; Cui, Yijun; Liu, Weiqiang; O'Neill, Maire Modelling Attack Analysis of Configurable Ring Oscillator (CRO) PUF Designs 2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP) International Conference on Digital Signal Processing English Proceedings Paper 23rd IEEE International Conference on Digital Signal Processing (DSP) NOV 19-21, 2018 Shanghai, PEOPLES R CHINA IEEE Physical Unclonable Functions (PUFs) have emerged as a lightweight security primitive for resource constrained devices. However, conventional delay-based Physical Unclonable Functions (PUFs) are vulnerable to machine learning (ML) based modelling attacks. Although ML resistant PUF designs have been proposed, they often suffer from large overheads and are difficult to implement on FPGA. Lightweight ML resistant FPGA compatible designs have been proposed which make use of combined multi-PUF designs, incorporating a set of weak PUFs to obscure the challenge to a strong PUF in order to increase the difficulty of model building. In such designs any unreliability in the main PUF is amplified by unreliability in the masking PUFs. For this reason strong PUFs suitable for FPGA that can achieve high reliability, such as the Configurable Ring Oscillator (CRO) PUF, are a promising option. In this paper a mathematical model of the CRO PUF is presented. We show that models of traditional CRO PUFs can be trained to above 99% prediction rate using the Linear Regression and CMA-ES strategies. A proposed multi-PUF design based on the previously proposed arbiter MPUF is evaluated with the same methods. It is shown that even with challenge obfuscation the CRO PUF can be predicted with greater than 90% accuracy. It is shown that with the addition of a second XORed PUF the ML resistance can be increased further with a maximum prediction rate of 86%. [Miskelly, Jack; Gu, Chongyan; O'Neill, Maire] Queens Univ Belfast, ECIT, CSIT, Belfast, Antrim, North Ireland; [Ma, Qingqing; Cui, Yijun; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Nanjing, Jiangsu, Peoples R China Queens University Belfast; Nanjing University of Aeronautics & Astronautics Miskelly, J (corresponding author), Queens Univ Belfast, ECIT, CSIT, Belfast, Antrim, North Ireland. jmiskelly08@qub.ac.uk; cgu01@qub.ac.uk; martin@nuaa.edu.cn; yijun.cui@nuaa.edu.cn; liuweiqiang@nuaa.edu.cn; moneill@ecit.qub.ac.uk GU, CHONGYAN/ABD-5437-2021 GU, CHONGYAN/0000-0002-3028-8004 Institute for Information & communications Technology Promotion(IITP) - Korean government(MSIT) [2016-0-00399]; EPSRC [EP/N508664/-CSIT2]; Nature Science Foundation of Jiangsu Province [BK20151477]; National Natural Science Foundation China [61771239] Institute for Information & communications Technology Promotion(IITP) - Korean government(MSIT); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Nature Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); National Natural Science Foundation China(National Natural Science Foundation of China (NSFC)) This work was partly supported by the Institute for Information & communications Technology Promotion(IITP) grant funded by the Korean government(MSIT) (No. 2016-0-00399, Study on secure key hiding technology for IoT devices [KeyHAS Project]), by the EPSRC (EP/N508664/-CSIT2), by Nature Science Foundation of Jiangsu Province (BK20151477) and by National Natural Science Foundation China (61771239). 22 8 8 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1546-1874 INT CONF DIGIT SIG 2018.0 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM0NT 2023-03-23 WOS:000458909600100 0
C Li, F; Chen, Y; Xie, R; Ben Abdesslem, F; Lindgren, A IEEE Li, Fei; Chen, Yang; Xie, Rong; Ben Abdesslem, Fehmi; Lindgren, Anders Understanding Service Integration of Online Social Networks: A Data-Driven Study 2018 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) International Conference on Pervasive Computing and Communications English Proceedings Paper IEEE International Conference on Pervasive Computing and Communications (PerCom) MAR 19-23, 2018 Athens, GREECE IEEE Service Integration; Online Social Networks; Cross-site Linking; High PageRank Users; Prediction; Medium The cross-site linking function is widely adopted by online social networks (OSNs). This function allows a user to link her account on one OSN to her accounts on other OSNs. Thus, users are able to sign in with the linked accounts, share contents among these accounts and import friends from them. It leads to the service integration of different OSNs. This integration not only provides convenience for users to manage accounts of different OSNs, but also introduces usefulness to OSNs that adopt the cross-site linking function. In this paper, we investigate this usefulness based on users' data collected from a popular OSN called Medium. We conduct a thorough analysis on its social graph, and find that the service integration brought by the cross site linking function is able to change Medium's social graph structure and attract a large number of new users. However, almost none of the new users would become high PageRank users (PageRank is used to measure a user's influence in an OSN). To solve this problem, we build a machine-learning-based model to predict high PageRank users in Medium based on their Twitter data only. This model achieves a high F1-score of 0.942 and a high area under the curve (AUC) of 0.986. Based on it, we design a system to assist new OSNs to identify and attract high PageRank users from other well-established OSNs through the cross-site linking function. [Li, Fei; Chen, Yang; Xie, Rong] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Ben Abdesslem, Fehmi; Lindgren, Anders] RISE SICS, Kista, Sweden Fudan University; RISE Research Institutes of Sweden Li, F (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China. feili14@fudan.edu.cn; chenyang@fudan.edu.cn; xieronglucy@fudan.edu.cn; fehmi.ben.abdesslem@ri.se; anders.lindgren@ri.se National Natural Science Foundation of China [61602122, 71731004]; Natural Science Foundation of Shanghai [16ZR1402200]; Shanghai Pujiang Program [16PJ1400700] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Pujiang Program(Shanghai Pujiang Program) This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700). Yang Chen is the corresponding author. 21 1 1 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2474-2503 978-1-5386-3227-7 INT CONF PERVAS COMP 2018.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BP1XG 2023-03-23 WOS:000541062400163 0
C Qin, J; Liu, T; Wang, ZM; Zou, QJ; Chen, LM; Hong, C Wang, Y; Zhu, G; Han, Q; Wang, H; Song, X; Lu, Z Qin, Jing; Liu, Tong; Wang, Zumin; Zou, Qijie; Chen, Liming; Hong, Chang Speech Recognition for Parkinson's Disease Based on Improved Genetic Algorithm and Data Enhancement Technology DATA SCIENCE (ICPCSEE 2022), PT I Communications in Computer and Information Science English Proceedings Paper 8th International Conference on Pioneering Computer Scientists, Engineers and Educators (ICPCSEE) AUG 19-22, 2022 SW Petr Univ, Chengdu, PEOPLES R CHINA Univ Elect Sci & Technol China,Natl Acad Guo Ding Inst Data Sci SW Petr Univ Parkinson's disease speech signal detection; Support vector machine; SMOTE algorithm; Genetic algorithm Parkinson's disease is one of the most destructive diseases to the nervous system. Speech disorder is one of the typical symptoms of Parkinson's disease. Approximately 90% of Parkin-son's patients develop some degree of speech disorder, which affects speech function faster than any other subsystem of the body. Screening Parkinson's disease by sound is a very effective method that has attracted a growing number of researchers over the past decade. Patients with Parkinson's disease could be identified by recording the sound signal of the pronunciation of words, extracting appropriate features and identifying the disturbance in their voices. This paper proposes an improved genetic algorithm combined with a data enhancement method for Parkinson's speech signal recognition. Specifically, the methods first extract representative speech signal features through the L1 regularization SVM and then enhance the representative feature data by the SMOTE algorithm. Following this, both original and enhanced features are used to train an SVM classifier for speech signal recognition. An improved genetic algorithm was applied to find the optimal parameters of the SVM. The effectiveness of our proposed model is demonstrated by using Parkinson's disease audio data set from the UCI machine learning library, and compared with the most advanced methods, our proposed method has the best performance. [Qin, Jing; Liu, Tong; Wang, Zumin; Zou, Qijie] Dalian Univ, Dalian 116000, Peoples R China; [Wang, Zumin; Chen, Liming] Ulster Univ, Belfast 100166, North Ireland; [Wang, Zumin; Hong, Chang] Third Peoples Hosp Dalian, Dalian 116000, Peoples R China Dalian University; Ulster University Wang, ZM (corresponding author), Dalian Univ, Dalian 116000, Peoples R China.;Wang, ZM (corresponding author), Ulster Univ, Belfast 100166, North Ireland.;Wang, ZM (corresponding author), Third Peoples Hosp Dalian, Dalian 116000, Peoples R China. qinjing@dlu.edu.cn; zumingwang@dlu.edu.cn Youth Fund Project of the National Natural Fund of China [62002038] Youth Fund Project of the National Natural Fund of China This work was supported by the Youth Fund Project of the National Natural Fund of China under Grant 62002038. 24 0 0 5 5 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 1865-0929 1865-0937 978-981-19-5194-7; 978-981-19-5193-0 COMM COM INF SC 2022.0 1628.0 273 286 10.1007/978-981-19-5194-7_21 0.0 14 Computer Science, Information Systems; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Operations Research & Management Science BU2ZQ 2023-03-23 WOS:000889375000021 0
J Buetti-Dinh, A; Herold, M; Christel, S; El Hajjami, ME; Delogu, F; Ilie, O; Bellenberg, S; Wilmes, P; Poetsch, A; Sand, W; Vera, M; Pivkin, IV; Friedman, R; Dopson, M Buetti-Dinh, Antoine; Herold, Malte; Christel, Stephan; El Hajjami, Mohamed; Delogu, Francesco; Ilie, Olga; Bellenberg, Soren; Wilmes, Paul; Poetsch, Ansgar; Sand, Wolfgang; Vera, Mario; Pivkin, Igor V.; Friedman, Ran; Dopson, Mark Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations BMC BIOINFORMATICS English Article Biological signalling simulations; Gene regulatory networks; Approximate Bayesian computation; Machine learning; Biomining; Acidophiles; Multispecies bacterial community interactions EXPRESSION; SEQUENCE; INTEGRATION; METASTASIS; ROBUSTNESS; RESISTANCE; PROGRESS; METALS; S100A4; MODEL Background Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications. [Buetti-Dinh, Antoine; Ilie, Olga; Pivkin, Igor V.] Univ Svizzera Italiana, Inst Computat Sci, Fac Informat, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland; [Buetti-Dinh, Antoine; Ilie, Olga; Pivkin, Igor V.] Swiss Inst Bioinformat, Quartier Sorge Batiment Genopode, CH-1015 Lausanne, Switzerland; [Buetti-Dinh, Antoine; Friedman, Ran] Linnaeus Univ, Dept Chem & Biomed Sci, Hus Vita, SE-39182 Kalmar, Sweden; [Buetti-Dinh, Antoine; Friedman, Ran] Linnaeus Univ, Ctr Biomat Chem, Hus Vita, SE-39182 Kalmar, Sweden; [Buetti-Dinh, Antoine; Christel, Stephan; Bellenberg, Soren; Dopson, Mark] Linnaeus Univ, Ctr Ecol & Evolut Microbial Model Syst, Hus Vita, SE-39182 Kalmar, Sweden; [Herold, Malte; Wilmes, Paul] Univ Luxembourg, Luxembourg Ctr Syst Biomed, Belvaux, Luxembourg; [Poetsch, Ansgar] Ruhr Univ Bochum, Plant Biochem, Bochum, Germany; [El Hajjami, Mohamed; Poetsch, Ansgar] QNLM, Ctr Marine & Mol Biotechnol, Qingdao, Peoples R China; [Delogu, Francesco] Norwegian Univ Life Sci, Fac Chem Biotechnol & Food Sci, Oslo, Norway; [Poetsch, Ansgar] Ocean Univ China, Coll Marine Life Sci, Qingdao, Peoples R China; Univ Duisburg Essen, Biofilm Ctr, Fac Chem, Essen, Germany; [Sand, Wolfgang] Donghua Univ, Coll Environm Sci & Engn, Shanghai, Peoples R China; [Sand, Wolfgang] Min Acad, Freiberg, Germany; [Sand, Wolfgang] Tech Univ Freiberg, Freiberg, Germany; [Vera, Mario] Pontificia Univ Catolica Chile, Inst Biol & Med Engn, Sch Engn, Santiago, Chile; [Vera, Mario] Pontificia Univ Catolica Chile, Inst Biol & Med Engn, Sch Med, Santiago, Chile; [Vera, Mario] Pontificia Univ Catolica Chile, Inst Biol & Med Engn, Sch Biol Sci, Santiago, Chile; [Vera, Mario] Pontificia Univ Catolica Chile, Dept Hydraul & Environm Engn, Santiago, Chile Universita della Svizzera Italiana; Swiss Institute of Bioinformatics; Linnaeus University; Linnaeus University; Linnaeus University; University of Luxembourg; Ruhr University Bochum; Qingdao National Laboratory for Marine Science & Technology; Norwegian University of Life Sciences; Ocean University of China; University of Duisburg Essen; Donghua University; Technical University Freiberg; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile Buetti-Dinh, A (corresponding author), Univ Svizzera Italiana, Inst Computat Sci, Fac Informat, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland.;Buetti-Dinh, A (corresponding author), Swiss Inst Bioinformat, Quartier Sorge Batiment Genopode, CH-1015 Lausanne, Switzerland.;Buetti-Dinh, A (corresponding author), Linnaeus Univ, Dept Chem & Biomed Sci, Hus Vita, SE-39182 Kalmar, Sweden.;Buetti-Dinh, A (corresponding author), Linnaeus Univ, Ctr Biomat Chem, Hus Vita, SE-39182 Kalmar, Sweden.;Buetti-Dinh, A (corresponding author), Linnaeus Univ, Ctr Ecol & Evolut Microbial Model Syst, Hus Vita, SE-39182 Kalmar, Sweden. antoine.buetti@sib.swiss Buetti-Dinh, Antoine/HNB-4313-2023; Poetsch, Ansgar/A-1862-2013; Wilmes, Paul/B-1707-2017; Dopson, Mark/A-7028-2013; Friedman, Ran/A-1582-2008; Christel, Stephan/AAX-1075-2021 Buetti-Dinh, Antoine/0000-0002-6469-0296; Poetsch, Ansgar/0000-0002-7540-3475; Wilmes, Paul/0000-0002-6478-2924; Friedman, Ran/0000-0001-8696-3104; Christel, Stephan/0000-0003-0021-2452; Delogu, Francesco/0000-0002-7211-8011; Herold, Malte/0000-0003-2627-0159 Bundesministerium fur Bildung und Forschung (BMBF) [031A600A]; Vetenskapsradet [2014-6545]; Luxembourg National Research Fund (FNR) [INTER/SYSAPP/14/05]; Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx); Swiss National Science Foundation [205321_173020]; Swiss Platform for Advanced Scientific Computing; Fondecyt [1161007, 1190892] Bundesministerium fur Bildung und Forschung (BMBF)(Federal Ministry of Education & Research (BMBF)); Vetenskapsradet(Swedish Research Council); Luxembourg National Research Fund (FNR)(Luxembourg National Research Fund); Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx); Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); Swiss Platform for Advanced Scientific Computing; Fondecyt(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT) This work was supported by Bundesministerium fur Bildung und Forschung (BMBF, 031A600A and B), Vetenskapsradet (contract 2014-6545), the Luxembourg National Research Fund (FNR) (INTER/SYSAPP/14/05), and the Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx) under the frame of ERASysAPP. The calculations were performed on resources provided by the Swiss National Supercomputing Centre (CSCS). IVP acknowledges the support from Swiss National Science Foundation and Swiss Platform for Advanced Scientific Computing. ABD and IVP acknowledge support from the Swiss National Science Foundation grant 205321_173020. MV acknowledges support from Fondecyt 1161007 and 1190892 grants. 75 7 7 5 15 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics JAN 21 2020.0 21.0 1 23 10.1186/s12859-019-3337-9 0.0 15 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology KL7MZ 31964336.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000513604400001 0
J Lu, YS; Qi, Y; Qi, SY; Li, Y; Song, HY; Liu, YH Lu, Youshui; Qi, Yong; Qi, Saiyu; Li, Yue; Song, Hongyu; Liu, Yuhao Say No to Price Discrimination: Decentralized and Automated Incentives for Price Auditing in Ride-Hailing Services IEEE TRANSACTIONS ON MOBILE COMPUTING English Article Pricing; Insurance; Mobile computing; Smart contracts; Data protection; Distributed ledger; Auditing; blockchain; smart contract; ride-hailing; game theory; insurance scheme; decentralized system SECURE As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users' personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchase Fair Price Insurance in the form of Price Auditing Contract, then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible. [Lu, Youshui; Qi, Yong; Song, Hongyu; Liu, Yuhao] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China; [Qi, Saiyu] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China; [Li, Yue] Vrije Univ Amsterdam, Dept Econ, NL-1081 HV Amsterdam, Netherlands; [Li, Yue] ABN AMRO Bank NV, Risk Modelling Dept, Amsterdam, Netherlands Xi'an Jiaotong University; Xidian University; Vrije Universiteit Amsterdam Lu, YS (corresponding author), Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China.;Qi, SY (corresponding author), Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China. lucienlu@me.com; qiy@mail.xjtu.edu.cn; syqi@connect.ust.hk; yue.li@vu.nl; hysong0101@stu.xjtu.edu.cn; lyuhao@stu.xjtu.edu.cn Li, Yue/0000-0002-5624-7235; LU, YOUSHUI/0000-0002-6598-7606 National key R&D Program of China [2018YFB1402700]; National Natural Science Foundation of China [61672412, 61602363] National key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National key R&D Program of China under Grant No.2018YFB1402700. It was also partially supported by the National Natural Science Foundation of China under Grant No. 61672412 and No. 61602363. 46 9 9 5 22 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1536-1233 1558-0660 IEEE T MOBILE COMPUT IEEE. Trans. Mob. Comput. FEB 1 2022.0 21.0 2 663 680 10.1109/TMC.2020.3008315 0.0 18 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications YC9JL hybrid 2023-03-23 WOS:000740000200019 0
J Cai, X; Bao, YS; Petropoulos, GP; Lu, F; Lu, QF; Zhu, LH; Wu, Y Cai, Xi; Bao, Yansong; Petropoulos, George P.; Lu, Feng; Lu, Qifeng; Zhu, Liuhua; Wu, Ying Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks REMOTE SENSING English Article GIIRS; hyperspectral; Earth Observation; Artificial Neural Network; temperature; humidity; profile ATMOSPHERIC-TEMPERATURE; MOISTURE PROFILES; SATELLITE This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm(-1), and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder's (AIRS') products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally. [Cai, Xi; Bao, Yansong; Zhu, Liuhua; Wu, Ying] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, CMA Key Lab Aerosol Cloud Precipitat, Nanjing 210044, Peoples R China; [Cai, Xi; Bao, Yansong; Zhu, Liuhua; Wu, Ying] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China; [Petropoulos, George P.] Harokopio Univ Athens, Dept Geog, El Venizelou 70, Athens 17671, Greece; [Lu, Feng; Lu, Qifeng] China Meteorol Adm, Natl Satellite Meteorol Ctr, Key Lab Radiometr Calibrat & Validat Environm Sat, Beijing 100081, Peoples R China Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; China Meteorological Administration Bao, YS (corresponding author), Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, CMA Key Lab Aerosol Cloud Precipitat, Nanjing 210044, Peoples R China.;Bao, YS (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China. xcai@nuist.edu.cn; ysbao@nuist.edu.cn; gpetropoulos@hua.gr; lufeng@cma.gov.cn; luqf@cma.gov.cn; zhulh@nuist.edu.cn; yingwu@nuist.edu.cn Petropoulos, George P./N-5810-2019 Petropoulos, George P./0000-0003-1442-1423 National Key Research and Development Program of China [2017YFC15017043, 2018YFC1407200, 2016YFA0600703]; National Natural Science Foundation of China [41975046] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Key Research and Development Program of China (2017YFC15017043, 2018YFC1407200, 2016YFA0600703), and National Natural Science Foundation of China (41975046). 53 11 12 1 22 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUN 2020.0 12.0 11 1872 10.3390/rs12111872 0.0 24 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology MC6LX gold 2023-03-23 WOS:000543397000177 0
J Tao, Y; Muller, JP; Conway, SJ; Xiong, ST Tao, Yu; Muller, Jan-Peter; Conway, Susan J.; Xiong, Siting Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover REMOTE SENSING English Article mars; 3D mapping; large area mapping; digital terrain model; deep learning; MADNet; HRSC; CTX; HiRISE; Oxia Planum; ExoMars; Rosalind Franklin rover; landing site; planetary mapping; 3D reconstruction ORBITER LASER ALTIMETER; MARS; SURFACE We demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express's High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter's Context Camera (CTX), and the High Resolution Imaging Science Experiment (HiRISE) imaging data over the ExoMars 2022 Rosalind Franklin rover's landing site at Oxia Planum on Mars. MADNet takes a single orbital optical image as input, provides pixelwise height predictions, and uses a separate coarse Digital Terrain Model (DTM) as reference, to produce a DTM product from the given input image. Initially, we demonstrate the resultant 25 m/pixel HRSC DTM mosaic covering an area of 197 km x 182 km, providing fine-scale details to the 50 m/pixel HRSC MC-11 level-5 DTM mosaic. Secondly, we demonstrate the resultant 12 m/pixel CTX MADNet DTM mosaic covering a 114 km x 117 km area, showing much more detail in comparison to photogrammetric DTMs produced using the open source in-house developed CASP-GO system. Finally, we demonstrate the resultant 50 cm/pixel HiRISE MADNet DTM mosaic, produced for the first time, covering a 74.3 km x 86.3 km area of the 3-sigma landing ellipse and partially the ExoMars team's geological characterisation area. The resultant MADNet HiRISE DTM mosaic shows fine-scale details superior to existing Planetary Data System (PDS) HiRISE DTMs and covers a larger area that is considered difficult for existing photogrammetry and photoclinometry pipelines to achieve, especially given the current limitations of stereo HiRISE coverage. All of the resultant DTM mosaics are co-aligned with each other, and ultimately with the Mars Global Surveyor's Mars Orbiter Laser Altimeter (MOLA) DTM, providing high spatial and vertical congruence. In this paper, technical details are presented, issues that arose are discussed, along with a visual evaluation and quantitative assessments of the resultant DTM mosaic products. [Tao, Yu; Muller, Jan-Peter] Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Holmbury St Mary, Surrey RH5 6NT, England; [Conway, Susan J.] Univ Nantes, CNRS, UMR 6112, Lab Planetol & Geodynam, F-44300 Nantes, France; [Xiong, Siting] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Minist Nat Resources, Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Xiong, Siting] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China University of London; University College London; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); Nantes Universite; Shenzhen University; Ministry of Natural Resources of the People's Republic of China; Shenzhen University; Shenzhen University; Shenzhen University Tao, Y (corresponding author), Univ Coll London, Dept Space & Climate Phys, Mullard Space Sci Lab, Imaging Grp, Holmbury St Mary, Surrey RH5 6NT, England. yu.tao@ucl.ac.uk; j.muller@ucl.ac.uk; susan.conway@univ-nantes.fr; xiongsiting@szu.edu.cn Muller, Jan-Peter/0000-0002-5077-3736; Tao, Yu/0000-0001-9170-6655 UKSA Aurora programme [ST/S001891/1]; STFC MSSL [ST/K000977/1]; French Space Agency CNES; Shenzhen Scientific Research and Development Funding Programme [JCYJ20190808120005713]; China Postdoctoral Science Foundation [2019M663073] UKSA Aurora programme; STFC MSSL; French Space Agency CNES(Centre National D'etudes Spatiales); Shenzhen Scientific Research and Development Funding Programme; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The research leading to these results is receiving funding from the UKSA Aurora programme (2018-2021) under grant no. ST/S001891/1, as well as partial funding from the STFC MSSL Consolidated Grant ST/K000977/1. S.C. is grateful to the French Space Agency CNES for supporting her HiRISE related work. S.X. has received funding from the Shenzhen Scientific Research and Development Funding Programme (grant No. JCYJ20190808120005713) and China Postdoctoral Science Foundation (grant No. 2019M663073). 44 6 6 6 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13.0 16 3270 10.3390/rs13163270 0.0 26 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology UH7PN Green Published, gold 2023-03-23 WOS:000690117900001 0
J Noori, N; de Jong, M; Hoppe, T Noori, Negar; de Jong, Martin; Hoppe, Thomas Towards an Integrated Framework to Measure Smart City Readiness: The Case of Iranian Cities SMART CITIES English Article smart city; urban transition; theory of change; technological readiness; social readiness; political readiness; Iranian cities BIG DATA; TRANSITIONS; TECHNOLOGY; ACCEPTANCE; PATHWAYS; MODEL This paper introduces an indicator system to measure and assess smart city readiness. Analyzing smart city initiatives in Iran as case studies, the theoretical framework we present reflects on how cities explore the possibility of becoming smart, and prepare themselves to begin implementing the transition towards becoming a smart city. This theoretical framework is then applied to four Iranian cities aspiring to become smart and that already possess credible smart city brands. The findings reveal that the most significant difficulty in Iran is associated with the political context. The changing urban governance model is the most important factor in Iranian smart cities' readiness. Utilization of open data policies and data sharing, as well as making reforms in government structures are all considered a sine qua non to gain momentum. Based on the results of our empirical analysis a Theory of Change is developed to address the cities' technological, socio-economic, and political readiness vis-a-vis the desired transition. The framework for measuring smart city readiness and the Theory of Change provide practical guidelines to developing systematic roadmaps for developing and implementing smart city policies. [Noori, Negar; de Jong, Martin] Erasmus Univ, Erasmus Sch Law, NL-3000 DR Rotterdam, Netherlands; [Noori, Negar; de Jong, Martin] Erasmus Univ, Rotterdam Sch Management, NL-3000 DR Rotterdam, Netherlands; [de Jong, Martin] Fudan Univ, Inst Global Publ Policy, Shanghai 200433, Peoples R China; [Hoppe, Thomas] Delft Univ Technol, Fac Technol Policy & Management TPM, NL-2628 BX Delft, Netherlands Erasmus University Rotterdam; Erasmus University Rotterdam; Fudan University; Delft University of Technology Hoppe, T (corresponding author), Delft Univ Technol, Fac Technol Policy & Management TPM, NL-2628 BX Delft, Netherlands. noori@law.eur.nl; w.m.jong@law.eur.nl; T.Hoppe@tudelft.nl Erasmus Initiative for the Dynamics of Inclusive Prosperity Erasmus Initiative for the Dynamics of Inclusive Prosperity Funding for this study was provided by the Erasmus Initiative for the Dynamics of Inclusive Prosperity. 68 9 9 0 11 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2624-6511 SMART CITIES-BASEL Smart Cities SEP 2020.0 3.0 3 676 704 10.3390/smartcities3030035 0.0 29 Engineering, Electrical & Electronic; Urban Studies Emerging Sources Citation Index (ESCI) Engineering; Urban Studies TX0NA Green Submitted, Green Published, gold 2023-03-23 WOS:000682786300007 0
J Liu, HC; van Oosterom, P; Tijssen, T; Commandeur, T; Wang, W Liu, Haicheng; van Oosterom, Peter; Tijssen, Theo; Commandeur, Tom; Wang, Wen Managing large multidimensional hydrologic datasets: A case study comparing NetCDF and SciDB JOURNAL OF HYDROINFORMATICS English Article chunked storage structure; hydrologic dataset; NetCDF; SciDB SYSTEM Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure are benchmarked to determine the best solution for storing and querying large MD hydrologic datasets. Expert consultancy was conducted to establish benchmark sets, with the HydroNET-4 system being utilized to provide the benchmark environment. In the final benchmark tests, the effect of data storage configurations, elaborating chunk size, dimension order (spatio-temporal clustering) and compression on the query performance, is explored. Results indicate that for big hydrologic MD data management, the properly chunked NetCDF-4 solution without compression is, in general, more efficient than the SciDB DBMS. However, benefits of a DBMS should not be neglected, for example, the integration with other data types, smart caching strategies, transaction support, scalability, and out-of-the-box support for parallelization. [Liu, Haicheng; van Oosterom, Peter; Tijssen, Theo; Commandeur, Tom] Delft Univ Technol, Fac Architecture & Built Environm, Julianalaan 134, NL-2628 BL Delft, Netherlands; [Wang, Wen] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China Delft University of Technology; Hohai University Liu, HC (corresponding author), Delft Univ Technol, Fac Architecture & Built Environm, Julianalaan 134, NL-2628 BL Delft, Netherlands. h.liu-6@tudelft.nl Wang, Wen/A-3729-2013 China National Key Research and Development Program [2017YFC0405801-02]; Hydrologic BV China National Key Research and Development Program; Hydrologic BV The authors would like to acknowledge the funding support from China National Key Research and Development Program (Grant No. 2017YFC0405801-02) and thank Hydrologic BV for the financial and technical support during this research. 24 0 0 3 10 IWA PUBLISHING LONDON ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND 1464-7141 1465-1734 J HYDROINFORM J. Hydroinform. SEP 2018.0 20.0 5 1058 1070 10.2166/hydro.2018.136 0.0 13 Computer Science, Interdisciplinary Applications; Engineering, Civil; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Environmental Sciences & Ecology; Water Resources GU4GD Bronze 2023-03-23 WOS:000445239900004 0
J Shome, D; Kar, T; Mohanty, SN; Tiwari, P; Muhammad, K; AlTameem, A; Zhang, YZ; Saudagar, AKJ Shome, Debaditya; Kar, T.; Mohanty, Sachi Nandan; Tiwari, Prayag; Muhammad, Khan; AlTameem, Abdullah; Zhang, Yazhou; Saudagar, Abdul Khader Jilani COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article vision transformer; COVID-19; deep learning; data science; healthcare; interpretability; transfer learning; grad-CAM In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient's X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare. [Shome, Debaditya; Kar, T.] KIIT Deemed Univ, Sch Elect Engn, Bhubaneswar 751024, Odisha, India; [Mohanty, Sachi Nandan] Vardhaman Coll Engn Autonomous, Dept Comp Sci & Engn, Hyderabad 501218, Telangana, India; [Tiwari, Prayag] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland; [Muhammad, Khan] Sungkyunkwan Univ, Coll Comp & Informat, Sch Convergence, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 03063, South Korea; [AlTameem, Abdullah; Saudagar, Abdul Khader Jilani] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11432, Saudi Arabia; [Zhang, Yazhou] Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450001, Peoples R China Kalinga Institute of Industrial Technology (KIIT); Vardhaman College of Engineering; Aalto University; Sungkyunkwan University (SKKU); Imam Mohammad Ibn Saud Islamic University (IMSIU); Zhengzhou University of Light Industry Muhammad, K (corresponding author), Sungkyunkwan Univ, Coll Comp & Informat, Sch Convergence, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 03063, South Korea.;Saudagar, AKJ (corresponding author), Imam Mohammad Ibn Saud Islamic Univ IMSIU, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11432, Saudi Arabia. sachinandan09@gmail.com; prayag.tiwari@aalto.fi; khan.muhammad@ieee.org; yzzhang@zzuli.edu.cn; aksaudagar@imamu.edu.sa Saudagar, Abdul/HHY-9939-2022; Muhammad, Khan/L-9059-2016; Pandey, Hari Mohan/M-9658-2015; Tiwari, Prayag/N-6261-2017 Muhammad, Khan/0000-0003-4055-7412; Pandey, Hari Mohan/0000-0002-9128-068X; Tiwari, Prayag/0000-0002-2851-4260; Kar, Tejaswini/0000-0001-9020-3626; Saudagar, Abdul Khader Jilani/0000-0003-4205-3621; Muhammad, Khan/0000-0002-5302-1150; Nandan Mohanty, Dr.Sachi/0000-0002-4939-0797 Deputyship for Research AMP; Innovation, Ministry of Education in Saudi Arabia [959] Deputyship for Research AMP; Innovation, Ministry of Education in Saudi Arabia This work was supported by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 959. 51 22 22 3 26 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health NOV 2021.0 18.0 21 11086 10.3390/ijerph182111086 0.0 14 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health WY0HQ 34769600.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000718967900001 0
J Xia, XQ; Ji, JF; Zhang, CS; Yang, ZF; Shi, HD Xia, Xueqi; Ji, Junfeng; Zhang, Chaosheng; Yang, Zhongfang; Shi, Huading Carbonate bedrock control of soil Cd background in Southwestern China: Its extent and influencing factors based on spatial analysis CHEMOSPHERE English Article Soil; Cadmium; Background; Carbonate; Spatial analysis ELEMENTS Previous studies have found that Cadmium (Cd) may be condensed during the processes of the weathering and soil formation over carbonate bedrock, which is a typical phenomenon in the southwest region of China. However, the extent of the high value background and the geographical factors influencing its spatial distribution are still unknown. This study collected data from the Multi-Purpose Regional Geochemical Survey (MPRGS) and the Regional Geochemistry-National Reconnaissance Program (RGNR) to investigate the extent and its controlling factors based on spatial analysis. It was found that the RGNR is an ideal data source for regional Cd background studies. Spatially, the high Cd area (Cd > 1.5 m/kg with reference of the GB15618-2018 soil environmental quality in China, 68.9 x 103 km2) coincide well with the carbonate distribution. In the total area (68.9 x 103 km2) with high Cd concentration, 89.8% is related to carbonates. The composition of parent rock, climate conditions, and landuse are controlling factors of the enrichment. The high Cd background effect of limestone is higher than that of dolomite and their effects decease with increasing detritus component they contain. Warm and humid climate in forest land helps to enrich Cd during weathering. Soil Cd concentration related to carbonate background can be predicted by major element concentrations, which is useful for differentiating Cd background from human pollution in the future soil pollution monitoring. Modelling by artificial neural network (ANN) is recommended rather than tradition multiple linear methods (LM) as the latter may have the effect of collinearity. [Xia, Xueqi; Yang, Zhongfang] China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China; [Xia, Xueqi] Minist Nat Resources, Zhejiang Inst Geol Survey, Technol Innovat Ctr Arable Land Assessment & Rest, Hangzhou 311203, Peoples R China; [Xia, Xueqi; Yang, Zhongfang] China Geol Survey, Kunming Nat Resources Comprehens Invest Ctr, Kunming 650100, Yunnan, Peoples R China; [Ji, Junfeng] Nanjing Univ, Sch Earth Sci & Engn, Nanjing 210023, Peoples R China; [Zhang, Chaosheng] Natl Univ Ireland, Int Network Environm & Hlth, Sch Geog Archaeol & Irish Studies, Galway, Ireland; [Zhang, Chaosheng] Natl Univ Ireland, Ryan Inst, Galway, Ireland; [Shi, Huading] Minist Ecol & Environm, Tech Ctr Soil Agr & Rural Ecol & Environm, Beijing, Peoples R China China University of Geosciences; Ministry of Natural Resources of the People's Republic of China; China Geological Survey; Nanjing University; Ollscoil na Gaillimhe-University of Galway; Ollscoil na Gaillimhe-University of Galway Xia, XQ (corresponding author), China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China. xiaxueqi@cugb.edu.cn xia, xue/HNR-4662-2023; 夏, 学齐/AAF-4800-2021 夏, 学齐/0000-0002-8913-5045; Zhang, Chaosheng/0000-0002-7913-4858 National Key R & D Program of China [2017YFD0800303]; Fundamental Research Funds for the Central Universities [2652018128]; Land Quality Geochemical Investigation in Chuxiong Area, Central Yunnan [DD20208070]; Results Integration of Land Quality Geological Survey in Zhejiang Province [2019007] National Key R & D Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Land Quality Geochemical Investigation in Chuxiong Area, Central Yunnan; Results Integration of Land Quality Geological Survey in Zhejiang Province This work was supported by the National Key R & D Program of China (2017YFD0800303) , the Fundamental Research Funds for the Central Universities (2652018128) , Land Quality Geochemical Investigation in Chuxiong Area, Central Yunnan (DD20208070) , and the Results Integration of Land Quality Geological Survey in Zhejiang Province ( [Shengzi] 2019007) . The Authors are grateful to the three anonymous reviewers for their suggestions, which have highly improved the quality of the paper. 33 4 4 16 44 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-6535 1879-1298 CHEMOSPHERE Chemosphere MAR 2022.0 290.0 133390 10.1016/j.chemosphere.2021.133390 0.0 9 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology YZ5EE 34942213.0 2023-03-23 WOS:000755498600002 0
J Li, YY; Zhang, YR; Pan, A; Han, MC; Veglianti, E Li, Yaya; Zhang, Yuru; Pan, An; Han, Minchun; Veglianti, Eleonora Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms TECHNOLOGY IN SOCIETY English Article Industrial robot application; Carbon emission reduction; Heterogeneity characteristics; Influence mechanism ENERGY-CONSUMPTION; DECOMPOSITION ANALYSIS; EMPIRICAL-EVIDENCE; RENEWABLE ENERGY; 4.0 TECHNOLOGIES; ECONOMIC-GROWTH; CO2 EMISSIONS; URBANIZATION; INTENSITY; CHINA Industrial robots are a key enabling technology of Industry 4.0 and the artificial intelligence revolution, which is of great significance in sustainable development. Existing research has focused on the economic effects of industrial robot application, but research on the environmental effects remains insufficient. Based on the environmental Kuznets curve (EKC) model and using sample data from 35 countries from 1993 to 2017, this paper empirically examines the carbon emission reduction effects of industrial robot application. Three key findings emerged. First, the application of industrial robots significantly reduces carbon intensity. The application of industrial robots leads to increased productivity, the optimisation of factor structures, and technological innovation in production, which improve energy efficiency and reduce carbon intensity. Second, there is a twodimensional heterogeneity in the carbon intensity reduction effects of industrial robot application in terms of the application fields and possible countries for application. Compared to other fields, the application of industrial robots in manufacturing, agriculture, and electricity, gas, and water supply fields significantly promotes carbon intensity reduction. Furthermore, industrial robots in developed countries have better emission reduction effects than in developing countries. Third, the application of industrial robots has a dual-channel mediating mechanism for carbon intensity reduction: first, there is a mediating role of green total factor productivity and energy intensity; second, absorptive capacity plays a moderating role. On the one hand, high absorptive capacity brings about a better innovation environment and enhances the effects of carbon emission reduction; on the other hand, the application of industrial robots promotes carbon intensity reduction by positively influencing the improvement of green total factor productivity and energy intensity. Finally, policy recommendations are provided based on the results. [Li, Yaya; Zhang, Yuru] Jiangsu Univ, Sch Finance & Econ, Zhenjiang, Jiangsu, Peoples R China; [Pan, An] Zhongnan Univ Econ & Law, Sch Econ, Wuhan, Peoples R China; [Han, Minchun] Huazhong Univ Sci & Technol, Sch Econ, Wuhan, Peoples R China; [Veglianti, Eleonora] Univ Catholic Lille, Fac Management Econ & Sci, Lille, France Jiangsu University; Zhongnan University of Economics & Law; Huazhong University of Science & Technology; Universite Catholique de Lille Pan, A (corresponding author), Zhongnan Univ Econ & Law, Sch Econ, Wuhan, Peoples R China. yizhi19881107@126.com; zyr_281528@163.com; panan0819@zuel.edu.cn; minchunhust@163.com; eleonora.veglianti@univ-catholille.fr Veglianti, Eleonora/GOE-4503-2022; Li, Yaya/GPC-6058-2022 Veglianti, Eleonora/0000-0002-9153-6745; Li, Yaya/0000-0002-1921-8478; Zhang, Yuru/0000-0003-2399-8064; PAN, AN/0000-0002-2392-2505 National Natural Science Foun-dation of China [71704069, 71802099]; Philosophy and Social Science Excellent Innovation Team Construction Foundation of Jiangsu Province [SJSZ2020-20]; Fundamental Research Funds for the Central Universities of Zhongnan University of Economics and Law [2722022BY004] National Natural Science Foun-dation of China(National Natural Science Foundation of China (NSFC)); Philosophy and Social Science Excellent Innovation Team Construction Foundation of Jiangsu Province; Fundamental Research Funds for the Central Universities of Zhongnan University of Economics and Law Funding This research was supported by the National Natural Science Foun-dation of China (Grant numbers: 71704069, 71802099) , Philosophy and Social Science Excellent Innovation Team Construction Foundation of Jiangsu Province (Grant number: SJSZ2020-20) , and the Fundamental Research Funds for the Central Universities of Zhongnan University of Economics and Law (Grant number: 2722022BY004) . 75 9 9 36 60 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0160-791X 1879-3274 TECHNOL SOC Technol. Soc. AUG 2022.0 70.0 102034 10.1016/j.techsoc.2022.102034 0.0 12 Social Issues; Social Sciences, Interdisciplinary Social Science Citation Index (SSCI) Social Issues; Social Sciences - Other Topics 2X3DE 2023-03-23 WOS:000825087400001 0
J Ahmed, I; Khan, MS; Ma, VC; Magsi, H; Gunawan, R; Ilyas, AMO; Lashari, NUR; Wassan, N; Paidi, S; Ali, Z; Law, AWL; Zhang, YP; Cho, WC; Alda, M; Bergink, V; Barman, I; Lau, C Ahmed, Irfan; Khan, Muhammad Shehzad; Ma, Victor; Magsi, Hina; Gunawan, Renardi; Ilyas, Abdul-Mojeed Olabisi; Lashari, Najeeb ur Rehman; Wassan, Naveed; Paidi, Santosh; Ali, Zulfiqar; Law, Alan W. L.; Zhang, Yanpeng; Cho, William C. C.; Alda, Martin; Bergink, Veerle; Barman, Ishan; Lau, Condon Lithium in breast milk transiently affects the renal electrolytic balance of infants BIPOLAR DISORDERS English Article analytical chemistry; bipolar disorder; endocrinology; lithium; pediatrics; psychiatry; renal electrolytic imbalance BIPOLAR I DISORDER; POSTPARTUM PSYCHOSIS; THYROID-DYSFUNCTION; ELEMENTAL ANALYSIS; MOOD STABILIZERS; MEDICATION; ASSOCIATION; PREGNANCY; EXPOSURE; MANIA BackgroundThe use of lithium during breast-feeding has not been comprehensively investigated in humans due to concerns about lithium toxicity. ProcedureWe analyzed lithium in the kidneys of nursed pups of lithium medicated mothers, using analytical spectroscopy in a novel rat model. The mothers were healthy rats administered lithium via gavage (1000 mg/day Li2CO3 per 50 kg body weight). ResultsLithium was detected in the breast milk, and in the blood of pups (0.08 mM), of lithium-exposed dams at post-natal day 18 (P18), during breast-feeding. No lithium was detected after breast-feeding, at P25 (4 days after cessation of nursing). The lithium pups blood had elevated urea nitrogen at P18 and reduced total T-4 at P18 and P25, indicating a longer-term effect on the kidneys and the thyroid gland. Multivariate machine-learning analysis of spectroscopy data collected from the excised kidneys of pups showed elevated potassium in lithium-exposed animals both during- and after breast-feeding. The elevated renal potassium was associated with low nephrin expression in the kidneys measured immunohistochemically during breast-feeding. After lithium exposure is stopped, the filtration of lithium from the kidneys reverses these effects. Our study showed that breastfeeding during lithium use has an effect on the kidneys of the offspring in rats. [Ahmed, Irfan; Khan, Muhammad Shehzad; Ilyas, Abdul-Mojeed Olabisi; Law, Alan W. L.; Barman, Ishan; Lau, Condon] City Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China; [Ahmed, Irfan; Magsi, Hina] Sukkur IBA Univ, Dept Elect Engn, Sukkur, Pakistan; [Ma, Victor] Queen Elizabeth Hosp, Dept Clin Oncol, Hong Kong, Peoples R China; [Gunawan, Renardi] City Univ Hong Kong, Dept Biomed Sci, Hong Kong, Peoples R China; [Gunawan, Renardi] City Univ Hong Kong, Tung Biomed Sci Ctr, Hong Kong, Peoples R China; [Ilyas, Abdul-Mojeed Olabisi] Hong Kong Ctr Cerebro Cardiovasc Hlth Engn COCHE, Hong Kong, Peoples R China; [Ilyas, Abdul-Mojeed Olabisi] Fed Univ Oye Ekiti, Dept Phys, Oye, Ekiti, Nigeria; [Lashari, Najeeb ur Rehman] Shenzhen Univ, Inst Adv Study, Shenzhen, Peoples R China; [Lashari, Najeeb ur Rehman] Jackson State Univ, Dept Chem Phys & Atmospher Sci, Jackson, MS USA; [Paidi, Santosh] Univ Calif Berkeley, Sch Optometry, Berkeley, CA USA; [Ali, Zulfiqar] Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China; [Zhang, Yanpeng] Xi An Jiao Tong Univ, Key Lab Phys Elect & Devices, Minist Educ, Xian, Peoples R China; [Zhang, Yanpeng] Xi An Jiao Tong Univ, Shaanxi Key Lab Informat Photon Tech, Xian, Peoples R China; [Alda, Martin] Dalhousie Univ, Dept Psychiat, Halifax, NS, Canada; [Alda, Martin] Natl Inst Mental Hlth, Klecany, Czech Republic; [Bergink, Veerle] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY USA; [Bergink, Veerle] Erasmus MC, Dept Psychiat, Rotterdam, Netherlands; [Barman, Ishan] Johns Hopkins Univ, Dept Oncol, Sch Med, Baltimore, MA USA; [Barman, Ishan] Johns Hopkins Univ Sch Med, Dept Radiol & Radiol Sci, Baltimore, MA USA City University of Hong Kong; Sukkur IBA University; City University of Hong Kong; City University of Hong Kong; Shenzhen University; Jackson State University; University of California System; University of California Berkeley; University of Hong Kong; Xi'an Jiaotong University; Xi'an Jiaotong University; Dalhousie University; National Institute of Mental Health - Czech Republic; Icahn School of Medicine at Mount Sinai; Erasmus University Rotterdam; Erasmus MC; Johns Hopkins University Barman, I; Lau, C (corresponding author), City Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China. ibarman@jhu.edu; condon.lau@cityu.edu.hk , Hina Magsi/0000-0002-9629-0967; GUNAWAN, Renardi/0000-0001-6901-9015; Ali, Zulfiqar/0000-0001-9641-3165 City University of Hong Kong; [7200414]; [9610338] City University of Hong Kong(City University of Hong Kong); ; ACKNOWLEDGEMENTS This research was supported by start-up funding from the City University of Hong Kong (project numbers 7200414 and 9610338). 55 0 0 1 1 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1398-5647 1399-5618 BIPOLAR DISORD Bipolar Disord. FEB 2023.0 25.0 1 56 65 10.1111/bdi.13275 0.0 NOV 2022 10 Clinical Neurology; Neurosciences; Psychiatry Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychiatry 9E4PZ 36409044.0 2023-03-23 WOS:000890430900001 0
J Zhao, GZ; Bi, YX; Wang, LF; Han, B; Wang, X; Xiao, QB; Cai, JT; Zhan, Y; Chen, XB; Tang, J; Wang, JJ Zhao GuoZe; Bi YaXin; Wang LiFeng; Han Bing; Wang Xiao; Xiao QiBin; Cai JunTao; Zhan Yan; Chen XiaoBin; Tang Ji; Wang JiJun Advances in alternating electromagnetic field data processing for earthquake monitoring in China SCIENCE CHINA-EARTH SCIENCES English Article seismo-electromagnetic field; alternating EM field; data processing; wavelet; stereoscopic observation ELECTRICAL-RESISTIVITY; TIBETAN PLATEAU; CRUST; LONGMENSHAN; DEFORMATION; MECHANISM; DEMETER; MARGIN; SIGNAL The alternating electromagnetic (EM) field is one of the most sensitive physical fields related to earthquakes. There have been a number of publications reporting EM anomalies associated with earthquakes. With increasing applications and research of artificial-source extremely low frequency EM and satellite EM technologies in earthquake studies, the amount of observed data from the alternating EM method increases rapidly and exponentially, so it is imperative to develop suitable and effective methods for processing and analyzing the influx of big data. This paper presents research on the self-adaptive filter and wavelet techniques and their applications to analyzing EM data obtained from ground measurements and satellite observations, respectively. Analysis results show that the self-adaptive filter method can identify both natural-and artificial-source EM signals, and enhance the ratio between signal and noise of EM field spectra, apparent resistivity, and others. The wavelet analysis is capable of detecting possible correlation between EM anomalies and seismic events. These techniques are effective in processing and analyzing massive data obtained from EM observations. [Zhao GuoZe; Wang LiFeng; Han Bing; Xiao QiBin; Cai JunTao; Zhan Yan; Chen XiaoBin; Tang Ji; Wang JiJun] China Earthquake Adm, Inst Geol, State Key Lab Earthquake Dynam, Beijing 100029, Peoples R China; [Bi YaXin] Univ Ulster, Fac Comp & Engn, Comp Sci Res Inst, Belfast BT37 0QB, Antrim, North Ireland; [Wang Xiao] Beijing Qilu Inst Geol & Mineral Resources, Beijing 102218, Peoples R China China Earthquake Administration; Ulster University Zhao, GZ (corresponding author), China Earthquake Adm, Inst Geol, State Key Lab Earthquake Dynam, Beijing 100029, Peoples R China. zhaogz@ies.ac.cn Tang, Ji/AAT-7182-2021; CHEN, Xiaobin/AAX-5130-2021 National Natural Science Foundation of China [41374077, 41074047]; CEA-NASCC Dragon Project III [10671]; Special Public Benefit Program for Earthquake Study [200808010] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CEA-NASCC Dragon Project III; Special Public Benefit Program for Earthquake Study This work was supported by the National Natural Science Foundation of China (Grant Nos. 41374077, 41074047), CEA-NASCC Dragon Project III (Grant No. 10671), and Special Public Benefit Program for Earthquake Study (Grant No. 200808010). We are grateful to Prof. Chen Rong for his encouragement and valuable suggestions, Prof. Chen Yuntai at CEA, and Prof. Terry Andserson of the University of Ulster for their comments. We also thank reviewers for their suggestions on revision of the manuscript. 73 6 9 3 24 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-7313 1869-1897 SCI CHINA EARTH SCI Sci. China-Earth Sci. FEB 2015.0 58.0 2 172 182 10.1007/s11430-014-5012-3 0.0 11 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology AZ3IN 2023-03-23 WOS:000348120900002 0
J Kolesnyk, V; Peterka, J; Alekseev, O; Neshta, A; Xu, JY; Lysenko, B; Sahul, M; Martinovic, J; Hrbal, J Kolesnyk, Vitalii; Peterka, Jozef; Alekseev, Oleksandr; Neshta, Anna; Xu, Jinyang; Lysenko, Bohdan; Sahul, Martin; Martinovic, Jozef; Hrbal, Jakub Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks MATERIALS English Article CFRP; Ti alloy stacks; thermocouple method; drilling temperatures; hole diameter; out of roundness; ANN analysis SURFACE-ROUGHNESS PREDICTION; ARTIFICIAL NEURAL-NETWORK; PROCESS PARAMETERS; INTERFACE DAMAGE; TOOL; COMPOSITE; DELAMINATION; PRECISION; GEOMETRY; QUALITY Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L-9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters-hole quality relationship. [Kolesnyk, Vitalii; Alekseev, Oleksandr; Neshta, Anna; Lysenko, Bohdan] Sumy State Univ, Dept Mfg Engn Machines & Tools, Rymskogo Korsakova Str 2, UA-40007 Sumy, Ukraine; [Peterka, Jozef; Sahul, Martin; Martinovic, Jozef; Hrbal, Jakub] Slovak Univ Technol Bratislava, Fac Mat Sci & Technol, Ul Jana Bottu C 2781-25, Trnava 91723, Slovakia; [Xu, Jinyang] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China Ministry of Education & Science of Ukraine; Sumy State University; Slovak University of Technology Bratislava; Shanghai Jiao Tong University Peterka, J (corresponding author), Slovak Univ Technol Bratislava, Fac Mat Sci & Technol, Ul Jana Bottu C 2781-25, Trnava 91723, Slovakia. xujinyang@sjtu.edu.cn; xujinyang@sjtu.edu.cn; xujinyang@sjtu.edu.cn; xujinyang@sjtu.edu.cn; xujinyang@sjtu.edu.cn; xujinyang@sjtu.edu.cn; martin.sahul@stuba.sk; xujinyang@sjtu.edu.cn; jakub.hrbal@stuba.sk Peterka, Jozef/Y-7014-2019; Xu, Jinyang/G-8731-2011; Kolesnyk, Vitalii/ABG-3045-2020; Sahul, Martin/AAQ-7546-2021; Алєксєєв, Олександр/HLW-2516-2023 Peterka, Jozef/0000-0001-6258-6113; Xu, Jinyang/0000-0001-7364-9837; Kolesnyk, Vitalii/0000-0002-0417-3801; Hrbal, Jakub/0000-0002-9667-6472 VEGA [1/0019/20]; KEGA [006STU-4/2021]; National Scholarship Program of the Slovak Republic (SAIA) VEGA(Vedecka grantova agentura MSVVaS SR a SAV (VEGA)); KEGA; National Scholarship Program of the Slovak Republic (SAIA) This research was funded by the Project of VEGA 1/0019/20 and the Project of KEGA 006STU-4/2021. The scholarship stay of the first author was covered by the National Scholarship Program of the Slovak Republic (SAIA). 90 7 7 28 49 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials MAR 2022.0 15.0 5 1940 10.3390/ma15051940 0.0 21 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics ZW9HA 35269169.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000771514200001 0
J Han, Z; Hong, DF; Gao, LR; Roy, SK; Zhang, B; Chanussot, J Han, Zhu; Hong, Danfeng; Gao, Lianru; Roy, Swalpa Kumar; Zhang, Bing; Chanussot, Jocelyn Reinforcement Learning for Neural Architecture Search in Hyperspectral Unmixing IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Computer architecture; Training; Hyperspectral imaging; Convolution; Decoding; Network architecture; Computational modeling; Deep learning (DL); hyperspectral unmixing (HU); multiobjective optimization; neural architecture search (NAS); reinforcement learning (RL) In this letter, a novel neural architecture search (NAS) method based on reinforcement learning (RL), called RLNAS, is devised to realize the automatic architecture design in the field of hyperspectral unmixing (HU). This method first trains the search network in the constructed self-supervised datasets based on hyperspectral images. The block-based searching and weight-sharing strategies are then introduced to reduce the computational cost in the training phase. The final optimal architecture is obtained by optimizing the multiobjective reward function to balance the trade-off between accuracy and computational efficiency. Compared with the state-of-the-art unmixing algorithms, the proposed RLNAS method can yield better unmixing results on the synthetic and real hyperspectral datasets, which verifies its effectiveness and superiority. In addition, the proposed method offers promising potential of the NAS for HU. [Han, Zhu; Zhang, Bing] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Han, Zhu; Zhang, Bing] Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China; [Han, Zhu; Zhang, Bing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Hong, Danfeng; Gao, Lianru] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China; [Roy, Swalpa Kumar] Jalpaiguri Govt Engn Coll, Dept Comp Sci & Engn, Jalpaiguri 735102, India; [Chanussot, Jocelyn] Univ Grenoble Alpes, INRIA, CNRS, LJK,Grenoble INP, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Jalpaiguri Government Engineering College; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Chinese Academy of Sciences Gao, LR (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China. hanzhu19@mails.ucas.ac.cn; hongdf@aircas.ac.cn; gaolr@aircas.ac.cn; swalpa@cse.jgec.ac.in; zb@radi.ac.cn; jocelyn@hi.is Roy, Swalpa Kumar/AAX-6467-2020; Han, Zhu/ABH-5886-2022 Roy, Swalpa Kumar/0000-0002-6580-3977; Han, Zhu/0000-0002-8602-864X; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [42030111, 62161160336]; MIAI@Grenoble Alpes [ANR-19-P3IA-0003]; AXA Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); MIAI@Grenoble Alpes; AXA Research Fund(AXA Research Fund) This work was supported in part by the National Natural Science Foundation of China under Grant 42030111 and Grant 62161160336, in part by the MIAI@Grenoble Alpes under Grant ANR-19-P3IA-0003, and in part by the AXA Research Fund. 26 1 1 7 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19.0 6012705 10.1109/LGRS.2022.3199583 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 4F0WE 2023-03-23 WOS:000848236100003 0
J Ullah, F; Jabbar, S; Mostarda, L Ullah, Farhan; Jabbar, Sohail; Mostarda, Leonardo An intelligent decision support system for software plagiarism detection in academia INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS English Article artificial intelligence; information security; intelligent system; latent semantic analysis; software similarity SOURCE CODE The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre-processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics. [Ullah, Farhan] Northwestern Polytech Univ, Sch Software, Xian 710072, Shaanxi, Peoples R China; [Jabbar, Sohail] Univ Faisalabad, Dept Computat Sci, Faisalabad, Pakistan; [Mostarda, Leonardo] Camerino Univ, Comp Sci Dept, Camerino, Italy Northwestern Polytechnical University; University of Camerino Ullah, F (corresponding author), Northwestern Polytech Univ, Sch Software, Xian 710072, Shaanxi, Peoples R China. farhankhan.cs@yahoo.com Ullah, Farhan/AAM-3866-2021 , farhan/0000-0002-1030-1275; Jabbar, Sohail/0000-0002-2127-1235 41 4 4 1 15 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0884-8173 1098-111X INT J INTELL SYST Int. J. Intell. Syst. JUN 2021.0 36.0 6 2730 2752 10.1002/int.22399 0.0 FEB 2021 23 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RT5LI gold 2023-03-23 WOS:000620183400001 0
J Yu, ZF; Guo, SQ; Deng, F; Yan, Q; Huang, KK; Liu, JK; Chen, F Yu, Zhaofei; Guo, Shangqi; Deng, Fei; Yan, Qi; Huang, Keke; Liu, Jian K.; Chen, Feng Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All IEEE TRANSACTIONS ON CYBERNETICS English Article Hidden Markov models; Biological neural networks; Mathematical model; Neurons; Cybernetics; Markov processes; Brain modeling; Hidden Markov models (HMMs); neural implementation; posterior inference; spiking neural network; winner-take-all (WTA) circuits NEURONS; UNCERTAINTY; BRAIN; COMPUTATION; ADAPTATION; PLASTICITY; CIRCUITS Hidden Markov models (HMMs) underpin the solution to many problems in computational neuroscience. However, it is still unclear how to implement inference of HMMs with a network of neurons in the brain. The existing methods suffer from the problem of being nonspiking and inaccurate. Here, we build a precise equivalence between the inference equation of HMMs with time-invariant hidden variables and the dynamics of spiking winner-take-all (WTA) neural networks. We show that the membrane potential of each spiking neuron in the WTA circuit encodes the logarithm of the posterior probability of the hidden variable in each state, and the firing rate of each neuron is proportional to the posterior probability of the HMMs. We prove that the time course of the neural firing rate can implement posterior inference of HMMs. Theoretical analysis and experimental results show that the proposed WTA circuit can get accurate inference results of HMMs. [Yu, Zhaofei] Peking Univ, Sch Elect Engn & Comp Sci, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China; [Yu, Zhaofei; Guo, Shangqi; Deng, Fei; Yan, Qi; Chen, Feng] Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Automat, Beijing 100084, Peoples R China; [Guo, Shangqi; Deng, Fei; Yan, Qi; Chen, Feng] Tsinghua Univ, Beijing Innovat Ctr Future Chip, Beijing 100084, Peoples R China; [Guo, Shangqi; Deng, Fei; Yan, Qi; Chen, Feng] Tsinghua Univ, LSBDPA Beijing Key Lab, Beijing 100084, Peoples R China; [Huang, Keke] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China; [Liu, Jian K.] Univ Leicester, Dept Neurosci Psychol & Behav, Ctr Syst Neurosci, Leicester LE1 7HA, Leics, England; [Liu, Jian K.] Graz Univ Technol, Inst Theoret Comp Sci, A-8010 Graz, Austria Peking University; Tsinghua University; Tsinghua University; Tsinghua University; Central South University; University of Leicester; Graz University of Technology Chen, F (corresponding author), Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Automat, Beijing 100084, Peoples R China.;Liu, JK (corresponding author), Univ Leicester, Dept Neurosci Psychol & Behav, Ctr Syst Neurosci, Leicester LE1 7HA, Leics, England. yzf714@126.com; gsq15@mails.tsinghua.edu.cn; dengf15@mails.tsinghua.edu.cn; qyan15@mails.tsinghua.edu.cn; huangkeke@csu.edu.cn; jian.liu@leicester.ac.uk; chenfeng@tsinghua.edu.cn Guo, Shangqi/AAJ-3066-2021; Liu, Jian K./E-1748-2013 Liu, Jian K./0000-0002-5391-7213; Huang, Keke/0000-0003-3553-3424 National Post-Doctoral Program for Innovative Talents [BX20180005]; China Post-Doctoral Science Foundation [2018M630036]; National Natural Science Foundation of China [61671266, 61703439, 61327902]; Tsinghua University Initiative Scientific Research Program; Human Brain Project of the European Union [604102, 720270] National Post-Doctoral Program for Innovative Talents; China Post-Doctoral Science Foundation(China Postdoctoral Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tsinghua University Initiative Scientific Research Program; Human Brain Project of the European Union This work was supported in part by the National Post-Doctoral Program for Innovative Talents under Grant BX20180005, in part by the China Post-Doctoral Science Foundation under Grant 2018M630036, in part by the National Natural Science Foundation of China under Grant 61671266, Grant 61703439, and Grant 61327902, in part by the Tsinghua University Initiative Scientific Research Program, and in part by the Human Brain Project of the European Union under Grant 604102 and Grant 720270. 50 22 24 0 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. MAR 2020.0 50.0 3 1347 1354 10.1109/TCYB.2018.2871144 0.0 8 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science KH9AI 30295641.0 Green Submitted, Green Accepted 2023-03-23 WOS:000510941100039 0
J Barber, S; Lima, LAM; Sakagami, Y; Quick, J; Latiffianti, E; Liu, YC; Ferrari, R; Letzgus, S; Zhang, XJ; Hammer, F Barber, Sarah; Lima, Luiz Andre Moyses; Sakagami, Yoshiaki; Quick, Julian; Latiffianti, Effi; Liu, Yichao; Ferrari, Riccardo; Letzgus, Simon; Zhang, Xujie; Hammer, Florian Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study ENERGIES English Article wind energy; digitalisation; collaboration; co-innovation; machine learning; fault detection SCADA DATA; TURBINES; CREATION; SYSTEM; MODEL In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific challenges, which are defined by challenge providers within a topical space and made available to participants via a digital platform. The data required in order to solve a particular challenge are provided by the challenge providers under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP's existing solution in terms of Total Prediction Costs (saving up to euro120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers. [Barber, Sarah; Hammer, Florian] Eastern Switzerland Univ Appl Sci, Inst Energy Technol, Oberseestr 10, CH-8640 Rapperswil, Switzerland; [Lima, Luiz Andre Moyses] Voltalia, 84 bd Sebastopol, F-75003 Paris, France; [Sakagami, Yoshiaki] Fed Inst Santa Catarina, Mauro Ramos 950, BR-88020300 Florianopolis, SC, Brazil; [Quick, Julian] Univ Colorado, Turbulence & Energy Syst Lab, Boulder, CO 80309 USA; [Latiffianti, Effi] Texas A&M Univ, Dept Ind & Syst Engn, College Stn, TX 77843 USA; [Latiffianti, Effi] Inst Teknol Sepuluh Nopember, Surabaya 60111, Indonesia; [Liu, Yichao] Elect Power Res Inst EPRI Europe, NexusUCD, Block 9&10 Belfield Off Pk Beech Hill Rd, Dublin D04 V2N9, Ireland; [Ferrari, Riccardo] Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 2, NL-2628 CD Delft, Netherlands; [Letzgus, Simon] Tech Univ Berlin, Machine Learning Grp, Str 17 Juni 135, D-10623 Berlin, Germany; [Zhang, Xujie] Zhejiang Sci Tech Univ, Fac Mech Engn & Automat, Hangzhou 310018, Peoples R China Instituto Federal de Santa Catarina (IFSC); University of Colorado System; University of Colorado Boulder; Texas A&M University System; Texas A&M University College Station; Institut Teknologi Sepuluh Nopember; Delft University of Technology; Technical University of Berlin; Zhejiang Sci-Tech University Barber, S (corresponding author), Eastern Switzerland Univ Appl Sci, Inst Energy Technol, Oberseestr 10, CH-8640 Rapperswil, Switzerland. sarah.barber@ost.ch; l.lima@voltalia.com; yoshi@ifsc.edu.br; julian.quick@colorado.edu; latiffianti@tamu.edu; yliu1@epri.com; r.ferrari@tudelft.nl; simon.letzgus@tu-berlin.de; zhang_xj_97@foxmail.com; florian.hammer@ost.ch Quick, Julian/GSI-4210-2022 Quick, Julian/0000-0002-1460-9808; Barber, Sarah/0000-0002-7329-6434; Sakagami, Yoshiaki/0000-0003-1669-9453; Ferrari, Riccardo/0000-0003-3615-5445; Liu, Yichao/0000-0002-4175-7638 Research Council of Norway through the AIMWIND project [312486]; Fulbright Scholarship; Indonesian Government (DIKTI-Funded Fulbright); OST; European Union Horizon 2020 fund through the WATEREYE project [851207] Research Council of Norway through the AIMWIND project(Research Council of Norway); Fulbright Scholarship; Indonesian Government (DIKTI-Funded Fulbright); OST; European Union Horizon 2020 fund through the WATEREYE project E.L.'s research is supported by a Fulbright Scholarship in collaboration with the Indonesian Government (DIKTI-Funded Fulbright). The APC was funded by OST. R.M.G.F.'s research was partially supported by the European Union Horizon 2020 fund through the WATEREYE project (Grant No. 851207) and by the Research Council of Norway through the AIMWIND project (Grant No. 312486). 72 0 0 12 14 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies AUG 2022.0 15.0 15 5638 10.3390/en15155638 0.0 32 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 3S7BJ Green Published, gold 2023-03-23 WOS:000839747200001 0
J Zhang, T; Xie, PC; Dong, YY; Liu, ZW; Zhou, F; Pan, DJ; Huang, ZY; Zhai, QC; Gu, Y; Wu, QY; Tanaka, N; Obata, Y; Bradley, A; Lelliott, CJ; Nutter, LMJ; McKerlie, C; Flenniken, AM; Champy, MF; Sorg, T; Herault, Y; De Angelis, MH; Durner, VG; Mallon, AM; Brown, SDM; Meehan, T; Parkinson, HE; Smedley, D; Lloyd, KCK; Yan, J; Gao, X; Seong, JK; Wang, CKL; Sedlacek, R; Liu, Y; Rozman, J; Yang, L; Xu, Y Zhang, Tao; Xie, Pancheng; Dong, Yingying; Liu, Zhiwei; Zhou, Fei; Pan, Dejing; Huang, Zhengyun; Zhai, Qiaocheng; Gu, Yue; Wu, Qingyu; Tanaka, Nobuhiko; Obata, Yuichi; Bradley, Allan; Lelliott, Christopher J.; Nutter, Lauryl M. J.; McKerlie, Colin; Flenniken, Ann M.; Champy, Marie-France; Sorg, Tania; Herault, Yann; De Angelis, Martin Hrabe; Durner, Valerie Gailus; Mallon, Ann-Marie; Brown, Steve D. M.; Meehan, Terry; Parkinson, Helen E.; Smedley, Damian; Lloyd, K. C. Kent; Yan, Jun; Gao, Xiang; Seong, Je Kyung; Wang, Chi-Kuang Leo; Sedlacek, Radislav; Liu, Yi; Rozman, Jan; Yang, Ling; Xu, Ying Sanger Inst Mouse Genetics Project High-throughput discovery of genetic determinants of circadian misalignment PLOS GENETICS English Article SUPRACHIASMATIC NUCLEUS; CLOCK; MUTATION; RHYTHM; PROKINETICIN-2; TEMPERATURE; MELANOPSIN; RECEPTORS; SCREENS; MODELS Author summary Synchronization to environmental changes such as day and night cycles and seasonal cycles is critical for survival. Organisms have therefore evolved a specialized circadian system to anticipate and adapt to daily changes in the environment. Loss of synchrony between the internal circadian clock and environment day and night changes is responsible for jet lag, but may also promote sleep disorders, metabolic disorders and many diseases. The availability of large amounts of mouse data from the International Mouse Phenotype Consortium provides new opportunities to identify novel genetic components of mouse behaviour and metabolism. In this study, we performed a high-throughput identification of genetic components of circadian misalignment by developing a machine learning-based algorithm. By analyzing the indirect calorimetry parameters from more than 2000 C57BL/6N mice and mice from 750 mutant lines, we identified 5 genes involved in circadian misalignment of activity and feeding behaviour. Further analyzing genetic knock-out mice for one of these genes, we were able to validate our screening method by functional studies. Our systemic analysis thus paves the way for searching the genetic determinants for circadian misalignment. Circadian systems provide a fitness advantage to organisms by allowing them to adapt to daily changes of environmental cues, such as light/dark cycles. The molecular mechanism underlying the circadian clock has been well characterized. However, how internal circadian clocks are entrained with regular daily light/dark cycles remains unclear. By collecting and analyzing indirect calorimetry (IC) data from more than 2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC), we show that the onset time and peak phase of activity and food intake rhythms are reliable parameters for screening defects of circadian misalignment. We developed a machine learning algorithm to quantify these two parameters in our misalignment screen (SyncScreener) with existing datasets and used it to screen 750 mutant mouse lines from five IMPC phenotyping centres. Mutants of five genes (Slc7a11, Rhbdl1, Spop, Ctc1 and Oxtr) were found to be associated with altered patterns of activity or food intake. By further studying the Slc7a11(tm1a/tm1a) mice, we confirmed its advanced activity phase phenotype in response to a simulated jetlag and skeleton photoperiod stimuli. Disruption of Slc7a11 affected the intercellular communication in the suprachiasmatic nucleus, suggesting a defect in synchronization of clock neurons. Our study has established a systematic phenotype analysis approach that can be used to uncover the mechanism of circadian entrainment in mice. [Zhang, Tao; Xie, Pancheng; Dong, Yingying; Liu, Zhiwei; Zhou, Fei; Pan, Dejing; Huang, Zhengyun; Zhai, Qiaocheng; Gu, Yue; Yang, Ling; Xu, Ying] Soochow Univ, Cambridge Suda Genom Resource Ctr, Jiangsu Key Lab Neuropsychiat Dis, Med Coll, Suzhou, Jiangsu, Peoples R China; [Wu, Qingyu] Soochow Univ, Collaborat Innovat Ctr Hematol, Cyrus Tang Hematol Ctr, Suzhou, Peoples R China; [Wu, Qingyu; Xu, Ying] Soochow Univ, State Key Lab Radiat Med & Prevent, Med Coll, Suzhou, Peoples R China; [Tanaka, Nobuhiko; Obata, Yuichi] RIKEN, BioResource Ctr, Tsukuba, Ibaraki, Japan; [Bradley, Allan; Lelliott, Christopher J.] Wellcome Trust Sanger Inst, Wellcome Genome Campus, Hinxton, England; [Nutter, Lauryl M. J.; McKerlie, Colin; Flenniken, Ann M.] Ctr Phenogen, Toronto, ON, Canada; [Champy, Marie-France; Sorg, Tania; Herault, Yann] ICS, CELPHEDIA, PHENOMIN, Illkirch Graffenstaden, France; [De Angelis, Martin Hrabe; Durner, Valerie Gailus; Rozman, Jan] Helmholtz Zentrum Munchen, Inst Expt Genet, German Res Ctr Environm Hlth, German Mouse Clin, Munich, Germany; [De Angelis, Martin Hrabe; Sedlacek, Radislav; Rozman, Jan] Czech Acad Sci, Inst Mol Genet, Czech Ctr Phenogen, Vestec, Czech Republic; [Mallon, Ann-Marie; Brown, Steve D. M.] MRC, Harwell Inst, Mammalian Genet Unit, Harwell, Berks, England; [Mallon, Ann-Marie; Brown, Steve D. M.] Mary Lyon Ctr, Harwell, Berks, England; [Meehan, Terry; Parkinson, Helen E.] European Bioinformat Inst, European Mol Biol Lab, Hinxton, England; [Smedley, Damian] Queen Mary Univ London, Sch Med & Dent, London, England; [Lloyd, K. C. Kent] Univ Calif Davis, Sch Med, Davis, CA 95616 USA; [Lloyd, K. C. Kent] Univ Calif Davis, Mouse Biol Program, Davis, CA 95616 USA; [Yan, Jun; Gao, Xiang] Nanjing Univ, Nanjing Biomed Res Inst, Collaborat Innovat Ctr Genet & Dev, SKL Pharmaceut Biotechnol & Model Anim Res Ctr, Nanjing, Peoples R China; [Seong, Je Kyung] Seoul Natl Univ, Coll Vet Med, Seoul, South Korea; [Seong, Je Kyung] Korea Mouse Phenotyping Ctr, Seoul, South Korea; [Wang, Chi-Kuang Leo] NARLabs, Natl Lab Anim Ctr, Taipei, Taiwan; [Liu, Yi] Univ Texas Southwestern Med Ctr Dallas, Dept Physiol, Dallas, TX USA; [Rozman, Jan] German Ctr Diabet Res DZD, Neuherberg, Germany Soochow University - China; Soochow University - China; Soochow University - China; RIKEN; Wellcome Trust Sanger Institute; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Czech Academy of Sciences; Institute of Molecular Genetics of the Czech Academy of Sciences; MRC Harwell; European Molecular Biology Laboratory (EMBL); University of London; Queen Mary University London; University of California System; University of California Davis; University of California System; University of California Davis; Nanjing University; Seoul National University (SNU); University of Texas System; University of Texas Southwestern Medical Center Dallas Yang, L; Xu, Y (corresponding author), Soochow Univ, Cambridge Suda Genom Resource Ctr, Jiangsu Key Lab Neuropsychiat Dis, Med Coll, Suzhou, Jiangsu, Peoples R China.;Xu, Y (corresponding author), Soochow Univ, State Key Lab Radiat Med & Prevent, Med Coll, Suzhou, Peoples R China.;Rozman, J (corresponding author), Helmholtz Zentrum Munchen, Inst Expt Genet, German Res Ctr Environm Hlth, German Mouse Clin, Munich, Germany.;Rozman, J (corresponding author), Czech Acad Sci, Inst Mol Genet, Czech Ctr Phenogen, Vestec, Czech Republic.;Rozman, J (corresponding author), German Ctr Diabet Res DZD, Neuherberg, Germany. jan.rozman@img.cas.cz; lyang@suda.edu.cn; yingxu@suda.edu.cn Yan, Jun/Q-3031-2017; Xu, Ying/AAF-3081-2019; Smedley, Damian/AAL-8172-2021; Rozman, Jan/A-2540-2010; Herault, Yann/B-5500-2012; Xu, Ying/A-3009-2013; de Angelis, Martin Hrabe/F-5531-2012 Yan, Jun/0000-0002-1303-1952; Xu, Ying/0000-0002-6689-7768; Rozman, Jan/0000-0002-8035-8904; Herault, Yann/0000-0001-7049-6900; Xu, Ying/0000-0002-6689-7768; de Angelis, Martin Hrabe/0000-0002-7898-2353; Mallon, Ann-Marie/0000-0003-4047-4019; Parkinson, Helen/0000-0003-3035-4195; Seong, Je Kyung/0000-0003-1177-6958; Wu, Qingyu/0000-0003-0561-9315; Nutter, Lauryl/0000-0001-9619-146X; Meehan, Terrence/0000-0003-1980-3228; Zhou, Fei/0000-0003-1857-8831; Sorg, Tania/0000-0001-6974-4735 Ministry of Science and Technology [2018YFA0801100]; National Natural Science Foundation of China [31630091, 31871185, 31600958, 11671417]; National Center for International Research [2017B01012]; NIH [UM1HG006370]; German Center for Diabetes Research (DZD) [01KX1012]; EU [653961]; MRC [MC_U142684172, MC_U142684171] Funding Source: UKRI Ministry of Science and Technology(Ministry of Science, ICT & Future Planning, Republic of Korea); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Center for International Research; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); German Center for Diabetes Research (DZD); EU(European Commission); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptto the end of Funding information as: This work was supported by grants from the Ministry of Science and Technology (2018YFA0801100 to YX) and the National Natural Science Foundation of China (31630091 to Y.X, 31871185 to Y.D., 31600958 to Z.L., 11671417 to L.Y.). We also thank the Priority Academic Program Development of the Jiangsu Higher Education Institutes (PAPD) and National Center for International Research (2017B01012). AMM, TFM, DS, HP, PF and the IMPC Data Coordination Centre are supported by the NIH Common fund grant (UM1HG006370). Infrafrontier grant 01KX1012, support by the German Center for Diabetes Research (DZD), EU Horizon2020: IPAD-MD funding 653961 (MHA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 46 7 9 4 10 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1553-7404 PLOS GENET PLoS Genet. JAN 2020.0 16.0 1 e1008577 10.1371/journal.pgen.1008577 0.0 25 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity KN5UX 31929527.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000514903300023 0
J Zhang, HC; Zang, N; Cao, Y; Wang, YB; Zhang, LQ; Huang, B; Mathiopoulos, PT Zhang, Hanchao; Zang, Ning; Cao, Yun; Wang, Yuebin; Zhang, Liqiang; Huang, Bo; Mathiopoulos, P. Takis DGCC-EB: Deep Global Context Construction With an Enabled Boundary for Land Use Mapping of CSMA IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Graph convolutional network (GCN); high spatial resolution; land-use mapping (LUM); transfer learning; vision transformer (ViT) GRAPH CONVOLUTIONAL NETWORKS; NEURAL-NETWORK; RESOLUTION; COVER; CLASSIFICATION; IMAGES Land use mapping (LUM) of a coal mining subsidence area (CMSA) is a significant task. The application of convolutional neural networks (CNNs) has become prevalent in LUM, which can achieve promising performances. However, CNNs cannot process irregular data; as a result, the boundary information is overlooked. The graph convolutional network (GCN) flexibly operates with irregular regions to capture the contextual relations among neighbors. However, the global context is not considered in the GCN. In this article, we develop the deep global context construction with enabled boundary (DGCC-EB) for the LUM of the CMSA. An original Google Earth image is partitioned into nonoverlapping processing units. The DGCC-EB extracts the preliminary features from the processing unit that are further divided into nonoverlapping superpixels with irregular edges. The superpixel features are generated and then embedded into the GCN and vision transformer (ViT). In the GCN, the graph convolution is applied to superpixel features; therefore, the boundary information of objects can be preserved. In the ViT, the multihead attention blocks and positional encoding build the global context among the superpixel features. The feature constraint is calculated to fuse the advantages of the features extracted from the GCN and ViT. To improve the LUM accuracy, the cross-entropy (CE) loss is calculated. The DGCC-EB integrates all modules into a whole end-to-end framework and is then optimized by a customized algorithm. The results of case studies show that the proposed DGCC-EB obtained the acceptable overall accuracy (OA) (89.06%/88.68%) and Kappa (0.86/0.87) values for Shouzhou city and Zezhou city, respectively. [Zhang, Hanchao; Zang, Ning; Wang, Yuebin] China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China; [Cao, Yun] China Meteorol Adm, Natl Satellite Meteorol Ctr, Natl Ctr Space Weather, Key Lab Radiometr Calibrat & Validat Environm Sate, Beijing 100081, Peoples R China; [Cao, Yun] Innovat Ctr FengYun Meteorol Satellite FYSIC, Beijing 100081, Peoples R China; [Zhang, Liqiang] Beijing Normal Univ, Fac Geog, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China; [Huang, Bo] Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China; [Mathiopoulos, P. Takis] Natl & Kapodestrian Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece China University of Geosciences; China Meteorological Administration; Beijing Normal University; Chinese University of Hong Kong; National & Kapodistrian University of Athens Wang, YB (corresponding author), China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China. cy12160019@163.com; xxgcdxwyb@163.com ; Huang, Bo/H-9874-2014 Wang, Yuebin/0000-0002-6978-4558; Zhang, Liqiang/0000-0002-4175-7590; Huang, Bo/0000-0002-5063-3522 National Natural Science Foundation of China [42171388]; Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes [AR2213] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes This work was supported in part by the National Natural Science Foundation of China under Grant 42171388 and in part by the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes under Grant AR2213. 66 0 0 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5634915 10.1109/TGRS.2022.3224733 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 7C7QN 2023-03-23 WOS:000900003000004 0
J Liu, YX; Zhang, YZ; Bhanu, B; Coleman, S; Kerr, D Liu, Yixiu; Zhang, Yunzhou; Bhanu, Bir; Coleman, Sonya; Kerr, Dermot Multi-level cross-view consistent feature learning for person re-identification NEUROCOMPUTING English Article Person re-identification; Local deep features; Cross-view consistent word learning; Multi-level metric learning; ID-based cross-view multi-level dictionary learning NETWORKS Person re-identification plays an important role in searching for a specific person in a camera network with non-overlapping cameras. The most critical problem for re-identification is feature representation. In this paper, a multi-level cross-view consistent feature learning framework is proposed for person re identification. First, local deep, LOMO and SIFT features are extracted to form multi-level features. Specifically, local features from the lower and higher layers of a convolutional neural network (CNN) are extracted, these features complement each other as they extract apparent and semantic properties. Second, an ID-based cross-view multi-level dictionary learning (IDB-CMDL) is carried out to obtain sparse and discriminant feature representation. Third, a cross-view consistent word learning is performed to get the cross-view consistent BoVW histograms from sparse feature representation. Finally, a multi-level metric learning fuses multiple BoVW histograms, and learns the sample distance in the subspace for ranking. Experiments on the public CUHK03, Market1501, and DukeMTMC-ReID datasets show results that are superior to many state-of-the-art methods for person re-identification. (c) 2021 Elsevier B.V. All rights reserved. Person re-identification plays an important role in searching for a specific person in a camera network with non-overlapping cameras. The most critical problem for re-identification is feature representation. In this paper, a multi-level cross-view consistent feature learning framework is proposed for person reidentification. First, local deep, LOMO and SIFT features are extracted to form multi-level features. Specifically, local features from the lower and higher layers of a convolutional neural network (CNN) are extracted, these features complement each other as they extract apparent and semantic properties. Second, an ID-based cross-view multi-level dictionary learning (IDB-CMDL) is carried out to obtain sparse and discriminant feature representation. Third, a cross-view consistent word learning is performed to get the cross-view consistent BoVW histograms from sparse feature representation. Finally, a multi-level metric learning fuses multiple BoVW histograms, and learns the sample distance in the subspace for ranking. Experiments on the public CUHK03, Market1501, and DukeMTMC-ReID datasets show results that are superior to many state-of-the-art methods for person re-identification. [Liu, Yixiu; Zhang, Yunzhou] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China; [Bhanu, Bir] Univ Calif Riverside, Riverside, CA 92521 USA; [Coleman, Sonya; Kerr, Dermot] Univ Ulster, Intelligent Syst Res Ctr, Derry, Londonderry, North Ireland Northeastern University - China; University of California System; University of California Riverside; Ulster University Zhang, YZ (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China. zhangyunzhou@mail.neu.edu.cn National Natural Science Foundation of China [61973066]; Equipment Preresearch Fundation [61403120111]; Distinguished Creative Talent Program of Liaoning Colleges and Universities [LR2019027]; Fundamental Research Funds for the Central Universities [N172608005, N182608004, N2004022] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Equipment Preresearch Fundation; Distinguished Creative Talent Program of Liaoning Colleges and Universities; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work is supported by National Natural Science Foundation of China (No. 61973066) , Equipment Preresearch Fundation (61403120111) , Distinguished Creative Talent Program of Liaoning Colleges and Universities (LR2019027) , and Fundamental Research Funds for the Central Universities (N172608005, N182608004, N2004022) . 64 3 3 5 37 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing MAY 7 2021.0 435.0 1 14 10.1016/j.neucom.2021.01.010 0.0 JAN 2021 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science RC5CK Green Accepted 2023-03-23 WOS:000632819000001 0
J Sabatini, FM; Lenoir, J; Hattab, T; Arnst, EA; Chytry, M; Dengler, J; De Ruffray, P; Hennekens, SM; Jandt, U; Jansen, F; Jimenez-Alfaro, B; Kattge, J; Levesley, A; Pillar, VD; Purschke, O; Sandel, B; Sultana, F; Aavik, T; Acic, S; Acosta, ATR; Agrillo, E; Alvarez, M; Apostolova, I; Khan, MASA; Arroyo, L; Attorre, F; Aubin, I; Banerjee, A; Bauters, M; Bergeron, Y; Bergmeier, E; Biurrun, I; Bjorkman, AD; Bonari, G; Bondareva, V; Brunet, J; Carni, A; Casella, L; Cayuela, L; Cerny, T; Chepinoga, V; Csiky, J; Custerevska, R; De Bie, E; de Gasper, AL; De Sanctis, M; Dimopoulos, P; Dolezal, J; Dziuba, T; El-Sheikh, MAM; Enquist, B; Ewald, J; Fazayeli, F; Field, R; Finckh, M; Gachet, S; Galan-de-Mera, A; Garbolino, E; Gholizadeh, H; Giorgis, M; Golub, V; Alsos, IG; Grytnes, JA; Guerin, GR; Gutierrez, AG; Haider, S; Hatim, MZ; Herault, B; Mendoza, GH; Holzel, N; Homeier, J; Hubau, W; Indreica, A; Janssen, JAM; Jedrzejek, B; Jentsch, A; Jurgens, N; Kacki, Z; Kapfer, J; Karger, DN; Kavgaci, A; Kearsley, E; Kessler, M; Khanina, L; Killeen, T; Korolyuk, A; Kreft, H; Kuhl, HS; Kuzemko, A; Landucci, F; Lengyel, A; Lens, F; Lingner, DV; Liu, HY; Lysenko, T; Mahecha, MD; Marceno, C; Martynenko, V; Moeslund, JE; Mendoza, AM; Mucina, L; Muller, JV; Munzinger, JM; Naqinezhad, A; Noroozi, J; Nowak, A; Onyshchenko, V; Overbeck, GE; Partel, M; Pauchard, A; Peet, RK; Penuelas, J; Perez-Haase, A; Peterka, T; Petrik, P; Peyre, G; Phillips, OL; Prokhorov, V; Rasomavicius, V; Revermann, R; Rivas-Torres, G; Rodwell, JS; Ruprecht, E; Rusina, S; Samimi, C; Schmidt, M; Schrodt, F; Shan, HH; Shirokikh, P; Sibik, J; Silc, U; Sklenar, P; Skvorc, Z; Sparrow, B; Sperandii, MG; Stancic, Z; Svenning, JC; Tang, ZY; Tang, CQ; Tsiripidis, I; Vanselow, KA; Martinez, RV; Vassilev, K; Velez-Martin, E; Venanzoni, R; Vibrans, AC; Violle, C; Virtanen, R; von Wehrden, H; Wagner, V; Walker, DA; Waller, DM; Wang, HF; Wesche, K; Whitfeld, TJS; Willner, W; Wiser, SK; Wohlgemuth, T; Yamalov, S; Zobel, M; Bruelheide, H Sabatini, Francesco Maria; Lenoir, Jonathan; Hattab, Tarek; Arnst, Elise Aimee; Chytry, Milan; Dengler, Juergen; De Ruffray, Patrice; Hennekens, Stephan M.; Jandt, Ute; Jansen, Florian; Jimenez-Alfaro, Borja; Kattge, Jens; Levesley, Aurora; Pillar, Valerio D.; Purschke, Oliver; Sandel, Brody; Sultana, Fahmida; Aavik, Tsipe; Acic, Svetlana; Acosta, Alicia T. R.; Agrillo, Emiliano; Alvarez, Miguel; Apostolova, Iva; Arfin Khan, Mohammed A. S.; Arroyo, Luzmila; Attorre, Fabio; Aubin, Isabelle; Banerjee, Arindam; Bauters, Marijn; Bergeron, Yves; Bergmeier, Erwin; Biurrun, Idoia; Bjorkman, Anne D.; Bonari, Gianmaria; Bondareva, Viktoria; Brunet, Jorg; Carni, Andraz; Casella, Laura; Cayuela, Luis; Cerny, Tomas; Chepinoga, Victor; Csiky, Janos; Custerevska, Renata; De Bie, Els; de Gasper, Andre Luis; De Sanctis, Michele; Dimopoulos, Panayotis; Dolezal, Jiri; Dziuba, Tetiana; El-Sheikh, Mohamed Abd El-Rouf Mousa; Enquist, Brian; Ewald, Joerg; Fazayeli, Farideh; Field, Richard; Finckh, Manfred; Gachet, Sophie; Galan-de-Mera, Antonio; Garbolino, Emmanuel; Gholizadeh, Hamid; Giorgis, Melisa; Golub, Valentin; Alsos, Inger Greve; Grytnes, John-Arvid; Guerin, Gregory Richard; Gutierrez, Alvaro G.; Haider, Sylvia; Hatim, Mohamed Z.; Herault, Bruno; Hinojos Mendoza, Guillermo; Hoelzel, Norbert; Homeier, Juergen; Hubau, Wannes; Indreica, Adrian; Janssen, John A. M.; Jedrzejek, Birgit; Jentsch, Anke; Juergens, Norbert; Kacki, Zygmunt; Kapfer, Jutta; Karger, Dirk Nikolaus; Kavgaci, Ali; Kearsley, Elizabeth; Kessler, Michael; Khanina, Larisa; Killeen, Timothy; Korolyuk, Andrey; Kreft, Holger; Kuehl, Hjalmar S.; Kuzemko, Anna; Landucci, Flavia; Lengyel, Attila; Lens, Frederic; Lingner, Debora Vanessa; Liu, Hongyan; Lysenko, Tatiana; Mahecha, Miguel D.; Marceno, Corrado; Martynenko, Vasiliy; Moeslund, Jesper Erenskjold; Monteagudo Mendoza, Abel; Mucina, Ladislav; Muller, Jonas V.; Munzinger, Jerome; Naqinezhad, Alireza; Noroozi, Jalil; Nowak, Arkadiusz; Onyshchenko, Viktor; Overbeck, Gerhard E.; Partel, Meelis; Pauchard, Anibal; Peet, Robert K.; Penuelas, Josep; Perez-Haase, Aaron; Peterka, Tomas; Petrik, Petr; Peyre, Gwendolyn; Phillips, Oliver L.; Prokhorov, Vadim; Rasomavicius, Valerijus; Revermann, Rasmus; Rivas-Torres, Gonzalo; Rodwell, John S.; Ruprecht, Eszter; Rusina, Solvita; Samimi, Cyrus; Schmidt, Marco; Schrodt, Franziska; Shan, Hanhuai; Shirokikh, Pavel; Sibik, Jozef; Silc, Urban; Sklenar, Petr; Skvorc, Zeljko; Sparrow, Ben; Sperandii, Marta Gaia; Stancic, Zvjezdana; Svenning, Jens-Christian; Tang, Zhiyao; Tang, Cindy Q.; Tsiripidis, Ioannis; Vanselow, Kim Andre; Vasquez Martinez, Rodolfo; Vassilev, Kiril; Velez-Martin, Eduardo; Venanzoni, Roberto; Vibrans, Alexander Christian; Violle, Cyrille; Virtanen, Risto; von Wehrden, Henrik; Wagner, Viktoria; Walker, Donald A.; Waller, Donald M.; Wang, Hua-Feng; Wesche, Karsten; Whitfeld, Timothy J. S.; Willner, Wolfgang; Wiser, Susan K.; Wohlgemuth, Thomas; Yamalov, Sergey; Zobel, Martin; Bruelheide, Helge sPlotOpen - An environmentally balanced, open-access, global dataset of vegetation plots GLOBAL ECOLOGY AND BIOGEOGRAPHY English Article; Data Paper big data; biodiversity; biogeography; database; functional traits; macroecology; vascular plants; vegetation plots PHYTOSOCIOLOGICAL DATABASE; GRASSLAND VEGETATION; WETLAND VEGETATION; FOREST VEGETATION; DRY GRASSLANDS; PLANT; BIODIVERSITY; DIVERSITY; ARCHIVE; EXTINCTION Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called 'sPlot', compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01-40,000 m(2). Time period and grain 1888-2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot-level records. Software format Three main matrices (.csv), relationally linked. [Sabatini, Francesco Maria; Jandt, Ute; Haider, Sylvia; Kuehl, Hjalmar S.; Mahecha, Miguel D.; Virtanen, Risto; Wesche, Karsten; Bruelheide, Helge] German Ctr Integrat Biodivers Res iDiv, Puschstr 4, D-04103 Leipzig, Germany; [Sabatini, Francesco Maria; Jandt, Ute; Haider, Sylvia; Bruelheide, Helge] Martin Luther Univ Halle Wittenberg, Inst Biol, Halle, Germany; [Lenoir, Jonathan] Univ Picardie Jules Verne, Ecol & Dynam Syst Anthropises EDYSAN, CNRS, UMR 7058, Amiens, France; [Hattab, Tarek] Univ Montpellier, MARBEC, CNRS, IFREMER, Sete, France; [Hattab, Tarek] IRD, Sete, France; [Arnst, Elise Aimee; Wiser, Susan K.] Manaaki Whenua Landcare Res, Lincoln, New Zealand; [Chytry, Milan; Landucci, Flavia; Marceno, Corrado; Peterka, Tomas] Masaryk Univ, Dept Bot & Zool, Brno, Czech Republic; [Dengler, Juergen] Zurich Univ Appl Sci ZHAW, Vegetat Ecol Grp, Inst Nat Resource Sci IUNR, Wadenswil, Switzerland; [Dengler, Juergen] Univ Bayreuth, Bayreuth Ctr Ecol & Environm Res BayCEER, Bayreuth, Germany; [De Ruffray, Patrice] Univ Strasbourg, CNRS, Inst Biol Mol Plantes, Strasbourg, France; [Hennekens, Stephan M.] Wageningen Environm Res, Wageningen, Netherlands; [Jansen, Florian] Univ Rostock, Fac Agr & Environm Sci, Rostock, Germany; [Jimenez-Alfaro, Borja] Univ Oviedo, Res Unit Biodivers, CSIC, UO,PA, Mieres, Spain; [Kattge, Jens] Max Planck Inst Biogeochem, Jena, Germany; [Levesley, Aurora; Phillips, Oliver L.] Univ Leeds, Sch Geog, Leeds, W Yorkshire, England; [Pillar, Valerio D.] Univ Fed Rio Grande do Sul, Dept Ecol, Porto Alegre, RS, Brazil; [Purschke, Oliver] Martin Luther Univ Halle Wittenberg, Med Sch, Interdisciplinary Ctr Hlth Sci, Inst Med Epidemiol Biometr & Informat IMEBI, Halle, Germany; [Sandel, Brody] Santa Clara Univ, Dept Biol, Santa Clara, CA 95053 USA; [Sultana, Fahmida; Arfin Khan, Mohammed A. S.] Shahjalal Univ Sci & Technol, Sylhet, Bangladesh; [Aavik, Tsipe; Partel, Meelis; Zobel, Martin] Univ Tartu, Inst Ecol & Earth Sci, Tartu, Estonia; [Acic, Svetlana] Univ Belgrade, Dept Bot, Fac Agr, Belgrade, Serbia; [Acosta, Alicia T. R.; Sperandii, Marta Gaia] Roma Tre Univ, Dept Sci, Rome, Italy; [Agrillo, Emiliano; Casella, Laura] ISPRA Italian Natl Inst Environm Protect & Res, Biodivers Conservat Dept, Rome, Italy; [Alvarez, Miguel] Univ Bonn, INRES, Bonn, Germany; [Apostolova, Iva; Vassilev, Kiril] Bulgarian Acad Sci, Inst Biodivers & Ecosyst Res, Dept Plant & Fungal Divers & Resources, Sofia, Bulgaria; [Arroyo, Luzmila] Univ Autonoma Gabriel Rene Moreno, Direcc Carrera Biol, Santa Cruz, Bolivia; [Attorre, Fabio; De Sanctis, Michele] Sapienza Univ Rome, Dept Environm Biol, Rome, Italy; [Aubin, Isabelle] Nat Resources Canada, Great Lakes Forestry Ctr, Canadian Forest Serv, Sault Ste Marie, ON, Canada; [Banerjee, Arindam] Univ Illinois, Dept Comp Sci, Urbana, IL USA; [Bauters, Marijn] Univ Ghent, Isotope Biosci Lab UGent, Dept Green Chem & Technol, ISOFYS, Ghent, Belgium; [Bauters, Marijn; Kearsley, Elizabeth] Univ Ghent, Dept Environm Computat & Appl Vegetat Ecol UGent, CAVELab, Ghent, Belgium; [Bergeron, Yves] Univ Quebec Abitibi Temiscamingue, Forest Res Inst, Rouyn Noranda, PQ, Canada; [Bergmeier, Erwin] Univ Gottingen, Vegetat Ecol & Phytodivers, Gottingen, Germany; [Biurrun, Idoia; Marceno, Corrado] Univ Basque Country UPV EHU, Plant Biol & Ecol, Bilbao, Spain; [Bjorkman, Anne D.] Univ Gothenburg, Dept Biol & Environm Sci, Gothenburg, Sweden; [Bjorkman, Anne D.] Gothenburg Global Biodivers Ctr, Gothenburg, Sweden; [Bonari, Gianmaria] Free Univ Bozen Bolzano, Bolzano, Italy; [Bondareva, Viktoria] Inst Ecol Volga River Basin, Lab Phytodivers Problem & Phytocoenol, Toljatty, Russia; [Brunet, Jorg] Swedish Univ Agr Sci, Southern Swedish Forest Res Ctr, Alnarp, Sweden; [Carni, Andraz] Slovenian Acad Sci & Arts, Res Ctr, Inst Biol, Ljubljana, Slovenia; [Carni, Andraz] Univ Nova Gorica, Sch Viticulture & Enol, Nova Gorica, Slovenia; [Cayuela, Luis] Univ Rey Juan Carlos, Dept Biol & Geol, Phys & Inorgan Chem, Mostoles, Spain; [Cerny, Tomas; Hatim, Mohamed Z.] Czech Univ Life Sci Prague, Fac Forestry & Wood Sci, Dept Forest Ecol, Prague 6, Czech Republic; [Chepinoga, Victor] RAS, Cent Siberian Bot Garden, SB, Novosibirsk, Russia; [Csiky, Janos] Univ Pecs, Dept Ecol, Pecs, Hungary; [Custerevska, Renata] Ss Cyril & Methodius Univ, Fac Nat Sci & Math, Ins Biol, Skopje, North Macedonia; [De Bie, Els] Res Inst Nat & Forest INBO, Biotope Divers, Brussels, Belgium; [de Gasper, Andre Luis] Univ Reg Blumenau, Blumenau, Brazil; [Dimopoulos, Panayotis] Univ Patras, Div Plant Biol, Dept Biol, Lab Bot, Patras, Greece; [Dolezal, Jiri] Czech Acad Sci, Inst Bot, Dept Funct Ecol, Trebon, Czech Republic; [Dziuba, Tetiana] Natl Acad Sci Ukraine, MG Kholodny Inst Bot, Geobotany & Ecol, Kiev, Ukraine; [El-Sheikh, Mohamed Abd El-Rouf Mousa] King Saud Univ, Bot & Microbiol Dept, Coll Sci, Riyadh, Saudi Arabia; [El-Sheikh, Mohamed Abd El-Rouf Mousa] Damanhour Univ, Bot Dept, Fac Sci, Damanhour, Egypt; [Enquist, Brian] Univ Arizona, Ecol & Evolutionary Biol, Tucson, AZ USA; [Ewald, Joerg] Univ Appl Sci, Hsch Weihenstephan Triesdorf, Freising Weihenstephan, Germany; [Fazayeli, Farideh] Google LLC, Mountain View, CA USA; [Fazayeli, Farideh] Univ Minnesota Twin Cities, Minneapolis, MN USA; [Field, Richard; Schrodt, Franziska] Univ Nottingham, Sch Geog, Nottingham, England; [Finckh, Manfred; Juergens, Norbert] Univ Hamburg, Inst Plant Sci & Microbiol, Biodivers Ecol & Evolut Plants, Hamburg, Germany; [Gachet, Sophie] Avignon Univ, Aix Marseille Univ, CNRS, IRD,IMBE, Marseille, France; [Galan-de-Mera, Antonio] Univ CEU San Pablo, Lab Bot, Madrid, Spain; [Galan-de-Mera, Antonio] Univ Privada Antonio Guillermo Urrelo, Lab Bot, Cajamarca, Peru; [Galan-de-Mera, Antonio] Estudios Fitogeog Peru, Herbario AQP, Arequipa, Peru; [Garbolino, Emmanuel] Nova Sophia Regus Nova, Climpact Data Sci CDS, Sophia Antipolis, France; [Gholizadeh, Hamid] Univ Mazandaran, Dept Plant Biol, Babolsar, Iran; [Giorgis, Melisa] Inst Multidisciplinario Biol Vegetal IMBIV, Ecol Vegetal & Fitogeog, CONICET, Cordoba, Argentina; [Golub, Valentin] Russian Acad Sci, Samara Fed Res Ctr, Inst Ecol Volga River Basin, Lab Phytocoenol, Toljatty, Russia; [Alsos, Inger Greve] UiT Arctic Univ Norway, Arctic Univ Museum Norway, Tromso, Norway; [Grytnes, John-Arvid] Univ Bergen, Dept Biol Sci, Bergen, Norway; [Guerin, Gregory Richard] Univ Adelaide, Sch Biol Sci, Adelaide, SA, Australia; [Gutierrez, Alvaro G.] Univ Chile, Fac Ciencias Agron, Dept Ciencias Ambient & Recursos Nat Renovables, Santiago, Chile; [Hatim, Mohamed Z.] Wageningen Univ, Plant Ecol & Nat Conservat Grp, Environm Sci Dept, Wageningen, Netherlands; Tanta Univ, Fac Sci, Bot & Microbiol Dept, Tanta, Egypt; [Herault, Bruno] CIRAD, UPR Forets & Soc, Yamoussoukro, Cote Ivoire; [Herault, Bruno] Univ Montpellier, CIRAD, Montpellier, France; [Herault, Bruno] Inst Natl Polytechn Felix Houphouet Boigny, INP HB, Yamoussoukro, Cote Ivoire; [Hinojos Mendoza, Guillermo] ASES Ecol & Sustainable Serv, Aubenas, France; [Hoelzel, Norbert; Jedrzejek, Birgit] Univ Munster, Inst Landscape Ecol, Munster, Germany; [Homeier, Juergen] Univ Gottingen, Plant Ecol & Ecosyst Res, Gottingen, Germany; [Hubau, Wannes] Univ Ghent, Lab Wood Biol UGent, Dept Environm, WoodLab, Ghent, Belgium; [Hubau, Wannes] Royal Museum Cent Africa, Serv Wood Biol, Tervuren, Belgium; [Indreica, Adrian] Transilvania Univ Brasov, Dept Silviculture, Brasov, Romania; [Janssen, John A. M.] Wageningen Univ & Res, Wageningen Environm Res Alterra, Wageningen, Netherlands; [Jentsch, Anke] Univ Bayreuth, Bayreuth Ctr Ecol & Environm Res, Disturbance Ecol, Bayreuth, Germany; [Kacki, Zygmunt] Univ Wroclaw, Bot Garden, Wroclaw, Poland; [Kapfer, Jutta] Norwegian Inst Bioecon Res, Tromso, Norway; [Karger, Dirk Nikolaus] Swiss Fed Inst Forest Snow & Landscape Res WSL, Biodivers & Conservat Biol, Birmensdorf, Switzerland; [Kavgaci, Ali] Karabuk Univ, Fac Foresty, Kilavuzlar Koyu Ote Karsi Univ Kampusu Merkez, Karabuk, Turkey; [Kessler, Michael] Univ Zurich, Dept Systemat & Evolutionary Bot, Zurich, Switzerland; [Khanina, Larisa] Russian Acad Sci, RAS, Branch MV Keldysh Inst Appl Math, Inst Math Problems Biol, Pushchino, Russia; [Killeen, Timothy] Univ Autonoma Gabriel Rene Moreno, Museo Hist Nat Noel Kempff Mercado, Santa Cruz, Bolivia; [Korolyuk, Andrey] Russian Acad Sci, Siberian Branch, Cent Siberian Bot Garden, Geosyst Lab, Novosibirsk, Russia; [Kreft, Holger] Univ Gottingen, Dept Biodivers Macroecol & Biogeog, Gottingen, Germany; [Kuehl, Hjalmar S.] Max Planck Inst Evolutionary Anthropol, Leipzig, Germany; [Kuzemko, Anna] Natl Acad Sci Ukraine, MG Kholodny Inst Bot, Dept Geobot & Ecol, Kiev, Ukraine; [Lengyel, Attila] Inst Ecol & Bot, Ctr Ecol Res, Vacratot, Hungary; [Lens, Frederic] Res Grp Funct Traits, Nat Biodivers Ctr, Leiden, Netherlands; [Lens, Frederic] Leiden Univ, Inst Biol Leiden, Leiden, Netherlands; [Lingner, Debora Vanessa; Vibrans, Alexander Christian] Univ Reg Blumenau, Dept Engn Florestal, Blumenau, Brazil; [Liu, Hongyan; Tang, Zhiyao] Peking Univ, Coll Urban & Environm Sci, Beijing, Peoples R China; [Lysenko, Tatiana] RAS, Komarov Bot Inst, Lab Vegetat Sci, St Petersburg, Russia; [Lysenko, Tatiana] RAS, Branch Samara Sci Ctr, Lab Phytodivers Problems, Inst Ecol Volga River Basin, Tolyatti, Russia; [Lysenko, Tatiana] RAS, Grp Ecol Living Organisms, Tobolsk Complex Sci Stn, Ural Branch, Tobolsk, Russia; [Mahecha, Miguel D.] Univ Leipzig, Remote Sensing Ctr Earth Syst Res, Leipzig, Germany; [Martynenko, Vasiliy; Shirokikh, Pavel] Russian Acad Sci, Ufa Inst Biol, Ufa Fed Sci Ctr, Ufa, Russia; [Moeslund, Jesper Erenskjold] Aarhus Univ, Dept Biosci, Ronde, Denmark; [Monteagudo Mendoza, Abel; Vasquez Martinez, Rodolfo] Jardin Bot Missouri Oxapampa, Oxapampa, Pasco, Peru; [Monteagudo Mendoza, Abel] Univ Nacl San Antonio Abad Cusco, Cuzco, Peru; [Mucina, Ladislav] Murdoch Univ, Perth, WA, Australia; [Mucina, Ladislav] Stellenbosch Univ, Dept Geog & Environm Studies, Stellenbosch, South Africa; [Muller, Jonas V.] Royal Bot Gardens, Conservat Sci, Richmond, Ardingly, England; [Munzinger, Jerome] Univ Montpellier, CNRS, CIRAD, INRAE,AMAP, Montpellier, France; [Naqinezhad, Alireza] Univ Mazandaran, Dept Plant Biol, Mazandaran, Iran; [Noroozi, Jalil; Willner, Wolfgang] Univ Vienna, Dept Bot & Biodivers Res, Vienna, Austria; [Nowak, Arkadiusz] Polish Acad Sci, Ctr Biodivers Conservat, Bot Garden, Warsaw, Poland; [Nowak, Arkadiusz] Univ Opole, Inst Biol, Opole, Poland; [Onyshchenko, Viktor] Natl Acad Sci Ukraine, MG Kholodny Inst Bot, Kiev, Ukraine; [Overbeck, Gerhard E.] Univ Fed Rio Grande do Sul, Dept Bot, Porto Alegre, RS, Brazil; [Pauchard, Anibal] Univ Concepcion, Fac Ciencias Forestales, Lab Invas Biol LIB, Concepcion, Chile; [Pauchard, Anibal] Inst Ecol & Biodivers IEB, Santiago, Chile; [Peet, Robert K.] Univ N Carolina, Dept Biol, Chapel Hill, NC 27515 USA; [Penuelas, Josep] CSIC, Global Ecol Unit, CREAF, UAB, Bellaterra, Catalonia, Spain; [Penuelas, Josep] CREAF, Cerdanyola Del Valles, Spain; [Perez-Haase, Aaron] Cent Univ Catalonia, Dept Biosci, Univ Vic, Barcelona, Spain; [Perez-Haase, Aaron] Univ Barcelona, Dept Evolutionary Biol Ecol & Environm Sci, Barcelona, Spain; [Petrik, Petr] Czech Acad Sci, Inst Bot, Dept Vegetat Ecol, Pruhonice, Czech Republic; [Peyre, Gwendolyn] Univ Andes, Dept Civil & Environm Engn, Bogota, Colombia; [Prokhorov, Vadim] Kazan Fed Univ, Inst Environm Sci, Kazan, Russia; [Rasomavicius, Valerijus] Nat Res Ctr, Inst Bot, Vilnius, Lithuania; [Revermann, Rasmus] Univ Hamburg, Inst Plant Sci & Microbiol, Biodivers Ecol & Evolut Plants, Hamburg, Germany; [Kattge, Jens; Revermann, Rasmus] Namibia Univ Sci & Technol, Fac Nat Resources & Spatial Sci, Windhoek, Namibia; [Rivas-Torres, Gonzalo] Univ San Francisco Quito, COCIBA, Quito, Ecuador; [Ruprecht, Eszter] Babe Bolyai Univ, Fac Biol & Geol, Hungarian Dept Biol & Ecol, Cluj Napoca, Romania; [Rusina, Solvita] Univ Latvia, Fac Geog & Earth Sci, Riga, Latvia; [Samimi, Cyrus] Univ Bayreuth, Bayreuth Ctr Ecol & Environm Res BayCEER, Climatol, Bayreuth, Germany; [Schmidt, Marco] Stadt Frankfurt Main Magistrat, Palmengarten, Frankfurt, Germany; [Shan, Hanhuai] Microsoft, Redmond, WA USA; [Sibik, Jozef] Slovak Acad Sci, Inst Bot, Plant Sci & Biodivers Ctr, Bratislava, Slovakia; [Silc, Urban] Slovenian Acad Sci & Arts ZRC SAZU, Inst Biol, Res Ctr, Ljubljana, Slovenia; [Sklenar, Petr] Charles Univ Prague, Dept Bot, Prague, Czech Republic; [Skvorc, Zeljko] Univ Zagreb, Fac Forestry & Wood Technol, Zagreb, Croatia; [Sparrow, Ben] Univ Adelaide, TERN, Adelaide, SA, Australia; [Sperandii, Marta Gaia] CSIC, Ctr Invest Desertificac, UV, GV, Moncada, Spain; [Stancic, Zvjezdana] Univ Zagreb, Fac Geotech Engn, Varazhdin, Croatia; [Svenning, Jens-Christian] Aarhus Univ, Dept Biol, Aarhus C, Denmark; [Tang, Cindy Q.] Yunnan Univ, Sch Ecol & Environm Sci, Kunming, Yunnan, Peoples R China; [Tsiripidis, Ioannis] Aristotle Univ Thessaloniki, Sch Biol, Thessaloniki, Greece; [Vanselow, Kim Andre] Univ Erlangen Nurnberg, Dept Geog, Erlangen, Germany; [Velez-Martin, Eduardo] ILEX Consultoria Cient, Porto Alegre, RS, Brazil; [Venanzoni, Roberto] Univ Perugia, Dept Chem Biol & Biotechnol, Perugia, Italy; [Violle, Cyrille] Univ Paul Valery Montpellier 3, CEFE, Univ Montpellier, CNRS,EPHE,IRD, Montpellier, France; [Virtanen, Risto] Univ Oulu, Ecol & Genet Res Unit, Biodivers Unit, Oulu, Finland; [Virtanen, Risto] Helmholtz Ctr Environm Res, Dept Physiol Divers, UFZ, Leipzig, Germany; [von Wehrden, Henrik] Leuphana Univ Luneburg, Inst Ecol, Luneburg, Germany; [Wagner, Viktoria] Univ Alberta, Dept Biol Sci, Edmonton, AB, Canada; [Walker, Donald A.] Univ Alaska, Inst Arctic Biol, Fairbanks, AK 99775 USA; [Waller, Donald M.] Univ Wisconsin, Bot, Madison, WI USA; [Wang, Hua-Feng] Hainan Univ, Coll Trop Crops, Hainan Key Lab Sustainable Utilizat Trop Bioresou, Haikou, Hainan, Peoples R China; [Wesche, Karsten] Senckenberg Museum Nat Hist Gorlitz, Bot Dept, Gorlitz, Germany; [Wesche, Karsten] Tech Univ Dresden, Int Inst Zittau, Zittau, Germany; [Whitfeld, Timothy J. S.] Univ Minnesota, Bell Museum, St Paul, MN 55108 USA; [Wohlgemuth, Thomas] Swiss Fed Inst Forest Snow & Landscape Res WSL, Forest Dynam, Birmensdorf, Switzerland; [Yamalov, Sergey] Russian Acad Sci, Ufa Sci Ctr, Lab Wild Growing Flora, South Ural Bot Garden Inst, Ufa, Russia Martin Luther University Halle Wittenberg; Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Picardie Jules Verne (UPJV); Centre National de la Recherche Scientifique (CNRS); Ifremer; Universite de Montpellier; Institut de Recherche pour le Developpement (IRD); Landcare Research - New Zealand; Masaryk University Brno; Zurich University of Applied Sciences; University of Bayreuth; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Wageningen University & Research; University of Rostock; Consejo Superior de Investigaciones Cientificas (CSIC); University of Oviedo; Max Planck Society; University of Leeds; Universidade Federal do Rio Grande do Sul; Martin Luther University Halle Wittenberg; Santa Clara University; Shahjalal University of Science & Technology (SUST); University of Tartu; Tartu University Institute of Ecology & Earth Sciences; University of Belgrade; Italfarmaco; Roma Tre University; Italian Institute for Environmental Protection & Research (ISPRA); University of Bonn; Bulgarian Academy of Sciences; Sapienza University Rome; Natural Resources Canada; Canadian Forest Service; Great Lakes Forestry Centre; University of Illinois System; University of Illinois Urbana-Champaign; Ghent University; Ghent University; University of Quebec; University Quebec Abitibi-Temiscamingue; University of Gottingen; University of Basque Country; University of Gothenburg; University of Gothenburg; Free University of Bozen-Bolzano; Samara Federal Research Scientific Center; Swedish University of Agricultural Sciences; National Institute of Biology - Slovenia; Slovenian Academy of Sciences & Arts (SASA); University of Nova Gorica; Universidad Rey Juan Carlos; Czech University of Life Sciences Prague; Central Siberian Botanical Garden; Russian Academy of Sciences; University of Pecs; Saints Cyril & Methodius University of Skopje; Research Institute for Nature & Forest; Universidade Regional de Blumenau (FURB); University of Patras; Czech Academy of Sciences; Institute of Botany of the Czech Academy of Sciences; National Academy of Sciences Ukraine; M.G. Kholodny Institute of Botany, NAS of Ukraine; King Saud University; Egyptian Knowledge Bank (EKB); Damanhour University; University of Arizona; University of Minnesota System; University of Minnesota Twin Cities; University of Nottingham; University of Hamburg; Avignon Universite; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); UDICE-French Research Universities; Aix-Marseille Universite; San Pablo CEU University; Universidad Privada Antonio Guillermo Urrelo; University of Mazandaran; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Russian Academy of Sciences; Samara Federal Research Scientific Center; UiT The Arctic University of Tromso; University of Bergen; University of Adelaide; Universidad de Chile; Wageningen University & Research; Egyptian Knowledge Bank (EKB); Tanta University; CIRAD; CIRAD; Universite de Montpellier; University of Munster; University of Gottingen; Ghent University; Royal Museum for Central Africa; Transylvania University of Brasov; Wageningen University & Research; University of Bayreuth; University of Wroclaw; Norwegian Institute of Bioeconomy Research; Swiss Federal Institutes of Technology Domain; Swiss Federal Institute for Forest, Snow & Landscape Research; Karabuk University; University of Zurich; Russian Academy of Sciences; Pushchino Scientific Center for Biological Research (PSCBI) of the Russian Academy of Sciences; Central Siberian Botanical Garden; Russian Academy of Sciences; University of Gottingen; Max Planck Society; National Academy of Sciences Ukraine; M.G. Kholodny Institute of Botany, NAS of Ukraine; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Centre for Ecological Research; Naturalis Biodiversity Center; Leiden University; Leiden University - Excl LUMC; Universidade Regional de Blumenau (FURB); Peking University; Russian Academy of Sciences; Komarov Botanical Institute, Russian Academy of Sciences; Russian Academy of Sciences; Samara Federal Research Scientific Center; Russian Academy of Sciences; Tobolsk Complex Scientific Station Ural Branch of the Russian Academy of Sciences; Leipzig University; Russian Academy of Sciences; Institute of Biology Ufa Research Centre RAS; Aarhus University; Universidad Nacional de San Antonio Abad del Cusco; Murdoch University; Stellenbosch University; Royal Botanic Gardens, Kew; Centre National de la Recherche Scientifique (CNRS); CIRAD; INRAE; Universite de Montpellier; University of Mazandaran; University of Vienna; Polish Academy of Sciences; University of Opole; National Academy of Sciences Ukraine; M.G. Kholodny Institute of Botany, NAS of Ukraine; Universidade Federal do Rio Grande do Sul; Universidad de Concepcion; University of North Carolina; University of North Carolina Chapel Hill; Autonomous University of Barcelona; Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Consejo Superior de Investigaciones Cientificas (CSIC); Centro de Investigacion Ecologica y Aplicaciones Forestales (CREAF); Universitat de Vic - Universitat Central de Catalunya (UVic-UCC); University of Barcelona; Czech Academy of Sciences; Institute of Botany of the Czech Academy of Sciences; Universidad de los Andes (Colombia); Kazan Federal University; Nature Research Center - Lithuania; University of Hamburg; Namibia University of Science & Technology; Babes Bolyai University from Cluj; University of Latvia; University of Bayreuth; Microsoft; Slovak Academy of Sciences; National Institute of Biology - Slovenia; Slovenian Academy of Sciences & Arts (SASA); Charles University Prague; University of Zagreb; University of Adelaide; Consejo Superior de Investigaciones Cientificas (CSIC); University of Valencia; CSIC-GV-UV - Centro de Investigaciones sobre Desertificacion (CIDE); University of Zagreb; Aarhus University; Yunnan University; Aristotle University of Thessaloniki; University of Erlangen Nuremberg; University of Perugia; UDICE-French Research Universities; Universite PSL; Ecole Pratique des Hautes Etudes (EPHE); Institut Agro; Montpellier SupAgro; CIRAD; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite Paul-Valery; Universite de Montpellier; University of Oulu; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ); Leuphana University Luneburg; University of Alberta; University of Alaska System; University of Alaska Fairbanks; University of Wisconsin System; University of Wisconsin Madison; Hainan University; Senckenberg Gesellschaft fur Naturforschung (SGN); Technische Universitat Dresden; University of Minnesota System; University of Minnesota Twin Cities; Swiss Federal Institutes of Technology Domain; Swiss Federal Institute for Forest, Snow & Landscape Research; Institute of Biochemistry & Genetics of Ufa Science Centre of the RAS; Russian Academy of Sciences Sabatini, FM (corresponding author), German Ctr Integrat Biodivers Res iDiv, Puschstr 4, D-04103 Leipzig, Germany. francesco.sabatini@botanik.uni-halle.de Tang, Cindy/AAC-3343-2022; Stančić, Zvjezdana/HFZ-9949-2022; Biurrun, Idoia/L-7590-2014; Zobel, Martin/H-1336-2015; Wohlgemuth, Thomas/E-6588-2016; Kessler, Michael/A-3605-2009; Apostolova, Iva/AET-0431-2022; Penuelas, Josep/D-9704-2011; Korolyuk, Andrey/I-7831-2018; Haider, Sylvia/M-2990-2014; Samimi, Cyrus/H-8085-2012; Šilc, Urban/ABC-8488-2021; Kreft, Holger/A-4736-2008; Chepinoga, Victor/N-3666-2017; Gholizadeh, Hamid/AGO-1658-2022; Pärtel, Meelis/D-5493-2012; Vibrans, Alexander C./C-3089-2013; Indreica, Adrian/ABD-4876-2020; Sibik, Jozef/F-1717-2011; РАН, СамНЦ/AAO-2584-2020; Kuzemko, Anna/B-3068-2017; Tsiripidis, Ioannis/AAA-4465-2021; Hérault, Bruno/B-2765-2011; Attorre, Fabio/H-5351-2012; Ćušterevska, Renata/AAA-3533-2022; Field, Richard/A-8093-2010; Rivas-Torres, Gonzalo/AAZ-3146-2021; Bjorkman, Anne D./H-2211-2016; Sabatini, Francesco Maria/H-3290-2019; Dengler, Jürgen/B-6604-2008; El-sheikh, Mohamed/AAO-4652-2020; Lenoir, Jonathan/AAE-8441-2019; Wang, Huifang E/AAY-2120-2021; Kattge, Jens/J-8283-2016; Wesche, Karsten/ABG-7299-2021; Ćušterevska, Renata/AFN-7305-2022; Enquist, Brian J/B-6436-2008; Pillar, Valerio/B-9872-2008; Yamalov, Sergey M/P-3847-2015; Pauchard, Anibal/B-5034-2008; Kreft, Holger/AAY-7573-2020; Jandt, Ute/S-7886-2016; Svenning, Jens-Christian/C-8977-2012; Gutierrez, Alvaro G./AAC-9385-2019; Bonari, Gianmaria/L-2245-2016; Karger, Dirk Nikolaus/ABD-5181-2021; Mahecha, Miguel D/F-2443-2010; Venanzoni, Roberto/F-3703-2014; Virtanen, Risto/G-1810-2010; Chytrý, Milan/J-4954-2012; Müller, Jonas/GQA-3371-2022; Phillips, Oliver/A-1523-2011; Aavik, Tsipe/H-1277-2015; Onyshchenko, Viktor/N-1901-2016; Sperandii, Marta Gaia/K-5617-2019; Lysenko, Tatiana M/N-3629-2017; Homeier, Jürgen/G-1662-2011; Overbeck, Gerhard/D-8695-2013; Bruelheide, Helge/G-3907-2013; Tarek, Hattab/K-2992-2014; Jansen, Florian/J-5463-2015 Tang, Cindy/0000-0003-3789-6771; Stančić, Zvjezdana/0000-0002-6124-811X; Biurrun, Idoia/0000-0002-1454-0433; Zobel, Martin/0000-0001-7957-6704; Wohlgemuth, Thomas/0000-0002-4623-0894; Kessler, Michael/0000-0003-4612-9937; Apostolova, Iva/0000-0002-2701-175X; Penuelas, Josep/0000-0002-7215-0150; Haider, Sylvia/0000-0002-2966-0534; Samimi, Cyrus/0000-0001-7001-7893; Šilc, Urban/0000-0002-3052-699X; Kreft, Holger/0000-0003-4471-8236; Chepinoga, Victor/0000-0003-3809-7453; Pärtel, Meelis/0000-0002-5874-0138; Vibrans, Alexander C./0000-0002-8789-5833; Sibik, Jozef/0000-0002-5949-862X; Kuzemko, Anna/0000-0002-9425-2756; Tsiripidis, Ioannis/0000-0001-9373-676X; Hérault, Bruno/0000-0002-6950-7286; Attorre, Fabio/0000-0002-7744-2195; Ćušterevska, Renata/0000-0002-3849-6983; Field, Richard/0000-0003-2613-2688; Bjorkman, Anne D./0000-0003-2174-7800; Sabatini, Francesco Maria/0000-0002-7202-7697; Dengler, Jürgen/0000-0003-3221-660X; Lenoir, Jonathan/0000-0003-0638-9582; Wang, Huifang E/0000-0002-4060-8649; Kattge, Jens/0000-0002-1022-8469; Wesche, Karsten/0000-0002-0088-6492; Enquist, Brian J/0000-0002-6124-7096; Pillar, Valerio/0000-0001-6408-2891; Pauchard, Anibal/0000-0003-1284-3163; Kreft, Holger/0000-0003-4471-8236; Jandt, Ute/0000-0002-3177-3669; Svenning, Jens-Christian/0000-0002-3415-0862; Gutierrez, Alvaro G./0000-0001-8928-3198; Bonari, Gianmaria/0000-0002-5574-6067; Mahecha, Miguel D/0000-0003-3031-613X; Venanzoni, Roberto/0000-0002-7768-0468; Virtanen, Risto/0000-0002-8295-8217; Chytrý, Milan/0000-0002-8122-3075; Phillips, Oliver/0000-0002-8993-6168; Aavik, Tsipe/0000-0001-5232-3950; Onyshchenko, Viktor/0000-0001-9079-7241; Sperandii, Marta Gaia/0000-0002-2507-5928; Lysenko, Tatiana M/0000-0001-6688-1590; Homeier, Jürgen/0000-0001-5676-3267; Overbeck, Gerhard/0000-0002-8716-5136; Bruelheide, Helge/0000-0003-3135-0356; Nowak, Arkadiusz/0000-0001-8638-0208; De Bie, Els/0000-0001-7679-743X; Peet, Robert/0000-0003-2823-6587; Tarek, Hattab/0000-0002-1420-5758; Arnst, Elise/0000-0003-2388-7428; Jansen, Florian/0000-0002-0331-5185; Willner, Wolfgang/0000-0003-1591-8386; Noroozi, Jalil/0000-0003-4124-2359; Skvorc, Zeljko/0000-0002-2848-1454; Andrey, Korolyuk/0000-0003-4646-4698 Agence Nationale de la Recherche [ANR-07-BDIV-0006, ANR-07-BDIV-0008, ANR-07-BDIV-0010]; European Research Council [291585, ERC-SyG-2013-610028 IMBALANCE-P]; Villum Fonden [16549]; Deutsche Forschungsgemeinschaft [DFG FZT 118, 202548816, DFG Ho3296-2, DFG Ho3296-4, DFG VA 749/1-1, DFG VA 749/4-1, DFG WE 2601/3-1 3-2 4-1 4-2]; Narodowe Centrum Nauki [2017/25/B/NZ8/00572]; Latvia grant [AAP2016/B041//Zd2016/AZ03]; NSF [DEB-0415383]; Horizon 2020 Framework Programme [640176]; U.S. National Science Foundation [DBI-0213794, DBI-9905838]; Grantova Agentura Ceske Republiky [19-28491X]; German Centre for Integrative Biodiversity Research [50170649_7]; Fundacion BBVA [BIOCON08_044]; Akademie Ved Ceske Republiky [RVO 67985939]; Spanish Research Agency [AEI/10.13039/501100011033]; National Research, Development and Innovation Office, Hungar [PD--12399]; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung [20BD21_184131]; Basque Government [640176]; Russian Foundation for Basic Research [16-04-00747a]; Brazil's National Council of Scientific and Technological Development [307689/2014-0, 310022/2015-0]; Volkswagen Foundation [AZ I/81 976] Agence Nationale de la Recherche(French National Research Agency (ANR)); European Research Council(European Research Council (ERC)European Commission); Villum Fonden(Villum Fonden); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); Narodowe Centrum Nauki(National Science Centre, Poland); Latvia grant; NSF(National Science Foundation (NSF)); Horizon 2020 Framework Programme; U.S. National Science Foundation(National Science Foundation (NSF)); Grantova Agentura Ceske Republiky(Grant Agency of the Czech Republic); German Centre for Integrative Biodiversity Research; Fundacion BBVA(BBVA Foundation); Akademie Ved Ceske Republiky; Spanish Research Agency(Spanish Government); National Research, Development and Innovation Office, Hungar; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung(Austrian Science Fund (FWF)); Basque Government(Basque Government); Russian Foundation for Basic Research(Russian Foundation for Basic Research (RFBR)); Brazil's National Council of Scientific and Technological Development; Volkswagen Foundation(Volkswagen) Agence Nationale de la Recherche, Grant/Award Number: ANR-07-BDIV-0006, ANR-07-BDIV-0008 and ANR-07-BDIV-0010; H2020 European Research Council, Grant/Award Number: ERC Advanced Grant 291585 T-FORCES and ERC-SyG-2013-610028 IMBALANCE-P; Villum Fonden, Grant/Award Number: 16549; Deutsche Forschungsgemeinschaft, Grant/Award Number: DFG FZT 118, 202548816, DFG Ho3296-2, DFG Ho3296-4, DFG VA 749/1-1, DFG VA 749/4-1 and DDFG WE 2601/3-1 3-2 4-1 4-2; Narodowe Centrum Nauki, Grant/Award Number: 2017/25/B/NZ8/00572; Latvia grant, Grant/Award Number: AAP2016/B041//Zd2016/AZ03; NSF, Grant/Award Number: DEB-0415383; Horizon 2020 Framework Programme, Grant/Award Number: 640176; U.S. National Science Foundation, Grant/Award Number: DBI-0213794 and DBI-9905838; Grantova Agentura Ceske Republiky, Grant/Award Number: 19-28491X; German Centre for Integrative Biodiversity Research, Grant/Award Number: 50170649_#7; Fundacion BBVA, Grant/Award Number: BIOCON08_044; Akademie Ved Ceske Republiky, Grant/Award Number: RVO 67985939; Spanish Research Agency, Grant/Award Number: AEI/10.13039/501100011033; National Research, Development and Innovation Office, Hungar, Grant/Award Number: PD--12399; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung, Grant/Award Number: 20BD21_184131; Basque Government, Grant/Award Number: 640176; Russian Foundation for Basic Research, Grant/Award Number: 16-04-00747a; Brazil's National Council of Scientific and Technological Development, Grant/Award Number: 307689/2014-0 and 310022/2015-0; Volkswagen Foundation, Grant/Award Number: AZ I/81 976 133 18 19 20 98 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1466-822X 1466-8238 GLOBAL ECOL BIOGEOGR Glob. Ecol. Biogeogr. SEP 2021.0 30.0 9 1740 1764 10.1111/geb.13346 0.0 JUN 2021 25 Ecology; Geography, Physical Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Physical Geography UA4FB Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000664540200001 0
J Deng, ZH; Chen, QH; Zhang, LY; Zong, Y; Zhou, KL; Fu, ZC Deng, Zhihua; Chen, Qihong; Zhang, Liyan; Zong, Yi; Zhou, Keliang; Fu, Zhichao Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems APPLIED ENERGY English Article Proton exchange membrane fuel cell system; Data driven modeling; Nonlinear subspace identification method; Linear parameter varying system; Kernel method PREDICTION; IDENTIFICATION; LPV Proton exchange membrane fuel cell systems are widely used to drive vehicles. It is indispensable to establish accurate and simple control oriented model of the system. However, this control oriented model is difficult to build due to the characteristic of strongly nonlinear, coupled and time - varying in proton exchange membrane fuel cell systems. To solve this problem, nonlinear subspace identification method is proposed to discover linear parameter varying model for the proton exchange membrane fuel cell system. The modeling process can be divided into two stages. In the first stage, algebraic transformation method is proposed to transform the nonlinear model to linear parameter varying model. In the second stage, kernel method based subspace identification is presented to identify parameter matrices of the proton exchange membrane fuel cell system. The established model is demonstrated by a 50 kW on board proton exchange membrane fuel cell system under real driving condition. The results show that mean absolute percent error and root mean squared error are as low as 9.6941e - 06 and 0.0957, respectively. Therefore, the control oriented model perfectly agrees with real data. Compared with the prediction error method and recursive neural network nonlinear auto - regressive moving average model with exogenous method, modeling accuracy and speed of the proposed method are the best. Moreover, linear control theory can be employed to design controller based on the linear parameter varying model, which greatly simplifies controller design for proton exchange membrane fuel cell systems. [Deng, Zhihua; Chen, Qihong; Zhang, Liyan; Zhou, Keliang; Fu, Zhichao] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China; [Zong, Yi] Tech Univ Denmark, Ctr Elect Power & Energy, DK-4000 Roskilde, Denmark; [Fu, Zhichao] CSIC, Wuhan Inst Marine Elect Prop, Wuhan 430064, Peoples R China Wuhan University of Technology; Technical University of Denmark; China Shipbuilding Industry Corporation Chen, QH; Zhang, LY (corresponding author), Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China. chenqh@whut.edu.cn; zlywhut@whut.edu.cn chen, qian/HKE-2403-2023; Chen, QH/GXH-3158-2022; Zong, Yi/G-8555-2017 Zong, Yi/0000-0003-1262-3558 National Natural Science Foundation of China [61673306, 61673305]; National Key Research and Development Program of China [2019YFB1504703]; Danish Agency for Science and Higher Education [8073-00026B]; Fundamental Research Funds for the Central Universities [2020-YB-034] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Danish Agency for Science and Higher Education; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors would like to thank the support of National Natural Science Foundation of China under grant 61673306, 61673305, National Key Research and Development Program of China under grant 2019YFB1504703, and the project granted by the Danish Agency for Science and Higher Education (No. 8073-00026B). This work is also supported by the Fundamental Research Funds for the Central Universities under grant 2020-YB-034. 22 8 8 11 46 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy NOV 1 2020.0 277.0 115540 10.1016/j.apenergy.2020.115540 0.0 8 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering OC8GC 2023-03-23 WOS:000579393800019 0
J Luan, F; Xu, X; Liu, HT; Cordeiro, MNDS Luan, Feng; Xu, Xuan; Liu, Huitao; Dias Soeiro Cordeiro, Maria Natalia Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach CHEMOSPHERE English Article Mixture toxicity; Non-polar narcotic chemical; Quantitative structure-activity relationships; Multiple linear regression method (MLR); Radial basis function neural network (RBFNN) QSPR MODELS; MULTIOBJECTIVE OPTIMIZATION; VALIDATION; HYDROPHOBICITY; DESCRIPTORS; PARAMETERS; TOXICOLOGY; PROFILES; EFFICACY; METRICS Environmental contaminants are frequently encountered as mixtures, and research on mixture toxicity is a hot topic until now. In the present study, the mixture toxicity of non-polar narcotic chemical was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNNs) from molecular descriptors that are calculated and be defined as composite descriptors according to the fractional concentrations of the mixture components. The statistical parameters provided by the MLR model were R-2 = 0.9512, RMS = 0.3792, F = 1402.214 and LOOq(2) = 0.9462 for the training set, and R-2 = 0.9453, RMS = 03458, F = 276.671 and q(ext)(2) = 0.9450 for the external test set. The RBFNN model gave the following statistical results, namely: R-2 = 0.9779, RMS = 0.2498, F = 3188.202 and LOOq(2) = 0.9746 for the training set, and R-2 = 0.9763, RMS = 0.2358, F = 660.631 and q(ext)(2) = 0.9745, for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction mixture toxicity of non-polar narcotic chemicals. The RBFNN model gave even improved results. In addition, epsilon(LUMO+1) (the energy of the second lowest unoccupied molecular orbital) and PPSA (total charge weighted partial positively surface area) were found to have high correlation with the mixture toxicity. (c) 2012 Elsevier Ltd. All rights reserved. [Luan, Feng; Xu, Xuan; Liu, Huitao] Yantai Univ, Dept Appl Chem, Yantai 264005, Peoples R China; [Dias Soeiro Cordeiro, Maria Natalia] Univ Porto, Fac Sci, Dept Chem & Biochem, REQUIMTE, P-4169007 Oporto, Portugal Yantai University; Universidade do Porto Cordeiro, MNDS (corresponding author), Univ Porto, Fac Sci, Dept Chem & Biochem, REQUIMTE, P-4169007 Oporto, Portugal. fluan@sina.com; ncordeir@fc.up.pt Cordeiro, Maria Natália D. S./A-7413-2012; Luan, Feng/K-5522-2013 Cordeiro, Maria Natália D. S./0000-0003-3375-8670; Luan, Feng/0000-0003-0254-2973 Natural Science Foundation of shandong Province [ZR2012BM016]; Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) [SFRH/BPD/63666/2009]; European Social Found; Fundação para a Ciência e a Tecnologia [SFRH/BPD/63666/2009] Funding Source: FCT Natural Science Foundation of shandong Province(Natural Science Foundation of Shandong Province); Portuguese Fundacao para a Ciencia e a Tecnologia (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); European Social Found(European Social Fund (ESF)); Fundação para a Ciência e a Tecnologia This work was financially supported by the Natural Science Foundation of shandong Province (Grant No. ZR2012BM016). F. Luan also acknowledges the Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) for the grant SFRH/BPD/63666/2009 co-financed by the European Social Found. 38 22 22 0 73 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-6535 CHEMOSPHERE Chemosphere FEB 2013.0 90.0 6 1980 1986 10.1016/j.chemosphere.2012.10.065 0.0 7 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 092PB 23177708.0 2023-03-23 WOS:000315133400030 0
J Wei, JQ; Li, XY; Liu, L; Christensen, TR; Jiang, ZY; Ma, YJ; Wu, XC; Yao, HY; Lopez-Blanco, E Wei, Junqi; Li, Xiaoyan; Liu, Lei; Christensen, Torben Rojle; Jiang, Zhiyun; Ma, Yujun; Wu, Xiuchen; Yao, Hongyun; Lopez-Blanco, Efren Radiation, soil water content, and temperature effects on carbon cycling in an alpine swamp meadow of the northeastern Qinghai-Tibetan Plateau BIOGEOSCIENCES English Article ECOSYSTEM CO2 EXCHANGE; DIOXIDE EXCHANGE; EDDY-COVARIANCE; TERRESTRIAL ECOSYSTEMS; NITROGEN ADDITION; SANJIANG PLAIN; WETLAND; FLUXES; CLIMATE; RESPIRATION Predicted intensified climate warming will likely alter the ecosystem net carbon (C) uptake of the Qinghai-Tibetan Plateau (QTP). Variations in C sink-source responses to climate warming have been linked to water availability; however, the mechanisms by which net C uptake responds to soil water content in saturated swamp meadow ecosystems remain unclear. To explore how soil moisture and other environmental drivers modulate net C uptake in the QTP, field measurements were conducted using the eddy covariance technique in 2014, 2015, 2017, and 2018. The alpine swamp meadow presented in this study was a persistent and strong C sink of CO2 (-168.0 +/- 62.5 g C m(-2) yr(-1), average +/- standard deviation) across the entire 4-year study period. A random forest machine-learning analysis suggested that the diurnal and seasonal variations of net ecosystem exchange (NEE) and gross primary productivity (GPP) were regulated by temperature and net radiation. Ecosystem respiration (Re), however, was found mainly regulated by the variability of soil water content (SWC) at different temporal aggregations, followed by temperature, the second contributing driver. We further explored how Re is controlled by nearly saturated soil moisture and temperature comparing two different periods featuring almost identical temperatures and significant differences on SWC and vice versa. Our data suggest that, despite the relatively abundant water supply, periods with a substantial decrease in SWC or increase in temperature produced higher Re and therefore weakened the C sink strength. Our results reveal that nearly saturated soil conditions during the growing seasons can help maintain lower ecosystem respiration rates and thus enhance the overall C sequestration capacity in this alpine swamp meadow. We argue that soil respiration and subsequent ecosystem C sink magnitude in alpine swamp meadows could likely be affected by future changes in soil hydrological conditions caused by permafrost degradation or accelerated thawing-freezing cycling due to climate warming. [Wei, Junqi; Li, Xiaoyan; Wu, Xiuchen; Yao, Hongyun] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; [Wei, Junqi; Li, Xiaoyan; Wu, Xiuchen; Yao, Hongyun] Beijing Normal Univ, Fac Geog Sci, Sch Nat Resources, Beijing 100875, Peoples R China; [Li, Xiaoyan] Peoples Govt Qinghai Prov, Acad Plateau Sci & Sustainabil, Xining 810008, Peoples R China; [Li, Xiaoyan] Beijing Normal Univ, Xining 810008, Peoples R China; [Li, Xiaoyan] Qinghai Normal Univ, Xining 810008, Peoples R China; [Li, Xiaoyan] Qinghai Normal Univ, Key Lab Tibetan Plateau Land Surface Proc & Ecol, Minist Educ, Xining 810016, Peoples R China; [Liu, Lei] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Liu, Lei] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Christensen, Torben Rojle; Lopez-Blanco, Efren] Aarhus Univ, Arctic Res Ctr, Dept Ecosci, DK-4000 Roskilde, Denmark; [Christensen, Torben Rojle] Univ Oulu, Oulanka Res Stn, Oulu, Finland; [Jiang, Zhiyun] South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China; [Ma, Yujun] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China; [Lopez-Blanco, Efren] Greenland Inst Nat Resources, Dept Environm & Minerals, Nuuk 3900, Greenland Beijing Normal University; Beijing Normal University; Beijing Normal University; Qinghai Normal University; Qinghai Normal University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Aarhus University; University of Oulu; South China Normal University; Sun Yat Sen University; Greenland Institute of Natural Resources Li, XY (corresponding author), Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China.;Li, XY (corresponding author), Beijing Normal Univ, Fac Geog Sci, Sch Nat Resources, Beijing 100875, Peoples R China.;Li, XY (corresponding author), Peoples Govt Qinghai Prov, Acad Plateau Sci & Sustainabil, Xining 810008, Peoples R China.;Li, XY (corresponding author), Beijing Normal Univ, Xining 810008, Peoples R China.;Li, XY (corresponding author), Qinghai Normal Univ, Xining 810008, Peoples R China.;Li, XY (corresponding author), Qinghai Normal Univ, Key Lab Tibetan Plateau Land Surface Proc & Ecol, Minist Educ, Xining 810016, Peoples R China. xyli@bnu.edu.cn Li, Xiaoyan/K-8544-2012; López-Blanco, Efrén/ABA-9934-2020 Li, Xiaoyan/0000-0002-7454-7821; López-Blanco, Efrén/0000-0002-3796-8408; Christensen, Torben R./0000-0002-4917-148X National Natural Science Foundation of China [NSFC 41730854, 41971029]; Second Tibetan Plateau Scientific Expedition and Research Program [STEP 2019QZKK0306]; Strategic Priority Research Program of Chinese Academy of Sciences [XDA20100102]; State Key Laboratory of Earth Surface Processes and Resource Ecology; China Scholarship Council [201906040130]; Faculty of Technical Sciences from Aarhus University; Greenland Research Council - Danish Program for Arctic Research [80.35] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Second Tibetan Plateau Scientific Expedition and Research Program; Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); State Key Laboratory of Earth Surface Processes and Resource Ecology; China Scholarship Council(China Scholarship Council); Faculty of Technical Sciences from Aarhus University; Greenland Research Council - Danish Program for Arctic Research The study was financially supported by the National Natural Science Foundation of China (NSFC 41730854 and 41971029), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP 2019QZKK0306), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100102), and projects from the State Key Laboratory of Earth Surface Processes and Resource Ecology. This study also received financial support from the China Scholarship Council (no. 201906040130) and the Faculty of Technical Sciences from Aarhus University. Additionally, Efren Lopez-Blanco was funded by the Greenland Research Council, grant number 80.35, financed by the Danish Program for Arctic Research. 80 2 2 24 77 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1726-4170 1726-4189 BIOGEOSCIENCES Biogeosciences FEB 10 2022.0 19.0 3 861 875 10.5194/bg-19-861-2022 0.0 15 Ecology; Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology ZD4DZ gold, Green Submitted 2023-03-23 WOS:000758151400001 0
J Yang, M; Huang, LC; Huang, HP; Tang, H; Zhang, N; Yang, HM; Wu, JH; Mu, F Yang, Meng; Huang, Lichao; Huang, Haiping; Tang, Hui; Zhang, Nan; Yang, Huanming; Wu, Jihong; Mu, Feng Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution NUCLEIC ACIDS RESEARCH English Article SUSCEPTIBILITY LOCI; WIDE ASSOCIATION; VARIANTS; EXPRESSION; VERTEBRATE; DATABASE; ALLELES; DISEASE; RARE Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution. [Yang, Meng; Huang, Lichao; Huang, Haiping; Tang, Hui; Zhang, Nan; Mu, Feng] BGI Shenzhen, MGI, Shenzhen 518083, Peoples R China; [Yang, Meng] Univ Copenhagen, Dept Biol, DK-2200 Copenhagen, Denmark; [Wu, Jihong] Fudan Univ, Eye & ENT Hosp, Dept Ophthalmol, Shanghai Med Coll, Shanghai, Peoples R China; [Wu, Jihong] Sci & Technol Commiss Shanghai Municipal, Shanghai Key Lab Visual Impairment & Restorat, Shanghai, Peoples R China; [Wu, Jihong] Fudan Univ, Key Lab Myopia, Chinese Acad Med Sci, Natl Hlth Commiss, Shanghai, Peoples R China; [Yang, Huanming] BGI Shenzhen, Shenzhen 518083, Peoples R China; [Yang, Huanming] BGI Shenzhen, Guangdong Prov Academician Workstn BGI Synthet Ge, Shenzhen 518120, Peoples R China Beijing Genomics Institute (BGI); University of Copenhagen; Fudan University; Science & Technology Commission of Shanghai Municipality (STCSM); Chinese Academy of Medical Sciences - Peking Union Medical College; Fudan University; Beijing Genomics Institute (BGI); Beijing Genomics Institute (BGI) Yang, M; Mu, F (corresponding author), BGI Shenzhen, MGI, Shenzhen 518083, Peoples R China.;Yang, M (corresponding author), Univ Copenhagen, Dept Biol, DK-2200 Copenhagen, Denmark.;Wu, JH (corresponding author), Fudan Univ, Eye & ENT Hosp, Dept Ophthalmol, Shanghai Med Coll, Shanghai, Peoples R China.;Wu, JH (corresponding author), Sci & Technol Commiss Shanghai Municipal, Shanghai Key Lab Visual Impairment & Restorat, Shanghai, Peoples R China.;Wu, JH (corresponding author), Fudan Univ, Key Lab Myopia, Chinese Acad Med Sci, Natl Hlth Commiss, Shanghai, Peoples R China. yangmeng1@genomics.cn; jihongwu@fdu.edu.cn; mufeng@mgi-tech.com Huang, Lichao/0000-0001-9319-821X Guangdong Provincial Academician Workstation of BGI Synthetic Genomics [2017B090904014]; Program of Shanghai Academic Research Leader [20XD1401100]; Program for Outstanding Medical Academic Leader [2019LJ01] Guangdong Provincial Academician Workstation of BGI Synthetic Genomics; Program of Shanghai Academic Research Leader; Program for Outstanding Medical Academic Leader This research is supported by Guangdong Provincial Academician Workstation of BGI Synthetic Genomics [2017B090904014]; Jihong Wu is supported by Program of Shanghai Academic Research Leader [20XD1401100] and Program for Outstanding Medical Academic Leader [2019LJ01]. 85 1 1 13 17 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0305-1048 1362-4962 NUCLEIC ACIDS RES Nucleic Acids Res. AUG 12 2022.0 50.0 14 10.1093/nar/gkac326 0.0 MAY 2022 19 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 3S5XZ 35536244.0 Green Submitted, Green Accepted, gold 2023-03-23 WOS:000793030700001 0
J Wang, ZC; Wang, QQ; Duan, S; Zhang, YL; Zhao, LM; Zhang, SJ; Hao, LSQ; Li, Y; Wang, XD; Wang, CS; Zhang, N; Bachert, C; Zhang, L; Lan, F Wang, Zaichuan; Wang, Qiqi; Duan, Su; Zhang, Yuling; Zhao, Limin; Zhang, Shujian; Hao, Liusiqi; Li, Yan; Wang, Xiangdong; Wang, Chenshuo; Zhang, Nan; Bachert, Claus; Zhang, Luo; Lan, Feng A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion FRONTIERS IN IMMUNOLOGY English Article chronic rhinosinusitis with nasal polyps; diagnostic model; nasal secretion; IL-5; blood eosinophils CHRONIC RHINOSINUSITIS; ENTEROTOXINS; SURGERY BackgroundPredicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable. MethodsTo optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2(low) and Th2(high)CRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2(high)CRSwNP using NasSecs biomarkers. ResultsConsidering the area under receiver operating characteristic curve (AUC) for IL-5 in NasSec, nasal pack in polyvinyl alcohol (PVA) was superior to other materials for NasSec collection. When Th2(low) and Th2(high)CRSwNP clusters were defined, logistic regression and decision tree model for prediction of Th2(high)CRSwNP demonstrated high AUCs values of 0.92 and 0.90 respectively using biomarkers of NasSecs. Consequently, the pre-pruned decision tree model; based on the levels of IL-5 in NasSec (<= 15.04 pg/mL), blood eosinophil count (<= 0.475*10(9)/L) and absence of comorbid asthma; was chosen to define Th2(low)CRSwNP from Th2(high)CRSwNP for routine clinical use. ConclusionsTaken together, a decision tree model based on a combination of NasSec biomarkers and clinical features can accurately define type 2 CRSwNP patients and therefore may be of benefit to patients in receiving appropriate therapies in daily clinical practice. [Wang, Zaichuan; Wang, Qiqi; Duan, Su; Zhang, Yuling; Zhao, Limin; Zhang, Shujian; Hao, Liusiqi; Li, Yan; Wang, Xiangdong; Wang, Chenshuo; Zhang, Luo; Lan, Feng] Beijing Inst Otolaryngol, Beijing Key Lab Nasal Dis, Beijing, Peoples R China; [Wang, Zaichuan; Zhang, Yuling; Zhao, Limin; Zhang, Shujian; Hao, Liusiqi; Wang, Xiangdong; Wang, Chenshuo; Zhang, Luo] Capital Med Univ, Beijing TongRen Hosp, Dept Otolaryngol Head & Neck Surg, Beijing, Peoples R China; [Duan, Su; Zhang, Luo] Capital Med Univ, Beijing TongRen Hosp, Dept Allergy, Beijing, Peoples R China; [Zhang, Nan; Bachert, Claus] Univ Ghent, Dept Otorhinolaryngol, Upper Airways Res Lab, Ghent, Belgium Capital Medical University; Capital Medical University; Ghent University Zhang, L; Lan, F (corresponding author), Beijing Inst Otolaryngol, Beijing Key Lab Nasal Dis, Beijing, Peoples R China.;Zhang, L (corresponding author), Capital Med Univ, Beijing TongRen Hosp, Dept Otolaryngol Head & Neck Surg, Beijing, Peoples R China.;Zhang, L (corresponding author), Capital Med Univ, Beijing TongRen Hosp, Dept Allergy, Beijing, Peoples R China. dr.luozhang@139.com; fenglanent@126.com 32 0 0 5 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-3224 FRONT IMMUNOL Front. Immunol. DEC 21 2022.0 13.0 1054201 10.3389/fimmu.2022.1054201 0.0 12 Immunology Science Citation Index Expanded (SCI-EXPANDED) Immunology 7N1YA 36618395.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000907140600001 0
J Cheng, YY; Zhou, KF; Wang, JL; Cui, SC; Yan, JN; De Maeyer, P; van de Voorde, T Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; Yan, Jining; De Maeyer, Philippe; Van de Voorde, Tim Regional metal pollution risk assessment based on a long short-term memory model: A case study of the South Altai Mountain mining area, China JOURNAL OF CLEANER PRODUCTION English Article Copper mines; Heavy metal pollution; Risk assessment; LSTM model; Southern Altai mountains HEAVY-METALS; MINERAL-RESOURCES; ECOLOGICAL RISK; SEWAGE IRRIGATION; HEALTH-RISK; SOIL; CONTAMINATION; ECOSYSTEM; IMPACT; IDENTIFICATION The discharge of wastewater and waste rock in mining production activities is a significant hidden cause of soil heavy metal pollution. The accumulation of heavy metals in soil occurs through a variety of processes, and exposure to these metals can permanently damage the human body. Due to multiple factors, such as the for-mation causes, sources, and distribution trends of heavy metals in mineral resources, reasonably applying different machine learning methods to monitor and evaluate heavy metal pollution remains challenging. In this paper, we choose the copper mining area in the southern Altai Mountains of China as the study area, and 19 different types of spatial data are uniformly managed in a distributed database to improve monitoring efficiency. Furthermore, we propose a heavy metal pollution evaluation framework based on a stacked long short-term memory (LSTM) model, which considers spatial data correlations and extracts spatial clustering features. In-formation is screened through state updating in the framework, the short-term memory features of long se-quences are extracted, and an effective prediction model is established. The results show that 26 of the 31 mining occurrences in the study area are in moderate-and high-pollution-risk grids, suggesting that the spatial distri-bution of copper mines is consistent with the predicted spatial distribution of pollution risk. Overall, these results show that using the optimized stacked LSTM model to integrate multisource geological features and mine the internal rules of feature information has a positive effect on improving the risk assessment of heavy metal pollution. [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao] Xinjiang Key Lab Mineral Resources & Digital Geol, Urumqi 830011, Peoples R China; [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao] Chinese Acad Sci, Xinjiang Res Ctr Mineral Resources, Urumqi 830011, Peoples R China; [Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; De Maeyer, Philippe; Van de Voorde, Tim] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Univ Ghent, Dept Geog, B-9000 Ghent, Belgium; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China; [Cheng, Yinyi; De Maeyer, Philippe; Van de Voorde, Tim] Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium; [Yan, Jining] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ghent University; Ghent University; China University of Geosciences Wang, JL (corresponding author), Xinjiang Key Lab Mineral Resources & Digital Geol, Urumqi 830011, Peoples R China. wangjinlin@ms.xjb.ac.cn Van de Voorde, Tim/0000-0002-9324-5087 Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China [2021A03001-3]; Key Project of National Natural Science Foundation of China -Xinjiang Joint Fund [U1803241]; Chinese Academy of Sciences President's International Fellowship Initiative (PIFI Grant) [2017VCA0002]; Xinjiang Laboratory of Mineral Resources and Digital Geology of the Chinese Academy of Sciences Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China; Key Project of National Natural Science Foundation of China -Xinjiang Joint Fund; Chinese Academy of Sciences President's International Fellowship Initiative (PIFI Grant); Xinjiang Laboratory of Mineral Resources and Digital Geology of the Chinese Academy of Sciences This study was funded by the Science and Technology Major Project of Xinjiang Uygur Autonomous Region, China (2021A03001-3), the Key Project of National Natural Science Foundation of China -Xinjiang Joint Fund (No. U1803241), and Chinese Academy of Sciences President's International Fellowship Initiative (PIFI Grant No. 2017VCA0002). We would like to thank Xinjiang Laboratory of Mineral Resources and Digital Geology of the Chinese Academy of Sciences for guidance and full support. 80 0 0 22 22 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. DEC 15 2022.0 379.0 2 134755 10.1016/j.jclepro.2022.134755 0.0 OCT 2022 13 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 5X6DN 2023-03-23 WOS:000878689300002 0
J Liu, TY; Wang, Y; Yang, YK; Sun, M; Fan, WH; Bunger, C; Wu, C Liu, Tianyu; Wang, Yu; Yang, Yukang; Sun, Ming; Fan, Wenhui; Bunger, Cody; Wu, Cheng A multi-scale keypoint estimation network with self-supervision for spinal curvature assessment of idiopathic scoliosis from the imperfect dataset ARTIFICIAL INTELLIGENCE IN MEDICINE English Article Spinal curvature assessment; Keypoint estimation; Self-supervision; Imperfect dataset SEGMENTATION Idiopathic scoliosis (IS) is a common lifetime disease, which exhibits an obvious deformity of spinal curvature to seriously affect heart and lung function. Accurate radiographic assessment of spinal curvature is vitally important for the clinical diagnosis and treatment planning of idiopathic scoliosis. Deep learning algorithms have been widely adopted to the medical image analysis with the remarkable advancement in computer vision. The automated methods can improve the efficiency of clinical diagnosis to relieve the burden of doctors, which have advantage in dealing with the tedious and repetitive tasks. However, existing methods usually require sufficiently large training datasets with strict annotation, which are costly and laborious especially for medical images. Moreover, the medical images of serious IS always contain the blurry and occlusive parts, which would make the accurate and robust estimation of the spinal curvature more difficult. In this paper, a dot annotation approach is presented to train the spinal curvature assessment model, rather than using strict annotation of IS X-ray images. We develop a multi-scale keypoint estimation network to reduce the requirement for large training datasets, in which the Squeeze-and-Excitation (SE) blocks are incorporated to improve the representational capacity of the model. Then, a self-supervision module is designed to alleviate the blurry and occlusive problem, and we use the two-view radiographic assessments of IS to generate a 3D spinal curvature. Finally, extensive experiments are conducted on a collected clinical dataset, in which we obtain 81.5 AP and the average E-d between the predicted keypoints and the ground truths is 0.43, making an improvement over the mainstream approaches. [Liu, Tianyu; Yang, Yukang; Fan, Wenhui; Wu, Cheng] Tsinghua Univ, Dept Automat, Beijing, Peoples R China; [Wang, Yu] Peking Univ First Hosp, Dept Orthopaed, Beijing, Peoples R China; [Sun, Ming; Bunger, Cody] Aarhus Univ Hosp, Dept Orthopaed, Aarhus, Denmark Tsinghua University; Aarhus University Wang, Y (corresponding author), Peking Univ First Hosp, Dept Orthopaed, Beijing, Peoples R China. wangyuspine@pkufh.com Yang, Yukang/0000-0001-6807-3052; Sun, Ming/0000-0002-7763-447X Interdisciplinary Clinical Research Project of Peking University First Hospital [2019CR41]; PKU-Baidu Fund [2020BD042]; Health Research Foundation of Central Denmark Interdisciplinary Clinical Research Project of Peking University First Hospital; PKU-Baidu Fund; Health Research Foundation of Central Denmark This research was partially supported by Interdisciplinary Clinical Research Project of Peking University First Hospital (No. 2019CR41) and PKU-Baidu Fund (No. 2020BD042). The authors are particularly grateful for the support of Health Research Foundation of Central Denmark 40 1 1 3 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0933-3657 1873-2860 ARTIF INTELL MED Artif. Intell. Med. MAR 2022.0 125.0 102235 10.1016/j.artmed.2021.102235 0.0 FEB 2022 10 Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Medical Informatics 2Q1NR 35241256.0 2023-03-23 WOS:000820196100001 0
C Pouriyeh, S; Allahyari, M; Cheng, G; Arabnia, HR; Atzori, M; Kochut, K IEEE Pouriyeh, Seyedamin; Allahyari, Mehdi; Cheng, Gong; Arabnia, Hamid Reza; Atzori, Maurizio; Kochut, Krys R-LDA: Profiling RDF datasets using Knowledge-based Topic Modeling 2019 13TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) IEEE International Conference on Semantic Computing English Proceedings Paper 13th IEEE International Conference on Semantic Computing (ICSC) JAN 30-FEB 01, 2019 Newport Beach, CA IEEE,IEEE Comp Soc RDF profiling; Topic modeling; Ontology Recently, Linked Open Data (LOD) has experienced an exponential growth via publishing huge volume of datasets on the Web. This vast amount of information needs to be searched, queried, and interlinked easier than before. It is recommended that potential data publishers provide recapitulative information about their datasets published on the Web. This information, which functions as metadata, will facilitate those datasets to be discovered easily. As it is not always the case, we are faced with a large number of datasets without a proper profile, leading to a high demand for different data profiling techniques. In this paper, we focus on RDF dataset profiling utilizing unsupervised machine learning techniques, namely knowledge based topic modeling. We also investigate the use of Wikipedia categories to represent the topics identified in an RDF dataset. In the proposed model, we extract a number of representative topics for an RDF dataset and annotate them with Wikipedia categories. The union of the assigned categories serves as a profile of the dataset, in a sense that it provides an overall characterization of the content of the dataset. [Pouriyeh, Seyedamin] Kennesaw State Univ, Dept Informat Technol, Marietta, GA 30060 USA; [Allahyari, Mehdi] Georgia Southern Univ, Dept Comp Sci, Statesboro, GA USA; [Cheng, Gong] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China; [Arabnia, Hamid Reza; Kochut, Krys] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA; [Atzori, Maurizio] Univ Cagliari, Math CS Dept, Cagliari, Italy University System of Georgia; Kennesaw State University; University System of Georgia; Georgia Southern University; Nanjing University; University System of Georgia; University of Georgia; University of Cagliari Pouriyeh, S (corresponding author), Kennesaw State Univ, Dept Informat Technol, Marietta, GA 30060 USA. spouriye@kennesaw.edu; mallahyari@georgiasouthern.edu; gcheng@nju.edu.cn; hra@uga.edu; atzori@unica.it; kkochut@uga.edu Pouriyeh, Seyedamin/0000-0002-5746-2914 11 2 2 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2325-6516 978-1-5386-6783-5 IEEE INT C SEMANT CO 2019.0 146 149 10.1109/ICSC.2019.00033 0.0 4 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM6QG 2023-03-23 WOS:000467270600024 0
J Zhang, ZG; Csanyi, G; Alfe, D; Zhang, YG; Li, J; Liu, J Zhang, Zhigang; Csanyi, Gabor; Alfe, Dario; Zhang, Yigang; Li, Juan; Liu, Jin Free Energies of Fe-O-Si Ternary Liquids at High Temperatures and Pressures: Implications for the Evolution of the Earth's Core Composition GEOPHYSICAL RESEARCH LETTERS English Article Earth's core; evolution; free energy; first-principles; iron-alloying liquids; machine learning ACCRETION; DIFFERENTIATION; BOUNDARY; SILICON; STATE; NI Solubility of oxygen and silicon in the iron-alloying liquids is important for constraining the core composition over the Earth's history. In this study, we systematically simulated the free energies of Fe-O-Si ternary liquids from 3000 K, 55 GPa to 6000 K, and 330 GPa. We found that temperature and pressure have remarkable influences on the free energies, and the nonideality of mixing is important for the chemical potentials even at high temperatures. Equilibrating with SiO2 phase, Fe-O-Si liquids significantly enhance the solubility of Si and O simultaneously with increasing temperature. Considering the secular cooling of the Earth's core, this leads to high precipitation rates of SiO2 once it was saturated, which would efficiently drive ancient geodynamo. If the evolution of Earth's core started from an oxygen-poor composition, later incorporations of oxygen seem to be needed to reach a core composition compatible with geophysical observations. [Zhang, Zhigang; Zhang, Yigang; Li, Juan] Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China; [Zhang, Zhigang; Zhang, Yigang; Li, Juan] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Key Lab Computat Geodynam, Beijing, Peoples R China; [Csanyi, Gabor] Univ Cambridge, Dept Engn, Cambridge, England; [Alfe, Dario] UCL, Dept Earth Sci, London, England; [Alfe, Dario] UCL, London Ctr Nanotechnol, London, England; [Alfe, Dario] Univ Napoli Federico II, Dipartimento Fis Ettore Pancini, Naples, Italy; [Liu, Jin] Ctr High Pressure Sci & Technol Adv Res, Beijing, Peoples R China; [Liu, Jin] CAS Ctr Excellence Deep Earth Sci, Guangzhou, Peoples R China Chinese Academy of Sciences; Institute of Geology & Geophysics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Cambridge; University of London; University College London; University of London; University College London; University of Naples Federico II Zhang, ZG (corresponding author), Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China.;Zhang, ZG (corresponding author), Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Key Lab Computat Geodynam, Beijing, Peoples R China. zgzhang@mail.iggcas.ac.cn Alfe`, Dario/C-1521-2008; Liu, Jin/C-6558-2011; zhang, zhi gang/E-5331-2014 Alfe`, Dario/0000-0002-9741-8678; Liu, Jin/0000-0002-1670-8199; zhang, zhi gang/0000-0001-8666-1026 Strategic Priority Research Program (B) of Chinese Academy of Sciences [XDB18000000]; key research program of IGGCAS [IGGCAS-201904] Strategic Priority Research Program (B) of Chinese Academy of Sciences; key research program of IGGCAS The authors are grateful to two anonymous reviewers for their constructive comments and to Steven Jacobsen for handling our manuscript. This work was supported by the Strategic Priority Research Program (B) of Chinese Academy of Sciences (XDB18000000) and the key research program of IGGCAS (IGGCAS-201904). Simulations were carried out on the computational facilities in the Computer Simulation Lab of IGGCAS and Tianhe-2 at the National Supercomputer Center of China (NSCC) in Guangzhou. 52 3 3 3 12 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. FEB 28 2022.0 49.0 4 e2021GL096749 10.1029/2021GL096749 0.0 11 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology ZO3WU Green Published 2023-03-23 WOS:000765659300045 0
J Feng, QH; Hao, QH; Sbert, M; Yi, YG; Wei, Y; Dai, JY Feng, Qinghe; Hao, Qiaohong; Sbert, Mateu; Yi, Yugen; Wei, Ying; Dai, Jiangyan Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval SENSORS English Article visual sensor; image retrieval; human visual system; local parallel cross pattern CLASSIFICATION; FUSION Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in parallel and cross manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results. [Feng, Qinghe; Wei, Ying] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China; [Hao, Qiaohong; Sbert, Mateu] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China; [Sbert, Mateu] Univ Girona, Inst Informat & Applicat, Girona 17017, Spain; [Yi, Yugen] Jiangxi Normal Univ, Sch Software, Nanchang 330022, Jiangxi, Peoples R China; [Dai, Jiangyan] Weifang Univ, Sch Comp Engn, Weifang 261061, Peoples R China Northeastern University - China; Tianjin University; Universitat de Girona; Jiangxi Normal University; Weifang University Wei, Y (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China.;Dai, JY (corresponding author), Weifang Univ, Sch Comp Engn, Weifang 261061, Peoples R China. 1510377@stu.neu.edu.cn; qiaohonghao@gmail.com; mateusbert@mac.com; yiyg510@jxnu.edu.cn; weiying@ise.neu.edu.cn; daijy@wfu.edu.cn Sbert, Mateu/G-6711-2011 Sbert, Mateu/0000-0003-2164-6858; Feng, Qinghe/0000-0002-9124-761X; Yi, Yugen/0000-0001-9828-0319 National Nature Science Foundation of China [61871106]; Fundamental Research Grant Scheme for the Central Universities [130204003]; Project of Shandong Province Higher Educational Science and Technology Program [J16LN68]; Shandong Province Natural Science Foundation [ZR2017QF011]; National Key Technology Research and Development Programme of the Ministry of Science and Technology of China [2014BAI17B02] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Grant Scheme for the Central Universities; Project of Shandong Province Higher Educational Science and Technology Program; Shandong Province Natural Science Foundation(Natural Science Foundation of Shandong Province); National Key Technology Research and Development Programme of the Ministry of Science and Technology of China This research was funded by the National Nature Science Foundation of China, grant number [61871106], the Fundamental Research Grant Scheme for the Central Universities, grant number [130204003], the Project of Shandong Province Higher Educational Science and Technology Program, grant number [J16LN68], the Shandong Province Natural Science Foundation, grant number [ZR2017QF011] and the National Key Technology Research and Development Programme of the Ministry of Science and Technology of China, grant number [2014BAI17B02]. 48 7 7 1 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JAN 2 2019.0 19.0 2 315 10.3390/s19020315 0.0 21 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HL2WI 30646611.0 Green Submitted, Green Published, gold, Green Accepted 2023-03-23 WOS:000458569300098 0
J Santos, J; Pedersen, ML; Ulusoy, B; Weinell, CE; Pedersen, HC; Petersen, PM; Dam-Johansen, K; Pedersen, C Santos, Joaquim; Pedersen, Morten Lysdahlgaard; Ulusoy, Burak; Weinell, Claus Erik; Pedersen, Henrik Chresten; Petersen, Paul Michael; Dam-Johansen, Kim; Pedersen, Christian A Tunable Hyperspectral Imager for Detection and Quantification of Marine Biofouling on Coated Surfaces SENSORS English Article hyperspectral imaging; biofouling; spectral library; classification; fouling control coatings; led illumination; pixelwise calibration CORAL; CLASSIFICATION; CLASSIFIERS; CALIBRATION; MACHINE; ALGAE; TOOL Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces. [Santos, Joaquim; Pedersen, Henrik Chresten; Petersen, Paul Michael; Pedersen, Christian] Tech Univ Denmark, Dept Elect & Photon Engn DTU Electro, DK-4000 Roskilde, Denmark; [Pedersen, Morten Lysdahlgaard; Ulusoy, Burak; Weinell, Claus Erik; Dam-Johansen, Kim] Tech Univ Denmark, Dept Chem & Biochem Engn DTU Chem Engn, CoaST, DK-2800 Lyngby, Denmark; [Pedersen, Morten Lysdahlgaard; Ulusoy, Burak] Sino Danish Ctr Educ & Res, Beijing 100093, Peoples R China; [Pedersen, Morten Lysdahlgaard; Ulusoy, Burak] Univ Chinese Acad Sci, Sino Danish Coll, Beijing 100049, Peoples R China Technical University of Denmark; Technical University of Denmark; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Pedersen, C (corresponding author), Tech Univ Denmark, Dept Elect & Photon Engn DTU Electro, DK-4000 Roskilde, Denmark. chrp@dtu.dk Petersen, Paul Michael/E-8636-2010; Erik Weinell, Claus/Q-5922-2018 Santos, Joaquim/0000-0003-3855-2954; Pedersen, Christian/0000-0001-7238-489X; Lysdahlgaard Pedersen, Morten/0000-0003-1443-9826; Erik Weinell, Claus/0000-0001-7178-4808; Ulusoy, Burak/0000-0003-3951-3041; Dam-Johansen, Kim/0000-0003-1658-6176 Technical University of Denmark (DTU); Sino-DanishCenter (SDC); Hempel Foundation Technical University of Denmark (DTU); Sino-DanishCenter (SDC); Hempel Foundation This project was funded by the Technical University of Denmark (DTU), the Sino-DanishCenter (SDC). Furthermore, the project was also partially funded by the Hempel Foundation through financial support to DTU CoaST (The Hempel Foundation Coating Science and Technology Center). 63 0 0 4 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors SEP 2022.0 22.0 18 7074 10.3390/s22187074 0.0 27 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation 4R4QB 36146436.0 Green Published, gold 2023-03-23 WOS:000856749700001 0
J Keighobadi, J; Xu, B; Alfi, A; Arabkoohsar, A; Nazmara, G Keighobadi, Javad; Xu, Bin; Alfi, Alireza; Arabkoohsar, Ahmad; Nazmara, Gholamreza Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics ISA TRANSACTIONS English Article Compound learning control; FAT-based control; Prespecified control; Output constraint; Backstepping algorithm; Robotic manipulator NONLINEAR DISTURBANCE OBSERVER; ROBUST TRACKING CONTROL; TASK-SPACE CONTROL; SURFACE CONTROL; IMPEDANCE CONTROL; NEURAL-NETWORK; CONTROL DESIGN; SYSTEMS In the framework of the backstepping algorithm, this article proposes a new function approximation technique (FAT)-based compound learning control law for electrically-driven robotic manipulators with output constraint. The Fourier series expansion is adopted in the learning-based design to approximate unknown terms in the system description. The accuracy of FAT approximation is also studied by defining an identification error, which is derived from a serial-parallel identifier. Furthermore, the output constraint is taken into account by integrating the error transformation, the performance function and the dynamic surface control in a compact framework. Following this idea, new compound adaptation laws are then constructed. The proposed compound learning controller confirms that all the signals of the overall system are uniformly ultimately bounded, ensuring the tracking error within the predefined bounds during operation. Different simulation scenarios applied to a robotic manipulator with motor dynamics illustrate the capability of the control algorithm.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved. [Keighobadi, Javad; Alfi, Alireza; Nazmara, Gholamreza] Shahrood Univ Technol, Fac Elect Engn, Shahrood, Iran; [Xu, Bin] Northwestern Polytech Univ, Sch Automation, Xian, Shaanxi, Peoples R China; [Arabkoohsar, Ahmad] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark Shahrood University of Technology; Northwestern Polytechnical University; Aalborg University Alfi, A (corresponding author), Shahrood Univ Technol, Fac Elect Engn, Shahrood, Iran. javad_keighobadi@yahoo.com; binxu@nwpu.edu.cn; a_alfi@shahroodut.ac.ir; ahm@et.aau.dk; rezanazmara89@gmail.com keighobadi, javad/0000-0001-6474-5499; Arabkoohsar, Ahmad/0000-0002-8753-5432 51 3 3 6 15 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0019-0578 1879-2022 ISA T ISA Trans. DEC 2022.0 131.0 246 263 10.1016/j.isatra.2022.04.027 0.0 DEC 2022 18 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation 7L6FZ 35525606.0 2023-03-23 WOS:000906060400001 0
J Ali, MS; Gunasekaran, N; Zhu, QX Ali, M. Syed; Gunasekaran, N.; Zhu, Quanxin State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control FUZZY SETS AND SYSTEMS English Article Lyapunov method; Linear matrix inequality; Sampled-data control; T-S fuzzy neural network; Time-varying delay TIME-VARYING DELAYS; DEPENDENT STABILITY-CRITERIA; H-INFINITY CONTROL; ASYMPTOTIC STABILITY; CONTROL-SYSTEMS; STABILIZATION; DISCRETE In this paper, we are concerned with the problem of state estimation of Takagi-Sugeno (T-S) fuzzy delayed neural networks with Markovian jumping parameters via sampled-data control. Based on the fuzzy-model-based control approach and linear matrix inequality (LMI) technique, several novel conditions are derived to guarantee the stability of the suggested system. A new class of Lyapunov functional, which contains integral terms, is constructed to derive delay-dependent stability criteria. Some characteristics of the sampling input delay are proposed based on the input delay approach. Numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results. (C) 2016 Elsevier B.V. All rights reserved. [Ali, M. Syed; Gunasekaran, N.] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India; [Zhu, Quanxin] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany Thiruvalluvar University; Nanjing Normal University; Nanjing Normal University; University of Bielefeld Zhu, QX (corresponding author), Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China.;Zhu, QX (corresponding author), Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China. syedgru@gmail.com; gunasmaths@gmail.com; zqx22@126.com Syed Ali, M/K-9858-2014; GUNASEKARAN, NALLAPPAN/ADF-4590-2022; , NALLAPPAN/AAA-4406-2020; Zhu, Quanxin/X-3230-2018; Ali, Syed/AAF-1750-2019 GUNASEKARAN, NALLAPPAN/0000-0003-0375-7917; , NALLAPPAN/0000-0003-0375-7917; Zhu, Quanxin/0000-0003-3130-4923; Ali, Syed/0000-0003-3747-3082 Department of Science and Technology (DST) [SR/FTP/MS-039/2011]; National Natural Science Foundation of China [61374080]; Alexander von Humboldt Foundation of Germany [CHN/1163390]; Priority Academic Program Development of Jiangsu Higher Education Institutions Department of Science and Technology (DST)(Department of Science & Technology (India)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation of Germany(Alexander von Humboldt Foundation); Priority Academic Program Development of Jiangsu Higher Education Institutions This work was jointly supported by the Department of Science and Technology (DST), under research project No. SR/FTP/MS-039/2011, the National Natural Science Foundation of China (61374080), the Alexander von Humboldt Foundation of Germany (Fellowship CHN/1163390), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. 52 122 123 2 60 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0165-0114 1872-6801 FUZZY SET SYST Fuzzy Sets Syst. JAN 1 2017.0 306.0 87 104 10.1016/j.fss.2016.03.012 0.0 18 Computer Science, Theory & Methods; Mathematics, Applied; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics ED0PM 2023-03-23 WOS:000388544400005 0
J Yang, Q; Hou, Z; Wang, Y; Zhao, Y; Delgado, J Yang, Q.; Hou, Z.; Wang, Y.; Zhao, Y.; Delgado, J. A comparative study of shallow groundwater level simulation with WA-ANN and ITS model in a coastal island of south China ARABIAN JOURNAL OF GEOSCIENCES English Article Groundwater table depth; Time series analysis; WA-ANN; ITS NEURAL-NETWORKS Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA-ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R (2)), Nash-Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA-ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA-ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies. [Yang, Q.; Hou, Z.; Wang, Y.; Zhao, Y.] Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China; [Delgado, J.] Univ A Coruna, Escuela Ingn Caminos, La Coruna 15192, Spain Jilin University; Universidade da Coruna Yang, Q (corresponding author), Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China. qyang@udc.es Delgado Martin, Jordi/H-7819-2015 Delgado Martin, Jordi/0000-0002-8261-8087 National Natural Science Foundation of China (NSFC) [41402202]; Specialized Research Fund for the Doctoral Program of Higher Education [3M213BU81425]; Seed Science Foundation from Ministry of Education of China [450060488107] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)); Seed Science Foundation from Ministry of Education of China This research was financially supported by the National Natural Science Foundation of China (NSFC) with grant number 41402202, Specialized Research Fund for the Doctoral Program of Higher Education (3M213BU81425), and Seed Science Foundation from Ministry of Education of China (450060488107). Special gratitude is given to editor Abdullah and Pradhan for their efforts on treating and evaluating the work, and the valuable comments of the other two anonymous reviewers are also greatly acknowledged. 34 11 11 2 20 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1866-7511 1866-7538 ARAB J GEOSCI Arab. J. Geosci. SEP 2015.0 8.0 9 6583 6593 10.1007/s12517-014-1706-2 0.0 11 Geosciences, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Geology CP9PP 2023-03-23 WOS:000360227000010 0
C Bonner, S; Brennan, J; Kureshi, I; Theodoropoulos, G; McGough, AS; Obara, B Abe, N; Liu, H; Pu, C; Hu, X; Ahmed, N; Qiao, M; Song, Y; Kossmann, D; Liu, B; Lee, K; Tang, J; He, J; Saltz, J Bonner, Stephen; Brennan, John; Kureshi, Ibad; Theodoropoulos, Georgios; McGough, Andrew Stephen; Obara, Boguslaw Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) IEEE International Conference on Big Data English Proceedings Paper IEEE International Conference on Big Data (Big Data) DEC 10-13, 2018 Seattle, WA IEEE,IEEE Comp Soc,Expedia Grp,Baidu,Squirrel AI Learning,Ankura,Springer graph representation learning; dynamic graphs Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However, many of the machine learning models designed to capture knowledge about the structure of these graphs ignore this rich temporal information when creating representations of the graph. This results in models which do not perform well when used to make predictions about the future state of the graph - especially when the delta between time stamps is not small. In this work, we explore a novel training procedure and an associated unsupervised model which creates graph representations optimised to predict the future state of the graph. We make use of graph convolutional neural networks to encode the graph into a latent representation, which we then use to train our temporal offset reconstruction method, inspired by auto-encoders, to predict a later time point - multiple time steps into the future. Using our method, we demonstrate superior performance for the task of future link prediction compared with none-temporal state-of-the-art baselines. We show our approach to be capable of outperforming non-temporal baselines by 38% on a real world dataset. [Bonner, Stephen; Brennan, John; Obara, Boguslaw] Univ Durham, Dept Comp Sci, Durham, England; [McGough, Andrew Stephen] Newcastle Univ, Sch Comp, Newcastle, England; [Theodoropoulos, Georgios] SUSTech, Sch Comp Sci & Engn, Shenzhen, Peoples R China; [Kureshi, Ibad] InlecomSystems, Brussels, Belgium Durham University; Newcastle University - UK; Southern University of Science & Technology Bonner, S (corresponding author), Univ Durham, Dept Comp Sci, Durham, England. s.a.r.bonner@durham.ac.uk; j.d.brennan@durham.ac.uk; ibad.kureshi@durham.ac.uk; georgios@sustec.edu.cn; stephen.mcgough@newcastle.ac.uk; boguslaw.obara@durham.ac.uk Bonner, Stephen/0000-0001-6008-358X; McGough, Stephen/0000-0001-5626-0934; Obara, Boguslaw/0000-0003-4084-7778 NVIDIA Corporation; Engineering and Physical Sciences Research Council UK (EPSRC) NVIDIA Corporation; Engineering and Physical Sciences Research Council UK (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. Additionally we thank the Engineering and Physical Sciences Research Council UK (EPSRC) for funding. 36 8 9 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2639-1589 978-1-5386-5035-6 IEEE INT CONF BIG DA 2018.0 3737 3746 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BM7WO Green Submitted 2023-03-23 WOS:000468499303114 0
J Han, DL; Hou, HY; Wu, H; Lai, JHK Han, Donglin; Hou, Huiying (Cynthia); Wu, Hao; Lai, Joseph H. K. Modelling Tourists' Acceptance of Hotel Experience-Enhancement Smart Technologies SUSTAINABILITY English Article hotel; experience-enhancement smart technology; structural equation modelling; technology acceptance model; tourist SELF-SERVICE TECHNOLOGY; INFORMATION-TECHNOLOGY; BEHAVIORAL INTENTION; USER ACCEPTANCE; BIG DATA; SATISFACTION; PERCEPTIONS; APPS; TAM; EXTENSION Whereas the use of specific smart technologies in various sectors of the tourism industry has been under greater scrutiny in recent years, research that investigates tourists' acceptance of smart technologies applied as a whole to hotels is largely underexplored. To address this shortfall, a study with a focus on tourists' acceptance of experience-enhancement smart technologies has been conducted. A technology acceptance model based conceptual framework was developed, followed by a series of interviews with the managerial staff of ten hotels in Hong Kong that adopt experience-enhancement smart technologies. Based on the interview findings, a questionnaire was designed for use in a survey in which data were collected face-to-face from 312 tourists. Structural equation modelling was utilised to reveal the interrelationships between nine technology acceptance parameters. This study contributes knowledge to both real-world practice and research in tourism management. [Han, Donglin] Renmin Univ China, Sch Int Studies, Beijing 100872, Peoples R China; [Hou, Huiying (Cynthia)] Delft Univ Technol, Dept Management Built Environm, NL-2628 CD Delft, Netherlands; [Wu, Hao] Univ Melbourne, Fac Architecture Bldg & Planning, Melbourne, Vic 3010, Australia; [Lai, Joseph H. K.] Hong Kong Polytech Univ, Fac Construct & Environm, Dept Bldg Serv Engn, Hong Kong, Peoples R China Renmin University of China; Delft University of Technology; University of Melbourne; Hong Kong Polytechnic University Hou, HY (corresponding author), Delft Univ Technol, Dept Management Built Environm, NL-2628 CD Delft, Netherlands. donglin2006@gmail.com; h.hou@tudelft.nl; haow@unimelb.edu.au; bejlai@polyu.edu.hk Hou, Cynthia/AAV-2722-2021; Wu, Hao/A-4706-2019 Hou, Cynthia/0000-0002-4215-2673; Wu, Hao/0000-0002-3221-2118; HAN, Donglin/0000-0003-0982-8384; Lai, Joseph/0000-0003-0526-4331 84 2 2 15 62 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability APR 2021.0 13.0 8 4462 10.3390/su13084462 0.0 19 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology RU7MB Green Published, gold 2023-03-23 WOS:000645326700001 0
C Schweitzer, P; Chu, RB; Jiang, YY; Bonnet, C; Berviller, Y; Weber, S; Tisserand, E; Chabert, A IEEE Schweitzer, Patrick; Chu, Ruobo; Jiang, Yueyang; Bonnet, Christophe; Berviller, Yves; Weber, Serge; Tisserand, Etienne; Chabert, Alexis An algorithm for the detection of DC series-arc faults using a Convolution Neural Network 2022 IEEE 67TH HOLM CONFERENCE ON ELECTRICAL CONTACTS (HLM) Electrical Contacts-IEEE Holm Conference on Electrical Contacts English Proceedings Paper IEEE 67th Holm Conference on Electrical Contacts (HLM) OCT 23-26, 2022 Tampa, FL Inst Elect & Elect Engineers, Elect Packaging Soc,Heraeus Deutschland GmbH & Co. KG,Hindustan Platinum,Checon,DODUCO Contacts & Refining GmbH,NAECO,TE Connect,Umicore Elect Mat USA,Metalor Technologies USA,Allied Holdings,Molex,Auburn Univ,Beijing Univ Posts & Telecommunicat,CNRS,Deublin Co,Friedrich Alexander Univ,Georgia Inst Technol,Harbin Inst Technol,Intel Corp,Kazak British Tech Univ,Leibniz Inst Plasma Sci & Technol,Littelfuse,Mat Engn Ctr Saarland,NAECO,Nexteer Automot,Ostwestfalen Lippe Univ Appl Sci & Arts,RD Malucci Consulting,Siemens AG,Tech Hochschule Ostwestfalen Lippe, Ostwestfalen Lippe Univ Appl Sci & Arts,Timron Sci Consulting Inc,Univ Lorraine,Univ Southampton,Xian Jiaotong Univ,Xian Univ Technol Series-arcing fault; DC grid; fault detection; LeNet5 model Due to the highly dangerous nature of electrical failures, and more specifically electric arc faults, the detection of such problems has become absolutely necessary. In contrast to the majority of methods proposed in scientific literature that are based on frequency analysis, we propose a method of detection based on CNN models (LeNet5 - 28*28 and 64*64 images). For this method, the line current must first be recorded (the dataset is composed of about 11000 signatures with and without arc faults). Series-arc faults are produced in circuits that comprise a 270-Volt DC supply voltage and loads that are mainly resistive. The selected sections of the current signals are then transformed into a 2D matrix (images). Then, the network must then be trained, tested and validated using the dataset. The performance of this method must also be also studied and discussed. In fact, the results of the detection process must then be presented using a confusion matrix in order to provide more precise information. Experimental results show that the method that we are proposing can effectively detect arcing faults. [Schweitzer, Patrick; Jiang, Yueyang; Bonnet, Christophe; Berviller, Yves; Weber, Serge; Tisserand, Etienne; Chabert, Alexis] Univ Lorraine, CNRS, Inst Jean Lamour IJL, Nancy, France; [Chu, Ruobo] Huaqiao Univ, Lab Proc Monitoring & Syst Optimizat Mech & Elect, Quanzhou, Peoples R China; [Chabert, Alexis] IRT St Exupery, Toulouse, France Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Huaqiao University Schweitzer, P (corresponding author), Univ Lorraine, CNRS, Inst Jean Lamour IJL, Nancy, France. 12 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1062-6808 2158-9992 978-1-6654-5966-2 ELECTR CONTACT 2022.0 87 90 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BU4QY 2023-03-23 WOS:000904911700013 0
C Su, EZ; Cai, SQ; Li, PW; Xie, LH; Li, HZ IEEE Su, Enze; Cai, Siqi; Li, Peiwen; Xie, Longhan; Li, Haizhou Auditory Attention Detection with EEG Channel Attention 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol SPEECH; BRAIN; CHAOS Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 883% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin across all test cases. [Su, Enze; Cai, Siqi; Li, Peiwen; Xie, Longhan] South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou, Guangdong, Peoples R China; [Cai, Siqi; Li, Haizhou] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore; [Li, Haizhou] Univ Bremen, Machine Listening Lab, Bremen, Germany South China University of Technology; National University of Singapore; University of Bremen Xie, LH (corresponding author), South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou, Guangdong, Peoples R China. enzesu@hotmail.com; elesiqi@nus.edu.sg; lintean@qq.com; melhxie@scut.edu.cn; haizhou.li@nus.edu.sg Cai, Siqi/0000-0003-3282-9246 Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain) [A18A2b0046, A1687b0033]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy (University Allowance, EXC 2077, University of Bremen, Germany); National Natural Science Foundation of China [52075177] Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy (University Allowance, EXC 2077, University of Bremen, Germany)(German Research Foundation (DFG)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research work is supported by Programmatic Grant No. A18A2b0046 and A1687b0033 from the Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain). The work by Haizhou Li is also funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy (University Allowance, EXC 2077, University of Bremen, Germany). The work by Longhan Xie is also funded by the National Natural Science Foundation of China (Grant No. 52075177). 23 1 1 9 21 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 5804 5807 10.1109/EMBC46164.2021.9630508 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34892439.0 Green Submitted 2023-03-23 WOS:000760910505095 0
C Bing, ZS; Jiang, ZY; Cheng, L; Cai, CX; Huang, K; Knoll, A Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A IEEE Bing, Zhenshan; Jiang, Zhuangyi; Cheng, Long; Cai, Caixia; Huang, Kai; Knoll, Alois End to End Learning of a Multi-layered SNN Based on R-STDP for a Target Tracking Snake-like Robot 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 20-24, 2019 Montreal, CANADA Bosch,DJI,Kinova,Mercedes Benz,Samsung,Argo AI,Clearpath Robot,Element AI,Fetch Robot,Huawei,iRobot,KUKA,Quanser,SICK,Toyota Res Inst,Uber,Waymo,Zhejiang Lab,Amazon,Applanix,Cloudminds,Honda Res Inst,MathWorks,Ouster This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach. Since the key of R-STDP is to use rewards to modulate synapse strengthens, we first propose a general way to propagate the reward back through a multi-layered SNN. Upon the proposed approach, we build up an SNN controller that drives a snake-like robot for performing target tracking tasks. We demonstrate the practicability and advantage of our approach in terms of lateral tracking accuracy by comparing it to other state-of-the-art learning algorithms for SNNs based on R-STDP. [Bing, Zhenshan; Jiang, Zhuangyi; Knoll, Alois] Tech Univ Munich, Dept Informat, Munich, Germany; [Cheng, Long; Huang, Kai] Sun Yat Sen Univ, Minist Educ, Key Lab Machine Intelligence & Adv Comp, Guangzhou, Guangdong, Peoples R China; [Cheng, Long; Huang, Kai] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Cai, Caixia] ASTAR, Inst Infocomm Res I2R, Singapore, Singapore Technical University of Munich; Sun Yat Sen University; Sun Yat Sen University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R) Cai, CX (corresponding author), ASTAR, Inst Infocomm Res I2R, Singapore, Singapore. bing@in.tum.de; jiangz@in.tum.de; chenglong3@mail.sysu.edu.cn; cai_caixia@i2r.a-star.edu.sg; huangk36@mail.sysu.edu.cn; knoll@in.tum.de Knoll, Alois/AAN-8417-2021; Bing, Zhenshan/AAF-7965-2020 Knoll, Alois/0000-0003-4840-076X; European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) [720270]; Chinese Scholarship Council European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020); Chinese Scholarship Council(China Scholarship Council) The research leading to these results has received funding from the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under grant agreement No. 720270 (Human Brain Project, HBP) and the Chinese Scholarship Council. 20 14 14 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-5386-6026-3 IEEE INT CONF ROBOT 2019.0 9645 9651 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BO1CN Green Published 2023-03-23 WOS:000494942307013 0
C Vemuganti, S; Ozdagli, A; Liu, BD; Bajric, A; Moreu, F; Brake, MRW; Troyer, K Caicedo, J; Pakzad, S Vemuganti, Shreya; Ozdagli, Ali; Liu, Bideng; Bajric, Anela; Moreu, Fernando; Brake, Matthew R. W.; Troyer, Kevin Sensing and Rating of Vehicle-Railroad Bridge Collision DYNAMICS OF CIVIL STRUCTURES, VOL 2 Conference Proceedings of the Society for Experimental Mechanics Series English Proceedings Paper 35th IMAC Conference and Exposition on Structural Dynamics JAN 30-FEB 02, 2017 Garden Grove, CA Soc Expt Mech,IMAC Railway bridges; Structural health monitoring; Finite element model; Impact detection; Neural networks COMPOSITE STRUCTURES; TRANSFORM Overhead collisions of trucks with low-clearance railway bridges cause more than half of the railway traffic interruptions over bridges in the United States. Railroad owners are required to characterize the damage caused by such events and assess the safety of subsequent train crossings. However, damage characterization is currently visual (subjective) and becomes difficult in remote locations where collisions are not reported and inspections are not performed following the impact. To mitigate these shortcomings, this paper presents a new impact definition and rating strategy for automatically and remotely quantify damage. This research proposes an impact rating strategy based on the information that best describes the consequences of vehicle-railway bridge collisions. A series of representative impacts were simulated using numerical finite element models of a steel railway bridge. Railway owners provided information about the bridge and impact characterization based on railway industry experience. The resulting nonlinear dynamic responses were evaluated with the proposed rating strategy to assess the effect of these impacts. In addition, a neural network methodology was implemented on a simplified numerical model to identify spatial characteristics of the impact damage. [Vemuganti, Shreya; Ozdagli, Ali; Moreu, Fernando] Univ New Mexico, Dept Civil Engn, Albuquerque, NM 87131 USA; [Liu, Bideng] Inst Disaster Prevent, Dept Disaster Prevent Engn, Beijing, Peoples R China; [Bajric, Anela] Tech Univ Denmark, Dept Mech Engn, Lyngby, Denmark; [Brake, Matthew R. W.] Rice Univ, Dept Mech Engn, Houston, TX USA; [Troyer, Kevin] Sandia Natl Labs, Component Sci & Mech Dept, POB 5800, Albuquerque, NM 87185 USA University of New Mexico; Institute of Disaster Prevention; Technical University of Denmark; Rice University; United States Department of Energy (DOE); Sandia National Laboratories Vemuganti, S (corresponding author), Univ New Mexico, Dept Civil Engn, Albuquerque, NM 87131 USA. shreya.vemuganti@gmail.com Brake, Matthew RW/Q-6224-2017; /AAT-6112-2021 Brake, Matthew RW/0000-0002-9421-7790; Moreu, Fernando/0000-0002-7105-7843; Ozdagli, Ali/0000-0002-2708-6532; Vemuganti, Shreya/0000-0003-1270-6839; Liu, Bideng/0000-0001-8418-3913 U.S. Department of Energy's National Nuclear Security Administration [DE-AC04-94AL85000] U.S. Department of Energy's National Nuclear Security Administration(National Nuclear Security Administration) Developed at the Nonlinear Mechanics and Dynamics (NOMAD) Research Institute, which was organized by Sandia National Laboratories and hosted by University of New Mexico. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. 22 2 2 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2191-5644 2191-5652 978-3-319-54777-0; 978-3-319-54776-3 C PROC SOC EXP MECH 2017.0 227 234 10.1007/978-3-319-54777-0_28 0.0 8 Engineering, Civil; Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BK1SI Green Submitted 2023-03-23 WOS:000432208200028 0
J Satz, AL; Brunschweiger, A; Flanagan, ME; Gloger, A; Hansen, NJV; Kuai, L; Kunig, VBK; Lu, XJ; Madsen, D; Marcaurelle, LA; Mulrooney, C; O'Donovan, G; Sakata, S; Scheuermann, J Satz, Alexander L.; Brunschweiger, Andreas; Flanagan, Mark E.; Gloger, Andreas; Hansen, Nils J., V; Kuai, Letian; Kunig, Verena B. K.; Lu, Xiaojie; Madsen, Daniel; Marcaurelle, Lisa A.; Mulrooney, Carol; O'Donovan, Gary; Sakata, Sylvia; Scheuermann, Jorg DNA-encoded chemical libraries NATURE REVIEWS METHODS PRIMERS English Review SMALL-MOLECULE LIBRARIES; SOLID-PHASE SYNTHESIS; LIGAND-TARGET PAIRS; BINDING MODE; POTENT CLASS; DISCOVERY; IDENTIFICATION; SELECTION; TECHNOLOGY; RECEPTOR DNA-encoded chemical library (DECL) technology is used by the pharmaceutical industry to discover small molecules capable of modulating biologically relevant targets. DECL synthesis starts with an oligonucleotide that contains a chemical linker moiety, and proceeds through iterative cycles of DNA barcode elongation and chemical synthesis. DECL selections require little protein, minimal assay development and no specialized instrumentation. Parallel DECL selections can be easily conducted, making it possible to directly compare results across different conditions. The acquisition of building blocks is a large impediment when setting up a successful DECL platform. A potential solution is the sharing of building blocks between different labs, or the high-throughput parallel synthesis of novel building blocks. DNA-compatible reactions are required to join the building blocks together, and numerous academic labs have recently taken up this challenge. DECLs exist as unpurified mixtures, complicating data analysis. Machine learning may provide an improved ability to interrogate these data. DECL selections are largely limited to soluble purified proteins. However, progress has been made towards cell surface and in-cell selections. Publication guidelines are needed to better enable reproducibility; for example, the quantification of amplifiable DNA by quantitative PCR, and more complete datasets and building block lists, should be provided. [Satz, Alexander L.; Kuai, Letian] WuXi HitS, WuXi AppTec, Martinsried, Germany; [Brunschweiger, Andreas; Kunig, Verena B. K.] TU Dortmund Univ, Fac Chem & Chem Biol, Dortmund, Germany; [Flanagan, Mark E.; Sakata, Sylvia] Pfizer Inc, Med Design External Res Solut, San Diego, CA USA; [Gloger, Andreas; Scheuermann, Jorg] Swiss Fed Inst Technol, Dept Chem & Appl Biosci, Inst Pharmaceut Sci, Zurich, Switzerland; [Hansen, Nils J., V; Madsen, Daniel] Vipergen ApS, Copenhagen, Denmark; [Kunig, Verena B. K.] Serengen GmbH, Dortmund, Germany; [Lu, Xiaojie] Chinese Acad Sci, State Key Lab Drug Res, Shanghai, Peoples R China; [Marcaurelle, Lisa A.; Mulrooney, Carol] GlaxoSmithKline, Encoded Lib Technol, NCE Mol Discovery, Med Sci & Technol, Cambridge, MA USA; [O'Donovan, Gary] AbbVie, Drug Discovery Sci & Technol, Chicago, IL USA Dortmund University of Technology; Pfizer; Swiss Federal Institutes of Technology Domain; ETH Zurich; Chinese Academy of Sciences; GlaxoSmithKline; AbbVie Satz, AL (corresponding author), WuXi HitS, WuXi AppTec, Martinsried, Germany. alex.satz@wuxiapptec.com Scheuermann, Jorg/N-8693-2016 Scheuermann, Jorg/0000-0001-7746-4717; Gloger, Andreas/0000-0002-6148-098X; Marcaurelle, Lisa/0000-0003-1926-5343 115 8 8 8 8 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-8449 NAT REV METHOD PRIME Nat. Rev. Method. Prim. JAN 17 2022.0 2.0 1 3 10.1038/s43586-021-00084-5 0.0 17 Multidisciplinary Sciences Emerging Sources Citation Index (ESCI) Science & Technology - Other Topics 6M0GJ 2023-03-23 WOS:000888555700002 0
J Zhao, Q; Chen, MQ; Gu, ZH; Luan, SY; Zeng, HB; Chakrabory, S Zhao, Qingling; Chen, Mingqiang; Gu, Zonghua; Luan, Siyu; Zeng, Haibo; Chakrabory, Samarjit CAN Bus Intrusion Detection Based on Auxiliary Classifier GAN and Out-of-distribution Detection ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS English Article Automotive security; controller area network; intrusion detection; deep learning; GAN DETECTION SYSTEM; ANOMALY DETECTION; FD MESSAGES; NETWORKS The Controller Area Network (CAN) is a ubiquitous bus protocol present in the Electrical/Electronic (E/E) systems of almost all vehicles. It is vulnerable to a range of attacks once the attacker gains access to the bus through the vehicle's attack surface. We address the problem of Intrusion Detection on the CAN bus and present a series of methods based on two classifiers trained with Auxiliary Classifier Generative Adversarial Network (ACGAN) to detect and assign fine-grained labels to Known Attacks and also detect the Unknown Attack class in a dataset containing a mixture of (Normal + Known Attacks + Unknown Attack) messages. The most effective method is a cascaded two-stage classification architecture, with the multi-class Auxiliary Classifier in the first stage for classification of Normal and Known Attacks, passing Out-of-Distribution (OOD) samples to the binary Real-Fake Classifier in the second stage for detection of the Unknown Attack class. Performance evaluation demonstrates that our method achieves both high classification accuracy and low runtime overhead, making it suitable for deployment in the resource-constrained in-vehicle environment. [Zhao, Qingling; Chen, Mingqiang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens In, Minist Educ,PCA Lab, Nanjing 210094, Jiangsu, Peoples R China; [Zhao, Qingling; Chen, Mingqiang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Jiangsu, Peoples R China; [Gu, Zonghua; Luan, Siyu] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Zeng, Haibo] Virginia Tech, Dept Elect & Comp Engn, Blacksburg, VA 24061 USA; [Chakrabory, Samarjit] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA Nanjing University of Science & Technology; Nanjing University of Science & Technology; Umea University; Virginia Polytechnic Institute & State University; University of North Carolina; University of North Carolina Chapel Hill Zhao, Q (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens In, Minist Educ,PCA Lab, Nanjing 210094, Jiangsu, Peoples R China.;Zhao, Q (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Jiangsu, Peoples R China.;Gu, ZH (corresponding author), Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden. ada_zhao@njust.edu.cn; chenmingqiang@njust.edu.cn; zonghua.gu@umu.se; siyu.luan@umu.se; hbzeng@vt.edu; samarjit@cs.unc.edu Gu, Zonghua/0000-0003-4228-2774 NSFC [61902185]; Jiangsu Provincial NSF [BK20190448]; NSF [2038960] NSFC(National Natural Science Foundation of China (NSFC)); Jiangsu Provincial NSF; NSF(National Science Foundation (NSF)) Q. Zhao and M. Chen'sworkwas supported by NSFC Grant No. 61902185 and Jiangsu Provincial NSF Grant No. BK20190448. S. Chakraborty's work was supported by NSF Grant No. 2038960. 59 1 1 9 9 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1539-9087 1558-3465 ACM T EMBED COMPUT S ACM Trans. Embed. Comput. Syst. JUL 2022.0 21.0 4 45 10.1145/3540198 0.0 30 Computer Science, Hardware & Architecture; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5E8PF Green Published 2023-03-23 WOS:000865883500011 0
J Xiong, XJ; Feng, YC Xiong, Xinjia; Feng, Yongchang Process quality indices: new metrics for process quality capability with zero-loss baseline TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE English Article process capability; process quality indices Q(p); Q(pk); zero-loss; quality loss function; zero-defects The traditional process capability indices C-p /C-pk for evaluating process capability used in the current industry are based on the probability model of a process distribution, which makes it difficult for many processes whose actual output with non-normal distribution to apply these indices. In this paper, new process quality indices (PQI) Q(p) /Q(pk) are proposed for measuring the quality capability of the process. The new PQI are derived from Taguchi's quality loss function model and do not need to estimate the probability distribution of the process output. The traditional process capability indices C-p /C-pk are zero-defect indices, it focus on meeting the process specification requirements, while the new PQI Q(p) /Q(pk) are zero-loss indices, it focus on approaching the process target value. The construction of the new PQI is logical, simple, and practical. The PQI approach is more suitable for quality management in the Era of big data. [Xiong, Xinjia] Shanghai Gengyin Intelligent Technol Dev Ctr, Shanghai, Peoples R China; [Feng, Yongchang] TUV Rheinland eV, Acad & Life Care, Cologne, Germany Xiong, XJ (corresponding author), Shanghai Gengyin Intelligent Technol Dev Ctr, Shanghai, Peoples R China. singa.xiong@leanlead.com FENG, YONGCHANG/0000-0002-7696-6683; Xiong, Xinjia/0000-0002-7186-6376 14 0 0 0 7 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1478-3363 1478-3371 TOTAL QUAL MANAG BUS Total Qual. Manag. Bus. Excell. JUL 4 2022.0 33.0 9-10 975 993 10.1080/14783363.2021.1911634 0.0 APR 2021 19 Management Social Science Citation Index (SSCI) Business & Economics 2L0XR hybrid 2023-03-23 WOS:000642573900001 0
J Bontempelli, A; Giunchiglia, F; Passerini, A; Teso, S Bontempelli, Andrea; Giunchiglia, Fausto; Passerini, Andrea; Teso, Stefano Human-in-the-loop handling of knowledge drift DATA MINING AND KNOWLEDGE DISCOVERY English Article Concept drift; Hierarchical classification; Human-in-the-loop; Interactive machine learning NEURAL-NETWORKS We introduce and study knowledge drift (KD), a special form of concept drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, the introduction of a new is-a relation between two concepts might be confused with changes to those individual concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in human-in-the-loop applications like smart personal assistants the user knows what kind of drift occurred recently, if any. Motivated by this observation, we introduce trckd, a novel approach that combines two automated stages-drift detection and adaptation-with a new interactive disambiguation stage in which the user is asked to refine the machine's understanding of recently detected KD. In addition, trckd implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that, when the structure of the concept hierarchy drifts, a handful of queries to the user are often enough to substantially improve prediction performance on both synthetic and realistic data. [Bontempelli, Andrea; Giunchiglia, Fausto; Passerini, Andrea; Teso, Stefano] Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy; [Giunchiglia, Fausto] Jilin Univ, Changchun, Peoples R China University of Trento; Jilin University Bontempelli, A (corresponding author), Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy. andrea.bontempelli@unitn.it; fausto.giunchiglia@unitn.it; andrea.passerini@unitn.it; stefano.teso@unitn.it Bontempelli, Andrea/0000-0001-7037-5797; Teso, Stefano/0000-0002-2340-9461 European Union [823783]; DELPhi-DiscovEring Life Patterns project by the MIUR [1062]; TAILOR - EU Horizon 2020 research and innovation programme [952215] European Union(European Commission); DELPhi-DiscovEring Life Patterns project by the MIUR(Ministry of Education, Universities and Research (MIUR)); TAILOR - EU Horizon 2020 research and innovation programme We are grateful to the anonymous reviewers for helping us to improve the paper. The research of FG and AP has received funding from the European Union's Horizon 2020 FET Proactive project WeNet-The Internet of us, grant Agreement No. 823783. The research of AB and ST has received funding from the DELPhi-DiscovEring Life Patterns project funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) 2017-DD n. 1062 del 31.05.2019. The research of ST and AP was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No. 952215. 46 0 0 0 1 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1384-5810 1573-756X DATA MIN KNOWL DISC Data Min. Knowl. Discov. SEP 2022.0 36.0 5 1865 1884 10.1007/s10618-022-00845-0 0.0 JUL 2022 20 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5D1AT Green Submitted, Green Published, hybrid 2023-03-23 WOS:000833439800001 0
J Li, R; Napolitano, NR; Feng, HC; Li, R; Amaro, V; Xie, LH; Tortora, C; Bilicki, M; Brescia, M; Cavuoti, S; Radovich, M Li, Rui; Napolitano, Nicola R.; Feng, Haicheng; Li, Ran; Amaro, Valeria; Xie, Linghua; Tortora, Crescenzo; Bilicki, Maciej; Brescia, Massimo; Cavuoti, Stefano; Radovich, Mario Galaxy morphoto-Z with neural Networks (GaZNets) I. Optimized accuracy and outlier fraction from imaging and photometry ASTRONOMY & ASTROPHYSICS English Article surveys; galaxies: general; techniques: photometric; galaxies: photometry KILO-DEGREE SURVEY; DATA RELEASE; REDSHIFTS; MACHINE; SELECTION; CATALOG; DESIGN; SPACE; EVOLUTION; CLUSTERS Aims. In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new machine learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. Methods. As a first application of this tool, we estimate photo-z for a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on similar to 140 000 galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG_AUTO < 21) and low-redshift (z < 0.8) systems; however, we could use similar to 6500 galaxies in the range 0.8 < z < 3 to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the nine-band magnitudes and colors from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. Results. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD = 0.014 for lower redshift and NMAD = 0.041 for higher redshift galaxies) and a low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a similar to 10%-35% improvement in precision at different redshifts and a similar to 45% reduction in the fraction of outliers. We finally discuss the finding that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to 0.3%. [Li, Rui; Feng, Haicheng; Li, Ran] Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China; [Li, Rui; Li, Ran] Chinese Acad Sci, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China; [Napolitano, Nicola R.; Amaro, Valeria; Xie, Linghua] Sun Yat Sen Univ, Sch Phys & Astron, Zhuhai Campus,2 Daxue Rd, Zhuhai, Peoples R China; [Napolitano, Nicola R.] CSST Sci Ctr Guangdong Hong Kong Macau Great Bay, Zhuhai 519082, Peoples R China; [Feng, Haicheng] Chinese Acad Sci, Yunnan Observ, Kunming 650011, Yunnan, Peoples R China; [Tortora, Crescenzo; Brescia, Massimo; Cavuoti, Stefano] INAF, Osservatorio Astron Capodimonte, Salita Moiariello 16, I-80131 Naples, Italy; [Bilicki, Maciej] Polish Acad Sci, Ctr Theoret Phys, Al Lotnikow 32-46, PL-02668 Warsaw, Poland; [Cavuoti, Stefano] Ist Nazl Fis Nucl, Sez Napoli, Via Cinthia 9, I-80126 Naples, Italy; [Radovich, Mario] INAF, Osservatorio Astron Padova, Via Osservatorio 5, I-35122 Padua, Italy Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Sun Yat Sen University; Chinese Academy of Sciences; Yunnan Astronomical Observatory, NAOC, CAS; Istituto Nazionale Astrofisica (INAF); Polish Academy of Sciences; Center for Theoretical Physics - Polish Academy of Sciences; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale Astrofisica (INAF); University of Padua Li, R (corresponding author), Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China. liruiww@gmail.com Cavuoti, Stefano/B-4650-2017; Brescia, Massimo/J-2774-2019 Cavuoti, Stefano/0000-0002-3787-4196; Brescia, Massimo/0000-0001-9506-5680 National Nature Science Foundation of China [11988101, 11773032, 12022306]; China Manned Space Project [CMS-CSST-2021-B01, CMS-CSST-2021-A01]; K.C. Wong Education Foundation; One hundred top talent program of Sun Yat-sen University [71000-18841229]; Polish National Science Center [2020/38/E/ST9/00395, 2018/30/E/ST9/00698, 2018/31/G/ST9/03388, 2020/39/B/ST9/03494]; Polish Ministry of Science and Higher Education [DIR/WK/2018/12]; ESO Telescopes at the La Silla Paranal Observatory [177.A-3016, 177.A-3017, 177.A-3018, 179.A-2004] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Manned Space Project; K.C. Wong Education Foundation; One hundred top talent program of Sun Yat-sen University; Polish National Science Center; Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); ESO Telescopes at the La Silla Paranal Observatory Rui Li and Ran Li acknowledges the support of National Nature Science Foundation of China (Nos 11988101,11773032,12022306), the science research grants from the China Manned Space Project (No CMS-CSST-2021-B01,CMS-CSST-2021-A01) and the support from K.C. Wong Education Foundation. N.R.N. acknowledge financial support from the One hundred top talent program of Sun Yat-sen University grant N. 71000-18841229. M.B. is supported by the Polish National Science Center through grants no. 2020/38/E/ST9/00395, 2018/30/E/ST9/00698, 2018/31/G/ST9/03388 and 2020/39/B/ST9/03494, and by the Polish Ministry of Science and Higher Education through grant DIR/WK/2018/12. This work is based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017, 177.A-3018 and 179.A-2004, and on data products produced by the KiDS consortium. 94 0 0 1 1 EDP SCIENCES S A LES ULIS CEDEX A 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE 0004-6361 1432-0746 ASTRON ASTROPHYS Astron. Astrophys. OCT 11 2022.0 666.0 A85 10.1051/0004-6361/202244081 0.0 12 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics 5E7XA hybrid, Green Submitted 2023-03-23 WOS:000865835600012 0
J Ran, YH; Cheng, GD; Dong, YH; Hjort, J; Lovecraft, AL; Kang, SC; Tan, MB; Li, X Ran, Youhua; Cheng, Guodong; Dong, Yuanhong; Hjort, Jan; Lovecraft, Amy Lauren; Kang, Shichang; Tan, Meibao; Li, Xin Permafrost degradation increases risk and large future costs of infrastructure on the Third Pole COMMUNICATIONS EARTH & ENVIRONMENT English Article ALASKA PUBLIC INFRASTRUCTURE; ROADBED COOLING APPROACH; CLIMATE-CHANGE; ECONOMIC-ASSESSMENT; SUSTAINABILITY; REGIONS; IMPACT; LINE The damage to infrastructure caused by near-surface permafrost degradation is directly related to the well-being of 10 million people and the sustainable development on the Qinghai-Tibet Plateau, the Third Pole of the Earth. Here we identify the economic damage caused by permafrost degradation to infrastructure on the Qinghai-Tibet Plateau by integrating data-driven projection, multihazard index, and lifespan replacement model. We found that additional cost of approximately $6.31 billion will be needed to maintain the service function of current infrastructure under the historical scenario (SSP245) by 2090. While 20.9% of these potential costs can be saved with strategic adaptations. Controlling global warming to below 1.5 degrees C will reduce the costs by $1.32 billion relative to the 2 degrees C target of Paris Agreement. These findings highlight the importance of mitigating global warming and of investment in the adaptation and maintenance of infrastructure on the Qinghai-Tibet Plateau, which has a sparse population but is a climate hotspot. Controlling warming to below 1.5 degrees C instead of 2 degrees C could reduce the costs of infrastructure damage from permafrost degradation in the Qinghai-Tibet Plateau by $1.32 billion, according to statistical and machine learning-based model projections [Ran, Youhua; Cheng, Guodong; Kang, Shichang; Tan, Meibao] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Cheng, Guodong; Li, Xin] Chinese Acad Sci, Inst Tibetan Plateau Res, State Key Lab Tibetan Plateau Earth Syst Resource, Beijing 100101, Peoples R China; [Dong, Yuanhong] CCCC First Highway Consultants Co LTD, State Key Lab Rd Engn Safety & Hlth Cold & High A, Xian 710075, Peoples R China; [Hjort, Jan] Univ Oulu, Geog Res Unit, POB 8000, FI-90014 Oulu, Finland; [Lovecraft, Amy Lauren] Univ Alaska, Ctr Arctic Policy Studies, Fairbanks, AK 99775 USA Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS; University of Oulu; University of Alaska System; University of Alaska Fairbanks Ran, YH (corresponding author), Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China. ranyh@lzb.ac.cn Li, Xin/F-7473-2011; li, xin/HHS-9461-2022; Ran, Youhua/Q-8041-2017 Li, Xin/0000-0003-2999-9818; Ran, Youhua/0000-0001-7774-4612; Hjort, Jan/0000-0002-4521-2088 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19070204]; National Natural Science Foundation of China [42071421]; Academy of Finland [315519]; Academy of Finland (AKA) [315519] Funding Source: Academy of Finland (AKA) Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This study was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDA19070204) and the National Natural Science Foundation of China projects (grant numbers 42071421). JH received funding from the Academy of Finland (grant number 315519). We thank Dr. Defu Zou and Dr. Qiangqiang Pang for providing ground ice content data for the QTP. The authors also thank the associate editor and three anonymous reviewers for their insightful comments and suggestions that helped improve this paper. 52 2 2 20 20 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-4435 COMMUN EARTH ENVIRON Commun. Earth Environ. OCT 13 2022.0 3.0 1 238 10.1038/s43247-022-00568-6 0.0 10 Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences 5F7IV gold 2023-03-23 WOS:000866486300001 0
J Herold, DM; Nowicka, K; Pluta-Zaremba, A; Kummer, S Herold, David M.; Nowicka, Katarzyna; Pluta-Zaremba, Aneta; Kummer, Sebastian COVID-19 and the pursuit of supply chain resilience: reactions and lessons learned from logistics service providers (LSPs) SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL English Article Resilience; Logistics management; Supply chain vulnerability; Logistics service provider; COVID-19; Supply chain resilience; Supply chain management BIG DATA ANALYTICS; SEEKING KNOWLEDGE; MANAGEMENT; BLOCKCHAIN; FRAMEWORK; FUTURE; PHENOMENOLOGY; CONSUMPTION; SIMULATION; INNOVATION Purpose The purpose of this paper is to provide new insights into the reactions and lessons learned with regard to the COVID-19 pandemic in terms of how logistics service providers (LSPs) managed to maintain supply chains resilience and what focus areas have been changed to keep operations functional and uphold financial stability. Design/methodology/approach Based on data-gathering techniques in interpretive research this study collected primary data via semi-structured interviews, interviewing informants from selected LSPs that operate on a global scale. Findings The results show that LSPs have built their reactions and actions to the COVID-19 outbreak around five main themes: create revenue streams, enhance operational transport flexibility, enforce digitalization and data management, optimize logistics infrastructure and optimize personnel capacity. These pillars build the foundation to LSP resilience that enables supply chains to stay resilient during an external shock of high impact and low probability. Originality/value The results of this study provide insights into how LSPs have managed the downsides and found innovative ways to overcome operational and financial challenges during the COVID-19 outbreak. As one of the first studies that specially focuses on the role of LSPs during the COVID-19 pandemic, this study categorizes the LSPs' reactions and provides a lessons learned framework from a managerial perspective. From a theoretical perspective, this paper discusses the strategic role of LSPs in supply chain management and thereby extends current supply chain literature with a focus on LSP resilience. [Herold, David M.] Vienna Univ Econ & Business, Inst Transport & Logist Management, Vienna, Austria; [Herold, David M.] Griffith Univ, Dept Business Strategy & Innovat, Brisbane, Qld, Australia; [Nowicka, Katarzyna; Pluta-Zaremba, Aneta] Warsaw Sch Econ, Dept Logist, Warsaw, Poland; [Kummer, Sebastian] Jilin Univ, Dept Logist, Changchun, Peoples R China Vienna University of Economics & Business; Griffith University; Warsaw School of Economics; Jilin University Herold, DM (corresponding author), Vienna Univ Econ & Business, Inst Transport & Logist Management, Vienna, Austria.;Herold, DM (corresponding author), Griffith Univ, Dept Business Strategy & Innovat, Brisbane, Qld, Australia. dherold@wu.ac.at; Katarzyna.Nowicka@sgh.waw.pl; Aneta.Pluta@sgh.waw.pl; sebastian.kummer@gmx.at ; Herold, David Martin/N-4218-2013 Kummer, Sebastian/0000-0002-4840-5256; Pluta-Zaremba, Aneta/0000-0002-8869-9017; Herold, David Martin/0000-0002-4023-2282 99 24 24 23 108 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 1359-8546 1758-6852 SUPPLY CHAIN MANAG Supply Chain Manag. SEP 6 2021.0 26.0 6 SI 702 714 10.1108/SCM-09-2020-0439 0.0 JUN 2021 13 Business; Management Social Science Citation Index (SSCI) Business & Economics UL6FK Green Submitted, Green Accepted, hybrid 2023-03-23 WOS:000662319600001 0
C Farooq, K; Karasek, J; Atassi, H; Hussain, A; Yang, P; MacRae, C; Mahmud, M; Luo, B; Slack, W IEEE Farooq, Kamran; Karasek, Jan; Atassi, Hicham; Hussain, Amir; Yang, Peipei; MacRae, Calum; Mahmud, Mufti; Luo, Bin; Slack, Warner A Novel Cardiovascular Decision Support Framework for Effective Clinical Risk Assessment 2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN HEALTHCARE AND E-HEALTH (CICARE) English Proceedings Paper IEEE Symposium on Computational Intelligence in Healthcare and e-health DEC 09-12, 2014 Orlando, FL IEEE,IEEE Computat Intelligence Soc CORONARY-ANGIOGRAPHY The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics ( RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinicians effectively distinguish acute angina patients from those with other causes of chest pain. Key to our new approach is ( 1) an intelligent prospective clinical decision support framework for primary and secondary care clinicians, ( 2) learning from missing/impartial clinical data using Bernoulli mixture models and Expectation Maximisation ( EM) techniques, ( 3) utilisation of state-of-the-art feature section, pattern recognition and data mining techniques for the development of intelligent risk prediction models for cardiovascular patients. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating clinical risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. A comparative analysis case study of machine learning methods was carried out for predicting RACPC clinical outcomes using real patient data acquired from Raigmore Hospital in Inverness, UK. The proposed framework was also validated using the University of Cleveland's Heart Disease dataset which contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Experiments with the Cleveland database ( based on 18 clinical features of 270 patients) were concentrated on attempting to distinguish the presence of heart disease ( values 1, 2, 3, 4) from absence ( value 0). The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. As part of these case studies, various online RACPC risk assessment prototypes have been developed which are being deployed in the clinical setting ( NHS Highland) for clinical trial purposes. [Farooq, Kamran; Hussain, Amir] Univ Stirling, Comp Sci & Math Div, Stirling, Scotland; [Karasek, Jan; Atassi, Hicham] Brno Univ Technol, Dept Telecommun, Brno, Czech Republic; [Yang, Peipei] Chinese Acad Sci, Natl Lab Pattern Recognit, Beijing, Peoples R China; [MacRae, Calum] Harvard Med Sch, Brigham & Womens Hosp, Boston, MA USA; [Mahmud, Mufti] Univ Antwerp, Theoret Neurobiol & Neuroengn Lab, B-2610 Antwerp, Belgium; [Mahmud, Mufti] Jahangirnagar Univ, Inst Informat Technol, Dhaka 1342, Bangladesh; [Mahmud, Mufti] Univ Stirling, COSIPRA Lab, Stirling, Scotland; [Luo, Bin] Anhui Univ, Sch Comp Sci & Technol, Hefei, Anhui, Peoples R China; [Slack, Warner] Harvard Med Sch, Beth Israel Clin Informat Div, Boston, MA USA University of Stirling; Brno University of Technology; Chinese Academy of Sciences; Harvard University; Brigham & Women's Hospital; Harvard Medical School; University of Antwerp; Jahangirnagar University; University of Stirling; Anhui University; Harvard University; Harvard Medical School Farooq, K (corresponding author), Univ Stirling, Comp Sci & Math Div, Stirling, Scotland. kfa@cs.stir.ac.uk; karasekj@gmail.com; atassi@feec.vutbr.cz; ahu@cs.stir.ac.uk; ppyang@nlpr.ia.ac.cn; cmacrae@partners.org; muftimahmud@gmail.com; luobinahu@gmail.com; wslack@bidmc.harvard.edu Hussain, Amir/AAG-6299-2020; Mahmud/C-7752-2012; lu, bin/HPE-4790-2023; Luo, Bin/Y-1233-2018 Hussain, Amir/0000-0002-8080-082X; Mahmud/0000-0002-2037-8348; Luo, Bin/0000-0001-5948-5055 14 2 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-5375-2 2014.0 117 124 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BF3TI 2023-03-23 WOS:000380576000017 0
J Sun, SJ; Veltri, GA; Wang, F Sun, Shaojing; Veltri, Giuseppe A.; Wang, Fan Representations of electronic cigarettes in Chinese media BMC PUBLIC HEALTH English Article E-cigarettes; Tobacco promotion; Chinese media; Text-mining approach; Tobacco control NEWSPAPER COVERAGE; SMOKING; NEWS Background: Electronic cigarettes (E-cigarettes) have become a debated issue for tobacco control over recent years. In this study we investigate how Chinese newspapers have covered E-cigarettes over the past ten years. Methods: The study analyses the salience, patterns and content of news articles pertaining to E-cigarettes in regional and national Chinese outlets. A total of 476 articles are examined via content analysis and supervised automatic text analysis. The manual content analysis generates a coding scheme, which is then validated and applied to machine learning. The whole research methodology demonstrates satisfying human-human and human-to-computer reliabilities. Results: The study reveals that E-cigarettes have not received enough attention in terms of its salience in the media, though the amount of coverage has been growing. A large share of the articles is published around May of each year - which is when the No Tobacco Day of the WHO takes place. The results point to four major themes on E-cigarettes: nicotine/constituents/features, tobacco control/regulation, children's use of E-cigarettes, and tobacco market/industry. Conclusions: Overall, E-cigarettes have not been a topic at the top of media agenda; however, we have identified a considerable growth of coverage about the potential concerns regarding young people's adoption of E-cigarettes advocated by parents and educators. [Sun, Shaojing] Fudan Univ, Sch Journalism, Shanghai, Peoples R China; [Veltri, Giuseppe A.] Univ Trento, Dept Sociol & Social Res, Trento, Italy; [Wang, Fan] Fudan Univ, Sch Publ Hlth, Minist Educ, Key Lab Publ Hlth Safety, Shanghai 200032, Peoples R China Fudan University; University of Trento; Fudan University Wang, F (corresponding author), Fudan Univ, Sch Publ Hlth, Minist Educ, Key Lab Publ Hlth Safety, Shanghai 200032, Peoples R China. wangfan512@126.com Veltri, Giuseppe Alessandro/R-1984-2018 Veltri, Giuseppe Alessandro/0000-0002-9472-2236 Shanghai Health and Family Planning Commission [15GWZK1001] Shanghai Health and Family Planning Commission This study was supported by the grant construction of the key disciplines of the fourth round public health three-year action plan from Shanghai Health and Family Planning Commission (15GWZK1001). 36 5 7 2 17 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2458 BMC PUBLIC HEALTH BMC Public Health JUN 13 2018.0 18.0 727 10.1186/s12889-018-5644-x 0.0 11 Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Public, Environmental & Occupational Health GJ4YA 29895266.0 gold, Green Published 2023-03-23 WOS:000435387200003 0
J Liu, MM; Wu, HJ; Yu, ZX; Liao, HL; Deng, SH Liu, Meimei; Wu, Hongjian; Yu, Zexin; Liao, Hanlin; Deng, Sihao Description and Prediction of Multi-layer Profile in Cold Spray Using Artificial Neural Networks JOURNAL OF THERMAL SPRAY TECHNOLOGY English Review artificial neural networks; cold spray additive manufacturing; multi-layer profile; profile control DEPOSITION; PARAMETERS; VELOCITY; STRATEGY Cold spray is a newly developed solid-state metal deposition technology, which allows for preparing various functional coatings and repairing damaged metal components, as well as fabricating freestanding parts. In order to obtain the deposits with the desired shape and accuracy, the coating profile, including its thickness and distribution, is an important indicator to monitor and control. In this work, an artificial neural network (ANN) model has been employed to describe and predict the multi-layer profile of cold-sprayed deposits. Compared to conventional feature-based modeling methods, the ANN model is capable of simulating a complete track profile on defined substrate morphologies. The superiority of the ANN approach is further emphasized by its ability to simulate a multi-layer profile, which differs from previous works that focus on single-layer profiles. It is essential for guiding the coating formation and fabrication of near-net-shape parts. The results imply that the ANN model is well trained and capable of predicting multi-layer profiles with acceptable accuracy. It can be used for profile control during cold spray additive manufacturing. [Liu, Meimei; Wu, Hongjian; Liao, Hanlin; Deng, Sihao] Univ Bourgogne Franche Comte, UTBM, CNRS, ICB UMR 6303, F-90010 Belfort, France; [Liu, Meimei] Suzhou Univ Sci & Technol, Sch Mech Engn, Suzhou 215009, Peoples R China; [Yu, Zexin] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215123, Peoples R China Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); Universite de Bourgogne; Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Suzhou University of Science & Technology; Soochow University - China Deng, SH (corresponding author), Univ Bourgogne Franche Comte, UTBM, CNRS, ICB UMR 6303, F-90010 Belfort, France. lmm89zx@163.com; sihao.deng@utbm.fr China Scholarship Council [201604490072, 201701810152] China Scholarship Council(China Scholarship Council) The authors gratefully appreciate the support from the China Scholarship Council (Grant No. 201604490072 and No. 201701810152). 23 4 4 3 36 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1059-9630 1544-1016 J THERM SPRAY TECHN J. Therm. Spray Technol. AUG 2021.0 30.0 6 1453 1463 10.1007/s11666-021-01212-z 0.0 JUN 2021 11 Materials Science, Coatings & Films Science Citation Index Expanded (SCI-EXPANDED) Materials Science UC6YI 2023-03-23 WOS:000658159600001 0
J Hu, RY; Zhu, XF; Cheng, DB; He, W; Yan, Y; Song, JK; Zhang, SC Hu, Rongyao; Zhu, Xiaofeng; Cheng, Debo; He, Wei; Yan, Yan; Song, Jingkuan; Zhang, Shichao Graph self-representation method for unsupervised feature selection NEUROCOMPUTING English Article Dimensionality reduction; Alternating direction method multipliers; Locality preserving projection; Self-representation CLASSIFICATION; REGRESSION Both subspace learning methods and feature selection methods are often used for removing irrelative features from high-dimensional data. Studies have shown that feature selection methods have interpretation ability and subspace learning methods output stable performance. This paper proposes a new unsupervised feature selection by integrating a subspace learning method (i.e., Locality Preserving Projection (LPP)) into a new feature selection method (i.e., a sparse feature-level self-representation method), aim at simultaneously receiving stable performance and interpretation ability. Different from traditional sample-level self-representation where each sample is represented by all samples and has been popularly used in machine learning and computer vision. In this paper, we propose to represent each feature by its relevant features to conduct feature selection via devising a feature-level self-representation loss function plus an l(2,1)-norm regularization term. Then we add a graph regularization term (i.e., LPP) into the resulting feature selection model to simultaneously conduct feature selection and subspace learning. The rationale of the LPP regularization term is that LPP preserves the original distribution of data after removing irrelative features. Finally, we conducted experiments on UCI data sets and other real data sets and the experimental results showed that the proposed approach outperformed all comparison algorithms. (C) 2016 Elsevier B.V. All rights reserved. [Hu, Rongyao; Zhu, Xiaofeng; Cheng, Debo; He, Wei; Zhang, Shichao] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Guangxi, Peoples R China; [Yan, Yan; Song, Jingkuan] Univ Trento, Dept Comp Sci & Informat Engn, Trento, Italy Guangxi Normal University; University of Trento Zhu, XF; Zhang, SC (corresponding author), Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Guangxi, Peoples R China. hu_No1@126.com; seanzhuxf@gmail.com; 15676209686@163.com; risehnhew@163.com; yan.yan@unitn.it; jingkuan.song1@unitn.it; zhangsc@mailbox.gxnu.edu.cn Zhang, Shichao/AAA-7608-2020; Hu, Rongyao/AAH-3834-2020; Cheng, Debo/T-9106-2019; Zhu, Xiaofeng/HII-5291-2022; Cheng, Debo/Y-5226-2019 Hu, Rongyao/0000-0001-9989-1103; Zhu, Xiaofeng/0000-0001-6840-0578; Cheng, Debo/0000-0002-0383-1462 National Natural Science Foundation of China [61263035, 61573270, 61450001]; China 973 Program [2013CB329404]; Guangxi Natural Science Foundation [2012GXNSFGA060004, 2015GXNSFCB139011]; Guangxi Higher Institutions' Program of Introducing 100 High-Level Overseas Talents; Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; Innovation Project of Guangxi Graduate Education [YCSZ2016046] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China 973 Program(National Basic Research Program of China); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Guangxi Higher Institutions' Program of Introducing 100 High-Level Overseas Talents; Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; Innovation Project of Guangxi Graduate Education This work was supported in part by the National Natural Science Foundation of China (Grant Nos: 61263035, 61573270 and 61450001), the China 973 Program (Grant no: 2013CB329404), the Guangxi Natural Science Foundation (Grant no: 2012GXNSFGA060004 and 2015GXNSFCB139011), the Guangxi Higher Institutions' Program of Introducing 100 High-Level Overseas Talents, the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing, and Innovation Project of Guangxi Graduate Education under grant YCSZ2016046. 47 118 119 5 66 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 12 2017.0 220.0 130 137 10.1016/j.neucom.2016.05.081 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science EF7JF 2023-03-23 WOS:000390505200015 0
C Jia, Y; Jin, SG; Yan, QY; Savi, P IEEE Jia, Yan; Jin, Shuanggen; Yan, Qingyun; Savi, Patrizia The Sensitivity Analysis on GNSS-R Soil Moisture Retrieval 2021 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS 2021) English Proceedings Paper Photonics and Electromagnetics Research Symposium (PIERS) NOV 21-25, 2021 Hangzhou, PEOPLES R CHINA IEEE,Zhejiang Univ, Electromagnet Acad,Coll Informat Sci & Elect Engn,Zhejiang Key Lab Adv Microelectron Intelligent Syst & Applicat,Zhejiang Univ, Univ Illinois Urbana Champaign Inst,Natl Engn Res Ctr Opt Instruments,Shanghai Ideaoptics Corp Ltd,IEEE Geoscience & Remote Sensing Soc,IEEE Antennas & Propagat Soc,IEEE Photon Soc,Electromagnet Acad REFLECTED SIGNALS; REFLECTOMETRY; SYSTEM; OCEAN The use of bistatic reflected global navigation satellite system (GNSS) signals as a means of sensing the Earth's surface is attracting widespread interest. It has the advantages of non-contact, large coverage area, and real-time which have attracted much attention during recent years. These reflected signals contain the information of the reflecting surface and therefore were applied to investigate the properties of the observed object, such as soil moisture (SM). Machine learning (ML) methods are featured with flexibility and are good at handling non-linear problems, modeling complex interactions between inputs and outputs, and have been rise attention for the GNSS-R SM retrieval field. The contribution of different input variables to SM is quite significant for optimizing the ML-based SM retrieval. In this paper, the typical random forest (RF) algorithm was adopted to evaluate the weight of input variables for ML-based SM retrieval. A simulation data set was built for training RF models, since the simulated data provide sufficient samples and show a more accurate relationship between the inputs and outputs. The SM predictions made by the RF methods are evaluated and compared with the simulation data set. The results show the contribution of a single variable to soil moisture retrieval, which can help with the ML-based GNSS-R SM retrieval to overcome the complex auxiliary variable problem. [Jia, Yan] Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China; [Jin, Shuanggen] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Jin, Shuanggen; Yan, Qingyun] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Savi, Patrizia] Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy Nanjing University of Posts & Telecommunications; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Nanjing University of Information Science & Technology; Polytechnic University of Turin Jia, Y (corresponding author), Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China. Jin, Shuanggen/B-8094-2008 Jin, Shuanggen/0000-0002-5108-4828 18 0 0 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-7247-7 2021.0 2307 2311 10.1109/PIERS53385.2021.9694804 0.0 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BT0ZS 2023-03-23 WOS:000795902300380 0
J Zhang, XT; Messenger, C; Korsakova, N; Chan, ML; Hu, YM; Zhang, JD Zhang, Xue-Ting; Messenger, Chris; Korsakova, Natalia; Chan, Man Leong; Hu, Yi-Ming; Zhang, Jian-dong Detecting gravitational waves from extreme mass ratio inspirals using convolutional neural networks PHYSICAL REVIEW D English Article ASTROPHYSICS Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of -principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability toward different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs toward more realistic EMRI data analysis. [Zhang, Xue-Ting; Hu, Yi-Ming; Zhang, Jian-dong] Sun Yat Sen Univ Zhuhai Campus, MOE Key Lab TianQin Mission, TianQin Res Ctr Gravitat Phys, Zhuhai 519082, Peoples R China; [Zhang, Xue-Ting; Hu, Yi-Ming; Zhang, Jian-dong] Sun Yat Sen Univ Zhuhai Campus, Sch Phys & Astron, Frontiers Sci Ctr TianQin, Gravitat Wave Res Ctr CNSA, Zhuhai 519082, Peoples R China; [Messenger, Chris] Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow G12 8QQ, Lanark, Scotland; [Korsakova, Natalia] Observ Cote Azur, ARTEMIS, Blvd Observ, F-06304 Nice, France; [Chan, Man Leong] Fukuoka Univ, Dept Appl Phys, Nanakuma 8-19-1, Fukuoka 8140180, Japan University of Glasgow; UDICE-French Research Universities; Universite Cote d'Azur; Observatoire de la Cote d'Azur; Fukuoka University Hu, YM (corresponding author), Sun Yat Sen Univ Zhuhai Campus, MOE Key Lab TianQin Mission, TianQin Res Ctr Gravitat Phys, Zhuhai 519082, Peoples R China.;Hu, YM (corresponding author), Sun Yat Sen Univ Zhuhai Campus, Sch Phys & Astron, Frontiers Sci Ctr TianQin, Gravitat Wave Res Ctr CNSA, Zhuhai 519082, Peoples R China. huyiming@mail.sysu.edu.cn Hu, Yi-Ming/ABD-6909-2021 Hu, Yi-Ming/0000-0002-7869-0174; Zhang, Jian-dong/0000-0002-5930-6739 Guangdong Major Project of Basic and Applied Basic Research [2019B030302001]; Natural Science Foundation of China [12173104, 11805286, 11690022]; Science and Technology Facilities Council [ST/V005634/1]; Science and Technology Facilities Council UK [ST/R002770/1]; CNES fellowship; Royal Society [IEC\NSFC\211371] Guangdong Major Project of Basic and Applied Basic Research; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); Science and Technology Facilities Council UK(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); CNES fellowship; Royal Society(Royal Society of London) The authors thank Hui-Min Fan, Lianggui Zhu, En-Kun Li, Jianwei Mei, and Micheal Williams for helpful dis-cussions. This work has been supported by Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2019B030302001) , the Natural Science Foundation of China (Grants No. 12173104, No. 11805286, and No. 11690022) . C. M. is supported by the Science and Technology Facilities Council Grant No. ST/V005634/1.The authors would like to thank the Gravitational-wave Excellence through Alliance Training (GrEAT) network for facilitating this collaborative project. The GrEAT network is funded by the Science and Technology Facilities Council UK Grant No. ST/R002770/1. N. K. would like to thank thesupport of the CNES fellowship. The authors acknowledge the uses of the calculating utilities of NumPy [89] , SciPy [90] , and the plotting utilities of MATPLOTLIB [91] . The collabo-ration is partially supported by the Royal Society international exchanges 2021 Cost Share (IEC\NSFC\211371) . 89 2 2 3 3 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D JUN 24 2022.0 105.0 12 123027 10.1103/PhysRevD.105.123027 0.0 12 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics 2W2GL Green Submitted 2023-03-23 WOS:000824348700001 0
J Hu, R; Yan, Z; Ding, WX; Yang, LT Hu, Rui; Yan, Zheng; Ding, Wenxiu; Yang, Laurence T. A survey on data provenance in IoT WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS English Article cyberization; internet of things; distributed architecture; intelligent service; data provenance BIG DATA PROVENANCE; INTERNET; ANALYTICS; SECURE; THINGS; MANAGEMENT; LINEAGE Internet of Things (IoT), as a typical representation of cyberization, enables the interconnection of physical things and the Internet, which provides intelligent and advanced services for industrial production and human lives. However, it also brings new challenges to IoT applications due to heterogeneity, complexity and dynamic nature of IoT. Especially, it is difficult to determine the sources of specified data, which is vulnerable to inserted attacks raised by different parties during data transmission and processing. In order to solve these issues, data provenance is introduced, which records data origins and the history of data generation and processing, thus possible to track the sources and reasons of any problems. Though some related researches have been proposed, the literature still lacks a comprehensive survey on data provenance in IoT. In this paper, we first propose a number of design requirements of data provenance in IoT by analyzing the features of IoT data and applications. Then, we provide a deep-insight review on existing schemes of IoT data provenance and employ the requirements to discuss their pros and cons. Finally, we summarize a number of open issues to direct future research. [Hu, Rui; Yan, Zheng; Ding, Wenxiu] Xidian Univ, Sch Cyber Engn, State Key Lab ISN, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Yang, Laurence T.] Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada Xidian University; Aalto University Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab ISN, Xian 710071, Shaanxi, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland. zheng.yan@aalto.fi Ding, Wenxiu/AAP-5812-2021; zheng, yan/GQY-6668-2022; yang, zheng/HGC-7753-2022 Ding, Wenxiu/0000-0002-8531-9226; Yan, Zheng/0000-0002-9697-2108 Aalto University; National Natural Science Foundation of China [61672410, 61802293, U1536202]; National Postdoctoral Program for Innovative Talents [BX20180238]; China Postdoctoral Science Foundation [2018 M633461]; Academy of Finland [308087, 314203]; Key Lab of Information Network Security, Ministry of Public Security [C18614]; open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China [CLDL-20182119]; Shaanxi Innovation Team Project [2018TD-007]; 111 project [B16037] Aalto University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Academy of Finland(Academy of Finland); Key Lab of Information Network Security, Ministry of Public Security; open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China; Shaanxi Innovation Team Project; 111 project(Ministry of Education, China - 111 Project) Open access funding provided by Aalto University. This work is supported in part by the National Natural Science Foundation of China under Grants 61672410, 61802293 and U1536202, the National Postdoctoral Program for Innovative Talents under grant BX20180238, the Project funded by China Postdoctoral Science Foundation under grant 2018 M633461, the Academy of Finland under Grants 308087 and 314203, the Key Lab of Information Network Security, Ministry of Public Security under grant No. C18614, the open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China under grant CLDL-20182119, Shaanxi Innovation Team Project under grant 2018TD-007, and the 111 project under grant B16037. 66 24 24 2 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1386-145X 1573-1413 WORLD WIDE WEB World Wide Web MAR 2020.0 23.0 2 SI 1441 1463 10.1007/s11280-019-00746-1 0.0 NOV 2019 23 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science KT5KY Green Published, hybrid 2023-03-23 WOS:000500400700003 0
J Tao, LF; Yang, C; Cheng, YJ; Lu, C; Ragulskis, M Tao, Laifa; Yang, Chao; Cheng, Yujie; Lu, Chen; Ragulskis, Minvydas Machine component health prognostics with only truncated histories using geometrical metric approach MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Time-continuous relevant isometric mapping; Truncated histories; Manifold space; Health prognostics; Survival probability DEGRADATION; PREDICTION; SYSTEMS; MODEL Prognostics plays a vital part in modern decision making for maintenance. Many related valuable approaches have been reported by scientists with both truncated and failure histories. However, for cases where the actual asserts have no failure histories, one important topic of prognostics is to focus on modeling with only truncated histories. Here we first describe an algorithm called time-continuous relevant isometric mapping (TRIM) to establish a manifold space where the health state evolutionary laws within truncated histories can be cognized. Unlike classical methods, such as isometric feature mapping, TRIM involves the vital element of state evolution (time), establishes a state evolutionary manifold space by utilizing both local geometrical structures and global isometric features of a given truncated data set. Meantime, two geometrical metrics, neighborhood geodesic distance (NGD) and cumulative geodesic distance, were defined and used in this study to indicate the health state of a given component. Then the feed-forward neural network (FFNN) was trained with inputs from the NGD series. The corresponding target vectors (survival probabilities) of FFNN were estimated by intelligent product limit estimator using truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual component. To validate the proposed method, case study was conducted by using the degradation data generated by a bearing test rig. Results demonstrate that the proposed method can highlight the intrinsic health state evolutionary laws by TRIM even with only truncated histories. The more accuracy prognostics results can be consequently achieved based on the cognition of the evolutionary laws. (C) 2017 Elsevier Ltd. All rights reserved. [Tao, Laifa; Yang, Chao; Cheng, Yujie] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing, Peoples R China; [Lu, Chen] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Lu, Chen] Sci & Technol Lab Reliabil & Environm Engn, Beijing, Peoples R China; [Tao, Laifa] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON, Canada; [Ragulskis, Minvydas] Kaunas Univ Technol, Res Grp Math & Numer Anal Dynam Syst, Kaunas, Lithuania Beihang University; Beihang University; University of Toronto; Kaunas University of Technology Cheng, YJ (corresponding author), Beihang Univ, Sch Aeronaut Sci & Engn, Beijing, Peoples R China. chengyujie@buaa.edu.cn Lu, Chen/B-6310-2016; Ragulskis, Minvydas/A-1546-2008; Tao, Laifa/F-6037-2015 Ragulskis, Minvydas/0000-0002-3348-9717; Tao, Laifa/0000-0002-9232-7216 Fundamental Research Funds for the Central Universities [YWF-14-KKX-015, YWF-16-BJ-J-18]; National Natural Science Foundation of China [61603016, 51575021]; China Postdoctoral Science Foundation [166482]; Technology Foundation Program of National Defense [Z132013B002] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Technology Foundation Program of National Defense This study was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. YWF-14-KKX-015 and YWF-16-BJ-J-18), the National Natural Science Foundation of China (Grant Nos. 61603016 and 51575021), the China Postdoctoral Science Foundation (Grant No. 166482), as well as the Technology Foundation Program of National Defense (Grant No. Z132013B002). 20 3 3 0 18 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. DEC 2018.0 113.0 SI 168 179 10.1016/j.ymssp.2017.01.052 0.0 12 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering GQ9DT 2023-03-23 WOS:000442066800014 0
J Yang, YY; Chen, ZL; Wu, TV; Sempey, A; Batsale, JC Yang, Yingying; Chen, Zhoulin; Wu, Tingting Vogt; Sempey, Alain; Batsale, Jean-Christophe In situ methodology for thermal performance evaluation of building wall: A review INTERNATIONAL JOURNAL OF THERMAL SCIENCES English Review Thermal performance evaluation; Building wall; In situ; Non-destructive testing; Transient method; Front face measurement ULTRASONIC WAVE-PROPAGATION; HEAT-TRANSFER COEFFICIENT; INFRARED THERMOGRAPHY; ENERGY PERFORMANCE; THERMOPHYSICAL PROPERTIES; DEFECT DETECTION; U-VALUE; NONDESTRUCTIVE EVALUATION; INSULATION MATERIALS; NEURAL-NETWORK The thermal performance of the building envelope is critical to reducing the energy consumption of buildings and increasing the building energy efficiency. Studies on the evaluation methodology for thermal performance of building wall is of great significance for new building accreditation, retrofitting energy efficiency of old build-ings, energy conversation, etc. This work reviews recent developments in methodology of thermal performance evaluation for building wall in situ, which consists of steady-state method, transient method and other non-destructive evaluation methods (e.g., terahertz radiation method). Both fundamental theories and measure-ment equipment and systems are analysed. The non-contact transient method exploiting the front face mea-surement is defined as a fast, less-instrument, and less invasive method, with the largest potentiality to be extensively employed in the future. This review will provide a useful reference for the research on the thermal performance evaluation of building wall in situ, guiding to select an appropriate evaluation method to employ in engineering. [Yang, Yingying; Chen, Zhoulin] Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China; [Wu, Tingting Vogt; Sempey, Alain; Batsale, Jean-Christophe] Univ Bordeaux, Lab I2M, UMR 5295, CNRS, F-33400 Talence, France; [Wu, Tingting Vogt] Esplanade Arts & Metiers, F-33400 Talence, France University of Shanghai for Science & Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Universite de Bordeaux Wu, TV (corresponding author), Univ Bordeaux, Lab I2M, UMR 5295, CNRS, F-33400 Talence, France.;Wu, TV (corresponding author), Esplanade Arts & Metiers, F-33400 Talence, France. tingting.vogt-wu@u-bordeaux.fr VOGT-WU, Tingting/0000-0001-9752-9872; Sempey, Alain/0000-0002-1274-3253 National Natural Science Foundation of China [52006146]; European Union?s Horizon 2020 research and innovation program [637221] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union?s Horizon 2020 research and innovation program This work was supported by the National Natural Science Foundation of China [grant numbers 52006146] , the European Union?s Horizon 2020 research and innovation program [grant numbers 637221] . The sole responsibility for the content of this paper lies with the authors. 155 0 0 10 17 ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER ISSY-LES-MOULINEAUX 65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE 1290-0729 1778-4166 INT J THERM SCI Int. J. Therm. Sci. NOV 2022.0 181.0 107687 10.1016/j.ijthermalsci.2022.107687 0.0 JUL 2022 14 Thermodynamics; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Engineering 3F2WW 2023-03-23 WOS:000830532000001 0
C Liu, X; Deng, CW; Zhao, BJ; Chanussot, J IEEE Liu, Xun; Deng, Chenwei; Zhao, Baojun; Chanussot, Jocelyn MULTIMODAL-TEMPORAL FUSION: BLENDING MULTIMODAL REMOTE SENSING IMAGES TO GENERATE IMAGE SERIES WITH HIGH TEMPORAL RESOLUTION 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Multimodal-Temporal Fusion; Cross-modality Image Translation; Image Time Series; Temporal Resolution; Deep Neural Networks This paper aims to tackle a general but interesting cross modality problem in remote sensing community: can multimodal images help to generate synthetic images in time series and improve temporal resolution? To this end, we explore multimodal-temporal fusion, in which we attempt to leverage the availability of additional cross-modality images to simulate the missing images in time series. We propose a multimodal-temporal fusion framework, and mainly focus on two kinds of information for the simulation: intra-modal cross-modality information and inter-modal temporal information. To exploit the cross-modality information, we adopt available paired images and learn a mapping between different modality images using a deep neural network. Considering temporal dependency among time-series images, we formulate a temporal constraint in the learning to encourage temporal consistent results. Experiments are conducted on two cross-modality image simulation applications (SAR to visible and visible to SWIR), and both visual and quantitative results demonstrate that the proposed model can successfully simulate missing images with cross-modality data. [Liu, Xun; Deng, Chenwei; Zhao, Baojun] Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, Grenoble, France Beijing Institute of Technology; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Liu, X (corresponding author), Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China. National Natural Science Foundation of China (NSFC) [31727901] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China (NSFC) under Grant 91438203 and National Natural Science Foundation of China (NSFC) under Grant 31727901. 5 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 10079 10082 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH 2023-03-23 WOS:000519270601064 0
J Chen, Z; Ye, F; Fu, WX; Ke, YT; Hong, HY Chen, Zhuo; Ye, Fei; Fu, Wenxi; Ke, Yutian; Hong, Haoyuan The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China NATURAL HAZARDS English Article Landslide susceptibility; DEM spatial resolution; Frequency ratio; Index of entropy; Weight of evidence; Slope units ANALYTICAL HIERARCHY PROCESS; RAINFALL-INDUCED LANDSLIDES; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; LOGISTIC-REGRESSION; FREQUENCY RATIO; RANDOM FOREST; MULTICRITERIA EVALUATION; DETERMINISTIC MODEL; HYBRID INTEGRATION The selection of an appropriate map resolution is highly important for landslide susceptibility assessment. No consistent objective criteria, however, are currently applied to the choice of map resolution. This research, in conjunction with slope units, explores the effect of digital elevation model (DEM) resolution on susceptibility modelling using three statistical models (frequency ratio, index of entropy, and weight of evidence). Seven different spatial resolutions (30, 40, 50, 60, 70, 80, and 90 m) and three statistical models are investigated. For each resolution, we compare the performance of the three models using area under curve (AUC) analysis. The results show that, independent of the statistical models, the best performances are produced at 70 m DEM resolution. This highlights that finer resolutions do not necessarily lead to higher predictive accuracy in landslide susceptibility mapping. Rather, the frequency ratio model seems to be optimal for the coarser resolutions (i.e. 70, 80, and 90 m). [Chen, Zhuo; Ye, Fei; Fu, Wenxi] Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Dept Geotech Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Peoples R China; [Ke, Yutian] Univ Paris Saclay, CNRS, Univ Paris Sud, UMR 8148,GEOPS, F-91405 Orsay, France; [Hong, Haoyuan] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China; [Hong, Haoyuan] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China; [Hong, Haoyuan] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China Sichuan University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Universite Paris Saclay; Nanjing Normal University Ye, F (corresponding author), Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Dept Geotech Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Peoples R China. yefei16scu@163.com KE, Yutian/AAG-9474-2019; Hong, Haoyuan/C-8455-2014 KE, Yutian/0000-0002-9098-9419; Hong, Haoyuan/0000-0001-6224-069X; chen, Zhuo/0000-0003-4466-3992 National Nature Science Foundation of China [41772321]; Basic Geological Survey Project of China Geological Survey [DD20160272]; International Cooperation Project of Sichuan Province [2018HH0082] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Basic Geological Survey Project of China Geological Survey; International Cooperation Project of Sichuan Province This study was supported by the National Nature Science Foundation of China (Grant Number 41772321), the Basic Geological Survey Project of China Geological Survey (Grant Number DD20160272) and the International Cooperation Project of Sichuan Province (Grant Number 2018HH0082). 131 24 26 4 34 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0921-030X 1573-0840 NAT HAZARDS Nat. Hazards APR 2020.0 101.0 3 853 877 10.1007/s11069-020-03899-9 0.0 MAR 2020 25 Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Geology; Meteorology & Atmospheric Sciences; Water Resources LB0IK 2023-03-23 WOS:000520808000002 0
J Gu, GY; Zhu, LM; Su, CY; Ding, H; Fatikow, S Gu, Guo-Ying; Zhu, Li-Min; Su, Chun-Yi; Ding, Han; Fatikow, Sergej Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article Creep; feedback control; feedforward control; hysteresis; piezo-actuated nanopositioning stage; piezoelectric actuator; vibrational dynamics ATOMIC-FORCE MICROSCOPY; DYNAMIC SURFACE CONTROL; REPRESENTING SMART ACTUATORS; UNCERTAIN NONLINEAR-SYSTEMS; ROBUST ADAPTIVE-CONTROL; MOTION TRACKING CONTROL; NEURAL-NETWORK CONTROL; PIEZOELECTRIC ACTUATORS; VIBRATION COMPENSATION; HYSTERESIS COMPENSATION Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies. [Gu, Guo-Ying; Zhu, Li-Min; Ding, Han] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Sch Mech Engn, Shanghai 200240, Peoples R China; [Su, Chun-Yi] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada; [Fatikow, Sergej] Carl von Ossietzky Univ Oldenburg, Div Microrobot & Control Engn, D-26129 Oldenburg, Germany Shanghai Jiao Tong University; Concordia University - Canada; Carl von Ossietzky Universitat Oldenburg Gu, GY; Zhu, LM; Ding, H (corresponding author), Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Sch Mech Engn, Shanghai 200240, Peoples R China.;Su, CY (corresponding author), Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada.;Fatikow, S (corresponding author), Carl von Ossietzky Univ Oldenburg, Div Microrobot & Control Engn, D-26129 Oldenburg, Germany. guguoying@sjtu.edu.cn; zhulm@sjtu.edu.cn; cysu@alcor.concordia.ca; hding@sjtu.edu.cn; fatikow@uni-oldenburg.de Gu, Guoying/L-8995-2013; Su, Chun-Yi/AAF-6064-2019; Zhu, Li/GXH-9801-2022 Gu, Guoying/0000-0002-7778-4523; Su, Chun-Yi/0000-0002-1869-5563; National Natural Science Foundation of China [51405293, 91023047]; China Postdoctoral Science Foundation [2014T70415] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was in part supported by the National Natural Science Foundation of China under Grants 51405293 and 91023047 and the China Postdoctoral Science Foundation funded project under Grant 2014T70415. 203 382 393 16 343 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. JAN 2016.0 13.0 1 313 332 10.1109/TASE.2014.2352364 0.0 20 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems DJ8DS 2023-03-23 WOS:000374443300030 0
C Shi, ZQ; Lin, HB; Liu, L; Liu, RJ; Han, JQ; Shi, AY Int Speech Commun Assoc Shi, Ziqiang; Lin, Huibin; Liu, Liu; Liu, Rujie; Han, Jiqing; Shi, Anyan Deep Attention Gated Dilated Temporal Convolutional Networks with Intra-Parallel Convolutional Modules for End-to-End Monaural Speech Separation INTERSPEECH 2019 Interspeech English Proceedings Paper Interspeech Conference SEP 15-19, 2019 Graz, AUSTRIA speech separation; cocktail party problem; temporal convolutional neural network; gating mechanism; parallel convolutional networks PERFORMANCE Monaural speech separation techniques are far from satisfactory and are a challenging task due to interference from multiple sources. Recently the deep dilated temporal convolutional networks (TCN) has proven to be very effective in sequence modeling. This work explores how to extend TCN to result a new, state-of-the-art monaural speech separation method. First, a new gating mechanism is introduced and added to generate a gated TCN. The gated activation controls the flow of information. Further in order to combine multiple training models to reduce the performance variance and improve the effect of speech separation, we propose to use the principle of ensemble learning in the gated TCN architecture by replacing the convolutional modules corresponding to each dilated factor with multiple identical branches of the convolutional components. For the sake of objectivity, we propose to train the network by directly optimizing in a permutation invariant training (PIT) style of the utterance level signal-to-distortion ratio (SDR). Our experiments with the public WSJ0-2mix data corpus resulted in an 18.2 dB improvement in SDR, indicating that our proposed network can improve the performance of speaker separation tasks. [Shi, Ziqiang; Lin, Huibin; Liu, Liu; Liu, Rujie] Fujitsu Res & Dev Ctr, Madrid, Spain; [Han, Jiqing] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China; [Shi, Anyan] ShuangFeng First, Loudi, Hunan, Peoples R China Harbin Institute of Technology Shi, ZQ (corresponding author), Fujitsu Res & Dev Ctr, Madrid, Spain. shiziqiang@cn.fujitsu.com 33 24 24 2 2 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2019.0 3183 3187 10.21437/Interspeech.2019-1373 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4LP 2023-03-23 WOS:000831796403065 0
J Guo, YJ; Ni, BB; Fu, S; Wang, DD; Shprits, YY; Zhelavskaya, IS; Feng, MH; Guo, DY Guo, Yingjie; Ni, Binbin; Fu, Song; Wang, Dedong; Shprits, Y. Y.; Zhelavskaya, I. S.; Feng, Minghang; Guo, Deyu Identification of Controlling Geomagnetic and Solar Wind Factors for Magnetospheric Chorus Intensity Using Feature Selection Techniques JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS English Article VAN ALLEN PROBES; GLOBAL DISTRIBUTION; ELECTRON-DENSITY; WAVE MODEL; ACCELERATION; GENERATION; PREDICTION Using over-5-year EMFISIS wave measurements from Van Allen Probes, we present a detailed survey to identify the controlling factors among the geomagnetic indices and solar wind parameters for the 1-min root mean square amplitudes of lower band chorus (LBC) and upper band chorus (UBC). A set of important features are automatically determined by feature selection techniques, namely, Random Forest and Maximum Relevancy Minimum Redundancy. Our analysis results indicate the AE index with zero-time-delay dominates the intensity evolution of LBC and UBC, consistent with the evidence that chorus waves prefer to occur and amplify during enhanced substorm periods. Regarding solar wind parameters, solar wind speed and IMF B-z are identified as the controlling factors for chorus wave intensity. Using the combination of all these important features, a predictive neural network model of chorus wave intensity is established to reconstruct the temporal variations of chorus wave intensity, for which application of Random Forest produces the overall best performance. Plain Language Summary Whistler mode chorus waves are electromagnetic waves observed in the low-density region near the geomagnetic equator outside the plasmapause. The dynamics of Earth's radiation belts are largely influenced by chorus waves owing to their dual contributions to both radiation belt electron acceleration and loss. In this study, we use feature selection techniques to identify the controlling geomagnetic and solar wind factors for magnetospheric chorus waves. Feature selection techniques implement the processes which can select the features most influential to the output. In this study, the inputs are geomagnetic indices and solar wind parameters and the output is the chorus wave intensity. The results indicate that AE index with zerotime delay dominates the chorus wave intensity. Furthermore, solar wind speed and IMF B-z are identified as the most important solar wind drivers for chorus wave intensity. On basis of the combination of all these important geomagnetic and solar wind controlling factors, we develop a neural network model of chorus wave intensity, and find that the model with the inputs identified using the Random Forest method produces the overall best performance. [Guo, Yingjie; Ni, Binbin; Fu, Song; Feng, Minghang; Guo, Deyu] Wuhan Univ, Sch Elect Informat, Dept Space Phys, Wuhan, Peoples R China; [Ni, Binbin] Chinese Acad Sci, Ctr Excellence Comparat Planetol, Hefei, Peoples R China; [Wang, Dedong; Shprits, Y. Y.; Zhelavskaya, I. S.] GFZ German Res Ctr Geosci, Sect 2 7 Space Phys & Space Weather, Potsdam, Germany; [Shprits, Y. Y.; Zhelavskaya, I. S.] Univ Potsdam, Inst Phys & Astron, Potsdam, Germany; [Shprits, Y. Y.] Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA USA Wuhan University; Chinese Academy of Sciences; Helmholtz Association; Helmholtz-Center Potsdam GFZ German Research Center for Geosciences; University of Potsdam; University of California System; University of California Los Angeles Ni, BB; Fu, S (corresponding author), Wuhan Univ, Sch Elect Informat, Dept Space Phys, Wuhan, Peoples R China.;Ni, BB (corresponding author), Chinese Acad Sci, Ctr Excellence Comparat Planetol, Hefei, Peoples R China. bbni@whu.edu.cn; fusion@whu.edu.cn Wang, Dedong/AAF-4803-2019 Wang, Dedong/0000-0002-4213-4037; Shprits, Yuri/0000-0002-9625-0834; Guo, Deyu/0000-0001-5059-5717; Guo, Yingjie/0000-0001-5441-7395 National Natural Science Foundation of China [42188101, 42025404, 41974186, 42174188, 41674163]; B-type Strategic Priority Program of the Chinese Academy of Sciences [XDB41000000]; pre-research projects on Civil Aerospace Technologies - China National Space Administration [D020303, D020308, D020104]; National Key R&D Program of China [2018YFC1407303] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); B-type Strategic Priority Program of the Chinese Academy of Sciences; pre-research projects on Civil Aerospace Technologies - China National Space Administration; National Key R&D Program of China This work was supported by the National Natural Science Foundation of China (grant nos. 42188101, 42025404, 41974186, 42174188, 41674163), the B-type Strategic Priority Program of the Chinese Academy of Sciences (grant no. XDB41000000), the pre-research projects on Civil Aerospace Technologies (grant nos. D020303, D020308, D020104) funded by the China National Space Administration, the National Key R&D Program of China (2018YFC1407303). Song Fu thanks Xiangning Chu for constructive discussions. 54 2 3 1 4 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-9380 2169-9402 J GEOPHYS RES-SPACE J. Geophys. Res-Space Phys. JAN 2022.0 127.0 1 e2021JA029926 10.1029/2021JA029926 0.0 14 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics ZF4OW Green Published 2023-03-23 WOS:000759550200019 0
J Xu, X; Tong, XH; Plaza, A; Zhong, YF; Xie, H; Zhang, LP Xu, Xiong; Tong, Xiaohua; Plaza, Antonio; Zhong, Yanfei; Xie, Huan; Zhang, Liangpei Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Hyperspectral imaging; spectral unmixing (SU); subpixel mapping REMOTELY-SENSED IMAGES; HOPFIELD NEURAL-NETWORK; PARTICLE SWARM OPTIMIZATION; SPATIAL-RESOLUTION; SENSING IMAGERY; GENETIC ALGORITHM; MAP MODEL; PIXEL; INUNDATION; CLASSIFICATION Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been obvious that the utilized abundance map has a strong impact on the subsequent subpixel mapping procedure. However, limited attention has been given to the impact of the different aspects in the spectral unmixing model on the subpixel mapping performance. In this paper, a detailed quantitative assessment of different aspects in linear spectral mixture analysis, such as the criteria used to determine the types of pixels, the abundance sum-to-one constraint in the unmixing, and the accuracy of the utilized abundance maps, is investigated. This is accomplished by designing an experimental procedure with replaceable components. A total of six hyperspectral images (four synthetic and two real) were utilized in our experiments. By investigating these critical issues, we can further improve the performance of subpixel mapping techniques. [Xu, Xiong; Tong, Xiaohua; Xie, Huan] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Xu, Xiong; Plaza, Antonio] Univ Exremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain; [Zhong, Yanfei; Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China Tongji University; Wuhan University Tong, XH (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. xvxiong@tongji.edu.cn; xhtong@tongji.edu.cn; aplaza@unex.es; zhongyanfei@whu.edu.cn; huanxie@tongji.edu.cn; zlp62@whu.edu.cn Plaza, Antonio/C-4455-2008; xie, huan/GQQ-4398-2022 Plaza, Antonio/0000-0002-9613-1659; China Postdoctoral Science Foundation [2014M560353, 2015T80450]; National Natural Science Foundation of China [41401398, 41325005, 41201426, 41171352, 41171327]; Fund of Shanghai Outstanding Academic Leaders Program [12XD1404900]; Kwang-Hua Foundation of College of Civil Engineering, Tongji University China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fund of Shanghai Outstanding Academic Leaders Program; Kwang-Hua Foundation of College of Civil Engineering, Tongji University This work was supported in part by China Postdoctoral Science Foundation under Grant 2014M560353 and Grant 2015T80450, in part by the National Natural Science Foundation of China under Grant 41401398, Grant 41325005, Grant 41201426, Grant 41171352, and Grant 41171327, in part by the Fund of Shanghai Outstanding Academic Leaders Program under Grant 12XD1404900, and in part by Kwang-Hua Foundation of College of Civil Engineering, Tongji University. 47 10 11 3 39 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. APR 2017.0 10.0 4 1589 1600 10.1109/JSTARS.2016.2624560 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology ER6VY 2023-03-23 WOS:000398948400028 0
C Gros, TP; Hermanns, H; Hoffmann, J; Klauck, M; Kohl, MA; Wolf, V Shoham, S; Vizel, Y Gros, Timo P.; Hermanns, Holger; Hoffmann, Joerg; Klauck, Michaela; Koehl, Maximilian A.; Wolf, Verena MOGYM: Using Formal Models for Training and Verifying Decision-making Agents COMPUTER AIDED VERIFICATION (CAV 2022), PT II Lecture Notes in Computer Science English Proceedings Paper 34th International Conference on Computer-Aided Verification (CAV) held as part of the Federated Logic Conference (FLoC) AUG 07-10, 2022 Haifa, ISRAEL Formal Methods; Statistical Model Checking; Reinforcement Learning CHECKING; GO MOGYM, is an integrated toolbox enabling the training and verification of machine-learned decision-making agents based on formal models, for the purpose of sound use in the real world. Given a formal representation of a decision-making problem in the JANI format and a reach-avoid objective, MOGYM (a) enables training a decision-making agent with respect to that objective directly on the model using reinforcement learning (RL) techniques, and (b) it supports rigorous assessment of the quality of the induced decision-making agent by means of deep statistical model checking (DSMC). MOGYM implements the standard interface for training environments established by OpenAI Gym, thereby connecting to the vast body of existing work in the RL community. In return, it makes accessible the large set of existing JANI model checking benchmarks to machine learning research. It thereby contributes an efficient feedback mechanism for improving in particular reinforcement learning algorithms. The connective part is implemented on top of Momba. For the DSMC quality assurance of the learned decision-making agents, a variant of the statistical model checker modes of the MODEST TOOLSET is leveraged, which has been extended by two new resolution strategies for non-determinism when encountered during statistical evaluation. [Gros, Timo P.; Hermanns, Holger; Hoffmann, Joerg; Klauck, Michaela; Koehl, Maximilian A.; Wolf, Verena] Saarland Univ, Saarland Informat Campus, Saarbrucken, Germany; [Hermanns, Holger] Inst Intelligent Software, Guangzhou, Peoples R China Saarland University Klauck, M; Kohl, MA (corresponding author), Saarland Univ, Saarland Informat Campus, Saarbrucken, Germany. timopgros@cs.uni-saarland.de; hermanns@cs.uni-saarland.de; hoffmann@cs.uni-saarland.de; klauck@cs.uni-saarland.de; koehl@cs.uni-saarland.de; wolf@cs.uni-saarland.de Klauck, Michaela/0000-0002-6353-227X German Research Foundation (DFG) [389792660, TRR 248]; European Regional Development Fund (ERDF); Key-Area Research and Development Program of Guangdong Province [2018B010107004] German Research Foundation (DFG)(German Research Foundation (DFG)); European Regional Development Fund (ERDF)(European Commission); Key-Area Research and Development Program of Guangdong Province Authors are listed alphabetically. This work was partially supported by the German Research Foundation (DFG) under grant No. 389792660 as part of TRR 248, by the European Regional Development Fund (ERDF), and by the Key-Area Research and Development Program Grant 2018B010107004 of Guangdong Province. 67 2 2 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-13188-2; 978-3-031-13187-5 LECT NOTES COMPUT SC 2022.0 13372.0 430 443 10.1007/978-3-031-13188-2_21 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU0LR hybrid 2023-03-23 WOS:000870310500021 0
J Luo, HS; Li, TR; Liu, B; Wang, B; Unger, H Luo, Huaishao; Li, Tianrui; Liu, Bing; Wang, Bin; Unger, Herwig Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Aspect term extraction; dependency syntactic tree; sentiment analysis; recursive neural network BACKPROPAGATION Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency tree. In this paper, we first propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is then developed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets. [Luo, Huaishao; Li, Tianrui; Wang, Bin] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Sichuan, Peoples R China; [Li, Tianrui] Southwest Jiaotong Univ, Inst Artificial Intelligence, Chengdu 611756, Sichuan, Peoples R China; [Li, Tianrui] Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Sichuan, Peoples R China; [Liu, Bing] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA; [Unger, Herwig] Fern Univ Hagen, Fac Math & Comp Sci, D-58097 Hagen, Germany Southwest Jiaotong University; Southwest Jiaotong University; Southwest Jiaotong University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Fern University Hagen Li, TR (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Sichuan, Peoples R China.;Li, TR (corresponding author), Southwest Jiaotong Univ, Inst Artificial Intelligence, Chengdu 611756, Sichuan, Peoples R China.;Li, TR (corresponding author), Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Sichuan, Peoples R China. huaishaoluo@gmail.com; trli@swjtu.edu.cn; liub@uic.edu; binwang007@gmail.com; herwig.unger@gmail.com luo, huaishao/GQY-9658-2022; Li, Tianrui/A-4889-2012; Li, Tianrui/F-4974-2019 luo, huaishao/0000-0003-4297-1270; Li, Tianrui/0000-0001-7780-104X National Key R&D Program of China [2017YFB1401401] National Key R&D Program of China This work was supported by the National Key R&D Program of China under Grant 2017YFB1401401. 65 32 37 2 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. JUL 2019.0 27.0 7 1201 1212 10.1109/TASLP.2019.2913094 0.0 12 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering HX7EY Green Submitted 2023-03-23 WOS:000467568600008 0
J Wang, SH; Yang, M; Du, SD; Yang, JQ; Liu, B; Gorriz, JM; Ramirez, J; Yuan, TF; Zhang, YD Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M.; Ramirez, Javier; Yuan, Ti-Fei; Zhang, Yudong Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning FRONTIERS IN COMPUTATIONAL NEUROSCIENCE English Article unilateral hearing loss; sensorineural hearing loss; wavelet entropy; support vector machine; directed acyclic graph; confusion matrix; computer aided diagnosis PATHOLOGICAL BRAIN DETECTION; ALZHEIMERS-DISEASE; TSALLIS ENTROPY; DECISION TREE; CLASSIFICATION; CHILDREN; MORPHOMETRY; GENERATION; TRANSFORM; DIAGNOSIS Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss. [Wang, Shuihua; Du, Sidan] Nanjing Univ, Sch Elect Sci & Engn, Nanjing, Jiangsu, Peoples R China; [Wang, Shuihua; Yuan, Ti-Fei; Zhang, Yudong] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China; [Wang, Shuihua] Hunan Police Acad, Hunan Prov Key Lab Network Invest Technol, Changsha, Hunan, Peoples R China; [Yang, Ming] Nanjing Med Univ, Nanjing Childrens Hosp, Dept Radiol, Nanjing, Jiangsu, Peoples R China; [Yang, Ming] Quanzhou Norma Univ, Key Lab Intelligent Comp & Informat Proc Fujian, Quanzhou, Peoples R China; [Yang, Jiquan] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing, Jiangsu, Peoples R China; [Liu, Bin] Southeast Univ, Zhong Da Hosp, Dept Radiol, Nanjing, Jiangsu, Peoples R China; [Gorriz, Juan M.; Ramirez, Javier] Univ Granada, Dept Signal Theory Networking & Commun, Granada, Spain; [Yuan, Ti-Fei] Zhejiang Univ, State Key Lab CAD & CG, Hangzhou, Zhejiang, Peoples R China; [Zhang, Yudong] State Stat Bur, Key Lab Stat Informat Technol & Data Min, Chengdu, Peoples R China Nanjing University; Nanjing Normal University; Nanjing Medical University; Quanzhou Normal University; Southeast University - China; University of Granada; Zhejiang University Du, SD (corresponding author), Nanjing Univ, Sch Elect Sci & Engn, Nanjing, Jiangsu, Peoples R China.;Yuan, TF; Zhang, YD (corresponding author), Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China.;Gorriz, JM; Ramirez, J (corresponding author), Univ Granada, Dept Signal Theory Networking & Commun, Granada, Spain.;Yuan, TF (corresponding author), Zhejiang Univ, State Key Lab CAD & CG, Hangzhou, Zhejiang, Peoples R China.;Zhang, YD (corresponding author), State Stat Bur, Key Lab Stat Informat Technol & Data Min, Chengdu, Peoples R China. coff128@nju.edu.cn; goiriz@ugr.es; javierrp@ugr.es; ytf0707@126.com; zhangyudong@njnu.edu.cn Ramírez, Javier/B-1836-2012; Gorriz, Juan Manuel/C-2385-2012; Wang, Shuihua/G-7326-2016; Zhang, Yudong/I-7633-2013 Ramírez, Javier/0000-0002-6229-2921; Gorriz, Juan Manuel/0000-0001-7069-1714; Wang, Shuihua/0000-0003-4713-2791; Zhang, Yudong/0000-0002-4870-1493; Tsolaki, Magda/0000-0002-2072-8010 NSFC [61602250, 61271231, 51407095, 61503188]; Open Fund of Key laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172016K17]; Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau [SDL201608]; Program of Natural Science Research of Jiangsu Higher Education Institutions [16KJB520025]; Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology [2016WLZC013]; Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing [BD201607]; Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]; Natural Science Foundation of Jiangsu Province [BK20150983] NSFC(National Natural Science Foundation of China (NSFC)); Open Fund of Key laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University; Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau; Program of Natural Science Research of Jiangsu Higher Education Institutions; Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology; Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing; Nanjing Normal University Research Foundation for Talented Scholars; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province) This paper was supported by NSFC (61602250, 61271231, 51407095, 61503188), Open Fund of Key laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (93K172016K17), Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau, (SDL201608), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Natural Science Foundation of Jiangsu Province (BK20150983). 67 57 58 1 27 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5188 FRONT COMPUT NEUROSC Front. Comput. Neurosci. OCT 19 2016.0 10.0 106 10.3389/fncom.2016.00106 0.0 11 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology DZ2AW 27807415.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000385645300001 0
J Grojean, C; Paul, A; Qian, ZN Grojean, Christophe; Paul, Ayan; Qian, Zhuoni Resurrecting b(b)over-barh with kinematic shapes JOURNAL OF HIGH ENERGY PHYSICS English Article Higgs Physics; Beyond Standard Model HIGGS PRODUCTION; TOP-QUARK; ASSOCIATION The associated production of a bb (b) over bar pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, y(b). However, the signal is shrouded by several background processes including the irreducible Zh, Z -> b (b) over bar background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the y(b)-sensitive production modes from both the irreducible and the QCD-QED backgrounds using the b (b) over bar gamma gamma final state. We draw a page from game theory and use Shapley values to make Boosted Decision Trees interpretable in terms of kinematic measurables and provide physics insights into the variances in the kinematic shapes of the different channels that help us complete this feat. Adding interpretability to the machine learning algorithm opens up the black-box and allows us to cherry-pick only those kinematic variables that matter most in the analysis. We resurrect the hope of constraining the size and, possibly, the phase of y(b) using kinematic shape studies of b (b) over barh production with the full HL-LHC data and at FCC-hh. [Grojean, Christophe; Paul, Ayan; Qian, Zhuoni] Deutsch Elektronen Synchrotron DESY, Notkestr 85, D-22607 Hamburg, Germany; [Grojean, Christophe; Paul, Ayan] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany; [Qian, Zhuoni] Shandong Univ, Dept Phys, Jinan 250100, Shandong, Peoples R China Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Humboldt University of Berlin; Shandong University Qian, ZN (corresponding author), Deutsch Elektronen Synchrotron DESY, Notkestr 85, D-22607 Hamburg, Germany.;Qian, ZN (corresponding author), Shandong Univ, Dept Phys, Jinan 250100, Shandong, Peoples R China. christophe.grojean@desy.de; ayan.paul@desy.de; zhuoni.qian@desy.de Qian, Zhuoni/AAS-7689-2021 Qian, Zhuoni/0000-0002-2914-7255; Paul, Ayan/0000-0002-2156-4062 69 8 8 1 12 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. APR 15 2021.0 4 139 10.1007/JHEP04(2021)139 0.0 37 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics RP0NI Green Published, gold 2023-03-23 WOS:000641433300001 0
J Gao, T; Zhu, SL; Liu, J; Shen, J; Shen, JL; Yang, SG; Xiong, PC Gao, Tian; Zhu, Shanliang; Liu, Jing; Shen, Jun; Shen, Jialie; Yang, Shuguo; Xiong, Pengcheng A new context-aware approach for automatic Chinese poetry generation KNOWLEDGE-BASED SYSTEMS English Article Poetry generation; Neural networks; Seq2seq; Keyword team Chinese poetry has been a favorite literary genre for thousands of years. Chinese ancient poetry is still being read and practiced, and many famous ancient Chinese poets are honored and adorned. Recently, deep learning has been widely adopted for poetry generation. In this paper, we present a new contextaware Chinese poetry generation method based on sequence-to-sequence framework. We generate a new concept called keyword team, which is a combination of all the keywords to capture the context of the Chinese poetry. Then we use the keyword, the keyword team and the previously generated lines to generate the present line in the poetry. We find that, by including keyword teams into the generation of the poetry, it can additionally perceive the keywords of preceding and succeeding lines to generate the present line, which can effectively improve the adhesion among the overall lines. The comprehensive evaluation results show that our proposed model outperforms many of the state-of-the-art poetry generation models. (C) 2021 Elsevier B.V. All rights reserved. [Gao, Tian; Zhu, Shanliang; Yang, Shuguo; Xiong, Pengcheng] Qingdao Univ Sci & Technol, Sch Math & Phys, Res Inst Math & Interdisciplinary Sci, Qingdao 266061, Peoples R China; [Liu, Jing] Weifang Univ Sci & Technol, Weifang 262700, Peoples R China; [Shen, Jun] Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW 2522, Australia; [Shen, Jialie] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Qingdao University of Science & Technology; University of Wollongong; Queens University Belfast Zhu, SL (corresponding author), Qingdao Univ Sci & Technol, Sch Math & Phys, Res Inst Math & Interdisciplinary Sci, Qingdao 266061, Peoples R China. znushanliang@qust.eau.cn Zhu, Shanliang/0000-0002-6194-3614; Yang, Shuguo/0000-0003-3363-0044; Shen, Jun/0000-0002-9403-7140 30 0 0 4 17 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. NOV 28 2021.0 232.0 107409 10.1016/j.knosys.2021.107409 0.0 SEP 2021 11 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WB4NP 2023-03-23 WOS:000703550700001 0
J Razavi, M; Leverrier, A; Ma, XF; Qi, B; Yuan, ZL Razavi, Mohsen; Leverrier, Anthony; Ma, Xiongfeng; Qi, Bing; Yuan, Zhiliang Quantum key distribution and beyond: introduction JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS English Editorial Material DISCRIMINATION; PROBABILITY; SYSTEM This feature issue presents a collection of recent theoretical and experimental developments in the field of quantum key distribution (QKD) and its extension to other quantum cryptography protocols and devices. It encompasses work on a variety of QKD protocols, including continuous-variable, measurement-device independent, and twin-field QKD, as well as other newly proposed protocols, in platforms ranging from optical fiber through to wireless indoor and satellite links. It covers examples of hacking strategies and their countermeasures as well as applications of machine learning techniques in designing quantum networks. It also includes new developments in efficient superconducting photon-number resolving detectors as well as fast quantum random number generators. Distinctively, this feature issue demonstrates how different expertise in science and engineering can come together to produce an outcome that hopefully takes us one step closer to the wide-scale deployment of quantum communications technologies. (C) 2019 Optical Society of America [Razavi, Mohsen] Univ Leeds, Sch Elect & Elect Engn, Leeds LS2 9JT, W Yorkshire, England; [Leverrier, Anthony] Inria Paris, Paris, France; [Ma, Xiongfeng] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Ctr Quantum Informat, Beijing 100084, Peoples R China; [Qi, Bing] Oak Ridge Natl Lab, Computat Sci & Engn Div, Quantum Informat Sci Grp, Oak Ridge, TN 37831 USA; [Qi, Bing] Univ Tennessee, Dept Phys & Astron, Knoxville, TN 37996 USA; [Yuan, Zhiliang] Toshiba Res Europe Ltd, 208 Cambridge Sci Pk, Cambridge CB4 0GZ, England University of Leeds; Tsinghua University; United States Department of Energy (DOE); Oak Ridge National Laboratory; University of Tennessee System; University of Tennessee Knoxville; Toshiba Corporation Razavi, M (corresponding author), Univ Leeds, Sch Elect & Elect Engn, Leeds LS2 9JT, W Yorkshire, England. m.razavi@leeds.ac.uk qi, bing/HMD-7871-2023; Leverrier, Anthony/B-1937-2009; Yuan, Zhiliang/AAY-7430-2021; Yuan, Zhiliang/HHZ-6646-2022; Ma, Xiongfeng/B-2000-2008 Leverrier, Anthony/0000-0002-6707-1458; Yuan, Zhiliang/0000-0001-5276-9151; Yuan, Zhiliang/0000-0001-5276-9151; Ma, Xiongfeng/0000-0002-9441-4006 19 7 8 1 50 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0740-3224 1520-8540 J OPT SOC AM B J. Opt. Soc. Am. B-Opt. Phys. MAR 1 2019.0 36.0 3 QKD1 QKD2 10.1364/JOSAB.36.00QKD1 0.0 2 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics HN3XG Green Accepted 2023-03-23 WOS:000460117900020 0
J Xu, XJ; Tatge, L; Xu, XL; Liu, Y Xu, Xiaojing; Tatge, Lukas; Xu, Xiaolin; Liu, Ying Blockchain applications in the supply chain management in German automotive industry PRODUCTION PLANNING & CONTROL English Article; Early Access Blockchain technology; case study; supply chain management; German automotive industry TECHNOLOGY ADOPTION; E-BUSINESS; BIG DATA; INFORMATION; CHALLENGES; FRAMEWORK; FUTURE; DELPHI; SUSTAINABILITY; PERFORMANCE The automotive industry appears to be a particularly attractive application case for blockchain technology adoption. Blockchain is one of the innovations that has much potential to change current business processes in German original equipment manufacturers (OEMs) and enable new services for their customers. This paper combines collective case study and in-depth interviews to explore the potentials and existing challenges of blockchain technology-based applications at German OEMs. The results suggest that blockchain applications have advantages in aggregating product information, securing transaction information, and establishing a reliable supply chain. Based on the TOE (technology, organisation and environment) model, our case study shows that the biggest obstacles for blockchain technology adoption in the automotive supply chain include: technology immaturity, lack of guidance and industry standards, non-cooperation of chain members, and legislative ambiguity. Based on auto manufacturers investigated, blockchain technology is perceived to have great potentials in reducing process costs, ensure product quality, and enhance the automotive supply chain's visibility and digitisation. [Xu, Xiaojing; Xu, Xiaolin; Liu, Ying] Nanjing Univ, Business Sch, Business Adm, Nanjing, Peoples R China; [Tatge, Lukas] Univ Gottingen, Business Adm, Gottingen, Germany Nanjing University; University of Gottingen Liu, Y (corresponding author), Nanjing Univ, Business Sch, Business Adm, Nanjing, Peoples R China. liuying@njmu.edu.cn Xu, Xiaojing/0000-0003-0469-0224 61 7 7 28 63 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0953-7287 1366-5871 PROD PLAN CONTROL Prod. Plan. Control 10.1080/09537287.2022.2044073 0.0 MAR 2022 15 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science ZL6SG 2023-03-23 WOS:000763804500001 0
C Zeng, ZQ; Liu, S; Wang, L IEEE Zeng, Zhuoqi; Liu, Steven; Wang, Lei UWB NLOS identification with feature combination selection based on genetic algorithm 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) IEEE International Symposium on Consumer Electronics English Proceedings Paper IEEE International Conference on Consumer Electronics (ICCE) JAN 11-13, 2019 Las Vegas, NV IEEE localization; UWB; CIR; NLOS identification; genetic algorithm; SVM; feature combination selection Non-line-of-sight (NLOS) identification is very important for accurate localization based on ultra-wide band (UWB) system. One of the most widely used approach for NLOS detection is based on machine learning algorithms with features extracted from the channel impulse response (CIR). Features, such as kurtosis, mean excess delay, root mean delay, energy and rise time are discussed in a lot of papers. Other features, like signal to noise ratio, form factor and crest factor etc. are barely discussed but they are also very useful parameters for NLOS detection. In this paper 18 useful features are discussed in total. The support vector machine (SVM) is used for the identification of the NLOS condition. Since the identification accuracy does not always improve with an increase in the number of used features, in this paper the best feature combination is selected based on genetic algorithm. By reducing the used features, not only the accuracy improves, but also the computation complexity is reduced. The experimental results show that, the RMS delay, maximal amplitude, received signal energy, distance between MS and BS, peak to start of the received pulses time delay are the optimal combination leading to best accuracy. [Zeng, Zhuoqi] Bosch China Investment Ltd, CR RTC5 AP, Shanghai, Peoples R China; [Liu, Steven] Univ Kaiserslautern, Inst Control Syst, Kaiserslautern, Germany; [Wang, Lei] Tongji Univ, Sino German Sch Postgrad Studies, Shanghai, Peoples R China Bosch; University of Kaiserslautern; Tongji University Zeng, ZQ (corresponding author), Bosch China Investment Ltd, CR RTC5 AP, Shanghai, Peoples R China. Zhuoqi.ZENG@cn.bosch.com; sliu@eit.uni-kl.de; leiwang@tongji.edu.cn Liu, Steven/0000-0002-3407-2745 National Key R&D Program of China [2017YFE0100900] National Key R&D Program of China This paper is supported by National Key R&D Program of China. No. 2017YFE0100900. 8 9 9 1 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2158-3994 978-1-5386-7910-4 I SYMP CONSUM ELECTR 2019.0 5 Computer Science, Cybernetics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM3ZO 2023-03-23 WOS:000462912600170 0
J Yakimovich, A; Beaugnon, A; Huang, Y; Ozkirimli, E Yakimovich, Artur; Beaugnon, Anael; Huang, Yi; Ozkirimli, Elif Labels in a haystack: Approaches beyond supervised learning in biomedical applications PATTERNS English Review TECHNOLOGIES Recent advances in biomedical machine learning demonstrate great potential for data-driven techniques in health care and biomedical research. However, this potential has thus far been hampered by both the scarcity of annotated data in the biomedical domain and the diversity of the domain's subfields. While unsupervised learning is capable of finding unknown patterns in the data by design, supervised learning requires human annotation to achieve the desired performance through training. With the latter performing vastly better than the former, the need for annotated datasets is high, but they are costly and laborious to obtain. This review explores a family of approaches existing between the supervised and the unsupervised problem setting. The goal of these algorithms is to make more efficient use of the available labeled data. The advantages and limitations of each approach are addressed and perspectives are provided. [Yakimovich, Artur] Roche Prod Ltd, Roche Pharma Int Informat, Welwyn Garden City, Herts, England; [Beaugnon, Anael] Roche Boulogne Billancourt, Roche Pharma Int Informat, Boulogne, France; [Huang, Yi] Roche China Holding Ltd, Roche Pharma Int Informat, Shanghai, Peoples R China; [Ozkirimli, Elif] F Hoffmann La Roche & Cie AG, Roche Pharma Int Informat, Kaiseraugst, Switzerland Roche Holding; Roche Holding Yakimovich, A (corresponding author), Roche Prod Ltd, Roche Pharma Int Informat, Welwyn Garden City, Herts, England.;Beaugnon, A (corresponding author), Roche Boulogne Billancourt, Roche Pharma Int Informat, Boulogne, France.;Huang, Y (corresponding author), Roche China Holding Ltd, Roche Pharma Int Informat, Shanghai, Peoples R China.;Ozkirimli, E (corresponding author), F Hoffmann La Roche & Cie AG, Roche Pharma Int Informat, Kaiseraugst, Switzerland. artur.yakimovich@roche.com; anael.beaugnon@roche.com; anael.beaugnon@roche.com; elif.ozkirimli@roche.com Yakimovich, Artur/AAA-5565-2020 Yakimovich, Artur/0000-0003-2458-4904 139 1 1 1 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns DEC 10 2021.0 2.0 12 10.1016/j.patter.2021.100383 0.0 DEC 2021 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science XQ1UT 34950904.0 Green Accepted 2023-03-23 WOS:000731338600002 0
J Tu, B; Zhou, CL; Kuang, WL; Chen, SY; Plaza, A Tu, Bing; Zhou, Chengle; Kuang, Wenlan; Chen, Siyuan; Plaza, Antonio Multiattribute Sample Learning for Hyperspectral Image Classification Using Hierarchical Peak Attribute Propagation IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Training; Hyperspectral imaging; Annotations; Labeling; Manuals; Deep learning; Costs; Guided filtering; hyperspectral classification; label propagation; multiattribute sample learning; superpixel REPRESENTATION; MACHINE; NETWORK; CNN Although hyperspectral image (HSI) classification methods have gained popularity in the remote sensing community, their performance tends to be limited by the quantity and quality of training samples. Actually, the production (i.e., capture and annotation) of training samples requires abundant labor costs and time consumption. Multiattribute sample learning can effectively solve the cost problem for the acquisition of training samples; for this, this article proposes a novel hierarchical peak attribute propagation (HPAP) method for the annotation of multiattribute samples, which is composed of the following key technologies. First, superpixel-oriented guided filtering (SOGF) is designed to improve the distinguishability of spectral features between pixels. Then, peak attribute-driven manual labeling is performed on each multiattribute region to provide prior knowledge for label propagation. Next, peak attribute-based label propagation is applied to each multiattribute region to achieve the single-label information of pixels. Finally, multiattribute samples with a single class label are augmented to the training process of HSI supervised model to enhance their classification performance. Experimental results on several public HSI datasets indicated that the proposed HPAP method can obtain higher annotation accuracy for multiattribute samples and promote the classification accuracy of HSI classification supervised models. [Tu, Bing; Zhou, Chengle; Kuang, Wenlan; Chen, Siyuan] Hunan Inst Sci & Technol, Sch Informat Sci & Engn, Yueyang 414000, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain Hunan Institute of Science & Technology; Universidad de Extremadura Chen, SY (corresponding author), Hunan Inst Sci & Technol, Sch Informat Sci & Engn, Yueyang 414000, Peoples R China. tubing@hnist.edu.cn; chengle_zhou@foxmail.com; wenlan_kuang@foxmail.com; siyuan@hnist.edu.cn; aplaza@unex.es Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Zhou, Chengle/0000-0003-3107-5446 National Natural Science Foundation of China [61977022]; Science Foundation for Distinguished Young Scholars of Hunan Province [2020JJ2017]; Key Research and Development Program of Hunan Province [2019SK2012]; Foundation of Department of Water Resources of Hunan Province [XSKJ2021000-12, XSKJ2021000-13]; Natural Science Foundation of Hunan Province [2020JJ4340, 2021JJ40226]; Foundation of Education Bureau of Hunan Province [19A200, 20B257, 20B266, 21B0595, 21B0590]; Open Fund of Education Department of Hunan Province [20K062]; Junta de Extremadura [GR18060]; Spanish Ministerio de Spanish Ministerio de Ciencia e Innovacin [PID2019-110315RB-I00] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation for Distinguished Young Scholars of Hunan Province(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Key Research and Development Program of Hunan Province; Foundation of Department of Water Resources of Hunan Province; Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Foundation of Education Bureau of Hunan Province; Open Fund of Education Department of Hunan Province; Junta de Extremadura; Spanish Ministerio de Spanish Ministerio de Ciencia e Innovacin This work was supported in part by the National Natural Science Foundation of China under Grant 61977022; in part by the Science Foundation for Distinguished Young Scholars of Hunan Province under Grant 2020JJ2017; in part by the Key Research and Development Program of Hunan Province under Grant 2019SK2012; in part by the Foundation of Department of Water Resources of Hunan Province under Grant XSKJ2021000-12 and Grant XSKJ2021000-13; in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ4340 and Grant 2021JJ40226; in part by the Foundation of Education Bureau of Hunan Province under Grant 19A200, Grant 20B257, Grant 20B266, Grant 21B0595, and Grant 21B0590; in part by the Open Fund of Education Department of Hunan Province under Grant 20K062; in part by the Junta de Extremadura under Grant GR18060; and in part by the Spanish Ministerio de Spanish Ministerio de Ciencia e Innovacin under Project PID2019-110315RB-I00 (APRISA). The Associate Editor coordinating the review process was Dr. Mohamad Forouzanfar. 51 0 0 3 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71.0 6502617 10.1109/TIM.2022.3176286 0.0 17 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 2O1SC 2023-03-23 WOS:000818845300006 0
J Xie, CJ; Zhao, HJ; Li, KB; Zhang, ZB; Lu, XX; Peng, HD; Wang, DH; Chen, J; Zhang, X; Wu, D; Gu, YZ; Yuan, J; Zhang, L; Lu, JC Xie, Chaojun; Zhao, Hongjun; Li, Kuibiao; Zhang, Zhoubin; Lu, Xiaoxiao; Peng, Huide; Wang, Dahu; Chen, Jin; Zhang, Xiao; Wu, Di; Gu, Yuzhou; Yuan, Jun; Zhang, Lin; Lu, Jiachun The evidence of indirect transmission of SARS-CoV-2 reported in Guangzhou, China BMC PUBLIC HEALTH English Article Novel coronavirus disease; Severe acute respiratory syndrome coronavirus 2; Snot-oral transmission; Indirect transmission WUHAN BackgroundMore than 2 months have passed since the novel coronavirus disease 2019 (COVID-19) first emerged in Wuhan, China. With the migration of people, the epidemic has rapidly spread within China and throughout the world. Due to the severity of the epidemic, undiscovered transmission of COVID-19 deserves further investigation. The aim of our study hypothesized possible modes of SARS-CoV-2 transmission and how the virus may have spread between two family clusters within a residential building in Guangzhou, China.MethodsIn a cross-sectional study, we monitored and traced confirmed patients and their close contacts from January 11 to February 5, 2020 in Guangzhou, China, including 2 family cluster cases and 61 residents within one residential building. The environmental samples of the building and the throat swabs from the patients and from their related individuals were collected for SARS-CoV-2 and tested with real-time reverse transcriptase polymerase chain reaction (RT-PCR). The relevant information was collected and reported using big data tools.ResultsThere were two notable family cluster cases in Guangzhou, which included 3 confirmed patients (family No.1: patient A, B, C) and 2 confirmed patients (family No.2: patient D, E), respectively. None of patients had contact with other confirmed patients before the onset of symptoms, and only patient A and patient B made a short stop in Wuhan by train. Home environment inspection results showed that the door handle of family No.1 was positive of SARS-CoV-2. The close contacts of the 5 patients all tested negative of SARS-CoV-2 and in good health, and therefore were released after the official medical observation period of 14-days. Finally, according to the traceability investigation through applying big data analysis, we found an epidemiological association between family No.1 and family No.2, in which patient D (family No.2) was infected through touching an elevator button contaminated by snot with virus from patient A (family No.1) on the same day.ConclusionsContaminants with virus from confirmed patients can pollute the environment of public places, and the virus can survive on the surface of objects for a short period of time. Therefore, in addition to the conventional droplet transmission, there is also indirect contact transmission such as snot-oral transmission that plays a crucial role in community spread of the virus. [Xie, Chaojun; Zhao, Hongjun; Li, Kuibiao; Zhang, Zhoubin; Wang, Dahu; Zhang, Xiao; Wu, Di; Gu, Yuzhou; Yuan, Jun; Zhang, Lin; Lu, Jiachun] Guangzhou Med Univ, Inst Publ Hlth, 195 Dongfengxi Rd, Guangzhou 510182, Peoples R China; [Xie, Chaojun; Li, Kuibiao; Zhang, Zhoubin; Wang, Dahu; Zhang, Xiao; Wu, Di; Gu, Yuzhou; Yuan, Jun; Zhang, Lin] Guangzhou Ctr Dis Control & Prevent, 1 Qide Rd, Guangzhou 510440, Peoples R China; [Zhao, Hongjun; Lu, Jiachun] Guangzhou Med Univ, Affiliated Hosp 1, State Key Lab Resp Dis, 195 Dongfengxi Rd, Guangzhou 510182, Peoples R China; [Lu, Xiaoxiao] Ludwig Maximilian Univ LMU, Fac Languages & Literatures, Dept English & Amer Studies, Munich, Germany; [Peng, Huide; Chen, Jin] Baiyun Dist Ctr Dis Control & Prevent, Guangzhou, Peoples R China Guangzhou Medical University; Guangzhou Medical University; State Key Laboratory of Respiratory Disease; University of Munich Zhang, L; Lu, JC (corresponding author), Guangzhou Med Univ, Inst Publ Hlth, 195 Dongfengxi Rd, Guangzhou 510182, Peoples R China.;Zhang, L (corresponding author), Guangzhou Ctr Dis Control & Prevent, 1 Qide Rd, Guangzhou 510440, Peoples R China.;Lu, JC (corresponding author), Guangzhou Med Univ, Affiliated Hosp 1, State Key Lab Resp Dis, 195 Dongfengxi Rd, Guangzhou 510182, Peoples R China. 378563318@qq.com; jclu@gzhmu.edu.com Wu, Di/0000-0003-2462-4232 National Natural Science Foundation of China [81872694, 81673267, 81473040]; Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program [2017BT01S155]; Health Industry Scientific Research Project of Gansu Province [GSWS KY2018-18] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program; Health Industry Scientific Research Project of Gansu Province This study was supported by the National Natural Science Foundation of China.81872694, 81673267 and 81473040 (J. Lu); Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program 2017BT01S155 (J Lu); Health Industry Scientific Research Project of Gansu Province GSWS KY2018-18(H Zhao). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 20 57 57 3 14 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2458 BMC PUBLIC HEALTH BMC Public Health AUG 5 2020.0 20.0 1 1202 10.1186/s12889-020-09296-y 0.0 9 Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED) Public, Environmental & Occupational Health NC2FQ 32758198.0 gold, Green Accepted 2023-03-23 WOS:000561030300002 0
J Bo, LJ; Capponi, A; Liao, HF Bo, Lijun; Capponi, Agostino; Liao, Huafu LARGE SAMPLE MEAN-FIELD STOCHASTIC OPTIMIZATION br SIAM JOURNAL ON CONTROL AND OPTIMIZATION English Article Key words; mean -field stochastic optimization; relaxed controls; Fokker-Planck-Kolmogorov; equation; Gamma -convergence; deep residual neural networks; inf OPTIMAL BILINEAR CONTROL; NEURAL-NETWORKS; PRINCIPLE; DRIVEN We study a class of sampled stochastic optimization problems, where the underlying state process has diffusive dynamics of the mean-field type. We establish the existence of optimal relaxed controls when the sample set has finite size. The core of our paper is to prove, via \Gamma convergence, that the minimizer of the finite sample relaxed problem converges to that of the limiting optimization problem as the sample size tends to infinity. We connect the limit of the sampled objective functional to the unique solution, in the trajectory sense, of a nonlinear Fokker-PlanckKolmogorov equation in a random environment. We highlight the connection between the minimizers of our optimization problems and the optimal training weights of a deep residual neural network. [Bo, Lijun] Xidian Univ, Sch Math & Stat, Xian 710126, Shaanxi Provinc, Peoples R China; [Capponi, Agostino] Columbia Univ, Dept Ind Engn & Operat Res, New York, NY 10027 USA; [Liao, Huafu] Humboldt Univ, Dept Math, Unter Den Linden 6, D-10099 Berlin, Germany Xidian University; Columbia University; Humboldt University of Berlin Bo, LJ (corresponding author), Xidian Univ, Sch Math & Stat, Xian 710126, Shaanxi Provinc, Peoples R China. lijunbo@xidian.edu.cn; ac3827@columbia.edu; huafu.liao@hu-berlin.de Natural Science Foundation of China [11971368, 11961141009]; National Center for Applied Mathematics in Shaanxi; National Science Foundation [DMS-1716145] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Center for Applied Mathematics in Shaanxi; National Science Foundation(National Science Foundation (NSF)) The first author is supported by the Natural Science Foundation of China under grants 11971368, 11961141009 and National Center for Applied Mathematics in Shaanxi (NCAMS) . The second author is supported by the National Science Foundation under grant DMS-1716145. 50 0 0 1 1 SIAM PUBLICATIONS PHILADELPHIA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA 0363-0129 1095-7138 SIAM J CONTROL OPTIM SIAM J. Control Optim. 2022.0 60.0 4 2538 2573 10.1137/21M1424937 0.0 36 Automation & Control Systems; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Mathematics 4Z5VH Green Submitted 2023-03-23 WOS:000862275500020 0
C Liu, Y; Bao, K; Wang, QF; Zio, E IEEE Liu, Yue; Bao, Ke; Wang, Qiufang; Zio, Enrico Application of FTIR Method to Monitor the Service Condition of Used Diesel Engine Lubricant Oil 2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019) English Proceedings Paper 4th Annual International Conference on System Reliability and Safety (ICSRS) NOV 20-22, 2019 Rome, ITALY IEEE FTIR; lubricant oil condition monitor; oxidation; oil replacement interval prediction INFRARED-SPECTROSCOPY FTIR method is a common term used to monitor the lubricant oil service condition. The E2412-04 standard approved by ASTM presents a method for calculating oil condition monitoring parameters using FTIR spectroscopy. In this work, the values of ASTM parameters for 35 diesel engine oil samples were obtained to analyze the relationship between the ASTM parameters, such as the oxidation value, water content and other parameters, and the mileage of the vehicle. The driving environment of vehicle is demonstrated to be influential to lubricant oil oxidation using the ratio of oxidation rate in plateau area and in high temperature region. It is proposed that oxidation and nitration values are suitable for oil degradation analysis and oil replacement interval prediction. Further, considering the mileage, load ratio, temperature, air pressure and interference factors, the neural network of oil oxidation is constructed, and the method of oil replacement interval prediction is proposed. [Liu, Yue; Bao, Ke; Wang, Qiufang] China North Vehicle Res Inst, Beijing, Peoples R China; [Zio, Enrico] Politecn Milan, Dept Energy, Milan, Italy; [Zio, Enrico] PSL Res Univ, MINES ParisTech, CRC, Sophia Antipolis, France; [Zio, Enrico] Kyung Hee Univ, Dept Nucl Engn, Coll Engn, Seoul, South Korea Polytechnic University of Milan; UDICE-French Research Universities; Universite PSL; MINES ParisTech; Kyung Hee University Liu, Y (corresponding author), China North Vehicle Res Inst, Beijing, Peoples R China. emma_yue@qq.com; enrico.zio@polimi.it CSC CSC The authors would like to thank the CSC for the financial support and thank the LASAR Lab for the technical support. 13 0 0 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-4781-9 2019.0 175 180 6 Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BP2XQ 2023-03-23 WOS:000545634000028 0
C Wang, YQ; Guo, JW; Yan, DM; Wang, K; Zhang, XP IEEE Comp Soc Wang, Yiqun; Guo, Jianwei; Yan, Dong-Ming; Wang, Kai; Zhang, Xiaopeng A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes with Incompatible Shape Structures 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 16-20, 2019 Long Beach, CA IEEE,CVF,IEEE Comp Soc Constructing a robust and discriminative local descriptor for 3D shapes is a key component of many computer vision applications. Although existing learning-based approaches can achieve good performance in specific benchmarks, they usually fail to learn sufficient information from shapes with different types and structures (e.g., spatial resolution, connectivity, and transformation). To solve this issue, we present a more discriminative local descriptor for deformable 3D shapes with incompatible structures. Based on spectral embedding using the Laplace-Beltrami framework on the surface, we construct a novel local spectral feature that exhibits high resilience to changes in mesh resolution, triangulation, and transformation. The multiscale local spectral features around each vertex are then encoded into a geometry image called vertex spectral image in a compact manner. Such vertex spectral images can be efficiently trained to learn local descriptors using a triplet neural network. Then, we present a new benchmark dataset for training and evaluation by extending the widely used FAUST dataset. We utilize a remeshing approach to generate modified shapes with different structures. Furthermore, we evaluate the proposed approach thoroughly and conduct an in-depth comparison to demonstrate that our approach outperforms recent state-of-the-art methods on this benchmark. [Wang, Yiqun; Guo, Jianwei; Yan, Dong-Ming; Zhang, Xiaopeng] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China; [Wang, Yiqun; Guo, Jianwei; Yan, Dong-Ming; Zhang, Xiaopeng] Univ Chinese Acad Sci, Beijing, Peoples R China; [Wang, Kai] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, Grenoble, France Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS) Guo, JW; Yan, DM (corresponding author), Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China.;Guo, JW; Yan, DM (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China. jianwei.guo@nlpr.ia.ac.cn; yandong-ming@gmail.com Beijing Natural Science Foundation [L182059, 4184102]; National Natural Science Foundation of China [61620106003, 61772523, 61802406, 61761003]; French National Research Agency [ANR-11-LABX-0025-01]; State Key Lab of CAD&CG, Zhejiang University [A1909] Beijing Natural Science Foundation(Beijing Natural Science Foundation); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); French National Research Agency(French National Research Agency (ANR)); State Key Lab of CAD&CG, Zhejiang University This work is partially funded by Beijing Natural Science Foundation (L182059, 4184102), National Natural Science Foundation of China (61620106003, 61772523, 61802406 and 61761003), the French National Research Agency (ANR-11-LABX-0025-01), and the Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1909). 38 1 2 0 4 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-7281-3293-8 PROC CVPR IEEE 2019.0 6224 6233 10.1109/CVPR.2019.00639 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO8XD Green Submitted 2023-03-23 WOS:000529484006043 0
J Sun, GL; Boateng, GO; Ayepah-Mensah, DI; Liu, GS; Wei, J Sun, Guolin; Boateng, Gordon Owusu; Ayepah-Mensah, Daniel; Liu, Guisong; Wei, Jiang Autonomous Resource Slicing for Virtualized Vehicular Networks With D2D Communications Based on Deep Reinforcement Learning IEEE SYSTEMS JOURNAL English Article Device-to-device communication; Resource management; Quality of service; Throughput; Vehicle dynamics; Virtualization; Machine learning; Deep reinforcement learning (DRL); network slicing; resource aggregation; resource allocation; V2V communication TO-DEVICE COMMUNICATION; CELLULAR NETWORKS; POWER ALLOCATION; LTE; 5G; SELECTION Considering bandwidth-hungry and low latency requirement of vehicular communications applications, we propose a novel dynamic reinforcement learning-based slicing framework and optimization solutions for efficient resource provisioning in virtualized network for D2D-based vehicle-to-vehicle (V2V) communication. The aim is to balance resource utilization and quality of service (QoS) satisfaction levels for multiple slices. The slicing framework is designed as a three-stage layered framework. In the first stage, we propose dynamic deep reinforcement learning-based virtual resource allocation scheme to allocate distinct resources to slices. In the second stage, we aggregate the D2D resource portion of the slice resource for D2D-based V2V communication. In the third stage, due to the computational complexity and signaling overhead of the physical resource allocation, we transform the problem into a convex optimization problem and solve with an alternating direction method of multipliers-based distributed algorithm. Performance results are provided in terms of resource utilization, QoS satisfaction and throughput to show the benefit of integrating resource slices dedicated to supporting interslice D2D-based V2V communication in vehicular network. [Sun, Guolin; Boateng, Gordon Owusu; Ayepah-Mensah, Daniel; Liu, Guisong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China; [Liu, Guisong] Univ Elect Sci & Technol China, Zhongshan Inst, Sch Comp Sci, Zhongshan 528400, Peoples R China; [Wei, Jiang] German Res Ctr Artificial Intelligence DFKI GmbH, Kaiserslautern 122, D-67663 Kaiserslautern, Germany; [Wei, Jiang] Tech Univ TU, Dept Elect & Informat Technol EIT, D-67663 Kaiserslautern, Germany University of Electronic Science & Technology of China; University of Electronic Science & Technology of China; German Research Center for Artificial Intelligence (DFKI) Sun, GL (corresponding author), Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China. guolin.sun@uestc.edu.cn; boatenggordon48@gmail.com; ayeps2000@gmail.com; lgs@uestc.edu.cn; wei.jiang@dfki.de Boateng, Gordon Owusu/U-2969-2019; Jiang, Wei/ADI-7167-2022 Boateng, Gordon Owusu/0000-0002-7923-367X; Ayepah-Mensah, Daniel/0000-0001-9159-0509; Liu, Guisong/0000-0003-2360-0466 National Natural Science Research Foundation of China [61771098]; Fundamental Research Funds for the Central Universities [ZYGX2018J068]; Department of Science and Technology of Sichuan province [2017GFW0128,8ZDYF2265, 2018JYO578, 2017JY0007]; ZTE Innovation Research Fund for Universities Program 2016 National Natural Science Research Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Department of Science and Technology of Sichuan province; ZTE Innovation Research Fund for Universities Program 2016 This work was supported in part by the National Natural Science Research Foundation of China under Grant 61771098, in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2018J068, in part by the fund from the Department of Science and Technology of Sichuan province under Grant 2017GFW0128,8ZDYF2265, 2018JYO578, and 2017JY0007, and in part by the ZTE Innovation Research Fund for Universities Program 2016. 34 15 15 3 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-8184 1937-9234 IEEE SYST J IEEE Syst. J. DEC 2020.0 14.0 4 4694 4705 10.1109/JSYST.2020.2982857 0.0 12 Computer Science, Information Systems; Engineering, Electrical & Electronic; Operations Research & Management Science; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science; Telecommunications PB0HU 2023-03-23 WOS:000596009700006 0
J Cai, H; Liu, B; Chen, JT; Naviner, L; Zhou, YL; Wang, Z; Yang, J Cai, Hao; Liu, Bo; Chen, Juntong; Naviner, Lirida; Zhou, Yongliang; Wang, Zhen; Yang, Jun A survey of in-spin transfer torque MRAM computing SCIENCE CHINA-INFORMATION SCIENCES English Review spin-transfer torque-magnetoresistive random access memory; in-memory computing; magnetic tunnel junction; analog computing; nonvolatile memory; Boolean logic; neural network SPINTRONIC PROCESSING UNIT; MAGNETIC TUNNEL-JUNCTIONS; ORBIT TORQUE; STT-MRAM; MEMORY; CIRCUIT; SYSTEM; SRAM In traditional von Neumann computing architectures, the essential transfer of data between the processor and memory hierarchies limits the computational efficiency of next-generation system-on-a-chip. The emerging in-memory computing (IMC) approach addresses this issue and facilitates the movement of significant data and rapid computations. Among the different memory types, intrinsic energy efficiency is demonstrated by in-magnetic random access memory (MRAM) computing with a low-power spintronic magnetic tunnel junction device and hybrid integration at an advanced complementary metal-oxide semiconductor node. This study reviews state-of-the-art techniques for managing IMC with an emphasis on spin-transfer torque-MRAM computing via design schemes at the bit-cell, circuit, and system levels. In addition, this study presents effective design techniques and potential challenges and demonstrates the existing limitations of in-MRAM computing and potential methods for overcoming these issues. This study also considers the design technology co-optimization from the IMC perspective. [Cai, Hao; Liu, Bo; Chen, Juntong; Zhou, Yongliang; Yang, Jun] Southeast Univ, Natl ASIC Syst Engn Res Ctr, Nanjing 210096, Peoples R China; [Naviner, Lirida] Telecom Paris, Lab Traitement & Commun Informat, F-91120 Palaiseau, France; [Wang, Zhen] Nanjing Prochip Elect Technol Co Ltd, Nanjing 210000, Peoples R China Southeast University - China; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Yang, J (corresponding author), Southeast Univ, Natl ASIC Syst Engn Res Ctr, Nanjing 210096, Peoples R China. dragon@seu.edu.cn Cai, Hao/L-7083-2019; Yang, Jun/HKV-8050-2023 Naviner, Lirida/0000-0002-6320-4153 National Natural Science Foundation of China [61904028] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by National Natural Science Foundation of China (Grant No. 61904028). 107 10 10 25 78 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-733X 1869-1919 SCI CHINA INFORM SCI Sci. China-Inf. Sci. JUN 2021.0 64.0 6 160402 10.1007/s11432-021-3220-0 0.0 15 Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering SD5JK 2023-03-23 WOS:000651408700001 0
C Xu, YC; Geraud, T; Puybareau, E; Bloch, I; Chazalon, J Crimi, A; Bakas, S; Kuijf, H; Menze, B; Reyes, M Xu, Yongchao; Geraud, Thierry; Puybareau, Elodie; Bloch, Isabelle; Chazalon, Joseph White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2017 Lecture Notes in Computer Science English Proceedings Paper 3rd International Workshop on Brain-Lesion (BrainLes) held jointly at the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI) SEP 14, 2017 Quebec City, CANADA Univ Penn, Ctr Biomed Image Comp & Analyt,Univ Med Ctr Utrecht, Image Sci Inst 3D brain MRI; Lesion segmentation; White matter hyperintensities; Mathematical morphology; Fully convolutional network SMALL VESSEL DISEASE; CONNECTED OPERATORS; AUTOMATIC SEGMENTATION; LESIONS; IMAGE In this paper, we propose a fast automatic method that segments white matter hyperintensities (WMH) in 3D brain MR images, using a fully convolutional network (FCN) and transfer learning. This FCN is the Visual Geometry Group neural network (VGG for short) pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI WMH Challenge. We consider three images for each slice of the volume to segment: the T1 slice, the FLAIR slice, and the result of a morphological operator that emphasizes small bright structures. These three 2D images are assembled to form a 2D color image, that inputs the FCN to obtain the 2D segmentation of the corresponding slice. We process all slices, and stack the results to form the 3D output segmentation. With such a technique, the segmentation of WMH on a 3D brain volume takes about 10 s including pre- processing. Our technique was ranked 6-th over 20 participants at the MICCAI WMH Challenge. [Xu, Yongchao; Geraud, Thierry; Puybareau, Elodie; Chazalon, Joseph] EPITA, Res & Dev Lab LRDE, Le Kremlin Bicetre, France; [Xu, Yongchao; Bloch, Isabelle] Univ Paris Saclay, Telecom ParisTech, LTCI, Palaiseau, France; [Xu, Yongchao] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China; [Chazalon, Joseph] Univ La Rochelle, La Rochelle, France IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay; Huazhong University of Science & Technology; La Rochelle Universite Geraud, T (corresponding author), EPITA, Res & Dev Lab LRDE, Le Kremlin Bicetre, France. thierry.geraud@lrde.epita.fr Géraud, Thierry/AAT-8485-2020 25 5 5 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-75238-9; 978-3-319-75237-2 LECT NOTES COMPUT SC 2018.0 10670.0 501 514 10.1007/978-3-319-75238-9_42 0.0 14 Computer Science, Theory & Methods; Clinical Neurology; Neurosciences; Neuroimaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Neurosciences & Neurology BK2EH 2023-03-23 WOS:000432603300042 0
J Shen, B; Choi, TM; Minner, S Shen, Bin; Choi, Tsan-Ming; Minner, Stefan A review on supply chain contracting with information considerations: information updating and information asymmetry INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH English Review supply chain contracting; demand information updating; supply information updating; information asymmetry QUANTITY FLEXIBILITY CONTRACTS; BIG DATA ANALYTICS; COST INFORMATION; QUICK RESPONSE; DEMAND INFORMATION; LEAD-TIME; COMPONENT PROCUREMENT; OPERATIONS MANAGEMENT; PERISHABLE PRODUCT; MASS CUSTOMIZATION Supply chain contracting and the use of information are undoubtedly two critical and influential areas in modern supply chain management. However, relatively little is known about supply chain contracting mechanisms with different information settings. To fill this gap, we review and classify the related supply chain contracting literature into three categories with respect to different kinds of information considerations, namely (i) demand information updating, (ii) supply information updating and (iii) information asymmetry. We report the publication trend and classify the commonly studied supply chain contracts with the use of information such as pricing contracts, commitment contracts and menu of contracts. We discuss how contracting and the use of information influence each other in the supply chain. Moreover, we review the major application areas of information usage and report the historical development of major related topics. Finally, we propose several important future research directions. [Shen, Bin] Donghua Univ, Glorious Sun Sch Business & Management, Shanghai, Peoples R China; [Choi, Tsan-Ming] Hong Kong Polytech Univ, Business Div, Inst Text & Clothing, Hong Kong, Peoples R China; [Minner, Stefan] Tech Univ Munich, Sch Management, Logist & Supply Chain Management, Munich, Germany Donghua University; Hong Kong Polytechnic University; Technical University of Munich Choi, TM (corresponding author), Hong Kong Polytech Univ, Business Div, Inst Text & Clothing, Hong Kong, Peoples R China. jason.choi@polyu.edu.hk 穆, 儒/HGC-6534-2022; Choi, Tsan-Ming/P-6065-2014 Choi, Tsan-Ming/0000-0003-3865-7043; Shen, Bin/0000-0002-1420-0658 Research Grants Council (Hong Kong) under the General Research Fund [PolyU 152294/16E]; Chenguang Program - Shanghai Education Development Foundation; Shanghai Municipal Education Commission [15CG34] Research Grants Council (Hong Kong) under the General Research Fund; Chenguang Program - Shanghai Education Development Foundation; Shanghai Municipal Education Commission(Shanghai Municipal Education Commission (SHMEC)) This work was supported by the Research Grants Council (Hong Kong) under the General Research Fund [grant number PolyU 152294/16E], and sponsored by Chenguang Program supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (15CG34). 190 157 157 75 291 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0020-7543 1366-588X INT J PROD RES Int. J. Prod. Res. AUG 29 2019.0 57.0 15-16 SI 4898 4936 10.1080/00207543.2018.1467062 0.0 39 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Operations Research & Management Science IO0FM 2023-03-23 WOS:000479054800014 0
J Sun, SC; Bao, ZY; Li, RY; Sun, DZ; Geng, HH; Huang, XF; Lin, JH; Zhang, PX; Ma, R; Fang, L; Zhang, XH; Zhao, XX Sun, Shichang; Bao, Zhiyuan; Li, Ruoyu; Sun, Dezhi; Geng, Haihong; Huang, Xiaofei; Lin, Junhao; Zhang, Peixin; Ma, Rui; Fang, Lin; Zhang, Xianghua; Zhao, Xuxin Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation BIORESOURCE TECHNOLOGY English Article N2O emission; A/O process; DO; BP-ANN model; Simulation NITROUS-OXIDE EMISSION; AUTOTROPHIC PARTIAL NITRIFICATION; CARBON SOURCE; PARTIAL NITRITATION; DISSOLVED-OXYGEN; NEURAL-NETWORKS; REMOVAL; GENERATION; REACTOR; DENITRIFICATION In order to make a better understanding of the characteristics of N2O emission in A/O wastewater treatment plant, full-scale and pilot-scale experiments were carried out and a back propagation artificial neural network model based on the experimental data was constructed to make a precise prediction of N2O emission. Results showed that, N2O flux from different units followed a descending order: aerated grit tank > oxic zone >> anoxic zone > final clarifier > primary clarifier, but 99.4% of the total emission of N2O (1.60% of N-load) was monitored from the oxic zone due to its big surface area. A proper DO control could reduce N2O emission down to 0.21% of N-load in A/O process, and a two-hidden-layers back propagation model with an optimized structure of 4: 3: 9: 1 could achieve a good simulation of N2O emission, which provided a new method for the prediction of N2O emission during wastewater treatment. [Sun, Shichang; Geng, Haihong; Huang, Xiaofei; Lin, Junhao; Zhang, Peixin; Ma, Rui; Fang, Lin; Zhang, Xianghua; Zhao, Xuxin] Shenzhen Univ, Coll Chem & Enviromental Engn, Shenzhen 518060, Peoples R China; [Sun, Shichang] Shenzhen Univ, Coll Optoelect Engn, Shenzhen 518060, Peoples R China; [Bao, Zhiyuan; Li, Ruoyu; Sun, Dezhi] Beijing Forestry Univ, Beijing Key Lab Source Control Technol Water Poll, Beijing, Peoples R China; [Zhang, Xianghua] Univ Rennes 1, Inst Chem Sci, Lab Glasses & Ceram, F-35042 Rennes, France Shenzhen University; Shenzhen University; Beijing Forestry University; Universite de Rennes Ma, R (corresponding author), 3688 Nanhai Rd, Shenzhen 518060, Peoples R China. marui067@163.com Zhang, Xianghua/A-1487-2018; Sun, Shichang/AAE-6064-2022 ZHANG, Xiang-Hua/0000-0002-2180-6543 National Natural Science Foundation of China [51708357]; China Postdoctoral Science Foundation [2017M612738] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This paper was supported by the National Natural Science Foundation of China (Grant No. 51708357) and China Postdoctoral Science Foundation (Grant No. 2017M612738). 38 25 27 18 135 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0960-8524 1873-2976 BIORESOURCE TECHNOL Bioresour. Technol. NOV 2017.0 244.0 1 800 809 10.1016/j.biortech.2017.08.054 0.0 10 Agricultural Engineering; Biotechnology & Applied Microbiology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels FG6YD 28830043.0 2023-03-23 WOS:000410545300097 0
C Zhang, CS; Bi, JJ; Soda, P Hu, XH; Shyu, CR; Bromberg, Y; Gao, J; Gong, Y; Korkin, D; Yoo, I; Zheng, JH Zhang, Chongsheng; Bi, Jingjun; Soda, Paolo Feature Selection and Resampling in Class Imbalance Learning: Which Comes First? An Empirical Study in the Biological Domain 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) IEEE International Conference on Bioinformatics and Biomedicine-BIBM English Proceedings Paper Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) NOV 13-16, 2017 Kansas City, MI IEEE,IEEE Comp Soc,IEEE Tech Comm Computat Life Sci Class imbalance exists in many applications of bioinformatics and biomedicine, while dimension reduction in the feature space is often needed when building prediction models on a dataset. When the above two issues need to be considered simultaneously for skewed/imbalanced datasets, practitioners and researchers in machine learning may raise the following question: should feature selection be conducted before or after the resampling methods for combating the skewness of a dataset? While feature selection and class imbalance learning have been widely studied in the literature, little study has jointly investigated them. This paper presents a first empirical study on the performance of the two opposing pipelines for binary imbalance learning, i.e., first feature selection then resampling, or first resampling then feature selection. We carry out the study on 35 publicly available datasets belonging to the biological field, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classifiers. Our experiments reveal that, there is no constant winner between the two pipelines, practitioners should test both pipelines in order to derive the best classification model for imbalance learning, in particular, the resampling before feature selection pipeline should not be neglected; but we also show that, the feature selection before resampling pipeline outperforms the other in more cases than not. [Zhang, Chongsheng; Bi, Jingjun] Henan Univ, Big Data Res Ctr, Kaifeng, Peoples R China; [Soda, Paolo] Univ Campus Biomed Roma, Dept Engn, Rome, Italy Henan University; University Campus Bio-Medico - Rome Italy Zhang, CS (corresponding author), Henan Univ, Big Data Res Ctr, Kaifeng, Peoples R China. chongsheng.zhang@yahoo.com; 2834335964@qq.com; p.soda@unicampus.it Soda, Paolo/K-8126-2016 Soda, Paolo/0000-0003-2621-072X Italian Republic [CN13MO4]; National Science Foundation of China (NSFC) [41401466]; Henan University [xxjc20140005]; People's Republic of China [CN13MO4] Italian Republic; National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Henan University; People's Republic of China This work has been carried out under the financial support of the Executive Programme for Scientific and Technological Cooperation Between the Italian Republic and the People's Republic of China for the years 2013-2015 (project code CN13MO4). It is also partially funded by the National Science Foundation of China (NSFC) under Grant no. 41401466 and Henan University under Grant no. xxjc20140005. 22 14 14 1 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-1125 2156-1133 978-1-5090-3050-7 IEEE INT C BIOINFORM 2017.0 933 938 6 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Mathematical & Computational Biology BJ6DG 2023-03-23 WOS:000426504100174 0
J Arico, P; Reynal, M; Di Flumeri, G; Borghini, G; Sciaraffa, N; Imbert, JP; Hurter, C; Terenzi, M; Ferreira, A; Pozzi, S; Betti, V; Marucci, M; Telea, AC; Babiloni, F Arico, Pietro; Reynal, Maxime; Di Flumeri, Gianluca; Borghini, Gianluca; Sciaraffa, Nicotine; Imbert, Jean-Paul; Hurter, Christophe; Terenzi, Michela; Ferreira, Ana; Pozzi, Simone; Betti, Viviana; Marucci, Matteo; Telea, Alexandru C.; Babiloni, Fabio How Neurophysiological Measures Can be Used to Enhance the Evaluation of Remote Tower Solutions FRONTIERS IN HUMAN NEUROSCIENCE English Article EEG; ECG; GSR; eye blink; mental workload; remote tower air traffic management; machine learning; asSWLDA ALPHA BAND POWER; MENTAL WORKLOAD; PASSIVE BCI; EEG; PERFORMANCE; AROUSAL; ATTENTION; INDEXES; ANXIETY; MEMORY New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an electroencephalography (EEG)-based neurophysiological index (W-EEG index) in comparing two different solutions (i.e., Normal and Augmented) in terms of experienced workload. In this regard, 16 professional air traffic controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e., operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (W-EEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioral effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioral and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g. , design process) could allow a more holistic and accurate evaluation of new solutions. [Arico, Pietro; Di Flumeri, Gianluca; Borghini, Gianluca; Babiloni, Fabio] Sapienza Univ Rome, Dept Mol Med, Rome, Italy; [Arico, Pietro; Di Flumeri, Gianluca; Borghini, Gianluca; Sciaraffa, Nicotine; Babiloni, Fabio] BrainSigns Srl, Rome, Italy; [Arico, Pietro; Di Flumeri, Gianluca; Borghini, Gianluca; Sciaraffa, Nicotine; Betti, Viviana; Babiloni, Fabio] IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Rome, Italy; [Reynal, Maxime; Imbert, Jean-Paul; Hurter, Christophe] Univ Toulouse, French Civil Aviat Univ ENAC, Toulouse, France; [Sciaraffa, Nicotine] Sapienza Univ Rome, Dept Anat Histol Forens & Orthoped Sci, Rome, Italy; [Terenzi, Michela; Ferreira, Ana; Pozzi, Simone] DeepBlue Srl, Rome, Italy; [Betti, Viviana; Marucci, Matteo] Sapienza Univ Rome, Dept Psychol, Rome, Italy; [Marucci, Matteo] Braintrends Ltd, Appl Neurosci, Rome, Italy; [Telea, Alexandru C.] Univ Groningen, Dept Math & Comp Sci, Groningen, Netherlands; [Babiloni, Fabio] Hangzhou Dianzi Univ, Coll Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China Sapienza University Rome; IRCCS Santa Lucia; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Ecole Nationale de l'Aviation Civile (ENAC); Sapienza University Rome; Sapienza University Rome; University of Groningen; Hangzhou Dianzi University Arico, P (corresponding author), Sapienza Univ Rome, Dept Mol Med, Rome, Italy.;Arico, P (corresponding author), BrainSigns Srl, Rome, Italy.;Arico, P (corresponding author), IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Rome, Italy. pietro.arico@uniroma1.it Hurter, Christophe/AHB-0811-2022; Di Flumeri, Gianluca/I-7196-2019; Reynal, Maxime/AAH-3000-2021; MARUCCI, Matteo/AAB-8769-2019; Borghini, Gianluca/AAA-4687-2019; Betti, Viviana/K-5516-2016 Hurter, Christophe/0000-0003-4318-6717; Di Flumeri, Gianluca/0000-0003-4426-051X; Reynal, Maxime/0000-0002-0413-1927; MARUCCI, Matteo/0000-0001-6431-9238; Borghini, Gianluca/0000-0001-8560-5671; Betti, Viviana/0000-0003-3429-2104; Arico, Pietro/0000-0002-3831-6620 European Commission [699379, 826232, 723386, 814961]; Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana European Commission(European CommissionEuropean Commission Joint Research Centre); Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana This work is co-financed by the European Commission by Horizon2020 projects Sesar-06-2015 the embodied reMOte Tower, MOTO,'' GA n. 699379; WORKINGAGE: Smart Working environments for all Ages'' (GA n. 826232); SIMUSAFE'': Simulator Of Behavioral Aspects For Safer Transport (GA n. 723386); SAFEMODE: Strengthening synergies between Aviation and maritime in the area of human Factors towards achieving more Efficient and resilient MODE of transportation'' (GA n. 814961); BRAINSAFEDRIVE: A Technology to detect Mental States during Drive for improving the Safety of the road'' (Italy-Sweden collaboration) with a grant of Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana. 61 19 19 0 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5161 FRONT HUM NEUROSCI Front. Hum. Neurosci. SEP 6 2019.0 13.0 303 10.3389/fnhum.2019.00303 0.0 16 Neurosciences; Psychology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychology IV9KD 31551735.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000484581900001 0
J Wang, YX; Liu, JQ; Chang, XL; Rodriguez, RJ; Wang, JH Wang, Yixiang; Liu, Jiqiang; Chang, Xiaolin; Rodriguez, Ricardo J.; Wang, Jianhua DI-AA: An interpretable white-box attack for fooling deep neural networks INFORMATION SCIENCES English Article Adversarial example; Deep learning; Interpretability; Robustness; White-box attack ADVERSARIAL ATTACKS White-box adversarial example (AE) attacks on deep neural networks (DNNs) have a more powerful destructive capacity than black-box attacks using AE strategies. However, few studies have been conducted on the generation of low-perturbation adversarial examples from the interpretability perspective. Specifically, adversaries who conducted attacks lacked interpretation from the point of view of DNNs, and the perturbation was not further considered. To address these, we propose an interpretable white-box AE attack approach, DI-AA, which not only explores the application of the interpretable method of deep Taylor decomposition in selecting the most contributing features but also adopts the Lagrangian relaxation optimization of the logit output and Lp norm to make the perturbation more unnoticeable. We compare DI-AA with eight baseline attacks on four representative data -sets. Experimental results reveal that our approach can (1) attack nonrobust models with low perturbation, where the perturbation is closer to or lower than that of the state-of-the-art white-box AE attacks; (2) evade the detection of the adversarial-training robust models with the highest success rate; (3) be flexible in the degree of AE generation saturation. Additionally, the AE generated by DI-AA can reduce the accuracy of the robust black-box models by 16-31 % in the black-box manner.(c) 2022 Elsevier Inc. All rights reserved. [Wang, Yixiang; Liu, Jiqiang; Chang, Xiaolin; Wang, Jianhua] Beijing Jiaotong Univ, Beijing Key Lab Secur & Privacy Intelligent Trans, Beijing 100044, Peoples R China; [Rodriguez, Ricardo J.] Univ Zaragoza, Dept Comp Sci & Syst Engn, Zaragoza, Spain Beijing Jiaotong University; University of Zaragoza Liu, JQ; Chang, XL (corresponding author), Beijing Jiaotong Univ, Beijing Key Lab Secur & Privacy Intelligent Trans, Beijing 100044, Peoples R China. 18112047@bjtu.edu.cn; jqliu@bjtu.edu.cn; xlchang@bjtu.edu.cn; rjrodriguez@unizar.es; 20112051@bjtu.edu.cn Rodriguez, Ricardo/GXF-0596-2022 Wang, Jianhua/0000-0002-2773-3429 National Key R&D Program of China [2020YFB2103802] National Key R&D Program of China We would like to thank the editors and anonymous reviewers for their helpful comments. This work was supported in part by National Key R&D Program of China, under Grant 2020YFB2103802. 50 0 0 27 27 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 2022.0 610.0 14 32 10.1016/j.ins.2022.07.157 0.0 AUG 2022 19 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4F2JE Green Submitted 2023-03-23 WOS:000848341500002 0
J Zhang, JQ; Jin, ZH; Teng, S; Chen, GF; Bassir, D Zhang, Jiqiao; Jin, Zihan; Teng, Shuai; Chen, Gongfa; Bassir, David Structural damage detection based on decision-level fusion with multi-vibration signals MEASUREMENT SCIENCE AND TECHNOLOGY English Article structural damage detection; decision-level fusion; convolutional neural network; vibration signal NEURAL-NETWORKS; DEEP; IDENTIFICATION; CURVATURE; MODEL When a structure is damaged, its vibration signals change. If a single vibration signal is used for structural damage detection (SDD), it may sometimes lead to low detection accuracy. To avoid this phenomenon, this paper presents a SDD method based on decision-level fusion (DLF) with multi-vibration signals. In this study, acceleration (ACC), strain (E), displacement (DIS), and the fusion signal of all three of these signals (ACC, E and DIS), are studied. The damage information can be extracted from the vibration signal of a structure by using convolution neural networks (CNN). The above four vibration signals are used as the inputs to train four CNN models, and each model outputs a corresponding result. Finally, a DLF strategy is used to fuse the detection results of each CNN. To demonstrate the effectiveness and correctness of the proposed method, a steel frame bridge is investigated with numerical simulations and vibration experiments. The research shows that the damage detection method based on DLF with multi-vibration signals can effectively improve the accuracy of the CNN damage detection. [Zhang, Jiqiao; Jin, Zihan; Teng, Shuai; Chen, Gongfa] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China; [Bassir, David] UTBM, IRAMAT, UMR 7065, CNRS, Rue Leupe, F-90010 Belfort, France; [Bassir, David] Univ Paris Saclay, ENS, Ctr Borelli, 4 Ave Sci, F-91190 Gif Sur Yvette, France Guangdong University of Technology; CEA; Centre National de la Recherche Scientifique (CNRS); Universite Bordeaux-Montaigne; Universite de Orleans; Universite de Technologie de Belfort-Montbeliard (UTBM); UDICE-French Research Universities; Universite Paris Saclay Chen, GF (corresponding author), Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China. gongfa.chen@gdut.edu.cn BASSIR, David/D-9637-2018 BASSIR, David/0000-0002-5364-9992 Program of Study Abroad for Young Scholars in Guangdong University of Technology [220410009] Program of Study Abroad for Young Scholars in Guangdong University of Technology The support from the Program of Study Abroad for Young Scholars in Guangdong University of Technology (Grant No. 220410009) is acknowledged. 38 0 0 8 17 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0957-0233 1361-6501 MEAS SCI TECHNOL Meas. Sci. Technol. OCT 1 2022.0 33.0 10 105112 10.1088/1361-6501/ac7940 0.0 15 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 2Z6PY 2023-03-23 WOS:000826698500001 0
J Giannakopoulos, P; Montandon, ML; Herrmann, FR; Hedderich, D; Gaser, C; Kellner, E; Rodriguez, C; Haller, S Giannakopoulos, Panteleimon; Montandon, Marie-Louise; Herrmann, Francois R.; Hedderich, Dennis; Gaser, Christian; Kellner, Elias; Rodriguez, Cristelle; Haller, Sven Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers EUROPEAN RADIOLOGY English Article Alzheimer; BrainAGE; Normal-pressure hydrocephalus; MTA; Microbleeds MEDIAL TEMPORAL ATROPHY; WHITE-MATTER CHANGES; CEREBRAL MICROBLEEDS; DISEASE; PET; AGE; PROGRESSION; DEMENTIA; CLASSIFICATION; MACHINE Objectives Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls. Methods A prospective study including 80 individuals (46 females, mean age 73.4 +/- 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers. Results AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%. Conclusions The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs. [Giannakopoulos, Panteleimon; Rodriguez, Cristelle] Univ Geneva, Dept Psychiat, Geneva, Switzerland; [Giannakopoulos, Panteleimon; Montandon, Marie-Louise; Rodriguez, Cristelle] Univ Geneva Hosp, Med Direct, Geneva, Switzerland; [Montandon, Marie-Louise; Herrmann, Francois R.] Geneva Univ Hosp, Dept Rehabil & Geriatr, Geneva, Switzerland; [Montandon, Marie-Louise; Herrmann, Francois R.] Univ Geneva, Geneva, Switzerland; [Hedderich, Dennis] Tech Univ Munich, Sch Med, Dept Diagnost & Intervent Neuroradiol, Klinikum Rechts Isar, Munich, Germany; [Gaser, Christian] Jena Univ Hosp, Dept Psychiat, Jena, Germany; [Kellner, Elias] Univ Freiburg, Med Ctr, Fac Med, Dept Radiol,Med Phys, Freiburg, Germany; [Haller, Sven] CIMC Ctr Imagerie Med Cornavin, Geneva, Switzerland; [Haller, Sven] Uppsala Univ, Dept Surg Sci, Radiol, Uppsala, Sweden; [Haller, Sven] Univ Geneva, Fac Med, Geneva, Switzerland; [Haller, Sven] Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China University of Geneva; University of Geneva; University of Geneva; University of Geneva; Technical University of Munich; Friedrich Schiller University of Jena; University of Freiburg; Uppsala University; University of Geneva; Capital Medical University Haller, S (corresponding author), CIMC Ctr Imagerie Med Cornavin, Geneva, Switzerland.;Haller, S (corresponding author), Uppsala Univ, Dept Surg Sci, Radiol, Uppsala, Sweden.;Haller, S (corresponding author), Univ Geneva, Fac Med, Geneva, Switzerland.;Haller, S (corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China. Sven.haller@me.com HERRMANN, Francois/M-7390-2019 HERRMANN, Francois/0000-0003-1312-1517; Haller, Sven/0000-0001-7433-0203 University of Geneva; Association Suisse pour la Recherche sur Alzheimer; Schmidheiny Foundation; Swiss National Foundation [320030-169390, 320030_169876] University of Geneva; Association Suisse pour la Recherche sur Alzheimer; Schmidheiny Foundation; Swiss National Foundation(Swiss National Science Foundation (SNSF)) Open access funding provided by University of Geneva. This research was carried out with help of an unrestricted grant from Association Suisse pour la Recherche sur Alzheimer, an unrestricted grant from the Schmidheiny Foundation, and the Swiss National Foundation grant (Grant No. 320030-169390, and 320030_169876). 49 1 1 0 2 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0938-7994 1432-1084 EUR RADIOL Eur. Radiol. NOV 2022.0 32.0 11 7833 7842 10.1007/s00330-022-08798-0 0.0 APR 2022 10 Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Radiology, Nuclear Medicine & Medical Imaging 6G5BC 35486172.0 Green Published, Green Accepted, hybrid 2023-03-23 WOS:000788961400004 0
J Qian, YG; Wang, JM; Wang, HJ; Cu, ZQ; Wang, B; Zeng, SN; Swaileh, W Qian, Yagua; Wang, Jiamin; Wang, Haijiang; Cu, Zhaoquan; Wang, Bin; Zeng, Shaonin; Swaileh, Wassim Visually imperceptible adversarial patch attacks COMPUTERS & SECURITY English Article Deep neural network; Adversarial example; Contributing feature region; Adversarial patch The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. However, most of these algorithms modified the global or local region of pixels without taking network explanations into account. Hence, the perturbations are redundant, which are easily detected by human eyes. In this paper, we propose a novel method to generate local region perturbations. The main idea is to find a contributing feature region (CFR) of an image by simulating the human attention mechanism and then add perturbations to CFR. Furthermore, a soft mask matrix is designed on the basis of an activation map to finely represent the contributions of each pixel in CFR. With this soft mask, we develop a new loss function with inverse temperature to search for optimal perturbations in CFR. Due to the network explanations, the perturbations added to CFR are more effective than those added to other regions. Extensive experiments conducted on CIFAR-10 and ILSVRC2012 demonstrate the effectiveness of the proposed method, including attack success rate, imperceptibility, and transferability. (c) 2022 Elsevier Ltd. All rights reserved. [Qian, Yagua; Wang, Jiamin; Wang, Haijiang] Zhejiang Univ Sci & Technol Hangzhou, Sch Big Data Sci, Zhejiang 310023, Peoples R China; [Cu, Zhaoquan] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China; [Wang, Bin] Zhejiang Key Lab Multidimens Percept Technol Appli, Hangzhou 310052, Zhejiang, Peoples R China; [Zeng, Shaonin] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst, Huzhou 313000, Zhejiang, Peoples R China; [Swaileh, Wassim] CNRS, IRISA Lab, INSA Rennes, UMR 6074, F-35000 Rennes, France Guangzhou University; University of Electronic Science & Technology of China; Centre National de la Recherche Scientifique (CNRS); Institut National des Sciences Appliquees de Rennes; Universite de Rennes Wang, B (corresponding author), Zhejiang Key Lab Multidimens Percept Technol Appli, Hangzhou 310052, Zhejiang, Peoples R China. qianyaguan@zust.edu; 1621099083@qq.com; wanghaijiangyes@163.com; zqgu@gzhu.edu.cn; wangbin2@hikvision.com; wassim.swaileh@cyu.fr Key Program of Zhejiang Provin- cial Natural Science Foundation of China; Major Research Plan of the National Natural Science Foundation of China; National Key RD Program; Natural Science Foundation of China; Zhejiang Key RD Program; [LZ22F020 0 07]; [92167203]; [2018YFB210 040 0]; [61902082]; [61972357]; [2019C03135] Key Program of Zhejiang Provin- cial Natural Science Foundation of China; Major Research Plan of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key RD Program; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Key RD Program; ; ; ; ; ; This work is supported by the Key Program of Zhejiang Provin- cial Natural Science Foundation of China (No. LZ22F020 0 07) , Major Research Plan of the National Natural Science Foundation of China (No. 92167203) , National Key R&D Program (No. 2018YFB210 040 0) , Natural Science Foundation of China (No. 61902082 , No. 61972357) , and Zhejiang Key R&D Program (No.2019C03135) . 42 0 0 3 3 ELSEVIER ADVANCED TECHNOLOGY OXFORD OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0167-4048 1872-6208 COMPUT SECUR Comput. Secur. DEC 2022.0 123.0 102943 10.1016/j.cose.2022.102943 0.0 OCT 2022 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5R5WN 2023-03-23 WOS:000874580700003 0
J Lu, J; Meng, YC; Timmermans, H; Zhang, AM Lu, Jing; Meng, Yucan; Timmermans, Harry; Zhang, Anming Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE English Article Hesitancy; Airport choice; Multi-airport region; Multinomial logit; Random forest; Deep reinforcement learning RANDOM REGRET-MINIMIZATION; STATED PREFERENCE SURVEY; WILLINGNESS-TO-PAY; DESTINATION CHOICE; CART ABANDONMENT; DECISION-MAKING; NEURAL-NETWORKS; TRAVEL; AIRLINE; PASSENGERS Noticing that people may become more hesitant about their airport choice when exposed to abundant information and multi-channel on-line sales opportunities, this paper analyzes hesitancy in the choice of transfer airport in a multi-airport region using three approaches: a multinomial logit model, a random forests algorithm, and deep reinforcement learning. These methods are applied to the outcomes of a stated choice experiment, which was designed to survey the iterative airport choice process under hesitancy. The results indicate that a quarter of the passengers show evidence of hesitancy. Further, the deep reinforcement learning approach is found to provide the highest accuracy in representing the stated choices; the random forests algorithm also gives good results in capturing the reconsideration decision, while the multinomial logit model seems less accurate in representing the choice behavior of interest. Moreover, hesitancy involved a switch from flight-related attributes to ground transport-related attributes. Managerial implications for airlines and airports are discussed. [Lu, Jing; Meng, Yucan; Timmermans, Harry] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, Nanjing 211106, Peoples R China; [Timmermans, Harry] Eindhoven Univ Technol, Dept Built Environm, NL-5600 MB Eindhoven, Netherlands; [Zhang, Anming] Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada Nanjing University of Aeronautics & Astronautics; Eindhoven University of Technology; University of British Columbia Zhang, AM (corresponding author), Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada. Zhang, Anming/0000-0002-9457-8414 National Natural Science Foundation of China [71701094] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We would like to express our sincere thanks to two anonymous reviewers for their valuable comments and suggestions. This research was sponsored by the National Natural Science Foundation of China (Grant No. 71701094) . 123 4 4 12 37 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0965-8564 1879-2375 TRANSPORT RES A-POL Transp. Res. Pt. A-Policy Pract. MAY 2021.0 147.0 230 250 10.1016/j.tra.2021.03.006 0.0 MAR 2021 21 Economics; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Transportation RN9WE 2023-03-23 WOS:000640702500002 0
J Wang, LF; Wu, HQ; Liu, DY; Boutat, D; Chen, YM Wang, Li-Fei; Wu, Huaiqin; Liu, Da-Yan; Boutat, Driss; Chen, Yi-Ming Lur'e Postnikov Lyapunov functional technique to global Mittag-Leffler stability of fractional-order neural networks with piecewise constant argument NEUROCOMPUTING English Article Fractional neural network; Mittag-Leffler stability; Piecewise constant argument; Lur'e Postnikov type Lyapunov function; LMIs TIME-VARYING DELAYS; DIFFERENTIAL-EQUATIONS; GENERALIZED TYPE; SYSTEMS In this paper, the global Mittag-Leffler stability issue of fractional-order neural networks (FNNs) with piecewise constant argument is investigated. Firstly, a new inequality with respect to the fractional derivative of integer-order variable upper limit integral is proposed, which not only is favorable to the construction of Lyapunov function but also enriches the fractional order calculus theory. Secondly, based on topological degree theory, the existence and uniqueness of equilibrium point is certified. In addition, under some suitable assumptions, by applying Picard successive approximation technique, the proof of the existence and uniqueness of solution with the initial value is given. Moreover, by introducing integral term into Lyapunov functional, Lur'e Postnikov type Lyapunov functional is constructed, and a sufficient condition is addressed in terms of linear matrix inequalities (LMIs) to guarantee that the considered FNNs are Mittag-Leffler stable. Finally, an example is given to demonstrate the validity of the obtained results. (C) 2018 Elsevier B.V. All rights reserved. [Wang, Li-Fei; Wu, Huaiqin; Chen, Yi-Ming] Yanshan Univ, Sch Sci, Qinhuangdao 066001, Peoples R China; [Wang, Li-Fei; Liu, Da-Yan; Boutat, Driss] Univ Orleans, INSA Ctr Val de Loire, PRISME EA 4229, F-18020 Bourges, France; [Chen, Yi-Ming] Loire Valley Inst Adv Studies, Orleans, France; [Chen, Yi-Ming] INSA, PRISME, 88 Blvd Lahitolle, F-18000 Bourges, France Yanshan University; Universite de Orleans Wu, HQ (corresponding author), Yanshan Univ, Sch Sci, Qinhuangdao 066001, Peoples R China. huaiqinwu@ysu.edu.cn Wu, Huaiqin/AAU-6064-2020 National Natural Science Foundation of China [61573306]; Postgraduate Innovation Project of Hebei province of China [00302-6370019]; High level talent support project of Hebei province of China [C2015003054]; LE STUDIUM RESEARCH PROFESSORSHIP award of Centre Val de Loire region in France National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Postgraduate Innovation Project of Hebei province of China; High level talent support project of Hebei province of China; LE STUDIUM RESEARCH PROFESSORSHIP award of Centre Val de Loire region in France This work was jointly supported by the National Natural Science Foundation of China (61573306), the Postgraduate Innovation Project of Hebei province of China (00302-6370019), High level talent support project of Hebei province of China (C2015003054) and the LE STUDIUM RESEARCH PROFESSORSHIP award of Centre Val de Loire region in France. 39 25 25 1 33 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing AUG 9 2018.0 302.0 23 32 10.1016/j.neucom.2018.03.050 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science GG2AK Green Accepted 2023-03-23 WOS:000432491700003 0
J Zhao, Y; Li, L; Lanteri, S; Viquerat, J Zhao, Ying; Li, Liang; Lanteri, Stephane; Viquerat, Jonathan Dynamic metasurface control using Deep Reinforcement Learning MATHEMATICS AND COMPUTERS IN SIMULATION English Article Dynamic metasurface; Artificial neural networks; Deep reinforcement learning; Proximal policy optimization NETWORKS Dynamic metasurface is an emerging concept for achieving a flexible control of electromagnetic waves. Generalized sheet transition conditions (GSTCs) can be used to model the relationship between the electromagnetic response and surface susceptibility parameters characterizing a metasurface. However, when it comes to the inverse problem of designing and controlling a metasurface in a space-time varying context based on GSTCs, the dynamic synthesis of the susceptibility parameters is a difficult and non-intuitive task. In this paper, we transform the inverse problem of solving dynamic susceptibility parameters into a sequence of control problems. Based on FDTD numerical simulations, a Deep Reinforcement Learning (DRL) framework using a proximal policy optimization (PPO) algorithm and a fully connected neural network is designed to control the susceptibility parameters intelligently and efficiently, promoting the further expansion of the application range of metasurface and thus helping realize more flexible and effective control of electromagnetic waves. We provide numerical results in a one-dimensional setting to show the applicability, correctness and effectiveness of the proposed approach.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved. [Zhao, Ying; Li, Liang] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu, Peoples R China; [Lanteri, Stephane] INRIA, 2004 Route Lucioles,BP 93, F-06902 Sophia Antipolis, France; [Viquerat, Jonathan] CEMEF Mines ParisTech, 1 Rue Claude Daunesse,BP207, F-06904 Sophia Antipolis, France University of Electronic Science & Technology of China; Inria; UDICE-French Research Universities; Universite PSL; MINES ParisTech Li, L (corresponding author), Univ Elect Sci & Technol China, Sch Math Sci, Chengdu, Peoples R China. Yingzhao@std.uestc.edu.cn; plum_liliang@uestc.edu.cn; Stephane.Lanteri@inria.fr; jonathan.viquerat@mines-paristech.fr Li, Liang/N-3713-2016 Li, Liang/0000-0002-3847-0310 37 0 0 21 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-4754 1872-7166 MATH COMPUT SIMULAT Math. Comput. Simul. JUL 2022.0 197.0 377 395 10.1016/j.matcom.2022.02.016 0.0 MAR 2022 19 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics 0N7CD Green Submitted 2023-03-23 WOS:000782991000005 0
J Jia, JX; Chen, JS; Zheng, XR; Wang, YM; Guo, SX; Sun, HB; Jiang, CH; Karjalainen, M; Karila, K; Duan, ZY; Wang, TH; Xu, C; Hyyppa, J; Chen, YW Jia, Jianxin; Chen, Jinsong; Zheng, Xiaorou; Wang, Yueming; Guo, Shanxin; Sun, Haibin; Jiang, Changhui; Karjalainen, Mika; Karila, Kirsi; Duan, Zhiyong; Wang, Tinghuai; Xu, Chong; Hyyppa, Juha; Chen, Yuwei Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Spatial resolution; Hyperspectral imaging; Agriculture; Signal to noise ratio; Forestry; Instruments; Signal resolution; Hyperspectral imager; parameter optimization; system design; tradeoff SIGNAL-TO-NOISE; NEAREST-NEIGHBOR; OPERATIONAL METHOD; FEATURE-SELECTION; NEURAL-NETWORK; CLASSIFICATION; ALGORITHMS; IMPACT; DISCRIMINATION; FRAMEWORK Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications. [Jia, Jianxin; Jiang, Changhui; Karjalainen, Mika; Karila, Kirsi; Hyyppa, Juha; Chen, Yuwei] Finnish Geospatial Res Inst, Dept Photogrammetry & Remote Sensing, Kyrkslatt 02430, Finland; [Jia, Jianxin; Chen, Jinsong; Zheng, Xiaorou; Guo, Shanxin] Chinese Acad Sci, Ctr Geospatial Informat, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Wang, Yueming] Chinese Acad Sci, Shanghai Inst Tech Phys, Key Lab Space Act Optoelect Technol, Shanghai 200083, Peoples R China; [Sun, Haibin] Chinese Acad Sci, Shanghai Inst Tech Phys, Key Lab Intelligent Infrared Percept, Shanghai 200083, Peoples R China; [Duan, Zhiyong] Zhengzhou Univ, Sch Phys & Microelect, Zhengzhou 450001, Peoples R China; [Wang, Tinghuai] Huawei Helsinki Res Ctr, Helsinki 00180, Finland; [Xu, Chong] Jihua Lab, Foshan 528200, Peoples R China The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; Shanghai Institute of Technical Physics, CAS; Chinese Academy of Sciences; Shanghai Institute of Technical Physics, CAS; Zhengzhou University; Ji Hua Laboratory Chen, YW (corresponding author), Finnish Geospatial Res Inst, Dept Photogrammetry & Remote Sensing, Kyrkslatt 02430, Finland.;Zheng, XR (corresponding author), Chinese Acad Sci, Ctr Geospatial Informat, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China.;Wang, YM (corresponding author), Chinese Acad Sci, Shanghai Inst Tech Phys, Key Lab Space Act Optoelect Technol, Shanghai 200083, Peoples R China. jianxin.jia@nls.fi; xiaorou.zheng@siat.ac.cn; wangym@mail.sitp.ac.cn; yuwei.chen@nls.fi 陈, 雨薇/HKF-1175-2023 Jia, Jianxin/0000-0003-4366-4547; Wang, Yueming/0000-0003-0791-4836; Guo, Shanxin/0000-0001-8911-0166 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030301, 181811KYSB20160113, XDA22030202]; Academy of Finland [336145]; Strategic Research Council [314312]; Beijing Municipal Science and Technology Commission [Z181100001018036]; Shanghai Science and Technology Foundations [18590712600]; Huawei Project [4020G-1214, 4020G-1215]; Jihua Laboratory [X190211TE190] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Academy of Finland(Academy of Finland); Strategic Research Council; Beijing Municipal Science and Technology Commission(Beijing Municipal Science & Technology Commission); Shanghai Science and Technology Foundations; Huawei Project(Huawei Technologies); Jihua Laboratory This work was supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA19030301, Grant 181811KYSB20160113, and Grant XDA22030202; in part by the Academy of Finland projects under Grant 336145; in part by the Strategic Research Council project under Grant 314312; in part by the Beijing Municipal Science and Technology Commission under Grant Z181100001018036; in part by the Shanghai Science and Technology Foundations under Grant 18590712600; in part by the Jihua Laboratory under Grant X190211TE190; and in part by the Huawei Project under Grant 4020G-1214 and Grant 4020G-1215. 66 14 14 5 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3096999 0.0 JUL 2021 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YL6FH hybrid 2023-03-23 WOS:000733501600001 0
C Yang, K; Zhao, WJ; Antoniou, C IEEE Yang, Kui; Zhao, Wenjing; Antoniou, Constantinos Utilizing Import Vector Machines to Identify Dangerous Pro-active Traffic Conditions 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) SEP 20-23, 2020 ELECTR NETWORK IEEE TIME CRASH PREDICTION; SEVERITY; WEATHER Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in real-time crash risk analysis, which is a hot topic to reduce traffic accidents. Historical crash data and corresponding traffic data from Shanghai Urban Expressway System were employed and matched. Traffic conditions are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a normal traffic condition) based on 5-minute measurements of average speed, volume and occupancy. The IVM algorithm is trained to build the classifier and its performance is compared to the popular and successfully applied technique of Support Vector Machines (SVMs). The main findings indicate that IVMs could successfully be employed in real-time identification of dangerous pro-active traffic conditions. Furthermore, similar to the support points of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM, and its classification rates are similar to those of SVMs. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large. [Yang, Kui; Antoniou, Constantinos] Tech Univ Munich, Dept Civil Geo & Environm Engn, Munich, Germany; [Zhao, Wenjing] Cent South Univ, Sch Traff & Transportat Engn, Changsha, Peoples R China Technical University of Munich; Central South University Yang, K (corresponding author), Tech Univ Munich, Dept Civil Geo & Environm Engn, Munich, Germany. ku.yang@tum.de; stacy_zhao@126.com; c.antoniou@tum.de European Union [814761] European Union(European Commission) Research supported by the European Union's Horizon 2020 research and innovation programme i-DREAMS under grant agreement No 814761. 31 3 3 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-7281-4149-7 IEEE INT C INTELL TR 2020.0 6 Engineering, Electrical & Electronic; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Transportation BS0GS 2023-03-23 WOS:000682770700108 0
J Guo, ZY; Shi, DW; Quevedo, DE; Shi, L Guo, Ziyang; Shi, Dawei; Quevedo, Daniel E.; Shi, Ling Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Secure sate estimation; Gaussian mixture model; integrity attack; clustering CYBER-PHYSICAL SYSTEMS We consider the problem of estimating the state of a linear time-invariant Gaussian system using N sensors, where a subset of the sensors can potentially be compromised by an adversary. In this case, locating the compromised sensors is of crucial importance for obtaining an accurate state estimate. Inspired by the clustering algorithm in machine learning, we propose a Gaussian-mixture-model-based (GMM-based) detection mechanism. It clusters the local state estimate autonomously and provides a belief for each sensor, based on which measurements from different sensors can be fused accordingly. When a subset of the sensors are under the optimal innovation-based deception attacks, we derive the remote estimation error covariance recursions under different detection mechanisms, e.g., distributed chi(2) false-data detector, centralized chi(2) false-data detector, and GMM-based detection algorithm. The performance of the proposed GMM-based detection algorithm is further evaluated through average belief in the same attack scenario. Moreover, we discuss applications of GMM-based detection algorithm on other attack scenarios, e.g., false-data injection attack, replay attack, and is an element of-stealthy attack. Simulation examples are provided to demonstrate the developed results. [Guo, Ziyang; Shi, Ling] Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China; [Shi, Dawei] Beijing Inst Technol, MIIT Key Lab Drive & Control Mot Syst, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China; [Quevedo, Daniel E.] Paderborn Univ, Dept Elect Engn EIM E, Paderborn, Germany Hong Kong University of Science & Technology; Beijing Institute of Technology; University of Paderborn Shi, DW (corresponding author), Beijing Inst Technol, MIIT Key Lab Drive & Control Mot Syst, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China. zguoae@connect.ust.hk; daweishi@bit.edu.cn; dquevedo@ieee.org; eesling@ust.hk Quevedo, Daniel E/A-8454-2008 Quevedo, Daniel E/0000-0002-4804-5481 Hong Kong RGC General Research Fund [16222716]; National Natural Science Foundation of China [61503027] Hong Kong RGC General Research Fund; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The work by Z. Guo and L. Shi was supported by a Hong Kong RGC General Research Fund 16222716. The work by D. Shi was supported by National Natural Science Foundation of China under Grant 61503027. 45 32 34 8 52 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. JAN 1 2019.0 67.0 1 194 207 10.1109/TSP.2018.2879037 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering HC7NR 2023-03-23 WOS:000451988800002 0
J Lu, T; Lu, XS; Salonen, HD; Zhang, QL Lu, Tao; Lu, Xiaoshu; Salonen, Heidi; Zhang, Qunli Novel hybrid modeling approach for utilizing simple linear regression models to solve multi-input nonlinear problems of indoor humidity modeling BUILDING AND ENVIRONMENT English Article Indoor humidity modelling; Simple linear regression model; Hybrid modeling; Extended outdoor absolute humidity MOISTURE BUFFER VALUE; RELATIVE-HUMIDITY; NEURAL-NETWORK; TEMPERATURE; HEAT; VENTILATION; AIR; PREDICTION; BUILDINGS; CLIMATE Investigating indoor humidity is important because abnormal moisture levels can damage building structures and result in poor indoor air quality. Outdoor humidity, ventilation rate, and internal moisture load are the three dominant factors affecting indoor humidity. State-of-the-art methods, particularly full-scale field studies for determining these three factors and indoor humidity can be both time consuming and labor intensive. This study proposes a radically new methodology to effectively model the influence of these three factors on indoor humidity for a mechanically ventilated building/space. The methodology starts with a simple linear regression (SLR) constructed by measuring indoor and outdoor humidity and then hybrids a novel analytical approach that accurately predicts the impact of ventilation rate and internal moisture load on indoor humidity. The proposed upgraded SLR model was successfully validated with high accuracy by both experiments and numerical simulations using TRNSYS commercial software. The results demonstrate the ability of the developed SLR to accurately model indoor humidity and account for the moisture exchange between indoor air and building structures/ furnishings. Inferring these relationships and their influences on indoor humidity presents a challenging task. The developed model is generic and unique and supports fast and inexpensive field studies by ensuring that the measurements of indoor and outdoor humidity are sufficient to derive field tests of the impacts of outdoor humidity, ventilation rate, and internal moisture loads on indoor humidity. The proposed model can be further developed as a standardized moisture assessment tool for benchmarking building performance. [Lu, Tao; Lu, Xiaoshu] Univ Vaasa, Dept Elect Engn & Energy Technol, POB 700, FIN-65101 Vaasa, Finland; [Lu, Xiaoshu] Jilin Univ, Coll Construct Engn, Changchun 130026, Peoples R China; [Lu, Xiaoshu; Zhang, Qunli] Beijing Univ Civil Engn & Architecture, Beijing Key Lab Heating Ventilating & Air Conditi, Beijing 100044, Peoples R China; [Lu, Xiaoshu; Salonen, Heidi] Aalto Univ, Dept Civil Engn, POB 11000, Espoo 02150, Finland University of Vaasa; Jilin University; Beijing University of Civil Engineering & Architecture; Aalto University Lu, T (corresponding author), Univ Vaasa, Dept Elect Engn & Energy Technol, POB 700, FIN-65101 Vaasa, Finland. tao.lu@uwasa.fi Salonen, Heidi/G-4685-2016 Lu, Xiaoshu/0000-0002-1928-8580; Lu, Tao/0000-0003-2917-6029 Academy of Finland [324023, 330150]; Project Catalyst Screening And Its Mechanism In The Molten Salt Pyrolysis of PUR [331682]; National Natural Science Foundation of China [41972324]; Academy of Finland (AKA) [324023] Funding Source: Academy of Finland (AKA) Academy of Finland(Academy of Finland); Project Catalyst Screening And Its Mechanism In The Molten Salt Pyrolysis of PUR; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This research was supported by the Academy of Finland (STARCLUB, Grant No. 324023, CleanSchool, Grant No. 330150, and Project Catalyst Screening And Its Mechanism In The Molten Salt Pyrolysis of PUR, Grant No. 331682) and National Natural Science Foundation of China (Grant No. 41972324) . 45 1 1 2 3 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-1323 1873-684X BUILD ENVIRON Build. Environ. APR 1 2022.0 213.0 108856 10.1016/j.buildenv.2022.108856 0.0 FEB 2022 11 Construction & Building Technology; Engineering, Environmental; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering 0J7GH hybrid, Green Published 2023-03-23 WOS:000780269500003 0
J Wang, GY; Marano, S; Zhu, J; Xu, ZW Wang, Guanyu; Marano, Stefano; Zhu, Jiang; Xu, Zhiwei Target Localization by Unlabeled Range Measurements IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Target localization; range measurements; unlabeled sensing; unordered signal processing SENSOR; ALGORITHMS; DECISIONS In this paper, the unlabeled target localization problem in a wireless sensor network is addressed. Sensors of the network are deployed in a two-dimensional surveyed area and measure their distance to a target. A central unit processes the data delivered by the sensors and is tasked to infer the position of a target, but it must do so without knowing the association between the received data and the identity of the delivering sensors. This setting of unlabeled inference is attracting much attention in recent years due to its relevance to emerging applications involving big data. The present article exploits the underlying geometrical structure of the inference problem to develop two unlabeled localization approaches based on range measurements, referred to as partition and intersection points methods, respectively. The design of these methods is based on analytical results while their performance assessment is conducted by computer experiments. We find that the partition method should be preferred in the regime of low signal-to-noise ratio and networks consisting of a small number of sensors, while the intersection points method is more suitable to solve the target localization problem with large networks. [Wang, Guanyu; Zhu, Jiang; Xu, Zhiwei] Zhejiang Univ, Engn Res Ctr Ocean Sensing Technol & Equipment, Minist Educ, Zhoushan, Peoples R China; [Wang, Guanyu; Zhu, Jiang; Xu, Zhiwei] Zhejiang Univ, Ocean Coll, Key Lab Ocean Observat Imaging Testbed Zhejiang P, Zhoushan 316021, Peoples R China; [Marano, Stefano] Univ Salerno, Dept Elect & Informat Engn & Appl Math, I-84084 Fisciano, Italy Zhejiang University; Zhejiang University; University of Salerno Zhu, J (corresponding author), Zhejiang Univ, Engn Res Ctr Ocean Sensing Technol & Equipment, Minist Educ, Zhoushan, Peoples R China. guanyuwang@zju.edu.cn; marano@unisa.it; jiangzhu16@zju.edu.cn; xuzw@zju.edu.cn MARANO, Stefano/0000-0002-5307-0980; Wang, Guanyu/0000-0001-7610-8104 Zhejiang Provincial Natural Science Foundation of China [LQ18F010001]; National Science Foundation of China [61901415, 61731019]; Fundamental Research Funds for the Central Universities Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Rajawat Ketan. This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant LQ18F010001, in part by the National Science Foundation of China under Grants 61901415 and 61731019, and the Fundamental Research Funds for the Central Universities. 57 4 4 1 4 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2020.0 68.0 6607 6620 10.1109/TSP.2020.3038230 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering PC6YI 2023-03-23 WOS:000597144000007 0
J Rentschler, T; Gries, P; Behrens, T; Bruelheide, H; Kuhn, P; Seitz, S; Shi, XZ; Trogisch, S; Scholten, T; Schmidt, K Rentschler, Tobias; Gries, Philipp; Behrens, Thorsten; Bruelheide, Helge; Kuehn, Peter; Seitz, Steffen; Shi, Xuezheng; Trogisch, Stefan; Scholten, Thomas; Schmidt, Karsten Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China PLOS ONE English Article FOREST BIODIVERSITY; ECOSYSTEM SERVICES; DEPTH FUNCTIONS; EROSION; PREDICTION; STORAGE; MATTER; WATER; DIVERSITY; CLIMATE As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks. Previous three-dimensional SOC modelling approaches usually averaged each depth increment for multi-layer two-dimensional predictions. Therefore, these models are limited in their vertical resolution and thus in the interpretability of the soil as a volume as well as in the accuracy of the SOC stock predictions. So far, only few approaches used spatially modelled depth functions for SOC predictions. This study implemented and evaluated an approach that compared polynomial, logarithmic and exponential depth functions using non-linear machine learning techniques, i.e. multivariate adaptive regression splines, random forests and support vector machines to quantify SOC stocks spatially and depth-related in the context of biodiversity and ecosystem functioning research. The legacy datasets used for modelling include profile data for SOC and bulk density (BD), sampled at five depth increments (0-5, 5-10, 10-20, 20-30, 30-50 cm). The samples were taken in an experimental forest in the Chinese subtropics as part of the biodiversity and ecosystem functioning (BEF) China experiment. Here we compared the depth functions by means of the results of the different machine learning approaches obtained based on multi-layer 2D models as well as 3D models. The main findings were (i) that 3rd degree polynomials provided the best results for SOC and BD (R-2 = 0.99 and R-2 = 0.98; RMSE = 0.36% and 0.07 g cm(-3)). However, they did not adequately describe the general asymptotic trend of SOC and BD. In this respect the exponential (SOC: R-2 = 0.94; RMSE = 0.56%) and logarithmic (BD:R-2 = 84; RMSE = 0.21 g cm(-3)) functions provided more reliable estimates. (ii) random forests with the exponential function for SOC correlated better with the corresponding 2.5D predictions (R-2 : 0.96 to 0.75), compared to the 3rd degree polynomials (R-2 : 0.89 to 0.15) which support vector machines fitted best. We recommend not to use polynomial functions with sparsely sampled profiles, as they have many turning points and tend to overfit the data on a given profile. This may limit the spatial prediction capacities. Instead, less adaptive functions with a higher degree of generalisation such as exponential and logarithmic functions should be used to spatially map sparse vertical soil profile datasets. We conclude that spatial prediction of SOC using exponential depth functions, in conjunction with random forests is well suited for 3D SOC stock modelling, and provides much finer vertical resolutions compared to 2.5D approaches. [Rentschler, Tobias; Gries, Philipp; Behrens, Thorsten; Kuehn, Peter; Seitz, Steffen; Scholten, Thomas; Schmidt, Karsten] Univ Tubingen, Dept Geosci, Chair Soil Sci & Geomorphol, Tubingen, Germany; [Rentschler, Tobias; Schmidt, Karsten] Univ Tubingen, SFB 1070 RESOURCECULTURES, Tubingen, Germany; [Bruelheide, Helge; Trogisch, Stefan] Martin Luther Univ Halle Wittenberg, Inst Biol, Dept Geobot & Bot Garden, Halle, Germany; [Bruelheide, Helge; Trogisch, Stefan] German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany; [Shi, Xuezheng] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Jiangsu, Peoples R China Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Martin Luther University Halle Wittenberg; Chinese Academy of Sciences; Institute of Soil Science, CAS Rentschler, T (corresponding author), Univ Tubingen, Dept Geosci, Chair Soil Sci & Geomorphol, Tubingen, Germany.;Rentschler, T (corresponding author), Univ Tubingen, SFB 1070 RESOURCECULTURES, Tubingen, Germany. t.rentschler@uni-tuebingen.de Seitz, Steffen/B-7627-2016; Schmidt, Karsten/G-8190-2015; Kühn, Peter/A-9146-2009; Rentschler, Tobias/AAX-1600-2021; Bruelheide, Helge/G-3907-2013 Seitz, Steffen/0000-0003-4911-3906; Schmidt, Karsten/0000-0003-0337-3024; Kühn, Peter/0000-0002-4417-5633; Rentschler, Tobias/0000-0003-3878-5539; Bruelheide, Helge/0000-0003-3135-0356; Behrens, Thorsten/0000-0002-9287-9395 German Research Foundation [DFG FOR 891/2, DFG FOR 891/3]; Sino-German Centre for Research Promotion in Beijing, China [GZ 698, GZ 699]; University of Tubingen, Germany (PROMOS); Open Access Publishing Fund of the University of Tubingen German Research Foundation(German Research Foundation (DFG)); Sino-German Centre for Research Promotion in Beijing, China; University of Tubingen, Germany (PROMOS); Open Access Publishing Fund of the University of Tubingen This study was funded by the German Research Foundation (DFG FOR 891/2 and 3). Travel grants were financed by the Sino-German Centre for Research Promotion in Beijing, China (GZ 698 and 699) and the University of Tubingen, Germany (PROMOS). Support was also received from the Open Access Publishing Fund of the University of Tubingen. 90 15 15 7 28 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One AUG 20 2019.0 14.0 8 e0220881 10.1371/journal.pone.0220881 0.0 23 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics IW5OC 31430307.0 Green Submitted, gold, Green Published 2023-03-23 WOS:000485026800016 0
J Li, B; Wang, Y; Wang, T; He, XM; Kazak, JK Li, Bo; Wang, Yue; Wang, Tong; He, Xiaoman; Kazak, Jan K. Scenario Analysis for Resilient Urban Green Infrastructure LAND English Article green infrastructure; resilience management; biodiversity; scenario analysis; cellular automata model WEASEL MUSTELA-SIBIRICA; RED-BILLED LEIOTHRIX; LAND-COVER CHANGE; CELLULAR-AUTOMATA; ECOLOGICAL RESILIENCE; MODEL; OPTIMIZATION; SIMULATION; CHINA; AREA With the advancement of urbanization, the stress on the green infrastructure around the urban agglomeration has intensified, which causes severe ecological problems. The uncertainty of urban growth makes it difficult to achieve effective protection only by setting protection red lines and other rigid measures. It is of practical significance to optimize the resilience of the stressed green infrastructure. To this end, we explore a scenario simulation analysis method for the resilience management of green infrastructure under stress. This research applies artificial neural network cellular automata to simulate the impacts of the Chang-Zhu-Tan urban agglomeration expansion on the green infrastructure in 2030 in three scenarios: no planning control, urban planning control, and ecological protection planning control. Based on the analysis, we identify four green infrastructure areas under stress and formulate resilience management measures, respectively. The results show that: (1) The distribution pattern of green infrastructure under stress is different in three scenarios. Even in the scenario of ecological protection planning and control, urban growth can easily break through the ecological protection boundary; (2) Residential, industrial, and traffic facility land are the main types of urban land causing green infrastructure stress, while forest, shrub, and wetland are the main types of the stressed green infrastructure; (3) Efficient protection of green infrastructure and the management of the urban growth boundary should be promoted by resilient management measures such as urban planning adjustment, regulatory detailed planning, development strength control and setting up the ecological protection facilities for the stressed green infrastructure areas of the planning scenarios and the no-planning control scenarios, for the areas to be occupied by urban land, and for the important ecological corridors. The results of this study provide an empirical foundation for formulating policies and the methods of this study can be applied to urban ecological planning and green infrastructure management practice in other areas as well. [Li, Bo; Wang, Yue; He, Xiaoman] Cent South Univ, Sch Architecture & Art, Changsha 410083, Peoples R China; [Wang, Tong] Delft Univ Technol, Fac Architecture & Built Environm, NL-2628 BL Delft, Netherlands; [Kazak, Jan K.] Wroclaw Univ Environm & Life Sci, Inst Spatial Management, PL-50357 Wroclaw, Poland Central South University; Delft University of Technology; Institute of Spatial Management & Housing; Wroclaw University of Environmental & Life Sciences Wang, T (corresponding author), Delft Univ Technol, Fac Architecture & Built Environm, NL-2628 BL Delft, Netherlands. twang-12@tudelft.nl Wang, Tong/HPD-4929-2023; Kazak, Jan/S-7783-2016 Kazak, Jan/0000-0002-1864-9954; li, bo/0000-0003-0763-3583; Wang, Tong/0000-0003-2599-5000 Hunan Province Philosophy and Social Science Foundation of China [09yba158] Hunan Province Philosophy and Social Science Foundation of China This research was supported by the Hunan Province Philosophy and Social Science Foundation of China (09yba158). And the article processing costs are waived. 92 1 1 33 33 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-445X LAND-BASEL Land SEP 2022.0 11.0 9 1481 10.3390/land11091481 0.0 19 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology 4S7QK gold, Green Published 2023-03-23 WOS:000857630500001 0
J Wang, WP; Wang, CY; Wang, Z; Yuan, MM; Luo, X; Kurths, J; Gao, Y Wang, Weiping; Wang, Chunyang; Wang, Zhen; Yuan, Manman; Luo, Xiong; Kurths, Juergen; Gao, Yang Abnormal detection technology of industrial control system based on transfer learning APPLIED MATHEMATICS AND COMPUTATION English Article Industrial control network; Anomaly detection; Instance migration; TrAdaBoost; Unbalanced sample TRADABOOST; CLASSIFICATION; ALGORITHM In industrial control systems, industrial infrastructure is often attacked by hackers. Due to the serious sample imbalance in industrial control data, the traditional machine learning method has poor performance in anomaly detection. In this paper, TrAdaboost algorithm is applied to industrial control anomaly detection. The samples that are easy to classify are taken as the source domain data, and the samples with poor classification effect are taken as the target domain. The source domain data is used to guide the target domain data training. Then, we improve the traditional TrAdaboost algorithm from two aspects of initial weight and final classifier, and apply it to industrial control anomaly detection. Finally, the performance of the algorithm on two different industrial control data sets is verified. And the improved algorithm is compared with other traditional algorithms. The experimental results show that the improved TrAdaboost algorithm has a significant advantage in predicting categories with a small sample size. This algorithm can accurately identify a few abnormal samples. Moreover, the F1 value, recall and precision value of the improved TrAdaboost algorithm on the two data sets have been significantly improved. This indicates that the improved TrAdaboost algorithm greatly improves the overall prediction accuracy of the model. (C) 2021 Published by Elsevier Inc. [Wang, Weiping; Wang, Chunyang; Yuan, Manman; Luo, Xiong] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Wang, Weiping; Wang, Chunyang; Yuan, Manman; Luo, Xiong] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China; [Wang, Weiping; Wang, Chunyang; Yuan, Manman; Luo, Xiong] Univ Sci & Technol Beijing, Shunde Grad Sch, Foshan 528399, Peoples R China; [Wang, Weiping; Wang, Chunyang; Yuan, Manman; Luo, Xiong] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China; [Wang, Zhen] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China; [Kurths, Juergen] Humboldt Univ, Inst Phys, D-10099 Berlin, Germany; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Gao, Yang] China Informat Technol Secur Evaluat Ctr, Beijing 10083, Peoples R China University of Science & Technology Beijing; University of Science & Technology Beijing; University of Science & Technology Beijing; Northwestern Polytechnical University; Humboldt University of Berlin; Potsdam Institut fur Klimafolgenforschung Wang, WP (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.;Wang, WP (corresponding author), Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.;Wang, WP (corresponding author), Univ Sci & Technol Beijing, Shunde Grad Sch, Foshan 528399, Peoples R China.;Wang, WP (corresponding author), Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China.;Wang, Z (corresponding author), Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China. weipingwangjt@ustb.edu.cn; nkzhenwang@163.com National Natural Science Foundation of China [81961138010, U1803263, 11931015, U1836106]; Fundamental Research Funds for the Central Universities [FRF-TP-19-005A3, QNXM20210036, FRF-IDRY-20-022]; Technological Innovation Foundation of Shunde Graduate School, USTB [BK19BF006, BK20BF010, BK21BF001]; National Natural Science Foundation for Distinguished Young Scholars [62025602]; Fok Ying-Tong Education Foundation of China [171105]; Key Area RAMP;D Program of Guangdong Province [2019B010137004]; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province [2020TD-013] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Technological Innovation Foundation of Shunde Graduate School, USTB; National Natural Science Foundation for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Fok Ying-Tong Education Foundation of China; Key Area RAMP;D Program of Guangdong Province; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province This work was supported in part by the National Natural Science Foundation of China under Grant 81961138010, U1803263, 11931015 and U1836106, in part by the Fundamental Research Funds for the Central Universities under Grant FRF-TP-19-005A3, QNXM20210036, and FRF-IDRY-20-022, in part by the Technological Innovation Foundation of Shunde Graduate School, USTB, under Grant BK19BF006, Grant BK20BF010, and Grant BK21BF001, in part by National Natural Science Foundation for Distinguished Young Scholars under Grant 62025602, in part by Fok Ying-Tong Education Foundation of China under Grant 171105, in part by Key Area R&D Program of Guangdong Province under Grant 2019B010137004, in part by Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province under Grant 2020TD-013. 30 7 7 12 30 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0096-3003 1873-5649 APPL MATH COMPUT Appl. Math. Comput. JAN 1 2022.0 412.0 126539 10.1016/j.amc.2021.126539 0.0 AUG 2021 14 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics US0VH 2023-03-23 WOS:000697154500003 0
C Zhang, JQ; Gao, X; Chen, H; Chen, L; Wang, L; Luo, JD; Fu, XM IEEE Zhang, Jiaquan; Gao, Xin; Chen, Hui; Chen, Lei; Wang, Liang; Luo, Jar-Der; Fu, Xiaoming A Data-Driven Analysis of K-12 Students' Participation and Learning Performance on an Online Supplementary Learning Platform 30TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2021) IEEE International Conference on Computer Communications and Networks English Proceedings Paper 30th International Conference on Computer Communications and Networks (ICCCN) JUL 19-22, 2021 ELECTR NETWORK IEEE,IEEE Commun Soc SOCIOECONOMIC-STATUS; PRIVATE; EDUCATION; DEMAND Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Different from public schools, the private online education provides diverse courses and satisfy differentiated demands of the students. Students' behavior and performance in online supplementary learning are relevant to not only personal attributes, but also some factors such as city levels, grades and family situation. Existing studies mostly rely on panel survey/questionnaire data and few studied online private tutoring. In this paper, with 11,392 anonymous K-12 students' 3-year learning data from one of the world's largest online extra-curricular education platforms, we uncover students' online learning behaviors and infer the impact of students' home location, family socioeconomic situation and attended school's reputation/rank on the students' private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students' performance and behaviors using machine learning algorithms with different groups of features, showing students' online learning performance can be predicted with MAE< 10%. [Zhang, Jiaquan; Fu, Xiaoming] Univ Gottingen, Gottingen, Germany; [Gao, Xin; Luo, Jar-Der] Tsinghua Univ, Beijing, Peoples R China; [Chen, Hui] Beijing Foreign Studies Univ, Beijing, Peoples R China; [Chen, Lei; Wang, Liang] TAL Educ Grp, Beijing, Peoples R China University of Gottingen; Tsinghua University; Beijing Foreign Studies University Zhang, JQ (corresponding author), Univ Gottingen, Gottingen, Germany. jzhang@cs.uni-goettingen.de; gao-x18@mails.tsinghua.edu.cn; chenhui@bfsu.edu.cn; chenlei.ray@tal.com; wangliang1@tal.com; jdluo@tsinghua.edu.cn; fu@cs.uni-goettingen.de Fu, Xiaoming/B-7208-2016; Fu, Xiaoming/AAD-2828-2022 Fu, Xiaoming/0000-0002-8012-4753; Fu, Xiaoming/0000-0002-8012-4753; Luo, Jar-Der/0000-0002-1387-9849; Chen, Hui/0000-0003-3015-0248; Zhang, Jiaquan/0000-0001-9179-9425 Fundamental Research Funds for Central Universities [2020JJ014, YY19SSK05]; Tsinghua-Gottingen Student Exchange Project [IDS-SSP-2017001]; EU H2020 COSAFE project [824019] Fundamental Research Funds for Central Universities(Fundamental Research Funds for the Central Universities); Tsinghua-Gottingen Student Exchange Project; EU H2020 COSAFE project This work is partly supported by Fundamental Research Funds for Central Universities (No. 2020JJ014, YY19SSK05), Tsinghua-Gottingen Student Exchange Project (No. IDS-SSP-2017001) and EU H2020 COSAFE project (No. 824019). Hui Chen is the corresponding author. 29 1 1 2 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1095-2055 978-0-7381-1330-2 IEEE IC COMP COM NET 2021.0 10.1109/ICCCN52240.2021.9522295 0.0 9 Computer Science, Hardware & Architecture Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS2HZ 2023-03-23 WOS:000701532600089 0
J Wong, JYY; Imani, P; Grigoryan, H; Bassig, BA; Dai, YF; Hu, W; Blechter, B; Rahman, ML; Ji, BT; Duan, HW; Niu, Y; Ye, M; Jia, XW; Meng, T; Bin, P; Downward, G; Meliefste, K; Leng, SG; Fu, W; Yang, JF; Ren, DZ; Xu, J; Zhou, BS; Hosgood, HD; Vermeulen, R; Zheng, YX; Silverman, DT; Rothman, N; Rappaport, SM; Lan, Q Wong, Jason Y. Y.; Imani, Partow; Grigoryan, Hasmik; Bassig, Bryan A.; Dai, Yufei; Hu, Wei; Blechter, Batel; Rahman, Mohammad L.; Ji, Bu-Tian; Duan, Huawei; Niu, Yong; Ye, Meng; Jia, Xiaowei; Meng, Tao; Bin, Ping; Downward, George; Meliefste, Kees; Leng, Shuguang; Fu, Wei; Yang, Jufang; Ren, Dianzhi; Xu, Jun; Zhou, Baosen; Hosgood, H. Dean; Vermeulen, Roel; Zheng, Yuxin; Silverman, Debra T.; Rothman, Nathaniel; Rappaport, Stephen M.; Lan, Qing Exposure to diesel engine exhaust and alterations to the Cys34/Lys525 adductome of human serum albumin ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY English Article Diesel engine exhaust; Cys34 adductome; Adductomics; Machine learning; Environmental epidemiology; Biomarkers INCIDENT LUNG-CANCER; OCCUPATIONAL-EXPOSURE; RISK-FACTOR; DISEASE; MARKERS; CYS34; HOMOCYSTEINE; PARTICLES; PIPELINE; BENZENE We investigated whether exposure to carcinogenic diesel engine exhaust (DEE) was associated with altered adduct levels in human serum albumin (HSA) residues. Nano-liquid chromatography-high resolution mass spectrometry (nLC-HRMS) was used to measure adducts of Cys34 and Lys525 residues in plasma samples from 54 diesel engine factory workers and 55 unexposed controls. An untargeted adductomics and bioinformatics pipeline was used to find signatures of Cys34/Lys525 adductome modifications. To identify adducts that were altered between DEE-exposed and unexposed participants, we used an ensemble feature selection approach that ranks and combines findings from linear regression and penalized logistic regression, then aggregates the important findings with those determined by random forest. We detected 40 Cys34 and 9 Lys525 adducts. Among these findings, we found evidence that 6 Cys34 adducts were altered between DEE-exposed and unexposed participants (i.e., 841.75, 851.76, 856.10, 860.77, 870.43, and 913.45). These adducts were biologically related to antioxidant activity. [Wong, Jason Y. Y.; Bassig, Bryan A.; Hu, Wei; Blechter, Batel; Rahman, Mohammad L.; Ji, Bu-Tian; Silverman, Debra T.; Rothman, Nathaniel; Lan, Qing] NCI, Div Canc Epidemiol & Genet, NIH, Rockville, MD 20850 USA; [Imani, Partow; Grigoryan, Hasmik; Rappaport, Stephen M.] Univ Calif Berkeley, Sch Publ Hlth, Berkeley, CA 94720 USA; [Dai, Yufei; Duan, Huawei; Niu, Yong; Ye, Meng; Jia, Xiaowei; Meng, Tao; Bin, Ping] Chinese Ctr Dis Control & Prevent, Natl Inst Occupat Hlth & Poison Control, Beijing, Peoples R China; [Downward, George; Meliefste, Kees; Vermeulen, Roel] Univ Utrecht, Inst Risk Assessment Sci, Div Environm Epidemiol, Utrecht, Netherlands; [Leng, Shuguang] Univ New Mexico, Comprehens Canc Ctr, Canc Control & Populat Sci, Albuquerque, NM 87131 USA; [Leng, Shuguang] Univ New Mexico, Comprehens Canc Ctr, Sch Med, Div Epidemiol Biostat & Prevent Med,Dept Internal, Albuquerque, NM 87131 USA; [Fu, Wei; Yang, Jufang; Ren, Dianzhi] Chaoyang Ctr Dis Control & Prevent, Chaoyang, Liaoning, Peoples R China; [Xu, Jun] Univ Hong Kong, Sch Publ Hlth, Hong Kong, Peoples R China; [Zhou, Baosen] China Med Univ, Shenyang, Liaoning, Peoples R China; [Hosgood, H. Dean] Albert Einstein Coll Med, Div Epidemiol, New York, NY USA; [Zheng, Yuxin] Qingdao Univ, Sch Publ Hlth, Qingdao, Peoples R China National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); University of California System; University of California Berkeley; Chinese Center for Disease Control & Prevention; National Institute of Occupational Health & Poison Control, Chinese Center for Disease Control & Prevention; Utrecht University; University of New Mexico; University of New Mexico; University of Hong Kong; China Medical University; Yeshiva University; Qingdao University Wong, JYY (corresponding author), NCI, Div Canc Epidemiol & Genet, 9609 Med Ctr Dr, Rockville, MD 20850 USA. jason.wong@nih.gov Downward, George/0000-0001-6642-7048 National Cancer Institute [P42ES04705]; National Insti-tute for Environmental Health Sciences National Cancer Institute(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)); National Insti-tute for Environmental Health Sciences(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Environmental Health Sciences (NIEHS)) This work was supported by intramural funding from the National Cancer Institute, as well as grant P42ES04705 from the National Insti-tute for Environmental Health Sciences (to SMR) . 48 0 0 3 3 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1382-6689 1872-7077 ENVIRON TOXICOL PHAR Environ. Toxicol. Pharmacol. OCT 2022.0 95.0 103966 10.1016/j.etap.2022.103966 0.0 8 Environmental Sciences; Pharmacology & Pharmacy; Toxicology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Pharmacology & Pharmacy; Toxicology 7T9ND 36067935.0 2023-03-23 WOS:000911765600011 0
J Hu, GS; Peng, XJ; Yang, YX; Hospedales, TM; Verbeek, J Hu, Guosheng; Peng, Xiaojiang; Yang, Yongxin; Hospedales, Timothy M.; Verbeek, Jakob Frankenstein: Learning Deep Face Representations Using Small Data IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Face recognition; deep learning; small training data RECOGNITION Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large training sets are needed with millions of labeled images. For some applications, such as near-infrared (NIR) face recognition, such large training data sets are not publicly available and difficult to collect. In this paper, we propose a method to generate very large training data sets of synthetic images by compositing real face images in a given data set. We show that this method enables to learn models from as few as 10 000 training images, which perform on par with models trained from 500 000 images. Using our approach, we also obtain state-of-the-art results on the CASIA NIR-VIS2.0 heterogeneous face recognition data set. [Hu, Guosheng; Verbeek, Jakob] Univ Grenoble Alpes, INRIA, CNRS, Grenoble INP,LJK, F-38000 Grenoble, France; [Peng, Xiaojiang] Hengyang Normal Univ, Hengyang 421008, Peoples R China; [Yang, Yongxin] Queen Mary Univ London, Elect Engn & Comp Sci, London E1 4NS, England; [Hospedales, Timothy M.] Univ Edinburgh, Edinburgh EH8 9JS, Midlothian, Scotland Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria; Hengyang Normal University; University of London; Queen Mary University London; University of Edinburgh Peng, XJ (corresponding author), Hengyang Normal Univ, Hengyang 421008, Peoples R China. guosheng.hu@inria.fr; xiaojiangp@gmail.com; yongxin.yang@qmul.ac.uk; t.hospedales@ed.ac.uk; jakob.verbeek@inria.fr yang, yongxin/HNB-4716-2023; Peng, Xiaojiang/AAN-8100-2020 Hospedales, Timothy/0000-0003-4867-7486; Yang, Yongxin/0000-0003-4134-8559 European Unions Horizon Research and Innovation Program [640891]; Science and Technology Plan Project of Hunan Province [2016TP1020]; Natural Science Foundation of China [61502152]; French research agency [ANR-16-CE23-0006, ANR-11-LABX-0025-01] European Unions Horizon Research and Innovation Program; Science and Technology Plan Project of Hunan Province; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); French research agency(French National Research Agency (ANR)) This work was supported in part by European Unions Horizon 2020 Research and Innovation Program under Grant 640891, in part by the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020, in part by the Natural Science Foundation of China under Grant 61502152, and in part by the French research agency contracts under Grant ANR-16-CE23-0006 and Grant ANR-11-LABX-0025-01. 68 63 64 1 50 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. JAN 2018.0 27.0 1 293 303 10.1109/TIP.2017.2756450 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering FM0ZW 28952941.0 Green Submitted, Green Accepted 2023-03-23 WOS:000414699100009 0
J Akusok, A; Baek, S; Miche, Y; Bjork, KM; Nian, R; Lauren, P; Lendasse, A Akusok, Anton; Baek, Stephen; Miche, Yoan; Bjork, Kaj-Mikael; Nian, Rui; Lauren, Paula; Lendasse, Amaury ELMVIS plus : Fast nonlinear visualization technique based on cosine distance and extreme learning machines NEUROCOMPUTING English Article Visualization; Nonlinear Dimensionality Reduction; Cosine Distance; Extreme Learning Machines; Big Data; Projection DIMENSIONALITY REDUCTION; NETWORKS; FIT This paper presents a fast algorithm and an accelerated toolbox(1) for data visualization. The visualization is stated as an assignment problem between data samples and the same number of given visualization points. The mapping function is approximated by an Extreme Learning Machine, which provides an error for a current assignment. This work presents a new mathematical formulation of the error function based on cosine similarity. It provides a closed form equation for a change of error for exchanging assignments between two random samples (called a swap), and an extreme speed-up over the original method even for a very large corpus like the MNIST Handwritten Digits dataset. The method starts from random assignment, and continues in a greedy optimization algorithm by randomly swapping pairs of samples, keeping the swaps that reduce the error. The toolbox speed reaches a million of swaps per second, and thousands of model updates per second for successful swaps in GPU implementation, even for very large dataset like MNIST Handwritten Digits. (C) 2016 Elsevier B.V. All rights reserved. [Akusok, Anton; Baek, Stephen; Lendasse, Amaury] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA; [Akusok, Anton; Lendasse, Amaury] Univ Iowa, Iowa Informat Initiat, Iowa City, IA USA; [Miche, Yoan] Nokia, Bell Labs, Espoo, Finland; [Miche, Yoan] Aalto Univ, Sch Sci, Dept Informat & Comp Sci, FI-00076 Espoo, Finland; [Bjork, Kaj-Mikael] Arcada Univ Appl Sci, Risklab, Helsinki, Finland; [Nian, Rui] Ocean Univ China, Sch Informat Sci & Engn, Qingdao, Peoples R China; [Lauren, Paula] Oakland Univ, Sch Engn & Comp Sci, Rochester, MN USA University of Iowa; University of Iowa; Nokia Corporation; Nokia Finland; Aalto University; Arcada University of Applied Sciences; Ocean University of China; Oakland University Akusok, A; Lendasse, A (corresponding author), Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA. anton-akusok@uiowa.edu; amaury-lendasse@uiowa.edu Baek, Stephen/A-6316-2016 Baek, Stephen/0000-0002-4758-4539; Baek, Stephen/0000-0001-5225-7965 44 12 13 0 23 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 12 2016.0 205.0 247 263 10.1016/j.neucom.2016.04.039 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DQ1HQ 2023-03-23 WOS:000378952500023 0
J Fang, SD; Du, ZD; Fang, YT; Huang, YJ; Chen, Y; Eeckhout, L; Temam, O; Li, HW; Chen, YJ; Wu, CY Fang, Shuangde; Du, Zidong; Fang, Yuntan; Huang, Yuanjie; Chen, Yang; Eeckhout, Lieven; Temam, Olivier; Li, Huawei; Chen, Yunji; Wu, Chengyong Performance Portability Across Heterogeneous SoCs Using a Generalized Library-Based Approach ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION English Article Design; Performance; Languages; Experimentation; SoC; heterogeneity; performance portability; library-based programming; approximate computing COMPONENT-BASED DESIGN; POWER; LANGUAGE; MODEL Because of tight power and energy constraints, industry is progressively shifting toward heterogeneous system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous programming approaches and languages. Libraries, on the other hand, provide a simple way to let programmers take advantage of complex architectures, which does not require programmers to acquire new accelerator-specific or domain-specific languages. Increasingly, library-based, also called algorithm-centric, programming approaches propose to generalize the usage of libraries and to compose programs around these libraries, instead of using libraries as mere complements. In this article, we present a software framework for achieving performance portability by leveraging a generalized library-based approach. Inspired by the notion of a component, as employed in software engineering and HW/SW codesign, we advocate nonexpert programmers to write simple wrapper code around existing libraries to provide simple but necessary semantic information to the runtime. To achieve performance portability, the runtime employs machine learning (simulated annealing) to select the most appropriate accelerator and its parameters for a given algorithm. This selection factors in the possibly complex composition of algorithms used in the application, the communication among the various accelerators, and the tradeoff between different objectives (i.e., accuracy, performance, and energy). Using a set of benchmarks run on a real heterogeneous SoC composed of a multicore processor and a GPU, we show that the runtime overhead is fairly small at 5.1% for the GPU and 6.4% for the multi-core. We then apply our accelerator selection approach to a simulated SoC platform containing multiple inexact accelerators. We show that accelerator selection together with hardware parameter tuning achieves an average 46.2% energy reduction and a speedup of 2.1 x while meeting the desired application error target. [Fang, Shuangde; Du, Zidong; Fang, Yuntan; Huang, Yuanjie; Li, Huawei; Chen, Yunji; Wu, Chengyong] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China; [Chen, Yunji] Microsoft Res, Beijing, Peoples R China; [Eeckhout, Lieven] Univ Ghent, Dept Elect & Informat Syst, B-9000 Ghent, Belgium; [Temam, Olivier] INRIA, Saclay, France; [Fang, Shuangde; Du, Zidong; Fang, Yuntan; Huang, Yuanjie; Li, Huawei; Chen, Yunji; Wu, Chengyong] Chinese Acad Sci, ICT, SKL Comp Architecture, Beijing, Peoples R China; [Fang, Shuangde; Du, Zidong; Fang, Yuntan; Huang, Yuanjie] Chinese Acad Sci, Grad Sch, Beijing, Peoples R China Chinese Academy of Sciences; Institute of Computing Technology, CAS; Microsoft; Ghent University; Inria; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Fang, SD (corresponding author), Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing, Peoples R China. fangshuangde@ict.ac.cn Li, Huawei/A-3306-2013 Li, Huawei/0000-0001-8082-4218; Chen, Yunji/0000-0003-3925-5185 National Natural Science Foundation of China (NSFC) [61176040, 61221062]; NSFC [61221062, 61222204, 61303158, 60873057, 60921002, 60925009, 61033009, 61202055]; Strategic Priority Research Program of CAS [XDA06010403]; China 10000-talents program; National High Technology Research and Development Program of China [2012AA010902]; National Basic Research Program of China [2011CB302504]; European Research Council under the European Community / ERC [259295]; Google Faculty Research Award; Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI); China 1000-talents program National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); NSFC(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of CAS; China 10000-talents program; National High Technology Research and Development Program of China(National High Technology Research and Development Program of China); National Basic Research Program of China(National Basic Research Program of China); European Research Council under the European Community / ERC(European Research Council (ERC)); Google Faculty Research Award(Google Incorporated); Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI); China 1000-talents program We would like to thank the anonymous reviewers for their valuable feedback. Y. Fang and H. Li are supported by the National Natural Science Foundation of China (NSFC) under grants No. 61176040 and 61221062. Yunji Chen is supported by the NSFC under grants No. 61222204, 61221062, and 61303158, the Strategic Priority Research Program of CAS under grant No. XDA06010403, and the China 10000-talents program. S. Fang, Z. Du, Y. Huang, and C. Wu are supported by the NSFC under grants No. 60873057, 60921002, 60925009, 61033009, and 61202055; the National High Technology Research and Development Program of China under grant No. 2012AA010902; and the National Basic Research Program of China under grant No. 2011CB302504. L. Eeckhout is supported by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement No. 259295. O. Temam is supported by a Google Faculty Research Award, the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI), and the China 1000-talents program. 63 2 2 1 10 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 1544-3566 1544-3973 ACM T ARCHIT CODE OP ACM Trans. Archit. Code Optim. JUN 2014.0 11.0 2 165 189 10.1145/2608253 0.0 25 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science AO1IU Bronze, Green Published 2023-03-23 WOS:000341066600007 0
J Guo, Y; Li, KW; Zhao, B; Shen, JD; Bloss, WJ; Azzi, M; Zhang, YP Guo, Yong; Li, Kangwei; Zhao, Bin; Shen, Jiandong; Bloss, William J.; Azzi, Merched; Zhang, Yinping Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020 CHEMOSPHERE English Article Air quality; Clean air action; Weather normalization; Random forest model; Meteorological influence OZONE PROCESS INSIGHTS; YANGTZE-RIVER DELTA; METEOROLOGICAL NORMALIZATION; SPATIOTEMPORAL VARIATIONS; ANTHROPOGENIC EMISSIONS; FIELD EXPERIMENTS; EASTERN CHINA; RANDOM FOREST; POLLUTION; TRENDS China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O-3 showed significant declines from 2013 to 2020, with PM2.5 annual decline rates of 6-9% in most cities. In contrast, O-3 concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O-3 to NO2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O-3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O-3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. [Guo, Yong; Zhang, Yinping] Tsinghua Univ, Dept Bldg Sci, Beijing, Peoples R China; [Guo, Yong; Zhang, Yinping] Beijing Key Lab Indoor Air Qual Evaluat & Control, Beijing, Peoples R China; [Li, Kangwei] Univ Lyon, Univ Claude Bernard Lyon 1, IRCELYON, CNRS, F-69626 Villeurbanne, France; [Zhao, Bin] Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100084, Peoples R China; [Shen, Jiandong] Hangzhou Environm Monitoring Ctr Stn, Hangzhou 310007, Peoples R China; [Bloss, William J.] Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England; [Azzi, Merched] New South Wales Dept Planning Ind & Environm, POB 29, Lidcombe, NSW 1825, Australia Tsinghua University; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Tsinghua University; University of Birmingham Li, KW (corresponding author), Univ Lyon, Univ Claude Bernard Lyon 1, IRCELYON, CNRS, F-69626 Villeurbanne, France. likangweizju@foxmail.com Bloss, William/N-1305-2014; Zhao, Bin/AAQ-9989-2021; Li, Kangwei/AAX-2847-2021 Bloss, William/0000-0002-3017-4461; Zhao, Bin/0000-0001-8438-9188; Li, Kangwei/0000-0001-7084-3861; Azzi, Merched/0000-0002-8357-4426; Guo, Yong/0000-0003-4059-3683 52 2 2 32 44 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-6535 1879-1298 CHEMOSPHERE Chemosphere AUG 2022.0 300.0 134608 10.1016/j.chemosphere.2022.134608 0.0 APR 2022 10 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 5Y6ZJ 35430204.0 2023-03-23 WOS:000879429900002 0
J Li, JX; Zhou, AJ; Liao, YF; Zhao, ZX; Mao, XL; Zhang, SX Li, Jingxia; Zhou, Aijun; Liao, Yongfeng; Zhao, Zixin; Mao, Xiaoli; Zhang, Shuoxin Forest Ecological Diversity Change Prediction Discrete Dynamic Model DISCRETE DYNAMICS IN NATURE AND SOCIETY English Article Forest ecosystem is the most important terrestrial ecosystem type of species diversity in the world. The protection and sustainable use of forest ecological diversity is of great significance to forest protection and sustainable management. In order to maintain biodiversity, the traditional concept of forest resources management must be changed. In recent years, the mining and application of big data have become the frontier content of international ecological diversity and macroecology research. By establishing the discrete dynamic model of forest ecological diversity change, this study finds a method that can keep the number of trees stable in a short time and ensure the sustainable development of forest resources. This article studies and constructs the forest information system and constructs an open-source WebGIS (network geographic information system) scheme for the field of spatial information and supporting OGC (occupationally generated content). At the same time, the grey theory GM (1,1) model is used to predict the development trend of forest resources in China. The results show that under the promotion of policies and research projects, China's forestry development has realized the double growth of forest area and volume, and the forest coverage rate has reached a new level. [Li, Jingxia; Mao, Xiaoli; Zhang, Shuoxin] Northwest A&F Univ, Coll Forestry, Yangling 712100, Shaanxi, Peoples R China; [Li, Jingxia; Liao, Yongfeng] Gansu Forestry Technol Coll, Tianshui 741020, Peoples R China; [Zhou, Aijun] Tianshui Normal Univ, Tianshui 741000, Peoples R China; [Zhao, Zixin] Acad Belle, Arti Milano Brera, I-20121 Milan, Italy Northwest A&F University - China; Tianshui Normal University Zhang, SX (corresponding author), Northwest A&F Univ, Coll Forestry, Yangling 712100, Shaanxi, Peoples R China. sxzhang@nwsuaf.edu.cn Zhang, Shuoxin/0000-0003-2452-6253 Gansu Province Science and Technology Program: Funding Construction of Urban and Rural Biodiversity Conservation System based on Ecological Mapping [21JR7RE906]; Gansu Provincial Science and Technology Program [21YF5FE203] Gansu Province Science and Technology Program: Funding Construction of Urban and Rural Biodiversity Conservation System based on Ecological Mapping; Gansu Provincial Science and Technology Program This work was supported by Gansu Province Science and Technology Program: Funding Construction of Urban and Rural Biodiversity Conservation System based on Ecological Mapping (project no. 21JR7RE906) and Gansu Provincial Science and Technology Program: Study on Vegetation and Human Activity Changes in Gansu Section of the Yellow River Based on Multi-Source Remote Sensing Images (project no. 21YF5FE203). 24 2 2 5 11 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1026-0226 1607-887X DISCRETE DYN NAT SOC Discrete Dyn. Nat. Soc. JAN 19 2022.0 2022.0 4869363 10.1155/2022/4869363 0.0 11 Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Science & Technology - Other Topics 0C7PY gold 2023-03-23 WOS:000775502100006 0
C Hu, WS; Li, HC; Ma, TY; Du, Q; Plaza, A; Emery, WJ IEEE Hu, Wen-Shuai; Li, Heng-Chao; Ma, Tian-Yu; Du, Qian; Plaza, Antonio; Emery, William J. HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON TENSOR-TRAIN CONVOLUTIONAL LONG SHORT-TERM MEMORY IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) SEP 26-OCT 02, 2020 ELECTR NETWORK Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Geoscience & Remote Sensing Soc Classification; convolutional long short-term memory; tensor-train decomposition; hyperspectral image NEURAL-NETWORKS In recent years, deep learning models have shown great advantages for hyperspectral images (HSIs) classification, in which long short-term memory (LSTM) has attracted plenty of attentions for its characteristic of modeling long-range dependencies. However, for the 2-D extended architecture of it (namely 2-D convolutional LSTM, ConvLSTM2D), it is the special gate structures of ConvLSTM2D that leads to a large number of training parameters and high requirements for device storage. To address this shortcoming, in this paper, a lightweight ConvLSTM2D cell is developed by using tensor-train decomposition (TTD) for the compression of training parameters, which is named TT-ConvLSTM2D and further applied to two state-of-the-art ConvLSTM2D-based HSI classification models for verifying its superiority. Experiments on a widely-used Indian Pines HSI data set are conducted, whose results demonstrate that the proposed TT-ConvLSTM2D cell can effectively reduce the number of the parameters and memory requirements of the whole models within a small range of accuracy degradation. [Hu, Wen-Shuai; Li, Heng-Chao; Ma, Tian-Yu] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Caceres 10003, Spain; [Emery, William J.] Univ Colorado, Dept Aerosp Engn Sci, Boulder, CO 80309 USA Southwest Jiaotong University; Mississippi State University; Universidad de Extremadura; University of Colorado System; University of Colorado Boulder Hu, WS (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China. du, qian/GYJ-7090-2022; Plaza, Antonio/C-4455-2008; Du, Qian/AAB-8840-2022 Plaza, Antonio/0000-0002-9613-1659; National Natural Science Foundation of China [61871335] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Thanks to the Program for the National Natural Science Foundation of China under Grant 61871335. 14 2 2 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-7281-6374-1 INT GEOSCI REMOTE SE 2020.0 858 861 10.1109/IGARSS39084.2020.9324095 0.0 4 Computer Science, Artificial Intelligence; Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Optics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Environmental Sciences & Ecology; Geology; Remote Sensing; Optics BR6WH 2023-03-23 WOS:000664335300192 0
J Abdulkareem, KH; Mutlag, AA; Dinar, AM; Frnda, J; Mohammed, MA; Zayr, FH; Lakhan, A; Kadry, S; Khattak, HA; Nedoma, J Abdulkareem, Karrar Hameed; Mutlag, Ammar Awad; Dinar, Ahmed Musa; Frnda, Jaroslav; Mohammed, Mazin Abed; Zayr, Fawzi Hasan; Lakhan, Abdullah; Kadry, Seifedine; Khattak, Hasan Ali; Nedoma, Jan Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE English Article CLASSIFICATION COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates. [Abdulkareem, Karrar Hameed] Al Muthanna Univ, Coll Agr, Samawah 66001, Iraq; [Abdulkareem, Karrar Hameed] Univ Warith Al Anbiyaa, Coll Engn, Karbala, Iraq; [Mutlag, Ammar Awad] Minist Educ, Pure Sci Dept, Gen Directorate Curricula, Baghdad, Iraq; [Dinar, Ahmed Musa] Univ Technol Iraq, Engn Dept, Baghdad, Iraq; [Frnda, Jaroslav] Univ Zilina, Fac Operat & Econ Transport & Commun, Dept Quantitat Methods & Econ Informat, Zilina, Slovakia; [Frnda, Jaroslav; Nedoma, Jan] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Dept Telecommun, Ostrava, Czech Republic; [Mohammed, Mazin Abed] Univ Anbar, Coll Comp Sci & Informat Technol, 11, Ramadi, Anbar, Iraq; [Zayr, Fawzi Hasan] Univ Wasit, Coll Med, Dept Biochem, Wasit, Iraq; [Lakhan, Abdullah] Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China; [Kadry, Seifedine] Noroff Univ Coll, Kristiansand, Norway; [Khattak, Hasan Ali] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci, Islamabad 44500, Pakistan Al-Muthanna University; University of Technology- Iraq; University of Zilina; Technical University of Ostrava; University of Anbar; Wasit University; Wenzhou University; National University of Sciences & Technology - Pakistan Abdulkareem, KH (corresponding author), Al Muthanna Univ, Coll Agr, Samawah 66001, Iraq.;Abdulkareem, KH (corresponding author), Univ Warith Al Anbiyaa, Coll Engn, Karbala, Iraq. khak9784@mu.edu.iq; ammar.awad14@gmail.com; ahmed.m.dinar@uotechnology.edu.iq; jaroslav.frnda@fpedas.uniza.sk; mazinalshujeary@uoanbar.edu.iq; falfahdawi@uowasit.edu.iq; abdullahrazalakhan@gmail.com; seifedine.kadry@noroff.no; hasan.alikhattak@seecs.edu.pk; jan.nedoma@vsb.cz Kadry, Seifedine/C-7437-2011; Mohammed, Mazin Abed/E-3910-2018; Frnda, Jaroslav/M-1454-2019; Abdulkareem, Karrar Hameed/V-1741-2017; Dinar, Ahmed/O-7385-2019; Khattak, Hasan Ali/N-4656-2014 Kadry, Seifedine/0000-0002-1939-4842; Mohammed, Mazin Abed/0000-0001-9030-8102; Frnda, Jaroslav/0000-0001-6065-3087; Abdulkareem, Karrar Hameed/0000-0001-7302-2049; Dinar, Ahmed/0000-0002-9082-0875; Khattak, Hasan Ali/0000-0002-8198-9265; LAKHAN, ABDULLAH/0000-0002-1833-1364 Slovak Grant Agency for Science (VEGA) [1/0157/21] Slovak Grant Agency for Science (VEGA)(Vedecka grantova agentura MSVVaS SR a SAV (VEGA)) AcknowledgmentsThis research was funded by Slovak Grant Agency for Science (VEGA), under Grant no. 1/0157/21. 47 4 4 7 8 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1687-5265 1687-5273 COMPUT INTEL NEUROSC Comput. Intell. Neurosci. JUL 19 2022.0 2022.0 5012962 10.1155/2022/5012962 0.0 17 Mathematical & Computational Biology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology; Neurosciences & Neurology 3M0ZT 35875731.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000835188300017 0
J Kang, J; Fernandez-Beltran, R; Kang, XD; Ni, JE; Plaza, A Kang, Jian; Fernandez-Beltran, Ruben; Kang, Xudong; Ni, Jingen; Plaza, Antonio Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images With Label Noise IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Deep metric learning; image characterization; image retrieval; label noise; remote sensing (RS) SCENE CLASSIFICATION; POINT CLOUDS; OBJECT DETECTION; REGISTRATION; REPRESENTATION; NETWORK; DOMAIN Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions for training these models require accurate annotations. However, the presence of noise in such annotations (also known as label noise) cannot be avoided in large-scale RS benchmark archives, resulting from geo-location/registration errors, land-cover changes, and diverse knowledge background of annotators. To overcome the influence of noisy labels on the learning process of deep models, we propose a new loss function called noise-tolerant deep neighborhood embedding which can accurately encode the semantic relationships among RS scenes. Specifically, we target at maximizing the leave-one-out K-NN score for uncovering the inherent neighborhood structure among the images in feature space. Moreover, we down-weight the contribution of potential noisy images by learning their localized structure and pruning the images with low leave-one-out K-NN scores. Based on our newly proposed loss function, classwise features can be more robustly discriminated. Our experiments, conducted on two benchmark RS datasets, validate the effectiveness of the proposed approach on three different RS scene interpretation tasks, including classification, clustering, and retrieval. The codes of this article will he publicly available from https://github.com/jiankang1991. [Kang, Jian; Ni, Jingen] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China; [Fernandez-Beltran, Ruben] Univ Jaume 1, Inst New Imaging Technol, Castellon de La Plana 12071, Spain; [Kang, Xudong] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Escuela Politecn, Hyperspectral Comp Lab, Caceres 10003, Spain Soochow University - China; Universitat Jaume I; Hunan University; Universidad de Extremadura Ni, JE (corresponding author), Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China. kangjian_1991@outlook.com; rufernan@uji.es; xudong_kang@163.com; jni@suda.edu.cn; aplaza@unex.es Plaza, Antonio/C-4455-2008; Fernandez-Beltran, Ruben/GLT-5907-2022; kang, jian/V-3055-2019 Plaza, Antonio/0000-0002-9613-1659; kang, jian/0000-0001-6284-3044 National Natural Science Foundation of China [61871179]; Ministry of Science, Innovation, and Universities of Spain [RTI2018-098651-B-C54]; Valencian Government of Spain [GV/2020/167]; FEDER-Junta de Extremadura [GR18060]; European Union under the H2020 EOXPOSURE Project [734541] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science, Innovation, and Universities of Spain(Spanish Government); Valencian Government of Spain; FEDER-Junta de Extremadura(European Commission); European Union under the H2020 EOXPOSURE Project This work was supported in part by the National Natural Science Foundation of China under Grant 61871179, in part by the Ministry of Science, Innovation, and Universities of Spain under Grant RTI2018-098651-B-C54, in part by the Valencian Government of Spain under Grant GV/2020/167, in part by FEDER-Junta de Extremadura under Ref. GR18060, and in part by the European Union under the H2020 EOXPOSURE Project 734541. 74 5 5 5 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 2551 2562 10.1109/JSTARS.2021.3056661 0.0 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology QM9MJ gold, Green Published 2023-03-23 WOS:000622097100007 0
C Zhu, ZH; Wang, LS; Magnier, BT; Zhu, L; Zhang, DF; Yu, LQ Wang, L; Dou, Q; Fletcher, PT; Speidel, S; Li, S Zhu, Zhonghang; Wang, Liansheng; Magnier, Baptiste; Zhu, Lei; Zhang, Defu; Yu, Lequan Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D Medical Image Classification MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT III Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 18-22, 2022 Singapore, SINGAPORE MICCAI Soc Multi-modality learning; 3D medical images; Reinforcement learning; Classification Multi-modality 3D medical images play an important role in the clinical practice. Due to the effectiveness of exploring the complementary information among different modalities, multi-modality learning has attracted increased attention recently, which can be realized by Deep Learning (DL) models. However, it remains a challenging task for two reasons. First, the prediction confidence of multi-modality learning network cannot be guaranteed when the model is trained with weakly-supervised volume-level labels. Second, it is difficult to effectively exploit the complementary information across modalities and also preserve the modality-specific properties when fusion. In this paper, we present a novel Reinforcement Learning (RL) driven approach to comprehensively address these challenges, where two Recurrent Neural Networks (RNN) based agents are utilized to choose reliable and informative features within modality (intra-learning) and explore complementary representations across modalities (inter-learning) with the guidance of dynamic weights. These agents are trained via Proximal Policy Optimization (PPO) with the confidence increment of the prediction as the reward. We take the 3D image classification as an example and conduct experiments on a multi-modality brain tumor MRI data. Our approach outperforms other methods when employing the proposed RL-based multi-modality representation learning. [Zhu, Zhonghang; Wang, Liansheng; Zhang, Defu] Xiamen Univ, Sch Informat, Dept Comp Sci, Xiamen, Peoples R China; [Magnier, Baptiste] Univ Montpellier, IMT Mines Ales, Euromov Digital Hlth Motion, Ales, France; [Zhu, Lei] Hong Kong Univ Sci & Technol Guangzhou, Guangzhou, Peoples R China; [Zhu, Lei] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China; [Yu, Lequan] Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong, Peoples R China Xiamen University; IMT - Institut Mines-Telecom; IMT Mines Ales; Universite de Montpellier; Hong Kong University of Science & Technology (Guangzhou); Hong Kong University of Science & Technology; University of Hong Kong Wang, LS (corresponding author), Xiamen Univ, Sch Informat, Dept Comp Sci, Xiamen, Peoples R China. zzhonghang@stu.xmu.edu.cn; lswang@xmu.edu.cn; baptiste.magnier@mines-ales.fr; leizhu@ust.hk; dfzhang@xmu.edu.cn; lqyu@hku.hk Wang, Liansheng/HGA-8949-2022 Wang, Liansheng/0000-0002-2096-454X National Key Research and Development Program of China [2019YFE0113900] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China (2019YFE0113900). 19 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-16437-8; 978-3-031-16436-1 LECT NOTES COMPUT SC 2022.0 13433.0 604 613 10.1007/978-3-031-16437-8_58 0.0 10 Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging BU0DI Green Submitted 2023-03-23 WOS:000867397400058 0
J Wang, Y; Li, X; Zhang, LD; Mo, D Wang, Yue; Li, Xiang; Zhang, Linda L.; Mo, Daniel Configuring products with natural language: a simple yet effective approach based on text embeddings and multilayer perceptron INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH English Article Product customisation; needs-based configuration; text embedding; multilayer perceptron; natural language processing OPTIMIZATION; DESIGN; SYSTEM Product configurators are recognised as critical toolkits enabling customers to co-create products with companies. Most available product configurators require customers to select suitable product attributes from predefined options. However, customers usually find the selection processes frustrating due to their lack of product knowledge. In view of the fact that customers often express their needs in imprecise and vague natural language, we define a new needs-based configuration mechanism and propose an implementation approach based on text embeddings and multilayer perceptron. Specifically, we leverage the massive amount of product reviews by encoding them into text embeddings. A multilayer perceptron is trained to map text embeddings to product attribute options. Experiment results indicate that the mapping has good generalisation capability to map customer needs into product configurations. The performance of our approach is comparable to that of deep learning-based approaches but with much higher efficiency in terms of computational complexity. Our needs-based configuration thus provides a quick and effective means of facilitating product customisation. It also demonstrates an innovative way of utilising customer resources in unstructured text to co-create products with companies. [Wang, Yue; Mo, Daniel] Hang Seng Univ Hong Kong, Dept Supply Chain & Informat Management, Hong Kong, Peoples R China; [Li, Xiang] City Univ Hong Kong, Dept Informat Syst, Hong Kong, Peoples R China; [Zhang, Linda L.] Univ Lille, IESEG Sch Management, CNRS, UMR 9221,LEM Lille Econ Management, Lille, France Hang Seng University of Hong Kong; City University of Hong Kong; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Humanities & Social Sciences (INSHS); IESEG School of Management; Universite de Lille - ISITE; Universite de Lille Wang, Y (corresponding author), Hang Seng Univ Hong Kong, Dept Supply Chain & Informat Management, Hong Kong, Peoples R China. yuewang@hsu.edu.hk Wang, Yue/0000-0002-0185-6172; Mo, Daniel/0000-0001-8307-9065 Hong Kong Research Grant Council Faculty Development Scheme [UGC/FDS14/E06/18] Hong Kong Research Grant Council Faculty Development Scheme This work was supported byHong Kong Research Grant Council Faculty Development Scheme [grant number UGC/FDS14/E06/18]. 53 6 6 10 24 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0020-7543 1366-588X INT J PROD RES Int. J. Prod. Res. SEP 2 2022.0 60.0 17 5394 5406 10.1080/00207543.2021.1957508 0.0 JUL 2021 13 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 4L0AR 2023-03-23 WOS:000679852700001 0
C Guerraoui, R; Kermarrec, AM; Ruas, O; Taiani, F Assoc Comp Machinery Guerraoui, Rachid; Kermarrec, Anne-Marie; Ruas, Olivier; Taiani, Francois Smaller, Faster & Lighter KNN Graph Constructions WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020) English Proceedings Paper 29th Web Conference (WWW) APR 20-24, 2020 Taipei, TAIWAN Assoc Comp Machinery,Quanta Comp,Taiwan Mobile Co Ltd,Chunghwa Telecom,Microsoft,Taipei City Govt, Dept Informat Technol,Zoom,FET,Web4Good,ELTA HD,ELTA Technol Co Ltd,Facebook,Yahoo Res,Pinterest We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard's index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that GoldFinger can drastically accelerate a large range of existing KNN algorithms with little to no overhead. As a side effect, we also show that the compact representation of the data protects users' privacy for free by providing k-anonymity and /-diversity. Our extensive evaluation of the resulting approach on several realistic datasets shows that our approach delivers speedups of up to 78.9% compared to the use of raw data while only incurring a negligible to moderate loss in terms of KNN quality. To convey the practical value of such a scheme, we apply it to item recommendation and show that the loss in recommendation quality is negligible. [Guerraoui, Rachid; Kermarrec, Anne-Marie] Ecole Polytech Fed Lausanne, Lausanne, Switzerland; [Kermarrec, Anne-Marie] Mediego, Cesson Sevigne, France; [Ruas, Olivier] Peking Univ, Beijing, Peoples R China; [Taiani, Francois] Univ Rennes, INRIA, CNRS, IRISA, Rennes, France Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Peking University; Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Rennes Guerraoui, R (corresponding author), Ecole Polytech Fed Lausanne, Lausanne, Switzerland. rachid.guerraoui@epfl.ch; anne-marie.kermarrec@epfl.ch; olivier.ruas@pku.edu.cn; francois.taiani@irisa.fr Taïani, François/AAQ-7684-2021 Taïani, François/0000-0002-9692-5678; Ruas, Olivier/0000-0002-6862-9046; Guerraoui, Rachid/0000-0002-4794-8902 45 1 1 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-7023-3 2020.0 1060 1070 10.1145/3366423.3380184 0.0 11 Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BQ9OC Green Published 2023-03-23 WOS:000626273301012 0
J Chan, CH; Zhu, JY; Chow, CSL; Fu, KW Chan, Chung-hong; Zhu, Junior Yuner; Chow, Cassius Siu-lun; Fu, King-wa The intertwined cyberbalkanizations of Facebook pages and their audience: an analysis of Facebook pages and their audience during the 2014 Hong Kong Occupy Movement JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE English Article Cyberbalkanization; Social media; Political polarization; Audience analysis NEWS CONSUMPTION; POLITICAL COMMUNICATION; MEDIA REPERTOIRES; NETWORKS; AGE; PATTERNS; OPINION This study tests a hypothesis that information sources (e.g., Facebook pages) that share information more frequently with each other have high level of audience overlapping. This association is also hypothesized to be politically motivated. To test the empirical relationship, a Facebook pages sharing network was created using the information shared between 1453 Facebook pages during a social movement in Hong Kong. The sharing frequency between two pages was denoted as the page-level edge weight. The audience of Facebook pages-commenters and likers of the page's posts-were collected. The Jaccard similarity coefficient between two pages was measured as the audience-level edge weight. Using network regression analysis, the page-level and audience-level edge weights were significantly associated. To show this relationship is politically motivated, 1076 audience members were randomly selected and with their political preferences labeled by inferring from their Facebook profile pictures. Using machine learning models, the repertoires of Facebook pages that they have interacted with can predict their political preferences. Our study demonstrated that selective sharing between information source is associated with the division of their audiences into enclaved subgroups with similar political ideologies. [Chan, Chung-hong] Univ Mannheim, Mannheimer Zentrum Europa Sozialforsch, Mannheim, Germany; [Chan, Chung-hong; Zhu, Junior Yuner; Chow, Cassius Siu-lun; Fu, King-wa] Univ Hong Kong, Journalism & Media Studies Ctr, Hong Kong, Peoples R China University of Mannheim; University of Hong Kong Chan, CH (corresponding author), Univ Mannheim, Mannheimer Zentrum Europa Sozialforsch, Mannheim, Germany.;Chan, CH (corresponding author), Univ Hong Kong, Journalism & Media Studies Ctr, Hong Kong, Peoples R China. chung-hong.chan@mzes.uni-mannheim.de ZHU, Yuner/HKP-9826-2023; Chan, Chung-hong/D-6946-2013 Chan, Chung-hong/0000-0002-6232-7530; ZHU, Yuner/0000-0003-2772-2188 Public Policy Research Funding Scheme of the Central Policy Unit of the Government of the Hong Kong Special Administrative Region [A8.009.14A]; HKU SPACE Postgraduate Fund Public Policy Research Funding Scheme of the Central Policy Unit of the Government of the Hong Kong Special Administrative Region; HKU SPACE Postgraduate Fund This research project (Project Number: 2013.A8.009.14A) is funded by the Public Policy Research Funding Scheme of the Central Policy Unit of the Government of the Hong Kong Special Administrative Region. Part of the first author's PhD studentship is supported by the HKU SPACE Postgraduate Fund. 70 0 0 1 2 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2432-2717 2432-2725 J COMPUT SOC SCI J. Comput. Soc. Sci. JUL 2019.0 2.0 2 183 205 10.1007/s42001-019-00043-x 0.0 23 Social Sciences, Mathematical Methods Emerging Sources Citation Index (ESCI) Mathematical Methods In Social Sciences VK4TL 2023-03-23 WOS:000704309200005 0
C Roukh, A; Bellatreche, L; Tziritas, N; Ordonez, C Carretero, J; GarciaBlas, J; Ko, RKL; Mueller, P; Nakano, K Roukh, Amine; Bellatreche, Ladjel; Tziritas, Nikos; Ordonez, Carlos Energy-Aware Query Processing on a Parallel Database Cluster Node ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2016 Lecture Notes in Computer Science English Proceedings Paper 16th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP) DEC 14-16, 2016 Granada, SPAIN Univ Madrid Carlos III,Univ Granada CONSUMPTION In the last few years, we have been seeing a significant increase in research about the energy efficiency of hardware and software components in both centralized and parallel platforms. In data centers, DBMSs are one of the major energy consumers, in which, a large amount of data is queried by complex queries running daily. Having green nodes is a pre-condition to design an energy-aware parallel database cluster. Generally, the most existing DBMSs focus on high-performance during query optimization phase, while usually ignoring the energy consumption of the queries. In this paper, we propose a methodology, supported by a tool called EnerQuery, that makes nodes of parallel database clusters saving energy when optimizing queries. To show its effectiveness, we implement our proposal on the top of PostgreSQL DBMS query optimizer. A mathematical cost model based on a machine learning technique is defined and used to estimate the energy consumption of SQL queries. [Roukh, Amine] Univ Mostaganem, Mostaganem, Algeria; [Bellatreche, Ladjel] Univ Poitiers, LIAS ISAE ENSMA, Poitiers, France; [Tziritas, Nikos] Chinese Acad Sci, Shenzhen, Peoples R China; [Ordonez, Carlos] Univ Houston, Houston, TX USA Universite Abdelhamid Ibn Badis de Mostaganem; Ecole Nationale Superieure de Mecanique et d'Aerotechnique (ISAE-ENSMA); Universite de Poitiers; Chinese Academy of Sciences; University of Houston System; University of Houston Roukh, A (corresponding author), Univ Mostaganem, Mostaganem, Algeria. roukh.amine@univ-mosta.dz; bellatreche@ensma.fr; nikolaos@siat.ac.cn; ordonez@cs.uh.edu 14 4 4 0 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-49583-5; 978-3-319-49582-8 LECT NOTES COMPUT SC 2016.0 10048.0 260 269 10.1007/978-3-319-49583-5_20 0.0 10 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG5WL 2023-03-23 WOS:000389797300020 0
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D.; Scharfa, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavia, C.; Schier, S.; Schillo, C.; Schioppaa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneidera, B.; Schnoor, U.; Schoeffel, L.; Schoeningb, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulona, H. -C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuria, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixasa, J. M.; Sekhniaidzea, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesaria, N.; Serfon, C.; Serin, L.; Serkina, L.; Sessaa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikha, N. W.; Shana, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shawa, K.; Shaw, S. M.; Shcherbakovaa, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shirabe, S.; Shiyakova, M.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaiia, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; ; ATLAS Collaboration Study of hard double-parton scattering in four-jet events in pp collisions root s=7 TeV with the ATLAS experiment JOURNAL OF HIGH ENERGY PHYSICS English Article Hadron-Hadron scattering (experiments) (P)OVER-BAR-P COLLISIONS; COLLIDER; CALORIMETER; EQUATIONS Inclusive four-jet events produced in proton-proton collisions at a centre-of mass energy of root s = 7 TeV are analysed for the presence of hard double-parton scattering using data corresponding to an integrated luminosity of 37.3 pb(-1), collected with the ATLAS detector at the LHC. The contribution of hard double-parton scattering to the production of four -jet events is extracted using an artificial neural network, assuming that hard double-parton scattering can be approximated by an uncorrelated overlaying of dijet events. For events containing at least four jets with transverse momentum PT >= 20 GeV and pseudorapidity vertical bar eta vertical bar <= 4.4, and at least one having pT >= 42.5 GeV, the contribution of hard double-parton scattering is estimated to be fDps = 0.092(-0.011)(+0.0005) (stat.) (+0.03337)(-0.011) (syst.). After combining this measurement with those of the inclusive dijet and four -jet cross -sections in the appropriate phase space regions, the effective cross-section, sigma(eff,) was determined to be sigma(eff) = 14.9(-1.0)(+1.2) (stat.) (+5.1)(-3.8) (syst.) mb. This result is consistent within the quoted uncertainties with previous measurements of sigma(eff), performed at centre-of-mass energies between 63 GeV and 8 TeV using various final states, and it corresponds to 21(-6%)(+7) of the total inelastic cross-section measured at root s = 7 TeV. The distributions of the observables sensitive to the contribution of hard double-parton scattering, corrected for detector effects, are also provided. [Jackson, P.; Leel, L.; Petridis, A.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia; [Bouffard, J.; Ernst, J.; Fischer, A.; Guindon, S.; Jain, V.] SUNY Albany, Dept Phys, Albany, NY 12222 USA; [Czodrowski, P.; Dassoulas, J.; Dehghanian, N.; Gingrich, D. M.; Jabbar, S.; Karamaoun, A.; Moore, R. W.; Pinfold, J. L.] Univ Alberta, Dept Phys, Edmonton, AB, Canada; [Cakira, O.; Ciftcia, A. K.; Yildiza, H. 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D.; Tsipolitis, G.; Vlachos, S.] Natl Tech Univ Athens, Dept Phys, Zografos, Greece; [Andeen, T.; Ilchenko, Y.; Narayan, R.; Onyisi, P. U. E.] Univ Texas Austin, Dept Phys, Austin, TX 78712 USA; [Abdinov, O.; Khalil-Zada, F.] Azerbaijan Acad Sci, Inst Phys, Baku, Azerbaijan; [Anjos, N.; Bosman, M.; Casado, M. P.; Casolino, M.; Cavallaro, E.; Cavalli-Sforza, M.; Farooque, T.; Perez, S. Fernandez; Fischer, C.; Fracchia, S.; Gerbaudo, D.; Grinstein, S.; Rozas, A. Juste; Korolkov, I.; Lange, J. C.; Paz, I. Lopez; Martinez, M.; Mir, L. M.; Pages, A. Pacheco; Aranda, C. Padilla; Riu, I.; Rizzi, C.; Perez, A. Rodriguez; Sorin, V.; Terzo, S.; Tripiana, M. F.; Tsiskaridze, S.; Valery, L.] IFAE, Barcelona, Spain; [Agatonovic-Jovin, T.; Bogavac, D.; Bokan, P.; Dimitrievska, A.; Krstic, J.; Marjanovic, M.; Sijacki, Dj.; Simic, Lj.; Vranjes, N.; Milosavljevic, M. Vranjes; Zivkovic, L.] Univ Belgrade, Inst Phys, Belgrade, Serbia; [Buanes, T.; Dale, O.; Eigen, G.; Liebig, W.; Lipniacka, A.; Maeland, S.; Latour, B. Martin Dit; Smestad, L.; Stugu, B.; Yang, Z.; Zalieckas, J.] Univ Bergen, Dept Phys & Technol, Bergen, Norway; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Heim, T.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Lawrence Berkeley Natl Lab, Div Phys, Berkeley, CA USA; [Amadio, B. T.; Axen, B.; Barnett, R. M.; Beringer, J.; Brosamer, J.; Calafiura, P.; Cerutti, F.; Ciocio, A.; Clarke, R. N.; Cooke, M.; Duffield, E. M.; Einsweiler, K.; Farrell, S.; Gabrielli, A.; Garcia-Sciveres, M.; Gilchriese, M.; Haber, C.; Heim, T.; Heinemann, B.; Hinchliffe, I.; Hinman, R. R.; Holmes, T. R.; Jeanty, L.; Lavrijsen, W.; Leggett, C.; Marshall, Z.; Ohm, C. C.; Griso, S. Pagan; Potamianos, K.; Pranko, A.; Shapiro, M.; Sood, A.; Tibbetts, M. J.; Trottier-McDonald, M.; Tsulaia, V.; Viel, S.; Wang, H.; Yao, W-M.; Yu, D. R.] Univ Calif Berkeley, Berkeley, CA USA; [Biedermann, D.; Dietrich, J.; Giorgi, F. M.; Grancagnolo, S.; Herbert, G. H.; Hristova, I.; Kind, O. M.; Kolanoski, H.; Lacker, H.; Lohse, T.; Mergelmeyer, S.; Nikiforov, A.; Rehnisch, L.; Rieck, P.; Schulz, H.; Sperlich, D.; Stamm, S.; zur Nedden, M.] Humboldt Univ, Dept Phys, Berlin, Germany; [Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Meloni, F.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.] Univ Bern, Albert Einstein Ctr Fundamental Phys, Bern, Switzerland; [Beck, H. P.; Cervelli, A.; Ereditato, A.; Haug, S.; Meloni, F.; Miucci, A.; Mullier, G. A.; Rimoldi, M.; Stramaglia, M. E.; Weber, M. S.] Univ Bern, High Energy Phys Lab, Bern, Switzerland; [Allport, P. P.; Andari, N.; Bella, L. Aperio; Baca, M. J.; Bracinik, J.; Broughton, J. H.; Casadei, D.; Charlton, D. G.; Daniells, A. C.; Foster, A. G.; Gonella, L.; Hawkes, C. M.; Head, S. J.; Hillier, S. J.; Levy, M.; Mudd, R. D.; Quijada, J. A. Murillo; Newman, P. R.; Nikolopoulos, K.; Owen, R. E.; Slater, M.; Thomas, J. P.; Thompson, P. D.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Wilson, J. A.] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England; [Arik, M.; Istina, S.; Ozcana, V. E.] Bogazici Univ, Dept Phys, Istanbul, Turkey; [Beddall, A.; Bingul, A.] Gaziantep Univ, Dept Engn Phys, Gaziantep, Turkey; [Cetind, S. A.] Istanbul Bilgi Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Beddall, A. J.] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkey; [Losada, M.; Moreno, D.; Navarro, G.; Sandoval, C.] Univ Antonio Narino, Ctr Invest, Bogota, Colombia; [Alberghi, G. L.; Bellagamba, L.; Biondi, S.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruschi, M.; Cioccaa, C.; D'amena, G.; De Castroa, S.; Fabbria, F.; Fabbria, L.; Franchinia, M.; Gabriellia, A.; Giacobbea, B.; Giorgia, F. M.; Grafstroma, P.; Manghia, F. Lasagni; Massaa, I.; Massaa, L.; Mengarellia, A.; Negrinia, M.; Piccininia, M.; Polinia, A.; Rinaldia, L.; Romanoa, M.; Sbarraa, C.; Sbrizzia, A.; Semprini-Cesaria, N.; Sidotia, A.; Siolia, M.; Spighia, R.; Tupputia, S. A.; Ucchiellia, G.; Valentinettia, S.; Villaa, M.; Vittoria, C.; Zoccolia, A.] INFN, Sez Bologna, Bologna, Italy; [Alberghi, G. L.; Biondi, S.; Cioccaa, C.; D'amena, G.; De Castroa, S.; Fabbria, F.; Fabbria, L.; Franchinia, M.; Gabriellia, A.; Grafstroma, P.; Manghia, F. Lasagni; Massaa, I.; Massaa, L.; Mengarellia, A.; Piccininia, M.; Romanoa, M.; Sbrizzia, A.; Semprini-Cesaria, N.; Sidotia, A.; Siolia, M.; Tupputia, S. A.; Ucchiellia, G.; Valentinettia, S.; Villaa, M.; Vittoria, C.; Zoccolia, A.] Univ Bologna, Dipartimento Fis & Astron, Bologna, Italy; [Arslan, O.; Bechtle, P.; Bernlochner, F. U.; Brock, I.; Bruscino, N.; Caudron, J.; Cioara, I. A.; Cristinziani, M.; Davey, W.; Desch, K.; Dingfelder, J.; Gaycken, G.; Geich-Gimbel, Ch.; Ghneimat, M.; Grefe, C.; Hagebock, S.; Hansen, M. C.; Hohn, D.; Huegging, F.; Janssen, J.; Kostyukhin, V. V.; Kroseberg, J.; Kruger, H.; Lantzsch, K.; Lenz, T.; Leyko, A. M.; Liebal, J.; Moles-Valls, R.; Obermann, T.; Pohl, D.; Ricken, O.; Sarrazin, B.; Schaepe, S.; Schopf, E.; Schultens, M. J.; Schwindt, T.; Seema, P.; Stillings, J. A.; Von Toerne, E.; Wagner, P.; Wang, T.; Wermes, N.; Wiik-Fuchs, L. A. M.; Winter, B. T.; Wong, K. H. Yau; Yuen, S. P. Y.; Zhang, R.] Univ Bonn, Inst Phys, Bonn, Germany; [Ahlen, S. P.; Black, K. M.; Butler, J. M.; Dell'Asta, L.; Kruskal, M.; Long, B. A.; Shank, J. T.; Yan, Z.; Youssef, S.] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; [Amelung, C.; Amundsen, G.; Barone, G.; Bensinger, J. R.; Bianchini, L.; Blocker, C.; Dhaliwal, S.; Goblirsch-Kolb, M.; Herde, H.; Loew, K. M.; Sciolla, G.; Venturini, A.] Brandeis Univ, Dept Phys, Waltham, MA 02254 USA; [Coutinho, Y. Amaral; Calobaa, L. P.; Maidantchika, C.; Marroquima, F.; Nepomucenoa, A. A.; Seixasa, J. M.] Univ Fed Rio de Janeiro, COPPE, EE, IF, Rio De Janeiro, Brazil; [Cerqueirab, A. S.; Filhob, L. Manhaes De Andrade; Peralvab, B. S.] Univ Fed Juiz de Fora, Elect Circuits Dept, Juiz de Fora, Brazil; [Do Valec, M. A. B.] Univ Fed Sao Joao del Rei, Sao Joao Del Rei, Brazil; [Donadellid, M.; Navarrod, J. L. La Rosa; Leited, M. A. L.] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil; [Adams, D. L.; Assamagan, K.; Begel, M.; Buttinger, W.; Chen, H.; Chernyatin, V.; Debbe, R.; Elmsheuser, J.; Ernst, M.; Gibbard, B.; Iakovidis, G.; Klimentov, A.; Kouskoura, V.; Kravchenko, A.; Lanni, F.; Lee, C. A.; Liu, H.; Lynn, D.; Ma, H.; Maeno, T.; Mountricha, E.; Nevski, P.; Nilsson, P.; Damazio, D. Oliveira; Paige, F.; Panitkin, S.; Perepelitsa, D. V.; Pleier, M. -A.; Polychronakos, V.; Protopopescu, S.; Purohit, M.; Radeka, V.; Rajagopalan, S.; Redlinger, G.; Snyder, S.; Steinberg, P.; Stucci, S. A.; Takai, H.; Tricoli, A.; Undrus, A.; Wenaus, T.; Xu, L.; Ye, S.] Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; Transilvania Univ Brasov, Brasov, Romania; [Alexa, C.; Caprinib, I.; Caprinib, M.; Chitanb, A.; Ciubancanb, M.; Constantinescub, S.; Ditab, P.; Ditab, S.; Dobreb, M.; Jinarub, A.; Martoiub, V. S.; Maurerb, J.; Olariub, A.; Panteab, D.; Rotarub, M.; Stoiceab, G.; Tudoracheb, A.; Tudoracheb, V.] Natl Inst Phys & Nucl Engn, Bucharest, Romania; [Popeneciuc, G. A.] Natl Inst Res & Dev Isotop & Mol Technol, Dept Phys, Cluj Napoca, Romania; Univ Politehn Bucuresti, Bucharest, Romania; [Gravilae, P. M.] West Univ Timisoara, Timisoara, Romania; [Sola, J. D. Bossio; Marceca, G.; Garzon, G. Otero Y.; Piegaia, R.; Sacerdoti, S.] Univ Buenos Aires, Dept Fis, Buenos Aires, DF, Argentina; [Arratia, M.; Barlow, N.; Batley, J. R.; Brochu, F. M.; Brunt, Bh; Carter, J. R.; Chapman, J. D.; Cottin, G.; Gillam, T. P. S.; Hill, J. C.; Kaneti, S.; Lester, C. G.; Malone, C.; Mueller, T.; Parker, M. A.; Potter, C. J.; Robinson, D.; Rosten, J. H. N.; Ward, C. P.; Yusuff, I.] Univ Cambridge, Cavendish Lab, Cambridge, England; [Bellerive, A.; Cree, G.; Di Valentino, D.; Gillberg, D.; Koffas, T.; Lacey, J.; Leight, W. A.; Nomidis, I.; Oakham, F. G.; Pasztor, G.; Ruiz-Martinez, A.; Vincter, M. G.; Weber, S. A.] Carleton Univ, Dept Phys, Ottawa, ON, Canada; [Aleksa, M.; Gonzalez, B. Alvarez; Amoroso, S.; Anders, G.; Anghinolfi, F.; Arnaez, O.; Avolio, G.; Baak, M. A.; Backhaus, M.; Barak, L.; Barisits, M. -S; Beermann, T. A.; Beltramello, O.; Bianco, M.; Bogaerts, J. A.; Bortfeldt, J.; Boveia, A.; Boyd, J.; Burckhart, H.; Camarda, S.; Campana, S.; Garrido, M. D. M. Capeans; Carli, T.; Carrillo-Montoya, G. D.; Catinaccio, A.; Cattai, A.; Cerv, M.; Chisholm, A. S.; Chromek-Burckhart, D.; Conti, G.; Cortes-Gonzalez, A.; Dell'Acqua, A.; Deviveiros, P. O.; Di Girolamo, A.; Di Girolamo, B.; Di Nardo, R.; Dittus, F.; Dobos, D.; Dudarev, A.; Duhrssen, M.; Effert, T.; Ellis, N.; Elsing, M.; Faltova, J.; Farthouat, P.; Fassnacht, P.; Feng, E. J.; Francis, D.; Fressard-Batraneanu, S. M.; Froidevaux, D.; Gadatsch, S.; Goossens, L.; Gorini, B.; Gray, H. M.; Gumpert, C.; Hawkings, R. J.; Helary, L.; Helsens, C.; Correia, A. M. Henriques; Hervas, L.; Hoecker, A.; Huhtinen, M.; Iengo, P.; Jakobsen, S.; Klioutchnikova, T.; Krasznahorkay, A.; Lapoire, C.; Lassnig, M.; Miotto, G. Lehmann; Lenzi, B.; Lichard, P.; Malyukov, S.; Manousos, A.; Mapelli, L.; Marzin, A.; Berlingen, J. Montejo; Morgenstern, S.; Mornacchi, G.; Nairz, A. M.; Nessi, M.; Nordberg, M.; Palestini, S.; Pauly, T.; Pernegger, H.; Petersen, B. A.; Pommes, K.; Poppleton, A.; Poulard, G.; Poveda, J.; Astigarraga, M. E. Pozo; Rammensee, M.; Raymond, M.; Rembser, C.; Ritsch, E.; Roe, S.; Ruthmann, N.; Salzburger, A.; Schaefer, D.; Schlenker, S.; Schmieden, K.; Sforza, F.; Sanchez, C. A. Solans; Spigo, G.; Starz, S.; Stelzer, H. J.; Teischinger, F. A.; Ten Kate, H.; Unal, G.; Vandelli, W.; Voss, R.; Vuillermet, R.; Wells, P. S.; Wengler, T.; Wenig, S.; Werner, P.; Wilkens, H. G.; Wotschack, J.; Young, C. J. S.; Zwalinski, L.] CERN, Geneva, Switzerland; [Alison, J.; Anderson, K. J.; Bryant, P.; Toro, R. Camacho; Cheng, Y.; Dandoy, J. R.; Facini, G.; Gardner, R. W.; Kapliy, A.; Kim, Y. K.; Krizka, K.; Li, H. L.; Merritt, F. S.; Miller, D. W.; Oreglia, M. J.; Pilcher, J. E.; Saxon, J.; Shochet, M. J.; Stark, G. H.; Swiatlowski, M.; Vukotic, I.; Wu, M.] Univ Chicago, Enrico Fermi Inst, 5640 S Ellis Ave, Chicago, IL 60637 USA; [Blunier, S.; Diaza, M. A.; Ochoa-Ricouxa, J. P.] Pontificia Univ Catolica Chile, Dept Fis, Santiago, Chile; [Brooks, W. K.; Carquinb, E.; Kuleshovb, S.; Lopezb, J. A.; Pezoab, R.; Prokoshinb, F.; Loyolab, J. E. Salazar; Arayab, S. Tapia; Vasquezb, G. A.; Whiteb, R.] Univ Tecn Federico Santa Maria, Dept Fis, Valparaiso, Chile; [Bai, Y.; da Costa, J. Barreiro Guimaraes; Chenga, H. J.; Fanga, Y.; Jina, S.; Lia, Q.; Lianga, Z.; Merinoa, J. Llorente; Loua, X.; Mansoura, J. D.; Ouyanga, Q.; Penga, C.; Rena, H.; Shana, L. Y.; Suna, X.; Xu, D.; Zhua, H.; Zhuanga, X.] Chinese Acad Sci, Inst High Energy Phys, Beijing, Peoples R China; [Chenb, S.; Wangb, C.; Zhangb, H.] Nanjing Univ, Dept Phys, Nanjing, Jiangsu, Peoples R China; [Chenc, X.; Zhouc, N.] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Ganguly, S.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] Clermont Univ, Phys Corpusculaire Lab, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Ganguly, S.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] Univ Blaise Pascal, Clermont Ferrand, France; [Boumediene, D.; Busato, E.; Calvet, D.; Calvet, S.; Chomont, A. R.; Donini, J.; Ganguly, S.; Gris, Ph.; Madar, R.; Pallin, D.; Saez, S. M. Romano; Santoni, C.; Simon, D.; Vazeille, F.] CNRS, IN2P3, Clermont Ferrand, France; [Alkire, S. P.; Angerami, A.; Brooijmans, G.; Carbone, R. M.; Clark, M. R.; Cole, B.; Hughes, E. W.; Iordanidou, K.; Klein, M. H.; Mohapatra, S.; Ochoa, I.; Parsons, J. A.; Smith, M. N. K.; Smith, R. W.; Tuts, P. M.; Wang, T.; Zhou, L.] Columbia Univ, Nevis Lab, Irvington, NY USA; [Alonso, A.; Besjes, G. J.; Dam, M.; Galster, G.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Monk, J.; Mortensen, S. S.; Pedersen, L. E.; Petersen, T. C.; Pingel, A.; Wiglesworth, C.; Xella, S.] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark; [Cairoa, V. M.; Calleaa, G.; Capuaa, M.; Crosettia, G.; Del Gaudioa, M.; La Rotondaa, L.; Mastroberardinoa, A.; Palazzoa, S.; Policicchioa, A.; Salvatorea, D.; Scarfonea, V.; Schioppaa, M.; Susinnoa, G.; Tassia, E.] INFN, Grp Collegato Cosenza, Lab Nazl Frascati, Frascati, Italy; [Cairoa, V. M.; Calleaa, G.; Capuaa, M.; Crosettia, G.; Del Gaudioa, M.; La Rotondaa, L.; Mastroberardinoa, A.; Palazzoa, S.; Policicchioa, A.; Salvatorea, D.; Scarfonea, V.; Schioppaa, M.; Susinnoa, G.; Tassia, E.] Univ Calabria, Dipartimento Fis, Arcavacata Di Rende, Italy; [Adamczyk, L.; Bold, T.; Dabrowskia, W.; Gacha, G. P.; Grabowska-Bolda, I.; Kisielewskaa, D.; Kopernya, S.; Kowalskia, T. Z.; Mindura, B.; Przybyciena, M.; Zemlaa, A.] AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland; [Palkab, M.; Richter-Wasb, E.] Jagiellonian Univ, Marian Smoluchowski Inst Phys, Krakow, Poland; [Banas, E.; de Renstrom, P. A. Bruckman; Burka, K.; Chwastowski, J. J.; Derendarz, D.; Godlewski, J.; Gornicki, E.; Hajduk, Z.; Kaczmarska, A.; Knapik, J.; Korcyl, K.; Kowalewska, A. B.; Malecki, Pa.; Olszewski, A.; Olszowska, J.; Stanecka, E.; Staszewski, R.; Trzebinski, M.; Trzupek, A.; Wolter, M. W.; Wosiek, B. K.; Wozniak, K. W.; Zabinski, B.] Polish Acad Sci, Inst Nucl Phys, Krakow, Poland; [Cao, T.; Firan, A.; Gupta, R.; Hetherly, J. W.; Kama, S.; Kehoe, R.; Sekula, S. J.; Stroynowski, R.; Varol, T.; Wang, H.; Ye, J.; Zhao, X.; Zhou, L.] Southern Methodist Univ, Dept Phys, Dallas, TX USA; [Izen, J. M.; Leyton, M.; Meirose, B.; Reeves, K.] Univ Texas Dallas, Dept Phys, Richardson, TX 75083 USA; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Britzger, D.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E. M.; Lohwasser, K.; Madsen, A.; Medinnis, M.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Rauch, D. M.; Robinson, J. E. M.; Schaefer, R.; Schmitt, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Wang, J.; Zakharchuk, N.] DESY, Hamburg, Germany; [Asbah, N.; Behr, J. K.; Bertsche, C.; Bessner, M.; Bloch, I.; Britzger, D.; Deterre, C.; Cornell, S. Diez; Dutta, B.; Dyndal, M.; Eckardt, C.; Ferrando, J.; Filipuzzi, M.; Flaschel, N.; Bravo, A. Gascon; Gasnikova, K.; Glazov, A.; Gregor, I. M.; Haleem, M.; Hamnett, P. G.; Hiller, K. H.; Howarth, J.; Huang, Y.; Katzy, J.; Keller, J. S.; Kondrashova, N.; Kuhl, T.; Lobodzinska, E. M.; Lohwasser, K.; Madsen, A.; Medinnis, M.; Monig, K.; Garcia, R. F. Naranjo; Naumann, T.; O'Rourke, A. A.; Peschke, R.; Peters, K.; Pirumov, H.; Poley, A.; Rauch, D. M.; Robinson, J. E. M.; Schaefer, R.; Schmitt, S.; South, D.; Stanescu-Bellu, M.; Stanitzki, M. M.; Styles, N. A.; Tackmann, K.; Trofymov, A.; Wang, J.; Zakharchuk, N.] DESY, Zeuthen, Germany; [Burmeister, I.; Cinca, D.; Dette, K.; Erdmann, J.; Esch, H.; Gossling, C.; Homann, M.; Klingenberg, R.; Kroeninger, K.] Tech Univ Dortmund, Lehrstuhl Expt Phys 4, Dortmund, Germany; [Duschinger, D.; Friedrich, F.; Gutschow, C.; Hauswald, L.; Kobel, M.; Mader, W. F.; Novgorodova, O.; Siegert, F.; Socher, F.; Straessner, A.; Vest, A.; Wahrmund, S.] Tech Univ Dresden, Inst Kern & Teilchenphys, Dresden, Germany; [Arce, A. T. H.; Benjamin, D. P.; Bjergaard, D. M.; Bocci, A.; Goshaw, A. T.; Kajomovitz, E.; Kotwal, A.; Kruse, M. C.; Li, L.; Li, S.; Liu, M.; Oh, S. H.] Duke Univ, Dept Phys, Durham, NC 27706 USA; [Bristow, T. M.; Clark, P. J.; Dias, F. A.; Edwards, N. C.; Gao, Y.; Walls, F. M. Garay; Glaysher, P. C. F.; Harrington, R. D.; Leonidopoulos, C.; Martin, V. J.; Mijovic, L.; Mills, C.; Pino, S. A. Olivares; Washbrook, A.; Wynne, B. M.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland; [Antonelli, M.; Beretta, M.; Bilokon, H.; Chiarella, V.; Curatolo, M.; Esposito, B.; Gatti, C.; Laurelli, P.; Maccarrone, G.; Mancini, G.; Sansoni, A.; Testa, M.; Vilucchi, E.] INFN, Lab Nazl Frascati, Frascati, Italy; [Arnold, H.; Betancourt, C.; Boehler, M.; Bruneliere, R.; Buehrer, F.; Burgard, C. D.; Buscher, D.; Cardillo, F.; Coniavitis, E.; Consorti, V.; Dang, N. P.; Dao, V.; Di Simone, A.; Glatzer, J.; Gonella, G.; Herten, G.; Hirose, M.; Jakobs, K.; Javurek, T.; Jenni, P.; Kiss, F.; Koneke, K.; Kopp, A. K.; Kuehn, S.; Landgraf, U.; Luedtke, C.; Nagel, M.; Pagacova, M.; Parzefall, U.; Ronzani, M.; Rosbach, K.; Ruehr, F.; Rurikova, Z.; Sammel, D.; Schillo, C.; Schnoor, U.; Schumacher, M.; Sommer, P.; Sundermann, J. E.; Ta, D.; Temming, K. K.; Tornambe, P.; Tsiskaridze, V.; Weiser, C.; Werner, M.; Zhang, L.; Zimmermann, S.] Albert Ludwigs Univ, Fak Math & Phys, Freiburg, Germany; [Ancu, L. S.; De Mendizabal, J. Bilbao; Calace, N.; Chatterjee, A.; Clark, A.; Coccaro, A.; Delitzsch, C. M.; Della Volpe, D.; Ferrere, D.; Golling, T.; Gonzalez-Sevilla, S.; Gramling, J.; Guescini, F.; Iacobucci, G.; Katre, A.; Khoo, T. J.; Lanfermann, M. C.; Lionti, A. E.; March, L.; Mermod, P.; Nackenhorst, O.; Paolozzi, L.; Ristic, B.; Schramm, S.; Sfyrla, A.; Wu, X.] Univ Geneva, Sect Phys, Geneva, Switzerland; [Barberis, D.; Darboa, G.; Favaretoa, A.; Parodia, A. Ferretto; Gagliardia, G.; Gaudielloa, A.; Gemmea, C.; Guidoa, E.; Miglioranzia, S.; Morettinia, P.; Oidea, H.; Osculatia, B.; Parodia, F.; Passaggioa, S.; Rossia, L. P.; Sanninoa, M.; Schiavia, C.] Ist Nazl Fis Nucl, Sez Genova, Genoa, Italy; [Barberis, D.; Favaretoa, A.; Parodia, A. Ferretto; Gagliardia, G.; Gaudielloa, A.; Guidoa, E.; Miglioranzia, S.; Oidea, H.; Osculatia, B.; Parodia, F.; Sanninoa, M.; Schiavia, C.] Univ Genoa, Dipartimento Fis, Genoa, Italy; [Tskhadadzea, E. G.] Iv Javakhishvili Tbilisi State Univ, E Andronikashvili Inst Phys, Tbilisi, Georgia; [Djobavab, T.; Durglishvilib, A.; Khubuab, J.; Mosidzeb, M.] Tbilisi State Univ, Inst High Energy Phys, Tbilisi, Georgia; [Duren, M.; Heinz, C.; Kreutzfeldt, K.; Stenzel, H.] Justus Liebig Univ Giessen, Inst Phys 2, Giessen, Germany; [Alshehri, A. A.; Bates, R. L.; Blue, A.; Boutle, S. K.; Madden, W. D. Breaden; Britton, D.; Buckley, A. G.; Bussey, P.; Buttar, C. M.; Buzatu, A.; Crawley, S. J.; D'Auria, S.; Doyle, A. T.; Gul, U.; Knue, A.; Mullen, P.; O'Shea, V.; Owen, M.; Pollard, C. S.; Qin, G.; Quilty, D.; Ravenscroft, T.; Robson, A.; St Denis, R. D.; Stewart, G. A.; Thompson, A. S.] Univ Glasgow, SUPA Sch Phys & Astron, Glasgow, Lanark, Scotland; [Bindi, M.; Bisanz, T.; Blumenschein, U.; Brandt, G.; De Maria, A.; Drechsler, E.; Graber, L.; Grosse-Knetter, J.; Janus, M.; Kareem, M. J.; Kawamura, G.; Lai, S.; Lemmer, B.; Magradze, E.; Mantoani, M.; Mchedlidze, G.; Llacer, M. Moreno; Musheghyan, H.; Quadt, A.; Rieger, J.; Rosien, N. -A.; Rzehorz, G. F.; Shabalina, E.; Stolte, P.; Veatch, J.; Weingarten, J.; Zinonos, Z.] Georg August Univ, Inst Phys 2, Gottingen, Germany; [Albrand, S.; Berlendis, S.; Bethani, A.; Camincher, C.; Collot, J.; Crepe-Renaudin, S.; Delsart, P. A.; Gabaldon, C.; Genest, M. H.; Gordon, H. A.; Gradin, P. O. J.; Hostachy, J-Y.; Ledroit-Guillon, F.; Lleres, A.; Lucotte, A.; Malek, F.; Petit, E.; Stark, J.; Trocme, B.; Wu, M.] Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subatom & Cosmol, Grenoble, France; [Chan, S. K.; Clark, B. L.; Franklin, M.; Giromini, P.; Huth, J.; Ippolito, V.; Lazovich, T.; Mateos, D. Lopez; Morii, M.; Rogan, C. S.; Roloff, J.; Skottowe, H. P.; Sun, S.; Tolley, E.; Tong, B.; Tuna, A. N.; Zambito, S.] Harvard Univ, Lab Particle Phys & Cosmol, Cambridge, MA 02138 USA; [Barnovska-Blenessy, Z.; Gao, J.; Geng, C.; Guo, Y.; Han, L.; Hu, Q.; Jiang, Y.; Li, B.; Li, C.; Liu, J. B.; Liu, M.; Liu, Y. L.; Liu, Y.; Peng, H.; Song, H. Y.; Wang, W.; Zhang, G.; Zhang, R.; Zhao, Z.; Zhu, Y.] Univ Sci & Technol China, Dept Modern Phys, Hefei, Anhui, Peoples R China; [Andrei, V.; Antel, C.; Baas, A. E.; Brandt, O.; Djuvslanda, J. I.; Dunforda, M.; Geislera, M. P.; Hankea, P.; Jongmannsa, J.; Klugea, E. -E.; Langa, V. S.; Meiera, K.; Theenhausena, H. Meyer Zu; Villara, D. I. Narrias; Sahinsoya, M.; Scharfa, V.; Schultz-Coulona, H. -C.; Stamena, R.; Starovoitova, P.; Sucheka, S.; Wesselsa, M.] Heidelberg Univ, Kirchhoff Inst Phys, Heidelberg, Germany; [Anders, C. F.; De Limab, D. E. Ferreira; Giulinib, M.; Kolbb, M.; Lisovyib, M.; Schaetzelb, S.; Schoeningb, A.; Sosab, D.] Heidelberg Univ, Inst Phys, Heidelberg, Germany; [Kretzc, M.; Kugelc, A.] Heidelberg Univ, ZITI Inst Tech Informat, Mannheim, Germany; [Nagasaka, Y.] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan; [Bortolotto, V.; Chana, Y. L.; Castilloa, L. R. Flores; Lua, H.; Salvucciab, A.; Tsuia, K. M.] Chinese Univ Hong Kong, Dept Phys, Shatin, Hong Kong, Peoples R China; [Bortolotto, V.; Orlandob, N.; Salvucciab, A.; Tub, Y.] Univ Hong Kong, Dept Phys, Hong Kong, Hong Kong, Peoples R China; [Bortolotto, V.; Prokofievc, K.; Salvucciab, A.] Hong Kong Univ Sci & Technol, Dept Phys, Kowloon, Hong Kong, Peoples R China; [Calfayan, P.; Choi, K.; Evans, H.; Gagnon, P.; Kopeliansky, R.; Lammers, S.; Martinez, N. Lorenzo; Luehring, F.; Ogren, H.; Penwell, J.; Weinert, B.; Zieminska, D.] Indiana Univ, Dept Phys, Bloomington, IN 47405 USA; [Guenther, J.; Iwanski, W.; Jansky, R.; Kneringer, E.; Lukas, W.; Milic, A.] Leopold Franzens Univ, Inst Astro & Teilchenphys, Innsbruck, Austria; [Argyropoulos, S.; Benitez, J.; Mallik, U.; Zaidan, R.] Univ Iowa, Iowa City, IA USA; [Chen, C.; Cochran, J.; De Lorenzi, F.; Jiang, H.; Krumnack, N.; Pluth, D.; Prell, S.; Werner, M. D.; Yu, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA; [Ahmadov, F.; Aleksandrov, I. N.; Annovi, A.; Bednyakov, V. A.; Boyko, I. R.; Budagov, I. A.; Chelkov, G. A.; Cheplakov, A.; Chizhov, M. V.; Dedovich, D. V.; Demichev, M.; Gongadze, A.; Gostkin, M. I.; Huseynov, N.; Javadov, N.; Karpov, S. N.; Karpova, Z. M.; Khramov, E.; Kruchonak, U.; Kukhtin, V.; Ladygin, E.; Lyubushkin, V.; Minashvili, I. A.; Mineev, M.; Peshekhonov, V. D.; Plotnikova, E.; Potrap, I. N.; Pozdnyakov, V.; Rusakovich, N. A.; Sadykov, R.; Sapronov, A.; Shiyakova, M.; Soloshenko, A.; Turchikhin, S.; Vinogradov, V. B.; Yeletskikh, I.; Zhernchugov, A.; Zimine, N. I.] JINR Dubna, Joint Inst Nucl Res, Dubna, Russia; [Amako, K.; Aoki, M.; Arai, Y.; Hanagaki, K.; Honda, T.; Ikegami, Y.; Ikeno, M.; Iwasaki, H.; Kanzaki, J.; Kondo, T.; Kono, T.; Makida, Y.; Nagai, R.; Nagano, K.; Nakamura, K.; Nozaki, M.; Odaka, S.; Okuyama, T.; Sasaki, O.; Suzuki, S.; Takubo, Y.; Tanaka, S.; Terada, S.; Tokushuku, K.; Tsuno, S.; Unno, Y.; Usui, J.; Yamamoto, A.; Yasu, Y.] KEK, High Energy Accelerator Res Org, Tsukuba, Ibaraki, Japan; [Chen, Y.; Hasegawa, M.; Kido, S.; Kurashige, H.; Maeda, J.; Ochi, A.; Shimizu, S.; Tanioka, R.; Yamazaki, Y.; Yuan, L.] Kobe Univ, Grad Sch Sci, Kobe, Hyogo, Japan; [Kunigo, T.; Monden, R.; Sumida, T.; Tashiro, T.] Kyoto Univ, Fac Sci, Kyoto, Japan; [Takashima, R.] Kyoto Univ Educ, Kyoto, Japan; [Kawagoe, K.; Oda, S.; Otono, H.; Shirabe, S.; Tojo, J.] Kyushu Univ, Dept Phys, Fukuoka, Japan; [Verzini, M. J. Alconada; Alonso, F.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Univ Nacl La Plata, Inst Fis La Plata, La Plata, Buenos Aires, Argentina; [Verzini, M. J. Alconada; Alonso, F.; Arduh, F. A.; Dova, M. T.; Hoya, J.; Monticelli, F.; Wahlberg, H.] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina; [Barton, A. E.; Beattie, M. D.; Bertram, I. A.; Borissov, G.; Bouhova-Thacker, E. V.; Dearnaley, W. J.; Fox, H.; Grimm, K.; Henderson, R. C. W.; Hughes, G.; Jones, R. W. L.; Kartvelishvili, V.; Long, R. E.; Love, P. A.; Muenstermann, D.; Parker, A. J.; Skinner, M. B.; Smizanska, M.; Walder, J.; Wharton, A. M.] Univ Lancaster, Dept Phys, Lancaster, England; [Aliev, M.; Bachas, K.; Chiodini, G.; Gorinia, E.; Longoa, L.; Primaveraa, M.; Realea, M.; Spagnoloa, S.; Venturaa, A.] INFN, Sez Lecce, Lecce, Italy; [Aliev, M.; Bachas, K.; Gorinia, E.; Longoa, L.; Realea, M.; Spagnoloa, S.; Venturaa, A.] Univ Solent, Dipartimento Matemat & Fis, Lecce, Italy; [Affolder, A. A.; Anders, J. K.; Burdin, S.; D'Onofrio, M.; Dervan, P.; Gwilliam, C. B.; Hayward, H. S.; Jones, T. J.; King, B. T.; Klein, M.; Klein, U.; Kretzschmar, J.; Laycock, P.; Lehan, A.; Maxfield, S. J.; Mehta, A.; Readioff, N. P.; Vossebeld, J. H.] Univ Liverpool, Oliver Lodge Lab, Liverpool, Merseyside, England; [Cindro, V.; Filipcic, A.; Gorisek, A.; Kanjir, L.; Kersevan, B. P.; Kramberger, G.; Macek, B.; Mandic, I.; Mikuz, M.; Muskinja, M.; Sfiligoj, T.; Sokhrannyi, G.] Jozef Stefan Inst, Dept Phys, Ljubljana, Slovenia; [Armitage, L. J.; Bevan, A. J.; Bona, M.; Hays, J. M.; Hickling, R.; Landon, M. P. J.; Lewis, D.; Lloyd, S. L.; Morris, J. D.; Nooney, T.; Piccaro, E.; Rizvi, E.; Sandbach, R. L.] Queen Mary Univ London, Sch Phys & Astron, London, England; [Berry, T.; Boisvert, V.; Brooks, T.; Connelly, I. A.; Cowan, G.; Giannelli, M. Faucci; Gadomski, S.; George, S.; Gibson, S. M.; Kempster, J. J.; Kilby, C. R.; Vazquez, J. G. Panduro; Pastore, Fr.; Savage, G.; Sowden, B. C.; Spano, F.; Teixeira-Dias, P.; Thomas-Wilsker, J.] Royal Holloway Univ London, Dept Phys, Surrey, England; [Bell, A. S.; Butterworth, J. M.; Campanelli, M.; Christodoulou, V.; Cooper, B. D.; Davison, P.; Falla, R. J.; Freeborn, D.; Gregersen, K.; Grout, Z. J.; Ortiz, N. G. Gutierrez; Hesketh, G. G.; Jiggins, S.; Konstantinidis, N.; Korn, A.; Kucuk, H.; Leney, K. J. C.; Martyniuk, A. C.; McClymont, L. I.; Mcfayden, J. A.; Nurse, E.; Richter, S.; Scanlon, T.; Sherwood, P.; Simmons, B.; Wardrope, D. R.; Waugh, B. M.] UCL, Dept Phys & Astron, London, England; [Greenwood, Z. D.; Grossi, G. C.; Jana, D. K.; Sawyer, L.] Louisiana Tech Univ, Ruston, LA 71270 USA; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] UPMC, Lab Phys Nucl & Hautes Energies, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] Univ Paris Diderot, Paris, France; [Beau, T.; Bomben, M.; Calderini, G.; Crescioli, F.; De Cecco, S.; Demilly, A.; Derue, F.; Francavilla, P.; Krasny, M. W.; Lacour, D.; Laforge, B.; Laplace, S.; Le Dortz, O.; Lefebvre, G.; Solis, A. Lopez; Luzi, P. M.; Malaescu, B.; Marchiori, G.; Nikolic-Audit, I.; Ocariz, J.; Pandini, C. E.; Pires, S.; Ridel, M.; Roos, L.; Trincaz-Duvoid, S.; Varouchas, D.; Yap, Y. C.] CNRS, IN2P3, Paris, France; [Akesson, T. P. A.; Bocchetta, S. S.; Bryngemark, L.; Doglioni, C.; Hedberg, V.; Jarlskog, G.; Lytken, E.; Mjornmark, J. U.; Smirnova, O.; Viazlo, O.] Lund Univ, Fys Inst, Lund, Sweden; [Barreiro, F.; Lopez, S. Calvente; Cueto, A.; Del Peso, J.; Glasman, C.; Terron, J.] Univ Autonoma Madrid, Dept Fis Teor C15, Madrid, Spain; [Artz, S.; Becker, M.; Bertella, C.; Blum, W.; Buscher, V.; Cuth, J.; Dudder, A. Chr.; Endner, O. C.; Ertel, E.; Fiedler, F.; Torregrosa, E. Fullana; Geisen, M.; Groh, S.; Heck, T.; Jakobi, K. B.; Kaluza, A.; Karnevskiy, M.; Kleinknecht, K.; Kopke, L.; Lin, T. H.; Masetti, L.; Mattmann, J.; Meyer, C.; Moritz, S.; Pleskot, V.; Rave, S.; Reiss, A.; Schaeffer, J.; Schafer, U.; Schmitt, C.; Schmitz, S.; Schott, M.; Schuh, N.; Schulte, A.; Simioni, E.; Simon, M.; Tapprogge, S.; Urrejola, P.; Webb, S.; Yildirim, E.; Zimmermann, C.; Zinser, M.] Johannes Gutenberg Univ Mainz, Inst Phys, Mainz, Germany; [Annovi, A.; Barnes, S. L.; Bielski, R.; Cox, B. E.; Da Via, C.; Dann, N. S.; Forcolin, G. T.; Forti, A.; Ponce, J. M. Iturbe; Li, X.; Loebinger, F. K.; Marsden, S. P.; Masik, J.; Sanchez, F. J. Munoz; Neep, T. J.; Oh, A.; Ospanov, R.; Pater, J. R.; Peters, R. F. Y.; Pilkington, A. D.; Pin, A. W. J.; Price, D.; Qin, Y.; Queitsch-Maitland, M.; Raine, J. A.; Schweiger, H.; Shaw, S. M.; Tomlinson, L.; Watts, S.; Wilk, F.; Woudstra, M. J.; Wyatt, T. R.] Univ Manchester, Sch Phys & Astron, Manchester, Lancs, England; [Aad, G.; Alstaty, M.; Barbero, M.; Calandri, A.; Calvet, T. P.; Coadou, Y.; Diaconu, C.; Djama, F.; Ellajosyula, V.; Feligioni, L.; Hadef, A.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagy, E.; Pralavorio, P.; Rodina, Y.; Rozanov, A.; Talby, M.; Theveneaux-Pelzer, T.; Torres, R. E. Ticse; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.; Wang, C.] Aix Marseille Univ, CPPM, Marseille, France; [Aad, G.; Alstaty, M.; Barbero, M.; Calandri, A.; Calvet, T. P.; Coadou, Y.; Diaconu, C.; Djama, F.; Ellajosyula, V.; Feligioni, L.; Hadef, A.; Hallewell, G. D.; Hubaut, F.; Kahn, S. J.; Knoops, E. B. F. G.; Le Guirriec, E.; Liu, J.; Liu, K.; Madaffari, D.; Monnier, E.; Muanza, S.; Nagy, E.; Pralavorio, P.; Rodina, Y.; Rozanov, A.; Talby, M.; Theveneaux-Pelzer, T.; Torres, R. E. Ticse; Tisserant, S.; Toth, J.; Touchard, F.; Vacavant, L.; Wang, C.] CNRS, IN2P3, Marseille, France; [Bellomo, M.; Bernard, N. R.; Brau, B.; Dallapiccola, C.; Moyse, E. J. W.; Pais, P.; Pettersson, N. E.; Picazio, A.; Willocq, S.] Univ Massachusetts, Dept Phys, Amherst, MA 01003 USA; [Belanger-Champagne, C.; Chuinard, A. J.; Corriveau, F.; Keyes, R. A.; Lefebvre, B.; Mantifel, R.; Prince, S.; Robertson, S. H.; ; University of Adelaide; State University of New York (SUNY) System; State University of New York (SUNY) Albany; University of Alberta; Ankara University; Istanbul Aydin University; TOBB Ekonomi ve Teknoloji University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; United States Department of Energy (DOE); Argonne National Laboratory; University of Arizona; University of Texas System; University of Texas Arlington; National & Kapodistrian University of Athens; National Technical University of Athens; University of Texas System; University of Texas Austin; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); University of Belgrade; University of Bergen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; 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Universidade Nova de Lisboa; Universidade Nova de Lisboa; Czech Academy of Sciences; Institute of Physics of the Czech Academy of Sciences; Czech Technical University Prague; Charles University Prague; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Rome Tor Vergata; Istituto Nazionale di Fisica Nucleare (INFN); Roma Tre University; Hassan II University of Casablanca; Cadi Ayyad University of Marrakech; Mohammed First University of Oujda; Mohammed V University in Rabat; CEA; UDICE-French Research Universities; Universite Paris Saclay; University of California System; University of California Santa Cruz; University of Washington; University of Washington Seattle; Shandong University; Shanghai Jiao Tong University; University of Sheffield; Shinshu University; Universitat Siegen; Simon Fraser University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Comenius University Bratislava; Slovak Academy of Sciences; University of Cape Town; University of Johannesburg; University of Witwatersrand; Stockholm University; Oskar Klein Centre; Royal Institute of Technology; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of Sussex; University of Sydney; Academia Sinica - Taiwan; Technion Israel Institute of Technology; Tel Aviv University; Aristotle University of Thessaloniki; University of Tokyo; University of Tokyo; Tokyo Metropolitan University; Tokyo Institute of Technology; Tomsk State University; University of Toronto; Istituto Nazionale di Fisica Nucleare (INFN); University of Trento; University of British Columbia; York University - Canada; University of Tsukuba; University of Tsukuba; Tufts University; University of California System; University of California Irvine; Istituto Nazionale di Fisica Nucleare (INFN); Abdus Salam International Centre for Theoretical Physics (ICTP); University of Udine; Uppsala University; University of Illinois System; University of Illinois Urbana-Champaign; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Fisica Corpuscular (IFIC); University of Valencia; University of Valencia; University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Microelectronica de Barcelona (IMB-CNM); University of Valencia; Consejo Superior de Investigaciones Cientificas (CSIC); University of British Columbia; University of Victoria; University of Warwick; Waseda University; Weizmann Institute of Science; University of Wisconsin System; University of Wisconsin Madison; University of Wurzburg; University of Wuppertal; Yale University; Yerevan Physics Institute; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); University of London; King's College London; Azerbaijan National Academy of Sciences (ANAS); Institute of Physics of the Azerbaijan National Academy of Sciences; Novosibirsk State University; University of British Columbia; University of Louisville; An Najah National University; California State University System; California State University Fresno; University of Fribourg; Autonomous University of Barcelona; Universidade do Porto; Tomsk State University; Parthenope University Naples; Horia Hulubei National Institute of Physics & Nuclear Engineering; Peter the Great St. Petersburg Polytechnic University; University of Michigan System; University of Michigan; Council for Scientific & Industrial Research (CSIR) - South Africa; University of Louisiana System; Louisiana Technical University; ICREA; Osaka University; National Tsing Hua University; FOM National Institute for Subatomic Physics; Radboud University Nijmegen; University of Texas System; University of Texas Austin; European Organization for Nuclear Research (CERN); Georgian Technical University; Ochanomizu University; Manhattan College; Durham University; Academia Sinica - Taiwan; Shandong University; California State University System; California State University Sacramento; Moscow Institute of Physics & Technology; University of Geneva; Eotvos Lorand University; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; International School for Advanced Studies (SISSA); University of South Carolina System; University of South Carolina Columbia; Barcelona Institute of Science & Technology; Institute for High Energy Physics (IFAE); Sun Yat Sen University; Bulgarian Academy of Sciences; Lomonosov Moscow State University; Academia Sinica - Taiwan; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Stanford University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Universiti Malaya; UDICE-French Research Universities; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3) Aaboud, M (corresponding author), Univ Mohamed Premier, Fac Sci, Oujda, Morocco.;Aaboud, M (corresponding author), LPTPM, Oujda, Morocco. Gutierrez, Phillip/C-1161-2011; O'Shea, Val/G-1279-2010; Li, Hao/GPS-9834-2022; Ferrer, Antonio/H-2942-2015; Prokoshin, Fedor/E-2795-2012; La Rosa, Alessandro/I-1856-2013; Warburton, Andreas/N-8028-2013; de la Torre Perez, Hector/ABG-6942-2020; Peleganchuk, Sergey V/J-6722-2014; Burka, Klaudia/Q-4624-2017; Davidek, Tomas/P-2697-2017; Alberich, Leonor Cerdá/ABG-8534-2020; Romaniouk, Anatoli/Q-6674-2017; Busato, Emmanuel/AAZ-4423-2020; Nessi, Marzio/L-5194-2017; Hejbal, Jiri/H-1358-2014; Burdin, Sergey/AAZ-9062-2021; Lazzaroni, Massimo/N-3675-2015; Mikel Eukeni, Pozo Astigarraga/AAZ-8345-2021; Smirnova, Oxana/A-4401-2013; Chwastowski, Janusz J./I-4480-2012; Price, Darren D/E-6162-2012; Zhemchugov, Alexey/N-1717-2017; Maevskiy, Artem/P-4101-2016; Donadelli, Marisilvia/H-3710-2016; Torregrosa, Esteban Fullana/A-7305-2016; Villaplana Perez, Miguel/B-5772-2014; Glasman, Claudia/Y-8858-2019; da via, cinzia/AAS-3978-2021; Trzupek, Adam/N-2448-2018; Kantserov, Vadim/M-9761-2015; Mamuzic, Judita/U-3509-2017; Sapronov, Andrey/HGB-0392-2022; Lacasta, Carlos/C-7254-2008; Buzykaev, Alexey/HIK-0117-2022; Mikestikova, Marcela/H-1996-2014; Talyshev, Alexey/HGC-6910-2022; Benekos, Nektarios/J-8629-2017; Muino, Patricia Conde/F-7696-2011; Cavaliere, Viviana/CAG-2384-2022; Fassi, Farida/F-3571-2016; Pages, Andres Pacheco/C-5353-2011; Barreiro, Fernando/D-9808-2012; Balaji, S/J-1864-2019; Llácer, María Moreno/AAU-6582-2020; Ros, Eduardo/P-2625-2018; Ventura, Andrea/A-9544-2015; Grancagnolo, Sergio/J-3957-2015; Wozniak, Krzysztof W/P-4475-2017; Brooks, William K/C-8636-2013; Nevski, Pavel/M-6292-2015; Llácer, María Moreno/AAQ-7522-2020; Zanzi, Daniele/AAY-4102-2020; Busato, Emmanuel/AAS-5459-2020; Carquin, Edson/GLU-9641-2022; Trzebinski, Maciej/AAO-6821-2021; Jiménez, Yesenia Hernández/ABH-1107-2020; Cheremushkina, Evgeniya/E-9881-2018; Caprini, Irinel/AAP-3760-2020; Bachas, Konstantinos/C-8101-2019; Dolejší, Jiří/P-1509-2017; Wu, Xin/ABH-1729-2020; Cheng, Hok-Chuen/GNP-8341-2022; spagnolo, stefania/A-6359-2012; Smirnov, Sergei Y/F-1014-2011; Kosek, Tomáš/Q-1906-2017; Jones, Roger WL/H-5578-2011; Amorim, Antonio/C-8460-2013; Gladilin, Leonid/B-5226-2011; Wang, Hui/HMU-9512-2023; Fassouliotis, Dimitrios/AAL-7081-2021; b, m/HPE-0989-2023; Huseynov, Nazim/AAE-4663-2019; Chelkov, Georgy A./G-9934-2019; CAKIR, Orhan/L-1893-2015; Staszewski, Rafał/AAO-6844-2021; Jiménez Peña, Javier/AFY-1817-2022; Testa, Marianna/L-9768-2019; Urbán, Susana Cabrera/H-1376-2015; Legger, Federica/HNC-2568-2023; Gongadze, Alexi/T-9162-2017; Kuze, Masahiro/V-4251-2018; García, Josu Cantero/AAS-9810-2020; Artoni, Giacomo/ABA-2164-2020; Price, Darren/AAB-5991-2021; Fuster, Juan/W-6189-2018; de la Hoz, Santiago Gonzalez/AHC-7576-2022; Bates, Richard L/D-6596-2013; Ippolito, Valerio/L-1435-2016; Svatos, Michal/ABA-2041-2020; Schernau, Michael/GRY-3808-2022; Francavilla, Paolo/HKE-1206-2023; Tsybychev, Dmitri/J-3733-2017; Cottin, Giovanna/AFR-3846-2022; MEONI, Evelin/ABD-9498-2021; Reznicek, Pavel/C-1989-2017; Laycock, Paul/AAL-8261-2021; Giuli, Francesco/HJI-6649-2023; Mindur, Bartosz/A-2253-2017; Lagouri, Theodota/AAC-7358-2021; de Groot, Nicolo/A-2675-2009; Costa, Maria Jose/H-5962-2015; Benekos, Nektarios/I-9928-2017; Kostyukhin, Vadim/ABA-9033-2020; Zhukov, Konstantin I/M-6027-2015; Bassalat, Ahmed Azzam/HHY-9901-2022; Mitsou, Vasiliki A./D-1967-2009; Bruscino, Nello/ABA-8980-2021; Doyle, Anthony T/C-5889-2009; Crispin-Ortuzar, Mireia/AAN-7108-2020; Albert, Justin/J-4152-2017; Lee, Shih-Chang/AAV-7016-2021; Camarri, Paolo/M-7979-2015; Lefebvre, Benoit/ABB-2272-2021; Sopczak, Andre/I-4951-2015; Snesarev, Andrei/H-5090-2013; Kazanin, Vasily F./HHM-2056-2022; Kodys, Peter/P-2636-2017; Potamianos, Karolos/V-2525-2019; Gao, Y./GWZ-3665-2022; Herten, Gregor/HNQ-9546-2023; Ohm, Christian/AAU-6572-2020; De, Kaushik/N-1953-2013; Ibáñez, Carlos Escobar/B-3761-2017; Li, Liang/O-1107-2015; Kostyukhin, Vadim/F-3171-2019; Beddall, Andrew John/AAE-5820-2022; Bin Yusuff, Imran/ABA-5986-2020; Klimentov, Alexei A/I-4270-2013; Giagu, Stefano/H-6455-2013; Sanchez, Francisca J. Munoz/AAH-3909-2019; Marcisovsky, Michal/AAM-2404-2020; Grinstein, Sebastian/ABE-1880-2020; Kepka, Oldrich/ABF-5327-2021; Coccaro, Andrea/AAB-6990-2021; Sadykov, Renat/AAN-9602-2020; Mashinistov, Ruslan/M-8356-2015; Barberis, Dario/ABF-1310-2021; Gonella, Laura/GLR-3838-2022; do Amaral Coutinho, Yara/AAU-7857-2021; zhang, jin/GXV-9154-2022; SULIN, Vladimir V/J-6966-2014; Staroba, Pavel/G-8850-2014; Derendarz, Dominik/AAO-3512-2021; Lysak, Roman/H-2995-2014; Doležal, Zdeněk/K-6861-2017; Vandelli, Wainer/ABI-8185-2020; Boyko, Igor R/J-3659-2013; Tassi, Enrico/AAJ-9661-2020; beddall, ayda/AAG-6531-2020; Padilla, Cristobal/C-3218-2017; Ungaro, Francesca C/G-4554-2018; Tikhomirov, Vladimir/M-6194-2015; Coadou, Yann/G-2263-2010; Nordberg, Markus/L-4255-2017; Vaniachine, Alexandre/H-7796-2013; Annovi, Alberto/AAA-8638-2020; Kaczmarska, Anna/B-2753-2019; Nevski, Pavel/AAH-1839-2019; Dyndal, Mateusz/AAB-1528-2020; Garcia, Carmen/W-2465-2018; Oh, Alexander/HHZ-4386-2022; Kharlamova, Tatyana/AAQ-5430-2020; Norjoharuddeen, Nurfikri/S-3109-2018; Gonzalez, Barbara Alvarez/ABG-7021-2020; Cavaliere, Viviana/CAE-8597-2022; Guo, Jun/O-5202-2015; della Volpe, Domenico/B-4482-2012; de Renstrom, Pawel Bruckman/AAG-7725-2021; Fullana Torregrosa, Esteban/AAC-9079-2022; Belyaev, Nikita/N-3676-2016; Chizhov, Mihail/CAG-2076-2022; Billoud, Thomas/AAY-1569-2020; Capua, Marcella/A-8549-2015; Kamenshchikov, Andrey/R-1112-2017; Dam, Mogens/C-2081-2015; Berta, Peter/AAL-7109-2020; Castillo, Luis Roberto Flores/W-3928-2018; Amoroso, Simone/AAW-4334-2021; Berger, N/ABE-4064-2020; Gongadze, Alexi/AAY-5201-2021; Petrucci, Fabrizio/G-8348-2012; Capua, Marcella/AAJ-8419-2020; Introzzi, Gianluca/K-2497-2015; Franchini, Matteo/AAC-9259-2021; Trzebinski, Maciej/W-1748-2018; Valero, Alberto/G-9866-2015; Bosman, Martine/J-9917-2014; arik, metin/T-4193-2019; de la Hoz, Santiago Gonzalez/E-2494-2016; Slovák, Radim/P-9217-2017; Di Nardo, Roberto/J-4993-2012; Dabrowski, Wladyslaw/AAS-6369-2020; Banaś, Elżbieta A/W-4583-2017; Bobrovnikov, Victor/AAB-8328-2022; Monzani, Simone/D-6328-2017; Castro, Nuno/AAB-3648-2019; Quijada, Javier Alberto Murillo/AAS-4234-2021; Malek, Fairouz/E-4410-2018; Alexa, Calin/F-6345-2010; Ventura, Andrea/CAH-0226-2022; Górnicki, Edward/C-4075-2019; Sýkora, Tomáš/Q-3174-2017; Berge, David/P-2179-2017; Woźniak, Krzysztof/AAQ-6119-2021; de Almeida, Fernando Marroquim Leao/D-6947-2019; Donadelli, Marisilvia/ABD-5330-2021; Ahmadov, Faig N/B-3723-2018; li, li/GPX-3938-2022; Olszewski, Andrzej J/W-1825-2018; Buttar, Craig/AAG-5392-2021; perez, miguel/GXF-5077-2022; Krasnopevtsev, Dimitrii/R-2157-2017; Gravila, Paul/HLP-6245-2023; do Vale, Maria Aline Barros/D-2357-2019; Pásztor, Gabriella/D-9716-2016; Fiorini, Luca/W-6250-2018; Zenin, Oleg/E-6116-2018; Evangelos, Gazis/L-3966-2017; Gabrielli, Alessandro/H-4931-2012; Bogdanchikov, Alexander/AAB-9414-2022; Perez, Miguel Villaplana/B-2717-2015; Maj, Klaudia/Q-2724-2019; Torro Pastor, Emma/AAB-5979-2021; Kartvelishvili, Vakhtang G/K-2312-2013; Ozcan, Veysi Erkcan/AAS-4508-2020; Carquin López, Edson/GZG-4712-2022; Fabbri, Laura/H-3442-2012; Casado, Pilar/H-1484-2015; Masik, Jiri/AAE-1203-2021; kucuk, hilal/AAM-5530-2020; Duarte-Campderrós, Jordi/F-5025-2018; Ciftci, Abbas Kenan/AAC-5487-2022; Alimonti, Gianluca/AAG-4603-2020; Olszowska, Jolanta/P-2889-2017; Vranjes Milosavljevic, Marija/F-9847-2016; Gustavino, Giuliano/C-3242-2016; McKee, Shawn/B-6435-2012; Stanecka, Ewa/V-6242-2018; Wolters, Helmut/M-4154-2013; Carli, Ina/C-2189-2017; Worm, Steven/I-3575-2012; Sykora, Ivan/T-5252-2018; Baldin, Evgenii/A-6186-2014; Hrabovsky, Miroslav/G-6714-2014; Carvalho, Joao/M-4060-2013; Darbo, Giovanni/C-8175-2012; Zenis, Tibor/T-5270-2018; Wosiek, Barbara/K-5811-2017; Maeda, Junpei/B-8131-2018; xella, stefania/E-6752-2015; Aguilar Saavedra, Juan Antonio/F-1256-2016; Garcia Navarro, Jose Enrique/H-6339-2015; Vos, Marcel/G-8123-2015; Rebuzzi, Daniela Marcella/D-9727-2018; Faltova, Jana/P-6842-2017; Malecki, Pawel/W-1710-2018; Carquin, Edson/G-5221-2015; Wolter, Marcin/A-7412-2012; LIVAN, Michele/D-7531-2012; Petersen, Troels/P-5538-2015; Kukla, Romain/P-9760-2016; Vykydal, Zdenek/H-6426-2016; Hamal, Petr/G-5540-2014; Leitner, Rupert/C-2004-2017; Britton, David/F-2602-2010; Monk, James/A-9021-2017; , Alex/N-8715-2016; Zabinski, Bartlomiej/Q-6483-2017; Gorelov, Igor/J-9010-2015; Turecek, Daniel/N-4149-2017; Sioli, Maximiliano/Q-1597-2016; Konig, Adriaan/B-6439-2013; Hansen, Peter Henrik/C-2098-2015; Negrini, Matteo/C-8906-2014; Schultz-Coulon, Hans-Christian/X-5006-2018; ezzi, mohammed/F-9820-2017; Vanadia, Marco/K-5870-2016; Korcyl, Krzysztof/W-2111-2018; Mlynarikova, Michaela/P-2701-2017; Tartarelli, Giuseppe Francesco/A-5629-2016; Juste, Aurelio/I-2531-2015; Soldatov, Evgeny/E-3990-2017; Blue, Andrew/C-9882-2016; cerri, alessandro/Q-6884-2016; Slawinska, Magdalena/W-2551-2018; Mendes Saraiva, Joao Gentil/D-1596-2017; Goncalo, Jose/M-3153-2016; Penc, Ondrej/H-3032-2014; Izen, Joseph/A-4392-2018 Gutierrez, Phillip/0000-0003-2326-3877; O'Shea, Val/0000-0001-7183-1205; Ferrer, Antonio/0000-0003-0532-711X; Prokoshin, Fedor/0000-0001-6389-5399; La Rosa, Alessandro/0000-0001-6291-2142; Warburton, Andreas/0000-0002-2298-7315; de la Torre Perez, Hector/0000-0002-4516-5269; Peleganchuk, Sergey V/0000-0003-0907-7592; Burka, Klaudia/0000-0003-4819-9226; Davidek, Tomas/0000-0002-3770-8307; Alberich, Leonor Cerdá/0000-0002-5567-4278; Busato, Emmanuel/0000-0003-2174-8512; Hejbal, Jiri/0000-0002-4600-3659; Burdin, Sergey/0000-0003-4831-4132; Lazzaroni, Massimo/0000-0002-4094-1273; Mikel Eukeni, Pozo Astigarraga/0000-0002-3069-3077; Smirnova, Oxana/0000-0003-2517-531X; Chwastowski, Janusz J./0000-0002-6190-8376; Zhemchugov, Alexey/0000-0002-3360-4965; Maevskiy, Artem/0000-0003-1652-8005; Donadelli, Marisilvia/0000-0001-8329-4240; Torregrosa, Esteban Fullana/0000-0003-3082-621X; Villaplana Perez, Miguel/0000-0002-0048-4602; Glasman, Claudia/0000-0003-2025-3817; da via, cinzia/0000-0001-6154-7323; Trzupek, Adam/0000-0001-6938-5867; Kantserov, Vadim/0000-0001-8255-416X; Mamuzic, Judita/0000-0002-3203-4243; Lacasta, Carlos/0000-0002-2623-6252; Buzykaev, Alexey/0000-0002-5458-5564; Mikestikova, Marcela/0000-0003-1277-2596; Talyshev, Alexey/0000-0001-8560-3756; Muino, Patricia Conde/0000-0002-9187-7478; Cavaliere, Viviana/0000-0002-4297-8539; Fassi, Farida/0000-0002-6423-7213; Pages, Andres Pacheco/0000-0001-8210-1734; Barreiro, Fernando/0000-0002-3021-0258; Balaji, S/0000-0002-7324-4853; Llácer, María Moreno/0000-0003-1113-3645; Ros, Eduardo/0000-0003-2812-9554; Ventura, Andrea/0000-0002-3368-3413; Grancagnolo, Sergio/0000-0001-8490-8304; Wozniak, Krzysztof W/0000-0003-1171-0887; Brooks, William K/0000-0001-6161-3570; Llácer, María Moreno/0000-0001-9045-5305; Zanzi, Daniele/0000-0002-1222-7937; Busato, Emmanuel/0000-0003-2174-8512; Trzebinski, Maciej/0000-0002-5151-7101; Jiménez, Yesenia Hernández/0000-0001-9844-6200; Caprini, Irinel/0000-0003-3343-3200; Bachas, Konstantinos/0000-0002-9047-6517; Dolejší, Jiří/0000-0001-5821-7067; Wu, Xin/0000-0001-7655-389X; Cheng, Hok-Chuen/0000-0002-8912-4389; spagnolo, stefania/0000-0001-7482-6348; Smirnov, Sergei Y/0000-0002-6778-073X; Kosek, Tomáš/0000-0003-1639-5564; Jones, Roger WL/0000-0002-6427-3513; Amorim, Antonio/0000-0003-0638-2321; Gladilin, Leonid/0000-0001-9422-8636; CAKIR, Orhan/0000-0002-9016-138X; Staszewski, Rafał/0000-0001-7708-9259; Jiménez Peña, Javier/0000-0002-8705-628X; Legger, Federica/0000-0003-1400-0709; Kuze, Masahiro/0000-0001-8858-8440; García, Josu Cantero/0000-0001-8449-1019; Artoni, Giacomo/0000-0002-3477-4499; Price, Darren/0000-0003-2750-9977; Ippolito, Valerio/0000-0001-5126-1620; Svatos, Michal/0000-0002-7199-3383; Francavilla, Paolo/0000-0003-1164-6870; Cottin, Giovanna/0000-0002-5308-5808; MEONI, Evelin/0000-0002-6934-3752; Reznicek, Pavel/0000-0003-4017-9829; Mindur, Bartosz/0000-0002-5511-2611; Benekos, Nektarios/0000-0001-7831-8762; Kostyukhin, Vadim/0000-0002-0490-9209; Zhukov, Konstantin I/0000-0003-2468-9634; Bassalat, Ahmed Azzam/0000-0002-0129-1423; 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Boutle, Sarah/0000-0002-8732-2963; Wengler, Thorsten/0000-0002-4375-5265; Blanchard, Jean Baptiste/0000-0003-0886-9125; Erdmann, Johannes/0000-0002-8073-2740; PERINI, LAURA/0000-0003-3715-0523; Rzehorz, Gerhard/0000-0002-3372-9565; Slawinska, Magdalena/0000-0002-9386-9092; Winter, Benedict Tobias/0000-0001-9606-7688; Heelan, Louise/0000-0002-4879-0131; Tal Hod, Noam/0000-0001-5241-0544; Lester, Christopher Gorham/0000-0001-5770-4883; Backhaus, Malte/0000-0002-5888-2304; Vetterli, Michel/0000-0002-7223-2965; Vincter, Manuella/0000-0002-5338-8972; Giuli, Francesco/0000-0002-8506-274X; Trincaz-Duvoid, Sophie/0000-0001-5913-0828; Nakamura, Tomoaki/0000-0002-7414-1071; Peters, Krisztian/0000-0002-7654-1677; Farina, Edoardo Maria/0000-0003-3037-9288; Mendes Saraiva, Joao Gentil/0000-0002-7006-0864; Goncalo, Jose/0000-0002-3826-3442; Stabile, Alberto/0000-0002-6868-8329; Karpova, Zoya/0000-0003-0254-4629; Shimojima, Makoto/0000-0002-8738-1664; Vranjes, Nenad/0000-0001-5415-5225; Lee, Lawrence/0000-0002-5590-335X; Besson, Nathalie/0000-0001-9248-6252; Konstantinidis, Nikolaos/0000-0002-4140-6360; Whalen, Kathleen/0000-0002-9383-8763; Penc, Ondrej/0000-0002-5433-3981; Morii, Masahiro/0000-0001-9324-057X; Majewski, Stephanie/0000-0002-6871-3395; Izen, Joseph/0000-0002-9846-5601; Truong, Thi Ngoc Loan/0000-0001-8249-7150; ADACHI, SHUNSUKE/0000-0002-0400-7555; Palka, Marek/0000-0002-7185-3540; Formica, Andrea/0000-0001-8308-2643; Lisovyi, Mykhailo/0000-0002-3014-5855; Sukharev, Andrey/0000-0002-2210-0913; Levchenko, Mikhail/0000-0002-5495-0656; Cindro, Vladimir/0000-0002-2037-7185; Yang, Hongtao/0000-0003-3554-7113; Lucotte, Arnaud/0000-0002-5992-0640; Ghneimat, Mazuza/0000-0002-4931-2764; Akesson, Torsten/0000-0003-4141-5408; Rimoldi, Marco/0000-0003-1165-7940; Lister, Alison/0000-0002-1552-3651; McCarthy, Thomas G/0000-0002-1182-3526; Guenther, Jaroslav/0000-0003-3189-3959; LeBlanc, Matt/0000-0001-5977-6418; Weingarten, Jens/0000-0003-2165-871X ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia, Russian Federation; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada; Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union; FP7, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Idex, France; ANR, France; Region Auvergne, France; Fondation Partager le Savoir, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme; Thales programme; Aristeia programme; EU-ESF; Greek NSRF; BSF, Israel; GIF, Israel; Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Spain; Generalitat Valenciana, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; ICREA Funding Source: Custom; Fundação para a Ciência e a Tecnologia [CERN/FIS-NUC/0005/2015] Funding Source: FCT; Grants-in-Aid for Scientific Research [16H06493] Funding Source: KAKEN; Division Of Physics; Direct For Mathematical & Physical Scien [1119200] Funding Source: National Science Foundation; Division Of Physics; Direct For Mathematical & Physical Scien [1410972] Funding Source: National Science Foundation; Science and Technology Facilities Council [ST/M001431/1, ST/M000753/1, ST/N000463/1, ST/N000277/1, ATLAS, ST/K001329/1, ST/L003449/1, ST/K003666/1, GRIDPP, PP/E000444/1, ST/L001144/1, ST/N000307/1, ST/N000331/1, ST/L005662/1, ST/N000420/1, ST/N000234/1, ST/J004804/1, ST/J005533/1, ST/L006162/1, ST/I005846/1, 1366825, ST/K001361/1, ST/L000997/1, ST/M006417/1, ST/K001329/1 ATLAS] Funding Source: researchfish; STFC [ST/N000331/1, ST/N000420/1, ST/K001361/1, ST/N000277/1, ST/N000234/1, ST/L003449/1, ST/K001329/1, ST/M001431/1, ST/L005662/1, ST/L001144/1, ST/I005846/1, ST/L006162/1, ST/M006417/1, ST/K003666/1, ST/N000463/1, ST/L000997/1, ST/M000753/1, ST/N000307/1, ST/J005533/1, ST/J004804/1] Funding Source: UKRI ANPCyT, Argentina(ANPCyT); YerPhI, Armenia; ARC, Australia(Australian Research Council); BMWFW, Austria; FWF, Austria(Austrian Science Fund (FWF)); ANAS, Azerbaijan(Azerbaijan National Academy of Sciences (ANAS)); SSTC, Belarus; CNPq, Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); FAPESP, Brazil(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); NSERC, Canada(Natural Sciences and Engineering Research Council of Canada (NSERC)); NRC, Canada; CFI, Canada(Canada Foundation for Innovation); CERN; CONICYT, Chile(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)); CAS, China(Chinese Academy of Sciences); MOST, China(Ministry of Science and Technology, China); NSFC, China(National Natural Science Foundation of China (NSFC)); COLCIENCIAS, Colombia(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSMT CR, Czech Republic(Ministry of Education, Youth & Sports - Czech RepublicCzech Republic Government); MPO CR, Czech Republic(Czech Republic Government); VSC CR, Czech Republic(Czech Republic Government); DNRF, Denmark(National Research Foundation of Korea); DNSRC, Denmark(Danish Natural Science Research Council); IN2P3-CNRS, France(Centre National de la Recherche Scientifique (CNRS)); CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany(Federal Ministry of Education & Research (BMBF)); HGF, Germany; MPG, Germany(Max Planck Society); GSRT, Greece(Greek Ministry of Development-GSRT); RGC, Hong Kong SAR, China(Hong Kong Research Grants Council); ISF, Israel(Israel Science Foundation); I-CORE, Israel; Benoziyo Center, Israel; INFN, Italy(Istituto Nazionale di Fisica Nucleare (INFN)); MEXT, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); JSPS, Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); CNRST, Morocco; FOM, Netherlands(FOM (The Netherlands)Netherlands Government); NWO, Netherlands(Netherlands Organization for Scientific Research (NWO)Netherlands Government); RCN, Norway; MNiSW, Poland(Ministry of Science and Higher Education, Poland); NCN, Poland; FCT, Portugal(Fundacao para a Ciencia e a Tecnologia (FCT)); MNE/IFA, Romania; MES of Russia, Russian Federation(Russian Federation); NRC KI, Russian Federation; JINR; MESTD, Serbia(Ministry of Education, Science & Technological Development, Serbia); MSSR, Slovakia; ARRS, Slovenia(Slovenian Research Agency - Slovenia); MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain(Spanish Government); SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland(Swiss National Science Foundation (SNSF)); Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan(Ministry of Science and Technology, Taiwan); TAEK, Turkey(Ministry of Energy & Natural Resources - Turkey); STFC, United Kingdom(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE, United States of America(United States Department of Energy (DOE)); NSF, United States of America(National Science Foundation (NSF)); BCKDF, Canada; Canada Council, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; FQRNT, Canada(FQRNT); Ontario Innovation Trust, Canada; EPLANET, European Union; ERC, European Union(European Research Council (ERC)); FP7, European Union; Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France(French National Research Agency (ANR)); Idex, France; ANR, France(French National Research Agency (ANR)); Region Auvergne, France(Region Auvergne-Rhone-Alpes); Fondation Partager le Savoir, France; DFG, Germany(German Research Foundation (DFG)); AvH Foundation, Germany(Alexander von Humboldt Foundation); Herakleitos programme; Thales programme(Thales Group); Aristeia programme; EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); BSF, Israel(US-Israel Binational Science Foundation); GIF, Israel(German-Israeli Foundation for Scientific Research and Development); Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Spain(Generalitat de Catalunya); Generalitat Valenciana, Spain(Center for Forestry Research & Experimentation (CIEF)); Royal Society, United Kingdom(Royal Society of London); Leverhulme Trust, United Kingdom(Leverhulme Trust); ICREA(ICREA); Fundação para a Ciência e a Tecnologia; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Division Of Physics; Direct For Mathematical & Physical Scien(National Science Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences (MPS)); Science and Technology Facilities Council(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); STFC(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)) We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. 75 41 41 3 47 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1029-8479 J HIGH ENERGY PHYS J. High Energy Phys. NOV 21 2016.0 11 110 10.1007/JHEP11(2016)110 0.0 52 Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Physics ES1OG Green Published, Green Submitted, Green Accepted, gold 2023-03-23 WOS:000399296300002 0
J Chen, F; Liu, S; Appolloni, A Chen, Fan; Liu, Sen; Appolloni, Andrea Horizontal Coordination of I-LNGOs in the Humanitarian Supply Chain: An Evolutionary Game Approach SUSTAINABILITY English Article sustainability; humanitarian supply chain; NGO; coordination; evolutionary game COLLABORATIVE PERFORMANCE; ORGANIZATIONAL CULTURE; SWIFT TRUST; BIG DATA; RELIEF; DISASTER; LOGISTICS; MANAGEMENT; COOPERATION; PROCUREMENT Disasters accompanied by heavy casualties and huge economic losses directly result in the disruption or delay of economic development. Considering the urgent need for reducing losses and accelerating the process of social recovery, international nongovernment organizations (INGOs) and local NGOs (LNGOs) with different resource endowments should achieve organizational coordination to improve the relief efficiency and sustainability of the humanitarian supply chain. Due to conflicting interests and expectations, this coordination is hard to achieve. In this study, we first establish an evolutionary game model between INGOs and LNGOs to determine the influencing factors and explore the interaction of NGOs in a dynamic way. Our results show that: (1) coordination by resource sharing can improve the sustainability of the humanitarian supply chain; (2) coordination willingness is affected by the behavior of other players, which can nevertheless achieve equilibrium under certain conditions; and (3) the important factors and optimal strategies of players are highlighted in the dynamic model. This study provides several insights into the theory of organizational coordination in the humanitarian supply chain regarding sustainability. [Chen, Fan] Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China; [Liu, Sen] Yunnan Univ Finance & Econ, Sch Logist, Kunming 650221, Yunnan, Peoples R China; [Appolloni, Andrea] Univ Roma Tor Vergata, Fac Econ, Dept Management & Law, I-00133 Rome, Italy; [Appolloni, Andrea] Natl Res Council CNR, Inst Res Innovat & Serv Dev IRISS, I-80134 Naples, Italy Huazhong University of Science & Technology; Yunnan University of Finance & Economics; University of Rome Tor Vergata; Consiglio Nazionale delle Ricerche (CNR); Istituto di Ricerca su Innovazione e Servizi per lo Sviluppo (IRISS-CNR) Liu, S (corresponding author), Yunnan Univ Finance & Econ, Sch Logist, Kunming 650221, Yunnan, Peoples R China.;Appolloni, A (corresponding author), Univ Roma Tor Vergata, Fac Econ, Dept Management & Law, I-00133 Rome, Italy.;Appolloni, A (corresponding author), Natl Res Council CNR, Inst Res Innovat & Serv Dev IRISS, I-80134 Naples, Italy. d201780928@hustedu.cn; liusencool@ynufe.edu.cn; andrea.appolloni@uniroma2.it Appolloni, Andrea/L-3607-2014 Appolloni, Andrea/0000-0001-5741-398X National Natural Science Foundation Council of China [71862035, 71502159]; Yunnan Fundamental Research Project [2019FB085] National Natural Science Foundation Council of China(National Natural Science Foundation of China (NSFC)); Yunnan Fundamental Research Project This research was funded by the National Natural Science Foundation Council of China under Projects No. 71862035 and No. 71502159; the Yunnan Fundamental Research Project under grant No. 2019FB085. 84 12 12 8 37 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability AUG 2020.0 12.0 15 5953 10.3390/su12155953 0.0 20 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology MZ2UP Green Published, gold 2023-03-23 WOS:000558979700001 0
J Acar-Denizli, N; Horvath, IF; Mandl, T; Priori, R; Vissink, A; Hernandez-Molina, G; Armagan, B; Praprotnik, S; Sebastian, A; Bartoloni, E; Rischmueller, M; Pasoto, SG; Nordmark, G; Nakamura, H; Trevisani, VFM; Retamozo, S; Carsons, SE; Maure-Noia, B; Sanchez-Berna, I; Lopez-Dupla, M; Fonseca-Aizpuru, E; Diaz, SM; Vazquez, M; Cuiza, PED; Campo, BD; Ng, WF; Rasmussen, A; Dong, X; Li, X; Baldini, C; Seror, R; Gottenberg, JE; Kruize, AA; Sandhya, P; Gandolfo, S; Kwok, SK; Kvarnstrom, M; Solans, R; Sene, D; Suzuki, Y; Isenberg, DA; Valim, V; Hofauer, B; Giacomelli, R; Devauchelle-Pensec, V; Atzeni, F; Gheita, TA; Morel, J; Izzo, R; Kalyoncu, U; Szanto, A; Olsson, P; Bootsma, H; Ramos-Casals, M; Kostov, B; Brito-Zeron, P Acar-Denizli, N.; Horvath, I. -F.; Mandl, T.; Priori, R.; Vissink, A.; Hernandez-Molina, G.; Armagan, B.; Praprotnik, S.; Sebastian, A.; Bartoloni, E.; Rischmueller, M.; Pasoto, S. G.; Nordmark, G.; Nakamura, H.; Fernandes Moca Trevisani, V.; Retamozo, S.; Carsons, S. E.; Maure-Noia, B.; Sanchez-Berna, I.; Lopez-Dupla, M.; Fonseca-Aizpuru, E.; Melchor Diaz, S.; Vazquez, M.; Diaz Cuiza, P. E.; de Miguel Campo, B.; Ng, W. -F.; Rasmussen, A.; Dong, X.; Li, X.; Baldini, C.; Seror, R.; Gottenberg, J. -E.; Kruize, A. A.; Sandhya, P.; Gandolfo, S.; Kwok, S. -K.; Kvarnstrom, M.; Solans, R.; Sene, D.; Suzuki, Y.; Isenberg, D. A.; Valim, V.; Hofauer, B.; Giacomelli, R.; Devauchelle-Pensec, V.; Atzeni, F.; Gheita, T. A.; Morel, J.; Izzo, R.; Kalyoncu, U.; Szanto, A.; Olsson, P.; Bootsma, H.; Ramos-Casals, M.; Kostov, B.; Brito-Zeron, P. Sjogren Big Data Consortium Systemic phenotype related to primary Sjogren's syndrome in 279 patients carrying isolated anti-La/SSB antibodies CLINICAL AND EXPERIMENTAL RHEUMATOLOGY English Article primary Sjogren's syndrome; isolated La/SSB auto-antibodies; anti-Ro/SSA antibodies; systemic disease; ESSDAI; big data DISEASE-ACTIVITY INDEX; CLASSIFICATION CRITERIA; LOWER PREVALENCE; DIAGNOSIS; MANIFESTATIONS; CONSENSUS; LA; AUTOANTIBODIES; IDENTIFICATION; LYMPHOMA Objective. To evaluate the systemic phenotype associated with the presence of isolated anti-La/SSB antibodies in a large international registry of patients with primary Sjogren's syndrome (pSS) fulfilling the 2002 classification criteria. Methods. The Big Data Sjogren Project Consortium is an international, multicentre registry created in 2014. Baseline clinical information from leading centres on clinical research in SS of the 5 continents was collected. Combination patterns of anti-Ro/SSA-La/SSB antibodies at the time of diagnosis defined the following four immunological phenotypes: double positive (combined Ro/SSA and La/SSB,) isolated anti-Ro/SSA, isolated anti-La/SSB, and immunonegative. Results. The cohort included 12,084 patients (11,293 females, mean 52.4 years) with recorded ESSDAI scores available. Among them, 279 (2.3%) had isolated anti-La/SSB antibodies. The mean total ESSDAI score at diagnosis of patients with pSS carrying isolated anti-La/SSB was 6.0, and 80.4% of patients had systemic activity (global ESSDAI score >= 1) at diagnosis. The domains with the highest frequency of active patients were the biological (42.8%), glandular (36.8%) and articular (31.2%) domains. Patients with isolated anti-La/ SSB showed a higher frequency of active patients in all ESSDAI domains but two (articular and peripheral nerve) in comparison with immune-negative patients, and even a higher absolute frequency in six clinical ESSDAI domains in comparison with patients with isolated anti-Ro/SSA. In addition, patients with isolated anti-La/SSB showed a higher frequency of active patients in two ESSDAI domains (pulmonary and glandular) with respect to the most active immunological subset (double-positive antibodies). Meanwhile, systemic activity detected in patients with isolated anti-La/SSB was overwhelmingly low. Even in ESSDAI domains where patients with isolated anti-La/SSB had the highest frequencies of systemic activity (lymphadenopathy and muscular), the percentage of patients with moderate or high activity was lower in comparison with the combined Ro/SSA and La/SSB group. Conclusion. Patients carrying isolated La/SSB antibodies represent a very small subset of patients with a systemic SS phenotype characterised by a significant frequency of active patients in most clinical ESSDAI domains but with a relative low frequency of the highest severe organ-specific involvements. Primary SS still remains the best clinical diagnosis for this subset of patients. [Acar-Denizli, N.; Kostov, B.] Univ Politecn Cataluna, Dept Stat & Opera tions Res, Barcelona, Spain; [Horvath, I. -F.; Szanto, A.] Univ Debrecen, Div Clin Immunol, Fac Med, Debrecen, Hungary; [Mandl, T.; Olsson, P.] Lund Univ, Skane Univ Hosp Malmo, Dept Rheumatol, Lund, Sweden; [Priori, R.; Izzo, R.] Sapienza Univ Rome, Rheumatol Clin, Dept Internal Med & Med Specialties, Rome, Italy; [Vissink, A.] Univ Groningen, Univ Med Ctr Groningen, Dept Oral & Maxillofacial Surg, Groningen, Netherlands; [Hernandez-Molina, G.] Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Immunol & Rheumatol Dept, Mexico City, DF, Mexico; [Armagan, B.; Kalyoncu, U.] Hacettepe Univ, Fac Med, Dept Internal Med, Ankara, Turkey; [Praprotnik, S.] Univ Med Ctr, Dept Rheumatol, Ljubljana, Slovenia; [Sebastian, A.] Wroclaw Med Univ, Dept Rheumatol & Internal Med, Wroclaw, Poland; [Bartoloni, E.] Univ Perugia, Dept Med, Rheumatol Unit, Perugia, Italy; [Rischmueller, M.] Univ Adelaide, Queen Elizabeth Hosp, Dept Rheumatol, Discipline Med, Adelaide, SA, Australia; [Pasoto, S. G.] Univ Sao Paulo, Fac Med, Hosp Clin HCFMUSP, Rheumatol Div, Sao Paulo, Brazil; [Nordmark, G.] Uppsala Univ, Dept Med Sci, Rheumatol, Uppsala, Sweden; [Nakamura, H.] Nagasaki Univ, Grad Sch Biomed Sci, Div Adv Prevent Med Sci, Dept Immunol & Rheumatol, Nagasaki, Japan; [Fernandes Moca Trevisani, V.] Univ Fed Sao Paulo, Sao Paulo, Brazil; [Retamozo, S.] Inst Modelo Cardiol Privado SRL, Cordoba, Argentina; [Retamozo, S.] Inst Univ Ciencias Biomed Cordoba IUCBC, Cordoba, Argentina; [Carsons, S. E.] NYU Long Isl Sch Med, Div Rheumatol Allergy & Immunol, Mineola, NY USA; [Maure-Noia, B.] Complexo Hosp Univ Vigo, Dept Autoimmune Dis, Vigo, Spain; [Sanchez-Berna, I.] Hosp Rey Juan Carlos de Mostoles, Dept Internal Med, Madrid, Spain; [Lopez-Dupla, M.] Hosp Joan 23, Dept Internal Med, Tarragona, Spain; [Fonseca-Aizpuru, E.] Hosp Cabuenes, Dept Internal Med, Gijon, Spain; [Melchor Diaz, S.] Hosp 12 Octubre, Dept Rheumatol, Madrid, Spain; [Vazquez, M.] Hosp Clin, Dept Rheumatol, San Lorenzo, Paraguay; [Diaz Cuiza, P. E.] Seguro Social Univ, Dept Reumatol, Sucre, Bolivia; [Diaz Cuiza, P. E.] Consultorio Privado Reumatol, Sucre, Bolivia; [de Miguel Campo, B.] Hosp 12 Octubre, Dept Internal Med, Madrid, Spain; [Ng, W. -F.] Newcastle Univ, Inst Cellular Med, Newcastle Upon Tyne, Tyne & Wear, England; [Ng, W. -F.] NIHR Biomed Res Ctr, Newcastle Upon Tyne, Tyne & Wear, England; [Rasmussen, A.] Oklahoma Med Res Fdn, Genes & Human Dis Res Program, 825 NE 13th St, Oklahoma City, OK 73104 USA; [Dong, X.] Peking Union Med Coll Hosp, Dept Rheumatol, Beijing, Peoples R China; [Li, X.] Anhui Prov Hosp, Dept Rheumatol & Immunol, Hefei, Anhui, Peoples R China; [Baldini, C.] Univ Pisa, Rheumatol Unit, Pisa, Italy; [Seror, R.] Univ Paris Sud, Hop Univ Paris Sud, AP HP, Ctr Immunol Viral Infect & Autoimmune Dis,INSERM, Paris, France; [Gottenberg, J. -E.] Univ Strasbourg, CNRS, Strasbourg Univ Hosp, Dept Rheumatol, Strasbourg, France; [Kruize, A. A.] Univ Med Ctr Utrecht, Dept Rheumatol & Clin Immunol, Utrecht, Netherlands; [Sandhya, P.] Christian Med Coll & Hosp, Dept Clin Immunol & Rheumatol, Vellore, Tamil Nadu, India; [Gandolfo, S.] Univ Hosp Santa Maria della Misericordia, Dept Med & Biol Sci, Clin Rheumatol, Udine, Italy; [Kwok, S. -K.] Catholic Univ Korea, Seoul St Marys Hosp, Div Rheumatol, Dept Internal Med,Coll Med, Seoul, South Korea; [Kvarnstrom, M.] Karolinska Inst, Div Expt Rheumatol, Dept Med, Stockholm, Sweden; [Kvarnstrom, M.] Karolinska Univ Hosp, Stockholm, Sweden; [Solans, R.] Hosp Valle De Hebron, Dept Internal Med, Barcelona, Spain; [Sene, D.] Univ Paris VII, Hop Lariboisiere, AP HP, Serv Med Interne 2, Paris, France; [Suzuki, Y.] Kanazawa Univ Hosp, Div Rheumatol, Kanazawa, Ishikawa, Japan; [Isenberg, D. A.] UCL, Div Med, Ctr Rheumatol, London, England; [Valim, V.] Univ Fed Espirito Santo, Dept Med, Vitoria, Brazil; [Hofauer, B.] Tech Univ Munich, Otorhinolaryngol Head & Neck Surg, Munich, Germany; [Giacomelli, R.] Univ Aquila, Sch Med, Clin Unit Rheumatol, Laquila, Italy; [Devauchelle-Pensec, V.] Brest Univ, Rheumatol Dept, INSERM 1227, Brest, France; [Atzeni, F.] Univ Messina, IRCCS Galeazzi Orthopaed Inst, Milan & Rheumatol Unit, Messina, Italy; [Gheita, T. A.] Cairo Univ, Kasr Al Ainy Sch Med, Rheumatol Dept, Cairo, Egypt; [Morel, J.] Teaching Hosp, Dept Rheumatol, Montpellier, France; [Morel, J.] Univ Montpellier, Montpellier, France; [Bootsma, H.] Univ Groningen, Univ Med Ctr Groningen, Dept Rheumatol & Clin Immunol, Groningen, Netherlands; [Ramos-Casals, M.; Brito-Zeron, P.] Univ Barcelona, Sjogren Syndrome Res Grp AGAUR, Lab Autoimmune Dis Josep Font, IDIBAPS CELLEX,Dept Autoimmune Dis,ICMiD,Hosp Cli, Barcelona, Spain; [Kostov, B.] Consorci Atencio Primaria Salut Barcelona Esquerr, Primary Care Ctr Corts, Inst Invest Biomed August Pi & Sunyer IDIBAPS, Primary Healthcare Transversal Res Grp, Barcelona, Spain; [Brito-Zeron, P.] Hosp CIMA Sanitas, Dept Med, Autoimmune Dis Unit, Barcelona, Spain Universitat Politecnica de Catalunya; University of Debrecen; Lund University; Skane University Hospital; Sapienza University Rome; University of Groningen; Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran - Mexico; Hacettepe University; University Medical Centre Ljubljana; Wroclaw Medical University; University of Perugia; University of Adelaide; Universidade de Sao Paulo; Uppsala University; Nagasaki University; Universidade Federal de Sao Paulo (UNIFESP); Complexo Hospitalario Universitario de Vigo; Universitat Rovira i Virgili; Hospital de Cabuenes; Hospital Universitario 12 de Octubre; Hospital Universitario 12 de Octubre; Newcastle University - UK; Oklahoma Medical Research Foundation; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College Hospital; University of Pisa; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Paul-Brousse - APHP; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CHU Strasbourg; UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Utrecht University; Utrecht University Medical Center; Christian Medical College & Hospital (CMCH) Vellore; Hospital Santa Maria della Misericordia; Catholic University of Korea; Seoul St. Mary's Hospital; Karolinska Institutet; Karolinska Institutet; Karolinska University Hospital; Hospital Universitari Vall d'Hebron; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Lariboisiere-Fernand-Widal - APHP; Kanazawa University; University of London; University College London; Universidade Federal do Espirito Santo; Technical University of Munich; University of L'Aquila; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; IRCCS Istituto Ortopedico Galeazzi; University of Messina; Egyptian Knowledge Bank (EKB); Cairo University; Universite de Montpellier; CHU de Montpellier; Universite de Montpellier; University of Groningen; University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS; University of Barcelona; Hospital Clinic de Barcelona; IDIBAPS Ramos-Casals, M (corresponding author), Hosp Clin Barcelona, Servei Malalties Autoimmunes Sistem, C Villarroel 170, Barcelona 08036, Spain. mramos@clinic.cat Retamozo, Soledad/AFT-7300-2022; Ramos-Casals, Manuel/HJH-4887-2023; sene, damien/R-3970-2017; Mandl, Thomas/AAS-1548-2020; Gheita, Tamer/HHC-8095-2022; Armagan, Berkan/HNQ-2829-2023; Acar-Denizli, Nihan/D-5954-2019; PASOTO, SANDRA/E-1213-2012; Giacomelli, Roberto/AAB-9962-2022; Nordmark, Gunnel/A-9652-2016; Sebastian, Agata/AAX-9590-2020 Ramos-Casals, Manuel/0000-0001-5709-6734; sene, damien/0000-0001-8640-4580; Mandl, Thomas/0000-0002-7143-7088; Gheita, Tamer/0000-0002-1155-9729; Acar-Denizli, Nihan/0000-0002-0012-8632; Nordmark, Gunnel/0000-0002-3829-7431; Pasoto, Sandra/0000-0002-7343-6804; Kvarnstrom, Marika/0000-0002-4948-8380; Gandolfo, Saviana/0000-0001-9406-3632; Sebastian, Agata/0000-0001-6332-8714; Olsson, Peter/0000-0001-7318-833X 33 7 7 0 1 CLINICAL & EXPER RHEUMATOLOGY PISA VIA SANTA MARIA 31, 56126 PISA, ITALY 0392-856X 1593-098X CLIN EXP RHEUMATOL Clin. Exp. Rheumatol. 2020.0 38.0 4 126 S85 S94 10 Rheumatology Science Citation Index Expanded (SCI-EXPANDED) Rheumatology OF4ML 33095152.0 2023-03-23 WOS:000581183900014 0
J Nie, LX; Li, C; Marzani, F; Wang, HB; Thibouw, F; Grayeli, AB Nie, Leixin; Li, Chao; Marzani, Franck; Wang, Haibin; Thibouw, Francois; Grayeli, Alexis Bozorg Classification of Wideband Tympanometry by Deep Transfer Learning With Data Augmentation for Automatic Diagnosis of Otosclerosis IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Wideband tympanometry; deep learning; data augmentation; transfer learning; otosclerosis EUSTACHIAN-TUBE; REFLECTANCE; ABSORBENCY; CT Otosclerosis is a common disease of the middle ear leading to stapedial fixation. Its rapid and non-invasive diagnosis could be achieved through wideband tympanometry (WBT), but the interpretation of the raw data provided by this tool is complex and time-consuming. Convolutional neural networks (CNN) could potentially be applied to this situation to help the clinicians categorize WBT data. A dataset containing 135 samples from 80 patients with otosclerosis and 55 controls was obtained. We designed a lightweight CNN to categorize samples into the otosclerosis and control. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.95 +/- 0.011, and the F1-score was 0.89 +/- 0.031 (r = 10). The performance was further improved by data augmentation schemes and transfer learning strategies (AUC: 0.97 +/- 0.010, F1-score: 0.94 +/- 0.016, p < 0.05, ANOVA). Finally, the most relevant diagnostic features employed by the CNN were assessed via the activation pattern heatmaps. These results are crucial for the visual interpretation of WBT graphic outputs which clinicians use in routine, and for a better understanding of the WBT signal in relation to the ossicular mechanics. [Nie, Leixin; Li, Chao; Wang, Haibin] Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China; [Nie, Leixin; Marzani, Franck; Grayeli, Alexis Bozorg] Univ Bourgogne Franche Comte, ImViA EA7535, F-21078 Dijon, France; [Thibouw, Francois; Grayeli, Alexis Bozorg] Dijon Univ Hosp, Otolaryngol Dept, F-21000 Dijon, France Chinese Academy of Sciences; Institute of Acoustics, CAS; Universite de Bourgogne; CHU Dijon Bourgogne Nie, LX (corresponding author), Chinese Acad Sci, Inst Acoust, State Key Lab Acoust, Beijing 100190, Peoples R China. nieleixin@mail.ioa.ac.cn; lichao8601@hotmail.com; franck.marzani@u-bourgogne.fr; whb@mail.ioa.ac.cn; francois.thibouw@chu-dijon.fr; alexis.bozorggrayeli@chu-dijon.fr China Scholarship Council; CAS Pioneer Hundred Talents Program China Scholarship Council(China Scholarship Council); CAS Pioneer Hundred Talents Program This work was supported in part by the China Scholarship Council and in part by the CAS Pioneer Hundred Talents Program. 46 2 3 3 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. FEB 2022.0 26.0 2 888 897 10.1109/JBHI.2021.3093007 0.0 10 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics ZY1DZ 34181561.0 2023-03-23 WOS:000772331200037 0
J Fathalla, A; Salah, A; Li, KL; Li, KQ; Francesco, P Fathalla, Ahmed; Salah, Ahmad; Li, Kenli; Li, Keqin; Francesco, Piccialli Deep end-to-end learning for price prediction of second-hand items KNOWLEDGE AND INFORMATION SYSTEMS English Article LSTM; ARIMA; SARIMA; Linear regression; Time series analysis; Price prediction; Second-hand items TIME-SERIES; MODEL Recent years have witnessed the rapid development of online shopping and ecommerce websites, e.g., eBay and OLX. Online shopping markets offer millions of products for sale each day. These products are categorized into many product categories. It is crucial for sellers to correctly estimate the price of the second-hand item. State-of-the-art methods can predict the price of only one item category. In addition, none of the existing methods utilized the price range of a given second-hand item in the prediction task, as there are several advertisements for the same product at different prices. In this vein, as the first contribution, we propose a deep model architecture for predicting the price of a second-hand item based on the image and textual description of the item for different sets of item types. This proposed method utilizes a deep neural network involving long short-term memory (LSTM) and convolutional neural network architectures for price prediction. The proposed model achieved a better mean absolute error accuracy score in comparison with the support vector machine baseline model. In addition, the second contribution includes twofold. First, we propose forecasting the minimum and maximum prices of the second-hand item. The models used for the forecasting task utilize linear regression, LSTM, and seasonal autoregressive integrated moving average methods. Second, we propose utilizing the model of the first contribution in predicting the item quality score. Then, the item quality score and the forecasted minimum and maximum prices are combined to provide the item's final predicted price. Using a dataset crawled from a website for second-hand items, the proposed method of combining the predicted second-hand item quality score with the forecasted minimum and maximum price outperforms the other models in all of the used accuracy metrics with a significant performance gap. [Fathalla, Ahmed; Salah, Ahmad; Li, Kenli; Li, Keqin] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China; [Fathalla, Ahmed; Salah, Ahmad; Li, Kenli; Li, Keqin] Natl Supercomp Ctr Changsha, Changsha, Hunan, Peoples R China; [Fathalla, Ahmed] Suez Canal Univ, Fac Sci, Dept Math, Ismailia, Egypt; [Salah, Ahmad] Zagazig Univ, Fac Comp & Informat, Sharkia, Egypt; [Li, Keqin] SUNY, New York, NY 10018 USA; [Francesco, Piccialli] Univ Naples Federico II, Dept Elect Engn & Informat Technol, Naples, Italy Hunan University; Egyptian Knowledge Bank (EKB); Suez Canal University; Egyptian Knowledge Bank (EKB); Zagazig University; State University of New York (SUNY) System; SUNY Community College; SUNY Maritime College; SUNY Optometry; University of Naples Federico II Fathalla, A; Li, KQ (corresponding author), Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China.;Fathalla, A; Li, KQ (corresponding author), Natl Supercomp Ctr Changsha, Changsha, Hunan, Peoples R China.;Fathalla, A (corresponding author), Suez Canal Univ, Fac Sci, Dept Math, Ismailia, Egypt.;Li, KQ (corresponding author), SUNY, New York, NY 10018 USA. fathalla@hnu.edu.cn; ahmad@hnu.edu.cn; lkl@hnu.edu.cn; lik@newpaltz.edu; francesco.piccialli@unina.it Salah, Ahmad/K-8761-2017; Salah, Ahmad/GPT-0358-2022; Fathalla, Ahmed/ABE-9610-2021 Salah, Ahmad/0000-0003-3433-7640; Fathalla, Ahmed/0000-0001-5432-5407; Xiao, Guoqing/0000-0001-5008-4829 National Key R&D Program of China [SQ2018YFB020061]; NSFC [61860206011, 61625202, 61602170, 61750110531] National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)) This research is partly supported by the National Key R&D Program of China (Grants: SQ2018YFB020061) and the NSFC (61860206011, 61625202, 61602170, and 61750110531). 64 12 12 2 29 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0219-1377 0219-3116 KNOWL INF SYST Knowl. Inf. Syst. DEC 2020.0 62.0 12 4541 4568 10.1007/s10115-020-01495-8 0.0 JUL 2020 28 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science OI6KS 2023-03-23 WOS:000552172400001 0
J Wu, W; Zhang, Y; Jiang, J; Lucas, MV; Fonzo, GA; Rolle, CE; Cooper, C; Chin-Fatt, C; Krepel, N; Cornelssen, CA; Wright, R; Toll, RT; Trivedi, HM; Monuszko, K; Caudle, TL; Sarhadi, K; Jha, MK; Trombello, JM; Deckersbach, T; Adams, P; McGrath, PJ; Weissman, MM; Fava, M; Pizzagalli, DA; Arns, M; Trivedi, MH; Etkin, A Wu, Wei; Zhang, Yu; Jiang, Jing; Lucas, Molly, V; Fonzo, Gregory A.; Rolle, Camarin E.; Cooper, Crystal; Chin-Fatt, Cherise; Krepel, Noralie; Cornelssen, Carena A.; Wright, Rachael; Toll, Russell T.; Trivedi, Hersh M.; Monuszko, Karen; Caudle, Trevor L.; Sarhadi, Kamron; Jha, Manish K.; Trombello, Joseph M.; Deckersbach, Thilo; Adams, Phil; McGrath, Patrick J.; Weissman, Myrna M.; Fava, Maurizio; Pizzagalli, Diego A.; Arns, Martijn; Trivedi, Madhukar H.; Etkin, Amit An electroencephalographic signature predicts antidepressant response in major depression NATURE BIOTECHNOLOGY English Article TRANSCRANIAL MAGNETIC STIMULATION; SINGLE-TRIAL EEG; BRAIN-FUNCTION; OSCILLATIONS; MECHANISMS; MODERATORS; BIOMARKERS; CONFLICT; NETWORKS; OUTCOMES The efficacy of an antidepressant is predicted from an EEG signature. Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression. [Wu, Wei] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou, Guangdong, Peoples R China; [Wu, Wei; Zhang, Yu; Jiang, Jing; Lucas, Molly, V; Fonzo, Gregory A.; Rolle, Camarin E.; Cornelssen, Carena A.; Wright, Rachael; Toll, Russell T.; Trivedi, Hersh M.; Monuszko, Karen; Caudle, Trevor L.; Sarhadi, Kamron; Etkin, Amit] Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA; [Wu, Wei; Zhang, Yu; Jiang, Jing; Lucas, Molly, V; Fonzo, Gregory A.; Rolle, Camarin E.; Cornelssen, Carena A.; Wright, Rachael; Toll, Russell T.; Trivedi, Hersh M.; Monuszko, Karen; Caudle, Trevor L.; Sarhadi, Kamron; Etkin, Amit] Stanford Univ, Wu Tsai Neurosci Inst, Stanford, CA 94305 USA; [Wu, Wei; Zhang, Yu; Jiang, Jing; Lucas, Molly, V; Fonzo, Gregory A.; Rolle, Camarin E.; Cornelssen, Carena A.; Wright, Rachael; Toll, Russell T.; Trivedi, Hersh M.; Monuszko, Karen; Caudle, Trevor L.; Sarhadi, Kamron; Etkin, Amit] Vet Affairs Palo Alto Healthcare Syst, Palo Alto, CA 94304 USA; [Wu, Wei; Zhang, Yu; Jiang, Jing; Lucas, Molly, V; Fonzo, Gregory A.; Rolle, Camarin E.; Cornelssen, Carena A.; Wright, Rachael; Toll, Russell T.; Trivedi, Hersh M.; Monuszko, Karen; Caudle, Trevor L.; Sarhadi, Kamron; Etkin, Amit] Sierra Pacific Mental Illness Res Educ & Clin Ctr, Palo Alto, CA 94304 USA; [Cooper, Crystal; Chin-Fatt, Cherise; Jha, Manish K.; Trombello, Joseph M.; Trivedi, Madhukar H.] Univ Texas Southwestern Med Ctr Dallas, Dept Psychiat, Dallas, TX USA; [Cooper, Crystal; Chin-Fatt, Cherise; Trombello, Joseph M.; Trivedi, Madhukar H.] Univ Texas Southwestern Med Ctr Dallas, ODonnell Brain Inst, Dallas, TX 75390 USA; [Krepel, Noralie; Arns, Martijn] Brainclin Fdn, Res Inst Brainclin, Nijmegen, Netherlands; [Krepel, Noralie] Maastricht Univ, Fac Psychol & Neurosci, Dept Cognit Neurosci, Maastricht, Netherlands; [Deckersbach, Thilo; Fava, Maurizio; Pizzagalli, Diego A.] Harvard Med Sch, Dept Psychiat, Belmont, MA USA; [Deckersbach, Thilo; Fava, Maurizio; Pizzagalli, Diego A.] McLean Hosp, 115 Mill St, Belmont, MA 02178 USA; [Adams, Phil; McGrath, Patrick J.; Weissman, Myrna M.] Columbia Univ, Coll Phys & Surg, New York State Psychiat Inst, New York, NY USA; [Adams, Phil; McGrath, Patrick J.; Weissman, Myrna M.] Columbia Univ, Coll Phys & Surg, Dept Psychiat, New York, NY USA; [Arns, Martijn] Univ Utrecht, Dept Expt Psychol, Utrecht, Netherlands; [Arns, Martijn] NeuroCare Grp Netherlands, Nijmegen, Netherlands South China University of Technology; Stanford University; Stanford University; US Department of Veterans Affairs; Veterans Health Administration (VHA); VA Palo Alto Health Care System; University of Texas System; University of Texas Southwestern Medical Center Dallas; University of Texas System; University of Texas Southwestern Medical Center Dallas; Maastricht University; Harvard University; McLean Hospital; Columbia University; New York State Psychiatry Institute; Columbia University; Utrecht University Etkin, A (corresponding author), Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA.;Etkin, A (corresponding author), Stanford Univ, Wu Tsai Neurosci Inst, Stanford, CA 94305 USA.;Etkin, A (corresponding author), Vet Affairs Palo Alto Healthcare Syst, Palo Alto, CA 94304 USA.;Etkin, A (corresponding author), Sierra Pacific Mental Illness Res Educ & Clin Ctr, Palo Alto, CA 94304 USA. amitetkin@stanford.edu Trivedi, Madhukar/A-9029-2013; Zhang, Yu/J-1796-2015; Etkin, Amit/ABD-5761-2021; Fava, Maurizio/AAH-3548-2020; Fonzo, Gregory/AAG-8824-2020; Jha, Manish Kumar/ABI-3594-2020 Trivedi, Madhukar/0000-0002-2983-1110; Zhang, Yu/0000-0003-4087-6544; Jha, Manish Kumar/0000-0002-2764-2391; Fonzo, Gregory/0000-0002-0213-1034; Toll, Russell/0000-0002-7655-6668; Lucas, Molly/0000-0001-7824-2219; Arns, Martijn/0000-0002-0610-7613; Jiang, Jing/0000-0002-2991-2076; Trivedi, Hersh/0000-0002-2583-2300 National Institute of Mental Health of the National Institutes of Health (NIH) [U01MH092221, U01MH092250]; Stanford Neurosciences Institute; Hersh Foundation; NIH [DP1 MH116506]; National Key Research and Development Plan of China [2017YFB1002505]; National Natural Science Foundation of China [61876063, 61836003] National Institute of Mental Health of the National Institutes of Health (NIH); Stanford Neurosciences Institute; Hersh Foundation; NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Key Research and Development Plan of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We thank C.J. Keller and S. Kim. The EMBARC study was supported by the National Institute of Mental Health of the National Institutes of Health (NIH) under award nos, U01MH092221 (M.H.T.) and U01MH092250 (P.J.M., R.V.P. and M.M.W.). Data from the second depressed cohort were acquired under R01MH103324 (A.E.) and Big Idea in Neuroscience research funds from the Stanford Neurosciences Institute (A.E.). This work was also funded in part by the Hersh Foundation (M.H.T.). A.E. and W.W. were additionally funded by NIH grant no. DP1 MH116506. W.W. was also funded by National Key Research and Development Plan of China (grant no. 2017YFB1002505) and the National Natural Science Foundation of China (grant nos. 61876063 and 61836003). 70 89 90 41 198 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 1087-0156 1546-1696 NAT BIOTECHNOL Nat. Biotechnol. APR 2020.0 38.0 4 439 + 10.1038/s41587-019-0397-3 0.0 FEB 2020 15 Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology LI2GF 32042166.0 2023-03-23 WOS:000512528900001 0
J Fiorio, L; Zagheni, E; Abel, G; Hill, J; Pestre, G; Letouze, E; Cai, JX Fiorio, Lee; Zagheni, Emilio; Abel, Guy; Hill, Johnathan; Pestre, Gabriel; Letouze, Emmanuel; Cai, Jixuan Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data DEMOGRAPHY English Article Migration; Mobility; Big data; Methods MOBILITY; INTERVALS; LOCATION Georeferenced digital trace data offer unprecedented flexibility in migration estimation. Because of their high temporal granularity, many migration estimates can be generated from the same data set by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the United States, and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical data sets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics. [Fiorio, Lee; Hill, Johnathan] Univ Washington, Dept Geog, Seattle, WA 98195 USA; [Zagheni, Emilio] Max Planck Inst Demog Res, Rostock, Germany; [Abel, Guy] Shanghai Univ, Asian Demog Res Inst, Shanghai, Peoples R China; [Abel, Guy; Cai, Jixuan] Wittgenstein Ctr IIASA, Int Inst Appl Syst Anal, VID OAW WU, Vienna, Austria; [Pestre, Gabriel; Letouze, Emmanuel] Data Pop Alliance, New York, NY USA; [Letouze, Emmanuel] MIT, Media Lab, Cambridge, MA 02139 USA; [Cai, Jixuan] Chinese Univ Hong Kong, Dept Geog & Resource Management, Vienna, Austria University of Washington; University of Washington Seattle; Max Planck Society; Shanghai University; International Institute for Applied Systems Analysis (IIASA); Massachusetts Institute of Technology (MIT) Fiorio, L (corresponding author), Univ Washington, Dept Geog, Seattle, WA 98195 USA. fiorio@uw.edu; guy.abel@shu.edu.cn Shanahan Endowment Fellowship; Eunice Kennedy Shriver National Institute of Child Health and Human Development training grant [T32 HD007543]; Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant [R2 HD042828]; Jubilaumsfonds der Stadt Wien fur die Osterreichische Akademie der Wissenschaften (STEProjekts) [STE0059]; National Science Foundation of China [41871142]; Ernst Mach Grant; Max Planck Institute of Demographic Research (MPIDR); World Bank; French Development Agency Shanahan Endowment Fellowship; Eunice Kennedy Shriver National Institute of Child Health and Human Development training grant; Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant; Jubilaumsfonds der Stadt Wien fur die Osterreichische Akademie der Wissenschaften (STEProjekts); National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ernst Mach Grant; Max Planck Institute of Demographic Research (MPIDR); World Bank(The World Bank India); French Development Agency Support for this re-search came from a Shanahan Endowment Fellowship, a Eunice Kennedy Shriver National Institute of Child Health and Human Development training grant (T32 HD007543), and a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant (R2 HD042828) to the Center for Studies in Demography and Ecology (CSDE) at the University of Washington. Additional support came from Jubilaumsfonds der Stadt Wien fur die Osterreichische Akademie der Wissenschaften (STEProjekts STE0059), the National Science Foundation of China, General Program (No. 41871142), and an Ernst Mach Grant. Support was also provided by the Max Planck Institute of Demographic Research (MPIDR), the World Bank, and the French Development Agency. The authors are also grateful to Sonatel for facilitation access to the CDRs used in the study. Finally, the authors would like to express gratitude to the intellectual communities at both CSDE and the MPIDR, with-out which this work would not have been possible. 39 12 12 4 15 DUKE UNIV PRESS DURHAM 905 W MAIN ST, STE 18-B, DURHAM, NC 27701 USA 0070-3370 1533-7790 DEMOGRAPHY Demography FEB 2021.0 58.0 1 51 74 10.1215/00703370-8917630 0.0 24 Demography Social Science Citation Index (SSCI) Demography RO8UU 33834241.0 gold, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000641316900003 0
J E, HY; Cui, Y; Pedrycz, W; Fayek, AR; Li, ZW; Li, JB E, Hanyu; Cui, Ye; Pedrycz, Witold; Fayek, Aminah Robinson; Li, Zhiwu; Li, Jinbo Design of fuzzy rule-based models with fuzzy relational factorization EXPERT SYSTEMS WITH APPLICATIONS English Article Fuzzy relational factorization; High dimensional data; Dimensionality reduction BIG DATA ANALYTICS; SYSTEMS; DIMENSIONALITY This study opens an original avenue of designing interpretable fuzzy rule-based models realized in the presence of data characterized by a large number of attributes (input variables). In the presence of such high-dimensional data, the development of rule-based models faces serious challenges both at the design and interpretability level. To address these two problems, we develop a new architecture of the rules and establish a two-step design process. First, the dimensionality reduction is realized with the aid of a fuzzy relational matrix factorization which transforms original fuzzy set-based encoded variables and yields a logic-oriented level of activation of individual rules. The results of relational factorization are logic expressions built over the encoded input variables forming condition parts of the rules. Second, the conclusions of the rules are built by optimizing local functions (both constant and linear functions are considered). A number of experiments are reported along with comparative studies involving a standard Takagi-Sugeno (TS) model. [E, Hanyu; Cui, Ye; Pedrycz, Witold; Li, Jinbo] Univ Alberta, Fac Engn, Dept Elect & Comp Engn, 9211 116 St NW, Edmonton, AB T6G 1H9, Canada; [Pedrycz, Witold] Polish Acad Sci Warsaw, Syst Res Inst, PL-01447 Warsaw, Poland; [Fayek, Aminah Robinson] Univ Alberta, Fac Engn, Dept Civil & Environm Engn, 11328 89 Ave NW, Edmonton, AB T6G 2J7, Canada; [Li, Zhiwu] Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macau, Peoples R China University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; University of Alberta; Macau University of Science & Technology Li, ZW (corresponding author), Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macau, Peoples R China. hanyu6@ualberta.ca; ycui7@ualberta.ca; wpedrycz@ualberta.ca; aminah@ualberta.ca; zwli@must.edu.mo; jinbo@ualberta.ca E, Hanyu/0000-0002-5567-6126; Cui, Ye/0000-0003-4402-5341 University of Alberta's Future Energy Systems research initiative; Canada First Research Excellence Fund [FES-T11-P01] University of Alberta's Future Energy Systems research initiative; Canada First Research Excellence Fund As a part of the University of Alberta's Future Energy Systems research initiative, this research was supported by the Canada First Research Excellence Fund, grant number FES-T11-P01, held by Dr. Aminah Robinson Fayek. 31 0 0 4 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 15 2022.0 206.0 117904 10.1016/j.eswa.2022.117904 0.0 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 3I8IT 2023-03-23 WOS:000832953800002 0
J Ha, MH; Wang, C; Pedrycz, W Ha Minghu; Wang Chao; Pedrycz, Witold The theoretical foundations of statistical learning theory based on fuzzy random samples in Sugeno measure space TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL English Article Bounds on the rate of uniform convergence of learning process; fuzzy random variables; key theorem; Sugeno measure RANDOM-VARIABLES; LARGE NUMBERS; RANDOM SETS Statistical learning theory is regarded as an appropriate theory to deal with learning problems on small samples, and it has now become a novel research interest of the machine learning field. However, the theory is based on real-valued random samples and established on probability measure space; it rarely deals with learning problems based on fuzzy random samples and established on Sugeno measure space. It is well known that fuzzy random samples and Sugeno measure space are interesting and important extensions of real-valued random samples and probability measure space, respectively. Therefore, the statistical learning theory based on fuzzy random samples in Sugeno measure space is further discussed in this paper. Firstly, based on definitions of the distribution function and the expected value of fuzzy random variables in Sugeno measure space, the Hoeffding inequality of fuzzy random variables is proved. Secondly, for the sake of completeness of the paper, the key theorem of learning theory based on fuzzy random samples in Sugeno measure space is introduced. Finally, the bounds on the rate of uniform convergence of a learning process based on fuzzy random samples in Sugeno measure space are constructed. [Ha Minghu] Hebei Univ, Coll Math & Comp Sci, Baoding, Peoples R China; [Wang Chao] Hebei Univ, Coll Phys Sci & Technol, Baoding, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland Hebei University; Hebei University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Ha, MH (corresponding author), Hebei Univ, Coll Math & Comp Sci, Baoding, Peoples R China. Minghuha@yahoo.com.cn National Natural Science Foundation of China [60773062, 61073121]; Natural Science Foundation of Hebei Province of China [F2008000633, 2010000318]; Education Department of Hebei Province of China [2005001D] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Education Department of Hebei Province of China This work was supported by the National Natural Science Foundation of China (Nos. 60773062 and 61073121), the Natural Science Foundation of Hebei Province of China (Nos. F2008000633 and 2010000318), and the Key Scientific Research Project of Education Department of Hebei Province of China (No. 2005001D). 22 0 0 0 10 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0142-3312 T I MEAS CONTROL Trans. Inst. Meas. Control JUL 2012.0 34.0 5 520 526 10.1177/0142331211403796 0.0 7 Automation & Control Systems; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Instruments & Instrumentation 953PY 2023-03-23 WOS:000304884400002 0
J Zhang, YY; Jin, SG; Wang, N; Zhao, JR; Guo, HW; Pellikka, P Zhang, Yuanyuan; Jin, Shuanggen; Wang, Ning; Zhao, Jiarui; Guo, Hongwei; Pellikka, Petri Total Phosphorus and Nitrogen Dynamics and Influencing Factors in Dongting Lake Using Landsat Data REMOTE SENSING English Article water quality; Dongting Lake; Landsat; influencing factors WATER-QUALITY; TAIHU LAKE; INDEX; EUTROPHICATION; CYANOBACTERIA; CHINA Total phosphorus (TP) and total nitrogen (TN) reflect the state of eutrophication. However, traditional point-based water quality monitoring methods are time-consuming and labor-intensive, and insufficient to estimate and assess water quality at a large scale. In this paper, we constructed machine learning models for TP and TN inversion using measured data and satellite imagery band reflectance, and verified it by in situ data. Atmospheric correction was performed on the Landsat Top of Atmosphere (TOP) data by removing the effect of the adjacency effect and correcting differences between Landsat sensors. Then, using the established model, the TP and TN patterns in Dongting Lake with a spatial resolution of 30 m from 1996 to 2021 were derived for the first time. The annual and monthly spatio-temporal variation characteristics of TP and TN in Dongting Lake were investigated in details, and the influences of hydrometeorological elements on water quality variations were analyzed. The results show that the established empirical model can accurately estimate TP with coefficient (R-2) >= 0.70, root mean square error (RMSE) <= 0.057 mg/L, mean relative error (MRE) <= 0.23 and TN with R-2 >= 0.73, RMSE <= 0.48 mg/L and MRE <= 0.20. From 1996 to 2021, TP in Dongting Lake showed a downward trend and TN showed an upward trend, while the summer value was much higher than the other seasons. Furthermore, the influencing factors on TP and TN variations were investigated and discussed. Between 1996 and 2003, the main contributors to the change of water quality in Dongting Lake were external inputs such as water level and flow. The significant changes in water quantity and sediment characteristics following the operation of the Three Gorges Dam (TGD) in 2003 also had an impact on the water quality in Dongting Lake. [Zhang, Yuanyuan; Jin, Shuanggen; Wang, Ning] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China; [Jin, Shuanggen; Zhao, Jiarui] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; [Jin, Shuanggen] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China; [Guo, Hongwei] Nankai Univ, Coll Environm Sci & Engn, Sino Canada Joint R&D Ctr Water & Environm Safety, Tianjin 300457, Peoples R China; [Pellikka, Petri] Univ Helsinki, Dept Geosci & Geog, FI-00014 Helsinki, Finland; [Pellikka, Petri] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS; Henan Polytechnic University; Nankai University; University of Helsinki; Wuhan University Jin, SG (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China.;Jin, SG (corresponding author), Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China.;Jin, SG (corresponding author), Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China. sgjin@shao.ac.cn Jin, Shuanggen/B-8094-2008 Jin, Shuanggen/0000-0002-5108-4828; Pellikka, Petri/0000-0002-5996-9268; Wang, Ning/0000-0002-9933-0725 Strategic Priority Research Program Project of the Chinese Academy of Sciences [XDA23040100]; Jiangsu Natural Resources Development Special Project [JSZRHYKJ202002] Strategic Priority Research Program Project of the Chinese Academy of Sciences; Jiangsu Natural Resources Development Special Project The work was supported by the Strategic Priority Research Program Project of the Chinese Academy of Sciences (Grant No. XDA23040100) and Jiangsu Natural Resources Development Special Project (Grant No. JSZRHYKJ202002). 61 0 0 17 17 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. NOV 2022.0 14.0 22 5648 10.3390/rs14225648 0.0 28 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 6K8OY gold, Green Submitted 2023-03-23 WOS:000887755300001 0
J Zheng, XR; Jia, JX; Guo, SX; Chen, JS; Sun, LY; Xiong, YF; Xu, WN Zheng, Xiaorou; Jia, Jianxin; Guo, Shanxin; Chen, Jinsong; Sun, Luyi; Xiong, Yingfei; Xu, Wenna Full Parameter Time Complexity (FPTC): A Method to Evaluate the Running Time of Machine Learning Classifiers for Land Use/Land Cover Classification IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Prediction algorithms; Support vector machines; Time complexity; Training; Remote sensing; Radio frequency; Task analysis; Algorithm running time; full parameter time complexity (FPTC); land use; land cover (LULC) classification; Sentinel-2a; traditional time complexity (TTC) ALGORITHMS In emergency responses to natural disasters, actionable information provided by remote sensing images is crucial to help emergencymanagers become aware of the situation and assess the magnitude of the damage. Without the accurate prediction of time consumption, choosing an algorithm for land use/land cover (LULC) classification under these emergency circumstances could be blind and subjective. Here, we proposed a full parameter time complexity (FPTC) analysis and the corresponding coefficient. to estimate the actual running time of the LULC classification without actually running the code. The FPTC of five general algorithms is derived in this article. After derivation, the FPTC of k-nearest neighbors (kNN) is F(nv + nlog(2) u), the FPTC of logistic regression (LR) is F(Qm(2)vn), the FPTC of classification and regression tree (CART) is F((m+ 1)nvlog(2)n), the FPTC of random forest (RF) is F(s(m+ 1)nvlog(2)n), and the FPTC of support vector machine (SVM) is F(m(2)Qv (n + k)). The results show a strong linear relationship between the actual running time and FPTC [R-squared: kNN(0.991), LR(0.997), CART (0.999), RF (1.000), and SVM (0.999)], with different data size. The average root-mean-squared error between the real running time and the estimated running time is 3.34 s, which demonstrates the effectiveness of FPTC. Combining FPTCwith the corresponding coefficient., the running time of the classification can be precisely predicted, which will help emergency managers quickly choose algorithms in response to natural disasters with available remote sensing data and limited time. [Zheng, Xiaorou; Jia, Jianxin; Guo, Shanxin; Chen, Jinsong; Sun, Luyi; Xiong, Yingfei; Xu, Wenna] Chinese Acad Sci, Ctr Geospatial Informat, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China; [Zheng, Xiaorou; Xiong, Yingfei; Xu, Wenna] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Jia, Jianxin; Guo, Shanxin; Chen, Jinsong; Sun, Luyi] Shenzhen Engn Lab Ocean Environm Big Data Anal &, Shenzhen 518055, Peoples R China; [Jia, Jianxin] Finnish Geospatial Res Inst, Dept Photogrammetry & Remote Sensing, Krikkonummi 02430, Finland Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI) Guo, SX (corresponding author), Chinese Acad Sci, Ctr Geospatial Informat, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China.;Guo, SX (corresponding author), Shenzhen Engn Lab Ocean Environm Big Data Anal &, Shenzhen 518055, Peoples R China. xiaorou.zheng@siat.ac.cn; jx.jia@siat.ac.cn; sx.guo@siat.ac.cn; js.chen@siat.ac.cn; ly.sun@siat.ac.cn; yf.xiong@siat.ac.cn; wn.xu@siat.ac.cn Jia, Jianxin/AAX-7969-2021; Guo, Shanxin/AAV-6414-2021; Sun, Luyi/AAM-1855-2020 Sun, Luyi/0000-0003-4575-0836; Guo, Shanxin/0000-0001-8911-0166 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030301]; National Key Research and Development Program of China [2017YFB0504203]; Natural Science Foundation of China Project [41601212, 41801360, 41771403, 41801358]; Fundamental Research Foundation of Shenzhen Technology and Innovation Council [KCXFZ202002011006298] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); National Key Research and Development Program of China; Natural Science Foundation of China Project(National Natural Science Foundation of China (NSFC)); Fundamental Research Foundation of Shenzhen Technology and Innovation Council This work was supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA19030301, in part by the National Key Research and Development Program of China under Grant 2017YFB0504203, in part by the Natural Science Foundation of China Project under Grant 41601212, Grant 41801360, Grant 41771403, and Grant 41801358, and in part by the Fundamental Research Foundation of Shenzhen Technology and Innovation Council under Grant KCXFZ202002011006298. 35 11 11 1 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 2222 2235 10.1109/JSTARS.2021.3050166 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology QG1UD gold 2023-03-23 WOS:000617374800006 0
J Takac, M; Knott, A; Stokes, S Takac, Martin; Knott, Alistair; Stokes, Stephanie What can Neighbourhood Density effects tell us about word learning? Insights from a connectionist model of vocabulary development JOURNAL OF CHILD LANGUAGE English Article In this paper, we investigate the effect of neighbourhood density (ND) on vocabulary size in a computational model of vocabulary development. A word has a high ND if there are many words phonologically similar to it. High ND words are more easily learned by infants of all abilities (e.g. Storkel, 2009; Stokes, 2014). We present a neural network model that learns general phonotactic patterns in the exposure language, as well as specific word forms and, crucially, mappings between word meanings and word forms. The network is faster at learning frequent words, and words containing high-probability phoneme sequences, as human word learners are, but, independently of this, the network is also faster at learning words with high ND, and, when its capacity is reduced, it learns high ND words in preference to other words, similarly to late talkers. We analyze the model and propose a novel explanation of the ND effect, in which word meanings play an important role in generating word-specific biases on general phonological trajectories. This explanation leads to a new prediction about the origin of the ND effect in infants. [Takac, Martin] Comenius Univ, Bratislava, Slovakia; [Takac, Martin; Knott, Alistair] Univ Otago, Dunedin, New Zealand; [Stokes, Stephanie] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China Comenius University Bratislava; University of Otago; University of Hong Kong Takac, M (corresponding author), Comenius Univ, Ctr Cognit Sci, Bratislava 84248, Slovakia. takac@ii.fmph.uniba.sk Takáč, Martin/T-4443-2018 Takáč, Martin/0000-0002-3956-962X; Stokes, Stephanie Fay/0000-0002-1200-8064; Knott, Alistair/0000-0003-3036-4668 Marsden fund of New Zealand [13-UOO-048]; Slovak VEGA agency [1/0898/14] Marsden fund of New Zealand(Royal Society of New ZealandMarsden Fund (NZ)); Slovak VEGA agency(Vedecka grantova agentura MSVVaS SR a SAV (VEGA)) We are grateful to the Marsden fund of New Zealand for Grant 13-UOO-048 and Slovak VEGA agency for grant 1/0898/14 ( Martin Takac). Big thanks to Jen Hay and Pat LaShell for the statistical analyses in this paper. We would also like to thank Igor Farkas for valuable discussions on neural network issues. We also thank the anonymous reviewers of this paper for their numerous helpful comments. Address for correspondence: Martin Takac, Comenius University - Centre for Cognitive Science, Mlynska dolina, Bratislava 84248, Slovakia. e-mail: takac@ii.fmph.uniba.sk 34 6 6 0 11 CAMBRIDGE UNIV PRESS NEW YORK 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA 0305-0009 1469-7602 J CHILD LANG J. Child Lang. MAR 2017.0 44.0 2 346 379 10.1017/S0305000916000052 0.0 34 Psychology, Developmental; Linguistics; Psychology, Experimental Social Science Citation Index (SSCI) Psychology; Linguistics EQ6QO 26884360.0 2023-03-23 WOS:000398208100004 0
C Chen, Q; Liu, Z; Zhang, Y; Fu, KR; Zhao, QJ; Du, HW Assoc Advancement Artificial Intelligence Chen, Qian; Liu, Ze; Zhang, Yi; Fu, Keren; Zhao, Qijun; Du, Hongwei RGB-D Salient Object Detection via 3D Convolutional Neural Networks THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence FEB 02-09, 2021 ELECTR NETWORK Assoc Advancement Artificial Intelligence IMAGE; FUSION; MODEL RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly guarantees sufficient cross-modal fusion ability. In this paper, we make the first attempt in addressing RGB-D SOD through 3D convolutional neural networks. The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams. Specifically, RD3D first conducts pre-fusion across RGB and depth modalities through an inflated 3D encoder, and later provides in-depth feature fusion by designing a 3D decoder equipped with rich back-projection paths (RBPP) for leveraging the extensive aggregation ability of 3D convolutions. With such a progressive fusion strategy involving both the encoder and decoder, effective and thorough interaction between the two modalities can be exploited and boost the detection accuracy. Extensive experiments on six widely used benchmark datasets demonstrate that RD3D performs favorably against 14 state-of-the-art RGB-D SOD approaches in terms of four key evaluation metrics. Our code will be made publicly available: https://github.com/PPOLYpubki/RD3D. [Chen, Qian; Liu, Ze; Du, Hongwei] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei, Anhui, Peoples R China; [Zhang, Yi] Inst Natl Sci Appl Rennes, Rennes, France; [Fu, Keren; Zhao, Qijun] Sichuan Univ, Coll Comp Sci, Chengdu, Sichuan, Peoples R China; [Fu, Keren; Zhao, Qijun] Sichuan Univ, Natl Key Lab Fundamental Sci Synthet Vis, Chengdu, Sichuan, Peoples R China Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universite de Rennes; Sichuan University; Sichuan University Fu, KR (corresponding author), Sichuan Univ, Coll Comp Sci, Chengdu, Sichuan, Peoples R China.;Fu, KR (corresponding author), Sichuan Univ, Natl Key Lab Fundamental Sci Synthet Vis, Chengdu, Sichuan, Peoples R China. poly@mail.ustc.edu.cn; liuze@mail.ustc.edu.cn; yi.zhang1@insa-rennes.fr; fkrsuper@scu.edu.cn; qjzhao@scu.edu.cn; duhw@ustc.edu.cn Fu, Keren/HPG-4742-2023 Fu, Keren/0000-0002-3195-2077 NSFC [61703077, 61773270, 61971005]; Fundamental Research Funds for the Central Universities [YJ201755] NSFC(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the NSFC, under No. 61703077, 61773270, 61971005, the Fundamental Research Funds for the Central Universities No. YJ201755. 62 41 41 1 5 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-866-4 AAAI CONF ARTIF INTE 2021.0 35.0 1063 1071 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Education & Educational Research BR9ZM 2023-03-23 WOS:000680423501017 0
J Tian, SZ; Li, GL; Ning, D; Jing, K; Xu, YW; Yang, Y; Fullwood, MJ; Yin, PF; Huang, GY; Plewczynski, D; Zhai, JX; Dai, ZW; Chen, W; Zheng, MZ Tian, Simon Zhongyuan; Li, Guoliang; Ning, Duo; Jing, Kai; Xu, Yewen; Yang, Yang; Fullwood, Melissa J.; Yin, Pengfei; Huang, Guangyu; Plewczynski, Dariusz; Zhai, Jixian; Dai, Ziwei; Chen, Wei; Zheng, Meizhen MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification BRIEFINGS IN BIOINFORMATICS English Article multi-way chromatin interactions; single-molecule; micro-domains; visualization; machine learning; chromatin extrusion; super-enhancers GENOME; ORGANIZATION; ALGORITHMS; PRINCIPLES The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure. [Tian, Simon Zhongyuan; Ning, Duo; Jing, Kai; Xu, Yewen; Yang, Yang; Yin, Pengfei; Huang, Guangyu; Zhai, Jixian; Dai, Ziwei; Zheng, Meizhen] Southern Univ Sci & Technol, Sch Life Sci, Shenzhen, Peoples R China; [Li, Guoliang] Huazhong Agr Univ, Coll Informat, Wuhan, Peoples R China; [Fullwood, Melissa J.] Natl Univ Singapore, Canc Sci Inst Singapore, Singapore, Singapore; [Fullwood, Melissa J.] Nanyang Technol Univ, Sch Biol Sci, Singapore, Singapore; [Fullwood, Melissa J.] ASTAR, Inst Mol & Cell Biol, Singapore, Singapore; [Plewczynski, Dariusz] Univ Warsaw Technol, Lab Funt & Struct & Coputat Genom, Fac Math & Infomat Sci, Warsaw, Poland; [Plewczynski, Dariusz] Univ Warsaw, Lab Funt & Struct & Coputat Genom, Ctr New Technol, Warsaw, Poland; [Chen, Wei] Southern Univ Sci Technol, Sch Life Sci, Chair Dept Biol, Syst Biol, Shenzhen, Peoples R China Southern University of Science & Technology; Huazhong Agricultural University; National University of Singapore; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Molecular & Cell Biology (IMCB); University of Warsaw; Southern University of Science & Technology Tian, SZ; Chen, W; Zheng, MZ (corresponding author), Southern Univ Sci Technol, Shenzen Key Lab Gene Regulat & Syst Biol, Dept Biol, Sch Life Sci, Shenzhen 518055, Peoples R China. tianzy3@sustech.edu.cn; chenw@sustech.edu.cn; zhengmz@sustech.edu.cn Li, Guoliang/D-8014-2012; Zhai, Jixian/HCH-9272-2022 Li, Guoliang/0000-0003-1601-6640; zheng, meizhen/0000-0001-5569-1812 National Natural Science Foundation of China [32170644]; Shenzhen Innovation Committee of Science and Technology [ZDSYS20200811144002008]; National Research Foundation Singapore; Singapore Ministry of Education; Ministry of Education Tier II grant [T2EP30120-0020]; Warsaw University of Technology within the Excellence Initiative-Research University(IDUB) programme; Polish National Science Centre [2019/35/O/ST6/02484, 2020/37/B/NZ2/03757]; EU National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shenzhen Innovation Committee of Science and Technology; National Research Foundation Singapore(National Research Foundation, Singapore); Singapore Ministry of Education(Ministry of Education, Singapore); Ministry of Education Tier II grant; Warsaw University of Technology within the Excellence Initiative-Research University(IDUB) programme; Polish National Science Centre; EU(European Commission) National Natural Science Foundation of China (32170644); Shenzhen Innovation Committee of Science and Technology (ZDSYS20200811144002008). M.J.F. is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative and by a Ministry of Education Tier II grant awarded to M.J.F. (T2EP30120-0020). D.P. is co-supported by Warsaw University of Technology within the Excellence Initiative-Research University (IDUB) programme, co-supported by Polish National Science Centre (2019/35/O/ST6/02484 and 2020/37/B/NZ2/03757) and EU-funded the Marie Sklodowska-Curie action (MSCA) Innovative Training Network. The authors are grateful to Dr Yijun Ruan for suggestions on manuscript organization and Dr Daniel Capurso for suggestions on the improvement of MCI-view functions. The authors thank Dr Minji Kim for editing and polishing this manuscript. 49 1 1 5 7 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. NOV 2022.0 23.0 6 10.1093/bib/bbac380 0.0 SEP 2022 14 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology 8A4VG 36094071.0 Green Submitted 2023-03-23 WOS:000852754600001 0
C Kuang, K; Zhang, L; Li, JY; Li, HW; Chen, JJ; Du, B; Yang, JC Wang, L; Dou, Q; Fletcher, PT; Speidel, S; Li, S Kuang, Kaiming; Zhang, Li; Li, Jingyu; Li, Hongwei; Chen, Jiajun; Du, Bo; Yang, Jiancheng What Makes for Automatic Reconstruction of Pulmonary Segments MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT I Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 18-22, 2022 Singapore, SINGAPORE MICCAI Soc Pulmonary segments; Surface reconstruction; Implicit fields ANATOMY 3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. In this paper, we investigate what makes for automatic reconstruction of pulmonary segments. First and foremost, we formulate, clinically and geometrically, the anatomical definitions of pulmonary segments, and propose evaluation metrics adhering to these definitions. Second, we propose ImPulSe (Implicit Pulmonary Segment), a deep implicit surface model designed for pulmonary segment reconstruction. The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing. Compared with canonical segmentation methods, ImPulSe outputs continuous predictions of arbitrary resolutions with higher training efficiency and fewer parameters. Lastly, we experiment with different network inputs to analyze what matters in the task of pulmonary segment reconstruction. Our code is available at https://github.com/M3DV/ImPulSe. [Kuang, Kaiming; Zhang, Li; Chen, Jiajun; Yang, Jiancheng] Dianei Technol, Shanghai, Peoples R China; [Yang, Jiancheng] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Li, Jingyu; Du, Bo] Wuhan Univ, Wuhan, Hubei, Peoples R China; [Li, Hongwei] Tech Univ Munich, Munich, Germany; [Yang, Jiancheng] Ecole Polytech Fed Lausanne, Lausanne, Switzerland Shanghai Jiao Tong University; Wuhan University; Technical University of Munich; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne Yang, JC (corresponding author), Dianei Technol, Shanghai, Peoples R China.;Yang, JC (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China.;Yang, JC (corresponding author), Ecole Polytech Fed Lausanne, Lausanne, Switzerland. jekyll4168@sjtu.edu.cn Yang, Jiancheng/HJG-5773-2022; Li, Hongwei Bran/HJH-5317-2023 Yang, Jiancheng/0000-0003-4455-7145; Li, Hongwei Bran/0000-0002-5328-6407 National Science Foundation of China [82071990, 61976238]; Swiss National Science Foundation National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Swiss National Science Foundation(Swiss National Science Foundation (SNSF)) This work was supported in part by National Science Foundation of China (82071990, 61976238). This work was also supported in part by a Swiss National Science Foundation grant. We would like to thank Lei Liang for his generous help in proofreading, and the anonymous (meta-)reviewers for their valuable comments. 34 0 0 3 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-16431-6; 978-3-031-16430-9 LECT NOTES COMPUT SC 2022.0 13431.0 495 505 10.1007/978-3-031-16431-6_47 0.0 11 Computer Science, Interdisciplinary Applications; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging BU0DN Green Submitted 2023-03-23 WOS:000867524300047 0
J Peng, J; Xia, CY; Xu, YW; Li, XJ; Wu, X; Han, X; Chen, XL; Chen, YC; Cui, Z Peng, Jing; Xia, Chaoyang; Xu, Yuanwei; Li, Xiaojie; Wu, Xi; Han, Xiao; Chen, Xinlai; Chen, Yucheng; Cui, Zhe A Multi-Task Network for Cardiac Magnetic Resonance Image Segmentation and Classification INTELLIGENT AUTOMATION AND SOFT COMPUTING English Article Multi-task; cardiac magnetic resonance imaging; convolutional neural networks; image classification; semantic segmentation Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder-decoder structure that obtains high-and low-level features at the same time. The encoder employs a feature pyramid module (FPM) and dense atrous convolution (DAC) to extract features from images with variable scales for classification. Meanwhile, the decoder leverages the subpixel layer to recover spatial information caused by downsampling in the encoder for segmentation. The approach was verified using 654 magnetic resonance images. It achieved a Dice similarity coefficient (DSC) metric of 82.14% on segmentation and a classification accuracy of 95.72%, with an area under the receiver operating characteristic curve (AUC) of 97.88%. The proposed method can aid in the segmentation of cardiac magnetic images and improve the classification accuracy of cardiopathy. [Peng, Jing; Han, Xiao; Cui, Zhe] Univ Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu 610041, Peoples R China; [Peng, Jing; Xia, Chaoyang; Li, Xiaojie; Wu, Xi] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610225, Peoples R China; [Xu, Yuanwei; Chen, Yucheng] Sichuan Univ, West China Hosp, Dept Cardiol, Chengdu 610041, Peoples R China; [Peng, Jing; Han, Xiao; Cui, Zhe] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Chen, Xinlai; Chen, Yucheng] Dept Expt Rheumatol, NL-6525 GA Nijmegen, Netherlands Chengdu Institute of Computer Application, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chengdu University of Information Technology; Sichuan University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Cui, Z (corresponding author), Univ Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu 610041, Peoples R China.;Cui, Z (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. cuizhe@casit.com.cn Sichuan Science and Technology Program [2019JDRC0077, 2020YFG0009, 2019ZDZX0005, 2019JDJQ0002]; 1.3.5 Project for discipline of excellence, West China Hospital, Sichuan University [ZYJC18013, Z2018A08] Sichuan Science and Technology Program; 1.3.5 Project for discipline of excellence, West China Hospital, Sichuan University This work was supported by the Sichuan Science and Technology Program (Grant No. 2019JDRC0077, 2020YFG0009, 2019ZDZX0005, and 2019JDJQ0002) , and the 1.3.5 Project for discipline of excellence, West China Hospital, Sichuan University (ZYJC18013 and Z2018A08) . 32 3 3 2 10 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1079-8587 2326-005X INTELL AUTOM SOFT CO Intell. Autom. Soft Comput. 2021.0 30.0 1 259 272 10.32604/iasc.2021.016749 0.0 14 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science TR9KY hybrid 2023-03-23 WOS:000679278600018 0
J Zhong, CM; Malinen, M; Miao, DQ; Franti, P Zhong, Caiming; Malinen, Mikko; Miao, Duoqian; Franti, Pasi A fast minimum spanning tree algorithm based on K-means INFORMATION SCIENCES English Article Minimum spanning tree; Clustering; Manifold learning; K-means HIGH-DIMENSIONAL DATA; CLUSTER VALIDITY; SPACE; IMPLEMENTATION; SEGMENTATION; COMPLEXITY; DESIGN; GRAPHS; FOREST Minimum spanning trees (MSTs) have long been used in data mining, pattern recognition and machine learning. However, it is difficult to apply traditional MST algorithms to a large dataset since the time complexity of the algorithms is quadratic. In this paper, we present a fast MST (FMST) algorithm on the complete graph of N points. The proposed algorithm employs a divide-and-conquer scheme to produce an approximate MST with theoretical time complexity of O(N-1.5), which is faster than the conventional MST algorithms with O(N-2). It consists of two stages. In the first stage, called the divide-and-conquer stage, K-means is employed to partition a dataset into root N clusters. Then an exact MST algorithm is applied to each cluster and the produced root N MSTs are connected in terms of a proposed criterion to form an approximate MST. In the second stage, called the refinement stage, the clusters produced in the first stage form root N - 1 neighboring pairs, and the dataset is repartitioned into root N - 1 clusters with the purpose of partitioning the neighboring boundaries of a neighboring pair into a cluster. With the root N - 1 clusters, another approximate MST is constructed. Finally, the two approximate MSTs are combined into a graph and a more accurate MST is generated from it. The proposed algorithm can be regarded as a framework, since any exact MST algorithm can be incorporated into the framework to reduce its running time. Experimental results show that the proposed approximate MST algorithm is computationally efficient, and the approximation is close to the exact MST so that in practical applications the performance does not suffer. (C) 2014 Elsevier Inc. All rights reserved. [Zhong, Caiming] Ningbo Univ, Coll Sci & Technol, Ningbo 315211, Zhejiang, Peoples R China; [Malinen, Mikko; Franti, Pasi] Univ Eastern Finland, Sch Comp, FIN-80101 Joensuu, Finland; [Miao, Duoqian] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China Ningbo University; University of Eastern Finland; Tongji University Zhong, CM (corresponding author), Ningbo Univ, Coll Sci & Technol, Ningbo 315211, Zhejiang, Peoples R China. zhongcaiming@nbu.edu.cn Miao, Duoqian/L-7378-2019 Natural Science Foundation of China [61175054]; Center for International Mobility (CIMO); K.C. Wong Magna Fund in Ningbo University Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Center for International Mobility (CIMO); K.C. Wong Magna Fund in Ningbo University This work was partially supported by the Natural Science Foundation of China (No. 61175054), the Center for International Mobility (CIMO), and sponsored by K.C. Wong Magna Fund in Ningbo University. 54 53 59 2 53 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. FEB 20 2015.0 295.0 1 17 10.1016/j.ins.2014.10.012 0.0 17 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science AW8XP Green Published 2023-03-23 WOS:000346543000001 0
J Yang, ZX; Ye, Q; Stoter, J; Nan, LL Yang, Zexin; Ye, Qin; Stoter, Jantien; Nan, Liangliang Enriching Point Clouds with Implicit Representations for 3D Classification and Segmentation REMOTE SENSING English Article point cloud; semantic segmentation; object classification; implicit representation Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit representations due to the discrepancy in data structures between implicit fields and point clouds. In this work, we propose a new point cloud representation by integrating the 3D Cartesian coordinates with the intrinsic geometric information encapsulated in its implicit field. Specifically, we parameterize the continuous unsigned distance field around each point into a low-dimensional feature vector that captures the local geometry. Then we concatenate the 3D Cartesian coordinates of each point with its encoded implicit feature vector as the network input. The proposed method can be plugged into an existing network architecture as a module without trainable weights. We also introduce a novel local canonicalization approach to ensure the transformation-invariance of encoded implicit features. With its local mechanism, our implicit feature encoding module can be applied to not only point clouds of single objects but also those of complex real-world scenes. We have validated the effectiveness of our approach using five well-known point-based deep networks (i.e., PointNet, SuperPoint Graph, RandLA-Net, CurveNet, and Point Structuring Net) on object-level classification and scene-level semantic segmentation tasks. Extensive experiments on both synthetic and real-world datasets have demonstrated the effectiveness of the proposed point representation. [Yang, Zexin; Ye, Qin] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Yang, Zexin; Stoter, Jantien; Nan, Liangliang] Delft Univ Technol, 3D Geoinformat Res Grp, NL-2628 BL Delft, Netherlands Tongji University; Delft University of Technology Ye, Q (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. yeqin@tongji.edu.cn Stoter, Jantien/0000-0002-1393-7279; Yang, Zexin/0000-0001-9047-6691; Nan, Liangliang/0000-0002-5629-9975 55 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JAN 2023.0 15.0 1 61 10.3390/rs15010061 0.0 21 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 7P9ZQ gold, Green Published 2023-03-23 WOS:000909057100001 0
C Chang, V; Wang, Z; Xu, QA; Golightly, L; Liu, B; Arami, M Baudier, P; Arami, M; Chang, V Chang, Victor; Wang, Zhi; Xu, Qianwen Ariel; Golightly, Lewis; Liu, Ben S.; Arami, Mitra Smart Home based on Internet of Things and Ethical Issues PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON FINANCE, ECONOMICS, MANAGEMENT AND IT BUSINESS (FEMIB) English Proceedings Paper 3rd International Conference on Finance, Economics, Management and IT Business (FEMIB) APR 25-26, 2021 ELECTR NETWORK INSTICC Smart Home; Ethics; Internet of Things; Data Analytics BIG DATA; CHALLENGES With the growing popularity of the Internet of Things, smart home applications are developing gradually and bring convenience to users' lives. In this paper, first, an overview of IoT and widely agreed and accepted three-layer architecture of IoT are introduced. Next, this paper discusses the basic features of the smart home and data analytics in a smart home with the benefits and analysis process. Security and privacy are the key ethical problems in the smart home, such as security attack, analysis of non-sensitive data, improper information collection and data abuse. Additionally, the user perception of privacy issues is included in this paper. Furthermore, this paper recommends suitable suggestions for improving ethical issues. [Chang, Victor; Xu, Qianwen Ariel; Golightly, Lewis] Teesside Univ, Sch Comp Engn & Digital Technol, Artificial Intelligence & Informat Syst Res Grp, Middlesbrough, Cleveland, England; [Wang, Zhi; Xu, Qianwen Ariel] Xian Jiaotong Liverpool Univ, IBSS, Suzhou, Peoples R China; [Liu, Ben S.] Quinnipiac Univ, Dept Mkt, Hamden, CT 06518 USA; [Arami, Mitra] EM Normandie Business Sch, Le Havre, France University of Teesside; Xi'an Jiaotong-Liverpool University; Quinnipiac University Chang, V (corresponding author), Teesside Univ, Sch Comp Engn & Digital Technol, Artificial Intelligence & Informat Syst Res Grp, Middlesbrough, Cleveland, England. Chang, Victor/AAC-7582-2019 Chang, Victor/0000-0002-8012-5852; Liu, Ben Shaw-Ching/0000-0002-2950-9607; Golightly, Lewis/0000-0002-2189-335X VC Research [VCR 0000095] VC Research This work is supported by VC Research (grant number VCR 0000095). 25 0 0 5 5 SCITEPRESS SETUBAL AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL 978-989-758-507-4 2021.0 57 64 10.5220/0010178100570064 0.0 8 Business; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) Business & Economics; Computer Science BT3MZ hybrid 2023-03-23 WOS:000821067300005 0
J Chen, Z; Guo, SZ; Wang, J; Li, YB; Lu, ZH Chen, Zhe; Guo, Shize; Wang, Jian; Li, Yubai; Lu, Zhonghai Toward FPGA Security in IoT: A New Detection Technique for Hardware Trojans IEEE INTERNET OF THINGS JOURNAL English Article Electromagnetic (EM) side channel; field programmable gate array (FPGA); hardware Trojan (HT) detection; Internet of Things (IoT) security INTERNET; THINGS Nowadays, field programmable gate array (FPGA) has been widely used in Internet of Things (IoT) since it can provide flexible and scalable solutions to various IoT requirements. Meanwhile, hardware Trojan (HT), which may lead to undesired chip function or leak sensitive information, has become a great challenge for FPGA security. Therefore, distinguishing the Trojan-infected FPGAs is quite crucial for reinforcing the security of IoT. To achieve this goal, we propose a clock-tree-concerned technique to detect the HTs on FPGA. First, we present an experimental framework which helps us to collect the electromagnetic (EM) radiation emitted by FPGA clock tree. Then, we propose a Trojan identifying approach which extracts the mathematical feature of obtained EM traces, i.e., 2-D principal component analysis (2DPCA) in this paper, and automatically isolates the Trojan-infected FPGAs from the Trojan-free ones by using a BP neural network. Finally, we perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid in detecting HTs on FPGA. Specifically, for the trust-hub benchmarks, we can find out the FPGA with always on Trojans (100% detection rate) while identifying the triggered Trojans with high probability (by up to 92%). In addition, we give a thorough discussion on how the experimental setup, such as probe step size, scanning area, and chip ambient temperature, affects the Trojan detection rate. [Chen, Zhe; Guo, Shize; Wang, Jian; Li, Yubai] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-16440 Stockholm, Sweden University of Electronic Science & Technology of China; Royal Institute of Technology Wang, J (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China. wangjian3630@uestc.edu.cn Lu, Zhonghai/AAP-2154-2020 Lu, Zhonghai/0000-0003-0061-3475 National Natural Science Foundation of China [61671110] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61671110. (Corresponding author: Jian Wang.) 35 18 19 9 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. AUG 2019.0 6.0 4 7061 7068 10.1109/JIOT.2019.2914079 0.0 8 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications IN8VL 2023-03-23 WOS:000478957600108 0
J Zhang, K; Qian, Z; Yang, Y; Chen, M; Zhong, T; Zhu, R; Lv, GN; Yan, JY Zhang, Kai; Qian, Zhen; Yang, Yue; Chen, Min; Zhong, Teng; Zhu, Rui; Lv, Guonian; Yan, Jinyue Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis SUSTAINABLE CITIES AND SOCIETY English Article Ensemble learning; Street view image; Image classification model; Road noise barrier; Sustainable Transport Infrastructure DISPERSION Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-theart deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs). [Zhang, Kai; Qian, Zhen; Yang, Yue; Chen, Min; Zhong, Teng; Lv, Guonian] Nanjing Normal Univ, Minist Educ PRC, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China; [Zhang, Kai; Qian, Zhen; Yang, Yue; Chen, Min; Zhong, Teng; Lv, Guonian] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China; [Zhang, Kai; Qian, Zhen; Yang, Yue; Chen, Min; Zhong, Teng; Lv, Guonian] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China; [Chen, Min] Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China; [Zhu, Rui] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China; [Yan, Jinyue] Malardalen Univ, Sch Business Soc & Engn, S-72123 Vasteras, Sweden; [Yan, Jinyue] KTH Royal Inst Technol, Dept Chem Engn, S-10044 Stockholm, Sweden Nanjing Normal University; Nanjing Normal University; Hong Kong Polytechnic University; Malardalen University; Royal Institute of Technology Chen, M (corresponding author), Nanjing Normal Univ, Sch Geog, 1 Wenyuan Rd, Nanjing 210023, Peoples R China. chenmin0902@163.com Chen, Min/A-8806-2016 Chen, Min/0000-0001-8922-8789; Zhu, Rui/0000-0002-9965-0948; Qian, Zhen/0000-0002-0423-7430 National Natural Science Foundation of China [U1811464]; Strategic Hiring Scheme at the Hong Kong Polytechnic University [P0036221]; General Research Fund [15602619] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Hiring Scheme at the Hong Kong Polytechnic University; General Research Fund This work was supported by the Joint Fund Project of National Natural Science Foundation of China (Grant U1811464), Strategic Hiring Scheme (Grant No. P0036221) at the Hong Kong Polytechnic University, and the General Research Fund (Grant No. 15602619). 64 5 5 6 28 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. MAR 2022.0 78.0 103598 10.1016/j.scs.2021.103598 0.0 DEC 2021 13 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels XU7YN 2023-03-23 WOS:000734475700003 0
J Yang, LR; Cai, JC Yang, Lirong; Cai, Jiacheng A method to identify wet ball mill?s load based on CEEMDAN, RCMDE and SRNN classification MINERALS ENGINEERING English Article Ball mill?s load; CEEMDAN; RCMDE; SRNN Ball mill plays a key role in mineral processing plant, and its load identification for optimal control has great significance for the energy consumption reduction and production efficiency improvement. The vibration signal of ball mill shell contains abundant load information, which can be used to identify ball mill load. However, due to the non-linear and non-stationary characteristics of vibration signals, as well as the heavy background noises, the load identification becomes a challenging task in practice. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), and stacked recurrent neural network (SRNN) is proposed. First, CEEMDAN algorithm is used to decompose the ball mill?s vibration signals and obtain the intrinsic mode function (IMF) components. Then, the sensitive IMF components are selected through the correlation coefficient method, and the signal is reconstructed with the sensitive IMF components. Secondly, the RCMDE of the reconstructed signal is calculated to obtain the load feature vector, and the dimension of the feature vector is reduced by principle component analysis (PCA). Thirdly, the SRNN is applied to establish a load recognition model, taking the feature vector as its input and the load state as its output. The experimental results show encouraging accuracy to apply this approach to recognize the wet ball mill?s load, with a recognition rate of 98.67%. [Yang, Lirong] Jiangxi Univ Sci & Technol, Sch Mech & Elect Engn, Ganzhou 341000, Peoples R China; [Yang, Lirong] Jiangxi Min & Met Electromech Engn Technol Res Ct, Ganzhou 341000, Peoples R China; [Cai, Jiacheng] KTH Royal Inst Technol, Dept Machine Design, S-10044 Stockholm, Sweden Jiangxi University of Science & Technology; Royal Institute of Technology Yang, LR (corresponding author), Jiangxi Univ Sci & Technol, Sch Mech & Elect Engn, Ganzhou 341000, Peoples R China. candy_yang_jx@163.com; jcai@kth.se National Natural Science Foundation of China [51464017]; Education Department of Jiangxi Provincial [GJJ190452] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Education Department of Jiangxi Provincial This work was supported by the National Natural Science Foundation of China [grant number 51464017] and Education Department of Jiangxi Provincial [grant number GJJ190452]. 29 2 3 2 19 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0892-6875 MINER ENG Miner. Eng. MAY 1 2021.0 165.0 106852 10.1016/j.mineng.2021.106852 0.0 MAR 2021 10 Engineering, Chemical; Mineralogy; Mining & Mineral Processing Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mineralogy; Mining & Mineral Processing RH7RG 2023-03-23 WOS:000636410300007 0
J Xiao, Z; Nie, ZY; Song, C; Chronopoulos, AT Xiao, Zheng; Nie, Zhenyu; Song, Chao; Chronopoulos, Anthony Theodore An extended attention mechanism for scene text recognition EXPERT SYSTEMS WITH APPLICATIONS English Article Scene text recognition; Attention on attention; Deep neural network; Encoder-decoder framework Scene text recognition (STR) refers to obtaining text information from natural text images. The task is more challenging than the optical character recognition(OCR) due to the variability of scenes. Attention mechanism, which assigns different weights to each feature vector at each time step, guides the text recognition decoding process. However, when the given query and the key/value are not related, the generated attention result will contain irrelevant information, which could lead the model to give wrong results. In this paper, we propose an extended attention-based framework for STR tasks. In particular, we have integrated an extended attention mechanism named Attention on Attention (AoA), which is able to determine the relevance between attention results and queries, into both the encoder and the decoder of a common text recognition framework. By two separate linear functions, the AoA module generates an information vector and an attention gate using the attention result and the current context. Then AoA adds new attention by applying element-wise multiplication to acquire final attended information. Our method is compared with seven benchmarks over eight datasets. Experimental results show that our method outperforms all the seven benchmarks, by 6.7% and 1.4% than the worst and best works on average. [Xiao, Zheng; Nie, Zhenyu; Song, Chao] Hunan Univ, Coll Informat Sci & Engn, Changsha, Peoples R China; [Chronopoulos, Anthony Theodore] Univ Texas, Dept Comp Sci, San Antonio, TX 78249 USA; [Chronopoulos, Anthony Theodore] Univ Patras, Comp Engn & Informat, Rion 26500, Greece Hunan University; University of Texas System; University of Patras Xiao, Z (corresponding author), Hunan Univ, Coll Informat Sci & Engn, Changsha, Peoples R China. zxiao@hnu.edu.cn; niezhenyu@hnu.edu.cn; song_chao@hnu.edu.cn; anthony.chronopoulos@utsa.edu Song, Chao/0000-0002-8141-8585; Xiao, Zheng/0000-0003-1144-7599 National Natural Science Foundation of China [61872129]; Natural Science Foundation of Hunan Province, China [2019JJ40024] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hunan Province, China(Natural Science Foundation of Hunan Province) Acknowledgments This work is supported by National Natural Science Foundation of China (No. 61872129) and Natural Science Foundation of Hunan Province, China (Grant No. 2019JJ40024) . 45 1 1 8 18 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. OCT 1 2022.0 203.0 117377 10.1016/j.eswa.2022.117377 0.0 MAY 2022 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 1T7UF 2023-03-23 WOS:000804932400004 0
J Avtar, R; Aggarwal, R; Kharrazi, A; Kumar, P; Kurniawan, TA Avtar, Ram; Aggarwal, Ridhika; Kharrazi, Ali; Kumar, Pankaj; Kurniawan, Tonni Agustiono Utilizing geospatial information to implement SDGs and monitor their Progress ENVIRONMENTAL MONITORING AND ASSESSMENT English Article Sustainable development goals; Geospatial data and techniques; Geographic information system; Remote sensing; And indicators OIL-SPILL DETECTION; SUSTAINABLE DEVELOPMENT; HEALTH-CARE; GIS; POVERTY; BIOMASS; IDENTIFICATION; SETTLEMENTS; CHALLENGES; ACCESS It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs. [Avtar, Ram] Hokkaido Univ, Fac Environm Earth Sci, Sapporo, Hokkaido 0600810, Japan; [Aggarwal, Ridhika] United Nations Univ, Inst Adv Study Sustainabil, Tokyo 1508925, Japan; [Kharrazi, Ali] Int Inst Appl Syst Anal, Adv Syst Anal Grp, Schlosspl 1, A-2361 Laxenburg, Austria; [Kharrazi, Ali] CMCC Fdn, Euro Mediterranean Ctr Climate Change, I-30175 Venice, Italy; [Kharrazi, Ali] Ca Foscari Univ Venice, I-30175 Venice, Italy; [Kumar, Pankaj] Inst Global Environm Strategies, Nat Resources & Ecosyst Serv, Hayama, Kanagawa 2400115, Japan; [Kurniawan, Tonni Agustiono] Xiamen Univ, Key Lab Coastal & Wetland Ecosyst, Minist Educ, Coll Environm & Ecol, Xiamen 361102, Fujian, Peoples R China Hokkaido University; United Nations University; International Institute for Applied Systems Analysis (IIASA); Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC); Universita Ca Foscari Venezia; Xiamen University Avtar, R (corresponding author), Hokkaido Univ, Fac Environm Earth Sci, Sapporo, Hokkaido 0600810, Japan. ram@ees.hokudai.ac.jp Kharrazi, Ali/M-4097-2018; Kumar, Pankaj/B-2854-2016; Kurniawan, Tonni Agustiono/B-8172-2008; kumar, Pankaj/HPF-8395-2023; Avtar, Ram/C-7394-2012 Kharrazi, Ali/0000-0002-5881-2568; Kurniawan, Tonni Agustiono/0000-0003-1362-6339; Kurniawan, Kurniawan/0000-0002-3039-0404; Avtar, Ram/0000-0003-3653-5771 Office for Developing Future Research Leaders (L-Station); Hokkaido University; Faculty of Environmental Earth Science Office for Developing Future Research Leaders (L-Station); Hokkaido University; Faculty of Environmental Earth Science This work is supported by the Office for Developing Future Research Leaders (L-Station), Hokkaido University and Faculty of Environmental Earth Science. The authors would like to thank Ashwani Aggarwal, Huynh Vuong ThuMinh, and students of UNU for their support. The authors extend sincere gratitude to the editor and anonymous reviewers for their constructive comments and valuable suggestions. 104 36 36 4 73 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0167-6369 1573-2959 ENVIRON MONIT ASSESS Environ. Monit. Assess. JAN 2020.0 192.0 1 35 10.1007/s10661-019-7996-9 0.0 21 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology KI4HQ 31828438.0 Green Accepted 2023-03-23 WOS:000511311100020 0
J Lyu, Y; Yang, MY; Vosselman, G; Xia, GS Lyu, Ye; Yang, Michael Ying; Vosselman, George; Xia, Gui-Song Video object detection with a convolutional regression tracker ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Video object detection; Plug & Play; Convolutional regression tracker; Deep learning; Tracking Video object detection is a fundamental research task for scene understanding. Compared with object detection in images, object detection in videos has been less researched due to shortage of labelled video datasets. As frames in a video clip are highly correlated, a larger quantity of video labels are needed to have good data variation, which are not always available as the labels are much more expensive to attain. Regarding the above-mentioned problem, it is easy to train an image object detector, but not always possible to train a video object detector if there are insufficient video labels for certain classes. In order to deal with this problem and improve the performance of an image object detector for the classes without video labels, we propose to augment a well-trained image object detector with an efficient and effective class-agnostic convolutional regression tracker for the video object detection task. The tracker learns to track objects by reusing the features from the image object detector, which is a light-weighted increment to the detector, with only a slight speed drop for the video object detection task. The performance of our model is evaluated on the large-scale ImageNet VID dataset. Our strategy improves the mean average precision (mAP) score for the image object detector by around 5% and around 3% for the image object detector plus Seq-NMS post-processing. [Lyu, Ye; Yang, Michael Ying; Vosselman, George] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands; [Xia, Gui-Song] Wuhan Univ, Sch Comp Sci, State Key Lab LIESMARS, Wuhan, Hubei, Peoples R China University of Twente; Wuhan University Yang, MY (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands.;Xia, GS (corresponding author), Wuhan Univ, Sch Comp Sci, State Key Lab LIESMARS, Wuhan, Hubei, Peoples R China. michael.yang@utwente.nl; guisong.xia@whu.edu.cn Xia, Gui-Song/HII-9232-2022; Vosselman, George/D-3985-2009; Yang, Michael/AAC-6698-2019 Xia, Gui-Song/0000-0001-7660-6090; Vosselman, George/0000-0001-8813-8028; Yang, Michael/0000-0002-0649-9987 46 5 5 2 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. JUN 2021.0 176.0 139 150 10.1016/j.isprsjprs.2021.04.004 0.0 APR 2021 12 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology SJ4AV Green Published, hybrid 2023-03-23 WOS:000655474600011 0
C Han, WJ; Jiang, T; Li, Y; Schuller, B; Ruan, HB IEEE Han, Wenjing; Jiang, Tao; Li, Yan; Schuller, Bjoern; Ruan, Huabin ORDINAL LEARNING FOR EMOTION RECOGNITION IN CUSTOMER SERVICE CALLS 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING International Conference on Acoustics Speech and Signal Processing ICASSP English Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing MAY 04-08, 2020 Barcelona, SPAIN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Signal Proc Soc speech emotion recognition; ordinal classification; consistent rank logits; VGGish Approaches toward ordinal speech emotion recognition (SER) tasks are commonly based on the categorical classification algorithms, where the rank-order emotions are arbitrarily treated as independent categories. To employ the ordinal information between emotional ranks, we propose to model the ordinal SER tasks under a COnsistent RAnk Logits (CORAL) based deep learning framework. Specifically, a multi-class ordinal SER task is transformed into a series of binary SER sub-tasks predicting whether an utterance's emotion is larger than a rank. All the sub-tasks are jointly solved by one single network with a mislabelling cost defined as the the sum of the individual cross-entropy loss for each sub-task. Having the VGGish as our basic network structure, via minimizing above CORAL based cost, a VGGish-CORAL network is implemented in this contribution. Experimental results on a real-world call center dataset and the widely used IEMOCAP corpus demonstrate the effectiveness of VGGish-CORAL compared to the categorical VGGish. [Han, Wenjing; Jiang, Tao; Li, Yan] Kuaishou Technol Corp, Beijing, Peoples R China; [Schuller, Bjoern] Imperial Coll London, GLAM Grp Language Audio & Mus, London, England; [Schuller, Bjoern] Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, Augsburg, Germany; [Ruan, Huabin] Tsinghua Univ, Sch Life Sci, Beijing, Peoples R China Imperial College London; University of Augsburg; Tsinghua University Ruan, HB (corresponding author), Tsinghua Univ, Sch Life Sci, Beijing, Peoples R China. ruanhuabin@tsinghua.edu.cn National Natural Science Foundation of China [61802226] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Grant No. 61802226). 28 8 8 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1520-6149 978-1-5090-6631-5 INT CONF ACOUST SPEE 2020.0 6494 6498 5 Acoustics; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Acoustics; Engineering BQ7HU Green Submitted 2023-03-23 WOS:000615970406151 0
J Zhong, JP; Liu, ZG; Wang, HR; Liu, WQ; Yang, C; Han, ZW; Nunez, A Zhong, Junping; Liu, Zhigang; Wang, Hongrui; Liu, Wenqiang; Yang, Cheng; Han, Zhiwei; Nunez, Alfredo A Looseness Detection Method for Railway Catenary Fasteners Based on Reinforcement Learning Refined Localization IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Component segmentation; looseness detection; railway catenary fasteners; reinforcement learning (RL) Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [Zhong, Junping; Liu, Zhigang; Liu, Wenqiang; Yang, Cheng; Han, Zhiwei] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China; [Wang, Hongrui; Nunez, Alfredo] Delft Univ Technol, Sect Railway Engn, NL-2628 Delft, Netherlands Southwest Jiaotong University; Delft University of Technology Han, ZW (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China. zhongjunping@my.swjtu.edu.cn; liuzg_cd@126.com; h.wang-8@tudelft.nl; liuwq_2009@126.com; yangc@my.swjtu.edu.cn; zw.han@my.swjtu.edu.cn; a.a.nunezvicencio@tudelft.nl Wang, Hongrui/D-2091-2016; Wang, Hongrui/AAQ-5219-2021; Nunez, Alfredo/B-8671-2009 Wang, Hongrui/0000-0001-7194-4728; Wang, Hongrui/0000-0001-7194-4728; Nunez, Alfredo/0000-0001-5610-6689 National Natural Science Foundation of China [U51977182]; Science and Technology Innovation Talents of Sichuan Science and Technology Plan [2021JDRC0008] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Innovation Talents of Sichuan Science and Technology Plan This work was supported in part by the National Natural Science Foundation of China under Grant U51977182 and in part by the Science and Technology Innovation Talents of Sichuan Science and Technology Plan under Grant 2021JDRC0008. The Associate Editor coordinating the review process was Qiang Miao. 33 0 0 5 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70.0 3518913 10.1109/TIM.2021.3086913 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation SX1YF Green Published 2023-03-23 WOS:000665007000004 0
J Zhang, J; Yang, YI; Noe, F Zhang, Jun; Yang, Yi Isaac; Noe, Frank Targeted Adversarial Learning Optimized Sampling JOURNAL OF PHYSICAL CHEMISTRY LETTERS English Article FREE-ENERGY LANDSCAPES; MOLECULAR-DYNAMICS; MONTE-CARLO; ALGORITHMS Boosting transitions of rare events is critical to simulations of chemical and biophysical dynamic systems in order to close the time scale gaps between theoretical modeling and experiments. We present a novel approach, called targeted adversarial learning optimized sampling (TALOS), to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free-energy barrier is lowered. Combining statistical mechanics and generative learning, TALOS formulates a competing game between a sampling engine and a virtual discriminator, enables unsupervised construction of bias potentials, and seeks for an optimal transport plan that transforms the system into a target. Through multiple experiments, we show that on-the fly training of TALOS benefits from the state-of-art optimization techniques in deep learning and thus is efficient, robust, and interpretable. TALOS is also closely connected to the actor-critic reinforcement learning and hence leads to a new way of flexibly manipulating the many-body Hamiltonian systems. [Zhang, Jun; Noe, Frank] Free Univ Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany; [Yang, Yi Isaac] Swiss Fed Inst Technol, Dept Chem & Appl Biosci, USI Campus,Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland; [Yang, Yi Isaac] USI, Inst Computat Sci, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland; [Yang, Yi Isaac] Inst Syst Biol, Shenzhen Bay Lab, Shenzhen 518055, Peoples R China; [Noe, Frank] Free Univ Berlin, Dept Phys, Arnimallee 6, D-14195 Berlin, Germany Free University of Berlin; Universita della Svizzera Italiana; Shenzhen Bay Laboratory; Free University of Berlin Zhang, J; Noe, F (corresponding author), Free Univ Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany.;Noe, F (corresponding author), Free Univ Berlin, Dept Phys, Arnimallee 6, D-14195 Berlin, Germany. jun.zhang@fu-berlin.de; frank.noe@fu-berlin.de Yang, Yi Isaac/F-3803-2015 Yang, Yi Isaac/0000-0002-5599-0975; Zhang, Jun/0000-0002-8760-6747 Alexander von Humboldt Foundation; European Commission [ERC CoG 772230, ERC-2014-ADG-670227]; Swiss National Science Foundation [NCCR MARVEL 51NF40_141828] Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); European Commission(European CommissionEuropean Commission Joint Research Centre); Swiss National Science Foundation(Swiss National Science Foundation (SNSF)) The authors thank Michele Parrinello, Yi Qin Gao, Omar Valsson, Giovanni Maria Piccini, and Haiyang Niu for useful discussion. Molecular dynamic simulations were carried out on the Euler Cluster at ETH Ziirich. This research was supported by Alexander von Humboldt Foundation, the European Commission (ERC CoG 772230 ScaleCell and ERC-2014-ADG-670227 VARMET), and the Swiss National Science Foundation (NCCR MARVEL 51NF40_141828). 41 27 27 4 37 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 1948-7185 J PHYS CHEM LETT J. Phys. Chem. Lett. OCT 3 2019.0 10.0 19 5791 5797 10.1021/acs.jpclett.9b02173 0.0 8 Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics JC3PB 31522495.0 2023-03-23 WOS:000489189500017 0
J Wu, QX; McGinnity, TM; Maguire, L; Cai, RT; Chen, MG Wu, QingXiang; McGinnity, T. M.; Maguire, Liam; Cai, Rongtai; Chen, Meigui A visual attention model based on hierarchical spiking neural networks NEUROCOMPUTING English Article; Proceedings Paper 7th International Conference on Intelligent Computing (ICIC) AUG 11-14, 2011 Zhengzhou, PEOPLES R CHINA IEEE Computat Intelligence Soc,Int Neural Network Soc,Natl Sci Fdn China Visual attention; Spiking neural network; Receptive field; Visual system MECHANISMS Based on the information processing functionalities of spiking neurons, hierarchical spiking neural networks are proposed to simulate visual attention. Using spiking neural networks inspired by the visual system, an image can be decomposed into multiple visual image components. Based on specific visual image components and image features, a visual attention system is proposed to extract attention areas according to top-down volition-controlled signals. The hierarchical spiking neural networks are constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the networks are detailed in this paper. Simulation results show that the attention system is able to perform visual attention of objects based on specific image components or features, and a demonstration shows how the attention system can detect a house in a visual image. Using the proposed saliency index, attention areas of interest can be extracted from spike rate maps of multiple visual pathways, such as ON/OFF colour pathways. According to this visual attention principle, the visual image processing system can quickly focus on specific areas while ignoring other areas. (C) 2012 Elsevier B.V. All rights reserved. [Wu, QingXiang; Cai, Rongtai; Chen, Meigui] Fujian Normal Univ, Sch Phys & OptoElect Technol, Fuzhou 350007, Peoples R China; [Wu, QingXiang; McGinnity, T. M.; Maguire, Liam] Univ Ulster, Sch Comp & Intelligent Syst, Intelligent Syst Res Ctr, Magee BT48 7JL, Londonderry, North Ireland Fujian Normal University; Ulster University Wu, QX (corresponding author), Fujian Normal Univ, Sch Phys & OptoElect Technol, Fuzhou 350007, Peoples R China. q.wu@ulster.ac.uk; tm.mcginnity@ulster.ac.uk; lp.maguire@ulster.ac.uk; rtcai@fjnu.edu.cn; mgchen@fjnu.edu.cn Maguire, Liam/0000-0001-6153-3298; McGinnity, Thomas Martin/0000-0002-9897-4748 29 27 29 3 27 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 20 2013.0 116.0 SI 3 12 10.1016/j.neucom.2012.01.046 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science 172AI 2023-03-23 WOS:000320971900002 0
J Tariq, M; Ahmad, H; Sahoo, SK; Kashuri, A; Nofal, TA; Hsu, CH Tariq, Muhammad; Ahmad, Hijaz; Sahoo, Soubhagya Kumar; Kashuri, Artion; Nofal, Taher A.; Hsu, Ching-Hsien Inequalities of Simpson-Mercer-type including Atangana-Baleanu fractional operators and their applications AIMS MATHEMATICS English Article Jensen-Mercer inequality; Simpson's inequality; s-convex functions; Atangana-Baleanu fractional operator; Holder's inequalities; q-digamma functions; Modified Bessel functions HERMITE-HADAMARD INEQUALITIES; CONVEX-FUNCTIONS; CALCULUS Fractional operators with integral inequalities have attracted the interest of several mathematicians. Fractional inequalities are best utilized in mathematical science with their features and wide range of applications in optimization, modeling, engineering and artificial intelligence. In this article, we consider new variants of Simpson-Mercer type inequalities involving the Atangana-Baleanu (A-B) fractional integral operator for s-convex functions. First, an integral identity, which acts as an auxiliary result for the main results is proved in the frame of fractional operator. Employing this new identity, some estimations of Simpson-Mercer type for s-convex functions in the second sense are discussed. In addition, we study various new applications on Modified Bessel functions, special means and q-digamma functions. These applications confirm the effectiveness and validity of the results and also bring a different dimension to the study. [Tariq, Muhammad] Mehran Univ Engn & Technol, Dept Basic Sci & Related Studies, Jamshoro 76062, Pakistan; [Ahmad, Hijaz] Int Telemat Univ Uninettuno, Sect Math, Corso Vittorio Emanuele II 39, I-00186 Rome, Italy; [Sahoo, Soubhagya Kumar] Siksha O Anusandhan Univ, Inst Tech Educ & Res, Dept Math, Bhubaneswar 751030, India; [Kashuri, Artion] Univ Ismail Qemali, Fac Tech & Nat Sci, Dept Math, Vlora 9400, Albania; [Nofal, Taher A.] Taif Univ, Coll Sci, Dept Math, POB 11099, Taif 21944, Saudi Arabia; [Hsu, Ching-Hsien] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan; [Hsu, Ching-Hsien] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan; [Hsu, Ching-Hsien] Foshan Univ, Sch Math & Big Data, Guangdong Hong Kong Macao Joint Lab Intelligent M, Foshan 528000, Peoples R China Mehran University Engineering & Technology; UNINETTUNO; Siksha 'O' Anusandhan University; Taif University; Asia University Taiwan; China Medical University Taiwan; China Medical University Hospital - Taiwan; Foshan University Sahoo, SK (corresponding author), Siksha O Anusandhan Univ, Inst Tech Educ & Res, Dept Math, Bhubaneswar 751030, India. soubhagyakumarsahoo@soa.ac.in Tariq, Muhammad/AFB-8576-2022; Ahmad, Hijaz/H-5958-2018 Ahmad, Hijaz/0000-0002-5438-5407 Taif University Researches Supporting Project, Taif University, Taif, Saudi Arabia [TURSP-2020/031] Taif University Researches Supporting Project, Taif University, Taif, Saudi Arabia The authors received financial support from Taif University Researches Supporting Project number (TURSP-2020/031) , Taif University, Taif, Saudi Arabia. 55 5 5 4 4 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA 2473-6988 AIMS MATH AIMS Math. 2022.0 7.0 8 15159 15181 10.3934/math.2022831 0.0 23 Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 2G3MP gold 2023-03-23 WOS:000813501900002 0
C Wang, G; Manhardt, F; Tombari, F; Ji, XY IEEE COMP SOC Wang, Gu; Manhardt, Fabian; Tombari, Federico; Ji, Xiangyang GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF 6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-toend trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an indepth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://git.io/GDR-Net. [Wang, Gu; Ji, Xiangyang] Tsinghua Univ, BNRist, Beijing, Peoples R China; [Wang, Gu; Manhardt, Fabian; Tombari, Federico] Tech Univ Munich, Munich, Germany; [Tombari, Federico] Google, Mountain View, CA 94043 USA Tsinghua University; Technical University of Munich; Google Incorporated Wang, G (corresponding author), Tsinghua Univ, BNRist, Beijing, Peoples R China.;Wang, G (corresponding author), Tech Univ Munich, Munich, Germany. wangg16@mails.tsinghua.edu.cn; fabian.manhardt@tum.de; tombari@in.tum.de; xyji@tsinghua.edu.cn Wang, Gu/ABG-9917-2021; Wang, Gu/GXW-3328-2022 Wang, Gu/0000-0002-0759-0782; Wang, Gu/0000-0002-0759-0782 China Scholarship Council (CSC) [201906210393]; National Key R&D Program of China [2018AAA0102801] China Scholarship Council (CSC)(China Scholarship Council); National Key R&D Program of China We thank Zhigang Li, Xingyu Liu at Tsinghua University for their helpful discussion, and Nikolas Brasch at Technical University of Munich for proofreading. We also thank anonymous reviewers for their constructive comments. This work was supported in part by China Scholarship Council (CSC) Grant #201906210393, and in part by the National Key R&D Program of China under Grant 2018AAA0102801. 66 32 32 13 20 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 16606 16616 10.1109/CVPR46437.2021.01634 0.0 11 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5UT Green Submitted 2023-03-23 WOS:000742075006081 0
J Wang, YL; Zhang, Q; Astier, HPAG; Nickle, C; Soni, S; Alami, FA; Borrini, A; Zhang, ZY; Honnigfort, C; Braunschweig, B; Leoncini, A; Qi, DC; Han, YM; del Barco, E; Thompson, D; Nijhuis, CA Wang, Yulong; Zhang, Qian; Astier, Hippolyte P. A. G.; Nickle, Cameron; Soni, Saurabh; Alami, Fuad A.; Borrini, Alessandro; Zhang, Ziyu; Honnigfort, Christian; Braunschweig, Bjorn; Leoncini, Andrea; Qi, Dong-Cheng; Han, Yingmei; del Barco, Enrique; Thompson, Damien; Nijhuis, Christian A. Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour NATURE MATERIALS English Article DEVICE To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component. To realize electronic operations beyond the von Neumann bottleneck, a new type of switch that can mimic self-learning is needed. Here, the authors demonstrate all-in-one-place logic and memory operations based on dynamic molecular switch that can emulate brain-like synaptic and Pavlovian response, bringing the field a step closer to molecular-scale hardware. [Wang, Yulong; Zhang, Qian; Astier, Hippolyte P. A. G.; Zhang, Ziyu; Leoncini, Andrea; Han, Yingmei; Nijhuis, Christian A.] Natl Univ Singapore, Dept Chem, Singapore, Singapore; [Nickle, Cameron; del Barco, Enrique] Univ Cent Florida, Dept Phys, Orlando, FL 32816 USA; [Soni, Saurabh; Alami, Fuad A.; Borrini, Alessandro; Nijhuis, Christian A.] Univ Twente, Hybrid Mat Optoelect Grp, Dept Mol & Mat, MESA Inst,Nanotechnol Mol Ctr, Enschede, Netherlands; [Soni, Saurabh; Alami, Fuad A.; Borrini, Alessandro; Nijhuis, Christian A.] Univ Twente, Fac Sci & Technol, Ctr Brain Inspired Nano Syst, Enschede, Netherlands; [Honnigfort, Christian; Braunschweig, Bjorn] Westfalische Wilhelms Univ Munster, Inst Phys Chem, Munster, Germany; [Honnigfort, Christian; Braunschweig, Bjorn] Westfalische Wilhelms Univ Munster, Ctr Soft Nanosci, Munster, Germany; [Qi, Dong-Cheng] Queensland Univ Technol, Sch Chem & Phys, Brisbane, Qld, Australia; [Thompson, Damien] Univ Limerick, Bernal Inst, Dept Phys, Limerick, Ireland; [Zhang, Qian] Chongqing Univ, Sch Chem & Chem Engn, Chongqing, Peoples R China National University of Singapore; State University System of Florida; University of Central Florida; University of Twente; University of Twente; University of Munster; University of Munster; Queensland University of Technology (QUT); University of Limerick; Chongqing University Nijhuis, CA (corresponding author), Natl Univ Singapore, Dept Chem, Singapore, Singapore.;del Barco, E (corresponding author), Univ Cent Florida, Dept Phys, Orlando, FL 32816 USA.;Nijhuis, CA (corresponding author), Univ Twente, Hybrid Mat Optoelect Grp, Dept Mol & Mat, MESA Inst,Nanotechnol Mol Ctr, Enschede, Netherlands.;Nijhuis, CA (corresponding author), Univ Twente, Fac Sci & Technol, Ctr Brain Inspired Nano Syst, Enschede, Netherlands.;Thompson, D (corresponding author), Univ Limerick, Bernal Inst, Dept Phys, Limerick, Ireland. delbarco@ucf.edu; damien.thompson@ul.ie; nijhuis@utwente.nl ; Soni, Saurabh/B-4217-2018; Qi, Dongchen/A-7052-2008 Borrini, Alessandro/0000-0001-5468-0038; Soni, Saurabh/0000-0002-8159-9128; Alami, Fuad/0000-0002-9237-5082; Qi, Dongchen/0000-0001-8466-0257 Ministry of Education (MOE) [MOE2018-T2-1-088, MOE2019-T2-1-137]; Prime Minister's Office, Singapore, under its Medium Sized Centre program; Science Foundation Ireland (SFI) [15/CDA/3491, 12/RC/2275_P2]; US National Science Foundation [1916874]; Australian Research Council [FT160100207]; Deutsche Forschungsgemeinschaft (German Research Foundation) [433682494-SFB 1459] Ministry of Education (MOE)(Ministry of Education, Singapore); Prime Minister's Office, Singapore, under its Medium Sized Centre program; Science Foundation Ireland (SFI)(Science Foundation Ireland); US National Science Foundation(National Science Foundation (NSF)); Australian Research Council(Australian Research Council); Deutsche Forschungsgemeinschaft (German Research Foundation)(German Research Foundation (DFG)) We thank the Ministry of Education (MOE, awards no. MOE2018-T2-1-088 and no. MOE2019-T2-1-137) and the Prime Minister's Office, Singapore, under its Medium Sized Centre program for supporting this research. D.T. acknowledges support from Science Foundation Ireland (SFI) under awards no. 15/CDA/3491 and no. 12/RC/2275_P2 and supercomputing resources at the SFI/Higher Education Authority Irish Center for High-End Computing (ICHEC). E.d.B. and C.N. acknowledge support from the US National Science Foundation (grant no. ECCS#1916874). D.Q. acknowledges the support of the Australian Research Council (grant no. FT160100207). C.H. and B.B. gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft (German Research Foundation) Project-ID 433682494-SFB 1459. 46 1 1 26 26 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 1476-1122 1476-4660 NAT MATER Nat. Mater. DEC 2022.0 21.0 12 1403 + 10.1038/s41563-022-01402-2 0.0 NOV 2022 19 Chemistry, Physical; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Physics 7Q6PO 36411348.0 Green Submitted 2023-03-23 WOS:000886185300001 0
J Sun, W; Zhao, F; Hu, T; Wu, ZQ; Xu, Y; Dong, Y; Zheng, BQ; Wang, CM; Yan, WJ; Zhu, XL; Wu, J; McKay, MJ; Arozarena, I; Alos, L; Teixido, C; Chen, Y Sun, Wei; Zhao, Fang; Hu, Tu; Wu, Zhiqiang; Xu, Yu; Dong, Yan; Zheng, Biqiang; Wang, Chunmeng; Yan, Wangjun; Zhu, Xiaoli; Wu, Jian; McKay, Michael J.; Arozarena, Imanol; Alos, Llucia; Teixido, Cristina; Chen, Yong Oncogenic alterations reveal key strategies for precision oncology in melanoma treatment ANNALS OF TRANSLATIONAL MEDICINE English Article; Early Access Melanoma; genetic alterations; next-generation sequencing panels (NGS panels); machine learning; prognostic predictors OPEN-LABEL; CHECKMATE 037; CHEMOTHERAPY; SURVIVAL; IPILIMUMAB; NIVOLUMAB; TRIAL Background: Molecular profiling with next-generation sequencing (NGS) has been applied in multiple solid tumors, including melanomas, to identify potential drug targets. However, the association between clinical outcomes and the molecular alterations has not yet been fully clarified.Methods: A total of 108 patients with melanoma were included in this study, 95 of whom had both sequencing data and clinical outcomes were collected. We analyzed the genetic alterations of 108 malignant melanoma patients using the OncoCare panel, which covers 559 genes.Results: A model was also established to predict side effects through a combination analysis of clinical data and somatic variants, yielding an area under the receiver operating characteristic curve (AUROC) score of 0.8. We also identified EGFR mutation was excellent predictor for progression-free survival (PFS) for patient who received immunotherapy (log-rank P=0.01), while tumor mutation burden (TMB) was found to not be significantly associated with PFS (log-rank P=0.87). Combining clinical features with genetic analysis, we found that patients carrying both DNA POLD1/ALOX12B or POLD1/PTPRT mutations had a significantly lower survival rate.Conclusions: Overall, these results demonstrate the benefits of applying NGS clinical panels and shed light on future directions of personalized therapeutics for the treatment of melanoma. [Sun, Wei; Hu, Tu; Wu, Zhiqiang; Xu, Yu; Dong, Yan; Zheng, Biqiang; Wang, Chunmeng; Yan, Wangjun; Chen, Yong] Fudan Univ, Dept Musculoskeletal Oncol, Shanghai Canc Ctr, 270 Dongan Rd, Shanghai 200032, Peoples R China; [Sun, Wei; Hu, Tu; Wu, Zhiqiang; Xu, Yu; Dong, Yan; Zheng, Biqiang; Wang, Chunmeng; Yan, Wangjun; Zhu, Xiaoli; Chen, Yong] Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China; [Zhao, Fang] Univ Hosp Essen, Dept Dermatol, Essen, Germany; [Zhu, Xiaoli] Fudan Univ, Dept Pathol, Shanghai Canc Ctr, Shanghai, Peoples R China; [Wu, Jian] MyGenostics Inc, Beijing, Peoples R China; [McKay, Michael J.] Northern Canc Serv, Dept Radiat Oncol, Burnie, Australia; [Arozarena, Imanol] Complejo Hosp Navarra, Navarrabiomed Fdn Miguel Servet Idisna, Pamplona, Spain; [Alos, Llucia; Teixido, Cristina] Hosp Clin Barcelona, Dept Pathol, Barcelona, Spain Fudan University; Fudan University; University of Duisburg Essen; Fudan University; Navarrabiomed; Servicio Navarro de Salud - Osasunbidea; University of Barcelona; Hospital Clinic de Barcelona Chen, Y (corresponding author), Fudan Univ, Dept Musculoskeletal Oncol, Shanghai Canc Ctr, 270 Dongan Rd, Shanghai 200032, Peoples R China. chenyong@fudan.edu.cn AME Melanoma Collaborative Group; Shanghai Committee of Science and Technology, China; National Natural Science Foundation of China; [19411951700]; [LG-QS-202205-11]; [82272857]; [81802636] AME Melanoma Collaborative Group; Shanghai Committee of Science and Technology, China(Shanghai Science & Technology Committee); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; ; ; Acknowledgments The authors appreciate the academic support from AME Melanoma Collaborative Group. Funding: This work was financially supported by the Shanghai Committee of Science and Technology, China (Grant No. 19411951700, to Yong Chen) , LinGang Laboratory (Grant No. LG-QS-202205-11, to Wei Sun) , and the National Natural Science Foundation of China (Grant Nos. 82272857 and 81802636, to Wei Sun) . 33 0 0 1 1 AME PUBLISHING COMPANY SHATIN FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA 2305-5839 2305-5847 ANN TRANSL MED ANN. TRANSL. MED. 10.21037/atm-22-5346 0.0 NOV 2022 14 Oncology; Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Oncology; Research & Experimental Medicine 6P8AT 36544693.0 Green Submitted, gold 2023-03-23 WOS:000891149200001 0
J Pang, SC; Zhuang, Y; Qiao, SB; Wang, FY; Wang, SD; Lv, ZH Pang, Shanchen; Zhuang, Yu; Qiao, Sibo; Wang, Fuyu; Wang, Shudong; Lv, Zhihan DCTGM: A Novel Dual-channel Transformer Graph Model for miRNA-disease Association Prediction COGNITIVE COMPUTATION English Article; Early Access Attention; Graph neural network; Attention mechanism; miRNA-disease association MICRORNAS Studies have shown that as non-coding RNAs, miRNAs regulate all levels of life activities and most pathological processes. Therefore, identifying disease-related miRNAs is essential for disease diagnosis and treatment. However, traditional biological experiments are highly uncertain and time-consuming. Hence, advanced intelligent computational models are needed to address this problem. We propose a dual-channel transformer graph model, named DCTGM, to learn multi-scale representations for miRNA-disease association prediction. Specifically, DCTGM includes a transformer encoder (TE) and GraphSAGE encoder (GE). The TE intensely captures the important interaction information between miRNA-disease pairs, and the GE aggregates multi-hop neighbor information of miRNA-disease association heterograph to enrich node features. Then, an attention module is proposed to aggregate the dual-channel interactive representations, and we adopt a multi-layer perceptron (MLP) to predict the miRNA-disease association scores. The fivefold cross-validation experimental results demonstrate that our proposed DCTGM achieves the AP of 92.735%, F1 of 84.430%, accuracy of 85.255%, and ROC of 93.012%. In addition, we conduct case studies on brain neoplasms, kidney neoplasms, and breast neoplasms. The extensive experiments show that the dbDEMC database validates 100% of the top 20 predicted miRNAs associated with these diseases. This model can effectively predict the potential mirNA-disease association. Experiments have shown that miRNA associated with a new disease can also be predicted. [Pang, Shanchen; Zhuang, Yu; Qiao, Sibo; Wang, Fuyu; Wang, Shudong] China Univ Petr, Sch Comp Sci & Technol, Qingdao, Peoples R China; [Lv, Zhihan] Uppsala Univ, Dept Game Design Fac Arts, Uppsala, Sweden China University of Petroleum; Uppsala University Qiao, SB (corresponding author), China Univ Petr, Sch Comp Sci & Technol, Qingdao, Peoples R China. siboqiao@126.com Lv, Zhihan/I-3187-2014 Lv, Zhihan/0000-0003-2525-3074 National Natural Science Foundation of China [61873281] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This study is funded by the National Natural Science Foundation of China (No.61873281). 41 0 0 8 8 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1866-9956 1866-9964 COGN COMPUT Cogn. Comput. 10.1007/s12559-022-10092-6 0.0 DEC 2022 10 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology 7C9WK 2023-03-23 WOS:000900154100001 0
C Wang, H; Teng, Z; Wu, CD; Coleman, S IEEE Wang, Huan; Teng, Zi; Wu, Chengdong; Coleman, Sonya Facial Landmarks and Generative Priors Guided Blind Face Restoration 2022 IEEE 20TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) IEEE International Conference on Industrial Informatics INDIN English Proceedings Paper 20th IEEE International Conference on Industrial Informatics (INDIN) JUL 25-28, 2022 ELECTR NETWORK IEEE Blind face restoration; deep neural networks; generative adversarial network; facial priors transformation Blind face restoration (BFR) from severely degraded face images is important in face image processing, and has attracted increasing attention due to its wide applications. However, due to the complex unknown degradations in real-world scenarios, existing priors-based methods tend to restore faces with unstable quality. In this paper, we propose a Facial Landmarks and Generative Priors Guided Blind Face Restoration Network (FGPNet) to seamlessly integrate the advantages of generative priors and face-specific geometry priors. Specifically, we pretrain a high-quality (HQ) face synthesis generative adversarial network (GAN) and a landmarks prediction network, and then embed them into a U-shaped deep neural network (DNN) as decoder priors to guide face restoration, during which the generative priors can provide adequate details and the landmarks priors provide geometry and semantic information. Furthermore, we design facial priors fusion (FPF) blocks to incorporate the prior features from pretrained face synthesis GAN and landmarks prediction network in an adaptive and progressive manner, making our FGPNet exhibits good generalization in real-world application. Experiments demonstrate the superiority of our FGPNet in comparison to state-of-the-arts, and also show its potential in handling real-world low-quality images from several practical applications. [Wang, Huan; Teng, Zi; Wu, Chengdong] Northeastern Univ, Shenyang, Peoples R China; [Coleman, Sonya] Ulster Univ, Coleraine, Londonderry, North Ireland Northeastern University - China; Ulster University Wang, H (corresponding author), Northeastern Univ, Shenyang, Peoples R China. wanghuan@stu.neu.edu.cn; tengzi@stu.neu.edu.cn; wuchengdong@mail.neu.edu.cn; sa.coleman@ulster.ac.uk 30 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1935-4576 978-1-7281-7568-3 IEEE INTL CONF IND I 2022.0 101 106 10.1109/INDIN51773.2022.9976126 0.0 6 Computer Science, Interdisciplinary Applications; Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BU4UB 2023-03-23 WOS:000907121600016 0
J Wang, XL; Xu, JH; Lv, ML; Zhang, L; Zhao, ZL Wang, Xiaolin; Xu, Jihui; Lv, Maolong; Zhang, Lei; Zhao, Zilong Barrier Lyapunov function-based fixed-time FTC for high-order nonlinear systems with predefined tracking accuracy NONLINEAR DYNAMICS English Article High-order nonlinear systems; Fixed-time stability; High-order tan-type BLF; Predefined tracking accuracy ADAPTIVE-CONTROL; OUTPUT TRACKING; FAULT-DETECTION; STABILIZATION This article proposes a fixed-time adaptive fault-tolerant control methodology for a larger class of high-order (powers are positive odd integers) nonlinear systems subject to asymmetric time-varying state constraints and actuator faults. In contrast with the state-of-the-art control methodologies, the distinguishing features of this study lie in that: (a) high-order asymmetric time-varying tan-type barrier Lyapunov function (BLF) is devised such that the state variables can be convergent to the preassigned compact sets all the time provided their initial values remain therein, which not only preserves the constraints satisfaction, but warrants the validity of the adopted neural network approximator; (b) the proposed control design ensures the tracking errors converge to specified residual sets within fixed time and makes the size of the convergence regions of tracking errors adjustable a priori by means of a new BLF-based tuning function and a projection operator; (c) a variable-separable lemma is delicately embedded into the control design to extract the control terms in a linear-like fashion which not only overcomes the difficulty that virtual control signals appear in a non-affine manner, but also solves the problem of actuator faults. Comparative simulations results finally validate the effectiveness of the proposed scheme. [Wang, Xiaolin; Xu, Jihui; Zhang, Lei] Air Force Engn Univ, Dept Equipment Management, Xian 710051, Shaanxi, Peoples R China; [Wang, Xiaolin; Xu, Jihui; Zhang, Lei] Air Force Engn Univ, UAV Engn Coll, Xian 710051, Shaanxi, Peoples R China; [Lv, Maolong] Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China; [Lv, Maolong] Air Force Engn Univ, Coll Aeronaut Engn, Xian 710038, Shaanxi, Peoples R China; [Zhao, Zilong] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, Mourik Broekmanweg 6, NL-2628 XE Delft, Netherlands Air Force Engineering University; Air Force Engineering University; Air Force Engineering University; Air Force Engineering University; Delft University of Technology Xu, JH (corresponding author), Air Force Engn Univ, Dept Equipment Management, Xian 710051, Shaanxi, Peoples R China.;Xu, JH (corresponding author), Air Force Engn Univ, UAV Engn Coll, Xian 710051, Shaanxi, Peoples R China.;Lv, ML (corresponding author), Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China. wangxiaolin_95@163.com; changan6018@163.com; M.Lyu@tudelft.nl; jxzl80926@163.com; Z.Zhao-8@tudelft.nl wang, xiao/HGB-7081-2022 wang, xiao/0000-0002-4088-3341 Excellent Doctoral Dissertation Foundation of Air Force Engineering University [KGD120121017] Excellent Doctoral Dissertation Foundation of Air Force Engineering University This work was supported by the Excellent Doctoral Dissertation Foundation of Air Force Engineering University under Grant KGD120121017. 49 0 0 21 33 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. SEP 2022.0 110.0 1 381 394 10.1007/s11071-022-07627-9 0.0 JUN 2022 14 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics 4G7IR hybrid, Green Published, Green Submitted 2023-03-23 WOS:000815562800002 0
J Ji, SP; Zhang, ZL; Zhang, C; Wei, SQ; Lu, M; Duan, YL Ji, Shunping; Zhang, Zhili; Zhang, Chi; Wei, Shiqing; Lu, Meng; Duan, Yulin Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images INTERNATIONAL JOURNAL OF REMOTE SENSING English Article LAND-COVER CLASSIFICATION; TIME-SERIES; MAXIMUM-LIKELIHOOD Precise crop classification from multi-temporal remote sensing images has important applications such as yield estimation and food transportation planning. However, the mainstream convolutional neural networks based on 2D convolution collapse the time series information. In this study, a 3D fully convolutional neural network (FCN) embedded with a global pooling module and channel attention modules is proposed to extract discriminative spatiotemporal presentations of different types of crops from multi-temporal high-resolution satellite images. Firstly, a novel 3D FCN structure is introduced to replace 2D FCNs as well as to improve current 3D convolutional neural networks (CNNs) by providing a mean to learn distinctive spatiotemporal representations of each crop type from the reshaped multi-temporal images. Secondly, to strengthen the learning significance of the spatiotemporal representations, our approach includes 3D channel attention modules, which regulate the between-channel consistency of the features from the encoder and the decoder, and a 3D global pooling module, which selects the most distinctive features at the top of the encoder. Experiments were conducted using two data sets with different types of crops and time spans. Our results show that our method outperformed in both accuracy and efficiency, several mainstream 2D FCNs as well as a recent 3D CNN designed for crop classification. The experimental data and source code are made openly available at http://study.rsgis.whu.edu.cn/pages/download/.. [Ji, Shunping; Zhang, Zhili; Zhang, Chi; Wei, Shiqing] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Hubei, Peoples R China; [Lu, Meng] Univ Utrecht, Dept Phys Geog, Fac Geosci, Utrecht, Netherlands; [Duan, Yulin] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 10008, Peoples R China Wuhan University; Utrecht University; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS Duan, YL (corresponding author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 10008, Peoples R China. duanyulin@caas.cn Lu, Meng/AAF-1711-2019 Lu, Meng/0000-0002-6850-581X; Duan, Yulin/0000-0003-4364-3041 National Key Research and Development Program of China [2018YFB0505003] National Key Research and Development Program of China This work was supported by the the National Key Research and Development Program of China [2018YFB0505003]. 45 18 19 7 106 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. APR 17 2020.0 41.0 8 3162 3174 10.1080/01431161.2019.1699973 0.0 13 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology JZ5FQ 2023-03-23 WOS:000505127900001 0
J Liu, C; Cheng, G; Chen, XH; Pang, YS Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN SENSORS English Article planetary gear; partition; feature extraction; degradation; VMD; SVD; CNN NEURAL-NETWORKS; IDENTIFICATION; MATRIX; DECOMPOSITION; RECOGNITION; TRANSFORM; ENTROPY; SVD Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. [Liu, Chang; Cheng, Gang] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China; [Chen, Xihui] Hohai Univ, Coll Mech & Elect Engn, Changzhou 213022, Peoples R China; [Pang, Yusong] Delft Univ Technol, Fac Mech Maritime & Mat Engn, NL-2628 CD Delft, Netherlands China University of Mining & Technology; Hohai University; Delft University of Technology Cheng, G (corresponding author), China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China. jsxzlc@foxmail.com; chg@cumt.edu.cn; chenxh@hhu.edu.cn; Y.Pang@tudelft.nl Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities [2015YC02] Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research was funded by [a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions] and [Fundamental Research Funds for the Central Universities] grant number [2015YC02]. 30 48 55 15 88 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors MAY 2018.0 18.0 5 1523 10.3390/s18051523 0.0 20 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation GJ7PP 29751671.0 Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000435580300222 0
J Xing, XJ; Guo, JW; Nan, LL; Gu, QY; Zhang, XP; Yan, DM Xing, Xuejun; Guo, Jianwei; Nan, Liangliang; Gu, Qingyi; Zhang, Xiaopeng; Yan, Dong-Ming Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS English Article Pose estimation; Three-dimensional displays; Robustness; Robot kinematics; Image segmentation; Deep learning; Clustering algorithms; 3-D point cloud; 6-D pose estimation; multisubpopulation particle swarm optimization (MSPSO); point pair features (PPF) The point pair feature (PPF) is widely used in manufacturing for estimating 6-D poses. The key to the success of PPF matching is to establish correct 3-D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. However, efficient sampling of point pairs has been overlooked in existing frameworks. In this article, we propose a revised PPF matching pipeline to improve the efficiency of 6-D pose estimation. Our basic idea is that the valid scene reference points are lying on the object's surface and the previously sampled reference points can provide prior information for locating new reference points. The novelty of our approach is a new sampling algorithm for selecting scene reference points based on the multisubpopulation particle swarm optimization guided by a probability map. We also introduce an effective pose clustering and hypotheses verification method to obtain the optimal pose. Moreover, we optimize the progressive sampling for multiframe point clouds to improve processing efficiency. The experimental results show that our method outperforms previous methods by 6.6%, 3.9% in terms of accuracy on the public DTU and LineMOD datasets, respectively. We further validate our approach by applying it in a real robot grasping task. [Xing, Xuejun; Guo, Jianwei; Gu, Qingyi; Yan, Dong-Ming] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; [Xing, Xuejun; Zhang, Xiaopeng] Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China; [Guo, Jianwei; Gu, Qingyi; Yan, Dong-Ming] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China; [Nan, Liangliang] Delft Univ Technol, NL-2628 BL Delft, Netherlands Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Delft University of Technology Guo, JW (corresponding author), Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.;Guo, JW (corresponding author), Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China. xingxuejun2019@ia.ac.cn; jianwei.guo@nlpr.ia.ac.cn; liangliang.nan@tudelft.nl; qingyi.gu@ia.ac.cn; xiaopeng.zhang@ia.ac.cn; dongming.yan@nlpr.ia.ac.cn Guo, Jianwei/0000-0002-3376-1725; Nan, Liangliang/0000-0002-5629-9975 National Key RD Program [2018YFB2100602]; National Natural Science Foundation of China [62172416, 62172415, 61802406, 61972459]; Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering, Tsinghua University [sklhse-2020-D-07]; Scientific Instrument Developing Project of the Chinese Academy of Sciences [YJKYYQ20200045]; Open Research Projects of Zhejiang Lab [2021KE0AB07, TC210H00L/42] National Key RD Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering, Tsinghua University; Scientific Instrument Developing Project of the Chinese Academy of Sciences; Open Research Projects of Zhejiang Lab This work is supported in part by the National Key R&D Program under Grant 2018YFB2100602, in part by the National Natural Science Foundation of China under Grant 62172416, Grant 62172415, Grant 61802406, and Grant 61972459, in part by the Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering, Tsinghua University under Grant sklhse-2020-D-07, in part by the Scientific Instrument Developing Project of the Chinese Academy of Sciences under Grant YJKYYQ20200045, and in part by the Open Research Projects of Zhejiang Lab under Grant 2021KE0AB07 and Project TC210H00L/42. 50 1 1 13 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0046 1557-9948 IEEE T IND ELECTRON IEEE Trans. Ind. Electron. OCT 2022.0 69.0 10 10281 10291 10.1109/TIE.2021.3121721 0.0 11 Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation 0Z1VH 2023-03-23 WOS:000790866600059 0
C Guo, YA; Han, J; Zhang, ZX; Schuller, B; Ma, YD ACM Guo, Ya'nan; Han, Jing; Zhang, Zixing; Schuller, Bjoern; Ma, Yide Exploring A New Method for Food Likability Rating Based on DT-CWT Theory ICMI'18: PROCEEDINGS OF THE 20TH ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION English Proceedings Paper 20th ACM International Conference on Multimodal Interaction (ICMI) OCT 16-20, 2018 Boulder, CO Assoc Comp Machinery,Assoc Comp Machinery SIGCHI,Openstream,Microsoft,Univ Colorado Boulder, Inst Cognit Sci,audEERING Likability Sub-Challenges; Double Tree Complex Wavelet Transformation; Pre-Trained CNNs In this paper, we mainly investigate subjects' food likability based on audio-related features as a contribution to EAT - the ICMI 2018 Eating Analysis and Tracking challenge. Specifically, we conduct 4-level Double Tree Complex Wavelet Transform decomposition of an audio signal, and obtain five sub-audio signals with frequencies ranging from low to high. For each sub-audio signal, not only 'traditional' functional-based features but also deep learning-based features via pretrained CNNs based on SliCQ-nonstationary Gabor transform and a cochleagram map, are calculated. Besides, the original audio signals based Bag-of-Audio-Words features extracted by the openXBOW toolkit are used to enhance the model as well. Finally, the early fusion of all these three kinds of features can lead to promising results, yielding the highest UAR of 79.2 % by means of a leave-one-speaker-out cross-validation, which holds a 12.7 % absolute gain compared with the baseline of 66.5 % UAR. [Guo, Ya'nan; Han, Jing; Schuller, Bjoern] Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, Augsburg, Germany; [Zhang, Zixing; Schuller, Bjoern] Imperial Coll London, GLAM, London, England; [Guo, Ya'nan; Ma, Yide] Lanzhou Univ, Sch Informat Sci Engn, Lanzhou, Gansu, Peoples R China University of Augsburg; Imperial College London; Lanzhou University Guo, YA (corresponding author), Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, Augsburg, Germany.;Guo, YA (corresponding author), Lanzhou Univ, Sch Informat Sci Engn, Lanzhou, Gansu, Peoples R China. yanan.guo@student.uni-augsburg.de Han, Jing/AAB-3944-2020 Han, Jing/0000-0001-5776-6849 China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) This work is partially supported by the China Scholarship Council (CSC). 30 3 3 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-5692-3 2018.0 569 573 10.1145/3242969.3243684 0.0 5 Computer Science, Cybernetics; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BL9OE Green Published 2023-03-23 WOS:000457913100086 0
J Luo, XH; Liu, Z; Wei, WJ; Ye, LW; Zhang, TH; Xu, LH; Wang, JJ Luo, Xinhui; Liu, Zhi; Wei, Weijie; Ye, Linwei; Zhang, Tianhong; Xu, Lihua; Wang, Jijun Few-shot personalized saliency prediction using meta-learning IMAGE AND VISION COMPUTING English Article Personalized saliency prediction; Few-shot learning; Meta-learning; Deep learning; Hard samples VISUAL SALIENCY; MODEL; RECOGNITION Personalized saliency maps (PSMs) reflect the gaze patterns of different subjects. Current works of saliency prediction explore the common trend in fixation distribution across all observers as a task. Personalized saliency prediction takes the personal preference of each individual into account. In other words, it considers each subject as a task. Due to the difficulty of obtaining individual labeled data, limited works are focusing on personalized saliency prediction. Our goal is to train a model that can quickly adapt to a new subject by using a few labeled data from the new subject. This paper proposes a meta-learning-based method to solve the few-shot personalized saliency prediction problem and predict better saliency maps of different subjects. Our method learns model parameters to fast adapt to new subjects. In addition, we design a Hard Samples Selection (HSS) strategy to make the training process more effective. Specifically, due to the adaptability of the model to different subjects is reflected in the value of the loss function during training, we regard subjects with high loss function values as hard samples. Then we can select hard samples online and retrain the model based on them to improve the saliency prediction performance. Experimental results show that our proposed method is better than existing methods on the PSM dataset for personalized saliency prediction. (c) 2022 Elsevier B.V. All rights reserved. [Luo, Xinhui; Liu, Zhi] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Key Lab Specialty Fiber Opt & Opt Access Networks, Joint Int Res Lab Specialty Fiber Opt & Adv Commun, Shanghai 200444, Peoples R China; [Luo, Xinhui; Liu, Zhi] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China; [Wei, Weijie] Univ Amsterdam, Atlas Lab, NL-1098 XH Amsterdam, Netherlands; [Ye, Linwei] Wenzhou Univ, Coll Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China; [Zhang, Tianhong; Xu, Lihua; Wang, Jijun] Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Sch Med, Shanghai Key Lab Psychot Disorders, Shanghai 200030, Peoples R China Shanghai University; Shanghai University; University of Amsterdam; Wenzhou University; Shanghai Jiao Tong University Liu, Z (corresponding author), Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Key Lab Specialty Fiber Opt & Opt Access Networks, Joint Int Res Lab Specialty Fiber Opt & Adv Commun, Shanghai 200444, Peoples R China. liuzhisjtu@163.com LIU, Zhi/D-4518-2012 LIU, Zhi/0000-0002-8428-1131 National Natural Science Founda-tion of China [62171269, 82171544]; Scienceand Technology Commission of Shanghai Municipality [21S31903100] National Natural Science Founda-tion of China(National Natural Science Foundation of China (NSFC)); Scienceand Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) Acknowledgments This work was supported by the National Natural Science Founda-tion of China under Grants 62171269 and 82171544, and by the Scienceand Technology Commission of Shanghai Municipality under Grant 21S31903100. 51 2 2 7 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0262-8856 1872-8138 IMAGE VISION COMPUT Image Vis. Comput. AUG 2022.0 124.0 104491 10.1016/j.imavis.2022.104491 0.0 JUN 2022 15 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Optics 2J7XL 2023-03-23 WOS:000815864800004 0
J Zou, XT; Wu, S; Bakker, EM; Wang, XZ Zou, Xitao; Wu, Song; Bakker, Erwin M.; Wang, Xinzhi Multi-label enhancement based self-supervised deep cross-modal hashing NEUROCOMPUTING English Article Multi-modal retrieval; Deep cross-modal hashing; Multi-label semantic learning NETWORK Deep cross-modal hashing which integrates deep learning and hashing into cross-modal retrieval, achieves better performance than traditional cross-modal retrieval methods. Nevertheless, most previous deep cross-modal hashing methods only utilize single-class labels to compute the semantic affinity across modalities but overlook the existence of multiple category labels, which can capture the semantic affinity more accurately. Additionally, almost all existing cross-modal hashing methods straightforwardly employ all modalities to learn hash functions but neglect the fact that original instances in all modalities may contain noise. To avoid the above weaknesses, in this paper, a novel multi-label enhancement based self-supervised deep cross-modal hashing (MESDCH) approach is proposed. MESDCH first propose a multi-label semantic affinity preserving module, which uses ReLU transformation to unify the similarities of learned hash representations and the corresponding multi-label semantic affinity of original instances and defines a positive-constraint Kullback-Leibler loss function to preserve their similarity. Then this module is integrated into a self-supervised semantic generation module to further enhance the perfor-mance of deep cross-modal hashing. Extensive evaluation experiments on four well-known datasets demonstrate that the proposed MESDCH achieves state-of-the-art performance and outperforms several excellent baseline methods in the application of cross-modal hashing retrieval. Code is available at: https://github.com/SWU-CS-MediaLab/MESDCH. (c) 2021 Elsevier B.V. All rights reserved. [Zou, Xitao; Wu, Song; Wang, Xinzhi] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China; [Zou, Xitao] Chongqing Three Gorges Univ, Coll Comp Sci & Engn, Chongqing 404100, Peoples R China; [Bakker, Erwin M.] Leiden Univ, LIACS Media Lab, Leiden, Netherlands; [Zou, Xitao] Chongqing Three Gorges Univ, Key Lab Intelligent Informat Proc & Control, Chongqing Municipal Inst Higher Educ, Chongqing 404100, Peoples R China Southwest University - China; Chongqing Three Gorges University; Leiden University; Leiden University - Excl LUMC; Chongqing Three Gorges University Wu, S (corresponding author), Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China. songwuswu@swu.edu.cn National Natural Science Foundation of China [61806168]; Fundamental Research Funds for the Central Universities [SWU117059]; Venture & Innovation Support Program for Chongqing Overseas Returnees [CX2018075] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Venture & Innovation Support Program for Chongqing Overseas Returnees This work was supported by the National Natural Science Foundation of China (61806168), Fundamental Research Funds for the Central Universities (SWU117059), and Venture & Innovation Support Program for Chongqing Overseas Returnees (CX2018075). 72 5 5 6 24 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 7 2022.0 467.0 138 162 10.1016/j.neucom.2021.09.053 0.0 OCT 2021 25 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WL0QO 2023-03-23 WOS:000710121100012 0
J Zhao, KS; Xu, Z; Yan, M; Xue, L; Li, W; Catolino, G Zhao, Kunsong; Xu, Zhou; Yan, Meng; Xue, Lei; Li, Wei; Catolino, Gemma A compositional model for effort-aware Just-In-Time defect prediction on android apps IET SOFTWARE English Article fault tolerance; software performance evaluation; software quality SELECTION FRAMEWORK; COMPONENT ANALYSIS; QUALITY Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just-in-Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high-quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state-of-the-art cost-sensitive cross-entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort-aware performance indicators in most cases. [Zhao, Kunsong; Xu, Zhou; Yan, Meng] Chongqing Univ, Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China; [Zhao, Kunsong] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China; [Xu, Zhou; Yan, Meng] Chongqing Univ, Sch Big Data & Software Engn, Chongqing, Peoples R China; [Xue, Lei] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Li, Wei] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China; [Catolino, Gemma] Tilburg Univ, Jheronimus Acad Data Sci, Tilburg, Netherlands Chongqing University; Wuhan University; Chongqing University; Hong Kong Polytechnic University; Jiangnan University; Tilburg University Xu, Z; Yan, M (corresponding author), Chongqing Univ, Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China. zhouxullx@cqu.edu.cn; mengy@cqu.edu.cn National Key Research and Development Project [2018YFB2101200]; National Natural Science Foundation of China [62002034, 62002306]; Fundamental Research Funds for the Central Universities [2020CDCGRJ072, 2020CDJQY-A021, JUSRP121073]; China Postdoctoral Science Foundation [2020M673137]; Special Funds for the Central Government to Guide Local Scientific and Technological Development [YDZX20195000004725]; Natural Science Foundation of Chongqing in China [cstc2020jcyj-bshX0114]; Key Project of Technology Innovation and Application Development of Chongqing [cstc2019jscx-mbdxX0020]; HKPolyU Start-up Fund [ZVU7]; CCF-Tencent Open Research Fund [ZDCK]; European Commission [825,040] National Key Research and Development Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Special Funds for the Central Government to Guide Local Scientific and Technological Development; Natural Science Foundation of Chongqing in China(Natural Science Foundation of Chongqing); Key Project of Technology Innovation and Application Development of Chongqing; HKPolyU Start-up Fund; CCF-Tencent Open Research Fund; European Commission(European CommissionEuropean Commission Joint Research Centre) This study was supported in part by the National Key Research and Development Project (No.2018YFB2101200), the National Natural Science Foundation of China (Nos.62002034, 62002306), the Fundamental Research Funds for the Central Universities (Nos.2020CDCGRJ072, 2020CDJQY-A021, and JUSRP121073), China Postdoctoral Science Foundation (No.2020M673137), the Special Funds for the Central Government to Guide Local Scientific and Technological Development (No.YDZX20195000004725), the Natural Science Foundation of Chongqing in China (No.cstc2020jcyj-bshX0114), the Key Project of Technology Innovation and Application Development of Chongqing (No.cstc2019jscx-mbdxX0020), HKPolyU Start-up Fund (No.ZVU7), CCF-Tencent Open Research Fund (No.ZDCK), and the European Commission grant (No.825,040) RADON. 66 5 6 2 13 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-8806 1751-8814 IET SOFTW IET Softw. JUN 2022.0 16.0 3 259 278 10.1049/sfw2.12040 0.0 AUG 2021 20 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1G4KF Green Published, gold 2023-03-23 WOS:000687765100001 0
J Vivone, G; Dalla Mura, M; Garzelli, A; Restaino, R; Scarpa, G; Ulfarsson, MO; Alparone, L; Chanussot, J Vivone, Gemine; Dalla Mura, Mauro; Garzelli, Andrea; Restaino, Rocco; Scarpa, Giuseppe; Ulfarsson, Magnus O.; Alparone, Luciano; Chanussot, Jocelyn A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE English Article Image resolution; Multiresolution analysis; Sensors; Principal component analysis; Optimization; Benchmark testing PAN-SHARPENING METHOD; SENSING IMAGE FUSION; DISCRETE WAVELET TRANSFORM; SPARSE REPRESENTATION; QUALITY ASSESSMENT; LANDSAT TM; MULTIRESOLUTION; REGRESSION; MODEL; OPTIMIZATION Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the state of the art hard to achieve. Thus, to fill this gap, we propose a new benchmark consisting of recent advances in MS pansharpening. In particular, optimized classical approaches [multiresolution analysis (MRA) and component substitution (CS)] are compared with methods belonging to the third generation of pansharpening, represented by variational optimization-based (VO) and machine learning (ML) techniques. The benchmark is tested on different scenarios (from urban to rural areas) acquired by different commercial sensors [i.e., IKONOS (IK), GeoEye-1 (GE-1), and WorldView-3 (WV-3)]. Both quantitative and qualitative assessments and the computational burden are analyzed in this article, and all of the implementations have been collected in a MATLAB toolbox that is made available to the community. [Vivone, Gemine] Inst Methodol Environm Anal, Tito, Italy; [Vivone, Gemine; Restaino, Rocco] Univ Salerno, Dept Informat Engn Elect Engn & Appl Math, Fisciano, Italy; [Dalla Mura, Mauro] Grenoble Inst Technol, GIPSA Lab, St Martin Dheres, France; [Garzelli, Andrea] Univ Siena, Dept Informat Engn & Math, Siena, Italy; [Scarpa, Giuseppe] Univ Federico II, Dept Elect Engn & Informat Technol, Naples, Italy; [Ulfarsson, Magnus O.] Univ Iceland, Fac Elect & Comp Engn, Reykjavik, Iceland; [Alparone, Luciano] Univ Florence, Dept Informat Engn, Florence, Italy; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); University of Salerno; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); University of Siena; University of Naples Federico II; University of Iceland; University of Florence; Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Chinese Academy of Sciences Vivone, G (corresponding author), Inst Methodol Environm Anal, Tito, Italy.;Vivone, G (corresponding author), Univ Salerno, Dept Informat Engn Elect Engn & Appl Math, Fisciano, Italy. gemine.vivone@imaa.cnr.it; mauro.dalla-mura@gipsa-lab.grenoble-inp.fr; andrea.garzelli@unisi.it; restaino@unisa.it; giscarpa@unina.it; mou@hi.is; luciano.alparone@unifi.it; jocelyn.chanussot@grenoble-inp.fr Dalla Mura, Mauro/AAA-1938-2020; VIVONE, Gemine/ABE-5121-2020; Garzelli, Andrea/A-1528-2016 Dalla Mura, Mauro/0000-0002-9656-9087; VIVONE, Gemine/0000-0001-9542-0638; Garzelli, Andrea/0000-0003-2332-780X; Ulfarsson, Magnus/0000-0002-0461-040X; Restaino, Rocco/0000-0003-2788-577X; ALPARONE, Luciano/0000-0002-8984-938X; Chanussot, Jocelyn/0000-0003-4817-2875 NVIDIA Corporation NVIDIA Corporation We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan XP GPU used for this research and the DigitalGlobe Foundation for providing the WorldView-3 NY data set. 142 89 89 17 53 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2473-2397 2168-6831 IEEE GEOSC REM SEN M IEEE Geosci. Remote Sens. Mag. MAR 2021.0 9.0 1 53 81 10.1109/MGRS.2020.3019315 0.0 29 Geochemistry & Geophysics; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Remote Sensing; Imaging Science & Photographic Technology RP5JJ 2023-03-23 WOS:000641764700005 0
J Viet-Ha Nhu; Mohammadi, A; Shahabi, H; Bin Ahmad, B; Al-Ansari, N; Shirzadi, A; Geertsema, M; Kress, VR; Karimzadeh, S; Kamran, KV; Chen, W; Nguyen, H Viet-Ha Nhu; Mohammadi, Ayub; Shahabi, Himan; Bin Ahmad, Baharin; Al-Ansari, Nadhir; Shirzadi, Ataollah; Geertsema, Marten; Kress, Victoria R.; Karimzadeh, Sadra; Kamran, Khalil Valizadeh; Chen, Wei; Nguyen, Hoang Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms FORESTS English Article landslide detection; remote sensing technique; decision tree; prediction accuracy; Cameron Highlands; Malaysia SUPPORT VECTOR MACHINE; FUZZY INFERENCE SYSTEM; BIOGEOGRAPHY-BASED OPTIMIZATION; ANALYTICAL HIERARCHY PROCESS; ARTIFICIAL NEURAL-NETWORKS; SPATIAL PREDICTION; FREQUENCY RATIO; RADAR INTERFEROMETRY; STATISTICAL-ANALYSIS; SHALLOW LANDSLIDES We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area. [Viet-Ha Nhu] Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City 700000, Vietnam; [Viet-Ha Nhu] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City 700000, Vietnam; [Mohammadi, Ayub; Karimzadeh, Sadra; Kamran, Khalil Valizadeh] Univ Tabriz, Dept Remote Sensing, Tabriz 5166616471, Iran; [Mohammadi, Ayub; Karimzadeh, Sadra; Kamran, Khalil Valizadeh] Univ Tabriz, GIS, Tabriz 5166616471, Iran; [Shahabi, Himan] Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran; [Shahabi, Himan] Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj 6617715175, Iran; [Bin Ahmad, Baharin] Univ Teknol Malaysia UTM, Fac Built Environm & Surveying, Dept Geoinformat, Johor Baharu 81310, Malaysia; [Al-Ansari, Nadhir] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; [Shirzadi, Ataollah] Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj 6617715175, Iran; [Geertsema, Marten] British Columbia Minist Forests Lands Nat Resourc, Prince George, BC V2L 1R5, Canada; [Kress, Victoria R.] Univ Northern British Columbia, Dept Nat Resources & Environm Studies, 3333 Univ Way, Prince George, BC V2N 4Z9, Canada; [Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Nguyen, Hoang] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam Ton Duc Thang University; Ton Duc Thang University; University of Tabriz; University of Tabriz; University of Kurdistan; University of Kurdistan; Universiti Teknologi Malaysia; Lulea University of Technology; University of Kurdistan; University of Northern British Columbia; Xi'an University of Science & Technology; Ministry of Natural Resources of the People's Republic of China; Duy Tan University Shahabi, H (corresponding author), Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran.;Shahabi, H (corresponding author), Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj 6617715175, Iran.;Al-Ansari, N (corresponding author), Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden. nhuvietha@tdtu.edu.vn; mohammadi.ayub@tabrizu.ac.ir; h.shahabi@uok.ac.ir; baharinahmad@utm.my; nadhir.alansari@ltu.se; a.shirzadi@uok.ac.ir; marten.geertsema@gov.bc.ca; kressv@unbc.ca; sa.karimzadeh@tabrizu.ac.ir; valizadeh@tabrizu.ac.ir; chenwei0930@xust.edu.cn; nguyenhoang23@duytan.edu.vn Mohammadi, Ayub/AAX-8561-2021; Karimzadeh, Sadra/AAW-4140-2020; Shahabi, Himan/J-1591-2017; Kamran, Khalil Valizadeh/AFG-3057-2022; Nguyen, Hoang/AAQ-6799-2021; Chen, Wei/ABB-8669-2020 Mohammadi, Ayub/0000-0001-8848-8917; Karimzadeh, Sadra/0000-0002-5645-0188; Shahabi, Himan/0000-0001-5091-6947; Kamran, Khalil Valizadeh/0000-0003-4648-842X; Nguyen, Hoang/0000-0001-6122-8314; Chen, Wei/0000-0002-5825-1422; Al-Ansari, Nadhir/0000-0002-6790-2653; Shirzadi, Ataollah/0000-0001-9668-8687; Shirzadi, Ataollah/0000-0003-1666-1180 University of Kurdistan, Iran [GRC98-04469-1] University of Kurdistan, Iran This research was supported by the University of Kurdistan, Iran, based on grant number GRC98-04469-1. 161 39 39 6 46 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4907 FORESTS Forests AUG 2020.0 11.0 8 830 10.3390/f11080830 0.0 28 Forestry Science Citation Index Expanded (SCI-EXPANDED) Forestry NL1GN gold, Green Submitted 2023-03-23 WOS:000567172400001 0
J AboElenein, NM; Piao, S; Noor, A; Ahmed, PN AboElenein, Nagwa M.; Piao, Songhao; Noor, Alam; Ahmed, Pir Noman MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation SIGNAL PROCESSING-IMAGE COMMUNICATION English Article Brain tumor segmentation; U-Net; Full convolutional network; Inception; Residual Module; Attention Gate BRAIN-TUMOR SEGMENTATION Gliomas are the largest prevalent and destructive of brain tumors and have crucial parts for the diagnosing and treating of MRI brain tumors during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive brain tumor segmentation, but this role remains challenging due to the differing severity and appearance of gliomas. Therefore, we proposed a novel encoder-decoder architecture called Multi Inception Residual Attention U-Net (MIRAU-Net) in this work. It integrates residual inception modules with attention gates into U-Net to further enhance brain tumor segmentation performance. Encoder-decoder is connected in this architecture through Inception Residual pathways to decrease the distance between their maps of features. We use the weight cross-entropy and generalized Dice (GDL) with focal Tversky loss functions to resolve the class imbalance problem. The evaluation performance of MIRAU-Net checked with Brats 2019 and obtained mean dice similarities of 0.885 for the whole tumor, 0.879 for the core area, and 0.818 for the enhancement tumor. Experiment results reveal that the suggested MIRAU-Net beats its baselines and provides better efficiency than recent techniques for brain tumor segmentation. [AboElenein, Nagwa M.; Piao, Songhao; Ahmed, Pir Noman] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China; [AboElenein, Nagwa M.] Menoufia Univ, Fac Comp & Informat, Shibin Al Kawm 32511, Egypt; [Noor, Alam] Politecn Porto, CISTER Res Ctr, ISEP, Porto, Portugal Harbin Institute of Technology; Egyptian Knowledge Bank (EKB); Menofia University; Polytechnic Institute of Porto Piao, S (corresponding author), Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China. piaosh@hit.edu.cn Noor, Alam/B-2353-2019 Noor, Alam/0000-0002-0077-6509; /0000-0002-5485-3012 32 4 4 3 18 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0923-5965 1879-2677 SIGNAL PROCESS-IMAGE Signal Process.-Image Commun. FEB 2022.0 101.0 116553 10.1016/j.image.2021.116553 0.0 NOV 2021 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering XF8WA Green Published 2023-03-23 WOS:000724345500014 0
J Zhao, W; Tao, T; Zio, E Zhao, Wei; Tao, Tao; Zio, Enrico System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection APPLIED SOFT COMPUTING English Article Reliability prediction; Time series forecasting; Support vector regression; Parameter selection; Analytic selection; Genetic algorithms TIME-SERIES PREDICTION; NEURAL-NETWORK; MACHINES; OPTIMIZATION; TUTORIAL; NOISE We address the problem of system reliability prediction, based on an available series of failure time data. We consider support vector regression (SVR) as solution approach, for its known performance on time series forecasting. However, SVR parameters selection is very critical for obtaining satisfactory forecasting. Currently, two different ways are followed to set the values of SVR parameters. One way is that of choosing parameters based on prior knowledge or experts experience on the problem at hand: this is a simple and quick, practical way but often not optimal in complex situations and for non-expert users. Another way is that of searching the values of the parameters via some intelligent methods of optimization of the SVR regression performance: for doing this efficiently, one must avoid problems like divergence, slow convergence, local optima, etc. In this paper, we propose the combination of an analytic selection (AS) method of prior selection followed by a genetic algorithm (GA) for intelligent optimization. The combination of these two methods allows utilizing the available prior knowledge by AS for guiding the GA optimization process so as to avoid divergence and local optima, and accelerate convergence. To show the effectiveness of the method, some simulation experiments are designed, based on artificial or real reliability datasets. The results show the superiority of our proposed ASGA method to the traditional GA method, in terms of prediction accuracy, convergence speed and robustness. (C) 2015 Elsevier B.V. All rights reserved. [Zhao, Wei; Tao, Tao] Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China; [Zio, Enrico] Ecole Cent Paris, European Fdn New Energy Elect France, Chair Syst Sci & Energet Challenge, Paris, France; [Zio, Enrico] Supelec, Paris, France; [Zio, Enrico] Politecn Milan, Dipartimento Energia, I-20133 Milan, Italy Beihang University; Electricite de France (EDF); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay; Polytechnic University of Milan Zhao, W (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China. zhaowei203@buaa.edu.cn Zio, Enrico/0000-0002-7108-637X Fundamental Research Funds for the Central University [YWF-14-DZXY-030] Fundamental Research Funds for the Central University The work of this paper is supported by the Fundamental Research Funds for the Central University, LIA2MCIS project Development of Optimized Methods for the Reliability Prediction of Industrial Components and Systems (YWF-14-DZXY-030). 41 39 41 2 51 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. MAY 2015.0 30.0 792 802 10.1016/j.asoc.2015.02.026 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science CD7TQ 2023-03-23 WOS:000351296200068 0
C Jiang, ZH; Crookes, D; Green, BD; Zhang, SP; Zhou, HY DeMarsico, M; DiBaja, GS; Fred, A Jiang, Zheheng; Crookes, Danny; Green, Brian Desmond; Zhang, Shengping; Zhou, Huiyu Behavior Recognition in Mouse Videos using Contextual Features Encoded by Spatial-temporal Stacked Fisher Vectors ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS English Proceedings Paper 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM) FEB 24-26, 2017 Porto, PORTUGAL Mouse Behavior Recognition; Spatial-temporal Stacked Fisher Vector; Gaussian Mixture Model; Contextual Features; Spatio-temporal Interest Points CLASSIFICATION; MODELS Manual measurement of mouse behavior is highly labor intensive and prone to error. This investigation aims to efficiently and accurately recognize individual mouse behaviors in action videos and continuous videos. In our system each mouse action video is expressed as the collection of a set of interest points. We extract both appearance and contextual features from the interest points collected from the training datasets, and then obtain two Gaussian Mixture Model (GMM) dictionaries for the visual and contextual features. The two GMM dictionaries are leveraged by our spatial-temporal stacked Fisher Vector (FV) to represent each mouse action video. A neural network is used to classify mouse action and finally applied to annotate continuous video. The novelty of our proposed approach is: (i) our method exploits contextual features from spatiotemporal interest points, leading to enhanced performance, (ii) we encode contextual features and then fuse them with appearance features, and (iii) location information of a mouse is extracted from spatio-temporal interest points to support mouse behavior recognition. We evaluate our method against the database of Jhuang et al. (Jhuang et al., 2010) and the results show that our method outperforms several state-of-the-art approaches. [Jiang, Zheheng; Crookes, Danny; Zhou, Huiyu] Queens Univ Belfast, Sch EEECS, Belfast, Antrim, North Ireland; [Green, Brian Desmond] Queens Univ Belfast, Sch Biol Sci, Belfast, Antrim, North Ireland; [Zhang, Shengping] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai, Peoples R China Queens University Belfast; Queens University Belfast; Harbin Institute of Technology Jiang, ZH (corresponding author), Queens Univ Belfast, Sch EEECS, Belfast, Antrim, North Ireland. zjiang01@qub.ac.uk; d.crookes@qub.ac.uk; b.green@qub.ac.uk; shengping.zhang@gmail.com; h.zhou@ecit.qub.ac.uk Zhou, Huiyu/O-2692-2014 Zhou, Huiyu/0000-0003-1634-9840 UK EPSRC [EP/N011074/1]; National Natural Science Foundation of China [61300111] UK EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This project is supported by UK EPSRC under Grant EP/N011074/1 and National Natural Science Foundation of China under Grant 61300111. 23 1 1 0 3 SCITEPRESS SETUBAL AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL 978-989-758-222-6 2017.0 259 269 10.5220/0006244602590269 0.0 11 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI6GD Green Submitted, hybrid 2023-03-23 WOS:000413240500029 0
J Fu, YC; Xu, ZP; Zhu, F; Liu, Q; Zhou, XK Fu, Yuchen; Xu, Zhipeng; Zhu, Fei; Liu, Quan; Zhou, Xiaoke Learn to Human-level Control in Dynamic Environment Using Incremental Batch Interrupting Temporal Abstraction COMPUTER SCIENCE AND INFORMATION SYSTEMS English Article hierarchical reinforcement learning; option; reinforcement learning; online learning; dynamic environment The temporal world is characterized by dynamic and variance. A lot of machine learning algorithms are difficult to be applied to practical control applications directly, while hierarchical reinforcement learning can be used to deal with them. Meanwhile, it is a commonplace to have some partial solutions available, called options, which are learned from knowledge or predefined by the system, to solve sub-tasks of the problem. The option can be reused for policy determination in control. Many traditional semi-Markov decision process methods take advantage of it. But most of them treat the option as a primitive object. However, due to the uncertainty and variability of the environment, they are unable to deal with real world control problems effectively. Based on the idea of interrupting option under the prerequisite for dynamic environment, a Q-learning control method which uses temporal abstraction, named as I-QOption, is introduced. The I-QOption approach combines the idea of interruption with the characteristics of dynamic environment so as to be able to learn and improve control policy in dynamic environment. The Q-learning framework helps to learn from interaction with raw data and achieving human-level control. The I-QOption algorithm is applied to grid world, a benchmark dynamic environment evaluation testing. The experiment results show that the proposed algorithm can learn and improve policy effectively in dynamic environment. [Fu, Yuchen; Xu, Zhipeng; Zhu, Fei; Liu, Quan] Soochow Univ, Sch Comp Sci & Technol, Shizi St 1,Box 158, Suzhou 215006, Peoples R China; [Zhu, Fei] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou, Peoples R China; [Zhu, Fei; Liu, Quan] Jilin Univ, Collaborat Innovat Ctr Novel Software Technol & I, Changchun 130012, Peoples R China; [Zhu, Fei] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China; [Zhou, Xiaoke] Univ Basque Country, Leioa, Spain Soochow University - China; Soochow University - China; Jilin University; Jilin University; University of Basque Country Fu, YC (corresponding author), Soochow Univ, Sch Comp Sci & Technol, Shizi St 1,Box 158, Suzhou 215006, Peoples R China. yuchenfu@suda.edu.cn; zhufei@suda.edu.cn; quanliu@suda.edu.cn; xzhou001@ikasle.ehu.eus National Natural Science Foundation of China [61303108, 61373094, 61472262]; High School Natural Foundation of Jiangsu [13KJB520020]; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172014K04]; Suzhou Industrial application of basic research program part [SYG201422]; Provincial Key Laboratory for Computer Information Processing Technology, Soochow University [KJS1524] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); High School Natural Foundation of Jiangsu; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University; Suzhou Industrial application of basic research program part; Provincial Key Laboratory for Computer Information Processing Technology, Soochow University This paper is supported by National Natural Science Foundation of China (61303108, 61373094, 61472262), High School Natural Foundation of Jiangsu(13KJB520020), Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University(93K172014K04), Suzhou Industrial application of basic research program part (SYG201422), Provincial Key Laboratory for Computer Information Processing Technology, Soochow University(KJS1524). 31 3 3 1 15 COMSIS CONSORTIUM NOVI SAD UNIV NOVI SAD, FAC TECH SCI, TRG DOSITEJA OBRADOVICA 6, NOVI SAD, 21000, SERBIA 1820-0214 COMPUT SCI INF SYST Comput. Sci. Inf. Syst. JUN 2016.0 13.0 2 561 577 10.2298/CSIS160210015F 0.0 17 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science DR6SL gold, Green Submitted 2023-03-23 WOS:000380031500013 0
J Wei, Z; Tao, T; Ding, ZS; Zio, E Wei, Zhao; Tao, Tao; Ding ZhuoShu; Zio, Enrico A dynamic particle filter-support vector regression method for reliability prediction RELIABILITY ENGINEERING & SYSTEM SAFETY English Article Time series regression; Support vector machines; Particle filter; Reliability prediction TRACKING SOFTWARE-RELIABILITY; TIME-SERIES PREDICTION; NEURAL-NETWORK; SYSTEMS RELIABILITY; GENETIC ALGORITHMS; ARIMA MODELS; MACHINES Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF-SVR method has superior prediction performance over that of standard SVR. Four application results show that PF-SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. (C) 2013 Elsevier Ltd. All rights reserved. [Wei, Zhao; Tao, Tao; Ding ZhuoShu] Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China; [Zio, Enrico] Politecn Milan, Dept Energy, I-20133 Milan, Italy; [Zio, Enrico] Ecole Cent Paris, Paris, France; [Zio, Enrico] Supelec, Paris, France Beihang University; Polytechnic University of Milan; UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay Wei, Z (corresponding author), Beihang Univ, Sch Elect & Informat Engn, Grp 203, Beijing 100191, Peoples R China. zhaowei203@buaa.edu.cn Zio, Enrico/0000-0002-7108-637X China NSFC [71231001] China NSFC(National Natural Science Foundation of China (NSFC)) The participation of Enrico Zio to this research is partially supported by the China NSFC under grant number 71231001. 39 97 102 4 93 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. NOV 2013.0 119.0 109 116 10.1016/j.ress.2013.05.021 0.0 8 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 215PY 2023-03-23 WOS:000324224000013 0
J Liu, H; Song, WQ; Niu, YH; Zio, E Liu, He; Song, Wanqing; Niu, Yuhui; Zio, Enrico A generalized cauchy method for remaining useful life prediction of wind turbine gearboxes MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article Generalized Cauchy process; Long-range dependent; Fractal; Gearbox degradation; Remaining useful life The accurate estimate of the Remaining Useful Life (RUL) of mechanical tools is a fundamental problem in Engineering. This prediction often implies the knowledge and application of sophisticated mathematical methods based on fractal and Long-Range Dependence (LRD) stochastic processes. However, the existing RUL prediction methods based on stochastic model cannot simultaneously consider the fractal and LRD characteristics of the equipment degradation process. This paper describes a new RUL prediction model based on the Generalized Cauchy (GC) process, which is a stochastic process with independent parameters. That is, the GC process uses the fractal dimension D and Hurst index H to describe the fractal and LRD characteristics of the degradation sequence, respectively. Then, the GC process is taken as the diffusion term, describing the uncertainty of the degradation sequence, to establish the GC degradation model, and the power law and exponential forms are used to describe the nonlinear drift of the degradation sequence. The stochastic volatility of the degradation sequence causes the equipment RUL unable to be predicted for a long time. This article uses the largest Lyapunov index to reveal the maximum prediction range of RUL. The analysis of actual equipment degradation verifies the effectiveness of the degradation model based on power law drift and GC process. The prediction results of the comparative case show that the prediction performance of the GC degradation model is better than Brownian motion, fractional Brownian motion, and long short-term memory neural network. (c) 2020 Elsevier Ltd. All rights reserved. [Liu, He; Song, Wanqing; Niu, Yuhui] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China; [Zio, Enrico] Politecn Milan, Energy Dept, Via La Masa 34-3, I-20156 Milan, Italy; [Zio, Enrico] PSL Res Univ, CRC, MINES ParisTech, Sophia Antipolis, France Shanghai University of Engineering Science; Polytechnic University of Milan; UDICE-French Research Universities; Universite PSL; MINES ParisTech Song, WQ (corresponding author), Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China. nh324310@163.com; swqls@126.com; n18321023297@163.com; enrico.zio@polimi.it 55 39 41 20 171 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. MAY 15 2021.0 153.0 107471 10.1016/j.ymssp.2020.107471 0.0 20 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering QA5YO 2023-03-23 WOS:000613521500001 0
J Liu, ZJ; Xu, JM; Li, J; Plaza, A; Zhang, SQ; Wang, LZ Liu, Zhenjie; Xu, Jianming; Li, Jun; Plaza, Antonio; Zhang, Shaoquan; Wang, Lizhe Moving Ship Optimal Association for Maritime Surveillance: Fusing AIS and Sentinel-2 Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Marine vehicles; Artificial intelligence; Sensors; Remote sensing; Optical sensors; Surveillance; Target tracking; Data fusion; domain adaptation; maritime surveillance (MS); ship detection; target association; time lag; you only look once (YOLO) IDENTIFICATION SYSTEM AIS; SPACE-BORNE SAR; HETEROGENEOUS DATA; OPTIMAL TRANSPORT; DOMAIN AWARENESS; SOCIAL MEDIA; TRACKING; IMAGES; INTEGRATION; ALGORITHM Nowadays, a variety of different sources can be combined together to measure and monitor maritime human activities. Reliable data fusion techniques are essential to associate the targets from different systems for maritime surveillance. In particular, the fusion of data from Sentinel-2 satellites and the automatic identification system (AIS) has attracted wide attention due to their public availability and complementarity. However, most traditional methods for target association are not suitable for this particular case, due to the time lag phenomenon of Sentinel-2 data. In this study, we first construct two new datasets for the detection of moving ships and their wakes based on Sentinel-2 images. Combined with the detection results obtained by the you only look once (YOLOv5) model, the position and course information of the detected ships are first extracted. After carefully analyzing the time lag phenomenon of Sentinel-2 data, we develop a new domain adaptation-based method for target association based on the fusion of Sentinel-2 and AIS data, called moving ship optimal association (MSOA). Different from standard domain adaptation methods only for representation alignment, the proposed MSOA is able to align representation, time, and position simultaneously. A case study is provided in which the newly proposed method is tested over the Port of Long Beach, USA. Experimental results demonstrate that both moving ships and wakes are well detected. Specifically, our newly proposed MSOA exhibits more accurate and robust performance when compared to traditional methods, and the detected ships without corresponding AIS tracks can also be detected by our MSOA. Moreover, the real sensing time and time lag of Sentinel-2 data are deduced with high accuracy. Overall, it can be concluded that our MSOA provides a new perspective for accurate target association based on heterogeneous data fusion. [Liu, Zhenjie; Xu, Jianming] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Li, Jun; Wang, Lizhe] China Univ Geosci, Sch Comp Sci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10071, Spain; [Zhang, Shaoquan] Nanchang Inst Technol, Sch Informat Engn, Jiangxi Prov Key Lab Water Informat Cooperat Sens, Nanchang 330099, Jiangxi, Peoples R China Sun Yat Sen University; China University of Geosciences; Universidad de Extremadura; Nanchang Institute Technology Li, J (corresponding author), China Univ Geosci, Sch Comp Sci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China. lijuncug@cug.edu.cn Plaza, Antonio/C-4455-2008; Wang, Lizhe/L-7453-2014 Plaza, Antonio/0000-0002-9613-1659; Wang, Lizhe/0000-0003-2766-0845 National Science Foundation for Distinguished Young Scholars of China [T2225019]; National Natural Science Foundation of China [61901208, 62141105] National Science Foundation for Distinguished Young Scholars of China(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Science Foundation for Distinguished Young Scholars of China under Grant T2225019 and in part by the National Natural Science Foundation of China under Grant 61901208 and Grant 62141105. 113 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5635218 10.1109/TGRS.2022.3227938 0.0 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 7G3EC 2023-03-23 WOS:000902411200026 0
J Lei, XM; Siringoringo, DM; Sun, Z; Fujino, Y Lei, Xiaoming; Siringoringo, Dionysius M.; Sun, Zhen; Fujino, Yozo Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL English Article; Early Access Structural health monitoring; displacement; multistep ahead prediction; deep learning; cable-stayed bridge; surrogate model SUSPENSION BRIDGE; LONG; TEMPERATURE; IDENTIFICATION Displacement is an essential indicator of the functioning and safety of long-span cable-supported bridges under operational conditions. However, displacement estimation is challenging as these bridges are simultaneously subjected to various loading conditions such as temperature, wind, and vehicles. This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. Monitoring data of a cable-stayed bridge are collected to validate the proposed approach, including comprehensive measurements of the various loads and the displacement responses. Characteristics of temperature, wind, and vehicle loads are taken as the input, and the displacement responses at the mid-span of the main girder and top of the two pylons are taken as the output. The results showed the effectiveness of the proposed approach with an accuracy higher than 95%, which clearly outperformed other models such as long short-term memory networks in accuracy and efficiency. The effects of different types of loads are also investigated, and the wind load is found to be the most influential. Furthermore, multistep ahead prediction is carried out using the proposed approach, and good accuracy is achieved even 5 min ahead. The proposed approach can shed light on early warning of the malfunction of the bridge. [Lei, Xiaoming] Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China; [Lei, Xiaoming] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Siringoringo, Dionysius M.; Fujino, Yozo] Yokohama Natl Univ, Inst Adv Sci, Yokohama, Kanagawa, Japan; [Sun, Zhen] Univ Porto, Fac Engn FEUP, Construct ViBest, R Dr Roberto Frias, P-4200465 Porto, Portugal Tongji University; Hong Kong Polytechnic University; Yokohama National University; Universidade do Porto Sun, Z (corresponding author), Univ Porto, Fac Engn FEUP, Construct ViBest, R Dr Roberto Frias, P-4200465 Porto, Portugal. sun@fe.up.pt Lei, Xiaoming/Y-7123-2019 Lei, Xiaoming/0000-0003-0963-3237; Siringoringo, Dionysius M./0000-0003-2267-0673; Sun, Zhen/0000-0002-2053-4902 FCT Project SAFESUSPENSE [POCI-01-0145-FEDER-031054]; JSPS [18K04320]; Taisei Foundation for application of Machine Learning FCT Project SAFESUSPENSE; JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Taisei Foundation for application of Machine Learning The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge financial supports from the FCT Project SAFESUSPENSE (reference POCI-01-0145-FEDER-031054). The second author acknowledges the support from JSPS grant-in-Aid Kakenhi C no. 18K04320 and Taisei Foundation for application of Machine Learning in this research. 59 1 1 19 23 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 1475-9217 1741-3168 STRUCT HEALTH MONIT Struct. Health Monit. 10.1177/14759217221116637 0.0 AUG 2022 17 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3P2CF 2023-03-23 WOS:000837346700001 0
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Bruno, G; Bury, F; Caputo, C; David, P; Delaere, C; Delcourt, M; Donertas, IS; Giammanco, A; Lemaitre, V; Mondal, K; Prisciandaro, J; Taliercio, A; Teklishyn, M; Vischia, P; Wertz, S; Wuyckens, S; Alves, GA; Hensel, C; Moraes, A; Alda, WL; Das Chagas, EBB; Malbouisson, HB; Carvalho, W; Chinellato, J; Coelho, E; Da Costa, EM; Da Silveira, GG; Damiao, DD; De Souza, SF; Martins, J; Figueiredo, DM; Herrera, CM; Mundim, L; Nogima, H; Teles, PR; Rosas, LJS; Santoro, A; Do Amaral, MS; Sznajder, A; Thiel, M; De Araujo, FTD; Pereira, AV; Bernardes, CA; Calligarisa, L; Tomeia, TRFP; Gregores, EM; Lemos, DS; Mercadantea, PG; Novaesa, SF; Padulaa, SS; Aleksandrov, A; Antchev, G; Atanasov, I; Hadjiiska, R; Iaydjiev, P; Misheva, M; Rodozov, M; Shopova, M; Sultanov, G; Dimitrov, A; Ivanov, T; Litov, L; Pavlov, B; Petkov, P; Petrov, A; Cheng, T; Fang, W; Guo, Q; Mittal, M; Wang, H; Yuan, L; Ahmad, M; Bauer, G; Hu, Z; Wang, Y; Yi, K; Chapon, E; Chen, GM; Chen, HS; Chen, M; Javaid, T; Kapoor, A; Leggat, D; Liao, H; Liu, ZA; Sharma, R; Spiezia, A; Tao, J; Thomas-Wilsker, J; Wang, J; Zhang, H; Zhang, S; Zhao, J; Agapitos, A; Ban, Y; Chen, C; Huang, Q; Levin, A; Li, Q; Lu, M; Lyu, X; Mao, Y; Qian, SJ; Wang, D; Wang, Q; Xiao, J; You, Z; Gao, X; Okawa, H; Xiao, M; Avila, C; Cabrera, A; Florez, C; Fraga, J; Sarkar, A; Delgado, MAS; Jaramillo, J; Guisao, JM; Ramirez, F; Alvarez, JDR; Gonzalez, CAS; Arbelaez, NV; Giljanovic, D; Godinovic, N; Lelas, D; Puljak, I; Antunovic, Z; Kovac, M; Sculac, T; Brigljevic, V; Ferencek, D; Majumder, D; Roguljic, M; Starodumov, A; Susa, T; Ather, MW; Attikis, A; Erodotou, E; Ioannou, A; Kole, G; Kolosova, M; Konstantinou, S; Mousa, J; Nicolaou, C; Ptochos, F; Razis, PA; Rykaczewski, H; Saka, H; Tsiakkouri, D; Finger, M; Finger, M; Kveton, A; Tomsa, J; Ayala, E; Jarrin, EC; Abdelalim, AA; Abu Zeid, S; Elgammal, S; Mahmoud, MA; Mohammed, Y; Bhowmik, S; De Oliveira, ACA; Dewanjee, RK; Ehataht, K; Kadastik, M; Pata, J; Raidal, M; Veelken, C; Eerola, P; Forthomme, L; 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A.; Lokos, S.; Major, P.; Mandal, K.; Mehta, A.; Pasztor, G.; Suranyi, O.; Veres, G. I.; Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Teyssier, D.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Csorgo, T.; Nemes, F.; Novak, T.; Choudhury, S.; Komaragiri, J. R.; Kumar, D.; Panwar, L.; Tiwari, P. C.; Bahinipati, S.; Dash, D.; Kar, C.; Mal, P.; Mishra, T.; Bindhu, V. K. Muraleedharan Nair; Nayak, A.; Sur, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chaudhary, G.; Chauhan, S.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, S.; Kumari, P.; Meena, M.; Sandeep, K.; Singh, J. B.; Virdi, A. K.; Ahmed, A.; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Kumar, A.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, A.; Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhowmik, D.; Dutta, S.; Ghosh, S.; Gomber, B.; Maity, M.; Nandan, S.; Palit, P.; Rout, P. 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P.; Tosi, N.; Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Cassese, A.; Ceccarelli, R.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Fiori, F.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.; Benussi, L.; Bianco, S.; Piccolo, D.; Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.; Benaglia, A.; Beschi, A.; Brivio, F.; Cetorelli, F.; Ciriolo, V.; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Monti, F.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; de Fatisa, T. Tabarelli; Valsecchi, D.; Zuolo, D.; Buontempo, S.; Cavallo, N.; De Iorioa, A.; Fabozzi, F.; Fienga, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.; Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Layer, L.; Margoni, M.; Meneguzzo, A. T.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Bilei, G. M.; Ciangottini, D.; Fano, L.; Lariccia, P.; Mantovani, G.; Mariani, V.; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bertacchi, V.; Bianchini, L.; Boccali, T.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. 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H.; Teague, D.; Trembath-Reichert, S.; Vetens, W. CMS Collaboration First measurement of the cross section for top quark pair production with additional charm jets using dileptonic final states in pp collisions at root s=13 TeV PHYSICS LETTERS B English Article CMS; Top quark; Dilepton; Charm quark; Charm-tagging; Heavy-flavour FRAGMENTATION The first measurement of the inclusive cross section for top quark pairs (tt) produced in association with two additional charm jets is presented. The analysis uses the dileptonic final states of tt events produced in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 41.5 fb-1, recorded by the CMS experiment at the LHC. A new charm jet identification algorithm provides input to a neural network that is trained to distinguish among tt events with two additional charm (ttcc), bottom (ttbb), and light-flavour or gluon (ttLL) jets. By means of a template fitting procedure, the inclusive ttcc, ttbb, and ttLL cross sections are simultaneously measured, together with their ratios to the inclusive tt + two jets cross section. This provides measurements of the ttcc and ttbb cross sections of 10.1 +/- 1.2 (stat) +/- 1.4 (syst) pb and 4.54 +/- 0.35 (stat) +/- 0.56 (syst) pb, respectively, in the full phase space. The results are compared and found to be consistent with predictions from two different matrix element generators with next-to-leading order accuracy in quantum chromodynamics, interfaced with a parton shower simulation. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. [Sirunyan, A. M.; Tumasyan, A.] Yerevan Phys Inst, Yerevan, Armenia; [Adam, W.; Bergauer, T.; Dragicevic, M.; Del Valle, A. Escalante; Fruehwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I.; Pitters, F. 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B.; Lattaud, H.; Lesauvage, A.; Lethuillier, M.; Mirabito, L.; Shchablo, K.; Torterotot, L.; Touquet, G.; Vander Donckt, M.; Viret, S.] Univ Claude Bernard Lyon 1, Univ Lyon, CNRS, IN2P3,Inst Phys Nucl Lyon, Villeurbanne, France; [Khvedelidze, A.; Tsamalaidze, Z.] Georgian Tech Univ, Tbilisi, Georgia; [Feld, L.; Klein, K.; Lipinski, M.; Meuser, D.; Pauls, A.; Rauch, M. P.; Schulz, J.; Teroerde, M.] Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; [Eliseev, D.; Erdmann, M.; Fackeldey, P.; Fischer, B.; Ghosh, S.; Hebbeker, T.; Hoepfner, K.; Keller, H.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Mocellin, G.; Mondal, S.; Mukherjee, S.; Noll, D.; Novak, A.; Pook, T.; Pozdnyakov, A.; Rath, Y.; Reithler, H.; Roemer, J.; Schmidt, A.; Schuler, S. C.; Sharma, A.; Wiedenbeck, S.; Zaleski, S.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; [Dziwok, C.; Fluegge, G.; Ahmad, W. Haj; Hlushchenko, O.; Kress, T.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.; Ziemons, T.] Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; [Petersen, H. Aarup; Martin, M. Aldaya; Asmuss, P.; Babounikau, I.; Baxter, S.; Behnke, O.; Martinez, A. Bermudez; Bin Anuar, A. A.; Borras, K.; Botta, V.; Brunner, D.; Campbell, A.; Cardini, A.; Connor, P.; Rodriguez, S. Consuegra; Danilov, V.; De Wit, A.; Defranchis, M. M.; Didukh, L.; Damiani, D. Dominguez; Eckerlin, G.; Eckstein, D.; Banos, L. I. Estevez; Gallo, E.; Geiser, A.; Giraldi, A.; Grohsjean, A.; Guthoff, M.; Harb, A.; Jafari, A.; Jomhari, N. Z.; Jung, H.; Kasem, A.; Kasemann, M.; Kaveh, H.; Kleinwort, C.; Knolle, J.; Kruecker, D.; Lange, W.; Lenz, T.; Lidrych, J.; Lipka, K.; Lohmann, W.; Madlener, T.; Mankel, R.; Melzer-Pellmann, I. -A.; Metwally, J.; Meyer, A. B.; Meyer, M.; Mnich, J.; Mussgiller, A.; Myronenko, V.; Otarid, Y.; Adan, D. Perez; Pflitsch, S. K.; Pitzl, D.; Raspereza, A.; Saggio, A.; Saibel, A.; Savitskyi, M.; Scheurer, V.; Schwanenberger, C.; Singh, A.; Ricardo, R. E. Sosa; Tonon, N.; Turkot, O.; Vagnerini, A.; Van de Klundert, M.; Walsh, R.; Walter, D.; Wen, Y.; Wichmann, K.; Wissing, C.; Wuchterl, S.; Zenaiev, O.; Zlebcik, R.] DESY, Hamburg, Germany; [Aggleton, R.; Bein, S.; Benato, L.; Benecke, A.; De Leo, K.; Dreyer, T.; Eich, M.; Feindt, F.; Froehlich, A.; Garbers, C.; Garutti, E.; Gunnellini, P.; Haller, J.; Hinzmann, A.; Karavdina, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kutzner, V.; Lange, J.; Lange, T.; Malara, A.; Niemeyer, C. E. N.; Nigamova, A.; Rodriguez, K. J. Pena; Rieger, O.; Schleper, P.; Schroeder, M.; Schumann, S.; Schwandt, J.; Schwarz, D.; Sonneveld, J.; Stadie, H.; Steinbrueck, G.; Tews, A.; Vormwald, B.; Zoi, I.] Univ Hamburg, Hamburg, Germany; [Bechtel, J.; Berger, T.; Butz, E.; Caspart, R.; Chwalek, T.; De Boer, W.; Dierlamm, A.; Droll, A.; El Morabit, K.; Faltermann, N.; Floeh, K.; Giffels, M.; Gosewisch, J. O.; Gottmann, A.; Hartmann, F.; Heidecker, C.; Husemann, U.; Katkov, I.; Keicher, P.; Koppenhoefer, R.; Maier, S.; Metzler, M.; Mitra, S.; Mueller, Th.; Musich, M.; Neukum, M.; Quast, G.; Rabbertz, K.; Rauser, J.; Savoiu, D.; Schaefer, D.; Schnepf, M.; Seith, D.; Shvetsov, I.; Simonis, H. J.; Ulrich, R.; Van der Linden, J.; Von Cube, R. F.; Wassmer, M.; Weber, M.; Wieland, S.; Wolf, R.; Wozniewski, S.; Wunsch, S.] Karlsruher Inst Technol, Karlsruhe, Germany; [Anagnostou, G.; Asenov, P.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Paspalaki, G.; Stakia, A.] NCSR Demokritos, Inst Nucl & Particle Phys INPP, Aghia Paraskevi, Greece; [Diamantopoulou, M.; Karasavvas, D.; Karathanasis, G.; Kontaxakis, P.; Koraka, C. K.; Manousakis-Katsikakis, A.; Panagiotou, A.; Papavergou, I.; Saoulidou, N.; Theofilatos, K.; Tziaferi, E.; Vellidis, K.; Vourliotis, E.] Natl & Kapodistrian Univ Athens, Athens, Greece; [Bakas, G.; Kousouris, K.; Papakrivopoulos, I.; Tsipolitis, G.; Zacharopoulou, A.] Natl Tech Univ Athens, Athens, Greece; [Evangelou, I.; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Manitara, K.; Manthos, N.; Papadopoulos, I.; Strologas, J.] Univ Ioannina, Ioannina, Greece; [Csanad, M.; Gadallah, M. M. A.; Lokos, S.; Major, P.; Mandal, K.; Mehta, A.; Pasztor, G.; Suranyi, O.; Veres, G. I.] Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; [Bartok, M.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.] Wigner Res Ctr Phys, Budapest, Hungary; [Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Teyssier, D.] Inst Nucl Res ATOMKI, Debrecen, Hungary; [Raics, P.; Trocsanyi, Z. L.; Ujvari, B.] Univ Debrecen, Inst Phys, Debrecen, Hungary; [Csorgo, T.; Nemes, F.; Novak, T.] Eszterhazy Karoly Univ, Karoly Robert Campus, Gyongyos, Hungary; [Choudhury, S.; Komaragiri, J. R.; Kumar, D.; Panwar, L.; Tiwari, P. C.] Indian Inst Sci IISc, Bangalore, Karnataka, India; [Bahinipati, S.; Dash, D.; Kar, C.; Mal, P.; Mishra, T.; Bindhu, V. K. Muraleedharan Nair; Nayak, A.; Sur, N.; Swain, S. K.] HBNI, Natl Inst Sci Educ & Res, Bhubaneswar, India; [Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chaudhary, G.; Chauhan, S.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, S.; Kumari, P.; Meena, M.; Sandeep, K.; Singh, J. B.; Virdi, A. K.] Panjab Univ, Chandigarh, India; [Ahmed, A.; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Kumar, A.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Shah, A.] Univ Delhi, Delhi, India; [Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhowmik, D.; Dutta, S.; Ghosh, S.; Gomber, B.; Maity, M.; Nandan, S.; Palit, P.; Rout, P. K.; Saha, G.; Sahu, B.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.] HBNI, Saha Inst Nucl Phys, Kolkata, India; [Behera, P. K.; Behera, S. C.; Kalbhor, P.; Muhammad, A.; Pradhan, R.; Pujahari, P. R.; Sharma, A.; Sikdar, A. K.] Indian Inst Technol Madras, Madras, Tamil Nadu, India; [Dutta, D.; Jha, V.; Kumar, V.; Mishra, D. K.; Naskar, K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.] Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; [Aziz, T.; Dugad, S.; Mohanty, G. B.; Sarkar, U.] Tata Inst Fundamental Res, Mumbai, Maharashtra, India; [Banerjee, S.; Bhattacharya, S.; Chatterjee, S.; Chudasama, R.; Guchait, M.; Karmakar, S.; Kumar, S.; Majumder, G.; Mazumdar, K.; Mukherjee, S.; Roy, D.] Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; [Dube, S.; Kansal, B.; Pandey, S.; Rane, A.; Rastogi, A.; Sharma, S.] Indian Inst Sci Educ & Res IISER, Pune, Maharashtra, India; [Bakhshiansohi, H.; Zeinali, M.] Isfahan Univ Technol, Dept Phys, Esfahan, Iran; [Chenarani, S.; Etesami, S. M.; Khakzad, M.; Najafabadi, M. Mohammadi] Inst Res Fundamental Sci IPM, Tehran, Iran; [Felcini, M.; Grunewald, M.] Univ Coll Dublin, Dublin, Ireland; [Abbrescia, M.; Aly, R.; Aruta, C.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Fiore, L.; Gelmi, A.; Gul, M.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Margjeka, I.; Mastrapasqua, V.; Merlin, J. A.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Simonea, F. M.; Venditti, R.; Verwilligen, P.] INFN, Sez Bari, Bari, Italy; [Abbrescia, M.; Aly, R.; Aruta, C.; De Palma, M.; Di Florio, A.; Di Pilato, A.; Elmetenawee, W.; Gelmi, A.; Ince, M.; Lezki, S.; Margjeka, I.; Mastrapasqua, V.; My, S.; Nuzzo, S.; Pompili, A.; Selvaggi, G.; Simonea, F. M.] Univ Bari, Bari, Italy; [Creanza, D.; De Filippis, N.; Iaselli, G.; Maggi, G.; Pugliese, G.] Politecn Bari, Bari, Italy; [Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Ciocca, C.; Cuffiani, M.; Dallavalle, G. M.; Diotalevi, T.; Fabbri, F.; Fanfani, A.; Fontanesi, E.; Giacomelli, P.; Giommi, L.; Grandi, C.; Guiducci, L.; Iemmi, F.; Lo Meo, S.; Marcellini, S.; Masetti, G.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossia, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.] INFN, Sez Bologna, Bologna, Italy; [Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cuffiani, M.; Diotalevi, T.; Fanfani, A.; Fontanesi, E.; Giommi, L.; Guiducci, L.; Iemmi, F.; Navarria, F. L.; Primavera, F.; Rossia, A. M.; Rovelli, T.; Siroli, G. P.] Univ Bologna, Bologna, Italy; [Albergo, S.; Costa, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.] INFN, Sez Catania, Catania, Italy; [Albergo, S.; Costa, S.; Potenza, R.; Tricomi, A.; Tuve, C.] Univ Catania, Catania, Italy; [Barbagli, G.; Cassese, A.; Ceccarelli, R.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Fiori, F.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Meschini, M.; Paoletti, S.; Seidita, R.; Sguazzoni, G.; Viliani, L.] INFN, Sez Firenze, Florence, Italy; [Ceccarelli, R.; Ciulli, V.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Lizzo, M.; Seidita, R.] Univ Firenze, Florence, Italy; [Benussi, L.; Bianco, S.; Piccolo, D.] INFN, Lab Nazl Frascati, Frascati, Italy; [Bozzo, M.; Ferro, F.; Mulargia, R.; Robutti, E.; Tosi, S.] INFN, Sez Genova, Genoa, Italy; [Bozzo, M.; Mulargia, R.; Tosi, S.] Univ Genoa, Genoa, Italy; [Benaglia, A.; Beschi, A.; Brivio, F.; Cetorelli, F.; Ciriolo, V.; De Guio, F.; Dinardo, M. E.; Dini, P.; Gennai, S.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Monti, F.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; de Fatisa, T. Tabarelli; Valsecchi, D.; Zuolo, D.] INFN, Sez Milano Bicocca, Milan, Italy; [Beschi, A.; Brivio, F.; Cetorelli, F.; Ciriolo, V.; De Guio, F.; Dinardo, M. E.; Ghezzi, A.; Govoni, P.; Guzzi, L.; Monti, F.; Paganoni, M.; Ragazzi, S.; de Fatisa, T. Tabarelli; Valsecchi, D.; Zuolo, D.] Univ Milano Bicocca, Milan, Italy; [Buontempo, S.; Cavallo, N.; De Iorioa, A.; Fabozzi, F.; Fienga, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Paolucci, P.; Rossi, B.; Sciacca, C.] INFN, Sez Napoli, Naples, Italy; [De Iorioa, A.; Iorio, A. O. M.; Lista, L.; Sciacca, C.] Univ Napoli Federico II, Naples, Italy; [Cavallo, N.; Fabozzi, F.] Univ Basilicata, Potenza, Italy; [Meola, S.] Univ G Marconi, Rome, Italy; [Azzi, P.; Bacchetta, N.; Bisello, D.; Bortignon, P.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Manzano, P. De Castro; Dorigo, T.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Layer, L.; Margoni, M.; Meneguzzo, A. T.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Strong, G.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] INFN, Sez Padova, Padua, Italy; [Bisello, D.; Bragagnolo, A.; Carlin, R.; Gasparini, F.; Gasparini, U.; Hoh, S. Y.; Margoni, M.; Meneguzzo, A. T.; Presilla, M.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tosi, M.; Yarar, H.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.] Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; [Aime, C.; Braghieri, A.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.] INFN, Sez Pavia, Pavia, Italy; [Aime, C.; Calzaferri, S.; Fiorina, D.; Montagna, P.; Ratti, P.; Ressegotti, M.; Riccardi, C.; Vitulo, P.] Univ Pavia, Pavia, Italy; [Bilei, G. M.; Ciangottini, D.; Fano, L.; Mantovani, G.; Mariani, V.; Menichelli, M.; Moscatelli, F.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Spiga, D.; Tedeschi, T.] INFN, Sez Perugia, Perugia, Italy; [Ciangottini, D.; Fano, L.; Mantovani, G.; Mariani, V.; Piccinelli, A.; Rossi, A.; Santocchia, A.; Tedeschi, T.] Univ Perugia, Perugia, Italy; [Androsov, K.; Azzurri, P.; Bagliesi, G.; Bertacchi, V.; Bianchini, L.; Bossini, E.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Di Domenico, M. R.; Donato, S.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Ramirez-Sanchez, G.; Rizzi, A.; Rolandi, G.; Chowdhury, S. Roy; Scribano, A.; Shafiei, N.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Turini, N.; Venturi, A.; Verdini, P. G.] INFN, Sez Pisa, Pisa, Italy; [Ciocci, M. A.; Messineo, A.; Rizzi, A.; Shafiei, N.; Tonelli, G.] Univ Pisa, Pisa, Italy; [Bertacchi, V.; Ligabue, F.; Manca, E.; Mandorli, G.; Ramirez-Sanchez, G.; Rolandi, G.; Chowdhury, S. Roy] Scuola Normale Super Pisa, Pisa, Italy; [Di Domenico, M. R.; Turini, N.] Univ Siena, Siena, Italy; [Cavallari, F.; Cipriani, M.; Del Re, D.; Di Marco, E.; Longo, E.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Tramontano, R.] INFN, Sez Roma, Rome, Italy; [Cipriani, M.; Del Re, D.; Longo, E.; Organtini, G.; Paramatti, R.; Quaranta, C.; Rahatlou, S.; Santanastasio, F.; Soffi, L.; Tramontano, R.] Sapienza Univ Roma, Rome, Italy; [Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Biino, C.; Cappati, A.; Cartiglia, N.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Legger, F.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, L. Pinna; Ruspa, M.; Salvatico, R.; Siviero, F.; Sola, V.; Solano, A.; Soldi, D.; Staiano, A.; Tornago, M.; Trocino, D.] INFN, Sez Torino, Turin, Italy; [Amapane, N.; Argiro, S.; Bellan, R.; Bellora, A.; Antequera, J. Berenguer; Cappati, A.; Costa, M.; Covarelli, R.; Kiani, B.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Angioni, L. Pinna; Salvatico, R.; Siviero, F.; Solano, A.; Soldi, D.; Tornago, M.; Trocino, D.] Univ Torino, Turin, Italy; [Arcidiacono, R.; Arneodo, M.; Ruspa, M.] Univ Piemonte Orientale, Novara, Italy; [Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Vazzoler, F.] INFN, Sez Trieste, Trieste, Italy; [Candelise, V.; Da Rold, A.; Della Ricca, G.; Vazzoler, F.] Univ Trieste, Trieste, Italy; [Dogra, S.; Huh, C.; Kim, B.; Kim, D. H.; Kim, G. N.; Lee, J.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S. I.; Radburn-Smith, B. C.; Sekmen, S.; Yang, Y. C.] Kyungpook Natl Univ, Daegu, South Korea; [Kim, H.; Moon, D. H.] Chonnam Natl Univ, Inst Univ & Elementary Particles, Kwangju, South Korea; [Francois, B.; Kim, T. J.; Park, J.] Hanyang Univ, Seoul, South Korea; [Cho, S.; Choi, S.; Go, Y.; Hong, B.; Lee, K.; Lee, K. S.; Lim, J.; Park, J.; Park, S. K.; Yoo, J.] Korea Univ, Seoul, South Korea; [Goh, J.; Gurtu, A.] Kyung Hee Univ, Dept Phys, Seoul, South Korea; [Kim, H. S.; Kim, Y.] Sejong Univ, Seoul, South Korea; [Almond, J.; Bhyun, J. H.; Choi, J.; Jeon, S.; Kim, J.; Kim, J. S.; Ko, S.; Kwon, H.; Lee, H.; Lee, S.; Nam, K.; Oh, B. H.; Oh, M.; Oh, S. B.; Seo, H.; Yang, U. K.; Yoon, I.] Seoul Natl Univ, Seoul, South Korea; [Jeon, D.; Kim, J. H.; Ko, B.; Lee, J. S. H.; Park, I. C.; Roh, Y.; Song, D.; Watson, I. J.] Univ Seoul, Seoul, South Korea; [Ha, S.; Yoo, H. D.] Yonsei Univ, Dept Phys, Seoul, South Korea; [Choi, Y.; Hwang, C.; Jeong, Y.; Lee, H.; Lee, Y.; Yu, I.] Sungkyunkwan Univ, Suwon, South Korea; [Maghrbi, Y.] Amer Univ Middle East AUM, Coll Engn & Technol, Dasman, Kuwait; [Veckalns, V.] Riga Tech Univ, Riga, Latvia; [Ambrozas, M.; Juodagalvis, A.; Rinkevicius, A.; Tamulaitis, G.; Vaitkevicius, A.] Vilnius Univ, Vilnius, Lithuania; [Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.] Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; [Benitez, J. F.; Castaneda Hernandez, A.; Murillo Quijada, J. A.; Valencia Palomo, L.] Univ Sonora UNISON, Hermosillo, Sonora, Mexico; [Ayala, G.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Lopez-Fernandez, R.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Sanchez-Hernandez, A.] Ctr Invest Estudios Avanzados IPN, Mexico City, DF, Mexico; [Carrillo Moreno, S.; Oropeza Barrera, C.; Ramirez-Garcia, M.; Vazquez Valencia, F.] Univ Iberoamer, Mexico City, DF, Mexico; [Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.] Benemerita Univ Autonoma Puebla, Puebla, Mexico; [Mijuskovic, J.; Raicevic, N.] Univ Montenegro, Podgorica, Montenegro; [Krofcheck, D.] Univ Auckland, Auckland, New Zealand; [Bheesette, S.; Butler, P. H.] Univ Canterbury, Christchurch, New Zealand; [Ahmad, A.; Asghar, M. I.; Awais, A.; Awan, M. I. M.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; [Avati, V.; Grzanka, L.; Malawski, M.] AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; [Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Gorski, M.; Kazana, M.; Szleper, M.; Traczyk, P.; Zalewski, P.] Natl Ctr Nucl Res, Otwock, Poland; [Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Walczak, M.] Univ Warsaw, Inst Expt Phys, Fac Phys, Warsaw, Poland; [Araujo, M.; Bargassa, P.; Bastos, D.; Boletti, A.; Faccioli, P.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Niknejad, T.; Seixas, J.; Shchelina, K.; Toldaiev, O.; Varela, J.] Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; [Afanasiev, S.; Budkouski, D.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavine, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Palichik, V.; Perelygin, V.; Savina, M.; Seitova, D.; Shalaev, V.; Shmatov, S.; Shulha, S.; Smirnov, V.; Teryaev, O.; Voytishin, N.; Zarubin, A.; Zhizhin, I.] Joint Inst Nucl Res, Dubna, Russia; [Gavrilov, G.; Golovtcov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sosnov, D.; Sulimov, V.; Uvarov, L.; Volkov, S.; Vorobyev, A.] Petersburg Nucl Phys Inst, St Petersburg, Russia; [Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Pivovarov, G.; Tlisov, D.; Toropin, A.] Inst Nucl Res, Moscow, Russia; [Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Nikitenko, A.; Popov, V.; Safronov, G.; Spiridonov, A.; Stepennov, A.; Toms, M.; Vlasov, E.; Zhokin, A.] NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; [Aushev, T.] Moscow Inst Phys & Technol, Moscow, Russia; [Chistov, R.; Danilov, M.; Oskin, A.; Parygin, P.; Polikarpov, S.] Natl Res Nucl Univ Moscow Engn Phys Inst MEPhI, Moscow, Russia; [Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Terkulov, A.] PN Lebedev Phys Inst, Moscow, Russia; [Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V.; Korneeva, N.; Lokhtin, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Volkov, P.] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; [Blinov, V.; Dimova, T.; Kardapoltsev, L.; Ovtin, I.; Skovpen, Y.] Novosibirsk State Univ NSU, Novosibirsk, Russia; [Azhgirey, I.; Bayshev, I.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.] Inst High Energy Phys Natl Res Ctr Kurchatov Inst, Protvino, Russia; [Babaev, A.; Iuzhakov, A.; Okhotnikov, V.; Sukhikh, L.] Natl Res Tomsk Polytech Univ, Tomsk, Russia; [Borchsh, V.; Ivanchenko, V.; Tcherniaev, E.] Tomsk State Univ, Tomsk, Russia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.] Univ Belgrade:, Fac Phys, Belgrade, Serbia; [Adzic, P.; Dordevic, M.; Milenovic, P.; Milosevic, J.] VINCA Inst Nucl Sci, Belgrade, Serbia; [Aguilar-Benitez, M.; Alcaraz Maestre, J.; Alvarez Fernandez, A.; Bachiller, I.; Barrio Luna, M.; Bedoya, Cristina F.; Carrillo Montoya, C. A.; Cepeda, M.; Cerrada, M.; Colino, N.; De La cruz, B.; Delgado Peris, A.; Fernandez Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Leon Holgado, J.; Moran, D.; Navarro Tobar, A.; Perez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I.; Romero, L.; Sanchez Navas, S.; Soares, M. S.; Urda Gomez, L.; Willmott, C.] Ctr Invest Energet Medioambient & Tecnol CIEMAT, Madrid, Spain; [Albajar, C.; de Troconiz, J. F.; Reyes-Almanza, R.] Univ Autonoma Madrid, Madrid, Spain; [Alvarez Gonzalez, B.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Palencia Cortezon, E.; Ramon Alvarez, C.; Ripoll Sau, J.; Rodriguez Bouza, V.; Sanchez Cruz, S.; Trapote, A.] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaci Asturias ICTEA, Oviedo, Spain; [Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernandez Madrazo, C.; Fernandez Manteca, P. J.; Garcia Alonso, A.; Gomez, G.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Ricci-Tam, F.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vizan Garcia, J. M.] Univ Cantabria, CSIC, Inst Fis Cantabria IFCA, Santander, Spain; [Jayananda, M. K.; Kailasapathy, B.; Sonnadara, D. U. J.; Wickramarathna, D. D. C.] Univ Colombo, Colombo, Sri Lanka; [Dharmaratna, W. G. D.; Liyanage, K.; Perera, N.; Wickramage, N.] Univ Ruhuna, Dept Phys, Matara, Sri Lanka; [Alvarez Fernandez, A.; Aarrestad, T. K.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Baechler, J.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Beni, N.; Bianco, M.; Bocci, A.; Brondolin, E.; Camporesi, T.; Garrido, M. Capeans; Cerminara, G.; Chhibra, S. S.; Cristella, L.; d'Enterria, D.; Dabrowski, A.; Daci, N.; David, A.; De Roeck, A.; Deile, M.; Di Maria, R.; Dobson, M.; Dunser, M.; Dupont, N.; Elliott-Peisert, A.; Emriskova, N.; Fallavollita, F.; Fasanella, D.; Fiorendi, S.; Florent, A.; Franzoni, G.; Fulcher, J.; Funk, W.; Giani, S.; Gigi, D.; Gill, K.; Glege, F.; Gouskos, L.; Guilbaud, M.; Haranko, M.; Hegeman, J.; Iiyama, Y.; Innocente, V.; James, T.; Janot, P.; Kaspar, J.; Kieseler, J.; Komm, M.; Kratochwil, N.; Lange, C.; Laurila, S.; Lecoq, P.; Long, K.; Lourenco, C.; Malgeri, L.; Mallios, S.; Mannelli, M.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Quast, T.; Rabady, D.; Racz, A.; Rieger, M.; Rovere, M.; Sakulin, H.; Salfeld-Nebgen, J.; Scarfi, S.; Schaefer, C.; Schwick, C.; Selvaggi, M.; Sharma, A.; Silva, P.; Snoeys, W.; Sphicas, P.; Summers, S.; Tavolaro, V. R.; Treille, D.; Tsirou, A.; Van Onsem, G. P.; Verzetti, M.; Wozniak, K. A.; Zeuner, W. D.] CERN, European Org Nucl Res, Geneva, Switzerland; [Caminada, L.; Ebrahimi, A.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Missiroli, M.; Rohe, T.] Paul Scherrer Inst, Villigen, Switzerland; [Backhaus, M.; Berger, P.; Calandri, A.; Chernyavskaya, N.; De Cosa, A.; Dissertori, G.; Dittmar, M.; Donega, M.; Dorfer, C.; Gadek, T.; Espinosa, T. A. Gomez; Grab, C.; Hits, D.; Lustermann, W.; Lyon, A. -M.; Manzoni, R. A.; Meinhard, M. T.; Micheli, F.; Nessi-Tedaldi, F.; Niedziela, J.; Pauss, F.; Perovic, V.; Perrin, G.; Pigazzini, S.; Ratti, M. G.; Reichmann, M.; Reissel, C.; Reitenspiess, T.; Ristic, B.; Ruini, D.; Becerra, D. A. Sanz; Schoenenberger, M.; Stampf, V.; Steggemann, J.; Wallny, R.; Zhu, D. H.] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; [Amsler, C.; Botta, C.; Brzhechko, D.; Canelli, M. F.; Del Burgo, R.; Heikkilae, J. K.; Huwiler, M.; Jofrehei, A.; Kilminster, B.; Leontsinis, S.; Macchiolo, A.; Meiring, P.; Mikuni, V. M.; Molinatti, U.; Neutelings, I.; Rauco, G.; Reimers, A.; Robmann, P.; Schweiger, K.; Takahashi, Y.] Univ Zurich, Zurich, Switzerland; [Adloff, C.; Kuo, C. M.; Lin, W.; Roy, A.; Sarkar, T.; Yu, S. S.] Natl Cent Univ, Chungli, Taiwan; [Ceard, L.; Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Hou, W. -S.; Li, Y. Y.; Lu, R. -S.; Paganis, E.; Psallidas, A.; Steen, A.; Yazgan, E.] Natl Taiwan Univ NTU, Taipei, Taiwan; [Asavapibhop, B.; Asawatangtrakuldee, C.; Srimanobhas, N.] Chulalongkorn Univ, Dept Phys, Fac Sci, Bangkok, Thailand; [Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dolek, F.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Gokbulut, G.; Guler, Y.; Guler, E. Gurpinar; Hos, I.; Isik, C.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Onengut, G.; Ozdemir, K.; Polatoz, A.; Simsek, A. E.; Tali, B.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.] Cukurova Univ, Sci & Art Fac, Dept Phys, Adana, Turkey; [Isildak, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.] Middle East Tech Univ, Dept Phys, Ankara, Turkey; [Akgun, B.; Atakisi, I. O.; Gulmez, E.; Kaya, M.; Kaya, O.; Ozcelik, O.; Tekten, S.; Yetkin, E. A.] Bogazici Univ, Istanbul, Turkey; [Cakir, A.; Cankocak, K.; Komurcu, Y.; Sen, S.] Istanbul Tech Univ, Istanbul, Turkey; [Sen, F. Aydogmus; Cerci, S.; Kaynak, B.; Ozkorucuklu, S.; Cerci, D. Sunar] Istanbul Univ, Istanbul, Turkey; [Grynyov, B.] Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; [Levchuk, L.] Kharkov Inst Phys & Technol, Natl Sci Ctr, Kharkov, Ukraine; [Bhal, E.; Bologna, S.; Brooke, J. J.; Bundock, A.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Krikler, B.; Paramesvaran, S.; Sakuma, T.; El Nasr-Storey, S. Seif; Smith, V. J.; Stylianou, N.; Taylor, J.; Titterton, A.] Univ Bristol, Bristol, Avon, England; [Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Ellis, K. V.; Harder, K.; Harper, S.; Linacre, J.; Manolopoulos, K.; Newbold, D. M.; Olaiya, E.; Petyt, D.; Reis, T.; Schuh, T.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.] Rutherford Appleton Lab, Didcot, Oxon, England; [Bainbridge, R.; Bloch, P.; Bonomally, S.; Borg, J.; Breeze, S.; Buchmuller, O.; Cepaitis, V.; Chahal, G. S.; Colling, D.; Dauncey, P.; Davies, G.; Della Negra, M.; Fedi, G.; Hall, G.; Iles, G.; Langford, J.; Lyons, L.; Magnan, A. -M.; Malik, S.; Martelli, A.; Milosevic, V.; Nash, J.; Palladino, V.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Tapper, A.; Uchida, K.; Virdee, T.; Wardle, N.; Webb, S. N.; Winterbottom, D.; Zecchinelli, A. G.] Imperial Coll, London, England; [Cole, J. E.; Khan, A.; Kyberd, P.; Mackay, C. K.; Reid, I. D.; Teodorescu, L.; Zahid, S.] Brunel Univ, Uxbridge, Middx, England; [Abdullin, S.; Brinkerhoff, A.; Call, K.; Caraway, B.; Dittmann, J.; Hatakeyama, K.; Kanuganti, A. R.; Madrid, C.; McMaster, B.; Pastika, N.; ; Yerevan Physics Institute; Belarusian State University; University of Antwerp; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ghent University; Universite Catholique Louvain; Centro Brasileiro de Pesquisas Fisicas; Universidade do Estado do Rio de Janeiro; Universidade Estadual Paulista; Bulgarian Academy of Sciences; University of Sofia; Beihang University; Tsinghua University; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Peking University; Sun Yat Sen University; Chinese Academy of Sciences; Fudan University; Fudan University; Zhejiang University; Universidad de los Andes (Colombia); Universidad de Antioquia; University of Split; University of Split; University of Cyprus; Charles University Prague; Escuela Politecnica Nacional Ecuador; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Egyptian Network of High Energy Physics (ENHEP); Egyptian Knowledge Bank (EKB); Fayoum University; National Institute of Chemical Physics & Biophysics (NICPB); University of Helsinki; Helsinki Institute of Physics; Lappeenranta University of Technology; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Institut Polytechnique de Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Georgian Technical University; RWTH Aachen University; RWTH Aachen University; RWTH Aachen University; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); University of Hamburg; Helmholtz Association; Karlsruhe Institute of Technology; National Centre of Scientific Research Demokritos; National & Kapodistrian University of Athens; National Technical University of Athens; University of Ioannina; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; University of Debrecen; Eszterhazy Karoly Catholic University; Indian Institute of Science (IISC) - Bangalore; Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); Panjab University; University of Delhi; Homi Bhabha National Institute; Saha Institute of Nuclear Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Bhabha Atomic Research Center (BARC); Tata Institute of Fundamental Research (TIFR); Tata Institute of Fundamental Research (TIFR); Indian Institute of Science Education & Research (IISER) Pune; Isfahan University of Technology; University College Dublin; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Istituto Nazionale di Fisica Nucleare (INFN); University of Bologna; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Florence; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); University of Genoa; Istituto Nazionale di Fisica Nucleare (INFN); University of Milano-Bicocca; Istituto Nazionale di Fisica Nucleare (INFN); University of Naples Federico II; University of Basilicata; Guglielmo Marconi University; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua; University of Trento; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; Istituto Nazionale di Fisica Nucleare (INFN); University of Perugia; Istituto Nazionale di Fisica Nucleare (INFN); University of Pisa; Scuola Normale Superiore di Pisa; University of Siena; Istituto Nazionale di Fisica Nucleare (INFN); Sapienza University Rome; Istituto Nazionale di Fisica Nucleare (INFN); University of Turin; University of Eastern Piedmont Amedeo Avogadro; Istituto Nazionale di Fisica Nucleare (INFN); University of Trieste; Kyungpook National University; Chonnam National University; Hanyang University; Korea University; Kyung Hee University; Sejong University; Seoul National University (SNU); University of Seoul; Yonsei University; Sungkyunkwan University (SKKU); American University of Middle East; Riga Technical University; Vilnius University; Universiti Malaya; Universidad de Sonora; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Universidad Iberoamericana Ciudad de Mexico; Benemerita Universidad Autonoma de Puebla; University of Montenegro; University of Auckland; University of Canterbury; National Centre for Physics - Pakistan; Quaid I Azam University; AGH University of Science & Technology; National Centre for Nuclear Research; University of Warsaw; Laboratorio de Instrumentacao e Fisica Experimental de Particulas; Joint Institute for Nuclear Research - Russia; National Research Centre - Kurchatov Institute; Petersburg Nuclear Physics Institute; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Moscow Institute of Physics & Technology; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Lomonosov Moscow State University; Tomsk Polytechnic University; Tomsk State University; University of Belgrade; University of Belgrade; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Autonomous University of Madrid; University of Oviedo; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Colombo; University Ruhuna; European Organization for Nuclear Research (CERN); Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Zurich; National Central University; National Taiwan University; Chulalongkorn University; Cukurova University; Middle East Technical University; Bogazici University; Istanbul Technical University; Istanbul University; National Academy of Sciences Ukraine; Institute for Scintillation Materials of the National Academy of Sciences of Ukraine; Kharkov Institute of Physics & Technology; University of Bristol; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); STFC Rutherford Appleton Laboratory; Imperial College London; Brunel University; Baylor University; Catholic University of America; University of Alabama System; University of Alabama Tuscaloosa; Boston University; Brown University; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; University of California System; University of California San Diego; University of California System; University of California Santa Barbara; California Institute of Technology; Carnegie Mellon University; University of Colorado System; University of Colorado Boulder; Cornell University; United States Department of Energy (DOE); University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; State University System of Florida; Florida State University; Florida Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Iowa; Johns Hopkins University; University of Kansas; Kansas State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Maryland; University of Maryland College Park; Massachusetts Institute of Technology (MIT); University of Minnesota System; University of Minnesota Twin Cities; University of Mississippi; University of Nebraska System; University of Nebraska Lincoln; State University of New York (SUNY) System; State University of New York (SUNY) Buffalo; Northeastern University; Northwestern University; University of Notre Dame; University System of Ohio; Ohio State University; Princeton University; University of Puerto Rico; University of Puerto Rico Mayaguez; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Rice University; University of Rochester; Rutgers State University New Brunswick; University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; Texas Tech University System; Texas Tech University; Vanderbilt University; University of Virginia; Wayne State University; University of Wisconsin System; University of Wisconsin Madison; Technische Universitat Wien; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Universite Libre de Bruxelles; CEA; UDICE-French Research Universities; Universite Paris Saclay; Universidade Estadual de Campinas; Universidade Federal do Rio Grande do Sul; Universidade Federal de Mato Grosso do Sul; Nanjing Normal University; University of Iowa; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; Joint Institute for Nuclear Research - Russia; Egyptian Knowledge Bank (EKB); Helwan University; Egyptian Knowledge Bank (EKB); Zewail City of Science & Technology; Egyptian Knowledge Bank (EKB); Ain Shams University; Egyptian Knowledge Bank (EKB); British University in Egypt; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Erzincan Binali Yildirim University; European Organization for Nuclear Research (CERN); RWTH Aachen University; University of Hamburg; Isfahan University of Technology; Brandenburg University of Technology Cottbus; Lomonosov Moscow State University; Egyptian Knowledge Bank (EKB); Assiut University; Eszterhazy Karoly Catholic University; University of Debrecen; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Institute for Nuclear Research; Eotvos Lorand University; Eotvos Lorand Research Network; Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bhubaneswar; Institute of Physics Bhubaneswar (IOPB); Shoolini University; University of Hyderabad; Visva Bharati University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology (IIT) - Madras; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Sharif University of Technology; University of Science & Technology of Mazandaran; Istituto Nazionale di Fisica Nucleare (INFN); Universita degli Studi di Bari Aldo Moro; Politecnico di Bari; Italian National Agency New Technical Energy & Sustainable Economics Development; University of Naples Federico II; Riga Technical University; Russian Academy of Sciences; Institute for Nuclear Research of the Russian Academy of Sciences; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Peter the Great St. Petersburg Polytechnic University; State University System of Florida; University of Florida; Imperial College London; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Moscow Institute of Physics & Technology; California Institute of Technology; Russian Academy of Sciences; Budker Institute of Nuclear Physics; University of Belgrade; Eastern University, Sri Lanka; Istituto Nazionale di Fisica Nucleare (INFN); University of Pavia; National & Kapodistrian University of Athens; University of Zurich; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); Universite de Savoie; Sirnak University; Tsinghua University; Near East University; Beykent University; Istanbul Aydin University; Mersin University; Piri Reis University; Adiyaman University; Ozyegin University; Izmir Institute of Technology; Necmettin Erbakan University; Bozok University; Marmara University; Kafkas University; Istanbul Bilgi University; Hacettepe University; Vrije Universiteit Brussel; University of Southampton; Monash University; Karamanoglu Mehmetbey University; Bingol University; Georgian Technical University; Sinop University; Mimar Sinan Guzel Sanatlar University; Erciyes University; Qatar Foundation (QF); Texas A&M University Qatar; Kyungpook National University Sirunyan, AM (corresponding author), Yerevan Phys Inst, Yerevan, Armenia. 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Dinardo, Mauro Emanuele/0000-0002-8575-7250; Cappati, Alessandra/0000-0003-4386-0564; Primavera, Federica/0000-0001-6253-8656; Faltermann, Nils/0000-0001-6506-3107; Lee, Sung-Won/0000-0002-3388-8339; Wu, Zhenbin/0000-0003-2165-9501; Ruiz Jimeno, Alberto/0000-0002-3639-0368; Sahasransu, Abanti Ranadhir/0000-0003-1505-1743; Covarelli, Roberto/0000-0003-1216-5235; Lee, Jason/0000-0002-2153-1519; Martinez Ruiz del Arbol, Pablo/0000-0002-7737-5121; Fernandez Ramos, Juan Pablo/0000-0002-0122-313X; Wilson, Graham/0000-0003-0917-4763; Schweiger, Korbinian/0000-0002-5846-3919; Schulte, Jan-Frederik/0000-0003-4421-680X; Jafari, Abideh/0000-0001-7327-1870; Zuolo, Davide/0000-0003-3072-1020; Novaes, Sergio/0000-0003-0471-8549; De Iorio, Agostino/0000-0002-9258-1345; Meyer, Arnd/0000-0001-9598-6623; Manca, Elisabetta/0000-0001-8946-655X; Cavallari, Francesca/0000-0002-1061-3877; Hamel de Monchenault, Gautier/0000-0002-3872-3592; Pigazzini, Simone/0000-0002-8046-4344; Dimova, Tatyana/0000-0002-9560-0660; Ricci-Tam, Francesca/0000-0001-9750-7702; Estevez Banos, Luis Ignacio/0000-0001-6195-3102; Tosi, Silvano/0000-0002-7275-9193; Tapper, Alexander/0000-0003-4543-864X; Prosper, Harrison Bertrand/0000-0002-4077-2713; Montagna, Paolo Maria/0000-0001-9647-9420; Consuegra Rodriguez, Sandra/0000-0002-1383-1837; Giammanco, Andrea/0000-0001-9640-8294; Blekman, Freya/0000-0002-7366-7098; Melo da Costa, Eliza/0000-0002-5016-6434; Meridiani, Paolo/0000-0002-8480-2259; Grzanka, Leszek/0000-0002-3599-854X; Shi, Kun/0000-0001-9833-5674; Brigljevic, Vuko/0000-0001-5847-0062; Erdmann, Martin/0000-0002-1653-1303; Tonelli, Guido Emilio/0000-0003-2606-9156; Rizzi, Andrea/0000-0002-4543-2718; Safonov, Alexei/0000-0001-9497-5471; Hurtado Anampa, Kenyi/0000-0002-9779-3566; CETORELLI, FLAVIA/0000-0002-3061-1553; Donega, Mauro/0000-0001-9830-0412; Hernandez Calama, Jose Maria/0000-0001-6436-7547; Gerosa, Raffaele/0000-0001-8359-3734; Caminada, Lea/0000-0001-5677-6033; Mitra, Soureek/0000-0002-3060-2278; Palladino, Vito/0000-0002-9786-9620; Gonzalez Lopez, Oscar/0000-0002-4532-6464; Puerta Pelayo, Jesus/0000-0001-7390-1457; Perez Prada, Maximilian/0000-0002-2831-463X; Vami, Tamas Almos/0000-0002-0959-9211; Dharmaratna, Welathantri/0000-0002-6366-837X; Sosa Ricardo, Rafael Eduardo/0000-0002-2240-6699; Nash, Jordan/0000-0003-0607-6519; MEHTA, ANKITA/0000-0002-0433-4484; Bachtis, Michail/0000-0003-3110-0701; Konecki, Marcin/0000-0001-9482-4841; Malik, Sudhir/0000-0002-6356-2655; Abbiendi, Giovanni/0000-0003-4499-7562; Forthomme, Laurent/0000-0002-3302-336X; Bhatnagar, Vipin/0000-0002-8392-9610; Zhang, Zhicai/0000-0002-1630-0986; Snoeys, Walter/0000-0003-3541-9066; Backhaus, Malte/0000-0002-5888-2304; Mousa, Jehad/0000-0002-2978-2718; Malgeri, Luca/0000-0002-0113-7389; Grandi, Claudio/0000-0001-5998-3070; Rolandi, Luigi (Gigi)/0000-0002-0635-274X; Sharma, Ram Krishna/0000-0003-1181-1426; HAJ AHMAD (Vael HAJAHMAD), Wael/0000-0003-1491-0446; Monteno, Marco/0000-0002-3521-6333; D'Hondt, Jorgen/0000-0002-9598-6241; ALCARAZ MAESTRE, JUAN/0000-0003-0914-7474; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226; MD Ali, Mohd Adli/0000-0001-7203-0610; Faccioli, Pietro/0000-0003-1849-6692; Csanad, Mate/0000-0002-3154-6925; Sola, Valentina/0000-0001-6288-951X; Nguyen, Thong Q./0000-0003-3954-5131; Hinzmann, Andreas/0000-0002-2633-4696; Kole, Gouranga/0000-0002-3285-1497; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; Mnich, Joachim/0000-0001-7242-8426 BMBWF (Austria); FWF (Austria); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); CERN; CAS (China); MOST (China); NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal); JINR (Dubna); MON (Russia); ROSATOM (Russia); RAS (Russia); RFBR (Russia); NRC KI (Russia); MESTD (Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain); MoSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (USA); NSF (USA); Marie-Curie pro-gramme; European Research Council and Horizon 2020 Grant [675440, 724704, 752730, 765710]; Leventis Foundation; A.P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technolo-gie (IWT-Belgium); F.R.S.-FNRS (Belgium); FWO (Belgium) under the Excellence of Science - EOS - be.h project [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemein-schaft (DFG) under Germany's Excellence Strategy - EXC 2121 Quantum Universe [390833306]; Lendulet (Momentum) Programme (Hungary); Janos Bolyai Research Scholarship of the Hun-garian Academy of Sciences (Hungary); New National Excellence Program uNKP (Hungary); NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]; Council of Science and Industrial Research, India; HOMING PLUS pro-gramme of the Foundation for Polish Science (Poland); European Union (Poland); Regional Development Fund (Poland); Mobility Plus pro-gramme of the Ministry of Science and Higher Education (Poland); National Science Center (Poland) [2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406]; National Priorities Research Program by Qatar National Research Fund; Ministry of Science and Higher Education (Russia) [0723-2020-0041]; Tomsk Polytechnic University Com-petitiveness Enhancement Program; Programa Estatal de Fo-mento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2015-0509]; Programa Severo Ochoa del Principado de Asturias; Thalis programme; Aristeia programme; EU-ESF; Greek NSRF; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA) BMBWF (Austria); FWF (Austria)(Austrian Science Fund (FWF)); FWO (Belgium)(FWO); CNPq (Brazil)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); CAPES (Brazil)(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)); FAPERJ (Brazil)(Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)); FAPERGS (Brazil)(Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS)); FAPESP (Brazil)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); MES (Bulgaria); CERN; CAS (China)(Chinese Academy of Sciences); MOST (China)(Ministry of Science and Technology, China); NSFC (China)(National Natural Science Foundation of China (NSFC)); COLCIENCIAS (Colombia)(Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERC PUT (Estonia); ERDF (Estonia); Academy of Finland (Finland)(Academy of Finland); MEC (Finland); HIP (Finland); CEA (France)(French Atomic Energy Commission); CNRS/IN2P3 (France)(Centre National de la Recherche Scientifique (CNRS)); BMBF (Germany)(Federal Ministry of Education & Research (BMBF)); DFG (Germany)(German Research Foundation (DFG)); HGF (Germany); GSRT (Greece)(Greek Ministry of Development-GSRT); NKFIA (Hungary); DAE (India)(Department of Atomic Energy (DAE)); DST (India)(Department of Science & Technology (India)); IPM (Iran); SFI (Ireland)(Science Foundation Ireland); INFN (Italy)(Istituto Nazionale di Fisica Nucleare (INFN)); MSIP (Republic of Korea); NRF (Republic of Korea)(National Research Foundation of Korea); MES (Latvia); LAS (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand)(New Zealand Ministry of Business, Innovation and Employment (MBIE)); PAEC (Pakistan); MSHE (Poland); NSC (Poland); FCT (Portugal)(Fundacao para a Ciencia e a Tecnologia (FCT)); JINR (Dubna); MON (Russia); ROSATOM (Russia); RAS (Russia)(Russian Academy of Sciences); RFBR (Russia)(Russian Foundation for Basic Research (RFBR)); NRC KI (Russia); MESTD (Serbia)(Ministry of Education, Science & Technological Development, Serbia); SEIDI (Spain); CPAN (Spain); PCTI (Spain); FEDER (Spain)(European CommissionSpanish Government); MoSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter (Thailand); IPST (Thailand); STAR (Thailand); NSTDA (Thailand); TUBITAK (Turkey)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)); TAEK (Turkey)(Ministry of Energy & Natural Resources - Turkey); NASU (Ukraine); STFC (United Kingdom)(UK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)); DOE (USA)(United States Department of Energy (DOE)); NSF (USA)(National Science Foundation (NSF)); Marie-Curie pro-gramme(European Commission); European Research Council and Horizon 2020 Grant; Leventis Foundation; A.P. Sloan Foundation(Alfred P. Sloan Foundation); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Belgian Federal Science Policy Office(Belgian Federal Science Policy Office); Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)(Fonds de la Recherche Scientifique - FNRS); Agentschap voor Innovatie door Wetenschap en Technolo-gie (IWT-Belgium)(Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)); F.R.S.-FNRS (Belgium)(Fonds de la Recherche Scientifique - FNRS); FWO (Belgium) under the Excellence of Science - EOS - be.h project(FWO); Beijing Municipal Science & Technology Commission(Beijing Municipal Science & Technology Commission); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Deutsche Forschungsgemein-schaft (DFG) under Germany's Excellence Strategy - EXC 2121 Quantum Universe(German Research Foundation (DFG)); Lendulet (Momentum) Programme (Hungary); Janos Bolyai Research Scholarship of the Hun-garian Academy of Sciences (Hungary); New National Excellence Program uNKP (Hungary); NKFIA (Hungary); Council of Science and Industrial Research, India(Council of Scientific & Industrial Research (CSIR) - India); HOMING PLUS pro-gramme of the Foundation for Polish Science (Poland); European Union (Poland); Regional Development Fund (Poland); Mobility Plus pro-gramme of the Ministry of Science and Higher Education (Poland); National Science Center (Poland)(National Science Centre, Poland); National Priorities Research Program by Qatar National Research Fund(Qatar National Research Fund (QNRF)); Ministry of Science and Higher Education (Russia); Tomsk Polytechnic University Com-petitiveness Enhancement Program; Programa Estatal de Fo-mento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu; Programa Severo Ochoa del Principado de Asturias(Principality of Asturias); Thalis programme; Aristeia programme; EU-ESF(European Commission); Greek NSRF(Greek Ministry of Development-GSRT); Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation(The Welch Foundation); Weston Havens Foundation (USA) We congratulate our colleagues in the CERN accelerator depart-ments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS in-stitutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construc-tion and operation of the LHC and the CMS detector provided by the following funding agencies: BMBWF and FWF (Austria) ; FNRS and FWO (Belgium) ; CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil) ; MES (Bulgaria) ; CERN; CAS, MOST, and NSFC (China) ; COLCIENCIAS (Colombia) ; MSES and CSF (Croatia) ; RIF (Cyprus) ; SENESCYT (Ecuador) ; MoER, ERC PUT and ERDF (Estonia) ; Academy of Finland, MEC, and HIP (Finland) ; CEA and CNRS/IN2P3 (France) ; BMBF, DFG, and HGF (Germany) ; GSRT (Greece) ; NKFIA (Hun-gary) ; DAE and DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP and NRF (Republic of Korea) ; MES (Latvia) ; LAS (Lithuania) ; MOE and UM (Malaysia) ; BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico) ; MOS (Montenegro) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE and NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON, ROSATOM, RAS, RFBR, and NRC KI (Russia) ; MESTD (Serbia) ; SEIDI, CPAN, PCTI, and FEDER (Spain) ; MoSTR (Sri Lanka) ; Swiss Funding Agencies (Switzerland) ; MST (Taipei) ; ThEPCenter, IPST, STAR, and NSTDA (Thailand) ; TUBITAK and TAEK (Turkey) ; NASU (Ukraine) ; STFC (United Kingdom) ; DOE and NSF (USA) . Individuals have received support from the Marie-Curie pro-gramme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, and 765710 (Eu-ropean Union) ; the Leventis Foundation; the A.P. Sloan Foun-dation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the Agentschap voor Innovatie door Wetenschap en Technolo-gie (IWT-Belgium) ; the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS - be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemein-schaft (DFG) under Germany's Excellence Strategy - EXC 2121 Quantum Universe - 390833306; the Lendulet (Momentum) Programme and the Janos Bolyai Research Scholarship of the Hun-garian Academy of Sciences, the New National Excellence Program uNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary) ; the Council of Science and Industrial Research, India; the HOMING PLUS pro-gramme of the Foundation for Polish Science, cofinanced from Eu-ropean Union, Regional Development Fund, the Mobility Plus pro-gramme of the Ministry of Science and Higher Education, the Na-tional Science Center (Poland) , contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the Na-tional Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, project no.; 0723-2020-0041 (Russia) ; the Tomsk Polytechnic University Com-petitiveness Enhancement Program; the Programa Estatal de Fo-mento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA) . 65 2 2 7 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0370-2693 1873-2445 PHYS LETT B Phys. Lett. B SEP 10 2021.0 820.0 136565 10.1016/j.physletb.2021.136565 0.0 AUG 2021 30 Astronomy & Astrophysics; Physics, Nuclear; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics WP4SU Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000713124400026 0
J Wang, WX; Wang, MF; Li, HX; Zhao, H; Wang, K; He, CT; Wang, J; Zheng, SF; Chen, JB Wang, Weixing; Wang, Mengfei; Li, Hongxia; Zhao, Heng; Wang, Kevin; He, Changtao; Wang, Jun; Zheng, Sifan; Chen, Jiabin Pavement crack image acquisition methods and crack extraction algorithms: A review JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING-ENGLISH EDITION English Review Highway engineering; Pavement crack; Image acquisition; Image processing; Crack extraction CONCRETE; FRACTURE; FUSION; LEVEL; ENHANCEMENT; SURFACES; RADAR; CLASSIFICATION; FUZZY; NSCT The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made. (C) 2019 Periodical Offices of Changan University. Publishing services by Elsevier B.V. on behalf of Owner. [Wang, Weixing; Wang, Mengfei; Li, Hongxia; Zhao, Heng] Changan Univ, Sch Informat Engn, Xian 710064, Shaanxi, Peoples R China; [Wang, Weixing; Wang, Kevin] Royal Inst Technol, S-10044 Stockholm, Sweden; [He, Changtao] Sichuan Jiuzhou Elect Grp Co Ltd, Mianyang 621000, Sichuan, Peoples R China; [Wang, Jun] Sichuan Univ, Sch Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China; [Zheng, Sifan; Chen, Jiabin] Liming Vocat Univ, Intelligent Mfg Engn Coll, Quanzhou 362000, Fujian, Peoples R China Chang'an University; Royal Institute of Technology; Sichuan University Wang, WX (corresponding author), Changan Univ, Sch Informat Engn, Xian 710064, Shaanxi, Peoples R China. znn525d@qq.com National Natural Science Fund in China [61170147]; National Natural Science Key Fund in China [U1401252] National Natural Science Fund in China(National Natural Science Foundation of China (NSFC)); National Natural Science Key Fund in China This research is financially supported by the National Natural Science Fund in China (grant No. 61170147) and the National Natural Science Key Fund in China (grant No. U1401252). 127 47 54 15 52 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGHENG DISTRICT 100717, PEOPLES R CHINA 2095-7564 J TRAFFIC TRANSP ENG J. Traffic Transp. Eng.-Engl. Ed. DEC 2019.0 6.0 6 535 556 10.1016/j.jtte.2019.10.001 0.0 22 Engineering, Civil; Transportation Science & Technology Emerging Sources Citation Index (ESCI) Engineering; Transportation JX9RG gold 2023-03-23 WOS:000504063700001 0
J Yuan, K; Jurado-Sanchez, B; Escarpa, A Yuan, Kaisong; Jurado-Sanchez, Beatriz; Escarpa, Alberto Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review JOURNAL OF NANOBIOTECHNOLOGY English Review Nanotechnology; Cancer and pathogens; Medical diagnosis; SERS; Plasmonic metal; Nano-hybrid; Magnetic materials; SERS tags WALLED CARBON NANOTUBES; LABEL-FREE DETECTION; SENSITIVE SERS DETECTION; SILVER NANOPARTICLES; GOLD NANOPARTICLES; CANCER-CELLS; FACILE SYNTHESIS; REAL-TIME; MAGNETIC MICROSPHERES; RECOGNITION ELEMENTS Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nano structures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates. [Yuan, Kaisong] Shantou Univ, Bioanalyt Lab, Med Coll, 22 Xinling Rd, Shantou 515041, Peoples R China; [Yuan, Kaisong; Jurado-Sanchez, Beatriz; Escarpa, Alberto] Univ Alcala, Dept Analyt Chem Phys Chem & Chem Engn, Madrid 28802, Spain; [Jurado-Sanchez, Beatriz; Escarpa, Alberto] Univ Alcala, Chem Res Inst Andres M del Rio, Madrid 28802, Spain Shantou University; Universidad de Alcala; Universidad de Alcala Jurado-Sanchez, B; Escarpa, A (corresponding author), Univ Alcala, Dept Analyt Chem Phys Chem & Chem Engn, Madrid 28802, Spain.;Jurado-Sanchez, B; Escarpa, A (corresponding author), Univ Alcala, Chem Res Inst Andres M del Rio, Madrid 28802, Spain. beatriz.jurado@uah.es; alberto.escarpa@uah.es Escarpa Miguel, Jesus Alberto/N-2293-2016 Escarpa Miguel, Jesus Alberto/0000-0002-7302-0948 Spanish Ministry of Economy, Industry and Competitiveness; EU [RYC-2015-17558]; MCIN/AEI; Community of Madrid [PID2020-118154GB-I00]; DISCOVER-UAH-CM Project [CM/JIN/2021-012, S2018/NMT-4349]; Community of Madrid (CAM) [REACT UE-CM2021-01]; European Union (UE), through the European Regional Development Fund (ERDF); SUMC Scientific Research Initiation; [510858045] Spanish Ministry of Economy, Industry and Competitiveness(Spanish Government); EU(European Commission); MCIN/AEI; Community of Madrid(Comunidad de Madrid); DISCOVER-UAH-CM Project; Community of Madrid (CAM)(Comunidad de Madrid); European Union (UE), through the European Regional Development Fund (ERDF); SUMC Scientific Research Initiation; This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness [Grant Numbers RYC-2015-17558, co-financed by EU (B.J.S)], Grant PID2020-118154GB-I00 funded by MCIN/AEI/10.13039/501100011033 (A. E and B.J.S); the Community of Madrid [Grant Numbers CM/JIN/2021-012 (B.J.S), TRANSNANOAVANSENS, S2018/NMT-4349 (A.E)]. B. J.S. and A. Escarpa acknowledge funding from DISCOVER-UAH-CM Project (Ref. REACT UE-CM2021-01), co-founded by Community of Madrid (CAM) and European Union (UE), through the European Regional Development Fund (ERDF) and supported as part of the EU's response to COVID-19 pandemic. K. Yuan acknowledge funding from SUMC Scientific Research Initiation Grant (Grant Number 510858045) 249 0 0 35 35 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1477-3155 J NANOBIOTECHNOL J. Nanobiotechnol. DEC 22 2022.0 20.0 1 10.1186/s12951-022-01711-3 0.0 28 Biotechnology & Applied Microbiology; Nanoscience & Nanotechnology Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Science & Technology - Other Topics 7F7LW 36544151.0 Green Accepted, gold 2023-03-23 WOS:000902025400002 0
J Herazo-Padilla, N; Augusto, V; Dalmas, B; Xie, XL; Bongue, B Herazo-Padilla, Nilson; Augusto, Vincent; Dalmas, Benjamin; Xie, Xiaolan; Bongue, Bienvenu A Decision-Tree-Based Bayesian Approach for Chance-Constrained Health Prevention Budget Rationing IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article Medical tests; Statistics; Sociology; Bayes methods; Electrocardiography; Medical services; Decision trees; Bayesian inference; decision tree; diabetes; electrocardiogram (ECG); health prevention; healthcare; machine learning; medical test rationing RISK; HYPERTENSION; READMISSION; FRAMEWORK; SELECTION; MEDICINE; CARE Medical test selection is a recurring problem in health prevention and consists of proposing a set of tests to each subject for diagnosis and treatment of pathologies. The problem is characterized by the unknown risk probability distribution across the population and two contradictory objectives: minimizing the number of tests and giving the medical test to all at-risk populations. This article sets this problem in a general framework of chance-constrained medical test rationing with unknown subject distribution over an attribute space and unknown risk probability but with a given sample population. A new approach combining decision-tree and Bayesian inference is proposed to allocate relevant medical tests according to the subjects' profile. Case studies on screening of hypertension and diabetes are conducted, and the performance of the proposed approach is evaluated. Significant savings on unnecessary tests are achieved with limited numbers of subjects needing but not receiving necessary tests. [Herazo-Padilla, Nilson] Univ Rosario, Grp Invest Direcc & Gerencia, Escuela Adm, Bogota 111711, Colombia; [Augusto, Vincent; Dalmas, Benjamin; Xie, Xiaolan] Univ Clermont Auvergne, Ctr CIS, CNRS, UMR 6158 LIMOS, F-42023 St Etienne, France; [Xie, Xiaolan] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200030, Peoples R China; [Bongue, Bienvenu] Univ Lyon, Univ Jean Monnet, Ctr Tech Appui & Format Ctr Examen Sante CETAF, Sainbiose Dysfonct Vasc & Hemostase,INSERM U1059, F-42100 St Etienne, France Universidad del Rosario; Centre National de la Recherche Scientifique (CNRS); Universite Clermont Auvergne (UCA); Shanghai Jiao Tong University; Institut National de la Sante et de la Recherche Medicale (Inserm) Herazo-Padilla, N (corresponding author), Univ Rosario, Grp Invest Direcc & Gerencia, Escuela Adm, Bogota 111711, Colombia. nilson.herazo@urosario.edu.co; augusto@emse.fr; benjamin.dalmas@emse.fr; xie@emse.fr; bienvenu.bongue@cetaf.fr Augusto, Vincent/0000-0002-5063-0831; Herazo-Padilla, Nilson/0000-0001-8936-342X; Xie, Xiaolan/0000-0002-6579-1523 Conference for Funders of Prevention of Autonomy Loss for the Elderly (CFPPA) of the Loire Department; CETAF Conference for Funders of Prevention of Autonomy Loss for the Elderly (CFPPA) of the Loire Department; CETAF Manuscript received June 30, 2020; revised October 14, 2020 and February 2, 2021; accepted March 22, 2021. This article was recommended for publication by Associate Editor K. Liu and Editor J. Li upon evaluation of the reviewers' comments. This work was supported in part by the CETAF and in part by the Conference for Funders of Prevention of Autonomy Loss for the Elderly (CFPPA) of the Loire Department (November 2017). (Corresponding author: Nilson Herazo-Padilla.) 30 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. JUL 2022.0 19.0 3 2322 2338 10.1109/TASE.2021.3069800 0.0 APR 2021 17 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems 3Q9CV 2023-03-23 WOS:000733485900001 0
J Wang, JQ; Zhu, T; Gan, JY; Chen, LL; Ning, HS; Wan, YP Wang, Jinqiang; Zhu, Tao; Gan, Jingyuan; Chen, Liming Luke; Ning, Huansheng; Wan, Yaping Sensor Data Augmentation by Resampling in Contrastive Learning for Human Activity Recognition IEEE SENSORS JOURNAL English Article Contrastive learning; human activity recognition (HAR); resampling; sensor data augmentation; wearable sensors NETWORKS While deep learning models have contributed to the advancement of sensor-based human activity recognition (HAR), it usually requires large amounts of annotated sensor data to extract robust features. To alleviate the limitations of data annotation, contrastive learning has been applied to sensor-based HAR. One of the essential factors of contrastive learning is data augmentation, significantly impacting the performance of pretraining. However, current popular augmentation methods do not achieve competitive performance in contrastive learning for sensor-based HAR. Motivated by this issue, we propose a new sensor data augmentation method by resampling, which introduces variable domain information and simulates realistic activity data by varying the sampling frequency to maximize the coverage of the sampling space. The resampling augmentation method was evaluated in supervised learning and contrastive learning [SimCLR for HAR (SimCLRHAR) and MoCo for HAR (MoCoHAR)]. In the experiment, we use four datasets, UCI-HAR, MotionSense, USC-HAD, and MobiAct, using the mean F1-score as the evaluation metric for downstream tasks. The experimental results show that the resampling data augmentation outperforms all state-of-the-art augmentation methods in supervised learning and contrastive learning with a small amount of labeled data. The results also demonstrate that not all data augmentation methods have positive effects in contrastive learning frameworks. [Wang, Jinqiang; Zhu, Tao; Gan, Jingyuan; Wan, Yaping] Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China; [Wang, Jinqiang; Zhu, Tao; Gan, Jingyuan; Wan, Yaping] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang 421001, Peoples R China; [Wang, Jinqiang; Zhu, Tao; Gan, Jingyuan; Wan, Yaping] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang 421001, Peoples R China; [Chen, Liming Luke] Ulster Univ, Sch Comp, Newtownabbey BT37 0QB, North Ireland; [Ning, Huansheng] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China University of South China; Ulster University; University of Science & Technology Beijing Zhu, T (corresponding author), Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.;Zhu, T (corresponding author), Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang 421001, Peoples R China.;Zhu, T (corresponding author), Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang 421001, Peoples R China. tzhu@usc.edu.cn; l.chen@ulster.ac.uk; ninghuansheng@ustb.edu.cn Chen, Liming (Luke)/0000-0003-0200-7989; Wang, Jinqiang/0000-0002-3711-0743 National Natural Science Foundation of China [62006110, 61872038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 62006110 and Grant 61872038 47 0 0 5 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. DEC 1 2022.0 22.0 23 22994 23008 10.1109/JSEN.2022.3214198 0.0 15 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics 6T3IG Green Published, Green Submitted 2023-03-23 WOS:000893571900073 0
J Marcial, J; Zhang, Y; Zhao, X; Xu, H; Mesbah, A; Nienhuis, ET; Szenknect, S; Neuefeind, JC; Lin, J; Qi, L; Migdisov, AA; Ewing, RC; Dacheux, N; McCloy, JS; Guo, X Marcial, J.; Zhang, Y.; Zhao, X.; Xu, H.; Mesbah, A.; Nienhuis, E. T.; Szenknect, S.; Neuefeind, J. C.; Lin, J.; Qi, L.; Migdisov, A. A.; Ewing, R. C.; Dacheux, N.; McCloy, J. S.; Guo, X. Thermodynamic non-ideality and disorder heterogeneity in actinide silicate solid solutions NPJ MATERIALS DEGRADATION English Article 1ST EXPERIMENTAL-DETERMINATION; THORITE MINERAL GROUP; X-RAY; METASOMATIZED GRANITE; ACTIVITY-COEFFICIENTS; ELECTRONIC-STRUCTURE; SOLUBILITY CONSTANT; CRYSTAL-STRUCTURE; COFFINITE; ZIRCON Non-ideal thermodynamics of solid solutions can greatly impact materials degradation behavior. We have investigated an actinide silicate solid solution system (USiO4-ThSiO4), demonstrating that thermodynamic non-ideality follows a distinctive, atomic-scale disordering process, which is usually considered as a random distribution. Neutron total scattering implemented by pair distribution function analysis confirmed a random distribution model for U and Th in first three coordination shells; however, a machine-learning algorithm suggested heterogeneous U and Th clusters at nanoscale (similar to 2 nm). The local disorder and nanosized heterogeneous is an example of the non-ideality of mixing that has an electronic origin. Partial covalency from the U/Th 5f-O 2p hybridization promotes electron transfer during mixing and leads to local polyhedral distortions. The electronic origin accounts for the strong non-ideality in thermodynamic parameters that extends the stability field of the actinide silicates in nature and under typical nuclear waste repository conditions. [Marcial, J.; McCloy, J. S.] Washington State Univ, Sch Mech & Mat Engn, Pullman, WA 99164 USA; [Zhang, Y.; Neuefeind, J. C.] Oak Ridge Natl Lab, Neutron Scattering Div, Oak Ridge, TN USA; [Zhang, Y.] NIST, Mat Measurement Sci Div, Gaithersburg, MD 20899 USA; [Zhao, X.; Guo, X.] Washington State Univ, Dept Chem, Pullman, WA 99164 USA; [Zhao, X.; Guo, X.] Washington State Univ, Alexandra Navrotsky Inst Expt Thermodynam, Pullman, WA 99164 USA; [Zhao, X.; Nienhuis, E. T.; McCloy, J. S.; Guo, X.] Washington State Univ, Mat Sci & Engn Program, Pullman, WA 99164 USA; [Xu, H.; Migdisov, A. A.] Los Alamos Natl Lab, Earth & Environm Sci Div, Los Alamos, NM USA; [Mesbah, A.; Szenknect, S.; Dacheux, N.] Univ Montpellier, ICSM, CRNS, CEA,ENSCM,Site Marcoule, Bagnols Sur Ceze, France; [Lin, J.] Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai, Peoples R China; [Qi, L.] Univ Michigan, Dept Mat Sci & Engn, Ann Arbor, MI 48109 USA; [Ewing, R. C.] Stanford Univ, Dept Geol Sci, Stanford, CA 94305 USA Washington State University; United States Department of Energy (DOE); Oak Ridge National Laboratory; National Institute of Standards & Technology (NIST) - USA; Washington State University; Washington State University; Washington State University; United States Department of Energy (DOE); Los Alamos National Laboratory; CEA; Universite de Montpellier; Chinese Academy of Sciences; Shanghai Institute of Applied Physics, CAS; University of Michigan System; University of Michigan; Stanford University Guo, X (corresponding author), Washington State Univ, Dept Chem, Pullman, WA 99164 USA.;Guo, X (corresponding author), Washington State Univ, Alexandra Navrotsky Inst Expt Thermodynam, Pullman, WA 99164 USA.;Guo, X (corresponding author), Washington State Univ, Mat Sci & Engn Program, Pullman, WA 99164 USA. x.guo@wsu.edu McCloy, John/HJB-3380-2022; mesbah, adel/A-4559-2011; Migdisov, Artaches/C-2794-2018; Szenknect, Stephanie/B-6970-2016; Guo, Xiaofeng/D-6365-2015 mesbah, adel/0000-0002-6905-2402; Migdisov, Artaches/0000-0001-7734-2082; Szenknect, Stephanie/0000-0003-3691-3621; Guo, Xiaofeng/0000-0003-3129-493X; Qi, Liang/0000-0002-6038-2907 ORAP at Washington State University; Department of Energy (DOE) Waste Treatment & Immobilization Plant (WTP) Federal Project Office [DE-EM0002904, DE-EM0003207]; DOE Office of Nuclear Energy, Nuclear Energy University Programs [DE-NE0008689]; Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory (LANL) [20180007 DR]; National Nuclear Security Administration of the U.S. DOE [89233218CNA000001]; Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy [DE-AC05- 00OR22725]; Alexandra Navrotsky Institute for Experimental Thermodynamics; WSU-PNNL Nuclear Science and Technology Institute; Department of Chemistry ORAP at Washington State University; Department of Energy (DOE) Waste Treatment & Immobilization Plant (WTP) Federal Project Office(United States Department of Energy (DOE)); DOE Office of Nuclear Energy, Nuclear Energy University Programs(United States Department of Energy (DOE)); Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory (LANL); National Nuclear Security Administration of the U.S. DOE(National Nuclear Security Administration); Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy(United States Department of Energy (DOE)); Alexandra Navrotsky Institute for Experimental Thermodynamics; WSU-PNNL Nuclear Science and Technology Institute; Department of Chemistry This work was supported by the institutional funds from the Department of Chemistry and New Faculty Seed Grant from ORAP at Washington State University. Funding for E.T.N. and J.M. was provided by the Department of Energy (DOE) Waste Treatment & Immobilization Plant (WTP) Federal Project Office under the direction of Dr. Albert A. Kruger, contract numbers DE-EM0002904 and DE-EM0003207. Funding for J.S.M.C. was provided by DOE Office of Nuclear Energy, Nuclear Energy University Programs, award # DE-NE0008689. Research presented in this article was also supported by the Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory (LANL) under project number 20180007 DR (to H.X.). LANL, an affirmative action/equal opportunity employer, is managed by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. DOE under contract 89233218CNA000001. A portion of this research used resources at the Spallation Neutron Source, the Oak Ridge National Laboratory, was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy, under Contract No. DE-AC05- 00OR22725. We also acknowledge the support from Alexandra Navrotsky Institute for Experimental Thermodynamics and the WSU-PNNL Nuclear Science and Technology Institute. We thank Dr. Francisco Espinosa for discussion and Mr. Ke (Edward) Zhao for help with visualization. 121 2 2 1 12 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2397-2106 NPJ MAT DEGRAD NPJ Mater. Degrad. JUN 18 2021.0 5.0 1 34 10.1038/s41529-021-00179-0 0.0 14 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science SV3WH Green Published, Green Submitted, gold 2023-03-23 WOS:000663752200001 0
J Kelishomi, AE; Garmabaki, AHS; Bahaghighat, M; Dong, JM Kelishomi, Aghil Esmaeili; Garmabaki, A. H. S.; Bahaghighat, Mahdi; Dong, Jianmin Mobile User Indoor-Outdoor Detection through Physical Daily Activities SENSORS English Article sensor-based indoor-outdoor detection; location-based services; human daily activity; smartphone motion sensors; machine learning; context awareness An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications. [Kelishomi, Aghil Esmaeili; Dong, Jianmin] Xi An Jiao Tong Univ, MOE Key Lab Intelligent & Network Secur, Xian 710049, Shaanxi, Peoples R China; [Garmabaki, A. H. S.] Lulea Univ Technol, Div Operat & Maintenance Engn, S-97187 Lulea, Sweden; [Bahaghighat, Mahdi] Raja Univ, Dept Elect Engn, Qazvin 34148, Iran Xi'an Jiaotong University; Lulea University of Technology Kelishomi, AE (corresponding author), Xi An Jiao Tong Univ, MOE Key Lab Intelligent & Network Secur, Xian 710049, Shaanxi, Peoples R China. ashil@sei.xjtu.edu.cn; amir.garmabaki@ltu.se; m.bahaghighat@raja.ac.ir; jianmind23@stu.xjtu.edu.cn Bahaghighat/ABB-4778-2020 Esmaeili Kelishomi, Aghil/0000-0003-1362-2020; Bahaghighat, Mahdi/0000-0002-1813-8417; Garmabaki, Amir/0000-0003-2976-5229 Natural Science Foundation of China [NSFC 61772415] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported in part by the Natural Science Foundation of China (NSFC 61772415). We would like to express our thankfulness Prof. Zhongmin Cai and anonymous reviewers for their valuable suggestions in improving the paper. 47 28 29 1 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 1 2019.0 19.0 3 511 10.3390/s19030511 0.0 29 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HN1IR 30691148.0 Green Accepted, gold, Green Published, Green Submitted 2023-03-23 WOS:000459941200074 0
J Li, X; Han, PF; Chen, WQ; Gao, CN; Wang, S; Song, T; Niu, MY; Rodriguez-Paton, A Li, Xue; Han, Peifu; Chen, Wenqi; Gao, Changnan; Wang, Shuang; Song, Tao; Niu, Muyuan; Rodriguez-Paton, Alfonso MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network BRIEFINGS IN BIOINFORMATICS English Article; Early Access protein-protein interactions; multi-scale architecture; ResNet; multi-feature fusion Protein-protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets. [Li, Xue; Han, Peifu; Chen, Wenqi; Gao, Changnan; Wang, Shuang] China Univ Petr East China, Sch Comp Sci & Technol, Qingdao, Peoples R China; [Song, Tao] China Univ Petr, Qingdao, Peoples R China; [Niu, Muyuan] Qingdao Univ, Teachers Coll, Qingdao, Peoples R China; [Rodriguez-Paton, Alfonso] Univ Politecn Madrid, Fac Informat, Madrid, Spain China University of Petroleum; China University of Petroleum; Qingdao University; Universidad Politecnica de Madrid Song, T (corresponding author), China Univ Petr, Sch Comp Sci & Technol, Qingdao 266580, Peoples R China. t.song@upm.es Song, Tao/T-7360-2018 Song, Tao/0000-0002-0130-3340; Li, Xue/0000-0002-1489-3095 57 1 1 7 7 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. 10.1093/bib/bbac524 0.0 DEC 2022 10 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology 6W6LK 36502435.0 2023-03-23 WOS:000895839400001 0
C Fang, WD; Zhu, CS; Chen, W; Zhang, WX; Rodrigues, JJPC IEEE Fang, Weidong; Zhu, Chunsheng; Chen, Wei; Zhang, Wuxiong; Rodrigues, Joel J. P. C. BDTMS: Binomial Distribution-based Trust Management Scheme for Healthcare-oriented Wireless Sensor Network 2018 14TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) International Wireless Communications and Mobile Computing Conference English Proceedings Paper 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC) JUN 25-29, 2018 Limassol, CYPRUS Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Cyprus Sect,Frederick Univ wireless sensor network (WSN); healthcare; trust management; internal attack; On-Off attack BIG DATA; CLOUD; CITY Healthcare-oriented wireless sensor network (HWSN) is one of the applications of wireless sensor networks in e-health. It not only can better achieve the physiological information of people, but also more efficiently reduce the latency regarding information collection and transmission. However, similar to other distributed networks, it also faces enormous security challenges, especially from internal attacks. It is difficult to distinguish many attack behaviors from interference in the complex healthcare scenarios, such as On-Off attack. In this paper, we propose a Binomial Distribution-based Trust Management Scheme (BDTMS) for HWSN. The proposed method can rapidly detect and effectively defend against On-Off attacks. In addition, the proposed method is also applicable to defending against bad mouthing attacks. Simulation results show that, compared with the Time-window-based Resilient Trust Management Scheme (TRTMS), our proposed BDTMS achieves better performance in defending against On-Off attack under obstacle movement, especially with higher detection accuracy. [Fang, Weidong] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Wireless Sensor Network & Commun, Shanghai 200051, Peoples R China; [Zhu, Chunsheng] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada; [Chen, Wei] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China; [Zhang, Wuxiong] Shanghai Res Ctr Wireless Commun, Shanghai 201210, Peoples R China; [Rodrigues, Joel J. P. C.] Natl Inst Telecommun Inatel, Santa Rita Do Sapucai, Brazil; [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, Lisbon, Portugal; [Rodrigues, Joel J. P. C.] Univ Fortaleza UNIFOR, Fortaleza, Ceara, Brazil Chinese Academy of Sciences; Shanghai Institute of Microsystem & Information Technology, CAS; University of British Columbia; China University of Mining & Technology; Instituto Nacional de Telecomunicacoes (INATEL); Instituto de Telecomunicacoes; Universidade Fortaleza Fang, WD (corresponding author), Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Wireless Sensor Network & Commun, Shanghai 200051, Peoples R China. wd_fang@126.com; cszhu@ece.ubc.ca; chenw@cumt.edu.cn; wuxiong.zhang@wico.sh; joeljr@ieee.org Rodrigues, Joel J. P. C./A-8103-2013; Zhu, Chunsheng/P-2182-2019 Rodrigues, Joel J. P. C./0000-0001-8657-3800; Zhu, Chunsheng/0000-0001-8041-0197 National Natural Science Foundation of China [61571004]; Shanghai Natural Science Foundation [17ZR1429100]; Science and Technology Innovation Program of Shanghai [17511105903, 17DZ1200302, 17DZ2281600]; FCT - Fundacao para a Ciencia e a Tecnologia [UID/EEA/500008/2013]; Brazilian National Council for Research and Development (CNPq) [309335/2017-5]; Finep; Funttel, under the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicacoes - CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes - Inatel), Brazil [01.14.0231.00] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Natural Science Foundation(Natural Science Foundation of Shanghai); Science and Technology Innovation Program of Shanghai; FCT - Fundacao para a Ciencia e a Tecnologia(Fundacao para a Ciencia e a Tecnologia (FCT)); Brazilian National Council for Research and Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Finep(Financiadora de Inovacao e Pesquisa (Finep)); Funttel, under the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicacoes - CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes - Inatel), Brazil This work is partially supported by the National Natural Science Foundation of China (No. 61571004), the Shanghai Natural Science Foundation (No. 17ZR1429100), the Science and Technology Innovation Program of Shanghai (No. 17511105903, No. 17DZ1200302, No. 17DZ2281600), National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the UID/EEA/500008/2013 Project; by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5; and by Finep, with resources from Funttel, Grant no. 01.14.0231.00, under the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicacoes - CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes - Inatel), Brazil. 19 16 16 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2376-6492 978-1-5386-2070-0 INT WIREL COMMUN 2018.0 382 387 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BL1DS 2023-03-23 WOS:000447259500065 0
J Zhang, HR; Li, PR; Zhang, ZW; Li, WJ; Chen, JY; Song, X; Shibasaki, R; Yan, JY Zhang, Haoran; Li, Peiran; Zhang, Zhiwen; Li, Wenjing; Chen, Jinyu; Song, Xuan; Shibasaki, Ryosuke; Yan, Jinyue Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis CITIES English Article COVID-19; Metropolitan lockdown; Epidemiological performance; Economic damage Lockdown measures have been a panacea for pandemic control but also a violent poison for economies. Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governments meet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive and quantified guides. Based on millions of financial transaction records, and billions of mobility data, we tracked spatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sided framework to assess the epidemiological performance and economic damage of different lockdown policies. We found that the pandemic duration under the strictest lockdown is less about two months than that under the lightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economically efficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue that cutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helped governments figure out the Pareto optimal solution set of lockdown strategy. [Zhang, Haoran; Li, Peiran; Zhang, Zhiwen; Li, Wenjing; Chen, Jinyu; Song, Xuan; Shibasaki, Ryosuke] Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan; [Zhang, Haoran; Yan, Jinyue] Malardalen Univ, Sch Business Soc & Engn, SE-72123 Vasteras, Sweden; [Song, Xuan] Southern Univ Sci & Technol, Southern Univ Sci & Technol Univ Tokyo Joint Res, Dept Comp & Engn, Shenzhen 518055, Guangdong, Peoples R China University of Tokyo; Malardalen University; Southern University of Science & Technology Song, X (corresponding author), Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan. songxuan@csis.u-tokyo.ac.jp [19K15260] Acknowledgments KAKENHI Grant-in-Aid for Early-Career Scientists (19K15260) . 30 8 8 15 28 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0264-2751 1873-6084 CITIES Cities JAN 2022.0 120.0 103502 10.1016/j.cities.2021.103502 0.0 11 Urban Studies Social Science Citation Index (SSCI) Urban Studies 1B8JS 34703071.0 Green Accepted 2023-03-23 WOS:000792678800002 0
J Richter, F; Chen, MQ; Schaub, P; Wust, F; Zhang, D; Schneider, S; Gross, GA; Mader, P; Dovzhenko, O; Palme, K; Kohler, JM; Cao, JL Richter, Felix; Chen, Minqian; Schaub, Patrick; Wuest, Florian; Zhang, Di; Schneider, Steffen; Gross, G. Alexander; Maeder, Patrick; Dovzhenko, Oleksandr; Palme, Klaus; Koehler, J. Michael; Cao, Jialan Induction of embryogenic development in haploid microspore stem cells in droplet-based microfluidics LAB ON A CHIP English Article SEGMENTED FLOW; IN-VITRO; BRASSICA; CHIP This work presents the application of droplet-based microfluidics for the cultivation of microspores from Brassica napus using the doubled haploid technology. Under stress conditions (e.g. heat shock) or by chemical induction a certain fraction of the microspores can be reprogrammed and androgenesis can be induced. This process is an important approach for plant breeding because desired plant properties can be anchored in the germline on a genetic level. However, the reprogramming rate of the microspores is generally very low, increasing it by specific stimulation is, therefore, both a necessary and challenging task. In order to accelerate the optimisation and development process, the application of droplet-based microfluidics can be a promising tool. Here, we used a tube-based microfluidic system for the generation and cultivation of microspores inside nL-droplets. Different factors like cell density, tube material and heat shock conditions were investigated to improve the yield of vital plant organoids. Evaluation and analysis of the stimuli response were done on an image base aided by an artificial intelligence cell detection algorithm. Droplet-based microfluidics allowed us to apply large concentration programs in small test volumes and to screen the best conditions for reprogramming cells by the histone deacetylase inhibitor trichostatin A and for enhancing the yield of vital microspores in droplets. An enhanced reprogramming rate was found under the heat shock conditions at 32 degrees C for about 3 to 6 days. In addition, the comparative experiment with MTP showed that droplet cultivation with lower cell density (<10 cells per droplet) or adding media after 3 or 6 days significantly positively affects the microspore growth and embryo rate inside 120 nL droplets. Finally, the developed embryos could be removed from the droplets and further grown into mature plants. Overall, we demonstrated that the droplet-based tube system is suitable for implementation in an automated, miniaturized system to achieve the induction of embryogenic development in haploid microspore stem cells of Brassica napus. [Richter, Felix; Schneider, Steffen; Gross, G. Alexander; Koehler, J. Michael; Cao, Jialan] Tech Univ Ilmenau, Inst Chem & Biotechnol, Dept Phys Chem & Microreact Technol, D-98693 Ilmenau, Germany; [Chen, Minqian; Maeder, Patrick] Tech Univ Ilmenau, Inst Comp & Syst Engn, Dept Software Engn Safety Crit Syst, D-98693 Ilmenau, Germany; [Schaub, Patrick; Dovzhenko, Oleksandr; Palme, Klaus] ScreenSYS GmbH, D-79104 Freiburg, Germany; [Wuest, Florian; Zhang, Di; Palme, Klaus] Albert Ludwigs Univ Freiburg, Fac Biol, Inst Biol 2, D-79104 Freiburg, Germany; [Palme, Klaus] Albert Ludwigs Univ Freiburg, BIOSS Ctr Biol Signalling Studies, D-79104 Freiburg, Germany; [Palme, Klaus] Shandong Agr Univ, Coll Life Sci, Sino German Joint Res Ctr Agr Biol, Daizong St 61, Tai An 271018, Shandong, Peoples R China Technische Universitat Ilmenau; Technische Universitat Ilmenau; University of Freiburg; University of Freiburg; Shandong Agricultural University Cao, JL (corresponding author), Tech Univ Ilmenau, Inst Chem & Biotechnol, Dept Phys Chem & Microreact Technol, D-98693 Ilmenau, Germany. jialan.cao@tu-ilmenau.de Wust, Florian/0000-0002-9941-5550 Thuringer Aufbaubank, Project Screen In Drop Line [2016FE9016] Thuringer Aufbaubank, Project Screen In Drop Line This research was funded by Thuringer Aufbaubank, Project Screen In Drop Line, Grant number: 2016FE9016. 24 0 0 13 13 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1473-0197 1473-0189 LAB CHIP Lab Chip NOV 8 2022.0 22.0 22 4292 4305 10.1039/d2lc00788f 0.0 SEP 2022 15 Biochemical Research Methods; Chemistry, Multidisciplinary; Chemistry, Analytical; Nanoscience & Nanotechnology; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Chemistry; Science & Technology - Other Topics; Instruments & Instrumentation 6C5SA 36196753.0 2023-03-23 WOS:000864259300001 0
J Borry, M; Cordova, B; Perri, A; Wibowo, M; Honap, TP; Ko, J; Yu, J; Britton, K; Girdland-Flink, L; Power, RC; Stuijts, I; Salazar-Garcia, DC; Hofman, C; Hagan, R; Kagone, TS; Meda, N; Carabin, H; Jacobson, D; Reinhard, K; Lewis, C; Kostic, A; Jeong, C; Herbig, A; Hubner, A; Warinner, C Borry, Maxime; Cordova, Bryan; Perri, Angela; Wibowo, Marsha; Honap, Tanvi Prasad; Ko, Jada; Yu, Jie; Britton, Kate; Girdland-Flink, Linus; Power, Robert C.; Stuijts, Ingelise; Salazar-Garcia, Domingo C.; Hofman, Courtney; Hagan, Richard; Kagone, Therese Samdapawinde; Meda, Nicolas; Carabin, Helene; Jacobson, David; Reinhard, Karl; Lewis, Cecil; Kostic, Aleksandar; Jeong, Choongwon; Herbig, Alexander; Huebner, Alexander; Warinner, Christina CoproID predicts the source of coprolites and paleofeces using microbiome composition and host DNA content PEERJ English Article Coprolite; Paleofeces; Microbiome; Endogenous DNA; Archeology; Machine learning; Nextflow; Gut; Human; Dog MOLECULAR ANALYSIS; ANCIENT DNA; DIET; DOGS; DIVERSITY; CULTURE; GENOME Shotgun metagenomics applied to archaeological feces (paleofeces) can bring new insights into the composition and functions of human and animal gut microbiota from the past. However, paleofeces often undergo physical distortions in archaeological sediments, making their source species difficult to identify on the basis of fecal morphology or microscopic features alone. Here we present a reproducible and scalable pipeline using both host and microbial DNA to infer the host source of fecal material. We apply this pipeline to newly sequenced archaeological specimens and show that we are able to distinguish morphologically similar human and canine paleofeces, as well as non-fecal sediments, from a range of archaeological contexts. [Borry, Maxime; Cordova, Bryan; Hagan, Richard; Jeong, Choongwon; Herbig, Alexander; Huebner, Alexander; Warinner, Christina] Max Planck Inst Sci Human Hist, Dept Archaeogenet, Jena, Germany; [Perri, Angela] Univ Durham, Dept Archaeol, Durham, England; [Perri, Angela; Britton, Kate; Power, Robert C.] Max Planck Inst Evolutionary Anthropol, Dept Human Evolut, Leipzig, Germany; [Wibowo, Marsha; Kostic, Aleksandar] Joslin Diabet Ctr, Sect Pathophysiol & Mol Pharmacol, Boston, MA 02215 USA; [Wibowo, Marsha; Kostic, Aleksandar] Joslin Diabet Ctr, Sect Islet Cell & Regenerat Biol, Boston, MA 02215 USA; [Wibowo, Marsha; Kostic, Aleksandar] Harvard Med Sch, Dept Microbiol, Boston, MA 02115 USA; [Honap, Tanvi Prasad; Hofman, Courtney; Jacobson, David; Lewis, Cecil] Univ Oklahoma, Dept Anthropol, Norman, OK 73019 USA; [Honap, Tanvi Prasad; Hofman, Courtney; Jacobson, David; Lewis, Cecil] Univ Oklahoma, LMAMR, Norman, OK 73019 USA; [Ko, Jada; Warinner, Christina] Harvard Univ, Dept Anthropol, Cambridge, MA 02138 USA; [Yu, Jie] Wuhan Univ, Dept Hist, Wuhan, Peoples R China; [Britton, Kate; Girdland-Flink, Linus] Univ Aberdeen, Dept Archaeol, Aberdeen, Scotland; [Girdland-Flink, Linus] Liverpool John Moores Univ, Sch Nat Sci & Psychol, Liverpool, Merseyside, England; [Power, Robert C.] Ludwig Maximilians Univ Munchen, Inst Vor & Fruhgeschichtl Archaol & Prov Rom Arch, Munich, Germany; [Stuijts, Ingelise] Discovery Programme, Dublin, Ireland; [Salazar-Garcia, Domingo C.] IKERBASQUE Basque Fdn Sci, Grp Invest Prehist IT 1223 19, UPV EHU, Vitoria, Spain; [Salazar-Garcia, Domingo C.] Univ Valencia, Dept Prehist Arqueol & Hist Antiga, Valencia, Spain; [Kagone, Therese Samdapawinde; Meda, Nicolas] Ctr Muraz, Bobo Dioulasso, Burkina Faso; [Carabin, Helene] Univ Montreal, Fac Med Vet, Dept Pathol & Microbiol, St Hyacinthe, PQ, Canada; [Reinhard, Karl] Univ Nebraska, Sch Nat Resources, Lincoln, NE USA; [Jeong, Choongwon] Seoul Natl Univ, Sch Biol Sci, Seoul, South Korea; [Warinner, Christina] Friedrich Schiller Univ Jena, Fac Biol Sci, Jena, Germany Durham University; Max Planck Society; Harvard University; Joslin Diabetes Center, Inc.; Harvard University; Joslin Diabetes Center, Inc.; Harvard University; Harvard Medical School; University of Oklahoma System; University of Oklahoma - Norman; University of Oklahoma System; University of Oklahoma - Norman; Harvard University; Wuhan University; University of Aberdeen; Liverpool John Moores University; University of Munich; Basque Foundation for Science; University of Basque Country; University of Valencia; Centre Muraz; Universite de Montreal; University of Nebraska System; University of Nebraska Lincoln; Seoul National University (SNU); Friedrich Schiller University of Jena Borry, M; Warinner, C (corresponding author), Max Planck Inst Sci Human Hist, Dept Archaeogenet, Jena, Germany.;Warinner, C (corresponding author), Harvard Univ, Dept Anthropol, Cambridge, MA 02138 USA.;Warinner, C (corresponding author), Friedrich Schiller Univ Jena, Fac Biol Sci, Jena, Germany. borry@shh.mpg.de; warinner@fas.harvard.edu Power, Robert C/I-3862-2019; Ko, Jada/HDM-3390-2022; Britton, Kate/A-3654-2010; Reinhard, Karl/AAT-7881-2020 Power, Robert C/0000-0001-7425-5709; Salazar-Garcia, Domingo C./0000-0002-8017-0194; Warinner, Christina/0000-0002-4528-5877; Kostic, Aleksandar/0000-0002-0837-4360; Hagan, Richard/0000-0003-1270-100X; Britton, Kate/0000-0002-9478-5966; Jeong, Choongwon/0000-0003-3049-2352; Hubner, Alexander/0000-0003-3572-9996 US National Institutes of Health [R01GM089886]; Deutsche Forschungsgemeinschaft EXC 2051 [390713860]; Max Planck Society US National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); Deutsche Forschungsgemeinschaft EXC 2051(German Research Foundation (DFG)); Max Planck Society(Max Planck SocietyFoundation CELLEX) This work was supported by the US National Institutes of Health R01GM089886 (to Christina Warinner and Cecil Lewis), the Deutsche Forschungsgemeinschaft EXC 2051 #390713860 (to Christina Warinner), and the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 56 17 18 0 11 PEERJ INC LONDON 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND 2167-8359 PEERJ PeerJ APR 17 2020.0 8.0 e9001 10.7717/peerj.9001 0.0 23 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics LE1ZE 32337106.0 gold, Green Accepted, Green Submitted, Green Published 2023-03-23 WOS:000526522100007 0
J Xiao, FY; Pedrycz, W Xiao, Fuyuan; Pedrycz, Witold Negation of the Quantum Mass Function for Multisource Quantum Information Fusion With its Application to Pattern Classification IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Entropy; Quantum mechanics; Hilbert space; Decision making; Measurement uncertainty; Evidence theory; Cognition; Complex evidence theory; quantum decision; quantum basic belief assignment; quantum negation; quantum entropy; multisource quantum information fusion; uncertainty; pattern classification ENTROPY; BELIEF; PROBABILITY; PERSPECTIVE; MODEL In artificial intelligence systems, a question on how to express the uncertainty in knowledge remains an open issue. The negation scheme provides a new perspective to solve this issue. In this paper, we study quantum decisions from the negation perspective. Specifically, complex evidence theory (CET) is considered to be effective to express and handle uncertain information in a complex plane. Therefore, we first express CET in the quantum framework of Hilbert space. On this basis, a generalized negation method is proposed for quantum basic belief assignment (QBBA), called QBBA negation. In addition, a QBBA entropy is revisited to study the QBBA negation process to reveal the variation tendency of negation iteration. Meanwhile, the properties of the QBBA negation function are analyzed and discussed along with special cases. Then, several multisource quantum information fusion (MSQIF) algorithms are designed to support decision making. Finally, these MSQIF algorithms are applied in pattern classification to demonstrate their effectiveness. This is the first work to design MSQIF algorithms to support quantum decision making from a new perspective of negation , which provides promising solutions to knowledge representation, uncertainty measure, and fusion of quantum information. [Xiao, Fuyuan] Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] King Abgudulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland Chongqing University; University of Alberta; King Abdulaziz University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Xiao, FY (corresponding author), Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China. xiaofuyuan@cqu.edu.cn; wpedrycz@ualberta.ca National Natural Science Foundation of China [62003280]; Chongqing Talents: Exceptional Young Talents Project [cstc2022ycjh-bgzxm0070]; Chongqing Overseas Scholars Innovation Program [cx2022024] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chongqing Talents: Exceptional Young Talents Project; Chongqing Overseas Scholars Innovation Program This work was supported in part by the National Natural Science Foundation of China under Grant 62003280, in part by Chongqing Talents: Exceptional Young Talents Project under Grant cstc2022ycjh-bgzxm0070, and in part by Chongqing Overseas Scholars Innovation Program under Grant cx2022024. 60 35 35 19 25 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. FEB 1 2023.0 45.0 2 2054 2070 10.1109/TPAMI.2022.3167045 0.0 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 7U8OK 35420983.0 2023-03-23 WOS:000912386000047 0
J Wu, YQ; Xiang, MS; Bi, SL; Liu, XW; Yu, J; Hon, M; Sharma, S; Li, TD; Huang, Y; Liu, K; Zhang, XF; Li, YG; Ge, ZS; Tian, ZJ; Zhang, JH; Zhang, JW Wu, Yaqian; Xiang, Maosheng; Bi, Shaolan; Liu, Xiaowei; Yu, Jie; Hon, Marc; Sharma, Sanjib; Li, Tanda; Huang, Yang; Liu, Kang; Zhang, Xianfei; Li, Yaguang; Ge, Zhishuai; Tian, Zhijia; Zhang, Jinghua; Zhang, Jianwei Mass and age of red giant branch stars observed with LAMOST and Kepler MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article asteroseismology; stars: evolution; stars: fundamental parameters STELLAR ATMOSPHERIC PARAMETERS; CHEMICAL EVOLUTION; SOLAR NEIGHBORHOOD; COMPONENT ANALYSIS; TELESCOPE LAMOST; K2 MISSION; ASTEROSEISMOLOGY; OSCILLATIONS; ABUNDANCES; ISOCHRONES Obtaining accurate and precise masses and ages for large numbers of giant stars is of great importance for unraveling the assemblage history of the Galaxy. In this paper, we estimate masses and ages of 6940 red giant branch (RGB) stars with asteroseismic parameters deduced from Kepler photometry and stellar atmospheric parameters derived from LAMOST spectra. The typical uncertainties of mass is a few per cent, and that of age is similar to 20 per cent. The sample stars reveal two separate sequences in the age-[alpha/Fe] relation - a high-alpha sequence with stars older than similar to 8 Gyr and a low-alpha sequence composed of stars with ages ranging from younger than 1 Gyr to older than 11 Gyr. We further investigate the feasibility of deducing ages and masses directly from LAMOST spectra with a machine learning method based on kernel based principal component analysis, taking a sub-sample of these RGB stars as a training data set. We demonstrate that ages thus derived achieve an accuracy of similar to 24 per cent. We also explored the feasibility of estimating ages and masses based on the spectroscopically measured carbon and nitrogen abundances. The results are quite satisfactory and significantly improved compared to the previous studies. [Wu, Yaqian; Bi, Shaolan; Liu, Kang; Zhang, Xianfei; Li, Yaguang; Ge, Zhishuai; Zhang, Jinghua; Zhang, Jianwei] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China; [Xiang, Maosheng; Li, Tanda] Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China; [Liu, Xiaowei] Peking Univ, Dept Astron, Beijing 100871, Peoples R China; [Yu, Jie; Sharma, Sanjib; Li, Tanda] Univ Sydney, Sch Phys, Sydney, NSW 2000, Australia; [Yu, Jie; Li, Tanda] Aarhus Univ, Dept Phys & Astron, Stellar Astrophys Ctr, Ny Munkegade 120, DK-8000 Aarhus C, Denmark; [Hon, Marc] Univ New South Wales, Sch Phys, Sydney, NSW 2052, Australia Beijing Normal University; Chinese Academy of Sciences; National Astronomical Observatory, CAS; Peking University; University of Sydney; Aarhus University; University of New South Wales Sydney Wu, YQ; Bi, SL (corresponding author), Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China.;Xiang, MS (corresponding author), Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China. wuyaqian@mail.bnu.edu.cn; msxiang@nao.cas.cn; bisl@bnu.edu.cn Li, Tanda/HJP-6535-2023; Xiang, Maosheng/AAX-5982-2020 Li, Tanda/0000-0001-6396-2563; Hon, Marc Teng Yen/0000-0003-2400-6960; Sharma, Sanjib/0000-0002-0920-809X; Tian, Zhijia/0000-0003-0220-7112; Zhang, Jianwei/0000-0003-2357-6259; Wu, Yaqian/0000-0002-8337-4117; Li, Yaguang/0000-0003-3020-4437; Yu, Jie/0000-0002-0007-6211 National Natural Science Foundation of China (NSFC) [U1631236]; Chinese Academy of Sciences (CAS) [U1631236]; National Natural Science Foundation of China [11273007, 10933002]; Fundamental Research Funds for the Central Universities; Youth Scholars Program of Beijing Normal University; National Development and Reform Commission National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS)(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Youth Scholars Program of Beijing Normal University; National Development and Reform Commission This work is supported by the Joint Research Fund in Astronomy (U1631236) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS), and grants 11273007 and 10933002 from the National Natural Science Foundation of China, and the Fundamental Research Funds for the Central Universities and Youth Scholars Program of Beijing Normal University. Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. 76 27 27 0 6 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. APR 2018.0 475.0 3 3633 3643 10.1093/mnras/stx3296 0.0 11 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics FY8VA Green Submitted, Bronze 2023-03-23 WOS:000427141900054 0
J Li, WJ; Meng, WZ; Su, CH; Kwok, LF Li, Wenjuan; Meng, Weizhi; Su, Chunhua; Kwok, Lam For Towards False Alarm Reduction Using Fuzzy If-Then Rules for Medical Cyber Physical Systems IEEE ACCESS English Article False alarm reduction; fuzzy if-then rules; alarm filter; medical cyber-physical systems; wireless sensor networks; machine learning technique INTRUSION; NETWORKS; FILTER Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. Its process control is often referred to as embedded systems. Generally, CPS and Internet of Things have the same basic architecture, whereas the former shows a higher combination and coordination between physical and computational elements, i.e., wireless sensor networks can be a vital part of CPS applications. With the rapid development, CPS has been applied to healthcare industry, where a wide range of medical sensors are used within a healthcare organization. However, these sensors may generate a large number of false alarms in practice, which could significantly reduce the system effectiveness. Targeting on this issue, in this work, we attempt to design a Medical Fuzzy Alarm Filter (named MFAFilter) for healthcare environments by means of fuzzy logic, especially fuzzy if-then rules, which could handle the vague and imprecise among data. In the evaluation, we conducted two major experiments to explore the performance of our approach in a simulated and a real network environment, respectively. Experimental results demonstrate that the use of fuzzy if-then rules could achieve a better accuracy as compared to the traditional supervised algorithms, and that our designed filter is effective in the practical environment. [Li, Wenjuan; Kwok, Lam For] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; [Li, Wenjuan; Meng, Weizhi] Tech Univ Denmark, DTU Compute, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark; [Su, Chunhua] Univ Aizu, Div Comp Sci, Aizu Wakamatsu, Fukushima 9658580, Japan City University of Hong Kong; Technical University of Denmark; University of Aizu Meng, WZ (corresponding author), Tech Univ Denmark, DTU Compute, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark. weme@dtu.dk Li, Wenjuan/AAE-4027-2019; Meng, Weizhi/N-9638-2019 Li, Wenjuan/0000-0003-3745-5669; Meng, Weizhi/0000-0003-4384-5786 JSPS [KAKENHI WAKATE B-15K16005]; University of Aizu [P-21]; Grants-in-Aid for Scientific Research [15K16005] Funding Source: KAKEN JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); University of Aizu; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported in part by JSPS Grants-in-Aid for Scientific Research under Grant KAKENHI WAKATE B-15K16005 and in part by the Competitive Research Funding from the University of Aizu under Grant P-21. 40 16 16 0 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 6530 6539 10.1109/ACCESS.2018.2794685 0.0 10 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications FZ3IJ gold, Green Submitted 2023-03-23 WOS:000427480400001 0
J Jiang, W; Li, C; Li, YJ; Peng, HD Jiang, Wei; Li, Chao; Li, Yanjun; Peng, Huadong Metabolic Engineering Strategies for Improved Lipid Production and Cellular Physiological Responses in Yeast Saccharomyces cerevisiae JOURNAL OF FUNGI English Review metabolic engineering; synthetic biology; yeast; triacylglycerol; cellular physiology; fatty acid MULTIPARAMETER FLOW-CYTOMETRY; FATTY-ACID BIOSYNTHESIS; BETA-OXIDATION CYCLE; YARROWIA-LIPOLYTICA; TRIACYLGLYCEROL ACCUMULATION; OLEAGINOUS YEAST; RICINOLEIC ACID; DIACYLGLYCEROL ACYLTRANSFERASE; MEMBRANE-FLUIDITY; ESCHERICHIA-COLI Microbial lipids have been a hot topic in the field of metabolic engineering and synthetic biology due to their increased market and important applications in biofuels, oleochemicals, cosmetics, etc. This review first compares the popular hosts for lipid production and explains the four modules for lipid synthesis in yeast, including the fatty acid biosynthesis module, lipid accumulation module, lipid sequestration module, and fatty acid modification module. This is followed by a summary of metabolic engineering strategies that could be used for enhancing each module for lipid production. In addition, the efforts being invested in improving the production of value-added fatty acids in engineered yeast, such as cyclopropane fatty acid, ricinoleic acid, gamma linoleic acid, EPA, and DHA, are included. A discussion is further made on the potential relationships between lipid pathway engineering and consequential changes in cellular physiological properties, such as cell membrane integrity, intracellular reactive oxygen species level, and mitochondrial membrane potential. Finally, with the rapid development of synthetic biology tools, such as CRISPR genome editing tools and machine learning models, this review proposes some future trends that could be employed to engineer yeast with enhanced intracellular lipid production while not compromising much of its cellular health. [Jiang, Wei; Peng, Huadong] Monash Univ, Dept Chem & Biol Engn, Clayton, Vic 3800, Australia; [Jiang, Wei; Peng, Huadong] Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, DK-2800 Lyngby, Denmark; [Li, Chao] East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200234, Peoples R China; [Li, Yanjun] Tianjin Univ Sci & Technol, Coll Biotechnol, Tianjin 300457, Peoples R China; [Li, Yanjun] Tianjin Univ Sci & Technol, Key Lab Ind Fermentat Microbiol, Minist Educ, Tianjin 300457, Peoples R China Monash University; Technical University of Denmark; East China University of Science & Technology; Tianjin University Science & Technology; Tianjin University Science & Technology Peng, HD (corresponding author), Monash Univ, Dept Chem & Biol Engn, Clayton, Vic 3800, Australia.;Peng, HD (corresponding author), Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, DK-2800 Lyngby, Denmark. jiangw@biosustain.dtu.dk; lichao@ecust.edu.cn; yjli@tust.eud.cn; hdpeng@dtu.dk Peng, Huadong/G-1357-2015 Peng, Huadong/0000-0003-0635-0493 Monash University; Graduate Research International Travel Award (GRITA); European Union [899987]; National Key Research and Development Program of China [2021YFC2100900, 2021YFC2103500] Monash University(Monash University); Graduate Research International Travel Award (GRITA); European Union(European Commission); National Key Research and Development Program of China W.J. and H.P. were financially supported by Monash University for Monash Graduate Scholarship (MGS) and Monash International Tuition Scholarships (MITS) and W.J. received a Graduate Research International Travel Award (GRITA). HP has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 899987. Y.J.L. and C.L. were financially supported by the National Key Research and Development Program of China with grant no. 2021YFC2100900 and 2021YFC2103500, respectively. 158 3 3 13 23 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2309-608X J FUNGI J. Fungi MAY 2022.0 8.0 5 427 10.3390/jof8050427 0.0 22 Microbiology; Mycology Science Citation Index Expanded (SCI-EXPANDED) Microbiology; Mycology 1O5RN 35628683.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000801389300001 0
J Jiang, ZX; Chi, CY; Zhan, YY Jiang, Zhixue; Chi, Chengying; Zhan, Yunyun Let Knowledge Make Recommendations for You IEEE ACCESS English Article Feature extraction; Semantics; Deep learning; Computational modeling; Data models; Data mining; Knowledge representation; Knowledge graph; recommendation system; knowledge feature representation The knowledge graph can make more accurate personalized recommendations for the recommendation system, but it is also interpretative and has traces to follow. The purpose of the recommendation system is to recommend a series of unobserved items for users. At present, recommendation systems based on knowledge graphs are mainly implemented in two ways: Embedding-based and path-based. Embedding methods usually directly use information from the knowledge graph to enrich the representation of an item or user. Still, it failed to introduce multi-hop relations, and it is challenging to use semantic network information. A path-based recommendation algorithm utilizes the knowledge graph to gain multi-hop knowledge and compare the similarity between users or items to improve the recommendation effect. This paper (1) Aiming at the problem of how the recommendation algorithm effectively utilizes the semantically related information of knowledge, a self-attention-based knowledge representation learning model is designed to learn the semantic information of the entity-relationship by using the overall triplet of the entity-relationship to achieve high-quality knowledge features, Which brings more and more helpful information to the recommendation. (2) Constructing a content recommendation model with unified, embedded behavior and knowledge features, using historical user preferences combined with knowledge graphs to dynamically learn knowledge features to bring users more accurate and diverse recommendations. (3) Aiming at the problem of knowledge feature representation learning, a self-attention-based knowledge representation learning model is proposed. Focusing on the difference in the importance of triples for determining entity semantics, the self-attention mechanism is used to learn semantics from triples to improve knowledge features. The quality of the representation provides high-quality auxiliary information for the recommendation system. The model's performance is demonstrated through link prediction and triple classification experiments to prove the feasibility of the method proposed in this article. [Jiang, Zhixue; Chi, Chengying] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114031, Peoples R China; [Zhan, Yunyun] Technol Univ Dublin, Coll Sci & Hlth, Dublin D08 X622 2, Ireland University of Science & Technology Liaoning Chi, CY (corresponding author), Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114031, Peoples R China. chichengying@ustl.edu.cn National Natural Science Foundation of China (NSFC) [61672138] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61672138. 23 3 3 9 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9.0 118194 118204 10.1109/ACCESS.2021.3106914 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications UK7WI Green Submitted, gold 2023-03-23 WOS:000692176300001 0
J Pan, SL; Trentesaux, D; McFarlane, D; Montreuil, B; Ballot, E; Huang, GQ Pan, Shenle; Trentesaux, Damien; McFarlane, Duncan; Montreuil, Benoit; Ballot, Eric; Huang, George Q. Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet COMPUTERS IN INDUSTRY English Article Interoperability; Interconnection; Physical internet; Digitalization; Logistics; Supply Chain management; Bibliometric review; State-of-the-art; Research avenues SERVICE; BLOCKCHAINS; NETWORKS Interoperability is playing an increasing role for today?s logistics and supply chain management (LSCM) because of the trends of cooperation or coopetition. Especially, digital interoperability concerning data or information exchange becomes a key enabler for the next evolutions that will massively rely upon digitalization, artificial intelligence, and autonomous systems. The notion of Physical Internet (PI) is one such evolution, an innovative worldwide logistic paradigm aimed at interconnecting and coordinating logistics networks for efficiency and sustainability. This paper investigates how digital interoperability can help interconnect logistics and supply networks as well as the operational solutions for sustainable development, and examines the new challenges and research opportunities for digital interoperability under the PI paradigm. To this end, we study the most relevant technologies for digital interoperabil-ity in LSCM, via a bibliometric analysis based on 208 papers published during 2010?2020. The results reveal that the present state-of-the-art solutions of digital interoperability are not fully aligned with PI requirements and show new challenges, research gaps and opportunities that need further discussion. Accordingly, several research avenues are suggested to advance research and applications in this area, and to achieve interconnection in logistics and supply networks for sustainability. ? 2021 Elsevier B.V. All rights reserved. [Pan, Shenle; Ballot, Eric] PSL Res Univ, CGS Ctr Gest Sci, MINES ParisTech, i3 UMR CNRS 9217, 60 Bd St Michel, F-75006 Paris, France; [Trentesaux, Damien] Univ Polytech Hauts de France, LAMIH UMR CNRS 8201, F-59313 Valenciennes 9, France; [McFarlane, Duncan] Univ Cambridge, Inst Mfg, 17 Charles Babbage Rd, Cambridge CB3 0FS, England; [Montreuil, Benoit] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Supply Chain & Logist Inst, Phys Internet Ctr, Atlanta, GA 30332 USA; [Huang, George Q.] Univ Hong Kong, Dept Ind & Mfg Syst Engn, HKU ZIRI Lab Phys Internet, Hong Kong, Peoples R China UDICE-French Research Universities; Universite PSL; MINES ParisTech; University of Cambridge; University System of Georgia; Georgia Institute of Technology; University of Hong Kong Pan, SL (corresponding author), PSL Res Univ, CGS Ctr Gest Sci, MINES ParisTech, i3 UMR CNRS 9217, 60 Bd St Michel, F-75006 Paris, France. shenle.pan@mines-paristech.fr; damien.trentesaux@uphf.fr; dcm@eng.cam.ac.uk; benoit.montreuil@isye.gatech.edu; eric.ballot@mines-paristech.fr; gqhuang@hku.hk Pan, Shenle/D-6571-2018 Pan, Shenle/0000-0002-6568-3709 72 15 15 12 85 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0166-3615 1872-6194 COMPUT IND Comput. Ind. JUN 2021.0 128.0 103435 10.1016/j.compind.2021.103435 0.0 MAR 2021 14 Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science RH5ZL Green Published 2023-03-23 WOS:000636296300002 0
J Hu, XS; Deng, ZW; Lin, XK; Xie, Y; Teodorescu, R Hu, Xiaosong; Deng, Zhongwei; Lin, Xianke; Xie, Yi; Teodorescu, Remus Research directions for next-generation battery management solutions in automotive applications RENEWABLE & SUSTAINABLE ENERGY REVIEWS English Article Battery management; Batteries; Electric vehicles; Energy storage; Sustainable energy ORTHOGONAL COLLOCATION; STATE; MECHANISMS; SIMULATION; STRATEGY; SYSTEMS; MODELS; ISSUES Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance. [Hu, Xiaosong; Deng, Zhongwei; Xie, Yi] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China; [Lin, Xianke] Univ Ontario Inst Technol, Dept Automot Mech & Mfg Engn, Oshawa, ON L1G 0C5, Canada; [Teodorescu, Remus] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark Chongqing University; Ontario Tech University; Aalborg University Hu, XS; Xie, Y (corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China. xiaosonghu@ieee.org; claudexie@cqu.edu.cn Hu, Xiao/GQI-0007-2022; Deng, Zhongwei/AAS-2636-2020; Teodorescu, Remus/O-5224-2015 Hu, Xiao/0000-0003-1128-4099; Deng, Zhongwei/0000-0002-5834-1583; Teodorescu, Remus/0000-0002-2617-7168 National Natural Science Foundation of China [51875054]; Chongqing Natural Science Foundation for Distinguished Young Scholars [cstc2019jcyjjq0010] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chongqing Natural Science Foundation for Distinguished Young Scholars This work was supported in part by the National Natural Science Foundation of China (No. 51875054) , and Chongqing Natural Science Foundation for Distinguished Young Scholars (No. cstc2019jcyjjq0010) . 25 6 7 14 46 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1364-0321 1879-0690 RENEW SUST ENERG REV Renew. Sust. Energ. Rev. DEC 2021.0 152.0 111695 10.1016/j.rser.2021.111695 0.0 SEP 2021 10 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels WI7FW 2023-03-23 WOS:000708524100001 0
J Qi, R; Zhu, BE; Han, ZK; Gao, Y Qi, Rui; Zhu, Beien; Han, Zhongkang; Gao, Yi High-Throughput Screening of Stable Single-Atom Catalysts in CO2 Reduction Reactions ACS CATALYSIS English Article single-atom catalysts; high-throughput screening density functional theory; oxygen vacancy; stability; CO2 reduction reaction TOTAL-ENERGY CALCULATIONS; SUPPORT INTERACTIONS; METHANOL SYNTHESIS; METAL-OXIDE; ADSORPTION; CONVERSION; TRENDS; CARBON; TIO2 Single-atom catalysts (SACs) have been extensively applied in CO2 reduction reactions (CO(2)RRs) due to their unique activity/ selectivity and maximum atom efficiency. To form and stabilize SACs, introducing oxygen vacancies (V-O) on metal oxide surfaces is a common strategy. However, there is a lack of studies on whether the single atoms (SAs) can be stably anchored on V-O sites under real reaction conditions, which hinders the rational design of SACs for practical usage. Herein, we combine the first-principles calculations and an artificial intelligence approach to high-throughput screen the stability and activity of 3d, 4d, and Sd transition metal (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, Au, and Hg) SAs on eight defective metal oxide surfaces (ZnO(100), rutile TiO2 (110), Co3O4 (001), CoO(100), MnO(100), NiO(100), MgO(100), and ZrO2 (111)) during the CO2RR. By evaluating the anchor energies of the 232 catalytic systems, 100 kinds of SACs are stably anchored by V-O in vacuum, but only 28 of them remain stable with the adsorption of intermediates of the CO2RR (*COOH, *OCHO, *CO, *CHO, and *H). By subgroup discovery analysis, we elucidate that the stability is attributed to the electronegativity and number of outer electrons of SA, the d-band center of metal oxides, and the relative coordination number of the adsorbed species together. In the further analysis of the selectivity and activity for the CO2 conversion to CO, the V-O-ZrO2 (111)-supported Os SAC is predicted as most promising in electrocatalysis and Ru/VO-ZrO2 (111) exhibits excellent catalytic performances in the reverse water-gas shift reaction. [Qi, Rui; Zhu, Beien; Gao, Yi] Chinese Acad Sci, Shanghai Inst Appl Phys, Key Lab Interfacial Phys & Technol, Shanghai 201800, Peoples R China; [Qi, Rui] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Han, Zhongkang] Fritz Haber Inst Max Planck Gesell, D-14195 Berlin, Germany; [Zhu, Beien; Gao, Yi] Chinese Acad Sci, Shanghai Adv Res Inst, Interdisciplinary Res Ctr, Zhangjiang Lab, Shanghai 201210, Peoples R China Chinese Academy of Sciences; Shanghai Institute of Applied Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Max Planck Society; Fritz Haber Institute of the Max Planck Society; Chinese Academy of Sciences; Shanghai Advanced Research Institute, CAS Zhu, BE; Gao, Y (corresponding author), Chinese Acad Sci, Shanghai Inst Appl Phys, Key Lab Interfacial Phys & Technol, Shanghai 201800, Peoples R China.;Zhu, BE; Gao, Y (corresponding author), Chinese Acad Sci, Shanghai Adv Res Inst, Interdisciplinary Res Ctr, Zhangjiang Lab, Shanghai 201210, Peoples R China. zhubeien@zjlab.org.cn; gaoyi@zjlab.org.cn Gao, Yi/A-8888-2009; gao, yi/HCI-8298-2022 Gao, Yi/0000-0001-6015-5694; Zhu, Beien/0000-0002-0126-0854 Youth Innovation Promotion Association, CAS; Key Research Program of Frontier Sciences, CAS [ZDBS-LY-7012] Youth Innovation Promotion Association, CAS; Key Research Program of Frontier Sciences, CAS B.Z. thanks the Youth Innovation Promotion Association, CAS, and the Key Research Program of Frontier Sciences, CAS, Grant No. ZDBS-LY-7012, for financial support. All calculations were performed at the National Supercomputing Center in Guangzhou (NSCC-GZ), Shanghai, and Tianjin. 62 6 6 92 125 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2155-5435 ACS CATAL ACS Catal. JUL 15 2022.0 12.0 14 8269 8278 10.1021/acscatal.2c02149 0.0 JUN 2022 10 Chemistry, Physical Science Citation Index Expanded (SCI-EXPANDED) Chemistry 6S1UI 2023-03-23 WOS:000824056800001 0
C Guo, J; Qian, K; Xu, HJ; Janott, C; Schuller, B; Matsuoka, S IEEE Guo, Jian; Qian, Kun; Xu, Huijie; Janott, Christoph; Schuller, Bjoern; Matsuoka, Satoshi GPU-Based Fast Signal Processing for Large Amounts of Snore Sound Data 2016 IEEE 5TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS English Proceedings Paper 5th IEEE Global Conference on Consumer Electronics OCT 11-14, 2016 Kyoto, JAPAN IEEE Snore sound (SnS) data has been demonstrated to carry very important information for diagnosis and evaluation of sleep related breathing disorders with high prevalence, such as Primary Snoring and Obstructive Sleep Apnea (OSA) a serious chronic sleep disorder with a big community. With the increasing number of collected SnS data from subjects, how to handle such large amount of data is a big challenge, and a huge opportunity for further study on optimally combining signal processing techniques with machine learning algorithms. In this study, we utilize the Graphics Processing Unit (GPU) to process a large amount of SUS data collected from hospitals in Germany (37 subjects, 38.34 hours, 15.10 GB). Experimental results prove that, our GPU-based platform significantly speeds up the audio processing for features extraction of SnS data, compared with the traditional Central Processing Unit (CPU) system. [Guo, Jian; Qian, Kun; Matsuoka, Satoshi] Tokyo Inst Technol, Dept Math & Comp Sci, Tokyo, Japan; [Qian, Kun; Janott, Christoph] Tech Univ Munich, Dept Elect & Comp Engn, D-80290 Munich, Germany; [Xu, Huijie] Beijing Hosp, Dept Otolaryngol, Beijing, Peoples R China; [Schuller, Bjoern] Imperial Coll London, Dept Comp, Machine Learning Grp, London, England Tokyo Institute of Technology; Technical University of Munich; Beijing Hospital; Imperial College London Guo, J (corresponding author), Tokyo Inst Technol, Dept Math & Comp Sci, Tokyo, Japan. guo.j.ae@m.titech.ac.jp; qian.k.aa@m.titech.ac.jp; xhi0531@163.com; c.janott@gmx.net; schuller@ieee.org; matsu@is.titech.ac.jp Qian, Kun/AAC-4279-2019; Janott, Christoph/W-6227-2019 China Scholarship Council (CSC); National Natural Science Foundation of China [61271410]; European Union [338164, 645378] China Scholarship Council (CSC)(China Scholarship Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union(European Commission) This work is supported by China Scholarship Council (CSC), National Natural Science Foundation of China under grant No. 61271410, and the European Union's Seventh Framework and Horizon 2020 Programmes under grant agreements No.338164 (ERC Starting Grant iHEARu) and No. 645378 (ARIA-VALUSPA). 12 0 0 0 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-2333-2 2016.0 2 Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BG8HR 2023-03-23 WOS:000392288200188 0
J Han, SY; Huang, JL; Foppiano, F; Prehn, C; Adamski, J; Suhre, K; Li, Y; Matullo, G; Schliess, F; Gieger, C; Peters, A; Wang-Sattler, R Han, Siyu; Huang, Jialing; Foppiano, Francesco; Prehn, Cornelia; Adamski, Jerzy; Suhre, Karsten; Li, Ying; Matullo, Giuseppe; Schliess, Freimut; Gieger, Christian; Peters, Annette; Wang-Sattler, Rui TIGER: technical variation elimination for metabolomics data using ensemble learning architecture BRIEFINGS IN BIOINFORMATICS English Article metabolomics; machine learning; ensemble learning; predictive modelling; longitudinal analysis MASS-SPECTROMETRY; METABOLITES; CHROMATOGRAPHY; IMPUTATION; REGRESSION; PLATFORM; MODELS; SERUM Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis. [Han, Siyu] Techn Univ Munich, Munich, Germany; [Han, Siyu; Huang, Jialing; Foppiano, Francesco; Wang-Sattler, Rui] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth GmbH, Munich, Germany; [Huang, Jialing; Foppiano, Francesco; Peters, Annette] Ludwig Maximilian Univ Munich, Munich, Germany; [Prehn, Cornelia] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth GmbH, Metabol Lab Metabraorraes & Proteom Core Fac, Munich, Germany; [Adamski, Jerzy] Natl Univ Singapore, Singapore, Singapore; [Adamski, Jerzy] Univ Ljubljana, Ljubljana, Slovenia; [Adamski, Jerzy] Tech Univ Munich, Munich, Germany; [Suhre, Karsten] Weill Cornell Med, New York, NY USA; [Suhre, Karsten] Bioinform Core, Doha, Qatar; [Li, Ying] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China; [Matullo, Giuseppe] Turin Univ, Complex Dis & Populat Med Unit, Turin, Italy; [Schliess, Freimut] Profile Inst Stoffwechselforschung GmbH, Neuss, Germany; [Gieger, Christian] Helmholtz Zentrum Munchen, Res Unit Mol Epidemiol, German Res Environm Hlth (GmbH, Munich, Germany; [Peters, Annette] Helmholtz Zentrurn Munchen, German Res Ctr Environm Hlth GmbH, Inst Epidemiol, Munich, Germany Technical University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; National University of Singapore; University of Ljubljana; Technical University of Munich; Cornell University; Weill Cornell Medicine; Jilin University; University of Turin; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health Wang-Sattler, R (corresponding author), Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth GmbH, Munich, Germany. rui.wang-sattler@helmholtz-muenchen.de Peters, Annette/A-6117-2011; Wang-Sattler, Rui/B-5354-2014; MATULLO, Giuseppe/K-6383-2016 Peters, Annette/0000-0001-6645-0985; Prehn, Cornelia/0000-0002-1274-4715; Wang-Sattler, Rui/0000-0002-8794-8229; MATULLO, Giuseppe/0000-0003-0674-7757 Helmholtz Zentrum Munchen -German Research Center for Environmental Health - German Federal Ministry of Education and Research; State of Bavaria; Munich Center of Health Sciences (MC - Health), Ludwig-Maximilians Universitat, LMUinnovativ; German Federal Ministry of Health (Berlin, Germany); German Federal Ministry of Education and Research; European Union - European Institute of Innovation and Technology (EIT) [210997-iPDM-GO]; European Union [821508] Helmholtz Zentrum Munchen -German Research Center for Environmental Health - German Federal Ministry of Education and Research; State of Bavaria; Munich Center of Health Sciences (MC - Health), Ludwig-Maximilians Universitat, LMUinnovativ; German Federal Ministry of Health (Berlin, Germany); German Federal Ministry of Education and Research(Federal Ministry of Education & Research (BMBF)); European Union - European Institute of Innovation and Technology (EIT); European Union(European Commission) The KORA study was initiated and financed by Helmholtz Zentrum Munchen -German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC -Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ.; The German Diabetes Center is supported by the German Federal Ministry of Health (Berlin, Germany). This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD).; Part of this study was supported by the funding from the European Union's Horizon 2020 research and innovation programmes: The 210997-iPDM-GO EIT Health Innovation Project supported by the European Institute of Innovation and Technology (EIT), a body of the European Union; The Innovative Medicines Initiative 2 (IMI2), project CARDIATEAM funded under the Grant Agreement No. 821508. 64 2 2 2 5 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1467-5463 1477-4054 BRIEF BIOINFORM Brief. Bioinform. MAR 10 2022.0 23.0 2 bbab535 10.1093/bib/bbab535 0.0 16 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology 1S6ZE 34981111.0 hybrid 2023-03-23 WOS:000804196500102 0
J Verlan, S; Zhang, GX Verlan, Sergey; Zhang, Gexiang A tutorial on the formal framework for spiking neural P systems NATURAL COMPUTING English Article; Early Access Spiking neural P systems; Membrane computing; Formal framework; Tutorial MEMBRANE SYSTEMS The model of Spiking Neural P systems (SNP systems) is a widespread computational model in the area of membrane computing. It has numerous applications, especially related to machine learning. Most of these applications require a custom variant of SNP systems, differing by the rule form and by semantics. The model of network of cells and the formal framework for SNP systems were developed to help the analysis of such custom models, to compare and relate them to each other and to other models of computing. The model specifies the data structure, the rules and the update procedure, while the formal framework concentrates on how the input, output and the choice of the update strategy are handled. Together, these concepts specify a concrete instance of a network of cells that strongly bisimulates the desired model, thus making easier the process of the creation of new models and the extension of existing ones. Since the formal framework is rather generic, it might be slightly complex to use it for concrete cases. This paper provides a tutorial that explains the model of networks of cells and the basic concepts used in the formal framework for SNP systems. It gives a series of examples for the analysis of existing models, their bisimulation and their extension by different features. [Verlan, Sergey] Univ Paris Est Creteil, LACL, F-94010 Creteil, France; [Zhang, Gexiang] Chengdu Univ Informat Technol, Sch Automat, Chengdu 610225, Peoples R China Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Chengdu University of Information Technology Verlan, S (corresponding author), Univ Paris Est Creteil, LACL, F-94010 Creteil, France. verlan@u-pec.fr; zhgxdylan@126.com National Natural Science Foundation of China [61972324]; Sichuan Science and Technology Program [2021YFS0313, 2021YFG0133] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Sichuan Science and Technology Program The work of GZ was supported by the National Natural Science Foundation of China (61972324) and Sichuan Science and Technology Program (2021YFS0313, 2021YFG0133). 36 0 0 8 9 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1567-7818 1572-9796 NAT COMPUT Nat. Comput. 10.1007/s11047-022-09896-0 0.0 JUL 2022 14 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2T4WI 2023-03-23 WOS:000822476600002 0
J Kang, LL; Jiang, QQ; Peng, CH; Sia, CL; Liang, TP Kang, Lele; Jiang, Qiqi; Peng, Chih-Hung; Sia, Choon Ling; Liang, Ting-Peng Managing Change with the Support of Smart Technology: A Field Investigation of Ride-Hailing Services JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS English Article Smart Services; Ride-Hailing Services; Smart Technology; Postadoption Behaviors; Openness to RHS Change; Mixed Data and Method BIG DATA ANALYTICS; INFORMATION-TECHNOLOGY; ORGANIZATIONAL-CHANGE; JOB-SATISFACTION; QUALITY; READINESS; OPENNESS; SYSTEM; ANTECEDENTS; PERCEPTIONS With the support of smart technology, IT-enabled services have become smart and have progressively disrupted existing markets. Ride-hailing services (RHSs) are widely regarded as representative of these IT-enabled services. However, few studies on IT-enabled services investigate how the technological attributes of smart technology influence service performance in a continuously changing environment. We developed our research model according to Wixom and Todd's model, the literature on change management, and the literature on information system postadoption behavior. We conducted a large-scale field study by surveying 380 drivers from major metropolises in mainland China and a post hoc qualitative interpretation to validate our model. We found that smart technological attributes of RHS systems (i.e., monitoring, control, advisory support, and responsive support) positively influence functionality and content quality, which in turn influence service quality. In addition, service quality positively influences drivers' postadoption attitudes and behaviors, including openness to RHS change, job satisfaction, and continuous usage intentions. Our findings provide important theoretical and practical implications. [Kang, Lele] Nanjing Univ, Sch Informat Management, Nanjing, Jiangsu, Peoples R China; [Kang, Lele] Nanjing Univ, Inst Data Res Humanities & Social Sci, Nanjing, Jiangsu, Peoples R China; [Jiang, Qiqi] Copenhagen Business Sch, Dept Digitalizat, Frederiksberg, Denmark; [Peng, Chih-Hung] Natl Chengchi Univ, Management Informat Syst, Taipei, Taiwan; [Sia, Choon Ling] Natl Taiwan Univ, Taipei, Taiwan; [Sia, Choon Ling] City Univ Hong Kong, Ctr Social Media Mkt & Business Intelligence, Hong Kong, Peoples R China; [Liang, Ting-Peng] Natl Sun Yat Sen Univ, Elect Commerce Res Ctr, Kaohsiung, Taiwan Nanjing University; Nanjing University; Copenhagen Business School; National Chengchi University; National Taiwan University; City University of Hong Kong; National Sun Yat Sen University Kang, LL (corresponding author), Nanjing Univ, Sch Informat Management, Nanjing, Jiangsu, Peoples R China.;Kang, LL (corresponding author), Nanjing Univ, Inst Data Res Humanities & Social Sci, Nanjing, Jiangsu, Peoples R China. lelekang@nju.edu.cn; qj.digi@cbs.dk; chpeng@nccu.edu.tw; siachoonling@ntu.edu.tw; tpliang@mail.nsysu.edu.tw National Natural Science Foundation of China [71702133, 71704078, 72072087, 71532015]; Institute of Data Research in Humanities and Social Sciences at Nanjing University; Ministry of Science and Technology of Taiwan [MOST 108-2410-H-004-227-MY2]; Yushan Scholar grant [NTU-109V0701] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Institute of Data Research in Humanities and Social Sciences at Nanjing University; Ministry of Science and Technology of Taiwan(Ministry of Science and Technology, Taiwan); Yushan Scholar grant The authors would like to thank the senior editor, Hsing Kenneth Cheng, and two anonymous reviewers for helpful comments provided throughout the review process of this article. The work described in this paper was partially supported by the National Natural Science Foundation of China (#71702133, #71704078, #72072087, #71532015), the Institute of Data Research in Humanities and Social Sciences at Nanjing University, the Ministry of Science and Technology of Taiwan (Grant MOST 108-2410-H-004-227-MY2), and a Yushan Scholar grant NTU-109V0701. 85 9 9 11 42 ASSOC INFORMATION SYSTEMS ATLANTA GEORGIA STATE UNIV, 35 BROAD STREET, STE 916-917, ATLANTA, GA 30303 USA 1536-9323 1558-3457 J ASSOC INF SYST J. Assoc. Inf. Syst. 2020.0 21.0 6 1594 1620 10.17705/1jais.00647 0.0 27 Computer Science, Information Systems; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science OP8CT 2023-03-23 WOS:000588317500008 0
C Demir, F; Sengur, A; Lu, H; Amiriparian, S; Cummins, N; Schuller, B IEEE Demir, Fatih; Sengur, Abdulkadir; Lu, Hao; Amiriparian, Shahin; Cummins, Nicholas; Schuller, Bjoern COMPACT BILINEAR DEEP FEATURES FOR ENVIRONMENTAL SOUND RECOGNITION 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP) English Proceedings Paper International Conference on Artificial Intelligence and Data Processing (IDAP) SEP 28-30, 2018 Inonu Univ, Malatya, TURKEY Inonu Univ, Comp Sci Dept,IEEE Turkey Sect,Anatolian Sci Inonu Univ Environmental sound classification; deep spectrum features; convolutional neural networks; compact bilinear pooling CLASSIFICATION Environmental sound recognition (ESR) has extensive various civilian and military applications. Existing ESR methods generally tackle this problem by employing various signal processing and machine learning methods. Herein, an ESR paradigm based on feature extraction from pre-trained deep convolutional neural networks (CNN), the derivation of higher-order statistics by compact bilinear pooling and normalisation. In particular, we consider two deep ImageNet architectures for deep feature extraction, and the Random Maclaurin (RM) to produce the compact bilinear features. A support vector machine (SVM) with homogeneous mapping is used in the classification stage. Two publicly available environmental sound datasets are used to verify the efficacy of the approach namely, ESC-50 and ESC-10. We compare the proposed method with various previous state-of-the-art methods. Presented results indicate the suitability of the higher-order statistics of DEEP SPECTRUM representations for ESR classification tasks. [Demir, Fatih; Sengur, Abdulkadir] Firat Univ, Technol Elazig, Elect & Elect Engn Dept, Elazig, Turkey; [Lu, Hao] Huazhong Univ Sci & Technol, Natl Key Lab Sci & Technol Multispectral Informat, Sch Automat, Wuhan 430074, Hubei, Peoples R China; [Amiriparian, Shahin; Cummins, Nicholas; Schuller, Bjoern] Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, Augsburg, Germany; [Amiriparian, Shahin] Tech Univ Munich, Machine Intelligence & Signal Proc Grp, Munich, Germany; [Schuller, Bjoern] Imperial Coll London, GLAM, London, England Firat University; Huazhong University of Science & Technology; University of Augsburg; Technical University of Munich; Imperial College London Sengur, A (corresponding author), Firat Univ, Technol Elazig, Elect & Elect Engn Dept, Elazig, Turkey. ksengur@firat.edu.tr Demir, Fatih/AAY-4785-2021; Cummins, Nicholas/AAC-6431-2019; Amiriparian, Shahin/AFN-4830-2022; Lu, Hao/AFL-6512-2022; Demir, Fatih/M-3012-2017; Sengur, Abdulkadir/Q-8023-2019 Demir, Fatih/0000-0003-3210-3664; Cummins, Nicholas/0000-0002-1178-917X; Amiriparian, Shahin/0000-0002-1129-8223; Sengur, Abdulkadir/0000-0003-1614-2639 European Unions's Seventh Framework and Horizon 2020 Programmes [338164] European Unions's Seventh Framework and Horizon 2020 Programmes This work was supported by the European Unions's Seventh Framework and Horizon 2020 Programmes under grant agreement No. 338164 (ERC StG iHEARu). 25 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-6878-8 2018.0 5 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BM0FD 2023-03-23 WOS:000458717400058 0
J Morante, M; Kopsinis, Y; Theodoridis, S; Protopapas, A Morante, Manuel; Kopsinis, Yannis; Theodoridis, Sergios; Protopapas, Athanassios Information Assisted Dictionary Learning for fMRI Data Analysis IEEE ACCESS English Article Functional magnetic resonance imaging; Task analysis; Machine learning; Brain modeling; Data analysis; Hemodynamics; Standards; Dictionary learning; fMRI; semi-blind; sparsity; weighted norms INDEPENDENT COMPONENT ANALYSIS; MATRIX FACTORIZATION; CEREBRAL-CORTEX; SPATIAL ICA; BRAIN; ORGANIZATION; SPARSITY; CONNECTIVITY; ALGORITHMS; SEPARATION In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function. In addition, the method bypasses one of the major drawbacks of the DL methods; namely, the selection of the sparsity-related regularization parameters. Under the proposed formulation, the associated regularization parameters bear a direct relation to the number of the activated voxels for each one of the sources & x2019; spatial maps. This natural interpretation facilitates fine-tuning of the related parameters and allows for exploiting external information from brain atlases. The proposed method is evaluated against several other popular techniques, including the classical General Linear Model (GLM). The obtained performance gains are quantitatively demonstrated via a novel realistic synthetic fMRI dataset as well as real data from a challenging experimental design. [Morante, Manuel; Theodoridis, Sergios] Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece; [Morante, Manuel] Comp Technol Inst & Press Diophantus CTI, Patras 26504, Greece; [Kopsinis, Yannis] Libra MLI Ltd, Edinburgh EH6 6JG, Midlothian, Scotland; [Theodoridis, Sergios] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China; [Protopapas, Athanassios] Univ Oslo, Dept Special Needs Educ, N-0318 Oslo, Norway National & Kapodistrian University of Athens; Chinese University of Hong Kong, Shenzhen; University of Oslo Morante, M (corresponding author), Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece.;Morante, M (corresponding author), Comp Technol Inst & Press Diophantus CTI, Patras 26504, Greece. morante3@gmail.com Morante, Manuel/0000-0001-8935-0186; Kopsinis, Yannis/0000-0003-2733-9357 European Union's Seventh Framework Programme (H2020-MSCA-ITN-2014), through the Marie Sklodowska-Curie Fellowship within the Innovative Training Network (MacSeNet) [642685] European Union's Seventh Framework Programme (H2020-MSCA-ITN-2014), through the Marie Sklodowska-Curie Fellowship within the Innovative Training Network (MacSeNet) This work was supported by the European Union's Seventh Framework Programme (H2020-MSCA-ITN-2014), through the Marie Sklodowska-Curie Fellowship within the Innovative Training Network (MacSeNet), under Grant agreement no. 642685. 64 3 3 2 5 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 90052 90068 10.1109/ACCESS.2020.2994276 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LV8XZ Green Submitted, Green Published, gold 2023-03-23 WOS:000538727700095 0
J Li, WJ; Meng, WZ; Furnell, S Li, Wenjuan; Meng, Weizhi; Furnell, Steven Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities * PATTERN RECOGNITION LETTERS English Article Behavioral user authentication; Touch gestures; Usable security; Smartphone security; Social networking; Machine learning The Internet of Things (IoT) allows various embedded devices and smart sensors to be connected with each other, which provides a basis for building smart cities. The IoT-enabled smart city can greatly benefit people's daily lives, where smartphone is one of the most widely used IoT devices. For example, people can use the phone to check their financial account, store personal data and communicate with peers. Thus it is very important to safeguard the phones from unauthorized access. To complement traditional textual passwords, touch behavioral authentication has attracted much attention while it is still a challenge on how to build a robust scheme in practice. This is because users' touch actions are often dynamic and hard to model. For this challenge, previous work has proved that touch actions could become consistent when users interact with social networking applications. Motivated by this observation, in this work, we perform a study to investigate users' touch behavior within Email applications on smartphones (with Email being one of the most important and widely used means in connecting with others). The study results with 60 participants validate the former observation that users' touch behavioral deviation can be greatly decreased when they play Email applications. (c) 2021 Elsevier B.V. All rights reserved. [Li, Wenjuan] Guangzhou Univ, Inst Artificial Intelligence & Blockchain, Guangzhou, Peoples R China; [Li, Wenjuan; Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark; [Furnell, Steven] Univ Nottingham, Fac Sci, Nottingham, England Guangzhou University; Technical University of Denmark; University of Nottingham Meng, WZ (corresponding author), Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark. weme@dtu.dk Li, Wenjuan/0000-0003-3745-5669 National Natural Science Foundation of China [61802077] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) We would like to thank all participants for their hard work during the user study. This work was partially supported by National Natural Science Foundation of China (No. 61802077) . 31 8 8 2 13 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. APR 2021.0 144.0 35 41 10.1016/j.patrec.2021.01.019 0.0 JAN 2021 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science QQ1LR Green Accepted, Green Published 2023-03-23 WOS:000624287500006 0
J Su, YS; Wu, TF; Xu, F; Paun, A Su, Yansen; Wu, Tingfang; Xu, Fei; Paun, Andrei Spiking Neural P Systems with Rules on Synapses Working in Sum Spikes Consumption Strategy FUNDAMENTA INFORMATICAE English Article Bio-inspired computing; Membrane computing; Spiking neural network; Spiking neural P system; Universality PRE-COMPUTED RESOURCES; UNIFORM SOLUTIONS; SUBSET SUM; ASTROCYTES; NETWORKS; NEURONS; SAT Spiking neural P systems with rules on synapses (RSSN P systems, for short) are a class of distributed and parallel computation models inspired by the way in which neurons process and communicate information with each other by means of spikes, where neurons only contain spikes and the evolution rules are on synapses. RSSN P systems have been proved to be Turing universal, using the strategy that restricts all the applied rules to consume the same number of spikes from the given neuron, termed as equal spikes consumption strategy. In this work, in order to avoid imposing the equal spikes consumption restriction on the application of rules, a new strategy for rule application, termed as sum spikes consumption strategy, is considered in RSSN P systems, where a maximal set of enabled rules from synapses starting from the same neuron is nondeterministically chosen to be applied, in the sense that no further synapse can use any of its rules, and the sum of these numbers of spikes that all the applied rules consume is removed from the neuron. In this way, the proposed strategy avoids checking whether all the applied rules consume the same number of spikes from the given neuron. The computation power of RSSN P systems working in the proposed strategy is investigated, and it is proved that such systems characterize the semilinear sets of natural numbers, i.e., such systems are not universal. Furthermore, RSSN P systems with weighted synapses working in the proposed strategy are proved to be Turing universal. These results show that the weight on synapses is a powerful ingredient of RSSN P systems in terms of the computation power, which makes RSSN P systems working in sum spikes consumption strategy become universal from non-universality. [Su, Yansen] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China; [Wu, Tingfang; Xu, Fei] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China; [Paun, Andrei] Univ Bucharest, Dept Comp Sci, Str Acad 14,Sect 1, Bucharest 010014, Romania; [Paun, Andrei] Natl Inst Res & Dev Biol Sci Bucuresti, Bioinformat Dept, Bucharest, Romania Anhui University; Huazhong University of Science & Technology; University of Bucharest Wu, TF (corresponding author), Huazhong Univ Sci & Technol, Key Lab Image Informat Proc & Intelligent Control, Educ Minist China, Sch Automat, Wuhan 430074, Hubei, Peoples R China. suyansen1985@163.com; tfwu@hust.edu.cn; fei_xu@hust.edu.cn; apaun@fmi.unibuc.ro Paun, Andrei/A-5292-2008 Paun, Andrei/0000-0002-1644-8198 National Natural Science Foundation of China [61320106005, 61502004, 61672033, 61502186]; ANCS [POC P 37 257] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ANCS This work was supported by National Natural Science Foundation of China (61320106005, 61502004, 61672033, and 61502186) and ANCS under award number POC P 37 257 (MoDASyS). 60 8 9 5 57 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 0169-2968 1875-8681 FUND INFORM Fundam. Inform. 2017.0 156.0 2 187 208 10.3233/FI-2017-1604 0.0 22 Computer Science, Software Engineering; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics FL2IK 2023-03-23 WOS:000414038400005 0
J Zheng, R; Brunius, C; Shi, L; Zafar, H; Paulson, L; Landberg, R; Naluai, AT Zheng, Rui; Brunius, Carl; Shi, Lin; Zafar, Huma; Paulson, Linda; Landberg, Rikard; Naluai, Asa Torinsson Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome ANALYTICA CHIMICA ACTA English Article Biobank; Pre-centrifugation management; Plasma; Sample quality; Untargeted metabolomics; Machine learning MASS-SPECTROMETRY; BLOOD; ANALYTES; IMPACT; SERUM Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 degrees C; classification rate 87%) and time (5-210 min; Q(2) - 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5-210 min), and the variation at 4 degrees C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of precentrifugation temperature (classification rate 94%) and delay time (Q(2) - 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed. (C) 2021 Published by Elsevier B.V. [Zheng, Rui; Brunius, Carl] Uppsala Univ, Dept Surg Sci, Uppsala, Sweden; [Zheng, Rui; Brunius, Carl; Shi, Lin; Landberg, Rikard] Chalmers Univ Technol, Dept Biol & Biol Engn, Div Food & Nutr Sci, Gothenburg, Sweden; [Brunius, Carl] Chalmers Univ Technol, Chalmers Mass Spectrometry Infrastruct, Gothenburg, Sweden; [Shi, Lin] Shaanxi Normal Univ, Sch Food Engn & Nutr Sci, Xian, Peoples R China; [Zafar, Huma; Paulson, Linda; Naluai, Asa Torinsson] Sahlgrens Univ Hosp, Biobank West, Gothenburg, Vastra Gotaland, Sweden; [Landberg, Rikard] Umea Univ, Dept Publ Hlth & Clin Med, Umea, Sweden; [Naluai, Asa Torinsson] Univ Gothenburg, Sahlgrenska Acad, Biobank Core Facil, Inst Biomed, Gothenburg, Sweden Uppsala University; Chalmers University of Technology; Chalmers University of Technology; Shaanxi Normal University; Sahlgrenska University Hospital; Umea University; University of Gothenburg Shi, L (corresponding author), Chalmers Univ Technol, Dept Biol & Biol Engn, Div Food & Nutr Sci, Gothenburg, Sweden. shlin@chalmers.se SHI, LIN/HJP-5440-2023; Torinsson Naluai, Asa/P-3269-2015 SHI, LIN/0000-0001-9709-3394; Zheng, Rui/0000-0003-2891-9273; Torinsson Naluai, Asa/0000-0002-0504-6492 China Scholarship Council [201706150099] China Scholarship Council(China Scholarship Council) We would like to thank Marina Frojd, Annika Johansson at Gothia Forum in Gothenburg and Kerstin Enquist Olsson and Anders Johansson together with Biobanken Norr in Umea, Sweden for collecting samples together with Biobank West. We would like to thank the Uppsala Multidisciplinary Center for Advanced Computational Science (Uppmax) for providing the computational infrastructure and resources. Rui Zheng received funding from the China Scholarship Council (No. 201706150099). 45 4 4 1 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0003-2670 1873-4324 ANAL CHIM ACTA Anal. Chim. Acta OCT 16 2021.0 1182.0 338968 10.1016/j.aca.2021.338968 0.0 AUG 2021 11 Chemistry, Analytical Science Citation Index Expanded (SCI-EXPANDED) Chemistry UM9RT 34602206.0 Green Published, hybrid 2023-03-23 WOS:000693662300010 0
J Fan, Z; Li, C; Chen, Y; Wei, JH; Loprencipe, G; Chen, XP; Di Mascio, P Fan, Zhun; Li, Chong; Chen, Ying; Wei, Jiahong; Loprencipe, Giuseppe; Chen, Xiaopeng; Di Mascio, Paola Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture MATERIALS English Article pavement cracking; automatic crack detection; encoder-decoder; deep learning; U-net; hierarchical feature; dilated Convolution 3D ASPHALT SURFACES; DAMAGE DETECTION; NEURAL-NETWORKS; INSPECTION; SYSTEM; ALGORITHM; ROBOT; MODEL Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms. [Fan, Zhun; Li, Chong; Chen, Ying; Wei, Jiahong] Shantou Univ, Coll Engn, Dept Elect & Informat Engn, Key Lab Digital Signal & Image Proc Guangdong Pro, Shantou 515063, Peoples R China; [Li, Chong; Loprencipe, Giuseppe; Di Mascio, Paola] Sapienza Univ Rome, Dept Civil Construct & Environm Engn, I-00184 Rome, Italy; [Chen, Xiaopeng] Pusan Natl Univ, Dept Ind Engn, Busan 609735, South Korea Shantou University; Sapienza University Rome; Pusan National University Loprencipe, G (corresponding author), Sapienza Univ Rome, Dept Civil Construct & Environm Engn, I-00184 Rome, Italy. zfan@stu.edu.cn; chongli1217@163.com; C.L.19ychen1@stu.edu.cn; 19jhwei@stu.edu.cn; giuseppe.loprencipe@uniromal.it; xiaopengchen388@gmail.com; paola.dimascio@uniromal.it Giuseppe, Loprencipe/C-2223-2013; 陈, 晓鹏/AAC-3999-2021 Giuseppe, Loprencipe/0000-0003-1003-8849; 陈, 晓鹏/0000-0001-5412-8876; di mascio, paola/0000-0001-8814-8605; Fan, Zhun/0000-0002-4232-8229 Science and Technology Planning Project of Guangdong Province of China [180917144960530]; Project of Educational Commission of Guangdong Province of China [2017KZDXM032]; State Key Lab of Digital Manufacturing Equipment and Technology [DMETKF2019020]; Project of Robot Automatic Design Platform combining Multi-Objective Evolutionary Computation and Deep Neural Network [2019A050519008]; China Scholarship Council (CSC) Science and Technology Planning Project of Guangdong Province of China; Project of Educational Commission of Guangdong Province of China; State Key Lab of Digital Manufacturing Equipment and Technology; Project of Robot Automatic Design Platform combining Multi-Objective Evolutionary Computation and Deep Neural Network; China Scholarship Council (CSC)(China Scholarship Council) This work was supported by the Science and Technology Planning Project of Guangdong Province of China under grant 180917144960530, by the Project of Educational Commission of Guangdong Province of China under grant 2017KZDXM032, by the State Key Lab of Digital Manufacturing Equipment and Technology under grant DMETKF2019020, by the Project of Robot Automatic Design Platform combining Multi-Objective Evolutionary Computation and Deep Neural Network under grant 2019A050519008, and by the China Scholarship Council (CSC) in 2019. 82 45 45 19 70 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials JUL 2020.0 13.0 13 2960 10.3390/ma13132960 0.0 18 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics MT5JI 32630713.0 gold, Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000555009200001 0
J Ashfaq, M; Tandon, A; Zhang, QY; Jabeen, F; Dhir, A Ashfaq, Muhammad; Tandon, Anushree; Zhang, Qingyu; Jabeen, Fauzia; Dhir, Amandeep Doing good for society! How purchasing green technology stimulates consumers toward green behavior: A structural equation modeling-artificial neural network approach BUSINESS STRATEGY AND THE ENVIRONMENT English Article; Early Access ANT Forest; delight; ECM; green habit; perceived entertainment; TTFM EXPECTATION-CONFIRMATION MODEL; CLOUD COMPUTING ADOPTION; CONTINUANCE INTENTION; CUSTOMER DELIGHT; SOCIAL MEDIA; ACCEPTANCE MODEL; HYBRID SEM; FIT; SATISFACTION; ANTECEDENTS Many countries have recognized the urgent need to address environmental problems, such as air pollution, waste disposal, global warming, and natural resource depletion, through the application of green technology. ANT Forest is one such technological initiative that has gained academic attention for its potential to minimize adverse environmental impacts and promote sustainable green behavior by involving people in eco-friendly activities. We built an integrated framework to understand users' continuance intention (CI) toward ANT Forest based on the expectation-confirmation model (ECM) and the task-technology fit model (TTFM). Using structural equation modeling (SEM), we analyzed survey data from 353 ANT Forest users. We then included the SEM results as components of an artificial neural network (ANN) to understand users' CI toward ANT Forest. The results from the SEM analysis revealed a series of sequential associations: (a) green habit as an individual characteristic and perceived entertainment as a technology characteristic significantly affect perceived green task-technology fit (GTTF), (b) perceived GTTF strongly and positively influences confirmation and CI, (c) confirmation is positively associated with users' satisfaction and delight, (d) delight significantly impacts satisfaction, and (e) perceived usefulness (PU) and satisfaction are strong determinants of CI. An ANN analysis further confirmed these findings. The study discusses managerial implications along with future research directions. [Ashfaq, Muhammad; Zhang, Qingyu] Shenzhen Univ, Coll Management, Res Inst Business Analyt & Supply Chain Managemen, Shenzhen, Peoples R China; [Tandon, Anushree] Univ Turku, Turku Sch Econ, Turku, Finland; [Jabeen, Fauzia] Abu Dhabi Univ, Coll Business Adm, Abu Dhabi, U Arab Emirates; [Dhir, Amandeep] Univ Agder, Sch Business & Law, Dept Management, Kristiansand, Norway; [Dhir, Amandeep] Norwegian Sch Hotel Management, Fac Social Sci, Stavanger, Norway; [Dhir, Amandeep] North West Univ, Optentia Res Focus Area, Vanderbijlpark, South Africa Shenzhen University; University of Turku; Abu Dhabi University; University of Agder; Universitetet i Stavanger; North West University - South Africa Zhang, QY (corresponding author), Shenzhen Univ, Coll Management, Res Inst Business Analyt & Supply Chain Managemen, Shenzhen, Peoples R China.;Dhir, A (corresponding author), Univ Agder, Sch Business & Law, Dept Management, Kristiansand, Norway. q.yu.zhang@gmail.com; nmandeep.dhir@uia.no Jabeen, Fauzia/B-2327-2012; tandon, anushree/AAP-8057-2020; Zhang, Qingyu/ABE-8037-2020; Dhir, Amandeep/F-1826-2013 Jabeen, Fauzia/0000-0002-3505-5955; tandon, anushree/0000-0002-7319-418X; Zhang, Qingyu/0000-0001-9739-3899; Ashfaq, Muhammad/0000-0001-7602-0647; Dhir, Amandeep/0000-0002-6006-6058 Key Project of National Social Science Foundation of China [21AGL014]; Natural Science Foundation of Guangdong-Guangdong Basic and Applied Basic Research Foundation [2021A1515011894]; Guangdong 13th-FiveYear-Plan Philosophical and Social Science Fund [GD20CGL28]; Shenzhen Science and Technology Program [JCYJ20210324093208022] Key Project of National Social Science Foundation of China; Natural Science Foundation of Guangdong-Guangdong Basic and Applied Basic Research Foundation; Guangdong 13th-FiveYear-Plan Philosophical and Social Science Fund; Shenzhen Science and Technology Program This research was partially funded by Key Project of National Social Science Foundation of China (21AGL014); Natural Science Foundation of Guangdong-Guangdong Basic and Applied Basic Research Foundation (2021A1515011894); Guangdong 13th-FiveYear-Plan Philosophical and Social Science Fund (GD20CGL28); and Shenzhen Science and Technology Program (JCYJ20210324093208022). 121 2 2 24 40 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0964-4733 1099-0836 BUS STRATEG ENVIRON Bus. Strateg. Environ. 10.1002/bse.3188 0.0 JUL 2022 18 Business; Environmental Studies; Management Social Science Citation Index (SSCI) Business & Economics; Environmental Sciences & Ecology 2R4GH hybrid 2023-03-23 WOS:000821070400001 0
J Poirier, C; Ben Hamed, S; Garcia-Saldivar, P; Kwok, SC; Meguerditchian, A; Merchant, H; Rogers, J; Wells, S; Fox, AS Poirier, Colline; Ben Hamed, Suliann; Garcia-Saldivar, Pamela; Kwok, Sze Chai; Meguerditchian, Adrien; Merchant, Hugo; Rogers, Jeffrey; Wells, Sara; Fox, Andrew S. Beyond MRI: on the scientific value of combining non-human primate neuroimaging with metadata NEUROIMAGE English Article Magnetic resonance imaging; Behaviour; Genetics; Physiology; Phylogenetics; BIDS PRIMARY MOTOR CORTEX; CHIMPANZEES PAN-TROGLODYTES; HEART-RATE-VARIABILITY; RESTING-STATE FMRI; BIMANUAL COORDINATED ACTIONS; MACAQUES MACACA-MULATTA; BABOONS PAPIO-ANUBIS; HAND PREFERENCES; HUMAN HANDEDNESS; NEUROANATOMICAL ASYMMETRIES Sharing and pooling large amounts of non-human primate neuroimaging data offer new exciting opportunities to understand the primate brain. The potential of big data in non-human primate neuroimaging could however be tremendously enhanced by combining such neuroimaging data with other types of information. Here we describe metadata that have been identified as particularly valuable by the non-human primate neuroimaging community, including behavioural, genetic, physiological and phylogenetic data. [Poirier, Colline] Biosci Inst, Newcastle Upon Tyne 6, Tyne & Wear, England; [Poirier, Colline] Ctr Behav & Evolut, Fac Med Sci, Newcastle Upon Tyne 6, Tyne & Wear, England; [Ben Hamed, Suliann] Univ Lyon, Inst Sci Cognit Marc Jeannerod, CNRS, UMR 5229, Lyon, France; [Garcia-Saldivar, Pamela; Merchant, Hugo] UNAM, Inst Neurobiol, Campus Juriquilla Blvd Juriquilla 3001, Queretaro, Qro, Mexico; [Kwok, Sze Chai] East China Normal Univ, Shanghai Key Lab Brain Funct Genom,Shanghai Chang, Key Lab Brain Funct Genom Minist Educ,Sch Psychol, Shanghai Key Lab Magnet Resonance Affiliated Ment, Shanghai, Peoples R China; [Kwok, Sze Chai] Duke Kunshan Univ, Duke Inst Brain Sci, Div Nat & Appl Sci, Kunshan, Jiangsu, Peoples R China; [Kwok, Sze Chai] NYU Shanghai, NYU ECNU Inst Brain & Cognit Sci, Shanghai, Peoples R China; [Meguerditchian, Adrien] Univ Aix Marseille, Lab Psychol Cognit, UMR 7290, Inst Language Commun & Brain,CNRS, F-13331 Marseille, France; [Rogers, Jeffrey] Baylor Coll Med, Human Genome Sequencing Ctr, Houston, TX 77030 USA; [Rogers, Jeffrey] Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA; [Wells, Sara] MRC Harwell Inst, Ctr Macaques, Salisbury, Wilts, England; [Fox, Andrew S.] Univ Calif Davis, Dept Psychol, Calif Natl Primate Res Ctr, Davis, CA 95616 USA Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Universidad Nacional Autonoma de Mexico; East China Normal University; Duke Kunshan University; NYU Shanghai; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Aix-Marseille Universite; Baylor College of Medicine; Baylor College of Medicine; MRC Harwell; University of California System; University of California Davis Poirier, C (corresponding author), Biosci Inst, Newcastle Upon Tyne 6, Tyne & Wear, England.;Poirier, C (corresponding author), Ctr Behav & Evolut, Fac Med Sci, Newcastle Upon Tyne 6, Tyne & Wear, England. colline.poirier@ncl.ac.uk Meguerditchian, Adrien/AAB-6367-2022; Meguerditchian, Adrien/H-3047-2013; Ben Hamed, Suliann/O-6070-2014; Merchant, Hugo/E-8703-2019 Meguerditchian, Adrien/0000-0003-3754-6747; Meguerditchian, Adrien/0000-0003-3754-6747; Ben Hamed, Suliann/0000-0003-1510-7284; Merchant, Hugo/0000-0002-3488-9501; Fox, Andrew/0000-0003-0695-3323; Kwok, Sze Chai/0000-0002-7439-1193; Garcia-Saldivar, Pamela/0000-0003-3274-4955; Rogers, Jeffrey/0000-0002-7374-6490 Newcastle University Research Fellowship; Center National pour la Recherche Scientifique (CNRS); National Natural Science Foundation of China [32071060]; Science and Technology Commission of Shanghai Municipality [201409002800]; Consejo Nacional de Ciencia y tecnologia [A1-S-8430]; European Research Council under the European Union [716931 -GESTIMAGE -ERC-2016-STG]; Consejo Nacional de Ciencia y tecnologia studentship [280464]; Medical Research Council (UK) [A430]; California National Primate Research Center [P51OD011107]; JORISS project grants; MRC [MC_UP_1504/1] Funding Source: UKRI Newcastle University Research Fellowship; Center National pour la Recherche Scientifique (CNRS)(Centre National de la Recherche Scientifique (CNRS)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)); Consejo Nacional de Ciencia y tecnologia(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); European Research Council under the European Union(European Research Council (ERC)); Consejo Nacional de Ciencia y tecnologia studentship; Medical Research Council (UK)(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); California National Primate Research Center; JORISS project grants; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) Colline Poirier was supported by a Newcastle University Research Fellowship. Suliann Ben Hamed was support by a funding from the Center National pour la Recherche Scientifique (CNRS). Sze Chai Kwok was supported by the National Natural Science Foundation of China (Grant No. 32071060), the Science and Technology Commission of Shanghai Municipality (grant number: 201409002800) and JORISS project grants. Hugo Merchant was supported by Consejo Nacional de Ciencia y tecnologia A1-S-8430. Adrien Meguerditchian was supported by fundings from the European Research Council under the European Union's Horizon 2020 research and innovation programm grant agreement No 716931 -GESTIMAGE -ERC-2016-STG. Pamela Garcia-Saldivar was supported by a Consejo Nacional de Ciencia y tecnologia studentship number 280464. Sara Wells was supported by core funding from the Medical Research Council (UK), to the Centre for Macaques (A430). Andrew S. Fox was supported by the California National Primate Research Center (P51OD011107). 139 6 6 2 3 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage MAR 2021.0 228.0 117679 10.1016/j.neuroimage.2020.117679 0.0 JAN 2021 11 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging QG6VO 33359343.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000617722700008 0
S Zheng, QH; Chen, L; Burgos, D Zheng, Q; Chen, L; Burgos, D Zheng, Qinhua; Chen, Li; Burgos, Daniel Zheng, Q; Chen, L; Burgos, D Innovative and Revolutionary Potential of MOOCs DEVELOPMENT OF MOOCS IN CHINA Lecture Notes in Educational Technology English Article; Book Chapter The phenomenal development of MOOCs in recent years has made an earthquake in the field of higher education globally. In essence, it is less a localized revolution that simply applies internet technologies into education, or 'a discussion on teaching reformation and new teaching models' happening within the superficial structures of universities, than a systematic revolution in education driven by the dominant genes of the free, open internet, and an innovation of the organization/service mode of higher education by use of the recessive genes such as big data and organization process reengineering (Guo et al. 2013). The new model of online education, represented by MOOCs, is now demonstrating its power by breaking through the traditional educational system and models and has become an important driving force in reform and development of higher education internationally. To promote the educational reform by making use of the opportunities brought by MOOCs, it will be fundamental to get an accurate understanding of the innovative nature of MOOCs through analysis. MOOCs take different forms in practice. These different forms share similarities but also have many differences. In this chapter, based on the case studies of cMOOCs and xMOOCs, we will try to analyze the innovative nature of MOOCs and discuss their potential in revolutionizing higher education. [Zheng, Qinhua; Chen, Li] Beijing Normal Univ, Beijing, Peoples R China; [Burgos, Daniel] Univ Int La Rioja UNIR, Logrono, La Rioja, Spain Beijing Normal University; Universidad Internacional de La Rioja (UNIR) Zheng, QH (corresponding author), Beijing Normal Univ, Beijing, Peoples R China. Burgos, Daniel/AAL-6447-2020; ZHENG, Qin/T-2925-2019 Burgos, Daniel/0000-0003-0498-1101; 19 0 0 0 1 SPRINGER-VERLAG SINGAPORE PTE LTD SINGAPORE 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE 2196-4963 978-981-10-6586-6; 978-981-10-6585-9 LECT N EDUC TECHNOL 2018.0 25 35 10.1007/978-981-10-6586-6_3 0.0 10.1007/978-981-10-6586-6 11 Education & Educational Research Book Citation Index – Social Sciences & Humanities (BKCI-SSH) Education & Educational Research BK6MT 2023-03-23 WOS:000440576900004 0
J Bai, H; Yu, HL; Yu, G; Rocha, A; Huang, X Bai, Hua; Yu, Hualong; Yu, Guang; Rocha, Alvaro; Huang, Xing Analysis on an Auto Increment Detection System of Chinese Disaster Weibo Text JOURNAL OF UNIVERSAL COMPUTER SCIENCE English Article Social media; Disasters; Emergency management; Word2vector SOCIAL MEDIA; EVENT DETECTION; EARTHQUAKE; TWITTER; CHALLENGES With the rapid development of Internet information technology, the advantages of social media in terms of speed, content, form, and effect of communication are becoming increasingly significant. In recent years, more and more researchers have paid attention to the special value and role of social media tools in disaster information emergency management. Weibo is the most widely used Chinese social media tool. To effectively mine and apply the emergency function of disaster situation microblogs, a disaster situation information discovery and collection system capable of online incremental identification and collection are constructed for massive and disordered disaster microblog text streams. First, based on the deep learning-trained word vector model and a large-scale corpus, an unsupervised short-text feature representation method of disaster situation Weibo information is developed. According to the experimental results of the feature combination test and the training set scale test, the SVM algorithm was selected for disaster microblog information classification, which realized effective identification of disaster situation micro-bloggings. Then, the temporal information similarity and geographic information similarity are used to improve the single text similarity algorithm, and a Chinese disaster event online real-time detection model is constructed. Furthermore, the disaster-affected areas can be achieved in real-time based on the detection results. By crawling and classifying the micro-bloggings from the disaster-affected areas, it is possible to realize the incremental identification and collection of online disaster situation Weibo information. Finally, the empirical analysis of disaster events such as the Leshan Earthquake shows that the real -time intelligent identification and collection system for disaster situation Weibo micro-bloggings developed in this paper can obtain large-scale and useful data for disaster emergency management, which proving that this system is effective and efficient. [Bai, Hua] Fujian Normal Univ, Fuzhou, Fujian, Peoples R China; [Yu, Hualong; Yu, Guang] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China; [Rocha, Alvaro] Univ Lisbon, Lisbon, Portugal; [Huang, Xing] Southwest Univ Sci & Technol, Mianyang, Sichuan, Peoples R China Fujian Normal University; Harbin Institute of Technology; Universidade de Lisboa; Southwest University of Science & Technology - China Rocha, A (corresponding author), Univ Lisbon, Lisbon, Portugal. baihua1727@163.com; yuhualong@hit.edu.cn; yug@hit.edu.cn; amr@iseg.ulisboa.pt; huangxing6213@126.com Rocha, Alvaro/0000-0002-0750-8187 National Natural Science Foundation of China [71774041, 71531013]; Natural Science Foundation of Fujian Province of China [31185015]; Social Science Planning Project of Fujian Province of China [FJ2018C008] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province); Social Science Planning Project of Fujian Province of China This work was supported by the National Natural Science Foundation of China (Grant Nos. 71774041 and 71531013) , Natural Science Foundation of Fujian Province of China (Grant No. 31185015) , and Social Science Planning Project of Fujian Province of China (Grant No. FJ2018C008) . 42 3 3 2 20 GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM GRAZ INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA 0948-695X J UNIVERS COMPUT SCI J. Univers. Comput. Sci. 2020.0 27.0 2 230 252 10.3897/jucs.65106 0.0 23 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science UH0BO Green Submitted, gold 2023-03-23 WOS:000689606700008 0
J Pan, YJ; Bast, D; Pollin, S Pan, Yujian; Bast, De; Pollin, Sofie Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Array calibration; indoor positioning; massive multiple-input and multiple-output (MIMO); multipath; near-field LOCALIZATION; CALIBRATION In this article, we focus on indoor direct positioning using a practical massive multiple-input and multiple-output (MIMO) array with imperfections. First, the imperfect array needs to be calibrated in an anechoic chamber from the view of array signal processing. This brings inconveniences due to the large size of massive MIMO systems. To solve this problem, we propose onsite calibration, where a banded calibration matrix, calibrating both phase-gain error and mutual coupling, can be estimated by performing least-squares on the measure near-field channels, i.e., the site survey data. The feasibility of this calibration method can be explained by that the non-line-of-sight (NLOS) channel is able to be modeled using Rayleigh fading; i.e., the sum of the multipath components can be treated as Gaussian noise. Then, to estimate the user position, we propose a new two-stage direct positioning scheme. In the first stage, the coarse position is obtained via global search-based conventional beamforming (CBF) using a global calibration matrix. In the second stage, fine positioning is achieved via local search-based multiple signal classification (MUSIC) using a local calibration matrix. The proposed method is computationally efficient, insensitive to model order selection, free of dense site survey, and robust to partial line-of-sight (LOS) blocking. Finally, the proposed method is verified by practical measurements from a distributed array (DA), uniform linear array (ULA), and uniform rectangular array (URA), and compared with a deep learning positioning-method. The results show that the DA with the proposed method performs the best, achieving sub-centimeter accuracy (typically below 5 mm). [Pan, Yujian] Hangzhou Dianzi Univ, Sch Elect & Informat, Hangzhou 310018, Peoples R China; [Bast, De; Pollin, Sofie] Katholieke Univ Leuven, Dept Elect Engn, B-3001 Leuven, Belgium; [Pollin, Sofie] IMEC, B-3001 Leuven, Belgium Hangzhou Dianzi University; KU Leuven; IMEC Pan, YJ (corresponding author), Hangzhou Dianzi Univ, Sch Elect & Informat, Hangzhou 310018, Peoples R China. pyj@hdu.edu.cn; sdebast@esat.kuleuven.be; spollin@esat.kuleuven.be De Bast, Sibren/0000-0002-9456-4709; Pan, Yujian/0000-0002-6700-3500 National Natural Science Foundation of China [61701145]; China Scholarship Council (CSC) [201908330075]; Research Foundation Flanders (FWO) Strategic Basic Research Ph.D. Fellowship [1SA1619N] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC)(China Scholarship Council); Research Foundation Flanders (FWO) Strategic Basic Research Ph.D. Fellowship(FWO) This work was supported in part by the National Natural Science Foundation of China under Contract 61701145. The work of Yujian Pan was supported by the China Scholarship Council (CSC) under Grant 201908330075. The work of Sibren De Bast was supported by the Research Foundation Flanders (FWO) Strategic Basic Research (SB) Ph.D. Fellowship under Grant 1SA1619N. 34 4 4 2 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70.0 5502011 10.1109/TIM.2021.3067963 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation UX0QO hybrid 2023-03-23 WOS:000700553800001 0
J Shu, X; Li, G; Zhang, YJ; Shen, JW; Chen, Z; Liu, YG Shu, Xing; Li, Guang; Zhang, Yuanjian; Shen, Jiangwei; Chen, Zheng; Liu, Yonggang Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles JOURNAL OF POWER SOURCES English Article State of health; Voltage prediction; Mixed kernel function; Fixed size least squares support vector machine; Lithium-ion battery SUPPORT VECTOR MACHINE; CAPACITY ESTIMATION; PREDICTION; STRATEGY; MODEL In this study, a machine learning method is proposed for online diagnosis of battery state of health. A prediction model for future voltage profiles is established based on the extreme learning machine algorithm with the short-term charging data. A fixed size least squares-based support vector machine with a mixed kernel function is employed to learn the dependency of state of health on feature variables generated from the charging voltage profile without preprocessing data. The simulated annealing method is employed to search and optimize the key parameters of the fixed size least squares support vector machine and the mixed kernel function. By this manner, the proposed algorithm requires only partial random and discontinuous charging data, enabling practical online diagnosis of state of health. The model training and experimental validation are conducted with different kernel functions, and the influence of voltage range and noise are also investigated. The results indicate that the proposed method can not only maintain the state of health estimation error within 2%, but also improve robustness and reliability. [Shu, Xing; Shen, Jiangwei; Chen, Zheng] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China; [Li, Guang; Chen, Zheng] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England; [Zhang, Yuanjian] Queens Univ Belfast, Sir William Wright Technol Ctr, Belfast BT9 5BS, Antrim, North Ireland; [Liu, Yonggang] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China; [Liu, Yonggang] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China Kunming University of Science & Technology; University of London; Queen Mary University London; Queens University Belfast; Chongqing University; Chongqing University Chen, Z (corresponding author), Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.;Liu, YG (corresponding author), Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China. shuxing92@kust.edu.cn; g.li@qmul.ac.uk; y.zhang@qub.ac.uk; shenjiangwei6@163.com; chen@kust.edu.cn; andylyg@umich.edu Li, Guang/AAC-6883-2021; Shu, Xing/AAA-6901-2021; Chen, Zheng/AAO-6454-2020; Zhang, Yuanjian/HKN-4832-2023 Chen, Zheng/0000-0002-1634-7231; Zhang, Yuanjian/0000-0001-5563-8480; Shu, Xing/0000-0003-1845-1988; Li, Guang/0000-0003-1068-0479 National Natural Science Foundation of China [61763021, 51775063]; National Key R&D Program of China [2018YFB0104000]; EU [845102-HOEMEV-H2020-MSCA-IF-2018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; EU(European Commission) This work was supported in part by the National Natural Science Foundation of China (No. 61763021 and No. 51775063), in part by the National Key R&D Program of China (No. 2018YFB0104000), and in part by the EU-funded Marie Sklodowska-Curie Individual Fellowships Project under Grant 845102-HOEMEV-H2020-MSCA-IF-2018. 38 39 41 5 63 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0378-7753 1873-2755 J POWER SOURCES J. Power Sources SEP 30 2020.0 471.0 228478 10.1016/j.jpowsour.2020.228478 0.0 11 Chemistry, Physical; Electrochemistry; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels; Materials Science MO4QD Green Submitted 2023-03-23 WOS:000551511800019 0
C Fan, SY; Wang, YS; Luo, Y; Schmitt, A; Yu, SH IEEE COMP SOC Fan, Siyu; Wang, Yisen; Luo, Yuan; Schmitt, Alexander; Yu, Shenghua Improving Gravitational Wave Detection with 2D Convolutional Neural Networks 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 25th International Conference on Pattern Recognition (ICPR) JAN 10-15, 2021 ELECTR NETWORK Int Assoc Pattern Recognit,IEEE Comp Soc,Italian Assoc Comp Vis Pattern Recognit & Machine Learning Signal analysis; Deep learning; Neural networks MATCHED-FILTER; VESSELS; IMAGES Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9). [Fan, Siyu; Luo, Yuan] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China; [Wang, Yisen] Peking Univ, Key Lab Machine Percept MoE, Sch EECS, Beijing, Peoples R China; [Schmitt, Alexander] Katholieke Univ Leuven, Fac Econ & Business FEB, Brussels, Belgium; [Yu, Shenghua] Chinese Acad Sci, Joint Lab Radio Astron Technol, Natl Astron Observ, Beijing, Peoples R China Shanghai Jiao Tong University; Peking University; KU Leuven; Chinese Academy of Sciences; National Astronomical Observatory, CAS Fan, SY (corresponding author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China. fansiyu@sjtu.edu.cn; yisen.wang@pku.edu.cn; luoyuan@cs.sjtu.edu.cn; info@a-schmitt.com; shenghuayu@bao.ac.cn China Program of International ST Cooperation [2016YFE0100300] China Program of International ST Cooperation This work was supported in part by China Program of International S&T Cooperation 2016YFE0100300. 33 1 1 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1051-4651 978-1-7281-8808-9 INT C PATT RECOG 2021.0 7103 7110 10.1109/ICPR48806.2021.9412180 0.0 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology BR9MO 2023-03-23 WOS:000678409207033 0
J Lv, ZH; Qiao, L; You, I Lv, Zhihan; Qiao, Liang; You, Ilsun 6G-Enabled Network in Box for Internet of Connected Vehicles IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article 6G mobile communication; 5G mobile communication; Satellites; Artificial intelligence; Communication channels; Wireless networks; Base stations; 6G communication network; communication channel; channel characteristics; channel performance WIRELESS COMMUNICATIONS; MILLIMETER-WAVE; STAR SENSOR; 6G; COMMUNICATION; VISION; 5G; OPPORTUNITIES; REQUIREMENTS; CHALLENGES Objective: To realize the full coverage, full spectrum, and full application, of 6G networks, the channel measurement, channel characteristics, and channel research of the 6G-oriented full-spectrum full-scene wireless network are explored. Methods: At present, constructing an information network covering three dimensions of sea, land and air is one of the research directions of 6G technology development. Meanwhile, the full coverage of three dimensions of communication requires multiple channels including multiple frequency bands and multiple scenarios, mainly including terahertz, light band, satellite, unmanned aerial vehicle, ocean, high-speed rail, and vehicle-to-vehicle. For different channels, different parameters are analyzed and targeted discussed according to channel characteristics. Also, there are many other problems of wireless communication channel that need to be solved. Here, only the above problems are analyzed in detail. Because of the current development of the Internet of Vehicles, the interconnection of connected vehicles is analyzed, and this scenario is also the most widely common scenario in daily life. Results: For the above-mentioned communication channels, the relevant measurement and modeling results of the 6G channel are shown; besides, the characteristics, time, and space correlation functions are obtained for each channel. Conclusion: The existing research results of 6G-oriented wireless channel measurement and modeling are analyzed; the research methods are summarized; new ideas are proposed to provide researchers with important references. [Lv, Zhihan; Qiao, Liang] Qingdao Univ, Sch Data Sci & Software Engn, Qingdao 266071, Peoples R China; [Lv, Zhihan] Warsaw Univ Technol, Inst Comp Sci, Artificial Intelligence Dept, PL-00661 Warsaw, Poland; [You, Ilsun] Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea Qingdao University; Warsaw University of Technology; Soonchunhyang University You, I (corresponding author), Soonchunhyang Univ, Dept Informat Secur Engn, Asan 31538, South Korea. lvzhihan@gmail.com; leonqiaoove@gmail.com; isyou@sch.ac.kr Lv, Zhihan/I-3187-2014; Lv, Zhihan/GLR-6000-2022 Lv, Zhihan/0000-0003-2525-3074; Lv, Zhihan/0000-0003-2525-3074; Qiao, Liang/0000-0002-8188-886X; You, Ilsun/0000-0002-0604-3445 Key Research and Development Plan Major Scientific and Technological Innovation Projects of Shandong Province [2019JZZY020101]; Soonchunhyang University Research Fund Key Research and Development Plan Major Scientific and Technological Innovation Projects of Shandong Province; Soonchunhyang University Research Fund This work was supported in part by the Key Research and Development Plan Major Scientific and Technological Innovation Projects of Shandong Province under Grant 2019JZZY020101 and in part by the Soonchunhyang University Research Fund. 46 43 43 3 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. AUG 2021.0 22.0 8 5275 5282 10.1109/TITS.2020.3034817 0.0 8 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation TY8BD 2023-03-23 WOS:000684003100048 0
C Chen, Q; Wang, KB; Bian, ZJ; Cremer, I; Xu, G; Guo, YJ Obaidat, MS; Oren, T; Merkuryev, Y Chen, Qian; Wang, Kebing; Bian, Zhaojuan; Cremer, Illia; Xu, Gen; Guo, Yejun Cluster Performance Simulation for Spark Deployment Planning, Evaluation and Optimization SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS, SIMULTECH 2016 Advances in Intelligent Systems and Computing English Proceedings Paper 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH) JUL 29-31, 2016 Lisbon, PORTUGAL Spark simulation; Cluster simulation; Performance modelling; Memory modelling; In-Memory computing; Big data; Capacity planning; IoT As the most active project in the Hadoop ecosystem these days [1], Spark is a fast and general purpose engine for large-scale data processing. Spark runs programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk [2]. However, Spark performance is impacted by many factors especially memory and JVM related, which makes capacity planning and tuning for Spark clusters extremely difficult. Current estimation based solution are highly dependent on experience which are trial-and-error and far from efficient and accurate. Here, we propose a novel Spark simulator based on CSMethod [3], extension with a fine-grained multi-layered memory subsystem, well suitable for this scenario. The whole Spark application execution life cycle is simulated, hardware activities derived from software operations are dynamically mapped onto architecture models for processors, storage, and network devices. Experimental results with several popular micro benchmarks and a real case IoT workloads demonstrate that our Spark Simulator achieves high accuracy with an average error rate below 7%, with light weight computing resource. Case studies are also demonstrated to show the simulator's capability. [Chen, Qian; Wang, Kebing; Bian, Zhaojuan; Xu, Gen; Guo, Yejun] Intel Corp, Software & Serv Grp, Shanghai, Peoples R China; [Cremer, Illia] Intel Corp, Software & Serv Grp, Nantes, France Intel Corporation; Intel Corporation Chen, Q (corresponding author), Intel Corp, Software & Serv Grp, Shanghai, Peoples R China. charles.chen@intel.com; kebing.wang@intel.com; bianny.bian@intel.com; illia.cremer@intel.com; gen.xu@intel.com; yejun.a.guo@intel.com 12 0 1 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2194-5357 2194-5365 978-3-319-69832-8; 978-3-319-69831-1 ADV INTELL SYST 2018.0 676.0 34 51 10.1007/978-3-319-69832-8_3 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BK4OJ 2023-03-23 WOS:000437042300003 0
J Ibrar, M; Wang, L; Muntean, GM; Chen, JH; Shah, N; Akbar, A Ibrar, Muhammad; Wang, Lei; Muntean, Gabriel-Miro; Chen, Jenhui; Shah, Nadir; Akbar, Aamir IHSF: An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems IEEE INTERNET OF THINGS JOURNAL English Article Fog computing (FC); hybrid software-defined networking (SDN); IoT; link failure; machine learning SOFTWARE-DEFINED FOG; NETWORKING The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a K-nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio. [Ibrar, Muhammad; Wang, Lei] Dalian Univ Technol, Sch Software, Dalian 116024, Peoples R China; [Wang, Lei] Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116024, Peoples R China; [Wang, Lei] Peng Cheng Lab, Ctr Underwater Robot, Shenzhen 518066, Peoples R China; [Muntean, Gabriel-Miro] Dublin City Univ, Sch Elect Engn, Dublin 09, Ireland; [Chen, Jenhui] Chang Gung Univ, Coll Engn, Dept Comp Sci & Informat Engn, Kweishan 33302, Taiwan; [Chen, Jenhui] Chang Gung Univ, Artificial Intelligence Res Ctr, Kweishan 33302, Taiwan; [Chen, Jenhui] Chang Gung Mem Hosp, Ctr Artificial Intelligence Med, Taoyuan 333423, Taiwan; [Shah, Nadir] COMSATS Univ Islamabad, Dept Comp Sci, Wah Campus, Taxila 4700, Pakistan; [Akbar, Aamir] Abdul Wali Khan Univ Mardan, Dept Comp Sci, Mardan 26110, Pakistan Dalian University of Technology; Dalian University of Technology; Peng Cheng Laboratory; Dublin City University; Chang Gung University; Chang Gung University; Chang Gung Memorial Hospital; COMSATS University Islamabad (CUI) Wang, L (corresponding author), Dalian Univ Technol, Sch Software, Dalian 116024, Peoples R China.;Wang, L (corresponding author), Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116024, Peoples R China.;Chen, JH (corresponding author), Chang Gung Univ, Coll Engn, Dept Comp Sci & Informat Engn, Kweishan 33302, Taiwan.;Chen, JH (corresponding author), Chang Gung Univ, Artificial Intelligence Res Ctr, Kweishan 33302, Taiwan. mibrar@mail.dlut.edu.cn; lei.wang@dlut.edu.cn; gabriel.muntean@dcu.ie; jhchen@mail.cgu.edu.tw; nadirshah82@gmail.com; amirakbar@awkum.edu.pk Ibrar, Muhammad/ABI-7976-2020; Chen, Jenhui/B-4521-2009; Shah, Nadir/AAB-5764-2022; Akbar, Aamir/AAC-8033-2020 Ibrar, Muhammad/0000-0002-6813-5003; Chen, Jenhui/0000-0002-8372-9221; Shah, Nadir/0000-0003-1173-4272; Akbar, Aamir/0000-0002-9421-7379; Muntean, Gabriel-Miro/0000-0002-9332-4770 National Key Research and Development Plan [2017YFC0821003-2]; Dalian Science and Technology Innovation Fund [2019J11CY004]; Science Foundation Ireland [16/SP/3804, 12/RC/2289_P2] National Key Research and Development Plan; Dalian Science and Technology Innovation Fund; Science Foundation Ireland(Science Foundation Ireland) This work was supported in part by the National Key Research and Development Plan under Grant 2017YFC0821003-2, and in part by the Dalian Science and Technology Innovation Fund under Grant 2019J11CY004. The work of Gabriel-Miro Muntean was supported by the Science Foundation Ireland through Research under Grant 16/SP/3804 (Enable) and Grant 12/RC/2289_P2 (Insight). 38 11 11 2 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2327-4662 IEEE INTERNET THINGS IEEE Internet Things J. MAR 1 2021.0 8.0 5 3130 3142 10.1109/JIOT.2020.3024560 0.0 13 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications QL9SN 2023-03-23 WOS:000621420700009 0
J Pei, EC; Oveneke, MC; Zhao, Y; Jiang, DM; Sahli, H Pei, Ercheng; Oveneke, Meshia Cedric; Zhao, Yong; Jiang, Dongmei; Sahli, Hichem Monocular 3D Facial Expression Features for Continuous Affect Recognition IEEE TRANSACTIONS ON MULTIMEDIA English Article Face recognition; Three-dimensional displays; Feature extraction; Solid modeling; Emotion recognition; Optical imaging; Strain; 3D morphable model; 3D scene flow; continuous affect recognition FACE RECOGNITION; MOTION; IMAGE; MODEL Automated facial expression analysis from image sequences for continuous emotion recognition is a very challenging task due to the loss of the three-dimensional information during the image formation process. State-of-the-art relied on estimating dynamic textures features and convolutional neural network features to derive spatio-temporal features. Despite their great success, such features are insensitive to micro facial muscle deformations and are affected by identity, face pose, illumination variation, and self-occlusion. In this work, we argue that retrieving, from image sequences, 3D facial spatio-temporal information, which describes the natural facial muscle deformation, provides a semantical and efficient way of representation and is useful for emotion recognition. In this paper, we propose a framework for extracting three-dimensional facial spatio-temporal features from monocular image sequences using an extended 3D Morphable Model (3DMM) which disentangles the identity factor from the facial expressions of a specific person. An LSTM model is used to evaluate the effectiveness of the proposed spatio-temporal features on video-based facial expression recognition task and continuous affect recognition task. Experimental results, on the AFEW6.0 datasets for facial expression recognition, and the RECOLA and SEMAINE datasets for continuous emotion prediction, illustrate the potential of the proposed 3D spatio-temporal features for facial expressions analysis and continuous affect recognition, as well as their efficiency compared to recent state-of-the-art features. [Pei, Ercheng; Zhao, Yong; Jiang, Dongmei] Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Key Lab Speech & Image Informat Proc, NPU VUB Joint AVSP Res Lab,Sch Comp Sci, Youyi Xilu 127, Xian 710072, Peoples R China; [Pei, Ercheng; Zhao, Yong] Vrije Univ Brussel VUB, Dept ETRO, Pl Laan 2, B-1050 Brussels, Belgium; [Oveneke, Meshia Cedric; Sahli, Hichem] Vrije Univ Brussel, Dept ETRO, Pl Laan 2, B-1050 Brussels, Belgium; [Jiang, Dongmei] Peng ChengLab, Shenzhen 518055, Guangdong, Peoples R China; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel; Vrije Universiteit Brussel; IMEC Jiang, DM (corresponding author), Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Shaanxi Key Lab Speech & Image Informat Proc, NPU VUB Joint AVSP Res Lab,Sch Comp Sci, Youyi Xilu 127, Xian 710072, Peoples R China. peiercheng@mail.nwpu.edu.cn; mcovenek@etrovub.be; yzhao@etrovub.be; jiangdm@nwpu.edu.cn; hsahli@etrovub.be ZHAO, YONG/0000-0003-2644-952X; Jiang, Dongmei/0000-0002-6238-8499; Sahli, Hichem/0000-0002-1774-2970 Chinese Scholarship Council [201706290115]; Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD-14] Chinese Scholarship Council(China Scholarship Council); Shaanxi Provincial International Science and Technology Collaboration Project This work was supported in part by the Chinese Scholarship Council under Grant 201706290115 and in part by the Shaanxi Provincial International Science and Technology Collaboration Project under Grant 2017KW-ZD-14. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sen-Ching Samson Cheung. 85 0 0 5 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia 2021.0 23.0 3540 3550 10.1109/TMM.2020.3026894 0.0 11 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications WJ5OB 2023-03-23 WOS:000709093100009 0
J Zhao, B; Liu, DR; Alippi, C Zhao, Bo; Liu, Derong; Alippi, Cesare Sliding-Mode Surface-Based Approximate Optimal Control for Uncertain Nonlinear Systems With Asymptotically Stable Critic Structure IEEE TRANSACTIONS ON CYBERNETICS English Article Optimal control; Perturbation methods; Nonlinear systems; Uncertainty; Cost function; Stability analysis; Adaptive systems; Adaptive critic designs; adaptive dynamic programming (ADP); optimal control; sliding mode surface (SMS); unknown mismatched perturbations DECENTRALIZED TRACKING CONTROL; GUARANTEED COST CONTROL; MULTIAGENT SYSTEMS; CONTROL SCHEME; HJB SOLUTION; STABILIZATION; DESIGN; STATE; DYNAMICS; GAMES This article develops a novel sliding-mode surface (SMS)-based approximate optimal control scheme for a large class of nonlinear systems affected by unknown mismatched perturbations. The observer-based perturbation estimation procedure is employed to establish the online updated value function. The solution to the Hamilton-Jacobi-Bellman equation is approximated by an SMS-based critic neural network whose weights error dynamics is designed to be asymptotically stable by nested update laws. The sliding-mode control strategy is combined with the approximate optimal control design procedure to obtain a faster control action. The stability is proved based on the Lyapunov's direct method. The simulation results show the effectiveness of the developed control scheme. [Zhao, Bo] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China; [Zhao, Bo] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Liu, Derong] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China; [Alippi, Cesare] Politecn Milan, Dipartimento Elettron & Informaz, I-20133 Milan, Italy; [Alippi, Cesare] Univ Svizzera Italiana, Fac Informat, CH-6900 Lugano, Switzerland Beijing Normal University; Chinese Academy of Sciences; Institute of Automation, CAS; Guangdong University of Technology; Polytechnic University of Milan; Universita della Svizzera Italiana Liu, DR (corresponding author), Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China. zhaobo@bnu.edu.cn; derong@gdut.edu.cn; cesare.alippi@polimi.it Liu, Derong/A-5304-2010; L, Deron/AAH-7493-2021 Liu, Derong/0000-0003-3715-4778; L, Deron/0000-0003-3715-4778; Zhao, Bo/0000-0002-7684-7342; ALIPPI, CESARE/0000-0003-3819-0025 National Natural Science Foundation of China [61973330, 61603387, 61773075, 61533017, U1501251]; Fundamental Research Funds for the Central Universities [2019NTST25]; Early Career Development Award of SKLMCCS [20180201]; State Key Laboratory of Synthetical Automation for Process Industries [2019-KF-23-03] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Early Career Development Award of SKLMCCS; State Key Laboratory of Synthetical Automation for Process Industries This work was supported in part by the National Natural Science Foundation of China under Grant 61973330, Grant 61603387, Grant 61773075, Grant 61533017, and Grant U1501251, in part by the Fundamental Research Funds for the Central Universities under Grant 2019NTST25, in part by the Early Career Development Award of SKLMCCS under Grant 20180201, and in part by the State Key Laboratory of Synthetical Automation for Process Industries under Grant 2019-KF-23-03. 55 28 29 9 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. JUN 2021.0 51.0 6 2858 2869 10.1109/TCYB.2019.2962011 0.0 12 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science SE4TE 31945008.0 2023-03-23 WOS:000652065400001 0
J Akhoondzadeh, M; De Santis, A; Marchetti, D; Piscini, A; Jin, S Akhoondzadeh, Mehdi; De Santis, Angelo; Marchetti, Dedalo; Piscini, Alessandro; Jin, Shuanggen Anomalous seismo-LAI variations potentially associated with the 2017 M-w=7.3 Sarpol-e Zahab (Iran) earthquake from Swarm satellites, GPS-TEC and climatological data ADVANCES IN SPACE RESEARCH English Article Pre-earthquake anomaly; Ionosphere; Swarm satellites; GPS TEC; LAIC mechanism IONOSPHERIC ANOMALIES; TIME The M-w = 7.3 earthquake near the Iran-Iraq border in west Iran (34.911 degrees N, 45.959 degrees E) occurred at 18:18:17 UTC (LT = UTC + 03:30), November 12, 2017 as the result of oblique-thrust faulting at mid-crustal depth (similar to 19 km). Median, Kalman filter and Neural Network, as three standard, classical and intelligent methods, have been implemented to investigate three months of GPS Total Electron Content (TEC) measurements and to detect the striking anomalous variations around the time and location of the mentioned earth-quake. The first method detects unusual variations, 9 days before the event, between 21:00 and 22:00 UTC. The other two methods of Kalman filter and Neural Network detect another clear anomaly on 11 days preceding the earthquake at 16:00 UTC. These findings are two of the outstanding results of GPS-TEC precursor analysis. This paper also presents the results of Swarm satellites (Alpha, Bravo and Charlie) data analysis inside the Dobrovolsky area around the Iran earthquake epicenter during the period from 1 August to 30 November 2017. The time series and orbital analysis of six measured parameters including electron density, electron temperature, magnetic scalar and vectors (X, Y, Z) components indicate irregular variations between 8 and 11 days prior to the occurrence of the earthquake. Since the variations of the solar and geomagnetic indices follow a normal behaviour during the whole period of the observed ionospheric anomalies between 8 and 11 days before the earthquake, it can be concluded that multi-precursors analysis has an important role to acknowledge the seismo-LAI (Lithospheric-Atmospheric-Ionospheric) anomalies associated to strong earthquakes such as this case. Furthermore, some physical and chemical atmospheric parameters from a climatological database are investigated and some interesting anomalies above two standard deviations prior to the earthquake are found. This paper shows not only anomalies in atmosphere and ionosphere but also a contemporary analysis of different data sources to detect the possible Lithosphere Atmosphere Ionosphere Coupling (LAIC) effects. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved. [Akhoondzadeh, Mehdi] Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Remote Sensing Div, Tehran, Iran; [De Santis, Angelo; Marchetti, Dedalo; Piscini, Alessandro] Ist Nazl Geofis & Vulcanol, Rome, Italy; [Marchetti, Dedalo; Jin, Shuanggen] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Jiangsu, Peoples R China University of Tehran; Istituto Nazionale Geofisica e Vulcanologia (INGV); Nanjing University of Information Science & Technology Akhoondzadeh, M (corresponding author), Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Remote Sensing Div, Tehran, Iran. makhonz@ut.ac.ir Marchetti, Dedalo/Z-4159-2019; Jin, Shuanggen/B-8094-2008; De Santis, Angelo/AAG-2895-2021 Marchetti, Dedalo/0000-0002-5457-3379; Jin, Shuanggen/0000-0002-5108-4828; Iran National Science Foundation (INSF) [53729]; ESA Iran National Science Foundation (INSF)(Iran National Science Foundation (INSF)); ESA(European Space Agency) The authors would like to acknowledge the ESA for the Swarm data, NASA Jet Propulsion Laboratory for the TEC data (ftp://cddis.gsfc.nasa.gov/), solar and geomagnetic indices (ftp://ftp.ngdc.noaa.gov/). This work was supported financially by Iran National Science Foundation (INSF) and founded with project No. 53729. This work was undertaken also in the framework of the ESA funded project SAFE (Swarm for Earthquake study) and ASI founded project LIMADOU-Science. Two anonymous referees are thanked for their suggestions and comments that improved the work significantly. 22 35 37 1 13 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. JUL 1 2019.0 64.0 1 143 158 10.1016/j.asr.2019.03.020 0.0 16 Engineering, Aerospace; Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences IE1DT 2023-03-23 WOS:000472126800011 0
J Jiang, HD; Christie, M; Wang, X; Liu, LB; Wang, B; Chen, BQ Jiang, Hongda; Christie, Marc; Wang, Xi; Liu, Libin; Wang, Bin; Chen, Baoquan Camera Keyframing with Style and Control ACM TRANSACTIONS ON GRAPHICS English Article Virtual Cinematography; Camera Behaviors; Machine Learning We present a novel technique that enables 3D artists to synthesize camera motions in virtual environments following a camera style, while enforcing user-designed camera keyframes as constraints along the sequence. To solve this constrained motion in-betweening problem, we design and train a camera motion generator from a collection of temporal cinematic features (camera and actor motions) using a conditioning on target keyframes. We further condition the generator with a style code to control how to perform the interpolation between the keyframes. Style codes are generated by training a second network that encodes different camera behaviors in a compact latent space, the camera style space. Camera behaviors are defined as temporal correlations between actor features and camera motions and can be extracted from real or synthetic film clips. We further extend the system by incorporating a fine control of camera speed and direction via a hidden state mapping technique. We evaluate our method on two aspects: i) the capacity to synthesize style-aware camera trajectories with user defined keyframes; and ii) the capacity to ensure that in-between motions still comply with the reference camera style while satisfying the keyframe constraints. As a result, our system is the first style-aware keyframe in-betweening technique for camera control that balances style-driven automation with precise and interactive control of keyframes. [Jiang, Hongda; Liu, Libin; Chen, Baoquan] Peking Univ, Beijing, Peoples R China; [Christie, Marc; Wang, Xi] Univ Rennes, INRIA, CNRS, IRISA, Rennes, France; [Wang, Bin] Beijing Inst Gen Artificial Intelligence, Beijing, Peoples R China Peking University; Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Rennes Chen, BQ (corresponding author), Peking Univ, Beijing, Peoples R China.;Wang, B (corresponding author), Beijing Inst Gen Artificial Intelligence, Beijing, Peoples R China. jianghd@pku.edu.cn; marc.christie@irisa.fr; xi.wang@inria.fr; libin.liu@pku.edu.cn; binwangbuaa@gmail.com; baoquan@pku.edu.cn Liu, Libin/0000-0003-2280-6817 National Key R&D Program of China [2019YFF0302902] National Key R&D Program of China We want to thank Anthony Mirabile and Yulong Zhang for the various support and helpful discussions throughout this project, as well as Yu Xiong for his help processing the MovieNet dataset. Furthermore, we wish to thank the anonymous reviewers for their constructive comments. This work was supported in part by the National Key R&D Program of China (2019YFF0302902). 31 1 1 0 10 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0730-0301 1557-7368 ACM T GRAPHIC ACM Trans. Graph. DEC 2021.0 40.0 6 209 10.1145/3478513.3480533 0.0 13 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science XN9WB Green Submitted 2023-03-23 WOS:000729846700015 0
C Liu, J; Zio, E IEEE Liu, Jie; Zio, Enrico KNN-FSVM for Fault Detection in High-Speed Trains 2018 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM) English Proceedings Paper IEEE International Conference on Prognostics and Health Management (ICPHM) JUN 11-13, 2018 Seattle, WA IEEE prognostics and health management; high-speed train; fuzzy SVM; fuzzy membership calculation; imbalanced data SUPPORT VECTOR MACHINES; BIG DATA; PREDICTION; SYSTEM; ALGORITHMS Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. A real case study concerning fault detection in a braking system of a high-speed train is considered. [Liu, Jie] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Zio, Enrico] Univ Paris Saclay, Cent Supelec, Lab Genie Ind, Gif Sur Yvette, France Beihang University; UDICE-French Research Universities; Universite Paris Saclay Liu, J (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China. liujie805@buaa.edu.cn; enrico.zio@centralesupelec.fr 42 0 0 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5386-1165-4 2018.0 7 Health Care Sciences & Services; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Health Care Sciences & Services; Engineering BP1JT 2023-03-23 WOS:000539546400039 0
J Deng, WL; Rolls, ET; Ji, XX; Robbins, TW; Banaschewski, T; Bokde, ALW; Bromberg, U; Buechel, C; Desrivieres, S; Conrod, P; Flor, H; Frouin, V; Gallinat, J; Garavan, H; Gowland, P; Heinz, A; Ittermann, B; Martinot, JL; Lemaitre, H; Nees, F; Orfanos, DP; Poustka, L; Smolka, MN; Walter, H; Whelan, R; Schumann, G; Feng, JF Deng, Wanlu; Rolls, Edmund T.; Ji, Xiaoxi; Robbins, Trevor W.; Banaschewski, Tobias; Bokde, Arun L. W.; Bromberg, Uli; Buechel, Christian; Desrivieres, Sylvane; Conrod, Patricia; Flor, Herta; Frouin, Vincent; Gallinat, Juergen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Ittermann, Bernd; Martinot, Jean-Luc; Lemaitre, Herve; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Poustka, Luise; Smolka, Michael N.; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Feng, Jianfeng Separate Neural Systems for Behavioral Change and for Emotional Responses to Failure During Behavioral Inhibition HUMAN BRAIN MAPPING English Article inhibition; impulsive behavior; orbitofrontal cortex; cingulate cortex; insula; depression INFERIOR FRONTAL-CORTEX; DORSOLATERAL PREFRONTAL CORTEX; STOP-SIGNAL INHIBITION; ORBITOFRONTAL CORTEX; NON-REWARD; WORKING-MEMORY; FUNCTIONAL-ORGANIZATION; NONHUMAN-PRIMATES; HUMAN BRAIN; MOTOR AREA To analyze the involvement of different brain regions in behavioral inhibition and impulsiveness, differences in activation were investigated in fMRI data from a response inhibition task, the stop-signal task, in 1709 participants. First, areas activated more in stop-success (SS) than stop-failure (SF) included the lateral orbitofrontal cortex (OFC) extending into the inferior frontal gyrus (ventrolateral prefrontal cortex, BA 47/12), and the dorsolateral prefrontal cortex (DLPFC). Second, the anterior cingulate and anterior insula (AI) were activated more on failure trials, specifically in SF versus SS. The interaction between brain region and SS versus SF activations was significant (P=5.6 * 10(-8)). The results provide new evidence from this big data investigation consistent with the hypotheses that the lateral OFC is involved in the stop-related processing that inhibits the action; that the DLPFC is involved in attentional processes that influence task performance; and that the AI and anterior cingulate are involved in emotional processes when failure occurs. The investigation thus emphasizes the role of the human lateral OFC BA 47/12 in changing behavior, and inhibiting behavior when necessary. A very similar area in BA47/12 is involved in changing behavior when an expected reward is not obtained, and has been shown to have high functional connectivity in depression. (C) 2017 Wiley Periodicals, Inc. [Deng, Wanlu; Feng, Jianfeng] Fudan Univ, Shanghai Ctr Math Sci, Shanghai, Peoples R China; [Deng, Wanlu; Rolls, Edmund T.; Feng, Jianfeng] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England; [Rolls, Edmund T.] Oxford Ctr Computat Neurosci, Oxford, England; [Ji, Xiaoxi; Feng, Jianfeng] Fudan Univ, Sch Life Sci, Sch Math Sci, Ctr Computat Syst Biol, Shanghai, Peoples R China; [Ji, Xiaoxi; Feng, Jianfeng] Fudan Univ, Collaborat Innovat Ctr Brain Sci, Shanghai, Peoples R China; [Robbins, Trevor W.] Univ Cambridge, Dept Psychol, Cambridge CB2 3EB, England; [Banaschewski, Tobias; Nees, Frauke; Poustka, Luise] Heidelberg Univ, Med Fac Mannheim, Cent Inst Mental Hlth, Dept Child & Adolescent Psychiat & Psychotherapy, D-68159 Mannheim, Germany; [Bokde, Arun L. W.] Trinity Coll Dublin, Sch Med, Discipline Psychiat, Dublin, Ireland; [Bokde, Arun L. W.] Trinity Coll Dublin, Trin Coll Inst Neurosci, Dublin, Ireland; [Bromberg, Uli; Buechel, Christian] Univ Med Ctr Hamburg Eppendorf, D-20246 Hamburg, Germany; [Desrivieres, Sylvane] Kings Coll London, Inst Psychiat Psychol & Neurosci, Social Genet & Dev Psychiat Ctr, Med Res Council, London, England; [Conrod, Patricia] Univ Montreal, CHU Ste Justine Hosp, Dept Psychiat, Montreal H3T 1C4, PQ, Canada; [Conrod, Patricia] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Psychol Med & Psychiat, London, England; [Flor, Herta] Heidelberg Univ, Med Fac Mannheim, Cent Inst Mental Hlth, Dept Cognit & Clin Neurosci, Mannheim, Germany; [Frouin, Vincent] CEA Saclay Ctr, Neurospin, CEA, Paris, France; [Gallinat, Juergen] Univ Med Ctr Hamburg Eppendorf UKE, Dept Psychiat & Psychotherapy, D-20246 Hamburg, Germany; [Garavan, Hugh] Univ Vermont, Dept Psychiat, Burlington, VT 05405 USA; [Garavan, Hugh] Univ Vermont, Dept Psychol, Burlington, VT 05405 USA; [Gowland, Penny] Univ Nottingham, Sch Phys & Astron, Sir Peter Mansfield Imaging Ctr, Univ Pk, Nottingham, England; [Heinz, Andreas; Walter, Henrik] Charite Univ Med Berlin, Dept Psychiat & Psychotherapy, Campus Charite Mitte,Charitepl 1, Berlin, Germany; [Ittermann, Bernd; Walter, Henrik] PTB, Braunschweig, Germany; [Ittermann, Bernd] PTB, Berlin, Germany; [Martinot, Jean-Luc; Lemaitre, Herve] Univ Paris 05, Univ Paris Sud, Sorbonne Paris Cite, INSERM,Unit 1000,Neuroimaging & Psychiat, Paris, France; [Martinot, Jean-Luc; Lemaitre, Herve] Maison Solenn, Paris, France; [Poustka, Luise] Med Univ Vienna, Dept Child & Adolescent Psychiat & Psychotherapy, Vienna, Austria; [Smolka, Michael N.] Tech Univ Dresden, Dept Psychiat & Neuroimaging Ctr, Dresden, Germany; [Whelan, Robert] Univ Coll Dublin, Dept Psychol, Dublin, Ireland Fudan University; University of Warwick; University of Oxford; Fudan University; Fudan University; University of Cambridge; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; Trinity College Dublin; Trinity College Dublin; University of Hamburg; University Medical Center Hamburg-Eppendorf; UK Research & Innovation (UKRI); Medical Research Council UK (MRC); University of London; King's College London; Universite de Montreal; University of London; King's College London; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; CEA; University of Hamburg; University Medical Center Hamburg-Eppendorf; University of Vermont; University of Vermont; University of Nottingham; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Physikalisch-Technische Bundesanstalt (PTB); Physikalisch-Technische Bundesanstalt (PTB); Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Cite; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Cochin - APHP; Medical University of Vienna; Technische Universitat Dresden; University College Dublin Feng, JF (corresponding author), Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England. Jianfeng.Feng@warwick.ac.uk Martinot, Jean-Luc/N-3704-2018; Conrod, Patricia J/P-4187-2018; Heinz, Andreas/ABB-7736-2020; Banaschewski, Tobias/ABE-5985-2020; Nees, Frauke/AAZ-2150-2020; Walter, Henrik/O-2612-2013; Lemaitre, Herve/ABB-5427-2020; Robbins, Trevor W/A-7551-2008; Gowland, Penny A./I-9016-2012; Desrivières, Sylvane/ABC-4336-2021 Heinz, Andreas/0000-0001-5405-9065; Banaschewski, Tobias/0000-0003-4595-1144; Lemaitre, Herve/0000-0002-5952-076X; Gowland, Penny A./0000-0002-4900-4817; Desrivières, Sylvane/0000-0002-9120-7060; Nees, Frauke/0000-0002-7796-8234; Schumann, Gunter/0000-0003-4905-5523; Flor, Herta/0000-0003-4809-5398; Walter, Henrik/0000-0002-9403-6121; Robbins, Trevor/0000-0003-0642-5977; Rolls, Edmund/0000-0003-3025-1292; Bokde, Arun/0000-0003-0114-4914; Whelan, Robert/0000-0002-2790-7281 Key Program of National Natural Science Foundation of China [NSFC 91630314]; National High Technology Research and Development Program of China [2015AA02050]; key project of Shanghai Science & Technology Innovation Plan [15JC1400101, 16JC1420402]; European Union [LSHM-CT-2007-037286]; FP7 projects IMAGEMEND (Imaging genetics for mental Disorders) [602450]; AGGRESSOTYPE [602805]; MATRICS [603016]; Innovative Medicine Initiative Project EU-AIMS [115300-2]; Medical Research Council [93558]; Consortium on Vulnerability to Externalizing Disorders and Addictions [c-VEDA] [MR/N000390/1]; VR, Sweden; FORTE, Sweden; FORMAS, Sweden; Medical Research Council; Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge); National Institute for Health Research (NIHR); Biomedical Research Centre at South London and Maudsley; NHS Foundation Trust and King's College London; Bundesministerium fur Bildung und Forschung [BMBF 01GS08152, 01EV0711, eMED SysAlc01ZX1311A]; Forschungsnetz AERIAL; Deutsche Forschungsgemeinschaft (DFG) [SM 80/7-1, SM 80/7-2, SFB 940/1]; ANR [AF12-NEUR0008-01 - WM2NA, ANR-12-SAMA-0004]; Fondation de France; Fondation pour la Recherche Medicale; Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hopitaux-de-Paris; INSERM; Paris Sud University IDEX; National Institutes of Health, U.S.A. (Axon) [MH085772-01A1]; NIH [U54 EB020403]; cross-NIH alliance, Big Data to Knowledge Centres of Excellence; MRC [MR/N000390/1, G0901858] Funding Source: UKRI; Medical Research Council [MR/N000390/1, G0901858] Funding Source: researchfish Key Program of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National High Technology Research and Development Program of China(National High Technology Research and Development Program of China); key project of Shanghai Science & Technology Innovation Plan; European Union(European Commission); FP7 projects IMAGEMEND (Imaging genetics for mental Disorders); AGGRESSOTYPE; MATRICS; Innovative Medicine Initiative Project EU-AIMS; Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Consortium on Vulnerability to Externalizing Disorders and Addictions [c-VEDA]; VR, Sweden(Swedish Research Council); FORTE, Sweden; FORMAS, Sweden(Swedish Research Council Formas); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge); National Institute for Health Research (NIHR)(National Institute for Health Research (NIHR)); Biomedical Research Centre at South London and Maudsley; NHS Foundation Trust and King's College London; Bundesministerium fur Bildung und Forschung(Federal Ministry of Education & Research (BMBF)); Forschungsnetz AERIAL; Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); ANR(French National Research Agency (ANR)); Fondation de France(Fondation de France); Fondation pour la Recherche Medicale(Fondation pour la Recherche Medicale); Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hopitaux-de-Paris; INSERM(Institut National de la Sante et de la Recherche Medicale (Inserm)); Paris Sud University IDEX; National Institutes of Health, U.S.A. (Axon)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); cross-NIH alliance, Big Data to Knowledge Centres of Excellence; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) Contract grant sponsor: Key Program of National Natural Science Foundation of China; Contract grant number: NSFC No. 91630314; Contract grant sponsor: National High Technology Research and Development Program of China (863); Contract grant number: 2015AA02050; Contract grant sponsor: The key project of Shanghai Science & Technology Innovation Plan (No. 15JC1400101; No. 16JC1420402); Contract grant sponsor: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology); Contract grant number: LSHM-CT-2007-037286; Contract grant sponsor: the FP7 projects IMAGEMEND (Imaging genetics for mental Disorders); Contract grant number: 602450; Contract grant sponsor: AGGRESSOTYPE; Contract grant number: 602805; Contract grant sponsor: MATRICS; Contract grant number: 603016; Contract grant sponsor: the Innovative Medicine Initiative Project EU-AIMS; Contract grant number: 115300-2; Contract grant sponsor: the Medical Research Council Grants Developmental pathways into adolescent substance abuse''; Contract grant number: 93558; Contract grant sponsor: Consortium on Vulnerability to Externalizing Disorders and Addictions [c-VEDA]; Contract grant number: MR/N000390/1; Contract grant sponsor: VR, Sweden; Contract grant sponsor: FORTE, Sweden; Contract grant sponsor: FORMAS, Sweden; Contract grant sponsor: the Medical Research Council and the Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge); Contract grant sponsor: the National Institute for Health Research (NIHR); Contract grant sponsor: Biomedical Research Centre at South London and Maudsley; Contract grant sponsor: NHS Foundation Trust and King's College London; Contract grant sponsor: the Bundesministerium fur Bildung und Forschung; Contract grant numbers: BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Contract grant sponsor: Forschungsnetz AERIAL; Contract grant sponsor: the Deutsche Forschungsgemeinschaft (DFG grants); Contract grant numbers: SM 80/7-1, SM 80/7-2, and SFB 940/1; Contract grant sponsor: ANR (project AF12-NEUR0008-01 - WM2NA, and ANR-12-SAMA-0004); Contract grant sponsor: the Fondation de France, the Fondation pour la Recherche Medicale; Contract grant sponsor: the Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Contract grant sponsor: the Assistance-Publique-Hopitaux-de-Paris and INSERM (interface grant); Contract grant sponsor: Paris Sud University IDEX 2012; Contract grant sponsor: the National Institutes of Health, U.S.A. (Axon, Testosterone and Mental Health during Adolescence); Contract grant number: MH085772-01A1; Contract grant sponsor: NIH Consortium grant; Contract grant number: U54 EB020403; Contract grant sponsor: cross-NIH alliance, Big Data to Knowledge Centres of Excellence. 63 26 27 0 14 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. JUL 2017.0 38.0 7 3527 3537 10.1002/hbm.23607 0.0 11 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging EZ8GR 28429498.0 Green Accepted, Bronze, Green Published, Green Submitted 2023-03-23 WOS:000404963400016 0
C Yang, MJ; Zheng, HR; Wang, HY; McClean, S; Mayagoitia, RE Zhou, ZH; Wang, W; Kumar, R; Toivonen, H; Pei, J; Huang, JZ; Wu, X Yang, Mingjing; Zheng, Huiru; Wang, Haiying; McClean, Sally; Mayagoitia, Ruth E. Statistical and machine learning approach to assessing the environmental impact on walking patterns 2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) English Proceedings Paper 14th IEEE International Conference on Data Mining (IEEE ICDM) DEC 14-17, 2014 Shenzhen, PEOPLES R CHINA Baidu,HUAWEI,PINGAN,IBM Res,KNIME,Alberta Innovates Ctr Machine Learning,IEEE,IEEE Comp Soc gait pattern; accelerometer; urban walking; classification GAIT It has been recongised that subjects may change their gait pattern when walking in different environments. This paper investigated the impact of walking environments on gait monitoring and analysis. A tri-axial accelerometer attached to subject's lower back was used to record gait pattern while walking in 5 different urban environments (quiet street, busy street, cobbled street, dark street and checkerboard floor). Forty-one young students participated the experiment. For each trial, a total of 33 gait features were extracted, of which 11 were derived from the entire walking trial and 22 were computed for each stride cycle. Statistics analysis showed that 7 out of 11 features extracted from each trial were significantly different across the five environments. The obtained results suggested that different environments have various impacts on gait features extracted from accelerometer data. To further access the impact, a multi-layer perceptrons based hierarchical classification approach was proposed to discriminate stride cycles taken from different walking environments. The classification accuracy of each level ranged from 98.26% to 65.62% with the discrimination of walking in quiet environment achieving the best performance. [Yang, Mingjing] Fuzhou Univ, Sch Phys & Informat Engn, Fuzhou, Peoples R China; [Yang, Mingjing] Univ Ulster, Sch Comp & Math, Belfast, Antrim, North Ireland; [Zheng, Huiru; Wang, Haiying; McClean, Sally] Univ Ulster, Comp Sci Res Inst, Belfast, Antrim, North Ireland; [Mayagoitia, Ruth E.] Kings Coll London, Div Hlth & Social Care Res, London, England Fuzhou University; Ulster University; Ulster University; University of London; King's College London Yang, MJ (corresponding author), Fuzhou Univ, Sch Phys & Informat Engn, Fuzhou, Peoples R China. yang-m@email.ulster.ac.uk; h.zheng@ulster.ac.uk; hy.wang@ulster.ac.uk; si.mcclean@ulster.ac.uk; ruth.mayagoitia-hill@kcl.ac.uk Wang, Haiying/E-1335-2016 Zheng, Huiru/0000-0001-7648-8709; Wang, Haiying/0000-0001-8358-9065 11 0 1 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4799-4274-9 2014.0 231 235 10.1109/ICDMW.2014.169 0.0 5 Computer Science, Information Systems Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BG4XT 2023-03-23 WOS:000389255100033 0
J Hu, KX; Wang, YH; Li, WD; Wang, LH Hu, Kaixiong; Wang, Yanghui; Li, Weidong; Wang, Lihui CNN-BiLSTM enabled prediction on molten pool width for thin-walled part fabrication using Laser Directed Energy Deposition JOURNAL OF MANUFACTURING PROCESSES English Article Laser Directed Energy Deposition (LDED); Molten pool width; Data driven approach; Additive manufacturing (AM) SINGLE-TRACK; MODEL; POWDER; CHALLENGES; LSTM Laser Directed Energy Deposition (LDED) is a promising metal Additive Manufacturing (AM) technology capable of fabricating thin-walled parts to support some high-value applications. Accurate and efficient prediction on the molten pool width is critical to support in-situ control of LDED for part quality assurance. Nevertheless, owing to the intricate physical mechanisms of the process, it is challenging to designing an effective approach to accomplish the prediction target. To tackle the issue, in this research, a new data model-driven predictive approach, which is enabled by a hybrid machine learning model namely CNN-BiLSTM, is presented. High prediction accuracy and efficiency are achievable through innovative measures in the research, that is, (i) the CNN-BiLSTM model is designed and configured by addressing the characteristics of the LDED process; (ii) process parameters related to the deposition and heat accumulation phenomena during the LDED process are extensively considered to strengthen the prediction accuracy. Experiments for thin-walled part fabrication were conducted to validate and benchmark the approach. In average, 4.286% of the mean absolute percentage error (MAPE) was acquired, and the prediction time took by the approach was only 0.04% of that by a finite element analysis (FEA) approach. Compared to the LSTM model, the BiLSTM model and the CNN-LSTM model, MAPEs of the CNN-BiLSTM model were improved by 27.0%, 17.3% and 12.6%, respectively. It demonstrates that the approach is competent in producing good-quality thin-walled parts using the LDED process. [Hu, Kaixiong; Wang, Yanghui] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [Li, Weidong] Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China; [Wang, Lihui] Royal Inst Technol, Dept Prod, Stockholm, Sweden Wuhan University of Technology; University of Shanghai for Science & Technology; Royal Institute of Technology Li, WD (corresponding author), Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China. weidongli@usst.edu.cn Wang, Lihui/O-3907-2014 Wang, Lihui/0000-0001-8679-8049; Wang, Yanghui/0000-0002-5387-791X; hu, kaixiong/0000-0001-9904-6137 National Natural Science Foundation of China [51975444] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was sponsored by the National Natural Science Foundation of China (Project No. 51975444). We would thank the anonymous reviewers for the detailed and constructive comments to improve the quality of the manuscript. 35 1 1 28 37 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1526-6125 2212-4616 J MANUF PROCESS J. Manuf. Process. JUN 2022.0 78.0 32 45 10.1016/j.jmapro.2022.04.010 0.0 APR 2022 14 Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Engineering 2U0CE 2023-03-23 WOS:000822831600001 0
J Morawska, L; Miller, W; Riley, M; Vardoulakis, S; Zhu, YG; Marks, GB; Garnawat, P; Kumar, P; Thynell, M Morawska, Lidia; Miller, Wendy; Riley, Matt; Vardoulakis, Sotiris; Zhu, Yong-Guan; Marks, Guy B.; Garnawat, Prachi; Kumar, Prashant; Thynell, Marie How to build Urbanome, the genome of the city? SCIENCE OF THE TOTAL ENVIRONMENT English Article Urban system; Future cities; City performance framework; Characterization of the city; City performance indicators; Ranking of the cities NUCLEOTIDE-SEQUENCE; EXPOSOME The world's population is shifting to the cities, and consequently, cities worldwide are growing in number and in size. Cities are complex systems, making it extremely difficult to build and run cities in a way that all the elements of the system operate in harmony. Recently a concept of urbanome, the genome of the city was proposed to address this complexity. Here we first explore this concept and analogy, taking advantage of the potential of other 'omics, modern data collection techniques, Big Data analysis methods and a transdisciplinary approach. Then, we propose a theoretical approach to build the urbanome as a means of quantifying and qualifying population outcomes, being a function of the form of an urban area including the built environment, the physical and social services it provides, and the population density. [Morawska, Lidia] Queensland Univ Technol, Int Lab Air Qual & Hlth ILAQH, Brisbane, Qld, Australia; [Morawska, Lidia] Queensland Univ Technol, Fac Sci, Brisbane, Qld, Australia; [Morawska, Lidia; Miller, Wendy] Queensland Univ Technol, Inst Hlth Biomed Innovat IHBI, Brisbane, Qld, Australia; [Riley, Matt] NSW Dept Planning Ind & Environm, Climate & Atmospher Sci, Sydney, NSW, Australia; [Vardoulakis, Sotiris] Australian Natl Univ, Natl Ctr Epidemiol & Populat Hlth, Canberra, ACT, Australia; [Zhu, Yong-Guan] Chinese Acad Sci, Inst Urban Environm, Xiamen, Fujian, Peoples R China; [Marks, Guy B.] Univ New South Wales, South Western Sydney Clin Sch, Sydney, NSW, Australia; [Marks, Guy B.] Univ Sydney, Woolcock Inst Med Res, Sydney, NSW, Australia; [Garnawat, Prachi] RMIT Univ, Melbourne, Vic, Australia; [Morawska, Lidia; Kumar, Prashant] Univ Surrey, Fac Engn & Phys Sci, Global Ctr Clean Air Res GCARE, Dept Civil & Environm Engn, Guildford GU2 7XH, Surrey, England; [Thynell, Marie] Univ Gothenburg, Sch Global Studies, Gothenburg, Sweden Queensland University of Technology (QUT); Queensland University of Technology (QUT); Queensland University of Technology (QUT); Australian National University; Chinese Academy of Sciences; Institute of Urban Environment, CAS; University of New South Wales Sydney; University of Sydney; Woolcock Institute of Medical Research; Royal Melbourne Institute of Technology (RMIT); University of Surrey; University of Gothenburg Morawska, L (corresponding author), Queensland Univ Technol, Brisbane, Qld 4001, Australia. l.morawska@qut.edu.au Kumar, Prashant/C-6357-2011; Marks, Guy B./F-5058-2013; Zhu, Yong-Guan/A-1412-2009; Morawska, Lidia/B-4140-2011 Kumar, Prashant/0000-0002-2462-4411; Marks, Guy B./0000-0002-8976-8053; Zhu, Yong-Guan/0000-0003-3861-8482; Morawska, Lidia/0000-0002-0594-9683 48 0 0 0 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0048-9697 1879-1026 SCI TOTAL ENVIRON Sci. Total Environ. MAR 1 2022.0 810.0 152310 10.1016/j.scitotenv.2021.152310 0.0 DEC 2021 7 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology YD2RZ 34914999.0 Green Submitted 2023-03-23 WOS:000740225200014 0
J Ding, ZZ; Xu, DH; Tu, CF; Zhao, HJ; Moze, M; Aioun, F; Guillemard, F Ding, Zhezhang; Xu, Donghao; Tu, Chenfeng; Zhao, Huijing; Moze, Mathieu; Aioun, Francois; Guillemard, Franck Driver Identification Through Heterogeneity Modeling in Car-Following Sequences IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Vehicles; Data models; Automobiles; Analytical models; Feature extraction; Time series analysis; Indexes; Driver identification; heterogeneity modeling; machine learning TIME-SERIES CLASSIFICATION; DRIVING STYLE; BEHAVIOR; PATTERNS Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. This research proposes a driver identification method by modeling such heterogeneities in car following sequences. It is assumed that all drivers share a pool of driver states; under each state, a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intra-driver heterogeneity, while the difference between the driver profile of different drivers depicts the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a method of driver identification is proposed to take both intra- and inter-driver heterogeneity into consideration, and a method is developed to jointly learn parameters in behavioral feature extractor, driver states, and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers is demonstrated and discussed. [Ding, Zhezhang; Xu, Donghao; Tu, Chenfeng; Zhao, Huijing] Peking Univ, Sch AI, Key Lab Machine Percept MOE, Beijing 100084, Peoples R China; [Moze, Mathieu; Aioun, Francois; Guillemard, Franck] Grp PSA, F-78140 Velizy Villacoublay, France Peking University Zhao, HJ (corresponding author), Peking Univ, Sch AI, Key Lab Machine Percept MOE, Beijing 100084, Peoples R China. zhaohj@pku.edu.cn Groupe PSA's OpenLab Program (Multimodal Perception and Reasoning for Intelligent Vehicles); NSFC [61973004] Groupe PSA's OpenLab Program (Multimodal Perception and Reasoning for Intelligent Vehicles); NSFC(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Groupe PSA's OpenLab Program (Multimodal Perception and Reasoning for Intelligent Vehicles) and in part by the NSFC under Grant 61973004. 47 1 1 7 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. OCT 2022.0 23.0 10 17143 17156 10.1109/TITS.2022.3151410 0.0 MAR 2022 14 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 5G4AU 2023-03-23 WOS:000764846100001 0
C Song, KH; De Jonckheere, J; Zeng, XY; Koehl, L; Yuan, XJ; Zhao, X Song, Kehui; De Jonckheere, Julien; Zeng, Xianyi; Koehl, Ludovic; Yuan, Xiaojie; Zhao, Xin Development of a data-based interactive medical expert system for supporting pregnancy consultations: general architecture and methodology IFAC PAPERSONLINE English Proceedings Paper 14th International-Federation-of-Automatic-Control (IFAC) Symposium on Analysis, Design, and Evaluation of Human Machine Systems (HMS) SEP 16-19, 2019 Tallinn, ESTONIA Int Federat Automat Control,Int Federat Automat Control Tech Comm 4 5 Human Machine Syst,Int Federat Automat Control Tech Comm 1 2 Adapt & Learning Syst,Int Federat Automat Control Tech Comm 2 1 Control Design,Int Federat Automat Control Tech Comm 9 2 Social Impact Automat expert system; big data; knowledge base; fetal movement; wearable system FETAL MOVEMENTS Innovations in telecommunication and online service solutions facilitate the emergence of remote medical diagnosis and treatment consultations. Using information and communication technologies (ICT's), timely e-health services to individuals are becoming a viable alternative to traditional healthcare solutions, which reduces pressure on the health system. In our scenario for pregnant women, developing an interactive medical expert system connecting to a wearable device for supporting pregnancy consultations based on fetal movement signals is essential. In this paper, we demonstrate the general architecture of MES-SPC (Medical Expert System for Supporting Pregnancy Consultations), which consist of data collection layer, data management layer and application service layer. The three highlighted parts in our system includes: (1) The development of an IoT-based wearable devices for automatic fetal movement signal acquisition and identification. (2) An interface collecting real-time status of pregnant women through online conversations led by doctors. (3) The construction of a self-updating knowledge base including case-based reasoning, rule-based inference and knowledge graph search to assist doctors for decision making. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved. [Song, Kehui; Zeng, Xianyi; Koehl, Ludovic; Zhao, Xin] ENSAIT, GEMTEX, F-59100 Roubaix, France; [De Jonckheere, Julien] CHRU Lille, CIC IT 1403, INSERM, Maison Reg Rech Clin, F-59000 Lille, France; [Song, Kehui; Yuan, Xiaojie] Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Lille - ISITE; CHU Lille; Universite de Lille; Nankai University Song, KH (corresponding author), ENSAIT, GEMTEX, F-59100 Roubaix, France.;Song, KH (corresponding author), Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China. kehui.song@ensait.fr Zeng, Xianyi/0000-0002-3236-6766 French National Research Agency (ANR) [IOTFetMov ANR-14-CE24-0035-01]; [61772289] French National Research Agency (ANR)(French National Research Agency (ANR)); This research was supported by the project IOTFetMov ANR-14-CE24-0035-01 of the French National Research Agency (ANR) and National Natural Science Foundation of China (No. 61772289). 10 1 1 2 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2019.0 52.0 19 67 72 10.1016/j.ifacol.2019.12.109 0.0 6 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems JY3BS gold 2023-03-23 WOS:000504294500013 0
J Wang, XL; Gaudoin, O; Doyen, L; Berenguer, C; Xie, M Wang, Xiaolin; Gaudoin, Olivier; Doyen, Laurent; Berenguer, Christophe; Xie, Min Modeling multivariate degradation processes with time-variant covariates and imperfect maintenance effects APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY English Article degradation path adjustment; imperfect maintenance; multivariate Wiener process; piecewise constant covariates; time scale adjustment NONPERIODIC INSPECTIONS; COMPLEX-SYSTEMS; OPTIMAL-DESIGN; BIG DATA; RELIABILITY; POLICY; TESTS; PRODUCTS; SUBJECT; REPAIR This article proposes two types of degradation models that are suitable for describing multivariate degrading systems subject to time-variant covariates and imperfect maintenance activities. A multivariate Wiener process is constructed as a baseline model, on top of which two types of models are developed to meaningfully characterize the time-variant covariates and imperfect maintenance effects. The underlying difference between the two models lies in the way of capturing the influences of covariates and maintenance: The first model reflects these impacts in the degradation rates/paths directly, whereas the second one describes the impacts by modifying the time scales governing the degradation processes. In each model, two particular imperfect maintenance models are presented, which differ in the extent of reduction in degradation level or virtual age. The two degradation models are then compared in certain special cases. The proposed multivariate degradation models pertain to complex industrial systems whose health deterioration can be characterized by multiple performance characteristics and can be altered or affected by maintenance activities and operating/environmental conditions. [Wang, Xiaolin] Hong Kong Polytech Univ, Dept Logist & Maritime Studies, Kowloon, Hong Kong, Peoples R China; [Gaudoin, Olivier; Doyen, Laurent] Univ Grenoble Alpes, LJK, Grenoble INP, CNRS, Grenoble, France; [Berenguer, Christophe] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, Grenoble, France; [Xie, Min] City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon, Hong Kong, Peoples R China Hong Kong Polytechnic University; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); City University of Hong Kong Wang, XL (corresponding author), Hong Kong Polytech Univ, Dept Logist & Maritime Studies, Kowloon, Hong Kong, Peoples R China. xiao-lin.wang@polyu.edu.hk Xie, Min/A-5552-2011; BERENGUER, Christophe/E-4000-2017; Wang, Xiao-Lin/AAT-4317-2021 Xie, Min/0000-0002-8500-8364; BERENGUER, Christophe/0000-0003-3000-5390; Wang, Xiao-Lin/0000-0003-0100-8154 PHC PROCORE - France/Hong Kong Joint Research Scheme [42636VE, F-CityU103/18]; Hong Kong Research Grants Council under a Theme-based Research Scheme [T32-101/15-R]; Hong Kong Research Grants Council under General Research Fund [CityU 11203519]; National Natural Science Foundation of China [71971181] PHC PROCORE - France/Hong Kong Joint Research Scheme; Hong Kong Research Grants Council under a Theme-based Research Scheme(Hong Kong Research Grants Council); Hong Kong Research Grants Council under General Research Fund(Hong Kong Research Grants Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the PHC PROCORE - France/Hong Kong Joint Research Scheme (grant numbers 42636VE, F-CityU103/18), the Hong Kong Research Grants Council under a Theme-based Research Scheme (grant number T32-101/15-R) and a General Research Fund (grant number CityU 11203519), and the National Natural Science Foundation of China (grant number 71971181). The work was done while the first author was visiting the Univ. Grenoble Alpes, while at City University of Hong Kong. 64 10 10 8 46 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1524-1904 1526-4025 APPL STOCH MODEL BUS Appl. Stoch. Models. Bus. Ind. MAY 2021.0 37.0 3 SI 592 611 10.1002/asmb.2600 0.0 DEC 2020 20 Operations Research & Management Science; Mathematics, Interdisciplinary Applications; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Operations Research & Management Science; Mathematics SN2VE Green Submitted 2023-03-23 WOS:000597171100001 0
C Yuan, TX; Adjallah, KH; Sava, A; Wang, HF; Liu, LY IEEE Yuan, Tangxiao; Adjallah, Kondo Hloindo; Sava, Alexandre; Wang, Huifen; Liu, Linyan Issues of Intelligent Data Acquisition and Quality for Manufacturing Decision-Support in an Industry 4.0 Context PROCEEDINGS OF THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 2 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems-Technology and Applications-IDAACS English Proceedings Paper 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS) SEP 22-25, 2021 ELECTR NETWORK IEEE,Cracow Univ Technol, Fac Elect & Comp Engn,West Ukrainian Natl Univ, Res Inst Intelligent Comp Syst,Natl Acad Sci Ukraine, V M Glushkov Inst Cybernet,West Ukrainian Natl Univ, Dept Informat Comp Syst & Control,IEEE Ukraine Sect I & M CI Joint Soc Chapter,IEEE Ukraine Sect,IEEE Poland Sect,MDPI Sensors,River Publishers,EFENTO,Fachhochschule Dortmund Univ Appl Sci & Arts, Inst Digital Transformat Applicat & Living Domains,Dortmund Univ Appl Sci & Arts data quality; data source; data acquisition; manufacturing; decision-making; review BIG DATA Data quality plays an essential role in decision-making, as the latter may incorporate some risks in different application areas. In the context of industry 4.0, the amount, the versatility, and the speed of information flow for decision-making are important issues. The quality and, in particular, the dependability of data is paramount. This paper investigates the leading data quality characteristics in the industry 4.0 environment with the related issues due to various interactions. It proposes a taxonomy of data sources and flows, from acquisition to the information extraction level for decision-making. The authors highlight the specific issues of error and uncertainty propagation management as significant research challenges for designing intelligent data collection and acquisition systems for industrial manufacturing decision support within the framework of the new generation of industry development. They review data quality characteristics definition and assessment requirements, and suggest classifying time characteristics into four categories. Data quality assessment methods and models with relation to decision-making are also examined. They willfully left aside the investigation of data quality improvement processes for a future more detailed paper. [Yuan, Tangxiao; Wang, Huifen; Liu, Linyan] Nanjing Univ Sci & Technol, 200 Xiaolingwei St, Nanjing 210094, Peoples R China; [Yuan, Tangxiao; Adjallah, Kondo Hloindo; Sava, Alexandre] Univ Lorraine, LCOMS, 1 Route dArs Laquenexy, F-57078 Metz 03, France Nanjing University of Science & Technology; Universite de Lorraine Yuan, TX (corresponding author), Nanjing Univ Sci & Technol, 200 Xiaolingwei St, Nanjing 210094, Peoples R China.;Yuan, TX (corresponding author), Univ Lorraine, LCOMS, 1 Route dArs Laquenexy, F-57078 Metz 03, France. Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20 0259]; National Natural Science Foundation of China [51705256]; China Scholarship Council Postgraduate Research & Practice Innovation Program of Jiangsu Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work was supported in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province No.KYCX20 0259, in part by the National Natural Science Foundation of China under Grant 51705256, in part by the China Scholarship Council. 18 0 0 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2605-3 INT WORKSH INT DATA 2021.0 1200 1205 10.1109/IDAACS53288.2021.9660957 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT7GR 2023-03-23 WOS:000848390800108 0
J Han, B; He, B; Ma, MM; Sun, TT; Yan, TH; Lendasse, A Han, Bo; He, Bo; Ma, Mengmeng; Sun, Tingting; Yan, Tianhong; Lendasse, Amaury RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement MATHEMATICAL PROBLEMS IN ENGINEERING English Article NEURAL-NETWORK; ALGORITHM; ACCURATE For blended data, the robustness of extreme learning machine (ELM) is so weak because the coefficients (weights and biases) of hidden nodes are set randomly and the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different ensemble groups concurrently and then employs selective ensemble approach to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble approach is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the result has shown that RMSE-ELM significantly improves robustness with a rapid learning speed compared to representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP, and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future. [Han, Bo; He, Bo; Ma, Mengmeng; Sun, Tingting] Ocean Univ China, Sch Informat & Engn, Qingdao 266000, Shandong, Peoples R China; [Yan, Tianhong] China Jiliang Univ, Sch Mech & Elect Engn, Hangzhou 310018, Zhejiang, Peoples R China; [Lendasse, Amaury] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA; [Lendasse, Amaury] Univ Iowa, Iowa Informat Initiat, Iowa City, IA 52242 USA; [Lendasse, Amaury] Arcada Univ Appl Sci, Helsinki 00550, Finland Ocean University of China; China Jiliang University; University of Iowa; University of Iowa; Arcada University of Applied Sciences He, B (corresponding author), Ocean Univ China, Sch Informat & Engn, Qingdao 266000, Shandong, Peoples R China. bhe@ouc.edu.cn; thyan@163.com Yan, T. H./G-5773-2010; YAN, T. H./AAE-5215-2019 Yan, T. H./0000-0003-3916-3926; YAN, T. H./0000-0003-3916-3926 Natural Science Foundation of China [41176076, 31202036, 51379198, 51075377] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is partially supported by Natural Science Foundation of China (41176076, 31202036, 51379198, and 51075377). 36 2 2 0 13 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. 2014.0 2014.0 395686 10.1155/2014/395686 0.0 12 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics AY1FA gold, Green Submitted 2023-03-23 WOS:000347337800001 0
J Li, J; Cui, YJ; Gu, C; Wang, CH; Liu, WQ; Lombardi, F Li, Jiang; Cui, Yijun; Gu, Chongyan; Wang, Chenghua; Liu, Weiqiang; Lombardi, Fabrizio A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory IEEE OPEN JOURNAL OF NANOTECHNOLOGY English Article Physical unclonable function; Resistance; Nonvolatile memory; Hardware; Transistors; Semiconductor device modeling; Security; Physical unclonable function; hardware security; resistive random access memory; modeling attack 1T1R The physical unclonable function (PUF) is a promising low-cost hardware security primitive. Recent advances in nanotechnology have provided new opportunities for nanoscale PUF circuits. The resistive random access memory (RRAM) is extensively used in nanoscale circuits due to its low cost, non-volatility and easy integration with CMOS. This paper proposes a novel tristate hybrid PUF (TH-PUF) design based on a one-transistor-one-RRAM (1T1R) cell; this cell can be configured into two weak PUFs and a strong PUF using few control signals. To assess the proposed PUF design, a compact RRAM model at UMC 65xa0;nm technology is employed. Simulation results show that the proposed TH-PUF achieves good uniqueness, reliability as well as a higher gate usability compared with an entire CMOS PUFs. The number of challenge response pairs (CRPs) of the proposed TH-PUF is larger than other RRAM-based PUFs. Moreover, the TH-PUF is more resistant to a modeling machine learning attack than traditional PUF designs. [Li, Jiang; Cui, Yijun; Wang, Chenghua; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China; [Gu, Chongyan] Queens Univ Belfast, Ctr Secure Informat Technol, Belfast BT3 9DT, Antrim, North Ireland; [Lombardi, Fabrizio] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 40125 USA Nanjing University of Aeronautics & Astronautics; Queens University Belfast; Northeastern University Liu, WQ (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China. lijiang@nuaa.edu.cn; yijun.cui@nuaa.edu.cn; c.gu@qub.ac.uk; chwang@nuaa.edu.cn; liuweiqiang@nuaa.edu.cn; lombardi@ece.neu.edu GU, CHONGYAN/ABD-5437-2021 GU, CHONGYAN/0000-0002-3028-8004; Liu, Weiqiang/0000-0001-8398-8648; Lombardi, Fabrizio/0000-0003-3152-3245; Li, Jiang/0000-0002-2792-3951 National Natural Science Foundation of China [62022041, 61771239]; Engineering and Physical Sciences Research Council (EPSRC) [EP/N508664/-CSIT2] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported in part by the National Natural Science Foundation of China under Grants 62022041 and 61771239, and in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N508664/-CSIT2. (Jiang Li and Yijun Cui contributed equally to this work.) 36 3 3 0 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2644-1292 IEEE OPEN J NANOTECH IEEE Open J. Nanotechnol. 2021.0 2.0 31 40 10.1109/OJNANO.2021.3058169 0.0 10 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary Emerging Sources Citation Index (ESCI) Science & Technology - Other Topics; Materials Science TP6SH Green Published, gold 2023-03-23 WOS:000677726900005 0
J Liu, ZY; Hu, JY; Li, Y; Huang, Y Liu, Zhenyu; Hu, Jingyuan; Li, Yang; Huang, Yi Towards virtual dataspaces for material scientific data cloud CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE English Article scientific data cloud; virtual dataspaces; semantic integration; ontology; materials science; pay-as-you-go New challenges for large-scale data management and sharing have arisen because of the information evolution from massive data to big data. In this paper, we propose the concept of virtual dataspaces and domain scientific data cloud (DSDC) to address the challenges of management, reusability, and service of scientific data in scientific domain. The application of DSDC is also introduced, which is used in the materials science domain for e-Science applications. Virtual dataspaces is then defined, which is used to integrate and organize both structured and unstructured scientific data in cloud environment. To build the virtual dataspaces, a logical resources model based on ontology is discussed. And then a new mapping and evolutionary model of virtual dataspaces is described in a pay-as-you-go fashion. As such, we introduce a semi-automatic evolution method. A scientific data cloud application model is also proposed for defining service template. Finally, the virtual dataspaces and DSDC are verified by material service safety evaluation, and the results show that our approach is efficient in scientific data management and suitable for data-intensive application. Copyright (C) 2013 John Wiley & Sons, Ltd. [Liu, Zhenyu; Li, Yang; Huang, Yi] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Hu, Jingyuan] Univ Technol Troyes, Comp Sci & Commun Engn Dept, Troyes, France University of Science & Technology Beijing; Universite de Technologie de Troyes Hu, JY (corresponding author), Univ Technol Troyes, Comp Sci & Commun Engn Dept, Troyes, France. hjyfirst@163.com Science and Technology Program of Beijing [D08050303450802]; R & D Infrastructure and Facility Development Program [2005DKA32800]; National Key Technology R D Program [2008BAH29B02]; National Key Technology R & D Program during the Twelfth Five-year Plan Period [2011BAK08B04] Science and Technology Program of Beijing; R & D Infrastructure and Facility Development Program; National Key Technology R D Program(National Key Technology R&D Program); National Key Technology R & D Program during the Twelfth Five-year Plan Period This work has been supported by the Science and Technology Program of Beijing under Grant No.D08050303450802, The R & D Infrastructure and Facility Development Program under Grant No.2005DKA32800, The National Key Technology R & D Program under Grant No.2008BAH29B02, and The National Key Technology R & D Program during the Twelfth Five-year Plan Period under Grant No.2011BAK08B04. 37 3 3 0 12 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1532-0626 1532-0634 CONCURR COMP-PRACT E Concurr. Comput.-Pract. Exp. APR 25 2016.0 28.0 6 SI 1737 1750 10.1002/cpe.2961 0.0 14 Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science DJ2CN 2023-03-23 WOS:000374011400004 0
J Liu, Y; Tuytelaars, T Liu, Yu; Tuytelaars, Tinne Residual Tuning: Toward Novel Category Discovery Without Labels IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Training; Task analysis; Tuning; Semantics; Knowledge engineering; Visualization; Supervised learning; Disentangled representation; novel category discovery (NCD); residual tuning; semisupervised learning Discovering novel visual categories from a set of unlabeled images is a crucial and essential capability for intelligent vision systems since it enables them to automatically learn new concepts with no need for human-annotated supervision anymore. To tackle this problem, existing approaches first pretrain a neural network with a set of labeled images and then fine-tune the network to cluster unlabeled images into a few categorical groups. However, their unified feature representation hits a tradeoff bottleneck between feature preservation on labeled data and feature adaptation on unlabeled data. To circumvent this bottleneck, we propose a residual-tuning approach, which estimates a new residual feature from the pretrained network and adds it with a previous basic feature to compute the clustering objective together. Our disentangled representation approach facilitates adjusting visual representations for unlabeled images and overcoming forgetting old knowledge acquired from labeled images, with no need of replaying the labeled images again. In addition, residual-tuning is an efficient solution, adding few parameters and consuming modest training time. Our results on three common benchmarks show consistent and considerable gains over other state-of-the-art methods, and further reduce the performance gap to the fully supervised learning setup. Moreover, we explore two extended scenarios, including using fewer labeled classes and continually discovering more unlabeled sets, where the results further signify the advantages and effectiveness of our residual-tuning approach against previous approaches. Our code is available at https://github.com/liuyudut/ResTune. [Liu, Yu] Dalian Univ Technol, Int Sch Informat Sci & Engn, Dalian 116024, Peoples R China; [Tuytelaars, Tinne] Katholieke Univ Leuven, ESAT PSI, B-3000 Leuven, Belgium Dalian University of Technology; KU Leuven Liu, Y (corresponding author), Dalian Univ Technol, Int Sch Informat Sci & Engn, Dalian 116024, Peoples R China. liuyu8824@dlut.edu.cn Liu, Yu/AAD-4123-2019; Tuytelaars, Tinne/B-4319-2015 Liu, Yu/0000-0002-6003-9121; Tuytelaars, Tinne/0000-0003-3307-9723 National Natural Science Foundation of China [62102061]; Fundamental Research Funds for the Central Universities [DUT21RC(3)024]; Fonds Voor Wetenschappelijk Onderzoek (FWO) Project Structure from Semantics [G086617N] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Fonds Voor Wetenschappelijk Onderzoek (FWO) Project Structure from Semantics(FWO) This work was supported in part by the National Natural Science Foundation of China under Grant 62102061, in part by the Fundamental Research Funds for the Central Universities under Grant DUT21RC(3)024, and in part by the Fonds Voor Wetenschappelijk Onderzoek (FWO) Project Structure from Semantics under Grant G086617N. 68 1 1 4 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3140235 0.0 JAN 2022 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YR8DS 35073270.0 Green Accepted 2023-03-23 WOS:000750221900001 0
J Zheng, EL; Cui, S; Zhu, R; Wang, YJ; Zhu, Y; Kang, M Zheng, Enlai; Cui, Song; Zhu, Rui; Wang, Yongjian; Zhu, Yue; Kang, Min Thermal error modeling and compensation of multilink high-speed precision press system INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Multilink; Press; Thermal sensitive points; Thermal error; Online correction; Compensation MACHINE-TOOLS; REGRESSION; NETWORK; PREDICTION It is essential to develop the predictive model of thermal error for multilink high-speed precision press system (MHSPPS) in order to construct its compensation on the slider's position accuracy at the bottom dead point (BDP). In this work, based on the principal factor strategy and the clustering methodology, the locations of thermal sensitive points (TSPs) on the MHSPPS are determined and the corresponding temperature is taken as the feed in parameter of the thermal error model (TEM). An improved adaptive genetic algorithm (IAGA) incorporated with a back-propagation neural network (BPNN) is then presented so as to simulate and predict the thermal error of MHSPPS through the collected temperature of TSPs. Compared with the experimental results, accuracy of the predictive model forward goes beyond that of the traditional TEMs, such as multiple linear regression (MLR), genetic algorithm with BPNN (GA-BPNN), and particle swarm optimization with BPNN (PSO-BPNN) models, which proves effectiveness of the proposed model. In order to implement the compensation experiment, a novel adjustment mechanism of slider's BDP position for MHSPPS is designed and the compensation algorithm with online correction based on the IAGA-BPNN model is also proposed. Test results indicate that the maximum errors and the RMS errors of the slider's BDP position after compensation with online correction based on the proposed IAGA-BPNN model can be reduced to 93.33% and 98.75%, respectively, which verifies the authenticity of thermal error compensation based on the IAGA-BPNN model and online correction algorithm. [Zheng, Enlai; Wang, Yongjian; Zhu, Yue; Kang, Min] Nanjing Agr Univ, Dept Mech Engn, Coll Engn, 40 DianJiangTai Rd, Nanjing 210031, Peoples R China; [Cui, Song] Nanjing Inst Technol, Sch Automot & Rail Transit, Nanjing 211167, Peoples R China; [Zhu, Rui] Katholieke Univ Leuven, Dept Mech Engn, B-3001 Leuven, Belgium Nanjing Agricultural University; Nanjing Institute of Technology; KU Leuven Zheng, EL (corresponding author), Nanjing Agr Univ, Dept Mech Engn, Coll Engn, 40 DianJiangTai Rd, Nanjing 210031, Peoples R China. enlaizheng@njau.edu.cn Kang, Min/GOK-0957-2022 National Natural Science Foundation of China [52075269]; Fundamental Research Funds for the Central Universities [KYTZ201603]; Innovation Fund of Science and Technology for Outstanding Youth from College of Engineering, Nanjing Agricultural University [YQ201606]; Qing Lan Project of Jiangsu Province [80400103] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Innovation Fund of Science and Technology for Outstanding Youth from College of Engineering, Nanjing Agricultural University; Qing Lan Project of Jiangsu Province This work was supported by the following research projects: National Natural Science Foundation of China (Grant number 52075269), The Fundamental Research Funds for the Central Universities (Grant number KYTZ201603), The Innovation Fund of Science and Technology for Outstanding Youth from College of Engineering, Nanjing Agricultural University (Grant number YQ201606), and Qing Lan Project of Jiangsu Province (Grant number 80400103). 35 1 2 1 19 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. JAN 2021.0 112.0 5-6 1729 1743 10.1007/s00170-020-06442-z 0.0 JAN 2021 15 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering PS4WJ 2023-03-23 WOS:000604507400003 0
J Fang, P; Ma, XC; Li, XX; Qiu, XL; Gerhard, R; Zhang, XQ; Li, GL Fang, Peng; Ma, Xingchen; Li, Xiangxin; Qiu, Xunlin; Gerhard, Reimund; Zhang, Xiaoqing; Li, Guanglin Fabrication, Structure Characterization, and Performance Testing of Piezoelectret-Film Sensors for Recording Body Motion IEEE SENSORS JOURNAL English Article Forcemyography; motion registration; piezoelectret; film sensor; wearable CELLULAR POLYPROPYLENE FILMS; MYOELECTRIC CONTROL; POLYMER FOAMS; LARGE-AREA; FERROELECTRETS; RECOGNITION; COEFFICIENT; INFLATION; SYSTEMS; SOFT During muscle contractions, radial-force distributions are generated on muscle surfaces due to muscle-volume changes, from which the corresponding body motions can be recorded by means of so-called force myography (FMG). Piezo- or ferroelectrets are flexible piezoelectric materials with attractive materials and sensing properties. In addition to several other applications, they are suitable for detecting force variations by means of wearable devices. In this paper, we prepared piezoelectrets from cellular polypropylene films by optimizing the fabrication procedures, and developed an FMG-recording system based on piezoelectret sensors. Different hand and wrist movements were successfully detected on able-bodied subjects with the FMG system. The FMG patterns were evaluated and identified by means of linear discriminant analysis and artificial neural network algorithms, and average motion-classification accuracies of 96.1% and 94.8%, respectively, were obtained. This paper demonstrates the feasibility of using piezoelectret-film sensors for FMG and may thus lead to alternative methods for detecting body motion and to related applications, e.g., in biomedical engineering or structural-health monitoring. [Fang, Peng; Li, Xiangxin; Li, Guanglin] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sy, Shenzhen 518055, Peoples R China; [Ma, Xingchen; Zhang, Xiaoqing] Tongji Univ, Sch Phys Sci & Engn, Shanghai Key Lab Special Artificial Microstruct M, Shanghai 200092, Peoples R China; [Qiu, Xunlin; Gerhard, Reimund] Univ Potsdam, Inst Phys & Astron, Fac Sci, D-14476 Potsdam, Germany Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Tongji University; University of Potsdam Li, GL (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sy, Shenzhen 518055, Peoples R China.;Zhang, XQ (corresponding author), Tongji Univ, Sch Phys Sci & Engn, Shanghai Key Lab Special Artificial Microstruct M, Shanghai 200092, Peoples R China. x.zhang@tongji.edu.cn; gl.li@siat.ac.cn Gerhard, Reimund/F-7099-2011; Qiu, Xunlin/AAR-4532-2021; Fang, Peng/AFM-2530-2022 Gerhard, Reimund/0000-0002-1306-2249; Qiu, Xunlin/0000-0002-6315-2648; Ma, Xingchen/0000-0002-1448-2245; Zhang, Xiaoqing/0000-0001-9498-5709; Li, Guanglin/0000-0001-9016-2617; Samuel, Oluwarotimi/0000-0003-1945-1402 National Natural Science Foundation of China [91420301, 11374232]; Natural Science Foundation for Distinguished Young Scholars of Guangdong Province, China [2014A030306029]; Special Support Program for Eminent Professionals of Guangdong Province, China [2015TQ01C399]; Shenzhen Technology Development Grant [CXZZ20150505093829781] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation for Distinguished Young Scholars of Guangdong Province, China; Special Support Program for Eminent Professionals of Guangdong Province, China; Shenzhen Technology Development Grant This work was supported in part by the National Natural Science Foundation of China under Grant 91420301 and Grant 11374232, in part by the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province, China, under Grant 2014A030306029, in part by the Special Support Program for Eminent Professionals of Guangdong Province, China, under Grant 2015TQ01C399, and in part by the Shenzhen Technology Development Grant CXZZ20150505093829781. The associate editor coordinating the review of this paper and approving it for publication was Prof. Tarikul Islam. (Corresponding authors: Xiaoqing Zhang; Guanglin Li.) 55 27 27 7 67 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. JAN 1 2018.0 18.0 1 401 412 10.1109/JSEN.2017.2766663 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics FQ0VY 2023-03-23 WOS:000418076100046 0
J Zhou, YJ; Zheng, YL; Liu, YC; Pan, TB; Zhou, YB Zhou, Yujue; Zheng, Yonglai; Liu, Yongcheng; Pan, Tanbo; Zhou, Yubao A hybrid methodology for structural damage detection uniting FEM and 1D-CNNs: Demonstration on typical high-pile wharf MECHANICAL SYSTEMS AND SIGNAL PROCESSING English Article High-pile wharf; Pile foundations; Vibration; Structural health monitoring; Structural damage detection; Finite element modeling; 1D Convolutional neural networks MACHINE LEARNING ALGORITHMS Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or fixed classifiers would not be the optimal choice for all structural damaged scenarios. This paper proposes a novel, quick and precise real-time SDD framework for high-pile wharf foundations using a combination of finite element modeling and 1D convolutional neural networks (CNNs). The distinct advantage of this method lies in extracting the damage-related features from the raw displacement response directly and automatically, and the computational complexity of the compact 1D CNNs is significantly lower because the data processing involves only simple 1D operations. The results show that the presented 1D CNNs have a superior ability to accurately identify the occurrence and location of damage in real time. In addition, the comprehensive performance of the CNNs trained by the displacement response dataset in component form is significantly better than that based on the dataset in absolute value form. The results also demonstrated that although the proposed CNNs are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure, the vertical component still has a positive effect on the improvement of the generalization and robustness of the CNNs. [Zhou, Yujue] Sanming Univ, Dept Civil Engn, Sanming 365004, Peoples R China; [Zhou, Yujue; Zheng, Yonglai; Liu, Yongcheng; Pan, Tanbo] Tongji Univ, Civil Engn Coll, Dept Hydraul Engn, Shanghai 200092, Peoples R China; [Zhou, Yubao] Delft Univ Technol, Fac Civil Engn & Geosci, NL-2600 AA Delft, Netherlands Sanming University; Tongji University; Delft University of Technology Liu, YC (corresponding author), Tongji Univ, Civil Engn Coll, Dept Hydraul Engn, Shanghai 200092, Peoples R China. 1930156@tongji.edu.cn Liu, Yongcheng/0000-0001-9289-7131; Zhou, Yubao/0000-0003-2714-3848 Provincial Key Laboratory of Construction Materials & Structural Reinforcement, Sanming University, Sanming, China Provincial Key Laboratory of Construction Materials & Structural Reinforcement, Sanming University, Sanming, China This study was supported by programs of Provincial Key Laboratory of Construction Materials & Structural Reinforcement, Sanming University, Sanming, China. 30 1 1 7 18 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0888-3270 1096-1216 MECH SYST SIGNAL PR Mech. Syst. Signal Proc. APR 1 2022.0 168.0 108738 10.1016/j.ymssp.2021.108738 0.0 23 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0U8IB 2023-03-23 WOS:000787889800001 0
J Zhang, JJ; Meuret, Y; Wang, XG; Smet, KAG Zhang, Jingjing; Meuret, Youri; Wang, Xiangguo; Smet, Kevin A. G. Improved and Robust Spectral Reflectance Estimation LEUKOS English Article Color accuracy; dual imaging system; IES TM-30 sample set; reflectance estimation; spectral optimization ILLUMINATION; NOISE; PROJECTOR; SURFACE; SYSTEM; SPACE; SET Accurately estimating spectral reflectance functions from color camera images is a hot research subject that demonstrates tremendous potential for illuminating engineering and computer vision applications. However, the impact of the illumination spectrum and camera responsivity (system functions) on estimation accuracy has not been systematically studied so far, nor has the impact of a training spectral reflectance set. In this study, a dual imaging reflectance optimization system is used based on a neural network and optimal system functions that are respectively trained and optimized using several sample sets. Simulations showed that such optimal systems, trained and optimized with the IES TM-30 spectral reflectance set, can have a substantially higher estimation accuracy compared to real systems composed of commercially available projector spectra and camera responsivities and that they are sufficiently robust under small changes in system function peak wavelength and spectral width due to changes in working temperature or with passing time. An analysis of the impact of the specific sample set database adopted for neural net training on estimation accuracy showed that training with the IES set results in good and stable performance, even for other sample sets and different illumination spectra. Training with the spectrally uniform IES spectral reflectance set is therefore advised for general-purpose, high-accuracy reflectance estimation systems. A comparison with a state-of-the-art method shows that the proposed method has a higher color prediction accuracy and a significantly shorter running time for realistic images with high resolution. [Zhang, Jingjing; Wang, Xiangguo] China Univ Geosci, Sch Automat, Wuhan, Peoples R China; [Meuret, Youri; Smet, Kevin A. G.] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan, Peoples R China; [Smet, Kevin A. G.] Katholieke Univ Leuven, ESAT Light & Lighting Lab, Technol Campus Ghent,Gebr Smetstr 1, BE-9000 Ghent, Belgium China University of Geosciences; KU Leuven Smet, KAG (corresponding author), Katholieke Univ Leuven, ESAT Light & Lighting Lab, Technol Campus Ghent,Gebr Smetstr 1, BE-9000 Ghent, Belgium. kevin.smet@kuleuven.be Zhang, Jing/HII-4294-2022; Smet, Kevin/M-2610-2013 Meuret, Youri/0000-0002-2815-5915; Smet, Kevin/0000-0003-3825-6274 National Natural Science Foundation of China (NSFC) [61604135]; China Scholarship Council [201706415021]; Fundamental Research Funds for the Central Universities [CUGL180404]; KU Leuven National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); KU Leuven(KU Leuven) This work was supported by the National Natural Science Foundation of China (NSFC) (61604135), China Scholarship Council (201706415021), Fundamental Research Funds for the Central Universities (CUGL180404), and Internal funds KU Leuven. 92 6 6 0 11 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 1550-2724 1550-2716 LEUKOS Leukos OCT 2 2021.0 17.0 4 359 379 10.1080/15502724.2020.1798246 0.0 SEP 2020 21 Construction & Building Technology; Optics Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Optics TU8FV Green Accepted 2023-03-23 WOS:000569248400001 0
J Pan, XR; Gao, LE; Zhang, B; Yang, F; Liao, WZ Pan, Xuran; Gao, Lianru; Zhang, Bing; Yang, Fan; Liao, Wenzhi High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network SENSORS English Article high-resolution aerial imageries; semantic segmentation; densely connected convolutions; pyramid pooling module OBJECT DETECTION; CLASSIFICATION Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline. [Pan, Xuran; Gao, Lianru; Zhang, Bing] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Pan, Xuran; Yang, Fan] Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300401, Peoples R China; [Liao, Wenzhi] Univ Ghent, Dept Telecommun & Informat Proc, B-9000 Ghent, Belgium Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Hebei University of Technology; Ghent University Gao, LE (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. 201611901006@stu.hebut.edu.cn; gaolr@radi.ac.cn; zb@radi.ac.cn; 201621901026@stu.hebut.edu.cn; wliao@telin.ugent.be gao, lian ru/0000-0003-3888-8124 Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080302]; National Natural Science Foundation of China [91638201] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA19080302, and by the National Natural Science Foundation of China under Grant No. 91638201. 31 22 24 0 21 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors NOV 2018.0 18.0 11 3774 10.3390/s18113774 0.0 15 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HC1YJ 30400591.0 Green Accepted, Green Published, gold, Green Submitted 2023-03-23 WOS:000451598900184 0
J He, ZL; Thomassey, S; Zeng, XY; Xu, J; Yi, CH He, Zhenglei; Thomassey, Sebastien; Zeng, Xianyi; Xu, Jie; Yi, Changhai Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning JOURNAL OF MANUFACTURING SYSTEMS English Article Deep reinforcement learning; Deep Q-networks; Multi-objective; Optimization; Decision; Process; Textile Manufacturing COLOR FADING OZONATION; GAME-THEORY; YARN; SYSTEM; ALGORITHM; INTELLIGENCE; FRAMEWORK; TRACKING; STRATEGY; FABRICS Multi-objective optimization, such as quality, productivity, and cost, of the textile manufacturing process is increasingly challenging because of the growing complexity involved in the development of textile industry in the upcoming big data era. It is hard for traditional methods to deal with high-dimension decision space in this issue, and prior experts' knowledge is required as well as human intervention. This paper proposed a novel framework that transformed the textile process optimization problem into a stochastic game, and introduced deep Q-networks algorithm instead of current methods to approach it in a multi-agent system. The developed multi-agent reinforcement learning system applied a utilitarian selection mechanism to maximize the sum of all agents' rewards (obeying the increasing epsilon-greedy policy) in each state, to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the textile process. The case study result reflects that the proposed MARL system can achieve the optimal solutions for the textile ozonation process, and it performs better than the traditional approaches. [He, Zhenglei; Thomassey, Sebastien; Zeng, Xianyi] Univ Lille, ENSAIT, GEMTEX Lab Genie & Materiaux Text, Lille, France; [Xu, Jie; Yi, Changhai] Wuhan Text Univ, 1 st, Av Yangguang, Wuhan, Peoples R China; [Xu, Jie; Yi, Changhai] Natl Local Joint Engn Lab Adv Text Proc & Clean Pr, Wuhan, Peoples R China Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Universite de Lille; Wuhan Textile University He, ZL (corresponding author), Univ Lille, ENSAIT, GEMTEX Lab Genie & Materiaux Text, Lille, France. zhenglei.he@ensait.fr He, Zhenglei/ABA-6999-2020; Zeng, Xianyi/AAQ-1183-2021; Jie, Xu/AAB-5278-2019 He, Zhenglei/0000-0003-4751-7819; Zeng, Xianyi/0000-0002-3236-6766; National Key R&D Program of China [2019YFB1706300]; Scientific Research Project of Hubei Provincial Department of Education, China [Q20191707]; China Scholarship Council [201708420166] National Key R&D Program of China; Scientific Research Project of Hubei Provincial Department of Education, China; China Scholarship Council(China Scholarship Council) This research was supported by the funds from National Key R&D Program of China (Project NO: 2019YFB1706300) , and Scientific Research Project of Hubei Provincial Department of Education, China (Project NO: Q20191707) . The first author would like to express his gratitude to China Scholarship Council for supporting this study (CSC, Project NO. 201708420166) . 77 5 6 19 39 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-6125 1878-6642 J MANUF SYST J. Manuf. Syst. JAN 2022.0 62.0 939 949 10.1016/j.jmsy.2021.03.017 0.0 11 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science ZY8LH Green Submitted 2023-03-23 WOS:000772831900006 0
J Li, XC; Zhou, ZB; Guo, JQ; Wang, SG; Zhang, JS Li, Xiaocui; Zhou, Zhangbing; Guo, Junqi; Wang, Shangguang; Zhang, Junsheng Aggregated multi-attribute query processing in edge computing for industrial IoT applications COMPUTER NETWORKS English Article Multi-attribute aggregation query; Energy-aware IR-tree; Edge node routing graph; Edge computing SOFTWARE-DEFINED-NETWORKS; BIG DATA; FRAMEWORK; INTERNET; THINGS The popularity of smart things constructs sensing networks for the Internet of Things (IoT), and promotes intelligent decision-makings to support industrial IoT applications, where multi-attribute query processing is an essential ingredient. Considering the huge number of smart things and large-scale of the network, traditional query processing mechanisms may not be applicable, since they mostly depend on a centralized index tree structure. To remedy this issue, this article proposes a multi-attribute aggregation query mechanism in the context of edge computing, where an energy-aware IR-tree is constructed to process query processing in single edge networks, while an edge node routing graph is established to facilitate query processing for marginal smart things contained in contiguous edge networks. This decentralized and localized strategy has shown its efficiency and applicability of query processing in IoT sensing networks. Experimental evaluation results demonstrate that this technique performs better than the rivals in reducing the traffic and energy consumption of the network. (C) 2019 Elsevier B.V. All rights reserved. [Li, Xiaocui; Zhou, Zhangbing] China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China; [Zhou, Zhangbing] TELECOM SudParis, Comp Sci Dept, F-91001 Evry, France; [Guo, Junqi] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China; [Wang, Shangguang] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Zhang, Junsheng] Inst Sci & Tech Informat China, Beijing 100038, Peoples R China China University of Geosciences; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Beijing Normal University; Beijing University of Posts & Telecommunications Zhou, ZB (corresponding author), China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China.;Zhou, ZB (corresponding author), TELECOM SudParis, Comp Sci Dept, F-91001 Evry, France. lixiaocui@cugb.edu.cn; zbzhou@cugb.edu.cn; guojunqi@bnu.edu.cn; wangjunping@bupt.edu.cn; zhangjs@istic.ac.cn Wang, Shangguang/0000-0001-7245-1298 National Natural Science Foundation of China [61772479, 61662021]; Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China) This work was supported partially by the National Natural Science Foundation of China (Grant No. 61772479 and 61662021) and by the Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China). 40 15 17 0 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. MAR 14 2019.0 151.0 114 123 10.1016/j.comnet.2019.01.022 0.0 10 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HP5OF Green Published 2023-03-23 WOS:000461725700008 0
J Gao, P; Dong, HM; Liu, SM; Fan, XR; Jiang, C; Wang, YS; Margulies, D; Li, HF; Zuo, XN Gao, Peng; Dong, Hao-Ming; Liu, Si-Man; Fan, Xue-Ru; Jiang, Chao; Wang, Yin-Shan; Margulies, Daniel; Li, Hai-Fang; Zuo, Xi-Nian A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping SCIENTIFIC DATA English Article; Data Paper MOTION ARTIFACTS; ICA-AROMA; CONNECTOME; CONSTRUCTION; RELIABILITY; TEMPLATES; CHILDREN; ANATOMY; IMAGES The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culturerelated impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely'I See your Brains (ISYB)', to the public via the Science Data Bank (https://doi.org/10.11922/sciencedb.00740). [Gao, Peng; Li, Hai-Fang] Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan 030024, Peoples R China; [Dong, Hao-Ming; Fan, Xue-Ru; Wang, Yin-Shan; Zuo, Xi-Nian] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China; [Dong, Hao-Ming; Zuo, Xi-Nian] Natl Basic Sci Data Ctr, Beijing 100109, Peoples R China; [Liu, Si-Man; Zuo, Xi-Nian] Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China; [Jiang, Chao] Capital Normal Univ, Sch Psychol, Beijing 100048, Peoples R China; [Margulies, Daniel] CNRS, UMR 7225, Brain & Spinal Cord Inst, Frontlab, Paris, France; [Zuo, Xi-Nian] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neurosci Res Ctr, Beijing 100875, Peoples R China; [Zuo, Xi-Nian] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning 530001, Peoples R China Taiyuan University of Technology; Beijing Normal University; Chinese Academy of Sciences; Institute of Psychology, CAS; Capital Normal University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); Beijing Normal University; Nanning Normal University Li, HF (corresponding author), Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan 030024, Peoples R China.;Zuo, XN (corresponding author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.;Zuo, XN (corresponding author), Natl Basic Sci Data Ctr, Beijing 100109, Peoples R China.;Zuo, XN (corresponding author), Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China.;Zuo, XN (corresponding author), Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neurosci Res Ctr, Beijing 100875, Peoples R China.;Zuo, XN (corresponding author), Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning 530001, Peoples R China. lihaifang@tyut.edu.cn; xinian.zuo@bnu.edu.cn Zuo, Xi-Nian/A-7273-2009; Margulies, Daniel/L-4719-2016 Zuo, Xi-Nian/0000-0001-9110-585X; Gao, Peng/0000-0002-7908-6075; Margulies, Daniel/0000-0002-8880-9204 National Natural Science Foundation of China [61976150]; Major Project of National Social Science Foundation of China [20ZD296]; Start-up Funds for Leading Talents at Beijing Normal University; National Basic Science Data Center Chinese Data-sharing Warehouse for In-vivo Imaging Brain [NBSDC-DB-15]; Child Brain-Mind Development Cohort Study in China Brain Initiative [SQ2021AAA010024] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Project of National Social Science Foundation of China; Start-up Funds for Leading Talents at Beijing Normal University; National Basic Science Data Center Chinese Data-sharing Warehouse for In-vivo Imaging Brain; Child Brain-Mind Development Cohort Study in China Brain Initiative This work was supported in part by the National Natural Science Foundation of China (61976150), the Major Project of National Social Science Foundation of China (20&ZD296), the National Basic Science Data Center Chinese Data-sharing Warehouse for In-vivo Imaging Brain (NBSDC-DB-15), the Child Brain-Mind Development Cohort Study in China Brain Initiative (SQ2021AAA010024) and the Start-up Funds for Leading Talents at Beijing Normal University. 72 1 1 16 19 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2052-4463 SCI DATA Sci. Data JUN 9 2022.0 9.0 1 286 10.1038/s41597-022-01413-3 0.0 10 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 1Z9BH 35680932.0 gold, Green Accepted 2023-03-23 WOS:000809109900005 0
J Chen, F; Zhou, R; Van de Voorde, T; Chen, XZ; Bourgeois, J; Gheyle, W; Goossens, R; Yang, J; Xu, WB Chen, Fen; Zhou, Rui; Van de Voorde, Tim; Chen, Xingzhuang; Bourgeois, Jean; Gheyle, Wouter; Goossens, Rudi; Yang, Jian; Xu, Wenbo Automatic detection of burial mounds (kurgans) in the Altai Mountains ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Kurgans; Stone mounds; Altai Mountains; Remote sensing archaeology; Object detection ARCHAEOLOGICAL FEATURES; IMAGERY; IDENTIFICATION; CLASSIFICATION; EXTRACTION; IKONOS-2; VALLEY; MARKS; TOMBS The Altai Mountains are one of the most impressive and valuable archaeological areas in the world. Kurgans (burial mounds) of ancient civilizations, which are scattered across the vast Altai area, are an exceptionally valuable source of information for archaeology. These precious archaeological resources, which sometimes have been preserved intact in the permafrost underground for over two millennia, are now under various threats, such as natural disasters, farmland expansion, touristic development, and most notably global warming. A detailed map or inventory of the mounds is essential but is still not available. In this study, we test the deep convolutional neural network (CNN) technique for automatic detection of stone mounds from high-resolution satellite images in four regions in the Altai Mountains. We propose three improvement techniques to increase the performance of off-the-shelf object detection methods that are originally proposed for daily-life objects. Our results demonstrate that it is feasible to apply CNN to detect stone mounds, and the detection results are good enough to capture their spatial distribution. CNN-based object detection can largely narrow down the search area for archaeologists in yet un-surveyed regions, and is therefore useful for preparing field survey campaigns and directing archaeological fieldwork. We also applied the method to an un-surveyed Altai Mountain area and successfully discovered stone mounds that are yet undocumented. Our method can potentially be applied to construct an inventory for all stone mounds present in the whole Altai Mountain region. [Chen, Fen; Zhou, Rui; Chen, Xingzhuang; Xu, Wenbo] Univ Elect Sci & Technol China, Sch Resources & Environm, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China; [Van de Voorde, Tim; Goossens, Rudi] Univ Ghent, Dept Geog, Krijgslaan 281,S8, B-9000 Ghent, Belgium; [Bourgeois, Jean; Gheyle, Wouter] Univ Ghent, Dept Archaeol, Sint Pieternieuwstr 35, B-9000 Ghent, Belgium; [Bourgeois, Jean] Fellow Presidents Int Fellowship Initiat, Beijing, Peoples R China; [Yang, Jian] Chinese Acad Sci, Aerosp Informat Res Inst, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China University of Electronic Science & Technology of China; Ghent University; Ghent University; Chinese Academy of Sciences Chen, F (corresponding author), Univ Elect Sci & Technol China, Sch Resources & Environm, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China. chenfen@uestc.edu.cn Van de Voorde, Tim/AAG-1657-2019 Van de Voorde, Tim/0000-0002-9324-5087; Bourgeois, Jean/0000-0002-7590-7200 National Key Research and Development Program of China [2017YFC0821900]; Chinese Academy of Sciences President's International Fellowship Initiative [2020VCA0015] National Key Research and Development Program of China; Chinese Academy of Sciences President's International Fellowship Initiative The authors would like to thank the anonymous reviewers for their valuable and helpful comments. This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0821900, and in part by the Chinese Academy of Sciences President's International Fellowship Initiative under Grant 2020VCA0015. 60 5 5 2 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. AUG 2021.0 177.0 217 237 10.1016/j.isprsjprs.2021.05.010 0.0 MAY 2021 21 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology SR3ZD 2023-03-23 WOS:000660980400015 0
C Feng, WZ; Dong, YX; Huang, TL; Yin, ZQ; Cheng, X; Kharlamov, E; Tang, J ACM Feng, Wenzheng; Dong, Yuxiao; Huang, Tinglin; Yin, Ziqi; Cheng, Xu; Kharlamov, Evgeny; Tang, Jie GRAND plus : Scalable Graph Random Neural Networks PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22) English Proceedings Paper 31st ACM Web Conference (WWW) APR 25-29, 2022 ELECTR NETWORK Assoc Comp Machinery,ACM SIGWEB,LIRIS,Univ Lyon,Inst Natl Sci Appliquees,Eurecom Graph Neural Networks; Scalability; Semi-Supervised Learning Graph neural networks (GNNs) have been widely adopted for semisupervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-ofthe-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets. [Feng, Wenzheng; Dong, Yuxiao; Huang, Tinglin; Tang, Jie] Tsinghua Univ, Beijing, Peoples R China; [Yin, Ziqi] Beijing Inst Technol, Beijing, Peoples R China; [Cheng, Xu] Tencent Inc, Hangzhou, Peoples R China; [Kharlamov, Evgeny] Bosch Ctr Artificial Intelligence, Berlin, Germany Tsinghua University; Beijing Institute of Technology; Tencent Tang, J (corresponding author), Tsinghua Univ, Beijing, Peoples R China. fwz17@mails.tsinghua.edu.cn; yuxiaod@tsinghua.edu.cn; tl091@tsinghua.edu.cn; ziqiyin18@gmail.com; alexcheng@tencent.com; evgeny.kharlamov@de.bosch.com; jietang@tsinghua.edu.cn NSFC [61825602]; Tsinghua-Bosch Joint ML Center NSFC(National Natural Science Foundation of China (NSFC)); Tsinghua-Bosch Joint ML Center The work is supported by the NSFC for Distinguished Young Scholar (61825602) and Tsinghua-Bosch Joint ML Center. 34 0 0 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-9096-5 2022.0 3248 3258 10.1145/3485447.3512044 0.0 11 Computer Science, Cybernetics; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT8BZ Green Submitted, Bronze 2023-03-23 WOS:000852713003032 0
C Zhao, JP; Datcu, M; Zhang, ZH; Xiong, HL; Yu, WX IEEE Zhao, Juanping; Datcu, Mihai; Zhang, Zenghui; Xiong, Huilin; Yu, Wenxian LEARNING PHYSICAL SCATTERING PATTERNS FROM POLSAR IMAGES BY USING COMPLEX-VALUED CNN 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 28-AUG 02, 2019 Yokohama, JAPAN Inst Elect & Elect Engineers,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Polarimetric synthetic aperture radar (PolSAR) images; physical scattering pattern; complex-valued convolutional neural network (CNN); H-A-alpha target decomposition; F-SAR Full-polarimetric synthetic aperture radar (SAR) images have the ability to provide physical patterns of the earth observation, no more than geometric information. In order to learn physical patterns from non-full-polarimetric SAR images, a complex-valued CNN is leveraged to learn a model containing physical parameters. The parameters are learned from the original complex scattering matrix of full-polarimetric SAR images and they can be adopted to extract physical patterns from non-full-polarimetric SAR images. Cloude and Pottier's H-alpha division, as the annotation principle, is computed by way of coherence matrix. We perform experiments on (German Aerospace Center) DLR's full-polarimetric, airborne F-SAR data, demonstrating that extracting physical patterns from non-full-polarimetric images is feasible. The comparative results illustrate that: 1) The best physical categoric patterns can be extracted from HV and VH polarimetric images in general, while performance from HH and VV polarimetric images are limited; 2) Cross-polarimetric SAR images have greater ability for surface and volume scattering, while co-polarimetric ones are better for multiple scattering extraction. [Zhao, Juanping; Datcu, Mihai] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany; [Zhao, Juanping; Zhang, Zenghui; Xiong, Huilin; Yu, Wenxian] Shanghai Jiao Tong Univ, Shanghai Key Lab Intelligent Sensing & Recognit, Shanghai 200240, Peoples R China Helmholtz Association; German Aerospace Centre (DLR); Shanghai Jiao Tong University Zhao, JP (corresponding author), German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany.;Zhao, JP (corresponding author), Shanghai Jiao Tong Univ, Shanghai Key Lab Intelligent Sensing & Recognit, Shanghai 200240, Peoples R China. zhang, zenghui/GVS-8983-2022 China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) Thanks to the China Scholarship Council (CSC) for funding. 6 4 5 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-5386-9154-0 INT GEOSCI REMOTE SE 2019.0 10019 10022 4 Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Geology; Remote Sensing BO5ZH Green Accepted 2023-03-23 WOS:000519270601049 0
J Jiang, HW; Li, YY; Zhou, C; Hong, HY; Glade, T; Yin, KL Jiang, Hongwei; Li, Yuanyao; Zhou, Chao; Hong, Haoyuan; Glade, Thomas; Yin, Kunlong Landslide Displacement Prediction Combining LSTM and SVR Algorithms: A Case Study of Shengjibao Landslide from the Three Gorges Reservoir Area APPLIED SCIENCES-BASEL English Article Shengjibao Landslide; displacement prediction; Three Gorges Reservoir area; long short-term memory neural networks; support vector regression; ensemble model EXTREME LEARNING-MACHINE; MEMORY NEURAL-NETWORK; SUPPORT VECTOR MACHINE; TIME-SERIES ANALYSIS; DECISION TREE; RAINFALL; REGRESSION; KERNEL; MODEL Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models; the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models. [Jiang, Hongwei; Yin, Kunlong] China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China; [Li, Yuanyao] China Univ Geosci, Inst Geol Survey, Wuhan 430074, Peoples R China; [Zhou, Chao] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China; [Hong, Haoyuan; Glade, Thomas] Univ Vienna, Dept Geog & Reg Res, ENGAGE Geomorph Syst & Risk Res, A-1010 Vienna, Austria; [Hong, Haoyuan] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China China University of Geosciences; China University of Geosciences; China University of Geosciences; University of Vienna; Nanjing Normal University Yin, KL (corresponding author), China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China. jianghongwei@cug.edu.cn; liyuanyao2004@163.com; zhouchao@cug.edu.cn; hong_haoyuan@outlook.com; thomas.glade@univie.ac.at; yinkl@cug.edu.cn Hong, Haoyuan/C-8455-2014; Glade, Thomas/H-6325-2017; JIANG, HONGWEI/ABG-1306-2021 Hong, Haoyuan/0000-0001-6224-069X; Glade, Thomas/0000-0001-9676-4416; JIANG, HONGWEI/0000-0002-5828-9324; Jiang, HongWei/0000-0003-3374-6549; Zhou, Chao/0000-0002-4702-4021 National Key R&D Program of China [292 2018YFC0809400] National Key R&D Program of China This research was funded by the National Key R&D Program of China (No.292 2018YFC0809400). 49 13 15 10 43 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel NOV 2020.0 10.0 21 7830 10.3390/app10217830 0.0 21 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics OR0HL gold 2023-03-23 WOS:000589156200001 0
J Song, G; Ai, ZT; Liu, ZX; Zhang, GQ Song, Ge; Ai, Zhengtao; Liu, Zhengxuan; Zhang, Guoqiang A systematic literature review on smart and personalized ventilation using CO2 concentration monitoring and control ENERGY REPORTS English Review Personalized ventilation; Smart control; CO2 concentration; Thermal comfort; Energy saving DEMAND-CONTROLLED VENTILATION; THERMAL COMFORT PREDICTION; CARBON-DIOXIDE; NATURAL VENTILATION; SKIN TEMPERATURE; ENERGY SAVINGS; PERFORMANCE; OCCUPANCY; MODEL; RATES Smart and personalized ventilation systems have been demonstrated with high performance in creating a healthy and energy-efficient indoor environment, but they have been rarely comprehensively summarized and explored in previous studies. With the progressive development of various terminal devices and control technologies, personalized ventilation based on intelligent control is potentially a promising way to achieve efficient control and energy savings in human micro-environments. This study comprehensively summarizes and analyzes the recent studies and common utilization forms of smart ventilation and PV systems that are based on CO2 concentration control, to pave path and provide some guidelines for their integration application for reducing energy consumption and improving indoor thermal comfort. Research shows that the combination of personalized ventilation and smart ventilation is an essential development for ventilation systems. Smart ventilation with demand control logic based on CO2 concentration has been mature enough to effectively improve the effectiveness and comfortable performance of personalized ventilation. However, switching from traditional air conditioning systems to personalized ventilation still requires improved sensors and intelligent control algorithms. In addition, this paper also summarizes the exploratory studies and potential application analysis of machine-learning theories to improve intelligent control of personalized ventilation. To this end, this paper identifies future tendencies for advanced theories, integrated systems, and devices in personalized ventilation systems. (C) 2022 Published by Elsevier Ltd. [Song, Ge; Ai, Zhengtao; Liu, Zhengxuan; Zhang, Guoqiang] Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China; [Liu, Zhengxuan] Delft Univ Technol, Fac Architecture & Built Environm, Julianalaan 134, NL-2628 BL Delft, Netherlands Hunan University; Delft University of Technology Liu, ZX; Zhang, GQ (corresponding author), Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China. zhengxuanliu@hnu.edu.cn; gqzhang@hnu.edu.cn Zhengxuan, Liu/AAW-3873-2021 Zhengxuan, Liu/0000-0002-2761-5078 138 1 1 5 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4847 ENERGY REP Energy Rep. NOV 2022.0 8.0 7523 7536 10.1016/j.egyr.2022.05.243 0.0 JUN 2022 14 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 5U8UO gold 2023-03-23 WOS:000876816600001 0
J Toenders, YJ; Kottaram, A; Dinga, R; Davey, CG; Banaschewski, T; Bokde, ALW; Quinlan, EB; Desrivieres, S; Flor, H; Grigis, A; Garavan, H; Gowland, P; Heinz, A; Bruhl, R; Martinot, JL; Martinot, MLP; Nees, F; Orfanos, DP; Lemaitre, H; Paus, T; Poustka, L; Hohmann, S; Frohner, JH; Smolka, MN; Walter, H; Whelan, R; Stringaris, A; van Noort, B; Penttila, J; Grimmer, Y; Insensee, C; Becker, A; Schumann, G; Schmaal, L Toenders, Yara J.; Kottaram, Akhil; Dinga, Richard; Davey, Christopher G.; Banaschewski, Tobias; Bokde, Arun L. W.; Quinlan, Erin Burke; Desrivieres, Sylvane; Flor, Herta; Grigis, Antoine; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Bruehl, Rudiger; Martinot, Jean-Luc; Martinot, Marie-Laure Paillere; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Lemaitre, Herve; Paus, Tomas; Poustka, Luise; Hohmann, Sarah; Froehner, Juliane H.; Smolka, Michael N.; Walter, Henrik; Whelan, Robert; Stringaris, Argyris; van Noort, Betteke; Penttila, Jani; Grimmer, Yvonne; Insensee, Corinna; Becker, Andreas; Schumann, Gunter; Schmaal, Lianne IMAGEN Consortium Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING English Article COMPREHENSIVE DEVELOPMENTAL MODEL; CORTICAL SURFACE-AREA; MAJOR DEPRESSION; MENTAL-HEALTH; SUBTHRESHOLD DEPRESSION; BRAIN VOLUMES; USE DISORDERS; LIFE EVENTS; 1ST ONSET; HIGH-RISK BACKGROUND: Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. METHODS: A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). RESULTS: The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. CONCLUSIONS: This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables. [Toenders, Yara J.; Kottaram, Akhil; Schmaal, Lianne] Univ Melbourne, Orygen, Parkville, Vic, Australia; [Toenders, Yara J.; Kottaram, Akhil; Schmaal, Lianne] Univ Melbourne, Ctr Youth Mental Hlth, Parkville, Vic, Australia; [Davey, Christopher G.] Univ Melbourne, Dept Psychiat, Parkville, Vic, Australia; [Dinga, Richard] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands; [Banaschewski, Tobias; Nees, Frauke; Hohmann, Sarah; Grimmer, Yvonne] Heidelberg Univ, Dept Child & Adolescent Psychiat & Psychotherapy, Med Fac Mannheim, Cent Inst Mental Hlth, Heidelberg, Germany; [Flor, Herta; Nees, Frauke] Heidelberg Univ, Med Fac Mannheim, Inst Cognit & Clin Neurosci, Cent Inst Mental Hlth, Heidelberg, Germany; [Flor, Herta] Univ Mannheim, Sch Social Sci, Dept Psychol, Mannheim, Germany; [Heinz, Andreas; Walter, Henrik] Charite Univ Med Berlin, Dept Psychiat & Psychotherapy CCM, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Free Univ Berlin, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Humboldt Univ, Berlin, Germany; [Heinz, Andreas; Walter, Henrik] Berlin Inst Hlth, Berlin, Germany; [Bruehl, Rudiger] Phys Tech Bundesanstalt, Braunschweig, Germany; [Bruehl, Rudiger] Phys Tech Bundesanstalt, Berlin, Germany; [Nees, Frauke] Univ Kiel, Univ Med Ctr Schleswig Holstein, Inst Med Psychol & Med Sociol, Kiel, Germany; [Poustka, Luise; Insensee, Corinna; Becker, Andreas] Univ Med Ctr Gottingen, Dept Child & Adolescent Psychiat & Psychotherapy, Gottingen, Germany; [Froehner, Juliane H.; Smolka, Michael N.] Tech Univ Dresden, Dept Psychiat, Dresden, Germany; [Froehner, Juliane H.; Smolka, Michael N.] Tech Univ Dresden, Neuroimaging Ctr, Dresden, Germany; [van Noort, Betteke] Siemens Villa, Hsch Gesundheit & Med, MSB Med Sch Berlin, Berlin, Germany; [Bokde, Arun L. W.] Trinity Coll Dublin, Discipline Psychiat, Sch Med, Dublin, Ireland; [Whelan, Robert] Trinity Coll Dublin, Trinity Coll Inst Neurosci, Dublin, Ireland; [Whelan, Robert] Trinity Coll Dublin, Sch Psychol, Dublin, Ireland; [Whelan, Robert] Trinity Coll Dublin, Global Brain Hlth Inst, Dublin, Ireland; [Quinlan, Erin Burke; Desrivieres, Sylvane] Kings Coll London, SGDP Ctr, Inst Psychiat Psychol & Neurosci, Ctr Populat Neurosci & Precis Med PONS, London, England; [Gowland, Penny] Univ Nottingham, Sir Peter Mansfield Imaging Ctr, Sch Phys & Astron, Univ Pk, Nottingham, England; [Grigis, Antoine; Orfanos, Dimitri Papadopoulos; Lemaitre, Herve] Univ Paris Saclay, CEA, NeuroSpin, Gif Sur Yvette, France; [Martinot, Jean-Luc; Martinot, Marie-Laure Paillere] Univ Paris Saclay, Inst Natl Sante & Rech Med, INSERM U A10 Trajectoires Dev Psychiat, Ecole Normale Super Paris Saclay,CNRS,Ctr Borelli, Gif Sur Yvette, France; [Martinot, Marie-Laure Paillere] Sorbonne Univ, Pitie Salpetriere Hosp, AP HP, Dept Child & Adolescent Psychiat, Paris, France; [Lemaitre, Herve] Univ Bordeaux, CEA, CNRS, UMR 5293,Inst Malad Neurodegenerat, Bordeaux, France; [Garavan, Hugh] Univ Vermont, Dept Psychiat, Burlington, VT USA; [Garavan, Hugh] Univ Vermont, Dept Psychol, Burlington, VT 05405 USA; [Stringaris, Argyris] NIMH, NIH, Bethesda, MD 20892 USA; [Paus, Tomas] Univ Toronto, Dept Psychol, Toronto, ON, Canada; [Paus, Tomas] Univ Toronto, Dept Psychiat, Toronto, ON, Canada; [Paus, Tomas] Holland Bloorview Kids Rehabil Hosp Toronto, Bloorview Res Inst, Toronto, ON, Canada; [Penttila, Jani] Psychosocial Serv Adolescent Outpatient Clin, Dept Social & Hlth Care, Kauppakatu Lahti, Finland; [Schumann, Gunter] Humboldt Univ, Campus Charite Mitte, Dept Psychiat & Psychotherapy, PONS Res Grp, Berlin, Germany; [Schumann, Gunter] Leibniz Inst Neurobiol, Magdeburg, Germany; [Schumann, Gunter] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence IS, Shanghai, Peoples R China Orygen, The National Centre of Excellence in Youth Mental Health; University of Melbourne; Orygen, The National Centre of Excellence in Youth Mental Health; University of Melbourne; University of Melbourne; Radboud University Nijmegen; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; Central Institute of Mental Health; Ruprecht Karls University Heidelberg; University of Mannheim; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Berlin Institute of Health; Physikalisch-Technische Bundesanstalt (PTB); Physikalisch-Technische Bundesanstalt (PTB); University of Kiel; Schleswig Holstein University Hospital; University of Gottingen; Technische Universitat Dresden; Technische Universitat Dresden; Trinity College Dublin; Trinity College Dublin; Trinity College Dublin; Trinity College Dublin; University of London; King's College London; University of Nottingham; CEA; UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Pitie-Salpetriere - APHP; UDICE-French Research Universities; Sorbonne Universite; CEA; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); UDICE-French Research Universities; Universite de Bordeaux; University of Vermont; University of Vermont; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); University of Toronto; University of Toronto; University of Toronto; Holland Bloorview Kids Rehabilitation Hospital; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Leibniz Institut fur Neurobiologie (LIN); Fudan University Toenders, YJ (corresponding author), Univ Melbourne, Orygen, Parkville, Vic, Australia.;Toenders, YJ (corresponding author), Univ Melbourne, Ctr Youth Mental Hlth, Parkville, Vic, Australia. yara.toenders@orygen.org.au Heinz, Andreas/ABB-7736-2020; van Noort, Betteke Maria/GLR-5205-2022; Gowland, Penny/I-9016-2012; Desrivieres, Sylvane/B-7399-2011 Heinz, Andreas/0000-0001-5405-9065; van Noort, Betteke Maria/0000-0002-9768-3383; Nees, Frauke/0000-0002-7796-8234; Gowland, Penny/0000-0002-4900-4817; Toenders, Yara Jo/0000-0002-4117-1143; Stringaris, Argyris/0000-0002-6264-8377; Whelan, Robert/0000-0002-2790-7281; Walter, Henrik/0000-0002-9403-6121; Desrivieres, Sylvane/0000-0002-9120-7060; Flor, Herta/0000-0003-4809-5398; Quinlan, Erin Burke/0000-0003-2927-1632; Schumann, Gunter/0000-0003-4905-5523 MQ Brighter Futures Award [MQBFC/2]; National Institute of Mental Health of the National Institutes of Health [R01MH117601]; National Health and Medical Research Council (NHMRC) Career Development Fellowship [1140764]; Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne; NHMRC Career Development Award [141738]; European Union [LSHM-CT-2007-037286]; ERC [695313]; Human Brain Project [785907, 945539]; Medical Research Council [MR/N000390/1]; National Institutes of Health (NIH) [R01DA049238, 01GS08152, 01EE1406A]; National Institute for Health Research Biomedical Research Centre at South London; Lilly; Maudsley NHS Foundation Trust and Kings College London; Medice; Bundesministerium fuer Bildung und Forschung (BMBF) [01EE1406A, 01EV0711, 01EE1406B, 01GL1745B]; Novartis; Deutsche Forschungsgemeinschaft (DFG) [SM 80/7-2, AAPG2019 - GeBra, 80/7-2, IMAC-Mind 01GL1745B]; Shire; Medical Research Foundation and Medical Research Council [ANR-18-NEUR00002-01 - ADORe, SFB 940]; ANR [TRR 265, NE 1383/14-1]; Eranet Neuron [MR/R00465X/1, MR/S020306/1]; Fondation de France [5U54EB020403-05]; Fondation pour la Recherche Medicale [1R56AG058854-01]; Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hppitaux-de-Paris; INSERM; Paris Sud University IDEX; Fondation de lAvenir [ANR-12-SAMA-0004]; Federation pour la Recherche sur le Cerveau; National Institutes of Health, Science Foundation Ireland [AAPG2019]; USA (Axon, Testosterone and Mental Health during Adolescence) [AF12-NEUR0008-01 - WM2NA]; NIH Consortium [ANR-18-NEUR00002-01]; cross-NIH alliance; [00081242]; [DPA20140629802]; [AP-RM-17-013]; [16/ERCD/3797]; [RO1 MH085772-01A1]; [U54 EB020403]; Medical Research Council [MR/N000390/1] Funding Source: researchfish MQ Brighter Futures Award; National Institute of Mental Health of the National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); National Health and Medical Research Council (NHMRC) Career Development Fellowship(National Health and Medical Research Council (NHMRC) of Australia); Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne(University of Melbourne); NHMRC Career Development Award(National Health and Medical Research Council (NHMRC) of Australia); European Union(European Commission); ERC(European Research Council (ERC)European Commission); Human Brain Project(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH)); Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); National Institutes of Health (NIH)(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Institute for Health Research Biomedical Research Centre at South London; Lilly(Eli Lilly); Maudsley NHS Foundation Trust and Kings College London; Medice; Bundesministerium fuer Bildung und Forschung (BMBF)(Federal Ministry of Education & Research (BMBF)); Novartis(Novartis); Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); Shire; Medical Research Foundation and Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); ANR(French National Research Agency (ANR)); Eranet Neuron; Fondation de France(Fondation de France); Fondation pour la Recherche Medicale(Fondation pour la Recherche Medicale); Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA); Assistance-Publique-Hppitaux-de-Paris; INSERM(Institut National de la Sante et de la Recherche Medicale (Inserm)); Paris Sud University IDEX; Fondation de lAvenir; Federation pour la Recherche sur le Cerveau; National Institutes of Health, Science Foundation Ireland(Science Foundation Ireland); USA (Axon, Testosterone and Mental Health during Adolescence); NIH Consortium(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); cross-NIH alliance; ; ; ; ; ; ; Medical Research Council(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) This work was supported by the MQ Brighter Futures Award (MQBFC/2 [to LS] ) , the National Institute of Mental Health of the National Institutes of Health (Award Number R01MH117601 [to LS] ) , a National Health and Medical Research Council (NHMRC) Career Development Fellowship (1140764 [to LS] ) , the Dame Kate Campbell Fellowship from the Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne, and an NHMRC Career Development Award (141738 [to CGD] ) . This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (LSHM-CT-2007-037286) , the Horizon 2020-funded ERC Advanced Grant STRATIFY (Brain network based stratification of reinforcement-related disorders) (695313) , Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539) , the Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1) , the National Institutes of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers) , the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministerium fuer Bildung und Forschung (BMBF Grant Nos. 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B) , the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1) , the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1) , and the NIH-funded ENIGMA (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01) . Further support was provided by grants from the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra) , the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe) , the Fondation de France (00081242) , the Fondation pour la Recherche Medicale (DPA20140629802) , the Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA) , the Assistance-Publique-Hppitaux-de-Paris and INSERM (interface grant) , Paris Sud University IDEX 2012, the Fondation de l'Avenir (grant AP-RM-17-013) , the Federation pour la Recherche sur le Cerveau, the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797) , USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1) , and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. TB served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speakers fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire & Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. The present work is unrelated to the above grants and relationships. LP served in an advisory or consultancy role for Roche and Viforpharm and received speakers fee from Shire. She received royalties from Hogrefe, Kohl-hammer, and Schattauer. The present work is unrelated to the above grants and relationships. All other authors report no biomedical financial interests or po-tential conflicts of interest. 52 2 2 4 5 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2451-9022 2451-9030 BIOL PSYCHIAT-COGN N Biol. Psychiat.-Cogn. Neurosci. Neuroimag. APR 2022.0 7.0 4 376 384 10.1016/j.bpsc.2021.03.005 0.0 APR 2022 9 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 1U0XK 33753312.0 Green Accepted, Green Published 2023-03-23 WOS:000805143700006 0
C Pacheco, F; Cerrada, M; Li, C; Sanchez, RV; Cabrera, D; de Oliveira, JV IEEE Pacheco, Fannia; Cerrada, Mariela; Li, Chuan; Sanchez, Rene Vinicio; Cabrera, Diego; de Oliveira, Jose Valente Clustering algorithm using rough set theory for unsupervised feature selection 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 24-29, 2016 Vancouver, CANADA IEEE,IEEE Computat Intelligence Soc,Int Nueral Network Soc,Evolutionary Programming Soc,IET,IEEE BigData,Gulf Univ Sci & Technol RELATIVE DEPENDENCY Nowadays, the available data to describe real world problems grows in considerable manner, due to the amount of measurable characteristics (features) that can be collected. Machine learning techniques are widely used to extract valuable knowledge from data, but their performance might decrease when the proper features are not selected. Feature selection is introduced to search relations to disclose possible redundant or irrelevant features in a case study; this search is performed either in a supervised or unsupervised manner. In the present work, we propose an unsupervised feature selection algorithm using: (1) relative dependency to search similarities between features, (2) a clustering algorithm to group similar features, and (3) a procedure to select the most representative feature to obtain a reduced feature space. The relative dependency degree between pairs of attributes is used to compute a similarity measure. This measure is used by a clustering algorithm to perform attribute clustering through KNN and prototype based clustering. The proposal is tested with well-known benchmarks, and compared with classic supervised and unsupervised feature selection techniques. Additionally, a real world application in fault diagnosis for rotating machinery is evaluated by our proposal. [Pacheco, Fannia] Univ Politecn Salesiana, GIDTEC Mech Engn Dept, Cuenca, Ecuador; [Cerrada, Mariela; Li, Chuan; Sanchez, Rene Vinicio; Cabrera, Diego] GIDTEC, Merida, Venezuela; [Cerrada, Mariela] Univ Los Andes, Merida, Venezuela; [Li, Chuan] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing, Peoples R China; [de Oliveira, Jose Valente] Univ Algrave, CEOT, Faro, Portugal Universidad Politecnica Salesiana; University of Los Andes Venezuela; Chongqing Technology & Business University Pacheco, F (corresponding author), Univ Politecn Salesiana, GIDTEC Mech Engn Dept, Cuenca, Ecuador. fannikaro@gmail.com; cerradam@ula.ve; lcctbu@126.com; rsanchezl-dcabrera@ups.edu.ec; jvo@ualg.pt Pacheco, Fannia/AAQ-9953-2020; L., René Vinicio Sánchez/O-5259-2019; Valente de Oliveira, José/B-6426-2008 Pacheco, Fannia/0000-0001-6997-0222; L., René Vinicio Sánchez/0000-0003-0395-9228; Valente de Oliveira, José/0000-0001-5337-5699; Li, Chuan/0000-0003-0004-1497 GIDTEC project [017-007-2015-11-05]; Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador GIDTEC project; Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador The work was sponsored in part by the GIDTEC project called Desarrollo de una herramienta computacional basada en modelos de computacion inteligente para el monitoreo en maquinaria rotativa No. 017-007-2015-11-05, and the Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador. The experimental work was developed at the GIDTEC research group lab of the Universidad Politcnica Salesiana de Cuenca, Ecuador. 17 2 2 0 8 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-5090-0619-9 IEEE IJCNN 2016.0 3493 3499 7 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BH3QM 2023-03-23 WOS:000399925503094 0
J Wen, JF; Zhou, ZB; Shi, ZS; Wang, JP; Duan, YC; Zhang, YQ Wen, Jinfeng; Zhou, Zhangbing; Shi, Zhensheng; Wang, Junping; Duan, Yucong; Zhang, Yaqiang Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus IEEE ACCESS English Article Scientific workflow; community discovery clustering; abstract activity; cross-workflow fragment recommendation BIG DATA; SEARCH Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments. [Wen, Jinfeng; Zhou, Zhangbing; Zhang, Yaqiang] China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China; [Wen, Jinfeng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Zhou, Zhangbing] TELECOM SudParis, Comp Sci Dept, F-91011 Evry, France; [Shi, Zhensheng] PetroChina Res Inst Petr Explorat & Dev, Langfang 065007, Peoples R China; [Wang, Junping] Chinese Acad Sci, Inst Automat, Lab Precis Sensing, Beijing 100190, Peoples R China; [Wang, Junping] Chinese Acad Sci, Inst Automat, Control Ctr, Beijing 100190, Peoples R China; [Duan, Yucong] Hainan Univ, Coll Informat Sci & Technol, Haikou 570228, Hainan, Peoples R China China University of Geosciences; Beijing University of Posts & Telecommunications; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; China National Petroleum Corporation; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; Institute of Automation, CAS; Hainan University Zhou, ZB (corresponding author), China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China.;Zhou, ZB (corresponding author), TELECOM SudParis, Comp Sci Dept, F-91011 Evry, France. zhangbing.zhou@gmail.com shi, zhensheng/GXN-3967-2022 National Natural Science Foundation of China [61772479, 61662021]; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications [SKLNST-2018-1-13]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China This work was supported in part by the National Natural Science Foundation of China under Grant 61772479 and Grant 61662021, in part by the Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications under Grant SKLNST-2018-1-13, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China. 45 4 4 0 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 40530 40546 10.1109/ACCESS.2018.2857482 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GQ6WJ gold 2023-03-23 WOS:000441868800018 0
C Wu, BX; Gu, JD; Li, ZF; Cai, D; He, XF; Liu, W Avidan, S; Brostow, G; Cisse, M; Farinella, GM; Hassner, T Wu, Boxi; Gu, Jindong; Li, Zhifeng; Cai, Deng; He, Xiaofei; Liu, Wei Towards Efficient Adversarial Training on Vision Transformers COMPUTER VISION, ECCV 2022, PT XIII Lecture Notes in Computer Science English Proceedings Paper 17th European Conference on Computer Vision (ECCV) OCT 23-27, 2022 Tel Aviv, ISRAEL Robustness; Adversarial training; Vision transformer Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversarial examples like CNNs. To build robust ViTs, an intuitive way is to apply adversarial training since it has been shown as one of the most effective ways to accomplish robust CNNs. However, one major limitation of adversarial training is its heavy computational cost. The self-attention mechanism adopted by ViTs is a computationally intense operation whose expense increases quadratically with the number of input patches, making adversarial training on ViTs even more time-consuming. In this work, we first comprehensively study fast adversarial training on a variety of vision transformers and illustrate the relationship between the efficiency and robustness. Then, to expediate adversarial training on ViTs, we propose an efficient Attention Guided Adversarial Training mechanism. Specifically, relying on the specialty of self-attention, we actively remove certain patch embeddings of each layer with an attention-guided dropping strategy during adversarial training. The slimmed self-attention modules accelerate the adversarial training on ViTs significantly. With only 65% of the fast adversarial training time, we match the state-of-the-art results on the challenging ImageNet benchmark. [Wu, Boxi; Cai, Deng; He, Xiaofei] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Zhejiang, Peoples R China; [Gu, Jindong] Univ Munich, Munich, Germany; [Li, Zhifeng; Liu, Wei] Tencent Data Platform, Shenzhen, Peoples R China Zhejiang University; University of Munich Cai, D (corresponding author), Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Zhejiang, Peoples R China.;Liu, W (corresponding author), Tencent Data Platform, Shenzhen, Peoples R China. boxiwu@zju.edu.cn; michaelzfli@tencent.com; dengcai@cad.zju.edu.cn; xiaofeihe@cad.zju.edu.cn; wl2223@columbia.edu National Key Research and Development Program of China [2018AAA0101400]; National Nature Science Foundation of China [62036009, U1909203, 61936006, 62133013]; Innovation Capability Support Program of Shaanxi [2021TD-05] National Key Research and Development Program of China; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Capability Support Program of Shaanxi This work was supported in part by The National Key Research and Development Program of China (Grant Nos: 2018AAA0101400), in part by The National Nature Science Foundation of China (Grant Nos: 62036009, U1909203, 61936006, 62133013), in part by Innovation Capability Support Program of Shaanxi (Program No. 2021TD-05). 74 1 1 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-19777-2; 978-3-031-19778-9 LECT NOTES COMPUT SC 2022.0 13673.0 307 325 10.1007/978-3-031-19778-9_18 0.0 19 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU4GB Green Submitted 2023-03-23 WOS:000897100100018 0
J Cheng, TL; Qin, HC; Cheng, Z; Yan, D; Jia, LC; Jiang, JH; Li, J Cheng, Tianliang; Qin, Hongchuan; Cheng, Zhe; Yan, Dong; Jia, Lichao; Jiang, Jianhua; Li, Jian Fault prognosis control of solid oxide fuel cell system based on health evaluation INTERNATIONAL JOURNAL OF HYDROGEN ENERGY English Article SOFC system; Performance degradation principle; Fault evolution mechanism; Multi-mode model; Health evaluation system; Control optimization TURBINE HYBRID SYSTEM; CONTROL STRATEGY; SOFC SYSTEM; MODEL; DIAGNOSIS; DEGRADATION; POWER; VALIDATION; NETWORK; DESIGN High cost and short life-span hinder the large-scale commercial application of solid oxide fuel cell technology. System performance inevitably degrades and gradually evolves into faults in the long run, leading to the change of system input-output characteristics. The control system designed based on initial conditions faces with problems such as parameter mismatch, inaccurate control and even wrong control laws. This paper proposes an adaptive control strategy to enable the system controller to cope with degradation and fault phenomena in real systems. The system characteristics under different degradation and fault conditions are investigated. A novel health evaluation system is developed, where an early fault identification and pretreatment approach based on optimal operation point optimization model and fuzzy neural network are adopted for control optimization. Simulation results show that the enhanced system controller can improve system efficiency by about 2% and prolong life-span significantly with the reality of degradation and fault mechanisms. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. [Cheng, Tianliang; Qin, Hongchuan; Cheng, Zhe; Jiang, Jianhua] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Educ, Minist Image Proc & Intelligent Control, Wuhan 430074, Peoples R China; [Yan, Dong; Jia, Lichao; Li, Jian] Huazhong Univ Sci & Technol, Sch Mat Sci & Engn, State Key Lab Mat Proc & Dye & Mold Technol, Wuhan 430074, Peoples R China; [Cheng, Tianliang] Forschungszentrum Julich GmbH, Electrochem Proc Engn IEK 14, Inst Energy & Climate Res, Julich, Germany; [Cheng, Tianliang] Rhein Westfal TH Aachen, Fac Mech Engn, Aachen, Germany Huazhong University of Science & Technology; Huazhong University of Science & Technology; Helmholtz Association; Research Center Julich; RWTH Aachen University Jiang, JH (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Educ, Minist Image Proc & Intelligent Control, Wuhan 430074, Peoples R China. jiangjh@hust.edu.cn Jia, Lichao/A-4137-2019 Cheng, Tianliang/0000-0001-5925-4521 55 1 1 9 22 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-3199 1879-3487 INT J HYDROGEN ENERG Int. J. Hydrog. Energy NOV 8 2021.0 46.0 77 38425 38450 10.1016/j.ijhydene.2021.09.103 0.0 OCT 2021 26 Chemistry, Physical; Electrochemistry; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels WN2LK 2023-03-23 WOS:000711605300010 0
J Wang, ZJ; Hu, H; Zhou, W Wang, Zhaojing; Hu, Hao; Zhou, Wei RFID Enabled Knowledge-Based Precast Construction Supply Chain COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING English Article RADIO-FREQUENCY IDENTIFICATION; MANAGEMENT; SYSTEM; TECHNOLOGY; IMPACT; MODEL; CAD Radio frequency identification (RFID) helps improve supply chain efficiency by providing item-level identification and real-time information. Today, barcode continues to be the main identification technology for precast construction applications. In this research, we investigate the data-driven mechanisms and benefits of utilizing RFID in knowledge-based precast construction supply chains. With computer-aided self-learning capability, we simulate three models for manual-, barcode-, and RFID-enabled precast construction supply chain. The results of 100 precast wall-panel construction in a two-echelon precast construction supply chain reveal that the knowledge-based RFID system could generate 62.0% saving of operational costs, which is 29.0% higher than that of a barcode-based system. As a result, the computer-aided adaptive learning mechanism based on RFID is verifiable to improve the overall operational performance by reducing lead time, operational errors, and costs. Due to the lack of existing literature of data technology utilization in the precast construction industry, our findings in this research could improve the decision making regarding technology selection, as well as help with the operationalization of RFID and transformation to intelligent precast construction management in big data environment. [Wang, Zhaojing; Hu, Hao] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai, Peoples R China; [Zhou, Wei] ESCP Europe, Dept Informat & Operat Management, Paris, France Shanghai Jiao Tong University; heSam Universite; ESCP Business School Hu, H (corresponding author), Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai, Peoples R China. hhu@sjtu.edu.cn Zhou, Wei/U-6509-2019; Wang, Zhaojing/AGZ-2542-2022; Hu, Hao/L-1378-2015; wang, zhaojing/AAG-1892-2019 Hu, Hao/0000-0002-1103-0243; wang, zhaojing/0000-0001-6705-6082 Key Technology and Development Program in Shanghai of the Shanghai Commission of Science and Technology [14DZ1207107] Key Technology and Development Program in Shanghai of the Shanghai Commission of Science and Technology The authors express their sincere gratitude to Shanghai Jiangong Construction Material Company for assistance in observation and conducting on-site interviews. They also thank the project engineers for valuable information and useful data. Additionally, this research was supported by the Key Technology and Development Program in Shanghai (14DZ1207107) of the Shanghai Commission of Science and Technology. 43 43 44 9 128 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1093-9687 1467-8667 COMPUT-AIDED CIV INF Comput.-Aided Civil Infrastruct. Eng. JUN 2017.0 32.0 6 499 514 10.1111/mice.12254 0.0 16 Computer Science, Interdisciplinary Applications; Construction & Building Technology; Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Construction & Building Technology; Engineering; Transportation EY5KD 2023-03-23 WOS:000404015200004 0
J Wang, XQ; Gu, Y Wang, Xuqian; Gu, Yu Classification of macular abnormalities using a lightweight CNN-SVM framework MEASUREMENT SCIENCE AND TECHNOLOGY English Article macular abnormalities; optical coherence tomography; lightweight CNN; attention mechanism; support vector machine DIABETIC-RETINOPATHY; DEGENERATION Macular abnormalities are the main reason for central vision loss, especially in elderly people. Due to global population aging, a heavy burden will be placed on the health care system. Therefore, it is urgent and necessary to develop an automatic and intelligent tool to identify macular abnormalities. Optical coherence tomography is a non-invasive rapid imaging technique to diagnose macular abnormalities. We propose a lightweight convolutional neural network-support vector machine (CNN-SVM) framework consisting of a novel lightweight CNN backbone and an SVM classifier for the accurate detection of macular abnormalities. The CNN-SVM framework achieves excellent performance based on various metrics (precision, recall, F1-score, and accuracy) with an accuracy of 99.8% and demonstrates strong interpretability using heatmap visualization, outperforming several state-of-the-art models (Joint-Attention Network, OpticNet, MobileNet-V3, DenseNet-169, ResNet-50, lesion-aware CNN, Atten-ResNet, least-squares generative adversarial network and others). The proposed CNN-SVM framework is a feasible and reliable tool for the classification of macular abnormalities and shows potential for diagnostic ophthalmology in clinical practice. [Wang, Xuqian] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China; [Gu, Yu] Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China; [Gu, Yu] Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China; [Gu, Yu] Goethe Univ, Inst Inorgan & Analyt Chem, Dept Chem, Max von Laue Str 9, D-60438 Frankfurt, Germany Beijing University of Chemical Technology; Beijing University of Chemical Technology; Guangdong University of Petrochemical Technology; Goethe University Frankfurt Gu, Y (corresponding author), Beijing Univ Chem Technol, Beijing Adv Innovat Ctr Soft Matter Sci & Engn, Beijing 100029, Peoples R China.;Gu, Y (corresponding author), Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China.;Gu, Y (corresponding author), Goethe Univ, Inst Inorgan & Analyt Chem, Dept Chem, Max von Laue Str 9, D-60438 Frankfurt, Germany. guyu@mail.buct.edu.cn National Natural Science Foundation of China [61876059] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors would like to thank the National Natural Science Foundation of China (Grant No. 61876059) for its supports. 43 0 0 14 30 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0957-0233 1361-6501 MEAS SCI TECHNOL Meas. Sci. Technol. JUN 1 2022.0 33.0 6 65702 10.1088/1361-6501/ac5876 0.0 11 Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation ZX2MD 2023-03-23 WOS:000771733000001 0
J Strait, EJ; Barr, JL; Baruzzo, M; Berkery, JW; Buttery, RJ; de Vries, PC; Eidietis, NW; Granetz, RS; Hanson, JM; Holcomb, CT; Humphreys, DA; Kim, JH; Kolemen, E; Kong, M; Lanctot, MJ; Lehnen, M; Lerchel, E; Logan, NC; Maraschek, M; Okabayashi, M; Park, JK; Pau, A; Pautasso, G; Poli, FM; Rea, C; Sabbagh, SA; Sauter, O; Schuster, E; Sheikh, UA; Sozzi, C; Turco, F; Turnbull, AD; Wang, ZR; Wehner, WP; Zeng, L Strait, E. J.; Barr, J. L.; Baruzzo, M.; Berkery, J. W.; Buttery, R. J.; de Vries, P. C.; Eidietis, N. W.; Granetz, R. S.; Hanson, J. M.; Holcomb, C. T.; Humphreys, D. A.; Kim, J. H.; Kolemen, E.; Kong, M.; Lanctot, M. J.; Lehnen, M.; Lerchel, E.; Logan, N. C.; Maraschek, M.; Okabayashi, M.; Park, J. K.; Pau, A.; Pautasso, G.; Poli, F. M.; Rea, C.; Sabbagh, S. A.; Sauter, O.; Schuster, E.; Sheikh, U. A.; Sozzi, C.; Turco, F.; Turnbull, A. D.; Wang, Z. R.; Wehner, W. P.; Zeng, L. Progress in disruption prevention for ITER NUCLEAR FUSION English Article tokamak; stability; control; disruption; ITER INTEGRATED CONTROL; JET; OPERATION Key plasma physics and real-time control elements needed for robustly stable operation of high fusion power discharges in ITER have been demonstrated in recent research worldwide. Recent analysis has identified the current density profile as the main drive for disruptive instabilities in discharges simulating ITER's baseline scenario with high and low external torque. Ongoing development of model-based profile control and active control of magnetohydrodynamic instabilities is improving the stability of multiple scenarios. Significant advances have been made toward real-time physics-based prediction of instabilities, including path-oriented analysis, active sensing, and machine learning techniques for prediction that are beginning to go beyond simple disruption mitigation trigger applications. Active intervention contributes to prevention of disruptions, including forced rotation of magnetic islands to prevent wall locking, and localized heating/current drive to shrink the islands. Stable discharge rampdowns have been achieved with the fastest ITER-like scaled current ramp rates, while maintaining an X-point configuration. These elements are being integrated into stable operating scenarios and new event-handling systems for off-normal events in order to develop the physics basis and techniques for robust control in ITER. [Strait, E. J.; Barr, J. L.; Buttery, R. J.; Eidietis, N. W.; Humphreys, D. A.; Lanctot, M. J.; Turnbull, A. D.] Gen Atom, POB 85608, San Diego, CA 92186 USA; [Baruzzo, M.] Consorzio RFX, Corso Stati Uniti 4, I-35127 Padua, Italy; [Berkery, J. W.; Hanson, J. M.; Sabbagh, S. A.; Turco, F.] Columbia Univ, New York, NY 10027 USA; [de Vries, P. C.; Lehnen, M.] ITER Org, Route Vinon Sur Verdon, F-13067 St Paul Les Durance, France; [Granetz, R. S.; Rea, C.] MIT, Plasma Sci & Fus Ctr, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Holcomb, C. T.] Lawrence Livermore Natl Lab, Livermore, CA 94551 USA; [Kim, J. H.] Natl Fus Res Inst, Daejon, South Korea; [Kolemen, E.; Logan, N. C.; Okabayashi, M.; Park, J. K.; Poli, F. M.; Wang, Z. R.] Princeton Plasma Phys Lab, Princeton, NJ 08543 USA; [Kong, M.; Sauter, O.; Sheikh, U. A.] Ecole Polytech Fed Lausanne, Swiss Plasma Ctr, CH-1015 Lausanne, Switzerland; [Lerchel, E.] CCFE Fus Assoc, Culham Sci Ctr, Abingdon, Oxon, England; [Lerchel, E.] Assoc Eurofus Belgian State, TEC Partner, LPP ERM KMS, Brussels, Belgium; [Maraschek, M.; Pautasso, G.] Max Planck Inst Plasma Phys, D-85748 Garching, Germany; [Pau, A.] Univ Cagliari, Elect & Elect Engn Dept, I-09123 Cagliari, Italy; [Schuster, E.; Wehner, W. P.] Lehigh Univ, Bethlehem, PA 18015 USA; [Sozzi, C.] Univ Milano Bicocca, Inst Fis Plasma, Milan, Italy; [Zeng, L.] Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Anhui, Peoples R China; [Lanctot, M. J.] US DOE, Washington, DC 20585 USA General Atomics & Affiliated Companies; Columbia University; ITER; Massachusetts Institute of Technology (MIT); United States Department of Energy (DOE); Lawrence Livermore National Laboratory; National Fusion Research Institute (NFRI); Princeton University; United States Department of Energy (DOE); Princeton Plasma Physics Laboratory; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Culham Science Centre; UK Atomic Energy Authority; Max Planck Society; University of Cagliari; Lehigh University; Consiglio Nazionale delle Ricerche (CNR); Istituto Fisica del Plasma Piero Caldirola (IFP-CNR); University of Milano-Bicocca; Chinese Academy of Sciences; Hefei Institutes of Physical Science, CAS; United States Department of Energy (DOE) Strait, EJ (corresponding author), Gen Atom, POB 85608, San Diego, CA 92186 USA. strait@fusion.gat.com Barr, Jayson L./AAE-5496-2022; Rea, Cristina/Q-3025-2019; Sheikh, Umar/GXH-8882-2022; Sauter, Olivier/AAA-1949-2022; poli, francesca/C-2226-2008; Kong, Mengdi/ABI-5544-2020; Berkery, John/AAW-5817-2020 Barr, Jayson L./0000-0001-7768-5931; Rea, Cristina/0000-0002-9948-2649; Sauter, Olivier/0000-0002-0099-6675; poli, francesca/0000-0003-3959-4371; Kong, Mengdi/0000-0002-2004-3513; Berkery, John/0000-0002-8062-3210; Logan, Nikolas/0000-0002-3268-7359; Eidietis, Nicholas/0000-0003-0167-5053; Kolemen, Egemen/0000-0003-4212-3247 US DOE [DE-FC02-04ER54698, DE-SC0008520, DE-SC0016372, DE-FG02-04ER54761, DE-AC52-07NA27344, DE-SC0015878, DE-SC0014264, DE-FG02-99ER54524, DE-FOA-0001498, DE-AC02-09CH11466, DE-FC02-99ER54512, DE-SC0010720, DE-SC0010492]; Euratom research and training programme 2014-2018 [633053]; Swiss National Science Foundation US DOE(United States Department of Energy (DOE)); Euratom research and training programme 2014-2018; Swiss National Science Foundation(Swiss National Science Foundation (SNSF)) Work supported in part by US DOE under DE-FC02-04ER54698, DE-SC0008520, DE-SC0016372, DE-FG02-04ER54761, DE-AC52-07NA27344, DE-SC0015878, DE-SC0014264, DE-FG02-99ER54524, DE-FOA-0001498, DE-AC02-09CH11466, DE-FC02-99ER54512, DE-SC0010720, DE-SC0010492. DIII-D data shown in this paper can be obtained in digital format by following the links at https://fusion.gat.com/global/D3D_DMP. This work has been carried out in part within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No. 633053. This work was supported in part by the Swiss National Science Foundation. 100 42 42 2 77 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0029-5515 1741-4326 NUCL FUSION Nucl. Fusion NOV 2019.0 59.0 11 112012 10.1088/1741-4326/ab15de 0.0 12 Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Physics IC2RU Green Submitted 2023-03-23 WOS:000470808200012 0
J Caggiani, L; Camporeale, R; Ottomanelli, M; Szeto, WY Caggiani, Leonardo; Camporeale, Rosalia; Ottomanelli, Michele; Szeto, Wai Yuen A modeling framework for the dynamic management of free-floating bike-sharing systems TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES English Article Free-floating bike sharing systems; Spatio-temporal clustering; Non-linear autoregressive neural network forecasting; Decision Support System; Dynamic fleet relocation STATIC REPOSITIONING PROBLEM; REBALANCING PROBLEM; FLOW PREDICTION; PASSENGER FLOW; OPTIMIZATION; ALGORITHM; NETWORKS; LOCATION; DESIGN Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems. [Caggiani, Leonardo; Camporeale, Rosalia; Ottomanelli, Michele] Polytech Univ Bari, Dept Civil Environm Land Bldg Engn & Chem DICATEC, Viale Orabona 4, I-70125 Bari, Italy; [Szeto, Wai Yuen] Univ Hong Kong, Dept Civil Engn, Rm 618,Halting Wong Bldg, Pokfulam, Hong Kong, Peoples R China; [Szeto, Wai Yuen] Univ Hong Kong, Shenzhen Inst Res & Innovat, Shenzhen, Peoples R China Politecnico di Bari; University of Hong Kong; University of Hong Kong Ottomanelli, M (corresponding author), Polytech Univ Bari, Dept Civil Environm Land Bldg Engn & Chem DICATEC, Viale Orabona 4, I-70125 Bari, Italy. leonardo.caggiani@poliba.it; rosalia.camporeale@poliba.it; michele.ottomanelli@poliba.it; ceszeto@hku.hk Szeto, Wai Yuen/E-9886-2010; Camporeale, Rosalia/I-4583-2019; Ottomanelli, Michele/D-4253-2011 Szeto, Wai Yuen/0000-0001-7059-3532; Camporeale, Rosalia/0000-0001-7449-2085; Ottomanelli, Michele/0000-0002-4460-9980; Caggiani, Leonardo/0000-0001-7390-5191 Fondo di Sviluppo e Coesione - APQ Ricerca Regione Puglia, Programma regionale a sostegno della specializzazione intelligente e della sostenibilita sociale ed ambientale - FutureInResearch - Italy [3T23CY7]; National Natural Science Foundation of China - China [71771194] Fondo di Sviluppo e Coesione - APQ Ricerca Regione Puglia, Programma regionale a sostegno della specializzazione intelligente e della sostenibilita sociale ed ambientale - FutureInResearch - Italy; National Natural Science Foundation of China - China(National Natural Science Foundation of China (NSFC)) This research has been carried out within the co-funded intervention Fondo di Sviluppo e Coesione 2007-2013 - APQ Ricerca Regione Puglia, Programma regionale a sostegno della specializzazione intelligente e della sostenibilita sociale ed ambientale - FutureInResearch - Italy, project No. 3T23CY7 and also funded by a grant from National Natural Science Foundation of China China (No. 71771194). 68 148 157 26 181 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0968-090X TRANSPORT RES C-EMER Transp. Res. Pt. C-Emerg. Technol. FEB 2018.0 87.0 159 182 10.1016/j.trc.2018.01.001 0.0 24 Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Transportation GA7EB 2023-03-23 WOS:000428496300011 0
J Chen, X; Zhang, J; Fu, CW; Fekete, JD; Wang, YH Chen, Xin; Zhang, Jian; Fu, Chi-Wing; Fekete, Jean-Daniel; Wang, Yunhai Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS English Article Data visualization; Sampling methods; Coherence; Reservoirs; Scalability; Visual analytics; Rivers; Scatterplots; sampling; pyramid; progressive visualization; streaming visualization; scalability; big data DENSITY; EXPLORATION; MODEL We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data. [Chen, Xin; Wang, Yunhai] Shandong Univ, Jinan, Shandong, Peoples R China; [Zhang, Jian] Chinese Acad Sci, CNIC, Beijing, Peoples R China; [Fu, Chi-Wing] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China; [Fekete, Jean-Daniel] Univ Paris Saclay, CNRS, INRIA, LISN, Gif Sur Yvette, France Shandong University; Chinese Academy of Sciences; Chinese University of Hong Kong; Centre National de la Recherche Scientifique (CNRS); Inria; UDICE-French Research Universities; Universite Paris Saclay Wang, YH (corresponding author), Shandong Univ, Jinan, Shandong, Peoples R China. chenxin199634@gmail.com; zhangjian@sccas.cn; cwfu@cse.cuhk.edu.hk; Jean-Daniel.Fekete@inria.fr; cloudseawang@gmail.com Fekete, Jean-Daniel/N-9175-2018 Fekete, Jean-Daniel/0000-0003-3770-8726 NSFC [61772315, 61861136012]; High-End Foreign Experts Project [G20190015039]; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [VRLAB2020C08]; CAS grant [GJHZ1862] NSFC(National Natural Science Foundation of China (NSFC)); High-End Foreign Experts Project; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University; CAS grant This work is supported by the grants of the NSFC (61772315, 61861136012), the High-End Foreign Experts Project (No. G20190015039), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No.VRLAB2020C08), and the CAS grant (GJHZ1862). 48 2 2 1 7 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1077-2626 1941-0506 IEEE T VIS COMPUT GR IEEE Trans. Vis. Comput. Graph. JAN 2022.0 28.0 1 593 603 10.1109/TVCG.2021.3114880 0.0 11 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science XU0IB 34587089.0 Green Submitted 2023-03-23 WOS:000733959000067 0
C Zhang, XY; Gong, Y; Li, ZW; Liu, X; Pan, SY; Li, J IEEE Zhang, Xinyu; Gong, Yan; Li, Zhiwei; Liu, Xuan; Pan, Shuyue; Li, Jun Multi-Modal Attention Guided Real-Time Lane Detection 2021 6TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2021) English Proceedings Paper 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) JUL 03-05, 2021 Chongqing, PEOPLES R CHINA IEEE,Chongqing Univ,IEEE Robot & Automat Soc,IEEE Syst Man & Cybernet Soc,IEEE RAS Tech Comm Neuro Robot Syst,IEEE SMC Tech Comm Bio Mechatron & Bio Robot Syst,Chinese Assoc Automat, Tech Comm Robot Intelligence,Sensors,Unitree Robot,Springer,Publishing House Elect Ind,Quanser,Gaitech Robot Multimodal fusion; Attention mechanism; Lane detection; LSTM; Convolutional Neural Network Multimodal data fusion is becoming a trend for the field of autonomous driving, especially for lane detection. In the process of driving, sensors often encounter problems such as modality imbalance, changing illumination and so on. Therefore, it is worthwhile to study the problems of applying multimodal fusion for lane detection and modality imbalance in the fusion process. In this paper, we propose a novel multimodal model for lane detection, in which attention mechanism is embedded into network to balance multimodal feature fusion and to improve detection capability. In addition, we use multi-frame input and long short-term memory (LSTM) network to solve the shadow interference, vehicles occlusion and mark degradation. At the same time, the network can be applied to the task of lane detection. In order to verify the effect of multimodal application and attention mechanism on fusion, we have designed adequate experiments on processed continuous scene KITTI dataset. The results show that precision increases by about 15% when LiDAR is added compared with RGB only. Besides, attention mechanism obviously improves the performance of multi-modal detection by balancing multi-modal features. [Zhang, Xinyu; Gong, Yan; Li, Jun] Tsinghua Univ, Sch Vehicle & Mobil, Beijing, Peoples R China; [Zhang, Xinyu; Liu, Xuan] Tsinghua Univ, Mengshi Intelligent Vehicle Team, Beijing, Peoples R China; [Gong, Yan] Northeastern Univ, Shenyang, Peoples R China; [Li, Zhiwei] Tsinghua Univ, Beijing, Peoples R China; [Li, Zhiwei] Tsinghua Univ, Mengshi Intelligent Vehicle Team, Fus Sensing, Beijing, Peoples R China; [Liu, Xuan] Beijing Jiaotong Univ, Sch Software Engn, Beijing, Peoples R China; [Pan, Shuyue] Daimler AG, Autonomous Driving Syst, Stuttgart, Germany; [Li, Jun] China Soc Automot Engineers, Beijing, Peoples R China Tsinghua University; Tsinghua University; Northeastern University - China; Tsinghua University; Tsinghua University; Beijing Jiaotong University; Daimler AG Zhang, XY (corresponding author), Tsinghua Univ, Sch Vehicle & Mobil, Beijing, Peoples R China.;Zhang, XY (corresponding author), Tsinghua Univ, Mengshi Intelligent Vehicle Team, Beijing, Peoples R China. xyzhang@tsinghua.edu.cn; gongyan2020@foxmail.com; lizhiwei713818@163.com; 18301011@bjtu.edu.cn; Shuyue.pan@daimler.com; lijun19580326@126.com Gong, Yan/0000-0002-3148-8286 National High Technology Research and Development Program of China [2018YFE0204300]; Beijing Science and Technology Plan Project [Z191100007419008, 2019GQG1010] National High Technology Research and Development Program of China(National High Technology Research and Development Program of China); Beijing Science and Technology Plan Project This work was supported by the National High Technology Research and Development Program of China under Grant No. 2018YFE0204300, and the Beijing Science and Technology Plan Project(Z191100007419008), and the Guoqiang Research Institute Project(2019GQG1010).(Xinyu Zhang and Yan Gong contributed equally to this work.) (Corresponding author: Xinyu Zhang) 47 0 0 5 14 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-0-7381-3364-5 2021.0 146 153 10.1109/ICARM52023.2021.9536157 0.0 8 Automation & Control Systems; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Engineering; Robotics BS5CJ 2023-03-23 WOS:000728141500024 0
J Pei, EC; Jiang, DM; Sahli, H Pei, Ercheng; Jiang, Dongmei; Sahli, Hichem An efficient model-level fusion approach for continuous affect recognition from audiovisual signals NEUROCOMPUTING English Article Continuous affect recognition; Multi-modal fusion; Model-level fusion; Adaptive weight network; Side information EMOTION; NETWORKS Continuous affect recognition has a huge potential in human computer interaction applications. How to efficiently fuse speech and facial information for inferring the affective state of a person from data captured in real-world conditions is a very important issue for continuous affect recognition. Currently, late fusion is usually used in multi-modal continuous affect recognition to improve system performance. However, late fusion ignores the complementarity and redundancy between multiple streams from the different modalities. In this work, we propose an efficient model-level fusion approach for audiovisual continuous affect recognition. First, we propose a LSTM based model-level fusion approach for audiovisual continuous affect recognition. Our approach considers the complementarity and redundancy between multiple streams from different modalities. In addition, our model can efficiently incorporate side information such as gender using adaptive weight network. At last, we design a deep supervision based effective optimization strategy for training the proposed audiovisual continuous affect recognition model. We demonstrate the effectiveness of our approach on the RECOLA dataset. Our experimental results show that the proposed adaptive weight network improves the performance compared to a plain neural network without adaptive weights. Our approach obtains remarkable improvements on both arousal and valence in terms of concordance correlation coefficient (CCC) compared to state-of-the-art early fusion and model-level fusion approaches. Therefore, we believe that our proposed approach gives a promising direction for further improving the performance of audiovisual continuous affect recognition. (C) 2019 Elsevier B.V. All rights reserved. [Pei, Ercheng; Jiang, Dongmei] Northwestern Polytech Univ, Sch Comp Sci, VUB NPU Joint AVSP Lab, Xian 710072, Shaanxi, Peoples R China; [Pei, Ercheng; Sahli, Hichem] Vrije Univ Brussel, Dept ETRO, B-1050 Brussels, Belgium; [Sahli, Hichem] Interuniv Microelect Ctr IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel; IMEC Pei, EC (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, VUB NPU Joint AVSP Lab, Xian 710072, Shaanxi, Peoples R China. peiercheng@mail.nwpu.edu.cn; jiangdm@nwpu.edu.cn; hsahli@etrovub.be Sahli, Hichem/0000-0002-1774-2970 Chinese Scholarship Council (CSC) [201706290115]; Shaanxi Provincial International Science and Technology Collaboration Project [2017KW-ZD14]; VUB Interdisciplinary Research Program through the EMOApp project Chinese Scholarship Council (CSC)(China Scholarship Council); Shaanxi Provincial International Science and Technology Collaboration Project; VUB Interdisciplinary Research Program through the EMOApp project This work is supported by the Chinese Scholarship Council (CSC) (grant 201706290115), the Shaanxi Provincial International Science and Technology Collaboration Project (grant 2017KW-ZD14), the VUB Interdisciplinary Research Program through the EMOApp project. 97 6 6 5 48 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing FEB 1 2020.0 376.0 42 53 10.1016/j.neucom.2019.09.037 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science JX0KW 2023-03-23 WOS:000503433800004 0
J Yu, R; Huo, LN; Huang, HG; Yuan, Y; Gao, BT; Liu, YJ; Yu, LF; Li, HA; Yang, LY; Ren, LL; Luo, YQ Yu, Run; Huo, Langning; Huang, Huaguo; Yuan, Yuan; Gao, Bingtao; Liu, Yujie; Yu, Linfeng; Li, Haonan; Yang, Liyuan; Ren, Lili; Luo, Youqing Early detection of pine wilt disease tree candidates using time-series of spectral signatures FRONTIERS IN PLANT SCIENCE English Article pine wilt disease; unmanned aerial vehicle; hyperspectral images; multi-temporal data; remote sensing; early detection; machine learning BEETLE; REFLECTANCE; SPRUCE; FOREST; PLANT; SENESCENCE; STRESS; VECTOR Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75-0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring. [Yu, Run; Gao, Bingtao; Liu, Yujie; Yu, Linfeng; Li, Haonan; Yang, Liyuan; Ren, Lili; Luo, Youqing] Beijing Forestry Univ, Beijing Key Lab Forest Pest Control, Beijing, Peoples R China; [Huo, Langning] Swedish Univ Agr Sci, Dept Forest Resource Management, Umea, Sweden; [Huang, Huaguo] Beijing Forestry Univ, Res Ctr Forest Management Engn State Forestry, Grassland Adm, Beijing, Peoples R China; [Yuan, Yuan] French Natl Res Inst Agr Food & Environm INRAE, Zoologie Forestiere Ctr Rech Orleans, Orleans, France; [Ren, Lili; Luo, Youqing] Beijing Forestry Univ, French Natl Res Inst Agr Food & Environm INRAE, Sino French Joint Lab Invas Forest Pests Eurasia, Beijing, Peoples R China Beijing Forestry University; Swedish University of Agricultural Sciences; Beijing Forestry University; INRAE; Beijing Forestry University Luo, YQ (corresponding author), Beijing Forestry Univ, Beijing Key Lab Forest Pest Control, Beijing, Peoples R China.;Luo, YQ (corresponding author), Beijing Forestry Univ, French Natl Res Inst Agr Food & Environm INRAE, Sino French Joint Lab Invas Forest Pests Eurasia, Beijing, Peoples R China. youqingluo@126.com yu, linfeng/GZL-3328-2022 Huo, Langning/0000-0001-8432-8609 National Key Research & Development Program of China [2021YFD1400900]; Major emergency science and Technology Project of National Forestry and Grassland Administration [ZD202001] National Key Research & Development Program of China; Major emergency science and Technology Project of National Forestry and Grassland Administration This work was funded by the National Key Research & Development Program of China (2021YFD1400900), Major emergency science and Technology Project of National Forestry and Grassland Administration (ZD202001). 55 1 1 30 30 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-462X FRONT PLANT SCI Front. Plant Sci. OCT 13 2022.0 13.0 1000093 10.3389/fpls.2022.1000093 0.0 16 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences 5U3SO 36311089.0 gold, Green Published 2023-03-23 WOS:000876471100001 0
J Lei, YX; Karimi, HR; Chen, XF Lei, Yongxiang; Karimi, Hamid Reza; Chen, Xiaofang A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application NEUROCOMPUTING English Article PT prediction; LSTM; Aluminum electrolysis; Recurrent neural network; Self-supervised learning (SSL) This article studies the influence of pot temperature or electrolyte temperature in the aluminum reduction production. Specifically, these indexes reflect the distribution of the physical and energy field of the reduction cell, the current efficiency, and the lifespan of the aluminum reduction cell. Therefore, the pot temperature detection and identification are two critical and significant issues in the whole production process of aluminum electrolysis. However, due to the low measurement accuracy and high maintenance costs with the thermocouple sensor in the practical production process, the real-time measurement of pot temperature index is still a major challenge, which motivate us to develop a self-supervised soft sensor method based on deep long-short term memory (LSTM). Under the constraint of the limited samples, the proposed method achieves a competitive performance. First, the input variables are selected according to the expert experience. Then, a deep self-supervised model is built. Finally, the proposed self-supervised LSTM model is applied to real-time detection in an industrial electrolysis production case. The performance in the experiment outperforms other existing methods in terms of both accuracy and robustness aspects. (C) 2022 Elsevier B.V. All rights reserved. [Lei, Yongxiang; Chen, Xiaofang] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China; [Lei, Yongxiang; Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy Central South University; Polytechnic University of Milan Chen, XF (corresponding author), Cent South Univ, Sch Automat, Changsha 410083, Peoples R China.;Karimi, HR (corresponding author), Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy. hamidreza.karimi@polimi.it; xiaofangchen@csu.edu.cn Natural National Foundation of China [62133016]; China Scholarship Council (CSC) [202006370101]; Italian Ministry of Education through the Project Department of Excellence LIS4.0 Lightweight and Smart Structures for Industry 4.0 Natural National Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council (CSC)(China Scholarship Council); Italian Ministry of Education through the Project Department of Excellence LIS4.0 Lightweight and Smart Structures for Industry 4.0 This work is supported by the Natural National Foundation of China under Grant No. 62133016, the China Scholarship Council (CSC) No. 202006370101, and in part by the Italian Ministry of Education through the Project Department of Excellence LIS4.0 Lightweight and Smart Structures for Industry 4.0. The authors also would like to thank the SHANDONG WEIQIAO Co. in China for supporting the industrial data. 31 4 4 2 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 1 2022.0 502.0 177 185 10.1016/j.neucom.2022.06.080 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8X0WT 2023-03-23 WOS:000931742000016 0
J Nan, Y; Friederici, AD Nan, Yun; Friederici, Angela D. Differential roles of right temporal cortex and broca's area in pitch processing: Evidence from music and mandarin HUMAN BRAIN MAPPING English Article fMRI; musicians; tone CROSS-LINGUISTIC PET; CONGENITAL AMUSIA; CORTICAL ORGANIZATION; FUNCTIONAL MRI; NEURAL BASIS; HUMAN BRAIN; LANGUAGE; SPEECH; PERCEPTION; TONE Superior temporal and inferior frontal cortices are involved in the processing of pitch information in the domain of language and music. Here, we used fMRI to test the particular roles of these brain regions in the neural implementation of pitch in music and in tone language (Mandarin) with a group of Mandarin speaking musicians whose pertaining experiences in pitch are similar across domains. Our findings demonstrate that the neural network for pitch processing includes the pars triangularis of Broca's area and the right superior temporal gyrus (STG) across domains. Within this network, pitch sensitive activation in Broca's area is tightly linked to the behavioral performance of pitch congruity judgment, thereby reflecting controlled processes. Activation in the right STG is independent of performance and more sensitive to pitch congruity in music than in tone language, suggesting a domain-specific modulation of the perceptual processes. These observations provide a first glimpse at the cortical pitch processing network shared across domains. Hum Brain Mapp 34:2045-2054, 2013. (c) 2012 Wiley Periodicals, Inc. [Nan, Yun] Beijing Normal Univ, Natl Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China; [Friederici, Angela D.] MPI Human Cognit & Brain Sci, Dept Neuropsychol, Leipzig, Germany Beijing Normal University; Max Planck Society Nan, Y (corresponding author), Beijing Normal Univ, Natl Key Lab Cognit Neurosci & Learning, 19 Xin Wai St, Beijing 100875, Peoples R China. dr.yunnan@gmail.com National Natural Science Foundation of China [30700225] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Contract grant sponsor: National Natural Science Foundation of China; Contract grant number: 30700225. 54 30 32 3 41 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1065-9471 1097-0193 HUM BRAIN MAPP Hum. Brain Mapp. SEP 2013.0 34.0 9 2045 2054 10.1002/hbm.22046 0.0 10 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging 203UZ 22431306.0 Green Published 2023-03-23 WOS:000323320800005 0
J Zhang, XY; Li, Z; Su, CY; Chen, XK; Wang, JG; Xia, LL Zhang, Xiuyu; Li, Zhi; Su, Chun-Yi; Chen, Xinkai; Wang, Jianguo; Xia, Linlin Robust Adaptive Neural Control for a Class of Time-Varying Delay Systems with Backlash-like Hysteresis Input ASIAN JOURNAL OF CONTROL English Article Dynamic surface control; unknown time-varying delay; prescribed tracking error performance; backlash-like hysteresis DYNAMIC SURFACE CONTROL; UNCERTAIN NONLINEAR-SYSTEMS; UNKNOWN HYSTERESIS; STABILIZATION; ACTUATORS This paper proposes a robust adaptive dynamic surface control (DSC) scheme for a class of time-varying delay systems with backlash-like hysteresis input. The main features of the proposed DSC method are that 1) by using a transformation function, the prescribed transient performance of the tracking error can be guaranteed; 2) by estimating the norm of the unknown weighted vector of the neural network, the computational burden can be greatly reduced; 3) by using the DSC method, the explosion of complexity problem is eliminated. It is proved that the proposed scheme guarantees all the closed-loop signals being uniformly ultimately bounded. The simulation results show the validity of the proposed control scheme. [Zhang, Xiuyu; Wang, Jianguo; Xia, Linlin] Northeast Dianli Univ, Sch Automat Engn, Chuanying 132012, Jilin, Peoples R China; [Li, Zhi] Eindhoven Univ Technol, Dept Elect Engn, POB 513, NL-5600 MB Eindhoven, Netherlands; [Su, Chun-Yi] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada; [Chen, Xinkai] Shibaura Inst Technol, Dept Elect & Informat Syst, Minuma Ku, Saitama, Saitama 3378570, Japan Northeast Electric Power University; Eindhoven University of Technology; Concordia University - Canada; Shibaura Institute of Technology Zhang, XY (corresponding author), Northeast Dianli Univ, Sch Automat Engn, Chuanying 132012, Jilin, Peoples R China. zhangxiuyu80@163.com Li, Zhi/M-6440-2019; Li, Zhi/V-7904-2017; Su, Chun-Yi/AAF-6064-2019 Li, Zhi/0000-0001-9057-9623; Li, Zhi/0000-0001-9057-9623; Su, Chun-Yi/0000-0002-1869-5563; Chen, Xinkai/0000-0001-7381-9760 National Natural Science Foundation of China [61304015, U1201244, 61228301, 61411140039, 51176028]; China Postdoctoral Science Foundation [2013M540839]; Emerging Industries of Strategic Importance of Guangdong Province of China [2012A090100012]; Nature Science Foundation of Jilin Province [20140101059JC]; Outstanding Young Scholar Project of Jilin City [2013625002]; Twelfth Five Year Scientific Research Plan of Jilin Province [[2014]111]; Grants-in-Aid for Scientific Research [24560553, 15K06152] Funding Source: KAKEN National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Emerging Industries of Strategic Importance of Guangdong Province of China; Nature Science Foundation of Jilin Province; Outstanding Young Scholar Project of Jilin City; Twelfth Five Year Scientific Research Plan of Jilin Province; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported in part by the National Natural Science Foundation of China under Grants 61304015, U1201244, 61228301, 61411140039, and 51176028, in part by the China Postdoctoral Science Foundation under Grant 2013M540839, in part by the Emerging Industries of Strategic Importance of Guangdong Province of China under Grant 2012A090100012, in part by the Nature Science Foundation of Jilin Province under Grant 20140101059JC, in part by the Outstanding Young Scholar Project of Jilin City under Grant 2013625002, in part by the Twelfth Five Year Scientific Research Plan of Jilin Province under Grant [2014]111. 34 11 11 1 33 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1561-8625 1934-6093 ASIAN J CONTROL Asian J. Control MAY 2016.0 18.0 3 1087 1101 10.1002/asjc.1182 0.0 15 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems DM8KV Green Published 2023-03-23 WOS:000376612500025 0
J Ahmad, F; Tang, XW; Qiu, JN; Wroblewski, P; Ahmad, M; Jamil, I Ahmad, Feezan; Tang, Xiao-Wei; Qiu, Jiang-Nan; Wroblewski, Piotr; Ahmad, Mahmood; Jamil, Irfan Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation MATHEMATICAL BIOSCIENCES AND ENGINEERING English Article machine learning algorithm; Tree Augmented Naive-Bayes; sensitivity analysis; slope stability prediction SUPPORT VECTOR MACHINE; SOIL; NETWORK; SUSCEPTIBILITY; EMBANKMENTS; ALGORITHM Predicting slope stability is critical for identifying terrain that is prone to landslides and mitigating the damage caused by landslides. The relationships between factors that determine slope instability are complicated and multi-factorial, so it is sometimes difficult to mathematically characterize slope stability. In this paper, new Tree Augmented Naive-Bayes (TAN) model was developed to predict slope stability subjected to circular failures based on six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, unit weight, and slope angle. A total 87 slope stability case records obtained from published literature was used to train and test the proposed TAN model. According to the results of the performance indices-accuracy, precision, recall, F-score and Matthews correlation coefficient, the established TAN model was proven to be better at predicting slope stability with acceptable accuracy than other formerly developed empirical models in the literature. Furthermore, the slope height was revealed as the most sensitive factor in a sensitivity analysis. [Ahmad, Feezan; Tang, Xiao-Wei] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116024, Peoples R China; [Qiu, Jiang-Nan] Dalian Univ Technol, Fac Econ & Management, Dalian 116024, Peoples R China; [Wroblewski, Piotr] Univ Technol & Econ H Chodkowska Warsaw, Fac Engn, Jutrzenki 135, PL-02231 Warsaw, Poland; [Wroblewski, Piotr] Mil Univ Technol, Fac Mechatron Armament & Aerosp, Sylwestra Kaliskiego 2, PL-00908 Warsaw, Poland; [Ahmad, Mahmood] Int Islamic Univ Malaysia, Dept Civil Engn, Fac Engn, Jalan Gombak, Selangor 50728, Malaysia; [Ahmad, Mahmood] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Bannu 28100, Pakistan; [Jamil, Irfan] Univ Engn & Technol Peshawar, Dept Civil Engn, Peshawar 25120, Pakistan Dalian University of Technology; Dalian University of Technology; Military University of Technology in Warsaw; International Islamic University Malaysia; University of Peshawar Tang, XW (corresponding author), Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116024, Peoples R China.;Qiu, JN (corresponding author), Dalian Univ Technol, Fac Econ & Management, Dalian 116024, Peoples R China. tangxw@dlut.edu.cn; qiujn@dlut.edu.cn Wróblewski, Piotr/AAP-9921-2021; Ahmad, Mahmood/HHD-1034-2022 Wróblewski, Piotr/0000-0001-5811-8333; Ahmad, Mahmood/0000-0002-1139-1831 National Key Research and Development Plan of China [2021YFB2600703] National Key Research and Development Plan of China The work was supported by the National Key Research and Development Plan of China under Grant No. 2021YFB2600703. 64 5 5 27 40 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES 1547-1063 1551-0018 MATH BIOSCI ENG Math. Biosci. Eng. 2022.0 19.0 5 4526 4546 10.3934/mbe.2022209 0.0 21 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology ZP1ZC 35430826.0 gold 2023-03-23 WOS:000766223800003 0
C Shen, G; Yang, MQ; Zhang, BY Peruzzini, M; Pellicciari, M; Bil, C; Stjepandic, J; Wognum, N Shen, Gang; Yang, Mingqi; Zhang, Biyong Ballistocardiogram-Based Heart Rate Variation Monitoring Using Unsupervised Learning TRANSDISCIPLINARY ENGINEERING METHODS FOR SOCIAL INNOVATION OF INDUSTRY 4.0 Advances in Transdisciplinary Engineering English Proceedings Paper 25th ISPE Inc International Conference on Transdisciplinary Engineering JUL 03-06, 2018 Univ Modena & Reggio Emilia, Modena, ITALY ISPE Inc,Int Soc Transdisciplinary Engn,IOS Press,PROSTEP AG Univ Modena & Reggio Emilia ballistocardiographs; L1/L2 norm minimization; unsupervised learning Continuous heart rate variation monitoring is an important measure to diagnose and prevent the fatal cardiovascular diseases. Due to the recent development of the wearable sensory devices, ballistocardiographic (BCG) records can be collected over a long period of time without the restrictions to the patient's normal activities. However, the physical activities of the patient may severely interfere with the BCG sampling and thus leading to the degrading of the signal quality. In this paper, we introduce a novel approach to recovering the beat-to-beat heart interval estimates by applying an unsupervised machine learning framework consisting of the bag of words model and the n-gram model. First, we adopt L1 and L2 norm minimization based linear filters to reveal the global signal patterns. Then we learn the cluster centers of the signal segments containing local peaks with a Gaussian mixture approach. N-gram model is applied to detect the repetitive pattern of the cluster centers within the signals, the learned heartbeat model acts as a further filter by the convolution with the time-varying signals. Finally, auto-correlation based interval prediction is used with the help of a Gaussian prior. The proposed approach is tested using the data collected from a number of subjects with different ages and genders. We compare the resulting estimates with the blood pressure oximeter references, and the experiments show that the proposed algorithm is able to provide reliable and accurate estimates with the standard error for the interval estimate of 12.2ms. [Shen, Gang; Yang, Mingqi] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China; [Zhang, Biyong] Hangzhou Bobo Technol, Hangzhou, Zhejiang, Peoples R China; [Zhang, Biyong] Eindhoven Univ Technol, Eindhoven, Netherlands Huazhong University of Science & Technology; Eindhoven University of Technology Shen, G (corresponding author), Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China. gang_shen@hust.edu.cn 18 0 0 0 1 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 2352-7528 978-1-61499-898-3; 978-1-61499-897-6 ADV TRANSDISCIPL ENG 2018.0 7.0 320 329 10.3233/978-1-61499-898-3-320 0.0 10 Engineering, Industrial Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM7OM 2023-03-23 WOS:000468226300032 0
J Meng, QY; Zhao, MF; Zhang, LL; Shi, WX; Su, C; Bruzzone, L Meng, Qingyan; Zhao, Maofan; Zhang, Linlin; Shi, Wenxu; Su, Chen; Bruzzone, Lorenzo Multilayer Feature Fusion Network With Spatial Attention and Gated Mechanism for Remote Sensing Scene Classification IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Nonhomogeneous media; Image analysis; Logic gates; Residual neural networks; Convolution; Remote sensing; Fuses; Gated mechanism; multilayer feature fusion; remote sensing (RS); scene classification; spatial attention Remote sensing (RS) scene classification has attracted extensive attention due to its large number of applications. Recently, convolutional neural network (CNN) methods have shown impressive ability of feature learning in the RS scene classification. However, the performance is still limited by large-scale variance and complex background. To address these problems, we present a multilayer feature fusion network with spatial attention and gated mechanism (MLF2Net_SAGM) for RS scene classification. First, the backbone is employed to extract multilayer convolutional features. Then, a residual spatial attention module (RSAM) is proposed to enhance discriminative regions of the multilayer feature maps, and the key areas can be harvested. Finally, the multilayer spatial calibration features are fused to form the final feature map, and a gated fusion module (GFM) is designed to eliminate feature redundancy and mutual exclusion (FRME). To verify the effectiveness of the proposed method, we conduct comparative experiments based on three widely used RS image scene classification benchmarks. The results show that the direct fusion of multilayer features via element-wise addition leads to FRME, whereas our method fuses multilayer features more effectively and improves the performance of scene classification. [Meng, Qingyan; Zhao, Maofan; Zhang, Linlin; Shi, Wenxu; Su, Chen] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China; [Meng, Qingyan; Zhao, Maofan; Zhang, Linlin; Shi, Wenxu; Su, Chen] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Meng, Qingyan; Zhang, Linlin] Chinese Acad Sci, Aerosp Informat Res Inst, Hainan Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China; [Zhao, Maofan; Bruzzone, Lorenzo] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Trento Zhao, MF (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China. mengqy@radi.ac.cn; mfzhao1998@163.com; zhangl1@radi.ac.cn; shiwenxu20@mails.ucas.edu.cn; suchen21@mails.ucas.edu.cn; lorenzo.bruzzone@unitn.it ; Bruzzone, Lorenzo/A-2076-2012 Shi, Wenxu/0000-0001-6229-7995; Zhao, Maofan/0000-0001-6094-9128; Bruzzone, Lorenzo/0000-0002-6036-459X Hainan Provincial Department of Science and Technology [ZDKJ2019006]; National Natural Science Foundation of China [42171357]; Major Projects of High Resolution Earth Observation Systems of National Science and Technology [05-Y30B01-9001-19/20-1]; China Scholarship Council [202104910460] Hainan Provincial Department of Science and Technology; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Projects of High Resolution Earth Observation Systems of National Science and Technology; China Scholarship Council(China Scholarship Council) This work was supported in part by the Hainan Provincial Department of Science and Technology under Grant ZDKJ2019006, in part by the National Natural Science Foundation of China under Grant 42171357, in part by the Major Projects of High Resolution Earth Observation Systems of National Science and Technology under Grant 05-Y30B01-9001-19/20-1, and in part by the China Scholarship Council under Grant 202104910460. 24 2 2 7 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19.0 6510105 10.1109/LGRS.2022.3173473 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 1M7TI 2023-03-23 WOS:000800169600003 0
J Amani, M; Moghimi, A; Mirmazloumi, SM; Ranjgar, B; Ghorbanian, A; Ojaghi, S; Ebrahimy, H; Naboureh, A; Nazari, ME; Mahdavi, S; Moghaddam, SHA; Asiyabi, RM; Ahmadi, SA; Mehravar, S; Mohseni, F; Jin, SG Amani, Meisam; Moghimi, Armin; Mirmazloumi, S. Mohammad; Ranjgar, Babak; Ghorbanian, Arsalan; Ojaghi, Saeid; Ebrahimy, Hamid; Naboureh, Amin; Nazari, Mohsen Eslami; Mahdavi, Sahel; Moghaddam, Sayyed Hamed Alizadeh; Asiyabi, Reza Mohammadi; Ahmadi, Seyed Ali; Mehravar, Soroosh; Mohseni, Farzane; Jin, Shuanggen Ocean Remote Sensing Techniques and Applications: A Review (Part I) WATER English Review remote sensing; ocean; ocean wind; ocean current; ocean wave; sea level; ocean tide; ship detection SEA-LEVEL RISE; HIGH-FREQUENCY RADAR; SIGNIFICANT WAVE HEIGHT; SYNTHETIC-APERTURE RADAR; SHIP DETECTION; SURFACE CURRENTS; TIDAL FLATS; WIND-SPEED; SATELLITE ALTIMETRY; GPS SIGNALS Oceans cover over 70% of the Earth's surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study. [Amani, Meisam; Mahdavi, Sahel] WSP Environm & Infrastruct Canada Ltd, Ottawa, ON K2E 7L5, Canada; [Moghimi, Armin; Ranjgar, Babak; Ghorbanian, Arsalan; Ahmadi, Seyed Ali; Mohseni, Farzane] KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Dept Photogrammetry & Remote Sensing, Tehran, Iran; [Mirmazloumi, S. Mohammad] Ctr Tecnol Telecomun Catalunya, Geomat Res Unit, CERCA, Ave Gauss 7, E-08860 Castelldefels, Spain; [Ojaghi, Saeid] Univ Sherbrooke, Fac Letters & Human Sci, Dept Appl Geomat, Sherbrooke, PQ J1K 2R1, Canada; [Ebrahimy, Hamid] Shahid Beheshti Univ, Fac Earth Sci, Ctr Remote Sensing & GIS Res, Tehran 1983969411, Iran; [Naboureh, Amin] Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applict, Chengdu 610041, Peoples R China; [Nazari, Mohsen Eslami] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NF A1C 5S7, Canada; [Moghaddam, Sayyed Hamed Alizadeh] Queen's Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada; [Asiyabi, Reza Mohammadi] Univ POLITEHN Bucharest UPB, Res Ctr Spatial Informat, CEOSpaceTech, Bucharest 011061, Romania; [Mehravar, Soroosh] Univ Coll Engn, Univ Tehran, Dept Geomat, Tehran 1439957131, Iran; [Jin, Shuanggen] Henan Polytechn Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China; [Jin, Shuanggen] Chinese Acad Sci, Shanghai Astronom Observ, Shanghai 200030, Peoples R China K. N. Toosi University of Technology; Centre Tecnologic Telecomunicacions Catalunya (CTTC); University of Sherbrooke; Shahid Beheshti University; Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS; Memorial University Newfoundland; University of Tehran; Henan Polytechnic University; Chinese Academy of Sciences; Shanghai Astronomical Observatory, CAS Amani, M (corresponding author), WSP Environm & Infrastruct Canada Ltd, Ottawa, ON K2E 7L5, Canada. meisam.amani@wsp.com Jin, Shuanggen/B-8094-2008; Moghimi, Armin/AAF-8581-2021; Ghorbanian, Arsalan/AAH-5707-2019; Ahmadi, Seyed Ali/J-7885-2019; Amani, Meisam/X-4606-2018; Mirmazloumi, S. Mohammad/GRS-9320-2022 Jin, Shuanggen/0000-0002-5108-4828; Moghimi, Armin/0000-0002-0455-4882; Ghorbanian, Arsalan/0000-0001-8406-683X; Ahmadi, Seyed Ali/0000-0003-3920-2390; Amani, Meisam/0000-0002-9495-4010; Ranjgar, Babak/0000-0002-8324-4339; Naboureh, Amin/0000-0003-1214-2359; Mirmazloumi, S. Mohammad/0000-0001-5310-5859 346 2 2 28 28 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water NOV 2022.0 14.0 21 3400 10.3390/w14213400 0.0 51 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources 6C9FE Green Published, gold 2023-03-23 WOS:000882309600001 0
J Hu, F; Trivedi, RH Hu, Feng; Trivedi, Rohit H. Mapping hotel brand positioning and competitive landscapes by text-mining user-generated content INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT English Article Brand positioning; Competitive landscape; Repertory grid analysis; Text mining; User-generated content REPERTORY GRID TECHNIQUE; BIG DATA; CUSTOMER SATISFACTION; ONLINE REVIEWS; IMAGE; ANTECEDENTS; PERSPECTIVE; MAP This study uncovers hotel brand positioning and competitive landscape mapping by text-mining user-generated content (UGC). Rather than relying on a single dimension of consumer evaluation, the current study detects brand attributes by using both customer preferences as well as perceptual performance to develop meaningful insights. For this, the study combines content analysis and repertory grid analysis (RGA) to answer three key research issues. 111,986 hotel reviews from two biggest Chinese cities are used to explore and visualize the competitive landscape of six selected hotel brands across three hotel categories. Findings from the study will not only advance the existing literature on brand positioning and competitive landscape mapping but also help practitioners in developing brand positioning strategies to fight competitors within and across hotel categories. [Hu, Feng] Zhejiang Normal Univ, Xingzhi Coll, Jinhua 31004, Zhejiang, Peoples R China; [Hu, Feng] Univ Hamburg, Von Melle Pk 5, D-20146 Hamburg, Germany; [Trivedi, Rohit H.] Univ Bradford, Emm Lane, Bradford BD9 4JL, W Yorkshire, England Zhejiang Normal University; University of Hamburg; University of Bradford Trivedi, RH (corresponding author), Univ Bradford, Emm Lane, Bradford BD9 4JL, W Yorkshire, England. hufeng@zjnu.cn; R.Trivedil@bradford.ac.uk Trivedi, Rohit/0000-0001-9013-1458 84 31 31 15 89 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0278-4319 1873-4693 INT J HOSP MANAG Int. J. Hosp. Manag. JAN 2020.0 84.0 102317 10.1016/j.ijhm.2019.102317 0.0 13 Hospitality, Leisure, Sport & Tourism Social Science Citation Index (SSCI) Social Sciences - Other Topics JW8UX Green Accepted 2023-03-23 WOS:000503323100001 0
J Xiong, WX; Schindelhauer, C; So, HC; Bordoy, J; Gabbrielli, A; Liang, JL Xiong, Wenxin; Schindelhauer, Christian; So, Hing Cheung; Bordoy, Joan; Gabbrielli, Andrea; Liang, Junli TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization SIGNAL PROCESSING English Article Source localization; Non-line-of-sight; Time-difference-of-arrival; Robust model transformation; Neural network; Nonconvex optimization TOA-BASED LOCALIZATION; TARGET LOCALIZATION; NETWORKS This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at bypassing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the l(1)-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement. (C) 2020 Elsevier B.V. All rights reserved. [Xiong, Wenxin; Schindelhauer, Christian; Bordoy, Joan; Gabbrielli, Andrea] Univ Freiburg, Dept Comp Sci, D-79110 Freiburg, Germany; [So, Hing Cheung] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China; [Liang, Junli] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China University of Freiburg; City University of Hong Kong; Northwestern Polytechnical University Xiong, WX (corresponding author), Univ Freiburg, Dept Comp Sci, D-79110 Freiburg, Germany. xiongw@informatik.uni-freiburg.de; schindel@informatik.uni-freiburg.de; hcso@ee.cityu.edu.hk; bordoy@informatik.uni-freiburg.de; andre_gabe@hotmail.it; liangjunli@nwpu.edu.cn Bordoy, Joan/0000-0003-3575-6513; Gabbrielli, Andrea/0000-0002-3167-760X German Federal Ministry of Education and Research (BMBF) [FKZ:16SV8162] German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)) This work was supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the ASSIST ALL project under Grant FKZ:16SV8162. 50 13 14 2 20 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0165-1684 1872-7557 SIGNAL PROCESS Signal Process. JAN 2021.0 178.0 107774 10.1016/j.sigpro.2020.107774 0.0 10 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering OH2UR Green Submitted 2023-03-23 WOS:000582425200003 0
J Chen, X; Qiu, CP; Guo, WY; Yu, AZ; Tong, XC; Schmitt, M Chen, Xin; Qiu, Chunping; Guo, Wenyue; Yu, Anzhu; Tong, Xiaochong; Schmitt, Michael Multiscale Feature Learning by Transformer for Building Extraction From Satellite Images IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Feature extraction; Buildings; Transformers; Satellites; Semantics; Windows; Training; Attention; building extraction; satellite remote sensing; semantic segmentation; transformer SEGMENTATION Extracting buildings from very high-resolution satellite images is a challenging yet important task for applications such as urban monitoring. Multiscale feature learning proves to be a potential solution toward accurate extraction of buildings. This study exploits a powerful multiscale feature learning module, a hierarchical vision transformer by shifted windows (swin), as a backbone within a building extraction network. To this end, we first designed a general structure for building extraction, consisting of a backbone to extract multiscale features and a head network to fuse and refine features. Then, we integrated swin into the structure as a backbone and utilized channel-wise and spatial-wise enhancement in a head network. Experimental results show that our method achieves improvements regarding both F1-score and intersection over union (IoU) compared to the multiple attending path neural network (MAP-Net), which is the current state-of-the-art (SOTA) algorithm for building extraction from remote sensing images. Our study thus confirms the potential of swin transformers as backbones for semantic segmentation tasks based on satellite images. [Chen, Xin; Qiu, Chunping; Guo, Wenyue; Yu, Anzhu; Tong, Xiaochong] PLA Strateg Support Force Informat Engn Univ, Sch Surveying & Mapping, Zhengzhou 450001, Peoples R China; [Schmitt, Michael] Bundeswehr Univ Munich, Dept Aerosp Engn, D-85579 Neubiberg, Germany PLA Information Engineering University; Bundeswehr University Munich Qiu, CP (corresponding author), PLA Strateg Support Force Informat Engn Univ, Sch Surveying & Mapping, Zhengzhou 450001, Peoples R China. chunping.qiu@aliyun.com ; Schmitt, Michael/J-7326-2013 Chen, Xin/0000-0001-8332-9132; Schmitt, Michael/0000-0002-0575-2362 National Key Research and Development Program of China [2018YFB0505304]; Natural Science Foundation of Henan Province of China [212300410096]; National Natural Science Foundation of China [42101458, 41801388, 42130112] National Key Research and Development Program of China; Natural Science Foundation of Henan Province of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0505304; in part by the Natural Science Foundation of Henan Province of China under Grant 212300410096; and in part by the National Natural Science Foundation of China under Grant 42101458, Grant 41801388, and Grant 42130112. (Xin Chen and Chunping Qiu contributed equally to this work.) 24 17 17 67 114 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19.0 2503605 10.1109/LGRS.2022.3142279 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZB4CV 2023-03-23 WOS:000756792900009 0
J Lin, SB; Wang, YG; Zhou, DX Lin, Shao-Bo; Wang, Yu Guang; Zhou, Ding-Xuan DISTRIBUTED FILTERED HYPERINTERPOLATION FOR NOISY DATA ON THE SPHERE SIAM JOURNAL ON NUMERICAL ANALYSIS English Article distributed learning; filtered hyperinterpolation; noisy data; big data; sphere SCATTERED DATA INTERPOLATION; ZONAL FUNCTION NETWORKS; POLYNOMIAL-APPROXIMATION; QUADRATURE-FORMULAS; KERNELS; FIELDS; SPACES Problems in astrophysics, space weather research, and geophysics usually need to analyze big noisy data on the sphere. This paper develops distributed filtered hyperinterpolation for noisy data on the sphere, which assigns the data fitting task to multiple servers to find a good approximation of the mapping of input and output data. For each server, the approximation is a filtered hyperinterpolation on the sphere by a small proportion of quadrature nodes. The distributed strategy allows parallel computing for data processing and model selection. It reduces computational cost for each server while preserving the approximation capability compared to the filtered hyperinterpolation. We prove a quantitative relation between the approximation capability of distributed filtered hyperinterpolation and the numbers of input data and servers. Numerical examples show the efficiency and accuracy of the proposed method. [Lin, Shao-Bo] Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China; [Wang, Yu Guang] Univ New South Wales, Sch Math & Stat, Sydney, NSW, Australia; [Wang, Yu Guang] Max Planck Inst Math Sci, Inselstr 22, D-04103 Leipzig, Germany; [Zhou, Ding-Xuan] City Univ Hong Kong, Sch Data Sci, Tat Chee Ave, Hong Kong, Peoples R China; [Zhou, Ding-Xuan] City Univ Hong Kong, Dept Math, Tat Chee Ave, Hong Kong, Peoples R China Xi'an Jiaotong University; University of New South Wales Sydney; Max Planck Society; City University of Hong Kong; City University of Hong Kong Lin, SB (corresponding author), Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China. sblin1983@gmail.com; yuguang.wang@unsw.edu.au; mazhou@cityu.edu.hk ; Wang, Yu Guang/K-6558-2014 Lin, Shaobo/0000-0001-5122-9153; Wang, Yu Guang/0000-0002-7450-0273 National Natural Science Foundation of China [61876133]; Australian Research Council [DP180100506]; Research Grant Council of Hong Kong [CityU 11306318]; National Science Foundation [DMS-1439786] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Australian Research Council(Australian Research Council); Research Grant Council of Hong Kong(Hong Kong Research Grants Council); National Science Foundation(National Science Foundation (NSF)) The work of the first author was supported by National Natural Science Foundation of China grant 61876133. The work of the second author was supported by the Australian Research Council under Discovery Project DP180100506. The work of the third author was supported by Research Grant Council of Hong Kong project CityU 11306318. This material is based on work supported by National Science Foundation grant DMS-1439786 while the second author was in residence at the Institute for Computational and Experimental Research in Mathematics in Providence, Rhode Island, during the Point Configurations in Geometry, Physics, and Computer Science program. 55 1 1 0 3 SIAM PUBLICATIONS PHILADELPHIA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA 0036-1429 1095-7170 SIAM J NUMER ANAL SIAM J. Numer. Anal. 2021.0 59.0 2 634 659 10.1137/19M1281095 0.0 26 Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Mathematics RV7TE Green Submitted 2023-03-23 WOS:000646030400002 0
J Xu, X; Guan, ZG; Li, Z; Sulowicz, M; Krolczyk, G; Dai, TC; Zhao, XZ Xu, Xiang; Guan, Zhengguo; Li, Zhixiong; Sulowicz, Maciej; Krolczyk, Grzegorz; Dai, Tiancan; Zhao, Xinze Machine learning calculation model for hydrodynamic lubrication characteristics of a miter gate bottom pivot ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS English Article Miter gate; Bottom pivot; Hydrodynamic lubrication; Genetic algorithm BEARINGS; OPTIMIZATION; PREDICTION; DESIGN The bottom pivot is a vital support device in the miter gate but often subject to poor lubrication and wear failures. Calculating the hydrodynamic lubrication characteristics of the bottom pivot is a complex threedimensional (3D) problem, and most of existing models adopt simplified assumptions to reduce the calculation difficulty. To solve this issue, this work develops a 3D model to calculate the hydrodynamic lubrication characteristics of the miter gate bottom pivot. The finite difference method is used to solve the oil film thickness and pressure distribution based on the spherical coordinates Reynolds equation. The component forces in three directions are calculated from the pressure distribution and compared with the theoretical values to generate the calculation difference. Then, the genetic algorithm (GA) is used to minimize the difference to determine the optimal initial parameters for the 3D model. The analysis results show that the calculation accuracy can be significantly improved by using the optimal initial model parameters. When our initial pressure is 5.64MPa, the results meet the engineering accuracy requirements. [Xu, Xiang; Guan, Zhengguo; Dai, Tiancan; Zhao, Xinze] China Three Gorges Univ, Coll Mech & Power Engn, Yichang 443002, Peoples R China; [Xu, Xiang; Guan, Zhengguo; Dai, Tiancan; Zhao, Xinze] China Three Gorges Univ, Hubei Key Lab Hydroelect Machinery Design & Mainte, Yichang 443002, Peoples R China; [Li, Zhixiong; Krolczyk, Grzegorz] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Sulowicz, Maciej] Cracow Univ Technol, Dept Elect Engn, PL-31155 Krakow, Poland; [Zhao, Xinze] China Three Gorges Univ, Yichang, Peoples R China China Three Gorges University; China Three Gorges University; Opole University of Technology; Yonsei University; Cracow University of Technology; China Three Gorges University Li, Z (corresponding author), Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland.;Zhao, XZ (corresponding author), China Three Gorges Univ, Yichang, Peoples R China. zhixiong.li@yonsei.ac.kr; xzzhao@ctgu.edu.cn Sulowicz, Maciej/A-6120-2015; Krolczyk, Grzegorz/D-6709-2013; Li, Zhixiong/G-8418-2018 Sulowicz, Maciej/0000-0001-6436-1110; Krolczyk, Grzegorz/0000-0002-2967-1719; Li, Zhixiong/0000-0002-7265-0008 National Natural Science Foundation of China [52175177]; China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Open Fund [2019KJX07]; National Science Centre under Project [2020/37/K/ST8/02748] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Open Fund; National Science Centre under Project This research was funded by the National Natural Science Foundation of China (Grant NO. 52175177) , China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Open Fund (2019KJX07) . The research leading to these results has received funding from the Norway Grants 2014-2021 operated by National Science Centre under Project Contract No 2020/37/K/ST8/02748. 19 0 0 1 3 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0955-7997 1873-197X ENG ANAL BOUND ELEM Eng. Anal. Bound. Elem. SEP 2022.0 142.0 1 9 10.1016/j.enganabound.2022.05.024 0.0 JUN 2022 9 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics 2G0TU 2023-03-23 WOS:000813313700001 0
C Zhuang, YM; Li, SQ; Peters, TV; Zhang, CG IEEE Zhuang, Yimeng; Li, Shuqin; Peters, Tom Vincent; Zhang, Chenguang Improving Monte-Carlo Tree Search for Dots-and-Boxes with a novel board representation and artificial neural networks 2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND GAMES (CIG) IEEE Conference on Computational Intelligence and Games English Proceedings Paper IEEE Conference on Computational Intelligence and Games (CIG) AUG 31-SEP 02, 2015 Tainan, TAIWAN IEEE,IEEE Computat Intelligence Soc,Taiwanese Assoc Artificial Intelligence,Taiwan Fuzzy Syst Assoc,Taiwan Comp Game Assoc,IEEE CIS Tainan Chapter,Taiwanese Minist Sci & Technol,Tainan City Bur Educ,Taiwanese Minist Educ,Tunghai Univ, Kaohsiung City Bur Educ,Natl Chiao Tung Univ,Natl Univ Tainan,Chang Jung Christian Univ,National Dong Hwa Univ,National Cheng Kung Univ,Osaka Prefecture Univ Dots-and-Boxes; Monte-Carlo Tree Search; board representation; artificial neural network Dots-and-Boxes is a well-known paper-and-pencil game for two players. It reaches a high level of complexity posing an interesting challenge for AI development. Previous board representation techniques for Dots-and-Boxes rely on data structures like arrays or linked lists to facilitate operations on the board. These representation techniques usually lack for the ability to incrementally update information required for efficient move generation during search. To address this problem a novel board representation for Dots-and-Boxes is proposed in this paper. It utilizes game-specific knowledge to classify distinct conditions on the board and its implementation is based on disjoint-sets. Besides the novel board representation this paper treats optimizations for Monte-Carlo Tree Search (MCTS) focusing on artificial neural networks. Finally we implemented our proposed approach in a new program called QDab and conducted experiments showing that the new board representation improves the efficiency of basic operations on the board by more than 6 times. Further tests against other implementations show the superior playing strength of our approach. [Zhuang, Yimeng; Li, Shuqin; Zhang, Chenguang] Beijing Informat Sci & Technol Univ, Beijing, Peoples R China; [Peters, Tom Vincent] Univ Bremen, D-28359 Bremen, Germany Beijing Information Science & Technology University; University of Bremen Zhuang, YM (corresponding author), Beijing Informat Sci & Technol Univ, Beijing, Peoples R China. yimmon.zhuang@gmail.com; lishuqin2005@bistu.edu.cn; dots@vincent-peters.de; zhangchenguang0322@outlook.com 22 5 5 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2325-4270 978-1-4799-8622-4 IEEE CONF COMPU INTE 2015.0 314 321 8 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BE8HL 2023-03-23 WOS:000376490300037 0
J Zheng, K; Li, JS; Ding, L; Yang, JF; Zhang, XC; Zhang, X Zheng, Kai; Li, Jiansheng; Ding, Lei; Yang, Jianfeng; Zhang, Xucheng; Zhang, Xun Cloud and Snow Segmentation in Satellite Images Using an Encoder-Decoder Deep Convolutional Neural Networks ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article satellite image; semantic segmentation; encoder-decoder; CNN; TH-1; cloud and snow detection; label quality The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder-decoder architecture-ED-CNN-is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images. [Zheng, Kai; Li, Jiansheng; Yang, Jianfeng; Zhang, Xucheng; Zhang, Xun] PLA Strateg Support Force Informat Engn Univ, Inst Surveying & Mapping, Zhengzhou 450001, Peoples R China; [Ding, Lei] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy PLA Information Engineering University; University of Trento Li, JS (corresponding author), PLA Strateg Support Force Informat Engn Univ, Inst Surveying & Mapping, Zhengzhou 450001, Peoples R China. kaizheng90@foxmail.com; ljs2021@vip.henu.edu.cn; lei.ding@unitn.it; marsyjf@126.com; banditex1990@126.com; zx121777@163.com 23 4 4 1 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. JUL 2021.0 10.0 7 462 10.3390/ijgi10070462 0.0 14 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Physical Geography; Remote Sensing TO4GE gold 2023-03-23 WOS:000676871500001 0
J Hang, RL; Liu, QS; Hong, DF; Ghamisi, P Hang, Renlong; Liu, Qingshan; Hong, Danfeng; Ghamisi, Pedram Cascaded Recurrent Neural Networks for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Gated recurrent unit (GRU); hyperspectral image (HSI) classification; recurrent neural network (RNN); spectral feature; spectral-spatial feature COLLABORATIVE REPRESENTATION By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs) recently. However, most of these models only input the whole spectral bands into RNNs directly, which may not fully explore the specific properties of HSIs. In this paper, we propose a cascaded RNN model using gated recurrent units to explore the redundant and complementary information of HSIs. It mainly consists of two RNN layers. The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from nonadjacent spectral bands. To improve the discriminative ability of the learned features, we design two strategies for the proposed model. Besides, considering the rich spatial information contained in HSIs, we further extend the proposed model to its spectral- spatial counterpart by incorporating some convolutional layers. To test the effectiveness of our proposed models, we conduct experiments on two widely used HSIs. The experimental results show that our proposed models can achieve better results than the compared models. [Hang, Renlong; Liu, Qingshan] Nanjing Univ Informat Sci & Technol, Sch Automat, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Jiangsu, Peoples R China; [Hong, Danfeng] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany; [Hong, Danfeng] Tech Univ Munich, Signal Proc Earth Observat, D-80333 Munich, Germany; [Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany Nanjing University of Information Science & Technology; Helmholtz Association; German Aerospace Centre (DLR); Technical University of Munich; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Liu, QS (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Automat, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Jiangsu, Peoples R China. renlong_hang@163.com; qsliu@nuist.edu.cn; danfeng.hong@dlr.de; p.ghamisi@gmail.com Liu, Qingqing/HMV-4816-2023; Liu, Qing/GWC-9222-2022; Ghamisi, Pedram/ABD-5419-2021; liu, qingqing/HHD-0360-2022; Hong, Danfeng/U-6082-2019 Hong, Danfeng/0000-0002-3212-9584 Natural Science Foundation of China [61532009, 61825601]; Natural Science Foundation of Jiangsu Province, China [BK20180786] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province, China(Natural Science Foundation of Jiangsu Province) This work was supported in part by the Natural Science Foundation of China under Grant 61532009 and Grant 61825601 and in part by the Natural Science Foundation of Jiangsu Province, China, under Grant BK20180786. 50 248 252 15 104 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2019.0 57.0 8 5384 5394 10.1109/TGRS.2019.2899129 0.0 11 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology IK7XA Green Accepted, Green Submitted 2023-03-23 WOS:000476805800013 0
J Breve, F; Zhao, L; Quiles, M; Pedrycz, W; Liu, JM Breve, Fabricio; Zhao, Liang; Quiles, Marcos; Pedrycz, Witold; Liu, Jiming Particle Competition and Cooperation in Networks for Semi-Supervised Learning IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Semi-supervised learning; particles competition and cooperation; network-based methods; label propagation UNLABELED DATA; CLASSIFICATION Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a divide-and-conquer effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. [Breve, Fabricio; Zhao, Liang] Univ Sao Paulo, Dept Computat, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP, Brazil; [Breve, Fabricio] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Computat DEMAC, Inst Geosci & Exact Sci IGCE, BR-13506900 Rio Claro, SP, Brazil; [Quiles, Marcos] Fed Univ Sao Paulo Unifesp, Dept Sci & Technol DCT, BR-12231280 Sao Jose Dos Campos, SP, Brazil; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland; [Liu, Jiming] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China Universidade de Sao Paulo; Universidade Estadual Paulista; Universidade Federal de Sao Paulo (UNIFESP); University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; Hong Kong Baptist University Breve, F (corresponding author), Univ Sao Paulo, Dept Computat, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, Brazil. fabricio@icmc.usp.br; zhao@icmc.usp.br; quiles@unifesp.br; pedrycz@ee.ualberta.ca; jiming@comp.hkbu.edu.hk Zhao, Liang/D-5131-2011; Zhao, Liang/T-9147-2019; Quiles, Marcos Goncalves/L-6131-2018; Breve, Fabricio/C-6970-2012 Zhao, Liang/0000-0002-1502-6604; Quiles, Marcos Goncalves/0000-0001-8147-554X; Breve, Fabricio/0000-0002-1123-9784 State of Sao Paulo Research Foundation (FAPESP); Brazilian National Council of Technological and Scientific Development (CNPq) State of Sao Paulo Research Foundation (FAPESP)(Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)); Brazilian National Council of Technological and Scientific Development (CNPq)(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)) This work is supported by the State of Sao Paulo Research Foundation (FAPESP) and the Brazilian National Council of Technological and Scientific Development (CNPq). 55 47 49 2 11 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. SEP 2012.0 24.0 9 1686 1698 10.1109/TKDE.2011.119 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 976CJ 2023-03-23 WOS:000306557800011 0
J Li, YL; Cheng, ZA; Chen, XX; Long, YY; Li, XQ; Li, FY; Li, JF; Twiefel, J Li, Yanle; Cheng, Zinan; Chen, Xiaoxiao; Long, Yangyang; Li, Xiaoqiang; Li, Fangyi; Li, Jianfeng; Twiefel, Jens Constitutive modeling and deformation analysis for the ultrasonic-assisted incremental forming process INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Incremental sheet forming; Ultrasonic-assisted forming; Acoustic softening; Constitutive model; Finite element simulation ALUMINUM; VIBRATIONS; ACOUSTOPLASTICITY; SIMULATION; PARAMETERS; EVOLUTION; BEHAVIOR; SPIF The application of high-frequency vibration on the incremental forming process could cause changes in the plasticity of material which may contribute to the reduction of forming force, the increase of formability, and the improvement of surface finish. The present work aims to deepen the understanding of the softening effect to facilitate the accurate prediction of the ultrasonic-assisted forming process. First, a theoretical model describing the relationship between the stress and strain during the ultrasonic-assisted incremental sheet forming (UISF) was established based on the theory of crystal plasticity. In particular, the acoustic softening effect was reflected by adjusting the thermal activation process and the dislocation density evolution process. Then, the constitutive model parameters were identified through the back propagation (BP) neural network based on the experimental results. In addition, the developed model was used to simulate the UISF process by ANSYS/LS-DYNA software, and the effect of ultrasonic vibration on the deformation behavior was revealed. The results show that the FE model with the modified constitutive model considering the softening effect can improve the prediction accuracy. [Li, Yanle; Cheng, Zinan; Chen, Xiaoxiao; Li, Fangyi; Li, Jianfeng] Shandong Univ, Key Lab High Efficiency & Clean Mech Mfg, Minist Educ, Sch Mech Engn, Jinan 250061, Shandong, Peoples R China; [Li, Yanle; Cheng, Zinan; Chen, Xiaoxiao; Li, Fangyi; Li, Jianfeng] Shandong Univ, Natl Demonstrat Ctr Expt Mech Engn Educ, Jinan 250061, Shandong, Peoples R China; [Long, Yangyang; Twiefel, Jens] Leibniz Univ Hannover, Inst Dynam & Vibrat Res, Appelstr 11, D-30167 Hannover, Germany; [Li, Xiaoqiang] Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China Shandong University; Shandong University; Leibniz University Hannover; Beihang University Li, XQ (corresponding author), Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China. lixiaoqiang@buaa.edu.cn Chen, xiaoxiao/GRS-0925-2022 National Natural Science Foundation of China [51605258]; Postdoctoral Innovation Project of Shandong Province [201701011]; Young Scholars Program of Shandong University [2018WLJH55]; State Key Laboratory of High Performance Complex Manufacturing, Central South University [Kfkt2017-04] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Postdoctoral Innovation Project of Shandong Province; Young Scholars Program of Shandong University; State Key Laboratory of High Performance Complex Manufacturing, Central South University This work is financially supported by National Natural Science Foundation of China (51605258), Postdoctoral Innovation Project of Shandong Province (201701011), Young Scholars Program of Shandong University (2018WLJH55), and State Key Laboratory of High Performance Complex Manufacturing, Central South University (Kfkt2017-04). 30 8 10 7 49 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. OCT 2019.0 104.0 5-8 2287 2299 10.1007/s00170-019-04031-3 0.0 13 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering JE0GN 2023-03-23 WOS:000490367800039 0
C Jin, Z; Luo, HL; Luo, L; Zou, WB; Li, X; Steinbach, E IEEE Jin, Zhi; Luo, Haili; Luo, Lei; Zou, Wenbin; Li, Xia; Steinbach, Eckehard Information Fusion based Quality Enhancement for 3D Stereo Images Using CNN 2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) European Signal Processing Conference English Proceedings Paper European Signal Processing Conference (EUSIPCO) SEP 03-07, 2018 Rome, ITALY European Assoc Signal Processing,IEEE Signal Processing Soc,ROMA TRE Univ Degli Studi,MathWorks,Amazon Devices COMPRESSION Stereo images provide users with a vivid 3D watching experience. Supported by per-view depth maps, 3D stereo images can be used to generate any required intermediate view between the given left and right stereo views. However, 3D stereo images lead to higher transmission and storage cost compared to single view images. Based on the binocular suppression theory, mixed-quality stereo images can alleviate this problem by employing different compression ratios on the two views. This causes noticeable visual artifacts when a high compression ratio is adopted and limits free-viewpoint applications. Hence, the low quality image at the receiver side needs to be enhanced to match the high quality one. To address this problem, in this paper we propose an end-to-end fully Convolutional Neural Network (CNN) for enhancing the low quality images in quality asymmetric stereo images by exploiting inter-view correlation. The proposed network achieves an image quality boost of up to 4.6dB and 3.88dB PSNR gain over ordinary JPEG for QF10 and 20, respectively, and an improvement of up to 2.37dB and 2.05dB over the state-of-the-art CNN-based results for QF10 and 20, respectively. [Jin, Zhi; Luo, Haili; Zou, Wenbin; Li, Xia] Shenzhen Univ, Coll Informat Engn, Shenzhen, Peoples R China; [Luo, Lei] Chongqing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Chongqing, Peoples R China; [Jin, Zhi; Steinbach, Eckehard] Tech Univ Munich, Chair Media Technol, Munich, Germany Shenzhen University; Chongqing University of Posts & Telecommunications; Technical University of Munich Zou, WB (corresponding author), Shenzhen Univ, Coll Informat Engn, Shenzhen, Peoples R China. jinzhi_126@163.com; wzouszu@sina.com National Natural Science Foundation of China (NSFC) [61771321, 61701313, 6171101606, 61472257]; Natural Science Foundation of Shenzhen [KQJSCX20170327151357330, JCYJ20170818091621856, JCYJ20170302145906843]; TUM University Foundation Fellowship (TUFF); China Postdoctoral Science Foundation [2017M622778] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shenzhen; TUM University Foundation Fellowship (TUFF); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported by the National Natural Science Foundation of China (NSFC) (61771321, 61701313, 6171101606, 61472257); Natural Science Foundation of Shenzhen (KQJSCX20170327151357330, JCYJ20170818091621856, JCYJ20170302145906843); TUM University Foundation Fellowship (TUFF) and China Postdoctoral Science Foundation Grants (2017M622778). 15 1 1 0 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 2076-1465 978-90-827970-1-5 EUR SIGNAL PR CONF 2018.0 1447 1451 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BL7TU 2023-03-23 WOS:000455614900291 0
J Liu, D; Ye, M; Zhang, JW Liu, Dan; Ye, Mao; Zhang, Jianwei Improving Action Recognition Using Sequence Prediction Learning INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE English Article Action recognition; sequence prediction learning; action predictor ENSEMBLE Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predictors can then output the action trends at the next time step. According to the predictions of these action predictors at each time step and the removal rule, the poor predictors can be eliminated step by step, and the IDentity(ID) number of the last predictor left is considered as the label of the action sequence to be categorized. We compare the proposed action recognition algorithm using sequence prediction learning with other methods on two publicly available datasets. Our experimental results consistently demonstrate the feasibility and effectiveness of the suggested method. It also proves the importance of prediction learning for action recognition. [Liu, Dan; Ye, Mao] Univ Elect Sci & Technol China, Key Lab NeuroInformat, Minist Educ, Sch Comp Sci & Engn,Ctr Robot, Chengdu 611731, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Dept Informat, Inst Tech Aspects Multimodal Syst TAMS, D-22527 Hamburg, Germany University of Electronic Science & Technology of China; University of Hamburg Ye, M (corresponding author), Univ Elect Sci & Technol China, Key Lab NeuroInformat, Minist Educ, Sch Comp Sci & Engn,Ctr Robot, Chengdu 611731, Peoples R China. liudankaicy5@gmail.com; cvlab.uestc@gmail.com; zhang@informatik.uni-hamburg.de zhang, jian/HPD-1712-2023 Ye, Mao/0000-0003-4760-8702 National Key R&D Program of China [2018YFC0831800]; National Natural Science Foundation of China [61773093]; Important Science and Technology Innovation Projects in Chengdu [2018-YF0800039-GX]; Research Programs of Sichuan Science and Technology Department [17ZDYF3184]; German Science Foundation (DFG); National Science Foundation of China (NSFC) [Sonderforschungsbereich Transregio 169] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Important Science and Technology Innovation Projects in Chengdu; Research Programs of Sichuan Science and Technology Department; German Science Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China (2018YFC0831800), National Natural Science Foundation of China (61773093), Important Science and Technology Innovation Projects in Chengdu (2018-YF0800039-GX), Research Programs of Sichuan Science and Technology Department (17ZDYF3184) and the German Science Foundation (DFG) and National Science Foundation of China (NSFC) in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169. 43 0 0 0 20 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0218-0014 1793-6381 INT J PATTERN RECOGN Int. J. Pattern Recognit. Artif. Intell. NOV 2020.0 34.0 12 2050029 10.1142/S0218001420500299 0.0 18 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science PH0SV 2023-03-23 WOS:000600134700007 0
C Li, Y; Zeng, XY; Gao, ZQ; Lin, LY; Tao, J; Han, J; Cheng, X; Tahoori, M; Zeng, XY IEEE Li, Yan; Zeng, Xiaoyoung; Gao, Zhengqi; Lin, Liyu; Tao, Jun; Han, Jun; Cheng, Xu; Tahoori, Mehdi; Zeng, Xiaoyang Exploring a Bayesian Optimization Framework Compatible with Digital Standard Flow for Soft-Error-Tolerant Circuit PROCEEDINGS OF THE 2020 57TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC) Design Automation Conference DAC English Proceedings Paper 57th ACM/IEEE Design Automation Conference (DAC) JUL 20-24, 2020 ELECTR NETWORK IEEE,ACM DESIGN Soft error is a major reliability concern in advanced technology nodes. Although mitigating Soft Error Rate (SER) will inevitably sacrifice area and power, few studies paid attention to optimization methods to explore trade-offs between area, power and SER. This paper proposes an optimization framework based on Bayesian approach for soft-error-tolerant circuit design. It comprises two steps:1) data preprocessing and 2) Bayesian optimization. In the preprocessing step, a strategy incorporating k-means algorithm and a novel sequencing algorithm is used to cluster Flip-Flops (FFs) with similar SER in order to reduce the dimensionality for the subsequent step. Bayesian Neural Network (BNN) is the applied surrogate model for acquiring the posterior distribution of three design metrics, while the Lower confidence bound (LCB) functions are employed as acquisition functions to select the next point based on BNN when optimizing. Finally, the non-dominated sorting genetic algorithm (NSGA-II) is used to search the Pareto Optimal Front (POF) solutions of three LCB functions. Experimental results demonstrate the proposed framework has a 1.4x improvement in accuracy and a 70% reduction in SER with acceptable increases in power and area. [Li, Yan; Zeng, Xiaoyoung; Gao, Zhengqi; Lin, Liyu; Tao, Jun; Han, Jun; Cheng, Xu; Zeng, Xiaoyang] Fudan Univ, Sch Microelect, State Key Lab ASIC & Syst, Shanghai, Peoples R China; [Li, Yan; Tahoori, Mehdi] Karlsruhe Inst Technol, Chair Dependable Nano Comp, Karlsruhe, Germany Fudan University; Helmholtz Association; Karlsruhe Institute of Technology Tao, J; Han, J; Zeng, XY (corresponding author), Fudan Univ, Sch Microelect, State Key Lab ASIC & Syst, Shanghai, Peoples R China.;Tahoori, M (corresponding author), Karlsruhe Inst Technol, Chair Dependable Nano Comp, Karlsruhe, Germany. mehdi.tahoori@kit.edu; xyzeng@fudan.edu.cn Li, Yan/AAC-7041-2021 Li, Yan/0000-0002-8918-7320 National Natural Science Foundation of China (NSFC) [61525401, 11690045, 61874032]; State Key Laboratory of Intense Pulsed Radiation Simulation [SKLIPR1614]; State Key Lab. of ASIC and System [2018MS005]; China Scholarship Council National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Intense Pulsed Radiation Simulation; State Key Lab. of ASIC and System; China Scholarship Council(China Scholarship Council) This work was supported partly by National Natural Science Foundation of China (NSFC) research project 61525401, 11690045 and 61874032, partly by the research project SKLIPR1614 from the State Key Laboratory of Intense Pulsed Radiation Simulation, partly by the research project 2018MS005 from the State Key Lab. of ASIC and System and partly by China Scholarship Council. 20 2 2 3 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0738-100X 978-1-7281-1085-1 DES AUT CON 2020.0 6 Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BR0CR 2023-03-23 WOS:000628528400201 0
J Shen, LS; Tian, Y; Zhang, LG; Yin, GS; Shuai, T; Liang, S; Wu, ZF Shen, Linshan; Tian, Ye; Zhang, Liguo; Yin, Guisheng; Shuai, Tong; Liang, Shuo; Wu, Zhuofei Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition CMC-COMPUTERS MATERIALS & CONTINUA English Article Semi-supervised learning; SAR target recognition; threshold filtering; out-of-class data The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data, especially in the field of Synthetic Aperture Radar (SAR) image recognition. In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model, this paper proposes a semi-supervised learning method of threshold filtering. In the training process, through the two selections of data by the model, unlabeled data outside the category is filtered out to optimize the performance of the model. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and compared with existing several state-of-the-art semi-supervised classification approaches, the superiority of our method was confirmed, especially when the unlabeled data contained a large amount of out-of-category data. [Shen, Linshan; Tian, Ye; Zhang, Liguo; Yin, Guisheng] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China; [Zhang, Liguo] Xidian Univ, Xian 710000, Peoples R China; [Shuai, Tong; Liang, Shuo] CETC, Res Inst 54, Shijiazhuang 050000, Hebei, Peoples R China; [Wu, Zhuofei] Univ Bologna, I-40100 Bologna, Italy Harbin Engineering University; Xidian University; China Electronics Technology Group; University of Bologna Tian, Y (corresponding author), Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China. tianye168@hrbeu.edu.cn 吴, 卓霏/GXN-0954-2022 National Key R&D Program of China [2021YFC3320302]; Fundamental Research [JCKY2020210B019]; Natural Science Foundation of Heilongjiang Province [F2018006]; Network threat depth analysis software [KY10800210013] National Key R&D Program of China; Fundamental Research; Natural Science Foundation of Heilongjiang Province(Natural Science Foundation of Heilongjiang Province); Network threat depth analysis software Our research is funded by National Key R&D Program of China (2021YFC3320302), Fundamental Research (JCKY2020210B019), Natural Science Foundation of Heilongjiang Province (No. F2018006), and Network threat depth analysis software (KY10800210013). 22 0 0 2 6 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1546-2218 1546-2226 CMC-COMPUT MATER CON CMC-Comput. Mat. Contin. 2022.0 73.0 1 465 476 10.32604/cmc.2022.027488 0.0 12 Computer Science, Information Systems; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Materials Science 1W4CG gold 2023-03-23 WOS:000806722000013 0
J Li, Z; Lu, TD; He, XX; Montillet, JP; Tao, R Li, Zhen; Lu, Tieding; He, Xiaoxing; Montillet, Jean-Philippe; Tao, Rui An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series ADVANCES IN SPACE RESEARCH English Article XGBoost; LSTM; GNSS; Time series; Forecasting DEFORMATION The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNNLSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %similar to 52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %similar to 54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series. (c) 2022 Published by Elsevier B.V. on behalf of COSPAR. [Li, Zhen; Lu, Tieding; Tao, Rui] East China Univ Technol, Sch Geodesy & Geomat, Nanchang, Peoples R China; [Lu, Tieding] Minist Nat Resources, Key Lab Mine Environm Monitoring & Improving Poyan, 418 Guanglan Rd, Nanchang 330013, Peoples R China; [He, Xiaoxing] Jiangxi Univ Sci & Technol, Sch Civil & Surveying & Mapping Engn, Ganzhou, Peoples R China; [Montillet, Jean-Philippe] Phys Meteorol Observ Davos World Radiat Ctr PMOD W, Davos, Switzerland; [Montillet, Jean-Philippe] Univ Beira Interior, Space & Earth Geodet Anal Lab SEGAL, Covilha, Portugal East China University of Technology; Ministry of Natural Resources of the People's Republic of China; Jiangxi University of Science & Technology; Universidade da Beira Interior Lu, TD (corresponding author), East China Univ Technol, Sch Geodesy & Geomat, Nanchang, Peoples R China.;Lu, TD (corresponding author), Minist Nat Resources, Key Lab Mine Environm Monitoring & Improving Poyan, 418 Guanglan Rd, Nanchang 330013, Peoples R China. tdlu@whu.edu National Natural Science Foundation China; Jiangxi University of Science and Technology high-level talent research startup project; [42061077]; [42064001]; [42104023]; [205200100564] National Natural Science Foundation China(National Natural Science Foundation of China (NSFC)); Jiangxi University of Science and Technology high-level talent research startup project; ; ; ; Acknowledgement National Natural Science Foundation China (42061077; 42064001; 42104023) , Jiangxi University of Science and Technology high-level talent research startup project (205200100564) . 34 0 0 2 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. JAN 1 2023.0 71.0 1 912 935 10.1016/j.asr.2022.08.038 0.0 DEC 2022 24 Engineering, Aerospace; Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences 7L4IK Bronze 2023-03-23 WOS:000905931400001 0
J Yang, X; Li, HM; Wang, Y; Liang, XW; Chen, CY; Zhou, X; Zeng, FY; Fang, JH; Frangi, A; Chen, ZY; Ni, D Yang, Xin; Li, Haoming; Wang, Yi; Liang, Xiaowen; Chen, Chaoyu; Zhou, Xu; Zeng, Fengyi; Fang, Jinghui; Frangi, Alejandro; Chen, Zhiyi; Ni, Dong Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound MEDICAL IMAGE ANALYSIS English Article Ultrasound; Segmentation; Ambiguity; Contrastive learning; Semi-supervised learning IMAGES Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms stateof-the-art methods in various evaluation metrics for both the ovary and follicles. (c) 2021 Elsevier B.V. All rights reserved. [Yang, Xin; Li, Haoming; Wang, Yi; Chen, Chaoyu; Zhou, Xu; Frangi, Alejandro; Ni, Dong] Shenzhen Univ, Hlth Ctr, Sch Biomed Engn, Shenzhen, Guangdong, Peoples R China; [Yang, Xin; Li, Haoming; Wang, Yi; Chen, Chaoyu; Zhou, Xu; Ni, Dong] Shenzhen Univ, Med UltraSound Image Comp MUSIC Lab, Shenzhen, Guangdong, Peoples R China; [Chen, Zhiyi] Univ South China, Inst Med Imaging, Hengyang, Hunan, Peoples R China; [Liang, Xiaowen; Zeng, Fengyi; Fang, Jinghui; Chen, Zhiyi] Guangzhou Med Univ, Dept Ultrasound Med, Affiliated Hosp 3, Guangzhou, Peoples R China; [Frangi, Alejandro] Univ Leeds, Ctr Computat Imaging & Simulat Technol Biomed CIS, Sch Comp, Leeds, W Yorkshire, England; [Frangi, Alejandro] Katholieke Univ Leuven, Med Imaging Res Ctr MIRC, Elect Engn Dept, Univ Hosp Gasthuisberg, Leuven, Belgium Shenzhen University; Shenzhen University; University of South China; Guangzhou Medical University; University of Leeds; KU Leuven; University Hospital Leuven Ni, D (corresponding author), Shenzhen Univ, Hlth Ctr, Sch Biomed Engn, Shenzhen, Guangdong, Peoples R China.;Ni, D (corresponding author), Shenzhen Univ, Med UltraSound Image Comp MUSIC Lab, Shenzhen, Guangdong, Peoples R China. zhiyi_chen@usc.edu.cn; nidong@szu.edu.cn Li, Hao/GPS-9834-2022; Frangi, Alejandro F/C-6500-2008 Frangi, Alejandro F/0000-0002-2675-528X National Key R&D Program of China [2019YFC0118300]; Shenzhen Peacock Plan [KQTD2016053112051497, KQJSCX20180328095606003] National Key R&D Program of China; Shenzhen Peacock Plan This work was supported by the grant from National Key R&D Program of China (No. 2019YFC0118300) , Shenzhen Peacock Plan (No. KQTD2016053112051497, KQJSCX20180328095606003) . 34 4 5 8 32 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 1361-8415 1361-8423 MED IMAGE ANAL Med. Image Anal. OCT 2021.0 73.0 102134 10.1016/j.media.2021.102134 0.0 JUL 2021 10 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging UY1AG 34246847.0 2023-03-23 WOS:000701263600003 0
J Sun, Z; Siringoringo, DM; Chen, SZ; Lu, J Sun, Zhen; Siringoringo, Dionysius M.; Chen, Shi-zhi; Lu, Jun Cumulative displacement-based detection of damper malfunction in bridges using data-driven isolation forest algorithm ENGINEERING FAILURE ANALYSIS English Article Cumulative displacement; Viscous damper; Isolation forest; Anomaly detection; Temperature variation LONG-TERM DISPLACEMENT; EXPANSION JOINTS; IDENTIFICATION Long-span cable-supported bridges are flexible and prone to significant deformations under temperature, wind, and vehicle loads. Such large deformations induce frequent longitudinal movement at the end of the bridge girder, which can cause malfunction or failure in the components such as expansion joints, bearings, or longitudinal viscous dampers. This paper develops a condition assessment approach using the girder end displacement measurements, which primarily includes four steps, namely, data preprocessing, feature characterization, datasets preparation and splitting, and anomaly detection. The data preprocessing step considers the physics-based correlation between the temperature and displacement, which is used to pre-check noisy data issues in the displacement monitoring data. Subsequently, hourly cumulative displacement is calculated as the feature to feed into the machine learning model. The third step prepares the datasets and splits them into training and testing datasets. Then the isolation forest algorithm is implemented for anomaly detection with well-tuned parameters of the number of decision trees and the number of samples in each decision tree. Structural health monitoring data of a suspension bridge are collected to verify the proposed approach, and the detection targets are specified as viscous damper malfunction and a specific holiday period. The proposed approach demonstrates its ability to detect both events successfully, which can shed light on the predictive and preventive maintenance of the bridge. [Sun, Zhen] Univ Porto, Fac Engn FEUP, Construct ViBest, R Dr Roberto Frias, P-4200465 Porto, Portugal; [Siringoringo, Dionysius M.] Yokohama Natl Univ, Inst Adv Sci, Yokohama, Kanagawa, Japan; [Chen, Shi-zhi] Changan Univ, Sch Highway, Xian 710064, Shaanxi, Peoples R China; [Lu, Jun] Jiangsu Transportat Inst, State Key Lab Safety & Hlth Serv Long Span Bridge, Nanjing 211112, Peoples R China Universidade do Porto; Yokohama National University; Chang'an University Chen, SZ (corresponding author), Changan Univ, Sch Highway, Xian 710064, Shaanxi, Peoples R China. szchen@chd.edu.cn Siringoringo, Dionysius M./0000-0003-2267-0673; Sun, Zhen/0000-0002-2053-4902 FCT Project SAFESUSPENSE [POCI-01-0145-FEDER-031054]; JSPS [18K04320]; Taisei Foundation for the application of Machine Learning FCT Project SAFESUSPENSE; JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Taisei Foundation for the application of Machine Learning The authors acknowledge financial support from the FCT Project SAFESUSPENSE (reference POCI-01-0145-FEDER-031054). The second author acknowledges the support from JSPS Grant-in-Aid Kakenhi C No. 18K04320 and Taisei Foundation for the application of Machine Learning in this research. The kind support from the case-study bridge is also acknowledged. The opinions in this paper are of the authors and do not necessarily reflect that of the bridge operator. 52 0 0 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1350-6307 1873-1961 ENG FAIL ANAL Eng. Fail. Anal. JAN 2023.0 143.0 B 106849 10.1016/j.engfailanal.2022.106849 0.0 15 Engineering, Mechanical; Materials Science, Characterization & Testing Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science 8O4CD 2023-03-23 WOS:000925782600001 0
J Cajo, R; Zhao, SQ; Birs, I; Espinoza, V; Fernandez, E; Plaza, D; Salcan-Reyes, G Cajo, Ricardo; Zhao, Shiquan; Birs, Isabela; Espinoza, Victor; Fernandez, Edson; Plaza, Douglas; Salcan-Reyes, Gabriela An Advanced Fractional Order Method for Temperature Control FRACTAL AND FRACTIONAL English Article EnergyPlus; MLE plus; building simulation; energy-efficient building; model predictive control; fractional order cost function MODEL-PREDICTIVE CONTROL; NEURAL-NETWORK; BIFURCATIONS; SYSTEM Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios. [Cajo, Ricardo; Plaza, Douglas; Salcan-Reyes, Gabriela] Escuela Super Politecn Litoral ESPOL, Fac Ingn Electr & Comp, Campus Gustavo Galindo Km 30-5 Via Perimetral,POB, Guayaquil 090150, Ecuador; [Zhao, Shiquan] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China; [Birs, Isabela] Tech Univ Cluj Napoca, Automat Dept, Cluj Napoca 400114, Romania; [Birs, Isabela] Univ Ghent, DySC Res Grp, B-9052 Ghent, Belgium; [Espinoza, Victor] Escuela Super Politecn Litoral ESPOL, Fac Ingn Mecan & Ciencias Prod, Campus Gustavo Galindo Km 30-5 Via Perimetral,POB, Guayaquil 090150, Ecuador; [Fernandez, Edson] Univ Appl Sci & Arts Southern Switzerland, Dept Innovat Technol, Via Serafino Balestra 16, CH-6900 Lugano, Switzerland Escuela Superior Politecnica del Litoral; Harbin Engineering University; Technical University of Cluj Napoca; Ghent University; Escuela Superior Politecnica del Litoral Cajo, R (corresponding author), Escuela Super Politecn Litoral ESPOL, Fac Ingn Electr & Comp, Campus Gustavo Galindo Km 30-5 Via Perimetral,POB, Guayaquil 090150, Ecuador. rcajo@espol.edu.ec ESPOL University; Romanian Ministry of Education and Research, CNCS-UEFISCDI [PN-III-P1-1.1-PD-2021-0204] ESPOL University; Romanian Ministry of Education and Research, CNCS-UEFISCDI(Consiliul National al Cercetarii Stiintifice (CNCS)Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI)) This research was funded by ESPOL University under project nr. FIEC-10-2021. This research was also supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2021-0204. Gabriela Salcan-Reyes acknowledges the financial support from ESPOL University. 51 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2504-3110 FRACTAL FRACT Fractal Fract. FEB 2023.0 7.0 2 172 10.3390/fractalfract7020172 0.0 13 Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Mathematics 9H0YG gold 2023-03-23 WOS:000938566600001 0
J Deng, J; Yang, M; Ma, RJ; Zhu, XL; Fan, JH; Yuan, GF; Wang, ZF Deng, Jie; Yang, Ming; Ma, Rongjiang; Zhu, Xiaolin; Fan, Jianhua; Yuan, Guofeng; Wang, Zhifeng Validation of a simple dynamic thermal performance characterization model based on the piston flow concept for flat-plate solar collectors SOLAR ENERGY English Article Flat-plate solar collector; Dynamic thermal performance model; Piston flow concept; Ordinary differential equation (ODE) NEURAL-NETWORK; REGRESSION A simple dynamic characterization model of flat-plate solar collectors based on the piston flow concept is used both to identify the collector characteristic parameters and to predict the dynamic thermal performance. The heat transport time originally defined as (1 e(-1))(-1) tau(C) by Amrizal et al. (2012) for the model turns out to be the collector static response time constant tau(C) by analytical derivation. The nonlinear least squares method is applied to determine the characteristic parameters of a flat-plate solar air collector previously tested by the authors. Then the obtained parameters are used to predict the dynamic behavior of the collector outlet temperature. The model coefficients particularly C-3 in the simple dynamic characterization model are examined by the collector dynamic prediction under variable meteorological conditions. Meanwhile, the prediction accuracy of the simple dynamic model based on the first-order difference method is compared to that of the numerical solution of the collector ordinary differential equation (ODE) model using the fourth-order Runge-Kutta method. The improved thermal inertia model (TIM) on the basis of closed-form solution presented by Deng et al. (2016a) is also considered. The results show that the prediction performance of the simple dynamic model is nearly as accurate as the ODE numerical solution and the TIM by Deng et al. (2016a) except some special conditions such as sharply changed solar irradiance and collector inlet temperature. (C) 2016 Elsevier Ltd. All rights reserved. [Deng, Jie; Yang, Ming; Zhu, Xiaolin; Yuan, Guofeng; Wang, Zhifeng] Chinese Acad Sci, Inst Elect Engn, Key Lab Solar Thermal Energy & Photovolta Syst, Beijing 100190, Peoples R China; [Deng, Jie; Fan, Jianhua; Yuan, Guofeng] Tech Univ Denmark, Dept Civil Engn, DK-2800 Lyngby, Denmark; [Ma, Rongjiang] Tsinghua Univ, Dept Bldg Sci, Beijing 100084, Peoples R China; [Wang, Zhifeng] Beijing Engn Res Ctr Solar Thermal Power, Beijing 100190, Peoples R China Chinese Academy of Sciences; Institute of Electrical Engineering, CAS; Technical University of Denmark; Tsinghua University Deng, J (corresponding author), Chinese Acad Sci, Inst Elect Engn, Beijing 100190, Peoples R China. deng-jie2@163.com Ma, Rongjiang/AAH-7174-2020; Deng, Jie/S-8339-2019 Ma, Rongjiang/0000-0003-3170-7298; Deng, Jie/0000-0001-6896-8622; Fan, Jianhua/0000-0003-0936-6677 Project in Chinese National Science & Technology Pillar Program [2015BAA02B4]; National Natural Science Foundation of China [51506193, 51476164]; Guangdong Innovative and Entrepreneurial Research Team Program [2013N070]; China Scholarship Council (CSC) [201504910177] Project in Chinese National Science & Technology Pillar Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Innovative and Entrepreneurial Research Team Program; China Scholarship Council (CSC)(China Scholarship Council) This work was supported by the Project in Chinese National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (No. 2015BAA02B4), the National Natural Science Foundation of China (Nos. 51506193 and 51476164), Guangdong Innovative and Entrepreneurial Research Team Program (No. 2013N070) and China Scholarship Council (CSC No. 201504910177). 20 10 10 0 9 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0038-092X SOL ENERGY Sol. Energy DEC 1 2016.0 139.0 171 178 10.1016/j.solener.2016.09.040 0.0 8 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels EE2BO Green Submitted 2023-03-23 WOS:000389388900020 0
C Xiao, ST; Feng, JS; Liu, LQ; Nie, XC; Wang, W; Yan, SC; Kassim, A IEEE Xiao, Shengtao; Feng, Jiashi; Liu, Luoqi; Nie, Xuecheng; Wang, Wei; Yan, Shuicheng; Kassim, Ashraf Recurrent 3D-2D Dual Learning for Large-pose Facial Landmark Detection 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) IEEE International Conference on Computer Vision English Proceedings Paper 16th IEEE International Conference on Computer Vision (ICCV) OCT 22-29, 2017 Venice, ITALY IEEE,IEEE Comp Soc Despite remarkable progress of face analysis techniques, detecting landmarks on large-pose faces is still difficult due to self-occlusion, subtle landmark difference and incomplete information. To address these challenging issues, we introduce a novel recurrent 3D-2D dual learning model that alternatively performs 2D-based 3D face model refinement and 3D-to-2D projection based 2D landmark refinement to reliably reason about self-occluded landmarks, precisely capture the subtle landmark displacement and accurately detect landmarks even in presence of extremely large poses. The proposed model presents the first loop-closed learning framework that effectively exploits the informative feedback from the 3D-2D learning and its dual 2D-3D refinement tasks in a recurrent manner. Benefiting from these two mutual-boosting steps, our proposed model demonstrates appealing robustness to large poses (up to profile pose) and outstanding ability to capture fine-scale landmark displacement compared with existing 3D models. It achieves new state-of-the-art on the challenging AFLW benchmark. Moreover, our proposed model introduces a new architectural design that economically utilizes intermediate features and achieves 4x faster speed than its deep learning based counterparts. [Xiao, Shengtao; Feng, Jiashi; Nie, Xuecheng; Yan, Shuicheng; Kassim, Ashraf] Natl Univ Singapore, Singapore, Singapore; [Liu, Luoqi; Yan, Shuicheng] Qihoo 360, Beijing, Peoples R China; [Wang, Wei] Univ Trento, Trento, Italy National University of Singapore; Qihoo 360 Technology; University of Trento Xiao, ST (corresponding author), Natl Univ Singapore, Singapore, Singapore. xiao_shengtao@u.nus.edu; elefjia@nus.edu.sg; liuluoqi@360.cn; niexuecheng@u.nus.edu; wei.wang@unitn.it; eleyans@nus.edu.sg; ashraf@nus.edu.sg Yan, Shuicheng/HCI-1431-2022; Wang, Wei/AAK-5521-2021; Feng, Jiashi/AGX-6209-2022 Wang, Wei/0000-0002-5477-1017; National University of Singapore [R-263-000-C08-133]; Ministry of Education of Singapore AcRF grant [R-263-000-C21-112] National University of Singapore(National University of Singapore); Ministry of Education of Singapore AcRF grant(Ministry of Education, Singapore) The work of Jiashi Feng was partially supported by National University of Singapore startup grant R-263-000-C08-133 and Ministry of Education of Singapore AcRF Tier One grant R-263-000-C21-112. 31 31 31 0 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1550-5499 978-1-5386-1032-9 IEEE I CONF COMP VIS 2017.0 1642 1651 10.1109/ICCV.2017.181 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BJ4TW 2023-03-23 WOS:000425498401074 0
J Uddin, MN; Li, BX; Ali, Z; Kefalas, P; Khan, I; Zada, I Uddin, Md Nasir; Li, Bixin; Ali, Zafar; Kefalas, Pavlos; Khan, Inayat; Zada, Islam Software defect prediction employing BiLSTM and BERT-based semantic feature SOFT COMPUTING English Article Software reliability; Software defect prediction; BERT; BiLSTM; Attention mechanism; Data augmentation MACHINE; LSTM Recent years, software defect prediction systems are becoming quite popular since they improve software reliability by identifying the potential bugs in the code. Several models were introduced in literature that aim to support the developers. Unfortunately, these models consider the manually constructed code features and input into machine learning-based classifiers. Moreover, these baseline approaches ignore the semantic and contextual information of the source code. With this paper we present a software defect prediction model that address all these issues. The model employs bidirectional long-short term memory network (BiLSTM) and BERT-based semantic feature (SDP-BB) that captures the semantic features of code to predict defects in the corresponding software. In particular, it utilizes the BiLSTM to exploit contextual information from the embedded token vectors learned through BERT model. Moreover, it utilizes an attention mechanism to capture salient features of the nodes. This is done through a data augmentation technique for generating more training data. We evaluated our approach against state-of-the-art models using ten open-source projects in terms of F1-score in fault prediction. The experiments evaluated the performance of full-token and AST-node data processing methods conducting the length of coverage on each project from 50 to 90% in both within-project defect prediction (WPDP) and cross-project defect prediction (CPDP) experiments. The results indicate that the proposed method outperforms competing models. [Uddin, Md Nasir; Li, Bixin; Ali, Zafar] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China; [Kefalas, Pavlos] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki, Greece; [Khan, Inayat] Univ Buner, Dept Comp Sci, Buner 19290, Pakistan; [Zada, Islam] Int Islamic Univ IIU, Dept Comp Sci & Software Engn, Islamabad, Pakistan Southeast University - China; Aristotle University of Thessaloniki; International Islamic University, Pakistan Uddin, MN; Ali, Z (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China. nasir_uddin@seu.edu.cn; bx.li@seu.edu.cn; zafarali@seu.edu.cn; kefalasp@csd.auth.gr; inayat_khan@uop.edu.pk; islam.zada@iiu.edu.pk Khan, Inayat/AAZ-2156-2020; ZADA, ISLAM/GLT-3832-2022; Ali, Zafar/D-7320-2017; Kefalas, Pavlos/H-1602-2017 Khan, Inayat/0000-0003-0895-9665; ZADA, ISLAM/0000-0001-6472-8795; Ali, Zafar/0000-0002-6404-645X; Kefalas, Pavlos/0000-0002-7197-1416; UDDIN, MD NASIR/0000-0002-0493-9803 74 5 5 13 32 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1432-7643 1433-7479 SOFT COMPUT Soft Comput. AUG 2022.0 26.0 16 SI 7877 7891 10.1007/s00500-022-06830-5 0.0 FEB 2022 15 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science 2Y7RD 2023-03-23 WOS:000758963300001 0
J Wan, H; Wang, H; Scotney, B; Liu, J; Ng, WWY Wan, Huan; Wang, Hui; Scotney, Bryan; Liu, Jun; Ng, Wing W. Y. Within-class multimodal classification MULTIMEDIA TOOLS AND APPLICATIONS English Article Within-class multimodality; Linear discriminant analysis; Subclass discriminant analysis; Separability-oriented subclass discriminant analysis In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is within-class multimodal. One example is face recognition where a face (i.e. a class) may be presented in frontal view or side view, corresponding to different modalities. This issue has been largely ignored in the literature or at least under studied. How to address the within-class multimodality issue is still an unsolved problem. In this paper, we present an extensive study of within-class multimodality classification. This study is guided by a number of research questions, and conducted through experimentation on artificial data and real data. In addition, we establish a case for within-class multimodal classification that is characterised by the concurrent maximisation of between-class separation, between-subclass separation and within-class compactness. Extensive experimental results show that within-class multimodal classification consistently leads to significant performance gains when within-class multimodality is present in data. Furthermore, it has been found that within-class multimodal classification offers a competitive solution to face recognition under different lighting and face pose conditions. It is our opinion that the case for within-class multimodal classification is established, therefore there is a milestone to be achieved in some machine learning algorithms (e.g. Gaussian mixture model) when within-class multimodal classification, or part of it, is pursued. [Wan, Huan; Wang, Hui; Liu, Jun] Ulster Univ, Sch Comp, Jordanstown, North Ireland; [Scotney, Bryan] Ulster Univ, Sch Comp, Coleraine, Londonderry, North Ireland; [Ng, Wing W. Y.] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China Ulster University; Ulster University; South China University of Technology Wan, H (corresponding author), Ulster Univ, Sch Comp, Jordanstown, North Ireland. h.wang@ulster.ac.uk; h.wang@ulster.ac.uk; bw.scotney@ulster.ac.uk; j.liu@ulster.ac.uk; wingng@ieee.org Liu, Jun/C-1338-2011; Wang, Hui/HMU-9512-2023 Liu, Jun/0000-0001-8859-5405; Wang, Hui/0000-0003-2633-6015; Ng, Wing W. Y./0000-0003-0783-3585 EU [700381, H2020-EU.3.7.] EU(European Commission) The work is partially funded by EU Horizon 2020 project Analysis System for Gathered Raw Data (Project Acronym: ASGARD, Project ID: 700381, Project Call: H2020-EU.3.7. - Secure societies - Protecting freedom and security of Europe and its citizens) 32 1 1 1 5 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. OCT 2020.0 79.0 39-40 29327 29352 10.1007/s11042-020-09238-1 0.0 AUG 2020 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering NY7SQ Green Submitted, hybrid 2023-03-23 WOS:000559640400002 0
J Liu, XY; Dai, SH; Fiumara, G; De Meo, P Liu, Xiaoyang; Dai, Shanghong; Fiumara, Giacomo; De Meo, Pasquale Target-specific sentiment analysis method combining word-masking data enhancement and adversarial learning COMPUTER JOURNAL English Article; Early Access adversarial learning; target-specific sentiment classification; data enhancement; adversarial examples Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of adversarial samples. A novel target-specific sentiment classification method is proposed. Firstly, the method of masking target entities is applied to replace synonyms and insert words randomly; secondly, the target-specific sentiment classification model of adversarial learning is constructed with six baseline models; finally, we combine data enhancement and adversarial learning to construct target-specific sentiment classification model. Experimental results show that Macro-F1 values are improved by 0.30-2.91, 0.88-2.42 and 0.13-1.94% compared to the six baseline models by using Laptop14, Restaurant14 and Twitter original datasets, respectively, using Adversarial learning. Using word-masking data enhancement samples and Adversarial learning from Laptop14, Restaurant14 and Twitter shows that Macro-F1 values are improved by 0.9-2.64, 1.59-3.09 and 0.18-1.71% compared to the six baseline (SC), respectively. Our method can effectively improve the quality of samples, it improves the classification performance and the capability of adversarial samples defense. [Liu, Xiaoyang; Dai, Shanghong] Chongqing Univ Technol, Sch Comp Sci & Engn, 69 Red Rd, Chongqing 400054, Peoples R China; [Fiumara, Giacomo] Univ Messina, MIFT Dept, Vle F Stagno DAlcontres 31, I-98166 Messina, Italy; [De Meo, Pasquale] Univ Messina, Dept Comp Sci, Vle F Stagno DAlcontres 31, I-98166 Messina, Italy Chongqing University of Technology; University of Messina; University of Messina Liu, XY (corresponding author), Chongqing Univ Technol, Sch Comp Sci & Engn, 69 Red Rd, Chongqing 400054, Peoples R China. lxy3103@163.com Fiumara, Giacomo/AAF-2653-2019 Fiumara, Giacomo/0000-0003-1528-7203; De Meo, Pasquale/0000-0001-7421-216X Science and Technology Research Project of Chongqing Municipal Education Commission [KJZD-K202001101]; Humanities and Social Sciences Research Project of Chongqing Municipal Education Commission [20SKGH166]; Graduate Innovation Fund of Chongqing University of Technology [clgycx20202091]; Chongqing Banan District Science and Technology Bureau Science and Technology Talents Special Project [2020.58]; General Project of Chongqing Natural Science Foundation [cstc2021jcyj-msxmX0162, GJK2021028]; 2020 Chongqing Municipal Human Resources and Social Security Bureau of Innovation Project for Returned Overseas Person [cx2020031]; Party Construction Project of Chongqing University of Technology [DJ20190306, 2020412] Science and Technology Research Project of Chongqing Municipal Education Commission; Humanities and Social Sciences Research Project of Chongqing Municipal Education Commission; Graduate Innovation Fund of Chongqing University of Technology; Chongqing Banan District Science and Technology Bureau Science and Technology Talents Special Project; General Project of Chongqing Natural Science Foundation; 2020 Chongqing Municipal Human Resources and Social Security Bureau of Innovation Project for Returned Overseas Person; Party Construction Project of Chongqing University of Technology Science and Technology Research Project of Chongqing Municipal Education Commission (KJZD-K202001101); Humanities and Social Sciences Research Project of Chongqing Municipal Education Commission (20SKGH166); Graduate Innovation Fund of Chongqing University of Technology (clgycx20202091); Chongqing Banan District Science and Technology Bureau Science and Technology Talents Special Project(2020.58); General Project of Chongqing Natural Science Foundation (cstc2021jcyj-msxmX0162); 2021 National Education Examination Research Project (GJK2021028); 2020 Chongqing Municipal Human Resources and Social Security Bureau of Innovation Project for Returned Overseas Person (cx2020031); Party Construction Project of Chongqing University of Technology (DJ20190306); 2020 National Statistical Science Research Projec t(2020412). 31 0 0 7 9 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0010-4620 1460-2067 COMPUT J Comput. J. 10.1093/comjnl/bxac066 0.0 JUN 2022 17 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1V9RV 2023-03-23 WOS:000806422300001 0
J Doellinger, J; Prabhakaran, VS; Fu, LC; Spies, M Doellinger, Johannes; Prabhakaran, Vishnu Suganth; Fu, Liangcheng; Spies, Markus Environment-Aware Multi-Target Tracking of Pedestrians IEEE ROBOTICS AND AUTOMATION LETTERS English Article Deep Learning in Robotics and Automation; Semantic Scene Understanding; Social Human-Robot Interaction When navigating mobile robotic systems in dynamic environments, the ability to predict where pedestrians will move in the next few seconds is crucial. To tackle this problem, many solutions have been developed which take the environment's influence on human navigation behavior into account. However, few do this in a way which seamlessly generalizes to new environments where no prior observations of pedestrians are available. In this letter, we propose a novel method that uses convolutional neural networks (CNNs) to predict local statistics about the direction of likely human motion. Specifically, the network takes crops of a floor plan as an input and computes a cell-wise probability distributions over directions conditioned on the direction in the last time step. These environment-aware motion models can be used to improve the performance of existing tracking algorithms. We evaluated our approach with two different existing trackers, a discrete, grid-based filter as well as a particle filter and show that using our proposed method as motion model considerably improves prediction quality in previously unseen test environments. Even though we train the CNN entirely in simulation, our experiments suggest that the learned models generalize to real pedestrian data. [Doellinger, Johannes; Prabhakaran, Vishnu Suganth; Spies, Markus] Bosch Ctr Artificial Intelligence, D-71272 Renningen, Germany; [Prabhakaran, Vishnu Suganth] Univ Stuttgart, Dept Comp Sci, D-70569 Stuttgart, Germany; [Fu, Liangcheng] Meituan Dianping Grp, Beijing 100102, Peoples R China University of Stuttgart Doellinger, J (corresponding author), Bosch Ctr Artificial Intelligence, D-71272 Renningen, Germany. johannes.doellinger@de.bosch.com; vishnusuganth@gmail.com; fuliangcheng@meituan.com; markus.spies2@de.bosch.com Prabhakaran, Vishnu Suganth/0000-0002-6171-1100 Bosch Center for Artificial Intelligence Bosch Center for Artificial Intelligence This work was supported by the Bosch Center for Artificial Intelligence. 24 1 2 1 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. APR 2019.0 4.0 2 1831 1837 10.1109/LRA.2019.2898039 0.0 7 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics HM8AV 2023-03-23 WOS:000459701700010 0
J Ji, DF; Li, CX; Zhai, CH; Dong, Y; Katsanos, EI; Wang, W Ji, Duofa; Li, Chenxi; Zhai, Changhai; Dong, You; Katsanos, Evangelos I.; Wang, Wei Prediction of Ground-Motion Parameters for the NGA-West2 Database Using Refined Second-Order Deep Neural Networks BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA English Article SHALLOW CRUSTAL EARTHQUAKES; HORIZONTAL COMPONENTS; MODEL; EQUATIONS; PGA; VARIABILITY; EUROPE One of the key elements within seismic hazard analysis is the establishment of appropriate ground-motion models (GMMs), which are used to predict the levels of ground-motion intensities by considering various parameters (e.g., source, path, and site). Many empirical GMMs were derived on the basis of a predefined linear or nonlinear equation that is heavily dependent on the a priori knowledge of a functional form that varies between the modelers' choices. To overcome this issue, this study develops a deep neural network (DNN) trained by the recordings from the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation-West2 Project (NGA-West2) database. To this end, we collected 20,900 ground motion recordings from the database and randomly split them into the training, validation, and testing datasets. The refined second-order neuron is proposed to solve the problem, and the Adam optimizer is used to optimize the performance of the model. The prediction errors are evaluated by three performance indicators (i.e., R2, root mean square error, mean absolute error), and the predictive results are compared with previous GMMs developed based on the PEER NGA-West2 database. The between-event and within-event standard deviations (SDs) as well as total SDs are calculated and compared. Based on the comparisons, our model maintains consistent performance (e.g., the dependence of predicted intensity measures on seismological and site-specific parameters) with the compared GMM. Its relatively small total SDs, especially for longer periods, confirm that the proposed model is associated with better predictive power. [Ji, Duofa; Li, Chenxi; Zhai, Changhai; Wang, Wei] Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China; [Dong, You] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China; [Katsanos, Evangelos I.] Tech Univ Denmark, Dept Civil Engn, Lyngby, Denmark Harbin Institute of Technology; Hong Kong Polytechnic University; Technical University of Denmark Zhai, CH (corresponding author), Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin, Heilongjiang, Peoples R China. zch-hit@hit.edu.cn Katsanos, Evangelos/0000-0003-2574-5327 Funds for Creative Research Groups of National Natural Science Foundation of China [51921006]; National Natural Science Foundation of China [52008142]; China Postdoctoral Science Foundation [2018M641834, 2019T120272]; Natural Science Foundation of Heilongjiang Province of China [LH2020E057] Funds for Creative Research Groups of National Natural Science Foundation of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Natural Science Foundation of Heilongjiang Province of China(Natural Science Foundation of Heilongjiang Province) The authors thank the two anonymous reviewers for helpful comments and considerations and the Pacific Earthquake Engineering Research Center (PEER) researchers for sharing the Next Generation Attenuation-West2 Project (NGA-West2) flatfile. The financial support from the Funds for Creative Research Groups of National Natural Science Foundation of China (Grant Number 51921006) , the National Natural Science Foundation of China (Grant Number 52008142) , the China Postdoctoral Science Foundation funded project (Grant Numbers 2018M641834, 2019T120272) , and the Natural Science Foundation of Heilongjiang Province of China (Grant Number LH2020E057) for current research are gratefully acknowledged. 48 3 3 9 23 SEISMOLOGICAL SOC AMER ALBANY 400 EVELYN AVE, SUITE 201, ALBANY, CA 94706-1375 USA 0037-1106 1943-3573 B SEISMOL SOC AM Bull. Seismol. Soc. Amer. DEC 2021.0 111.0 6 3278 3296 10.1785/0120200388 0.0 19 Geochemistry & Geophysics Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics XD2YG 2023-03-23 WOS:000722575200002 0
J Cheng, YX; Xiang, Y; Chen, WZ; Hassan, H; Alelaiwi, A Cheng, Yuxia; Xiang, Yang; Chen, Wenzhi; Hassan, Houcine; Alelaiwi, Abdulhameed Efficient cache resource aggregation using adaptive multi-level exclusive caching policies FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Buffer cache; Multi-level; Exclusive caching; Adaptive policy MANAGEMENT Multi-level buffer cache hierarchies are now commonly seen in most client/server cluster configurations, especially in today's big data application deployment. However, multi-level caching policies deployed so far typically use independent cache replacement algorithms in each level, which has two major drawbacks: (1) File blocks may be redundantly cached on multiple levels, reducing the actual aggregate cache usable size; (2) Less accurate replacement decisions at lower level caches due to weakened locality. Inefficient cache resource usage may result in noticeable performance degradation for big data applications. To address these problems, we propose new adaptive multi-level exclusive caching policies that can dynamically adjust replacement and placement decisions in response to changing access patterns. (1) First, to capture locality information in multi-level cache hierarchies, we propose a Reuse Distance based Adaptive Replacement Caching (ReDARC) algorithm that adopts reuse distance as the means of locality measure and adaptively balances between the Small Reuse Distance (SRD) set and Large Reuse Distance (LRD) set. (2) Second, to achieve exclusive caching and make global caching decisions, we propose an Adaptive Level-Aware Caching Algorithm (ALACA) that works collaboratively with ReDARC. The ALACA algorithm uses an adaptive probabilistic PUSH technique that allows lower caches to push blocks to higher caches and appropriately decide blocks' caching locations with the ReDARC algorithm. In this way, we achieve multi-level exclusive caching with significant cache performance improvement. Our trace-driven simulation experiments show that the policies we proposed achieve a reduction of the client average response time of 8 percent to 56 percent over other multi-level cache schemes. (C) 2017 Elsevier B.V. All rights reserved. [Cheng, Yuxia; Xiang, Yang] Deakin Univ, 221 Burwood Highway, Burwood, Vic 3125, Australia; [Chen, Wenzhi] Zhejiang Univ, Coll Comp Sci & Technol, Zheda Rd 38, Hangzhou, Zhejiang, Peoples R China; [Hassan, Houcine] Univ Politecn Valencia, Camino Vera S-N, Valencia 46022, Spain; [Alelaiwi, Abdulhameed] King Saud Univ, Riyadh 11543, Saudi Arabia Deakin University; Zhejiang University; Universitat Politecnica de Valencia; King Saud University Cheng, YX (corresponding author), Deakin Univ, 221 Burwood Highway, Burwood, Vic 3125, Australia. yuxia.cheng@deakin.edu.au hassan, houcine/AAG-6212-2019; Xiang, Yang/M-3527-2019; xiang, yang/HKN-0533-2023 Xiang, Yang/0000-0001-5252-0831; International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia [ISPP0069] International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069. 30 1 1 0 10 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. SEP 2018.0 86.0 964 974 10.1016/j.future.2017.09.044 0.0 11 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GL9LK 2023-03-23 WOS:000437555800084 0
J Chen, LH; Tang, R; Anisetti, M; Yang, XM Chen, Lihui; Tang, Rui; Anisetti, Marco; Yang, Xiaomin A lightweight iterative error reconstruction network for infrared image super-resolution in smart grid SUSTAINABLE CITIES AND SOCIETY English Article Convolutional neural networks; Deep learning; Smart grid; Infrared image super-resolution ENHANCEMENT; DICTIONARY Thermal infrared (IR) images are widely used in smart grids for numerous applications. These applications prefer high-resolution (HR) IR images since HR IR images benefit the performance. However, HR IR imaging devices are extremely expensive. To save the cost of upgrading imaging devices, an iterative error reconstruction network (IERN) is proposed to improve the resolution of IR images. We first achieve efficient dense connections based on linearly compressive skip links. Slightly sacrificing the performance, the efficient dense connections can mark-edly reduce the parameters and computations of the vanilla dense connections. Then, an iterative error recon-struction mechanism is proposed to boost the performance, which enables IERN to restore many more textures and edges. Specifically, an initial SR image, high-level features, and up-sampled features are obtained firstly. Secondly, a SR error image is acquired by reconstructing the errors between the initial high-level features and the back-projected features from the up-sampled features. Thirdly, a new SR image is obtained by adding the SR error image to the initial SR image. Iterating the above process, the final SR image is achieved when the number of iterations reaches to the iteration threshold. Experimental results reveal the superiority of the proposed method over state-of-the-art methods. [Chen, Lihui; Tang, Rui; Yang, Xiaomin] Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China; [Anisetti, Marco] Univ Milan, Dipartimento Informat DI, Via Celoria 18, I-20133 Milan, MI, Italy Sichuan University; University of Milan Yang, XM (corresponding author), Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China. arielyang@scu.edu.cn yang, xiao/HJI-7815-2023; Anisetti, Marco/AAC-9656-2021 Anisetti, Marco/0000-0002-5438-9467 National Natural Science Foundation of China [61701327] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is sponsored by the National Natural Science Foundation of China (grant no. 61701327). 63 5 6 3 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2210-6707 2210-6715 SUSTAIN CITIES SOC Sust. Cities Soc. MAR 2021.0 66.0 102520 10.1016/j.scs.2020.102520 0.0 JAN 2021 13 Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels RH3VQ 2023-03-23 WOS:000636151000004 0
J Zhou, CM; Huang, BD; Hassan, H; Franti, P Zhou, Chengmin; Huang, Bingding; Hassan, Haseeb; Franti, Pasi Attention-based advantage actor-critic algorithm with prioritized experience replay for complex 2-D robotic motion planning JOURNAL OF INTELLIGENT MANUFACTURING English Article Motion planning; Path planning; Reinforcement learning; Intelligent robot; Deep learning CONTINUOUS COLLISION DETECTION Robotic motion planning in dense and dynamic indoor scenarios constantly challenges the researchers because of the motion unpredictability of obstacles. Recent progress in reinforcement learning enables robots to better cope with the dense and unpredictable obstacles by encoding complex features of the robot and obstacles into the encoders like the long-short term memory (LSTM). Then these features are learned by the robot using reinforcement learning algorithms, such as the deep Q network and asynchronous advantage actor critic algorithm. However, existing methods depend heavily on expert experiences to enhance the convergence speed of the networks by initializing them via imitation learning. Moreover, those approaches based on LSTM to encode the obstacle features are not always efficient and robust enough, therefore sometimes causing the network overfitting in training. This paper focuses on the advantage actor critic algorithm and introduces an attention-based actor critic algorithm with experience replay algorithm to improve the performance of existing algorithm from two perspectives. First, LSTM encoder is replaced by a robust encoder attention weight to better interpret the complex features of the robot and obstacles. Second, the robot learns from its past prioritized experiences to initialize the networks of the advantage actor-critic algorithm. This is achieved by applying the prioritized experience replay method, which makes the best of past useful experiences to improve the convergence speed. As results, the network based on our algorithm takes only around 15% and 30% experiences to get rid of the early-stage training without the expert experiences in cases with five and ten obstacles, respectively. Then it converges faster to a better reward with less experiences (near 45% and 65% of experiences in cases with ten and five obstacles respectively) when comparing with the baseline LSTM-based advantage actor critic algorithm. Our source code is freely available at the GitHub (https://github.com/CHUENGMINCHOU/AW-PER-A2C). [Zhou, Chengmin; Franti, Pasi] Univ Eastern Finland, Sch Comp, Joensuu, Finland; [Zhou, Chengmin; Huang, Bingding; Hassan, Haseeb] Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen, Peoples R China; [Franti, Pasi] Univ Eastern Finland, Sch Comp, Machine Learning Grp, Joensuu, Finland University of Eastern Finland; Shenzhen Technology University; University of Eastern Finland Franti, P (corresponding author), Univ Eastern Finland, Sch Comp, Joensuu, Finland.;Huang, BD (corresponding author), Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen, Peoples R China.;Franti, P (corresponding author), Univ Eastern Finland, Sch Comp, Machine Learning Grp, Joensuu, Finland. huangbingding@sztu.edu.cn; franti@cs.uef.fi Franti, Pasi/0000-0002-9554-2827 University of Eastern Finland (UEF) including Kuopio University Hospital University of Eastern Finland (UEF) including Kuopio University Hospital Open access funding provided by University of Eastern Finland (UEF) including Kuopio University Hospital. The authors did not receive support from any organization for the submitted work. 48 0 0 17 18 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0956-5515 1572-8145 J INTELL MANUF J. Intell. Manuf. JAN 2023.0 34.0 1 151 180 10.1007/s10845-022-01988-z 0.0 AUG 2022 30 Computer Science, Artificial Intelligence; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 7P8HF hybrid 2023-03-23 WOS:000836986000002 0
J Zheng, Y; Wang, JW; You, S; Li, XM; Bindner, HW; Munster, M Zheng, Yi; Wang, Jiawei; You, Shi; Li, Ximei; Bindner, Henrik W.; Munster, Marie Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties APPLIED ENERGY English Article Data-driven; Robust optimization; Day-ahead operation; Wind; hydrogen system; DRCCP UNIT COMMITMENT; STORAGE; POWER; ELECTROLYSIS; OPTIMIZATION Hydrogen is believed as a promising energy carrier that contributes to deep decarbonization, especially for the sectors hard to be directly electrified. A grid-connected wind/hydrogen system is a typical configuration for hydrogen production. For such a system, a critical barrier lies in the poor cost-competitiveness of the produced hydrogen. Researchers have found that flexible operation of a wind/hydrogen system is possible thanks to the excellent dynamic properties of electrolysis. This finding implies the system owner can strategically participate in day-ahead power markets to reduce the hydrogen production cost. However, the uncertainties from imperfect prediction of the fluctuating market price and wind power reduce the effectiveness of the offering strategy in the market. In this paper, we proposed a decision-making framework, which is based on data-driven robust chance constrained programming (DRCCP). This framework also includes multi-layer perception neural network (MLPNN) for wind power and spot electricity price prediction. Such a DRCCP-based decision framework (DDF) is then applied to make the day-ahead decision for a wind/hydrogen system. It can effectively handle the uncertainties, manage the risks and reduce the operation cost. The results show that, for the daily operation in the selected 30 days, offering strategy based on the framework reduces the overall operation cost by 24.36%, compared to the strategy based on imperfect prediction. Besides, we elaborate the parameter selections of the DRCCP to reveal the best parameter combination to obtain better optimization performance. The efficacy of the DRCCP method is also highlighted by the comparison with the chance-constrained programming method. [Zheng, Yi; Wang, Jiawei; You, Shi; Li, Ximei; Bindner, Henrik W.] Tech Univ Denmark, Dept Wind & Energy Syst, DK-2800 Lyngby, Denmark; [Munster, Marie] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark; [Li, Ximei] Harbin Inst Technol, Sch Energy Sci & Engn, 92 West Dazhi St, Harbin 150001, Peoples R China Technical University of Denmark; Technical University of Denmark; Harbin Institute of Technology Wang, JW (corresponding author), Tech Univ Denmark, Dept Wind & Energy Syst, DK-2800 Lyngby, Denmark. yiazh@dtu.dk; jiawang@dtu.dk Wang, Jiawei/HMO-9885-2023; Bindner, Henrik W./N-8202-2017; Munster, Marie/K-9249-2017 Wang, Jiawei/0000-0002-1891-0657; Bindner, Henrik W./0000-0003-1545-6994; Munster, Marie/0000-0002-5543-547X Synergies Utilizing renewable Power Regionally by means of Power To Gas project; European Union [775970]; VILLUM FONDEN, Denmark [37177] Synergies Utilizing renewable Power Regionally by means of Power To Gas project; European Union(European Commission); VILLUM FONDEN, Denmark(Villum Fonden) The authors would like to acknowledge financial support from Synergies Utilizing renewable Power Regionally by means of Power To Gas project, which received funding in the framework of the joint programming initiative ERA-Net Smart Energy Systems' focus initiative Integrated, Regional Energy Systems, with support from the European Union's Horizon 2020 research and innovation program under grant agreement No 775970. This work is also funded by a research grant (37177) from VILLUM FONDEN, Denmark. Project title: Data-driven Approaches for enhancing resilience of Multi-Energy systems (DAME) . 31 0 0 20 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy JAN 1 2023.0 329.0 120201 10.1016/j.apenergy.2022.120201 0.0 12 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 6F9ZK Green Published 2023-03-23 WOS:000884419800001 0
J Zhang, J; Ziegler, M Zhang, Jing; Ziegler, Matthias How do the big five influence scholastic performance? A big five-narrow traits model or a double mediation model LEARNING AND INDIVIDUAL DIFFERENCES English Article Big Five-narrow traits model; Double mediation model; Self-beliefs; Learning approaches; Scholastic performance PERCEIVED SELF-EFFICACY; ACADEMIC-PERFORMANCE; PERSONALITY-TRAITS; 5-FACTOR MODEL; FIT INDEXES; UNIVERSITY-STUDENTS; JOB-PERFORMANCE; ACHIEVEMENT; INTELLIGENCE; SCHOOL The current study develops the Big Five-Narrow Traits Model [B5NT] suggesting a general mechanism by which the Big Five affects scholastic performance. Moreover, the B5NT is compared to the Double Mediation Model that was also suggested to explain said mechanism. In both models self-beliefs (i.e., subject-specific self-efficacy and subject-specific self-concept) and learning approaches are seen as relevant mediators, but are sequenced differently. Data were collected from 836 Chinese secondary school students. The results strongly support the B5NT. Across three subjects (Mathematics, Chinese, English), subject-specific self-concept significantly mediated the influences of Openness and Conscientiousness on grades while a surface-learning approach mediated the influences of Openness, Extraversion, and Neuroticism. A deep-learning approach also significantly mediated the relations of Openness and Conscientiousness with grades but only in Mathematics and Chinese. In addition, Neuroticism also influenced Math grades via Math self-concept. Agreeableness did not predict grades directly or indirectly. Implications are discussed, along with limitations and suggestions for future research. (C) 2016 Elsevier Inc. All rights reserved. [Zhang, Jing] East China Normal Univ, Fac Educ Sci, 3663 Zhongshan N Rd, Shanghai 200062, Peoples R China; [Zhang, Jing; Ziegler, Matthias] Humboldt Univ, Inst Psychol, Rudower Chaussee 18, D-12489 Berlin, Germany East China Normal University; Humboldt University of Berlin Zhang, J (corresponding author), East China Normal Univ, Fac Educ Sci, 3663 Zhongshan N Rd, Shanghai 200062, Peoples R China.;Zhang, J (corresponding author), Humboldt Univ, Inst Psychol, Rudower Chaussee 18, D-12489 Berlin, Germany. zhangjapple@hotmail.com Zhang, Jing/0000-0001-5403-5442 Peak Discipline Construction Project of Education at East China Normal University; Humboldt-Universitat zu Berlin; China Scholarship Council [201206140015] Peak Discipline Construction Project of Education at East China Normal University; Humboldt-Universitat zu Berlin; China Scholarship Council(China Scholarship Council) This work was sponsored by Peak Discipline Construction Project of Education at East China Normal University. We acknowledge the support of Humboldt-Universitat zu Berlin and China Scholarship Council (Study Number: 201206140015). We also thank the school principals, teachers and students whose support made this study possible. 75 28 29 3 60 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1041-6080 1873-3425 LEARN INDIVID DIFFER Learn. Individ. Differ. AUG 2016.0 50.0 93 102 10.1016/j.lindif.2016.07.011 0.0 10 Psychology, Educational Social Science Citation Index (SSCI) Psychology DY7TN 2023-03-23 WOS:000385332300011 0
J Chen, SQ; Zhong, X; Dorn, S; Ravikumar, N; Tao, QH; Huang, XL; Lell, M; Kachelriess, M; Maier, A Chen, Shuqing; Zhong, Xia; Dorn, Sabrina; Ravikumar, Nishant; Tao, Qinghua; Huang, Xiaolin; Lell, Michael; Kachelriess, Marc; Maier, Andreas Improving Generalization Capability of Multiorgan Segmentation Models Using Dual-Energy CT IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES English Article Data augmentation; dual-energy computed tomography (DECT); deep learning; multiorgan segmentation IMAGES Multiorgan segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft-tissue contrast, noise, and artifacts. Devising automatic multiorgan segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multicenter/scanner analyses. In this study, we investigate the use of dual-energy CT (DECT) images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e., at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in the frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using DECT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with p <= 0.03. Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available DECT data. [Chen, Shuqing; Zhong, Xia; Ravikumar, Nishant; Maier, Andreas] Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany; [Dorn, Sabrina; Kachelriess, Marc] German Canc Res Ctr, Dept Xray Imaging & Computed Tomog, D-69120 Heidelberg, Germany; [Dorn, Sabrina; Kachelriess, Marc] Heidelberg Univ, Med Fac, D-69117 Heidelberg, Germany; [Tao, Qinghua; Huang, Xiaolin] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China; [Tao, Qinghua; Huang, Xiaolin] Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai 200240, Peoples R China; [Lell, Michael] Univ Hosp Nurnberg, Inst Radiol & Nucl Med, D-91054 Erlangen, Germany; [Lell, Michael] Paracelsus Med Univ, Dept Radiol, D-90419 Nurnberg, Germany University of Erlangen Nuremberg; Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Shanghai Jiao Tong University; Shanghai Jiao Tong University; University of Erlangen Nuremberg Chen, SQ (corresponding author), Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany. shuqing.chen@fau.de Tao, Qinghua/0000-0001-9705-7748; Chen, Shuqing/0000-0003-4100-2988; Maier, Andreas/0000-0002-9550-5284; Ravikumar, Nishant/0000-0003-0134-107X German Research Foundation (DFG) [KA 1678/20, LE 2763/2, MA 4898/5]; National Natural Science Foundation of China [61977046]; National Key Research and Development Project [2018AAA0100702] German Research Foundation (DFG)(German Research Foundation (DFG)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Project This work was supported in part by the German Research Foundation (DFG) under Grant KA 1678/20, Grant LE 2763/2, and Grant MA 4898/5; in part by the National Natural Science Foundation of China under Grant 61977046; and in part by the National Key Research and Development Project under Grant 2018AAA0100702. 46 2 2 0 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2469-7311 2469-7303 IEEE T RADIAT PLASMA IEEE Trans. Radiat. Plasma Med. Sci. JAN 2022.0 6.0 1 79 86 10.1109/TRPMS.2021.3055199 0.0 8 Radiology, Nuclear Medicine & Medical Imaging Emerging Sources Citation Index (ESCI) Radiology, Nuclear Medicine & Medical Imaging YB0VL 2023-03-23 WOS:000738740600011 0
J Dong, JX; Roth, S; Schiele, B Dong, Jiangxin; Roth, Stefan; Schiele, Bernt DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Deconvolution; Image restoration; Feature extraction; Kernel; Standards; Neural networks; Transform coding; Image deblurring; wiener deconvolution; feature-based deconvolution; multi-scale cascaded feature refinement; saturation and JPEG artifacts SHAKEN; FIELDS We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin. [Dong, Jiangxin] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China; [Dong, Jiangxin; Schiele, Bernt] Max Planck Inst Informat, Dept Comp Vis & Machine Learning, D-66123 Saarbrucken, Germany; [Roth, Stefan] Tech Univ Darmstadt, Dept Comp Sci, D-64289 Darmstadt, Germany Nanjing University of Science & Technology; Max Planck Society; Technical University of Darmstadt Dong, JX (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China. dongjxjx@gmail.com; stefan.roth@visinf.tu-darmstadt.de; schiele@mpi-inf.mpg.de European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [866008] European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme(European Research Council (ERC)) This work was supported in part by European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under Grant 866008. 77 8 8 7 13 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. DEC 1 2022.0 44.0 12 9960 9976 10.1109/TPAMI.2021.3138787 0.0 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 6A4XX 34962863.0 2023-03-23 WOS:000880661400100 0
J Wu, HB; Zheng, KY; Sfarra, S; Liu, Y; Yao, Y Wu, Haibin; Zheng, Kaiyi; Sfarra, Stefano; Liu, Yi; Yao, Yuan Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Feature extraction; Testing; Temperature; Data analysis; Principal component analysis; Decoding; Heating systems; Autoencoder; deep learning; feature extraction; multiview learning; nondestructive testing (NDT); thermographic data analysis PRINCIPAL COMPONENT THERMOGRAPHY; ENHANCEMENT Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly visualizing thermal images is difficult owing to the nonuniform background and noise. Recently, data analysis methods have been introduced to thermal image processing, including principal component analysis, which is known for its good performance in dimensionality reduction, feature extraction, and noise reduction. However, most of these methods can only extract linear features. In this article, a multiview learning-based autoencoder, which can process not only nonlinear features but also sequential attributes, is utilized in thermographic data analysis. After extracting the low-dimensional features by multiview learning, a background elimination step is conducted to highlight the locations and shapes of the defects. The experimental results demonstrate the feasibility of the proposed method. [Wu, Haibin] Natl Taiwan Univ, Grad Inst Commun Engn, Taipei 10617, Taiwan; [Zheng, Kaiyi] Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [Sfarra, Stefano] Univ Aquila, Dept Ind & Informat Engn & Econ, I-67100 Laquila, Italy; [Liu, Yi] Zhejiang Univ Technol, Inst Proc Equipment & Control Engn, Hangzhou 310023, Peoples R China; [Yao, Yuan] Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30013, Taiwan National Taiwan University; Jiangsu University; University of L'Aquila; Zhejiang University of Technology; National Tsing Hua University Yao, Y (corresponding author), Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30013, Taiwan. r07921092@ntu.edu.tw; beihai722@126.com; stefano.sfarra@univaq.it; yliuzju@zjut.edu.cn; yyao@mx.nthu.edu.tw Liu, Yi/F-8444-2014; Yao, Yuan/A-8769-2015 Yao, Yuan/0000-0002-0025-6175; Liu, Yi/0000-0002-4066-689X Ministry of Science and Technology, ROC [MOST 108-2221-E-007-068-MY3, TII-194916] Ministry of Science and Technology, ROC This work was supported by Ministry of Science and Technology, ROC under Grant MOST 108-2221-E-007-068-MY3. Paper no. TII-194916. 26 18 19 9 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. SEP 2020.0 16.0 9 5996 6003 10.1109/TII.2019.2963795 0.0 8 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering MC0CY 2023-03-23 WOS:000542966300036 0
J Liu, Y; Munteanu, CR; Blanco, EF; Tan, ZL; del Riego, AS; Pazos, A Liu, Yong; Munteanu, Cristian R.; Fernandez Blanco, Enrique; Tan, Zhiliang; Santos del Riego, Antonino; Pazos, Alejandro Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices MOLECULAR INFORMATICS English Article QSAR; nucleotide binding proteins; Star Graph; topological indice SEQUENCE-BASED PREDICTOR; PROTEIN-COUPLED RECEPTORS; SUPPORT VECTOR MACHINES; WEB SERVER; CANCER BIOMARKERS; COMPLEX NETWORKS; MODEL; QSAR; CLASSIFICATION; BIOINFORMATICS The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. [Liu, Yong; Munteanu, Cristian R.; Fernandez Blanco, Enrique; Santos del Riego, Antonino; Pazos, Alejandro] Univ A Coruna, Fac Comp Sci, Dept Informat & Commun Technol, La Coruna 15071, Spain; [Liu, Yong] Autonomous Univ State Mexico, Fac Vet Med & Anim Sci, Toluca 50090, Mexico; [Liu, Yong; Tan, Zhiliang] Chinese Acad Sci, Inst Subtrop Agr, Key Lab Subtrop Agroecol Engn, Changsha 410125, Hunan, Peoples R China Universidade da Coruna; Universidad Autonoma del Estado de Mexico; Chinese Academy of Sciences; Institute of Subtropical Agriculture, CAS Munteanu, CR (corresponding author), Univ A Coruna, Fac Comp Sci, Dept Informat & Commun Technol, Campus Elvina S-N, La Coruna 15071, Spain. crm.publish@gmail.com Pazos, Alejandro/AAI-5887-2020; Sierra, Alejandro Pazos/K-8927-2017; Fernandez-Blanco, Enrique/F-6085-2014; Munteanu, Cristian-Robert/G-1714-2011; Santos-del-Riego, Antonino/I-7844-2015 Sierra, Alejandro Pazos/0000-0003-2324-238X; Fernandez-Blanco, Enrique/0000-0003-3260-8734; Munteanu, Cristian-Robert/0000-0002-5628-2268; Santos-del-Riego, Antonino/0000-0002-3646-8145 Mexican Council for Science and Technology (CONACyT); Galician Network for Colorectal Cancer Research (REGICC) - Xunta de Galicia [R2014/039]; Collaborative Project on Medical Informatics (CIMED) - Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation [PI13/00280]; European Regional Development Funds (FEDER); Galician Network of Drugs R+D REGID (Xunta de Galicia) [R2014/025] Mexican Council for Science and Technology (CONACyT)(Consejo Nacional de Ciencia y Tecnologia (CONACyT)); Galician Network for Colorectal Cancer Research (REGICC) - Xunta de Galicia(Xunta de Galicia); Collaborative Project on Medical Informatics (CIMED) - Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation; European Regional Development Funds (FEDER)(European Commission); Galician Network of Drugs R+D REGID (Xunta de Galicia)(Xunta de Galicia) Yong Liu acknowledges the Mexican Council for Science and Technology (CONACyT) for financial support for Ph.D. studies. This work was supported by the Galician Network for Colorectal Cancer Research (REGICC) (Ref. R2014/039), funded by Xunta de Galicia, by the Collaborative Project on Medical Informatics (CIMED) PI13/00280, funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016, and the European Regional Development Funds (FEDER). The authors acknowledge the support offered by the Galician Network of Drugs R+D REGID (Xunta de Galicia R2014/025). 63 4 4 0 12 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1868-1743 1868-1751 MOL INFORM Mol. Inf. NOV 2015.0 34.0 11-12 736 741 10.1002/minf.201500064 0.0 6 Chemistry, Medicinal; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy; Computer Science; Mathematical & Computational Biology CW9KW 27491034.0 Green Submitted 2023-03-23 WOS:000365318600004 0
J Bai, CG; Dallasega, P; Orzes, G; Sarkis, J Bai, Chunguang; Dallasega, Patrick; Orzes, Guido; Sarkis, Joseph Industry 4.0 technologies assessment: A sustainability perspective INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS English Article Industry 4.0; Technology; Sustainability; Hesitant fuzzy set; Cumulative prospect theory; VIKOR BIG DATA; FUTURE; INNOVATION; INTERNET; THINGS; VIKOR The fourth industrial revolution, also labelled Industry 4.0, was beget with emergent and disruptive intelligence and information technologies. These new technologies are enabling ever-higher levels of production efficiencies. They also have the potential to dramatically influence social and environmental sustainable development. Organizations need to consider Industry 4.0 technologies contribution to sustainability. Sufficient guidance, in this respect, is lacking in the scholarly or practitioner literature. In this study, we further examine Industry 4.0 technologies in terms of application and sustainability implications. We introduce a measures framework for sustainability based on the United Nations Sustainable Development Goals; incorporating various economic, environmental and social attributes. We also develop a hybrid multi-situation decision method integrating hesitant fuzzy set, cumulative prospect theory and VIKOR. This method can effectively evaluate Industry 4.0 technologies based on their sustainable performance and application. We apply the method using secondary case information from a report of the World Economic Forum. The results show that mobile technology has the greatest impact on sustainability in all industries, and nanotechnology, mobile technology, simulation and drones have the highest impact on sustainability in the automotive, electronics, food and beverage, and textile, apparel and footwear industries, respectively. Our recommendation is to take advantage of Industry 4.0 technology adoption to improve sustainability impact but each technology needs to be carefully evaluated as specific technology will variably influence industry and sustainability dimensions. Investment in such technologies should consider appropriate priority investment and championing. [Bai, Chunguang] Univ Elect Sci & Technol China, Sch Management & Econ, West Hitech Zone, 2006 Xiyuan Ave, Chengdu 611731, Peoples R China; [Dallasega, Patrick; Orzes, Guido] Free Univ Bozen Bolzano, Fac Sci & Technol, Piazza Univ 5, I-39100 Bolzano, Italy; [Sarkis, Joseph] Worcester Polytech Inst, Foisie Business Sch, 100 Inst Rd, Worcester, MA 01609 USA; [Sarkis, Joseph] Hanken Sch Econ, Humlog Inst, Helsinki, Finland University of Electronic Science & Technology of China; Free University of Bozen-Bolzano; Worcester Polytechnic Institute; Hanken School of Economics Bai, CG (corresponding author), Univ Elect Sci & Technol China, Sch Management & Econ, West Hitech Zone, 2006 Xiyuan Ave, Chengdu 611731, Peoples R China. Cbai@uestc.edu.cn; patrick.dallasega@unibz.it; guido.orzes@unibz.it; jsarkis@wpi.edu Sarkis, Joseph/F-4508-2014; Bai, Chunguang/E-3296-2012; Orzes, Guido/AHB-5726-2022; Orzes, Guido/E-4868-2012 Sarkis, Joseph/0000-0003-0143-804X; Bai, Chunguang/0000-0002-9461-1632; Orzes, Guido/0000-0001-7740-3885 National Natural Science Foundation of China Project [71772032, 71472031]; European Union [734713] National Natural Science Foundation of China Project(National Natural Science Foundation of China (NSFC)); European Union(European Commission) This work is supported by the National Natural Science Foundation of China Project (71772032, 71472031).; This work was also supported by the European Union's Horizon 2020 research and innovation program under the Marie SklodowskaCurie grant agreement No. 734713 (Project title: SME 4.0-Industry 4.0 for SMEs). The authors also thank the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano for covering the Open Access publication costs. 64 249 250 106 352 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0925-5273 1873-7579 INT J PROD ECON Int. J. Prod. Econ. NOV 2020.0 229.0 107776 10.1016/j.ijpe.2020.107776 0.0 15 Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Operations Research & Management Science OH1MF hybrid 2023-03-23 WOS:000582335000018 0
J Hu, XL; Zhang, JW; Li, JM; Zhang, B Hu, Xiaolin; Zhang, Jianwei; Li, Jianmin; Zhang, Bo Sparsity-Regularized HMAX for Visual Recognition PLOS ONE English Article INDEPENDENT COMPONENT ANALYSIS; OBJECT RECOGNITION; NEURONS; REPRESENTATION; ARCHITECTURE; EMERGENCE; FEATURES; STRIATE; SHIFT; MODEL About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC) or independent component analysis (ICA), two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC) and medial temporal lobe (MTL). Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision. [Hu, Xiaolin; Li, Jianmin; Zhang, Bo] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China; [Hu, Xiaolin; Li, Jianmin; Zhang, Bo] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Zhang, Jianwei] Univ Hamburg, Dept Informat, Hamburg, Germany Tsinghua University; Tsinghua University; University of Hamburg Hu, XL (corresponding author), Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. xlhu@tsinghua.edu.cn Hu, Xiaolin/K-2443-2013; Tong, Raymond Kai-yu/C-3546-2009 Tong, Raymond Kai-yu/0000-0003-4375-653X National Basic Research Program (973 Program) of China [2013CB329403, 2012CB316301]; National Natural Science Foundation of China [61273023]; Beijing Natural Science Foundation [4132046]; Tsinghua University Initiative Scientific Research Program [20121088071]; Deutsche Forschungsgemeinschaft-Chinese Ministry of Education International Research Training Group IGK 1247 CINACS National Basic Research Program (973 Program) of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Tsinghua University Initiative Scientific Research Program; Deutsche Forschungsgemeinschaft-Chinese Ministry of Education International Research Training Group IGK 1247 CINACS This work was supported by the National Basic Research Program (973 Program) of China (Grant Nos. 2013CB329403 and 2012CB316301), the National Natural Science Foundation of China (Grant No. 61273023), the Beijing Natural Science Foundation (Grant No. 4132046), the Tsinghua University Initiative Scientific Research Program (Grant No. 20121088071), and the Deutsche Forschungsgemeinschaft-Chinese Ministry of Education International Research Training Group IGK 1247 CINACS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 46 43 46 0 19 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JAN 2 2014.0 9.0 1 e81813 10.1371/journal.pone.0081813 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 286ID 24392078.0 Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000329460100002 0
J Su, YC; Li, J; Plaza, A; Marinoni, A; Gamba, P; Chakravortty, S Su, Yuanchao; Li, Jun; Plaza, Antonio; Marinoni, Andrea; Gamba, Paolo; Chakravortty, Somdatta DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep autoencoder network (DAEN); deep learning; endmember identification; hyperspectral unmixing; variational autoencoder (VAE) SPECTRAL MIXTURE ANALYSIS; ENDMEMBER EXTRACTION; FAST ALGORITHM Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance. [Su, Yuanchao] Sun Yat Sen Univ, Sch Geog & Planning, Ctr Integrated Geog Informat Anal, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Li, Jun] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Li, Jun] Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10071, Spain; [Marinoni, Andrea] UiT Arctic Univ Norway, Dept Phys & Technol, Ctr Integrated Remote Sensing & Forecasting Arcti, Earth Observat Grp, N-9037 Tromso, Norway; [Gamba, Paolo] Univ Pavia, Dept Elect Comp & Biomed Engn, Telecommun & Remote Sensing Lab, I-27100 Pavia, Italy; [Chakravortty, Somdatta] Maulana Abul Kalam Azad Univ Technol, Dept Informat Technol, Kolkata 700064, India Sun Yat Sen University; Hunan University; Universidad de Extremadura; UiT The Arctic University of Tromso; University of Pavia; Maulana Abul Kalam Azad University of Technology Li, J (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. suych3@mail2.sysu.edu.cn; lijun48@mail.sysu.edu.cn; aplaza@unex.es; andrea.marinoni@unipv.it; paolo.gamba@unipv.it; csomdatta@rediffmail.com Li, Jun/E-6840-2019; Gamba, Paolo E/G-1959-2010; Chakravortty, Somdatta/GVS-3700-2022; Su, Yuanchao/ABD-6108-2021; Li, Jun/AAD-7233-2020; Plaza, Antonio/C-4455-2008; Marinoni, Andrea/F-4610-2017 Li, Jun/0000-0003-1613-9448; Gamba, Paolo E/0000-0002-9576-6337; Su, Yuanchao/0000-0002-4776-0862; Li, Jun/0000-0003-1613-9448; Plaza, Antonio/0000-0002-9613-1659; Chakravortty, Somdatta/0000-0002-9121-4949; Marinoni, Andrea/0000-0001-6789-0915 National Natural Science Foundation of China [61771496]; Guangdong Provincial Natural Science Foundation [2016A030313254]; National Key Research and Development Program of China [2017YFB0502900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 61771496, in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254, and in part by the National Key Research and Development Program of China under Grant 2017YFB0502900. 55 122 129 20 113 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUL 2019.0 57.0 7 4309 4321 10.1109/TGRS.2018.2890633 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology IF9TS 2023-03-23 WOS:000473436000012 0
J Ren, ZW; Mukherjee, M; Lloret, J; Venu, P Ren, Zhenwen; Mukherjee, Mithun; Lloret, Jaime; Venu, P. Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Clustering; cognitive computing; graph learning; industrial Internet of Things (IIoT); multiple kernel clustering (MKC) IDENTIFICATION; ROBUST In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method. [Ren, Zhenwen] Southwest Univ Sci & Technol, Dept Natl Def Sci & Technol, Mianyang 621010, Sichuan, Peoples R China; [Ren, Zhenwen] Nanjing Univ Sci & Technol, Dept Comp Sci & Engn, Nanjing 210094, Peoples R China; [Mukherjee, Mithun] Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing 210044, Peoples R China; [Lloret, Jaime] Univ Politecn Valencia, Inst Invest Gest Integrada Zonas Costeras, Valencia 46022, Spain; [Lloret, Jaime] Staffordshire Univ, Sch Comp & Digital Technol, Stoke On Trent ST4 2DE, Staffs, England; [Venu, P.] SCMS Grp Educ Inst, SCMS Sch Engn & Technol, Dept Mech Engn, Ernakulam 683582, India Southwest University of Science & Technology - China; Nanjing University of Science & Technology; Nanjing University of Information Science & Technology; Universitat Politecnica de Valencia; Staffordshire University Mukherjee, M (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing 210044, Peoples R China. rzw@njust.edu.cn; m.mukherjee@ieee.org; jlloret@dcom.upv.es; venu@scmsgroup.org Lloret, Jaime/H-3994-2013; P, Venu/AAW-1614-2021 Lloret, Jaime/0000-0002-0862-0533; P, Venu/0000-0001-9466-6740; P, Venu/0000-0003-3507-1389; REN, ZHEN WEN/0000-0003-3791-9750 Sichuan Science and Technology Program [2020ZDZX0014, 2019ZDZX0119]; Key Lab of Film and TV Media Technology of Zhejiang Province [2020E10015] Sichuan Science and Technology Program; Key Lab of Film and TV Media Technology of Zhejiang Province This work was supported in part by Sichuan Science and Technology Program under Grant 2020ZDZX0014 and Grant 2019ZDZX0119 and in part by the Key Lab of Film and TV Media Technology of Zhejiang Province under Grant 2020E10015. 30 15 16 7 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. APR 2021.0 17.0 4 2956 2963 10.1109/TII.2020.3010357 0.0 8 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering PS3GR Green Published 2023-03-23 WOS:000607814600065 0
J Deng, LW; Sun, H; Sun, R; Zhao, Y; Su, H Deng, Liwei; Sun, Hao; Sun, Rui; Zhao, Yan; Su, Han Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY English Article Similar subtrajectory search; graph neural networks; reading comprehension DISTANCE; PATTERNS Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly. [Deng, Liwei; Sun, Hao; Sun, Rui] Univ Elect Sci & Technol China, Chengdu, Peoples R China; [Zhao, Yan] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark; [Su, Han] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Denmark; [Su, Han] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Chengdu, Denmark University of Electronic Science & Technology of China; Aalborg University Zhao, Y (corresponding author), Aalborg Univ, Dept Comp Sci, Aalborg, Denmark. deng_liwei@std.uestc.edu.cn; sunhao19990814@gmail.com; sunrui@std.uestc.edu.cn; yanz@cs.aau.dk; hansu@uestc.edu.cn Deng, Liwei/AAN-9893-2020 Deng, Liwei/0000-0002-3996-5569; Sun, Hao/0000-0001-8456-7925 NSFC [61972069, 61836007, 61832017] NSFC(National Natural Science Foundation of China (NSFC)) This work is supported by NSFC (No. 61972069, 61836007, 61832017). 53 0 0 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 2157-6904 2157-6912 ACM T INTEL SYST TEC ACM Trans. Intell. Syst. Technol. JUN 2022.0 13.0 3 35 10.1145/3456723 0.0 22 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1H7RC 2023-03-23 WOS:000796735600002 0
J Zhang, CZ; Wang, JR; Yen, GG; Zhao, CQ; Sun, QY; Tang, Y; Qian, F; Kurths, J Zhang, Chongzhen; Wang, Jianrui; Yen, Gary G.; Zhao, Chaoqiang; Sun, Qiyu; Tang, Yang; Qian, Feng; Kurths, Juergen When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey PATTERNS English Review GENERATIVE ADVERSARIAL NETWORKS; IMAGE SUPERRESOLUTION; DOMAIN ADAPTATION; MODEL; NAVIGATION; INTERPOLATION; ROBOTICS With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making, and control for autonomous systems have improved significantly in recent years. When autonomous systems consider the performance of accuracy and transferability, several AI methods, such as adversarial learning, reinforcement learning (RL), and meta-learning, show their powerful performance. Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability. Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. Transferability means that when a well-trained model is transferred to other testing domains, the accuracy is still good. Firstly, we introduce some basic concepts of transfer learning and then present some preliminaries of adversarial learning, RL, and meta-learning. Secondly, we focus on reviewing the accuracy or transferability or both of these approaches to show the advantages of adversarial learning, such as generative adversarial networks, in typical computer vision tasks in autonomous systems, including image style transfer, image super-resolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection, and person re-identification. We furthermore review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation, and robotic manipulation. Finally, we discuss several challenges and future topics for the use of adversarial learning, RL, and meta-learning in autonomous systems. [Zhang, Chongzhen; Wang, Jianrui; Zhao, Chaoqiang; Sun, Qiyu; Tang, Yang; Qian, Feng] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China; [Yen, Gary G.] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74075 USA; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Kurths, Juergen] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany East China University of Science & Technology; Oklahoma State University System; Oklahoma State University - Stillwater; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin Tang, Y (corresponding author), East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China. yangtang@ecust.edu.cn Sun, Qiyu/0000-0002-1401-8768; Yen, Gary/0000-0001-8851-5348; Zhao, Chaoqiang/0000-0002-3651-2177 National Key Research and Development Program of China [2018YFC0809302]; National Natural Science Foundation of China [61988101, 61751305, 61673176]; Program of Introducing Talents of Discipline to Universities (the 111 Project) [B17017] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program of Introducing Talents of Discipline to Universities (the 111 Project)(Ministry of Education, China - 111 Project) The authors would like to thank the Editor-in-Chief, Scientific Editor, and anonymous referees for their helpful comments and suggestions, which have greatly improved this paper. This work was supported by the National Key Research and Development Program of China under grant 2018YFC0809302; the National Natural Science Foundation of China under grant nos. 61988101, 61751305, and 61673176; and by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under grant B17017. 280 12 13 8 31 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2666-3899 PATTERNS Patterns JUL 10 2020.0 1.0 4 100050 10.1016/j.patter.2020.100050 0.0 28 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Emerging Sources Citation Index (ESCI) Computer Science SH0KV 33205114.0 Green Submitted, gold 2023-03-23 WOS:000653827800003 0
J Zhang, X; Lin, W; Yao, JM; Jiao, CF; Romero, AM; Rodriguez, TR; Hergert, H Zhang, X.; Lin, W.; Yao, J. M.; Jiao, C. F.; Romero, A. M.; Rodriguez, T. R.; Hergert, H. Optimization of the generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double-beta decay: Ridge regression for nuclei with axial deformation PHYSICAL REVIEW C English Article PROJECTION Background: The generator coordinate method (GCM) is an important tool of choice for modeling large -amplitude collective motion in atomic nuclei. Recently, it has attracted increasing interest as it can be exploited to extend ab initio methods to the collective excitations of medium-mass and heavy deformed nuclei, as well as the nuclear matrix elements (NME) of candidates for neutrinoless double-beta (0 nu beta beta) decay.Purpose: The computational complexity of the GCM increases rapidly with the number of collective co-ordinates. It imposes a strong restriction on the applicability of the method. We aim to exploit statistical machine-learning (ML) algorithms to speed up GCM calculations and ultimately provide a more efficient description of nuclear energy spectra and other observables such as the NME of 0 nu beta beta decay without loss of accuracy. Method: In this work, we propose a subspace-reduction algorithm that employs optimal statistical ML models as surrogates for exact quantum-number projection calculations for norm and Hamiltonian kernels. The model space of the original GCM is reduced to a subspace relevant for nuclear low energy spectra and the NME of ground state to ground state 0 nu beta beta decay based on the orthogonality condition (OC) and the energy-transition -orthogonality procedure (ENTROP), respectively. Nuclear energy spectra are determined by the GCM through the configuration mixing within this subspace. For simplicity, the polynomial ridge regression (RR) algorithm is used to learn the norm and Hamiltonian kernels of axially deformed configurations. The efficiency and accuracy of this algorithm are illustrated for 76Ge and 76Se by comparing results obtained using the optimal RR models to direct GCM calculations. The nonrelativistic Gogny force D1S and relativistic energy density functional PC-PK1, a valence-space shell-model Hamiltonian, and a modern nuclear interaction derived from chiral effective field theory are employed.Results: The low-lying energy spectra of 76Ge and 76Se, as well as the 0 nu beta beta-decay NME between their ground states, are computed. The results show that the performance of the GCM+OC/ENTROP+RR is more robust than that of the GCM + RR alone, and the former can reproduce the results of the original GCM calculation accurately with a significantly reduced computational cost.Conclusions: Statistical ML algorithms, when implemented properly, can accelerate GCM calculations without loss of accuracy. In applications with axially deformed states, the computation time can be reduced by a factor of three to nine for energy spectra and NMEs, respectively. This factor is expected to increase significantly with the number of employed generator coordinates. [Zhang, X.; Lin, W.; Yao, J. M.; Jiao, C. F.] Sun Yat sen Univ, Sch Phys & Astron, Zhuhai 519082, Peoples R China; [Romero, A. M.] Univ N Carolina, Dept Phys & Astron, Chapel Hill, NC 27516 USA; [Romero, A. M.] Univ Barcelona UB, Dept Fis Quant & Astrofis FQA, c Marti i Franques, 1, Barcelona 08028, Spain; [Romero, A. M.] Univ Barcelona UB, Inst Ciencies Cosmos ICCUB, c Marti i Franques, 1, Barcelona 08028, Spain; [Rodriguez, T. R.] Univ Complutense Madrid, Dept Estruct Mat Fis Term & Elect, E-28040 Madrid, Spain; [Rodriguez, T. R.] Univ Autonoma Madrid, Dept Fis Teor, E-28049 Madrid, Spain; [Rodriguez, T. R.] UAM, Ctr Invest Avanzada Fis Fundamental CIAFF, E-28049 Madrid, Spain; [Hergert, H.] Michigan State Univ, Facil Rare Isotope Beams, E Lansing, MI 48824 USA; [Hergert, H.] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA Sun Yat Sen University; University of North Carolina; University of North Carolina Chapel Hill; University of Barcelona; University of Barcelona; Complutense University of Madrid; Autonomous University of Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - UAM - Institut de Fisica Teorica (IFT); Autonomous University of Madrid; Michigan State University; Michigan State University Yao, JM (corresponding author), Sun Yat sen Univ, Sch Phys & Astron, Zhuhai 519082, Peoples R China. yaojm8@sysu.edu.cn National Natural Science Foundation of China [12141501, 12275369]; Fundamental Research Funds for the Central Universities, Sun Yat-sen University; Spanish MCIN [PGC2018-094583-B-I00, PID2021-127890NB-I00]; U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE- SC0017887]; NextGenerationEU/PRTR; [DE-SC0018083] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, Sun Yat-sen University(Fundamental Research Funds for the Central Universities); Spanish MCIN; U.S. Department of Energy, Office of Science, Office of Nuclear Physics(United States Department of Energy (DOE)); NextGenerationEU/PRTR; We thank B. Bally, J. J. Chen, J. Engel, Z. M. Niu, L. G. Pang, R. Wirth, X. L. Zhang, and Y. N. Zhang for fruitful discussions. This work is partly supported by the National Natural Science Foundation of China (Grants No. 12141501 and No. 12275369) and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University. A.M.R. acknowledges the support from NextGenerationEU/PRTR funding. The work of T.R.R. was funded by the Span- ish MCIN under Contracts No. PGC2018-094583-B-I00 and No. PID2021-127890NB-I00. H.H. acknowledges the support of the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Awards No. DE- SC0017887 and No. DE-SC0018083 (NUCLEI SciDAC-4 Collaboration) . 92 0 0 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9985 2469-9993 PHYS REV C Phys. Rev. C FEB 8 2023.0 107.0 2 24304 10.1103/PhysRevC.107.024304 0.0 14 Physics, Nuclear Science Citation Index Expanded (SCI-EXPANDED) Physics 8Y9MP Green Submitted 2023-03-23 WOS:000933015400002 0
J Jiang, YW; Wang, HK; Sun, XB; Li, CS; Wu, TN Jiang, Yuwei; Wang, Hongkai; Sun, Xiaobang; Li, Congsheng; Wu, Tongning Evaluation of Chinese populational exposure to environmental electromagnetic field based on stochastic dosimetry and parametric human modelling ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH English Article; Early Access Specific absorption rate; Radiofrequency electromagnetic field; Anatomical model; Deterministic simulation; Surrogate model; Polynomial chaos expansion POLYNOMIAL CHAOS EXPANSIONS; SLICE-BASED SUPINE; DIELECTRIC-PROPERTIES; BIOLOGICAL TISSUES; ANATOMICAL MODELS; FAR-FIELD; FREQUENCY; ABSORPTION; DEFORMATION This study aimed to estimate the distribution of the whole-body averaged specific absorption rate (WBSAR) using several measurable physique parameters for Chinese adult population exposed to environmental electromagnetic fields (EMFs) of current wireless communication frequencies, and to discuss the effects of these physique parameters in the frequency-dependent dosimetric results. The physique distribution of Chinese adults was obtained from the National Physical Fitness and Health Database comprising 81,490 adult samples. The number of physique parameters used to construct the surrogate model was reduced to three via mutual information analysis. A stochastic method with 40 deterministic simulations was used to generate frequency-dependent and gender-specific surrogate models for WBSAR via polynomial chaos expansion. In the simulations, we constructed anatomically correct models conforming to the targeted physique parameters via deformable human modelling technique, which was based on deep learning from the image database including 767 Chinese adults. Thereafter, we analysed the sensitivity of the physique parameters to WBSAR by covariance-based Sobol decomposition. The results indicated that the generated models were consistent with the targeted physique parameters. The estimated dosimetric results were validated using finite-difference time-domain simulations (the error was < 6% across all the investigated frequencies for WBSAR). The novelty of the study included that it demonstrated the feasibility of estimating the individual WBSAR using a limited number of physique parameters with the aid of surrogate modelling. In addition, the population-based distribution of the WBSAR in Chinese adults was firstly presented in the manuscript. The results also indicated that the different combinations of physique parameter, dependent on genders and frequencies, significantly influenced the WBSAR, although the general conservativeness of the guidelines of the International Commission on Non-Ionizing Radiation and Protection can be confirmed in the surveyed population. [Jiang, Yuwei; Li, Congsheng; Wu, Tongning] China Acad Informat & Commun Technol, 52 Huayuan Bei Rd, Beijing 100191, Peoples R China; [Wang, Hongkai; Sun, Xiaobang] Dalian Univ Technol, Sch Biomed Engn, Dalian 116024, Peoples R China; [Sun, Xiaobang] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland China Academy of Information & Communication Technology; Dalian University of Technology; University of Jyvaskyla Wu, TN (corresponding author), China Acad Informat & Commun Technol, 52 Huayuan Bei Rd, Beijing 100191, Peoples R China. wutongning@caict.ac.cn Sun, Xiaobang/0000-0001-8797-6337; Wu, Tongning/0000-0002-9894-9518 National Natural Science Foundation of China; [61971445]; [62271508] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; This study was supported by the National Natural Science Foundation of China (grant no. 61971445, 62271508). 51 0 0 3 3 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0944-1344 1614-7499 ENVIRON SCI POLLUT R Environ. Sci. Pollut. Res. 10.1007/s11356-023-25153-y 0.0 JAN 2023 16 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 7S5LO 36609755.0 2023-03-23 WOS:000910794900002 0
J Li, N; Zare, M; Yi, CK; Jimenez, R Li, Ning; Zare, Masoud; Yi, Congke; Jimenez, Rafael Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH English Article rock pillar; Logistic Model Trees (LMT); stability prediction; cross-validation SPATIAL PREDICTION; CROWN PILLAR; REGRESSION; STRENGTH; MINE; CLASSIFICATION; DESIGN; SIZE; ROOM Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of underground pillar stability is important. This paper presents a novel application of Logistic Model Trees (LMT) to predict underground pillar stability. Seven parameters-pillar width, pillar height, ratio of pillar width to height, uniaxial compressive strength of rock, average pillar stress, underground depth, and Bord width-are employed to construct LMTs for rock and coal pillars. The LogitBoost algorithm is applied to train on two data sets of rock and coal pillar case histories. The two models are validated with (i) 10-fold cross-validation and with (ii) another set of new case histories. Results suggest that the accuracy of the proposed LMT is the highest among other common machine learning methods previously employed in the literature. Moreover, a sensitivity analysis indicates that the average stress, p, and the ratio of pillar width to height, r, are the most influential parameters for the proposed models. [Li, Ning] Anhui Univ Sci & Technol, Sch Min Engn, Huainan 232001, Peoples R China; [Zare, Masoud] United Consulting Grp Ltd, Norcross, GA 30071 USA; [Yi, Congke; Jimenez, Rafael] Tech Univ Madrid, ETSI Caminos Canales & Puertos, Madrid 28040, Spain Anhui University of Science & Technology; Universidad Politecnica de Madrid Zare, M (corresponding author), United Consulting Grp Ltd, Norcross, GA 30071 USA. nli@aust.edu.cn; mzare@unitedconsulting.com; congke.yi@alumnos.upm.es; rafael.jimenez@upm.es Spanish Ministry of Science and Innovation [PID2019-108060RB-I00] Spanish Ministry of Science and Innovation(Ministry of Science and Innovation, Spain (MICINN)Spanish Government) FundingThis research was funded by research startup of Anhui University of Science and Technology grant number (11696), youth funds of Anhui University of Science and Technology grant number (QN2019116), China Postdoctoral Fund grant number (2020M681975). And the APC was funded by (2020M681975). 55 2 2 11 24 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1660-4601 INT J ENV RES PUB HE Int. J. Environ. Res. Public Health FEB 2022.0 19.0 4 2136 10.3390/ijerph19042136 0.0 19 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Public, Environmental & Occupational Health ZT4GE 35206322.0 Green Accepted, gold 2023-03-23 WOS:000769115700001 0
J Garcia, SL; Lauritsen, J; Zhang, ZY; Bandak, M; Dalgaard, MD; Nielsen, RL; Daugaard, G; Gupta, R Garcia, Sara L.; Lauritsen, Jakob; Zhang, Zeyu; Bandak, Mikkel; Dalgaard, Marlene D.; Nielsen, Rikke L.; Daugaard, Gedske; Gupta, Ramneek Prediction of Nephrotoxicity Associated With Cisplatin-Based Chemotherapy in Testicular Cancer Patients JNCI CANCER SPECTRUM English Article GENETIC POLYMORPHISMS; POPULATION; MECHANISMS; MORTALITY; TOXICITY; TARGET; DRUGS; RISK Background: Cisplatin-based chemotherapy may induce nephrotoxicity. This study presents a random forest predictive model that identifies testicular cancer patients at risk of nephrotoxicity before treatment. Methods: Clinical data and DNA from saliva samples were collected for 433 patients. These were genotyped on Illumina Human Omni Express Exome-8 v1.2 (964 193 markers). Clinical and genomics-based random forest models generated a risk score for each individual to develop nephrotoxicity defined as a 20% drop in isotopic glomerular filtration rate during chemotherapy. The area under the receiver operating characteristic curve was the primary measure to evaluate models. Sensitivity, specificity, and positive and negative predictive values were used to discuss model clinical utility. Results: Of 433 patients assessed in this study, 26.8% developed nephrotoxicity after bleomycin-etoposide-cisplatin treatment. Genomic markers found to be associated with nephrotoxicity were located at NAT1, NAT2, and the intergenic region of CNTN6 and CNTN4. These, in addition to previously associated markers located at ERCC1, ERCC2, and SLC22A2, were found to improve predictions in a clinical feature-trained random forest model. Using only clinical data for training the model, an area under the receiver operating characteristic curve of 0.635 (95% confidence interval [CI] = 0.629 to 0.640) was obtained. Retraining the classifier by adding genomics markers increased performance to 0.731 (95% CI = 0.726 to 0.736) and 0.692 (95% CI = 0.688 to 0.696) on the holdout set. Conclusions: A clinical and genomics-based machine learning algorithm improved the ability to identify patients at risk of nephrotoxicity compared with using clinical variables alone. Novel genetics associations with cisplatin-induced nephrotoxicity were found for NAT1, NAT2, CNTN6, and CNTN4 that require replication in larger studies before application to clinical practice. [Garcia, Sara L.; Zhang, Zeyu; Dalgaard, Marlene D.; Nielsen, Rikke L.; Gupta, Ramneek] Tech Univ Denmark, Dept Hlth Technol, Lyngby, Denmark; [Lauritsen, Jakob; Bandak, Mikkel; Daugaard, Gedske] Copenhagen Univ Hosp, Dept Oncol, Blegdamsvej 9, DK-2100 Copenhagen, Denmark; [Zhang, Zeyu] Univ Chinese Acad Sci, Inst Genet & Dev Biol, Key Lab Genet Network Biol, Beijing, Peoples R China; [Nielsen, Rikke L.] Univ Chinese Acad Sci, Sino Danish Ctr Educ & Res, Eastern Yanqihu Campus, Beijing, Peoples R China Technical University of Denmark; University of Copenhagen; Chinese Academy of Sciences; Institute of Genetics & Developmental Biology, CAS; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Lauritsen, J (corresponding author), Copenhagen Univ Hosp, Dept Oncol, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. jakob.lauritsen@regionh.dk Gupta, Ramneek/G-7278-2012 Gupta, Ramneek/0000-0001-6841-6676; Nielsen, Rikke Linnemann/0000-0003-0173-2134; Lauritsen, Jakob/0000-0002-9985-174X; Dalgaard, Marlene Danner/0000-0002-4036-6408; Daugaard, Gedske/0000-0002-9618-9180; Ludovico Garcia, Sara/0000-0002-2784-2318 Danish cancer society [R40A2119]; Idella Foundation; Sino-Danish Center for Education and Research Danish cancer society(Danish Cancer Society); Idella Foundation; Sino-Danish Center for Education and Research This work was supported by the Danish cancer society (R40A2119). SLG was supported by Idella Foundation. ZZ and RLN were supported by Sino-Danish Center for Education and Research. 47 9 9 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 2515-5091 JNCI CANCER SPECT JNCI Cancer Spectr. JUN 2020.0 4.0 3 pkaa032 10.1093/jncics/pkaa032 0.0 8 Oncology Emerging Sources Citation Index (ESCI) Oncology PS6DW 32617516.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000608017500012 0
J Najafzadeh, M; Noori, R; Afroozi, D; Ghiasi, B; Hosseini-Moghari, SM; Mirchi, A; Haghighi, AT; Klove, B Najafzadeh, Mohammad; Noori, Roohollah; Afroozi, Diako; Ghiasi, Behzad; Hosseini-Moghari, Seyed-Mohammad; Mirchi, Ali; Haghighi, Ali Torabi; Klove, Bjorn A comprehensive uncertainty analysis of model-estimated longitudinal and lateral dispersion coefficients in open channels JOURNAL OF HYDROLOGY English Article Open channel; Machine learning models; Rates of mixing; Uncertainty NATURAL STREAMS; WATER-QUALITY; PREDICTING DISPERSION; SOLUTE TRANSPORT; RIVER; STRAIGHT; PARAMETERS; EQUATION The complexity of pollutant-mixing mechanism in open channels generates large uncertainty in estimation of longitudinal and lateral dispersion coefficients (K-x and K-y). Therefore, K-x and K-y estimation in rivers should be accompanied by an uncertainty analysis, a subject mainly ignored in previous studies. We introduce a method based on thorough analysis of different calibration datasets, resampled from a global database of tracer studies, to determine the uncertainty associated with five applicable intelligent models for estimation of K-x and K-y (model tree, evolutionary polynomial regression (EPR), gene expression programming, multivariate adaptive regression splines (MARS), and support vector machine (SVM)). Our findings suggest that SVM gives least uncertainty in both K-x and K-y estimation, while EPR and MARS generate most uncertainty in K-x and K-y estimation, respectively. By considering significant uncertainty in the model estimations, we suggest that the methodology we introduce here for uncertainty determination of the models be incorporated in empirical studies on estimation of K-x and K-y in rivers. [Najafzadeh, Mohammad; Afroozi, Diako] Grad Univ Adv Technol, Fac Civil & Surveying Engn, Dept Water Engn, Kerman 76315116, Iran; [Noori, Roohollah; Haghighi, Ali Torabi; Klove, Bjorn] Univ Oulu, Fac Technol, Water Energy & Environm Engn Res Unit, Oulu 90014, Finland; [Ghiasi, Behzad] Univ Tehran, Coll Engn, Sch Environm, Tehran 1417853111, Iran; [Hosseini-Moghari, Seyed-Mohammad] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; [Mirchi, Ali] Oklahoma State Univ, Dept Biosyst & Agr Engn, 111 Agr Hall, Stillwater, OK 74078 USA; [Noori, Roohollah] Iran Univ Sci & Technol, Sch Civil Engn, Tehran 1684613114, Iran Graduate University of Advanced Technology; University of Oulu; University of Tehran; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Oklahoma State University System; Oklahoma State University - Stillwater; Iran University Science & Technology Noori, R (corresponding author), Univ Oulu, Fac Technol, Water Energy & Environm Engn Res Unit, Oulu 90014, Finland.;Noori, R (corresponding author), Iran Univ Sci & Technol, Sch Civil Engn, Tehran 1684613114, Iran. roohollah.noori@oulu.fi Najafzadeh, Mohammad/K-1192-2019 Najafzadeh, Mohammad/0000-0002-4100-9699; Ghiasi, Behzad/0000-0002-1408-0633 Arctic Interactions (ArcI) Visit Grant program, Profi 4, University of Oulu Arctic Interactions (ArcI) Visit Grant program, Profi 4, University of Oulu The first author acknowledges the support from the Arctic Interactions (ArcI) Visit Grant program, Profi 4, University of Oulu. 77 18 18 3 8 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. DEC 2021.0 603.0 A 126850 10.1016/j.jhydrol.2021.126850 0.0 SEP 2021 9 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources WF4YZ Green Published, hybrid 2023-03-23 WOS:000706313000036 0
J Li, R; Wang, LB; Zhang, C; Duan, CX; Zheng, SY Li, Rui; Wang, Libo; Zhang, Ce; Duan, Chenxi; Zheng, Shunyi A(2)-FPN for semantic segmentation of fine-resolution remotely sensed images INTERNATIONAL JOURNAL OF REMOTE SENSING English Article semantic segmentation; deep learning; attention mechanism LAND-COVER; DEEP; NETWORK The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network (A(2)-FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our A(2)-FPN in segmentation accuracy. Code is available at . [Li, Rui; Wang, Libo; Zheng, Shunyi] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Peoples R China; [Zhang, Ce] Univ Lancaster, Lancaster Environm Ctr, Lancaster, England; [Zhang, Ce] UK Ctr Ecol & Hydrol, Lancaster, England; [Duan, Chenxi] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Hengelosestr 99, NL-7514 AE Enschede, Netherlands Wuhan University; Lancaster University; UK Centre for Ecology & Hydrology (UKCEH); University of Twente Duan, CX (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Hengelosestr 99, NL-7514 AE Enschede, Netherlands. c.duan@utwente.nl Li, Rui/ADH-9550-2022; Zheng, sy/GRS-3136-2022; Wang, Libo/GLR-6297-2022; Wang, Libo/GLR-5763-2022 Li, Rui/0000-0001-7858-3160; Wang, Libo/0000-0001-8096-6531; Duan, Chenxi/0000-0003-0056-3295; Zhang, Ce/0000-0001-5100-3584 National Natural Science Foundation of China [41671452] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China [No.41671452]. 43 7 7 15 33 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. FEB 1 2022.0 43.0 3 1131 1155 2030071 10.1080/01431161.2022.2030071 0.0 25 Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing; Imaging Science & Photographic Technology ZI4TZ Green Submitted, Green Accepted, hybrid 2023-03-23 WOS:000761615900001 0
J Liu, YW; Zhang, SW; Mu, XD; Ding, ZG; Schober, R; Al-Dhahir, N; Hossain, E; Shen, XM Liu, Yuanwei; Zhang, Shuowen; Mu, Xidong; Ding, Zhiguo; Schober, Robert; Al-Dhahir, Naofal; Hossain, Ekram; Shen, Xuemin Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Article NOMA; Next generation networking; MIMO communication; Silicon carbide; Interference; 6G mobile communication; Time division multiple access; 6G; next generation multiple access (NGMA); non-orthogonal multiple access (NOMA); multi-antenna techniques; mathematical optimization; machine learning MASSIVE-MIMO-NOMA; INTELLIGENT REFLECTING SURFACE; OPTIMAL RESOURCE-ALLOCATION; POWER ALLOCATION; WIRELESS NETWORKS; SUM-RATE; BEAMFORMING DESIGN; SPATIAL MODULATION; COOPERATIVE NOMA; BEAMSPACE MIMO Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work. [Liu, Yuanwei] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Zhang, Shuowen] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Peoples R China; [Mu, Xidong] Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China; [Ding, Zhiguo] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England; [Schober, Robert] Friedrich Alexander Univ Erlangen Nurnberg FAU, Inst Digital Commun, D-91054 Erlangen, Germany; [Al-Dhahir, Naofal] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA; [Hossain, Ekram] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB R2M 2J8, Canada; [Shen, Xuemin] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada University of London; Queen Mary University London; Hong Kong Polytechnic University; Beijing University of Posts & Telecommunications; University of Manchester; University of Erlangen Nuremberg; University of Texas System; University of Texas Dallas; University of Manitoba; University of Waterloo Liu, YW (corresponding author), Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England. yuanwei.liu@qmul.ac.uk; shuowen.zhang@polyu.edu.hk; muxidong@bupt.edu.cn; zhiguo.ding@manchester.ac.uk; robert.schober@fau.de; aldhahir@utdallas.edu; ekram.hossain@umanitoba.ca; sshen@uwaterloo.ca Shen, Xuemin/AAH-2564-2020; Liu, Yuanwei/AAA-2444-2020; Mu, Xidong/ABD-8992-2021; Aldhahir, Naofal/HCH-5192-2022 Shen, Xuemin/0000-0002-4140-287X; Liu, Yuanwei/0000-0002-6389-8941; Mu, Xidong/0000-0001-8351-360X; Al-Dhahir, Naofal/0000-0001-9894-8382 255 33 33 11 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. APR 2022.0 40.0 4 1037 1071 10.1109/JSAC.2022.3145234 0.0 35 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications ZU7AW Green Submitted 2023-03-23 WOS:000769993400005 0
J Shao, HK; Zhong, DX; Du, XF; Du, SY; Veldhuis, RNJ Shao, Huikai; Zhong, Dexing; Du, Xuefeng; Du, Shaoyi; Veldhuis, Raymond N. J. Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Biometrics; few-shot learning; information security; meta-learning; palmprint recognition UNCONSTRAINED PALMPRINT; DEEP; PCANET Palmprint is one of the discriminant biometric modalities of humans. Recently, deep learning-based palmprint recognition algorithms have improved the accuracy and robustness of recognition results to a new level. Most of them require a large amount of labeled training samples to guarantee satisfactory performance. However, getting enough labeled data is difficult due to time consumption and privacy issues. Therefore, in this article, a novel meta-Siamese network (MSN) is proposed to exploit few-shot learning for small-sample palmprint recognition. During each episode-based training iteration, a few images are selected as sample and query sets to simulate the support and testing sets in the test set. Specifically, the model is trained episodically with a flexible framework to learn both the feature embedding and deep similarity metric function. In addition, two distance-based losses are introduced to assist the optimization. After training, the model can learn the ability to get similarity scores between two images for few-shot testing. Adequate experiments conducted on several constrained and unconstrained benchmark palmprint databases show that MSN can obtain competitive improvements compared with baseline methods, where the best accuracy can be up to 100%. [Shao, Huikai; Zhong, Dexing] Xi An Jiao Tong Univ, Sch Automat Sci & Engn, Xian 710049, Peoples R China; [Zhong, Dexing] Pazhou Lab, Guangzhou 510335, Peoples R China; [Zhong, Dexing] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China; [Du, Xuefeng] Univ Wisconsin, Dept Comp Sci, Madison, WI 53201 USA; [Du, Shaoyi] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China; [Veldhuis, Raymond N. J.] Univ Twente, Fac Elect Engn Math & Comp Sci, NL-7500 AE Enschede, Netherlands Xi'an Jiaotong University; Pazhou Lab; Nanjing University; University of Wisconsin System; University of Wisconsin Madison; Xi'an Jiaotong University; University of Twente Zhong, DX (corresponding author), Xi An Jiao Tong Univ, Sch Automat Sci & Engn, Xian 710049, Peoples R China.;Zhong, DX (corresponding author), Pazhou Lab, Guangzhou 510335, Peoples R China.;Zhong, DX (corresponding author), Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China. shaohuikai@stu.xjtu.edu.cn; bell@xjtu.edu.cn; dxfsxl@163.com; dushaoyi@gmail.com; r.n.j.veldhuis@utwente.nl Zhong, Dexing/0000-0002-6806-6300; Du, Shaoyi/0000-0002-7092-0596 National Natural Science Foundation of China [61105021]; Natural Science Foundation of Zhejiang Province [LGF19F030002]; Natural Science Foundation of Shaanxi Province [2020JM-073]; Fundamental Research Funds for the Central Universities [xzy022020051]; China Scholarship Council National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province); Natural Science Foundation of Shaanxi Province(Natural Science Foundation of Shaanxi Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) This work was supported in part by the National Natural Science Foundation of China under Grant 61105021, in part by the Natural Science Foundation of Zhejiang Province under Grant LGF19F030002, in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JM-073, in part by the Fundamental Research Funds for the Central Universities under Grant xzy022020051, and in part by the China Scholarship Council. The Associate Editor coordinating the review process was Hongrui Wang. 63 4 4 4 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70.0 5009812 10.1109/TIM.2021.3076850 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation YR3HC 2023-03-23 WOS:000749884500008 0
J Khosravi, K; Golkarian, A; Booij, MJ; Barzegar, R; Sun, W; Yaseen, ZM; Mosavi, A Khosravi, Khabat; Golkarian, Ali; Booij, Martijn J.; Barzegar, Rahim; Sun, Wei; Yaseen, Zaher Mundher; Mosavi, Amir Improving daily stochastic stream flow prediction: comparison of novel hybrid data-mining algorithms HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES English Article streamflow modelling; M5P; random forest; M5Rule; bagging; Attribute Selected Classifier; data mining; Taleghan catchment ARTIFICIAL NEURAL-NETWORKS; MACHINE LEARNING-METHODS; SUPPORT VECTOR MACHINE; RUNOFF; MODEL; SIMULATION; CATCHMENT; ENSEMBLE; RIVER; ANN In the current paper, the efficiency of three new standalone data-mining algorithms [M5 Prime (M5P), Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of bagging (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with an autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from the period 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all of the the data were used (i.e. R and Q - with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, BA-M5P outperformed the other models. [Khosravi, Khabat; Golkarian, Ali] Ferdowsi Univ Mashhad, Fac Nat Resources & Environm, Dept Watershed Management Engn, Mashhad 9177948974, Razavi Khorasan, Iran; [Booij, Martijn J.] Univ Twente, Fac Engn Technol, Water Engn & Management, Enschede, Netherlands; [Barzegar, Rahim] McGill Univ, Dept Bioresource Engn, Montreal, PQ, Canada; [Barzegar, Rahim] Wilfrid Laurier Univ, Dept Geog & Environm Studies, Waterloo, ON, Canada; [Sun, Wei] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China; [Yaseen, Zaher Mundher] Al Ayen Univ, Ctr Sci Res, New Era & Dev Civil Engn Res Grp, Thi Qar 64001, Iraq; [Mosavi, Amir] Norwegian Univ Life Sci, Sch Econ & Business, As, Norway; [Mosavi, Amir] Obuda Univ, John Von Neumann Fac Informat, Budapest, Hungary Ferdowsi University Mashhad; University of Twente; McGill University; Wilfrid Laurier University; Sun Yat Sen University; Norwegian University of Life Sciences; Obuda University Golkarian, A (corresponding author), Ferdowsi Univ Mashhad, Fac Nat Resources & Environm, Dept Watershed Management Engn, Mashhad 9177948974, Razavi Khorasan, Iran. golkarian@um.ac.ir Yaseen, Zaher Mundher/G-7029-2018; Booij, Martijn J./C-7753-2011; Sun, Wei/K-9385-2017; Golkarian, Ali/ABE-9408-2021; Mosavi, Amir/I-7440-2018 Yaseen, Zaher Mundher/0000-0003-3647-7137; Booij, Martijn J./0000-0001-6208-9045; Golkarian, Ali/0000-0002-8797-0434; Mosavi, Amir/0000-0003-4842-0613 Ferdowsi University of Mashhad, Mashhad, Iran Ferdowsi University of Mashhad, Mashhad, Iran This research project was supported by Ferdowsi University of Mashhad, Mashhad, Iran. 97 14 14 1 11 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0262-6667 2150-3435 HYDROLOG SCI J Hydrol. Sci. J.-J. Sci. Hydrol. JUL 4 2021.0 66.0 9 1457 1474 10.1080/02626667.2021.1928673 0.0 JUL 2021 18 Water Resources Science Citation Index Expanded (SCI-EXPANDED) Water Resources UA1VI Green Published 2023-03-23 WOS:000673162800001 0
J Xue, Y; Tong, YL; Yuan, ZM; Su, SB; Slowik, A; Toglaw, S Xue, Yu; Tong, Yiling; Yuan, Ziming; Su, Shoubao; Slowik, Adam; Toglaw, Sam Handwritten Character Recognition Based on Improved Convolutional Neural Network INTELLIGENT AUTOMATION AND SOFT COMPUTING English Article Convolutional neural networks; handwritten character recognition; tensorflow; optimizer CLASSIFICATION; ALGORITHM Because of the characteristics of high redundancy, high parallelism and nonlinearity in the handwritten character recognition model, the convolutional neural networks (CNNs) are becoming the first choice to solve these complex problems. The complexity, the types of characters, the character similarity of the handwritten character dataset, and the choice of optimizers all have a great impact on the network model, resulting in low accuracy, high loss, and other problems. In view of the existence of these problems, an improved LeNet-5 model is proposed. Through increasing its convolutional layers and fully connected layers, higher quality features can be extracted. Secondly, a more complex dataset called EMNIST is selected and many experiments are carried out. After many experiments, the Adam optimization algorithm is finally chosen to optimize the network model. Then, for processing character similarity problems on the pre-processed EMNIST dataset, the dataset is divided into different parts and to be processed. A better-divided result is selected after the comparative experiments. Finally, the high accuracy recognition of handwritten characters is achieved. The experimental results show that the recognition accuracy of the handwritten characters reached at 88% in the test set, and the loss is low. [Xue, Yu; Tong, Yiling; Yuan, Ziming] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Xue, Yu; Su, Shoubao] Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 211169, Peoples R China; [Slowik, Adam] Koszalin Univ Technol, Dept Elect & Comp Sci, Sniadeckich 2, PL-75453 Koszalin, Poland; [Toglaw, Sam] Australian Coll Kuwait, Fac Business, Kuwait, State Of Kuwait, Kuwait Nanjing University of Information Science & Technology; Jinling Institute of Technology; Koszalin University of Technology Xue, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.;Xue, Y (corresponding author), Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 211169, Peoples R China. xueyu@nuist.edu.cn Su, Shoubao/0000-0003-2036-7646 National Natural Science Foundation of China [61876089, 61876185, 61902281, 61375121]; Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software [2019DS301]; Engineering Research Center of Digital Forensics, Ministry of Education; Key Research and Development Program of Nanjing Jiangbei New Area [ZDYF20200129]; Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software; Engineering Research Center of Digital Forensics, Ministry of Education; Key Research and Development Program of Nanjing Jiangbei New Area; Priority Academic Program Development of Jiangsu Higher Education Institutions Funding Statement: This work was partially supported by the National Natural Science Foundation of China (61876089, 61876185, 61902281, 61375121) , the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software (No.2019DS301) , the Engineering Research Center of Digital Forensics, Ministry of Education, the Key Research and Development Program of Nanjing Jiangbei New Area (ZDYF20200129) , and the Priority Academic Program Development of Jiangsu Higher Education Institutions. 31 3 3 2 20 TECH SCIENCE PRESS HENDERSON 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA 1079-8587 2326-005X INTELL AUTOM SOFT CO Intell. Autom. Soft Comput. 2021.0 29.0 2 497 509 10.32604/iasc.2021.016884 0.0 13 Automation & Control Systems; Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science UI7BS hybrid 2023-03-23 WOS:000690758200012 0
J Chen, QY; Fu, YX; Song, WQ; Cheng, KF; Lu, ZH; Zhang, C; Li, L Chen, Qinyu; Fu, Yuxiang; Song, Wenqing; Cheng, Kaifeng; Lu, Zhonghai; Zhang, Chuan; Li, Li An Efficient Streaming Accelerator for Low Bit-Width Convolutional Neural Networks ELECTRONICS English Article low bit-width convolutional neural networks; parallel streaming architecture; coarse grain task partitioning; reconfigurable; VLSI ARCHITECTURE Convolutional Neural Networks (CNNs) have been widely applied in various fields, such as image recognition, speech processing, as well as in many big-data analysis tasks. However, their large size and intensive computation hinder their deployment in hardware, especially on the embedded systems with stringent latency, power, and area requirements. To address this issue, low bit-width CNNs are proposed as a highly competitive candidate. In this paper, we propose an efficient, scalable accelerator for low bit-width CNNs based on a parallel streaming architecture. With a novel coarse grain task partitioning (CGTP) strategy, the proposed accelerator with heterogeneous computing units, supporting multi-pattern dataflows, can nearly double the throughput for various CNN models on average. Besides, a hardware-friendly algorithm is proposed to simplify the activation and quantification process, which can reduce the power dissipation and area overhead. Based on the optimized algorithm, an efficient reconfigurable three-stage activation-quantification-pooling (AQP) unit with the low power staged blocking strategy is developed, which can process activation, quantification, and max-pooling operations simultaneously. Moreover, an interleaving memory scheduling scheme is proposed to well support the streaming architecture. The accelerator is implemented with TSMC 40 nm technology with a core size of . It can achieve TOPS/W energy efficiency and area efficiency at 100.1mW, which makes it a promising design for the embedded devices. [Chen, Qinyu; Fu, Yuxiang; Song, Wenqing; Cheng, Kaifeng; Li, Li] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210093, Jiangsu, Peoples R China; [Lu, Zhonghai] KTH Royal Inst Technol, S-10044 Stockholm, Sweden; [Zhang, Chuan] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China Nanjing University; Royal Institute of Technology; Southeast University - China Fu, YX; Li, L (corresponding author), Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210093, Jiangsu, Peoples R China. cqy@smail.nju.edn.cn; yuxiangfu@nju.edu.cn; wq_song@smail.nju.edn.cn; chengkaifeng@nju.edu.cn; zhonghai@kth.se; chzhang@seu.edu.cn; lili@nju.edu.cn Lu, Zhonghai/AAP-2154-2020 National Nature Science Foundation of China [61176024]; project of the Integration of Industry, Education and Research of Jiangsu Province [BY2015069-05]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics; Fundamental Research Funds for the Central Universities National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); project of the Integration of Industry, Education and Research of Jiangsu Province; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the National Nature Science Foundation of China under Grant No. 61176024; The project on the Integration of Industry, Education and Research of Jiangsu Province BY2015069-05; The project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics; and the Fundamental Research Funds for the Central Universities. 36 4 4 3 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics APR 2019.0 8.0 4 371 10.3390/electronics8040371 0.0 22 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics HX9TJ Green Submitted, gold 2023-03-23 WOS:000467751100002 0
J Li, BY; Xu, WS; Xu, ZY; Li, JX; Peng, P Li, Boyang; Xu, Weisheng; Xu, Zhiyu; Li, Jiaxin; Peng, Peng A two-domain coordinated sentence similarity scheme for question-answering robots regarding unpredictable outliers and non-orthogonal categories APPLIED INTELLIGENCE English Article Natural language processing (NLP); Question-answering robot(Qabot); Similarity analysis; Structure domain; Semantics domain; Multi-kernel CNN enhanced BERT (MC-BERT) It is crucial and challenging for the question-answering robot (Qabot) to match the customer-input questions with the priori identification questions due to highly diversified expressions, especially in the case of Chinese. This article proposes a coordinated scheme to analyze the similarity between sentences in two independent domains instead of a single deep learning model. In the structure domain, the BLEU and data preprocessing are applied for binary analysis to discriminate the unpredictable outliers (illegal questions) to existing library. In the semantics domain, the MC-BERT model, which integrates the BERT encoder and the Multi-kernel convolutional top classifier, is developed to handle the non-orthogonality of class identification questions. The two-domain analyses are in parallel and the two similarity scores are coordinated for the final response. The linguistic features of Chinese are also taken into account. A realistic case of Qabot on energy trading service and finance is numerically studied. Computational results validate the effectiveness and accuracy of the proposed algorithm: Top-1 and Top-3 accuracies are 90.5% and 95.5%, respectively, which are significantly superior to the latest published results. [Li, Boyang] Tongji Univ, Das Chinesisch Deutsche Hsch Kolleg, Shanghai 200092, Peoples R China; [Li, Boyang] Tech Univ Munich, Elect Engn & Informat Technol, D-80333 Munich, Germany; [Xu, Weisheng; Xu, Zhiyu; Li, Jiaxin; Peng, Peng] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China; [Xu, Weisheng] Tongji Univ, Informatizat Off, Shanghai 200092, Peoples R China; [Xu, Zhiyu] Tongji Univ, Natl Comp & Informat Technol Pract Educ Demonstra, Shanghai 201084, Peoples R China Tongji University; Technical University of Munich; Tongji University; Tongji University; Tongji University Xu, ZY (corresponding author), Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.;Xu, ZY (corresponding author), Tongji Univ, Natl Comp & Informat Technol Pract Educ Demonstra, Shanghai 201084, Peoples R China. 1831437@tongji.edu.cn; xuweisheng@tongji.edu.cn; xuzhiyu@tongji.edu.cn; lijx@tongji.edu.cn; peng.peng@tongji.edu.cn National Natural Science Foundation of China [61973238, 61773292]; Innovation Program of Science and Technology Commission of Shanghai Municipality [19DZ1209200] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovation Program of Science and Technology Commission of Shanghai Municipality This work was funded by National Natural Science Foundation of China under Grant No. 61973238, 61773292, and Innovation Program of Science and Technology Commission of Shanghai Municipality under Grant No.19DZ1209200. 32 1 1 2 13 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-669X 1573-7497 APPL INTELL Appl. Intell. DEC 2021.0 51.0 12 8928 8944 10.1007/s10489-021-02269-7 0.0 APR 2021 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science WT4TD 2023-03-23 WOS:000640755200002 0
J Ren, QS; Kolwankar, KM; Samal, A; Jost, J Ren, Quansheng; Kolwankar, Kiran M.; Samal, Areejit; Jost, Juergen Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity PHYSICAL REVIEW E English Article OSCILLATIONS We study the interplay of topology and dynamics in a neural network connected with spike-timing-dependent plasticity (STDP) synapses. Stimulated with periodic spike trains, the STDP-driven network undergoes a synaptic pruning process and evolves to a residual network. We examine the variation of topological and dynamical properties of the residual network by varying two key parameters of STDP: synaptic delay and the ratio between potentiation and depression. Our extensive numerical simulations of the leaky integrate-and-fire model show that there exists two regions in the parameter space. The first corresponds to fixed-point configurations, where the distribution of peak synaptic conductances and the firing rate of neurons remain constant over time. The second corresponds to oscillating configurations, where both topological and dynamical properties vary periodically, which is a result of a fixed point becoming a limit cycle via a Hopf bifurcation. This leads to interesting questions regarding the implications of these rhythms in the topology and dynamics of the network for learning and cognitive processing. [Ren, Quansheng] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China; [Ren, Quansheng; Kolwankar, Kiran M.; Samal, Areejit; Jost, Juergen] Max Planck Inst Math Sci, D-04103 Leipzig, Germany; [Kolwankar, Kiran M.] Ramniranjan Jhunjhunwala Coll, Dept Phys, Bombay 400086, Maharashtra, India; [Samal, Areejit] CNRS, Lab Phys Theor & Modeles Stat, F-91405 Orsay, France; [Samal, Areejit] Univ Paris Sud, UMR 8626, F-91405 Orsay, France; [Jost, Juergen] Santa Fe Inst, Santa Fe, NM 87501 USA Peking University; Max Planck Society; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); UDICE-French Research Universities; Universite Paris Saclay; The Santa Fe Institute Ren, QS (corresponding author), Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China. qsren@pku.edu.cn; kiran.kolwankar@gmail.com; samal@mis.mpg.de; jost@mis.mpg.de Samal, Areejit/AAC-2485-2020; Kolwankar, Kiran M/K-5543-2017; Samal, Areejit/E-5422-2011 Kolwankar, Kiran M/0000-0001-5050-7713; Samal, Areejit/0000-0002-6796-9604 NSF China [61104142]; Department of Science and Technology (DST), India; CNRS-MPG Joint Program in Systems Biology [CNRS GDRE513]; Volkswagen Foundation, Germany NSF China(National Natural Science Foundation of China (NSFC)); Department of Science and Technology (DST), India(Department of Science & Technology (India)); CNRS-MPG Joint Program in Systems Biology; Volkswagen Foundation, Germany(Volkswagen) We thank the anonymous reviewers for their constructive comments which helped improve the manuscript. Q.R. acknowledges support from NSF China (Grant No. 61104142); K.M.K. from the Department of Science and Technology (DST), India; A.S. from the CNRS-MPG Joint Program in Systems Biology (CNRS GDRE513); and J.J. from the Volkswagen Foundation, Germany. 26 8 8 1 16 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 1539-3755 1550-2376 PHYS REV E Phys. Rev. E NOV 6 2012.0 86.0 5 2 56103 10.1103/PhysRevE.86.056103 0.0 9 Physics, Fluids & Plasmas; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Physics 041GV 23214839.0 hybrid 2023-03-23 WOS:000311388600001 0
J Zheng, QY; Wang, XW; Bao, CB; Ji, YP; Liu, H; Meng, QC; Pan, QW Zheng, Qinyue; Wang, Xinwei; Bao, Chunbing; Ji, Yunpeng; Liu, Hua; Meng, Qingchun; Pan, Qiuwei A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions TRANSBOUNDARY AND EMERGING DISEASES English Article COVID-19; epicentre; local epidemic control; mathematical modelling; population movement Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic. [Zheng, Qinyue; Bao, Chunbing; Meng, Qingchun] Shandong Univ, Sch Management, Shandong Key Lab Social Supernetwork Computat & D, Jinan, Peoples R China; [Wang, Xinwei] Dalian Univ Technol, Dept Engn Mech, State Key Lab Struct Anal Ind Equipment, Dalian, Peoples R China; [Ji, Yunpeng; Pan, Qiuwei] Northwest Minzu Univ, Biomed Res Ctr, Key Lab Biotechnol & Bioengn, State Ethn Affairs Commiss, Lanzhou, Peoples R China; [Ji, Yunpeng; Pan, Qiuwei] Erasmus MC Univ Med Ctr, Dept Gastroenterol & Hepatol, Rotterdam, Netherlands; [Liu, Hua] Northwest Minzu Univ, Sch Math & Comp Sci, Lanzhou, Peoples R China Shandong University; Dalian University of Technology; Northwest Minzu University; Erasmus University Rotterdam; Erasmus MC; Northwest Minzu University Meng, QC (corresponding author), Shandong Univ, Sch Management, 27 Shanda Nanlu, Jinan 250100, Peoples R China.;Pan, QW (corresponding author), Erasmus MC, Room Na 1005,Wytemaweg 80, NL-3015 CN Rotterdam, Netherlands. meqich@163.com; q.pan@erasmusmc.nl Wang, Xinwei/U-1246-2019 Wang, Xinwei/0000-0003-2791-0170; Pan, Qiuwei/0000-0001-9982-6184 18 3 3 7 29 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1865-1674 1865-1682 TRANSBOUND EMERG DIS Transbound. Emerg. Dis. MAR 2022.0 69.0 2 549 558 10.1111/tbed.14019 0.0 FEB 2021 10 Infectious Diseases; Veterinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Infectious Diseases; Veterinary Sciences 0A8GS 33539678.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000619941700001 0
J Wang, YS; Wang, XM; Ran, YJ; Michalski, R; Jia, T Wang, Yansong; Wang, Xiaomeng; Ran, Yijun; Michalski, Radoslaw; Jia, Tao CasSeqGCN: Combining network structure and temporal sequence to predict information cascades EXPERT SYSTEMS WITH APPLICATIONS English Article Cascade prediction; Information cascade; Information popularity prediction; Deep learning; Graph convolutional networks EVENT DETECTION; TWITTER; POPULARITY One important task in the study of information cascade is to predict the future recipients of a message given its past spreading trajectory. While the network structure serves as the backbone of the spreading, an accurate prediction can hardly be made without the knowledge of the dynamics on the network. The temporal information in the spreading sequence captures many hidden features, but predictions based on sequence alone have their limitations. Recent efforts start to explore the possibility of combining both the network structure and the temporal feature. Here, we propose a new end-to-end prediction method CasSeqGCN in which the structure and temporal feature are simultaneously taken into account. A cascade is divided into multiple snapshots which record the network topology and the state of nodes. The graph convolutional network (GCN) is used to learn the representation of a snapshot. A novel aggregation method based on dynamic routing is proposed to aggregate node representation and the long short-term memory (LSTM) model is used to extract temporal information. CasSeqGCN predicts the future cascade size more accurately compared with other state-of-art baseline methods. The ablation study demonstrates that the improvement mainly comes from the design of the input and the GCN layer. We explicitly design an experiment to show the quality of the cascade representation learned by our approach is better than other methods. Our work proposes a new approach to combine the structural and temporal features, which not only gives a useful baseline model for future studies of cascade prediction, but also brings new insights on a wide collection of problems related with dynamics on and of the network. [Wang, Yansong; Wang, Xiaomeng; Ran, Yijun; Jia, Tao] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China; [Michalski, Radoslaw] Wroclaw Univ Sci & Technol, Fac Informat & Commun Technol, Dept Artificial Intelligence, PL-50370 Wroclaw, Poland Southwest University - China; Wroclaw University of Science & Technology Jia, T (corresponding author), Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China. yansong0682@email.swu.edu.cn; wxm1706@swu.edu.cn; ranyij288@email.swu.edu.cn; radoslaw.michalski@pwr.edu.pl; tjia@swu.edu.cn Michalski, Radoslaw/I-6978-2013; Jia, Tao/C-3215-2014 Michalski, Radoslaw/0000-0002-0106-655X; Jia, Tao/0000-0002-2337-2857 National Natural Science Foundation of China (NSFC) [62006198]; Industry-University-Research Innovation Fund for Chinese Universities [2021ALA03016]; University Innovation Research Group of Chongqing [CXQT21005]; National Science Centre, Poland [2021/41/B/HS6/02798] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Industry-University-Research Innovation Fund for Chinese Universities; University Innovation Research Group of Chongqing; National Science Centre, Poland(National Science Centre, Poland) We thank Ling Zhan for helpful discussions in this work. The work is supported by the National Natural Science Foundation of China (NSFC) (No. 62006198), the Industry-University-Research Innovation Fund for Chinese Universities (No. 2021ALA03016), the University Innovation Research Group of Chongqing (No. CXQT21005) and the National Science Centre, Poland (Grant No. 2021/41/B/HS6/02798). 79 1 1 11 12 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 15 2022.0 206.0 117693 10.1016/j.eswa.2022.117693 0.0 JUN 2022 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 3I8MU Green Submitted, hybrid 2023-03-23 WOS:000832964300001 0
J Esporcatte, LPG; Salomao, MQ; Lopes, BT; Vinciguerra, P; Vinciguerra, R; Roberts, C; Elsheikh, A; Dawson, DG; Ambrosio, R Esporcatte, Louise Pellegrino Gomes; Salomao, Marcella Q.; Lopes, Bernardo T.; Vinciguerra, Paolo; Vinciguerra, Riccardo; Roberts, Cynthia; Elsheikh, Ahmed; Dawson, Daniel G.; Ambrosio, Renato, Jr. Biomechanical diagnostics of the cornea EYE AND VISION English Review Corneal biomechanics; Corneal ectasia; Corneal imaging OPTICAL COHERENCE TOMOGRAPHY; OCULAR RESPONSE ANALYZER; IN-VIVO; SUBCLINICAL KERATOCONUS; DEFORMATION PARAMETERS; CROSS-LINKING; THICKNESS; ECTASIA; MICROSCOPY; PRESSURE Corneal biomechanics has been a hot topic for research in contemporary ophthalmology due to its prospective applications in diagnosis, management, and treatment of several clinical conditions, including glaucoma, elective keratorefractive surgery, and different corneal diseases. The clinical biomechanical investigation has become of great importance in the setting of refractive surgery to identify patients at higher risk of developing iatrogenic ectasia after laser vision correction. This review discusses the latest developments in the detection of corneal ectatic diseases. These developments should be considered in conjunction with multimodal corneal and refractive imaging, including Placido-disk based corneal topography, Scheimpflug corneal tomography, anterior segment tomography, spectral-domain optical coherence tomography (SD-OCT), very-high-frequency ultrasound (VHF-US), ocular biometry, and ocular wavefront measurements. The ocular response analyzer (ORA) and the Corvis ST are non-contact tonometry systems that provide a clinical corneal biomechanical assessment. More recently, Brillouin optical microscopy has been demonstrated to provide in vivo biomechanical measurements. The integration of tomographic and biomechanical data into artificial intelligence techniques has demonstrated the ability to increase the accuracy to detect ectatic disease and characterize the inherent susceptibility for biomechanical failure and ectasia progression, which is a severe complication after laser vision correction. [Esporcatte, Louise Pellegrino Gomes; Salomao, Marcella Q.; Lopes, Bernardo T.; Ambrosio, Renato, Jr.] Rio de Janeiro Corneal Tomog & Biomech Study Grp, Rio De Janeiro, Brazil; [Esporcatte, Louise Pellegrino Gomes; Salomao, Marcella Q.; Ambrosio, Renato, Jr.] Inst Olhos Renato Ambrosio, Rua Conde Bonfim 211-712, BR-20520050 Rio De Janeiro, RJ, Brazil; [Esporcatte, Louise Pellegrino Gomes] Hosp Sao Vicente Paulo, Dept Ophthalmol, Rio De Janeiro, Brazil; [Salomao, Marcella Q.; Ambrosio, Renato, Jr.] Brazilian Study Grp Artificial Intelligence & Cor, Rio De Janeiro, Brazil; [Salomao, Marcella Q.; Ambrosio, Renato, Jr.] Brazilian Study Grp Artificial Intelligence & Cor, Maceio, Alagoas, Brazil; [Salomao, Marcella Q.; Ambrosio, Renato, Jr.] Univ Fed Sao Paulo, Dept Ophthalmol, Sao Paulo, Brazil; [Salomao, Marcella Q.] Inst Benjamin Constant, Rio De Janeiro, Brazil; [Lopes, Bernardo T.; Vinciguerra, Riccardo; Elsheikh, Ahmed] Univ Liverpool, Sch Engn, Liverpool L69 3GH, Merseyside, England; [Vinciguerra, Paolo] Humanitas Univ, Dept Biomed Sci, Rozzano, Italy; [Vinciguerra, Paolo] Humanitas Clin & Res Ctr, Eye Ctr, Rozzano, Italy; [Vinciguerra, Riccardo] Humanitas San Pio X Hosp, Dept Ophthalmol, Milan, Italy; [Roberts, Cynthia] Ohio State Univ, Dept Ophthalmol & Visual Sci, Dept Biomed Engn, Columbus, OH 43210 USA; [Elsheikh, Ahmed] Beihang Univ, Sch Biol Sci & Biomed Engn, Beijing, Peoples R China; [Elsheikh, Ahmed] Moorfields Eye Hosp NHS Fdn Trust, NIHR Biomed Res Ctr Ophthalmol, London, England; [Elsheikh, Ahmed] UCL Inst Ophthalmol, London, England; [Dawson, Daniel G.] Univ Florida, Dept Ophthalmol, Gainesville, FL USA; [Ambrosio, Renato, Jr.] Fed Univ State Rio de Janeiro UNIRIO, Dept Ophthalmol, Rio De Janeiro, Brazil Universidade Federal de Sao Paulo (UNIFESP); University of Liverpool; Humanitas University; IRCCS Humanitas Research Hospital; IRCCS Humanitas Research Hospital; University System of Ohio; Ohio State University; Beihang University; University of London; University College London; Moorfields Eye Hospital NHS Foundation Trust; University of London; University College London; State University System of Florida; University of Florida; Universidade Federal do Estado do Rio de Janeiro Ambrosio, R (corresponding author), Rio de Janeiro Corneal Tomog & Biomech Study Grp, Rio De Janeiro, Brazil.;Ambrosio, R (corresponding author), Inst Olhos Renato Ambrosio, Rua Conde Bonfim 211-712, BR-20520050 Rio De Janeiro, RJ, Brazil. dr.renatoambrosio@gmail.com Ambrósio, Renato/AAI-6715-2020; Elsheikh, Ahmed/AAT-1476-2020; VINCIGUERRA, PAOLO/AAF-5775-2019; Pellegrino, Louise/AAI-6202-2021; Vinciguerra, Riccardo/AFO-7782-2022 Ambrósio, Renato/0000-0001-6919-4606; Elsheikh, Ahmed/0000-0001-7456-1749; Vinciguerra, Riccardo/0000-0002-2280-9981 EPSRC [EP/N014499/1, EP/H052046/1] Funding Source: UKRI EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) 93 29 31 2 20 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2326-0254 EYE VISION Eye Vis. FEB 5 2020.0 7.0 1 9 10.1186/s40662-020-0174-x 0.0 12 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology KM1DX 32042837.0 gold, Green Published 2023-03-23 WOS:000513858700001 0
J Li, FH; Zheng, DQ; Zhao, TJ; Pedrycz, W Li, Fenghuan; Zheng, Dequan; Zhao, Tiejun; Pedrycz, Witold A novel approach for anomaly detection in data streams: Fuzzy-statistical detection mode JOURNAL OF INTELLIGENT & FUZZY SYSTEMS English Article Anomaly detection; statistical test; self-adaptive; fuzzy set theory; unsupervised ARRHYTHMIA DETECTION; INTRUSION DETECTION; OUTLIER DETECTION; NEURAL-NETWORK; SYSTEM; ALGORITHMS; DIAGNOSIS; REPRESENTATION subsequences that exhibit departures from normal state of operation. In this paper, to solve the problems of unknown data distribution, control limit determination, multiple parameters, training data and fuzziness of 'anomaly', a self-adaptive and unsupervised model is developed for finding anomalies in data streams. A salient feature is a synergistic combination of both statistical and fuzzy set-based techniques. Anomaly detection problem is viewed as a certain statistical hypothesis testing which is realized in an unsupervised mode. At the same time, 'anomaly' is a much more complex concept and as such can be described with fuzzy set theory. Fuzzy sets bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Because of the fuzzification, parameters determination is self-adaptive and no parameter needs to be specified by the user, what's more, there is no need to consider the data distribution in statistical hypothesis testing in this paper. The approach is validated with a number of experiments, which help to quantify the performance of constructed algorithm. [Li, Fenghuan; Zheng, Dequan; Zhao, Tiejun] Harbin Inst Technol, MOE MS Key Lab Nat Language Proc & Speech, Harbin 150006, Heilongjiang, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2G7, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland Harbin Institute of Technology; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Zhao, TJ (corresponding author), Harbin Inst Technol, MOE MS Key Lab Nat Language Proc & Speech, Harbin 150006, Heilongjiang, Peoples R China. tjzhao@hit.edu.cn National Natural Science Foundation of China [61173073]; International S&T Cooperation Program of China [2014DFA11350] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); International S&T Cooperation Program of China This work is supported by the National Natural Science Foundation of China under Grant No. 61173073 and International S&T Cooperation Program of China under Grant No. 2014DFA11350. 46 4 4 1 19 IOS PRESS AMSTERDAM NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS 1064-1246 1875-8967 J INTELL FUZZY SYST J. Intell. Fuzzy Syst. 2016.0 30.0 5 2611 2622 10.3233/IFS-151910 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DJ4JP 2023-03-23 WOS:000374171500011 0
J Xu, CQ; Zhao, J; Muntean, GM Xu, Changqiao; Zhao, Jia; Muntean, Gabriel-Miro Congestion Control Design for Multipath Transport Protocols: A Survey IEEE COMMUNICATIONS SURVEYS AND TUTORIALS English Article Multipath transport protocol; multipath congestion control; TCP; friendliness; load-balancing PATH TCP; SCTP; TRANSMISSION; ARCHITECTURE; THROUGHPUT; DIVERSITY; STABILITY; ALGORITHM; SERVICES; MODEL High-quality services over wired and wireless networks require high bitrate network content delivery. Multipath transport protocols utilize multiple paths to support increased throughput for network applications. Ubiquitous mobile devices with multiple interfaces such as WiFi and 4G/5G cellular, and data centers supporting big data analysis and cloud-computing, motivate adoption of multipath transmission in current and future network architectures. Congestion control mechanisms play a fundamental role in these multipath transport protocols. Diverse approaches have been proposed in the literature, differing in terms of their goals, principles, performance, and mostly in how various issues are addressed in their design. This paper presents a survey of congestion control solutions for multipath transport protocols and discusses the multipath congestion control design in order to address the need for some desirable properties including TCP-friendliness, load balancing, stability, and Pareto optimality. Existing window-based and rate-based multipath congestion control algorithms are investigated and categorized based on their theoretical algorithm design. Next, this paper discusses the congestion control mechanisms used in diverse multipath transport protocols in the context of different network scenarios. Finally, the trends in the future multipath congestion control design are presented. [Xu, Changqiao] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Zhao, Jia] Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada; [Muntean, Gabriel-Miro] Dublin City Univ, Sch Elect Engn, Performance Engn Lab, Dublin 9, Ireland Beijing University of Posts & Telecommunications; Simon Fraser University; Dublin City University Xu, CQ (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. cqxu@bupt.edu.cn; zhaojiaz@sfu.ca; gabriel.muntean@dcu.ie Muntean, Gabriel-Miro/U-6783-2019 Muntean, Gabriel-Miro/0000-0002-9332-4770 National Natural Science Foundation of China [61522103, 61372112]; Beijing Natural Science Foundation [4142037] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 61522103 and Grant 61372112, and in part by the Beijing Natural Science Foundation under Grant 4142037. 129 88 90 3 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1553-877X IEEE COMMUN SURV TUT IEEE Commun. Surv. Tutor. 2016.0 18.0 4 2948 2969 10.1109/COMST.2016.2558818 0.0 22 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications ED9OS 2023-03-23 WOS:000389202500022 0
J Lu, YB; Maulik, R; Gao, T; Dietrich, F; Kevrekidis, IG; Duan, JQ Lu, Yubin; Maulik, Romit; Gao, Ting; Dietrich, Felix; Kevrekidis, Ioannis G.; Duan, Jinqiao Learning the temporal evolution of multivariate densities via normalizing flows CHAOS English Article MODEL-REDUCTION; IDENTIFICATION; EQUATIONS In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporally evolving probability distributions (e.g., those produced by integrating local or nonlocal Fokker-Planck equations). We analyze this evolution through machine learning assisted construction of a time-dependent mapping that takes a reference distribution (say, a Gaussian) to each and every instance of our evolving distribution. If the reference distribution is the initial condition of a Fokker-Planck equation, what we learn is the time-T map of the corresponding solution. Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time. We demonstrate that this approach can approximate probability density function evolutions in time from observed sampled data for systems driven by both Brownian and Levy noise. We present examples with two- and three-dimensional, uni- and multimodal distributions to validate the method. Published under an exclusive license by AIP Publishing. [Lu, Yubin; Gao, Ting] Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Peoples R China; [Lu, Yubin; Gao, Ting] Huazhong Univ Sci & Technol, Ctr Math Sci, Wuhan 430074, Peoples R China; [Maulik, Romit] Argonne Natl Lab, Argonne Leadership Comp Facil, Lemont, IL 60439 USA; [Maulik, Romit; Duan, Jinqiao] IIT, Dept Appl Math, Coll Comp, Chicago, IL 60616 USA; [Dietrich, Felix] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany; [Kevrekidis, Ioannis G.] Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21211 USA; [Kevrekidis, Ioannis G.] Johns Hopkins Univ, Dept Chem & Biomol Engn, Baltimore, MD 21211 USA Huazhong University of Science & Technology; Huazhong University of Science & Technology; United States Department of Energy (DOE); Argonne National Laboratory; Illinois Institute of Technology; Technical University of Munich; Johns Hopkins University; Johns Hopkins University Maulik, R (corresponding author), Argonne Natl Lab, Argonne Leadership Comp Facil, Lemont, IL 60439 USA.;Maulik, R (corresponding author), IIT, Dept Appl Math, Coll Comp, Chicago, IL 60616 USA. rmaulik@anl.gov Maulik, Romit/0000-0001-9731-8936; Dietrich, Felix/0000-0002-2906-1769; Gao, Ting/0000-0001-7840-7060; Lu, Yubin/0000-0002-1117-8758 U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research [DE-AC02-06CH11357]; Margaret Butler Fellowship at the Argonne Leadership Computing Facility; US DOE; ARO MURI U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research(United States Department of Energy (DOE)); Margaret Butler Fellowship at the Argonne Leadership Computing Facility; US DOE(United States Department of Energy (DOE)); ARO MURI(MURI) We would like to thank Xiaoli Chen, William McClure, and Stanley Nicholson for helpful discussions. This material was based upon the work supported by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under Contract No. DE-AC02-06CH11357. This research was funded in part and used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract No. DE-AC02-06CH11357. R.M. acknowledges support from the Margaret Butler Fellowship at the Argonne Leadership Computing Facility. The work of I.G.K was partially supported by the US DOE and by an ARO MURI. 64 1 1 3 9 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1054-1500 1089-7682 CHAOS Chaos MAR 2022.0 32.0 3 33121 10.1063/5.0065093 0.0 17 Mathematics, Applied; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mathematics; Physics 0O5XX 35364835.0 Green Submitted 2023-03-23 WOS:000783599900003 0
J Sha, C; Ren, CH; Malekian, R; Wu, M; Huang, HP; Ye, N Sha, Chao; Ren, Chunhui; Malekian, Reza; Wu, Min; Huang, Haiping; Ye, Ning A Type of Virtual Force-Based Energy-Hole Mitigation Strategy for Sensor Networks IEEE SENSORS JOURNAL English Article Sensor networks; virtual force; energy-hole mitigation; path selection; node position optimization ALLEVIATING ALGORITHM; EFFICIENT; LIFETIME; SCHEME In the era of Big Data and Mobile Internet, how to ensure the terminal devices (e.g., sensor nodes) work steadily for a long time is one of the key issues to improve the efficiency of the whole network. However, a lot of facts have shown that the unattended equipments are prone to failure due to energy exhaustion, physical damage and other reasons. This may result in the emergence of energy-hole, seriously affecting network performance and shortening its lifetime. To reduce data redundancy and avoid the generation of sensing blind areas, a type of Virtual Force based Energy-hole Mitigation strategy (VFEM) is proposed in this paper. Firstly, the virtual force (gravitation and repulsion) between nodes is introduced that makes nodes distribute as uniformly as possible. Secondly, in order to alleviate the energy-hole problem, the network is divided into several annuluses with the same width. Then, another type of virtual force, named virtual gravity generated by annulus, is proposed to further optimize the positions of nodes in each annulus. Finally, with the help of the data forwarding area, the optimal paths for data uploading can be selected out, which effectively balances energy consumption of nodes. Experiment results show that, VFEM has a relatively good performance on postponing the generation time of energy-holes as well as prolonging the network lifetime compared with other typical energy-hole mitigation methods. [Sha, Chao; Ren, Chunhui; Wu, Min; Huang, Haiping; Ye, Ning] Nanjing Univ Posts & Telecommun, Sch Comp Sci Software & Cyberspace Secur, Nanjing 210003, Jiangsu, Peoples R China; [Malekian, Reza] Malmo Univ, Dept Comp Sci & Media Technol, S-20506 Malmo, Sweden; [Malekian, Reza] Malmo Univ, Internet Things & People Res Ctr, S-20506 Malmo, Sweden Nanjing University of Posts & Telecommunications; Malmo University; Malmo University Sha, C (corresponding author), Nanjing Univ Posts & Telecommun, Sch Comp Sci Software & Cyberspace Secur, Nanjing 210003, Jiangsu, Peoples R China. shac@njupt.edu.cn huang, haiping/AAM-8251-2021; Malekian, Reza/F-7647-2015 Malekian, Reza/0000-0002-2763-8085; Sha, Chao/0000-0002-2347-2073 National Natural Science Foundation of P. R. China [61872194, 61872196]; Jiangsu Natural Science Foundation for Excellent Young Scholar [BK20160089]; Six Talent Peaks Project of Jiangsu Province [0JNHB-095]; 333 Project of Jiangsu Province [SJCX18_0295]; Qing Lan Project of Jiangsu Province [SJCX18_0295]; Innovation Project for Postgraduate of Jiangsu Province [SJCX18_0295]; 1311 Talents Project of Nanjing University of Posts and Telecommunications National Natural Science Foundation of P. R. China(National Natural Science Foundation of China (NSFC)); Jiangsu Natural Science Foundation for Excellent Young Scholar; Six Talent Peaks Project of Jiangsu Province; 333 Project of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project of Jiangsu Province; Innovation Project for Postgraduate of Jiangsu Province; 1311 Talents Project of Nanjing University of Posts and Telecommunications This work was supported in part by the National Natural Science Foundation of P. R. China under Grant 61872194 and Grant 61872196, in part by the Jiangsu Natural Science Foundation for Excellent Young Scholar under Grant BK20160089, in part by the Six Talent Peaks Project of Jiangsu Province under Grant 0JNHB-095, in part by the 333 Project of Jiangsu Province, Qing Lan Project of Jiangsu Province, Innovation Project for Postgraduate of Jiangsu Province under Grant SJCX18_0295, and in part by the 1311 Talents Project of Nanjing University of Posts and Telecommunications. 35 10 11 0 8 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. JAN 15 2020.0 20.0 2 1105 1119 10.1109/JSEN.2019.2945595 0.0 15 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics KY1QE Green Submitted 2023-03-23 WOS:000522345700060 0
J Bellenberg, S; Buetti-Dinh, A; Galli, V; Ilie, O; Herold, M; Christel, S; Boretska, M; Pivkin, IV; Wilmes, P; Sand, W; Vera, M; Dopson, M Bellenberg, Soren; Buetti-Dinh, Antoine; Galli, Vanni; Ilie, Olga; Herold, Malte; Christel, Stephan; Boretska, Mariia; Pivkin, Igor V.; Wilmes, Paul; Sand, Wolfgang; Vera, Mario; Dopson, Mark Automated Microscopic Analysis of Metal Sulfide Colonization by Acidophilic Microorganisms APPLIED AND ENVIRONMENTAL MICROBIOLOGY English Article bioleaching; biofilm formation; biofilm dispersal; image analysis; microbe-mineral interaction; quorum sensing; diffusible soluble factor; biofilms; fluorescent image analysis; microbe-mineral interactions CYCLIC DI-GMP; ACIDITHIOBACILLUS FERROOXIDANS; THIOBACILLUS-FERROOXIDANS; XANTHOMONAS-CAMPESTRIS; LEACHING PATTERNS; BIOFILM FORMATION; SP-NOV; BACTERIA; PYRITE; IMAGE Industrial biomining processes are currently focused on metal sulfides and their dissolution, which is catalyzed by acidophilic iron(II)- and/or sulfur-oxidizing microorganisms. Cell attachment on metal sulfides is important for this process. Biofilm formation is necessary for seeding and persistence of the active microbial community in industrial biomining heaps and tank reactors, and it enhances metal release. In this study, we used a method for direct quantification of the mineral-attached cell population on pyrite or chalcopyrite particles in bioleaching experiments by coupling high-throughput, automated epifluorescence microscopy imaging of mineral particles with algorithms for image analysis and cell quantification, thus avoiding human bias in cell counting. The method was validated by quantifying cell attachment on pyrite and chalcopyrite surfaces with axenic cultures of Acidithiobacillus caldus, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans. The method confirmed the high affinity of L. ferriphilum cells to colonize pyrite and chalcopyrite surfaces and indicated that biofilm dispersal occurs in mature pyrite batch cultures of this species. Deep neural networks were also applied to analyze biofilms of different microbial consortia. Recent analysis of the L. ferriphilum genome revealed the presence of a diffusible soluble factor (DSF) family quorum sensing system. The respective signal compounds are known as biofilm dispersal agents. Biofilm dispersal was confirmed to occur in batch cultures of L. ferriphilum and S. thermosulfidooxidans upon the addition of DSF family signal compounds. IMPORTANCE The presented method for the assessment of mineral colonization allows accurate relative comparisons of the microbial colonization of metal sulfide concentrate particles in a time-resolved manner. Quantitative assessment of the mineral colonization development is important for the compilation of improved mathematical models for metal sulfide dissolution. In addition, deep-learning algorithms proved that axenic or mixed cultures of the three species exhibited characteristic biofilm patterns and predicted the biofilm species composition. The method may be extended to the assessment of microbial colonization on other solid particles and may serve in the optimization of bioleaching processes in laboratory scale experiments with industrially relevant metal sulfide concentrates. Furthermore, the method was used to demonstrate that DSF quorum sensing signals directly influence colonization and dissolution of metal sulfides by mineral-oxidizing bacteria, such as L. ferriphilum and S. thermosulfidooxidans. [Bellenberg, Soren; Boretska, Mariia; Sand, Wolfgang] Univ Duisburg Essen, Biofilm Ctr, Fak Chem, Essen, Germany; [Buetti-Dinh, Antoine; Ilie, Olga; Pivkin, Igor V.] Univ Svizzera Italiana, Fac Informat, Inst Computat Sci, Lugano, Switzerland; [Buetti-Dinh, Antoine; Ilie, Olga; Pivkin, Igor V.] Swiss Inst Bioinformat, Lausanne, Switzerland; [Galli, Vanni] Univ Appl Sci Southern Switzerland, Inst Informat Syst & Networking, Manno, Switzerland; [Herold, Malte; Wilmes, Paul] Univ Luxembourg, Luxembourg Ctr Syst Biomed, Belvaux, Luxembourg; [Christel, Stephan; Dopson, Mark] Linnaeus Univ, Ctr Ecol & Evolut Microbial Model Syst, Kalmar, Sweden; [Sand, Wolfgang] Donghua Univ, Shanghai, Peoples R China; [Sand, Wolfgang] Tech Univ Bergakad Freiberg, Freiberg, Germany; [Vera, Mario] Pontificia Univ Catolica Chile, Inst Biol & Med Engn, Sch Engn, Dept Hydraul & Environm Engn, Santiago, Chile; [Vera, Mario] Pontificia Univ Catolica Chile, Dept Hydraul & Environm Engn, Inst Biol & Med Engn, Sch Med, Santiago, Chile; [Vera, Mario] Pontificia Univ Catolica Chile, Dept Hydraul & Environm Engn, Inst Biol & Med Engn, Sch Biol Sci, Santiago, Chile University of Duisburg Essen; Universita della Svizzera Italiana; Swiss Institute of Bioinformatics; University of Luxembourg; Linnaeus University; Donghua University; Technical University Freiberg; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile Bellenberg, S (corresponding author), Univ Duisburg Essen, Biofilm Ctr, Fak Chem, Essen, Germany.;Buetti-Dinh, A (corresponding author), Univ Svizzera Italiana, Fac Informat, Inst Computat Sci, Lugano, Switzerland.;Buetti-Dinh, A (corresponding author), Swiss Inst Bioinformat, Lausanne, Switzerland. soeren.bellenberg@uni-due.de; antoine.buetti.dinh@usi.ch Christel, Stephan/AAX-1075-2021; Wilmes, Paul/B-1707-2017; Dopson, Mark/A-7028-2013 Christel, Stephan/0000-0003-0021-2452; Wilmes, Paul/0000-0002-6478-2924; Buetti-Dinh, Antoine/0000-0002-6469-0296; Bellenberg, Soeren/0000-0003-2842-3315 Bundesministerium fur Bildung und Forschung (BMBF) [031A600A, 031A600B]; Vetenskapsradet [2014-6545]; Luxembourg National Research Fund (FNR) [INTER/SYSAPP/14/05]; Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx); Fondecyt [1161007] Bundesministerium fur Bildung und Forschung (BMBF)(Federal Ministry of Education & Research (BMBF)); Vetenskapsradet(Swedish Research Council); Luxembourg National Research Fund (FNR)(Luxembourg National Research Fund); Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx); Fondecyt(Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT) This project was supported by Bundesministerium fur Bildung und Forschung (BMBF, grants 031A600A and 031A600B), Vetenskapsradet (contract 2014-6545), the Luxembourg National Research Fund (FNR) (grant INTER/SYSAPP/14/05), and the Swiss Initiative in Systems Biology (SystemsX.ch, SysMetEx) under the frame of ERASysAPP. M.V. acknowledges support from Fondecyt 1161007 grant. 59 14 14 2 45 AMER SOC MICROBIOLOGY WASHINGTON 1752 N ST NW, WASHINGTON, DC 20036-2904 USA 0099-2240 1098-5336 APPL ENVIRON MICROB Appl. Environ. Microbiol. OCT 2018.0 84.0 20 e01835-18 10.1128/AEM.01835-18 0.0 15 Biotechnology & Applied Microbiology; Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Microbiology GV6GS 30076195.0 Green Published, Green Accepted 2023-03-23 WOS:000446206900020 0
J Yang, LH; Liu, J; Wang, YM; Wang, H; Martinez, L Yang, Long-Hao; Liu, Jun; Wang, Ying-Ming; Wang, Hui; Martinez, Luis Enhancing extended belief rule-based systems for classification problems using decomposition strategy and overlap function INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Extended belief rule-based system; Overlap function; Decomposition strategy; One-versus-one; Classification MULTICLASS CLASSIFICATION; IMPACT Multi-class and multi-attribute are two important features of classification problems and have different effects on the requirements and performance of the classifier. Decomposition strategy and overlap function are two effective ways to enhance the performance of classifiers, because the former decomposes a complex multi-class problem into multiple simple sub-problems; the latter uses various functions to specify the conjunctive relationship of input variables in a multi-attribute problem. Extended belief rule-based system (EBRBS) is an advanced rule-based system that has been widely used in classification problems. In order to apply decomposition strategies and overlap functions to enhance the performance of EBRBSs, the present work focuses on the investigative research and comparative evaluation of the commonly used one-versus-one (OVO) decomposition strategy and five common overlap functions to improve the performance of EBRBSs on multi-class and multi-attribute problems. More specifically, three typical kinds of EBRBSs, namely original EBRBS (O-EBRBS), EBRBS with dynamic rule activation (DRA-EBRBS), and a latest EBRBS for big data (Micro-EBRBS), are selected to conduct extensive experimental studies on twenty classification problems. To best of our knowledge, this present work is the first time to provide a meaningful and useful study in revealing the potential capability of the EBRBSs with decomposition strategy and overlap function for multi-class and multi-attribute problems. Experimental results demonstrate that the square product overlap function and the OVO strategy can enhance the performance of EBRBSs over others for twenty classification problems. [Yang, Long-Hao; Wang, Ying-Ming] Fuzhou Univ, Decis Sci Inst, Fuzhou, Peoples R China; [Wang, Ying-Ming] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China; [Yang, Long-Hao; Liu, Jun; Wang, Hui; Martinez, Luis] Ulster Univ, Sch Comp, Coleraine, Londonderry, North Ireland; [Yang, Long-Hao; Martinez, Luis] Univ Jaen, Dept Comp Sci, Jaen, Spain Fuzhou University; Fuzhou University; Ulster University; Universidad de Jaen Wang, YM (corresponding author), Fuzhou Univ, Decis Sci Inst, Fuzhou, Peoples R China.;Wang, YM (corresponding author), Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China. msymwang@hotmail.com Wang, Hui/HMU-9512-2023; Liu, Jun/C-1338-2011; wang, yan/GSE-6489-2022; wang, yingying/GRS-3058-2022; Wang, Ying/HJI-2509-2023; Martinez, Luis/A-1746-2009 Liu, Jun/0000-0001-8859-5405; Martinez, Luis/0000-0003-4245-8813; Wang, Hui/0000-0003-2633-6015 National Natural Science Foundation of China [72001043, 61773123]; Natural Science Foundation of Fujian Province of China [2020J05122]; Humanities and Social Science Foundation of the Ministry of Education of China [20YJC630188]; Social Science Planning Fund Project of Fujian Province of China [FJ2019C032]; Chengdu International Science Cooperation Project [2020-GH02-00064-HZ]; Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project [PGC2018-099402-B-I00] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province); Humanities and Social Science Foundation of the Ministry of Education of China(Ministry of Education, China); Social Science Planning Fund Project of Fujian Province of China; Chengdu International Science Cooperation Project; Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project This research was supported by the National Natural Science Foundation of China (Nos. 72001043 and 61773123), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188), the Social Science Planning Fund Project of Fujian Province of China (No. FJ2019C032), the Chengdu International Science Cooperation Project (No. 2020-GH02-00064-HZ), and the Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project PGC2018-099402-B-I00. 45 1 1 3 8 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. MAR 2022.0 13.0 3 SI 811 837 10.1007/s13042-021-01355-z 0.0 JUN 2021 27 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 1T4LY Green Accepted 2023-03-23 WOS:000661445900001 0
J Chen, HT; Jiang, B; Ding, SX; Lu, NY; Chen, W Chen, Hongtian; Jiang, Bin; Ding, Steven X.; Lu, Ningyun; Chen, Wen Probability-Relevant Incipient Fault Detection and Diagnosis Methodology With Applications to Electric Drive Systems IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY English Article Fault detection; Principal component analysis; Bayes methods; Standards; Mathematical analysis; Bayesian inference; electric drive systems; incipient faults; Kullback-Leibler divergence (KLD); probability-relevant principal component analysis (PRPCA) COMPONENT STATISTICAL-ANALYSIS; NONLINEAR UNCERTAIN SYSTEMS; INFORMATION By dealing with the crowding problem caused by incipient faults, this brief will develop a new fault detection and diagnosis (FDD) scheme called probability-relevant principal component analysis from the probability view point. The proposed methodology cooperates with Kullback-Leibler divergence from the information field and Bayesian inference from the machine learning domain. Compared with the standard FDD methods under the framework of multivariate statistical analysis, this new FDD scheme is more sensitive to faults under an acceptable false alarm ratio, especially to incipient faults; moreover, it is more accurate in diagnosing faults with the aid of improved fault detectability. The effectiveness of the proposed FDD method is illustrated by mathematical analysis and geometric descriptions, and validated via a numerical example and a real experimental setup on the electric drive system of a high-speed train. [Chen, Hongtian; Jiang, Bin; Lu, Ningyun] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Jiangsu, Peoples R China; [Chen, Hongtian; Jiang, Bin; Lu, Ningyun] Nanjing Univ Aeronaut & Astronaut, Jiangsu Key Lab Internet Things & Control Technol, Nanjing 211106, Jiangsu, Peoples R China; [Ding, Steven X.] Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany; [Chen, Wen] Wayne State Univ, Div Engn Technol, Detroit, MI 48202 USA Nanjing University of Aeronautics & Astronautics; Nanjing University of Aeronautics & Astronautics; University of Duisburg Essen; Wayne State University Jiang, B (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Jiangsu, Peoples R China. chtbaylor@163.com; binjiang@nuaa.edu.cn; steven.ding@uni-due.de; luningyun@nuaa.edu.cn; wchenc@wayne.edu CHEN, Hongtian/F-4957-2014; Ding, Steven X./ABF-2356-2020; Alidadi, Mehdi/HJZ-0235-2023 CHEN, Hongtian/0000-0002-8600-9668; Alidadi, Mehdi/0000-0001-5183-7829; Ding, Steven/0000-0002-5149-5918 National Natural Science Foundation of China [61490703, 61374141, 61573180]; Priority Academic Program Development of Jiangsu Higher Education Institutions; Funding of Jiangsu Innovation Program for Graduate Education [KYLX16_0378] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Priority Academic Program Development of Jiangsu Higher Education Institutions; Funding of Jiangsu Innovation Program for Graduate Education This work was supported in part by the National Natural Science Foundation of China under Grant 61490703, Grant 61374141, and Grant 61573180, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and in part by the Funding of Jiangsu Innovation Program for Graduate Education under Grant KYLX16_0378. 18 95 111 7 67 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6536 1558-0865 IEEE T CONTR SYST T IEEE Trans. Control Syst. Technol. NOV 2019.0 27.0 6 2766 2773 10.1109/TCST.2018.2866976 0.0 8 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering JG8AD 2023-03-23 WOS:000492299000045 0
J Cheridito, P; Jentzen, A; Riekert, A; Rossmannek, F Cheridito, Patrick; Jentzen, Arnulf; Riekert, Adrian; Rossmannek, Florian A proof of convergence for gradient descent in the training of artificial neural networks for constant functions JOURNAL OF COMPLEXITY English Article Artificial neural networks; Nonconvex optimization; Nonsmooth optimization; Gradient methods; Machine learning Gradient descent (GD) optimization algorithms are the standard ingredients that are used to train artificial neural networks (ANNs). However, even in the case of the most basic variant of GD optimization algorithms, the plain vanilla GD method, it remains until today an open problem to prove or disprove the conjecture that GD converges in the training of ANNs. In this article we solve this problem in the special situation where the target function under consideration is a constant function. More specifically, in the case of constant target functions we prove in the training of rectified fully-connected feedforward ANNs with one-hidden layer that the risk function of the GD method does indeed converge to zero. A key contribution of this work is also to explicitly specify a Lyapunov function for the gradient flow system of the ANN parameters. This Lyapunov function is the central tool in our convergence proof. (C) 2022 Elsevier Inc. All rights reserved. [Cheridito, Patrick; Riekert, Adrian; Rossmannek, Florian] Swiss Fed Inst Technol, Dept Math, Zurich, Switzerland; [Jentzen, Arnulf; Riekert, Adrian] Univ Munster, Appl Math Inst Anal & Numer, Munster, Germany; [Jentzen, Arnulf] Chinese Univ Hong Kong Shenzhen, Sch Data Sci, Shenzhen, Peoples R China; [Jentzen, Arnulf] Chinese Univ Hong Kong Shenzhen, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Munster; Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong, Shenzhen Jentzen, A (corresponding author), Univ Munster, Appl Math Inst Anal & Numer, Munster, Germany.;Jentzen, A (corresponding author), Chinese Univ Hong Kong Shenzhen, Sch Data Sci, Shenzhen, Peoples R China.;Jentzen, A (corresponding author), Chinese Univ Hong Kong Shenzhen, Shenzhen Res Inst Big Data, Shenzhen, Peoples R China. patrick.cheridito@math.ethz.ch; ajentzen@cuhk.edu.cn; ariekert@uni-muenster.de; florian.rossmannek@math.ethz.ch Rossmannek, Florian/0000-0001-5772-5086; Riekert, Adrian/0000-0002-1458-3388 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [EXC 2044-390685587] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) Benno Kuckuck is gratefully acknowledged for several helpful suggestions. This work has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC 2044-390685587, Mathematics Munster: Dynamics-Geometry-Structure. 34 5 5 0 1 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0885-064X 1090-2708 J COMPLEXITY J. Complex. OCT 2022.0 72.0 101646 10.1016/j.jco.2022.101646 0.0 JUN 2022 26 Computer Science, Theory & Methods; Mathematics, Applied; Mathematics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics 2D7HX Green Submitted 2023-03-23 WOS:000811714300010 0
J Zhou, Y; Jing, WP; Wang, J; Chen, GS; Scherer, R; Damasevicius, R Zhou, Ying; Jing, Weipeng; Wang, Jian; Chen, Guangsheng; Scherer, Rafal; Damasevicius, Robertas MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution IET IMAGE PROCESSING English Article NEURAL-NETWORK High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real-time effective algorithm is proposed, namely MSAR-Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large-scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine-grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high-resolution remote sensing image restoration. It can meet the requirements of many hard real-time tasks, so that remote sensing images can play a greater value for human activities. [Zhou, Ying; Jing, Weipeng; Chen, Guangsheng] Northeast Forestry Univ, Harbin, Peoples R China; [Zhou, Ying; Jing, Weipeng; Chen, Guangsheng] State Forestry Adm, Key Lab Forestry Data Sci & Cloud Comp, Harbin, Peoples R China; [Wang, Jian] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China; [Scherer, Rafal] Czestochowa Tech Univ, Dept Intelligent Comp Syst, Czestochowa, Poland; [Damasevicius, Robertas] Silesian Tech Univ, Gliwice, Silesia, Poland Northeast Forestry University - China; Chinese Academy of Sciences; Technical University Czestochowa; Silesian University of Technology Jing, WP (corresponding author), Northeast Forestry Univ, Harbin, Peoples R China. jwp@nefu.edu.cn Damaševičius, Robertas/E-1387-2017; Scherer, Rafal/F-6745-2012; Chen, Guangsheng/AAC-4462-2022 Damaševičius, Robertas/0000-0001-9990-1084; Scherer, Rafal/0000-0001-9592-262X; Heilongjiang Province Applied Technology Research and Development Program Major Project [GA18B301, GA20A301]; Fundamental Research Funds for the Central Universities [2572017PZ04]; China State Forestry Administration Forestry Industry Public Welfare Project [201504307]; National Natural Science Foundation of China [31770768] Heilongjiang Province Applied Technology Research and Development Program Major Project; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China State Forestry Administration Forestry Industry Public Welfare Project; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Heilongjiang Province Applied Technology Research and Development Program Major Project, Grant/Award Numbers: GA18B301, GA20A301; Fundamental Research Funds for the Central Universities, Grant/AwardNumber: 2572017PZ04; China State Forestry Administration Forestry Industry Public Welfare Project, Grant/Award Number: 201504307; National Natural Science Foundation of China, Grant/Award Number: 31770768 27 6 6 1 18 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. FEB 2022.0 16.0 3 659 668 10.1049/ipr2.12224 0.0 APR 2021 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology YR2ZW gold 2023-03-23 WOS:000640653400001 0
J Yang, J; Wang, QH; Zhang, ZY; Long, LH; Ezhilarasan, R; Karp, JM; Tsirigos, A; Snuderl, M; Wiestler, B; Wick, W; Miao, YS; Huse, JT; Sulman, EP Yang, Jie; Wang, Qianghu; Zhang, Ze-Yan; Long, Lihong; Ezhilarasan, Ravesanker; Karp, Jerome M.; Tsirigos, Aristotelis; Snuderl, Matija; Wiestler, Benedikt; Wick, Wolfgang; Miao, Yinsen; Huse, Jason T.; Sulman, Erik P. DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas NATURE COMMUNICATIONS English Article POINT MUTATIONS; PROGRESSION; LANDSCAPE; PATTERN Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies. No clinical assay currently exists to classify glioma tumours based on gene expression. Here, the authors develop a DNA methylation-based classifier, Unified Diagnostic Pipeline (UniD) that identifies genomic alterations and gene expression subtypes of gliomas. [Yang, Jie; Zhang, Ze-Yan; Ezhilarasan, Ravesanker; Karp, Jerome M.; Sulman, Erik P.] NYU Grossman Sch Med, Dept Radiat Oncol, New York, NY 10016 USA; [Yang, Jie; Zhang, Ze-Yan; Ezhilarasan, Ravesanker; Karp, Jerome M.; Sulman, Erik P.] NYU Langone Hlth, Brain & Spine Tumor Ctr, Laura & Isaac Perlmutter Canc Ctr, New York, NY 10016 USA; [Yang, Jie] MD Anderson Canc Ctr UTHlth Grad Sch Biomed Sci, Quantitat Sci Program, Houston, TX USA; [Wang, Qianghu] Nanjing Med Univ, Dept Bioinformat, Sch Biomed Engn & Informat, Jiangsu Collaborat Innovat Ctr Canc Personalized, Nanjing, Peoples R China; [Long, Lihong] Univ Texas MD Anderson Canc Ctr, Dept Neurosurg, Houston, TX 77030 USA; [Tsirigos, Aristotelis; Snuderl, Matija] NYU Grossman Sch Med, Dept Pathol, New York, NY USA; [Tsirigos, Aristotelis] NYU Grossman Sch Med, Appl Bioinformat Lab, New York, NY USA; [Wiestler, Benedikt] Tech Univ Munich, Dept Neuroradiol, Munich, Germany; [Wick, Wolfgang] Heidelberg Univ, German Canc Res Ctr DKFZ, Heidelberg, Germany; [Wick, Wolfgang] Heidelberg Univ, Dept Neurol, Heidelberg, Germany; [Wick, Wolfgang] Heidelberg Univ, NCT Neurooncol Program, Heidelberg, Germany; [Miao, Yinsen] Rice Univ, Dept Stat, Houston, TX 77251 USA; [Huse, Jason T.] Univ Texas MD Anderson Canc Ctr, Dept Pathol, Houston, TX 77030 USA; [Huse, Jason T.] Univ Texas MD Anderson Canc Ctr, Dept Translat Mol Pathol, Houston, TX 77030 USA NYU Langone Medical Center; Nanjing Medical University; University of Texas System; UTMD Anderson Cancer Center; Technical University of Munich; Helmholtz Association; German Cancer Research Center (DKFZ); Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center Sulman, EP (corresponding author), NYU Grossman Sch Med, Dept Radiat Oncol, New York, NY 10016 USA.;Sulman, EP (corresponding author), NYU Langone Hlth, Brain & Spine Tumor Ctr, Laura & Isaac Perlmutter Canc Ctr, New York, NY 10016 USA. erik.sulman@nyulangone.org Wick, Wolfgang/AAA-2545-2020; Wang, Qianghu/HJI-9741-2023 Wick, Wolfgang/0000-0002-6171-634X; Yang, Jie/0000-0003-2241-5066; Tsirigos, Aristotelis/0000-0002-7512-8477; Zhang, Zeyan/0000-0002-7896-3013 National Brain Tumor Society DefeatGBM Consortium; [R01 CA190121-01] National Brain Tumor Society DefeatGBM Consortium; Funding from R01 CA190121-01 (EPS) and the National Brain Tumor Society DefeatGBM Consortium (EPS, QW). 44 2 2 1 2 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2041-1723 NAT COMMUN Nat. Commun. JUL 29 2022.0 13.0 1 4410 10.1038/s41467-022-31827-x 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 3U2VK 35906213.0 Green Accepted, gold 2023-03-23 WOS:000840832000020 0
J He, ZZ; Xu, J; Shi, HR; Wu, SX He, Zhizhou; Xu, Jing; Shi, Haoran; Wu, Shuxiang m5CRegpred: Epitranscriptome Target Prediction of 5-Methylcytosine (m5C) Regulators Based on Sequencing Features GENES English Article 5-methylcytosine; machine learning; readers RNA 5-METHYLCYTOSINE; WEB SERVER; SITES; CANCER; METHYLTRANSFERASE; IDENTIFICATION; METHYLATION; DATABASE; PSEKNC; MODES 5-methylcytosine (m5C) is a common post-transcriptional modification observed in a variety of RNAs. m5C has been demonstrated to be important in a variety of biological processes, including RNA structural stability and metabolism. Driven by the importance of m5C modification, many projects focused on the m5C sites prediction were reported before. To better understand the upstream and downstream regulation of m5C, we present a bioinformatics framework, m5CRegpred, to predict the substrate of m5C writer NSUN2 and m5C readers YBX1 and ALYREF for the first time. After features comparison, window lengths selection and algorism comparison on the mature mRNA model, our model achieved AUROC scores 0.869, 0.724 and 0.889 for NSUN2, YBX1 and ALYREF, respectively in an independent test. Our work suggests the substrate of m5C regulators can be distinguished and may help the research of m5C regulators in a special condition, such as substrates prediction of hyper- or hypo-expressed m5C regulators in human disease. [He, Zhizhou; Xu, Jing; Wu, Shuxiang] Fujian Med Univ, Sch Basic Med Sci, Key Lab, Minist Educ Gastrointestinal Canc, Fuzhou 350004, Peoples R China; [He, Zhizhou] Univ Calif Santa Cruz, Dept Mol Cell & Dev Biol, Santa Cruz, CA 95064 USA; [Shi, Haoran] Justus Liebig Univ Giessen, Res Ctr BioSyst Land Use & Nutr IFZ, Inst Appl Microbiol, Heinrich Buff Ring 26-32, D-35392 Giessen, Germany; [Wu, Shuxiang] Fujian Med Univ, Sch Basic Med Sci, Dept Med Microbiol, Fujian Key Lab Tumor Microbiol, Fuzhou 350004, Peoples R China Fujian Medical University; University of California System; University of California Santa Cruz; Justus Liebig University Giessen; Fujian Medical University Wu, SX (corresponding author), Fujian Med Univ, Sch Basic Med Sci, Key Lab, Minist Educ Gastrointestinal Canc, Fuzhou 350004, Peoples R China.;Shi, HR (corresponding author), Justus Liebig Univ Giessen, Res Ctr BioSyst Land Use & Nutr IFZ, Inst Appl Microbiol, Heinrich Buff Ring 26-32, D-35392 Giessen, Germany.;Wu, SX (corresponding author), Fujian Med Univ, Sch Basic Med Sci, Dept Med Microbiol, Fujian Key Lab Tumor Microbiol, Fuzhou 350004, Peoples R China. zhe54@ucsc.edu; xjing955@fjmu.edu.cn; Haoran.Shi@umwelt.uni-giessen.de; wushuxiang@fjmu.edu.cn Shi, Haoran/0000-0001-5997-6946 Scientific Research Foundation for Advanced Talents of Fujian Medical University [XRCZX2020012] Scientific Research Foundation for Advanced Talents of Fujian Medical University Scientific Research Foundation for Advanced Talents of Fujian Medical University (XRCZX2020012). 79 2 2 3 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4425 GENES-BASEL Genes APR 2022.0 13.0 4 677 10.3390/genes13040677 0.0 13 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity 0Q7ZP 35456483.0 gold, Green Accepted 2023-03-23 WOS:000785132100001 0
J Obaied, A; Bocklund, B; Zomorodpoosh, S; Zhang, L; Otis, R; Liu, ZK; Roslyakova, I Obaied, A.; Bocklund, B.; Zomorodpoosh, S.; Zhang, L.; Otis, R.; Liu, Zi-Kui; Roslyakova, I Thermodynamic re-assessment of pure chromium using modified segmented regression model CALPHAD-COMPUTER COUPLING OF PHASE DIAGRAMS AND THERMOCHEMISTRY English Article Chromium; Thermodynamic properties; Third generation; CALPHAD databases, sigmoid function; Segmented regression HEAT-CAPACITY; HIGH-TEMPERATURES; SGTE DATA; 0 K; ELEMENTS; METALS; PHASES; ANTIFERROMAGNETISM; SOFTWARE; VANADIUM Chromium is considered an essential addition element in multiple industrial applications. Therefore, obtaining an accurate and robust description of its properties is important. Recently, several attempts were made to improve the thermodynamic description of pure elements and binary systems in the frame of the third generation CALPHAD databases. Moreover, it became increasingly important to develop mathematical tools that can allow researchers more access to each phase of the material over the entire temperature range. Thus, the recently proposed segmented regression model was modified using the so-called sigmoid function for the case of pure Cr. A comprehensive analysis of phase equilibria and thermodynamic properties of pure Cr was used to develop an updated unary description from 0 K to 6000 K using the proposed modified segmented regression (MSR) model. In addition, two newly developed machine learning tools were utilized to analyze the datasets used in this assessment to determine any possible outliers. [Obaied, A.; Zomorodpoosh, S.; Roslyakova, I] Ruhr Univ Bochum, ICAMS, Univ Str 150, D-44801 Bochum, Germany; [Bocklund, B.; Liu, Zi-Kui] Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA; [Zhang, L.] Cent South Univ, State Key Lab Powder Met, Changsha 410083, Hunan, Peoples R China; [Otis, R.] CALTECH, Jet Prop Lab, Engn & Sci Directorate, 4800 Oak Grove Dr, Pasadena, CA 91109 USA Ruhr University Bochum; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Central South University; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL) Obaied, A (corresponding author), Ruhr Univ Bochum, ICAMS, Univ Str 150, D-44801 Bochum, Germany. abdulmonem.obaied@rub.de Roslyakova, Irina/GWR-1991-2022; Liu, Zi-Kui/A-8196-2009 Roslyakova, Irina/0000-0003-4329-6502; Liu, Zi-Kui/0000-0003-3346-3696; Bocklund, Brandon/0000-0002-3264-8413 IMPRS-SurMat; NASA Space Technology Research Fellowship [80NSSC18K1168]; National Science Foundation [CMMI-1825538]; Office of Naval Research [N00014-17-1-2567]; German Research Foundation (DFG) [SFB TR-103]; Sino-German Collaboration Group IMPRS-SurMat; NASA Space Technology Research Fellowship; National Science Foundation(National Science Foundation (NSF)); Office of Naval Research(Office of Naval Research); German Research Foundation (DFG)(German Research Foundation (DFG)); Sino-German Collaboration Group A. Obaied acknowledges funding from IMPRS-SurMat. B. Bocklund was supported by a NASA Space Technology Research Fellowship (grant number 80NSSC18K1168). Zi-Kui Liu is partially supported by National Science Foundation (Grant CMMI-1825538) and Office of Naval Research (Grant N00014-17-1-2567). This work has also been supported by the Collaborative Research Center Superalloys Single Crystal (SFB TR-103 project C6) of the German Research Foundation (DFG) and the Sino-German Collaboration Group. A part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. 80 8 8 0 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0364-5916 1873-2984 CALPHAD Calphad-Comput. Coupling Ph. Diagrams Thermochem. JUN 2020.0 69.0 101762 10.1016/j.calphad.2020.101762 0.0 14 Thermodynamics; Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Chemistry; Materials Science; Metallurgy & Metallurgical Engineering LR8SX Bronze 2023-03-23 WOS:000535967900013 0
C Guo, YN; Zhao, ZP; Ma, YD; Schuller, BW Int Speech Commun Assoc Guo, Ya'nan; Zhao, Ziping; Ma, Yide; Schuller, Bjorn W. Speech Augmentation via Speaker-Specific Noise in Unseen Environment INTERSPEECH 2019 Interspeech English Proceedings Paper Interspeech Conference SEP 15-19, 2019 Graz, AUSTRIA Emotion Recognition; Speech Augmentation; Speech decomposition; Bidirectional LSTM-Attention Speech augmentation is a common and effective strategy to avoid overfitting and improve on the robustness of an emotion recognition model. In this paper, we investigate for the first time the intrinsic attributes in a speech signal using the multi-resolution analysis theory and the Hilbert-Huang Spectrum, with the goal of developing a robust speech augmentation approach from raw speech data. Specifically, speech decomposition in a double tree complex wavelet transform domain is realized, to obtain sub-speech signals; then, the Hilbert Spectrum using Hilbert-Huang Transform is calculated for each sub-band to capture the noise content in unseen environments with the voice restriction to 100-4000 Hz; finally, the speech-specific noise that varies with the speaker individual, scenarios, environment, and voice recording equipment, can be reconstructed from the top two high-frequency sub-bands to enhance the raw signal. Our proposed speech augmentation is demonstrated using five robust machine learning architectures based on the RAVDESS database, achieving up to 9.3% higher accuracy compared to the performance on raw data for an emotion recognition task. [Guo, Ya'nan; Zhao, Ziping; Schuller, Bjorn W.] Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, Augsburg, Germany; [Guo, Ya'nan; Ma, Yide] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China; [Zhao, Ziping] Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin, Peoples R China; [Schuller, Bjorn W.] Imperial Coll London, GLAM Grp Language Audio & Mus, London, England University of Augsburg; Lanzhou University; Tianjin Normal University; Imperial College London Zhao, ZP (corresponding author), Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, Augsburg, Germany.;Zhao, ZP (corresponding author), Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin, Peoples R China. guoyn10@gmail.com Schuller, Bjorn/0000-0002-6478-8699 China Scholarship Council (CSC); Key Program of the Natural Science Foundation of Tianjin [18JCZDJC36300] China Scholarship Council (CSC)(China Scholarship Council); Key Program of the Natural Science Foundation of Tianjin The work presented in this paper was substantially supported by the China Scholarship Council (CSC) and the Key Program of the Natural Science Foundation of Tianjin (Grant No. 18JCZDJC36300). We thank the help from our colleagues Manuel Milling as well as Dr. Judith Dineley. 26 0 0 1 1 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2019.0 1781 1785 10.21437/Interspeech.2019-2712 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4LP Green Published 2023-03-23 WOS:000831796401188 0
J Cheng, J; Park, JH; Karimi, HR; Shen, H Cheng, Jun; Park, Ju H.; Karimi, Hamid Reza; Shen, Hao A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals IEEE TRANSACTIONS ON CYBERNETICS English Article Exponentially synchronization; Markovian jumping neural network (MJNN); reliable control; sampled-data (SD); time-varying delay STABILITY ANALYSIS; H-INFINITY; ASYMPTOTIC STABILITY; RELIABLE CONTROL; STATE ESTIMATION; NEUTRAL-TYPE; SYSTEMS; DISCRETE This paper investigates the problem of sampled-data (SD) exponentially synchronization 14w a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure. [Cheng, Jun] Hubei Univ Nationalities, Sch Sci, Enshi City 445000, Hubei, Peoples R China; [Cheng, Jun] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China; [Park, Ju H.] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea; [Karimi, Hamid Reza] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy; [Shen, Hao] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243002, Peoples R China Hubei Minzu University; University of Electronic Science & Technology of China; Yeungnam University; Polytechnic University of Milan; Anhui University of Technology Park, JH (corresponding author), Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea. jcheng6819@126.com; jessie@ynu.ac.kr; hrkarimi@ieee.org; haoshen10@gmail.com Cheng, Jun/AAE-6928-2019; shen, hao/HGF-0551-2022; Karimi, Hamid Reza/Y-2403-2019; Shen, Hao/S-3348-2019; Park, Ju H./J-8796-2012 Cheng, Jun/0000-0002-3483-747X; Shen, Hao/0000-0001-7024-6573; Park, Ju H./0000-0002-0218-2333 National Natural Science Foundation of China [NSFC 11626093]; Natural Science Foundation of Hubei Provinces of China [2016CFB211]; Basic Science Research Programs through the National Research Foundation of Korea - Ministry of Education [NRF-2017R1A2B2004671]; National Research Foundation of Korea [2017R1A2B2004671, 22A20130000136] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Provinces of China(Natural Science Foundation of Hubei Province); Basic Science Research Programs through the National Research Foundation of Korea - Ministry of Education; National Research Foundation of Korea(National Research Foundation of Korea) The work of J. Cheng was supported in part by the National Natural Science Foundation of China under Grant NSFC 11626093, and in part by the Natural Science Foundation of Hubei Provinces of China under Grant 2016CFB211. The work of J. H. Park was supported by the Basic Science Research Programs through the National Research Foundation of Korea funded by the Ministry of Education under Grant NRF-2017R1A2B2004671. 46 173 173 5 58 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2267 2168-2275 IEEE T CYBERNETICS IEEE T. Cybern. AUG 2018.0 48.0 8 2232 2244 10.1109/TCYB.2017.2729581 0.0 13 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science GN7XS 28783655.0 2023-03-23 WOS:000439363600002 0
J Chen, H; Wang, TL; Cao, JW; Vidal, PP; Yang, YM Chen, Hao; Wang, Tianlei; Cao, Jiuwen; Vidal, Pierre-Paul; Yang, Yimin Dynamic Quaternion Extreme Learning Machine IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS English Article Quaternions; Neurons; Algebra; Training; Optimization; Computer architecture; Circuits and systems; Quaternion algebra; quaternion EM-ELM; dynamic quaternion ELM; quaternion GHR ATTRACTOR; COMPLEX Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization-based Q-ELM (QEM-ELM) that only needs to optimize the output weights of the newly added neuron is developed in this brief. On this basis, a dynamic network construction scheme is further extended on Q-ELM, leading to a novel DQ-ELM, where the hidden nodes can be dynamically recruited or deleted according to the significance to network performance. The network parameters can be optimized and the architecture can be self-adapted simultaneously. Simulation results on many benchmark datasets demonstrate that the proposed QEM-ELM and DQ-ELM achieve good generalization performance by preserving a compact network size. [Chen, Hao; Wang, Tianlei; Cao, Jiuwen; Vidal, Pierre-Paul] Hangzhou Dianzi Univ, Machine Learning & I Hlth Int Cooperat Base Zheji, Hangzhou 310018, Peoples R China; [Chen, Hao; Wang, Tianlei; Cao, Jiuwen; Vidal, Pierre-Paul] Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Peoples R China; [Vidal, Pierre-Paul] Univ Paris 05, Plateforme Sensorimotricite, F-75270 Paris, France; [Vidal, Pierre-Paul] Univ Paris 05, Cognit & Act Grp, F-75270 Paris, France; [Yang, Yimin] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada Hangzhou Dianzi University; Hangzhou Dianzi University; UDICE-French Research Universities; Universite Paris Cite; UDICE-French Research Universities; Universite Paris Cite; Lakehead University Cao, JW (corresponding author), Hangzhou Dianzi Univ, Machine Learning & I Hlth Int Cooperat Base Zheji, Hangzhou 310018, Peoples R China.;Cao, JW (corresponding author), Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Peoples R China. 840948059@qq.com; tianlei.wang.cn@gmail.com; jwcao@hdu.edu.cn; pierre-paul.vidal@parisdescartes.fr; yyang48@lakeheadu.ca Yang, Yimin/A-3060-2019 Yang, Yimin/0000-0002-1131-2056; Wang, Tianlei/0000-0002-4498-4326 National Natural Sciences Foundation of China [U1909209, 61503104]; Open Research Projects of Zhejiang Lab [2021MC0AB04] National Natural Sciences Foundation of China(National Natural Science Foundation of China (NSFC)); Open Research Projects of Zhejiang Lab This work was supported in part by the National Natural Sciences Foundation of China under Grant U1909209 and Grant 61503104, and in part by the Open Research Projects of Zhejiang Lab under Grant 2021MC0AB04. 29 1 1 12 28 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1549-7747 1558-3791 IEEE T CIRCUITS-II IEEE Trans. Circuits Syst. II-Express Briefs AUG 2021.0 68.0 8 3012 3016 10.1109/TCSII.2021.3067014 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering TS3CS 2023-03-23 WOS:000679532300056 0
J Mutter, S; Casey, AE; Zhen, SQ; Shi, ZM; Makinen, VP Mutter, Stefan; Casey, Aaron E.; Zhen, Shiqi; Shi, Zumin; Makinen, Ville-Petteri Multivariable Analysis of Nutritional and Socio-Economic Profiles Shows Differences in Incident Anemia for Northern and Southern Jiangsu in China NUTRIENTS English Article anemia; subtypes; subgroups; China; grains; Jiangsu; geographical divide; iron; rice; wheat IRON INTAKE; DIETARY PATTERNS; SOY-SAUCE; ADULTS; ASSOCIATION; PREVALENCE; COHORT; RISK; POPULATION; ABSORPTION Anemia is a prevalent public health problem associated with nutritional and socio-economic factors that contribute to iron deficiency. To understand the complex interplay of risk factors, we investigated a prospective population sample from the Jiangsu province in China. At baseline, three-day food intake was measured for 2849 individuals (20 to 87 years of age, mean age 47 +/- 14, range 20-87 years, 64% women). At a five-year follow-up, anemia status was re-assessed for 1262 individuals. The dataset was split and age-matched to accommodate cross-sectional (n = 2526), prospective (n = 837), and subgroup designs (n = 1844). We applied a machine learning framework (self-organizing map) to define four subgroups. The first two subgroups were primarily from the less affluent North: the High Fibre subgroup had a higher iron intake (35 vs. 21 mg/day) and lower anemia incidence (10% vs. 25%) compared to the Low Vegetable subgroup. However, the predominantly Southern subgroups were surprising: the Low Fibre subgroup showed a lower anemia incidence (10% vs. 27%), yet also a lower iron intake (20 vs. 28 mg/day) compared to the High Rice subgroup. These results suggest that interventions and iron intake guidelines should be tailored to regional, nutritional, and socio-economic subgroups. [Mutter, Stefan; Casey, Aaron E.; Makinen, Ville-Petteri] SAHMRI, Adelaide, SA 5000, Australia; [Mutter, Stefan; Casey, Aaron E.; Makinen, Ville-Petteri] Univ Adelaide, Sch Biol Sci, Adelaide, SA 5005, Australia; [Zhen, Shiqi] Jiangsu Prov Ctr Dis Control & Prevent, Dept Nutr & Foodborne Dis Prevent, Nanjing 210009, Jiangsu, Peoples R China; [Shi, Zumin] Univ Adelaide, Sch Med, Adelaide, SA 5005, Australia; [Makinen, Ville-Petteri] Univ Oulu, Computat Med, Fac Med, Oulu 90014, Finland; [Makinen, Ville-Petteri] Bioctr Oulu, Oulu 90014, Finland South Australian Health & Medical Research Institute (SAHMRI); University of Adelaide; Jiangsu Provincial Center for Disease Control & Prevention; University of Adelaide; University of Oulu; University of Oulu Makinen, VP (corresponding author), SAHMRI, Adelaide, SA 5000, Australia.;Makinen, VP (corresponding author), Univ Adelaide, Sch Biol Sci, Adelaide, SA 5005, Australia.;Makinen, VP (corresponding author), Univ Oulu, Computat Med, Fac Med, Oulu 90014, Finland.;Makinen, VP (corresponding author), Bioctr Oulu, Oulu 90014, Finland. stefan.mutter@sahmri.com; aaron.casey@sahmri.com; cdczsq@163.com; zumin.shi@adelaide.edu.au; ville-petteri.makinen@sahmri.com Mutter, Stefan/AAS-2110-2020; Shi, Zumin/A-1093-2009 Mutter, Stefan/0000-0002-8293-6982; Shi, Zumin/0000-0002-3099-3299; Casey, Aaron/0000-0003-3224-2162 45 5 5 0 4 MDPI AG BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6643 NUTRIENTS Nutrients OCT 2017.0 9.0 10 1153 10.3390/nu9101153 0.0 14 Nutrition & Dietetics Science Citation Index Expanded (SCI-EXPANDED) Nutrition & Dietetics FM0DM 29065474.0 gold, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000414629900109 0
J Kuo, CY; Schaarschmidt, A; Cui, YD; Asfour, T; Matsubara, T Kuo, Cheng-Yu; Schaarschmidt, Andreas; Cui, Yunduan; Asfour, Tamim; Matsubara, Takamitsu Uncertainty-Aware Contact-Safe Model-Based Reinforcement Learning IEEE ROBOTICS AND AUTOMATION LETTERS English Article Machine learning for robot control; probabilistic inference; reinforcement learning This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity. Operating these unreliable policies in a contact-rich environment could cause damage to the robot and its surroundings. To alleviate the risk of causing damage through unexpected intensive physical contacts, we present the contact-safe MBRL that associates the probabilistic Model Predictive Control's (pMPC) control limits with the model uncertainty so that the allowed acceleration of controlled behavior is adjusted according to learning progress. Control planning with such uncertainty-aware control limits is formulated as a deterministic MPC problem using a computation-efficient approximated GP dynamics and an approximated inference technique. Our approach's effectiveness is evaluated through bowl mixing tasks with simulated and real robots, scooping tasks with a real robot as examples of contact-rich manipulation skills. [Kuo, Cheng-Yu; Matsubara, Takamitsu] Nara Inst Sci & Technol, Grad Sch Sci & Technol, Nara 6300192, Japan; [Schaarschmidt, Andreas; Asfour, Tamim] Karlsruhe Inst Technol, Inst Anthropomat & Robot, D-76131 Karlsruhe, Germany; [Cui, Yunduan] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 1068, Peoples R China Nara Institute of Science & Technology; Helmholtz Association; Karlsruhe Institute of Technology; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS Kuo, CY (corresponding author), Nara Inst Sci & Technol, Grad Sch Sci & Technol, Nara 6300192, Japan. kuo.cheng-yu.jy5@is.naist.jp; ufthim@student.kit.edu; yd.cui@siat.ac.cn; asfour@kit.edu; takam-m@is.naist.jp Asfour, Tamim/0000-0003-4879-7680; Matsubara, Takamitsu/0000-0003-3545-4814; Kuo, Cheng-Yu/0000-0002-3085-0343 32 4 4 3 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. APR 2021.0 6.0 2 3918 3925 10.1109/LRA.2021.3065271 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics RK6JU Green Submitted 2023-03-23 WOS:000638400900003 0
J Yuan, QJ; Gao, JN; Wu, DL; Zhang, SH; Mamitsuka, H; Zhu, SF Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank BIOINFORMATICS English Article; Proceedings Paper 24th Annual Conference on Intelligent Systems for Molecular Biology (ISMB) JUL 08-12, 2016 Orlando, FL INTERACTION NETWORKS; FEATURES; KERNELS Motivation: Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. [Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China; [Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Zhang, Shihua] Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China; [Mamitsuka, Hiroshi] Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Japan; [Mamitsuka, Hiroshi] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Zhu, Shanfeng] Fudan Univ, Ctr Computat Syst Biol, Shanghai, Peoples R China Fudan University; Fudan University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Kyoto University; Aalto University; Fudan University Zhu, SF (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Ctr Computat Syst Biol, Shanghai, Peoples R China. zhusf@fudan.edu.cn Mamitsuka, Hiroshi/R-1110-2016 Mamitsuka, Hiroshi/0000-0002-6607-5617 37 90 90 2 22 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics JUN 15 2016.0 32.0 12 18 27 10.1093/bioinformatics/btw244 0.0 10 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics DR2KV 27307615.0 Green Published, Green Submitted, hybrid 2023-03-23 WOS:000379734300003 0
J Hu, S; Zhou, QS; Li, JY; Wang, YB; Roncoli, C; Zhang, LH; Lehe, L Hu, Simon; Zhou, Qishen; Li, Junyi; Wang, Yibing; Roncoli, Claudio; Zhang, Lihui; Lehe, Lewis High Time-Resolution Queue Profile Estimation at Signalized Intersections Based on Extended Kalman Filtering IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Estimation; Queueing analysis; Vehicle dynamics; Sensors; Data models; Real-time systems; Heuristic algorithms; Connected vehicles (CVs); queue profile estimation; dynamic shockwave propagation model; data-fusion-based shockwave sensing; extended Kalman filtering (EKF) LENGTH ESTIMATION; PREDICTION; VEHICLES; MODELS The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5%, and of 0.5 vehicle at the MPR of 40%. [Hu, Simon; Zhou, Qishen; Li, Junyi] Zhejiang Univ, ZJU UIUC Inst, Haining 314400, Peoples R China; [Hu, Simon; Zhou, Qishen; Li, Junyi] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China; [Hu, Simon; Zhou, Qishen; Li, Junyi] Alibaba Zhejiang Univ, Joint Res Inst Frontier Technol, Hangzhou 310058, Peoples R China; [Wang, Yibing; Zhang, Lihui] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China; [Roncoli, Claudio] Aalto Univ, Dept Built Environm, Espoo 02150, Finland; [Lehe, Lewis] Univ Illinois, Civil & Environm Engn Dept, Urbana, IL 61801 USA Zhejiang University; Zhejiang University; Zhejiang University; Aalto University; University of Illinois System; University of Illinois Urbana-Champaign Wang, YB (corresponding author), Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China. simonhu@zju.edu.cn; qishenzhou@intl.zju.edu.cn; junyili@intl.zju.edu.cn; wangyibing@zju.edu.cn; claudio.roncoli@aalto.fi; lihuizhang@zju.edu.cn; lehe@illinois.edu 周, 启申/ADH-5560-2022; Li, Junyi/AAC-6269-2022; Roncoli, Claudio/Q-6313-2016; Wang, Yibing/J-9389-2013 周, 启申/0000-0001-8074-4111; Roncoli, Claudio/0000-0002-9381-3021; Hu, Simon/0000-0002-9832-6679; Lehe, Lewis/0000-0001-8029-1706; Wang, Yibing/0000-0001-9937-2055; Li, Junyi/0000-0002-8267-9939 National Key Research and Development Program of China [2018YFB1600500, 2017YFE9134700]; Zhejiang University-University of Illinois at Urbana-Champaign (ZJU-UIUC) Joint Research Centre Project of Zhejiang University [DREMES202001]; National Natural Science Foundation of China [71771200, 52131202]; Provincial Key Research and Development Program of Zhejiang [2021C01012]; Academy of Finland [328216, 347200]; Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies; Academy of Finland (AKA) [328216] Funding Source: Academy of Finland (AKA) National Key Research and Development Program of China; Zhejiang University-University of Illinois at Urbana-Champaign (ZJU-UIUC) Joint Research Centre Project of Zhejiang University; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Provincial Key Research and Development Program of Zhejiang; Academy of Finland(Academy of Finland); Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies; Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1600500 and Grant 2017YFE9134700, in part by the Zhejiang University-University of Illinois at Urbana-Champaign (ZJU-UIUC) Joint Research Centre Project of Zhejiang University under Grant DREMES202001, in part by the National Natural Science Foundation of China under Grant 71771200 and Grant 52131202, in part by the Provincial Key Research and Development Program of Zhejiang under Grant 2021C01012, in part by Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and in part by the Academy of Finland through the Projects ULTRA under Grant 328216 and AIforLEssAuto under Grant 347200. 59 0 0 6 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. NOV 2022.0 23.0 11 21274 21290 10.1109/TITS.2022.3173515 0.0 MAY 2022 17 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 6A6GZ 2023-03-23 WOS:000800804800001 0
J Sebastiani, M; Di Flumeri, G; Arico, P; Sciaraffa, N; Babiloni, F; Borghini, G Sebastiani, Marika; Di Flumeri, Gianluca; Arico, Pietro; Sciaraffa, Nicolina; Babiloni, Fabio; Borghini, Gianluca Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers BRAIN SCIENCES English Article vigilance; mental states assessment; high-resolution EEG; machine learning; psychomotor vigilance task; stepwise linear discriminant analysis; air traffic controllers; out-of-the-loop; ATM PERFORMANCE; TASK; DECREMENT; ATTENTION; OSCILLATIONS; SYSTEM; AUTOMATION; WORKLOAD; ALPHA; BCI Vigilance degradation usually causes significant performance decrement. It is also considered the major factor causing the out-of-the-loop phenomenon (OOTL) occurrence. OOTL is strongly related to a high level of automation in operative contexts such as the Air Traffic Management (ATM), and it could lead to a negative impact on the Air Traffic Controllers' (ATCOs) engagement. As a consequence, being able to monitor the ATCOs' vigilance would be very important to prevent risky situations. In this context, the present study aimed to characterise and assess the vigilance level by using electroencephalographic (EEG) measures. The first study, involving 13 participants in laboratory settings allowed to find out the neurophysiological features mostly related to vigilance decrements. Those results were also confirmed under realistic ATM settings recruiting 10 professional ATCOs. The results demonstrated that (i) there was a significant performance decrement related to vigilance reduction; (ii) there were no substantial differences between the identified neurophysiological features in controlled and ecological settings, and the EEG-channel configuration defined in laboratory was able to discriminate and classify vigilance changes in ATCOs' vigilance with high accuracy (up to 84%); (iii) the derived two EEG-channel configuration was able to assess vigilance variations reporting only slight accuracy reduction. [Sebastiani, Marika; Di Flumeri, Gianluca; Arico, Pietro; Sciaraffa, Nicolina; Babiloni, Fabio; Borghini, Gianluca] BrainSigns Srl, Lungotevere Michelangelo 9, I-00192 Rome, Italy; [Di Flumeri, Gianluca; Arico, Pietro; Sciaraffa, Nicolina; Babiloni, Fabio; Borghini, Gianluca] Sapienza Univ Rome, Dept Mol Med, Piazzale Aldo Moro 5, I-00185 Rome, Italy; [Di Flumeri, Gianluca; Arico, Pietro; Borghini, Gianluca] IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Via Ardeatina 306, I-00179 Rome, Italy; [Babiloni, Fabio] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China Sapienza University Rome; IRCCS Santa Lucia; Hangzhou Dianzi University Borghini, G (corresponding author), BrainSigns Srl, Lungotevere Michelangelo 9, I-00192 Rome, Italy.;Borghini, G (corresponding author), Sapienza Univ Rome, Dept Mol Med, Piazzale Aldo Moro 5, I-00185 Rome, Italy.;Borghini, G (corresponding author), IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Via Ardeatina 306, I-00179 Rome, Italy. marikasebastiani@hotmail.it; gianluca.diflumeri@uniroma1.it; pietro.arico@uniroma1.it; nicolina.sciaraffa@uniroma1.it; fabio.babiloni@uniroma1.it; gianluca.borghini@uniroma1.it Borghini, Gianluca/AAA-4687-2019; Di Flumeri, Gianluca/I-7196-2019; Sciaraffa, Nicolina/AAT-9691-2020; Babiloni, Fabio/E-5169-2015 Borghini, Gianluca/0000-0001-8560-5671; Di Flumeri, Gianluca/0000-0003-4426-051X; Babiloni, Fabio/0000-0002-4962-176X; Sebastiani, Marika/0000-0002-9398-4541; Arico, Pietro/0000-0002-3831-6620 European Commission by Horizon2020 project Sesar-01-2015 Automation in ATM, STRESS [699381]; European Commission by Horizon2020 project Sesar-06-2015 High Performing Airport Operations, MOTO [699379]; European Commission by Horizon2020 project WORKINGAGE: Smart Working environments for all Ages [826232]; European Commission by Horizon2020 project SIMUSAFE: Simulator Of Behavioural Aspects For Safer Transport [723386]; European Commission by Horizon2020 project SAFEMODE: Strengthening synergies between Aviation and maritime in the area of human Factors towards achieving more Efficient and resilient MODE of transportation [814961]; European Commission by Horizon2020 project BRAINSAFEDRIVE: A Technology to detect Mental States during Drive for improving the Safety of the road (Italy-Sweden collaboration); Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana European Commission by Horizon2020 project Sesar-01-2015 Automation in ATM, STRESS; European Commission by Horizon2020 project Sesar-06-2015 High Performing Airport Operations, MOTO; European Commission by Horizon2020 project WORKINGAGE: Smart Working environments for all Ages; European Commission by Horizon2020 project SIMUSAFE: Simulator Of Behavioural Aspects For Safer Transport; European Commission by Horizon2020 project SAFEMODE: Strengthening synergies between Aviation and maritime in the area of human Factors towards achieving more Efficient and resilient MODE of transportation; European Commission by Horizon2020 project BRAINSAFEDRIVE: A Technology to detect Mental States during Drive for improving the Safety of the road (Italy-Sweden collaboration); Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana This work is co-financed by the European Commission by Horizon2020 projects Sesar-01-2015 Automation in ATM, STRESS (GA n. 699381), Sesar-06-2015 High Performing Airport Operations, MOTO (GA n. 699379), WORKINGAGE: Smart Working environments for all Ages (GA n. 826232); SIMUSAFE: Simulator Of Behavioural Aspects For Safer Transport (GA n. 723386); SAFEMODE: Strengthening synergies between Aviation and maritime in the area of human Factors towards achieving more Efficient and resilient MODE of transportation(GA n. 814961), BRAINSAFEDRIVE: A Technology to detect Mental States during Drive for improving the Safety of the road (Italy-Sweden collaboration) with a grant of Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana. 90 8 8 1 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3425 BRAIN SCI Brain Sci. JAN 2020.0 10.0 1 48 10.3390/brainsci10010048 0.0 22 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology KL0NW 31952181.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000513130500018 0
J Borghini, G; Arico, P; Di Flumeri, G; Sciaraffa, N; Babiloni, F Borghini, Gianluca; Arico, Pietro; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Babiloni, Fabio Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User's States Assessment SENSORS English Article brain activity; EEG; human tissues conductibility; electrical activity; human body; machine-learning analysis; individual alpha frequency; correlation; biopotentials; mental states assessment ALPHA-BAND; EEG; OSCILLATIONS; BCI Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body. Since no works have been found in this direction, the proposed study aimed at overcoming this lack of evidence and seeking analogies between the brain activity and the electrical activity of non-cerebral locations, such as the neck and wrists, to determine if i) cerebral parameters can be estimated from non-cerebral sites, and if ii) non-cerebral sensors can replace cerebral sensors for the evaluation of the users under specific experimental conditions, such as eyes open or closed. In fact, the use of cerebral sensors requires high-qualified personnel, and reliable recording systems, which are still expensive. Therefore, the possibility to use cheaper and easy-to-use equipment to estimate cerebral parameters will allow making some brain-based applications less invasive and expensive, and easier to employ. The results demonstrated the occurrence of significant correlations and analogies between cerebral and non-cerebral electrical activity. Furthermore, the same discrimination and classification accuracy were found in using the cerebral or non-cerebral sites for the user's status assessment. [Borghini, Gianluca; Arico, Pietro; Di Flumeri, Gianluca; Babiloni, Fabio] Sapienza Univ Rome, Dept Mol Med, Piazzale Aldo Moro 5, I-00185 Rome, Italy; [Borghini, Gianluca; Arico, Pietro; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Babiloni, Fabio] BrainSigns Srl, Via Sesto Celere, I-00152 Rome, Italy; [Borghini, Gianluca; Arico, Pietro] IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Via Ardeatina 306, I-00179 Rome, Italy; [Sciaraffa, Nicolina] Sapienza Univ Rome, Dept Anat Histol Forens & Orthoped Sci, Piazzale Aldo Moro 5, I-00185 Rome, Italy; [Babiloni, Fabio] Hangzhou Dianzi Univ, Coll Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China Sapienza University Rome; IRCCS Santa Lucia; Sapienza University Rome; Hangzhou Dianzi University Borghini, G (corresponding author), Sapienza Univ Rome, Dept Mol Med, Piazzale Aldo Moro 5, I-00185 Rome, Italy.;Borghini, G (corresponding author), BrainSigns Srl, Via Sesto Celere, I-00152 Rome, Italy.;Borghini, G (corresponding author), IRCCS Fdn Santa Lucia, Neuroelect Imaging & BCI Lab, Via Ardeatina 306, I-00179 Rome, Italy. gianluca.borghini@uniroma1.it; pietro.arico@uniroma1.it; gianluca.diflumeri@uniroma1.it; nicolina.sciaraffa@brainsigns.com; fabio.babiloni@uniroma1.it Aricò, Pietro/K-9005-2016; Borghini, Gianluca/AAA-4687-2019; Di Flumeri, Gianluca/I-7196-2019; Sciaraffa, Nicolina/AAT-9691-2020; Babiloni, Fabio/E-5169-2015 Borghini, Gianluca/0000-0001-8560-5671; Di Flumeri, Gianluca/0000-0003-4426-051X; Babiloni, Fabio/0000-0002-4962-176X; Arico, Pietro/0000-0002-3831-6620 European Commission [723386]; project BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road (Italy-Sweden collaboration) European Commission(European CommissionEuropean Commission Joint Research Centre); project BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road (Italy-Sweden collaboration) This research was funded by the European Commission through the Horizon2020 project SIMUSAFE (GA n. 723386), and the project BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road (Italy-Sweden collaboration). 34 10 10 0 1 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 1 2019.0 19.0 3 704 10.3390/s19030704 0.0 12 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation HN1IR 30744081.0 Green Published, Green Accepted, Green Submitted, gold 2023-03-23 WOS:000459941200267 0
J Huo, ZN; Mei, G; Casolla, G; Giampaolo, F Huo, Zenan; Mei, Gang; Casolla, Giampaolo; Giampaolo, Fabio Designing an efficient parallel spectral clustering algorithm on multi-core processors in Julia JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING English Article Clustering algorithm; Spectral clustering; Parallel algorithm; Multi-core processors; Julia language MACHINE; GPU; POINT; SPARK Spectral clustering is widely used in data mining, machine learning and other fields. It can identify the arbitrary shape of a sample space and converge to the global optimal solution. Compared with the traditional k-means algorithm, the spectral clustering algorithm has stronger adaptability to data and better clustering results. However, the computation of the algorithm is quite expensive. In this paper, an efficient parallel spectral clustering algorithm on multi-core processors in the Julia language is proposed, and we refer to it as juPSC. The Julia language is a high-performance, open-source programming language. The juPSC is composed of three procedures: (1) calculating the affinity matrix, (2) calculating the eigenvectors, and (3) conducting k-means clustering. Procedures (1) and (3) are computed by the efficient parallel algorithm, and the COO format is used to compress the affinity matrix. Two groups of experiments are conducted to verify the accuracy and efficiency of the juPSC. Experimental results indicate that (1) the juPSC achieves speedups of approximately 14x similar to 18x on a 24-core CPU and that (2) the serial version of the juPSC is faster than the Python version of scikit-learn. Moreover, the structure and functions of the juPSC are designed considering modularity, which is convenient for combination and further optimization with other parallel computing platforms. (C) 2020 Elsevier Inc. All rights reserved. [Huo, Zenan; Mei, Gang] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China; [Casolla, Giampaolo] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy; [Giampaolo, Fabio] CINI, Bari, Italy China University of Geosciences; University of Naples Federico II Mei, G (corresponding author), China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China. gang.mei@cugb.edu.cn Mei, Gang/C-9124-2016 Mei, Gang/0000-0003-0026-5423; Giampaolo, Fabio/0000-0001-5414-3435; Huo, Zenan/0000-0003-3750-1709 National Natural Science Foundation of China [11602235, 41772326]; Fundamental Research Funds for China Central Universities, China [2652018091, 2652018107, 2652018109] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for China Central Universities, China This research was jointly supported by the National Natural Science Foundation of China (Grant Numbers: 11602235 and 41772326), and the Fundamental Research Funds for China Central Universities, China (Grant Numbers: 2652018091, 2652018107, and 2652018109). The authors would like to thank the editor and the reviewers for their contributions. 56 10 10 1 12 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0743-7315 1096-0848 J PARALLEL DISTR COM J. Parallel Distrib. Comput. APR 2020.0 138.0 211 221 10.1016/j.jpdc.2020.01.003 0.0 11 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science KN9VN 2023-03-23 WOS:000515195500017 0
J Haut, JM; Paoletti, ME; Plaza, J; Plaza, A; Li, J Haut, Juan Mario; Paoletti, Mercedes E.; Plaza, Javier; Plaza, Antonio; Li, Jun Visual Attention-Driven Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep learning (DL); feature extraction; hyper-spectral image (HSI) classification; residual neural networks; visual attention SPECTRAL-SPATIAL CLASSIFICATION; REMOTE-SENSING IMAGES; SUPPORT VECTOR MACHINES; FEATURE-EXTRACTION; TARGET DETECTION; NEURAL-NETWORKS; SALIENCY; ALGORITHMS; REGIONS; FIELDS Deep neural networks (DNNs), including convolutional neural networks (CNNs) and residual networks (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that perform advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through more detailed filtering of the features extracted by the operational layers of the network. This is of significant interest for analyzing remotely sensed hyperspectral images (HSIs), characterized by their very high spectral dimensionality. However, few efforts have been conducted in the literature in order to adapt visual attention methods to remotely sensed HSI data analysis. In this paper, we introduce a new visual attention-driven technique for the HSI classification. Specifically, we incorporate attention mechanisms to a ResNet in order to better characterize the spectral-spatial information contained in the data. Our newly proposed method calculates a mask that is applied to the features obtained by the network in order to identify the most desirable ones for classification purposes. Our experiments, conducted using four widely used HSI data sets, reveal that the proposed deep attention model provides competitive advantages in terms of classification accuracy when compared to other state-of-the-art methods. [Haut, Juan Mario; Paoletti, Mercedes E.; Plaza, Javier; Plaza, Antonio] Univ Extremadura, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Escuela Politecn, Caceres 10003, Spain; [Li, Jun] Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Ctr Integrated Geog Informat Anal, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China Universidad de Extremadura; Sun Yat Sen University Li, J (corresponding author), Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Ctr Integrated Geog Informat Anal, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China. juanmariohaut@unex.es; mpaoletti@unex.es; jplaza@unex.es; aplaza@unex.es; lijun48@mail.sysu.edu.cn Plaza, Antonio/C-4455-2008; Paoletti, Mercedes Eugenia/AAE-1946-2019; Plaza, Javier/C-4452-2008; Li, Jun/AAD-7233-2020; Haut, Juan M./AAD-9028-2019 Plaza, Antonio/0000-0002-9613-1659; Paoletti, Mercedes Eugenia/0000-0003-1030-3729; Plaza, Javier/0000-0002-2384-9141; Li, Jun/0000-0003-1613-9448; Haut, Juan M./0000-0001-6701-961X Ministerio de Educacion; Junta de Extremadura [GR18060]; European Union [734541]; National Natural Science Foundation of China [61771496]; Guangdong Provincial Natural Science Foundation [2016A030313254]; National Key Research and Development Program of China [2017YFB0502900] Ministerio de Educacion; Junta de Extremadura; European Union(European Commission); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Natural Science Foundation(National Natural Science Foundation of Guangdong Province); National Key Research and Development Program of China This work was supported in part by the Ministerio de Educacion (Resolucion de 26 de diciembre de 2014 y de 19 de noviembre de 2015), de la Secretaria de Estado de Educacion, Formacion Profesional y Universidades, por la que se convocan ayudas para la formacion de profesorado universitario, de los subprogramas de Formacion y de Movilidad incluidos en el Programa Estatal de Promocion del Talento y su Empleabilidad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016, in part supported by Junta de Extremadura (Decreto 14/2018, de 6 de febrero, por el que se establecen las bases reguladoras de las ayudas para la realizacion de actividades de investigacion y desarrollo tecnologico, de divulgacion y de transferencia de conocimiento por los Grupos de Investigacion de Extremadura) under Grant GR18060, in part by the European Union's Horizon 2020 Research And Innovation Programme under Grant Agreement 734541 (EOXPOSURE), in part by the National Natural Science Foundation of China under Grant 61771496, in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254, and in part by the National Key Research and Development Program of China under Grant 2017YFB0502900. 94 126 128 15 116 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing OCT 2019.0 57.0 10 8065 8080 10.1109/TGRS.2019.2918080 0.0 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology JD2UB Green Submitted 2023-03-23 WOS:000489829200058 0
J Boada, MJL; Boada, BL; Zhang, H Boada, Maria Jesus L.; Boada, Beatriz L.; Zhang, Hui Event-triggering H-infinity-based observer combined with NN for simultaneous estimation of vehicle sideslip and roll angles with network-induced delays NONLINEAR DYNAMICS English Article Nonlinear vehicle dynamics; H-infinity estimation; Event-triggered; Neural network; LPV; Model parameter uncertainty Nowadays, vehicles are being fitted with systems that improve their maneuverability, stability, and comfort in order to reduce the number of accidents. Improving these aspects is of particular interest thanks to the incorporation of autonomous vehicles onto the roads. The knowledge of vehicle sideslip and roll angles, which are among the main causes of road accidents, is necessary for a proper design of a lateral stability and roll-over controller system. The problem is that these two variables cannot be measured directly through sensors installed in current series production vehicles due to their high costs. For this reason, their estimation is fundamental. In addition, there is a time delay in the relaying of information between the different vehicle systems, such as, sensors, actuators and controllers, among others. This paper presents the design of an H-infinity-based observer that simultaneously estimates both the sideslip angle and the roll angle of a vehicle for a networked control system, with networked transmission delay based on an event-triggered communication scheme combined with neural networks (NN). To deal with the vehicle nonlinearities, NN and linear-parameter-varying techniques are considered alongside uncertainties in parameters. Both simulation and experimental results are carried out to prove the performance of observer design. [Boada, Maria Jesus L.; Boada, Beatriz L.] Univ Carlos III Madrid, Dept Mech Engn, Res Inst Vehicle Safety ISVA, Avda Univ 30, Madrid 28911, Spain; [Zhang, Hui] Beihang Univ, Sch Transportat Sci & Engn, Dept Automot Engn, Beijing 100083, Peoples R China Universidad Carlos III de Madrid; Beihang University Boada, MJL (corresponding author), Univ Carlos III Madrid, Dept Mech Engn, Res Inst Vehicle Safety ISVA, Avda Univ 30, Madrid 28911, Spain. mjboada@ing.uc3m.es; bboada@ing.uc3m.es; huizhang285@gmail.com LOPEZ BOADA, BEATRIZ/C-9049-2016; BOADA, MARIA JESUS L./B-9206-2008 LOPEZ BOADA, BEATRIZ/0000-0002-8061-068X; BOADA, MARIA JESUS L./0000-0001-5377-0023; Zhang, Hui/0000-0002-2501-712X Agencia Estatal de Investigacion (AEI) of the Ministry of Science and Innovation of the Government of Spain [RTI2018-095143-B-C21] Agencia Estatal de Investigacion (AEI) of the Ministry of Science and Innovation of the Government of Spain This work was supported by the Agencia Estatal de Investigacion (AEI) of the Ministry of Science and Innovation of the Government of Spain through the project RTI2018-095143-B-C21. 42 1 1 4 19 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. FEB 2021.0 103.0 3 2733 2752 10.1007/s11071-021-06269-7 0.0 FEB 2021 20 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics QS8PJ hybrid, Green Published 2023-03-23 WOS:000618591100002 0
J Zhao, XD; Liu, L; Wang, YB; Zhang, LQ; Plaza, A Zhao, Xudong; Liu, Li; Wang, Yuebin; Zhang, Liqiang; Plaza, Antonio (DSL)-L-4: Deep Semisupervised Shared Subspace Learning for Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Constraint; hyperspectral image (HSI) classification; semisupervised; shared subspace learning (SL) AUTOENCODERS; PROGRESS; NETWORK Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The classification methods based on deep learning have attracted more and more attention. However, classification accuracy is seriously affected by the quantity of labeled data and redundant information. Therefore, a deep semisupervised shared subspace learning ((DSL)-L-4) model is developed to overcome these problems in this article. (DSL)-L-4 is composed of two parts. First, the basic feature extraction (BFE) network is constructed to preliminary extract high-dimensional-space features of multiscale data and fusion them to one shared subspace. Then, a deep shared subspace learning (DSSL) network is proposed to obtain a deeper and more representative low-dimensional subspace. Moreover, to obtain a more representative subspace and alleviate dependence on labeled samples, the regular, irregular constraint, and cross-entropy (CE) loss are integrated into the model. The regular constraint is adopted to reconstruct the multiscale patches to ensure the quality of the subspace in an unsupervised manner. The irregular constraint can well embed labeled and unlabeled samples into the procedure of subspace learning (SL). Then, the CE loss is used to extract more discriminative subspace using the limited labeled samples. Finally, we perform experiments on three widely used HSI datasets. Compared with the basic SL model, the (DSL)-L-4's classification accuracy on the popular Salinas, Indian Pines, and PaviaU datasets are increased by 4.68%, 4.25%, and 1.57%, respectively. [Zhao, Xudong; Liu, Li; Wang, Yuebin] China Univ Geosci Beijing, Sch Land Sci & Technol, Beijing 100083, Peoples R China; [Zhang, Liqiang] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Plaza, Antonio] Univ Extremadura, Hyperspectral Comp Lab, Dept Comp Technol & Commun, Caceres 10071, Spain China University of Geosciences; Beijing Normal University; Universidad de Extremadura Wang, YB (corresponding author), China Univ Geosci Beijing, Sch Land Sci & Technol, Beijing 100083, Peoples R China. cugbxudongzhao@163.com; cugbliuli@163.com; xxgcdxwyb@163.com; zhanglq@bnu.edu.cn; aplaza@unex.es Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Wang, Yuebin/0000-0002-6978-4558; Zhao, Xiaobin/0000-0002-9828-1976; Zhang, Liqiang/0000-0002-4175-7590 National Natural Science Foundation of China [42171388, 41801241]; Fundamental Research Funds for the Central Universities [292018029, 375201906]; Key Research and Development Projects of Shanxi Province [201903D121142]; Guizhou Science and Technology Plan Project [Qiankehezhicheng [2020] 4Y022] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Key Research and Development Projects of Shanxi Province; Guizhou Science and Technology Plan Project This work was supported in part by the National Natural Science Foundation of China under Grant 42171388 and Grant 41801241, in part by the Fundamental Research Funds for the Central Universities under Grant 292018029 and Grant 375201906, in part by the Key Research and Development Projects of Shanxi Province under Grant 201903D121142, and in part by the Guizhou Science and Technology Plan Project under Grant Qiankehezhicheng [2020] 4Y022. 47 7 7 5 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3115354 0.0 OCT 2021 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YP5SW 2023-03-23 WOS:000732799600001 0
J Ghasemian, A; Zhang, P; Clauset, A; Moore, C; Peel, L Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks PHYSICAL REVIEW X English Article STOCHASTIC BLOCKMODELS; RECONSTRUCTION; PREDICTION The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information. [Ghasemian, Amir; Clauset, Aaron] Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA; [Zhang, Pan] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China; [Clauset, Aaron] Univ Colorado, BioFrontiers Inst, Boulder, CO 80305 USA; [Clauset, Aaron; Moore, Cristopher] Santa Fe Inst, 1399 Hyde Pk Rd, Santa Fe, NM 87501 USA; [Peel, Leto] Catholic Univ Louvain, ICTEAM, Ave George Lemaitre 4, B-1348 Louvain, Belgium; [Peel, Leto] Univ Namur, naXys, Rempart Vierge 8, B-5000 Namur, Belgium University of Colorado System; University of Colorado Boulder; Chinese Academy of Sciences; Institute of Theoretical Physics, CAS; University of Colorado System; University of Colorado Boulder; The Santa Fe Institute; Universite Catholique Louvain; University of Namur Ghasemian, A (corresponding author), Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA. Zhang, Pan/O-7877-2018; Ghasemian, Amir/GYU-2849-2022; Peel, Leto/R-1402-2018 Ghasemian, Amir/0000-0002-3515-3504; Peel, Leto/0000-0003-4740-7492; Zhang, Pan/0000-0001-8496-2730; Moore, Cristopher/0000-0002-2062-1942; CLAUSET, AARON/0000-0002-3529-8746 BioFrontiers IT [NIH 1S10OD012300]; National Science Foundation [IIS-1452718]; U.S. Air Force Office of Scientific Research (AFOSR) [FA9550-12-1-0432]; Defense Advanced Research Projects Agency (DARPA); Army Research Office (ARO) [W911NF-12-R-0012]; John Templeton Foundation; Div Of Information & Intelligent Systems; Direct For Computer & Info Scie & Enginr [1452718] Funding Source: National Science Foundation BioFrontiers IT; National Science Foundation(National Science Foundation (NSF)); U.S. Air Force Office of Scientific Research (AFOSR)(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)); Defense Advanced Research Projects Agency (DARPA)(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Army Research Office (ARO); John Templeton Foundation; Div Of Information & Intelligent Systems; Direct For Computer & Info Scie & Enginr(National Science Foundation (NSF)NSF - Directorate for Computer & Information Science & Engineering (CISE)) The authors thank Elchanan Mossel and Andrey Lokhov for helpful conversations, and acknowledge the BioFrontiers Computing Core at the University of Colorado Boulder for providing High Performance Computing resources (NIH 1S10OD012300) supported by BioFrontiers IT. This work was supported in part by Grant No. IIS-1452718 (A. G., A. C.) from the National Science Foundation, Grant No. FA9550-12-1-0432 from the U.S. Air Force Office of Scientific Research (AFOSR) and the Defense Advanced Research Projects Agency (DARPA) (L. P.), Contract No. W911NF-12-R-0012 from the Army Research Office (ARO) (C. M.), and the John Templeton Foundation (P. Z., C. M.). A. G. and P. Z. are joint principal authors, with the remaining authors appearing alphabetically. 58 69 70 0 25 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2160-3308 PHYS REV X Phys. Rev. X JUL 13 2016.0 6.0 3 31005 10.1103/PhysRevX.6.031005 0.0 9 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics DR0CW Green Submitted, gold 2023-03-23 WOS:000379576600001 0
J Castillo, E; Calvino, A; Nogal, M; Lo, HK Castillo, Enrique; Calvino, Aida; Nogal, Maria; Lo, Hong K. On the Probabilistic and Physical Consistency of Traffic Random Variables and Models COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING English Article TRAVEL-TIME RELIABILITY; NEURAL-NETWORK MODEL; ORIGIN-DESTINATION MATRICES; BAYESIAN-INFERENCE; FLOW; ASSIGNMENT; METHODOLOGY; CAPACITY; DELAY; DESIGN In this article we deal with the probabilistic and physical consistency of traffic-related random variables and models. We analyze and discuss the conditions for a model to be consistent from two different points of view: probabilistic and physical (dimensional analysis). The first, leads us to the concept of stability in general and reproductivity in particular because, for example, origin-destination (OD) and link flows are the sum of route flows and route travel times are the sum of link travel times. This implies stability with respect to sums (reproductivity). Normal models are justified because when the number of summands increases the averages approach the normal distribution. Similarly, stability with respect to minimum or maximum operations arises in practice. From the dimensional analysis point of view, some models are demonstrated not to be convenient. In particular, it is shown that some families of distributions are valid only for dimensionless variables. All these and other problems are discussed and some proposed models in the literature are analyzed from these two points of view. When some families fail to satisfy the desired properties, alternative models are provided via extension of the original families. Finally, some simple examples and conclusions are given to summarize the analysis. [Castillo, Enrique; Calvino, Aida; Nogal, Maria] Univ Cantabria, Dept Appl Math & Computat Sci, E-39005 Santander, Spain; [Lo, Hong K.] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Clear Water Bay, Hong Kong, Peoples R China Universidad de Cantabria; Hong Kong University of Science & Technology Castillo, E (corresponding author), Univ Cantabria, Dept Appl Math & Computat Sci, E-39005 Santander, Spain. castie@unican.es Castillo, Enrique F/A-7858-2008; Nogal, Maria/ABE-2639-2021; Calviño, Aida/F-7030-2016; Lo, Hong K/C-5578-2013 Castillo, Enrique F/0000-0002-8570-0844; Nogal, Maria/0000-0001-5405-0626; Calviño, Aida/0000-0002-9224-9406; Lo, Hong K/0000-0002-3015-6431 Spanish Ministry of Science and Technology [TRA2010-17818] Spanish Ministry of Science and Technology(Ministry of Science and Innovation, Spain (MICINN)Spanish Government) The authors are indebted to the Spanish Ministry of Science and Technology (Project TRA2010-17818) for partial support of this work, and to the referees for their interesting comments that allowed us to improve the article substantially. 78 38 38 1 15 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1093-9687 1467-8667 COMPUT-AIDED CIV INF Comput.-Aided Civil Infrastruct. Eng. AUG 2014.0 29.0 7 496 517 10.1111/mice.12061 0.0 22 Computer Science, Interdisciplinary Applications; Construction & Building Technology; Engineering, Civil; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Construction & Building Technology; Engineering; Transportation AN3DZ 2023-03-23 WOS:000340466600003 0
C Sciaraffa, N; Germano, D; Giorgi, A; Ronca, V; Vozzi, A; Borghini, G; Di Flumeri, G; Babiloni, F; Arico, P IEEE Sciaraffa, Nicolina; Germano, Daniele; Giorgi, Andrea; Ronca, Vincenzo; Vozzi, Alessia; Borghini, Gianluca; Di Flumeri, Gianluca; Babiloni, Fabio; Arico, Pietro Mental Effort Estimation by Passive BCI: A Cross-Subject Analysis 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) IEEE Engineering in Medicine and Biology Society Conference Proceedings English Proceedings Paper 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) NOV 01-05, 2021 ELECTR NETWORK IEEE Engn Med & Biol Soc,IEEE,Elsevier,Inst Engn & Technol Despite the technological advancements, the employment of passive brain computer interface (BCI) out of the laboratory context is still challenging. This is largely due to methodological reasons. On the one hand, machine learning methods have shown their potential in maximizing performance for user mental states classification. On the other hand, the issues related to the necessary and frequent calibration of algorithms and to the temporal resolution of the measurement (i.e. how long it takes to have a reliable state measure) are still unsolved. This work explores the performances of a passive BCI system for mental effort monitoring consisting of three frontal electroencephalographic (EEG) channels. In particular, three calibration approaches have been tested: an intra-subject approach, a cross-subject approach, and a free-calibration procedure based on the simple average of theta activity over the three employed channels. A Random Forest model has been employed in the first two cases. The results obtained during multi-tasking have shown that the cross-subject approach allows the classification of low and high mental effort with an AUC higher than 0.9, with a related time resolution of 45 seconds. Moreover, these performances are not significantly different from the intra-subject approach although they are significantly higher than the calibration-free approach. In conclusion, these results suggest that a light (three EEG channels) passive BCI system based on a Random Forest algorithm and cross-subject calibration could be a simple and reliable tool for out-of-the-lab employment. [Sciaraffa, Nicolina; Borghini, Gianluca; Di Flumeri, Gianluca; Babiloni, Fabio; Arico, Pietro] Sapienza Univ Rome, Dept Mol Med, Rome, Italy; [Germano, Daniele; Giorgi, Andrea; Ronca, Vincenzo; Vozzi, Alessia; Borghini, Gianluca; Di Flumeri, Gianluca; Babiloni, Fabio; Arico, Pietro] BrainSigns Srl, Rome, Italy; [Ronca, Vincenzo; Vozzi, Alessia] Sapienza Univ Rome, Dept Anat Histol Forens & Orthoped Sci, Rome, Italy; [Babiloni, Fabio] Hangzhou Dianzi Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China Sapienza University Rome; Sapienza University Rome; Hangzhou Dianzi University Ronca, V (corresponding author), BrainSigns Srl, Rome, Italy.;Ronca, V (corresponding author), Sapienza Univ Rome, Dept Anat Histol Forens & Orthoped Sci, Rome, Italy. nicolina.sciaraffa@uniroma1.it; vincenzo.ronca@uniroma1.it; gianluca.borghini@uniroma1.it; gianluca.diflumeri@uniroma1.it; fabio.babiloni@uniroma1.it; pietro.arico@uniroma1.it Di Flumeri, Gianluca/I-7196-2019 Di Flumeri, Gianluca/0000-0003-4426-051X; Borghini, Gianluca/0000-0001-8560-5671; Giorgi, Andrea/0000-0001-6220-3389 European Commission by Horizon2020 projects MINDTOOTH [950998]; H2020-SESAR-2019-2 projects: ``ARTIMATION'' [894238]; WORKINGAGE [826232]; SI-MUSAFE [723386]; SAFEMODE [814961]; BRAIN-SAFEDRIVE (Italy-Sweden collaboration); Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana European Commission by Horizon2020 projects MINDTOOTH; H2020-SESAR-2019-2 projects: ``ARTIMATION''; WORKINGAGE; SI-MUSAFE; SAFEMODE; BRAIN-SAFEDRIVE (Italy-Sweden collaboration); Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana This work is co-financed by the European Commission by Horizon2020 projects MINDTOOTH (GA n. 950998). H2020-SESAR-2019-2 projects: ``ARTIMATION,'' (GA n. 894238); WORKINGAGE (GA n. 826232); SI-MUSAFE (GA n. 723386); SAFEMODE (GA n. 814961), BRAIN-SAFEDRIVE (Italy-Sweden collaboration) with a grant of Ministero dell'Istruzione dell'Universita e della Ricerca della Repubblica Italiana. 17 1 1 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1557-170X 1558-4615 978-1-7281-1179-7 IEEE ENG MED BIO 2021.0 906 909 10.1109/EMBC46164.2021.9630613 0.0 4 Engineering, Biomedical; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BS7HB 34891437.0 Bronze, Green Submitted 2023-03-23 WOS:000760910500212 0
J Varga-Szemes, A; Schoepf, UJ; Maurovich-Horvat, P; Wang, R; Xu, L; Dargis, DM; Emrich, T; Buckler, AJ Varga-Szemes, Akos; Schoepf, U. Joseph; Maurovich-Horvat, Pal; Wang, Rui; Xu, Lei; Dargis, Danielle M.; Emrich, Tilman; Buckler, Andrew J. Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve* INTERNATIONAL JOURNAL OF CARDIOLOGY English Article Coronary artery disease; Coronary computed tomography angiography; Computational fractional flow reserve; Invasive coronary angiography; Plaque morphology Background: To evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR. Methods: Patients (n = 113, 59 +/- 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR. Results: A total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis. Conclusions: Our results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches. (c) 2021 Elsevier B.V. All rights reserved. [Varga-Szemes, Akos; Schoepf, U. Joseph; Dargis, Danielle M.; Emrich, Tilman] Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Ashley River Tower,25 Courtenay Dr,MSC 226, Charleston, SC 29425 USA; [Maurovich-Horvat, Pal] Semmelweis Univ, MTA SE Cardiovasc Imaging Res Grp, Heart & Vasc Ctr, Budapest, Hungary; [Wang, Rui; Xu, Lei] Capital Med Univ, Beijing Anzhen Hosp, Dept Radiol, Beijing, Peoples R China; [Emrich, Tilman] Johannes Gutenberg Univ Mainz, Univ Med Ctr, Dept Diagnost & Intervent Radiol, Mainz, Germany; [Emrich, Tilman] German Ctr Cardiovasc Res, Partner Site Rhine Main, Mainz, Germany; [Buckler, Andrew J.] Elucid Bioimaging Inc, Boston, MA USA Medical University of South Carolina; Semmelweis University; Capital Medical University; Johannes Gutenberg University of Mainz; German Centre for Cardiovascular Research Schoepf, UJ (corresponding author), Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Ashley River Tower,25 Courtenay Dr,MSC 226, Charleston, SC 29425 USA. schoepf@musc.edu Emrich, Tilman/0000-0003-4156-7727; Varga-Szemes, Akos/0000-0002-2781-7462 39 1 1 3 10 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0167-5273 1874-1754 INT J CARDIOL Int. J. Cardiol. MAY 15 2021.0 331.0 307 315 10.1016/j.ijcard.2021.01.040 0.0 MAR 2021 9 Cardiac & Cardiovascular Systems Science Citation Index Expanded (SCI-EXPANDED) Cardiovascular System & Cardiology RJ4UR 33529657.0 2023-03-23 WOS:000637596700004 0
J Kang, J; Fernandez-Beltran, R; Duan, PH; Liu, SC; Plaza, AJ Kang, Jian; Fernandez-Beltran, Ruben; Duan, Puhong; Liu, Sicong; Plaza, Antonio J. Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Deep learning (DL); metric learning; remote sensing (RS); scene characterization; self-supervised learning; unsupervised learning LAND-COVER; SCENE CLASSIFICATION; REPRESENTATION; EXTRACTION; FUSION Convolutional neural networks (CNNs) have achieved great success when characterizing remote sensing (RS) images. However, the lack of sufficient annotated data (together with the high complexity of the RS image domain) often makes supervised and transfer learning schemes limited from an operational perspective. Despite the fact that unsupervised methods can potentially relieve these limitations, they are frequently unable to effectively exploit relevant prior knowledge about the RS domain, which may eventually constrain their final performance. In order to address these challenges, this article presents a new unsupervised deep metric learning model, called spatially augmented momentum contrast (SauMoCo), which has been specially designed to characterize unlabeled RS scenes. Based on the first law of geography, the proposed approach defines spatial augmentation criteria to uncover semantic relationships among land cover tiles. Then, a queue of deep embeddings is constructed to enhance the semantic variety of RS tiles within the considered contrastive learning process, where an auxiliary CNN model serves as an updating mechanism. Our experimental comparison, including different state-of-the-art techniques and benchmark RS image archives, reveals that the proposed approach obtains remarkable performance gains when characterizing unlabeled scenes since it is able to substantially enhance the discrimination ability among complex land cover categories. The source codes of this article will be made available to the RS community for reproducible research. [Kang, Jian] Harbin Inst Technol, Res Inst Elect Engn Technol, Harbin 150001, Peoples R China; [Fernandez-Beltran, Ruben] Univ Jaume 1, Inst New Imaging Technol, Castellon De La Plana 12071, Spain; [Duan, Puhong] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Liu, Sicong] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China; [Plaza, Antonio J.] Univ Extremadura, Dept Technol Computers & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10003, Spain Harbin Institute of Technology; Universitat Jaume I; Hunan University; Tongji University; Universidad de Extremadura Liu, SC (corresponding author), Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China. kangjian_1991@outlook.com; rufernan@uji.es; sicong.liu@tongji.edu.cn; aplaza@unex.es Fernandez-Beltran, Ruben/GLT-5907-2022; Plaza, Antonio/C-4455-2008; kang, jian/V-3055-2019; Liu, Sicong/J-7094-2013 Plaza, Antonio/0000-0002-9613-1659; kang, jian/0000-0001-6284-3044; Liu, Sicong/0000-0003-1612-4844 National Key Research and Development Program of China [2018YFB 050500]; Spanish Ministry of Economy [RTI2018-098651B-C54]; FEDER-Junta de Extremadura [GR18060]; European Union [734541] National Key Research and Development Program of China; Spanish Ministry of Economy(Spanish Government); FEDER-Junta de Extremadura(European Commission); European Union(European Commission) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB 050500, in part by the Spanish Ministry of Economy under Grant RTI2018-098651B-C54, in part by the FEDER-Junta de Extremadura under Grant GR18060, and in part by the European Union under the H2020 EOXPOSURE Project under Grant 734541. 68 44 44 14 79 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing MAR 2021.0 59.0 3 2598 2610 10.1109/TGRS.2020.3007029 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology QN2TP Green Published 2023-03-23 WOS:000622319000054 0
J Zhao, JJ; Jing, XY; Yan, Z; Pedrycz, W Zhao, Jingjing; Jing, Xuyang; Yan, Zheng; Pedrycz, Witold Network traffic classification for data fusion: A survey INFORMATION FUSION English Article Traffic classification; Machine learning; Security management; Data fusion FEATURE-SELECTION APPROACH; DATA-COLLECTION; SECURITY MEASUREMENT; INTERNET; IDENTIFICATION; VECTOR Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However, the existing literature lacks a thorough survey to summarize, compare and analyze the recent advances of network traffic classification in order to deliver a holistic perspective. This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation-based classification, behavior-based classification, payload-based classification, and port-based classification. A series of criteria are proposed for the purpose of evaluating the performance of existing traffic classification methods. For each specified category, we analyze and discuss the details, advantages and disadvantages of its existing methods, and also present the traffic features commonly used. Summaries of investigation are offered for providing a holistic and specialized view on the state-of-art. For convenience, we also cover a discussion on the mostly used datasets and the traffic features adopted for traffic classification in the review. At the end, we identify a list of open issues and future directions in this research field. [Zhao, Jingjing; Jing, Xuyang; Yan, Zheng; Pedrycz, Witold] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo, Finland; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada Xidian University; Aalto University; University of Alberta Yan, Z (corresponding author), Xidian Univ, 119 POX,2 South Taibai Rd, Xian 710071, Peoples R China. zyan@xidian.edu.cn yang, zheng/HGC-7753-2022; zheng, yan/GQY-6668-2022 Yan, Zheng/0000-0002-9697-2108 National Natural Science Foundation of China [62072351]; Academy of Finland [308087, 335262]; Shaanxi Innovation Team Project [2018TD007]; 111 Project [B16037]; Huawei Technologies Group Co., Ltd.; Academy of Finland (AKA) [308087] Funding Source: Academy of Finland (AKA) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Shaanxi Innovation Team Project; 111 Project(Ministry of Education, China - 111 Project); Huawei Technologies Group Co., Ltd.(Huawei Technologies); Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) This work was supported in part by the National Natural Science Foundation of China under Grant 62072351; in part by the Academy of Finland under Grant 308087 and Grant 335262; in part by the Shaanxi Innovation Team Project under Grant 2018TD007; and in part by the 111 Project under Grant B16037, as well as Huawei Technologies Group Co., Ltd. 143 20 20 11 34 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion AUG 2021.0 72.0 22 47 10.1016/j.inffus.2021.02.009 0.0 FEB 2021 26 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science RN8OE hybrid, Green Published 2023-03-23 WOS:000640610500002 0
C Diba, A; Sharma, V; Safdari, R; Lotfi, D; Sarfraz, MS; Stiefelhagen, R; Van Gool, L IEEE Diba, Ali; Sharma, Vivek; Safdari, Reza; Lotfi, Dariush; Sarfraz, M. Saquib; Stiefelhagen, Rainer; Van Gool, Luc Vi(2)CLR: Video and Image for Visual Contrastive Learning of Representation 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) English Proceedings Paper 18th IEEE/CVF International Conference on Computer Vision (ICCV) OCT 11-17, 2021 ELECTR NETWORK IEEE,CVF,IEEE Comp Soc In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion. The proposed neural network architecture and objectives are designed to obtain two different Convolutional Neural Networks for solving visual recognition tasks in the domain of videos and images. Our method called Video/Image for Visual Contrastive Learning of Representation(Vi(2)CLR) uses unlabeled videos to exploit dynamic and static visual cues for self-supervised and instances similarity/dissimilarity learning. Vi(2)CLR optimization pipeline consists of visual clustering part and representation learning based on groups of similar positive instances within a cluster and negative ones from other clusters and learning visual clusters and their distances. We show how a joint self-supervised visual clustering and instance similarity learning with 2D (image) and 3D (video) CovNet encoders yields such robust and near to supervised learning performance. We extensively evaluate the method on downstream tasks like large scale action recognition, image and object classification on datasets like Kinetics, ImageNet, Pascal VOC'07 and UCF101 and achieve outstanding results compared to state-of-the-art self-supervised methods. [Diba, Ali; Van Gool, Luc] Katholieke Univ Leuven, Leuven, Belgium; [Diba, Ali; Safdari, Reza; Lotfi, Dariush; Van Gool, Luc] Sensifai, Xuzhou, Jiangsu, Peoples R China; [Sarfraz, M. Saquib; Stiefelhagen, Rainer] Karlsruhe Inst Technol, Karlsruhe, Germany; [Sarfraz, M. Saquib] Daimler TSS, Ulm, Germany; [Sharma, Vivek] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Sharma, Vivek] Harvard Med Sch, Boston, MA 02115 USA; [Van Gool, Luc] Swiss Fed Inst Technol, Zurich, Switzerland KU Leuven; Helmholtz Association; Karlsruhe Institute of Technology; Massachusetts Institute of Technology (MIT); Harvard University; Harvard Medical School; Swiss Federal Institutes of Technology Domain; ETH Zurich Diba, A (corresponding author), Katholieke Univ Leuven, Leuven, Belgium.;Diba, A (corresponding author), Sensifai, Xuzhou, Jiangsu, Peoples R China. 85 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2812-5 2021.0 1482 1492 10.1109/ICCV48922.2021.00153 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT1IF 2023-03-23 WOS:000797698901066 0
J Fan, LX; Marculescu, D; Bailer, W; Chen, YR Fan, Lixin; Marculescu, Diana; Bailer, Werner; Chen, Yurong Editorial: Special Issue on Compact Deep Neural Networks With Industrial Applications IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Editorial Material Special issues and sections; Artificial neural networks; Neural networks; Deep learning; Industry applications; Training data; Convolutional neural networks; Biological neural networks; Quantization (signal) The twenty-five papers in this special issue focus on industrial applications that deplpoy compact deep neural networks. Artificial neural netorks have been adopted for a broad range of tasks in areas like multimedia analysis and processing, media coding, data analytics, etc. Their recent success is based on the feasibility of processing much larger and complex neural networks (deep neural networks, DNNs) than in the past, and the availability of large-scale training data sets. As a consequence, the large memory footprint of trained neural networks and the high computational complexity of performing inference cannot be neglected. Many applications require the deployment of a particularly trained network instance, potentially to a larger number of devices, which may have limitations in terms of processing power and memory. For such applications, compact representations of neural networks are of increasing relevance. In spite of active research in the area, there are still open questions to be clarified concerning, for example, how to train neural networks with optimal performance while achieving compact representations, and how to achieve representations that do not only allow for compact transmission, but also for efficient inference. This issue sheds light on these important questions and provides answers that may be useful for both practitioners and researchers in the field. [Fan, Lixin] WeBank, Shenzhen, Guangdong, Peoples R China; [Marculescu, Diana] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA; [Marculescu, Diana] Univ Texas Austin, Motorola Regents Chair Elect & Comp Engn, Austin, TX 78712 USA; [Bailer, Werner] Joanneum Res Digital, A-8010 Graz, Austria; [Chen, Yurong] Intel Labs China, Intel Corp, Beijing 100190, Peoples R China; [Chen, Yurong] Intel Labs China, Cognit Comp Lab, Beijing 100190, Peoples R China University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Intel Corporation; Intel Corporation Fan, LX (corresponding author), WeBank, Shenzhen, Guangdong, Peoples R China. Marculescu, Diana/Q-4925-2019 Marculescu, Diana/0000-0002-5734-4221 0 0 0 0 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. MAY 2020.0 14.0 4 605 608 10.1109/JSTSP.2020.3006323 0.0 4 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NJ2ES 2023-03-23 WOS:000565858900001 0
J Cheng, L; Yang, JJ; Chen, XW; Cao, MQ; Zhou, H; Sun, Y Cheng, Long; Yang, Junjian; Chen, Xuewu; Cao, Mengqiu; Zhou, Hang; Sun, Yu How could the station-based bike sharing system and the free-floating bike sharing system be coordinated? JOURNAL OF TRANSPORT GEOGRAPHY English Article Station-based bike sharing system; Free-floating bike sharing system; Support vector machine; Coordinated development; Land use FEATURE-SELECTION; DEMAND; MODEL; MACHINE; USAGE The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes. [Cheng, Long; Yang, Junjian; Chen, Xuewu; Zhou, Hang; Sun, Yu] Southeast Univ, Sch Transportat, Nanjing, Peoples R China; [Cheng, Long] Univ Ghent, Dept Geog, Ghent, Belgium; [Cao, Mengqiu] Univ Westminster, Sch Architecture & Cities, London, England; [Cao, Mengqiu] UCL, Bartlett Sch Planning, London, England; [Cao, Mengqiu] London Sch Econ & Polit Sci, Dept Stat, London, England Southeast University - China; Ghent University; University of Westminster; University of London; University College London; University of London; London School Economics & Political Science Chen, XW (corresponding author), Si Pai Lou 2, Nanjing 210096, Peoples R China. chenxuewu@seu.edu.cn Cao, Mengqiu/0000-0001-8670-4735; Cheng, Long/0000-0003-3720-4093 National Key R&D Program of China [2018YFB1601300]; National Natural Science Foundation of China [71801041, 51808392]; EPSRC [EP/R035148/1]; SCUE Research Fund; University of Westminster; EPSRC [EP/R035199/1, EP/R035148/1] Funding Source: UKRI National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); SCUE Research Fund; University of Westminster; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research is funded by the National Key R&D Program of China (Project No.: 2018YFB1601300), the National Natural Science Foundation of China (Project No.: 71801041 and 51808392), the EPSRC (EPSRC Reference: EP/R035148/1), the SCUE Research Fund, and School Funding from the University of Westminster. Thanks to the extensive comments from the anonymous reviewers, which have significantly improved the paper. 44 14 14 13 42 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0966-6923 1873-1236 J TRANSP GEOGR J. Transp. Geogr. DEC 2020.0 89.0 102896 10.1016/j.jtrangeo.2020.102896 0.0 8 Economics; Geography; Transportation Social Science Citation Index (SSCI) Business & Economics; Geography; Transportation QA1XR Green Accepted 2023-03-23 WOS:000613242900001 0
J Mei, QC; Fernandez, J; Xiang, LL; Gao, ZX; Yu, PM; Baker, JS; Gu, YD Mei, Qichang; Fernandez, Justin; Xiang, Liangliang; Gao, Zixiang; Yu, Peimin; Baker, Julien S.; Gu, Yaodong Dataset of lower extremity joint angles, moments and forces in distance running HELIYON English Article Running biomechanics; Lower extremity; Contact forces; Principal component analysis; Statistical parametric mapping FUNCTIONAL DATA-ANALYSIS; FOOT STRIKE PATTERNS; CONTACT FORCES; ELITE; KNEE; COMPONENT; MUSCLE; KINEMATICS; RUNNERS; COORDINATION This study presents a database of joint angles, moments, and forces of the lower extremity from distance running at a submaximal speed in recreational runners. Twenty recreational runners participated in two experimental sessions, specifically pre and post a 5k treadmill run, with a synchronous collection of markers trajectories and ground reaction forces for both limbs in walking and running trials. The raw data in C3D files could be used for musculoskeletal modelling. Extra datasets of joint angles, moments, and forces are presented ready-for-use in MAT files, which could be as reference for study of biomechanical alterations from distance running. Applying advanced data processing techniques (Machine Learning algorithms) to these datasets (C3D & MAT), such as Principal Component Analysis, could extract key features of variation, thus potentially being applied for corre-lation with accelerometric and gyroscope parameters from wearable sensors during field running. Dataset of multi-segmental foot could be another contribution for the investigation of foot complex biomechanics from distance running. The dataset from Asian males may also be used for population-based studies of running biomechanics. [Mei, Qichang; Fernandez, Justin; Xiang, Liangliang; Gao, Zixiang; Yu, Peimin; Gu, Yaodong] Ningbo Univ, Fac Sports Sci, Ningbo, Peoples R China; [Mei, Qichang; Fernandez, Justin; Xiang, Liangliang; Gao, Zixiang; Yu, Peimin; Gu, Yaodong] Ningbo Univ, Res Acad Grand Hlth, Ningbo, Peoples R China; [Mei, Qichang; Fernandez, Justin; Xiang, Liangliang; Yu, Peimin; Gu, Yaodong] Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand; [Fernandez, Justin] Univ Auckland, Dept Engn Sci, Auckland, New Zealand; [Gao, Zixiang] Univ Pannonia, Fac Engn, Veszprem, Hungary; [Baker, Julien S.] Hong Kong Baptist Univ, Dept Sport & Phys Educ, Hong Kong, Peoples R China Ningbo University; Ningbo University; University of Auckland; University of Auckland; University of Pannonia; Hong Kong Baptist University Mei, QC; Gu, YD (corresponding author), Ningbo Univ, Fac Sports Sci, Ningbo, Peoples R China.;Mei, QC; Gu, YD (corresponding author), Ningbo Univ, Res Acad Grand Hlth, Ningbo, Peoples R China.;Mei, QC; Gu, YD (corresponding author), Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand. meiqichang@outlook.com; guyaodong@hotmail.com Mei, Qichang/0000-0003-1113-877X National Natural Science Foundation of China [12202216]; NSFC (Natural Science Foundation of China) - RSE (The Royal Society of Edinburgh) Joint Project [81911530253]; K. C. Wong Magna Fund in Ningbo University; Ministry of Foreign Affairs and Trade (New Zealand); China Scholarship Council (CSC) National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NSFC (Natural Science Foundation of China) - RSE (The Royal Society of Edinburgh) Joint Project; K. C. Wong Magna Fund in Ningbo University; Ministry of Foreign Affairs and Trade (New Zealand); China Scholarship Council (CSC)(China Scholarship Council) This study was sponsored by National Natural Science Foundation of China (No. 12202216), NSFC (Natural Science Foundation of China) - RSE (The Royal Society of Edinburgh) Joint Project (No. 81911530253) and K. C. Wong Magna Fund in Ningbo University. QM of this paper was supported by the New Zealand-China. Doctoral Research Scholarship issued from the Ministry of Foreign Affairs and Trade (New Zealand). LX, ZG and PM are currently supported by the China Scholarship Council (CSC). 95 0 0 2 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2405-8440 HELIYON Heliyon NOV 2022.0 8.0 11 e11517 10.1016/j.heliyon.2022.e11517 0.0 NOV 2022 11 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 7I7PX 36406689.0 gold 2023-03-23 WOS:000904079500002 0
J Li, CY; Xie, HB; Fan, XH; Xu, RYD; Van Huffel, S; Mengersen, K Li, Caoyuan; Xie, Hong-Bo; Fan, Xuhui; Xu, Richard Yi Da; Van Huffel, Sabine; Mengersen, Kerrie Kernelized Sparse Bayesian Matrix Factorization IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Sparse matrices; Bayes methods; Matrix decomposition; Covariance matrices; Data models; Kernel; Computational modeling; Image restoration; low-rankness; matrix factorization; sparse Bayesian learning; variational Bayesian (VB) inference AUTOMATIC RELEVANCE DETERMINATION; LOW-RANK; REPRESENTATION; COMPLETION; MODELS Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption. [Li, Caoyuan] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China; [Li, Caoyuan; Xu, Richard Yi Da] Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia; [Xie, Hong-Bo; Mengersen, Kerrie] Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, Brisbane, Qld 4001, Australia; [Fan, Xuhui] Univ New South Wales, Sch Math & Stat, Sydney, NSW 2033, Australia; [Van Huffel, Sabine] Katholieke Univ Leuven, ESAT Stadius Div, Dept Elect Engn, B-3001 Leuven, Belgium Beijing Institute of Technology; University of Technology Sydney; Queensland University of Technology (QUT); University of New South Wales Sydney; KU Leuven Xie, HB; Mengersen, K (corresponding author), Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, Brisbane, Qld 4001, Australia. hongbo.xie@qut.edu.au Fan, Xu/GSE-2196-2022; Mengersen, Kerrie/AHC-4096-2022; Van Huffel, Sabine/T-2282-2017 Li, Caoyuan/0000-0001-6600-2664; XIE, HONGBO/0000-0002-7739-3047; Xu, Richard Yi Da/0000-0003-2080-4762; Van Huffel, Sabine/0000-0001-5939-0996; Fan, Xuhui/0000-0002-7558-7200 Australian Research Council (ARC) Laureate Program; ARC Centre of Excellence Program Australian Research Council (ARC) Laureate Program(Australian Research Council); ARC Centre of Excellence Program(Australian Research Council) This work was supported by the Australian Research Council (ARC) Laureate Program and the ARC Centre of Excellence Program. 53 4 4 1 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JAN 2021.0 32.0 1 391 404 10.1109/TNNLS.2020.2978761 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering RO6OG 32203037.0 2023-03-23 WOS:000641162100031 0
J Dornaika, F; Moujahid, A; Wang, K; Feng, X Dornaika, F.; Moujahid, A.; Wang, K.; Feng, X. Efficient deep discriminant embedding: Application to face beauty prediction and classification ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE English Article Manifold learning; Feature subset selection; Multi-layer architecture; Discriminant embedding; Dimensionality reduction; Image-based face beauty analysis; Classification DIMENSIONALITY REDUCTION; FACIAL ATTRACTIVENESS; FEATURE-SELECTION; RECOGNITION Inspired by deep learning architectures, we introduce a multi-layer local discriminant embedding algorithm that integrates feature selection as a main step to capture the most relevant and discriminant features of an input face image or face descriptor. The proposed framework allows to transform any linear method to a deep variant via a cascaded feature extraction and selection architecture able to convert weak and noisy descriptors to strong ones. As a case study, the local discriminant embedding (LDE) projection is adopted as a linear feature extraction method. The resulting framework can be considered as an efficient deep discriminant embedding technique. To validate this framework, we have considered two different computer vision problems: face beauty prediction which involves both classification and regression tasks, and face recognition which is a classical classification problem. Experiments conducted on different public benchmark databases show that this approach enhances the performance of the LDE algorithm and provides a discriminating strategy to solve the dimensionality reduction problem. For face beauty regression, our proposed framework achieved on average an improvement of about 5% and 7% with respect to two other configurations where only VGG-face and VGG-face followed by LDE have been considered. For face beauty classification, the proposed algorithm outperformed many classical manifold learning techniques reaching in some databases improvements of about 10%. [Dornaika, F.; Moujahid, A.; Wang, K.; Feng, X.] Univ Basque Country UPV EHU, San Sebastian, Spain; [Dornaika, F.; Moujahid, A.; Wang, K.; Feng, X.] Ikerbasque, San Sebastian, Spain; [Dornaika, F.; Moujahid, A.; Wang, K.; Feng, X.] Northwestern Polytech Univ, Xian, Peoples R China University of Basque Country; Northwestern Polytechnical University Moujahid, A (corresponding author), Univ Basque Country UPV EHU, San Sebastian, Spain. abdelmalik.moujahid@ehu.eus Moujahid, Abdelmalik/A-7626-2012 Moujahid, Abdelmalik/0000-0002-4971-3613; Dornaika, Fadi/0000-0001-6581-9680 52 7 7 4 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0952-1976 1873-6769 ENG APPL ARTIF INTEL Eng. Appl. Artif. Intell. OCT 2020.0 95.0 103831 10.1016/j.engappai.2020.103831 0.0 11 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering PY7JB 2023-03-23 WOS:000612217200007 0
J Khan, AT; Cao, XW; Brajevic, I; Stanimirovic, PS; Katsikis, VN; Li, S Khan, Ameer Tamoor; Cao, Xinwei; Brajevic, Ivona; Stanimirovic, Predrag S.; Katsikis, Vasilios N.; Li, Shuai Non-linear Activated Beetle Antennae Search: A novel technique for non-convex tax-aware portfolio optimization problem EXPERT SYSTEMS WITH APPLICATIONS English Article Beetle Antennae Search; Portfolio selection; Tax-liability; Optimization; Finance problem NEURAL-NETWORK; SELECTION The non-convex tax-aware portfolio optimization problem is traditionally approximated as a convex problem, which compromises the quality of the solution and converges to a local-minima instead of global minima. In this paper, we proposed a non-deterministic meta-heuristic algorithm called Non-linear Activated Beetle Antennae Search (NABAS). NABAS explores the search space at the given gradient estimate measure until it is smaller than a threshold known as Activation Threshold, which increases its convergence rate and avoids local minima. To test the validity of NABAS, we formulated an optimization-based tax-aware portfolio problem. The objective is to maximize the profit and minimize the risk and tax liabilities and fulfill other constraints. We collected stock data of 20 companies from the NASDAQ stock market and performed a simulation using MATLAB. A comprehensive comparison is made with BAS, PSO, and GA algorithms. The results also showed that a better-optimized portfolio is achieved with a non-convex problem than a convex problem. [Khan, Ameer Tamoor] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China; [Cao, Xinwei; Li, Shuai] Swansea Univ, Dept Elect & Elect Engn, Swansea, W Glam, Wales; [Brajevic, Ivona] Univ Business Acad, Fac Appl Management Econ & Finance, Novi Sad, Serbia; [Stanimirovic, Predrag S.] Univ Nis, Fac Sci & Math, Dept Comp, Nish, Serbia; [Katsikis, Vasilios N.] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math & Informat, Athens, Greece Hong Kong Polytechnic University; Swansea University; University of Nis; National & Kapodistrian University of Athens Cao, XW (corresponding author), Swansea Univ, Dept Elect & Elect Engn, Swansea, W Glam, Wales. ameer.khan@connect.polyu.hk; cxw.amethyst@163.com; ivona.brajevic@mef.edu.rs; pecko@pmf.ni.ac.rs; vaskatsikis@econ.uoa.gr; Shuaili@ieee.org Khan, Ameer Tamoor/GQI-2589-2022; Brajević, Ivona/A-6966-2019; Katsikis, Vasilios/AAD-7216-2019 Khan, Ameer Tamoor/0000-0001-6838-992X; Brajević, Ivona/0000-0002-2999-3187; Katsikis, Vasilios/0000-0002-8208-9656; Stanimirovic, Predrag S./0000-0003-0655-3741 46 4 4 1 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. JUL 1 2022.0 197.0 116631 10.1016/j.eswa.2022.116631 0.0 MAR 2022 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 1A9ZT 2023-03-23 WOS:000792106000003 0
J Chen, ZP; Tondi, B; Li, XL; Ni, RR; Zhao, Y; Barni, M Chen, Zhipeng; Tondi, Benedetta; Li, Xiaolong; Ni, Rongrong; Zhao, Yao; Barni, Mauro Secure Detection of Image Manipulation by Means of Random Feature Selection IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Adversarial signal processing; adversarial machine learning; image manipulation detection; feature selection; multimedia forensics and counter-forensics; secure classification; randomization-based adversarial detection IDENTIFICATION; DECISION; TRACES; GAME We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector. Specifically, we assume that the attacker knows the architecture of the detector, the training data, and the class of features V the detector can rely on. In order to get an advantage in his race of arms with the attacker, the analyst designs the detector by relying on a subset of features chosen at random in V. Given its ignorance about the exact feature set, the adversary attacks a version of the detector based on the entire feature set. In this way, the effectiveness of the attack diminishes since there is no guarantee that attacking a detector working in the full feature space will result in a successful attack against the reduced-feature detector. We theoretically prove that, thanks to random feature selection, the security of the detector significantly increases at the expense of a negligible loss of performance in the absence of attacks. We also provide an experimental validation of the proposed procedure by focusing on the detection of two specific kinds of image manipulations, namely adaptive histogram equalization and median filtering. The experiments confirm the gain in security at the expense of a negligible loss of performance in the absence of attacks. [Chen, Zhipeng; Li, Xiaolong; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Chen, Zhipeng; Li, Xiaolong; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Chen, Zhipeng] Tangshan Normal Univ, Dept Comp Sci, Tangshan 063000, Peoples R China; [Tondi, Benedetta; Barni, Mauro] Univ Siena, Dept Informat Engn & Math, I-53100 Siena, Italy Beijing Jiaotong University; Beijing Jiaotong University; Tangshan Normal University; University of Siena Zhao, Y (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. chzhpeng@hotmail.com; benedettatondi@gmail.com; lixl@bjtu.edu.cn; rrni@bjtu.edu.cn; yzhao@bjtu.edu.cn; barni@dii.unisi.it li, xiao/HJP-5134-2023; li, xiao/GSN-6181-2022; Li, xiaolong/GRS-9148-2022 Zhao, Yao/0000-0002-8581-9554; Chen, Zhipeng/0000-0001-7156-4485; BARNI, Mauro/0000-0002-7368-0866 National Key Research and Development of China [2016YFB0800404]; National Science Foundation of China [61572052, 61672090, U1736213]; Fundamental Research Funds for the Central Universities [2018JBZ001]; Key Research and Development of Hebei Province of China [17210332]; Defense Advanced Research Projects Agency (DARPA); Air Force Research Laboratory (AFRL) [FA8750-16-2-0173] National Key Research and Development of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Key Research and Development of Hebei Province of China; Defense Advanced Research Projects Agency (DARPA)(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Air Force Research Laboratory (AFRL)(United States Department of DefenseUS Air Force Research Laboratory) This work was supported in part by the National Key Research and Development of China under Grant 2016YFB0800404, in part by the National Science Foundation of China under Grant 61572052, Grant 61672090, and Grant U1736213, in part by the Fundamental Research Funds for the Central Universities under Grant 2018JBZ001, in part by the Key Research and Development of Hebei Province of China under Grant 17210332, and in part by Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory (AFRL) under Grant FA8750-16-2-0173. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Patrizio Campisi. (Corresponding author: Yao Zhao.) 50 10 13 0 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. SEP 2019.0 14.0 9 2454 2469 10.1109/TIFS.2019.2901826 0.0 16 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IC4WB Green Submitted 2023-03-23 WOS:000470967000006 0
J Dina, NE; Tahir, MA; Bajwa, SZ; Amin, I; Valev, VK; Zhang, LW Dina, Nicoleta Elena; Tahir, Muhammad Ali; Bajwa, Sadia Z.; Amin, Imran; Valev, Ventsislav K.; Zhang, Liwu SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis BIOSENSORS & BIOELECTRONICS English Article SERS; Antibiotic susceptibility test; SERS-based biosensors; PoC biosensing; Multidrug resistant pathogens; Clinical diagnosis ENHANCED RAMAN-SPECTROSCOPY; RESISTANT STAPHYLOCOCCUS-AUREUS; METHICILLIN-RESISTANT; CHEMICAL-ANALYSIS; FACILE SYNTHESIS; SCATTERING SERS; IDENTIFICATION; BACTERIA; SILVER; GOLD Emerging antibiotic resistant bacteria constitute one of the biggest threats to public health. Surface-enhanced Raman scattering (SERS) is highly promising for detecting such bacteria and for antibiotic susceptibility testing (AST). SERS is fast, non-destructive (can probe living cells) and it is technologically flexible (readily integrated with robotics and machine learning algorithms). However, in order to integrate into efficient point-of -care (PoC) devices and to effectively replace the current culture-based methods, it needs to overcome the challenges of reliability, cost and complexity. Recently, significant progress has been made with the emergence of both new questions and new promising directions of research and technological development. This article brings together insights from several representative SERS-based AST studies and approaches oriented towards clinical PoC biosensing. It aims to serve as a reference source that can guide progress towards PoC routines for identifying antibiotic resistant pathogens. In turn, such identification would help to trace the origin of sporadic infections, in order to prevent outbreaks and to design effective medical treatment and preventive procedures. [Dina, Nicoleta Elena] Natl Inst Res & Dev Isotop & Mol Technol, Dept Mol, Cluj Napoca 400293, Romania; [Dina, Nicoleta Elena] Natl Inst Res & Dev Isotop & Mol Technol, Biomol Dept, Cluj Napoca 400293, Romania; [Tahir, Muhammad Ali; Zhang, Liwu] Fudan Univ, Dept Environm Sci & Engn, Shanghai Key Lab Atmospher Particle Pollut & Preve, Shanghai 200433, Peoples R China; [Bajwa, Sadia Z.; Amin, Imran] Natl Inst Biotechnol & Genet Engn NIBGE, POB 577,Jhang Rd, Faisalabad 38000, Pakistan; [Valev, Ventsislav K.] Univ Bath, Ctr Photon & Photon Mat, Dept Phys, Bath BA2 7AY, England; [Valev, Ventsislav K.] Univ Bath, Ctr Therapeut Innovat, Bath, England; [Valev, Ventsislav K.] Univ Bath, Ctr Nanosci & Nanotechnol, Bath, England; [Zhang, Liwu] Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; Fudan University; Pakistan Institute of Engineering & Applied Science; University of Bath; University of Bath; University of Bath Dina, NE (corresponding author), Natl Inst Res & Dev Isotop & Mol Technol, Dept Mol, Cluj Napoca 400293, Romania.;Dina, NE (corresponding author), Natl Inst Res & Dev Isotop & Mol Technol, Biomol Dept, Cluj Napoca 400293, Romania.;Zhang, LW (corresponding author), Fudan Univ, Dept Environm Sci & Engn, Shanghai Key Lab Atmospher Particle Pollut & Preve, Shanghai 200433, Peoples R China.;Valev, VK (corresponding author), Univ Bath, Ctr Photon & Photon Mat, Dept Phys, Bath BA2 7AY, England. nicoleta.dina@itim-cj.ro; vkv23@bath.ac.uk; zhanglw@fudan.edu.cn Dina, Nicoleta Elena/E-3434-2016 Dina, Nicoleta Elena/0000-0002-0435-2105 Ministry of Research, Innovation and Digitization [PN-III-P1- 1.1-TE-2019-0910]; Subprogramme 1.2-Institutional Performance-Funding Projects for Excellence in RDI within PNCDI III [37PFE/30.12.2021]; Royal Society [RGF\EA\180228, ICA\R1\201088]; EPSRC [EP/T001046/1]; Higher Education Commission of Pakistan [6117] Ministry of Research, Innovation and Digitization; Subprogramme 1.2-Institutional Performance-Funding Projects for Excellence in RDI within PNCDI III; Royal Society(Royal Society of London); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Higher Education Commission of Pakistan(Higher Education Commission of Pakistan) N.E. Dina acknowledges the financial support provided by the Ministry of Research, Innovation and Digitization, project number PN-III-P1- 1.1-TE-2019-0910 and the Subprogramme 1.2-Institutional Performance-Funding Projects for Excellence in RDI, Contract No. 37PFE/30.12.2021, within PNCDI III. V.K.V. acknowledges support from the Royal Society through the University Research Fellowships and the Royal Society grant RGF\EA\180228, as well as the EPSRC grant EP/T001046/1. V.K.V and L.Z. acknowledge the International Collaboration Awards 2020 of the Royal Society (No. ICA\R1\201088) . Imran Amin and Sadia Z. Bajwa are thankful to the Higher Education Commission of Pakistan for funding via Grant No. 6117. 168 4 4 65 65 ELSEVIER ADVANCED TECHNOLOGY OXFORD OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0956-5663 1873-4235 BIOSENS BIOELECTRON Biosens. Bioelectron. JAN 1 2023.0 219.0 114843 10.1016/j.bios.2022.114843 0.0 21 Biophysics; Biotechnology & Applied Microbiology; Chemistry, Analytical; Electrochemistry; Nanoscience & Nanotechnology Science Citation Index Expanded (SCI-EXPANDED) Biophysics; Biotechnology & Applied Microbiology; Chemistry; Electrochemistry; Science & Technology - Other Topics 6F0HF 36327563.0 2023-03-23 WOS:000883750800001 0
J Dong, JW; Metternicht, G; Hostert, P; Fensholt, R; Chowdhury, RR Dong, Jinwei; Metternicht, Graciela; Hostert, Patrick; Fensholt, Rasmus; Chowdhury, Rinku Roy Remote sensing and geospatial technologies in support of a normative land system science: status and prospects CURRENT OPINION IN ENVIRONMENTAL SUSTAINABILITY English Review ECOSYSTEM SERVICES; NATIONAL-SCALE; COVER; INFORMATION; INTEGRATION; POLICY; PALSAR; TOOLS; CROP Changes of land systems are largely a consequence of human decision making at multiple scales, from local land management to national land use planning and global trade agreements. Improved understanding of the status, trend, and consequences of land changes is relevant to the engagement of Land System Science (LSS) in transformations for sustainability. While remote sensing has been a major tool for documenting land cover change, new opportunities are emerging to understand land as a socio-ecological system by integrating crowd-sourced social sensing data (e.g. through mobile technologies). Increased access to remote sensing time series and social sensing data are improving mapping of land and providing new information on the coupled social-ecological system for better land governance. Given advances in data availability and machine learning algorithms, land mapping efforts have evolved to more sophisticated analysis strategies. In addition, the emergence of planetary-scale geospatial analysis platforms facilitates regional and global land change monitoring in a rapid, scalable, and convenient way. This review aims to document current status and prospects on data, algorithms, and processing platforms in the era of 'Big Earth Data', providing examples of how it can contribute to LSS at the interface of normative and policy concerns, including those in support of internationally agreed environmental goals and land governance. [Dong, Jinwei] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; [Metternicht, Graciela] Univ New South Wales, Sch Biol Earth & Environm Sci, Sydney, NSW, Australia; [Hostert, Patrick] Humboldt Univ, Geog Dept, Unter Linden 6, D-10099 Berlin, Germany; [Hostert, Patrick] Humboldt Univ, Integrat Res Inst Transformat Human Environm Syst, Unter Linden 6, D-10099 Berlin, Germany; [Fensholt, Rasmus] Univ Copenhagen, Dept Geosci & Nat Resource Management, Oester Voldgade 10, DK-1350 Copenhagen K, Denmark; [Chowdhury, Rinku Roy] Clark Univ, Grad Sch Geog, Worcester, MA 01610 USA Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; University of New South Wales Sydney; Humboldt University of Berlin; Humboldt University of Berlin; University of Copenhagen; Clark University Dong, JW (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China. dongjw@igsnrr.ac.cn Fensholt, Rasmus/L-7951-2014; Dong, Jinwei/C-4949-2009 Fensholt, Rasmus/0000-0003-3067-4527; Dong, Jinwei/0000-0001-5687-803X Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19040301]; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences [QYZDB-SSW-DQC005] Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Key Research Program of Frontier Sciences of the Chinese Academy of Sciences Jinwei Dong was supported by the Strategic Priority Research Program (XDA19040301), the Key Research Program of Frontier Sciences (QYZDB-SSW-DQC005) of the Chinese Academy of Sciences. We thank Erle Ellis, Peter Messerli, Ole Mertz, and Navin Ramankutty for their valuable comments on the original design of this review. 80 29 32 5 58 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1877-3435 1877-3443 CURR OPIN ENV SUST Curr. Opin. Environ. Sustain. JUN 2019.0 38.0 SI 44 52 10.1016/j.cosust.2019.05.003 0.0 9 Green & Sustainable Science & Technology; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology IQ0DD 2023-03-23 WOS:000480419900008 0
J Li, D; Cohen, JB; Qin, K; Xue, Y; Rao, LL Li, Ding; Cohen, Jason Blake; Qin, Kai; Xue, Yong; Rao, Lanlan Absorbing Aerosol Optical Depth From OMI/TROPOMI Based on the GBRT Algorithm and AERONET Data in Asia IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Aerosols; MODIS; Spatial resolution; Pollution measurement; Satellites; Correlation; Scattering; Absorbing aerosol optical depth (AAOD); Asia; machine learning; Moderate Resolution Imaging Spectro-Radiometer (MODIS); TROPOspheric Monitoring Instrument (TROPOMI) Quantifying the concentration of absorbing aerosol is essential for pollution tracking and calculation of atmospheric radiative forcing. To quickly obtain absorbing aerosol optical depth (AAOD) with high-resolution and high-accuracy, the gradient boosted regression trees (GBRT) method based on the joint data from Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectro-Radiometer (MODIS), and AErosol RObotic NETwork (AERONET) is used for TROPOspheric Monitoring Instrument (TROPOMI). Compared with the ground-based data, the correlation coefficient of the results is greater than 0.6 and the difference is generally within +/- 0.04. Compared with OMI data, the underestimation has been greatly improved. By further restricting the impact factors, three valid conclusions can be drawn: 1) the model with more spatial difference information achieves better results than the model with more temporal difference information; 2) the training dataset with a high cloud fraction (0.1-0.4) can partly improve the performance of GBRT results; and 3) when aerosol optical depth (AOD) is less than 0.3, the perform of retrieved AAODs is still good by comparing with ground-based measurements. The novel finding is expected to contribute to regional and even urban anthropogenic pollution research. [Li, Ding; Cohen, Jason Blake; Qin, Kai; Xue, Yong] China Univ & Min & Technol, Sch Environm & Spatial Informat, Xuzhou 221000, Jiangsu, Peoples R China; [Rao, Lanlan] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany China University of Mining & Technology; Helmholtz Association; German Aerospace Centre (DLR) Cohen, JB; Qin, K (corresponding author), China Univ & Min & Technol, Sch Environm & Spatial Informat, Xuzhou 221000, Jiangsu, Peoples R China. lidinf@cumt.edu.cn; jasonbc@alum.mit.edu; qinkai@cumt.edu.cn; yxue@cumt.edu.cn; lanlan.rao@dlr.de Cohen, Jason Blake/0000-0002-9889-8175 National Natural Science Foundation of China [41975041, 42075147]; Shanxi Province Major Science and Technique Program [202101090301013]; Fundamental Research Funds for the Central Universities [2021QN1033] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanxi Province Major Science and Technique Program; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 41975041 and Grant 42075147, in part by the Shanxi Province Major Science and Technique Program under Grant 202101090301013, and in part by the Fundamental Research Funds for the Central Universities under Grant 2021QN1033. 0 0 0 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2023.0 61.0 4100210 10.1109/TGRS.2022.3231699 0.0 10 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 8C9QC 2023-03-23 WOS:000917934300016 0
J Govindan, K; Kannan, D; Jorgenser, TB; Nielsen, TS Govindan, Kannan; Kannan, Devika; Jorgenser, Thomas Ballegard; Nielsen, Tim Straarup Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW English Review SupplyChain4; 0; Industry4; SupplyChainPerformance; SystematicLiteratureReview; Contentanalysis; Technologies; Concepts BIG DATA; FOOD-INDUSTRY; DIGITAL TWIN; TECHNOLOGY; LOGISTICS; MANAGEMENT; IOT; DIGITIZATION; INTERNET; CONTEXT Companies require a greater understanding of the Supply Chain (SC) benefits that can be gained from industry 4.0 (I4.0) and, more specifically, which technologies and concepts that can improve certain SC performance measures. A state-of-the-art systematic literature review (SLR) has been done on supply chain performance measurement linked with various industry 4.0 technologies. Based on the findings of the review through content analysis, this paper presents a framework for exploring the usage of I4.0 technologies to identify the potential supply chain performance measures. This framework includes the dimensions of Procurement 4.0, Manufacturing 4.0, Lo-gistics 4.0, and Warehousing 4.0. As a scientific contribution, this study has validated the pro-posed framework through case studies, where the existing studies are limited. Finally, several fruitful future possible extensions have been discussed based on the proposed framework. [Govindan, Kannan] Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai 201306, Peoples R China; [Govindan, Kannan; Kannan, Devika; Jorgenser, Thomas Ballegard; Nielsen, Tim Straarup] Univ Southern Denmark, Ctr Sustainable Supply Chain Engn, Dept Technol & Innovat, Campusvej 55, Odense, Denmark; [Govindan, Kannan] Univ Southern Denmark, Danish Inst Adv Study, Campusvej 55, Odense, Denmark; [Govindan, Kannan] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea Shanghai Maritime University; University of Southern Denmark; University of Southern Denmark; Yonsei University Kannan, D (corresponding author), Univ Southern Denmark, Ctr Sustainable Supply Chain Engn, Dept Technol & Innovat, Campusvej 55, Odense, Denmark. deka@iti.sdu.dk Kannan, Devika/Q-6233-2018; govindan, kannan/M-5996-2017 Kannan, Devika/0000-0003-4365-0313; govindan, kannan/0000-0002-6204-1196 275 4 4 32 50 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1366-5545 1878-5794 TRANSPORT RES E-LOG Transp. Res. Pt. e-Logist. Transp. Rev. AUG 2022.0 164.0 102725 10.1016/j.tre.2022.102725 0.0 JUL 2022 41 Economics; Engineering, Civil; Operations Research & Management Science; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Engineering; Operations Research & Management Science; Transportation 3B6HC hybrid, Green Submitted 2023-03-23 WOS:000828038500002 0
J Jing, XY; Zhao, JJ; Zheng, QH; Yan, Z; Pedrycz, W Jing, Xuyang; Zhao, Jingjing; Zheng, Qinghua; Yan, Zheng; Pedrycz, Witold A reversible sketch-based method for detecting and mitigating amplification attacks JOURNAL OF NETWORK AND COMPUTER APPLICATIONS English Article Amplification attacks; Reversible sketch; Bloom filter; Chinese remainder theorem; Network security SECURITY DATA-COLLECTION; DRDOS ATTACKS; DEFENSE Amplification attacks bring serious threats to network security due to their characteristics of anonymity and amplification. How to detect amplification attacks attracts more and more attention. However, as the age of networking for big data is coming, traditional amplification attack detection methods become inefficient due to the impact of big-volume network traffic that swamp significant signals of attacks. The premise of accurate effective attack detection is efficiently processing big-volume traffic. In this paper, we propose a meaningful work that applies sketch technique to detect and mitigate amplification attacks. This step enables the detection method to handle big-volume network traffic. We use a Chinese Reminder Theorem based Reversible Sketch to directly collect network traffic and then monitor the abrupt changes in one-to-one mapping between request packets and response packets to identify amplification attack traffic. The detection mechanism is robust and efficient since it does not need to record complicated traffic features and makes full use of the basic characteristic of amplification attacks. We examine the performance of our method through a series of experiments conducted on simulation and real world traffic. The results denote that the method can accurately detect and mitigate amplification attacks. [Jing, Xuyang; Zhao, Jingjing; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China; [Zheng, Qinghua] Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Helsinki, Finland; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Computat Intelligence, Edmonton, AB, Canada Xidian University; Xi'an Jiaotong University; Aalto University; University of Alberta Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China. zheng.yan@aalto.fi zheng, yan/GQY-6668-2022; yang, zheng/HGC-7753-2022; Jing, Xuyang/AAP-5757-2021 Jing, Xuyang/0000-0003-2274-0969; Yan, Zheng/0000-0002-9697-2108 National Key Research and Development Program of China [2016YFB0800700]; NSFC [61672410, U1536202]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; Academy of Finland [308087, 314203]; 111 project [B08038, B16037]; open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China [CLDL-20182119] National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China; Academy of Finland(Academy of Finland); 111 project(Ministry of Education, China - 111 Project); open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China This work is sponsored by the National Key Research and Development Program of China (grant 2016YFB0800700), the NSFC (grants 61672410 and U1536202), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), Academy of Finland (grants 308087 and 314203), the open grant of the Tactical Data Link Lab of the 20th Research Institute of China Electronics Technology Group Corporation, P.R. China (grant No. CLDL-20182119), and the 111 project (grants B08038 and B16037). 39 14 14 0 6 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1084-8045 J NETW COMPUT APPL J. Netw. Comput. Appl. SEP 15 2019.0 142.0 15 24 10.1016/j.jnca.2019.06.007 0.0 10 Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science IU2QB Green Submitted 2023-03-23 WOS:000483423800003 0
J Bu, L; Zhao, DB; Alippi, C Bu, Li; Zhao, Dongbin; Alippi, Cesare An Incremental Change Detection Test Based on Density Difference Estimation IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Change detection; incremental computing; incremental least squares density difference change detection method (LSDD-Inc); probability density function (pdf)-free CLASSIFIERS; SHIFTS; MODEL We propose incremental least squares density difference (LSDD) change detection method, an incremental test to detect changes in stationarity based on the difference between the unknown prechange and the post-change probability density functions (pdfs). The method is computationally light and, hence, adequate to process continuous datastreams, as those emerging from the Internet of Things and the big data framework. The incremental change detection test operates on two nonoverlapping data windows to estimate the LSDD between the two pdfs. We construct a theoretical framework that shows how the distribution of LSDD values follows a linear combination of chi(2) distributions and provides thresholds to control false positive rates. The proposed test can operate online, with needed estimates and thresholds computed incrementally as fresh samples come. Comprehensive experiments validate the effectiveness of the test both in detecting abrupt and drift types of changes. [Bu, Li; Zhao, Dongbin] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; [Bu, Li; Zhao, Dongbin] Univ Chinese Acad Sci, Beijing 100190, Peoples R China; [Alippi, Cesare] Politecn Milan, I-20133 Milan, Italy; [Alippi, Cesare] Univ Svizzera Italiana, CH-6904 Lugano, Switzerland Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Polytechnic University of Milan; Universita della Svizzera Italiana Bu, L (corresponding author), Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.;Bu, L (corresponding author), Univ Chinese Acad Sci, Beijing 100190, Peoples R China. bulipolly@gmail.com; dongbin.zhao@ia.ac.cn; cesare.alippi@polimi.it zhao, dong bin/0000-0001-8218-9633 National Natural Science Foundation of China [61573353, 61533017, 61603382] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61573353, Grant 61533017, and Grant 61603382. 38 7 7 1 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. OCT 2017.0 47.0 10 2714 2726 10.1109/TSMC.2017.2682502 0.0 13 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science FH4CT 2023-03-23 WOS:000411098200009 0
J Jiang, YL; Jiang, DY; Costabel, U; Dai, HP; Wang, C Jiang, Yale; Jiang, Dingyuan; Costabel, Ulrich; Dai, Huaping; Wang, Chen A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis FRONTIERS IN MEDICINE English Article sarcoidosis; transcriptome; interferon; IL-17; machine learning EXPRESSION ANALYSIS; PERIPHERAL-BLOOD; GENE; IMMUNITY; PACKAGE Sarcoidosis is a granulomatous disease of unknown etiology, immunologically characterized by a Th1 immune response. Transcriptome-wide expression studies in various types of sarcoid tissues contributed to better understanding of disease mechanisms. We performed a systematic database search on Gene Expression Omnibus (GEO) and utilized transcriptomic data from blood and sarcoidosis-affected tissues in a meta-analysis to identify a cross-tissue, cross-platform signature. Datasets were further separated into training and testing sets for development of a diagnostic classifier for sarcoidosis. A total of 690 differentially expressed genes were identified in the analysis among various tissues. 29 of the genes were robustly associated with sarcoidosis in the meta-analysis both in blood and in lung-associated tissues. Top genes included LINC01278 (P = 3.11 x 10(-13)), GBP5 (P = 5.56 x 10(-07)), and PSMB9 (P = 1.11 x 10(-06)). Pathway enrichment analysis revealed activated IFN-gamma, IL-1, and IL-18, autophagy, and viral infection response. IL-17 was observed to be enriched in peripheral blood specific signature genes. A 16-gene classifier achieved excellent performance in the independent validation data (AUC 0.711-0.964). This study provides a cross-tissue meta-analysis for expression profiles of sarcoidosis and identifies a diagnostic classifier that potentially can complement more invasive procedures. [Jiang, Yale; Jiang, Dingyuan; Dai, Huaping; Wang, Chen] China Japan Friendship Hosp, Dept Pulm & Crit Care Med, Beijing, Peoples R China; [Jiang, Yale] Chinese Acad Med Sci Peking Union Med Coll, Clin Trial Ctr, Canc Hosp, Natl Canc Ctr,Natl Clin Res Ctr Canc, Beijing, Peoples R China; [Jiang, Yale] Tsinghua Univ, Sch Med, Beijing, Peoples R China; [Jiang, Dingyuan; Dai, Huaping; Wang, Chen] Chinese Acad Med Sci, Inst Resp Med, Natl Ctr Resp Med, Natl Clin Res Ctr Resp Dis, Beijing, Peoples R China; [Costabel, Ulrich] Univ Hosp, Ruhrlandklin, Ctr Interstitial & Rare Lung Dis, Dept Pneumol, Essen, Germany; [Wang, Chen] Peking Univ Joint Ctr Life Sci, Tsinghua Univ, Beijing, Peoples R China; [Wang, Chen] Peking Union Med Coll, Beijing, Peoples R China China-Japan Friendship Hospital; Chinese Academy of Medical Sciences - Peking Union Medical College; Cancer Institute & Hospital - CAMS; Peking Union Medical College; Tsinghua University; Chinese Academy of Medical Sciences - Peking Union Medical College; University of Duisburg Essen; Tsinghua University; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College Dai, HP; Wang, C (corresponding author), China Japan Friendship Hosp, Dept Pulm & Crit Care Med, Beijing, Peoples R China.;Dai, HP; Wang, C (corresponding author), Chinese Acad Med Sci, Inst Resp Med, Natl Ctr Resp Med, Natl Clin Res Ctr Resp Dis, Beijing, Peoples R China.;Wang, C (corresponding author), Peking Univ Joint Ctr Life Sci, Tsinghua Univ, Beijing, Peoples R China.;Wang, C (corresponding author), Peking Union Med Coll, Beijing, Peoples R China. daihuaping@ccmu.edu.cn; cyh-birm@263.net National Natural Science Foundation of China; Elite Medical Professionals Project of China-Japan Friendship Hospital; [82170080]; [81870056]; [ZRJY2021-GG11] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Elite Medical Professionals Project of China-Japan Friendship Hospital; ; ; Funding This work was supported by the National Natural Science Foundation of China (Grant Nos. 82170080 and 81870056) and the Elite Medical Professionals Project of China-Japan Friendship Hospital (No. ZRJY2021-GG11). 34 0 0 1 1 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-858X FRONT MED-LAUSANNE Front. Med. SEP 20 2022.0 9.0 960266 10.3389/fmed.2022.960266 0.0 10 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine 5P1OP 36203777.0 gold 2023-03-23 WOS:000872929000001 0
J Wang, LL; Li, Q; Lam, J; Wang, Z Wang, Liangliang; Li, Qiang; Lam, James; Wang, Zheng Tactual Recognition of Soft Objects From Deformation Cues IEEE ROBOTICS AND AUTOMATION LETTERS English Article Force; Strain; Feature extraction; Grasping; Robots; Eigenvalues and eigenfunctions; Data analysis; Soft object recognition; tactual grasping; force-displacement curve; functional data analysis ROBOTIC MANIPULATION; PERCEPTION; DISCRIMINATION; IDENTIFICATION; CLASSIFICATION; PARAMETERS Recognition of soft objects is a critical step to tailor policy for dexterous manipulation. Deformation property is one of the valuable tactile cues for inferring the identities of soft objects, especially those having similar appearance features. In this work, we implemented tactual grasping for discriminating soft objects from the response curves of indentation displacement in relation to grasping force. Unlike some existing methods defining certain local features from the force-displacement observations, we viewed the force-displacement curve as continuous function rather than discrete observations. We proposed to use functional data analysis for classifying the kind of force-displacement curve data. Functional principal component analysis (FPCA) was used to extract multivariate features from force-displacement curves. Different traditional machine learning models were trained for classifying the multivariate features and the best one was selected based on cross-validation. Case study demonstrated that our method could accurately distinguish eight different soft objects. The FPCA based features outperformed those manually defined features. We indicated that the force had crucial influence on the classification accuracy and proposed how to select the proper force for the classification task. Overall, this work provides practical guidelines on tactual recognition of soft objects. [Wang, Liangliang; Lam, James] Univ Hong Kong, Dept Mech Engn, Hong Kong 999077, Peoples R China; [Li, Qiang] Bielefeld Univ, Ctr Cognit Interact Technol ogy CITEC, D-33619 Bielefeld, Germany; [Wang, Zheng] Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China University of Hong Kong; University of Bielefeld; Southern University of Science & Technology Wang, Z (corresponding author), Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China. wangll41101321@gmail.com; qli@techfak.uni-bielefeld.de; jlam@hku.hk; wangz@sustech.edu.cn Lam, James/B-1684-2008; Wang, Zheng/H-6239-2011 Lam, James/0000-0002-0294-0640; Wang, Zheng/0000-0002-7726-0770 Science, Technology and Innovation Commission of Shenzhen Municipality [ZDSYS20200811143601004]; NSFC [51975268]; Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); SUSTECH-Taizhou Innovation Fund; SUSTECH Institute of Robotics Fund; DEXMAN Project - Deutsche Forschungsgemeinschaft (DFG) [410916101]; SUSTECH-AISONO Joint Lab Science, Technology and Innovation Commission of Shenzhen Municipality; NSFC(National Natural Science Foundation of China (NSFC)); Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); SUSTECH-Taizhou Innovation Fund; SUSTECH Institute of Robotics Fund; DEXMAN Project - Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); SUSTECH-AISONO Joint Lab Thisworkwas supported in part by the Science, Technology and Innovation Commission of Shenzhen Municipality under Grant ZDSYS20200811143601004, in part by the NSFC under Grant 51975268, in part by the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), SUSTECH-AISONO Joint Lab, SUSTECH-Taizhou Innovation Fund, and in part by the SUSTECH Institute of Robotics Fund. The work of Qiang Li was supported by the DEXMAN Project 410916101 funded by the Deutsche Forschungsgemeinschaft (DFG). 31 1 1 3 27 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. JAN 2022.0 7.0 1 96 103 10.1109/LRA.2021.3119393 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics WJ5GM Green Submitted 2023-03-23 WOS:000709073100006 0
J Lin, HQ; Yan, Z; Chen, Y; Zhang, LF Lin, Huaqing; Yan, Zheng; Chen, Yu; Zhang, Lifang A Survey on Network Security-Related Data Collection Technologies IEEE ACCESS English Article Network security; security-related data; data collection technologies; large-scale heterogeneous networks TRAFFIC PREDICTION; TRUST MANAGEMENT; CAPTURE; SYSTEM Security threats and economic loss caused by network attacks, intrusions, and vulnerabilities have motivated intensive studies on network security. Normally, data collected in a network system can reflect or can be used to detect security threats. We define these data as network security-related data. Studying and analyzing security-related data can help detect network attacks and intrusions, thus making it possible to further measure the security level of the whole network system. Obviously, the first step in detecting network attacks and intrusions is to collect security-related data. However, in the context of big data and 5G, there exist a number of challenges in collecting these security-related data. In this paper, we first briefly introduce network security-related data, including its definition and characteristics, and the applications of network data collection. We then provide the requirements and objectives for security-related data collection and present a taxonomy of data collection technologies. Moreover, we review existing collection nodes, collection tools, and collection mechanisms in terms of network data collection and analyze them based on the proposed requirements and objectives toward high quality security-related data collection. Finally, we discuss open research issues and conclude with suggestions for future research directions. [Lin, Huaqing; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng; Zhang, Lifang] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Chen, Yu] Univ Calif Irvine, Dept Informat, Irvine, CA 92697 USA Xidian University; Aalto University; University of California System; University of California Irvine Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland. zyan@xidian.edu.cn yang, zheng/HGC-7753-2022 Chen, Yu/0000-0002-0612-1482; Yan, Zheng/0000-0002-9697-2108 National Key Research and Development Program of China [2016YFB0800704]; NSFC [61672410, U1536202]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; 111 project [B08038, B16037]; Academy of Finland [308087] National Key Research and Development Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China; 111 project(Ministry of Education, China - 111 Project); Academy of Finland(Academy of Finland) This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800704, in part by the NSFC under Grant 61672410 and Grant U1536202, in part by the Project supported by the Natural Science Basic Research Plan in Shaanxi Province of China under Program 2016ZDJC-06, in part by the 111 project under Grant B08038 and Grant B16037, and in part by the Academy of Finland under Grant 308087. 70 33 37 9 59 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 18345 18365 10.1109/ACCESS.2018.2817921 0.0 21 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications GD9ML gold, Green Submitted, Green Published 2023-03-23 WOS:000430836700001 0
C Cheng, F; Cao, JW; Wang, JZ; Wang, DP; Vidal, PP IEEE Cheng, Fei; Cao, Jiuwen; Wang, Jianzhong; Wang, Danping; Vidal, Pierre-Paul Excavation Devices Recognition with ELM-AE based Multi-Feature Learning Algorithm 2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP) International Conference on Digital Signal Processing English Proceedings Paper 23rd IEEE International Conference on Digital Signal Processing (DSP) NOV 19-21, 2018 Shanghai, PEOPLES R CHINA IEEE ELM-AE; Feature fusion; MFCC; LPCC; Excavation device recognition MFCC FEATURES; MACHINE Underground power cable suffered severe external damage caused by road excavation devices In urban cities nowadays. Thus, establishing a monitoring system which can automatically detect these potential damages becomes urgent and important. Recently, acoustic signal recognition based algorithms have been extensively investigated for excavation device detection. However, the current research mainly focuses on single acoustic feature extraction method, which may not be sufficient in characterizing the acoustic signals of excavation devices. In this paper, we introduce two novel feature fusion methods based on the principle component analysis (PCA) and the artificial neural network based stacked autoencoder with extreme learning machine algorithm (ELM-AE), respectively. The concatenated feature consisted of LPCC and MFCC features are employed as the original inputs for the two feature learning algorithms. Experiments on acoustics by four representative excavation devices are presented. Extensive comparisons by conducting tests on the single domain features LPCC and MFCC, the directly concatenated LPCC+MFCC features, the LPCC+MFCC feature with the PCA fusion method, and the LPCC+MFCC feature with the stacked ELM-AEs fusion method, respectively, are provided. Experimental results reveal that the combined feature vector performs better than only using the single domain features. Meanwhile, the stacked ELM-AEs feature fusion method wins the best recognition accuracy. [Cheng, Fei; Cao, Jiuwen; Wang, Jianzhong; Wang, Danping; Vidal, Pierre-Paul] Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310018, Zhejiang, Peoples R China; [Cao, Jiuwen] Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Zhejiang, Peoples R China; [Wang, Danping] Univ Paris 05, Plateforme Sensorimotricite, F-75270 Paris, France; [Vidal, Pierre-Paul] Univ Paris 05, COGNAC G COGNit & ACt Grp, F-75270 Paris, France Hangzhou Dianzi University; Hangzhou Dianzi University; UDICE-French Research Universities; Universite Paris Cite; UDICE-French Research Universities; Universite Paris Cite Cao, JW (corresponding author), Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310018, Zhejiang, Peoples R China.;Cao, JW (corresponding author), Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Zhejiang, Peoples R China. jwcao@hdu.edu.cn National Natural Science Foundation of China [61503104, U1509205, GK150906299001/019] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (61503104, U1509205), and Hangzhou Smart City Research Center or Zhejiang/Zhejiang Satan City Regional Collaborative innovation Center (GK150906299001/019). 14 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1546-1874 INT CONF DIGIT SIG 2018.0 5 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BM0NT 2023-03-23 WOS:000458909600193 0
C Wang, TB; Zhang, DQ; Wang, Z; Jia, JB; Ni, HB; Zhou, XS Ma, J; Yang, LT; Ning, H; Li, A Wang, Tianben; Zhang, Daqing; Wang, Zhu; Jia, Jiangbo; Ni, Hongbo; Zhou, Xinshe Recognizing Gait Pattern of Parkinson's Disease Patients Based on Fine-grained Movement Function Features IEEE 12TH INT CONF UBIQUITOUS INTELLIGENCE & COMP/IEEE 12TH INT CONF ADV & TRUSTED COMP/IEEE 15TH INT CONF SCALABLE COMP & COMMUN/IEEE INT CONF CLOUD & BIG DATA COMP/IEEE INT CONF INTERNET PEOPLE AND ASSOCIATED SYMPOSIA/WORKSHOPS English Proceedings Paper 12th IEEE Int Conf Ubiquitous Intelligence & Comp/12th IEEE Int Conf Autonom & Trusted Comp/15th IEEE Int Conf Scalable Comp & Commun & Associated Workshops/IEEE Int Conf Cloud & Big Data Comp/IEEE Int Conf Internet People AUG 10-14, 2015 Beijing, PEOPLES R CHINA Inst Elect & Elect Engn,IEEE Comp Soc,NSFC,IEEE Tech Comm Scalable Comp,Beijing Municipal Sci & Technol Commiss,Zheziang Univ,Tsinghua Univ,China Univ Petroleum,China Univ Geosciences,Shanghai Univ,Peking Univ,Beijing Union Univ,Tsinghua Sci & Technol,Sci China Press,NE Univ,Univ Sci & Technol Beijing,Cybermat & Brain Informat S&T Int Cooperat Base Parkinson's Disease; gait phases; stability; symmetry harmony; gait pattern recognition STABILITY; RHYTHMICITY; ATTENTION; HARMONY Parkinson's Disease (PD) is one of the typical movement disorder diseases, which has a serious impact on the daily lives of elderly people. In this paper, we propose a novel framework for PD gait pattern recognition. The key idea of our approach is to distinguish PD gait patterns from healthy individuals by accurately extracting gait features that indicate three aspects of movement function, i.e., stability, symmetry and harmony. Concretely, our framework contains three steps: gait phase discrimination, feature extraction and selection and pattern classification. In the first step, we put forward a key event based method to discriminate four gait phases from plantar pressure data. In the second step, based on the gait phases, we extract and select gait features that can indicate stability, symmetry and harmony of movement function. In the third step, we recognize PD gait pattern by employing BP neural network. We evaluate the framework using a real plantar pressure dataset that contains 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework outperforms the baseline approach by 32.7% on average in terms of Precision, 42.2% on average in terms of Recall, and 24.0% on average in terms of AUC. [Wang, Tianben; Zhang, Daqing; Wang, Zhu; Jia, Jiangbo; Ni, Hongbo; Zhou, Xinshe] Northwestern Polytech Univ, Sch Comp Sci, Xian, Shaanxi, Peoples R China; [Zhang, Daqing] Telecom SudParis, Inst Mines Telecom, CNRS SAMOVAR, F-91000 Evry, France Northwestern Polytechnical University; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Wang, TB (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Xian, Shaanxi, Peoples R China. wangtianbengx@163.com; daqing.zhang@telecom-sudparis.eu; transitwang@gmail.com; jiajiangbo758@gmail.com; nihb@nwpu.edu.cn; zhouxs@nwpu.edu.cn Wang, Tao/GQZ-6437-2022 Wang, Tao/0000-0003-3271-3331 State Key Program of National Natural Science of China [61332013] State Key Program of National Natural Science of China(National Natural Science Foundation of China (NSFC)) The authors would like to express our special appreciation to W. Zhao, Y. Song, J. Deng, for their assistance in the experiments. This work is supported by State Key Program of National Natural Science of China (No. 61332013). 24 34 35 1 5 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-4673-7211-4 2015.0 1 10 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.26 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI4DG 2023-03-23 WOS:000411670500001 0
C Wu, C; Chen, X; Zhou, YZ; Li, NY; Fu, XM; Zhang, YX IEEE Wu, Chao; Chen, Xu; Zhou, Yuezhi; Li, Ningyuan; Fu, Xiaoming; Zhang, Yaoxue Spice: Socially-Driven Learning-Based Mobile Media Prefetching IEEE INFOCOM 2016 - THE 35TH ANNUAL IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS IEEE INFOCOM English Proceedings Paper 35th IEEE Annual International Conference on Computer Communications (IEEE INFOCOM) APR 10-14, 2016 San Francisco, CA IEEE,IEEE Commun Soc Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide Quality of Experience (QoE) support for mobile OSN services, in this paper we propose a socially-driven learning-based framework, namely Spice, for media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17,000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 67.2% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve speed-up of a factor of 1000 over the local data training execution on smartphones. [Wu, Chao; Zhou, Yuezhi; Zhang, Yaoxue] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Chen, Xu; Fu, Xiaoming] Univ Goettingen, Inst Comp Sci, Gottingen, Germany; [Li, Ningyuan] Zhongyuan Univ Technol, Software Coll, Zhengzhou, Peoples R China Tsinghua University; University of Gottingen; Zhongyuan University of Technology Wu, C (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China. Fu, Xiaoming/B-7208-2016; Fu, Xiaoming/AAD-2828-2022; Chen, Xu/GXW-3072-2022 Fu, Xiaoming/0000-0002-8012-4753; Fu, Xiaoming/0000-0002-8012-4753; 18 0 0 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0743-166X 978-1-4673-9953-1 IEEE INFOCOM SER 2016.0 9 Computer Science, Information Systems; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BG6EN 2023-03-23 WOS:000390154400239 0
J He, ST; Li, CB; Liu, XB; Chen, XY; Shahidehpour, M; Chen, T; Zhou, B; Wu, QW He, Shengtao; Li, Canbing; Liu, Xubin; Chen, Xinyu; Shahidehpour, Mohammad; Chen, Tao; Zhou, Bin; Wu, Qiuwei A per-unit curve rotated decoupling method for CNN-TCN based day-ahead load forecasting IET GENERATION TRANSMISSION & DISTRIBUTION English Article NEURAL-NETWORKS; MODEL The existing load forecasting method based on the per-unit curve static decoupling (PCSD) would easily lead to the deviation and translation of forecasting results. To tackle this challenge, a per-unit curve rotated decoupling (PCRD) method is proposed for day-ahead load forecasting with convolutional neural network and temporal convolutional network framework. The PCRD method decomposes the load into three parts: the rotated per-unit load curve, the 0 AM load, and the daily average load. The shape feature of the load curve is extracted by CNN, the temporal features of the 0 AM load and daily average load are extracted by TCN. The rotation operation is to rotate the per-unit load curve at the midpoint of the curve until the first load point is aligned to the same point, in order to improve the similarity of per-unit load curves and to alleviate the deflection of forecasting results. The 0 AM load can verify the accuracy of the daily average load, which alleviates the translation of forecasting results. Several experimental results show that the proposed method has higher accuracy and stability than the existing PCSD method. After repeated experiments on multiple data sets, the generalization ability of the model is also verified. [He, Shengtao; Chen, Tao; Zhou, Bin] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China; [Li, Canbing] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Liu, Xubin; Chen, Xinyu] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China; [Shahidehpour, Mohammad] IIT, Dept Elect & Comp Engn, Chicago, IL 60616 USA; [Wu, Qiuwei] Tech Univ Denmark, Dept Elect Engn, Ctr Elect Power & Energy CEE, DK-2800 Lyngby, Denmark Hunan University; Shanghai Jiao Tong University; Huazhong University of Science & Technology; Illinois Institute of Technology; Technical University of Denmark Liu, XB (corresponding author), Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China. liuxubin@hust.edu.cn chen, xinyu/HNR-2708-2023 Wu, Qiuwei/0000-0001-7935-2567 National Natural Science Foundation of China [51722701] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) National Natural Science Foundation of China, Grant/Award Number: 51722701 36 5 5 5 28 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-8687 1751-8695 IET GENER TRANSM DIS IET Gener. Transm. Distrib. OCT 2021.0 15.0 19 2773 2786 10.1049/gtd2.12214 0.0 MAY 2021 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering UL6LK Green Published, gold 2023-03-23 WOS:000657799100001 0
J Wang, XH; Chen, J; Wei, Q; Richard, C Wang, Xiuheng; Chen, Jie; Wei, Qi; Richard, Cedric Hyperspectral Image Super-Resolution via Deep Prior Regularization With Parameter Estimation IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Superresolution; Hyperspectral imaging; Optimization; Tensors; Spatial resolution; Degradation; Matrix decomposition; HSI super-resolution; deep learning; fusion; Sylvester equation; regularization parameter estimation ILL-POSED PROBLEMS; MULTISPECTRAL IMAGES; FUSION; RESOLUTION Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical model and deep prior information. Specifically, a novel, yet effective two-stream fusion network is designed to serve as a regularizer for the fusion problem. This fusion problem is formulated as an optimization problem whose solution can be obtained by solving a Sylvester equation. Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and the learned prior to reconstruct the final HR HSI. Experimental results on both simulated and real data demonstrate the superiority of the proposed method over other state-of-the-art methods on both quantitative and qualitative comparisons. [Wang, Xiuheng; Chen, Jie] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China; [Wei, Qi] JP Morgan, New York, NY 10017 USA; [Richard, Cedric] Univ Cote dAzur, OCA, CNRS, F-06108 Nice, France Northwestern Polytechnical University; JP Morgan Chase & Company; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Cote d'Azur; Observatoire de la Cote d'Azur Chen, J (corresponding author), Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China. xiuheng.wang@mail.nwpu.edu.cn; dr.jie.chen@ieee.org; bjweiqi@gmail.com; cedric.richard@unice.fr Wang, Xiuheng/0000-0002-0115-5034 National Key Research and Development Program of China [2018AAA0102200]; 111 Project [B18041] National Key Research and Development Program of China; 111 Project(Ministry of Education, China - 111 Project) The work of Jie Chen was supported in part by the National Key Research and Development Program of China under Grant 2018AAA0102200 and in part by the 111 Project under Grant B18041. This article was recommended by Associate Editor Z. Li. 68 11 11 8 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. APR 2022.0 32.0 4 1708 1723 10.1109/TCSVT.2021.3078559 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 0H8IP Green Submitted 2023-03-23 WOS:000778973700004 0
C Song, RJ; Vanthienen, J; Cui, WP; Wang, Y; Huang, L Abramowicz, W; Corchuelo, R Song, Rongjia; Vanthienen, Jan; Cui, Weiping; Wang, Ying; Huang, Lei A DMN-Based Method for Context-Aware Business Process Modeling Towards Process Variability BUSINESS INFORMATION SYSTEMS, PT I Lecture Notes in Business Information Processing English Proceedings Paper 22nd International Conference on Business Information Systems (BIS) JUN 26-28, 2019 Seville, SPAIN Univ Seville,Poznan Univ Econ & Business, Dept Informat Syst Decision modeling; Context-aware business process; Process modeling; Process variability Business process modeling traditionally has not paid much attention to the interactive features considering the dynamism of the environment in which a business process is embedded. As context-awareness is accommodated in business process modeling, decisions are still considered within business processes in a traditional way. Moreover, context-aware business process modeling excessively relies on expert knowledge, due to a lack of a methodological way to guide its whole procedure. Lately, BPM (Business Process Management) is moving towards the separation of concerns paradigm by externalizing the decisions from the process flow. Most notably, the introduction of DMN (Decision Model and Notation) standard provides a solution and technique to model decisions and the process separately but consistently integrated. The DMN technique supports the ability to extract and operationalize value from data analytics since the value of data analytics lies in improving decision-making. In this paper, a DMN-based method is proposed for the separate consideration of decisions and business processes, which allows to model context into decisions as context-aware business process models for achieving business process variability. Using this method, the role of analytics in improving some part of the decision making can also be integrated in the context-aware business process modeling, which increases the potential for using big data and analytics to improve decision-making. Moreover, a formal presentation of DMN is extended with the context concept to set the theoretical foundation for the proposed DMN-based method. [Song, Rongjia; Wang, Ying; Huang, Lei] Beijing Jiaotong Univ, Dept Informat Management, Beijing, Peoples R China; [Song, Rongjia; Vanthienen, Jan] Katholieke Univ Leuven, Dept Decis Sci & Informat Management, Leuven, Belgium; [Cui, Weiping] State Grid Corp China, State Grid Energy Res Inst, Beijing, Peoples R China Beijing Jiaotong University; KU Leuven; State Grid Corporation of China Song, RJ (corresponding author), Beijing Jiaotong Univ, Dept Informat Management, Beijing, Peoples R China.;Song, RJ (corresponding author), Katholieke Univ Leuven, Dept Decis Sci & Informat Management, Leuven, Belgium. rjsong@bjtu.edu.cn; jan.vanthienen@kuleuven.be; cuiweiping@sgeri.sgcc.com.cn; ywang1@bjtu.edu.cn; lhuang@bjtu.edu.cn huang, lei/GQP-8739-2022; Vanthienen, Jan/A-1668-2018 Song, Rongjia/0000-0001-8594-5641; Vanthienen, Jan/0000-0002-3867-7055 Natural Science Foundation of China [71502010] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the Natural Science Foundation of China under Grant 71502010. 20 4 4 3 8 SPRINGER-VERLAG BERLIN BERLIN HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY 1865-1348 978-3-030-20485-3; 978-3-030-20484-6 LECT NOTES BUS INF P 2019.0 353.0 176 188 10.1007/978-3-030-20485-3_14 0.0 13 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO0HA Green Accepted 2023-03-23 WOS:000490868600014 0
J Liu, JX; Hua, YF; Yang, RX; Luo, YL; Lu, H; Wang, YH; Yang, S; Ding, XM Liu, Junxiu; Hua, Yifan; Yang, Rixing; Luo, Yuling; Lu, Hao; Wang, Yanhu; Yang, Su; Ding, Xuemei Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance FRONTIERS IN NEUROSCIENCE English Article neuroanimats; spiking neural networks; reinforcement learning; spike-timing-dependent plasticity; robot DOPAMINE; MODEL; CIRCUITS; NEURONS; STDP Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario. [Liu, Junxiu; Hua, Yifan; Luo, Yuling; Lu, Hao; Wang, Yanhu] Guangxi Normal Univ, Sch Elect Engn, Guilin, Peoples R China; [Yang, Rixing] Guangxi Normal Univ, Coll Innovat & Entrepreneurship, Guilin, Peoples R China; [Yang, Su] Swansea Univ, Dept Comp Sci, Swansea, Wales; [Ding, Xuemei] Ulster Univ, Sch Comp Engn & Intelligent Syst, Derry, North Ireland Guangxi Normal University; Guangxi Normal University; Swansea University; Ulster University Yang, RX (corresponding author), Guangxi Normal Univ, Coll Innovat & Entrepreneurship, Guilin, Peoples R China. ee2022gxnu@gxnu.edu.cn Lu, Hao/0000-0001-6887-1767 National Natural Science Foundation of China [61976063]; Guangxi Natural Science Foundation [2022GXNSFFA035028] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province) Funding This research was partially supported by the National Natural Science Foundation of China under Grant No. 61976063 and the Guangxi Natural Science Foundation under Grant No. 2022GXNSFFA035028. 41 1 1 6 7 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-453X FRONT NEUROSCI-SWITZ Front. Neurosci. JUN 30 2022.0 16.0 905596 10.3389/fnins.2022.905596 0.0 13 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology 2Z5ZX 35844210.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000826656800001 0
J Aicheler, F; Li, J; Hoene, M; Lehmann, R; Xu, GW; Kohlbacher, O Aicheler, Fabian; Li, Jia; Hoene, Miriam; Lehmann, Rainer; Xu, Guowang; Kohlbacher, Oliver Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches ANALYTICAL CHEMISTRY English Article 2-DIMENSIONAL GAS-CHROMATOGRAPHY; MASS-SPECTROMETRY; PLASMA LIPIDOMICS; FATTY-ACIDS; SYSTEM; OLIGONUCLEOTIDES; SOFTWARE; DATABASE; PROFILE Identification of lipids in nontargeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (t(R)) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves a correlation coefficient between predicted and experimental test sample retention times of r = 0.989. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95% of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles. [Aicheler, Fabian; Kohlbacher, Oliver] Univ Tubingen, Quantitat Biol Ctr, Appl Bioinformat Ctr Bioinformat, D-72076 Tubingen, Baden Wurttembe, Germany; [Aicheler, Fabian; Kohlbacher, Oliver] Univ Tubingen, Dept Comp Sci, D-72076 Tubingen, Baden Wurttembe, Germany; [Li, Jia; Xu, Guowang] Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Liaoning, Peoples R China; [Hoene, Miriam; Lehmann, Rainer] Univ Tubingen Hosp, Dept Internal Med 4, Div Clin Chem & Pathobiochem, D-72076 Tubingen, Baden Wurttembe, Germany; [Lehmann, Rainer; Kohlbacher, Oliver] Univ Tubingen, Helmholtz Ctr Munich, Inst Diabet Res & Metab Dis, Dept Mol Diabetol, D-72076 Tubingen, Baden Wurttembe, Germany; [Lehmann, Rainer; Kohlbacher, Oliver] German Ctr Diabet Res DZD, D-72076 Tubingen, Baden Wurttembe, Germany Eberhard Karls University of Tubingen; Eberhard Karls University of Tubingen; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Eberhard Karls University of Tubingen; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health Xu, GW (corresponding author), Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Liaoning, Peoples R China. xugw@dicp.ac.cn; oliver.kohlbacher@uni-tuebingen.de Kohlbacher, Oliver/AAF-3297-2021; Xu, Guowang/L-6910-2014; Lehmann, Rainer/AAF-4281-2019; Kohlbacher, Oliver/B-7310-2008 Kohlbacher, Oliver/0000-0003-1739-4598; Xu, Guowang/0000-0003-4298-3554; Lehmann, Rainer/0000-0003-1218-6810; Kohlbacher, Oliver/0000-0003-1739-4598 Sino-German Center for Research Promotion by DFG [GZ 753, LE 1391/1-1]; Sino-German Center for Research Promotion by NSFC; German Federal Ministry of Education and Research (BMBF) [01GI0925]; BMBF [BIOMARKERS - FKZ 01GI1104A]; European Commission FP7MARINA [236215]; DFG [KO 2313/6-1]; foundation and creative research group project by NSFC [21175132, 21321064] Sino-German Center for Research Promotion by DFG(German Research Foundation (DFG)); Sino-German Center for Research Promotion by NSFC; German Federal Ministry of Education and Research (BMBF)(Federal Ministry of Education & Research (BMBF)); BMBF(Federal Ministry of Education & Research (BMBF)); European Commission FP7MARINA(European Commission); DFG(German Research Foundation (DFG)); foundation and creative research group project by NSFC This work was funded in part by a grant from the Sino-German Center for Research Promotion (GZ 753 by DFG and NSFC to G.X. and R.L. and LE 1391/1-1 by DFG to R.L.); by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Centre for Diabetes Research (DZD e.V., Grant 01GI0925); by BMBF grant BIOMARKERS - FKZ 01GI1104A; by the European Commission FP7MARINA (Grant No. 236215); by DFG grant KO 2313/6-1; and by the foundation and creative research group project (No. 21175132 and No. 21321064 by NSFC to G.X.). 53 58 59 3 139 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0003-2700 1520-6882 ANAL CHEM Anal. Chem. AUG 4 2015.0 87.0 15 7698 7704 10.1021/acs.analchem.5b01139 0.0 7 Chemistry, Analytical Science Citation Index Expanded (SCI-EXPANDED) Chemistry CO6PF 26145158.0 2023-03-23 WOS:000359277900031 0
J Asif, MR; Qi, C; Wang, TX; Fareed, MS; Khan, S Asif, Muhammad Rizwan; Qi, Chun; Wang, Tiexiang; Fareed, Muhammad Sadiq; Khan, Subhan License plate detection for multi-national vehicles - a generalized approach MULTIMEDIA TOOLS AND APPLICATIONS English Article Intelligent vision system; License plate detection; Local image features; Multi-national vehicles; Vehicle identification; Vehicle rear lights RECOGNITION; TRACKING; NETWORK License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles. [Asif, Muhammad Rizwan; Qi, Chun; Wang, Tiexiang; Fareed, Muhammad Sadiq] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China; [Asif, Muhammad Rizwan] Aarhus Univ, Dept Engn, Aarhus N, Denmark; [Khan, Subhan] Univ New South Wales UNSW, Sch Mech & Mfg Engn, Sydney, NSW, Australia Xi'an Jiaotong University; Aarhus University; University of New South Wales Sydney Qi, C (corresponding author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China. rizwansheikh123@hotmail.com; qichun@mail.xjtu.edu.cn; subhan.khan@unsw.edu.au Khan, Subhan/AFL-0450-2022 Khan, Subhan/0000-0002-0979-3751; Asif, Muhammad Rizwan/0000-0003-1385-8041 National Natural Science Foundation of China [61572395, 61675161] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the National Natural Science Foundation of China (Grant No. 61572395 and 61675161). 28 4 4 2 25 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. DEC 2019.0 78.0 24 35585 35606 10.1007/s11042-019-08199-4 0.0 22 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering JX9MV 2023-03-23 WOS:000504051800060 0
C Li, XP; Ao, HX; Belaroussi, R; Gruyer, D IEEE Li, Xuanpeng; Ao, Huanxuan; Belaroussi, Rachid; Gruyer, Dominique Fast Semi-Dense 3D Semantic Mapping with Monocular Visual SLAM 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) IEEE International Conference on Intelligent Transportation Systems-ITSC English Proceedings Paper 20th IEEE International Conference on Intelligent Transportation Systems (ITSC) OCT 16-19, 2017 Yokohama, JAPAN IEEE Fast 3D reconstruction with semantic information on road scenarios involves issues of geometry and appearance in the field of computer vision. An important idea is that fusion of geometry and appearance would boost the performance of each other. Stereo cameras and RGB-D sensors are widely used to realise 3D reconstruction and trajectory tracking, but they need heavy computation cost and storage in a dense way. Moreover, they lack flexibility of seamless switch between various scaled environments, i.e., indoor and outdoor scenes. Besides, autonomous vehicle would benefit from semantic information on the road, but it is hard to be sufficiently accurate and consistent in the 3D mapping. We address this challenge by fusion of direct Simultaneous Localisation and Mapping (SLAM) from a monocular camera in a semi-dense way and the state-of-the-art approaches of deep neural network. In our approach, 2D semantic information is transferred to 3D mapping via correspondence between consecutive Keyframes with spatial consistency. Since there are lots of semantic redundancies in consecutive frames, it is not necessary to process semantic segmentation for each frame in the entire sequence. Consequently, the segmentation could run in a reasonable speed (about 20Hz). We thoroughly evaluate our methods on road scene datasets. The experiments show a promising 3D semantic mapping in various road scenes. [Li, Xuanpeng] Southeast Univ, Fac Sch Instrument Sci & Engn, Nanjing 210006, Jiangsu, Peoples R China; [Ao, Huanxuan] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210006, Jiangsu, Peoples R China; [Belaroussi, Rachid; Gruyer, Dominique] IFSTTAR, Fac COSYS LIVIC, 25 Allee Marronniers, F-78000 Versailles, France Southeast University - China; Southeast University - China; Universite Gustave-Eiffel Li, XP (corresponding author), Southeast Univ, Fac Sch Instrument Sci & Engn, Nanjing 210006, Jiangsu, Peoples R China. li_xuanpeng@seu.edu.cn; aohuanxuan@163.com; rachid.belaroussi@ifsttar.fr; dominique.gruyer@ifsttar.fr LI, Xuanpeng/O-6873-2018; Gruyer, Dominique/L-3416-2016 LI, Xuanpeng/0000-0001-9320-0658; Jiangsu Basic Research Programs, China [BK20160700] Jiangsu Basic Research Programs, China This work was supported by the Jiangsu Basic Research Programs, China (Grant No. BK20160700). 20 8 9 4 15 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0009 978-1-5386-1526-3 IEEE INT C INTELL TR 2017.0 6 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Transportation Science & Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Transportation BK1WR 2023-03-23 WOS:000432373000316 0
J Zhao, M; Shi, SK; Chen, J; Dobigeon, N Zhao, Min; Shi, Shuaikai; Chen, Jie; Dobigeon, Nicolas A 3-D-CNN Framework for Hyperspectral Unmixing With Spectral Variability IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article 3-D-convolutional neural network (CNN); end-member variability; hyperspectral imaging; structured sparsity; unmixing; weight uncertainty ENDMEMBER VARIABILITY; AUTOENCODER; BUNDLES; MODEL; CNN Hyperspectral unmixing plays an important role in hyperspectral image processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. The hyperspectral image contains spatial information in neighborhood regions, and spectral signatures existing in the region also have a high correlation. However, most autoencoder (AE)-based unmixing methods are pixel-to-pixel methods and ignore these priors. It is helpful to add spectral-spatial information into unmixing methods. A recent trend to deal with this problem is to use convolutional neural networks (CNNs). Our proposed framework uses 3-D-CNN-based networks to jointly learn spectral-spatial priors. Moreover, previous AE-based unmixing methods use fixed spectral signatures for each pure material. In our work, we use a carefully designed decoder to cope with the endmember variability issue, and variational inference strategy is applied to add uncertainty property into endmembers. To avoid overfitting, we use structured sparsity regularizers to the encoder networks, and l(2,1) -loss is added to the estimated abundances to guarantee the sparseness. Experimental results on both simulated and real data demonstrate the effectiveness of our proposed method. [Zhao, Min; Shi, Shuaikai; Chen, Jie] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China; [Chen, Jie] Blueye Intelligence, Zhenjiang 212000, Jiangsu, Peoples R China; [Dobigeon, Nicolas] Univ Toulouse, Inst Rech Informat Toulouse, Inst Natl Polytech Toulouse,Ctr Natl Rech Sci, Ecole Natl Super Electrotech Elect Informat Hydra, F-31071 Toulouse, France; [Dobigeon, Nicolas] Inst Univ France IUF, F-75231 Paris, France Northwestern Polytechnical University; Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Institut National Polytechnique de Toulouse; Universite Toulouse 1 Capitole; Universite de Toulouse - Jean Jaures; Centre National de la Recherche Scientifique (CNRS); Institut Universitaire de France Chen, J (corresponding author), Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China.;Chen, J (corresponding author), Blueye Intelligence, Zhenjiang 212000, Jiangsu, Peoples R China. minzhao@mail.nwpu.edu.cn; shuaikai_shi@mail.nwpu.edu.cn; dr.jie.chen@ieee.org; nicolas.dobigeon@enseeiht.fr Shi, Shuaikai/0000-0003-1985-6530 NSFC [62171380]; 111 Project [B18041]; ANR3IA Artificial and Natural Intelligence Toulouse Institute (ANITI) [ANITI ANR-19-PI3A-0004]; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University; China Scholarship Council NSFC(National Natural Science Foundation of China (NSFC)); 111 Project(Ministry of Education, China - 111 Project); ANR3IA Artificial and Natural Intelligence Toulouse Institute (ANITI); Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University; China Scholarship Council(China Scholarship Council) The work of Jie Chen was supported in part by NSFC under Grant 62171380 and in part by the 111 Project under Grant B18041. The work of Nicolas Dobigeon was supported in part by the ANR3IA Artificial and Natural Intelligence Toulouse Institute (ANITI) under Grant ANITI ANR-19-PI3A-0004. The work of Min Zhao and Shuaikai Shi was supported in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University and in part by the China Scholarship Council. 61 2 2 11 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5521914 10.1109/TGRS.2022.3141387 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZR6BD Green Submitted 2023-03-23 WOS:000767865000016 0
J Man, Y; Shen, WH; Chen, XQ; Long, Z; Corriou, JP Man, Yi; Shen, Wenhao; Chen, Xiaoquan; Long, Zhou; Corriou, Jean-Pierre Dissolved oxygen control strategies for the industrial sequencing batch reactor of the wastewater treatment process in the papermaking industry ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY English Article ACTIVATED-SLUDGE; MODEL; REMOVAL; SYSTEM This paper proposes two strategies for controlling the dissolved oxygen concentration in the sequential batch reactor of the wastewater treatment process in the papermaking industry. Based on a previously developed sequential batch reactor simulation model, a fuzzy logic system and a neural network are integrated with a PID controller separately. According to the criteria defined in benchmark simulation model no. 1 (BSM1), three control strategies, namely, conventional PID, developed BP (back propagation)-PID and fuzzy-PID, applied in two cases with and without noise, have been investigated and assessed. For the cases without noise, both the developed controllers, i. e. BP-PID and fuzzy-PID, are superior to PID, wherein the fuzzy-PID controller not only improves the control performance but also reduces the aeration energy consumption. The results of further simulation with the interference of white noise reveal that the developed fuzzy-PID controller not only has better manipulated and controlled indexes than those of the BP-PID controller, it also demonstrates a good ability for anti-interference. These simulation results reveal the superiority of the developed fuzzy-PID control strategy in the dissolved oxygen control of the sequential batch reactor of the wastewater treatment process in the papermaking industry. [Man, Yi; Shen, Wenhao; Chen, Xiaoquan; Long, Zhou] South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510640, Guangdong, Peoples R China; [Corriou, Jean-Pierre] Univ Lorraine, UMR CNRS 7274, Lab React & Genie Proc, 1,Rue Grandville BP 451, F-54001 Nancy, France South China University of Technology; Universite de Lorraine Shen, WH (corresponding author), South China Univ Technol, State Key Lab Pulp & Paper Engn, Guangzhou 510640, Guangdong, Peoples R China. ppwhshen@scut.edu.cn Corriou, Jean-Pierre/AAB-5978-2020; Man, Yi/B-4243-2019 Corriou, Jean-Pierre/0000-0003-4780-3313; Man, Yi/0000-0002-3745-9128 Research Funds of the Natural Science Foundation of Guangdong, China [2016A030313478, 2017A030310562]; State Key Laboratory of Pulp and Paper Engineering, South China University of Technology [2017ZD03]; Science and Technology Program of Guangzhou, China [201607010050]; China Postdoctoral Science Foundation [2017M612668] Research Funds of the Natural Science Foundation of Guangdong, China; State Key Laboratory of Pulp and Paper Engineering, South China University of Technology; Science and Technology Program of Guangzhou, China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) The research was supported by the Research Funds of the Natural Science Foundation of Guangdong, China (No. 2016A030313478 and 2017A030310562), the State Key Laboratory of Pulp and Paper Engineering, South China University of Technology (No. 2017ZD03), the Science and Technology Program of Guangzhou, China (No. 201607010050), and the China Postdoctoral Science Foundation (No. 2017M612668). 23 21 21 5 62 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2053-1400 2053-1419 ENVIRON SCI-WAT RES Environ. Sci.-Wat. Res. Technol. MAY 1 2018.0 4.0 5 654 662 10.1039/c8ew00035b 0.0 9 Engineering, Environmental; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology; Water Resources GE7SS 2023-03-23 WOS:000431434000005 0
C Liu, Y; Li, ZL; Shi, CX; Tang, BH; Wu, H; Li, QS IEEE Liu, Yu; Li, Zhao-Liang; Shi, Chun-Xiang; Tang, Bo-Hui; Wu, Hua; Li, Qingsheng RELATION BETWEEN CUMULONIMBUS(CB) PREICITIATION AND CLOUD DYNAMICAL FEATURES OVER HUAIHE RIVER BASIN OF CHINA BASED ON FY-2C IMAGE 2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) IEEE International Symposium on Geoscience and Remote Sensing IGARSS English Proceedings Paper IEEE International Geoscience and Remote Sensing Symposium (IGARSS) JUL 21-26, 2013 Melbourne, AUSTRALIA IEEE,Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc Cumulonimbus; Precipitation; FY-2C; Dynamic features; cloud patch CONVECTIVE INITIATION; TRACKING; ALGORITHM The crowning objective of this research are to analyze precipitation character of Cb for different dynamical characters in Huai river basin(HRB) with China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. Firstly, 5 cloud patch dynamic parameters with respect to life stage and moving parameters are derived based on the Cb tracking method the author has proposed by combing artificial neural network (ANN) cloud classification[1], and cross-correlation-based approach to track Cb patch motion. Secondly, Cb precipitation over different life cycles and motion characters are analyzed. The result shows that: 1) Rain probability has a similar variation to rain rate, and rain rate is generally not more than 6 mm/hour, and probability is randomly higher than 50%. 2) Both rain rate and probability of single Cb is lower than that of complicated Cb which involves cell-merger and cell-split of some minor Cb patches. 3) Motion features such as horizontal moving speed of cloud patch (HMSP), horizontal moving direction of cloud patch (HMDP), and vertical moving character of cloud patch (VMCP) have no obvious impact on rain. [Li, Zhao-Liang] LSIIT, ENSPS, 5 Bld Sebastien Brant, F-67412 Illkirch Graffenstaden, France; [Liu, Yu; Tang, Bo-Hui; Wu, Hua; Li, Qingsheng] Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Li, Zhao-Liang] Chinese Acad Agr Sci, Inst Agro Resources & Regional Planning, Beijing 100081, Peoples R China; [Shi, Chun-Xiang] China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; China Meteorological Administration Li, ZL (corresponding author), LSIIT, ENSPS, 5 Bld Sebastien Brant, F-67412 Illkirch Graffenstaden, France. lizl@igsnrr.ac.cn National Natural Science Foundation of China [41231170]; independent innovation project of State Key Laboratory of Resource and Environmental Information System, CAS [088RA800KA] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); independent innovation project of State Key Laboratory of Resource and Environmental Information System, CAS This work was jointly supported by the National Natural Science Foundation of China under Grants 41231170 and the independent innovation project of State Key Laboratory of Resource and Environmental Information System, CAS (Project No. 088RA800KA) 8 0 0 1 4 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-6996 978-1-4799-1114-1 INT GEOSCI REMOTE SE 2013.0 554 557 10.1109/IGARSS.2013.6721216 0.0 4 Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Geology; Remote Sensing BB7JO 2023-03-23 WOS:000345638900136 0
C Peng, KK; Li, XY; Gao, L; Wang, X; Gao, YP ASME Peng, Kunkun; Li, Xinyu; Gao, Liang; Wang, Xi (Vincent); Gao, Yiping A NEW JOINT DATA-MODEL DRIVEN DYNAMIC SCHEDULING ARCHITECTURE FOR INTELLIGENT WORKSHOP PROCEEDINGS OF THE ASME 14TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, 2019, VOL 1 English Proceedings Paper 14th ASME International Manufacturing Science and Engineering Conference JUN 10-14, 2019 Erie, PA ASME, Mfg Engn Div Intelligent manufacturing; Ontelligent workshop; Joint Data-Model Driven; Dynamic scheduling; Dynamic events FLOWSHOP Intelligent manufacturing plays a significant role in Industry 4.0. Dynamic shop scheduling is a key problem and hot research topic in the intelligent manufacturing systems, which is NP-hard. However, traditional shop scheduling mode, dynamic event prediction approach, scheduling model and scheduling algorithm, cannot cope with increasingly complicated problems under kinds of scales production disruptions in the real-world production. To deal with these problems, this paper proposes a new joint data-model driven dynamic scheduling architecture for intelligent workshop. The architecture includes four new and key characteristics in the aspects of scheduling mode, dynamic event prediction, scheduling model and algorithm. More specifically, the new scheduling mode introduces data analytics methods to quickly and accurately deal with the dynamic events encountered in the production process. The new prediction model improves the deep learning method, and further applies it predict the dynamic events accurately to provide reliable input to the dynamic scheduling. The new scheduling model proposes a new hybrid rescheduling and inverse scheduling model, which can cope with almost scales of abnormal production problems. The new scheduling algorithm hybridizes dynamic programming and intelligent optimization algorithm, which can overcome the disadvantages of the two methods based on the analysis of solution space. The dynamic programming is employed to provide high-quality initial solutions for the intelligent optimization algorithm by reducing the computation time greatly. To sum up, the presented architecture is a new attempt to understand the problem domain knowledge and broaden the solving idea, which can also provide new theories and technologies to manufacturing system optimization and promote the applications of the theoretical results. [Peng, Kunkun; Li, Xinyu; Gao, Liang; Gao, Yiping] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Peoples R China; [Peng, Kunkun] Wuhan Univ Sci & Technol, Sch Management, Wuhan, Peoples R China; [Wang, Xi (Vincent)] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden Huazhong University of Science & Technology; Wuhan University of Science & Technology; Royal Institute of Technology Li, XY (corresponding author), Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Peoples R China. lixinyu@mail.hust.edu.cn Gao, Yiping/HMO-5446-2023 Natural Science Foundation of China (NSFC) [51775216, 51705177, 51711530038]; Natural Science Foundation of Hubei Province [2018CFA078]; Program for RUST Academic Frontier Youth Team Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province(Natural Science Foundation of Hubei Province); Program for RUST Academic Frontier Youth Team This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant nos. 51775216, 51705177 and 51711530038, the Natural Science Foundation of Hubei Province under Grant 2018CFA078, and the Program for RUST Academic Frontier Youth Team. 17 0 0 0 2 AMER SOC MECHANICAL ENGINEERS NEW YORK THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA 978-0-7918-5874-5 2019.0 V001T02A034 6 Engineering, Manufacturing; Engineering, Mechanical Conference Proceedings Citation Index - Science (CPCI-S) Engineering BO2FR 2023-03-23 WOS:000505633900073 0
J Reumschussel, JM; von Saldern, JGR; Li, YQ; Paschereit, CO; Orchini, A Reumschuessel, Johann Moritz; von Saldern, Jakob G. R.; Li, Yiqing; Paschereit, Christian Oliver; Orchini, Alessandro Gradient-Free Optimization in Thermoacoustics: Application to a Low-Order Model JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME English Article FREQUENCY-DOMAIN; DYNAMICS; PROGRESS Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimization can avoid cumbersome theoretical studies as input parameters are purely adapted based on output data. As no knowledge about the objective function is provided to the algorithms, this approach might reveal unconventional solutions to complex problems that were out of scope of classical solution strategies. In this study, the potential of these optimization methods on thermoacoustic problems is examined. The optimization algorithms are applied to a generic low-order thermoacoustic can-combustor model with several fuel injectors at different locations. We use three optimization algorithms - the well established downhill simplex method, the recently proposed explorative gradient method, and an evolutionary algorithm - to find optimal fuel distributions across the fuel lines while maintaining the amount of consumed fuel constant. The objective is to have minimal pulsation amplitudes. We compare the results and efficiency of the gradient-free algorithms. Additionally, we employ model-based linear stability analysis to calculate the growth rates of the dominant thermoacoustic modes. This allows us to highlight general and thermoacoustic-specific features of the optimization methods and results. The findings of this study show the potential of gradient-free optimization methods on combustor design for tackling thermoacoustic problems, and motivate further research in this direction. [Reumschuessel, Johann Moritz; Paschereit, Christian Oliver] Tech Univ Berlin, Chair Fluid Dynam, D-10623 Berlin, Germany; [von Saldern, Jakob G. R.] Tech Univ Berlin, Lab Flow Instabilities & Dynam, D-10623 Berlin, Germany; [Li, Yiqing] Harbin Inst Technol, Ctr Turbulence Control, Shenzhen 518055, Peoples R China; [Orchini, Alessandro] Tech Univ Berlin, Inst Fluid Mech & Tech Acoust, D-10623 Berlin, Germany Technical University of Berlin; Technical University of Berlin; Harbin Institute of Technology; Technical University of Berlin Reumschussel, JM (corresponding author), Tech Univ Berlin, Chair Fluid Dynam, D-10623 Berlin, Germany. reumschuessel@tu-berlin.de; j.vonsaldern@tu-berlin.de; anne.yiqingli@outlook.com; oliver.paschereit@tu-berlin.de; a.orchini@tu-berlin.de Bundesministerium fur Wirtschaft und Ene rgie [03EE5013E]; Deutsche Forschungsgemeinschaft [422037803] Bundesministerium fur Wirtschaft und Ene rgie; Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)) Bundesministerium fur Wirtschaft und Ene rgie (Grant No. 03EE5013E; Funder ID: 10.13039/501100006360).; Deutsche Forschungsgemeinschaft (Grant No. 422037803; Funder ID: 10.13039/501100001659). 32 2 2 2 4 ASME NEW YORK TWO PARK AVE, NEW YORK, NY 10016-5990 USA 0742-4795 1528-8919 J ENG GAS TURB POWER J. Eng. Gas. Turbines Power-Trans. ASME MAY 1 2022.0 144.0 5 51004 10.1115/1.4052087 0.0 9 Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Engineering 0Z2GT 2023-03-23 WOS:000790896400003 0
J Kolbe, C; Thies, B; Turini, N; Liu, ZY; Bendix, J Kolbe, Christine; Thies, Boris; Turini, Nazli; Liu, Zhiyu; Bendix, Joerg Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit-Part 2: Precipitation Rates with Elektro-L2 and Insat-3D REMOTE SENSING English Article precipitation retrieval; Insat-3D; Elektro-L2; GPM IMERG; machine learning; random forest GAUGE OBSERVATIONS; PASSIVE MICROWAVE; SATELLITE; RAINFALL; PRODUCTS; CLIMATE; VARIABILITY; DATASETS; REGION; INDIA We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MW precipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independent MW precipitation with IMERG's IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found. [Kolbe, Christine; Thies, Boris; Turini, Nazli; Bendix, Joerg] Philipps Univ Marburg, Dept Geog, Lab Climatol & Remote Sensing, Deutschhausstr 12, D-35032 Marburg, Germany; [Liu, Zhiyu] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China Philipps University Marburg; Hohai University Kolbe, C (corresponding author), Philipps Univ Marburg, Dept Geog, Lab Climatol & Remote Sensing, Deutschhausstr 12, D-35032 Marburg, Germany. christine.kolbe@geo.uni-marburg.de; boris.thies@geo.uni-marburg.de; nazli.turini@geo.uni-marburg.de; liuzhiyu2019@mwr.gov.cn; bendix@mailer.uni-marburg.de Kolbe, Christine/0000-0003-0207-4946; Bendix, Jorg/0000-0001-6559-2033 German Research Foundation (DFG) [BE1780/46-1, TH1531/6-1] German Research Foundation (DFG)(German Research Foundation (DFG)) This study was conducted within the project Precipitation patterns, snow and glacier response in High Asia and their variability on sub-decadal time scales-Remote Sens. of precipitation (prime-RS). This study and the APC were funded by the German Research Foundation (DFG projects: BE1780/46-1 and TH1531/6-1). 56 4 4 3 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUL 2020.0 12.0 13 2114 10.3390/rs12132114 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology MN4OY gold 2023-03-23 WOS:000550824200001 0
J Lu, YY; Kowarschik, M; Huang, XL; Chen, SQ; Ren, QS; Fahrig, R; Hornegger, J; Maier, A Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Chen, Shuqing; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES English Article Ensemble learning; machine learning; material decomposition; multienergy X-ray imaging RECONSTRUCTION METHOD; ESTIMATOR Material decomposition allows the reconstruction of material-specific images in spectral X-ray imaging, which requires efficient decomposition models. Due to the presence of nonideal effects in X-ray imaging systems, it is difficult to explicitly estimate the imaging systems for material decomposition tasks. As an alternative to previous empirical material decomposition methods, we investigated material decomposition using ensemble learning methods in this paper. Three ensemble methods with two decision trees as the base learning algorithms were investigated to perform material decomposition in both simulation and experiment. The results were quantitatively evaluated for comparison studies. In general, the results demonstrate that the proposed ensemble learning methods often outperform their base learning algorithms, and rarely reduce performance. Compared to the reference methods and its base learning algorithm, the performance of the Boosting method using REPTree with regularization is improved by over 42% and 13%, respectively, in the noiseless simulated scenario of the XCAT phantom with cardiac and respiratory motion, and over 36% and 17%, respectively, in the noisy scenario. Simultaneously, the performance is improved by over 9% and 8%, respectively, in the original torso phantom scenario, and over 13% and 12%, respectively, in the denoising scenario. The results indicate that ensemble learning with gradient descent optimization algorithms is more appropriate for material decomposition tasks. Further studies are encouraged to facilitate future efforts toward clinical applications. [Lu, Yanye; Chen, Shuqing; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas] Univ Erlangen Nurnberg, Dept Comp Sci, Pattern Recognit Lab, D-91058 Erlangen, Germany; [Lu, Yanye; Kowarschik, Markus; Fahrig, Rebecca] Siemens Healthineers, Adv Therapies, D-91301 Forchheim, Germany; [Huang, Xiaolin] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China; [Ren, Qiushi] Peking Univ, Dept Biomed Engn, Beijing 100871, Peoples R China University of Erlangen Nuremberg; Siemens AG; Shanghai Jiao Tong University; Peking University Lu, YY (corresponding author), Univ Erlangen Nurnberg, Dept Comp Sci, Pattern Recognit Lab, D-91058 Erlangen, Germany. yanye.lu@fau.de Lu, Yanye/ABF-8769-2020; Maier, Andreas/AAV-6505-2021; Hornegger, Joachim/H-2465-2017; Chen, Shuqing/L-6324-2019 Lu, Yanye/0000-0002-3063-8051; Maier, Andreas/0000-0002-9550-5284; Hornegger, Joachim/0000-0002-1834-8844; Chen, Shuqing/0000-0003-4100-2988; Huang, Xiaolin/0000-0003-4285-6520 NVIDIA Corporation NVIDIA Corporation The authors would like to thank the anonymous reviewers for their constructive comments that improved significantly the quality of this paper, and the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this paper. 34 10 11 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2469-7311 2469-7303 IEEE T RADIAT PLASMA IEEE Trans. Radiat. Plasma Med. Sci. MAY 2018.0 2.0 3 194 204 10.1109/TRPMS.2018.2805328 0.0 11 Radiology, Nuclear Medicine & Medical Imaging Emerging Sources Citation Index (ESCI) Radiology, Nuclear Medicine & Medical Imaging HI0RE 2023-03-23 WOS:000456149400005 0
J Cao, D; Zhao, JB; Hu, WH; Zhang, YC; Liao, QS; Chen, Z; Blaabjerg, F Cao, Di; Zhao, Junbo; Hu, Weihao; Zhang, Yingchen; Liao, Qishu; Chen, Zhe; Blaabjerg, Frede Robust Deep Gaussian Process-Based Probabilistic Electrical Load Forecasting Against Anomalous Events IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Forecasting; Load forecasting; Uncertainty; Probabilistic logic; Global Positioning System; Artificial neural networks; Training; Anomalous events; deep Gaussian process (GP) regression; limited data; probabilistic load forecasting; uncertainty; quantification NEURAL-NETWORK; MACHINES The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties. [Cao, Di; Hu, Weihao; Liao, Qishu] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China; [Zhao, Junbo] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA; [Zhang, Yingchen] Natl Renewable Energy Lab, Golden, CO 80401 USA; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark University of Electronic Science & Technology of China; Mississippi State University; United States Department of Energy (DOE); National Renewable Energy Laboratory - USA; Aalborg University Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China. caodi@std.uestc.edu.cn; junbo@ece.msstate.edu; whu@uestc.edu.cn; yingchen.zhang@nrel.gov; liaoqishu@std.uestc.edu.cn; zch@et.aau.dk; fbl@et.aau.dk Zhao, Junbo/R-8341-2019; Blaabjerg, Frede/A-5008-2008 Zhao, Junbo/0000-0002-8498-9666; Hu, Weihao/0000-0002-7019-7289; Chen, Zhe/0000-0002-2919-4481; Blaabjerg, Frede/0000-0001-8311-7412 National Key Research and Development Program of China [2018YFE0127600, TII-21-1652] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2018YFE0127600. Paper no. TII-21-1652. 28 3 3 15 62 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. FEB 2022.0 18.0 2 1142 1153 10.1109/TII.2021.3081531 0.0 12 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering WO6LW Green Published 2023-03-23 WOS:000712564700044 0
J Liu, JY; Liu, XW; Yang, YX; Guo, XF; Kloft, M; He, LZ Liu, Jiyuan; Liu, Xinwang; Yang, Yuexiang; Guo, Xifeng; Kloft, Marius; He, Liangzhong Multiview Subspace Clustering via Co-Training Robust Data Representation IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Kernel; Eigenvalues and eigenfunctions; Clustering algorithms; Redundancy; Machine learning algorithms; Robustness; Dermatology; Eigendecomposition; multiview clustering; robust representation; subspace clustering SPARSE; SEGMENTATION; GRAPH Taking the assumption that data samples are able to be reconstructed with the dictionary formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a consensus reconstruction matrix via exploring complementary information across multiple views. Most of them directly operate on the original data observations without preprocessing, while others operate on the corresponding kernel matrices. However, they both ignore that the collected features may be designed arbitrarily and hard guaranteed to be independent and nonoverlapping. As a result, original data observations and kernel matrices would contain a large number of redundant details. To address this issue, we propose an MSC algorithm that groups samples and removes data redundancy concurrently. In specific, eigendecomposition is employed to obtain the robust data representation of low redundancy for later clustering. By utilizing the two processes into a unified model, clustering results will guide eigendecomposition to generate more discriminative data representation, which, as feedback, helps obtain better clustering results. In addition, an alternate and convergent algorithm is designed to solve the optimization problem. Extensive experiments are conducted on eight benchmarks, and the proposed algorithm outperforms comparative ones in recent literature by a large margin, verifying its superiority. At the same time, its effectiveness, computational efficiency, and robustness to noise are validated experimentally. [Liu, Jiyuan; Liu, Xinwang; Yang, Yuexiang; Guo, Xifeng] Natl Univ Def Technol, Coll Comp Sci, Changsha 410072, Peoples R China; [Kloft, Marius] Tech Univ Kaiserslautern, Dept Comp Sci, D-67653 Kaiserslautern, Germany; [He, Liangzhong] China Mobile Su Zhou Software Technol Co Ltd, Suzhou 215009, Peoples R China National University of Defense Technology - China; University of Kaiserslautern Liu, XW (corresponding author), Natl Univ Def Technol, Coll Comp Sci, Changsha 410072, Peoples R China. liujiyuan13@nudt.edu.cn; xinwangliu@nudt.edu.cn; yyx@nudt.edu.cn; guoxifeng1990@163.com; kloft@cs.unikl.de; heliangzhong@cmss.chinamobile.com LIU, Xinwang/0000-0001-9066-1475; Guo, Xifeng/0000-0002-3556-1516; Liu, Jiyuan/0000-0001-5702-4941 National Key Research and Development Program of China [2020AAA0107100]; National Natural Science Foundation of China [61922088, 61906020, 61773392]; Education Ministry-China Mobile Research Funding [MCM20170404]; German Research Foundation (DFG) [KL 2698/2-1]; Federal Ministry of Science and Education (BMBF) [01IS18051A, 031B0770E] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Education Ministry-China Mobile Research Funding; German Research Foundation (DFG)(German Research Foundation (DFG)); Federal Ministry of Science and Education (BMBF)(Federal Ministry of Education & Research (BMBF)) This work was supported in part by the National Key Research and Development Program of China under Project 2020AAA0107100; in part by the National Natural Science Foundation of China under Project 61922088, Project 61906020, and Project 61773392; in part by the Education Ministry-China Mobile Research Funding under Project MCM20170404; in part by the German Research Foundation (DFG) under Award KL 2698/2-1; and in part by the Federal Ministry of Science and Education (BMBF) under Award 01IS18051A and Award 031B0770E. 52 22 22 6 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. OCT 2022.0 33.0 10 5177 5189 10.1109/TNNLS.2021.3069424 0.0 APR 2021 13 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5C3XB 33835924.0 2023-03-23 WOS:000733512700001 0
J Qu, YQ; Zhu, ZL; Montzka, C; Chai, LN; Liu, SM; Ge, Y; Liu, J; Lu, Z; He, XL; Zheng, J; Han, T Qu, Yuquan; Zhu, Zhongli; Montzka, Carsten; Chai, Linna; Liu, Shaomin; Ge, Yong; Liu, Jin; Lu, Zheng; He, Xinlei; Zheng, Jie; Han, Tian Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China JOURNAL OF HYDROLOGY English Article Soil moisture; Downscaling; Methods comparison; Qinghai-Tibet plateau REGRESSION NEURAL-NETWORKS; LAND-SURFACE TEMPERATURE; L-BAND RADIOMETER; HIGH-RESOLUTION; AMSR-E; RANDOM FOREST; SATELLITE; ALGORITHM; SMOS; RADAR Microwave remote sensing is able to retrieve soil moisture (SM) at an adequate level of accuracy. However, these microwave remotely sensed SM products usually have a spatial resolution of tens of kilometers which cannot satisfy the requirements of fine to medium scale applications such as agricultural irrigation and local water resource management. Several SM downscaling methods have been proposed to solve this mismatch by downscaling the coarse-scale SM to fine-scale (several kilometers or hundreds of meters). Although studies have been conducted over different climatic zones and from different data sets with good results, there is still a lack of a comprehensive comparison and evaluation between them to guide the production of high-resolution and high-accuracy SM data. Therefore, in this study we compared several SM downscaling methods (from 0.25 degrees to 0.01 degrees) based on polynormal fitting, physical model, machine learning and geostatistics over the Qinghai-Tibet plateau where there is a wide range of climate conditions from four aspects, that is, comparison with the original microwave product, comparison with in situ measurements, inter-comparison based on three-cornered hat (TCH) method, and a spatial feasibility analysis. The comparison results show that the method based on a physical model, in this case the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) method, has the highest ability on preserving the coarse-scale feature of original microwave SM product, while to some extent, this ability could be a disadvantage for improving the accuracy of the downscaling results. In addition, soil evaporation efficiency (SEE) alone is not sufficient to represent SM spatial patterns over complex land surface. Geostatistics based area-to-area regression Kriging (ATARK) introduces the highest uncertainty caused by the overcorrection during the residual interpolation process while this process can also improve correlation (R) and correct the bias as well as provide more feasible spatial patterns and details. Two machine learning methods, the random forest (RF) and Gaussian process regression (GPR) show high stability on all comparison results but provide smoother spatial patterns. The multivariate statistical regression (MSR) method performs worst due to the fact that its simple linear regression model could not meet the requirement of SM fitting on complicated land surface. Moreover, all five downscaling methods show a declining accuracy after downscaling. This phenomenon may be caused by the spatial mismatch on fine-scale. In addition, this could also be caused by the tendency that downscaled results will usually provide more spatial details from downscaling predictors, while they cannot capture the temporal changes of the microwave SM product well. In general, this phenomenon tends to be more significant over heterogeneous land surface. All in all, five widely used soil moisture downscaling methods were compared based on a comprehensive comparison scheme to add to the body of knowledge in applicability of downcaling methods under different weather conditions. [Qu, Yuquan; Zhu, Zhongli; Chai, Linna; Liu, Shaomin; Liu, Jin; Lu, Zheng; He, Xinlei; Zheng, Jie] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; [Qu, Yuquan; Montzka, Carsten] Forschungszentrum Julich, Inst Bio & Geosci Agrosphere IBG 3, D-52428 Julich, Germany; [Ge, Yong] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Ge, Yong; Han, Tian] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Han, Tian] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lab Remote Sensing & Geospatial Sci, Lanzhou 730000, Gansu, Peoples R China Beijing Normal University; Helmholtz Association; Research Center Julich; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences Zhu, ZL (corresponding author), Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China. zhuzl@bnu.edu.cn XU, TONGREN/0000-0002-1744-8974; Liu, Shaomin/0000-0003-3627-9288; Qu, Yuquan/0000-0002-5470-7840; Lu, Zheng/0000-0002-0287-5826 National Natural Science Foundation of China [41671336]; Strategic Priority Research Program of Chinese Academy of Sciences [XDA20100101]; State Key Laboratory of Earth Surface Processes and Resource Ecology [2017-FX04] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); State Key Laboratory of Earth Surface Processes and Resource Ecology This study was supported by the National Natural Science Foundation of China (41671336), Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100101) and State Key Laboratory of Earth Surface Processes and Resource Ecology (2017-FX04). Meanwhile, we appreciate the Qinghai-Tibet plateau Monitoring Network, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences and HiWATER for providing soil moisture in situ data sets. 93 18 18 11 57 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. JAN 2021.0 592.0 125616 10.1016/j.jhydrol.2020.125616 0.0 21 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources RM7NE 2023-03-23 WOS:000639844900018 0
J Tu, YD; Xu, Y; Li, JZ; Hao, QY; Liu, XS; Qi, DY; Bao, CX; He, TC; Gao, F; Zhang, WJ Tu, Yudi; Xu, Yan; Li, Junzi; Hao, Qiaoyan; Liu, Xiaosong; Qi, Dianyu; Bao, Chunxiong; He, Tingchao; Gao, Feng; Zhang, Wenjing Ultrathin Single-Crystalline 2D Perovskite Photoconductor for High-Performance Narrowband and Wide Linear Dynamic Range Photodetection SMALL English Article 2D materials; 2D perovskites; linear dynamic range; narrowband; photodetectors GRAPHENE For next-generation Internet-of-Everything applications, for example, artificial-neural-network image sensors, artificial retina, visible light communication, on-chip light interconnection, and flexible devices, etc., high-performance microscale photodetectors are in urgent demands. 2D material (2DM) photodetectors have been researched and demonstrated impressive performances. However, they have not met the demands in filterless narrowband photoresponse, wide linear dynamic range (LDR), ultralow dark current, and large on/off ratio, which are key performances for these applications. 2D Ruddlesden-Popper perovskites (2D-RPPs) are recently highlighted photovoltaic and optoelectronic materials. Embedding ultrathin 2D-RPPs into 2DM photodetectors holds potentials to improve these performances. Herein, a single-crystalline ultrathin (PEA)(2)PbI4 is integrated into a vertical-stacked graphene-(PEA)(2)PbI4-graphene micro photoconductor (V-PEPI-PC). V-PEPI-PC exhibits narrowband photoresponses at 517 nm with a full-width-at-half-maximum of 15 nm and a wide LDR of 122 dB. Due to the multiple quantum wells in (PEA)(2)PbI4, V-PEPI-PC demonstrates an ultralow dark current of 1.1 x 10(-14) A (44 pA mm(-2)), a high specific detectivity of 1.2 x 10(13) Jones, and a high on/off ratio of 1.6 x 10(6). Owing to the short vertical channel, V-PEPI-PC shows a fast response rise time of 486 mu s. Therefore, the vertical-stacked photodetectors based on hybrid 2D-RPPs and 2DMs may have great potentials in future optoelectronics. [Tu, Yudi; Xu, Yan; Li, Junzi; Hao, Qiaoyan; Liu, Xiaosong; Qi, Dianyu; Bao, Chunxiong; Zhang, Wenjing] Shenzhen Univ, Inst Microscale Optoelect, Int Collaborat Lab 2D Mat Optoelect Sci & Technol, Minist Educ, Shenzhen 518060, Peoples R China; [Xu, Yan; Bao, Chunxiong; Gao, Feng] Linkoping Univ, Dept Phys Chem & Biol IFM, SE-58183 Linkoping, Sweden; [He, Tingchao] Shenzhen Univ, Coll Phys & Optoelect Engn, Shenzhen 518060, Peoples R China Shenzhen University; Linkoping University; Shenzhen University Zhang, WJ (corresponding author), Shenzhen Univ, Inst Microscale Optoelect, Int Collaborat Lab 2D Mat Optoelect Sci & Technol, Minist Educ, Shenzhen 518060, Peoples R China. wjzhang@szu.edu.cn Dianyu, Qi/GAA-5332-2022; Gao, Feng/AAF-6971-2019; TU, YUDI/GPG-2746-2022; He, Tingchao/L-1990-2018 Gao, Feng/0000-0002-2582-1740; He, Tingchao/0000-0003-1040-0596; Tu, Yudi/0000-0003-2340-713X China Postdoctoral Science Foundation [2019M653016]; Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20190808142603561]; 1000 Talents Program for Young Scientists of China, Shenzhen Peacock Plan [KQTD2016053112042971]; Educational Commission of Guangdong Province [2016KCXTD006] China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Science, Technology and Innovation Commission of Shenzhen Municipality; 1000 Talents Program for Young Scientists of China, Shenzhen Peacock Plan; Educational Commission of Guangdong Province This work was supported by China Postdoctoral Science Foundation (Grant No. 2019M653016), Science, Technology and Innovation Commission of Shenzhen Municipality (Grant No. JCYJ20190808142603561), the 1000 Talents Program for Young Scientists of China, Shenzhen Peacock Plan (Grant No. KQTD2016053112042971), the Educational Commission of Guangdong Province (Grant No. 2016KCXTD006). The authors sincerely appreciate Prof. Takashi Ichii in Kyoto University for the suggestion in making the transimpedance amplifier. 46 14 14 15 150 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1613-6810 1613-6829 SMALL Small DEC 2020.0 16.0 52 2005626 10.1002/smll.202005626 0.0 NOV 2020 8 Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Science & Technology - Other Topics; Materials Science; Physics PM2TQ 33283445.0 2023-03-23 WOS:000594736600001 0
J Deng, JW; Li, YX; Sun, YY; Lei, R; Yang, TA Deng, Jianwei; Li, Yaxin; Sun, Yangyang; Lei, Run; Yang, Tianan Public service motivation as a mediator of the relationship between job stress and presenteeism: a cross-sectional study from Chinese public hospitals BMC HEALTH SERVICES RESEARCH English Article Challenge stress; Hindrance stress; PSM; Presenteeism; Healthcare service; Big data SICKNESS ABSENCE; WORK; CHALLENGE; HINDRANCE; HEALTH; PERFORMANCE; SATISFACTION; EMPLOYEES; BEHAVIOR; SUPPORT Background Job stress is a strong indicator of presenteeism, but few studies have examined its diverse effects and mediators on presenteeism. This study explored the relationships between job stress, public service motivation (PSM) and presenteeism and how job stress and PSM influence presenteeism in a large national sample of Chinese healthcare workers. Methods A cross-sectional survey including 1392 healthcare workers from 11 Class A tertiary hospitals in eastern, central and western China was used in the analysis. Descriptive statistical analysis, correlation analysis and structural equation modeling were used to test the research hypothesis. Results Hindrance stress was inversely associated with PSM (beta = - 0.27; P < 0.001) but significantly positively associated with presenteeism (beta = 0.35; P < 0.001). PSM was directly inversely associated with presenteeism (beta = - 0.35; P < 0.001). PSM partially mediated the relation of hindrance stress with presenteeism. Conclusions The findings suggest that efforts to prevent presenteeism among healthcare workers in China should emphasize PSM improvement and reduction of hindrance stress. [Deng, Jianwei; Li, Yaxin; Sun, Yangyang; Lei, Run; Yang, Tianan] Beijing Inst Technol, Sch Management & Econ, 5 South Zhongguancun St, Beijing 100081, Peoples R China; [Deng, Jianwei; Yang, Tianan] Sustainable Dev Res Inst Econ & Soc Beijing, 5 South Zhongguancun St, Beijing 100081, Peoples R China; [Yang, Tianan] Tech Univ Munich, Sch Management, Chair Sport & Hlth Management, Uptown Munich Campus D,Georg Brauchle Ring 60-62, D-80992 Munich, Germany Beijing Institute of Technology; Technical University of Munich Yang, TA (corresponding author), Beijing Inst Technol, Sch Management & Econ, 5 South Zhongguancun St, Beijing 100081, Peoples R China.;Yang, TA (corresponding author), Sustainable Dev Res Inst Econ & Soc Beijing, 5 South Zhongguancun St, Beijing 100081, Peoples R China.;Yang, TA (corresponding author), Tech Univ Munich, Sch Management, Chair Sport & Hlth Management, Uptown Munich Campus D,Georg Brauchle Ring 60-62, D-80992 Munich, Germany. yangtianan@gmail.com Yang, Tianan/P-6973-2019 Yang, Tianan/0000-0001-6994-354X National Science Funding of China [71804009, 71974011, 91746116, 71972012, 71603018]; Beijing Social Science Foundation [17GLC043, 17JDGLB008]; MOE Project of Humanities and Social Sciences [16YJC630017]; Special Plan for Basic Research of Beijing Institute of Technology [20192142002]; Special Fund for Joint Development Program of Beijing Municipal Commission of Education National Science Funding of China; Beijing Social Science Foundation; MOE Project of Humanities and Social Sciences; Special Plan for Basic Research of Beijing Institute of Technology; Special Fund for Joint Development Program of Beijing Municipal Commission of Education This study was supported by National Science Funding of China (grant no. 71804009, 71974011, 91746116, 71972012, 71603018), Beijing Social Science Foundation (grant no. 17GLC043, 17JDGLB008), MOE Project of Humanities and Social Sciences (grant no. 16YJC630017), Special Plan for Basic Research of Beijing Institute of Technology (grant no. 20192142002), Special Fund for Joint Development Program of Beijing Municipal Commission of Education. The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. 51 8 9 9 37 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1472-6963 BMC HEALTH SERV RES BMC Health Serv. Res. SEP 3 2019.0 19.0 1 625 10.1186/s12913-019-4483-5 0.0 8 Health Care Sciences & Services Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Health Care Sciences & Services IU7PU 31481038.0 gold, Green Published 2023-03-23 WOS:000483776600003 0
J Luo, J; Xue, R; Hu, JL; El Baz, D Luo, Jia; Xue, Rui; Hu, Jinglu; El Baz, Didier Combating the Infodemic: A Chinese Infodemic Dataset for Misinformation Identification HEALTHCARE English Article COVID-19; infodemic data; misinformation identification; deep learning Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually. Therefore, instead of developing techniques for infodemic detection, this paper aims at constructing a Chinese infodemic dataset, infodemic 2019, by collecting widely spread Chinese infodemic during the COVID-19 outbreak. Each record is labeled as true, false or questionable. After a four-time adjustment, the original imbalanced dataset is converted into a balanced dataset by exploring the properties of the collected records. The final labels achieve high intercoder reliability with healthcare workers' annotations and the high-frequency words show a strong relationship between the proposed dataset and pandemic diseases. Finally, numerical experiments are carried out with RNN, CNN and fastText. All of them achieve reasonable performance and present baselines for future works. [Luo, Jia; Xue, Rui] Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China; [Luo, Jia; Hu, Jinglu] Waseda Univ, Grad Sch Informat Prod & Syst, Kitakyushu, Fukuoka 8080135, Japan; [El Baz, Didier] Univ Toulouse, CNRS, LAAS CNRS, F-31031 Toulouse, France Beijing University of Technology; Waseda University; Centre National de la Recherche Scientifique (CNRS); Universite de Toulouse Xue, R (corresponding author), Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China. luo.jia.621125@gmail.com; ruixue.bjut@foxmail.com; jinglu@waseda.jp; elbaz@laas.fr EL BAZ, Didier/0000-0003-0427-0692; Luo, Jia/0000-0003-3914-8865 Beijing Municipal Education Commission [SM202110005011, SM202010005004]; International Research Cooperation Seed Fund of Beijing University of Technology [B38]; Japan Society for the Promotion of Science [P19800] Beijing Municipal Education Commission(Beijing Municipal Commission of Education); International Research Cooperation Seed Fund of Beijing University of Technology; Japan Society for the Promotion of Science(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science) This work is supported by the Beijing Municipal Education Commission (Grant No.SM202110005011 and Grant No.SM202010005004), the International Research Cooperation Seed Fund of Beijing University of Technology (Grant No. B38) and the Japan Society for the Promotion of Science (Grant No. P19800). 29 3 3 21 81 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-9032 HEALTHCARE-BASEL Healthcare SEP 2021.0 9.0 9 1094 10.3390/healthcare9091094 0.0 13 Health Care Sciences & Services; Health Policy & Services Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Health Care Sciences & Services UX2MD 34574868.0 gold, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000700678800001 0
J Hu, DL; Zhang, YK; Liu, J; Du, CP; Zhang, JL; Luo, SH; Quan, GT; Liu, QG; Chen, Y; Luo, LM Hu, Dianlin; Zhang, Yikun; Liu, Jin; Du, Changping; Zhang, Jinglin; Luo, Shouhua; Quan, Guotao; Liu, Qiegen; Chen, Yang; Luo, Limin SPECIAL: Single-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING English Article Limited-angle CT reconstruction; measurement consistency; enhanced adversarial learning; high-frequency constraint; projection correction LOW-DOSE CT; CONVOLUTIONAL NEURAL-NETWORK; IMAGE-RECONSTRUCTION; DOMAIN; NET; TOMOGRAPHY; CNN Limited-angle CT is an indispensable tool for some practical applications when the projection data can be only collected within a limited-angle range due to the constraints of scanning conditions. However, the limited-angle scanning mode will lead to severely degraded images with excessive artifacts. Meanwhile, existing methods fail to reconstruct satisfactory images in limited-angle CT because of the unguaranteed measurement consistency caused by serious projection missing. In this paper, we developed a method termed Single-shot Projection Error Correction Integrated Adversarial Learning (SPECIAL) progressive-improvement strategy, which could effectively combine the complementary information contained in the image domain and projection domain, and greatly improve the reconstructions at the expense of small computational cost. Specifically, enhanced adversarial learning is used in different stages to remove artifacts without losing high-frequency component. A projection error correction module is used to boost the performance in high-attenuation tissue restoration. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method could make a promising improvement on artifact removal, edge preservation and tiny structure restoration. [Hu, Dianlin; Zhang, Yikun; Du, Changping; Luo, Limin] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Hu, Dianlin; Zhang, Yikun; Du, Changping; Luo, Limin] Ctr Rech Informat Biomed Sino Francais LIA CRIBs, F-3500 Rennes, France; [Hu, Dianlin; Zhang, Yikun; Du, Changping; Chen, Yang; Luo, Limin] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China; [Liu, Jin] Anhui Polytech Univ, Coll Comp & Informat, Wuhu 241000, Peoples R China; [Zhang, Jinglin] Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300131, Peoples R China; [Luo, Shouhua] Southeast Univ, Dept Biomed Engn, Nanjing 210096, Peoples R China; [Quan, Guotao] United Imaging Healthcare Co Ltd, CT RPA Dept, Shanghai 201807, Peoples R China; [Liu, Qiegen] Nanchang Univ, Dept Elect Informat Engn ing, Nanchang 330031, Jiangxi, Peoples R China; [Chen, Yang] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Peoples R China; [Chen, Yang] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China Southeast University - China; Universite de Rennes; Southeast University - China; Anhui Polytechnic University; Hebei University of Technology; Southeast University - China; Nanchang University; Southeast University - China; Southeast University - China Chen, Y (corresponding author), Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Peoples R China.;Chen, Y (corresponding author), Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China. dianlin@seu.edu.cn; yikun@seu.edu.cn; liujin@ahpu.edu.cn; 220191716@seu.edu.cn; jinglin.zhang37@gmail.com; luoshouhua@seu.edu.cn; guotao.quan@united-imaging.com; liuqiegen@ncu.edu.cn; chenyang.list@seu.edu.cn; luo.list@seu.edu.cn Yikun, Zhang/HNI-6209-2023 Zhang, Yikun/0000-0002-4048-4869; quan, guotao/0000-0002-1079-0718; Liu, Jin/0000-0003-1884-671X State's Key Project of Research and Development Plan [2017YFC0109202, 2017YFA0104302]; National Natural Science Foundation [61871117]; Science and Technology Program of Guangdong [2018B030333001] State's Key Project of Research and Development Plan; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Guangdong Manuscript received December 7, 2020; revised April 27, 2021 and June 28, 2021; accepted July 12, 2021. Date of publication July 26, 2021; date of current version August 4, 2021. This work was supported in part by the State's Key Project of Research and Development Plan under Grants 2017YFC0109202 and 2017YFA0104302, in part by the National Natural Science Foundation under Grant 61871117, in part by the Science and Technology Program of Guangdong under Grant 2018B030333001. (Corresponding authors: Qiegen Liu; Yang Chen.) 54 10 10 7 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2573-0436 2333-9403 IEEE T COMPUT IMAG IEEE Trans. Comput. Imaging 2021.0 7.0 734 746 10.1109/TCI.2021.3098922 0.0 13 Engineering, Electrical & Electronic; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Imaging Science & Photographic Technology TU6DI 2023-03-23 WOS:000681124400002 0
J Rahman, MA; Rashid, MM; Le Kernec, J; Philippe, B; Barnes, SJ; Fioranelli, F; Yang, SF; Romain, O; Abbasi, QH; Loukas, G; Imran, M Abdur Rahman, Md.; Rashid, Md. Mamunur; Le Kernec, Julien; Philippe, Bruno; Barnes, Stuart J.; Fioranelli, Francesco; Yang, Shufan; Romain, Olivier; Abbasi, Qammer H.; Loukas, George; Imran, Muhammad A Secure Occupational Therapy Framework for Monitoring Cancer Patients' Quality of Life SENSORS English Article cyber-physical occupational therapy system; distributed medical big data; health IoT sensors; therapeutic kinematic data; quality of life LUNG-CANCER; MOTOR IMAGERY; PALLIATIVE CARE; SMART CONTRACTS; ACCESS-CONTROL; FOLLOW-UP; BLOCKCHAIN; OUTCOMES; IMPLEMENTATION; INFORMATION Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as after cancer treatment. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient's situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one's home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development. [Abdur Rahman, Md.] Univ Prince Mugrin, Dept Cyber Secur & Forens Comp, Coll Comp & Cyber Sci C3S, Madinah 41499, Saudi Arabia; [Rashid, Md. Mamunur; Barnes, Stuart J.] Kings Coll London, Kings Business Sch, Consumer & Org Digital Analyt CODA Res Ctr, London WC2B 4BG, England; [Le Kernec, Julien; Fioranelli, Francesco; Yang, Shufan; Abbasi, Qammer H.; Imran, Muhammad] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Le Kernec, Julien; Romain, Olivier] Univ Cergy Pontoise, Univ Paris Seine, Lab ETIS, ENSEA,CNRS,UMR8051, F-95000 Paris, France; [Le Kernec, Julien] Univ Elect Sci & Technol China, Sch Informat & Commun, Chengdu 610000, Peoples R China; [Philippe, Bruno] Rene Dubos Hosp, Dept Pneumol, F-95300 Pontoise, France; [Loukas, George] Univ Greenwich, Comp & Math Sci, London SE1 09LS, England University of Prince Mugrin; University of London; King's College London; University of Glasgow; Centre National de la Recherche Scientifique (CNRS); CY Cergy Paris Universite; University of Electronic Science & Technology of China; Centre Hospitalier Rene Dubos, Pontoise; University of Greenwich Rahman, MA (corresponding author), Univ Prince Mugrin, Dept Cyber Secur & Forens Comp, Coll Comp & Cyber Sci C3S, Madinah 41499, Saudi Arabia. m.arahman@upm.edu.sa; mamun.rashid@kcl.ac.uk; Julien.LeKernec@glasgow.ac.uk; bruno.philippe@ght-novo.fr; stuart.barnes@kcl.ac.uk; Francesco.Fioranelli@glasgow.ac.uk; Shufan.Yang@glasgow.ac.uk; olivier.romain@u-cergy.fr; Qammer.Abbasi@glasgow.ac.uk; G.Loukas@greenwich.ac.uk; Muhammad.Imran@glasgow.ac.uk Fioranelli, Francesco/Y-8612-2019; Loukas, George/AAC-4291-2021; Yang, Shufan/AAI-6707-2021; Abbasi, Qammer H./I-8644-2019; Rahman, Abdur/AAG-9302-2019; Imran, Muhammad Ali/I-4832-2012; Yang, Shufan/AAF-7942-2021 Loukas, George/0000-0003-3559-5182; Abbasi, Qammer H./0000-0002-7097-9969; Rahman, Abdur/0000-0002-4105-0368; Imran, Muhammad Ali/0000-0003-4743-9136; Yang, Shufan/0000-0003-0531-2903; Rashid, Md Mamunur/0000-0001-6249-1181; romain, olivier/0000-0002-2172-1865; le kernec, julien/0000-0003-2124-6803; Fioranelli, Francesco/0000-0001-8254-8093 Consumer and Organisational Digital Analytics (CODA) Research Centre, King's Business School, King's College London; EPSRC [EP/R041679/1] Funding Source: UKRI Consumer and Organisational Digital Analytics (CODA) Research Centre, King's Business School, King's College London; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This research was funded by Consumer and Organisational Digital Analytics (CODA) Research Centre, King's Business School, King's College London. 107 9 9 1 16 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors DEC 2019.0 19.0 23 5258 10.3390/s19235258 0.0 30 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Chemistry; Engineering; Instruments & Instrumentation KD1BI 31795384.0 Green Accepted, gold, Green Published, Green Submitted 2023-03-23 WOS:000507606200201 0
J Hu, JF; Huang, TZ; Deng, LJ; Jiang, TX; Vivone, G; Chanussot, J Hu, Jin-Fan; Huang, Ting-Zhu; Deng, Liang-Jian; Jiang, Tai-Xiang; Vivone, Gemine; Chanussot, Jocelyn Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Superresolution; Tensors; Spatial resolution; Hyperspectral imaging; Computer architecture; Training; Learning systems; Attention module (AM); deep convolutional neural network (CNN); hyperspectral image (HSI) super-resolution; image fusion; pixelShuffle (PS) PAN-SHARPENING METHOD; LOW-RANK; VARIATIONAL APPROACH; FUSION; INJECTION; DECOMPOSITION; REGRESSION Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI's scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html. [Hu, Jin-Fan; Huang, Ting-Zhu; Deng, Liang-Jian] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China; [Jiang, Tai-Xiang] Southwestern Univ Finance & Econ, FinTech Innovat Ctr, Sch Econ Informat Engn, Financial Intelligence & Financial Engn Res Key L, Chengdu 610074, Peoples R China; [Vivone, Gemine] CNR, CNR IMAA, Inst Methodol Environm Anal, I-85050 Tito, Italy; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, CNRS, Inria,LJK, F-38000 Grenoble, France University of Electronic Science & Technology of China; Southwestern University of Finance & Economics - China; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR); Chinese Academy of Sciences; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria Huang, TZ; Deng, LJ (corresponding author), Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China. hujf0206@163.com; tingzhuhuang@126.com; liangjian.deng@uestc.edu.cn; taixiangjiang@gmail.com; gvivone@unisa.it; jocelyn.chanussot@grenoble-inp.fr VIVONE, Gemine/ABE-5121-2020; huang, ting/GRR-3141-2022 VIVONE, Gemine/0000-0001-9542-0638; Hu, Jinfan/0000-0003-3969-0945; Huang, Ting-Zhu/0000-0001-7766-230X; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [61772003, 61702083, 12001446, 61876203]; National Key Research and Development Program of China [2020YFA0714001]; Key Projects of Applied Basic Research in Sichuan Province [2020YJ0216]; Fundamental Research Funds for the Central Universities [JBK2102001]; MIAI@Grenoble Alpes [ANR19-P3IA-0003] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Key Projects of Applied Basic Research in Sichuan Province; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); MIAI@Grenoble Alpes This work was supported in part by the National Natural Science Foundation of China under Grant 61772003, Grant 61702083, Grant 12001446, and Grant 61876203; in part by the National Key Research and Development Program of China under Grant 2020YFA0714001; in part by the Key Projects of Applied Basic Research in Sichuan Province under Grant 2020YJ0216; and in part by the Fundamental Research Funds for the Central Universities under Grant JBK2102001; and in part by MIAI@Grenoble Alpes under Grant ANR19-P3IA-0003. 81 31 31 13 43 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. DEC 2022.0 33.0 12 7251 7265 10.1109/TNNLS.2021.3084682 0.0 JUN 2021 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 6S5MN 34106864.0 2023-03-23 WOS:000733542900001 0
J Liao, ZY; Couillet, R; Mahoney, MW Liao, Zhenyu; Couillet, Romain; Mahoney, Michael W. A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent* JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT English Article random matrix theory and extensions; analysis of algorithms; learning theory; deep learning STATISTICAL-MECHANICS; REGRESSION This article characterizes the exact asymptotics of random Fourier feature (RFF) regression, in the realistic setting where the number of data samples n, their dimension p, and the dimension of feature space N are all large and comparable. In this regime, the random RFF Gram matrix no longer converges to the well-known limiting Gaussian kernel matrix (as it does when N -> infinity alone), but it still has a tractable behavior that is captured by our analysis. This analysis also provides accurate estimates of training and test regression errors for large n, p, N. Based on these estimates, a precise characterization of two qualitatively different phases of learning, including the phase transition between them, is provided; and the corresponding double descent test error curve is derived from this phase transition behavior. These results do not depend on strong assumptions on the data distribution, and they perfectly match empirical results on real-world data sets. [Liao, Zhenyu] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan, Peoples R China; [Couillet, Romain] Univ Grenoble Alpes, CNRS, INRIA, Grenoble INP,LIG, F-38000 Grenoble, France; [Mahoney, Michael W.] Univ Calif Berkeley, ICSI, Berkeley, CA 94720 USA; [Mahoney, Michael W.] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA Huazhong University of Science & Technology; Inria; UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); University of California System; University of California Berkeley; University of California System; University of California Berkeley Liao, ZY (corresponding author), Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan, Peoples R China. zhenyu_liao@hust.edu.cn Liao, Zhenyu/AES-7133-2022 LIAO, Zhenyu/0000-0002-1915-8559 Fundamental Research Funds for the Central Universities of China [2021XXJS110]; CCF-Hikvision Open Fund [20210008]; MIAI LargeDATA chair at University Grenoble-Alpes [ANR-19-P3IA-0003]; HUAWEI LarDist project; DARPA; IARPA [W911NF20C0035]; NSF; ONR via its BRC on RandNLA Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); CCF-Hikvision Open Fund; MIAI LargeDATA chair at University Grenoble-Alpes; HUAWEI LarDist project(Huawei Technologies); DARPA(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); IARPA; NSF(National Science Foundation (NSF)); ONR via its BRC on RandNLA Z L would like to acknowledge the Fundamental Research Funds for the Central Universities of China (No. 2021XXJS110) and CCF-Hikvision Open Fund (20210008) for providing partial support of this work. R C would like to acknowledge the MIAI LargeDATA chair (ANR-19-P3IA-0003) at University Grenoble-Alpes as well as the HUAWEI LarDist project for providing partial support of this work. M W M would like to acknowledge DARPA, IARPA (Contract W911NF20C0035), NSF, and ONR via its BRC on RandNLA for providing partial support of this work. Our conclusions do not necessarily reflect the position or the policy of our sponsors, and no official endorsement should be inferred. 52 0 0 1 3 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1742-5468 J STAT MECH-THEORY E J. Stat. Mech.-Theory Exp. DEC 2021.0 2021.0 12 124006 10.1088/1742-5468/ac3a77 0.0 38 Mechanics; Physics, Mathematical Science Citation Index Expanded (SCI-EXPANDED) Mechanics; Physics XW4UD Green Submitted 2023-03-23 WOS:000735615400001 0
J Ma, S; Zeng, SY; Dong, X; Chen, JN; Olsson, G Ma, Shuai; Zeng, Siyu; Dong, Xin; Chen, Jining; Olsson, Gustaf Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING English Article influent load prediction; wavelet de-noising; power spectrum density; autoregressive model; time-frequency analysis; wastewater treatment ACTIVATED-SLUDGE PROCESS; CONTROL STRATEGIES; NITROGEN REMOVAL; MODEL SELECTION; NEURAL-NETWORK; PERSPECTIVE; SYSTEMS; ICA The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia (NH3) concentrations: 1) data filtering using wavelet decomposition and reconstruction; 2) typical cycle identification using power spectrum density analysis; 3) fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1) for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles (especially cycles longer than 1 day); 2) when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3) the periodicity influence is larger than rainfalls; 4) the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods. [Ma, Shuai; Zeng, Siyu; Dong, Xin; Chen, Jining] Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100084, Peoples R China; [Olsson, Gustaf] Lund Univ, Dept Ind Elect Engn & Automat, SE-22100 Lund, Sweden Tsinghua University; Lund University Chen, JN (corresponding author), Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100084, Peoples R China. jchen1@tsinghua.edu.cn project Technologies Development and Demonstration for Pollution Load Reduction and Water Quality Improvement [2011ZX07302-001]; Ministry of Science and Technology, China project Technologies Development and Demonstration for Pollution Load Reduction and Water Quality Improvement; Ministry of Science and Technology, China(Ministry of Science and Technology, China) This paper was supported by the project Technologies Development and Demonstration for Pollution Load Reduction and Water Quality Improvement (2011ZX07302-001) funded by the Ministry of Science and Technology, China. 35 5 5 4 50 HIGHER EDUCATION PRESS BEIJING NO 4 DEWAI DAJIE, BEIJING 100120, PEOPLES R CHINA 2095-2201 2095-221X FRONT ENV SCI ENG Front. Env. Sci. Eng. FEB 2014.0 8.0 1 128 136 10.1007/s11783-013-0598-9 0.0 9 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology 296TT 2023-03-23 WOS:000330209000013 0
J Du, L; Zhang, Y; Chen, Y; Zhu, J; Yang, Y; Zhang, HL Du, Li; Zhang, Ying; Chen, Yang; Zhu, Jie; Yang, Yi; Zhang, Hong-Liang Role of Microglia in Neurological Disorders and Their Potentials as a Therapeutic Target MOLECULAR NEUROBIOLOGY English Review Microglia; Neurodegenerative disorders; M1/M2 polarization; Inflammation; Microglial activation EXPERIMENTAL AUTOIMMUNE ENCEPHALOMYELITIS; AMYOTROPHIC-LATERAL-SCLEROSIS; CENTRAL-NERVOUS-SYSTEM; NF-KAPPA-B; 1-METHYL-4-PHENYL-1,2,3,6-TETRAHYDROPYRIDINE MOUSE MODEL; RESIDENT MACROPHAGES ORIGINATE; AGGREGATED ALPHA-SYNUCLEIN; GENE-EXPRESSION SIGNATURE; MESENCHYMAL STEM-CELLS; ALZHEIMERS-DISEASE Microglia are resident macrophage-like immune cells in the central nervous system (CNS) and play a vital role in both physiological and pathological conditions, including restoring the integrity of the CNS and promoting the progression of neurodegenerative disorders. Upon stimulation, microglia typically convert from a surveillant to an activated phenotype. The major function of microglia is to maintain homeostasis and normal function of the CNS, both during development and in response to CNS injury. Microglia regulate multiple aspects of inflammation, such as repair, cytotoxicity, regeneration, and immunosuppression due to their different kind of activation states or phenotypes. Although microglia are involved in almost all neurodegenerative disorders, the mechanisms for microglial activation and their potential contributions to neuronal degeneration remain a matter of intense debate. In inflammatory process of the CNS, polarized M1 microglia can produce proinflammatory cytokines, neurotoxic molecules, which contribute to dysfunction of neural network and promoting inflammation reaction, whereas polarized M2 microglia secrete antiinflammatory mediators and neurotrophic factors that are involved in restoring homeostasis. Modulation of microglial activation for therapeutic purposes might be realized via suppressing the deleterious effects of these cells, while simultaneously retaining their protective functions. Here, we summarize the functions of microglia and discuss dual role of microglia in neurodegenerative diseases as well as multiple sclerosis. [Du, Li; Zhang, Ying; Chen, Yang; Zhu, Jie; Yang, Yi; Zhang, Hong-Liang] Jilin Univ, Hosp 1, Dept Neurol, Neurosci Ctr, Xinmin St 71, Changchun 130021, Jilin, Peoples R China; [Zhu, Jie] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden; [Zhang, Hong-Liang] Natl Nat Sci Fdn China, Dept Life Sci, Beijing, Peoples R China Jilin University; Karolinska Institutet; National Natural Science Foundation of China (NSFC) Yang, Y; Zhang, HL (corresponding author), Jilin Univ, Hosp 1, Dept Neurol, Neurosci Ctr, Xinmin St 71, Changchun 130021, Jilin, Peoples R China.;Zhang, HL (corresponding author), Natl Nat Sci Fdn China, Dept Life Sci, Beijing, Peoples R China. doctoryangyi@163.com; drzhl@hotmail.com Zhang, Hongliang/AAN-3077-2020 Zhang, Hongliang/0000-0001-9205-5559; Du, Li/0000-0002-2916-1014 223 79 81 2 51 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0893-7648 1559-1182 MOL NEUROBIOL Mol. Neurobiol. DEC 2017.0 54.0 10 7567 7584 10.1007/s12035-016-0245-0 0.0 18 Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology FM8MF 27830532.0 2023-03-23 WOS:000415341900002 0
J Li, XY; He, Y; Fioranelli, F; Jing, XJ Li, Xinyu; He, Yuan; Fioranelli, Francesco; Jing, Xiaojun Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Radar; Semantics; Feature extraction; Training; Spectrogram; Data models; Activity recognition; Human activity recognition (HAR); radar micro-Doppler (MD) effect; semisupervised learning; transfer learning (TL) HUMAN-MOTION RECOGNITION; DOMAIN ADAPTATION; FALL-DETECTION; CLASSIFICATION Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels. Insufficient labeled data often limit the generalization of DL models. As a result, the performance of DL models will drop when being applied to a new scenario. In this sense, only labeling a small portion of data in the large-scale radar dataset is more feasible. In this article, we propose a semisupervised transfer learning (TL) algorithm, ``joint domain and semantic transfer learning (JDS-TL),'' for radar-based HAR, which is composed of two modules: unsupervised domain adaptation (DA) and supervised semantic transfer. By employing a sparsely labeled dataset to train the HAR model, the proposed method alleviates the need of labeling a significantly large number of radar signals. We adopt a public radar micro-Doppler spectrogram dataset including six human activities to evaluate JDS-TL. Experiments show that the proposed JDS-TL is able to recognize the six activities with an average accuracy of 87.6% when there are only 10% instances labeled in the training dataset. Ablation analysis also demonstrates the efficiency of the DA and the semantic transfer modules. [Li, Xinyu; He, Yuan; Jing, Xiaojun] Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv BUPT, Beijing 100876, Peoples R China; [Fioranelli, Francesco] Delft Univ Technol, Dept Microelect, NL-2628 Delft, Netherlands Beijing University of Posts & Telecommunications; Delft University of Technology He, Y (corresponding author), Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv BUPT, Beijing 100876, Peoples R China. lixinyu@bupt.edu.cn; yuanhe@bupt.edu.cn; f.fioranelli@tudelft.nl; jxiaojun@bupt.edu.cn Fioranelli, Francesco/Y-8612-2019 Fioranelli, Francesco/0000-0001-8254-8093; he, yuan/0000-0001-7578-8515; Li, Xinyu/0000-0002-1919-2327 National Nature Science Foundation of China [61901049]; Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D.; Students Foundation [CX2020208] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D.; Students Foundation This work was supported in part by the National Nature Science Foundation of China under Grant 61901049 and in part by the Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D. Students Foundation under Grant CX2020208. 36 18 18 8 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3090106 0.0 JUN 2021 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YG3HV 2023-03-23 WOS:000733461900001 0
J Li, J; Shi, YQ; Wu, RR; Xie, SD Li, Jing; Shi, Yuqi; Wu, Rongrong; Xie, Shaodong Temperature-Dependent Coalescence of Individual Nonpolar Gold Nanoparticles in Liquid ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS English Article; Early Access ambient VOCs; variations; wintertime; Beijing; air quality improvements VOLATILE ORGANIC-COMPOUNDS; FINE PARTICULATE MATTER; CHINA; EMISSIONS; OZONE; TRENDS; PM2.5; VOC; MODEL Air quality in China significantly improved after strict regulations were put in place. To assess the efficacy of volatile organic compound (VOC) control strategies, online measurements of wintertime ambient VOCs at a Beijing urban location were taken in December and January from 2014 to 2019. We assessed the changes in mixing ratios of 81 VOC species and cancer risks of 10 hazard species. The impact of meteorology on VOC mixing ratios was decoupled using a machine-learning approach. The highest and lowest VOC mixing ratios were observed in wintertime 2017 (December 2016 and January 2017) and 2019 (December 2018 and January 2019), respectively, with a decrease in 63.5%. Both emission control policies and meteorological conditions helped reduce VOC mixing ratios in the winter in Beijing. Alkenes and alkynes decreased significantly from 2015 to 2019. In contrast, the contribution of aromatics increased considerably from 2015 to 2017, and many aromatics had higher mixing ratios in 2019 than in 2015, indicating that aromatics may be a key group for future air quality improvement. The cancer risks due to VOC exposure in the wintertime in Beijing in 2019 were lower than in other years but were still much higher than the safety level. [Li, Jing; Shi, Yuqi; Wu, Rongrong; Xie, Shaodong] Peking Univ, Coll Environm Sci & Engn, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100871, Peoples R China; [Li, Jing] Peking Univ, Sch Publ Hlth, Inst Child & Adolescent Hlth, Beijing 100191, Peoples R China; [Wu, Rongrong] Forschungszentrum Julich, Inst Energy & Climate Res, D-52428 Julich, Germany Peking University; Peking University; Helmholtz Association; Research Center Julich Xie, SD (corresponding author), Peking Univ, Coll Environm Sci & Engn, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100871, Peoples R China. sdxie@pku.edu.cn National Natural Science Foundation of China [91544106] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant 91544106) . 29 0 0 12 12 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2328-8930 ENVIRON SCI TECH LET Environ. Sci. Technol. Lett. 10.1021/acs.estlett.2c00919 0.0 JAN 2023 6 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology 7W1JG 2023-03-23 WOS:000913269900001 0
J Liu, XF; Wang, CC; Meng, Y; Wang, H; Fu, M; Bourennane, S Liu, Xuefeng; Wang, Congcong; Meng, Yue; Wang, Hao; Fu, Min; Bourennane, Salah Target Detection Based on Spectral Derivation in HSI Shadow Region Classified by Convolutional Neural Networks CANADIAN JOURNAL OF REMOTE SENSING English Article AUTOMATED CLOUD; IMAGES; IDENTIFICATION; REMOVAL Because of the low reflection value of the shadow regions in hyperspectral image (HSI), these regions were deleted directly or ignored in target detection or classification. There are some studies on improving the reflectivity of shadow regions, but it is still difficult to determine the actual substances contained in shadow regions. In this paper, an improved target detection method in shadow regions in HSI is proposed by combining the convolution neural network (CNN) and the adaptive cosine consistency estimation (ACE) of the spectral derivative image. Firstly, the derivative image could be obtained by deriving the original HSI in the spectral dimension. The following steps would be performed simultaneously on the original HSI and the derivative image. Next, the shadow regions in HSI could be determined through two-dimensional (2D) CNN model whose main parameters have been adjusted to optimize the network performance. Finally, the substances contained in the shadow regions would be detected by ACE algorithm. The performance of the proposed method was evaluated and analyzed on the real-world HSIs. The experiments show that the spectral derivation can help to improve the target detection and it is worth taking into account the shadow regions for processing HSI data. [Liu, Xuefeng; Wang, Congcong; Meng, Yue; Wang, Hao] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao, Shandong, Peoples R China; [Fu, Min] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China; [Bourennane, Salah] Ecole Cent Marseille, Inst Fresnel, Marseille, France Qingdao University of Science & Technology; Ocean University of China; UDICE-French Research Universities; Aix-Marseille Universite Bourennane, S (corresponding author), Ecole Cent Marseille, Inst Fresnel, Marseille, France. salah.bourennane@fresnel.fr FU, MINGYU/GRS-3707-2022; Fu, Ming/HMD-6061-2023 Fu, Ming/0000-0003-2734-6725; /0000-0001-7919-9486 49 3 3 1 17 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0703-8992 1712-7971 CAN J REMOTE SENS Can. J. Remote Sens. NOV 2 2019.0 45.0 6 782 794 10.1080/07038992.2019.1697221 0.0 DEC 2019 13 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing KH6SQ 2023-03-23 WOS:000503138200001 0
J Dong, SX; Quan, YH; Feng, W; Dauphin, G; Gao, LR; Xing, MD Dong, Shuxian; Quan, Yinghui; Feng, Wei; Dauphin, Gabriel; Gao, Lianru; Xing, Mengdao A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Feature extraction; Training; Clustering algorithms; Data mining; Classification algorithms; Hyperspectral imaging; Support vector machines; Convolutional neural network (CNN); hyperspectral image classification (HSIC); pixel cluster; small training set; spectral-spatial fusion (SSF) FEATURE-EXTRACTION; ROTATION FOREST; ENSEMBLE; NETWORKS Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. To solve this problem, a pixel cluster CNN and spectral-spatial fusion (SSF) algorithm for hyperspectral image classification with small-size training samples is proposed in this article. First, spatial information is extracted by the gray level co-occurrence matrix. Then, spatial information and spectral information are fused by means of bands superposition, forming spectral-spatial features. To expand the number of training samples, the pixels after SSF are combined into pixel clusters according to a certain rule. Finally, a CNN framework is utilized to extract effective features from the pixel clusters. Experiments based on three standard HSIs demonstrate that the proposed algorithm can get better performance than the conventional CNN and also outperforms other studied algorithms in the case of small training set. [Dong, Shuxian; Quan, Yinghui; Feng, Wei] Xidian Univ, Sch Elect Engn, Dept Remote Sensing Sci & Technol, Xian 710071, Peoples R China; [Dauphin, Gabriel] Univ Paris XIII, Inst Galilee, Lab Informat Proc & Transmiss, F-93430 Villetaneuse, France; [Gao, Lianru] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Xing, Mengdao] Xidian Univ, Acad Adv Interdisciplinary Res, Xian 710071, Peoples R China Xidian University; Universite Paris 13; Chinese Academy of Sciences; Xidian University Quan, YH; Feng, W (corresponding author), Xidian Univ, Sch Elect Engn, Dept Remote Sensing Sci & Technol, Xian 710071, Peoples R China. sxdong@stu.xidian.edu.cn; yhquan@mail.xidian.edu.cn; wfeng@xidian.edu.cn; gabriel.dauphin@univ-paris13.fr; gaolr@aircas.ac.cn; xmd@xidian.edu.cn xing, mengdao/A-6449-2019 xing, mengdao/0000-0002-4084-0915; Dauphin, Gabriel/0000-0002-0677-6702 National Natural Science Foundation of China [61772397, 12005169]; National Key R&D Program of China [2016YFE0200400]; Open Research Fund of Key Laboratory of Digital Earth Science [2019LDE005]; Science, and Technology Innovation Team of Shaanxi Province [2019TD-002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Open Research Fund of Key Laboratory of Digital Earth Science; Science, and Technology Innovation Team of Shaanxi Province This work was supported in part by the National Natural Science Foundation of China under Grant 61772397 andGrant 12005169, in part by the National Key R&D Program of China under Grant 2016YFE0200400, in part by the Open Research Fund of Key Laboratory of Digital Earth Science under Grant 2019LDE005, and in part by the Science, and Technology Innovation Team of Shaanxi Province under Grant 2019TD-002. 47 13 13 7 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021.0 14.0 4101 4114 10.1109/JSTARS.2021.3068864 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology RU3XE gold 2023-03-23 WOS:000645081200008 0
J Xia, WS; Han, DZ; Li, D; Wu, ZD; Han, B; Wang, JX Xia, Wanshang; Han, Dezhi; Li, Dun; Wu, Zhongdai; Han, Bing; Wang, Junxiang An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification ANNALS OF APPLIED BIOLOGY English Article; Early Access convolutional neural networks; ensemble learning; insect classification; vision transformer AUTOMATIC CLASSIFICATION; RECOGNITION Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large-scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state-of-the-art methods and has a high practical application value. [Xia, Wanshang; Han, Dezhi; Li, Dun] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; [Li, Dun] Inst Polytech Paris, IMT, Telecom SudParis, Paris, France; [Wu, Zhongdai; Han, Bing] Shanghai Ship & Shipping Res Inst Co Ltd, Shanghai, Peoples R China; [Wu, Zhongdai; Wang, Junxiang] COSCO Shipping Technol Co Ltd, Shanghai, Peoples R China Shanghai Maritime University; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Han, DZ (corresponding author), Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China. dzhan@shmtu.edu.cn 夏, 万尚/HPC-7797-2023 夏, 万尚/0000-0003-0935-4764 National Natural Science Foundation of China [61873160, 61672338]; National Key R&D Program of China [2021YFC2801000]; Natural Science Foundation of Shanghai [21ZR1426500] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai) This work is supported by the National Natural Science Foundation of China under Grants nos. 61873160 and 61672338, National Key R&D Program of China under No. 2021YFC2801000, Natural Science Foundation of Shanghai under Grant No. 21ZR1426500. 42 0 0 9 9 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0003-4746 1744-7348 ANN APPL BIOL Ann. Appl. Biol. 10.1111/aab.12804 0.0 OCT 2022 15 Agriculture, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Agriculture 5C5HH 2023-03-23 WOS:000864290400001 0
J Qiao, SB; Pang, SC; Luo, G; Sun, Y; Yin, WJ; Pan, SL; Lv, ZH Qiao, Sibo; Pang, Shanchen; Luo, Gang; Sun, Yi; Yin, Wenjing; Pan, Silin; Lv, Zhihan DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography COMPLEX & INTELLIGENT SYSTEMS English Article; Early Access Convolutional neural network; Echocardiography; Fetal four chambers; Semantic segmentation; Transformer LEFT-VENTRICLE; AUTOMATIC SEGMENTATION; IMAGE SEGMENTATION; CHALLENGE; UNET Echocardiography is essential in evaluating fetal cardiac anatomical structures and functions when clinicians conduct early treatment and screening for congenital heart defects, a common and intricate fetal malformation. Nevertheless, the prenatal detection rate of fetal CHD remains low since the peculiarities of fetal cardiac structures and the variousness of fetal CHD. Precisely segmenting four cardiac chambers can assist clinicians in analyzing cardiac morphology and further facilitate CHD diagnosis. Hence, we design a dual-path chain multi-scale gated axial-transformer network (DPC-MSGATNet) that simultaneously models global dependencies and local visual cues for fetal ultrasound (US) four-chamber (FC) views and further accurately segments four chambers. Our DPC-MSGATNet includes a global and a local branch that simultaneously operates on an entire FC view and image patches to learn multi-scale representations. We design a plug-and-play module, Interactive dual-path chain gated axial-transformer (IDPCGAT), to enhance the interactions between global and local branches. In IDPCGAT, the multi-scale representations from the two branches can complement each other, capturing the same region's salient features and suppressing feature responses to maintain only the activations associated with specific targets. Extensive experiments demonstrate that the DPC-MSGATNet exceeds seven state-of-the-art convolution-and transformer-based methods by a large margin in terms of F1 and IoU scores on our fetal FC view dataset, achieving a F1 score of 96.87% and an IoU score of 93.99%. [Qiao, Sibo; Pang, Shanchen; Yin, Wenjing] China Univ Petr, Sch Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China; [Luo, Gang; Sun, Yi; Pan, Silin] Qingdao Women & Childrens Hosp, Heart Ctr, Qingdao 266034, Shandong, Peoples R China; [Lv, Zhihan] Uppsala Univ, Fac Arts, Dept Game Design, S-75236 Uppsala, Sweden China University of Petroleum; Uppsala University Pang, SC (corresponding author), China Univ Petr, Sch Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China.;Pan, SL (corresponding author), Qingdao Women & Childrens Hosp, Heart Ctr, Qingdao 266034, Shandong, Peoples R China. siboqiao@126.com; pangsc@upc.edu.cn; lg1989jay@163.com; 706666596@qq.com; winsyin@126.com; silinpan@126.com; lvzhihan@gmail.com Lv, Zhihan/I-3187-2014 Lv, Zhihan/0000-0003-2525-3074 China University of Petroleum (East China) [22CX04036A]; National Natural Science Foundation of China [61873281] China University of Petroleum (East China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors are thankful to the Innovation fund project for graduate students of China University of Petroleum (East China) (No. 22CX04036A), and National Natural Science Foundation of China (No. 61873281) for their financial supports. 60 0 0 5 5 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 2199-4536 2198-6053 COMPLEX INTELL SYST COMPLEX INTELL. SYST. 10.1007/s40747-023-00968-x 0.0 JAN 2023 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8B3AV gold 2023-03-23 WOS:000916800200004 0
J Wang, C; Shen, MM; Yao, C Wang, Ci; Shen, Minmin; Yao, Chen No-reference quality assessment for DCT-based compressed image JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Compression distortion; Probability model; No-reference estimate; Objective quality assessment; Image quality assessment; Gaussian distribution; Uniform distribution; Noise variance STRUCTURAL SIMILARITY; DISTRIBUTIONS; INFORMATION; TRANSFORM; DOMAIN; NOISE A blind/no-reference (NR) method is proposed in this paper for image quality assessment (IQA) of the images compressed in discrete cosine transform (DCT) domain. When an image is measured by structural similarity (SSIM), two variances, i.e. mean intensity and variance of the image, are used as features. However, the parameters of original copies are actually unavailable in NR applications; hence SSIM is not widely applicable. To extend SSIM in general cases, we apply Gaussian model to fit quantization noise in spatial domain, and directly estimate noise distribution from the compressed version. Benefit from this rearrangement, the revised SSIM does not require original image as the reference. Heavy compression always results in some zero-value DCT coefficients, which need to be compensated for more accurate parameter estimate. By studying the quantization process, a machine-learning based algorithm is proposed to estimate quantization noise taking image content into consideration. Compared with state-of-the-art algorithms, the proposed IQA is more heuristic and efficient. With some experimental results, we verify that the proposed algorithm (provided no reference image) achieves comparable efficacy to some full reference (FR) methods (provided the reference image), such as SSIM. (C) 2015 Elsevier Inc. All rights reserved. [Wang, Ci] E China Normal Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China; [Shen, Minmin] Univ Konstanz, INCIDE Ctr, Constance, Germany; [Shen, Minmin] S China Univ Technol, Sch Software Engn, Guangzhou 510641, Guangdong, Peoples R China; [Yao, Chen] Minist Publ Secur, Res Inst 3, Beijing, Peoples R China; [Yao, Chen] Shanghai Key Lab Digital Media Proc & Transmiss, Shanghai, Peoples R China East China Normal University; University of Konstanz; South China University of Technology; Ministry of Public Security (China) Yao, C (corresponding author), Minist Publ Secur, Res Inst 3, Beijing, Peoples R China. yaochensing@126.com National Nature Science Foundation of China [60902072, 61302121, 61201446]; Opening Project of Shanghai Key Laboratory of Digital Media Processing and Transmission National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Opening Project of Shanghai Key Laboratory of Digital Media Processing and Transmission This work is supported by National Nature Science Foundation of China, Nos. 60902072, 61302121, 61201446 as well as the Opening Project of Shanghai Key Laboratory of Digital Media Processing and Transmission. 26 7 7 2 23 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. APR 2015.0 28.0 53 59 10.1016/j.jvcir.2015.01.006 0.0 7 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science CD8DI 2023-03-23 WOS:000351325000007 0
J Yan, B; Li, Y; Li, L; Yang, XC; Li, TQ; Yang, G; Jiang, MF Yan, Bin; Li, Yang; Li, Lin; Yang, Xiaocheng; Li, Tie-qiang; Yang, Guang; Jiang, Mingfeng Quantifying the impact of Pyramid Squeeze Attention mechanism and filtering approaches on Alzheimer's disease classification COMPUTERS IN BIOLOGY AND MEDICINE English Article Alzheimer's disease; Pyramid squeeze attention mechanism; Image filtering; Fully convolutional network; Image classification DIAGNOSIS; ALGORITHM Brain medical imaging and deep learning are important foundations for diagnosing and predicting Alzheimer's disease. In this study, we explored the impact of different image filtering approaches and Pyramid Squeeze Attention (PSA) mechanism on the image classification of Alzheimer's disease. First, during the image pre-processing, we register MRI images and remove skulls, then apply median filtering, Gaussian blur filtering, and anisotropic diffusion filtering to obtain different experimental images. After that, we add the Squeeze and Excitation (SE) mechanism and Pyramid Squeeze Attention (PSA) mechanism to the Fully Convolutional Network (FCN) model respectively, to obtain each MRI image's corresponding feature information of disease probability map. Besides, we also construct Multi-Layer Perceptron (MLP) model's framework, combining feature information of disease probability map with age, gender, and Mini-Mental State Examination (MMSE) of each sample, to get the final classification performance of model. Among them, the accuracy of the MLP-C model combining anisotropic diffusion filtering with the Pyramid Squeeze Attention mechanism can reach 98.85%. The corresponding quantitative experimental results show that different image filtering approaches and attention mechanisms provide effective assistance for the diagnosis and classification of Alzheimer's disease. [Yan, Bin; Li, Yang; Li, Lin; Yang, Xiaocheng; Jiang, Mingfeng] Zhejiang Sci Tech Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China; [Li, Tie-qiang] Karolinska Inst, Dept Clin Sci Intervent & Technol, S-17177 Stockholm, Sweden; [Yang, Guang] Royal Brompton Hosp, Cardiovasc Res Ctr, London SW3 6NP, England; [Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London SW7 2AZ, England Zhejiang Sci-Tech University; Karolinska Institutet; Royal Brompton Hospital; Imperial College London Jiang, MF (corresponding author), Zhejiang Sci Tech Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China. tie-qiang.li@ki.se; g.yang@imperial.ac.uk; m.jiang@zstu.edu.cn Yang, Guang/S-5032-2016 Yang, Guang/0000-0001-7344-7733; Li, Tie-Qiang/0000-0002-4866-5904; Li, Yang/0000-0001-9678-1320; jiang, mingfeng/0000-0003-3013-4790 National Natural Science Foundation of China [62011530130, 61672466, 62101497]; Joint Fund of Zhejiang Provincial Natural Science Foundation [LSZ19F010001]; Key Research and Development Program of Zhejiang Province [2020C03060]; 521 Talents project of Zhejiang Sci- Tech University; ERC IMI [101005122]; MRC [MC/PC/21013]; Royal Society [IEC\ NSFC\211235]; UKRI Future Leaders Fellowship [MR/V023799/1]; H2020 [952172] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Joint Fund of Zhejiang Provincial Natural Science Foundation; Key Research and Development Program of Zhejiang Province; 521 Talents project of Zhejiang Sci- Tech University(Zhejiang Sci-Tech University); ERC IMI; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Royal Society(Royal Society of London); UKRI Future Leaders Fellowship(UK Research & Innovation (UKRI)); H2020 This work is supported in part by the National Natural Science Foundation of China (62011530130, 61672466 and 62101497) , Joint Fund of Zhejiang Provincial Natural Science Foundation (LSZ19F010001) , the Key Research and Development Program of Zhejiang Province (2020C03060) , the 521 Talents project of Zhejiang Sci- Tech University, the ERC IMI (101005122) , the H2020 (952172) , the MRC (MC/PC/21013) , the Royal Society (IEC\ NSFC\211235) , and the UKRI Future Leaders Fellowship (MR/V023799/1) . 60 1 1 20 25 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0010-4825 1879-0534 COMPUT BIOL MED Comput. Biol. Med. SEP 2022.0 148.0 105944 10.1016/j.compbiomed.2022.105944 0.0 AUG 2022 14 Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology 3Y9RT 35969934.0 2023-03-23 WOS:000844060700002 0
C Chen, YF; Shen, C; Wang, C; Zhang, Y USENIX Assoc Chen, Yufei; Shen, Chao; Wang, Cong; Zhang, Yang Teacher Model Fingerprinting Attacks Against Transfer Learning PROCEEDINGS OF THE 31ST USENIX SECURITY SYMPOSIUM English Proceedings Paper 31st USENIX Security Symposium AUG 10-12, 2022 Boston, MA USENIX Assoc,Meta,Google,NSF,Baidu,Chainlink,IBM,Intel Secur,Red Canary,Mercari,Paloalto,Technol Innovat Inst Transfer learning has become a common solution to address training data scarcity in practice. It trains a specified student model by reusing or fine-tuning early layers of a well-trained teacher model that is usually publicly available. However, besides utility improvement, the transferred public knowledge also brings potential threats to model confidentiality, and even further raises other security and privacy issues. In this paper, we present the first comprehensive investigation of the teacher model exposure threat in the transfer learning context, aiming to gain a deeper insight into the tension between public knowledge and model confidentiality. To this end, we propose a teacher model fingerprinting attack to infer the origin of a student model, i.e., the teacher model it transfers from. Specifically, we propose a novel optimization-based method to carefully generate queries to probe the student model to realize our attack. Unlike existing model reverse engineering approaches, our proposed fingerprinting method neither relies on fine-grained model outputs, e.g., posteriors, nor auxiliary information of the model architecture or training dataset. We systematically evaluate the effectiveness of our proposed attack. The empirical results demonstrate that our attack can accurately identify the model origin with few probing queries. Moreover, we show that the proposed attack can serve as a stepping stone to facilitating other attacks against machine learning models, such as model stealing.(1) [Chen, Yufei; Shen, Chao] Xi An Jiao Tong Univ, Xian, Peoples R China; [Chen, Yufei; Wang, Cong] City Univ Hong Kong, Hong Kong, Peoples R China; [Zhang, Yang] CISPA Helmholtz Ctr Informat Secur, Saarbrucken, Germany Xi'an Jiaotong University; City University of Hong Kong Chen, YF (corresponding author), Xi An Jiao Tong Univ, Xian, Peoples R China.;Chen, YF (corresponding author), City Univ Hong Kong, Hong Kong, Peoples R China. Chen, Yufei/0000-0003-3786-928X National Key Research and Development Program of China [2020AAA0107702]; National Natural Science Foundation of China [U21B2018, 62161160337, 62132011]; Shaanxi Province Key Industry Innovation Program [2021ZDLGY01-02]; Research Grants Council of Hong Kong [N_CityU139/21, R602120F, R1012-21]; Helmholtz Association [ZT-I-OO1 4] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shaanxi Province Key Industry Innovation Program; Research Grants Council of Hong Kong(Hong Kong Research Grants Council); Helmholtz Association(Helmholtz Association) This work is supported by the National Key Research and Development Program of China (2020AAA0107702), National Natural Science Foundation of China (U21B2018, 62161160337, 62132011), Shaanxi Province Key Industry Innovation Program (2021ZDLGY01-02), the Research Grants Council of Hong Kong under Grants N_CityU139/21, R602120F, R1012-21, and the Helmholtz Association within the project Trustworthy Federated Data Analytics (TFDA) (funding number ZT-I-OO1 4). Chao Shen, Cong Wang, and Yang Zhang are the corresponding authors. 51 0 0 0 0 USENIX ASSOC BERKELEY SUITE 215, 2560 NINTH ST, BERKELEY, CA 94710 USA 978-1-939133-31-1 2022.0 3593 3610 18 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT8TA 2023-03-23 WOS:000855237505018 0
J Yue, ML; Benaggoune, K; Meng, JW; Azib, T; Zhu, D Yue, Meiling; Benaggoune, Khaled; Meng, Jianwen; Azib, Toufik; Zhu, Dan Data-driven fuel cell performance prediction by transfer learning and dynamic time warping ENERGY REPORTS English Article; Proceedings Paper 5th International Conference on Electrical Engineering and Green Energy (CEEGE) JUN 08-11, 2022 Berlin, GERMANY Dynamic time warping; Proton exchange membrane fuel cell; Performance prediction; Transfer learning The increase of world energy demand contributes to the development of renewable energy sources. Fuel cells, which use hydrogen to deliver energy, have seen a promising future. Fuel cells have been investigated since the last 50 years but they are still relatively absent for the commercial use due to the large exploitation cost and poor durability. One of the reasons is that the health state of a running fuel cell is hard to evaluate due to the system complexity and insufficient data. To tackle this problem, this paper proposes a data-driven fuel cell performance prediction method based on transfer learning and dynamic time warping. Historical data is trained in the source domain to build a model that can be transferred to the target domain and can be fine-tuned with only a small volume of online measurement. To improve the model recognition, dynamic time warping technology is applied to quantify the similarity between two different time series so that similar instances can be selected to build the neural network prediction model. Results show that, compared to traditional prediction method, the proposed prediction method has improved the prediction accuracy by almost 50%. (C) 2022 The Author(s). Published by Elsevier Ltd. [Yue, Meiling] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China; [Yue, Meiling; Benaggoune, Khaled] Univ Bourgogne Franche Comte, CNRS, FEMTO ST Inst, F-25000 Besancon, France; [Meng, Jianwen; Azib, Toufik] Ecole Super Tech Aeronaut & Construct Automobile, F-78066 Paris, France; [Zhu, Dan] Changan Univ, Sch Automobile, Xian 710044, Shaanxi, Peoples R China Beijing Jiaotong University; Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Chang'an University Yue, ML (corresponding author), Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China. yueml@bjtu.edu.cn Meng, Jianwen/AFC-4054-2022 Meng, Jianwen/0000-0001-5539-464X; YUE, MEILING/0000-0002-4624-4743 Fundamental Research Funds for the Central Universities, China [2021RC279] Fundamental Research Funds for the Central Universities, China(Fundamental Research Funds for the Central Universities) This work has been supported by the Fundamental Research Funds for the Central Universities, China [grant number 2021RC279], China. 13 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4847 ENERGY REP Energy Rep. NOV 2022.0 8.0 13 940 947 10.1016/j.egyr.2022.08.075 0.0 AUG 2022 8 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Energy & Fuels 5E4LN gold 2023-03-23 WOS:000865597700052 0
J Lan, XY; Zhao, E; Li, ZL; Labed, J; Nerry, F Lan, Xinyu; Zhao, Enyu; Li, Zhao-Liang; Labed, Jelila; Nerry, Francoise Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor IEEE ACCESS English Article Land surface temperature; Land surface; Mixture models; Ocean temperature; Hyperspectral imaging; Atmospheric modeling; Temperature sensors; Hyperspectral thermal infrared; land surface temperature retrieval; IASI; deep learning ATMOSPHERIC PROFILES; PHYSICAL RETRIEVAL; NEURAL-NETWORKS; INFRARED DATA; EMISSIVITY; PARAMETERS; ALGORITHM; SELECTION; PRODUCTS A deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction. [Lan, Xinyu; Li, Zhao-Liang; Labed, Jelila; Nerry, Francoise] Univ Strasbourg, CNRS, ICube Lab, UMR 7357, F-67412 Illkirch Graffenstaden, France; [Zhao, Enyu] Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116026, Peoples R China Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); UDICE-French Research Universities; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Dalian Maritime University Zhao, E (corresponding author), Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116026, Peoples R China. zhaoenyu@dlmu.edu.cn LAN, XIN/GRX-8159-2022 Labed, Jelila/0000-0001-9226-1977; ZHAO, Enyu/0000-0001-7165-1861 National Natural Science Foundation of China [41801231] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 41801231. 47 0 1 4 16 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2020.0 8.0 218122 218130 10.1109/ACCESS.2020.3040780 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications PD6KF Green Submitted, gold 2023-03-23 WOS:000597790600001 0
C Robaglia, BM; Destounis, A; Coupechoux, M; Tsilimantos, D IEEE Robaglia, Benoit-Marie; Destounis, Apostolos; Coupechoux, Marceau; Tsilimantos, Dimitrios Deep Reinforcement Learning for Scheduling Uplink IoT Traffic with Strict Deadlines 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Communications Conference (GLOBECOM) DEC 07-11, 2021 Madrid, SPAIN IEEE,Huawei,Vodafone,Intel,ZTE,Qualcomm,Assia,IMEC,Keysight Technologies,PRIME Alliance,Rohde & Schwarz,Vantage Towers,Guard,Usal Bisite Res Grp Multiple Access; Reinforcement Learning; Proximal Policy Optimization; POMDP; Internet of Things; Wireless sensor networks; scheduling SPECTRUM ACCESS This paper considers the Multiple Access problem where N Internet of Things (IoT) devices share a common wireless medium towards a central Base Station (BS). We propose a Reinforcement Learning (RL) method where the BS is the agent and the devices are part of the environment. A device is allowed to transmit only when the BS decides to schedule it. Besides the information packets, devices send additional messages like the delay or the number of discarded packets since their last transmission. This information is used to design the RL reward function and constitutes the next observation that the agent can use to schedule the next device. Leveraging RL allows us to learn the sporadic and heterogeneous traffic patterns of the IoT devices and an optimal scheduling policy that maximizes the channel throughput. We adapt the Proximal Policy Optimization (PPO) algorithm with a Recurrent Neural Network (RNN) to handle the partial observability of our problem and exploit the temporal correlations of the users' traffic. We demonstrate the performance of our model through simulations on different number of heterogeneous devices with periodic traffic and individual latency constraints. We show that our RL algorithm outperforms traditional scheduling schemes and distributed medium access algorithms. [Robaglia, Benoit-Marie; Destounis, Apostolos; Tsilimantos, Dimitrios] Huawei Technol Co Ltd, Math & Algorithm Sci Lab, Paris Res Ctr, Shenzhen, Guangdong, Peoples R China; [Robaglia, Benoit-Marie; Coupechoux, Marceau] Inst Polytech Paris, Telecom Paris, LTCI, Palaiseau, France Huawei Technologies; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Robaglia, BM (corresponding author), Huawei Technol Co Ltd, Math & Algorithm Sci Lab, Paris Res Ctr, Shenzhen, Guangdong, Peoples R China.;Robaglia, BM (corresponding author), Inst Polytech Paris, Telecom Paris, LTCI, Palaiseau, France. 27 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 2576-6813 978-1-7281-8104-2 IEEE GLOB COMM CONF 2021.0 10.1109/GLOBECOM46510.2021.9685561 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BT0PJ Green Submitted 2023-03-23 WOS:000790747202149 0
J Qiao, MM; Xia, GY; Cui, T; Xu, Y; Fan, CL; Su, Y; Li, YB; Han, SY Qiao, Mengmeng; Xia, Guoyi; Cui, Tao; Xu, Yang; Fan, Chenlong; Su, Yuan; Li, Yibo; Han, Shaoyun Machine learning and experimental testing for prediction of breakage rate of maize kernels based on components contents JOURNAL OF CEREAL SCIENCE English Article Maize kernels; Breakage rate; Components contents; Support vector regression MECHANICAL-PROPERTIES; SUMMER MAIZE; CORN; HARDNESS; YIELD; TIME A high breakage rate (BR) of maize kernels is the main problem during the direct harvest of maize kernels which causes massive grain losses. Thus, a solution was provided for reducing BR in harvest by predicting BR to select a suitable harvest time. The BR prediction models of maize kernels based on moisture, protein and starch contents were studied by using multivariate polynomial regression, stepwise polynomial regression, support vector regression (SVR) and extreme learning regression. SVR with radial basis function (rbf-SVR) was selected for further analysis. The performances of 7 different rbf-SVR models with single and multiple combinations of three components contents were evaluated. The rbf-SVR model constructed with moisture, protein and starch contents (rbf-SVRMs + Pr + St), which were regarded as predictor variables, generated the most accurate BR estimate. The correlation coefficients of the correction set and prediction set were 0.8921 and 0.8776, respectively. The root mean square errors of the correction set and prediction set were 1.3898% and 1.3767%, respectively. The adjusted R2 was 0.7851. The average classification accuracy was 82.17%. As a result, the rbf-SVRMs + Pr + St model can comprehensively evaluate BR and guide the selection of appropriate harvest time, to reduce the BR. [Qiao, Mengmeng; Cui, Tao; Xu, Yang; Fan, Chenlong; Su, Yuan; Li, Yibo; Han, Shaoyun] China Agr Univ, Coll Engn, 17 Qinghua East Rd, Beijing 100083, Peoples R China; [Xia, Guoyi] Univ Bremen, Bibliothekstr 1, D-28359 Bremen, Germany China Agricultural University; University of Bremen Xu, Y (corresponding author), China Agr Univ, Coll Engn, 17 Qinghua East Rd, Beijing 100083, Peoples R China. S20193071129@cau.edu.cn; xuyang@cau.edu.cn China Agriculture Research System of MOF and MARA; Ministry of Science and Technology of the People's Republic of China [2016YFD0300302] China Agriculture Research System of MOF and MARA(Ministry of Oceans & Fisheries (MOF), Republic of Korea); Ministry of Science and Technology of the People's Republic of China(Ministry of Science and Technology, China) This work was supported financially by China Agriculture Research System of MOF and MARA, and Ministry of Science and Technology of the People's Republic of China (2016YFD0300302). 29 0 0 8 8 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0733-5210 1095-9963 J CEREAL SCI J. Cereal Sci. NOV 2022.0 108.0 103582 10.1016/j.jcs.2022.103582 0.0 OCT 2022 8 Food Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Food Science & Technology 6I2QD 2023-03-23 WOS:000885974100002 0
J Fenstermacher, ME; Abbate, J; Abe, S; Abrams, T; Adams, M; Adamson, B; Aiba, N; Akiyama, T; Aleynikov, P; Allen, E; Allen, S; Anand, H; Anderson, J; Andrew, Y; Andrews, T; Appelt, D; Arbon, R; Ashikawa, N; Ashourvan, A; Aslin, M; Asnis, Y; Austin, M; Ayala, D; Bak, J; Bandyopadhyay, I; Banerjee, S; Barada, K; Bardoczi, L; Barr, J; Bass, E; Battaglia, D; Battey, A; Baumgartner, W; Baylor, L; Beckers, J; Beidler, M; Belli, E; Berkery, J; Bernard, T; Bertelli, N; Beurskens, M; Bielajew, R; Bilgili, S; Biswas, B; Blondel, S; Boedo, J; Bogatu, I; Boivin, R; Bolzonella, T; Bongard, M; Bonnin, X; Bonoli, P; Bonotto, M; Bortolon, A; Bose, S; Bosviel, N; Bouwmans, S; Boyer, M; Boyes, W; Bradley, L; Brambila, R; Brennan, D; Bringuier, S; Brodsky, L; Brookman, M; Brooks, J; Brower, D; Brown, G; Brown, W; Burke, M; Burrell, K; Butler, K; Buttery, R; Bykov, I; Byrne, P; Cacheris, A; Callahan, K; Callen, J; Campbell, G; Candy, J; Canik, J; Cano-Megias, P; Cao, N; Carayannopoulos, L; Carlstrom, T; Carrig, W; Carter, T; Cary, W; Casali, L; Cengher, M; Paz, GC; Chaban, R; Chan, V; Chapman, B; Char, I; Chattopadhyay, A; Chen, R; Chen, J; Chen, X; Chen, X; Chen, J; Chen, M; Chen, J; Chen, Z; Choi, M; Choi, W; Choi, G; Chousal, L; Chrobak, C; Chrystal, C; Chung, Y; Churchill, R; Cianciosa, M; Clark, J; Clement, M; Coda, S; Cole, A; Collins, C; Conlin, W; Cooper, A; Cordell, J; Coriton, B; Cote, T; Cothran, J; Creely, A; Crocker, N; Crowe, C; Crowley, B; Crowley, T; Cruz-Zabala, D; Cummings, D; Curie, M; Curreli, D; Dal Molin, A; Dannels, B; Dautt-Silva, A; Davda, K; De Tommasi, G; De Vries, P; Degrandchamp, G; Degrassie, J; Demers, D; Denk, S; Depasquale, S; Deshazer, E; Diallo, A; Diem, S; Dimits, A; Ding, R; Ding, S; Ding, W; Do, T; Doane, J; Dong, G; Donovan, D; Drake, J; Drews, W; Drobny, J; Du, X; Du, H; Duarte, V; Dudt, D; Dunn, C; Duran, J; Dvorak, A; Effenberg, F; Eidietis, N; Elder, D; Eldon, D; Ellis, R; Elwasif, W; Ennis, D; Erickson, K; Ernst, D; Fasciana, M; Fedorov, D; 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Hollocombe, J; Holm, A; Holmes, I; Holtrop, K; Honda, M; Hong, R; Hood, R; Horton, A; Horvath, L; Hosokawa, M; Houshmandyar, S; Howard, N; Howell, E; Hoyt, D; Hu, W; Hu, Y; Hu, Q; Huang, J; Huang, Y; Hughes, J; Human, T; Humphreys, D; Huynh, P; Hyatt, A; Ibanez, C; Ibarra, L; Icasas, R; Ida, K; Igochine, V; In, Y; Inoue, S; Isayama, A; Izacard, O; Izzo, V; Jackson, A; Jacobsen, G; Jaervinen, A; Jalalvand, A; Janhunen, J; Jardin, S; Jarleblad, H; Jeon, Y; Ji, H; Jian, X; Joffrin, E; Johansen, A; Johnson, C; Johnson, T; Jones, C; Joseph, I; Jubas, D; Junge, B; Kalb, W; Kalling, R; Kamath, C; Kang, J; Kaplan, D; Kaptanoglu, A; Kasdorf, S; Kates-Harbeck, J; Kazantzidis, P; Kellman, A; Kellman, D; Kessel, C; Khumthong, K; Kim, E; Kim, H; Kim, J; Kim, S; Kim, J; Kim, H; Kim, K; Kim, C; Kimura, W; King, M; King, J; Kinsey, J; Kirk, A; Kiyan, B; Kleiner, A; Klevarova, V; Knapp, R; Knolker, M; Ko, W; Kobayashi, T; Koch, E; Kochan, M; Koel, B; Koepke, M; Kohn, A; Kolasinski, R; Kolemen, E; Kostadinova, E; Kostuk, M; Kramer, G; Kriete, D; Kripner, L; Kubota, S; Kulchar, J; Kwon, K; La Haye, R; Laggner, F; Lan, H; Lantsov, R; Lao, L; Esquisabel, AL; Lasnier, C; Lau, C; Leard, B; Lee, J; Lee, R; Lee, M; Lee, M; Lee, Y; Lee, C; Lee, J; Lee, S; Lehnen, M; Leonard, A; Leppink, E; Lesher, M; Lestz, J; Leuer, J; Leuthold, N; Li, X; Li, K; Li, E; Li, G; Li, L; Li, Z; Li, J; Li, Y; Lin, Z; Lin, D; Liu, X; Liu, J; Liu, Y; Liu, T; Liu, Y; Liu, C; Liu, Z; Liu, C; Liu, D; Liu, A; Liu, D; Loarte-Prieto, A; Lodestro, L; Logan, N; Lohr, J; Lombardo, B; Lore, J; Luan, Q; Luce, T; Di Cortemiglia, TL; Luhmann, N; Lunsford, R; Luo, Z; Lvovskiy, A; Lyons, B; Ma, X; Madruga, M; Madsen, B; Maggi, C; Maheshwari, K; Mail, A; Mailloux, J; Maingi, R; Major, M; Makowski, M; Manchanda, R; Marini, C; Marinoni, A; Maris, A; Markovic, T; Marrelli, L; Martin, E; Mateja, J; Matsunaga, G; Maurizio, R; Mauzey, P; Mauzey, D; Mcardle, G; Mcclenaghan, J; Mccollam, K; Mcdevitt, C; Mckay, K; Mckee, G; Mclean, A; Mehta, V; Meier, E; 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Lasa; Lasnier, C.; Lau, C.; Leard, B.; Lee, J.; Lee, R.; Lee, M.; Lee, M.; Lee, Y.; Lee, C.; Lee, J.; Lee, S.; Lehnen, M.; Leonard, A.; Leppink, E.; Lesher, M.; Lestz, J.; Leuer, J.; Leuthold, N.; Li, X.; Li, K.; Li, E.; Li, G.; Li, L.; Li, Z.; Li, J.; Li, Y.; Lin, Z.; Lin, D.; Liu, X.; Liu, J.; Liu, Y.; Liu, T.; Liu, Y.; Liu, C.; Liu, Z.; Liu, C.; Liu, D.; Liu, A.; Liu, D.; Loarte-Prieto, A.; Lodestro, L.; Logan, N.; Lohr, J.; Lombardo, B.; Lore, J.; Luan, Q.; Luce, T.; Di Cortemiglia, T. 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Ruiz; Rupani, R.; Saarelma, S.; Sabbagh, S.; Sachdev, J.; Saenz, J.; Saib, S.; Salewski, M.; Salmi, A.; Sammuli, B.; Samuell, C.; Sandorfi, A.; Sang, C.; Sarff, J.; Sauter, O.; Schaubel, K.; Schmitz, L.; Schmitz, O.; Schneider, J.; Schroeder, P.; Schultz, K.; Schuster, E.; Schwartz, J.; Sciortino, F.; Scotti, F.; Scoville, J.; Seltzman, A.; Seol, S.; Sfiligoi, I; Shafer, M.; Sharapov, S.; Shen, H.; Sheng, Z.; Shepard, T.; Shi, S.; Shibata, Y.; Shin, G.; Shiraki, D.; Shousha, R.; Si, H.; Simmerling, P.; Sinclair, G.; Sinha, J.; Sinha, P.; Sips, G.; Sizyuk, T.; Skinner, C.; Sladkomedova, A.; Slendebroek, T.; Slief, J.; Smirnov, R.; Smith, J.; Smith, S.; Smith, D.; Snipes, J.; Snoep, G.; Snyder, A.; Snyder, P.; Solano, E.; Solomon, W.; Song, J.; Sontag, A.; Soukhanovskii, V; Spendlove, J.; Spong, D.; Squire, J.; Srinivasan, C.; Stacey, W.; Staebler, G.; Stagner, L.; Stange, T.; Stangeby, P.; Stefan, R.; Stemprok, R.; Stephan, D.; Stillerman, J.; Stoltzfus-Dueck, T.; Stonecipher, W.; Storment, S.; Strait, E.; Su, D.; Sugiyama, L.; Sun, Y.; Sun, P.; Sun, Z.; Sun, A.; Sundstrom, D.; Sung, C.; Sungcoco, J.; Suttrop, W.; Suzuki, Y.; Suzuki, T.; Svyatkovskiy, A.; Swee, C.; Sweeney, R.; Sweetnam, C.; Szepesi, G.; Takechi, M.; Tala, T.; Tanaka, K.; Tang, X.; Tang, S.; Tao, Y.; Tao, R.; Taussig, D.; Taylor, T.; Teixeira, K.; Teo, K.; Theodorsen, A.; Thomas, D.; Thome, K.; Thorman, A.; Thornton, A.; Ti, A.; Tillack, M.; Timchenko, N.; Tinguely, R.; Tompkins, R.; Tooker, J.; De Sousa, A. Torrezan; Trevisan, G.; Tripathi, S.; Ochoa, A. Trujillo; Truong, D.; Tsui, C.; Turco, F.; Turnbull, A.; Umansky, M.; Unterberg, E.; Vaezi, P.; Vail, P.; Valdez, J.; Valkis, W.; Van Compernolle, B.; Van Galen, J.; Van Kampen, R.; Van Zeeland, M.; Verdoolaege, G.; Vianello, N.; Victor, B.; Viezzer, E.; Vincena, S.; Wade, M.; Waelbroeck, F.; Wai, J.; Wakatsuki, T.; Walker, M.; Wallace, G.; Waltz, R.; Wampler, W.; Wang, L.; Wang, H.; Wang, Y.; Wang, H.; Wang, Z.; Wang, H.; Wang, Z.; Wang, Y.; Wang, G.; Ward, S.; Watkins, M.; Watkins, J.; Wehner, W.; Wei, Y.; Weiland, M.; Weisberg, D.; Welander, A.; White, A.; White, R.; Wiesen, S.; Wilcox, R.; Wilks, T.; Willensdorfer, M.; Wilson, H.; Wingen, A.; Wolde, M.; Wolff, M.; Woller, K.; Wolz, A.; Wong, H.; Woodruff, S.; Wu, M.; Wu, Y.; Wukitch, S.; Wurden, G.; Xiao, W.; Xie, R.; Xing, Z.; Xu, X.; Xu, C.; Xu, G.; Yan, Z.; Yang, X.; Yang, S.; Yokoyama, T.; Yoneda, R.; Yoshida, M.; You, K.; Younkin, T.; Yu, J.; Yu, M.; Yu, G.; Yuan, Q.; Zaidenberg, L.; Zakharov, L.; Zamengo, A.; Zamperini, S.; Zarnstorff, M.; Zeger, E.; Zeller, K.; Zeng, L.; Zerbini, M.; Zhang, L.; Zhang, X.; Zhang, R.; Zhang, B.; Zhang, J.; Zhang, J.; Zhao, L.; Zhao, B.; Zheng, Y.; Zheng, L.; Zhu, B.; Zhu, J.; Zhu, Y.; Zhu, Y.; Zsutty, M.; Zuin, M. DIII-D Team DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy NUCLEAR FUSION English Article DIII-D; tokamak; fusion energy; plasma physics; core-edge integration MITIGATION; SCENARIOS DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I (p) steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L-H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at similar to 8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I (p) beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate beta (N) in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation. [Fenstermacher, M. E.; Allen, S.; Brown, G.; Dimits, A.; Ghosh, D.; Holcomb, C.; Joseph, I; Kamath, C.; Lasnier, C.; Lodestro, L.; Logan, N.; Mclean, A.; Meyer, W.; Orvis, W.; Rensink, M.; Rognlien, T.; Samuell, C.; Scotti, F.; Soukhanovskii, V; Umansky, M.; Victor, B.; Wolff, M.; Xu, X.; Zhu, B.] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA; [Abbate, J.; Ashourvan, A.; Battaglia, D.; Bertelli, N.; Bortolon, A.; Boyer, M.; Brown, W.; Churchill, R.; Clement, M.; Cordell, J.; Depasquale, S.; Diallo, A.; Dong, G.; Duarte, V; Effenberg, F.; Ellis, R.; Erickson, K.; Feibush, E.; Ferraro, N.; Fox, W.; Gilson, E.; Grierson, B.; Guttenfelder, W.; Hager, R.; Hammett, G.; Haskey, S.; Hawryluk, R.; Hu, Q.; Human, T.; Jardin, S.; Kleiner, A.; Kolemen, E.; Kramer, G.; Laggner, F.; Liu, C.; Lunsford, R.; Maingi, R.; Mauzey, D.; Menard, J.; Misleh, A.; Mueller, D.; Nagy, A.; Nazikian, R.; Neilson, G.; Nespoli, F.; Okabayashi, M.; Palacios, L.; Pankin, A.; Park, J.; Podesta, M.; Poli, F.; Raines, T.; Reiman, A.; Ren, Y.; Rozenblat, R.; Sachdev, J.; Sinha, P.; Skinner, C.; Stoltzfus-Dueck, T.; Sun, Z.; Sweetnam, C.; Wang, Z.; White, R.; Xu, C.; Yang, S.] Princeton Plasma Phys Lab, POB 451, Princeton, NJ 08543 USA; [Abe, S.; Arbon, R.; Bose, S.; Brennan, D.; Choi, W.; Conlin, W.; Dudt, D.; Fu, Y.; Glasser, A.; Goodman, A.; Izacard, O.; Ji, H.; Jubas, D.; Kiyan, B.; Koel, B.; Liu, D.; Liu, A.; Nelson, A.; Schwartz, J.; Shousha, R.; Svyatkovskiy, A.; Vail, P.; Wai, J.; Xing, Z.; Zarnstorff, M.] Princeton Univ, Princeton, NJ 08544 USA; [Abrams, T.; Adams, M.; Adamson, B.; Akiyama, T.; Allen, E.; Anand, H.; Anderson, J.; Andrews, T.; Appelt, D.; Ayala, D.; Bardoczi, L.; Barr, J.; Belli, E.; Boivin, R.; Brambila, R.; Bringuier, S.; Brodsky, L.; Brookman, M.; Burrell, K.; Buttery, R.; Byrne, P.; Campbell, G.; Candy, J.; Carlstrom, T.; Carrig, W.; Cary, W.; Casali, L.; Cengher, M.; Chen, X.; Chrystal, C.; Coriton, B.; Crowe, C.; Crowley, B.; Cummings, D.; Dautt-Silva, A.; Degrassie, J.; Doane, J.; Drake, J.; Drews, W.; Du, X.; Eidietis, N.; Eldon, D.; Ferron, J.; Garofalo, A.; Gattuso, A.; Geng, D.; Glass, F.; Glibert, D.; Gohil, P.; Gomez, R.; Gomez, S.; Gonzales, E.; Gorelov, Y.; Greenfield, C.; Groebner, R.; Grosnickle, W.; Grunloh, H.; Guo, H.; Gupta, P.; Guterl, J.; Guzman, T.; Haar, S.; Hall, T.; Halpern, F.; Hansink, M.; Harris, A.; Hicok, J.; Hill, D.; Holland, L.; Holmes, I; Holtrop, K.; Hoyt, D.; Humphreys, D.; Huynh, P.; Hyatt, A.; Ibanez, C.; Ibarra, L.; Icasas, R.; Izzo, V; Jacobsen, G.; Junge, B.; Kaplan, D.; Kellman, A.; Kellman, D.; Khumthong, K.; King, M.; Knapp, R.; Koch, E.; Kostuk, M.; Kulchar, J.; La Haye, R.; Lao, L.; Lee, R.; Leonard, A.; Lesher, M.; Leuer, J.; Liu, Y.; Liu, C.; Lohr, J.; Lombardo, B.; Lvovskiy, A.; Lyons, B.; Madruga, M.; Mail, A.; Mauzey, P.; Mcclenaghan, J.; Meneghini, O.; Minet, G.; Moeller, C.; Morris, P.; Moser, A.; Munaretto, S.; Murphy, C.; Muscatello, C.; Nelson, D.; Nguyen, R.; Noraky, S.; Norausky, N.; Odstrcil, T.; Ogas, D.; Ogorman, T.; Olofsson, E.; Oneill, R.; Osborne, T.; Pace, D.; Parks, P.; Pawley, C.; Paz-Soldan, C.; Petty, C.; Pierce, D.; Piglowski, D.; Pinsker, R.; Ponce, D.; Prater, R.; Rauch, J.; Raymond, A.; Reinhold, S.; Renteria, J.; Roberts, R.; Ruane, J.; Rupani, R.; Saarelma, S.; Saenz, J.; Sammuli, B.; Schaubel, K.; Schroeder, P.; Schultz, K.; Scoville, J.; Shen, H.; Shepard, T.; Sinclair, G.; Sips, G.; Smith, J.; Smith, S.; Snyder, A.; Snyder, P.; Solomon, W.; Squire, J.; Staebler, G.; Stefan, R.; Stemprok, R.; Stephan, D.; Stonecipher, W.; Strait, E.; Su, D.; Sundstrom, D.; Sungcoco, J.; Tao, R.; Taussig, D.; Taylor, T.; Teixeira, K.; Thomas, D.; Thome, K.; Tompkins, R.; Tooker, J.; De Sousa, A. 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Luda; Plank, U.; Poli, E.; Reich, M.; Stange, T.; Suttrop, W.; Weiland, M.; Willensdorfer, M.] Max Planck Inst Plasma Phys, Garching, Germany; [Andrew, Y.] Imperial Coll London, London, England; [Ashikawa, N.; Bak, J.; Choi, M.; Hahn, S.; Ida, K.; Jeon, Y.; Kang, J.; Kim, J.; Kim, H.; Ko, W.; Kobayashi, T.; Lee, M.; Lee, Y.; Ohdachi, S.; Suzuki, Y.; Tanaka, K.; You, K.; Yu, M.] Natl Inst Fus Sci, Toki, Gifu, Japan; [Aslin, M.] Mt Holyoke Coll, S Hadley, MA USA; [Asnis, Y.; Canal, G. Paganini] Univ Sao Paulo, Inst Phys, Sao Paulo, Brazil; [Austin, M.; Chen, Z.; Curie, M.; Fitzpatrick, R.; Gentle, K.; Halfmoon, M.; Hassan, E.; Hatch, D.; Houshmandyar, S.; Janhunen, J.; Merlo, G.; Rowan, W.; Waelbroeck, F.; Xie, R.; Zhao, B.; Zheng, L.] Univ Texas Austin, Austin, TX 78712 USA; [Bandyopadhyay, I] ITER India, Ahmadabad, Gujarat, India; [Banerjee, S.; Chaban, R.; Mckay, K.; Mordijck, S.; Pandit, R.; Reksoatmodjo, R.] Coll William & Mary, Williamsburg, VA 23187 USA; [Barada, K.; Bradley, L.; Brower, D.; Callahan, K.; Carter, T.; Chen, J.; Crocker, N.; Ding, W.; Hong, R.; Kubota, S.; Lantsov, R.; Michael, C.; Nguyen, X.; Peebles, W.; Pratt, Q.; Rhodes, T.; Schmitz, L.; Sladkomedova, A.; Storment, S.; Tang, S.; Tripathi, S.; Vincena, S.; Wang, G.; Wong, H.; Yoneda, R.; Zeger, E.; Zeng, L.] Univ Calif Los Angeles, Los Angeles, CA USA; [Bass, E.; Boedo, J.; Bykov, I; Chousal, L.; Do, T.; Hanson, M.; Holland, C.; Hollmann, E.; Jian, X.; Jones, C.; Moyer, R.; Orlov, D.; Perillo, R.; Popovic, Z.; Rudakov, D.; Sfiligoi, I; Smirnov, R.; Tillack, M.; Tsui, C.; Vaezi, P.; Valkis, W.] Univ Calif San Diego, San Diego, CA 92103 USA; [Battey, A.; Berkery, J.; Boyes, W.; Hanson, J.; Navratil, G.; Park, Y.; Sabbagh, S.; Turco, F.; Wei, Y.] Columbia Univ, New York, NY 10027 USA; [Baumgartner, W.; Bielajew, R.; Biswas, B.; Bonoli, P.; Cao, N.; Carayannopoulos, L.; Chen, X.; Creely, A.; Denk, S.; Ernst, D.; Garnier, D.; Granetz, R.; Greenwald, M.; Howard, N.; Hughes, J.; Kalb, W.; Leppink, E.; Marinoni, A.; Maris, A.; Montes, K.; Pan, Q.; Porkolab, M.; Rea, C.; Rice, J.; Fernandez, P. Rodriguez; Rosenthal, A.; Rost, J.; Sciortino, F.; Seltzman, A.; Stillerman, J.; Sugiyama, L.; Sweeney, R.; Tinguely, R.; Wallace, G.; White, A.; Wilks, T.; Woller, K.; Wukitch, S.; Zhu, J.] MIT, Cambridge, MA 02139 USA; [Baylor, L.; Beidler, M.; Canik, J.; Cianciosa, M.; Collins, C.; Dannels, B.; Dvorak, A.; Elwasif, W.; Gray, T.; Green, D.; Herfindal, J.; Horton, A.; Kessel, C.; Kim, K.; Lau, C.; Lore, J.; Martin, E.; Park, J.; Reinke, M.; Shafer, M.; Shiraki, D.; Spong, D.; Unterberg, E.; Wade, M.; Wilcox, R.; Wingen, A.] Oak Ridge Natl Lab, Oak Ridge, TN USA; [Beckers, J.; Bouwmans, S.; Slief, J.; Van Galen, J.] Eindhoven Univ Technol, Eindhoven, Netherlands; [Bernard, T.; Ding, S.; Fedorov, D.; Gallo, A.; Gu, S.; Knolker, M.; Li, Z.; Ma, X.; Marini, C.; Maurizio, R.; Neiser, T.; Pelton, S.; Slendebroek, T.; Stagner, L.; Trevisan, G.] Oak Ridge Associated Univ, Oak Ridge, TN 37831 USA; [Bilgili, S.; Koepke, M.; Nogami, S.] West Virginia Univ, Morgantown, WV 26506 USA; [Blondel, S.; Butler, K.; Cacheris, A.; Davda, K.; Donovan, D.; Duran, J.; Esquisabel, A. Lasa; Lee, S.; Messer, S.; Nichols, J.; Ren, J.; Younkin, T.; Zamperini, S.] Univ Tennessee, Knoxville, TN USA; [Bogatu, I; Kim, J.; Zhao, L.] Far Tech Inc, San Diego, CA 92121 USA; [Bolzonella, T.; Bonotto, M.; Marrelli, L.; Pigatto, L.; Piovesan, P.; Vianello, N.; Zuin, M.] Consorzio RFX, Padua, Italy; [Bongard, M.; Burke, M.; Callen, J.; Chapman, B.; Cote, T.; Diem, S.; Frerichs, H.; Hinson, E.; Kriete, D.; Major, M.; Mccollam, K.; Mckee, G.; Morton, L.; Navarro, M.; Nornberg, M.; Pandya, M.; Qin, X.; Sarff, J.; Schmitz, O.; Smith, D.; Sontag, A.; Swee, C.; Yan, Z.] Univ Wisconsin, Madison, WI 53706 USA; [Bonnin, X.; De Vries, P.; Hosokawa, M.; Kim, S.; Lehnen, M.; Loarte-Prieto, A.; Luce, T.; Pinches, S.; Pitts, R.; Sinha, J.; Snipes, J.] ITER Org, Paris, France; [Bosviel, N.] SUNY Stony Brook, Stony Brook, NY 11794 USA; [Brooks, J.; Hassanein, A.; Mateja, J.; Sizyuk, T.] Purdue Univ, W Lafayette, IN 47907 USA; [Cano-Megias, P.; Cruz-Zabala, D.; Garcia Munoz, M.; Viezzer, E.] Univ Seville, Seville, Spain; [Paz, G. Cespedes] Univ Ingn & Tecnol, Lima, Peru; [Char, I; Chung, Y.; Mehta, V; Neiswanger, W.; Schneider, J.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Chattopadhyay, A.; Manchanda, R.] Inst Plasma Res, Ahmadabad, Gujarat, India; [Chen, R.; Chen, J.; Ding, R.; Gong, X.; Guo, W.; Hu, W.; Hu, Y.; Huang, J.; Huang, Y.; Lan, H.; Li, X.; Li, K.; Li, E.; Li, G.; Liu, X.; Liu, J.; Liu, Y.; Luo, Z.; Pan, C.; Qian, J.; Ren, Q.; Sheng, Z.; Si, H.; Sun, Y.; Sun, P.; Tao, Y.; Ti, A.; Wang, L.; Wang, H.; Wang, Y.; Wu, M.; Yuan, Q.; Zhang, L.; Zhang, X.; Zhang, R.; Zhang, B.; Zhang, J.; Zhang, J.] ASIPP, Hefei, Peoples R China; [Chen, J.] Peking Univ, Beijing, Peoples R China; [Chen, M.; Luhmann, N.; Wang, Y.; Yu, G.; Zheng, Y.; Zhu, Y.] Univ Calif Davis, Davis, CA 95616 USA; [Choi, G.; Degrandchamp, G.; Gage, K.; Garcia, A.; Hansen, E.; Hayashi, W.; Heidbrink, W.; Lestz, J.; Lin, Z.; Lin, D.; Liu, D.; Zhu, Y.] Univ Calif Irvine, Irvine, CA USA; [Chrobak, C.] Commonwealth Fus Syst, Cambridge, MA USA; [Clark, J.] Univ Liverpool, Liverpool, Merseyside, England; [Coda, S.; Cooper, A.; Sauter, O.] Ecole Polytech Fed Lausanne, Lausanne, Switzerland; [Cole, A.; Woodruff, S.] Woodruff Sci Inc, Santa Fe, NM USA; [Cothran, J.] Convergint Technol, Schaumburg, IL USA; [Crowley, T.; Demers, D.; Fimognari, P.] Xantho Technol LLC, Madison, WI USA; [Curreli, D.; Drobny, J.; Moynihan, C.] Univ Illinois, Urbana, IL USA; [Dal Molin, A.; Nocente, M.] Univ Milano Bicocca, Milan, Italy; [De Tommasi, G.; Pironti, A.] CREATE Consortium, Chesterfield, MO USA; [Deshazer, E.; Dunn, C.; Liu, Z.; Piper, N.; Roveto, J.; Stacey, W.] Georgia Tech, Atlanta, GA USA; [Du, H.; Gao, Q.; Hao, G.; Li, J.; Sun, A.] Southwestern Inst Phys, Chengdu, Peoples R China; [Elder, D.; Stangeby, P.] Univ Toronto, Toronto, ON, Canada; [Ennis, D.; Johnson, C.] Auburn Univ, Auburn, AL 36849 USA; [Fasciana, M.] Politecn Torino, Turin, Italy; [Ferreira, J.; Nave, F.] Inst Super Tecn, Lisbon, Portugal; [Finkenthal, D.] Palomar Coll, San Marcos, CA USA; [Fox, P.; Giroud, C.; Hollocombe, J.; Horvath, L.; Jackson, A.; Kirk, A.; Kochan, M.; Maggi, C.; Mailloux, J.; Mcardle, G.; Sharapov, S.; Srinivasan, C.; Szepesi, G.; Thornton, A.] CCFE, Oxford, England; [Frassinetti, L.] Kungliga Tekniska Hgsk, Stockholm, Sweden; [Frye, H.] San Diego State Univ, San Diego, CA 92182 USA; [Giacomelli, L.] CNR EURATOM, Ist Fis Plasma, Milan, Italy; [Gibson, S.] Univ Durham, Durham, England; [Graber, V; Leard, B.; Morosohk, S.; Martinez, A. Pajares; Rafiq, T.; Schuster, E.; Wang, Z.; Wang, H.] Lehigh Univ, Bethlehem, PA 18015 USA; [Groth, M.; Holm, A.; Jaervinen, A.] Aalto Univ, Espoo, Finland; [Han, H.; Shin, G.] Korea Natl Fus Res Ctr, Seoul, South Korea; [Hansen, C.; Johansen, A.; Kaptanoglu, A.; Raman, R.; Teo, K.] Univ Washington, Seattle, WA 98195 USA; [Harvey, R.; Kinsey, J.; Pigarov, A.] CompX, Dallas, TX USA; [Hood, R.; Kolasinski, R.; Myers, C.; Nygren, R.; Truong, D.; Wampler, W.; Watkins, J.] Sandia Natl Labs, Albuquerque, NM USA; [Howell, E.; King, J.] Tech X Corp, Boulder, CO USA; [In, Y.; Kwon, K.; Ra, O.] Ulsan Natl Inst Sci & Technol, Hefei, Peoples R China; [Jalalvand, A.; Klevarova, V; Verdoolaege, G.] Univ Ghent, Ghent, Belgium; [Jarleblad, H.; Madsen, B.; Salewski, M.] Tech Univ Denmark, Lyngby, Denmark; [Joffrin, E.; Moreau, D.; Peysson, Y.] CEA Cadarache, St Paul Les Durance, France; [Johnson, T.] Drake Univ, Des Moines, IA 50311 USA; [Kalling, R.] Kalling Software, Kirkland, WA USA; [Kasdorf, S.; Parker, S.] Univ Colorado, Boulder, CO USA; [Kates-Harbeck, J.] Harvard Univ, Cambridge, MA 02138 USA; [Kazantzidis, P.] Natl Tech Univ Athens, Athens, Greece; [Kim, E.] Coventry Univ, Coventry, W Midlands, England; [Kim, H.] EUROfusion, Brussels, Belgium; [Kim, C.] SLS2 Consulting, San Diego, CA USA; [Kimura, W.] STI Optron Inc, Bellevue, WA USA; [Kohn, A.] Univ Stuttgart, Stuttgart, Germany; [Kostadinova, E.] Baylor Univ, CASPER, Waco, TX 76798 USA; [Kripner, L.; Markovic, T.] Inst Plasma Phys AS CR, Prague, Czech Republic; [Lee, J.] Alphawave Res, New York, NY USA; [Lee, M.] Harvey Mudd Coll, Claremont, CA USA; [Lee, C.; Lee, J.] Seoul Natl Univ, Seoul, South Korea; [Li, L.] Donghua Univ, Shanghai, Peoples R China; [Li, Y.; Patel, B.; Ward, S.; Wilson, H.] Univ York, York, N Yorkshire, England; [Liu, T.; Luan, Q.; Sang, C.; Yang, X.] Dalian Univ Technol, Dalian, Peoples R China; [Maheshwari, K.; Saib, S.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Makowski, M.; Porter, G.] AKIMA Infrastruct Serv, Albuquerque, NM USA; [Mcdevitt, C.; Tang, X.; Wurden, G.] Los Alamos Natl Lab, Santa Fe, NM USA; [Meier, E.] Zap Energy Inc, Everett, WA USA; [Michoski, C.] US DOE, Washington, DC 20585 USA; [Milne, P.] D TACQ Solut Ltd, New York, NY USA; [Mitrishkin, Y.] Inst Control Sci Russian Acad, Shanghai, Peoples R China; [Morales, M.] Fus Energy Joint Undertaking, Oxford, England; [Mrazkova, T.] Palomar Sci Instruments Inc, San Marcos, TX USA; [Burgos, J. Munoz] Astro Fus Spectre LLC, Oxford, England; [Murphy, K.] Univ British Columbia, Vancouver, BC, Canada; [Nandipati, G.] Pacific Northwest Natl Lab, Richland, WA 99352 USA; [Neff, A.] ORISE, Oak Ridge, TN USA; [Nguyen, L.] Michigan State Univ, E Lansing, MI 48824 USA; [Okamoto, M.] Ishikawa Natl Coll Technol, Tsubata, Ishikawa, Japan; [Olavson, L.] Peak Tech Staffing, Pittsburgh, PA USA; [Omullane, M.] Univ Strathclyde, Glasgow, Lanark, Scotland; [Parsons, M.] Penn State Univ, University Pk, PA 16802 USA; [Pizzo, J.; Seol, S.] Rensselaer Polytech Inst, Troy, NY 12181 USA; [Powers, C.; Powers, S.] Univ Southern Calif, Los Angeles, CA 90007 USA; [Pusztai, I] Chalmers Univ Technol, Gothenburg, Sweden; [Robinson, J.; Wolz, A.] Univ Virginia, Charlottesville, VA 22903 USA; [Rosiello, S.] Univ Napoli Federico II, Naples, Italy; [Ruiz, J. Ruiz] Univ Oxford, Oxford, England; [Salmi, A.; Tala, T.] VTT Tech Res Ctr, Oulu, Finland; [Sandorfi, A.] Jefferson Lab, Newport News, VA USA; [Chan, V; Shi, S.; Wu, Y.; Xu, G.] Univ Sci & Technol China, Hefei, Peoples R China; [Shibata, Y.] Gifu Coll, Natl Inst Technol, Gifu, Japan; [Simmerling, P.] Univ Connecticut, Storrs, CT USA; [Snoep, G.; Van Kampen, R.] DIFFER, Eindhoven, Netherlands; [Solano, E.] CIEMAT, Madrid, Spain; [Song, J.] Hanyang Univ, Seoul, South Korea; [Spendlove, J.] Brigham Young Univ, Provo, UT 84602 USA; [Sung, C.] Korea Adv Inst Sci & Technol, Daejeon, South Korea; [Theodorsen, A.] UiT Arctic Univ Norway, Tromso, South Korea; [Thorman, A.] Australian Natl Univ, Canberra, ACT, Australia; [Timchenko, N.] RRC Kurchatov Inst, Moscow, Russia; [Ochoa, A. 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Coventry University; University of Stuttgart; Baylor University; Czech Academy of Sciences; Institute of Plasma Physics of the Czech Academy of Sciences; Claremont Colleges; Harvey Mudd College; Seoul National University (SNU); Donghua University; University of York - UK; Dalian University of Technology; University of California System; University of California Berkeley; United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); University of British Columbia; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Oak Ridge Associated Universities; United States Department of Energy (DOE); Oak Ridge Institute for Science & Education; Michigan State University; University of Strathclyde; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Rensselaer Polytechnic Institute; University of Southern California; Chalmers University of Technology; University of Virginia; University of Naples Federico II; University of Oxford; VTT Technical Research Center Finland; United States Department of Energy (DOE); Jefferson National Accelerator; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Connecticut; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas; Hanyang University; Brigham Young University; Korea Advanced Institute of Science & Technology (KAIST); Australian National University; National Research Centre - Kurchatov Institute; Helmholtz Association; Research Center Julich; Zhejiang University; University of Tokyo; University of Michigan System; University of Michigan; Italian National Agency New Technical Energy & Sustainable Economics Development Fenstermacher, ME (corresponding author), Lawrence Livermore Natl Lab, Livermore, CA 94550 USA. fenstermacher1@llnl.gov Groth, Mathias/G-2227-2013; Leuthold, Nils/GXV-3617-2022; Barr, Jayson L./AAE-5496-2022; 顾, 帅/HNS-5348-2023; De Tommasi, Gianmaria/A-8787-2012; Stoltzfus-Dueck, Timothy/AGX-2338-2022; Bortolon, Alessandro/GNP-2880-2022; Marinoni, Alessandro/AFQ-6558-2022; Duran, Jonah/HMP-2604-2023; Munaretto, Stefano/AAU-5252-2021; Cote, Tyler/ACU-8496-2022; Haskey, Shaun/GRE-8150-2022; Bertelli, Nicola/GNP-5633-2022; Marini, Claudio/AGO-3012-2022; Sung, Choongki/AAA-2676-2022; Dudt, Daniel/GNH-3945-2022; Järvinen, Aaro Einari/AAB-8448-2021; Cruz Zabala, Diego Jose/HKM-9479-2023; Perillo, Renato/J-1267-2019; guangzhou, hao/HNP-3595-2023; Vianello, Nicola/B-6323-2008; Willensdorfer, Matthias/J-7202-2012; Callahan, Kelly/GXV-2892-2022; Laggner, Florian/I-9826-2016; Effenberg, Florian/AAC-1841-2019; Hong, Rongjie/ABI-5988-2020; Salewski, Mirko/C-7104-2008; Biswas, Bodhi/GYU-0192-2022; Kleiner, Andreas/AAB-5902-2019; Orlov, Dmitri M/D-2406-2016; DAL MOLIN, ANDREA/X-1667-2018; Koel, Bruce/H-3857-2013; Haskey, Shaun/M-1469-2015; Tsui, Cedric Kar-Wai/L-6784-2017; Kohn-Seemann, Alf/A-6717-2010; Unterberg, Ezekial/F-5240-2016 Barr, Jayson L./0000-0001-7768-5931; 顾, 帅/0000-0001-5159-939X; De Tommasi, Gianmaria/0000-0002-8509-7176; Stoltzfus-Dueck, Timothy/0000-0003-2587-6298; Bortolon, Alessandro/0000-0002-0094-0209; Duran, Jonah/0000-0003-2139-0215; Munaretto, Stefano/0000-0003-1465-0971; Bertelli, Nicola/0000-0002-9326-7585; Marini, Claudio/0000-0003-4819-7765; Sung, Choongki/0000-0002-8122-0018; Järvinen, Aaro Einari/0000-0002-4525-8158; Cruz Zabala, Diego Jose/0000-0001-5925-5153; Perillo, Renato/0000-0001-6696-5970; Vianello, Nicola/0000-0003-4401-5346; Willensdorfer, Matthias/0000-0002-1080-4200; Laggner, Florian/0000-0003-1601-2973; Effenberg, Florian/0000-0002-4846-4598; Hong, Rongjie/0000-0003-4750-8015; Salewski, Mirko/0000-0002-3699-679X; Biswas, Bodhi/0000-0001-7993-3732; Kleiner, Andreas/0000-0002-5800-8027; Orlov, Dmitri M/0000-0002-2230-457X; DAL MOLIN, ANDREA/0000-0003-0471-1718; Sarff, John/0000-0001-8883-6678; Wilcox, Robert/0000-0003-1369-1739; Lvovskiy, Andrey/0000-0002-3649-1169; Koel, Bruce/0000-0002-0032-4991; Seltzman, Andrew/0000-0002-7725-2981; Haskey, Shaun/0000-0002-9978-6597; Younkin, Timothy/0000-0002-7471-6840; Duarte, Vinicius/0000-0001-8096-7518; Lestz, Jeff/0000-0002-6975-1537; Casali, Livia/0000-0003-2924-3674; Ferraro, Nathaniel/0000-0002-6348-7827; Neiser, Tom/0000-0002-8763-3016; /0000-0003-1004-5782; Choi, Gyung Jin/0000-0003-0044-1650; Brookman, Michael/0000-0002-5838-1630; Tsui, Cedric Kar-Wai/0000-0002-7346-8312; Slief, Jelle/0000-0003-3454-1473; Bose, Sayak/0000-0001-8093-9322; Grierson, Brian/0000-0001-5918-6506; Kohn-Seemann, Alf/0000-0002-1192-2057; Parsons, Matthew/0000-0002-2428-3215; Maurizio, Roberto/0000-0001-9896-6732; Ren, Jun/0000-0002-2256-1007; Fenstermacher, Max/0000-0001-5635-7330; Cianciosa, Mark/0000-0001-6211-5311; Jeon, YoungMu/0000-0002-7374-3759; Sinclair, Gregory/0000-0003-4195-177X; Hu, Qiming/0000-0002-8877-4988; Halpern, Federico/0000-0002-0387-6038; Barada, Kshitish/0000-0001-7724-8491; /0000-0002-7361-1131; Unterberg, Ezekial/0000-0003-1353-8865 US Department of Energy, Office of Science, Office of Fusion Energy Sciences [DE-FC0204ER54698, DE-AC52-07NA27344] US Department of Energy, Office of Science, Office of Fusion Energy Sciences(United States Department of Energy (DOE)) This material is based upon work supported by the US Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Awards DE-FC0204ER54698 and DE-AC52-07NA27344. 80 5 5 22 42 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0029-5515 1741-4326 NUCL FUSION Nucl. Fusion APR 1 2022.0 62.0 4 42024 10.1088/1741-4326/ac2ff2 0.0 22 Physics, Fluids & Plasmas Science Citation Index Expanded (SCI-EXPANDED) Physics 0R4UI Green Published, Green Submitted, Green Accepted, hybrid 2023-03-23 WOS:000785591600001 0
J Fazio, M; Buzachis, A; Galletta, A; Celesti, A; Wan, JF; Longo, A; Villari, M Fazio, Maria; Buzachis, Alina; Galletta, Antonino; Celesti, Antonio; Wan, Jiafu; Longo, Antonella; Villari, Massimo A Map-Reduce Approach for the Dijkstra Algorithm in SDN Over Osmotic Computing Systems INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING English Article Osmotic computing; SDN; Dijkstra; Hadoop; Map-reduce Osmotic Computing represents a glue solution able to manage the deployment and orchestration of interconnected microelements across heterogeneous physical and virtual infrastructures (e.g., IoT, Edge and Cloud nodes) according to the behavior of hardware and software components during the time. The adoption of Osmotic Computing is challenging, but addressing networking issues is a key research topic due to the emergence of new problems in terms of QoS requirements. In this paper, we analyze how to exploit well-known networking solutions, such as the Dijkstra's algorithm, and Big Data oriented technologies, such as the Hadoop and MapReduce, to provide efficient newtorking functionalities in Osmotic Computing. In particular, our objective is to minimize the routing path computation time in the software defined network (SDN) at the basis of microelement networking, as well as to ensure a global view and a high level of dynamism of our network topology. To accomplish this task, we process routing tables through a MapReduce based implementation of the Dijkstra's algorithm whenever a topology change occurs, and we export routing results into the SDN. Our experimental results show that our networking strategy drastically reduces the best path computation time whenever the network of microelements is very large. [Fazio, Maria; Buzachis, Alina; Galletta, Antonino; Celesti, Antonio; Villari, Massimo] Univ Messina, MIFT, Messina, Italy; [Wan, Jiafu] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Peoples R China; [Fazio, Maria; Celesti, Antonio] Grp Nazl Calcolo Sci GNCS, Ist Nazl Alta Matemat INdAM F Severi, Rome, Italy; [Longo, Antonella] Univ Salento, Dipartimento Ingn Innovaz, Lecce, Italy University of Messina; South China University of Technology; University of Salento Fazio, M (corresponding author), Univ Messina, MIFT, Messina, Italy.;Fazio, M (corresponding author), Grp Nazl Calcolo Sci GNCS, Ist Nazl Alta Matemat INdAM F Severi, Rome, Italy. mfazio@unime.it; abuzachis@unime.it; angalletta@unime.it; acelesti@unime.it; mejwan@scut.edu.cn; antonella.longo@unisalento.it; mvillari@unime.it Galletta, Antonino/HKM-4636-2023; Longo, Antonella/A-5335-2009; Wan, Jiafu/I-3059-2016 Longo, Antonella/0000-0002-6902-0160; Wan, Jiafu/0000-0001-9188-4179; Fazio, Maria/0000-0003-3574-1848 C4E C4E This work has been supported by C4E. 40 0 0 0 4 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0885-7458 1573-7640 INT J PARALLEL PROG Int. J. Parallel Program. JUN 2021.0 49.0 3 347 375 10.1007/s10766-021-00693-3 0.0 MAR 2021 29 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science RZ0LE 2023-03-23 WOS:000626403100001 0
J Gong, SM; Wu, CW; Duan, YJ; Tang, JY; Wu, PF Gong, Siming; Wu, Changwu; Duan, Yingjuan; Tang, Juyu; Wu, Panfeng A Comprehensive Pan-Cancer Analysis for Pituitary Tumor-Transforming Gene 1 FRONTIERS IN GENETICS English Article PTTG1; prognosis; immune infiltration; enrichment analysis; big data; pan-cancer analysis EPITHELIAL-MESENCHYMAL TRANSITION; PROMOTES MIGRATION; PTTG1 ONCOGENE; MUTANT P53; INVASION; FIBROBLASTS; METASTASIS; PATHWAY; TARGET; CELLS Pituitary tumor-transforming gene 1 (PTTG1) encodes a multifunctional protein that is involved in many cellular processes. However, the potential role of PTTG1 in tumor formation and its prognostic function in human pan-cancer is still unknown. The analysis of gene alteration, PTTG1 expression, prognostic function, and PTTG1-related immune analysis in 33 types of tumors was performed based on various databases such as The Cancer Genome Atlas database, the Genotype-Tissue Expression database, and the Human Protein Atlas database. Additionally, PTTG1-related gene enrichment analysis was performed to investigate the potential relationship and possible molecular mechanisms between PTTG1 and tumors. Overexpression of PTTG1 may lead to tumor formation and poor prognosis in various tumors. Consequently, PTTG1 acts as a potential oncogene in most tumors. Additionally, PTTG1 is related to immune infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, PTTG1 could be potential biomarker for both prognosis and outcomes of tumor treatment and it could also be a promising target in tumor therapy. [Gong, Siming; Tang, Juyu; Wu, Panfeng] Cent South Univ, Natl Clin Res Ctr Geriatr Disorders, Dept Orthpaed, Xiangya Hosp, Changsha, Peoples R China; [Gong, Siming; Wu, Changwu] Univ Leipzig, Inst Anat, Leipzig, Germany; [Duan, Yingjuan] Univ Leipzig, Fac Chem & Mineral, Leipzig, Germany Central South University; Leipzig University; Leipzig University Wu, PF (corresponding author), Cent South Univ, Natl Clin Res Ctr Geriatr Disorders, Dept Orthpaed, Xiangya Hosp, Changsha, Peoples R China. wupanfeng@csu.edu.cn 63 1 2 1 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. FEB 25 2022.0 13.0 843579 10.3389/fgene.2022.843579 0.0 13 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity ZR4OP 35281830.0 Green Accepted, gold 2023-03-23 WOS:000767764700001 0
C Ying, Z; Wang, LJ; Yi, AJ; Li, F; Nuppenau, EA Xiaosheng, Q; Amahmid, O Ying, Zhang; Wang, Lijun; Yi, AiJun; Li, Fei; Nuppenau, Ernst-August Study on desertification reversal factors in Maowusu sandy land in China 2020 2ND INTERNATIONAL CONFERENCE ON WATER RESOURCES AND ENVIRONMENTAL ENGINEERING (ICWREE2020) E3S Web of Conferences English Proceedings Paper 2nd International Conference on Water Resources and Environmental Engineering (ICWREE) SEP 11, 2020 ELECTR NETWORK Based on the data of China's economic and social big data platform from 2000 to 2019, this paper studies and analyzes the development status and influencing factors of desertification in Maowusu sandy land in China. Based on the exploratory analysis (EFA) and the dummy variable regression model (DVRM), the research result shows that the annual precipitation is the main climate factor affecting the vegetation coverage in this area. And for every 100 mm increase in annual precipitation, vegetation coverage will increase by 10%. In addition, the annual average temperature also has a significant impact on the vegetation coverage. For every 1 degrees C increase in the annual average temperature, the vegetation coverage will increase by 2.5%. Analysis of policy factors shows that the policy effects of the 2005 National Desert Control Plan (2005-2010) and the 2011 National Desert Control Plan (2011-2020) etc. can increase vegetation coverage by 3.4% and 4.7% respectively compared with the base period level in 2000. The study reveals the important role of climate and policy factors in the reversion of desertification in Maowusu sandy land. The study is of great significance and value to desertification management and related policy-making in China. [Ying, Zhang] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China; [Wang, Lijun] Nanjing Univ Aeronaut & Astronaut, Sch Econ & Management, Nanjing 211106, Peoples R China; [Yi, AiJun] Jiangsu Ocean Univ, Sch Business, Lianyungang 222005, Peoples R China; [Li, Fei] Beijing Forestry Univ, Sch Landscape Architecture, Beijing 100083, Peoples R China; [Nuppenau, Ernst-August] Giessen Univ, Inst Agr Polit & Monetary Res, D-35390 Giessen, Germany Beijing Forestry University; Nanjing University of Aeronautics & Astronautics; Jiangsu Ocean University; Beijing Forestry University Ying, Z (corresponding author), Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China. zhangyin@bjfu.edu.cn Zhang, Ying/0000-0002-9964-9381 National Key Research and Development Program [2016YFC0500905]; Ministry of Science and Technology P. R. China; Beijing Natural Science Foundation [8172034] National Key Research and Development Program; Ministry of Science and Technology P. R. China(Ministry of Science and Technology, China); Beijing Natural Science Foundation(Beijing Natural Science Foundation) The study is supported by the National Key Research and Development Program (2016YFC0500905) of Ministry of Science and Technology P. R. China, and Beijing Natural Science Foundation (8172034) the authors would like to express heartfelt thanks to them. 27 0 0 2 6 E D P SCIENCES CEDEX A 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A, FRANCE 2267-1242 E3S WEB CONF 2020.0 199.0 15 10.1051/e3sconf/202019900015 0.0 11 Engineering, Environmental; Environmental Sciences; Water Resources Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Environmental Sciences & Ecology; Water Resources BR6BY Green Published, gold 2023-03-23 WOS:000659491300014 0
J Li, ML; He, HW; Feng, L; Chen, Y; Yan, M Li, Menglin; He, Hongwen; Feng, Lei; Chen, Yong; Yan, Mei Hierarchical predictive energy management of hybrid electric buses based on driver information JOURNAL OF CLEANER PRODUCTION English Article Predictive energy management; Driver behavior; Driver type identification; Hierarchical control STATE-OF-CHARGE; DRIVING-BEHAVIOR; MODEL; STRATEGIES To improve the energy efficiency of hybrid electric city buses, a hierarchical predictive energy management strategy (HP-EMS) based on driver behavior and type is proposed in this paper. Within the model predictive control (MPC) framework, the k-Nearest Neighbor (kNN) method is applied to identify the driver type, and the deep neural network (DNN) is adopted to predict future speed based on the historical speed, driver type, and driver behavior. Combined with the city bus driving characteristics, the hierarchical strategy aims to reduce the frequent starts of the engine. The upper-level controller implements a rule-based strategy to limit the engine start-stop frequency. The lower-level controller uses dynamic programming (DP) to search for the best control strategy in the prediction horizon. Simulation results show that, compared with speed prediction without driver information, the new method can effectively improve the accuracy of future speed prediction, and RMSE between the prediction and measurement drops from 1.58 m/s to 1.45 m/s. The HP-EMS without driver information can reduce the number of engine starts by 30% while increase only 2% energy consumption compared with predictive energy management without hierarchical control. The paper also studies the benefits of considering driver behavior and type. The same HP-EMS controller is implemented with and without driver behavior and type. The one with the additional information reduces the energy consumption by 3.34% compared to the one without the information. (C) 2020 Elsevier Ltd. All rights reserved. [Li, Menglin; He, Hongwen; Yan, Mei] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China; [Li, Menglin; Feng, Lei; Yan, Mei] KTH Royal Inst Technol, Dept Machine Design, SE-10044 Stockholm, Sweden; [Chen, Yong] Beijing Informat Sci & Technol Univ, Beijing 100192, Peoples R China Beijing Institute of Technology; Royal Institute of Technology; Beijing Information Science & Technology University He, HW (corresponding author), Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China. hwhebit@bit.edu.cn Li, Menglin/L-1791-2015; He, Hongwen/Y-2860-2019; yan, mei/AAA-1379-2022 Li, Menglin/0000-0002-9248-9020; Yan, Mei/0000-0002-7620-3838; Chen, Yong/0000-0002-7776-4549; Feng, Lei/0000-0001-5703-5923 National Key R&D Program of China [2018YFB0105900]; National Natural Science Foundation of China of China [51675042]; Project for capacity construction of science and technology innovation service-Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles [PXM2019_014224 _000005]; Project for capacity construction of science and technology innovation service-Construction of scientific research base -Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles [PXM2018_014224 _000011] National Key R&D Program of China; National Natural Science Foundation of China of China(National Natural Science Foundation of China (NSFC)); Project for capacity construction of science and technology innovation service-Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles; Project for capacity construction of science and technology innovation service-Construction of scientific research base -Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles This work is supported by National Key R&D Program of China. (Grand No. 2018YFB0105900) in part, National Natural Science Foundation of China of China (grant number 51675042), Project for capacity construction of science and technology innovation service-Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles (PXM2019_014224 _000005) and Project for capacity construction of science and technology innovation service-Construction of scientific research base -Beijing laboratory construction-Beijing Laboratory for New Energy Vehicles (PXM2018_014224 _000011). Any opinions expressed in this paper are solely those of the authors and do not represent those of the sponsors. The authors would also like to thank the reviewers for their corrections and helpful suggestions. 35 16 16 7 30 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. OCT 1 2020.0 269.0 122374 10.1016/j.jclepro.2020.122374 0.0 14 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology ND0MS 2023-03-23 WOS:000561603100086 0
J Wang, Z; Hu, YF; Yan, SY; Wang, ZH; Hou, RJ; Wu, C Wang, Zhao; Hu, Yifan; Yan, Shiyang; Wang, Zhihao; Hou, Ruijie; Wu, Chao Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems ELECTRONICS English Article federated learning; medical data security; ring topology; deep generative model; privacy preserving; non-IID By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets. [Wang, Zhao; Hu, Yifan; Wang, Zhihao; Hou, Ruijie; Wu, Chao] Zhejiang Univ, Sch Publ Affairs, Hangzhou 310027, Peoples R China; [Wang, Zhao] Zhejiang Post & Telecommun Construct Co Ltd, Hangzhou 310016, Peoples R China; [Yan, Shiyang] INRIA, F-78150 Le Chesnay Rocquencourt, France Zhejiang University; Inria Wang, Z; Wu, C (corresponding author), Zhejiang Univ, Sch Publ Affairs, Hangzhou 310027, Peoples R China.;Wang, Z (corresponding author), Zhejiang Post & Telecommun Construct Co Ltd, Hangzhou 310016, Peoples R China. zhao_wang@zju.edu.cn; yifan_hu@zju.edu.cn; elyotyan@gmail.com; zhihao_wang@zju.edu.cn; ruijie.hou@zju.edu.cn; chao.wu@zju.edu.cn Wang, Zhao/0000-0002-7144-1511 National Key Research and Development Project of China [2021ZD0110400]; National Natural Science Foundation of China [U19B2042]; Ningbo Natural Science Foundation [2021J167] National Key Research and Development Project of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ningbo Natural Science Foundation This research was funded by National Key Research and Development Project of China (2021ZD0110400), National Natural Science Foundation of China (U19B2042) and Ningbo Natural Science Foundation (2021J167). 39 3 3 7 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics MAY 2022.0 11.0 10 1548 10.3390/electronics11101548 0.0 11 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics 1P1NK gold 2023-03-23 WOS:000801784200001 0
J Xu, JH; Zhou, W; Chen, ZB; Ling, SY; Le Callet, P Xu, Jiahua; Zhou, Wei; Chen, Zhibo; Ling, Suiyi; Le Callet, Patrick Binocular Rivalry Oriented Predictive Autoencoding Network for Blind Stereoscopic Image Quality Measurement IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article 3-D human vision; predictive autoencoding; quality measurement; Siamese encoder-decoder; stereoscopic image quality VISUAL-CORTEX; RECONSTRUCTION; INTEGRATION; VISION Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3-D contents. Compared with conventional methods that are relied on handcrafted features, deep-learning-oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3-D human visual system (HVS) in network design. In this article, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/no-reference SIQM. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions, we adopt the encoder-decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, a quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types. [Xu, Jiahua; Zhou, Wei; Chen, Zhibo] Univ Sci & Technol China, CAS Key Lab Technol Geospatial Informat Proc & Ap, Hefei 230027, Peoples R China; [Ling, Suiyi; Le Callet, Patrick] Univ Nantes, Lab Sci Numer Nantes, Equipe Image Percept & Interact, F-44300 Nantes, France Chinese Academy of Sciences; University of Science & Technology of China, CAS; Nantes Universite Chen, ZB (corresponding author), Univ Sci & Technol China, CAS Key Lab Technol Geospatial Informat Proc & Ap, Hefei 230027, Peoples R China. xujiahua@mail.ustc.edu.cn; weichou@mail.ustc.edu.cn; chenzhibo@ustc.edu.cn; suiyi.ling@univ-nantes.fr; patrick.lecallet@univ-nantes.fr Le Callet, Patrick/F-5772-2010 Xu, Jiahua/0000-0002-1668-9792; Ling, suiyi/0000-0002-5306-9189; Zhou, Wei/0000-0003-3641-1429 NSFC [U1908209, 61632001]; National Key Research and Development Program of China [2018AAA0101400]; Alibaba Corporate NSFC(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Alibaba Corporate This work was supported in part by NSFC under Grant U1908209 and Grant 61632001; in part by the National Key Research and Development Program of China 2018AAA0101400; and in part by Alibaba Corporate. The Associate Editor coordinating the review process was Yuhua Cheng. (Jiahua Xu and Wei Zhou contributed equally to this work.) 77 11 12 0 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2021.0 70.0 5001413 10.1109/TIM.2020.3026443 0.0 13 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation OZ4PT Green Submitted 2023-03-23 WOS:000594910700065 0
J Gan, ZY; Sun, W; Liao, KM; Yang, X Gan, Ziyu; Sun, Wei; Liao, Kaimin; Yang, Xuan Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article; Early Access Strain; Probabilistic logic; Deformable models; Motion estimation; Unsupervised learning; Image registration; Supervised learning; Cardiac motion estimation; compact support radial basis function (CSRBF); deep learning (DL); deformable registration; probabilistic learning LEARNING FRAMEWORK; REGULARIZATION; STRAIN Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy. [Gan, Ziyu; Liao, Kaimin; Yang, Xuan] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China; [Sun, Wei] Radboud Univ Nijmegen, Med Ctr, NL-6500 Nijmegen, Netherlands Shenzhen University; Radboud University Nijmegen Yang, X (corresponding author), Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China. ganziyu@email.szu.edu.cn; sunweidemail@gmail.com; liaokaimin2019@email.szu.edu.cn; yangxuan@szu.edu.cn Sun, Wei/0000-0002-4385-1636 National Natural Science Foundation of China [61871269] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61871269. 55 0 0 5 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. 10.1109/TNNLS.2022.3141119 0.0 JAN 2022 15 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering YR9HT 35073271.0 hybrid 2023-03-23 WOS:000750300100001 0
J Meraghni, S; Terrissa, LS; Yue, ML; Ma, J; Jemei, S; Zerhouni, N Meraghni, Safa; Terrissa, Labib Sadek; Yue, Meiling; Ma, Jian; Jemei, Samir; Zerhouni, Noureddine A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction INTERNATIONAL JOURNAL OF HYDROGEN ENERGY English Article Deep learning; Digital twin; Prognostics; Proton exchange membrane fuel cell; Remaining useful life FAULT-DIAGNOSIS; DEGRADATION PREDICTION; MANAGEMENT; OPTIMIZATION; DESIGN; SYSTEM Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. [Meraghni, Safa; Terrissa, Labib Sadek] Univ Biskra, LINFI Lab, Biskra, Algeria; [Meraghni, Safa; Yue, Meiling; Jemei, Samir; Zerhouni, Noureddine] Univ Bourgogne Franche Comte, FEMTO ST Inst, UMR CNRS 6174, Besancon, France; [Yue, Meiling; Jemei, Samir; Zerhouni, Noureddine] Univ Bourgogne Franche Comte, FCLAB Res Federat, FR CNRS 3539, Besancon, France; [Ma, Jian] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China; [Ma, Jian] Natl Key Lab Sci & Technol Reliabil & Environm En, Beijing, Peoples R China Universite Mohamed Khider Biskra; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Centre National de la Recherche Scientifique (CNRS); Universite de Franche-Comte; Universite de Technologie de Belfort-Montbeliard (UTBM); Beihang University Yue, ML (corresponding author), Univ Bourgogne Franche Comte, FEMTO ST Inst, UMR CNRS 6174, Besancon, France. meiling.yue@femto-st.fr Jemeï, Samir/ACR-4931-2022 YUE, MEILING/0000-0002-4624-4743; JEMEI, Samir/0000-0003-0195-903X French region of Bourgogne Franche-Comte [2016Y-04574]; EIPHI Graduate School of the Region Bourgogne-Franche-Comte, France [ANR-17-EURE-0002] French region of Bourgogne Franche-Comte; EIPHI Graduate School of the Region Bourgogne-Franche-Comte, France The authors would like to thank the project PHyTie (grant number 2016Y-04574) funded by the French region of Bourgogne Franche-Comte for the support. This work has also been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002) of the Region Bourgogne-Franche-Comte, France. 41 35 38 32 157 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-3199 1879-3487 INT J HYDROGEN ENERG Int. J. Hydrog. Energy JAN 6 2021.0 46.0 2 2555 2564 10.1016/j.ijhydene.2020.10.108 0.0 10 Chemistry, Physical; Electrochemistry; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Electrochemistry; Energy & Fuels PO4OM Green Submitted 2023-03-23 WOS:000605147300093 0
J Radev, ST; Graw, F; Chen, SM; Mutters, NT; Eichel, VM; Barnighausen, T; Kothe, U Radev, Stefan T.; Graw, Frederik; Chen, Simiao; Mutters, Nico T.; Eichel, Vanessa M.; Baernighausen, Till; Koethe, Ullrich OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany PLOS COMPUTATIONAL BIOLOGY English Article Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases: In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations. [Radev, Stefan T.] Heidelberg Univ, Inst Psychol, Heidelberg, Germany; [Graw, Frederik] Heidelberg Univ, BioQuant Ctr Quantitat Biol, Heidelberg, Germany; [Chen, Simiao; Baernighausen, Till] Heidelberg Inst Global Hlth, Heidelberg, Germany; [Chen, Simiao] Chinese Acad Med Sci & Peking Union Med Coll, Beijing, Peoples R China; [Mutters, Nico T.] Univ Hosp Bonn, Inst Hyg & Publ Hlth, Bonn, Germany; [Eichel, Vanessa M.] Univ Hosp Heidelberg, Ctr Infect Dis, Heidelberg, Germany; [Baernighausen, Till] Harvard TH Chan Sch Publ Hlth, Boston, MA USA; [Baernighausen, Till] Africa Hlth Res Inst, Durban, South Africa; [Koethe, Ullrich] Heidelberg Univ, Comp Vis & Learning Lab, Heidelberg, Germany Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Ruprecht Karls University Heidelberg; Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; University of Bonn; Ruprecht Karls University Heidelberg; Harvard University; Harvard T.H. Chan School of Public Health; Africa Health Research Institute; Ruprecht Karls University Heidelberg Radev, ST (corresponding author), Heidelberg Univ, Inst Psychol, Heidelberg, Germany. stefan.radev93@gmail.com Chen, Simiao/O-4017-2017 Chen, Simiao/0000-0003-0555-2157; Koethe, Ullrich/0000-0001-6036-1287; Radev, Stefan/0000-0002-6702-9559; Mutters, Nico/0000-0002-0156-9595 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [GRK 2277]; Google Cloud; Bill & Melinda Gates Foundation [INV-006261]; Alexander von Humboldt-Foundation; Chica and Heinz Schaller Foundation; Informatics for Life - Klaus Tschira Foundation; DFG; Alexander von Humboldt Foundation - German Federal Ministry of Education and Research Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)); Google Cloud(Google Incorporated); Bill & Melinda Gates Foundation(Bill & Melinda Gates FoundationCGIAR); Alexander von Humboldt-Foundation(Alexander von Humboldt Foundation); Chica and Heinz Schaller Foundation; Informatics for Life - Klaus Tschira Foundation; DFG(German Research Foundation (DFG)); Alexander von Humboldt Foundation - German Federal Ministry of Education and Research SR was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; grant number GRK 2277 Statistical Modeling in Psychology) and the Covid-19 Research Grants by Google Cloud. SC was supported by Bill & Melinda Gates Foundation (Project INV-006261) and Alexander von Humboldt-Foundation. FG was supported by a Fellowship from the Chica and Heinz Schaller Foundation. UK was supported by Informatics for Life funded by the Klaus Tschira Foundation and by the Cluster of Excellence STRUCTURES funded by DFG. TB was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 50 5 5 1 6 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1553-734X 1553-7358 PLOS COMPUT BIOL PLoS Comput. Biol. OCT 2021.0 17.0 10 e1009472 10.1371/journal.pcbi.1009472 0.0 26 Biochemical Research Methods; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Mathematical & Computational Biology YS5OI 34695111.0 gold, Green Published, Green Accepted, Green Submitted 2023-03-23 WOS:000750725600006 0
J Pietronudo, MC; Zhou, FL; Caporuscio, ARA; La Ragione, SE; Risitano, MRCL Pietronudo, Maria Cristina; Zhou, Fuli; Caporuscio, Andrea; La Ragione, Giuseppe; Risitano, Marcello New emerging capabilities for managing data-driven innovation in healthcare: the role of digital platforms EUROPEAN JOURNAL OF INNOVATION MANAGEMENT English Article Digital platform; Innovation management; Dynamic capabilities; Healthcare industry; Case study BIG DATA; MANAGEMENT; COMPETITION; TECHNOLOGY; STRATEGY; ICT; INTERMEDIARIES; ARCHITECTURE; PERFORMANCE; NETWORKS Purpose This article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital platforms in addressing data challenges and fostering data-driven innovation in the health sector. Design/methodology/approach For exploring the role of platforms, the authors propose a theoretical model based on the platform's dynamic capabilities, assuming that, because of their set of capabilities, platforms may trigger innovation practices in actor interactions. To corroborate the theoretical framework, the authors present a detailed in-depth case study analysis of Apheris, an innovative data-driven digital platform operating in the healthcare scenario. Findings The paper finds that the innovative data-driven digital platform can be used to revolutionize established practices in the health sector (a) accelerating research and innovation; (b) overcoming challenges related to healthcare data. The case study demonstrates how data and intellectual property sharing can be privacy-compliant and enable new capabilities. Originality/value The paper attempts to fill the gap between the use of the data-driven digital platform and the critical innovation practices in the healthcare industry. [Pietronudo, Maria Cristina; Caporuscio, Andrea; La Ragione, Giuseppe; Risitano, Marcello] Univ Naples Parthenope, Dept Management & Quantitat Studies, Naples, Italy; [Zhou, Fuli] Zhengzhou Univ Light Ind, Coll Econ & Management, Zhengzhou, Peoples R China Parthenope University Naples; Zhengzhou University of Light Industry Pietronudo, MC (corresponding author), Univ Naples Parthenope, Dept Management & Quantitat Studies, Naples, Italy. mariacristina.pietronudo@uniparthenope.it Pietronudo, Maria Cristina/0000-0001-5040-2225; Risitano, Marcello/0000-0001-9579-5931 99 0 0 28 32 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 1460-1060 1758-7115 EUR J INNOV MANAG Eur. J. Innov. Manag. AUG 2 2022.0 25.0 6 867 891 10.1108/EJIM-07-2021-0327 0.0 25 Business; Management Social Science Citation Index (SSCI) Business & Economics 3N7ZM hybrid 2023-03-23 WOS:000836364700001 0
J Guo, CH; Lin, SF; Huang, ZS; Yao, YH Guo, Chaohui; Lin, Shaofu; Huang, Zhisheng; Yao, Yahong Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT plus BiLSTM HEALTH INFORMATION SCIENCE AND SYSTEMS English Article Adversarial training; BERT plus BiLSTM; Time feature; Sentiment analysis; Depression HEALTH With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the Tree Hole. The purpose of this article is to support the Tree Hole rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of Tree Hole named Zou Fan, this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of Tree Hole messages in multiple time dimensions is positively correlated to emotion. The longer the Tree Hole is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of Tree Hole rescue, volunteers should focus on the long-formed Tree Hole and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events. [Guo, Chaohui; Lin, Shaofu; Yao, Yahong] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China; [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands Beijing University of Technology; Vrije Universiteit Amsterdam Lin, SF (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China. linshaofu@bjut.edu.cn Lin, Shaofu/0000-0002-3653-6531 32 0 0 10 11 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 2047-2501 HEALTH INF SCI SYST Health Inf. Sci. Syst. JUL 13 2022.0 10.0 1 15 10.1007/s13755-022-00184-w 0.0 10 Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Medical Informatics 2W6OC 35846171.0 Green Accepted, Green Published, Bronze 2023-03-23 WOS:000824640700001 0
J Chang, HL; Li, LS; Huang, JX; Zhang, QP; Chin, KS Chang, Haoliang; Li, Lishuai; Huang, Jianxiang; Zhang, Qingpeng; Chin, Kwai-Sang Tracking traffic congestion and accidents using social media data: A case study of Shanghai ACCIDENT ANALYSIS AND PREVENTION English Article Traffic accident; Traffic congestion; Kernel density estimation; Social media data; Geographic information science; Natural language processing SENTIMENT ANALYSIS; TRANSIT; TRANSPORTATION; IDENTIFICATION; INFORMATION; COLLISIONS; DENSITY; SUPPORT Traffic congestion and accidents take a toll on commuters' daily experiences and society. Locating the venues prone to congestion and accidents and capturing their perception by public members is invaluable for transport policy-makers. However, few previous methods consider user perception toward the accidents and congestion in finding and profiling the accident- and congestion-prone areas, leaving decision-makers unaware of the subsequent behavior responses and priorities of retrofitting measures. This study develops a framework to identify and characterize the accident- and congestion-prone areas heatedly discussed on social media. First, we use natural language processing and deep learning to detect the accident- and congestion-relevant Chinese microblogs posted on Sina Weibo, a Chinese social media platform. Then a modified Kernel Density Estimation method considering the sentiment of microblogs is employed to find the accident- and congestion-prone regions. The results show that the 'congestion-prone areas' discussed on social media are mainly distributed throughout the historical urban core and the Northwest of Pudong New Area, in reasonably good agreements with actual congestion records. In contrast, the 'accident-prone areas' are primarily found in locations with severe accidents. Finally, the above venues are characterized in spatio-temporal and semantic aspects to understand the nature of the incidents and assess the priority level for mitigation measures. The outcomes can provide a reference for traffic authorities to inform resource allocation and prioritize mitigation measures in future traffic management. [Chang, Haoliang; Chin, Kwai-Sang] City Univ Hong Kong, Dept Adv Design & Syst Engn, Kowloon, Hong Kong, Peoples R China; [Li, Lishuai] Delft Univ Technol, Fac Aerosp Engn, Postbus 5, NL-2600AA Delft, Netherlands; [Li, Lishuai; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China; [Huang, Jianxiang] Univ Hong Kong, Dept Urban Planning & Design, Pokfulam Rd, Hong Kong, Peoples R China City University of Hong Kong; Delft University of Technology; City University of Hong Kong; University of Hong Kong Chang, HL (corresponding author), City Univ Hong Kong, Dept Adv Design & Syst Engn, Kowloon, Hong Kong, Peoples R China. hlchang4-c@my.cityu.edu.hk Li, Lishuai/K-8472-2014; Zhang, Qingpeng/D-4682-2011; Huang, Jianxiang/K-6400-2015 Li, Lishuai/0000-0002-0990-5119; Zhang, Qingpeng/0000-0002-6819-0686; Huang, Jianxiang/0000-0002-0027-3944; CHANG, Haoliang/0000-0001-8794-9072 Hong Kong Research Grant Council Theme-Based Research Scheme [T32-101/15-R]; Hong Kong Research Grant Council General Research Fund [11209717] Hong Kong Research Grant Council Theme-Based Research Scheme; Hong Kong Research Grant Council General Research Fund(Hong Kong Research Grants Council) Acknowledgements This work was supported by the Hong Kong Research Grant Council Theme-Based Research Scheme (Grant No. T32-101/15-R) and the Hong Kong Research Grant Council General Research Fund (Project No. 11209717) . 56 1 1 19 35 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0001-4575 1879-2057 ACCIDENT ANAL PREV Accid. Anal. Prev. MAY 2022.0 169.0 106618 10.1016/j.aap.2022.106618 0.0 FEB 2022 17 Ergonomics; Public, Environmental & Occupational Health; Social Sciences, Interdisciplinary; Transportation Social Science Citation Index (SSCI) Engineering; Public, Environmental & Occupational Health; Social Sciences - Other Topics; Transportation 0Z7OL 35231867.0 2023-03-23 WOS:000791263000009 0
C Gao, Y; Dong, J; Isabella, O; Zeman, M; Zhang, GQ IEEE Gao, Y.; Dong, J.; Isabella, O.; Zeman, M.; Zhang, G. Q. Rapid Method of Estimating the Solar Irradiance Spectra with Potential Lighting Applications 2016 13TH CHINA INTERNATIONAL FORUM ON SOLID STATE LIGHTING (SSLCHINA 2016) English Proceedings Paper 13th China International Forum on Solid State Lighting (SSLChina) NOV 15-17, 2016 Beijing, PEOPLES R CHINA CLOUD CLASSIFICATION Diverse solar irradiance spectra can be observed under different conditions of time, date, location, weather, etc. Since the solar irradiance spectrum is required by certain scientific and engineering applications, obtaining accurate spectral data is essential. Measurements by spectrophotometers are able to achieve accurate real-time data with high resolution, but at high expense. While in some engineering applications, the requirements on accuracy and resolution are much lower than that in a typical scientific research. Therefore, a rapid method of estimating the solar spectrum is proposed based on an available spectral model in this paper. In order to achieve fast estimation, we simplify the input parameters of this model into five key inputs, including latitude and longitude, altitude, date and time, sky and ground type. The first three parameters are easy to obtain from GPS and the internet. Sky and ground types include common types of sky and ground, which can be input manually or processed automatically by analyzing a digital image of target sky or ground. The automatic input is realized through dominant color extraction or by training an artificial neural network. Results show that the proposed rapid method can generate different spectral power distributions based on distinct input conditions. Two device frameworks are also proposed to implement the rapid method, which is applicable to many fields. LED lighting is one of the most prominent applications. Users can easily share local sunlight with each other through an APP in mobile phones. [Gao, Y.; Isabella, O.; Zeman, M.] Delft Univ Technol, PVMD DIMES, Delft, Netherlands; [Gao, Y.; Dong, J.] State Key Lab Solid State Lighting, Changzhou, Peoples R China; [Gao, Y.; Dong, J.] Delft Univ Technol, Beijing Res Ctr, Beijing, Peoples R China; [Zhang, G. Q.] Delft Univ Technol, Dept Microelect, Delft, Netherlands Delft University of Technology; Delft University of Technology Dong, J (corresponding author), State Key Lab Solid State Lighting, Changzhou, Peoples R China.;Dong, J (corresponding author), Delft Univ Technol, Beijing Res Ctr, Beijing, Peoples R China. jfeidong@hotmail.com 11 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-5090-5611-8 2016.0 90 93 4 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BG8RD 2023-03-23 WOS:000392657000024 0
J Schafer, D; Chen, SP; Che, Q Schaefer, Dagmar; Chen, Shih-pei; Che, Qun What is Local Knowledge? Digital Humanities and Yuan Dynasty Disasters in Imperial China's Local Gazetteers JOURNAL OF CHINESE HISTORY English Article local gazetteers; historical disasters; mulberry crop failures; digital humanities; Big Data HISTORY; SONG This paper focuses on the historical politics of disaster records in Chinese local gazetteers (difangzhi(sic)(sic)(sic)). Using records of mulberry crop failures as examples, the authors ask how gazetteer editors collated Yuan disaster records-initially collected to help prevent disasters and authorize the legitimacy of dynastic rule-in gazetteers and, in so doing, made them into 'local' knowledge. Digital humanities methods allow for both qualitative and quantitative analyses, and the authors deploy them to demonstrate how, in structured texts like the Chinese local gazetteers, they could help combine close reading of specific sections and larger-scale analysis of regional patterns. In the first part, the authors show how disasters were recorded in a Yuan Zhenjiang gazetteer to facilitate taxation and disaster prevention locally-a strategy rarely traceable in subsequent gazetteers until the Qing. In the second part, the authors shifted their perspective to the historical accumulation of data and what that reveals about the reception of Yuan disasters: whereas local gazetteers from the north generate long chronologies of mulberry disasters from the Ming to the Qing, others depict the south as disaster-free. [Schaefer, Dagmar; Chen, Shih-pei] Max Planck Inst Hist Sci, Berlin, Germany; [Che, Qun] Shanghai Jiao Tong Univ, Shanghai, Peoples R China Max Planck Society; Shanghai Jiao Tong University Schafer, D (corresponding author), Max Planck Inst Hist Sci, Berlin, Germany. dschaefer@mpiwg-berlin.mpg.de 141 0 0 0 8 CAMBRIDGE UNIV PRESS NEW YORK 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA 2059-1632 2059-1640 J CHINESE HIST J. Chin. Hist. JUL 2020.0 4.0 2 SI 391 429 PII S2059163220000316 10.1017/jch.2020.31 0.0 39 History; Asian Studies Emerging Sources Citation Index (ESCI) History; Asian Studies NE6PL hybrid, Green Published 2023-03-23 WOS:000562722500006 0
J Li, HG; Galayko, D; Trocan, M Li, Honggui; Galayko, Dimitri; Trocan, Maria Multi-level adaptive neuro-fuzzy inference system-based reconstruction of 1D ISOMAP representations FUZZY SETS AND SYSTEMS English Article Adaptive neuro-fuzzy inference system; Multi-level; Isometric feature mapping; 1D representations; Data reconstruction IMPULSIVE NOISE SUPPRESSION; ANFIS; PREDICTION; CANCELLATION; MODELS; IMAGE; CLASSIFICATION; IDENTIFICATION; ENSEMBLES; PIXELS This paper addresses the inverse problem of isometric feature mapping (ISOMAP) via multi-level adaptive neuro-fuzzy inference system (ML-ANFIS). ISOMAP is a conventional nonlinear dimensionality reduction (NLDR) method, which prospects for low dimensional interior structure embedded in high dimensional data space. The inverse problem of ISOMAP reconstructs the original high dimensional data from the related low dimensional ISOMAP representations and holds promising applications in data representations, generation, compression and visualization. Because the reconstruction of 1D ISOMAP representations is ill posed and undetermined, ML-ANFIS is wielded to augment the recovery quality of general ISOMAP reconstruction algorithm. By linearly combing inputs with nonlinear weights as output, ML-ANFIS can efficiently achieve the latent nonlinear relationship between the low-performance result of general ISOMAP reconstruction algorithm and its original data. The membership functions and fuzzy rules of ML-ANFIS can be automatically learned by gradient descent method as deep learning. It is demonstrated by the experimental results that, in the situation of 1D representations, the proposed method is superior to the state-of-the-art methods, such as nearest neighbor (NN), discrete cosine transformation (DCT), sparse representations (SR) and classical ANFIS algorithms, in the reconstruction performance of video data. (c) 2020 Elsevier B.V. All rights reserved. [Li, Honggui] Yangzhou Univ, Yangzhou 225000, Jiangsu, Peoples R China; [Galayko, Dimitri] Sorbonne Univ, F-75020 Paris, France; [Trocan, Maria] Inst Super Elect Paris, F-75006 Paris, France Yangzhou University; UDICE-French Research Universities; Sorbonne Universite Li, HG (corresponding author), Yangzhou Univ, Yangzhou 225000, Jiangsu, Peoples R China. hgli@yzu.edu.cn; dimitri.galayko@sorbonne-universite.fr; maria.trocan@isep.fr Li, Honggui/ABC-6529-2021 Li, Honggui/0000-0002-6294-1581 49 4 4 2 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0165-0114 1872-6801 FUZZY SET SYST Fuzzy Sets Syst. MAY 15 2021.0 411.0 155 173 10.1016/j.fss.2020.11.002 0.0 MAR 2021 19 Computer Science, Theory & Methods; Mathematics, Applied; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematics RC3MM 2023-03-23 WOS:000632708000007 0
J Liu, YS; Chen, H; Duan, WY; Zhang, XY; He, XL; Nielsen, RAM; Ma, L; Zhai, WW Liu, Yunsong; Chen, Hui; Duan, Wenyuan; Zhang, Xinyi; He, Xionglei; Nielsen, Rasmus; Ma, Liang; Zhai, Weiwei Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus VIRUSES-BASEL English Article passage adaptation; vaccine efficacy; epistasis; fitness landscape; convergent evolution AMINO-ACID SUBSTITUTIONS; UNITED-STATES; ANTIGENIC DRIFT; HEMAGGLUTININ; EVOLUTION; BINDING; IDENTIFICATION; GLYCOPROTEIN; PROTECTION; MUTATIONS Seasonal H3N2 influenza evolves rapidly, leading to an extremely poor vaccine efficacy. Substitutions employed during vaccine production using embryonated eggs (i.e., egg passage adaptation) contribute to the poor vaccine efficacy (VE), but the evolutionary mechanism remains elusive. Using an unprecedented number of hemagglutinin sequences (n = 89,853), we found that the fitness landscape of passage adaptation is dominated by pervasive epistasis between two leading residues (186 and 194) and multiple other positions. Convergent evolutionary paths driven by strong epistasis explain most of the variation in VE, which has resulted in extremely poor vaccines for the past decade. Leveraging the unique fitness landscape, we developed a novel machine learning model that can predict egg passage substitutions for any candidate vaccine strain before the passage experiment, providing a unique opportunity for the selection of optimal vaccine viruses. Our study presents one of the most comprehensive characterizations of the fitness landscape of a virus and demonstrates that evolutionary trajectories can be harnessed for improved influenza vaccines. [Liu, Yunsong; Duan, Wenyuan; Zhang, Xinyi; Ma, Liang; Zhai, Weiwei] Chinese Acad Sci, Inst Zool, Key Lab Zool Systemat & Evolut, Beijing 100101, Peoples R China; [Liu, Yunsong; Duan, Wenyuan; Zhang, Xinyi; Zhai, Weiwei] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Chen, Hui] Agcy Sci Technol & Res, Genome Inst Singapore, Human Genet, Singapore 138672, Singapore; [He, Xionglei] Sun Yat Sen Univ, Sch Life Sci, State Key Lab Biocontrol, MOE Key Lab Gene Funct & Regulat, Guangzhou 510275, Peoples R China; [Nielsen, Rasmus] Univ Calif Berkeley, Dept Integrat Biol, Berkeley, CA 94707 USA; [Nielsen, Rasmus] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94707 USA; [Nielsen, Rasmus] Univ Copenhagen, Globe Inst, DK-1350 Copenhagen, Denmark; [Zhai, Weiwei] Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China Chinese Academy of Sciences; Institute of Zoology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Agency for Science Technology & Research (A*STAR); A*STAR - Genome Institute of Singapore (GIS); Sun Yat Sen University; University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of Copenhagen; Chinese Academy of Sciences Zhai, WW (corresponding author), Chinese Acad Sci, Inst Zool, Key Lab Zool Systemat & Evolut, Beijing 100101, Peoples R China.;Zhai, WW (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;Zhai, WW (corresponding author), Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China. weiweizhai@ioz.ac.cn Liu, Yunsong/0000-0002-2837-8625; Duan, Wenyuan/0000-0002-3032-5864 National Key R&D Program of China [2019YFA0709501]; National Natural Science Foundation of China [31970566]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDPB17]; National Key R&D program of China [2018YFC1406902, 2018YFC0910400] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); National Key R&D program of China M.L. is supported by the National Key R&D Program of China (grant no. 2019YFA0709501) and National Natural Science Foundation of China (grant no. 11971459). W.Z. is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDPB17), the National Natural Science Foundation of China (grant no. 31970566) and National Key R&D program of China (grant no. 2018YFC1406902 and 2018YFC0910400). 56 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1999-4915 VIRUSES-BASEL Viruses-Basel SEP 2022.0 14.0 9 2065 10.3390/v14092065 0.0 16 Virology Science Citation Index Expanded (SCI-EXPANDED) Virology 4V9JH 36146872.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000859785600001 0
J Sun, X; Yu, YB; Yang, YT; Dong, JY; Bohm, C; Chen, XE Sun, Xin; Yu, Yongbo; Yang, Yuting; Dong, Junyu; Boehm, Christian; Chen, Xueen Modeling and analysis of the ocean dynamic with Gaussian complex network* CHINESE PHYSICS B English Article complex networks; ocean dynamic; Gaussian mixture model; physical processes CLIMATE The techniques for oceanographic observation have made great progress in both space-time coverage and quality, which make the observation data present some characteristics of big data. We explore the essence of global ocean dynamic via constructing a complex network with regard to sea surface temperature. The global ocean is divided into discrete regions to represent the nodes of the network. To understand the ocean dynamic behavior, we introduce the Gaussian mixture models to describe the nodes as limit-cycle oscillators. The interacting dynamical oscillators form the complex network that simulates the ocean as a stochastic system. Gaussian probability matching is suggested to measure the behavior similarity of regions. Complex network statistical characteristics of the network are analyzed in terms of degree distribution, clustering coefficient and betweenness. Experimental results show a pronounced sensitivity of network characteristics to the climatic anomaly in the oceanic circulation. Particularly, the betweenness reveals the main pathways to transfer thermal energy of El Nino-Southern oscillation. Our works provide new insights into the physical processes of ocean dynamic, as well as climate changes and ocean anomalies. [Sun, Xin; Yu, Yongbo; Yang, Yuting; Dong, Junyu] Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266000, Peoples R China; [Dong, Junyu] Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Qingdao 266000, Peoples R China; [Boehm, Christian] Ludwig Maximilian Univ Munich, Inst Informat, D-803318192 Munich, Germany; [Chen, Xueen] Ocean Univ China, Coll Phys & Environm Oceanog, Qingdao 266000, Peoples R China Ocean University of China; University of Munich; Ocean University of China Dong, JY (corresponding author), Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266000, Peoples R China.;Dong, JY (corresponding author), Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Qingdao 266000, Peoples R China. dongjunyu@ouc.edu.cn Böhm, Christian/HDN-5925-2022 National Natural Science Foundation of China [U1706218, 61971388, L1824025] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Project supported by the National Natural Science Foundation of China (Grant Nos. U1706218, 61971388, and L1824025). 38 3 3 13 34 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1674-1056 1741-4199 CHINESE PHYS B Chin. Phys. B SEP 2020.0 29.0 10 108901 10.1088/1674-1056/aba27d 0.0 10 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics NY3TS 2023-03-23 WOS:000576316900001 0
J Wu, JQ; Niu, ZB; Li, X; Huang, LZ; Nielsen, PS; Liu, XF Wu, Junqi; Niu, Zhibin; Li, Xiang; Huang, Lizhen; Nielsen, Per Sieverts; Liu, Xiufeng Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach ENERGY English Article Demand-side management; Visual analysis; Spatiotemporal patterns; Consumption variability; Segmentation; Demand shift patterns VISUALIZATION; EFFICIENCY; PATTERNS; TRAVEL Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, seg-mentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human-client-server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region. [Wu, Junqi; Niu, Zhibin] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China; [Nielsen, Per Sieverts; Liu, Xiufeng] Tech Univ Denmark, Dept Technol Management & Econ, Lyngby, Denmark; [Huang, Lizhen] Norwegian Univ Sci & Technol, Dept Mfg & Civil Engn, Trondheim, Norway; [Li, Xiang] Univ Calif Berkeley, Berkeley, CA USA Tianjin University; Technical University of Denmark; Norwegian University of Science & Technology (NTNU); University of California System; University of California Berkeley Niu, ZB (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.;Liu, XF (corresponding author), Tech Univ Denmark, Dept Technol Management & Econ, Lyngby, Denmark. zniu@tju.edu.cn; xiuli@dtu.dk Nielsen, Per Sieverts/E-7100-2017; Tomm, He/E-5457-2013 Nielsen, Per Sieverts/0000-0003-4090-7400; Liu, Xiufeng/0000-0001-5133-6688; Li, Xiang/0000-0001-5936-8457 China Natural Science Foundation [61802278]; Horizon 2020 Research and Innovation Program of the European Union [754462]; Flexible Energy Denmark (FED) project - Innovations fonden [8090-00069B] China Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Horizon 2020 Research and Innovation Program of the European Union; Flexible Energy Denmark (FED) project - Innovations fonden The research was supported by the China Natural Science Foundation (61802278) , the Flexibility for Smart Urban Energy Systems (FlexSUS) project funded by Horizon 2020 Research and Innovation Program of the European Union under the Marie Sklodowska-Curie Grant Agreement (754462) , and the Flexible Energy Denmark (FED) project (8090-00069B) funded by Innovations fonden. 60 0 0 5 5 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0360-5442 1873-6785 ENERGY Energy JAN 15 2023.0 263.0 D 125939 10.1016/j.energy.2022.125939 0.0 15 Thermodynamics; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Energy & Fuels 7Y5PQ Green Published 2023-03-23 WOS:000914931400002 0
C Yang, SQ; Qin, HF; El-Yacoubi, MA; Liu, CW Wang, J; Tang, Y; Wang, FY Yang, Shuqiang; Qin, Huafeng; El-Yacoubi, Mmounim A.; Liu, Chongwen Cross-Modality Domain Adaptation for hand-vein recognition 2021 INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SOCIAL INTELLIGENCE (ICCSI) English Proceedings Paper International Conference on Cyber-physical Social Intelligence (ICCSI) DEC 18-20, 2021 Beijing, PEOPLES R CHINA Assoc Global Intelligent Sci & Technol,Chinese Assoc Automat,IEEE Sys Man & Cybernet Soc Palm-vein Authentication; Domain Adaptation; GAN; CNN FEATURE-EXTRACTION; FINGER Palm-vein recognition has attracted increasing attention over the last years. Although deep learning-based approaches, such as Convolutional Neural Networks (CNN), have been shown to be effective for feature representation, thereby achieving good performance in vein verification tasks, they typically are trained on large labeled datasets. In general, labeling vein images is expensive and time cost, and typical hand-tuned approaches for data augmentation can not collect the complex variations in such images. To address this problem, a novel unsupervised domain adaptation approach, named CycleGAN-based domain adaptation (CGAN-DA), is proposed to automatically extract discriminant from the palm-vein network, without the need of any image annotation. Our proposed CGAN-DA allows a learning scheme that ensures a synergistic fusion of adaptations image-wise and feature-wise. Concretely, we transform the image appearance across two domains (palm-vein image domain and retinal image domain), in order to enhance the domain-invariance of the extracted features for the palm-vein segmentation task. Without using any annotation from the target domain (palm-vein images), our model learning is guided by several adversarial losses, a cycle consistence loss and a segmentation loss. Our experimental on the public CASIA palm-vein dataset show that our approach is capable of achieving state-of-the art verification accuracy. [Yang, Shuqiang] Luoyang Normal Univ, Coll Pyys & Elect informat, Luoyang, Peoples R China; [Qin, Huafeng; Liu, Chongwen] Chongqing Technol & Business Univ, Artificial Intelligence Sch, Chongqing, Peoples R China; [El-Yacoubi, Mmounim A.] Univ Paris Saclay, CNRS, Telecom SudParis, SAMOVAR, Paris, France Luoyang Normal University; Chongqing Technology & Business University; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; UDICE-French Research Universities; Universite Paris Saclay Yang, SQ (corresponding author), Luoyang Normal Univ, Coll Pyys & Elect informat, Luoyang, Peoples R China. 27ii07933@qq.com; qinhuafengfeng@163.com; mounirn.el_yacoubi@telecom-sudparis.eu; liuchongwen@ctbu.edu.cn 16 0 0 2 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-2621-3 2021.0 10.1109/ICCSI53130.2021.9736171 0.0 6 Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT2WC 2023-03-23 WOS:000814718500059 0
J Ran, XC; Chen, WD; Yvert, B; Zhang, SM Ran, Xingchen; Chen, Weidong; Yvert, Blaise; Zhang, Shaomin A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding COMPUTERS IN BIOLOGY AND MEDICINE English Article Dimensionality reduction; Autoencoder; Brain-computer interface; Neural decoding LOCAL-FIELD POTENTIALS; COMPONENT ANALYSIS; CORTICAL CONTROL; MOVEMENT; REPRESENTATION; SPEECH; TETRAPLEGIA; DYNAMICS Objective: As the scale of neural recording increases, Brain-computer interfaces (BCIs) are restrained by high-dimensional neural features, so dimensionality reduction is required as a preprocess of neural features. In this context, we propose a novel framework based on deep learning to reduce the dimensionality of neural features that are typically extracted from electrocorticography (ECoG) or local field potential (LFP). Approach: A high-performance autoencoder was implemented by chaining convolutional layers to deal with spatial and frequency dimensions with bottleneck long short-term memory (LSTM) layers to deal with the temporal dimension of the features. Furthermore, this autoencoder is combined with a fully connected layer to regularize the training. Main results: By applying the proposed method to two different datasets, we found that this dimensionality reduction method largely outperforms kernel principal component analysis (KPCA), partial least square (PLS), preferential subspace identification (PSID), and latent factor analysis via dynamical systems (LFADS). Besides, the new features obtained by our method can be applied to various BCI decoders, without significant differences in decoding performance. Significance: A novel method is proposed as a reliable tool for efficient dimensionality reduction of neural signals. Its high performance and robustness are promising to enhance the decoding accuracy and long-term stability of online BCI systems based on large-scale neural recordings. [Ran, Xingchen; Chen, Weidong; Zhang, Shaomin] Zhejiang Univ, Qiushi Acad Adv Studies, Hangzhou, Peoples R China; [Ran, Xingchen; Zhang, Shaomin] Zhejiang Univ, Zhejiang Prov Key Lab Cardiocerebral Vasc Detect, Minist Educ, Dept Biomed Engn,Key Lab Biomed Engn, Hangzhou, Peoples R China; [Yvert, Blaise] INSERM, Grenoble, France; [Yvert, Blaise] Univ Grenoble Alpes, BrainTech Lab U1205, Grenoble, France Zhejiang University; Zhejiang University; Institut National de la Sante et de la Recherche Medicale (Inserm); Communaute Universite Grenoble Alpes; UDICE-French Research Universities; Universite Grenoble Alpes (UGA) Yvert, B (corresponding author), INSERM, Grenoble, France.;Yvert, B (corresponding author), Univ Grenoble Alpes, BrainTech Lab U1205, Grenoble, France.;Zhang, SM (corresponding author), Zhejiang Univ, Hangzhou, Peoples R China. blaise.yvert@inserm.fr; shaomin@zju.edu.cn Ran, Xingchen/0000-0002-3868-0713 National Key R&D Program of China [2022ZD0208604, 2017YFE0195500, 2021YFF1200805]; National Natural Science Foundation of China [31371001]; Key R&D Program of Zhejiang Province of China [2021C03003, 2021C03050, 2022C03029]; French National Research Agency [ANR-16-CE190005-01] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key R&D Program of Zhejiang Province of China; French National Research Agency(French National Research Agency (ANR)) This research is supported by the National Key R&D Program of China (2022ZD0208604, 2017YFE0195500, 2021YFF1200805), the National Natural Science Foundation of China (31371001), the Key R&D Program of Zhejiang Province of China (2021C03003, 2021C03050, 2022C03029), the French National Research Agency (ANR-16-CE190005-01). 74 0 0 12 14 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0010-4825 1879-0534 COMPUT BIOL MED Comput. Biol. Med. SEP 2022.0 148.0 105871 10.1016/j.compbiomed.2022.105871 0.0 AUG 2022 12 Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology 6L4ZJ 35933960.0 2023-03-23 WOS:000888192600002 0
J Madec, S; Jin, XL; Lu, H; De Solan, B; Liu, SY; Duyme, F; Heritier, E; Baret, F Madec, Simon; Jin, Xiuliang; Lu, Hao; De Solan, Benoit; Liu, Shouyang; Duyme, Florent; Heritier, Emmanuelle; Baret, Frederic Ear density estimation from high resolution RGB imagery using deep learning technique AGRICULTURAL AND FOREST METEOROLOGY English Article Wheat ear density; Object detection; Object counting; Convolutional neural networks; Phenotyping; Broad-sense heritability HIGH-THROUGHPUT Wheat ear density estimation is an appealing trait for plant breeders. Current manual counting is tedious and inefficient. In this study we investigated the potential of convolutional neural networks (CNNs) to provide accurate ear density using nadir high spatial resolution RGB images. Two different approaches were investigated, either using the Faster-RCNN state-of-the-art object detector or with the TasselNet local count regression network. Both approaches performed very well (rRMSE approximate to 6%) when applied over the same conditions as those prevailing for the calibration of the models. However, Faster-RCNN was more robust when applied to a dataset acquired at a later stage with ears and background showing a different aspect because of the higher maturity of the plants. Optimal spatial resolution for Faster-RCNN was around 0.3 mm allowing to acquire RGB images from a UAV platform for high-throughput phenotyping of large experiments. Comparison of the estimated ear density with in-situ manual counting shows reasonable agreement considering the relatively small sampling area used for both methods. Faster-RCNN and in-situ counting had high and similar heritability (H-2 approximate to 85%), demonstrating that ear density derived from high resolution RGB imagery could replace the traditional counting method. [Madec, Simon; Jin, Xiuliang; Liu, Shouyang; Baret, Frederic] UMT CAPTE, UMR EMMAH, INRA, Avignon, France; [Lu, Hao] Huazhong Univ Sci & Technol, Sch Automat, Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Hubei, Peoples R China; [De Solan, Benoit; Duyme, Florent; Heritier, Emmanuelle] Inst Vegetal, ARVALIS, Avignon, France INRAE; Huazhong University of Science & Technology Madec, S (corresponding author), UMT CAPTE, UMR EMMAH, INRA, Avignon, France. simon.madec@inra.fr Jin, Xiuliang/H-1773-2012; Baret, Frédéric/AAJ-3213-2020; Lu, Hao/AFL-6512-2022; MADEC, Simon/AAE-7893-2019; Jin, Xiuliang/L-2802-2019 Baret, Frédéric/0000-0002-7655-8997; MADEC, Simon/0000-0002-5367-184X; Jin, Xiuliang/0000-0003-2720-6247 Programme d'investissement d'Avenir PHENOME [ANR-11-INBS-012]; Breedwheat [ANR-10-BTR-03]; France Agrimer; Fonds de Soutien l'Obtention Vegetale; French ministry of Agriculture Programme d'investissement d'Avenir PHENOME(French National Research Agency (ANR)); Breedwheat; France Agrimer; Fonds de Soutien l'Obtention Vegetale; French ministry of Agriculture This study was supported by Programme d'investissement d'Avenir PHENOME (ANR-11-INBS-012) and Breedwheat (ANR-10-BTR-03) with participation of France Agrimer and Fonds de Soutien l'Obtention Vegetale. The work was completed within the UMT-CAPTE funded by the French ministry of Agriculture. 33 115 132 21 206 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0168-1923 1873-2240 AGR FOREST METEOROL Agric. For. Meteorol. JAN 15 2019.0 264.0 225 234 10.1016/j.agrformet.2018.10.013 0.0 10 Agronomy; Forestry; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Forestry; Meteorology & Atmospheric Sciences HE0BL 2023-03-23 WOS:000452931700019 0
J Guo, YC; Werner, M; Baulin, VA Guo, Yachong; Werner, Marco; Baulin, Vladimir A. Efficient enumeration-selection computational strategy for adaptive chemistry SCIENTIFIC REPORTS English Article POLYMERS; BILAYERS; DYNAMERS; DESIGN; WATER; MODEL Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dynamic adaptive behavior of chemical systems, reconstruction of 3D structures from diffraction data are examples of difficult to solve optimal design or inverse search problems. Nature inspires evolution strategies to solve design problems that are based on selection of successful adaptations and heritable traits over generations. To exploit this strategy in the creation of new materials, a concept of adaptive chemistry was proposed to provide a route for synthesis of self-adapting molecules that can fit to their environment. We propose a computational method of an efficient exhaustive search exploiting massive parallelization on modern GPUs, which finds a solution for an inverse problem by solving repetitively a direct problem in the mean field approximation. One example is the search for a composition of a copolymer that allows the polymer to translocate through a lipid membrane at a minimal time. Another example is a search of a copolymer sequence that maximizes the polymer load in the micelle defined by the radial core-shell potentials. The length and the composition of the sequence are adjusted to fit into the restricted environment. Hydrogen bonding is another pathway of adaptation to the environment through reversible links. A linear polymer that interacts with water through hydrogen bonds adjusts the position of hydrogen bonds along the chain as a function of the concentration field around monomers. In the last example, branching of the molecules is adjusted to external fields, providing molecules with annealed topology, that can be flexibly changed by changing external conditions. The method can be generalized and applied to a broad spectrum of design problems in chemistry and physics, where adaptive behavior in multi-parameter space in response to environmental conditions lead to non-trivial patterns or molecule architectures and compositions. It can further be combined with machine learning or other optimization techniques to explore more efficiently the parameter space. [Guo, Yachong] Nanjing Univ, Kuang Yaming Honors Sch, Nanjing 210023, Peoples R China; [Werner, Marco] Leibniz Inst Polymerforsch Dresden eV, Hohe Str 6, D-01069 Dresden, Germany; [Baulin, Vladimir A.] Univ Rovira & Virgili, Dept Quim Fis & Inorgan, Marcelli Domingo S-N, Tarragona 43007, Spain Nanjing University; Leibniz Institut fur Polymerforschung Dresden; Universitat Rovira i Virgili Baulin, VA (corresponding author), Univ Rovira & Virgili, Dept Quim Fis & Inorgan, Marcelli Domingo S-N, Tarragona 43007, Spain. vladimir.baulin@urv.cat Ministerio de Ciencia, Innovacion y Universidades of the Spanish Government - MCIN/ AEI [PID2020-114347RB-C33]; Natural Science Foundation of Jiangsu Province [BK20221437]; Science and Technology Innovation 2030 [2021ZD0201301]; Academic Consortium 21 (SPF) Ministerio de Ciencia, Innovacion y Universidades of the Spanish Government - MCIN/ AEI; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Science and Technology Innovation 2030; Academic Consortium 21 (SPF) The authors gratefully acknowledge Prof. Jean-Marie Lehn for valuable comments that improved the manuscript. VAB acknowledges financial assistance from the Ministerio de Ciencia, Innovacion y Universidades of the Spanish Government through research project PID2020-114347RB-C33, financed by MCIN/ AEI 10.13039/501100011033. YG acknowledges funding from Natural Science Foundation of Jiangsu Province (BK20221437), Science and Technology Innovation 2030 (2021ZD0201301) and Academic Consortium 21 (SPF). 43 0 0 2 4 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep AUG 29 2022.0 12.0 1 14664 10.1038/s41598-022-17938-x 0.0 13 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 4E0ZW 36038758.0 Green Accepted, gold 2023-03-23 WOS:000847564200034 0
J Li, XZ; Li, ZH; Fioranelli, F; Yang, SF; Romain, O; Le Kernec, J Li, Xingzhuo; Li, Zhenghui; Fioranelli, Francesco; Yang, Shufan; Romain, Olivier; Le Kernec, Julien Hierarchical Radar Data Analysis for Activity and Personnel Recognition REMOTE SENSING English Article radar sensors; activity classification; gait analysis; personnel recognition; mircro-doppler; machine learning HUMAN-MOTION RECOGNITION; MICRO-DOPPLER; CLASSIFICATION; FEATURES Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people. [Li, Xingzhuo; Li, Zhenghui; Yang, Shufan; Le Kernec, Julien] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland; [Li, Xingzhuo] Univ Elect Sci & Technol China, Glasgow Coll UESTC, Chengdu 610000, Peoples R China; [Fioranelli, Francesco] Delft Univ Technol, MS3 Microwave Sensing Signals & Syst, NL-2628 CD Delft, Netherlands; [Romain, Olivier; Le Kernec, Julien] Univ Cergy Pontoise, ETIS Signal & Informat Proc Lab, F-95000 Cergy, France; [Le Kernec, Julien] Univ Elect Sci & Technol China, Sch Informat & Commun, Chengdu 610000, Peoples R China University of Glasgow; University of Electronic Science & Technology of China; Delft University of Technology; CY Cergy Paris Universite; University of Electronic Science & Technology of China Le Kernec, J (corresponding author), Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland.;Le Kernec, J (corresponding author), Univ Cergy Pontoise, ETIS Signal & Informat Proc Lab, F-95000 Cergy, France.;Le Kernec, J (corresponding author), Univ Elect Sci & Technol China, Sch Informat & Commun, Chengdu 610000, Peoples R China. 2288849L@student.gla.ac.uk; 2227284L@student.gla.ac.uk; F.Fioranelli@tudelft.nl; Shufan.Yang@glasgow.ac.uk; olivier.romain@cyu.fr; Julien.lekernec@glasgow.ac.uk Yang, Shufan/AAF-7942-2021; Fioranelli, Francesco/Y-8612-2019; Yang, Shufan/AAI-6707-2021 Yang, Shufan/0000-0003-0531-2903; le kernec, julien/0000-0003-2124-6803; Fioranelli, Francesco/0000-0001-8254-8093 British Council [515095884]; Campus France Alliance France-UK [44764WK-PHC]; EPSRC [EP/R041679/1] Funding Source: UKRI British Council(The British Council in India); Campus France Alliance France-UK; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) The authors would like to thank the British Council 515095884 and Campus France 44764WK-PHC Alliance France-UK for their financial support. 33 11 11 1 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUL 2020.0 12.0 14 2237 10.3390/rs12142237 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology MW6ME Green Published, Green Accepted, gold 2023-03-23 WOS:000557147500001 0
J Zhang, SK; Shi, GY; Liu, ZJ; Zhao, ZW; Wu, ZL Zhang, Shu-kai; Shi, Guo-you; Liu, Zheng-jiang; Zhao, Zhi-wei; Wu, Zhao-lin Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity OCEAN ENGINEERING English Article Data-driven; AIS trajectories; Automatic routing; DBSCAN; Marine navigation INFERENCE Motivated by new technologies, such as Internet of Things, Big Data, Cloud Computing, data-driven based smart maritime related research is attracting more and more attention. With the establishment of China Automatic Identification System (AIS) Asia-Pacific Data Center and its network with IALA-Net, worldwide ship trajectory data is becoming increasingly available, thus providing plenty of materials for data-driven based smart maritime researches. This paper presents a novel approach to automatic maritime routing algorithm, given a set of ship trajectories, infer a routable road network by combining data-driven based algorithms, specially by focusing on: (i) After appropriate pre-processing, simplify AIS trajectory data using Douglas-Peucker algorithm, which can simplify AIS trajectory data by extracting characteristic points, in the meanwhile, compressing redundant information and improving subsequent processing efficiency. (ii) By setting similarity metric of characteristic points, cluster them using DBSCAN algorithm, consequently, deduce general behavior and spatial pattern of trajectories following the similar route. Also, the turning nodes of routes can be obtained in this step. Unlike past published works that focus on partitioned trajectory and require dense-sampling, trajectory point based method proposed in this paper has the merits of robustness to noise and disparity, low computational complexity and space complexity, (iii) Determine the connectivity of turning nodes according to ship trajectories. In this step, isolated turning nodes are linked together to form a coherent chain modeled as a directed graph. (iv) Given a starting location, infer the optimal route to the destination using Ant Colony Algorithm. By comparing the main route generated automatically and the macroscopic traffic flow situation, the result indicate that the method proposed in this paper is effective. [Zhang, Shu-kai] Shanghai Maritime Univ, Merchant Marine Coll, 1550 Haigang Ave, Shanghai 201306, Peoples R China; [Zhang, Shu-kai] China Merchants Shekou Ind Zone Holdings Co LTD, Div Cruise Business, China Merchants Grp, Shenzhen, Peoples R China; [Shi, Guo-you; Liu, Zheng-jiang; Wu, Zhao-lin] Dalian Maritime Univ, Nav Coll, Dalian, Peoples R China; [Zhao, Zhi-wei] Dalian Maritime Univ, Transportat Management Coll, Dalian, Peoples R China; [Zhao, Zhi-wei] Univ Southern Denmark, Ctr Maritime Hlth & Soc, Esbjerg, Denmark Shanghai Maritime University; Dalian Maritime University; Dalian Maritime University; University of Southern Denmark Zhang, SK (corresponding author), Shanghai Maritime Univ, Merchant Marine Coll, 1550 Haigang Ave, Shanghai 201306, Peoples R China. zhangshukai@cmhk.com Zhao, Zhiwei/E-8129-2019 National Natural Science Foundation of China [51709165] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (grant number 51709165). 27 61 70 17 93 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0029-8018 OCEAN ENG Ocean Eng. MAY 1 2018.0 155.0 240 250 10.1016/j.oceaneng.2018.02.060 0.0 11 Engineering, Marine; Engineering, Civil; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography GE9XE 2023-03-23 WOS:000431583500019 0
J Zhuo, Y; Hariyani, S; You, SH; Dorenbos, P; Brgoch, J Zhuo, Ya; Hariyani, Shruti; You, Shihai; Dorenbos, Pieter; Brgoch, Jakoah Machine learning 5d-level centroid shift of Ce3+ inorganic phosphors JOURNAL OF APPLIED PHYSICS English Article CRYSTALLINE ENVIRONMENT; LUMINESCENCE PROPERTIES; ENERGIES; PHOTOLUMINESCENCE; DEPENDENCE; DESIGN; SR; BA Information on the 5d level centroid shift ((c)) of rare-earth ions is critical for determining the chemical shift and the Coulomb repulsion parameter as well as predicting the luminescence and thermal response of rare-earth substituted inorganic phosphors. The magnitude of (c) depends on the binding strength between the rare-earth ion and its coordinating ligands, which is difficult to quantify a priori and makes phosphor design particularly challenging. In this work, a tree-based ensemble learning algorithm employing extreme gradient boosting is trained to predict (c) by analyzing the optical properties of 160 Ce3+ substituted inorganic phosphors. The experimentally measured (c) of these compounds was featurized using the materials' relative permittivity ((r)), average electronegativity, average polarizability, and local geometry. Because the number of reported (r) values is limited, it was necessary to utilize a predicted relative permittivity ((r,SVR)) obtained from a support vector regressor trained on data from similar to 2800 density functional theory calculations. The remaining features were compiled from open-source databases and by analyzing the rare-earth coordination environment from each Crystallographic Information File. The resulting ensemble model could reliably estimate (c) and provide insight into the optical properties of Ce3+-activated inorganic phosphors. [Zhuo, Ya; Hariyani, Shruti; You, Shihai; Brgoch, Jakoah] Univ Houston, Dept Chem, Houston, TX 77204 USA; [You, Shihai] Xiamen Univ, Coll Mat, Xiamen 361005, Fujian, Peoples R China; [Dorenbos, Pieter] Delft Univ Technol, Fac Appl Sci, Dept Radiat Sci & Technol, Mekelweg 15, NL-2629 JB Delft, Netherlands University of Houston System; University of Houston; Xiamen University; Delft University of Technology Brgoch, J (corresponding author), Univ Houston, Dept Chem, Houston, TX 77204 USA. jbrgoch@uh.edu Zhuo, Ya/AAN-6580-2021 Zhuo, Ya/0000-0003-2554-498X; Hariyani, Shruti/0000-0002-4707-8863; You, Shihai/0000-0002-1537-7148 National Science Foundation (NSF) [DMR 18-47701]; Welch Foundation [E-1981]; Texas Center for Superconductivity at the University of Houston (TcSUH) National Science Foundation (NSF)(National Science Foundation (NSF)National Research Foundation of Korea); Welch Foundation(The Welch Foundation); Texas Center for Superconductivity at the University of Houston (TcSUH) The authors acknowledge the National Science Foundation (NSF) (No. DMR 18-47701). This work was also supported by the Welch Foundation (No. E-1981) and the Texas Center for Superconductivity at the University of Houston (TcSUH). The authors declare no competing financial interest. 45 13 13 6 47 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0021-8979 1089-7550 J APPL PHYS J. Appl. Phys. JUL 7 2020.0 128.0 1 10.1063/5.0012434 0.0 7 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics MM2OT Green Submitted 2023-03-23 WOS:000549997000001 0
J Wu, Y; Bighashdel, A; Chen, G; Dubbelman, G; Jancura, P Wu, Ya; Bighashdel, Ariyan; Chen, Guang; Dubbelman, Gijs; Jancura, Pavol Continual Pedestrian Trajectory Learning With Social Generative Replay IEEE ROBOTICS AND AUTOMATION LETTERS English Article Trajectory; Task analysis; Predictive models; Training; Data models; Mobile robots; Learning systems; Continual Learning; Intelligent Transportation Systems; Deep Learning Methods Learning to predict the trajectories of pedestrians is essential for improving safety and efficiency of mobile robots. The prediction is challenging since the robot needs to operate in multiple environments in which the motion patterns of pedestrians are different between environments. Existing pedestrian trajectory prediction models heavily rely on the availability of representative data samples during training. In the presence of additional training data from a new environment, these models must be retrained on all datasets to avoid catastrophic forgetting of the knowledge obtained from the already supported environments. In this paper, we address this catastrophic forgetting problem in the context of learning to predict the trajectories of pedestrians. We propose a pseudo-rehearsal approach based on a novel Generative Replay (GR) model, referred to as Social-GR. The proposed method is consistent with crowd motion patterns and is free of any explicit reference to past experiences. To demonstrate the problem of catastrophic forgetting and evaluate our solution, we develop the Continual Trajectory Prediction Benchmark, which consists of four tasks, each representing a real-world pedestrian trajectory dataset from a different environment. By conducting several experiments, we show that our proposed Social-GR approach significantly outperforms other continual learning methods that depend on explicit experience replay, including the state-of-the-art conditional-GR model. We further illustrate the robustness of our proposed approach to mitigating catastrophic forgetting by switching the order of environments and employing a more complex prediction model. [Wu, Ya; Chen, Guang] Tongji Univ, Dept Automot Engn, Shanghai 201804, Peoples R China; [Wu, Ya] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 AZ Eindhoven, Netherlands; [Bighashdel, Ariyan; Dubbelman, Gijs; Jancura, Pavol] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 AZ Eindhoven, Netherlands Tongji University; Eindhoven University of Technology; Eindhoven University of Technology Chen, G (corresponding author), Tongji Univ, Dept Automot Engn, Shanghai 201804, Peoples R China. wuya@tongji.edu.cn; a.bighashdel@tue.nl; guang@in.tum.de; gijsdubbelman@gmail.com; p.jancura@tue.nl Dubbelman, Gijs/0000-0001-6635-3245; Bighashdel, Ariyan/0000-0002-9871-2633; Jancura, Pavol/0000-0002-9219-5014; Wu, Ya/0000-0002-5376-4448 Dutch Research Council (NWO) [EINF-3745]; National Natural Science Foundation of China [61906138]; Shanghai Rising Star Program [21QC1400900] Dutch Research Council (NWO)(Netherlands Organization for Scientific Research (NWO)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Rising Star Program This work was supported in part by Dutch Research Council (NWO) through SURF Cooperative and Dutch National e-infrastructure under Grant EINF-3745, in part by the National Natural Science Foundation of China under Grant 61906138, and in part by Shanghai Rising Star Program under Grant 21QC1400900. 31 0 0 2 2 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. FEB 2023.0 8.0 2 848 855 10.1109/LRA.2022.3231833 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics 7R6GG 2023-03-23 WOS:000910168800004 0
J Zhang, J; Liu, YM; Zhang, SP; Poppe, R; Wang, M Zhang, Jun; Liu, Yamei; Zhang, Shengping; Poppe, Ronald; Wang, Meng Light Field Saliency Detection With Deep Convolutional Networks IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Saliency detection; light field; micro-lens images; angular changes; deep neural network OBJECT DETECTION Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light field image benefits from the additional modeling of angular patterns. For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection. However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs. In addition, current light field datasets are not sufficiently large to train CNNs. To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps. Compared to current publicly available light field saliency datasets [1], [2], our new dataset is larger, of higher quality, contains more variation and more types of light field inputs. This makes our dataset suitable for training deeper networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images. We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency. Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets. [Zhang, Jun; Liu, Yamei; Wang, Meng] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230601, Peoples R China; [Zhang, Jun; Liu, Yamei; Wang, Meng] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230601, Peoples R China; [Zhang, Shengping] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China; [Poppe, Ronald] Univ Utrecht, Dept Informat & Comp Sci, NL-3584 Utrecht, Netherlands Hefei University of Technology; Hefei University of Technology; Harbin Institute of Technology; Utrecht University Zhang, J (corresponding author), Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230601, Peoples R China. zhangjun1126@gmail.com; liuyamei@mail.hfut.edu.cn; s.zhang@hit.edu.cn; r.w.poppe@uu.nl; eric.mengwang@gmail.com Poppe, Ronald/0000-0002-0843-7878; Liu, Yamei/0000-0002-2125-7230 National Natural Science Foundation of China [61876057, 61872112, 61725203, 61732008]; China Scholarship Council [201806695016]; National Key Research and Development Program of China [2018YFC0806802, 2018YFC0832105] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 61876057, Grant 61872112, Grant 61725203 and Grant 61732008, in part by the China Scholarship Council under Grant 201806695016, and in part by the National Key Research and Development Program of China under Grant 2018YFC0806802 and Grant 2018YFC0832105. 69 45 48 5 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2020.0 29.0 4421 4434 10.1109/TIP.2020.2970529 0.0 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering LE2AI 32031938.0 Green Submitted 2023-03-23 WOS:000526525100003 0
J Lin, C; Zhang, SJ; You, SD; Liu, XX; Zhu, ZY Lin, Cong; Zhang, Shijie; You, Shaodi; Liu, Xiaoxiang; Zhu, Zhiyu Real-time foreground object segmentation networks using long and short skip connections INFORMATION SCIENCES English Article Foreground segmentation; Lightweight model; Real-time inference; Surveillance CHANGE DETECTION ALGORITHMS; NEURAL-NETWORK Foreground object segmentation is an important task with various applications in outdoor surveillance and navigation. Most existing methods focus on accuracy and therefore, are computationally expensive and low in speed, making them difficult to use in actual applications. In this study, we aim to address the issue of accuracy and efficiency trade-off. In particular, in contrast with existing methods that use fine-tuning routine on heavyweight pretrained models and/or optimization techniques to enhance results, we propose a lightweight end-to-end network that can be trained from scratch effectively and efficiently. First, long and short skip connections are used among convolutional blocks and within the bottleneck block. By doing so, information flow within the networks is enhanced during the training stage, and thus, the use rate of parameters in the model is increased, allowing a more compact and efficient network design. Second, we use feature fusions based on element-wise summing before each up-sampling layer to reduce the size of the decoder, accelerate the up-sampling process, and stabilize training convergence. Our proposed method is tested rigorously. In particular, we achieved 1000 times higher speed compared with state-of-the-art methods on CD2014 and SBI2015 datasets with comparable accuracy. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [Lin, Cong; Liu, Xiaoxiang] Jinan Univ, Sch Intelligent Syst Sci & Engn, Zhuhai, Peoples R China; [Zhang, Shijie] Tianjin Med Univ, Sch Basic Med Sci, Dept Pharmacol, Tianjin, Peoples R China; [You, Shaodi] Univ Amsterdam, Fac Sci, Informat Inst, Amsterdam, Netherlands; [Zhu, Zhiyu] Jiangsu Univ Sci & Technol, Elect & Informat Colleage, Zhenjiang, Jiangsu, Peoples R China Jinan University; Tianjin Medical University; University of Amsterdam; Jiangsu University of Science & Technology You, SD (corresponding author), Univ Amsterdam, Fac Sci, Informat Inst, Amsterdam, Netherlands. conglin@jnu.edu.cn; shijie.zhang@tmu.edu.cn; s.you@uva.nl; tlxx@jnu.edu.cn; zzy@just.edu.cn zhang, shijie/HGF-0836-2022; Shaodi, YOU/AAA-4524-2022 Shaodi, YOU/0000-0001-8973-645X Fundamental Research Funds for the Central Universities of China [11618316]; Science & Technology Development Fund of Tianjin Education Commission for Higher Education [2018KJ082]; National Natural Science Foundation of China [62006101] Fundamental Research Funds for the Central Universities of China(Fundamental Research Funds for the Central Universities); Science & Technology Development Fund of Tianjin Education Commission for Higher Education; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 11618316), the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (Grant No. 2018KJ082), and the National Natural Science Foundation of China (Grant No. 62006101). 49 0 0 2 4 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 2021.0 571.0 543 559 10.1016/j.ins.2021.01.044 0.0 JUN 2021 17 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science TY1MQ hybrid, Green Published 2023-03-23 WOS:000683548900010 0
J Fang, GH; Guo, YX; Huang, XF; Rutten, M; Yuan, Y Fang, Guohua; Guo, Yuxue; Huang, Xianfeng; Rutten, Martine; Yuan, Yu Combining Grey Relational Analysis and a Bayesian Model Averaging Method to Derive Monthly Optimal Operating Rules for a Hydropower Reservoir WATER English Article grey relational analysis; input variables selection; Bayesian Model Averaging method; monthly optimal operating rules; hydropower generation; model selecting uncertainty FUZZY INFERENCE SYSTEM; MULTIPLE LINEAR-REGRESSION; VARIABLE SELECTION METHOD; NEURAL-NETWORK MODELS; PERFORMANCE-CHARACTERISTICS; MUTUAL INFORMATION; OPTIMIZATION; PREDICTION; CLASSIFICATION; CLASSIFIERS Various regression models are currently applied to derive functional forms of operating rules for hydropower reservoirs. It is necessary to analyze and evaluate the model selecting uncertainty involved in reservoir operating rules for efficient hydropower generation. Moreover, selecting the optimal input variables from a large number of candidates to characterize an output variable can lead to a more accurate operation simulation. Therefore, this paper combined the Grey Relational Analysis (GRA) method and the Bayesian Model Averaging (BMA) method to select input variables and derive the monthly optimal operating rules for a hydropower reservoir. The monthly input variables were first filtered according to the relationship between the preselected output and input variables based on the reservoir optimal deterministic trajectory using GRA. Three models, Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM), Adaptive Neural Fuzzy Inference System (ANFIS), and Multiple Linear Regression Analysis (MLRA) model, were further implemented to derive individual monthly operating rules. BMA was applied to determine the final monthly operating rules by analyzing the uncertainty of selecting individual models with different weights. A case study of Xinanjiang Reservoir in China shows that the combination of the two methods can achieve high-efficiency hydropower generation and optimal utilization of water resources. [Fang, Guohua; Guo, Yuxue; Huang, Xianfeng; Yuan, Yu] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210098, Jiangsu, Peoples R China; [Rutten, Martine] Rotterdam Univ Appl Sci, Water Management, NL-3015 GG Rotterdam, Netherlands Hohai University Guo, YX (corresponding author), Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210098, Jiangsu, Peoples R China. ghfang@hhu.edu.cn; yuxueguo@hhu.edu.cn; hxfhuang2005@163.com; M.M.Rutten@tudelft.nl; yuanyu@hhu.edu.cn Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Postgraduate Education Innovation Project of Jiangsu Province [2016B05327] Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Postgraduate Education Innovation Project of Jiangsu Province This research was funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Postgraduate Education Innovation Project of Jiangsu Province (2016B05327). 58 5 5 6 25 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4441 WATER-SUI Water AUG 2018.0 10.0 8 1099 10.3390/w10081099 0.0 20 Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Water Resources GY3OR Green Submitted, gold 2023-03-23 WOS:000448462700133 0
J Zhang, L; Su, H; Zio, E; Jiang, LX; Fan, L; Zhang, JJ Zhang, Li; Su, Huai; Zio, Enrico; Jiang, Luxin; Fan, Lin; Zhang, Jinjun A graph structure feature-based framework for the pattern recognition of the operational states of integrated energy systems EXPERT SYSTEMS WITH APPLICATIONS English Article Integrated energy system; Time series pattern recognition; Shapelets; Time-graphlets TIME-SERIES; COMPLEX NETWORKS; VISIBILITY GRAPH; CLASSIFICATION; MODEL The recognition of operational state patterns is crucial for the safe, reliable and profitable operational management of integrated energy systems (IES). However, considering that the monitored operational data are often not labelled, supervised machine learning classification methods cannot be directly applied. In this study, based on the idea of graph theory, a framework of interpretable time series pattern recognition methods based on graph structural features is proposed. Firstly, the time series data is segmented into local subsequences by a shapeletbased time series classification method. Secondly, time-graphlets are constructed by means of a visibility graph algorithm and an original graph structure feature-based representation method is proposed. Then, pattern recognition of the different operational states of IES is performed in the form of multidimensional feature vectors. Thirdly, an association rule model is designed to analyze the supply and demand fluctuations, price fluctuations and couplings between multiple energy subsystems, based on which the pattern recognition results are annotated to match the operational characteristics of the IES. Fourthly, a shapelet-based state transfer network graph is constructed, and the complex network centrality metrics are used to analyze the operating characteristics and key operating states of the system over time. The new method is applied for pattern recognition of operational states in a Spanish IES. The results show that the proposed method can increase the interpretability of the recognition results and provide explanatory labels for the operational data. Furthermore, the vulnerable operational states of the system can be revealed, which allows managers to monitor the operational state of the system and take early defensive measures against the more threatening operational patterns. This suggests that the methodological framework presented in this paper is a promising approach to pattern recognition that can provide interpretable annotations for unsupervised operational data of IES and their safe management. [Zhang, Li; Su, Huai; Jiang, Luxin; Fan, Lin; Zhang, Jinjun] China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Beijing 102249, Peoples R China; [Zio, Enrico] Politecn Milan, Dipartimento Energia, Via Masa 34, I-20156 Milan, Italy; [Zio, Enrico] PSL Res Univ, Mines ParisTech, CRC, Sophia Antipolis, France China University of Petroleum; Polytechnic University of Milan; UDICE-French Research Universities; Universite PSL; MINES ParisTech Su, H; Zhang, JJ (corresponding author), China Univ Petr, Natl Engn Lab Pipeline Safety, MOE Key Lab Petr Engn, Beijing Key Lab Urban Oil & Gas Distribut Technol, Beijing 102249, Peoples R China. suhuai1990@163.com; enrico.zio@polimi.it; zhangjj@cup.edu.cn National Natural Science Foundation of China [51904316]; China University of Petroleum, Beijing [2462018YJRC038] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China University of Petroleum, Beijing Thanks to ENTSOE, a public portal for Transmission Service Operator (TSO) for the public data. This work is supported by the National Natural Science Foundation of China (51904316) and the research fund provided by the China University of Petroleum, Beijing (2462018YJRC038). We finally thank the reviewers who allowed us to improve the quality of this work through their comments and suggestions. 58 0 0 16 16 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. MAR 1 2023.0 213.0 B 119039 10.1016/j.eswa.2022.119039 0.0 17 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Operations Research & Management Science 5W3UF 2023-03-23 WOS:000877842700004 0
J Zhang, XY; Dong, X; Zeng, J; Hou, SG; Smeets, PCJP; Reijmer, CH; Wang, YT Zhang, Xueying; Dong, Xu; Zeng, Jing; Hou, Shugui; Smeets, Paul C. J. P.; Reijmer, Carleen H.; Wang, Yetang Spatiotemporal Reconstruction of Antarctic Near-Surface Air Temperature from MODIS Observations JOURNAL OF CLIMATE English Article Atmosphere; Antarctica; Neural networks GLOBAL ATMOSPHERIC REANALYSES; HEMISPHERE ANNULAR MODE; NEURAL-NETWORKS; POLAR WRF; IN-SITU; CLIMATE; ICE; WIND; VARIABILITY; VALIDATION MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05 degrees x 0.05 degrees, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09 degrees C, a mean absolute error of 2.23 degrees C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001-18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model. [Zhang, Xueying; Dong, Xu; Zeng, Jing; Wang, Yetang] Shandong Normal Univ, Coll Geog & Environm, Jinan, Peoples R China; [Hou, Shugui] Shanghai Jiao Tong Univ, Sch Oceanog, Shanghai, Peoples R China; [Smeets, Paul C. J. P.; Reijmer, Carleen H.] Univ Utrecht, Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands; [Zeng, Jing] Shandong Inst Food & Drug Control, Jinan, Peoples R China Shandong Normal University; Shanghai Jiao Tong University; Utrecht University Wang, YT (corresponding author), Shandong Normal Univ, Coll Geog & Environm, Jinan, Peoples R China. yetangwang@sdnu.edu.cn Hou, Shugui/0000-0002-0905-3542 National Natural Science Foundation of China [41971081, 42122047]; National Key Research and Development Program of China [2020YFA0608202]; Strategic Priority Research Program of the Chinese Academy of Sciences [XAD19070103]; Project for Outstanding Youth Innovation Team in the Universities of Shandong Province [2019KJH011]; Basic Research Fund of the Chinese Academy of Meteorological Sciences [2021Y021, 2021Z006] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Project for Outstanding Youth Innovation Team in the Universities of Shandong Province; Basic Research Fund of the Chinese Academy of Meteorological Sciences Funding this work was the National Natural Science Foundation of China (41971081, 42122047), the National Key Research and Development Program of China (2020YFA0608202), the Strategic Priority Research Program of the Chinese Academy of Sciences (XAD19070103), the Project for Outstanding Youth Innovation Team in the Universities of Shandong Province (2019KJH011), and the Basic Research Fund of the Chinese Academy of Meteorological Sciences (Grant 2021Y021 and 2021Z006). 73 2 2 15 15 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693, UNITED STATES 0894-8755 1520-0442 J CLIMATE J. Clim. SEP 1 2022.0 35.0 17 5537 5553 10.1175/JCLI-D-21-0786.1 0.0 17 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences 4X2TW 2023-03-23 WOS:000860701300005 0
J Sirunyan, AM; Tumasyan, A; Adam, W; Bergauer, T; Dragicevic, M; Ero, J; Del Valle, AE; Fruhwirth, R; Jeitler, M; Krammer, N; Lechner, L; Liko, D; Mikulec, I; Pitters, FM; Rad, N; Schieck, J; Schofbeck, R; Spanring, M; Templ, S; Waltenberger, W; Wulz, CE; Zarucki, M; Chekhovsky, V; Litomin, A; Makarenko, V; Gonzalez, JS; Darwish, MR; De Wolf, EA; Di Croce, D; Janssen, X; Kello, T; Lelek, A; Pieters, M; Sfar, HR; Van Haevermaet, H; Van Mechelen, P; Van Putte, S; Van Remortel, N; Blekman, F; Bols, ES; Chhibra, SS; D'Hondt, J; De Clercq, J; Lontkovskyi, D; Lowette, S; Marchesini, I; Moortgat, S; Morton, A; Muller, D; Python, Q; Tavernier, S; Van Doninck, W; Van Mulders, P; Beghin, D; Bilin, B; Clerbaux, B; De Lentdecker, G; Dorney, B; Favart, L; Grebenyuk, A; Kalsi, AK; Makarenko, I; Moureaux, L; Petre, L; Popov, A; Postiau, N; Starling, E; Thomas, L; Vander Velde, C; Vanlaer, P; Vannerom, D; Wezenbeek, L; Cornelis, T; Dobur, D; Gruchala, M; Khvastunov, I; Niedziela, M; Roskas, C; Skovpen, K; 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CMS Collaboration Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at root s=13TeV EUROPEAN PHYSICAL JOURNAL C English Article BROKEN SYMMETRIES; LIKELIHOOD METHOD; MISSING MOMENTUM; EVENTS; RECONSTRUCTION; MASS; COLLISIONS; SEARCH; PAIR The rate for Higgs (H) bosons production in association with either one (tH) or two (t (t) over barH) top quarks is measured in final states containing multiple electrons, muons, or tau leptons decaying to hadrons and a neutrino, using proton-proton collisions recorded at a center-of-mass energy of 13 TeV by the CMS experiment. The analyzed data correspond to an integrated luminosity of 137 fb(-1). The analysis is aimed at events that contain H -> WW, H -> tt, or H -> ZZ decays and each of the top quark(s) decays either to lepton+jets or all-jet channels. Sensitivity to signal is maximized by including ten signatures in the analysis, depending on the lepton multiplicity. The separation among tH, t (t) over barH, and the backgrounds is enhanced through machine-learning techniques and matrix-element methods. The measured production rates for the ttH and tH signals correspond to 0.92 +/- 0.19 (stat)(-0.13)(+0.17) (syst) and 5.7 +/- 2.7 (stat)+/- 3.0 (syst) of their respective standard model (SM) expectations. The corresponding observed (expected) significance amounts to 4.7 (5.2) standard deviations for t (t) over barH, and to 1.4 (0.3) for tH production. Assuming that the Higgs boson coupling to the tau lepton is equal in strength to its expectation in the SM, the coupling y(t) of the Higgs boson to the top quark divided by its SM expectation, kappa(t) = y(t)/y(t)(SM), is constrained to be within -0.9 0.34 ng/ml at the cost of increased hematological side effects. Concomitant ADT and its duration are also discussed, possibly advantageous (at least in terms of metastasis-free survival) for PSA rates over 0.6 ng/ml, taking into account life expectancy and cardiovascular comorbidities. Intensified regimens, for instance, with new-generation hormone therapies, could further improve outcomes in carefully selected patients. Finally, recent advances in molecular imaging, as well as upcoming breakthroughs in genomics and artificial intelligence tools, could soon reshuffle the cards of the current therapeutic strategy. [Giraud, Nicolas; Benziane-Ouaritini, Nicolas; Sargos, Paul] Inst Bergonie, Radiat Oncol Dept, Bordeaux, France; [Schick, Ulrike] Univ Hosp, Radiat Oncol Dept, Brest, France; [Beauval, Jean-Baptiste] Clin Croix Sud, Urol Dept, Toulouse, France; [Chaddad, Ahmad] Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin, Peoples R China; [Niazi, Tamim; Faye, Mame Daro] McGill Univ, Dept Oncol, Div Radiat Oncol, Montreal, PQ, Canada; [Supiot, Stephane] Inst Cancerol Ouest, Radiat Oncol Dept, Nantes St, Herblain, France; [Latorzeff, Igor] Clin Pasteur, Radiat Oncol Dept, Toulouse, France UNICANCER; Institut Bergonie; CHU Brest; Guilin University of Electronic Technology; McGill University Sargos, P (corresponding author), Inst Bergonie, Radiat Oncol Dept, Bordeaux, France. p.sargos@bordeaux.unicancer.fr 63 0 0 0 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. NOV 18 2021.0 11.0 781040 10.3389/fonc.2021.781040 0.0 9 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology XK0SD 34881187.0 gold, Green Accepted 2023-03-23 WOS:000727184900001 0
C Guo, TT; Lim, EG; Lopez-Benitez, M; Ma, F; Yu, LM IEEE Guo Tiantian; Lim, Eng Gee; Lopez-Benitez, Miguel; Ma Fei; Yu Limin UNDERWATER ACOUSTIC SENSING WITH RATIONAL ORTHOGONAL WAVELET PULSE AND AUDITORY FREQUENCY CEPSTRAL COEFFICIENT-BASED FEATURE EXTRACTION 2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) International Computer Conference on Wavelet Active Media Technology and Information Processing English Proceedings Paper 19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) DEC 16-18, 2022 Univ Elect Sci & Technol China, Chengdu, PEOPLES R CHINA Hunan First Normal Univ,Natl Nat Sci Fdn China,Natl High Technol Res & Dev Program China,China Int Talent Exchange Fdn,IEEE,IEEE Chengdu Sect Univ Elect Sci & Technol China Tracking; Underwater communication; CNN; Mel frequency cepstral coefficient (MFCC); Gammatone frequency cepstral coefficient (GFCC); Rational orthogonal wavelet (ROW) Active pulse design, target detection and classification play an essential role in underwater acoustic sensing. This paper addresses the system design with three kinds of pulse signals, including continuous wave (CW), linear frequency modulation (LFM) signal and rational orthogonal wavelet (ROW) signal. The detector design has an architecture of feature extraction and convolutional neural network (CNN) based classification. A geometric underwater channel model is adopted to facilitate the generation of training datasets with designated geometric underwater environment parameters. The simulated received pulse signals are converted into feature maps as the input of the classifier. This paper applies the acoustic features, Short Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to construct different feature maps. A lightweight CNN model is used as the classifier. Experiments demonstrate the superiority of the ROW wavelet pulse signals and the proposed algorithm in target localization and underwater signal classification. [Guo Tiantian; Lim, Eng Gee; Yu Limin] Xian Jiaotong Liverpool Univ XJTLU, Sch Adv Technol, Dept Commun & Networking, Suzhou, Peoples R China; [Guo Tiantian; Lopez-Benitez, Miguel] Univ Liverpool, Dept Elect Engn & Elect, Liverpool, Merseyside, England; [Lopez-Benitez, Miguel] Antonio de Nebrija Univ, Artificial Intelligence & Emergent Syst ARIES Res, Madrid 28040, Spain; [Ma Fei] Xian Jiaotong Liverpool Univ XJTLU, Sch Sci, Dept Appl Math, Suzhou, Peoples R China Xi'an Jiaotong-Liverpool University; University of Liverpool; Universidad Antonio de Nebrija; Xi'an Jiaotong-Liverpool University Guo, TT (corresponding author), Xian Jiaotong Liverpool Univ XJTLU, Sch Adv Technol, Dept Commun & Networking, Suzhou, Peoples R China.;Guo, TT (corresponding author), Univ Liverpool, Dept Elect Engn & Elect, Liverpool, Merseyside, England. Tiantian.guo19@student.xjtlu.edu.cn; Enggee.lim@xjtlu.edu.cn; M.Lopez-Benitez@liverpool.ac.uk; Fei.ma@xjtlu.edu.cn; Limin.yu@xjtlu.edu.cn Research Enhancement Fund of XJTLU [REF-19-01-04]; National Natural Science Foundation of China (NSFC) [61501380]; AI University Research Center (AI-URC); XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund [KSFP-02]; Jiangsu Data Science and Cognitive Computational Engineering Research Centre; Suzhou Municipal Key Laboratory Broadband Wireless Access Technology; ARIES Research Centre Research Enhancement Fund of XJTLU; National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); AI University Research Center (AI-URC); XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund; Jiangsu Data Science and Cognitive Computational Engineering Research Centre; Suzhou Municipal Key Laboratory Broadband Wireless Access Technology; ARIES Research Centre This research was partially funded by Research Enhancement Fund of XJTLU (REF-19-01-04), National Natural Science Foundation of China (NSFC) (Grant No. 61501380), and by AI University Research Center (AI-URC) and XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund (KSFP-02), Jiangsu Data Science and Cognitive Computational Engineering Research Centre, ARIES Research Centre, and Suzhou Municipal Key Laboratory Broadband Wireless Access Technology. 7 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2576-8972 2576-8964 978-1-6654-9389-5 I COMP CONF WAVELET 2022.0 10.1109/ICCWAMTIP56608.2022.10016489 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Telecommunications BU7AE 2023-03-23 WOS:000932922500011 0
J Shen, ZJ; Deifalla, AF; Kaminski, P; Dyczko, A Shen, Zhongjie; Deifalla, Ahmed Farouk; Kaminski, Pawel; Dyczko, Artur Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning MATERIALS English Article UHSC; building material; compressive strength; soft computing technique; concrete HIGH-PERFORMANCE CONCRETE; MECHANICAL-PROPERTIES; PREDICTION; IMPROVEMENT In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R-2). The model's performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R-2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R-2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study's findings. [Shen, Zhongjie] Xijing Univ, Xian 710123, Peoples R China; [Deifalla, Ahmed Farouk] Future Univ Egypt, Fac Engn & Technol, Struct Engn & Construct Management Dept, Cairo 11835, Egypt; [Kaminski, Pawel] AGH Univ Sci & Technol, Fac Civil Engn & Resource Management, Mickiewicza 30, PL-30059 Krakow, Poland; [Dyczko, Artur] Polish Acad Sci, Mineral & Energy Econ Res Inst, J Wybickiego 7a, PL-31261 Krakow, Poland Xijing University; Egyptian Knowledge Bank (EKB); Future University in Egypt; AGH University of Science & Technology; Polish Academy of Sciences; Mineral & Energy Economy Research Institute of the Polish Academy of Sciences Shen, ZJ (corresponding author), Xijing Univ, Xian 710123, Peoples R China.;Deifalla, AF (corresponding author), Future Univ Egypt, Fac Engn & Technol, Struct Engn & Construct Management Dept, Cairo 11835, Egypt. zhongjieshen@outlook.com; ahmed.deifalla@fue.edu.eg; pkamin@agh.edu.pl; arturdyczko@min-pan.krakow.pl Kamiński, Paweł/T-7756-2017; Dyczko, Artur/GON-7386-2022 Kamiński, Paweł/0000-0002-1450-5881; Dyczko, Artur/0000-0001-6387-5339; deifalla, ahmed/0000-0003-1758-3743; shen, zhongjie/0000-0002-8904-7918 49 13 13 3 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1944 MATERIALS Materials MAY 2022.0 15.0 10 3523 10.3390/ma15103523 0.0 19 Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics 1Q1NC 35629548.0 gold, Green Accepted 2023-03-23 WOS:000802462400001 0
J Zhu, SY; Bing, JX; Min, XP; Lin, C; Zeng, XX Zhu, Siyi; Bing, Jiaxin; Min, Xiaoping; Lin, Chen; Zeng, Xiangxiang Prediction of Drug-Gene Interaction by Using Metapath2vec FRONTIERS IN GENETICS English Article drug-gene; ADR; heterogeneous network; link prediction; representation learning; network embedding GUILT-BY-ASSOCIATION; MODEL Heterogeneous information networks (HINs) currently play an important role in daily life. HINs are applied in many fields, such as science research, e-commerce, recommendation systems, and bioinformatics. Particularly, HINs have been used in biomedical research. Algorithms have been proposed to calculate the correlations between drugs and targets and between diseases and genes. Recently, the interaction between drugs and human genes has become an important subject in the research on drug efficacy and human genomics. In previous studies, numerous prediction methods using machine learning and statistical prediction models were proposed to explore this interaction on the biological network. In the current work, we introduce a representation learning method into the biological heterogeneous network and use the representation learning models metapath2vec and metapath2vec++ on our dataset. We combine the adverse drug reaction (ADR) data in the drug-gene network with causal relationship between drugs and ADRs. This article first presents an analysis of the importance of predicting drug-gene relationships and discusses the existing prediction methods. Second, the skip-gram model commonly used in representation learning for natural language processing tasks is explained. Third, the metapath2vec and metapath2vec++ models for the example of drug-gene-ADR network are described. Next, the kernelized Bayesian matrix factorization algorithm is used to complete the prediction. Finally, the experimental results of both models are compared with Katz, CATAPULT, and matrix factorization, the prediction visualized using the receiver operating characteristic curves are presented, and the area under the receiver operating characteristic values for three varying algorithm parameters are calculated. [Zhu, Siyi; Bing, Jiaxin; Min, Xiaoping; Lin, Chen; Zeng, Xiangxiang] Xiamen Univ, Dept Comp Sci, Xiamen, Peoples R China; [Zeng, Xiangxiang] Univ Politecn Madrid, Dept Inteligencia Artificial, Madrid, Spain Xiamen University; Universidad Politecnica de Madrid Min, XP (corresponding author), Xiamen Univ, Dept Comp Sci, Xiamen, Peoples R China. mxp@xmu.edu.cn zeng, xiangxiang/H-3771-2014; Lin, Chen/GRR-2799-2022 zeng, xiangxiang/0000-0003-1081-7658; National Natural Science Foundation of China [61472333, 61772441, 61472335, 61272152, 41476118]; Project of marine economic innovation and development in Xiamen [16PFW034SF02]; Natural Science Foundation of the Higher Education Institutions of Fujian Province [JZ160400]; Natural Science Foundation of Fujian Province [2017J01099]; President Fund of Xiamen University [20720170054]; Juan de la Cierva position [IJCI-2015-26991] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Project of marine economic innovation and development in Xiamen; Natural Science Foundation of the Higher Education Institutions of Fujian Province; Natural Science Foundation of Fujian Province(Natural Science Foundation of Fujian Province); President Fund of Xiamen University; Juan de la Cierva position The work was supported by the National Natural Science Foundation of China (Grant Nos. 61472333, 61772441, 61472335, 61272152, and 41476118), Project of marine economic innovation and development in Xiamen (No. 16PFW034SF02), Natural Science Foundation of the Higher Education Institutions of Fujian Province (No. JZ160400), Natural Science Foundation of Fujian Province (No. 2017J01099), President Fund of Xiamen University (No. 20720170054). XZ is supported by Juan de la Cierva position (code: IJCI-2015-26991). 29 20 20 2 23 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. JUL 31 2018.0 9.0 248. 10.3389/fgene.2018.00248 0.0 10 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity GO8RD 30108606.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000440360200001 0
J Li, YS; Han, D; Wang, BY; Sun, JF Li, Yansong; Han, Dong; Wang, Baoying; Sun, Jianfei An Industrial Scale Simulation Method for Predicting the Contamination Evolution in Long-Distance Multiproduct Pipelines JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE English Article Multiproduct pipelines; Successive transportation; Contamination evolution; Industrial scale simulation method (ISSM) LONGITUDINAL DISPERSION; TURBULENT; FLOW; NETWORKS An industrial scale simulation method (ISSM) has been proposed to predict the contamination evolution and oil quality distribution in multiproduct pipelines. The proposed ISSM consists of two subalgorithms: computational domain tracking method (CDTM) and spatial domain conversion method (SDCM). The computational effort can be greatly reduced by CDTM. SDCM is implemented for flow simulation in industrial scale pipelines. The basic concept of SDCM is adaptively adjusting the spatial mesh resolution according to the spatial distribution of contamination. The communication between spatial meshes with different resolutions is achieved by machine learning technology in SDCM. The validation of training and predicting process in SDCM indicates the artificial neural networks (ANN)-based method could efficiently construct the implicit function between the contamination concentration and spatial coordinates in pipelines. Then, the detailed implementation of ISSM, which is a hybrid of CDTM and SDCM, is given. The accuracy and efficiency are validated by both the direct numerical simulation results and practical experiments in industry multiproduct pipelines. Compared with the direct numerical method, ISSM could greatly improve the computational efficiency. Finally, two applications, mixing dynamics of miscible oil interface and oil quality evolution within long-distance pipelines, are carried out. The results reveal that ISSM can capture the asymmetric distribution phenomenon of the contamination concentration along the pipeline. Furthermore, the quality index of oil product can be tracked within the present framework of ISSM, which could be potentially used to design a quality-based scheduling scheme for industrial multiproduct pipelines. [Li, Yansong] China Univ Petr, Beijing Key Lab Urban Oil & Gas Distribut Technol, Beijing 102249, Peoples R China; [Han, Dong] China Natl Offshore Oil Corp China Ltd, Hainan Branch, Haikou 570311, Peoples R China; [Wang, Baoying] Univ Politecn Cataluna, Dept Appl Phys, Barcelona 08014, Spain; [Sun, Jianfei] Zhejiang Zheneng Natiral Gas Operat Co Ltd, 1751 Binsheng Rd, Hangzhou 310027, Peoples R China China University of Petroleum; China National Offshore Oil Corporation (CNOOC); Universitat Politecnica de Catalunya Li, YS (corresponding author), China Univ Petr, Beijing Key Lab Urban Oil & Gas Distribut Technol, Beijing 102249, Peoples R China. cyliyansong@126.com; handong6@cnooc.com.cn; baoying.wang@upc.edu; sjf752940949@outlook.com Wang, Baoying/0000-0002-6229-0336 National Natural Science Foundation of China [~51474228]; China Scholarship Council [~201806440152] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council) This work is supported by National Natural Science Foundation of China (No.~51474228). B. Y. Wang who was sponsored by the China Scholarship Council (No.~201806440152). 47 0 0 1 1 ASCE-AMER SOC CIVIL ENGINEERS RESTON 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA 1949-1190 1949-1204 J PIPELINE SYST ENG J. Pipel. Syst. Eng. Pract. FEB 1 2023.0 14.0 1 4022054 10.1061/(ASCE)PS.1949-1204.0000696 0.0 16 Engineering, Civil; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Water Resources 7B7UX 2023-03-23 WOS:000899335300012 0
C He, XF; Hua, Y; Song, T; Zhang, ZP; Xue, ZG; Ma, RH; Robertson, N; Guan, HB ACM He, Xufeng; Hua, Yang; Song, Tao; Zhang, Zongpu; Xue, Zhengui; Ma, Ruhui; Robertson, Neil; Guan, Haibing Unsupervised Video Summarization with Attentive Conditional Generative Adversarial Networks PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) English Proceedings Paper 27th ACM International Conference on Multimedia (MM) OCT 21-25, 2019 Nice, FRANCE Assoc Comp Machinery,ACM SIGMM video summarization; generative adversarial networks; video analysis; deep learning With the rapid growth of video data, video summarization technique plays a key role in reducing people's efforts to explore the content of videos by generating concise but informative summaries. Though supervised video summarization approaches have been well studied and achieved state-of-the-art performance, unsupervised methods are still highly demanded due to the intrinsic difficulty of obtaining high-quality annotations. In this paper, we propose a novel yet simple unsupervised video summarization method with attentive conditional Generative Adversarial Networks (GANs). Firstly, we build our framework upon Generative Adversarial Networks in an unsupervised manner. Specifically, the generator produces high-level weighted frame features and predicts frame-level importance scores, while the discriminator tries to distinguish between weighted frame features and raw frame features. Furthermore, we utilize a conditional feature selector to guide GAN model to focus on more important temporal regions of the whole video frames. Secondly, we are the first to introduce the frame-level multi-head self-attention for video summarization, which learns long-range temporal dependencies along the whole video sequence and overcomes the local constraints of recurrent units, e.g., LSTMs. Extensive evaluations on two datasets, SumMe and TVSum, show that our proposed framework surpasses state-of-the-art unsupervised methods by a large margin, and even outperforms most of the supervised methods. Additionally, we also conduct the ablation study to unveil the influence of each component and parameter settings in our framework. [He, Xufeng; Song, Tao; Zhang, Zongpu; Xue, Zhengui; Ma, Ruhui; Guan, Haibing] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Hua, Yang; Robertson, Neil] Queens Univ Belfast, Belfast, Antrim, North Ireland Shanghai Jiao Tong University; Queens University Belfast Song, T (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. hexufeng@sjtu.edu.cn; Y.Hua@qub.ac.uk; songt333@sjtu.edu.cn; zhangz-z-p@sjtu.edu.cn; zhenguixue@sjtu.edu.cn; ruhuima@sjtu.edu.cn; N.Robertson@qub.ac.uk; hbguan@sjtu.edu.cn hua, yang/GSE-0594-2022; guan, haibing/G-8142-2011 guan, haibing/0000-0002-4714-7400; Hua, Yang/0000-0001-5536-503X; , Tao/0000-0002-5965-3140 National NSF of China [61525204, 61732010, 61872234]; Shanghai Key Laboratory of Scalable Computing and Systems National NSF of China(National Natural Science Foundation of China (NSFC)); Shanghai Key Laboratory of Scalable Computing and Systems This work was supported in part by National NSF of China (NO. 61525204, 61732010, 61872234) and Shanghai Key Laboratory of Scalable Computing and Systems. 52 18 18 1 4 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-6889-6 2019.0 2296 2304 10.1145/3343031.3351056 0.0 9 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BO2ZT Green Accepted 2023-03-23 WOS:000509743400292 0
J Dornaika, F Dornaika, F. On the use of high-order feature propagation in Graph Convolution Networks with Manifold Regularization INFORMATION SCIENCES English Article Graph-based semi-supervised learning; Graph Convolution Networks (GCN); Graph Convolution Networks with Manifold; Regularization (GCNMR); Feature propagation; Label prediction; Manifold regularization; Semi-supervised image classification FRAMEWORK Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called GCNs with Manifold Regularization (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature propagation into each GCN layer. High-order feature propagation over the graph can fully exploit the structured information provided by the latter at all the GCN's layers. It fully exploits the clustering assumption, which is valid for structured data but not well exploited in GCNs. Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects, faces and digits: Extended Yale, PF01, Caltech101 and MNIST. We will also consider three citation networks. The proposed scheme performs well compared to several semi-supervised methods. With respect to the recent GCNMR approach, the average improvements were 2.2%, 4.5%, 1.0% and 10.6% on Extended Yale, PF01, Caltech101 and MNIST, respectively. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). [Dornaika, F.] Henan Univ, Kaifeng, Peoples R China; [Dornaika, F.] Univ Basque Country, UPV EHU, San Sebastian, Spain; [Dornaika, F.] Basque Fdn Sci, Ikerbasque, Bilbao, Spain Henan University; University of Basque Country; Basque Foundation for Science Dornaika, F (corresponding author), Univ Basque Country, San Sebastian, Spain. fadi.dornaika@ehu.eus University of the Basque Country UPV/EHU [GIU19/027] University of the Basque Country UPV/EHU This work is supported in part by the University of the Basque Country UPV/EHU grant GIU19/027. 48 4 4 3 16 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. JAN 2022.0 584.0 467 478 10.1016/j.ins.2021.10.041 0.0 NOV 2021 12 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science XA5AB hybrid, Green Published 2023-03-23 WOS:000720658400020 0
J Wu, Q; Zhang, LQ; Cichocki, A Wu, Qiang; Zhang, Liqing; Cichocki, Andrzej Multifactor sparse feature extraction using Convolutive Nonnegative Tucker Decomposition NEUROCOMPUTING English Article; Proceedings Paper 18th International Conference on Neural Information Processing (ICONIP) NOV 13-17, 2011 Shanghai, PEOPLES R CHINA Asia Pacific Neural Network Assembly,Shanghai Jiao Tong Univ,Natl Nat Sci Fdn China,Shanghai Hyron Software Co Ltd,Microsoft Res Asia,Hitachi Res & Dev Corp,Fujitsu Res & Dev Ctr Co Ltd Nonnegative Tensor Decomposition; Convolutive Tucker Model; Alternating Least Squares (ALS); Feature extraction; Robustness SHIFTED FACTOR-ANALYSIS; N-WAY GENERALIZATION; HIGHER-ORDER TENSOR; PART III; ALGORITHMS; MATRIX; YOUNG Multilinear algebra of the higher-order tensor has been proposed as a potential mathematical framework for machine learning to investigate the relationships among multiple factors underlying the observations. One popular model Nonnegative Tucker Decomposition (NTD) allows us to explore the interactions of different factors with nonnegative constraints. In order to reduce degeneracy problem of tensor decomposition caused by component delays, convolutive tensor decomposition model is an appropriate model for exploring temporal correlations. In this paper, a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model is proposed using an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition. The patterns across columns in convolutive tensor model are investigated to represent audio and image considering multiple factors. We employ the K-CNTD algorithm to extract the shift-invariant sparse features in different subspaces for robust speaker recognition and Alzheimer's Disease(AD) diagnosis task. The experimental results confirm the validity of our proposed algorithm and indicate that it is able to improve the speaker recognition performance especially in noisy conditions and has potential application on AD diagnosis. (C) 2013 Elsevier B.V. All rights reserved. [Wu, Qiang] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China; [Zhang, Liqing] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, MOE Microsoft Key Lab Intelligent Comp & Intellig, Shanghai 200030, Peoples R China; [Cichocki, Andrzej] BSI RIKEN, Lab Adv Brain Signal Proc, Saitama, Japan; [Cichocki, Andrzej] Warsaw Univ Technol, Dept EE, PL-00661 Warsaw, Poland Shandong University; Microsoft; Shanghai Jiao Tong University; RIKEN; Warsaw University of Technology Wu, Q (corresponding author), Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China. wuqiang@sdu.edu.cn; lqzhang@sjtu.edu.cn; cia@brain.riken.jp Cichocki, Andrzej/A-1545-2015; Cichocki, Andrzej/AAI-4209-2020 Cichocki, Andrzej/0000-0002-8364-7226; 41 12 12 0 15 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing APR 10 2014.0 129.0 SI 17 24 10.1016/j.neucom.2013.04.049 0.0 8 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S) Computer Science AB9RD 2023-03-23 WOS:000332132400003 0
J Jia, CZ; Wang, LM; Meng, EL; Chen, LM; Liu, YS; Jia, WQ; Bao, YT; Liu, ZG Jia, Chengzhen; Wang, Lingmei; Meng, Enlong; Chen, Liming; Liu, Yushan; Jia, Wenqiang; Bao, Yutao; Liu, Zhenguo Combining LIDAR and LADRC for intelligent pitch control of wind turbines RENEWABLE ENERGY English Article LIDAR; RBFNNFIR; LADRC; Speed fluctuation; Load moment; Pitch control DISTURBANCE REJECTION; FEEDFORWARD CONTROL; PREDICTIVE CONTROL; DESIGN; PLANT At present, most of the pitch control methods are based on PI controller, the pitch control system has poor disturbance resistance, and the research of variable parameter feedforward based on Light detection and ranging (LIDAR) and the Linear Active Disturbance Rejection controller (LADRC) composite control is rarely studied to reduce the blade root load, so this paper conceives a hybrid intelligent and adaptive pitch control approach to reduce a wind turbine generator speed fluctuation and its blade root load. Specifically, we combine the Radial Basis Neural Network and Finite Impulse Response filter (RBFNNFIR) based on LIDAR wind measurement. We then use a variable bandwidth of LADRC controller. Overall the approach enables and facilitates self-adaption and self-adjustment. We use Matlab s-function to call the multi-freedom mathematical wind turbine model based on FAST code, the composite intelligent control algorithm is established in Simulink. Initial results from the statistical analysis of the experiments under different turbulent wind conditions shows that the hybrid intelligent pitch control approach can reduce the generator speed fluctuation by about 40.8%, and the blade root max value of load moment by about 13.1%, compared with the baseline values of the traditional variable gain PI control algorithm. (C) 2021 The Authors. Published by Elsevier Ltd. [Jia, Chengzhen; Wang, Lingmei; Liu, Yushan] Shanxi Univ, Sch Comp & Informat Technol, 92 Wucheng St, Taiyuan 030006, Shanxi, Peoples R China; [Jia, Chengzhen; Wang, Lingmei; Meng, Enlong; Liu, Yushan] Wind Turbine Monitoring & Diag Engn Technol Res C, Taiyuan 030013, Peoples R China; [Chen, Liming] Ulster Univ, Sch Comp, Belfast BT37 0QB, Antrim, North Ireland; [Jia, Wenqiang] Taiyuan Heavy Ind New Energy Equipment Co Ltd, Taiyuan 030000, Peoples R China; [Bao, Yutao] Qinghai Green Power Distributed Energy Co Ltd, Xining 810001, Peoples R China; [Meng, Enlong; Liu, Zhenguo] Shanxi Univ, Automat Dept, Taiyuan 030013, Peoples R China Shanxi University; Ulster University; Shanxi University Wang, LM (corresponding author), Shanxi Univ, Sch Comp & Informat Technol, 92 Wucheng St, Taiyuan 030006, Shanxi, Peoples R China. 13546468676@163.com Jia, Chengzhen/GXG-9377-2022; bao, yutao/HHS-5896-2022 Jia, Chengzhen/0000-0001-7526-2492; Chen, Liming (Luke)/0000-0003-0200-7989 National Natural Science Foundation of China [U1810126]; Qinghai Key R&D and transformation projects [2019-GX-C27] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Qinghai Key R&D and transformation projects This work was jointly supported by the National Natural Science Foundation of China (No.U1810126) and Qinghai Key R&D and transformation projects (No.2019-GX-C27). 37 10 10 4 14 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0960-1481 1879-0682 RENEW ENERG Renew. Energy MAY 2021.0 169.0 1091 1105 10.1016/j.renene.2021.01.065 0.0 JAN 2021 15 Green & Sustainable Science & Technology; Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Energy & Fuels QM5RA hybrid, Green Published 2023-03-23 WOS:000621835200007 0
J She, JH; Guzman-Miranda, H; Huang, V; Chen, AC; Karnouskos, S; Dunai, L; Ma, CB; Tisan, A; Yokota, S She, Jinhua; Guzman-Miranda, Hipolito; Huang, Victor; Chen, Allen C.; Karnouskos, Stamatis; Dunai, Larisa; Ma, Chengbin; Tisan, Alin; Yokota, Sho A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY English Article Industries; Industrial electronics; Human factors; Standards; Education; Ethics; System-on-chip; Cross-disciplinary technology; electronic systems on chip; human factors; resilience and security for industrial applications; professional education; technology ethics; standards; sustainable society DATA-PARALLEL APPLICATIONS; DATA ANALYTICS; ENERGY MANAGEMENT; BIG DATA; ACCELERATOR; NETWORK; IOT; ARCHITECTURES; OPTIMIZATION; PERFORMANCE How to build a sustainable society in view of industrial electronics has been discussed from energy, information and communication technologies, cyber-physical systems (CPSs), and other viewpoints. This paper presents a cross-disciplinary view that integrates the fields of human factors, professional education, electronic systems on chip, resilience and security for industrial applications, technology ethics and society, and standards. After explaining the efforts and challenges in these fields, this paper shows a methodology for cross-disciplinary technology that integrates the technical committees in Cluster 4, Industrial Electronics Society. A project, which was launched in March 2022, implements a 'Proof of Concept' trial of the methodology. [She, Jinhua] Tokyo Univ Technol, Hachioji, Tokyo, Japan; [Guzman-Miranda, Hipolito] Univ Seville, Seville, Spain; [Huang, Victor] Sage Technol Resources, Cupertino, CA 95014 USA; [Chen, Allen C.] Innovatech Solut Inc, Monroe Township, NJ USA; [Karnouskos, Stamatis] SAP, Walldorf, Germany; [Dunai, Larisa] Univ Politecn Valencia, Valencia, Spain; [Ma, Chengbin] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Tisan, Alin] Royal Holloway Univ London, Egham, Surrey, England; [Yokota, Sho] Toyo Univ, Bunkyo Ku, Tokyo, Japan Tokyo University of Technology; University of Sevilla; SAP; Universitat Politecnica de Valencia; Shanghai Jiao Tong University; University of London; Royal Holloway University London; Toyo University She, JH (corresponding author), Tokyo Univ Technol, Hachioji, Tokyo, Japan. she@stf.teu.ac.jp Karnouskos, Stamatis/D-3191-2016; She, Jinhua/I-9776-2014; Guzman-Miranda, Hipolito/F-2063-2010 Karnouskos, Stamatis/0000-0003-3519-8232; Yokota, Sho/0000-0002-8507-5620; Tisan, Alin/0000-0002-8280-2722; She, Jinhua/0000-0003-3165-5045; Dunai, Larisa/0000-0002-5076-0695; Guzman-Miranda, Hipolito/0000-0002-2896-5897 JSPS [22H03998] JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science) The work of Jinhua Sh was supported by JSPS Grant-in-Aid for Scientific Research B under Grant 22H03998 (Japan). 103 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2644-1284 IEEE OPEN J IND ELEC IEEE Open J. Ind. Electron. Soc. 2022.0 3.0 375 391 10.1109/OJIES.2022.3174218 0.0 17 Engineering, Electrical & Electronic Emerging Sources Citation Index (ESCI) Engineering 2E9GF gold 2023-03-23 WOS:000812528300003 0
J Li, WB; Li, Y; Chan, JCW Li, Wenbo; Li, Ying; Chan, Jonathan Cheung-Wai Thick Cloud Removal With Optical and SAR Imagery via Convolutional-Mapping-Deconvolutional Network IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Cloud removal; convolutional neural network (CNN); intensity normalization; multisource estimation REMOTELY-SENSED IMAGES; SATELLITE IMAGERY; INFORMATION; RECONSTRUCTION; CLASSIFICATION In this article, we proposed a thick cloud removal method for remote-sensing imagery based on multisource estimation. A convolutional-mapping-deconvolutional (CMD) network is proposed to estimate the cloud-free image directly from multisource reference images. Synthetic aperture radar (SAR) image and low-resolution heterogeneous (LRH) image, namely, image from a different optical sensor with lower spatial resolution, are used as reference images to recover the missing information in the cloud-contaminated high-resolution (HR) image. The CMD net is composed of three functional components: the convolutional layers for encoding, the mapping layer for feature transferring, and the deconvolutional layers for decoding. In the training procedure, HR images from cloud-free regions and their corresponding LRH and SAR reference images are used to train the CMD net. When the CMD net is fully trained, it is able to estimate the HR images with their corresponding LRH and SAR reference images. The LRH and SAR reference images are first encoded by the convolutional layers before being transferred to the feature space at HR by the mapping layer. The transferred features are then decoded into cloud-free HR image by the deconvolutional layers. Cloud-free regions in the cloud-contaminated HR image are used to further improve the estimated image via intensity normalization. At last, the cloudy pixels are replaced by their corresponding pixels from the estimated cloud-free HR image. Comparisons with several recently proposed multisource cloud removal methods show that our proposed method is superior as validated by quantitative indexes and visual inspections. [Li, Wenbo; Li, Ying] Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Sch Comp Sci, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Peoples R China; [Li, Wenbo; Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Northwestern Polytechnical University; Vrije Universiteit Brussel Li, Y (corresponding author), Northwestern Polytech Univ, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Sch Comp Sci, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Peoples R China. wlia@etrovub.be; lybyp@nwpu.edu.cn; jcheungw@etrovub.be Chan, Jonathan/0000-0002-3741-1124; Li, Wenbo/0000-0002-7432-0213 National Natural Science Foundation of China [61871460, 61876152]; Fundamental Research Funds for the Central Universities [3102019ghxm016]; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201957] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University This work was supported in part by the National Natural Science Foundation of China under Grant 61871460 and Grant 61876152, in part by the Fundamental Research Funds for the Central Universities under Grant 3102019ghxm016, and in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX201957. 38 13 16 8 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing APR 2020.0 58.0 4 2865 2879 10.1109/TGRS.2019.2956959 0.0 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology LV9FL 2023-03-23 WOS:000538748900046 0
J Chen, G; Xu, ZC; Li, ZJ; Tang, HJ; Qu, SQ; Ren, KJ; Knoll, A Chen, Guang; Xu, Zhongcong; Li, Zhijun; Tang, Huajin; Qu, Sanqing; Ren, Kejia; Knoll, Alois A Novel Illumination-Robust Hand Gesture Recognition System With Event-Based Neuromorphic Vision Sensor IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article Gesture recognition; Cameras; Lighting; Vision sensors; Neuromorphics; Robustness; Heuristic algorithms; Active LED marker (ALM); biologically inspired learning; biologically inspired signal processing; dynamic and active-pixel vision sensor (DAVIS); event-based neuromorphic vision; hand gesture recognition; illumination-robust system; wearable device REAL-TIME The hand gesture recognition system is a noncontact and intuitive communication approach, which, in turn, allows for natural and efficient interaction. This work focuses on developing a novel and robust gesture recognition system, which is insensitive to environmental illumination and background variation. In the field of gesture recognition, standard vision sensors, such as CMOS cameras, are widely used as the sensing devices in state-of-the-art hand gesture recognition systems. However, such cameras depend on environmental constraints, such as lighting variability and the cluttered background, which significantly deteriorates their performances. In this work, we propose an event-based gesture recognition system to overcome the detriment constraints and enhance the robustness of the recognition performance. Our system relies on a biologically inspired neuromorphic vision sensor that has microsecond temporal resolution, high dynamic range, and low latency. The sensor output is a sequence of asynchronous events instead of discrete frames. To interpret the visual data, we utilize a wearable glove as an interaction device with five high-frequency (>100 Hz) active LED markers (ALMs), representing fingers and palm, which are tracked precisely in the temporal domain using a restricted spatiotemporal particle filter algorithm. The latency of the sensing pipeline is negligible compared with the dynamics of the environment as the sensor's temporal resolution allows us to distinguish high frequencies precisely. We design an encoding process to extract features and adopt a lightweight network to classify the hand gestures. The recognition accuracy of our system is comparable to the state-of-the-art methods. To study the robustness of the system, experiments considering illumination and background variations are performed, and the results show that our system is more robust than the state-of-the-art deep learning-based gesture recognition systems. Note to Practitioners-This article addresses the robustness of the hand gesture recognition system that is important for gesture recognition-based applications. Existing methods rely on either the large-volume data to train a deep learning model or to restrict the applied environments (e.g., an ideal environment without dynamic background). However, a vision-based deep learning model requires large computational resources, while the ideal environment limits the practicality of the system. In this work, we introduce a biologically inspired neuromorphic vision sensor and an ALM glove and build a novel gesture recognition system to tackle the above issue. The neuromorphic vision sensor has a microsecond temporal resolution and a high dynamic range. With these properties, the sensing system of our prototype operates in a very low-latency space, which, in turn, ensures that our gesture recognition system is robust to illumination variance and dynamic background. Thus, this work is valuable to the research of illumination-robust gesture recognition systems. Preliminary experiments suggest that our system prototype is feasible, but it has not yet been incorporated into an online gesture recognition system nor tested with complex gestures. In future work, we will concentrate on the improvement of the signal processing methods that advance the current system to complex and practical applications. [Chen, Guang; Qu, Sanqing] Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China; [Chen, Guang; Knoll, Alois] Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, D-80333 Munich, Germany; [Xu, Zhongcong] Natl Univ Singapore, Elect & Comp Engn Dept, Singapore 119077, Singapore; [Li, Zhijun] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China; [Tang, Huajin] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China; [Ren, Kejia] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA Tongji University; Technical University of Munich; National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Zhejiang University; Johns Hopkins University Li, ZJ (corresponding author), Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China. zjli@ieee.org Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X; Ren, Kejia/0000-0002-6970-2268 European Union's Horizon 2020 Framework Programme for Research and Innovation [945539]; Shanghai AI Innovative Development Project 2018; National Science and Technology Major Project of the Ministry of Science and Technology of China [2018AAA0102900] European Union's Horizon 2020 Framework Programme for Research and Innovation; Shanghai AI Innovative Development Project 2018; National Science and Technology Major Project of the Ministry of Science and Technology of China This work was supported in part by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement 945539 (Human Brain Project SGA3), in part by the Shanghai AI Innovative Development Project 2018, and in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2018AAA0102900. 49 11 11 2 24 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. APR 2021.0 18.0 2 508 520 10.1109/TASE.2020.3045880 0.0 13 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems RK6KA 2023-03-23 WOS:000638401500012 0
J Flament, F; Maudet, A; Ye, CD; Zhang, YZ; Jiang, RW; Dubosc, S; Even, M; Tournery, S; Abric, A; De Boni, M; Delaunay, C; Aarabi, P Flament, Frederic; Maudet, Aurelie; Ye, Chengda; Zhang, Yuze; Jiang, Ruowei; Dubosc, Sarah; Even, Maxime; Tournery, Sylvia; Abric, Aurelie; De Boni, Maxime; Delaunay, Caroline; Aarabi, Parham Comparing the self-perceived effects of a facial anti-aging product to those automatically detected from selfie images of Chinese women of different ages and cities SKIN RESEARCH AND TECHNOLOGY English Article anti‐ aging efficacy; artificial intelligence; automatic grading system; Chinese women; self‐ perception Objective To assess the agreement, after 1-month application of a popular and efficient anti-aging product, between self-perceived facial signs of aging and those detected and graded by an automatic A.I-based system, using smartphones' selfie images. Material and Methods Of 1065 Chinese women, aged 18-60 years, from eight different Chinese cities were recruited. They were asked to apply daily, for 1 month, a referential anti-aging product onto their whole face. Selfie images were taken by all subjects at D-0 and D-28 and sent to our facilities for being analyzed through 10 different facial signs. At D-28, all subjects were asked to fill a questionnaire on the status of their faces, through six general statements. Results A global agreement between both approaches is reached, particularly among women older than 40 years where the severity of facial signs is already more pronounced or among younger women who present at least facial signs scored above one grading units. This limit becomes, therefore, a prerequisite in the recruitment of Chinese subjects in the case of anti-aging applied studies and possible automatically based on automatic grading system. When respecting such conditions, the positive effects of the product on most facial signs can be demonstrated after 28 days of successive applications. Conclusion Such methodological approach paves the road in fulfilling the need of consumers of a better transparency in the claims of an anti-aging product. [Flament, Frederic; Maudet, Aurelie; Delaunay, Caroline] LOreal Res & Innovat, 9 Rue Pierre Dreyfus, F-92110 Clichy, France; [Ye, Chengda; De Boni, Maxime] LOreal Res & Innovat, Shanghai, Peoples R China; [Zhang, Yuze; Jiang, Ruowei; Aarabi, Parham] LOreal Grp Co, ModiFace, Toronto, ON, Canada; [Dubosc, Sarah; Even, Maxime; Tournery, Sylvia] Lancome Int, Levallois Perret, France; [Abric, Aurelie] Eurosyn, Villebon Sur Yvette, France L'Oreal Group; L'Oreal Group; L'Oreal Group Flament, F (corresponding author), LOreal Res & Innovat, 9 Rue Pierre Dreyfus, F-92110 Clichy, France. frederic.flament@rd.loreal.com L'Oreal Group L'Oreal Group(L'Oreal Group) All costings of the present work were entirely met by L'Oreal Group 26 4 4 0 7 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0909-752X 1600-0846 SKIN RES TECHNOL Skin Res. Technol. SEP 2021.0 27.0 5 880 890 10.1111/srt.13037 0.0 APR 2021 11 Dermatology Science Citation Index Expanded (SCI-EXPANDED) Dermatology WJ0KA 33822402.0 2023-03-23 WOS:000637011700001 0
J Wen, J; Kohler, P; Duveiller, G; Parazoo, NC; Magney, TS; Hooker, G; Yu, L; Chang, CY; Sun, Y Wen, J.; Kohler, P.; Duveiller, G.; Parazoo, N. C.; Magney, T. S.; Hooker, G.; Yu, L.; Chang, C. Y.; Sun, Y. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF) REMOTE SENSING OF ENVIRONMENT English Article SIP; Multi-instrument harmonization; Downscaling; GOME-2; SCIAMACHY; Machine learning; CDF matching EMPIRICAL MODE DECOMPOSITION; SUN-INDUCED FLUORESCENCE; GROSS PRIMARY PRODUCTION; LIGHT-USE EFFICIENCY; FIELD SPECTROSCOPY; CANOPY SCATTERING; RED FLUORESCENCE; PHOTOSYNTHESIS; RETRIEVAL; DROUGHT Several decade-long satellite retrievals of solar-induced chlorophyll fluorescence (SIP) have become available during the past few years, but understanding the long-term dynamics of SIF and elucidating its co-variation with historical gross primary production (GPP) remains a challenge. Part of the challenge is due to the lack of direct comparability among these SIF products as they are derived from various satellite platforms with different retrieval methods, instruments characteristics, overpass time, and viewing-illumination geometries. This study presents a framework that circumvents these discrepancies and allows the harmonization of SIF products from multiple instruments to achieve long-term coverage. We demonstrate this framework by fusing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) onboard MetOp-A developed at German Research Center for Geosciences (GFZ). We first downscale both original SIF datasets from their native resolutions to 0.05 degrees ((SIF) over bar (GOME2_005) and (SIF) over bar (SCIA_005) respectively) using machine learning (ML) algorithms imposed with regionalization constraints to account for the varying relationships between predictors and SIF in space and time. We then apply the cumulative distribution function (CDF) matching technique to correct the offset between (SIF) over bar (GOME2_005) and (SIF) over bar (SCIA_005) inherited from the original instrumental discrepancies to generate a harmonized SIF time series from 2002 to present ((SIF) over bar (005)). Finally, we quantify the uncertainty of (SIF) over bar (005). (SIF) over bar (005) is validated with 1) the original retrievals to ensure the spatial and temporal variabilities are preserved, 2) airborne SIF derived from the Chlorophyll Fluorescence Imaging Spectrometer (CFIS, R-2 = 0.73), and 3) ground-based SIF measurements at a subalpine coniferous forest (R-2 = 0.91). The SIFyield derived from (SIF) over bar (005) has high seasonal consistency with the ground measurements (R-2 = 0.93), suggesting that the harmonized product (SIF) over bar (005) carries physiological information beyond the absorbed photosynthetically active radiation. Additionally, (SIF) over bar (005) has a good capability for large-scale stress monitoring as demonstrated with several major historical drought and heatwave events. The framework developed in this study sets the stage for future development of even more advanced SIF products from all SIP-capable satellite platforms once issues related to inter-sensor calibration are resolved and SIF physiology is better understood. [Wen, J.; Yu, L.; Chang, C. Y.; Sun, Y.] Cornell Univ, Sch Integrat Plant Sci, Soil & Crop Sci Sect, Ithaca, NY 14850 USA; [Kohler, P.; Magney, T. S.] CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA; [Duveiller, G.] JRC, European Commiss, Via E Fermi 2749, I-21027 Ispra, VA, Italy; [Parazoo, N. C.] CALTECH, Jet Prop Lab, Pasadena, CA USA; [Magney, T. S.] Univ Calif Davis, Dept Plant Sci, Davis, CA 95616 USA; [Hooker, G.] Cornell Univ, Dept Stat & Data Sci, Ithaca, NY USA; [Yu, L.] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China; [Yu, L.] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China Cornell University; California Institute of Technology; European Commission Joint Research Centre; EC JRC ISPRA Site; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); University of California System; University of California Davis; Cornell University; Chinese Academy of Sciences; Computer Network Information Center, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Sun, Y (corresponding author), Cornell Univ, Sch Integrat Plant Sci, Soil & Crop Sci Sect, Ithaca, NY 14850 USA. ys776@cornell.edu Duveiller, Gregory/A-4932-2016; Köhler, Philipp/AAV-1936-2020 Duveiller, Gregory/0000-0002-6471-8404; Köhler, Philipp/0000-0002-7820-1318; Sun, Ying/0000-0002-9819-1241; Magney, Troy/0000-0002-9033-0024; Chang, Christine/0000-0001-6609-3527 USAID Feed the Future program [7200AA18CA00014]; Earth Sciences Division MEaSUREs program; Earth Science U.S. Participating Investigator [NNX15AH95G]; European Commission Joint Research Centre by the Copernicus-2 - DG-GROW [5054]; OCO-2 Science Team at the Jet Propulsion Laboratory, California Institute of Technology; National Science Foundation [DMS-1712554, TRIPODS 1740882]; 2018 China Scholarship Council (CSC)-IBM Future Data Scientist Scholarship Program; USDA-NIFA postdoctoral fellowship [2018-67012-27985] USAID Feed the Future program(United States Agency for International Development (USAID)); Earth Sciences Division MEaSUREs program; Earth Science U.S. Participating Investigator; European Commission Joint Research Centre by the Copernicus-2 - DG-GROW(European CommissionEuropean Commission Joint Research Centre); OCO-2 Science Team at the Jet Propulsion Laboratory, California Institute of Technology; National Science Foundation(National Science Foundation (NSF)); 2018 China Scholarship Council (CSC)-IBM Future Data Scientist Scholarship Program; USDA-NIFA postdoctoral fellowship J. Wen and Y. Sun are funded by the USAID Feed the Future program 7200AA18CA00014. Y. Sun, N.C. Parazoo, and T. S. Magney acknowledge the support from Earth Sciences Division MEaSUREs program. P. Kohler is funded by the Earth Science U.S. Participating Investigator (Grant Number: NNX15AH95G). G. Duveiller is funded at the European Commission Joint Research Centre by the Copernicus-2 administrative agreement (nr. 5054) supported by DG-GROW. T. S. Magney is supported by the OCO-2 Science Team at the Jet Propulsion Laboratory, California Institute of Technology. G. Hooker is funded with National Science Foundation DMS-1712554 and TRIPODS 1740882. L. Yu is funded by the 2018 China Scholarship Council (CSC)-IBM Future Data Scientist Scholarship Program. C. Y. Chang is supported by USDA-NIFA postdoctoral fellowship (Grant Number: 2018-67012-27985). We thank C. Frankenberg for providing CFIS SIF retrievals and the helpful discussion on analyzing and interpreting the PhotoSpec SIP measured at Niwot Ridge. Data used in this study: MODIS products are from https://lpdaac.usgs.gov/; MERRA-2 PAR product is from: https://disc.gsfc.nasa.gov/; GOME-2 and SCIAMACHY SIF are from ftp://fluo.gps.caltech.edu/data/Philipp/; airborne CFIS SIF is from ftp://fluo.gps.caltech.edu/data/CFIS/; tower SIP measurements is from: https://data.caltech.edu/records/1231; SIFLUE is from https://jeodpp.jrc.ec.europa.eu.The harmonized long-term record of SIF005 can be downloaded from https://cornell.box.com/s/gkp4moy4grvgsus1g5oz7u5lc30i7o41. 109 39 43 17 67 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. MAR 15 2020.0 239.0 111644 10.1016/j.rse.2020.111644 0.0 24 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology KT0IO Bronze 2023-03-23 WOS:000518694400023 0
J Iswarya, M; Raja, R; Zhu, Q; Niezabitowski, M; Alzabut, J; Maharajan, C Iswarya, M.; Raja, R.; Zhu, Q.; Niezabitowski, M.; Alzabut, J.; Maharajan, C. Existence, Uniqueness, and Exponential Stability of Uncertain Delayed Neural Networks with Inertial Term: Nonreduced Order Case MATHEMATICAL PROBLEMS IN ENGINEERING English Article ROBUST STABILITY; MIXED DELAYS; SYNCHRONIZATION; DISCRETE; DYNAMICS; CRITERIA In this work, we mainly focus on uncertain delayed neural network system with inertial term. Here, the existence, uniqueness, and exponential stability of inertial neural networks are derived without shifting the second order differential system into first order through substituting variables. Initially, we construct a proper Lyapunov-Krasovskii functional to investigate the stability of novel uncertain delayed inertial neural networks, which is different from the classical Lyapunov functional approach. By utilizing the ICirchhoff's matrix tree theorem, Cauchy-Schwartz inequality, homeomorphism theorem, and some inequality techniques, the necessary and sufficient conditions are derived for the designed framework Subsequently, to exhibit the strength of this outcome, we framed a quantitative example. [Iswarya, M.] Alagappa Univ, Dept Math, Karaikkudi 630003, Tamil Nadu, India; [Raja, R.] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630003, Tamil Nadu, India; [Zhu, Q.] Hunan Normal Univ, Sch Math & Stat, Changsha 410081, Hunan, Peoples R China; [Zhu, Q.] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China; [Niezabitowski, M.] Silesian Tech Univ, Fac Automat Control Elect & Comp Sci, Dept Automat Control & Robot, Akad 16, PL-44100 Gliwice, Poland; [Alzabut, J.] Prince Sultan Univ, Dept Math & Gen Sci, Riyadh 11586, Saudi Arabia; [Alzabut, J.] Ostim Tech Univ, Grp Math, Fac Engn, TR-06374 Ankara, Turkey; [Maharajan, C.] VSB Engn Coll, Dept Math, Karur, India Alagappa University; Alagappa University; Hunan Normal University; Chengdu University; Silesian University of Technology; Prince Sultan University; Ostim Technical University Zhu, Q (corresponding author), Hunan Normal Univ, Sch Math & Stat, Changsha 410081, Hunan, Peoples R China.;Zhu, Q (corresponding author), Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China. iswaryajm19@gmail.com; rajarchm2012@gmail.com; zqx22@126.com; michal.niezabitowski@polsl.pl; jalzabut@psu.edu.sa; c.maharajan369369@gmail.com NIEZABITOWSKI, MICHAŁ/H-3407-2012; Raja, R./Y-7657-2019 NIEZABITOWSKI, MICHAŁ/0000-0003-4553-5351; Raja, R./0000-0003-0830-4933; Alzabut, Prof. Dr. Jehad/0000-0002-5262-1138; Manickam, Iswarya/0000-0002-1420-8094 RUSA-Phase 2.0 grant [F.24-51/2014-U]; policy (TN Multi-Gen), Dept. of Edn. Govt. of India, UGC-SAP (DRS-I) [F.510/8/DRS-I/2016(SAP-I)]; DST (FIST- Phase I) [SR/FIST/MS-I/2018-17]; National Science Centre in Poland [DEC-2017/25/B/ST7/02888]; Prince Sultan University RUSA-Phase 2.0 grant; policy (TN Multi-Gen), Dept. of Edn. Govt. of India, UGC-SAP (DRS-I); DST (FIST- Phase I); National Science Centre in Poland(National Science Centre, Poland); Prince Sultan University The article has been written with the joint partial financial support of the RUSA-Phase 2.0 grant sanctioned vide letter no. F.24-51/2014-U, policy (TN Multi-Gen), Dept. of Edn. Govt. of India, UGC-SAP (DRS-I) vide letter no. F.510/8/DRS-I/2016(SAP-I), and DST (FIST- Phase I) vide letter no. SR/FIST/MS-I/2018-17 and the National Science Centre in Poland under Grant no. DEC-2017/25/B/ST7/02888 and J. Alzabut would like to thank Prince Sultan University for supporting this work. 45 1 1 2 17 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1024-123X 1563-5147 MATH PROBL ENG Math. Probl. Eng. MAY 7 2021.0 2021.0 5560763 10.1155/2021/5560763 0.0 15 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics SM5TJ gold 2023-03-23 WOS:000657667300001 0
C Zhang, ZP; Hua, Y; Song, T; Xue, ZG; Ma, RH; Robertson, N; Guan, HB ACM Zhang, Zongpu; Hua, Yang; Song, Tao; Xue, Zhengui; Ma, Ruhui; Robertson, Neil; Guan, Haibing Tracking-assisted Weakly Supervised Online Visual Object Segmentation in Unconstrained Videos PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18) English Proceedings Paper 26th ACM Multimedia Conference (MM) OCT 22-26, 2018 Seoul, SOUTH KOREA Assoc Comp Machinery,ACM SIGMM Video Object Segmentation; Visual Object Tracking; Video Analysis; Deep Learning This paper tackles the task of online video object segmentation with weak supervision, i.e., labeling the target object and background with pixel-level accuracy in unconstrained videos, given only one bounding box information in the first frame. We present a novel tracking-assisted visual object segmentation framework to achieve this. On the one hand, initialized with a given bounding box in the first frame, the auxiliary object tracking module guides the segmentation module frame by frame by providing motion and region information, which is usually missing in semi-supervised methods. Moreover, compared with the unsupervised approach, our approach with such minimum supervision can focus on the target object without bringing unrelated objects into the final results. On the other hand, the video object segmentation module also improves the robustness of the visual object tracking module by pixel-level localization and objectness information. Thus, segmentation and tracking in our framework can mutually help each other in an online manner. To verify the generality and effectiveness of the proposed framework, we evaluate our weakly supervised method on two cross-domain datasets, i.e., the DAVIS and VOT2016 datasets, with the same configuration and parameter setting. Experimental results show the top performance of our method, which is even better than the leading semi-supervised methods. Furthermore, we conduct the extensive ablation study on our approach to investigate the influence of each component and main parameters. [Zhang, Zongpu; Song, Tao; Xue, Zhengui; Ma, Ruhui; Guan, Haibing] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Hua, Yang; Robertson, Neil] Queens Univ Belfast, Belfast, Antrim, North Ireland; [Xue, Zhengui] Ulster Univ, Belfast, Antrim, North Ireland Shanghai Jiao Tong University; Queens University Belfast; Ulster University Song, T (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. zhangz-z-p@sjtu.edu.cn; Y.Hua@qub.ac.uk; songt333@sjtu.edu.cn; zhenguixue@sjtu.edu.cn; ruhuima@sjtu.edu.cn; N.Robertson@qub.ac.uk; hbguan@sjtu.edu.cn hua, yang/GSE-0594-2022; guan, haibing/G-8142-2011 guan, haibing/0000-0002-4714-7400; Hua, Yang/0000-0001-5536-503X; , Tao/0000-0002-5965-3140 National NSF of China [61525204, 61732010]; Shanghai Key Laboratory of Scalable Computing and Systems National NSF of China(National Natural Science Foundation of China (NSFC)); Shanghai Key Laboratory of Scalable Computing and Systems This work was supported in part by National NSF of China (NO. 61525204, 61732010) and Shanghai Key Laboratory of Scalable Computing and Systems. 45 7 7 1 1 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-5665-7 2018.0 941 949 10.1145/3240508.3240638 0.0 9 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO2ZN Green Accepted 2023-03-23 WOS:000509665700108 0
J Saigre-Tardif, C; Faqiri, R; Zhao, HT; Li, LL; del Hougne, P Saigre-Tardif, Chloe; Faqiri, Rashid; Zhao, Hanting; Li, Lianlin; del Hougne, Philipp Intelligent meta-imagers: From compressed to learned sensing APPLIED PHYSICS REVIEWS English Review SYNTHETIC-APERTURE RADAR; METASURFACE ANTENNA; PHASE RETRIEVAL; CODED-APERTURE; MATRIX DESIGN; FIELD; OPTIMIZATION; CAPACITY; MODEL; RECONSTRUCTION Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of intelligence in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step over-the-air. We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors. [Saigre-Tardif, Chloe; Faqiri, Rashid; del Hougne, Philipp] Univ Rennes, CNRS, UMR 6164, IETR, F-35000 Rennes, France; [Saigre-Tardif, Chloe] INSA Rennes, F-35000 Rennes, France; [Faqiri, Rashid] Imperial Coll London, Dept Phys, Blackett Lab, London SW7 2AZ, England; [Zhao, Hanting; Li, Lianlin] Peking Univ, Dept Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National des Sciences Appliquees de Rennes; Universite de Rennes; Imperial College London; Peking University del Hougne, P (corresponding author), Univ Rennes, CNRS, UMR 6164, IETR, F-35000 Rennes, France. philipp.del-hougne@univ-rennes1.fr 241 15 16 76 112 AIP Publishing MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 1931-9401 APPL PHYS REV Appl. Phys. Rev. MAR 2022.0 9.0 1 11314 10.1063/5.0076022 0.0 24 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics ZQ4EU Green Submitted, Green Published, hybrid 2023-03-23 WOS:000767060700002 0
J Chen, XQ; Wu, XY; Prasad, DK; Wu, B; Postolache, O; Yang, YS Chen, Xinqiang; Wu, Xingyu; Prasad, Dilip K.; Wu, Bing; Postolache, Octavian; Yang, Yongsheng Pixel-Wise Ship Identification From Maritime Images via a Semantic Segmentation Model IEEE SENSORS JOURNAL English Article Depth separable convolution; intelligent visual navigation; pixel-wise ship identification; semantic segmentation; smart ship NETWORK Accurately identifying ships from maritime surveillance videos attracts increasing attention in the smart ship community, considering that the videos provide informative yet easily understandable spatial-temporal traffic information for varied maritime traffic participants. Previous studies (e.g., ship detection and ship tracking) are conducted by learning distinct features from training samples labeled in terms of bounding boxes, and thus, background pixels may be wrongly trained as ship features. To bridge the gap, we propose a novel approach for achieving a pixel-wise ship segmentation and identification task through a novel design of U-Net deep learning architecture (denoted as EU-Net). The encoder of the EU-Net extracts distinct ship features from input maritime images, and its decoder outputs ship segmentation results in the pixel-wise manner. The proposed EU-Net model consists of encoder and decoder parts via the help of a convolution layer, a depth separable convolution layer, a softmax layer, and so on. More specifically, the EU-Net model identifies each pixel into ship or non-ship as the final output. Experimental results suggest that our proposed model can accurately identify ship (in terms of pixels), considering that the ship segmentation accuracies were larger than 99%. The proposed ship segmentation framework can be further adaptively deployed in the ship sensing system for maritime traffic situation awareness and intelligent visual navigation in a smart ship era. [Chen, Xinqiang; Yang, Yongsheng] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 210036, Peoples R China; [Wu, Xingyu] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 210036, Peoples R China; [Prasad, Dilip K.] UiT Arctic Univ Norway, Dept Comp Sci, N-9019 Tromso, Norway; [Wu, Bing] Wuhan Univ Technol, Natl Engn Res Ctr Water Transportat Safety, Wuhan 430063, Peoples R China; [Postolache, Octavian] ISCTE Inst Univ Lisboa, P-1049001 Lisbon, Portugal; [Postolache, Octavian] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal Shanghai Maritime University; Shanghai Maritime University; UiT The Arctic University of Tromso; Wuhan University of Technology; Instituto Universitario de Lisboa; Instituto de Telecomunicacoes Wu, B (corresponding author), Wuhan Univ Technol, Natl Engn Res Ctr Water Transportat Safety, Wuhan 430063, Peoples R China. chenxinqiang@stu.shmtu.edu.cn; wuxingyu2@stu.shmtu.edu.cn; dilipprasad@gmail.com; bing.wu@whut.edu.cn; opostolache@lx.it.pt; yangys@shmtu.edu.cn Lang, Ming/HIK-0758-2022; Postolache, Octavian Adrian/ADC-7853-2022; Prasad, Dilip K./B-5838-2009 Postolache, Octavian Adrian/0000-0001-5055-6347; Prasad, Dilip K./0000-0002-3693-6973 National Key Research and Development Program of China [2019YFB1600605]; National Natural Science Foundation of China [52102397, 52071200, 51978069, 62176150]; China Postdoctoral Science Foundation [2021M700790]; Fund of National Engineering Research Center for Water Transport Safety [A2022003] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fund of National Engineering Research Center for Water Transport Safety This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1600605; in part by the National Natural Science Foundation of China under Grant 52102397, Grant 52071200, Grant 51978069, and Grant 62176150; in part by the China Postdoctoral Science Foundation under Grant 2021M700790; and in part by the Fund of National Engineering Research Center for Water Transport Safety under Grant A2022003. The associate editor coordinating the review of this article and approving it for publication was Dr. Brajesh Kumar Kaushik. (Corresponding author: Bing Wu.) 31 2 2 3 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. SEP 15 2022.0 22.0 18 18180 18191 10.1109/JSEN.2022.3195959 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics 5Z6UR 2023-03-23 WOS:000880106500090 0
J Kaiser, MS; Chowdhury, ZI; Al Mamun, S; Hussain, A; Mahmud, M Kaiser, M. Shamim; Chowdhury, Zamshed Iqbal; Al Mamun, Shamim; Hussain, Amir; Mahmud, Mufti A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair COGNITIVE COMPUTATION English Article Surface EMG signals; Rehabilitation; Neuro-fuzzy system; Solar-powered wheelchair; Wheelchair navigation USERS This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography (sEMG) technique. The raw sEMG signals are collected from the upper limb muscles which are then processed, characterized, and classified to extract necessary features for the generation of control signals to be used for the automated movement of the wheelchair. An artificial neural network-based classifier is constructed to classify the patterns and features extracted from the raw sEMG signals. The classification accuracy of the extracted parameters from the sEMG signals is found to be relatively high in comparison with the existing methods. The extracted parameters used to generate control signals that are then fed into a microcomputer-based control system (MiCS). A solar-powered wheelchair prototype is developed, and the above MiCS is introduced to control its maneuver using the sEMG signals. The prototype is then thoroughly tested with sEMG signals from patients of different age groups. Also, the life cycle cost analysis of the proposed wheelchair revealed that it is financially feasible and cost-effective. [Kaiser, M. Shamim; Chowdhury, Zamshed Iqbal; Al Mamun, Shamim] Jahangirnagar Univ, Inst Informat Technol, Dhaka 1342, Bangladesh; [Hussain, Amir] Univ Stirling, Stirling FK9 4LA, Scotland; [Hussain, Amir] Anhui Univ, Hefei 230601, Anhui, Peoples R China; [Mahmud, Mufti] Univ Padua, Dept Biomed Sci, NeuroChip Lab, I-35131 Padua, Italy; [Mahmud, Mufti] Univ Antwerp, Theoret Neurobiol & Neuroengn, B-2610 Antwerp, Belgium; [Mahmud, Mufti] Univ Stirling, COSIPRA Lab, Stirling FK9 4LA, Scotland Jahangirnagar University; University of Stirling; Anhui University; University of Padua; University of Antwerp; University of Stirling Mahmud, M (corresponding author), Univ Padua, Dept Biomed Sci, NeuroChip Lab, I-35131 Padua, Italy.;Mahmud, M (corresponding author), Univ Antwerp, Theoret Neurobiol & Neuroengn, B-2610 Antwerp, Belgium.;Mahmud, M (corresponding author), Univ Stirling, COSIPRA Lab, Stirling FK9 4LA, Scotland. muftimahmud@gmail.com Mahmud/C-7752-2012; Kaiser, M. Shamim/F-5750-2012; Hussain, Amir/AAG-6299-2020; Mamun, Shamim Al/N-1323-2018 Mahmud/0000-0002-2037-8348; Kaiser, M. Shamim/0000-0002-4604-5461; Hussain, Amir/0000-0002-8080-082X; Mamun, Shamim Al/0000-0002-2017-7087 41 34 35 3 29 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1866-9956 1866-9964 COGN COMPUT Cogn. Comput. OCT 2016.0 8.0 5 SI 946 954 10.1007/s12559-016-9398-4 0.0 9 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology EA1KR Green Accepted 2023-03-23 WOS:000386351500015 0
J Qiu, QH; Zhang, M Qiu, Qihang; Zhang, Mu Using Content Analysis to Probe the Cognitive Image of Intangible Cultural Heritage Tourism: An Exploration of Chinese Social Media ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION English Article intangible cultural heritage; tourism; cognition; social media; big data; semantic network analysis The industry of intangible cultural heritage (ICH) tourism continues to grow, and social media can serve as an essential tool to promote this trend. Although ICH tourism development is outstanding in China, the language structure and restricted use of social media render ICH difficult for non-Chinese speakers to understand. Using content analysis, this study investigates the structure and relationships among cognitive elements of ICH tourism based on 9074 blogs posted between 2011 and 2020 on Weibo.com, one of the most popular social media platforms in China. The main analysis process consisted of matrix construction, dimension classification, and semantic network analysis. Findings indicated that the cognitive image of ICH tourism on social media can be divided into seven dimensions: institutions, ICH and inheritors, tourism products, traditional festivals and seasons, tourism facilities and services, visitors, and regions. This network vividly illustrates ICH tourism and depicts the roles of organizers, residents, inheritors, and tourists. Among these elements, institutions hold the greatest power to regulate and control ICH tourism activities, and folklore appears to be the most common type of ICH resource that can be developed into tourism activities. Practically, the results offer insight for policymakers regarding ways to better balance the relationships among heritage protection, the business economy, and people's well-being. Such strategies can promote the industrialization of ICH tourism. In addition, through content analysis, this paper confirms the effectiveness of social media in providing a richer understanding of ICH tourism. [Qiu, Qihang] Adam Mickiewicz Univ, Fac Human Geog & Planning, PL-61680 Poznan, Poland; [Zhang, Mu] Jinan Univ, Shenzhen Tourism Coll, Shenzhen 518053, Peoples R China Adam Mickiewicz University; Jinan University Zhang, M (corresponding author), Jinan Univ, Shenzhen Tourism Coll, Shenzhen 518053, Peoples R China. qihang.qiu@amu.edu.pl; zhangmu@jnu.edu.cn Qiu, Qihang/GMW-4485-2022 Qiu, Qihang/0000-0002-7821-8670; Zhang, Mu/0000-0003-4682-8156 Guangdong Provincial Science and Technology Plan Project [2018A070712022] Guangdong Provincial Science and Technology Plan Project This research was funded by Funding: The Guangdong Provincial Science and Technology Plan Project (Grant No. 2018A070712022). 54 11 11 20 70 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2220-9964 ISPRS INT J GEO-INF ISPRS Int. J. Geo-Inf. APR 2021.0 10.0 4 240 10.3390/ijgi10040240 0.0 19 Computer Science, Information Systems; Geography, Physical; Remote Sensing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Physical Geography; Remote Sensing RR4IS gold 2023-03-23 WOS:000643064600001 0
J Senthilraj, S; Raja, R; Zhu, QX; Samidurai, R; Yao, ZS Senthilraj, S.; Raja, R.; Zhu, Quanxin; Samidurai, R.; Yao, Zhangsong New delay-interval-dependent stability criteria for static neural networks with time-varying delays NEUROCOMPUTING English Article Static neural network; Lyapunov functional; Time-varying delay; Delay-interval-dependent stability; Delay partitioning approach EXPONENTIAL STABILITY; PARAMETERS This paper introduces an effective approach to study the stability of static neural networks with interval time-varying delay using delay partitioning approach and tighter integral inequality lemma. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, some suitable Lyapunov-Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the stability analysis issue for the considered system. These conditions are expressed in the framework of linear matrix inequalities, which heavily depend on the lower and upper bounds of the time-varying delay. It is shown, by comparing with existing approaches, that the delay-partitioning approach can largely reduce the conservatism of the stability results. Finally, three examples are given to show the effectiveness of the theoretical results. (C) 2016 Elsevier B.V. All rights reserved. [Senthilraj, S.; Samidurai, R.] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India; [Raja, R.] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630004, Tamil Nadu, India; [Zhu, Quanxin] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany; [Yao, Zhangsong] Nanjing Xiaozhuang Univ, Sch Math & Informat Technol, Nanjing 211171, Jiangsu, Peoples R China Thiruvalluvar University; Alagappa University; Nanjing Normal University; Nanjing Normal University; University of Bielefeld; Nanjing Xiaozhuang University Yao, ZS (corresponding author), Nanjing Xiaozhuang Univ, Sch Informat Engn, Nanjing 211171, Jiangsu, Peoples R China. yaozhsong@163.com S, Senthilraj/AAT-2712-2020; Raja, R./Y-7657-2019; Zhu, Quanxin/X-3230-2018 Raja, R./0000-0003-0830-4933; Zhu, Quanxin/0000-0003-3130-4923; S, Senthilraj/0000-0002-2883-1864; R, Samidurai/0000-0003-0265-9426 Department of Science and Technology (DST), Government of India, New Delhi [SB/EMEQ-181/2013]; National Natural Science Foundation of China [61374080]; Alexander von Humboldt Foundation of Germany [CHN/1163390]; Priority Academic Program Development of Jiangsu Higher Education Institutions Department of Science and Technology (DST), Government of India, New Delhi(Department of Science & Technology (India)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation of Germany(Alexander von Humboldt Foundation); Priority Academic Program Development of Jiangsu Higher Education Institutions The work of fourth author was supported by the Department of Science and Technology (DST), Government of India, New Delhi, for its nancial support through the research project grant No. SB/EMEQ-181/2013. Quanxin Zhu's work was jointly supported by the National Natural Science Foundation of China (61374080), the Alexander von Humboldt Foundation of Germany (Fellowship CHN/1163390), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. 40 17 17 0 18 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing APR 19 2016.0 186.0 1 7 10.1016/j.neucom.2015.12.063 0.0 7 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DJ7BG 2023-03-23 WOS:000374366300001 0
J Fang, X; Sauter, DA; Heerdink, MW; van Kleef, GA Fang, Xia; Sauter, Disa A.; Heerdink, Marc W.; van Kleef, Gerben A. Culture Shapes the Distinctiveness of Posed and Spontaneous Facial Expressions of Anger and Disgust JOURNAL OF CROSS-CULTURAL PSYCHOLOGY English Article culture; facial expressions; emotion; posed; spontaneous EMOTION RECOGNITION; BASIC EMOTIONS; PERCEPTION; UNIVERSALITY; SPECIFICITY; BLIND; INDIVIDUALISM; HETEROGENEITY; AMERICANS; INNATE There is a growing consensus that culture influences the perception of facial expressions of emotion. However, relatively few studies have examined whether and how culture shapes the production of emotional facial expressions. Drawing on prior work on cultural differences in communication styles, we tested the prediction that people from the Netherlands (a low-context culture) produce facial expressions that are more distinct across emotions compared to people from China (a high-context culture). Furthermore, we examined whether the degree of distinctiveness varies across posed and spontaneous expressions. Dutch and Chinese participants were instructed to either pose facial expressions of anger and disgust, or to share autobiographical events that elicited spontaneous expressions of anger or disgust. Using a supervised machine learning approach to categorize expressions based on the patterns of activated facial action units, we showed that both posed and spontaneous facial expressions of anger and disgust were more distinct when produced by Dutch compared to Chinese participants. Yet, the distinctiveness of posed and spontaneous expressions differed in their sources. The difference in the distinctiveness of posed expressions appears to be due to a larger array of facial expression prototypes for each emotion in Chinese culture than in Dutch culture. The difference in the distinctiveness of spontaneous expressions, however, appears to reflect the greater similarity of expressions of anger and disgust from the same Chinese individual than from the same Dutch individual. The implications of these findings are discussed in relation to cross-cultural emotion communication, including via cultural products. [Fang, Xia] Zhejiang Univ, Hangzhou, Peoples R China; [Sauter, Disa A.; Heerdink, Marc W.; van Kleef, Gerben A.] Univ Amsterdam, Amsterdam, Netherlands Zhejiang University; University of Amsterdam Fang, X (corresponding author), Zhejiang Univ, Dept Psychol & Behav Sci, 148 Tianmushan Rd, Hangzhou 310058, Peoples R China. x.fang@zju.edu.cn Fang, Xia/AEH-2940-2022 Fang, Xia/0000-0001-8485-2910; Van Kleef, Gerben/0000-0003-0823-7654 China Scholarship Council; Netherlands Organisation for Scientific Research (NWO) [275-70-033, 452-09-010] China Scholarship Council(China Scholarship Council); Netherlands Organisation for Scientific Research (NWO)(Netherlands Organization for Scientific Research (NWO)) The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was facilitated by a scholarship from the China Scholarship Council awarded to the first author, and grants from the Netherlands Organisation for Scientific Research (NWO) awarded to the second (275-70-033) and last (452-09-010) authors. 52 0 0 12 19 SAGE PUBLICATIONS INC THOUSAND OAKS 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA 0022-0221 1552-5422 J CROSS CULT PSYCHOL J. Cross-Cult. Psychol. JUN 2022.0 53.0 5 471 487 220221221095208 10.1177/00220221221095208 0.0 APR 2022 17 Psychology, Social Social Science Citation Index (SSCI) Psychology 1D6EJ Green Published 2023-03-23 WOS:000789418600001 0
J Glowacz, A; Tadeusiewicz, R; Legutko, S; Caesarendra, W; Irfan, M; Liu, H; Brumercik, F; Gutten, M; Sulowicz, M; Daviu, JAA; Sarkodie-Gyan, T; Fracz, P; Kumar, A; Xiang, JW Glowacz, Adam; Tadeusiewicz, Ryszard; Legutko, Stanislaw; Caesarendra, Wahyu; Irfan, Muhammad; Liu, Hui; Brumercik, Frantisek; Gutten, Miroslav; Sulowicz, Maciej; Antonino Daviu, Jose Alfonso; Sarkodie-Gyan, Thompson; Fracz, Pawel; Kumar, Anil; Xiang, Jiawei Fault diagnosis of angle grinders and electric impact drills using acoustic signals APPLIED ACOUSTICS English Article Degradation; Acoustic; Fault diagnosis; Bearings; Power tool; Ventilation NEURAL-NETWORK; CLASSIFICATION; TRANSFORM; ALGORITHM; SYSTEM Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical motors is an important task, because it allows saving a large amount of money and time. An analysis of acoustic signals is a promising tool to improve the accuracy of fault diagnosis. It is essential to analyze acoustic signals to assess the state of the motor. In this paper, three electric impact drills (EID) were analyzed using acoustic signals: healthy EID, EID with damaged rear bearing, EID with damaged front bearing. Three angle grinders (AG) were analyzed: healthy AG, AG with 1 blocked air inlet, AG with 2 blocked air inlets. The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components). Acoustic features vectors were classified by the nearest neighbor classifier and Naive Bayes classifier. The classification accuracy were in the range of 89.33-97.33% for three electric impact drills. The classification accuracy were in the range of 90.66-100% for three angle grinders. The presented method is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools. It can be also useful for diagnosis of similar power tools. (C) 2021 Elsevier Ltd. All rights reserved. [Glowacz, Adam] AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Dept Automat Control & Robot, Al A Mickiewicza 30, PL-30059 Krakow, Poland; [Tadeusiewicz, Ryszard] AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Dept Biocybernet & Biomed Engn, Al A Mickiewicza 30, PL-30059 Krakow, Poland; [Legutko, Stanislaw] Poznan Univ Tech, Fac Mech Engn & Management, 3 Piotrowo St, PL-60965 Poznan, Poland; [Caesarendra, Wahyu] Univ Brunei Darussalam, Fac Integrated Technol, Gadong BE1410, Brunei; [Irfan, Muhammad] Najran Univ Saudi Arabia, Coll Engn, Elect Engn Dept, Najran 61441, Saudi Arabia; [Liu, Hui] China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China; [Brumercik, Frantisek] Univ Zilina, Fac Mech Engn, Dept Design & Machine Elements, Univ 1, Zilina 01026, Slovakia; [Gutten, Miroslav] Univ Zilina, Dept Measurement & Applicat Elect, Fac Elect Engn, Univ 1, Zilina 01026, Slovakia; [Sulowicz, Maciej] Cracow Univ Technol, Fac Elect & Comp Engn, Warszawska 24 St, PL-31155 Krakow, Poland; [Antonino Daviu, Jose Alfonso] Univ Politecn Valencia, Dept Elect Engn, Inst Tecnol Energia, Camino Vera S-N, Valencia 46022, Spain; [Sarkodie-Gyan, Thompson] Univ Texas El Paso, Coll Engn, Lab Ind Metrol & Automat, El Paso, TX 79968 USA; [Fracz, Pawel] Opole Univ Technol, Dept Mfg Engn & Automat Prod, PL-45758 Opole, Poland; [Kumar, Anil] Amity Univ Uttar Pradesh, Noida 201313, India; [Xiang, Jiawei] Wenzhou Univ, Coll Mech & Elect Engn, Wenzhou 325035, Peoples R China AGH University of Science & Technology; AGH University of Science & Technology; Poznan University of Technology; University Brunei Darussalam; Najran University; China Jiliang University; University of Zilina; University of Zilina; Cracow University of Technology; Universitat Politecnica de Valencia; University of Texas System; University of Texas El Paso; Opole University of Technology; Amity University Noida; Wenzhou University Glowacz, A (corresponding author), AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Dept Automat Control & Robot, Al A Mickiewicza 30, PL-30059 Krakow, Poland. adglow@agh.edu.pl; rtad@agh.edu.pl; stanislaw.legutko@put.poznan.pl; wahyu.caesarendra@ubd.edu.bn; miditta@nu.edu.sa; hui.liu@cjlu.edu.cn; frantisek.brumercik@fstroj.uniza.sk; miroslav.gutten@feit.uniza.sk; msulowicz@pk.edu.pl; joanda@die.upv.es; tsarkodi@utep.edu; jwxiang@wzu.edu.cn Glowacz, Adam/N-6462-2013; Irfan, Muhammad/AAB-6847-2019; Sulowicz, Maciej/A-6120-2015; liu, hui/K-2142-2015; Xiang, Jiawei/G-1177-2011; Kumar, Anil/O-3741-2017; Brumerčík, František/AAE-3161-2021; Gutten, Miroslav/ABC-8531-2021; Legutko, Stanislaw/J-6532-2013; Glowacz, Adam/I-1024-2019 Glowacz, Adam/0000-0003-0546-7083; Irfan, Muhammad/0000-0003-4161-6875; Sulowicz, Maciej/0000-0001-6436-1110; liu, hui/0000-0002-4265-7241; Xiang, Jiawei/0000-0003-4028-985X; Kumar, Anil/0000-0001-6675-1657; Brumerčík, František/0000-0001-7475-3724; Legutko, Stanislaw/0000-0001-8973-5035; Glowacz, Adam/0000-0003-0546-7083; Tadeusiewicz, Ryszard/0000-0001-9675-5819 Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital [AICO/019/224] Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, ' Ciencia y Sociedad Digital, (project AICO/019/224). 53 86 86 24 96 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0003-682X 1872-910X APPL ACOUST Appl. Acoust. AUG 2021.0 179.0 108070 10.1016/j.apacoust.2021.108070 0.0 APR 2021 14 Acoustics Science Citation Index Expanded (SCI-EXPANDED) Acoustics SE4FX 2023-03-23 WOS:000652029800034 0
J Ali, M; Jung, LT; Sodhro, AH; Laghari, AA; Belhaouari, SB; Gillani, Z Ali, Munwar; Jung, Low Tang; Sodhro, Ali Hassan; Laghari, Asif Ali; Belhaouari, Samir Birahim; Gillani, Zeeshan A Confidentiality-based data Classification-as-a-Service (C2aaS) for cloud security ALEXANDRIA ENGINEERING JOURNAL English Article Data confidentiality; Security level; Cloud storage method; Data classification NETWORK Rapid development and massive use of Information Technology (IT) have since pro-duced a massive amount of electronic data. In tandem, the demand for data outsourcing and the associated data security is increasing exponentially. Small organizations are often finding it expen-sive to save and process their huge amount of data, and keep the data secure from unauthorized access. Cloud computing is a suitable and affordable platform to provide services on user demand. The cloud platform is preferable used by individuals, Small, and Medium Enterprises (SMEs) that cannot afford large-scale hardware, software, and security maintenance cost. Storage and process-ing of big data in the cloud are becoming the key appealing features to SMEs and individuals. How-ever, the processing of big data in the cloud is facing two issues such as security of stored data and system overload due to the volume of the data. These storage methods are plain text storage and encrypted text storage. Both methods have their strengths and limitations. The fundamental issue in plain text storage is the high risk of data security breaches; whereas, in encrypted text storage, the encryption of complete file data may cause system overload. This paper propose a feasible solu-tion to address these issues with a new service model called Confidentiality-based Classification-as -a-Service (C2aaS) that performs data processing by treating data dynamically according to the data security level in preparation for data storing in the cloud. In comparison to the conventional methods, our proposed service model is strongly showing good security for confidential data and is proficient in reducing cloud system overloading.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). [Ali, Munwar] Shaheed Benazir Bhutto Univ, Dept Informat Technol, Shaheed Benazirabad, Sindh, Pakistan; [Jung, Low Tang] Univ Teknol PETRONAS, CIS Dept, Seri Iskandar, Perak, Malaysia; [Sodhro, Ali Hassan] Kristianstad Univ, Dept Comp Sci, SE-29188 Kristianstad, Sweden; [Sodhro, Ali Hassan] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518000, Peoples R China; [Laghari, Asif Ali] Sindh Madressatul Islam Univ, Dept Comp Sci, Karachi, Pakistan; [Belhaouari, Samir Birahim] Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Tech, Ar Rayyan, Qatar; [Gillani, Zeeshan] COMSATS Univ Lahore Campus, Dept Comp Sci, Lahore, Pakistan Universiti Teknologi Petronas; Kristianstad University; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar Belhaouari, SB (corresponding author), Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Tech, Ar Rayyan, Qatar. munwar.ali@sbbusba.edu.pk; lowtanjung@utp.edu.my; ali.hassan.sodhro@ltu.se; sbelhaouari@hbku.edu.qa; zeeshangillani@cuilahore.edu.pk Laghari, Asif Ali/AAF-5893-2020 35 0 0 0 0 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1110-0168 2090-2670 ALEX ENG J Alex. Eng. J. FEB 2023.0 64.0 749 760 10.1016/j.aej.2022.10.056 0.0 DEC 2022 12 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering 8D7NQ 2023-03-23 WOS:000918475700001 0
J Sun, J; Yang, KF; Chen, C; Shen, JF; Yang, Y; Wu, XH; Norton, T Sun, Jun; Yang, Kaifeng; Chen, Chen; Shen, Jifeng; Yang, Yu; Wu, Xiaohong; Norton, Tomas Wheat head counting in the wild by an augmented feature pyramid networks-based convolutional neural network COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Wheat head counting; CNN; Multi-scale feature; Dense targets detection; Wheat yield estimation WINTER-WHEAT; GRAIN-YIELD; MODEL Wheat head counting plays an important role in crop yield estimation, which also meets great challenges of high density, scale variation, and illumination difference. In this paper, an improved wheat head counting network (WHCnet) was proposed to count wheat heads from the top view in the wild, with a novel object counting by detection pipeline. Firstly, data enhancement such as contrast adjustment and Gaussian blur were conducted to improve the generalization ability. Secondly, the Augmented Feature Pyramid Networks (AugFPN) was used to pool the original information adaptively, which made full use of the underlying information and solved the problem of poor detection of small wheat heads. Finally, cascaded Intersection over Union (IoU) threshold was used to make the IoU of the training model as close as possible to the input target, which could effectively remove negative samples in the complex background, reduce the noise bounding box and improve the target positioning accuracy of occluded wheat heads. Experiments on the test set image showed that the proposed method had an average error rate of 3.7% and an AP of 95.17%, which was significantly better than other state-of-the-art methods. Besides, the average counting time for a single wheat field image (3968 x 2976) obtained by an un-maimed aerial vehicle was 87 ms, and the counting accuracy was 95.8%, which effectively verified the generalization ability of the model. These results indicate that the proposed method can meet the requirements of wheat head counting in the field environment and provide reliable reference data for wheat yield estimation. [Sun, Jun; Yang, Kaifeng; Chen, Chen; Shen, Jifeng; Yang, Yu; Wu, Xiaohong] Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [Norton, Tomas] Katholieke Univ Leuven, Div Measure Model & Manage Bioresponses M3 Biores, Kasteelpk Arenberg 30, B-3001 Leuven, Belgium Jiangsu University; KU Leuven Sun, J; Chen, C (corresponding author), Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China.;Norton, T (corresponding author), Katholieke Univ Leuven, Div Measure Model & Manage Bioresponses M3 Biores, Kasteelpk Arenberg 30, B-3001 Leuven, Belgium. sun2000jun@sina.com; ainicc1987@163.com; tomas.norton@kuleuven.be Shen, Jifeng/GVS-6113-2022; Norton, Tomas/Q-3803-2017 Shen, Jifeng/0000-0002-4356-1831; Norton, Tomas/0000-0002-0161-3189 Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD-2018-87]; Project of Faculty of Agricultural Equipment of Jiangsu University [4121680001]; Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology [4091600030]; Natio-nal Natural Science Foundation of China [32102598] Priority Academic Program Development of Jiangsu Higher Education Institutions; Project of Faculty of Agricultural Equipment of Jiangsu University; Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology; Natio-nal Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was partially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD-2018-87) ; the Project of Faculty of Agricultural Equipment of Jiangsu University (4121680001) ; the Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology (4091600030) ; and the Natio-nal Natural Science Foundation of China (32102598) . 40 3 3 8 21 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. FEB 2022.0 193.0 106705 10.1016/j.compag.2022.106705 0.0 JAN 2022 9 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science YX7MS Green Published 2023-03-23 WOS:000754283100004 0
J Yang, JX; Zhao, YQ; Chan, JCW; Xiao, L Yang, Jingxiang; Zhao, Yong-Qiang; Chan, Jonathan Cheung-Wai; Xiao, Liang A Multi-Scale Wavelet 3D-CNN for Hyperspectral Image Super-Resolution REMOTE SENSING English Article super-resolution; hyperspectral; wavelet; multi-scale; CNN RESOLUTION; INTERPOLATION Super-resolution (SR) is significant for hyperspectral image (HSI) applications. In single-frame HSI SR, how to reconstruct detailed image structures in high resolution (HR) HSI is challenging since there is no auxiliary image (e.g., HR multispectral image) providing structural information. Wavelet could capture image structures in different orientations, and emphasis on predicting high-frequency wavelet sub-bands is helpful for recovering the detailed structures in HSI SR. In this study, we propose a multi-scale wavelet 3D convolutional neural network (MW-3D-CNN) for HSI SR, which predicts the wavelet coefficients of HR HSI rather than directly reconstructing the HR HSI. To exploit the correlation in the spectral and spatial domains, the MW-3D-CNN is built with 3D convolutional layers. An embedding subnet and a predicting subnet constitute the MW-3D-CNN, the embedding subnet extracts deep spatial-spectral features from the low resolution (LR) HSI and represents the LR HSI as a set of feature cubes. The feature cubes are then fed to the predicting subnet. There are multiple output branches in the predicting subnet, each of which corresponds to one wavelet sub-band and predicts the wavelet coefficients of HR HSI. The HR HSI can be obtained by applying inverse wavelet transform to the predicted wavelet coefficients. In the training stage, we propose to train the MW-3D-CNN with L1 norm loss, which is more suitable than the conventional L2 norm loss for penalizing the errors in different wavelet sub-bands. Experiments on both simulated and real spaceborne HSI demonstrate that the proposed algorithm is competitive with other state-of-the-art HSI SR methods. [Yang, Jingxiang; Xiao, Liang] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China; [Yang, Jingxiang; Zhao, Yong-Qiang] Northwestern Polytech Univ Shenzhen, Inst Res & Dev, Shenzhen 518057, Peoples R China; [Chan, Jonathan Cheung-Wai] Vrije Univ Brussel, Dept Elect & Informat, B-1050 Brussels, Belgium Nanjing University of Science & Technology; Vrije Universiteit Brussel Zhao, YQ (corresponding author), Northwestern Polytech Univ Shenzhen, Inst Res & Dev, Shenzhen 518057, Peoples R China. zhaoyq@nwpu.edu.cn xiao, liang/G-2968-2010 xiao, liang/0000-0003-0178-9384; Chan, Jonathan/0000-0002-3741-1124 National Natural Science Foundation of China [61771391, 61371152, 61511140292]; South Korean National Research Foundation [61511140292]; Fundamental Research Funds for the Central Universities [3102015ZY045]; Jiangsu Provincial Social Developing Project [BE 2018727]; China Scholarship Council [201506290120]; Science, Technology and Innovation Commission of Shenzhen Manicipality [JCYJ20170815162956949]; Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University [CX201621] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); South Korean National Research Foundation; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Jiangsu Provincial Social Developing Project; China Scholarship Council(China Scholarship Council); Science, Technology and Innovation Commission of Shenzhen Manicipality; Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University This work is supported by the National Natural Science Foundation of China (61771391, 61371152), the National Natural Science Foundation of China and South Korean National Research Foundation Joint Funded Cooperation Program (61511140292), the Fundamental Research Funds for the Central Universities (3102015ZY045), the Jiangsu Provincial Social Developing Project (BE 2018727), the China Scholarship Council for joint PhD students (201506290120), the Science, Technology and Innovation Commission of Shenzhen Manicipality (JCYJ20170815162956949), and the Innovation Foundation of Doctor Dissertation of Northwestern Polytechnical University (CX201621). 61 30 30 5 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUL 1 2019.0 11.0 13 1557 10.3390/rs11131557 0.0 22 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology IL1HA gold 2023-03-23 WOS:000477049000049 0
J Zuo, JX; Zhao, B; Guo, H; Xie, DQ Zuo, Junxiang; Zhao, Bin; Guo, Hua; Xie, Daiqian A global coupled cluster potential energy surface for HCl plus OH <-> Cl + H2O PHYSICAL CHEMISTRY CHEMICAL PHYSICS English Article RATE COEFFICIENTS; MODE SPECIFICITY; VIBRATIONAL-EXCITATION; CHEMISTRY; KINETICS; DYNAMICS; TEMPERATURES; SELECTIVITY; SCATTERING; ACCURATE A new and more accurate full-dimensional global potential energy surface (PES) for the ground electronic state of the ClH2O system is developed by fitting 15 777 points obtained using an explicitly correlated unrestricted coupled-cluster method with single, double, and perturbative triple excitations (UCCSD(T)-F12b). The fitting is carried out using the permutation invariant polynomial-neural network (PIP-NN) method and has an error of 6.9 meV. The new PES has a slightly lower barrier for the atmospherically important HCl + OH -> Cl + H2O reaction than the previous PES based on multi-reference configuration interaction (MRCI) calculations. As a result, it should provide a better characterization of the kinetics. Quantum dynamical calculations of reaction probabilities for both the forward and reverse reactions are performed on this new PES and compared with those on the MRCI PES. They reveal notable differences, resulting apparently from subtle differences in the PESs. [Zuo, Junxiang; Xie, Daiqian] Nanjing Univ, Sch Chem & Chem Engn, Inst Theoret & Computat Chem, Key Lab Mesoscop Chem, Nanjing 210093, Jiangsu, Peoples R China; [Zhao, Bin; Guo, Hua] Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA; [Xie, Daiqian] Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China; [Zhao, Bin] Univ Bielefeld, Fak Chem, Theoret Chem, Univ Str 25, D-33615 Bielefeld, Germany Nanjing University; University of New Mexico; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Bielefeld Xie, DQ (corresponding author), Nanjing Univ, Sch Chem & Chem Engn, Inst Theoret & Computat Chem, Key Lab Mesoscop Chem, Nanjing 210093, Jiangsu, Peoples R China.;Xie, DQ (corresponding author), Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China. dqxie@nju.edu.cn Guo, H./J-2685-2014; Zuo, Junxiang/ABD-1731-2021 Guo, H./0000-0001-9901-053X; Zuo, Junxiang/0000-0002-2967-125X National Natural Science Foundation of China [21590802, 91421315, 21273104]; Chinese Ministry of Science and Technology [2013CB834601]; U.S. Department of Energy [DE-SC0015997] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Ministry of Science and Technology(Ministry of Science and Technology, China); U.S. Department of Energy(United States Department of Energy (DOE)) This work was supported by financial support from National Natural Science Foundation of China (Grant No. 21590802, 91421315, and 21273104 to DX), the Chinese Ministry of Science and Technology (2013CB834601 to DX), and U.S. Department of Energy (Grant No. DE-SC0015997 to HG). 61 17 17 4 109 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 1463-9076 1463-9084 PHYS CHEM CHEM PHYS Phys. Chem. Chem. Phys. APR 21 2017.0 19.0 15 9770 9777 10.1039/c7cp00920h 0.0 8 Chemistry, Physical; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Physics ES2UJ 28327711.0 2023-03-23 WOS:000399383100009 0
J Liu, YX; Heuvelink, GBM; Bai, ZG; He, P Liu, Yingxia; Heuvelink, Gerard B. M.; Bai, Zhanguo; He, Ping Uncertainty quantification of nitrogen use efficiency prediction in China using Monte Carlo simulation and quantile regression forests COMPUTERS AND ELECTRONICS IN AGRICULTURE English Article Nitrogen use efficiency; Input data; Model uncertainty; Monte Carlo; Quantile regression forests FERTILIZER; EMISSIONS; INVENTORY; MODEL Nitrogen use efficiency (NUE) plays an essential role in food security and environmental sustainability. With the development of technology, NUE prediction by models has come available. However, the prediction uncertainty of NUE models is still poorly understood. This study aimed to analyze uncertainty in NUE predictions obtained from a random forest machine learning model. Input and model uncertainties were quantified using Monte Carlo simulation in three scenarios and quantile regression forests (QRF), respectively, to analyze how these un-certainties propagate to the NUE predictions for 31 provinces in China from 1978 to 2015. Two NUE indicators were considered: the partial factor productivity of nitrogen (PFPN) and the partial nutrient balance of nitrogen (PNBN). The results indicated that the prediction uncertainty for both NUE indicators decreased over time. In 2015, PFPN had a higher 90% prediction interval ratio (PIR90) of input data in south and west China and a higher 90% prediction interval width (PIW90) in south and east-coastal China, while PNBN had a higher PIR90 in north China and a higher PIW90 in northeast China. The NUE prediction uncertainty propagated from QRF models had similar spatial patterns as those resulting from uncertainty in input data. NUE in most provinces had smaller input uncertainty than model uncertainty, except PNBN, which had smaller model uncertainty than input un-certainty after 2010. Generally, PNBN had higher input uncertainty contributions than PFPN in 2015, especially in south and northeast China. Overall, the uncertainties in NUE predictions were substantial. A series of rec-ommendations were made to improve the accuracy of NUE predictions. These may be applied by the govern-ment, in order to inform sustainable nitrogen management in agroecological systems. [Liu, Yingxia; He, Ping] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Plant Nutr & Fertilizer, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China; [Liu, Yingxia; Heuvelink, Gerard B. M.] Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands; [Liu, Yingxia; Heuvelink, Gerard B. M.; Bai, Zhanguo] ISRIC World Soil Informat, POB 353, NL-6700 AJ Wageningen, Netherlands Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Wageningen University & Research He, P (corresponding author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Plant Nutr & Fertilizer, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China. heping02@caas.cn National Natural Science Foundation of China [31972515]; Earmarked fund for the China Agriculture Research System [CARS-09-P31]; China Scholarship Council (CSC) [201903250115] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Earmarked fund for the China Agriculture Research System; China Scholarship Council (CSC)(China Scholarship Council) We thank Rong Jiang and Dainius Masiliunas for their contributions to data collection and code programming. This work was supported by the National Natural Science Foundation of China (No. 31972515) and the Earmarked fund for the China Agriculture Research System (No. CARS-09-P31). We are especially acknowledgeable to financial support from the China Scholarship Council (CSC) [201903250115]. 59 0 0 7 7 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0168-1699 1872-7107 COMPUT ELECTRON AGR Comput. Electron. Agric. JAN 2023.0 204.0 107533 10.1016/j.compag.2022.107533 0.0 12 Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Agriculture; Computer Science 7C8WB 2023-03-23 WOS:000900085200002 0
J Chen, WT; Wang, YX; Li, XJ; Zou, Y; Liao, YW; Yang, JC Chen, Weitao; Wang, Yanxin; Li, Xianju; Zou, Yi; Liao, Yiwei; Yang, Juncang Land use/land cover change and driving effects of water environment system in Dunhuang Basin, northwestern China ENVIRONMENTAL EARTH SCIENCES English Article Land use/land cover changes (LUCC); Remote sensing; Machine learning algorithm; Dunhuang Basin; Groundwater; Landsat; RapidEye CLASSIFICATION; AREA The Dunhuang Basin, located in northwestern China, is famous for its oases and geological remains. However, some problems of the eco-environment have raised public concern in recent decades. Land use/land cover change (LUCC) has been considered essential reference for studying eco-environment across the world. In the present study, the land use/land cover was divided into natural water, salt marshes, Aeluropus littoralis, natural vegetation, barren land, and desertified land. The LUCC was analyzed using four temporal Landsat images (from around 1975, 1990, 2000, 2010, respectively) and RapidEye images in 2010. Firstly, vegetation degeneration is the most serious problem, and 926.74 km(2) turned into bare land in the past 35 years. The total area of bare land increased mainly occurred during 1975-1990. The area of desertified land increased rapidly from 2000 to 2010. Secondly, wetlands have experienced extreme shrinking; some areas degenerated into salt marshes, subsequently vanished. Salt marsh areas have been continually decreasing and gradually degenerating into saline and alkaline lands and bare land. In relation to the driving forces of LUCC, according to collected data and interpretation results by remote sensing images, the surface water environment is destructive due to three reservoirs impede surface water supplementation to the soil and natural vegetation. In addition, excessive pumping of groundwater occurred in the study area. Based on the local soil profiles of vadose zones and dynamic change of groundwater level, the groundwater flow system is another key factor, which developed along with the spatial distribution of groundwater recharge, runoff, and discharge conditions. Furthermore, large-scale activities connected to the reclamation of commercial farmlands have also promoted the LUCC. [Chen, Weitao; Zou, Yi] China Univ Geosci, Fac Comp Sci, Wuhan 430074, Peoples R China; [Chen, Weitao; Zou, Yi] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China; [Chen, Weitao] Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7500 AE Enschede, Netherlands; [Wang, Yanxin] China Univ Geosci, Sch Environm Studies, Wuhan 430074, Peoples R China; [Wang, Yanxin] China Univ Geosci, State Key Lab Biogeol & Environm Geol, Wuhan 430074, Peoples R China; [Li, Xianju] China Univ Geosci, Fac Informat Engn, Wuhan 430074, Peoples R China; [Liao, Yiwei] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China; [Yang, Juncang] Gansu Prov Inst Geol Environm Monitoring, Lanzhou 730050, Peoples R China China University of Geosciences; China University of Geosciences; University of Twente; China University of Geosciences; China University of Geosciences; China University of Geosciences; Huazhong University of Science & Technology; Chinese Academy of Sciences; Institute of Geology & Geophysics, CAS Chen, WT (corresponding author), China Univ Geosci, Fac Comp Sci, Wuhan 430074, Peoples R China.;Chen, WT (corresponding author), China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China.;Chen, WT (corresponding author), Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7500 AE Enschede, Netherlands. wtchen@cug.edfyhu.cn Li, Xianju/0000-0001-7785-2541; Chen, Weitao/0000-0002-6272-1618; Liao, Yiwei/0000-0003-0572-4449 Fundamental Research Funds for the Central Universities; China University of Geosciences (Wuhan) [CUGL150417]; Foundation for Innovative Research Groups of the National Natural Science [41521001]; China Scholarship Council [201406415051] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China University of Geosciences (Wuhan); Foundation for Innovative Research Groups of the National Natural Science; China Scholarship Council(China Scholarship Council) The research was supported jointly by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUGL150417), Foundation for Innovative Research Groups of the National Natural Science (No. 41521001), and the China Scholarship Council (No. 201406415051). We wish to thank Kong Lingfeng, Zhao Duohui, Liu Bo, and Yan Zezhou for helping to conduct out the in situ training sampling investigation. We also wish to thank reviewers for the comments. 33 21 21 0 87 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1866-6280 1866-6299 ENVIRON EARTH SCI Environ. Earth Sci. JUN 2016.0 75.0 12 1027 10.1007/s12665-016-5809-9 0.0 11 Environmental Sciences; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Water Resources DQ0CI 2023-03-23 WOS:000378865400034 0
J Yan, SB; Fang, JT; Chen, YC; Xie, Y; Zhang, SY; Zhu, XH; Fang, F Yan, Shibai; Fang, Juntao; Chen, Yongcai; Xie, Yong; Zhang, Siyou; Zhu, Xiaohui; Fang, Feng Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer BMC CANCER English Article Ovarian cancer (OV); Single-sample gene set enrichment analysis (ssGSEA); Immune infiltration; Prognosis TUMOR-ASSOCIATED MACROPHAGES; MAINTENANCE THERAPY; PARP INHIBITORS; EXPRESSION; MICROENVIRONMENT; BLOCKADE; CELLS; RADIOTHERAPY; ASSOCIATION; VALIDATION BackgroundOvarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.MethodsBased on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.ResultsA prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P=0.013). The high and low-risk groups showed significantly different distribution (P<0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).ConclusionThe present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment. [Yan, Shibai] Guangxi Med Univ, Dept Med Oncol, Affiliated Hosp 1, Nanning 530021, Guangxi Zhuang, Peoples R China; [Fang, Juntao] Univ Med Ctr Utrecht, Dept Cardiol, Lab Expt Cardiol, NL-3584 CX Utrecht, Netherlands; [Chen, Yongcai; Xie, Yong; Zhang, Siyou; Fang, Feng] First Peoples Hosp Foshan, Dept Obstet & Gynecol, 81 Lingnan North Ave, Foshan 528000, Guangdong, Peoples R China; [Zhu, Xiaohui] Shenzhen Technol Univ, Dept Pharmacol, Coll Pharm, Shenzhen 518118, Guangdong, Peoples R China Guangxi Medical University; Utrecht University; Utrecht University Medical Center; Shenzhen Technology University Fang, F (corresponding author), First Peoples Hosp Foshan, Dept Obstet & Gynecol, 81 Lingnan North Ave, Foshan 528000, Guangdong, Peoples R China.;Zhu, XH (corresponding author), Shenzhen Technol Univ, Dept Pharmacol, Coll Pharm, Shenzhen 518118, Guangdong, Peoples R China. zxh2681731@163.com; fangfeng_go@163.com National Natural Science Foundation of China [81603147] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China (Nos. 81603147, funder by XZ, proofread and revised the manuscript). 95 13 13 1 13 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1471-2407 BMC CANCER BMC Cancer DEC 7 2020.0 20.0 1 1205 10.1186/s12885-020-07695-3 0.0 17 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology PE0BW 33287740.0 gold 2023-03-23 WOS:000598039800012 0
J Pan, KK; Palensky, P; Esfahani, PM Pan, Kaikai; Palensky, Peter; Esfahani, Peyman Mohajerin Dynamic Anomaly Detection With High-Fidelity Simulators: A Convex Optimization Approach IEEE TRANSACTIONS ON SMART GRID English Article Mathematical models; Data models; Tools; Power system dynamics; Anomaly detection; Numerical models; System dynamics; Anomaly detection; data-assisted model-based; diagnosis filter; model mismatch; high-fidelity simulator; convex optimization DATA INJECTION ATTACKS; MOVING-TARGET DEFENSE; STATE ESTIMATION; FAULT-DIAGNOSIS; CYBER ATTACKS; SMART GRIDS; MITIGATION; GENERATION; AREA The main objective of this article is to develop scalable dynamic anomaly detectors with high-fidelity simulators of power systems. On the one hand, models in high-fidelity simulators are typically intractable if one opts to describe them in a mathematical formulation in order to apply existing model-based approaches from the anomaly detection literature. On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of power systems. In this study, we combine tools from these two mainstream approaches to develop a data-assisted model-based diagnosis filter utilizing both the knowledge from a picked abstract model and also the data of simulation results from high-fidelity simulators. The proposed diagnosis filter aims to achieve two desired features: (i) performance robustness with respect to model mismatch; (ii) high scalability. To this end, we propose a tractable (convex) optimization-based reformulation in which decisions are the filter parameters, the model-based information introduces feasible sets, and the data from the simulator forms the objective function to-be-minimized regarding the effect of model mismatch on the filter performance. To validate the theoretical results, we implement the developed diagnosis filter in DIgSILENT PowerFactory to detect false data injection attacks on the Automatic Generation Control measurements in the three-area IEEE 39-bus system. [Pan, Kaikai] Zhejiang Univ, Coll Elect Engn, Hangzhou 310058, Peoples R China; [Palensky, Peter] Delft Univ Technol, Dept Elect Sustainable Energy, NL-2600 AA Delft, Netherlands; [Esfahani, Peyman Mohajerin] Delft Univ Technol, Delft Ctr Syst & Control, NL-2600 AA Delft, Netherlands Zhejiang University; Delft University of Technology; Delft University of Technology Pan, KK (corresponding author), Zhejiang Univ, Coll Elect Engn, Hangzhou 310058, Peoples R China. pankaikai@zju.edu.cn; p.palensky@tudelft.nl; p.mohajerinesfahani@tudelft.nl Palensky, Peter/J-7238-2013 Palensky, Peter/0000-0003-3183-4705 Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform) [ZJUNGICS2021024]; ERC [TRUST-949796] Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform); ERC(European Research Council (ERC)European Commission) The work of Kaikai Pan was supported by the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform) under Project ZJUNGICS2021024. The work of Peyman Mohajerin Esfahani was supported by the ERC under Grant TRUST-949796. Paper no. TSG-00710-2021. 44 0 0 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3053 1949-3061 IEEE T SMART GRID IEEE Trans. Smart Grid MAR 2022.0 13.0 2 1500 1515 10.1109/TSG.2021.3129074 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering ZH9BI Green Published, hybrid, Green Submitted 2023-03-23 WOS:000761224400059 0
C Shi, YF; Xu, K; Niessner, M; Rusinkiewicz, S; Funkhouser, T Ferrari, V; Hebert, M; Sminchisescu, C; Weiss, Y Shi, Yifei; Xu, Kai; Niessner, Matthias; Rusinkiewicz, Szymon; Funkhouser, Thomas PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction COMPUTER VISION - ECCV 2018, PT VIII Lecture Notes in Computer Science English Proceedings Paper 15th European Conference on Computer Vision (ECCV) SEP 08-14, 2018 Munich, GERMANY RGB-D registration; Co-planarity; Loop closure We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural network that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from other images. We train the network on 10 million triplets of coplanar and non-coplanar patches, and evaluate on a new coplanarity benchmark created from commodity RGB-D scans. Experiments show that our learned descriptor outperforms alternatives extended for this new task by a significant margin. In addition, we demonstrate the benefits of coplanarity matching in a robust RGBD reconstruction formulation. We find that coplanarity constraints detected with our method are sufficient to get reconstruction results comparable to state-of-the-art frameworks on most scenes, but outperform other methods on established benchmarks when combined with traditional keypoint matching. [Shi, Yifei; Xu, Kai; Rusinkiewicz, Szymon; Funkhouser, Thomas] Princeton Univ, Princeton, NJ 08544 USA; [Shi, Yifei; Xu, Kai] Natl Univ Def Technol, Changsha, Peoples R China; [Niessner, Matthias] Tech Univ Munich, Munich, Germany; [Funkhouser, Thomas] Google, Mountain View, CA 94043 USA Princeton University; National University of Defense Technology - China; Technical University of Munich; Google Incorporated Xu, K (corresponding author), Princeton Univ, Princeton, NJ 08544 USA.;Xu, K (corresponding author), Natl Univ Def Technol, Changsha, Peoples R China. kevin.kai.xu@gmail.com Rusinkiewicz, Szymon/0000-0002-4253-2588 NSF [VEC 1539014/1539099, IIS 1421435, CHS 1617236]; NSFC [61532003, 61572507, 61622212]; Google; Intel; China Scholarship Council; Pixar; Amazon; Facebook NSF(National Science Foundation (NSF)); NSFC(National Natural Science Foundation of China (NSFC)); Google(Google Incorporated); Intel(Intel Corporation); China Scholarship Council(China Scholarship Council); Pixar; Amazon; Facebook(Facebook Inc) We are grateful to Min Liu, Zhan Shi, Lintao Zheng, and Maciej Halber for their help on data preprocessing. We also thank Yizhong Zhang for the early discussions. This work was supported in part by the NSF (VEC 1539014/1539099, IIS 1421435, CHS 1617236), NSFC (61532003, 61572507, 61622212), Google, Intel, Pixar, Amazon, and Facebook. Yifei Shi was supported by the China Scholarship Council. 46 15 15 3 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-01237-3; 978-3-030-01236-6 LECT NOTES COMPUT SC 2018.0 11212.0 767 784 10.1007/978-3-030-01237-3_46 0.0 18 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BQ4SC Green Submitted 2023-03-23 WOS:000594226800046 0
C Liang, HZ; Li, S; Ma, XJ; Hendrich, N; Gerkmann, T; Sun, FC; Zhang, JW IEEE Liang, Hongzhuo; Li, Shuang; Ma, Xiaojian; Hendrich, Norman; Gerkmann, Timo; Sun, Fuchun; Zhang, Jianwei Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) IEEE International Conference on Intelligent Robots and Systems English Proceedings Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) NOV 04-08, 2019 Macau, PEOPLES R CHINA IEEE,RSJ In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring. [Liang, Hongzhuo; Li, Shuang; Hendrich, Norman; Zhang, Jianwei] Univ Hamburg, Dept Informat, TAMS Tech Aspects Multimodal Syst, Hamburg, Germany; [Ma, Xiaojian] Univ Calif Los Angeles, Dept Stat, Ctr Vis Cognit Learning & Auton, Los Angeles, CA USA; [Ma, Xiaojian; Sun, Fuchun] Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing Natl Res Ctr Informat Sci & Technol BNRi, Beijing, Peoples R China; [Gerkmann, Timo] Univ Hamburg, Dept Informat, SP Signal Proc, Hamburg, Germany University of Hamburg; University of California System; University of California Los Angeles; Tsinghua University; University of Hamburg Liang, HZ (corresponding author), Univ Hamburg, Dept Informat, TAMS Tech Aspects Multimodal Syst, Hamburg, Germany. Gerkmann, Timo/I-3353-2014 Gerkmann, Timo/0000-0002-8678-4699; Hendrich, Norman/0000-0003-0499-886X German Research Foundation (DFG); National Science Foundation of China (NSFC) in project Crossmodal Learning [DFG TRR-169/NSFC 61621136008]; STEP2DYNA [691154] German Research Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC) in project Crossmodal Learning(National Natural Science Foundation of China (NSFC)); STEP2DYNA This research was funded by the German Research Foundation (DFG) and the National Science Foundation of China (NSFC) in project Crossmodal Learning, DFG TRR-169/NSFC 61621136008. And it was partially supported by project STEP2DYNA (691154). 25 8 8 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2153-0858 978-1-7281-4004-9 IEEE INT C INT ROBOT 2019.0 5333 5339 7 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Robotics BP2QS Green Submitted 2023-03-23 WOS:000544658404046 0
J Senthilraj, S; Raja, R; Zhu, QX; Samidurai, R; Yao, ZS Senthilraj, S.; Raja, R.; Zhu, Quanxin; Samidurai, R.; Yao, Zhangsong Delay-interval-dependent passivity analysis of stochastic neural networks with Markovian jumping parameters and time delay in the leakage term NONLINEAR ANALYSIS-HYBRID SYSTEMS English Article Passivity analysis; Stochastic neural network; Markovian jumping parameters; Leakage delay; Linear matrix inequality STABILITY ANALYSIS; NEUTRAL TYPE; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DISCRETE In this paper, the problem of passivity analysis is investigated for a class of stochastic neural networks with Markovian jumping parameters and time delay in the leakage term. The discrete delay is assumed to be time-varying and belongs to,a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing appropriate Lyapunov-Krasovskii functionals, and employing Newton-Leibniz formulation and the free weighting matrix method, several delay-dependent criteria for checking the passivity of the addressed neural networks are established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Two examples are given to show the effectiveness and less conservatism of the proposed criteria. (C) 2016 Elsevier Ltd. All rights reserved. [Senthilraj, S.; Samidurai, R.] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India; [Raja, R.] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630004, Tamil Nadu, India; [Zhu, Quanxin] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China; [Zhu, Quanxin] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany; [Yao, Zhangsong] Nanjing Xiaozhuang Univ, Sch Math & Informat Technol, Nanjing 211171, Jiangsu, Peoples R China Thiruvalluvar University; Alagappa University; Nanjing Normal University; Nanjing Normal University; University of Bielefeld; Nanjing Xiaozhuang University Zhu, QX (corresponding author), Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China.;Zhu, QX (corresponding author), Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China. senthilraj106@gmail.com; antony.raja67@yahoo.com; zqx22@126.com; samidurair@gmail.com S, Senthilraj/AAT-2712-2020; Zhu, Quanxin/X-3230-2018; Raja, R./Y-7657-2019 Zhu, Quanxin/0000-0003-3130-4923; Raja, R./0000-0003-0830-4933; R, Samidurai/0000-0003-0265-9426; S, Senthilraj/0000-0002-2883-1864 National Natural Science Foundation of China [61374080]; Alexander von Humboldt Foundation of Germany [CHN/1163390]; Qing Lan Project of Jiangsu; Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Alexander von Humboldt Foundation of Germany(Alexander von Humboldt Foundation); Qing Lan Project of Jiangsu; Priority Academic Program Development of Jiangsu Higher Education Institutions This work was jointly supported by the National Natural Science Foundation of China (61374080), the Alexander von Humboldt Foundation of Germany (Fellowship CHN/1163390), Qing Lan Project of Jiangsu and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. 42 22 22 0 39 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1751-570X 1878-7460 NONLINEAR ANAL-HYBRI Nonlinear Anal.-Hybrid Syst. NOV 2016.0 22.0 262 275 10.1016/j.nahs.2016.05.002 0.0 14 Automation & Control Systems; Mathematics, Applied Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Mathematics DU1ER 2023-03-23 WOS:000381951500019 0
C Zheng, S; Prisacariu, VA; Averkiou, M; Cheng, MM; Mitra, NJ; Shotton, J; Torr, PHS; Rother, C Gall, J; Gehler, P; Leibe, B Zheng, Shuai; Prisacariu, Victor Adrian; Averkiou, Melinos; Cheng, Ming-Ming; Mitra, Niloy J.; Shotton, Jamie; Torr, Philip H. S.; Rother, Carsten Object Proposals Estimation in Depth Image Using Compact 3D Shape Manifolds PATTERN RECOGNITION, GCPR 2015 Lecture Notes in Computer Science English Proceedings Paper 37th German Conference on Pattern Recognition (GCPR) OCT 07-10, 2015 Aachen, GERMANY Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement. [Zheng, Shuai; Prisacariu, Victor Adrian; Cheng, Ming-Ming; Torr, Philip H. S.] Univ Oxford, Oxford, England; [Averkiou, Melinos; Mitra, Niloy J.] UCL, London, England; [Shotton, Jamie] Microsoft Res, Cambridge, England; [Rother, Carsten] Tech Univ Dresden, Dresden, Germany; [Cheng, Ming-Ming] Nankai Univ, Tianjin 300071, Peoples R China University of Oxford; University of London; University College London; Microsoft; Technische Universitat Dresden; Nankai University Zheng, S (corresponding author), Univ Oxford, Oxford, England. shuai.zheng@eng.ox.ac.uk Cheng, Ming-Ming/A-2527-2009; Zheng, Shuai/G-9395-2014 Cheng, Ming-Ming/0000-0001-5550-8758; Zheng, Shuai/0000-0002-1636-631X; Averkiou, Melinos/0000-0003-1814-7134 40 8 8 0 9 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-24947-6; 978-3-319-24946-9 LECT NOTES COMPUT SC 2015.0 9358.0 196 208 10.1007/978-3-319-24947-6_16 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BE0ZS hybrid 2023-03-23 WOS:000367589300016 0
J Hu, B; Yu, XH; Guan, ZH; Kurths, J; Chen, GR Hu, Bin; Yu, Xinghuo; Guan, Zhi-Hong; Kurths, Jurgen; Chen, Guanrong Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Switches; Network topology; Topology; Adaptive control; Markov processes; Artificial neural networks; Distributed adaptive control; heterogeneous nonlinear dynamics; Markovian switching network; neural network (NN) MULTIAGENT SYSTEMS; STABILITY ANALYSIS; CONSENSUS; SYNCHRONIZATION; IDENTIFICATION; OPTIMIZATION; PARAMETERS While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance. [Hu, Bin] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Britton Chance Ctr Biomed Photon, Wuhan 430074, Peoples R China; [Yu, Xinghuo] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia; [Guan, Zhi-Hong] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China; [Kurths, Jurgen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany; [Kurths, Jurgen] Humboldt Univ, Inst Phys, D-10099 Berlin, Germany; [Chen, Guanrong] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China Huazhong University of Science & Technology; Royal Melbourne Institute of Technology (RMIT); Huazhong University of Science & Technology; Potsdam Institut fur Klimafolgenforschung; Humboldt University of Berlin; City University of Hong Kong Guan, ZH (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China. bhu@hust.edu.cn; x.yu@rmit.edu.au; zhguan@mail.hust.edu.cn; juergen.kurths@pik-potsdam.de; eegchen@cityu.edu.hk Yu, Xinghuo/D-8423-2013 Yu, Xinghuo/0000-0001-8093-9787; GUAN, Zhi-Hong/0000-0001-7997-0314; Hu, Bin/0000-0002-8851-4561; Kurths, Juergen/0000-0002-5926-4276 Australian Research Council's Discovery Program [DP200101199]; National Natural Science Foundation of China [61976099, 61976100, 61633011, 61672245, 61672244] Australian Research Council's Discovery Program(Australian Research Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by Australian Research Council's Discovery Program under Grant DP200101199 and in part by the National Natural Science Foundation of China under Grant 61976099, Grant 61976100, Grant 61633011, Grant 61672245, and Grant 61672244. 34 3 3 3 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. MAY 2021.0 32.0 5 2157 2168 10.1109/TNNLS.2020.3001009 0.0 12 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering RX7JY 32568715.0 2023-03-23 WOS:000647397200028 0
J Nan, Y; Li, FY; Tang, P; Zhang, GY; Zeng, CH; Xie, GT; Liu, ZH; Yang, G Nan, Yang; Li, Fengyi; Tang, Peng; Zhang, Guyue; Zeng, Caihong; Xie, Guotong; Liu, Zhihong; Yang, Guang Automatic fine-grained glomerular lesion recognition in kidney pathology PATTERN RECOGNITION English Article Deep convolutional neural network; Glomerulus segmentation; Fine-grained lesion classification; Uncertainty assessment; Kidney pathology CLASSIFICATION Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task. In this paper, we introduce a scheme to recognize fine-grained glomeruli lesions from whole slide images. First, a focal instance structural similarity loss is proposed to drive the model to locate all types of glomeruli precisely. Then an Uncertainty Aided Apportionment Network is designed to carry out the fine-grained visual classification without bounding-box annotations. This double branch-shaped structure extracts common features of the child class from the parent class and produces the uncertainty factor for reconstituting the training dataset. Results of slide-wise evaluation illustrate the effectiveness of the entire scheme, with an 8-22% improvement of the mean Average Precision compared with remarkable detection methods. The comprehensive results clearly demonstrate the effectiveness of the proposed method. (C) 2022 The Authors. Published by Elsevier Ltd. [Nan, Yang; Yang, Guang] Imperial Coll London, Natl Heart & Lung Inst, London, England; [Li, Fengyi; Zhang, Guyue; Xie, Guotong] PingAn Healthcare Technol, Shanghai, Peoples R China; [Tang, Peng] Tech Univ Munich, Dept Informat, Munich, Germany; [Zeng, Caihong; Liu, Zhihong] Nanjing Univ, Sch Med, Natl Clin Res Ctr Kidney Dis, Nanjing, Peoples R China Imperial College London; Technical University of Munich; Nanjing University Nan, Y; Yang, G (corresponding author), Imperial Coll London, Natl Heart & Lung Inst, London, England.;Xie, GT (corresponding author), PingAn Healthcare Technol, Shanghai, Peoples R China.;Liu, ZH (corresponding author), Nanjing Univ, Sch Med, Natl Clin Res Ctr Kidney Dis, Nanjing, Peoples R China. y.nan20@imperial.ac.uk; xieguotong@pingan.com.cn; liuzhihong@nju.edu.cn; g.yang@imperial.ac.uk ; Yang, Guang/S-5032-2016 Nan, Yang/0000-0002-4542-3336; Yang, Guang/0000-0001-7344-7733 39 1 1 3 4 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUL 2022.0 127.0 108648 10.1016/j.patcog.2022.108648 0.0 MAR 2022 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 2S0IV Green Submitted, Green Published, hybrid 2023-03-23 WOS:000821486500010 0
J Tang, XP; Pu, Y; Peng, HN; Li, KX; Faouzi, S; Lu, TJ; Pu, D; Cerezo, M; Xu, JG; Li, L; Robert, C; Shen, SS Tang, Xinpu; Pu, Yi; Peng, Haoning; Li, Kaixiu; Faouzi, Sara; Lu, Tianjian; Pu, Dan; Cerezo, Michael; Xu, Jianguo; Li, Lu; Robert, Caroline; Shen, Shensi Spatial patterns of the cap-binding complex eIF4F in human melanoma cells COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL English Article MESSENGER-RNA LOCALIZATION; INITIATION-FACTOR 4E; ENDOPLASMIC-RETICULUM; TRANSLATION; GRANULES; TRANSLOCATION; PROTEINS; GROWTH; BRAF As a central node of protein synthesis, the cap-binding complex, eukaryotic translation initiation factor 4 F (eIF4F), is involved in cell homeostasis, development and tumorigenesis. A large body of literature exists on the regulation and function of eIF4F in cancer cells, however the intracellular localization patterns of this complex are largely unknown. Since different subsets of mRNAs are translated in distinct subcellular compartments, understanding the distribution of translation initiation factors in the cell is of major interest. Here, we developed an in situ detection method for eIF4F at the single cell level. By using an image-based spot feature analysis pipeline as well as supervised machine learning, we identify five distinct spatial patterns of the eIF4F translation initiation complex in human melanoma cells. The quantity of eIF4F complex per cell correlated with the global mRNA translation activity, and its variation is dynamically regulated by cell state or extracellular stimuli. In contrast, the spatial patterns of eIF4F complexes at the single cell level could distinguish melanoma cells harboring different oncogenic driver mutations. This suggests that different tumorigenic contexts differentially regulate the subcellular localization of mRNA translation, with specific localization of eIF4F potentially associated with melanoma cell chemoresistance.(c) 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). [Tang, Xinpu; Pu, Yi; Peng, Haoning; Li, Kaixiu; Lu, Tianjian; Shen, Shensi] Sichuan Univ, Inst Thorac Oncol, West China Hosp, Chengdu, Peoples R China; [Tang, Xinpu; Xu, Jianguo] Sichuan Univ, Dept Neurosurg, West China Hosp, Chengdu, Peoples R China; [Tang, Xinpu; Shen, Shensi] Sichuan Univ, West China Hosp, Natl Clin Res Ctr Geriatr, Chengdu, Peoples R China; [Pu, Yi] Sichuan Univ, Dept Burn Surg, West China Hosp, Chengdu, Peoples R China; [Peng, Haoning; Lu, Tianjian; Shen, Shensi] Sichuan Univ, Dept Thorac Surg, West China Hosp, Chengdu, Peoples R China; [Faouzi, Sara; Robert, Caroline] Gustave Roussy Canc Campus, INSERM U981, Villejuif, France; [Pu, Dan; Li, Lu] Sichuan Univ, Lung Canc Ctr, West China Hosp, Chengdu, Peoples R China; [Cerezo, Michael] Univ Cote Azur, Nice, France; [Cerezo, Michael] Ctr Mediterraneen Med Mol C3M, Equipe 12, INSERM U1065, Nice, France; [Robert, Caroline] Gustave Roussy Canc Campus, Dermatol Unit, Villejuif, France Sichuan University; Sichuan University; Sichuan University; Sichuan University; Sichuan University; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; UNICANCER; Gustave Roussy; Sichuan University; UDICE-French Research Universities; Universite Cote d'Azur; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Cote d'Azur; UNICANCER; Gustave Roussy Shen, SS (corresponding author), Sichuan Univ, Inst Thorac Oncol, West China Hosp, Chengdu, Peoples R China.;Shen, SS (corresponding author), Sichuan Univ, West China Hosp, Natl Clin Res Ctr Geriatr, Chengdu, Peoples R China.;Robert, C (corresponding author), Gustave Roussy Canc Campus, INSERM U981, Villejuif, France.;Li, L (corresponding author), Sichuan Univ, Lung Canc Ctr, West China Hosp, Chengdu, Peoples R China. windflower1991@vip.163.com; Caroline.Robert@gustaveroussy.fr; shenshensi@wchscu.cn CEREZO, Michael/0000-0002-3687-2009; Pu, Yi/0000-0002-0875-4160 Ensemble contre le Melanome; Foundation Credit Mutuel; Foundation Carrefour; Natural Science Foundation of China (NSFC) [82172794]; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University [Y2022JC002] Ensemble contre le Melanome; Foundation Credit Mutuel; Foundation Carrefour; Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University We thank Ensemble contre le Melanome; The Foundation Credit Mutuel, The Foundation Carrefour, Olivier and Christian Courtin and the association Vaincre le Melanome for their constant support (C.R.). S.S. is supported by the Natural Science Foundation of China (NSFC, 82172794) and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (grant no. Y2022JC002). S.S. thanks the constant support from Sebastien Bazin. We acknowledge the editing support of Life Science Editors. 47 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2001-0370 COMPUT STRUCT BIOTEC Comp. Struct. Biotechnol. J.. 2023.0 21.0 1157 1168 10.1016/j.csbj.2023.01.040 0.0 FEB 2023 12 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology 8W2IO 36789267.0 gold 2023-03-23 WOS:000931163600001 0
J Wei, SG; Hengl, T; de Jesus, JM; Yuan, H; Dai, YJ Wei Shangguan; Hengl, Tomislav; de Jesus, Jorge Mendes; Yuan, Hua; Dai, Yongjiu Mapping the global depth to bedrock for land surface modeling JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS English Article depth to bedrock; land surface model; earth system model; machine learning; spatial prediction model SOIL THICKNESS; TERRAIN; CLIMATE; WATER Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas. [Wei Shangguan; Yuan, Hua; Dai, Yongjiu] Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China; [Hengl, Tomislav; de Jesus, Jorge Mendes] ISRIC World Soil Informat, Wageningen, Netherlands Sun Yat Sen University Dai, YJ (corresponding author), Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China. daiyj6@mail.sysu.edu.cn Yuan, Hua/Y-8074-2019; Dai, Yongjiu/D-6261-2014; Hengl, Tomislav/AAE-9320-2019; Shangguan, Wei/C-5157-2008 Yuan, Hua/0000-0003-2524-598X; Dai, Yongjiu/0000-0002-3588-6644; Hengl, Tomislav/0000-0002-9921-5129; Shangguan, Wei/0000-0001-7622-8210; Mendes de Jesus, Jorge Samuel/0000-0001-9890-067X Natural Science Foundation of China [41575072, 41405096]; R&D Special Fund for Nonprofit Industry (Meteorology) [GYHY201206013, GYHY201306066]; Dutch government Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); R&D Special Fund for Nonprofit Industry (Meteorology); Dutch government This work was supported by the Natural Science Foundation of China (under grants 41575072, 41405096) and R&D Special Fund for Nonprofit Industry (Meteorology, GYHY201206013, GYHY201306066). ISRIC is a nonprofit-making organization, core-funded by the Dutch government, with a mandate to serve the international community as custodian of global soil information and to increase awareness and understanding of the role of soils in major global issues. 59 132 134 10 47 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 1942-2466 J ADV MODEL EARTH SY J. Adv. Model. Earth Syst. MAR 2017.0 9.0 1 65 88 10.1002/2016MS000686 0.0 24 Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Meteorology & Atmospheric Sciences ES7UH gold 2023-03-23 WOS:000399756400004 0
J He, LX; Liu, Y; Bi, SF; Wang, L; Broggi, M; Beer, M He, Longxue; Liu, Yong; Bi, Sifeng; Wang, Li; Broggi, Matteo; Beer, Michael Estimation of failure probability in braced excavation using Bayesian networks with integrated model updating UNDERGROUND SPACE English Article Failure probability; Braced excavation; Bayesian networks; Stochastic model updating; Sensitivity analysis RESPONSE-SURFACE; WALL DEFLECTION; PARAMETERS A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations. The model integrates a Bayesian network and distanced-based Bayesian model updating. In the network, the movement of a retaining wall is selected as the indicator of failure, and the observed ground surface settlement is used to update the soil parameters. The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis. An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors. The proposed model effectively estimates the uncertainty of influential factors. A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach. The update results facilitate accurate estimates according to the target value, from which the corresponding probabilities of failure are obtained. The proposed model enables failure probabilities to be determined with real-time result updating. [He, Longxue; Bi, Sifeng; Broggi, Matteo; Beer, Michael] Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany; [Liu, Yong] Wuhan Univ, Inst Engn Risk & Disaster Prevent, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China; [Wang, Li] Dalian Jiaotong Univ, Sch Civil & Safety Engn, Dalian, Liaoning, Peoples R China; [Beer, Michael] Univ Liverpool, Inst Risk & Uncertainty, Liverpool, Merseyside, England; [Beer, Michael] Tongji Univ, Int Joint Res Ctr Engn Reliabil & Stochast Mech, Shanghai, Peoples R China Leibniz University Hannover; Wuhan University; Dalian Jiaotong University; University of Liverpool; Tongji University Beer, M (corresponding author), Leibniz Univ Hannover, Inst Risk & Reliabil, Hannover, Germany. beer@irz.uni-hannover.de Liu, Yong/R-4019-2017; BI, Sifeng/D-7137-2018 Liu, Yong/0000-0003-1006-7842; BI, Sifeng/0000-0002-8600-8649; Broggi, Matteo/0000-0002-3683-3907; Beer, Michael/0000-0002-0611-0345 Chinese Scholarship Council Chinese Scholarship Council(China Scholarship Council) This work is supported by the Chinese Scholarship Council. 36 12 12 6 21 KEAI PUBLISHING LTD BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGHENG DISTRICT 100717, PEOPLES R CHINA 2096-2754 2467-9674 UNDERGR SPACE Undergr. Space DEC 2020.0 5.0 4 315 323 10.1016/j.undsp.2019.07.001 0.0 9 Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering NM0HP Green Published, gold, Green Accepted 2023-03-23 WOS:000567786600004 0
J Fan, Z; Ji, PP; Zhang, J; Segets, D; Chen, DR; Chen, SC Fan, Zheng; Ji, Pan-pan; Zhang, Jie; Segets, Doris; Chen, Da-Ren; Chen, Sheng-Chieh Wavelet neural network modeling for the retention efficiency of sub-15 nm nanoparticles in ultrafiltration under small particle to pore diameter ratio JOURNAL OF MEMBRANE SCIENCE English Article Wavelet artificial neuron network; Ultrafiltration; Particle to pore diameter ratio; Modified extended DLVO model; Quench molecular dynamics simulation BOX-COX TRANSFORMATION; OPERATING-CONDITIONS; MOLECULAR-DYNAMICS; GOLD NANOPARTICLES; QUANTUM DOTS; MEMBRANE; DLVO; MICROFILTRATION; PREDICTION; FILTRATION Ultrafiltration (UF) using membranes with a small ratio of particle to pore diameter (PPD) would be very desirable for energy saving. The nanoparticle (NP) retention efficiency of membranes with a small PPD ratio depends on various physical and chemical properties of NPs, membranes and solutions as well as the filtration conditions. Until now, no simple model is available for the calculation of NP retention efficiency in UF membranes, besides, it is unlikely to conduct experiments covering all conditions for obtaining the efficiency. The artificial neural network (ANN) has been attracting much attention for studying the performance of a highly nonlinear system. In this study, a wavelet ANN model was developed to predict the NP retentions in membranes for the dead-ended UFs under different conditions. A total of 13 parameters, including the membrane features, particle properties, water solution characteristics, operating conditions, etc., are considered as ANN inputs and the NP retention efficiency as the output. A total of 200 datasets with high quality from literature were selected, in which 50% were for the model training, 30% for the model validation and the remaining 20% for the model testing. A high correlation between the output and inputs was obtained and the significance of the 13 parameters on the NP retention was ranked. A case study was performed to further validate the trained ANN model in the prediction of the retention efficiency of 10 nm gold NPs in a 50 nm pore sized polycarbonate track etched (PCTE) membrane at different pH conditions (5-9). Focusing on the variation of pH, an excellent agreement between the model prediction and the calculation by the modified extended Derjaguin Landau Verwey Overbeek-Maxwell (MEDLVO-Maxwell) model originating from classical and extended DLVO theory was obtained. The interaction energy in the MEDLVO-Maxwell model was based on the separation distance calculated by a quench molecular dynamics (MD) simulation. This study illustrates and validates the application of ANN modeling on the NP retention efficiency prediction in the UF with small PPD ratios. [Fan, Zheng; Ji, Pan-pan] Xian Shiyou Univ, Coll Chem & Chem Engn, 18 Dianzi 2nd Rd, Xian 710065, Shaanxi, Peoples R China; [Fan, Zheng; Zhang, Jie; Chen, Da-Ren; Chen, Sheng-Chieh] Virginia Commonwealth Univ, Dept Mech & Nucl Engn, 401 West Main St, Richmond, VA 23284 USA; [Segets, Doris] Univ Duisburg Essen UDE, Nanoparticle Proc Grp, Carl Benz Str 199, D-47057 Duisburg, Germany Xi'an Shiyou University; Virginia Commonwealth University; University of Duisburg Essen Chen, SC (corresponding author), Virginia Commonwealth Univ, Dept Mech & Nucl Engn, 401 West Main St, Richmond, VA 23284 USA. scchen@vcu.edu Chen, Sheng-Chieh Shawn/AAH-8820-2020 Zhang, Jie/0000-0002-9499-8830 China Scholarship Council (CSC) [201908610135] China Scholarship Council (CSC)(China Scholarship Council) The authors thank the support of members of the Center for Filtration Research: 3 M Corporation, Applied Materials Inc., BASF Corporation, Boeing Company, Corning Inc., China Yancheng Environmental Protection Science and Technology City, Cummins Filtration Inc., Donaldson Company, Inc., Entegris, Inc., Ford Motor Company, W. L. Gore & Associates Inc., Guangxi Wat Yuan Filtration System Co., Ltd, MSP Corporation; Samsung Electronics Co., Ltd., Shigematsu Works Co. Ltd.; TSI Inc.; W. L. Gore & Associates, Inc., Xinxiang Shengda Filtration Technique Co. Ltd., and the affiliate member National Institute for Occupational Safety and Health (NIOSH). Z.F. and P.P.J. acknowledge the funding from China Scholarship Council (CSC, award number: 201908610135). 80 56 56 13 51 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0376-7388 1873-3123 J MEMBRANE SCI J. Membr. Sci. OCT 1 2021.0 635.0 119503 10.1016/j.memsci.2021.119503 0.0 JUN 2021 11 Engineering, Chemical; Polymer Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Polymer Science TP4IO 2023-03-23 WOS:000677558500002 0
J Liu, KX; Zeng, XY; Bruniaux, P; Wang, JP; Kamalha, E; Tao, XY Liu, Kaixuan; Zeng, Xianyi; Bruniaux, Pascal; Wang, Jianping; Kamalha, Edwin; Tao, Xuyuan Fit evaluation of virtual garment try-on by learning from digital pressure data KNOWLEDGE-BASED SYSTEMS English Article Digital clothing pressure; Support vector machines; Naive Bayes; Active learning; Ease allowance; Real try-on ASSEMBLY LINES; FASHION; MACHINE; SYSTEM; OPTIMIZATION; ALGORITHM; SEARCH; DESIGN; RETAIL; MODEL Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies. (C) 2017 Elsevier B.V. All rights reserved. [Liu, Kaixuan] Xian Polytech Univ, Apparel & Art Design Coll, Xian 710048, Shaanxi, Peoples R China; [Liu, Kaixuan; Wang, Jianping] Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China; [Liu, Kaixuan; Zeng, Xianyi; Bruniaux, Pascal; Kamalha, Edwin; Tao, Xuyuan] Univ Lille 1, F-59000 Lille, France; [Liu, Kaixuan; Zeng, Xianyi; Bruniaux, Pascal; Kamalha, Edwin; Tao, Xuyuan] ENSAIT, GEMTEX Lab, F-59100 Roubaix, France Xi'an Polytechnic University; Donghua University; Universite de Lille - ISITE; Universite de Lille; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT) Liu, KX (corresponding author), Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China. L40260611@hotmail.com thomassey, sebastien/AAZ-9320-2020; Kamalha, Edwin/E-6825-2017; Liu, kaixuan/GQH-8342-2022 thomassey, sebastien/0000-0002-5556-7173; Kamalha, Edwin/0000-0002-2923-8886; liu, kaixuan/0000-0003-4105-8324; Zeng, Xianyi/0000-0002-3236-6766 China Scholarship Council [201406630077]; Fundamental Research Funds for the Central Universities [CUSF-DH-D-2015082] China Scholarship Council(China Scholarship Council); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This paper was financially supported by China Scholarship Council (No. 201406630077) and the Fundamental Research Funds for the Central Universities (No.CUSF-DH-D-2015082). 28 41 42 8 86 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. OCT 1 2017.0 133.0 174 182 10.1016/j.knosys.2017.07.007 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FI2NM 2023-03-23 WOS:000411775600013 0
J Zhou, N; Cheng, H; Pedrycz, W; Zhang, Y; Liu, HP Zhou, Nan; Cheng, Hong; Pedrycz, Witold; Zhang, Yong; Liu, Huaping Discriminative sparse subspace learning and its application to unsupervised feature selection ISA TRANSACTIONS English Article Machine learning; Feature selection; Subspace learning; Unsupervised learning; Kernel learning DIMENSIONALITY REDUCTION; MUTUAL INFORMATION; ALGORITHM; REPRESENTATION; FACTORIZATION; RECOGNITION; CRITERIA In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters' assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved. [Zhou, Nan; Cheng, Hong; Zhang, Yong] Univ Elect Sci & Technol China, Sch Automat Engn, Ctr Robot, Chengdu 611731, Sichuan, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Liu, Huaping] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland University of Electronic Science & Technology of China; University of Alberta; Tsinghua University; King Abdulaziz University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Cheng, H (corresponding author), Univ Elect Sci & Technol China, Sch Automat Engn, Ctr Robot, Chengdu 611731, Sichuan, Peoples R China. nzhouuestc@gmail.com; hcheng@uestc.edu.cn; wpedrycz@ualberta.ca; mathzy@uestc.edu.cn; hpliu@tsinghua.edu.cn 42 12 12 2 32 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0019-0578 1879-2022 ISA T ISA Trans. MAR 2016.0 61.0 104 118 10.1016/j.isatra.2015.12.011 0.0 15 Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering; Instruments & Instrumentation DI3PI 26803552.0 2023-03-23 WOS:000373410400009 0
J Liu, CY; Springer, D; Li, Q; Moody, B; Juan, RA; Chorro, FJ; Castells, F; Roig, JM; Silva, I; Johnson, AEW; Syed, Z; Schmidt, SE; Papadaniil, CD; Hadjileontiadis, L; Naseri, H; Moukadem, A; Dieterlen, A; Brandt, C; Tang, H; Samieinasab, M; Samieinasab, MR; Sameni, R; Mark, RG; Clifford, GD Liu, Chengyu; Springer, David; Li, Qiao; Moody, Benjamin; Juan, Ricardo Abad; Chorro, Francisco J.; Castells, Francisco; Roig, Jose Millet; Silva, Ikaro; Johnson, Alistair E. W.; Syed, Zeeshan; Schmidt, Samuel E.; Papadaniil, Chrysa D.; Hadjileontiadis, Leontios; Naseri, Hosein; Moukadem, Ali; Dieterlen, Alain; Brandt, Christian; Tang, Hong; Samieinasab, Maryam; Samieinasab, Mohammad Reza; Sameni, Reza; Mark, Roger G.; Clifford, Gari D. An open access database for the evaluation of heart sound algorithms PHYSIOLOGICAL MEASUREMENT English Article heart sound; phonocardiogram (PCG); database; heart sound classification; heart sound segmentation; PhysioNet/CinC Challenge TIME-FREQUENCY REPRESENTATIONS; ARTIFICIAL NEURAL-NETWORK; SEGMENTATION ALGORITHM; FEATURE-EXTRACTION; WAVELET TRANSFORM; SIGNAL; CLASSIFICATION; IDENTIFICATION; DIAGNOSIS; FEATURES In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed. [Liu, Chengyu; Li, Qiao; Clifford, Gari D.] Emory Univ, Dept Biomed Informat, Atlanta, GA 30322 USA; [Springer, David] Univ Oxford, Dept Engn Sci, Inst Biomed Engn, Oxford, England; [Moody, Benjamin; Silva, Ikaro; Mark, Roger G.] MIT, Inst Med Engn & Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA; [Juan, Ricardo Abad; Clifford, Gari D.] Georgia Inst Technol, Dept Biomed Engn, Atlanta, GA 30332 USA; [Juan, Ricardo Abad; Castells, Francisco; Roig, Jose Millet] Univ Politecn Valencia, ITACA Inst, Valencia, Spain; [Chorro, Francisco J.] Univ Valencia, Clin Hosp, Serv Cardiol, INCLIVA, Valencia, Spain; [Syed, Zeeshan] Univ Michigan, Dept Biomed Engn, Ann Arbor, MI 48109 USA; [Schmidt, Samuel E.] Aalborg Univ, Dept Hlth Sci & Technol, Aalborg, Denmark; [Papadaniil, Chrysa D.; Hadjileontiadis, Leontios] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Thessaloniki, Greece; [Naseri, Hosein] KN Toosi Univ Technol, Dept Mech Engn, Tehran, Iran; [Moukadem, Ali; Dieterlen, Alain] Univ Haute Alsace, MIPS Lab, Mulhouse, France; [Brandt, Christian] Hosp Univ Strasbourg, Mulhouse, France; [Tang, Hong] Dalian Univ Technol, Fac Elect & Elect Engn, Dalian, Peoples R China; [Samieinasab, Maryam; Sameni, Reza] Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran; [Samieinasab, Mohammad Reza] Isfahan Univ Med Sci, Dept Med, Esfahan, Iran Emory University; University of Oxford; Massachusetts Institute of Technology (MIT); University System of Georgia; Georgia Institute of Technology; Universitat Politecnica de Valencia; University of Valencia; University of Michigan System; University of Michigan; Aalborg University; Aristotle University of Thessaloniki; K. N. Toosi University of Technology; Universites de Strasbourg Etablissements Associes; Universite de Haute-Alsace (UHA); Dalian University of Technology; Shiraz University; Isfahan University Medical Science Clifford, GD (corresponding author), Emory Univ, Dept Biomed Informat, Atlanta, GA 30322 USA.;Clifford, GD (corresponding author), Georgia Inst Technol, Dept Biomed Engn, Atlanta, GA 30332 USA. gari@gatech.edu Sameni, Reza/P-4144-2018; mirmosaiebi, m/V-5969-2017; Clifford, Gari/GRJ-0376-2022; Chorro, Francisco J/AAB-6766-2020; Schmidt, Samuel E/A-1377-2011; Millet-Roig, Jose/ABC-8862-2021; Samieinasab, Mohammad Reza/V-5973-2017; Castells, Francisco/AAT-1899-2021; Castells, Francisco/S-3258-2018; Hadjileontiadis, Leontios/AAF-3448-2020 Sameni, Reza/0000-0003-4913-6825; Schmidt, Samuel E/0000-0002-0917-634X; Millet-Roig, Jose/0000-0002-8879-003X; Samieinasab, Mohammad Reza/0000-0002-9804-9608; Castells, Francisco/0000-0001-5044-3545; Castells, Francisco/0000-0001-5044-3545; Clifford, Gari/0000-0002-5709-201X; Liu, Chengyu/0000-0003-1965-3020 National Institutes of Health (NIH) grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) [R01-EB001659]; National Institute of General Medical Sciences [R01GM104987] National Institutes of Health (NIH) grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB); National Institute of General Medical Sciences(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS)) This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences. 66 266 285 16 126 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0967-3334 1361-6579 PHYSIOL MEAS Physiol. Meas. DEC 2016.0 37.0 12 2181 2213 10.1088/0967-3334/37/12/2181 0.0 33 Biophysics; Engineering, Biomedical; Physiology Science Citation Index Expanded (SCI-EXPANDED) Biophysics; Engineering; Physiology EE1EO 27869105.0 Green Published, Green Submitted, Green Accepted 2023-03-23 WOS:000389324000002 0
J Feng, XR; Li, HC; Wang, R; Du, Q; Jia, XP; Plaza, A Feng, Xin-Ru; Li, Heng-Chao; Wang, Rui; Du, Qian; Jia, Xiuping; Plaza, Antonio Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Review Hyperspectral imaging; Mixture models; Indexes; Libraries; Earth; Data mining; Cost function; Deep learning; hyperspectral unmixing; linear mixture model; nonnegative matrix factorization SPECTRAL MIXTURE ANALYSIS; CONSTRAINED SPARSE NMF; TENSOR FACTORIZATION; ENDMEMBER VARIABILITY; COMPONENT ANALYSIS; FAST ALGORITHM; EXTRACTION; REGRESSION; AUTOENCODERS; IMAGERY Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions, including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude this article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. [Feng, Xin-Ru; Wang, Rui] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China; [Li, Heng-Chao] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Peoples R China; [Du, Qian] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA; [Jia, Xiuping] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia; [Plaza, Antonio] Univ Extremadura, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Escuela Politecn, Caceres 10071, Spain Southwest Jiaotong University; Southwest Jiaotong University; Mississippi State University; University of New South Wales Sydney; Universidad de Extremadura Li, HC (corresponding author), Southwest Jiaotong Univ, Sch Informat Sci & Technol, Natl Engn Lab Integrated Transportat Big Data App, Chengdu 611756, Peoples R China. fengxinru@my.swjtu.edu.cn; hcli@home.swjtu.edu.cn; wangruinew@163.com; du@ece.msstate.edu; x.jia@adfa.edu.au; aplaza@unex.es du, qian/GYJ-7090-2022; Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; Feng, Xin-Ru/0000-0001-8799-667X National Natural Science Foundation of China [61871335, 61801404]; Fundamental Research Funds for the Central Universities [2682020ZT35] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61871335 and Grant 61801404, and in part by the Fundamental Research Funds for the Central Universities under Grant 2682020ZT35. 193 3 3 28 43 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2022.0 15.0 4414 4436 10.1109/JSTARS.2022.3175257 0.0 23 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology 2D5KY Green Submitted, gold 2023-03-23 WOS:000811587000005 0
J Chen, L; Li, ZD; Zeng, T; Zhang, YH; Zhang, SQ; Huang, T; Cai, YD Chen, Lei; Li, ZhanDong; Zeng, Tao; Zhang, Yu-Hang; Zhang, ShiQi; Huang, Tao; Cai, Yu-Dong Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features FRONTIERS IN GENETICS English Article protein subcellular location; protein-protein interaction network; GO enrichment; KEGG enrichment; feature selection; classification algorithm AMINO-ACID-COMPOSITION; NEAREST-NEIGHBOR CLASSIFICATION; COMPLEX-I; FEATURE-SELECTION; CYTOPLASMIC FILAMENTS; MALATE-DEHYDROGENASE; STRING DATABASE; NDUFS3 SUBUNIT; CELL; LOCALIZATION Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance. [Chen, Lei; Cai, Yu-Dong] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China; [Chen, Lei] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China; [Li, ZhanDong] Jilin Engn Normal Univ, Coll Food Engn, Changchun, Peoples R China; [Zeng, Tao; Huang, Tao] Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Biomed Big Data Ctr,CAS Key Lab Computat Biol, Shanghai, Peoples R China; [Zhang, Yu-Hang] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA USA; [Zhang, ShiQi] Univ Copenhagen, Dept Biostat, Copenhagen, Denmark; [Huang, Tao] Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai, Peoples R China Shanghai University; Shanghai Maritime University; Jilin Engineering Normal University; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS; Harvard University; Brigham & Women's Hospital; Harvard Medical School; University of Copenhagen; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; University of Chinese Academy of Sciences, CAS Cai, YD (corresponding author), Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Biomed Big Data Ctr,CAS Key Lab Computat Biol, Shanghai, Peoples R China.;Huang, T (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai, Peoples R China. tohuangtao@126.com; cai_yud@126.com li, zhandong/ABG-6019-2020 li, zhandong/0000-0002-0171-4473; Zeng, Tao/0000-0002-0295-3994 Strategic Priority Research Program of Chinese Academy of Sciences [XDA26040304, XDB38050200]; National Key R&D Program of China [2018YFC0910403]; Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences [202002] Strategic Priority Research Program of Chinese Academy of Sciences(Chinese Academy of Sciences); National Key R&D Program of China; Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA26040304, XDB38050200), National Key R&D Program of China (2018YFC0910403), the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences (202002). 106 1 1 1 2 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-8021 FRONT GENET Front. Genet. NOV 5 2021.0 12.0 783128 10.3389/fgene.2021.783128 0.0 13 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity 6P0TP 34804131.0 Green Accepted, gold 2023-03-23 WOS:000890649100001 0
J Zhou, N; Xu, YY; Cheng, H; Fang, J; Pedrycz, W Zhou, Nan; Xu, Yangyang; Cheng, Hong; Fang, Jun; Pedrycz, Witold Global and local structure preserving sparse subspace learning: An iterative approach to unsupervised feature selection PATTERN RECOGNITION English Article Machine learning; Feature selection; Subspace learning; Unsupervised learning DIMENSIONALITY REDUCTION; DISCRIMINANT; RECOVERY As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of the data, and few studies simultaneously focus on global and local structures as the both of them contain important information. In this paper, we propose a global and local structure preserving sparse subspace learning (GLOSS) model for unsupervised feature selection. The model can simultaneously realize feature selection and subspace learning. In addition, we develop a greedy algorithm to establish a generic combinatorial model, and an iterative strategy based on an accelerated block coordinate descent is used to solve the GLoSS problem. We also provide whole iterate sequence convergence analysis of the proposed iterative algorithm. Extensive experiments are conducted on real-world datasets to show the superiority of the proposed approach over several state-of-the-art unsupervised feature selection approaches. (C) 2015 Elsevier Ltd. All rights reserved. [Zhou, Nan; Cheng, Hong; Fang, Jun] Univ Elect Sci & Technol China, Ctr Robot, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China; [Zhou, Nan; Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2R3, Canada; [Xu, Yangyang] Univ Waterloo, Dept Combinator & Optimizat, Waterloo, ON N2L 3G1, Canada; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland University of Electronic Science & Technology of China; University of Alberta; University of Waterloo; King Abdulaziz University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences Cheng, H (corresponding author), Univ Elect Sci & Technol China, Ctr Robot, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China. nzhouuestc@gmail.com; yangyang.xu@uwaterloo.ca; hcheng@uestc.edu.cn; JunFang@uestc.edu.cn; wpedrycz@ualberta.ca Xu, Yangyang/R-6164-2019 AFOSR; NSERC; CRC AFOSR(United States Department of DefenseAir Force Office of Scientific Research (AFOSR)); NSERC(Natural Sciences and Engineering Research Council of Canada (NSERC)); CRC(Australian GovernmentDepartment of Industry, Innovation and ScienceCooperative Research Centres (CRC) Programme) The authors would like to thank two anonymous referees for their careful reviews and constructive comments. Y. Xu is partially supported by AFOSR. W. Pedrycz is partially supported by NSERC and CRC. 60 59 63 1 53 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. MAY 2016.0 53.0 87 101 10.1016/j.patcog.2015.12.008 0.0 15 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering DE8KU Bronze, Green Submitted 2023-03-23 WOS:000370885700008 0
J Qammar, A; Karim, A; Ning, HS; Ding, JG Qammar, Attia; Karim, Ahmad; Ning, Huansheng; Ding, Jianguo Securing federated learning with blockchain: a systematic literature review ARTIFICIAL INTELLIGENCE REVIEW English Review; Early Access Federated learning; Blockchain; Security; Privacy; Blockchain-based FL; Systematic literature review TECHNOLOGY; CHALLENGES; FRAMEWORK Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed. [Qammar, Attia; Ning, Huansheng] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China; [Karim, Ahmad] Bahauddin Zakariya Univ, Dept Informat Technol, Multan, Pakistan; [Ding, Jianguo] Blekinge Inst Technol, Dept Comp Sci, Karlskrona, Sweden University of Science & Technology Beijing; Bahauddin Zakariya University; Blekinge Institute Technology Ding, JG (corresponding author), Blekinge Inst Technol, Dept Comp Sci, Karlskrona, Sweden. ahmadkarim@bzu.edu.pk; ninghuansheng@ustb.edu.cn; jianguo.ding@bth.se Ding, Jianguo/AAJ-1814-2021 Ding, Jianguo/0000-0002-8927-0968 Blekinge Institute of Technology Blekinge Institute of Technology(General Electric) Open access funding provided by Blekinge Institute of Technology. 91 0 0 97 97 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. 10.1007/s10462-022-10271-9 0.0 SEP 2022 35 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4O0IP 36160367.0 Green Accepted, hybrid, Green Published 2023-03-23 WOS:000854393600001 0
J Xu, JS; Chen, ZQ; Lou, HX; Shen, GJ; Pumir, A Xu, Jinshan; Chen, Zhenqin; Lou, Hangxiao; Shen, Guojiang; Pumir, Alain Review on EHG signal analysis and its application in preterm diagnosis BIOMEDICAL SIGNAL PROCESSING AND CONTROL English Review Preterm diagnosis; Electrohysterogram; feature extraction; sample imbalance UTERINE SMOOTH-MUSCLE; ELECTRICAL-ACTIVITY; INTRAUTERINE PRESSURE; PREDICTION; LABOR; PREGNANCY; BIRTH; RISK; ELECTROHYSTEROGRAM; PROPAGATION Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk deliveries, combined with appropriate medication appears as the way to treat the problem, although effective early diagnostic methods remain elusive. Since the strong contractions that drive the fetus out of the uterus can be traced back to the electrical activity of uterine muscle cells, the external recording of these activities in the form of electrohysterogram (EHG) opens a new research direction for the development of preterm diagnostic methods. This review focuses on EHG signal analysis and its application to preterm birth diagnostic methods, and in particular on the analysis of such signals using machine learning techniques. We introduce the publicly available databases of EHG recordings, used as a testing ground for this approach, as well as the methods to extract meaningful information from the raw data. We stress the problem of imbalance, due to the comparatively small number of patients suffering from preterm births, and discuss techniques to mitigate these effects. Last, we discuss the possibility to identify and characterize contractions directly from EHG signals. We conclude by discussing new research directions, in particular based on a biophysical description of uterus contraction towards the end of pregnancy, and their possible applications to extract more effective features and for better dealing with the shortage of training examples. [Xu, Jinshan; Chen, Zhenqin; Lou, Hangxiao; Shen, Guojiang] Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China; [Pumir, Alain] Univ Lyon, Univ Claude Bernard, CNRS, Lab Phys,ENS Lyon, F-69342 Lyon, France; [Xu, Jinshan] Zhejiang Lab, Res Ctr Social Expt, Hangzhou 311121, Peoples R China Zhejiang University of Technology; Centre National de la Recherche Scientifique (CNRS); Ecole Normale Superieure de Lyon (ENS de LYON); UDICE-French Research Universities; Universite Claude Bernard Lyon 1; Zhejiang Laboratory Shen, GJ (corresponding author), Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China.;Pumir, A (corresponding author), Univ Lyon, Univ Claude Bernard, CNRS, Lab Phys,ENS Lyon, F-69342 Lyon, France. gjshen1975@zjut.edu.cn; alain.pumir@ens-lyon.fr chen, zhenqin/0000-0001-8419-0638 111 5 5 5 17 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1746-8094 1746-8108 BIOMED SIGNAL PROCES Biomed. Signal Process. Control JAN 2022.0 71.0 B 103231 10.1016/j.bspc.2021.103231 0.0 OCT 2021 12 Engineering, Biomedical Science Citation Index Expanded (SCI-EXPANDED) Engineering WM0LH Bronze 2023-03-23 WOS:000710786300006 0
J Elahe, MF; Kabir, MA; Mahmud, SMH; Azim, R Elahe, Md Fazla; Kabir, Md Alamgir; Mahmud, S. M. Hasan; Azim, Riasat Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle ELECTRONICS English Article charging station; electric vehicle; forecast; power demand; privacy The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research. [Elahe, Md Fazla] Daffodil Int Univ, Dept Software Engn, Dhaka 1216, Bangladesh; [Elahe, Md Fazla; Kabir, Md Alamgir; Mahmud, S. M. Hasan] Ctr Adv Machine Learning & Applicat CAMLAs, Dhaka 1229, Bangladesh; [Kabir, Md Alamgir] Malardalen Univ, Sch Innovat Design & Engn, Artificial Intelligence & Intelligent Syst Res Grp, Hogskoleplan 1, S-72220 Vasteras, Sweden; [Mahmud, S. M. Hasan] Amer Int Univ Bangladesh AIUB, Dept Comp Sci, Dhaka 1229, Bangladesh; [Azim, Riasat] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China Daffodil International University; Malardalen University; American International University Bangladesh (AIUB); Hunan University Kabir, MA (corresponding author), Ctr Adv Machine Learning & Applicat CAMLAs, Dhaka 1229, Bangladesh.;Kabir, MA (corresponding author), Malardalen Univ, Sch Innovat Design & Engn, Artificial Intelligence & Intelligent Syst Res Grp, Hogskoleplan 1, S-72220 Vasteras, Sweden. md.alamgir.kabir@mdu.se Kabir, Md Alamgir/ABA-2963-2020; ELAHE, MD FAZLA/GRR-5608-2022 Kabir, Md Alamgir/0000-0002-7136-6339; ELAHE, MD FAZLA/0000-0002-0839-5946 Maelardalen University Maelardalen University We would like to acknowledge Maelardalen University for supporting the publication charges. 27 0 0 5 5 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics JAN 2023.0 12.0 1 55 10.3390/electronics12010055 0.0 18 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics 7R9MZ gold 2023-03-23 WOS:000910390400001 0
J Wang, T; Sabourin, C; Madani, K Wang, Ting; Sabourin, Cristophe; Madani, Kurosh A NOVEL PATH PLANNING APPROACH FOR MULTI-ROBOT BASED TRANSPORTATION INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION English Article Multi-robot team; rigid formation; path planning; logistic transportation; machine-learning; Q-learning SYSTEMS A path planning approach has been proposed for a team of robots which formed a rigid geometry formation to navigate in the unspecified environment. It may benefit for the transportation in the logistic application. In the proposed approach, the integral rigid formation is considered as a virtual structure with virtual rigid links between robots. The path planning is based on a two-level control strategy, where the higher level controls the formation and the lower one is in charge of holding the shape of the formation. The demonstration of the proposed concept's viability has been done on the basis of both simulation and experimentation involving real robots. The simulation is performed accordingly to a scenario involving a multi-robot team including three two-wheeled robots. In the considered scenario, the three robots form a rigid formation and navigate in the constrained structure in an unmatched environment (unknown by the group of robots at the initial time step). The real robots' based demonstrator involves two two-wheeled robots within a rigid object's transportation scenario. The considered scenario assumes that the two involved robots transport a voluminous rigid object (materialized by a long paper box in our experimental demonstration) from an initial location to a final location. Both the simulation and the experimental evaluations show that our path planning approach can be successfully applied to the logistic transportation. [Wang, Ting] Nanjing Univ, Sch Astron & Space Sci, Nanjing, Jiangsu, Peoples R China; [Sabourin, Cristophe; Madani, Kurosh] Univ PARIS EST Creteil UPEC, Signals Images & Intelligent Syst Lab LISSI EA 35, Senart FB Inst Technol, Lieusaint, France Nanjing University; Universite Paris-Est-Creteil-Val-de-Marne (UPEC) Wang, T (corresponding author), Nanjing Univ, Sch Astron & Space Sci, Nanjing, Jiangsu, Peoples R China. wangting0310@nju.edu.cn; sabourin@u-pec.fr; madani@u-pec.fr sabourin, christophe/A-3263-2013 sabourin, christophe/0000-0002-7316-0801 32 2 2 2 24 ACTA PRESS CALGARY 2509 DIEPPE AVE SW, BLDG B6, STE 101, CALGARY, AB T3E 7J9, CANADA 0826-8185 1925-7090 INT J ROBOT AUTOM Int. J. Robot. Autom. 2013.0 28.0 3 218 225 10.2316/Journal.206.2013.3.206-3756 0.0 8 Automation & Control Systems; Robotics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Robotics 210VT 2023-03-23 WOS:000323863600002 0
J Xu, JS; Chen, ZQ; Zhang, JP; Lu, YP; Yang, X; Pumir, A Xu, Jinshan; Chen, Zhenqin; Zhang, Jinpeng; Lu, Yanpei; Yang, Xi; Pumir, Alain Realistic preterm prediction based on optimized synthetic sampling of EHG signal COMPUTERS IN BIOLOGY AND MEDICINE English Article Preterm prediction; Uterine electrohysterogram; Synthetic sampling; Sample balance coefficient DELIVERY; TERM Preterm labor is the leading cause of neonatal morbidity and mortality in newborns and has attracted significant research attention from many scientific areas. The relationship between uterine contraction and the underlying electrical activities makes uterine electrohysterogram (EHG) a promising direction for detecting and predicting preterm births. However, due to the scarcity of EHG signals, especially those leading to preterm births, synthetic algorithms have been used to generate artificial samples of preterm birth type in order to eliminate bias in the prediction towards normal delivery, at the expense of reducing the feature effectiveness in automatic preterm detection based on machine learning. To address this problem, we quantify the effect of synthetic samples (balance coefficient) on the effectiveness of features and form a general performance metric by using several feature scores with relevant weights that describe their contributions to class segregation. In combination with the activation/inactivation functions that characterize the effect of the abundance of training samples on the accuracy of the prediction of preterm and normal birth delivery, we obtained an optimal sample balance coefficient that compromises the effect of synthetic samples in removing bias toward the majority group (i.e., normal delivery and the side effect of reducing the importance of features). A more realistic predictive accuracy was achieved through a series of numerical tests on the publicly available TPEHG database, therefore demonstrating the effectiveness of the proposed method. [Xu, Jinshan; Chen, Zhenqin; Zhang, Jinpeng; Lu, Yanpei; Yang, Xi] Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China; [Xu, Jinshan] Res Ctr AI Social Expt, Zhejiang Lab, Hangzhou 311321, Peoples R China; [Pumir, Alain] ENS Lyon, Lab Phys, F-69007 Lyon, France Zhejiang University of Technology; Zhejiang Laboratory; Ecole Normale Superieure de Lyon (ENS de LYON) Yang, X (corresponding author), Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Peoples R China. xyang@zjut.edu.cn chen, zhenqin/0000-0001-8419-0638; XU, Jinshan/0000-0002-7707-9074; Pumir, Alain/0000-0001-9946-7353 National Natural Science Foundation of China [61873238, 62073293]; Zhejiang Provincial Natural Science Foundation of China [LY20F030018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work was supported by the National Natural Science Foundation of China under Grant No. 61873238, 62073293 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LY20F030018. 42 4 4 1 10 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0010-4825 1879-0534 COMPUT BIOL MED Comput. Biol. Med. SEP 2021.0 136.0 104644 10.1016/j.compbiomed.2021.104644 0.0 JUL 2021 8 Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology UR7PP 34271407.0 Green Submitted 2023-03-23 WOS:000696936800001 0
J Xu, XH; Shen, YH; Chen, WY; Gong, YM; Wang, HW Xu, Xianhao; Shen, Yaohan; Chen, Wanying (Amanda); Gong, Yeming; Wang, Hongwei Data-driven decision and analytics of collection and delivery point location problems for online retailers OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE English Article Data-driven decision making and analytics; Customer behavior data; Machine learning; Collection and delivery points; Facility location FACILITY LOCATION; PERSPECTIVE; NETWORKS; PICKUP; URBAN The location of collection and delivery points (CDPs), impacted by online customers' demand data, plays an important role for online retailers. While previous delivery points optimization researches do not use customer behavior data, we propose new models, integrating with customer behavior data analysis, to optimize collection and delivery points for online retailers. We explore a real customer behavior data and use totally 257,685 users' records (212,062 records for training set and 45,623 records test set). We first estimate the customer purchase probability by five data mining models. Based on the estimation results, we establish two facility location models to respectively optimize the attended and unattended CDPs locations with the objective of cost minimization. Our numerical experiments make a quantitative analysis of customer service level and location cost. Our results can further help online retailers to decide the suitable CDPs with trading off the consumer service level and the total logistics cost. We make interesting contributions: (i) we analyze real customer behavior data and find that gradient boosting trees algorithm outperform other four algorithms when estimating customers' purchase probabilities; (ii) We propose a new data-driven method integrating data mining models and facility location models to determine CDP locations for online retailers. (c) 2020 Elsevier Ltd. All rights reserved. [Xu, Xianhao; Shen, Yaohan; Wang, Hongwei] Huazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China; [Chen, Wanying (Amanda)] Zhejiang Gongshang Univ, Hangzhou 310018, Peoples R China; [Gong, Yeming] EMLYON Business Sch, 23 Ave Guy Collongue, F-69134 Ecully, France Huazhong University of Science & Technology; Zhejiang Gongshang University; EMLYON Business School Chen, WY (corresponding author), Zhejiang Gongshang Univ, Hangzhou 310018, Peoples R China. wanyingchen@mail.zjgsu.edu.cn GONG, Yeming/I-7148-2012 GONG, Yeming/0000-0001-9270-5507 National Natural Science Foundation of China [71620107002, 71821001, 71971095]; National Social Science Foundation of China [16ZDA013]; Ministry of Education, Humanities and Social Sciences of China [17YJC630013]; Zhejiang Natural Science Foundation [LQ19G030 0 04]; Qianjiang River Talent Scholarship [QJC1802002]; Business Intelligence Center of EMLYON National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Social Science Foundation of China; Ministry of Education, Humanities and Social Sciences of China; Zhejiang Natural Science Foundation(Natural Science Foundation of Zhejiang Province); Qianjiang River Talent Scholarship; Business Intelligence Center of EMLYON Yaohan Shen and Xianhao Xu are first author of this paper. We thank Haitao Li for discussion in the earlier version. This research was supported in part by the National Natural Science Foundation of China [grant number 71620107002, 71821001, 71971095], the National Social Science Foundation of China [grant number 16ZDA013], the Ministry of Education, Humanities and Social Sciences of China [grant number 17YJC630013] and Zhejiang Natural Science Foundation [grant number LQ19G030 0 04], Qianjiang River Talent Scholarship (grant number QJC1802002).Yeming GONG is supported by Business Intelligence Center of EMLYON. 38 10 10 13 69 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0305-0483 1873-5274 OMEGA-INT J MANAGE S Omega-Int. J. Manage. Sci. APR 2021.0 100.0 102280 10.1016/j.omega.2020.102280 0.0 JAN 2021 12 Management; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Operations Research & Management Science PY8GE 2023-03-23 WOS:000612278900015 0
J Huang, Y; Wang, XF; Zhang, Y; Chiavetta, D; Porter, AL Huang, Ying; Wang, Xuefeng; Zhang, Yi; Chiavetta, Denise; Porter, Alan L. Big data driven tech mining and ST&I management: an introduction SCIENTOMETRICS English Editorial Material GTM; Tech Mining; Competitive Technical Intelligence; Science, Technology & Innovation; Intelligent Bibliometrics Since the first Global Tech Mining (GTM) conference was held in Atlanta in 2011, the GTM conference has created a platform to connect tech mining researchers, exchange ideas and research progress, and promote collaborations. When it came to its 10th anniversary in 2020, COVID-19 forced the GTM conference into an online format. In tumultuous times for ST&I research activity, the GTM conference sought to focus on several issues: How to better collect and combine multiple large data sources? How to analyze these data effectively? And how to utilize these results more powerfully in ST&I management? In this collection, 15 papers are selected after evaluating by the science advisory committee, the guest editor team, and our peer review experts to address the following aspects regarding tech mining: (1) DATA: Maximizing the potential of traditional and novel data; (2) METHODS: Advancing and integrating methods; (3) APPLICATIONS: Innovative analyses translating to usefulintelligence. [Huang, Ying] Wuhan Univ, Ctr Studies Informat Resources, Sch Informat Management, Wuhan, Peoples R China; [Huang, Ying] Wuhan Univ, Ctr Sci Technol & Educ Assessment CSTEA, Wuhan, Peoples R China; [Huang, Ying] Katholieke Univ Leuven, Ctr R&D Monitoring ECOOM, Leuven, Belgium; [Huang, Ying] Katholieke Univ Leuven, Dept MSI, Leuven, Belgium; [Wang, Xuefeng] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China; [Zhang, Yi] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Sydney, NSW, Australia; [Chiavetta, Denise; Porter, Alan L.] Search Technol Inc, Peachtree Corners, GA USA; [Porter, Alan L.] Georgia Inst Technol, Program Sci Technol & Innovat Policy STIP, Atlanta, GA 30332 USA Wuhan University; Wuhan University; KU Leuven; KU Leuven; Beijing Institute of Technology; University of Technology Sydney; University System of Georgia; Georgia Institute of Technology Huang, Y (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, Sch Informat Management, Wuhan, Peoples R China.;Huang, Y (corresponding author), Wuhan Univ, Ctr Sci Technol & Educ Assessment CSTEA, Wuhan, Peoples R China.;Huang, Y (corresponding author), Katholieke Univ Leuven, Ctr R&D Monitoring ECOOM, Leuven, Belgium.;Huang, Y (corresponding author), Katholieke Univ Leuven, Dept MSI, Leuven, Belgium. ying.huang@whu.edu.cn Zhang, Yi/0000-0002-7731-0301 7 0 0 11 15 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0138-9130 1588-2861 SCIENTOMETRICS Scientometrics SEP 2022.0 127.0 9 5227 5231 10.1007/s11192-022-04507-2 0.0 AUG 2022 5 Computer Science, Interdisciplinary Applications; Information Science & Library Science Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Information Science & Library Science 4L1UW 36036022.0 Bronze, Green Accepted 2023-03-23 WOS:000843996000001 0
C Cai, SQ; Su, EZ; Song, YH; Xie, LH; Li, HZ Int Speech Commun Assoc Cai, Siqi; Su, Enze; Song, Yonghao; Xie, Longhan; Li, Haizhou Low Latency Auditory Attention Detection with Common Spatial Pattern Analysis of EEG Signals INTERSPEECH 2020 Interspeech English Proceedings Paper Interspeech Conference OCT 25-29, 2020 Shanghai, PEOPLES R CHINA Auditory attention detection (AAD); convolutional neural networks (CNN); electroencephalogram (EEG); common spatial pattern (CSP) SPEECH A listener listens to one speech stream at a time in a multi-speaker scenario. EEG-based auditory attention detection (AAD) aims to identify to which speech stream the listener has attended using EEG signals. The performance of linear modeling approaches is limited due to the non-linear nature of the human auditory perception. Furthermore, the real-world applications call for low latency AAD solutions in noisy environments. In this paper, we propose to adopt common spatial pattern (CSP) analysis to enhance the discriminative ability of EEG signals. We study the use of convolutional neural network (CNN) as the non-linear solution. The experiments show that it is possible to decode auditory attention within 2 seconds, with a competitive accuracy of 80.2%, even in noisy acoustic environments. The results are encouraging for brain-computer interfaces, such as hearing aids, which require real-time responses, and robust AAD in complex acoustic environments. [Cai, Siqi; Su, Enze; Song, Yonghao; Xie, Longhan] South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou, Peoples R China; [Cai, Siqi; Li, Haizhou] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore; [Li, Haizhou] Univ Bremen, Machine Listening Lab, Bremen, Germany South China University of Technology; National University of Singapore; University of Bremen Cai, SQ (corresponding author), South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou, Peoples R China.;Cai, SQ (corresponding author), Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore. siqi.cai@u.nus.edu; Enze.Su@mail.scut.edu.cn; Yonghao.Song@mail.scut.edu.cn; melhxie@scut.edu.cn; haizhou.li@nus.edu.sg Human-Robot Interaction Phase 1 [192 25 00054]; National Research Foundation (NRF) Singapore; AI Speech Lab [AISG-100E-2018-006]; NRF Singapore; Human Robot Collaborative AI for AME [A18A2b0046]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Human-Robot Interaction Phase 1; National Research Foundation (NRF) Singapore(National Research Foundation, Singapore); AI Speech Lab; NRF Singapore(National Research Foundation, Singapore); Human Robot Collaborative AI for AME; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)) This work is supported by Human-Robot Interaction Phase 1 (Grant No.192 25 00054), National Research Foundation (NRF) Singapore under the National Robotics Programme; AI Speech Lab (Award No. AISG-100E-2018-006), NRF Singapore under the AI Singapore Programme; Human Robot Collaborative AI for AME (Grant No. A18A2b0046), NRF Singapore. The work by H. Li is also partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy (University Allowance, EXC 2077, University of Bremen, Germany). 34 10 10 1 1 ISCA-INT SPEECH COMMUNICATION ASSOC BAIXAS C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE 2308-457X INTERSPEECH 2020.0 2772 2776 10.21437/Interspeech.2020-2496 0.0 5 Audiology & Speech-Language Pathology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering Conference Proceedings Citation Index - Science (CPCI-S) Audiology & Speech-Language Pathology; Computer Science BT4QT 2023-03-23 WOS:000833594102181 0
J Rhif, M; Ben Abbes, A; Farah, IR; Martinez, B; Sang, YF Rhif, Manel; Ben Abbes, Ali; Farah, Imed Riadh; Martinez, Beatriz; Sang, Yanfang Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review APPLIED SCIENCES-BASEL English Review wavelet transform; non stationary; time series; time-frequency; decomposition; applied sciences EMPIRICAL MODE DECOMPOSITION; INFORMATION; EXTRACTION; SPECTRUM; RETURNS; MARKETS; NOISE; ECG Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed. [Rhif, Manel; Ben Abbes, Ali; Farah, Imed Riadh] Ecole Natl Sci Informat, Lab RIADI, La Manouba 2010, Tunisia; [Ben Abbes, Ali] Univ Sherbrooke, Ctr Applicat & Rech Teledetect CARTEL, Sherbrooke, PQ J1K 2R1, Canada; [Farah, Imed Riadh] IMT Atlantique, Lab ITI Dept, F-29238 Brest, France; [Martinez, Beatriz] Univ Valencia, Dept Fis Terra & Termodinam, E-46100 Valencia, Spain; [Sang, Yanfang] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China Universite de la Manouba; University of Sherbrooke; IMT - Institut Mines-Telecom; IMT Atlantique; University of Valencia; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS Rhif, M (corresponding author), Ecole Natl Sci Informat, Lab RIADI, La Manouba 2010, Tunisia.;Sang, YF (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China. manel.rhif@ensi-uma.tn; ali.ben.abbes@usherbrooke.ca; riadh.farah@ensi.rnu.tn; Beatriz.Martinez@uv.es; sangyf@igsnrr.ac.cn farah, imed riadh/AAL-7809-2021; sang, yan-fang/I-8277-2016; MARTINEZ, BEATRIZ/F-7800-2016 farah, imed riadh/0000-0001-9114-5659; sang, yan-fang/0000-0001-6770-9311; MARTINEZ, BEATRIZ/0000-0002-6354-9899; Ben Abbes, Ali/0000-0001-5714-7562 National Key Research and Development Program [2017YFA0603702]; National Natural Science Foundation of China [91647110]; Youth Innovation Promotion Association CAS [2017074] National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Youth Innovation Promotion Association CAS This study was financially supported by the National Key Research and Development Program (No. 2017YFA0603702), the National Natural Science Foundation of China (No. 91647110), and the Youth Innovation Promotion Association CAS (No. 2017074). 95 123 124 18 84 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel APR 1 2019.0 9.0 7 1345 10.3390/app9071345 0.0 22 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics HW2VN Green Submitted, gold 2023-03-23 WOS:000466547500080 0
J Bouvier, C; Souedet, N; Levy, J; Jan, C; You, Z; Herard, AS; Mergoil, G; Rodriguez, BH; Clouchoux, C; Delzescaux, T Bouvier, C.; Souedet, N.; Levy, J.; Jan, C.; You, Z.; Herard, A-S; Mergoil, G.; Rodriguez, B. H.; Clouchoux, C.; Delzescaux, T. Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain SCIENTIFIC REPORTS English Article TEXTURE CLASSIFICATION In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research. [Bouvier, C.; Souedet, N.; Jan, C.; You, Z.; Herard, A-S; Clouchoux, C.; Delzescaux, T.] Univ Paris Saclay, CNRS, CEA, MIRCen,Lab Malad Neurodegenerat, Fontenay Aux Roses, France; [Bouvier, C.; Mergoil, G.; Rodriguez, B. H.; Clouchoux, C.] Witsee, Paris, France; [Levy, J.] Hop Raymond Poincare, AP HP, Serv Med Phys & Readaptat, Garches, France; [Levy, J.] UVSQ, INSERM, Handicap Neuromusculaire, UMR 1179, Montigny Le Bretonneux, France; [You, Z.] Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian, Peoples R China CEA; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Raymond-Poincare - APHP; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; Xi'an University of Technology Delzescaux, T (corresponding author), Univ Paris Saclay, CNRS, CEA, MIRCen,Lab Malad Neurodegenerat, Fontenay Aux Roses, France. thierry.delzescaux@cea.fr Bouvier, Clement/FCE-0966-2022 Bouvier, Clement/0000-0003-2920-7585 French national funds PIA2 program [P112331-3422142] French national funds PIA2 program This work is supported by the French national funds PIA2 program under contract No. P112331-3422142 and NEOXIA. This work was granted access to the HPC resources of TGCC under the allocation 2021 [A0040310374] made by GENCI. 70 0 0 1 3 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep NOV 26 2021.0 11.0 1 22973 10.1038/s41598-021-02344-6 0.0 17 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics XD8AO 34836996.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000722925300030 0
J Wu, H; Luo, WT; Lin, AQ; Hao, FH; Olteanu-Raimond, AM; Liu, LF; Li, Y Wu, Hao; Luo, Wenting; Lin, Anqi; Hao, Fanghua; Olteanu-Raimond, Ana-Maria; Liu, Lanfa; Li, Yan SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images COMPUTERS ENVIRONMENT AND URBAN SYSTEMS English Article Urban functional zone mapping; SALT features; Ensemble learning; Volunteered geographic information REMOTE-SENSING IMAGERY; GOOGLE EARTH ENGINE; LAND-COVER CHANGE; SOCIOECONOMIC FEATURES; MOBILE PHONE; MULTISOURCE; SPACE; EXTRACTION; LANDSCAPE; STREET Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. [Wu, Hao; Luo, Wenting; Lin, Anqi; Hao, Fanghua; Liu, Lanfa; Li, Yan] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, 152 Luoyu Rd, Wuhan, Peoples R China; [Wu, Hao; Luo, Wenting; Lin, Anqi; Hao, Fanghua; Liu, Lanfa; Li, Yan] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan, Peoples R China; [Olteanu-Raimond, Ana-Maria] Univ Gustave Eiffel, LASTIG, ENSG, IGN, St Mande, France; [Lin, Anqi] Cent China Normal Univ, Room 318,10 Bldg,152 Luoyu Rd, Wuhan, Peoples R China Central China Normal University; Central China Normal University; Universite Gustave-Eiffel; Central China Normal University Lin, AQ (corresponding author), Cent China Normal Univ, Room 318,10 Bldg,152 Luoyu Rd, Wuhan, Peoples R China. haowu@ccnu.edu.cn; wentingluo@mails.ccnu.edu.cn; linanqi@mails.ccnu.edu.cn; fhhao@ccnu.edu.cn; maria.raimond@ign.fr; lanfa@ccnu.edu.cn; linda@mails.ccnu.edu.cn National Natural Science Foundation of China; Postdoctoral Innovation Talents Sup- port Program of China; China Postdoctoral Science Foundation; Fundamental Research Funds for the Central Universities; [42201468]; [42071358]; [BX20220128]; [2022M721283]; [CCNU22QN018] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Postdoctoral Innovation Talents Sup- port Program of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); ; ; ; ; Acknowledgements This work was supported by the National Natural Science Foundation of China [42201468, 42071358] , Postdoctoral Innovation Talents Sup- port Program of China [BX20220128] , China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018] . 77 0 0 36 36 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0198-9715 1873-7587 COMPUT ENVIRON URBAN Comput. Environ. Urban Syst. MAR 2023.0 100.0 101921 10.1016/j.compenvurbsys.2022.101921 0.0 16 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science; Regional & Urban Planning Social Science Citation Index (SSCI) Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science; Public Administration 7B5ZA 2023-03-23 WOS:000899209700001 0
J Chen, LH; Raitio, T; Valentini-Botinhao, C; Ling, ZH; Yamagishi, J Chen, Ling-Hui; Raitio, Tuomo; Valentini-Botinhao, Cassia; Ling, Zhen-Hua; Yamagishi, Junichi A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Deep generative architecture; hidden Markov model (HMM); modulation spectrum; postfilter; segmental quality; speech synthesis VOICE CONVERSION; NEURAL-NETWORKS The generated speech of hidden Markov model (HMM)-based statistical parametric speech synthesis still sounds muffled. One cause of this degradation in speech quality may be the loss of fine spectral structures. In this paper, we propose to use a deep generative architecture, a deep neural network (DNN) generatively trained, as a postfilter. The network models the conditional probability of the spectrum of natural speech given that of synthetic speech to compensate for such gap between synthetic and natural speech. The proposed probabilistic postfilter is generatively trained by cascading two restricted Boltzmann machines (RBMs) or deep belief networks (DBNs) with one bidirectional associative memory (BAM). We devised two types of DNN postfilters: one operating in the mel-cepstral domain and the other in the higher dimensional spectral domain. We compare these two new data-driven postfilters with other types of postfilters that are currently used in speech synthesis: a fixed mel-cepstral based postfilter, the global variance based parameter generation, and the modulation spectrum-based enhancement. Subjective evaluations using the synthetic voices of a male and female speaker confirmed that the proposed DNN-based postfilter in the spectral domain significantly improved the segmental quality of synthetic speech compared to that with conventional methods. [Chen, Ling-Hui; Ling, Zhen-Hua] Univ Sci & Technol China, NELSLIP, Hefei 230027, Peoples R China; [Chen, Ling-Hui] iFLYTEK Co Ltd, Hefei 230088, Peoples R China; [Raitio, Tuomo] Aalto Univ, Dept Signal Proc & Acoust, FI-02015 Espoo, Finland; [Valentini-Botinhao, Cassia; Yamagishi, Junichi] Univ Edinburgh, CSTR, Edinburgh EH8 9AB, Midlothian, Scotland Chinese Academy of Sciences; University of Science & Technology of China, CAS; Aalto University; University of Edinburgh Chen, LH (corresponding author), Univ Sci & Technol China, NELSLIP, Hefei 230027, Peoples R China. chenlh@mail.ustc.edu.cn; tuomo.raitio@aalto.fi; cvbotinh@inf.ed.ac.uk; zhling@ustc.edu.cn; jyamagis@inf.ed.ac.uk EU FP7 (FP7) project [287678]; Academy of Finland [256961, 284671]; National Natural Science Foundation of China [61273032]; EPSRC through Programme [EP/I031022/1 (NST), EP/J002526/1 (CAF)]; Engineering and Physical Sciences Research Council [EP/I031022/1, EP/J002526/1] Funding Source: researchfish; EPSRC [EP/I031022/1, EP/J002526/1] Funding Source: UKRI EU FP7 (FP7) project; Academy of Finland(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EPSRC through Programme(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) This work was supported by the EU FP7 (FP7/2007-13) project under Grant 287678 (Simple4All), by the Academy of Finland under Grants 256961 and 284671, by the National Natural Science Foundation of China under Grant 61273032), and by EPSRC through Programme Grants EP/I031022/1 (NST) and EP/J002526/1 (CAF). 42 33 33 0 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. NOV 2015.0 23.0 11 2003 2014 10.1109/TASLP.2015.2461448 0.0 12 Acoustics; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Acoustics; Engineering CQ8CY hybrid, Green Published 2023-03-23 WOS:000360835000025 0
J Guo, JJ; Ling, N; Li, Y; Li, KS; Ning, HL; Shen, QR; Guo, SW; Vandenkoornhuyse, P Guo, Junjie; Ling, Ning; Li, Yong; Li, Kaisong; Ning, Huiling; Shen, Qirong; Guo, Shiwei; Vandenkoornhuyse, Philippe Seed-borne, endospheric and rhizospheric core microbiota as predictors of plant functional traits across rice cultivars are dominated by deterministic processes NEW PHYTOLOGIST English Article bacteria; community assembly; plant– microbiota association; rhizosphere; root endosphere; seed habitat NICHE DIFFERENTIATION; FUNGAL COMMUNITIES; HOST-MICROBE; BACTERIAL; DIVERSITY; TRANSMISSION; MICROORGANISMS; DOMESTICATION; ENDOPHYTES; DISPERSION A host-plant and its associated microbiota depend on one another. However, the assembly process and the functioning of host-associated microbiota are poorly understood. Herein, rice was used as model plant to investigate the assemblage of bacterial microbiota, including those in the seed, root endosphere and rhizosphere. We also assessed the degree to which endosphere and rhizosphere communities were influenced by vertical transmission through seed and identified the core microbes that potentially associated with plant phenotypic properties. Plant microhabitat, rather than subspecies type, was the major driver shaping plant-associated bacterial microbiota. Deterministic processes were primarily responsible for community assembly in all microhabitats. The influence of vertical transmission from seed to root-associated bacterial communities appeared to be quite weak (endosphere) or even absent (rhizosphere). A core microbial community composed of 15 generalist species persisted across different microhabitats and represented key connectors in networks. Host-plant functional traits were linked to the relative abundance of these generalist core microbes and could be predicted from them using machine learning algorithms. Overall, bacterial microbiota is assembled by host-plant interactions in a deterministic-based manner. This study enhances our understanding of the driving mechanisms and associations of microbiota in various plant microhabitats and provides new perspectives to improve plant performance. [Guo, Junjie; Ling, Ning; Li, Kaisong; Ning, Huiling; Shen, Qirong; Guo, Shiwei] Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Solid Organ Waste, Jiangsu Prov Key Lab Solid Organ Waste Utilizat, Nanjing 210095, Jiangsu, Peoples R China; [Ling, Ning; Vandenkoornhuyse, Philippe] Univ Rennes 1, EcoBio, UMR 6553, CNRS, Campus Beaulieu Ave Gen Leclerc, F-35042 Rennes, France; [Li, Yong] Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Hubei, Peoples R China Nanjing Agricultural University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Rennes; Huazhong Agricultural University Ling, N (corresponding author), Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Solid Organ Waste, Jiangsu Prov Key Lab Solid Organ Waste Utilizat, Nanjing 210095, Jiangsu, Peoples R China.;Ling, N (corresponding author), Univ Rennes 1, EcoBio, UMR 6553, CNRS, Campus Beaulieu Ave Gen Leclerc, F-35042 Rennes, France. nling@njau.edu.cn Ling, Ning/AAF-3633-2019; Guo, Junjie/CAG-8295-2022 Ling, Ning/0000-0003-1250-4073; Guo, Junjie/0000-0002-8670-3924; Li, Yong/0000-0002-3595-6673 National Natural Science Foundation of China [41977080, 31902114, U2003210]; Natural Science Foundation of Jiangsu Province [BK20190543]; China Postdoctoral Science Foundation [2019M651861]; Young Elite Scientists Sponsorship Program by CAST [2019QNRC001]; Innovative Research Team Development Plan of the Ministry of Education of China [IRT_17R56]; Fundamental Research Funds for the Central Universities [KYT201802]; French National programme EC2CO (Ecosphere Continentale et Cotiere) of the CNRS [12669] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Young Elite Scientists Sponsorship Program by CAST; Innovative Research Team Development Plan of the Ministry of Education of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); French National programme EC2CO (Ecosphere Continentale et Cotiere) of the CNRS This work was supported by the National Natural Science Foundation of China (41977080, 31902114, U2003210), the Natural Science Foundation of Jiangsu Province (BK20190543), the China Postdoctoral Science Foundation (2019M651861), the Young Elite Scientists Sponsorship Program by CAST (2019QNRC001), the Innovative Research Team Development Plan of the Ministry of Education of China (grant no. IRT_17R56), and the Fundamental Research Funds for the Central Universities (grant no. KYT201802). This work was also supported by a grant from the French National programme EC2CO (Ecosphere Continentale et Cotiere) of the CNRS (HOLOBIONT - no. 12669). We especially thank Dr Matthieu Barret from Institut National de la Recherche Agronomique, France, for valuable comments on the manuscript. We also thank American Journal Experts for polishing the language. Finally, we are grateful for the constructive comments of three anonymous reviewers. The authors declare that they have no conflicts of interest. 87 24 24 36 147 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0028-646X 1469-8137 NEW PHYTOL New Phytol. JUN 2021.0 230.0 5 2047 2060 10.1111/nph.17297 0.0 MAR 2021 14 Plant Sciences Science Citation Index Expanded (SCI-EXPANDED) Plant Sciences RU4BS 33626176.0 Green Published 2023-03-23 WOS:000634729500001 0
J Toivonen, L; Karppinen, S; Schuez-Havupalo, L; Waris, M; He, QS; Hoffman, KL; Petrosino, JF; Dumas, O; Camargo, CA; Hasegawa, K; Peltola, V Toivonen, Laura; Karppinen, Sinikka; Schuez-Havupalo, Linnea; Waris, Matti; He, Qiushui; Hoffman, Kristi L.; Petrosino, Joseph F.; Dumas, Orianne; Camargo, Carlos A., Jr.; Hasegawa, Kohei; Peltola, Ville Longitudinal Changes in Early Nasal Microbiota and the Risk of Childhood Asthma PEDIATRICS English Article AIRWAY MICROBIOTA; PATHOGENIC BACTERIA; RESPIRATORY-TRACT; BRONCHIOLITIS; INFECTION; SEVERITY; SYMPTOMS; PROFILE; HEALTH; COHORT In this birth cohort of 704 children, we identified distinct longitudinal nasal microbiota profiles in early life that were associated with differential risks of developing asthma. OBJECTIVES: Although the airway microbiota is a highly dynamic ecology, the role of longitudinal changes in airway microbiota during early childhood in asthma development is unclear. We aimed to investigate the association of longitudinal changes in early nasal microbiota with the risk of developing asthma. METHODS: In this prospective, population-based birth cohort study, we followed children from birth to age 7 years. The nasal microbiota was tested by using 16S ribosomal RNA gene sequencing at ages 2, 13, and 24 months. We applied an unsupervised machine learning approach to identify longitudinal nasal microbiota profiles during age 2 to 13 months (the primary exposure) and during age 2 to 24 months (the secondary exposure) and examined the association of these profiles with the risk of physician-diagnosed asthma at age 7 years. RESULTS: Of the analytic cohort of 704 children, 57 (8%) later developed asthma. We identified 4 distinct longitudinal nasal microbiota profiles during age 2 to 13 months. In the multivariable analysis, compared with the persistentMoraxelladominance profile during age 2 to 13 months, the persistentMoraxellasparsity profile was associated with a significantly higher risk of asthma (adjusted odds ratio, 2.74; 95% confidence interval, 1.20-6.27). Similar associations were observed between the longitudinal changes in nasal microbiota during age 2 to 24 months and risk of asthma. CONCLUSIONS: Children with an altered longitudinal pattern in the nasal microbiota during early childhood had a high risk of developing asthma. Our data guide the development of primary prevention strategies (eg, early identification of children at high risk and modification of microbiota) for childhood asthma. These observations present a new avenue for risk modification for asthma (eg, microbiota modification). [Toivonen, Laura; Camargo, Carlos A., Jr.; Hasegawa, Kohei] Massachusetts Gen Hosp, Dept Emergency Med, 125 Nashua St,Suite 920, Boston, MA 02114 USA; [Toivonen, Laura; Camargo, Carlos A., Jr.; Hasegawa, Kohei] Harvard Univ, Harvard Med Sch, Boston, MA 02115 USA; [Toivonen, Laura; Karppinen, Sinikka; Schuez-Havupalo, Linnea; Peltola, Ville] Turku Univ Hosp, Dept Paediat & Adolescent Med, Turku, Finland; [Toivonen, Laura; Karppinen, Sinikka; Schuez-Havupalo, Linnea; Peltola, Ville] Univ Turku, Turku, Finland; [Waris, Matti] Univ Turku, Inst Biomed, Virol Unit, Turku, Finland; [He, Qiushui] Univ Turku, Dept Microbiol Virol & Immunol, Turku, Finland; [He, Qiushui] Univ Turku, Inst Biomed, Turku, Finland; [He, Qiushui] Capital Med Univ, Dept Med Microbiol, Beijing, Peoples R China; [Hoffman, Kristi L.; Petrosino, Joseph F.] Baylor Coll Med, Dept Mol Virol & Microbiol, Alkek Ctr Metagen & Microbiome Res, Houston, TX 77030 USA; [Dumas, Orianne] INSERM, U1168, VIMA Aging & Chron Dis Epidemiol & Publ Hlth Appr, Villejuif, France; [Dumas, Orianne] Univ Versailles St Quentin En Yvelines, UMR S 1168, Montigny Le Bretonneux, France Harvard University; Massachusetts General Hospital; Harvard University; Harvard Medical School; University of Turku; University of Turku; University of Turku; University of Turku; University of Turku; Capital Medical University; Baylor College of Medicine; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite Paris Saclay Toivonen, L (corresponding author), Massachusetts Gen Hosp, Dept Emergency Med, 125 Nashua St,Suite 920, Boston, MA 02114 USA. laura.toivonen@utu.fi Waris, Matti E/A-6418-2008; Camargo, Carlos A./C-2145-2008; Dumas, Orianne/R-8636-2016 Camargo, Carlos A./0000-0002-5071-7654; Dumas, Orianne/0000-0001-8423-2826 University of Turku; Abo Akademi University; Turku University Hospital; Academy of Finland [123571, 140251, 277535, 324926]; Finnish Medical Foundation; Paivikki and Sakari Sohlberg Foundation; Foundation for Pediatric Research; Emil Aaltonen Foundation; Paulo Foundation; Allergy Research Foundation; Juho Vainio Foundation; Tampere Tuberculosis Foundation; Finnish Anti-Tuberculosis Association; Orion Research Foundation; Maud Kuistila Memorial Foundation; Specified Government Transfers, Hospital District of Southwest Finland; Vaino and Laina Kivi Foundation; Academy of Finland (AKA) [324926] Funding Source: Academy of Finland (AKA) University of Turku; Abo Akademi University; Turku University Hospital; Academy of Finland(Academy of Finland); Finnish Medical Foundation; Paivikki and Sakari Sohlberg Foundation; Foundation for Pediatric Research; Emil Aaltonen Foundation; Paulo Foundation; Allergy Research Foundation; Juho Vainio Foundation; Tampere Tuberculosis Foundation; Finnish Anti-Tuberculosis Association; Orion Research Foundation; Maud Kuistila Memorial Foundation; Specified Government Transfers, Hospital District of Southwest Finland; Vaino and Laina Kivi Foundation; Academy of Finland (AKA)(Academy of FinlandFinnish Funding Agency for Technology & Innovation (TEKES)) Supported by the University of Turku; the Abo Akademi University; the Turku University Hospital; the Academy of Finland (grants 123571, 140251, 277535, and 324926); the Finnish Medical Foundation; the Paivikki and Sakari Sohlberg Foundation; the Foundation for Pediatric Research; the Emil Aaltonen Foundation; the Paulo Foundation; the Allergy Research Foundation; the Juho Vainio Foundation; the Tampere Tuberculosis Foundation; the Finnish Anti-Tuberculosis Association; the Orion Research Foundation; the Maud Kuistila Memorial Foundation; Research Funds from Specified Government Transfers, Hospital District of Southwest Finland; and the Vaino and Laina Kivi Foundation. The funding sources had no involvement in the study design, collection, analysis, interpretation of data, writing of the report, or the decision to submit the article for publication. 41 15 15 1 8 AMER ACAD PEDIATRICS ELK GROVE VILLAGE 141 NORTH-WEST POINT BLVD,, ELK GROVE VILLAGE, IL 60007-1098 USA 0031-4005 1098-4275 PEDIATRICS Pediatrics OCT 2020.0 146.0 4 e20200421 10.1542/peds.2020-0421 0.0 11 Pediatrics Science Citation Index Expanded (SCI-EXPANDED) Pediatrics OG1VI 32934151.0 2023-03-23 WOS:000581680200011 0
J Guo, B; Liu, Y; Ouyang, Y; Zheng, VW; Zhang, DQ; Yu, ZW Guo, Bin; Liu, Yan; Ouyang, Yi; Zheng, Vincent W.; Zhang, Daqing; Yu, Zhiwen Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey IEEE ACCESS English Article Crowdsourced business intelligence; consumer behaviors; competitive intelligence; crowd intelligence; commercial site recommendation; brand trending STREET CENTRALITY; ONLINE REVIEWS; BIG DATA; RETAIL; LOCATION; BEHAVIOR; RANKING; NETWORK; DESIGN Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI. [Guo, Bin; Liu, Yan; Ouyang, Yi; Yu, Zhiwen] Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China; [Zheng, Vincent W.] Adv Digital Sci Ctr, Singapore 138632, Singapore; [Zhang, Daqing] Telecom SudParis, Inst Mines Telecom, Dept Reseaux & Serv Multimedia Mobiles, F-91011 Evry, France Northwestern Polytechnical University; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Guo, B (corresponding author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China. guobin.keio@gmail.com Guo, Bin/0000-0001-6097-2467; Yu, Zhiwen/0000-0002-9905-3238 National Key R&D Program of China [2017YFB1001800]; National Natural Science Foundation of China [61772428, 61725205] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China under Grant 2017YFB1001800, and in part by the National Natural Science Foundation of China under Grant 61772428 and Grant 61725205. 159 10 10 6 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 26606 26630 10.1109/ACCESS.2019.2901027 0.0 25 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications HQ0YH gold, Green Submitted 2023-03-23 WOS:000462121800001 0
J Ma, YP; Jiang, XY; Xia, ZQ; Gabbouj, M; Feng, XY Ma, Yupeng; Jiang, Xiaoyue; Xia, Zhaoqiang; Gabbouj, Moncef; Feng, Xiaoyi CasQNet: Intrinsic Image Decomposition Based on Cascaded Quotient Network IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Feature extraction; Image decomposition; Image reconstruction; Task analysis; Lighting; Shape; Image analysis; Intrinsic image; deep neural network; cascaded network; quotient network; image pyramid; U-net RETINEX Intrinsic image analysis plays an important role for image understanding, since it can provide accurate reflectance, shape and illumination information of the scene. However, intrinsic image analysis is an ill-posed problem which need to apply extra constrains for the decomposition of reflectance image and shading image from a single image. Recently deep neural networks are introduced for intrinsic image analysis, which can produce two intrinsic components simultaneously. In fact, the mutually exclusive relationship between reflectance image and shading image is not only a constraint for decomposition but also can improve the decomposition results. However, this relationship is always omitted in the current networks. In order to address this problem, we propose a novel deep network called as Cascaded Quotient Network (CasQNet) for intrinsic image decomposition. The CasQNet consists of two sub-networks: a Pyramid Mini-U-Net (PyNet) that specifically extracts the reflectance image in multi-scale and a Shading Optimization Network (SoNet) that optimizes the resulting shading. These two sub-networks are cascaded by a quotient operation, which directly enforces the mutually exclusive relationship between reflectance image and shading image in the network architecture. In PyNet, the task of reconstructing reflectance image is achieved by a series of nested multi-scale U-Nets, which simplified the learning task for each U-Net. SoNet is designed to address the unsmooth and blur problems of extreme points caused by the quotient operation. PyNet and SoNet are trained alternately and finally jointed in cascaded structure. Furthermore, we combine multiple loss functions, which consist of data loss, correlation loss and reconstruction loss, for improving the learning effectiveness. To evaluate our proposed algorithm, extensive experiments are performed on three datasets, i.e., ShapeNet, BOLD Surface and MIT Intrinsic Image datasets. Qualitative and quantitative results show that our model achieves the best performance compared to the state-of-the-art methods. [Ma, Yupeng; Jiang, Xiaoyue; Xia, Zhaoqiang; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Peoples R China; [Gabbouj, Moncef] Tampere Univ Technol, Dept Signal Proc, Tampere 33720, Finland Northwestern Polytechnical University; Tampere University Feng, XY (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Peoples R China. mayupenga@mail.nwpu.edu.cn; xjiang@nwpu.edu.cn; zxia@nwpu.edu.cn; fengxiao@nwpu.edu.cn Xia, Zhaoqiang/AAC-4021-2019; Gabbouj, Moncef/G-4293-2014 Xia, Zhaoqiang/0000-0003-0630-3339; Gabbouj, Moncef/0000-0002-9788-2323 National Natural Science Foundation of China [61502388, 61702419]; Key Research and Development Program of Shaanxi [2018ZDXM-GY-186, 2020GY-050] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Shaanxi This work was supported in part by the National Natural Science Foundation of China under Grant 61502388 and Grant 61702419; and in part by the Key Research and Development Program of Shaanxi under Grant 2018ZDXM-GY-186 and Grant 2020GY-050. This article was recommended by Associate Editor L. Marcenaro. 55 3 3 2 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. JUL 2021.0 31.0 7 2661 2674 10.1109/TCSVT.2020.3024687 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering TF3AO 2023-03-23 WOS:000670583300012 0
J Jia, YZ; Liu, BW; Dou, WC; Xu, XL; Zhou, XK; Qi, LY; Yan, Z Jia, Yongzhe; Liu, Bowen; Dou, Wanchun; Xu, Xiaolong; Zhou, Xiaokang; Qi, Lianyong; Yan, Zheng CroApp: A CNN-Based Resource Optimization Approach in Edge Computing Environment IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS English Article Computational modeling; Optimization; Servers; Adaptation models; Solid modeling; Surveillance; Edge computing; Computation sharing; convolutional neural networks (CNNs); edge computing; model pruning; resource optimization With the emergence of various convolutional neural network (CNN)-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimizes the CNN model on edge servers and deploys the optimized model on devices in an edge computing environment. However, most of them only optimize the resource consumption within or across models solely, whereas neglecting the other side. In this article, we propose a novel CNN-based resource optimization approach (CroApp) that not only optimizes the resource consumption within the CNN model but also pays attention to resource optimization across the applications. Specifically, we adopt model compression as the inner-model optimization method, as well as computation sharing as the intermodel optimization method. First, during inner-model optimization, the CroApp prunes unnecessary parameters within the model on edge servers to reduce the scale of the model. Then, during intermodel optimization, the CroApp trains a set of shareable models based on the pruned model and sends these shareable models to end devices. Finally, the CroApp adaptively adjusts the shared models to reduce resource consumption. The experimental results show that the CroApp outperforms the state-of-the-art approaches in terms of resource reduction, scalability, and application performance. [Jia, Yongzhe; Liu, Bowen; Dou, Wanchun] Nanjing Univ, Dept Comp Sci & Technol, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China; [Xu, Xiaolong] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China; [Zhou, Xiaokang] Shiga Univ, Fac Data Sci, Hikone 5228522, Japan; [Zhou, Xiaokang] RIKEN, Ctr Adv Intelligence Project, Tokyo 1030027, Japan; [Qi, Lianyong] Qufu Normal Univ, Sch Comp Sci, Rizhao 276826, Peoples R China; [Yan, Zheng] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China; [Yan, Zheng] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland Nanjing University; Nanjing University of Information Science & Technology; Shiga University; RIKEN; Qufu Normal University; Xidian University; Xidian University; Aalto University Zhou, XK (corresponding author), Shiga Univ, Fac Data Sci, Hikone 5228522, Japan.;Zhou, XK (corresponding author), RIKEN, Ctr Adv Intelligence Project, Tokyo 1030027, Japan.;Yan, Z (corresponding author), Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China.;Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland. jiayz@smail.nju.edu.cn; liubw@smail.nju.edu.cn; douwc@nju.edu.cn; xlxu@nuist.edu.cn; zhou@biwako.shiga-u.ac.jp; lianyongqi@qfnu.edu.cn; zyan@xidian.edu.cn Jia, Yongzhe/GVT-8365-2022; yang, zheng/HGC-7753-2022; zheng, yan/GQY-6668-2022 Liu, Bowen/0000-0001-5949-9756; Yan, Zheng/0000-0002-9697-2108; Xu, Xiaolong/0000-0003-4879-9803 National Key Research and Development Program of China [2020YFB1707601] National Key Research and Development Program of China This work was supported by the National Key Research and Development Program of China under Grant 2020YFB1707601. 30 1 1 4 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1551-3203 1941-0050 IEEE T IND INFORM IEEE Trans. Ind. Inform. SEP 2022.0 18.0 9 6300 6307 10.1109/TII.2022.3154473 0.0 8 Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 2D5RG Green Accepted 2023-03-23 WOS:000811603400060 0
J Cao, L; Wang, YF; Zhang, B; Jin, Q; Vasilakos, AV Cao, Liang; Wang, Yufeng; Zhang, Bo; Jin, Qun; Vasilakos, Athanasios V. GCHAR: An efficient Group-based Context-aware human activity recognition on smartphone JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING English Article Human Activity Recognition (HAR); Hierarchical classifier; Context awareness; Machine learning With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including health monitoring, self-managing system and fitness tracking. However, one of the main issues of the existing HAR schemes is that the classification accuracy is relatively low, and in order to improve the accuracy, high computation overhead is needed. In this paper, an efficient Group based Context-aware classification method for human activity recognition on smartphones, GCHAR is proposed, which exploits hierarchical group-based scheme to improve the classification efficiency, and reduces the classification error through context awareness rather than the intensive computation. Specifically, GCHAR designs the two-level hierarchical classification structure, i.e., inter-group and inner group, and utilizes the previous state and transition logic (so-called context awareness) to detect the transitions among activity groups. In comparison with other popular classifiers such as RandomTree, Bagging, J48, BayesNet, KNN and Decision Table, thorough experiments on the realistic dataset (UCI HAR repository) demonstrate that GCHAR achieves the best classification accuracy, reaching 94.1636%, and time consumption in training stage of GCHAR is four times shorter than the simple Decision Table and is decreased by 72.21% in classification stage in comparison with BayesNet. (C) 2017 Elsevier Inc. All rights reserved. [Cao, Liang; Wang, Yufeng; Zhang, Bo] Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China; [Jin, Qun] Waseda Univ, Tokyo, Japan; [Vasilakos, Athanasios V.] Lulea Univ Technol, Lulea, Sweden Nanjing University of Posts & Telecommunications; Waseda University; Lulea University of Technology Wang, YF (corresponding author), Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China. wfwang1974@gmail.com; zhangb@njupt.edu.cn; jin@waseda.jp; th.vasilakos@gmail.com Vasilakos, Athanasios/0000-0003-1902-9877 NSFC [61171092]; JiangSu Educational Bureau Project [14KJA510004]; Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute); Huawei Innovation Research Program; NUPTSF [NY215177, NY217089] NSFC(National Natural Science Foundation of China (NSFC)); JiangSu Educational Bureau Project; Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute); Huawei Innovation Research Program(Huawei Technologies); NUPTSF The work was sponsored by the NSFC Grant 61171092, JiangSu Educational Bureau Project 14KJA510004, Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute), Huawei Innovation Research Program, and NUPTSF (Grant No. NY215177 and NY217089). 49 76 77 0 18 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0743-7315 1096-0848 J PARALLEL DISTR COM J. Parallel Distrib. Comput. AUG 2018.0 118.0 1 67 80 10.1016/j.jpdc.2017.05.007 0.0 14 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science GH9QG 2023-03-23 WOS:000434003300008 0
J Vrachimis, SG; Eliades, DG; Taormina, R; Kapelan, Z; Ostfeld, A; Liu, SM; Kyriakou, M; Pavlou, P; Qiu, MN; Polycarpou, MM Vrachimis, Stelios G.; Eliades, Demetrios G.; Taormina, Riccardo; Kapelan, Zoran; Ostfeld, Avi; Liu, Shuming; Kyriakou, Marios; Pavlou, Pavlos; Qiu, Mengning; Polycarpou, Marios M. Battle of the Leakage Detection and Isolation Methods JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT English Article OPTIMAL SENSOR PLACEMENT; WATER; PRESSURE; MODEL; NETWORKS; LOCALIZATION; LOCATION A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose. (C) 2022 American Society of Civil Engineers. [Vrachimis, Stelios G.; Polycarpou, Marios M.] Univ Cyprus, Dept Elect & Comp Engn, KIOS Res & Innovat Ctr Excellence, CY-2109 Nicosia, Cyprus; [Eliades, Demetrios G.; Kyriakou, Marios; Pavlou, Pavlos] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, CY-2109 Nicosia, Cyprus; [Taormina, Riccardo] Delft Univ Technol, Fac Civil Engn & Cieosci, Dept Water Management, Stevinweg 1, NL-2628 CN Delft, Netherlands; [Kapelan, Zoran] Delft Univ Technol, Dept Water Management, Stevinweg 1, NL-2628 CN Delft, Netherlands; [Ostfeld, Avi; Qiu, Mengning] Technion Israel Inst Technol, Fac Civil & Environm Engn, IL-32000 Haifa, Israel; [Liu, Shuming] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China University of Cyprus; University of Cyprus; Delft University of Technology; Delft University of Technology; Technion Israel Institute of Technology; Tsinghua University Vrachimis, SG (corresponding author), Univ Cyprus, Dept Elect & Comp Engn, KIOS Res & Innovat Ctr Excellence, CY-2109 Nicosia, Cyprus. vrachimis.stelios@ucy.ac.cy; eldemet@ucy.ac.cy; r.taormina@tudelft.nl; z.kapelan@tudelft.nl; ostfeld@tx.technion.ac.il; shumingliu@tsinghua.edu.cn; kiriakou.marios@ucy.ac.cy; pavlou.v.pavlos@ucy.ac.cy; mengning.qiu@campus.technion.ac.il; mpolycar@ucy.ac.cy Taormina, Riccardo/AFK-0550-2022; Eliades, Demetrios/H-2417-2013 Kapelan, Zoran/0000-0002-0934-4470; Taormina, Riccardo/0000-0002-1550-504X; Vrachimis, Stelios/0000-0001-8862-5205; Pavlou, Pavlos/0000-0001-8740-4187; Eliades, Demetrios/0000-0001-6184-6366 European Research Council (ERC) under the ERC Synergy Grant-Water-Futures' [951424]; European Union [739551]; Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy; Interreg V-A Greece-Cyprus 2014-2020 program; European Union (ERDF); National Funds of Greece and Cyprus under project SmartWater2020; Water Analytics Project [ENTERPRISES/0916/23]; Republic of Cyprus through the Research and Innovation Foundation; Deutsche Forschungsgemeinschaft (DFG); European Regional Development Fund European Research Council (ERC) under the ERC Synergy Grant-Water-Futures'(European Research Council (ERC)); European Union(European Commission); Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy; Interreg V-A Greece-Cyprus 2014-2020 program; European Union (ERDF)(European Commission); National Funds of Greece and Cyprus under project SmartWater2020; Water Analytics Project; Republic of Cyprus through the Research and Innovation Foundation; Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)); European Regional Development Fund(European Commission) This work was supported by the European Research Council (ERC) under the ERC Synergy Grant-Water-Futures' (Grant Agreement No. 951424); by the European Union Horizon 2020 programme Grant Agreement No. 739551 (KIOS CoE), and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy; by the Interreg V-A Greece-Cyprus 2014-2020 program, co-financed by the European Union (ERDF) and National Funds of Greece and Cyprus under project SmartWater2020; by the Water Analytics Project ENTERPRISES/0916/23 which is cofinanced by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation; and by the Deutsche Forschungsgemeinschaft (DFG). 61 3 3 7 7 ASCE-AMER SOC CIVIL ENGINEERS RESTON 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA 0733-9496 1943-5452 J WATER RES PLAN MAN J. Water Resour. Plan. Manage.-ASCE DEC 1 2022.0 148.0 12 4022068 10.1061/(ASCE)WR.1943-5452.0001601 0.0 20 Engineering, Civil; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Water Resources 5H7XT Green Submitted 2023-03-23 WOS:000867888300012 0
J Hu, JF; Huang, TZ; Deng, LJ; Dou, HX; Hong, DF; Vivone, G Hu, Jin-Fan; Huang, Ting-Zhu; Deng, Liang-Jian; Dou, Hong-Xia; Hong, Danfeng; Vivone, Gemine Fusformer: A Transformer-Based Fusion Network for Hyperspectral Image Super-Resolution IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Transformers; Hyperspectral imaging; Task analysis; Computer architecture; Training; Superresolution; Feature extraction; Hyperspectral image super-resolution (HISR); image fusion; remote sensing; Transformer NET Hyperspectral image super-resolution (HISR) is to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), aiming to obtain a high-resolution hyperspectral image (HR-HSI). Recently, various convolution neural network (CNN)-based techniques have been successfully applied to address the HISR problem. However, these methods generally only consider the relation of a local neighborhood by convolution kernels with a limited receptive field, thus ignoring the global relationship in a feature map. In this letter, we design a Transformer-based architecture (called Fusformer) for the HISR problem, which is the first attempt to apply the Transformer architecture to this task to the best of our knowledge. Because of the excellent ability of feature representations, especially by the self-attention (SA) in the Transformer, our approach can globally explore the intrinsic relationship within features. Considering the specific HISR problem, since the LR-HSI holds the primary spectral information, our method estimates the spatial residual between the upsampled low-resolution multispectral image (LR-MSI) and the desired HR-HSI, reducing the burden of training the whole data in a smaller mapping space. Various experiments show that our approach outperforms current state-of-the-art (SOTA) HISR methods. The code is available at https://github.com/J-FHu/Fusformer. [Hu, Jin-Fan; Huang, Ting-Zhu; Deng, Liang-Jian] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China; [Dou, Hong-Xia] Xihua Univ, Sch Sci, Chengdu 610039, Peoples R China; [Hong, Danfeng] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China; [Vivone, Gemine] CNR, Inst Methodol Environm Anal, I-85050 Tito, Italy University of Electronic Science & Technology of China; Xihua University; Chinese Academy of Sciences; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie per l'Analisi Ambientale (IMAA-CNR) Huang, TZ; Deng, LJ (corresponding author), Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China. hujf0206@163.wm; tingzhuhuang@126.com; liangjian.deng@uestc.edu.cn; hongxia.dou@mail.xhu.edu.cn; hongdf@aircas.ac.cn; gemine.vivone@imaa.cnr.it huang, ting/GRR-3141-2022; VIVONE, Gemine/ABE-5121-2020 VIVONE, Gemine/0000-0001-9542-0638 National Natural Science Foundation of China [12171072, 61702083, 61876203]; Key Projects of Applied Basic Research in Sichuan Province [2020YJ0216]; National Key Research and Development Program of China [2020YFA0714001] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Projects of Applied Basic Research in Sichuan Province; National Key Research and Development Program of China This work was supported in part by the National Natural Science Foundation of China under Grant 12171072, Grant 61702083, and Grant 61876203; in part by the Key Projects of Applied Basic Research in Sichuan Province under Grant 2020YJ0216; and in part by the National Key Research and Development Program of China under Grant 2020YFA0714001. 37 4 4 29 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. 2022.0 19.0 6012305 10.1109/LGRS.2022.3194257 0.0 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3R1OF Green Submitted 2023-03-23 WOS:000838688000011 0
J Piscitelli, MS; Brandi, S; Capozzoli, A; Xiao, F Piscitelli, Marco Savino; Brandi, Silvio; Capozzoli, Alfonso; Xiao, Fu A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings BUILDING SIMULATION English Article anomaly detection; data analytics; energy management; pattern recognition; prediction models FAULT-DETECTION; CLUSTER; PREDICTION; MANAGEMENT; KNOWLEDGE; MODEL In this paper, a tool for the detection and diagnosis of anomalous electrical daily energy patterns relative to a transformer substation of a university campus was developed and tested. Through an innovative pattern recognition analysis consisting in a multi-step clustering process, six clusters of anomalous daily load profiles were identified and isolated in two-year historical data of total electrical energy consumption. The infrequent electrical load profiles were found to be strongly affected, in terms of both shape and magnitude, by the energy consumption behaviour related to the heating/cooling mechanical room. Then, a fault-free predictive model, which uses artificial neural network (ANN) in combination with a Regression Tree, was developed to detect anomalous trends of the electrical energy consumption. The model was able to detect the 93.7% of the anomalous profiles and only the 5% of fault-free days were wrongly predicted as anomalous. Eventually, a diagnosis phase was conceived and validated with a testing data set. A number of daily abnormal load profiles were detected and compared with the centroids of the anomalous clusters identified in the pattern-recognition stage. The work led to the development of a flexible intelligent tool useful for operating a continuous commissioning of the campus facilities. [Piscitelli, Marco Savino; Brandi, Silvio; Capozzoli, Alfonso] Politecn Torino, Dept Energy Galileo Ferraris, TEBE Res Grp, Turin, Italy; [Piscitelli, Marco Savino; Xiao, Fu] Hong Kong Polytech Univ, Dept Bldg Serv Engn, Hong Kong, Peoples R China Polytechnic University of Turin; Hong Kong Polytechnic University Capozzoli, A (corresponding author), Politecn Torino, Dept Energy Galileo Ferraris, TEBE Res Grp, Turin, Italy. alfonso.capozzoli@polito.it Piscitelli, Marco Savino/GVT-2238-2022; Capozzoli, Alfonso/AAH-1469-2019; Brandi, Silvio/GWQ-5008-2022; Xiao, Fu/F-7680-2014 Piscitelli, Marco Savino/0000-0002-0013-5656; Capozzoli, Alfonso/0000-0002-0083-4983; Brandi, Silvio/0000-0002-8329-3229; Xiao, Fu/0000-0002-3779-3943 Research Grant Council of the Hong Kong SAR [152133/19E] Research Grant Council of the Hong Kong SAR(Hong Kong Research Grants Council) The authors express their gratitude to Living Lab of PoliTo for providing data and to Eng. Giovanni Carioni for the support in data preparation and collection. The authors also gratefully acknowledge the support of this research by the Research Grant Council of the Hong Kong SAR (152133/19E). 39 19 19 2 24 TSINGHUA UNIV PRESS BEIJING B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA 1996-3599 1996-8744 BUILD SIMUL-CHINA Build. Simul. FEB 2021.0 14.0 1 SI 131 147 10.1007/s12273-020-0650-1 0.0 MAY 2020 17 Thermodynamics; Construction & Building Technology Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Construction & Building Technology PE2LE 2023-03-23 WOS:000532622300002 0
J Jiang, CR; Xiao, JM; Xie, YC; Tillo, T; Huang, KZ Jiang, Chenru; Xiao, Jimin; Xie, Yanchun; Tillo, Tammam; Huang, Kaizhu Siamese network ensemble for visual tracking NEUROCOMPUTING English Article Ensemble Siamese Tracker; Siamese instance search Tracker; Model updating; CNN OBJECT TRACKING Visual object tracking is a challenging task considering illumination variation, occlusion, rotation, deformation and other problems. In this paper, we extend a Siamese INstance search Tracker (SINT) with model updating mechanism to improve its tracking robustness. SINT uses convolutional neural network (CNN) features, and compares the new frame features with the target features in the first frame. The candidate region with the highest similarity score is considered as the tracking result. However, SINT is not robust against large target variation because the matching model is not updated during the whole tracking process. To combat this defect, we propose an Ensemble Siamese Tracker (EST), where the final similarity score is also affected by the similarity with tracking results in recent frames instead of solely considering the first frame. Tracking results in recent frames are used to adjust the model for continuous target change. Meanwhile, we combine large displacement optical flow method with EST to further improve the performance (called EST+). We test the proposed EST and EST+ on a standard tracking benchmark OTB. It turns out the average overlap ratio of EST and EST+ increase 2.72% and 3.55% respectively compared with SINT on OTB 2013, which contains 51 video sequences. For the OTB 100, the average overlap ratio gain is 4.2%. (C) 2017 Elsevier B.V. All rights reserved. [Jiang, Chenru; Xiao, Jimin; Xie, Yanchun; Huang, Kaizhu] Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou, Peoples R China; [Tillo, Tammam] Libera Univ Bolzano Bozen, Dept Comp Sci, Bolzano, Italy Xi'an Jiaotong-Liverpool University; Free University of Bozen-Bolzano Xiao, JM (corresponding author), Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou, Peoples R China. Chenru.Jiang11@student.xjtlu.edu.cn; Jimin.Xiao@xjtlu.edu.cn; Yanchun.Xie@xjtlu.edu.cn; ttillo@unibz.it; Kaizhu.Huang@xjtlu.edu.cn Huang, Kaizhu/O-4721-2014 Huang, Kaizhu/0000-0002-3034-9639 National Natural Science Foundation of China [61501379, 61210006, 61501074]; Jiangsu Science and Technology Programme [BK20150375] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Science and Technology Programme This work was supported by National Natural Science Foundation of China (Nos. 61501379, 61210006 and 61501074), and Jiangsu Science and Technology Programme under the BK20150375. 34 22 24 6 42 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 31 2018.0 275.0 2892 2903 10.1016/j.neucom.2017.10.043 0.0 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FQ5AI 2023-03-23 WOS:000418370200263 0
J Jiang, CH; Chen, S; Chen, YW; Liu, D; Bo, YM Jiang, Changhui; Chen, Shuai; Chen, Yuwei; Liu, Di; Bo, Yuming An UWB Channel Impulse Response De-Noising Method for NLOS/LOS Classification Boosting IEEE COMMUNICATIONS LETTERS English Article Noise reduction; Signal to noise ratio; Feature extraction; Distance measurement; IP networks; Convolution; Boosting; UWB; NLOS; CNN; de-noising MITIGATION; IDENTIFICATION With the booming demand of indoor position information, Wireless signals (Wi-Fi, Bluetooth, Ultra-Wide-Band etc.) are investigated to construct Indoor Positioning System (IPS). Among these wireless signals, UWB (Ultra-Wide-Band) is recognized as the most promising technology to construct IPS with decimeter-level positioning accuracy. However, there are various objects in indoor environments, UWB signals might be reflected by these surrounding objects. These None-Line-Of-Sight (NLOS) signals will induce additional errors to the distance measurements between the anchor and agent. Therefore, NLOS/LOS (Line-Of-Sight) signals classification should be carried out for identifying the NLOS reception. In UWB based IPS, the distance information is extracted through the Channel Impulse Response (CIR) waveforms. NLOS reception will lead to different CIR waveforms compared with that from the LOS signal, this difference could be characterized for NLOS/LOS signals classification. However, the hardware circuits, signal transmitting path and the NLOS reception will bring about noises to the CIR waveforms, which might influence the signal type identification. Motivated by this problem, in this letter, a reversible transformation method was proposed for de-nosing the CIR data, and a Conventional Neural Network (CNN) was employed to identify the NLOS signal. Raw and de-noised CIR dataset were both input to the CNN for assessing the effect of the de-noising method. With dataset collected from seven different sites, the results showed that the reversible transformation method was able to increase the Signal Noise Ratio (SNR) values of these CIR datasets. After the de-noising, the CNN performed higher classification accuracy with less convolutional layers. Moreover, the de-noising operation brought about higher NLOS signal identification accuracy increase than that of LOS signals. [Jiang, Changhui; Chen, Yuwei] Finnish Geospatial Res Inst, Dept Photogrammetry & Remote Sensing, Masala 02430, Finland; [Chen, Shuai; Bo, Yuming] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China; [Liu, Di] Nanjing Inst Technol, Sch Automat, Nanjing 210094, Peoples R China The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); Nanjing University of Science & Technology; Nanjing Institute of Technology Chen, YW (corresponding author), Finnish Geospatial Res Inst, Dept Photogrammetry & Remote Sensing, Masala 02430, Finland.;Chen, S (corresponding author), Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China. changhui.jiang1992@gmail.com; chenshuai@njust.edu.cn; yuwei.chen@nls.fi; liudinust@163.com; byming@njust.edu.cn 陈, 雨薇/HKF-1175-2023; Jiang, Chanhgui/AAH-8314-2019 jiang, changhui/0000-0002-4788-2464 National Natural Science Foundation of China [61601225] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors acknowledge the support of National Natural Science Foundation of China (Grant No. 61601225). 18 9 12 6 49 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1089-7798 1558-2558 IEEE COMMUN LETT IEEE Commun. Lett. NOV 2020.0 24.0 11 2513 2517 10.1109/LCOMM.2020.3009659 0.0 5 Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Telecommunications OR0XE 2023-03-23 WOS:000589200500030 0
J Perez-Rathke, A; Sun, Q; Wang, BS; Boeva, V; Shao, ZF; Liang, J Perez-Rathke, Alan; Sun, Qiu; Wang, Boshen; Boeva, Valentina; Shao, Zhifeng; Liang, Jie CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci from deconvolved single cells GENOME BIOLOGY English Article Chromatin folding; Polymer modeling; Super-enhancers; Topologically associated domains; Epigenomics; Histone modifications; Sequential Monte Carlo sampling; Gibbs sampling; Bayesian inference; Machine learning; CHROMATIX 3-DIMENSIONAL GENOME STRUCTURES; SUPER-ENHANCERS; DNA ELEMENTS; IDENTITY; ENCYCLOPEDIA; CONFORMATION; PRINCIPLES; PREDICTION; MODEL; MAP Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore unsuitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvolving Hi-C data and identifies significant many-body interactions. For a diverse set of highly active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions. [Perez-Rathke, Alan; Wang, Boshen; Liang, Jie] Univ Illinois, Richard & Loan Hill Dept Bioengn, Chicago, IL 60607 USA; [Sun, Qiu] Shanghai Jiao Tong Univ, Shanghai Ctr Syst Biomed, Shanghai, Peoples R China; [Boeva, Valentina] Paris Descartes Univ, Inst Cochin, UMR S1016, INSERM U1016,CNRS,UMR 8104, F-75014 Paris, France; [Boeva, Valentina] Swiss Fed Inst Technol, Dept Comp Sci, Zurich, Switzerland; [Shao, Zhifeng] Shanghai Jiao Tong Univ, Sch Biomed Engn, State Key Lab Oncogenes, Shanghai, Peoples R China; [Shao, Zhifeng] Shanghai Jiao Tong Univ, Sch Biomed Engn, BioID Ctr, Shanghai, Peoples R China University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Shanghai Jiao Tong University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Cochin - APHP; Swiss Federal Institutes of Technology Domain; ETH Zurich; Shanghai Jiao Tong University; Shanghai Jiao Tong University Liang, J (corresponding author), Univ Illinois, Richard & Loan Hill Dept Bioengn, Chicago, IL 60607 USA. jliang@uic.edu Liang, Jie/HJA-3759-2022; Shao, Zhifeng/B-6075-2013; Boeva, Valentina/C-2855-2017 Boeva, Valentina/0000-0002-4382-7185; Liang, Jie/0000-0002-2773-6427 NIH [R21AI126308, R21AI137865, R01CA204962, R35GM127084]; [CAS-18H100000104]; [NSFC-81627801] NIH(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); ; We thank Daniel M. Czajkowsky and Gamze Gursoy for helpful discussions, Lin Du for the help with A/B compartment calling, and Kimberly Beiting for her editing. This work is supported by grants NIH R21AI126308, R21AI137865, R01CA204962, and R35GM127084 (to JL) and CAS-18H100000104 and NSFC-81627801 (to ZFS). 77 8 8 0 4 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1474-760X GENOME BIOL Genome Biol. JAN 16 2020.0 21.0 1 13 10.1186/s13059-019-1904-z 0.0 17 Biotechnology & Applied Microbiology; Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Biotechnology & Applied Microbiology; Genetics & Heredity KL8NA 31948478.0 Green Published, Green Submitted, gold 2023-03-23 WOS:000513673700002 0
J Wen, T; Pelusi, D; Deng, Y Wen, Tao; Pelusi, Danilo; Deng, Yong Vital spreaders identification in complex networks with multi-local dimension KNOWLEDGE-BASED SYSTEMS English Article Complex network; Vital spreaders identification; Multi-local dimension IDENTIFYING INFLUENTIAL NODES; COMMON-CAUSE FAILURE; SOCIAL NETWORKS; INFLUENCE MAXIMIZATION; GENERALIZED DIMENSIONS; RELIABILITY ASSESSMENT; NEURAL-NETWORK; SYSTEMS; PREDICTION; CENTRALITY The important nodes identification has been an interesting problem in this issue. Several centrality methods have been proposed to solve this problem, but most previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and q plays a weighting coefficient for this partition information. MLD would have different expressions with different values of q, and it would degenerate to local information dimension and variant of local dimension when q = 1 when q = 0 respectively, both of which have been effective identification methods for influential nodes. Thus, MLD would be a more general method which can degenerate to some existing centrality methods. In addition, different from classical methods, the node with low MLD would be more important in the network. Some real-world and theoretical complex networks and comparison methods are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method. (C) 2020 Elsevier B.V. All rights reserved. [Wen, Tao; Deng, Yong] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China; [Pelusi, Danilo] Univ Teramo, Fac Commun Sci, I-64100 Teramo, Italy University of Electronic Science & Technology of China; University of Teramo Deng, Y (corresponding author), Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China. dengentropy@uestc.edu.cn Wen, Tao/T-4653-2019; Deng, Yong/A-9193-2015 Wen, Tao/0000-0002-8490-1628; National Natural Science Foundation of China [61973332, 61573290, 61503237] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors greatly appreciate the reviewers' suggestions and editor's encouragement. This work is partially supported by the National Natural Science Foundation of China (grant nos. 61973332, 61573290, and 61503237). 73 40 40 6 52 ELSEVIER AMSTERDAM RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. MAY 11 2020.0 195.0 105717 10.1016/j.knosys.2020.105717 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KZ9EN Green Submitted 2023-03-23 WOS:000523561500017 0
J Wang, H; Alattas, KA; Mohammadzadeh, A; Sabzalian, MH; Aly, AA; Mosavi, A Wang, Hong; Alattas, Khalid A.; Mohammadzadeh, Ardashir; Sabzalian, Mohammad Hosein; Aly, Ayman A.; Mosavi, Amir Comprehensive review of load forecasting with emphasis on intelligent computing approaches ENERGY REPORTS English Review Load forecasting; Electrical load; Mid-term load forecasting; Fuzzy systems; Neural networks; Learning algorithms SUPPORT VECTOR REGRESSION; ARTIFICIAL NEURAL-NETWORK; SHORT-TERM; ELECTRICITY DEMAND; POWER-SYSTEMS; MODELS; LONG; ENERGY In this paper, a comprehensive review is presented for mid-term load forecasting. The basic loads and effective factors are studied, and then several classifications are presented for forecasting approaches. The main advantages and drawbacks of the approaches are analyzed. The neuro-fuzzy-based approaches are investigated in more detail, and their limitations are studied. Finally, some aspects are presented in the use of neuro-fuzzy systems for load forecasting. The main contributions are that: (1) A comprehensive review is presented such that both classical methods and new neuro-fuzzy approaches are investigated. (2) The basic methods are studied in details, and their achievements and drawbacks are discussed. (3) Some models and suggestions are presented for future practical applications. (4) Some categories are introduced for better evaluation of various methods. (C) 2022 The Author(s). Published by Elsevier Ltd. [Wang, Hong] Hainan Univ, Sch Tourism, Haikou 570228, Peoples R China; [Alattas, Khalid A.] Univ Jeddah, Dept Comp Sci & Artificial Intelligence, Coll Comp Sci & Engn, Jeddah 23890, Saudi Arabia; [Mohammadzadeh, Ardashir] Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China; [Sabzalian, Mohammad Hosein] UNICAMP Univ Campinas, FEEC Sch Elect & Comp Engn, Dept Syst & Energy, LabREI Smart Grid Lab, Campinas, Brazil; [Aly, Ayman A.] Taif Univ, Dept Mech Engn, Coll Engn, Taif 21944, Saudi Arabia; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary; [Mosavi, Amir] Obuda Univ, Budapest, Hungary Hainan University; University of Jeddah; Shenyang University of Technology; Universidade Estadual de Campinas; Taif University; Obuda University; Obuda University Mohammadzadeh, A (corresponding author), Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China.;Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary. a.mzadeh@ieee.org; amir.mosavi@uni-obuda.hu Sabzalian, Mohammadhosein/AGZ-1425-2022; Mosavi, Amir/I-7440-2018; Aly, Ayman A./AAZ-9459-2021; Mohammadzadeh, Ardashir/AEN-2013-2022 Mosavi, Amir/0000-0003-4842-0613; Aly, Ayman A./0000-0002-8383-8014; Mohammadzadeh, Ardashir/0000-0001-5173-4563 National Social Science Foundation [21BJY112]; Social Science Program of Hainan Province [HNSK(YB)19-10]; Program of the Ministry of Culture and Tourism [TYETP201552] National Social Science Foundation; Social Science Program of Hainan Province; Program of the Ministry of Culture and Tourism This study is supported by the project of the National Social Science Foundation (21BJY112); the Social Science Program of Hainan Province (HNSK(YB)19-10) and the Program of the Ministry of Culture and Tourism (TYETP201552). 69 1 1 19 19 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4847 ENERGY REP Energy Rep. NOV 2022.0 8.0 13189 13198 10.1016/j.egyr.2022.10.016 0.0 OCT 2022 10 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 6G5BP gold 2023-03-23 WOS:000884770900012 0
J Zhang, LF; Hong, XP; Arandjelovic, O; Zhao, GY Zhang, Liangfei; Hong, Xiaopeng; Arandjelovic, Ognjen; Zhao, Guoying Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition IEEE TRANSACTIONS ON AFFECTIVE COMPUTING English Article Emotion recognition; long-term optical flow; temporal aggregator; self-attention mechanism ATTENTION Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions is a difficult task in affective computing. The early work based on handcrafted spatio-temporal features which showed some promise, has recently been superseded by different deep learning approaches which now compete for the state of the art performance. Nevertheless, the problem of capturing both local and global spatio-temporal patterns remains challenging. To this end, herein we propose a novel spatio-temporal transformer architecture - to the best of our knowledge, the first purely transformer based approach (i.e., void of any convolutional network use) for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal aggregator for temporal dimension analysis, and a classification head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, namely SMIC-HS, CASME II and SAMM, shows that the proposed approach consistently outperforms the state of the art, and is the first framework in the published literature on micro-expression recognition to achieve the unweighted F1-score greater than 0.9 on any of the aforementioned data sets. The source code is available at https://github.com/Vision-Intelligence-and-Robots-Group/SLSTT. [Zhang, Liangfei; Arandjelovic, Ognjen] Univ St Andrews, Sch Comp Sci, St Andrews KY16 9AJ, Scotland; [Hong, Xiaopeng] Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China; [Zhao, Guoying] Univ Oulu, Oulu 90570, Finland University of St Andrews; Harbin Institute of Technology; University of Oulu Hong, XP (corresponding author), Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China. lz36@st-andrews.ac.uk; hongxiaopeng@ieee.org; oa7@st-andrews.ac.uk; guoying.zhao@oulu.fi HONG, Xiaopeng/K-3594-2017 HONG, Xiaopeng/0000-0002-0611-0636; Zhao, Guoying/0000-0003-3694-206X; Arandjelovic, Ognjen/0000-0002-9314-194X China Scholarship Council - University of St Andrews Scholarships [201908060250]; National Key Research and Development Project of China [2019YFB1312000]; National Natural ScienceFoundation of China [62076195]; FundamentalResearch Funds for the Central Universities [AUGA5710011522] China Scholarship Council - University of St Andrews Scholarships(China Scholarship Council); National Key Research and Development Project of China; National Natural ScienceFoundation of China(National Natural Science Foundation of China (NSFC)); FundamentalResearch Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the China Scholarship Council - University of St Andrews Scholarships under Grant 201908060250 funds Liangfei Zhang forher PhD, in part by the National Key Research and Development Project of China under Grant 2019YFB1312000, in part by the National Natural ScienceFoundation of China under Grant 62076195, and in part by the FundamentalResearch Funds for the Central Universities under Grant AUGA5710011522 75 0 0 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3045 IEEE T AFFECT COMPUT IEEE Trans. Affect. Comput. OCT 1 2022.0 13.0 4 1973 1985 10.1109/TAFFC.2022.3213509 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Computer Science 6S4GV Green Accepted, Green Submitted 2023-03-23 WOS:000892948500022 0
J Ren, XX; Cao, JJ; Xu, XH; Gong, Y Ren, Xinxin; Cao, Jingjing; Xu, Xianhao; Gong, Yeming (Yale) A two-stage model for forecasting consumers' intention to purchase with e-coupons JOURNAL OF RETAILING AND CONSUMER SERVICES English Article Electronic coupon redemption; Consumer segmentation; Logistic regression; Isolation forest; Data-driven strategy SEGMENTATION; SHOPPERS; PATTERNS E-coupons (electronic coupons) have been a mainstay of online marketing to attract consumers and promote them to repeat purchase, distributing right e-coupons to right consumers is of critical importance. In big data era, analyzing consumers preferences for e-coupons by their online behavior and the impact of data imbalance caused by low active consumers are rarely studied. Thus, we propose a two-stage hybrid model. Firstly, consumer segmentation is implemented to analyze behavioral characteristics for each segment and distinguish low active consumers, then models are constructed for different consumer segments. The proposed model is applied to a real online consumption data. Consumers are aggregated into four segments: potential e-coupons user, low discount sensitive user, high discount sensitive user (including discount preference and fixed preference). The first one is defined as low active consumer segment and others are high active consumer segments. Isolation forest model and logistic regression model are respectively constructed for them. Result shows that data imbalance is effectively relieved, prediction performance is also significantly better than the traditional approaches. Finally, e-coupons' usage characteristics for each consumer segment are summarized, according to that, companies can increase sales and improve consumer satisfaction as well. [Ren, Xinxin; Xu, Xianhao] Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China; [Cao, Jingjing] Wuhan Univ Technol, Sch Logist Engn, Wuhan 430063, Peoples R China; [Gong, Yeming (Yale)] EMLYON Business Sch, 23 Ave Guy Collogue, F-69130 Ecully, France Huazhong University of Science & Technology; Wuhan University of Technology; EMLYON Business School Xu, XH (corresponding author), Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China. renxinxin@hust.edu.cn; bettycao@whut.edu.cn; xxhao@hust.edu.cn; gong@em-lyon.com GONG, Yeming/I-7148-2012 GONG, Yeming/0000-0001-9270-5507 National Natural Science Foundation of China [71620107002, 71821001, 71971095, 61502360] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported in part by the National Natural Science Foundation of China (grant number 71620107002), (grant number 71821001), (grant number 71971095), (grant number 61502360). 34 3 3 23 85 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0969-6989 1873-1384 J RETAIL CONSUM SERV J. Retail. Consum. Serv. MAR 2021.0 59.0 102289 10.1016/j.jretconser.2020.102289 0.0 JAN 2021 12 Business Social Science Citation Index (SSCI) Business & Economics PU0BE 2023-03-23 WOS:000608972200026 0
J Ding, F; Yu, KP; Gu, ZH; Li, XJ; Shi, YQ Ding, Feng; Yu, Keping; Gu, Zonghua; Li, Xiangjun; Shi, Yunqing Perceptual Enhancement for Autonomous Vehicles: Restoring Visually Degraded Images for Context Prediction via Adversarial Training IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Context prediction; autonomous Vehicle; image processing; deep learning; generative adversarial network DEGRADATION; INTERNET Realizing autonomous vehicles is one of the ultimate dreams for humans. However, perceptual information collected by sensors in dynamic and complicated environments, in particular, vision information, may exhibit various types of degradation. This may lead to mispredictions of context followed by more severe consequences. Thus, it is necessary to improve degraded images before employing them for context prediction. To this end, we propose a generative adversarial network to restore images from common types of degradation. The proposed model features a novel architecture with an inverse and a reverse module to address additional attributes between image styles. With the supplementary information, the decoding for restoration can be more precise. In addition, we develop a loss function to stabilize the adversarial training with better training efficiency for the proposed model. Compared with several state-of-the-art methods, the proposed method can achieve better restoration performance with high efficiency. It is highly reliable for assisting in context prediction in autonomous vehicles. [Ding, Feng] Nanchang Univ, Sch Management, Nanchang 330031, Jiangxi, Peoples R China; [Yu, Keping] Waseda Univ, Global Informat & Telecommun Inst, Tokyo 1698555, Japan; [Gu, Zonghua] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Li, Xiangjun] Nanchang Univ, Sch Software, Nanchang 330047, Jiangxi, Peoples R China; [Shi, Yunqing] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07101 USA Nanchang University; Waseda University; Umea University; Nanchang University; New Jersey Institute of Technology Yu, KP (corresponding author), Waseda Univ, Global Informat & Telecommun Inst, Tokyo 1698555, Japan. fengding@ncu.edu.cn; keping.yu@aoni.waseda.jp; zonghua.gu@umu.se; lixiangjun@ncu.edu.cn; yun-qing.shi@njit.edu Yu, Keping/GVT-7847-2022 Yu, Keping/0000-0001-5735-2507; Gu, Zonghua/0000-0003-4228-2774 Japan Society for the Promotion of Science (JSPS) [JP18K18044, 21K17736]; National Natural Science Foundation of China [61762062]; Science and Technology Innovation Platform Project of Jiangxi Province [20181BCD40005]; Major Discipline Academic and Technical Leader Training Plan Project of Jiangxi Province [20172BCB22030]; Jiangxi Province Natural Science Foundation of China [20192BAB207019]; Jiangxi Double Thousand Plan [JXSQ201901075]; Grants-in-Aid for Scientific Research [21K17736] Funding Source: KAKEN Japan Society for the Promotion of Science (JSPS)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Innovation Platform Project of Jiangxi Province; Major Discipline Academic and Technical Leader Training Plan Project of Jiangxi Province; Jiangxi Province Natural Science Foundation of China; Jiangxi Double Thousand Plan; Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work was supported in part by the Japan Society for the Promotion of Science (JSPS) Grants-inAid for Scientific Research (KAKENHI) under Grant JP18K18044 and Grant 21K17736, in part by the National Natural Science Foundation of China under Grant 61762062, in part by the Science and Technology Innovation Platform Project of Jiangxi Province under Grant 20181BCD40005, in part by the Major Discipline Academic and Technical Leader Training Plan Project of Jiangxi Province under Grant 20172BCB22030, in part by the Jiangxi Province Natural Science Foundation of China under Grant 20192BAB207019, and in part by the Jiangxi Double Thousand Plan under Grant JXSQ201901075. 50 67 67 20 26 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. JUL 2022.0 23.0 7 9430 9441 10.1109/TITS.2021.3120075 0.0 OCT 2021 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 3R1QN 2023-03-23 WOS:000732910600001 0
J You, RH; Liu, YX; Mamitsuka, H; Zhu, SF You, Ronghui; Liu, Yuxuan; Mamitsuka, Hiroshi; Zhu, Shanfeng BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text BIOINFORMATICS English Article QUERY EXPANSION Motivation: With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers from three major drawbacks: FullMeSH (i) uses Learning To Rank, which is time-consuming, (ii) can capture some pre-defined sections only in full text and (iii) ignores the whole MEDLINE database. Results: We propose a computationally lighter, full text and deep-learning-based MeSH indexing method, BERTMeSH, which is flexible for section organization in full text. BERTMeSH has two technologies: (i) the state-of-the-art pre-trained deep contextual representation, Bidirectional Encoder Representations from Transformers (BERT), which makes BERTMeSH capture deep semantics of full text. (ii) A transfer learning strategy for using both full text in PubMed Central (PMC) and title and abstract (only and no full text) in MEDLINE, to take advantages of both. In our experiments, BERTMeSH was pre-trained with 3 million MEDLINE citations and trained on similar to 1.5 million full texts in PMC. BERTMeSH outperformed various cutting-edge baselines. For example, for 20 K test articles of PMC, BERTMeSH achieved a Micro F-measure of 69.2%, which was 6.3% higher than FullMeSH with the difference being statistically significant. Also prediction of 20 K test articles needed 5 min by BERTMeSH, while it took more than 10 h by FullMeSH, proving the computational efficiency of BERTMeSH. [You, Ronghui; Liu, Yuxuan; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [You, Ronghui; Liu, Yuxuan; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [Mamitsuka, Hiroshi] Kyoto Univ, Bioinformat Ctr, Inst Chem Res, Uji, Kyoto, Japan; [Mamitsuka, Hiroshi] Aalto Univ, Dept Comp Sci, Espoo, Finland; [Zhu, Shanfeng] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, Shanghai Inst Artificial Intelligence Algorithms, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Fudan Univ, Key Lab Computat Neurosci & Brain Inspired Intell, Minist Educ, Shanghai 200433, Peoples R China; [Zhu, Shanfeng] Chinese Acad Sci, Biomed Big Data Ctr, CAS MPG Partner Inst Computat Biol, Shanghai Inst Nutr & Hlth,Key Lab Computat Biol, Shanghai 200031, Peoples R China Fudan University; Fudan University; Kyoto University; Aalto University; Fudan University; Fudan University; Fudan University; Chinese Academy of Sciences; Shanghai Institute of Nutrition & Health, CAS; Shanghai Institutes for Biological Sciences, CAS; Max Planck Society Zhu, SF (corresponding author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Shanghai Inst Artificial Intelligence Algorithms, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Fudan Univ, Key Lab Computat Neurosci & Brain Inspired Intell, Minist Educ, Shanghai 200433, Peoples R China.;Zhu, SF (corresponding author), Chinese Acad Sci, Biomed Big Data Ctr, CAS MPG Partner Inst Computat Biol, Shanghai Inst Nutr & Hlth,Key Lab Computat Biol, Shanghai 200031, Peoples R China. zhusf@fudan.edu.cn Mamitsuka, Hiroshi/R-1110-2016 Mamitsuka, Hiroshi/0000-0002-6607-5617 National Natural Science Foundation of China [61572139, 61872094]; Shanghai Municipal Science and Technology Major Project [2018SHZDZX01, 2017SHZDZX01]; Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences; 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; JST ACCEL [JPMJAC1503]; MEXT Kakenhi [19H04169]; AIPSE by Academy of Finland; ZJLab National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shanghai Municipal Science and Technology Major Project; Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences; 111 Project(Ministry of Education, China - 111 Project); key project of Shanghai Science Technology; JST ACCEL(Japan Science & Technology Agency (JST)); MEXT Kakenhi(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)); AIPSE by Academy of Finland; ZJLab This work was supported by National Natural Science Foundation of China [numbers 61572139, 61872094 to S.Z.]; Shanghai Municipal Science and Technology Major Project [No. 2017SHZDZX01]; and Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. R.Y. and Y.L. have been supported by the 111 Project [No. B18015]; the key project of Shanghai Science & Technology [No. 16JC1420402]; Shanghai Municipal Science and Technology Major Project [No. 2018SHZDZX01]; and ZJLab.; H.M. has been supported in part by JST ACCEL [grant number JPMJAC1503], MEXT Kakenhi [grant numbers 19H04169] and AIPSE by Academy of Finland. 29 4 5 3 14 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics MAR 1 2021.0 37.0 5 684 692 10.1093/bioinformatics/btaa837 0.0 9 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics SE5IK 32976559.0 Green Submitted, Green Accepted 2023-03-23 WOS:000652105500013 0
J Shao, Q; Xia, CY; Wang, L; Li, HJ Shao, Qian; Xia, Chengyi; Wang, Lin; Li, Huijia A new propagation model coupling the offline and online social networks NONLINEAR DYNAMICS English Article Propagation model; Multilayer network; Offline and online networks; Mean-field approximation; Monte Carlo simulation AWARENESS DIFFUSION; SPREADING PROCESSES; DYNAMICS; SYNCHRONIZATION; THRESHOLD; NEURON; PHASE Although rapid advancements of online networking technologies and softwares (e.g., 5G, Internet of Things, Big data) have substantially facilitated our communications with others, the regular face-to-face contact is still an important communication channel in our daily life. Modeling the information diffusion through both online and offline interactions has become a challenging topic that has attracted much attention from the field of industrial informatics. To this end, we characterize the coupling effect of online and offline communication by developing a multilayer network propagation model, which considers three novel aspects: two distinct contagions taking place at two types of social software; competition and dynamic processes between them under the joint influence of individual selection and information dissemination; and information coupling between offline and online social networks. Moreover, a mean-field method is provided to analyze the critical threshold, and extensive Monte Carlo simulations are performed to demonstrate the theoretical predictions and explore the rich dynamical phenomena. Current results are conducive to understanding the transmission of information or epidemic spreading within the structured population, and further motivating the design of networked hardware and software with high performance. [Shao, Qian; Xia, Chengyi] Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300384, Peoples R China; [Shao, Qian; Xia, Chengyi] Tianjin Univ Technol, Minist Educ, Key Lab Comp Vis & Syst, Tianjin 300384, Peoples R China; [Wang, Lin] Inst Pasteur, CNRS, UMR2000, F-75015 Paris, France; [Li, Huijia] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100081, Peoples R China Tianjin University of Technology; Tianjin University of Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Cite; Le Reseau International des Instituts Pasteur (RIIP); Institut Pasteur Paris; Central University of Finance & Economics Xia, CY (corresponding author), Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300384, Peoples R China. cyxia@email.tjut.edu.cn Li, Hui-Jia/AAW-9398-2021; Wang, Lin/B-4145-2013; Xia, Chengyi/A-2752-2012 Wang, Lin/0000-0002-5371-2138; Li, Huijia/0000-0003-1000-717X; Xia, Chengyi/0000-0003-2686-5072 National Natural Science Foundation of China (NSFC) [61773286] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) We will acknowledge the funding support of the National Natural Science Foundation of China (NSFC) under Grant No. 61773286. 58 10 10 6 34 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0924-090X 1573-269X NONLINEAR DYNAM Nonlinear Dyn. NOV 2019.0 98.0 3 2171 2183 10.1007/s11071-019-05315-9 0.0 NOV 2019 13 Engineering, Mechanical; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mechanics JS1SW 2023-03-23 WOS:000495040800004 0
J Li, L; Xia, ZQ; Jiang, XY; Ma, YP; Roli, F; Feng, XY Li, Lei; Xia, Zhaoqiang; Jiang, Xiaoyue; Ma, Yupeng; Roli, Fabio; Feng, Xiaoyi 3D face mask presentation attack detection based on intrinsic image analysis IET BIOMETRICS English Article neural nets; feature extraction; face recognition; image texture; reflectance image; face mask; intrinsic image analysis; face presentation attacks; face recognition systems; image reflectance; face image; intrinsic image decomposition algorithm LIVENESS DETECTION Face presentation attacks have become a major threat against face recognition systems and many countermeasures have been proposed over the past decade. However, most of them are devoted to 2D face presentation attack detection, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. Therefore, in this study, the authors propose a novel 3D face mask presentation attack detection method based on analysis of image reflectance. In the proposed method, the face image is first processed with intrinsic image decomposition algorithm to compute its reflectance image. Then, the intensity distribution histograms are extracted from three orthogonal planes to represent the intensity differences of reflectance images between the real face and 3D face mask. After that, given that the reflectance image of a smooth surface is more sensitive to illumination changes, 1D convolutional neural network is used to characterise how different materials or surfaces react differently to illumination changes. Extensive experiments with the public available 3DMAD database demonstrate the effectiveness of the proposed method for distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods. [Li, Lei; Xia, Zhaoqiang; Jiang, Xiaoyue; Ma, Yupeng; Feng, Xiaoyi] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China; [Roli, Fabio] Univ Cagliari, Dept Elect & Elect Engn, Cagliari, Italy Northwestern Polytechnical University; University of Cagliari Xia, ZQ (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China. zxia@nwpu.edu.cn Li, Lei/0000-0003-4498-6126; ROLI, FABIO/0000-0003-4103-9190 47 9 10 5 27 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 2047-4938 2047-4946 IET BIOMETRICS IET Biom. MAY 2020.0 9.0 3 100 108 10.1049/iet-bmt.2019.0155 0.0 9 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science LL2LJ Green Submitted, Bronze 2023-03-23 WOS:000531387600002 0
J Guo, CP; Pierson, JM; Liu, H; Song, J Guo, Chaopeng; Pierson, Jean-Marc; Liu, Hui; Song, Jie Frequency Selection Approach for Energy Aware Cloud Database IEEE ACCESS English Article Cloud database system; dynamic voltage and frequency scaling; energy efficiency; frequency selection; optimization REAL-TIME TASKS A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using dynamic voltage and frequency scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of the cloud systems in terms of resource provisioning. In the approach, two algorithms, genetic algorithm (GA) and Monte Carlo tree search algorithm (MCTS), are proposed. A cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages. The algorithms have great scalability, in which the GA can be applied to thousands of nodes and the MCTS can be applied to hundreds of nodes. Both algorithms have high accuracy compared with optimal solutions (up to 99.9% and 99.6% for GA and MCTS, respectively). According to an optimality bound analysis, 26% of energy can be saved at most using our frequency selection approach. [Guo, Chaopeng; Pierson, Jean-Marc] Univ Toulouse, Inst Rech Informat Toulouse, F-31062 Toulouse, France; [Liu, Hui] Northeastern Univ, Sch Met, Shenyang 110819, Liaoning, Peoples R China; [Song, Jie] Northeastern Univ, Software Coll, Shenyang 110819, Liaoning, Peoples R China Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Institut National Polytechnique de Toulouse; Universite Toulouse 1 Capitole; Universite de Toulouse - Jean Jaures; Centre National de la Recherche Scientifique (CNRS); Northeastern University - China; Northeastern University - China Song, J (corresponding author), Northeastern Univ, Software Coll, Shenyang 110819, Liaoning, Peoples R China. songjie@mail.neu.edu.cn Pierson, Jean-Marc/V-2290-2019; PIERSON, Jean-Marc/AAQ-4009-2020 PIERSON, Jean-Marc/0000-0001-8948-0474; Guo, Chaopeng/0000-0001-5022-5919 30 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 1927 1942 10.1109/ACCESS.2018.2885765 0.0 16 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HH4MB gold, Green Submitted, Green Accepted 2023-03-23 WOS:000455695600001 0
J Sanin, C; Zhang, HX; Shafiq, I; Waris, MM; de Oliveira, CS; Szczerbicki, E Sanin, Cesar; Zhang Haoxi; Shafiq, Imran; Waris, Md Maqbool; de Oliveira, Caterine Silva; Szczerbicki, Edward Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE English Article Decisional DNA; Set of experience knowledge structure; Cyber Physical Systems; Internet of Things; Factory 4.0; Knowledge representation; Knowledge engineering; Decision making; Artificial intelligence; Virtual engineering object; Virtual engineering process; Virtual engineering factory DESIGN; WEB Cyber Physical Systems and Internet of Things have grown significant attention from industry and academia during the past decade. The main reason behind this interest is the capabilities of such technologies to revolutionize human life since they appear as seamlessly integrating classical networks, networked objects and people to create more efficient environments. However, enhancing these technologies with intelligent skills becomes an even more interesting and enticing scenario. In this paper, we propose and illustrate through a number of case studies how Decisional DNA, a multi-domain knowledge structure based on experience, can be implemented as a comprehensive embedded knowledge representation for Internet of Things and Cyber Physical Systems. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The main advantages of using Decisional DNA are as follows: (i) offers a standardized form of the collected knowledge and experience, (ii) provides versatility and dynamicity of the knowledge structure, (iii) stipulates storage of day-to-day explicit experience in a single configuration, (iv) delivers transportability and shareability of the knowledge, and (v) provides predicting capabilities based on the collected experience. Consequently, test and results of the presented implementation of Decisional DNA case studies support it as a technology that can improve and be applied to the aforementioned technologies enhancing them with intelligence by predicting capabilities and facilitating knowledge engineering processes. (C) 2018 Elsevier B.V. All rights reserved. [Sanin, Cesar; Waris, Md Maqbool; de Oliveira, Caterine Silva] Univ Newcastle, Fac Engn & Built Environm, Univ Dr, Callaghan, NSW 2208, Australia; [Zhang Haoxi] Chengdu Univ Informat Technol, Coll Informat Secur Engn, 24 Block 1,Xuefu Rd, Chengdu 610225, Sichuan, Peoples R China; [Shafiq, Imran] Aligarh Muslim Univ, Aligarh, Uttar Pradesh, India; [Szczerbicki, Edward] Gdansk Univ Technol, Gdansk, Poland University of Newcastle; Chengdu University of Information Technology; Aligarh Muslim University; Fahrenheit Universities; Gdansk University of Technology Sanin, C (corresponding author), Univ Newcastle, Fac Engn & Built Environm, Univ Dr, Callaghan, NSW 2208, Australia. Cesar.Sanin@newcastle.edu.au; haoxi@cuit.edu.cn; sishafiq.bp@amu.ac.in; MohammadMaqbool.Waris@uon.edu.au; Caterine.SilvadeOliveira@uon.edu.au; edward.szczerbicki@zie.pg.gda.pl Sanin, Cesar/AAI-2962-2020 Sanin, Cesar/0000-0001-8515-417X; Zhang, Haoxi/0000-0002-1341-1912 53 28 30 0 38 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. MAR 2019.0 92.0 604 616 10.1016/j.future.2018.01.062 0.0 13 Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science HF6TG 2023-03-23 WOS:000454370600053 0
J Ma, D; Guo, RZ; Jing, Y; Zheng, Y; Zhao, ZG; Yang, JH Ma, Ding; Guo, Renzhong; Jing, Ying; Zheng, Ye; Zhao, Zhigang; Yang, Jiahao Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery REMOTE SENSING English Article urban hotspot delineation; Zipf’ s law; intra-urban scaling; street nodes; VIIRS imagery A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots-street-based and NTL-based hotspots-being generated across 20 major cities in China. We find that Zipf's law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development. [Ma, Ding; Guo, Renzhong; Zheng, Ye; Zhao, Zhigang; Yang, Jiahao] Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China; [Ma, Ding] Univ Gavle, Fac Engn & Sustainable Dev, S-80176 Gavle, Sweden; [Jing, Ying] Zhejiang Univ, Sch Econ, Ningbo Inst Technol, Ningbo 315000, Peoples R China Shenzhen University; University of Gavle; Zhejiang University Zhao, ZG (corresponding author), Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China. dingma@szu.edu.cn; guorz@szu.edu.cn; y.crystal@nit.zju.edu.cn; zhengye@szu.edu.cn; zhaozgrisc@szu.edu.cn; 2060325018@email.szu.edu.cn Ma, Ding/0000-0001-9328-9584 National Nature Science Foundation of China [42001180]; China Postdoctoral Science Foundation [2019M663038]; National Key Research and Development Program of China [2018YFB2100705] National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); National Key Research and Development Program of China This research was financially supported by the National Nature Science Foundation of China (grant no. 42001180) and the China Postdoctoral Science Foundation (grant no. 2019M663038), and the National Key Research and Development Program of China [Grant No. 2018YFB2100705]. 46 4 4 6 30 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. APR 2021.0 13.0 7 1322 10.3390/rs13071322 0.0 19 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology RL2GH Green Published, gold 2023-03-23 WOS:000638797600001 0
J Wang, L; Sun, Z; Dai, XY; Zhang, YX; Hu, HH Wang, Le; Sun, Zao; Dai, Xiaoyong; Zhang, Yixin; Hu, Hai-hua Retaining users after privacy invasions The roles of institutional privacy assurances and threat-coping appraisal in mitigating privacy concerns INFORMATION TECHNOLOGY & PEOPLE English Article Survey; Information management; Mobile social media; Individual; Consumer behaviour; Social media; Privacy; End users; Hypothesis testing; Threat-coping appraisal; Communication privacy management theory SELF-DISCLOSURE; ONLINE PRIVACY; CONSUMER PRIVACY; SOCIAL NETWORKS; E-COMMERCE; BIG DATA; INFORMATION; TRUST; INTERNET; MODEL Purpose The purpose of this paper is to facilitate understanding of how to mitigate the privacy concerns of users who have experienced privacy invasions. Design/methodology/approach Drawing on the communication privacy management theory, the authors developed a model suggesting that privacy concerns form through a cognitive process involving threat-coping appraisals, institutional privacy assurances and privacy experiences. The model was tested using data from an empirical survey with 913 randomly selected social media users. Findings Privacy concerns are jointly determined by perceived privacy risks and privacy self-efficacy. The perceived effectiveness of institutional privacy assurances in terms of established privacy policies and privacy protection technology influences the perceptions of privacy risks and privacy self-efficacy. More specifically, privacy invasion experiences are negatively associated with the perceived effectiveness of institutional privacy assurances. Research limitations/implications - Privacy concerns are conceptualized as general concerns that reflect an individual's worry about the possible loss of private information. The specific types of private information were not differentiated. Originality/value This paper is among the first to clarify the specific mechanisms through which privacy invasion experiences influence privacy concerns. Privacy concerns have long been viewed as resulting from individual actions. The study contributes to literature by linking privacy concerns with institutional privacy practice. [Wang, Le; Sun, Zao; Dai, Xiaoyong] Xi An Jiao Tong Univ, Sch Econ & Finance, Xian, Shaanxi, Peoples R China; [Zhang, Yixin] Univ Gothenburg, Dept Appl IT, Gothenburg, Sweden; [Hu, Hai-hua] Xian Univ Architecture & Technol, Sch Management, Xian, Shaanxi, Peoples R China Xi'an Jiaotong University; Royal Institute of Technology; University of Gothenburg; Xi'an University of Architecture & Technology Hu, HH (corresponding author), Xian Univ Architecture & Technol, Sch Management, Xian, Shaanxi, Peoples R China. wangle@xjtu.edu.cn; sunz@mail.xjtu.edu.cn; daixy87@gmail.com; sarah.yixin.zhang@gmail.com; huhaihua@vip.163.com Wang, Le/CAF-7080-2022; Wang, Le/E-9199-2017 Wang, Le/0000-0003-0327-7315; Wang, Le/0000-0003-0327-7315 National Natural Science Foundation of China [71502070, 71804140]; Humanity and Social Science foundation of Ministry of Education [18YJC630180]; China Postdoctoral Science Foundation [2018T111079, 2017M623203]; Fundamental Research Funds for the Central Universities [SK2018004, SK2018071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Humanity and Social Science foundation of Ministry of Education; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported by the National Natural Science Foundation of China (71502070, 71804140), Humanity and Social Science foundation of Ministry of Education (18YJC630180), China Postdoctoral Science Foundation (2018T111079, 2017M623203), and Fundamental Research Funds for the Central Universities (SK2018004, SK2018071). 90 14 15 30 115 EMERALD GROUP PUBLISHING LTD BINGLEY HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND 0959-3845 1758-5813 INFORM TECHNOL PEOPL Inf. Technol. People DEC 2 2019.0 32.0 6 1679 1703 10.1108/ITP-01-2018-0020 0.0 25 Information Science & Library Science Social Science Citation Index (SSCI) Information Science & Library Science JN6IB 2023-03-23 WOS:000496998700015 0
J Yin, YY; Xu, HR; Liang, TT; Chen, MM; Gao, HH; Longo, A Yin, Yuyu; Xu, Haoran; Liang, Tingting; Chen, Manman; Gao, Honghao; Longo, Antonella Leveraging Data Augmentation for Service QoS Prediction in Cyber-physical Systems ACM TRANSACTIONS ON INTERNET TECHNOLOGY English Article QoS prediction; data augmentation; cyber-physical systems; neural network With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity. [Yin, Yuyu; Xu, Haoran; Liang, Tingting; Chen, Manman] Hangzhou Dianzi Univ, 1158,2nd St,Baiyang St, Hangzhou 310018, Peoples R China; [Gao, Honghao] Shanghai Univ, Shanghai, Peoples R China; [Longo, Antonella] Univ Salento, Lecce, Italy Hangzhou Dianzi University; Shanghai University; University of Salento Liang, TT (corresponding author), Hangzhou Dianzi Univ, 1158,2nd St,Baiyang St, Hangzhou 310018, Peoples R China. yinyuyu@hdu.edu.cn; xuhaoran@hdu.edu.cn; liangtt@hdu.edu.cn; manman@hdu.edu.cn; gaohonghao@shu.edu.cn; antonella.longo@unisalento.it Gao, Honghao/AAX-4529-2020; Longo, Antonella/A-5335-2009; xu, hao/GWQ-7394-2022 Gao, Honghao/0000-0001-6861-9684; Longo, Antonella/0000-0002-6902-0160; yuyu, yin/0000-0001-7565-4111 NSFC [61872119]; Key Research & Development Plan of Zhejiang Province [2019C03134]; Fundamental Research Funds for the Provincial Universities of Zhejiang [GK199900299012-025, GK209907299001-020] NSFC(National Natural Science Foundation of China (NSFC)); Key Research & Development Plan of Zhejiang Province; Fundamental Research Funds for the Provincial Universities of Zhejiang This work is supported in part by NSFC under Grant No. 61872119, Key Research & Development Plan of Zhejiang Province under Grant No. 2019C03134, and Fundamental Research Funds for the Provincial Universities of Zhejiang under Grants No. GK199900299012-025 and No. GK209907299001-020. 43 6 6 0 3 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 1533-5399 1557-6051 ACM T INTERNET TECHN ACM Trans. Internet. Technol. JUN 2021.0 21.0 2 35 10.1145/3425795 0.0 25 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science WQ2BS 2023-03-23 WOS:000713626000010 0
J Li, J; Cui, YJ; Wang, CH; Gu, CY; Liu, WQ Li, Jiang; Cui, Yijun; Wang, Chenghua; Gu, Chongyan; Liu, Weiqiang A Fully Configurable PUF Using Dynamic Variations of Resistive Crossbar Arrays IEEE TRANSACTIONS ON NANOTECHNOLOGY English Article Physical unclonable function (PUF); resistive random access memory (RRAM); authentication; reconfigurability; modelling attacks AUTHENTICATION; LIGHTWEIGHT; ROBUST A resistive random access memory (RRAM) as an emerging nanoelectronic device, is widely used for memory and physical unclonable function (PUF) applications. The compatibility of RRAM PUFs with memory architectures can be exploited to reduce the hardware overhead. Therefore, an intrinsic PUF using dynamic variations of resistive crossbar arrays is presented in this paper. Based on an improved sense amplifier (SA), the proposed intrinsic RRAM PUF can be fully configured as a memory cell or a PUF cell, leading to a minimal design overhead. Using the device-to-device (D2D) variation of an RRAM, a significant number of challenge-response pairs (CRPs) is generated with a flexible configuration of the resistive crossbar arrays. The proposed RRAM PUF can be refreshed to a new instance relying on the cycle-to-cycle (C2C) variation of an RRAM. An efficient challenge generation method is presented to improve the security of the proposed RRAM PUF. To verify the performance of the proposed RRAM PUF, 20 instances are simulated and implemented using a compact Spice model and a UMC 65 nm CMOS process technology, respectively. The simulation results show that the proposed RRAM PUF exhibits good performances with a high uniqueness, reliability, and reconfigurability. The randomness of the PUF is evaluated by the National Institute of Standards and Technology (NIST) and autocorrelation function (ACF) tests. Moreover, the experimental results show that the proposed RRAM PUF achieves a good resistance against machine learning (ML) attacks. [Li, Jiang; Cui, Yijun; Wang, Chenghua; Liu, Weiqiang] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China; [Gu, Chongyan] Queens Univ Belfast, Ctr Secure Informat Technol, Belfast BT7 1NN, North Ireland Nanjing University of Aeronautics & Astronautics; Queens University Belfast Cui, YJ; Liu, WQ (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China. lijiang@nuaa.edu.cn; yijun.cui@nuaa.edu.cn; chwang@nuaa.edu.cn; c.gu@qub.ac.uk; liuweiqiang@nuaa.edu.cn Li, Jiang/0000-0002-2792-3951; Liu, Weiqiang/0000-0001-8398-8648 Natural Science Foundation of Jiangsu Province [BK20210287]; National Natural Science Foundation of China [62104107, 62022041, 62134002]; EPSRC New Investigator Award [EP/X009602/1]; Royal Society [IEC\NSFC\211024] Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); EPSRC New Investigator Award(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Society(Royal Society of London) This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210287, in part by the National Natural Science Foundation of China under Grants 62104107, 62022041, and 62134002, in part by the EPSRC New Investigator Award under Grant EP/X009602/1, and in part by the Royal Society under Grant IEC\NSFC\211024. The review of this article was arranged by the guest editors of the Special Issue for NANOARCH2021. 37 0 0 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1536-125X 1941-0085 IEEE T NANOTECHNOL IEEE Trans. Nanotechnol. 2022.0 21.0 737 746 10.1109/TNANO.2022.3221372 0.0 10 Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Science & Technology - Other Topics; Materials Science; Physics 6S3VW Green Accepted 2023-03-23 WOS:000892919800005 0
J Wang, YP; Li, WD; Fang, R; Zhu, HH; Peng, Q Wang, Yuping; Li, Weidong; Fang, Run; Zhu, Honghui; Peng, Qiao Capacities prediction and correlation analysis for lithium-ion battery-based energy storage system CONTROL ENGINEERING PRACTICE English Article Lithium-ion battery; Battery-based energy storage system; Capacity predictions; Battery parameter analysis; Data-driven model FAULT-DIAGNOSIS; MODEL; CHALLENGES Lithium-ion battery-based energy storage system plays a pivotal role in many low-carbon applications such as transportation electrification and smart grid. The performance of battery significantly depends on its capacities under different operational current cases, which would be affected and determined by its component parameters interacting with one another. Due to the complex interdependency of electrical, chemical, and mechanical dynamics within a battery, it is a key but challenging issue to predict battery capacities under various current cases and analyze correlations of key parameters within a battery. This paper proposes an XGBoost-based interpretable machine learning framework, which fills the gap of predicting and analyzing how battery capacities under different current rates with respect to battery component parameters of interest. The parameter importance ranking is obtained by using the Gini index within the XGBoost model, while the correlations of all parameter pairs are quantified by using the predictive measure of association. The proposed framework is tested in two popular lithium-ion battery types with three various current levels. Illustrative results show that the proposed XGBoost-based framework is able to not only produce satisfactory capacity prediction performance but also provide reliable importance as well as correlation quantifications of involved battery component parameters. These could promote the prediction and analysis of battery capacities under different current rates, further benefitting the monitoring and optimization of battery management for wider low-carbon applications. [Wang, Yuping; Li, Weidong; Zhu, Honghui] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China; [Li, Weidong] Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China; [Fang, Run] Wuhan Univ, Elect Informat Sch, Wuhan, Peoples R China; [Peng, Qiao] Queens Univ Belfast, Belfast, North Ireland Wuhan University of Technology; University of Shanghai for Science & Technology; Wuhan University; Queens University Belfast Li, WD (corresponding author), Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Peoples R China.;Li, WD (corresponding author), Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China.;Peng, Q (corresponding author), Queens Univ Belfast, Belfast, North Ireland. yuping.wang@whut.edu.cn; weidongli@usst.edu.cn; 2016202120061@whu.edu.cn; h2_zhu@163.com; qpeng01@qub.ac.uk Wang, Yu/GZL-9655-2022 Peng, Qiao/0000-0002-3837-3932 National Natural Science Founda-tion of China [51975444] National Natural Science Founda-tion of China(National Natural Science Foundation of China (NSFC)) Acknowledgments This research is sponsored by the National Natural Science Founda-tion of China (Project No. 51975444) . 44 0 0 14 21 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0967-0661 1873-6939 CONTROL ENG PRACT Control Eng. Practice AUG 2022.0 125.0 105224 10.1016/j.conengprac.2022.105224 0.0 JUN 2022 10 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering 2C7VH 2023-03-23 WOS:000811070600003 0
J Lin, K; Gao, J; Li, YH; Savaglio, C; Fortino, G Lin, Kai; Gao, Jian; Li, Yihui; Savaglio, Claudio; Fortino, Giancarlo Multi-Granularity Collaborative Decision With Cognitive Networking in Intelligent Transportation Systems IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article; Early Access Decision making; Collaboration; Multitasking; Task analysis; Real-time systems; Granular computing; Vehicle dynamics; Cognitive networking; granular computing; real-time decision; deep learning; intelligent transportation system INTERNET; PREDICTION; RADIO Cognitive networking is a valuable enabler to improve the capability of intelligent transportation system (ITS) by analyzing and utilizing the heterogeneous traffic information. However, the significant increase in the amount of decision-making tasks makes it difficult to guarantee real-time performance of decision response. This paper focuses on the problem of the quality and real-time assurance of collaborative decision-making response in large-scale ITS during multi-task parallelism execution. First, a collaborative decision architecture with cognitive networking is developed, which introduces the advanced 6G communication technology to enhance information interaction capability of vehicle-road-cloud collaboration, and lays the foundation for multi-task real-time decision-making with inevitable fuzzy information in the perception process. Then, a multi-task parallel multi-granularity collaborative decision model (MPMCD) is designed to improve knowledge discovery ability for decision-making process by building multi-granularity information structures. An AI-driven cognitive networking collaborative decision-making (ACNCD) algorithm is further proposed based on MPMCD model to support multi-task parallel vehicle-road-cloud collaborative real-time decision. Extensive simulation experiments are carried out to evaluate ACNCD algorithm in terms of several performance criteria including decision response time, accuracy, and accident rate. The obtained results show that the comprehensive decision-making performance of ACNCD outperforms other relevant existing algorithms. [Lin, Kai; Gao, Jian; Li, Yihui] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China; [Savaglio, Claudio] Univ Calabria, Inst High Performance Comp & Networking, Italian Natl Res Council ICAR CNR, I-87036 Arcavacata Di Rende, Italy; [Fortino, Giancarlo] Univ Calabria, Dept Informat Modeling Elect & Syst DIMES, I-87036 Arcavacata Di Rende, Italy Dalian University of Technology; Consiglio Nazionale delle Ricerche (CNR); Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR-CNR); University of Calabria; University of Calabria Lin, K (corresponding author), Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China. link@dlut.edu.cn; 22009183@mail.dlut.edu.cn; 928982000@mail.dlut.edu.cn; claudio.savaglio@icar.cnr.it; g.fortino@unical.it Fortino, Giancarlo/J-2950-2017; Savaglio, Claudio/D-2519-2017 Fortino, Giancarlo/0000-0002-4039-891X; Savaglio, Claudio/0000-0001-5092-0823 National Key Research and Development Program of China [2020YFB170458]; Fundamental Research Funds for the Central Universities [DUT19JC22, DUT18RC(3)069]; National Natural Science Foundation of China [U1908214]; Liaoning Province Higher Education Innovative Talent Support Program; Italian Ministry of Education, University and Research (MIUR), Research Projects of National Interest (PRIN) [CUP H24I17000070001] National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Liaoning Province Higher Education Innovative Talent Support Program; Italian Ministry of Education, University and Research (MIUR), Research Projects of National Interest (PRIN)(Ministry of Education, Universities and Research (MIUR)) This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB170458, in part by the Fundamental Research Funds for the Central Universities under Grant DUT19JC22 and Grant DUT18RC(3)069, in part by the National Natural Science Foundation of China under Grant U1908214, in part by the Liaoning Province Higher Education Innovative Talent Support Program, and in part by the Italian Ministry of Education, University and Research (MIUR), Research Projects of National Interest (PRIN) 2017 Project Fluidware under Grant CUP H24I17000070001. The Associate Editor for this article was Y. Zhang. 30 1 1 14 25 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. 10.1109/TITS.2022.3151754 0.0 MAR 2022 11 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation ZR5RA 2023-03-23 WOS:000767838600001 0
C Li, HW; Xue, FF; Chaitanya, K; Luo, SD; Ezhov, I; Wiestler, B; Zhang, JG; Menze, B DeBruijne, M; Cattin, PC; Cotin, S; Padoy, N; Speidel, S; Zheng, Y; Essert, C Li, Hongwei; Xue, Fei-Fei; Chaitanya, Krishna; Luo, Shengda; Ezhov, Ivan; Wiestler, Benedikt; Zhang, Jianguo; Menze, Bjoern Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT II Lecture Notes in Computer Science English Proceedings Paper International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 27-OCT 01, 2021 ELECTR NETWORK CANCER Radiomics can quantify the properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low-level image features. Alternatively, deep learning-based representations are derived from supervised learning but require expensive annotations and often suffer from overfitting and data imbalance issues. In this work, we address the challenge of learning the representation of a 3D medical image for an effective quantification under data imbalance. We propose a self-supervised representation learning framework to learn high-level features of 3D volumes as a complement to existing radiomics features. Specifically, we demonstrate how to learn image representations in a self-supervised fashion using a 3D Siamese network. More importantly, we deal with data imbalance by exploiting two unsupervised strategies: a) sample re-weighting, and b) balancing the composition of training batches. When combining the learned self-supervised feature with traditional radiomics, we show significant improvement in brain tumor classification and lung cancer staging tasks covering MRI and CT imaging modalities. Codes are available in https://github.com/hongweilibran/imbalanced-SSL. [Li, Hongwei; Ezhov, Ivan; Menze, Bjoern] Tech Univ Munich, Dept Comp Sci, Munich, Germany; [Li, Hongwei; Menze, Bjoern] Univ Zurich, Dept Quantitat Biomed, Zurich, Switzerland; [Chaitanya, Krishna] Swiss Fed Inst Technol, Zurich, Switzerland; [Xue, Fei-Fei; Zhang, Jianguo] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China; [Luo, Shengda] Macau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China; [Wiestler, Benedikt] Tech Univ Munich, Klinikum Rechts Isar, Munich, Germany Technical University of Munich; University of Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; Southern University of Science & Technology; Macau University of Science & Technology; Technical University of Munich Zhang, JG (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China. hongwei.li@tum.de; zhangjg@sustech.edu.cn Li, Hongwei Bran/HJH-5317-2023 Li, Hongwei Bran/0000-0002-5328-6407; zhang, jianguo/0000-0001-9317-0268; Menze, Bjoern/0000-0003-4136-5690 Helmut Horten Foundation; TRABIT network under the EU Marie Sklodowska-Curie program [765148]; Macau University of Science and Technology [FRG-18-020-FI]; DFG [SFB-824]; Clinical Research Priority Program (CRPP) Grant on Artificial Intelligence in Oncological Imaging Network, University of Zurich Helmut Horten Foundation; TRABIT network under the EU Marie Sklodowska-Curie program; Macau University of Science and Technology; DFG(German Research Foundation (DFG)); Clinical Research Priority Program (CRPP) Grant on Artificial Intelligence in Oncological Imaging Network, University of Zurich This work was supported by Helmut Horten Foundation. I. E. was supported by the TRABIT network under the EU Marie Sklodowska-Curie program (Grant ID: 765148). S. L. was supported by the Faculty Research Grant (NO. FRG-18-020-FI) at Macau University of Science and Technology. B. W. and B. M. were supported through the DFG, SFB-824, subproject B12. K. C. was supported by Clinical Research Priority Program (CRPP) Grant on Artificial Intelligence in Oncological Imaging Network, University of Zurich. 24 4 4 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-87196-3; 978-3-030-87195-6 LECT NOTES COMPUT SC 2021.0 12902.0 36 46 10.1007/978-3-030-87196-3_4 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Biomedical; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging BS3JX Green Submitted 2023-03-23 WOS:000712020700004 0
C Zeng, YP; Zhang, H; Fang, ZM; Liu, XD; Crociani, L; Vizzari, G ACM Zeng, Yiping; Zhang, Hui; Fang, Zhiming; Liu, Xiaodong; Crociani, Luca; Vizzari, Giuseppe Lane-formation in counter-flow based on DBSCAN PROCEEDINGS OF THE 4TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON SAFETY AND RESILIENCE (EM-GIS 2018) English Proceedings Paper 4th ACM SIGSPATIAL International Workshop on Safety and Resilience (EM-GIS) NOV 06, 2018 Seattle, WA ACM SIGSPATIAL Large-scale activities; DBSCAN; Lane formation; Counter-flow POTENTIAL-FIELD; SIMULATION; DRIVEN; BEHAVIOR; MODEL With(1) the development of society, more and more large-scale activities are conducted in the cities. The issue that how to avoid emergency in respect of human performance has attracted a lot of researchers. It is easy to find the counter-flow in large-scale activities. Bi-directional movement has attracted a lot of researchers to investigate. More attention is paid to the existence of lane formation in respect of experiments and simulations. However, few attentions are drawn to the definition of lane formation in counter-flow. In this paper, a method based on deep learning technique(DBSCAN) is proposed to define lane formation. Lane formation based on DBSCAN is used to gather the pedestrians in a group, and order parameters and time step are taken into consideration in order to obtain the number of lane formation explicitly. Then proposed method is compared with previous lane formation definition. All the lanes defined by previous method are quantified during movement process, which could reflect self-organization in time, while method introduced in this manuscript are measured after movement, which needs more data after event. The proposed method has the ability to calculate the number of the lanes. The work in this paper is intended to better understand movement during counter-flow in a corridor and develop a technical foundation for evacuation strategies in large-scale activities. [Zeng, Yiping; Zhang, Hui] Tsinghua Univ, Inst Publ Safety Res, Beijing, Peoples R China; [Fang, Zhiming] Univ Shanghai Sci & Technol, Business Sch, Shanghai, Peoples R China; [Liu, Xiaodong] Peoples Publ Secur Univ China, Publ Order Sch, Beijing, Peoples R China; [Crociani, Luca; Vizzari, Giuseppe] Univ Milano Bicocca, CSAI Res Ctr, Milan, Italy Tsinghua University; University of Shanghai for Science & Technology; People's Public Security University of China; University of Milano-Bicocca Zhang, H (corresponding author), Tsinghua Univ, Inst Publ Safety Res, Beijing, Peoples R China. ypzeng@mail.tsinghua.edu.cn; zhhui@tsinghua.edu.cn; zhmfang2015@163.com; skdlxd@163.com; luca.crociani@disco.unimib.it; giuseppe.vizzari@unimib.it Vizzari, Giuseppe/0000-0002-7916-6438 36 2 2 0 1 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-6044-9 2018.0 3 10.1145/3284103.3284106 0.0 6 Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS1OZ 2023-03-23 WOS:000694006700003 0
J Wang, TT; Pan, ZX; Ge, M; Yang, Z; Li, HZ Wang, Tingting; Pan, Zexu; Ge, Meng; Yang, Zhen; Li, Haizhou Time-Domain Speech Separation Networks With Graph Encoding Auxiliary IEEE SIGNAL PROCESSING LETTERS English Article Time-domain analysis; Encoding; Speech recognition; Convolution; Speech enhancement; Signal to noise ratio; Transforms; Source separation; single channel; deep learning; graph signal processing; graph neural networks FILTERBANK End-to-end time-domain speech separation with masking strategy has shown its performance advantage, where a 1-D convolutional layer is used as the speech encoder to encode a sliding window of waveform to a latent feature representation, i.e. an embedding vector. A large window leads to low resolution in the speech processing, on the other hand, a small window offers high resolution but at the expense of high computational cost. In this work, we propose a graph encoding technique to model the fine structural knowledge of speech samples in a window of reasonable size. Specifically, we build a graph representation for each latent representation, and encode the structural details with a graph convolutional network encoder. The encoded graph feature representation complements the original latent feature representation and benefits the separation and reconstruction of speech. Experiments on various models and datasets show that our proposed encoding technique significantly improves the speech quality over other time-domain speech encoders. [Pan, Zexu; Li, Haizhou] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119077, Singapore; [Ge, Meng] Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300072, Peoples R China; [Wang, Tingting; Yang, Zhen] Nanjing Univ Posts & Telecommun, Dept Commun & Informat Engn, Nanjing 210023, Peoples R China; [Li, Haizhou] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China; [Li, Haizhou] Univ Bremen, D-28359 Bremen, Germany; [Li, Haizhou] Kriston AI, Xiamen 361021, Peoples R China; [Li, Haizhou] Natl Univ Singapore, Singapore 119077, Singapore National University of Singapore; Tianjin University; Nanjing University of Posts & Telecommunications; Chinese University of Hong Kong, Shenzhen; University of Bremen; National University of Singapore Ge, M (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300072, Peoples R China. 2018010215@njupt.edu.cn; pan_zexu@u.nus.edu; gemeng@tju.edu.cn; yangz@njupt.edu.cn; haizhou.li@nus.edu.sg National Natural Science Foundation of China [62071242, 62271432]; Internal Project Fund Shenzhen Research Institute of Big Data under Grant [T00120220002]; Guangdong Provincial Key Laboratory of Big Data Computing; Chinese University of Hong Kong [B10120210117-KP02]; Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX19-0897]; DFG (German Research Foundation); University Allowance; University of Bremen National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Internal Project Fund Shenzhen Research Institute of Big Data under Grant; Guangdong Provincial Key Laboratory of Big Data Computing; Chinese University of Hong Kong(Chinese University of Hong Kong); Postgraduate Research and Practice Innovation Program of Jiangsu Province; DFG (German Research Foundation)(German Research Foundation (DFG)); University Allowance; University of Bremen This work was supported in part by the National Natural Science Foundation of China under Grants 62071242 and 62271432, in part by Internal Project Fund Shenzhen Research Institute of Big Data under Grant T00120220002, in part by the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen under Grant B10120210117-KP02, in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX19-0897, and in part by DFG (German Research Foundation) through Germany's Excellence Strategy (University Allowance, EXC 2077, University of Bremen). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Joseph Keshet. 41 0 0 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1070-9908 1558-2361 IEEE SIGNAL PROC LET IEEE Signal Process. Lett. 2023.0 30.0 110 114 10.1109/LSP.2023.3243764 0.0 5 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 9E9NL 2023-03-23 WOS:000937104700002 0
C Ahmed, H; Zhang, Y; Zafar, MS; Sheikh, N; Tai, ZY Li, G; Shen, HT; Yuan, Y; Wang, X; Liu, H; Zhao, X Ahmed, Hasnat; Zhang, Yangyang; Zafar, Muhammad Shoaib; Sheikh, Nasrullah; Tai, Zhenying Node Embedding over Attributed Bipartite Graphs KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT (KSEM 2020), PT I Lecture Notes in Computer Science English Proceedings Paper 13th International Conference on Knowledge Science, Engineering and Management (KSEM) AUG 28-30, 2020 Hangzhou, PEOPLES R CHINA Attributed bipartite graphs; Network embedding; Link prediction; Classification This work investigates the modeling of attributes along with network structure for representation learning of the bipartite networks. Most of the attributed network representation learning (NRL) works consider the homogeneous type network only; However, these methods, when apply to bipartite type networks, may not be beneficial to learn an informative representation of nodes for predictive analysis. Hence, we propose a BIGAT2VEC framework that examines the internode relationships in the form of direct and indirect relations between two different as well as the same node type of bipartite network to preserve both structure and attribute context. In BIGAT2VEC, learning is enforced on two levels: (1) direct inter-node relationship between nodes of different type (either through the edge or attribute similarities perspective) by minimizing the probabilities through KL divergence; (2) indirect inter-node relationship within same node type (either through 2nd order neighborhood proximity and attributes similarities perspective) by employing shallow neural network model through maximizing the probabilities. These two levels are separately optimized, and we leverage its learned embeddings through late fusion to further execute the network mining tasks such as link prediction, node classification (multi-class and multilabel), and visualization. We perform extensive experiments on various datasets and evaluate our method with several baselines. The results show the BIGAT2VEC efficacy as compare to other (non)attributed representation learning methods. [Ahmed, Hasnat; Zhang, Yangyang; Zafar, Muhammad Shoaib] Beihang Univ BUAA, Sch Comp Sci & Engn, Beijing 100191, Peoples R China; [Sheikh, Nasrullah] Univ Trento, Trento, Italy; [Zhang, Yangyang; Tai, Zhenying] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China Beihang University; University of Trento; Beihang University Tai, ZY (corresponding author), Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China. hasnatsatti@buaa.edu.cn; zhangyy@act.buaa.edu.cn; mszafar.dcs@outlook.com; nasrullah.sheikh@unitn.it; taizy@act.buaa.edu.cn 9 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-55130-8; 978-3-030-55129-2 LECT NOTES COMPUT SC 2020.0 12274.0 202 210 10.1007/978-3-030-55130-8_18 0.0 9 Computer Science, Information Systems; Computer Science, Theory & Methods; Operations Research & Management Science Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Operations Research & Management Science BU2SL 2023-03-23 WOS:000886461700018 0
J Li, YB; Tao, FL Li, Yibo; Tao, Fulu Interactions of genotype, environment and management on wheat traits and grain yield variations in different climate zones across China AGRICULTURAL SYSTEMS English Article Agronomy; Management practices; Yield potential; Cultivar traits; Trade-offs PAST 3 DECADES; WINTER-WHEAT; RICE YIELDS; PHYSIOLOGICAL TRAITS; AGRONOMIC MANAGEMENT; GENETIC-IMPROVEMENT; NIGHT TEMPERATURE; MAIZE HYBRIDS; STAY-GREEN; TRENDS CONTEXT: Concerns about the effects of global warming on crop yields have risen due to stagnating yield progress in recent years. Understanding crop traits and grain yield variations in different climate zones is critical to gain insights into crop response and adaptation mechanisms to ongoing climate change. However, the in-teractions of genotype, environment and management on crop traits and historical yield progress are lacking.OBJECTIVE: This study is to investigate the variations and trends in wheat traits in different climate zones across China, trade-offs among phenological and yield-related traits, the effects of genotype, environment and man-agement interactions on wheat traits, as well as the contributions of traits, management, climate and genetic changes to grain yield.METHODS: The genotype, environment and management interactions on wheat traits and grain yield variations were analyzed based on long-term field trials during 2003-2016 at five representative stations across China. And then, a machine learning method was applied to quantify the relative contributions of these factors to wheat traits and grain yield variations.RESULTS AND CONCLUSIONS: A significant warming trend during the reproductive growth period and a decreasing trend in solar radiation were observed. Significant trends and variations in wheat traits and signifi-cant interactions between genotype, station and year were discovered. Compared with fertilization, grain yield under no-fertilization was significantly lower by 80.3% at five representative stations across China. With local farmer's practices, about 3.5-40.9%, 19.6-58.3% and 0.1-8.2% of the yield variations were associated with agricultural management practices, climate change and cultivar shifts, respectively, contradicting some previous studies which highlight the weight of genetic yield improvement.SIGNIFICANCE: The findings are valuable for understanding the mechanisms underlying crop traits and grain yield variations, for breeders to breed desirable traits, and for agronomists to adopt suitable cultivars and agricultural management practices. [Li, Yibo; Tao, Fulu] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; [Li, Yibo; Tao, Fulu] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Tao, Fulu] Nat Resources Inst Finland Luke, Helsinki, Finland Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Natural Resources Institute Finland (Luke) Tao, FL (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China. taofl@igsnrr.ac.cn National Natural Science Foundation of China; [31761143006]; [41571493] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; Acknowledgements This study is supported by the National Natural Science Foundation of China (Project Nos. 31761143006, 41571493) . 85 0 0 38 38 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0308-521X 1873-2267 AGR SYST Agric. Syst. DEC 2022.0 203.0 103521 10.1016/j.agsy.2022.103521 0.0 14 Agriculture, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Agriculture 4Z7GW 2023-03-23 WOS:000862373300001 0
J Sun, YC; Yan, HL; Lu, C; Bie, RF; Zhou, ZB Sun, Yunchuan; Yan, Hongli; Lu, Cheng; Bie, Rongfang; Zhou, Zhangbing Constructing the Web of Events from raw data in the Web of Things MOBILE INFORMATION SYSTEMS English Article Web of Events; Web of Things; restful; information extraction; mobile MANAGEMENT; INTERNET An exciting paradise of data is emerging into our daily life along with the development of the Web of Things. Nowadays, volumes of heterogeneous raw data are continuously generated and captured by trillions of smart devices like sensors, smart controls, readers and other monitoring devices, while various events occur in the physical world. It is hard for users including people and smart things to master valuable information hidden in the massive data, which is more useful and understandable than raw data for users to get the crucial points for problems-solving. Thus, how to automatically and actively extract the knowledge of events and their internal links from the big data is one key challenge for the future Web of Things. This paper proposes an effective approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things, which starts with grasping the critical data for useful events by filtering data with well-defined event types in the schema. A case study in the context of smart campus is presented to show the application of proposed approach for the extraction of events and their internal semantic links. [Sun, Yunchuan; Yan, Hongli] Beijing Normal Univ, Sch Business, Beijing 100875, Peoples R China; [Lu, Cheng; Bie, Rongfang] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China; [Zhou, Zhangbing] China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China; [Zhou, Zhangbing] Inst Mines TELECOM TELECOM SudParis, Dept Comp Sci, Paris, France Beijing Normal University; Beijing Normal University; China University of Geosciences; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris Bie, RF (corresponding author), Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China. rfbie@bnu.edu.cn Sun, Yunchuan/AAN-8060-2020 National Natural Science Foundation of China [61171014]; Fundamental Research Funds for the Central Universities National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This research is supported by National Natural Science Foundation of China (61171014) and the Fundamental Research Funds for the Central Universities. 45 54 54 0 46 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1574-017X 1875-905X MOB INF SYST Mob. Inf. Syst. 2014.0 10.0 1 105 125 10.1155/2014/517486 0.0 21 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications AA4TP 2023-03-23 WOS:000331089400006 0
J Li, XM; Shi, DQ; Diao, XL; Xu, H Li, Xiaomin; Shi, Daqian; Diao, Xiaolei; Xu, Hao SCL-MLNet: Boosting Few-Shot Remote Sensing Scene Classification via Self-Supervised Contrastive Learning IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Remote sensing; Task analysis; Measurement; Training; Fuses; Adaptation models; Feature representation; few-shot learning; remote sensing (RS) scene classification; self-supervised contrastive learning (SCL) CONVOLUTIONAL NEURAL-NETWORK Few-shot classification aims at recognizing novel categories from low data regimes based on prior knowledge. However, the existing methods for few-shot scene classification have limitations on using few annotated data and do not fully consider the intra-class samples with classification targets in different sizes, which lead to poor feature representation. To address these problems, this study introduces an end-to-end framework called self-supervised contrastive learning-based metric learning network (SCL-MLNet) for few-shot remote sensing (RS) scene classification. On one hand, we weave self-supervised contrastive learning into few-shot classification algorithms through multi-task learning, enabling feature extractors to learn representative image features from few annotated samples. Moreover, we devise a new loss function to train the proposed model end-to-end and speed up the convergence of the model. On the other hand, considering the differences between intra-class samples, we introduce a novel attention module embedded in the feature extractor to fuse multi-scale spatial features from the classification targets in different sizes. In our experiments, SCL-MLNet is evaluated on three public benchmark datasets. The results demonstrate that SCL-MLNet achieves state-of-the-art performance for few-shot remote sensing scene classification. [Li, Xiaomin; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China; [Shi, Daqian; Diao, Xiaolei] Univ Trento, Dept Informat Engn & Comp Sci, I-38122 Trento, Italy Jilin University; University of Trento Xu, H (corresponding author), Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China. xuhao@jlu.edu.cn Diao, Xiaolei/0000-0002-3269-8103 National Natural Science Foundation of China [62077027]; Ministry of Science and Technology of the People's Republic of China [2018YFC2002500]; Jilin Province Development and Reform Commission, China [2019C053-1]; Education Department of Jilin Province, China [JJKH20200993K]; Department of Science and Technology of Jilin Province, China [20200801002GH]; European Union [823783] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Ministry of Science and Technology of the People's Republic of China(Ministry of Science and Technology, China); Jilin Province Development and Reform Commission, China; Education Department of Jilin Province, China; Department of Science and Technology of Jilin Province, China; European Union(European Commission) This work was supported in part by the National Natural Science Foundation of China under Grant 62077027, in part by the Ministry of Science and Technology of the People's Republic of China under Grant 2018YFC2002500, in part by the Jilin Province Development and Reform Commission, China under Grant 2019C053-1, in part by the Education Department of Jilin Province, China under Grant JJKH20200993K, in part by the Department of Science and Technology of Jilin Province, China under Grant 20200801002GH, and in part by the European Union's Horizon 2020 Future and Emerging Technologies (FET) Proactive project WeNet-The Internet of us under Grant 823783. (Corresponding author: Hao Xu.) 50 10 10 19 52 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 10.1109/TGRS.2021.3109268 0.0 SEP 2021 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology YP2XM 2023-03-23 WOS:000732784000001 0
C Laroui, M; Sellami, A; Nour, B; Moungla, H; Afifi, H; Hacene, SB IEEE Laroui, Mohammed; Sellami, Akrem; Nour, Boubakr; Moungla, Hassine; Afifi, Hossam; Hacene, Sofiane B. Driving Path Stability in VANETs 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) IEEE Global Communications Conference English Proceedings Paper IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV DEC 09-13, 2018 Abu Dhabi, U ARAB EMIRATES IEEE VANET; Stability of communication path; SVR MODEL Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution. [Laroui, Mohammed; Hacene, Sofiane B.] Univ Djillali Liabes Sidi Bel Abbes, Evotionary Engn & Distributed Informat Syst Lab, Sidi Bel Abbes, Algeria; [Laroui, Mohammed; Sellami, Akrem; Moungla, Hassine] Paris Descartes Univ, LIPADE, Sorbonne Paris Cite, Paris, France; [Nour, Boubakr] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China; [Moungla, Hassine; Afifi, Hossam] Telecom SudParis, Inst Mines Telecom, Nanoinnov CEA Saclay, CNRS,UMR 5157, Gif Sur Yvette, France University Djillali Liabes Sidi Bel Abbes; UDICE-French Research Universities; Universite Paris Cite; Beijing Institute of Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Laroui, M (corresponding author), Univ Djillali Liabes Sidi Bel Abbes, Evotionary Engn & Distributed Informat Syst Lab, Sidi Bel Abbes, Algeria.;Laroui, M (corresponding author), Paris Descartes Univ, LIPADE, Sorbonne Paris Cite, Paris, France. mohammed.laroui@univ-sba.dz; akrem.sellami@parisdescartes.fr; n.boubakr@bit.edu.cn; hassine.moungla@parisdescartes.fr; hossam.afifi@telecom-sudparis.eu; boukli@univ-sba.dz Sofiane, BOUKLI-HACENE/D-4750-2012; Nour, Boubakr/AAE-1240-2019 Sofiane, BOUKLI-HACENE/0000-0002-9760-3806; 24 5 5 0 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2334-0983 978-1-5386-4727-1 IEEE GLOB COMM CONF 2018.0 6 Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Telecommunications BM5YU 2023-03-23 WOS:000465774301147 0
C Menouer, T; Khedimi, A; Cerin, C; Jiang, CF Qiu, M Menouer, Tarek; Khedimi, Amina; Cerin, Christophe; Jiang, Congfeng Cloud Allocation and Consolidation Based on a Scalability Metric ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT III Lecture Notes in Computer Science English Proceedings Paper 20th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP) OCT 02-04, 2020 New York, NY Springer LNCS,Columbia Univ,N Amer Chinese Talents Assoc,Longxiang High Tech Grp Inc Cloud computing; Resource management and scheduling; Performance of cloud systems; Consolidation of servers; Scalability In this paper, we present a new allocation and resource consolidation system based on a scalability metric. According to cloud computing principles, the end users rent computing and big data analytic services with a pay-as-you-go cost model. However, when users' data size increases or when the application stresses the memory or requires more computing power, they need to scale their rental to achieve approximately the same performance, such as task completion time and normalized system throughput. In this paper, we propose to delegate the responsibility to scale-up and scale-out the cloud system to a new component of the cloud orchestrator. The decision is taken on a metric that quantifies the scalability of the cloud system consistently under different system expansion configurations. The scalability metric is defined as a ratio between the new and the current situation, over the size of the i-th workload in terms of CPU cores and a performance metric. The considered metrics are the waiting time in the queue and the average resource (cores) usage rate during task execution. To validate our approach, we conduct experiments by emulation, on a real platform. The experimental results demonstrate the validity of the proposed general automatic strategies for cloud allocation and consolidation. [Menouer, Tarek] Umanis Res & Innovat, 7 Rue Paul Vaillant Couturier, F-92300 Levallois Perret, France; [Khedimi, Amina; Cerin, Christophe] Univ Sorbonne Paris Nord, LIPN, CNRS, UMR 7030, F-93430 Villetaneuse, France; [Jiang, Congfeng] Hangzhou Dianzi Univ, Hangzhou 310018, Zhejiang, Peoples R China Centre National de la Recherche Scientifique (CNRS); Hangzhou Dianzi University Menouer, T (corresponding author), Umanis Res & Innovat, 7 Rue Paul Vaillant Couturier, F-92300 Levallois Perret, France. tmenouer@umanis.com; Khedimi@lipn.univ-paris13.fr; christophe.cerin@lipn.univ-paris13.fr; cjiang@hdu.edu.cn Menouer, Tarek/0000-0003-0066-1733 scientific interest group (GIS); CNRS; RENATER scientific interest group (GIS); CNRS(Centre National de la Recherche Scientifique (CNRS)); RENATER We thank the Grid5000 team for their help to use the testbed. Grid5000 is supported by a scientific interest group (GIS) hosted by INRIA and including CNRS, RENATER and several Universities as well as other organizations. 30 0 0 0 0 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-030-60248-2; 978-3-030-60247-5 LECT NOTES COMPUT SC 2020.0 12454.0 381 395 10.1007/978-3-030-60248-2_26 0.0 15 Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS4GW 2023-03-23 WOS:000719296800026 0
J Xu, DT; Zhou, HY; Zhang, QL; Baker, JS; Ugbolue, UC; Radak, Z; Ma, X; Gusztav, F; Wang, MZ; Gu, YD Xu, Datao; Zhou, Huiyu; Zhang, Qiaolin; Baker, Julien S.; Ugbolue, Ukadike C.; Radak, Zsolt; Ma, Xin; Gusztav, Fekete; Wang, Meizi; Gu, Yaodong A new method proposed to explore the feline's paw bones of contributing most to landing pattern recognition when landed under different constraints FRONTIERS IN VETERINARY SCIENCE English Article animal biomechanics; cat paws; feline landing; post-processing of finite element analysis; feature engineering techniques; metaheuristic optimization algorithms GAIT Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage felines have nine lives seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics. [Xu, Datao; Zhou, Huiyu; Zhang, Qiaolin; Wang, Meizi; Gu, Yaodong] Ningbo Univ, Fac Sports Sci, Ningbo, Peoples R China; [Xu, Datao; Gusztav, Fekete] Eotvos Lorand Univ, Savaria Inst Technol, Szombathely, Hungary; [Xu, Datao; Gusztav, Fekete] Univ Pannonia, Fac Engn, Veszprem, Hungary; [Zhou, Huiyu; Ugbolue, Ukadike C.] Univ West Scotland, Sch Hlth & Life Sci, Paisley, Scotland; [Baker, Julien S.] Hong Kong Baptist Univ, Dept Sport & Phys Educ, Kowloon, Hong Kong, Peoples R China; [Radak, Zsolt] Univ Phys Educ, Res Inst Sport Sci, Budapest, Hungary; [Ma, Xin] Fudan Univ, Huashan Hosp, Dept Orthoped, Shanghai, Peoples R China; [Wang, Meizi] Obuda Univ, Fac Hlth & Safety, Budapest, Hungary Ningbo University; Eotvos Lorand University; University of Pannonia; University of West Scotland; Hong Kong Baptist University; University of Physical Education; Fudan University; Obuda University Gu, YD (corresponding author), Ningbo Univ, Fac Sports Sci, Ningbo, Peoples R China. guyaodong@nbu.edu.cn Ugbolue, Ukadike/0000-0002-6640-4126 Zhejiang Provincial Key Research and Development Program of China [2021C03130]; Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars [LR22A020002]; Philosophy and Social Sciences Project of Zhejiang Province, China [22QNYC10ZD, 22NDQN223YB]; Educational Science Planning Project of Zhejiang Province [2021SCG083]; Fundamental Research Funds for the Provincial Universities of Zhejiang [SJWY2022014]; Public Welfare Science and Technology Project of Ningbo, China [2021S134]; Teaching Research Project of Ningbo University [JYXMXZD2022008, JYXMXYB2021018]; K. C. Wong Magna Fund in Ningbo University Zhejiang Provincial Key Research and Development Program of China; Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars; Philosophy and Social Sciences Project of Zhejiang Province, China; Educational Science Planning Project of Zhejiang Province; Fundamental Research Funds for the Provincial Universities of Zhejiang; Public Welfare Science and Technology Project of Ningbo, China; Teaching Research Project of Ningbo University; K. C. Wong Magna Fund in Ningbo University This study was sponsored by the Zhejiang Provincial Key Research and Development Program of China (2021C03130), Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars (LR22A020002), Philosophy and Social Sciences Project of Zhejiang Province, China (22QNYC10ZD and 22NDQN223YB), Educational Science Planning Project of Zhejiang Province (2021SCG083), the Fundamental Research Funds for the Provincial Universities of Zhejiang (SJWY2022014), Public Welfare Science and Technology Project of Ningbo, China (2021S134), Teaching Research Project of Ningbo University (JYXMXZD2022008 and JYXMXYB2021018), and K. C. Wong Magna Fund in Ningbo University. 50 0 0 3 3 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2297-1769 FRONT VET SCI Front. Vet. Sci. OCT 10 2022.0 9.0 1011357 10.3389/fvets.2022.1011357 0.0 15 Veterinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Veterinary Sciences 5T8PA 36299631.0 Green Accepted, gold 2023-03-23 WOS:000876119800001 0
J Wang, RY; Dong, PX; Dong, GP; Xiao, X; Huang, JW; Yang, LC; Yu, YJ; Dong, GH Wang, Ruoyu; Dong, Pengxin; Dong, Guoping; Xiao, Xiang; Huang, Jingwen; Yang, Linchuan; Yu, Yunjiang; Dong, Guang-Hui Exploring the impacts of street-level greenspace on stroke and cardiovascular diseases in Chinese adults ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY English Article Street -level greenspace; Tecent Street View; CVDs; Stroke; Difference in vegetation type AIR-POLLUTION; PHYSICAL-ACTIVITY; BLOOD-PRESSURE; URBAN; GREENNESS; HYPERTENSION; OBESITY; SCENES; SPACE; RISK In recent years, cardiovascular diseases (CVDs) have become the primary cause of death in the world. Existing studies have found that greenspace is important for the prevention of CVDs and stroke. However, since they only focus on large green infrastructure (e.g., urban parks) or the general greenspace (usually being evaluated through normalized difference vegetation index), little information exists regarding the association between street-level greenspace and CVDs (stroke). In this study, the CVDs and stroke data of participants were retrieved from the 33 Chinese Community Health Study. We measured participants' exposure to street-level greenspace exposure using street view images and machine learning technique. Multilevel logistic regressions were applied. While con-trolling for confounders, we found that higher level of street-level greenspace exposure was associated with lower CVDs prevalence. However, street-level greenspace exposure was associated with stroke prevalence only for females. The associations were stronger among females, younger adults, participants with educational attainment above high school, physically active participants and participants who were not overweight. None of the mediators (air pollution, physical exercise, and BMI) can explain the associations between street-level greenspace exposure and CVDs (stroke) prevalence. Our findings suggest that street-level vegetation should be increased to cope with the rapid growth of the CVDs burdens. Also, the differences between the effect of street -level trees and grasses should be noted before formulating specific urban planning policies. [Wang, Ruoyu] Queens Univ Belfast, UKCRC Ctr Excellence Publ Hlth, Ctr Publ Hlth, Belfast, North Ireland; [Dong, Pengxin] Guangxi Med Univ, Nursing Coll, Nanning 530021, Peoples R China; [Dong, Guoping] Guangzhou Huashang Coll, Sch Accounting, Guangzhou 511300, Peoples R China; [Xiao, Xiang] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China; [Huang, Jingwen; Dong, Guang-Hui] Sun Yat Sen Univ, Guangdong Prov Engn Technol Res Ctr Environm & Hlt, Sch Publ Hlth, Dept Occupat & Environm Hlth, Guangzhou 510080, Peoples R China; [Yang, Linchuan] Southwest Jiaotong Univ, Sch Architecture & Design, Dept Urban & Rural Planning, Chengdu, Peoples R China; [Yu, Yunjiang] Minist Environm Protect, South China Inst Environm Sci, State Environm Protect Key Lab Environm Pollut Hlt, Guangzhou 510655, Peoples R China; [Dong, Guang-Hui] Sun Yat Sen Univ, Guangdong Prov Engn Technol Res Ctr Environm Pollu, Sch Publ Hlth, Dept Prevent Med, 74 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China Queens University Belfast; Guangxi Medical University; Hong Kong Baptist University; Sun Yat Sen University; Southwest Jiaotong University; Sun Yat Sen University Yu, YJ (corresponding author), Minist Environm Protect, South China Inst Environm Sci, State Environm Protect Key Lab Environm Pollut Hlt, Guangzhou 510655, Peoples R China.;Dong, GH (corresponding author), Sun Yat Sen Univ, Guangdong Prov Engn Technol Res Ctr Environm Pollu, Sch Publ Hlth, Dept Prevent Med, 74 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China. r.wang@qub.ac.uk; dongpx2021@hotmail.com; donggp97@hotmail.com; xiaoxiang@life.hkbu.edu.hk; huangjw98@mail2.sysu.edu.cn; yanglc0125@swjtu.edu.cn; yuyunjiang@scies.org; donggh512@hotmail.com Yang, Linchuan/ABF-1874-2021 Yang, Linchuan/0000-0001-6070-9044 National Natural Science Foundation of China [M-0420, 82073502, 81872582, 81872583]; Natural Science Foundation of Guangdong Province [2021A1515012212, 2021A151011754, 2021B1515020015, 2020A1515011131, 2019A050510017, 2018B05052007, 2017A090905042] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province) This work was supported by the National Natural Science Foundation of China (M-0420, 82073502, 81872582, and 81872583) , Natural Science Foundation of Guangdong Province (No. 2021A1515012212; No. 2021A151011754; No. 2021B1515020015; No. 2020A1515011131; No. 2019A050510017; No. 2018B05052007; No. 2017A090905042) . 56 1 1 35 35 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0147-6513 1090-2414 ECOTOX ENVIRON SAFE Ecotox. Environ. Safe. SEP 15 2022.0 243.0 113974 10.1016/j.ecoenv.2022.113974 0.0 AUG 2022 10 Environmental Sciences; Toxicology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Toxicology 4I9CX 35988381.0 gold, Green Published 2023-03-23 WOS:000850871500003 0
J Guo, RR; Li, J; Chen, CT; Xiao, M; Liao, MH; Hu, YN; Liu, Y; Li, D; Zou, J; Sun, DP; Torre, V; Zhang, Q; Chai, RJ; Tang, ML Guo, Rongrong; Li, Jian; Chen, Chuntao; Xiao, Miao; Liao, Menghui; Hu, Yangnan; Liu, Yun; Li, Dan; Zou, Jun; Sun, Dongping; Torre, Vincent; Zhang, Qi; Chai, Renjie; Tang, Mingliang Biomimetic 3D bacterial cellulose-graphene foam hybrid scaffold regulates neural stem cell proliferation and differentiation COLLOIDS AND SURFACES B-BIOINTERFACES English Article Graphene; Bacterial cellulose; Three-dimensional culture; Neural stem cell; Differentiation Neural stem cell (NSC)-based therapy is a promising candidate for treating neurodegenerative diseases and the preclinical researches call an urgent need for regulating the growth and differentiation of such cells. The recognition that three-dimensional culture has the potential to be a biologically significant system has stimulated an extraordinary impetus for scientific researches in tissue engineering and regenerative medicine. Here, A novel scaffold for culturing NSCs, three-dimensional bacterial cellulose-graphene foam (3D-BC/G), which was prepared via in situ bacterial cellulose interfacial polymerization on the skeleton surface of porous graphene foam has been reported. 3D-BC/G not only supports NSC growth and adhesion, but also maintains NSC stemness and enhances their proliferative capacity. Further phenotypic analysis indicated that 3D-BC/G induces NSCs to selectively differentiate into neurons, forming a neural network in a short amount of time. The scaffold has good biocompatibility with primary cortical neurons enhancing the neuronal network activities. To explore the underlying mechanisms, RNA-Seq analysis to identify genes and signaling pathways was performed and it suggests that 3D-BC/G offers a more promising three-dimensional conductive substrate for NSC research and neural tissue engineering, and the repertoire of gene expression serves as a basis for further studies to better understand NSC biology. [Guo, Rongrong; Zhang, Qi] Soochow Univ, Med Coll, Sch Radiat Med & Protect, Suzhou 215123, Jiangsu, Peoples R China; [Guo, Rongrong; Zhang, Qi] Soochow Univ, Med Coll, Sch Radiol & Interdisciplinary Sci RAD X, Collaborat Innovat Ctr Radiat Med,Jiangsu Higher, Suzhou 215123, Jiangsu, Peoples R China; [Guo, Rongrong] Kunming Univ Sci & Technol, Inst Primate Translat Med, Yunnan Key Lab Primate Biomed Res, Kunming 650500, Yunnan, Peoples R China; [Li, Dan] Southeast Univ, Sch Life Sci & Technol, State Key Lab Bioelect, Jiangsu Prov High Tech Key Lab Biomed Res, Nanjing 210096, Peoples R China; [Guo, Rongrong; Li, Jian; Liao, Menghui; Hu, Yangnan; Liu, Yun; Li, Dan; Chai, Renjie; Tang, Mingliang] Southeast Univ, Sch Life Sci & Technol, Key Lab Dev Genes & Human Dis, Minist Educ, Nanjing 210096, Peoples R China; [Guo, Rongrong; Liao, Menghui; Hu, Yangnan; Liu, Yun; Chai, Renjie; Tang, Mingliang] Nantong Univ, Coinnovat Ctr Neuroregenerat, Nantong 226001, Peoples R China; [Xiao, Miao; Tang, Mingliang] Soochow Univ, Affiliated Hosp 1, Med Coll, Inst Cardiovasc Sci, Suzhou 215000, Peoples R China; [Xiao, Miao; Tang, Mingliang] Soochow Univ, Affiliated Hosp 1, Med Coll, Dept Cardiovasc Surg, Suzhou 215000, Peoples R China; [Chen, Chuntao; Sun, Dongping] Nanjing Univ Sci & Technol, Minist Educ, Sch Chem Engn, Inst Chemicobiol & Funct Mat,Key Lab Soft Chem &, 200 Xiao Ling Wei St, Nanjing 210094, Jiangsu, Peoples R China; [Xiao, Miao; Torre, Vincent] Int Sch Adv Studies SISSA, Via Bonomea 265, I-34136 Trieste, Italy; [Zou, Jun] Soochow Univ, Affiliated Hosp 1, Dept Orthopaed Surg, 188 Shizi St, Suzhou 215006, Jiangsu, Peoples R China; [Chai, Renjie] Chinese Acad Sci, Inst Stem Cell & Regenerat, Beijing, Peoples R China; [Chai, Renjie] Capital Med Univ, Beijing Key Lab Neural Regenerat & Repair, Beijing 100069, Peoples R China Soochow University - China; Soochow University - China; Kunming University of Science & Technology; Southeast University - China; Southeast University - China; Nantong University; Soochow University - China; Soochow University - China; Nanjing University of Science & Technology; International School for Advanced Studies (SISSA); Soochow University - China; Chinese Academy of Sciences; Capital Medical University Zhang, Q (corresponding author), Soochow Univ, Med Coll, Sch Radiat Med & Protect, Suzhou 215123, Jiangsu, Peoples R China.;Zhang, Q (corresponding author), Soochow Univ, Med Coll, Sch Radiol & Interdisciplinary Sci RAD X, Collaborat Innovat Ctr Radiat Med,Jiangsu Higher, Suzhou 215123, Jiangsu, Peoples R China.;Chai, RJ; Tang, ML (corresponding author), Southeast Univ, Inst Life Sci, Key Lab Dev Genes & Human Dis, Minist Educ, Nanjing 210096, Peoples R China. qzhang2012@suda.edu.cn; renjiec@seu.edu.cn; mltang@suda.edu.cn Xiao, Miao/GRS-1411-2022 Xiao, Miao/0000-0002-4072-8106 Major State Basic Research Development Program of China [2015CB965000]; National Natural Science Foundation of China [81970883, 31571530, 81622013, 81470692, 31500852, 31871322, 91839101, 82030029, 81970882, 51803092]; Strategic Priority Research Program of the Chinese Academy of Science [XDA16010302, XDA16010303]; Jiangsu Province Natural Science Foundation [BK20200862, BK20181435, BE2019711]; Boehringer Ingelheim Pharma GmbH; Yingdong Huo Education Foundation; Fundamental Research Funds for the Central Universities [2242017K41042, 2242017K3DN23, 2242017K41041]; Scientific Research Foundation of the Graduate School of Southeast University [YBJJ1739]; Shenzhen Fundamental Research Program [JCYJ20190814093401920]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX17_0050] Major State Basic Research Development Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Science(Chinese Academy of Sciences); Jiangsu Province Natural Science Foundation(Natural Science Foundation of Jiangsu Province); Boehringer Ingelheim Pharma GmbH(Boehringer Ingelheim); Yingdong Huo Education Foundation; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Scientific Research Foundation of the Graduate School of Southeast University; Shenzhen Fundamental Research Program; Postgraduate Research & Practice Innovation Program of Jiangsu Province This work was supported by grants from the Major State Basic Research Development Program of China (2015CB965000), the National Natural Science Foundation of China (No. 81970883, 31571530, 81622013, 81470692, 31500852, 31871322), National Natural Science Foundation of China (91839101, Nos. 82030029, 81970882, 51803092), the Strategic Priority Research Program of the Chinese Academy of Science (XDA16010302, XDA16010303), the Jiangsu Province Natural Science Foundation (BK20200862, BK20181435, BE2019711), Boehringer Ingelheim Pharma GmbH, the Yingdong Huo Education Foundation, the Fundamental Research Funds for the Central Universities (2242017K41042, 2242017K3DN23, 2242017K41041), the Scientific Research Foundation of the Graduate School of Southeast University (YBJJ1739), Shenzhen Fundamental Research Program (JCYJ20190814093401920), and the Fundamental Research Funds for the Central Universities and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0050). 74 51 51 24 146 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0927-7765 1873-4367 COLLOID SURFACE B Colloid Surf. B-Biointerfaces APR 2021.0 200.0 111590 10.1016/j.colsurfb.2021.111590 0.0 JAN 2021 9 Biophysics; Chemistry, Physical; Materials Science, Biomaterials Science Citation Index Expanded (SCI-EXPANDED) Biophysics; Chemistry; Materials Science RF0TY 33529926.0 2023-03-23 WOS:000634562100002 0
J Taormina, R; Chau, KW; Sethi, R Taormina, Riccardo; Chau, Kwok-wing; Sethi, Rajandrea Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE English Article Artificial neural networks; Groundwater levels; Coastal aquifer system; Venice lagoon; Simulation WATER; PREDICTION; MODEL; EVAPOTRANSPIRATION; VARIABLES; DEPTH; RIVER Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After initialising the model with groundwater elevations observed at a given time, the developed FNN should able to reproduce water level variations using only the external input variables, which have been identified as rainfall and evapotranspiration. To achieve this purpose, the models are first calibrated on a training dataset to perform 1-h ahead predictions of future groundwater levels using past observed groundwater levels and external inputs. Simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. The results show that the developed FNN can accurately reproduce groundwater depths of the shallow aquifer for several months. The study suggests that such network can be used as a viable alternative to physical-based models to simulate the responses of the aquifer under plausible future scenarios or to reconstruct long periods of missing observations provided past data for the influencing variables is available. (C) 2012 Elsevier Ltd. All rights reserved. [Taormina, Riccardo; Chau, Kwok-wing] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Kowloon, Hong Kong, Peoples R China; [Sethi, Rajandrea] Politecn Torino, DIATI, I-10129 Turin, Italy Hong Kong Polytechnic University; Polytechnic University of Turin Chau, KW (corresponding author), Hong Kong Polytech Univ, Dept Civil & Struct Engn, Kowloon, Hong Kong, Peoples R China. cekwchau@polyu.edu.hk Taormina, Riccardo/AFK-0550-2022; Sexton, Susan/E-9348-2012; Sethi, Rajandrea/J-8593-2015; Sethi, Rajandrea/GYU-7974-2022; Chau, Kwok-wing/E-5235-2011 Sethi, Rajandrea/0000-0003-0927-4801; Sethi, Rajandrea/0000-0003-0927-4801; Chau, Kwok-wing/0000-0001-6457-161X; Taormina, Riccardo/0000-0002-1550-504X Magistrato delle Acque di Venezia Magistrato delle Acque di Venezia The monitoring activities were financed by Magistrato delle Acque di Venezia, and coordinated by CORILA. We want to acknowledge Pierpaolo Campostrini and Caterina Dabala of CORILA, Franco Belosi of ISAC CNR and Alessandro Casasso, Tommaso Baldarelli, Chiara Santi, Silvia Delforno for the maintenance of the data set over time. 34 295 298 11 119 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0952-1976 1873-6769 ENG APPL ARTIF INTEL Eng. Appl. Artif. Intell. DEC 2012.0 25.0 8 1670 1676 10.1016/j.engappai.2012.02.009 0.0 7 Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science; Engineering 045NF Green Accepted 2023-03-23 WOS:000311702800013 0
J Niu, BZ; Dong, J; Dai, ZP; Liu, YQ Niu, Baozhuang; Dong, Jian; Dai, Zhipeng; Liu, Yaoqi Sales data sharing to improve product development efficiency in cross-border e-commerce ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS English Article Product development; Data sharing; Constrained optimization; Arm's length principle; Co-opetitive supply chain SUPPLY-CHAIN; TAX; COURNOT; IMPACT; COMPETITION; STRATEGIES; DECISIONS; CONTRACTS; PRICE Product development is critical in production research, which requires the developer's real-time knowledge of product sales. However, in cross-border e-commerce, the multinational firm (MNF) is in upstream and it is the e-commerce platforms such as JD.com who accumulate the sales big data. Then, whether the platforms have incentives to share the sales data vertically to improve the product development efficiency, especially when the MNF may establish a retail division that competes with the e-commerce platform? In this paper, we build a three-stage optimization model by incorporating a MNF's product development effort, the constraint of arm's length principle, the benefit of cross-border tax-planning, and the co-opetition relationship between the MNF and an e-commerce platform. We find that, both the MNF and its retail division benefit from the platform's sales data sharing, but the platform's data sharing decision critically depends on the value of its information advantage and the MNF's product development efficiency. We find there exist win-win-win opportunities for all the supply chain parties with the platform's sales data sharing. [Niu, Baozhuang; Dong, Jian; Dai, Zhipeng] South China Univ Technol, Sch Business Adm, Guangzhou 510640, Peoples R China; [Liu, Yaoqi] Montpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier, France South China University of Technology; Montpellier Business School; Universite de Montpellier Dai, ZP (corresponding author), South China Univ Technol, Sch Business Adm, Guangzhou 510640, Peoples R China. bmniubz@scut.edu.cn; dongjian677@yeeh.net; zhipeng_dai@yeah.net; yq.liu@montpellier-bs.com Liu, Yaoqi/GSM-9664-2022; Niu, Baozhuang/HMD-9527-2023 National Natural Science Foundation of China [72125006, 71822202]; Fundamental Research Funds for the Central Universities, SCUT; Guangdong Basic and Applied Basic Research Foundation [2021A1515011980] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities, SCUT(Fundamental Research Funds for the Central Universities); Guangdong Basic and Applied Basic Research Foundation The authors are grateful to the editor and reviewers for their helpful comments. This work was supported by the National Natural Science Foundation of China (72125006, 71822202), the Fundamental Research Funds for the Central Universities, SCUT, and Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011980). 61 4 4 39 94 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1567-4223 1873-7846 ELECTRON COMMER R A Electron. Commer. Res. Appl. JAN-FEB 2022.0 51.0 101112 10.1016/j.elerap.2021.101112 0.0 DEC 2021 16 Business; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Computer Science YL7IT 2023-03-23 WOS:000746062400002 0
J Abdellatif, M; Peel, H; Cohn, AG; Fuentes, R Abdellatif, Mohamed; Peel, Harriet; Cohn, Anthony G.; Fuentes, Raul Combining block-based and pixel-based approaches to improve crack detection and localisation AUTOMATION IN CONSTRUCTION English Article Crack discretisation; Noise tolerance; Accurate crack localization; Pavement distress; Crack repair system; CrackIT RECOGNITION; INSPECTION A variety of civil engineering applications require the identification of cracks in roads and buildings. In such cases, it is frequently helpful for the precise location of cracks to be identified as labelled parts within an image to facilitate precision repair for example. CrackIT is known as a crack detection algorithm that allows a user to choose between a block-based or a pixel-based approach. The block-based approach is noise-tolerant but is not accurate in edge localization while the pixel-based approach gives accurate edge localisation but is not noise-tolerant. We propose a new approach that combines both techniques and retains the advantages of each. The new method is evaluated on three standard crack image datasets. The method was compared with the CrackIT method and three deep learning methods namely, HED, RCF and the FPHB. The new approach outperformed the existing arts and reduced the discretisation errors significantly while still being noise-tolerant. [Abdellatif, Mohamed; Peel, Harriet] Univ Leeds, Sch Civil Engn, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England; [Cohn, Anthony G.] Qingdao Univ Sci & Technol, Luzhong Inst Safety Environm Protect Engn & Mat, Zibo 255000, Peoples R China; [Abdellatif, Mohamed; Cohn, Anthony G.] Univ Leeds, Sch Comp, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England; [Cohn, Anthony G.] Qingdao Univ Sci & Technol, Sch Mech & Elect Engn, Qingdao 260061, Peoples R China; [Cohn, Anthony G.] Tongji Univ, Dept Comp Sci & Technol, Shanghai 211985, Peoples R China; [Cohn, Anthony G.] Shandong Univ, Sch Civil Engn, Jinan 250061, Peoples R China; [Fuentes, Raul] Rhein Westfal TH Aachen, Inst Geotech Engn, Mies van der Rohe Str 1, D-52074 Aachen, Germany University of Leeds; Qingdao University of Science & Technology; University of Leeds; Qingdao University of Science & Technology; Tongji University; Shandong University; RWTH Aachen University Abdellatif, M (corresponding author), Univ Leeds, Sch Civil Engn, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England. m.abdellatif@leeds.ac.uk; cnhap@leeds.ac.uk; a.g.cohn@leeds.ac.uk; raul.fuentes@rwth-aachen.de Abdellatif, Mohamed/D-5416-2014 Abdellatif, Mohamed/0000-0002-7641-4723 UK Engineering and Physical Sciences Research Council (EPSRC) [EP/NO10523/1]; Alan Turing Institute fellowship UK Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Alan Turing Institute fellowship This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC), award reference: EP/NO10523/1. We thank Dr. Bilal Kaddouh for fruitful discussion about the crack detection requirements for 3D asphalt printer. The third author is partially supported by an Alan Turing Institute fellowship which is gratefully aknowledged. 48 11 12 7 34 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0926-5805 1872-7891 AUTOMAT CONSTR Autom. Constr. FEB 2021.0 122.0 103492 10.1016/j.autcon.2020.103492 0.0 14 Construction & Building Technology; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering PQ9II Green Published, hybrid 2023-03-23 WOS:000606854700006 0
J Yu, Y; Ma, GH; Hao, ZL; Wang, SF; Niu, FL; Zio, E Yu, Yu; Ma, Guohang; Hao, Zulong; Wang, Shengfei; Niu, Fenglei; Zio, Enrico Correlation analysis for screening key parameters for passive system reliability analysis ANNALS OF NUCLEAR ENERGY English Article Passive system reliability analysis; Correlation analysis; Natural circulation; Thermal-hydraulic model; AP1000 ANALYTIC HIERARCHY PROCESS; MONTE-CARLO SIMULATION; FAILURE PROBABILITY Passive systems are widely used in new generation nuclear power plants to enhance their safety. Reliability of passive system operating based on natural circulation must be assessed in terms of functional failure. The functional failure probability evaluation requires repeatedly running the thermal-hydraulic (T-H) code which simulates the system responses under different values of the input parameters. In practice, repeated running of the code is quite costly in terms of running time and artificial neural network (ANN) has been proposed to replace the T-H model. However, the number of input parameters can be too large to satisfy the requirement of the ANN. In this paper we illustrate a systematic methodology to screen the key parameters for passive system operation based on correlation analysis for reducing the number of inputs. Correlation analysis is a well-known statistical method to assess the relationships among parameters. In the case of interest for passive system reliability, we consider the T-H model as a relationship between model inputs and outputs, which can be used in correlation analysis. With this method, key parameters can be screened with limited numbers of samples. The passive containment cooling system in AP1000 is analyzed and 4 parameters are identified as important ones from 47 inputs. (C) 2014 Elsevier Ltd. All rights reserved. [Yu, Yu; Ma, Guohang; Hao, Zulong; Wang, Shengfei; Niu, Fenglei] North China Elect Power Univ, Beijing Key Lab Pass Safety Technol Nucl Energy, Sch Nucl Sci & Technol, Beijing 102206, Peoples R China; [Zio, Enrico] Politecn Milan, Dept Energy, I-20133 Milan, Italy North China Electric Power University; Polytechnic University of Milan Yu, Y (corresponding author), North China Elect Power Univ, Beijing Key Lab Pass Safety Technol Nucl Energy, Sch Nucl Sci & Technol, Box 78,2 Beinong Rd, Beijing 102206, Peoples R China. yuyu2011@ncepu.edu.cn Zio, Enrico/0000-0002-7108-637X; WANG, Shengfei/0000-0001-8568-1711 National Natural Science Foundation of China [51206042, 91326108]; Fundamental Research Funds for the Central Universities [12ZX05] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work is supported by The National Natural Science Foundation of China (51206042), (91326108) and The Fundamental Research Funds for the Central Universities (12ZX05). 30 20 23 0 11 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0306-4549 ANN NUCL ENERGY Ann. Nucl. Energy MAR 2015.0 77.0 23 29 10.1016/j.anucene.2014.10.035 0.0 7 Nuclear Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Nuclear Science & Technology CB6FR 2023-03-23 WOS:000349723400002 0
J Wang, WS; Zong, F; Yao, BZ Wang, Wensi; Zong, Fang; Yao, Baozhen A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Real-time systems; Reliability; Dispatching; Public transportation; Monitoring; Data-driven transit demand prediction; proactive real-time control; dispatching time SERVICE RELIABILITY; CONTROL FRAMEWORK; BUS ROUTE; CORRIDORS; MODEL With the technological innovation, public transit has truly entered the era of big data. Bus operators have been possible to monitor the system conditions and obtain the real-time transit demand. This situation inspires us to rethink the transit demand prediction and real-time control problems. This article proposes a proactive real-time control method based on data-driven transit demand prediction with multi-source traffic data. A proactive control strategy is to predict the possible disturbance in the future by monitoring and inferring the system operation, and takes measures in advance to prevent the disturbance from disrupting the service regularity. Firstly, the further service reliability is assessed based on the evolution of the latest service reliabilities, to justify whether to conduct control actions. Secondly, if a control action is required, predict the transit demand and the number of alighting passengers. Thirdly, according to the predicted results, the bus dispatching time is optimized by minimizing passenger waiting time. A calculation process is introduced to solve the problem and the effectiveness of the proposed method is evaluated with the data of a real transit route. The results show that the control method based on transit demand prediction suits the needs of real-time control in the smart transit context. [Wang, Wensi] Dalian Maritime Univ, Transportat Engn Coll, Dalian 116026, Peoples R China; [Zong, Fang] Jilin Univ, Coll Transportat, Changchun 130022, Peoples R China; [Yao, Baozhen] Dalian Univ Technol, Sch Automot Engn, Dalian 116024, Peoples R China; [Yao, Baozhen] Eindhoven Univ Technol, Dept Built Environm, NL-5600 MB Eindhoven, Netherlands Dalian Maritime University; Jilin University; Dalian University of Technology; Eindhoven University of Technology Zong, F (corresponding author), Jilin Univ, Coll Transportat, Changchun 130022, Peoples R China.;Yao, BZ (corresponding author), Dalian Univ Technol, Sch Automot Engn, Dalian 116024, Peoples R China. wangwensiwws@163.com; zongfang@jlu.edu.cn; yaobaozhen@dlut.edu.cn yao, Baozhen/I-9650-2014; zong, fang/GQZ-6104-2022; Zong, Fang/GXF-5614-2022 Zing, Fang/0000-0003-0180-3078 National Natural Science Foundation of China [71961137008, U1811463, 61873109]; Social Science Foundation of the Ministry of Education [18YJA630157] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Social Science Foundation of the Ministry of Education This work was supported in part by the National Natural Science Foundation of China under Grant 71961137008, Grant U1811463, and Grant 61873109 and in part by the Social Science Foundation of the Ministry of Education under Grant 18YJA630157. 59 0 0 8 42 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. APR 2021.0 22.0 4 2404 2416 10.1109/TITS.2020.3028415 0.0 13 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Transportation RI8ZB 2023-03-23 WOS:000637194100040 0
J Zhang, Y; Farnoosh, A Zhang, Yue; Farnoosh, Arash Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China ENERGY POLICY English Article Electricity market; Spot prices; Futures prices; Hedging strategy; Renewable energy COMMODITY FUTURES; BIG DATA; PRICES; CONSUMPTION; PREDICTION; DEMAND; MODELS; POWER; SPOT Though the electricity market in China has gone through several reforms in the last few decades, the market is still not completely liberalized. The wholesale prices are regulated and for renewable it is based on feed-in tariff; there is not yet a competitive spot or derivative market concerning the generation side. Furthermore, with great potential, renewable energy is being gradually promoted by the government to compete freely with conventional energies. However, it is hard for a renewable generator to survive without subsidy. So, in this paper we propose a new round of revolution in power sector to introduce electricity futures into China with the expectation of perfecting the market and providing a proper hedging tool for renewable plants. We make an estimation of the risk premium and then simulate the futures prices in China's market. To support the establishment of the futures contracts, we also propose two pricing mechanisms: Demand-side price & Opportunity cost price and study their effects on the futures. Finally, some suggestions with regard to the construction of futures market in China and the operational strategy for renewable plants are given. [Zhang, Yue] China Univ Petr, Beijing 102249, Peoples R China; [Farnoosh, Arash] IFP Energies Nouvelles, IFP Sch, 228-232 Ave Napoleon Bonaparte, F-92852 Rueil Malmaison, France China University of Petroleum; IFP Energies Nouvelles Zhang, Y (corresponding author), China Univ Petr, Beijing 102249, Peoples R China. zhangyueno.8@163.com; arash.farnoosh@ifpen.fr FARNOOSH, Arash/0000-0003-3162-5575 53 9 10 4 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0301-4215 1873-6777 ENERG POLICY Energy Policy SEP 2019.0 132.0 678 690 10.1016/j.enpol.2019.06.011 0.0 13 Economics; Energy & Fuels; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Energy & Fuels; Environmental Sciences & Ecology IU2QV Green Submitted 2023-03-23 WOS:000483425800063 0
J Niu, BZ; Dong, J; Liu, YQ Niu, Baozhuang; Dong, Jian; Liu, Yaoqi Incentive alignment for blockchain adoption in medicine supply chains TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW English Article Blockchain technology; Social goods; Supply chain competition; Information sharing DEMAND SIGNAL; BIG DATA; INFORMATION; TECHNOLOGY; QUALITY; DECISIONS; TIME; EQUILIBRIUM; COMPETITION; STRATEGIES In recent years, blockchain technology has been increasingly adopted in OTC medicine supply chains, enabling customers to track the entire process from raw material purchasing to finished medicine distribution. This improves the brand image and hence expands the market. With the use of blockchain, information transparency can be achieved because data are stored immutably and safely in a distributed database that is accessible by all supply chain members. However, will the incentives for supply chain members to participate in blockchain for larger-scale demand come at the cost of information disclosure? In this paper, to investigate the supply chain members' incentive alignment opportunities towards the adoption of blockchain technology, we consider a two-stage supply chain comprising two medicine manufacturers and a common retailer that has more accurate demand information than the manufacturers have. We find that, interestingly, the retailer has incentives to participate in blockchain when the manufacturers' competition is mild and the demand variance is low. We further investigate the impact of blockchain on total surplus and customer surplus and find that the adoption of blockchain always benefits customers and society; therefore, blockchain can be particularly useful for social goods such as OTC medicine. [Niu, Baozhuang; Dong, Jian] South China Univ Technol, Sch Business Adm, Guangzhou 510640, Peoples R China; [Liu, Yaoqi] Univ Montpellier, Montpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier, France South China University of Technology; Montpellier Business School; Universite de Montpellier Liu, YQ (corresponding author), Univ Montpellier, Montpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier, France. bmniubz@scut.edu.cn; dongjian677@yeah.net; yq.liu@montpellier-bs.com Liu, Yaoqi/GSM-9664-2022; Niu, Baozhuang/HMD-9527-2023 NSFC Excellent Young Scientists Fund [71822202]; Chang Jiang Scholars Program; Fundamental Research Funds for the Central Universities NSFC Excellent Young Scientists Fund; Chang Jiang Scholars Program(Program for Changjiang Scholars & Innovative Research Team in University (PCSIRT)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) The authors are grateful to the editor and reviewers for their helpful comments. This work was supported by NSFC Excellent Young Scientists Fund (No. 71822202), Chang Jiang Scholars Program (Niu Baozhuang 2017), and the Fundamental Research Funds for the Central Universities. The corresponding author is Yaoqi Liu. 53 28 28 51 159 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1366-5545 1878-5794 TRANSPORT RES E-LOG Transp. Res. Pt. e-Logist. Transp. Rev. AUG 2021.0 152.0 102276 10.1016/j.tre.2021.102276 0.0 JUL 2021 32 Economics; Engineering, Civil; Operations Research & Management Science; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Engineering; Operations Research & Management Science; Transportation US9FP 2023-03-23 WOS:000697731100002 0
J Du, N; Manjunath, N; Shuai, Y; Burger, D; Skorupa, I; Schuffny, R; Mayr, C; Basov, DN; Di Ventra, M; Schmidt, OG; Schmidt, H Du, Nan; Manjunath, Niveditha; Shuai, Yao; Buerger, Danilo; Skorupa, Ilona; Schueffny, Rene; Mayr, Christian; Basov, Dimitri N.; Di Ventra, Massimiliano; Schmidt, Oliver G.; Schmidt, Heidemarie Novel implementation of memristive systems for data encryption and obfuscation JOURNAL OF APPLIED PHYSICS English Article CLOUD With the rise of big data handling, new solutions are required to drive cryptographic algorithms for maintaining data security. Here, we exploit the nonvolatile, nonlinear resistance change in BiFeO3 memristors [Shuai et al., J. Appl. Phys. 109, 124117 (2011)] by applying a voltage for the generation of second and higher harmonics and develop a new memristor-based encoding system from it to encrypt and obfuscate data. It is found that a BiFeO3 memristor in high and low resistance state can be used to generate two clearly distinguishable sets of second and higher harmonics as recently predicted theoretically [Cohen et al., Appl. Phys. Lett. 100, 133109 (2012)]. The computed autocorrelation of encrypted data using higher harmonics generated by a BiFeO3 memristor shows that the encoded data distribute randomly. (C) 2014 AIP Publishing LLC. [Du, Nan; Manjunath, Niveditha; Buerger, Danilo; Skorupa, Ilona; Schmidt, Oliver G.; Schmidt, Heidemarie] Tech Univ Chemnitz, Fac Elect Engn & Informat Technol, D-09107 Chemnitz, Germany; [Shuai, Yao] Univ Elect Sci & Technol China, State Key Lab Elect Thin Films & Integrated Devic, Chengdu 610054, Peoples R China; [Schueffny, Rene] Tech Univ Dresden, Dept Elect Engn & Informat Technol, Dresden, Germany; [Mayr, Christian] Univ Zurich, Inst Neuroinformat, Neuromorph Cognit Syst Grp, CH-8057 Zurich, Switzerland; [Mayr, Christian] ETH, CH-8057 Zurich, Switzerland; [Basov, Dimitri N.; Di Ventra, Massimiliano] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA; [Schmidt, Oliver G.] IFW Dresden, Inst Integrat Nanosci, D-01069 Dresden, Germany Technische Universitat Chemnitz; University of Electronic Science & Technology of China; Technische Universitat Dresden; University of Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of California System; University of California San Diego; Institute for Integrative Nanosciences (IIN); Leibniz Institute for Solid State & Materials Research Dresden Du, N (corresponding author), Tech Univ Chemnitz, Fac Elect Engn & Informat Technol, D-09107 Chemnitz, Germany. n.du@s2012.tu-chemnitz.de; heidemarie.schmidt@etit.tu-chemnitz.de Skorupa, Ilona/O-7848-2014; Schmidt, Heidemarie/E-4627-2012; Schmidt, Oliver G/W-4877-2019; Bürger, Danilo/O-7833-2014; Di Ventra, Massimiliano/E-1667-2011 Bürger, Danilo/0000-0003-0685-5301; Di Ventra, Massimiliano/0000-0001-9416-189X; Du, Nan/0000-0002-7775-7795; Schmidt, Oliver G/0000-0001-9503-8367 BMWi-ZIM [VP2999601ZG2]; Deutsche Forschungs gemeinschaft [SCHM 1663/4-1, BU 2956/1-1]; Networking Fund of the Helmholtz Association [VH-VI-422] BMWi-ZIM; Deutsche Forschungs gemeinschaft(German Research Foundation (DFG)); Networking Fund of the Helmholtz Association N.D. acknowledges the funding by BMWi-ZIM (VP2999601ZG2). H.S. and D.B. are grateful for the financial support from the Deutsche Forschungs gemeinschaft (SCHM 1663/4-1, BU 2956/1-1) and the Networking Fund of the Helmholtz Association (VH-VI-422). The authors would like to thank Professor Mathias Schubert from University of Nebraska-Lincoln for pointing out the initial flaws in the encoding technique. 34 9 9 1 46 AMER INST PHYSICS MELVILLE 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA 0021-8979 1089-7550 J APPL PHYS J. Appl. Phys. MAR 28 2014.0 115.0 12 124501 10.1063/1.4869262 0.0 7 Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Physics AE3TP Green Accepted 2023-03-23 WOS:000333901100084 0
C Fu, X; Gui, G; Wang, Y; Ohtsuki, T; Adebisi, B; Gacanin, H; Adachi, F IEEE Fu, Xue; Gui, Guan; Wang, Yu; Ohtsuki, Tomoaki; Adebisi, Bamidele; Gacanin, Haris; Adachi, Fumiyuki Lightweight Network and Model Aggregation for Automatic Modulation Classification in Wireless Communications 2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) IEEE Wireless Communications and Networking Conference English Proceedings Paper IEEE Wireless Communications and Networking Conference (WCNC) MAR 29-APR 01, 2021 Nanjing, PEOPLES R CHINA IEEE Automatic modulation classification (AMC); lightweight network; model aggregation DEEP; CNN This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN. [Fu, Xue; Gui, Guan; Wang, Yu] NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China; [Ohtsuki, Tomoaki] Keio Univ, Dept Informat & Comp Sci, Yokohama, Kanagawa, Japan; [Adebisi, Bamidele] Manchester Metropolitan Univ, Fac Sci & Engn, Manchester, Lancs, England; [Gacanin, Haris] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, Aachen, Germany; [Adachi, Fumiyuki] Tohoku Univ, Res Org Elect Commun, Sendai, Miyagi, Japan Nanjing University of Posts & Telecommunications; Keio University; Manchester Metropolitan University; RWTH Aachen University; Tohoku University Fu, X (corresponding author), NJUPT, Coll Telecommun & Informat Engn, Nanjing, Peoples R China. Gui, Guan/AAG-3593-2019; Adachi, Fumiyuki/ABD-7025-2021 Gui, Guan/0000-0001-7428-4980; Adebisi, Bamidele/0000-0001-9071-9120 27 1 1 1 6 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1525-3511 978-1-7281-9505-6 IEEE WCNC 2021.0 10.1109/WCNC49053.2021.9417592 0.0 6 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BS2OB 2023-03-23 WOS:000704226500346 0
J Jin, Z; Iqbal, MZ; Zou, WB; Li, X; Steinbach, E Jin, Zhi; Iqbal, Muhammad Zafar; Zou, Wenbin; Li, Xia; Steinbach, Eckehard Dual-Stream Multi-Path Recursive Residual Network for JPEG Image Compression Artifacts Reduction IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Image coding; Training; Transform coding; Automobiles; Task analysis; Hafnium; Dual-stream; structure-texture decomposition; compression artifacts reduction; recursive residual network DEBLOCKING; REMOVAL JPEG is the most widely used lossy image compression standard. When using JPEG with high compression ratios, visual artifacts cannot be avoided. These artifacts not only degrade the user experience but also negatively affect many low-level image processing tasks. Recently, convolutional neural network (CNN)-based compression artifact removal approaches have achieved significant success, however, at the cost of high computational complexity due to an enormous number of parameters. To address this issue, we propose a dual-stream recursive residual network (STRRN) which consists of structure and texture streams for separately reducing the specific artifacts related to high-frequency or low-frequency image components. The outputs of these streams are combined and fed into an aggregation network to further enhance the restored images. By using parameter sharing, the proposed network reduces the total number of training parameters significantly. Moreover, experiments conducted on five commonly used datasets confirm that the proposed STRRN can efficiently reduce the compression artifacts, while using up to 4.6 times less training parameters and 5 times less running time compared to the state-of-the-art approaches. [Jin, Zhi; Zou, Wenbin; Li, Xia] Shenzhen Univ, Shenzhen Key Lab Adv Machine Learning & Applicat, Guangdong Key Lab Intelligent Informat Proc Inst, Coll Elect & Informat Engn,Inst Artificial Intell, Shenzhen 518060, Peoples R China; [Jin, Zhi] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China; [Iqbal, Muhammad Zafar; Steinbach, Eckehard] Tech Univ Munich, Chair Media Technol, D-80333 Munich, Germany Shenzhen University; Sun Yat Sen University; Technical University of Munich Zou, WB (corresponding author), Shenzhen Univ, Shenzhen Key Lab Adv Machine Learning & Applicat, Guangdong Key Lab Intelligent Informat Proc Inst, Coll Elect & Informat Engn,Inst Artificial Intell, Shenzhen 518060, Peoples R China. jinzh26@mail.sysu.edu.cn; mzafar.iqbal@tum.de; wzou@szu.edu.cn; lixia@szu.edu.cn; eckehard.steinbach@tum.de Steinbach, Eckehard/0000-0001-8853-2703 NSFC [61701313, 61771321, 61871273, 61872429]; Natural Science Foundation of Guangdong Province [2020A1515010959]; Natural Science Foundation of Shenzhen [JCYJ20170818091621856, JCYJ2020N294]; Interdisciplinary Innovation Team of Shenzhen University; key Project of DEGP [2018KCXTD027] NSFC(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); Natural Science Foundation of Shenzhen; Interdisciplinary Innovation Team of Shenzhen University; key Project of DEGP This work was supported in part by the NSFC Project (61701313, 61771321, 61871273 and 61872429), in part by the key Project of DEGP (2018KCXTD027), in part by the Natural Science Foundation of Guangdong Province (2020A1515010959), in part by Natural Science Foundation of Shenzhen (JCYJ20170818091621856 and JCYJ2020N294) and in part by the Interdisciplinary Innovation Team of Shenzhen University. 43 11 11 0 10 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. FEB 2021.0 31.0 2 467 479 10.1109/TCSVT.2020.2982174 0.0 13 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering QC7WT 2023-03-23 WOS:000615044400004 0
J Wang, CH; Zhou, XD; Liu, PF; Lu, GY; Wang, HN; Oeser, M Wang, Chonghui; Zhou, Xiaodong; Liu, Pengfei; Lu, Guoyang; Wang, Hainian; Oeser, Markus Study on pre-compaction of pavement graded gravels via imaging technologies, artificial intelligent and numerical simulations CONSTRUCTION AND BUILDING MATERIALS English Article Pavement compaction; Discrete element; Image technology; Aggregate; Artificial intelligent; Deep learning; Asphalt pavement ASPHALT; PERFORMANCE; DYNAMICS; BITUMEN; ADHESION Pavement compaction cannot be neglected during the motorway manufacture stage because it can determine pavement service quality and durability. Concerning the compaction scenario, the paving compaction is responsible for offering the preliminary strength of the pavement. Ignoring paving compaction quality control can lead to over compaction. This paper introduces an integral system to study and simulate the paving compaction of asphalt motorways in Discrete Element Model two-dimensional (DEM2D). This method includes the whole procedure from aggregate image acquisition database establishment to the DEM2D simulation of paving compaction. To this end, this study fulfils the creation of the aggregate database applied in DEM via the Aggregate Image Measuring System (AIMS) method. In addition, the artificial intelligent (AI) technology called Generative Adversarial Networks (GANs) method is proposed to expand the developed DEM aggregate database. Three different approaches are applied to calibrate the accuracy of the extended database. According to the aggregate database, the pavement paving compaction with different aggregate gradations can be simulated in DEM2D. [Wang, Chonghui; Liu, Pengfei; Oeser, Markus] Rhein Westfal TH Aachen, Inst Highway Engn, D-52074 Aachen, Germany; [Zhou, Xiaodong] Michigan Technol Univ, Dept Civil & Environm Engn, Houghton, MI 49931 USA; [Lu, Guoyang] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Wang, Chonghui; Wang, Hainian] Changan Univ, Sch Highway, Xian 710064, Peoples R China; [Oeser, Markus] Ferderal Highway Res Inst, D-51427 Bergisch Gladbach, Germany RWTH Aachen University; Michigan Technological University; Hong Kong Polytechnic University; Chang'an University Liu, PF (corresponding author), Rhein Westfal TH Aachen, Inst Highway Engn, D-52074 Aachen, Germany. liu@isac.rwth-aachen.de Lu, Guoyang/0000-0001-9012-1751 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [FOR 2089/2, OE 514/1-2, OE 514/15-1, 459436571]; National Key Research and Development Program of China [2021YFB2601000]; China Scholarship Council [201706560034] Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)(German Research Foundation (DFG)); National Key Research and Development Program of China; China Scholarship Council(China Scholarship Council) This paper is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -FOR 2089/2, OE 514/1-2 and OE 514/15-1 Project -ID 459436571. This study is also based on the part of the research project carried out at the request of the National Key Research and Development Program of China under research project No. 2021YFB2601000. The first author of this manuscript also appreciates the support from the China Scholarship Council (Grant No. 201706560034) . The authors are solely responsible for the content. 55 0 0 27 32 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-0618 1879-0526 CONSTR BUILD MATER Constr. Build. Mater. AUG 22 2022.0 345.0 128380 10.1016/j.conbuildmat.2022.128380 0.0 JUL 2022 12 Construction & Building Technology; Engineering, Civil; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Construction & Building Technology; Engineering; Materials Science 3X3GN 2023-03-23 WOS:000842929200005 0
J Cheng, C; Mahnken, R Cheng, C.; Mahnken, R. A modified Zerilli-Armstrong model as the asymmetric visco-plastic part of a multi-mechanism model for cutting simulations ARCHIVE OF APPLIED MECHANICS English Article Cutting simulations; Zerilli– Armstrong model; Johnson– Cook model; Asymmetric visco-plasticity; Coupled effects ARTIFICIAL NEURAL-NETWORK; CONSTITUTIVE MODEL; JOHNSON-COOK; FLOW-STRESS; FCC METALS; DEFORMATION-BEHAVIOR; STRAIN RATES; ALLOY; PLASTICITY; BCC The Zerilli-Armstrong (ZA) model is modified to describe asymmetric visco-plastic material properties as part of a multi-mechanism model (MMM) for cutting simulations. This is done as an improvement of an existing modified Johnson-Cook (JC) model. Based on the modification of the ZA model by Samantatray et al. for elevated-temperature behaviour, we replace the hardening stress by the sum of nonlinear and linear isotropic hardening stresses using internal variables, thus consider history effects. Furthermore, weighting functions related to stress modes are applied taking asymmetric effect of strength into account. For calibrating the modified ZA model, experimental data in a wide range of strain rates and temperatures as well as under different loading types are used. Moreover, a systematic comparative study on the modified JC and ZA model is made regarding their dependence on strain, strain rate, and temperature. Finally, the modified ZA model is validated by comparing temperatures and cutting forces of cutting simulations with those of hard turning experiments. [Cheng, C.] Yangzhou Univ, Coll Mech Engn, Yangzhou 225127, Jiangsu, Peoples R China; [Mahnken, R.] Univ Paderborn, Chair Engn Mech, Warburger Str 100, D-33098 Paderborn, Germany Yangzhou University; University of Paderborn Cheng, C (corresponding author), Yangzhou Univ, Coll Mech Engn, Yangzhou 225127, Jiangsu, Peoples R China. chuncheng@yzu.edu.cn Cheng, Chun/0000-0001-7145-8893 Deutsche Forschungsgemeinschaft (DFG) [SPP 1480] Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)) This paper is an extension of the research work based on investigations of priority point program 1480 (SPP 1480), which is kindly supported by the Deutsche Forschungsgemeinschaft (DFG). 54 1 1 2 7 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0939-1533 1432-0681 ARCH APPL MECH Arch. Appl. Mech. SEP 2021.0 91.0 9 3869 3888 10.1007/s00419-021-01982-6 0.0 MAY 2021 20 Mechanics Science Citation Index Expanded (SCI-EXPANDED) Mechanics TW6RA 2023-03-23 WOS:000654318500002 0
J Mukherjee, S; Lu, DC; Raghavan, B; Breitkopf, P; Dutta, S; Xiao, MY; Zhang, WH Mukherjee, Sougata; Lu, Dongcheng; Raghavan, Balaji; Breitkopf, Piotr; Dutta, Subhrajit; Xiao, Manyu; Zhang, Weihong Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING English Review Large-scale structural topology optimization has always suffered from prohibitively high computational costs that have till date hindered its widespread use in industrial design. The first and major contributor to this problem is the cost of solving the Finite Element equations during each iteration of the optimization loop. This is compounded by the frequently very fine 3D models needed to accurately simulate mechanical or multi-physical performance. The second issue stems from the requirement to embed the high-fidelity simulation within the iterative design procedure in order to obtain the optimal design. The prohibitive number of calculations needed as a result of both these issues, is often beyond the capacities of existing industrial computers and software. To alleviate these issues, the last decade has opened promising pathways into accelerating the topology optimization procedure for large-scale industrial sized problems, using a variety of techniques, including re-analysis, multi-grid solvers, model reduction, machine learning and high-performance computing, and their combinations. This paper attempts to give a comprehensive review of the research activities in all of these areas, so as to give the engineer both an understanding as well as a critical appreciation for each of these developments. [Mukherjee, Sougata; Dutta, Subhrajit] Natl Inst Technol Silchar, Dept Civil Engn, Silchar, India; [Lu, Dongcheng; Xiao, Manyu] Northwestern Polytech Univ, Sch Math & Stat, Dept Appl Math, Xian Key Lab Scient Computat & Appl Stat, Xian 710072, Peoples R China; [Raghavan, Balaji] Inst Natl Sci Appl Rennes, Lab Genie Civil & Genie Mecan, EA 3913, Rennes, France; [Breitkopf, Piotr] Univ Technol Compiegne, CNRS, Lab Roberval FRE UTC 2012, Compiegne, France; [Zhang, Weihong] Northwestern Polytech Univ, State IJR Ctr Aerosp Design & Addit Mfg, Xian, Peoples R China National Institute of Technology (NIT System); National Institute of Technology Silchar; Northwestern Polytechnical University; Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); Picardie Universites; Universite de Technologie de Compiegne; Northwestern Polytechnical University Xiao, MY (corresponding author), Northwestern Polytech Univ, Sch Math & Stat, Dept Appl Math, Xian Key Lab Scient Computat & Appl Stat, Xian 710072, Peoples R China. manyuxiao@nwpu.edu.cn Dutta, Subhrajit/N-6525-2016 Dutta, Subhrajit/0000-0001-8877-0840 National Natural Science Foundation of China [11620101002, 11972166]; Fundamental Research Funds for the Central Universities [310201911cx029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This study was supported by the National Natural Science Foundation of China (Grant No. 11620101002 and Grant No. 11972166) and the Fundamental Research Funds for the Central Universities (Grant No. 310201911cx029). 241 19 20 8 60 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1134-3060 1886-1784 ARCH COMPUT METHOD E Arch. Comput. Method Eng. DEC 2021.0 28.0 7 4549 4571 10.1007/s11831-021-09544-3 0.0 JAN 2021 23 Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Mathematics WW0GQ 2023-03-23 WOS:000612595600001 0
J Chen, L; Jiang, X; Liu, XZ; Haardt, M Chen, Lin; Jiang, Xue; Liu, Xingzhao; Haardt, Martin Reweighted Low-Rank Factorization With Deep Prior for Image Restoration IEEE TRANSACTIONS ON SIGNAL PROCESSING English Article Image restoration; Tensors; Matrix decomposition; Minimization; Electronics packaging; Correlation; Training data; Image restoration; low-rank recovery; matrix and tensor factorization; (re)weighted nuclear norm; deep image prior; Augmented direction method of multipliers (ADMM) MATRIX COMPLETION; ALGORITHM; RECOVERY The low-rank recovery is a powerful tool to restore images from incomplete and corrupted observations. Conventional low-rank recovery techniques employ the reweighted nuclear norm minimization, which requires performing the full singular value decomposition and thus is computationally expensive. Using the scheme of bilinear factorization, we propose the Reweighted Low-rank Matrix Factorization (RLMF) method for single channel image restoration. The RLMF method can not only inherit the computational efficiency of bilinear factorization, but also incorporate the empirical distribution of the singular values in natural images. Then, considering the correlation between image channels, we generalize the reweighted nuclear norm from matrices to tensors, and develop the Reweighted Low-rank Tensor Factorization (RLTF) method for multichannel image restoration. Moreover, we enhance the RLMF and RLTF methods by introducing the deep image prior information, which is capable of capturing the implicit image structure through the neural network architecture to improve restoration accuracy. Experimental results show the computational efficiency of the proposed low-rank factorization scheme, and the superior restoration accuracy of the proposed methods compared with the state-of-the-art methods. [Chen, Lin; Jiang, Xue; Liu, Xingzhao] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Haardt, Martin] Ilmenau Univ Technol, D-98684 Ilmenau, Germany Shanghai Jiao Tong University; Technische Universitat Ilmenau Jiang, X (corresponding author), Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China. linchenee@sjtu.edu.cn; xuejiang@sjtu.edu.cn; xzh-liu@sjtu.edu.cn; martin.haardt@tu-ilmenau.de Haardt, Martin/0000-0001-7810-975X; Chen, Lin/0000-0001-5144-7855; Liu, Xingzhao/0000-0002-4533-3904 National Natural Science Foundation of China [61971279, 62022054] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grants 61971279 and 62022054. 54 2 2 10 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1053-587X 1941-0476 IEEE T SIGNAL PROCES IEEE Trans. Signal Process. 2022.0 70.0 3514 3529 10.1109/TSP.2022.3183466 0.0 16 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 3D2YG 2023-03-23 WOS:000829173000002 0
J Dey, N; Ashour, AS; Shi, FQ; Fong, SJ; Tavares, JMRS Dey, Nilanjan; Ashour, Amira S.; Shi, Fuqian; Fong, Simon James; Tavares, Joao Manuel R. S. Medical cyber-physical systems: A survey JOURNAL OF MEDICAL SYSTEMS English Article Networked medical device systems; Wireless sensor networks; Medical internet of things; Body area networks; Security assurance; Patient monitoring WIRELESS SENSOR NETWORKS; SECURITY; PRIVACY Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included. [Dey, Nilanjan] Techno India Coll Technol, Dept Informat Technol, Kolkata 740000, W Bengal, India; [Ashour, Amira S.] Tanta Univ, Dept Elect & Elect Commun Engn, Fac Engn, Tanta, Egypt; [Shi, Fuqian] Wenzhou Med Univ, Coll Informat & Engn, Wenzhou 325035, Peoples R China; [Fong, Simon James] Univ Macau, Dept Comp & Informat Sci, Data Analyt & Collaborat Comp Lab, Taipa, Macau Sar, Peoples R China; [Tavares, Joao Manuel R. S.] Univ Porto, Fac Engn, Dept Engn Mecan, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Porto, Portugal Egyptian Knowledge Bank (EKB); Tanta University; Wenzhou Medical University; University of Macau; Universidade do Porto Ashour, AS (corresponding author), Tanta Univ, Dept Elect & Elect Commun Engn, Fac Engn, Tanta, Egypt. neelanjan.dey@gmail.com; amirasashour@yahoo.com; sfq@wmu.edu.cn; ccfong@umac.mo; tavares@fe.up.pt Ashour, Amira S./T-5454-2019; Tavares, João Manuel R.S./M-5305-2013; Fong, Simon/C-9388-2009 Ashour, Amira S./0000-0003-3217-6185; Tavares, João Manuel R.S./0000-0001-7603-6526; Fong, Simon/0000-0002-1848-7246 78 75 78 2 40 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0148-5598 1573-689X J MED SYST J. Med. Syst. APR 2018.0 42.0 4 74 10.1007/s10916-018-0921-x 0.0 13 Health Care Sciences & Services; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Health Care Sciences & Services; Medical Informatics GA4XP 29525900.0 Green Submitted 2023-03-23 WOS:000428337000014 0
J Yang, JH; Wang, K; Li, RF; Qin, ZH; Perner, P Yang, Jianhua; Wang, Ke; Li, Ruifeng; Qin, Zhonghao; Perner, Petra A novel fast combine-and-conquer object detector based on only one-level feature map COMPUTER VISION AND IMAGE UNDERSTANDING English Article Deep learning; Object detection; Face detection; Person detection; Combine-and-conquer In this paper, we present a conceptually simple, flexible, and efficient Combine-and-Conquer detection frame-work. The proposed framework is composed of a very simple one-level detection pipeline. We modularized the proposed framework into four parts, Backbone, Neck, Feature Aggregation and Detection Head. To verify the performance of this framework, we design a simple yet strong detector, CC-Det. First CC-Det deploys a backbone network to encode the input image, and then uses a neck network to extract rich features and enlarge the receptive field. Next, a feature aggregation network is deployed to aggregate multi-scale features into one feature map. Finally, only one detection head is deployed on the one-level feature map to output a heatmap and bounding boxes. Compared with existing multi-level detectors such as RetinaNet and FCOS, CC-Det achieves excellent performance with much fewer parameters of model and much lower FLOPs. In addition, CC-Det also achieves better trade-off among speed, accuracy and model size without any elaborate special design, compared to other one-level detectors. Moreover, CC-Det is easy to generalize to other tasks with minor modifications and achieves state-of-the-art performance. Excellent results are presented on COCO, PASCAL VOC, WiderFace and CrowdHuman datasets. [Yang, Jianhua; Wang, Ke; Li, Ruifeng; Qin, Zhonghao] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China; [Perner, Petra] Inst Comp Vis & Appl Comp Sci IBaI, Dresden, Germany Harbin Institute of Technology Li, RF (corresponding author), Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China. lrf100@hit.edu.cn National Key Research and Development Program of China; National Natural Science Foundation of China; State Key Laboratory of Robotics and System (HIT); [2019YFB1310004]; [62176072]; [SKLRS202111B] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Robotics and System (HIT); ; ; Acknowledgments This work was supported in part by the National Key Research and Development Program of China (No. 2019YFB1310004) , National Natural Science Foundation of China (62176072) and Self-Planned Task NO. SKLRS202111B of State Key Laboratory of Robotics and System (HIT) . 45 0 0 5 5 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1077-3142 1090-235X COMPUT VIS IMAGE UND Comput. Vis. Image Underst. NOV 2022.0 224.0 103561 10.1016/j.cviu.2022.103561 0.0 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 5J5PS 2023-03-23 WOS:000869094100009 0
C Duan, R; Fu, CH; Alexis, K; Kayacan, E IEEE Duan, Ran; Fu, Changhong; Alexis, Kostas; Kayacan, Erdal Online Recommendation-based Convolutional Features for Scale-Aware Visual Tracking 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) IEEE International Conference on Robotics and Automation ICRA English Proceedings Paper IEEE International Conference on Robotics and Automation (ICRA) MAY 30-JUN 05, 2021 Xian, PEOPLES R CHINA IEEE,IEEE Robot & Automat Soc,So Univ Sci & Technol,Harbin Inst Technol,Xian Jiaotong Univ,Univ Toronto,Nat Sci & Engn Res Council Canada,Chinese Univ Hong Kong,DJI,Biomimet Intelligence & Robot,Toyota Res Inst,Baidu,Mech Mind Robot Technologies,Facebook AI,Nokov,Manycore,Franka Emika,Zhejiang Univ, Huzhou Inst,Jing Tian,ATI Ind Automat,Amber,Direct Drive Tech,Inceptio,Flexiv,Deeproute AI,Unitree,Waymo,Kuka,Pudu Robot,Deep Robot,Nvidia,MathWorks MODEL In this paper, we develop an online learning-based visual tracking framework that can optimize the target model and estimate the scale variation for object tracking. We propose a recommender-based tracker, which is capable of selecting the representative convolutional neural network (CNN) layers and feature maps autonomously. In addition, the proposed recommender computes the weights of these layers and feature maps. A discriminative target percept of each recommended layer is reconstructed by the weighted sum of the recommended feature maps. Then the target model of the correlation filter is updated by the weighted sum of the target percepts. Thus, a sub-network is extracted from the pre-trained CNN backbone for the tracking process of a specific target. To deal with scale changes, we propose a spatiotemporal-based min-channel method to estimate the target size variation directly from CNN features. Experimental results on 50 benchmark datasets and video data from a rescue drone demonstrate that the proposed tracker is quite competitive with the state-of-the-art CNN-based trackers in terms of accuracy, scale adaptation, and robustness for UAV-related applications. [Duan, Ran] Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong, Peoples R China; [Fu, Changhong] Tongji Univ, Sch Mech Engn, Shanghai, Peoples R China; [Alexis, Kostas] Univ Nevada, Reno, NV 89557 USA; [Alexis, Kostas] NTNU, Trondheim, Norway; [Kayacan, Erdal] Aarhus Univ, Dept Engn, Artificial Intelligence Robot Lab AiR Lab, DK-8000 Aarhus C, Denmark Hong Kong Polytechnic University; Tongji University; Nevada System of Higher Education (NSHE); University of Nevada Reno; Aarhus University Kayacan, E (corresponding author), Aarhus Univ, Dept Engn, Artificial Intelligence Robot Lab AiR Lab, DK-8000 Aarhus C, Denmark. ran-sn.duan@connect.polyu.hk; changhongfu@tongji.edu.cn; konstantinos.alexis@ntnu.no; erdal@eng.au.dk Alexis, Kostas/HKM-4911-2023; Kayacan, Erdal/D-6256-2014 Alexis, Kostas/0000-0002-9989-298X; Kayacan, Erdal/0000-0002-4873-2513; Duan, Ran/0000-0001-9948-6714 27 3 3 1 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1050-4729 2577-087X 978-1-7281-9077-8 IEEE INT CONF ROBOT 2021.0 14206 14212 10.1109/ICRA48506.2021.9562065 0.0 7 Automation & Control Systems; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems; Robotics BS8DT 2023-03-23 WOS:000771405405096 0
J Dai, HZ; Cao, JW; Wang, TL; Deng, MQ; Yang, ZX Dai, Haozhen; Cao, Jiuwen; Wang, Tianlei; Deng, Muqing; Yang, Zhixin Multilayer one-class extreme learning machine NEURAL NETWORKS English Article One-class classification; OC-ELM; ML-OCELM; Kernel learning; Outlier/anomaly detection REGRESSION; APPROXIMATION; OUTLIERS; SUPPORT; CHOICE One-class classification has been found attractive in many applications for its effectiveness in anomaly or outlier detection. Representative one-class classification algorithms include the one-class support vector machine (SVM), Naive Parzen density estimation, autoencoder (AE), etc. Recently, the one-class extreme learning machine (OC-ELM) has been developed for learning acceleration and performance enhancement. But existing one-class algorithms are generally less effective in complex and multi-class classifications. To alleviate the deficiency, a multilayer neural network based one-class classification with ELM (in short, as ML-OCELM) is developed in this paper. The stacked AEs are employed in ML-OCELM to exploit an effective feature representation for complex data. The effective kernel based learning framework is also investigated in the stacked AEs of ML-OCELM, leading to a multilayer kernel based OC-ELM (in short, as MK-OCELM). The MK-OCELM has advantages of less human-intervention parameters and good generalization performance. Experiments on 13 benchmark UCI classification datasets and a real application on urban acoustic classification (UAC) are carried out to show the superiority of the proposed ML-OCELM/ MK-OCELM over the OC-ELM and several state-of-the-art algorithms. (C) 2019 Elsevier Ltd. All rights reserved. [Dai, Haozhen; Cao, Jiuwen; Wang, Tianlei; Deng, Muqing] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China; [Dai, Haozhen; Cao, Jiuwen; Wang, Tianlei; Deng, Muqing] Hangzhou Dianzi Univ, Artificial Intelligence Inst, Hangzhou 310018, Zhejiang, Peoples R China; [Cao, Jiuwen] Univ Wuppertal, Sch Elect Informat & Media Engn, D-42119 Wuppertal, Germany; [Yang, Zhixin] Univ Macau, Fac Sci & Technol, State Key Lab Internet Things Smart City, Macau, Peoples R China Hangzhou Dianzi University; Hangzhou Dianzi University; University of Wuppertal; University of Macau Cao, JW (corresponding author), Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China. jwcao@hdu.edu.cn Yang, Zhi-Xin/AAV-1335-2020 Yang, Zhi-Xin/0000-0001-9151-7758 NNSF of China [61503104, U15092051]; K. C. Wong Education Foundation, DAAD, Germany NNSF of China(National Natural Science Foundation of China (NSFC)); K. C. Wong Education Foundation, DAAD, Germany This work was supported by the NNSF of China (61503104, U15092051) and by the K. C. Wong Education Foundation, DAAD, Germany. 49 43 45 5 42 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. JUL 2019.0 115.0 11 22 10.1016/j.neunet.2019.03.004 0.0 12 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology HZ5FM 30921561.0 2023-03-23 WOS:000468877100002 0
J Li, J; Ji, HF; Cao, L; Zang, D; Gu, R; Xia, B; Wu, Q Li, Jie; Ji, Hongfei; Cao, Lei; Zang, Di; Gu, Rong; Xia, Bin; Wu, Qiang EVALUATION AND APPLICATION OF A HYBRID BRAIN COMPUTER INTERFACE FOR REAL WHEELCHAIR PARALLEL CONTROL WITH MULTI-DEGREE OF FREEDOM INTERNATIONAL JOURNAL OF NEURAL SYSTEMS English Article Electroencephalogram; hybrid brain-computer interface; wheelchair; parallel control; multi-degree of freedom SINGLE-TRIAL EEG; FUZZY SYNCHRONIZATION LIKELIHOOD; WAVELET-CHAOS METHODOLOGY; MILD COGNITIVE IMPAIRMENT; ASYNCHRONOUS BCI; NEURAL-NETWORK; MOTOR IMAGERY; DIAGNOSIS; FRACTALITY; P300 There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi- degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real- world. [Li, Jie; Ji, Hongfei; Cao, Lei; Zang, Di] Tongji Univ, Dept Comp Sci & Technol, Shanghai 200092, Peoples R China; [Gu, Rong] Tongji Univ, Dept Elect Sci & Technol, Shanghai 200092, Peoples R China; [Xia, Bin] Shanghai Maritime Univ, Inst Informat Engn, Shanghai 201306, Peoples R China; [Xia, Bin] Univ Tubingen, Inst Med Psychol & Behav Neurobiol, D-72076 Tubingen, Germany; [Wu, Qiang] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China Tongji University; Tongji University; Chinese Academy of Sciences; Shanghai Maritime University; Eberhard Karls University of Tubingen; Eberhard Karls University Hospital; Shandong University Ji, HF (corresponding author), Tongji Univ, Dept Comp Sci & Technol, 4800 Cao An Highway, Shanghai 200092, Peoples R China. nijanice@gmail.com; jh410@duke.edu; scallray@163.com; zangdi@tongji.edu.cn; gurong2001@163.com; bin.xia@uni-tuebingen.de; wuqiang@sdu.edu.cn cao, lei/0000-0002-8427-4548 Fundamental Research Funds for the Central Universities [0800219202]; National Natural Science Foundation of China [61105122, 61103071, 61272271, 61305060]; Excellent Youth and Middle Age Scientists Fund of Shandong Province [BS2012DX020]; Natural Science Foundation Program of Shanghai [11ZR1440200, 12ZR1434000]; Specialized Research Fund for the Doctoral Program of Higher Education [20110072120065, 20130131120025] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Excellent Youth and Middle Age Scientists Fund of Shandong Province; Natural Science Foundation Program of Shanghai; Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)) The work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 0800219202), the National Natural Science Foundation of China (Grant Nos. 61105122, 61103071, 61272271 and 61305060), the Excellent Youth and Middle Age Scientists Fund of Shandong Province (Grant No. BS2012DX020), Natural Science Foundation Program of Shanghai (Grant Nos. 11ZR1440200 and 12ZR1434000) and Specialized Research Fund for the Doctoral Program of Higher Education (Grant Nos. 20110072120065 and 20130131120025). 62 51 52 9 148 WORLD SCIENTIFIC PUBL CO PTE LTD SINGAPORE 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE 0129-0657 1793-6462 INT J NEURAL SYST Int. J. Neural Syst. JUN 2014.0 24.0 4 1450014 10.1142/S0129065714500142 0.0 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science AE8VU 24694169.0 2023-03-23 WOS:000334281800004 0
J Ling, H; Kovacs, GG; Vonsattel, JPG; Davey, K; Mok, KY; Hardy, J; Morris, HR; Warner, TT; Holton, JL; Revesz, T Ling, Helen; Kovacs, Gabor G.; Vonsattel, Jean Paul G.; Davey, Karen; Mok, Kin Y.; Hardy, John; Morris, Huw R.; Warner, Thomas T.; Holton, Janice L.; Revesz, Tamas Astrogliopathy predominates the earliest stage of corticobasal degeneration pathology BRAIN English Article corticobasal degeneration; tau; astrocytic plaque; progressive supranuclear palsy; neurofibrillary tangles PROGRESSIVE SUPRANUCLEAR PALSY; LEWY BODY DISEASE; PARKINSONS-DISEASE; NEUROPATHOLOGIC CRITERIA; FRONTOTEMPORAL DEMENTIA; PTDP-43 PATHOLOGY; SUBSTANTIA-NIGRA; MOUSE-BRAIN; TAUOPATHY; INCLUSIONS Animal models have shown that tau seeding and propagation are strain-and neural network-specific. The study of preclinical cases is valuable to gain insights into early pathological features of corticobasal degeneration and its progression. Three preclinical corticobasal degeneration cases and six age-matched end-stage corticobasal degeneration cases were included in this study. Tau immunohistochemistry performed in 20 brain regions and quantitative assessment of regional tau load using image analysis were performed. Semi-quantitative grading of tau-positive cellular lesions and neuronal loss in the frontal, parietal and temporal cortices, striatum, substantia nigra and subthalamic nucleus were assessed. All preclinical cases were clinically asymptomatic but had widespread tau lesions in the typically affected regions in corticobasal degeneration and the pathognomonic astrocytic plaques were the most prominent lesion type in the anterior frontal and striatal regions. Mean total tau load (sum of all regional tau load) of end-stage corticobasal degeneration cases were nine times greater than that of the preclinical cases (P = 0.04) and less tau load was found in all regions of the preclinical cases. An anterior-to-posterior tau load ratio in the frontal cortex in preclinical cases was 12-fold greater than in end-stage corticobasal degeneration cases. Relatively greater tau burden in the anterior frontal cortex, striatum and subthalamic nucleus suggests the striatal afferent connection to the dorsolateral prefrontal cortex and basal ganglia circuitry are the earliest neural network connections affected by corticobasal degeneration-related tau pathology. Differential distribution of the tau pathology to selective cortical regions in these preclinical cases implies phenotypic presentation may be predetermined at a very early stage of the disease process. Neuronal loss of the substantia nigra was either absent or very mild in the preclinical cases and was moderate to severe in end-stage corticobasal degeneration cases (P < 0.05). Our findings suggest that a threshold of pathological burden in the 'right' anatomical regions needs to be reached before the onset of clinical symptoms. The early prominence of the astrocytic plaques in relation to sparse neuronal lesions leads one to speculate that corticobasal degeneration may begin as an astrogliopathy at a very early disease stage but neuronal lesions gradually take over as the predominant lesion type in advanced disease. [Ling, Helen; Davey, Karen; Warner, Thomas T.; Holton, Janice L.; Revesz, Tamas] UCL, UCL Inst Neurol, Queen Sq Brain Bank Neurol Disorders, London, England; [Ling, Helen; Davey, Karen; Warner, Thomas T.; Holton, Janice L.; Revesz, Tamas] UCL, UCL Inst Neurol, Reta Lila Weston Inst Neurol Studies, London, England; [Ling, Helen; Mok, Kin Y.; Hardy, John; Warner, Thomas T.; Holton, Janice L.; Revesz, Tamas] UCL, UCL Inst Neurol, Dept Mol Neurosci, London, England; [Kovacs, Gabor G.] Med Univ Vienna, Inst Neurol, Vienna, Austria; [Vonsattel, Jean Paul G.] Columbia Univ, Med Ctr, Taub Inst Res AD & Aging Brain, New York, NY USA; [Mok, Kin Y.] Hong Kong Univ Sci & Technol, Div Life Sci, Hong Kong, Hong Kong, Peoples R China; [Morris, Huw R.] UCL, UCL Inst Neurol, Dept Clin Neurosci, London, England University of London; University College London; University of London; University College London; University of London; University College London; Medical University of Vienna; Columbia University; Hong Kong University of Science & Technology; University of London; University College London Revesz, T (corresponding author), UCL Inst Neurol, Queen Sq Brain Bank Neurol Disorders, 1 Wakefield St, London WC1N 1PJ, England. t.revesz@ucl.ac.uk Mok, Kin/U-6893-2019; Holton, Janice L/F-6831-2011; Mok, Kin Ying/F-5860-2012; Hardy, John/C-2451-2009; Warner, Thomas/A-1454-2013; Revesz, Tamas/A-8732-2010; Kovacs, Gabor/A-7468-2013; Morris, Huw R/B-8527-2008 Mok, Kin/0000-0003-3145-880X; Holton, Janice L/0000-0002-3882-5249; Mok, Kin Ying/0000-0003-3145-880X; Hardy, John/0000-0002-3122-0423; Warner, Thomas/0000-0001-6195-6995; Revesz, Tamas/0000-0003-2501-0259; Kovacs, Gabor/0000-0003-3841-5511; Morris, Huw R/0000-0002-5473-3774 Karin & Sten Mortstedt CBD Solutions research grant [512385]; MRC [G0901254, G0701075] Funding Source: UKRI Karin & Sten Mortstedt CBD Solutions research grant; MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)) This project is funded by Karin & Sten Mortstedt CBD Solutions research grant (Grant code: 512385). H.L. presented this work at the British Neuropathological Society Annual Meeting and the European Congress of Neuropathology in 2016. 51 83 83 0 5 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0006-8950 1460-2156 BRAIN Brain DEC 2016.0 139.0 12 3237 3252 10.1093/brain/aww256 0.0 16 Clinical Neurology; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology EI7US 27797812.0 Green Submitted, Bronze 2023-03-23 WOS:000392709800023 0
J Tu, SS; Waqas, M; Lin, QQ; Rehman, SU; Hanif, M; Xiao, CB; Butt, MM; Chang, CC Tu, Shanshan; Waqas, Muhammad; Lin, Qiangqiang; Rehman, Sadaqat Ur; Hanif, Muhammad; Xiao, Chuangbai; Butt, M. Majid; Chang, Chin-Chen Tracking area list allocation scheme based on overlapping community algorithm COMPUTER NETWORKS English Article Location management; Cellular networks; Tracking area list; Graph partitioning; Overlapping community Reducing the singling overhead for tracking and mobile paging devices is a challenging issue in the study of location management of cellular networks. Cellular networks have become massive generators of data, and in the forthcoming years, this data is expected to increase drastically. Big data-based intelligence and analytics can improve network operational efficiency and user service quality. This work proposes to exploit massive handover and paging data from cellular networks to minimize singling due to user mobility. In this paper, we offer a new holistic tracking area lists (TAL) management methodology, considering group user mobility behavior and paging characteristics. Firstly, a series of graphs showing the evolution of user mobility and traffic is built from handover and paging statistics in the network management system (NMS). Then, the TAL allocation problem is formulated as a classical graph partitioning problem, which is then solved by detecting overlapping communities algorithm based on game theory. Results show that the proposed method can effectively reduce the location management singling overhead and improve the TAL configuration efficiency. [Tu, Shanshan; Waqas, Muhammad; Lin, Qiangqiang; Rehman, Sadaqat Ur; Xiao, Chuangbai] Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China; [Waqas, Muhammad; Hanif, Muhammad] Ghulam Ishaq Khan GIK Inst Engn Sci & Technol, Fac Comp Sci & Engn, Topi 23460, Pakistan; [Butt, M. Majid] Trinity Coll Dublin, Sch Engn, Dublin, Ireland; [Chang, Chin-Chen] Feng Chia Univ, Dept Informat Engn & Comp Sci, Taichung, Taiwan; [Chang, Chin-Chen] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China Beijing University of Technology; GIK Institute Engineering Science & Technology; Trinity College Dublin; Feng Chia University; Hangzhou Dianzi University Lin, QQ (corresponding author), Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China. sstu@bjut.edu.cn; engr.waqas2079@gmail.com; 969118264@qq.com; engr.sidkhan@gmail.com; majidb.pk@gmail.com Waqas, Muhammad/AAZ-6651-2020; Rehman, Sadaqat ur/AAQ-9508-2020; Butt, Majid/L-9723-2014 Rehman, Sadaqat ur/0000-0001-7823-3814; Butt, Majid/0000-0003-2875-1714; Waqas, Muhammad/0000-0003-0814-7544 National Key R&D Program of China [2018YFB0803600]; National Natural Science Foundation of China [61801008]; Beijing Municipal Natural Science Foundation [L172049]; Scientific Research Common Program of Beijing Municipal Commission of Education [KM201910005025] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Scientific Research Common Program of Beijing Municipal Commission of Education(Beijing Municipal Commission of Education) This work was partially supported by the National Key R&D Program of China (No.2018YFB0803600), National Natural Science Foundation of China (No.61801008), Beijing Municipal Natural Science Foundation (No.L172049), Scientific Research Common Program of Beijing Municipal Commission of Education (No.KM201910005025). 23 3 4 0 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. MAY 22 2020.0 173.0 107182 10.1016/j.comnet.2020.107182 0.0 9 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications LJ2VN 2023-03-23 WOS:000530027500003 0
J Li, F; Shugurov, I; Busam, B; Li, ML; Yang, SW; Ilic, S Li, Fu; Shugurov, Ivan; Busam, Benjamin; Li, Minglong; Yang, Shaowu; Ilic, Slobodan PolarMesh: A Star-Convex 3D Shape Approximation for Object Pose Estimation IEEE ROBOTICS AND AUTOMATION LETTERS English Article Three-dimensional displays; Shape; Pose estimation; Solid modeling; Neural networks; Training; Task analysis; Shape representation; object pose estimation In this letter, we introduce PolarMesh as a star-convex approximation of a 3D object based on spherical projection and can be applied to monocular object pose and shape estimation. The proposed PolarMesh can be stored in a discrete 2D map that allows a trivial conversion between it and the object surface, as the adjacent map elements form faces of the 3D model. The fixed-grid 2D nature of this object surface approximation ensures that it can be estimated from an image patch of the object with an auto-encoder neural network. Moreover, the PolarMesh not only encodes the object shape but also its orientation. Consequently, it is used for accurate estimation of object orientation and can be extended to the full 6D pose estimation using a projection-based method for translation. An exhaustive quantitative evaluation on LineMod, LineMod-Occlusion and HomebrewedDB shows the competitive performance of the proposed method compared to other monocular RGB pose estimation pipelines. Furthermore, we tested the generalization capabilities of the representation on the recent NOCS dataset for category-level pose estimation and achieved very compelling results in terms of predicted rotations. [Li, Fu; Li, Minglong; Yang, Shaowu] Natl Univ Def Technol, Coll Comp Sci & Technol, Inst Quantum Informat, Changsha 410073, Peoples R China; [Li, Fu; Li, Minglong; Yang, Shaowu] Natl Univ Def Technol, Coll Comp Sci & Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China; [Li, Fu; Shugurov, Ivan; Busam, Benjamin; Ilic, Slobodan] Tech Univ Munich, Dept Informat, D-85748 Garching, Germany; [Shugurov, Ivan; Ilic, Slobodan] Siemens AG, D-81739 Munich, Germany National University of Defense Technology - China; National University of Defense Technology - China; Technical University of Munich; Siemens AG; Siemens Germany Yang, SW (corresponding author), Natl Univ Def Technol, Coll Comp Sci & Technol, Inst Quantum Informat, Changsha 410073, Peoples R China.;Yang, SW (corresponding author), Natl Univ Def Technol, Coll Comp Sci & Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China. lifull@nudt.edu.cn; ivan.shugurov@tum.de; b.busam@tum.de; liminglong10@nudt.edu.cn; shaowu.yang@nudt.edu.cn; slobodan.ilic@siemens.com Busam, Benjamin/ADC-5306-2022 Busam, Benjamin/0000-0002-0620-5774; Shugurov, Ivan/0000-0001-5413-6622; Ilic, Slobodan/0000-0002-3413-1936; Fu, Li/0000-0001-6228-4289 National Natural Science Foundation of China [61803375, 91948303] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) Thisworkwas supported by the National Natural Science Foundation of China under Grants 61803375 and 91948303. 56 2 2 1 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. APR 2022.0 7.0 2 4416 4423 10.1109/LRA.2022.3147880 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics ZH9CX 2023-03-23 WOS:000761228500023 0
J Liu, W; Placke, T; Chau, KT Liu, Wei; Placke, Tobias; Chau, K. T. Overview of batteries and battery management for electric vehicles ENERGY REPORTS English Review Available online xxxx; Electric vehicles; Electric propulsion; Rechargeable batteries; Battery management; Wireless power transfer LITHIUM-ION BATTERY; STATE-OF-CHARGE; PREDICTIVE TORQUE CONTROL; ENERGY-STORAGE SYSTEMS; POWER TRANSFER SYSTEMS; OPEN-CIRCUIT VOLTAGE; ELECTROCHEMICAL MODEL; WIRELESS POWER; NEURAL-NETWORK; KALMAN FILTER Popularization of electric vehicles (EVs) is an effective solution to promote carbon neutrality, thus combating the climate crisis. Advances in EV batteries and battery management interrelate with government policies and user experiences closely. This article reviews the evolutions and challenges of (i) state-of-the-art battery technologies and (ii) state-of-the-art battery management technologies for hybrid and pure EVs. The key is to reveal the major features, pros and cons, new technological breakthroughs, future challenges, and opportunities for advancing electric mobility. This critical review envisions the development trends of battery chemistry technologies, technologies regarding batteries, and technologies replacing batteries. Wherein, lithium-ion batteries, lithium-metal batteries (such as solid state batteries), and technologies beyond lithium ('post-lithium') will be actively explored in the next decades. Meanwhile, the data-driven electrothermal model is promising and identified with an impressive performance. Technologies of move-and-charge and wireless power drive will help alleviate the overdependence of batteries. Finally, future high-energy batteries and their management technologies will actively embrace the information and energy internet for data and energy sharing. [Liu, Wei; Chau, K. T.] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Room LG203,Chow Yei Ching Bldg, Hong Kong, Peoples R China; [Placke, Tobias] Univ Munster, MEET Battery Res Ctr, Corrensstr 46, D-48149 Munster, Germany University of Hong Kong; University of Munster Chau, KT (corresponding author), Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Room LG203,Chow Yei Ching Bldg, Hong Kong, Peoples R China. ktchau@eee.hku.hk LIU, Wei/AAH-2263-2019; Placke, Tobias/HDN-1338-2022; Placke, Tobias/M-4067-2017 LIU, Wei/0000-0003-2556-3689; Placke, Tobias/0000-0002-2097-5193 Research Grants Council of the Hong Kong Special Administrative Region, China [T23-701/20-R] Research Grants Council of the Hong Kong Special Administrative Region, China(Hong Kong Research Grants Council) This work was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. T23-701/20-R). The authors would like to appreciate Zhichao Hua for collecting some important information, Hongliang Pang for preparing parts of this article, and Calvin C.T. Chow for proofreading the whole article. 252 23 24 132 243 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2352-4847 ENERGY REP Energy Rep. NOV 2022.0 8.0 4058 4084 10.1016/j.egyr.2022.03.016 0.0 MAR 2022 27 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 1D7ZF gold 2023-03-23 WOS:000794015500003 0
J Guo, F; Zhao, QJ; Li, X; Kuang, XH; Zhang, JW; Han, YH; Tan, YA Guo, Feng; Zhao, Qingjie; Li, Xuan; Kuang, Xiaohui; Zhang, Jianwei; Han, Yahong; Tan, Yu-an Detecting adversarial examples via prediction difference for deep neural networks INFORMATION SCIENCES English Article Deep neural network; Adversarial example; Image recognition; Prediction difference Deep neural networks (DNNs) perform effectively in many computer vision tasks. However, DNNs are found to be vulnerable to adversarial examples which are generated by adding imperceptible perturbations to original images. To address this problem, we propose a novel defense method, transferability prediction difference (TPD), to drastically improve the adversarial robustness of DNNs with small sacrificing verified accuracy. We find out that the adversarial examples have lager prediction difference for various DNN models due to their various complicated decision boundaries, which can be used to identify the adversarial examples by converging decision boundaries to a prediction difference threshold. We adopt the K-means clustering algorithm on benign data to determine transferability prediction difference threshold, by which we can detect adversarial examples accurately and efficiently. Furthermore, TPD method neither modifies the target model nor needs to take knowledge of adversarial attacks. We perform four state-of-the-art adversarial attacks (FGSM, BIM, JSMA and C&W) to evaluate TPD models trained on MNIST and CIFAR-10 and the average detection accuracy is 96.74% and 86.61%. The results show that TPD model has high detection ratio on the demonstrably advanced white-box adversarial examples while keeping low false positive rate on benign examples. (C) 2019 Elsevier Inc. All rights reserved. [Guo, Feng; Zhao, Qingjie; Tan, Yu-an] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China; [Zhao, Qingjie; Zhang, Jianwei] Univ Hamburg, Dept Informat, Hamburg, Germany; [Li, Xuan] Fujian Normal Univ, Coll Math & Informat, Fuzhou, Fujian, Peoples R China; [Kuang, Xiaohui] Natl Key Lab Sci & Technol Informat Syst Secur, Beijing, Peoples R China; [Han, Yahong] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China Beijing Institute of Technology; University of Hamburg; Fujian Normal University; Tianjin University Zhao, QJ (corresponding author), Beijing Inst Technol, Beijing, Peoples R China. 2120171113@bit.edu.cn; zhaoqi@bit.edu.cn; zhang@informatik.uni-hamburg.de; yahong@tju.edu.cn; tan2008@bit.edu.cn zhang, jian/HPD-1712-2023 Han, Yahong/0000-0003-2768-1398; Tan, Yu-An/0000-0001-6404-8853 National Natural Science Foundation of China [61876019]; China Scholarship Council [201706035021]; German Research Foundation in project Crossmodal Learning [TRR-169] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); German Research Foundation in project Crossmodal Learning This work was supported by the National Natural Science Foundation of China (no. 61876019), the China Scholarship Council (201706035021), the German Research Foundation in project Crossmodal Learning (TRR-169). 33 20 22 1 32 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. OCT 2019.0 501.0 182 192 10.1016/j.ins.2019.05.084 0.0 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science IQ3PP 2023-03-23 WOS:000480663900012 0
J Wu, JC; Wang, TJ; Uckermann, O; Galli, R; Schackert, G; Cao, LC; Czarske, J; Kuschmierz, R Wu, Jiachen; Wang, Tijue; Uckermann, Ortrud; Galli, Roberta; Schackert, Gabriele; Cao, Liangcai; Czarske, Juergen; Kuschmierz, Robert Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis SCIENTIFIC REPORTS English Article SELF-CALIBRATION; ENHANCEMENT; MICROSCOPY; ENDOSCOPE; PROBE; CARS Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery. [Wu, Jiachen; Wang, Tijue; Czarske, Juergen; Kuschmierz, Robert] Tech Univ Dresden, Lab Measurement & Sensor Syst Tech, D-01069 Dresden, Germany; [Wu, Jiachen; Cao, Liangcai] Tsinghua Univ, Dept Precis Instruments, State Key Lab Precis Measurement Technol & Instru, Beijing 100084, Peoples R China; [Uckermann, Ortrud; Schackert, Gabriele] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dept Neurosurg, Dresden, Germany; [Uckermann, Ortrud] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Fac Med, Div Med Biol,Dept Psychiat, Dresden, Germany; [Galli, Roberta] Tech Univ Dresden, Fac Med Carl Gustav Carus, Dept Med Phys & Biomed Engn, Dresden, Germany; [Czarske, Juergen; Kuschmierz, Robert] Tech Univ Dresden, Competence Ctr BIOLAS, Dresden, Germany; [Czarske, Juergen] Tech Univ Dresden, Excellence Cluster Phys Life, Dresden, Germany; [Czarske, Juergen] Tech Univ Dresden, Sch Sci, Fac Phys, Dresden, Germany; [Uckermann, Ortrud; Schackert, Gabriele; Czarske, Juergen; Kuschmierz, Robert] Tech Univ Dresden, Else Kroner Fresenius Ctr Digital Hlth, Dresden, Germany Technische Universitat Dresden; Tsinghua University; Technische Universitat Dresden; Carl Gustav Carus University Hospital; Technische Universitat Dresden; Carl Gustav Carus University Hospital; Technische Universitat Dresden; Technische Universitat Dresden; Technische Universitat Dresden; Technische Universitat Dresden; Technische Universitat Dresden Wu, JC; Czarske, J; Kuschmierz, R (corresponding author), Tech Univ Dresden, Lab Measurement & Sensor Syst Tech, D-01069 Dresden, Germany.;Wu, JC (corresponding author), Tsinghua Univ, Dept Precis Instruments, State Key Lab Precis Measurement Technol & Instru, Beijing 100084, Peoples R China.;Czarske, J; Kuschmierz, R (corresponding author), Tech Univ Dresden, Competence Ctr BIOLAS, Dresden, Germany.;Czarske, J (corresponding author), Tech Univ Dresden, Excellence Cluster Phys Life, Dresden, Germany.;Czarske, J (corresponding author), Tech Univ Dresden, Sch Sci, Fac Phys, Dresden, Germany.;Czarske, J; Kuschmierz, R (corresponding author), Tech Univ Dresden, Else Kroner Fresenius Ctr Digital Hlth, Dresden, Germany. jiachen.wu@mailbox.tu-dresden.de; robert.kuschmierz@tu-dresden.de Galli, Roberta/0000-0002-0633-0321 German Science Foundation [DFG Cz55/47-1, Cz55/48-1]; Edmund Koch, TU Dresden German Science Foundation(German Research Foundation (DFG)); Edmund Koch, TU Dresden We would like to thank the assistance and valuable discussion from Julian Lich and Tom Glosemeyer. We thank the German Science Foundation (DFG Cz55/47-1, Cz55/48-1) for extensive funding of the project and Edmund Koch, TU Dresden for support. 68 1 1 11 11 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2045-2322 SCI REP-UK Sci Rep NOV 7 2022.0 12.0 1 18846 10.1038/s41598-022-23490-5 0.0 12 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 5Z3ZQ 36344626.0 gold, Green Accepted 2023-03-23 WOS:000879914800019 0
C Yuan, YL; Adhatarao, SS; Lin, MK; Yuan, YC; Liu, ZL; Fu, XM IEEE Yuan, Yali; Adhatarao, Sripriya Srikant; Lin, Mingkai; Yuan, Yachao; Liu, Zheli; Fu, Xiaoming ADA: Adaptive Deep Log Anomaly Detector IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS IEEE INFOCOM English Proceedings Paper 39th IEEE International Conference on Computer Communications (IEEE INFOCOM) JUL 06-09, 2020 ELECTR NETWORK IEEE Anomaly detection; deep neural networks; logs; online training; unsupervised; log-normal; threshold Large private and government networks are often subjected to attacks like data extrusion and service disruption. Existing anomaly detection systems use offline supervised learning and employ experts for labeling. Hence they cannot detect anomalies in real-time. Even though unsupervised algorithms are increasingly used nowadays, they cannot readily adapt to newer threats. Moreover, many such systems also suffer from high cost of storage and require extensive computational resources. In this paper, we propose ADA: Adaptive Deep Log Anomaly Detector, an unsupervised online deep neural network framework that leverages LSTM networks and regularly adapts to newer log patterns to ensure accurate anomaly detection. In ADA, an adaptive model selection strategy is designed to choose pareto-optimal configurations and thereby utilize resources efficiently. Further, a dynamic threshold algorithm is proposed to dictate the optimal threshold based on recently detected events to improve the detection accuracy. We also use the predictions to guide storage of abnormal data and effectively reduce the overall storage cost. We compare ADA with state-of-the-art approaches through leveraging the Los Alamos National Laboratory cyber security dataset and show that ADA accurately detects anomalies with high F1-score similar to 95% and it is 97 times faster than existing approaches and incurs very low storage cost. [Yuan, Yali; Adhatarao, Sripriya Srikant; Yuan, Yachao; Fu, Xiaoming] Univ Gottingen, Gottingen, Germany; [Lin, Mingkai] Nanjing Univ, Nanjing, Peoples R China; [Liu, Zheli] Nankai Univ, Tianjin, Peoples R China University of Gottingen; Nanjing University; Nankai University Yuan, YL (corresponding author), Univ Gottingen, Gottingen, Germany. yali.yuan@cs.uni-goettingen.de; adhatarao@cs.uni-goettingen.de; mingkai@smail.nju.edu.cn; yachao.yuan@uni-goettingen.de; liuzheli@nankai.edu.cn; fu@cs.uni-goettingen.de LIU, zhe/HGD-6875-2022; Yuan, Yali/ABC-2029-2020; Fu, Xiaoming/AAD-2828-2022; Yuan, Yachao/ABE-5182-2020; Fu, Xiaoming/B-7208-2016 Fu, Xiaoming/0000-0002-8012-4753; Yuan, Yachao/0000-0001-7498-002X; Fu, Xiaoming/0000-0002-8012-4753 EU H2020 COSAFE project [824019]; China Scholarship Council [201706050095] EU H2020 COSAFE project; China Scholarship Council(China Scholarship Council) This work has been funded by EU H2020 COSAFE project (Grant ID: 824019) and China Scholarship Council (Grant ID: 201706050095). The authors also appreciate Ricardo Zimmer, Trevor Khwam Tabougua and Shahroz Shahjahan for their helpful inputs on the topic of adaptive threshold. 33 9 9 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0743-166X 978-1-7281-6412-0 IEEE INFOCOM SER 2020.0 2449 2458 10 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BQ8NB 2023-03-23 WOS:000620945800248 0
J Bing, ZS; Meschede, C; Chen, G; Knoll, A; Huang, K Bing, Zhenshan; Meschede, Claus; Chen, Guang; Knoll, Alois; Huang, Kai Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle NEURAL NETWORKS English Article Spiking neural network; End-to-end learning; R-STDP; Lane keeping Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of SNNs in robotic fields are limited due to the lack of practical training methods. In this paper, we therefore introduce both indirect and direct end-to-end training methods of SNNs for a lane-keeping vehicle. First, we adopt a policy learned using the Deep Q-Learning (DQN) algorithm and then subsequently transfer it to an SNN using supervised learning. Second, we adopt the reward-modulated spike-timing-dependent plasticity (R-STDP) for training SNNs directly, since it combines the advantages of both reinforcement learning and the well-known spike-timing-dependent plasticity (STDP). We examine the proposed approaches in three scenarios in which a robot is controlled to keep within lane markings by using an event-based neuromorphic vision sensor. We further demonstrate the advantages of the R-STDP approach in terms of the lateral localization accuracy and training time steps by comparing them with other three algorithms presented in this paper. (C) 2019 Elsevier Ltd. All rights reserved. [Bing, Zhenshan; Huang, Kai] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China; [Bing, Zhenshan; Meschede, Claus; Knoll, Alois] Tech Univ Munich, Dept Comp Sci, Munich, Germany; [Chen, Guang] Tongji Univ, Sch Automot Studies, Shanghai, Peoples R China Sun Yat Sen University; Technical University of Munich; Tongji University Huang, K (corresponding author), Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China. bingzs@mail.sysu.edu.cn; claus.meschede@tum.de; guangchen@tongji.edu.cn; knoll@in.tum.de; huangk36@mail.sysu.edu.cn Bing, Zhenshan/AAF-7965-2020; Knoll, Alois/AAN-8417-2021 Knoll, Alois/0000-0003-4840-076X Shenzhen Research [JCYJ20180507182508857]; European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under the Specic Grant [720270]; Chinese Scholarship Council Shenzhen Research; European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under the Specic Grant; Chinese Scholarship Council(China Scholarship Council) The research leading to these results has received funding from the Shenzhen Research JCYJ20180507182508857, the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under the Specic Grant Agreement No. 720270 (Human Brain Project SGA1), and the Chinese Scholarship Council. 35 22 22 2 43 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. JAN 2020.0 121.0 21 36 10.1016/j.neunet.2019.05.019 0.0 16 Computer Science, Artificial Intelligence; Neurosciences Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Neurosciences & Neurology JT3WE 31526952.0 Green Submitted 2023-03-23 WOS:000500922700004 0
J Ding, EJ; Cheng, YH; Xiao, CC; Liu, ZY; Yu, WL Ding, Enjie; Cheng, Yuhao; Xiao, Chengcheng; Liu, Zhongyu; Yu, Wanli Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network APPLIED SCIENCES-BASEL English Article dynamic neural networks; light-weight convolutional neural networks; channel redundancy in convolutional neural networks Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters. [Ding, Enjie; Cheng, Yuhao; Xiao, Chengcheng; Liu, Zhongyu] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221008, Jiangsu, Peoples R China; [Ding, Enjie; Cheng, Yuhao; Xiao, Chengcheng; Liu, Zhongyu] China Univ Min & Technol, IOT Percept Mine Res Ctr, Xuzhou 221008, Jiangsu, Peoples R China; [Yu, Wanli] Univ Bremen, Inst Electrodynam & Microelect, D-28359 Bremen, Germany China University of Mining & Technology; China University of Mining & Technology; University of Bremen Liu, ZY (corresponding author), China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221008, Jiangsu, Peoples R China.;Liu, ZY (corresponding author), China Univ Min & Technol, IOT Percept Mine Res Ctr, Xuzhou 221008, Jiangsu, Peoples R China. enjied@cumt.edu.cn; TS19060020A31@cumt.edu.cn; TS2006055A31@cumt.edu.cn; TB17060009B4@cumt.edu.cn; wyu@uni-bremen.de Yu, Wanli/L-8410-2018 Yu, Wanli/0000-0003-1299-6223 Research on On-line identification of Coal Gangue Based on terahertz detection technology [52074273]; State Key Research Development Program of China [2017 YFC0804400, 2017YFC0804401] Research on On-line identification of Coal Gangue Based on terahertz detection technology; State Key Research Development Program of China(State Key Development Program for Basic Research of China) This research was funded by Research on On-line identification of Coal Gangue Based on terahertz detection technology (grant number NO.52074273), and by the State Key Research Development Program of China (grant number NO. 2017 YFC0804400, NO. 2017YFC0804401). 44 2 2 0 20 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel APR 2021.0 11.0 7 3111 10.3390/app11073111 0.0 15 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Materials Science; Physics RK5SR gold 2023-03-23 WOS:000638356400001 0
J Chen, L; Zhan, WJ; Tian, W; He, YH; Zou, Q Chen, Long; Zhan, Wujing; Tian, Wei; He, Yuhang; Zou, Qin Deep Integration: A Multi-Label Architecture for Road Scene Recognition IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Large-scale dataset; data imbalance; multi-label classification; road scene recognition Deep convolutional neural networks have been applied by automobile industries, Internet giants, and academic institutes to boost autonomous driving technologies; while progress has been witnessed in environmental perception tasks, such as object detection and driver state recognition, the scene-centric understanding and identification still remain a virgin land. This mainly encompasses two key issues: 1) the lack of shared large datasets with comprehensively annotated road scene information and 2) the difficulty to find effective ways to train networks concerning the bias of category samples, image resolutions, scene dynamics, and capturing conditions. In this paper, we make two contributions: 1) we introduce a large-scale dataset with over 110 k images, dubbed DrivingScene, covering traffic scenarios under different weather conditions, road structures, and environmental instances and driving places, which is the first large-scale dataset for multi-class traffic scenes classification and 2) we propose a multi-label neural network for road scene recognition, which incorporates both single- and multi-class classification modes into a multi-level cost function for training with imbalanced categories and utilizes a deep data integration strategy to improve the classification ability on hard samples. The experimental results on DrivingScene and PASCAL VOC demonstrate the effectiveness of the proposed approach in handling the challenge of data imbalance. [Chen, Long; Zhan, Wujing; He, Yuhang] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China; [Tian, Wei] Karlsruhe Inst Technol, Inst Measurement & Control Syst, D-76131 Karlsruhe, Germany; [Zou, Qin] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China Sun Yat Sen University; Helmholtz Association; Karlsruhe Institute of Technology; Wuhan University Chen, L (corresponding author), Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China. chen146@mail.sysu.edu.cn; zhanwj5@mail2.sysu.edu.cn; wei.tian@kit.edu; qzou@whu.edu.cn he, yh/HJA-5454-2022; Zou, Qin/GVU-2237-2022; Zou, Qin/AFM-0040-2022; Tian, Wei/T-8872-2017 Tian, Wei/0000-0002-5085-7219; Zou, Qin/0000-0001-7955-0782 National Key Research and Development Program of China [2018YFB1305002]; National Natural Science Foundation of China [61773414, 61872277] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305002 and in part by the National Natural Science Foundation of China under Grant 61773414 and Grant 61872277. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Shuicheng Yan. 43 48 51 8 64 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. OCT 2019.0 28.0 10 4883 4898 10.1109/TIP.2019.2913079 0.0 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IP8RF 31071033.0 2023-03-23 WOS:000480312800013 0
J Su, PF; Joutsensaari, J; Dada, L; Zaidan, MA; Nieminen, T; Li, XY; Wu, YS; Decesari, S; Tarkoma, S; Petaja, T; Kulmala, M; Pellikka, P Su, Peifeng; Joutsensaari, Jorma; Dada, Lubna; Zaidan, Martha Arbayani; Nieminen, Tuomo; Li, Xinyang; Wu, Yusheng; Decesari, Stefano; Tarkoma, Sasu; Petaja, Tuukka; Kulmala, Markku; Pellikka, Petri New particle formation event detection with Mask R-CNN ATMOSPHERIC CHEMISTRY AND PHYSICS English Article NUCLEATION MODE PARTICLES; GROWTH-RATES; SIZE; HYYTIALA; STATION; TOOL Atmospheric new particle formation (NPF) is an important source of climate-relevant aerosol particles which has been observed at many locations globally. To study this phenomenon, the first step is to identify whether an NPF event occurs or not on a given day. In practice, NPF event identification is performed visually by classifying the NPF event or non-event days from the particle number size distribution surface plots. Unfortunately, this day-by-day visual classification is time-consuming and labor-intensive, and the identification process renders subjective results. To detect NPF events automatically, we regard the visual signature (banana shape) which has been observed all over the world in NPF surface plots as a special kind of object, and a deep learning model called Mask R-CNN is applied to localize the spatial layouts of NPF events in their surface plots. Utilizing only 358 human-annotated masks on data from the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II station (Hyytiala, Finland), the Mask R-CNN model was successfully generalized for three SMEAR stations in Finland and the San Pietro Capofiume (SPC) station in Italy. In addition to the detection of NPF events (especially the strongest events), the presented method can determine the growth rates, start times, and end times for NPF events automatically. The automatically determined growth rates agree with the manually determined growth rates. The statistical results validate the potential of applying the proposed method to different sites, which will improve the automatic level for NPF event detection and analysis. Furthermore, the proposed automatic NPF event analysis method can minimize subjectivity compared with human-made analysis, especially when long-term data series are analyzed and statistical comparisons between different sites are needed for event characteristics such as the start and end times, thereby saving time and effort for scientists studying NPF events. [Su, Peifeng; Pellikka, Petri] Univ Helsinki, Dept Geosci & Geog, FIN-00014 Helsinki, Finland; [Su, Peifeng; Zaidan, Martha Arbayani; Nieminen, Tuomo; Li, Xinyang; Wu, Yusheng; Petaja, Tuukka; Kulmala, Markku; Pellikka, Petri] Univ Helsinki, Fac Sci, Inst Atmospher & Earth Syst Res INAR Phys, FIN-00014 Helsinki, Finland; [Joutsensaari, Jorma] Univ Eastern Finland, Dept Appl Phys, POB 1627, Kuopio 70211, Finland; [Dada, Lubna] Ecole Polytech Fed Lausanne EPFL Valais, Extreme Environm Res Lab, CH-1951 Sion, Switzerland; [Dada, Lubna] Paul Scheirer Inst, Lab Atmospher Chem, CH-5232 Villigen, Switzerland; [Zaidan, Martha Arbayani; Petaja, Tuukka; Kulmala, Markku] Nanjing Univ, Sch Atmospher Sci, Joint Int Res Lab Atmospher & Earth Syst Sci, Nanjing 210023, Peoples R China; [Nieminen, Tuomo] Univ Helsinki, Inst Atmospher & Earth Syst Res INAR Forest Sci, Fac Agr & Forestry, FIN-00014 Helsinki, Finland; [Decesari, Stefano] Natl Res Council Italy CNR, Inst Atmospher & Climate Sci, I-40129 Bologna, Italy; [Tarkoma, Sasu] Univ Helsinki, Fac Sci, Dept Comp Sci, FI-00014 Helsinki, Finland University of Helsinki; University of Helsinki; University of Eastern Finland; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Nanjing University; University of Helsinki; Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze dell'Atmosfera e del Clima (ISAC-CNR); University of Helsinki Su, PF (corresponding author), Univ Helsinki, Dept Geosci & Geog, FIN-00014 Helsinki, Finland.;Su, PF (corresponding author), Univ Helsinki, Fac Sci, Inst Atmospher & Earth Syst Res INAR Phys, FIN-00014 Helsinki, Finland. peifeng.su@helsinki.fi Petäjä, Tuukka/A-8009-2008; Dada, Lubna/X-7559-2018; Zaidan, Martha Arbayani/G-4575-2011 Petäjä, Tuukka/0000-0002-1881-9044; Dada, Lubna/0000-0003-1105-9043; Zaidan, Martha Arbayani/0000-0002-6348-1230; Li, Xinyang/0000-0002-5316-2017; Nieminen, Tuomo/0000-0002-2713-715X; Joutsensaari, Jorma/0000-0003-3460-1114 Business Finland [UIA03-240]; Academy of Finland [337549, 302958, 311932] Business Finland; Academy of Finland(Academy of Finland) This research has been supported by Business Finland (grant no. UIA03-240) and the Academy of Finland (grant nos. 337549, 302958, 311932). 48 5 5 0 9 COPERNICUS GESELLSCHAFT MBH GOTTINGEN BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY 1680-7316 1680-7324 ATMOS CHEM PHYS Atmos. Chem. Phys. JAN 25 2022.0 22.0 2 1293 1309 10.5194/acp-22-1293-2022 0.0 17 Environmental Sciences; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences YT1XN Green Submitted, Green Published, Green Accepted, gold 2023-03-23 WOS:000751160400001 0
J Dong, XC; Yetisen, AK; Kohler, MH; Dong, J; Wang, SJ; Jakobi, M; Zhang, XX; Koch, AW Dong, Xingchen; Yetisen, Ali K.; Koehler, Michael H.; Dong, Jie; Wang, Shengjia; Jakobi, Martin; Zhang, Xiaoxing; Koch, Alexander W. Microscale Spectroscopic Mapping of 2D Optical Materials ADVANCED OPTICAL MATERIALS English Review 2D materials; diffraction-limited imaging; optoelectronics; spectroscopy MULTILAYER BLACK PHOSPHORUS; TERAHERTZ SPECTROSCOPY; PERFECT ABSORPTION; RAMAN-SPECTROSCOPY; PHONON POLARITONS; LAYERED MATERIALS; MONOLAYER MOS2; BORON-NITRIDE; GRAPHENE; PHOTOLUMINESCENCE 2D materials have unique optical properties due to their ultrathin layered structures, and are emerging as promising materials for optoelectronic devices. To characterize and evaluate these properties, diffraction-limited spectroscopic mapping, also known as microscale spectroscopic imaging, has been developed to provide abundant spatial and spectral information. The usefulness of microscale spectroscopic mapping for unique property study of 2D optical materials is addressed. Advances in both mature and growing microscale spectroscopic mapping setups and methods covering broad ranges of the electromagnetic spectrum are introduced. Different forms of graphene, molybdenum disulphide (MoS2), and black phosphorus, including intrinsic and engineered structures made by different methods, are selected as varied representatives of 2D optical materials from the perspective of bandgap and anisotropy. A detailed discussion of microscale spectroscopic images and their unique optical property findings, including spatial adsorption and emission, excitonic behavior, light sensitivity, and plasmonic effects, is then given to demonstrate the advantages of spectral mapping over conventional point spectroscopy. The analysis also provides a generic view of microscale spectroscopic mapping selection in 2D-materials imaging. Furthermore, the challenges inherent in large-scale spectral data processing are explored, and the utilization of deep learning is suggested for possible application in academica and industry. [Dong, Xingchen; Yetisen, Ali K.; Koehler, Michael H.; Dong, Jie; Wang, Shengjia; Jakobi, Martin; Koch, Alexander W.] Tech Univ Munich, Inst Measurement Syst & Sensor Technol, D-80333 Munich, Germany; [Zhang, Xiaoxing] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400044, Peoples R China Technical University of Munich; Chongqing University Dong, XC (corresponding author), Tech Univ Munich, Inst Measurement Syst & Sensor Technol, D-80333 Munich, Germany. xingchen.dong@tum.de Wang, Shengjia/HDN-2010-2022; Yetisen, Ali/AAK-8833-2021; Yetisen, Ali K./HOI-0302-2023 Dong, Xingchen/0000-0001-6734-7568; Wang, Shengjia/0000-0003-2347-2898; Yetisen, Ali K./0000-0003-0896-267X; Zhang, Xiaoxing/0000-0001-5872-2039 152 17 17 10 101 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 2195-1071 ADV OPT MATER Adv. Opt. Mater. SEP 2019.0 7.0 18 1900324 10.1002/adom.201900324 0.0 20 Materials Science, Multidisciplinary; Optics Science Citation Index Expanded (SCI-EXPANDED) Materials Science; Optics IZ4XV 2023-03-23 WOS:000487087400012 0
J Ren, XQ; Liu, YY; Hu, YB; Li, ZJ Ren, Xiaoqian; Liu, Yueyue; Hu, Yingbai; Li, Zhijun Integrated Task Sensing and Whole Body Control for Mobile Manipulation With Series Elastic Actuators IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article; Early Access Manipulators; Task analysis; Manipulator dynamics; Robot sensing systems; Robots; Sensors; Navigation; Mobile manipulation; navigation; whole body control; SEAs FLEXIBLE-JOINT ROBOT; NAVIGATION; LOCALIZATION; SATURATION; SYSTEMS; INPUT In this paper, an integrated framework consisting of the sensing, navigation and control is proposed for an autonomous mobile manipulator driven by series elastic actuators (SEAs) to preform mobile manipulation tasks in unknown environments. First, ORB-SLAM2 technique is combined into the environment sensing by extracting the ORB features, automatic initialization, repositioning and loop detection for real-time posture estimation. Then, the navigation function is designed for generating collision-free trajectory in an environment with obstacles. To realize kinematic and dynamic control of the mobile manipulator with the developed SEA joints, the whole body dynamics is considered and described. And to handle dynamic uncertainties and the SEA inherent saturation limits, a novel adaptive neural network control considering the whole body dynamics is proposed. Without knowing the exact parameters of the whole body model, the designed controller merely requires the position and velocity of the actuators and links, which can make the tracking errors converge to zero and keep all signals uniformly bounded in the closed-loop system. The performance and efficiency of the proposed method are verified by extensive experiments. [Ren, Xiaoqian; Li, Zhijun] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China; [Ren, Xiaoqian; Li, Zhijun] Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230031, Peoples R China; [Liu, Yueyue] South China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510641, Peoples R China; [Hu, Yingbai] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany Chinese Academy of Sciences; University of Science & Technology of China, CAS; South China University of Technology; Technical University of Munich Li, ZJ (corresponding author), Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China.;Li, ZJ (corresponding author), Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230031, Peoples R China. ren0919@mail.ustc.edu.cn; lyy8313167@163.com; yingbai.hu@tum.de; zjli@ieee.org Ren, Xiao/HNS-0057-2023 Ren, Xiaoqian/0000-0002-0542-2144; Liu, Yueyue/0000-0002-7148-4167; Hu, Yingbai/0000-0003-2452-3570 National Natural Science Foundation of China [62133013, U2013601]; Anhui Provincial Natural Science Foundation; Anhui Energy-Internet Joint Program [2008085UD01]; Major Science and Technology Projects of Anhui Province [202103a05020004] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Anhui Provincial Natural Science Foundation(Natural Science Foundation of Anhui Province); Anhui Energy-Internet Joint Program; Major Science and Technology Projects of Anhui Province This work was supported in part by the National Natural Science Foundation of China under Grant 62133013 and Grant U2013601; in part by the Anhui Provincial Natural Science Foundation, Anhui Energy-Internet Joint Program, under Grant 2008085UD01; and in part by the Major Science and Technology Projects of Anhui Province under Grant 202103a05020004. 39 0 0 7 18 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. 10.1109/TASE.2022.3156127 0.0 MAR 2022 12 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems ZR5WW 2023-03-23 WOS:000767853800001 0
C Parisi, GI; Barros, P; Kerzel, M; Wu, HY; Yang, GC; Li, ZH; Liu, X; Wermter, S IEEE Parisi, German, I; Barros, Pablo; Kerzel, Matthias; Wu, Haiyan; Yang, Guochun; Li, Zhenghan; Liu, Xun; Wermter, Stefan A Computational Model of Crossmodal Processing for Conflict Resolution 2017 THE SEVENTH JOINT IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING AND EPIGENETIC ROBOTICS (ICDL-EPIROB) Joint IEEE International Conference on Development and Learning and Epigenetic Robotics ICDL-EpiRob English Proceedings Paper 7th joint IEEE International Conference on Development Learning and Epigenetic Robotic (IEEE ICDL-EpiRob) SEP 18-21, 2017 Univ Lisbon, Inst Super Tecnico, Lisbon, PORTUGAL IEEE Computat Intelligence Soc,Inst Syst & Robot,Univ Lisboa, Mind Brain Coll,Fundacao Ciencia Tecnologia,Robot Brain & Cognit Res Infrastructure Lab Univ Lisbon, Inst Super Tecnico INTEGRATION; VENTRILOQUISM The brain integrates information from multiple sensory modalities to form a coherent and robust perceptual experience in complex environments. This ability is progressively acquired and fine-tuned during developmental stages in a multisensory environment. A rich set of neural mechanisms supports the integration and segregation of multimodal stimuli, providing the means to efficiently solve conflicts across modalities. Therefore, there is the motivation to develop efficient mechanisms for robotic platforms that process multisensory signals and trigger robust sensory-driven motor behavior. In this paper, we implement a computational model of crossmodal integration in a sound source localization task that accounts also for audiovisual conflict resolution. Our model consists of two layers of reciprocally connected visual and auditory neurons and a layer with crossmodal neurons that learns to integrate (or segregate) audiovisual stimuli on the basis of spatial disparity. To validate our architecture, we propose a spatial localization task in which 30 subjects had to determine the location of the sound source in a virtual scenario with four animated avatars. We measured their accuracy and reaction time under different conditions for congruent and incongruent audiovisual stimuli. We used this study as a baseline to model human-like behavioral responses with a neural network architecture exposed to the same experimental conditions. [Parisi, German, I; Barros, Pablo; Kerzel, Matthias; Wermter, Stefan] Univ Hamburg, Dept Informat, Knowledge Technol, Hamburg, Germany; [Wu, Haiyan; Yang, Guochun; Li, Zhenghan; Liu, Xun] Chinese Acad Sci, CAS Key Lab Behav Sci, Beijing, Peoples R China; [Wu, Haiyan; Yang, Guochun; Li, Zhenghan; Liu, Xun] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China University of Hamburg; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Parisi, GI (corresponding author), Univ Hamburg, Dept Informat, Knowledge Technol, Hamburg, Germany. Yang, Guochun/H-6590-2018; Liu, Xun/C-2400-2009 Yang, Guochun/0000-0002-0516-8772; Liu, Xun/0000-0003-1366-8926 National Natural Science Foundation of China (NSFC); German Research Foundation (DFG) [TRR 169] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); German Research Foundation (DFG)(German Research Foundation (DFG)) This research was supported by National Natural Science Foundation of China (NSFC) and the German Research Foundation (DFG) under project Transregio Crossmodal Learning (TRR 169). The authors would like to thank Josip Josifovski for valuable technical help during the development of the four-figure scenario and Stefan Heinrich and Sascha Griffiths for a revision of early versions of the paper. 21 5 5 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-9484 978-1-5386-3715-9 J IEEE I C DEVELOP L 2017.0 33 38 6 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Robotics Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Robotics BO0LM 2023-03-23 WOS:000491967600004 0
C Escalante, HJ; Ponce-Lopez, V; Wan, J; Riegler, MA; Chen, BY; Clapes, A; Escalera, S; Guyon, I; Baro, X; Halvorsen, P; Muller, H; Larson, M IEEE Jair Escalante, Hugo; Ponce-Lopez, Victor; Wan, Jun; Riegler, Michael A.; Chen, Baiyu; Clapes, Albert; Escalera, Sergio; Guyon, Isabelle; Baro, Xavier; Halvorsen, Pal; Muller, Henning; Larson, Martha ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) International Conference on Pattern Recognition English Proceedings Paper 23rd International Conference on Pattern Recognition (ICPR) DEC 04-08, 2016 Mexican Assoc Comp Vis Robot & Neural Comp, Cancun, MEXICO Int Assoc Pattern Recognit,Int Conf Pattern Recognit, Org Comm,Elsevier,IBM Res,INTEL,CONACYT Mexican Assoc Comp Vis Robot & Neural Comp This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost 200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to allow researchers to keep making progress in the four tracks. [Jair Escalante, Hugo; Guyon, Isabelle] ChaLearn, Berkeley, CA 94708 USA; [Jair Escalante, Hugo] INAOE, Puebla, Mexico; [Ponce-Lopez, Victor; Escalera, Sergio; Baro, Xavier] UAB, Comp Vis Ctr, Barcelona, Spain; [Ponce-Lopez, Victor; Clapes, Albert; Escalera, Sergio] UB, Dept Math & Comp Sci, Barcelona, Spain; [Ponce-Lopez, Victor] Open Univ Catalonia, EIMT IN3, Barcelona, Spain; [Wan, Jun] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China; [Riegler, Michael A.; Halvorsen, Pal] Simula Res Lab, Fornebu, Norway; [Guyon, Isabelle] Univ Paris Saclay, Paris, France; [Muller, Henning] Univ Appl Sci Western Switzerland, Sierre, Switzerland; [Larson, Martha] Delft Univ Technol, Multimedia Informat Retrieval Lab, Delft, Netherlands; [Larson, Martha] Univ Calif Berkeley, Berkeley, CA 94720 USA Instituto Nacional de Astrofisica, Optica y Electronica; Autonomous University of Barcelona; Centre de Visio per Computador (CVC); University of Barcelona; UOC Universitat Oberta de Catalunya; Chinese Academy of Sciences; Institute of Automation, CAS; UDICE-French Research Universities; Universite Paris Saclay; University of Applied Sciences & Arts Western Switzerland; Delft University of Technology; University of California System; University of California Berkeley Escalante, HJ (corresponding author), ChaLearn, Berkeley, CA 94708 USA.;Escalante, HJ (corresponding author), INAOE, Puebla, Mexico. Riegler, Michael A/E-5443-2015; Baró, Xavier/A-4064-2011; Clapés, Albert/GMW-4956-2022; Clapés, Albert/AAY-8971-2020; Escalera, Sergio/L-2998-2015; Ponce-López, Víctor/F-9002-2016 Riegler, Michael A/0000-0002-3153-2064; Baró, Xavier/0000-0001-5338-3007; Clapés, Albert/0000-0002-4089-9060; Escalera, Sergio/0000-0003-0617-8873; Ponce-López, Víctor/0000-0002-4662-5722; Halvorsen, Pal/0000-0003-2073-7029 Universitat de Barcelona; INAOE; ChaLearn; Spanish projects [TIN2013-43478-P, TIN2015-66951-C2-2-R]; European Comission; EC FP7 CrowdRec [610594] Universitat de Barcelona; INAOE; ChaLearn; Spanish projects(Spanish Government); European Comission(European CommissionEuropean Commission Joint Research Centre); EC FP7 CrowdRec We would like to thank Marc Oliu, Ciprian Corneanu. The following initiatives supported the challenge: IAPR TC12, MediaEval, ImageCLEF, ChaLearn, CASIA. We are also grateful with the sponsors of this challenge: Universitat de Barcelona, INAOE and ChaLearn. This work has been partially supported by the Spanish projects TIN2013-43478-P, TIN2015-66951-C2-2-R and the European Comission Horizon 2020 granted project SEE.4C under call H2020-ICT-2015 as well as EC FP7 CrowdRec 610594. 18 48 48 0 2 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1051-4651 978-1-5090-4847-2 INT C PATT RECOG 2016.0 67 73 7 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BI1RM Green Submitted 2023-03-23 WOS:000406771300012 0
J Liu, JY; Lin, G; Rehtanz, C; Huang, SH; Zhou, Y; Li, Y Liu, Jiayan; Lin, Gang; Rehtanz, Christian; Huang, Sunhua; Zhou, Yang; Li, Yong Data-driven intelligent EV charging operating with limited chargers considering the charging demand forecasting INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS English Article Electric Vehicle; Charging Scheduling; Data-Driven; Charging Demand ELECTRIC VEHICLES; BATTERY DEGRADATION; ENERGY; MANAGEMENT; STATIONS; ARRIVAL; SCHEME Coordinated charging scheduling can improve the operating economics of charging stations and reduce the required amount of charging facilities. However, existing optimal scheduling schemes either simplify the charging station capacity modeling when taking into account the traffic uncertainty, or ignore the future charging demands when considering charging capacity limitation. To tackle this issue, a data-driven intelligent EV charging scheduling algorithm is proposed in this paper, by scheduling in response to the time-of-use (TOU) electricity price, the limitation of charging facilities, and detailed charger operating process is also considered. First, based on the neural network algorithm, a charging demand forecasting method is introduced to establish the charging task of the charging station. Then, according to the established task, an optimization model that considers the charging costs, battery degradation, and users' dissatisfaction comprehensively is proposed. The proposed model is formulated as a mixed-integer nonlinear programming problem, and a corresponding approach for solving the model is also proposed. Finally, the real-time operation process of the proposed scheduling method in the actual charging station is presented. By comparing with the existing methods, better effectiveness and performance of the proposed scheduling method are verified by simulation results. [Liu, Jiayan; Lin, Gang; Rehtanz, Christian; Zhou, Yang] TU Dortmund, Inst Energy Syst Energy Efficiency & Energy Econ, D-44227 Dortmund, Germany; [Huang, Sunhua] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China; [Li, Yong] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China Dortmund University of Technology; Shanghai Jiao Tong University; Hunan University Liu, JY; Lin, G (corresponding author), TU Dortmund, Inst Energy Syst Energy Efficiency & Energy Econ, D-44227 Dortmund, Germany. jiayan.liu@tu-dortmund.de; Gang.lin@tu-dortmund.de Zhou, Yang/0000-0002-1443-0672 KonVeEn [201806130201]; German Federal Ministry of Education and Research; 111 Project of China [03EK3057A]; China Scholarship Council [B17016] KonVeEn; German Federal Ministry of Education and Research(Federal Ministry of Education & Research (BMBF)); 111 Project of China(Ministry of Education, China - 111 Project); China Scholarship Council(China Scholarship Council) This work is based upon work in the project KonVeEn (Grant No. 03EK3057A, supported by the German Federal Ministry of Education and Research) , in part by the 111 Project of China under Grant B17016, and supported in part by China Scholarship Council (No. 201806130201) . 37 2 2 7 20 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0142-0615 1879-3517 INT J ELEC POWER Int. J. Electr. Power Energy Syst. OCT 2022.0 141.0 108218 10.1016/j.ijepes.2022.108218 0.0 APR 2022 15 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 1M2YU 2023-03-23 WOS:000799840600008 0
J Wang, WP; He, C; Wang, Z; Cheng, J; Mo, XS; Tian, K; Fan, DG; Luo, X; Yuan, MM; Kurths, J Wang, Weiping; He, Chang; Wang, Zhen; Cheng, Jun; Mo, Xishuo; Tian, Kuo; Fan, Denggui; Luo, Xiong; Yuan, Manman; Kurths, Juergen Dynamic analysis of disease progression in Alzheimer's disease under the influence of hybrid synapse and spatially correlated noise NEUROCOMPUTING English Article Alzheimer's disease; Associative memory; Synaptic plasticity; Hybrid synapse; Spatial correlated noise TRANSMISSION; NETWORKS; AUTAPSE; NEURONS Alzheimer's disease (AD), characterized by cognitive impairment, mainly affects middle-aged and elderly people. As the aging process of the world continues to intensify, AD harms people's life, economy and society more and more seriously. Therefore, it has become an urgent problem to study the pathogenesis of AD and seek treatment on this basis. Hybrid synapse, autapse and spatial correlated noise in diverse neural activities have been investigated separately, however, theoretically understanding combination of them still has not been fully studied. Here in this paper, a neural network with multiple associative memory abilities is established from the perspective of the degeneration of associative memory ability in AD patients under the conditions of hybrid synapse, autapse and spatial correlated noise. In order to explore the pathogenesis, a synaptic loss and synaptic compensation model are established to analyze the associative memory ability of AD in different degrees of disease. The simulation results demonstrate the effectiveness of the proposed models and pave a way in the study of dynamic mechanism with higher bio-interpretability in neural networks. (c) 2021 Elsevier B.V. All rights reserved. [Wang, Weiping; He, Chang; Mo, Xishuo; Tian, Kuo; Luo, Xiong; Yuan, Manman] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China; [Wang, Weiping; He, Chang; Mo, Xishuo; Tian, Kuo; Luo, Xiong; Yuan, Manman] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China; [Wang, Weiping; He, Chang; Mo, Xishuo; Tian, Kuo; Luo, Xiong; Yuan, Manman] Inst Artificial Intelligence, Beijing 100083, Peoples R China; [Wang, Weiping; Luo, Xiong; Yuan, Manman] Beijing Univ Sci & Technol, Shunde Grad Sch, Guangzhou 528399, Peoples R China; [Wang, Zhen] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China; [Cheng, Jun] Guangxi Normal Univ, Coll Math & Stat, Guilin 541006, Peoples R China; [Fan, Denggui] Univ Sci & Technol Beijing, Sch Math & Phys, Beijing 100083, Peoples R China; [Kurths, Juergen] Inst Phys, Humboldt Univ, D-10099 Berlin, Germany; [Kurths, Juergen] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany University of Science & Technology Beijing; University of Science & Technology Beijing; Northwestern Polytechnical University; Guangxi Normal University; University of Science & Technology Beijing; Humboldt University of Berlin; Potsdam Institut fur Klimafolgenforschung Wang, WP (corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.;Wang, Z (corresponding author), Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China. weipingwangjt@ustb.edu.cn; nkzhenwang@163.com mo, xishuo/HGB-6289-2022 Luo, Xiong/0000-0002-1929-8447 National Natural Science Foundation of China [81961138010, U1803263, 11931015, U1836106]; Funda-mental Research Funds for the Central Universities [FRFTP19005A3, QNXM20210036, FRFIDRY20022]; Technological Innovation Foundation of Shunde Graduate School, USTB [BK19BF006, BK20BF010]; National Natural Science Foundation for Distinguished Young Scholars [62025602]; Fok Ying-Tong Education Foundation of China [171105]; Key Area R & D Program of Guangdong Province [2019B010137004]; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province [2020TD013] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Funda-mental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Technological Innovation Foundation of Shunde Graduate School, USTB; National Natural Science Foundation for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Fok Ying-Tong Education Foundation of China; Key Area R & D Program of Guangdong Province; Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province This work was supported in part by the National Natural Science Foundation of China under Grant 81961138010, U1803263, 11931015 and Grant U1836106, in part by the Funda-mental Research Funds for the Central Universities under Grant FRFTP19005A3, QNXM20210036, and Grant FRFIDRY20022, in part by the Technological Innovation Foundation of Shunde Graduate School, USTB, under Grant BK19BF006 and Grant BK20BF010, in part by National Natural Science Foundation for Distinguished Young Scholars under Grant 62025602, in part by Fok Ying-Tong Education Foundation of China under Grant 171105, in part by Key Area R & D Program of Guangdong Province under Grant 2019B010137004, in part by Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shanxi Province under Grant 2020TD013. 44 1 1 1 14 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 7 2021.0 456.0 23 35 10.1016/j.neucom.2021.05.067 0.0 JUN 2021 13 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UD8SE 2023-03-23 WOS:000687471900003 0
J Galow, AM; Peleg, S Galow, Anne-Marie; Peleg, Shahaf How to Slow down the Ticking Clock: Age-Associated Epigenetic Alterations and Related Interventions to Extend Life Span CELLS English Review histone modification; DNA methylation; metabolism; longevity; health span; biomarker; age-associated disorders; methylation clock; DNAm age; accelerated epigenetic aging; microbiome; single-cell sequencing; deep learning HISTONE DEACETYLASE INHIBITORS; EPIGENOME-WIDE ASSOCIATION; DNA METHYLATION; GENE-EXPRESSION; C. ELEGANS; DIETARY RESTRICTION; PROMOTER HYPERMETHYLATION; NORDIHYDROGUAIARETIC ACID; METABOLIC-REGULATION; MAMMALIAN TARGET Epigenetic alterations pose one major hallmark of organismal aging. Here, we provide an overview on recent findings describing the epigenetic changes that arise during aging and in related maladies such as neurodegeneration and cancer. Specifically, we focus on alterations of histone modifications and DNA methylation and illustrate the link with metabolic pathways. Age-related epigenetic, transcriptional and metabolic deregulations are highly interconnected, which renders dissociating cause and effect complicated. However, growing amounts of evidence support the notion that aging is not only accompanied by epigenetic alterations, but also at least in part induced by those. DNA methylation clocks emerged as a tool to objectively determine biological aging and turned out as a valuable source in search of factors positively and negatively impacting human life span. Moreover, specific epigenetic signatures can be used as biomarkers for age-associated disorders or even as targets for therapeutic approaches, as will be covered in this review. Finally, we summarize recent potential intervention strategies that target epigenetic mechanisms to extend healthy life span and provide an outlook on future developments in the field of longevity research. [Galow, Anne-Marie] Res Inst Farm Anim Biol FBN, Inst Genome Biol, D-18196 Dummerstorf, Germany; [Peleg, Shahaf] Res Inst Farm Anim Biol FBN, Res Grp Epigenet Metab & Longev, D-18196 Dummerstorf, Germany; [Peleg, Shahaf] Qingdao Univ, Inst Neuroregenerat & Neurorehabil, Qingdao 266071, Peoples R China Forschungsinstitut fur Nutztierbiologie (FBN); Forschungsinstitut fur Nutztierbiologie (FBN); Qingdao University Galow, AM (corresponding author), Res Inst Farm Anim Biol FBN, Inst Genome Biol, D-18196 Dummerstorf, Germany.;Peleg, S (corresponding author), Res Inst Farm Anim Biol FBN, Res Grp Epigenet Metab & Longev, D-18196 Dummerstorf, Germany.;Peleg, S (corresponding author), Qingdao Univ, Inst Neuroregenerat & Neurorehabil, Qingdao 266071, Peoples R China. galow@fbn-dummerstorf.de; peleg@fbn-dummerstorf.de Galow, Anne-Marie/0000-0002-7968-3152; Peleg, Shahaf/0000-0003-2646-3807 DFG [458246576]; NSFC [81870900]; FBN; two Longevity Impetus grants from Norn Group; EU [ESF/14-BM-A55-0028/18] DFG(German Research Foundation (DFG)); NSFC(National Natural Science Foundation of China (NSFC)); FBN; two Longevity Impetus grants from Norn Group; EU(European Commission) SP lab is supported by the FBN, DFG grant (458246576) and NSFC grant (81870900). The Project was also supported by two Longevity Impetus grants from Norn Group. The work of AG is supported by the EU Social Fund (ESF/14-BM-A55-0028/18, grant approved 12 July 2018). 382 11 11 13 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4409 CELLS-BASEL Cells FEB 2022.0 11.0 3 468 10.3390/cells11030468 0.0 40 Cell Biology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Cell Biology 0F9OZ 35159278.0 gold, Green Accepted 2023-03-23 WOS:000777686700001 0
J Shi, CH; Chen, XYL; Huang, KH; Xiao, JH; Lu, HM; Stachniss, C Shi, Chenghao; Chen, Xieyuanli; Huang, Kaihong; Xiao, Junhao; Lu, Huimin; Stachniss, Cyrill Keypoint Matching for Point Cloud Registration Using Multiplex Dynamic Graph Attention Networks IEEE ROBOTICS AND AUTOMATION LETTERS English Article SLAM; deep learning metod HISTOGRAMS; MATRICES The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a novel and flexible graph network architecture to tackle the keypoint matching problem in an end-to-end fashion. Each layer of our multiplex dynamic graph attention network (MDGAT) utilizes an attention mechanism to dynamically construct a multiplex graph and reasons about the contextual information based on the point cloud data. It enriches the feature representation by recovering local information and by aggregating information along with the connections. We also design a scan matcher called MDGAT-matcher, which treats the registration problem as an optimal transport problem and uses the predictions of MDGAT as the cost. It builds upon sparse keypoints extracted from pairs of LiDAR scans. Eventually, MDGAT-matcher finds high-quality correspondences and at the same time handles non-matching points appropriately. We train our matcher using a novel gap loss guiding the network to learn a discriminative cognition about matching and non-matching 3D points. We thoroughly test our approach on the KITTI odometry benchmark. The experiments presented in this paper suggest that our approach outperforms state-of-the-art matching approaches and achieves a substantial improvement. [Shi, Chenghao; Huang, Kaihong; Xiao, Junhao; Lu, Huimin] Natl Univ Def Technol, Coll Intelligence Sci & Technol, Robot Res Ctr, Changsha 410073, Peoples R China; [Chen, Xieyuanli; Stachniss, Cyrill] Univ Bonn, D-53113 Bonn, Germany National University of Defense Technology - China; University of Bonn Huang, KH (corresponding author), Natl Univ Def Technol, Coll Intelligence Sci & Technol, Robot Res Ctr, Changsha 410073, Peoples R China. 421747029@qq.com; xieyuanli.chen@igg.uni-bonn.de; kaihong.huang11@outlook.com; junhao.xiao@ieee.org; lhmnew@nudt.edu.cn; cyrill.stachniss@igg.uni-bonn.de Chen, Xieyuanli/AAH-6401-2020 Chen, Xieyuanli/0000-0003-0955-6681; Shi, Chenghao/0000-0002-1462-2367; Xiao, Junhao/0000-0002-4751-539X; Huang, Kaihong/0000-0002-2424-8524; Lu, Huimin/0000-0002-6375-581X; Stachniss, Cyrill/0000-0003-1173-6972 National Natural Science Foundation of China [61773393, U1913202, U1813205] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grants 61773393, U1913202, and U1813205. 43 12 12 13 31 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. OCT 2021.0 6.0 4 8221 8228 10.1109/LRA.2021.3097275 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics UK8JN 2023-03-23 WOS:000692211400003 0
J Hong, WD; Zhou, XY; Jin, SC; Lu, YJ; Pan, JY; Lin, QY; Yang, SP; Xu, TT; Basharat, Z; Zippi, M; Fiorino, S; Tsukanov, V; Stock, S; Grottesi, A; Chen, Q; Pan, JY Hong, Wandong; Zhou, Xiaoying; Jin, Shengchun; Lu, Yajing; Pan, Jingyi; Lin, Qingyi; Yang, Shaopeng; Xu, Tingting; Basharat, Zarrin; Zippi, Maddalena; Fiorino, Sirio; Tsukanov, Vladislav; Stock, Simon; Grottesi, Alfonso; Chen, Qin; Pan, Jingye A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY English Article COVID-19; infection; pneumonia; severity; critically ill; predictor; machine learning MORTALITY; RISK; INTERLEUKIN-10; MODEL; SYSTEM; TRIAGE; IL-10 Background and AimsThe aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. MethodsClinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. ResultsUnivariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4(+) T, and CD8(+) T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4(+) T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. ConclusionsXGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia. [Hong, Wandong] Wenzhou Med Univ, Affiliated Hosp 1, Dept Gastroenterol & Hepatol, Wenzhou, Peoples R China; [Zhou, Xiaoying; Jin, Shengchun; Lu, Yajing; Pan, Jingyi; Lin, Qingyi; Yang, Shaopeng; Xu, Tingting] Wenzhou Med Univ, Sch Clin Med Sci 1, Wenzhou, Peoples R China; [Basharat, Zarrin] Univ Karachi, Int Ctr Chem & Biol Sci, Dr Panjwani Ctr Mol Med & Drug Res, Jamil ur RahmanCenter Genome Res, Karachi, Pakistan; [Zippi, Maddalena] Sandro Pertini Hosp, Unit Gastroenterol & Digest Endoscopy, Rome, Italy; [Fiorino, Sirio] Budrio Hosp, Internal Med Unit, Bologna, Italy; [Tsukanov, Vladislav] Sci Res Inst Med Problems North, Dept Gastroenterol, Krasnoyarsk, Russia; [Stock, Simon] World Mate Emergency Hosp, Dept Surg, Battambang, Cambodia; [Grottesi, Alfonso] Sandro Pertini Hosp, Unit Gen Surg, Rome, Italy; [Chen, Qin; Pan, Jingye] Wenzhou Med Univ, Affiliated Hosp 1, Dept Intens Care Unit, Wenzhou, Peoples R China Wenzhou Medical University; Wenzhou Medical University; University of Karachi; Ospedale Sandro Pertini; Russian Academy of Sciences; Krasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Sciences; Research Institute for Medical Problems in the North, Siberian Branch, Russian Academy of Sciences; Ospedale Sandro Pertini; Wenzhou Medical University Hong, WD (corresponding author), Wenzhou Med Univ, Affiliated Hosp 1, Dept Gastroenterol & Hepatol, Wenzhou, Peoples R China.;Pan, JY (corresponding author), Wenzhou Med Univ, Affiliated Hosp 1, Dept Intens Care Unit, Wenzhou, Peoples R China. xhnk-hwd@163.com; studypanjingye@sina.com Zippi, Maddalena/P-5144-2018; Basharat, Zarrin/U-3274-2019 Basharat, Zarrin/0000-0003-1785-3803; Hong, Wandong/0000-0001-6857-4252 86 3 3 6 10 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2235-2988 FRONT CELL INFECT MI Front. Cell. Infect. Microbiol. APR 12 2022.0 12.0 819267 10.3389/fcimb.2022.819267 0.0 13 Immunology; Microbiology Science Citation Index Expanded (SCI-EXPANDED) Immunology; Microbiology 0Z2YP 35493729.0 gold, Green Accepted 2023-03-23 WOS:000790942900001 0
J Shi, Z; Li, HH; Zhang, W; Chen, YX; Zeng, CY; Kang, XH; Xu, XP; Xia, ZK; Qing, B; Yuan, YC; Song, GD; Caldana, C; Hu, JY; Willmitzer, L; Li, Y Shi, Zhan; Li, Haohui; Zhang, Wei; Chen, Youxiang; Zeng, Chunyan; Kang, Xiuhua; Xu, Xinping; Xia, Zhenkun; Qing, Bei; Yuan, Yunchang; Song, Guodong; Caldana, Camila; Hu, Junyuan; Willmitzer, Lothar; Li, Yan A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies METABOLITES English Article metabolomics; clinical cohort; LC-MS; GC-MS; quality control; data normalization; data modeling BIOMARKER DISCOVERY; COLORECTAL-CANCER; ANNOTATION; DIAGNOSIS; QUANTIFICATION; SERUM; C-13; NMR As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine (TM) and Ulib(MS) library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow. [Shi, Zhan; Li, Haohui; Hu, Junyuan; Li, Yan] Metanotitia Inc, 59 Gaoxin South 9th Rd,Yuehai St, Shenzhen 518056, Peoples R China; [Zhang, Wei; Chen, Youxiang; Zeng, Chunyan; Kang, Xiuhua; Xu, Xinping] Nanchang Univ, Affiliated Hosp 1, 17 Yongwaizheng St, Nanchang 330209, Peoples R China; [Xia, Zhenkun; Qing, Bei; Yuan, Yunchang] Cent South Univ, Xiangya Hosp 2, Changsha 410011, Peoples R China; [Song, Guodong] Tianjin Med Univ, Hosp 2, 23 Pingjiang Rd, Tianjin 300211, Peoples R China; [Caldana, Camila; Willmitzer, Lothar] Max Planck Inst Mol Plant Physiol, Potsdam Sci Pk,Muehlenberg 1, D-14476 Potsdam, Germany Nanchang University; Central South University; Tianjin Medical University; Max Planck Society Hu, JY; Li, Y (corresponding author), Metanotitia Inc, 59 Gaoxin South 9th Rd,Yuehai St, Shenzhen 518056, Peoples R China.;Willmitzer, L (corresponding author), Max Planck Inst Mol Plant Physiol, Potsdam Sci Pk,Muehlenberg 1, D-14476 Potsdam, Germany. junyuan.hu@metanotitia.com; willmitzer@mpimp-golm.mpg.de; yan.li@metanotitia.com XU, XINPING/0000-0002-9568-4171 58 0 0 0 0 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2218-1989 METABOLITES Metabolites DEC 2022.0 12.0 12 1168 10.3390/metabo12121168 0.0 20 Biochemistry & Molecular Biology Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology 7H9MD 36557207.0 Green Accepted, gold 2023-03-23 WOS:000903519100001 0
J Meng, Z; Pedrini, G; Lv, XX; Ma, J; Nie, SP; Yuan, CJ Meng, Zhang; Pedrini, Giancarlo; Lv, Xiaoxu; Ma, Jun; Nie, Shouping; Yuan, Caojin DL-SI-DHM: a deep network generating the high-resolution phase and amplitude images from wide-field images OPTICS EXPRESS English Article STRUCTURED-ILLUMINATION; SUPERRESOLUTION; MICROSCOPY; ENHANCEMENT Structured illumination digital holographic microscopy (SI-DHM) is a high-resolution, label-free technique enabling us to image unstained biological samples. SI-DHM has high requirements on the stability of the experimental setup and needs long exposure time. Furthermore, image synthesizing and phase correcting in the reconstruction process are both challenging tasks. We propose a deep-learning-based method called DL-SI-DHM to improve the recording, the reconstruction efficiency and the accuracy of SI-DHM and to provide high-resolution phase imaging. In the training process, high-resolution amplitude and phase images obtained by phase-shifting SI-DHM together with wide-field amplitudes are used as inputs of DL-SI-DHM. The well-trained network can reconstruct both the high-resolution amplitude and phase images from a single wide-field amplitude image. Compared with the traditional SI-DHM, this method significantly shortens the recording time and simplifies the reconstruction process and complex phase correction, and frequency synthesizing are not required anymore. By comparsion, with other learning-based reconstruction schemes, the proposed network has better response to high frequencies. The possibility of using the proposed method for the investigation of different biological samples has been experimentally verified, and the low-noise characteristics were also proved. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement [Meng, Zhang; Nie, Shouping; Yuan, Caojin] Nanjing Normal Univ, Key Lab Optoelect Technol Jiangsu Prov, Nanjing 210023, Peoples R China; [Pedrini, Giancarlo] Univ Stuttgart, Inst Tech Opt, Pfaffenwaldring 9, D-70569 Stuttgart, Germany; [Lv, Xiaoxu] South China Normal Univ, Lab Nanophoton Funct Mat & Devices, Guangzhou 510006, Peoples R China; [Ma, Jun] Nanjing Univ Sci & Technol, Sch Elect Engn & Optoelect Tech, Nanjing 210094, Peoples R China Nanjing Normal University; University of Stuttgart; South China Normal University; Nanjing University of Science & Technology Yuan, CJ (corresponding author), Nanjing Normal Univ, Key Lab Optoelect Technol Jiangsu Prov, Nanjing 210023, Peoples R China. yuancj@njnu.edu.cn Pedrini, Giancarlo/0000-0002-3300-8362 National Natural Science Foundation of China [61775097, 61975081]; National Key Research and Development Program of China [2017YFB0503505]; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education [2017VGE02]; Sino-German Center for Research Promotion [GZ 1391] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education; Sino-German Center for Research Promotion National Natural Science Foundation of China (61775097, 61975081); National Key Research and Development Program of China (2017YFB0503505); Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education (2017VGE02); Sino-German Center for Research Promotion (GZ 1391). 34 3 3 6 18 Optica Publishing Group WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUN 21 2021.0 29.0 13 19247 19261 10.1364/OE.424718 0.0 15 Optics Science Citation Index Expanded (SCI-EXPANDED) Optics SV7VD 34266038.0 2023-03-23 WOS:000664025900003 0
C Wang, HL; Li, PY; Tao, WJ; Feng, BL; Shao, JM ACM Wang, Honglian; Li, Peiyan; Tao, Wujun; Feng, Bailin; Shao, Junming Learning Dynamic User Behavior Based on Error-driven Event Representation PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021) English Proceedings Paper 30th World Wide Web Conference (WWW) APR 12-23, 2021 ELECTR NETWORK Assoc Comp Machinery User modeling; behavior prediction; temporal network; deep learning Understanding the evolution of large graphs over time is of significant importance in user behavior understanding and prediction. Modeling user behavior with temporal networks has gained increasing attention in recent years since it allows capturing users' dynamic preferences and predicting their next actions. Recently, some approaches have been proposed to model user behavior. However, these methods suffer from two problems: they work on static data, which ignores the dynamic evolution, or they model the whole behavior sequences directly by recurrent neural networks and thus suffer from noisy information. To tackle these problems, we propose a dynamic user behavior learning algorithm called LDBR. It views user behaviors as a set of dynamic events and uses recent event embedding to predict future user behavior and infer the current semantic labels. Specifically, we propose a new strategy to automatically learn a good event embedding in behavior sequence by introducing a smooth sampling strategy and minimizing the temporal link prediction error. It is hard to obtain real-world datasets with evolving labels. Thus in this paper, we provide a new dynamic network dataset with evolving labels called Arxiv and make it publicly available. Based on the Arxiv dataset, we conduct a case study to verify the quality of event embedding. Extensive experiments on temporal link prediction tasks further demonstrate the effectiveness of the LDBR model. [Wang, Honglian; Tao, Wujun; Feng, Bailin; Shao, Junming] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst, Sch Comp Sci & Engn, Chengdu, Peoples R China; [Li, Peiyan] Univ Munich, Inst Comp Sci, Munich, Germany University of Electronic Science & Technology of China; University of Munich Shao, JM (corresponding author), Univ Elect Sci & Technol China, Yangtze Delta Reg Inst, Sch Comp Sci & Engn, Chengdu, Peoples R China. hlw@std.uestc.edu.cn; peiyan.li@dbs.ifi.lmu.de; wujuntao@std.uestc.edu.cn; fengbailin@std.uestc.edu.cn; junmshao@uestc.edu.cn Li, Peiyan/0000-0002-8677-559X Fundamental Research Funds for the Central Universities [ZYGX2019Z014]; National Natural Science Foundation of China [61976044, 52079026]; Fok Ying-Tong Education Foundation [161062]; Sichuan Science and Technology Program [2020YFH0037]; China Scholarship Council [201906070155] Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fok Ying-Tong Education Foundation(Fok Ying Tung Education Foundation); Sichuan Science and Technology Program; China Scholarship Council(China Scholarship Council) This work is supported by the Fundamental Research Funds for the Central Universities (ZYGX2019Z014), National Natural Science Foundation of China (61976044, 52079026), Fok Ying-Tong Education Foundation (161062), Sichuan Science and Technology Program (2020YFH0037) and China Scholarship Council (201906070155). 41 0 0 1 2 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8312-7 2021.0 2457 2465 10.1145/3442381.3450012 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS5MC 2023-03-23 WOS:000733621802042 0
C Zhang, KX; Sumbul, G; Demir, B IEEE Zhang, Kexin; Sumbul, Gencer; Demir, Begum AN APPROACH TO SUPER-RESOLUTION OF SENTINEL-2 IMAGES BASED ON GENERATIVE ADVERSARIAL NETWORKS 2020 MEDITERRANEAN AND MIDDLE-EAST GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (M2GARSS) English Proceedings Paper Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) MAR 09-11, 2020 Tunis, TUNISIA Inst Elect & Elect Engineers,IEEE, Geoscience & Remote Sensing Soc Sentinel-2 images; super-resolution; generative adversarial network; remote sensing This paper presents a generative adversarial network based super-resolution (SR) approach (which is called as S2GAN) to enhance the spatial resolution of Sentinel-2 spectral bands. The proposed approach consists of two main steps. The first step aims to increase the spatial resolution of the bands with 20m and 60m spatial resolutions by the scaling factors of 2 and 6, respectively. To this end, we introduce a generator network that performs SR on the lower resolution bands with the guidance of the bands associated to 10m spatial resolution by utilizing the convolutional layers with residual connections and a long skip-connection between inputs and outputs. The second step aims to distinguish SR bands from their ground truth bands. This is achieved by the proposed discriminator network, which alternately characterizes the high level features of the two sets of bands and applying binary classification on the extracted features. Then, we formulate the adversarial learning of the generator and discriminator networks as a min-max game. In this learning procedure, the generator aims to produce realistic SR bands as much as possible so that the discriminator incorrectly classifies SR bands. Experimental results obtained on different Sentinel-2 images show the effectiveness of the proposed approach compared to both conventional and deep learning based SR approaches. [Zhang, Kexin] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Sumbul, Gencer; Demir, Begum] Tech Univ Berlin, Berlin, Germany Shanghai Jiao Tong University; Technical University of Berlin Zhang, KX (corresponding author), Shanghai Jiao Tong Univ, Shanghai, Peoples R China. Sumbul, Gencer/B-9585-2018 Sumbul, Gencer/0000-0003-3690-3052 European Research Council [BigEarth-759764] European Research Council(European Research Council (ERC)European Commission) This work was supported by the European Research Council under the ERC Starting Grant BigEarth-759764. The authors would like to thank Yakun Li, DFKI GmbH, Germany and Dr. Hua Yang, Shanghai Jiao Tong University, China for the helpful suggestions. 10 4 4 1 2 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-7281-2190-1 2020.0 69 72 4 Agriculture, Multidisciplinary; Geosciences, Multidisciplinary; Remote Sensing; Water Resources Conference Proceedings Citation Index - Science (CPCI-S) Agriculture; Geology; Remote Sensing; Water Resources BQ5EA Green Submitted 2023-03-23 WOS:000604612500018 0
J Xiang, S; Wang, M; Jiang, XF; Xie, GQ; Zhang, ZQ; Tang, P Xiang, Shao; Wang, Mi; Jiang, Xiaofan; Xie, Guangqi; Zhang, Zhiqi; Tang, Peng Dual-Task Semantic Change Detection for Remote Sensing Images Using the Generative Change Field Module REMOTE SENSING English Article very-high-resolution remote sensing images; semantic change detection; generative change field; separable loss BUILDING CHANGE DETECTION; NETWORKS; MIXTURE; MODEL With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth's land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances. [Xiang, Shao; Wang, Mi; Jiang, Xiaofan; Xie, Guangqi; Zhang, Zhiqi] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China; [Tang, Peng] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany Wuhan University; Technical University of Munich Wang, M (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China. xiangshao@whu.edu.cn; wangmi@whu.edu.cn; 2014106190034@whu.edu.cn; xiegqrs@whu.edu.cn; zzq540@whu.edu.cn; tangp@in.tum.de Zhang, Zhiqi/HJZ-5295-2023; Xiang, Shao/V-3790-2018 Zhang, Zhiqi/0000-0003-1914-9430; TANG, Peng/0000-0003-4099-6677; Xiang, Shao/0000-0002-2797-1937; Xie, Guangqi/0000-0002-0292-1626 National Natural Science Foundation of China [61825103, 91838303, 91738302] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The study was supported by a grant from the National Natural Science Foundation of China under project 61825103, 91838303, and 91738302. 38 4 4 14 53 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2021.0 13.0 16 3336 10.3390/rs13163336 0.0 15 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology UH2YR gold, Green Published 2023-03-23 WOS:000689803700001 0
J Li, QM; Su, YJ; Gao, Y; Xie, F; Li, J Li, Qiming; Su, Yijing; Gao, Yin; Xie, Feng; Li, Jun OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Detectors; Training; Feature extraction; Proposals; Avalanche photodiodes; Head; Benchmark testing; Pedestrian detection; occlusion-aware; anchor-free; crowd scenes VEHICLE Although pedestrian detection has achieved promising performance with the development of deep learning techniques, it remains a great challenge to detect heavily occluded pedestrians in crowd scenes. Therefore, to make the anchor-free network pay more attention to learning the hard examples of occluded pedestrians, we propose a simple but effective Occlusion-aware Anchor-Free Network (namely OAF-Net) for pedestrian detection in crowd scenes. Specifically, we first design a novel occlusion-aware detection head, which includes three separate center prediction branches combining with the scale and offset prediction branches. In the detection head of OAF-Net, occluded pedestrian instances are assigned to the most suitable center prediction branch according to the occlusion level of human body. To optimize the center prediction, we accordingly propose a novel weighted Focal Loss where pedestrian instances are assigned with different weights according to their visibility ratios, so that the occluded pedestrians are up-weighted during the training process. Our OAF-Net is able to model different occlusion levels of pedestrian instances effectively, and can be optimized towards catching a high-level understanding of the hard training samples of occluded pedestrians. Experiments on the challenging CityPersons, Caltech, and CrowdHuman benchmarks sufficiently validate the efficacy of our OAF-Net for pedestrian detection in crowd scenes. [Li, Qiming; Su, Yijing; Gao, Yin; Li, Jun] Chinese Acad Sci, Quanzhou Inst Equipment Mfg, Haixi Inst, Lab Robot & Intelligent Syst, Quanzhou 362216, Fujian, Peoples R China; [Li, Qiming; Su, Yijing; Gao, Yin; Li, Jun] Fujian Sci & Technol, Innovat Lab Optoelect Informat China, Fuzhou 350108, Fujian, Peoples R China; [Xie, Feng] Inst Automat & Commun, Dept Traff & Assistance, D-39106 Magdeburg, Germany Chinese Academy of Sciences Li, J (corresponding author), Chinese Acad Sci, Quanzhou Inst Equipment Mfg, Haixi Inst, Lab Robot & Intelligent Syst, Quanzhou 362216, Fujian, Peoples R China. qimingli@fjirsm.ac.cn; suyj@fjirsm.ac.cn; yingao@fjirsm.ac.cn; feng.xie@ifak.eu; junli@fjirsm.ac.cn gao, Yin/D-4589-2019 gao, Yin/0000-0003-3902-0087; li, qiming/0000-0001-8317-5700 National Natural Science Foundation of China [62102394, 61906178, 62001452]; Natural Science Foundation of Fujian Province of China [2020J05083]; Science and Technology Program of Quanzhou [2020C071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province); Science and Technology Program of Quanzhou This work was supported in part by the National Natural Science Foundation of China under Grant 62102394, Grant 61906178, and Grant 62001452; in part by the Natural Science Foundation of Fujian Province of China under Grant 2020J05083; and in part by the Science and Technology Program of Quanzhou under Grant 2020C071. The Associate Editor for this article was H. G. Jung. 77 1 1 10 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. NOV 2022.0 23.0 11 21291 21300 10.1109/TITS.2022.3171250 0.0 MAY 2022 10 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 6A6GZ 2023-03-23 WOS:000795579800001 0
J Barsi, D; Perrone, A; Qu, YL; Ratto, L; Ricci, G; Sergeev, V; Zunino, P Barsi, Dario; Perrone, Andrea; Qu, Yonglei; Ratto, Luca; Ricci, Gianluca; Sergeev, Vitaliy; Zunino, Pietro Compressor and Turbine Multidisciplinary Design for Highly Efficient Micro-gas Turbine JOURNAL OF THERMAL SCIENCE English Article Micro-gas turbine; Multidisciplinary Optimization; Centrifugal Compressor; Centripetal Turbine CENTRIFUGAL-COMPRESSOR; FLOW; OPTIMIZATION; IMPELLER; SYSTEM Multidisciplinary design optimization (MDO) is widely employed to enhance turbomachinery components efficiency. The aim of this work is to describe a complete tool for the aero-mechanical design of a radial inflow turbine and a centrifugal compressor. The high rotational speed of such machines and the high exhaust gas temperature (only for the turbine) expose blades to really high stresses and therefore the aerodynamics design has to be coupled with the mechanical one through an integrated procedure. The described approach employs a fully 3D Reynolds Averaged Navier-Stokes (RANS) solver for the aerodynamics and an open source Finite Element Analysis (FEA) solver for the mechanical integrity assessment. Due to the high computational cost of both these two solvers, a meta model, such as an artificial neural network (ANN), is used to speed up the optimization design process. The interaction between two codes, the mesh generation and the post processing of the results are achieved via in-house developed scripting modules. The obtained results are widely presented and discussed. [Barsi, Dario; Perrone, Andrea; Ratto, Luca; Ricci, Gianluca; Zunino, Pietro] Univ Genoa, Dept Mech Energy Management & Transport Engn, Via Montallegro 1, I-16145 Genoa, Italy; [Qu, Yonglei] Harbin Engn Univ, Fac Power & Energy Engn, Harbin 150001, Heilongjiang, Peoples R China; [Sergeev, Vitaliy] Peter Great St Petersburg Polytech Univ, Inst Energy & Transport Syst, Polytech Skaya Str 29, St Petersburg 195251, Russia University of Genoa; Harbin Engineering University; Peter the Great St. Petersburg Polytechnic University Zunino, P (corresponding author), Univ Genoa, Dept Mech Energy Management & Transport Engn, Via Montallegro 1, I-16145 Genoa, Italy. pietro.zunino@unige.it Zunino, Pietro/AAU-9287-2020 Barsi, Dario/0000-0002-6397-6380; Sergeev, Vitaly/0000-0002-6289-325X 22 9 9 3 27 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1003-2169 1993-033X J THERM SCI J. Therm. Sci. JUN 2018.0 27.0 3 259 269 10.1007/s11630-018-1007-2 0.0 11 Thermodynamics; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Engineering GF3WW 2023-03-23 WOS:000431889400008 0
J Yang, ZX; Xia, JY; Luo, JS; Zhang, SH; Gunduz, D Yang, Zhixiong; Xia, Jing-Yuan; Luo, Junshan; Zhang, Shuanghui; Gunduz, Deniz A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming IEEE WIRELESS COMMUNICATIONS LETTERS English Article Array signal processing; Heuristic algorithms; Optimization; Computational complexity; Training; Signal to noise ratio; Neural networks; Multi-user MISO downlink; beamforming; implicit gradient descent; unsupervised learning NEURAL-NETWORKS; DEEP; DESIGN This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD. [Yang, Zhixiong; Xia, Jing-Yuan; Luo, Junshan; Zhang, Shuanghui] Natl Univ Def Technol, Coll Elect Engn, Changsha 410073, Peoples R China; [Gunduz, Deniz] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England; [Gunduz, Deniz] Univ Modena & Reggio Emilia, Enzo Ferrari Dept Engn, I-41121 Modena, Italy National University of Defense Technology - China; Imperial College London; Universita di Modena e Reggio Emilia Xia, JY; Zhang, SH (corresponding author), Natl Univ Def Technol, Coll Elect Engn, Changsha 410073, Peoples R China. d.gunduz@imperial.ac.uk Xia, Jingyuan/ABE-7885-2021; yang, zhixiong/HHZ-7100-2022 Xia, Jingyuan/0000-0003-4329-0354; Gunduz, Deniz/0000-0002-7725-395X National Natural Science Foundation of China [62171448, 61921001, 62131020, 62022091]; Natural Science Fund for Young Talents of Hunan Province (NSFYT) of Hunan [2020RC3029]; European Research Council through Project BEACON [677854]; CHIST-ERA [CHISTERA-18-SDCDN001, EPSRC-EP/T023600/1] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Fund for Young Talents of Hunan Province (NSFYT) of Hunan; European Research Council through Project BEACON; CHIST-ERA This work was supported in part by the National Natural Science Foundation of China under Project 62171448, Project 61921001, Project 62131020, and Project 62022091; in part by the Natural Science Fund for Young Talents of Hunan Province (NSFYT) of Hunan under Grant 2020RC3029; in part by the European Research Council through Project BEACON under Grant 677854; and in part by CHIST-ERA under Grant CHISTERA-18-SDCDN001 (funded by EPSRC-EP/T023600/1). 23 1 1 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-2337 2162-2345 IEEE WIREL COMMUN LE IEEE Wirel. Commun. Lett. SEP 2022.0 11.0 9 1895 1899 10.1109/LWC.2022.3186160 0.0 5 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 4K8TT Green Submitted 2023-03-23 WOS:000852215400029 0
J Peng, JY; Wang, DK; Kimmig, A; Langovoy, MA; Wang, JH; Ovtcharova, J Peng Jieyang; Wang Dongkun; Kimmig, Andreas; Langovoy, Mikhail A.; Wang Jiahai; Ovtcharova, Jivka A hybrid RNN model for mid-to-long term electricity demand forecasting incorporating weather influences AT-AUTOMATISIERUNGSTECHNIK German Article time series data; deep learning; energy optimization; word embedding ENERGY-CONSUMPTION; LOAD; TEMPERATURE In the daily operation of the city, the power supply should be non-interruptible, which brings challenges to the modern power management. Energy demand forecasting can optimize the strategy of power management and improve energy efficiency. The traditional Long Short-Term Memory model, based on the encodingdecoding structure, encodes all the historical information as a fixed-length vector, which will lead to the loss of information when the predicted value depends on the features of a long time ago. This is common in energy forecasting because of the periodicity of energy consumption. To solve the above problem and to fully realize the potential of the operational data from power plants in energy predictions, this paper proposes an energy prediction model based on the attention mechanism. Based on the traditional encoding-decoding architecture, the spatial- and temporal-attention mechanism is introduced to adaptively select the spatial and temporal characteristics most relevant to the predicted value. The experimental results show that the error of the hybrid model is 25.8 % lower than that of the traditional LSTM model when predicting the power demand of Shanghai in the next 100 days. In addition, the trend of the error of the hybrid model over time also shows stronger stability than the traditional model. [Peng Jieyang; Wang Jiahai] Tongji Univ, Chinesich Deutsch Zentrum Maschinenbau, Shanghai 200092, Peoples R China; [Wang Dongkun] Univ Stuttgart, Keplerstr 7, D-70174 Stuttgart, Germany; [Kimmig, Andreas; Langovoy, Mikhail A.; Ovtcharova, Jivka] Karlsruhe Inst Technol, Inst Informationsmanagement Ingn IMI, D-76133 Karlsruhe, Germany Tongji University; University of Stuttgart; Helmholtz Association; Karlsruhe Institute of Technology Wang, JH (corresponding author), Tongji Univ, Chinesich Deutsch Zentrum Maschinenbau, Shanghai 200092, Peoples R China. jieyang.peng@kit.edu; st165673@stud.uni-stuttgart.de; andreas.kimmig@kit.edu; mikhail.langovoy@kit.edu; jhwang@tongji.edu.cn; jivka.ovtcharova@kit.edu 20 1 1 2 22 WALTER DE GRUYTER GMBH BERLIN GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY 0178-2312 2196-677X AT-AUTOM AT-Autom. JAN 2021.0 69.0 1 73 83 10.1515/auto-2020-0033 0.0 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems PQ8XR 2023-03-23 WOS:000606826800007 0
J Wei, W; Xu, HS; Alpers, J; Rak, M; Hansen, C Wei Wei; Xu Haishan; Alpers, Julian; Rak, Marko; Hansen, Christian A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE English Article Slice-to-volume; Ultrasound; Image registration; Image classification; Image segmentation Background and Objective: Liver tumor ablation is often guided by ultrasound (US). Due to poor image quality, intraoperative US is fused with preoperative computed tomography or magnetic tomography (CT/MR) images to provide visual guidance. As of today, the underlying 2D US to 3D CT/MR registration problem remains a very challenging task. Methods: We propose a novel pipeline to address this registration problem. Contrary to previous work, we do not formulate the problem as a regression task, which for the given registration problem achieves a low performance regarding accuracy and robustness due to the limited US soft-tissue contrast and the inter-patient variability on liver vessels. Instead, we first estimate the US probe angle roughly by using a classification network. Given this coarse initialization, we then improve the registration by formulation of the problem as a segmentation task, estimating the US plane in the 3D CT/MR through segmentation. Results: We benchmark our approach on 1035 clinical images from 52 patients, yielding average registration errors of 11.6 degrees and 4.7 mm, which outperforms the state of the art SVR method[1]. Conclusion: Our results show the efficiency of the proposed registration pipeline, which has potential to improve the robustness and accuracy of intraoperative patient registration. (c) 2021 Elsevier B.V. All rights reserved. [Wei Wei; Alpers, Julian; Rak, Marko; Hansen, Christian] Univ Magdeburg, Fac Comp Sci & Res, Campus STIMULATE, Magdeburg, Germany; [Xu Haishan] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Hangzhou, Peoples R China Otto von Guericke University; Zhejiang University Hansen, C (corresponding author), Univ Magdeburg, Fac Comp Sci & Res, Campus STIMULATE, Magdeburg, Germany. hansen@isg.cs.uni-magdeburg.de Zhejiang Provincial Natural Science Foundation of China [LSY19H180012] Zhejiang Provincial Natural Science Foundation of China(Natural Science Foundation of Zhejiang Province) This work has been funded by Zhejiang Provincial Natural Science Foundation of China under Grant No. LSY19H180012. 29 3 3 4 22 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0169-2607 1872-7565 COMPUT METH PROG BIO Comput. Meth. Programs Biomed. JUL 2021.0 206.0 106117 10.1016/j.cmpb.2021.106117 0.0 MAY 2021 10 Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Biomedical; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Medical Informatics SU8XR 34022696.0 2023-03-23 WOS:000663415100004 0
C Fan, YL; Wang, HJ; Gemmeke, H; Hopp, T; Hesser, J Byram, BC; Ruiter, NV Fan, Yuling; Wang, Hongjian; Gemmeke, Hartmut; Hopp, Torsten; Hesser, Juergen DDN: Dual domain network architecture for non-linear ultrasound transmission tomography reconstruction MEDICAL IMAGING 2021: ULTRASONIC IMAGING AND TOMOGRAPHY Progress in Biomedical Optics and Imaging English Proceedings Paper SPIE Medical Imaging Conference - Ultrasonic Imaging and Tomography FEB 15-19, 2021 ELECTR NETWORK SPIE Ultrasound transmission tomography; deep learning; image reconstruction; dual domain Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction. [Fan, Yuling; Hesser, Juergen] Heidelberg Univ, Theodor Kutzer Ufer 1-3, D-68167 Mannheim, Germany; [Wang, Hongjian] Donghua Univ, 2999 North Renmin Rd, Shanghai 201620, Peoples R China; [Gemmeke, Hartmut; Hopp, Torsten] Karlsruhe Inst Technol KIT, Campus Nord,POB 3640, D-76021 Karlsruhe, Germany Ruprecht Karls University Heidelberg; Donghua University; Helmholtz Association; Karlsruhe Institute of Technology Wang, HJ (corresponding author), Donghua Univ, 2999 North Renmin Rd, Shanghai 201620, Peoples R China. hongjian.wang@dhu.edu.cn Hopp, Torsten/0000-0001-7324-1735 ''Fundamental Research Funds for the Central Universities from Donghua University [2232020D-36]; Donghua University [112-07-0053079]; Deutsche Forschungsgemeinschaft (DFG) [HE 3011/37-1, HO 5565/2-1] ''Fundamental Research Funds for the Central Universities from Donghua University; Donghua University; Deutsche Forschungsgemeinschaft (DFG)(German Research Foundation (DFG)) The research was supported by the Fundamental Research Funds for the Central Universities from Donghua University under grants no. 2232020D-36, the Young Teacher Research Startup Fund from Donghua University under grants no.112-07-0053079, the Deutsche Forschungsgemeinschaft (DFG) under grants no. HE 3011/37-1 and HO 5565/2-1. 16 0 0 0 2 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 1605-7422 978-1-5106-4034-4 PRO BIOMED OPT IMAG 2021.0 11602.0 1160209 10.1117/12.2580911 0.0 6 Engineering, Biomedical; Optics; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Engineering; Optics; Radiology, Nuclear Medicine & Medical Imaging BR8JY 2023-03-23 WOS:000672554900006 0
J Zhu, KF; Li, CQ; Asari, V; Saupe, D Zhu, Kongfeng; Li, Chengqing; Asari, Vijayan; Saupe, Dietmar No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Blocking artifact; discrete cosine transform (DCT); H.264/Advanced Video Coding (AVC); natural scene; no-reference (NR) measure; video quality assessment (VQA) PERCEPTUAL IMAGE; INFORMATION A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos. The model has two stages: 1) distortion measurement and 2) nonlinear mapping. In the first stage, an unsigned ac band, three frequency bands, and two orientation bands are generated from the DCT coefficients of each decoded frame in a video sequence. Six efficient frame-level features are then extracted to quantify the distortion of natural scenes. In the second stage, each frame-level feature of all frames is transformed to a corresponding video-level feature via a temporal pooling, then a trained multilayer neural network takes all video-level features as inputs and outputs, a score as the predicted quality of the video sequence. The proposed method was tested on videos with various compression types, content, and resolution in four databases. We compared our model with a linear model, a support-vector-regression-based model, a state-of-the-art training-based model, and a four popular full-reference metrics. Detailed experimental results demonstrate that the results of the proposed method are highly correlated with the subjective assessments. [Zhu, Kongfeng] Univ Nantes, Image & Video Commun Res Grp, F-44306 Nantes, France; [Saupe, Dietmar] Univ Konstanz, Dept Comp & Informat Sci, D-78457 Constance, Germany; [Li, Chengqing] Xiangtan Univ, Coll Informat Engn, Xiangtan 411100, Peoples R China; [Asari, Vijayan] Univ Dayton, Dept Elect & Comp Engn, Dayton, OH 45469 USA Nantes Universite; University of Konstanz; Xiangtan University; University of Dayton Zhu, KF (corresponding author), Univ Nantes, Image & Video Commun Res Grp, F-44306 Nantes, France. kongfeng.zhu@univ-nantes.fr; chengqingg@gmail.com; vasari1@udayton.edu; dietmar.saupe@uni-konstanz.de Li, Chengqing/B-9388-2008 Li, Chengqing/0000-0002-5385-7644 Alexander von Humboldt Foundation, Germany; Hunan Provincial Natural Science Foundation of China [2015JJ1013] Alexander von Humboldt Foundation, Germany(Alexander von Humboldt Foundation); Hunan Provincial Natural Science Foundation of China(Natural Science Foundation of Hunan Province) The work of C. Li was supported in part by the Alexander von Humboldt Foundation, Germany, and in part by the Hunan Provincial Natural Science Foundation of China (No. 2015JJ1013). This paper was recommended by Associate Editor P. Le Callet. 55 52 61 1 20 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. APR 2015.0 25.0 4 533 546 10.1109/TCSVT.2014.2363737 0.0 14 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering CF4DC Green Published 2023-03-23 WOS:000352497800001 0
J Kong, APH; Abutalebi, J; Lam, KSY; Weekes, B Kong, Anthony Pak-Hin; Abutalebi, Jubin; Lam, Karen Sze-Yan; Weekes, Brendan Executive and Language Control in the Multilingual Brain BEHAVIOURAL NEUROLOGY English Article SUBCORTICAL APHASIA; BILINGUAL APHASIA; LESIONS; RECOVERY; TASK; FMRI Neuroimaging studies suggest that the neural network involved in language control may not be specific to bi-/multilingualism but is part of a domain-general executive control system. We report a trilingual case of a Cantonese (L1), English (L2), and Mandarin (L3) speaker, Dr. T, who sustained a brain injury at the age of 77 causing lesions in the left frontal lobe and in the left temporo-parietal areas resulting in fluent aphasia. Dr. T's executive functions were impaired according to a modified version of the Stroop color-word test and the Wisconsin Card Sorting Test performance was characterized by frequent perseveration errors. Dr. T demonstrated pathological language switching and mixing across her three languages. Code switching in Cantonese was more prominent in discourse production than confrontation naming. Our case suggests that voluntary control of spoken word production in trilingual speakers shares neural substrata in the frontobasal ganglia system with domain-general executive control mechanisms. One prediction is that lesions to such a system would give rise to both pathological switching and impairments of executive functions in trilingual speakers. [Kong, Anthony Pak-Hin] Univ Cent Florida, Dept Commun Sci & Disorders, Orlando, FL 32816 USA; [Kong, Anthony Pak-Hin; Abutalebi, Jubin; Lam, Karen Sze-Yan; Weekes, Brendan] Univ Hong Kong, Commun Sci Lab, Pokfulam, Hong Kong, Peoples R China; [Abutalebi, Jubin] San Raffaele Univ, Dept Psychol, I-20132 Milan, Italy; [Abutalebi, Jubin] Ist Sci San Raffaele, I-20132 Milan, Italy State University System of Florida; University of Central Florida; University of Hong Kong; Vita-Salute San Raffaele University; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele Kong, APH (corresponding author), Univ Cent Florida, Dept Commun Sci & Disorders, HPA 2 106,POB 162215, Orlando, FL 32816 USA. antkong@ucf.edu Kong, Anthony Pak Hin/AAE-9449-2021; Abutalebi, Jubin/I-4933-2013 Kong, Anthony Pak Hin/0000-0002-6211-0358; Abutalebi, Jubin/0000-0003-3016-9184 32 27 28 1 52 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 0953-4180 1875-8584 BEHAV NEUROL Behav. Neurol. 2014.0 2014.0 527951 10.1155/2014/527951 0.0 7 Clinical Neurology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology AG8XP 24868121.0 gold, Green Published 2023-03-23 WOS:000335701900001 0
J Deng, Z; Jonetzko, Y; Zhang, LW; Zhang, JW Deng, Zhen; Jonetzko, Yannick; Zhang, Liwei; Zhang, Jianwei Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization SENSORS English Article grasping force control; force estimation; slip detection; multi-fingered robotic hands MANIPULATION; EXPERIENCE Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors. [Deng, Zhen; Zhang, Liwei] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China; [Deng, Zhen; Jonetzko, Yannick; Zhang, Liwei; Zhang, Jianwei] Univ Hamburg, Dept Informat, TAMS, D-22527 Hamburg, Germany Fuzhou University; University of Hamburg Zhang, LW (corresponding author), Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China.;Zhang, LW (corresponding author), Univ Hamburg, Dept Informat, TAMS, D-22527 Hamburg, Germany. yuchen8807@163.com; jonetzko@informatik.uni-hamburg.de; lw.zhang@fzu.edu.cn; zhang@informatik.uni-hamburg.de zhang, jian/HPD-1712-2023 Zhang, Liwei/0000-0003-3083-9002 German Science Foundation (DFG); National Science Foundation of China (NSFC) [Sonderforschungsbereich Transregio 169]; National Natural Science Foundation of China [61673115]; Natural Science Foundation of Fujian Province, China [2017J01749]; Program for New Century Excellent Talents in Fujian Province University, China German Science Foundation (DFG)(German Research Foundation (DFG)); National Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province); Program for New Century Excellent Talents in Fujian Province University, China(Program for New Century Excellent Talents in University (NCET)) This work was partly funded by the German Science Foundation (DFG) and National Science Foundation of China (NSFC) in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169. This work is supported by the National Natural Science Foundation of China (Project No.: 61673115), the Natural Science Foundation of Fujian Province, China (Project No.:2017J01749), and by the Program for New Century Excellent Talents in Fujian Province University, China. 34 19 19 13 51 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 2020.0 20.0 4 1050 10.3390/s20041050 0.0 21 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation KY3CM 32075193.0 gold, Green Published, Green Accepted 2023-03-23 WOS:000522448600101 0
J Xue, Y; Zhao, Y; Slowik, A Xue, Yu; Zhao, Yan; Slowik, Adam Classification Based on Brain Storm Optimization With Feature Selection IEEE ACCESS English Article Brain storm optimization (BSO) algorithm; classification; evolutionary computation (EC); feature selection CLASSIFIERS Classification is one of the most classic problems in machine learning. Due to the global optimization ability, evolutionary computation (EC) techniques have been successfully applied to solve many problems and the evolutionary classification model is one of the methods used to solve classification problems. Recently, some evolutionary algorithms (EAs) such as the fireworks algorithm (FWA) and brain storm optimization (BSO) algorithm have been employed to implement the evolutionary classification model and achieved the desired results. This means that it is feasible to use EC techniques to solve the classification problem directly. However, the existing evolutionary classification model still has some disadvantages. The limited datasets used in the experiment make the experimental results not convincing enough, and more importantly, the structure of the evolutionary classification model is closely related to the dimension of datasets, which may lead to poor classification performance, especially on large-scale datasets. Therefore, this paper aims at improving the structure of the evolutionary classification model to improve classification performance. Feature selection is an effective method to deal with large datasets, firstly, we introduce the concept of feature selection and use the different feature subsets to construct the structure of the evolutionary classification model. Then, the BSO algorithm is employed to implement the evolutionary classification model to search the optimal structure by search for the optimal feature subset. Moreover, the optimal weight parameters corresponding to the different structures are also searched by the BSO algorithm while searching the optimal feature subset. For verification of the classification effectiveness of the proposed method, 11 different datasets are selected for experiments. The results show that it is feasible to optimize the structure of the evolutionary classification model by introducing feature selection. Moreover, the new method has better classification performance than the original method, especially on large-scale or high-dimensional datasets. [Xue, Yu; Zhao, Yan] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Xue, Yu] Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 210044, Peoples R China; [Slowik, Adam] Koszalin Univ Technol, Dept Elect & Comp Sci, PL-75453 Koszalin, Poland Nanjing University of Information Science & Technology; Jinling Institute of Technology; Koszalin University of Technology Xue, Y (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.;Xue, Y (corresponding author), Jinling Inst Technol, Jiangsu Key Lab Data Sci & Smart Software, Nanjing 210044, Peoples R China. xueyu@nuist.edu.cn xue, yu/0000-0002-9069-7547; Slowik, Adam/0000-0003-2542-9842 National Natural Science Foundation of China [61876089, 61403206, 61876185, 61902281]; opening Project of Jiangsu Key Laboratory of Data Science and Smart Software [2019DS301]; Natural Science Foundation of Jiangsu Province [BK20141005]; Natural Science Foundation of the Jiangsu Higher Education Institutions of China [14KJB520025]; Engineering Research Center of Digital Forensics, Ministry of Education; Priority Academic Program Development of Jiangsu Higher Education Institutions; Postgraduate Research AMP; Practice Innovation Program of Jiangsu Province [KYCX20_0981, KYCX20_0979] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); opening Project of Jiangsu Key Laboratory of Data Science and Smart Software; Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Natural Science Foundation of the Jiangsu Higher Education Institutions of China(National Natural Science Foundation of China (NSFC)); Engineering Research Center of Digital Forensics, Ministry of Education; Priority Academic Program Development of Jiangsu Higher Education Institutions; Postgraduate Research AMP; Practice Innovation Program of Jiangsu Province This work was supported in part by the National Natural Science Foundation of China under Grant 61876089, Grant 61403206, Grant 61876185, and Grant 61902281; in part by the opening Project of Jiangsu Key Laboratory of Data Science and Smart Software under Grant 2019DS301; in part by the Natural Science Foundation of Jiangsu Province under BK20141005; in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025; in part by the Engineering Research Center of Digital Forensics, Ministry of Education; in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions; and in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_0981 and Grant KYCX20_0979. 28 0 0 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2021.0 9.0 16582 16590 10.1109/ACCESS.2020.3045970 0.0 9 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications QA6CP gold 2023-03-23 WOS:000613532000001 0
J Zahid, N; Sodhro, AH; Kamboh, UR; Alkhayyat, A; Wang, L Zahid, Noman; Sodhro, Ali Hassan; Kamboh, Usman Rauf; Alkhayyat, Ahmed; Wang, Lei AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system MATHEMATICAL BIOSCIENCES AND ENGINEERING English Article AI; IoT; Sustainable; reliable; routing; data rate; smart and connected; healthcare SENSOR NETWORKS; ENERGY; OPTIMIZATION; MECHANISM; TRANSMISSION; IOT Artificial Intelligence (AI) driven adaptive techniques are viable to optimize the resources in the Internet of Things (IoT) enabled wearable healthcare devices. Due to the miniature size and ability of wireless data transfer, Body Sensor Networks (BSNs) have become the center of attention in current medical media technologies. For a long-term and reliable healthcare system, high energy efficiency, transmission reliability, and longer battery lifetime of wearable sensors devices are required. There is a dire need for empowering sensor-based wearable techniques in BSNs from every aspect i.e., data collection, healthcare monitoring, and diagnosis. The consideration of protocol layers, data routing, and energy optimization strategies improves the efficiency of healthcare delivery. Hence, this work presents some key contributions. Firstly, it proposes a novel avant-garde framework to simultaneously optimize the energy efficiency, battery lifetime, and reliability for smart and connected healthcare. Secondly, in this study, an Adaptive Transmission Data Rate (ATDR) mechanism is proposed, which works on the average constant energy consumption by varying the active time of the sensor node to optimize the energy over the dynamic wireless channel. Moreover, a Self-Adaptive Routing Algorithm (SARA) is developed to adopt a dynamic source routing mechanism with an energy-efficient and shortest possible path, unlike the conventional routing methods. Lastly, real-time datasets are adopted for intensive experimental setup for revealing pervasive and cost-effective healthcare through wearable devices. It is observed and analysed that proposed algorithms outperform in terms of high energy efficiency, better reliability, and longer battery lifetime of portable devices. [Zahid, Noman] Univ Faisalabad, Off Res Innovat & Commercializat ORIC, Faisalabad, Punjab, Pakistan; [Sodhro, Ali Hassan] Kristianstad Univ, Dept Comp Sci, SE-29188 Kristianstad, Sweden; [Sodhro, Ali Hassan; Wang, Lei] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518000, Peoples R China; [Kamboh, Usman Rauf] Univ Faisalabad, Dept Computat Sci, Faisalabad, Punjab, Pakistan; [Alkhayyat, Ahmed] Islamic Univ, Coll Tech Engn, Najaf, Iraq Kristianstad University; Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; Islamic University College Sodhro, AH (corresponding author), Kristianstad Univ, Dept Comp Sci, SE-29188 Kristianstad, Sweden.;Sodhro, AH (corresponding author), Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518000, Peoples R China. ali.hassan_sodhro@hkr.se Zahid, Noman/ADI-9651-2022; alkhayyat, ahmed/B-6434-2018 Zahid, Noman/0000-0002-7304-398X; alkhayyat, ahmed/0000-0002-1270-4713; alkhayyat, ahmed/0000-0002-0962-3453 PIFI 2020, China [2020VBC0002] PIFI 2020, China This work is funded by Research grant of PIFI 2020 (2020VBC0002) , China. 39 10 10 13 17 AMER INST MATHEMATICAL SCIENCES-AIMS SPRINGFIELD PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES 1547-1063 1551-0018 MATH BIOSCI ENG Math. Biosci. Eng. 2022.0 19.0 4 3953 3971 10.3934/mbe.2022182 0.0 19 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology YZ9FL 35341282.0 gold 2023-03-23 WOS:000755775400001 0
J Zhang, RK; Zhang, C; Cao, ZG; Song, W; Tan, PS; Zhang, J; Wen, BH; Dauwels, J Zhang, Rongkai; Zhang, Cong; Cao, Zhiguang; Song, Wen; Tan, Puay Siew; Zhang, Jie; Wen, Bihan; Dauwels, Justin Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article; Early Access Task analysis; Costs; Routing; Reinforcement learning; Time factors; Training data; Market research; Travelling salesman problem; graph neural network; deep reinforcement learning ALGORITHM We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances. [Zhang, Rongkai; Wen, Bihan] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Zhang, Cong; Zhang, Jie] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; [Cao, Zhiguang; Tan, Puay Siew] ASTAR, Singapore Inst Mfg Technol SIMTech, Singapore 138632, Singapore; [Song, Wen] Shandong Univ, Inst Marine Sci & Technol, Qingdao 266237, Peoples R China; [Dauwels, Justin] Delft Univ Technol, Fac EEMCS, Dept Microelect, NL-2628 CD Delft, Netherlands Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; Agency for Science Technology & Research (A*STAR); A*STAR - Singapore Institute of Manufacturing Technology (SIMTech); Shandong University; Delft University of Technology Song, W (corresponding author), Shandong Univ, Inst Marine Sci & Technol, Qingdao 266237, Peoples R China. rongkai002@e.ntu.edu.sg; cong030@e.ntu.edu.sg; zhiguangcao@outlook.com; wensong@email.sdu.edu.cn; pstan@simtech.a-star.edu.sg; zhangj@ntu.edu.sg; bihan.wen@ntu.edu.sg; j.h.g.dauwels@tudelft.nl Wen, Bihan/B-3123-2017 Wen, Bihan/0000-0002-6874-6453; ZHANG, RONGKAI/0000-0002-0034-460X Agency for Science Technology and Research Career Development Fund [222D8235, C222812027]; IEO Decentralized GAP Project of Continuous Last-Mile Logistics (CLML) at the Singapore Institute of Manufacturing Technology [I22D1AG003]; Rapid-Rich Object Search (ROSE) Laboratory, Nanyang Technological University, Singapore; Ministry of Education, Singapore, under its Academic Research Fund Tier 1 [RG61/22]; National Natural Science Foundation of China [62102228]; Shandong Provincial Natural Science Foundation [ZR2021QF063] Agency for Science Technology and Research Career Development Fund; IEO Decentralized GAP Project of Continuous Last-Mile Logistics (CLML) at the Singapore Institute of Manufacturing Technology; Rapid-Rich Object Search (ROSE) Laboratory, Nanyang Technological University, Singapore(Nanyang Technological University); Ministry of Education, Singapore, under its Academic Research Fund Tier 1(Ministry of Education, Singapore); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Shandong Provincial Natural Science Foundation(Natural Science Foundation of Shandong Province) This work was supported in part by the Agency for Science Technology and Research Career Development Fund under Grant 222D8235 and Grant C222812027; in part by the IEO Decentralized GAP Project of Continuous Last-Mile Logistics (CLML) at the Singapore Institute of Manufacturing Technology under Grant I22D1AG003; in part by the Rapid-Rich Object Search (ROSE) Laboratory, Nanyang Technological University, Singapore; in part by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 under Project RG61/22; in part by the Start-Up Grant; in part by the National Natural Science Foundation of China under Grant 62102228; and in part by the Shandong Provincial Natural Science Foundation under Grant ZR2021QF063. 36 0 0 21 21 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. 10.1109/TITS.2022.3207011 0.0 SEP 2022 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 4Z6TW Green Submitted 2023-03-23 WOS:000862339400001 0
J Wang, QA; Zhao, TS; Zhao, C; Liu, W; Yang, L; Liu, YN; Sheng, DA; Xu, RX; Ge, YT; Tu, X; Gao, H; Zhao, CZ Wang, Qinan; Zhao, Tianshi; Zhao, Chun; Liu, Wen; Yang, Li; Liu, Yina; Sheng, Dian; Xu, Rongxuan; Ge, Yutong; Tu, Xin; Gao, Hao; Zhao, Cezhou Solid-State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing ADVANCED ELECTRONIC MATERIALS English Article in-memory computing; neuromorphic computing; recognition of image and ECG; synaptic transistor SYNAPTIC TRANSISTOR; NONVOLATILE MEMORY As the core component of an intelligent neuromorphic computer system, reliable synaptic devices process vast amounts of data with high computing speed and low energy consumption. In this work, the ion-doped eco-friendly solution-processed indium oxide (InOx)/aluminum oxide (AlOx) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlOx solid-state layer to facilitate the generation of electrical double layers and doped into InOx to improve the stability of long-term potentiation/depression cyclic update and enhance the synaptic plasticity. Finally, an artificial neural network simulator is well designed to electrocardiogram signal recognition based on the G(max)/G(min) ratio and nonlinearity of weight update curve. According to the results, the device possesses tremendous potential for biosignal prediction and neural intervention. Moreover, for the first time, the recognition accuracy of the abnormality of the cardiovascular can reach over 94.8% obtained from the confusion matrix. Consequently, this research article presents a stable and robust neuromorphic device for biosignal recognition based on solid-state EGTs via the synaptic long-term plasticity. [Wang, Qinan; Zhao, Tianshi; Zhao, Chun; Liu, Wen; Sheng, Dian; Xu, Rongxuan; Ge, Yutong; Zhao, Cezhou] Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China; [Wang, Qinan; Zhao, Tianshi; Sheng, Dian; Xu, Rongxuan; Ge, Yutong; Zhao, Cezhou] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England; [Yang, Li] Xian Jiaotong Liverpool Univ, Dept Chem, Suzhou 215123, Peoples R China; [Liu, Yina; Tu, Xin] Xian Jiaotong Liverpool Univ, Dept Appl Math, Suzhou 215123, Peoples R China; [Gao, Hao] Eindhoven Univ Technol, Dept Elect Engn, Den Dolech 2, NL-5612 AZ Eindhoven, Netherlands Xi'an Jiaotong-Liverpool University; University of Liverpool; Xi'an Jiaotong-Liverpool University; Xi'an Jiaotong-Liverpool University; Eindhoven University of Technology Zhao, C; Liu, W (corresponding author), Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China.;Liu, YN (corresponding author), Xian Jiaotong Liverpool Univ, Dept Appl Math, Suzhou 215123, Peoples R China. chun.zhao@xjtlu.edu.cn; wen.liu@xjtlu.edu.cn; yina.liu@xjtlu.edu.cn Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program [19KJB510059]; Natural Science Foundation of Jiangsu Province of China [BK20180242]; Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation [SYG201924]; University Research Development Fund [RDF-17-01-13]; Key Program Special Fund in XJTLU [KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07, KSF-A-18] Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program; Natural Science Foundation of Jiangsu Province of China(Natural Science Foundation of Jiangsu Province); Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation; University Research Development Fund; Key Program Special Fund in XJTLU This research was funded in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (19KJB510059), Natural Science Foundation of Jiangsu Province of China (BK20180242), the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924), University Research Development Fund (RDF-17-01-13), and the Key Program Special Fund in XJTLU (KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07, KSF-A-18). 39 0 0 17 39 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2199-160X ADV ELECTRON MATER Adv. Electron. Mater. JUL 2022.0 8.0 7 2101260 10.1002/aelm.202101260 0.0 APR 2022 10 Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics; Materials Science; Physics 3A4LH 2023-03-23 WOS:000780400900001 0
J Cao, XF; Qiu, BZ; Li, XL; Shi, ZL; Xu, GD; Xu, JL Cao, Xiaofeng; Qiu, Baozhi; Li, Xiangli; Shi, Zenglin; Xu, Guandong; Xu, Jianliang Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Balance principle; cluster boundary; high-dimensional space; unlimited lever DENSITY-FUNCTION; NEURAL-NETWORK; SUBSPACE; RECOGNITION; HYBRID The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (kNNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DH Blan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm. [Cao, Xiaofeng; Qiu, Baozhi; Li, Xiangli] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China; [Cao, Xiaofeng; Xu, Guandong] Univ Technol Sydney, Adv Analyt Inst, Sydney, NSW 2008, Australia; [Shi, Zenglin] Univ Amsterdam, Informat Inst, NL-1098 XH Amsterdam, Netherlands; [Xu, Jianliang] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China Zhengzhou University; University of Technology Sydney; University of Amsterdam; Hong Kong Baptist University Xu, JL (corresponding author), Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China. xiaofeng.cao@student.uts.edu.au; iebzqiu@zzu.edu.cn; iexlli@zzu.edu.cn; zenglin.shi@uva.nl; guandong.xu@uts.edu.au; xujl@comp.hkbu.edu.hk Xu, Guandong/ABE-6430-2020; Cao, Xiaofeng/ABI-2876-2020 Xu, Guandong/0000-0003-4493-6663; Cao, Xiaofeng/0000-0002-7391-0334; Xu, Jianliang/0000-0001-9404-5848 Australian Research Council [LP170100891]; HK-RGC [HKBU12200817, C1008-16G]; Australian Research Council [LP170100891] Funding Source: Australian Research Council Australian Research Council(Australian Research Council); HK-RGC(Hong Kong Research Grants Council); Australian Research Council(Australian Research Council) The work of G. Xu was supported by the Australian Research Council Linkage Project Scheme under Grant LP170100891. The work of J. Xu was supported by HK-RGC under Project HKBU12200817 and Project C1008-16G. 70 4 4 0 13 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JUN 2019.0 30.0 6 1867 1880 10.1109/TNNLS.2018.2874458 0.0 14 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering IA1PX 30387747.0 Green Published 2023-03-23 WOS:000469335200022 0
J Zhang, JW; Tai, L; Yun, P; Xiong, YF; Liu, M; Boedecker, J; Burgard, W Zhang, Jingwei; Tai, Lei; Yun, Peng; Xiong, Yufeng; Liu, Ming; Boedecker, Joschka; Burgard, Wolfram VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control IEEE ROBOTICS AND AUTOMATION LETTERS English Article Deep learning in robotics and automation; visual-based navigation; model learning for control In this letter, we deal with the reality gap from a novel perspective, targeting transferring deep reinforcement learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as first, no extra transfer steps are required during the expensive training of DRL agents in simulation; second, the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; and third, the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments. [Zhang, Jingwei; Xiong, Yufeng; Boedecker, Joschka; Burgard, Wolfram] Univ Freiburg, Dept Comp Sci, D-79110 Breisgau, Germany; [Tai, Lei; Yun, Peng; Liu, Ming] Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China University of Freiburg; Hong Kong University of Science & Technology Zhang, JW (corresponding author), Univ Freiburg, Dept Comp Sci, D-79110 Breisgau, Germany.;Tai, L (corresponding author), Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China. zhang@informatik.uni-freiburg.de; ltai@ust.hk; pyun@ust.hk; xiongy@informatik.uni-freiburg.de; eelium@ust.hk; jboedeck@informatik.uni-freiburg.de; burgard@informatik.uni-freiburg.de Liu, Ming/AAC-9891-2020; Tai, Lei/U-6396-2019 Liu, Ming/0000-0002-4500-238X; Tai, Lei/0000-0003-0221-3467; Boedecker, Joschka/0000-0002-3486-7345; Burgard, Wolfram/0000-0002-5680-6500; Yun, Peng/0000-0002-8163-267X Shenzhen Science, Technology and Innovation Commission [JCYJ20160428154842603]; BrainLinks-BrainTools cluster of excellence - DFG (German Research Foundation) [EXC 1086]; Research Grant Council of Hong Kong SAR Government, China [11210017, 16212815, 21202816]; National Natural Science Foundation of China [U1713211] Shenzhen Science, Technology and Innovation Commission; BrainLinks-BrainTools cluster of excellence - DFG (German Research Foundation)(German Research Foundation (DFG)); Research Grant Council of Hong Kong SAR Government, China(Hong Kong Research Grants Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the Shenzhen Science, Technology and Innovation Commission under Grant JCYJ20160428154842603; in part by the BrainLinks-BrainTools cluster of excellence funded by the DFG (German Research Foundation), under Grant EXC 1086; and in part by the Research Grant Council of Hong Kong SAR Government, China, under Grant 11210017, Grant 16212815, and Grant 21202816. The work of M. Liu was supported by the National Natural Science Foundation of China under Grant U1713211. 30 40 41 1 22 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2377-3766 IEEE ROBOT AUTOM LET IEEE Robot. Autom. Lett. APR 2019.0 4.0 2 1148 1155 10.1109/LRA.2019.2894216 0.0 8 Robotics Science Citation Index Expanded (SCI-EXPANDED) Robotics HL6BL Green Submitted 2023-03-23 WOS:000458818700026 0
C Biondi, G; Franzoni, V; Mancinelli, A; Milani, A; Niyogi, R Gervasi, O; Murgante, B; Misra, S; Rocha, AMAC; Garau, C Biondi, Giulio; Franzoni, Valentina; Mancinelli, Alessio; Milani, Alfredo; Niyogi, Rajdeep Hate Speech and Stereotypes with Artificial Neural Networks COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2022 WORKSHOPS, PART III Lecture Notes in Computer Science English Proceedings Paper 22nd International Conference on Computational Science and its Applications (ICCSA) JUL 04-07, 2022 Malaga, SPAIN Springer Int Publishing AG,Comp Open Access Journal,Computat Open Access Journal,Univ Malaga,Univ Perugia,Univ Basilicata,Monash Univ,Kyushu Sangyo Univ,Univ Minho,Univ Cagliari NLP; Affective computing; Ethical computing Information technology is ubiquitously integrated into all areas of human and social life. It becomes progressively critical to build trust in systems while exposing their limitations along with utility and values. The harmonic integration of applications into society will promote the ability of the individuals to positively adapt to change (resilience and response) only if, instead of imposing an AI-centric world on humans, the central goal is to rearrange AI to environments around all aspects of human life. Current AI architectures and applications are increasingly designed taking into account ethical issues, to support the educative role of advanced research tools in improving the interaction between individuals and ultimately in the betterment of society. This work aims at detecting hate speech and stereotypes in textual communication using Artificial Neural Networks and Natural Language Processing. Starting from data from the Evalita 2020 competition, we analyse 6851 tweets which include stereotypes and hate speech, including text and emoticons. Training our neural network with the Adam optimizer, we obtain very promising results, of accuracy for the hate speech task and the stereotype prediction task. [Biondi, Giulio; Franzoni, Valentina; Mancinelli, Alessio; Milani, Alfredo] Univ Perugia, Dept Math & Comp Sci, Perugia, Italy; [Franzoni, Valentina] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China; [Niyogi, Rajdeep] IIT Indian Inst Technol, Roorkee, Uttar Pradesh, India University of Perugia; Hong Kong Baptist University Franzoni, V (corresponding author), Univ Perugia, Dept Math & Comp Sci, Perugia, Italy.;Franzoni, V (corresponding author), Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. giulio.biondi@unipg.it; valentina.franzoni@dmi.unipg.it; alessio.mancinelli@studenti.unipg.it; alfredo.milani@unipg.it; rajdeep.niyogi@cs.iitr.ac.in Biondi, Giulio/0000-0002-1854-2196; Niyogi, Rajdeep/0000-0003-1664-4882 MIUR PRIN PHRAME Project; EmoRe MIUR PRIN PHRAME Project(Ministry of Education, Universities and Research (MIUR)); EmoRe This work has been supported by MIUR PRIN PHRAME Project, and EmoRe. 0 0 0 1 1 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-10545-6; 978-3-031-10544-9 LECT NOTES COMPUT SC 2022.0 13379.0 15 32 10.1007/978-3-031-10545-6_2 0.0 18 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU6IA 2023-03-23 WOS:000925182900002 0
J Zahmatkesh, S; Rezakhani, Y; Chofreh, AG; Karimian, M; Wang, CQ; Ghodrati, I; Hasan, M; Sillanpaa, M; Panchal, H; Khan, R Zahmatkesh, Sasan; Rezakhani, Yousof; Chofreh, Abdoulmohammad Gholamzadeh; Karimian, Melika; Wang, Chongqing; Ghodrati, Iman; Hasan, Mudassir; Sillanpaa, Mika; Panchal, Hitesh; Khan, Ramsha SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks CHEMOSPHERE English Article Mix matrix membrane; SARS-CoV-2; Wastewater treatment; Artificial neural network LOW DISSOLVED-OXYGEN; PARTIAL NITRIFICATION The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treat-ment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as per-formance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively. [Zahmatkesh, Sasan] Univ Sci & Technol Mazandaran, Dept Chem Engn, POB 48518-78195, Behshahr, Iran; [Zahmatkesh, Sasan] Tecnol Monterrey, Escuela Ingenieriay Ciencias, Puebla, Mexico; [Rezakhani, Yousof] Islamic Azad Univ, Dept Civil Engn, Pardis Branch, Pardis, Iran; [Chofreh, Abdoulmohammad Gholamzadeh] Brno Univ Technol, Fac Mech Engn, NETME Ctr, Sustainable Proc Integrat Lab, Techn 2896-2, Brno 61600, Czech Republic; [Karimian, Melika] Univ Eyvanekey, Fac Civil Engn Architecture & Urban Planning, Eyvanakey, Iran; [Wang, Chongqing] Zhengzhou Univ, Sch Chem Engn, Zhengzhou 450001, Peoples R China; [Ghodrati, Iman] Islamic Azad Univ, Dept Comp Engn, Bojnourd Branch, Bojnourd, Iran; [Hasan, Mudassir] King Khalid Univ, Coll Engn, Dept Chem Engn, Abha 61411, Saudi Arabia; [Sillanpaa, Mika] Univ Kebangsaan Malaysia, Fac Sci & Technol, Sch Appl Phys, Bangi 43600, Selangor, Malaysia; [Sillanpaa, Mika] Shoolini Univ, Int Res Ctr Nanotechnol Himalayan Sustainabil IRCN, Solan 173212, Himachal Prades, India; [Sillanpaa, Mika] Univ Johannesburg, Sch Min Met & Chem Engn, Dept Chem Engn, POB 17011, ZA-2028 Doornfontein, South Africa; [Panchal, Hitesh] Govt Engn Coll, Mech Engn Dept, Patan, Gujarat, India; [Khan, Ramsha] Shri Ramswaroop Mem Univ, Inst Technol, Fac Civil Engn, Barabanki 225003, UP, India University of Science & Technology of Mazandaran; Tecnologico de Monterrey; Islamic Azad University; Brno University of Technology; Zhengzhou University; Islamic Azad University; King Khalid University; Universiti Kebangsaan Malaysia; Shoolini University; University of Johannesburg; Shri Ramswaroop Memorial University Zahmatkesh, S (corresponding author), Univ Sci & Technol Mazandaran, Dept Chem Engn, POB 48518-78195, Behshahr, Iran.;Zahmatkesh, S (corresponding author), Tecnol Monterrey, Escuela Ingenieriay Ciencias, Puebla, Mexico. sasan-zahmatkesh@mazust.ac.ir Ghodrati, Iman/0000-0001-9594-7237; Hasan, Mudassir/0000-0002-7748-917X; Rezakhani, Yousof/0000-0002-0119-6231 EU [CZ.02.1.01/0.0/0.0/15_003/0000456]; Czech Ministry of Education, Youth and Sports Operational Pro- gramme Research, Development and Education - European Structural and Investment Funds; Czech Ministry of Education, Youth and Sports Operational Programme Research, Development and Education - European Structural and Investment Funds; Deanship of Scientific Research at King Khalid University, Abha-KSA [R.G.P. 2/189/43] EU(European Commission); Czech Ministry of Education, Youth and Sports Operational Pro- gramme Research, Development and Education - European Structural and Investment Funds; Czech Ministry of Education, Youth and Sports Operational Programme Research, Development and Education - European Structural and Investment Funds; Deanship of Scientific Research at King Khalid University, Abha-KSA Throughout this study, Sustainable Process Integration Laboratory-SPIL has been supported by the EU European Structural and Investment Funds as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, as well as the Czech Ministry of Education, Youth and Sports Operational Programme Research, Development and Education funded by the European Structural and Investment Funds, and This work was supported by the Deanship of Scientific Research at King Khalid University, Abha-KSA, for funding this research through General Research Project under grant number (R.G.P. 2/189/43). 36 4 4 4 4 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0045-6535 1879-1298 CHEMOSPHERE Chemosphere JAN 2023.0 310.0 136837 10.1016/j.chemosphere.2022.136837 0.0 8 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 7P8YL 36252897.0 Bronze, Green Accepted 2023-03-23 WOS:000908985100007 0
J Ghahramani, M; Zhou, MC; Wang, G Ghahramani, Mohammadhossein; Zhou, MengChu; Wang, Gang Urban sensing based on mobile phone data: approaches, applications, and challenges IEEE-CAA JOURNAL OF AUTOMATICA SINICA English Review Big data analysis; human mobility; origin destination matrices; smart infrastructure; urban planning HANDOVER INFORMATION; TRAVEL DEMAND; TIME; ALGORITHM Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in cellular data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, analyzing people ʼ s activities, e.g., counting them at fixed locations and tracking them by generating origin-destination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring, and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons. [Ghahramani, Mohammadhossein] Univ Coll Dublin, Insight Ctr Data Analyt, Dublin 4, Ireland; [Zhou, MengChu] New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA; [Zhou, MengChu; Wang, Gang] Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China; [Wang, Gang] China Telecom Macau, Macau 999078, Peoples R China University College Dublin; New Jersey Institute of Technology; Macau University of Science & Technology Zhou, MC (corresponding author), New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA. sepehr.ghahramani@ucd.ie; zhou@njit.edu; gangtt@189.cn Ghahramani, Mohammadhossein/P-9618-2019 Ghahramani, Mohammadhossein/0000-0002-2743-359X Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) [119/2014/A3] Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) This work was supported by Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) (119/2014/A3). 76 40 40 8 47 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9266 2329-9274 IEEE-CAA J AUTOMATIC IEEE-CAA J. Automatica Sin. MAY 2020.0 7.0 3 627 637 10.1109/JAS.2020.1003120 0.0 11 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems LL1SI Green Submitted 2023-03-23 WOS:000531334400001 0
J Chen, G; Liu, S; Ren, KJ; Qu, ZN; Fu, CH; Hinz, G; Knoll, A Chen, Guang; Liu, Shu; Ren, Kejia; Qu, Zhongnan; Fu, Changhong; Hinz, Gereon; Knoll, Alois Deep anticipation: lightweight intelligent mobile sensing for unmanned vehicles in IoT by recurrent architecture IET INTELLIGENT TRANSPORT SYSTEMS English Article remotely operated vehicles; neural nets; mobile computing; learning (artificial intelligence); recurrent neural nets; quality of service; Kalman filters; Internet of Things; IoT communication; neural architecture; deep anticipation; lightweight intelligent mobile sensing; unmanned vehicles; things techniques; automated vehicles; intelligent transportation system; on-board sensor perception system; individual mobile applications; mobile-sensing perception system; extensive intelligent system; mobile sensing perception; IoT data; mission-critical services; quality of service; communication challenge; packet delay variation; measurement noise; recurrent neural networks Integrating Internet of things (IoT) techniques into automated vehicles has been a vision in intelligent transportation system, there is however seldom researches addressing it. To this end, we envision a scenario: short-range on-board sensor perception system attached to individual mobile applications such as vehicles are connected via IoT and transferred to long-range mobile-sensing perception system, which can be used as part of a more extensive intelligent system surveilling the environment. However, the mobile sensing perception brings new challenges for how to efficiently analyse and intelligently interpret the deluge of IoT data in mission-critical services. Among these challenges, one bottelneck is the quality of service of IoT communication. In this article, we model the communication challenge as latency, packet delay variation and measurement noise which severely deteriorate the reliability and quality of IoT data. We propose a novel architecture that leverages recurrent neural networks and Kalman filtering to anticipate motions and interactions between objects. The model learns to develop a biased belief between prediction and measurement in different situations. We validate our neural architecture with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. The proposed neural architecture outperforms state-of-the-art work in both computation time and model complexity. [Chen, Guang; Ren, Kejia; Fu, Changhong] Tongji Univ, Shanghai 201804, Peoples R China; [Chen, Guang] State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China; [Chen, Guang; Hinz, Gereon; Knoll, Alois] Tech Univ Munich, Dept Informat, D-85748 Munich, Germany; [Liu, Shu; Qu, Zhongnan] Swiss Fed Inst Technol, CH-8092 Zurich, Switzerland Tongji University; Technical University of Munich; Swiss Federal Institutes of Technology Domain; ETH Zurich Chen, G (corresponding author), Tongji Univ, Shanghai 201804, Peoples R China.;Chen, G (corresponding author), State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China.;Chen, G (corresponding author), Tech Univ Munich, Dept Informat, D-85748 Munich, Germany. guang@in.tum.de Knoll, Alois/AAN-8417-2021; Liu, Shu/AFT-1390-2022 Knoll, Alois/0000-0003-4840-076X; Liu, Shu/0000-0002-9576-9335 Federal Ministry of Transport and Digital Infrastructure of Germany; Shanghai Automotive Industry Sci-Tech Development Program [1838]; Shanghai AI Innovative Development Project; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body [31815005] Federal Ministry of Transport and Digital Infrastructure of Germany; Shanghai Automotive Industry Sci-Tech Development Program; Shanghai AI Innovative Development Project; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The research leading to these results has partially received funding from the Federal Ministry of Transport and Digital Infrastructure of Germany in the frame of project Providentia, from the Shanghai Automotive Industry Sci-Tech Development Program according to Grant Agreement No. 1838, from the Shanghai AI Innovative Development Project, and from the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body according to Grant Agreement No. 31815005. 18 1 1 1 34 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-956X 1751-9578 IET INTELL TRANSP SY IET Intell. Transp. Syst. OCT 2019.0 13.0 10 1468 1474 10.1049/iet-its.2019.0208 0.0 7 Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation JA6JR 2023-03-23 WOS:000487949200002 0
J Diaz-Pinto, A; Ravikumar, N; Attar, R; Suinesiaputra, A; Zhao, YT; Levelt, E; Dall'Armellina, E; Lorenzi, M; Chen, QY; Keenan, TDL; Agron, E; Chew, EY; Lu, ZY; Gale, CP; Gale, RP; Plein, S; Frangi, AF Diaz-Pinto, Andres; Ravikumar, Nishant; Attar, Rahman; Suinesiaputra, Avan; Zhao, Yitian; Levelt, Eylem; Dall'Armellina, Erica; Lorenzi, Marco; Chen, Qingyu; Keenan, Tiarnan D. L.; Agron, Elvira; Chew, Emily Y.; Lu, Zhiyong; Gale, Chris P.; Gale, Richard P.; Plein, Sven; Frangi, Alejandro F. Predicting myocardial infarction through retinal scans and minimal personal information NATURE MACHINE INTELLIGENCE English Article CARDIOVASCULAR-DISEASE; HEMOGLOBIN A(1C); RISK; MORTALITY; HYPERTENSION; MASS In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example, retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (-32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (-53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80 +/- 0.02, sensitivity = 0.74 +/- 0.02, specificity = 0.71 +/- 0.03) using just the retinal images and demographic data. Our results indicate that one could identify patients at high risk of future myocardial infarction from retinal imaging available in every optician and eye clinic. Routine eye clinic imaging could help screen patients with cardiovascular risk as studies indicate strong associations between biomarkers in the retina and the heart. This potential is supported by a multimodal study, employing a deep learning model, that can infer cardiac functional indices based on retinal images and demographic data. [Diaz-Pinto, Andres; Ravikumar, Nishant; Attar, Rahman; Suinesiaputra, Avan; Frangi, Alejandro F.] Univ Leeds, Ctr Computat Imaging & Simulat Technol Biomed, Sch Comp, Leeds, W Yorkshire, England; [Diaz-Pinto, Andres; Ravikumar, Nishant; Attar, Rahman; Suinesiaputra, Avan; Levelt, Eylem; Dall'Armellina, Erica; Gale, Chris P.; Plein, Sven; Frangi, Alejandro F.] Univ Leeds, Sch Med, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England; [Zhao, Yitian] Chinese Acad Sci, Cixi Inst Biomed Engn, Ningbo Inst Mat Technol & Engn, Ningbo, Peoples R China; [Levelt, Eylem; Dall'Armellina, Erica; Gale, Chris P.; Plein, Sven] Leeds Teaching Hosp NHS Trust, Dept Cardiol, Leeds, W Yorkshire, England; [Lorenzi, Marco] Univ Cote Azur, Epione Project Team, Inria Sophia Antipolis, Biot, France; [Chen, Qingyu; Lu, Zhiyong] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA; [Keenan, Tiarnan D. L.; Agron, Elvira; Chew, Emily Y.] NEI, Div Epidemiol & Clin Applicat, NIH, Bethesda, MD 20892 USA; [Gale, Chris P.] Univ Leeds, Leeds Inst Data Analyt, Leeds, W Yorkshire, England; [Gale, Richard P.] York Teaching Hosp NHS Fdn Trust, Dept Ophthalmol, York, N Yorkshire, England; [Gale, Richard P.] Univ York, Dept Hlth Sci, York, N Yorkshire, England; [Frangi, Alejandro F.] Katholieke Univ Leuven, Dept Cardiovasc Sci, Leuven, Belgium; [Frangi, Alejandro F.] Katholieke Univ Leuven, Dept Elect Engn, Leuven, Belgium; [Frangi, Alejandro F.] Alan Turing Inst, London, England University of Leeds; University of Leeds; Chinese Academy of Sciences; Ningbo Institute of Materials Technology and Engineering, CAS; University of Leeds; UDICE-French Research Universities; Universite Cote d'Azur; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM); National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI); University of Leeds; University of York - UK; KU Leuven; KU Leuven Frangi, AF (corresponding author), Univ Leeds, Ctr Computat Imaging & Simulat Technol Biomed, Sch Comp, Leeds, W Yorkshire, England.;Frangi, AF (corresponding author), Univ Leeds, Sch Med, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England.;Frangi, AF (corresponding author), Katholieke Univ Leuven, Dept Cardiovasc Sci, Leuven, Belgium.;Frangi, AF (corresponding author), Katholieke Univ Leuven, Dept Elect Engn, Leuven, Belgium.;Frangi, AF (corresponding author), Alan Turing Inst, London, England. a.frangi@leeds.ac.uk Frangi, Alejandro F/C-6500-2008; Chen, Qingyu/Q-8343-2019 Frangi, Alejandro F/0000-0002-2675-528X; Chen, Qingyu/0000-0002-6036-1516; Levelt, Eylem/0000-0002-3799-7102; Diaz-Pinto, Andres/0000-0002-4865-8296; Gale, Chris/0000-0003-4732-382X; Keenan, Tiarnan/0000-0002-2253-1772; Ravikumar, Nishant/0000-0003-0134-107X Royal Academy of Engineering Chair in Emerging Technologies Scheme [CiET1819\19]; MedIAN Network - Engineering and Physical Sciences Research Council (EPSRC) [EP/N026993/1]; Intramural Research Program of National Library of Medicine, , National Institutes of Health; European Union's Horizon 2020 InSilc [777119]; EPSRC TUSCA [EP/V04799X/1]; BHF [FS/13/71/30378]; National Eye Institute, National Institutes of Health; EPSRC [EP/N026993/1] Funding Source: UKRI Royal Academy of Engineering Chair in Emerging Technologies Scheme; MedIAN Network - Engineering and Physical Sciences Research Council (EPSRC)(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Intramural Research Program of National Library of Medicine, , National Institutes of Health; European Union's Horizon 2020 InSilc; EPSRC TUSCA(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); BHF(British Heart Foundation); National Eye Institute, National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Eye Institute (NEI)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) A.F.F. is supported by the Royal Academy of Engineering Chair in Emerging Technologies Scheme (grant no. CiET1819\19), the MedIAN Network (grant no. EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC). This work was also supported by the Intramural Research Program of National Library of Medicine and National Eye Institute, National Institutes of Health. This research was also supported by the European Union's Horizon 2020 InSilc (grant no. 777119) and EPSRC TUSCA (grant no. EP/V04799X/1) programmes. E.D. acknowledges funding from the BHF grant FS/13/71/30378. 40 10 10 19 31 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 2522-5839 NAT MACH INTELL Nat. Mach. Intell. JAN 2022.0 4.0 1 55 + 10.1038/s42256-021-00427-7 0.0 JAN 2022 15 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science YQ3YR Green Accepted 2023-03-23 WOS:000746785300004 0
J Wright, C; Kagawa-Viviani, A; Gerlein-Safdi, C; Mosquera, GM; Poca, M; Tseng, H; Chun, KP Wright, Cynthia; Kagawa-Viviani, Aurora; Gerlein-Safdi, Cynthia; Mosquera, Giovanny M.; Poca, Maria; Tseng, Han; Chun, Kwok Pan Advancing ecohydrology in the changing tropics: Perspectives from early career scientists ECOHYDROLOGY English Article big data; climate change; land use; land cover; modelling; remote sensing; stable isotopes; tropical ecosystems WATER WORLDS HYPOTHESIS; LAND-COVER CHANGE; HEAT PULSE METHOD; SAP FLOW; STABLE-ISOTOPE; CLIMATE-CHANGE; DRY-SEASON; RUNOFF GENERATION; RESIDENCE TIMES; INVASIVE PLANTS Tropical ecosystems offer a unique setting for understanding ecohydrological processes, but to date, such investigations have been limited. The purpose of this paper is to highlight the importance of studying these processesspecifically, how they are being affected by the transformative changes taking place in the tropicsand to offer an agenda for future research. At present, the ongoing loss of native ecosystems is largely due to agricultural expansion, but parallel processes of afforestation are also taking place, leading to shifts in ecohydrological fluxes. Similarly, shifts in water availability due to climate change will affect both water and carbon fluxes in tropical ecosystems. A number of methods exist that can help us better understand how changes in land use and climate affect ecohydrological processes; these include stable isotopes, remote sensing, and process-based models. Still, our knowledge of the underlying physical mechanisms, especially those that determine the effects of scale on ecosystem processes, remains incomplete. We assert that development of a knowledge base concerning the effects of transformative change on ecological, hydrological, and biogeochemical processes at different spatio-temporal scales is an urgent need for tropical regions and should serve as a compass for emerging ecohydrologists. To reach this goal, we advocate a research agenda that expands the number and diversity of ecosystems targeted for ecohydrological investigations and connects researchers across the tropics. We believe that the use of big data and open source softwarealready an important integrative tool/skill for the young ecohydrologistwill be key in expanding research capabilities. [Wright, Cynthia] Texas A&M Univ, Dept Ecosyst Sci & Management, College Stn, TX 77843 USA; [Kagawa-Viviani, Aurora; Tseng, Han] Univ Hawaii Manoa, Dept Geog, Honolulu, HI 96822 USA; [Gerlein-Safdi, Cynthia] Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA; [Mosquera, Giovanny M.] Univ Cuenca, Dept Recursos Hidr & Ciencias Ambientales, Cuenca, Ecuador; [Mosquera, Giovanny M.] Univ Cuenca, Fac Ingn, Cuenca, Ecuador; [Mosquera, Giovanny M.] Justus Liebig Univ, Res Ctr BioSyst Land Use & Nutr IFZ, Inst Landscape Ecol & Resources Management ILR, Giessen, Germany; [Poca, Maria] Univ Nacl Cordoba, CONICET, Inst Multidisciplinario Biol Vegetal, Cordoba, Argentina; [Poca, Maria] Univ Nacl Cordoba, Fac Ciencias Exactas Fis & Nat, Catedra Biogeog, Dept Diversidad Biol & Ecol, Cordoba, Argentina; [Chun, Kwok Pan] Hong Kong Baptist Univ, Dept Geog, Kowloon Tong, Hong Kong, Peoples R China Texas A&M University System; Texas A&M University College Station; University of Hawaii System; University of Hawaii Manoa; Princeton University; Universidad de Cuenca; Universidad de Cuenca; Justus Liebig University Giessen; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of Cordoba; National University of Cordoba; Hong Kong Baptist University Wright, C (corresponding author), Texas A&M Univ, Dept Ecosyst Sci & Management, College Stn, TX 77843 USA. 曾, 涵/A-1115-2013; Mosquera, Giovanny/G-9319-2017; Gerlein-Safdi, Cynthia/AAL-7972-2020; Chun, Kwok/P-5782-2018; Poca, María/AAG-7843-2021 曾, 涵/0000-0003-0445-9284; Mosquera, Giovanny/0000-0002-4764-4685; Gerlein-Safdi, Cynthia/0000-0001-8160-6167; Chun, Kwok/0000-0001-9873-6240; Poca, María/0000-0001-9160-1036; Wright, Cynthia/0000-0003-2571-7334; Kagawa-Viviani, Aurora/0000-0001-8532-3017 NSF [DGE-1252521]; Dynamics of Coupled Natural and Human Systems (CNH) programmes [DEB-1413900]; Division of Research of Texas AM University; Ford Foundation Predoctoral Fellowship Program; NASA Headquarters under the Earth and Space Science Fellowship Program [14-EARTH14F-241]; PEI-STEP fellowship from the Princeton Environmental Institute; Central Research Office at the University of Cuenca (DIUC); German Research Foundation [DFG, BR2238/14-1]; Ecuadorian National Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) [PIC-13-ETAPA-001]; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) [4885]; Pacific Islands Climate Science Center; Faculty Development Fund [FRG1/17-18/005]; Hong Kong Baptist University Faculty Research [FRG2/15-16/085] NSF(National Science Foundation (NSF)); Dynamics of Coupled Natural and Human Systems (CNH) programmes; Division of Research of Texas AM University; Ford Foundation Predoctoral Fellowship Program; NASA Headquarters under the Earth and Space Science Fellowship Program(National Aeronautics & Space Administration (NASA)); PEI-STEP fellowship from the Princeton Environmental Institute; Central Research Office at the University of Cuenca (DIUC); German Research Foundation(German Research Foundation (DFG)); Ecuadorian National Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT); Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)(Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)); Pacific Islands Climate Science Center; Faculty Development Fund; Hong Kong Baptist University Faculty Research NSF Graduate Research Fellowship, Grant/Award Number: DGE-1252521; Dynamics of Coupled Natural and Human Systems (CNH) programmes, Grant/Award Number: DEB-1413900; Division of Research of Texas A&M University; Ford Foundation Predoctoral Fellowship Program; NASA Headquarters under the Earth and Space Science Fellowship Program, Grant/Award Number: 14-EARTH14F-241; PEI-STEP fellowship from the Princeton Environmental Institute; Central Research Office at the University of Cuenca (DIUC); German Research Foundation, Grant/Award Number: DFG, BR2238/14-1; Ecuadorian National Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT), Grant/Award Number: PIC-13-ETAPA-001; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Grant/Award Number: Resolution D No 4885; Pacific Islands Climate Science Center; Faculty Development Fund, Grant/Award Number: FRG1/17-18/005; Hong Kong Baptist University Faculty Research, Grant/Award Number: FRG2/15-16/085 228 28 28 4 26 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1936-0584 1936-0592 ECOHYDROLOGY Ecohydrology APR 2018.0 11.0 3 SI e1918 10.1002/eco.1918 0.0 18 Ecology; Environmental Sciences; Water Resources Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Water Resources GC9LT 2023-03-23 WOS:000430119600004 0
J Jombart, T; Ghozzi, S; Schumacher, D; Taylor, TJ; Leclerc, QJ; Jit, M; Flasche, S; Greaves, F; Ward, T; Eggo, RM; Nightingale, E; Meakin, S; Brady, OJ; Medley, GF; Hohle, M; Edmunds, WJ Jombart, Thibaut; Ghozzi, Stephane; Schumacher, Dirk; Taylor, Timothy J.; Leclerc, Quentin J.; Jit, Mark; Flasche, Stefan; Greaves, Felix; Ward, Tom; Eggo, Rosalind M.; Nightingale, Emily; Meakin, Sophie; Brady, Oliver J.; Medley, Graham F.; Hohle, Michael; Edmunds, W. John Ctr Math Modelling Infect Dis COVI Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES English Article ASMODEE; trendbreaker; surveillance; outbreak; algorithm; machine learning OUTBREAK DETECTION; SURVEILLANCE; ALGORITHM As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. [Jombart, Thibaut; Taylor, Timothy J.; Leclerc, Quentin J.; Jit, Mark; Flasche, Stefan; Eggo, Rosalind M.; Nightingale, Emily; Meakin, Sophie; Brady, Oliver J.; Medley, Graham F.; Edmunds, W. John] London Sch Hyg & Trop Med, Ctr Math Modelling Infect Dis, Dept Infect Dis Epidemiol, Keppel St, London WC1E 7HT, England; [Jombart, Thibaut] UK Publ Hlth Rapid Support Team, London WC1E 7HT, England; [Jombart, Thibaut] Imperial Coll London, MRC Ctr Global Infect Dis Anal, Sch Publ Hlth, Dept Infect Dis Epidemiol, London SW7 2DD, England; [Ghozzi, Stephane] Helmholtz Ctr Infect Res, Dept Epidemiol, D-38124 Braunschweig, Lower Saxony, Germany; [Schumacher, Dirk] Robert Koch Inst, Dept Infect Dis Epidemiol, DE-13353 Berlin, Germany; [Jit, Mark] Univ Hong Kong, Li Ka Shing Fac Med, Sch Publ Hlth, Hong Kong, Peoples R China; [Greaves, Felix; Ward, Tom] Joint Biosecur Ctr, Dept Hlth & Social Care, London SW1H 0EU, England; [Greaves, Felix] Imperial Coll London, Dept Primary Care & Publ Hlth, London W6 8RP, England; [Hohle, Michael] Stockholm Univ, Dept Math, S-11419 Stockholm, Sweden; [Schumacher, Dirk] Fed Inst Qual Assurance & Transparency Healthcare, Unit Med Biometry & Stat, Berlin, Germany University of London; London School of Hygiene & Tropical Medicine; Imperial College London; Helmholtz Association; Helmholtz-Center for Infection Research; Robert Koch Institute; University of Hong Kong; Imperial College London; Stockholm University Jombart, T (corresponding author), London Sch Hyg & Trop Med, Ctr Math Modelling Infect Dis, Dept Infect Dis Epidemiol, Keppel St, London WC1E 7HT, England.;Jombart, T (corresponding author), UK Publ Hlth Rapid Support Team, London WC1E 7HT, England.;Jombart, T (corresponding author), Imperial Coll London, MRC Ctr Global Infect Dis Anal, Sch Publ Hlth, Dept Infect Dis Epidemiol, London SW7 2DD, England.;Ghozzi, S (corresponding author), Helmholtz Ctr Infect Res, Dept Epidemiol, D-38124 Braunschweig, Lower Saxony, Germany. thibautjombart@gmail.com; stephane.ghozzi@helmholtz-hzi.de Nightingale, Emily/ABA-6907-2021; Leclerc, Quentin J/ABI-5611-2020; Jit, Mark/HDM-7797-2022; Brady, Oliver/P-9421-2019; Höhle, Michael/AFM-0575-2022; Greaves, Felix/A-1324-2012 Nightingale, Emily/0000-0002-9265-1841; Leclerc, Quentin J/0000-0003-4761-001X; Brady, Oliver/0000-0002-3235-2129; Höhle, Michael/0000-0002-0423-6702; Flasche, Stefan/0000-0002-5808-2606; Jit, Mark/0000-0001-6658-8255; Eggo, Rosalind M/0000-0002-0362-6717; Medley, Graham/0000-0002-0030-7278; Taylor, Tim/0000-0002-8587-7113; Greaves, Felix/0000-0001-9393-3122; Ghozzi, Stephane/0000-0002-3911-9573 Sir Henry Wellcome Fellowship - Wellcome Trust [206471/Z/17/Z]; HDR UK [MR/S003975/1]; Sir Henry Dale Fellowship - Wellcome Trust [208812/Z/17/Z]; Sir Henry Dale Fellowship - Royal Society [208812/Z/17/Z]; Global Challenges Research Fund (GCRF) project 'RECAP' [ES/P010873/1]; UK Public Health Rapid Support Team - United Kingdom Department of Health and Social Care; National Institute for Health Research (NIHR)-Health Protection Research Unit for Modelling Methodology; Bill and Melinda Gates foundation [INV-003174, OPP1183986, INV-001754]; NIHR [16/137/109, HPRU-2012-10096, 16/136/46]; European Commission project EpiPose [101003688]; Wellcome Trust [206471/Z/17/Z, 208812/Z/17/Z, 210758/Z/18/Z, 206250/Z/17/Z]; MRC [MC_PC_19065]; BBSRC LIDP [BB/M009513/1]; NTD Modelling Consortium [OPP1184344, OPP1180644, OPP1191821]; BMGF [OPP1157270]; DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme) [221303/Z/20/Z]; DTRA [HDTRA1-18-1-0051]; National Institute for Health Research (NIHR) using UK aid from the UK Government; ERC Starting Grant [757699]; European Union's Horizon 2020 research and innovation programme-project EpiPose [101003688]; Health Protection Research Unit for Immunisation [NIHR200929]; Health Protection Research Unit [HPRU-2012-10096, NIHR200929, PR-OD-1017-20002]; UK DHSC/UK Aid/NIHR [ITCRZ 03010]; UK MRC [MC_PC_19065, LID DTP MR/N013638/1, MR/P014658/1]; Department of Health and Social Care; Wellcome Trust [210758/Z/18/Z] Funding Source: researchfish Sir Henry Wellcome Fellowship - Wellcome Trust(Wellcome Trust); HDR UK; Sir Henry Dale Fellowship - Wellcome Trust(Wellcome Trust); Sir Henry Dale Fellowship - Royal Society; Global Challenges Research Fund (GCRF) project 'RECAP'; UK Public Health Rapid Support Team - United Kingdom Department of Health and Social Care; National Institute for Health Research (NIHR)-Health Protection Research Unit for Modelling Methodology; Bill and Melinda Gates foundation(Bill & Melinda Gates Foundation); NIHR(National Institute for Health Research (NIHR)); European Commission project EpiPose(European CommissionEuropean Commission Joint Research Centre); Wellcome Trust(Wellcome Trust); MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); BBSRC LIDP(UK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC)); NTD Modelling Consortium; BMGF; DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme); DTRA(United States Department of DefenseDefense Threat Reduction Agency); National Institute for Health Research (NIHR) using UK aid from the UK Government; ERC Starting Grant(European Research Council (ERC)); European Union's Horizon 2020 research and innovation programme-project EpiPose; Health Protection Research Unit for Immunisation; Health Protection Research Unit; UK DHSC/UK Aid/NIHR; UK MRC(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); Department of Health and Social Care; Wellcome Trust(Wellcome Trust) The named authors (O.J.B., W.J. E., R.M.E., T.J., M. J., S.M., E.N., T.J. T.) had the following sources of funding: O.J.B. was funded by a Sir Henry Wellcome Fellowship funded by the Wellcome Trust (grant no. 206471/Z/17/Z). R.M.E. receives funding from HDR UK (grant no. MR/S003975/1). S.F. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant no. 208812/Z/17/Z). T.J. receives funding from the Global Challenges Research Fund (GCRF) project ` RECAP' managed through RCUK and ESRC (ES/P010873/1), the UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care and from the National Institute for Health Research (NIHR)-Health Protection Research Unit for Modelling Methodology. M.J. receives funding from the Bill and Melinda Gates foundation (grant no.: INV-003174) and the NIHR (grant no.: 16/137/109 and HPRU-2012-10096). E.N. receives funding from the Bill and Melinda Gates Foundation (grant no.: OPP1183986). M.J. and W.J.E. receive funding from European Commission project EpiPose (grant no. 101003688). S.M. receives funding from the Wellcome Trust (grant no. 210758/Z/18/Z). S.F. receives funding from the Wellcome Trust (grant no. 208812/Z/17/Z). T.J. and T. J. T. receive funding from the MRC (grant no. MC_PC_19065). The following funding sources are acknowledged as providing funding for the working group authors. BBSRC LIDP (BB/M009513/1: D.S.). This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: M.Q.; INV-003174: K.P., M.J., Y.L.; NTD Modelling Consortium OPP1184344: C.A.B.P., G.F. M.; OPP1180644: S.R.P.; OPP1191821: K.O'R., M.A.). BMGF (OPP1157270: K.A.). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: K.v.Z.). DTRA (HDTRA1-18-1-0051: J. W. R.). Elrha R2HC/UK DFID/Wellcome Trust/this research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. ERC Starting Grant (#757699: J.C.E., M.Q., R. M.G.J.H.). This project has received funding from the European Union's Horizon 2020 research and innovation programme-project EpiPose (101003688: K.P., M.J., P.K., R. C.B., Y.L.). HDR UK (MR/S003975/1: R.M.E.). NIHR (16/136/46: B.J.Q.; 16/137/109: B.J.Q., C.D., F.Y.S., M.J., Y.L.; Health Protection Research Unit for Immunisation NIHR200929: N.G.D., F.G. S.; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: T.J., F.G. S.; NIHR200929: M.J.; PR-OD-1017-20002: A. R.). UK DHSC/UK Aid/NIHR (ITCRZ 03010: H. P.G.). UK MRC (LID DTP MR/N013638/1: G.R.G.-L., Q.J.L., N.R.W.; MC_PC_19065: A.G., N.G.D., R.M.E., S.C., T.J., Y.L.; MR/P014658/1: G.M.K.). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (T.J.). Wellcome Trust (206250/Z/17/Z: A.J.K., T.W.R.; 206471/Z/17/Z: O.J.B.; 208812/Z/17/Z: S.C., S.F.; 210758/Z/18/Z: S.F., S.A., J.H.). No funding (A.K.D., A.M.F., C.J.V.-A., D. C.T., S.H., Y.-W. D.C.). The UK Public Health Rapid Support Team is funded by UK aid from the Department of Health and Social Care and is jointly run by Public Health England and the London School of Hygiene and Tropical Medicine. The University of Oxford and King's College London are academic partners. 38 4 4 2 7 ROYAL SOC LONDON 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND 0962-8436 1471-2970 PHILOS T R SOC B Philos. Trans. R. Soc. B-Biol. Sci. JUL 19 2021.0 376.0 1829 20200266 10.1098/rstb.2020.0266 0.0 12 Biology Science Citation Index Expanded (SCI-EXPANDED) Life Sciences & Biomedicine - Other Topics SN9GL 34053271.0 Green Accepted, Green Published, Green Submitted 2023-03-23 WOS:000658592500005 0
J Cao, L; Sutcliffe, W; Van Tonder, R; Bernlochner, FU; Adachi, I; Aihara, H; Al Said, S; Asner, DM; Atmacan, H; Aushev, T; Ayad, R; Babu, V; Bauer, M; Behera, P; Belous, K; Bennett, J; Bessner, M; Bhardwaj, V; Bilka, T; Biswal, J; Bonvicini, G; Bozek, A; Bracko, M; Browder, TE; Campajola, M; Cervenkov, D; Chang, MC; Chang, P; Chekelian, V; Chen, A; Cheon, BG; Chilikin, K; Cho, HE; Cho, K; Cho, SJ; Choi, SK; Choi, Y; Choudhury, S; Cinabro, D; Cunliffe, S; Das, S; Dash, N; De Nardo, G; Di Capua, F; Dingfelder, J; Dolezal, Z; Dong, TV; Dubey, S; Eidelman, S; Epifanov, D; Ferber, T; Ferlewicz, D; Frey, A; Fulsom, BG; Garg, R; Gaur, V; Garmash, A; Giri, A; Goldenzweig, P; Guan, Y; Hadjivasiliou, C; Hara, T; Hartbrich, O; Hayasaka, K; Hayashii, H; Hedges, MT; Villanueva, MH; Hou, WS; Hsu, CL; Iijima, T; Inami, K; Ishikawa, A; Itoh, R; Iwasaki, M; Iwasaki, Y; Jacobs, WW; Jang, EJ; Jeon, HB; Jia, S; Jin, Y; Joo, CW; Joo, KK; Kang, KH; Karyan, G; Kawasaki, T; Kichimi, H; Kiesling, C; Kim, BH; Kim, CH; Kim, DY; Kim, HJ; Kim, KH; Kim, SH; Kim, YK; Kinoshita, K; Kodys, P; Konno, T; Korobov, A; Korpar, S; Kotchetkov, D; Kovalenko, E; Krizan, P; Kroeger, R; Krokovny, P; Kuhr, T; Kumar, M; Kumar, R; Kumara, K; Kuzmin, A; Kwon, YJ; Lalwani, K; Lange, JS; Lee, IS; Lee, SC; Lewis, P; Li, CH; Li, J; Li, LK; Li, YB; Li Gioi, L; Libby, J; Lieret, K; Liptak, Z; Liventsev, D; MacNaughton, J; MacQueen, C; Masuda, M; Matsuda, T; Matvienko, D; Merola, M; Metzner, F; Miyabayashi, K; Mizuk, R; Mohanty, GB; Moon, TJ; Mori, T; Mrvar, M; Mussa, R; Nakao, M; Natkaniec, Z; Natochii, A; Nayak, L; Nayak, M; Niiyama, M; Nisar, NK; Nishida, S; Nishimura, K; Ogawa, S; Ono, H; Onuki, Y; Oskin, P; Pakhlov, P; Pakhlova, G; Pang, T; Pardi, S; Park, CW; Park, H; Park, SH; Patra, S; Paul, S; Pedlar, TK; Pestotnik, R; Piilonen, LE; Podobnik, T; Popov, V; Prencipe, E; Prim, MT; Ritter, M; Rohrken, M; Rostomyan, A; Rout, N; Rozanska, M; Russo, G; Sahoo, D; Sakai, Y; Sandilya, S; Sangal, A; Santelj, L; Sanuki, T; Savinov, V; Schnell, G; Schueler, J; Schwanda, C; Schwartz, AJ; Seino, Y; Senyo, K; Sevior, ME; Shapkin, M; Sharma, C; Shen, CP; Shiu, JG; Simon, F; Sokolov, A; Solovieva, E; Stanic, S; Staric, M; Stottler, ZS; Strube, JF; Sumiyoshi, T; Takizawa, M; Tamponi, U; Tanida, K; Tenchini, F; Trabelsi, K; Uchida, M; Uglov, T; Unno, Y; Uno, S; Urquijo, P; Usov, Y; Vahsen, SE; Varner, G; Varvell, KE; Vinokurova, A; Vorobyev, V; Vossen, A; Waheed, E; Wang, CH; Wang, E; Wang, MZ; Wang, P; Watanabe, M; Watanuki, S; Wehle, S; Wiechczynski, J; Won, E; Xu, X; Yabsley, BD; Yan, W; Yang, SB; Ye, H; Yin, JH; Yuan, CZ; Yusa, Y; Zhang, ZP; Zhilich, V; Zhukova, V; Zhulanov, V Cao, L.; Sutcliffe, W.; Van Tonder, R.; Bernlochner, F. U.; Adachi, I; Aihara, H.; Al Said, S.; Asner, D. M.; Atmacan, H.; Aushev, T.; Ayad, R.; Babu, V; Bauer, M.; Behera, P.; Belous, K.; Bennett, J.; Bessner, M.; Bhardwaj, V; Bilka, T.; Biswal, J.; Bonvicini, G.; Bozek, A.; Bracko, M.; Browder, T. E.; Campajola, M.; Cervenkov, D.; Chang, M-C; Chang, P.; Chekelian, V; Chen, A.; Cheon, B. G.; Chilikin, K.; Cho, H. E.; Cho, K.; Cho, S-J; Choi, S-K; Choi, Y.; Choudhury, S.; Cinabro, D.; Cunliffe, S.; Das, S.; Dash, N.; De Nardo, G.; Di Capua, F.; Dingfelder, J.; Dolezal, Z.; Dong, T., V; Dubey, S.; Eidelman, S.; Epifanov, D.; Ferber, T.; Ferlewicz, D.; Frey, A.; Fulsom, B. G.; Garg, R.; Gaur, V; Garmash, A.; Giri, A.; Goldenzweig, P.; Guan, Y.; Hadjivasiliou, C.; Hara, T.; Hartbrich, O.; Hayasaka, K.; Hayashii, H.; Hedges, M. T.; Villanueva, M. Hernandez; Hou, W-S; Hsu, C-L; Iijima, T.; Inami, K.; Ishikawa, A.; Itoh, R.; Iwasaki, M.; Iwasaki, Y.; Jacobs, W. W.; Jang, E-J; Jeon, H. B.; Jia, S.; Jin, Y.; Joo, C. W.; Joo, K. K.; Kang, K. H.; Karyan, G.; Kawasaki, T.; Kichimi, H.; Kiesling, C.; Kim, B. H.; Kim, C. H.; Kim, D. Y.; Kim, H. J.; Kim, K-H; Kim, S. H.; Kim, Y-K; Kinoshita, K.; Kodys, P.; Konno, T.; Korobov, A.; Korpar, S.; Kotchetkov, D.; Kovalenko, E.; Krizan, P.; Kroeger, R.; Krokovny, P.; Kuhr, T.; Kumar, M.; Kumar, R.; Kumara, K.; Kuzmin, A.; Kwon, Y-J; Lalwani, K.; Lange, J. S.; Lee, I. S.; Lee, S. C.; Lewis, P.; Li, C. H.; Li, J.; Li, L. K.; Li, Y. B.; Li Gioi, L.; Libby, J.; Lieret, K.; Liptak, Z.; Liventsev, D.; MacNaughton, J.; MacQueen, C.; Masuda, M.; Matsuda, T.; Matvienko, D.; Merola, M.; Metzner, F.; Miyabayashi, K.; Mizuk, R.; Mohanty, G. B.; Moon, T. J.; Mori, T.; Mrvar, M.; Mussa, R.; Nakao, M.; Natkaniec, Z.; Natochii, A.; Nayak, L.; Nayak, M.; Niiyama, M.; Nisar, N. K.; Nishida, S.; Nishimura, K.; Ogawa, S.; Ono, H.; Onuki, Y.; Oskin, P.; Pakhlov, P.; Pakhlova, G.; Pang, T.; Pardi, S.; Park, C. W.; Park, H.; Park, S-H; Patra, S.; Paul, S.; Pedlar, T. K.; Pestotnik, R.; Piilonen, L. E.; Podobnik, T.; Popov, V; Prencipe, E.; Prim, M. T.; Ritter, M.; Rohrken, M.; Rostomyan, A.; Rout, N.; Rozanska, M.; Russo, G.; Sahoo, D.; Sakai, Y.; Sandilya, S.; Sangal, A.; Santelj, L.; Sanuki, T.; Savinov, V; Schnell, G.; Schueler, J.; Schwanda, C.; Schwartz, A. J.; Seino, Y.; Senyo, K.; Sevior, M. E.; Shapkin, M.; Sharma, C.; Shen, C. P.; Shiu, J-G; Simon, F.; Sokolov, A.; Solovieva, E.; Stanic, S.; Staric, M.; Stottler, Z. S.; Strube, J. F.; Sumiyoshi, T.; Takizawa, M.; Tamponi, U.; Tanida, K.; Tenchini, F.; Trabelsi, K.; Uchida, M.; Uglov, T.; Unno, Y.; Uno, S.; Urquijo, P.; Usov, Y.; Vahsen, S. E.; Varner, G.; Varvell, K. E.; Vinokurova, A.; Vorobyev, V.; Vossen, A.; Waheed, E.; Wang, C. H.; Wang, E.; Wang, M-Z; Wang, P.; Watanabe, M.; Watanuki, S.; Wehle, S.; Wiechczynski, J.; Won, E.; Xu, X.; Yabsley, B. D.; Yan, W.; Yang, S. B.; Ye, H.; Yin, J. H.; Yuan, C. Z.; Yusa, Y.; Zhang, Z. P.; Zhilich, V; Zhukova, V; Zhulanov, V Measurements of partial branching fractions of inclusive B -> XTI4PFTI4PF(u)(lx+)nu(l) decays with hadronic tagging PHYSICAL REVIEW D English Article CB-VERTICAL-BAR; ENERGY MOMENTS; CP-VIOLATION; HEAVY; ANNIHILATION; IDENTIFICATION; FACTORIZATION; (B)OVER-BAR; PHYSICS; MODEL We present measurements of partial branching fractions of inclusive semileptonic B -> X(u)l+nu(l) decays using the full Belle dataset of 711 fb(-1) of integrated luminosity at the gamma(4S) resonance and for l = e, mu. Inclusive semileptonic B -> X(u)l(l)(+nu) decays are Cabibbo-Kobayashi-Maskawa (CKM) suppressed and measurements are complicated by the large background from CKM favored B. Xclthorn.l transitions, which have a similar signature. Using machine learning techniques, we reduce this and other backgrounds effectively, while retaining access to a large fraction of the B -> X(c)l+nu(l) phase space and high signal efficiency. We measure partial branching fractions in three phase-space regions covering about 31% to 86% of the accessible B -> X(u)l(l)(+nu) phase space. The most inclusive measurement corresponds to the phase space with lepton energies of E-l(B) > 1 GeV, and we obtain Delta B(B -> X(u)l+nu(l)) = (1.59 +/- 0.07 +/- 0.16) x 10(-3) from a two-dimensional fit of the hadronic mass spectrum and the four-momentum-transfer squared distribution, with the uncertainties denoting the statistical and systematic error. We find vertical bar V-ub vertical bar = (4.10 +/- 0.09 +/- 0.22 +/- 0.15) x 10(-3) from an average of four calculations for the partial decay rate with the third uncertainty denoting the average theory error. This value is higher but compatible with the determination from exclusive semileptonic decays within 1.3 standard deviations. In addition, we report charmless inclusive partial branching fractions separately for B+ and B-0 mesons as well as for electron and muon final states. No isospin breaking or lepton flavor universality violating effects are observed. [Cao, L.; Sutcliffe, W.; Van Tonder, R.; Bernlochner, F. U.; Dingfelder, J.; Lewis, P.; Prim, M. T.] Univ Bonn, D-53115 Bonn, Germany; [Schnell, G.] Univ Basque Country UPV EHU, Dept Phys, Bilbao 48080, Spain; [Asner, D. M.; Nisar, N. K.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Eidelman, S.; Epifanov, D.; Garmash, A.; Korobov, A.; Kovalenko, E.; Krokovny, P.; Kuzmin, A.; Matvienko, D.; Usov, Y.; Vinokurova, A.; Vorobyev, V.; Zhilich, V; Zhulanov, V] Budker Inst Nucl Phys SB RAS, Novosibirsk 630090, Russia; [Bilka, T.; Cervenkov, D.; Dolezal, Z.; Kodys, P.] Charles Univ Prague, Fac Math & Phys, Prague 12116, Czech Republic; [Joo, K. K.] Chonnam Natl Univ, Gwangju 61186, South Korea; [Atmacan, H.; Guan, Y.; Kinoshita, K.; Li, L. K.; Sangal, A.; Schwartz, A. J.] Univ Cincinnati, Cincinnati, OH 45221 USA; [Babu, V; Cunliffe, S.; Ferber, T.; Karyan, G.; Rohrken, M.; Rostomyan, A.; Tenchini, F.; Wehle, S.; Ye, H.] DESY, D-22607 Hamburg, Germany; [Vossen, A.] Duke Univ, Durham, NC 27708 USA; [Chang, M-C] Fu Jen Catholic Univ, Dept Phys, Taipei 24205, Taiwan; [Dong, T., V; Jia, S.; Shen, C. P.] Fudan Univ, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai 200443, Peoples R China; [Dong, T., V; Jia, S.; Shen, C. P.] Fudan Univ, Inst Modern Phys, Shanghai 200443, Peoples R China; [Lange, J. S.] Justus Liebig Univ Giessen, D-35392 Giessen, Germany; [Frey, A.] Georg August Univ Gottingen, Phys Inst 2, D-37073 Gottingen, Germany; [Adachi, I; Hara, T.; Ishikawa, A.; Itoh, R.; Nakao, M.; Nishida, S.; Sakai, Y.; Uno, S.] SOKENDAI Grad Univ Adv Studies, Hayama, Kanagawa 2400193, Japan; [Choi, S-K; Jang, E-J] Gyeongsang Natl Univ, Jinju 52828, South Korea; [Cheon, B. G.; Cho, H. E.; Kim, C. H.; Lee, I. S.; Unno, Y.] Hanyang Univ, Dept Phys, Seoul 04763, South Korea; [Cheon, B. G.; Cho, H. E.; Kim, C. H.; Lee, I. S.; Unno, Y.] Hanyang Univ, Inst Nat Sci, Seoul 04763, South Korea; [Bessner, M.; Browder, T. E.; Dubey, S.; Hartbrich, O.; Hedges, M. T.; Kotchetkov, D.; Liptak, Z.; Natochii, A.; Nishimura, K.; Schueler, J.; Vahsen, S. E.; Varner, G.] Univ Hawaii, Honolulu, HI 96822 USA; [Adachi, I; Hara, T.; Ishikawa, A.; Itoh, R.; Iwasaki, Y.; Kichimi, H.; Liventsev, D.; Nakao, M.; Nishida, S.; Sakai, Y.; Uno, S.; Waheed, E.] High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki 3050801, Japan; [Takizawa, M.] High Energy Accelerator Res Org KEK, KEK Theory Ctr, J PARC Branch, Tsukuba, Ibaraki 3050801, Japan; [Aushev, T.; Mizuk, R.; Pakhlova, G.; Popov, V; Uglov, T.] Higher Sch Econ HSE, Moscow 101000, Russia; [Prencipe, E.] Forschungszentrum Julich, D-52425 Julich 52425, Germany; [Schnell, G.] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain; [Bhardwaj, V; Patra, S.] Indian Inst Sci Educ & Res Mohali, Sas Nagar 140306, India; [Choudhury, S.; Giri, A.; Nayak, L.; Sandilya, S.] Indian Inst Technol Hyderabad, Hyderabad 502285, Telangana, India; [Behera, P.; Dash, N.; Libby, J.; Rout, N.] Indian Inst Technol Madras, Chennai 600036, Tamil Nadu, India; [Jacobs, W. W.] Indiana Univ, Bloomington, IN 47408 USA; [Wang, P.; Yuan, C. Z.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China; [Mrvar, M.; Schwanda, C.] Inst High Energy Phys, A-1050 Vienna, Austria; [Belous, K.; Shapkin, M.; Sokolov, A.] Inst High Energy Phys, Protvino 142281, Russia; [Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.; Pardi, S.] Ist Nazl Fis Nucl, Sez Napoli, I-80126 Naples, Italy; [Mussa, R.; Tamponi, U.] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; [Tanida, K.] Japan Atom Energy Agcy, Adv Sci Res Ctr, Naka, Ibaraki 3191195, Japan; [Biswal, J.; Bracko, M.; Korpar, S.; Krizan, P.; Pestotnik, R.; Podobnik, T.; Santelj, L.; Staric, M.] J Stefan Inst, Ljubljana 1000, Slovenia; [Bauer, M.; Goldenzweig, P.; Metzner, F.] Karlsruher Inst Technol, Inst Expt Teilchenphys, D-76131 Karlsruhe, Germany; [Joo, C. W.] Univ Tokyo, Kavli Inst Phys & Math Universe WPI, Kashiwa, Chiba 2778583, Japan; [Al Said, S.] King Abdulaziz Univ, Fac Sci, Dept Phys, Jeddah 21589, Saudi Arabia; [Kawasaki, T.; Konno, T.] Kitasato Univ, Sagamihara, Kanagawa 2520373, Japan; [Cho, K.] Korea Inst Sci & Technol Informat, Daejeon 34141, South Korea; [Won, E.; Yang, S. B.; Yin, J. H.] Korea Univ, Seoul 02841, South Korea; [Niiyama, M.] Kyoto Sangyo Univ, Kyoto 6038555, Japan; [Jeon, H. B.; Kang, K. H.; Kim, H. J.; Lee, S. C.; Li, J.; Park, H.] Kyungpook Natl Univ, Daegu 41566, South Korea; [Trabelsi, K.; Watanuki, S.] Univ Paris Saclay, IJCLab, CNRS IN2P3, F-91405 Orsay, France; [Chilikin, K.; Eidelman, S.; Matvienko, D.; Mizuk, R.; Oskin, P.; Pakhlov, P.; Pakhlova, G.; Solovieva, E.; Uglov, T.; Vorobyev, V.; Zhukova, V] Russian Acad Sci, PN Lebedev Phys Inst, Moscow 119991, Russia; [Li, C. H.] Liaoning Normal Univ, Dalian 116029, Peoples R China; [Krizan, P.; Podobnik, T.; Santelj, L.] Univ Ljubljana, Fac Math & Phys, Ljubljana 1000, Slovenia; [Kuhr, T.; Lieret, K.; Ritter, M.] Ludwig Maximilians Univ Munchen, D-80539 Munich, Germany; [Das, S.; Kumar, M.; Lalwani, K.; Sharma, C.] Malaviya Natl Inst Technol Jaipur, Jaipur 302017, Rajasthan, India; [Bracko, M.; Korpar, S.] Univ Maribor, Maribor 2000, Slovenia; [Chekelian, V; Kiesling, C.; Li Gioi, L.; Paul, S.; Simon, F.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [Ferlewicz, D.; MacQueen, C.; Sevior, M. E.; Urquijo, P.] Univ Melbourne, Sch Phys, Melbourne, Vic 3010, Australia; [Bennett, J.; Villanueva, M. Hernandez; Kroeger, R.] Univ Mississippi, University, MS 38677 USA; [MacNaughton, J.; Matsuda, T.] Univ Miyazaki, Miyazaki 8892192, Japan; [Pakhlov, P.] Moscow Phys Engn Inst, Moscow 115409, Russia; [Iijima, T.; Inami, K.; Mori, T.] Nagoya Univ, Grad Sch Sci, Nagoya, Aichi 4648602, Japan; [Iijima, T.] Nagoya Univ, Kobayashi Maskawa Inst, Nagoya, Aichi 4648602, Japan; [Campajola, M.; De Nardo, G.; Di Capua, F.; Merola, M.; Russo, G.] Univ Napoli Federico II, I-80126 Naples, Italy; [Hayashii, H.; Miyabayashi, K.] Nara Womens Univ, Nara 6308506, Japan; [Chen, A.] Natl Cent Univ, Chungli 32054, Taiwan; [Wang, C. H.] Natl United Univ, Miaoli 36003, Taiwan; [Chang, P.; Hou, W-S; Shiu, J-G; Wang, M-Z] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan; [Bozek, A.; Natkaniec, Z.; Rozanska, M.; Wiechczynski, J.] H Niewodniczanski Inst Nucl Phys, PL-31342 Krakow, Poland; [Ono, H.] Nippon Dent Univ, Niigata 9518580, Japan; [Hayasaka, K.; Ono, H.; Seino, Y.; Watanabe, M.; Yusa, Y.] Niigata Univ, Niigata 9502181, Japan; [Stanic, S.] Univ Nova Gorica, Nova Gorica 5000, Slovenia; [Eidelman, S.; Epifanov, D.; Garmash, A.; Korobov, A.; Kovalenko, E.; Krokovny, P.; Kuzmin, A.; Matvienko, D.; Usov, Y.; Vinokurova, A.; Vorobyev, V.; Zhilich, V; Zhulanov, V] Novosibirsk State Univ, Novosibirsk 630090, Russia; [Iwasaki, M.] Osaka City Univ, Osaka 5588585, Japan; [Fulsom, B. G.; Hadjivasiliou, C.; Strube, J. F.] Pacific Northwest Natl Lab, Richland, WA 99352 USA; [Garg, R.] Panjab Univ, Chandigarh 160014, India; [Li, Y. B.] Peking Univ, Beijing 100871, Peoples R China; [Pang, T.; Savinov, V; Wang, E.] Univ Pittsburgh, Pittsburgh, PA 15260 USA; [Kumar, R.] Punjab Agr Univ, Ludhiana 141004, Punjab, India; [Masuda, M.] Osaka Univ, Res Ctr Nucl Phys, Osaka 5670047, Japan; [Takizawa, M.] RIKEN, Meson Sci Lab, Cluster Pioneering Res, Wako, Saitama 3510198, Japan; [Yan, W.; Zhang, Z. P.] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China; [Yan, W.; Zhang, Z. P.] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China; [Kim, B. H.; Kim, S. H.; Moon, T. J.] Seoul Natl Univ, Seoul 08826, South Korea; [Takizawa, M.] Showa Pharmaceut Univ, Tokyo 1948543, Japan; [Xu, X.] Soochow Univ, Suzhou 215006, Peoples R China; [Kim, D. Y.] Soongsil Univ, Seoul 06978, South Korea; [Choi, Y.; Park, C. W.] Sungkyunkwan Univ, Suwon 16419, South Korea; [Hsu, C-L; Varvell, K. E.; Yabsley, B. D.] Univ Sydney, Sch Phys, Sydney, NSW 2006, Australia; [Al Said, S.; Ayad, R.] Univ Tabuk, Fac Sci, Dept Phys, Tabuk 71451, Saudi Arabia; [Mohanty, G. B.; Sahoo, D.] Tata Inst Fundamental Res, Mumbai 400005, Maharashtra, India; [Paul, S.] Tech Univ Munich, Dept Phys, D-85748 Garching, Germany; [Nayak, M.] Tel Aviv Univ, Sch Phys & Astron, IL-69978 Tel Aviv, Israel; [Ogawa, S.] Toho Univ, Funabashi, Chiba 2748510, Japan; [Sanuki, T.] Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan; [Masuda, M.] Univ Tokyo, Earthquake Res Inst, Tokyo 1130032, Japan; [Aihara, H.; Jin, Y.; Onuki, Y.] Univ Tokyo, Dept Phys, Tokyo 1130033, Japan; [Uchida, M.] Tokyo Inst Technol, Tokyo 1528550, Japan; [Sumiyoshi, T.] Tokyo Metropolitan Univ, Tokyo 1920397, Japan; [Gaur, V; Piilonen, L. E.; Stottler, Z. S.] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA; [Bonvicini, G.; Cinabro, D.; Kumara, K.; Liventsev, D.] Wayne State Univ, Detroit, MI 48202 USA; [Senyo, K.] Yamagata Univ, Yamagata 9908560, Japan; [Cho, S-J; Kim, K-H; Kim, Y-K; Kwon, Y-J; Park, S-H] Yonsei Univ, Seoul 03722, South Korea; [Pedlar, T. K.] Luther Coll, Decorah, IA 52101 USA; [Liptak, Z.] Hiroshima Univ, Higashihiroshima, Hiroshima 7398530, Japan University of Bonn; University of Basque Country; United States Department of Energy (DOE); Brookhaven National Laboratory; Russian Academy of Sciences; Budker Institute of Nuclear Physics; Charles University Prague; Chonnam National University; University System of Ohio; University of Cincinnati; Helmholtz Association; Deutsches Elektronen-Synchrotron (DESY); Duke University; Fu Jen Catholic University; Fudan University; Chinese Academy of Sciences; Fudan University; Justus Liebig University Giessen; University of Gottingen; Graduate University for Advanced Studies - Japan; Gyeongsang National University; Hanyang University; Hanyang University; University of Hawaii System; High Energy Accelerator Research Organization (KEK); High Energy Accelerator Research Organization (KEK); HSE University (National Research University Higher School of Economics); Helmholtz Association; Research Center Julich; Basque Foundation for Science; Indian Institute of Science Education & Research (IISER) - Mohali; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Hyderabad; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Indiana University System; Indiana University Bloomington; Chinese Academy of Sciences; Institute of High Energy Physics, CAS; Austrian Academy of Sciences; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Fisica Nucleare (INFN); Japan Atomic Energy Agency; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Helmholtz Association; Karlsruhe Institute of Technology; University of Tokyo; King Abdulaziz University; Kitasato University; Korea Institute of Science & Technology Information (KISTI); Korea University; Kyoto Sangyo University; Kyungpook National University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Russian Academy of Sciences; Russian Academy of Science Lebedev Physical Institute; Liaoning Normal University; University of Ljubljana; University of Munich; National Institute of Technology (NIT System); Malaviya National Institute of Technology Jaipur; University of Maribor; Max Planck Society; University of Melbourne; University of Mississippi; University of Miyazaki; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Nagoya University; Nagoya University; University of Naples Federico II; Nara Womens University; National Central University; National United University; National Taiwan University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Nippon Dental University; Niigata University; University of Nova Gorica; Novosibirsk State University; Osaka Metropolitan University; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Panjab University; Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Punjab Agricultural University; Osaka University; RIKEN; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Seoul National University (SNU); Showa Pharmaceutical University; Soochow University - China; Soongsil University; Sungkyunkwan University (SKKU); University of Sydney; University of Tabuk; Tata Institute of Fundamental Research (TIFR); Technical University of Munich; Tel Aviv University; Toho University; Tohoku University; University of Tokyo; University of Tokyo; Tokyo Institute of Technology; Tokyo Metropolitan University; Virginia Polytechnic Institute & State University; Wayne State University; Yamagata University; Yonsei University; Hiroshima University Cao, L; Bernlochner, FU (corresponding author), Univ Bonn, D-53115 Bonn, Germany. cao@physik.uni-bonn.de; fiorian.bernlochner@uni-bonn.de Cervenkov, Daniel/D-2884-2017; Natkaniec, Zbigniew/AAP-2995-2021; Pakhlova, Galina V/C-5378-2014; Uglov, Timofey V/B-2406-2014; Tamponi, Umberto/AGG-5576-2022; Popov, Vitaliy/HLW-5177-2023; Mizuk, Roman/B-3751-2014; Zhukova, Valentina/C-8878-2016; Campajola, Marcello/V-1689-2019; hou, wei/GWM-4800-2022; Liptak, Zachary John/AAR-3980-2020; Pakhlov, Pavel N/K-2158-2013; Niiyama, Masayuki/HPE-8084-2023; Solovieva, Elena/B-2449-2014; Chilikin, Kirill/B-4402-2014; Nayak, Lopamudra/HPE-4040-2023; Popov, Vitaliy/C-9925-2016; Strube, Jan/E-9310-2015; Bilka, Tadeas/Q-3680-2017 Cervenkov, Daniel/0000-0002-1865-741X; Natkaniec, Zbigniew/0000-0003-0486-9291; Pakhlova, Galina V/0000-0001-7518-3022; Uglov, Timofey V/0000-0002-4944-1830; Zhukova, Valentina/0000-0002-8253-641X; Campajola, Marcello/0000-0003-2518-7134; Liptak, Zachary John/0000-0002-6491-8131; Pakhlov, Pavel N/0000-0001-7426-4824; Solovieva, Elena/0000-0002-5735-4059; Chilikin, Kirill/0000-0001-7620-2053; Popov, Vitaliy/0000-0003-0208-2583; Vahsen, Sven/0000-0003-1685-9824; Hsu, Chia-Ling/0000-0002-1641-430X; Nayak, Lopamudra/0000-0002-7739-914X; Cao, Lu/0000-0001-8332-5668; Belous, Konstantin/0000-0003-0014-2589; Nayak, Minakshi/0000-0002-2572-4692; Russo, Guido/0000-0001-5823-4393; Dong, Thanh/0000-0003-3043-1939; Dubey, Shawn/0000-0002-1345-0970; Metzner, Felix/0000-0002-0128-264X; Cunliffe, Sam/0000-0003-0167-8641; Yabsley, Bruce/0000-0002-2680-0474; Iwasaki, Masako/0000-0002-9402-7559; Browder, Thomas E/0000-0001-7357-9007; Zilic, Viktor/0000-0002-0907-5565; Sevior, Martin/0000-0002-4824-101X; Vinokurova, Anna/0000-0003-4220-8056; Hernandez Villanueva, Michel Enrique/0000-0002-6322-5587; Simon, Frank/0000-0002-5978-0289; Bernlochner, Florian/0000-0001-8153-2719; Bozek, Andrzej/0000-0002-5915-1319; Strube, Jan/0000-0001-7470-9301; Ono, Hiroaki/0000-0003-4486-0064; tamponi, umberto/0000-0001-6651-0706; Ferber, Torben/0000-0002-6849-0427; van Tonder, Raynette/0000-0002-7448-4816; Natochii, Andrii/0000-0002-1076-814X; Sandilya, Saurabh/0000-0002-4199-4369; Lange, Jens Soren/0000-0003-0234-0474; Bilka, Tadeas/0000-0003-1449-6986; Kovalenko, Evgeniy/0000-0001-8084-1931 DFG Emmy-Noether Grant [BE 6075/1-1]; Alexander von Humboldt Foundation; Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan; Japan Society for the Promotion of Science (JSPS); Tau-Lepton Physics Research Center of Nagoya University; Australian Research Council [DP180102629, DP170102389, DP170102204, DP150103061, FT130100303]; Austrian Federal Ministry of Education, Science and Research (FWF); FWF Austrian Science Fund [P 31361-N36]; National Natural Science Foundation of China [11435013, 11475187, 11521505, 11575017, 11675166, 11705209]; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) [QYZDJ-SSW-SLH011]; CAS Center for Excellence in Particle Physics (CCEPP); Shanghai Pujiang Program [18PJ1401000]; Shanghai Science and Technology Committee (STCSM) [19ZR1403000]; Ministry of Education, Youth and Sports of the Czech Republic [LTT17020]; Horizon 2020 ERC [884719, 947006]; Carl Zeiss Foundation; Deutsche Forschungsgemeinschaft; Excellence Cluster Universe; VolkswagenStiftung; Department of Atomic Energy [RTI 4002]; Department of Science and Technology of India; Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea [2016R1D1A1B01010135, 2016R1D1A1B02012900, 2018R1A2B3003643, 2018R1A6A1A06024970, 2018R1D1A1B07047294, 2019K1A3A7A09033840, 2019R1I1A3A01058933]; Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Polish Ministry of Science and Higher Education; National Science Center; Ministry of Science and Higher Education of the Russian Federation [14.W03.31.0026]; HSE University Basic Research Program, Moscow; University of Tabuk (Saudi Arabia) [S-1440-0321, S-02561438, S-0280-1439]; Slovenian Research Agency [J1-9124, P1-0135]; Ikerbasque, Basque Foundation for Science, Spain; Swiss National Science Foundation; Ministry of Education; Ministry of Science and Technology of Taiwan; U.S. Department of Energy; National Science Foundation DFG Emmy-Noether Grant(German Research Foundation (DFG)); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Japan Society for the Promotion of Science (JSPS)(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Tau-Lepton Physics Research Center of Nagoya University; Australian Research Council(Australian Research Council); Austrian Federal Ministry of Education, Science and Research (FWF); FWF Austrian Science Fund(Austrian Science Fund (FWF)); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS); CAS Center for Excellence in Particle Physics (CCEPP); Shanghai Pujiang Program(Shanghai Pujiang Program); Shanghai Science and Technology Committee (STCSM)(Science & Technology Commission of Shanghai Municipality (STCSM)Shanghai Science & Technology Committee); Ministry of Education, Youth and Sports of the Czech Republic(Ministry of Education, Youth & Sports - Czech Republic); Horizon 2020 ERC; Carl Zeiss Foundation; Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); Excellence Cluster Universe; VolkswagenStiftung(Volkswagen); Department of Atomic Energy(Department of Atomic Energy (DAE)); Department of Science and Technology of India(Department of Science & Technology (India)); Istituto Nazionale di Fisica Nucleare of Italy(Istituto Nazionale di Fisica Nucleare (INFN)); National Research Foundation (NRF) of Korea(National Research Foundation of Korea); Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project; Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Polish Ministry of Science and Higher Education(Ministry of Science and Higher Education, Poland); National Science Center(National Science Centre, Poland); Ministry of Science and Higher Education of the Russian Federation; HSE University Basic Research Program, Moscow; University of Tabuk (Saudi Arabia); Slovenian Research Agency(Slovenian Research Agency - Slovenia); Ikerbasque, Basque Foundation for Science, Spain; Swiss National Science Foundation(Swiss National Science Foundation (SNSF)); Ministry of Education; Ministry of Science and Technology of Taiwan(Ministry of Science and Technology, Taiwan); U.S. Department of Energy(United States Department of Energy (DOE)); National Science Foundation(National Science Foundation (NSF)) We thank Kerstin Tackmann, Frank Tackmann, Zoltan Ligeti, Ian Stewart, Thomas Mannel, and Keri Voss for useful discussions about the subject matter of this manuscript. L. C., W. S., R. V. T., and F. B. were supported by the DFG Emmy-Noether Grant No. BE 6075/1-1. W. S. was supported by the Alexander von Humboldt Foundation. We thank the KEKB group for the excellent operation of the accelerator, the KEK cryogenics group for the efficient operation of the solenoid, and the KEK computer group and the Pacific Northwest National Laboratory (PNNL) Environmental Molecular Sciences Laboratory (EMSL) computing group for strong computing support, and the National Institute of Informatics and Science Information NETwork 5 (SINET5) for valuable network support. We acknowledge support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, the Japan Society for the Promotion of Science (JSPS), and the Tau-Lepton Physics Research Center of Nagoya University; the Australian Research Council including Grants No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, and No. FT130100303; Austrian Federal Ministry of Education, Science and Research (FWF) and FWF Austrian Science Fund No. P 31361-N36; the National Natural Science Foundation of China under Contracts No. 11435013, No. 11475187, No. 11521505, No. 11575017, No. 11675166, and No. 11705209; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS), Grant No. QYZDJ-SSW-SLH011; the CAS Center for Excellence in Particle Physics (CCEPP); the Shanghai Pujiang Program under Grant No. 18PJ1401000; the Shanghai Science and Technology Committee (STCSM) under Grant No. 19ZR1403000; the Ministry of Education, Youth and Sports of the Czech Republic under Contract No. LTT17020; Horizon 2020 ERC Advanced Grant No. 884719 and ERC Starting Grant No. 947006 InterLeptons (European Union); the Carl Zeiss Foundation, the Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe, and the VolkswagenStiftung; the Department of Atomic Energy (Project Identification No. RTI 4002) and the Department of Science and Technology of India; the Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea Grants No. 2016R1D1A1B01010135, No. 2016R1D1A1B02012900, No. 2018R1A2B3003643, No. 2018R1A6A1A06024970, No. 2018R1D1A1B07047294, No. 2019K1A3A7A09033840, andNo. 2019R1I1A3A01058933; Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Science and Higher Education of the Russian Federation, Agreement 14.W03.31.0026, and the HSE University Basic Research Program, Moscow; University of Tabuk research Grants No. S-1440-0321, No. S-02561438, and No. S-0280-1439 (Saudi Arabia); the Slovenian Research Agency Grants No. J1-9124 and No. P1-0135; Ikerbasque, Basque Foundation for Science, Spain; the Swiss National Science Foundation; the Ministry of Education and the Ministry of Science and Technology of Taiwan; and the U.S. Department of Energy and the National Science Foundation. 75 6 6 0 8 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2470-0010 2470-0029 PHYS REV D Phys. Rev. D JUL 12 2021.0 104.0 1 12008 10.1103/PhysRevD.104.012008 0.0 32 Astronomy & Astrophysics; Physics, Particles & Fields Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics; Physics TI4PF hybrid, Green Submitted, Green Published, Green Accepted 2023-03-23 WOS:000672781100003 0
C Jiang, ZY; Bjornholm, M; Guo, JM; Dong, WB; Ren, ZR; Verma, AS IEEE Jiang, Zhiyu; Bjornholm, Marius; Guo, Jiamin; Dong, Wenbin; Ren, Zhengru; Verma, Amrit Shankar Damage identification of a jacket support structure for offshore wind turbines PROCEEDINGS OF THE 15TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2020) IEEE Conference on Industrial Electronics and Applications English Proceedings Paper 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) NOV 09-13, 2020 ELECTR NETWORK IEEE,Univ Agder,IEEE Ind Elect Chapter Singapore,IEEE Singapore Sect,IEEE Ind Elect Soc damage identification; jacket; structural modal analysis; artificial neural network; sensor JOINTS Offshore jacket structures are regarded as a suitable type of support structure for offshore wind turbines in immediate water depths. Because of the welded tubular members used and environmental conditions, offshore jackets are often subjected to fatigue damages during their service life. Underwater sensors can provide measurements of the structural vibration signals and provide an efficient way to detect damages at early stages. In this work, simplified forms of the damages are assumed, random damages are imposed on the jacket structure, and damaged indicators are established from combination of modal shapes. Then, a response surface is constructed mapping the damage indicators and damages. Given that the efficiency of the damage identification depends on the locations of the damages and the location and number of sensor locations, a sensitivity study is performed to vary sensor location, sensor quantity, and damage severity. It is found that the effect of damage identification is better when sensor locations are closer to damage locations, and this effect is more sensitive to sensor placements when damages occur in the upper structures. Additionally, the identification effect is more sensitive to damage severity than to occurrence of multiple damages. [Jiang, Zhiyu; Bjornholm, Marius] Univ Agder, Dept Engn Sci, Grimstad, Norway; [Guo, Jiamin] Shanghai Maritime Univ, Sch Ocean Sci & Engn, Shanghai, Peoples R China; [Dong, Wenbin] DNV GL, Hovik, Norway; [Ren, Zhengru] Norwegian Univ Sci & Technol, Dept Marine Technol, Trondheim, Norway; [Verma, Amrit Shankar] Delft Univ Technol, Dept Aerosp Struct & Mat, Delft, Netherlands University of Agder; Shanghai Maritime University; DNV; Norwegian University of Science & Technology (NTNU); Delft University of Technology Jiang, ZY (corresponding author), Univ Agder, Dept Engn Sci, Grimstad, Norway. zhiyu.jiang@uia.no; mariusbjornholm@gmail.com; jmguo@shmtu.edu.cn; Wenbin.Dong@dnvgl.com; zhengru.ren@ntnu.no; A.S.Verma@tudelft.nl Ren, Zhengru/ABB-8245-2021; Jiang, Zhiyu/B-1150-2018; Ren, Zhengru/AAD-5421-2019 Jiang, Zhiyu/0000-0002-8778-0999; Ren, Zhengru/0000-0001-5522-8094 19 2 2 2 7 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2156-2318 978-1-7281-5169-4 C IND ELECT APPL 2020.0 995 1000 6 Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Engineering BR3IM Green Submitted 2023-03-23 WOS:000646627000172 0
C Baeza-Yates, R; Jiang, D; Silvestri, F; Harrison, B Assoc Comp Machinery Baeza-Yates, Ricardo; Jiang, Di; Silvestri, Fabrizio; Harrison, Beverly Predicting The Next App That You Are Going To Use WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING English Proceedings Paper 8th ACM International Conference on Web Search and Data Mining (WSDM) JAN 31-FEB 06, 2015 Shanghai, PEOPLES R CHINA Assoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Informat Retrieval,Assoc Comp Machinery SIGMOD,Assoc Comp Machinery SIGWEB,Assoc Comp Machinery SIGKDD Aviate; Prediction; Mobile App; Machine Learning Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use. [Baeza-Yates, Ricardo; Jiang, Di; Silvestri, Fabrizio] Yahoo Labs, Barcelona, Spain; [Jiang, Di] HKUST, CS & Engn Dept, Hong Kong, Peoples R China; [Harrison, Beverly] Yahoo Labs, Sunnyvale, CA USA Hong Kong University of Science & Technology Baeza-Yates, R (corresponding author), Yahoo Labs, Barcelona, Spain. rbaeza@acm.org; dijiang@cse.ust.hk; silvestr@yahoo-inc.com; beverlyh@yahoo-inc.com Baeza-Yates, Ricardo/H-5692-2015; Silvestri, Fabrizio/AAI-3561-2020 Baeza-Yates, Ricardo/0000-0003-3208-9778; Silvestri, Fabrizio/0000-0001-7669-9055 20 100 108 0 2 ASSOC COMPUTING MACHINERY NEW YORK 1515 BROADWAY, NEW YORK, NY 10036-9998 USA 978-1-4503-3317-7 2015.0 285 294 10.1145/2684822.2685302 0.0 10 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BN6JI 2023-03-23 WOS:000485486900035 0
J Pu, JJ; Hakim, SG; Melville, JC; Su, YX Pu, Jane J.; Hakim, Samer G.; Melville, James C.; Su, Yu-Xiong Current Trends in the Reconstruction and Rehabilitation of Jaw following Ablative Surgery CANCERS English Review jaw reconstruction; microvascular reconstruction; free flaps; computer-assisted surgery; patient-specific implants; virtual surgical planning; 3D printing INFERIOR ALVEOLAR NERVE; COMPUTER-ASSISTED DESIGN; MANDIBULAR RECONSTRUCTION; FREE-FLAP; MAXILLOFACIAL RECONSTRUCTION; OSTEOCUTANEOUS FLAP; SPATIAL DEVIATIONS; AUGMENTED REALITY; FIBULA; OUTCOMES Simple Summary The Maxilla and mandible provide skeletal support for of the middle and lower third of our faces, allowing for the normal functioning of breathing, chewing, swallowing, and speech. The ablative surgery of jaws in the past often led to serious disfigurement and disruption in form and function. However, with recent strides made in computer-assisted surgery and patient-specific implants, the individual functional reconstruction of the jaw is evolving rapidly and the prompt rehabilitation of both the masticatory function and aesthetics after jaw resection has been made possible. In the present review, the recent advancements in jaw reconstruction technology and future perspectives will be discussed. The reconstruction and rehabilitation of jaws following ablative surgery have been transformed in recent years by the development of computer-assisted surgery and virtual surgical planning. In this narrative literature review, we aim to discuss the current state-of-the-art jaw reconstruction, and to preview the potential future developments. The application of patient-specific implants and the jaw-in-a-day technique have made the fast restoration of jaws' function and aesthetics possible. The improved efficiency of primary reconstructive surgery allows for the rehabilitation of neurosensory function following ablative surgery. Currently, a great deal of research has been conducted on augmented/mixed reality, artificial intelligence, virtual surgical planning for soft tissue reconstruction, and the rehabilitation of the stomatognathic system. This will lead to an even more exciting future for the functional reconstruction and rehabilitation of the jaw following ablative surgery. [Pu, Jane J.; Su, Yu-Xiong] Univ Hong Kong, Fac Dent, Div Oral & Maxillofacial Surg, Hong Kong, Peoples R China; [Hakim, Samer G.] Univ Hosp Lubeck, Dept Oral & Maxillofacial Surg, Ratzeburger Allee 160, D-23538 Lubeck, Germany; [Melville, James C.] Univ Texas Hlth Sci Ctr Houston, Dept Oral & Maxillofacial Surg, Houston, TX 77030 USA University of Hong Kong; University of Lubeck; University of Texas System; University of Texas Health Science Center Houston Su, YX (corresponding author), Univ Hong Kong, Fac Dent, Div Oral & Maxillofacial Surg, Hong Kong, Peoples R China. u3503024@connect.hku.hk; samer.hakim@uni-luebeck.de; james.c.melville@uth.tmc.edu; richsu@hku.hk Su, Yu-xiong/0000-0001-7143-3370; Melville, James/0000-0002-6527-4585 HKU Platform Technology Funding (PTF) [2020/21, 102010080]; Health and Medical Research Fund [08192096]; Food and Health Bureau, Hong Kong HKU Platform Technology Funding (PTF); Health and Medical Research Fund; Food and Health Bureau, Hong Kong This work is supported by HKU Platform Technology Funding (PTF) 2020/21 (No. 102010080), and Health and Medical Research Fund (Project no.: 08192096), Food and Health Bureau, Hong Kong. 82 0 0 7 8 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-6694 CANCERS Cancers JUL 2022.0 14.0 14 3308 10.3390/cancers14143308 0.0 16 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 3H9UK 35884369.0 gold, Green Accepted 2023-03-23 WOS:000832373200001 0
C Yeung, PH; Aliasi, M; Haak, M; Xie, WD; Namburete, AIL Wang, L; Dou, Q; Fletcher, PT; Speidel, S; Li, S Yeung, Pak-Hei; Aliasi, Moska; Haak, Monique; Xie, Weidi; Namburete, Ana I. L. Intergrowth 21St Consortium Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV Lecture Notes in Computer Science English Proceedings Paper 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) SEP 18-22, 2022 Singapore, SINGAPORE MICCAI Soc Freehand ultrasound; Slice to volume registration; Domain adaptation Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations in the 3D anatomical atlas. Next, we fine-tune it with 2D freehand ultrasound images using a novel unsupervised cycle consistency, which utilizes the fact that the overall displacement of a sequence of images in the 3D anatomical atlas is equal to the displacement from the first image to the last in that sequence. We demonstrate that AdLocUI can adapt to three different ultrasound datasets, acquired with different machines and protocols, and achieves significantly better localization accuracy than the baselines. AdLocUI can be used for sensorless 2D freehand ultrasound guidance by the bedside. The source code is available at https://github.com/pakheiyeung/AdLocUI. [Yeung, Pak-Hei] Univ Oxford, Oxford Machine Learning NeuroImaging Lab, Oxford, England; [Yeung, Pak-Hei; Namburete, Ana I. L.] Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England; [Aliasi, Moska; Haak, Monique] Leiden Univ, Med Ctr, Dept Obstet, Div Fetal Med, NL-2333 ZA Leiden, Netherlands; [Intergrowth 21St Consortium] Univ Oxford, Nuffield Dept Womens & Reprod Hlth, Oxford, England; [Xie, Weidi] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Xie, Weidi] Univ Oxford, Dept Engn Sci, Visual Geometry Grp, Oxford, England University of Oxford; University of Oxford; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; University of Oxford; Shanghai Jiao Tong University; University of Oxford Yeung, PH (corresponding author), Univ Oxford, Oxford Machine Learning NeuroImaging Lab, Oxford, England.;Yeung, PH (corresponding author), Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England. pak.yeung@pmb.ox.ac.uk Yeung, Pak Hei/GZG-5790-2022 RC Lee Centenary Scholarship; EPSRC Programme Grant Visual AI [EP/T028572/1]; UK Royal Academy of Engineering under its Engineering for Development Research Fellowship scheme; Academy of Medical Sciences RC Lee Centenary Scholarship; EPSRC Programme Grant Visual AI(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); UK Royal Academy of Engineering under its Engineering for Development Research Fellowship scheme; Academy of Medical Sciences(Academy of Medical Sciences (AMS)) PH. Yeung is grateful for support from the RC Lee Centenary Scholarship. W. Xie is supported by the EPSRC Programme Grant Visual AI (EP/T028572/1). A. Namburete is funded by the UK Royal Academy of Engineering under its Engineering for Development Research Fellowship scheme and the Academy of Medical Sciences. We thank Linde Hesse, Madeleine Wyburd and Nicola Dinsdale for their valuable suggestions and comments about the work. 25 0 0 3 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-031-16440-8; 978-3-031-16439-2 LECT NOTES COMPUT SC 2022.0 13434.0 207 217 10.1007/978-3-031-16440-8_20 0.0 11 Computer Science, Interdisciplinary Applications; Radiology, Nuclear Medicine & Medical Imaging Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Radiology, Nuclear Medicine & Medical Imaging BU0DG Green Submitted 2023-03-23 WOS:000867306400020 0
J Pacheco, F; de Oliveira, JV; Sanchez, RV; Cerrada, M; Cabrera, D; Li, C; Zurita, G; Artes, M Pacheco, Fannia; Valente de Oliveira, Jose; Sanchez, Rene-Vinicio; Cerrada, Mariela; Cabrera, Diego; Li, Chuan; Zurita, Grover; Artes, Mariano A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions NEUROCOMPUTING English Article Neural networks; Statistic tests; Classification; Fault diagnosis; Feature selection; Gearbox LINEAR DISCRIMINANT-ANALYSIS; NEURAL-NETWORK; SYSTEMS; TESTS; INTELLIGENCE; DESIGN Gearboxes are crucial devices in rotating power transmission systems with applications in a variety of industries. Gearbox faults can cause catastrophic physical consequences, long equipment downtimes, and severe production costs. Several artificial neural networks, learning algorithms, and feature selection methods have been used in the diagnosis of the gearbox healthy state. Given a specific gearbox, this study investigates how these approaches compare with each other in terms of the typical fault classification accuracy but also in terms of the area under curve (AUC), where the curve refers to the precision recall curve otherwise known as receiver operating characteristic (ROC) curve. In particular, the comparison aims at identifying whether there are statistically significant (dis)similarities among six feature selection methods, and seven pairs of neural nets with different learning rules. Genetic algorithm based, entropy based, linear discriminants, principal components, most neighbors first, and non-negative matrix factorization are the studied feature selection methods. Feed forward perceptrons, cascade forward, probabilistic nets, and radial basis function neural nets are evaluated. Six supervised and one unsupervised learning rules are considered. Both parametric and nonparametric statistical tests are employed. A ranking process is defined to elect the best approach, when available. An experimental setup was especially prepared to ensure operating conditions as realistic as possible. (C) 2016 Elsevier B.V. All rights reserved. [Pacheco, Fannia] Univ Politecn Salesiana, GIDTEC Mech Engn Dept, Cuenca, Ecuador; [Cerrada, Mariela] Univ Los Andes, Control Syst Dept, Merida, Venezuela; [Valente de Oliveira, Jose] Univ Algrave, CEOT, Faro, Portugal; [Li, Chuan] Chongqing Technol & Business Univ, Chongqing Key Lab Mfg Equipment Mech Design & Con, Chongqing, Peoples R China; [Valente de Oliveira, Jose; Sanchez, Rene-Vinicio; Cerrada, Mariela; Cabrera, Diego; Li, Chuan; Zurita, Grover] Univ Politecn Salesiana, Dept Mech Engn, Cuenca, Ecuador; [Artes, Mariano] UNED, Dept Mech, Madrid, Spain Universidad Politecnica Salesiana; University of Los Andes Venezuela; Chongqing Technology & Business University; Universidad Politecnica Salesiana; Universidad Nacional de Educacion a Distancia (UNED) Pacheco, F (corresponding author), Univ Politecn Salesiana, GIDTEC Mech Engn Dept, Cuenca, Ecuador. fannikaro@gmail.com; rsanchezl@ups.edu.ec; cerradam@ula.ve; dcabrera@ups.edu.ec; chuanli@21cn.com; gzuritav@ups.edu.ec Pacheco, Fannia/AAQ-9953-2020; Valente de Oliveira, José/B-6426-2008; Cerrada, Mariela/AFL-9564-2022; L., René Vinicio Sánchez/O-5259-2019; Cabrera, Diego/I-8837-2019 Pacheco, Fannia/0000-0001-6997-0222; Valente de Oliveira, José/0000-0001-5337-5699; Cerrada, Mariela/0000-0003-4379-8836; L., René Vinicio Sánchez/0000-0003-0395-9228; Cabrera, Diego/0000-0003-1023-871X; Zurita, Grover/0000-0003-1338-0092; Li, Chuan/0000-0003-0004-1497 GIDTEC project Desarrollo de una herramienta computacional basada en modelos de computacion inteligente para el monitoreo en maquinaria rotativa [017-007-2015-11-05]; Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador GIDTEC project Desarrollo de una herramienta computacional basada en modelos de computacion inteligente para el monitoreo en maquinaria rotativa; Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador The work was sponsored in part by the GIDTEC project called Desarrollo de una herramienta computacional basada en modelos de computacion inteligente para el monitoreo en maquinaria rotativa No. 017-007-2015-11-05, and the Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador. The experimental work was developed at the GIDTEC research group lab of the Universidad Politecnica Salesiana de Cuenca, Ecuador. 42 43 44 1 42 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUN 19 2016.0 194.0 192 206 10.1016/j.neucom.2016.02.028 0.0 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science DM7NU 2023-03-23 WOS:000376548100020 0
J Qiu, YQ; Tian, H; Tang, LL; Mazurczyk, W; Chang, CC Qiu, Yiqin; Tian, Hui; Tang, Lili; Mazurczyk, Wojciech; Chang, Chin-Chen Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords JOURNAL OF INFORMATION SECURITY AND APPLICATIONS English Article Steganalysis; Steganography; Deep neural network; Adaptive multi-rate speech; Distributed representations QUANTIZATION INDEX MODULATION; STEGANOGRAPHY; VOICE; SCHEME With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones. [Qiu, Yiqin; Tian, Hui; Tang, Lili] Huaqiao Univ, Sch Comp Sci & Technol, Xiamen 361021, Peoples R China; [Qiu, Yiqin; Tian, Hui; Tang, Lili] Huaqiao Univ, Xiamen Key Lab Data Secur & Blockchain Technol, Xiamen 361021, Peoples R China; [Qiu, Yiqin; Tian, Hui; Tang, Lili] Huaqiao Univ, Fujian Key Lab Big Data Intelligence & Secur, Xiamen 361021, Peoples R China; [Mazurczyk, Wojciech] Warsaw Univ Technol, Inst Comp Sci, Nowowiejska 15-19, PL-00665 Warsaw, Poland; [Chang, Chin-Chen] Feng Chia Univ, Dept Informat Engn & Comp Sci, Taichung 40724, Taiwan Huaqiao University; Huaqiao University; Huaqiao University; Warsaw University of Technology; Feng Chia University Tian, H (corresponding author), Huaqiao Univ, Sch Comp Sci & Technol, Xiamen 361021, Peoples R China. htian@hqu.edu.cn Qiu, Yiqin/0000-0001-6653-1734; , Tang Lili/0000-0003-0486-6322 National Natural Science Foundation of China [61972168] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61972168. 38 0 0 4 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2214-2126 2214-2134 J INF SECUR APPL J. Inf. Secur. Appl. AUG 2022.0 68.0 103250 10.1016/j.jisa.2022.103250 0.0 JUL 2022 11 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 5L3UE 2023-03-23 WOS:000870339800001 0
J Cui, YK; Long, D; Hong, Y; Zeng, C; Zhou, J; Han, ZY; Liu, RH; Wan, W Cui, Yaokui; Long, Di; Hong, Yang; Zeng, Chao; Zhou, Jie; Han, Zhongying; Liu, Ronghua; Wan, Wei Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau JOURNAL OF HYDROLOGY English Article FY-3B/MWRI; Soil moisture; Validation; Reconstruction; Tibetan Plateau SURFACE-TEMPERATURE; SATELLITE; MODEL; SIMULATIONS; ASSIMILATION; RETRIEVALS; VEGETATION; RESOLUTION; RECORDS; BASIN Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth's 'third pole'. Large-scale spatially consistent and temporally continuous soil moisture data sets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1 degrees x 1 degrees and 0.25 degrees x 0.25 degrees), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil, moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R-2 higher than 0.56, RMSE less than 0.1 cm(3) cm(-3), and Bias less than 0.07 cm(3) cm(-3) for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP. (C) 2016 Elsevier B.V. All rights reserved. [Cui, Yaokui; Long, Di; Hong, Yang; Zeng, Chao; Han, Zhongying; Wan, Wei] Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China; [Hong, Yang] Univ Oklahoma, Dept Civil Engn & Environm Sci, Norman, OK 73019 USA; [Zhou, Jie] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Zhou, Jie] Delft Univ Technol, Delft, Netherlands; [Liu, Ronghua] China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China Tsinghua University; University of Oklahoma System; University of Oklahoma - Norman; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS; Delft University of Technology; China Institute of Water Resources & Hydropower Research Long, D; Hong, Y (corresponding author), Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China. dlong@tsinghua.edu.cn; hongyang@tsinghua.edu.cn Zou, Pengfei/V-5059-2019; Cui, Yaokui/ABB-2268-2021; Wan, Wei/HMU-8460-2023; Zhou, Jie/G-6760-2011; Hong, Yang/D-5132-2009; Long, Di/N-8049-2013 Zhou, Jie/0000-0002-1199-4261; Hong, Yang/0000-0001-8720-242X; Long, Di/0000-0001-9033-5039 National Natural Science Foundation of China [91437214, 91547210, 51579128]; foundation of the State Key Laboratory of Hydroscience and Engineering of Tsinghua University [2016-KY-03] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); foundation of the State Key Laboratory of Hydroscience and Engineering of Tsinghua University This study was financially supported by the National Natural Science Foundation of China (Grant No. 91437214, 91547210 and 51579128) and the foundation of the State Key Laboratory of Hydroscience and Engineering of Tsinghua University under Grant 2016-KY-03. We are grateful to Prof. Kun Yang for his kindly providing the in situ soil moisture measurements. We also thank the National Snow and Ice Data Center (NSIDC) and National Satellite Meteorological Center (NSMC) for providing the remote sensing products. Reviewers and editors' comments that were helpful for improving this paper are highly appreciated. 47 63 67 9 100 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. DEC 2016.0 543.0 B 242 254 10.1016/j.jhydrol.2016.10.005 0.0 13 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Engineering; Geology; Water Resources EG0PY 2023-03-23 WOS:000390735900005 0
J Chatzidiakou, L; Krause, A; Kellaway, M; Han, YQ; Li, YL; Martin, E; Kelly, FJ; Zhu, T; Barratt, B; Jones, RL Chatzidiakou, Lia; Krause, Anika; Kellaway, Mike; Han, Yiqun; Li, Yilin; Martin, Elizabeth; Kelly, Frank J.; Zhu, Tong; Barratt, Benjamin; Jones, Roderic L. Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution ENVIRONMENTAL HEALTH English Article Portable sensor technologies; Multi-pollutant personal exposure; Automated time-activity classification LOW-COST; GPS; QUALITY; SENSORS; URBAN Background: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. Methods: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. Results: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score > 0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score > 0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. Conclusions: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies. [Chatzidiakou, Lia; Krause, Anika; Li, Yilin; Martin, Elizabeth; Jones, Roderic L.] Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England; [Krause, Anika] Univ Potsdam, Inst Chem, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany; [Kellaway, Mike] Atmospher Sensors Ltd, Bedford SG19 3SH, Beds, England; [Han, Yiqun; Kelly, Frank J.; Barratt, Benjamin] Imperial Coll London, MRC Ctr Environm & Hlth, Environm Res Grp, London W12 0BZ, England; [Zhu, Tong] Peking Univ, Coll Environm Sci & Engn, Ctr Environm & Hlth, BIC ESAT & SKL ESPC, Beijing 100871, Peoples R China University of Cambridge; University of Potsdam; Imperial College London; Peking University Chatzidiakou, L (corresponding author), Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England. ec571@cam.ac.uk ZHU, TONG/H-6501-2011 ZHU, TONG/0000-0002-2752-7924 Medical Research Council of the UK, COPE project; MRC-PHE Centre for Environment and Health [MR\L019744\1]; [MR\L01341X\1] Medical Research Council of the UK, COPE project(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); MRC-PHE Centre for Environment and Health(UK Research & Innovation (UKRI)Medical Research Council UK (MRC)); This research has been supported by the Medical Research Council of the UK (MR\L019744\1, COPE project) and MRC-PHE Centre for Environment and Health (MR\L01341X\1). 65 0 0 3 3 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1476-069X ENVIRON HEALTH-GLOB Environ. Health DEC 9 2022.0 21.0 1 125 10.1186/s12940-022-00939-8 0.0 21 Environmental Sciences; Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Public, Environmental & Occupational Health 6W8HQ 36482402.0 Green Submitted, Green Published, gold, Green Accepted 2023-03-23 WOS:000895967900002 0
J Yu, XX; Yan, Z; Zhang, R Yu, Xixun; Yan, Zheng; Zhang, Rui Verifiable outsourced computation over encrypted data INFORMATION SCIENCES English Article Outsourced computation; Verifiability; Privacy preservation; Fully homomorphic encryption; Polynomial factorization algorithm In recent years, cloud computing has become the most popular and promising service platform. A cloud user can outsource its heavy computation overhead to a cloud service provider (CSP) and let the CSP make the computation instead. In order to guarantee the correctness of the outsourced processing (e.g., machine learning and data mining), a proof should be provided by the CSP in order to make sure that the processing is carried out properly. On the other hand, from the security and privacy points of view, users will always encrypt their sensitive data first before they are outsourced to the CSP rather than sending the raw data directly. However, processing and verifying of encrypted data computation has always been a challenging problem. Homomorphic Encryption (HE) has been proposed to tackle this task on computations over encrypted data and ensure the confidentiality of the data. However, original HE cannot provide an efficient approach to verify the correctness of computation over encrypted data that is processed by CSP. In this paper, we propose a verifiable outsourced computation scheme over encrypted data with the help of fully homomorphic encryption and polynomial factorization algorithm. Our scheme protects user data security in outsourced processing and allows public verification on the computation result processed by CSP with zero knowledge. We then prove the security of our scheme and analyze its performance by comparing it with some latest related works. Performances analysis shows that our scheme reduces the overload of both the cloud users and the verifier. (C) 2018 Elsevier Inc. All rights reserved. [Yu, Xixun; Yan, Zheng] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; [Yan, Zheng] Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland; [Zhang, Rui] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA Xidian University; Aalto University; University of Delaware Yan, Z (corresponding author), Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China.;Yan, Z (corresponding author), Aalto Univ, Dept Commun & Networking, Espoo 02150, Finland. gabrielyu@126.com; zyan@xidian.edu.cn; ruizhang@udel.edu yang, zheng/HGC-7753-2022 Yan, Zheng/0000-0002-9697-2108 NSFC [61672410, U1536202]; Academy of Finland [308087]; Natural Science Basic Research Plan in Shaanxi Province of China [2016ZDJC-06]; Key Lab of Information Network Security, Ministry of Public Security [C18614]; 111 project [B08038, B16037]; China Scholarship Council NSFC(National Natural Science Foundation of China (NSFC)); Academy of Finland(Academy of Finland); Natural Science Basic Research Plan in Shaanxi Province of China; Key Lab of Information Network Security, Ministry of Public Security; 111 project(Ministry of Education, China - 111 Project); China Scholarship Council(China Scholarship Council) This work is sponsored by NSFC (grants 61672410 and U1536202), Academy of Finland (grant No. 308087), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), the Key Lab of Information Network Security, Ministry of Public Security (grant No. C18614) and the 111 project (grants B08038 and B16037), as well as the China Scholarship Council for two years study at University of Delaware. 44 18 19 0 24 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. APR 2019.0 479.0 372 385 10.1016/j.ins.2018.12.022 0.0 14 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science HK8HE Green Submitted 2023-03-23 WOS:000458229200023 0
J Ngabo, D; Wang, D; Iwendi, C; Anajemba, JH; Ajao, LA; Biamba, C Ngabo, Desire; Wang, Dong; Iwendi, Celestine; Anajemba, Joseph Henry; Ajao, Lukman Adewale; Biamba, Cresantus Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things ELECTRONICS English Article blockchain; cloud of things; fog layer; medical data; security mechanism; privacy; elliptic curve cryptography; distributed ledger; fog and edge computing FRAMEWORK; SYSTEM; IOT The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model. [Ngabo, Desire; Wang, Dong] Hunan Univ, Dept Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China; [Ngabo, Desire] Univ Rwanda, African Ctr Excellence Internet Things, POB 3900, Kigali, Rwanda; [Iwendi, Celestine] Univ Bolton, Sch Creat Technol, Bolton BL3 5AB, England; [Iwendi, Celestine] Coal City Univ Enugu, Dept Math & Comp Sci, Enugu 400231, Nigeria; [Anajemba, Joseph Henry] Hohai Univ, Coll Internet Things, Dept Commun Engn, Changzhou 230001, Peoples R China; [Ajao, Lukman Adewale] Fed Univ Technol, Dept Comp Engn, Minna 92011, Nigeria; [Biamba, Cresantus] Univ Gavle, Fac Educ & Business Studies, S-80176 Gavle, Sweden Hunan University; University of Rwanda; University of Bolton; Hohai University; University of Gavle Wang, D (corresponding author), Hunan Univ, Dept Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China.;Biamba, C (corresponding author), Univ Gavle, Fac Educ & Business Studies, S-80176 Gavle, Sweden. dngabo@hnu.edu.cn; wangd@hnu.edu.cn; celestine.iwendi@ieee.org; herinopallazo@ieee.org; ajao.wale@futminna.edu.ng; cresantus.biamba@hig.se Iwendi, Celestine/I-1989-2018; Ajao, Lukman Adewale/Q-1089-2018 Iwendi, Celestine/0000-0003-4350-3911; Desire, Ngabo/0000-0003-1958-6956; Biamba, Cresantus/0000-0003-3571-0347; Ajao, Lukman Adewale/0000-0003-1255-752X; Anajemba, Joseph Henry Arinze/0000-0002-1199-7446 National Natural Science Foundation of China [61301148, 61272061]; Fundamental Research Funds for the Central Research Funds for the Doctoral Program of Higher Education of China [201301-61110002, 20120161120019]; Hunan Natural Science Foundation of China [14- JJ7023]; University of Gavle, Sweden National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Research Funds for the Doctoral Program of Higher Education of China; Hunan Natural Science Foundation of China(Natural Science Foundation of Hunan Province); University of Gavle, Sweden This work was supported in part by the National Natural Science Foundation of China (Grants Nos.61301148 and 61272061), Fundamental Research Funds for the Central Research Funds for the Doctoral Program of Higher Education of China (Nos. 201301-61110002 & 20120161120019), the Hunan Natural Science Foundation of China (No.14- JJ7023) and University of Gavle, Sweden. 37 14 14 5 18 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2079-9292 ELECTRONICS-SWITZ Electronics SEP 2021.0 10.0 17 2110 10.3390/electronics10172110 0.0 17 Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Physics UP7LH Green Published, gold 2023-03-23 WOS:000695557300001 0
J Wang, XQ; Huang, WJ; Su, L; Xing, Y; Jessen, F; Sun, Y; Shu, N; Han, Y Wang, Xiaoqi; Huang, Weijie; Su, Li; Xing, Yue; Jessen, Frank; Sun, Yu; Shu, Ni; Han, Ying Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease MOLECULAR NEURODEGENERATION English Review subjective cognitive decline; Alzheimer's disease; neuroimaging; multimodal MRI; PET CEREBRAL-BLOOD-FLOW; MEDIAL TEMPORAL-LOBE; DEFAULT-MODE NETWORK; APOE EPSILON-4 CARRIERS; WHITE-MATTER INTEGRITY; NORMAL OLDER-ADULTS; MIDDLE-AGED ADULTS; MEMORY COMPLAINTS; AMYLOID-BETA; FUNCTIONAL CONNECTIVITY Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer's disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and theApolipoprotein E(ApoE) e4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility. [Wang, Xiaoqi; Sun, Yu; Han, Ying] Capital Med Univ, Xuanwu Hosp, Dept Neurol, Beijing 100053, Peoples R China; [Wang, Xiaoqi; Sun, Yu; Han, Ying] Beijing Inst Brain Disorders, Ctr Alzheimers Dis, Beijing, Peoples R China; [Huang, Weijie; Shu, Ni] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China; [Huang, Weijie; Shu, Ni] Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China; [Huang, Weijie; Shu, Ni] Beijing Normal Univ, Ctr Collaborat & Innovat Brain & Learning Sci, Beijing, Peoples R China; [Huang, Weijie; Shu, Ni] Beijing Normal Univ, Beijing Key Lab Brain Imaging & Connect, Beijing, Peoples R China; [Su, Li] Univ Cambridge, Dept Psychiat, Cambridge, England; [Su, Li] Southwest Univ, Sino Britain Ctr Cognit & Ageing Res, Chongqing, Peoples R China; [Xing, Yue] Univ Nottingham, Div Clin Neurosci, Radiol Sci, Nottingham, England; [Jessen, Frank] Univ Cologne, Fac Med, Dept Psychiat & Psychotherapy, D-50937 Cologne, Germany; [Jessen, Frank] German Ctr Neurodegenerat Dis DZNE, Bonn, Germany; [Jessen, Frank] Univ Cologne, Excellence Cluster Cellular Stress Responses Agin, Cologne, Germany; [Han, Ying] Natl Clin Res Ctr Geriatr Disorders, Beijing, Peoples R China Capital Medical University; Beijing Normal University; Beijing Normal University; Beijing Normal University; Beijing Normal University; University of Cambridge; Southwest University - China; University of Nottingham; University of Cologne; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE); University of Cologne Sun, Y; Han, Y (corresponding author), Capital Med Univ, Xuanwu Hosp, Dept Neurol, Beijing 100053, Peoples R China.;Sun, Y; Han, Y (corresponding author), Beijing Inst Brain Disorders, Ctr Alzheimers Dis, Beijing, Peoples R China.;Shu, N (corresponding author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.;Shu, N (corresponding author), Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China.;Shu, N (corresponding author), Beijing Normal Univ, Ctr Collaborat & Innovat Brain & Learning Sci, Beijing, Peoples R China.;Shu, N (corresponding author), Beijing Normal Univ, Beijing Key Lab Brain Imaging & Connect, Beijing, Peoples R China.;Han, Y (corresponding author), Natl Clin Res Ctr Geriatr Disorders, Beijing, Peoples R China. wuyou91@126.com; nshu@bnu.edu.cn; hanying@xwh.ccmu.edu.cn Su, Li/0000-0002-6347-3986; huang, weijie/0000-0002-2481-1188; Han, Ying/0000-0003-0377-7424 National Key Research and Development Program of China [2016YFC1306300, 2018YFC1312001]; National Natural Science Foundation of China [61633018, 81671761, 81871425]; Beijing Municipal Commission of Health and Family Planning [PXM2020_026283_000002]; Beijing Nature Science Foundation [7161009]; Fundamental Research Funds for the Central University [2017XTCX04] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Commission of Health and Family Planning; Beijing Nature Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central University This work was supported by the National Key Research and Development Program of China (2016YFC1306300, 2018YFC1312001), National Natural Science Foundation of China (61633018, 81671761, 81871425), Beijing Municipal Commission of Health and Family Planning (PXM2020_026283_000002), Beijing Nature Science Foundation (7161009) and Fundamental Research Funds for the Central University (2017XTCX04). 207 60 63 7 54 BMC LONDON CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 1750-1326 MOL NEURODEGENER Mol. Neurodegener. SEP 22 2020.0 15.0 1 55 10.1186/s13024-020-00395-3 0.0 27 Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology NV4DY 32962744.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000574275700001 0
J Ni, Q; Fan, Z; Zhang, L; Zhang, B; Zheng, XC; Zhang, YP Ni, Qin; Fan, Zhuo; Zhang, Lei; Zhang, Bo; Zheng, Xiaochen; Zhang, Yuping Daily Activity Recognition and Tremor Quantification from Accelerometer Data for Patients with Essential Tremor Using Stacked Denoising Autoencoders INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS English Article Essential tremor; Activity recognition; Stacked denoising autoencoder; Acceleration data; Deep learning ALGORITHM; MOVEMENT; DISORDER; MOBILE Human activity recognition (HAR) has received more and more attention, which is able to play an important role in many fields, such as healthcare and intelligent home. Thus, we have discussed an application of activity recognition in the healthcare field in this paper. Essential tremor (ET) is a common neurological disorder that can make people with this disease rise involuntary tremor. Nowadays, the disease is easy to be misdiagnosed as other diseases. We have combined the essential tremor and activity recognition to recognize ET patients' activities and evaluate the degree of ET for providing an auxiliary analysis toward disease diagnosis by utilizing stacked denoising autoencoder (SDAE) model. Meanwhile, it is difficult for model to learn enough useful features due to the small behavior dataset from ET patients. Thus, resampling techniques are proposed to alleviate small sample size and imbalanced samples problems. In our experiment, 20 patients with ET and 5 healthy people have been chosen to collect their acceleration data for activity recognition. The experimental results show the significant result on ET patients activity recognition and the SDAE model has achieved an overall accuracy of 93.33%. What's more, this model is also used to evaluate the degree of ET and has achieved the accuracy of 95.74%. According to a set of experiments, the model we used is able to acquire significant performance on ET patients activity recognition and degree of tremor assessment. [Ni, Qin; Fan, Zhuo; Zhang, Bo; Zhang, Yuping] Shanghai Normal Univ, Coll Informat Mech & Elect Engn, 100 Haisi Rd, Shanghai 201418, Peoples R China; [Zhang, Lei] Donghua Univ, Coll Informat Sci & Technol, 2999 North Renmin Rd, Shanghai 201620, Peoples R China; [Zheng, Xiaochen] Univ Politecn Madrid, Escuela Tecn Super Ingn Ind, Madrid, Spain Shanghai Normal University; Donghua University; Universidad Politecnica de Madrid Zhang, L (corresponding author), Donghua Univ, Coll Informat Sci & Technol, 2999 North Renmin Rd, Shanghai 201620, Peoples R China. niqin@shnu.edu.cn; 1000459457@smail.shnu.edu.cn; lei.zhang@dhu.edu.cn; zhangbo@shnu.edu.cn; xiaochen.zheng@alumnos.upm.es; yp_zhang@shnu.edu.cn ZHANG, YUTING/HOH-4131-2023; zhang, yu/HNS-5948-2023; Zhang, Lei/GRS-4023-2022; Zheng, Xiaochen/ABZ-1133-2022 National Natural Science Foundation of China [6210020445, 61901104]; Natural Science Foundation of Shanghai [21ZR1446900]; Shanghai Sailing Program [19YF1436800] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai); Shanghai Sailing Program This work was sponsored by the National Natural Science Foundation of China under Grant 6210020445, 61901104, Natural Science Foundation of Shanghai under Grant 21ZR1446900 and Shanghai Sailing Program under Grant 19YF1436800. 44 1 1 5 7 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 1875-6891 1875-6883 INT J COMPUT INT SYS Int. J. Comput. Intell. Syst. JAN 3 2022.0 15.0 1 1 10.1007/s44196-021-00052-7 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science ZN2IQ gold 2023-03-23 WOS:000764864700001 0
J Feng, SZ; Han, X; Ma, ZJ; Krolczyk, G; Li, ZX Feng, S. Z.; Han, X.; Ma, Z. J.; Krolczyk, Grzegorz; Li, Z. X. Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING English Article Data-driven learning; Fatigue crack propagation; XFEM; Stochastic model parameters FINITE-ELEMENT-METHOD; LAMINATED COMPOSITE PLATE; COVER MESHLESS METHOD; CRACK-GROWTH; FRACTURE-ANALYSIS; NUMERICAL-SIMULATION; STATIC FRACTURE; XFEM; PROPAGATION; STATIONARY Fatigue crack growth analysis using extended finite element method (XFEM) is an efficient way to predict the residual life of structures; however, when the structure parameters vary stochastically, it will be very hard to make accurate predictions. To bridge this research gap, this work proposed a data-driven learning algorithm to improve the prediction capacity of fatigue life by considering stochastic parameters of structures. In this new algorithm, the XFEM was firstly employed to generate a large amount of datasets that pair the structural responses with remaining fatigue life. Then, the back propagation neural network (BPNN) was employed to construct a fatigue life prediction model based on the XFEM datasets. Real-time prediction for the structural fatigue life was achieved using the constructed BPNN model without knowing the exact distribution functions of stochastic parameters. Several numerical examples were performed to evaluate the performance of the proposed algorithm. The analysis results demonstrate that the proposed data-driven algorithm can accurately predict the fatigue life of the structures with stochastic parameters. (c) 2020 Elsevier B.V. All rights reserved. [Feng, S. Z.; Han, X.] Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China; [Feng, S. Z.; Han, X.] Hebei Univ Technol, Sch Mech Engn, Tianjin 300130, Peoples R China; [Ma, Z. J.; Li, Z. X.] Univ Wollongong, Sch Mech Mat Mech & Biomed Engn, Wollongong, NSW 2522, Australia; [Krolczyk, Grzegorz] Opole Univ Technol, Dept Mfg Engn & Automat Prod, PL-45758 Opole, Poland; [Li, Z. X.] Ocean Univ China, Sch Engn, Qingdao 266100, Peoples R China Hebei University of Technology; Hebei University of Technology; University of Wollongong; Opole University of Technology; Ocean University of China Han, X (corresponding author), Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China.;Han, X (corresponding author), Hebei Univ Technol, Sch Mech Engn, Tianjin 300130, Peoples R China.;Li, ZX (corresponding author), Univ Wollongong, Sch Mech Mat Mech & Biomed Engn, Wollongong, NSW 2522, Australia.;Li, ZX (corresponding author), Ocean Univ China, Sch Engn, Qingdao 266100, Peoples R China. hanxu@hnu.edu.cn; zhixiong_li@uow.edu.au Krolczyk, Grzegorz/D-6709-2013; Ma, Zhenjun/M-5878-2014; Li, Zhixiong/G-8418-2018 Krolczyk, Grzegorz/0000-0002-2967-1719; Ma, Zhenjun/0000-0001-5996-7206; Li, Zhixiong/0000-0002-7265-0008; Han, Xu/0000-0002-8967-4372 Natural Science Foundation of Hebei Province of China [A2020202017]; Youth Foundation of Hebei Education Department [QN2020211]; Major Scientific and Technological Innovation Project of Shandong Province of China [2019JZZY010820]; Foundation Strengthening Program [2019-JCJQ-00] Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Youth Foundation of Hebei Education Department; Major Scientific and Technological Innovation Project of Shandong Province of China; Foundation Strengthening Program This work is supported by the Natural Science Foundation of Hebei Province of China (A2020202017), Youth Foundation of Hebei Education Department (QN2020211), Major Scientific and Technological Innovation Project of Shandong Province of China (No. 2019JZZY010820) and Foundation Strengthening Program (No: 2019-JCJQ-00). 55 20 21 3 41 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0045-7825 1879-2138 COMPUT METHOD APPL M Comput. Meth. Appl. Mech. Eng. DEC 1 2020.0 372.0 113373 10.1016/j.cma.2020.113373 0.0 15 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications; Mechanics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics; Mechanics OV9SA 2023-03-23 WOS:000592539200005 0
J Fang, HH; Li, F; Fu, HZ; Sun, X; Cao, XX; Lin, FB; Son, JM; Kim, SH; Quellec, G; Matta, S; Shankaranarayana, SM; Chen, YT; Wang, CH; Shah, NA; Lee, CY; Hsu, CC; Xie, H; Lei, BY; Baid, U; Innani, S; Dang, K; Shi, WX; Kamble, R; Singhal, N; Wang, CW; Lo, SC; Orlando, JI; Bogunovic, H; Zhang, XL; Xu, YW Fang, Huihui; Li, Fei; Fu, Huazhu; Sun, Xu; Cao, Xingxing; Lin, Fengbin; Son, Jaemin; Kim, Sunho; Quellec, Gwenole; Matta, Sarah; Shankaranarayana, Sharath M.; Chen, Yi-Ting; Wang, Chuen-Heng; Shah, Nisarg A.; Lee, Chia-Yen; Hsu, Chih-Chung; Xie, Hai; Lei, Baiying; Baid, Ujjwal; Innani, Shubham; Dang, Kang; Shi, Wenxiu; Kamble, Ravi; Singhal, Nitin; Wang, Ching-Wei; Lo, Shih-Chang; Orlando, Jose Ignacio; Bogunovic, Hrvoje; Zhang, Xiulan; Xu, Yanwu iChallenge-AMD Study Grp ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images IEEE TRANSACTIONS ON MEDICAL IMAGING English Article Lesions; Image segmentation; Biomedical optical imaging; Optical imaging; Retina; Diseases; Task analysis; AMD detection; optic disc segmentation; fovea localization; lesion segmentation FOVEA LOCALIZATION; OPTIC DISC; SEGMENTATION; NETWORK; CLASSIFICATION; RELIABILITY; ENSEMBLE; LESIONS; CUP Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. [Fang, Huihui; Sun, Xu; Cao, Xingxing; Xu, Yanwu] Baidu Inc, Intelligent Healthcare Unit, Beijing 100085, Peoples R China; [Li, Fei; Lin, Fengbin; Zhang, Xiulan] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangdong Prov Key Lab Ophthalmol & Visual Sci, Guangzhou 510623, Peoples R China; [Fu, Huazhu] Agcy Sci Technol & Res, Inst High Performance Comp, Singapore 138632, Singapore; [Son, Jaemin; Kim, Sunho] VUNO Inc, Seoul 06536, South Korea; [Quellec, Gwenole; Matta, Sarah] INSERM, UMR 1101, F-29200 Brest, France; [Matta, Sarah] Univ Bretagne Occidentale, Lab Traitement Informat Med, F-29200 Brest, France; [Shankaranarayana, Sharath M.] Zasti India Pvt Ltd, Chennai 600113, Tamil Nadu, India; [Chen, Yi-Ting] Graphen Inc, New York, NY 10110 USA; [Wang, Chuen-Heng] Muen Biomed & Optoelect Technol Inc, Taipei 115, Taiwan; [Shah, Nisarg A.] Indian Inst Technol Jodhpur, Dept Elect Engn, Karwar 342030, Rajasthan, India; [Lee, Chia-Yen] Natl United Univ, Dept Elect Engn, Miaoli 36003, Taiwan; [Hsu, Chih-Chung] Natl Cheng Kung Univ, Inst Data Sci, Dept Stat, Tainan 701, Taiwan; [Xie, Hai; Lei, Baiying] Shenzhen Univ, Sch Biomed Engn, Shenzhen 518060, Peoples R China; [Baid, Ujjwal; Innani, Shubham] SGGS Inst Engn & Technol, Nanded 431606, India; [Dang, Kang; Shi, Wenxiu] VoxelCloud Ltd, Shanghai 201114, Peoples R China; [Kamble, Ravi; Singhal, Nitin] AIRA Matrix, Thana 400604, Maharashtra, India; [Wang, Ching-Wei; Lo, Shih-Chang] Natl Taiwan Univ Sci & Technol, Grad Inst Biomed Engn, Taipei 106, Taiwan; [Orlando, Jose Ignacio] UNICEN, Yatiris Grp, PLADEMA Inst, CONICET, RA-7000 Tandil, Argentina; [Bogunovic, Hrvoje] Med Univ Vienna, Dept Ophthalmol & Optometry, Christian Doppler Lab Artificial Intelligence Ret, A-1090 Vienna, Austria Baidu; Sun Yat Sen University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bretagne Occidentale; Universite de Bretagne Occidentale; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Jodhpur; National United University; National Cheng Kung University; Shenzhen University; Shri Guru Gobind Singhji Institute of Engineering & Technology; National Taiwan University of Science & Technology; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Medical University of Vienna Xu, YW (corresponding author), Baidu Inc, Intelligent Healthcare Unit, Beijing 100085, Peoples R China.;Zhang, XL (corresponding author), Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangdong Prov Key Lab Ophthalmol & Visual Sci, Guangzhou 510623, Peoples R China. fanghuihuibit@163.com; lifei57@mail.sysu.edu.cn; hzfu@ieee.org; pamixsun@foxmail.com; caoxingxing@baidu.com; linfb52@163.com; woalsdnd@gmail.com; ksunho0660@vuno.co; gwenole.quellec@inserm.fr; sarah.matta@univ-brest.fr; sharathms.iit@gmail.com; timchen@graphen.ai; andrew.wang@mail2000.com.tw; shah.2@iitj.ac.in; leecyya@gmail.com; cchsu@gs.ncku.edu.tw; shehare@szu.edu.cn; leiby@szu.edu.cn; ujjwalbaid0408@gmail.com; 2016bec035@sggs.ac.in; kdang@voxelcloud.net.cn; 1553916604@qq.com; ravi.kamble@airamatrix.com; nitin.singhal@airamatrix.com; cwwang1979@gmail.com; edward70932@gmail.com; jiorlando@pladema.exa.unicen.edu.ar; hrvoje.bogunovic@meduniwien.ac.at; zhangxl2@mail.sysu.edu.cn; xuyanwu@baidu.com Fu, Huazhu/A-1411-2014; Wang, Ching-Wei/GXV-5212-2022; Fang, Huihui/GZG-9742-2022; Quellec, Gwenole/L-9946-2015 Fu, Huazhu/0000-0002-9702-5524; Wang, Ching-Wei/0000-0001-9992-6863; Sun, Xu/0000-0001-9155-0427; Fang, Huihui/0000-0003-3380-7970; Lo, Shih-Chang/0000-0003-2461-0498; Innani, Shubham/0000-0003-3616-0308; Quellec, Gwenole/0000-0003-1669-7140 High-Level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University [303020104]; Singapore A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund [A20H4b0141]; Young Talent Support Project of Guangzhou Association for Science and Technology (2022) High-Level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; Singapore A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund(Agency for Science Technology & Research (A*STAR)); Young Talent Support Project of Guangzhou Association for Science and Technology (2022) This work was supported in part by the High-Level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University, under Grant 303020104; in part by the Singapore A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund, under Grant A20H4b0141; and in part by the Young Talent Support Project of Guangzhou Association for Science and Technology (2022). 73 1 1 6 6 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0278-0062 1558-254X IEEE T MED IMAGING IEEE Trans. Med. Imaging OCT 2022.0 41.0 10 2828 2847 10.1109/TMI.2022.3172773 0.0 20 Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging 4Z7RE 35507621.0 Green Submitted 2023-03-23 WOS:000862400100023 0
J Ferriol-Galmes, M; Suarez-Varela, J; Paillisse, J; Shi, X; Xiao, SH; Cheng, XL; Barlet-Ros, P; Cabellos-Aparicio, A Ferriol-Galmes, Miquel; Suarez-Varela, Jose; Paillisse, Jordi; Shi, Xiang; Xiao, Shihan; Cheng, Xiangle; Barlet-Ros, Pere; Cabellos-Aparicio, Albert Building a Digital Twin for network optimization using Graph Neural Networks COMPUTER NETWORKS English Article Digital Twin; Graph Neural Networks; Network optimization; Deep Learning; Network modeling Network modeling is a critical component of Quality of Service (QoS) optimization. Current networks implement Service Level Agreements (SLA) by careful configuration of both routing and queue scheduling policies. However, existing modeling techniques are not able to produce accurate estimates of relevant SLA metrics, such as delay or jitter, in networks with complex QoS-aware queueing policies (e.g., strict priority, Weighted Fair Queueing, Deficit Round Robin). Recently, Graph Neural Networks (GNNs) have become a powerful tool to model networks since they are specifically designed to work with graph-structured data. In this paper, we propose a GNN-based network model able to understand the complex relationship between (i) the queueing policy (scheduling algorithm and queue sizes), (ii) the network topology, (iii) the routing configuration, and (iv) the input traffic matrix. We call our model TwinNet, a Digital Twin that can accurately estimate relevant SLA metrics for network optimization. TwinNet can generalize to its input parameters, operating successfully in topologies, routing, and queueing configurations never seen during training. We evaluate TwinNet over a wide variety of scenarios with synthetic traffic and validate it with real traffic traces. Our results show that TwinNet can provide accurate estimates of end-to-end path delays in 106 unseen real -world topologies, under different queuing configurations with a Mean Absolute Percentage Error (MAPE) of 3.8%, as well as a MAPE of 6.3% error when evaluated with a real testbed. We also showcase the potential of the proposed model for SLA-driven network optimization and what-if analysis. [Ferriol-Galmes, Miquel; Suarez-Varela, Jose; Paillisse, Jordi; Barlet-Ros, Pere; Cabellos-Aparicio, Albert] Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Catalunya, Spain; [Shi, Xiang; Xiao, Shihan; Cheng, Xiangle] Huawei Technol Co Ltd, Shenzhen, Peoples R China Universitat Politecnica de Catalunya; Huawei Technologies Ferriol-Galmes, M (corresponding author), Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Catalunya, Spain. miquel.ferriol@upc.edu Suárez-Varela, José/AAA-5418-2019 Suárez-Varela, José/0000-0002-7141-3414; Ferriol Galmes, Miquel/0000-0002-7806-2979 MCIN/ AEI [PID2020-118011GB-C21]; Catalan Institution for Research and Advanced Studies (ICREA), Spain; Government of Catalonia, Spain; European Social Fund MCIN/ AEI; Catalan Institution for Research and Advanced Studies (ICREA), Spain; Government of Catalonia, Spain(Generalitat de Catalunya); European Social Fund(European Social Fund (ESF)) This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/, Spain10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), Spain and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, Spain and the European Social Fund. 72 0 0 11 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. NOV 9 2022.0 217.0 109329 10.1016/j.comnet.2022.109329 0.0 14 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5I7JU Green Published, hybrid 2023-03-23 WOS:000868529300002 0
J Hang, JF; Sun, H; Yu, XH; Rodriguez-Andina, JJ; Yang, XQ Hang, Jingfan; Sun, Hao; Yu, Xinghu; Rodriguez-Andina, Juan J.; Yang, Xianqiang Surface Defect Detection in Sanitary Ceramics Based on Lightweight Object Detection Network IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY English Article Feature extraction; Ceramics; Surface treatment; Industrial electronics; Production; Object detection; Task analysis; Sanitary ceramic; surface defect detection; deep learning Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, real-time defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic one-stage SSD, YOLO V3 and two-stage Faster R-CNN methods. [Hang, Jingfan; Yang, Xianqiang] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Peoples R China; [Sun, Hao] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China; [Yu, Xinghu] Ningbo Inst Intelligent Equipment Technol Co Ltd, Ningbo 315200, Peoples R China; [Rodriguez-Andina, Juan J.] Univ Vigo, Dept Elect Technol, Vigo 36310, Spain Harbin Institute of Technology; Harbin Institute of Technology; Universidade de Vigo Yang, XQ (corresponding author), Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Peoples R China.;Rodriguez-Andina, JJ (corresponding author), Univ Vigo, Dept Elect Technol, Vigo 36310, Spain. Juan.J.R@ieee.org; xianqiangyang@hi.edu.cn yang, xianqiang/0000-0002-0036-6921; sun, hao/0000-0002-8372-4551; Hang, Jingfan/0000-0002-4219-6828; Rodriguez-Andina, Juan J./0000-0002-0919-1793 National Key R&D Program of China [2018YFB1308400]; National Natural Science Foundation of China [U20A20188] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key R&D Program of China under Grant 2018YFB1308400 and in part by the Joint Funds of the National Natural Science Foundation of China under Grant U20A20188. 26 1 1 7 7 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2644-1284 IEEE OPEN J IND ELEC IEEE Open J. Ind. Electron. Soc. 2022.0 3.0 473 483 10.1109/OJIES.2022.3193572 0.0 11 Engineering, Electrical & Electronic Emerging Sources Citation Index (ESCI) Engineering 3V6VU gold 2023-03-23 WOS:000841800300001 0
J Anande, G; Deshpande, NP; Mareschal, S; Batcha, AMN; Hampton, HR; Herold, T; Lehmann, S; Wilkins, MR; Wong, JWH; Unnikrishnan, A; Pimanda, JE Anande, Govardhan; Deshpande, Nandan P.; Mareschal, Sylvain; Batcha, Aarif M. N.; Hampton, Henry R.; Herold, Tobias; Lehmann, Soren; Wilkins, Marc R.; Wong, Jason W. H.; Unnikrishnan, Ashwin; Pimanda, John E. RNA Splicing Alterations Induce a Cellular Stress Response Associated with Poor Prognosis in Acute Myeloid Leukemia CLINICAL CANCER RESEARCH English Article OLDER PATIENTS; MUTATIONS; RISK; STEM; RECOMMENDATIONS; IDENTIFICATION; HEMATOPOIESIS; MODULATION; MANAGEMENT; CATALYSIS Purpose: RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are common in some hematologic cancers but are relatively uncommon in acute myeloid leukemia (AML, < 20% of patients). We examined whether RNA splicing differences exist in AML, even in the absence of splicing factor mutations. Experimental Design: We developed a bioinformatics pipeline to study alternative RNA splicing in RNA-sequencing data from large cohorts of patients with AML. Results: We have identified recurrent differential alternative splicing between patients with poor and good prognosis. These splicing events occurred even in patients without any discernible splicing factor mutations. Alternative splicing recurrently occurred in genes with specific molecular functions, primarily related to protein translation. Developing tools to predict the functional impact of alternative splicing on the translated protein, we discovered that approximately 45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced and the splicing of their target transcripts were altered. Studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and upregulation of inflammation-related genes. Finally, using machine learning techniques, we identified a splicing signature of four genes which refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort. Conclusions: Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance. [Anande, Govardhan; Hampton, Henry R.; Wong, Jason W. H.; Unnikrishnan, Ashwin; Pimanda, John E.] Univ New South Wales Sydney, Adult Canc Program, Lowy Canc Res Ctr, Sydney, NSW, Australia; [Anande, Govardhan; Hampton, Henry R.; Wong, Jason W. H.; Unnikrishnan, Ashwin; Pimanda, John E.] Univ New South Wales Sydney, Prince Wales Clin Sch, Sydney, NSW, Australia; [Deshpande, Nandan P.; Wilkins, Marc R.] Univ New South Wales Sydney, Sch Biotechnol & Biomol Sci, Sydney, NSW, Australia; [Mareschal, Sylvain; Lehmann, Soren] Karolinska Univ Hosp, Hematol Ctr, Stockholm, Sweden; [Mareschal, Sylvain; Lehmann, Soren] Karolinska Inst, Dept Med, Stockholm, Sweden; [Batcha, Aarif M. N.] Ludwig Maximilians Univ Munchen, Fac Med, Inst Med Data Proc Biometr & Epidemiol, Munich, Germany; [Batcha, Aarif M. N.] Ludwig Maximilians Univ Munchen, Data Integrat Future Med, Munich, Germany; [Herold, Tobias] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Med 3, Munich, Germany; [Herold, Tobias] Helmholtz Zentrum Munchen, Res Unit Apoptosis Hematopoiet Stem Cells, German Res Ctr Environm Hlth, Munich, Germany; [Lehmann, Soren] Uppsala Univ, Dept Med Sci, Uppsala, Sweden; [Wong, Jason W. H.] Univ Hong Kong, Li Ka Shing Fac Med, Sch Biomed Sci, Hong Kong, Peoples R China; [Pimanda, John E.] Univ New South Wales Sydney, Sch Med Sci, Dept Pathol, Sydney, NSW, Australia; [Pimanda, John E.] Prince Wales Hosp, Dept Haematol, Sydney, NSW, Australia University of New South Wales Sydney; University of New South Wales Sydney; University of New South Wales Sydney; Karolinska Institutet; Karolinska University Hospital; Karolinska Institutet; University of Munich; University of Munich; University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Uppsala University; University of Hong Kong; University of New South Wales Sydney Unnikrishnan, A; Pimanda, JE (corresponding author), UNSW Sydney, Room 217,Level 2,C25 Lowy Canc Res Ctr, Sydney, NSW 2052, Australia. Ashwin.unnikrishnan@unsw.edu.au; jpimanda@unsw.edu.au Herold, Tobias/AAD-1072-2019; Unnikrishnan, Ashwin/L-8632-2013; Wong, Jason/A-9466-2008 Herold, Tobias/0000-0002-9615-9432; Unnikrishnan, Ashwin/0000-0001-5168-8755; Mareschal, Sylvain/0000-0001-7492-7528; Anande, Govardhan/0000-0003-0633-600X; Deshpande, Nandan/0000-0002-0324-8728; Wong, Jason/0000-0003-2953-7728; Nazeer Batcha, Aarif Mohamed/0000-0002-7972-7506 University of New South Wales; Translational Cancer Research Network; National Health and Medical Research Council of Australia [APP1163815, APP1024364, 1043934, 1102589]; Leukemia & Lymphoma Society (USA); Anthony Rothe Memorial Trust; Cancer Institute of New South Wales/Translational Cancer Research Network; Wilhelm Sander Foundation [2013.086.2]; Helmholtz Zentrum M_unchen [G-509200-004]; German Cancer Consortium (Deutsches Konsortium f_ur Translationale Krebsforschung, Heidelberg, Germany); Deutsche Forschungsgemeinschaft [DFG SFB 1243]; BMBF [01ZZ1804B]; National Health and Medical Research Council of Australia [1102589] Funding Source: NHMRC University of New South Wales; Translational Cancer Research Network; National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia); Leukemia & Lymphoma Society (USA)(Leukemia and Lymphoma Society); Anthony Rothe Memorial Trust; Cancer Institute of New South Wales/Translational Cancer Research Network; Wilhelm Sander Foundation; Helmholtz Zentrum M_unchen(Helmholtz Association); German Cancer Consortium (Deutsches Konsortium f_ur Translationale Krebsforschung, Heidelberg, Germany); Deutsche Forschungsgemeinschaft(German Research Foundation (DFG)); BMBF(Federal Ministry of Education & Research (BMBF)); National Health and Medical Research Council of Australia(National Health and Medical Research Council (NHMRC) of Australia) The authors would like to thank Dr. Ling Zhong (Mark Wainwright Analytical Centre, University of New South Wales, New South Wales, Australia) for technical assistance rendered. The authors also thank Dr. Annatina Schnegg (University of New South Wales, New South Wales, Australia) for critical review and discussion of the manuscript. This work was facilitated by infrastructure provided by the NSW Government co-investment in the National Collaborative Research Infrastructure Scheme (NCRIS, Australia). The authors acknowledge the following funding support: G. Anande was supported by a postgraduate scholarship from the University of New South Wales, with additional funding from the Translational Cancer Research Network. A. Unnikrish-nan acknowledges funding support from the National Health and Medical Research Council of Australia (APP1163815), Leukemia & Lymphoma Society (USA) and Anthony Rothe Memorial Trust. J.E. Pimanda acknowledges funding from National Health and Medical Research Council of Australia (APP1024364, 1043934, 1102589), Cancer Institute of New South Wales/Translational Cancer Research Network and Anthony Rothe Memorial Trust. T. Herold is supported by the Wilhelm Sander Foundation (2013.086.2), the P (hysician Scientists GrantG-509200-004) from the Helmholtz Zentrum M_unchen and the German Cancer Consortium (Deutsches Konsortium f_ur Translationale Krebsforschung, Heidelberg, Germany) and a grant from Deutsche Forschungsgemeinschaft (DFG SFB 1243). A. M.N. Batcha was partially funded by the BMBF grant 01ZZ1804B (DIFUTURE). 49 14 15 6 15 AMER ASSOC CANCER RESEARCH PHILADELPHIA 615 CHESTNUT ST, 17TH FLOOR, PHILADELPHIA, PA 19106-4404 USA 1078-0432 1557-3265 CLIN CANCER RES Clin. Cancer Res. JUL 15 2020.0 26.0 14 3597 3607 10.1158/1078-0432.CCR-20-0184 0.0 11 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology MO2NT 32122925.0 Green Published 2023-03-23 WOS:000551370100014 0
J Chen, F; Tiwari, S; Mohammed, KS; Huo, WD; Jamroz, P Chen, Fu; Tiwari, Sunil; Mohammed, Kamel Si; Huo, Weidong; Jamroz, Pawel Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs RESOURCES POLICY English Article Minerals resource; Economic performance; Greener energy; Environmental policy; ML & ANN; China FINANCIAL DEVELOPMENT; NATURAL-RESOURCES; COORDINATED DEVELOPMENT; SUSTAINABILITY ASSESSMENT; CONSUMPTION EVIDENCE; CARRYING-CAPACITY; SYSTEM DYNAMICS; DUTCH DISEASE; IMPACT; GROWTH The relationship of mineral resources rent with economic performance, greener energy, and environmental policy is crucial because it could impact how welfare and eco-friendly are treated in a nation. This paper employed machine learning (ML) and Artificial Neural Networks (ANN) to predict minerals resourcesthrough-economic growth, the producer price index, market prices, the environmental policy stringency index (EPSI), and greener energy in China. The ML and ANN outputs showed how responsive quarterly minerals resource rent variations are too dynamic shifts in economic performance and renewable energy use. The more critical prediction accuracy of ML experiments compared to ANN experiments is highlighted by data from mean absolute percentage, mean square, root mean square, and root mean absolute errors, as well as the coefficient of determination. Though China is abundant in natural resources, there is a need to employ these resources efficiently to achieve long-term eco-friendly performance. In addition to comparing the results of ML and ANN, it also provided causality conclusions and policy implications. [Chen, Fu; Huo, Weidong] Liaoning Univ, Sch Finance & Trade, Shenyang 110136, Peoples R China; [Tiwari, Sunil] Cent Univ Kerala, Sch Business Studies, Dept Tourism Studies, Kasaragod, Kerala, India; [Mohammed, Kamel Si] Univ Ain Temouchent, Fac Econ & Management, Dept Econ, Ain Temouchent, Algeria; [Jamroz, Pawel] Univ Bialystok, Fac Econ & Finance, Bialystok, Poland Liaoning University; Central University of Kerala; Centre Universitaire Ain Temouchent; University of Bialystok Huo, WD (corresponding author), Liaoning Univ, Sch Finance & Trade, Shenyang 110136, Peoples R China. chenfu@lnu.edu.cn; tiwari.sunil10@outlook.com; kamel.simohammed@univ-temouchent.edu.dz; weidong_huo2001@sina.com; p.jamroz@uwb.edu.pl Tiwari, Sunil/HMW-0468-2023; Jamroz, Pawel/J-1627-2014 Tiwari, Sunil/0000-0002-0180-9237; si mohammed, kamel/0000-0002-4359-9151; Jamroz, Pawel/0000-0003-1839-4418 109 0 0 2 2 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0301-4207 1873-7641 RESOUR POLICY Resour. Policy MAR 2023.0 81.0 103307 10.1016/j.resourpol.2023.103307 0.0 JAN 2023 10 Environmental Studies Social Science Citation Index (SSCI) Environmental Sciences & Ecology 8N9IG 2023-03-23 WOS:000925458200001 0
J Ming, YR; Ding, WP; Pelusi, D; Wu, DR; Wang, YK; Prasad, M; Lin, CT Ming, Yurui; Ding, Weiping; Pelusi, Danilo; Wu, Dongrui; Wang, Yu-Kai; Prasad, Mukesh; Lin, Chin-Teng Subject adaptation network for EEG data analysis APPLIED SOFT COMPUTING English Article Brain-computer interface (BCI); Clustering; Deep learning; Domain adaptation (DA); Electroencephalograph (EEG); Sample selection; Subject adaptation network (SAN) Biosignals tend to display manifest intra- and cross-subject variance, which generates numerous challenges for electroencephalograph (EEG) data analysis. For instance, in the context of classification, the discrepancy between EEG data can make the trained model generalising poorly for new test subjects. In this paper, a subject adaptation network (SAN) inspired by the generative adversarial network (GAN) to mitigate different variances is proposed for analysing EEG data. First the challenges faced by traditional approaches employed for EEG signal processing are emphasised. Then the problem is formulated from mathematical perspective to highlight the key points in resolving such discrepancies. Third, the motivation behind and design principle of the SAN are described in an intuitive manner to reflect its suitability for analysing EEG data. Then after depicting the overall architecture of the SAN, several experiments are used to justify the practicality and efficiency of using the proposed model from different perspectives. For instance, an EEG dataset captured during a stereotypical neurophysiological experiment called the VEP oddball task is utilised to demonstrate the performance improvement achieved by running the SAN. (C) 2019 Elsevier B.V. All rights reserved. [Ming, Yurui; Wang, Yu-Kai; Prasad, Mukesh; Lin, Chin-Teng] Univ Technol Sydney, Ctr Artificial Intelligence, Sch Comp Sci, FEIT, Sydney, NSW 2007, Australia; [Ding, Weiping] Nantong Univ, Sch Comp Sci & Technol, Nantong 226019, Jiangsu, Peoples R China; [Pelusi, Danilo] Univ Teramo, Fac Commun Sci, I-64100 Teramo, Italy; [Wu, Dongrui] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China University of Technology Sydney; Nantong University; University of Teramo; Huazhong University of Science & Technology Lin, CT (corresponding author), Univ Technol Sydney, Ctr Artificial Intelligence, Sch Comp Sci, FEIT, Sydney, NSW 2007, Australia. yrming@gmial.com; ding.wp@ntu.edu.cn; dpelusi@unite.it; drwu@hust.edu.cn; Yukai.Wang@uts.edu.au; Mukesh.Prasad@uts.edu.au; Chin-Teng.Lin@uts.edu.au ; Wu, Dongrui/E-1956-2011; Lin, Chin-Teng (CT)/G-8129-2017 Ming, Yurui/0000-0003-3837-5580; Wu, Dongrui/0000-0002-7153-9703; Wang, YuKai/0000-0001-8390-2664; Prasad, Mukesh/0000-0002-7745-9667; Lin, Chin-Teng (CT)/0000-0001-8371-8197 Australian Research Council (ARC) [DP180100670, DP180100656]; National Natural Science Foundation of China [61976120]; Natural Science Foundation of Jiangsu Province [BK20151274]; Qing Lan Project of Jiangsu Province; NSW Defence Innovation Network; Office of Naval Research Global, US [ONRG-NICOP-N62909-19-1-2058]; NSW State Government of Australia [DINPP2019 S103/09] Australian Research Council (ARC)(Australian Research Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Qing Lan Project of Jiangsu Province; NSW Defence Innovation Network; Office of Naval Research Global, US(Office of Naval Research); NSW State Government of Australia This work was supported in part by the Australian Research Council (ARC) under discovery grant DP180100670 and DP180100656; The National Natural Science Foundation of China under Grant 61976120; The Natural Science Foundation of Jiangsu Province under Grant BK20151274; The Qing Lan Project of Jiangsu Province; NSW Defence Innovation Network and NSW State Government of Australia under the grant DINPP2019 S103/09; Office of Naval Research Global, US under Cooperative Agreement Number ONRG-NICOP-N62909-19-1-2058. 25 16 16 7 56 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. NOV 2019.0 84.0 105689 10.1016/j.asoc.2019.105689 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science JE5SW Green Submitted, Bronze 2023-03-23 WOS:000490753200011 0
J Huang, W; Oh, SK; Pedrycz, W Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold Fuzzy reinforced polynomial neural networks constructed with the aid of PNN architecture and fuzzy hybrid predictor based on nonlinear function NEUROCOMPUTING English Article Dynamic systems modeling; Fuzzy hybrid predictor; Particle swarm optimizer; Fuzzy reinforced polynomial neural networks; Polynomial neural networks EVOLUTIONARY ALGORITHM; DESIGN; IDENTIFICATION; FCM In the field of dynamic system identification and prediction, linear models (e.g., autoregressive models), nonlinear models (namely, neural networks models), and hybrid predictors (HPs) that are a hybridization of linear and nonlinear models have been proposed in the past. However, they are not completely free from limitations: they exhibit difficulties to describe high-order nonlinear relations between input and output variables. In this study, we propose fuzzy reinforced polynomial neural networks (FRPNNs), which are polynomial neural network architecture-based on fuzzy reinforced polynomial neurons (FRPNs) to overcome this limitation. The proposed FRPNs that consist of approximation part (AP) and compensation part (CP) arise as novel HPs. Here the CP for modeling nonlinear patterns can be regarded as forming the reinforced part for the AP that aims at capturing linear patterns, while AP is the linear polynomial neuron used in the conventional polynomial neural networks. In some sense, the overall FRPNNs are essentially generalized polynomial neural network architecture with novel HPs. The parameters considered in the design of the proposed fuzzy reinforced polynomial neural networks are optimized with the aid of the particle swarm optimization (PSO). The performance of FRPNNs is discussed involving time series and system identification datasets. Experimental results demonstrate that the proposed FRPNNs achieve at most the accuracy of 43.6% higher in comparison with the accuracy produced by some classical models reported in the literature. Crown Copyright (c) 2021 Published by Elsevier B.V. All rights reserved. [Huang, Wei] Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100081, Peoples R China; [Huang, Wei] Tianjin Univ Technol, Sch Comp & Commun Engn, Tianjin, Peoples R China; [Oh, Sung-Kwun] Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea; [Oh, Sung-Kwun] Linyi Univ, Res Ctr Big Data & Art Intelligence, Linyi 276005, Shandong, Peoples R China; [Pedrycz, Witold] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada; [Pedrycz, Witold] Polish Acad Sci, Syst Res Inst, Warsaw, Poland; [Pedrycz, Witold] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia Beijing Institute of Technology; Tianjin University of Technology; Suwon University; Linyi University; University of Alberta; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; King Abdulaziz University Oh, SK (corresponding author), Univ Suwon, Sch Elect & Elect Engn, Hwaseong Si 18323, Gyeonggi Do, South Korea.;Oh, SK (corresponding author), Linyi Univ, Res Ctr Big Data & Art Intelligence, Linyi 276005, Shandong, Peoples R China. huangwabc@163.com; ohsk@suwon.ac.kr; wpedrycz@ualberta.ca National Natural Science Foundation of China [61673295]; NaturalScience Foundation of Tianjin for Distinguished Young Scholars [19JCJQJC61500]; Natural Science Foundation of Tianjin [18JCYBJC85200]; Fundamental Research of Science and Technology on Complex Electronic System Simulation Laboratory [DXZT-JC-ZZ-2019-010]; National Research Foundation of Korea (NRF) - Korea Government (MSIT) [NRF-2021R1F1A1056102] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); NaturalScience Foundation of Tianjin for Distinguished Young Scholars; Natural Science Foundation of Tianjin(Natural Science Foundation of Tianjin); Fundamental Research of Science and Technology on Complex Electronic System Simulation Laboratory; National Research Foundation of Korea (NRF) - Korea Government (MSIT)(National Research Foundation of KoreaMinistry of Science, ICT & Future Planning, Republic of Korea) This work was supported in part by the National Natural Science Foundation of China under Grant 61673295, by the Natural Science Foundation of Tianjin for Distinguished Young Scholars under Grant 19JCJQJC61500, and by the Natural Science Foundation of Tianjin (General Program) under Grant 18JCYBJC85200, and by the Fundamental Research of Science and Technology on Complex Electronic System Simulation Laboratory under Grant DXZT-JC-ZZ-2019-010, and also by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102) . 43 1 2 2 19 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 11 2021.0 458.0 454 467 10.1016/j.neucom.2021.06.047 0.0 JUL 2021 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science UJ8XB 2023-03-23 WOS:000691561400020 0
J Jin, Y; Yan, D; Zhang, XX; An, JJ; Han, MJ Jin, Yuan; Yan, Da; Zhang, Xingxing; An, Jingjing; Han, Mengjie A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development BUILDING SIMULATION English Article lighting system; occupant behavior; data-driven; model predictive control ARTIFICIAL NEURAL-NETWORKS; CONTROL STRATEGY; ENERGY; SIMULATION; BEHAVIOR; PERFORMANCE; ALGORITHM; STATE The lighting system accounts for 8% of the total electricity consumption in commercial buildings in the United States and 12% of the total electricity consumption in public buildings globally. This consumption share can be effectively reduced using the demand-response control. The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared (PIR) sensor. However, the detection inaccuracy of the PIR sensor usually results in false-offs. To diminish the false-error frequency, the existing lighting system control simply deploys a delayed reaction period (e.g., 5 to 20 min), which is not sufficiently accurate for the demand-response operation. Therefore, in this research, a novel data-driven model predictive control (MPC) method that is based on the temporal sequential-based artificial neural network (TS-ANN) is proposed to overcome this challenge using an updated historical occupancy status. Using an office as case study, the proposed model is also compared with the traditional lighting system control method. In the proposed model, the occupancy data was trained to predict the occupancy pattern to improve the control. It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature. The simulation results indicated that the proposed method achieved higher accuracy (97.4%) and fewer false-offs (from 79.5 with traditional time delay method to 0.6 times per day) are achieved by the MPC model. The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation. [Jin, Yuan; Yan, Da; An, Jingjing] Tsinghua Univ, Sch Architecture, Beijing 100084, Peoples R China; [Zhang, Xingxing; Han, Mengjie] Dalarna Univ, Sch Ind Technol & Business Studies, S-79188 Falun, Sweden Tsinghua University; Dalarna University Yan, D (corresponding author), Tsinghua Univ, Sch Architecture, Beijing 100084, Peoples R China. yanda@tsinghua.edu.cn yu, yang/HIZ-9682-2022; Yan, Da/AAI-8879-2021 Yan, Da/0000-0003-2399-723X National Natural Science Foundation of China [51778321]; Innovative Research Groups of the National Natural Science Foundation of China [51521005]; Tsinghua University tutor research fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tsinghua University tutor research fund This study was supported by the National Natural Science Foundation of China (No. 51778321): Research on the quantitative description and simulation methodology of occupant behavior in buildings; the Innovative Research Groups of the National Natural Science Foundation of China (No. 51521005); also the Tsinghua University tutor research fund. 55 25 25 6 38 TSINGHUA UNIV PRESS BEIJING B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA 1996-3599 1996-8744 BUILD SIMUL-CHINA Build. Simul. FEB 2021.0 14.0 1 SI 219 235 10.1007/s12273-020-0638-x 0.0 MAY 2020 17 Thermodynamics; Construction & Building Technology Science Citation Index Expanded (SCI-EXPANDED) Thermodynamics; Construction & Building Technology PE2LE 2023-03-23 WOS:000532622300003 0
J Almasan, P; Xiao, SH; Cheng, XL; Shi, X; Barlet-Ros, P; Cabellos-Aparicio, A Almasan, Paul; Xiao, Shihan; Cheng, Xiangle; Shi, Xiang; Barlet-Ros, Pere; Cabellos-Aparicio, Albert ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning COMPUTER NETWORKS English Article Routing; Optimization; Deep Reinforcement Learning; Graph Neural Networks Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage the network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 s on average for topologies up to 100 links. [Almasan, Paul; Barlet-Ros, Pere; Cabellos-Aparicio, Albert] Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Barcelona, Spain; [Xiao, Shihan; Cheng, Xiangle; Shi, Xiang] Huawei Technol Co Ltd, Network Technol Lab, Shenzhen, Peoples R China Universitat Politecnica de Catalunya; Huawei Technologies Almasan, P (corresponding author), Univ Politecn Cataluna, Barcelona Neural Networking Ctr, Barcelona, Spain. felician.paul.almasan@upc.edu Almasan, Paul/0000-0003-3903-6759 Spanish I+D+i project TRAINER -A [PID2020-118011GB-C21, MCIN/AEI/10.13039/501100011033]; Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia; European Social Fund Spanish I+D+i project TRAINER -A; Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia; European Social Fund(European Social Fund (ESF)) This publication is part of the Spanish I+D+i project TRAINER -A (ref.PID2020-118011GB-C21) , funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. 46 1 1 5 6 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. SEP 4 2022.0 214.0 109166 10.1016/j.comnet.2022.109166 0.0 JUL 2022 11 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 3L2SH hybrid, Green Submitted, Green Published 2023-03-23 WOS:000834615600015 0
J Liu, Y; Munteanu, CR; Fernandez-Lozano, C; Pazos, A; Ran, T; Tan, ZL; Yu, YZ; Zhou, CS; Tang, SX; Gonzalez-Diaz, H Liu, Yong; Munteanu, Cristian R.; Fernandez-Lozano, Carlos; Pazos, Alejandro; Ran, Tao; Tan, Zhiliang; Yu, Yizun; Zhou, Chuanshe; Tang, Shaoxun; Gonzalez-Diaz, Humberto Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota FRONTIERS IN MICROBIOLOGY English Article electrokinetic properties; zeta potential; artificial neural networks; perturbation theory; predictive model; ruminal microbiome VITRO FERMENTATION CHARACTERISTICS; WASTE ACTIVATED-SLUDGE; FATTY-ACID PRODUCTION; IN-VITRO; STAPHYLOCOCCUS-EPIDERMIDIS; ANAEROBIC FERMENTATION; ALKYL POLYGLYCOSIDE; NONIONIC SURFACTANT; INTEGRIN BINDING; BIO-SURFACTANTS The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, xi; electrokinetic mobility, mu(e)), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, gamma). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N)) to establish a predictive artificial neural network (ANN) model. Concepts of dual-time series analysis, perturbation theory (PT), and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR) model for xi of rumen microbiota predicted xi for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential) of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension) of media, digestibility of substrate, and their metabolites (NH3-N, VFAs) in relation to environmental factors. [Liu, Yong; Ran, Tao; Tan, Zhiliang; Zhou, Chuanshe; Tang, Shaoxun] Chinese Acad Sci, Key Lab Agroecol Proc Subtrop Reg, Hunan Res Ctr Livestock & Poultry Sci,Inst Subtro, South Cent Expt Stn Anim Nutr & Feed Sci,Minist A, Changsha, Hunan, Peoples R China; [Liu, Yong; Munteanu, Cristian R.; Fernandez-Lozano, Carlos; Pazos, Alejandro] Univ A Coruna, Comp Sci Fac, RNASA IMEDIR, La Coruna, Spain; [Fernandez-Lozano, Carlos; Pazos, Alejandro] Complexo Hosp Univ A Coruna, Inst Invest Biomed A Coruna, La Coruna, Spain; [Tan, Zhiliang; Zhou, Chuanshe; Tang, Shaoxun] CICAPS, Hunan Coinnovat Ctr Anim Prod Safety, Changsha, Hunan, Peoples R China; [Yu, Yizun] Jiangxi Acad Sci, Inst Biol Resources, Nanchang, Jiangxi, Peoples R China; [Gonzalez-Diaz, Humberto] Univ Basque Country, UPV EHU, Dept Organ Chem 2, Leioa, Spain; [Gonzalez-Diaz, Humberto] Basque Fdn Sci, Ikerbasque, Bilbao, Spain Chinese Academy of Sciences; Universidade da Coruna; Complejo Hospitalario Universitario A Coruna; Universidade da Coruna; Instituto de Investigacion Biomedica de A Coruna (INIBIC); University of Basque Country; Basque Foundation for Science Tang, SX (corresponding author), Chinese Acad Sci, Key Lab Agroecol Proc Subtrop Reg, Hunan Res Ctr Livestock & Poultry Sci,Inst Subtro, South Cent Expt Stn Anim Nutr & Feed Sci,Minist A, Changsha, Hunan, Peoples R China.;Tang, SX (corresponding author), CICAPS, Hunan Coinnovat Ctr Anim Prod Safety, Changsha, Hunan, Peoples R China. shaoxuntang@163.com Fernandez-Lozano, Carlos/D-2469-2014; Munteanu, Cristian-Robert/G-1714-2011; Gonzalez-Diaz, Humbert G. Díaz/A-6785-2012; Pazos, Alejandro/AAI-5887-2020; Pazos Sierra, Alejandro/K-8927-2017 Fernandez-Lozano, Carlos/0000-0003-0413-5677; Munteanu, Cristian-Robert/0000-0002-5628-2268; Gonzalez-Diaz, Humbert G. Díaz/0000-0002-9392-2797; Pazos Sierra, Alejandro/0000-0003-2324-238X National Natural Science Foundation of China [31172234, 31260556]; Planned Science and Technology Project of Hu-nan Province [2015NK3041]; Jilin Province [2016SYHZ0022]; Chinese Academy of Sciences [2016SYHZ0022]; Hunan Provincial Creation Development Project [2013TF3006]; General Directorate of Culture; Education and University Management of Xunta de Galicia [GRC2014/049]; Galician Network for Colorectal Cancer Research [R2014/039]; Spanish Ministry of Economy, Industry and Competitiveness [FJCI-2015-26071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Planned Science and Technology Project of Hu-nan Province; Jilin Province; Chinese Academy of Sciences(Chinese Academy of Sciences); Hunan Provincial Creation Development Project; General Directorate of Culture; Education and University Management of Xunta de Galicia; Galician Network for Colorectal Cancer Research; Spanish Ministry of Economy, Industry and Competitiveness The present study was supported by grants from the National Natural Science Foundation of China (Grant No. 31172234 and No. 31260556), the Planned Science and Technology Project of Hu-nan Province (2015NK3041), the Technology Specialty Fund for Cooperation between Jilin Province and the Chinese Academy of Sciences (2016SYHZ0022), and the Hunan Provincial Creation Development Project (Grant No. 2013TF3006). This work was also supported by the General Directorate of Culture, the Education and University Management of Xunta de Galicia (Ref. GRC2014/049), and the Galician Network for Colorectal Cancer Research (Ref. R2014/039). CF acknowledges a Juan de la Cierva postdoctoral fellowship grant from the Spanish Ministry of Economy, Industry and Competitiveness (Ref. FJCI-2015-26071). 62 2 2 2 17 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-302X FRONT MICROBIOL Front. Microbiol. JUN 30 2017.0 8.0 1216 10.3389/fmicb.2017.01216 0.0 12 Microbiology Science Citation Index Expanded (SCI-EXPANDED) Microbiology EZ1ES 28713345.0 Green Published, gold, Green Accepted 2023-03-23 WOS:000404453500003 0
C Ghiassi, A; Birke, R; Han, R; Chen, LY IEEE Ghiassi, Amirmasoud; Birke, Robert; Han, Rui; Chen, Lydia Y. LABELNET: Recovering Noisy Labels 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 18-22, 2021 ELECTR NETWORK Int Neural Network Soc,IEEE Computat Intelligence Soc Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. Recent studies improve the robustness of deep models against noisy labels without the knowledge of true labels. In this paper, we advocate to derive a stronger classifier which proactively makes use of the noisy labels in addition to the original images - turning noisy labels into learning features. To such an end, we propose a novel framework, LABELNET, composed of Amateur and Expert, which iteratively learn from each other. Amateur is a regular image classifier trained by the feedback of Expert, which imitates how human experts would correct the predicted labels from Amateur using the noise pattern learnt from the knowledge of both the noisy and ground truth labels. The trained Amateur and Expert proactively leverage the images and their noisy labels to infer image classes. Our empirical evaluations on noisy versions of MNIST, CIFAR-10, CIFAR-100 and real-world data of Clothing1M show that the proposed model can achieve robust classification against a wide range of noise ratios and with as little as 20-50% training data, compared to state-of-theart deep models that solely focus on distilling the impact of noisy labels. [Ghiassi, Amirmasoud; Chen, Lydia Y.] Delft Univ Technol, Delft, Netherlands; [Birke, Robert] ABB Corp Res, Baden, Switzerland; [Han, Rui] Beijing Inst Technol, Beijing, Peoples R China Delft University of Technology; ABB; Beijing Institute of Technology Ghiassi, A (corresponding author), Delft Univ Technol, Delft, Netherlands. s.ghiassi@tudelft.nl; robert.birke@ch.abb.com; hanrui@bit.edu.cn; lydiaychen@ieee.org 33 0 0 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-0-7381-3366-9 IEEE IJCNN 2021.0 10.1109/IJCNN52387.2021.9533562 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BS4TO 2023-03-23 WOS:000722581702023 0
J Zhang, WB; Zhang, PL; Yu, YH; Li, XY; Biancardo, SA; Zhang, JY Zhang, Weibin; Zhang, Pulin; Yu, Yinghao; Li, Xiying; Biancardo, Salvatore Antonio; Zhang, Junyi Missing Data Repairs for Traffic Flow With Self-Attention Generative Adversarial Imputation Net IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS English Article Data imputation; spatio-temporal analysis; deep learning; generative adversarial network; self-attention PREDICTION With the rapid development of sensor technologies, time series data collected by multiple and spatially distributed sensors have been widely used in different research fields. Examples of such data include geo-tagged temperature data collected by temperature sensors, air pollutant monitoring data, and traffic data collected by road traffic sensors. Due to sensor failure, communication errors and storage loss, etc., data collected by sensors inevitably includes missing data. However, models commonly used in the analysis of such large-scale data often rely on complete data sets. This paper proposes a model for the imputation of missing data of traffic flow, which combines a self-attention mechanism, an auto-encoder, and a generative adversarial network, into a self-attention generative adversarial imputation net (SA-GAIN). The introduction of the self-attention mechanism can help the proposed model to effectively capture correlations between spatially-distributed sensors at different time points. Adversarial training through two neural networks, called generators and discriminators, allows the proposed model to generate imputed data close to the real data. In comparison with different imputation models, the proposed model shows the best performance in imputing missing data. [Zhang, Weibin; Zhang, Pulin; Yu, Yinghao] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Peoples R China; [Li, Xiying] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510006, Peoples R China; [Biancardo, Salvatore Antonio] Univ Naples Federico II, Sch Polytech & Basic Sci, I-80138 Naples, Italy; [Zhang, Junyi] Hiroshima Univ, Grad Sch Adv Sci & Engn, Higashihiroshima 7398511, Japan Nanjing University of Science & Technology; Sun Yat Sen University; University of Naples Federico II; Hiroshima University Li, XY (corresponding author), Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510006, Peoples R China. wbin.zhang@outlook.com; stslxy@mail.sysu.edu.cn; Salvatoreantonio.biancardo@unina.it; zjy@hiroshima-u.ac.jp Zhang, Pulin/HPD-7194-2023; Zhang, Y J/HLG-1022-2023; Zhang, Jennifer/HLQ-0725-2023; Zhang, Junyi/HDN-7815-2022 Biancardo, Salvatore Antonio/0000-0003-2567-7977 National Key Research and Development Program of China [2018YFB1601100]; National Natural Science Foundation of China [71971116] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1601100 and in part by the National Natural Science Foundation of China under Grant 71971116. 61 8 8 32 68 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1524-9050 1558-0016 IEEE T INTELL TRANSP IEEE Trans. Intell. Transp. Syst. JUL 2022.0 23.0 7 7919 7930 10.1109/TITS.2021.3074564 0.0 MAY 2021 12 Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Transportation 3R1QN 2023-03-23 WOS:000732106000001 0
J Li, EZ; Du, PJ; Samat, A; Meng, YP; Che, MQ Li, Erzhu; Du, Peijun; Samat, Alim; Meng, Yaping; Che, Meiqin Mid-Level Feature Representation via Sparse Autoencoder for Remotely Sensed Scene Classification IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Global feature descriptors; scene classification; sparse autoencoder (SAE); unsupervised feature learning NEURAL-NETWORKS Feature representation is a classic problem in the machine learning community due to the fact that different representations can entangle and hide more or less the different explanatory factors of variation behind the raw data. Especially for scene classification, its performance generally depends on the discriminative power of feature representation. Recently, unsupervised feature learning attracts tremendous attention because of its ability to learn feature representation automatically. However, reliable performance of feature representations by unsupervised learning always requires a large number of features and complex framework of mid-level feature representation. To alleviate such drawbacks, this paper presents a new framework of mid-level feature representation, which does not need learnmany convolutional features during the unsupervised feature learning process, and has few parameter settings. In detail, the unsupervised feature learning method, sparse autoencoder, is employed to learn relatively small number of convolutional features from input dataset, and then extended features are extracted from the learned features by a multiple normalized difference features extraction method to compose a derivative feature set. At mid-level feature representation stage, in order to avoid poor performance of standard pooling technology in solving problems brought by rotation and translation of scene images, global feature descriptors (histogram moments, mean, variance, standard deviation) are utilized to build mid-level feature representations of images. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing datasets. The results demonstrate that the approach is effective, and shows strong performance for remotely sensed scene classification. [Li, Erzhu; Du, Peijun; Meng, Yaping; Che, Meiqin] Nanjing Univ, State Adm Surveying Mapping & Geoinformat China, Key Lab Satellite Mapping Technol & Applicat, Nanjing 210093, Jiangsu, Peoples R China; [Li, Erzhu; Du, Peijun; Che, Meiqin] Nanjing Univ, Dept Geogrp Informat Sci, Nanjing 210093, Peoples R China; [Li, Erzhu; Du, Peijun; Meng, Yaping; Che, Meiqin] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China; [Li, Erzhu; Du, Peijun; Meng, Yaping; Che, Meiqin] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China; [Samat, Alim] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Li, Erzhu; Samat, Alim] Nanjing Univ, Cartog & Geog Informat Syst, Nanjing 210008, Jiangsu, Peoples R China; [Du, Peijun] China Univ Mining & Technol, Xuzhou, Peoples R China; [Che, Meiqin] Univ Pavia, Telecommun & Remote Sensing Lab, I-27100 Pavia, Italy Nanjing University; Nanjing University; Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Nanjing University; China University of Mining & Technology; University of Pavia Du, PJ (corresponding author), Nanjing Univ, State Adm Surveying Mapping & Geoinformat China, Key Lab Satellite Mapping Technol & Applicat, Nanjing 210093, Jiangsu, Peoples R China.;Du, PJ (corresponding author), Nanjing Univ, Dept Geogrp Informat Sci, Nanjing 210093, Peoples R China.;Du, PJ (corresponding author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.;Du, PJ (corresponding author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China. lierzhu2008@126.com; dupjrs@126.com; ypmengnju@126.com; lastrye00@gmail.com Samat, Alim/H-5309-2019 Samat, Alim/0000-0002-9091-6033; Du, Peijun/0000-0002-2488-2656 Jiangsu Provincial Natural Science Foundation [BK2012018]; Research Projects of China Geological Survey [12120113007500]; Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities Jiangsu Provincial Natural Science Foundation(Natural Science Foundation of Jiangsu Province); Research Projects of China Geological Survey; Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by Jiangsu Provincial Natural Science Foundation under Grant BK2012018, in part by the Research Projects of China Geological Survey under Grant 12120113007500, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and in part by the Fundamental Research Funds for the Central Universities. (Corresponding author: Peijun Du.) 48 24 24 2 32 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. MAR 2017.0 10.0 3 1068 1081 10.1109/JSTARS.2016.2621011 0.0 14 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology EN2YL 2023-03-23 WOS:000395876100022 0
J Jin, QG; Meng, ZP; Pham, TD; Chen, Q; Wei, LY; Su, R Jin, Qiangguo; Meng, Zhaopeng; Tuan D Pham; Chen, Qi; Wei, Leyi; Su, Ran DUNet: A deformable network for retinal vessel segmentation KNOWLEDGE-BASED SYSTEMS English Article Retinal blood vessel; Segmentation; DUNet; U-Net; Deformable convolution BLOOD-VESSELS; SUPERVISED METHOD; IMAGES Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels' scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases. (C) 2019 Elsevier B.V. All rights reserved. [Jin, Qiangguo; Meng, Zhaopeng; Chen, Qi; Su, Ran] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Software, Tianjin, Peoples R China; [Tuan D Pham] Linkoping Univ, Dept Biomed Engn, Linkoping, Sweden; [Wei, Leyi] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin, Peoples R China; [Meng, Zhaopeng] Tianjin Univ Tradit Chinese Med, Tianjin, Peoples R China Tianjin University; Linkoping University; Tianjin University; Tianjin University of Traditional Chinese Medicine Su, R (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Sch Comp Software, Tianjin, Peoples R China. ran.su@tju.edu.cn Wei, Leyi/Q-5699-2018; , Ran/L-2323-2015 Wei, Leyi/0000-0003-1444-190X; , Ran/0000-0001-5922-0364 National Natural Science Foundation of China [61702361]; Science and Technology Program of Tianjin, China [16ZXHLGX00170]; National Key Technology R&D Program of China [2018YFB1701700] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Program of Tianjin, China; National Key Technology R&D Program of China(National Key Technology R&D Program) This work is supported by the National Natural Science Foundation of China [Grant No. 61702361], the Science and Technology Program of Tianjin, China [Grant No. 16ZXHLGX00170], the National Key Technology R&D Program of China [Grant No. 2015BAH52F00] and the National Key Technology R&D Program of China [Grant No. 2018YFB1701700]. 67 320 337 44 173 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. AUG 15 2019.0 178.0 149 162 10.1016/j.knosys.2019.04.025 0.0 14 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science IE9IG Green Submitted 2023-03-23 WOS:000472687500013 0
J Zang, AQ; de Vega, M; Fu, Y; Wang, HL; Beltran, D Zang, Anqi; de Vega, Manuel; Fu, Yang; Wang, Huili; Beltran, David Language switching may facilitate the processing of negative responses FRONTIERS IN PSYCHOLOGY English Article language switching; negation processing; inhibitory mechanism; cognitive control; bilinguals BILINGUAL SPEECH PRODUCTION; SENTENCE COMPREHENSION; AUTOBIOGRAPHICAL MEMORY; FMRI EVIDENCE; INNER SPEECH; INHIBITION; MECHANISMS; BRAIN; PSYTOOLKIT; RETRIEVAL It has been proposed that processing sentential negation recruits the neural network of inhibitory control (de Vega et al., 2016; Beltran et al., 2021). In addition, inhibition mechanisms also play a role in switching languages for bilinguals (Kroll et al., 2015). Since both processes may share inhibitory resources, the current study explored for the first time whether and how language-switching influences the processing of negation. To this end, two groups of Spanish-English bilinguals participated in an encoding-verification memory task. They read short stories involving the same two protagonists (Montse and Jordi), referring to their activities in four different scenarios in Spanish or English. Following each story, the participants received verification questions requiring yes or no responses depending on whether a given fact was correctly referred to one of the protagonists. Some of the verification questions were in the story's original language (non-switch condition) and others in the alternate language (switch condition). Results revealed that language-switching facilitated negative responses compared to affirmative responses, exclusively for questions switching from dominant language (L1) to non-dominant language (L2). This effect might reflect that the domain-general mechanisms of inhibitory control are recruited at least partially for both language switch and negation process simultaneously, although this phenomenon is modulated by language dominance. [Zang, Anqi; de Vega, Manuel; Fu, Yang; Beltran, David] Univ La Laguna, Inst Univ Neurociencia, Santa Cruz De Tenerife, Spain; [Wang, Huili] Zhejiang Univ City Coll, Sch Foreign Languages, Hangzhou, Peoples R China; [Beltran, David] Univ Nacl Educ Distancia UNED, Dept Psicol Basica 1, Madrid, Spain Universidad de la Laguna; Zhejiang University City College; Universidad Nacional de Educacion a Distancia (UNED) Wang, HL (corresponding author), Zhejiang Univ City Coll, Sch Foreign Languages, Hangzhou, Peoples R China. huiliw1966@outlook.com Beltrán, David/K-9684-2014; Wang, Huili/HLH-1020-2023 Beltrán, David/0000-0003-1658-5915; Wang, Huili/0000-0002-3060-4476 Spanish MINECO; European Regional Development Funds; National Social Science Foundation of China; [RTI2018-098730-B-100-R]; [21FYYA002] Spanish MINECO(Spanish Government); European Regional Development Funds(European Commission); National Social Science Foundation of China; ; Funding This work was co-funded by the Spanish MINECO and the European Regional Development Funds (Grant Number RTI2018-098730-B-100-R to DB and MV), and by the National Social Science Foundation of China (Grant Number 21FYYA002). 72 0 0 5 5 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. SEP 6 2022.0 13.0 906154 10.3389/fpsyg.2022.906154 0.0 11 Psychology, Multidisciplinary Social Science Citation Index (SSCI) Psychology 4W9EH 36148105.0 gold, Green Accepted 2023-03-23 WOS:000860457300001 0
J Wang, JY; He, B; Wang, J; Li, TH Wang, Jingyu; He, Bo; Wang, Jing; Li, Tonghong Intelligent VNFs Selection Based on Traffic Identification in Vehicular Cloud Networks IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY English Article Vehicular cloud network; NFV; traffic identification; resource management; VNFs selection ARCHITECTURE; INTERNET; SDN Internet of Vehicles (IoV) has become a significant research area due to its specific features and applications such as efficient traffic management, road safety, and entertainment. Vehicles are expected to carry relatively more communication systems, onboard computing facilities, storage, and increased sensing power. Vehicular Cloud Network (VCN) is a hybrid technology that has a remarkable impact on IoV by instantly using vehicular resources. Merging software-defined networking with VCN is valuable to vehicular networks. Network function virtualization can be deployed across the servers and vehicles within the VCN to provide customized agile services. Its deployment requires an appropriate management strategy that often manifests as the difficult online decision making tasks. This paper proposes an intelligent Virtualized Network Functions (VNFs) selection strategy. For satisfying the quality requirements of different vehicular services, this strategy is based on the results of traffic identification that utilizes deep neural network and Multi-Grained Cascade Forest to distinguish service behaviors. According to the order of VNFs, the vehicular network is divided into several layers where each arrived packet needs to be queued. The scheduler of each layer selects a layer to host the next VNF for the packets in the queue. Our experiments demonstrate that the proposed traffic identification method increases the precision by 7.7% and improves the real-time performance. Furthermore, the proposed model of VNFs selection reduces network congestion compared to traditional scheduling models. [Wang, Jingyu; He, Bo; Wang, Jing] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China; [Li, Tonghong] Tech Univ Madrid, Dept Comp Sci, Madrid 28040, Spain Beijing University of Posts & Telecommunications; Universidad Politecnica de Madrid He, B (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. wangjingyu@bupt.edu.cn; hebo@bupt.edu.cn; cjwjy212@sohu.com; tonghong@fi.upm.es wang, jing/HJA-5384-2022 li, tonghong/0000-0003-1533-4954 National Natural Science Foundation of China [61671079, 61771068, 61471063]; Beijing Municipal Natural Science Foundation [4182041]; Ministry of Economy and Competitiveness [TIN2013-46883, TIN2016-80350-P]; Regional Government of Madrid [S2013/ICE-2894]; FSE; FEDER National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Municipal Natural Science Foundation(Beijing Natural Science Foundation); Ministry of Economy and Competitiveness; Regional Government of Madrid; FSE(European Social Fund (ESF)); FEDER(European Commission) This work was jointly supported in part by the National Natural Science Foundation of China under Grants 61671079, 61771068, and 61471063, in part by the Beijing Municipal Natural Science Foundation under Grant 4182041, in part by the Ministry of Economy and Competitiveness under Grants TIN2013-46883 and TIN2016-80350-P, and in part by the Regional Government of Madrid under project Cloud4BigData under Grant S2013/ICE-2894, cofunded by FSE and FEDER. The review of this paper was coordinated by the Guest Editors of the Special Section on Machine Learning-Based Internet of Vehicles. (Corresponding author: Bo He.) 30 20 20 2 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9545 1939-9359 IEEE T VEH TECHNOL IEEE Trans. Veh. Technol. MAY 2019.0 68.0 5 4140 4147 10.1109/TVT.2018.2880754 0.0 8 Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications; Transportation IB1HY 2023-03-23 WOS:000470017500005 0
J Yang, PP; Baracchi, D; Iuliani, M; Shullani, D; Ni, RR; Zhao, Y; Piva, A Yang, Pengpeng; Baracchi, Daniele; Iuliani, Massimo; Shullani, Dasara; Ni, Rongrong; Zhao, Yao; Piva, Alessandro Efficient Video Integrity Analysis Through Container Characterization IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Article Containers; Streaming media; Forensics; Nickel; Object recognition; Operating systems; Video forensics; video container; social media; integrity; authentication; video tampering; decision trees; machine learning Most video forensic techniques look for traces within the data stream that are, however, mostly ineffective when dealing with strongly compressed or low resolution videos. Recent research highlighted that useful forensic traces are also left in the video container structure, thus offering the opportunity to understand the life-cycle of a video file without looking at the media stream itself. In this article we introduce a container-based method to identify the software used to perform a video manipulation and, in most cases, the operating system of the source device. As opposed to the state of the art, the proposed method is both efficient and effective and can also provide a simple explanation for its decisions. This is achieved by using a decision-tree-based classifier applied to a vectorial representation of the video container structure. We conducted an extensive validation on a dataset of 7000 video files including both software manipulated contents (ffmpeg, Exiftool, Adobe Premiere, Avidemux, and Kdenlive), and videos exchanged through social media platforms (Facebook, TikTok, Weibo and YouTube). This dataset has been made available to the research community. The proposed method achieves an accuracy of 97.6% in distinguishing pristine from tampered videos and classifying the editing software, even when the video is cut without re-encoding or when it is downscaled to the size of a thumbnail. Furthermore, it is capable of correctly identifying the operating system of the source device for most of the tampered videos. [Yang, Pengpeng; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China; [Yang, Pengpeng; Ni, Rongrong; Zhao, Yao] Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China; [Baracchi, Daniele; Iuliani, Massimo; Shullani, Dasara; Piva, Alessandro] Univ Florence, Dept Informat Engn, I-50139 Florence, Italy; [Iuliani, Massimo; Piva, Alessandro] PIN Scrl, FORLAB, Multimedia Forens Lab, I-59100 Prato, Italy Beijing Jiaotong University; Beijing Jiaotong University; University of Florence Ni, RR (corresponding author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China.;Piva, A (corresponding author), Univ Florence, Dept Informat Engn, I-50139 Florence, Italy. 14120339@bjtu.edu.cn; daniele.baracchi@unifi.it; massimo.iuliani@unifi.it; dasara.shullani@unifi.it; rrni@bjtu.edu.cn; yzhao@bjtu.edu.cn; alessandro.piva@unifi.it Baracchi, Daniele/AHD-5162-2022 Zhao, Yao/0000-0002-8581-9554 National Key Research and Development of China [2016YFB0800404]; National NSF of China [U1936212, 61672090]; AFRL; DARPA [FA8750-16-2-0188]; Italian Ministry of Education, Universities and Research MIUR [2017Z595XS] National Key Research and Development of China; National NSF of China(National Natural Science Foundation of China (NSFC)); AFRL(United States Department of DefenseUS Air Force Research Laboratory); DARPA(United States Department of DefenseDefense Advanced Research Projects Agency (DARPA)); Italian Ministry of Education, Universities and Research MIUR(Ministry of Education, Universities and Research (MIUR)) This work was supported in part by the National Key Research and Development of China under Grant 2016YFB0800404, in part by National NSF of China under Grants U1936212 and 61672090, in part by AFRL, in part by DARPA under Grant FA8750-16-2-0188, and in part by the Italian Ministry of Education, Universities and Research MIUR under Grant 2017Z595XS. 24 6 6 1 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. AUG 2020.0 14.0 5 947 954 10.1109/JSTSP.2020.3008088 0.0 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering NG8BM Green Submitted 2023-03-23 WOS:000564205000004 0
J Wang, JT; Liu, LS; Lu, MZ; Okinda, C; Lovarelli, D; Guarino, M; Shen, MX Wang, Jintao; Liu, Longshen; Lu, Mingzhou; Okinda, Cedric; Lovarelli, Daniela; Guarino, Marcella; Shen, Mingxia The estimation of broiler respiration rate based on the semantic segmentation and video amplification FRONTIERS IN PHYSICS English Article broiler; respiration rate; computer vision; semantic segmentation; Euler video magnification MACHINE VISION Respiratory rate is an indicator of a broilers' stress and health status, thus, it is essential to detect respiratory rate contactless and stress-freely. This study proposed an estimation method of broiler respiratory rate by deep learning and machine vision. Experiments were performed at New Hope (Shandong Province, P. R. China) and Wen's group (Guangdong Province, P. R. China), and a total of 300 min of video data were collected. By separating video frames, a data set of 3,000 images was made, and two semantic segmentation models were trained. The single-channel Euler video magnification algorithm was used to amplify the belly fluctuation of the broiler, which saved 55% operation time compared with the traditional Eulerian video magnification algorithm. The contour features significantly related to respiration were used to obtain the signals that could estimate broilers' respiratory rate. Detrending and band-pass filtering eliminated the influence of broiler posture conversion and motion on the signal. The mean absolute error, root mean square error, average accuracy of the proposed respiratory rate estimation technique for broilers were 3.72%, 16.92%, and 92.19%, respectively. [Wang, Jintao] Henan Polytech Univ, Sch Mech & Power Engn, Jiaozuo, Henan, Peoples R China; [Wang, Jintao; Liu, Longshen; Lu, Mingzhou; Okinda, Cedric; Shen, Mingxia] Nanjing Agr Univ, Coll Engn, Lab Modern Facil Agr Technol & Equipment Engn Jian, Nanjing, Jiangsu, Peoples R China; [Okinda, Cedric] Masinde Muliro Univ Sci & Technol, Sch Engn & Built Environm, Dept Elect & Commun Engn, Kakamega, Kenya; [Lovarelli, Daniela; Guarino, Marcella] Univ Milan, Dept Sci & Environm Policy, Milan, Italy Henan Polytechnic University; Nanjing Agricultural University; University of Milan Shen, MX (corresponding author), Nanjing Agr Univ, Coll Engn, Lab Modern Facil Agr Technol & Equipment Engn Jian, Nanjing, Jiangsu, Peoples R China. mingxia@njau.edu.cn 26 0 0 6 6 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2296-424X FRONT PHYS-LAUSANNE Front. Physics DEC 21 2022.0 10.0 1047077 10.3389/fphy.2022.1047077 0.0 13 Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics 7N1JM gold, Green Published 2023-03-23 WOS:000907102300001 0
J Yuan, SH; Wang, YD; Liang, TC; Jiang, WC; Lin, S; Zhao, ZM Yuan, Shenghai; Wang, Yudong; Liang, Tiancai; Jiang, Wenchao; Lin, Sui; Zhao, Zhiming Real-time recognition and warning of mask wearing based on improved YOLOv5 R6.1 INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS English Article deep learning; early warning alert; mask-wearing; target detection; Yolov5 r6; 1 Since the new crown epidemic, mask-wearing has become a new normal in people's work and life. The inspection mechanism for mask-wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of pick-up on entry has led to the severe formalization of mask-wearing inspection. Manual detection of mask-wearing in an open and dynamic crowded environment is unrealistic, which is not only time-consuming and labor-intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real-time recognition and early warning method for mask-wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask-wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and recall of this method reach 92.9%, 94.1%, and 88.5% on average, and the average frames per second (FPS) reaches 117. Moreover, the mAP and FPS are improved by an average of 6.5% and 474% compared with algorithms based on RetinaNet, Attention-Retina, Single Shot multibox Detector, Fast-RCNN, YOLOv4, and YOLOv5. [Yuan, Shenghai; Wang, Yudong; Jiang, Wenchao; Lin, Sui] Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China; [Liang, Tiancai; Jiang, Wenchao] Guangzhou Univ, Inst Artificial Intelligence & Blockchain, Guangzhou, Peoples R China; [Jiang, Wenchao; Zhao, Zhiming] Univ Amsterdam, Syst & Network Engn Res Grp, Informat Inst, Amsterdam, Netherlands Guangdong University of Technology; Guangzhou University; University of Amsterdam Jiang, WC (corresponding author), Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China. jiangwenchao@gdut.edu.cn jiang, wen/GYI-9662-2022 sui, lin/0000-0002-0735-5416 Guangzhou Science and Technology Plan Project [201902020016]; Guangdong Science and Technology Plan Project [2019B010139001, 2021B1212100004]; Guangdong Natural Science Fund Project [2021A1515011243]; Research and Development Projects of National Key fields [2018YFB1004202] Guangzhou Science and Technology Plan Project; Guangdong Science and Technology Plan Project; Guangdong Natural Science Fund Project; Research and Development Projects of National Key fields This paper is supported partly by Guangzhou Science and Technology Plan Project (201902020016), Guangdong Science and Technology Plan Project (2019B010139001 and 2021B1212100004), Guangdong Natural Science Fund Project (2021A1515011243) and, and Research and Development Projects of National Key fields (2018YFB1004202). 50 0 0 41 47 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0884-8173 1098-111X INT J INTELL SYST Int. J. Intell. Syst. NOV 2022.0 37.0 11 9309 9338 10.1002/int.22994 0.0 AUG 2022 30 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4V0ZM 2023-03-23 WOS:000840416200001 0
J Xie, J; He, NJ; Fang, LY; Plaza, A Xie, Jie; He, Nanjun; Fang, Leyuan; Plaza, Antonio Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Free-scale convolutional neural networks (CNNs); fully connected layers (FCLs); remote sensing scene classification IMAGE RETRIEVAL; FEATURES Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by fully connected layers (FCLs) in the pretrained CNN model. Such a resizing process often discards key information in the scenes and thus deteriorates the classification performance. To address this issue, in this paper, we introduce a scale-free CNN (SF-CNN) for remote sensing scene classification. Specifically, the FCLs in the CNN model are first converted into convolutional layers, which not only allow the input images to be of arbitrary sizes but also retain the ability to extract discriminative features using a traditional sliding-window-based strategy. Then, a global average pooling (GAP) layer is added after the final convolutional layer so that input images of arbitrary size can be mapped to feature maps of uniform size. Finally, we utilize the resulting feature maps to create a new FCL that is fed to a softmax layer for final classification. Our experimental results conducted using several real data sets demonstrate the superiority of the proposed SF-CNN method over several well-known classification methods, including pretrained CNN-based ones. [Xie, Jie; He, Nanjun; Fang, Leyuan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Xie, Jie; He, Nanjun; Fang, Leyuan] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, E-10003 Caceres, Spain Hunan University; Hunan University; Universidad de Extremadura Fang, LY (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. xj_xj@hnu.edu.cn; henanjun@hnu.edu.cn; fangleyuan@gmail.com; aplaza@unex.es Xie, Jie/AAX-8665-2020; Plaza, Antonio/C-4455-2008 Plaza, Antonio/0000-0002-9613-1659; He, Nanjun/0000-0003-3105-6499; Xie, Jie/0000-0002-4710-4275; Fang, Leyuan/0000-0003-2351-4461 National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]; National Natural Science Foundation [61771192]; National Natural Science Foundation of Hunan Province [2018JJ3077]; Science and Technology Plan Project Fund of Hunan Province [CX2018B171, 2017RS3024, 2018TP1013]; Science and Technology Talents Program of Hunan Association for Science and Technology [2017TJ-Q09]; China Postdoctoral Science Foundation [2017T100597]; MINECO [TIN2015-63646-C5-5-R] National Natural Science Fund of China for International Cooperation and Exchanges; National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); National Natural Science Foundation of Hunan Province(Natural Science Foundation of Hunan Province); Science and Technology Plan Project Fund of Hunan Province; Science and Technology Talents Program of Hunan Association for Science and Technology; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); MINECO(Spanish Government) This work was supported in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, in part by the National Natural Science Foundation under Grant 61771192, in part by the National Natural Science Foundation of Hunan Province under Grant 2018JJ3077, in part by the Science and Technology Plan Project Fund of Hunan Province under Grant CX2018B171, Grant 2017RS3024, and Grant 2018TP1013, in part by the Science and Technology Talents Program of Hunan Association for Science and Technology under Grant 2017TJ-Q09, in part by the China Postdoctoral Science Foundation under Project 2017T100597, and in part by the MINECO Project under Grant TIN2015-63646-C5-5-R. 66 126 132 13 58 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2019.0 57.0 9 6916 6928 10.1109/TGRS.2019.2909695 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology IV3YB 2023-03-23 WOS:000484209000048 0
J Schlick, T; Portillo-Ledesma, S; Blaszczyk, M; Dalessandro, L; Ghosh, S; Hackl, K; Harnish, C; Kotha, S; Livescu, D; Masud, A; Matous, K; Moyeda, A; Oskay, C; Fish, J Schlick, Tamar; Portillo-Ledesma, Stephanie; Blaszczyk, Mischa; Dalessandro, Luke; Ghosh, Somnath; Hackl, Klaus; Harnish, Cale; Kotha, Shravan; Livescu, Daniel; Masud, Arif; Matous, Karel; Moyeda, Arturo; Oskay, Caglar; Fish, Jacob A MULTISCALE VISION-ILLUSTRATIVE APPLICATIONS FROM BIOLOGY TO ENGINEERING INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING English Article multiscale modeling; computer simulations; biology; engineering; structural mechanics; machine learning; applications of modeling to life and physical sciences FATIGUE-CRACK NUCLEATION; CONSTITUTIVE MODELS PHCMS; FINITE-ELEMENT-METHOD; WAVELET COLLOCATION METHOD; PLASTICITY FE SIMULATIONS; NONLOCAL DISPERSIVE MODEL; ADAPTIVE MESH REFINEMENT; LARGE-EDDY SIMULATION; HETEROGENEOUS MEDIA; ACOUSTIC METAMATERIALS Modeling and simulation have quickly become equivalent pillars of research along with traditional theory and experimentation. The growing realization that most complex phenomena of interest span many orders of spatial and temporal scales has led to an exponential rise in the development and application of multiscale modeling and simulation over the past two decades. In this perspective, the associate editors of the International Journal for Multiscale Computational Engineering and their co-workers illustrate current applications in their respective fields spanning biomolecular structure and dynamics, civil engineering and materials science, computational mechanics, aerospace and mechanical engineering, and more. Such applications are highly tailored, exploit the latest and ever-evolving advances in both computer hardware and software, and contribute significantly to science, technology, and medical challenges in the 21st century. [Schlick, Tamar; Portillo-Ledesma, Stephanie] NYU, Dept Chem, 4 Washington Pl, New York, NY 10003 USA; [Schlick, Tamar] NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10012 USA; [Schlick, Tamar] NYU Shanghai, NYU ECNU Ctr Computat Chem, Shanghai, Peoples R China; [Blaszczyk, Mischa; Hackl, Klaus] Ruhr Univ Bochum, Inst Mech Mat, D-44721 Bochum, Germany; [Dalessandro, Luke] Indiana Univ, Dept Intelligent Syst Engn, Bloomington, IN 47405 USA; [Ghosh, Somnath; Kotha, Shravan] Johns Hopkins Univ, Dept Civil & Syst Engn, Baltimore, MD 21218 USA; [Harnish, Cale; Matous, Karel] Univ Notre Dame, Dept Aerosp & Mech Engn, Notre Dame, IN 46556 USA; [Livescu, Daniel] Los Alamos Natl Lab, Comp & Computat Sci Div, Los Alamos, NM 87545 USA; [Masud, Arif] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA; [Moyeda, Arturo] Constructora Moyeda, Monterrey, Mexico; [Oskay, Caglar] Vanderbilt Univ, Dept Civil & Environm Engn, Nashville, TN 37235 USA; [Fish, Jacob] Columbia Univ, Dept Civil Engn & Engn Mech, New York, NY 10027 USA New York University; New York University; NYU Shanghai; Ruhr University Bochum; Indiana University System; Indiana University Bloomington; Johns Hopkins University; University of Notre Dame; United States Department of Energy (DOE); Los Alamos National Laboratory; University of Illinois System; University of Illinois Urbana-Champaign; Vanderbilt University; Columbia University Schlick, T (corresponding author), NYU, Dept Chem, 4 Washington Pl, New York, NY 10003 USA. schlick@nyu.edu Livescu, Daniel/B-8590-2009 Livescu, Daniel/0000-0003-2367-1547 National Science Foundation; Divisions of Mathematical Sciences and of Chemistry, National Institutes of Health [2030377]; National Institute of General Medical Sciences, and Philip-Morris International [R35GM122562]; National Science Foundation CMMI MOMS and CDSE programs [1435115]; Los Alamos National Laboratory (LANL) [369229, 370985] National Science Foundation(National Science Foundation (NSF)); Divisions of Mathematical Sciences and of Chemistry, National Institutes of Health(United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA); National Institute of General Medical Sciences, and Philip-Morris International; National Science Foundation CMMI MOMS and CDSE programs(National Science Foundation (NSF)); Los Alamos National Laboratory (LANL)(United States Department of Energy (DOE)Los Alamos National Laboratory) T.S. gratefully acknowledges funding from the National Science Foundation through RAPID Award No. 2030377 from the Divisions of Mathematical Sciences and of Chemistry, National Institutes of Health, R35GM122562 Award from the National Institute of General Medical Sciences, and Philip-Morris International. C.O. gratefully acknowl-edges funding from the National Science Foundation CMMI MOMS and CDS&E programs (Grant No. 1435115) . C.H., L.D., K.M., and D.L. gratefully acknowledge Los Alamos National Laboratory (LANL) for Award Nos. 369229 and 370985. 154 0 0 2 2 BEGELL HOUSE INC DANBURY 50 NORTH ST, DANBURY, CT 06810 USA 1543-1649 1940-4352 INT J MULTISCALE COM Int. J. Multiscale Comput. Eng. 2021.0 19.0 2 39 73 35 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Mathematics WF6IO 2023-03-23 WOS:000706406300004 0
J Zhang, ZX; Han, J; Qian, K; Janott, C; Guo, YN; Schuller, B Zhang, Zixing; Han, Jing; Qian, Kun; Janott, Christoph; Guo, Yanan; Schuller, Bjoern Snore-GANs: Improving Automatic Snore Sound Classification With Synthesized Data IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS English Article Gallium nitride; Informatics; Sleep apnea; Generators; Machine learning; Data models; Snore sound classification; obstructive sleep apnea; data augmentation; data synthesis One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional generative adversarial networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing realistic high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC. [Zhang, Zixing; Schuller, Bjoern] Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England; [Han, Jing; Qian, Kun; Guo, Yanan; Schuller, Bjoern] Univ Augsburg, ZDB Chair Embedded Intelligence Hlth Care & Wellb, D-86159 Augsburg, Germany; [Qian, Kun] Tech Univ Munich, MMK, Machine Intelligence & Signal Proc Grp, D-80333 Munich, Germany; [Qian, Kun] Univ Tokyo, Grad Sch Educ, Educ Physiol Lab, Tokyo 1138654, Japan; [Janott, Christoph] Tech Univ Munich, Inst Med Engn, D-85748 Garching, Germany; [Janott, Christoph; Schuller, Bjoern] AudEERING GmbH, D-82205 Gilching, Germany; [Guo, Yanan] Lanzhou Univ, Lanzhou 730000, Peoples R China Imperial College London; University of Augsburg; Technical University of Munich; University of Tokyo; Technical University of Munich; Lanzhou University Zhang, ZX (corresponding author), Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England. zixing.zhang@imperial.ac.uk; jing.han@informatik.uni-augsburg.de; qian@p.u-tokyo.ac.jp; cjanott@audeering.com; yanan.guo@informatik.uni-augsburg.de; bjoern.schuller@imperial.ac.uk Qian, Kun/AAC-4279-2019 Han, Jing/0000-0001-5776-6849; Qian, Kun/0000-0002-1918-6453; Schuller, Bjorn/0000-0002-6478-8699 U.K.'s Economic and Social Research Council [HJ-253479]; EU's Horizon 2020/EFPIA Innovative Medicines Initiative [115902]; ESRC [ES/R00398X/1] Funding Source: UKRI U.K.'s Economic and Social Research Council(UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)); EU's Horizon 2020/EFPIA Innovative Medicines Initiative; ESRC(UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)) This work was supported in part by the U.K.'s Economic and Social Research Council through the Research Grant HJ-253479 (ACLEW) and in part by the EU's Horizon 2020/EFPIA Innovative Medicines Initiative through Grant 115902 (RADAR-CNS). 63 22 22 1 17 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2194 2168-2208 IEEE J BIOMED HEALTH IEEE J. Biomed. Health Inform. JAN 2020.0 24.0 1 300 310 10.1109/JBHI.2019.2907286 0.0 11 Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Mathematical & Computational Biology; Medical Informatics KB6ZZ 30946682.0 Green Submitted 2023-03-23 WOS:000506642000030 0
J Radhakrishnan, S; Adam, J; Adamczyk, L; Adams, JR; Adkins, JK; Agakishiev, G; Aggarwal, MM; Ahammed, Z; Alekseev, I; Anderson, DM; Aoyama, R; Aparin, A; Arkhipkin, D; Ashraf, MU; Atetalla, F; Attri, A; Averichev, GS; Bai, X; Bairathi, V; Barish, K; Bassill, AJ; Behera, A; Beliwied, R; Bhasin, A; Bhati, AK; Bielcik, J; Bielcikova, J; Bland, LC; Bordyuzhin, IG; Brandenburg, JD; Aschenauer, EC; Brandin, AV; Brown, D; Bryslawskyj, J; Bunzarov, I; Butterworth, J; Caines, H; Sanchez, MCD; Cebra, D; Chakaberia, I; Chaloupka, P; Chan, BK; Chang, FH; Chang, Z; Chankova-Bunzarova, N; Chatterjee, A; Chattopadhyay, S; Chen, JH; Chen, X; Cheng, J; Cherney, M; Christie, W; Contin, G; Crawford, HJ; Csanad, M; Das, S; Dedovich, TG; Deppner, IM; Derevschikov, AA; Didenko, L; Dilks, C; Dong, X; Drachenberg, JL; Dunlop, JC; Efimov, LG; Elsey, N; Engelage, J; Eppley, G; Esha, R; Esumi, S; Ewigleben, J; Fatemi, R; Fazio, S; Federic, P; Fedorisin, J; Filip, P; Finch, E; Fisyak, Y; Flores, CE; Fulek, L; Gagliardi, CA; Galatyuk, T; Geurts, F; Gibson, A; Grosnick, D; Gunarathne, DS; Guo, Y; Gupta, A; Guryn, W; Hamad, AI; Flamed, A; Harlenderova, A; Evdokimov, O; Eyser, O; Harris, JW; He, L; Heppelmann, S; Herrmann, N; Hirsch, A; Holub, L; Hong, Y; Horvat, S; Huang, B; Huang, HZ; Huang, SL; Huang, T; Huang, X; Humanic, TJ; Huo, P; Igo, G; Jacobs, WW; Jentsch, A; Jia, J; Jiang, K; Jowzaee, S; Ju, X; Judd, EG; Kabana, S; Kagamaster, S; Kalinkin, D; Kang, K; Kapukchyan, D; Kauder, K; Ke, HW; Keane, D; Kechechyan, A; Kikola, DP; Kim, C; Kinghorn, TA; Kisel, I; Kisiel, A; Kocan, M; Kochenda, L; Kosarzewski, LK; Kraishan, AF; Kramarik, L; Krauth, L; Kravtsov, P; Krueger, K; Kulathunga, N; Kumar, L; Elayavalli, RK; Kvapil, J; Kwasizur, JH; Lacey, R; Landgraf, JM; Lauret, J; Lebedev, A; Lednicky, R; Lee, JH; Li, C; Li, W; Li, X; Li, Y; Liang, Y; Licenik, R; Lidrych, J; Lin, T; Lipiec, A; Lisa, MA; Liu, F; Liu, H; Liu, P; Liu, Y; Liu, Z; Ljubicic, T; Llope, WJ; Lomnitz, M; Longacre, RS; Luo, S; Luo, X; Ma, GL; Ma, L; Ma, R; Ma, YG; Magdy, N; Majka, R; Mallick, D; Margetis, S; Markert, C; Matis, HS; Matonoha, O; Mazer, JA; Meehan, K; Mei, JC; Minaev, NG; Mioduszewski, S; Mishra, D; Mohanty, B; Mondal, MM; Mooney, I; Moravcova, Z; Morozov, DA; Nasim, M; Nayak, K; Negrete, JD; Nelson, JM; Nemes, DB; Nie, M; Nigmatkulov, G; Niida, T; Nogach, LV; Nonaka, T; Odyniec, G; Ogawa, A; Oh, K; Oh, S; Okorokov, VA; Olvitt, D; Page, BS; Pak, R; Panebratsev, Y; Pawlik, B; Pei, H; Perkins, C; Pinter, RL; Pluta, J; Porter, J; Posik, M; Pruthi, NK; Przybycien, M; Putschke, J; Quintero, A; Radhakrishnan, SK; Ramachandran, S; Ray, RL; Reed, R; Ritter, HG; Roberts, JB; Rogachevskiy, OV; Romero, JL; Ruan, L; Rusnak, J; Sahoo, NR; Sahu, PK; Salur, S; Sandweiss, J; Schambach, J; Schmah, AM; Schmidke, WB; Schmitz, N; Schweid, BR; Seck, F; Seger, J; Sergeeva, M; Seto, R; Seyboth, P; Shah, N; Shahaliev, E; Shanmuganathan, PV; Shao, M; Shen, F; Shen, WQ; Shi, SS; Shou, QY; Sichtermann, EP; Siejka, S; Sikora, R; Simko, M; Rusnakova, O; Singh, J; Singha, S; Smirnov, D; Smirnov, N; Solyst, W; Sorensen, P; Spinka, HM; Srivastava, B; Stanislaus, TDS; Stewart, DJ; Strikhanov, M; Stringfellow, B; Suaide, AAP; Sugiura, T; Sumbera, M; Summa, B; Sun, XM; Sun, Y; Surrow, B; Svirida, DN; Szymanski, P; Tang, AH; Tang, Z; Taranenko, A; Tarnowsky, T; Thomas, JH; Timmins, AR; Tlusty, D; Todoroki, T; Tokarev, M; Tomkiel, CA; Trentalange, S; Tribble, RE; Tribedy, P; Tripathy, SK; Tsai, OD; Tu, B; Ullrich, T; Underwood, DG; Upsal, I; Van Buren, G; Vanek, J; Vasiliev, AN; Vassiliev, I; Videbaek, F; Vokal, S; Voloshin, SA; Vossen, A; Wang, F; Wang, G; Wang, P; Wang, Y; Webb, JC; Wen, L; Westfall, GD; Wieman, H; Wissink, SW; Witt, R; Wu, Y; Xiao, ZG; Xie, G; Xie, W; Xu, J; Xu, N; Xu, QH; Xu, YF; Xu, Z; Yang, C; Yang, Q; Yang, S; Yang, Y; Ye, Z; Yi, L; Yip, K; Yoo, IK; Zbroszczyk, H; Zha, W; Zhang, J; Zhang, L; Zhang, S; Zhang, XP; Zhang, Y; Zhang, Z; Zhao, J; Zhong, C; Zhou, C; Zhu, X; Zhu, Z; Zyzak, M Radhakrishnan, Sooraj; Adam, J.; Adamczyk, L.; Adams, J. R.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I; Anderson, D. M.; Aoyama, R.; Aparin, A.; Arkhipkin, D.; Ashraf, M. U.; Atetalla, F.; Attri, A.; Averichev, G. S.; Bai, X.; Bairathi, V; Barish, K.; Bassill, A. J.; Behera, A.; Beliwied, R.; Bhasin, A.; Bhati, A. K.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Brandenburg, J. D.; Aschenauer, E. C.; Brandin, A., V; Brown, D.; Bryslawskyj, J.; Bunzarov, I; Butterworth, J.; Caines, H.; Sanchez, M. Calderon de la Barca; Cebra, D.; Chakaberia, I; Chaloupka, P.; Chan, B. K.; Chang, F-H; Chang, Z.; Chankova-Bunzarova, N.; Chatterjee, A.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Contin, G.; Crawford, H. J.; Csanad, M.; Das, S.; Dedovich, T. G.; Deppner, I. M.; Derevschikov, A. A.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Dunlop, J. C.; Efimov, L. G.; Elsey, N.; Engelage, J.; Eppley, G.; Esha, R.; Esumi, S.; Ewigleben, J.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Filip, P.; Finch, E.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Galatyuk, T.; Geurts, F.; Gibson, A.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, A.; Guryn, W.; Hamad, A., I; Flamed, A.; Harlenderova, A.; Evdokimov, O.; Eyser, O.; Harris, J. W.; He, L.; Heppelmann, S.; Herrmann, N.; Hirsch, A.; Holub, L.; Hong, Y.; Horvat, S.; Huang, B.; Huang, H. Z.; Huang, S. L.; Huang, T.; Huang, X.; Humanic, T. J.; Huo, P.; Igo, G.; Jacobs, W. W.; Jentsch, A.; Jia, J.; Jiang, K.; Jowzaee, S.; Ju, X.; Judd, E. G.; Kabana, S.; Kagamaster, S.; Kalinkin, D.; Kang, K.; Kapukchyan, D.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Kikola, D. P.; Kim, C.; Kinghorn, T. A.; Kisel, I; Kisiel, A.; Kocan, M.; Kochenda, L.; Kosarzewski, L. K.; Kraishan, A. F.; Kramarik, L.; Krauth, L.; Kravtsov, P.; Krueger, K.; Kulathunga, N.; Kumar, L.; Elayavalli, R. Kunnawalkam; Kvapil, J.; Kwasizur, J. H.; Lacey, R.; Landgraf, J. M.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, C.; Li, W.; Li, X.; Li, Y.; Liang, Y.; Licenik, R.; Lidrych, J.; Lin, T.; Lipiec, A.; Lisa, M. A.; Liu, F.; Liu, H.; Liu, P.; Liu, Y.; Liu, Z.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, S.; Luo, X.; Ma, G. L.; Ma, L.; Ma, R.; Ma, Y. G.; Magdy, N.; Majka, R.; Mallick, D.; Margetis, S.; Markert, C.; Matis, H. S.; Matonoha, O.; Mazer, J. A.; Meehan, K.; Mei, J. C.; Minaev, N. G.; Mioduszewski, S.; Mishra, D.; Mohanty, B.; Mondal, M. M.; Mooney, I; Moravcova, Z.; Morozov, D. A.; Nasim, Md; Nayak, K.; Negrete, J. D.; Nelson, J. M.; Nemes, D. B.; Nie, M.; Nigmatkulov, G.; Niida, T.; Nogach, L., V; Nonaka, T.; Odyniec, G.; Ogawa, A.; Oh, K.; Oh, S.; Okorokov, V. A.; Olvitt, D., Jr.; Page, B. S.; Pak, R.; Panebratsev, Y.; Pawlik, B.; Pei, H.; Perkins, C.; Pinter, R. L.; Pluta, J.; Porter, J.; Posik, M.; Pruthi, N. K.; Przybycien, M.; Putschke, J.; Quintero, A.; Radhakrishnan, S. K.; Ramachandran, S.; Ray, R. L.; Reed, R.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O., V; Romero, J. L.; Ruan, L.; Rusnak, J.; Sahoo, N. R.; Sahu, P. K.; Salur, S.; Sandweiss, J.; Schambach, J.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Schweid, B. R.; Seck, F.; Seger, J.; Sergeeva, M.; Seto, R.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P., V; Shao, M.; Shen, F.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Siejka, S.; Sikora, R.; Simko, M.; Rusnakova, O.; Singh, J.; Singha, S.; Smirnov, D.; Smirnov, N.; Solyst, W.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stewart, D. J.; Strikhanov, M.; Stringfellow, B.; Suaide, A. A. P.; Sugiura, T.; Sumbera, M.; Summa, B.; Sun, X. M.; Sun, Y.; Surrow, B.; Svirida, D. N.; Szymanski, P.; Tang, A. H.; Tang, Z.; Taranenko, A.; Tarnowsky, T.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Todoroki, T.; Tokarev, M.; Tomkiel, C. A.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Tsai, O. D.; Tu, B.; Ullrich, T.; Underwood, D. G.; Upsal, I; Van Buren, G.; Vanek, J.; Vasiliev, A. N.; Vassiliev, I; Videbaek, F.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, F.; Wang, G.; Wang, P.; Wang, Y.; Webb, J. C.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y.; Xiao, Z. G.; Xie, G.; Xie, W.; Xu, J.; Xu, N.; Xu, Q. H.; Xu, Y. F.; Xu, Z.; Yang, C.; Yang, Q.; Yang, S.; Yang, Y.; Ye, Z.; Yi, L.; Yip, K.; Yoo, I-K; Zbroszczyk, H.; Zha, W.; Zhang, J.; Zhang, L.; Zhang, S.; Zhang, X. P.; Zhang, Y.; Zhang, Z.; Zhao, J.; Zhong, C.; Zhou, C.; Zhu, X.; Zhu, Z.; Zyzak, M. STAR Collaboration Measurements of open charm production in Au plus Au collisions at root s(NN)=200 GeV with the STAR experiment at RHIC NUCLEAR PHYSICS A English Article; Proceedings Paper 27th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter) MAY 13-19, 2018 Venice, ITALY Italian Inst Nucl Phys Quark-gluon plasma; charm quark production; charm quark hadronization; heavy flavor energy loss We report in these proceedings on the measurements of collision centrality and transverse momentum (p(T)) dependences of the Lambda(+/-)(c) production in Au+Au collisions at root s(NN) = 200 GeV, using the STAR Heavy Flavor Tracker (HFT). The Lambda(+/-)(c) signal significance is greatly improved with the addition of the high-statistics data set collected in 2016 and the use of a supervised machine learning method for topological reconstruction of the decay vertices. The measured Lambda(+/-)(c)/D-0 ratio shows a significant enhancement compared to the PYTHIA prediction for p+p collisions, across the measured p(T) range. We also report on the updated measurements of D-0 nuclear modification factors R-AA and R-CP using the 2014 data with the HFT. The measured D-0 R-AA in central collisions is lower than unity across the p(T) interval of the measurement. The D-0 yields show strong suppression at high p(T) (> 6 GeV/c) in central collisions, consistent with that of light flavor hadrons. The c (c) over bar production cross section per binary nucleon collision at midrapidity in Au+Au collisions at 200 GeV is extracted based on the various charm hadron measurements by STAR. It is observed that while the charm hadrochemistry is significantly modified in Au+Au collisions compared to p+p collisions, the c (c) over bar production cross section at midrapidity remains consistent between Au+Au and p+p collisions. [Radhakrishnan, Sooraj; Contin, G.; Dong, X.; Hong, Y.; Lomnitz, M.; Matis, H. S.; Odyniec, G.; Porter, J.; Radhakrishnan, S. K.; Ritter, H. G.; Schmah, A. M.; Sichtermann, E. P.; Thomas, J. H.; Wieman, H.; Xu, N.; Zhang, J.] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; [Drachenberg, J. L.] Abilene Christian Univ, Abilene, TX 79699 USA; [Adamczyk, L.; Fulek, L.; Przybycien, M.; Sikora, R.] AGH Univ Sci & Technol, FPACS, PL-30059 Krakow, Poland; [Alekseev, I; Bordyuzhin, I. G.; Svirida, D. N.] Alikhanov Inst Theoret & Expt Phys, Moscow 117218, Russia; [Krueger, K.; Spinka, H. M.; Underwood, D. G.] Argonne Natl Lab, Argonne, IL 60439 USA; [Arkhipkin, D.; Bland, L. C.; Aschenauer, E. C.; Chang, Z.; Christie, W.; Didenko, L.; Dunlop, J. C.; Fazio, S.; Fisyak, Y.; Guryn, W.; Eyser, O.; Jia, J.; Kauder, K.; Ke, H. W.; Landgraf, J. M.; Lauret, J.; Lebedev, A.; Lee, J. H.; Ljubicic, T.; Longacre, R. S.; Ma, R.; Ogawa, A.; Page, B. S.; Pak, R.; Ruan, L.; Schmidke, W. B.; Smirnov, D.; Sorensen, P.; Tang, A. H.; Todoroki, T.; Tribedy, P.; Ullrich, T.; Upsal, I; Van Buren, G.; Videbaek, F.; Webb, J. C.; Xu, Z.; Yang, S.; Yip, K.] Brookhaven Natl Lab, Upton, NY 11973 USA; [Crawford, H. J.; Engelage, J.; Judd, E. G.; Nelson, J. M.; Perkins, C.] Univ Calif Berkeley, Berkeley, CA 94720 USA; [Sanchez, M. Calderon de la Barca; Cebra, D.; Flores, C. E.; Heppelmann, S.; Kinghorn, T. A.; Meehan, K.; Romero, J. L.] Univ Calif Davis, Davis, CA 95616 USA; [Chan, B. K.; Esha, R.; Huang, H. Z.; Igo, G.; Nasim, Md; Sergeeva, M.; Trentalange, S.; Tsai, O. D.; Wang, G.; Wen, L.] Univ Calif Los Angeles, Los Angeles, CA 90095 USA; [Barish, K.; Bassill, A. J.; Bryslawskyj, J.; Kapukchyan, D.; Kim, C.; Krauth, L.; Negrete, J. D.; Seto, R.] Univ Calif Riverside, Riverside, CA 92521 USA; [Bai, X.; Das, S.; Liu, F.; Luo, X.; Nayak, K.; Nonaka, T.; Pei, H.; Shi, S. S.; Sun, X. M.; Tu, B.; Wang, Y.; Xu, J.; Zhang, L.] Cent China Normal Univ, Wuhan 430079, Hubei, Peoples R China; [Evdokimov, O.; Huang, B.; Luo, S.; Xie, G.; Ye, Z.] Univ Illinois, Chicago, IL 60607 USA; [Adam, J.; Cherney, M.; Seger, J.] Creighton Univ, Omaha, NE 68178 USA; [Bielcik, J.; Chaloupka, P.; Harlenderova, A.; Holub, L.; Kocan, M.; Kosarzewski, L. K.; Kramarik, L.; Kvapil, J.; Licenik, R.; Lidrych, J.; Matonoha, O.; Moravcova, Z.; Rusnakova, O.] Czech Tech Univ, FNSPE, Prague 11519, Czech Republic; [Galatyuk, T.; Seck, F.] Tech Univ Darmstadt, D-64289 Darmstadt, Germany; [Csanad, M.; Pinter, R. L.] Eotvos Lorand Univ, H-1117 Budapest, Hungary; [Kisel, I; Vassiliev, I; Zyzak, M.] FIAS, D-60438 Frankfurt, Germany; [Ma, L.] Fudan Univ, Shanghai 200433, Peoples R China; [Deppner, I. M.; Herrmann, N.] Heidelberg Univ, D-69120 Heidelberg, Germany; [Beliwied, R.; Kulathunga, N.; Timmins, A. R.] Univ Houston, Houston, TX 77204 USA; [Jacobs, W. W.; Kalinkin, D.; Kwasizur, J. H.; Liu, H.; Solyst, W.; Vossen, A.; Wissink, S. W.] Indiana Univ, Bloomington, IN 47408 USA; [Chen, X.; Zhang, J.] Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Gansu, Peoples R China; [Mondal, M. M.; Sahu, P. K.; Tripathy, S. K.] Inst Phys, Bhubaneswar 751005, India; [Bhasin, A.; Gupta, A.] Univ Jammu, Jammu 180001, India; [Agakishiev, G.; Aparin, A.; Averichev, G. S.; Bunzarov, I; Chankova-Bunzarova, N.; Dedovich, T. G.; Efimov, L. G.; Fedorisin, J.; Filip, P.; Kechechyan, A.; Lednicky, R.; Panebratsev, Y.; Rogachevskiy, O., V; Shahaliev, E.; Tokarev, M.; Vokal, S.] Joint Inst Nucl Res, Dubna 141980, Russia; [Atetalla, F.; Chakaberia, I; Guo, Y.; Hamad, A., I; Kabana, S.; Keane, D.; Liang, Y.; Margetis, S.; Singha, S.; Wu, Y.] Kent State Univ, Kent, OH 44242 USA; [Adkins, J. K.; Fatemi, R.; Ramachandran, S.] Univ Kentucky, Lexington, KY 40506 USA; [Brown, D.; Ewigleben, J.; Kagamaster, S.; Reed, R.; Shanmuganathan, P., V; Tomkiel, C. A.] Lehigh Univ, Bethlehem, PA 18015 USA; [Schmitz, N.; Seyboth, P.] Max Planck Inst Phys & Astrophys, D-80805 Munich, Germany; [Tarnowsky, T.; Westfall, G. D.] Michigan State Univ, E Lansing, MI 48824 USA; [Alekseev, I; Brandin, A., V; Kochenda, L.; Kravtsov, P.; Nigmatkulov, G.; Okorokov, V. A.; Strikhanov, M.; Taranenko, A.] Natl Res Nucl Univ MEPhI, Moscow 115409, Russia; [Bairathi, V; Mallick, D.; Mishra, D.; Mohanty, B.] HBNI, Natl Inst Sci Educ & Res, Jatni 752050, India; [Chang, F-H; Huang, T.; Yang, Y.] Natl Cheng Kung Univ, Tainan 70101, Taiwan; [Bielcikova, J.; Federic, P.; Rusnak, J.; Simko, M.; Sumbera, M.; Vanek, J.] CAS, Nucl Phys Inst, Rez 25068, Czech Republic; [Adams, J. R.; Humanic, T. J.; Lisa, M. A.] Ohio State Univ, Columbus, OH 43210 USA; [Pawlik, B.] Inst Nucl Phys PAN, PL-31342 Krakow, Poland; [Aggarwal, M. M.; Attri, A.; Bhati, A. K.; Kumar, L.; Pruthi, N. K.; Singh, J.] Panjab Univ, Chandigarh 160014, India; [Dilks, C.; Heppelmann, S.; Summa, B.] Penn State Univ, University Pk, PA 16802 USA; [Derevschikov, A. A.; Minaev, N. G.; Morozov, D. A.; Nogach, L., V; Vasiliev, A. N.] Inst High Energy Phys, Protvino 142281, Russia; [He, L.; Hirsch, A.; Srivastava, B.; Stringfellow, B.; Wang, F.; Xie, W.; Zhao, J.] Purdue Univ, W Lafayette, IN 47907 USA; [Oh, K.; Yoo, I-K] Pusan Natl Univ, Pusan 46241, South Korea; [Brandenburg, J. D.; Butterworth, J.; Eppley, G.; Geurts, F.; Roberts, J. B.; Tlusty, D.] Rice Univ, Houston, TX 77251 USA; [Mazer, J. A.; Salur, S.] Rutgers State Univ, Piscataway, NJ 08854 USA; [Suaide, A. A. P.] Univ Sao Paulo, Sao Paulo, Brazil; [Chen, X.; Jiang, K.; Ju, X.; Li, C.; Li, X.; Liu, Z.; Shao, M.; Sun, Y.; Tang, Z.; Wang, P.; Zha, W.; Zhang, S.; Zhang, Y.] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China; [Chakaberia, I; Mei, J. C.; Shen, F.; Upsal, I; Xu, Q. H.; Yang, C.; Yang, Q.; Yi, L.; Zhu, Z.] Shandong Univ, Jinan 266237, Shandong, Peoples R China; [Chen, J. H.; Li, W.; Liu, P.; Ma, G. L.; Ma, Y. G.; Nie, M.; Shah, N.; Shen, W. Q.; Shou, Q. Y.; Xu, Y. F.; Zhang, S.; Zhang, Z.; Zhong, C.; Zhou, C.] Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China; [Finch, E.] Southern Connecticut State Univ, New Haven, CT 06515 USA; [Behera, A.; Huang, S. L.; Huo, P.; Jia, J.; Lacey, R.; Liu, P.; Magdy, N.; Schweid, B. R.] SUNY Stony Brook, Stony Brook, NY 11794 USA; [Gunarathne, D. S.; Kraishan, A. F.; Olvitt, D., Jr.; Posik, M.; Quintero, A.; Surrow, B.] Temple Univ, Philadelphia, PA 19122 USA; [Anderson, D. M.; Gagliardi, C. A.; Flamed, A.; Lin, T.; Liu, Y.; Mioduszewski, S.; Sahoo, N. R.; Tribble, R. E.] Texas A&M Univ, College Stn, TX 77843 USA; [Jentsch, A.; Markert, C.; Ray, R. L.; Schambach, J.] Univ Texas Austin, Austin, TX 78712 USA; [Ashraf, M. U.; Cheng, J.; Huang, X.; Kang, K.; Li, Y.; Wang, Y.; Xiao, Z. G.; Zhang, X. P.; Zhu, X.] Tsinghua Univ, Beijing 100084, Peoples R China; [Aoyama, R.; Esumi, S.; Sugiura, T.] Univ Tsukuba, Tsukuba, Ibaraki 3058571, Japan; [Witt, R.] US Naval Acad, Annapolis, MD 21402 USA; [Gibson, A.; Grosnick, D.; Stanislaus, T. D. S.] Valparaiso Univ, Valparaiso, IN 46383 USA; [Ahammed, Z.; Chatterjee, A.; Chattopadhyay, S.] Variable Energy Cyclotron Ctr, Kolkata 700064, India; [Kikola, D. P.; Kisiel, A.; Lipiec, A.; Pluta, J.; Siejka, S.; Szymanski, P.; Zbroszczyk, H.] Warsaw Univ Technol, PL-00661 Warsaw, Poland; [Elsey, N.; Jowzaee, S.; Elayavalli, R. Kunnawalkam; Llope, W. J.; Mooney, I; Niida, T.; Putschke, J.; Voloshin, S. A.] Wayne State Univ, Detroit, MI 48201 USA; [Caines, H.; Harris, J. W.; Horvat, S.; Majka, R.; Nemes, D. B.; Oh, S.; Sandweiss, J.; Smirnov, N.; Stewart, D. J.] Yale Univ, New Haven, CT 06520 USA United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Abilene Christian University; AGH University of Science & Technology; National Research Centre - Kurchatov Institute; Alikhanov Institute for Theoretical & Experimental Physics; United States Department of Energy (DOE); Argonne National Laboratory; United States Department of Energy (DOE); Brookhaven National Laboratory; University of California System; University of California Berkeley; University of California System; University of California Davis; University of California System; University of California Los Angeles; University of California System; University of California Riverside; Central China Normal University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Creighton University; Czech Technical University Prague; Technical University of Darmstadt; Eotvos Lorand University; Fudan University; Ruprecht Karls University Heidelberg; University of Houston System; University of Houston; Indiana University System; Indiana University Bloomington; Chinese Academy of Sciences; Institute of Modern Physics, CAS; Institute of Physics Bhubaneswar (IOPB); University of Jammu; Joint Institute for Nuclear Research - Russia; University System of Ohio; Kent State University; Kent State University Kent; Kent State University Salem; University of Kentucky; Lehigh University; Max Planck Society; Michigan State University; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute); Homi Bhabha National Institute; National Institute of Science Education & Research (NISER); National Cheng Kung University; Czech Academy of Sciences; Nuclear Physics Institute of the Czech Academy of Sciences; University System of Ohio; Ohio State University; Polish Academy of Sciences; Institute of Nuclear Physics - Polish Academy of Sciences; Panjab University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; National Research Centre - Kurchatov Institute; Institute of High Energy Physics - IHEP; Purdue University System; Purdue University; Purdue University West Lafayette Campus; Pusan National University; Rice University; Rutgers State University New Brunswick; Universidade de Sao Paulo; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shandong University; Chinese Academy of Sciences; Shanghai Institute of Applied Physics, CAS; Connecticut State University System; Southern Connecticut State University; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin; Tsinghua University; University of Tsukuba; United States Department of Defense; United States Navy; United States Naval Academy; Valparaiso University; Variable Energy Cyclotron Centre; Warsaw University of Technology; Wayne State University; Yale University Radhakrishnan, S (corresponding author), Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA. Kabana, Sonia/ABF-5974-2020; Kumar, Lokesh/A-6154-2010; Nigmatkulov, Grigory/AAH-9428-2019; Aparin, Alexey/AAE-1455-2022; Suaide, Alexandre A P/L-6239-2016; Kisel, Ivan/H-7077-2017; Strikhanov, Mikhail N/P-7393-2014; Matis, Howard S/HMV-9747-2023; Vasiliev, Alexander N/N-4742-2017; Svirida, Dmitry N/R-4909-2016; Ashraf, Muhammad Usman/GNM-5453-2022; Bairathi, Vipul/AAR-3812-2021; Bielcikova, Jana/AAB-4862-2020; Zhao, Jie/AAD-3191-2022; YANG, CHI/AAF-6393-2021; Alekseev, Igor G/J-8070-2014; Taranenko, Arkadiy/AAH-9189-2019; Lednicky, Richard/K-4164-2013; Bhasin, Anju/AAS-4745-2021; Sahoo, Nihar Ranjan/ABF-3516-2021; Tokarev, Mikhail/GRE-9737-2022; Contin, Giacomo/HLW-2131-2023; Matis, Howard/AAO-2082-2021; Bordyuzhin, Igor/AGX-2537-2022; Ma, Yu-Gang/M-8122-2013 Kabana, Sonia/0000-0003-0568-5750; Kumar, Lokesh/0000-0002-2746-9840; Nigmatkulov, Grigory/0000-0003-2232-5124; Suaide, Alexandre A P/0000-0003-2847-6556; Kisel, Ivan/0000-0002-4808-419X; Strikhanov, Mikhail N/0000-0003-2586-0405; Matis, Howard S/0000-0003-0764-4389; Svirida, Dmitry N/0000-0002-0334-7304; Zhao, Jie/0000-0001-6408-2314; YANG, CHI/0000-0001-9976-8631; Alekseev, Igor G/0000-0003-3358-9635; Lednicky, Richard/0000-0001-6887-1804; Sahoo, Nihar Ranjan/0000-0003-4518-6630; Ma, Yu-Gang/0000-0002-0233-9900 22 6 7 0 1 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0375-9474 1873-1554 NUCL PHYS A Nucl. Phys. A FEB 2019.0 982.0 659 662 10.1016/j.nuclphysa.2018.10.050 0.0 4 Physics, Nuclear Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Physics HJ9JU Green Submitted, hybrid, Green Published 2023-03-23 WOS:000457515500150 0
J Jiang, Y; Zevenbergen, C; Ma, YC Jiang, Yong; Zevenbergen, Chris; Ma, Yongchi Urban pluvial flooding and stormwater management: A contemporary review of China's challenges and sponge cities strategy ENVIRONMENTAL SCIENCE & POLICY English Review Urban pluvial flooding; China; Stormwater management; Urban planning; Governance; Low impact development LOW IMPACT DEVELOPMENT; YANGTZE-RIVER DELTA; CLIMATE-CHANGE; WATER MANAGEMENT; EXTREME PRECIPITATION; URBANIZATION; ADAPTATION; DRAINAGE; GOVERNANCE; DESIGN In recent years, urban pluvial flooding caused by extreme rainfall has increasingly occurred across China. This paper reviews the challenges faced by China in addressing urban pluvial flooding and managing urban storm water, with a particular focus on a policy initiative termed sponge cities. The paper first synthetically presents pluvial flood disasters in urbanized areas, and analyses their causes and formation mechanisms. It then introduces China's sponge cities initiative and discusses policy implementation in relation to contemporary understanding of sustainable urban stormwater management and international experience with innovative practices. The initiative, while theoretically well grounded and appropriate by its design principles, is shown subject to diverse implementation challenges, ranging from technological complexity to limited or lack of governance capacity as reflected in management ideology, knowledge and capacity of learning, participatory and integrated governance, investment financing, implementation pathway, planning and organization, and project evaluation. The paper offers some strategies for addressing those challenges, which include: 1) continuous experiment-based deep learning through pilot and institutionalization of knowledge and information management with city-to-city peering learning mechanisms, 2) establishment of institutional mechanisms dedicated to participatory, coordinated and integrated governance of the policy initiative, 3) increased government role in creating favorable conditions for investments, and 4) appropriate plafining and an adaptive approach to policy implementation. The paper concludes that the sponge cities initiative can be an effective approach only if China commits to appropriate technical, governance, financial, and organizational measures to effectively address the challenges for policy implementation. [Jiang, Yong; Zevenbergen, Chris] IHE Delft Inst Water Educ, NL-2611 AX Delft, Netherlands; [Ma, Yongchi] Dalian Univ Technol, Dept Publ Management, Fac Humanities & Social Sci, Dalian 116024, Peoples R China IHE Delft Institute for Water Education; Dalian University of Technology Jiang, Y (corresponding author), IHE Delft Inst Water Educ, NL-2611 AX Delft, Netherlands. y.jiang@unesco-ihe.org jiang, yong/M-4318-2013; Ma, Yongchi/GRR-5230-2022; FENG, SHUXIAN/W-4643-2018 jiang, yong/0000-0001-6192-8206; IHE Delft Institute for Water Education; Grant of the Dalian Construction Science and Technology Planning Fund IHE Delft Institute for Water Education; Grant of the Dalian Construction Science and Technology Planning Fund This work was supported partially by IHE Delft Institute for Water Education and by the Grant 2013-19 of the Dalian Construction Science and Technology Planning Fund. 115 203 211 113 578 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1462-9011 1873-6416 ENVIRON SCI POLICY Environ. Sci. Policy FEB 2018.0 80.0 132 143 10.1016/j.envsci.2017.11.016 0.0 12 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology FU1VZ hybrid 2023-03-23 WOS:000423638900015 0
J Wang, ZX; Zhang, Z; Feng, Y; Hendriks, LEL; Miclea, RL; Gietema, H; Schoenmaekers, J; Dekker, A; Wee, L; Traverso, A Wang, Zhixiang; Zhang, Zhen; Feng, Ying; Hendriks, Lizza E. L.; Miclea, Razvan L.; Gietema, Hester; Schoenmaekers, Janna; Dekker, Andre; Wee, Leonard; Traverso, Alberto Generation of synthetic ground glass nodules using generative adversarial networks (GANs) EUROPEAN RADIOLOGY EXPERIMENTAL English Article Deep learning; Tomography (x-ray computed); Lung; Neural networks (computer); Solitary pulmonary nodule LUNG NODULES; CT IMAGES Background Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). Methods We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training. Results Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p >= 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data. Conclusions GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics. [Wang, Zhixiang; Zhang, Zhen; Dekker, Andre; Wee, Leonard; Traverso, Alberto] Maastricht Univ Med Ctr, GROW Sch Oncol & Reprod, Dept Radiat Oncol Maastro, Maastricht, Netherlands; [Zhang, Zhen] Tianjin Med Univ Canc Inst & Hosp, Dept Radiat Oncol, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc,Key Lab Canc Preventio, Tianjin, Peoples R China; [Feng, Ying] Capital Med Univ, Beijing Friendship Hosp, Dept Ultrasound, Beijing, Peoples R China; [Feng, Ying] Maastricht Univ, Dept Obstet & Gynecol, GROW Sch Oncol & Dev Biol, Med Ctr, Maastricht, Netherlands; [Hendriks, Lizza E. L.; Schoenmaekers, Janna] Maastricht Univ, GROW Sch Oncol & Reprod, Dept Pulm Dis, Med Ctr, Maastricht, Netherlands; [Miclea, Razvan L.; Gietema, Hester] Maastricht Univ, Dept Radiol & Nucl Med, Med Ctr, Maastricht, Netherlands Maastricht University; Maastricht University Medical Centre (MUMC); Tianjin Medical University; Capital Medical University; Maastricht University; Maastricht University; Maastricht University Wee, L; Traverso, A (corresponding author), Maastricht Univ Med Ctr, GROW Sch Oncol & Reprod, Dept Radiat Oncol Maastro, Maastricht, Netherlands. leonard.wee@maastro.nl; alberto.traverso@maastro.nl Zhang, Zhen/0000-0001-6335-9529; Wang, Zhixiang/0000-0002-4778-7539 Chinese Scholarship Council (CSC) Chinese Scholarship Council (CSC)(China Scholarship Council) We thank the Chinese Scholarship Council (CSC) for their financial support for studying abroad. 19 0 0 0 0 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2509-9280 EUR RADIOL EXP Eur. Radiol. Exp. NOV 30 2022.0 6.0 1 59 10.1186/s41747-022-00311-y 0.0 12 Radiology, Nuclear Medicine & Medical Imaging Emerging Sources Citation Index (ESCI) Radiology, Nuclear Medicine & Medical Imaging 6O5DH 36447082.0 gold 2023-03-23 WOS:000890261200001 0
J Wu, HH; Zhuo, FP; Qiao, HM; Venkataraman, LK; Zheng, MP; Wang, SZ; Huang, H; Li, B; Mao, XP; Zhang, QB Wu, Hong Hui; Zhuo, Fangping; Qiao, Huimin; Kodumudi Venkataraman, Lalitha; Zheng, Mupeng; Wang, Shuize; Huang, He; Li, Bo; Mao, Xinping; Zhang, Qiaobao Polymer-/Ceramic-based Dielectric Composites for Energy Storage and Conversion ENERGY & ENVIRONMENTAL MATERIALS English Review composite; dielectric; energy storage and conversion; ferroelectric PSEUDO-1ST-ORDER PHASE-TRANSITION; FREE PIEZOELECTRIC CERAMICS; HIGH-TEMPERATURE; BARIUM-TITANATE; THIN-FILMS; FERROELECTRIC POLYMERS; MULTILAYER CAPACITORS; DISCHARGE EFFICIENCY; THERMAL-STABILITY; CALORIC MATERIALS Dielectric composites boost the family of energy storage and conversion materials as they can take full advantage of both the matrix and filler. This review aims at summarizing the recent progress in developing high-performance polymer- and ceramic-based dielectric composites, and emphases are placed on capacitive energy storage and harvesting, solid-state cooling, temperature stability, electromechanical energy interconversion, and high-power applications. Emerging fabrication techniques of dielectric composites such as 3D printing, electrospinning, and cold sintering are addressed, following by highlighted challenges and future research opportunities. The advantages and limitations of the typical theoretical calculation methods, such as finite-element, phase-field model, and machine learning methods, for designing high-performance dielectric composites are discussed. This review is concluded by providing a brief perspective on the future development of composite dielectrics toward energy and electronic devices. [Wu, Hong Hui; Wang, Shuize; Mao, Xinping] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Collaborat Innovat Ctr Steel Technol, Beijing 100083, Peoples R China; [Zhuo, Fangping; Kodumudi Venkataraman, Lalitha] Tech Univ Darmstadt, Dept Mat & Earth Sci, D-64287 Darmstadt, Germany; [Qiao, Huimin] Sungkyunkwan Univ SKKU, Sch Adv Mat & Engn, Suwon 16419, South Korea; [Zheng, Mupeng] Beijing Univ Technol, Key Lab Adv Funct Mat, Minist Educ, Fac Mat & Mfg, Beijing 100124, Peoples R China; [Huang, He; Li, Bo] Univ Sci & Technol Beijing, Sch Mat Sci & Engn, Beijing 100083, Peoples R China; [Zhang, Qiaobao] Xiamen Univ, Dept Mat Sci & Engn, Coll Mat, Xiamen 361005, Fujian, Peoples R China University of Science & Technology Beijing; Technical University of Darmstadt; Sungkyunkwan University (SKKU); Beijing University of Technology; University of Science & Technology Beijing; Xiamen University Wang, SZ (corresponding author), Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Collaborat Innovat Ctr Steel Technol, Beijing 100083, Peoples R China.;Zhuo, FP (corresponding author), Tech Univ Darmstadt, Dept Mat & Earth Sci, D-64287 Darmstadt, Germany.;Huang, H (corresponding author), Univ Sci & Technol Beijing, Sch Mat Sci & Engn, Beijing 100083, Peoples R China.;Zhang, QB (corresponding author), Xiamen Univ, Dept Mat Sci & Engn, Coll Mat, Xiamen 361005, Fujian, Peoples R China. zhuo@ceramics.tu-darmstadt.de; wangshuize@ustb.edu.cn; hhuangae@connect.ust.hk; zhangqiaobao@xmu.edu.cn Zhuo, Fangping/GXF-7288-2022; , Woo/M-2215-2013; Huimin Qiao, Huimin Qiao/A-1206-2019; Zhuo, Fangping/Q-4161-2019; Venkataraman, Lalitha Kodumudi/D-4971-2017 Zhuo, Fangping/0000-0001-5194-320X; , Woo/0000-0002-1381-2281; Huimin Qiao, Huimin Qiao/0000-0001-9556-7832; Zhuo, Fangping/0000-0001-5194-320X; Venkataraman, Lalitha Kodumudi/0000-0002-4848-5436; Zhang, Qiaobao/0000-0002-3584-5201 State Key Lab of Advanced Metals and Materials [2020Z16]; Fundamental Research Funds for the Central Universities (USTB) [06500135]; National Research Foundation of Korea [2019R1I1A1A01063888]; Alexander von Humboldt Foundation; USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering; Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology [HKDNM2019013]; Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology [HKDNM2019013] State Key Lab of Advanced Metals and Materials; Fundamental Research Funds for the Central Universities (USTB); National Research Foundation of Korea(National Research Foundation of Korea); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering; Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology; Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology H.-H.W., F.Z. and H.Q. contributed equally to this work. This work was financially supported by the State Key Lab of Advanced Metals and Materials (No. 2020Z16) and the Fundamental Research Funds for the Central Universities (USTB: No. 06500135). Huimin Qiao thanks the National Research Foundation of Korea (No. 2019R1I1A1A01063888) for financial support. Fangping Zhuo would like to thank the Alexander von Humboldt Foundation for financial support. The computing work was supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering. Prof. Q. Zhang also acknowledges the financial support from the Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, and Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology (Grants No. HKDNM2019013). 278 25 25 193 409 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2575-0356 ENERGY ENVIRON MATER Energy Environ. Mater. APR 2022.0 5.0 2 486 514 10.1002/eem2.12237 0.0 JAN 2022 29 Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Materials Science 1B4TU 2023-03-23 WOS:000743101400001 0
J Sun, J; Li, J; Fujita, H; Ai, WG Sun, Jie; Li, Jie; Fujita, Hamido; Ai, Wenguo Multiclass financial distress prediction based on one-versus-one decomposition integrated with improved decision-directed acyclic graph JOURNAL OF FORECASTING English Article; Early Access class imbalance; decision-directed acyclic graph; decomposition and fusion method; multiclass financial distress prediction; one-versus-one SUPPORT VECTOR MACHINES; LISTING STATUS; TREE; RATIOS Although binary financial distress prediction (FDP) is widely explored, multiclass FDP (MFDP) lacks enough investigation. This paper designs a new fusion method of improved decision-directed acyclic graph (IDDAG) based on the principle of maximizing generalization ability and integrates it with the one-versus-one (OVO) decomposition method (OVO-IDDAG) to build MFDP models. Based on data of Chinese listed companies, we categorize corporate financial status into four states (financial soundness, financial pseudosoundness, moderate financial distress, and serious financial distress) and compare the MFDP performance of OVO-IDDAG with two benchmark methods, that is, the OVO decomposition method integrated with the max-win fusion method (OVO- max-win) and the OVO decomposition method integrated with the original DDAG fusion method (OVO-ODDAG). The experimental results show that the OVO-IDDAG method overall outperformances the benchmark methods. Among the six base classifiers of multivariate discriminant analysis, Logit, naive bayes, support vector machine, decision tree, and random forest and the three imbalance processing mechanisms of random oversampling, random undersampling, and synthetic minority oversampling technique, the combination of the random forest classifier with the imbalance processing mechanism of random undersampling or the support vector machine classifier with the imbalance processing mechanism of random oversampling or synthetic minority oversampling technique is more suitable for MFDP based on OVO-IDDAG. This study not only improves artificial intelligence methodology for multiclass classification but also enriches applications of intelligent systems to MFDP. [Sun, Jie; Li, Jie] Tianjin Univ Finance & Econ, Sch Accounting, Tianjin, Peoples R China; [Fujita, Hamido] HUTECH Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam; [Fujita, Hamido] Univ Granada, Andalusian Res Inst Data Sci & Computat Intelligen, Granada, Spain; [Fujita, Hamido] Iwate Prefectural Univ, Fac Software & Informat Sci, Takizawa, Japan; [Ai, Wenguo] Harbin Inst Technol, Sch Management, Harbin, Peoples R China Tianjin University of Finance & Economics; Ho Chi Minh City University of Technology (HUTECH); Vietnam National University Hochiminh City; University of Granada; Iwate Prefectural University; Harbin Institute of Technology Li, J (corresponding author), Tianjin Univ Finance & Econ, Sch Accounting, Tianjin, Peoples R China. lijietufe@foxmail.com National Natural Science Foundation of China; [72271174]; [71771162] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); ; ACKNOWLEDGMENT This research is supported by the National Natural Science Foundation of China (Grant numbers 72271174 and 71771162). 41 0 0 4 4 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0277-6693 1099-131X J FORECASTING J. Forecast. 10.1002/for.2937 0.0 DEC 2022 20 Economics; Management Social Science Citation Index (SSCI) Business & Economics 6W0FN 2023-03-23 WOS:000895411900001 0
J Xu, WX; Wang, JH; Fu, T; Gong, HR; Sobhani, A Xu, Wenxiang; Wang, Junhua; Fu, Ting; Gong, Hongren; Sobhani, Anae Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data ACCIDENT ANALYSIS AND PREVENTION English Article Aggressive driving prediction; Panel data; Hidden Markov model; Long short-term memory network; Attention mechanism TRAFFIC FLOW PREDICTION; ACCIDENT RISK; VEHICLE; MODEL; CRASH; ACCELERATION; PARAMETERS; SYSTEM; SAFETY Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network. HMM is applied to extract the driver's intention which leads to aggressive driving behavior; attention-based LSTM networks are applied in the multivariate-temporal aggressive driving behavior prediction. The method input uses panel data which contains observations about different cross-sections across time. In the case study, the model was trained based on the Shanghai Naturalistic Driving Study data. After comparing with other deep learning methods and normal LSTM, results show the proposed method provides good performance for aggressive driving behavior prediction (Mean of Accuracy = 80%), especially with the 2-sec time interval applied (Training Accuracy = 82% and Validation Accuracy = 84%). Also, the result shows that the attention mechanism can improve the result's interpretability, and using the driver's intentions as input can enhance the model accuracy. This method for predicting aggressive driving behavior that combines driver's intention, variable contribution sorting, and time-series processing. This method can be used in real-world applications for improving driving safety with the applications in the Advanced Driver Assistance Systems. [Xu, Wenxiang; Wang, Junhua; Fu, Ting; Gong, Hongren] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China; [Xu, Wenxiang; Wang, Junhua; Fu, Ting; Gong, Hongren] Tongji Univ, Coll Transportat Engn, 4800 Caoan Highway, Shanghai 201804, Peoples R China; [Sobhani, Anae] Univ Utrecht, Dept Human Geog & Planning, Social Urban Transit Sect, NL-3584 CB Utrecht, Netherlands Tongji University; Tongji University; Utrecht University Wang, JH (corresponding author), Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China. 1910907@tongji.edu.cn; benwjh@163.com; tingfu@tongji.edu.cn; hgong@tongji.edu.cn; a.sobhani@uu.nl Fu, Ting/0000-0002-7097-1262 Chinese National Natural Science Foundation [71871161]; Science and Technology Commission of Shanghai Municipality [19DZ1202100] Chinese National Natural Science Foundation(National Natural Science Foundation of China (NSFC)); Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) This research was supported by the Chinese National Natural Science Foundation (71871161) and the grant of Science and Technology Commission of Shanghai Municipality (19DZ1202100) . 69 2 2 13 48 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0001-4575 1879-2057 ACCIDENT ANAL PREV Accid. Anal. Prev. JAN 2022.0 164.0 106477 10.1016/j.aap.2021.106477 0.0 NOV 2021 14 Ergonomics; Public, Environmental & Occupational Health; Social Sciences, Interdisciplinary; Transportation Social Science Citation Index (SSCI) Engineering; Public, Environmental & Occupational Health; Social Sciences - Other Topics; Transportation YJ1CA 34813934.0 2023-03-23 WOS:000744274300003 0
J Ahmed, M; Raza, S; Mirza, MA; Aziz, A; Khan, MA; Khan, WU; Li, JB; Han, Z Ahmed, Manzoor; Raza, Salman; Mirza, Muhammad Ayzed; Aziz, Abdul; Khan, Manzoor Ahmed; Khan, Wali Ullah; Li, Jianbo; Han, Zhu A survey on vehicular task offloading: Classification, issues, and challenges JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES English Article Vehicular task offloading; cloud; Edge; Cloudlet; NR V2X; 802; 11bd ARTIFICIAL-INTELLIGENCE; VEHICLES CHALLENGES; EDGE; COMMUNICATION; NETWORKS; MEC; 5G; FRAMEWORK; SAFETY; MANAGEMENT Emerging vehicular applications with strict latency and reliability requirements pose high computing requirements, and current vehicles' computational resources are not adequate to meet these demands. In this scenario, vehicles can get help to process tasks from other resource-rich computing platforms, including nearby vehicles, fixed edge servers, and remote cloud servers. Nonetheless, different vehicular communication network (VCN) modes need to be utilized to access these computing resources, improving applications and networks' performance and quality of service (QoS). In this paper, we present a comprehensive survey on the vehicular task offloading techniques under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). For the task/computation offloading, we present the classification of methods under the V2V, V2I, and V2X communication domains. Besides, the task/computation offloading categories are each sub-categorized according to their schemes' objectives. Furthermore, the literature on vehicular task offloading is elaborated, compared, and analyzed from the perspectives of approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [Ahmed, Manzoor; Li, Jianbo] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China; [Raza, Salman] Natl Text Univ Faisalabad, Dept Comp Sci, Faisalabad, Pakistan; [Mirza, Muhammad Ayzed] Beijing Univ Posts & Telecommun, BUPT QMUL EM Theory & Applicat Int Res Lab, Beijing 100876, Peoples R China; [Aziz, Abdul] Islamia Univ Bahawalpur, Dept informat & Commun Engn, Bahawalpur 63100, Pakistan; [Khan, Manzoor Ahmed] UAE Univ, Dept Comp & Network Engn, Collage IT, Al Ain, U Arab Emirates; [Khan, Wali Ullah] Univ Luxembourg, Interdisciplinary Ctr Secur & Reliabil & Trust SnT, L-4365 Luxembourg City, Luxembourg; [Han, Zhu] Univ Houston, Houston, TX 77004 USA; [Han, Zhu] Kyung Hee Univ, Dept Comp Sci & Engn, Seoul, South Korea Qingdao University; National Textile University - Pakistan; Beijing University of Posts & Telecommunications; Islamia University of Bahawalpur; United Arab Emirates University; University of Luxembourg; University of Houston System; University of Houston; Kyung Hee University Ahmed, M (corresponding author), Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China. manzoor.achakzai@gmail.com; salmanraza@ntu.edu.pk; mamirza@bupt.edu.cn; abdul.aziz@iub.edu.pk; manzoor-khan@uaeu.ac.ae; waliullah.khan@uni.lu; lijianbo@qdu.edu.cn; zhan2@uh.edu Khan, Wali Ullah/0000-0003-1485-5141 189 2 2 7 11 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1319-1578 2213-1248 J KING SAUD UNIV-COM J. King Saud Univ.-Comput. Inf. Sci. JUL 2022.0 34.0 7 4135 4162 10.1016/j.jksuci.2022.05.016 0.0 JUN 2022 28 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science 3A6ZI gold 2023-03-23 WOS:000827405000020 0
J Ji, DY; Li, LQ; Fuchs, H; Hu, WP Ji, Deyang; Li, Liqiang; Fuchs, Harald; Hu, Wenping Engineering the Interfacial Materials of Organic Field-Effect Transistors for Efficient Charge Transport ACCOUNTS OF MATERIALS RESEARCH English Article ELECTRODES; MOBILITY; SEMICONDUCTOR; PHOTODIODES CONSPECTUS: The development of organic field-effect transistors (OFETs) has witnessed impressive advances in organic electronics, which has broad application prospects in artificial intelligence, information technology, energy storage, and medical treatments. How to reveal the interface behavior of charge carriers and how to obtain efficient charge transfer of OFETs are very important scientific issues, which are essential for the construction of high-performance OFETs and also for the further understanding of the intrinsic properties of organic semiconductor materials. To this end, the investigation of interfacial materials and related interface engineering is an effective and promising approach, offering potential capabilities of optimizing molecular packing of semiconductors, interface energy alignments, growth morphology, charge injection/output, and contact resistance for efficient charge transport. In this Account, we present our recent progress on engineering the interface materials of OFETs for efficient charge transport. Polymer dielectrics as indispensable components of OFETs have the capability of tuning the charge transport by modulating the interface, such as surface functionalization, chemical structural optimization, and self-assembly of functional groups into nanostructures. It is noted that the chemical-structure engineered polymer dielectrics endowing them with nanostructures on their surfaces represent a promising method for interface engineering. Besides pure polymer dielectrics, surface self-assembled monolayers also well-tune molecular packing of semiconductors for efficient photogenerated carrier transport in optoelectronic devices. In comparison with dielectric modulation, micro-/nanopatterning of source/drain electrodes is a novel and effective approach to optimize device interfaces and improve device performance for efficient charge transfer. Different from the bulk film, the monolayer semiconductor might produce highly efficient charge transport because of its two-dimensional nature with fewer grain boundaries and interface defects. Thanks to the charge density redistribution occurring at semiconductor/dielectric interface, the monolayer semiconductor on a pure polymer dielectric exhibited highly efficient charge transport. Interface engineering has promoted the significant progress of OFETs in the preceding years. We hope the above-mentioned methodologies of engineering the interfacial materials (e.g., the dielectric, semiconductor and electrode) to achieve efficient charge transport of OFETs in this Account will offer a new strategy for designing high-performance optoelectronic devices. [Fuchs, Harald] Westfalische Wilhelms Univ, Phys Inst, D-48149 Munster, Germany; [Fuchs, Harald] Ctr Nanotechnol, D-48149 Munster, Germany; [Fuchs, Harald] Northwestern Polytech Univ NW PU, Inst Flexible Elect IFE, Xian 710072, Peoples R China; [Hu, Wenping] Tianjin Univ, Sch Sci, Tianjin Key Lab Mol Optoelect Sci, Dept Chem, Tianjin 300072, Peoples R China; [Hu, Wenping] Tianjin Univ, Collaborat Innovat Ctr Chem Sci & Engn, Tianjin 300072, Peoples R China; [Ji, Deyang; Li, Liqiang] Tianjin Univ, Inst Mol Aggregat Sci, Dept Chem, Tianjin Key Lab Mol Optoelect Sci, Tianjin 300072, Peoples R China; [Ji, Deyang] Beijing Natl Lab Mol Sci, Beijing 100190, Peoples R China University of Munster; Tianjin University; Tianjin University; Tianjin University; Chinese Academy of Sciences Ji, DY; Li, LQ (corresponding author), Tianjin Univ, Inst Mol Aggregat Sci, Dept Chem, Tianjin Key Lab Mol Optoelect Sci, Tianjin 300072, Peoples R China.;Ji, DY (corresponding author), Beijing Natl Lab Mol Sci, Beijing 100190, Peoples R China. jideyang@tju.edu.cn; lilq@tju.edu.cn Li, Liqiang/H-9094-2013 Li, Liqiang/0000-0001-8399-3957; Ji, Deyang/0000-0002-8206-3130 Beijing National Laboratory for Molecular Sciences [BNLMS202006]; National Key Research and Development Program [2018YFA0703200, 2016YFB0401100]; National Natural Science Foundation of China [62004138, 21573277, 51503221, 21905199]; Tianjin Natural Science Foundation [19JCJQJC62600, 194214030036]; Key Research Program of Frontier Sciences of Chinese Academy of Sciences [QYZDB-SSWSLH031] Beijing National Laboratory for Molecular Sciences; National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Tianjin Natural Science Foundation(Natural Science Foundation of Tianjin); Key Research Program of Frontier Sciences of Chinese Academy of Sciences The authors are grateful to Beijing National Laboratory for Molecular Sciences (BNLMS202006), National Key Research and Development Program (2018YFA0703200, 2016YFB0401100), National Natural Science Foundation of China (62004138, 21573277, 51503221, 21905199), Tianjin Natural Science Foundation (19JCJQJC62600, 194214030036), and Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDB-SSWSLH031). 49 5 6 7 34 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 2643-6728 ACCOUNTS MATER RES Accounts Mater. Res. MAR 26 2021.0 2.0 3 159 169 10.1021/accountsmr.0c00112 0.0 FEB 2021 11 Chemistry, Multidisciplinary; Materials Science, Multidisciplinary Emerging Sources Citation Index (ESCI) Chemistry; Materials Science SD1EN 2023-03-23 WOS:000651110500005 0
J Wang, B; Dong, M; Ren, M; Wu, ZY; Guo, CX; Zhuang, TX; Pischler, O; Xie, JC Wang, Bin; Dong, Ming; Ren, Ming; Wu, Zhanyu; Guo, Chenxi; Zhuang, Tianxin; Pischler, Oliver; Xie, Jiacheng Automatic Fault Diagnosis of Infrared Insulator Images Based on Image Instance Segmentation and Temperature Analysis IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Insulators; Image segmentation; Training; Fault diagnosis; Fitting; Object segmentation; Inspection; Fault diagnosis; infrared detection; instance segmentation; insulator images; substation automation; temperature fitting OBJECT DETECTION As an onsite condition monitoring method, an infrared inspection can help to discover and analyze abnormal temperature increases in power equipment. For improving the efficiency of the onsite diagnosis of insulators in substations, this article proposes an automatic diagnosis method using instance segmentation and temperature analysis of infrared insulator images. For developing this method, thousands of infrared images from field inspection databases were collected to establish an annotated data set of insulator images. With the aid of the Mask R-convolutional neural network (CNN), it was possible to extract multiple insulators automatically in the infrared images. Transfer learning, as well as the dynamic learning rate algorithm, were then employed to realize the training process of Mask R-CNN with the annotated image data set. The result of the testing experiment showed that the mean Average Precision (mAP) of the model is 0.77, and the frame per second (FPS) is 5.07, which indicated great identification accuracy and computing speed of the proposed model. Next, function fitting was realized to extract the temperature distribution of each insulator. Finally, to evaluate the condition of each insulator, rules, which are based on the related standards, were established using machine language. This is the first time that the machine could independently realize fault analysis of multiple insulators in the infrared images, which is a great attempt to adapt the development of the Internet of Things and the tendency of predictive maintenance. Moreover, because of the universality of the model algorithm used, automatic infrared fault diagnosis for other power equipment could also be performed in a similar manner, which has significant potential applicability in the area of power equipment diagnosis. [Wang, Bin; Dong, Ming; Ren, Ming; Guo, Chenxi; Zhuang, Tianxin; Xie, Jiacheng] Xi An Jiao Tong Univ, Dept Elect Engn, Xian 710049, Peoples R China; [Wu, Zhanyu] China Acad Engn Phys, Key Lab Neutron Phys, Mianyang 621999, Sichuan, Peoples R China; [Wu, Zhanyu] China Acad Engn Phys, Inst Nucl Phys & Chem, Mianyang 621999, Sichuan, Peoples R China; [Pischler, Oliver] Graz Univ Technol, Inst High Voltage Engn & Syst Performance, A-8010 Graz, Austria Xi'an Jiaotong University; Chinese Academy of Engineering Physics; Chinese Academy of Engineering Physics; Graz University of Technology Ren, M (corresponding author), Xi An Jiao Tong Univ, Dept Elect Engn, Xian 710049, Peoples R China. renming@xjtu.edu.cn; wuzhanyujanet@foxmail.com; oliver.pischler@tugraz.at WANG, BIN/GWU-5483-2022 Ren, Ming/0000-0003-1147-1513; Wang, Bin/0000-0002-6311-5185; Guo, Chenxi/0000-0003-1529-8543; Zhuang, Tianxin/0000-0003-3275-2664; Xie, Jiacheng/0000-0002-8998-3140; Dong, Ming/0000-0001-9452-9463 National Key Research and Development Program of China [2017YFB0902705]; National Natural Science Foundation of China [51507130, 51777157]; Natural Science Basic Research Plan in Shaanxi Province of China [2016KW-072] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Plan in Shaanxi Province of China This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFB0902705), National Natural Science Foundation of China (Grant No. 51507130 and 51777157), and Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2016KW-072). The Associate Editor coordinating the review process was Amitava Chatterjee. 33 62 66 21 116 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. AUG. 2020.0 69.0 8 5345 5355 10.1109/TIM.2020.2965635 0.0 11 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation MD7AX 2023-03-23 WOS:000544124500007 0
J Chang, XM; Wu, JJ; Sun, HJ; Correia, GHD; Chen, JH Chang, Ximing; Wu, Jianjun; Sun, Huijun; Correia, Goncalo Homem de Almeida; Chen, Jianhua Relocating operational and damaged bikes in free-floating systems: A data-driven modeling framework for level of service enhancement TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE English Article Free-floating bike-sharing; Demand forecasting; Bike relocation; Multi-period optimization REPOSITIONING PROBLEM; SHARING SYSTEMS; NEIGHBORHOOD SEARCH; REBALANCING PROBLEM; DEMAND; ALGORITHM; NETWORK; USAGE; OPTIMIZATION; ARCHITECTURE Free-floating bike sharing is an innovative and sustainable travel mode, where shared bikes can be picked up and returned at any proper place on the streets and not just at docking stations. Nevertheless, in these systems, two major problems arise. One is the imbalance of free-floating shared bikes (FFSB) between zones due to one-way trips, the other is the damaged bikes that must be brought for repair. In this study, a modeling framework for dynamic relocating operational and damaged bikes is proposed that starts with predicting the number and location of shared bikes using deep learning algorithms. The demand forecasting model adopts the EncoderDecoder architecture embedded with the attention mechanism to further enhance the model's prediction ability and flexibility. Then, a data-driven optimization model for FFSB relocations is presented, where the multi-period optimization is applied to dynamically plan the relocation activities throughout the day. A new hybrid metaheuristic algorithm that incorporates variable neighborhood search (VNS) and enhanced simulated annealing (ESA) algorithm is developed for solving the relocating problem, in which satisfactory performance is observed from the numerical example. We test the proposed framework with the real-world FFSB data from Beijing, China. The results show that relocating both operational and damaged bikes timely decreases the probability of users finding damaged bikes in the system, but leads to higher relocation costs. For peak-hours, considering only the operational bikes for relocation is the most effective strategy given the limited relocation resources. It is urgent at those times of the day to focus on providing bikes to clients where they are undersupplied. [Chang, Ximing; Wu, Jianjun] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China; [Wu, Jianjun] Beijing Jiaotong Univ, Minist Transport, Key Lab Transport Ind Big Data Applicat Technol C, Beijing, Peoples R China; [Sun, Huijun] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing, Peoples R China; [Correia, Goncalo Homem de Almeida] Delft Univ Technol, Dept Transport & Planning, Delft, Netherlands; [Chen, Jianhua] Shenzhen Polytech, Shenzhen, Peoples R China Beijing Jiaotong University; Beijing Jiaotong University; Beijing Jiaotong University; Delft University of Technology; ShenZhen Polytechnic Wu, JJ (corresponding author), Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China. jjwu1@bjtu.edu.cn de Almeida Correia, Gonçalo Homem/AAN-9832-2020 de Almeida Correia, Gonçalo Homem/0000-0002-9785-3135 National Natural Science Foundation of China [91846202, 71890972/71890970]; State Key Laboratory of Rail Traffic Control and Safety [RCS2020ZZ001]; Fundamental Research Funds for the Central Universities [2021RC237]; 111 Project [B20071] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); State Key Laboratory of Rail Traffic Control and Safety; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); 111 Project(Ministry of Education, China - 111 Project) This work was supported by the National Natural Science Foundation of China (Nos 91846202, 71890972/71890970), and the State Key Laboratory of Rail Traffic Control and Safety (No. RCS2020ZZ001), the Fundamental Research Funds for the Central Universities (No. 2021RC237) and the 111 Project (No. B20071). The authors would like to thank the editor and anonymous referees for their valuable comments that help us to improve the quality of the paper. 66 9 9 11 38 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0965-8564 1879-2375 TRANSPORT RES A-POL Transp. Res. Pt. A-Policy Pract. NOV 2021.0 153.0 235 260 10.1016/j.tra.2021.09.010 0.0 SEP 2021 26 Economics; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Business & Economics; Transportation XQ7IK 2023-03-23 WOS:000731717800004 0
J Wei, XJ; Wang, X; Wei, G; Zhu, CW; Shi, Y Wei, Xin-Jiang; Wang, Xiao; Wei, Gang; Zhu, Cheng-Wei; Shi, Yu Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article jacking force; vertical tunneling method; artificial neural network; genetic algorithm DISPLACEMENT; STRENGTH The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R-2), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study. [Wei, Xin-Jiang; Wei, Gang] Zhejiang Univ City Coll, Dept Civil Engn, Hangzhou 310015, Peoples R China; [Wang, Xiao] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China; [Zhu, Cheng-Wei] Univ Bodenkultur Wien, Inst Geotech, Feistmantelstr 4, A-1180 Vienna, Austria; [Shi, Yu] Shanghai Fdn Engn Grp Co Ltd, Shanghai 200002, Peoples R China Zhejiang University City College; Zhejiang University; University of Natural Resources & Life Sciences, Vienna Wang, X (corresponding author), Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China. weixj@zucc.edu.cn; susanxiaowang@126.com; weig@zucc.edu.cn; chengwei.zhu@boku.ac.at; shiyushjc@126.com Zhu, Chengwei/ABD-7186-2021; 魏, 纲/GXV-1965-2022 33 2 2 1 7 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. JAN 2021.0 9.0 1 71 10.3390/jmse9010071 0.0 18 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography PW0CL gold 2023-03-23 WOS:000610344700001 0
J Liu, HZ; He, XQ; Li, QQ; Kratzer, S; Wang, JJ; Shi, TZ; Hu, ZW; Yang, C; Hu, SB; Zhou, QM; Wu, GF Liu, Huizeng; He, Xianqiang; Li, Qingquan; Kratzer, Susanne; Wang, Junjie; Shi, Tiezhu; Hu, Zhongwen; Yang, Chao; Hu, Shuibo; Zhou, Qiming; Wu, Guofeng Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing REMOTE SENSING OF ENVIRONMENT English Article Ultraviolet; Remote sensing reflectance; Ocean colour remote sensing; Colour index; Machine learning; Inherent optical properties ATMOSPHERIC CORRECTION ALGORITHM; INHERENT OPTICAL-PROPERTIES; WATER-LEAVING RADIANCE; ABSORPTION-COEFFICIENTS; RADIATION; VALIDATION; COASTAL; PHYTOPLANKTON; IMAGERY; INLAND In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92?1.00 and MAPD = 1.11%?10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms. [Liu, Huizeng; Li, Qingquan; Wang, Junjie; Shi, Tiezhu; Hu, Zhongwen; Yang, Chao; Hu, Shuibo; Wu, Guofeng] Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China; [Liu, Huizeng; Li, Qingquan; Wang, Junjie; Shi, Tiezhu; Hu, Zhongwen; Yang, Chao; Hu, Shuibo; Wu, Guofeng] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Liu, Huizeng; Li, Qingquan; Wang, Junjie; Shi, Tiezhu; Hu, Zhongwen; Yang, Chao; Hu, Shuibo; Wu, Guofeng] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [He, Xianqiang] Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China; [Kratzer, Susanne] Stockholm Univ, Dept Ecol Environm & Plant Sci, S-10691 Stockholm, Sweden; [Zhou, Qiming] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China; [Liu, Huizeng] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China Shenzhen University; Shenzhen University; Shenzhen University; Ministry of Natural Resources of the People's Republic of China; Stockholm University; Hong Kong Baptist University; Shenzhen University Wu, GF (corresponding author), Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China. guofeng.wu@szu.edu.cn Kratzer, Susanne/ABD-8128-2021; LIU, Huizeng/ABA-4759-2021; 邬国锋/B-8735-2018; Hu, Zhongwen/AAX-7567-2021; Zhou, Qiming/N-8735-2016 邬国锋/0000-0003-2275-6530; Zhou, Qiming/0000-0003-0934-0602; Li, Qingquan/0000-0002-2438-6046 Key-Area Research and Development Program of Guangdong Province [2020B1111020005]; National Natural Science Foundation of China [42001281, 41890854, 41890852, 41606199, 41971386]; National Key R&D Program of China [2017YFC0506200]; Hong Kong Research Grant Council (RGC) General Research Fund [12301820]; Swedish National Space Board [175/17, 61/17]; European Space Agency [ESRIN 12352/08/I-OL]; ARGANS ESA MERIS 4thRP Key-Area Research and Development Program of Guangdong Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key R&D Program of China; Hong Kong Research Grant Council (RGC) General Research Fund; Swedish National Space Board; European Space Agency(European Space AgencyEuropean Commission); ARGANS ESA MERIS 4thRP We greatly appreciate Prof. Lee Zhongping for his precious comments and suggestions during paper preparation. This study was supported by the Key-Area Research and Development Program of Guangdong Province (No. 2020B1111020005), the National Natural Science Foundation of China grants (Grant Nos. 42001281, 41890854, 41890852, 41606199 and 41971386) National Key R&D Program of China (No. 2017YFC0506200) and Hong Kong Research Grant Council (RGC) General Research Fund (12301820). We thank the NASA Ocean Biology Processing Group and SeaBASS for archiving and distributing the in situ matchup data. The authors are grateful to the MOBY team for providing the in situ data. The principal investigators, including those responsible for AERONET-OC observatories, are appreciated for sharing their field measurements. Specifically, we thank Joji Ishizaka, Kohei Arai and the JAXA GCOM-C project for maintaining and sharing the Rrs data from AERONET-OC station of ARIAKE_TOWER, and Nima Pahlevan for sharing data from the Grizzly_Bay station, and thank Prof. Giuseppe Zibordi for AERONET-OC stations of Casablana_Platform, Galata_Platform, Gloria, Gustav_dalen_Tower, Helsinki_lighthouse, Irbe_lighthouse and Section_7_Platform. We also would like to acknowledge the Swedish National Space Board, project Dnr 175/17 and 61/17 as well as the European Space Agency ESRIN 12352/08/I-OL and ARGANS ESA MERIS 4thRP. 66 7 8 5 38 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. JUN 1 2021.0 258.0 112404 10.1016/j.rse.2021.112404 0.0 MAR 2021 19 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology RI9HF Bronze 2023-03-23 WOS:000637215300001 0
J Flores, H; Hui, P; Nurmi, P; Lagerspetz, E; Tarkoma, S; Manner, J; Kostakos, V; Li, Y; Su, X Flores, Huber; Hui, Pan; Nurmi, Petteri; Lagerspetz, Eemil; Tarkoma, Sasu; Manner, Jukka; Kostakos, Vassilis; Li, Yong; Su, Xiang Evidence-Aware Mobile Computational Offloading IEEE TRANSACTIONS ON MOBILE COMPUTING English Article Mobile cloud computing; code offloading; cloud offload; big data; crowdsensing; mobile context modeling CLOUD; SYSTEM; OPPORTUNITIES; EXECUTION; ENERGY Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub. [Flores, Huber] Univ Helsinki, Dept Comp Sci, SF-00100 Helsinki, Finland; [Hui, Pan; Tarkoma, Sasu] Univ Helsinki, Comp Sci, SF-00100 Helsinki, Finland; [Hui, Pan] Hong Kong Univ Sci & Technol, HKUST DT Syst & Media Lab, Hong Kong, Hong Kong, Peoples R China; [Nurmi, Petteri; Lagerspetz, Eemil] Univ Helsinki, SF-00100 Helsinki, Finland; [Manner, Jukka] Aalto Univ, Dept Commun & Networking Comnet, Networking Technol, Espoo 02150, Finland; [Kostakos, Vassilis] Univ Melbourne, Sch Comp & Informat Syst, Human Comp Interact, Parkville, Vic 3010, Australia; [Li, Yong] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China; [Su, Xiang] Univ Oulu, Ctr Ubiquitous Comp, Comp Sci, Oulu 90014, Finland University of Helsinki; University of Helsinki; Hong Kong University of Science & Technology; University of Helsinki; Aalto University; University of Melbourne; Tsinghua University; University of Oulu Hui, P (corresponding author), Univ Helsinki, Comp Sci, SF-00100 Helsinki, Finland.;Hui, P (corresponding author), Hong Kong Univ Sci & Technol, HKUST DT Syst & Media Lab, Hong Kong, Hong Kong, Peoples R China. huber.flores@helsinki.fi; panhui@cse.ust.hk; petteri.nurmi@helsinki.fi; eemil.lagerspetz@helsinki.fi; sasu.tarkoma@helsinki.fi; jukka.manner@tkk.fi; vassilis.kostakos@oulu.fi; liyong07@tsinghua.edu.cn; xiang.su@ee.oulu.fi Nurmi, Petteri/AAV-4865-2020; Hui, Pan/AAK-6660-2020; Manner, Jukka/G-2350-2013; Flores, Huber/AAP-5274-2021; Kostakos, Vassilis/G-8997-2011; Tarkoma, Sasu/V-2345-2019; li, yong/HDN-3885-2022 Hui, Pan/0000-0001-6026-1083; Manner, Jukka/0000-0002-9133-473X; Flores, Huber/0000-0003-4551-629X; Kostakos, Vassilis/0000-0003-2804-6038; Tarkoma, Sasu/0000-0003-4220-3650; Nurmi, Petteri/0000-0001-8262-6434; Lagerspetz, Eemil/0000-0003-3875-8135 Research Grants Council of Hong Kong [26211515, 16214817]; Jorma Ollila Grant [201620040] Research Grants Council of Hong Kong(Hong Kong Research Grants Council); Jorma Ollila Grant The authors thank the anonymous reviewers for their insightful comments. This research has been supported, in part, by projects 26211515 and 16214817 from the Research Grants Council of Hong Kong. This work also was supported by the Jorma Ollila Grant 201620040. 37 24 24 1 7 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1536-1233 1558-0660 IEEE T MOBILE COMPUT IEEE. Trans. Mob. Comput. AUG 1 2018.0 17.0 8 1834 1850 10.1109/TMC.2017.2777491 0.0 17 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications GL7SB Green Accepted, Green Published 2023-03-23 WOS:000437402100008 0
J Pan, XY; Shen, HB Pan, Xiaoyong; Shen, Hong-Bin Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks BIOINFORMATICS English Article WEB SERVER; MESSENGER-RNAS; DNA; ACCESSIBILITY; RECOGNITION; SELECTION; GENOME; TDP-43; MODES; FUS Motivation: RNA-binding proteins (RBPs) take over 5-10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using patterns learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. Results: In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN runs 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding mitifs. [Pan, Xiaoyong] Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands; [Shen, Hong-Bin] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China; [Shen, Hong-Bin] Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China Erasmus University Rotterdam; Erasmus MC; Shanghai Jiao Tong University; Ministry of Education, China Pan, XY (corresponding author), Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands.;Shen, HB (corresponding author), Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China.;Shen, HB (corresponding author), Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China. xypan172436@gmail.com; hbshen@sjtu.edu.cn National Natural Science Foundation of China [61725302, 61671288, 91530321, 61603161, 61462018, 61762026]; Science and Technology Commission of Shanghai Municipality [16JC1404300, 17JC1403500] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Science and Technology Commission of Shanghai Municipality(Science & Technology Commission of Shanghai Municipality (STCSM)) This work was supported by the National Natural Science Foundation of China (No. 61725302, 61671288, 91530321, 61603161, 61462018, 61762026), and Science and Technology Commission of Shanghai Municipality (No. 16JC1404300, 17JC1403500). 60 104 105 6 67 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1367-4803 1460-2059 BIOINFORMATICS Bioinformatics OCT 15 2018.0 34.0 20 3427 3436 10.1093/bioinformatics/bty364 0.0 10 Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Science Citation Index Expanded (SCI-EXPANDED) Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics GY7II 29722865.0 Bronze 2023-03-23 WOS:000448782100002 0
J Zou, PX; Bricker, J; Uijttewaal, W Zou, Pengxu; Bricker, Jeremy; Uijttewaal, Wim Optimization of submerged floating tunnel cross section based on parametric Bezier curves and hybrid backpropagation - genetic algorithm MARINE STRUCTURES English Article Submerged floating tunnel; Cross-section optimization; Hybrid BP-GA; CFD CIRCULAR-CYLINDER; MULTIOBJECTIVE OPTIMIZATION; NUMERICAL-SIMULATION; FLOW; IMPLEMENTATION; MODEL; WIDE; TIME The cross-section geometry of a submerged floating tunnel (SFT) has a large effect on hydrodynamic characteristics, structural behavior and service level, making the tunnel cross section the primary factor in optimizing efficiency. Minimizing the mean drag and the dynamic variability in the lift of the SFT cross section under bi-directional (i.e., tidal) flow has a dramatic impact on the reduction of structural displacements and mooring loads. Based on a parametric Bezier curve dynamically comprising the leading-edge radius, tunnel height and width to define the SFT geometry, a sensitivity analysis of the Bezier curve parameters for a fixed aspect ratio with prototype dimensions under uniform flow conditions was conducted by applying Computational Fluid Dynamics (CFD), and the pressure distribution around the SFT cross-section surface was analyzed. A theoretical method comprising the Kirman vortex street parameters was employed to verify the CFD simulation results. In order to determine the SFT cross section with optimal hydrodynamic properties, the mean drag and Root Mean Square (RMS) lift coefficients were selected as optimization objectives, and four Bezier curve parameters were the input variables, in a neural network and genetic algorithm optimization process (a hybrid BP-GA structure), which is less likely to become trapped in local minima. The results show the optimal tunnel cross section has a mean drag and a RMS lift coefficient reduced by 0.9% and 6.3%, respectively, compared to the original CFD dataset. [Zou, Pengxu; Bricker, Jeremy; Uijttewaal, Wim] Delft Univ Technol, Dept Hydraul Engn, Delft, Netherlands; [Zou, Pengxu] CCCC FHDI Engn Co Ltd, Guangzhou, Peoples R China; [Zou, Pengxu] CCCC SFT Tech Joint Res Team, Zhuhai, Peoples R China Delft University of Technology Zou, PX (corresponding author), Delft Univ Technol, Dept Hydraul Engn, Delft, Netherlands. p.zou@tudelft.nl Uijttewaal, Wim/ABC-8328-2021; Uijttewaal, Wim/GNP-7281-2022 Uijttewaal, Wim/0000-0002-6043-8026; Zou, Pengxu/0000-0002-5610-367X; Bricker, Jeremy/0000-0002-7503-6652 China Communications Construction Company Ltd. (CCCC) China Communications Construction Company Ltd. (CCCC) The study presented in this paper was conducted in the submerged floating tunnel research project funded by China Communications Construction Company Ltd. (CCCC) and jointly carried out by universities, scientific research institutes, engineering consulting firms, design and construction companies in China and the Netherlands. The authors would like to acknowledge C.J. (Carlos) Simao Ferreira for his contribution in elucidating the knowledge of airfoil design. 43 7 7 4 14 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8339 1873-4170 MAR STRUCT Mar. Struct. NOV 2020.0 74.0 102807 10.1016/j.marstruc.2020.102807 0.0 19 Engineering, Marine; Engineering, Civil Science Citation Index Expanded (SCI-EXPANDED) Engineering NR4GZ hybrid 2023-03-23 WOS:000571523700003 0
J Akusok, A; Gritsenko, A; Miche, Y; Bjork, KM; Nian, R; Lauren, P; Lendasse, A Akusok, Anton; Gritsenko, Andrey; Miche, Yoan; Bjork, Kaj-Mikael; Nian, Rui; Lauren, Paula; Lendasse, Amaury Adding reliability to ELM forecasts by confidence intervals NEUROCOMPUTING English Article Extreme learning machines; Confidence; Confidence interval; Regression; Skin segmentation; Big data EXTREME LEARNING-MACHINE; SKIN SEGMENTATION; CLASSIFICATION; REGRESSION This paper proposes a way of providing transparent and interpretable results for ELM models by adding confidence intervals to the predicted outputs. In supervised learning, outputs are often random variables because they may depend on information that is unavailable, due to the presence of noise, or the projection function itself may be stochastic. Probability distribution of outputs is input dependent, and the observed output values are samples from that distribution. However, ELM predicts deterministic outputs. The proposed method addresses that problem by estimating predictive Confidence Intervals (Cls) at a confidence level alpha, such that random output values fall between these intervals with probability alpha. Assuming that the outputs are normally distributed, only a standard deviation is needed to compute CIs of a predicted output (the predicted output itself is a mean). Our method provides CIs for ELM predictions by estimating standard deviation of a random output for a particular input sample. It shows good results on both toy and real skin segmentation datasets, and compares well with the existing Confidence-weighted ELM methods. On a toy dataset, the predicted CIs accurately represent the variable variance of outputs. On a real dataset, CIs improve the precision-of a classification task at a cost of recall. (C) 2016 Elsevier B.V. All rights reserved. [Akusok, Anton; Gritsenko, Andrey; Lendasse, Amaury] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA; [Akusok, Anton; Gritsenko, Andrey; Lendasse, Amaury] Univ Iowa, Iowa Informat Initiat, Iowa City, IA 52242 USA; [Miche, Yoan] Nokia, Bell Labs, Espoo, Finland; [Miche, Yoan] Aalto Univ, Dept Informat & Comp Sci, Sch Sci, FI-00076 Aalto, Finland; [Akusok, Anton] Arcada Univ Appl Sci, Dept Business Management & Analyt, Helsinki, Finland; [Bjork, Kaj-Mikael] Arcada Univ Appl Sci, Risklab, Helsinki, Finland; [Nian, Rui] Ocean Univ China, Sch Informat Sci & Engn, Qingdao, Peoples R China; [Lauren, Paula] Oakland Univ, Sch Engn & Comp Sci, Rochester, MI 48309 USA University of Iowa; University of Iowa; Nokia Corporation; Nokia Finland; Aalto University; Arcada University of Applied Sciences; Arcada University of Applied Sciences; Ocean University of China; Oakland University Akusok, A (corresponding author), Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA.;Akusok, A (corresponding author), Univ Iowa, Iowa Informat Initiat, Iowa City, IA 52242 USA. anton-akusok@uiowa.edu; amaury-lendasse@uiowa.edu Gritsenko, Andrey/AAM-3383-2020 Gritsenko, Andrey/0000-0001-5074-7282; Lendasse, Amaury/0000-0001-5410-4751 Natural Science Foundation of PR China [31202036] Natural Science Foundation of PR China(National Natural Science Foundation of China (NSFC)) This work was partially supported by the Natural Science Foundation of PR China (31202036). 24 5 5 0 22 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JAN 5 2017.0 219.0 232 241 10.1016/j.neucom.2016.09.021 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science EG0PI 2023-03-23 WOS:000390734300022 0
C Rao, W; Fu, YH; Hu, YX; Xu, X; Jv, YK; Han, JY; Jiang, ZJ; Xie, L; Wang, YN; Watanabe, S; Tan, ZH; Bu, H; Yu, T; Shang, SD IEEE Rao, Wei; Fu, Yihui; Hu, Yanxin; Xu, Xin; Jv, Yvkai; Han, Jiangyu; Jiang, Zhongjie; Xie, Lei; Wang, Yannan; Watanabe, Shinji; Tan, Zheng-Hua; Bu, Hui; Yu, Tao; Shang, Shidong CONFERENCINGSPEECH CHALLENGE: TOWARDS FAR-FIELD MULTI-CHANNEL SPEECH ENHANCEMENT FOR VIDEO CONFERENCING 2021 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU) English Proceedings Paper IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) DEC 13-17, 2021 ELECTR NETWORK Inst Elect & Elect Engineers,Inst Elect & Elect Engineers Signal Proc Soc ConferencingSpeech challenge; multi-channel speech enhancement; multiple distributed microphone arrays; casual system; subjective evaluation CORPUS The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing. The challenge consists of two separate tasks: 1) Task 1 is multi-channel speech enhancement with single microphone array and focusing on practical application with real-time requirement and 2) Task 2 is multichannel speech enhancement with multiple distributed microphone arrays, which is a non-real-time track and does not have any constraints so that participants could explore any algorithms to obtain high speech quality. Targeting the real video conferencing room application, the challenge database was recorded from real speakers and all recording facilities were located by following the real setup of conferencing room. In this challenge, we open-sourced the list of open source clean speech and noise datasets, simulation scripts, and a baseline system for participants to develop their own system. The final ranking of the challenge will be decided by the subjective evaluation which is performed using Absolute Category Ratings (ACR) to estimate Mean Opinion Score (MOS), speech MOS (S-MOS), and noise MOS (N-MOS). This paper describes the challenge, tasks, datasets, subjective evaluation, and challenge results. The baseline system which is a complex ratio mask based neural network and its experimental results are also presented. [Rao, Wei; Han, Jiangyu; Jiang, Zhongjie; Wang, Yannan; Shang, Shidong] Tencent Ethereal Audio Lab, Shenzhen, Peoples R China; [Fu, Yihui; Hu, Yanxin; Jv, Yvkai; Xie, Lei] Northwestern Polytech Univ, Xian, Peoples R China; [Xu, Xin; Bu, Hui] Beijing Shell Shell Technol Co LTD, Beijing, Peoples R China; [Watanabe, Shinji] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Watanabe, Shinji] Johns Hopkins Univ, Baltimore, MD 21218 USA; [Tan, Zheng-Hua] Aalborg Univ, Aalborg, Denmark; [Yu, Tao] Tencent Ethereal Audio Lab, Seattle, WA USA Northwestern Polytechnical University; Carnegie Mellon University; Johns Hopkins University; Aalborg University Rao, W (corresponding author), Tencent Ethereal Audio Lab, Shenzhen, Peoples R China. Watanabe, Shinji/0000-0002-5970-8631 32 2 2 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 978-1-6654-3739-4 2021.0 679 686 10.1109/ASRU51503.2021.9688126 0.0 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BT0TC 2023-03-23 WOS:000792364700090 0
J Shi, R; Wang, X; Wu, Y; Xu, B; Zhao, TY; Trapp, C; Wang, XB; Unger, K; Zhou, C; Lu, S; Buchner, A; Schulz, GB; Cao, FJ; Belka, C; Su, C; Li, ML; Shu, YQ Shi, Run; Wang, Xin; Wu, Yang; Xu, Bin; Zhao, Tianyu; Trapp, Christian; Wang, Xuanbin; Unger, Kristian; Zhou, Cheng; Lu, Shun; Buchner, Alexander; Schulz, Gerald Bastian; Cao, Fengjun; Belka, Claus; Su, Chuan; Li, Minglun; Shu, Yongqian APOBEC-mediated mutagenesis is a favorable predictor of prognosis and immunotherapy for bladder cancer patients: evidence from pan-cancer analysis and multiple databases THERANOSTICS English Article APOBEC mutagenesis; Pan-cancer analysis; Immunotherapy; Prognosis DNA-DAMAGE RESPONSE; CLINICAL BENEFIT; TUMOR; IDENTIFICATION; EXPRESSION; SIGNATURES; MUTATIONS; SENSITIVITY; EVOLUTION; BLOCKADE Background: The APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) family-mediated mutagenesis is widespread in human cancers. However, our knowledge of the biological feature and clinical relevance of APOBECs and APOBEC mutagenesis in cancers remains limited. Methods: In this study, with a series of bioinformatic and statistical approaches, we performed a comprehensive analysis of multiple levels of data, including whole-exome sequencing (WES) and targeted next-generation sequencing (NGS), transcriptome (bulk RNA-seq and single-cell RNA-seq), immune signatures and immune checkpoint blockade (ICB) potential, patient survival and drug sensitivity, to reveal the distribution characteristics and clinical significance of APOBECs and APOBEC mutagenesis in pan-cancer especially bladder cancer (BLCA). Results: APOBEC mutagenesis dominates in the mutational patterns of BLCA. A higher enrichment score of APOBEC mutagenesis correlates with favorable prognosis, immune activation and potential ICB response in BLCA patients. APOBEC3A and 3B play a significant role in the malignant progression and cell differentiation within the tumor microenvironment. Furthermore, using machine learning approaches, a prognostic APOBEC mutagenesis-related model was established and validated in different BLCA cohorts. Conclusions: Our study illustrates the characterization of APOBECs and APOBEC mutagenesis in multiple cancer types and highlights its potential value as a promising biomarker for prognosis and immunotherapy in BLCA. [Shi, Run; Shu, Yongqian] Nanjing Med Univ, Dept Oncol, Affiliated Hosp 1, Nanjing, Peoples R China; [Wang, Xin] Nanjing Med Univ, Affiliated Canc Hosp, Jiangsu Inst Canc Res, Dept Oncol,Jiangsu Canc Hosp, Nanjing, Peoples R China; [Wu, Yang] Nanjing Med Univ, Affiliated Hosp 1, Pancreas Ctr, Nanjing, Peoples R China; [Xu, Bin] Shanghai Jiao Tong Univ, Sch Med, Shanghai Ninth Peoples Hosp, Dept Urol, Shanghai, Peoples R China; [Zhao, Tianyu] LMU Univ Hosp Munich, German Ctr Lung Res DZL, Comprehens Pneumol Ctr CPC Munich, Inst & Clin Occupat Social & Environm Med, Munich, Germany; [Zhao, Tianyu] German Res Ctr Environm Hlth, Inst Epidemiol, Helmholtz Zentrum Munchen, Neuherberg, Germany; [Trapp, Christian; Unger, Kristian; Belka, Claus; Li, Minglun] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiat Oncol, Munich, Germany; [Wang, Xuanbin] Hubei Univ Med, Renmin Hosp, Dept Pharmacol, Lab Chinese Herbal Pharmacol,Hubei Key Lab Wudang, Shiyan, Hubei, Peoples R China; [Unger, Kristian] German Res Ctr Environm Hlth GmbH, Res Unit Radiat Cytogenet, Helmholtz Ctr Munich, Neuherberg, Germany; [Zhou, Cheng] Southern Med Univ, Nanfang Hosp, Dept Radiat Oncol, Guangzhou, Peoples R China; [Lu, Shun] Univ Elect Sci & Technol China, Sch Med, Sichuan Canc Hosp, Dept Radiotherapy, Chengdu, Peoples R China; [Buchner, Alexander; Schulz, Gerald Bastian] Ludwig Maximilians Univ Munchen, Dept Urol, Univ Hosp, Munich, Germany; [Cao, Fengjun] Hubei Univ Med, Renmin Hosp, Dept Oncol, Shiyan, Hubei, Peoples R China; [Belka, Claus] German Canc Consortium DKTK, Munich, Germany; [Su, Chuan] Nanjing Med Univ, Dept Pathogen Biol & Immunol, Ctr Global Hlth, Jiangsu Prov Key Lab Pathogen Biol, Nanjing, Peoples R China Nanjing Medical University; Nanjing Medical University; Nanjing Medical University; Shanghai Jiao Tong University; University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; University of Munich; Hubei University of Medicine; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Southern Medical University - China; University of Electronic Science & Technology of China; University of Munich; Hubei University of Medicine; Helmholtz Association; German Cancer Research Center (DKFZ); Nanjing Medical University Shu, YQ (corresponding author), Nanjing Med Univ, Dept Oncol, Affiliated Hosp 1, Nanjing, Peoples R China.;Li, ML (corresponding author), Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiat Oncol, Munich, Germany.;Su, C (corresponding author), Nanjing Med Univ, Dept Pathogen Biol & Immunol, Ctr Global Hlth, Jiangsu Prov Key Lab Pathogen Biol, Nanjing, Peoples R China. chuansu@njmu.edu.cn; minglun.li@med.uni-muenchen.de; shuyongqian@csco.org.cn Buchner, Alexander/HKN-3430-2023; Wu, Yang/Y-7494-2019; Zhao, Tianyu/ACJ-4592-2022; Wang, Xuanbin/AHE-9819-2022; Trapp, Christian/HLX-6566-2023; Zhao, Tianyu/U-2559-2019; Li, Minglun/A-8086-2019 Buchner, Alexander/0000-0001-7895-7070; Wu, Yang/0000-0002-0906-9667; Zhao, Tianyu/0000-0002-2696-273X; Wang, Xuanbin/0000-0003-0769-2874; Zhao, Tianyu/0000-0002-2696-273X; 59 1 1 8 9 IVYSPRING INT PUBL LAKE HAVEN PO BOX 4546, LAKE HAVEN, NSW 2263, AUSTRALIA 1838-7640 THERANOSTICS Theranostics 2022.0 12.0 9 4181 4199 10.7150/thno.73235 0.0 19 Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Research & Experimental Medicine 2E6TC 35673559.0 Green Accepted, gold 2023-03-23 WOS:000812358800009 0
J Wan, C; Xu, Z; Wang, YL; Dong, ZY; Wong, KP Wan, Can; Xu, Zhao; Wang, Yelei; Dong, Zhao Yang; Wong, Kit Po A Hybrid Approach for Probabilistic Forecasting of Electricity Price IEEE TRANSACTIONS ON SMART GRID English Article Artificial neural network; bootstrap; electricity price forecasting; maximum likelihood estimation; prediction intervals EXTREME LEARNING-MACHINE; CONFIDENCE-INTERVAL ESTIMATION; PREDICTION INTERVALS; NETWORKS; WAVELET The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts to probabilistic interval forecasts can be of great importance to quantify the uncertainties of potential forecasts, thus effectively supporting the decision making activities against uncertainties and risks ahead. This paper proposes a hybrid approach to construct prediction intervals of MCPs with a two-stage formulation. In the first stage, extreme learning machine (ELM) is applied to estimate point forecasts of MCPs and model uncertainties involved. In the second stage, the maximum likelihood method is used to estimate the noise variance. A generalized and comprehensive evaluation framework for probabilistic electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market. [Wan, Can; Xu, Zhao] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China; [Wan, Can] Tech Univ Denmark, Ctr Elect Power & Energy CEE, DK-2800 Lyngby, Denmark; [Wang, Yelei] Zhejiang Univ, Sch Elect Engn, Hangzhou 310058, Zhejiang, Peoples R China; [Dong, Zhao Yang] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia; [Wong, Kit Po] Univ Western Australia, Sch Elect Elect & Comp Engn, Crawley, WA 6009, Australia Hong Kong Polytechnic University; Technical University of Denmark; Zhejiang University; University of Sydney; University of Western Australia Wan, C (corresponding author), Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China. can.wan@polyu.edu.hk; eezhaoxu@polyu.edu.hk; yeleiwang@zju.edu.cn; zydong@ieee.org; kitpo@ieee.org gao, xiaodan/K-3594-2015; yang, dong zhao/AFP-5094-2022 Dong, Zhao Yang/0000-0001-9659-0858; XU, Zhao/0000-0003-4480-7394 Hong Kong RGC GRF [515110, 528412, T23-407/13-N]; Hong Kong Ph.D. Fellowship Hong Kong RGC GRF(Hong Kong Research Grants Council); Hong Kong Ph.D. Fellowship This work is partially supported by Hong Kong RGC GRF grants no. 515110, 528412, and T23-407/13-N. The work of C. Wan is supported by a Hong Kong Ph.D. Fellowship. Paper no. TSG-00114-2013 31 152 158 3 64 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1949-3053 1949-3061 IEEE T SMART GRID IEEE Trans. Smart Grid JAN 2014.0 5.0 1 463 470 10.1109/TSG.2013.2274465 0.0 8 Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Engineering 287CR 2023-03-23 WOS:000329517300049 0
J Cheng, XG; Yang, B; Liu, GQ; Olofsson, T; Li, HB Cheng, Xiaogang; Yang, Bin; Liu, Guoqing; Olofsson, Thomas; Li, Haibo A Total Bounded Variation Approach to Low Visibility Estimation on Expressways SENSORS English Article total bounded variation; image spectrum; low visibility estimation; piece stationary; fog and haze IMAGE-RESTORATION; DISTANCE; ALGORITHM; RECOVERY Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pse udo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%. [Cheng, Xiaogang; Li, Haibo] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China; [Cheng, Xiaogang; Yang, Bin; Olofsson, Thomas] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden; [Cheng, Xiaogang; Li, Haibo] Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden; [Yang, Bin] Xian Univ Architecture & Technol, Sch Environm & Municipal Engn, Xian 710055, Shaanxi, Peoples R China; [Liu, Guoqing] Nanjing Tech Univ, Sch Phys & Math Sci, Nanjing 211800, Jiangsu, Peoples R China Nanjing University of Posts & Telecommunications; Umea University; Royal Institute of Technology; Xi'an University of Architecture & Technology; Nanjing Tech University Cheng, XG (corresponding author), Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.;Cheng, XG (corresponding author), Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden.;Cheng, XG (corresponding author), Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden. xiacheng@kth.se; bin.yang@umu.se; guoqing@njtech.edu.cn; thomas.olofsson@umu.se; haiboli@kth.se Yang, Bin/GQA-9324-2022 Yang, Bin/0000-0002-1579-7978; Olofsson, Thomas/0000-0002-8704-8538 National Natural Science Foundation of China [61401236]; Jiangsu Postdoctoral Science Foundation [1601039B]; Key Research Project of Jiangsu Science and Technology Department [BE2016001-3]; Ministry of Education-China Mobile Research Fund [MCM20150504]; Scientific Research Foundation of NUPT [NY214005] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Jiangsu Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Key Research Project of Jiangsu Science and Technology Department; Ministry of Education-China Mobile Research Fund; Scientific Research Foundation of NUPT This study was supported by the National Natural Science Foundation of China (No. 61401236), the Jiangsu Postdoctoral Science Foundation (No. 1601039B), the Key Research Project of Jiangsu Science and Technology Department (No. BE2016001-3), the Ministry of Education-China Mobile Research Fund (No. MCM20150504), and the Scientific Research Foundation of NUPT (No. NY214005). 38 7 7 1 10 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors FEB 2018.0 18.0 2 392 10.3390/s18020392 0.0 18 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation FZ4EM 29382181.0 Green Accepted, Green Published, Green Submitted, gold 2023-03-23 WOS:000427544000075 0
C Zhang, KC; Zhou, JB; Tao, DL; Karras, P; Li, Q; Xiong, H ASSOC COMP MACHINERY Zhang, Kaichen; Zhou, Jingbo; Tao, Donglai; Karras, Panagiotis; Li, Qing; Xiong, Hui Geodemographic Influence Maximization KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING English Proceedings Paper 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) AUG 23-27, 2020 ELECTR NETWORK Assoc Comp Machinery,ACM SIGMOD,ACM SIGKDD Given a set of locations in a city, on which ones should we place ads on so as to reach as many people as possible within a limited budget? Past research has addressed this question under the assumption that dense trajectory data are available to determine the reach of each ad. However, the data that are available in most industrial settings do not consist of dense, long-range trajectories; instead, they consist of statistics on people's short-range point-to-point movements. In this paper, we address the natural problem that arises on such data: given a distribution of population and point-to-point movement statistics over a network, find a set of locations within a budget that achieves maximum expected reach. We call this problem geodemographic influence maximization (GIM). We show that the problem is NP-hard, but its objective function is monotone and submodular, thus admits a greedy algorithm with a 1/2(1 - 1/e) approximation ratio. Still, this algorithm is inapplicable on large-scale data for high-frequency digital signage ads. We develop an efficient deterministic algorithm, Lazy-Sower, exploiting a novel, tight double-bounding scheme of marginal influence gain as well as the locality proprieties of the problem; a learning-based variant, NN-Sower, utilizes randomization and deep learning to further improve efficiency, with a slight loss of quality. Our exhaustive experimental study on two real-world urban datasets demonstrates the efficacy and efficiency of our solutions compared to baselines. [Zhang, Kaichen; Zhou, Jingbo] Baidu Res, Business Intelligence Lab, Beijing, Peoples R China; [Karras, Panagiotis] Aarhus Univ, Aarhus, Denmark; [Li, Qing] Baidu Inc, Beijing, Peoples R China; [Zhou, Jingbo] Natl Engn Lab Deep Learning Technol & Applicat, Beijing, Peoples R China; [Zhang, Kaichen] Beijing Univ Posts & Telecommun, Beijing, Peoples R China; [Tao, Donglai] Tsinghua Univ, Beijing, Peoples R China; [Xiong, Hui] Rutgers State Univ, Newark, NJ 07103 USA; [Zhang, Kaichen] Baidu Res, Beijing, Peoples R China Baidu; Aarhus University; Baidu; Beijing University of Posts & Telecommunications; Tsinghua University; Rutgers State University Newark; Rutgers State University New Brunswick; Baidu Zhou, JB (corresponding author), Baidu Res, Business Intelligence Lab, Beijing, Peoples R China.;Zhou, JB (corresponding author), Natl Engn Lab Deep Learning Technol & Applicat, Beijing, Peoples R China.;Xiong, H (corresponding author), Rutgers State Univ, Newark, NJ 07103 USA. zhoujingbo@baidu.com; hxiong@rutgers.edu Karras, Panagiotis/AFS-4578-2022 Xiong, Hui/0000-0001-6016-6465 National Natural Science Foundation of China [91746301, 61725205]; Danish Council for Independent Research [9041-00382B] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Danish Council for Independent Research(Det Frie Forskningsrad (DFF)) This work was supported in part by the National Natural Science Foundation of China (Grant No.91746301,61725205) and the Danish Council for Independent Research (Research Project 9041-00382B). 31 2 3 5 6 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7998-4 2020.0 2764 2774 10.1145/3394486.3403327 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LS 2023-03-23 WOS:000749552302076 0
J Wang, HL; Li, Q; Sun, HY; Chen, ZZ; Hao, YQ; Peng, JK; Yuan, ZH; Fu, JS; Jiang, Y Wang, Hanling; Li, Qing; Sun, Heyang; Chen, Zuozhou; Hao, Yingqian; Peng, Junkun; Yuan, Zhenhui; Fu, Junsheng; Jiang, Yong VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS English Article Edge-cloud collaborative computing; semantic compression; video analytics; task-oriented communication system NEURAL-NETWORK; EFFICIENT Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantic compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks. [Wang, Hanling; Peng, Junkun; Jiang, Yong] Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Guangdong, Peoples R China; [Wang, Hanling; Li, Qing; Chen, Zuozhou; Peng, Junkun; Jiang, Yong] Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China; [Sun, Heyang] Southeast Univ, Sch Software, Nanjing 211189, Jiangsu, Peoples R China; [Hao, Yingqian] Jilin Univ, Sch Software, Changchun 130012, Jilin, Peoples R China; [Yuan, Zhenhui] Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England; [Fu, Junsheng] Zenseact, S-41756 Gothenburg, Sweden Tsinghua University; Peng Cheng Laboratory; Southeast University - China; Jilin University; Northumbria University Li, Q (corresponding author), Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China. hl-wang21@mails.tsinghua.edu.cn; liq@pcl.ac.cn; 213182539@seu.edu.cn; chenzzh@pcl.ac.cn; haoyq5519@mails.jlu.edu.cn; pjk20@mails.tsinghua.edu.cn; zhenhui.yuan@northumbria.ac.uk; junsheng.fu@zenseact.com; jiangy@sz.tsinghua.edu.cn National Natural Science Foundation of China [61972189]; Major Key Project of Peng Cheng Laboratory (PCL) [PCL2021A03-1]; Shenzhen Key Laboratory of Software Defined Networking [ZDSYS20140509172959989] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Major Key Project of Peng Cheng Laboratory (PCL); Shenzhen Key Laboratory of Software Defined Networking This work was supported in part by the National Natural Science Foundation of China under Grant 61972189, in part by the Major Key Project of Peng Cheng Laboratory (PCL) under Grant PCL2021A03-1, and in part by the Shenzhen Key Laboratory of Software Defined Networking under Grant ZDSYS20140509172959989. 88 1 1 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8716 1558-0008 IEEE J SEL AREA COMM IEEE J. Sel. Areas Commun. JAN 2023.0 41.0 1 SI 90 106 10.1109/JSAC.2022.3221995 0.0 17 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications 8R5KV Green Accepted 2023-03-23 WOS:000927934500006 0
J Zhang, MS; Huang, J; Cao, Y; Xiong, CH; Mohammed, S Zhang, Mengshi; Huang, Jian; Cao, Yu; Xiong, Cai-Hua; Mohammed, Samer Echo State Network-Enhanced Super-Twisting Control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles IEEE-ASME TRANSACTIONS ON MECHATRONICS English Article Echo state network (ESN); lower limb exoskeleton; pneumatic muscle (PM) actuator; rehabilitation robotics; super-twisting control (STC) NONLINEAR DISTURBANCE OBSERVER; SLIDING-MODE CONTROL; TRACKING; ALGORITHM; SYSTEM In this article, a robust trajectory tracking control method is proposed for the passive gait training exoskeleton system driven by pneumatic muscles (PMs). Conventional model-based controllers suffer from limitations with respect to model uncertainties and external disturbances caused by PMs and complex robotic systems. An echo state network (ESN) is used in this study to approximate the model uncertainties and external disturbances of the exoskeleton system. Based on the approximation of ESN, a super-twisting control (STC) algorithm is designed to guarantee accurate tracking control at both hip and knee joint levels. Because there are both weight error and global approximation error in neural network approximation, a standard quadratic form cannot be obtained which plays an important role in the stability analysis of the traditional super twisting algorithm. To solve this issue, a dedicated positive definite matrix is constructed in this article, which bridges the ESN and STC by providing a parameter selection criteria. The stability with respect to the tracking problem of the exoskeleton system is then guaranteed according to the Lyapunov theorem. Both numerical simulations and experimental results present better tracking accuracy and robustness compared with the traditional sliding mode control and STC. [Zhang, Mengshi; Huang, Jian; Cao, Yu] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China; [Zhang, Mengshi; Huang, Jian; Cao, Yu] Educ Minist, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China; [Huang, Jian] Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518060, Peoples R China; [Xiong, Cai-Hua] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China; [Xiong, Cai-Hua] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China; [Mohammed, Samer] Univ Paris Est Creteil, F-94400 Vitry Sur Seine, France Huazhong University of Science & Technology; Huazhong University of Science & Technology; Huazhong University of Science & Technology; Huazhong University of Science & Technology; Universite Paris-Est-Creteil-Val-de-Marne (UPEC) Huang, J (corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China. dream_poem@hust.edu.cn; huangjan@mail.hust.edu.cn; cao_yu@mail.hust.edu.cn; chxiong@hust.edu.cn; samer.mohammed@u-pec.fr Huang, Jian/I-8521-2014 Huang, Jian/0000-0002-6267-8824; Zhang, Mengshi/0000-0002-0603-5525; Cao, Yu/0000-0002-3486-5518; Mohammed, Samer/0000-0001-6738-4529 International Science and Technology Cooperation Program of China [2017YFE0128300]; National Natural Science Foundation of China [62103157]; Technology Innovation Project of Hubei Province of China [2019AEA171]; China Postdoctoral Science Foundation [2021M691139]; Fund from Science, Technology, Innovation Commission of Shenzhen Municipality [2021Szvup090] International Science and Technology Cooperation Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Technology Innovation Project of Hubei Province of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fund from Science, Technology, Innovation Commission of Shenzhen Municipality This work was supported in part by the International Science and Technology Cooperation Program of China under Grant 2017YFE0128300, in part by the National Natural Science Foundation of China under Grant 62103157, in part by the Technology Innovation Project of Hubei Province of China under Grant 2019AEA171, in part by the China Postdoctoral Science Foundation under Grant 2021M691139, and in part by the Fund from Science, Technology, Innovation Commission of Shenzhen Municipality under Grant 2021Szvup090. 33 0 0 9 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1083-4435 1941-014X IEEE-ASME T MECH IEEE-ASME Trans. Mechatron. DEC 2022.0 27.0 6 1 5107 5118 10.1109/TMECH.2022.3172715 0.0 12 Automation & Control Systems; Engineering, Manufacturing; Engineering, Electrical & Electronic; Engineering, Mechanical Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering 9B2KS 2023-03-23 WOS:000934571800080 0
J Li, X; Zhao, Y; Zhou, XF; Zheng, K Li, Xiang; Zhao, Yan; Zhou, Xiaofang; Zheng, Kai Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing DATA SCIENCE AND ENGINEERING English Article Spatial crowdsourcing; Group task assignment; Social impact-based preference; Group consensus With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups' preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely. [Li, Xiang] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China; [Zhao, Yan] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark; [Zheng, Kai] Univ Elect Sci & Technol China, Chengdu, Peoples R China; [Zhou, Xiaofang] Univ Queensland, Brisbane, Qld, Australia Soochow University - China; Aalborg University; University of Electronic Science & Technology of China; University of Queensland Zhao, Y (corresponding author), Aalborg Univ, Dept Comp Sci, Aalborg, Denmark. xli96@stu.suda.edu.cn; yanzhao2019612@gmail.com; zxf@itee.uq.edu.au; zhengkai@uestc.edu.cn Zhou, Xiaofang/C-6169-2013 Zhou, Xiaofang/0000-0001-6343-1455; Zhao, Yan/0000-0002-0242-3707 Natural Science Foundation of China [61972069, 61532018, 61836007, 61832017] Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is partially supported by Natural Science Foundation of China (No. 61972069, 61532018, 61836007 and 61832017). 35 15 16 0 3 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2364-1185 2364-1541 DATA SCI ENG Data Sci. Eng. DEC 2020.0 5.0 4 SI 375 390 10.1007/s41019-020-00142-0 0.0 16 Computer Science, Information Systems; Computer Science, Theory & Methods Emerging Sources Citation Index (ESCI) Computer Science SL8IK gold, Green Published 2023-03-23 WOS:000657157000005 0
J Bai, SW; Fang, G; Zhou, J Bai, Shengwen; Fang, Gang; Zhou, Jie Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation JOURNAL OF MATERIALS PROCESSING TECHNOLOGY English Article Magnesium; Extrusion; Extrusion limit diagram; Hot shortness; Artificial neural networks HOT EXTRUSION; PORTHOLE DIE; EXTRUDABILITY; DEFORMATION; TEMPERATURE; ANN; PREDICTION; BEHAVIOR; PROFILE; SPEED Conventional extrusion limit diagram (ELD) involves only two extrusion process variables and as such it does not account for the combined effects of multiple process parameters on the extrusion process with respect to pressure requirement and extrudate temperature. Attempts were made in the present research to construct three-dimensional (3D) ELD for a magnesium alloy in the space of initial billet temperature, extrusion ratio and extrusion speed. A method to build 3D ELD by integrating finite element (FE) simulations, extrusion experiments and artificial neural networks (ANN) was developed. In addition to initial billet temperature, extrusion ratio and extrusion speed, the temperature difference between the extrusion tooling and billet, the size of the billet and the shape complexity of the extrudate were taken as the additional process variables and integrated into the equivalent initial billet temperature, extrusion ratio and extrusion speed. The FE simulations, verified by performing extrusion experiments to produce magnesium alloy rods, were used to generate datasets for training the ANN. The ANN then predicted the peak values of extrusion pressure and extrudate temperature over a wider range of extrusion conditions, based on which a 3D ELD for the magnesium alloy was constructed. The 3D ELD was finally validated by performing extrusion experiments to produce magnesium alloy tubes. The results demonstrated that the constructed 3D ELD was reliable and able to provide guidelines for the selection of appropriate extrusion conditions. [Bai, Shengwen; Fang, Gang] Tsinghua Univ, Dept Mech Engn, State Key Lab Tribol, Beijing 100084, Peoples R China; [Zhou, Jie] Delft Univ Technol, Dept Biomech Engn, Mekelweg 2, NL-2628 CD Delft, Netherlands Tsinghua University; Delft University of Technology Fang, G (corresponding author), Tsinghua Univ, Dept Mech Engn, State Key Lab Tribol, Beijing 100084, Peoples R China. fangg@tsinghua.edu.cn Zhou, Jie/J-3019-2012; Fang, Gang/L-5586-2013 Zhou, Jie/0000-0002-6484-9156; Fang, Gang/0000-0002-5409-3922 National Natural Science Foundation of China [51675300] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The authors (Gang Fang and Shengwen Bai) greatly appreciate the financial support of the National Natural Science Foundation of China (Project No. 51675300). 30 8 9 9 35 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0924-0136 1097-6787 J MATER PROCESS TECH J. Mater. Process. Technol. JAN 2020.0 275.0 116361 10.1016/j.jmatprotec.2019.116361 0.0 11 Engineering, Industrial; Engineering, Manufacturing; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering; Materials Science KQ7TN Green Published 2023-03-23 WOS:000517123200044 0
C Li, YW; Adamczewski, K; Li, W; Gu, SH; Timofte, R; Van Gool, L IEEE COMP SOC Li, Yawei; Adamczewski, Kamil; Li, Wen; Gu, Shuhang; Timofte, Radu; Van Gool, Luc Revisiting Random Channel Pruning for Neural Network Compression 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 18-24, 2022 New Orleans, LA IEEE,CVF,IEEE Comp Soc Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical problem, each being claimed effective in some ways. Yet, a benchmark to compare those algorithms directly is lacking, mainly due to the complexity of the algorithms and some custom settings such as the particular network configuration or training procedure. A fair benchmark is important for the further development of channel pruning. Meanwhile, recent investigations reveal that the channel configurations discovered by pruning algorithms are at least as important as the pre-trained weights. This gives channel pruning a new role, namely searching the optimal channel configuration. In this paper, we try to determine the channel configuration of the pruned models by random search. The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning. We show that this simple strategy works quite well compared with other channel pruning methods. We also show that under this setting, there are surprisingly no clear winners among different channel importance evaluation methods, which then may tilt the research efforts into advanced channel configuration searching methods. Code will be released at https: // github. com/ ofsoundof/ random_ channel_ pruning. [Li, Yawei; Timofte, Radu; Van Gool, Luc] Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland; [Adamczewski, Kamil] MPI IS, Stuttgart, Germany; [Li, Wen] UESTC, Chengdu, Peoples R China; [Gu, Shuhang] USYD, Camperdown, NSW, Australia; [Van Gool, Luc] Katholieke Univ Leuven, Leuven, Belgium Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Electronic Science & Technology of China; University of Sydney; KU Leuven Li, YW (corresponding author), Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland. yawei.li@vision.ee.ethz.ch; radu.timofte@vision.ee.ethz.ch; vangool@vision.ee.ethz.ch Timofte, Radu/0000-0002-1478-0402 ETH Zurich Fund; National Natural Science Foundation of China [62176047]; Amazon AWS grant ETH Zurich Fund(ETH Zurich); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Amazon AWS grant This work is partially supported by the ETH Zurich Fund (OK), the National Natural Science Foundation of China (Grant No. 62176047), and an Amazon AWS grant. 66 2 2 1 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-6946-3 PROC CVPR IEEE 2022.0 191 201 10.1109/CVPR52688.2022.00029 0.0 11 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BU0EQ Green Submitted 2023-03-23 WOS:000867754200021 0
J Long, J; Li, MM; Wang, XQ; Stein, A Long, Jiang; Li, Mengmeng; Wang, Xiaoqin; Stein, Alfred Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Agricultural fields; High-resolution satellite images; Multi-task network; BsiNet; Spatial group-wise enhancement CLASSIFICATION This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring three parallel decoders into a sin-gle encoder to improve computational efficiency. BsiNet learns three tasks: a core task for agricultural field identification and two auxiliary tasks for field boundary prediction and distance estimation, corresponding to mask, boundary, and distance tasks, respectively. A spatial group-wise enhancement module is incorporated to improve the identification of small fields. We conducted experiments on a GaoFen1 and three GaoFen2 satellite images collected in Xinjiang, Fujian, Shandong, and Sichuan provinces in China, and compared BsiNet with 13 different neural networks. Our results show that the agricultural fields extracted by BsiNet have the lowest global over-classification (GOC) of 0.062, global under-classification (GUC) of 0.042, and global total errors (GTC) of 0.062 for the Xinjiang dataset. For the Fujian dataset with irregular and complex fields, BsiNet outperformed the second-best method from the Xinjiang dataset analysis, yielding the lowest GTC of 0.291. It also produced satisfactory results on the Shandong and Sichuan datasets. Moreover, BsiNet has fewer parameters and faster computation than existing multi-task models (i.e., Psi-Net and ResUNet-a D7). We conclude that BsiNet can be used successfully in extracting agricultural fields from high-resolution satellite images and can be applied to different field settings. [Long, Jiang; Li, Mengmeng; Wang, Xiaoqin] Fuzhou Univ, Acad Digital China Fujian, Key Lab Spatial Data Min & Informat Sharing Minist, Fuzhou, Peoples R China; [Stein, Alfred] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands Fuzhou University; University of Twente Li, MM (corresponding author), Fuzhou Univ, Acad Digital China Fujian, Key Lab Spatial Data Min & Informat Sharing Minist, Fuzhou, Peoples R China. 205527028@fzu.edu.cn; mli@fzu.edu.cn; wangxq@fzu.edu.cn; a.stein@utwente.nl Long, Jiang/HLV-7740-2023 Long, Jiang/0000-0003-1969-0980; Li, Mengmeng/0000-0002-9083-0475 National Natural Science Foundation of China (NSFC) [42001283] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)) The research was funded by the National Natural Science Foundation of China (NSFC) with No. 42001283. 33 1 1 10 16 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1569-8432 1872-826X INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. AUG 2022.0 112.0 102871 10.1016/j.jag.2022.102871 0.0 13 Remote Sensing Science Citation Index Expanded (SCI-EXPANDED) Remote Sensing 3Z0AF gold 2023-03-23 WOS:000844083000001 0
J Lu, NJ; Hafezi, F; Torres-Netto, EA; Assaf, JF; Aslanides, IM; Awwad, ST; Cui, LL; Koppen, C Lu, Nan-Ji; Hafezi, Farhad; Torres-Netto, Emilio A.; Assaf, Jad F.; Aslanides, Ioannis M.; Awwad, Shady T.; Cui, Le-Le; Koppen, Carina Effect of fluence levels on prophylactic corneal cross-linking for laser in situ keratomileusis and transepithelial photorefractive keratectomy ACTA OPHTHALMOLOGICA English Article biomechanics; CXL; demarcation line; haze; keratoconus; LASIK; TransPRK MITOMYCIN-C APPLICATION; DEMARCATION LINE; RISK-ASSESSMENT; HIGH MYOPIA; ECTASIA; LASIK; DEPTH Purpose The purpose of this study is to assess the effect of various fluence levels on prophylactic corneal cross-linking (CXL) combined with femtosecond laser in situ keratomileusis (FS-LASIK-Xtra) or transepithelial photorefractive keratectomy (TransPRK-Xtra) on biomechanics, demarcation line (DL), and stromal haze. Methods Prospective analysis where two prophylactic CXL protocols (lower/higher fluence [LF/HF]: 30 mw/cm(2), 60/80 s, 1.8/2.4 J/cm(2)) were performed as part of either an FS-LASIK-Xtra or TransPRK-Xtra procedure. Data were collected preoperatively and at 1 week and 1, 3, and 6 months postoperatively. Main outcome measures were (1) dynamic corneal response parameters and the stress-strain index (SSI) from Corvis, (2) actual DL depth (ADL), and (3) stromal haze on OCT images analysed by a machine learning algorithm. Results Eighty-six eyes from 86 patients underwent FS-LASIK-Xtra-HF (21 eyes), FS-LASIK-Xtra-LF (21 eyes), TransPRK-Xtra-HF (23 eyes), and TransPRK-Xtra-LF (21 eyes). SSI increased similarly by around 15% in all groups 6 months postoperatively (p = 0.155). All other corneal biomechanical parameters were statistically significant worsening postoperatively, but the change was similar in all groups. At 1 month postoperatively, there was no statistical difference in mean ADL among four groups (p = 0.613), mean stromal haze was similar between the two FS-LASIK-Xtra groups, but higher in the TransPRK-Xtra-HF group compared with the TransPRK-Xtra-LF group. Conclusions FS-LASIK-Xtra and TransPRK-Xtra lead to a similar ADL and improve SSI equally. Lower fluence prophylactic CXL might be recommended as it achieves similar mean ADL with potentially less induced stromal haze, especially in TransPRK. The clinical relevance and applicability of such protocols remains to be assessed. [Lu, Nan-Ji; Hafezi, Farhad; Aslanides, Ioannis M.; Cui, Le-Le] Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Eye Hosp, 270 West Xueyuan Rd, Wenzhou 325027, Zhejiang, Peoples R China; [Lu, Nan-Ji; Hafezi, Farhad; Aslanides, Ioannis M.; Cui, Le-Le] Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Sch Ophthalmol & Optometry, Wenzhou, Zhejiang, Peoples R China; [Lu, Nan-Ji; Koppen, Carina] Univ Antwerp, Fac Med & Hlth Sci, Antwerp, Belgium; [Lu, Nan-Ji; Koppen, Carina] Antwerp Univ Hosp, Dept Ophthalmol, Edegern, Belgium; [Lu, Nan-Ji; Hafezi, Farhad; Torres-Netto, Emilio A.] ELZA Inst, Dietikon, Switzerland; [Hafezi, Farhad; Torres-Netto, Emilio A.] Univ Zurich, Ctr Appl Biotechnol & Mol Med, Lab Ocular Cell Biol, Zurich, Switzerland; [Hafezi, Farhad; Torres-Netto, Emilio A.] Univ Geneva, Fac Med, Geneva, Switzerland; [Assaf, Jad F.; Awwad, Shady T.] Amer Univ Beirut, Med Ctr, Beirut, Lebanon; [Aslanides, Ioannis M.] Emmetropia Mediterranean Eye Inst, Iraklion, Greece Wenzhou Medical University; Wenzhou Medical University; University of Antwerp; University of Zurich; University of Geneva; American University of Beirut Cui, LL (corresponding author), Wenzhou Med Univ, Natl Clin Res Ctr Ocular Dis, Eye Hosp, 270 West Xueyuan Rd, Wenzhou 325027, Zhejiang, Peoples R China. cll@mail.eye.ac.cn Assaf, Jad/0000-0003-4963-7339; lu, nanji/0000-0003-3096-9579 Chinese Scholarship Council [202008330323] Chinese Scholarship Council(China Scholarship Council) Chinese Scholarship Council, Grant/Award Number: 202008330323 32 0 0 2 2 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1755-375X 1755-3768 ACTA OPHTHALMOL Acta Ophthalmol. MAR 2023.0 101.0 2 E185 E196 10.1111/aos.15230 0.0 AUG 2022 12 Ophthalmology Science Citation Index Expanded (SCI-EXPANDED) Ophthalmology 9A5EG 36794626.0 2023-03-23 WOS:000839331300001 0
J Wu, PP; Liu, H; Li, XF; Fan, T; Zhang, XW Wu, Pingping; Liu, Hong; Li, Xiaofei; Fan, Ting; Zhang, Xuewu A Novel Lip Descriptor for Audio-Visual Keyword Spotting Based on Adaptive Decision Fusion IEEE TRANSACTIONS ON MULTIMEDIA English Article Audio-visual fusion; keyword spotting; noisy conditions; visual speech recognition SPEECH RECOGNITION; ALIGNMENT Keyword spotting remainsa challenge when applied to real-world environments with dramatically changing noise. In recent studies, audio-visual integration methods have demonstrated superiorities since visual speech is not influenced by acoustic noise. However, for visual speech recognition, individual utterance mannerisms can lead to confusion and false recognition. To solve this problem, a novel lip descriptor is presented involving both geometry-based and appearance-based features in this paper. Specifically, a set of geometry-based features is proposed based on an advanced facial landmark localization method. In order to obtain robust and discriminative representation, a spatiotemporal lip feature is put forward concerning similarities among textons and mapping the feature to intra-class subspace. Moreover, a parallel two-step keyword spotting strategy based on decision fusion is proposed in order to make the best use of audio-visual speech and adapt to diverse noise conditions. Weights generated using a neural network combine acoustic and visual contributions. Experimental results on the OuluVS dataset and PKU-AV dataset demonstrate that the proposed lip descriptor shows competitive performance compared to the state of the art. Additionally, the proposed audio-visual keyword spotting (AV-KWS) method based on decision-level fusion significantly improves the noise robustness and attains better performance than feature-level fusion, which is also capable of adapting to various noisy conditions. [Wu, Pingping; Liu, Hong] Peking Univ, Minist Educ, Key Lab Machine Percept, Beijing 100871, Peoples R China; [Wu, Pingping; Liu, Hong; Fan, Ting; Zhang, Xuewu] Peking Univ, Shenzhen Grad Sch, Engn Lab Intelligent Percept Internet Things ELIP, Beijing 100871, Peoples R China; [Li, Xiaofei] INRIA Grenoble Rhone Alpes, PERCEPTION Team, F-28330 Grenoble, France Peking University; Peking University; University Town of Shenzhen Liu, H (corresponding author), Peking Univ, Minist Educ, Key Lab Machine Percept, Beijing 100871, Peoples R China. wupingping@pku.edu.cn; hongliu@pku.edu.cn; xiaofei.li@inria.fr; fanting19900126@126.com; zhangxuewu@sz.pku.edu.cn Li, xiaofei/GXF-7187-2022 National Natural Science Foundation of China (NSFC) [61340046, 60875050, 60675025]; National High Technology Research and Development Programme of China (863 Programme) [2006AA04Z247]; Guangdong Natural Science Foundation of China [2015A030311034]; Specialized Research Fund for the Doctoral Programme of Higher Education [20130001110011] National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); National High Technology Research and Development Programme of China (863 Programme)(National High Technology Research and Development Program of China); Guangdong Natural Science Foundation of China(National Natural Science Foundation of Guangdong Province); Specialized Research Fund for the Doctoral Programme of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)) This work is supported by the National Natural Science Foundation of China (NSFC) under Grant 61340046, Grant 60875050, and Grant 60675025, by the National High Technology Research and Development Programme of China (863 Programme) under Grant 2006AA04Z247, by the Guangdong Natural Science Foundation of China under Grant 2015A030311034, and by the Specialized Research Fund for the Doctoral Programme of Higher Education under Grant 20130001110011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Martha Larson. 52 31 32 2 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia MAR 2016.0 18.0 3 326 338 10.1109/TMM.2016.2520091 0.0 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications DG2WP Green Submitted 2023-03-23 WOS:000371931600002 0
J Chen, YH; Ge, Y; Heuvelink, GBM; An, R; Chen, Y Chen, Yuehong; Ge, Yong; Heuvelink, Gerard B. M.; An, Ru; Chen, Yu Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Area-to-point kriging (ATPK); deconvolution; mixed object; remotely sensed imagery; superresolution mapping (SRM) MARKOV-RANDOM-FIELD; HOPFIELD NEURAL-NETWORK; SENSING IMAGERY; PARAMETER SELECTION; SPATIAL-RESOLUTION; SHIFTED IMAGES; PIXEL; ALGORITHM; SEGMENTATION; INFORMATION Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon-a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification. [Chen, Yuehong; An, Ru] Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China; [Ge, Yong] Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Heuvelink, Gerard B. M.] Wageningen Univ, Soil Geog & Landscape Grp, NL-6700 AA Wageningen, Netherlands; [Chen, Yu] Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China Hohai University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; University of Chinese Academy of Sciences, CAS; Wageningen University & Research; Nanjing Normal University Ge, Y (corresponding author), Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. chenyh@lreis.ac.cn; gey@lreis.ac.cn; gerard.heuvelink@wur.nl; anrunj@163.com; chenyu@lreis.ac.cn Heuvelink, Gerard/AAD-5916-2022 Heuvelink, Gerard/0000-0003-0959-9358 National Natural Science Foundation of China [41701376, 41471296]; Chinese Academy of Sciences [ZDRW-ZS-2016-6-3-4]; China Postdoctoral Science Foundation [2016M600356]; Fundamental Research Funds for the Central Universities [2017B11714]; State Key Laboratory of Resources and Environmental Information System, China National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences(Chinese Academy of Sciences); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Key Laboratory of Resources and Environmental Information System, China This work was supported in part by the National Natural Science Foundation of China under Grant 41701376 and Grant 41471296, in part by the Key Program of Chinese Academy of Sciences under Grant ZDRW-ZS-2016-6-3-4, in part by the China Postdoctoral Science Foundation under Grant 2016M600356, in part by the Fundamental Research Funds for the Central Universities under Grant 2017B11714, and in part by the State Key Laboratory of Resources and Environmental Information System, China. 55 24 25 3 37 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JAN 2018.0 56.0 1 328 340 10.1109/TGRS.2017.2747624 0.0 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology FR2BW 2023-03-23 WOS:000418873000027 0
J Chen, JZ; Xu, YH; Lu, SF; Liang, RH; Nan, LL Chen, Jiazhou; Xu, Yanghui; Lu, Shufang; Liang, Ronghua; Nan, Liangliang 3-D Instance Segmentation of MVS Buildings IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Three-dimensional displays; Image segmentation; Buildings; Solid modeling; Semantics; Feature extraction; Point cloud compression; 3-D urban scene; dataset; instance segmentation; multiview clustering CONVOLUTIONAL NETWORKS; SEMANTIC SEGMENTATION; BENCHMARK We present a novel 3-D instance segmentation framework for multiview stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes, the emphasis of this work lies in detecting and segmenting 3-D building instances even if they are attached and embedded in a large and imprecise 3-D surface model. Multiview red green blue (RGB) images are first enhanced to RGB height (RGBH) images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2-D instance segmentation neural network. Instance masks from different multiview images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multiview images. Based on these global masks, 3-D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field optimization. A new dataset that contains instance-level annotation for both 3-D urban scenes (roofs and buildings) and drone images (roofs) is provided. To the best of our knowledge, it is the first outdoor dataset dedicated to 3-D instance segmentation with much more annotations of attached 3-D buildings than existing datasets.(1) Quantitative evaluations and ablation studies have shown the effectiveness of all major steps and the advantages of our multiview framework over the orthophoto-based method. [Chen, Jiazhou; Xu, Yanghui; Lu, Shufang; Liang, Ronghua] Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310014, Peoples R China; [Nan, Liangliang] Delft Univ Technol, Fac Architecture & Built Environm, NL-2628 BL Delft, Netherlands Zhejiang University of Technology; Delft University of Technology Liang, RH (corresponding author), Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310014, Peoples R China. rhliang@zjut.edu.cn Chen, Jiazhou/0000-0003-2780-6146; Nan, Liangliang/0000-0002-5629-9975 National Natural Science Foundation of China [62172367]; Natural Science Foundation of Zhejiang Province [LGF22F020022] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Zhejiang Province(Natural Science Foundation of Zhejiang Province) This work was supported in part by the National Natural Science Foundation of China under Grant 62172367 and in part by the Natural Science Foundation of Zhejiang Province under Grant LGF22F020022. 69 1 1 10 12 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5704014 10.1109/TGRS.2022.3183567 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology 3Q7XZ Green Published, Green Submitted 2023-03-23 WOS:000838440500003 0
J Wang, T; Miao, ZC; Chen, YX; Zhou, Y; Shan, GC; Snoussi, H Wang, Tian; Miao, Zichen; Chen, Yuxin; Zhou, Yi; Shan, Guangcun; Snoussi, Hichem AED-Net: An Abnormal Event Detection Network ENGINEERING English Article Abnormal events detection; Abnormal event detection network; Principal component analysis network; Kernel principal component analysis ANOMALY DETECTION; CLASSIFICATION It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd's situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework's generalization capacity. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. [Wang, Tian; Miao, Zichen; Chen, Yuxin; Shan, Guangcun] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Zhou, Yi] Dalian Maritime Univ, Dept Elect Engn, Dalian 116026, Peoples R China; [Shan, Guangcun] Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China; [Shan, Guangcun] Beihang Univ, Int Res Inst Multidisciplinary Sci, Beijing 100191, Peoples R China; [Snoussi, Hichem] Univ Technol Troyes, CNRS, UMR STMR 6281, Inst Charles Delaunay LM2S, F-10010 Troyes, France Beihang University; Dalian Maritime University; Beihang University; Beihang University; Centre National de la Recherche Scientifique (CNRS); Universite de Technologie de Troyes Shan, GC (corresponding author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.;Shan, GC (corresponding author), Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China.;Shan, GC (corresponding author), Beihang Univ, Int Res Inst Multidisciplinary Sci, Beijing 100191, Peoples R China. gcshan@buaa.edu.cn Shan, Guangcun/D-8247-2011 Shan, Guangcun/0000-0002-3314-1614 National Key Research and Development Program of China [2016YFE0204200]; National Natural Science Foundation of China [61503017]; Fundamental Research Funds for the Central Universities [YWF-18-BJ-J-221]; Aeronautical Science Foundation of China [2016ZC51022]; Platform CAPSEC (capteurs pour la securite) - Region Champagne-Ardenne; FEDER (fonds europeen de developpement regional) National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Aeronautical Science Foundation of China; Platform CAPSEC (capteurs pour la securite) - Region Champagne-Ardenne(Region Grand-Est); FEDER (fonds europeen de developpement regional)(European Commission) This work is partially supported by the National Key Research and Development Program of China (2016YFE0204200), the National Natural Science Foundation of China (61503017), the Fundamental Research Funds for the Central Universities (YWF-18-BJ-J-221), the Aeronautical Science Foundation of China (2016ZC51022), and the Platform CAPSEC (capteurs pour la securite) funded by Region Champagne-Ardenne and FEDER (fonds europeen de developpement regional). 43 14 14 2 2 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2095-8099 2096-0026 ENGINEERING-PRC Engineering OCT 2019.0 5.0 5 930 939 10.1016/j.eng.2019.02.008 0.0 10 Engineering, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Engineering JG4PI Green Submitted, gold 2023-03-23 WOS:000492056100022 0
J Wang, L; Huang, W; Zhang, M; Pan, SR; Chang, XJ; Su, SW Wang, Li; Huang, Wei; Zhang, Miao; Pan, Shirui; Chang, Xiaojun; Su, Steven Weidong Pruning graph neural networks by evaluating edge properties KNOWLEDGE-BASED SYSTEMS English Article Graph neural networks; Network pruning; Model compression The emergence of larger and deeper graph neural networks (GNNs) makes their training and inference increasingly expensive. Existing GNN pruning methods simultaneously prune the graph adjacency matrix and the model weights on a pretrained neural network by directly leveraging the lottery-ticket hypothesis, but the benefits of such methods are mainly via weight pruning, and methods based on saliency metrics struggle to outperform random pruning when pruning only the graph adjacency matrix. This motivates us to use different scoring standards for graph edges and network weights during GNN pruning. Thus, rather than measuring the importance of graph edges based on saliency metrics, we formulate the performance of GNNs mathematically with respect to the properties of their edges, elucidating how the performance drop can be avoided by pruning negative edges and nonbridges. This leads to our simple but effective two-step method for GNN pruning, leveraging the saliency metrics for the network pruning while sparsifying the graph with preservation of the loss performance. Experimental results show the effectiveness and efficiency of the proposed method on both small-scale graph datasets (Cora, Citeseer, and PubMed) and a large-scale dataset (Ogbn-ArXiv), where our method saves up to 98% of floating-point operations per second (FLOPs) on the small graphs and 94% of FLOPs on the large one, with no significant drop in accuracy. (c) 2022 Elsevier B.V. All rights reserved. [Wang, Li; Su, Steven Weidong] Shandong First Med Univ & Shandong Acad Med Sci, Coll Artif Intelligence & Big Data Med Sci, 6699 Qingdao Rd, Jinan 250000, Shandong, Peoples R China; [Wang, Li; Huang, Wei; Chang, Xiaojun; Su, Steven Weidong] Univ Technol Sydney, Fac Engn & IT, 81 Broadway, Ultimo, NSW 2007, Australia; [Zhang, Miao] Aalborg Univ, Comp Sci Dept, Fredrik Bajers Vej 7K, DK-9220 Aalborg, Denmark; [Pan, Shirui] Griffith Univ, Sch Informat & Commun Technol, Parklands Dr, Southport, Qld 4222, Australia Shandong First Medical University & Shandong Academy of Medical Sciences; University of Technology Sydney; Aalborg University; Griffith University Su, SW (corresponding author), Shandong First Med Univ & Shandong Acad Med Sci, Coll Artif Intelligence & Big Data Med Sci, 6699 Qingdao Rd, Jinan 250000, Shandong, Peoples R China.;Su, SW (corresponding author), Univ Technol Sydney, Fac Engn & IT, 81 Broadway, Ultimo, NSW 2007, Australia. suweidong@sdfmu.edu.cn ; Chang, Xiaojun/A-2055-2015 Su, Steven/0000-0002-5720-8852; Huang, Wei/0000-0001-5674-7021; Chang, Xiaojun/0000-0002-7778-8807 Academic Promotion Project of Shandong First Medical University Academic Promotion Project of Shandong First Medical University This work was supported in part by Academic Promotion Project of Shandong First Medical University. 49 0 0 1 1 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. NOV 28 2022.0 256.0 109847 10.1016/j.knosys.2022.109847 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 4X0UD 2023-03-23 WOS:000860566400011 0
J Sun, J; Shi, R; Zhang, XW; Fang, D; Rauch, J; Lu, S; Wang, XB; Kasmann, L; Ma, J; Belka, C; Su, C; Li, ML Sun, Jing; Shi, Run; Zhang, Xiaowen; Fang, Da; Rauch, Josefine; Lu, Shun; Wang, Xuanbin; Kasmann, Lukas; Ma, Jing; Belka, Claus; Su, Chuan; Li, Minglun Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy ONCOIMMUNOLOGY English Article Papillary thyroid cancer; immune clusters; immunotherapy; prognosis GENOMIC CHARACTERIZATION; EXPRESSION; CARCINOMA; IMMUNOSURVEILLANCE; MICROENVIRONMENT; DIFFERENTIATION; SIGNATURES; SURVIVAL; ANTIBODY; THERAPY Although the vast majority of patients with papillary thyroid cancer (PTC) have a favorable prognosis when conventional treatments are implemented, local recurrence and distant metastasis of advanced PTCs still hamper the survival and clinical management in certain patients. As immune checkpoint blockade (ICB) therapy achieves a great success in some advanced cancers, we aimed to investigate the immune landscape in PTC and its potential implications for prognosis and immunotherapy. In this study, different algorithms were conducted to estimate immune infiltration in PTC samples. A series of bioinformatic and machine learning approaches were performed to identify PTC-specific immune-related genes (IRGs) and distinct immune clusters. Differences in intrinsic tumor immunogenicity and potential immunotherapy response were observed between distinct immune clusters. A prognostic immune-related signature (IRS) was established to predict progression-free survival (PFS). IRS exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, altered pathways, mutational patterns, and potential applicable drugs were analyzed in different immune-related risk groups. Our study gained some insight into the immune landscape of PTC, and provided some useful clues for introducing immune-based molecular classification into risk stratification and guiding ICB decision-making. [Sun, Jing; Zhang, Xiaowen; Fang, Da] Nanjing Univ, Med Sch, Drum Tower Hosp, Dept Endocrinol, Nanjing 210008, Peoples R China; [Sun, Jing; Rauch, Josefine; Kasmann, Lukas; Ma, Jing; Belka, Claus; Li, Minglun] Ludwig Maximilians Univ Munchen, Dept Radiat Oncol, Univ Hosp, Marchioninistr 15, D-81377 Munich, Germany; [Shi, Run] Nanjing Med Univ, Sch Clin Med 1, Nanjing, Peoples R China; [Lu, Shun] Univ Elect Sci & Technol China, Sichuan Canc Hosp, Sch Med, Dept Radiotherapy, Chengdu, Peoples R China; [Wang, Xuanbin] Hubei Univ Med, Renmin Hosp, Oncol Ctr, Lab Chinese Herbal Pharmacol,Hubei Key Lab Wudang, Shiyan, Peoples R China; [Belka, Claus] German Canc Consortium DKTK, Munich, Germany; [Su, Chuan] Nanjing Med Univ, Ctr Global Hlth, Dept Pathogen Biol & Immunol, Jiangsu Prov Key Lab Pathogen Biol, Nanjing, Peoples R China Nanjing University; University of Munich; Nanjing Medical University; University of Electronic Science & Technology of China; Hubei University of Medicine; Helmholtz Association; German Cancer Research Center (DKFZ); Nanjing Medical University Sun, J (corresponding author), Nanjing Univ, Med Sch, Drum Tower Hosp, Dept Endocrinol, Nanjing 210008, Peoples R China.;Sun, J; Li, ML (corresponding author), Ludwig Maximilians Univ Munchen, Dept Radiat Oncol, Univ Hosp, Marchioninistr 15, D-81377 Munich, Germany.;Su, C (corresponding author), Nanjing Med Univ, Ctr Global Hlth, Dept Pathogen Biol & Immunol, Jiangsu Prov Key Lab Pathogen Biol, Nanjing, Peoples R China. Jing.Sun@med.uni-muenchen.de; chuansu@njmu.edu.cn; minglun.li@med.uni-muenchen.de Lu, Shun/ABF-8341-2021; Li, Minglun/A-8086-2019; Wang, Xuanbin/AHE-9819-2022; Zhang, Xiaowen/GZL-4194-2022 Wang, Xuanbin/0000-0003-0769-2874; Kasmann, Lukas/0000-0001-6260-5249; Zhang, Xiaowen/0000-0002-0072-2439 China Scholarship Council (CSC) China Scholarship Council (CSC)(China Scholarship Council) We greatly thank the China Scholarship Council (CSC) for supporting the work of Jing Sun and Run Shi. 50 12 12 3 10 TAYLOR & FRANCIS INC PHILADELPHIA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 2162-402X ONCOIMMUNOLOGY OncoImmunology JAN 1 2021.0 10.0 1 e1964189 10.1080/2162402X.2021.1964189 0.0 14 Oncology; Immunology Science Citation Index Expanded (SCI-EXPANDED) Oncology; Immunology UM6LS 34513318.0 gold, Green Accepted 2023-03-23 WOS:000693442600001 0
J Zhou, TX; Ruan, S; Hu, HG Zhou, Tongxue; Ruan, Su; Hu, Haigen A literature survey of MR-based brain tumor segmentation with missing modalities COMPUTERIZED MEDICAL IMAGING AND GRAPHICS English Article Brain tumor segmentation; Missing modalities; Multi -modalities; Magnetic Resonance Imaging; Deep learning NETWORK; FUSION Multimodal MR brain tumor segmentation is one of the hottest issues in the community of medical image processing. However, acquiring the complete set of MR modalities is not always possible in clinical practice, due to the acquisition protocols, image corruption, scanner availability, scanning cost or allergies to certain contrast materials. The missing information can cause some restraints to brain tumor diagnosis, monitoring, treatment planning and prognosis. Thus, it is highly desirable to develop brain tumor segmentation methods to address the missing modalities problem. Based on the recent advancements, in this review, we provide a detailed analysis of the missing modality issue in MR-based brain tumor segmentation. First, we briefly introduce the biomedical background concerning brain tumor, MR imaging techniques, and the current challenges in brain tumor segmentation. Then, we provide a taxonomy of the state-of-the-art methods with five categories, namely, image synthesis-based method, latent feature space-based model, multi-source correlation -based method, knowledge distillation-based method, and domain adaptation-based method. In addition, the principles, architectures, benefits and limitations are elaborated in each method. Following that, the corresponding datasets and widely used evaluation metrics are described. Finally, we analyze the current challenges and provide a prospect for future development trends. This review aims to provide readers with a thorough knowledge of the recent contributions in the field of brain tumor segmentation with missing modalities and suggest potential future directions. [Zhou, Tongxue] Hangzhou Normal Univ, Sch Informat Sci & Technol, Hangzhou 311121, Peoples R China; [Ruan, Su] Univ Rouen Normandie, LITIS QuantIF, F-76183 Rouen, France; [Hu, Haigen] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China; [Hu, Haigen] Key Lab Visual Media Intelligent Proc Technol Zhej, Hangzhou 310023, Peoples R China Hangzhou Normal University; Universite de Rouen Normandie; Zhejiang University of Technology Hu, HG (corresponding author), Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China.;Hu, HG (corresponding author), Key Lab Visual Media Intelligent Proc Technol Zhej, Hangzhou 310023, Peoples R China. hghu@zjut.edu.cn National Natural Science Foundation of China [6220071321] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by National Natural Science Foundation of China (No. 6220071321) . 116 0 0 9 9 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0895-6111 1879-0771 COMPUT MED IMAG GRAP Comput. Med. Imaging Graph. MAR 2023.0 104.0 102167 10.1016/j.compmedimag.2022.102167 0.0 DEC 2022 14 Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging Science Citation Index Expanded (SCI-EXPANDED) Engineering; Radiology, Nuclear Medicine & Medical Imaging 7Y6VW 36584536.0 2023-03-23 WOS:000915015300001 0
J Ren, B; Zhao, YY; Hou, B; Chanussot, J; Jiao, LC Ren, Bo; Zhao, Yangyang; Hou, Biao; Chanussot, Jocelyn; Jiao, Licheng A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Task analysis; Data models; Data mining; Scattering; Mutual information; Covariance matrices; Land cover classification; mutual information; polarimetric synthetic aperture radar (PolSAR); self-supervised learning (SSL) IMAGE CLASSIFICATION; DECOMPOSITION Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data. [Ren, Bo; Zhao, Yangyang; Hou, Biao; Jiao, Licheng] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, CNRS, Inria,LJK, F-38000 Grenoble, France Xidian University; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Inria Ren, B (corresponding author), Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China. asdwer2046@126.com; yangzyyk@163.com; avcodec@163.com; jocelyn.chanussot@gipsa-lab.grenoble-inp.fr; lchjiao@mail.xidian.edu.cn Jiao, Licheng/0000-0003-3354-9617; Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [62001355, 61671350, 61771379, 61836009]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005]; Key Research and Development Program in Shaanxi Province of China [2019ZDLGY03-05]; China Postdoctoral Science Foundation [2018M633468] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Foundation for Innovative Research Groups of the National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program in Shaanxi Province of China; China Postdoctoral Science Foundation(China Postdoctoral Science Foundation) This work was supported in part by the National Natural Science Foundation of China under Grant 62001355, Grant 61671350, Grant 61771379, and Grant 61836009; in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005; in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY03-05; and in part by the China Postdoctoral Science Foundation Funded Project under Grant 2018M633468. (Corresponding author: Bo Ren.) 49 12 15 3 9 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing NOV 2021.0 59.0 11 9224 9237 10.1109/TGRS.2020.3048967 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology WN6BE 2023-03-23 WOS:000711850900026 0
J Ming, WL; Zhu, YH; Li, FY; Bai, YF; Gu, WJ; Liu, Y; Sun, X; Liu, XA; Liu, HD Ming, Wenlong; Zhu, Yanhui; Li, Fuyu; Bai, Yunfei; Gu, Wanjun; Liu, Yun; Sun, Xiao; Liu, Xiaoan; Liu, Hongde Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study GENES English Article radiogenomics; association analysis; breast cancer; DCE-MRI; pathway; radiomics; machine learning SIGNATURES; SURVIVAL; SUBTYPES; RNA Background: To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. Methods: Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters. Results: We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively. Conclusions: The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity. [Ming, Wenlong; Li, Fuyu; Bai, Yunfei; Gu, Wanjun; Sun, Xiao; Liu, Hongde] Southeast Univ, Sch Biol Sci & Med Engn, State Key Lab Bioelect, Nanjing 210096, Peoples R China; [Ming, Wenlong] German Canc Res Ctr, Div Med Image Comp, Neuenheimer Feld 280, D-69120 Heidelberg, Germany; [Zhu, Yanhui; Liu, Xiaoan] Nanjing Med Univ, Dept Breast Surg, Affiliated Hosp 1, 300 Guangzhou Rd, Nanjing 210029, Peoples R China; [Gu, Wanjun] Nanjing Univ Chinese Med, Collaborat Innovat Ctr Jiangsu Prov Canc Prevent &, Sch Artificial Intelligence & Informat Technol, Nanjing 210023, Peoples R China; [Liu, Yun] Nanjing Med Univ, Dept Informat, Affiliated Hosp 1, 300 Guangzhou Rd, Nanjing 210029, Peoples R China Southeast University - China; Helmholtz Association; German Cancer Research Center (DKFZ); Nanjing Medical University; Nanjing University of Chinese Medicine; Nanjing Medical University Liu, HD (corresponding author), Southeast Univ, Sch Biol Sci & Med Engn, State Key Lab Bioelect, Nanjing 210096, Peoples R China.;Liu, XA (corresponding author), Nanjing Med Univ, Dept Breast Surg, Affiliated Hosp 1, 300 Guangzhou Rd, Nanjing 210029, Peoples R China. liuxiaoan@njmu.edu.cn; liuhongde@seu.edu.cn Liu, Hongde/0000-0001-8768-764X National Natural Science Foundation of China [81830053, 61972084, 61871121] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Natural Science Foundation of China, grant number 81830053, 61972084, and 61871121. 57 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2073-4425 GENES-BASEL Genes JAN 2023.0 14.0 1 28 10.3390/genes14010028 0.0 19 Genetics & Heredity Science Citation Index Expanded (SCI-EXPANDED) Genetics & Heredity 8E8BK 36672769.0 gold, Green Accepted 2023-03-23 WOS:000919193200001 0
J Liu, SH; Tremblais, B; Carre, P; Zhou, NR; Wu, JH Liu, Sihang; Tremblais, Benoit; Carre, Phillippe; Zhou, Nanrun; Wu, Jianhua Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network MATHEMATICS English Article image reconstruction; discrete wavelet transform; interest point; convolutional neural network; conditional generative adversarial network A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points. [Liu, Sihang; Tremblais, Benoit; Carre, Phillippe] Univ Poitiers, Xlim, UMR CNRS 7252, F-86360 Poitiers, France; [Liu, Sihang; Zhou, Nanrun; Wu, Jianhua] Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China; [Zhou, Nanrun] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China Universite de Poitiers; Nanchang University; Shanghai University of Engineering Science Wu, JH (corresponding author), Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China. jhwu@ncu.edu.cn Wu, Jianhua/AFL-8480-2022; Zhou, Nanrun/HGC-4650-2022 Wu, Jianhua/0000-0002-4505-0568; National Natural Science Foundation of China [61861029] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by Nanrun Zhou, National Natural Science Foundation of China (grant number 61861029). 32 0 0 3 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2227-7390 MATHEMATICS-BASEL Mathematics OCT 2022.0 10.0 19 3591 10.3390/math10193591 0.0 19 Mathematics Science Citation Index Expanded (SCI-EXPANDED) Mathematics 5I1TN gold 2023-03-23 WOS:000868148200001 0
J Guo, T; Huang, ZX; Cheng, JY Guo, Tao; Huang, Zhixin; Cheng, Jingyuan LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system PERVASIVE AND MOBILE COMPUTING English Article Human Activity Recognition; Smart textile; Feature selection; Machine learning FLOATING SEARCH METHODS; FEATURE-SELECTION; BOUND ALGORITHMS; RECOGNITION Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital imageand video, that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile. (C) 2022 Elsevier B.V. All rights reserved. [Guo, Tao; Cheng, Jingyuan] Univ Sci & Technol China, Hefei, Peoples R China; [Huang, Zhixin] Univ Kassel, Kassel, Germany Chinese Academy of Sciences; University of Science & Technology of China, CAS; Universitat Kassel Cheng, JY (corresponding author), Univ Sci & Technol China, Hefei, Peoples R China. gtpplab@mail.ustc.edu.cn; zhixin.huang@uni-kassel.de; jingyuan@ustc.edu.cn guo, tao/0000-0003-1821-0666 National Natural Science Foundation of China [62072420]; Fundamental Research Funds for the Central Universities [2150110020] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) Our work is supported by the National Natural Science Foundation of China (Grant No. 62072420) and the Fundamental Research Funds for the Central Universities (Grant No. 2150110020) . 38 1 1 3 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1574-1192 1873-1589 PERVASIVE MOB COMPUT Pervasive Mob. Comput. FEB 2022.0 80.0 101540 10.1016/j.pmcj.2022.101540 0.0 JAN 2022 16 Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications YR4VJ 2023-03-23 WOS:000749989600003 0
J Zhao, WF; Li, MM; Wu, C; Zhou, W; Chu, GZ Zhao, Wufan; Li, Mengmeng; Wu, Cai; Zhou, Wen; Chu, Guozhong Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network REMOTE SENSING English Article urban function regions; high resolution satellite images; building extraction; road extraction; context-aware semantic segmentation LEARNING FRAMEWORK The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to 1.37% and 1.19% for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of 94%, implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions. [Zhao, Wufan; Wu, Cai; Zhou, Wen] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands; [Li, Mengmeng; Chu, Guozhong] Fuzhou Univ, Acad Digital China Fujian, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350108, Peoples R China University of Twente; Fuzhou University Li, MM (corresponding author), Fuzhou Univ, Acad Digital China Fujian, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350108, Peoples R China. mli@fzu.edu.cn Wu, Cai/0000-0002-5578-5525; Li, Mengmeng/0000-0002-9083-0475 Natural Science Foundation of Fujian Province, China [2021J01630] Natural Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province) This research was funded by the Natural Science Foundation of Fujian Province, China (Grant No. 2021J01630). 44 2 2 18 19 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. AUG 2022.0 14.0 16 3996 10.3390/rs14163996 0.0 18 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology 4A9CP Green Published, gold 2023-03-23 WOS:000845390000001 0
C Liu, WH; Emmerich, C; Zhang, LZ; Steil, J Luo, YZ; Chern, JS; Chen, XQ; Chen, L Liu, Weihui; Emmerich, Christian; Zhang, Lizhi; Steil, Jochen USER INTERFACES FOR LEARNING REDUNDANCY RESOLUTION IN HUMAN ROBOT INTERACTION FOURTH IAA CONFERENCE ON DYNAMICS AND CONTROL OF SPACE SYSTEMS 2018, PTS I-III Advances in the Astronautical Sciences English Proceedings Paper 4th International-Academy-of-Astronautics Conference on Dynamics and Control of Space Systems (DyCoSS '2018) MAY 21-23, 2018 Natl Univ Defense Technol, Changsha, PEOPLES R CHINA Int Acad Astronaut,Natl Univ Defense Technol Natl Univ Defense Technol TASK Redundant manipulator has good prospects for diverse and complex space operation tasks. However, teaching by demonstration of robot skills in the presence of redundancy is difficult and inefficient for non-expert users, especially with large and highly redundant manipulator, especially in complex confined scenarios. To master this task, two remote user interface systems are proposed and compared, which both comprise an input interface using mouse and keyboard, a visualization, an inverse kinematic solver and a machine learning component. The learner is used to generalize from a limited number of examples to a preferred redundancy resolution for a typical 7-DoF robot arm. The methods differ in the information that users can provide either through a novel approach to adjust Cartesian positions of certain key points on the robot's links and solve the inverse kinematics problem analytically, referred to as Keyposition interface. Or alternatively, users directly manipulate joint positions one-at-a-time along the manipulator and solve the inverse kinematics problem based on the pseudo inverse Jacobian matrix, referred to as Nullspace interface. A non-expert user study is conducted to compare and the quality of the obtained redundancy resolution between Cartesian and Nullspace interfaces. The results show superior performance of the Keyposition approach in terms of teaching redundancy resolution to avoid collisions with the constraint environment based on users' intuitive knowledge. And the study is furthermore accompanied by in-depth interviews with domain experts for overall evaluation and further improvement. [Liu, Weihui; Zhang, Lizhi] Beijing Inst Control Engn, Beijing, Peoples R China; [Emmerich, Christian] Bielefeld Univ, Res Inst Cognit & Robot, Bielefeld, Germany; [Steil, Jochen] Tech Univ Carolo Wilhelmina Braunschweig, Inst Robot & Proc Control, Braunschweig, Germany University of Bielefeld; Braunschweig University of Technology Liu, WH (corresponding author), Beijing Inst Control Engn, Beijing, Peoples R China. China Scholarship Council (CSC); CoR-Lab; European Unions [601116] China Scholarship Council (CSC)(China Scholarship Council); CoR-Lab; European Unions(European Commission) The authors wish to thank the colleagues in the Research Institute for Cognition and Robotics (Cor-Lab) for testing the system, many valuable suggestions and technical support. We are grateful to the 27 non-expert users and these two experts for participating in the study. W.L was supported by a Doctoral Scholarship from China Scholarship Council (CSC) and a grant from CoR-Lab. C.E. has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement no 601116 within the experiment CoHRoS in the project ECHORD++. 25 0 0 0 2 UNIVELT INC SAN DIEGO PO BOX 28130, SAN DIEGO, CA 92128 USA 1081-6003 978-0-87703-653-1 ADV ASTRONAUT SCI 2018.0 165.0 I - III 797 810 14 Engineering, Aerospace Conference Proceedings Citation Index - Science (CPCI-S) Engineering BN4XJ 2023-03-23 WOS:000482843500061 0
J Wang, JY; Zeng, ZG; Zhang, H; Barros, A; Miao, Q Wang, Jianyu; Zeng, Zhiguo; Zhang, Heng; Barros, Anne; Miao, Qiang An hybrid domain adaptation diagnostic network guided by curriculum pseudo labels for electro-mechanical actuator RELIABILITY ENGINEERING & SYSTEM SAFETY English Article EMA fault diagnosis; Hybrid domain adaptation; Inter-domain clustering; Intra-class domain clustering; Curriculum pseudo labeling FAULT-DIAGNOSIS; NEURAL-NETWORK Electro-mechanical actuator (EMA) usually operates in complex working conditions. When developing data -driven fault diagnosis models for EMA, training and testing data might come from different working condi-tions, reducing the generalization ability of traditional data-driven models. To address the challenge of domain difference between training and testing data, we propose a hybrid domain adaptation network, whose loss functions comprise of adversarial loss, triplet loss and cross-entropy loss. Adversarial loss and triplet loss can enhance the inter-domain and intra-class domain clustering, respectively. A softmax classifier with cross-entropy loss is used to predict pseudo labels for unlabeled target domain training samples. Compared to traditional transfer learning models that only reduces the global inter-domain difference between two domains, the strength of our model is that both the intra and inter-class domain difference are reduced. Curriculum pseudo labeling (CPL) is further applied to dynamically adjust thresholds for different classes during training phases. Compared to the fixed threshold in previous efforts, CPL can take into account the difference in pseudo label prediction and improve the performance of the developed model. The experiment results show that, compared to several transfer learning models, the developed model can achieve better classification accuracy in target domain. [Wang, Jianyu] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610065, Sichuan, Peoples R China; [Wang, Jianyu; Zeng, Zhiguo; Barros, Anne] Univ Paris Saclay, Lab Genie Ind, Cent Supelec, F-91190 Gif Sur Yvette, France; [Zhang, Heng; Miao, Qiang] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Sichuan, Peoples R China Sichuan University; UDICE-French Research Universities; Universite Paris Saclay; Sichuan University Zeng, ZG (corresponding author), Univ Paris Saclay, Lab Genie Ind, Cent Supelec, F-91190 Gif Sur Yvette, France.;Zhang, H (corresponding author), Sichuan Univ, Coll Elect Engn, Chengdu 610065, Sichuan, Peoples R China. zhiguo.zeng@centralesupelec.fr; hengzhang27@scu.edu.cn chaire of Risk and Resilience of Complex Systems (Chaire EDF, Orange and SNCF); National Natural Science Foundation of China [52075349]; Aeronautical Science Foundation of China [202106240078]; China Scholarship Council [201905019001] chaire of Risk and Resilience of Complex Systems (Chaire EDF, Orange and SNCF); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Aeronautical Science Foundation of China; China Scholarship Council(China Scholarship Council) The research of Anne Barros and Zhiguo Zeng are partially supported by the chaire of Risk and Resilience of Complex Systems (Chaire EDF, Orange and SNCF) . This work was also partially supported by the National Natural Science Foundation of China (No. 52075349) , the Aeronautical Science Foundation of China (No. 201905019001) , and the China Scholarship Council (No. Grant 202106240078) . 42 1 1 18 18 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0951-8320 1879-0836 RELIAB ENG SYST SAFE Reliab. Eng. Syst. Saf. DEC 2022.0 228.0 108770 10.1016/j.ress.2022.108770 0.0 SEP 2022 12 Engineering, Industrial; Operations Research & Management Science Science Citation Index Expanded (SCI-EXPANDED) Engineering; Operations Research & Management Science 5A0IR 2023-03-23 WOS:000862580900003 0
J Fang, Z; Zhang, Q; Tang, XA; Wang, AN; Baron, C Fang, Zhao; Zhang, Qiang; Tang, Xiaoan; Wang, Anning; Baron, Claude An implicit opinion analysis model based on feature-based implicit opinion patterns ARTIFICIAL INTELLIGENCE REVIEW English Article Natural language processing; Opinion mining; Product feature; Sentiment analysis PRODUCT FEATURE-EXTRACTION; SENTIMENT ANALYSIS; RECOMMENDATIONS; CONSUMERS With the rapid growth of social networks, mining customer opinions based on online reviews is crucial to understand consumer needs. Due to the richness of language expressions, customer opinions are often expressed implicitly. However, previous studies usually focus on mining explicit opinions to understand consumer needs. In this paper, we propose a novel implicit opinion analysis model to perform implicit opinion analysis of Chinese customer reviews at both the feature and review levels. First, we extract an implicit-opinionated review/clause dataset from raw review dataset and introduce the concept of the feature-based implicit opinion pattern (FBIOP). Secondly, we develop a clustering algorithm to construct product feature categories. Based on the constructed feature categories, FBIOPs can be mined from the extracted implicit-opinionated clause dataset. Thirdly, the sentiment intensity and polarity of each FBIOP are calculated by using the Chi squared test and pointwise mutual information. Fourthly, according to the resulting FBIOP polarities, the polarities of implicit opinions can be determined at both the feature and review levels. Car forum reviews written in Chinese are collected and labeled as the experimental dataset. The results show that the proposed model outperforms the traditional support vector machine model and the cutting-edge convolutional neural network model. [Fang, Zhao; Zhang, Qiang; Tang, Xiaoan; Wang, Anning] Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China; [Baron, Claude] CNRS, LAAS, 7 Ave Colonel Roche, F-31077 Toulouse, France; [Baron, Claude] Univ Toulouse, INSA Toulouse, Toulouse, France Hefei University of Technology; Centre National de la Recherche Scientifique (CNRS); Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National des Sciences Appliquees de Toulouse Zhang, Q (corresponding author), Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China. qiang_zhang@hfut.edu.cn BARON, Claude/AAI-3850-2021 zhang, qiang/0000-0003-2947-1351 60 6 6 3 32 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. AUG 2020.0 53.0 6 4547 4574 10.1007/s10462-019-09801-9 0.0 28 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science MK3PD 2023-03-23 WOS:000548694400020 0
J Bian, XQ; Zhang, Q; Zhang, L; Chen, L Bian, Xiao-Qiang; Zhang, Qian; Zhang, Lu; Chen, Ling A grey wolf optimizer-based support vector machine for the solubility of aromatic compounds in supercritical carbon dioxide CHEMICAL ENGINEERING RESEARCH & DESIGN English Article Solubility; Carbon dioxide; Grey wolf optimizer; Support vector machine EQUATION-OF-STATE; ARTIFICIAL NEURAL-NETWORK; DENSITY-BASED MODELS; SOLUTE SOLUBILITY; DRUG SOLUBILITY; SOLID COMPOUNDS; GENETIC ALGORITHM; PREDICTION; FLUIDS; HYDROCARBONS The prediction of solute solubility in supercritical carbon dioxide (SCCO2) is crucial for the development of supercritical applications. Many models have been developed to calculate the solubility of aromatic compounds. In this work, a grey wolf optimizer-based support vector machine (GWO-SVM) was proposed for correlating solute solubility in SCCO2. The proposed GWO-SVM model utilized the temperature, pressure and the density of SCCO2 as input parameters and the solubility of different solutes in SCCO2 as target parameter on the basis of gray correlation analysis. The new model successfully correlated solute solubility of 18 compounds (1148 data points including 814 training data points and 334 testing data points) in SCCO2, which were collected from the published literature. A comparison of the 27 commonly used empirical models and the proposed GWO-SVM model showed that the overall average absolute relative deviation of the proposed model is the lowest (3.20%). It was also found that the overall average absolute relative deviation is less dependent on material type for the proposed GWO-SVM model. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. [Bian, Xiao-Qiang; Zhang, Qian; Zhang, Lu] Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Peoples R China; [Bian, Xiao-Qiang] Univ Lorraine, Ecole Natl Super Ind Chim, Lab React & Genie Proc UPR 3349, 1 Rue Grandville,BP 20451, Nancy 9, France; [Chen, Ling] Southwest Petr Univ, Appl Tech Coll, Nanchong 637001, Peoples R China Southwest Petroleum University; Universite de Lorraine; Southwest Petroleum University Bian, XQ (corresponding author), Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Peoples R China. bxqiang3210_88@163.com Bian, Xiaoqiang/0000-0003-4995-8159 National Natural Science Foundation of China [51404205]; Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University) [PLN 1207] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University) This work was supported by National Natural Science Foundation of China (No. 51404205), and Open Fund (PLN 1207) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University). 65 23 26 3 51 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0263-8762 1744-3563 CHEM ENG RES DES Chem. Eng. Res. Des. JUL 2017.0 123.0 284 294 10.1016/j.cherd.2017.05.008 0.0 11 Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Engineering FA0YT 2023-03-23 WOS:000405163400026 0
J Tian, Y; Yang, X; Yang, J; Mao, KK; Yao, YJ; Liang, HS Tian, Yang; Yang, Xue; Yang, Juan; Mao, Kaikai; Yao, Yujia; Liang, Haoshen Evolution dynamic of intelligent construction strategy of coal mine enterprises in China HELIYON English Article Intelligent construction; Coal mine; Capacity replacement; Stochastic evolutionary game; Moran process SAFETY REGULATION; SYSTEM; GAME; INFORMATION; SUPPORT; MODEL The intelligent construction of coal mining enterprises is the fundamental strategy to prevent and curb major coal mine accidents, and the core technical support to realize the high-quality development of the coal industry. Considering the intelligent construction of coal mining enterprises enabled by emerging information technologies such as 5G and artificial intelligence, this paper constructs a Moran process stochastic evolutionary game model of intelligent construction of coal mining enterprises. Based on the fixed point probability of the Moran process, the probability of successful invasion of the intelligent construction strategy and the traditional production strategy in the limited coal mining enterprise group is calculated. By comparing the probability of individual fixed point and the probability of neutral mutation, the dominant condition of the strategy under strong selection and weak selection is obtained. The research shows that external stochastic factors, the number and scale of enterprises, the intensity of capacity replacement and the cost-benefit of intelligent construction are the main factors affecting the intelligent construction behavior of coal mining enterprises. When the intelligent construction of coal mining enterprises is in the cultivation period, the intelligent construction of underground coal mines dominated by stochastic factors can be effectively promoted by increasing the intensity of capacity reduction replacement. For the open-pit coal mine dominated by expected payoffs, reducing the number of mining rights and improving the concentration of open-pit coal mining industry will have a better effect on promoting its intelligent cultivation process. When the intelligent construction of coal mining enterprises is in the mature stage, with the improvement of the cost and benefits of intelligent construction, intelligent construction strategy will become a general consensus of coal mining enterprises. In addition, this paper analyzes the relevant parameters through specific examples to verify the effectiveness of the conclusions, which provides a scientific basis for effectively accelerating the intelligent construction of coal mining enterprises and promoting the transformation and upgrading of the coal industry. [Tian, Yang; Yang, Xue; Yang, Juan; Yao, Yujia] North China Univ Water Resources & Elect Power, Sch Management & Econ, Zhengzhou 450046, Peoples R China; [Mao, Kaikai] Henan Univ Econ & Law, Sch Finance, Zhengzhou 450046, Peoples R China; [Liang, Haoshen] Tech Univ Dresden, Helmholtzstr 10, D-01069 Dresden, Germany North China University of Water Resources & Electric Power; Henan University of Economics & Law; Technische Universitat Dresden Yang, J (corresponding author), North China Univ Water Resources & Elect Power, Sch Management & Econ, Zhengzhou 450046, Peoples R China.;Mao, KK (corresponding author), Henan Univ Econ & Law, Sch Finance, Zhengzhou 450046, Peoples R China. yangjuan1121@163.com; maokk2288@163.com National Natural Science Foundation of China [71573086]; Education Department of Henan Province [01]; North China University of Water Resources and Electric Power [01] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Education Department of Henan Province; North China University of Water Resources and Electric Power Xue Yang was supported by National Natural Science Foundation of China [71573086], The Education Department of Henan Province [01]. Yang Tian and Xue Yang were supported by North China University of Water Resources and Electric Power [01]. 52 2 2 12 12 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 2405-8440 HELIYON Heliyon OCT 2022.0 8.0 10 e10933 10.1016/j.heliyon.2022.e10933 0.0 9 Multidisciplinary Sciences Science Citation Index Expanded (SCI-EXPANDED) Science & Technology - Other Topics 9A4TT 36267372.0 gold, Green Accepted 2023-03-23 WOS:000934052900001 0
J Kan, RLD; Mak, ADP; Chan, SKW; Zhang, BBB; Fong, KNK; Kranz, GS Kan, Rebecca L. D.; Mak, Arthur D. P.; Chan, Sherry K. W.; Zhang, Bella B. B.; Fong, Kenneth N. K.; Kranz, Georg S. Protocol for a prospective open-label clinical trial to investigate the utility of concurrent TBS/fNIRS for antidepressant treatment optimisation BMJ OPEN English Article depression & mood disorders; clinical trials; neurology TRANSCRANIAL MAGNETIC STIMULATION; THETA-BURST STIMULATION; HUMAN MOTOR CORTEX; DEPRESSIVE SYMPTOMATOLOGY; QUICK INVENTORY; INTERINDIVIDUAL VARIABILITY; TREATMENT RESPONSE; CHINESE VERSIONS; QIDS-SR; SCALE Introduction Repetitive transcranial magnetic stimulation (rTMS) with theta burst stimulation (i.e. TBS) of the dorsolateral prefrontal cortex (DLPFC) is an innovative treatment for major depressive disorder (MDD). However, fewer than 50% of patients show sufficient response to this treatment; markers for response prediction are urgently needed. Research shows considerable individual variability in the brain responses to rTMS. However, whether differences in individual DLPFC modulation by rTMS can be used as a predictive marker for treatment response remains to be investigated. Here, we present a research programme that will exploit the combination of functional near-infrared spectroscopy (fNIRS) with brain stimulation. Concurrent TBS/fNIRS will allow us to systematically investigate TBS-induced modulation of blood oxygenation as a proxy for induced brain activity changes. The findings from this study will (1) elucidate the immediate effects of excitatory and inhibitory TBS on prefrontal activity in TBS treatment-naive patients with MDD and (2) validate the potential utility of TBS-induced brain modulation at baseline for the prediction of antidepressant response to 4 weeks of daily TBS treatment. Methods and analysis Open-label, parallel-group experiment consisting of two parts. In part 1, 70 patients and 37 healthy controls will be subjected to concurrent TBS/fNIRS. Intermittent TBS (iTBS) and continuous TBS (cTBS) will be applied on the left and right DLPFC, respectively. fNIRS data will be acquired before, during and several minutes after stimulation. In part 2, patients who participated in part 1 will receive a 4 week iTBS treatment of the left DLPFC, performed daily for 5 days per week. Psychometric evaluation will be performed periodically and at 1 month treatment follow-up. Statistical analysis will include a conventional, as well as a machine learning approach. Ethics and dissemination Ethics approval was obtained from the Institutional Review Board. Findings will be disseminated through scientific journals, conferences and university courses. [Kan, Rebecca L. D.; Zhang, Bella B. B.; Fong, Kenneth N. K.; Kranz, Georg S.] Hong Kong Polytech Univ, Dept Rehabil Sci, Hong Kong, Peoples R China; [Mak, Arthur D. P.] Chinese Univ Hong Kong, Dept Psychiat, Hong Kong, Peoples R China; [Chan, Sherry K. W.] Univ Hong Kong, Queen Mary Hosp, Dept Psychiat, Hong Kong, Peoples R China; [Kranz, Georg S.] Med Univ Vienna, Dept Psychiat & Psychotherapy, Vienna, Austria; [Kranz, Georg S.] Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China Hong Kong Polytechnic University; Chinese University of Hong Kong; University of Hong Kong; Medical University of Vienna; University of Hong Kong Kranz, GS (corresponding author), Hong Kong Polytech Univ, Dept Rehabil Sci, Hong Kong, Peoples R China.;Kranz, GS (corresponding author), Med Univ Vienna, Dept Psychiat & Psychotherapy, Vienna, Austria.;Kranz, GS (corresponding author), Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China. georg.kranz@polyu.edu.hk Mak, Arthur/V-6345-2017 Mak, Arthur/0000-0003-1627-6434; Kranz, Georg/0000-0002-3892-1804; Kan, Laidi/0000-0003-0894-0243; Zhang, Bella Bingbing/0000-0003-2580-3371 General Research Fund (GRF) under the University Grants Committee (UGC) of the Hong Kong Special Administrative Region [15100120] General Research Fund (GRF) under the University Grants Committee (UGC) of the Hong Kong Special Administrative Region This work is supported by the General Research Fund (GRF) under the University Grants Committee (UGC) of the Hong Kong Special Administrative Region, grant number 15100120. 41 0 0 4 10 BMJ PUBLISHING GROUP LONDON BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND 2044-6055 BMJ OPEN BMJ Open FEB 2022.0 12.0 2 e053896 10.1136/bmjopen-2021-053896 0.0 7 Medicine, General & Internal Science Citation Index Expanded (SCI-EXPANDED) General & Internal Medicine YZ0NH 35144953.0 Green Accepted, Green Published, gold 2023-03-23 WOS:000755179700022 0
J Shah, AA; Wu, DD; Korotkov, V; Jabeen, G Shah, Akber Aman; Wu, Desheng Dash; Korotkov, Vladimir; Jabeen, Gul Do Commercial Banks Benefited From the Belt and Road Initiative? A Three-Stage DEA-Tobit-NN Analysis IEEE ACCESS English Article Industry applications; modeling; neural networks; performance analysis; prediction algorithms DATA ENVELOPMENT ANALYSIS; 2-STAGE DEA; MEASURING EFFICIENCY; INSURANCE COMPANIES; NEURAL-NETWORKS; 2ND-STAGE DEA; PERFORMANCE; PRODUCTIVITY; MODELS; IMPACT The data envelopment analysis (DEA) treats decision-making units (DMUs) as black boxes: there is an unknown internal structure and transformation mechanism of input to output. Two-stage models have been proposed to resolve this problem by considering the internal structure of DMUs. However, each DMU has a different structure, and in two-stage models, the poor estimation of sub-models causes conflicts in the intermediate layer. Therefore, it is necessary to use additional tools to extract insight into opportunities to enhance the performance of DMUs. This paper presents a three-stage model employing DEA to evaluate efficiency, a Tobit regression model to identify the determinants, and a neural network (NN) to improve those determinants. Improvement in the determinants of a DMU enhances its efficiency. The developed model is applied to the empirical dataset of commercial banks from the countries that have joined the belt and road initiative (BRI), grouping them based on their economist intelligence unit (EIU) rating. The results provide valuable information on the efficiency enhancement process for banks to benefit from the BRI. [Shah, Akber Aman; Wu, Desheng Dash; Korotkov, Vladimir] Chinese Acad Sci, Sch Econ & Management, Beijing 100049, Peoples R China; [Wu, Desheng Dash] Stockholm Univ, Stockholm Business Sch, SE-10691 Stockholm, Sweden; [Jabeen, Gul] Karakoram Int Univ, Dept Comp Sci, Gilgit 15100, Pakistan Chinese Academy of Sciences; Stockholm University; Karakoram International University Wu, DD (corresponding author), Chinese Acad Sci, Sch Econ & Management, Beijing 100049, Peoples R China.;Wu, DD (corresponding author), Stockholm Univ, Stockholm Business Sch, SE-10691 Stockholm, Sweden. dash@risklab.ca jabeen, gul/N-4653-2019; Shah, Akber Aman/N-8939-2019; Shah, Akber Aman/AAA-6019-2020; Korotkov, Vladimir/I-2594-2016 jabeen, gul/0000-0002-1775-3995; Shah, Akber Aman/0000-0002-9036-0928; Korotkov, Vladimir/0000-0002-5965-6819 Ministry of Science and Technology of China [2016YFC0503606]; National Natural Science Foundation of China [71825007]; Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project [QYZDB-SSW-SYS021]; CAS Strategic Research and Decision Support System Development [GHJ-ZLZX-2019-33-3]; Marianne and Marcus Wallenberg Foundation [MMW 2015.0007]; International Partnership Program of CAS [211211KYSB20180042]; CAS-The World Academy of Science (TWAS) President's Fellowship Ministry of Science and Technology of China(Ministry of Science and Technology, China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project; CAS Strategic Research and Decision Support System Development; Marianne and Marcus Wallenberg Foundation; International Partnership Program of CAS; CAS-The World Academy of Science (TWAS) President's Fellowship This work was supported in part by the Ministry of Science and Technology of China under Grant 2016YFC0503606, in part by the National Natural Science Foundation of China under Grant 71825007, in part by the Chinese Academy of Sciences (CAS) Frontier Scienti ~ c Research Key Project under Grant QYZDB-SSW-SYS021, in part by the CAS Strategic Research and Decision Support System Development under Grant GHJ-ZLZX-2019-33-3, in part by the Marianne and Marcus Wallenberg Foundation under Grant MMW 2015.0007, and in part by the International Partnership Program of CAS under Grant 211211KYSB20180042. The work of A. A. Shah was supported by the CAS-The World Academy of Science (TWAS) President's Fellowship. 53 10 10 8 33 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2019.0 7.0 37936 37949 10.1109/ACCESS.2019.2897137 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Computer Science; Engineering; Telecommunications HS1SE gold 2023-03-23 WOS:000463641500001 0
C Gong, L; Sun, T; Li, XD; Lin, K; Diaz-Rodriguez, N; Filliat, D; Zhang, ZF; Zhang, JP Gong, Liang; Sun, Te; Li, Xudong; Lin, Ke; Diaz-Rodriguez, Natalia; Filliat, David; Zhang, Zhengfeng; Zhang, Junping Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments IFAC PAPERSONLINE English Proceedings Paper 3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS) DEC 03-05, 2020 Beijing, PEOPLES R CHINA Int Federat Automat Control, Tech Comm 9 1 Econ, Busines, & Financial Syst,Int Federat Automat Control, Tech Comm 9 2 Social Impact Automat,Int Federat Automat Control, Tech Comm 1 3 Discrete Event & Hybrid Syst,Int Federat Automat Control, Tech Comm 1 4 Stochastic Syst,Int Federat Automat Control, Tech Comm 1 5 Networked Syst,Int Federat Automat Control, Tech Comm 3 1 Comp Control,Int Federat Automat Control, Tech Comm 4 3 Robot,Int Federat Automat Control, Tech Comm 4 5 Human Machine Syst,Int Federat Automat Control, Tech Comm 7 3 Aerosp,Int Federat Automat Control, Tech Comm 9 5 Technol, Culture & Int Stabil deep learning; deep reinforcement learning; learning from demonstration (LfD); actor-critic framework; robotics Using direct reinforcement learning (RL) to accomplish a task can be very inefficient, especially in robotic configurations where interactions with the environment are lengthy and costly. Instead, learning from expert demonstration (LfD) is an alternative approach to gain better performance in an RL setting, which also greatly improves sample efficiency. We propose a novel demonstration learning framework for actor-critic based algorithms. Firstly, we put forward an environment pre-training paradigm to initialize the model parameters without interacting with the target environment, which effectively avoids the cold start problem in deep RL scenarios.Secondly, we design a general-purpose LfD framework for most of the mainstream actor-critic RL algorithms that include a policy network and a value function like PPO, SAC, TRPO, A3C. Thirdly,we build a dedicated model training platform to perform the human-robot interaction and numerical experimentation. We evaluate the method in six Mujoco simulated locomotion environments and our robot control simulation platform. Results show that several epochs of pre-training can improve the agent's performance over the early stage of training. Also, the final converged performance of the RL algorithm is also boosted by external demonstration. In general the sample efficiency is improved by 30% with the proposed method. Our demonstration pipeline makes full use of the exploration property of the RL algorithm, which is feasible for fast teaching robots in dynamic environments. Copyright (C) 2020 The Authors. [Gong, Liang; Sun, Te; Li, Xudong; Lin, Ke] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China; [Diaz-Rodriguez, Natalia; Filliat, David] Inst Polytech Paris, ENSTA, Comp Sci & Syst Engn Dept, Palaiseau, France; [Diaz-Rodriguez, Natalia; Filliat, David] Inst Polytech Paris, ENSTA, Inria Flowers Team, Palaiseau, France; [Zhang, Zhengfeng; Zhang, Junping] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China; [Zhang, Zhengfeng; Zhang, Junping] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China Shanghai Jiao Tong University; Institut Polytechnique de Paris; Institut Polytechnique de Paris; Fudan University; Fudan University Gong, L (corresponding author), Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China. gongliang_mi@sjtu.edu.cn zhang, ZY/HJH-6535-2023; zhang, zheng/HCH-9684-2022; Díaz-Rodríguez, Natalia/B-1843-2019 Díaz-Rodríguez, Natalia/0000-0003-3362-9326 National Natural Foundation of China [51775333]; Science Foundation of Shanghai Municipal Commission of Science and Technology [18391901000]; National Key Research and Development Project [2018YFB1305104] National Natural Foundation of China(National Natural Science Foundation of China (NSFC)); Science Foundation of Shanghai Municipal Commission of Science and Technology; National Key Research and Development Project This work was supported by National Natural Foundation of China (Grant No.51775333), the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.18391901000) as well as the National Key Research and Development Project (2018YFB1305104). 37 0 0 1 7 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 2405-8963 IFAC PAPERSONLINE IFAC PAPERSONLINE 2020.0 53.0 5 SI 271 278 10.1016/j.ifacol.2021.04.227 0.0 8 Automation & Control Systems Conference Proceedings Citation Index - Science (CPCI-S) Automation & Control Systems SL0EN gold, Green Submitted 2023-03-23 WOS:000656589700048 0
J Song, RJ; Huang, L; Cui, WP; Oskarsdottir, M; Vanthienen, J Song, Rongjia; Huang, Lei; Cui, Weiping; Oskarsdottir, Maria; Vanthienen, Jan Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models APPLIED SCIENCES-BASEL English Article; Proceedings Paper 4th IEEE International Conference on Applied System Invention (IEEE ICASI) APR 13-17, 2018 Tokyo, JAPAN IEEE,IEEE Tainan Sec Sensors Council,Taiwanese Inst Knowledge Innovat fraud detection; predictive modeling; bulk cargo theft; Bayesian network; port BIG DATA ANALYTICS; MANAGEMENT; LOGISTICS The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management. [Song, Rongjia; Huang, Lei] Beijing Jiaotong Univ, Dept Informat Management, Beijing 100044, Peoples R China; [Song, Rongjia; Vanthienen, Jan] Katholieke Univ Leuven, Dept Decis Sci & Informat Management, B-3000 Leuven, Belgium; [Cui, Weiping] State Grid Corp China, State Grid Energy Res Inst, Beijing 102200, Peoples R China; [Oskarsdottir, Maria] Reykjav Univ, Dept Comp Sci, IS-101 Reykjavik, Iceland Beijing Jiaotong University; KU Leuven; State Grid Corporation of China; Reykjavik University Song, RJ; Huang, L (corresponding author), Beijing Jiaotong Univ, Dept Informat Management, Beijing 100044, Peoples R China.;Song, RJ (corresponding author), Katholieke Univ Leuven, Dept Decis Sci & Informat Management, B-3000 Leuven, Belgium. rjsong@bjtu.edu.cn; lhuang@bjtu.edu.cn; cuiweiping@sgeri.sgcc.com.cn; mariaoskars@ru.is; jan.vanthienen@kuleuven.be huang, lei/GQP-8739-2022; Vanthienen, Jan/A-1668-2018 Vanthienen, Jan/0000-0002-3867-7055; Song, Rongjia/0000-0001-8594-5641 30 7 7 1 18 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2076-3417 APPL SCI-BASEL Appl. Sci.-Basel FEB 2020.0 10.0 3 1056 10.3390/app10031056 0.0 22 Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Chemistry; Engineering; Materials Science; Physics LC4PI Green Published, gold 2023-03-23 WOS:000525305900327 0
C Xue, ZB; Shen, L; Xue, ZB; Zeng, XY; Koehl, L IEEE Xue, Zhebin; Shen, Lei; Xue, Zhebin; Zeng, Xianyi; Koehl, Ludovic Development of an intelligent model to predict tactile properties from visual features of textile products 2015 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE) International Conference on Knowledge and Systems Engineering English Proceedings Paper 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) NOV 24-27, 2015 Taipei, TAIWAN Natl Taiwan Univ Sci & Technol,SW Jiaotong Univ,Taiwan Tech,IEEE Taipei Sect,Minist Sci & Technol,Univ Technol Sydney,IEEE Comp Soc textile products; sensory evaluation; visual representation; inclusion degree; borda's rule; adaptive network-based fuzzy inference system (ANFIS) FUZZY INFERENCE SYSTEM; ANFIS; OPTIMIZATION The development of virtual simulation techniques and digitized interactive platforms makes the purchasing of textile products as simple as a click. But some of our real desires about a fabric is still difficult to be satisfied, such as touch. So far, much research has been dedicated to providing consumers with a realistic simulation of fabric through virtual experiences. In the previous study, we have proved that fabrics' tactile properties could be well perceived through products' visual representations. On this basis, the present study is aimed to further study the mathematical model of predicting fabric tactile properties from samples' visual features. Two sensory experiments have been designed to extract the tactile properties and visual features of a set of textile samples represented in video clips. An intelligent method based on rough set and fuzzy set theories has been developed to extract principal visual features for the interpretation of each tactile property. Then a fuzzy neural network has been designed to build a mathematical model between each tactile property and the corresponding principal visual features. Extra experiments have been carried out to verify the effectiveness of the proposed model. The results have proved that it is possible to predict fabric tactile properties from samples' visual features with satisfactory accuracy. [Xue, Zhebin; Shen, Lei] Jiangnan Univ, Coll Text & Clothing, Wuxi, Peoples R China; [Xue, Zhebin; Zeng, Xianyi; Koehl, Ludovic] Ecole Natl Super Arts & Ind Text, Lab Genie & Mat Text, Roubaix, France Jiangnan University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT) Xue, ZB (corresponding author), Jiangnan Univ, Coll Text & Clothing, Wuxi, Peoples R China. zhebin.xue@hotmail.com Zeng, Xianyi/0000-0002-3236-6766 38 0 0 2 11 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-2508 978-1-4673-9323-2 INT CONF KNOWL SYS 2015.0 229 236 10.1109/ISKE.2015.57 0.0 8 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BF1GY 2023-03-23 WOS:000380396100039 0
J Jin, GF; Wang, TZ; Amirat, Y; Zhou, ZB; Xie, T Jin, Guangfeng; Wang, Tianzhen; Amirat, Yassine; Zhou, Zhibin; Xie, Tao A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter JOURNAL OF MARINE SCIENCE AND ENGINEERING English Article grid-connected inverter; open-circuit fault; current sensor fault; linear discriminant analysis; layering fault diagnosis COMPONENT ANALYSIS; IGBT Grid-connected inverters are the core equipment for connecting marine energy power generation systems to the public electric utility. The variation of current sensor fault severity will make fault samples multimodal. However, linear discriminant analysis assumes that the same fault is independent and identically distributed. To solve this problem, this paper proposes a layering linear discriminant analysis method based on traditional linear discriminant analysis. The proposed method divides the historical fault data based on the sensor fault severity layer-by-layer until the distribution of the same fault category in each subset is very close. Linear discriminant analysis is used to analyze historical fault data in each subgroup, and the kappa coefficient is applied as the basis for ending the training process. A BP neural network is employed to estimate the fault severity during the testing process, and the fault diagnosis sub-model is selected. The proposed method enables the accurate diagnosis of faults with different distributions in the same category and provides an accurate estimate of the sensor's fault severity degree. The estimated value of the sensor's fault degree can provide critical information for the maintenance of the equipment and can be used to correct the sensor's output. [Jin, Guangfeng; Wang, Tianzhen; Xie, Tao] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China; [Amirat, Yassine; Zhou, Zhibin] L BISEN, ISEN Yncrea Ouest, 20 Rue Cuirasse Bretagne, F-29200 Brest, France Shanghai Maritime University Wang, TZ (corresponding author), Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.;Amirat, Y (corresponding author), L BISEN, ISEN Yncrea Ouest, 20 Rue Cuirasse Bretagne, F-29200 Brest, France. 201930210074@stu.shmtu.edu.cn; tzwang@shmtu.edu.cn; yassine.amirat@isen-ouest.yncrea.fr; zhibin.zhou@isen-ouest.yncrea.fr; 201840210002@stu.shmtu.edu.cn AMIRAT, Yassine/C-4160-2019 AMIRAT, Yassine/0000-0002-2031-4088; Xie, Tao/0000-0001-7911-0517; wang, tianzhen/0000-0002-7525-8466 National Natural Science Foundation of China [62073213] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research was funded by the National Natural Science Foundation of China (grant no.s 62073213). 37 1 1 6 13 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2077-1312 J MAR SCI ENG J. Mar. Sci. Eng. JUL 2022.0 10.0 7 939 10.3390/jmse10070939 0.0 16 Engineering, Marine; Engineering, Ocean; Oceanography Science Citation Index Expanded (SCI-EXPANDED) Engineering; Oceanography 3G5ZV gold 2023-03-23 WOS:000831433300001 0
J Liu, KX; Wu, HH; Zhu, C; Wang, JP; Zeng, XY; Tao, XY; Bruniaux, P Liu, Kaixuan; Wu, Hanhan; Zhu, Chun; Wang, Jianping; Zeng, Xianyi; Tao, Xuyuan; Bruniaux, Pascal An evaluation of garment fit to improve customer body fit of fashion design clothing INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Fit evaluation; Clothing pressure; Data learning; BP-ANNs; Random forest; Bayesian classifier; Discriminant analysis; Virtual try-on SENSORY EVALUATION; PARAMETRIC DESIGN; EASE ALLOWANCE; NEURAL-NETWORK; FUZZY-LOGIC; SIZE; OPTIMIZATION; PATTERN; SYSTEM; SATISFACTION Currently, garment fit evaluation is one of the biggest bottlenecks for fashion design and manufacturing. In this paper, we proposed a garment fit prediction model using data learning technology based on Artificial Neural Networks. The inputs of the proposed model are digital clothing pressures measured by virtual try-on, while the output of the model is one of the three fit conditions-tight, fit, or loose. To acquire reliable learning data, virtual and real try-on experiments were carried out to collect input and output learning data, respectively. We collected 72 samples, each sample contains 20 clothing virtual pressure values and the corresponding fit values of the garment. After learning from the collected input and output experimental data, the proposed model can predict garment fit rapidly and automatically by inputting digital clothing pressures measured by virtual try-on. Test results showed that the prediction accuracy of fit evaluation model based on Back Propagation Artificial Neural Networks (BP-ANNs) is 93%. Compared with the 50% prediction accuracy of the traditional method, our proposed method has obvious advantages. This technology can be applied to the process of garment design and manufacturing to improve work efficiency. [Liu, Kaixuan; Wu, Hanhan; Zhu, Chun] Xian Polytech Univ, Apparel & Art Design Coll, Xian 710048, Peoples R China; [Wu, Hanhan] Shaanxi Fash Engn Univ, Fash Inst, Xian 712046, Peoples R China; [Liu, Kaixuan] Xian Polytech Univ, Shaanxi Key Lab Intelligent Clothing Design, Xian 710048, Peoples R China; [Liu, Kaixuan; Zeng, Xianyi; Tao, Xuyuan; Bruniaux, Pascal] ENSAIT, GEMTEX Lab, F-59100 Roubaix, France; [Zhu, Chun; Wang, Jianping] Donghua Univ, Fash & Art Design Inst, Shanghai 200051, Peoples R China; [Zhu, Chun; Wang, Jianping] Donghua Univ, Key Lab Clothing Design & Technol, Minist Educ, Shanghai 200051, Peoples R China Xi'an Polytechnic University; Xi'an Polytechnic University; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Donghua University; Donghua University Liu, KX (corresponding author), Xian Polytech Univ, Apparel & Art Design Coll, Xian 710048, Peoples R China.;Liu, KX (corresponding author), Xian Polytech Univ, Shaanxi Key Lab Intelligent Clothing Design, Xian 710048, Peoples R China.;Liu, KX (corresponding author), ENSAIT, GEMTEX Lab, F-59100 Roubaix, France. L40260611@hotmail.com zhu, chun/HJP-8759-2023; Liu, kaixuan/GQH-8342-2022 Zeng, Xianyi/0000-0002-3236-6766 National Natural Science Foundation of China, China [61806161]; Natural Science Basic Research Program of Shaanxi Province, China [2019JQ-848]; Innovation Ability Support Plan of Shaanxi ProvinceYoung Science and Technology Star Project, China [2020KJXX083]; Teaching Reform Research Project of Undergraduate and Higher Continuing Education in Shaanxi province [21BZ046]; Higher Education Science Research Project of Shaanxi Higher Education Society [XGH21143]; China National Endowment for the Arts, China [2018-A-05-(263)]; Social Science Fund Project of Shaanxi Province, China [2018K32]; Fundamental Research Funds for the Central Universities [2232021G-08]; International Cooperation Fund of Science and Technology Commission of Shanghai Municipality [21130750100]; Youth Innovation Team of Shaanxi Universities, China [21JP048] National Natural Science Foundation of China, China(National Natural Science Foundation of China (NSFC)); Natural Science Basic Research Program of Shaanxi Province, China; Innovation Ability Support Plan of Shaanxi ProvinceYoung Science and Technology Star Project, China; Teaching Reform Research Project of Undergraduate and Higher Continuing Education in Shaanxi province; Higher Education Science Research Project of Shaanxi Higher Education Society; China National Endowment for the Arts, China; Social Science Fund Project of Shaanxi Province, China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); International Cooperation Fund of Science and Technology Commission of Shanghai Municipality; Youth Innovation Team of Shaanxi Universities, China The work was financially supported by the National Natural Science Foundation of China, China (No. 61806161), the Natural Science Basic Research Program of Shaanxi Province, China (No. 2019JQ-848), the Innovation Ability Support Plan of Shaanxi ProvinceYoung Science and Technology Star Project, China (No. 2020KJXX083), the Teaching Reform Research Project of Undergraduate and Higher Continuing Education in Shaanxi province (No. 21BZ046), the Higher Education Science Research Project of Shaanxi Higher Education Society (No. XGH21143), China National Endowment for the Arts, China (No. 2018-A-05-(263)-0928), the Social Science Fund Project of Shaanxi Province, China (No. 2018K32), the Financial Support from the Fundamental Research Funds for the Central Universities (No. 2232021G-08), the Financial Support from International Cooperation Fund of Science and Technology Commission of Shanghai Municipality (No. 21130750100), and the Youth Innovation Team of Shaanxi Universities, China (No. 21JP048). 47 4 4 23 63 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. MAY 2022.0 120.0 3-4 2685 2699 10.1007/s00170-022-08965-z 0.0 FEB 2022 15 Automation & Control Systems; Engineering, Manufacturing Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Automation & Control Systems; Engineering 0O9KX 2023-03-23 WOS:000761870000003 0
J Molassiotis, A; Kwok, SWH; Leung, AYM; Tyrovolas, S Molassiotis, Alex; Kwok, Stephen W. H.; Leung, Angela Y. M.; Tyrovolas, Stefanos Associations between sociodemographic factors, health spending, disease burden, and life expectancy of older adults (70+years old) in 22 countries in the Western Pacific Region, 1995-2019: estimates from the Global Burden of Disease (GBD) Study 2019 GEROSCIENCE English Article Aging; Older adults; Western Pacific; Non-communicable diseases; Life expectancy; Disease burden; Sociodemographic factors RESPIRATORY HEALTH; CARE; COST; QUALITY; FUTURE; JAPAN The needs of the aging populations are putting increasing burden on healthcare particularly in the Western Pacific Region (WPR), which is the home of aging economies such as Hong Kong and Japan alongside rapid increases in older people in low- and middle-income countries (LMICs). However, little is known about the associations between sociodemographic factors, disease burden, and life expectancy in WPR. The current study conducted secondary analysis on the Global Burden of Disease (GBD) estimates of populations aged 70 years and older in WPR countries between 1995 and 2019. Correlation tests, linear mixed regressions and generalized additive mixture models were run to examine the associations of interest. Unsupervised machine learning was conducted to segment the data automatically, at cluster analysis. The sociodemographic development index was found an important factor to the disease burden in terms of Years Lived with Disability (YLD) rate and mortality rate among older adults. YLD rate and mortality rate of non-communicable diseases (NCDs) attributable to different risk groups were significant contributors to higher expectation of Lost Healthy Years (LHE) and shorter life expectancy respectively in the context of rising YLD rate, particularly in LMICs. Three clusters of countries with similar characteristics were identified. NCDs were the most significant contributors to shorter life expectancy within which the time living in poor health did not improve. Better management of NCDs in aging populations should be emphasized in all countries. The country clusters may help in a more regional strategic planning. [Molassiotis, Alex; Kwok, Stephen W. H.; Leung, Angela Y. M.; Tyrovolas, Stefanos] Hong Kong Polytech Univ, Sch Nursing, Kowloon, Hung Hom, Hong Kong, Peoples R China; [Tyrovolas, Stefanos] CIBERSAM, Fundacio St Joan de Deu, Parc Sanitari St Joan de Deu, Barcelona, Spain Hong Kong Polytechnic University; CIBER - Centro de Investigacion Biomedica en Red; CIBERSAM Molassiotis, A (corresponding author), Hong Kong Polytech Univ, Sch Nursing, Kowloon, Hung Hom, Hong Kong, Peoples R China. alex.molasiotis@polyu.edu.hk Molassiotis, Alex/0000-0001-6351-9991 46 1 1 9 17 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 2509-2715 2509-2723 GEROSCIENCE GeroScience APR 2022.0 44.0 2 SI 925 951 10.1007/s11357-021-00494-z 0.0 JAN 2022 27 Geriatrics & Gerontology Science Citation Index Expanded (SCI-EXPANDED) Geriatrics & Gerontology 1U7PL 35000094.0 hybrid 2023-03-23 WOS:000740387800001 0
J Xiang, XB; Yu, CY; Lapierre, L; Zhang, JL; Zhang, Q Xiang, Xianbo; Yu, Caoyang; Lapierre, Lionel; Zhang, Jialei; Zhang, Qin Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles INTERNATIONAL JOURNAL OF FUZZY SYSTEMS English Article Fuzzy logic control; Guidance and control; Unmanned surface vehicles; Autonomous underwater vehicles; Remotely operated vehicles PATH-FOLLOWING CONTROL; NEURAL-NETWORK CONTROL; SLIDING MODE CONTROL; VARYING FORMATION CONTROL; UNMANNED AERIAL VEHICLES; OF-SIGHT GUIDANCE; TRACKING CONTROL; OBSTACLE-AVOIDANCE; CONTROL DESIGN; SAMPLING APPARATUS Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots-marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi-Sugeno-Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art. [Xiang, Xianbo] Huazhong Univ Sci & Technol, Sch Naval Architecture & Ocean Engn, Wuhan 430074, Hubei, Peoples R China; [Yu, Caoyang; Zhang, Jialei] Huazhong Univ Sci & Technol, Sch Naval Architecture & Ocean Engn, Marine Engn, Wuhan 430074, Hubei, Peoples R China; [Lapierre, Lionel] CNRS, Dept Robot, LIRMM, UMII,UMR 5506, F-34095 Montpellier 5, France; [Zhang, Qin] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China Huazhong University of Science & Technology; Huazhong University of Science & Technology; Centre National de la Recherche Scientifique (CNRS); Universite Paul-Valery; Universite Perpignan Via Domitia; Universite de Montpellier; Huazhong University of Science & Technology Xiang, XB (corresponding author), Huazhong Univ Sci & Technol, Sch Naval Architecture & Ocean Engn, Wuhan 430074, Hubei, Peoples R China.;Yu, CY (corresponding author), Huazhong Univ Sci & Technol, Sch Naval Architecture & Ocean Engn, Marine Engn, Wuhan 430074, Hubei, Peoples R China. xbxiang@hust.edu.cn; ycyhust@hust.edu.cn Lapierre, Lionel/AHE-7859-2022; Xiang, Xianbo/O-7613-2019; Yu, Caoyang/AAB-4785-2020; Zhang, Jialei/AAX-3820-2021 Xiang, Xianbo/0000-0002-6215-9864; Yu, Caoyang/0000-0001-7448-1634; Zhang, Jialei/0000-0001-7999-5212 National Natural Science Foundation of China [51579111, 51209100]; Fundamental Research Funds for the Central Universities [2017KFYXJJ005]; State Key Laboratory of Ocean Engineering [1504]; International Exchanges of the UK Royal Society [IE161588] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); State Key Laboratory of Ocean Engineering; International Exchanges of the UK Royal Society This work is supported by National Natural Science Foundation of China (under Grant 51579111 and 51209100), the Fundamental Research Funds for the Central Universities (under Grant 2017KFYXJJ005), State Key Laboratory of Ocean Engineering (under Grant 1504), and International Exchanges of the UK Royal Society (under Grant IE161588). 217 207 215 92 558 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1562-2479 2199-3211 INT J FUZZY SYST Int. J. Fuzzy Syst. FEB 2018.0 20.0 2 SI 572 586 10.1007/s40815-017-0401-3 0.0 15 Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science FX9XU 2023-03-23 WOS:000426465100017 0
J Zheng, YN; Chen, WL; Lin, RF; Zhao, TS; Le Callet, P Zheng, Yannan; Chen, Weiling; Lin, Rongfu; Zhao, Tiesong; Le Callet, Patrick UIF: An Objective Quality Assessment for Underwater Image Enhancement IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Measurement; Indexes; Databases; Image quality; Visualization; Imaging; Task analysis; Image quality assessment (IQA); underwater image enhancement (UIE); underwater image processing MODEL Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images in CIELab space, we present the naturalness, sharpness, and structure indexes. Among them, the naturalness and sharpness indexes represent the visual improvements of enhanced images; the structure index indicates the structural similarity between the underwater images before and after UIE. We combine all indexes with a saliency-based spatial pooling and thus obtain the final UIF metric. To evaluate the proposed metric, we also establish a first-of-its-kind large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED). Experimental results confirm that the proposed UIF metric outperforms a variety of underwater and general-purpose image quality metrics. The database and source code are available at https://github.com/z21110008/UIF. [Zheng, Yannan; Chen, Weiling; Lin, Rongfu] Fuzhou Univ, Fujian Key Lab Intelligent Proc & Wireless Transm, Fujian 350108, Peoples R China; [Zhao, Tiesong] Fuzhou Univ, Fujian Key Lab Intelligent Proc & Wireless Transm, Fuzhou 350108, Peoples R China; [Zhao, Tiesong] Peng Cheng Lab, Shenzhen 518000, Peoples R China; [Le Callet, Patrick] Univ Nantes, Equipe Image Percept & Interact, Lab Sci Numer Nantes, F-44306 Nantes, France Fuzhou University; Fuzhou University; Peng Cheng Laboratory; Nantes Universite Zhao, TS (corresponding author), Fuzhou Univ, Fujian Key Lab Intelligent Proc & Wireless Transm, Fuzhou 350108, Peoples R China.;Zhao, TS (corresponding author), Peng Cheng Lab, Shenzhen 518000, Peoples R China. n191127059@fzu.edu.cn; weiling.chen@fzu.edu.cn; n191127021@fzu.edu.cn; t.zhao@fzu.edu.cn; patrick.lecallet@univ-nantes.fr National Science Foundation of China [62171134, 61901119]; Natural Science Foundation of Fujian Province, China [2022J02015] National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province, China(Natural Science Foundation of Fujian Province) This work was supported in part by the National Science Foundation of China under Grant 62171134 and Grant 61901119; and in part by the Natural Science Foundation of Fujian Province, China, under Grant 2022J02015. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Zhenzhong Chen. 57 0 0 32 40 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. 2022.0 31.0 5456 5468 10.1109/TIP.2022.3196815 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3X1AH 35951566.0 Green Submitted 2023-03-23 WOS:000842776300014 0
J Lin, Y; Rong, Y; Li, L; Li, FT; Zhang, HC; Yu, J Lin, Yi; Rong, Yu; Li, Lang; Li, Fengting; Zhang, Hanchao; Yu, Jie Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH English Article; Early Access Water resources; Spatiotemporal evolution; Ecological sensitivity; RS; GIS; The Mekong subregion in Cambodia RIVER; BIODIVERSITY; HYDROPOWER; INDEX Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020. [Lin, Yi; Rong, Yu; Li, Lang; Yu, Jie] Tongji Univ, Coll Surveying Mapping & Geoinformat, Shanghai 200092, Peoples R China; [Lin, Yi; Yu, Jie] Res Ctr Remote Sensing & Spatial Informat Technol, Shanghai 200092, Peoples R China; [Li, Lang] Univ Stuttgart, Inst Geodesy, D-70174 Stuttgart, Germany; [Li, Fengting] Tongji Univ, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China; [Zhang, Hanchao] Chinese Acad Surveying & Mapping, Beijing 100036, Peoples R China Tongji University; University of Stuttgart; Tongji University; Chinese Academy of Surveying & Mapping Yu, J (corresponding author), Tongji Univ, Coll Surveying Mapping & Geoinformat, Shanghai 200092, Peoples R China.;Yu, J (corresponding author), Res Ctr Remote Sensing & Spatial Informat Technol, Shanghai 200092, Peoples R China. linyi@tongji.edu.cn; ronv_228@tongji.edu.cn; lilang@tongji.edu.cn; fengting@tongji.edu.cn; zhanghc@casm.ac.cn; 2011_jieyu@tongji.edu.cn LI, feng/HIR-1703-2022 National Key Research and Development Program of China [2018YFB0505400]; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology, Ministry of Natural Resources, Beijing, China [2020-2-3]; National Natural Science Foundation of China [41771449]; Fundamental Research Funds for the Central Universities [2022-4-ZD-05]; China Scholarship Council [202106260141] National Key Research and Development Program of China; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology, Ministry of Natural Resources, Beijing, China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); China Scholarship Council(China Scholarship Council) This work was supported by the National Key Research and Development Program of China under Grant 2018YFB0505400, the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology, Ministry of Natural Resources, Beijing, China (No. 2020-2-3), the National Natural Science Foundation of China (No. 41771449), the Fundamental Research Funds for the Central Universities (No. 2022-4-ZD-05), and China Scholarship Council (No. 202106260141). 70 0 0 14 16 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0944-1344 1614-7499 ENVIRON SCI POLLUT R Environ. Sci. Pollut. Res. 10.1007/s11356-022-22469-z 0.0 AUG 2022 21 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology 3T0ZN 35962167.0 2023-03-23 WOS:000840012800001 0
J Duan, X; Xie, SY; Xie, XZ; Obermayer, K; Cui, YJ; Wang, ZZ Duan, Xu; Xie, Songyun; Xie, Xinzhou; Obermayer, Klaus; Cui, Yujie; Wang, Zhenzhen An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training FRONTIERS IN HUMAN NEUROSCIENCE English Article brain-computer interface; motor imagery; training protocol; feedback; Riemannian geometry BRAIN-COMPUTER INTERFACE Brain-computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect. [Duan, Xu; Xie, Songyun; Xie, Xinzhou; Cui, Yujie; Wang, Zhenzhen] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China; [Obermayer, Klaus] Tech Univ Berlin, Fac Elect Engn & Comp Sci, Berlin, Germany Northwestern Polytechnical University; Technical University of Berlin Xie, SY; Xie, XZ (corresponding author), Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China. syxie@nwpu.edu.cn; xinzhxie@nwpu.edu.cn Shaanxi Provincial Key RD Program [2021KWZ-02]; Key Research and Development Program of Shaanxi [2020ZDLGY04-01]; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201951] Shaanxi Provincial Key RD Program; Key Research and Development Program of Shaanxi; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University This research is supported partly by Shaanxi Provincial Key R&D Program (2021KWZ-02), the Key Research and Development Program of Shaanxi (2020ZDLGY04-01), and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (CX201951). 32 6 6 14 34 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 1662-5161 FRONT HUM NEUROSCI Front. Hum. Neurosci. JUN 7 2021.0 15.0 625983 10.3389/fnhum.2021.625983 0.0 13 Neurosciences; Psychology Science Citation Index Expanded (SCI-EXPANDED) Neurosciences & Neurology; Psychology SV3WC 34163337.0 Green Accepted, gold, Green Published 2023-03-23 WOS:000663751700001 0
J Zhao, XD; Wang, XY; Zhang, S; Zong, GD Zhao, Xudong; Wang, Xinyong; Zhang, Shuo; Zong, Guangdeng Adaptive Neural Backstepping Control Design for A Class of Nonsmooth Nonlinear Systems IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS English Article Adaptive control; backstepping; neural networks (NNs); nonsmooth nonlinear systems OUTPUT-FEEDBACK STABILIZATION; MULTIAGENT SYSTEMS; NETWORK CONTROL; DELAY SYSTEMS; STABILITY; STATE; TRACKING; INPUT This paper investigates the problems of backstepping control for a class of nonsmooth nonlinear systems. With the help of set-valued functions (or maps) and set-valued derivatives, a new Lyapunov criterion is developed for nonsmooth systems. The concept of semi-globally uniformly ultimately bounded (SGUUB) stability that is widely used for smooth nonlinear systems in lower triangular form is, for the first time, applied to the nonsmooth case, and such a concept provides a theory foundation for the subsequent backstepping control design. Then, two types of continuous controllers are designed based on the backstepping technique and adaptive neural network (NN) algorithm. In the light of Cellina approximate selection theorem and smooth approximation theorem for Lipschitz functions, the system under investigation is first transformed into an equivalent model. Then, the first type of controller can be efficiently designed by using the traditional backstepping technique. On the other hand, when the unknown uncertainty is taken into account in the equivalent systems, another type of controller is designed based on adaptive NN control strategy. It is proved that in both cases, all the closed-loop signals are SGUUB by using our proposed controllers. Finally, a numerical example with two types of controllers is supplied to show the availability of the provided control schemes. [Zhao, Xudong; Wang, Xinyong] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China; [Zhao, Xudong; Zhang, Shuo] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China; [Wang, Xinyong] Ecole Cent Lille, Domain Automat, F-59651 Lille, France; [Zong, Guangdeng] Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China Bohai University; Dalian University of Technology; Universite de Lille - ISITE; Centrale Lille; Universite de Lille; Qufu Normal University Zhao, XD (corresponding author), Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China. xdzhaohit@gmail.com; wangxinyong1993@gmail.com; shuozhang@dlut.edu.cn ZONG, GUANGDENG/AAA-3896-2020 ZONG, GUANGDENG/0000-0001-6498-5580 National Natural Science Foundation of China [61573069, 61722302] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the National Natural Science Foundation of China under Grant 61573069 and Grant 61722302. This paper was recommended by Associate Editor C.-C. Lim. 48 121 121 5 75 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2168-2216 2168-2232 IEEE T SYST MAN CY-S IEEE Trans. Syst. Man Cybern. -Syst. SEP 2019.0 49.0 9 1820 1831 10.1109/TSMC.2018.2875947 0.0 12 Automation & Control Systems; Computer Science, Cybernetics Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Computer Science IS2LE 2023-03-23 WOS:000481984300008 0
J Wang, ZT; Vincent, E; Serizel, R; Yan, YH Wang, Ziteng; Vincent, Emmanuel; Serizel, Romain; Yan, Yonghong Rank-1 constrained Multichannel Wiener Filter for speech recognition in noisy environments COMPUTER SPEECH AND LANGUAGE English Article Rank-1 Multichannel Wiener Filter; Speech recognition; Residual noise power; Deep neural network REDUCTION; ENHANCEMENT; PERSPECTIVE Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and the Generalized Eigenvalue (GEV) beamformer are popular signal processing techniques which can improve speech recognition performance. In this paper, we present an experimental study on these linear filters in a specific speech recognition task, namely the CHiME-4 challenge, which features real recordings in multiple noisy environments. Specifically, the rank-1 MWF is employed for noise reduction and a new constant residual noise power constraint is derived which enhances the recognition performance. To fulfill the underlying rank-1 assumption, the speech covariance matrix is reconstructed based on eigenvectors or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with alternative multichannel linear filters under the same framework, which involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask estimation. The proposed filter outperforms alternative ones, leading to a 40% relative Word Error Rate (WER) reduction compared with the baseline Weighted Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER reduction compared with the GEV-BAN method. The results also suggest that the speech recognition accuracy correlates more with the Mel-frequency cepstral coefficients (MFCC) feature variance than with the noise reduction or the speech distortion level. (C) 2017 Elsevier Ltd. All rights reserved. [Wang, Ziteng; Yan, Yonghong] Univ Chinese Acad Sci, Beijing 100190, Peoples R China; [Vincent, Emmanuel; Serizel, Romain] INRIA, F-54600 Villers Les Nancy, France Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Inria Wang, ZT (corresponding author), Univ Chinese Acad Sci, Beijing 100190, Peoples R China. wangziteng@hccl.ioa.ac.cn China Scholarship Council [201604910623]; CNRS; RENATER China Scholarship Council(China Scholarship Council); CNRS(Centre National de la Recherche Scientifique (CNRS)); RENATER We would like to thank the anonymous reviewers for their constructive comments. This work was supported by the China Scholarship Council (No. 201604910623). Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). 36 22 24 1 32 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0885-2308 1095-8363 COMPUT SPEECH LANG Comput. Speech Lang. MAY 2018.0 49.0 37 51 10.1016/j.csl.2017.11.003 0.0 15 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FS3WX Green Submitted 2023-03-23 WOS:000419716200003 0
J Taherdangkoo, R; Yang, HC; Akbariforouz, M; Sun, YN; Liu, Q; Butscher, C Taherdangkoo, Reza; Yang, Huichen; Akbariforouz, Mohammadreza; Sun, Yuantian; Liu, Quan; Butscher, Christoph Gaussian process regression to determine water content of methane: Application to methane transport modeling JOURNAL OF CONTAMINANT HYDROLOGY English Article Methane; Water content; Machine learning; Phase equilibrium; Gaussian process regression GAS-PHASE; SOLUBILITY; PREDICTION; PRESSURES; OPTIMIZATION; TEMPERATURES; EQUILIBRIUM; GROUNDWATER; NITROGEN; DENSITY The uncontrolled release of methane from natural gas wells may pose risks to shallow groundwater resources. Numerical modeling of methane migration from deep hydrocarbon formations towards shallow systems requires knowledge of phase behavior of the water-methane system, usually calculated by classic thermodynamic approaches. This study presents a Gaussian process regression (GPR) model to estimate water content of methane gas using pressure and temperature as input parameters. Bayesian optimization algorithm was implemented to tune hyper-parameters of the GPR model. The GPR predictions were evaluated with experimental data as well as four thermodynamic models. The results revealed that the predictions of the GPR are in good correspondence with experimental data having a MSE value of 3.127 x 10(-7) and R-2 of 0.981. Furthermore, the analysis showed that the GPR model exhibits an acceptable performance comparing with the well-known thermodynamic models. The GPR predicts the water content of methane over widespread ranges of pressure and temperature with a degree of accuracy needed for typical subsurface engineering applications. [Taherdangkoo, Reza; Butscher, Christoph] TU Bergakademie Freiberg, Inst Geotechn, Gustav Zeuner Str 1, D-09599 Freiberg, Germany; [Yang, Huichen; Liu, Quan] Univ Gottingen, Geosci Ctr, Dept Appl Geol, Goldschmidtstr. 3, D-37077 Gottingen, Germany; [Akbariforouz, Mohammadreza] Isfahan Univ Technol IUT, Dept Min Engn, Esfahan, Iran; [Sun, Yuantian] China Univ Min & Technol, Sch Mines, Xuzhou 221116, Jiangsu, Peoples R China Technical University Freiberg; University of Gottingen; Isfahan University of Technology; China University of Mining & Technology Taherdangkoo, R (corresponding author), TU Bergakademie Freiberg, Inst Geotechn, Gustav Zeuner Str 1, D-09599 Freiberg, Germany. reza.Taherdangkoo@ifgt.tu-freiberg.de Taherdangkoo, Reza/HIK-0120-2022 Taherdangkoo, Reza/0000-0002-0290-8106 61 4 4 1 4 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0169-7722 1873-6009 J CONTAM HYDROL J. Contam. Hydrol. DEC 2021.0 243.0 103910 10.1016/j.jconhyd.2021.103910 0.0 OCT 2021 8 Environmental Sciences; Geosciences, Multidisciplinary; Water Resources Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Water Resources WN3MS 34695717.0 2023-03-23 WOS:000711676800004 0
J Bi, H; Sun, JW; Jiang, YB; Ni, XY; Shu, HZ Bi, Hui; Sun, Jiawei; Jiang, Yibo; Ni, Xinye; Shu, Huazhong Structure boundary-preserving U-Net for prostate ultrasound image segmentation FRONTIERS IN ONCOLOGY English Article prostate ultrasound image segmentation; ASM-based key points selection; U-Net architecture; deep learning; shape prior Prostate cancer diagnosis is performed under ultrasound-guided puncture for pathological cell extraction. However, determining accurate prostate location remains a challenge from two aspects: (1) prostate boundary in ultrasound images is always ambiguous; (2) the delineation of radiologists always occupies multiple pixels, leading to many disturbing points around the actual contour. We proposed a boundary structure-preserving U-Net (BSP U-Net) in this paper to achieve precise prostate contour. BSP U-Net incorporates prostate shape prior to traditional U-Net. The prior shape is built by the key point selection module, which is an active shape model-based method. Then, the module plugs into the traditional U-Net structure network to achieve prostate segmentation. The experiments were conducted on two datasets: PH2 + ISBI 2016 challenge and our private prostate ultrasound dataset. The results on PH2 + ISBI 2016 challenge achieved a Dice similarity coefficient (DSC) of 95.94% and a Jaccard coefficient (JC) of 88.58%. The results of prostate contour based on our method achieved a higher pixel accuracy of 97.05%, a mean intersection over union of 93.65%, a DSC of 92.54%, and a JC of 93.16%. The experimental results show that the proposed BSP U-Net has good performance on PH2 + ISBI 2016 challenge and prostate ultrasound image segmentation and outperforms other state-of-the-art methods. [Bi, Hui; Ni, Xinye] Nanjing Med Univ, Affiliated Changzhou Peoples Hosp 2, Dept Radiat Oncol, Changzhou, Peoples R China; [Bi, Hui] Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou, Peoples R China; [Bi, Hui] Southeast Univ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China; [Sun, Jiawei; Ni, Xinye] Jiangsu Prov Engn Res Ctr Med Phys, Changzhou, Peoples R China; [Jiang, Yibo] Changzhou Inst Technol, Sch Elect & Informat Engn, Changzhou, Peoples R China; [Shu, Huazhong] Southeast Univ, Lab Image Sci & Technol, Nanjing, Peoples R China; [Shu, Huazhong] Ctr Rech Informat Biomedicale Sino francais, Rennes, France; [Shu, Huazhong] Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing, Peoples R China Nanjing Medical University; Changzhou University; Southeast University - China; Changzhou Institute of Technology; Southeast University - China; Southeast University - China Ni, XY (corresponding author), Nanjing Med Univ, Affiliated Changzhou Peoples Hosp 2, Dept Radiat Oncol, Changzhou, Peoples R China.;Ni, XY (corresponding author), Jiangsu Prov Engn Res Ctr Med Phys, Changzhou, Peoples R China. nxy@njmu.edu.cn National Natural Science Foundation of China [62171125, 62141401]; Natural Science Foundation of Jiangsu Province [BK20190159]; Science and Technology Project of Changzhou City [CE20215045]; Key Laboratory of Computer Network and Information Integration (Southeast University) of the Ministry of Education [K93-9-2021-08]; Key Medical Physics Laboratory of Changzhou [CM20193005]; 67th National Postdoctoral Program [2020M671277] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); Science and Technology Project of Changzhou City; Key Laboratory of Computer Network and Information Integration (Southeast University) of the Ministry of Education; Key Medical Physics Laboratory of Changzhou; 67th National Postdoctoral Program This work was supported by the National Natural Science Foundation of China (Grant No. 62171125, No. 62141401), the 67th National Postdoctoral Program (Grant No. 2020M671277), the Natural Science Foundation of Jiangsu Province (Grant No. BK20190159), the Science and Technology Project of Changzhou City (Grant No. CE20215045), the Key Laboratory of Computer Network and Information Integration (Southeast University) of the Ministry of Education (Grant No. K93-9-2021-08), and the Key MedicalPhysics Laboratory ofChangzhou(Grant No. CM20193005). 37 0 0 12 12 FRONTIERS MEDIA SA LAUSANNE AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND 2234-943X FRONT ONCOL Front. Oncol. JUL 28 2022.0 12.0 900340 10.3389/fonc.2022.900340 0.0 9 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 3U4YQ 35965563.0 gold 2023-03-23 WOS:000840978600001 0
J de Crecy-Lagard, V; de Hegedus, RA; Arighi, C; Babor, J; Bateman, A; Blaby, I; Blaby-Haas, C; Bridge, AJ; Burley, SK; Cleveland, S; Colwell, LJ; Conesa, A; Dallago, C; Danchin, A; de Waard, A; Deutschbauer, A; Dias, R; Ding, YS; Fang, G; Friedberg, I; Gerlt, J; Goldford, J; Gorelik, M; Gyori, BM; Henry, C; Hutinet, G; Jaroch, M; Karp, PD; Kondratova, L; Lu, ZY; Marchler-Bauer, A; Martin, MJ; McWhite, C; Moghe, GD; Monaghan, P; Morgat, A; Mungall, CJ; Natale, DA; Nelson, WC; O'Donoghue, S; Orengo, C; O'Toole, KH; Radivojac, P; Reed, C; Roberts, RJ; Rodionov, D; Rodionova, IA; Rudolf, JD; Saleh, L; Sheynkman, G; Thibaud-Nissen, F; Thomas, PD; Uetz, P; Vallenet, D; Carter, EW; Weigele, PR; Wood, V; Wood-Charlson, EM; Xu, J de Crecy-Lagard, Valerie; de Hegedus, Rocio Amorin; Arighi, Cecilia; Babor, Jill; Bateman, Alex; Blaby, Ian; Blaby-Haas, Crysten; Bridge, Alan J.; Burley, Stephen K.; Cleveland, Stacey; Colwell, Lucy J.; Conesa, Ana; Dallago, Christian; Danchin, Antoine; de Waard, Anita; Deutschbauer, Adam; Dias, Raquel; Ding, Yousong; Fang, Gang; Friedberg, Iddo; Gerlt, John; Goldford, Joshua; Gorelik, Mark; Gyori, Benjamin M.; Henry, Christopher; Hutinet, Geoffrey; Jaroch, Marshall; Karp, Peter D.; Kondratova, Liudmyla; Lu, Zhiyong; Marchler-Bauer, Aron; Martin, Maria-Jesus; McWhite, Claire; Moghe, Gaurav D.; Monaghan, Paul; Morgat, Anne; Mungall, Christopher J.; Natale, Darren A.; Nelson, William C.; O'Donoghue, Sean; Orengo, Christine; O'Toole, Katherine H.; Radivojac, Predrag; Reed, Colbie; Roberts, Richard J.; Rodionov, Dmitri; Rodionova, Irina A.; Rudolf, Jeffrey D.; Saleh, Lana; Sheynkman, Gloria; Thibaud-Nissen, Francoise; Thomas, Paul D.; Uetz, Peter; Vallenet, David; Carter, Erica Watson; Weigele, Peter R.; Wood, Valerie; Wood-Charlson, Elisha M.; Xu, Jin A roadmap for the functional annotation of protein families: a community perspective DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION English Article GENE ONTOLOGY; RESOURCE; DISCOVERY; UNIPROT; TOOL Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward. [de Crecy-Lagard, Valerie; Babor, Jill; Cleveland, Stacey; Dias, Raquel; Gorelik, Mark; Hutinet, Geoffrey; Jaroch, Marshall; Reed, Colbie] Univ Florida, Dept Microbiol & Cell Sci, Gainesville, FL 32611 USA; [de Hegedus, Rocio Amorin; Kondratova, Liudmyla] Univ Florida, Genet Inst, Gainesville, FL 32611 USA; [Arighi, Cecilia] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19713 USA; [Bateman, Alex; Martin, Maria-Jesus] European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Wellcome Genome Campus, Hinxton CB10 1SD, England; [Blaby, Ian] US DOE, Joint Genome Inst, Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; [Blaby-Haas, Crysten] Brookhaven Natl Lab, Biol Dept, Upton, NY 11973 USA; [Bridge, Alan J.; Morgat, Anne] Ctr Med Univ Geneva, SIB Swiss Inst Bioinformat, Swiss Prot Grp, CH-1211 Geneva 4, Switzerland; [Burley, Stephen K.] Rutgers State Univ, Inst Quantitat Biomed, RCSB Prot Data Bank, Piscataway, NJ 08854 USA; [Colwell, Lucy J.] Univ Cambridge, Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England; [Conesa, Ana] Inst Integrat Syst Biol, Spanish Natl Res Council, Valencia 46980, Spain; [Dallago, Christian] TUM Tech Univ Munich, I12, Dept Informat Bioinformat & Computat Biol, Boltzmannstr 3, D-85748 Garching, Germany; [Danchin, Antoine] Univ Hong Kong, Li KaShing Fac Med, Sch Biomed Sci, Pokfulam, 21 Sassoon Rd, Hong Kong 999077, Peoples R China; [de Waard, Anita] Elsevier, Res Collaborat Unit, Jericho, VT 05465 USA; [Deutschbauer, Adam; Mungall, Christopher J.; Wood-Charlson, Elisha M.] Lawrence Berkeley Natl Lab, Environm Genom & Syst Biol Div, Berkeley, CA 94720 USA; [Ding, Yousong] Univ Florida, Ctr Nat Prod Drug Discovery & Dev, Dept Med Chem, Gainesville, FL 32610 USA; [Fang, Gang] NYU Shanghai, Shanghai 200120, Peoples R China; [Friedberg, Iddo] Iowa State Univ, Dept Vet Microbiol & Prevent Med, Ames, IA 50011 USA; [Gerlt, John] Univ Illinois, Inst Genom Biol, Urbana, IL 61801 USA; [Gerlt, John] Univ Illinois, Dept Biochem, Urbana, IL 61801 USA; [Gerlt, John] Univ Illinois, Dept Chem, Urbana, IL 61801 USA; [Goldford, Joshua] MIT, Phys Living Syst, Cambridge, MA 02139 USA; [Gyori, Benjamin M.] Harvard Med Sch, Lab Syst Pharmacol, Boston, MA 02115 USA; [Henry, Christopher] Argonne Natl Lab, Math & Comp Sci Div, Argonne, IL 60439 USA; [Karp, Peter D.] SRI Int, Bioinformat Res Grp, Menlo Pk, CA 94025 USA; [Lu, Zhiyong; Marchler-Bauer, Aron; Thibaud-Nissen, Francoise] NIH, Natl Ctr Biotechnol Informat NCBI, Natl Lib Med NLM, 8600 Rockville Pike, Bethesda, MD 20817 USA; [McWhite, Claire] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08540 USA; [Moghe, Gaurav D.] Cornell Univ, Sch Integrat Plant Sci, Plant Biol Sect, Ithaca, NY 14853 USA; [Monaghan, Paul] Univ Florida, Dept Agr Educ & Commun, Gainesville, FL 32611 USA; [Natale, Darren A.] Georgetown Univ Med Ctr, Washington, DC 20007 USA; [Nelson, William C.] Pacific Northwest Natl Labs, Biol Sci Div, Richland, WA 99354 USA; [O'Donoghue, Sean] Univ NSW, Sch Biotechnol & Biomol Sci, Sydney, NSW 2052, Australia; [Orengo, Christine] UCL, Dept Struct & Mol Biol, London WC1E 6BT, England; [O'Toole, Katherine H.; Roberts, Richard J.; Saleh, Lana; Weigele, Peter R.] New England Biolabs Inc, Ipswich, MA 01938 USA; [Radivojac, Predrag] Northeastern Univ, Khoury Coll Comp Sci, Boston, MA 02115 USA; [Rodionov, Dmitri] Sanford Burnham Prebys Med Discovery Inst, La Jolla, CA 92037 USA; [Rodionova, Irina A.] Univ Calif San Diego, Dept Bioengn, Div Engn, La Jolla, CA 92093 USA; [Rudolf, Jeffrey D.] Univ Florida, Dept Chem, Gainesville, FL 32611 USA; [Sheynkman, Gloria] Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA USA; [Thomas, Paul D.] Univ Southern Calif, Dept Populat & Publ Hlth Sci, Los Angeles, CA 90033 USA; [Uetz, Peter] Virginia Commonwealth Univ, Ctr Biol Data Sci, Richmond, VA 23284 USA; [Vallenet, David] Univ Paris Saclay, Univ Evry, Inst Francois Jacob, CNRS,LABGeM,Genom Metab,CEA,Genoscope, F-91057 Evry, France; [Carter, Erica Watson; Xu, Jin] Univ Florida, Dept Plant Pathol, Citrus Res & Educ Ctr, 700 Expt Stn Rd, Lake Alfred, FL 33850 USA; [Wood, Valerie] Univ Cambridge, Dept Biochem, Cambridge CB2 1GA, England State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Delaware; European Molecular Biology Laboratory (EMBL); United States Department of Energy (DOE); Joint Genome Institute - JGI; Lawrence Berkeley National Laboratory; United States Department of Energy (DOE); Brookhaven National Laboratory; Swiss Institute of Bioinformatics; University of Geneva; Rutgers State University New Brunswick; University of Cambridge; Consejo Superior de Investigaciones Cientificas (CSIC); Technical University of Munich; University of Hong Kong; Reed Elsevier; Elsevier; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; State University System of Florida; University of Florida; NYU Shanghai; Iowa State University; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Massachusetts Institute of Technology (MIT); Harvard University; Harvard Medical School; United States Department of Energy (DOE); Argonne National Laboratory; SRI International; National Institutes of Health (NIH) - USA; Princeton University; Cornell University; State University System of Florida; University of Florida; Georgetown University; United States Department of Energy (DOE); Pacific Northwest National Laboratory; University of New South Wales Sydney; University of London; University College London; New England Biolabs; Northeastern University; Sanford Burnham Prebys Medical Discovery Institute; University of California System; University of California San Diego; State University System of Florida; University of Florida; University of Virginia; University of Southern California; Virginia Commonwealth University; CEA; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; State University System of Florida; University of Florida; University of Cambridge de Crecy-Lagard, V (corresponding author), Univ Florida, Dept Microbiol & Cell Sci, Gainesville, FL 32611 USA. vcrecy@ufl.edu Martín, María/HDL-9512-2022; xu, jin/O-6912-2019; Sheynkman, Gloria/AEA-8603-2022; Bridge, Alan/U-4209-2018; Arighi, Cecilia/J-2381-2015; Conesa, Ana/A-9559-2014 xu, jin/0000-0003-2289-4896; Burley, Stephen K./0000-0002-2487-9713; Blaby, Ian/0000-0002-1631-3154; Bateman, Alex/0000-0002-6982-4660; Bridge, Alan/0000-0003-2148-9135; Arighi, Cecilia/0000-0002-0803-4817; Conesa, Ana/0000-0001-9597-311X; Martin, Maria-Jesus/0000-0001-5454-2815; de Crecy-Lagard, Valerie/0000-0002-9955-3785 National Science Foundation [MCB-2129768]; National Institutes of Health Intramural Research Program, National Library of Medicine National Science Foundation(National Science Foundation (NSF)); National Institutes of Health Intramural Research Program, National Library of Medicine National Science Foundation (grant MCB-2129768) to V.d.C.-L; National Institutes of Health Intramural Research Program, National Library of Medicine (Z.L.). 121 5 5 3 7 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1758-0463 DATABASE-OXFORD Database AUG 12 2022.0 2022.0 baac062 10.1093/database/baac062 0.0 16 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology 3T2TW 35961013.0 Green Published, Green Accepted, gold 2023-03-23 WOS:000840134400001 0
C Han, ZH; Tan, HS; Jiang, SFHC; Fu, XM; Cao, WL; Lau, FCM IEEE Han, Zhenhua; Tan, Haisheng; Jiang, Shaofeng H-C; Fu, Xiaoming; Cao, Wanli; Lau, Francis C. M. Scheduling Placement-Sensitive BSP Jobs with Inaccurate Execution Time Estimation IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS IEEE INFOCOM English Proceedings Paper 39th IEEE International Conference on Computer Communications (IEEE INFOCOM) JUL 06-09, 2020 ELECTR NETWORK IEEE The Bulk Synchronous Parallel (BSP) paradigm is gaining tremendous importance recently because of the popularity of computations such as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we derive SPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present how SPIN solves it with a rounding-based randomized approximation approach. Our analysis indicates SPIN achieves a good performance guarantee efficiently. Moreover, SPIN is robust against misestimation of job execution time by theoretically bounding its negative impact. We implement SPIN on a production-trace driven testbed with 40 GPUs. Our extensive experiments show that SPIN can reduce the job makespan and the average job completion time by up to 3x and 4.68x, respectively. Our approach also demonstrates better robustness to execution time misestimation compared with heuristic baselines. [Han, Zhenhua; Tan, Haisheng; Cao, Wanli] Univ Sci & Technol China, Hefei, Anhui, Peoples R China; [Han, Zhenhua; Lau, Francis C. M.] Univ Hong Kong, Hong Kong, Peoples R China; [Jiang, Shaofeng H-C] Weizmann Inst Sci, Rehovot, Israel; [Fu, Xiaoming] Univ Gottingen, Gottingen, Germany Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Hong Kong; Weizmann Institute of Science; University of Gottingen Tan, HS (corresponding author), Univ Sci & Technol China, Hefei, Anhui, Peoples R China. hstan@ustc.edu.cn Fu, Xiaoming/B-7208-2016; Fu, Xiaoming/AAD-2828-2022 Fu, Xiaoming/0000-0002-8012-4753; Fu, Xiaoming/0000-0002-8012-4753; Jiang, Shaofeng/0000-0001-7972-827X National Key R&D Program of China [2018YFB0803400]; NSFC [61772489, 61751211]; Key Research Program of Frontier Sciences (CAS) [QYZDY-SSW-JSC002]; Hong Kong CRF [C703615G]; EU H2020 COSAFE project National Key R&D Program of China; NSFC(National Natural Science Foundation of China (NSFC)); Key Research Program of Frontier Sciences (CAS); Hong Kong CRF; EU H2020 COSAFE project This work is supported partly by the National Key R&D Program of China 2018YFB0803400, NSFC Grants 61772489, 61751211, Key Research Program of Frontier Sciences (CAS) No. QYZDY-SSW-JSC002, Hong Kong CRF Grant C703615G, and EU H2020 COSAFE project. 50 5 5 0 3 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 0743-166X 978-1-7281-6412-0 IEEE INFOCOM SER 2020.0 1053 1062 10 Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Telecommunications BQ8NB 2023-03-23 WOS:000620945800107 0
J Cao, LD; Zhu, P; Zhao, YS; Zhao, JH Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids JOURNAL OF HAZARDOUS MATERIALS English Article Toxicity; Ionic liquids; Quantitative structure-activity relationship; Extreme learning machine; Quantum chemistry descriptors OXYGEN-REDUCTION; IMIDAZOLIUM; CAPTURE; BIODEGRADABILITY; GRAPHENE; SOLVENTS; DATABASE; IMPACTS Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S-EP) and charge distribution area (S sigma-profile) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R-2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. [Cao, Lingdi] Forschungszentrum Julich, Helmholtz Inst Erlangen Nurnberg Renewable Energy, Egerlandstr 3, D-91058 Erlangen, Germany; [Zhu, Peng; Zhao, Yongsheng] Shanghai Jiao Tong Univ, Dept Micro Nanoelect, Key Lab Thin Film & Microfabricat, Minist Educ, Shanghai 200240, Peoples R China; [Zhu, Peng; Zhao, Yongsheng] Univ Calif Santa Barbara, Dept Chem Engn, Santa Barbara, CA 93106 USA; [Zhao, Jihong] Collaborat Innovat Ctr Environm Pollut Control &, Zhengzhou 450001, Henan, Peoples R China; [Zhao, Jihong] Xuchang Univ, Xuchang 461001, Henan, Peoples R China Shanghai Jiao Tong University; University of California System; University of California Santa Barbara; Xuchang University Zhao, YS (corresponding author), Shanghai Jiao Tong Univ, Dept Micro Nanoelect, Key Lab Thin Film & Microfabricat, Minist Educ, Shanghai 200240, Peoples R China.;Zhao, JH (corresponding author), Collaborat Innovat Ctr Environm Pollut Control &, Zhengzhou 450001, Henan, Peoples R China. yzhao01@ucsb.edu; zjh@zzuli.edu.cn China Postdoctoral Science Foundation [2017M621477, 2017M621476]; Intergovernmental International Scientific and Technological Cooperation of Shanghai [17520710200] China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Intergovernmental International Scientific and Technological Cooperation of Shanghai We are grateful for the financial support provided by the China Postdoctoral Science Foundation (2017M621477, 2017M621476), and the Intergovernmental International Scientific and Technological Cooperation of Shanghai (No. 17520710200). 54 54 57 11 129 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0304-3894 1873-3336 J HAZARD MATER J. Hazard. Mater. JUN 15 2018.0 352.0 17 26 10.1016/j.jhazmat.2018.03.025 0.0 10 Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Engineering; Environmental Sciences & Ecology GE8GI 29567407.0 2023-03-23 WOS:000431469800002 0
J Wu, X; Hong, DF; Chanussot, J Wu, Xin; Hong, Danfeng; Chanussot, Jocelyn Convolutional Neural Networks for Multimodal Remote Sensing Data Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Feature extraction; Laser radar; Synthetic aperture radar; Task analysis; Convolutional neural networks; Hyperspectral imaging; Network architecture; Classification; convolutional neural networks (CNNs); cross-channel; hyperspectral (HS); light detection and ranging (LiDAR); multimodal; reconstruction; remote sensing (RS); synthetic aperture radar (SAR) LAND-COVER CLASSIFICATION; LIDAR DATA; FUSION; FRAMEWORK; IMAGES In recent years, enormous research has been made to improve the classification performance of single-modal remote sensing (RS) data. However, with the ever-growing availability of RS data acquired from satellite or airborne platforms, simultaneous processing and analysis of multimodal RS data pose a new challenge to researchers in the RS community. To this end, we propose a deep-learning-based new framework for multimodal RS data classification, where convolutional neural networks (CNNs) are taken as a backbone with an advanced cross-channel reconstruction module, called CCR-Net. As the name suggests, CCR-Net learns more compact fusion representations of different RS data sources by the means of the reconstruction strategy across modalities that can mutually exchange information in a more effective way. Extensive experiments conducted on two multimodal RS datasets, including hyperspectral (HS) and light detection and ranging (LiDAR) data, i.e., the Houston2013 dataset, and HS and synthetic aperture radar (SAR) data, i.e., the Berlin dataset, demonstrate the effectiveness and superiority of the proposed CCR-Net in comparison with several state-of-the-art multimodal RS data classification methods. The codes will be openly and freely available at https://github.com/danfenghong/IEEE_TGRS_CCR-Net for the sake of reproducibility. [Wu, Xin] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China; [Wu, Xin] Beijing Inst Technol, Beijing Key Lab Fract Signals & Syst, Beijing 100081, Peoples R China; [Hong, Danfeng] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble Inst Technol, Grenoble INP, LJK,INRJA,CNRS, F-38000 Grenoble, France; [Chanussot, Jocelyn] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China Beijing Institute of Technology; Beijing Institute of Technology; Chinese Academy of Sciences; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; UDICE-French Research Universities; Universite Grenoble Alpes (UGA); Chinese Academy of Sciences Hong, DF (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. 040251522wuxin@163.com; hongdf@aircas.ac.cn; jocelyn@hi.is Chanussot, Jocelyn/0000-0003-4817-2875 National Natural Science Foundation of China [62101045]; China Postdoctoral Science Foundation [2021M690385]; MIAI@Grenoble Alpes [ANR19-P3IA-0003]; AXA Research Fund National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); MIAI@Grenoble Alpes; AXA Research Fund(AXA Research Fund) This work was supported in part by the National Natural Science Foundation of China under Grant 62101045, in part by the China Postdoctoral Science Foundation-funded Project 2021M690385, in part by MIAI@Grenoble Alpes under Grant ANR19-P3IA-0003, and in part by the AXA Research Fund. 48 55 55 78 124 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5517010 10.1109/TGRS.2021.3124913 0.0 10 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZB5PC 2023-03-23 WOS:000756892900024 0
J Fu, GH; Li, JQ; Wang, RQ; Ma, Y; Chen, YD Fu, Guanghui; Li, Jianqiang; Wang, Ruiqian; Ma, Yue; Chen, Yueda Attention-based full slice brain CT image diagnosis with explanations q NEUROCOMPUTING English Article Brain CT; Deep learning; Multi-label classification; Model interpretability CONVOLUTIONAL NEURAL-NETWORKS; CLASSIFICATION Diagnose brain diseases by brain CT images is one of the most common ways. But, it usually takes several (> 7) years to train a professional doctor because it is very challenging to diagnose brain diseases cor-rectly. The study on automated assistance of brain CT diagnosis is still limited. In this paper, we research the challenges of this task and propose a method by simulating human doctor diagnosis habits. Our method analyzes a full slice of brain CT images, instead of every single one, to take into account contin-uous changes of the whole brain structure, simulate the way the doctor diagnoses. To avoid redundancies in a thin slice scan, we propose a redundancy removal and data augmentation method that can both reduce computation complexity and improve performance without information loss. Doctors make a diagnosis by observing several key images and key points in them. Our method achieved this by two steps of attention mechanisms. It can highlight the images and key points that have significant impacts on the prediction and explain the results. We evaluated our method on two public datasets CQ500 and RSNA, which achieved 0.9262 and 0.8650 F1 score respectively. Moreover, an experienced doctor (with 29 years of experience) verified the promising clinical application value of the proposed method through manual experiments. (c) 2021 Elsevier B.V. All rights reserved. [Fu, Guanghui; Li, Jianqiang; Wang, Ruiqian] Beijing Univ Technol, Beijing 100124, Peoples R China; [Ma, Yue] Univ Paris Saclay, CNRS, LRI, Orsay, France; [Chen, Yueda] Tianjin Huanhu Hosp, Tianjin 300350, Peoples R China Beijing University of Technology; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay Li, JQ (corresponding author), Beijing Univ Technol, Beijing 100124, Peoples R China. fuguanghui@emails.bjut.edu.cn; lijianqiang@bjut.edu.cn; wangruiqian@emails.bjut.edu.cn; ma@lri.fr LI, JIAN/GRY-2197-2022; LI, Jing/HNB-5575-2023; Fu, Guanghui/ABA-7593-2021; li, jian/GSE-0245-2022; Li, Jing/GYU-5036-2022 Ma, Yue/0000-0002-2805-2473 National Key R&D Program of China [2020YFB2104402] National Key R&D Program of China q This study is supported by the National Key R&D Program of China with project no. 2020YFB2104402. 34 3 3 0 12 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 10 2021.0 452.0 263 274 10.1016/j.neucom.2021.04.044 0.0 MAY 2021 12 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science SU4EA 2023-03-23 WOS:000663091800005 0
J Bagante, F; Spolverato, G; Merath, K; Weiss, M; Alexandrescu, S; Marques, HP; Aldrighetti, L; Maithel, SK; Pulitano, C; Bauer, TW; Shen, F; Poultsides, GA; Soubrane, O; Martel, G; Koerkamp, BG; Guglielmi, A; Endo, I; Pawlik, TM Bagante, Fabio; Spolverato, Gaya; Merath, Katiuscha; Weiss, Matthew; Alexandrescu, Sorin; Marques, Hugo P.; Aldrighetti, Luca; Maithel, Shishir K.; Pulitano, Carlo; Bauer, Todd W.; Shen, Feng; Poultsides, George A.; Soubrane, Olivier; Martel, Guillaume; Koerkamp, B. Groot; Guglielmi, Alfredo; Endo, Itaru; Pawlik, Timothy M. Intrahepatic cholangiocarcinoma tumor burden: A classification and regression tree model to define prognostic groups after resection SURGERY English Article; Proceedings Paper 14th Annual American Academic Surgical Congress (ASC) of Society-of-University-Surgeons (SUS)/Association-for-Academic-Surgery (AAS) FEB 04-09, 2019 Houston, TX Soc Univ Surg,Assoc Acad Surg FEATURES; SURGERY; SCORE Background: Tumor burden is an important factor in defining prognosis among patients with primary and secondary liver cancers. Although the eighth edition of the American Joint Committee on Cancer staging system has changed the criteria for staging patients with intrahepatic cholangiocarcinoma to better define the effect of tumor burden on prognosis, the impact of intrahepatic cholangiocarcinoma tumor burden on overall survival has not been examined using a machine-learning tool. Methods: Patients who underwent resection of intrahepatic cholangiocarcinoma at 1 of 14 participating international hospitals between 1990 and 2015 were identified. Classical survival models and the Classification and Regression Tree model were used to identify groups of patients with a homogeneous risk of death and investigate the hierarchical association between variables and overall survival. Results: Among 1,116 patients included in the analysis, tumor size was <= 5 cm in 447 (40.1%) patients and >5 cm in 669 (59.9%) patients. Although 82.9% (n = 926) of patients had a single intrahepatic cholangiocarcinoma, 9.9% (n = 110) and 7.2% (n = 80) of patients had 2 and >= 3 tumors, respectively. Patients with intrahepatic cholangiocarcinoma tumors <= 5 cm and >5 cm had a 5-year overall survival of 51.7% and 32.6%, respectively (P < 0.001). Five-year overall survival decreased from 44.6% among patients with a single intrahepatic cholangiocarcinoma to 28.1% and 14.2% among patients with 2 and >= 3 intrahepatic cholangiocarcinomas, respectively (P < 0.001). Among the combinations of tumor size and intrahepatic cholangiocarcinoma tumor number used to estimate tumor burden, logarithmic transformation of tumor size (log tumor size) and intrahepatic cholangiocarcinoma tumor number had the highest concordance index. The Classification and Regression Tree model identified 8 classes of patients with a homogeneous risk of death, illustrating the hierarchical relationship between tumor burden (log tumor size and number of intrahepatic cholangiocarcinomas) and other factors associated with prognosis. Conclusion: Intrahepatic cholangiocarcinoma tumor size and number demonstrated a strong nonlinear association with survival after resection of intrahepatic cholangiocarcinoma. A log-model Classification and Regression Tree derived tumor burden score may be a better tool to estimate prognosis of patients undergoing curative-intent resection of intrahepatic cholangiocarcinoma. (C) 2019 Elsevier Inc. All rights reserved. [Bagante, Fabio; Merath, Katiuscha; Pawlik, Timothy M.] Ohio State Univ, Dept Surg, Wexner Med Ctr, Columbus, OH 43210 USA; [Bagante, Fabio; Spolverato, Gaya; Guglielmi, Alfredo] Univ Verona, Dept Surg, Verona, Italy; [Weiss, Matthew] Johns Hopkins Univ Hosp, Dept Surg, Baltimore, MD 21205 USA; [Alexandrescu, Sorin] Fundeni Clin Inst, Dept Surg, Bucharest, Romania; [Marques, Hugo P.] Curry Cabral Hosp, Dept Surg, Lisbon, Portugal; [Aldrighetti, Luca] Osped San Raffaele, Dept Surg, Milan, Italy; [Maithel, Shishir K.] Emory Univ, Dept Surg, Atlanta, GA 30322 USA; [Pulitano, Carlo] Univ Sydney, Royal Prince Alfred Hosp, Dept Surg, Sydney, NSW, Australia; [Bauer, Todd W.] Univ Virginia, Dept Surg, Charlottesville, VA USA; [Shen, Feng] Eastern Hepatobiliary Surg Hosp, Dept Surg, Shanghai, Peoples R China; [Poultsides, George A.] Stanford Univ, Dept Surg, Stanford, CA 94305 USA; [Soubrane, Olivier] Beaujon Hosp, AP HP, Dept Hepatobiliopancreat Surg & Liver Transplanta, Clichy, France; [Martel, Guillaume] Univ Ottawa, Dept Surg, Div Gen Surg, Ottawa, ON, Canada; [Koerkamp, B. Groot] Erasmus MC, Dept Surg, Rotterdam, Netherlands; [Endo, Itaru] Yokohama City Univ, Sch Med, Gastroenterol Surg Div, Yokohama, Kanagawa, Japan University System of Ohio; Ohio State University; University of Verona; Johns Hopkins University; Johns Hopkins Medicine; Institutul Clinic Fundeni; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele; Emory University; University of Sydney; University of Virginia; Naval Medical University; Stanford University; UDICE-French Research Universities; Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Hopital Universitaire Beaujon - APHP; University of Ottawa; Erasmus University Rotterdam; Erasmus MC; Yokohama City University Pawlik, TM (corresponding author), Ohio State Univ, Dept Surg, Urban Meyer III & Shelley Meyer Chair Canc Res, Wexner Med Ctr, 395 W 12th Ave,Suite 670, Columbus, OH 43210 USA. tim.pawlik@osumc.edu Shen, Feng/GSD-2508-2022; Pulitano, Carlo/HPB-9047-2023; Pawlik, Timothy/AAO-9335-2020; ALEXANDRESCU, SORIN/AAJ-3660-2021; Groot koerkamp, Bas/ABD-1621-2020; Soubrane, Olivier/ABB-4434-2021 ALEXANDRESCU, SORIN/0000-0002-2783-2769; Pulitano, Carlo/0000-0002-4420-4107; Groot Koerkamp, Bas/0000-0003-1917-6973; Pinto Marques, Hugo/0000-0003-3540-0488; Bagante, Fabio/0000-0002-5386-0958 26 35 38 0 10 MOSBY-ELSEVIER NEW YORK 360 PARK AVENUE SOUTH, NEW YORK, NY 10010-1710 USA 0039-6060 SURGERY Surgery DEC 2019.0 166.0 6 983 990 10.1016/j.surg.2019.06.005 0.0 8 Surgery Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) Surgery JS6JG 31326191.0 2023-03-23 WOS:000500410900004 0
C Hu, J; Wu, ZH; Tan, V; Lu, ZL; Zeng, MZ; Wu, EH Assoc Advancement Artificial Intelligence Hu, Jie; Wu, Ziheng; Tan, Vince; Lu, Zhilin; Zeng, Mengze; Wu, Enhua Elastic-Link for Binarized Neural Network THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence FEB 22-MAR 01, 2022 ELECTR NETWORK Assoc Advancement Artificial Intelligence Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 x 1 convolutional layers, for which standard binarization methods do not work well. Because 1 x 1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an ElasticLink (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids convergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy. [Hu, Jie; Wu, Enhua] ISCAS, State Key Lab Comp Sci, Selestat, France; [Hu, Jie; Wu, Enhua] Univ Chinese Acad Sci, Beijing, Peoples R China; [Lu, Zhilin] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China; [Wu, Ziheng] Alibaba Grp, Hangzhou, Peoples R China; [Tan, Vince; Zeng, Mengze] ByteDance Inc, Beijing, Peoples R China; [Wu, Enhua] Univ Macau, Taipa, Macao, Peoples R China Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Tsinghua University; Alibaba Group; University of Macau Wu, EH (corresponding author), ISCAS, State Key Lab Comp Sci, Selestat, France.;Wu, EH (corresponding author), Univ Chinese Acad Sci, Beijing, Peoples R China.;Wu, EH (corresponding author), Univ Macau, Taipa, Macao, Peoples R China. hujie@ios.ac.cn; zhoulou.wzh@alibaba-inc.com; vince.tan@bytedance.com; zeitzmz@gmail.com; ehw@um.edu.mo NSFC [62072449, 61972271]; Macao FDCT Grant [0018/2019/AKP]; Guangdong-Hongkong-Macao Joint Research Grant [2020B1515130004] NSFC(National Natural Science Foundation of China (NSFC)); Macao FDCT Grant; Guangdong-Hongkong-Macao Joint Research Grant The work is supported in part by NSFC Grants (62072449, 61972271), Macao FDCT Grant (0018/2019/AKP) and Guangdong-Hongkong-Macao Joint Research Grant (2020B1515130004). 34 0 0 0 0 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-876-3 AAAI CONF ARTIF INTE 2022.0 942 950 9 Computer Science, Artificial Intelligence Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BU3UU 2023-03-23 WOS:000893636201002 0
J Yuan, JF; Xie, H; Yang, DJ; Xiahou, X; Skibniewski, MJ; Huang, W Yuan, Jingfeng; Xie, Hong; Yang, Dujuan; Xiahou, Xiaer; Skibniewski, Miroslaw J.; Huang, Wei STRATEGY FORMULATION FOR THE SUSTAINABLE DEVELOPMENT OF SMART CITIES: A CASE STUDY OF NANJING, CHINA INTERNATIONAL JOURNAL OF STRATEGIC PROPERTY MANAGEMENT English Article smart cities; SWOT; AHP; strategy formulation; sustainable development SWOT ANALYSIS; BIG DATA; CITY; TECHNOLOGY; FRAMEWORK; BARRIERS; MODEL; INDIA Smart cities possess huge potential in future urban development. However, the critical problems in developing sustainable smart cities in China are the lack of clear strategies and effective strategy planning tools. Therefore, reasonable plans and strategics play important roles in helping the government develop sustainable smart cities. In this study, 16 SWOT (Strength, Weakness, Opportunity, and Threat) factors were identified. According to a structured questionnaire survey about SWOT factors, an integrated method, composed of SWOT and AHP (analytic hierarchy process) was conducted. A case study was conducted in Nanjing, China. The most powerful facilitators (strengths and opportunities) and the most powerful obstacles (weaknesses and threats) for developing Smart Nanjing City (SNC) were identified. The strategic intensity and value of the elements were confirmed. Moreover, proactive strategies were proposed including strengthening intelligent clusters, establishing governance ecosystem, and providing integrated services for SNC, which can be considered as policy suggestions for SNC, providing hybrid strategies for a planning approach integrating bottom-up and top-down design to develop smart cities, as a reference for other global cities. Moreover, the proposed AHP-SWOT hybrid method can be used as an effective quantitative strategy planning tool to help other authorities determine appropriate strategies for developing smart cities. [Yuan, Jingfeng; Xie, Hong; Xiahou, Xiaer] Southeast Univ, Sch Civil Engn, Dept Construct & Real Estate, Nanjing 210096, Peoples R China; [Yang, Dujuan] Tech Univ Eindhoven, Dept Built Environm, NL-5600 MB Eindhoven, Netherlands; [Skibniewski, Miroslaw J.] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA; [Skibniewski, Miroslaw J.] Polish Acad Sci, Inst Theoret & Appl Informat, PL-44100 Gliwice, Poland; [Skibniewski, Miroslaw J.] Chaoyang Univ Technol, Taichung 41349, Taiwan; [Huang, Wei] Sanjiang Univ, Sch Civil Engn, Nanjing 210012, Peoples R China Southeast University - China; Eindhoven University of Technology; University System of Maryland; University of Maryland College Park; Polish Academy of Sciences; Institute of Theoretical & Applied Informatics of the Polish Academy of Sciences; Chaoyang University of Technology; Sanjiang University Yuan, JF (corresponding author), Southeast Univ, Sch Civil Engn, Dept Construct & Real Estate, Nanjing 210096, Peoples R China. jingfeng-yuan@seu.edu.cn National Natural Science Foundation of China, NSFC [71671042, 71472037]; National Social Science Foundation of China [19CGL065]; Social Science Foundation of Jiangsu Province, China [13GLB005]; Fundamental Research Funds for the Central Universities; Postgraduate Science and Innovation Foundation of Jiangsu Province [KYLX_0206] National Natural Science Foundation of China, NSFC(National Natural Science Foundation of China (NSFC)); National Social Science Foundation of China; Social Science Foundation of Jiangsu Province, China; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Postgraduate Science and Innovation Foundation of Jiangsu Province This work was supported by the funding as follows:; - under Grant [71671042, and 71472037];; - under Grant [19CGL065];; - under Grant [13GLB005];; -;; - under Grant [No. KYLX_0206]. 68 6 6 6 32 VILNIUS GEDIMINAS TECH UNIV VILNIUS SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA 1648-715X 1648-9179 INT J STRATEG PROP M Int. J. Strateg. Prop. Manag. 2020.0 24.0 6 379 399 10.3846/ijspm.2020.13345 0.0 21 Management Social Science Citation Index (SSCI) Business & Economics PB1LV gold 2023-03-23 WOS:000596091800001 0
J Yang, M; Guo, YM; Bloom, MS; Dharmagee, SC; Morawska, L; Heinrich, J; Jalaludin, B; Markevychd, I; Knibbsf, LD; Lin, S; Lan, SH; Jalava, P; Komppula, M; Roponen, M; Hirvonen, MR; Guan, QH; Liang, ZM; Yu, HY; Hu, LW; Yang, BY; Zeng, XW; Dong, GH Yang, Mo; Guo, Yu-Ming; Bloom, Michael S.; Dharmagee, Shyamali C.; Morawska, Lidia; Heinrich, Joachim; Jalaludin, Bin; Markevychd, Iana; Knibbsf, Luke D.; Lin, Shao; Lan, Steve Hung; Jalava, Pasi; Komppula, Mika; Roponen, Marjut; Hirvonen, Maija-Riitta; Guan, Qi-Hua; Liang, Zi-Mian; Yu, Hong-Yao; Hu, Li-Wen; Yang, Bo-Yi; Zeng, Xiao-Wen; Dong, Guang-Hui Is PM1 similar to PM2.5? A new insight into the association of PM1 and PM2.5 with children's lung function ENVIRONMENT INTERNATIONAL English Article PM1; Lung function; Children; China AIR-POLLUTION; PARTICULATE MATTER; CHINESE CHILDREN; BLOOD-PRESSURE; AMBIENT PM1; HYPERTENSION; RESPONSES Experimental data suggests that PM1 is more toxic than PM2.5 although the epidemiologic evidence suggests that the health associations are similar. However, few objective exposure data are available to compare the associations of PM1 and PM2.5 with children lung function. Our objectives are a) to evaluate associations between long-term exposure to PM1, PM2.5 and children's lung function, and b) to compare the associations between PM1 and PM2.5. From 2012 to 2013, we enrolled 6,740 children (7-14 years), randomly recruited from primary and middle schools located in seven cities in northeast China. We measured lung function including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (MMEF) utilizing two portable electronic spirometers. We dichotomized continuous lung function measures according the expected values for gender and age. The spatial resolution at which PM1 and PM2.5 estimated were estimated using a machine learning method and the temporal average concentrations were averaged from 2009 to 2012. A multilevel regression model was used to estimate the associations of PM1, PM2.5 exposure and lung function measures, adjusted for confounding factors. Associations with lower lung function were consistently larger for PM1 than for PM2.5. Adjusted odds ratios (OR) per interquartile range greater PM1 ranged from 1.53 for MMEF (95% confidence interval [CI]: 1.20-1.96) to 2.14 for FEV1 (95% CI: 1.66-2.76) and ORs for PM2.5 ranged from 1.36 for MMEF (95%CI: 1.12-1.66) to 1.82 for FEV1 (95%CI: 1.49-2.22), respectively. PM1 and PM2.5 had significant associations with FVC and FEV1 in primary school children, and on PEF and MMEF in middle school children. Long-term PM1 and PM2.5 exposure can lead to decreased lung function in children, and the associations of PM1 are stronger than PM2.5. Therefore, PM1 may be more hazardous to children's respiratory health than PM2.5 exposure. [Yang, Mo; Bloom, Michael S.; Yu, Hong-Yao; Hu, Li-Wen; Yang, Bo-Yi; Zeng, Xiao-Wen; Dong, Guang-Hui] Sun Yat Sen Univ, Guangdong Prov Engn Technol Res Ctr Environm Poll, Sch Publ Hlth, Dept Occupat & Environm Hlth, Guangzhou 510080, Peoples R China; [Guo, Yu-Ming] Monash Univ, Sch Publ Hlth & Prevent Med, Dept Epidemiol & Prevent Med, Melbourne, Vic, Australia; [Bloom, Michael S.; Lin, Shao] Univ Albany State Univ New York, Dept Environm Hlth Sci, Rensselaer, NY USA; [Bloom, Michael S.; Lin, Shao] Univ Albany State Univ New York, Dept Epidemiol & Biostat, Rensselaer, NY USA; [Dharmagee, Shyamali C.] Univ Melbourne, Ctr Epidemiol & Biostat, Sch Populat & Global Hlth, Allergy & Lung Hlth Unit, Melbourne, Vic 3010, Australia; [Dharmagee, Shyamali C.] Murdoch Children Res Inst, Melbourne, Vic 3010, Australia; [Morawska, Lidia] Queensland Univ Technol QUT, Int Lab Air Qual & Hlth, GPO Box 2434, Brisbane, Qld 4001, Australia; [Heinrich, Joachim] Ludwig Maximilians Univ Munchen, Inst & Clin Occupat Social & Environm Med, Univ Hosp, Ziemssenstr 1, D-80336 Munich, Germany; [Heinrich, Joachim] German Ctr Lung Res, Comprehens Pneumol Ctr Munich, Ziemssenstr 1, D-80336 Munich, Germany; [Jalaludin, Bin] Ctr Air Qual & Hlth Res & Evaluat, Glebe, NSW 2037, Australia; [Jalaludin, Bin] South Western Sydney Local Hlth Dist, Populat Hlth, Liverpool, NSW 2170, Australia; [Jalaludin, Bin] Ingham Inst Appl Med Res, Liverpool, NSW 2170, Australia; [Jalaludin, Bin] Univ New South Wales, Sch Publ Hlth & Community Med, Kensington, NSW 2052, Australia; [Markevychd, Iana] Jagiellonian Univ, Inst Psychol, Krakow, Poland; [Knibbsf, Luke D.] Univ Queensland, Sch Publ Hlth, Herston, Qld 4006, Australia; [Lan, Steve Hung] Chinese Univ Hong Kong, Stanley Ho Big Data Decis Analyt Res Ctr, Inst Environm Energy & Sustainabil, Dept Geog & Resource Management, Shatin, Peoples R China; [Jalava, Pasi; Hirvonen, Maija-Riitta] Univ Eastern Finland, Dept Environm & Biol Sci, Kuopio, Finland; [Komppula, Mika] Finnish Meteorol Inst, Kuopio, Finland; [Roponen, Marjut; Guan, Qi-Hua; Liang, Zi-Mian] Foshan Ctr Dis Control & Prevent, 3 Yingyin Rd, Chancheng Dist, Foshan, Peoples R China Sun Yat Sen University; Monash University; State University of New York (SUNY) System; State University of New York (SUNY) System; University of Melbourne; Murdoch Children's Research Institute; Queensland University of Technology (QUT); University of Munich; Ingham Institute for Applied Medical Research; University of New South Wales Sydney; Jagiellonian University; University of Queensland; Chinese University of Hong Kong; University of Eastern Finland; Finnish Meteorological Institute Dong, GH (corresponding author), Sun Yat Sen Univ, Sch Publ Hlth, Dept Prevent Med, Guangzhou Key Lab Environm Pollut & Hlth Risk Ass, 74 Zhongshan 2nd Rd, Guangzhou 510080, Peoples R China. donggh5@mail.sysu.edu.cn Guo, Yuming/I-8353-2018; Komppula, Mika/AAC-2571-2021; Morawska, Lidia/B-4140-2011; Heinrich, Joachim/N-1720-2013 Guo, Yuming/0000-0002-1766-6592; Komppula, Mika/0000-0002-4550-3061; Morawska, Lidia/0000-0002-0594-9683; Yang, Mo/0000-0002-2607-779X; Heinrich, Joachim/0000-0002-9620-1629 National Natural Science Foundation of China [81872583, 81872582, 81972992]; National Key Research and Development Program of China [2018YFE0106900, 2018YFC1004300]; Guangdong Provincial Natural Science Foundation Team Project [2018B030312005]; Science and Technology Planning Project of Guangdong Province [2019A050510017, 2018B05052007]; Science and Technology Planning Project of Guangzhou [201803010054, 201807010032]; Fundamental Research Funds for the Central Universities [19ykjc01]; Science and Technology Program of Zhongshan [2019B1110] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Key Research and Development Program of China; Guangdong Provincial Natural Science Foundation Team Project; Science and Technology Planning Project of Guangdong Province; Science and Technology Planning Project of Guangzhou; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Science and Technology Program of Zhongshan This work was supported by the National Natural Science Foundation of China (grant numbers 81872583, 81872582, and 81972992), the National Key Research and Development Program of China (grant numbers 2018YFE0106900 and 2018YFC1004300), the Guangdong Provincial Natural Science Foundation Team Project (grant number 2018B030312005), the Science and Technology Planning Project of Guangdong Province (grant numbers 2019A050510017 and 2018B05052007), the Science and Technology Planning Project of Guangzhou (grant numbers 201803010054 and 201807010032), the Fundamental Research Funds for the Central Universities (grant number 19ykjc01), and Science and Technology Program of Zhongshan (grant number 2019B1110). The sponsor or funding organization had no role in the design or conduct of this research. 33 20 20 9 36 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0160-4120 1873-6750 ENVIRON INT Environ. Int. DEC 2020.0 145.0 106092 10.1016/j.envint.2020.106092 0.0 8 Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology OE6IJ 32916413.0 Green Published, gold 2023-03-23 WOS:000580632000013 0
J Xu, HZ; Peng, XR; Liu, YR; Lei, X; Yu, J Xu, Hong-Zhou; Peng, Xue-Rui; Liu, Yun-Rui; Lei, Xu; Yu, Jing Sleep Quality Modulates the Association between Dynamic Functional Network Connectivity and Cognitive Function in Healthy Older Adults NEUROSCIENCE English Article aging; dynamic functional network connectivity; sleep; cognitive function; machine learning BASAL GANGLIA; DEFAULT MODE; PREFRONTAL CORTEX; BRAIN; VARIABILITY; PATTERNS; PREDICTION; COMPONENTS is associated with changes in sleep, brain activity, and cognitive function, as well as the association among these factors; however, the precise nature of these changes has not been elucidated. This study systematically investigated the modulatory effect of sleep on the relationship between brain functional network connectivity (FNC) and cognitive function in older adults. In total, 107 community-dwelling healthy older adults were recruited and assigned into poor sleep and good sleep groups based on the Pittsburgh Sleep Quality Index. The static functional network connectivity (sFNC), the temporal variability of dynamic FNC (dFNC) from variance (dFNC-var), and the dFNC from clustering state (dFNC-state) were calculated. Corresponding cognition-predictive models were constructed for each sleep group. dFNC but not sFNC, was able to significantly predict the cognitive function in older adults. Specifically, sleep played a modulatory role in the association between dFNC and cognitive function, with sleep-specific variations at both microscopic (i.e., specific edges) and macroscopic levels (i.e., specific states) of dFNC. (c) 2021 IBRO. Published by Elsevier Ltd. All rights reserved. [Xu, Hong-Zhou; Peng, Xue-Rui; Liu, Yun-Rui; Lei, Xu; Yu, Jing] Southwest Univ, Fac Psychol, Tiansheng Rd, Chongqing 400715, Peoples R China; [Yu, Jing] Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Beijing, Peoples R China; [Peng, Xue-Rui] Tech Univ Dresden, Fac Psychol, Lifespan Dev Neurosci, Dresden, Germany; [Liu, Yun-Rui] Univ Basel, Fac Psychol, Ctr Cognit & Decis Sci, Basel, Switzerland Southwest University - China; Chinese Academy of Sciences; Institute of Psychology, CAS; Technische Universitat Dresden; University of Basel Yu, J (corresponding author), Southwest Univ, Fac Psychol, Tiansheng Rd, Chongqing 400715, Peoples R China. helen12@swu.edu.cn Peng, Xuerui/GYJ-5861-2022 Peng, Xue-Rui/0000-0002-8780-0910; Liu, Yunrui/0000-0002-1981-4691 National Natural Science Foundation of China [31971007, 61961160705, 31971028]; CAS Key Laboratory of Mental Health, Institute of Psychology [KLMH2019K02] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); CAS Key Laboratory of Mental Health, Institute of Psychology This work was supported by the National Natural Science Foundation of China (31971007, 61961160705, 31971028) and the CAS Key Laboratory of Mental Health, Institute of Psychology (KLMH2019K02). 70 1 1 4 19 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0306-4522 1873-7544 NEUROSCIENCE Neuroscience JAN 1 2022.0 480.0 131 142 10.1016/j.neuroscience.2021.11.018 0.0 NOV 2021 12 Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology XJ4GA 34785273.0 2023-03-23 WOS:000726747400012 0
J Jungemann, C; Bonani, F; Cea, SM; Gnani, E; Hong, SM; Jin, S; Liu, XY; Moroz, V; Verhulst, A Jungemann, Christoph; Bonani, Fabrizio; Cea, Stephen M.; Gnani, Elena; Hong, Sung-Min; Jin, Seonghoon; Liu, Xiaoyan; Moroz, Victor; Verhulst, Anne Special Issue on New Simulation Methodologies for Next-Generation TCAD Tools Foreword IEEE TRANSACTIONS ON ELECTRON DEVICES English Editorial Material Technology computer-aided design (TCAD) is an integral part of the development process of semiconductor technologies and devices, a field which has become increasingly complex and heterogeneous. Processing of integrated circuits requires nowadays over 400 process steps, and the resulting devices often have an intricate 3-D structure and contain various specifically designed materials. The full device behavior can only be understood by considering effects on all length scales from atomistic (material properties, interfaces, defects, and so on), to nanometric (quantum confinement, non-bulk properties, tunneling, ballistic transport, and so on), to full-chip dimensions (strain, heat transport, and so on), and time scales from femtoseconds (scattering, ferroelectric switching time, and so on) to seconds (trapping times, degradation, and so on). Voltages, currents, and charges have been scaled to such low levels that statistical effects and process variations have a strong impact. Devices based on new materials (e.g., 2-D crystals) and physical principles (ferroelectrics, magnetic materials, qubits, and so on) challenge standard TCAD approaches. While the simulation methods developed by the physics community can describe the basic device behavior, they often lack important simulation capabilities like, for example, transient simulations or integration with other TCAD tools, and are often too slow for daily use. Due to the complexity of semiconductor technology, it becomes more and more difficult to assess the impact of a change in processing or device structure on circuit performance by looking at a single aspect of an isolated device under idealized conditions. Instead, a TCAD tool chain is required which can handle realistic device structures embedded in a chip environment. New methodologies are required for all aspects of TCAD to ensure an efficient tool chain covering from atomistic effects to circuit behavior based on flexible simulation models that can handle new materials, device principles, and the ensuing large-scale simulations and that make use of artificial intelligence for well-chosen (sub)routines to decrease the overall simulation time. This Special Issue features six invited and 18 regular papers that address these problems. [Jungemann, Christoph] Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany; [Bonani, Fabrizio] Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy; [Cea, Stephen M.] Intel Corp, Hillsboro, OR 97124 USA; [Gnani, Elena] Univ Bologna, I-40126 Bologna, Italy; [Hong, Sung-Min] Gwangju Inst Sci & Technol, Gwangju 500712, South Korea; [Jin, Seonghoon] Samsung, San Jose, CA 95134 USA; [Liu, Xiaoyan] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China; [Moroz, Victor] Synopsys, Mountain View, CA 94043 USA; [Verhulst, Anne] IMEC, B-3001 Leuven, Belgium RWTH Aachen University; Polytechnic University of Turin; Intel Corporation; University of Bologna; Gwangju Institute of Science & Technology (GIST); Samsung; Peking University; Synopsys Inc; IMEC Jungemann, C (corresponding author), Rhein Westfal TH Aachen, Fac Elect Engn & Informat Technol, D-52062 Aachen, Germany. Jungemanncj@ithe.rwth-aachen.de Bonani, Fabrizio/AAS-2617-2021; Bonani, Fabrizio/I-5937-2012; liu, xiao/HKE-9880-2023; liu, xiao/HMD-7454-2023; Liu, XiaoYan/GQA-7216-2022 Bonani, Fabrizio/0000-0002-4964-0100; Bonani, Fabrizio/0000-0002-4964-0100; Jungemann, Christoph/0000-0002-3423-4046 0 0 0 1 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9383 1557-9646 IEEE T ELECTRON DEV IEEE Trans. Electron Devices NOV 2021.0 68.0 11 5346 5349 10.1109/TED.2021.3116395 0.0 4 Engineering, Electrical & Electronic; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Physics WN3AU Bronze 2023-03-23 WOS:000711645500006 0
J Yang, H; Niu, ZK; Xiao, SL; Fang, JF; Liu, ZY; Fainsin, D; Yi, LL Yang, Hang; Niu, Zekun; Xiao, Shilin; Fang, Jiafei; Liu, Zhiyang; Fainsin, David; Yi, Lilin Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network JOURNAL OF LIGHTWAVE TECHNOLOGY English Article Gallium nitride; Optical fibers; Optical fiber dispersion; Generative adversarial networks; Optical fiber amplifiers; Mathematical model; Optical fiber networks; Data-driven; deep learning; fiber channel modeling; generative adversarial network (GAN); split-step Fourier method (SSFM) In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our investigation focuses on joint channel effects of attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve the success of GAN for channel modeling, we modify the loss function, design the condition vector of input, and address the mode collapse for the long-haul transmission. The effective architecture, parameters, and training skills of GAN are also displayed in the article. The results show that the proposed method can learn the accurate transfer function of the fiber channel. The transmission distance of modeling can be up to 1000 km and can be extended to arbitrary distance theoretically. Moreover, GAN shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions. Comparing the complexity of GAN with the split-step Fourier method (SSFM), the total multiplication number is only 2% of SSFM and the running time is less than 0.1 seconds for 1000-km transmission, versus 400 seconds using the SSFM under the same hardware and software conditions, which highlights the remarkable reduction in complexity of the fiber channel modeling. [Yang, Hang; Niu, Zekun; Xiao, Shilin; Fang, Jiafei; Liu, Zhiyang; Yi, Lilin] Shanghai Jiao Tong Univ, State Key Lab Adv Tical Commun Syst & Networks, Dept Elect Engn, Shanghai 200240, Peoples R China; [Fainsin, David] IMT Atlantique, F-44307 Nantes, France Shanghai Jiao Tong University; IMT - Institut Mines-Telecom; IMT Atlantique Xiao, SL (corresponding author), Shanghai Jiao Tong Univ, State Key Lab Adv Tical Commun Syst & Networks, Dept Elect Engn, Shanghai 200240, Peoples R China. hangyang@sjtu.edu.cn; zekunniu@sjtu.edu.cn; slxiao@sjtu.edu.cn; jiafeifang@sjtu.edu.cn; dextermorgen@sjtu.edu.cn; david.fainsin@imt-atlantique.net; lilinyi@sjtu.edu.cn Yang, Hang/0000-0002-5524-4227; Liu, Zhiyang/0000-0003-1990-028X; Niu, Zekun/0000-0002-6760-6395 National Key R&D Program of China [2018YFB1800904]; National Nature Science Fund of China [62071295, 61775137, 61431009, 61433009]; National 863 Hi-tech Project of China National Key R&D Program of China; National Nature Science Fund of China(National Natural Science Foundation of China (NSFC)); National 863 Hi-tech Project of China(National High Technology Research and Development Program of China) This work was supported in part by the National Key R&D Program of China under Grant 2018YFB1800904, in part by the National Nature Science Fund of China under Grant 62071295, Grant 61775137, Grant 61431009, Grant 61433009, and in part by the National 863 Hi-tech Project of China. 35 21 21 6 30 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0733-8724 1558-2213 J LIGHTWAVE TECHNOL J. Lightwave Technol. MAR 1 2021.0 39.0 5 1322 1333 10.1109/JLT.2020.3037905 0.0 12 Engineering, Electrical & Electronic; Optics; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Optics; Telecommunications QL9QQ Green Submitted 2023-03-23 WOS:000621415800009 0
J Li, BH; Garicano, J; Zheng, Y; Valero, E Li, Binghua; Garicano-Mena, Jesus; Zheng, Yao; Valero, Eusebio Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases ENERGIES English Article modal decompositions; turbulent flows; flow reconstruction; machine learning; clustering algorithms; spatial agglomeration; dynamic mode decomposition; feature detection DIRECT NUMERICAL-SIMULATION; PROPER ORTHOGONAL DECOMPOSITION; CHANNEL FLOW; TURBULENCE; STATISTICS Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or numerical flow data on a data-driven manner. Performing a DMD analysis requires handling matrices V is an element of RnpxN, where np and N are indicative of the spatial and temporal resolutions. The DMD analysis of a complex flow field requires long temporal sequences of well resolved data, and thus the memory footprint may become prohibitively large. In this contribution, the effect that principled spatial agglomeration (i.e., reduction in np via clustering) has on the results derived from the DMD analysis is investigated. We compare twelve different clustering algorithms on three testcases, encompassing different flow regimes: a synthetic flow field, a ReD=60 flow around a cylinder cross section, and a Re tau approximate to 200 turbulent channel flow. The performance of the clustering techniques is thoroughly assessed concerning both the accuracy of the results retrieved and the computational performance. From this assessment, we identify DBSCAN/HDBSCAN as the methods to be used if only relatively high agglomeration levels are affordable. On the contrary, Mini-batch K-means arises as the method of choice whenever high agglomeration np/np<< 1 is possible. [Li, Binghua; Zheng, Yao] Zhejiang Univ, Ctr Engn & Sci Computat, Sch Aeronaut & Astronaut, Hangzhou 310027, Peoples R China; [Li, Binghua; Garicano-Mena, Jesus; Valero, Eusebio] Univ Politecn Madrid, ETSI Aeronaut & Espacio, Madrid 28040, Spain Zhejiang University; Universidad Politecnica de Madrid Li, BH (corresponding author), Zhejiang Univ, Ctr Engn & Sci Computat, Sch Aeronaut & Astronaut, Hangzhou 310027, Peoples R China.;Li, BH; Garicano, J (corresponding author), Univ Politecn Madrid, ETSI Aeronaut & Espacio, Madrid 28040, Spain. libh@zju.edu.cn; jesus.garicano.mena@upm.es; yao.zheng@zju.edu.cn; eusebio.valero@upm.es valero, eusebio/G-9802-2015; Garicano-Mena, Jesús/K-3019-2016 valero, eusebio/0000-0002-1627-6883; Garicano-Mena, Jesús/0000-0002-7422-5320; Zheng, Yao/0000-0003-2126-2897; Li, Binghua/0000-0003-2910-122X Projects: Drag Reduction via Turbulent Boundary Layer Flow Control (DRAGY), 2016-2019, China-EU Aeronautical Cooperation project [GA-690623]; Ministry of Industry and Information Technology (MIIT), China; Directorate -General for Research and Innovation (DG RTD), European Commission; SIMOPAIL, Ministry of Innovation, Spain [RTI-2018-097075-B-100]; China Scholarship Council (CSC) [201806320222] Projects: Drag Reduction via Turbulent Boundary Layer Flow Control (DRAGY), 2016-2019, China-EU Aeronautical Cooperation project; Ministry of Industry and Information Technology (MIIT), China; Directorate -General for Research and Innovation (DG RTD), European Commission; SIMOPAIL, Ministry of Innovation, Spain; China Scholarship Council (CSC)(China Scholarship Council) This work is funded by Projects: Drag Reduction via Turbulent Boundary Layer Flow Control (DRAGY, GA-690623), 2016-2019, China-EU Aeronautical Cooperation project, co-funded by Ministry of Industry and Information Technology (MIIT), China, and Directorate -General for Research and Innovation (DG RTD), European Commission; and SIMOPAIL (Ref. RTI-2018-097075-B-100), Ministry of Innovation, Spain.Mr. B. Li was supported by the China Scholarship Council (CSC, No.201806320222). 55 6 6 0 4 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies MAY 2020.0 13.0 9 2134 10.3390/en13092134 0.0 23 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels LR5MP Green Published, gold 2023-03-23 WOS:000535739300012 0
J Gonzalez-Raya, T; Solano, E; Sanz, M Gonzalez-Raya, Tasio; Solano, Enrique; Sanz, Mikel Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials QUANTUM English Article NOISE-INDUCED SYNCHRONIZATION; COHERENCE RESONANCE; QUANTUM; RELIABILITY; BEHAVIOR; NETWORK The Hodgkin-Huxley model describes the conduction of the nervous impulse through the axon, whose membrane's electric response can be described employing multiple connected electric circuits containing capacitors, voltage sources, and conductances. These conductances depend on previous depolarizing membrane voltages, which can be identified with a memory resistive element called memristor. Inspired by the recent quantization of the memristor, a simplified Hodgkin-Huxley model including a single ion channel has been studied in the quantum regime. Here, we study the quantization of the complete Hodgkin-Huxley model, accounting for all three ion channels, and introduce a quantum source, together with an output waveguide as the connection to a subsequent neuron. Our system consists of two memristors and one resistor, describing potassium, sodium, and chloride ion channel conductances, respectively, and a capacitor to account for the axon's membrane capacitance. We study the behavior of both ion channel conductivities and the circuit voltage, and we compare the results with those of the single channel, for a given quantum state of the source. It is remarkable that, in opposition to the single-channel model, we are able to reproduce the voltage spike in an adiabatic regime. Arguing that the circuit voltage is a quantum variable, we find a purely quantum-mechanical contribution in the system voltage's second moment. This work represents a complete study of the Hodgkin-Huxley model in the quantum regime, establishing a recipe for constructing quantum neuron networks with quantum state inputs. This paves the way for advances in hardware-based neuromorphic quantum computing, as well as quantum machine learning, which might be more efficient resource-wise. [Gonzalez-Raya, Tasio; Solano, Enrique; Sanz, Mikel] Univ Basque Country, Dept Phys Chem, UPV EHU, Apartado 644, Bilbao 48080, Spain; [Solano, Enrique] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China; [Solano, Enrique] Shanghai Univ, Phys Dept, Shanghai 200444, Peoples R China; [Solano, Enrique] Ikerbasque, Basque Fdn Sci, Maria Diaz de Haro 3, Bilbao 48013, Spain University of Basque Country; Shanghai University; Shanghai University; Basque Foundation for Science Sanz, M (corresponding author), Univ Basque Country, Dept Phys Chem, UPV EHU, Apartado 644, Bilbao 48080, Spain. mikel.sanz@ehu.eus Sanz, Mikel/W-8637-2019 Sanz, Mikel/0000-0003-1615-9035; Gonzalez-Raya, Tasio/0000-0001-9645-9681 Spanish Government (MCIU/AEI/FEDER, UE) [PGC2018-095113-B-I00]; Basque Government [IT986-16]; project QMiCS of the EU Flagship on Quantum Technologies [820505]; project OpenSuperQ of the EU Flagship on Quantum Technologies [820363]; EU FET Open Grant Quromorphic; U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program [ERKJ333] Spanish Government (MCIU/AEI/FEDER, UE); Basque Government(Basque Government); project QMiCS of the EU Flagship on Quantum Technologies; project OpenSuperQ of the EU Flagship on Quantum Technologies; EU FET Open Grant Quromorphic; U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program The authors acknowledge support from Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE) and Basque Government IT986-16. The authors also acknowledge support from the projects QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies, as well as the EU FET Open Grant Quromorphic. This work is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program, under field work proposal number ERKJ333. 46 3 3 1 11 VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF WIEN BOLTZMANNGASSE 3, WIEN, 1090, AUSTRIA 2521-327X QUANTUM-AUSTRIA Quantum JAN 20 2020.0 4.0 224 10.22331/q-2020-01-20-224 0.0 13 Quantum Science & Technology; Physics, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Physics KH4NG Green Submitted, gold 2023-03-23 WOS:000510624700001 0
C Wu, L; Ledent, A; Lei, YW; Kloft, M Assoc Advancement Artificial Intelligence Wu, Liang; Ledent, Antoine; Lei, Yunwen; Kloft, Marius Fine-grained Generalization Analysis of Vector-valued Learning THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE AAAI Conference on Artificial Intelligence English Proceedings Paper 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence FEB 02-09, 2021 ELECTR NETWORK Assoc Advancement Artificial Intelligence RADEMACHER COMPLEXITY Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss functions and low-noise condition. To understand the interaction between optimization and learning, we further use our results to derive the first generalization bounds for stochastic gradient descent with vector-valued functions. We apply our general results to multi-class classification and multi-label classification, which yield the first bounds with a logarithmic dependency on the output dimension for extreme multi-label classification with the Frobenius regularization. As a byproduct, we derive a Rademacher complexity bound for loss function classes defined in terms of a general strongly convex function. [Wu, Liang] Southwestern Univ Finance & Econ, Ctr Stat Res, Sch Stat, Chengdu 611130, Peoples R China; [Ledent, Antoine; Lei, Yunwen; Kloft, Marius] TU Kaiserslautern, Dept Comp Sci, D-67653 Kaiserslautern, Germany; [Lei, Yunwen] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England Southwestern University of Finance & Economics - China; University of Kaiserslautern; University of Birmingham Lei, YW (corresponding author), TU Kaiserslautern, Dept Comp Sci, D-67653 Kaiserslautern, Germany.;Lei, YW (corresponding author), Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England. wuliang@swufe.edu.cn; ledent@cs.uni-kl.de; y.lei@bham.ac.uk; kloft@cs.uni-kl.de Lei, Yunwen/V-2782-2018 Lei, Yunwen/0000-0002-5383-467X National Natural Science Foundation of China [61903309, 61806091, 11771012]; Fundamental Research Funds for the Central Universities [JBK1806002]; Alexander von Humboldt Foundation; Carl-Zeiss Foundation; German Research Foundation (DFG) [KL 2698/2-1]; Federal Ministry of Science and Education (BMBF) [01IS18051A, 031B0770E] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Alexander von Humboldt Foundation(Alexander von Humboldt Foundation); Carl-Zeiss Foundation; German Research Foundation (DFG)(German Research Foundation (DFG)); Federal Ministry of Science and Education (BMBF)(Federal Ministry of Education & Research (BMBF)) LW and YL acknowledge support by the National Natural Science Foundation of China (Grant Nos. 61903309, 61806091, 11771012) and the Fundamental Research Funds for the Central Universities (JBK1806002). YL also acknowledges support by the Alexander von Humboldt Foundation. MK acknowledges support by the Carl-Zeiss Foundation, by the German Research Foundation (DFG) award KL 2698/2-1, and by the Federal Ministry of Science and Education (BMBF) awards 01IS18051A and 031B0770E. The corresponding author is Yunwen Lei. 50 1 1 0 1 ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE PALO ALTO 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA 2159-5399 2374-3468 978-1-57735-866-4 AAAI CONF ARTIF INTE 2021.0 35.0 10338 10346 9 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Education & Educational Research BS0DR 2023-03-23 WOS:000681269802003 0
C Li, C; Zhang, Q; Zhao, ZP; Gu, L; Cummins, N; Schuller, B IEEE Li, Chao; Zhang, Qian; Zhao, Ziping; Gu, Li; Cummins, Nicholas; Schuller, Bjoern Analysing and Inferring of Intimacy Based on fNIRS Signals and Peripheral Physiological Signals 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) IEEE International Joint Conference on Neural Networks (IJCNN) English Proceedings Paper International Joint Conference on Neural Networks (IJCNN) JUL 14-19, 2019 Budapest, HUNGARY Intimacy; Functional near-infrared spectroscopy signals; Physiological signals; General Linear Model; Complex Brain Network Analysis Intimacy refers to a relatively long-lasting affinity relationship between individuals, which involves complex neuronal activities and physiological changes in the body. Recent advancements in the field of neuroimaging have demonstrated that functional near-infrared spectroscopy (fNIRS) has excellent potential for intimate relationship analysis. Signals such as fNIRS and physiological signals are increasingly utilised in this regard due to their consistency and complementarity. In this paper, first, we apply fNIRS and physiological database collected from 26 subjects when viewing lover, friend and stranger pictures to analyse and infer the intimacy. Then, the time domain information from both the fNIRS and physiological signals are utilised to exploit the representation of intimacy by General Linear Model (GLM) and Complex Brain Network Analysis (CBNA) methods. Based on these two methods, the intimacy can be analysed with different brain activation patterns. Finally, different machine learning techniques are utilised to predict the intimate relationship. The results demonstrate that multi-modal features are more efficient for intimacy research. Moreover, the average classification accuracy of ensemble learning is 98.72% whereas for KNN it is 91.03%. [Li, Chao; Zhang, Qian; Zhao, Ziping] Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin, Peoples R China; [Gu, Li] Tianjin Normal Univ, Inst Psychol & Behav, Tianjin, Peoples R China; [Cummins, Nicholas; Schuller, Bjoern] Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, Augsburg, Germany; [Schuller, Bjoern] Imperial Coll London, Grp Language Audio & Mus, London, England Tianjin Normal University; Tianjin Normal University; University of Augsburg; Imperial College London Zhao, ZP (corresponding author), Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin, Peoples R China. ztianjin@126.com Schuller, Bjorn/0000-0002-6478-8699 National Natural Science Foundation of China [61702370]; Key Program of the Natural Science Foundation of Tianjin [18JCZDJC36300]; Open Projects Program of the National Laboratory of Pattern Recognition; Senior Visiting Scholar Program of Tianjin Normal University National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Program of the Natural Science Foundation of Tianjin; Open Projects Program of the National Laboratory of Pattern Recognition; Senior Visiting Scholar Program of Tianjin Normal University The work presented in this paper was substantially supported by the National Natural Science Foundation of China (Grant No: 61702370), the Key Program of the Natural Science Foundation of Tianjin (Grant No. 18JCZDJC36300), the Open Projects Program of the National Laboratory of Pattern Recognition and the Senior Visiting Scholar Program of Tianjin Normal University.Chao Li and Qian Zhang share joint first authorship. Ziping Zhao is the corresponding author. 25 1 2 0 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2161-4393 978-1-7281-1985-4 IEEE IJCNN 2019.0 8 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering BO9HA 2023-03-23 WOS:000530893805093 0
J Liu, JG; Zhou, YL; Valach, A; Shortt, R; Kasak, K; Rey-Sanchez, C; Hemes, KS; Baldocchi, D; Lai, DYF Liu, Jiangong; Zhou, Yulun; Valach, Alex; Shortt, Robert; Kasak, Kuno; Rey-Sanchez, Camilo; Hemes, Kyle S.; Baldocchi, Dennis; Lai, Derrick Y. F. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half GLOBAL CHANGE BIOLOGY English Article carbon emissions; coastal wetland; eddy covariance; global warming potential; greenhouse gas; mangrove; methane; random forest NET ECOSYSTEM EXCHANGE; GLOBAL WARMING POTENTIALS; EDDY COVARIANCE TECHNIQUE; MAI-PO MARSHES; NITROUS-OXIDE; CARBON-DIOXIDE; ENVIRONMENTAL DRIVERS; TEMPORAL VARIATION; TROPICAL MANGROVE; FLUX MEASUREMENTS The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO2) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH4) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH(4)flux (F-CH4) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale F(CH4)in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F(CH4)reached a peak of over 0.1 g CH4-C m(-2) day(-1)during the summertime owing to a combination of high temperature and low salinity, while the wintertime F(CH4)was negligible. In this mangrove, the mean annual CH(4)emission was 11.7 +/- 0.4 g CH4-C m(-2) year(-1)while the annual net ecosystem CO(2)exchange ranged between -891 and -690 g CO2-C m(-2) year(-1), indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F(CH4)could offset the negative radiative forcing caused by CO(2)uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F(CH4)could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale F(CH4)in a mangrove wetland with long-term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH(4)emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests. [Liu, Jiangong; Zhou, Yulun; Lai, Derrick Y. F.] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China; [Valach, Alex; Shortt, Robert; Kasak, Kuno; Rey-Sanchez, Camilo; Baldocchi, Dennis] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA; [Kasak, Kuno] Univ Tartu, Inst Ecol & Earth Sci, Dept Geog, Tartu, Estonia; [Hemes, Kyle S.] Stanford Univ, Stanford Woods Inst Environm, Stanford, CA 94305 USA; [Lai, Derrick Y. F.] Chinese Univ Hong Kong, Ctr Environm Policy & Resource Management, Shatin, Hong Kong, Peoples R China Chinese University of Hong Kong; University of California System; University of California Berkeley; University of Tartu; Tartu University Institute of Ecology & Earth Sciences; Stanford University; Chinese University of Hong Kong Lai, DYF (corresponding author), Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China. dyflai@cuhk.edu.hk Kasak, Kuno/AAH-6189-2020; Baldocchi, Dennis/A-1625-2009; Liu, Jiangong/ABD-3102-2020; Valach, Alex/AAX-7438-2020; Lai, Derrick Y.F./B-1387-2009 Kasak, Kuno/0000-0002-0810-2154; Baldocchi, Dennis/0000-0003-3496-4919; Valach, Alex/0000-0003-4782-5766; Yulun, ZHOU/0000-0002-9607-2185; Lai, Derrick Y.F./0000-0002-1225-9904; Jiangong, Liu/0000-0002-4148-9522; Rey-Sanchez, Camilo/0000-0003-4762-9001 Eesti Teadusagentuur [PRG-352, PUTJD715]; Research Grants Council, University Grants Committee [CUHK458913] Eesti Teadusagentuur(Estonian Research Council); Research Grants Council, University Grants Committee Eesti Teadusagentuur, Grant/Award Number: PRG-352 and PUTJD715; Research Grants Council, University Grants Committee, Grant/Award Number: CUHK458913 154 17 17 28 162 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1354-1013 1365-2486 GLOBAL CHANGE BIOL Glob. Change Biol. SEP 2020.0 26.0 9 4998 5016 10.1111/gcb.15247 0.0 JUL 2020 19 Biodiversity Conservation; Ecology; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED) Biodiversity & Conservation; Environmental Sciences & Ecology MX8JP 32574398.0 2023-03-23 WOS:000548280100001 0
J Kostka, G; Zhang, CM Kostka, Genia; Zhang, Chunman Tightening the grip: environmental governance under Xi Jinping INTRODUCTION ENVIRONMENTAL POLITICS English Editorial Material Environmental governance; transition; environmental policy; environmental state; China INFORMATION DISCLOSURE; CHINA; IMPLEMENTATION; ENFORCEMENT; POLITICS; STATE; LAW In the space of a few years, China's global image with regard to environmental matters has significantly improved. Particularly since Xi Jinping's coming-to-power in 2012 China's reputation in the global climate change regime has improved markedly and it has gained accolades for a new determination to reverse environmental degradation at home. China's incipient green transformation is partly due to a new actor constellation in environmental governance, a striking feature of which is the prominence of ad hoc campaigns that offer quick results but that may undermine the creation of law-based enforcement mechanisms in the long term. Another development - China's increasing use of emerging technologies and big data analytics - has given rise to new forms of government-business alliances. These new players and innovative approaches have injected momentum into China's environmental governance system and suggest that, contrary to conventional wisdom, authoritarian regimes can be responsive to citizen demands under certain circumstances. Yet it remains to be seen whether long-term environmental goals can be met, due to a pervasive lack of accountability, the weakening of civil society and heavy constraints on public participation. [Kostka, Genia] Free Univ Berlin, Inst China Studies, Berlin, Germany; Fudan Univ, Inst Adv Study Social Sci, Shanghai, Peoples R China Free University of Berlin; Fudan University Kostka, G (corresponding author), Free Univ Berlin, Inst China Studies, Berlin, Germany. genia.kostka@fu-berlin.de Kostka, Genia/GQP-0139-2022 Kostka, Genia/0000-0002-3696-9513 42 86 86 10 66 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0964-4016 1743-8934 ENVIRON POLIT Environ. Polit. 2018.0 27.0 5 769 781 10.1080/09644016.2018.1491116 0.0 13 Environmental Studies; Political Science Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Government & Law GP2ZM Bronze 2023-03-23 WOS:000440707200001 0
J Wang, YW; Gogu, C; Binaud, N; Bes, C; Fu, J Wang, Yiwei; Gogu, Christian; Binaud, Nicolas; Bes, Christian; Fu, Jian A model-based prognostics method for fatigue crack growth in fuselage panels CHINESE JOURNAL OF AERONAUTICS English Article Aircraft fuselage panels; Extended Kalman filter; Fatigue crack propagation; Linearization method; Model-based prognostics REMAINING-USEFUL-LIFE; PARIS LAW CONSTANTS; NEURAL-NETWORK; KALMAN FILTER; PREDICTION; EVOLUTION; SYSTEM; REGRESSION; DAMAGE This paper proposes a model-based prognostics method that couples the Extended Kalman Filter (EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo (MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful. (C) 2019 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. [Wang, Yiwei; Fu, Jian] Beihang Univ, Sch Mech Engn & Automat, Beijing 100083, Peoples R China; [Wang, Yiwei; Gogu, Christian; Binaud, Nicolas; Bes, Christian] Univ Toulouse, Inst Clement Ader, CNRS, UMR 5312,INSA,UPS,ISAE,Mines Albi, F-31400 Toulouse, France Beihang University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); IMT - Institut Mines-Telecom; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; IMT Mines Albi; Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO); Universite Toulouse III - Paul Sabatier; Institut National des Sciences Appliquees de Toulouse Fu, J (corresponding author), Beihang Univ, Sch Mech Engn & Automat, Beijing 100083, Peoples R China. fujian@buaa.edu.cn Binaud, Nicolas/ABG-5530-2020 BINAUD, Nicolas/0000-0001-5535-3063 National Natural Science Foundation of China [51805262] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) The present work was partially funded by the National Natural Science Foundation of China (No. 51805262). 49 4 5 1 9 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 1000-9361 CHINESE J AERONAUT Chin. J. Aeronaut. FEB 2019.0 32.0 2 396 408 10.1016/j.cja.2018.11.010 0.0 13 Engineering, Aerospace Science Citation Index Expanded (SCI-EXPANDED) Engineering HM9HL hybrid 2023-03-23 WOS:000459794000015 0
J Bian, XQ; Han, B; Du, ZM; Jaubert, JN; Li, MJ Bian, Xiao-Qiang; Han, Bing; Du, Zhi-Min; Jaubert, Jean-Noel; Li, Ming-Jun Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams FUEL English Article Minimum miscibility pressure; CO2; Genetic algorithm; Support vector regression; Correlation EQUATION-OF-STATE; NEURAL-NETWORK MODEL; RESERVOIR FLUIDS; PVT PROPERTIES; PENG-ROBINSON; PREDICTION; DISPLACEMENT; PARAMETERS; SYSTEMS; K(IJ) Accurate knowledge of the minimum miscibility pressure (MMP) is essential in successful design of any miscible gas injection process, particularly in CO2 flooding. It is however well-acknowledged that experimental measurements are expensive, time-consuming, and cumbersome. As a direct consequence, a support vector regression model combined with genetic algorithm (GA-SVR) was proposed to predict pure and impure CO2-crude oil MMP. The accuracy and reliability of the proposed model were evaluated through 150 data sets collected in the open literature and compared with approaches commonly used to estimate the MMP (Lee correlation, Shokir correlation, Orr-Jensen correlation, Yellig-Metcalfe correlation, Alston correlation, Emera-Sarma correlation, Cronquist correlation, Kamari et al. correlation, and Fathinasab-Ayatollahi correlation). The results showed that the proposed model for predicting the MMP is in excellent agreement with experimental data and outperforms all the existing methods considered in this work in prediction of pure and impure CO2-oil MMP. Furthermore, outlier diagnosis was pet formed on the whole data sets to identify the applicable range of all models investigated in this work by detecting the probable doubtful MMP data. (C) 2016 Elsevier Ltd. All rights reserved. [Bian, Xiao-Qiang; Han, Bing; Du, Zhi-Min] Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploit, Chengdu 610500, Peoples R China; [Bian, Xiao-Qiang; Jaubert, Jean-Noel] Univ Lorraine, Ecole Natl Super Ind Chim, Lab React & Genie Proc, UMR 7274,CNRS, 1 Rue Grandville, F-54000 Nancy, France; [Li, Ming-Jun] China CNOOC Ltd Zhanjiang, Zhanjiang 524057, Peoples R China Southwest Petroleum University; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Lorraine; China National Offshore Oil Corporation (CNOOC) Bian, XQ; Du, ZM (corresponding author), Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploit, Chengdu 610500, Peoples R China. xiaoqiang.bian@univ-lorraine.fr; duzhimin@swpu.edu.cn JAUBERT, Jean-Noel/H-1399-2011 JAUBERT, Jean-Noel/0000-0001-7831-5684; Bian, Xiaoqiang/0000-0003-4995-8159 National Natural Science Foundation of China [51404205]; Open Fund (PLN 1207) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University); Specialized Research Fund for the Doctoral Program of Higher Education [20125121120002] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fund (PLN 1207) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University); Specialized Research Fund for the Doctoral Program of Higher Education(Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)) This work was supported by National Natural Science Foundation of China (No. 51404205), Open Fund (PLN 1207) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University), and Specialized Research Fund for the Doctoral Program of Higher Education (No. 20125121120002). 87 45 47 3 55 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0016-2361 1873-7153 FUEL Fuel OCT 15 2016.0 182.0 550 557 10.1016/j.fuel.2016.05.124 0.0 8 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering DQ3LA 2023-03-23 WOS:000379102600060 0
C Zou, X; Zheng, QK; Dong, YX; Guan, XY; Kharlamov, EG; Lu, JL; Tang, J ASSOC COMP MACHINERY Zou, Xu; Zheng, Qinkai; Dong, Yuxiao; Guan, Xinyu; Kharlamov, Evgeny; Lu, Jialiang; Tang, Jie TDGIA: Effective Injection Attacks on Graph Neural Networks KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING English Proceedings Paper 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) AUG 14-18, 2021 ELECTR NETWORK Assoc Comp Machinery,ACM SIGMOD,ACM SIGKDD Graph Neural Networks; Adversarial Machine Learning; Graph Injection Attack; Graph Mining; Graph Adversarial Attack Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. However, recent studies find that GNNs are vulnerable to adversarial attacks. In this paper, we study a recently-introduced realistic attack scenario on graphs graph injection attack (GIA). In the GIA scenario, the adversary is not able to modify the existing link structure or node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it. We present an analysis on the topological vulnerability of GNNs under GIA setting, based on which we propose the Topological Defective Graph Injection Attack (TDGIA) for effective injection attacks. TDGIA first introduces the topological defective edge selection strategy to choose the original nodes for connecting with the injected ones. It then designs the smooth feature optimization objective to generate the features for the injected nodes. Extensive experiments on large-scale datasets show that TDGIA can consistently and significantly outperform various attack baselines in attacking dozens of defense GNN models. Notably, the performance drop on target GNNs resultant from TDGIA is more than double the damage brought by the best attack solution among hundreds of submissions on KDD-CUP 2020. [Zou, Xu; Tang, Jie] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China; [Dong, Yuxiao] Facebook AI, Seattle, WA USA; [Zheng, Qinkai; Lu, Jialiang] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Guan, Xinyu] Biendata, New York, NY USA; [Kharlamov, Evgeny] Bosch Ctr Artificial Intelligence, Stuttgart, Germany Tsinghua University; Facebook Inc; Shanghai Jiao Tong University Tang, J (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China. zoux18@mails.tsinghua.edu.cn; qinkai.zheng1028@gmail.com; ericdongyx@gmail.com; guanxinyu@gmail.com; evgeny.kharlamov@de.bosch.com; jialiang.lu@sjtu.edu.cn; jietang@tsinghua.edu.cn National Natural Science Foundation of China [61836013]; National Science Foundation for Distinguished Young Scholars [61825602]; Tsinghua-Bosch Joint ML Center National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); National Science Foundation for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); Tsinghua-Bosch Joint ML Center This work is funded by National Natural Science Foundation of China(Key Program, No. 61836013), National Science Foundation for Distinguished Young Scholars (No. 61825602) and Tsinghua-Bosch Joint ML Center. We thank Yang Yang for helpful discussions, Shuwen Liu and Yicheng Zhao for the dataset preparation work on KDD CUP 2020, and all participants of KDD CUP 2020 ML2 Track. 26 4 4 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-8332-5 2021.0 2461 2471 10.1145/3447548.3467314 0.0 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BS6LU Green Submitted 2023-03-23 WOS:000749556802050 0
J Chao, ZC; Takaura, K; Wang, LP; Fujii, N; Dehaene, S Chao, Zenas C.; Takaura, Kana; Wang, Liping; Fujii, Naotaka; Dehaene, Stanislas Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain NEURON English Article NEURAL REPRESENTATION; PREFRONTAL CORTEX; GAMMA ACTIVITY; VISUAL-CORTEX; EEG-DATA; FEEDBACK; OSCILLATIONS; EXPECTATION; FEEDFORWARD; PERCEPTION According to predictive-coding theory, cortical areas continuously generate and update predictions of sensory inputs at different hierarchical levels and emit prediction errors when the predicted and actual inputs differ. However, predictions and prediction errors are simultaneous and interdependent processes, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using high-density electrocortico-graphy (ECoG) in monkeys during an auditory localglobal'' paradigm in which the temporal regularities of the stimuli were controlled at two hierarchical levels. We decomposed the broadband data and identified lower-and higher-level prediction-error signals in early auditory cortex and anterior temporal cortex, respectively, and a prediction-update signal sent from prefrontal cortex back to temporal cortex. The prediction-error and prediction-update signals were transmitted via gamma (>40 Hz) and alpha/beta (<30 Hz) oscillations, respectively. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented using distinct cortical areas and frequencies. [Chao, Zenas C.] Kyoto Univ, Grad Sch Med, Dept Neurosci, Kyoto 6068501, Japan; [Chao, Zenas C.] Kyoto Univ, Fac Med, Kyoto 6068501, Japan; [Chao, Zenas C.; Takaura, Kana; Fujii, Naotaka] RIKEN Brain Sci Inst, Wako, Saitama 3510198, Japan; [Wang, Liping] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Neurosci, Shanghai 200031, Peoples R China; [Dehaene, Stanislas] Univ Paris Saclay, Univ Paris Sud, INSERM, Cognit Neuroimaging Unit,CEA,DSV,I2BM,NeuroSpin C, F-91191 Gif Sur Yvette, France; [Dehaene, Stanislas] Coll France, F-75005 Paris, France Kyoto University; Kyoto University; RIKEN; Chinese Academy of Sciences; Shanghai Institutes for Biological Sciences, CAS; CEA; Institut National de la Sante et de la Recherche Medicale (Inserm); UDICE-French Research Universities; Universite Paris Saclay; UDICE-French Research Universities; Universite PSL; College de France Chao, ZC (corresponding author), Kyoto Univ, Grad Sch Med, Dept Neurosci, Kyoto 6068501, Japan.;Chao, ZC (corresponding author), Kyoto Univ, Fac Med, Kyoto 6068501, Japan.;Chao, ZC (corresponding author), RIKEN Brain Sci Inst, Wako, Saitama 3510198, Japan. zenas.c.chao@gmail.com Brain Science Project of the Center for Novel Science Initiatives (CNSI); Ministry of Education, Culture, Sports, Science, and Technology [21118002]; Inserm, CEA; Canadian Institute for Advanced Research (CIFAR); European Research Council (ERC); College de France; National institute of Natural Science (NINS) [BS261006]; Grants-in-Aid for Scientific Research [21118002] Funding Source: KAKEN Brain Science Project of the Center for Novel Science Initiatives (CNSI); Ministry of Education, Culture, Sports, Science, and Technology(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)); Inserm, CEA(French Atomic Energy CommissionInstitut National de la Sante et de la Recherche Medicale (Inserm)); Canadian Institute for Advanced Research (CIFAR)(Canadian Institute for Advanced Research (CIFAR)); European Research Council (ERC)(European Research Council (ERC)European Commission); College de France; National institute of Natural Science (NINS); Grants-in-Aid for Scientific Research(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) We thank Naomi Hasegawa and Tomonori Notoya for providing veterinary care and technical support. This work was supported by the Brain Science Project of the Center for Novel Science Initiatives (CNSI), National institute of Natural Science (NINS; Grant Number BS261006), and Ministry of Education, Culture, Sports, Science, and Technology Grant in Aid for Scientific Research on Innovative Areas (21118002). S.D. was supported by Inserm, CEA, College de France, the Canadian Institute for Advanced Research (CIFAR), and the European Research Council (ERC). 78 70 70 4 25 CELL PRESS CAMBRIDGE 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA 0896-6273 1097-4199 NEURON Neuron DEC 5 2018.0 100.0 5 1252 + 10.1016/j.neuron.2018.10.004 0.0 18 Neurosciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Neurosciences & Neurology HD1TV 30482692.0 Bronze 2023-03-23 WOS:000452295400024 0
J Baronchelli, I; Scarlata, CM; Rodriguez-Munoz, L; Bonato, M; Morselli, L; Vaccari, M; Carraro, R; Barrufet, L; Henry, A; Mehta, V; Rodighiero, G; Baruffolo, A; Bagley, M; Battisti, A; Colbert, J; Dai, YS; De Pascale, M; Dickinson, H; Malkan, M; Mancini, C; Rafelski, M; Teplitz, HI Baronchelli, I; Scarlata, C. M.; Rodriguez-Munoz, L.; Bonato, M.; Morselli, L.; Vaccari, M.; Carraro, R.; Barrufet, L.; Henry, A.; Mehta, V; Rodighiero, G.; Baruffolo, A.; Bagley, M.; Battisti, A.; Colbert, J.; Dai, Y. S.; De Pascale, M.; Dickinson, H.; Malkan, M.; Mancini, C.; Rafelski, M.; Teplitz, H., I Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES English Article WISP The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines. To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line. In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter). The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy. We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions. These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines. In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed. [Baronchelli, I; Bonato, M.; Morselli, L.; Baruffolo, A.; De Pascale, M.] INAF Osservatorio Astron Padova, Vicolo Osservatorio 5, I-35122 Padua, Italy; [Baronchelli, I; Rodriguez-Munoz, L.; Morselli, L.; Rodighiero, G.; Mancini, C.] Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy; [Scarlata, C. M.; Mehta, V] Univ Minnesota, MN Inst Astrophys, 116 Church St SE, Minneapolis, MN 55455 USA; [Bonato, M.] INAF Ist Radioastron, Via Gobetti 101, I-40129 Bologna, Italy; [Bonato, M.] Italian ALMA Reg Ctr, Via Gobetti 101, I-40129 Bologna, Italy; [Vaccari, M.] Interuniv Inst Data Intens Astron IDIA, Dept Phys & Astron, Cape Town, South Africa; [Vaccari, M.] Univ Western Cape, Robert Sobukwe Rd, ZA-7535 Cape Town, South Africa; [Carraro, R.] Univ Valparaiso, Inst Fis & Astron, Gran Bretana 1111, Valparaiso, Chile; [Barrufet, L.] Univ Geneva, Dept Astron, Chemin Pegasi 51, CH-1290 Versoix, Switzerland; [Henry, A.; Rafelski, M.] Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA; [Bagley, M.] Univ Texas Austin, Coll Nat Sci, 2515 Speedway, Austin, TX 78712 USA; [Battisti, A.] Australian Natl Univ, Res Sch Astron & Astrophys, Cotter Rd, Weston, ACT 2611, Australia; [Colbert, J.; Teplitz, H., I] CALTECH, IPAC, Mail Code 314-6,1200 E Calif Blvd, Pasadena, CA 91125 USA; [Dai, Y. S.] Chinese Acad Sci, South Amer Ctr Astron CASSACA NAOC, 20A Datun Rd, Beijing 100101, Peoples R China; [Dickinson, H.] Open Univ, Sch Phys Sci, Walton Hall, Milton Keynes MK7 6AA, Bucks, England; [Malkan, M.] Univ Calif Los Angeles, Dept Phys & Astron, Phys & Astron Bldg 3-714, Los Angeles, CA 90095 USA; [Mancini, C.] INAF IASF, Via Alfonso Corti 12, I-20133 Milan, Italy; [Rafelski, M.] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA Istituto Nazionale Astrofisica (INAF); University of Padua; University of Minnesota System; University of Minnesota Twin Cities; Istituto Nazionale Astrofisica (INAF); University of the Western Cape; Universidad de Valparaiso; University of Geneva; Space Telescope Science Institute; University of Texas System; University of Texas Austin; Australian National University; California Institute of Technology; Chinese Academy of Sciences; Open University - UK; University of California System; University of California Los Angeles; Johns Hopkins University Baronchelli, I (corresponding author), INAF Osservatorio Astron Padova, Vicolo Osservatorio 5, I-35122 Padua, Italy.;Baronchelli, I (corresponding author), Univ Padua, Dipartimento Fis & Astron, Vicolo Osservatorio 3, I-35122 Padua, Italy. ivano.baronchelli@inaf.it Vaccari, Mattia/R-3431-2016 Vaccari, Mattia/0000-0002-6748-0577; Rodriguez Munoz, Lucia/0000-0002-0192-5131; Barrufet, Laia/0000-0003-1641-6185; Battisti, Andrew/0000-0003-4569-2285; Henry, Alaina/0000-0002-6586-4446; Scarlata, Claudia/0000-0002-9136-8876; Mehta, Vihang/0000-0001-7166-6035; Dai, Y. Sophia/0000-0002-7928-416X; Baronchelli, Ivano/0000-0003-0556-2929; Baruffolo, Andrea/0000-0002-1114-4355; Carraro, Rosamaria/0000-0002-6089-1947; Bonato, Matteo/0000-0001-9139-2342 27 1 1 2 4 IOP Publishing Ltd BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0067-0049 1538-4365 ASTROPHYS J SUPPL S Astrophys. J. Suppl. Ser. DEC 2021.0 257.0 2 67 10.3847/1538-4365/ac250c 0.0 17 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics XM6WG Green Submitted, Green Accepted 2023-03-23 WOS:000728964100001 0
J Wang, H; Xu, YS; He, YG; Cai, YF; Chen, L; Li, YC; Sotelo, MA; Li, ZX Wang, Hai; Xu, Yansong; He, Youguo; Cai, Yingfeng; Chen, Long; Li, Yicheng; Sotelo, Miguel Angel; Li, Zhixiong YOLOv5-Fog: A Multiobjective Visual Detection Algorithm for Fog Driving Scenes Based on Improved YOLOv5 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT English Article Feature extraction; Detection algorithms; Atmospheric modeling; Autonomous vehicles; Training; Meteorology; Object detection; 2-D object detection; autonomous driving; complex traffic conditions; fog With the rapid development of deep learning in recent years, the level of automatic driving perception has also increased substantially. However, automatic driving perception under adverse conditions, such as fog, remains a significant obstacle. The existing fog-oriented detection algorithms are unable to simultaneously address the detection accuracy and detection speed. Based on improved YOLOv5, this work provides a multiobject detection network for fog driving scenes. We construct a synthetic fog dataset by using the dataset of a virtual scene and the depth information of the image. Second, we present a detection network for driving in fog based on improved YOLOv5. The ResNeXt model, which has been modified by structural re-parameterization, serves as the model's backbone. We build a new feature enhancement module (FEM) in response to the lack of features in fog scene images and use the attention mechanism to help the detection network pay more attention to the more useful features in the fog scenes. The test results show that the proposed fog multitarget detection network outperforms the original YOLOv5 in terms of detection accuracy and speed. The accuracy of the Real-world Task-driven Testing Set (RTTS) public dataset is 77.8%, and the detection speed is 31 frames/s, which is 14 frames faster as compared with the original YOLOv5. [Wang, Hai; Xu, Yansong] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang 212013, Jiangsu, Peoples R China; [He, Youguo; Cai, Yingfeng; Chen, Long; Li, Yicheng] Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China; [Sotelo, Miguel Angel] Univ Alcal, Dept Comp Engn, Alcal Henares, Madrid 28801, Spain; [Li, Zhixiong] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea; [Li, Zhixiong] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland Jiangsu University; Jiangsu University; Universidad de Alcala; Yonsei University; Opole University of Technology Cai, YF (corresponding author), Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China. wanghai1019@163.com; xu1157658889@163.com; hyg197715@163.com; caicaixiao0304@126.com; chenlong@ujs.edu.cn; liyucheng070@163.com; miguel.sotelo@uah.es; zhixiong.li@yonsei.ac.kr ; Sotelo, Miguel Angel/A-8663-2013 Cai, Yingfeng/0000-0002-0633-9887; Xu, Yansong/0000-0002-9652-2780; Sotelo, Miguel Angel/0000-0001-8809-2103; Wang, Hai/0000-0002-9136-8091 National Natural Science Foundation of China [U20A20333, 52072160, 51875255]; Key Research and Development Program of Jiangsu Province [BE2019010-2, BE2020083-3]; Jiangsu Province's Six Talent Peaks [TD-GDZB-022]; Zhenjiang Key Research and Development Program [GY2020006]; Narodowego Centrum Nauki Poland [2020/37/K/ST8/02748] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Program of Jiangsu Province; Jiangsu Province's Six Talent Peaks; Zhenjiang Key Research and Development Program; Narodowego Centrum Nauki Poland This work was supported in part by the National Natural Science Foundation of China under Grant U20A20333, Grant 52072160, and Grant 51875255; in part by the Key Research and Development Program of Jiangsu Province under Grant BE2019010-2 and Grant BE2020083-3; in part by the Jiangsu Province's Six Talent Peaks under Grant TD-GDZB-022; in part by the Zhenjiang Key Research and Development Program under Grant GY2020006; and in part by the Narodowego Centrum Nauki Poland under Grant 2020/37/K/ST8/02748. 54 5 5 65 73 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9456 1557-9662 IEEE T INSTRUM MEAS IEEE Trans. Instrum. Meas. 2022.0 71.0 2515612 10.1109/TIM.2022.3196954 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation 3X8FO 2023-03-23 WOS:000843270700021 0
J Salek, F; Hayashi, M; Winter, A Salek, Farzin; Hayashi, Masahito; Winter, Andreas Usefulness of adaptive strategies in asymptotic quantum channel discrimination PHYSICAL REVIEW A English Article STRONG CONVERSE; STEINS LEMMA; EXPONENTS; ENTROPY Adaptiveness is a key principle in information processing including statistics and machine learning. We investigate the usefulness adaptive methods in the framework of asymptotic binary hypothesis testing, when each hypothesis represents asymptotically many independent instances of a quantum channel, and the tests are based on using the unknown channel and observing outputs. Unlike the familiar setting of quantum states as hypotheses, there is a fundamental distinction between adaptive and nonadaptive strategies with respect to the channel uses, and we introduce a number of further variants of the discrimination tasks by imposing different restrictions on the test strategies. The following results are obtained: (1) We prove that for classical-quantum channels, adaptive and nonadaptive strategies lead to the same error exponents both in the symmetric (Chernoff) and asymmetric (Hoeffding, Stein) settings. (2) The first separation between adaptive and nonadaptive symmetric hypothesis testing exponents for quantum channels, which we derive from a general lower bound on the error probability for nonadaptive strategies; the concrete example we analyze is a pair of entanglement-breaking channels. (3) We prove, in some sense generalizing the previous statement, that for general channels adaptive strategies restricted to classical feed-forward and product state channel inputs are not superior in the asymptotic limit to nonadaptive product state strategies. (4) As an application of our findings, we address the discrimination power of an arbitrary quantum channel and show that adaptive strategies with classical feedback and no quantum memory at the input do not increase the discrimination power of the channel beyond nonadaptive tensor product input strategies. [Salek, Farzin] Tech Univ Munich, Zentrum Math, D-85748 Munich, Germany; [Hayashi, Masahito] Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China; [Hayashi, Masahito] Int Quantum Acad SIQA, Shenzhen 518048, Peoples R China; [Hayashi, Masahito] Nagoya Univ, Grad Sch Math, Nagoya, Aichi 4648602, Japan; [Winter, Andreas] Inst Catalana Recerca & Estudis Avancats ICREA, Pg Lluis Co 23, Barcelona 08010, Spain; [Winter, Andreas] Univ Autonoma Barcelona, Grp Informacio Quant, Dept Fis, Bellaterra 08193, Barcelona, Spain Technical University of Munich; Southern University of Science & Technology; Nagoya University; ICREA; Autonomous University of Barcelona Salek, F (corresponding author), Tech Univ Munich, Zentrum Math, D-85748 Munich, Germany.;Hayashi, M (corresponding author), Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China.;Hayashi, M (corresponding author), Int Quantum Acad SIQA, Shenzhen 518048, Peoples R China.;Hayashi, M (corresponding author), Nagoya Univ, Grad Sch Math, Nagoya, Aichi 4648602, Japan.;Winter, A (corresponding author), Inst Catalana Recerca & Estudis Avancats ICREA, Pg Lluis Co 23, Barcelona 08010, Spain.;Winter, A (corresponding author), Univ Autonoma Barcelona, Grp Informacio Quant, Dept Fis, Bellaterra 08193, Barcelona, Spain. farzin.salek@gmail.com; hayashi@sustech.edu.cn; andreas.winter@uab.cat Winter, Andreas/K-4200-2019 Winter, Andreas/0000-0001-6344-4870; Hayashi, Masahito/0000-0003-3104-1000 Peng Cheng Laboratory (PCL); DFG cluster of excellence 2111 (Munich Center for Quantum Science and Technology); Baidu-UAB collaborative project Learning of Quantum Hidden Markov Models; Spanish MINECO [FIS2016-86681-P, PID2019107609GB-I00/AEI/10.13039/501100011033]; FEDER funds; Generalitat de Catalunya [2017-SGR-1127]; Catalan Government within the ERDF Program of Catalunya [001-P-001644]; Guangdong Provincial Key Laboratory [2019B121203002]; JSPS [17H01280, 16KT0017]; Kayamori Foundation of Information Science Advancement Peng Cheng Laboratory (PCL); DFG cluster of excellence 2111 (Munich Center for Quantum Science and Technology); Baidu-UAB collaborative project Learning of Quantum Hidden Markov Models; Spanish MINECO(Spanish Government); FEDER funds(European Commission); Generalitat de Catalunya(Generalitat de Catalunya); Catalan Government within the ERDF Program of Catalunya; Guangdong Provincial Key Laboratory; JSPS(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science); Kayamori Foundation of Information Science Advancement(Kayamori Foundation of Informational Science Advancement) The authors thank Z. Puchala for drawing our attention to Example 2, from Ref. [15], and Z. Puchala and S. Pirandola for pointing out various pertinent previous works. F.S. is grateful to M. Berta for stimulating discussions during QIP 2020. He also acknowledges the hospitality and support of the Peng Cheng Laboratory (PCL) where the initial part of this work was done. A.W. thanks E. White and D. Colt for spirited conversations on issues around discrimination and inequalities. F.S. acknowledges support by the DFG cluster of excellence 2111 (Munich Center for Quantum Science and Technology). F.S. and A.W. acknowledge partial financial support by the Baidu-UAB collaborative project Learning of Quantum Hidden Markov Models, the Spanish MINECO (Projects No. FIS2016-86681-P and No. PID2019107609GB-I00/AEI/10.13039/501100011033) with the support of FEDER funds, and the Generalitat de Catalunya (Project No. 2017-SGR-1127). F.S. is also supported by the Catalan Government 001-P-001644 QuantumCAT within the ERDF Program of Catalunya. M.H. is supported in part by Guangdong Provincial Key Laboratory (Grant No. 2019B121203002), a JSPS Grant-in-Aids for Scientific Research (A) No. 17H01280 and for Scientific Research (B) No. 16KT0017, and Kayamori Foundation of Information Science Advancement. The authors thank Z. Puchala for drawing our attention to Example 2, from Ref. [15], and Z. Puchala and S. Pirandola for pointing out various pertinent previous works. F.S. is grateful to M. Berta for stimulating discussions during QIP 2020. He also acknowledges the hospitality and support of the Peng Cheng Laboratory (PCL) where the initial part of this work was done. A.W. thanks E. White and D. Colt for spirited conversations on issues around discrimination and inequalities. F.S. acknowledges support by the DFG cluster of excellence 2111 (Munich Center for Quantum Science and Technology). F.S. and A.W. acknowledge partial financial support by the Baidu-UAB collaborative project Learning of Quantum Hidden Markov Models, the Spanish MINECO (Projects No. FIS2016-86681-P and No. PID2019107609GB-I00/AEI/10.13039/501100011033) with the support of FEDER funds, and the Generalitat de Catalunya (Project No. 2017-SGR-1127). F.S. is also supported by the Catalan Government 001-P-001644 QuantumCAT within the ERDF Program of Catalunya. M.H. is supported in part by Guangdong Provincial Key Laboratory (Grant No. 2019B121203002), a JSPS Grant-in-Aids for Scientific Research (A) No. 17H01280 and for Scientific Research (B) No. 16KT0017, and Kayamori Foundation of Information Science Advancement. The authors thank Z. Puchala for drawing our attention to Example 2, from Ref. [15], and Z. Puchala and S. Pirandola for pointing out various pertinent previous works. F.S. is grateful to M. Berta for stimulating discussions during QIP 2020. He also acknowledges the hospitality and support of the Peng Cheng Laboratory (PCL) where the initial part of this work was done. A.W. thanks E. White and D. Colt for spirited conversations on issues around discrimination and inequalities. F.S. acknowledges support by the DFG cluster of excellence 2111 (Munich Center for Quantum Science and Technology). F.S. and A.W. acknowledge partial financial support by the Baidu-UAB collaborative project Learning of Quantum Hidden Markov Models, the Spanish MINECO (Projects No. FIS2016-86681-P and No. PID2019107609GB-I00/AEI/10.13039/501100011033) with the support of FEDER funds, and the Generalitat de Catalunya (Project No. 2017-SGR-1127). F.S.; is also supported by the Catalan Government 001-P-001644 QuantumCAT within the ERDF Program of Catalunya. M.H. is supported in part by Guangdong Provincial Key Laboratory (Grant No. 2019B121203002), a JSPS Grant-in-Aids for Scientific Research (A) No. 17H01280 and for Scientific Research (B) No. 16KT0017, and Kayamori Foundation of Information Science Advancement. 48 1 1 1 3 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2469-9926 2469-9934 PHYS REV A Phys. Rev. A FEB 14 2022.0 105.0 2 22419 10.1103/PhysRevA.105.022419 0.0 25 Optics; Physics, Atomic, Molecular & Chemical Science Citation Index Expanded (SCI-EXPANDED) Optics; Physics ZH8IT 2023-03-23 WOS:000761176100010 0
C Zhang, JC; Chen, JJ; Tang, H; Wang, W; Yan, Y; Sangineto, EE; Sebe, NC ASSOC COMP MACHINERY Zhang, Jichao; Chen, Jingjing; Tang, Hao; Wang, Wei; Yan, Yan; Sangineto, Enver; Sebe, Nicu Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA English Proceedings Paper 28th ACM International Conference on Multimedia (MM) OCT 12-16, 2020 ELECTR NETWORK ACM SIGMM,Assoc Comp Machinery Deep Learning; Generative Adversarial Networks; Image Translation; Gaze Correction; Gaze Animation We address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need of precise annotations of the gaze angle and the head pose. We created a new dataset called CelebAGaze consisting of two domains X, Y, where the eyes are either staring at the camera or somewhere else. Our method consists of three novel modules: the Gaze Correction module(GCM), the Gaze Animation module(GAM), and the Pretrained Autoencoder module (PAM). Specifically, GCM and GAM separately train a dual in-painting network using data from the domain X for gaze correction and data from the domain Y for gaze animation. Additionally, a Synthesis-As-Training method is proposed when training GAM to encourage the features encoded from the eye region to be correlated with the angle information, resulting in gaze animation achieved by interpolation in the latent space. To further preserve the identity information e.g., eye shape, iris color, we propose the PAM with an Autoencoder, which is based on Self-Supervised mirror learning where the bottleneck features are angle-invariant and which works as an extra input to the dual in-painting models. Extensive experiments validate the effectiveness of the proposed method for gaze correction and gaze animation in the wild and demonstrate the superiority of our approach in producing more compelling results than state-of-the-art baselines. Our code, the pretrained models and supplementary results are available at:https://github.com/zhangqianhui/GazeAnimation. [Zhang, Jichao; Tang, Hao; Sangineto, Enver; Sebe, Nicu] Univ Trento, Trento, Italy; [Chen, Jingjing] Shandong Univ, Jinan, Peoples R China; [Wang, Wei] Ecole Polytech Fed Lausanne, Lausanne, Switzerland; [Yan, Yan] Texas State Univ, San Marcos, TX USA; [Sebe, Nicu] Huawei Res Ireland, Dublin, Ireland University of Trento; Shandong University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Texas State University System; Texas State University San Marcos Zhang, JC (corresponding author), Univ Trento, Trento, Italy. chen, JJ/HGB-6029-2022 Sebe, Niculae/0000-0002-6597-7248 Italy-China collaboration project TALENT Italy-China collaboration project TALENT This work has been partially supported by the Italy-China collaboration project TALENT. 53 1 1 0 0 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES 978-1-4503-7988-5 2020.0 1588 1596 10.1145/3394171.3413981 0.0 9 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BT2LS Green Submitted 2023-03-23 WOS:000810735001072 0
J Jin, XZ; Lu, SY; Deng, C; Chadli, M Jin, Xiaozheng; Lue, Shaoyu; Deng, Chao; Chadli, Mohammed Distributed adaptive security consensus control for a class of multi-agent systems under network decay and intermittent attacks INFORMATION SCIENCES English Article Nonlinear multi-agent systems; Security consensus; Adaptive observers; Network decay and intermittent attacks This paper addresses the distributed adaptive security consensus control problem of a class of nonlinear multi-agent systems subject to network decay and intermittent attacks. The network communication is decayed and intermittently attacked by attackers, which may result in loss of transmission effectiveness and communication outage, respectively. A distributed adaptive consensus control strategy is firstly developed to ensure bounded con sensus of the nonlinear multi-agent systems in the case of normal networks. Then, a novel adaptive neural network (NN)-based observer is proposed to observe decayed and intermittent transmission signals of networks in such a way to recover the original signals. Based on the adaptive control and NN-based observer schemes, distributed security control strategies are developed to achieve the bounded consensus of the multi-agent systems under the influence of network decay and intermittent attacks. The efficiency of the designed adaptive security control strategies are illustrated by a multiple coupled nonlinear forced pendulum system. (C) 2020 Elsevier Inc. All rights reserved. [Jin, Xiaozheng] Qilu Univ Technol, Shandong Acad Sci, Sch Comp Sci & Technol, Jinan 250353, Shandong, Peoples R China; [Jin, Xiaozheng] Natl Supercomp Ctr Jinan, Shandong Comp Sci Ctr, Jinan 250014, Shandong, Peoples R China; [Jin, Xiaozheng] Shandong Prov Key Lab Comp Networks, Jinan 250014, Shandong, Peoples R China; [Lue, Shaoyu] HeFei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Anhui, Peoples R China; [Deng, Chao] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore; [Chadli, Mohammed] Univ Evry, Univ Paris Saclay, IBISC, F-91020 Evry, France Qilu University of Technology; Qilu University of Technology; Hefei University of Technology; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University; UDICE-French Research Universities; Universite Paris Saclay Deng, C (corresponding author), Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore. cdeng@ntu.edu.sg CHADLI, Mohammed/R-5490-2019 CHADLI, Mohammed/0000-0002-0140-5187; Deng, Chao/0000-0002-6148-1034 National Natural Science Foundation of China [61773149] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported in part by the National Natural Science Foundation of China under Grant 61773149. 38 36 36 19 99 ELSEVIER SCIENCE INC NEW YORK STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. FEB 8 2021.0 547.0 88 102 10.1016/j.ins.2020.08.013 0.0 15 Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science OT2JQ 2023-03-23 WOS:000590678600006 0
J Dharejo, FA; Deeba, F; Zhou, YC; Das, B; Jatoi, MA; Zawish, M; Du, Y; Wang, XZ Dharejo, Fayaz Ali; Deeba, Farah; Zhou, Yuanchun; Das, Bhagwan; Jatoi, Munsif Ali; Zawish, Muhammad; Du, Yi; Wang, Xuezhi TWIST-GAN: TowardsWavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY English Article Wavelet transform; neural networks; spatio-temporal; super resolution WAVELET TRANSFORM; SUPERRESOLUTION; CLASSIFICATION; SPARSITY; NETWORK Single Image Super-resolution (SISR) produces high-resolution imageswith fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 x 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers. [Dharejo, Fayaz Ali; Deeba, Farah; Zhou, Yuanchun; Du, Yi; Wang, Xuezhi] Chinese Acad Sci, Univ Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China; [Das, Bhagwan] Quaid e Awam Univ Engn Sci & Technol, Dept Elect Engn, Nawasbshah 67450, Sindh, Pakistan; [Jatoi, Munsif Ali] Salim Habib Univ, Dept Biomed Engn, Karachi 74900, Sindh, Pakistan; [Zawish, Muhammad] Walton Inst Informat & Commun Syst Sci, Waterford X91 WR86, Ireland Chinese Academy of Sciences; Computer Network Information Center, CAS; University of Chinese Academy of Sciences, CAS Zhou, YC; Du, Y (corresponding author), Chinese Acad Sci, Univ Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China. fayazdharejo@cnic.cn; deeba@cnic.cn; zyc@cnic.cn; engr.bhagwandas@hotmail.com; munsif.jatoi@shu.edu.pk; muhammad.zawish@waltoninstitute.ie; duyi@cnic.cn Dharejo, Fayaz Ali/AAN-2678-2020; Deeba, Farah/ADS-3256-2022 Dharejo, Fayaz Ali/0000-0001-7685-3913; Zawish, Muhammad/0000-0002-4994-3443; Wang, Xuezhi/0000-0001-5222-248X; Deeba, Farah/0000-0002-1987-2402; Zhou, Yuanchun/0000-0003-2144-1131 62 7 7 3 9 ASSOC COMPUTING MACHINERY NEW YORK 1601 Broadway, 10th Floor, NEW YORK, NY USA 2157-6904 2157-6912 ACM T INTEL SYST TEC ACM Trans. Intell. Syst. Technol. NOV 2021.0 12.0 6 71 10.1145/3456726 0.0 20 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science YY1AN Green Submitted 2023-03-23 WOS:000754524700004 0
J Du, YH; Yan, Y; Chen, S; Hua, Y Du, Yihan; Yan, Yan; Chen, Si; Hua, Yang Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation NEUROCOMPUTING English Article Visual tracking; LSTM network; Generative adversarial network; Data augmentation In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking. (C) 2019 Elsevier B.V. All rights reserved. [Du, Yihan; Yan, Yan] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China; [Du, Yihan] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing 100084, Peoples R China; [Chen, Si] Xiamen Univ Technol, Sch Comp & Informat Engn, Xiamen 361024, Fujian, Peoples R China; [Hua, Yang] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Xiamen University; Tsinghua University; Xiamen University of Technology; Queens University Belfast Yan, Y (corresponding author), Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China. yanyan@xmu.edu.cn hua, yang/GSE-0594-2022 Hua, Yang/0000-0001-5536-503X National Key R&D Program of China [2017YFB1302400]; National Natural Science Foundation of China [61571379, U1605252, 61872307]; Natural Science Foundation of Fujian Province of China [2017J01127 and2018J01576] National Key R&D Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Fujian Province of China(Natural Science Foundation of Fujian Province) This work was supported by the National Key R&D Program of China under Grant 2017YFB1302400, by the National Natural Science Foundation of China under Grants 61571379, U1605252 and 61872307, and by the Natural Science Foundation of Fujian Province of China under Grants 2017J01127 and2018J01576. 76 11 12 2 39 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing APR 7 2020.0 384.0 67 83 10.1016/j.neucom.2019.12.022 0.0 17 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science KM1CA Green Submitted 2023-03-23 WOS:000513853600006 0
C Cao, Y; Wang, LH; Zhang, J; Xia, H; Yang, F; Zhu, YM IEEE Cao, Ying; Wang, Lihui; Zhang, Jian; Xia, Hui; Yang, Feng; Zhu, Yuemin Accelerating multi-echo MRI in k-space with complex-valued diffusion probabilistic model 2022 16TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP2022), VOL 1 International Conference on Signal Processing English Proceedings Paper 16th IEEE International Conference on Signal Processing (ICSP) OCT 21-24, 2022 Beijing, PEOPLES R CHINA Inst Elect & Elect Engineers,Beijing Jiaotong Univ,IET,Union Radio Scientifique Int,Chinese Inst Elect,IEEE Beijing Sect,IET Beijing Local Network,Natl Nat Sci Fdn China,IEEE Signal Proc Soc Beijing Chapter,IEEE Comp Soc Beijing Chapter,Japan China Sci & Technol Exchange Assoc,CIE Signal Proc Soc,Shenzhen Univ, Intelligent Informat Inst,CIC Commun & Signal Proc Soc,Beijing Inst Elect Complex convolutional network; denoising diffusion probabilistic model; compressed sensing IMAGE-RECONSTRUCTION; SUSCEPTIBILITY Compressed sensing (CS) is an interesting technique for effectively accelerating multi-echo gradient-recalled-echo (ME-GRE) magnetic resonance imaging (MRI). However, how to reconstruct high-quality MRI from under-sampled k-space data is still a challenge issue. Considering the superiority of complex-valued convolutional neural network and the image generation ability of denoising diffusion probabilistic model (DDPM), in this work, we proposed a complex diffusion probabilistic model (CDPM) to realize the CS-MRI reconstruction in k-space. Specifically, we randomly generated a mask to under-sample the k-space data; those k-space data not acquired were taken as the input of forward diffusion model. We recover these unobserved k-space data through an inverse diffusion process, which was realized by a complex-valued UNet-like network. By comparing the complex-valued CDPM and real-valued DDPM models on the reconstruction of ME-GRE MRI, we validated that our proposed CDPM model outperforms the real-valued DDPM methods. It can effectively restore the image details with a sampling ratio of 25%. [Cao, Ying; Wang, Lihui; Zhang, Jian] Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Dia, State Key Lab Publ Big Data, Guiyang, Peoples R China; [Xia, Hui] Peoples Liberat Army Gen Hosp, Dept Thorac Cardio Surg, Med Ctr 4, Beijing 100048, Peoples R China; [Yang, Feng] Natl Inst Hlth, Lister Hill Natl Ctr Biomed Commun, Natl Lib Med, Bethesda, MD USA; [Zhu, Yuemin] Univ Lyon, CNRS, INSA Lyon, INSERM,CREATIS UMR 5220,U1294, F-69621 Lyon, France Guizhou University; Chinese People's Liberation Army General Hospital; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM); Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Wang, LH (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Dia, State Key Lab Publ Big Data, Guiyang, Peoples R China. cyversatile@163.com; wlh1984@gmail.com; 34044200@qq.com; xiahui304@163.com; feng.yang2@nih.gov; zhu@creatis.insa-lyon.fr National Nature Science Foundations of China [62161004]; Key Project of Guizhou Science and Technology Fund [QianKeHe ZK [2021] Key 002]; Nature Science Foundations of Guizhou Province [QianKeHe [2020]1Y255]; Qiannan Science and Technology Planning Project [[2018]16] National Nature Science Foundations of China(National Natural Science Foundation of China (NSFC)); Key Project of Guizhou Science and Technology Fund; Nature Science Foundations of Guizhou Province; Qiannan Science and Technology Planning Project This work was supported by the National Nature Science Foundations of China (Grant No.62161004), the Key Project of Guizhou Science and Technology Fund (QianKeHe ZK [2021] Key 002), Nature Science Foundations of Guizhou Province (QianKeHe [2020]1Y255), Qiannan Science and Technology Planning Project(No.[2018]16). 21 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-5221 978-1-6654-6056-9 INT CONF SIGN PROCES 2022.0 479 484 10.1109/ICSP56322.2022.9964484 0.0 6 Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU4ET 2023-03-23 WOS:000896487300094 0
C Zotti, C; Luo, ZM; Humbert, O; Lalande, A; Jodoin, PM Pop, M; Sermesant, M; Jodoin, PM; Lalande, A; Zhuang, X; Yang, G; Young, A; Bernard, O Zotti, Clement; Luo, Zhiming; Humbert, Olivier; Lalande, Alain; Jodoin, Pierre-Marc GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: ACDC AND MMWHS CHALLENGES Lecture Notes in Computer Science English Proceedings Paper 8th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM) SEP 10-14, 2017 Quebec, CANADA SciMedia Ltd,Imeka,Nvidia,Arterys Convolutional neural networks; MRI; Heart Segmentation HEART; VENTRICLE In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv grid architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 s with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm. [Zotti, Clement; Luo, Zhiming; Jodoin, Pierre-Marc] Univ Sherbrooke, Comp Sci Dept, Sherbrooke, PQ, Canada; [Lalande, Alain] Univ Bourgogne Franche Comte, Le2i, Dijon, France; [Humbert, Olivier] Ctr Antoine Lacassagne, Dept Nucl Med, Nice, France; [Luo, Zhiming] Xiamen Univ, Cognit Sci Dept, Xiamen, Peoples R China University of Sherbrooke; Universite de Bourgogne; UNICANCER; Centre Antoine Lacassagne; Xiamen University Zotti, C (corresponding author), Univ Sherbrooke, Comp Sci Dept, Sherbrooke, PQ, Canada. clement.zotti@usherbrooke.ca Lalande, Alain/T-1050-2017 22 31 32 0 3 SPRINGER INTERNATIONAL PUBLISHING AG CHAM GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 0302-9743 1611-3349 978-3-319-75541-0; 978-3-319-75540-3 LECT NOTES COMPUT SC 2018.0 10663.0 73 81 10.1007/978-3-319-75541-0_8 0.0 9 Cardiac & Cardiovascular Systems; Mathematical & Computational Biology; Statistics & Probability Conference Proceedings Citation Index - Science (CPCI-S) Cardiovascular System & Cardiology; Mathematical & Computational Biology; Mathematics BP4AI Green Submitted 2023-03-23 WOS:000550266300008 0
J Sabin, L; Gomez-Llanos, V; Morisset, C; Gomez-Gonzalez, VMA; Guerrero, MA; Todt, H; Fang, X Sabin, L.; Gomez-Llanos, V; Morisset, C.; Gomez-Gonzalez, V. M. A.; Guerrero, M. A.; Todt, H.; Fang, X. Catching a grown-up starfish planetary nebula - II. Plasma analysis and central star properties of PC 22 MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article stars: evolution; planetary nebulae: general; planetary nebulae: individual: PC22 EFFECTIVE COLLISION STRENGTHS; ELECTRON-IMPACT EXCITATION; TRANSITION-PROBABILITIES; ENERGY-LEVELS; QUANTITATIVE CLASSIFICATION; FORBIDDEN TRANSITIONS; GROUND CONFIGURATION; HYDROGENIC IONS; IRON PROJECT; H-DEFICIENT After performing the morpho-kinematic analysis of the planetary nebula (PN) PC 22, we now present its nebular and stellar analysis. The plasma investigation relies on the novel use of a Monte Carlo analysis associated with the pyneb code for the uncertainty propagation. The innermost region of the nebula shows electronic temperatures T-e approximate to 10 800 K using [N ii] and approximate to 13 000 K using [O iii] and electronic densities n(e) approximate to 600 cm(-3). We also used for the first time a machine learning algorithm to calculate ionization correction factors (ICFs) specifically adapted to PC 22. This has allowed us to have pioneer ICFs for (S+ + S++)/O++, Cl++/O++, and Ar3+ + Ar4+, as well as a possible new determination for the total abundance of neon. The study of the stellar spectrum revealed the presence of broad emission lines consistent with a Wolf-Rayet-type [WR] classification and more precisely a [WO1] subtype based on different qualitative and quantitative criteria. This classification is also coherent with the high stellar temperature derived from the reproduction of the ionization state of the gas with the Mexican Million Models data base (3MdB) and the best-fitting model obtained with the NLTE model atmosphere code PoWR. PC 22 is therefore a new addition to the [WO1]-subtype PNe. [Sabin, L.; Gomez-Llanos, V; Morisset, C.] Univ Nacl Autonoma Mexico, Inst Astron, AP 106, Ensenada 22800, Baja California, Mexico; [Gomez-Gonzalez, V. M. A.] UNAM Campus Morelia, Inst Radioastron & Astrofis IRyA, Apartado Postal 3-72, Morelia 58090, Michoacan, Mexico; [Gomez-Gonzalez, V. M. A.; Todt, H.] Univ Potsdam, Inst Phys & Astron, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany; [Guerrero, M. A.] CSIC, IAA, Inst Astrofis Andalucia, C Glorieta Astronomia S-N, E-18008 Granada, Spain; [Fang, X.] Chinese Acad Sci NAOC, Natl Astron Observ, Key Lab Opt Astron, Beijing 100101, Peoples R China; [Fang, X.] Univ Hong Kong, Fac Sci, Dept Phys, Hong Kong, Peoples R China; [Fang, X.] Univ Hong Kong, Fac Sci, Lab Space Res, Hong Kong, Peoples R China Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma de Mexico; University of Potsdam; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Astrofisica de Andalucia (IAA); Chinese Academy of Sciences; National Astronomical Observatory, CAS; University of Hong Kong; University of Hong Kong Sabin, L (corresponding author), Univ Nacl Autonoma Mexico, Inst Astron, AP 106, Ensenada 22800, Baja California, Mexico. lsabin@astro.unam.mx Sabin, Laurence/HDO-7492-2022; Guerrero, Martin/L-1482-2014 Guerrero, Martin/0000-0002-7759-106X; Morisset, Christophe/0000-0001-5801-6724; Gomez Gonzalez, Victor Mauricio Alfonso/0000-0001-8252-6548; Gomez-Llanos, Veronica/0000-0002-1825-8267 PAPIIT (Mexico) [IN101819]; CONACYT/CB2015 [254132]; UNAM/PAPIIT [IN101220]; Programa de Becas posdoctorales - Direccion General de Asuntos del Personal Academico of the Universidad Nacional Autonoma de Mexico (DGAPA, UNAM); Spanish Ministerio de Ciencias, Innovacion y Universidades [PGC2018-102184-B-I00]; National Aeronautics and Space Administration PAPIIT (Mexico)(Programa de Apoyo a Proyectos de Investigacion e Innovacion Tecnologica (PAPIIT)); CONACYT/CB2015; UNAM/PAPIIT(Programa de Apoyo a Proyectos de Investigacion e Innovacion Tecnologica (PAPIIT)Universidad Nacional Autonoma de Mexico); Programa de Becas posdoctorales - Direccion General de Asuntos del Personal Academico of the Universidad Nacional Autonoma de Mexico (DGAPA, UNAM); Spanish Ministerio de Ciencias, Innovacion y Universidades; National Aeronautics and Space Administration(National Aeronautics & Space Administration (NASA)) We thank the anonymous referee for his/her suggestions and comments that help improving the article. LS acknowledges support from PAPIIT grant IN101819 (Mexico). VGL and CM acknowledge grants CONACYT/CB2015 - 254132 and UNAM/PAPIIT - IN101220. VMAGG acknowledges support from the Programa de Becas posdoctorales funded by Direccion General de Asuntos del Personal Academico of the Universidad Nacional Autonoma de Mexico (DGAPA, UNAM). MAG acknowledges support of the grant PGC2018-102184-B-I00 of the Spanish Ministerio de Ciencias, Innovacion y Universidades. Also, we want to thank the OAN-SPM staff and the CATT for time allocation. This study is based on observations made with the 2.1m telescope of the Observatorio Astronomico Nacional at the Sierra de San Pedro Martir (OAN-SPM), which is a national facility operated by the Instituto de Astronomia of the Universidad Nacional Autonoma de Mexico. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We have also used archival observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA), and the Canadian Astronomy Data Centre (CADC/NRC/CSA). This paper has been edited using the Overleaf facility. 66 1 1 0 1 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. FEB 1 2022.0 511.0 1 1 19 10.1093/mnras/stab3649 0.0 19 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics ZU7QB Green Submitted 2023-03-23 WOS:000770033900001 0
C Li, YW; Li, W; Danelljan, M; Zhang, K; Gu, SH; Van Gool, L; Timofte, R IEEE COMP SOC Li, Yawei; Li, Wen; Danelljan, Martin; Zhang, Kai; Gu, Shuhang; Van Gool, Luc; Timofte, Radu The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 IEEE Conference on Computer Vision and Pattern Recognition English Proceedings Paper IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) JUN 19-25, 2021 ELECTR NETWORK IEEE,IEEE Comp Soc,CVF In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity. The LW-DNA models are identified without extra computational cost or training time compared with the original network. This constraint leads to controlled experiments which direct the focus to the importance of layer-wise specific channel configurations. LW-DNA models come with advantages related to overfitting, i.e. the relative relationship between model complexity and dataset size. Experiments are conducted on various networks and datasets for image classification, visual tracking and image restoration. The resultant LW-DNA models consistently outperform the baseline models. [Li, Yawei; Danelljan, Martin; Zhang, Kai; Van Gool, Luc; Timofte, Radu] Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland; [Li, Wen] UESTC, Chengdu, Peoples R China; [Gu, Shuhang] Univ Sydney, Camperdown, NSW, Australia; [Van Gool, Luc] Katholieke Univ Leuven, Leuven, Belgium Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Electronic Science & Technology of China; University of Sydney; KU Leuven Li, YW (corresponding author), Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland. yawei.li@vision.ee.ethz.ch; liwenbnu@gmail.com; martin.danelljan@vision.ee.ethz.ch; kai.zhang@vision.ee.ethz.ch; shuhanggu@gmail.com; vangool@vision.ee.ethz.ch; radu.timofte@vision.ee.ethz.ch Timofte, Radu/H-4438-2011; Zhang, Kai/GPK-2574-2022; Zhang, Kai/ABF-3950-2020; Timofte, Radu/AAK-6022-2021 Timofte, Radu/0000-0002-1478-0402; Zhang, Kai/0000-0002-6319-3722; Major Project for New Generation of AI [2018AAA0100400]; ETH Zurich Fund (OK); Huawei Technologies Oy (Finland) project, an Amazon AWS; Nvidia grant Major Project for New Generation of AI; ETH Zurich Fund (OK); Huawei Technologies Oy (Finland) project, an Amazon AWS(Huawei Technologies); Nvidia grant This work is partially supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400, the ETH Zurich Fund (OK), a Huawei Technologies Oy (Finland) project, an Amazon AWS grant, and an Nvidia grant. 54 7 7 1 1 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA 1063-6919 978-1-6654-4509-2 PROC CVPR IEEE 2021.0 2144 2153 10.1109/CVPR46437.2021.00218 0.0 10 Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Imaging Science & Photographic Technology BS5QS Green Accepted, Green Submitted 2023-03-23 WOS:000739917302034 0
J Huang, YY; Ciais, P; Luo, YQ; Zhu, D; Wang, YP; Qiu, CJ; Goll, DS; Guenet, B; Makowski, D; De Graaf, I; Leifeld, J; Kwon, MJ; Hu, J; Qu, LY Huang, Yuanyuan; Ciais, Phillipe; Luo, Yiqi; Zhu, Dan; Wang, Yingping; Qiu, Chunjing; Goll, Daniel S.; Guenet, Bertrand; Makowski, David; De Graaf, Inge; Leifeld, Jens; Kwon, Min Jung; Hu, Jing; Qu, Laiye Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown NATURE CLIMATE CHANGE English Article NITROUS-OXIDE FLUXES; CLIMATE-CHANGE; TROPICAL PEATLANDS; WETLAND EXTENT; CARBON STORAGE; PRESENT STATE; METHANE; SUBSIDENCE; DECOMPOSITION; NORTHERN The climate impact of water-table drawdown in peatlands is unclear as carbon dioxide emissions increase and methane emissions decrease due to drying. This study shows decreasing water-table depth results in net greenhouse gas emissions from global peatlands, despite reducing methane emissions. Water-table drawdown across peatlands increases carbon dioxide (CO2) and reduces methane (CH4) emissions. The net climatic effect remains unclear. Based on global observations from 130 sites, we found a positive (warming) net climate effect of water-table drawdown. Using a machine-learning-based upscaling approach, we predict that peatland water-table drawdown driven by climate drying and human activities will increase CO2 emissions by 1.13 (95% interval: 0.88-1.50) Gt yr(-1) and reduce CH4 by 0.26 (0.14-0.52) GtCO(2)-eq yr(-1), resulting in a net increase of greenhouse gas of 0.86 (0.36-1.36) GtCO(2)-eq yr(-1) by the end of the twenty-first century under the RCP8.5 climate scenario. This drops to 0.73 (0.2-1.2) GtCO(2)-eq yr(-1) under RCP2.6. Our results point to an urgent need to preserve pristine and rehabilitate drained peatlands to decelerate the positive feedback among water-table drawdown, increased greenhouse gas emissions and climate warming. [Huang, Yuanyuan; Ciais, Phillipe; Zhu, Dan; Qiu, Chunjing; Goll, Daniel S.; Kwon, Min Jung] Univ Paris Saclay, UVSQ, CNRS, CEA,Lab Sci Climat & Environm,LSCE IPSL, Gif Sur Yvette, France; [Huang, Yuanyuan; Wang, Yingping] CSIRO Oceans & Atmosphere, Aspendale, Vic, Australia; [Luo, Yiqi] No Arizona Univ, Dept Biol Sci, Ctr Ecosyst Sci & Soc, Box 5640, Flagstaff, AZ 86011 USA; [Guenet, Bertrand] PSL Res Univ, Ecole Normale Super, CNRS, UMR 8538,Lab Geol,IPSL, Paris, France; [Makowski, David] Univ Paris Saclay, AgroParisTech, INRAE, UMR MIA 518, Paris, France; [De Graaf, Inge] Univ Freiburg, Fac Environm & Nat Resources, Chair Environm Hydrol Syst, Freiburg, Germany; [De Graaf, Inge] Wageningen Univ & Res, Water Syst & Global Change Grp, Wageningen, Netherlands; [Leifeld, Jens] Agroscope, Climate & Agr Grp, Zurich, Switzerland; [Hu, Jing] Mississippi State Univ, Geosyst Res Inst, Mississippi State, MS 39762 USA; [Hu, Jing] Univ Florida, Soil & Water Sci Dept, Wetland Biogeochem Lab, Gainesville, FL USA; [Qu, Laiye] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing, Peoples R China; [Qu, Laiye] Univ Chinese Acad Sci, Beijing, Peoples R China UDICE-French Research Universities; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Commonwealth Scientific & Industrial Research Organisation (CSIRO); Northern Arizona University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Universite Paris Cite; Universite PSL; Ecole Normale Superieure (ENS); AgroParisTech; INRAE; UDICE-French Research Universities; Universite Paris Saclay; University of Freiburg; Wageningen University & Research; Swiss Federal Research Station Agroscope; Mississippi State University; State University System of Florida; University of Florida; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Huang, YY (corresponding author), Univ Paris Saclay, UVSQ, CNRS, CEA,Lab Sci Climat & Environm,LSCE IPSL, Gif Sur Yvette, France.;Huang, YY (corresponding author), CSIRO Oceans & Atmosphere, Aspendale, Vic, Australia. yuanyuanhuang2011@gmail.com Qu, laiye/HJA-3978-2022; Qiu, Chunjing/AAX-1465-2021; De Graaf, Inge/AAG-7199-2019; Leifeld, Jens/GYR-0349-2022; Kwon, Min Jung/GRF-4488-2022; Goll, Daniel/K-6620-2017; Zhu, Dan/J-4450-2019; Guenet, Bertrand/ABD-5907-2021; Wang, Ying/HJI-2509-2023; wang, yingping/A-9765-2011; Leifeld, Jens/A-3298-2014 De Graaf, Inge/0000-0001-7748-868X; Leifeld, Jens/0000-0002-7245-9852; Goll, Daniel/0000-0001-9246-9671; Zhu, Dan/0000-0002-5857-1899; Guenet, Bertrand/0000-0002-4311-8645; wang, yingping/0000-0002-4614-6203; Leifeld, Jens/0000-0002-7245-9852; Huang, Yuanyuan/0000-0003-4202-8071; Qiu, Chunjing/0000-0002-9951-3951 European Research Council Synergy project [SyG-2013-610028 IMBALANCE-P]; ANR CLAND Convergence Institute [ANR-16-CONV-0003] European Research Council Synergy project; ANR CLAND Convergence Institute(French National Research Agency (ANR)) Y.H., P.C., D.Z., C.Q. and D.S.G. received support from the European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and Y.H., P.C., D.Z., C.Q., D.S.G., B.G. and D.M. from the ANR CLAND Convergence Institute (ANR-16-CONV-0003). 42 34 36 30 107 NATURE PORTFOLIO BERLIN HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY 1758-678X 1758-6798 NAT CLIM CHANGE Nat. Clim. Chang. JUL 2021.0 11.0 7 618 + 10.1038/s41558-021-01059-w 0.0 JUN 2021 7 Environmental Sciences; Environmental Studies; Meteorology & Atmospheric Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences TE8JQ Green Submitted 2023-03-23 WOS:000658703200002 0
J Song, WW; Gao, Z; Dian, RW; Ghamisi, P; Zhang, YJ; Benediktsson, JA Song, Weiwei; Gao, Zhi; Dian, Renwei; Ghamisi, Pedram; Zhang, Yongjun; Benediktsson, Jon Atli Asymmetric Hash Code Learning for Remote Sensing Image Retrieval IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Codes; Feature extraction; Remote sensing; Databases; Semantics; Image retrieval; Hash functions; Asymmetric; deep neural network; hashing learning; remote sensing image; scene retrieval CLASSIFICATION; NETWORKS Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing learning as the current mainstream method has achieved satisfactory retrieval performance. On one hand, various deep neural networks are used to extract semantic features of remote sensing images. On the other hand, the hashing techniques are subsequently adopted to map the high-dimensional deep features to the low-dimensional binary codes. This kind of method attempts to learn one hash function for both the query and database samples in a symmetric way. However, with the number of database samples increasing, it is typically time-consuming to generate the hash codes of large-scale database images. In this article, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL generates the hash codes of query and database images in an asymmetric way. In more detail, the hash codes of query images are obtained by binarizing the output of the network, while the hash codes of database images are directly learned by solving the designed objective function. In addition, we combine the semantic information of each image and the similarity information of pairs of images as supervised information to train a deep hashing network, which improves the representation ability of deep features and hash codes. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency. The source code is available at https://github.com/weiweisong415/Demo_AHCL_for_TGRS2022. [Song, Weiwei] Peng Cheng Lab, Dept Math & Theories, Shenzhen 518000, Peoples R China; [Gao, Zhi; Zhang, Yongjun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; [Dian, Renwei] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China; [Dian, Renwei] Hunan Univ, Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China; [Ghamisi, Pedram] Helmholtz Zentrum Dresden Rossendorf HZDR, Explorat, Helmholtz Inst Freiberg Resource Technol HIF, D-09599 Freiberg, Germany; [Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, A-1030 Vienna, Austria; [Benediktsson, Jon Atli] Univ Iceland, Fac Elect & Comp Engn, IS-101 Reykjavik, Iceland Peng Cheng Laboratory; Wuhan University; Hunan University; Hunan University; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); University of Iceland Dian, RW (corresponding author), Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China. weiweisong415@gmail.com; gaozhinus@whu.edu.cn; drw@hnu.edu.cn; p.ghamisi@gmail.com; zhangyj@whu.edu.cn; benedikt@hi.is Zhang, Yongjun/HCH-7076-2022; Benediktsson, Jon Atli/F-2861-2010 Zhang, Yongjun/0000-0001-9845-4251; Song, Weiwei/0000-0001-5089-4127; Benediktsson, Jon Atli/0000-0003-0621-9647 Major Key Project of Peng Cheng Laboratory (PCL) [PCL2021A09]; National Natural Science Foundation of China [42192583]; Natural Science Foundation of Hubei Province [2020CFA003, 2021CFA088]; China Postdoctoral Science Foundation [2020M682553]; Fellowship of China National Postdoctoral Program for Innovative Talents [BX20200121] Major Key Project of Peng Cheng Laboratory (PCL); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Natural Science Foundation of Hubei Province(Natural Science Foundation of Hubei Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Fellowship of China National Postdoctoral Program for Innovative Talents This work was supported in part by the Major Key Project of Peng Cheng Laboratory (PCL) under Grant PCL2021A09, in part by the National Natural Science Foundation of China under Grant 42192583, in part by the Natural Science Foundation of Hubei Province under Grant 2020CFA003 and Grant 2021CFA088, in part by the China Postdoctoral Science Foundation under Grant 2020M682553, and in part by the Fellowship of China National Postdoctoral Program for Innovative Talents under Grant BX20200121. 50 8 8 7 19 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing 2022.0 60.0 5617514 10.1109/TGRS.2022.3143571 0.0 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology ZU8EP Green Submitted 2023-03-23 WOS:000770073000021 0
J Van, M; Ge, SS Van, Mien; Ge, Shuzhi Sam Adaptive Fuzzy Integral Sliding-Mode Control for Robust Fault-Tolerant Control of Robot Manipulators With Disturbance Observer IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Robustness; Uncertainty; Fuzzy logic; Sliding mode control; Manipulators; Adaptive neural network; backstepping control; control of robots; disturbance observer (DO); fault-tolerant control (FTC); integral sliding-mode control (ISMC) NONLINEAR-SYSTEMS; TRACKING CONTROL; DESIGN; ACCOMMODATION; DIAGNOSIS This article develops a new strategy for robust fault-tolerant control (FTC) of robot manipulators using an adaptive fuzzy integral sliding-mode control (ISMC) and a disturbance observer (DO). First, an ISMC is developed for the FTC system. The major features of the approach are discussed. Then, to enhance the performance of the system, a fuzzy logic system approximation and a DO are introduced to approximate the unknown nonlinear terms, which include the model uncertainty and fault components, and to estimate the compounded disturbance and then are integrated into the ISMC. Next, a switching term based on an adaptive two-layer supertwisting algorithm is designed to compensate the disturbance estimated error and guarantee stability and convergence of the whole system. The nominal controller of the ISMC is reconstructed using a backstepping control technique to achieve the stability for the nominal system based on the Lyapunov criterion. The computer simulation results demonstrate the effectiveness of the proposed approach. [Van, Mien] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland; [Ge, Shuzhi Sam] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore; [Ge, Shuzhi Sam] Natl Univ Singapore, Social Robot Lab, Smart Syst Inst, Singapore, Singapore; [Ge, Shuzhi Sam] Qingdao Univ, Inst Future, Qingdao, Peoples R China Queens University Belfast; National University of Singapore; National University of Singapore; Qingdao University Van, M (corresponding author), Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland. m.van@qub.ac.uk; samge@nus.edu.sg Van, Mien/0000-0001-9616-6061 Queen's University Belfast Queen's University Belfast This work was supported in part by the start-up grant of Queen's University Belfast. 64 34 34 13 64 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. MAY 2021.0 29.0 5 1284 1296 10.1109/TFUZZ.2020.2973955 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering RZ1BM Green Accepted 2023-03-23 WOS:000648333700028 0
J Jin, S; Chen, L; Sun, RC; McLoone, S Jin, Sheng; Chen, Liang; Sun, Rongchuan; McLoone, Sean A novel vSLAM framework with unsupervised semantic segmentation based on adversarial transfer learning APPLIED SOFT COMPUTING English Article Semantic SLAM; Dynamic environments; Dynamic object segmentation NEURAL-NETWORKS; MONOCULAR SLAM Significant progress has been made in the field of visual Simultaneous Localization and Mapping (vSLAM) systems. However, the localization accuracy of vSLAM can be significantly reduced in dynamic applications with mobile robots or passengers. In this paper, a novel semantic SLAM framework in dynamic environments is proposed to improve the localization accuracy. We incorporate a semantic segmentation model into the Oriented FAST and Rotated BRIEF-SLAM2 (ORB-SLAM2) system to filter out dynamic feature points, but we encounter one main challenge, i.e. the performance of a segmentation network well-trained with labeled datasets may decrease seriously in a real application without any labeled data due to the inconsistency between the source domain and the target domain. Therefore, we proposed an unsupervised semantic segmentation model with a Residual Neural Network (ResNet) structure, which is trained by the adversarial transfer learning method in the multi-level feature spaces. This work may be the first to perform multi-level feature space adversarial transfer learning for the semantic SLAM task in dynamic environments. In order to evaluate our method, images of indoor scenes from three datasets are used as the source domain, and the dynamic sequences of the TUM dataset are used as the target domain. The extensive experimental results show favorable performance against the state-of-the-art methods in terms of the absolute trajectory accuracy and image semantic segmentation quality. (C) 2020 Elsevier B.V. All rights reserved. [Jin, Sheng; Chen, Liang; Sun, Rongchuan] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China; [McLoone, Sean] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland Soochow University - China; Queens University Belfast Chen, L (corresponding author), Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China. ChenL@suda.edu.cn national natural science foundation of China [61673288] national natural science foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the national natural science foundation of China (61673288). 64 1 2 3 41 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 1568-4946 1872-9681 APPL SOFT COMPUT Appl. Soft. Comput. MAY 2020.0 90.0 106153 10.1016/j.asoc.2020.106153 0.0 17 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science LJ0ZK Green Accepted 2023-03-23 WOS:000529902300048 0
J Dang, SP; Wen, MW; Mumtaz, S; Li, J; Li, CZ Dang, Shuping; Wen, Miaowen; Mumtaz, Shahid; Li, Jun; Li, Chengzhong Enabling Multicarrier Relay Selection by Sensing Fusion and Cascaded ANN for Intelligent Vehicular Communications IEEE SENSORS JOURNAL English Article Artificial neural network (ANN); cooperative relaying; multi-carrier relay selection; sensing fusion (SF); vehicular communications PERFORMANCE ANALYSIS; ASSISTED OFDM; METHODOLOGY; WIRELESS; SYSTEMS Cooperative relaying has been adopted as one of the most important techniques to enhance the energy efficiency and coverage. Multi-carrier relay selection is an efficient method to allocate spatial/spectral resources in cooperative relay networks and provides diversity gain. However, the implementation of multi-carrier relay selection is not straightforward, and could render the high system complexity (for centralized implementation schemes) or long processing delay (for distributed implementation schemes). These issues hinder the promotion and implementation of multi-carrier relay selection for intelligent vehicular communications. To mitigate aforementioned issues, we propose an enabling technique of multi-carrier relay selection based on sensing fusion (SF) and cascaded artificial neural networks (CANNs) for intelligent vehicular communications. We employ two well-known multi-carrier relay selection schemes, i.e. bulk and per-subcarrier relay selection, to verify the effectiveness of the CANN based enabling technique. With the powerful processing ability with intelligent vehicles, the numerical results illustrate a promising vision of applying CANNs to enable multi-carrier relay selection for fast deployment in intelligent vehicular communication networks. [Dang, Shuping] Guangxi Huanan Commun Co Ltd, R&D Ctr, Nanning 530007, Peoples R China; [Dang, Shuping] King Abdullah Univ Sci & Technol KAUST, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia; [Wen, Miaowen] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Peoples R China; [Mumtaz, Shahid] Inst Telecomunicacoes, P-3810193 Aveiro, Portugal; [Li, Jun] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Peoples R China; [Li, Chengzhong] Guangxi Huanan Commun Co Ltd, R&D Ctr, Nanning 530007, Peoples R China King Abdullah University of Science & Technology; South China University of Technology; Universidade de Aveiro; Guangzhou University Wen, MW (corresponding author), South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Peoples R China. shuping.dang@kaust.edu.sa; eemwwen@scut.edu; smumtaz@av.it.pt; lijun52018@gzhu.edu.cn; chengzhong.li@hncom.ac.cn Mumtaz, Dr shahid/V-3603-2019 Mumtaz, Dr shahid/0000-0001-6364-6149; Li, Jun/0000-0003-3465-1621 Guangxi Natural Science Foundation [AD19245043]; Nanning Excellent Young Scientist Program [RC20190201]; Guangxi Beibu Gulf Economic Zone Major Talent Program; National Nature Science Foundation of China [61701127]; Pearl River Nova Program of Guangzhou [201806010171]; Natural Science Foundation of Guangdong Province [2018B030306005] Guangxi Natural Science Foundation(National Natural Science Foundation of Guangxi Province); Nanning Excellent Young Scientist Program; Guangxi Beibu Gulf Economic Zone Major Talent Program; National Nature Science Foundation of China(National Natural Science Foundation of China (NSFC)); Pearl River Nova Program of Guangzhou; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province) This work was supported in part by the Guangxi Natural Science Foundation under Grant AD19245043, in part by the Nanning Excellent Young Scientist Program under Grant RC20190201, in part by the Guangxi Beibu Gulf Economic Zone Major Talent Program, in part by the National Nature Science Foundation of China under Grant 61701127, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, and in part by the Natural Science Foundation of Guangdong Province under Grant 2018B030306005. 39 17 17 0 11 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1530-437X 1558-1748 IEEE SENS J IEEE Sens. J. JUL 15 2021.0 21.0 14 15614 15625 10.1109/JSEN.2020.2986322 0.0 12 Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied Science Citation Index Expanded (SCI-EXPANDED) Engineering; Instruments & Instrumentation; Physics TJ7BO Green Published 2023-03-23 WOS:000673632700011 0
J Adam, RS; Poel, D; Moreno, LF; Spronck, JMA; de Back, TR; Torang, A; Barila, PMG; Ten Hoorn, S; Markowetz, F; Wang, X; Verheul, HMW; Buffart, TE; Vermeulen, L Adam, Ronja S.; Poel, Dennis; Moreno, Leandro Ferreira; Spronck, Joey M. A.; de Back, Tim R.; Torang, Arezo; Barila, Patricia M. Gomez; Ten Hoorn, Sanne; Markowetz, Florian; Wang, Xin; Verheul, Henk M. W.; Buffart, Tineke E.; Vermeulen, Louis Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes MOLECULAR ONCOLOGY English Article colorectal cancer; consensus molecular subtypes; microRNA; miRNA BREAST-CANCER; MICRORNAS; EXPRESSION; MIR-592; MIR-200; CONTEXT; COLON Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of similar to 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows. [Adam, Ronja S.; Moreno, Leandro Ferreira; Spronck, Joey M. A.; de Back, Tim R.; Torang, Arezo; Barila, Patricia M. Gomez; Ten Hoorn, Sanne; Buffart, Tineke E.; Vermeulen, Louis] Amsterdam Univ Med Ctr, Ctr Expt & Mol Med CEMM, Canc Ctr Amsterdam, Lab Expt Oncol & Radiobiol LEXOR, Meibergdreef 9, NL-1105 AZ Amsterdam, Netherlands; [Adam, Ronja S.; Moreno, Leandro Ferreira; Spronck, Joey M. A.; de Back, Tim R.; Torang, Arezo; Barila, Patricia M. Gomez; Ten Hoorn, Sanne; Buffart, Tineke E.; Vermeulen, Louis] Amsterdam Univ Med Ctr, Amsterdam Gastroenterol & Metab, Meibergdreef 9, NL-1105 AZ Amsterdam, Netherlands; [Adam, Ronja S.; Moreno, Leandro Ferreira; Spronck, Joey M. A.; de Back, Tim R.; Torang, Arezo; Barila, Patricia M. Gomez; Ten Hoorn, Sanne; Vermeulen, Louis] Oncode Inst, Amsterdam, Netherlands; [Poel, Dennis; Verheul, Henk M. W.] Radboud Univ Nijmegen Med Ctr, Dept Med Oncol, Nijmegen, Netherlands; [Markowetz, Florian] Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England; [Wang, Xin] City Univ Hong Kong, Dept Biomed Sci, Kowloon Tong, Hong Kong, Peoples R China; [Wang, Xin] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China; [Buffart, Tineke E.] Netherlands Canc Inst, Dept Gastrointestinal Oncol, Amsterdam, Netherlands University of Amsterdam; Vrije Universiteit Amsterdam; Radboud University Nijmegen; Cancer Research UK; CRUK Cambridge Institute; University of Cambridge; City University of Hong Kong; City University of Hong Kong; Netherlands Cancer Institute Buffart, TE; Vermeulen, L (corresponding author), Amsterdam Univ Med Ctr, Ctr Expt & Mol Med CEMM, Canc Ctr Amsterdam, Lab Expt Oncol & Radiobiol LEXOR, Meibergdreef 9, NL-1105 AZ Amsterdam, Netherlands.;Buffart, TE; Vermeulen, L (corresponding author), Amsterdam Univ Med Ctr, Amsterdam Gastroenterol & Metab, Meibergdreef 9, NL-1105 AZ Amsterdam, Netherlands. t.buffart@amsterdamumc.nl; l.vermeulen@amsterdamumc.nl Gomez Barila, Patricia/0000-0003-3878-3373; Markowetz, Florian/0000-0002-2784-5308; Poel, Dennis/0000-0002-4395-2824; Spronck, Joey Matheus Antonius/0000-0001-9653-9542; Torang, Arezo/0000-0002-7912-3964; Adam, Ronja Sophia/0000-0001-8343-1882; de Back, Tim/0000-0002-5205-8031 New York Stem Cell Foundation; KWF [UVA2014-7245, 10529]; Maurits en Anna de Kock Stichting [2015-2]; European Research Council [ERG-StG 638193, ERC-PoC 841112]; ZonMw [Vidi 016.156.308]; CRUK core grant [C14303/A17197, A19274] New York Stem Cell Foundation; KWF; Maurits en Anna de Kock Stichting; European Research Council(European Research Council (ERC)European Commission); ZonMw(Netherlands Organization for Health Research and Development); CRUK core grant This work is supported by The New York Stem Cell Foundation and grants from KWF (UVA2014-7245 and 10529), the Maurits en Anna de Kock Stichting (2015-2), the European Research Council (ERG-StG 638193 and ERC-PoC 841112), and ZonMw (Vidi 016.156.308) to LV. Parts of this work were funded by CRUK core grant C14303/A17197 and A19274. 66 4 4 5 9 WILEY HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1574-7891 1878-0261 MOL ONCOL Mol. Oncol. JUL 2022.0 16.0 14 2693 2709 10.1002/1878-0261.13210 0.0 APR 2022 17 Oncology Science Citation Index Expanded (SCI-EXPANDED) Oncology 3B0TI 35298091.0 Green Published, Green Accepted 2023-03-23 WOS:000788504900001 0
J Wang, ZY; Zhang, YZ; Liu, Y; Liu, SC; Coleman, S; Kerr, D Wang, Zhenyu; Zhang, Yunzhou; Liu, Yan; Liu, Shichang; Coleman, Sonya; Kerr, Dermot MFC-Net : Multi-feature fusion cross neural network for salient object detection IMAGE AND VISION COMPUTING English Article Salient object detection; Cross network framework; Contextual feature transfer; Feature fusion ATTENTION Although methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach. (c) 2021 Published by Elsevier B.V. [Wang, Zhenyu; Liu, Yan] Northeastern Univ, Fac Robot Sci & Engn, Shenyang 110819, Peoples R China; [Zhang, Yunzhou] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China; [Liu, Shichang] SIASUN Robot & Automat CO Ltd, Shanghai, Peoples R China; [Coleman, Sonya; Kerr, Dermot] Univ Ulster, Intelligent Syst Res Ctr, Derry BT52 1SA, North Ireland Northeastern University - China; Northeastern University - China; Ulster University Zhang, YZ (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China. zhangyunzhou@mail.neu.edu.cn Wang, Zhenyu/0000-0001-9941-1480 Major Science and technology innovation engineering projects of Shandong Province [2019JZZY010128]; National Natural Science Foundation of China [61973066]; Open Fundation of Zhijiang Laboratory [2019KD0AD01/006]; Equipment Pre-research Fundation [61403120111]; Distinguished Creative Talent Programof Liaoning Colleges and Universities [LR2019027]; Fundamental Research Funds for the Central Universities [N182608004, N2004022] Major Science and technology innovation engineering projects of Shandong Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Open Fundation of Zhijiang Laboratory; Equipment Pre-research Fundation; Distinguished Creative Talent Programof Liaoning Colleges and Universities; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work is supported byMajor Science and technology innovation engineering projects of Shandong Province (2019JZZY010128), National Natural Science Foundation of China (No. 61973066), Open Fundation of Zhijiang Laboratory(No.2019KD0AD01/006), Equipment Pre-research Fundation (61403120111), Distinguished Creative Talent Programof Liaoning Colleges and Universities (LR2019027) and Fundamental Research Funds for the Central Universities (N182608004, N2004022). 65 1 1 8 29 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0262-8856 1872-8138 IMAGE VISION COMPUT Image Vis. Comput. SEP 2021.0 113.0 104243 10.1016/j.imavis.2021.104243 0.0 JUL 2021 10 Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Optics UA3CM Green Accepted 2023-03-23 WOS:000685039900009 0
J Jiang, GF; Wang, NQ; Zhang, YY; Zhang, YL; Yu, JB; Zhang, Y; Wei, Z; Xu, YC; Geisen, S; Friman, VP; Shen, QR Jiang, Gaofei; Wang, Ningqi; Zhang, Yaoyu; Zhang, Yuling; Yu, Jiabao; Zhang, Yong; Wei, Zhong; Xu, Yangchun; Geisen, Stefan; Friman, Ville-Petri; Shen, Qirong The relative importance of soil moisture in predicting bacterial wilt disease occurrence SOIL ECOLOGY LETTERS English Article Bacterial wilt disease; Soil moisture; Soil physicochemical properties; Rhizosphere bacterial communities; Ralstonia solanacearum; Random forest algorithm MICROBIAL COMMUNITY STRUCTURE; RALSTONIA-SOLANACEARUM; GROWTH; TEMPERATURE; INFECTION; TOMATO; PLANTS; HUMIDITY; SURVIVAL; IMPACTS Soil-borne plant diseases cause major economic losses globally. This is partly because their epidemiology is difficult to predict in agricultural fields, where multiple environmental factors could determine disease outcomes. Here we used a combination of field sampling and direct experimentation to identify key abiotic and biotic soil properties that can predict the occurrence of bacterial wilt caused by pathogenic Ralstonia solanacearum. By analyzing 139 tomato rhizosphere soils samples isolated from six provinces in China, we first show a clear link between soil properties, pathogen density and plant health. Specifically, disease outcomes were positively associated with soil moisture, bacterial abundance and bacterial community composition. Based on soil properties alone, random forest machine learning algorithm could predict disease outcomes correctly in 75% of cases with soil moisture being the most significant predictor. The importance of soil moisture was validated causally in a controlled greenhouse experiment, where the highest disease incidence was observed at 60% of maximum water holding capacity. Together, our results show that local soil properties can predict disease occurrence across a wider agricultural landscape, and that management of soil moisture could potentially offer a straightforward method for reducing crop losses to R. solanacearum. (C) Higher Education Press 2021. [Jiang, Gaofei; Wang, Ningqi; Zhang, Yaoyu; Zhang, Yuling; Yu, Jiabao; Wei, Zhong; Xu, Yangchun; Shen, Qirong] Nanjing Agr Univ, Natl Engn Res Ctr Organ Based Fertilizers, Jiangsu Collaborat Innovat Ctr Solid Organ Waste, Key Lab Plant Immun,Jiangsu Prov Key Lab Organ So, Nanjing 210095, Peoples R China; [Zhang, Yong] Southwest Univ, Coll Resources & Environm, Key Lab Efficient Utilizat Soil & Fertilizer Reso, Chongqing 400715, Peoples R China; [Geisen, Stefan] Wageningen Univ & Res, Lab Nematol, Wageningen, Netherlands; [Friman, Ville-Petri] Univ York, Dept Biol, York YO10 5DD, N Yorkshire, England Nanjing Agricultural University; Southwest University - China; Wageningen University & Research; University of York - UK Wei, Z (corresponding author), Nanjing Agr Univ, Natl Engn Res Ctr Organ Based Fertilizers, Jiangsu Collaborat Innovat Ctr Solid Organ Waste, Key Lab Plant Immun,Jiangsu Prov Key Lab Organ So, Nanjing 210095, Peoples R China. weizhong@njau.edu.cn Wei, Zhong/AAB-9103-2022 Friman, Ville-Petri/0000-0002-1592-157X National Natural Science Foundation of China [41922053, 42090062, 31972504, 42007038]; Fundamental Research Funds for the Central Universities [KJQN202116-KJQN202117, KYXK202009-KYXK202012]; Natural Science Foundation of Jiangsu Province [BK20190518, BK20180527, BK20200533]; China Postdoctoral Science Foundation [2019M651848]; Royal Society [RSG\R1\180213, CHL\R1\180031]; UKRI; Defra; Scottish Government, under the Strategic Priorities Fund Plant Bacterial Diseases programme at the University of York [BB/T010606/1]; NWO-Veni grant [016.Veni.181.078] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Natural Science Foundation of Jiangsu Province(Natural Science Foundation of Jiangsu Province); China Postdoctoral Science Foundation(China Postdoctoral Science Foundation); Royal Society(Royal Society of London); UKRI(UK Research & Innovation (UKRI)); Defra(Department for Environment, Food & Rural Affairs (DEFRA)); Scottish Government, under the Strategic Priorities Fund Plant Bacterial Diseases programme at the University of York; NWO-Veni grant We thank Dr. Alexandre Jousset and Dr. Zhipeng Liu for helpful discussions. This research was financially supported by the National Natural Science Foundation of China (41922053, 42090062, 31972504 and 42007038), and the Fundamental Research Funds for the Central Universities (KJQN202116-KJQN202117, KYXK202009-KYXK202012), the Natural Science Foundation of Jiangsu Province (BK20190518, BK20180527 and BK20200533), the China Postdoctoral Science Foundation (2019M651848) and technically supported by the Bioinformatics Center of Nanjing Agricultural University. S.G. is funded by the NWO-Veni grant (016.Veni.181.078 to S.G.). V.F. is funded by the Royal Society (RSG\R1\180213 and CHL\R1\180031) and jointly by a grant from UKRI, Defra, and the Scottish Government, under the Strategic Priorities Fund Plant Bacterial Diseases programme (BB/T010606/1) at the University of York. 73 7 7 5 21 SPRINGERNATURE LONDON CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND 2662-2289 2662-2297 SOIL ECOL LETT Soil Ecol. Lett. DEC 2021.0 3.0 4 SI 356 366 10.1007/s42832-021-0086-2 0.0 APR 2021 11 Ecology; Soil Science Emerging Sources Citation Index (ESCI) Environmental Sciences & Ecology; Agriculture XX7VA Bronze, Green Accepted 2023-03-23 WOS:000731813400001 0
J Zhang, JJ; Feng, T; Timmermans, H; Lin, ZK Zhang, Jiajia; Feng, Tao; Timmermans, Harry; Lin, Zhengkui Improved imputation of rule sets in class association rule modeling: application to transportation mode choice TRANSPORTATION English Article Rule merging; FP-tree; Class association rules; Transportation mode choice MIXED LOGIT MODEL; TRAVEL MODE; DECISION TREES; NEURAL-NETWORKS; PATTERNS; BEHAVIOR; GPS; SIMULATION; PREDICTION Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model. [Zhang, Jiajia; Lin, Zhengkui] Dalian Maritime Univ, Sch Maritime Econ & Management, Linghai 1, Dalian 116026, Liaoning, Peoples R China; [Zhang, Jiajia; Feng, Tao; Timmermans, Harry] Eindhoven Univ Technol, Urban Planning & Transportat Grp, POB 513, NL-5600 MB Eindhoven, North Brabant, Netherlands; [Feng, Tao] Hiroshima Univ, Grad Sch Adv Sci & Engn, Higashihiroshima, Hiroshima 7398529, Japan; [Timmermans, Harry] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, Nanjing 211106, Jiangsu, Peoples R China Dalian Maritime University; Eindhoven University of Technology; Hiroshima University; Nanjing University of Aeronautics & Astronautics Lin, ZK (corresponding author), Dalian Maritime Univ, Sch Maritime Econ & Management, Linghai 1, Dalian 116026, Liaoning, Peoples R China. j.zhang3@dlmu.edu.cn; t.feng@tue.nl; h.j.p.timmermans@tue.nl; zhengkuilin@dlmu.edu.cn Feng, Tao/ABD-7177-2020 Feng, Tao/0000-0002-5759-3164 China Scholarship Council China Scholarship Council(China Scholarship Council) This work was supported by China Scholarship Council. 60 0 0 13 28 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 0049-4488 1572-9435 TRANSPORTATION Transportation FEB 2023.0 50.0 1 63 106 10.1007/s11116-021-10238-9 0.0 NOV 2021 44 Engineering, Civil; Transportation; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Engineering; Transportation 8G0VK 2023-03-23 WOS:000722807500001 0
J Wang, MS; Chen, ZY; Rong, HH; Mu, L; Zhu, PY; Shi, ZL Wang, Mingshu; Chen, Zheyan; Rong, Helena Hang; Mu, Lan; Zhu, Pengyu; Shi, Zenglin Ridesharing accessibility from the human eye: Spatial modeling of built environment with street-level images COMPUTERS ENVIRONMENT AND URBAN SYSTEMS English Article Built environment; Accessibility; Ridesharing; Uber; Street -level image PUBLIC-TRANSIT; UBER; DEMAND; TRAVEL Scholarly interest in the accessibility of ridesharing services stems from debates within the transportation and planning communities on the inequality of access to transit and the growing digital divide embedded within novel forms of transit services. Contributing to such discussions, this paper considers the city of Atlanta as a case study and explores the links between the spatial disparity of accessibility to different Uber ridesharing products and features of the built environment extracted from Google Street View (GSV) imagery. The variability of wait time for an Uber service is used as a proxy of accessibility, while semantic image segmentation is performed on GSV imagery using a deep learning model DeepLabv3+ to identify notable spatial features captured at the eye -level perspective around service pick-up points. Results from spatial models show that proportions of built environment features such as buildings, vegetation, and terrains are associated with longer waiting times. In contrast, larger salient regions with foreground features are associated with shorter waiting times for several Uber service products. [Wang, Mingshu] Univ Glasgow, Sch Geog & Earth Sci, Glasgow G12 8QQ, Lanark, Scotland; [Chen, Zheyan] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands; [Rong, Helena Hang] Columbia Univ, Grad Sch Architecture Planning & Preservat, New York, NY USA; [Mu, Lan] Univ Georgia, Dept Geog, Athens, GA 30602 USA; [Zhu, Pengyu] Hong Kong Univ Sci & Technol, Div Publ Policy, Hong Kong, Peoples R China; [Shi, Zenglin] Univ Amsterdam, Video & Image Sense Lab, Amsterdam, Netherlands University of Glasgow; Utrecht University; Columbia University; University System of Georgia; University of Georgia; Hong Kong University of Science & Technology; University of Amsterdam Wang, MS (corresponding author), Univ Glasgow, Sch Geog & Earth Sci, Glasgow G12 8QQ, Lanark, Scotland. Mingshu.Wang@glasgow.ac.uk; z.chen1@uu.nl; hhr2112@columbia.edu; mulan@uga.edu; pengyuzhu@ust.hk; z.shi@uva.nl Wang, Mingshu/I-1533-2014 Wang, Mingshu/0000-0001-5260-3143 University of Glasgow Reinvigorating Research Funding [201644-20, 126438-01]; Innova-tion and Technology Commission; Government of the Hong Kong Special Administrative Region [GSP/035/20]; Public Policy Research (PPR) Grant Scheme, PICO, Hong Kong SAR Govern-ment [2021]; Natural Science Foundation of Guangdong Province [2021A1515011250]; 2022 Guangzhou Basic Research Scheme University of Glasgow Reinvigorating Research Funding; Innova-tion and Technology Commission; Government of the Hong Kong Special Administrative Region; Public Policy Research (PPR) Grant Scheme, PICO, Hong Kong SAR Govern-ment; Natural Science Foundation of Guangdong Province(National Natural Science Foundation of Guangdong Province); 2022 Guangzhou Basic Research Scheme This study was partially supported by the University of Glasgow Reinvigorating Research Funding (Grant No. 201644-20) and Research Enabling/Impact Generating Scheme (Grant No. 126438-01) ; Innova-tion and Technology Commission, the Government of the Hong Kong Special Administrative Region (Grant No. GSP/035/20) ; the Public Policy Research (PPR) Grant Scheme, PICO, Hong Kong SAR Govern-ment (Grant No. 2021.A7.021.21C) ; the Natural Science Foundation of Guangdong Province (Grant No. 2021A1515011250) ; and the 2022 Guangzhou Basic Research Scheme. The authors are very grateful for the editor and anonymous reviewers for their constructive comments. All remaining errors are our own. The usual disclaimers apply. 75 7 7 10 11 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0198-9715 1873-7587 COMPUT ENVIRON URBAN Comput. Environ. Urban Syst. OCT 2022.0 97.0 101858 10.1016/j.compenvurbsys.2022.101858 0.0 AUG 2022 10 Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science; Regional & Urban Planning Social Science Citation Index (SSCI) Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science; Public Administration 3U1IU Green Accepted, hybrid 2023-03-23 WOS:000840725500002 0
J Bhikha, C; Andreasen, A; Christensen, EI; Letts, RFR; Pantanowitz, A; Rubin, DM; Thomsen, JS; Zhai, XY Bhikha, Charita; Andreasen, Arne; Christensen, Erik I.; Letts, Robyn F. R.; Pantanowitz, Adam; Rubin, David M.; Thomsen, Jesper S.; Zhai, Xiao-Yue Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE English Article RECONSTRUCTION; ARCHITECTURE; REGISTRATION; MOUSE An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron. [Bhikha, Charita; Letts, Robyn F. R.; Pantanowitz, Adam; Rubin, David M.] Univ Witwatersrand, Sch Elect & Informat Engn, Biomed Engn Res Grp, ZA-2050 Johannesburg, South Africa; [Andreasen, Arne; Christensen, Erik I.; Thomsen, Jesper S.] Univ Aarhus, Dept Biomed, DK-8000 Aarhus C, Denmark; [Zhai, Xiao-Yue] China Med Univ, Dept Histol & Embryol, Shenyang 110122, Peoples R China University of Witwatersrand; Aarhus University; China Medical University Bhikha, C (corresponding author), Univ Witwatersrand, Sch Elect & Informat Engn, Biomed Engn Res Grp, Private Bag 3, ZA-2050 Johannesburg, South Africa. charita.bhikha@gmail.com Thomsen, Jesper/HKN-2794-2023 Letts, Robyn/0000-0001-6770-5070; Thomsen, Jesper Skovhus/0000-0001-9386-6679; Pantanowitz, Adam/0000-0002-4080-6389; Rubin, David/0000-0003-0316-9197 University of Witwatersrand; Danish Council for Independent Research, Medical Sciences [FSS 11-104255]; Novo Nordic Foundation; Danish Biotechnology Program, Daloon Foundation; European Commission (EU) [05085]; University of Aarhus Research Foundation; Maskinfabrikant Jochum Jensen og hustru Mette Marie Jensen; F. Poulsens Mindelegat; Soster og Verner Lipperts Fond; Bagenkop Nielsens Myopi-Fond University of Witwatersrand; Danish Council for Independent Research, Medical Sciences(Det Frie Forskningsrad (DFF)); Novo Nordic Foundation(Novo Nordisk Foundation); Danish Biotechnology Program, Daloon Foundation; European Commission (EU)(European CommissionEuropean Commission Joint Research Centre); University of Aarhus Research Foundation; Maskinfabrikant Jochum Jensen og hustru Mette Marie Jensen; F. Poulsens Mindelegat; Soster og Verner Lipperts Fond; Bagenkop Nielsens Myopi-Fond The authors would like to thank the University of Witwatersrand for providing the funding and resources required to carry out this research. The authors are grateful for the excellent technical assistance of Inger B. Kristoffersen and Birgitte Lundbol Grann. The establishment of the histological material was supported by grants from The Danish Council for Independent Research, Medical Sciences, FSS 11-104255 (to Erik I. Christensen), the Novo Nordic Foundation, the Danish Biotechnology Program, Daloon Foundation, the European Commission (EU Framework Program 6, EuroGene, Contract no. 05085), and the University of Aarhus Research Foundation. Arne Andreasen was supported by Maskinfabrikant Jochum Jensen og hustru Mette Marie Jensen, F. Poulsens Mindelegat, Soster og Verner Lipperts Fond, and Bagenkop Nielsens Myopi-Fond. The Amira visualization system was donated to Arne Andreasen by the Toyota Foundation. 31 2 2 0 2 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1748-670X 1748-6718 COMPUT MATH METHOD M Comput. Math. Method Med. 2015.0 2015.0 545809 10.1155/2015/545809 0.0 13 Mathematical & Computational Biology Science Citation Index Expanded (SCI-EXPANDED) Mathematical & Computational Biology CL8HC 26170896.0 Green Submitted, Green Published, gold 2023-03-23 WOS:000357214000001 0
J Li, ZM; Bejarbaneh, BY; Asteris, PG; Koopialipoor, M; Armaghani, DJ; Tahir, MM Li, Zimu; Bejarbaneh, Behnam Yazdani; Asteris, Panagiotis G.; Koopialipoor, Mohammadreza; Armaghani, Danial Jahed; Tahir, M. M. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass SOFT COMPUTING English Article TBM performance; Penetration rate; Gene expression programming; Neural network; Multivariate adaptive regression splines; Whale optimization algorithm TUNNEL BORING MACHINE; COMPRESSIVE STRENGTH; OPTIMIZATION TECHNIQUES; PERFORMANCE PREDICTION; BEHAVIOR; EXCAVATION; MODELS; DAMAGE The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM's performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R-2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R-2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R-2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were, respectively, defined as decision variables and objective function for the WOA technique to find the optimum values of PR. As a consequence of optimizing the GEP equation, the maximum value of PR rose from 3.75 m/h to 4.022 m/h, equivalent to an increase of 7.25% in PR value. The findings of this study verified the applicability of the proposed hybrid GEP and WOA approach in the site investigation phase of tunneling projects constructed by TBMs. [Li, Zimu] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China; [Bejarbaneh, Behnam Yazdani] Univ Teknol Malaysia, Fac Civil Engn, Dept Geotech & Transportat, Skudai 81310, Johor, Malaysia; [Asteris, Panagiotis G.] Sch Pedag & Technol Educ, Computat Mech Lab, Athens 14121, Greece; [Koopialipoor, Mohammadreza] Amirkabir Univ Technol, Fac Civil & Environm Engn, Tehran 15914, Iran; [Armaghani, Danial Jahed] Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia; [Tahir, M. M.] Univ Teknol Malaysia, UTM Construct Res Ctr, Sch Civil Engn, Inst Smart Infrastruct & Innovat Construct ISI, Johor Baharu 81310, Johor, Malaysia Southwest University - China; Universiti Teknologi Malaysia; ASPETE - School of Pedagogical & Technological Education; Amirkabir University of Technology; Universiti Malaya; Universiti Teknologi Malaysia Armaghani, DJ (corresponding author), Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia. lzm0709@email.swu.edu.cn; ybbehnam2@live.utm.my; asteris@aspete.gr; Mr.koopialipoor@aut.ac.ir; danialarmaghani@um.edu.my; mahmoodtahir@utm.my Bejarbaneh, Behnam Yazdani/AAF-2764-2020; Asteris, Panagiotis G./U-3798-2017 Bejarbaneh, Behnam Yazdani/0000-0002-8170-6296; Asteris, Panagiotis G./0000-0002-7142-4981 69 12 12 5 23 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1432-7643 1433-7479 SOFT COMPUT Soft Comput. SEP 2021.0 25.0 17 11877 11895 10.1007/s00500-021-06005-8 0.0 JUL 2021 19 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Computer Science TX9TV 2023-03-23 WOS:000673885600001 0
J Chen, BY; Cheng, XP; Kwan, MP; Schwanen, T Chen, Bi Yu; Cheng, Xue-Ping; Kwan, Mei-Po; Schwanen, Tim Evaluating spatial accessibility to healthcare services under travel time uncertainty: A reliability-based floating catchment area approach JOURNAL OF TRANSPORT GEOGRAPHY English Article Healthcare accessibility; Travel time uncertainty; Reliability; Big data analysis SPACE-TIME; GIS ENVIRONMENT; EQUITY; ACCESS; MODEL; DISPARITIES; PROVISION; PATTERNS; CHICAGO; SCALE Most existing healthcare accessibility studies ignore the travel time uncertainty that are commonly encountered in road networks. This study aims to examine the impacts of travel time uncertainty on healthcare accessibility. A reliability-based two-step floating catchment area (2SFCA) method is proposed to evaluate healthcare accessibility under travel time uncertainty. The proposed measure generalizes the conventional 2SFCA measure by explicitly considering individuals' reliability constraints when scheduling visits to healthcare facilities in the face of travel time uncertainty. The proposed measure is further used to investigate travel time uncertainty impacts in a comprehensive case study. A big dataset of taxi trajectories is collected in the case study to extract dynamic information on travel time distributions. The results of the case study highlight the significant but heterogeneous impacts of travel time uncertainty on healthcare accessibility for various parts of the city at different times of the day. They also have several methodological implications for the evaluation of healthcare accessibility under travel time uncertainty. [Chen, Bi Yu; Cheng, Xue-Ping] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Peoples R China; [Chen, Bi Yu; Cheng, Xue-Ping] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China; [Chen, Bi Yu; Schwanen, Tim] Univ Oxford, Sch Geog & Environm, Oxford, England; [Kwan, Mei-Po] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China; [Kwan, Mei-Po] Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3584 CB Utrecht, Netherlands Wuhan University; University of Oxford; Chinese University of Hong Kong; Chinese University of Hong Kong; Utrecht University Chen, BY (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Peoples R China. chen.biyu@whu.edu.cn Chen, Bi Yu/B-1803-2012; Schwanen, Tim/ABE-6070-2021; Kwan, Mei-Po/S-4162-2016 Chen, Bi Yu/0000-0003-3591-9968; Kwan, Mei-Po/0000-0001-8602-9258 National Key Research and Development Program [2017YFB0503600]; National Natural Science Foundation of China [41571149]; Research Committee on Research Sustainability of Major RGC Funding Scheme of the Chinese University of Hong Kong National Key Research and Development Program; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Research Committee on Research Sustainability of Major RGC Funding Scheme of the Chinese University of Hong Kong The authors are thankful to four anonymous referees for their comments and suggestions that improved the quality of this paper. Bi Yu Chen was supported by research grants from the National Key Research and Development Program (No. 2017YFB0503600) and the National Natural Science Foundation of China (No. 41571149). Mei-Po Kwan was supported by a grant from the Research Committee on Research Sustainability of Major RGC Funding Scheme of the Chinese University of Hong Kong. 59 34 35 17 61 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0966-6923 1873-1236 J TRANSP GEOGR J. Transp. Geogr. JUL 2020.0 87.0 102794 10.1016/j.jtrangeo.2020.102794 0.0 11 Economics; Geography; Transportation Social Science Citation Index (SSCI) Business & Economics; Geography; Transportation NH8LD 2023-03-23 WOS:000564914200004 0
J Xia, CK; Zhang, YZ; Coleman, SA; Weng, CY; Liu, HD; Liu, SC; Chen, IM Xia, Chongkun; Zhang, Yunzhou; Coleman, Sonya A.; Weng, Ching-Yen; Liu, Houde; Liu, Shichang; Chen, I-Ming Graph Wasserstein Autoencoder-Based Asymptotically Optimal Motion Planning With Kinematic Constraints for Robotic Manipulation IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING English Article; Early Access Planning; Robots; Collision avoidance; Kinematics; Neural networks; System dynamics; Probabilistic logic; Motion planning; graph wasserstein autoencoder; kinematic constraints; collision detection; robotic manipulation This paper presents a learning based motion planning method for robotic manipulation, aiming to solve the asymptotically-optimal motion planning problem with nonlinear kinematics in a complex environment. The core of the proposed method is based on a novel neural network model, i.e., graph wasserstein autoencoder (GraphWAE) network, which is used to represent the implicit sampling distributions of the configuration space (C-space) for sampling-based planning algorithms. Through learning the implicit distributions, we can guide the planning process to search or extend in the desired region to reduce the collision checks dramatically for fast and high-quality motion planning. The theoretical analysis and proofs are given to demonstrate the probabilistic completeness and asymptotic optimality of the proposed method. Numerical simulations and experiments are conducted to validate the effectiveness of the proposed method through a series of planning problems from 2D, 6D and 12D robot C-spaces in the challenging scenes. Results indicate that the proposed method can achieve better planning performance than the state-of-the-art planning algorithms. [Xia, Chongkun; Liu, Houde] Tsinghua Univ, Intelligent Control & Tele Sci Res Ctr, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China; [Zhang, Yunzhou] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China; [Coleman, Sonya A.] Ulster Univ, Sch Comp & Intelligent Syst, Derry BT48 7JL, North Ireland; [Weng, Ching-Yen] Univ Shanghai Sci & Technol, Inst Machine Intelligence, Shanghai 200000, Peoples R China; [Liu, Shichang] SIASUN Robot & Automat Co Ltd, Shenyang 110819, Peoples R China; [Chen, I-Ming] Nanyang Technol Univ, Robot Res Ctr, Singapore 639798, Singapore Tsinghua University; Northeastern University - China; Ulster University; University of Shanghai for Science & Technology; Nanyang Technological University & National Institute of Education (NIE) Singapore; Nanyang Technological University Liu, HD (corresponding author), Tsinghua Univ, Intelligent Control & Tele Sci Res Ctr, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China.;Zhang, YZ (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China. xiachongkun@sz.tsinghua.edu.cn; zhangyunzhou@mail.neu.edu.cn; sa.coleman@ulster.ac.uk; wengchingyen@usst.edu.cn; liu.hd@sz.tsinghua.edu.cn; liushichang@siasun.com; michen@ntu.edu.sg Chen, I-Ming/A-3811-2011 Chen, I-Ming/0000-0002-4831-3781 National Natural Science Foundation of China [61973066, 61803221, U1813216]; Equipment Pre-Research Foundation [61403120111]; Major Science and Technology Projects of Liaoning Province [2021JH1/10400049]; Foundation of Key Laboratory of Equipment Reliability [WD2C20205500306]; Fundamental Research Funds for the Central Universities [N2004022]; Guangdong Young Talent with Scientific and Technological Innovation [2019TQ05Z111] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Equipment Pre-Research Foundation; Major Science and Technology Projects of Liaoning Province; Foundation of Key Laboratory of Equipment Reliability; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Guangdong Young Talent with Scientific and Technological Innovation This work was supported in part by the National Natural Science Foundation of China under Grant 61973066, Grant 61803221, and Grant U1813216; in part by the Equipment Pre-Research Foundation under Grant 61403120111; in part by the Major Science and Technology Projects of Liaoning Province under Grant 2021JH1/10400049; in part by the Foundation of Key Laboratory of Equipment Reliability under Grant WD2C20205500306; in part by the Fundamental Research Funds for the Central Universities under Grant N2004022; and in part by the Guangdong Young Talent with Scientific and Technological Innovation under Grant 2019TQ05Z111. 33 0 0 7 14 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-5955 1558-3783 IEEE T AUTOM SCI ENG IEEE Trans. Autom. Sci. Eng. 10.1109/TASE.2022.3146967 0.0 FEB 2022 14 Automation & Control Systems Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems YX7PJ Green Submitted 2023-03-23 WOS:000754290000001 0
J Wong, WKM; Jiang, GZ; Sorensen, AE; Chew, YV; Lee-Maynard, C; Liuwantara, D; Williams, L; O'Connell, PJ; Dalgaard, LT; Ma, RC; Hawthorne, WJ; Joglekar, MV; Hardikar, AA Wong, Wilson K. M.; Jiang, Guozhi; Sorensen, Anja E.; Chew, Yi Vee; Lee-Maynard, Cody; Liuwantara, David; Williams, Lindy; O'Connell, Philip J.; Dalgaard, Louise T.; Ma, Ronald C.; Hawthorne, Wayne J.; Joglekar, Mugdha, V; Hardikar, Anandwardhan A. The long noncoding RNA MALAT1 predicts human islet isolation quality JCI INSIGHT English Article PANCREATIC DONORS; GENE-REGULATION; CELL FUNCTION; TRANSPLANTATION; MULTICENTER; ANNOTATION; EXPRESSION; VARIABLES; SCORE Human islet isolation is a cost- and resource-intensive program for generating islets for cell therapy in type 1 diabetes. However, only one-third of cadaveric pancreases get to clinical transplantation because of low quality/number of islets. There is a need to identify biomarkers that predict the quality of islets, before initiating their isolation. Here, we sequenced transcriptomes from 18 human islet preparations stratified into 3 groups (group 1: best quality/transplantable islets; group 2: intermediary quality; and group 3: inferior quality/nontransplantable islets) based on routine measurements, including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long noncoding RNAs (lncRNAs) that were significantly different across all group-wise comparisons (group 1 vs. group 2, group 2 vs. group 3, and group 1 vs. group 3). Two variants of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA were common across all comparisons. We then confirmed RNA-Seq findings in a validation set of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrated that assessing the levels of MALAT1 variants alone (receiver operator characteristic curve ADC: 0.83) offers higher specificity in predicting postisolation islet quality, further improving the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/BMI/North American Islet Donor Score. We present this resource of islet quality-stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical-grade (good manufacturing practice-grade) islet isolation. [Wong, Wilson K. M.; Lee-Maynard, Cody; Joglekar, Mugdha, V; Hardikar, Anandwardhan A.] Univ Sydney, Fac Med & Hlth, NHMRC Clin Trials Ctr, Diabet & Islet Biol Grp, Camperdown, NSW, Australia; [Jiang, Guozhi; Ma, Ronald C.] Chinese Univ Hong Kong, Prince Wales Hosp, Dept Med & Therapeut, Hong Kong, Peoples R China; [Sorensen, Anja E.; Dalgaard, Louise T.] Roskilde Univ, Dept Sci & Environm, Roskilde, Denmark; [Chew, Yi Vee; Liuwantara, David; Williams, Lindy; O'Connell, Philip J.; Hawthorne, Wayne J.] Univ Sydney, Ctr Transplant & Renal Res, Westmead Inst Med Res, 176 Hawkesbury Rd, Westmead, NSW, Australia; [Ma, Ronald C.] Chinese Univ Hong Kong, Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China University of Sydney; Chinese University of Hong Kong; Prince of Wales Hospital; Roskilde University; University of Sydney; Westmead Institute for Medical Research; Chinese University of Hong Kong Hawthorne, WJ (corresponding author), Univ Sydney, Ctr Transplant & Renal Res, Westmead Inst Med Res, 176 Hawkesbury Rd, Westmead, NSW, Australia.;Joglekar, MV; Hardikar, AA (corresponding author), Univ Sydney, NHMRC Clin Trials Ctr, Diabet & Islet Biol Grp, Level 6,Med Fdn Bldg,92-94 Parramatta Rd, Camperdown, NSW, Australia. wayne.hawthorne@sydney.edu.au; mugdha.joglekar@sydney.edu.au; anand.hardikar@sydney.edu.au Wong, Wilson/AAC-1438-2022; Joglekar, Mugdha V./AFP-0659-2022; O'Connell, Philip John/AFK-9353-2022; Ma, Ronald/C-2788-2009; Hardikar, Anandwardhan/ACF-7730-2022; Dalgaard, Louise/O-3788-2015 O'Connell, Philip John/0000-0002-2036-283X; Ma, Ronald/0000-0002-1227-803X; Sorensen, Anja Elaine/0000-0001-7557-5376; Wong, Wilson/0000-0001-7736-6834; Chew, Yi Vee/0000-0003-1484-8654; Dalgaard, Louise/0000-0002-3598-2775 Marian and E.H. Flack Trust [FLACK-2016-17]; Juvenile Diabetes Research Foundation (JDRF) Australia T1D CRN [4-CDA-2016-228-M-B]; JDRF International [3-APF-2016-178-A-N]; Australian Research Council [FT110100254]; Sydney Medical Foundation; Danish Diabetes Academy - Novo Nordisk Foundation; Postdoctoral Fellowship Scheme, The Chinese University of Hong Kong; University of Sydney; JDRF Australia; Australian Research Council [FT110100254] Funding Source: Australian Research Council Marian and E.H. Flack Trust; Juvenile Diabetes Research Foundation (JDRF) Australia T1D CRN; JDRF International(Juvenile Diabetes Research Foundation); Australian Research Council(Australian Research Council); Sydney Medical Foundation; Danish Diabetes Academy - Novo Nordisk Foundation; Postdoctoral Fellowship Scheme, The Chinese University of Hong Kong; University of Sydney(University of Sydney); JDRF Australia; Australian Research Council(Australian Research Council) This study was supported by the Marian and E.H. Flack Trust (to WJH; FLACK-2016-17), Juvenile Diabetes Research Foundation (JDRF) Australia T1D CRN (to AAH; 4-CDA-2016-228-M-B), JDRF International (to MVJ; 3-APF-2016-178-A-N), Australian Research Council (to AAH; FT110100254), Sydney Medical Foundation (to AAH and MVJ), Danish Diabetes Academy funded by the Novo Nordisk Foundation (to AAH, LTD, and AES). In addition, we acknowledge the Postdoctoral Fellowship Scheme, The Chinese University of Hong Kong (to GJ) and the University of Sydney postgraduate award and top-up scholarship from the JDRF Australia (to WKMW). 55 12 12 0 3 AMER SOC CLINICAL INVESTIGATION INC ANN ARBOR 2015 MANCHESTER RD, ANN ARBOR, MI 48104 USA 2379-3708 JCI INSIGHT JCI Insight AUG 22 2019.0 4.0 16 e129299 10.1172/jci.insight.129299 0.0 13 Medicine, Research & Experimental Science Citation Index Expanded (SCI-EXPANDED) Research & Experimental Medicine IS5YL 31361602.0 gold, Green Accepted, Green Published 2023-03-23 WOS:000482229000017 0
J Nie, PL; Xu, GQ; Jiao, LT; Liu, SY; Liu, J; Meng, WZ; Wu, HY; Feng, MQ; Wang, WZ; Jing, ZJ; Zheng, X Nie, Pengli; Xu, Guangquan; Jiao, Litao; Liu, Shaoying; Liu, Jian; Meng, Weizhi; Wu, Hongyue; Feng, Meiqi; Wang, Weizhe; Jing, Zhengjun; Zheng, Xi Sparse Trust Data Mining IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY English Article Data mining; Sparse matrices; Data models; Computational modeling; Collaboration; Big Data; Recommender systems; Anti-sparsification; recommendation system; sparse trust; trust model RECOMMENDER SYSTEM; CLOUD; MODEL As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem. [Nie, Pengli; Xu, Guangquan; Liu, Jian; Feng, Meiqi; Wang, Weizhe] Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Adv Networking TANK, Tianjin 300350, Peoples R China; [Xu, Guangquan; Jiao, Litao] Qingdao Huanghai Univ, Sch Big Data, Qingdao 266000, Peoples R China; [Liu, Shaoying] Hiroshima Univ, Sch Informat & Data Sci, Higashihiroshima 7398511, Japan; [Meng, Weizhi] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Copenhagen, Denmark; [Wu, Hongyue] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China; [Jing, Zhengjun] Jiangsu Univ Technol, Sch Comp Engn, Changzhou 213001, Peoples R China; [Zheng, Xi] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia Tianjin University; Hiroshima University; Technical University of Denmark; Tianjin University; Jiangsu University of Technology; Macquarie University Liu, J (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Adv Networking TANK, Tianjin 300350, Peoples R China.;Xu, GQ (corresponding author), Qingdao Huanghai Univ, Sch Big Data, Qingdao 266000, Peoples R China.;Wu, HY (corresponding author), Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China. pretty_pl@sina.com; losin@tju.edu.cn; jiaolitao_11@163.com; sliu@hiroshima-u.ac.jp; jianliu@tju.edu.cn; weme@dtu.dk; hongyue.wu@tju.edu.cn; fengmeiqi@tju.edu.cn; will@tju.edu.cn; zhengjun_jing@163.com; james.zheng@mq.edu.au Jing, Zhengjun/GWC-9082-2022 Wang, Weizhe/0000-0002-6910-687X; zheng, xi/0000-0002-2572-2355; Meng, Weizhi/0000-0003-4384-5786 National Key Research and Development Program of China [2019YFB2101700]; National Science Foundation of China [62172297, 61902276]; Key Research and Development Project of Sichuan Province [21SYSX0082]; Tianjin Intelligent Manufacturing Special Fund Project [20201159]; Natural Science Foundation of Tianjin City [19JCQNJC00200] National Key Research and Development Program of China; National Science Foundation of China(National Natural Science Foundation of China (NSFC)); Key Research and Development Project of Sichuan Province; Tianjin Intelligent Manufacturing Special Fund Project; Natural Science Foundation of Tianjin City(Natural Science Foundation of Tianjin) This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2101700, in part by the National Science Foundation of China under Grant 62172297 and Grant 61902276, in part by the Key Research and Development Project of Sichuan Province under Grant 21SYSX0082, in part by Tianjin Intelligent Manufacturing Special Fund Project under Grant 20201159, and in part by the Natural Science Foundation of Tianjin City under Grant 19JCQNJC00200. 57 0 0 10 29 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1556-6013 1556-6021 IEEE T INF FOREN SEC IEEE Trans. Inf. Forensic Secur. 2021.0 16.0 4559 4573 10.1109/TIFS.2021.3109412 0.0 15 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering UQ4ZX Green Published 2023-03-23 WOS:000696074300008 0
J Wang, T; Lei, SY; Jiang, YY; Chang, C; Snoussi, H; Shan, GC; Fu, Y Wang, Tian; Lei, Shiye; Jiang, Youyou; Chang, Choi; Snoussi, Hichem; Shan, Guangcun; Fu, Yao Accelerating temporal action proposal generation via high performance computing FRONTIERS OF COMPUTER SCIENCE English Article temporal convolution; temporal action proposal eneration; deep learning Temporal action proposal generation aims to output the starting and ending times of each potential action for long videos and often suffers from high computation cost. To address the issue, we propose a new temporal convolution network called Multipath Temporal ConvNet (MTCN). In our work, one novel high performance ring parallel architecture based is further introduced into temporal action proposal generation in order to respond to the requirements of large memory occupation and a large number of videos. Remarkably, the total data transmission is reduced by adding a connection between multiple-computing load in the newly developed architecture. Compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection tasks with multiple GPUs. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms-other state-of-art temporal action detection methods with high recall and high temporal precision. In addition, a time metric is further proposed here to evaluate the speed performancein the distributed training process. [Wang, Tian] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China; [Wang, Tian; Lei, Shiye] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China; [Jiang, Youyou] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China; [Chang, Choi] Gachon Univ, Dept Comp Engn, Seongnam 13120, South Korea; [Snoussi, Hichem] Univ Technol Troyes, Inst Charles Delaunay LM2S FRE CNRS 2019, F-10010 Troyes, France; [Shan, Guangcun] Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China; [Fu, Yao] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Peoples R China Beihang University; Beihang University; Tsinghua University; Gachon University; Universite de Technologie de Troyes; Beihang University; Chinese Academy of Sciences; Changchun Institute of Optics, Fine Mechanics & Physics, CAS Shan, GC (corresponding author), Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China. guangcunshan@hotmail.com National Key Research and Development Program of China [2016YFE0204200]; National Natural Science Foundation of China [61972016, 62032016]; Beijing Natural Science Foundation [L191007]; Fundamental Research Funds for the Central Universities [YWF-21-BJ-J-313, YWF-20-BJ-J-612]; Open Research Fund of Digital Fujian Environment Monitoring Internet of Things Laboratory Foundation [202004] National Key Research and Development Program of China; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Beijing Natural Science Foundation(Beijing Natural Science Foundation); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Open Research Fund of Digital Fujian Environment Monitoring Internet of Things Laboratory Foundation This work was partially supported by the National Key Research and Development Program of China (2016YFE0204200), the National Natural Science Foundation of China (Grant Nos. 61972016, 62032016), Beijing Natural Science Foundation (L191007), the Fundamental Research Funds for the Central Universities (YWF-21-BJ-J-313 and YWF-20-BJ-J-612), Open Research Fund of Digital Fujian Environment Monitoring Internet of Things Laboratory Foundation (202004). The experimental platform is provided by Marc Casas at the Barcelona Supercomputing Center (BSC). 35 1 1 3 31 HIGHER EDUCATION PRESS BEIJING CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R CHINA 2095-2228 2095-2236 FRONT COMPUT SCI-CHI Front.. Comput. Sci. AUG 2022.0 16.0 4 164317 10.1007/s11704-021-0173-7 0.0 10 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science XA4WM Green Submitted 2023-03-23 WOS:000720649100002 0
J Adenusi, H; Chass, GA; Passerini, S; Tian, KV; Chen, GH Adenusi, Henry; Chass, Gregory A. A.; Passerini, Stefano; Tian, Kun V. V.; Chen, Guanhua Lithium Batteries and the Solid Electrolyte Interphase (SEI)-Progress and Outlook ADVANCED ENERGY MATERIALS English Review; Early Access Helmholtz double layer; lithium metal batteries; lithium-ion batteries; solid electrolyte interphases; Stern Helmholtz layer RAY PHOTOELECTRON-SPECTROSCOPY; SURFACE-FILM FORMATION; LI-ION BATTERIES; CARBONATE-PROPYLENE CARBONATE; ORIENTED PYROLYTIC-GRAPHITE; ATOMIC-FORCE MICROSCOPY; IN-SITU; ETHYLENE CARBONATE; MOLECULAR-DYNAMICS; METAL ANODES Interfacial dynamics within chemical systems such as electron and ion transport processes have relevance in the rational optimization of electrochemical energy storage materials and devices. Evolving the understanding of fundamental electrochemistry at interfaces would also help in the understanding of relevant phenomena in biological, microbial, pharmaceutical, electronic, and photonic systems. In lithium-ion batteries, the electrochemical instability of the electrolyte and its ensuing reactive decomposition proceeds at the anode surface within the Helmholtz double layer resulting in a buildup of the reductive products, forming the solid electrolyte interphase (SEI). This review summarizes relevant aspects of the SEI including formation, composition, dynamic structure, and reaction mechanisms, focusing primarily on the graphite anode with insights into the lithium metal anode. Furthermore, the influence of the electrolyte and electrode materials on SEI structure and properties is discussed. An update is also presented on state-of-the-art approaches to quantitatively characterize the structure and changing properties of the SEI. Lastly, a framework evaluating the standing problems and future research directions including feasible computational, machine learning, and experimental approaches are outlined. [Adenusi, Henry; Chen, Guanhua] Hong Kong Quantum AI Lab, 17 Sci Pk West Ave, Hong Kong, Peoples R China; [Chass, Gregory A. A.] Queen Mary Univ London, Sch Phys & Chem Sci, Dept Chem, London E1, England; [Chass, Gregory A. A.; Tian, Kun V. V.] McMaster Univ, Dept Chem & Biol Chem, Hamilton, ON L8S 4L8, Canada; [Chass, Gregory A. A.; Tian, Kun V. V.] Univ British Columbia, Fac Land & Food Syst, Vancouver, BC V6T 1Z4, Canada; [Passerini, Stefano] Sapienza Univ Rome, Dept Chem, I-00185 Rome, Italy; [Passerini, Stefano] Helmholtz Inst Ulm, Helmholtzstr 11, D-89081 Ulm, Germany; [Passerini, Stefano] Karlsruhe Inst Technol, POB 3640, D-76021 Karlsruhe, Germany; [Tian, Kun V. V.] Sapienza Univ Rome, Dept Chem & Chem Sci Pharm, I-00185 Rome, Italy; [Chen, Guanhua] Univ Hong Kong, Dept Chem, Hong Kong, Peoples R China University of London; Queen Mary University London; McMaster University; University of British Columbia; Sapienza University Rome; Helmholtz Association; Karlsruhe Institute of Technology; Helmholtz Association; Karlsruhe Institute of Technology; Sapienza University Rome; University of Hong Kong Chen, GH (corresponding author), Hong Kong Quantum AI Lab, 17 Sci Pk West Ave, Hong Kong, Peoples R China.;Tian, KV (corresponding author), McMaster Univ, Dept Chem & Biol Chem, Hamilton, ON L8S 4L8, Canada.;Tian, KV (corresponding author), Univ British Columbia, Fac Land & Food Syst, Vancouver, BC V6T 1Z4, Canada.;Tian, KV (corresponding author), Sapienza Univ Rome, Dept Chem & Chem Sci Pharm, I-00185 Rome, Italy.;Chen, GH (corresponding author), Univ Hong Kong, Dept Chem, Hong Kong, Peoples R China. kun.tian@uniroma1.it; ghc@everest.hku.hk Adenusi, Henry/HGD-3095-2022; Tian, Kun V/C-7783-2017 Adenusi, Henry/0000-0002-9832-8834; Tian, Kun V/0000-0003-0102-0620 Hong Kong Quantum AI Lab Limited.; BEIS, UK; Helmholtz Association; Faculty of Land and Food Systems (University of British Columbia); Natural Sciences and Engineering Research Council; EU-Horizon 2020; RAL-ISIS neutron and muon facility, STFC, UK; AIR@InnoHK of Hong Kong government; [RGPIN 04598]; [299668]; [RB1710285]; [RB1710444]; [RB1710447]; [RB2010426]; [RB2010696] Hong Kong Quantum AI Lab Limited.; BEIS, UK; Helmholtz Association(Helmholtz Association); Faculty of Land and Food Systems (University of British Columbia); Natural Sciences and Engineering Research Council(Natural Sciences and Engineering Research Council of Canada (NSERC)); EU-Horizon 2020; RAL-ISIS neutron and muon facility, STFC, UK; AIR@InnoHK of Hong Kong government; ; ; ; ; ; ; The authors would like to thank Miss Stephanie Ferreira for her graphical expertise in constructing Figures 1-4, 6-8, and 11. H.A. acknowledges the Hong Kong Quantum AI Lab Limited for supporting his fellowship. G.A.C. acknowledges the EU-Horizon 2020 and BEIS, UK for supporting the FUNMIN project (ACT, No. 299668), and the RAL-ISIS neutron and muon facility, STFC, UK is thanked for supporting the following funding related to the evolution of the neutron science covered in this work: RB1710285, RB1710444, RB1710447, RB2010426, RB2010696. S.P. acknowledges the basic funding from the Helmholtz Association. K.V.T. thanks Prof. Bruno Botta (Sapienza University of Rome), Prof. David H. Farrar and the Department of Chemistry and Chemical Biology (McMaster University), as well as Prof. Rickey Y. Yada and the Faculty of Land and Food Systems (University of British Columbia) and the Natural Sciences and Engineering Research Council, Canada (RGPIN 04598, RYY) for support. G.C. acknowledges the support from the Hong Kong Quantum AI Lab Limited, AIR@InnoHK of Hong Kong government. 297 0 0 101 101 WILEY-V C H VERLAG GMBH WEINHEIM POSTFACH 101161, 69451 WEINHEIM, GERMANY 1614-6832 1614-6840 ADV ENERGY MATER Adv. Energy Mater. 10.1002/aenm.202203307 0.0 JAN 2023 23 Chemistry, Physical; Energy & Fuels; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Energy & Fuels; Materials Science; Physics 8B0IC 2023-03-23 WOS:000916615400001 0
J Gbongli, K; Xu, YA; Amedjonekou, KM Gbongli, Komlan; Xu, Yongan; Amedjonekou, Komi Mawugbe Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach SUSTAINABILITY English Article mobile-based money service; adoption; sustainability; technology acceptance model (TAM); structural equation modeling (SEM); artificial neural networks (ANN) USER ACCEPTANCE; SELF-EFFICACY; INFORMATION-TECHNOLOGY; PERSONAL INNOVATIVENESS; BEHAVIORAL INTENTION; FINANCIAL INCLUSION; CONSUMER ACCEPTANCE; SCALE DEVELOPMENT; PERCEIVED EASE; PLS-SEM This research is a pioneering study into the adoption of mobile-based money services for financial inclusion and sustainability in developing countries like Togo. Owing to their differences from more usual mobile-based banking and payment services, such technology is being aggressively promoted by providers of network telecommunication companies. However, the factors influencing its sustainable acceptance remain largely unknown. This paper extends the original Technology Acceptance Model (TAM), by integrating self-efficacy (SEMM), technology anxiety (TAMM), and personal innovativeness (PIMM). The research model is assessed with survey data of 539 actual and prospective mobile money users employing structural equation modeling-artificial neural networks (SEM-ANN) approach. A feed-forward-back-propagation (FFBP) multi-layer perceptron (MLP) ANN with significant predictors obtained from SEM as the input units and the root mean square of errors (RMSE) indicated that the ANN method achieves high prediction accuracy. The results present conclusive evidence that perceived ease-of-use (PEMM) is the most significant factor affecting consumers' attitudes to mobile-based money. While perceived usefulness (PUMM) and PIMM affect adoption decisions, their impact is much lower. Consumer attitudes and intentions were found to have a significant relationship with TAM. SEMM and TAMM; however, they showed mixed results. These findings will be useful to retain prevailing users and attract new ones. [Gbongli, Komlan] Univ Miskolc, Inst Finance & Accounting, Fac Econ, H-3515 Miskolc, Hungary; [Xu, Yongan] Southwestern Univ Finance & Econ, Sch Int Business, 55 Guanghuacun St, Chengdu 610074, Sichuan, Peoples R China; [Amedjonekou, Komi Mawugbe] York St John Univ, Sch Business, York Y031 7EX, N Yorkshire, England University of Miskolc; Southwestern University of Finance & Economics - China Gbongli, K (corresponding author), Univ Miskolc, Inst Finance & Accounting, Fac Econ, H-3515 Miskolc, Hungary. pzkgbong@uni-miskolc.hu Komlan, Gbongli/AAY-7887-2020 168 44 44 4 29 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability JUL 1 2019.0 11.0 13 3639 10.3390/su11133639 0.0 33 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology IL1IB Green Submitted, Green Accepted, gold 2023-03-23 WOS:000477051900138 0
J Djenouri, Y; Djenouri, D; Lin, JCW; Belhadi, A Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei; Belhadi, Asma Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms IEEE ACCESS English Article Apriori; frequent itemset mining; heuristic; parallel computing; support computing EFFICIENT ALGORITHM; MAPREDUCE; PATTERNS This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on Hadoop clusters (MR-SSFIM). EA-SSFIM and MR-SSFIM target sparse and big databases, respectively. The proposed approach (in all its variants) requires only one scan to extract the candidate itemsets, and it has the advantage to generate a fixed number of candidate itemsets independently from the value of the minimum support. This accelerates the scan process compared with existing approaches while dealing with sparse and big databases. Numerical results show that SSFIM outperforms the state-of-the-art FIM approaches while dealing with medium and large databases. Moreover, EA-SSFIM provides similar performance as SSFIM while considerably reducing the runtime for large databases. The results also reveal the superiority of MR-SSFIM compared with the existing HPC-based solutions for FIM using sparse and big databases. [Djenouri, Youcef] Southern Denmark Univ, Sch Math & Comp Sci, DK-5230 Odense, Denmark; [Djenouri, Youcef] Norwegian Univ Sci & Technol, Dept Comp Sci, N-7491 Trondheim, Norway; [Djenouri, Djamel] CERIST Res Ctr, Algiers 16028, Algeria; [Lin, Jerry Chun-Wei] Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen 150006, Peoples R China; [Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Dept Comp Math & Phys, N-5020 Bergen, Norway; [Belhadi, Asma] Univ Sci & Technol Houari Boumediene, RIMA, Algiers 16111, Algeria Norwegian University of Science & Technology (NTNU); Centre de Recherche sur l'Information Scientifique et Technique (CERIST); Harbin Institute of Technology; Western Norway University of Applied Sciences; University Science & Technology Houari Boumediene Lin, JCW (corresponding author), Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen 150006, Peoples R China.;Lin, JCW (corresponding author), Western Norway Univ Appl Sci, Dept Comp Math & Phys, N-5020 Bergen, Norway. jerrylin@ieee.org Lin, Jerry Chun-Wei/C-1514-2011; Djenouri, Djamel/AAP-7344-2020; Djenouri, Youcef/AAJ-2490-2020; Djenouri, Youcef/AAM-7999-2020; Belhadi, Asma/AAK-4580-2020 Djenouri, Djamel/0000-0001-9333-9860; Djenouri, Youcef/0000-0003-0135-7450; Lin, Jerry Chun-Wei/0000-0001-8768-9709 National Natural Science Foundation of China (NSFC) [61503092]; Shenzhen Technical Project [JCYJ20170307151733005, KQJSCX20170726103424709]; Algerian Ministry of Higher Education under DGRSDT; Norwegian University of Science and Technology (NTNU), through European Research Consortium for Informatics and Mathematics (ERCIM) National Natural Science Foundation of China (NSFC)(National Natural Science Foundation of China (NSFC)); Shenzhen Technical Project; Algerian Ministry of Higher Education under DGRSDT; Norwegian University of Science and Technology (NTNU), through European Research Consortium for Informatics and Mathematics (ERCIM) This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503092, in part by the Shenzhen Technical Project under Grant JCYJ20170307151733005 and Grant KQJSCX20170726103424709, in part by the Algerian Ministry of Higher Education under DGRSDT, and in part by the Norwegian University of Science and Technology (NTNU), through a postdoctoral fellowship from the European Research Consortium for Informatics and Mathematics (ERCIM). 39 18 18 0 3 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2169-3536 IEEE ACCESS IEEE Access 2018.0 6.0 68013 68026 10.1109/ACCESS.2018.2880275 0.0 14 Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications HD3XQ gold, Green Submitted 2023-03-23 WOS:000452459600001 0
J Yang, C; Simon, G; See, J; Berger, MO; Wang, WY Yang, Cong; Simon, Gilles; See, John; Berger, Marie-Odile; Wang, Wenyong WatchPose: A View-Aware Approach for Camera Pose Data Collection in Industrial Environments SENSORS English Article data acquisition; augmented reality; pose estimation; deep learning; industrial environments Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments. [Yang, Cong; Simon, Gilles; Berger, Marie-Odile] INRIA LORIA, MAGRIT Team, F-54600 Nancy, France; [See, John] Multimedia Univ, Fac Comp & Informat, Cyberjaya 63100, Selangor, Malaysia; [Wang, Wenyong] Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130000, Peoples R China Universite de Lorraine; Multimedia University; Northeast Normal University - China Wang, WY (corresponding author), Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130000, Peoples R China. cong.yang@loria.fr; gilles.simon@loria.fr; johnsee@mmu.edu.my; marie-odile.berger@loria.fr; wangwy546@nenu.edu.cn See, John/C-8633-2013 See, John/0000-0003-3005-4109 Region Lorraine; Universite de Lorraine [1.87.02.99.216.331/09] Region Lorraine(Region Grand-Est); Universite de Lorraine This work was funded by the Region Lorraine and Universite de Lorraine. Grant Number: 1.87.02.99.216.331/09 53 2 2 0 3 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUN 2020.0 20.0 11 3045 10.3390/s20113045 0.0 19 Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Engineering; Instruments & Instrumentation MQ2OZ 32471231.0 Green Submitted, gold, Green Accepted, Green Published 2023-03-23 WOS:000552737900038 0
J Wang, P; Emmerich, M; Li, R; Tang, K; Back, T; Yao, X Wang, Pu; Emmerich, Michael; Li, Rui; Tang, Ke; Baeck, Thomas; Yao, Xin Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION English Article Classification; evolutionary multiobjective algorithm; genetic programming; memetic algorithm; receiver operating characteristic (ROC) convex hull CLASSIFICATION; ALGORITHM The receiver operating characteristic (ROC) is commonly used to analyze the performance of classifiers in data mining. An important topic in ROC analysis is the ROC convex hull (ROCCH), which is the least convex majorant (LCM) of the empirical ROC curve and covers potential optima for a given set of classifiers. ROCCH maximization problems have been taken as multiobjective optimization problem (MOPs) in some previous work. However, the special characteristics of ROCCH maximization problem makes it different from traditional MOPs. In this paper, the difference will be discussed in detail and a new convex hull-based multiobjective genetic programming (CH-MOGP) is proposed to solve ROCCH maximization problems. Specifically, convex hull-based without redundancy sorting (CWR-sorting) is introduced, which is an indicator-based selection scheme that aims to maximize the area under the convex hull. A novel selection procedure is also proposed based on the proposed sorting scheme. It is hypothesized that by using a tailored indicator-based selection, CH-MOGP becomes more efficient for ROC convex hull approximation than algorithms that compute all Pareto optimal points. Empirical studies are conducted to compare CH-MOGP to both existing machine learning approaches and multiobjective genetic programming (MOGP) methods with classical selection schemes. Experimental results show that CH-MOGP outperforms the other approaches significantly. [Wang, Pu; Tang, Ke; Yao, Xin] Univ Sci & Technol China, Birmingham Joint Res Inst Intelligent Computat &, Sch Comp Sci & Technol, Nat Inspired Computat & Applicat Lab, Hefei 230027, Anhui, Peoples R China; [Emmerich, Michael; Li, Rui; Baeck, Thomas] Leiden Univ, Leiden Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands; [Yao, Xin] Univ Birmingham Edgbaston, Sch Comp Sci, Ctr Excellence Res Computat Intelligence & Applic, Birmingham B15 2TT, W Midlands, England Chinese Academy of Sciences; University of Science & Technology of China, CAS; Leiden University; Leiden University - Excl LUMC; University of Birmingham Wang, P (corresponding author), Univ Sci & Technol China, Birmingham Joint Res Inst Intelligent Computat &, Sch Comp Sci & Technol, Nat Inspired Computat & Applicat Lab, Hefei 230027, Anhui, Peoples R China. wuyou308@mail.ustc.edu.cn; m.t.m.emmerich@liacs.leidenuniv.nl; r.li@liacs.leidenuniv.nl; ketang@ustc.edu.cn; t.h.w.baeck@liacs.leidenuniv.nl; x.yao@cs.bham.ac.uk Emmerich, Michael/AAD-4791-2021; YAO, XIN/W-2158-2018; Baeck, Thomas/ABF-4706-2020 YAO, XIN/0000-0001-8837-4442; Emmerich, Michael/0000-0002-7342-2090; Back, Thomas/0000-0001-6768-1478 973 Program of China [2011CB707006]; National Natural Science Foundation of China [61175065, 61329302]; Program for New Century Excellent Talents in University [NCET-12-0512]; Science and Technological Fund of Anhui Province for Outstanding Youth [1108085J16]; EPSRC [EP/J017515/1]; European Union [247619]; Royal Society; Engineering and Physical Sciences Research Council [EP/J017515/1] Funding Source: researchfish; EPSRC [EP/J017515/1] Funding Source: UKRI 973 Program of China(National Basic Research Program of China); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Program for New Century Excellent Talents in University(Program for New Century Excellent Talents in University (NCET)); Science and Technological Fund of Anhui Province for Outstanding Youth; EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); European Union(European Commission); Royal Society(Royal Society of London); Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) Manuscript received May 24, 2013; revised November 3, 2013 and January 14, 2014; accepted January 28, 2014. Date of publication March 11, 2014; date of current version March 27, 2015. This work was supported in part by the 973 Program of China under Grant 2011CB707006, in part by the National Natural Science Foundation of China under Grants 61175065 and 61329302, in part by the Program for New Century Excellent Talents in University under Grant NCET-12-0512, in part by the Science and Technological Fund of Anhui Province for Outstanding Youth under Grant 1108085J16, in part by the EPSRC under Grant EP/J017515/1, and in part by the European Union Seventh Framework Programme under Grant 247619. The work of X. Yao was supported by a Royal Society Wolfson Research Merit Award. 38 31 34 0 15 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1089-778X 1941-0026 IEEE T EVOLUT COMPUT IEEE Trans. Evol. Comput. APR 2015.0 19.0 2 188 200 10.1109/TEVC.2014.2305671 0.0 13 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Science Citation Index Expanded (SCI-EXPANDED) Computer Science CF1BF 2023-03-23 WOS:000352277900003 0
C Wang, L; Wang, LH; Chen, QJ; Yang, F; Zhu, YM IEEE Wang, Li; Wang, Lihui; Chen, Qijian; Yang, Feng; Zhu, Yuemin Spatial Attention based Semantic Decouple Network for Breast Tumor Segmentation 2022 16TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP2022), VOL 1 International Conference on Signal Processing English Proceedings Paper 16th IEEE International Conference on Signal Processing (ICSP) OCT 21-24, 2022 Beijing, PEOPLES R CHINA Inst Elect & Elect Engineers,Beijing Jiaotong Univ,IET,Union Radio Scientifique Int,Chinese Inst Elect,IEEE Beijing Sect,IET Beijing Local Network,Natl Nat Sci Fdn China,IEEE Signal Proc Soc Beijing Chapter,IEEE Comp Soc Beijing Chapter,Japan China Sci & Technol Exchange Assoc,CIE Signal Proc Soc,Shenzhen Univ, Intelligent Informat Inst,CIC Commun & Signal Proc Soc,Beijing Inst Elect Breast tumor segmentation; DCE-MRI; deep learning; semantic decouple; multicenter dataset Breast tumor segmentation is of great significance for establishing a computer aided diagnosis system. Although existing studies have achieved some promising results for breast tumor segmentation with a single-center dataset, it is still challenging to improve the generalization capability of the segmentation model with multicenter datasets. To address this issue, we propose a spatial attention based semantic decouple network (SADnet) to segment the breast cancer tumors with datasets from different sites, which estimates the potential semantic clusters in breast cancer images based on multi-head spatial attention modules. The decoupled semantic clusters can overcome the influence of semantics interference on the segmentation and therefore promotes the segmentation performance. The experimental results show that SADnet for breast cancer segmentation achieves dice similarity coefficient (DSC) of 0.706 and sensitivity (SEN) of 0.755 on the external test set, which are 2.2% and 4.4% higher than state-of-the-art (SOTA) methods, respectively. In addition, it achieves an average surface distance (ASD) of 2.863, which is 11.2% lower than SOTA methods. These results show that the clustering strategy proposed in this paper is beneficial for multi-center breast tumor segmentation. [Wang, Li; Wang, Lihui; Chen, Qijian] Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, State Key Lab Publ Big Data, Guiyang, Peoples R China; [Yang, Feng] NIH, Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, Bethesda, MD USA; [Zhu, Yuemin] Univ Lyon, CNRS, INSA Lyon, INSERM,CREATIS,UMR 5220,U1206, F-69621 Lyon, France Guizhou University; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM); Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Institut National de la Sante et de la Recherche Medicale (Inserm); Institut National des Sciences Appliquees de Lyon - INSA Lyon Wang, LH (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, State Key Lab Publ Big Data, Guiyang, Peoples R China. wangli614079@163.com; lhwang2@gzu.edu.cn; forchenqijian@163.com; feng.yang2@nih.gov; zhu@creatis.insa-lyon.fr National Natural Science Foundations of China [62161004]; Guizhou Provincial Science and Technology Projects [QianKeHe ZK [2021] Key 002]; Natural Science Foundations of Guizhou Province [QianKeHe [2020]1Y255] National Natural Science Foundations of China(National Natural Science Foundation of China (NSFC)); Guizhou Provincial Science and Technology Projects; Natural Science Foundations of Guizhou Province This work was supported by the National Natural Science Foundations of China (Grant No.62161004), Guizhou Provincial Science and Technology Projects (QianKeHe ZK [2021] Key 002), Natural Science Foundations of Guizhou Province (QianKeHe [2020]1Y255). 16 0 0 1 1 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 2164-5221 978-1-6654-6056-9 INT CONF SIGN PROCES 2022.0 235 239 10.1109/ICSP56322.2022.9965308 0.0 5 Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications BU4ET 2023-03-23 WOS:000896487300046 0
J Li, H; Xu, SP; Wang, M; Chen, ZH; Ji, FF; Cheng, KW; Gao, ZY; Ding, Z; Yang, WJ Li, Hao; Xu, Shaopeng; Wang, Min; Chen, Ziheng; Ji, Fengfeng; Cheng, Kewei; Gao, Zhengyang; Ding, Zhao; Yang, Weijie Computational design of (100) alloy surfaces for the hydrogen evolution reaction JOURNAL OF MATERIALS CHEMISTRY A English Article TOTAL-ENERGY CALCULATIONS; ELECTROCATALYTIC MATERIALS; NANOPARTICLES; ADSORPTION; REDUCTION; ENSEMBLE; LIGAND; NANOCRYSTALS; ACTIVATION; NANOSHEETS With the rapid development of kinetically-controlled techniques, synthesis of cubic bimetallic catalysts with tunable components and compositions becomes possible. In recent years, many of the bimetallic alloy catalysts with (100) as the predominant facet have shown outstanding electrocatalytic activity and stability. However, alloying effects on the activity at (100) were less explored, compared to the well-studied close-packed surfaces. Here, using density functional theory (DFT) calculations, we explore the catalytic activity of bimetallic alloy (100) surfaces for the electrocatalytic hydrogen evolution reaction (HER). The ensemble and compositional effects of (100) facets alloyed by strong- (Pd and Pt) and weak-binding (Ag, Au, and Cu) transition metals were studied. Based on DFT calculations, the catalytic activities of bimetallic alloys for the HER were systematically investigated using H binding energy as the reaction descriptor, at all of the typical surface ensembles on (100). Our results suggest that PdxAg1-x and PdxAu1-x(100) have promising theoretical HER activity in acidic media, due to the presence of highly active four-fold ensembles which reach the peak of the HER volcano activity plot. The electronic structure and stability of the alloys with predicted promising HER activities were studied. Furthermore, based on the DFT-calculated database, we performed feature analysis and developed a machine learning model which can help to predict HER activities of those out-of-sample alloys. Most importantly, this study provides helpful guidelines for the design of (100) dominated bimetallic alloys towards promising HER activity. [Li, Hao; Xu, Shaopeng; Wang, Min; Ji, Fengfeng; Gao, Zhengyang; Yang, Weijie] North China Elect Power Univ, Sch Energy Power & Mech Engn, Dept Power Engn, Baoding 071003, Peoples R China; [Li, Hao] Tech Univ Denmark, Dept Phys, DK-2800 Lyngby, Denmark; [Chen, Ziheng] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA; [Cheng, Kewei] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA; [Ding, Zhao] IIT, Dept Mech Mat & Aerosp Engn, Chicago, IL 60616 USA North China Electric Power University; Technical University of Denmark; State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook; University of California System; University of California Los Angeles; Illinois Institute of Technology Yang, WJ (corresponding author), North China Elect Power Univ, Sch Energy Power & Mech Engn, Dept Power Engn, Baoding 071003, Peoples R China. yangwj@ncepu.edu.cn Yang, Wei/HIA-0360-2022; Li, Hao/J-6893-2013; Yang, Weijie/N-8956-2018 Li, Hao/0000-0002-7577-1366; Chen, Ziheng/0000-0002-2585-637X; Yang, Weijie/0000-0002-0232-1129 National Key Research and Development Program of China [2016YFA0202401]; Beijing Natural Science Foundation [2182066]; Natural Science Foundation of Hebei Province of China [B2018502067, E2020502023]; Fundamental Research Funds for the Central Universities [2020MS104]; North China Electric Power University National Key Research and Development Program of China; Beijing Natural Science Foundation(Beijing Natural Science Foundation); Natural Science Foundation of Hebei Province of China(Natural Science Foundation of Hebei Province); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); North China Electric Power University This work was supported by the National Key Research and Development Program of China (No. 2016YFA0202401), Beijing Natural Science Foundation (No. 2182066), the Natural Science Foundation of Hebei Province of China (No. B2018502067 and No. E2020502023), and the Fundamental Research Funds for the Central Universities (2020MS104). All of the computations were performed in The Computational Centre for North China Electric Power University. Hao Li acknowledges the research funds and computational resources provided by North China Electric Power University. We thank Ryan Ciufo (The University of Texas at Austin, USA) for improving the writing of our manuscript. 66 25 25 21 129 ROYAL SOC CHEMISTRY CAMBRIDGE THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND 2050-7488 2050-7496 J MATER CHEM A J. Mater. Chem. A SEP 21 2020.0 8.0 35 17987 17997 10.1039/d0ta04615a 0.0 11 Chemistry, Physical; Energy & Fuels; Materials Science, Multidisciplinary Science Citation Index Expanded (SCI-EXPANDED) Chemistry; Energy & Fuels; Materials Science NP0KU 2023-03-23 WOS:000569873400054 0
J Ching, NT; Ghobakhloo, M; Iranmanesh, M; Maroufkhani, P; Asadi, S Ching, Ng Tan; Ghobakhloo, Morteza; Iranmanesh, Mohammad; Maroufkhani, Parisa; Asadi, Shahla Industry 4.0 applications for sustainable manufacturing: A systematic literature review and a roadmap to sustainable development JOURNAL OF CLEANER PRODUCTION English Review Industry 4; 0 technologies; 0 applications; Sustainable manufacturing; Sustainable development; Sustainability; Literature review SUPPLY CHAIN; PRODUCT DESIGN; BIG DATA; TECHNOLOGIES; MANAGEMENT; MODEL; IMPLEMENTATION; FRAMEWORK; BARRIERS Industry 4.0 is transforming the manufacturing industry and the economics of value creation. A great deal of positive hype has built up around the sustainable development implications of Industry 4.0 technologies during the past few years. Expectations regarding the opportunities that Industry 4.0 offers for sustainable manufacturing are significantly high, but the lack of accurate understanding of the process through which Industry 4.0 technologies enable sustainable manufacturing is a fundamental barrier for businesses pursuing digitalization and sustainable thinking. The present study addresses this knowledge gap by developing a road map that explains how Industry 4.0 and the underlying digital technologies can be leveraged to support and facilitate the triple bottom line of sustainable manufacturing. To this purpose, the study conducted a systematic literature review and identified 15 sustainability functions through which Industry 4.0 contributes to sustainable manufacturing. Interpretive structural modeling was further applied to identify the relationships that may exist within the sustainability functions. The resulting sustainable manufacturing roadmap explains how, and in which order, various Industry 4.0 sustainability functions contribute to developing the economic, environmental, and social dimensions of sustainability. The resulting implications are expected to serve manufacturers, industrialists, and academia as a strategic guide for leveraging Industry 4.0 digital transformation to support sustainable development. [Ching, Ng Tan] Univ Tunku Abdul Rahman, Dept Mech & Mat Engn, Sungai Long Campus, Kajang, Selangor, Malaysia; [Ghobakhloo, Morteza] Kaunas Univ Technol, Sch Business & Econ, Kaunas, Lithuania; [Iranmanesh, Mohammad] Edith Cowan Univ, Sch Business & Law, Joondalup, WA 6027, Australia; [Maroufkhani, Parisa] Zhejiang Univ City Coll, Hangzhou, Peoples R China; [Asadi, Shahla] Univ Kebangsaan Malaysia, Ctr Software Technol & Management, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia Universiti Tunku Abdul Rahman (UTAR); Kaunas University of Technology; Edith Cowan University; Zhejiang University City College; Universiti Kebangsaan Malaysia Ghobakhloo, M (corresponding author), Kaunas Univ Technol, Sch Business & Econ, Kaunas, Lithuania. ngtc@utar.edu.my; morteza_ghobakhloo@yahoo.com; m.iranmanesh@ecu.edu.au; parisa.maroufkhani@gmail.com; asadi.shahla2003@gmail.com Asadi, Shahla/L-9423-2019; Iranmanesh, Mohammad/G-2321-2012 Iranmanesh, Mohammad/0000-0001-6964-6238 European Union [810318] European Union(European Commission) This research has been a part of a project that received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 810318. The opinions expressed in the paper are of the authors only and in no way reflect the European Commission's opinions. 91 22 22 31 64 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0959-6526 1879-1786 J CLEAN PROD J. Clean Prod. FEB 1 2022.0 334.0 130133 10.1016/j.jclepro.2021.130133 0.0 18 Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology 0E4FO 2023-03-23 WOS:000776638400007 0
J Sun, ZY; Han, Y; Wang, ZP; Chen, Y; Liu, P; Qin, ZA; Zhang, ZS; Wu, ZQ; Song, CB Sun, Zhenyu; Han, Yang; Wang, Zhenpo; Chen, Yong; Liu, Peng; Qin, Zian; Zhang, Zhaosheng; Wu, Zhiqiang; Song, Chunbao Detection of voltage fault in the battery system of electric vehicles using statistical analysis APPLIED ENERGY English Article Electric vehicle; Lithium-ion battery; Fault diagnosis; Three-layer detection; Confidence interval; Improved K-means EXTERNAL SHORT-CIRCUIT; ION POWER BATTERIES; THERMAL-RUNAWAY; DIAGNOSIS APPROACH; ENTROPY; CELLS; CONNECTION; MECHANISMS; FEATURES; CATHODE It is vital to detect the safety state and identify faults of the battery pack for the safe operation of electric vehicles. The voltage faults such as over-voltage and under-voltage imply more serious battery faults including short -circuit and thermal runaway. The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack. In the second layer, confidence interval estimation is applied to identify risky cells. In the third layer, correlation and variability of all cells in one battery pack are analyzed by using an improved K-means method to identify abnormal voltage fluctuation over a certain period. The validity and feasibility of the proposed method are verified by real vehicle data from the National Big Data Alliance of New Energy Vehicles. [Sun, Zhenyu; Wang, Zhenpo; Liu, Peng; Zhang, Zhaosheng; Wu, Zhiqiang; Song, Chunbao] Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China; [Sun, Zhenyu; Wang, Zhenpo; Liu, Peng; Zhang, Zhaosheng; Wu, Zhiqiang; Song, Chunbao] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China; [Sun, Zhenyu; Qin, Zian] Delft Univ Technol, Dept Elect Sustainable Energy, NL-2628 CD Delft, Netherlands; [Han, Yang] Univ Manchester, Dept Math, Manchester M13 9PL, Lancs, England; [Wang, Zhenpo; Liu, Peng] Beijing Inst Technol Chongqing Innovat Ctr, Chongqing 401120, Peoples R China; [Chen, Yong] Beijing Informat Sci & Technol Univ, Beijing 100192, Peoples R China Beijing Institute of Technology; Beijing Institute of Technology; Delft University of Technology; University of Manchester; Beijing Information Science & Technology University Wang, ZP (corresponding author), Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China.;Wang, ZP (corresponding author), Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China.;Han, Y (corresponding author), Univ Manchester, Dept Math, Manchester M13 9PL, Lancs, England. yang.han@manchester.ac.uk; wangzhenpo@bit.edu.cn Han, Yang/AIC-6375-2022 Han, Yang/0000-0002-4143-7765 China Scholarship Council [Contract202006030153]; Ministry of Science and Technology of the People's Republic of China [2019YFE0107900] China Scholarship Council(China Scholarship Council); Ministry of Science and Technology of the People's Republic of China(Ministry of Science and Technology, China) Acknowledgements This work is supported by the China Scholarship Council (Contract202006030153) , and the Ministry of Science and Technology of the People's Republic of China (Grant No. 2019YFE0107900) . 38 7 7 33 51 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-2619 1872-9118 APPL ENERG Appl. Energy FEB 1 2022.0 307.0 118172 10.1016/j.apenergy.2021.118172 0.0 JAN 2022 13 Energy & Fuels; Engineering, Chemical Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels; Engineering 1I4RN 2023-03-23 WOS:000797217300006 0
C Nguyen, TT; Trahay, F; Domke, J; Drozd, A; Vatai, E; Liao, JW; Wahib, M; Gerofi, B IEEE Nguyen, Truong Thao; Trahay, Francois; Domke, Jens; Drozd, Aleksandr; Vatai, Emil; Liao, Jianwei; Wahib, Mohamed; Gerofi, Balazs Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning 2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2022) International Parallel and Distributed Processing Symposium IPDPS English Proceedings Paper 36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS) MAY 30-JUN 03, 2022 ELECTR NETWORK IEEE,IEEE Comp Soc Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training epoch processing data samples in a random access fashion. Because this puts enormous pressure on the I/O subsystem, the most common approach to distributed SGD in HPC environments is to replicate the entire dataset to node local SSDs. However, due to rapidly growing data set sizes this approach has become increasingly infeasible. Surprisingly, the questions of why and to what extent random access is required have not received a lot of attention in the literature from an empirical standpoint. In this paper, we revisit data shuffling in DL workloads to investigate the viability of partitioning the dataset among workers and performing only a partial distributed exchange of samples in each training epoch. Through extensive experiments on up to 2,048 GPUs of ABCI and 4,096 compute nodes of Fugaku, we demonstrate that in practice validation accuracy of global shuffling can be maintained when carefully tuning the partial distributed exchange. We provide a solution implemented in PyTorch that enables users to control the proposed data exchange scheme. [Nguyen, Truong Thao; Wahib, Mohamed] Natl Inst Adv Ind Sci & Technol, Tokyo, Japan; [Trahay, Francois] Inst Polytech Paris, Telecom SudParis, Paris, France; [Domke, Jens; Drozd, Aleksandr; Vatai, Emil; Wahib, Mohamed; Gerofi, Balazs] RIKEN, Ctr Computat Sci, Tokyo, Japan; [Liao, Jianwei] Southwest Univ China, Coll Comp & Informat Sci, Chongqing, Peoples R China; [Domke, Jens; Drozd, Aleksandr; Vatai, Emil; Wahib, Mohamed; Gerofi, Balazs] Tokyo Inst Technol, Tokyo, Japan; [Drozd, Aleksandr] Amigawa GK, Tokyo, Japan National Institute of Advanced Industrial Science & Technology (AIST); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; RIKEN; Tokyo Institute of Technology Nguyen, TT (corresponding author), Natl Inst Adv Ind Sci & Technol, Tokyo, Japan. nguyen.truong@aist.go.jp; francois.trahay@telecom-sudparis.eu; jens.domke@riken.jp; aleksandr.drozd@riken.jp; emil.vatai@riken.jp; liaotoad@hotmail.com; mohamed.attia@aist.go.jp; bgerofi@riken.jp Vatai, Emil/G-8790-2017 Vatai, Emil/0000-0001-7494-5048 JST ACT-X [JPM-JAX190C]; JSET CREST [JPMJCR19F5]; JST PRESTO [JPMJPR20MA]; JSPS KAKENHI [JP21K17751, JP19K11993] JST ACT-X; JSET CREST; JST PRESTO(Japan Science & Technology Agency (JST)); JSPS KAKENHI(Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)) This work has been supported by JST ACT-X Grant Number JPM-JAX190C, JSET CREST Grant Number JPMJCR19F5, JST PRESTO Grant Number JPMJPR20MA and JSPS KAKENHI Grant Numbers JP21K17751 and JP19K11993. 48 0 0 0 0 IEEE NEW YORK 345 E 47TH ST, NEW YORK, NY 10017 USA 1530-2075 978-1-6654-8106-9 INT PARALL DISTRIB P 2022.0 1085 1096 10.1109/IPDPS53621.2022.00109 0.0 12 Computer Science, Hardware & Architecture; Computer Science, Theory & Methods Conference Proceedings Citation Index - Science (CPCI-S) Computer Science BT8NS Green Submitted 2023-03-23 WOS:000854096200101 0
J Yan, SH; Ihsan, U; Malaney, R; Sun, LL; Tomasin, S Yan, Shihao; Ihsan, Ullah; Malaney, Robert; Sun, Linlin; Tomasin, Stefano Location Verification for Future Wireless Vehicular Networks: Research Directions and Challenges IEEE NETWORK English Article; Early Access Ions; Millimeter wave communication; Integrated circuits; Global navigation satellite system; Wireless networks; Neural networks; Rician channels Vehicle location information obtained through the global navigation satellite system (GNSS) will play a pivotal role in emerging vehicular networks. This vital information is, however, susceptible to a host of unwanted manipulations, especially if a malicious entity is involved. The most obvious example of such manipulations is the forwarding by a malicious vehicle of false GNSS locations to other members of the network. Such events can lead to poor operational outcomes for the vehicular network, and in extreme cases even lead to catastrophic safety violations. Here, we highlight research efforts pursued in the past few years that have attempted to address this weakness in vehicular networks. We also discuss the importance of location verification in the wake of emerging wireless technologies, such as those being proposed for beyond 5G (B5G) wireless vehicular networks. In particular, we detail an opportunity to conduct location reporting and verification simultaneously with the aid of mmWave technology and discuss how emerging machine learning (ML) techniques will provide for location verification solutions where reliability levels will be commensurate with that required by the vehicular network paradigm. We close by discussing the potential enhancements for location verification within a future combined B5G-ML architecture. [Yan, Shihao] Edith Cowan Univ, Perth, WA, Australia; [Ihsan, Ullah] Xynoptik, Sydney, NSW, Australia; [Malaney, Robert] Univ New South Wales, Sydney, NSW, Australia; [Sun, Linlin] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing, Peoples R China; [Tomasin, Stefano] Univ Padua, Padua, Italy Edith Cowan University; University of New South Wales Sydney; Nanjing University of Science & Technology; University of Padua Sun, LL (corresponding author), Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing, Peoples R China. shihao.yan@unsw.edu.au; sunlinlin@njust.edu.cn 15 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. 10.1109/MNET.103.2100338 0.0 OCT 2022 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Telecommunications 5D6YV 2023-03-23 WOS:000865085300001 0
J Qi, P; Hu, HY; Zhu, L; Gao, LY; Yuan, JJ; Thakor, N; Bezerianos, A; Sun, Y Qi, Peng; Hu, Hongying; Zhu, Li; Gao, Lingyun; Yuan, Jingjia; Thakor, Nitish; Bezerianos, Anastasios; Sun, Yu EEG Functional Connectivity Predicts Individual Behavioural Impairment During Mental Fatigue IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING English Article Individualized prediction; functional connectivity; machine learning; regression; EEG SUSTAINED ATTENTION; NEURAL MECHANISMS; BRAIN; PERFORMANCE; MACHINE; OSCILLATIONS; ACTIVATION; VIGILANCE; DYNAMICS; PATTERNS Mental fatigue deteriorates ability to perform daily activities - known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully crossvalidated, data-driven analysis framework incorporating multivariate regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the fatigue-related behavioural impairment at individual level. EEG was recorded from 40 healthy adults as they performed a 30-min high-demanding sustained attention task. FC were constructed in different frequency bands using three widely-adoptedmethods (including coherence, phase log index (PLI), and partial directed coherence (PDC)) and contrasted between the most vigilant and fatigued states. The differences of individual FC (diff (FC)) were considered as features; whereas the TOT slop across the course of task and the differences of reaction time (Delta RT) between the most vigilant and fatigued states were chosen to represent behavioural impairments. Behaviourally, we found substantial inter-subject differences of impairments. Furthermore, we achieved significantly high accuracies for individualized prediction of behavioural impairments using diff (PDC). The identified top diff (PDC) features contributing to the individualized predictions were found mainly in theta and alpha bands. Further interrogation of diff (PDC) features revealed distinct patterns between the TOT slop and Delta RT prediction models, highlighting the complex neural mechanisms of mental fatigue. Overall, the current findings extended conventional brain-behavioural correlation analysis to individualized prediction of fatigue-related behavioural impairments, therebymoving a step forward towards development of applicable techniques for quantitative fatigue monitoring in real-world scenarios. [Qi, Peng] Tongji Univ, Dept Control Sci & Engn, Coll Elect & Informat Engn, Shanghai 200092, Peoples R China; [Hu, Hongying; Gao, Lingyun; Yuan, Jingjia; Sun, Yu] Zhejiang Univ, Dept Biomed Engn, Minist Educ China, Key Lab Biomed Engn, Hangzhou 310027, Peoples R China; [Zhu, Li] Zhejiang Univ, Coll Educ, Hangzhou 310027, Peoples R China; [Thakor, Nitish; Bezerianos, Anastasios] Natl Univ Singapore, Ctr Life Sci, Singapore 117456, Singapore; [Thakor, Nitish] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117456, Singapore; [Thakor, Nitish; Bezerianos, Anastasios] Natl Univ Singapore, Inst Hlth 1, Singapore 117456, Singapore; [Bezerianos, Anastasios] Univ Patras, Med Sch, Patras 26500, Greece; [Sun, Yu] Zhejiang Univ, Dept Radiol, Natl Clin Res Ctr Child Hlth, Sch Med,Childrens Hosp, Hangzhou 310052, Peoples R China Tongji University; Ministry of Education, China; Zhejiang University; Zhejiang University; National University of Singapore; National University of Singapore; National University of Singapore; University of Patras; Zhejiang University Sun, Y (corresponding author), Zhejiang Univ, Dept Biomed Engn, Minist Educ China, Key Lab Biomed Engn, Hangzhou 310027, Peoples R China. yusun@zju.edu.cn Bezerianos, Tasos/AAC-2499-2022; Thakor, Nitish/G-9110-2015; Qi, Peng/A-3816-2015 Bezerianos, Tasos/0000-0002-8199-6000; SUN, Yu/0000-0002-6666-8586; Thakor, Nitish/0000-0002-9981-9395; Qi, Peng/0000-0003-0514-9464 National Natural Science Foundation of China [51905379, 81801785]; Hundred Talents Program of Zhejiang University; Fundamental Research Funds for the Central Universities [2019FZJD005, 2020FZZX001-05]; Zhejiang Lab [2019KE0AD01]; National University of Singapore [R719-001-102-232]; Ministry of Education of Singapore [MOE2014-T2-1-115]; Natural Science Foundation of Shanghai [19ZR1478900] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hundred Talents Program of Zhejiang University; Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities); Zhejiang Lab; National University of Singapore(National University of Singapore); Ministry of Education of Singapore(Ministry of Education, Singapore); Natural Science Foundation of Shanghai(Natural Science Foundation of Shanghai) This work was supported in part by the National Natural Science Foundation of China under Grant 81801785, in part by the Hundred Talents Program of Zhejiang University, in part by the Fundamental Research Funds for the Central Universities under Grant 2019FZJD005 and Grant 2020FZZX001-05, in part by the Zhejiang Lab under Grant 2019KE0AD01, in part by the National University of Singapore under Grant R719-001-102-232, and in part by the Ministry of Education of Singapore under Grant MOE2014-T2-1-115. The work of Peng Qi was supported in part by the Natural Science Foundation of Shanghai under Grant 19ZR1478900 and in part by the National Natural Science Foundation of China under Grant 51905379. 60 6 6 9 50 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1534-4320 1558-0210 IEEE T NEUR SYS REH IEEE Trans. Neural Syst. Rehabil. Eng. SEPT 2020.0 28.0 9 2080 2089 10.1109/TNSRE.2020.3007324 0.0 10 Engineering, Biomedical; Rehabilitation Science Citation Index Expanded (SCI-EXPANDED) Engineering; Rehabilitation NN3ES 32746312.0 2023-03-23 WOS:000568675100021 0
J Grekousis, G; Feng, ZX; Marakakis, I; Lu, Y; Wang, RY Grekousis, George; Feng, Zhixin; Marakakis, Ioannis; Lu, Yi; Wang, Ruoyu Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach HEALTH & PLACE English Article spatial Machine learning; Random forest; COVID-19; USA CLASSIFICATION; MORTALITY; WILL A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local nonlinear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation. [Grekousis, George; Feng, Zhixin] Sun Yat Sen Univ, Sch Geog & Planning, Dept Urban & Reg Planning, Xingang Xi Rd, Guangzhou 510275, Peoples R China; [Grekousis, George] Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou, Peoples R China; [Grekousis, George] Guangdong Prov Engn Res Ctr Publ Secur & Disaster, Guangzhou, Peoples R China; [Marakakis, Ioannis] Natl Tech Univ Athens, Dept Geog & Reg Planning, Sch Rural & Surveying Engn, Zografou Campus, Athens 15780, Greece; [Lu, Yi] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China; [Lu, Yi] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China; [Wang, Ruoyu] Univ Edinburgh, Inst Geog, Sch Geosci, Edinburgh, Scotland Sun Yat Sen University; National Technical University of Athens; City University of Hong Kong; City University of Hong Kong; University of Edinburgh Grekousis, G; Feng, ZX (corresponding author), 135 Xingang Xi Rd, Guangzhou 510275, Haizhu, Peoples R China. geograik@gmail.com; frankfengs@outlook.com; imarakakis@gmail.com; yilu24@cityu.edu.hk; R.Wang-54@sms.ed.ac.uk LU, Yi/AAD-7750-2020 LU, Yi/0000-0001-7614-6661; Grekousis, George/0000-0003-2837-2335 83 15 15 26 47 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1353-8292 1873-2054 HEALTH PLACE Health Place MAR 2022.0 74.0 102744 10.1016/j.healthplace.2022.102744 0.0 FEB 2022 12 Public, Environmental & Occupational Health Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Public, Environmental & Occupational Health ZY7FP 35114614.0 Bronze, Green Accepted 2023-03-23 WOS:000772749400007 0
J Ji, ZY; Yang, C; Wang, HH; Armendariz-inigo, JE; Arce-Urriza, M Ji, Zhenyan; Yang, Chun; Wang, Huihui; Enrique Armendariz-inigo, Jose; Arce-Urriza, Marta BRScS: a hybrid recommendation model fusing multi-source heterogeneous data EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING English Article Multi-source heterogeneous data; Recommendation model; Social network ALGORITHM Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users' choices are usually affected by their direct and even indirect friends' preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-Nrecommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, and HRSc. The BRScS model is also scalable and can fuse new types of data easily. [Ji, Zhenyan; Yang, Chun] Beijing Jiaotong Univ, Sch Software Engn, Beijing 100044, Peoples R China; [Wang, Huihui] Jacksonville Univ, Dept Engn, Jacksonville, FL 32211 USA; [Enrique Armendariz-inigo, Jose] Univ Publ Navarra, Dept Stat Comp Sci & Math, Pamplona 31006, Spain; [Arce-Urriza, Marta] Univ Publ Navarra, Dept Business Management, Pamplona 31006, Spain Beijing Jiaotong University; Jacksonville University; Universidad Publica de Navarra; Universidad Publica de Navarra Ji, ZY (corresponding author), Beijing Jiaotong Univ, Sch Software Engn, Beijing 100044, Peoples R China. zhyji@bjtu.edu.cn Wang, Huihui/ABB-6935-2021 Wang, Huihui/0000-0002-4098-5313; Arce Urriza, Marta/0000-0002-5095-3788; Armendariz-Inigo, Jose Enrique/0000-0002-2666-7191 National Key Research and Development Program of China [2018YFC0831903]; Major Project of National Natural Science Foundation of China [51935002] National Key Research and Development Program of China; Major Project of National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by the National Key Research and Development Program of China under grant no. 2018YFC0831903 and Major Project of National Natural Science Foundation of China under grant no. 51935002. 40 5 6 4 16 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1687-1472 1687-1499 EURASIP J WIREL COMM EURASIP J. Wirel. Commun. Netw. JUN 16 2020.0 2020.0 1 124 10.1186/s13638-020-01716-2 0.0 17 Engineering, Electrical & Electronic; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Engineering; Telecommunications MB7ID gold, Green Submitted, Green Published 2023-03-23 WOS:000542772300001 0
J Sun, LC; Qin, H; Przystupa, K; Cui, YR; Kochan, O; Skowron, M; Su, J Sun, Lichao; Qin, Hang; Przystupa, Krzysztof; Cui, Yanrong; Kochan, Orest; Skowron, Mikolaj; Su, Jun A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques ENERGIES English Article feature selection; metaheuristic sine cosine algorithm (SCA); multilevel regulator OPTIMIZATION ALGORITHM Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases. [Sun, Lichao; Qin, Hang; Cui, Yanrong] Yangtze Univ, Comp Sch, Jingzhou 434023, Peoples R China; [Przystupa, Krzysztof] Lublin Univ Technol, Dept Automat, Nadbystrzycka 36, PL-20618 Lublin, Poland; [Przystupa, Krzysztof] State Sch Higher Educ, Pocztowa 54, PL-22100 Chelm, Poland; [Kochan, Orest; Su, Jun] Hubei Univ Technol, Sch Comp Sci, Wuhan 430068, Peoples R China; [Kochan, Orest] Lviv Polytech Natl Univ, Inst Comp Technol Automat & Metrol, Dept Measuring Informat Technol, UA-79013 Lvov, Ukraine; [Skowron, Mikolaj] AGH Univ Sci & Technol, Dept Elect & Power Engn, A Mickiewicza 30, PL-30059 Krakow, Poland Yangtze University; Lublin University of Technology; Hubei University of Technology; Ministry of Education & Science of Ukraine; Lviv Polytech National University; AGH University of Science & Technology Qin, H (corresponding author), Yangtze Univ, Comp Sch, Jingzhou 434023, Peoples R China.;Przystupa, K (corresponding author), Lublin Univ Technol, Dept Automat, Nadbystrzycka 36, PL-20618 Lublin, Poland.;Przystupa, K (corresponding author), State Sch Higher Educ, Pocztowa 54, PL-22100 Chelm, Poland.;Skowron, M (corresponding author), AGH Univ Sci & Technol, Dept Elect & Power Engn, A Mickiewicza 30, PL-30059 Krakow, Poland. sunlc@yangtzeu.edu.cn; hangqin100@hotmail.com; k.przystupa@pollub.pl; cyanr@yangtzeu.edu.cn; orest.v.kochan@lpnu.ua; mskowron@agh.edu.pl; sujuncs@hbut.edu.cn Skowron, Mikołaj/C-5534-2013; Kochan, Orest/I-9885-2018; Przystupa, Krzysztof/V-8850-2018 Kochan, Orest/0000-0002-3164-3821; Skowron, Mikolaj/0000-0002-7558-4237; Przystupa, Krzysztof/0000-0003-4361-2763 2021 Wuxi Science and Technology Innovation and Entrepreneurship Program 2021 Wuxi Science and Technology Innovation and Entrepreneurship Program This work was supported by the 2021 Wuxi Science and Technology Innovation and Entrepreneurship Program. 55 2 2 5 9 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 1996-1073 ENERGIES Energies MAY 2022.0 15.0 10 3485 10.3390/en15103485 0.0 24 Energy & Fuels Science Citation Index Expanded (SCI-EXPANDED) Energy & Fuels 1R8VH gold 2023-03-23 WOS:000803641000001 0
J Turpin, D; Ganet, M; Antier, S; Bertin, E; Xin, LP; Leroy, N; Wu, C; Xu, Y; Han, XH; Cai, HB; Li, HL; Lu, XM; Peng, QC; Wei, JY Turpin, Damien; Ganet, M.; Antier, S.; Bertin, E.; Xin, L. P.; Leroy, N.; Wu, C.; Xu, Y.; Han, X. H.; Cai, H. B.; Li, H. L.; Lu, X. M.; Peng, Q. C.; Wei, J. Y. Vetting the optical transient candidates detected by the GWAC network using convolutional neural networks MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY English Article methods: data analysis; surveys (transients:) neutron star mergers; transients: supernovae GAMMA-RAY BURSTS; CLASSIFICATION; TAROT The observation of the transient sky through a multitude of astrophysical messengers has led to several scientific breakthroughs in the last two decades, thanks to the fast evolution of the observational techniques and strategies employed by the astronomers. Now, it requires to be able to coordinate multiwavelength and multimessenger follow-up campaigns with instruments both in space and on ground jointly capable of scanning a large fraction of the sky with a high-imaging cadency and duty cycle. In the optical domain, the key challenge of the wide field-of-view telescopes covering tens to hundreds of square degrees is to deal with the detection, identification, and classification of hundreds to thousands of optical transient (OT) candidates every night in a reasonable amount of time. In the last decade, new automated tools based on machine learning approaches have been developed to perform those tasks with a low computing time and a high classification efficiency. In this paper, we present an efficient classification method using convolutional neural networks (CNNs) to discard many common types of bogus falsely detected in astrophysical images in the optical domain. We designed this tool to improve the performances of the OT detection pipeline of the Ground Wide field Angle Cameras (GWAC) telescopes, a network of robotic telescopes aiming at monitoring the OT sky down to R = 16 with a 15 s imaging cadency. We applied our trained CNN classifier on a sample of 1472 GWAC OT candidates detected by the real-time detection pipeline. [Turpin, Damien; Xin, L. P.; Wu, C.; Xu, Y.; Han, X. H.; Cai, H. B.; Li, H. L.; Lu, X. M.; Peng, Q. C.; Wei, J. Y.] Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Space Astron & Technol, Beijing 100012, Peoples R China; [Turpin, Damien] Univ Paris Saclay, Dept Astrophys Astrophys Instrumentat & Modelisat, CEA, CNRS, F-91191 Gif Sur Yvette, France; [Ganet, M.] ENSAE Paris, F-91120 Palaiseau, France; [Ganet, M.] HEC Paris, F-78350 Jouy En Josas, France; [Antier, S.] CNRS, Astroparticule & Cosmol, F-75013 Paris, France; [Bertin, E.] UPMC CNRS, UMR7095, Inst Astrophys Paris, F-75014 Paris, France; [Leroy, N.] Univ Paris Saclay, CNRS, IN2P3, IJCLab, F-91405 Orsay, France; [Xu, Y.; Wei, J. Y.] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Chinese Academy of Sciences; National Astronomical Observatory, CAS; CEA; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Saclay; Hautes Etudes Commerciales (HEC) Paris; UDICE-French Research Universities; Universite PSL; Universite Paris Cite; Observatoire de Paris; CEA; Centre National de la Recherche Scientifique (CNRS); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Earth Sciences & Astronomy (INSU); UDICE-French Research Universities; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute of Nuclear and Particle Physics (IN2P3); UDICE-French Research Universities; Universite Paris Saclay; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS Turpin, D (corresponding author), Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Space Astron & Technol, Beijing 100012, Peoples R China.;Turpin, D (corresponding author), Univ Paris Saclay, Dept Astrophys Astrophys Instrumentat & Modelisat, CEA, CNRS, F-91191 Gif Sur Yvette, France. dturpin@nao.cas.cn Xin, Li-Ping/0000-0002-9422-3437; Bertin, Emmanuel/0000-0002-3602-3664; TURPIN, Damien/0000-0003-1835-1522 Chinese Academy of Sciences (CAS) PIFI; CNES Postdoctoral Fellowship at Departement d'astrophysique at CEA-Saclay; CNRS/IJCLab; National Astronomical Observatories of China (NAOC, Beijing); CNES; National Natural Science Foundation of China [11533003, 11973055, U1931133]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB23040000]; Strategic Pionner Program on Space Science, Chinese Academy of Sciences [XDA15052600] Chinese Academy of Sciences (CAS) PIFI; CNES Postdoctoral Fellowship at Departement d'astrophysique at CEA-Saclay; CNRS/IJCLab; National Astronomical Observatories of China (NAOC, Beijing); CNES(Centre National D'etudes Spatiales); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Strategic Priority Research Program of the Chinese Academy of Sciences(Chinese Academy of Sciences); Strategic Pionner Program on Space Science, Chinese Academy of Sciences DT acknowledges the financial support from the Chinese Academy of Sciences (CAS) PIFI post-doctoral fellowship program (program C). DT was supported by the CNES Postdoctoral Fellowship at Departement d'astrophysique at CEA-Saclay. MG acknowledges the financial support of Laboratoire de l'Accelerateur Lineaire (the LAL is now the CNRS/IJCLab) and the National Astronomical Observatories of China (NAOC, Beijing). SA was supported by the CNES Postdoctoral Fellowship at Laboratoire AstroParticule et Cosmologie. This work was supported by the National Natural Science Foundation of China (grant numbers 11533003, 11973055 and U1931133) as well as the Strategic Priority Research Program of the Chinese Academy of Sciences grant no. XDB23040000 and the Strategic Pionner Program on Space Science, Chinese Academy of Sciences, grant no. XDA15052600. 52 9 9 0 4 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0035-8711 1365-2966 MON NOT R ASTRON SOC Mon. Not. Roy. Astron. Soc. SEP 2020.0 497.0 3 2641 2650 10.1093/mnras/staa2046 0.0 10 Astronomy & Astrophysics Science Citation Index Expanded (SCI-EXPANDED) Astronomy & Astrophysics NZ5JL Green Submitted 2023-03-23 WOS:000577137200012 0
J Yu, ZT; Shen, YM; Shi, JA; Zhao, HS; Cui, YW; Zhang, JH; Torr, P; Zhao, GY Yu, Zitong; Shen, Yuming; Shi, Jingang; Zhao, Hengshuang; Cui, Yawen; Zhang, Jiehua; Torr, Philip; Zhao, Guoying PhysFormer plus plus : Facial Video-Based Physiological Measurement with SlowFast Temporal Difference Transformer INTERNATIONAL JOURNAL OF COMPUTER VISION English Article; Early Access RPPG; Temporal difference transformer; SlowFast; Cross-attention; Periodic-attention HEART-RATE MEASUREMENT; NONCONTACT Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose two end-to-end video transformer based architectures, namely PhysFormer and PhysFormer++, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. To better exploit the temporal contextual and periodic rPPG clues, we also extend the PhysFormer to the two-pathway SlowFast based PhysFormer++ with temporal difference periodic and cross-attention transformers. Furthermore, we propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and PhysFormer++ and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. Unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer family can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community. [Yu, Zitong] Great Bay Univ, Dongguan 523000, Peoples R China; [Shen, Yuming; Torr, Philip] Univ Oxford, Oxford OX13PJ, England; [Shi, Jingang] Xi An Jiao Tong Univ, Xian 710049, Peoples R China; [Zhao, Hengshuang] Univ Hong Kong, Hong Kong, Peoples R China; [Cui, Yawen; Zhang, Jiehua; Zhao, Guoying] Univ Oulu, Oulu 90014, Finland University of Oxford; Xi'an Jiaotong University; University of Hong Kong; University of Oulu Zhao, GY (corresponding author), Univ Oulu, Oulu 90014, Finland. zitong.yu@ieee.org; ym_zmxncbv@hotmail.com; jingang@xjtu.edu.cn; hszhao@cs.hku.hk; yawen.cui@oulu.fi; jiehua.zhang@oulu.fi; philip.torr@eng.ox.ac.uk; guoying.zhao@oulu.fi Academy of Finland (Academy Professor project EmotionAI) [336116, 345122, ICT2023, 345948]; National Natural Science Foundation of China [62002283]; HKU Startup Fund; HKU Seed Fund for Basic Research; EPSRC grant: Turing AI Fellowship [EP/W002981/1]; EPSRC/MURI [EP/N019474/1]; Royal Academy of Engineering; FiveAI Academy of Finland (Academy Professor project EmotionAI)(Academy of Finland); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); HKU Startup Fund; HKU Seed Fund for Basic Research; EPSRC grant: Turing AI Fellowship(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); EPSRC/MURI(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)); Royal Academy of Engineering(Royal Academy of Engineering - UK); FiveAI This work was supported by the Academy of Finland (Academy Professor project EmotionAI with grant numbers 336116 and 345122, and ICT2023 project with grant number 345948), the National Natural Science Foundation of China (Grant No. 62002283), HKU Startup Fund, HKU Seed Fund for Basic Research, and the EPSRC grant: Turing AI Fellowship: EP/W002981/1, EPSRC/MURI grant EP/N019474/1. We would also like to thank the Royal Academy of Engineering and FiveAI. The authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. 87 0 0 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0920-5691 1573-1405 INT J COMPUT VISION Int. J. Comput. Vis. 10.1007/s11263-023-01758-1 0.0 FEB 2023 24 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science 8Z4KI hybrid, Green Submitted 2023-03-23 WOS:000933348800002 0
J Liu, Y; Guo, B; Zhang, DQ; Zeghlache, D; Chen, JM; Zhang, SZ; Zhou, D; Shi, XL; Yu, ZW Liu, Yan; Guo, Bin; Zhang, Daqing; Zeghlache, Djamal; Chen, Jingmin; Zhang, Sizhe; Zhou, Dan; Shi, Xinlei; Yu, Zhiwen MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY English Article Urban computing; optimal store placement; machine learning; knowledge transfer; meta-learning Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users' preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines. [Liu, Yan; Guo, Bin; Yu, Zhiwen] Northwestern Polytech Univ, Xian, Peoples R China; [Zhang, Daqing; Zeghlache, Djamal] Telecom SudParis, Evry, France; [Chen, Jingmin; Zhang, Sizhe; Zhou, Dan; Shi, Xinlei] Alibaba Grp, Hangzhou, Peoples R China Northwestern Polytechnical University; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Alibaba Group Guo, B (corresponding author), Northwestern Polytech Univ, Xian, Peoples R China. yan_emily@outlook.com; guob@nwpu.edu.cn; daqing.zhangmetwally555@yahoo.com; djamal.zeghlache@telecom-sudparis.eu; jingmin.cjm@alibaba-inc.com; jincheng.zsz@alibaba-inc.com; modan.zd@alibaba-inc.com; sxl110655@alibaba-inc.com; zhiwenyu@nwpu.edu.cn National Key R&D Program of China [2019YFB1703901]; National Science Fund for Distinguished Young Scholars [62025205]; National Natural Science Foundation of China [61960206008, 61772428, 61725205]; China Scholarship Council; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201958] National Key R&D Program of China; National Science Fund for Distinguished Young Scholars(National Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); China Scholarship Council(China Scholarship Council); Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University This work was supported by the National Key R&D Program of China (grant no. 2019YFB1703901), the National Science Fund for Distinguished Young Scholars (grant no. 62025205), the National Natural Science Foundation of China (grant nos. 61960206008, 61772428, 61725205), the China Scholarship Council, and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (grant no. CX201958). 52 0 0 3 6 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 2157-6904 2157-6912 ACM T INTEL SYST TEC ACM Trans. Intell. Syst. Technol. JUL 2021.0 12.0 3 28 10.1145/3447271 0.0 23 Computer Science, Artificial Intelligence; Computer Science, Information Systems Science Citation Index Expanded (SCI-EXPANDED) Computer Science WY9DN Green Published 2023-03-23 WOS:000719577700002 0
J Chen, HY; Li, C; Wang, G; Li, XY; Rahaman, MM; Sun, HZ; Hu, WM; Li, YX; Liu, WL; Sun, CH; Ai, SL; Grzegorzek, M Chen, Haoyuan; Li, Chen; Wang, Ge; Li, Xiaoyan; Rahaman, Md Mamunur; Sun, Hongzan; Hu, Weiming; Li, Yixin; Liu, Wanli; Sun, Changhao; Ai, Shiliang; Grzegorzek, Marcin GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection PATTERN RECOGNITION English Article Gastric histropathological image; Multi-scale visual transformer; Image detection In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images. GasHis-Transformer model consists of two key modules designed to extract global and local information using a position-encoded transformer model and a convolutional neural network with local convolution, respectively. A publicly available hematoxylin and eosin (H&E) stained gastric histopathological image dataset is used in the experiment. Furthermore, a Dropconnect based lightweight network is proposed to reduce the model size and training time of GasHis-Transformer for clinical applications with improved confidence. Moreover, a series of contrast and extended experiments verify the robustness, extensibility and stability of GasHis-Transformer. In conclusion, GasHis-Transformer demonstrates high global detection performance and shows its significant potential in GHID task. (C) 2022 Elsevier Ltd. All rights reserved. [Chen, Haoyuan; Li, Chen; Rahaman, Md Mamunur; Hu, Weiming; Li, Yixin; Liu, Wanli; Sun, Changhao; Ai, Shiliang] Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang, Peoples R China; [Wang, Ge] Rensselaer Polytech Inst, Biomed Imaging Ctr, Dept Biomed Engn, Troy, NY 12180 USA; [Rahaman, Md Mamunur] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia; [Li, Xiaoyan; Sun, Hongzan] China Med Univ, Shenyang, Peoples R China; [Sun, Changhao] Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Peoples R China; [Grzegorzek, Marcin] Univ Lubeck, Inst Med Informat, Lubeck, Germany Northeastern University - China; Rensselaer Polytechnic Institute; University of New South Wales Sydney; China Medical University; Chinese Academy of Sciences; Shenyang Institute of Automation, CAS; University of Lubeck Li, C (corresponding author), Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang, Peoples R China.;Wang, G (corresponding author), Rensselaer Polytech Inst, Biomed Imaging Ctr, Dept Biomed Engn, Troy, NY 12180 USA. lichen@bmie.neu.edu.cn; wangg6@rpi.edu li, xiaoyan/HPC-4813-2023; Rahaman, Md Mamunur/AAS-5300-2021; SUN, CHANG/GXM-3680-2022 Rahaman, Md Mamunur/0000-0003-2268-2092; Li, Chen/0000-0003-1545-8885; Sun, Hongzan/0000-0002-4724-5828; Wang, Ge/0000-0002-2656-7705 National Natural Science Foundation of China [61806047] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work is supported by National Natural Science Foundation of China (No. 61806047) . We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. We also thank Mr. Jinghua Zhang for his contribution to the revision of this paper. There is no con-flict of interest in this paper. 40 22 22 10 10 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. OCT 2022.0 130.0 108827 10.1016/j.patcog.2022.108827 0.0 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering 3J6TK Green Submitted 2023-03-23 WOS:000833526700007 0
J Yang, ER; Hao, F; Yang, YX; De Maio, C; Nasridinov, A; Min, GY; Yang, LT Yang, Erhe; Hao, Fei; Yang, Yixuan; De Maio, Carmen; Nasridinov, Aziz; Min, Geyong; Yang, Laurence T. T. Incremental Entity Summarization With Formal Concept Analysis IEEE TRANSACTIONS ON SERVICES COMPUTING English Article Knowledge graph; entity summarization; formal concept analysis; incremental algorithm?????????????? Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this article first formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7 percent of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of $F1-measure$F1-measure, $MAP$MAP, and $NDCG$NDCG. [Yang, Erhe; Yang, Yixuan] Minist Educ, Key Lab Modern Teaching Technol, Xian 710062, Peoples R China; [Yang, Erhe; Hao, Fei; Yang, Yixuan] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China; [Hao, Fei; Min, Geyong] Univ Exeter, Coll Engn, Dept Comp Sci Math & Phys Sci, Exeter EX4 4QF, England; [De Maio, Carmen] Univ Salerno, Dept Informat Engn Elect Engn & Appl Math, I-84084 Fisciano, Italy; [Nasridinov, Aziz] Chungbuk Natl Univ, Dept Comp Sci, Cheongju 28644, South Korea; [Yang, Laurence T. T.] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada Shaanxi Normal University; University of Exeter; University of Salerno; Chungbuk National University; Saint Francis Xavier University - Canada Hao, F (corresponding author), Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China.;Hao, F (corresponding author), Univ Exeter, Coll Engn, Dept Comp Sci Math & Phys Sci, Exeter EX4 4QF, England. erhyang@163.com; feehao@gmail.com; yxyang@snnu.edu.cn; cdemaio@unisa.it; aziz@chungbuk.ac.kr; g.min@exeter.ac.uk; ltyang@gmail.com Hao, Fei/0000-0001-5288-5523; Yang, Laurence T./0000-0002-7986-4244 National Natural Science Foundation of China [61702317]; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [840922]; Fundamental Research Funds for the Central Universities [GK202103080] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant(European CommissionEuropean Commission Joint Research Centre); Fundamental Research Funds for the Central Universities(Fundamental Research Funds for the Central Universities) This work was supported in part by the National Natural Science Foundation of China under Grant 61702317, in part by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 840922, and in part by the Fundamental Research Funds for the Central Universities under Grant GK202103080. This work reflects only the authors' view and the EU Commission is not responsible for any use that may be made of the information it contains. 36 0 0 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1939-1374 IEEE T SERV COMPUT IEEE Trans. Serv. Comput. NOV 1 2022.0 15.0 6 3289 3303 10.1109/TSC.2021.3090276 0.0 15 Computer Science, Information Systems; Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science 7B7CB Green Accepted 2023-03-23 WOS:000899285900014 0
J Luo, C; He, F; Ghezzi, C Luo, Chen; He, Fei; Ghezzi, Carlo Inferring software behavioral models with MapReduce SCIENCE OF COMPUTER PROGRAMMING English Article Model inference; Parametric trace; Log analysis; MapReduce In the real world practice, software systems are often built without developing any explicit upfront model. This can cause serious problems that may hinder: the almost inevitable future evolution, since at best the only documentation about the software is in the form of source code comments. To address this problem, research has been focusing on automatic inference of models by applying machine learning algorithms to execution logs. However, the logs generated by a real software system may be very large and the inference algorithm can exceed the processing capacity of,a single computer. This paper proposes a scalable, general approach to the inference of behavior models that can handle large execution logs via parallel and distributed algorithms implemented using the MapReduce programming model and executed on a cluster-of interconnected execution nodes. The approach consists of two distributed phases that perform trace slicing and model synthesis. For each phase, a distributed algorithm using MapReduce is developed. With the parallel data processing capacity of MapReduce, the problem of inferring behavior models from large logs can be efficiently solved. The technique is implemented on top of Hadoop. Experiments on Amazon clusters show efficiency and scalability of our approach. (C) 2017 Elsevier B.V. All rights reserved. [Luo, Chen; He, Fei] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, Key Lab Informat Syst Secur, Minist Educ,Sch Software, Beijing 100084, Peoples R China; [Luo, Chen] Univ Calif Irvine, Irvine, CA USA; [Ghezzi, Carlo] Politecn Milan, Milan, Italy; [Luo, Chen] Tsinghua Univ, Beijing, Peoples R China Tsinghua University; University of California System; University of California Irvine; Polytechnic University of Milan; Tsinghua University He, F (corresponding author), Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, Key Lab Informat Syst Secur, Minist Educ,Sch Software, Beijing 100084, Peoples R China. cluo8@uci.edu; hefei@tsinghua.edu.cn; carlo.ghezzi@polimi.it Luo, Chen/AAC-4300-2019; Luo, Chen/AAA-9615-2020 Luo, Chen/0000-0002-2180-7749; He, Fei/0000-0002-4266-875X Chinese National 973 Plan [2010CB328003]; NSF of China [61672310, 61272001, 91218302]; Chinese National Key Technology RD Program [SQ2012BAJY4052] Chinese National 973 Plan(National Basic Research Program of China); NSF of China(National Natural Science Foundation of China (NSFC)); Chinese National Key Technology RD Program(National Key Technology R&D Program) We thank Dr. Srdjan Krstic for his comments to our manuscript. This work was supported in part by the Chinese National 973 Plan (2010CB328003), the NSF of China (61672310, 61272001, 91218302) and the Chinese National Key Technology R&D Program (SQ2012BAJY4052). 70 7 7 2 10 ELSEVIER AMSTERDAM RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS 0167-6423 1872-7964 SCI COMPUT PROGRAM Sci. Comput. Program. OCT 1 2017.0 145.0 13 36 10.1016/j.scico.2017.04.004 0.0 24 Computer Science, Software Engineering Science Citation Index Expanded (SCI-EXPANDED) Computer Science EY9KG 2023-03-23 WOS:000404316900002 0
J Hu, JJ; Bao, YS; Liu, J; Liu, H; Petropoulos, GP; Katsafados, P; Zhu, LH; Cai, X Hu, Jingjing; Bao, Yansong; Liu, Jian; Liu, Hui; Petropoulos, George P.; Katsafados, Petros; Zhu, Liuhua; Cai, Xi Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region REMOTE SENSING English Article HIRAS; temperature retrieval; relative humidity retrieval; arctic; neural networks OPERATIONAL SOIL-MOISTURE The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic's climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60 degrees N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D's operational implementation range from within 60 degrees N to the Arctic region. [Hu, Jingjing; Bao, Yansong; Zhu, Liuhua; Cai, Xi] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, CMA Key Lab Aerosol Cloud Precipitat, Nanjing 210044, Peoples R China; [Hu, Jingjing; Bao, Yansong; Zhu, Liuhua; Cai, Xi] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China; [Liu, Jian; Liu, Hui] China Meteorol Adm, Key Lab Radiometr Calibrat & Validat Environm Sat, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China; [Petropoulos, George P.; Katsafados, Petros] Harokopio Univ Athens, Dept Geog, EI Venizelou 70, Athens 17671, Greece Nanjing University of Information Science & Technology; Nanjing University of Information Science & Technology; China Meteorological Administration; Harokopio University Athens Bao, YS (corresponding author), Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, CMA Key Lab Aerosol Cloud Precipitat, Nanjing 210044, Peoples R China.;Bao, YS (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China. 20191205001@nuist.edu.cn; ysbao@nuist.edu.cn; liujian@cma.cn; liuhui@cma.cn; gpetropoulos@hua.gr; pkatsaf@hua.gr; zhulh@nuist.edu.cn; xcai@nuist.edu.cn Petropoulos, George P./N-5810-2019 Petropoulos, George P./0000-0003-1442-1423; KATSAFADOS, PETROS/0000-0001-7452-0138; Liu, Hui/0000-0002-2843-165X National Key Research and Development Program of China [2018YFC1407204, 2018YFC1407200, 2017YFB0502800, 2017YFC1501704]; Graduate Research and Innovation Program in Jiangsu Province [KYCX20_0953]; National Natural Science Foundation of China [41975046]; European Union's Horizon 2020 Marie Sklodowska-Curie Project ENViSIoN-EO [752094] National Key Research and Development Program of China; Graduate Research and Innovation Program in Jiangsu Province; National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); European Union's Horizon 2020 Marie Sklodowska-Curie Project ENViSIoN-EO This study was supported by the National Key Research and Development Program of China (2018YFC1407204, 2018YFC1407200, 2017YFB0502800, 2017YFC1501704), Graduate Research and Innovation Program in Jiangsu Province (KYCX20_0953), and National Natural Science Foundation of China (41975046). G.P.P.'s participation was supported financially by the European Union's Horizon 2020 Marie Sklodowska-Curie Project ENViSIoN-EO (grant agreement No 752094). 48 2 2 3 41 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAY 2021.0 13.0 10 1884 10.3390/rs13101884 0.0 20 Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Science Citation Index Expanded (SCI-EXPANDED) Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology ST5LC gold 2023-03-23 WOS:000662483700001 0
C Tse, R; Monti, L; Im, M; Mirri, S; Pau, G; Salomoni, P Jiang, X; Fujita, H Tse, Rita; Monti, Lorenzo; Im, Marcus; Mirri, Silvia; Pau, Giovanni; Salomoni, Paola DeepClass: Edge Based Class Occupancy Detection Aided by Deep Learning and Image Cropping TWELFTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2020) Proceedings of SPIE English Proceedings Paper 12th International Conference on Digital Image Processing (ICDIP) MAY 19-22, 2020 ELECTR NETWORK Int Assoc Comp Sci & Informat Technol Image processing; Image objects recognition; smart sensing; Internet of Things; smart environments; ambient intelligence PEOPLE Detecting people's presence, monitoring their flows, and their activities, counting how many persons are in a specific place can be strategic goals in different contexts, providing useful insights for different purposes, including those ones related to the management of staying quality in indoor environments. In particular, having information about the actual and current occupancy of a specific room, in specific hours, could be strategic in providing interesting and helpful information for smart building management. In fact, this information could be needed to adequately set the Heat, Ventilation and Air Conditioning (HVAC), the alarm, the lighting systems, and other management issues also. In this context, the Internet of Things paradigm, together with the diffusion of the availability of sensors and smart objects, can provide significant support in monitoring and detecting daily life activities in various situations. Moreover, advancements and specific analysis in image processing can play a strategic role in guaranteeing and improving accuracy, whenever cameras are involved in these situations, to get pictures from the monitored environments. In this paper, we present a people counting approach we have defined and adopted to monitor persons' presence in smart campus classrooms, which is based on the use of cameras and Raspberry Pi platforms. Such an approach has been improved thanks to specific image processing strategies, to be generalized and adopted in different indoor environments, without the need for a specific training phase. The paper presents some evaluation tests we have conducted, showing the accuracy of our approach. [Tse, Rita; Im, Marcus] Macao Polytech Inst, Rua Luis Gonzaga Gomes, Macau, Peoples R China; [Monti, Lorenzo; Mirri, Silvia; Pau, Giovanni; Salomoni, Paola] Univ Bologna, Dept Comp Sci & Engn, Mura Anteo Zamboni 7, Bologna, Italy Macao Polytechnic University; University of Bologna Monti, L (corresponding author), Univ Bologna, Dept Comp Sci & Engn, Mura Anteo Zamboni 7, Bologna, Italy. IM, SIO KEI/0000-0002-5599-4300; Monti, Lorenzo/0000-0002-2087-0535; Pau, Giovanni/0000-0003-2216-7170 Macao Polytechnic Institute -Environmental Monitoring of UNESCO Coimbra Science Museum [RP/ESAP-01/2019] Macao Polytechnic Institute -Environmental Monitoring of UNESCO Coimbra Science Museum This work was supported in part by the Macao Polytechnic Institute -Environmental Monitoring of UNESCO Coimbra Science Museum (RP/ESAP-01/2019) and the authors would like to thank Massimo Donini and Luca Rispoli for their precious support. 28 5 5 1 7 SPIE-INT SOC OPTICAL ENGINEERING BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA 0277-786X 1996-756X 978-1-5106-3846-4 PROC SPIE 2020.0 11519.0 1151904 10.1117/12.2572948 0.0 8 Computer Science, Software Engineering; Optics; Imaging Science & Photographic Technology Conference Proceedings Citation Index - Science (CPCI-S) Computer Science; Optics; Imaging Science & Photographic Technology BQ4IG 2023-03-23 WOS:000589893500003 0
J Huang, L; Xiang, WN; Wu, JG; Traxler, C; Huang, JZ Huang, Lu; Xiang, Weining; Wu, Jianguo; Traxler, Christoph; Huang, Jingzhou Integrating GeoDesign with Landscape Sustainability Science SUSTAINABILITY English Article GeoDesign; Landscape Sustainability Science; landscape design; planning; ecosystem services; information technology; evaluation LAND SYSTEM ARCHITECTURE; ECOSYSTEM SERVICES; URBAN SUSTAINABILITY; DESIGN; FUTURE; ECOLOGY; TOOLS With an increasing world population and accelerated urbanization, the development of landscape sustainability remains a challenge for scientists, designers, and multiple stakeholders. Landscape sustainability science (LSS) studies dynamic relationships among landscape pattern, ecosystem services, and human well-being with spatially explicit methods. The design of a sustainable landscape needs both landscape sustainability-related disciplines and digital technologies that have been rapidly developing. GeoDesign is a new design method based on a new generation of information technology, especially spatial information technology, to design land systems. This paper discusses the suitability of GeoDesign for LSS to help design sustainable landscapes. Building on a review of LSS and GeoDesign, we conclude that LSS can utilize GeoDesign as a research method and the designed landscape as a research object to enrich and empower the spatially explicit methodology of LSS. To move forward, we suggest to integrate GeoDesign with LSS from six perspectives: strong/weak sustainability, multiple scales, ecosystem services, sustainability indicators, big data application, and the sense of place. Toward this end, we propose a LSS-based GeoDesign framework that links the six perspectives. We expect that this integration between GeoDesign and LSS will help advance the science and practice of sustainability and bring together many disciplines across natural, social, and design sciences. [Huang, Lu; Huang, Jingzhou] Hangzhou Dianzi Univ, Smart City Res Ctr Zhejiang Prov, Hangzhou 310012, Zhejiang, Peoples R China; [Xiang, Weining] Univ N Carolina, Dept Geog & Earth Sci, Charlotte, NC 28223 USA; [Wu, Jianguo] Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA; [Wu, Jianguo] Arizona State Univ, Sch Sustainabil, Tempe, AZ 85287 USA; [Traxler, Christoph] VRVis Zentrum Virtual Real & Visualisierung Forsc, A-1220 Vienna, Austria Hangzhou Dianzi University; University of North Carolina; University of North Carolina Charlotte; Arizona State University; Arizona State University-Tempe; Arizona State University; Arizona State University-Tempe Huang, L (corresponding author), Hangzhou Dianzi Univ, Smart City Res Ctr Zhejiang Prov, Hangzhou 310012, Zhejiang, Peoples R China. luhuang@hdu.edu.cn; wxiang@uncc.edu; Jingle.Wu@asu.edu; traxler@vrvis.at; huangjz@hdu.edu.cn Wu, Jianguo/G-6267-2010 Wu, Jianguo/0000-0002-1182-3024; Christoph, Traxler/0000-0003-4157-1232; Huang, Lu/0000-0002-4552-1403 National Natural Science Foundation of China [41701638]; Hangzhou Science and Technology Development Plan [20170834M30] National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)); Hangzhou Science and Technology Development Plan This research was funded by the National Natural Science Foundation of China (grant number 41701638) and the Hangzhou Science and Technology Development Plan (grant number 20170834M30). 66 21 27 10 81 MDPI BASEL ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND 2071-1050 SUSTAINABILITY-BASEL Sustainability FEB 1 2019.0 11.0 3 833 10.3390/su11030833 0.0 17 Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) Science & Technology - Other Topics; Environmental Sciences & Ecology HL7NY Green Published, Green Submitted, gold 2023-03-23 WOS:000458929500278 0
J Mohammadi, B; Linh, NTT; Pham, QB; Ahmed, AN; Vojtekova, J; Guan, YQ; Abba, SI; El-Shafie, A Mohammadi, Babak; Nguyen Thi Thuy Linh; Quoc Bao Pham; Ahmed, Ali Najah; Vojtekova, Jana; Guan, Yiqing; Abba, S., I; El-Shafie, Ahmed Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES English Article Streamflow; estimation; time series models; adaptive neuro-fuzzy inference system (ANFIS); shuffled frog leaping algorithm (SFLA) ARTIFICIAL-INTELLIGENCE APPROACH; RULE-BASED APPROACH; GENETIC ALGORITHM; PERFORMANCE EVALUATION; SEDIMENT TRANSPORT; MODEL; OPTIMIZATION; NETWORK; EVAPOTRANSPIRATION; FLOW Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input-output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R-2= 0.88; NS = 0.88; RMSE = 142.30 (m(3)/s); MAE = 88.94 (m(3)/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R-2= 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m(3)/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide. [Mohammadi, Babak; Guan, Yiqing] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Nguyen Thi Thuy Linh] Natl Cheng Kung Univ, Dept Hydraul & Ocean Engn, Tainan, Taiwan; [Nguyen Thi Thuy Linh] Thuyloi Univ, Fac Water Resource Engn, 175 Tay Son, Hanoi 100000, Vietnam; [Quoc Bao Pham] Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam; [Quoc Bao Pham] Duy Tan Univ, Fac Environm & Chem Engn, Danang 550000, Vietnam; [Ahmed, Ali Najah] Univ Tenaga Nas, Coll Engn, Inst Energy Infrastruct IEI, Civil Engn Dept, Kajang, Selangor, Malaysia; [Vojtekova, Jana] Constantine Philosopher Univ Nitra, Fac Nat Sci, Dept Geog & Reg Dev, Nitra, Slovakia; [Abba, S., I] Yusuf Maitama Sule Univ, Dept Phys Planning Dev, Kano, Nigeria; [El-Shafie, Ahmed] Univ Malaya, Fac Engn, Civil Engn Dept, Kuala Lumpur, Malaysia; [El-Shafie, Ahmed] United Arab Emirates Univ, Natl Water Ctr, Al Ain, U Arab Emirates Hohai University; National Cheng Kung University; Thuyloi University; Duy Tan University; Duy Tan University; Universiti Tenaga Nasional; Constantine the Philosopher University in Nitra; Universiti Malaya; United Arab Emirates University Pham, QB (corresponding author), Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam. phambaoquoc@duytan.edu.vn Pham, Quoc Bao/AAD-5611-2020; Abba, Sani Isah/C-8573-2017; El-Shafie, Ahmed/J-1799-2014; Ahmed, Ali Najah/J-9456-2017; NTT, Linh/AAJ-2377-2020; Mohammadi, Babak/G-4012-2018; Vojteková, Jana/O-3130-2019 Pham, Quoc Bao/0000-0002-0468-5962; Abba, Sani Isah/0000-0001-9356-2798; El-Shafie, Ahmed/0000-0001-5018-8505; Ahmed, Ali Najah/0000-0002-5618-6663; NTT, Linh/0000-0001-8631-9941; Mohammadi, Babak/0000-0001-8427-5965; Vojteková, Jana/0000-0002-8904-9673 83 59 59 2 32 TAYLOR & FRANCIS LTD ABINGDON 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND 0262-6667 2150-3435 HYDROLOG SCI J Hydrol. Sci. J.-J. Sci. Hydrol. JUL 26 2020.0 65.0 10 1738 1751 10.1080/02626667.2020.1758703 0.0 JUN 2020 14 Water Resources Science Citation Index Expanded (SCI-EXPANDED) Water Resources MV0UE 2023-03-23 WOS:000544173500001 0
J Wei, X; Wang, H; Guo, GD; Wan, H Wei, Xin; Wang, Hui; Guo, Gongde; Wan, Huan Multiplex image representation for enhanced recognition INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS English Article Multiplex image representation; Feature fusion; LBP; HOG; Face recognition FACE-RECOGNITION; CLASSIFICATION; SCALE A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532-539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image-joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition. [Wei, Xin; Guo, Gongde; Wan, Huan] Fujian Normal Univ, Sch Math & Comp Sci, Key Lab Network Secur & Cryptol, Fuzhou, Fujian, Peoples R China; [Wang, Hui] Univ Ulster, Sch Comp & Math, Belfast, Antrim, North Ireland Fujian Normal University; Ulster University Wei, X (corresponding author), Fujian Normal Univ, Sch Math & Comp Sci, Key Lab Network Secur & Cryptol, Fuzhou, Fujian, Peoples R China. xinwei.mail@qq.com; h.wang@ulster.ac.uk; ggd@fjnu.edu.cn; huanwan.mail@qq.com Wang, Hui/HMU-9512-2023 Wang, Hui/0000-0003-2633-6015 Hu Guozan Study-Abroad Grant for Graduates of Fujian Normal University Hu Guozan Study-Abroad Grant for Graduates of Fujian Normal University This work is partially supported by Hu Guozan Study-Abroad Grant for Graduates of Fujian Normal University. 35 5 7 0 8 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1868-8071 1868-808X INT J MACH LEARN CYB Int. J. Mach. Learn. Cybern. MAR 2018.0 9.0 3 SI 383 392 10.1007/s13042-015-0427-5 0.0 10 Computer Science, Artificial Intelligence Science Citation Index Expanded (SCI-EXPANDED) Computer Science FW5FT 2023-03-23 WOS:000425342400003 0
J Li, SY; Dario, P; Song, ZB Li, Shuyang; Dario, Paolo; Song, Zhibin Prediction of Passive Torque on Human Shoulder Joint Based on BPANN APPLIED BIONICS AND BIOMECHANICS English Article MODEL In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient's joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists. [Li, Shuyang; Dario, Paolo; Song, Zhibin] Tianjin Univ, Key Lab Mech Theory & Equipment Design, Minist Educ, Tianjin 300072, Peoples R China; [Dario, Paolo] Scuola Super Sant Anna, BioRobot Inst, Polo St Anna Valdera, Vle R Piaggio 34, I-56025 Pontedera, Italy Tianjin University; Scuola Superiore Sant'Anna Song, ZB (corresponding author), Tianjin Univ, Key Lab Mech Theory & Equipment Design, Minist Educ, Tianjin 300072, Peoples R China. lishuyang@tju.edu.cn; paolo.dario@santannapisa.it; songzhibin@tju.edu.cn National Key Research and Development Program of China [2018YFB1307803]; Natural Science Foundation of China [51775367, 51975401, 51535008] National Key Research and Development Program of China; Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This research is supported by the National Key Research and Development Program of China (No. 2018YFB1307803) and the Natural Science Foundation of China (Project No. 51775367, 51975401, 51535008). 17 1 1 6 12 HINDAWI LTD LONDON ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND 1176-2322 1754-2103 APPL BIONICS BIOMECH Appl. Bionics Biomech. AUG 28 2020.0 2020.0 8839791 10.1155/2020/8839791 0.0 10 Engineering, Biomedical; Robotics Science Citation Index Expanded (SCI-EXPANDED) Engineering; Robotics NQ0FA 32908611.0 Green Published, gold 2023-03-23 WOS:000570543500001 0
J Zhang, PY; Liu, FL; Jiang, CX; Benslimane, A; Gorricho, JL; Serrat-Fernandez, J Zhang, Peiying; Liu, Fanglin; Jiang, Chunxiao; Benslimane, Abderrahim; Gorricho, Juan-Luis; Serrat-Fernandez, Joan A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks MOBILE NETWORKS & APPLICATIONS English Article Internet of Things; Virtual network mapping; Genetic algorithm; Ant colony algorithm; Load balancing RESOURCE-ALLOCATION; DESIGN; ACCESS; NODE The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people's increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm's masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time. [Zhang, Peiying; Liu, Fanglin] China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China; [Jiang, Chunxiao] Tsinghua Univ, Sch Informat Sci & Technol, Beijing 100084, Peoples R China; [Benslimane, Abderrahim] Univ Avignon, Dept Comp Sci, LIA CERI, Avignon, France; [Gorricho, Juan-Luis; Serrat-Fernandez, Joan] Tech Univ Catalonia UPC, Coll Network Engn, Carrer Jordi Girona 1,3, Barcelona 108034, Spain China University of Petroleum; Tsinghua University; Avignon Universite; Universitat Politecnica de Catalunya Jiang, CX (corresponding author), Tsinghua Univ, Sch Informat Sci & Technol, Beijing 100084, Peoples R China. zhangpeiying@upc.edu.cn; liufanglin.upc@qq.com; jchx@tsinghua.edu.cn; abderrahim.benslimane@univ-avignon.fr; juanluis@entel.upc.edu; serrat@tsc.upc.edu National Key Research and Development Program of China [2020YFB1804800]; Major Scientific and Technological Projects of CNPC [ZD2019-183-006]; Shandong Provincial Natural Science Foundation [ZR2020MF006] National Key Research and Development Program of China; Major Scientific and Technological Projects of CNPC; Shandong Provincial Natural Science Foundation(Natural Science Foundation of Shandong Province) This work is partially supported by the National Key Research and Development Program of China under Grant 2020YFB1804800, partially supported by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-006, and partially supported by Shandong Provincial Natural Science Foundation under Grant ZR2020MF006. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation. 36 1 1 1 25 SPRINGER NEW YORK ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES 1383-469X 1572-8153 MOBILE NETW APPL Mobile Netw. Appl. FEB 2022.0 27.0 1 SI 124 138 10.1007/s11036-020-01714-0 0.0 FEB 2021 15 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Telecommunications Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Telecommunications ZY8TD Green Submitted, Green Published 2023-03-23 WOS:000615551700001 0
J Yi, XL; Zhang, SJ; Yang, T; Johansson, KH Yi, Xinlei; Zhang, Shengjun; Yang, Tao; Johansson, Karl H. Zeroth-order algorithms for stochastic distributed nonconvex optimization AUTOMATICA English Article Distributed nonconvex optimization; Gradient-free; Linear speedup; Polyak-Lojasiewicz condition; Stochastic optimization MULTIAGENT OPTIMIZATION; CONVEX-OPTIMIZATION In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(root p/(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p/(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms. It consequently enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relatively bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. (C) 2022 Elsevier Ltd. All rights reserved. [Yi, Xinlei; Johansson, Karl H.] KTH Royal Inst Technol & Digital Futures, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden; [Zhang, Shengjun] Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA; [Yang, Tao] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China University of North Texas System; University of North Texas Denton; Northeastern University - China Yang, T (corresponding author), Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China. xinleiy@kth.se; ShengjunZhang@my.unt.edu; yangtao@mail.neu.edu.cn; kallej@kth.se zhang, shengjun/HLG-5516-2023 Knut and Alice Wallenberg Foundation; Swedish Foundation for Strategic Research; Swedish Research Council; National Natural Science Foundation of China [62133003, 61991403, 61991400] Knut and Alice Wallenberg Foundation(Knut & Alice Wallenberg Foundation); Swedish Foundation for Strategic Research(Swedish Foundation for Strategic Research); Swedish Research Council(Swedish Research Council); National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) This work was supported by the Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research, the Swedish Research Council, and the National Natural Science Foundation of China under grants 62133003, 61991403, and 61991400. 81 1 1 16 17 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0005-1098 1873-2836 AUTOMATICA Automatica AUG 2022.0 142.0 110353 10.1016/j.automatica.2022.110353 0.0 MAY 2022 11 Automation & Control Systems; Engineering, Electrical & Electronic Science Citation Index Expanded (SCI-EXPANDED) Automation & Control Systems; Engineering 3F7NG Green Submitted 2023-03-23 WOS:000830850700015 0
J Luan, F; Xu, X; Cordeiro, MNDS; Liu, HT; Zhang, XY Luan, Feng; Xu, Xuan; Dias Soeiro Cordeiro, Maria Natalia; Liu, Huitao; Zhang, Xiaoyun QSAR Modeling for the Antimalarial Activity of 1,4-Naphthoquinonyl Derivatives as Potential Antimalarial Agents CURRENT COMPUTER-AIDED DRUG DESIGN English Article Antimalarial activity; dug design; malaria; molecular design; multiple linear regression (MLR); 1,4-Naphthoquinonyl derivatives; QSAR; radial basis function neural network (RBFNN) QUANTITATIVE STRUCTURE-ACTIVITY; 2,3-BIS(AZIRIDINYL)-1,4-NAPHTHOQUINONYL SULFONATE; ACYLATE DERIVATIVES; CROSS-VALIDATION; DRUG DESIGN; ARTEMISININ; INHIBITORS; MALARIA Malaria has been known as one of the major causes of morbidity and mortality on a large scale in tropical countries until now. In the past decades, many scientific groups have focused their attention on looking for ideal drugs to this disease. So far, this research area is still a hot topic. In the present study, the antimalarial activity of 1, 4-naphthoquinonyl derivatives was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNN). The derived QSAR models have been statistically validated both internally - by means of the Leave One Out (LOO) and Leave Many Out (LMO) cross-validation, and Y-scrambling techniques, as well as externally (by means of an external test set). The statistical parameters provided by the MLR model were R-2 =0.7876, LOOq(2) =0.7068, RMS =0.3377, R-0(2) =0.7876, k = 1.0000 for the training set, and R-2 =0.7648, q(ext)(2) =0.7597, RMS=0.2556, R-0(2)=0.7598, k=1.0417 for the external test set. The RBFNN model gave the following statistical results, namely: R-2=0.8338, LOOq(2)=0.5869, RMS=0.2781, R-0(2) = 0.8335, k=1.0000 for the training set, and R-2 =0.7586, q(ext)(2) =0.7189, RMS=0.2788, R-0(2)=0.7129, k=1.0284 for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction and virtual screening of 1, 4-naphoquinone derivatives with high antimalarial activity. In addition, the energies of the highest occupied molecular orbital were found to have high correlation with the activity. [Luan, Feng; Xu, Xuan; Liu, Huitao] Yantai Univ, Dept Appl Chem, Yantai 264005, Peoples R China; [Dias Soeiro Cordeiro, Maria Natalia] Univ Porto, REQUIMTE, Dept Chem & Biochem, P-4169007 Oporto, Portugal; [Zhang, Xiaoyun] Lanzhou Univ, Dept Chem, Lanzhou 730000, Peoples R China Yantai University; Universidade do Porto; Lanzhou University Luan, F (corresponding author), Yantai Univ, Dept Appl Chem, Yantai 264005, Peoples R China. fluan@sina.com; ncordeir@fc.up.pt Luan, Feng/K-5522-2013; Cordeiro, Maria Natália D. S./A-7413-2012 Luan, Feng/0000-0003-0254-2973; Cordeiro, Maria Natália D. S./0000-0003-3375-8670 Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) [SFRH/BPD/63666/2009]; European Social Found; National Natural Science Foundation of Shandong Province [ZR2012BM016]; Portuguese FCT [Pest-C/EQB/LA0006/2011]; Fundação para a Ciência e a Tecnologia [SFRH/BPD/63666/2009] Funding Source: FCT Portuguese Fundacao para a Ciencia e a Tecnologia (FCT)(Fundacao para a Ciencia e a Tecnologia (FCT)); European Social Found(European Social Fund (ESF)); National Natural Science Foundation of Shandong Province; Portuguese FCT(Fundacao para a Ciencia e a Tecnologia (FCT)); Fundação para a Ciência e a Tecnologia FL acknowledges the Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) for the grant SFRH/BPD/63666/2009 co-financed by the European Social Found. The authors would like to thank also Prof. Mary Hartigan Soeiro for her helpful suggestions. This work was financially supported by the National Natural Science Foundation of Shandong Province (grant No. ZR2012BM016) and by the Portuguese FCT (grant No. Pest-C/EQB/LA0006/2011). 52 5 5 0 19 BENTHAM SCIENCE PUBL LTD SHARJAH EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES 1573-4099 CURR COMPUT-AID DRUG Curr. Comput.-Aided Drug Des. MAR 2013.0 9.0 1 95 107 13 Chemistry, Medicinal; Computer Science, Interdisciplinary Applications Science Citation Index Expanded (SCI-EXPANDED) Pharmacology & Pharmacy; Computer Science 163YD 23157413.0 2023-03-23 WOS:000320373600009 0